From 82e82e0a6f27a478941200e048a67fd41a7b9790 Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 25 Jul 2024 17:09:30 +0000
Subject: [PATCH] Deployed d42dd328d2 to dev with MkDocs 1.6.0 and mike 2.1.1

---
 .../__pycache__/__main__.cpython-312.pyc      | Bin 26441 -> 26441 bytes
 dev/recipes/on-hoeffding-trees.ipynb          | 160 +++++-------------
 dev/recipes/on-hoeffding-trees/index.html     |  26 +--
 .../on-hoeffding-trees_12_0.svg               |  26 +--
 .../on-hoeffding-trees_19_0.png               | Bin 180906 -> 180558 bytes
 .../on-hoeffding-trees_21_0.png               | Bin 190612 -> 193069 bytes
 .../on-hoeffding-trees_23_0.png               | Bin 192254 -> 189983 bytes
 .../on-hoeffding-trees_25_0.png               | Bin 207997 -> 209370 bytes
 .../on-hoeffding-trees_27_0.png               | Bin 211576 -> 212191 bytes
 dev/search/search_index.json                  |   2 +-
 10 files changed, 65 insertions(+), 149 deletions(-)

diff --git a/dev/parse/__pycache__/__main__.cpython-312.pyc b/dev/parse/__pycache__/__main__.cpython-312.pyc
index 3f090da8043cc0fea35128a3b472126bffd63730..24a19ff0532568d994233e5fee2e71534f4b9cb1 100644
GIT binary patch
delta 21
bcmX?kj`8F<My}Jmyj%=GAkwyx%RU_dP~8S4

delta 21
bcmX?kj`8F<My}Jmyj%=G(3roG%RU_dQuqdY

diff --git a/dev/recipes/on-hoeffding-trees.ipynb b/dev/recipes/on-hoeffding-trees.ipynb
index 9843919fbb..54d77140cc 100644
--- a/dev/recipes/on-hoeffding-trees.ipynb
+++ b/dev/recipes/on-hoeffding-trees.ipynb
@@ -41,12 +41,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:56.494351Z",
-     "iopub.status.busy": "2023-12-04T17:48:56.493893Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.115871Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.115462Z"
-    },
     "tags": []
    },
    "outputs": [],
@@ -100,12 +94,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.117832Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.117681Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.128760Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.128511Z"
-    },
     "tags": []
    },
    "outputs": [
@@ -116,12 +104,12 @@
        "\n",
        "This dataset contains features from web pages that are classified as phishing or not.\n",
        "\n",
-       "    Name  Phishing                                                          \n",
-       "    Task  Binary classification                                             \n",
-       " Samples  1,250                                                             \n",
-       "Features  9                                                                 \n",
-       "  Sparse  False                                                             \n",
-       "    Path  /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz"
+       "    Name  Phishing                                                       \n",
+       "    Task  Binary classification                                          \n",
+       " Samples  1,250                                                          \n",
+       "Features  9                                                              \n",
+       "  Sparse  False                                                          \n",
+       "    Path  /Users/mastelini/Documents/river/river/datasets/phishing.csv.gz"
       ]
      },
      "execution_count": 2,
@@ -145,12 +133,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.130143Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.130063Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.179708Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.179486Z"
-    },
     "tags": []
    },
    "outputs": [
@@ -158,8 +140,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 37.6 ms, sys: 569 µs, total: 38.2 ms\n",
-      "Wall time: 39.1 ms\n"
+      "CPU times: user 64.8 ms, sys: 2.75 ms, total: 67.6 ms\n",
+      "Wall time: 68.1 ms\n"
      ]
     },
     {
@@ -322,7 +304,7 @@
     "\n",
     "for x, y in dataset:\n",
     "    model.learn_one(x, y)\n",
-    "    \n",
+    "\n",
     "model"
    ]
   },
@@ -339,12 +321,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.181238Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.181151Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.191191Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.190978Z"
-    },
     "scrolled": true,
     "tags": []
    },
@@ -381,12 +357,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.192658Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.192583Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.205983Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.205736Z"
-    },
     "scrolled": true,
     "tags": []
    },
@@ -518,12 +488,6 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.207360Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.207269Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.425988Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.425673Z"
-    },
     "scrolled": true,
     "tags": []
    },
@@ -534,7 +498,7 @@
        "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
        "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
        " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Generated by graphviz version 8.1.0 (20230707.0739)\n",
+       "<!-- Generated by graphviz version 12.0.0 (20240704.0754)\n",
        " -->\n",
        "<!-- Pages: 1 -->\n",
        "<svg width=\"292pt\" height=\"212pt\"\n",
@@ -559,9 +523,9 @@
        "<!-- 0&#45;&gt;1 -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>0&#45;&gt;1</title>\n",
-       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M71.7,-167.52C71.7,-167.52 71.7,-142.26 71.7,-142.26\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"75.2,-142.26 71.7,-132.26 68.2,-142.26 75.2,-142.26\"/>\n",
-       "<text text-anchor=\"middle\" x=\"58.95\" y=\"-157.24\" font-family=\"Times,serif\" font-size=\"7.00\">≤ 0.5455</text>\n",
+       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M71.7,-167.52C71.7,-167.52 71.7,-143.17 71.7,-143.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"75.2,-143.17 71.7,-133.17 68.2,-143.17 75.2,-143.17\"/>\n",
+       "<text text-anchor=\"middle\" x=\"58.95\" y=\"-157.69\" font-family=\"Times,serif\" font-size=\"7.00\">≤ 0.5455</text>\n",
        "</g>\n",
        "<!-- 2 -->\n",
        "<g id=\"node3\" class=\"node\">\n",
@@ -572,9 +536,9 @@
        "<!-- 0&#45;&gt;2 -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>0&#45;&gt;2</title>\n",
-       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M155.26,-167.52C155.26,-167.52 155.26,-136.35 155.26,-136.35\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"158.76,-136.35 155.26,-126.35 151.76,-136.35 158.76,-136.35\"/>\n",
-       "<text text-anchor=\"middle\" x=\"142.51\" y=\"-154.28\" font-family=\"Times,serif\" font-size=\"7.00\">&gt; 0.5455</text>\n",
+       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M155.26,-167.52C155.26,-167.52 155.26,-137.26 155.26,-137.26\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"158.76,-137.26 155.26,-127.26 151.76,-137.26 158.76,-137.26\"/>\n",
+       "<text text-anchor=\"middle\" x=\"142.51\" y=\"-154.74\" font-family=\"Times,serif\" font-size=\"7.00\">&gt; 0.5455</text>\n",
        "</g>\n",
        "<!-- 3 -->\n",
        "<g id=\"node4\" class=\"node\">\n",
@@ -588,9 +552,9 @@
        "<!-- 2&#45;&gt;3 -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>2&#45;&gt;3</title>\n",
-       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M142.26,-89.77C142.26,-89.77 142.26,-58.25 142.26,-58.25\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"145.76,-58.25 142.26,-48.25 138.76,-58.25 145.76,-58.25\"/>\n",
-       "<text text-anchor=\"middle\" x=\"129.51\" y=\"-76.36\" font-family=\"Times,serif\" font-size=\"7.00\">≤ 0.0909</text>\n",
+       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M142.26,-89.77C142.26,-89.77 142.26,-59.16 142.26,-59.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"145.76,-59.16 142.26,-49.16 138.76,-59.16 145.76,-59.16\"/>\n",
+       "<text text-anchor=\"middle\" x=\"129.51\" y=\"-76.82\" font-family=\"Times,serif\" font-size=\"7.00\">≤ 0.0909</text>\n",
        "</g>\n",
        "<!-- 4 -->\n",
        "<g id=\"node5\" class=\"node\">\n",
@@ -604,15 +568,15 @@
        "<!-- 2&#45;&gt;4 -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>2&#45;&gt;4</title>\n",
-       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M194.64,-89.77C194.64,-89.77 194.64,-58.25 194.64,-58.25\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"198.14,-58.25 194.64,-48.25 191.14,-58.25 198.14,-58.25\"/>\n",
-       "<text text-anchor=\"middle\" x=\"181.89\" y=\"-76.36\" font-family=\"Times,serif\" font-size=\"7.00\">&gt; 0.0909</text>\n",
+       "<path fill=\"none\" stroke=\"black\" stroke-width=\"0.6\" d=\"M194.64,-89.77C194.64,-89.77 194.64,-59.16 194.64,-59.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" stroke-width=\"0.6\" points=\"198.14,-59.16 194.64,-49.16 191.14,-59.16 198.14,-59.16\"/>\n",
+       "<text text-anchor=\"middle\" x=\"181.89\" y=\"-76.82\" font-family=\"Times,serif\" font-size=\"7.00\">&gt; 0.0909</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x10561d1d0>"
+       "<graphviz.graphs.Digraph at 0x157d06a90>"
       ]
      },
      "execution_count": 6,
@@ -637,12 +601,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.427606Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.427498Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.439934Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.439690Z"
-    },
     "tags": []
    },
    "outputs": [
@@ -675,12 +633,6 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.441374Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.441291Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.450415Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.450185Z"
-    },
     "tags": []
    },
    "outputs": [
@@ -732,12 +684,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.451796Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.451720Z",
-     "iopub.status.idle": "2023-12-04T17:48:57.462677Z",
-     "shell.execute_reply": "2023-12-04T17:48:57.462419Z"
-    },
     "tags": []
    },
    "outputs": [],
@@ -762,19 +708,19 @@
     "\n",
     "            # Convert timedelta object into seconds\n",
     "            r_time.append(checkpoint[\"Time\"].total_seconds())\n",
-    "            # Make sure the memory measurements are in MB\n",
+    "            # Make sure the memory measurements are in MiB\n",
     "            raw_memory = checkpoint[\"Memory\"]\n",
     "            memory.append(raw_memory * 2**-20)\n",
     "\n",
     "        ax[0].plot(step, error, label=model_name)\n",
     "        ax[1].plot(step, r_time, label=model_name)\n",
     "        ax[2].plot(step, memory, label=model_name)\n",
-    "    \n",
+    "\n",
     "    ax[0].set_ylabel(metric_name)\n",
     "    ax[1].set_ylabel('Time (seconds)')\n",
-    "    ax[2].set_ylabel('Memory (MB)')\n",
+    "    ax[2].set_ylabel('Memory (MiB)')\n",
     "    ax[2].set_xlabel('Instances')\n",
-    "    \n",
+    "\n",
     "    ax[0].grid(True)\n",
     "    ax[1].grid(True)\n",
     "    ax[2].grid(True)\n",
@@ -793,19 +739,13 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:48:57.464024Z",
-     "iopub.status.busy": "2023-12-04T17:48:57.463949Z",
-     "iopub.status.idle": "2023-12-04T17:49:07.647224Z",
-     "shell.execute_reply": "2023-12-04T17:49:07.646820Z"
-    },
     "scrolled": true,
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 3000x1500 with 3 Axes>"
       ]
@@ -834,34 +774,28 @@
    "source": [
     "In our example we use the `EBSTSplitter`, which is going to discussed later. For now, is enough to know that it is a mechanism to evaluate split candidates in the trees.\n",
     "\n",
-    "As we can see, our tree uses almost 10 MB to keep its structure. Let's say we wanted to limit our memory usage to 5 MB. How could we do that?\n",
+    "As we can see, our tree uses almost 10 MiB to keep its structure. Let's say we wanted to limit our memory usage to 5 MiB. How could we do that?\n",
     "\n",
     "Note that we are using a illustration case here. In real applications, data may be unbounded, so the trees might grow indefinitely.\n",
     "\n",
     "HTs expose some parameters related to memory management. The user can refer to the documentation for more details on that matter. Here, we are going to focus on two parameters:\n",
     "\n",
-    "- `max_size`: determines the maximum amount of memory (in MB) that the HT can use.\n",
+    "- `max_size`: determines the maximum amount of memory (in MiB) that the HT can use.\n",
     "- `memory_estimate_period`: intervals after which the memory-management is triggered.\n",
     "\n",
-    "We are going to limit our HTR to 5 MB and perform memory checks at intervals of 500 instances."
+    "We are going to limit our HTR to 5 MiB and perform memory checks at intervals of 500 instances."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:49:07.648955Z",
-     "iopub.status.busy": "2023-12-04T17:49:07.648846Z",
-     "iopub.status.idle": "2023-12-04T17:49:15.925716Z",
-     "shell.execute_reply": "2023-12-04T17:49:15.925436Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 3000x1500 with 3 Axes>"
       ]
@@ -905,18 +839,12 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:49:15.927542Z",
-     "iopub.status.busy": "2023-12-04T17:49:15.927463Z",
-     "iopub.status.idle": "2023-12-04T17:49:21.299561Z",
-     "shell.execute_reply": "2023-12-04T17:49:21.299292Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 3000x1500 with 3 Axes>"
       ]
@@ -986,18 +914,12 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:49:21.301499Z",
-     "iopub.status.busy": "2023-12-04T17:49:21.301395Z",
-     "iopub.status.idle": "2023-12-04T17:49:37.134205Z",
-     "shell.execute_reply": "2023-12-04T17:49:37.133921Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAC38AAAW7CAYAAAA93elSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAC4jAAAuIwF4pT92AAEAAElEQVR4nOzdd5wT1frH8W/qdtilV6kWEEEEFEEE7L0LKipYrope9XotqFfFgl4V+0+vWAHFgthQsCsIFqQIWECQrvS+PXV+fywZMkl2N9mW3eXz1rwy58yZmTPJLDkzefKMzTAMQwAAAAAAAAAAAAAAAAAAAACAWs2e7A4AAAAAAAAAAAAAAAAAAAAAAMpH8DcAAAAAAAAAAAAAAAAAAAAA1AEEfwMAAAAAAAAAAAAAAAAAAABAHUDwNwAAAAAAAAAAAAAAAAAAAADUAQR/AwAAAAAAAAAAAAAAAAAAAEAdQPA3AAAAAAAAAAAAAAAAAAAAANQBBH8DAAAAAAAAAAAAAAAAAAAAQB1A8DcAAAAAAAAAAAAAAAAAAAAA1AEEfwMAAAAAAAAAAAAAAAAAAABAHUDwNwAAAAAAAAAAAAAAAAAAAADUAQR/AwAAAAAAAAAAAAAAAAAAAEAdQPA3AAAAAAAAAAAAAAAAAAAAANQBBH8DAAAAAAAAAAAAAAAAAAAAQB1A8DcAAAAAAAAAAAAAAAAAAAAA1AEEfwMAAAAAAAAAAAAAAAAAAABAHUDwNwAAAAAAAAAAAAAAAAAAAADUAQR/AwAAAAAAAAAAAAAAAAAAAEAdQPA3AAAAAAAAAAAAAAAAAAAAANQBBH8DAAAAAAAAAAAAAAAAAAAAQB1A8DcAAAAAAAAAAAAAAAAAAAAA1AEEfwMAAAAAAAAAAAAAAAAAAABAHUDwNwAAAAAAAAAAAAAAAAAAAADUAQR/AwAAAAAAAAAAAAAAAAAAAEAdQPA3AAAAAAAAAAAAAAAAAAAAANQBBH8DAAAAAAAAAAAAAAAAAAAAQB1A8DcAAAAAAAAAAAAAAAAAAAAA1AHOZHcAAABUD8MwFAwGFQwGk90VAAAAAAAAAAAAAABQj9jtdtntdtlstmR3BQD2OQR/AwBQTxiGocLCQu3cuVO7du2Sx+NJdpcAAAAAAAAAAAAAAEA95nQ61aBBAzVs2FANGjSQ00lIIgBUN5thGEayOwEAAConLy9Pa9askdfrTXZXAAAAAAAAAAAAAADAPio7O1vt27eXw+FIdlcAoN4i+BsAgDouLy9PK1asUDAYTHZXAAAAAAAAAAAAAADAPi4tLU2dO3eW2+1OdlcAoF4i+BsAgDqMwG8AAAAAAAAAAAAAAFDbuFwu7b///kpLS0t2VwCg3iH4GwCAOsowDP3222/yer2WerfbrZycHGVnZ8vlcslutyephwAAAAAAAAAAAAAAoD4yDEN+v1/5+fnavXu38vLyFBmK2KBBA+2///5J6iEA1F/OZHcAAABUTGFhYVTgd3Z2tjp27CibzZakXgEAAAAAAAAAAAAAgH2B2+1Wenq6mjVrJo/Hoz///FMej8ecn5ubK6/XK7fbncReAkD9QypQAADqqJ07d1rKbrebwG8AAAAAAAAAAAAAAFDjUlJSdOCBB0bdnXz79u1J6hEA1F8EfwMAUEft2rXLUs7JySHwGwAAAAAAAAAAAAAAJIXL5VKjRo0sdTt27EhSbwCg/iL4GwCAOsgwDMutkiQpOzs7OZ0BAAAAAAAAAAAAAABQSeK6cEVFRVqyZIny8/OT1CMAqH+cye4AAABIXDAYjKpzuVxJ6AkAAAAAAAAAAAAAAECJlJQUS9lms2nx4sVatGiRGjdurPbt2+uAAw7gzuYAUAkEfwMAUAfFCv6227mhBwAAAAAAAAAAAAAASJ5YsQtFRUVKSUnR9u3btX37du3atUuHH344AeAAUEEEfwMAAAAAAAAAAAAAAAAAgGqxdu1aGYah7OxsNW/eXCtXrpQkAsABoIJIEQoAAAAAAAAAAAAAAAAAAKqF0+mUz+fT1q1b9ffff8swDK1cuVK//fZbsrsGAHUSwd8AAAAAAAAAAAAAAAAAAKBadO7cWW3btpXNZtOuXbu0ceNGSdKqVatkGEaSewcAdQ/B3wAAAAAAAAAAAAAAAAAAoFrY7XZlZ2erbdu2kqSdO3cqGAyqoKBA27dvT3LvAKDuIfgbAAAAAAAAAAAAAAAAAABUqwYNGiglJUXBYFC5ubmSpHXr1iW5VwBQ9xD8DQAAAAAAAAAAAAAAAAAAqpXNZlPDhg0lyQz+3rx5czK7BAB1EsHfAAAAAAAAAAAAAAAAAACg2qWnp0uSvF6vJMnj8SSzOwBQJxH8DQAAAAAAAAAAAAAAAAAAqp3dXhKyGAwGJe0NAgcAxI/gbwAAAAD7hBEjRshms5mPNWvWJLtLqIQ1a9ZY3s8RI0Yku0uoRuHv9aBBg8pse++991raz5w5s0b6CABAXcW4Cqh/Zs6cafm7vvfee8tsP2jQIEt7AACA2qx9+/bmuKV9+/bJ7g6qWaLnrHwXVDeEzjsMw0hyTwCg7nImuwMAAABAddm+fbsWLlyoNWvWaOfOnfJ4PGrYsKFycnLUrl079erVy7ytGAAAAICKYdwNAAAAxI/xc80IBoNaunSpli1bpvXr1ys/P1+S1LBhQ2VnZ6tdu3bq0aOHMjMzk9xTAAAAIHEEfwMAANRz7du319q1a83yjBkzys2aWtF1DRo0SN9++21FuxqXdu3alfkr/c2bN+vFF1/UlClT9Ouvv5a5LofDoR49euiCCy7QsGHD1KpVqyrubd03c+ZMDR48uErW1bBhQ+3atatK1gUAAFDbMO4uHePu8kWOu8t7/WtqXQAAANWF8XPpGD9XXDAY1PTp0/X666/r888/V25ubpnt7Xa7DjjgAJ122mkaOnSoevfuXUM9BQAAACrHnuwOAAAAAFWhsLBQd9xxh9q3b6977rmn3AvokhQIBPTzzz/rtttu03777acrrrhCGzdurIHeArj33nstt16cOXNmsruEfVT4cViRL5m5jSiAfQ3jblQXbt0O1A0zZ860jH/vvffehNfB3zuAfQnj55rz+eefq1u3bjrjjDM0ZcqUcgO/pZJg8T/++EOPPfaY+vTpo0MOOUTvv/++DMOogR4DSLY1a9ZYxrYjRoxIeB2DBg2yrAMAgJpC5m8AAADUecuXL9fZZ5+tJUuWxJyfnp6uZs2aqWnTpsrLy9PGjRu1e/duS5tAIKBXX31VkydP1sqVK9W8efOa6DoAAABQZzDuBgAAAOLH+Llm+Hw+3XTTTXruuedizrfZbGrUqJGaNGmihg0baseOHdq0aZPy8/Oj2v72228699xzddlll+nVV1+t7q4DAAAAFUbwNwAAAKrM448/rp07d8bV9vjjj7eUJ02aFNeF67S0NEv5119/1bHHHqutW7da6lu1aqWRI0fqlFNOUc+ePaN+bb9lyxZNmzZNH3zwgaZPn25m8igoKFBRUVFc+7Avat68uSZNmlShZV0uVxX3Bvuy9u3bk4EHMd17770Vyj4IAHUJ425UJcZVALgTE4D6jvFz/eXz+XTOOedo2rRplnq73a7zzz9fZ599to499lg1adIkatnNmzfrs88+0yeffKKpU6fK4/GY87Zs2VLtfUfFcec9lGXChAmaMGFCsrsBAEC1I/gbAAAAVaZXr14VXrZ///4J3+p39+7dOvPMMy0X0G02m+68807dcccdysjIKHXZZs2a6fLLL9fll1+uRYsWadSoUfriiy8q2v0qM2LECE2cOFGSNHDgwFr3BWxqaqqOO+64ZHcDAABgn8a4u/Jq+7gbAAAAVYfxc+XV1vHzLbfcEhX4PWDAAP3vf/9Tt27dyly2efPmGj58uIYPH66///5b//3vf/Xyyy/L6/VWZ5cBAACAKmFPdgcAAACAirryyiu1evVqs+x0OjVx4kSNGTOmzAvokQ499FB9/vnnev7558lODQAAAERg3A0AAADEj/FzzXjvvff0zDPPWOqGDh2qr776qtzA70ht2rTRc889p0WLFiW8LAAAAJAMZP4GAABAnfTRRx/p3XfftdSNGTNGl1xySYXXec0116hbt24JXYAHAAAA6jPG3QAAAED8GD/XjOLiYl177bWWuiOOOEJvvPGGHA5HhdfbpUsXzZ07V1OnTq1sFwEAAIBqRfA3AAAA6qRHH33UUu7Tp49uueWWSq/3qKOOqvQ6UDcVFRVp1qxZ+uOPP5Sfn6+cnBy1b99eAwcOrPQXK6tXr9bvv/+udevWaffu3XI6nWrUqJHatWunvn37KjMzs4r2ovrt2LFDP//8s1asWKHdu3fL7/crPT1dTZo0UYcOHXTwwQcrJycn2d2sVdatW6f58+dr8+bN2rlzpxo2bKgWLVqof//+atGiRY33Jy8vTwsXLtSyZcu0a9cueTwepaenm8d8165d1bx58yrZzuzZs/Xnn3+qoKBATZs2VceOHXX00UeTrSoOhmHol19+0dKlS7VlyxYVFBSoSZMmatOmjQYMGFDl/27k5+fr+++/14YNG7Rp0yalpqZq4MCBOuyww6p0OwDqHsbdKM/y5cu1ePFibdy4UXl5eXI6ncrIyFDr1q3VqVMnde3aVU5n8r6KWLZsmRYuXGj5PG3VqpWOOuooNWzYsNLr9/v9+v7777VixQpt3rxZqamp6tSpkwYMGKBGjRpVwR7Etn37ds2ZM0cbNmzQ1q1blZmZqZNOOkkHHHBAqcvk5+fr999/1x9//KHt27erqKhIDRs2VJMmTXTYYYeVuWxlzJs3T3/++afWr18vu92uTp06afDgweW+/sXFxfruu++0dOlS5eXlKScnRwcddJAGDBhQ48fUpk2b9PPPP2vNmjXKzc1VMBhUenq6mjVrpo4dO6pbt25VMj5bv369fvjhB61du1Z+v18tW7ZUt27d1KtXryrYi/rP6/Vqzpw5WrNmjbZu3apgMKimTZtq//33V9++fSsVDBjL+vXrtWDBAm3YsEHbt29Xdna2zj77bLVq1apKtwOgbmH8XDMmTJigLVu2mOWUlBRNnDixSv6tT0tL0wUXXJDQMsFgUH/++ad+//13bdiwQbm5uUpJSVGjRo3UuXNnHX744UpJSal032pKdY19asO5g8/n07Jly7RkyRJt2rRJeXl5ysjIUKNGjXTQQQfpsMMOq5Y+rFu3Tj/88IPWrVsnwzDUtGlTHXrooerZs6dsNluVb68sNXWNf/v27Zo9e7ZWrVql4uJitWjRQgcccID69esnu91eBXtSvwUCAc2fP18rVqzQli1b5PF41LRpU3Xo0EH9+/ev8n9TKnKOCQBIMgMAANQ5Xq/XmD9/vuXh9XqT3S3UUu3atTMkmY8ZM2bUinWFr0eSsXr16riXnTNnTtTyH330UYX7UpsMHz7c3KeBAwcmuzvGjBkzLK9zu3btqnwb55xzjmUbJ554ohEMBuNadteuXUaHDh0syz/00EMx24a/tuHH3K5du4ybbrrJyMjIiDquJBlut9sYOXKksXXr1rj3qaioyHj33XeNiy66yGjRokXM9YYeDofDOOGEExL+ewpfR6LHSmmvRVm++uor4/jjjzfsdnuZ+2Oz2YyDDjrIGDVqlLFu3TrLOiKPp0QekVavXm2ZP3z48Kg2GzduNBwOh9lmv/32i/vYCjdz5kzLtk499dRyl/F4PMZTTz1ldO3atczXqnfv3sbUqVMT7lNFLFiwwDj77LMNt9td7uvdoUMH45///Kfx+++/l7q+0aNHx/xM2LJli/GPf/zDSElJibnuRo0aGXfccYdRVFQUd98TOd5L61e4gQMHVug4HD16tGEYhjF+/PgKLR/Pv6FbtmwxbrrpJqNly5alrsftdhtnnHGGsXjx4rhfw8h9Dvn999+NCy+80EhPT4/azo033hj3+gEw7q5r6vO4O9F1xTOuilRcXGw8/PDDRqdOncr9/EtLSzOOOeYY4/nnn49aT+S4NN5HeX0sLi42xo4dW2b/nE6nceyxxxqzZ88ud39L28Y999xjNGnSJOb6HQ6HMXToUGPt2rWGYST+Oof/OxD+Hv7www/G6aefbrhcrqhtPvnkk1HrWbZsmXHvvfcaffv2NZxOZ5mva4sWLYz77rvP2LFjR9yvQ+TxFhov+f1+4+mnnzb233//mNtKT083br311phjwtzcXOO2224zGjRoEHPZpk2bGi+//HLcfayMd955xzjyyCPLPSYdDofRs2dP47777jO2b99e6vpKG5MtWrTIOOGEEwybzRZz/Z06dTLGjx8fd79Le18S7Ve4ivythn+GVdffu2EYxq+//mqcf/75RmZmZqnryc7ONv75z38amzdvjvt1DF8+/LNi2rRpxjHHHBPz/PiDDz6Ie/3Avo7xc91Sm8bPwWDQ6Ny5s+V1vuiii2q8H7m5ucbEiRONs88+28jJySnz8ywlJcU477zzjAULFsS9/oqM08PF8/keqarHPoZRdecO4UobK5dm69atxnPPPWecfPLJZY4XJBkZGRnGFVdcYfz5559xvWYhpY0b5s6daxxzzDGlbq9t27YJjfMqoyqu8Ycr7XuG1atXG+eff77l2nz4o1WrVsZjjz1m+P3+uPqd6N9CPN9/RH5uxPsIvVeR16DjfcTz7+fq1auNyy+/3GjUqFGp60lPTzcuvvhiY9WqVXG9hpH7XJlzzHjFimd4++23jTfeeMN44403jJdeesm4/fbbjXvuucd44403jMmTJ1d4WwCwr+KnVAAAAKhz3n//fUu5devWOuWUU5LUG1TWyy+/rHbt2pnlzz//PCpDTmmuvPJKrV692iwfd9xxuv322+Pe9pIlS9SjRw89+eSTKigoiNnG6/Xq+eefV9++fbVmzZq41nvUUUfpvPPO05tvvqlNmzaV2TYQCOiLL77Q4MGD9c9//lN+vz/u/tcEwzB0/fXX67jjjtOXX36pYDBYbvs//vhDjzzyiD7//PMa6mVsLVq00IknnmiW161bp5kzZya8nokTJ1rKw4cPL7P9Tz/9pIMOOkj/+te/tGTJklLbGYah+fPn68wzz9QZZ5xR6jFYFR5++GH16dNHH3zwgbxeb7ntV69erWeffVZvvvlmQtv59ddf1aNHD7300kvyeDwx2+zYsUP//e9/1aNHj7j/pvYVr7zyijp16qQnn3xSGzduLLWd1+vVRx99pJ49e+r++++v8PbeeOMN9ezZU2+99ZYKCwsrvB4A9RfjbsSybt06HXroobr99tu1cuXKctsXFRXpm2++0ciRI2tkrPv777+ra9euuvXWW8vsn9/v19dff60BAwbo8ssvl8/ni3sbodfg/vvv17Zt22K2CQQCmjx5srp3765vv/024f2I5dFHH1X//v318ccfx9XfadOm6cADD9S9996rOXPmlPv6b9q0SaNHj9YhhxyiefPmVbifBQUFOvnkk3XjjTfqzz//jNmmsLBQY8eO1QknnKCioiKzfuXKlerVq5ceffRR5ebmxlx269atuvLKK3XTTTdVuI/l8Xg8OuecczRkyBD9+OOP5bYPBAJauHChRo8erZ9//jmhbb311lvq06ePvvjiCxmGEbPNypUrddlll+mMM84odZy9L/L7/br++uvVo0cPTZkyRfn5+aW23bVrl5599ll17txZ06dPr9D2QufHp512mr755ptyz48B7HsYP9eMJUuWaMWKFZa6f/zjHzXejw4dOmj48OH64IMPtHPnzjLbejwevfvuu+rdu7ceeuihGuph/Kpr7FMbzh127typli1b6rrrrtOnn35a5nhBKhnLvvLKK+rWrZsmTJhQqW0/88wzOvLII/XNN9+U2uavv/7SZZddpquuuqraxhY1eY1/xowZ6t69u6ZMmaJAIBCzzYYNG3TLLbeof//+2rFjR0Lrr+/GjBmjAw88UK+++mqZr01hYaEmTZqkgw46SK+88kqFt5foOSYAoHZJ3r0WAQAAgAqaPXu2pXzqqadW+a17UXNycnL01ltv6eijjzYv6N511106+uijdeSRR5a63Lhx4/Tuu++a5ebNm2vSpElx3yJxzZo1uvDCCy3B2e3atVOzZs2Un5+v5cuXWy5Orly5Uuecc47mzp1b7m0fi4uLo+patWqlRo0aKTMzUwUFBVq3bp12795tafPcc8+psLBQr776alz7UBPuuecePfvss1H1jRo1Utu2bZWWlqaCggJt27atzGDVZBk+fLg++eQTs/zaa69p8ODBcS9fWFhoOc5ycnJ0xhlnlNr+448/1tChQy0BLJLkdrvVoUMHNWzYULm5uVqxYoXlC4yPP/5YxxxzjL799lulpqbG3b94vPLKK7rjjjui6rOystS+fXtlZGSoqKhIO3bs0N9//11qwEl5Nm/erGHDhlmOgzZt2qhFixbasWOH1qxZY/liYfny5TrmmGP03XffcVt0SXfffbfGjBkTVd+gQQO1bdtWGRkZ2rJliyVgPhgMavTo0dq+fbuefvrphLb3ySef6NJLLzXfE7vdrg4dOqhRo0basmWL/v7770rtD4D6gXE3IhUVFem4446LCui12+1q27atmjRpIofDodzcXP3999/lBlZUtfnz5+uEE06ICr5xuVxq3769GjZsqA0bNmjDhg2W+ePHj9fGjRs1depUud3uMrexadMmDR48WKtWrbLU22w2dejQQY0bN9b27du1evVqGYah3bt36/TTT9c777xTqX174YUXNGrUKLPsdrvL3Ccp9nlJWlqa2rZtq6ysLNlsNm3fvl1r1661jNPWr1+vQYMGaf78+erSpUtC/TQMQxdccIG+/PJLs65Vq1Zq1aqVCgsLtWzZMst51uzZs3XjjTfqxRdf1JYtW3TMMcdo3bp1kqJf08jX/KmnnlLv3r01bNiwhPoYjyuvvFIffPBBVH2zZs3UunVrpaSkKC8vT1u2bNHWrVsrvJ0ZM2bo0ksvNc8NHA6HOnTooOzs7Jjv68cff6xzzz1XH374YbnnpfVdYWGhzjnnnJgBSS1atFCLFi1kt9v1999/a8uWLea8vLw8nXnmmXrrrbd0/vnnJ7TNO++803J+nJaWpnbt2ikjI0Pr168v98ffAOo/xs81I/J1zsrK0tFHH13j/Ygca9lsNrVp00Y5OTlKT09XXl6eVq9ebfnBvWEY+s9//iO/36977rmnprtcquoY+9SWc4dAIBAVSO5wOLTffvupYcOGSklJ0e7du7V69WrLj+w8Ho8uu+wy2e12XXrppQlvd9y4cbrxxhvNclpamtq3b6/MzEytX78+apz30ksv6aCDDtK///3vhLdVnpq6xr9kyRINHTrUfC9tNpvat2+vxo0ba+vWrVq7dq2l/U8//aQTTzxR33zzjbKysiq83fogEAjoiiuuiEpEI0mNGzdW69at5Xa7tXHjRq1fv96c5/V6deWVVyo3NzfhH8dW5BwTAFC77NtXhgAAAFDnFBUVacGCBZa63r17J6k3qCpHHnmkxowZY2bt9vv9uvDCC7Vo0SJlZ2dHtf/1118tF7JsNptef/11NW/ePO5tXnHFFdq0aZPS0tJ066236uqrr7YEoO7cuVOPPPKIHn30UTMYduHChXrppZc0cuTIcte/33776fzzz9cpp5yi3r17q0GDBpb5hmHo119/1bhx4/Tiiy+aARDjx4/X6aefrrPPPjvufaku69ev1yOPPGKpu/rqq3XjjTfGDALZuXOnfvjhB02bNk1vvfVW1PwePXqYQSCvvfaaXn/9dXPeY489ph49elTxHkhnnHGGsrOztWvXLknSe++9p+eee07p6elxLf/BBx8oLy/PLA8dOlQpKSkx2/7+++9Rgd8DBgzQqFGjdOyxx1qCuvPy8vT222/r7rvv1ubNmyVJc+fO1U033aTnn38+0d0slcfj0W233WapO/fcc3XHHXfosMMOi/qxRF5enubNm6dPPvlEkyZNSmhbo0aNMi8IX3DBBRo9erQOOuggc/6GDRv01FNP6YknnjCP99WrV+uKK67Qp59+WpHdq7DHH3/cDAo7/vjjzfru3bvr8ccfL3W5jh07SpJOPPFE81geO3asvvjiC7PNpEmTSv23KC0tLWb9+PHjLYHfNptNl1xyia6//noddthhstv33rhtw4YNevbZZ/XYY4+Z2VieeeYZ9e/fX0OGDClzv8NdfvnlCgaDatiwoe655x5deumlatKkiTl/8+bNUV/IANi3MO5GLM8++6wleKNp06Z68MEHdd555yknJ8fS1jAMrVq1Sl999ZXef/99SzBwyG233aaLL75YknTxxReb46LQDztLE+uHY3l5eTr//PMtgd/p6em69957dcUVV6hRo0Zm/S+//KLRo0frww8/NOs+++wz3XPPPXr44YfLfA2uvvpqSxCy0+nULbfcouuvv97Sr/Xr1+uZZ57RE088oby8PF177bVlrrcsO3bsMM9/WrZsqTFjxuj888+3BEisXr065p1kHA6HTjrpJJ1xxhk65phj1LFjR8vYQip57T766CPdf//9Wr58uaSSwNqLLrpIP//8c9w/sJVKxvmhH6tdeOGFuvvuuy3nDtu3b9c999yj//3vf2bdyy+/rOuvv1633nqr1q1bp9TUVN12220aOXKkWrRoYbZbtmyZrrrqKs2aNcusu+WWWzRkyBC5XK64+1ieefPmWY4/p9NpnjOG37UqZPPmzZo9e7Y+/vhjTZkyJaFtXXbZZfL7/XK73brzzjt17bXXqmnTpub8X375Rf/5z380bdo0s2769Ol6/PHHLYEaNSH0N7x48WLdcsstZv0ll1xSZkBU6DyvKv/eJWnkyJGWwO/MzEzddNNNuuyyy9ShQwdL28WLF2vMmDHmD3tDwTU9e/ZU586dS912uCVLlpjBhvvvv78eeughnXbaaZbzvN9++y3uc00A9Q/j55oTGfzds2fPqPFNTTnooIN0/vnn6+STT1aPHj2iPgeCwaDmzp2rp59+Wm+//bZZf//99+vkk09Wnz59arrLUapr7FPV5w6V1bt3b51zzjk68cQTdfDBB0dd4/X7/fr22281duxYyxjjuuuu0+DBg9W2bdu4t7VixQoz8LtLly4aM2aMTjnlFMu44eeff9Y///lPS6b1u+++W8OHD1fjxo0ruptRqvoaf1muvfZa5efny2az6Z///KduueUW7bfffub8lStX6sEHH9T48ePNuvnz5+u2226r0mvi8XjjjTdUVFSkzZs3m2NUSTrhhBN06623lrrcwQcfLEm69NJLddRRR0mSbr75Zv3yyy9mm7KO38hjP+T++++3BH67XC5de+21uuqqq9S1a1dL25UrV2rs2LF68cUXze+ubrvtNh1xxBHq169fqdsOV5lzTABA7UHwNwAA+yB/IKiNu6OzP6FqtWyYKqcjORccy7JgwYIK3y4vVtawmrZq1Sp5vV5LXc+ePZPUm31PcXGxvvrqqwot26NHD8uX2JFuu+02zZgxw7ywunbtWl1xxRV67733LO0KCws1dOhQy/E4atQoSwBnPFatWqVGjRrps88+i3mRPScnRw8//LCysrJ01113mfUvvvhiucHfzz//vPr161dmZh+bzabu3bvrf//7n84++2ydfvrpZmaRhx9+uFYEf0+dOtVyq7977rlH9913X6ntc3JydOqpp+rUU0/V2LFjozIu5uTk6LjjjpMkfffdd5Z5vXr10qBBg6qu83ukpqZq6NCheuGFFySVBLa8//77lgu6ZYnMtDF8+PCY7fx+vy644AJL4Pd9992nu+++O2awTFZWlv7xj3/olFNO0eDBg80vQsaNG6errrqqyv5d+/rrry23h7z00ktjZg8J79cxxxyjY445Rg899FBC2Z9DgcJjxozRf/7zn6j5rVq10qOPPqq+fftqyJAhZgD4Z599pilTpiScda8yevXqFbM+/BgtS8uWLdWyZUtJigpU6d+/v9q3bx93X1atWqV//vOfZjktLU3vvfeeTj755JjtW7VqpYceekgnnXSSTjrpJPOYu/7663XGGWfEnTl+8+bNatGihWbMmGEJ0g9p3rx5Qj+oQQwBv5S7vvx2qLwGrSVH7bvMybgblVGZcffixYuruDd7hQd3pKSkaNasWTE/R6SS8W6nTp3UqVMnXX311frjjz+ixsddu3Y1v8gO/wxLTU2N6zM53J133mm5Q0bDhg01Y8aMmMdt9+7d9cEHH+iuu+7Sgw8+aNaPHTtWQ4YM0WGHHRZzGx9//LE++ugjs+xyufTBBx/o1FNPjWrbunVrPfLIIzrqqKN0zjnnaPXq1QntT7jQjxG7dOmib775xhIQHRIZ7CqVjHlWrlwZM2gnXFZWloYNG6Zzzz1X5557rnnnnEWLFumzzz4rdVwSS+g9eOyxx3TzzTdHzW/cuLGee+45FRUVmUEfhmFo6NChWrp0qTIzM/XJJ59owIABUcseeOCB+vTTT9W7d28tXbpUUkkm9unTp+uss86Ku4/liQxievHFF3XZZZeV2r558+Y677zzdN555+mJJ56wZFEvz9q1a5WSkqJPPvlExxxzTNT87t276+OPP9a///1vPfnkk2b9fffdpwsvvNASTFPdQn+TkRnHO3bsGNffa1X+vU+ePFmvvfaaWe7UqZM+//xzderUKWb7Hj16aMqUKRo7dqz549i8vDzdfPPNmjp1alzbDGU5HTBggD755BNlZmZGtenWrVtC+4Fo/qBfmws3J7sb9V7z9OZy2hk/VzXGzzUn9GO1kOpIKBGPadOmlXs90263q2/fvurbt69OPvlk89piIBDQY489psmTJ9dAT8tWXWOfqj53qKiMjAz9+OOP6tu3b5ntnE6njj32WB177LF64IEHzMzs+fn5evbZZ6MCqMsSysx84okn6v3334/547DDDjtMX331lfr162eewxUWFmrSpEmWjOGVVdXX+Muydu1a2Ww2vfbaazGvv3fq1EmvvvqqevTooX/9619m/QsvvKARI0boiCOOiHtbldW/f39JspxDSiXXfeMZm3bs2NFMFBIZ0J3o2PaHH36wJAZp0qSJed4TS6dOnTRu3DgNHjxYF110kYLBoPx+v0aOHBn39YCKnmMCAGqX2ndWBwAAqt3G3cUa8OiMZHej3pt922C1bVT7sv2EZ2eqi8KDGEOaNWuWhJ7smzZv3pxwkHXIBx98UOYX8qGLgj169DBvlfz+++/rueee03XXXWe2u/76680v+qWSrOEPPPBAhfr0yiuvlJtdZdSoURo3bpwZBLto0SJt3ry5zKDIWIEKZTn++ON16623mhf45s6dqyVLlkRldKhpkV+kJJKtMDMzM+aX4ckwfPhwM/hbUqkXnyOtX79eX3/9tVk+4IADSv2S4N1339Vvv/1mlq+++uq4bt3aunVrvffeezr00EPNL0oef/zxhLNul6Yy76Hb7TYvYMfrtNNOixn4He6cc87Rrbfeasmq+eSTT9Zo8Hdt8uijj1pu//vqq6/GFWB19NFH67HHHjP/fdyyZYsmTZqkK6+8Mu5tT5gwodQv3VAFctdLT3dPdi/2DTf+IuWUHdiYDIy7URmVGXdXp/CxxeDBgxP6HKnOz5xdu3bp1VdftdS98sor5QZcjRkzRj///LN5F5JgMKgnn3zScoeacM8884yl/J///Cdm4He4008/Xbfffrvly/yKcLlceuedd2J+KV+aRL+sT01N1euvv66OHTtq9+7dkkruUJJI8LdUcrecWIHf4caMGaOJEyeaY+DQOd4TTzxR5vlUenq67r77bl100UVm3aefflqlwd/hx3lmZmaZWa0jVSRL44MPPhgz8Dvc448/rjlz5piZIYuKijRu3Dg99NBDCW+vrjMMQ/fee69ZTk9PLzPwO9ytt96qefPmmcFoH3/8sZYvX64DDjggrm3n5OTonXfeqTXnuvXR5sLNOum9k5LdjXrvs3M/U+vM1snuRhTGz4hX5GtdVsKR6pRoIotLL71UX375pXnd7/3339fu3bvVsGHDauhd/Kpr7FNbzh3S0tLKDfyOdPfdd+vLL780s8xPmDAhoeBvqeTOoJMnTy7zriDp6el6+OGHLePtTz/9tEqDv2v6Gv91111X7rX3G2+8UXPmzDGz4RuGoaefflpvvvlmQtuqL+6//37zvMhut2vq1Klx3Tli6NChWrBggcaOHSup5K5BX331VdzB5xU5xwQA1C61LxUlAAAAUIZYF9Gzs7NrviOoFs2aNdOkSZMst+m8+eabzWwFb775piWoIzs7W2+99VZU5rF4HH744XEFCDidTp1zzjmWushbuFaFyAuiP/zwQ5VvI1HhWawlVemt1GvSkUceafky/+uvvzazr5Rl0qRJlsw1pWX9lqSnnnrKnE5PT9d///vfuPt3yCGH6MwzzzTLU6dONbNiV1ZNv4fxfgnyn//8Rw0aNDDLP/74o+VHHfuKHTt2WDIWHnnkkbrgggviXv4f//iH5YvkyDsllOWoo47SiSeeGHd7APsext2IJXxsUZvGhm+++ablx1T9+/fXueeeG9eyTzzxhKX8zjvvmIHP4SJ/GJidna1Ro0bFtY077rij0kE9Q4cOrZGswo0aNbIEnyR6XmKz2XT//feX265Vq1ZRAQ3t2rXT5ZdfXu6yp59+uuWcceHChQn1sTzhx7ndbrdsq6q1bt1aN9xwQ7ntbDZb1Fh7/Pjx5m3e9yWff/65/vjjD7N84403xhX4HRJ+Zy/DMPTBBx/Evex1111HcAyAUjF+rjmRr3Ui46xt27bpq6++iuuxatWqqu665Rqw3+/XvHnzqnwbiaqusU9tPXeI17Bhw8zpLVu2aMWKFQktf/vtt8d1bB5//PGWzNE///xzQtspT01eH05NTS0zq3i4hx9+2HKsvf/++9q1a1c19az2Wrp0qXk3XKnkvK9fv35xLz9q1CjL92OJXB+uqXNMAED1IfgbAAAAdUroVmThMjIyktATVJdjjz1Wd955p1n2eDwaMmSIFi1apGuuucbS9pVXXin3FualGTp0aNxtDz30UEv5r7/+qtA2yxKZla+qAxgqolWrVpZyVWWjTobwjDXBYDCufQkPyrXb7brkkktittu+fbvmzp1rlk877bSoWz2W54QTTjCn8/Pzq+z9r8n3sFevXnFnq8/MzIwKyPr222+ro1u12syZMy1fwJR2jJXG5XJp8ODBZvmHH34o9Va7kS688MKEtgVg38O4G7GEjy1mzZqldevWJbE3e0WOI+IJIA456KCDLF+ue71ezZkzJ6rdDz/8YAm0Pffcc5WamhrXNtLT06N+UJqomvzsDj83Wb9+vbZu3Rr3st27d487i3JkoMHZZ58th8NR7nKZmZlq3769Wa7q4zD8OM/NzdXHH39cpesPd8EFF8QdgDNgwADLnXk2bdqkZcuWVVfXaq1PPvnEUk50DN29e3dLAHcoo2c8GEMDKAvj55oT+Von8jrPmTNHxx9/fFyP8GuDVaW2XwOuyrFPbT13iFdl3iubzaYhQ4bE1dbhcOiQQw4xy1u3bpXH44l7W+WpyevDp556qho1ahRX23bt2mngwIFm2ePxxDwPq+9Cd6EKSXRs27hxY/Xq1cssM7YFgH0Lwd8AAAD7mBkzZsgwjAo9KhpkW5WysrKi6goKCpLQk/jZbLa4HxMnTjSX+/bbbxNatia0a9euwsdPIrfhvvfee3XUUUeZ5eXLl+uII46wXNgfOXJkpQIo4rltXkjkLVpjZQIszdy5c3XnnXfqlFNOUceOHdWoUSO5XK6o9y8lJcWy3LZt2+LeRnU5/vjjLeWbb75Zd911lzZt2pSkHlXcpZdeaskk8vrrr5fZfv78+VqyZIlZHjx4sNq2bRuz7XfffWcJBErk2ArZb7/9LOWqyoJ9zDHHWAJonnzySV177bXVkrko0VvdRrYPD6DfV0RejK/ssZObmxtXVnup5O4HAKoX4+6ax7i75DFjxoxq61f4+HD37t0aPHiwpkyZIp/PV23bjMdPP/1kKR9zzDEJLX/sscdayrGCDubPn28pJ5KNrSLtI1X2s3vXrl165ZVXdNlll+mwww5Ty5YtlZGREfMYj7yLTSLnJuGBB+Vp3LixpXzYYYdVaNnc3Ny4l4tH5HnQsGHD9Pjjj1dLJsJEx9DhATISY+iMjAwddNBBCa8j/Nwu3nOvrKwsdenSJeFtAYgf4+eaV1fHz5GvdbJf52AwqG+++UY333yzjjvuOLVr107Z2dlyOBxRr8WBBx5oWbY2XgOuqrFPbTx38Pl8+vjjj3X99dfr6KOPVps2bdSgQQPZ7fao9yryjnmJvFft27ePGuuWpTLfP5SnJq/xc304cVV9ffiPP/6I++5AXB8GgLqP4G8AAADUKbGyBlTlhTDUDg6HQ2+99ZblAqnX6zWnu3fvHnV79kRFXlAtS2T2mMhbJcYye/Zs9ejRQ0cccYT++9//6tNPP9Xq1au1c+dO+f3+cpevDbc47Nevn+XisN/v14MPPqjWrVtrwIABuvfee/X111/HzGxU27Rt29aSIfn333/XggULSm0fmdln+PDhpbaNDBa47bbbEvoCzGaz6dRTT7WsI9atgiuibdu2UZkvn3/+eXXq1Em9e/fW7bffrk8++aRKtpfoLSIj269evbrSfahrIo+dww8/POFjZ+zYsZZ1xPteRmYvAoBIjLsRy6233qr09HSzvGrVKg0ZMkTNmjXTBRdcoHHjxumXX36J+04UVcEwDMudeRo0aGDJCh2PHj16WMqxshJG/sAq0SDQygSNZmZmqkmTJhVatqCgQLfddptatGihK6+8UhMmTNDChQu1adMmFRYWxrWORM5NmjZtGnfb8GOpMsvGc36WiPPPP99yR5v8/Hzdcsstat68uU444QQ9/PDD+u6771RcXFzpbTGGTlz4GLqgoCBmsFZ5j3nz5pnriHf83K5duxr7ARCAuonxc82JfK2T+TpPnTpVnTt31rHHHqsnnnhCX3/9tdatW6fdu3fHNSauDdeAq2vsU5vOHQzD0CuvvKI2bdrojDPO0LPPPqvZs2dr/fr1ysvLiytYNpH3KpHvHqSKff8Qr5q8xs/YNnGR14ebNWuW8Nh2ypQp5vKBQCCuH8dW5hwTAFB7EPwNAACAOiXWRfQtW7YkoSeobm3atNH48eOj6jMyMjR58uS4b7FemsosX97F4BdeeEEDBw7UL7/8UuFtVOWtHSvjzTffVN++fS11wWBQ3333ne677z4dd9xxatSokY488kg9+OCDWrNmTXI6GofIAO7wjEXhfD6f3nrrLbOcmZlZZpb57du3V00Hw1Tll1bPPPOMTj/99Kj6BQsW6JFHHtGpp56qJk2aqGfPnrrzzjv1+++/V2g7iWSzidW+NnzZVdOSeew0aNCgyrcNoH5h3I1Y9t9/f7377rtRnyO7du3S5MmTNXLkSPXo0UNNmjTRueeeq7feeqvKA3MjRQbWJDomkRT1pffOnTuj2kSOVRo2bJjQNrKzsxPtlqmin9vbtm3TkUceqbFjx1bq/CKRZStznlXZc7yq4nK59PHHH0dl5vR6vfryyy91xx13aMCAAcrOztbgwYP11FNPafPmzRXaFmPoxBQUFFT5uTLjZwBVhfFzzYl8rRO5vnLaaaeVmkG+rOQPsdx5550666yzKhWwWhuuAVfX2Ke2nDsEg0FdeumluvLKKyv1N1lTY2Kp/O8fElVT1/gZ2yYuWdeHGdsCQP3gTHYHAABAzWvZMFWzbxtcfkNUSsuGteNLy/qmY8eOcrlcllsDLly4MKFbS9e0L7/8Mu62Y8eO1RdffCGpJLv1448/Xl3dqhNi3bKzXbt2tTpj7YwZMzRy5EjLBVqn06mjjjpKRxxxhNq1a6dmzZopNTVVKSkplmUjb8FYGzRp0kSzZs3Syy+/rCeeeEIrVqyIauP3+zVnzhzNmTNH99xzjy666CI99thjat68eRJ6XLpzzjlH1157rfLz8yVJb7/9th5//HG5XC5Lu08++cRyG8/zzjsvKvtKuOq4KF2VGW9SU1M1depUvf3223r00Ue1aNGiqDaGYWjRokVatGiR/vvf/+rUU0/VU089pc6dO8e9ncjMjeWJfE1D78u+JJnHTuRxj2rQoLV0Y8V/BIQENGid7B7US4y7UZqTTz5Zv//+ux544AG9+eabMT/Dd+7cqffff1/vv/++mjZtqrvvvlv//Oc/qyVrbuT2yxq3lSZymVhZ7yKDPdxud0LbiBz7J6Kin9vnn3++fv31V0td6I44Xbt2VZs2bZSZmam0tDTZ7XtzBb322mt6/fXXK9zf+qBjx476+eef9dRTT+l///tfVOZ3qeSYmDlzpmbOnKlRo0bp6quv1oMPPqisrKy4t8MYOjHVMX6ON7iK8XPNaJ7eXJ+d+1myu1HvNU+vXddr6gvGzzXngAMO0Pz5881yrGtd1W3ixIn673//a6lLS0vTgAEDdPjhh2u//fZTkyZNlJKSYhk3bt68WRdffHFNd7dc1TX2qQ3nDg888IAmTZpkqWvQoIEGDRqkXr16qW3btsrOzlZqaqrl837x4sW65ZZbqqQPyVZT1/gZ2yYuWdeHGdsCQP1A8DcAAPsgp8Outo0SOwEHaou0tDT17t1bP/74o1k3f/58XXnllUnsVdmOO+64uNuGX4TMyclJaNn6ZuXKlbr66quj6pcsWaJbb71VzzzzTBJ6Vb6bb77Z8uXxqaeeqnHjxqlNmzZlLlcbsryUxuVyaeTIkRo5cqTmz5+vr7/+WjNnztQPP/wQdQvBYDCoSZMm6auvvtLMmTOjMsYkU0ZGhs477zxNmDBBkrR161Z9+umnOuOMMyztIjOCl5f1J/Ki9r/+9S+deuqpleprx44dK7V8JJvNpgsvvFAXXnihlixZoi+//FIzZ87Ud999Zwl0D5k+fbpmzZql6dOna8CAAXFto7CwMKE+Rf64IzMzM6Hl64PIY2f8+PHl/ltRnh49elRqeVQhh1PKaZfsXgAVxrgbZWnTpo1eeOEFPfHEE+a4b9asWVq8eLECgYCl7datW3XDDTfo22+/1eTJk+VwOKq0L5FjiFg/IC1P5DKxAlgiM30nGpgQz623q9JHH32kmTNnmuWsrCw9//zzuvDCCy2B3rF8/fXX1dy7uiE9PV133nmnbr/9dn3//ff65ptvNHPmTP30009RWSm9Xq/+7//+T1988YVmzZqlZs2axbWNwsLChILF9/UxdOT4uVGjRpo8eXKSeoPq4LQ71TqTH/ahbmL8XHMGDBigN9980yz//PPPMgyjWn5oGIvX69WoUaMsdZdffrkeffTRcjMfL1u2rDq7VinVNfZJ5rnD5s2b9cgjj1jq7rjjDt15553ljqMi+1bX1cQ1fq4PJy49Pd3y+n/66adyOisXyteiRYvKdgsAUEcQ/A0AAIA6Z8CAAZaL6NOnT1cgEKjyIAIkj8/n0wUXXFBqgMT//d//6bjjjosK2k225cuXa+HChWa5W7duev/99+PKCrhjx47q7JpFohdhw/Xu3Vu9e/fWqFGjFAwGtXjxYn322WeaPHmyFi9ebLbbtGmTzjvvPC1evLjc4JKaNHz4cDP4WyoJ9A4/jnbs2KHp06eb5Xbt2mngwIFlrrNJkyaWcsuWLWt1AFnXrl3VtWtX3XjjjTIMQ3/88Ye++OILvfvuu/ruu+/Mdnl5eTrvvPO0cuXKuC68xwoiL0vkLS2zs7MTWr4+iDx2unbtqsMPPzxJvQGAaIy7UZ6MjAydeeaZOvPMMyWVBDh/9913mj59ut566y3t3LnTbPvee+/p8ccf12233ValfWjYsKHsdruZ3awit82OHMfk5OREtWnUqJGlvGHDBh166KFxb2PDhg0J96sy3n77bUv5hRde0IUXXhjXsjV5blIX2O12DRgwQAMGDNDo0aPl8/k0f/58ffbZZ3rzzTctmROXLVumESNG6JNPPolr3du2bUso+HtfH0NnZ2fL6XTK7/dLkoqKimr1uReAfQ/j55oRmaggLy9Ps2fP1tFHH10j2585c6Y2b95slk844QS98sorcS2byDirssHsFb0GXF1jn2ScO0ydOtUSuH7VVVfpoYceimvZ+jwmrq5r/FwfTlyTJk0s34Mddthhcf+QFACA2vMNPAAAABCns88+21L++++/9dln3BK2Prn99tstt+7s0aOHnn/+eUubyy+/XH///XdNd61Mc+bMsZSvvPLKuG8H//vvvye0rdTUVHM6MvNKebZu3ZpQ+9LY7Xb17NlTd9xxhxYtWqT33ntPaWlp5vzffvtNn3/+eZVsq6oMHDhQ7drtzcQ7bdo0yxcLb731lrxer1m+9NJLy/2ipUOHDpZyrNtm1lY2m01dunTRjTfeqNmzZ2vWrFmWgOQtW7bo9ddfj2tdv/32W0Lb/vXXXy3lyNdxX1CXjx0A+wbG3UhUgwYNdMopp+i5557TX3/9pcsuu8wy/8knn7TcJacq2Gw2tW3b1izn5uZqzZo1Ca0jPMBBkmW8GNK1a1dLOfxHn/FYtGhRQu0rK/zcpHHjxhoyZEjcyyZ6brKvcblcOvLII3Xfffdp+fLleu655yzBMJ9++qmWLl0a17oYQyfGZrNZ/j6Liopq/IcVAFAWxs81o2vXrurcubOl7qWXXqqx7UdeA7722mvjXjaRcVb49V8pedeAq3LsE64mzh1q6r2qy6ryGj9j28RxfRgAUBkEfwMAAKDO6du3r/r162epGzNmjJnpDXXbJ598oieffNIsZ2RkaPLkybrmmmt00UUXmfXbt2/XsGHDatXtF8MzvkiK+3aIkvTNN98ktK0GDRqUut2yBINB/fzzzwltK17nnHOObr75ZktdeCbpcJGZQqo6CKg0NptNl156qVn2er2WrIivvfaapX1429IMHjzYUk70vaxNBgwYoIcffthSV9p7GOnbb79NaFuR7ZOV8To8uL8ix2FljuX6dOwAqJ8Yd6MyMjIy9OKLL6p9+/Zm3aZNm0r9Mjv8MzXRz+S+fftayol+pka2j1yfFD1WmTZtWkLb+OijjxJqX1nh5widO3eOO+Nobm6uFixYUF3dqndsNpuuvfZay7mqVH1j6FmzZlnKyRhDV8W5XGX+3hlDA6jNGD/XDJvNpn//+9+WuilTpujPP/+ske3X1DXg8Ou/sbZblm3btiX8g8h4VHbsU5pEzx3iVZPX6+uLRK7xR6qL14eremyb6DoY2wIAKoPgbwAAANRJt956q6U8Z84cPfHEE5Ve77x586osIwYSt3HjRo0YMcJycezZZ581L8qOGzfOktVl1qxZeuCBB2q8n6WJvKgXnkG6LB6PR6+++mpC2wrPdrZu3bq4b0P56aefWm4jWNX69+9vKZd2q8eMjAxLuaK3Ia2I4cOHW8oTJ06UJP3xxx+aO3euWd+/f/+oLEKxtG7dWt26dTPLK1eu1KefflpFva158b6HkRYsWKAlS5bE1TY/P1/vvfeepW7gwIHxdbCKhR+LFTkOK3MsH3fccXI6nWb57bffjrrdKQAkG+NuVIbT6dQRRxxhqYtnfJjoZ3LkOGLChAlxL7ts2TJ9//33ZjklJSWqz5LUp08fNW/e3CzPmTMn7iDpuXPnat68eXH3qSqEn5vEe14iSa+++qqKi4uro0v1WkXH0G+//bZ8Pl9cbWfPnq1Vq1aZ5RYtWiQUwFRVquJcrjJ/7yeddJKl/Oyzzya8fQCoToyfa8aIESPUtGlTs+zxeDRixIgaCbSv6DXgzZs36/333497O2lpaZZ9/OWXX+Lev8mTJ8e9nYqo6NinLImcO8Srou/VokWL9OOPP1Zq23VZRd/f6dOnx/09xdq1ay3B3ykpKTF/hFvdqnpsm+g6Ise2L774YtznBwAAEPwNAACAOumss86Kuo3mHXfcobfeeqvC65w0aZIGDx6sgoKCynYPFRAMBjVs2DDLlxjDhg3TiBEjzHJWVpbefvttud1us27MmDFR2c+SpUWLFpZyvBkx7r777oQyt0jSYYcdZk4bhqEpU6aUu4zP59Po0aMT2k6iIi8E5+TkxGzXqFEjS3n16tXV1qdInTp10lFHHWWWf/rpJy1fvjwq63dkkHhZIr/Y+9e//qXdu3dXrqNJEu97GMuoUaPiavfggw9afoTQt29fdenSJe7tVKXwY7EiGZEqcyw3b95cl1xyiVkuKCjQddddl3AfAKA6Me5GZVVkfLh9+3bl5eXFvY0LL7zQ8oX77Nmz9eGHH8a1bGRWuyFDhqhhw4ZR7VwuV9St6EeOHFluoHRRUZGuueaauPpSlcLPTX7//Xft2rWr3GXWr1+v++67rxp7VX9VdAy9fv16PfPMM+W2Mwwjaqw9YsQIy11sakpVnMtV5u/9rLPOsvxI96efftLzzz+fcB8AoLowfq4ZaWlpUT8A+uGHHzR8+PBqD5ys6DXg66+/Xh6PJ6FthV8D3rlzp7788styl9m9e7ceeeSRhLaTqMpcP6zJ9VbkvQoEArr22msrtd26rqLvQ3FxcdzfP9x+++2WHzOcffbZys7OjruPVaVBgwaWuyRVdmyb6Dp69eplyf79119/6a677kq4DwCAfRPB3wAAAKizXn31VUv2Y7/fr4svvlj33ntvQr+sX7Vqlc455xxdcsklXEBPojFjxmjGjBlmuXPnzjG/wO3Vq5cefvhhsxwIBHTRRRfVimy5kbd1HTduXLm3pnzhhRf02GOPJbytU045xVK+//77y8z+4/f7ddVVVyV0C/frrrtOH3/8cdy3KfR4PFGBC7169YrZ9uCDD7aUI7NAV7fIwO4JEyZo0qRJZjk1NVVDhgyJe33Dhg2z7NPy5ct18skna8OGDXGvw+fzaeLEiVX65czdd9+tSZMmye/3x9XeMAw9/vjjlrrS3sNYpk2bpoceeqjMNh988IHGjh1rqfvXv/4V9zaqWvj7tm3bNs2cObPCy0vSu+++m9Dyd911l9LT083y5MmTdfXVVyeUoXPHjh0aM2aMPv7444S2DQDxYtwNSVq6dKlGjhyZ0K3X582bZ/lszc7OVseOHWO2Df9MNQwjofFhdna2Lr/8ckvd5Zdfrl9++aXM5UaPHq3p06ebZbvdrptuuqnU9jfddJPli/158+bpzDPP1JYtW2K237x5s04//XQtXLiwxoN0w89NvF6v7rjjjjLbb926VaeddlpcQeL13bBhwxK6Zf3OnTv18ssvW+oSGUP/5z//sZwLx3LzzTdbsk+mpqYm5UcFUsldqDIzM83y119/rZ07dya0jsr8vTscjqg7gN1444166aWXEurD8uXLddVVV2n9+vUJLQcA8WD8XDOGDBkS9SP6SZMm6cQTT9TSpUsTWldhYaH+/vvvuNpGXgN++OGHy82MfNddd8WVvCNS5DXgUaNGlXkMFRQUaOjQofrrr7/i3kZ1jH2q+9whXpHv1d13362ioqJS2wcCAV1++eX1Kut3dV7jj+W5557TG2+8UWabZ555Rm+//bZZttlsuvHGG+PeRlVyuVw64IADzPKiRYu0cuXKhNZR2evDDzzwgOz2veF7jz76qO6///643zNJ+vvvv3XrrbfW+B2nAADJRfA3AAAA6qzs7GxNnTpVjRs3NuuCwaDuu+8+HXDAAXrooYe0aNGimMtu27ZNr732ms455xwdeOCB+uCDD2qo13VbcXGxvvrqqwo/SgtOnj17tu6//36z7Ha79fbbbysrKytm+3/961+WC9/r16+PysKXDJ07d9aRRx5plvPy8nT00UdrypQpUcG3ixcv1tChQ3XNNdfIMIyEsx6fcsopat26tVnesGGDBg4cqJkzZ1ouCvr9fn355Zc66qijNGHCBElShw4d4trG999/rzPOOEMdOnTQLbfcopkzZ1qyNYf4fD599tln6t+/v+XiYosWLXTaaafFXHfPnj3VpEkTszxz5kwdc8wxGjdunD755JOoY6eqDRkyRGlpaWb5ySeftHwpctZZZ8XM+Fgah8Oh9957z7LMjz/+qG7dumn06NFavnx5zOU2b96sadOm6eqrr1br1q01YsSIhL+cKsuvv/6qSy65RK1bt9bIkSP12WefxfyhRDAY1HfffacTTjjBkiUzPT1dF110UVzbCn2p+Z///EcXXXSRli1bZpm/ceNGjRo1Sueff74CgYBZf8IJJ2jo0KEV2LuqccIJJ1jKZ599tm6//XZNmTJFX3zxheU4XLVqVdTygwYNksvlMssTJ07UOeeco1dffVWffvqpZfnvv/8+avmOHTvqlVdesdS9+OKLOuSQQ/TSSy/FvCuAYRhauXKlXn/9dZ1zzjlq06aN7r777lrxIxgA9RPjbkglQQDjxo3TgQceqIEDB+r//u//9Ntvv1k+10O2bt2qJ554Qscee6xl/vDhwy138QkX+Zk8cuRIXX/99Xrrrbf0+eefWz5TlyxZErX8gw8+qPbt25vlnTt3ql+/fnr88cejAlN/++03nXvuuZZzEKnkbi49e/Ys9TVo1qyZnn76aUvdF198oQMPPFAjR47UG2+8oU8//VSTJk3SNddcowMPPFBff/21JOnqq68udb3V4dJLL7WUx40bp+HDh2vt2rWW+ry8PL3yyivq3r27+XecrDuy1BbTp0/XoEGD1LVrV40ePVo//vhjzCCroqIiTZkyRUcccYTlde3Ro4f69OkT17batWsnj8ejk046Sffdd1/UOfOvv/6qM844Q08++aSl/p577rEEFdYku92uY4891izv2rVLffv21SOPPKKpU6dGncvFCgyv7N/7BRdcYPmb8vl8uuqqq3Tsscdq2rRpMQMkfT6fFi9erKeeekoDBgzQQQcdpJdeeqnas8MC2Dcxfq45TzzxhE466SRL3YwZM3TIIYdo2LBhevfdd7Vjx46YyxYXF2vOnDm688471bFjR3PcVp6BAwdaPof/+usv9e/fX19++aXluqxhGPrhhx90/PHH68EHH5SU+Dhr2LBhlqQBixcv1nHHHaeFCxdG7cv777+vPn366PPPP5fb7VabNm3i2kZ1jH2q+9whXmeffbblO4aff/5ZgwcP1k8//WRp5/f79fnnn+uII44w7w5ZX8bE1XmNP1K7du1kGIYuueQS3XDDDVE/Qli1apWuuOKKqEDvf/zjH+rbt28F9q5qhI9NA4GAjj76aN1333364IMP9OWXX1rGphs3boxa/vjjj7eUH3jgAV122WV6/fXX9dlnn1mWj5Wcp3///ua/ESGjR49Wnz599Pbbb8ccTwcCAS1dulQvvviiTjzxRHXo0EGPPfYYPxQCgH2NAQAA6hyv12vMnz/f8vB6vcnuFmqpdu3aGZLMx4wZM2rFusLXI8lYvXp1hde1ZMkS48ADD4xaZ+iRmZlpdOzY0ejTp4/RpUsXIycnp9S2DRs2NDZv3lzhvlTW8OHDzb4MHDgwaf0ImTFjRqmvVUUeH3zwQdQ2tm/fbrRp08bS7oknnii3b1u3bjVatWplWe7pp58utX34a5voMRf5OowePbrUtt9//73hcrliHoc9e/Y0evXqZTRv3twyLyMjw/j5558tdfG8/++++27M17lZs2ZG7969jUMOOcTIysqyzLv99tvjfi169OgRtW6bzWa0adPG6NGjh9G3b1+ja9euRmpqalQ7h8NhTJs2rcz+33///XEfO5FWr15tmT98+PByX69IF154Yanb+/TTTxNen2EYxjfffFPqvzFNmjQxunXrZhxxxBFGly5djKZNm8ZsV5F9Kc2ZZ54ZcxstW7Y0DjnkEKNv377GIYccYmRmZsZs9/zzz5e67tGjR1vavv3221F/k/vtt5/Rp08fo3Pnzobdbo9af7t27Yy//vqr3P1I5G8jsl/lfVZt377daNKkSVzHYWl/+5dffnlcy7dr167Ufjz++OMxXyNJRtu2bY2ePXsaffr0Mfbff/+ov+vQY/z48aWuf+DAgWX+TQGoPMbdjLsrI3K8WdZnRlWvK95x1cKFC2O+l2lpaUbnzp2NPn36GH369DHatWtn2Gy2qHb777+/kZubW2o/fD5fmcdXPH2cN29ezOPO5XIZBxxwgNG7d2+jdevWMdd50kknGR6PJ67X+IEHHoirn6HHWWedZaxcudJSd8UVV5S5jfB/Byp6PJxyyikx+9OxY0fjiCOOMA488EDD7XZb5l100UUJjacSOVeKlOi4LVx1jm0aNmwY9Zo5HA6jXbt2Rs+ePc3XLtZ5X3p6urFgwYK4+/3NN98YTqfTLDudTmP//fcv81g98cQTy70OmOj7kujrOXPmzJh/57Eesd7Xqvh793q9xpAhQ2Iu43Q6jU6dOhl9+vQxevbsaXTo0CHqWA89yvpcDG9XGz4rgPqG8TPj56ri8XiMq666qtTXzmazGU2bNjW6dOli9O3b1+jRo4fRrl27Uq/DSDKOOuoo4/fffy91m++8807M5XJycoxevXoZhx56qNGoUSPLvObNmxuzZs2K63Mu3GOPPRZzW61btzb69OljdO3a1UhLS7PMGzduXNyf79Ux9qnuc4dExsqPPvpozL40b97c6NOnj3HIIYcYDRo0sMw76KCDjKlTpyY0nqrMuKEy312Upzqv8Uf2e/r06Zbrljabzfw3rn379jHfh549exq7d+8ucx8S/S4g0ddz2bJlMfc/1qO066/HHHNMXMuXdWzcdNNNMZex2+1G+/btjV69ehm9evUyOnXqZKSnp8dsW9bnX1WcYyYiVjzD22+/bbzxxhvGG2+8Ybz00kvG7bffbtxzzz3GG2+8YUyePLna+wQA9Q2ZvwEAAFDndenSRQsWLNAtt9wSMxNEfn6+Vq1apXnz5mnp0qUxfyXvcrn0z3/+UytWrFCzZs1qotvY47LLLrPcUvPUU08t81brIU2aNNEbb7xhuR3ebbfdFpX1pKb169dPL730kiUTsFRyHC5cuFALFiywZPLNycnRtGnTyswwWJpY2QolacuWLZo/f75+/fVX5eXlmfW33HKLHnrooYS3E84wDP39999avHix5syZoyVLlqi4uNjSJicnR++9955OPfXUMtd155136uKLL65Ufypj+PDhMetbtmwZla0jXoMHD9a8efNiZvrbtm2bfvvtN/30009aunRpzEz4NptNbdu2rdC2E7Fx40b9+uuvmjNnjn799Vfl5+db5qelpWncuHEJ3Uq+efPm+vzzz9WqVSuzbt26dZo3b55WrFihYDBoad+5c2d98803cWchqi6NGjXSe++9V6l/+5966qkKHzMh//73v/XJJ5+oZcuWUfP++usvLVy4UPPmzdOff/5p+bsOSUlJ4fMLQLVj3I1YioqKtGLFCs2bN0/z5s3T2rVro25PfeSRR2r27Nml3tlHkpxOp9577z116tSpwn3p3bu3Zs2aFXV7eJ/Pp+XLl2v+/Plav3591HIjRozQRx99FHdmwbvuukvjx49XTk5Ome1sNpuuu+46vfPOO1HZExO5y0xFvfHGGzr88MOj6letWqWffvpJy5Ytk9frNesvuOACjR8/vtr7VRcFAgGtXbtWCxcuNF+7yIzRrVu31ldffaXDDjss7vUOHjxYr7/+unns+f1+/fnnn6Ueq6eccoo++OCDqPPNmjZw4EA9/fTTFe5HVfy9u1wuTZ48WY888ojlrk5Syeu4cuVKzZs3TwsXLtTq1astx3pIkyZNopYFgKrE+LlmuN1uvfDCC5o2bZoOOOCAqPmGYWjr1q1aunSp5syZo8WLF2vt2rVR16okqXv37nr77bc1e/Zsde3atdRtnn/++XrwwQdls9ks9Tt37tSCBQu0aNEiS8bxtm3b6quvvqrQdb+bbrpJ//jHP6Lq169fr3nz5mnJkiUqKiqSVHKHjqeeeqrSd52prrFPVZ07JOLWW2+N+Xps3rxZ8+bN06+//mrJhH3IIYfoyy+/VIMGDapk+7VRVV7jD9e1a1dNnTrVfO0MwzD/jVuzZk1U+z59+tSK1/qAAw7Q66+/rszMzAqv4/XXX0/obyGWJ554QhMnTlR2dralPhgMas2aNVqwYIEWLFiglStXxszOn5WVFbUsAKB+I/gbAAAA9UJGRobGjh2rNWvW6N577y3zwmyI0+lUnz599OSTT2rDhg36v//7PzVp0qQGeouQZ555Rh999JFZbtWqlSZMmBD38oMGDdJ//vMfs+zxeHTBBRdEBbLWtOHDh2vWrFk6+uijS22Tmpqqyy+/XL///rsGDRpU4W3dfffd+vjjj3XwwQeX2qZHjx765JNPNHbs2KgvJMry8ccf69lnn9Wpp54a10XDVq1a6dZbb9Xy5ct15plnltve4XDo9ddf13fffafrrrtOhx9+uJo0aaKUlJS4+1gZxx13nCVQOWTYsGFyOBwVXm+nTp00d+5cffTRRzrmmGPKDSJyOBw68sgjdf/992vFihV64IEHKrztSC+99JJeffVVnXvuuWrevHm57Rs1aqRrrrlGS5curdCXRN26ddOiRYt05ZVXlvo+5uTkaNSoUfrll1+iArOS5eijj9Yff/yhZ599Vqeffro6dOigrKwsy49LypKVlaXPP/9cn332mS6//HIdeuihatSoUcLBMCeeeKJWrVqlZ555Rt27dy/37zUzM1Onnnqqnn/+eW3cuFGnnHJKQtsDgIpg3L3v6t69u77//nuNGjVKvXr1ktPpLHeZfv366bXXXtP3338f11jk4IMP1i+//KLx48frvPPO0wEHHKCGDRsmNDbr1q2blixZorFjx5Y51nA6nTr22GM1e/ZsjR8/PuHP7REjRmj58uV68skndfTRR6tVq1ZyuVzKzMxU9+7ddcMNN2jx4sV69tln5XK5LME/Us0Ef2dnZ2vWrFm6++67y9zewQcfrDfffFNvvfVW3AHw9dncuXP16KOP6thjj1VGRka57Tt37qwHHnhAy5Yt05FHHpnw9i644ALNnTu3zB8TduzYUa+++qqmT59ea4KVr7/+ei1dulT33HOPjjnmGLVq1Urp6elxn3NWxd+7VPIj8NWrV+uWW27RfvvtV277Fi1a6OKLL9b777+vDRs2xPVvEwBUBuPnmnPqqadq6dKl+vDDD3XuuefGFchpt9vVtWtXXX/99Zo/f74WL16soUOHxrW9O++8U9OnT1ePHj1KbdOgQQPdfPPN+vXXX9WtW7e49yWyjy+++KImTJhQ5mfdgAED9MMPP+jGG29MaP3VMfapiXOHRIwbN06vvfZamecHzZo10wMPPKB58+YlPVlFVarua/yRBg8erMWLF+u8884rdVzXsmVLPfroo/rxxx/VuHHjhLdRHc477zwtX75cDz/8sE488US1bdtWmZmZcY9tW7VqpTlz5mjKlCm66KKLdPDBBys7OzuuYz/cpZdeqjVr1uiBBx6I+WOWSDk5OTrvvPP02muvadOmTTr00EMT2h4AoG6zGZE/owMAALWez+fTL7/8Yqnr3r170rP+ALXN1q1btXDhQq1Zs0Y7d+6Uz+dTgwYNlJOTo44dO+qwww6rNV+aov5as2aNvv/+e23cuFEej0fZ2dk68MAD1a9fP6Wnp1fptpYuXaq5c+dqy5Yt8vv9atmypQ4//PC4vlQqj2EYWr58uf7880+tW7dOubm5CgQCysrKUosWLdS9e3cdcMABcQfL7ksKCws1Z84c/fXXX9q+fbuKioqUmZmpJk2a6MADD1SXLl3i+mKlKqxevVrLli3T2rVrtXv3bnm9XmVmZqpp06Y65JBD1LVr17gvSN9777267777zPKMGTMsP2TIzc3V7Nmz9eeff6qgoEBNmjRRx44dNWjQIMYscdq6dat++uknbdq0Sdu3b1cwGFSDBg3UokULdenSRfvvvz+vJYBagXH3vqmgoEC///67Vq5cqc2bN6ugoEBOp1MNGzZUx44d1bNnTzVt2jTZ3dQff/yhhQsXasuWLSosLFTjxo3VunVrHXXUUTUSgB3y7LPP6vrrrzfLEyZMKPVONNWhuLhYP/74o5lV1O12q1WrVurTp09cQQX7qkAgoKVLl+rPP//U+vXrzTuwZGVlqXXr1jr00EPVoUOHuNc3aNAgffvtt2Y58iu6v//+W99//73WrVtnntN169ZNvXv3rpod2gesWLFCixYt0tatW7Vz507z36X99ttPXbp0Ufv27ZPdRQBg/FxDAoGAlixZomXLlmn9+vUqKCiQzWZTdna2cnJy1KpVKx122GGVyvYbsnTpUv3000/mddnGjRurS5cu6tu3b5X+uM4wDC1cuFALFy7Utm3bZBiG2rZtq379+iU0JilNVY99QmrLuYNhGFq0aJEWLFhgvn7NmjUzx1uVSQhSF1T1Nf4RI0Zo4sSJZnn16tWWsda2bds0e/ZsrVq1Sh6PR82aNdOBBx6o/v378z1CnEIZ/rds2aLt27fLbrerQYMGat26tbp06aJOnTrV2tcyVjzDihUrFAgEJJV8b7Jy5Uq53W4deOCBcjqdGjJkSDK6CgB1FsHftczKlSs1d+5c/f333/J6vcrJydFBBx2kfv36KTU1NdndAwDUEgR/AwCAZCsv+BsAAAB7nX322frwww/N8m+//Vbm3XtQP5UX/A0AAADUFeUFf2PfRvA3AFS/xO4vgWrz4Ycf6oEHHtDPP/8cc35mZqZGjBih0aNHV+stndq3b6+1a9dWybqGDx+uCRMmVMm6AAAAAAAAAACoi9asWaOPP/7YLDdq1EhdunRJYo8AAAAAAAAA1GW1894P+xCPx6OLL75YZ599dqmB35KUn5+vZ599Vl27dtWsWbNqsIcVV5W3LwIAAAAAAAAAoDZIJFuzz+fT8OHDzexmUknilNp6a24AAAAAAAAAtR9XF5MoGAxq6NCheuONNyz1DodDHTp00KGHHqqGDRta5m3dulUnn3yyfvzxx5rsaoWcdtppye4CAAAAAAAAAABV6rDDDtM777wjr9dbZrtVq1bpuOOOsyR0SUlJ0bXXXlvdXQQAAAAAAABQjzmT3YF92dixYzV16lRL3TXXXKO7775brVq1klQSID516lT961//0rp16yRJhYWFGjJkiH777beo4PDKeuONN1RUVJTwcu+++65eeOEFs9ysWTOdcsopVdk1AAAAAAAAAACSbtGiRRo6dKiys7N14oknqk+fPmrXrp0yMzOVl5endevWaebMmfr0008tGb8lacyYMercuXOSeg4AAAAAAACgPiD4O0m2b9+uBx980FL33//+V7fffrulzm636+yzz9bhhx+uo446SmvWrJEk/f3333riiSd03333VWm/+vfvX6Hl7rnnHkt52LBhcjo5vAAAAAAAAAAA9dOuXbs0efJkTZ48Oa72N9xwg/79739Xc68AAAAAAAAA1Hf2ZHdgX/Xoo48qLy/PLB999NEaNWpUqe1bt26tl19+2VL35JNPavv27dXWx3gtX75cP/74o6VuxIgRyekMAAAAAAAAAADVqHXr1gm1b9u2rcaPH6+nn35adjtfywAAAAAAAACoHFIzJ0EwGNT48eMtdffee69sNluZyx177LEaMGCAZs+eLUnKy8vTO++8o5EjR1ZbX+MxYcIES7lnz57q3r17cjoDAAAAAAAAAEA1+uuvv/Tjjz9qxowZmjt3rlauXKkNGzYoPz9fdrtdOTk5atasmfr27atjjz1WZ511ltxud7K7DQAAAAAAAKCesBmGYSS7E/ua7777TgMGDDDLHTt21IoVK8oN/pakiRMnWrJqn3DCCfr888+ro5txCQaDateunf7++2+z7umnn9YNN9yQtD5JJbfb/Pbbb81y27ZtlZKSksQeAUDVMgxDHo/HUte9e3e5XK4k9QgAAAAAAAAAAAAAAOzrfD6ffvnlF0vdnDlz5Pf7JUnFxcXasGGDnE6n9ttvPzkcDg0ePDgZXQWASvF4PPrrr7/M8sCBA5WdnV0j2ybzdxJMnz7dUj7++OPjCvwOtQ03c+ZMFRQUKCMjo8r6l4ivv/7aEvjtcrl00UUXJaUv4b799ludddZZye4GAFSb7OxsffXVV8nuBgAAAAAAAAAAAAAAQJnGjx+vvLw8SZLT6VTDhg0VDAY1bdo0GYah66+/Psk9BIDK+/DDD3XmmWfWyLbsNbIVWCxatMhS7tevX9zLtmrVSu3btzfLXq9XS5YsqaKeJW7ixImW8mmnnaYmTZokqTcAAAAAAAAAAAAAAAAAAABA/UXwdxIsXbrUUu7atWtCy0e2j1xfTcnNzdUHH3xgqRsxYkRS+gIAAAAAAAAAAAAAAAAAAADUd85kd2BfU1RUpHXr1lnq2rZtm9A6ItsvW7as0v2qiHfeeUeFhYVmuXnz5jrllFOS0pdIka/Rhx9+qM6dOyepN/u2/Px8zZ071ywffvjhyszMTGKPgPrBMAx5PJ5kdwMAAAAAAAAAAAAAAKBMl112mfx+vySpuLhYGzZskNPp1H777SeHw6HBgwcnuYdA5REnt+9ZsWKFzjrrLLOcaCxwZRD8XcO2bdsmwzDMssvlUrNmzRJaR+vWrS3lLVu2VEnfEjVx4kRLediwYXI6a8chlZKSYil37txZBx98cJJ6s2/Lzc3Vpk2bzHKXLl3UoEGDJPYIqB98Pp9++eWXZHcDAAAAAAAAAAAAAACgTE2aNFEgEJAkFRYWateuXXK73WratKmcTidxXagXiJNDZNxqdaodkbr7kPz8fEs5PT1dNpstoXVkZGSUuc6asHLlSn333XeWuhEjRlTLtrZs2aKtW7cmtMyKFSss5fz8fOXm5lZltxCngoKCMssAKib0i1gAAAAAAAAAAAAAAIDazOPxmMHfXq9XgUBAfr/frCeuC/UBcXL7nmTE7oYQ/F3DIt/s1NTUhNeRlpZW5jprwoQJEyzlww47TIcccki1bOt///uf7rvvvkqtY+7cuZZf1SB5wm9tAaDi7Ha7WrVqlexuAAAAAAAAAAAAAAAAlOmvv/6S1+uVVBL8vWvXLjkcDq1Zs0Z2u10zZsxIcg+BqkecXP23bt26pG3bnrQt76OKi4stZbfbnfA6IlPDFxUVVapPiTIMQ6+//rqlrrqyfgMAYjMMI6ouGAwmoScAAAAAAAAAAAAAAAAlYsUuhLJ+AwCqBsHfNSwy03foF02J8Hg8Za6zus2YMUNr1641y263WxdddFGN9gEA9nWGYUQFgPt8viT1BgAAAAAAAAAAAAAAIDq2LRgMEvwNAFXMmewO7GsyMzMt5chM4PGIzPQduc7qNnHiREv5tNNOU+PGjatte9dee63OP//8hJZZsWKFzjrrLLN8+OGHq0uXLlXcM8SjoKDAcguLww8/XBkZGUnsEVB/rFmzxhLwvWvXrhr/TAAAAAAAAAAAAAAAAAjZuXOnpez1etW+fXuzXFRUpEAgIJfLpfbt28vpdGrw4ME13Eug6hEnt+9ZunRp0rZN8HcNiwzKKywslGEYstlsca+joKCgzHVWp/z8fL333nuWuhEjRlTrNps1a6ZmzZpVah2ZmZlq0KBBFfUIlZGRkcF7AVSRRo0aafPmzWZ5586dat26dUKfKQAAAAAAAAAAAAAAAFXB5/Npx44dlrq8vDylpKSY5UAgIIfDIafTqZSUFDmdTmKJUC8RJ1f/JTNJpz1pW95HNWnSxBKU5/P5tGXLloTWsX79eku5soHRiZgyZYol+Lx58+Y6+eSTa2z7AIC9cnJyLGWv16tVq1bJMIwk9QgAAAAAAAAAAAAAAOyLPB6Pli1bpmAwaNYZhqHc3Nwk9goA6icyf9ewtLQ07bffflq7dq1Zt27dOjVv3jzudaxbt85SPuigg6qsf+WZOHGipTxs2DA5nRxGAJAM6enpcrvd8nq9Zt2uXbv022+/KScnR9nZ2XK5XLLb+a0XAAAAAAAAAAAAAACoOoZhyO/3Kz8/X7t371ZeXl5UsrrCwkL5/f4k9RCofoZhyG/45Q14tcuzS7nBXPkNv/zya/mu5WrnbKem6U2T3U3UQ0TtJsFBBx1kCf5esmSJ+vTpE/fyS5cujVpfTVi9erVmzZplqbvssstqZNsAgGg2m03t27fXihUrLL+c9Xq92rx5szZv3pzE3gEAAAAAAAAAAAAAgH2Vz+fTli1bkt0N1CNBIyhvwCtf0Gc++wK+kuewOm/QK19gz3NYm9B8T8BjzvcGvJZ1mnVh82LND1/ekBG7wzOkaw+9ViN7jKzZFwr7BIK/k+DQQw/V559/bpZ/+OEHDR8+PK5lN27cqDVr1phll8ulrl27VnUXY5o4caLl11m9evVSt27damTbAIDYsrKy1Llz56gAcAAAAAAAAAAAAAAAgGQoLi7W33//rUAgkOyuoBL8Qb8Z/OwJeGIGR/sCJcHUZnlPkLQn4NkbMB0ZTB0RQB0ejG0GaocFcIf6ETDq3vHkC/iS3QXUUwR/J8Fpp52mRx55xCx/9dVXMgxDNput3GW/+OILS3nw4MHKzMys8j5GMgxDr732mqVuxIgR1b5dAED5QgHga9askdfrTXZ3AAAAAAAAAAAAAADAPsgwDOXn52vTpk2lJrALJR8NxcrFEzNXkX74g355Ah4VB4rlCXjk8XtKnvfUeQMlQcWSZJNNNpvNfI6qk00l/1vrYrbd8xzaN5tsMmTIMIyYz6H+BhXcW2dIhmK3N5/D2vkNvxmEHQrSDn+OrAsFXJfXri4GW9c23gBxPKgeBH8nQb9+/dSkSRNt27ZNkrRq1SrNnDlTgwcPLnfZV155xVI+88wzq6WPkWbNmqXVq1ebZbfbrYsuuqhGtg0AKF9WVpa6deumoqIi7dixQ7t27VJxcXG1nCQBAAAAAAAAAAAAAAAYhiGv36uduTu1dfdWbd+9Xb6Ar/SAZcNQUUGRdhTtkNNwKrA9IGeqU5P/mKyAEVDQCJoPX9BnBmkX+4stQdvFgWJ5/J690wGPiv3FlnLQ4O7pSD5vkOBvVA+Cv5PAbrdrxIgReuyxx8y6++67T4MGDSozSO/rr7/W7NmzzXJWVpaGDBlSrX0NmThxoqV8+umnq1GjRjWybQBAfGw2m9LT05Wenq42bdpowYIF+vPPP7Vr1y5t3LhR6enpat++fbK7CaCO8ng8+uuvv8xy27ZtlZKSksQeAQCAiuJzHQCA+oHPdAAA6gc+0wFUl1AAtT/glzfolS/oK3kEfCXlgE8+wydvYM900GdpE/4cUHQGaH/AL6/fa2avjpcz3ylnkVMBT0A+w6dAakDv/vRuVe02UKuQ+RvVheDvJBk1apTGjRun/Px8SdK3336rRx55RLfffnvM9uvXr9eVV15pqbvxxhvVpEmTMrcTGUw+Y8YMDRo0KKG+FhYW6t13rR+wI0aMSGgdAICa16pVKy1btkzp6eny+XzatWuXioqK5Ha7k901AHVQIBCQ1+u1lAMBbvMFAEBdxOc6AAD1A5/pAADUD3ymA5CkgBGwBGD7g35rEHYwYl4gRt2eZ29gb6B3rWNIDq9DkhRMKcnM7cushf0Eqkit/DtEvUDwd5I0adJEd955p+68806z7o477tC6det01113qVWrVpKkYDCojz76SDfeeKPWrVtntm3VqpVuvvnmGunru+++q7y8PLPcokULnXTSSTWybQBAxTVv3tzMCpCRkaH8/Hxt3rxZbdq0KfNOEwAAAAAAAAAAAAAAIFoom7YZdB3wRwVnxxPEHQrgDs0PKpjsXasRjmKHbAGbZJMC7pIfuvgaEByLqmWTTW6HWy67S26HW067U267Wy6Hq6TO7pbbEfbYM89Sv6cuxZGyd/6eessyYXW+Ip9+nvezHDaHnHJq0IBBaprTNNkvB+opgr+TaNSoUfrhhx80bdo0s+7555/Xiy++qHbt2qlhw4ZavXq1du3aZVkuLS1N77zzjrKzs2uknxMnTrSUhw0bJqeTQwcAaju73a42bdpo5cqVatKkiQoKCszPlObNm5MBHAAAAAAAAAAAAABQpwWNoAJGQP6gXwEjoEAwYHn2G/6oukBwT31EXTzZtQMGmfkrJFgS+O0qcEmS/Gl+ySYF3UEFUnhN6zKnzRkdRL1nOsWREh1UHRYsHQrOTnGkmOVQXeRzeAC3226dHwrqDtU5bI6kJEXMzc3VOsfeBL8N3A2U4kip8X5g30AEbxLZ7XZNmTJFl112md5++22zPhAIaNWqVTGXady4sd59913179+/Rvq4bt06zZgxw1I3YsSIGtk2AKDyOnfurNWrVysrK0tt27bVX3/9pV27dmnXrl1KS0tTRkaGHI7kDHoB1C1er1f5+flmedu2bfyIBACAOorPdQAA6gc+0wEAqB/4TEd9EjSCZrB1MBhUQGHB1caeOmNv4HWoHB6EHTSCZnB2KLA7ECypD5XDpw0ZNbqPDjlqdHt1mU02KSjZA3bZvXazPpAWkD/DL0nyNPJISQpXSHGkyGl3yjAM8zgKTZvPMlTyv7W+Othkk81mU+i/kv9tZr3dZi+1ncPmMIOoQ4HU4c+WOrs7qk08y5YWwB3qF4CaRfB3kqWmpuqtt97SeeedpzFjxmjRokUx22VkZGj48OEaPXq0mjVrVmP9e+2112QYez+wevXqpW7dutXY9gEAldO4cWMNGDBAs2fPVsOGDWW327Vt2zYVFBSoqKhIRUVFye4igDoiEAgoLy/PLDscDjkcXNwCAKAu4nMdAID6gc90AADqBz7TURXMIGkjOtg6qGBUQGvQCFoDXMuqj5hnBsIahrluc7kaDMS2ySYnoW+1im1PFHcoONlSZ7PJcBlSmuTIcChVqTJaGMpqmSW73S67zS677CXTKim77C6lOEsCj1MdqUpxpCjVmSq33a1UZ6oZmGzWh7VLce6pd4S1c6ZY1lHZJHllBYpLssyz2+ylBnWHv14AEC8+AWuJc889V+eee65WrFihn376SevXr5fX61V2dra6dOmi/v37KzU1NeH1hgduV8Rdd92lu+66q1LrAAAkV+vWrc0A8KysLGVlZSkQCCg3N1cej6fkhD8YTHY3AdRyfr9fHo/HLGdnZ8vp5HQCAIC6iM91AADqBz7TAQCoH/hM37cFjID8Qb/58AV9ChiBkudgybM53/Bb2oayY/uD/qoPurZFPKPesMkmp90pp80ph8Mhl80lh91h1jntYY89ZYfdIZd9T7s9dWYw857g5ZjbstnkcDjkdDqVlZWllJQUc163bt10yCGH1OmgZ3Pf6+4uAKjDGC3WMp07d1bnzp2T3Q0AQD3TunVrHX/88Vq5cqX++usveTwe5eTkJLtbAOoQj8djufjcsmVLywUaAABQd/C5DgBA/cBnOgAA9QOf6bWTYRjyG35r8HX4w9gbrF3avFjzQ4HdoXLcQdu2PQ97de41aiOX3SWn3SmX3WWddrjMOvPh2Dvf7XDLbXfL5XDJbXfL7XCbyyeLw+FQq1at1K5dO7Vt27ZOB34DQLIR/A0AwD6icePGaty4sXr37q0tW7Zo48aN8ng88nq9lb5TBID6r6ioSDt37jTLLVu2VFpaWhJ7BAAAKorPdQAA6gc+0wEAqB/4TK86QSMoX9CnYn+xPH6PPAGPigPF8gQ88vj3TofXefweFQfD2vtL5vmCvqrrmE2SY88D9VYo4DrFkSKXw6UUe0pJAPaeulDwdYozJXqe3S23022pD7W32+p2xL/L5ZLb7VaTJk3UunVruVyuZHcJAOoFgr8BANjH2O12tWjRQi1atEh2VwDUIbm5uZbMI/369VODBg2S2CMAAFBRfK4DAFA/8JkOAED9UN8/0/1Bv7wBb0mwdcBjTseqK2++N+g1A7QLfAUq8BUo35dvThf6CuPLpG1TScQUUVP7BJtsewOyHSkl0/aw6T31brtbac40pbvSleZMM6fTnelmXWg63ZlumZ/mTJPDTnQ/AKDmMIwBAAAAAAAAAAAAAAAA9jGGYag4UKwif1HJw1e0dzrsUegvjFkfq31xoNgSwB0wAsneTdQSTrtTqY5UpThSlOpMLZl2pph14dOpzlQzODs0nepIjcqkbcmcXUq90+6UzWZL9u4DAFClCP4GAAAAAAAAAAAAAAAA6ghPwKN8b77yffmW5zxfngp8Bcrz5u2tD2tT4CuICtZG/WSTTZmuTKW70s1M1067Uy67Sy7H3mmzrpRpp90pl6P0+aHp8ODt8GDt8IBuMmMDAFB1CP4GAAAAAAAAAAAAAAAAqoFhGPIH/Sr0F6rYX2xm2i72F6vQX6iduTu12LtYXsOrYqNYfy75Uz6bzxK0nectCeoOTfuCvmTvFqqJ3WZXhitDma7Mvc/uDGW5sizlTFdmycO9t11omSx3llKdqbLb7MneHQAAUE0I/gYAAAAAAAAAAAAAAECdEggG5Av65A/6FTACChpB8zk0HQgGouaF6iPrgsGg/IY/Zr036FWxf2/QdlGgSEW+Iksgd2h++LxQXcAIxL9jy6rvNUPVcNvdSnWWZLhOc6aZ2a5TnalKc6SZ06mOPfNjlfcsExnMneZMk81mS/YuAgCAWo7gbwAAAAAAAAAAAAAAAFgEjaB8QZ+8Aa+8Aa98QZ98AZ+8wZJy6Dmyzhfwmcv5gtHTofnxlMOX8wf9lnkJBVRjn2WTTZmuTKW70s1A6wxnhpkxO/QIZc0OTae70pXuTI8K3E5xpMhhdyR7twAAwD6O4G8AAAAAAAAAAAAAAIA6JmgEVeQvUoGvQAW+AhX6ClXoLzTLoboCf9j8sLLH79kbwB0jsNsf9Cd7F7EPsNvsSnGkyO1wK8W+59mRohRnyt56h3XabXeXBGmXE8Cd4cogkzYAAKiXCP4GAAAAAAAAAAAAAACoJoZhqDhQrGJ/sYr8RTEfkfMig7ZDwdyWYG9/YbJ3DfVYKNN1+CPVGV2X5ip5Tnemm20ig7VLDeJ2uOW0OQnOBgAASBDB3wAAAAAAAAAAAAAAYJ/nD/pV6C80M2SHsmiHTxf5i1ToLywJ0vYVqThQXGoAd3i9ISPZu4d6LpTxOsuVpUx3pjJdmXufw6ZjBW6HP9Kd6Up1pspusyd7lwAAAFAKgr8BAAAAAAAAAAAAAECd4wv6VOAtUJ4vTwW+AuV588zM2Jbg7YiA7qhg7j2Ztb1Bb7J3CfsYu82uFEeKHAGHXDaXUm2papHTQg3TGirLlVUSzO3OihnEnenOLGnjzlCGM0MOuyPZuwMAAIAaQvA3AAAAAAAAAAAAAACoMYZhqMhfpDzvnqBtX57yvfnK9+XvfQ6f9uaXBHh7CyzzigPFyd4V1BN2m112m10Om8N8dtgdZtlpdyrNmaZUR6olS3aqM1WpztSoeWadM1VpjpIM26H5oXlpzjS57C7l5eVpxowZZl8GDxisBg0aJPHVAAAAQG1H8DcAAAAAAAAAAAAAAIgSNIIq9her0F+oIn+R9eEriq4Le8RaJjxLd9AIJnv3UEWcNqdcDpfcDrdcdpfcdnfJtMNlliOnnXanXHbX3odj73RoPbHmRZUdLjltTjnsjjKDt8PnOe3OqLY2my3ZLyMAAAAQN4K/AQAAAAAAAAAAAACo5fxBv7wBr4oDxfIGvPIEPPIEPKVOlzbP0sbvkSdYUhcrmJvM2nVTKKt0hitDGa4MpTvTzekMV4Y5L9WZagZqm4HbDndUncuxN6A7FLxtTttdctgdyd5lAAAAYJ9C8DcAAAAAAAAAAAAAAHEyDEP+oF/FgWJLEHVp5WJ/sSUgO9Y8M6C7lOW8Aa8CRiDZu45q4La7leYqCdZOdaSagdvprnRL0Ha6a8+0s2Q6vBw+P92ZTjA2AAAAUM8R/A0AAAAAAAAAAAAAqBMMw5Av6JMv6JM34C15BL0ldQGfpRyaX1ombI9/T11wb0bsYn/pWbXDy4aMZL8UqEFpzjSlO0sCrkPToSDtNFd00HZ4ObJNujPdLKc6U+W0E7YBAAAAIDGcRQAAAAAAAAAAAAAAKswX9KnQV1jy8Md4Dk2H1Rf5i/YGaAe98gV8lrI3EBbQHV4O+pK9u6jlUhwpZqB2KHt2KIN2KHg7lCE7PFt2afNSHalk0gYAAABQqxD8DQAAAAAAAAAAAAD1mGEY8gf9KgoUqdhfrGJ/sYr8RSoOFMvj96g4sKccNi8yeLvAV6AiX3R9oa+QgGxUCbvNrkxXprLcWcpwZSjTlalMd6alLnxeZF2WK0sZ7gy57K5k7woAAAAAVCuCvwEAAAAAAAAAAACgFvAGvCrwFZiPUNB1ga9Ahb5Csy48eLvYXyxPwGMJ3rYEc+9pEzACyd491DNpzjTLI92Zbq1zpUW1MYO6Q4Hde4K7M12ZSnOmyWazJXu3AAAAAKDWI/gbAAAAAAAAAAAAABIQyqRdHNgbeO3xlzwX+PcGa+f78i2B25EB3ea8Pcv4g/5k7xrqMLvNrhRHilIdqXI73EpxpJjP4dPhdaGHy+EqPYg7RjB3qiOVQG0AAAAASBKCvwEAAAAAAAAAAADUG4ZhqMhfpEJ/ofK9+SrwlwRYR2bC9gQ8Ztnj98SuL6MNmbRRGptsSnXuDcBOdaQqxbnn2ZGiFGeKUuwplrpU595nt91tlmO1LS2w22nn638AAAAA2Bdw9gcAAAAAAAAAAAAgqQLBgBmkHcqIne/Lt5Qt2bL3ZM+O1abQX6igEUz2LqEWcNvdJcHTYQHSpWXFLi0jtlnnjN0utI3wAG+X3UVWbAAAAABAtSH4GwAAAAAAAAAAAECF+II+FfoKle/LV743X/m+/JKg7PDpyHm+fBV4CyzlIn9RsncF1SDNmaZ0Z7rSXelKd6Yrw5WhNFdJXZozTW6HW267W26HWy67Sy6HS257ybTbEV0fKoeWczlc1vKeaZfDJafNSQA2AAAAAKBeIvgbAAAAAAAAAAAAqEcMw5Df8Msb8MoT8JjPpU2XV1fkL1K+tyRQO8+XZwZ3F/gKVBwoTvbuogqkOlKV6tzz2DMdHrQd/pzhyogK6o6cn+5MV6ozVXabPdm7BgAAAABAvUPwNwAAAAAAAAAAAJAkQSNoZs4OPRf4CvZmyN4zHf7I9+Vrd9Fubc7bLL/hl08+PfXpU/IZPjNoO2gEk71rqCJOu1MZrgxlODNKgqxdJVmz0xxpZsB2iiNFac40S/B2mjMtZlB3qjM1almCtAEAAAAAqDsI/gYAAAAAAAAAAABiMAxD3mB0JmyPv/Qs2eGPQl9hqcHboelCf2HVdJYE3LVKurMkA3aGK8PMhp3hzFCGe89zeH1oek99+LxMV6bcDneydwcAAAAAANQiBH8DAAAAAAAAAACgVjAMoySYOuiVL+CTL1iSyTry2Rv0yh/0l0yH6ktZJrzerNtT7w14VRwojhnEHZpG/ZHiSLFkyU5xpJjZsMPnhbJkW6adEW1D0xH1ac40pTnTyKQNAAAAAACqDcHfAAAAAAAAAAAAiMkwDBX5i5TrzVW+N1/5vnxLsHSx3xo4HT4v/BFZZ1kmfB1Bb7J3GbWITbaoLNiRGbJjPdJd6cp0ZVrK6c50Oe18NQoAAAAAAOo+rnAAAAAAAAAAAADUU76gT/nefOV585Tny1OeN29veU9dvjffDO62lH35yvfmK2AEkr0bqGPcdrcy3ZlmwHb4dKw6M1DbnWEJ7E5zpslmsyV7dwAAAAAAAGoVgr8BAAAAAAAAAACSwDAMeYMlma89AY88fo+KAkXy+D0qDsRX5wnsqd9TFwrsDgVyF/mLkr2bqKXsNrtSHClKcaTI7XBHTYfXpThSzIBtM1jbHRHMHRbQ7Xa4k717AAAAAAAA9RbB3wAAAAAAAAAAoF4JBAPyBX17H4G9096AV/6gP+a8UH34s2Xa2FMO+PZOl7FcZL0n4Nkb6L1n2pCR7JcLtZBNNjP7dSi4Ot2Vbnl2GS5tXLtRLptLLrnUvWt3NcxsGFcwt9vhltPO14QAAAAAAAB1EVd1AAAAAAAAAABAlQhlsg5lpzaDnAPF8gZKMlx7A969GazjaOcNeKMCtb1Bb3TQdti8oBFM9kuBfYjb7o4Ksk51psptdyvNlaYMZ4Yy3ZlKd6abmbEjA7ojA71Tnamy2+xlbjc3N1czNs8wy4P3G6wGDRpU9+4CAAAAAAAgyQj+BgAAAAAAAABgH+UL+lTgLVCBv0D53nwV+guV781Xgb+gpN5X8sj35ZvT4Y9Cf6E8/r1B256Ah0zWqBEOm0Muu0suh0tuu9t8djvcZr3LHl0Xauuyu5TqSC01K3ZZ81IcKUpxlpRddle5QdoAAAAAAABAVSL4GwAAAAAAAACAWi4QDKg4UKwif5GK/EUq9hebz8WBYhX6C611/pK60oK2Qw9PwJPsXUMdFRkcHV4OBUaXFkhtaeuIaOtMlcvuMgOr3Y6S4G2n3VkybS8J5HbYHcl+CQAAAAAAAICkIPgbAAAAAAAAAIBKMgzDDMwu8hep0F+4t+yzlgt9hWYgdyhg2xLQHdgbvB0K5PYGvcneRdQjma5MZbozleXOUpYrS1nuLGW6M5XpylQDd4OY80LlTHemUh2pstlsyd4NAAAAAAAAYJ9E8DcAAAAAAAAAYJ/hC/jMQOxCf6ElMDuyHB6sXV5Qd7G/WIaMZO8e6hmn3alUR6pSnalKcaQo1ZGqFGdKVJ057UxVhiujJFA7LFg7PKA7w5lB1mwAAAAAAACgDiP4GwAAAAAAAABQ64QyaRf4CpTvyzefC32Fe4O1fdag7fDg7JgB274i+Q1/sncNtYzT5pTT7pTL7pLL4TLLobrQdFnlyGenzVmyrj3TofZmoPaeAO4UR4rSnGlKcaRYgrpD8wjSBgAAAAAAABCJ4G8AAAAAAAAAQJUJBAMq8BeowGsN2s735VvqQo9Y80LPQSOY7N1BFbPJplRnqtwOt5m12u1wmxmtUxzWR6it2+6Wy+EqCdAOfzginmPN2/NwO9xR85x2p+w2e7JfFgAAAAAAAACIG8HfAAAAAAAAAACTYRgq8BUo15urPG+ecr25yvXkljyH1VmePSXPeb48FfmLkr0LqCSn3alMV6YyXBlRj0xXptJd6eb8NGfa3szVYdmrYwV0pzpS5bQ7ZbPZkr2LAAAAAAAAAFBnEfwNAAAAAAAAAPWML+gzA7ajgrR9eZZg7sh5ed48Mm7XMamOVKU6U5XmTFOqM1WpjlQzKDvDlaFMd6bSnenKdGcqw5mhDHeGMpyl1Lsy5La7CdAGAAAAAAAAgFqK4G8AAAAAAAAAqGUMw1CRvygq+3Z44HasrNyherJv1y6hQOw0Z5rSXekl0469deGB22adI1VprjQzkNsyL9R+T9C33WZP9i4CAAAAAAAAAGoIwd8AAAAAAAAAUMWCRlAFvgLleUsyaef78pXvzVeeL6/k2ZtnTud785Xry1WeJ88SxO0P+pO9G/sUt92tNFdYkLYz3RqsHVYXFcgdMR3eluBsAAAAAAAAAEBVIvgbAAAAAAAAAML4g34V+ApU6CssCdr2lQRr53tLpnO9ueZ0KLA7Msi7wFcgQ0ayd6VeSnemxwzIjqpzWYO1y1vGaedyOQAAAAAAAACg9uNqNgAAAAAAAIA6L2gEzWDt8KDt0HSBr0AFvoLYdd58FfoLzecif1Gyd6fecdqcynBnKNOVqQxXxHOsevfecqguw5WhdFc6WbQBAAAAAAAAAPs0gr8BAAAAAAAAJMQwDPmCPhX5i+QJeFTsL1ZxoLjkOWzaF/TJH/TLH/THnPYFffIbfvkCe+aFpo097cKnw5YPL5sB3/7CZL8s9Vq6M11Z7iw1SGmgLFfJcwP33kdoXqYrU1nurKhAbrfdLZvNluzdAAAAAAAAAACgziP4GwAAAAAAAKijgkZQ3oBX3qBX3oBXvoDPnPYG95QDXnkCHrO8u2C3fvX8Kr/88hperVy6UobDMIO2PX6PigJFKvYXlxnYbchI9u4jAQ6boyRAOxSoHRawHV4XGczdwN1Ame5MueyuZO8CAAAAAAAAAAAQwd8AAAAAAACo5QzDMDM9hx7h5fAs0EEjqEAwUPJsRD8bhhFdX0r70CNWvWEYJdMKmzaCMmRY28QxP3J9ZkB3wCtf0BcV3O0Jesxpv+Gv/Av8R+VXgZqR4kiJGZwdHrgdXm8+Uhoo3ZlO5m0AAAAAAAAAAOoBgr8BAAAAAECtER7k6zf8CgQD8gf9ex+G31IOGAEz6De8LvQsycxObBjRWYoj54VnMo5sX9Z6JJlBlTbZyqwvr52l7Z7nUMBwaDoUKGwp7wkyDvUxVA7vdyj4OFabyGDmUKB0aQHOgWDAGsgcK6g5uDeYOWgELUHalgDuPUHMvkD0vND7DtQHbrtbWe4sZbmzlOnKVKY705w2A7hTorNwh+pSHCnJ3gUAAAAAAAAAAJBktTr4+4477tBVV12lDh06JLsrAAAAAABAkj/oV6G/UIW+kkeBr0AF/gIV+Ar21oWX/XvaRJQ9fk/MQO5QwDYA1DYuuysqaDvLlaVMd6YZvB16znKX1EfOdzvcyd4NAAAAAAAAAABQx9Xq4O9HHnlEY8eO1aBBg3TVVVfp7LPPlsvlSna3AAAAAACoNkEjqGJ/sQr9hSryF6nQV/IceviCPgWNoAJGoOQ5GNg7HVEXqz5ULqsutH0zuNtXYJaLA8XJfokAIG422ZTpylS6K12ZrkxluDOU4cxQpjtT6c50ZbozleHKUIYro2T+nunwunRXOlm3AQAAAAAAAABArVGrg7+lklsQz5gxQzNmzFDjxo116aWX6sorr9RBBx2U7K4BAAAAAPZRhmGoOFAcFZhd6C9Uka8oui6sHJofVb8n0JvgagD1QYojRSmOFLkdbjntTrnsLjntzpKHzSmXw1XybHfJ6XDKZXNFt4sox5p22V1Kc6ZFBWuHArnTnGmy2WzJfjkAAAAAAAAAAACqTK0P/g4xDEPbtm3Tk08+qSeffFL9+vXTVVddpfPPP1+pqanJ7h4AAAAAIEkMw5Df8MfMgh1eLvYXW4KuwwO1wwOxYwZ0RwRtF/uLZchI9q4DQNxcdpfcDrfcdrecNqf8Xr/cKplultNM6SnpSnOkKdWZqhRHitKcaUpxpCjVmWqZTnWkWtrEap/iSJHdZk/2LgMAAAAAAAAAANRLtTr4+6mnntLLL7+s3377TZLMLD2GYeiHH37QDz/8oBtuuEEXX3yxrrzySvXo0SOZ3QUAAACAeiVoBOUP+uUJeOQJeOQL+Mxpb8BrfQ56zenI+eFtwueFr9Mf9EcFbZvTpQRzh6YJwgZQGrvNLrvNLofNYU6Hl6Oe7XvLNpstZrtQffizXSXrjZq22WVX2LTNLpvCpve0jzXfDNbeE7Admg4P4o5Vl+JIkcthbeOyuyzZr3NzczVjxgyzPHjAYDVo0CAZbxEAAAAAAAAAAAASVKuDv2+44QbdcMMN+umnn/Tiiy/qnXfeUUFBgSUIfPfu3frf//6n//3vf+rVq5euuuoqXXjhhcrIyEhy7wEAAAAgPoZhyB/0qyhQpGJ/cckjUGyZLvIXyRPwmNmri/3FJcHTQZ/8QX/Us/kw/PIFfPIbfkt9rPaWZfdk0gbqCrvNLqfNKafdKYfdIZfdZQbSSjKf9z7tDYQNXWeIahs2zyyH2kTUG0bJjxAif4wQWW8+R7YPWyxWW3NfbDKDg22ymf0IL4f6WF4b83nPdGnByXEFOIceZQQ62212Oe1OueyuqOeoOodLLptLLodLTltJudS2Ec/hgd6R7xMAAAAAAAAAAABQ19Xq4O+QI444QkcccYSefvppvfnmm3r55Zc1f/58SdZs4PPnz9eCBQv073//WxdeeKGuvPJK9enTJ5ldBwAAAFAPeQNe5Xpzle/NV74v3zKd581TnjdPBb6CkiDtsCDu8ADuUEB3KIg7YASSvVtApaQ705XhylC6K92cjlUO1aU6UuW0lwRrh4K2LY+wQG6n3SmXzWUpRy5nt9mT/RIAAAAAAAAAAAAAQLWrE8HfIZmZmbrqqqt01VVX6ZdfftGLL76oN998U7t27ZJUEghuGIby8/P18ssv6+WXX9Yhhxyiq666SsOGDVPDhg2TuwMAAAAAkiqUYbvQX6g8b54lWNsy7c1Xni9ses/8UJC3N+hN9q5gH2STTanOVLkdbjlsDjO7ceg5FAAdXuewOWS3l2TEDq+328OWC5+3p95ld0UFa0cGdGc49zy7MpTmTCP4GgAAAAAAAAAAAABqQJ0K/g7XvXt3Pfvss3rsscc0ZcoUvfzyy5o9e7YkazbwX375Rddff71uvfVWnX/++bryyit11FFHJbPrAAAAAFQyXg8aQQWMgPxBvzntC/r2Zsfeky07lD07Vibt8HJ4Nu2o8p52ZNhGdUtzplke6c50a50rRp0zTemusutSHanm+S4AAAAAAAAAAAAAYN9UZ4O/Q1JTU3XJJZfokksu0fLly/XSSy/ptdde09atWyXtzQZeVFSk119/Xa+//roOPPBA/eMf/9Cll16qxo0bJ3kPAAAAgKplGIb8hl8ev0eegEfegFfFgWJ5A155Ah7ro5w2ofqC4gJtzN8ov/wKKqh3Zr0jm92mQDCggLHnETYdNILyB/2W6ViB3kCyuO1upbliB2eHB1xb5sUI2o5sm+pMJQM2AAAAAAAAAAAAAKDa1Png73AHHHCAxo4dq4ceekgffvihXn75ZX311VeWTOCS9Mcff+iWW27RnXfeqXPOOUfXXnut+vfvn8yuAwAAYB9kGIa8Qa8KfYUq9BfGfg5Nx5hf5Cuylv1FZtB20AhWb+e3V+/qUXc4bU65HW6lOFLkdrgt02adPbouvL3b7pbT7pTT7pTdZpfD5pDD5iiZtjtils12kWWbQ3a7XU6b09LGbrMrxZFiBmo77fXqdBgAAAAAAAAAAAAAsI+ol992u1wunX/++Tr+/9n77/jI7vru/36fNl0jbW/27nrXDRe6DTZgY1NsSgJxQrswmBYMpECSm4sQIMaU2ISE3OSC0ExcgBgwoQeCKa4YY7pxxfb2XXubykiaesrvj3Nm5sxotCutpJ2R9Ho+fB7fcoq+R95dlXnP5zzvefrQhz6kj33sYzIMo+Xx2EEQqFKp6Mtf/rK+/OUv64wzztAHPvABPf/5z+/iygEAANBtru+2VMEuu+WJ1bJjFbPbK2TXz6n6URvNV7yKirWixmvjLcFtql9jKlJWSkk7qZSVUtpOK2WnGnOO6cg27QmtbdhyLEe2YTeC1S37J+sbE49tD20nrfDjJqwEIWoAAAAAAAAAAAAAAI6iBfkq/e23367Pfe5z+trXvqZyuTwh9F0Xrwh+11136QUveIFe9rKX6aqrrlIulzvq6wYAAMDhBUEQBqmjStf1EHXJLankllSsxfqHmq81+/GAN2FsHAlDhnKJnPqcvrBN9Cnn5JSxM2FQu75ZzbYe4k5aSaXsaGy1HRftNw2z27cIAAAAAAAAAAAAAAB6wIIJfx88eFDXXnutrrrqKj344IOSmkFvwzAUBIEMw9Dznvc8veENb9B9992nq6++Wrt27Wo55oYbbtDOnTt16623yrKsrt0PAABALwmCQGWvrLJbbgSo64Hpml9Tza/J9d2w79Umzh3hvOu7KnvllgB32SvLD/xuf0qwwKTtdGtwOwpyd+rnE3nlnFxLP+NkCGgDAAAAAAAAAAAAAIA5N+/D3z/60Y901VVX6Vvf+paq1WpL4Ltu+fLlev3rX683v/nN2rRpU2P+sssu0//8z//on//5n/XTn/60EQC/88479dnPflZvfetbj/r9AAAATMYPfHm+Jzdw5fmevMCT67vyAq/jfNWrhiFtr9yodF0PUsdD3PEwd/u+ePAa6CbHdMLq2Fa6pZJ2yziqll2vqN0yjlXSro9zThj0ziayckyn27cIAAAAAAAAAAAAAABwWPMy/P3oo4/q6quv1uc//3lt27ZN0sQq30EQ6Nxzz9Vb3vIWXXTRRXKciWEO0zT1R3/0R/qjP/ojXXPNNfrzP/9z+X5YRfL6668n/A0AACRJru+q4lUaweh6xet6OLoelq54lXB/LGDdcl50bNkrq+bVGqHtjiHu+lws1B0o6PanAguIYzpKWsnGlrASYd9ujlNWqjFveIb27dknS5ZMmdq8abMyqYwsw5Jt2jINc/K+ackyrEMeaxlW4zjbtJW0ko2gdtJKyjbn5Y8uAAAAAAAAAAAAAAAAs2reJCiCIND3vvc9fe5zn9P3vvc9eZ7Xscr3wMCALrnkEl166aU66aSTpnz9173udbr11lt1zTXXSJLuueeeWV0/AACzJQgCVf2qql641fyaKl4lHPtV1bxao1/xKqp5NdX8miQ1QpaGYcgyLJmG2dg6zU92rClTphnuj/fja2z02wLL8fGkxwWTHK+gUdG6/jmoedH9+83PR/1z0/h8tH2+4ue3f86qXrUZ4q4HtaPPH3C0mIapjJ0JNyejtJ1WxgnHWSfb6Mfn68elrFRLgDtpNgPd8aC3aZjTWlOhUNBNQzc1xuedfJ7y+fxs3zoAAAAAAAAAAAAAAAAOoefD3zt27NDnP/95XX311dq9e7ekMChmGEZLle+zzz5bl156qV7+8pcrmUwe0cd63vOe1wh/FwqF2boFAECXBUEgP/Ab1ZPdwG1UVPYCTzW/1lJ9uX5cY+x3rsI8oY0d13LOZG1sLS0B5VhQuT3AXPEqcn23259SYFGzTbtRETteGbs9YD1hs5ONkHbWyR4y3J20ki1vcAQAAAAAAAAAAAAAAACkHg9/X3jhhfrRj37UCHhLagl95/N5veY1r9Gll16q0047bcYfb+XKlY1+vBIpAGAi13dV82uNKsr1aszxuU79ehXqelsPNrf3W46d7Bp+rSVo3RLsbgtYA1gc0na6ZatXx047beMobB2fi1fFTtmt4e54yNsyrcMvBAAAAAAAAAAAAAAAAJgDPR3+vvHGGxsVD+NVvp/61Kfq0ksv1ate9SplMpkurxLAQuYHfmvw2KspUCAv8OQHvoIg7NdbP/DDTb58P2qD1i1+fL0idf2c9n0dr92+aeK1Ox3nBV64dt9r3lesonTNr8n13Alz9X57uNsP/G7/7wHQQxzTCTfLkW3YciynOWc6sk27sb9lPIXzElZi0sB2xs4o7YRtyk7JNMxufyoAAAAAAAAAAAAAAACAOdPT4e+6IAiUy+X0qle9Spdeeqme/OQnz8nHOfXUU3X11VfPybUBzL6aX9O3H/62qn61JZwdD2u3BJkn2RevLN1eXZqK0QAWgqSVVNpOK2WnwtZKNathW80q2Y39UVsPVMfn0nZ0jhNeJ2knZRt24w17AAAAAAAAAAAAAAAAi1rgS5WCVLWlBAWOMft6Pvx9+umn6y1veYte/epXq6+vb04/1urVq3XJJZfM6ccAMHs839P7f/b+bi8DwCJnm7bSVhiObmzWJG2sn7SSsgxLlmnJMizZpt0Y24bdcb7jcR3OqY8dy1HKSskyrW5/mgAAAAAAAAAAAAAAAHpXEEhuWaqMRltBqoy1jUelanyueVy2NKILRg/K9kuy/Yr0W0kv/BfpzD/v9p1hAerp8PfPfvYzPe1pT+v2MgD0KMd0ur0EoKfZpq2klVTCTDT+vniBp0CBvMCTH/gtmxd4CoJw33xSDzknrIQSZkIJK7zflrHlNPqN1mqOW/ZbCSWt5ITwdtpON+ft5tg2e/rbKQAAAAAAAAAAAAAAgIXL9yYJZI922NoD3bH56pjku0e8DCvaWlQKM7kzYFI9nVYi+A3gUOoVbudbUBXzT6dqy7bZWpm5vVJzy/H1/e3j2PGNsHJ7OPlw4eVYP34Nx3RkGuYR33M9BN4pIN4+F583ZDQv0tI1Wq5vGEbHfS19o/WcOtu0m6Ft06GqNQAAAAAAAAAAAAAAwHzj+2HgujzS3CYLb1fHJg90V8e6fSeTq4x2ewVYoHo6/A0Ah5OwEiq5pW4vA4dgyGiEkR3TkWM5jX58flrHRPvroenJgtb1gHY8rN3oHyKIXd9fbycLIS9khmHINvg2AQAAAAAAAAAAAAAAAB0EgVQdl8rDrQHuCduwVOpwTKUgBX6Xb2KOVXo4mI55jVQXgHnNNuffP2P1MLFlWDINM9xkyjTDdsK+aIufZxhGeM4k+zq18eNNw2xUko6HqusVldvD1ocLZh9q/2INTwMAAAAAAAAAAAAAAAA9qV51u6XSdlRZu9xeYXsknOsU7A68bt9Jb6PyN+ZIT6cmt2zZoje96U2N8Qc/+EE94xnPmPZ1br/9dv3jP/6jpLCS6bXXXqtjjjlm1tYJoHuOyx+nsdrYhPBxwkxMGkxOmImW4HM9rBw/frJ99UrQ7cHsjiFsGbLM1tA1IWgAAAAAAAAAAAAAAAAAR8z3w0B2aUgqDoWVtRsB7lhouzzSFu4ebd0UdPtOFj7C35gjPR3+/vSnP62bb75ZhmFo/fr1evrTn35E13n605+uRx55RLt27ZIkXXXVVXr/+98/iysF0C1fetGXur0EAAAAAAAAAAAAAAAAAJieIAirbxcHwyB3KWob46HO+8rDUuB3e/WLh5OVkn2xLScl821zfSr5tu57ZKdcK62amdYTn/Ys5VZu7PbqsUD1dPj7a1/7WqP/mte8RpZlHdF1bNvWa17zGv3TP/2TJOmrX/0q4W8AAAAAAAAAAAAAAAAAAHB4QSB5ValWktyK5JajNjaulSfOVwrNCt2dwt1+rdt3tjAZlpSqB7RjQe1ELpxPdApwd5hL5CRzarnVWqGgXUM3Ncb+qtOlfH6u7hCLXM+Gv7ds2aJt27Y1xi996UtndL2LLrqoEf5+8MEHtWfPHq1du3ZG1wQAAAAAAAAAAAAAAAAAAD3CrYaB60pBKkdtZTTWj81Xi7Gwdjm2VdpC3tGGuWenJ1TU7rxNrLzdMmenJMPo9t0Ac6Znw9/33HNPo59KpfTEJz5xRtd7whOeoFQqpXI5/Ef47rvvJvwNAAAAAAAAAAAAAAAAAEC3BYFUHWsNak8IbI8eer4ySki72+yUlOqXUgNR22FLt++Lxsm8ZCe6fAPA/NCz4e/t27dLkgzD0IYNG2Sa5oyuZ1mWNm7cqAceeECStHXr1hmvEQAAAAAAAAAAAAAAAACARc33Jg9ol0c6zLVX5I6OCfxu38niZphR9eyognaqrbr2hED3QGuQO5mXnFSXbwJYHHo2/D06Otro9/f3z8o149cpFAqzck0AAAAAAAAAAAAAAAAAAHqe70m1olQtSrVxqVZq9qvFcF9jf7HDsePhXGW0tUJ3dazbd7a4GWYU0O5vC2vHw9uxUHfL/tick5EMo9t3A2AKejb8nclkGv3ZCmrHA+WWZc3KNQEAAAAAAAAAAAAAAAAAOGJBIHk1yS2FIetaMWrjW7HZuuW2Y4pSrTxJYDsW7vYq3b5THJIRVtJOL5HSS8M2s7TDeKB1nMwT2gYWmZ4Nf69YsUKSFASBdu/erSAIZMzgHyjf97Vz587GePny5TNeIwAAAAAAAAAAAAAAAABgkfLcsPJ1eVgqDUvlkWiL9ePz9XB2p2B34HX3XjBzdkqyk1EbbU56kgB3NG70l0ipfsmkqC2Aw+vZ8PeGDRsa/dHRUf3sZz/T2WeffcTXu/POO1sqfx9zzDEzWh8AAAAAAAAAAAAAAAAAYB4LAqkyevjQ9mTz1dFDXx+9IZGTkn1hhexUvtlP5CQn1RrWtpNhYLs9xF0fO/Fx/Lgk1bcBHDU9G/5+2tOepkwmo1KpJEn62Mc+NqPw98c+9rFGP5FI6BnPeMaM1wgAAAAAAAAAAAAAAAAA6CK32hrQLg1H/eG2sPYk48Dv3tpxaIYVhbXzzbY9wN3o908yn6eaNoAFp2fD347j6Pzzz9d3v/tdSdI3vvENffWrX9XLX/7yaV/rK1/5ir7+9a/LiN5Z8+xnP1vpdHpW1wsAAAAAAAAAAAAAAAAAmAbfl6pjYfXtSiGqwl2I+rHxocLbtWKXbwId2akorN3XFuDuFNLuFO7OhxW4qaYNABP0bPhbkt797nfru9/9rgzDUBAEuuSSSzQ2NqY3vOENU77G5z//ef3lX/5l4xqGYejd7373HK4aAAAAAAAAAAAAAAAAABYo35e8iuRGWyO4HQ9vx+Zaxu37RyUF3b4jSJKTCbdERnKyYfA6kY3NxfYnch2C2/Uwd3/Y2olu3xEALFg9Hf4+66yzdNFFFzWqdlcqFf35n/+5vvCFL+iv//qvdcEFFyiTyUw4r1gs6n//93/17//+77rtttsaoW/DMPTHf/zHOuecc7pwNwAAAAAAAAAAAAAAAAAwi3xfqsaqZZdjIexKoRnQ9qpRW5Hcalsb39/edjjed7t914uPnQrD2HY6bJ1M1KZi/Ux4XEtQO9vc3zHIHdtPhW0AmDd6OvwtSddcc40eeOAB3XfffY3q3bfeeqtuvfVWmaapk046SStXrlQul9PY2Jj27dunBx98UL7vS1Ij+B0EgU477TRdd911Xb4jAAAAAAAAAAAAAAAAAIueW41C2iOtoe0JQe5C29xos18d7fZdoIURVsFO9Uupgajtl9IDUrI/rI7dHth20lG4u22uvtlpyTS7fWMAgB7S8+HvXC6nH/zgB7rooov0i1/8Qkb0DqMgCOR5nu677z7df//9jeODoPkYkHq17yAI9PSnP11f+9rX1NfXd9TvAQAAAAAAAAAAAAAAAMACEARSrRgFsMdiVbbjW0GqjnWej5/nlrt9N+jEyTYD2/XwdnuQu9N8ql9K5glqAwDmXM+HvyVp3bp1uv322/WP//iP+sQnPqHx8XFJagTB4+Lh8CAI1NfXp7e//e36x3/8R9n2vLhdAAAAAAAAAAAAAAAAALPF98MwdnUsDF5XowB2fFwdj83FAtst4yi8HfjdviMcimHFAtoDbYHt+Li9PxBW7bacri0dAICpmDdpaMdxdMUVV+j//t//q6uuuko33nij7rzzzkYQPC6Xy+mss87ShRdeqDe84Q3q7+/vwooBAAAAAAAAAAAAAAAAHBGv1gxcl9uqa8fD29XxWEg7Ft6Oj2vFbt8NpsOwwhB2MtpS+YmVtg8V7k5kpQ5FRQEAWCjmTfi7bsmSJXrnO9+pd77znfI8T4899pgGBwc1Ojqqvr4+LV26VKtXr5ZlWd1eKgAAAAAAAAAAAAAAALC4uJXWKtmV0Vh4uy3E3VJRu22fW+72neBIJPqkZF8U3u5rhrfr/Qnjvtagd7JPctKEtwEAOIR5F/6OsyxL69at07p167q9FAAAAAAAAAAAAAAAAGD+CALJq4aVs+sVtCfrx6tsdzyuPh4Lr4nushKtQWorIdnJWJuU7ERbm+xw3BSPT+TCAHciJ5kU7AQAYK7N6/A3AAAAAAAAAAAAAAAAsOj4nlQeCatll0fCytrlkc5zlfr86MTgtu92+07Qzsm2VsNuqaDd32EuH9sXzTmpbt8FAACYQ4S/AQAAAAAAAAAAAAAAgKPB96RasRHCNof3a8nYQ7L9khyvKOfu3ZKqhw92V8e6fSdol8hF4evYlsg1K3B33PKtQe9kXrKIcwEAgEPjuwUAAAAAAAAAAAAAAAAgzq2GAetKIQpqF8NxrRj2a/W58Wa/Za6trffdcsuHyUk6Jz6x7SjeI6Iq27kwpJ3IxgLbuYkB7USHuUbIOyuZVrfvBgAALBKEvwEAAAAAAAAAAAAAADD/+Z5UGW1u9fB2ZWyKc6PNea/S7btBJ3a6GcxO5JpB7UbbF7XZZli75Zi+5j4C2wAAYJ6ad+HvBx54QHfccYd+85vf6MCBAxoeHlalMr1vuA3D0I9//OM5WiEAAAAAAAAAAAAAAAAaPFdyS1Kt3Kx+XSs121qpub9xXIc5txQ7PjqmOt4McNeK3b5TTMbJTFI1O9ZP5TtX1Y6PLafbdwIAANB18yb8/a1vfUtXXnml7rrrrhldJwgCGYYxS6sCAAAAAAAAAAAAAACYJ4IgDEiXR6RqFMJ2K1Eb71fCoHXLuK2tHWZ/PLDtu92+c0xXvTJ2Y+uL9dv35Vr77WHuRJ9kzZuIEgAAE1RcT2NlV6NlV2MVV4VyrWU8Wq7pYKGoB7eaKrtS2ZOu2fk7Xfrs43XhaWu6vXwsQD3/nZXnebr00kt19dVXS2oNbwdB0DiuU6D7cPsBAAAAAAAAAAAAAADmFbcahrfLw822VO/H50fa5qPNr3V1+ZhDTlZK9UdbPmyT+ea4XkH7UMHtRFay05JpdvtuAACYkarrq1TzVKp6KlZdlWqeitUwxF0o16LQthuFuGsajY8rzXD3aMVV1fWn+FFjXz+HC9oxyFNJMDd6Pvz9jne8Q//5n/8pSRNC3/FAdzzoXXe4/QAAAAAAAAAAAAAAAEedW5FKQ9E23OyXh5tzk4W53VJXl4654ctUzcrIyS2VmVkSC233t4W4OwW7oz7VtQEA84zvBxotuxop1VQo11SOAtrFqhfru41+M8zttQW7fZWqbsu863c/MzpW5uknmBs9/V3fLbfcok9+8pONEHcikdA73/lOXXzxxdq4caNSqVRj349+9CM985nP1ODgoO6991795Cc/0X/+539q7969MgxDp5xyir70pS/pCU94QjdvCcBs+/ql4S8/EpnwHchO/R3JmfBdyU6m9V3LnfbbSYmnAwAAAAAAAAAAAACYDt+XqqOxEPchgtzx+dIQAe55y4i9Bp2JXn+Ov1adaX2Nut53Mm2vYTfPHa0EuvmOu+QbjmQYOu+885TP57t9owAATJnvBxqtuCqUahqJbcPF1vHE/VWNVlwt5Lq+oxXC35gbPR3+vuKKKySFVbtt29Z3vvMdPfe5z530+EQiodWrV2v16tV6znOeo8suu0zvfe979a//+q+6//77dc455+i73/2unvWsZx2tWwAw17beKo3umdk1DGviD98t41zzB/ZEn5RZKmWXS5nlUnZF2E8N8NgrAAAAAAAAAAAAYL6qFqXSoFQcnNi2zA21hrsDv9srx+EYlpTsCytjJ3NhPxG18e1Qc4lc9PpxetYLiwWFgnwzMavXBABACnOXNS9Q1fNVdWOb56nSMvZVi46pxObi5xRr3oQQdz3cPVquqQeKbPekUSp/Y470bPh7fHxcP/7xjxuVvd/85jcfMvjdSSKR0D//8z9r8+bNeutb36rR0VH9yZ/8ie69916tWrVqLpYN4Girjs/8GoEnVQrhdqQMKwyFZ5ZHwfBlYZtd0ew39i0PjzWtma8dAAAAAAAAAAAAQJPvNytuTwhyH5wY5K6P3XK3V754GaZkpyUnFbXxfnwuLdmpWJsJ9zvpKNg9SYjbTvEkaADAvOD7gcaqYQXt0XKsLdda+s05V6PlmsarXkuQO95Hd40R/sYc6dnw95133inP8yRJhmHoLW95yxFf69JLL9VPfvIT3XDDDRoaGtL73vc+ffazn52tpQLoliCQarMQ/p4NgSeN7w+3/VM5wegcFq+Pk3kplW9t633Lmeu7AQAAAAAAAAAAAHpLrSSNPhZuY49Jo3ub7eij0tjecCsNUY37SJl2GLS2k2FgerLWSR16v51svc7hAtyWQzgbABaRIAgrUZervko1T+Wap1K0levjaliduuYFcr1Arh+GmV0/kOv54bzvy/WCRr9Wn/d81aLjXC9o9GsTzgu/X3AsM9oM2ZapxCT9+jHOYfq2ZUTnhX3PDzQaC2oXolB3IR7kLtVUKNc0VnEVUEF73sgkLOWStvpStnIpRxlbKo4MKmVJKVs6efNGPWHD8m4vEwtUz4a/t2/f3uj39/frtNNOO+TxlUrlkPvf85736IYbblAQBPrSl76kf//3f1cqlZqVtQLoEq8q+fP13VFBVFngoHTgwemdaqcnBsNT9Xey909tn8O/fwAAAAAAAAAAAOgBldHWAHc83N2Y2ytVRrq90t5lWFKqP9zSA81+aqBtfqB1PpGNQthJyUpKVs9GSAAAXRAEgcYqrkZKNY2UwvDySBRSLlZclV1fpWossF3zVKr5sQC3p7IbtdF8PeRNwBndlLDMKLAdBbeTtvpSjvoaQe5wXA92h8c4jWPzKUfZpCXbMluuWygUdNNNNzXG552zQfl8/mjfHhaJnv3OfXBwUFJY9Xv9+vUdj7Ftu1EdvFw+9COYHv/4x2vVqlXau3evyuWy7rjjDp1//vmzu2gAR1e1R6p+H21uSRorhb/oOlJWInzcWSInJaM2kY36fbF+9Ei0RLbt2La+neTd+AAAAAAAAAAAAAif3lsZbRZCGj8gFQ+EbT3cHQ9498qTfrvNTocB7fSScIuHtA8Z6O4PX6/jtToAQAc1z1chCm+PRNWmwyB3rdEWyq3h7nrAu1CqySekjS4wDSmTsJVOWMokLKUdq61vKxPNpRNWaz9+TMdzLSVtq9u3CMxYz4a/XbdZzTebzXY8pq+vT0NDQzIMQ/v27TvsNY855hjt3RuGJR988EHC38B8ZznSOf9XqhWl6phULYaB8Np42Fbr8+PhMbVit1fcO7yqVBoMt9lg2lFAvC8KhWfD0HhqoPkLqszSZr+xLQ1/KWUnZ2cdAAAAAAAAAAAAmF2+J5WGJga5i4Ox/oFo/8Gw71W7veouMaJQ9pK2IHes33F+IKzADQBYlDw/CCtiV2MVtKud23pF7eIhjh8txyp0V71u3x4Wqb6Urf6009gGMmGbj821b/mUo0zSUsIyZfDGNuCQejb83d/f3+iPjY11PGZgYEBDQ0OSpO3btx/2mr7vN/rDw8MzWyCA7kv2See/Z+rH+34UFO8QEK/P17fJAuXlkfAXWOMHqEIQ57vh56Z8hI/bc7KxkHh7QLweEu8w76Rm9z4AAAAAAAAAAAAWKq8mlQtSZSRqC7F2pC3cHVXtLh4Ig9+Bf/jrLzSp/vA1qszSDm2HIHd6iZTMSyaVJAFgsfH8QMPFqoaKNQ0Vqxoar4ZtsdboD47XNFysarzaDHCXouB21V2EX2fRU0xDStimEpaphG0paZuxcbglbbM1qN0W6o5vfSlHlkl4G5hLPRv+Xr9+vSQpCAINDnauTHvyySdr69atkqSf//znh7ye53l66KGHGu8ISSapMgssOqYZVqVO5mbnerVS2y/AYpUNxve3zR0Mf3GGzmpRwL6wa3rn2enwTQCJbPg4u0Q2ttUrkOc67O/Uj1p+IQcAAAAAAAAAAHqNW22GtFtC2x1C3JPtd0vdvovuMJ2Jwe3GeFnncHdqQLJ6Nk4BAJhDNc/XULGq4WJNg+PVRqi73q+HuAdjxxTKNQVBt1eOhSrlmOpLOcqnbOWjYHU+ZYdt2lY+5SiXtCeEtRO2qWTbuCXMbVlybEMJy5Rtmd2+TQDT1LM/rZx66qmN/p49ezQ2NqZcrjWw+cQnPlHf//73FQSBbr/9du3Zs0dr167teL1vfOMbjQrihmFozZo1c7d4AIuDk5YGjg23qXArEx+H1/JovGhfaVCqjIa/hKO6+KG5pXCbzU+TnW4LhmckIwqEG4Yko7Uff8xMo29Mr2+YYSWI7PJwy7S3y8LqEjzSBgAAAAAAAACA3ua50RNlJ3kabacn0B7yKbXR2C13+856i5OV+laHW25VrL86em0lFuRO5HiNBQAWuCAIVKx6Gqu4Gqu4Gm+0Xqxfn4/mqq1zY5Wahos1jZbdbt/Oope0TaUTlhKWKccy5ViGbMuUbRpyLFO2ZcgxTTm2IduM9pvRfOx4x4zOqx9f71vN8ySp5gWqeb5cP1DV9eX6vmperO+G+2t+oJrrH7Jfv0a9X3N9yZDyKUd9UXg73xLkDsPb+XQU5m45LpxL2ASzAUzUs+HvjRs3avXq1XrsscckSb/85S/17Gc/u+WYl770pbriiitkGIZc19Vb3/pW/fd//7dsu/W2tmzZore//e0yDENB9DarZzzjGUflPgCgwU5K+bXhNlWeG1Zj6FShoTI6heoOBak6Onf3tBDVA+XFA91eSSvTCUPg9TB4dkUsIL5sYmA8NRBWuwcAAAAAAAAAYLELAsmrhoHqWjl8HaBWauuXwoD1hGPq8536UVsPdteKhLRnKtkv9a1qBrn7VkXt6ta5ZF+3VwoAC5br+Sq7vso1T5WoDTdflfic25zzA8kPAgWBFChoHQfhOGjMBQoU9uvz4TFBdEy4T41jAnm+VKo2g9vj1Xig29N41aXy9lGSTVjqTzvKJm2lE5ZSTrilHVNppzkO5yylE2bLOD6ftC2lE63zSduUafKmLQA4nJ4Nf0vS+eefr//6r/+SJH3ve9+bEP4+44wzdPrpp+uee+6RJH33u9/VU57yFL3pTW/SSSedpGq1qttvv12f/vSnNTo6qiAIZBiGzjnnHK1fv/5o3w4ATJ9lhxUJMkuP/Bq+HwbAW4Lho+FcdVyqjEWVI8Zi/fHomLHYMaNhG3izd3+YOr8mjT0WblNhWNGfnbbq4fWq5k6mWdk8kQ0rZCSy0TgX7Y/mLGdu7w0AAAAAAAAAcOSCQPK98PfIvit5UTulfi0sRNPo16JruVLgR5sXtUFszg+Pi49b9rfv85vrjB/je8220XebxzX6kxzTss+N+n6z79WaYW2RCOuaeIGb7Aqpb00s1B0Ld+dWha9TAACmxPMDjZVdFco1jZRqKpRrKpRqKpTcZr/sdgxxl11PlXgbO8b1+Zq5kFmmof6oqnR/2gkrTEfVqMNxON/fMhe2fSlbjkUROgDoBT0d/r7ooosa4e8bbrhBH/nIR2S0PY7pE5/4RCMUHgSBfv/73+sd73hHyzH10LckOY6jj3zkI3O+dgDoGaYZhn5T/TO/VhBIbqVDWLzejx5NWBkNg+alYak0NHGrFWe+Fhxa4Enj+8Nt/wyvZSUOExaPjbPLpf5jpPwxYZtdQQVyAAAAAAAAAItHEISB43oF6FqxWWG63m9UlC62zcePjx/Tts8th2Fnr9YMfANHUyIXhrkbTyxdHhakiT+hNL4/mZcMKngCQDvfDzRedVUouxop1loC24UozD3SIcwdBrxrGq3wPcBikLTNRnXssGJ2s61Xy87U+9G+bNKeEPCut9mENSF/BwCYf3o6/P2iF71IT37yk1WpVCRJd9xxh57xjGe0HPOsZz1L//Ef/6G3ve1tjS9MQew5HoZhyDAMBUEgx3H02c9+VmeeeebRuwkAWEgMQ3JS4ZZdfuTXqZWl8rBUHOwcDi+1z0fH1sZn604wHV413MrD0z/XSkj5tVL/sVJ+XRgI71/XOk7lZ33JAAAAAAAAABapeCjaa6uE7VWjEHU5bOsVod1KGMh2K7Mz5gmamE+cbPh7+mQ+eqJoPNC9rBnkjge6nXS3Vw0APaPq+rGQdtiOtIW0R2JB7nqYe6RU02i5JopsL0xpx9KSjKMl2YSWZBJR62ggk1A+ZbeEuVMJS5lYcLse4s4kLCVtS5ZJUBsAMFFPh7+TyaR++ctfHva4Sy+9VCeeeKL+7u/+Tr/97W9b9tWD4GeddZb+5V/+RWedddZcLBUAMB1OSnKiR/hNh1uZWE28VpxYebw6Pkk/NnbLc3JraONVpaFt4TaZZH8UCD8mFhA/tjnXt1ayE0drxQAAAAAAAMCR8dywgEW8WrNXC8PIvhtuQb3f3kb9oG3cst+VAr/RT5ZLOnX31rDStKTkLbdJieTc3V8QSApa28CfODdhn9r2+ZMc74Wfwwmh7Vrs89hpn9usfu3VFH1AYHFI5MLQdj283bHtn3x/Mi9ZPR0ZAABJYfZnpFTT7uGSHh0ua6hYnfTYySoaHyo+2+kU1w80WnYboe72EHc9wF2q8aavhS6bsCaEuJdkwvHSbBjoXppNaCDjaGl0XMqxur1sAMACt2B+kjvvvPP061//Wg899JDuvPNO7d27V0EQaPXq1Tr77LO1efPmbi8RADBTdlLqWxVuM1V/ISYeDu8UIK+Nt704MY2+1Hzx4lB93wsrmxcPSOMHpOJBaXz/4gmoV0akfSPSvvsmOcCQcquaYfBUv2Ta4WZYkmlFYys2bzb7jX1W7Jy2uQnndbrWNOcMSzLN2f98BbEXyAK/ualtHASS5YRVW+ZiHQAAAAAAANPle1GF5HLYepXWsVuW3Kgys4Lw9y0ywjROvN+Yq/fNKfbVOu9WmiHtelstTpyrlSaGumul6PeHpea8Xzuqn86kpOPjE/uP6ocHMF2mIyWyrZtT72eiNic5mbZjMlHIu29ieNskWAZgYSjXPO0ZLunRkXIj4L1nuKQ9I6XGfLFKyBrTl3YsZZO2csmwDft2cy7ROtefdrQk60TB7jDQnbT5egsA6D0LJvxdd8IJJ+iEE07o9jIAAL3OsiWrPwwS96IgCF88Kh6Qxg/GguFtAfHG3MHwBagFKZDGHgu33b/q9mKmyWgLiVvN8LmkjoHt9lB3e7D7SNaQ7Itt+UOPU/nO84k+QuQAJqp/vaq/caoyGntD1djUx9Vi+JSHVL+UGohexOyf2pbMz/9/n4KgrcJfve93mPPCryP1F3+ddOeyNAAAAAuJ53b4GbmtgnD9Z+aWasOTHRe0nRNdO15F2Kt26NdaqxBPq18Nv68zzGiL3pRe7xtG8/cGhhnbZ8bGHfa1XCMad6yuPFn/ENWYJ3yuYsd7bUHtwwa6o7mjHI4GgCmzEpKdDp9caqein7lT0Vy02am2fuyYRoA71xrYroe6nSxPuASwaHl+oH2jZe2JAt2PjpQa/T1Rf3B88kre6D0J21TKNpVyLKUcS0nblGUaMgxDpiGZhhG9VzMcGwrnTCMcxI8J29bjjMa+1mulHSsKaVvNwHZLeNtqhLizSVvZhCXbmuevHwAAMIkFF/4GAGBBMAwpmQu3JRundk6t1BoGLx6IBcQPRpWIirFq5/VxFLrzKnN6S4tTED1ytpsvbAZSpRBuM5XoawuO94UvXFiJsDJ/p3bCXDJ8kcNKxPrxNn5efS41/4OdQC/xaq0h7MqoVB1tDWRXxppzjWPbg9tR2/XHaRtTDIvnw0DMhEeEu62PCT/c48M77auP2x/bHvitoe0Jc9GxM/0cOpnoReVMVDEsM/lc4wXoyY6JWjvV/PfYcgiYAwCA6XMr0feWo63fU04Yj4U/s7aM69+jRmMCwwAwdwwr/Lmv/oTDeD/+JpQJb0wx1PKGlvZj6/vb38zSfkzL0xpNtTy5sfHkR2tq8y37Yk+OnBDcjkLeTqY5TwVtAJiycs3TaNnVaLkWta4K5VpjfGCsGgW8w2D3Y4WyPL/bv0dePHJJW/mUrXzaUT7lqC9lK52woqC2qZRtKRm19blkFOCuh7nrwe7241KOpYRlyjT5fTEAAN3Ws+Hv66+/Xn/xF3/RMr7gggu6uCIAAHqck5YGjg23I+G5YfXw6ngYBq+OTQyLV8cnP6YyKhX2SCO7CJIvVNXoxffRLnzslked5toej5qbQr/9kak5Ku1g9nm18M029a003KFif4dfcAedfuk9xeOCIHoTzzSC3Avu3+hAqoyE20i319Ilteix9MU5/Bim0wyCx0PhE/pO25t4DnGs6YQvrseDBe1Bg5Y5KzqnPlff73QeN17sNyQZzeBBp35jboYvWtQrg7Zvbr0fVf30Ks2+G+tPdl7gNT9/pj3J57XDvOkc4v+T03pNwwirzXdax6Rr7bTuQxzr19o+52aHz3+n+Un+v3U6xrSl3Eopv07qXyf1reVrPgB0EgThv8+1Yvhm8vjmto1rxbByc+PYcutctRgLcxea33cS2AaAzqxkM4TspFsDyPVK0vUnPcWrTrccn2z7Wchp/Xmq8XOV0/qzUvxnK8uJwtEUXQCAxaTm+S3B7UKppkJ83Ah0twe7w7lC2VXVPZIn1WKq0o6lfNpWfxTeDkPczTD3xH3hXD3oTaVrAAAWh54Nf2/fvl3Dw8OSpFwup+c+97ndXRAAAAudZUtWVB11JoIgDD2O7AyD4CO7w35hd3M8+qi6XykW80o92Di+f/auaTqxUHi67cUxp8OLZfF98RfMnLYX2SbZZyej6sMDUnqg2XdSs3dPmD2+L5WHpeJg+CSFeKi7eDB6wkJ8GwzDx8BCVX+SxaLIcB0mfNy+X0Ez4DzhDR/ziGHO7/VPypByq8IgeH6d1H9srH9M2OZWEXgBFjPfj4WW65WmC+EbcOJfC1paqePXiwlvKurwRqP4+Qpa3zjkx99ENIP5xlNKojcSNYLabYFufi4HgOiNq+nw9zZOKgxh17eO4/qxUTvpODNJmDtFhWkAwJwKgkCD41VtO1jU9oPjE9rh4qL4BV9XWaahfCoMaPeno4B2FNIOx7F9qdgxKVt9KUcJm99TAQCAw+vZ8HcqFYZgDMPQhg0bZFn8IgQAgHnBMKTs8nBb+6TOx3i1sEp4IxAebY3xTqlMiBJzzK+F4d7ycHfXYafCEHiqPwqFD7SGwyf0Y8cmctOvUhsEYUjErURVWett9ZBzztiINu7/vczAk2TI+c02KZWevPLqlKq3todg4mFKqSWM0qh6PZO5GK8mlQYnBrjHo6B3aXCBhiABHF4QVtpebHm4BftvXiCNPRZuu3/V+RDTkfJrpPwxYTC8HgqPt+klM68MD8wHnhtWfXYrUQXochQaLkfVoMuxuUn2S+EbIe1k7GkQyeiJA8m2p0Mk254UkTjEeU7r30PPjZ5uMiqV24Lbjf7o4eer3Xi0EYBFL/6UG8NqPrWmvTXiY6vtPFOuLx0YGlb95+hly5fLsafw0l/HJ1Ad8gRN/kaXyd4Ec6g3yJixN9LEr2VN8ub8WKXqQ+5rf8N+h31WojXQzZsAAQDzUBAE2jda0bYD49p+sKhtB8e1fTAMd28/UNRoxe32EueUZRpankvInubX8WCS74Em+87IkJRLtQa160Hu/iisHQ9419tswpLB75EAAMAc69nw95o1axr9ZDLZxZUAAIBZZznSkg3hNpnKaFglvLCrWTG8sCcMpfputPnNfuBJvheNvdgxXrRvsnPqx8eOWXRpL3SVW26G0qbLtKNgeBQOT2TDUHM9vB0PcMfnjuDPeFrSE+ITu6e/XAAAepJfk4Z3hNtknEwUBI+qhpt2GJgPgqiNNrWNOx0zYdw2V7+GnYqqRabDCpKNypHpti1WVdJOtR0TqzRpOUfrM7ow+F7s+6haGFJrCZPZ3XlDgFcLq1RXx6VqMezXitF4LJobn3y+XgG6Ediuh7grYXjb7/GAQD0wHnhR5WoAC1L977phRCHloPn1UkHz62Z7/4gZbVWa01Ii0+Fraoe5qRxnJ2MhbntWw8bFQkE/v+mmxvi8886Tk8/P2vUBAED3eH6gR0dK2n6wGG3jYcg7GpdqXreXOGeWZRNaM5DS2v601g6ktXYgpTWx/sq+lCyTcDUAAFjcejb8ffzxx0sK33m3ezfJEgAAFp1kn7Ty5HA72ny/LUzuhi8kxgPlk821hM07zUVjqVkFub6pbVyvgtQ+N5XjZIRh38pUKuEVWttyIVwvep/vNqtWA73EToWV6ZM5KdEXtblY29c6TuTC8Fl55PBbbbzbdwdgMaoVpYMPhdt8ZdqNgHjOTum8iifPTMozHWWG/lNK5SaGy+32oHl7yDwWNq8fe7gKmkEQvVmtGm3xfttcPXjdMl+ZeJ57qGvF52vNN8d1vG5sbirV8FsqiXaoKtpefbSlSml0TLyCqVeLwtpRSLs95F0dD9+ssJjV//8AR40R/f1NNP+uduwnJvm7nZj4d19B8/cEgR/9DsKPjev76sfFfkcRf+NQyzW81vmWpy6p7QlMk/RbKjWrrWpzh369Or+djFVRTkYV/ZPNsZ0KK/zHx4c6xkrOLBwdtAfFDxEaV9CsAk1lRgAAcBT4fqDRiqtCqaaRUk2Fck2Fkqu9QwX9Zo+hoaqhA2Xp//+HX2r3cEVVb+E9qS2TsLR2IK01/SmtG0hHoe5UFOwO51OO1e1lAgAA9LyeDX8/5SlP0XHHHaetW7dq3759uueee3Taaad1e1kAAGAxME1J5uKtjhgEUQizcIjHo4+E+91yW3Xpamv/cPsWe3gF6BWGFQaxk/m2kHY9vN03eXC7ZZwNj7fm8EdNrxa9UWV4amHx+CbFHhFuTwzltTw+3I4d02mf03qt+r7449jrj283rPBry4S59r7dPDb+ePf2871qGAqslWIBwXpbDAPy1Wh/o3+YY3q90iuAmfPd6Pu6gkxJLTVBxx6c3Y9Vr1pup8NgYnvYeqHwa3w/C0yXlYh9/5iPvgfta35fGd/ic04mVnG/HkDWJEHmDkFlIwoTH+64CcFuQifzjmGE3zMDAADMgSAIVKx6jdB2oVzTSLEe4q5pJJorRMHukVLsuFJNYxU3fA9aR/HvYUpH4W5mj2FIuaStfMpRX8pWPu1oTX9YrXtdrGr3uoG08mlbBm+8AwAAmLGeDX9L0ute9zpddtllkqQrrrhCX/rSl7q8IgAAgEXAMJqVHftWze3H8v3WqoxuJay66FabrVtqVkBsVD9s39rnY2OqBKNbEn2d30TS8RfbHeamelwi06yu3RLE7hDanhDcjgI386nSneVImaXhhtnjVluD4h0r43aqrHsE/fq160/E8GrR0zJq0diNPS0j2ufVmuOFwkpGlSYTzaqgViKar/ej/YYZfR6jz6Vfm+Rz7MaqFs/xUzwa1Uzjm9N2T/W5ZGuArl5RuWNFzkNU54zPT3aeW5YKu5tv+EB3uOVw01C3VwLgSNUrOU94IkCHOScT+36z/j1pX4dxLqzyDAAAAPQI3w80XKppcLyiA2NVHRyr6mCjX2mMD45XNVwMQ92uP2l6e97KJW31peqbo3zU9sXafLo+Xz/GaZyTTdgyzXny+2UAAIAFoqfD3+985zv15S9/Wffff7++/OUv66yzztJf/uVfdntZAAAAmC2mKZkpyUnN3cfw/WbF28lC4m4pChvWmgFDrxYbe4fYFx/Hwoot16uF1XVLwwsruLiY2CkpszwKHS8Lt+zyqB+byywLj0svCcOPwHxhR4Hd9JJur+TwfC/2b6/burUHyX03Fg6WphU0buzvdF7sWBkdAs/tYei2QHejcukcf57iX68mBMXb5gNv4ro7BdGtRFTl3pzb9c9UZVQa2S0VdoXtyK4wFB5v3XK3VwmgVxlWGGqWJv7737GdhUex199UU3/qSGOLzyWOYD4R/rxVD2x3DHTHQtxOKhz3+r/zAAAAQAdBEGi86ungWCzAPV7V4HhVB+Jh7rGqDoxVNVSsyluAYe66bMLShmVZbVyeCdtlGR27JKOBTCIMdKcc5VK2LILbAAAA805Ph79TqZS+//3v6/nPf77+8Ic/6O1vf7vuvPNOfeADH9CmTZu6vTwAAADMB6YZVZvLSZrjSuaHEwRhCLw8HAbByyOT9KNxvV/ffzSrmJt2GPqzw/Cfbzoar7jyjfBHiFw2Lcs0p1C1VZOEKScLXkb9eoXrlt851+eMiXMt81OZM6T0wMRAdyPU3TaXyB7Z5xHA7DOtcKNy6KHVP0+awzdY9bJkn7Ty5HDrJAik4mBbODzqN0Lie+a+gjow31jJKBxcrwqdjvWj0LBhxJ4gUX/CT71fifVj22wxHSmVjypO56Otr8PWaT4256SP7E06HZ9S0OnNRbHveetvCiJsDQAAcNR4fqCq66vq+qp4XqNf9fxm3/VVaRvH93tBoCCQ/CAMDvt+oEDhOPy2sHXsB1IQ/b60fa5+fH3sR98yphxT/WlH/WlHAxmn0e9PJxr9hH30v48MgkCjFVdDUah6qFjV4HgtHBerbfNVVT1fhgyZhmQYRvibWkMyo75hSIaMsI3mTDM2p2g+6puNvhFdRypWvSjQXVHFnYU3Z84j+ZSt45ZnG+HueNh7WTYhY7488REAAADT0tPh7+uuu06S9Bd/8Rf6p3/6J+3du1fXX3+9rr/+ep1xxhk688wzddxxxymfz8txOjxO/RBe+9rXzsWSAQAAgMkZhpTIhFt+7fTPd6tRSLw9KB71a6WoumwiCuFEVVs7ztXbTnPJKDDYNFYo6KabbmqMzzvvPOXz+Rl8MgAA6CLDkLLLwm3NEzof43vS2N4oHL4zCoXvlsb3R9cw27ZOc6YkIzY22va3j81m4NSthF/ba8WoLbWO3XJsX1GqlcNgLRYnOx2+WS2RkRK5sIJzIhv2E7F+p7C2new83x7ytpJzE1AOguhJBJXWkLhXaw2Ru5XmcTJiIe9YcLvbbwwK0yrdXQMAAEAPCoKgEZyutIWpKzVfVc9rzMf3h31vwrmVlnOjY+Lnem3X8Fo/5kKq9JxJWLFQuNMxLJ5POxrINAPjA9Fcvdpzqep1DG03w9y1xvzBaN5dQJ/D+WB5LqENy7LasCyjDUtbK3kPZHgCJQAAwGLU0+Hv173udS3vQjQMQ0H0ztm77rpLv/jFL4742oS/AQAAMO/YCSm3ItwAAMDcMq3wzVr5tdKxZ3R7NVPje82QuNsWFq8HxGsllUYH9fB9d8sMqrL8qo47ZrWShtfhvChg7pYnzulovNBvRG9iS0qWE76ZzXKab2xrzMU3Z2Lfbjt/wrHJSebr/ajohFeVPFfya2EIud42+m6HufZ97ddwo7la+HESWcnJRmHttm3S+cyEN+7NK4YRfp9rJyQe6gAAAOYB3w+rHhdKNY2UaiqUay2h5ErNawSTm/PeJP1w3Dy3Hmr2JgSdHctQwjLl2KYSlqmEHW2WqaTdHDtW6/5k7HjnEOfZpinXbw1NV9zWe5gYzvZaQtjx9Tc+B1HwGnOjWPVUrHp6dKQ87XP7krZqvq9yjf8/3ZRL2solLRluWVk70LKk9LRTN+mktUvDsPeyjPpS0yuGCAAAgIWvp8PfdUEQNELg7Y+kqYfBp6IeHuexNgAAAAAAAFhwTEtK5sLtEGqFgrYcWN0Yrz3nPCWn80SPIAirMLultqrkpYlzpt0aprY7Bazbwtv1cPd8DjQDAAAcgarrq1h1NV71VKq6Gq94Gq+6KlU9jVc9FSuuitUwFOwHgXw/kB9IfhAoCJp9P1A0Dvuef+j9E6+l8EEbtqV0wlTasZRO2GHrmEonLKUcK5oP21Ssn4n2J22zZ1+XdT1fo2VXI1GAu30rlGuNcHfLVqxptOJqGi9Rz5qaF6jmeVLVO/ofHAvSaMXt9hIWhJRjKp8Kq6nnU3aj2no+Va+8bjf290fz9bm+lC3bMlVof/Lm2cfy5E0AAAAcUs+Hv+vh7umEvA93LQAAAAAAAABHyDAkJxVu6SXdXg0AAMBREQRBWMnY9VWuearUwrZc81V2m+NSzQsD3JWwPx4Ftuuh7mJjHAt2R3Ouv7BeyzQMRYHx1nB42rGUSoThcNOQDBkyjPB4KRxH/8kwDBmx67XMxY5v7muOpTD4XijXw9vNat1jhF4BdNCXsrU8l9SybEJLswktyyW1PJfQsmxCS7IJ9dcD3LEQd9LmjcsAAAA4+no6/H311Vd3ewkAAAAAAAAAAAAA5pmq62v/WEX7CmXtLVS0f7SsQtlVpeap7NaD22F4u+JGIe5oXyXaV3FbA97UmZqeIFAj6A4A3ZCwTa3IJbUsCnAvi/ezYX95NLc0myDIDQAAgHmjp8Pfl1xySbeXAAAAAAAAAAAAAKBHVFxP+0cr2jcaBrv3jVa0t1DWvkJFe2Nzg+PVbi8VwDyXsE0lLVMJO9ySUetYpmzTCKvQR1XnzXo/qkxvGM05MypFbxhGVO2+uU+K5mLH16vfFytuVLW+udW83nsXimMZWpIJw9NLMgktzSW0NBNWyl6acZROWAoCKVD4phA/CBQoHASSfD9o2RftUqBAftDsB0H4FIb6tfxYP2mbjXD30mwirNadSyqbsGTUHwUAAAAALCA9Hf4GAAAAAAAAAAAAsPBVXE/7Ch1C3VG7P2qHirVuLxXAHElYrSHrhG12mLOUsEwlndZgdn0uYVnNOXviMe3XT9oTz0lYphzL6LnQcBAEKtU8jZRqGi7GQuHF1oD4cDwwXqw2+v4UcuOmIS1pBLcTWpJ1mqHueBvbn0vaPfe5AgAAABY6wt8AAAAAAAAAAAAAZiQIApVrvkZKNRXKNRWisGHYD6vXdpwrh3OjZbfbtwAcsYTVDBQnbVNJJxZQbsxbrcfYVue+Uw8yN49PWKZcP1DV9cPN82L9w8x7vqpuOF+L5ipedIzrqer5qrmBap4v2zIaa22GqWP3Y7feZyK29k5zrfcbhqzr12wJaEf3SID40AzDUCZhK5OwtaY/Pa1zgyDQWMVthMbr/x5bpqGl2URjy6ccmSb/HwAAAIBeR/gbAAAAAAAAAAAAQEPV9bVvtKy9hbL2FioaKlZbwtrNELer0Vigu+ZNoawspi3lmMombKUTlrIJWynHlGkaMg1DphEGQk1DsqK5+ji+3zIMmWb92OZ+I2pb90t+IJVrnkpVT6WoLdeifs1TqeqrXPNUrLpTqibc6wxDyqcc9adbt3zaUT5tT5jvTzuN4/tStmzL7PYtAIdkGIb6Uo76Uo6O7fZiAAAAAMwY4W8AAAAAAAAAAABgEQiCQIWyq72Fsh4bKeuxQll7o7YxLpR1cLyqYAEEeo8mw5BSthUGtZO2MglLmYStbNJS2gnbsGKvpWzCUiZ+TMIKg91tc5mkrbRjyerhKrxBEKjmBc2QeCww3hIej8bFajNIXnF9BUGgQGr8eQsUKAgUmwuijxNtsf2NeYUTQbSe+llBEP5/6UvZEwLb9WB3f9pRf8ZRLmFT7RgAAAAAMG8Q/gYAAAAAAAAAAADmuZrna89wqTXQHevvLVT02EhZpZrX7aUedUnb1Mp8UkuzSaUdUynHUtIO23pguz6XdKxw3jGVsi0lo7YxFzs36TSv4Vhhxe3FxjAMJWxDCdtUv5xuLwcAAAAAgEWhp8PfO3bsmLNrr1+/fs6uDQAAAAAAAAAAAMyGqutr/1hF+0cr2lcoa/9YRfsKFe0ZHNX920yNVA2NVKXRn/1Ui61Yd9I2tSqf0qp8Uiv7UloZtava2nzaXpTBbAAAAAAAsDD1dPh748aNc/KLGMMw5LrurF8XAAAAAAAAAAAAOJwgCFQou9o/Wta+0TDYvX+00ujvGy03xsPF2iGuZB61NR9NKScKdfeltCKf1Koo2B0Pda/oSymfItQNAAAAAAAWn54Of9cFwWKrUwAAAAAAAAAAAIBuCIJArh/I9QK5vi/PD1TzAnl+OA7ng2jej+YDufG+76vmBRoar7YEuuNB74rrd/tW51zCNtWfdpRP2cqnnajvKJ+2Y/1wfiDthJW78yn1JQl1AwAAAAAATGZehL+PRPsvhAiQAwAAAAAAAAAALA5BEGi4WNPu4ZIeHSlrz3BJe0ZK2jNc1qPDJT1WKKvi+h0D3D4vKbVIO5ZW9CXVXw9vp+2W0HY92J2Pwtz9abvRTzlWt5cPAAAAAACw4PR0+PuSSy6Z9jnFYlH79+/Xr3/9axUKBUlhEHzTpk161rOeNdtLBAAAAAAAAAAAwFFWrLphkHukFAa7G/1yFPIuqVxb+JW1Z8IwpGXZpFb3J7U6n9KqfCps+8N2dX84l09RhRsAAAAAAKCX9HT4++qrrz7ic4Mg0Pe+9z29//3v169+9Stt3bpVr3jFK/ThD394FlcIAAAAAAAAAACA2VTzfO0tlJsVu9tC3ntGShou1rq9zJ6WtM1GeHtNFOZeFQt0r+5PaWVfUo5ldnupAAAAAAAAmKaeDn/PhGEYetGLXqQLLrhAf/7nf65rr71WV155pTzP05VXXtnt5QEAAAAAAAAAACxKQRBouFjTjsFiY9sZtdsPFvXoSEl+0O1V9qaEZWpFX1Ir+pJakrZUHdmvvBOoPyGdc8bjtWnNUq3Op9SfdqjWDQAAAAAAsEAt2PB3nW3buuqqq/TAAw/o5z//uT760Y/q/PPP1/Of//xuLw0AAAAAAAAAAGBBqrq+9gyXwkB3Pdx9sBn0Hq243V5iT+lPO1oZhbqbbWrCOJ+2G6HuQqGgm266qXGNZ25eqnw+361bAAAAAAAAwFGy4MPfkmRZlj7wgQ/oggsukCS9+93vJvwNAAAAAAAAAABwhIIg0FCsenc83L1jcHFX73YsQ7ZpyjYN5VJ2I7y9YkKYO9mo4p20rW4vGwAAAAAAAPPEogh/S9L555+vvr4+jY6O6re//a0eeOABnXzyyd1eFgAAAAAAAAAAQE8qlGvaPVTSrqGSdg0VtXuopJ1DRe0cLM2r6t22aWh1f0pr+9NaM5DS2oG01vanlE87sk1TlmnIsQxZZhTatgzZphHNx/eHgW57smNNU6ZpdPt2AQAAAAAAsMAtmvC3ZVnauHGjfv/730uS7rrrLsLfAAAAAAAAAABgUQqCQIWSq51DRe0aKmn3cBjw3jVUigLfRRXK8yPcvTyX0NqBtNb014Pd6XA8kNK6gbSW55KyCGUDAAAAAABggVg04W9JSiaTjf6jjz7axZUAAAAAAAAAAADMnSAINFSsNSp2N6p3D9f7JY3Ng8rduaSttQMprYkC3WujgPeagbCS9+r+lFKO1e1lAgAAAAAAAEfNogp/79ixo9G37UV16wAAAAAAAAAAYBb5fqDxqqvRsquxStiOlmsaq7gai+YK5Xq/pvGqJwWSDMmQZBhG1LaOw/1GbD42jg7ouE9SzQ/06HCpEfAuVr0ufXamLmmbWr80o/VLMzo2atcvzeiYpWHYO59yur1EAAAAAAAAoKcsmgT0Lbfcon379jXGK1eu7OJqAAAAAAAAAABAtwRBoNGKq+HxmoaKVY2UalGIu9YS5m6GuFtD3fVjMDUr+5KNUPf6Zc2A9/qlGa3oS8oIU+0AAAAAAAAApmBRhL8PHjyot73tbTIMQ0EQSJLOPPPMLq8KAAAAAAAAAADMVLnmaahY1dB4TcOlqoaLYaB7uFjTcLGqobZ2uFjTcKkmzw+6vfQFI+V0rt69fmlGxyzJKJ2wur1EAAAAAAAAYMFY0OHv4eFh3XDDDfrABz6gPXv2SAofm3jqqafqpJNO6vLqAAAAAAAAAABAJ+Wap60HxvXI/jHtGS5Fge5meHso1lZcv9vLXfAs09Ca/pSOWZLWuoGMjlmS1oZlzUreK3JU7wYAAAAAAACOlp4Of59//vlHdF6pVNLevXu1Y8cOBUGgIAgaVb8Nw9CVV145yysFAAAAAAAAAADTEQSB9hYq2rJ/TI/sH9Mj+8e15cC4Htk3pj0jJQUU5j5qHMvQ2oG01g2kdcyStI5Zkmn2l2a0qi8p2zK7vUwAAAAAAAAA6vHw980333zElSKC2G+F68FvSbr88sv1whe+cFbWBwAAAAAAAAAADi1exXvL/ma7Zf+Yxqtet5e3KCRsMxbsroe8wwre65aktbIvJcukcjcAAAAAAAAwH/R0+Hsm6qHxeuXvdevW6eMf/7guuuiiLq8MAAAAAAAAAICFhSreIcs01JeylUva6ks56kvayqVs9aVsZRK2TEMKpOjzESgIwn5Q76s5VmMcxOabY8XPq58jaVku2VK9+9glaS3PJWUS7gYAAAAAAAAWhJ4PfwdH8BvhVCqlfD6vDRs26MlPfrJe+MIX6oUvfKEsy5qDFQIAAAAAAAAAsHjsK5R1756C7nu0oD/sHV0QVbxNQ83AdiO8bSuXcpRL2spHc2GQ22ns74vN51OOkrZ5xE80BQAAAAAAAICp6Onwt+/73V4CAAAAAAAAAACLUhAE2jFY1L17Crp3z0jUFrR/tNLtpR1SX9JWf8bRkkxCA7F2IJPQkmhc378kmu9L2lTGBgAAAAAAADAv9HT4GwAAAAAAAAAAzL2a5+vhfWMtQe/79xQ0WnG7ui7bNLR+WUYrcskwrJ0Nw9oD6Vi4OxuOB6KxY5ldXTMAAAAAAAAAzCXC3wAAAAAAAAAALCKlqqf7HwureN8XBb0feGxUVbd7T+Nclk1o04qsNi3PafPKsN20Iqtjl2YIcwMAAAAAAABADOFvAAAAAAAAAACOoprnq+b5Mg1DlmnINAyZhmQYxqx/rOFitaWa9717Ctqyf0x+MOsf6rAcy9D6pRltXpHTphVhuHvzipw2r8hqIJM4+gsCAAAAAAAAgHmI8DcAAAAAAAAAAJFS1VOhXFOp6qlUC7dy1VPZ9VSq+o25Ss1rPabmqVzzO8xF46rfGLuTJK9NQ7EweD0YHpszDVn1eVPhMbF5Izq2fvz+0Yp2D5eO8mewWcV7cxTwDqt553TskrRsqngDAAAAAAAAwIwQ/u4hjzzyiO666y7t2rVL1WpVS5Ys0cknn6yzzz5bqVSq28uT53n61a9+pfvuu0/79u1TrVZTLpfTMccco8c97nE6+eSTZZr84h4AAAAAAABA7wmCQCOlmh4dKeuxQlmPjcS2QrMdKdW6tkY/kHwvkNSFstxHYE1/SqesyevE1X3atDyrTVTxBgAAAAAAAIA519Ph77vvvlsvfelLG+Orr75a55577rSvc/PNN+sNb3iDpPCxmd///vd14oknztYyZ+yb3/ymPvjBD+rXv/51x/25XE6ve93rdNlll2n58uVHeXXS1q1b9dGPflTXX3+9hoeHJz0un8/rvPPO05vf/Ga98IUvPHoLBAAAAAAAALCouZ6vA2NVPTpS0t5CeWLAO+pXXL/bS52XDEM6bllWp6zN69S1/Tp1bV6nrs1rWS7Z7aUBAAAAAAAAwKLT0+HvT33qU9q2bZsk6ZRTTjmi4LckPfvZz1YymdSDDz4owzD0mc98Rv/6r/86iys9MpVKRW984xv1pS996ZDHjY2N6ROf+IS+8pWv6Gtf+5rOOeeco7I+3/f1kY98RJdffrkqlcphjy8UCvrWt76lVCpF+BsAAAAAAADAjARBoLGKq6HxmoaKVQ0Wqxoar2pvoaK9UZj70UJZe0fK2jdalj8/imX3PMcydOKqvijgHQa9T16TVy7Z0y8nAAAAAAAAAMCi0dO/rf3Wt74lKazWffHFF8/oWq997Wv1nve8R0EQ6Bvf+EbXw9++7+sVr3hF4x7rLMvS+vXr1d/fr61bt2pkZKSxb//+/XrBC16gH/3oRzrrrLPmdH21Wk2vfvWrdcMNN0zY19/frzVr1iifz2t0dFTbt29XsVic0/UAAAAAAAAAmL+CIFCx6mlwvKrhYq0R5B4cr4bB7vp8bDxUrKrmkeieS9mEpcetyTeC3qeszevEVX1K2Ga3lwYAAAAAAAAAmETPhr/vv/9+PfbYY43xS17ykhld7yUveYne8573SJK2b9+urVu36rjjjpvRNWfiox/96ITg91ve8ha9733v09q1ayWFAfFvfetbesc73qEdO3ZIkorFol7+8pfrnnvuUX9//5yt741vfGNL8Nu2bV166aW65JJL9NSnPlWGYTT2+b6vP/zhD/rBD36gr371qy37AAAAAAAAAMwPQRDI9QNVXV8V11e1vnmeyjVfVS82Vz/G81rGhbKroSi4HYa4a2HIu1hV1fW7fYuL2tJsoqWa96lr89q4LCvT5Pe5AAAAAAAAADCf9Gz4+7777mv0c7mcHve4x83oeo973OOUy+U0NjYmSbrnnnu6Fv4+ePCgPvzhD7fMXXHFFfr7v//7ljnTNPUnf/InOvPMM/XMZz5T27ZtkyTt2rVLH/vYx3T55ZfPyfq++MUv6gtf+EJjvHbtWn3/+9/X4x//+I7Hm6apk08+WSeffLLe/va3a2hoaE7WBQAAAADAZDw/0O6hkrYeHNe2A+PafrCoqudpeS6p1fmUVvWntDofbgMZZ9G8cTkIgjCMWaqpUK6pUHajftiOll0VyjVVar4yCUvZpK1cylYuaSmbqPdtZZO2+qI2k7AWzecPi1sQBCqUXQ2OV1VxPXl+IN+XvCAI+/XWD9rm1Lo/at3Ysb4fznmBWs+P7w8C+fX9bfOerwnHen6gIFCHY2Nr8IJmqNtrBrYrrteYCyi0PamUYyrtWEo5VrNN1PtmY745Vz/WVDox8TzHMuW3/HkI/4z5QeufEz/6sxD/f9n4/93+57Htz5RjGdq0PKdT1+W1Op/i328AAAAAAAAAWAB6Nvy9e/duSZJhGDr22GNnfD3DMLR+/fpGqLxeSbsb/vmf/1mjo6ON8TnnnKN3vetdkx6/bt06XXXVVXruc5/bmPu3f/s3/fVf/7WWLVs2q2s7cOCA/uZv/qYx7u/v1y233KLjjz9+ytdYsmTJrK4JAAAAAAApDL3tGS5p28FxbTtY1LYDYdB768Fx7RwsquZNLbGYsE2tykeh8CgQvro/pZX5ZkB8ZT6plGPN8R0dnu8HKta8Rnh7tBHerqlQcjXaEuiO72+Gu6ve7FbaNQwplwiD4NmkpVwUGM8m7GY/GfWT9b6lfMrRphU5rconCR+ia1zP12CxqoNjVR0YqzTaA41xvF+d9b8/6B2mIa3oSza+BoRtWqv7k1qdT2tFX0KZhN0IbCdtkwrZAAAAAAAAAICe0LPh73qFbknK5/Ozcs2+vr5GPx6+Ppp839fVV1/dMvf+97//sC96Puc5z9GznvUs3XbbbZLC9X/1q1/VW9/61lld34c//GEdOHCgMf6nf/qnaQW/AQAAAACYCd8P9Fih3Ah1bzswrq0Hitp2cFw7BouqujMPYlZdXzsHS9o5WDrkcUsyjlbFAuKN6uH9Sa3sC8OCA2lHpZoXbtWwLVajftVTseapXPVUrLoq1XyVqm7rMfV+7PxS43hP5VrvBU+DQBqtuBqtuEd0/vJcUqevy+v0df06bV2/Tj+mn2q0mJFyzdP+0YoOjld1YLSig+NhgLvT3FCxSmXrRSBhm1rTH/77vSb6t7veX9UftitySdmW2e2lAgAAAAAAAAAwbT0b/o4HtYeGhmblmsPDw41+IpGYlWtO1x133KH9+/c3xps2bdKzn/3sKZ37xje+sRH+lqRvfvObsxr+rlQquu666xrj1atX69JLL5216wMAAAAAIElBEGhvoaKtB8bDKt4Hxhv97QeLqsxCwHs2DBVrGirW9MBj3XkD+UJ1YKyimx7cr5sebP5+ZHkuEQbB64Hwdf1a008gfLHx/ECFUk3DpZqGilUNF6sajv4eNvthO1yqamg8nB+vet1eOo4SyzS0JONoaTYRVunOJ6M21RL2Hsg4/PsBAAAAAAAAAFiwejb8vXz5cknhC8I7d+5UrVaT4zhHfL1qtaqdO3c2xitWrJjxGo/E//zP/7SMn/e85035hYjnPe95LeObb75Z4+Pjymazs7K2b3zjGxocHGyMX/nKV8qyuv+IawAAAADA/DdSrOknD+7Vjffu1e0PHTjiqtFYmA6MVXXzg/t1cywQvizbFgg/pl9rCYT3HN8P5PqBXN+X6wfyvEA135fnB6q5gUZKUVC7U4C7GJsv1TRSqlGVexExDWkgk2iEuZdkEmGbDefi46WZsO1L2jJN/g0AAAAAAAAAACxuPRv+3rx5c6NfKpV0yy236LnPfe4RX++WW25RsVhsjDds2DCj9R2p3/72ty3js88+e8rnrl27Vhs3btS2bdskhYH2++67T2ecccasrK09mH7eeefNynUBAAAAAIvTnuGSfnjfXt1432O6c8ugPJ9UJ6bu4HhVt/xhv275QzMQvrQRCM83QuHrBtIEwtv4fqBCuabB8WpjGypWNThe0+B4RXuHx7VllynXl3xJ1+36nWRacr0owO0HqnlhgNv1A7leEM37sX4Y8iasvThZpqGEZSrpmEpYphJ2tFmmMglLS7NJLc06UZC7Gd5emm2GuvMphyA3AAAAAAAAAABHoGfD30996lPV39+vQqEgSbriiitmFP6+8sorG/1sNquzzjprxms8Evfff3/L+JRTTpnW+aecckoj/F2/3myFv3/xi1+0jJ/whCdIkjzP04033qhrr71Wv/nNb7Rr1y45jqMVK1boSU96kl7wghfoFa94hTKZzKysAwAAAAAwPwVBoD/sHdON9z6mG+/bq9/vHunKOlblk9q4LKu+lKN9o2XtLZS1f7Qisuch2zSUTzvKp2zl0476UrZStqXxqqvxiqfxiqvRiqvxiqti1ev2clsMjld16x/269ZYIHxJxmlUCD91bb/6Uq2/7uqUCzdkHPKYCacY8a4h2zLkWKZs01DCDlvHMsM5q95vHjOTcHoQBCrVvEmD3IPjNQ2NVzVYjPZF+w//591sdkcKR7w+hCzTkGUYMk1FrRGbC1vLDPfbpinTCM8xo/mWvmHIiPbH58NWHY6dOD/xuorWMDGwnYy2MLxtNebige7wGKsxZxHaBgAAAAAAAACga3o2/G2apl74whfq+uuvlyTdfPPN+rd/+zf9zd/8zbSv9bGPfUw33XRT44W2Cy64QI7jzOp6p6JUKmnHjh0tc8cee+y0rtF+/IMPPjjjdUnSyMiI/vCHPzTGlmVpw4YN2rJliy6++GL97Gc/63jOww8/rBtuuEHvfe97deWVV+o1r3nNrKwHAAAAADA/eH6gX+8YagS+tx8sHv6kWbCiL6njlmW1cXlGG5dnddyyrDZE40xi4q87XM/XgbGqHiuU9dhIGAh/rBC2extzFY1V3KOy/plI2mYjvN2XciYEufNtc/mWOUcpx5xyGNnzgygUHm6j5TAgPlZxNRbNTeiXXY1XXY1VPI2Vay3Hz4WhYk23PXRAtz10YE6uPxvq4XDbCqsl25Yh2zTbguPNY0zD0EipWbm74vrdvoUFyTCkJZmElucSWpZNanlfUsuyCa2I2uW5pJblwjabtJvh7rbwNdWrAQAAAAAAAADA0dSz4W9Jes973qOvfOUrCoJAQRDone98p4aGhnTZZZfJsqzDnu95nt7//vfriiuukGEYCoJApmnqfe9731FY/UQHDhxQEHsWruM4Wrly5bSusW7dupbxvn37ZmVtW7ZsaVlbX1+f7rvvPp199tkaGTl8pbY9e/bota99re69996WKusAAAAAgIWnXPP004cP6MZ79+pH9+/VwfHqnHycZdmENi7PauOyrI6LQt4bl2W1cXlWueT0fqVhW6ZW96e0uj8lHeJ92GMVtxkOHylr72hZe0fCoPhjhYr2jpS1f6wibwplxNOOpUzCUipq0wmrMRf2baUTpjIJu3mME+5r7dst4e6kffjficwWyzTC4Hhq5m+iHxqv6ve7R/T73SO6J2p3DZVmYZW9z/UDub4n1bq9koUvYZlhmDuXjLVhPx7mXpZLaGkmIdsyD39RAAAAAAAAAACAHtLT4e9TTjlFb37zm/XpT39ahmHI9319+MMf1vXXX6+3ve1tesELXqCTTz55wnkPPPCAvve97+lTn/pUI9RsRI9AfeMb36jHP/7xXbgbaWxsrGWcyWSm/djfbDZ7yGseqeHh4ZaxYRh68Ytf3Ah+ZzIZ/Z//8390zjnnaNmyZTp48KBuueUW/dd//ZdKpeYLtR/5yEe0bt06/dVf/dWsrEsKA+779+8//IExDz/8cMt4bGxMhQKPMO6G8fHxQ44BAMD8wNd0AIVSTbc9MqSf/OGgbn9kUKXa7FQiHkjbWr80rfVL0tqwNK31S1LasDStY5ek1Zfq/GsLv1JUoTIrH76jlSlpZSqh01cmJOUn7Pf8QAfHq9o3WlXZ9ZV2TKVsU+mE1dJO92f+Q/Mlv6JKsaI5vPU5ZUl64uqknrh6pfSU8M3ww8Wa7t87pvseHdN9j4Xb7uFydxeKnmGbhgbStvrTjvrTtgba2v60o4FovCzraGk2ob6kNbW/e0FVxTl64woAAEcLP6sDALAw8DUdAICFga/pi89s5XePhBHEyz33INd19ZznPEe33XZbo3q3pMaLONlsVsuXL1cul9PY2JgOHDjQ+EsTPzYIAj372c/WjTfeKNvuTub9F7/4hc4888zGeNWqVXrsscemdY1PfepTetvb3tYYv/jFL9Z3vvOdGa/tG9/4hi666KKO+57ylKfo61//utavXz9h37Zt2/SSl7xEd999d2MumUzq7rvv1oknnjjjdUnS+9//fl1++eUzusa///u/d1w/AAAAAGByQxXpniFDdw8aerhgyA9mHmZemgx0+pJApy0NdEw2UKan35aObhivSbvGDe0cl3aOG9o5ZuhgZTaD9DjaDAVK21I22jJ2ELaOlLXDfwda9jlhP2FKs/oeCgAAAAAAAAAAgFmyY8cO/fVf/3VjfM899+jUU089Kh+7519itW1b3/nOd/S6171O3/zmNxuh73qwe2xsbNL0fPzYP/uzP9PnP//5rgW/Jalcbq1clUgkpn2NZDLZMo5X3Z6JyT6HxxxzjH74wx9qyZIlHfdv3LhRP/7xj3X66ac3guyVSkX/8i//os9+9rOzsjYAAAAAwNHh+tLeknTvkKG7B03tHJ+d1OW6TKDTlwY6famvdRnCnDi0rCOdNBDopAFJCn//U3TDIPiusWYg/ACB8ClzjDBQnYvC1rkoXO2YkmVIpiFZRiCz0T902zKnQJYpmep8bMqS0nbYBwAAAAAAAAAAwMz1fPhbkvL5vL7+9a/rM5/5jK644grt2LFDkg75CNcgCBQEgTZu3Kj3vve9esMb3nC0ljupVCrVMq5Wp/9o2Uql9eHO7dc8UpNd56Mf/eikwe+65cuX68orr9TrXve6xtwXvvAFffzjH1c6nZ6V9QEAAAAAZsYPpNFaWM17qGpoqCINVwwNVaWhqB2rzU4601CgzXnp9KW+Tl8SaNns/OiKRSxjSyf1BzqpX4oHwneNG9o1Lu0cM7RzPPxzHX/E3YTH3QWHHHYUqLdSy6YCZRwpF1XKzjlRsNuWsk4QtVLObs4nrG6vGgAAAAAAAAAAALNlXoS/6y699FK96U1v0te//nXdeOONuu2227Rlyxa5rts4xrZtHX/88XrWs56lCy+8UC95yUtkmmYXV92Uy+Vaxu2VwKeivdJ3+zWPVKfrLF26VH/6p386pfNf8YpX6O1vf7tGRkYkhfd211136dxzz53x2t72trfpZS972bTOefjhh/XSl760MT7zzDP1uMc9bsZrwfSNj4/rrrvuaozPPPNMZbPZLq4IAAAcCb6mA70tCAKNll09NlrVoyNlPVaoNLZHo3bfaFWuP5Wo65FJ2qbO3rRE55+4TOccv1RLMs6cfSygm4IgkOsHqnn11m+O4/36Pi9QzT/EvrZ51/PlBlJf0tKSjKOBtBO1tpZmHOVStswZls/n6zoAAAsDX9MBAFgY+JoOAMDCwNf0xef+++/v2seeV+FvSbIsSy972ctawsCjo6MaHR1VX1+f+vr6uri6Q2sPWBeLRQVBcMgK5u3Gx8cPec3ZWpsknXXWWXKcqb1Yn0qldOaZZ+qHP/xhY+6Xv/zlrIS/V65cqZUrV87oGrlcTvl8fsZrwcxls1n+XwAAsADwNR04uoIg0GOFsrYeGNfuoZIeHSlrz3BJe6L20eGSxqveUV/XQMbRc05epeefukrPOmG5Mol592sGAOLrOgAACwVf0wEAWBj4mg4AwMLA1/SFb7byu0diQbwq2+uh77rly5fLMAwFQVhprVarad++fVq1atWUr7F79+6W8UxD0XWd1nDiiSdO6xonnXRSS/h73759M14XAAAAACwmYxVXW/ePa8uBMW3ZP64tB8a1Zf+Yth4YV7EL4e5O1g2k9fxTV+n5p6zWGRuXyLZ642lbAAAAAAAAAAAAALAYLIjw93yRTqe1fv16bd++vTG3Y8eOaYW/d+zY0TI++eSTZ2VtmzdvViKRULVabcxN910n7ccPDQ3NytoAAAAAYCHx/EC7h0p6pB7w3l8Peo9pb6HS7eV19Lg1eT3/lLDC9ylr8tN6ghUAAAAAAAAAAAAAYPYQ/j7KTj755Jbw93333aczzjhjyufff//9E643GyzL0oknnqh77rmnMVepTC90UC6XW8aZTGZW1gYAAHpL1fV155aDumvroAaLVSUsUwnblGMZcqJ+cy7sO7aphGV0mIsdF10jaVlybEMJy5RlGvM+YBgEgTw/UCDJoTousKgMjVcblbsb7f5xbT9YVNXzu728Q7JMQ0/dsETPP3W1nn/KKh27lJ/vAAAAAAAAAAAAAKAXEP4+yp74xCfqBz/4QWN8xx136JJLLpnSuY8++qi2bdvWGDuOo1NOOWXW1vbkJz+5Jfy9d+/eaZ2/b9++lvGyZctmZV0AAKD7Rss13fzgft14317d/MA+jVbco/JxDUONsLhdD5fH+uFmtLVh364faxqNoHm971imnFjfkFTzfNU8X1UvUNX1G+Oa56vqBqp6vmpu/Zj6vK+aF3Sec8O5qucrCML7STuW+lK28mlH+ajtSzX7+ZQzYX8+ZSufcpRPO0ra5rwPwwMLUcX19LudI/r1jiE9sq8Z9B4q1rq9tEmlHUtrBlJaN5DWmv6U1g6ktbY/rbUD6cZ8yrG6vUwAAAAAAAAAAAAAQJueDn/ffvvtOvfccxvjH/3oRzrvvPOmfZ2f/OQnet7znidJMgxDP//5z/WUpzxl1tY5HS9+8Yv1kY98pDH+0Y9+pCAIphTiufHGG1vG5513nnK53Kyt7Y//+I913XXXNca/+tWvpnV++/EnnXTSrKwLAAB0x95CWT+8b69uvG+vfvbIAdW84KivIQjCSuNVt7cr5E5VqeapVPO0b3R6T1ipS1im8ml70sB4f9rRuoG01i/LaMPSjJZmE4TFF6Bi1dWW/eN6eN+YHh0pK+WYWpJJaEk2oSUZp9HPJiz+/8+Rquvrd7uGdecjB/WzLQf1q+1DqvTQv1OWaWh1PtUIdTdD3mmtHUhpbX9aAxmHPx8AAAAAAAAAAAAAMA/1dPj7M5/5jIKoTOKZZ555RMFvSTr//PP1pCc9Sb/+9a8VBIE+97nPdS38ffbZZ2v58uU6cOCAJGnLli26+eabp3Rvn//851vGL3nJS2Z1bRdeeKFSqZTK5bIk6e6779ZDDz2kE0444bDn3nvvvbr//vtb5p797GfP6voAAMDcCoJAD+8b041R4Pt3O4e7vSS0qXq+DoxVdWCsOqXjswlL65dltX5pWhuWZXXs0jAUvn5pRuuWpOVY5hyvGEcqCAIdHK/qkX1jenj/mB7eN6ZH9o/rkX1j2j1cmtI1EpapgYyjpdlErE1oaaY5bgmNZxPqS9oEgjuour7u3jWsO7cc1J1bBvXL7YMq17oX9l6ScbRuSRjmbqncPRC2K3JJ2fz9BgAAAAAAAAAAAIAFqWfD377v63vf+14jePDqV796Rtd77Wtfq1//+tcyDEPf/va39elPf3o2ljltpmnqda97nf7lX/6lMXf55Zfr2c9+9iFDFj/+8Y912223NcZ9fX16+ctfPqtry2azuvjii3XVVVc15j70oQ/p2muvPey5H/jAB1rG5557rlauXDmr6wMAALPP8wP9ZsdQo8L31gPj3V4SZtF41dP9jxZ0/6OFCfss09DagZTWL81o/dKs1i/NaMOyMBi+fllG+ZTThRUvPp4faPdQSY9EAe8w5B0GvoeLtRldu+r52jdamValeds0NJBphsGXRCHx5bmkVvYltTKf0qp8SqvySS3PJRfsGwhqXj3sPag7txzUL7cNqVTzjuoaErap45ZltWlFtC3PNdr+DH8/AQAAAAAAAAAAAGCx6tnw9+9//3sNDQ1JkgzD0Ite9KIZXe9FL3qR3vGOdygIAu3du1cPPvigTjrppNlY6rS9613v0qc//WmNjY1Jkm655RZ95CMf0d///d93PH737t1605ve1DL39re/XcuXLz/kx2kPk990002HrcZ92WWX6Ytf/GKj+vd1112nc889V294wxsmPec//uM/9NWvfrVl7t3vfvchPw4AAOiecs3TTx8+oB/et1c/un/vlKtIY2Hx/EA7B0vaOVjST3Vwwv6BjKMNSzNhtfBlGa1ImzowYmh5KtBAogsLnufKNU9bD4w3w91R0HvrgXFV3O5VkG7n+oEOjFV0YOzwgXHDkJZlE1rZF4bBV+VTWplPaWVfshEQX5VPaVk20fNVqGuer9/vHtHPHjl41MPea/pTreHuFTltWp7V2oG0LJMq7AAAAAAAAAAAAACAVj0b/r7//vsb/YGBAW3atGlG19u8ebMGBgY0PDwsSbr33nu7Fv5evny5/uEf/kH/8A//0Jh797vfrR07dui9732v1q5dKymsfv7tb39bb3/727Vjx47GsWvXrtXf/d3fzcnajjnmGL3rXe/S5Zdf3ph705vepF//+td617vepWOPPbYxv2PHDl1xxRX6zGc+03KNV73qVbrgggvmZH0AAODIDBer+skD+/TD+/bqlj/sV7E681BjX8rW045bKss0VHV91bxAVddX1fOjcbiFc4Gqrhce4/ny/GAW7gpzabhY03BxRL/bNRKbtSRJaSvQaY/erSeuX6rT1vXrtHX9Om5ZVuYiDqpWXV/7Rsthpe1CWXsLFe0eLjVC3juHigoW2B/7IJAOjFV1YKyq+x6d/DjTUFg5PJ/Uqr5UVD08Cov31UPjSeVTjpK2ecgnIs0WNwp737llUD/bclC/3DY4K/8uTiabsMJQdyzkfdzysKJ3JtGzP5YDAAAAAAAAAAAAAHpQz77K/Nhjj0kKq1evW7duVq55zDHHNMLfu3fvnpVrHql3vetduuOOO/Td7363MfepT31Kn/3sZ7Vhwwb19/dr69atjfXWpdNpffWrX9XAwMCcre1973uffvWrXzXWFgSBPvnJT+o//uM/dNxxx2nZsmU6ePCgtmzZMuHcJz/5yfrsZz87Z2sDAABTt2uoqB/et1c/vG+vfr51cFYC16vzKT3vlFV6/qmr9LTjlilhH1k1X88PwmC456vm1ttAVc9T1W3dV/F8uV7QCJPXor7rhaHyWnRczW/2XT9onN/erwfS4/0gkBK2Kccy5FimEraphGXKsUw50XzSjsbRFh5jtI1NOXaHOcuUHwQaLbsaLddUKNVUKLtRW9No2VWhXFOhVG/D/b0aki95hn6xfUS/2N4MhueStk5Zm9dpa/t1+jFhu2lFbt5XLq55vg6MVbS3UNHeQrkR7N43Wm7OjVY0OE4F/cn4gcJQ/GhF96hwyGMNQ0raplKO1WhTtqWUYyrpWG3zUevU23BfsmVfc7/vB/rNzmH97JEw7D0+B2HvXNLWkzcs0QkrmwHvzStyWtmXPCqhdgAAAAAAAAAAAADAwtez4e9isdjoZ7PZWblm/DpjY2Ozcs0jZZqmbrjhBr3+9a/Xl7/85ca853kdQ9WStGzZMn3ta1/TM57xjDldm2VZ+trXvqZLL71U1157bWM+CAJt2bJl0vX98R//sb70pS8pl8vN6foAAMDkKq6nr/xip77yi526d8+hQ5ZTddKqvkbg+/R1/bMSYLRMQ5YZBjPRWRAEKtW8lkB4MyQeD4835/YVKtoxWFSpNncVjCczVnF119ZB3bV1sDGXdiydsjav06Pq4Kety+v4FTnZ1pG9aWA2uZ6vg+NV7W0Lc4fh7ubcwfFqz1bsTtimNi7LyPMDDRdrGipW1aPvF5iyIJDKNV/lmt/tpUxJLmnrjI1L9PRNy/T0Tct06tp8T/z5BgAAAAAAAAAAAAAsXD0b/u7v72/0Dx48OCvXHBxsBlEymcysXHMmUqmUrr/+ev3Zn/2ZPvShD+m3v/1tx+Oy2awuueQSXXbZZVq5cuVRWVsymdQ111yjV77ylfrQhz6kn/70px2PMwxDZ555pt773vfqxS9+8VFZGwAAmMj3A33n7j36lxsf1M7B0oyuZRjSUzcs0fNPWa3nnbJKG5fPzhvxMD2GYSiTsJVJ2Frdn5ryeUEQaP9YRTsHi9p+sKgdg0XtiNrtg0XtH63M4apblWqefrV9SL/aPtSYS9qmHremHgjP67R1/TpxVZ+cIwzMBkGgYtXTcKmmofGqhos1DZeqGirWNDxeDeeL0XzUDhWrGinV5k1Quj/t6PiVOW1ekdXxK3NRP6djlmRaKqv7flhdfrBY1VCxqqHx8PMQtvW5mgaLVQ0XqxocDz8n7nz5RPSAbMLSUzcu1Vmbw7D3aYS9AQAAAAAAAAAAAABHWc+Gv1esWCEpDHPs3LlTpVJJ6XT6iK9XLBa1ffv2RqXK+vV7wZ/+6Z/qT//0T/Xwww/r5z//uXbv3q1qtaqBgQE97nGP0zOe8QylUlMP/NQFs1Ci8MILL9SFF16o3bt362c/+5m2b9+ucrmsJUuWaM2aNXrGM55x1ALpAACgs9sfOqAr//d+3bP7yCt9J2xT55ywXM8/ZbXOf9xKLc8lZ3GFOJoMw9DKvpRW9qX0lA1LJ+wvVl3tHCyFYfCD42FIfDAMh+8aLKnqzW3F5Yrr67c7h/XbncONuYRl6uQ1fWF18LX9Oml1n1zPDwPcxViAezwW7G4EumtzvuajZd1AWpuigPfmFblG0HtZNjGlivumaag/46g/4+g4Te1NG0EQaLTiajgKhXcKjQ+OV7VvtKK9hbL2FSoL5vM9FZl62HvTMj1901Kdtq7/iN+oAAAAAAAAAAAAAADAbOjZ8PfJJ5/c6FerVd144416yUtecsTX+8EPfqBqtSopDMRs3rx5xmucbccff7yOP/74bi+jo3Xr1unP/uzPur0MAAAQc++eEV35/Qd020MHjuj8/rSj5zxupZ5/yio964QVyiZ79ltDzKJMwtZJq/t00uq+Cfs8P9DeQlnbDxajUPi4dgyWtOPguLYfHNdwyZ2TNVU9X3fvGtHdu0bm5Pq9xLEMbVzWGvDevCKnTSuyXfk7aBiG8ilH+ZSj9csO/3SkIAg0UqppbyEMg+8tlLVvtKJ9hXI4NxoGxPeNllXz5l9F8bRj6akblzQqe59O2BsAAAAAAAAAAAAA0GN6NuHz+Mc/XitXrtT+/fsVBIE++MEPzij8/aEPfUiGYSgIAg0MDOjMM8+cxdUCAAAcPTsHi/rXGx/UN3+7Z9rnrhtI6/mnrtLzTlmlMzculU2oETGWaWjtQFprB9I6a/Oyln2FQkH/88Ob9GhR2jluyO1bowf3FfXwvjH58y/jO6dSjqlV+ZRW9iW1fmlWm1dmdXwU9D52aWZeh4kNw9BAJqGBTKLjGwjqfD/QcKnWDIhHYfF6BfG9UWB8/2hFbhf/ANXD3k/fFIa9H38MYW8AAAAAAAAAAAAAQG/r2fC3JL30pS/VZz/7WUnSb37zG/3t3/6tPvaxj037On/7t3+r3/zmNzIMQ4Zh6KUvfemUHpsOAADQS4bGq/rETQ/rCz/brqrnT/m8U9bk9bxTVun5p67SKWvyfB+EI5axpc15aXM+0HnnnaR8Pq9S1dN9jxZ0754R/X7XiO7ZU9BDe0e7GuidKwnb1Kp8Uqv6UlqZT2plX0qr8qlwLmpX9KWUT9mL/u+ZaRpamk1oaTahx63JT3qc7wcaLFa1r1BRqeaqUvNVdj2Va77KtVjreo19lca+aL8b9iuur3LNV6W+z20eV//jmHYsPWXDEj1901KdtXmZTl83oIRN2BsAAAAAAAAAAAAAMH/0dPj7Pe95j6655hrVajUFQaCPf/zjOnDggD7xiU8on588QFBXKBT0F3/xF/qv//qvRtXvRCKh9773vUdh9QAAALOjXPP0nz/dqk/d/IhGy+6UzrFMQ68441i95ZzNWr8sM8crxGKWToRh2qdsWNKYK9c8PfjYqH6/e0T37B7RPXtG9OBjo6p5vRkIdyxDK6NA96q+MMS9Mp9qVO+uB7v7086iD3XPNtM0tDyX1PJccs4+RhAEcv1AFddXNmHx/xAAAAAAAAAAAAAAMK/1dPj72GOP1bvf/W5dfvnljfD2l770JX3729/Wa1/7Wr3whS/UU5/6VC1fvrxxzoEDB/TLX/5S3/ve9/SFL3xBhUJBQRCGTAzD0Lve9S4dd9xx3bolAACAKfP8QF/71U792w8f0mOF8pTPu+DUVXrnBSfr+JW5OVwdMLmUY+kJxw7oCccONOYqrqeH9o41A+G7R3T/Y6OqulOvYj9V/WlHAxlHA5mElmQcDaTr/UQ0H47rwe6BtCPTJBC8UBmGIccy5FhU+AYAAAAAAAAAAAAAzH89Hf6WpMsuu0z33HOP/vu//7sRAC8UCvrkJz+pT37yk5LCF/MzmYyKxWIj6C2pJfQdBIFe/vKX6/LLL+/KfQAAAExVEAT68f379JH/fUAP7Rub8nlP3bBE737hyXrKhqVzuDrgyCRtS6et69dp6/obczXP10N7x3TPnjAM/vvdI7r/0YLKtTAQnk1YGogC20syCfVnHC2p99NhuyTrqD8dhbyjeYsgNwAAAAAAAAAAAAAAWKB6PvwtSddff73e+c536uMf/3jLI7rr4e4gCDQ21hqMMgyjEfqWpL/7u7/TlVdeefQWDQAAcAR+s2NIV3zvAd21bXDK52xekdW7LjxZzztlVcv3SkCvcyxTp6zN65S1eb38qcdKCivej5ZrSicsJW2ryysEAAAAAAAAAAAAAADoLfMi/G3btv7t3/5NF154oS6//HLdeeedjX2dAk5BEDRC38985jN12WWX6TnPec5RWy8AAMB0bdk/po/+4EF9/57HpnzOqnxSf/PcE/VnTzlGtmXO4eqAo8cyDQ1kEt1eBgAAAAAAAAAAAAAAQE+aF+HvugsuuEAXXHCBfvGLX+jGG2/UbbfdpkceeUSDg4MaHR1VX1+fli5dqhNOOEHPetazdOGFF+pJT3pSt5cNAAAwqf2jFX38x3/Q9XftlOcHUzqnL2nrLc/erDc84zilE1RGBgAAAAAAAAAAAAAAABaLeRX+rjvjjDN0xhlnHPH5nufJsghKAQCA7hmruPrcrVv0udu2qFj1pnSOYxm6+Okb9Ffnn6ClWSojAwAAAAAAAAAAAAAAAIvNvAx/H6nf/e53uvbaa3X99dfr0Ucf7fZyAADAIlTzfF1/1w79+48f0oGx6pTPe8kT1+rvnneS1i/LzOHqAAAAAAAAAAAAAAAAAPSyBR/+PnDggL74xS/q2muv1d13393t5QAAgEXK9Xx987d79P9+8pC2HyxO+bxnHr9cf/+Ck3Xauv45XB0AAAAAAAAAAAAAAACA+WBBhr9d19V3vvMdXXPNNfrf//1fua6rIAga+w3D6OLqAADAYnKkoe9T1uT19y84WeecuGIOVwcAAAAAAAAAAAAAAABgPllQ4e9f/epXuvbaa3X99ddrcHBQkhqh73rgOx4CBwAAmCtHGvpeN5DW/++CE/WSJ6yTafKGNQAAAAAAAAAAAAAAAABN8z78vXfvXn3hC1/Qtddeq/vuu09Sa+DbMAwFQaAgCJTJZPTiF79Yr3rVq7q5ZAAAsIC5nq9vRaHvbdMIfQ9kHP3lecfrNWdtUNK25nCFAAAAAAAAAAAAAAAAAOareRn+rlar+ta3vqVrrrlGP/zhD+V5XktF73jgO5FI6IILLtArX/lKveQlL1Emk+niygEAwEJ1pKHvpG3qDc88Tm85d7P6084crhAAAAAAAAAAAAAAAADAfDevwt8///nPde211+orX/mKhoeHJbVW+Y6Pn/Oc5+hVr3qVLrroIg0MDHRjuQAAYBE40tC3ZRr6sycfo3c87wSt6U/P4QoBAAAAAAAAAAAAAAAALBQ9H/7es2ePrrvuOl177bX6wx/+IKk18B2v8l0PgEvSD3/4w66sFwAALA4zCX1f9KR1+svzj9eGZdk5XCEAAAAAAAAAAAAAAACAhaYnw9/lclnf+MY3dM011+gnP/mJfN+fNPCdy+X0J3/yJ9q0aZMuv/zyLq8cAAAsdK7n69u/26P/95OHtfXA+JTPI/QNAAAAAAAAAAAAAAAAYKZ6Kvz905/+VNdee61uuOEGFQoFSa1VvuuBb9u29fznP1+vfvWr9dKXvlTpdFo//vGPu7l0AACwwM0k9P0nT1qnvzzveG1cTugbAAAAAAAAAAAAAAAAwJHrevh7dIdZBwABAABJREFU586duvbaa3XdddfpkUcekaRJq3yfeeaZuvjii/XKV75Sy5cv7+ayAQDAIkHoGwAAAAAAAAAAAAAAAECv6Gr4+znPeY5uueWWRrhbmhj43rx5s1796lfr4osv1vHHH9/N5QIAgEWE0DcAAAAAAAAAAAAAAACAXtPV8PdNN93U6McD38uXL9crXvEKXXzxxXra057WxRUCAIDFxvV8fefuPfp/P35YWwh9AwAAAAAAAAAAAAAAAOghXQ1/S83QtyQ985nP1N///d/rggsukGVZXV4ZAABYDCqup20Hinp435ge3jemb/1297RD3y994jr91fmEvgEAAAAAAAAAAAAAAADMra6Hv6VmAPyOO+7QRz7yEe3Zs0cve9nL1N/f3+2lAQCAw3h436iuuWObfrltSEnH0jFL0jp2SSZsl4btuoG0Uk5339g1Uqrpkf1hwPuRfWON/o7Bovxg+tcj9A0AAAAAAAAAAAAAAADgaOt6+DsIAhmGIcMw5Pu+br/9dt1+++36q7/6K73whS/UxRdfrBe/+MVyHKfbSwUAADG/3DaoT9+yRT+6f2/L/O92Dnc8fmVfsiUQHgbEMzp2aVpr+tNK2OaM1xQEgfYWKlEV71E9sn887O8f0/7RyoyvL0mmIf3Jk44h9A0AAAAAAAAAAAAAAADgqOtq+Pv73/++rr76an37299WuVxuzAdBoEqlom9+85v65je/qf7+fr3sZS/Tq1/9ap1zzjldXDEAAIub7wf68QP79OlbHtGvtg9N69x9oxXtG63o1zuGJ+wzDWl1PqVjlmR0zNJ02MYqiK/pT8m2muHwmudrx2AxCnmHVbzDat7jGqu4M73Njuqh7788/3gdR+gbAAAAAAAAAAAAAAAAQBd0Nfx9wQUX6IILLlChUND111+va6+9VnfeeackyTAMSWEQfHh4WFdddZWuuuoqHXvssXr1q1+tV7/61TrllFO6uXwAABaNiuvpW7/Zo8/c+oge2T8+69f3A2nPSFl7Rsq6a9vE/ZZpaE1/SmsH0hocr2r7wXHVvGDW19EJoW8AAAAAAAAAAAAAAAAAvaKr4e+6fD6vSy+9VJdeeqkeeughXX311friF7+oXbt2SWoNgu/YsUNXXnmlrrzySj3+8Y/Xa17zGr3qVa/q5vIBAFiwRss1/dfPd+g/f7pVewuVrq3D8wPtGipp11DpqH1M05Be+qR1+qvzTyD0DQAAAAAAAAAAAAAAAKAn9ET4O+6EE07QP/3TP+nDH/6wfvzjH+vqq6/WN7/5TZVKYdjLMAwFQVjp83e/+53uvvtuvetd79KJJ57YzWUDALCg7CuU9Z8/3aYv3bldoxW328s5KmzT0IZlGW1ekdNp6/r1x09Yq42EvgEAAAAAAAAAAAAAAAD0kJ4Lf9cZhqHnPve5eu5zn6vR0VF95Stf0XXXXafbb7+9sV8Kq4F7nqcHHnigJRh+++2365nPfGbX1g8AwHz0yP4xfe7WLfr6r3er6vnTOveUNXmdc+IKPTZS0s6hknYNFbtaLXwy2YSlzStz2rwip+MbbVbrl2aVsM1uLw8AAAAAAAAAAAAAAAAAJtWz4e+4vr4+velNb9Kb3vQmbdmyRVdffbW++MUvavv27ZKaQfB6PwgCnXvuuVq7dq1e+cpX6hWveIWe+tSndmv5AAD0vF/vGNJnbnlEN963V9H7qKbsGccv01vO3axnHr+85WuyJJVrnvYMN8PgOwejdqik3UNFHRirzuJdtFqeS+r4ldlGyLse9F7Tn5qwTgAAAAAAAAAAAAAAAACYD+ZF+Dtu06ZN+uAHP6gPfvCDuummm3T11VfrG9/4hsbHxyWF4e96AHz37t362Mc+po997GPavHmzXvnKV+qVr3ylTjnllC7fBQAA3ef7gW7+wz59+uYtumvb4LTONQ3pBaev0VvO2azTj+mf9LiUY2nTipw2rch13F+suto9VNLOoaJ2DZW0czBqo/FwsXbYdRy7NKPjV+S0eWWupe3PONO6JwAAAAAAAAAAAAAAAADodfMu/B133nnn6bzzztP4+Li++tWv6rrrrtOtt96qIAgaFT2DqHzpww8/rA9/+MP68Ic/rNNOO02/+93vurl0AAC6pur6+s7v9ugztz6iP+wdm9a5SdvUy596rN70rOO0YVl2xmvJJGydsKpPJ6zq67h/tFzTrqFSIxi+d7SsjGNr88qsjl+Z08ZlWaUca8brAAAAAAAAAAAAAAAAAID5YF6Hv+uy2axe//rX6/Wvf722bduma665Rl/4whe0detWSZoQBL/nnnu6tlYAALplrOLqy3ft0Odv36pHR8rTOrc/7eiSszbotWdv1PJcco5WOFFfytHj1jh63Jr8UfuYAAAAAAAAAAAAAAAAANCrFkT4O27jxo16//vfr/e///269dZbdc011+i///u/NTo62u2lAQDQFftHK7rmjq36ws+2q1B2p3XuuoG03vjM4/SKM45VNrngvm0AAAAAAAAAAAAAAAAAgHllQae4zjnnHJ1zzjn65Cc/qRtuuEHXXXedbrrppm4vCwCAo2LXUFGfuWWLvvLLnaq6/rTOPXl1ny49d5Ne/Pi1cixzjlYIAAAAAAAAAAAAAAAAAJiOBR3+rkun03rta1+r1772tdqxY4e+8IUvdHtJAADMmYf3jelTNz+ib/12t1w/mNa5T9+0VG85d7POPXGFDMOYoxUCAAAAAAAAAAAAAAAAAI7Eogh/x61fv17vec97ur0MAABm3T27R/QfNz+s79/zmIJpZL4NQ7rw1NW69NzNeuKxA3O2PgAAAAAAAAAAAAAAAADAzCy68DcAAAvNL7cN6hM3PaybH9w/rfMStqk/ffIxevM5m3Tc8uwcrQ4AAAAAAAAAAAAAAAAAMFsIfwMAMA8FQaDbHjqgT970sH6+dXBa5+ZTtl5z1gZdcvZGrexLzdEKAQAAAAAAAAAAAAAAAACzjfA3AADziO8H+uH9e/XJmx7W3btGpnXuqnxSf/6sTXrlmeuVS/ItAAAAAAAAAAAAAAAAAADMNyS/AACYB1zP13fvflT/cfPD+sPesWmdu35pRm999mZd9OR1StrWHK0QAAAAAAAAAAAAAAAAADDXCH8DANDDKq6n//7Vbn36lke0Y7A4rXNPXJXTX5x3vF50+hrZljlHKwQAAAAAAAAAAAAAAAAAHC2EvwEA6EHFqqv/+vkOfe62LdpbqEzr3Ccc06+/OO94Pfdxq2SaxhytEAAAAAAAAAAAAAAAAABwtBH+BgCgh4yUarrujm36z59u1VCxNq1zn75pqf7yvBP0jOOXyTAIfQMAAAAAAAAAAAAAAADAQkP4GwCAHnBgrKL/vH3r/8fencfXXdX543/fpEnaJmnTfd9LW1pZStmEyqKAX1EWF5bBDURkcXScnyyuo6ioKI7KzLiAgMy4IQiouAKy2Fb2vZRC9zVtkzbNvn9+f7REbtO0SZPcNOnz+Xjk0X4+n/M5531wpud+bl733Pi/f6yOirrGDt371lkj4+MnT4t5k4Z2U3UAAAAAAAAAAADsD4S/AaAHbSiriZseXRG/enJN1DY0t/u+VCri9EPGxBUnTYs5Ywd3Y4UAAAAAAAAAAADsL4S/AaAHNDUn8YOHlsV//W1Z1De1P/TdLysVZ88dF5efNC2mjSjoxgoBAAAAAAAAAADY3wh/A0CGbdxeE5/61XPx+Mqt7b4nt19WnH/UhPjYCVNj/JCB3VgdAAAAAAAAAAAA+yvhbwDIoPtf3hRX3fV8lFU3tKt9fm52fODNk+Li+VNiZGH/bq4OAAAAAAAAAACA/ZnwNwBkQG1DU3zjj0vi9n+sblf7ooE5cdFxU+LC4ybH4IE53VwdAAAAAAAAAAAAvYHwNwB0s2WbK+MTv3w2lmws32vbEYV58bG3TI0LjpkY+XmWaQAAAAAAAAAAAP5JqgygHarqGuPBVzZHSUVdnDp7VEwYOrCnS6IXSJIkfv3U2vjy716OmoamPbYt7N8vrn77zDjnyAnRPyc7QxUCAAAAAAAAAADQmwh/A+zFmtLquPj2J+O1zZUREXHdH5fER98yJa55+6zIykr1cHXsr8prG+Jzd78Y972wca9t504sihvPn+tDBQAAAAAAAAAAAOyR8DfAHtQ2NMXH/u+pluB3RERTcxI/fmRFrCmtju+ed7hdmmnl2TXb4pO/ejbWbq3ZY7tUKuKKk6bFp06ZETnZWRmqDgAAAAAAAAAAgN5K+BtgD77xxyXxSnHFbq/96aXi2FT+WNz8oSNjWEFehitjf9TcnMSPHl0e//nXV6OxOdlj25GFefHd8w6P46cPz1B1AAAAAAAAAAAA9Ha2GQVow/0vb4rb/7F6j22eWVMW7/nholhZUpWhqthfbS6vjQ/d+kR8689L9xr8PnnmiPjTv71F8BsAAAAAAAAAAIAOEf4G2I3i7bVx1V3Pt6vt6tLqeM8PFsZTq7Z2c1Xsrx5aujne8f2/x4JlJXtsl5Odii++a3bceuFRdosHAAAAAAAAAACgw4S/AXbR1JzEp+54NsqqG9p9z7bqhrjgJ4/HH17Y2I2Vsb+pa2yKr973clx025NRWlW/x7ZThufHPVccHxfPnxKpVCpDFQIAAAAAAAAAANCX9OvpAgD2Nz98eFk8tqLju3jXNzbHx3/xTKzbNis+dsJUAd8+bmVJVXzil8/ES+vL99r2vUeMj2vPmhMFeZZdAAAAAAAAAAAA9p0UGsAbPL16a3z3gdfavP7+YybG06u3xSvFFW22+cafXok1W6vj2jPnRL9sX7DQF939zLr44r0vRVV90x7b5edmx3XvPiTOnjsuQ5UBAAAAAAAAAADQlwl/A+y0vaYhPvnL56KpOdnt9SMmFsWXz5wTtQ1NccXPn4m/v1bSZl8/f3xNbNxeG//1L3Mj327PfUZlXWN88d6X4p5n1++17aHjB8eN58+NycPzM1AZAAAAAAAAAAAABwJb0gJERJIk8bm7X4z1ZTW7vV6Y1y++f/7cyMnOisL+OXHrhUfFuUeO32Off3tlc5x30z9ic3ltd5RMhr24bnu868a/tyv4/bETpsZdlx0n+A0AAAAAAAAAAECXEv4GiIg7nlwbf3hxY5vXv/6eQ2LC0IEtxznZWXH9ew+NK0+bscd+X1pfHu/+waJ4dVNFl9XaG1TUNsTG7TVRXd/Y06V0WnNzEjc/uiLe88OFsaq0eo9thxfkxu0fOTo+d/rBkdvPEgsAAAAAAAAAAEDX6tfTBQD0tGWbK+LLv1/c5vXzjpwQZxw2ttX5VCoV//rWg2L8kIFx1V3PR0NTstv715fVxHt/sCh+9MF5cfz04V1W9/6korYhnli5NRYtL41/LC+NJcXlkSQR2VmpOHhMYcybOCSOmDQk5k0aEuOKBkQqlerpktulpLIuPv3r5+ORV7fste1bDhoe3zn3sBhZ2D8DlQEAAAAAAAAAAHAgEv4GDmi1DU3xr794Nmobmnd7feqI/PjSmbP32MfZc8fFqEH949L/eyrKa3e/03VFXWN8+NYn4pvvPTTeN298p+vuadX1jfHUqm3xjxWlsWh5aby0fns0NbcOvzc1J/HS+vJ4aX153P6P1RERMWpQXsybNCSOmLgjDD5n7OD9YpfspuYk1mytjlc2lseS4opYWlweT6zcGtuqG/Z4X7+sVFz19plxyVumRlZW7wi1AwAAAAAAAAAA0DsJfwMHtG/+6ZV4pbhit9dys7Piv/5lbgzM3fs/lW+eNizuvuK4uPC2J2PdtprdtmlsTuLKO5+Pdduq49/edlCv2f06IqKusSmeXVO2c2fvknhubVmbO53vzabyuvjji8XxxxeLIyIir19WHDp+8I6dwXfuED68IK8ry29lW1V9LCkuj1c2VsTS4op4pbg8lm6qaPNDAG2ZOHRg3Pgvc+PwCUXdUygAAAAAAAAAAAC8gfA3cMB64OVN8dNFq9q8/tnTZ8WcsYPb3d/0kYVx9xXHxUdvfypeWLe9zXbfe+C1WLu1Jr7xnkP2ix2vd6ehqTleWLc9/rG8JP6xojSeWrUt6ho7Foxur7rG5nhy1bZ4ctW2lnOThw3cEQbf+XPQyMLI3oddtesbm2P5lsp4ZWfQ+/UdvTeV13W67jMPGxvXvftNUdg/p9N9AQAAAAAAAAAAQHsIfwMHpOLttXHVXc+3ef2ts0bGhcdN7nC/Iwv7x68+dmx88pfPxQNLNrXZ7jfPrIuN22vihx+YF4MH9Hx4uKk5iZc3lMeinWHvJ1dujar6ph6rZ1VpdawqrY67n1kfERGFef3i8IlFLWHwwycUpYWukySJ4vLanQHv8h27eW+siOVbKqOxed92KG/LgJzs+MpZc+J988b3qt3bAQAAAAAAAAAA6P2Ev4EDTlNzEv9+x3Oxrbpht9dHFubFt9936D4Hewfm9osff3BefPW+l/e4s/ii5aVxzo8WxW0XHR3jigbs01j7qrk5iVc3V8SiZaXxjxWl8fiK0iivbcxoDR1RUdcYf3+tJP7+WklERKRSETNHFcbsMYNiXVlNLC2uiO01u//fsyvNHjMo/uuCuTFtREG3jwUAAAAAAAAAAAC7Ev4GDjg/emR5/GNF6W6vpVIR3z3v8BhWkNepMbKzUvGlM2bH+CED4ro/Lomkjc2nX91UGWf/z8K47cKj4k3jBndqzLZU1zfGii1V8drmili2uTJe3VQZz6zeFqVV9V0+1sjCvDhu2rB487RhMWv0oFhaXBFPr94WT6/ZFss2V3bZOEkS8UpxRbxSXNFlfe5J/5ys+OCxk+LKt8+MvH7ZGRkTAAAAAAAAAAAAdiX8DRxQnl69Lf7z/lfbvH75idPi+OnDu2SsVCoVH33L1BhXNCA+dcdzUdfYvNt2Wyrq4twf/yP+54Ij4uRZI/d5vO01DbFsc2Us2xnyfm1zZSzbXBnrttXsc597MzQ/N948dVgcO21YHDdtWEwdnp+2Y/phE4ri3KMmREREWXV9PLumbEcYfPW2eG5tWdQ0NHVbbfsqlYqYMiw/Zo4ujFmjB8WsMYVxzJShUTQwt6dLAwAAAAAAAAAA4AAn/A0cMLbXNMQnf/lsNDXvfhvuwycUxb+fOqPLx33HIWNi1OD+8dHbn4qtbey2XV3fFBff/mR85aw3xQeOndRmX0mSREll/W5D3psr6rq89l0V9u8Xx0zZEfQ+bvqwmDGyMLKyUnu/MSKKBubGybNGtgTcG5ua45XXdwbf+bO+rPuC6rszZGBOS8D74NGDYubowpgxqjAG5NrdGwAAAAAAAAAAgP2P8DdwQEiSJD5/z4tthosL8/rFf/3L3MjJzuqW8Y+YOCTuueK4uPC2J2NlSdVu2zQnEV+496VYu7U6rv5/s6K4vHZHuHtTRSzfUhmvbaqMZVsqo6y6oVtq3J2Budlx1OShcdy0YfHmacNiztjBkd3OsPfe9MvOijeNGxxvGjc4Pnzc5IiIKN5eG8+s+WcYfPGG7dHQtPuwfkfkZKdi+sjCOHh04Y4dvccMioNHF8aIwry0ncoBAAAAAAAAAABgfyb8DRwQ7nxqXdz3wsY2r3/t3W+KCUMHdmsNk4blx92XHxeX/O9T8dTqbW22+/GjK+K2RauivrG5W+vZnbx+WTFv0pCWsPeh44u6LRC/O6MH94/TDxkTpx8yJiIiahua4sX121vC4M+s3halbeye/roxg/vHrJ0B71mjC2PW6EExdUR+RucBAAAAAAAAAAAA3UH4G+jzlm2ujC/9bnGb18+ZNz7OOnxcRmoZkp8bP/voMfHpO5+PP+whjJ6p4He/rFTMnVgUb546LN48bXjMnVgU/XOyMzJ2e/TP2bHz+FGTh0bEjh3cV5dW7wiCr9kWW6vqo2hgTswa/c+g9+CBOT1cNQAAAAAAAAAAAHQP4W+gT6ttaIpP/PLZqGlo2u31qcPz48tnzsloTf1zsuO/zp8bE4YMjB89sjxj4+b1y4qpIwrioJEFMX1kQRw2oSiOmjwkBub2nqUglUrF5OH5MXl4frx33vieLgcAAAAAAAAAAAAyqvck/gD2wfV/fiWWbCzf7bXc7Ky48V/mRn5e5v8pzMpKxWfeMSsmDB0QX7z3pWhOuq7v/NzsmD6qMKaPKIiDRv0z7D1+yMDIzkp13UAAAAAAAAAAAABARgl/A33Wg0s2xW0LV7V5/Zp3zIo3jRucuYJ24/3HTIqxgwfEx3/xTFTX73538rYMGZgTB40sjGkj/xnwPmhUQYwe1D9SKSFvAAAAAAAAAAAA6GuEv4E+aVN5bVx11wttXj955oj4yPGTM1fQHpw8a2T8+tI3x6X/93SsL6tpdX3UoLw4aGRhTN8Z8J6+M+w9rCCvB6oFAAAAAAAAAAAAeorwN9DnNDUn8e93PBdbq+p3e31EYV58+5zD9qvdsd80bnA8+OkT43fPbYjNFbUxsrB/TB+1I+g9qH9OT5cHAAAAAAAAAAAA7AeEv4E+58ePLo9Fy0t3ey2VivjuuYfH8P1w1+z+Odlx7lETeroMAAAAAAAAAAAAYD+V1dMFAHSlZ9Zsi+/89dU2r196wrSYf9DwDFYEAAAAAAAAAAAA0DWEv4E+o7y2If7tV89GU3Oy2+uHTSiKT582I8NVAQAAAAAAAAAAAHQN4W+gT0iSJD5/z0uxdmvNbq8X5PWLG88/PHKy/bMHAAAAAAAAAAAA9E5SkECfcNfT6+L3z29o8/p1735TTBqWn8GKAAAAAAAAAAAAALqW8DfQ6y3fUhlf+t3iNq+/94jxcdbh4zJYEQAAAAAAAAAAAEDXE/4GerW6xqb45C+fjer6pt1enzI8P649a06GqwIAAAAAAAAAAADoesLfQK/2rT8vjcUbynd7LSc7FTeePzcK8vpluCoAAAAAAAAAAACArif8DfRaD72yOW5ZsLLN69f8v1lxyPjBGawIAAAAAAAAAAAAoPsIfwO9UmVdY1x55/NtXj9xxoj4yPFTMlgRAAAAAAAAAAAAQPcS/gZ6pYK8fnHduw+JwQNyWl0bXpAXN5xzWGRlpXqgMgAAAAAAAAAAAIDuIfwN9Fr/702j40//9pY4evLQtPP/ee5hMaIwr4eqAgAAAAAAAAAAAOgewt9Arza2aED88mPHxr+fMiOyUhGXnjA1TpgxoqfLAgAAAAAAAAAAAOhy/Xq6AIDOys5Kxb+dclCcNHNEHDxmUE+XAwAAAAAAAAAAANAthL+BPuOwCUU9XQIAAAAAAAAAAABAt8nq6QIAAAAAAAAAAAAAANg74W8AAAAAAAAAAAAAgF5A+BsAAAAAAAAAAAAAoBcQ/gYAAAAAAAAAAAAA6AWEvwEAAAAAAAAAAAAAegHhbwAAAAAAAAAAAACAXkD4GwAAAAAAAAAAAACgF+jX0wVAd6irq0s7XrZsWQ9VQmVlZaxZs6bleMmSJVFQUNCDFQEA+8KaDgB9h3UdAPoGazoA9A3WdADoG6zpB55dc6m75la7k/A3fdLatWvTjs8+++yeKQQAAAAAAAAAAACAPm3t2rVxxBFHZGSsrIyMAgAAAAAAAAAAAABApwh/AwAAAAAAAAAAAAD0AqkkSZKeLgK6WllZWTzyyCMtxxMmTIi8vLwerOjAtWzZsjj77LNbju+9996YPn16zxUEAOwTazoA9B3WdQDoG6zpANA3WNMBoG+wph946urqYu3atS3HJ554YhQVFWVk7H4ZGQUyrKioKM4666yeLoPdmD59esyZM6enywAAOsmaDgB9h3UdAPoGazoA9A3WdADoG6zpB4YjjjiiR8bN6pFRAQAAAAAAAAAAAADoEOFvAAAAAAAAAAAAAIBeQPgbAAAAAAAAAAAAAKAXEP4GAAAAAAAAAAAAAOgFhL8BAAAAAAAAAAAAAHoB4W8AAAAAAAAAAAAAgF5A+BsAAAAAAAAAAAAAoBcQ/gYAAAAAAAAAAAAA6AWEvwEAAAAAAAAAAAAAegHhbwAAAAAAAAAAAACAXkD4GwAAAAAAAAAAAACgF+jX0wUAfduIESPiS1/6UtoxAND7WNMBoO+wrgNA32BNB4C+wZoOAH2DNZ1MSiVJkvR0EQAAAAAAAAAAAAAA7FlWTxcAAAAAAAAAAAAAAMDeCX8DAAAAAAAAAAAAAPQCwt8AAAAAAAAAAAAAAL2A8DcAAAAAAAAAAAAAQC8g/A0AAAAAAAAAAAAA0AsIfwMAAAAAAAAAAAAA9ALC3wAAAAAAAAAAAAAAvYDwNwAAAAAAAAAAAABALyD8DQAAAAAAAAAAAADQCwh/AwAAAAAAAAAAAAD0AsLfAAAAAAAAAAAAAAC9gPA3AAAAAAAAAAAAAEAvIPwNAAAAAAAAAAAAANAL9OvpAoC+bfny5fHEE0/EunXror6+PoYMGRKzZs2K4447Lvr379/T5QFAj0mSJFatWhUvvvhirFu3LsrKyiIvLy+GDBkSBx10UBx11FFdvlZWVFTEwoUL49VXX43y8vIYMGBATJo0KY477rgYO3Zsl461ePHiePrpp2Pjxo3R1NQUw4YNize96U1xzDHHRL9+XfcYksk5AcD+ora2NhYtWhSvvPJKbNu2LXJzc2P8+PFxzDHHxNSpU7t0rEw912dyTgDQUUuXLo3nn38+1q1bF9XV1TFgwIAYNWpUzJgxIw477LDIy8vb576t6wDQverq6uLZZ5+NJUuWxLZt26KmpiYGDRoUI0eOjCOOOCKmT58eqVSq0+M0NjbG448/Hi+99FKUlpZGdnZ2jBkzJubNmxdz5szpgpn80/r16+Mf//hHrF69umU+M2bMiPnz50dBQUGXjZPJOQFAd/DM3Tlyf/u5BKAb3HPPPckRRxyRRMRufwoKCpJ//dd/TbZs2dLTpQJAxmzdujW59dZbk3PPPTcZPnx4m+tkRCQ5OTnJ2WefnTz88MOdHnfFihXJBz7wgSQ3N3e3Y6VSqeSkk05KHnnkkU6N09zcnNxyyy3JjBkz2pzXsGHDki984QtJZWVlr5gTAHTW+eef32qdmjRp0j71tXnz5uTjH/94kp+f3+ZaO2/evOTee+/tdN2Zeq7P5JwAoCPKy8uT6667LpkyZcoen99zc3OT+fPnJ9/73vc61L91HQC611NPPZW8//3vT/Ly8va4lo8bNy75j//4j6S0tHSfxqmoqEg+//nPJ0OHDm1zjJkzZya33npr0tzc3Kk5Pfzww8lJJ520x9clH/zgB5OVK1d2apxMzgmAA8u6deuSu+++O7nmmmuSk08+OSksLOyS98535Zm7d8yJzhH+BrpUbW1t8v73v3+PD9Bv/BkxYoRQFgAHhCuuuKLNoPLefj70oQ8l27dv36dx77jjjmTgwIHtGieVSiXXXHPNPr1Zu23btuTUU09t95ymTp2avPTSS/v1nACgs373u9/tdn3alzewH3roob1+eGzX1w91dXUdHieTz/WZmhMAdNTvf//7ZNSoUR16dh81alS7+7euW9cB6D5NTU3JNddck2RlZXV4Lf/Tn/7UobFeeOGFvX5Q7I0/b3/725OysrIOz6m5uTm56qqr2j1Ofn5+ctddd3V4nEzOCYADx4IFC5J3v/vdydixY/e6rnRF+Nszd++YE52XSpIkCYAu0NzcHO95z3vit7/9bdr57OzsmDhxYgwePDhWrlwZ27dvT7s+cODAeOCBB+LNb35zJssFgIw68sgj4+mnn251/vWvSRw1alQ0NDTE6tWrW62VERFHH310PPjggx36ysY777wzzj///Ghubk47P2LEiJgwYUJs3rw51q9fH7s+EnzqU5+K7373u+0ep6amJk466aR44okn0s7n5ubG5MmTIy8vL1asWBFVVVWt6li0aFFMnz59v5sTAHTW9u3bY86cObF+/fpW1yZNmhSrVq1qd18LFiyI0047LWpqatLOFxUVxZQpU2Lbtm2xdu3aaGpqSrv+nve8J+666652f311Jp/rMzUnAOio7373u/HpT3+61XNl//79Y+zYsTF8+PCoqamJjRs3RklJScv1UaNGRXFx8V77t67v+5wAoD0uueSS+MlPftLq/MCBA2PatGkxYMCAKC0tjRUrVrRa73Nzc+Pee++Nd7zjHXsdZ+nSpTF//vy01wMREQUFBTF16tSoqamJVatWRUNDQ9r1N7/5zfG3v/0t+vfv3+45feITn4j//u//TjuXSqVi/PjxMWLEiFizZk2rOrKzs+POO++Md7/73e0eJ5NzAuDA8b3vfS/+/d//vV1tO/re+a48c/eOOdFFejJ5DvQt3/zmN1t9wueyyy5L1q9f39Kmqakpufvuu5OJEyemtRs/frxPBAPQp82bN69l3SsqKkquuOKK5A9/+ENSXl6e1q6xsTF56KGHkre85S2t1tX3vve97R5v2bJlrb7y6bDDDkv+9re/pbV75ZVXkve85z2txvrNb37T7rEuu+yytHuzsrKSL37xi8nWrVtb2tTV1SW33XZbMmTIkLS2c+fOTRobG/e7OQFAZ11yySVpO269cU3qyO4lW7dubbUjyqRJk5J777037Zst1q5dm1x66aWt1r/vfOc77R4rU8/1mZwTAHTET37yk1brzjve8Y7kT3/6U1JbW9uq/fr165P/+7//S9773vcmEyZM2Gv/1nXrOgDd684772y11syePTv5wx/+kDQ0NKS13bx5c3Lttde2+sbOESNGpL23vTsNDQ3JIYccknbf0KFDk9tvvz2pr69vaVdaWpp8/vOfb7UL+Sc+8Yl2z+mOO+7Y7e8KXn311bR2DzzwQHLooYemtSssLExWrlzZrnEyOScADizf/e5329w5uqCgYJ/fO9+VZ+7eMSe6jvA30CVKSkqSwsLCtH/Yv/GNb7TZft26dcnkyZPT2v/Hf/xHBisGgMyaN29eMnny5OQnP/lJUl1dvdf2jY2Nycc+9rFWD1i7Bp3b8i//8i9p9x111FHJ9u3bd9u2ubm51VjTpk1r9Wb47ixZsiTJzs5Ou/cXv/hFm+1feumlpKioKK39rbfeul/NCQA666GHHkpSqVQSseNDUd/61rf2+Q3sz372s2n3TpkyJe3N1l1dd911ae0HDx68119aJ0lmn+szNScA6IjXXnst6d+/f8t6k5OTs8fn2121Z22yrlvXAeheb3rTm9LWmSOPPDKprKzc4z0PPvhg0q9fv7T7vv71r+/xnh//+Mdp7YcMGZIsXry4zfY///nP09r369evVXh7d+rq6lqt0Zdddlla2OuNysrKkiOPPDKt/Yc+9KG9jpPJOQFw4Hk9/F1YWJicdNJJyVVXXZXceeedyapVq5KHHnqoy8Lfnrl7x5zoOsLfQJe4+uqr0/5BP+GEE9p86HzdAw88kHZPYWFhUlJSkqGKASCz7rvvvqSurq5D9zQ2NrZ6o/aCCy7Y630vvfRS2q4bubm5ycsvv7zHe2pqapKDDjoobaybbrppr2Ode+65afd88IMf3Os9u+6kNmnSpLSdQ3p6TgDQGdXV1cm0adNa1p5/+7d/2+c3sDdv3txq55MHHnhgj/c0NzcnJ5xwQto9n/vc5/Y6Vqae6zM5JwDoiJNPPjltrfn1r3/dpf1b1zs3JwDYm+XLl6etLxGRPPHEE+26d9edMt/85je32bauri6ZMGFCWvtbbrllr2N84AMf6PB7/T/4wQ/S7jnooIOSmpqaPd6zePHitN3Ms7OzkyVLluzxnkzOCYADz7Jly5LFixcnTU1Nra51VfjbM3fvmBNdKysAOqm5uTluu+22tHNf/vKXI5VK7fG+t73tbfGWt7yl5biioiJ+/etfd0uNANDT3vnOd0Zubm6H7snOzo6rr7467dxf/vKXvd536623RnNzc8vx+eefHwcffPAe7+nfv3985jOfSTv3k5/8ZI/3bNu2Le6+++6W41QqFV/+8pf3Wt9FF10UkyZNajlevXp1PPDAA3u8J1NzAoDO+uIXvxjLly+PiIiJEyfG1772tX3u61e/+lVUVla2HJ9wwgnxtre9bY/3pFKp+NKXvpR27tZbb40kSdq8J5PP9ZmaEwB0xG9/+9t46KGHWo7POeecOOecc7p0DOv6DtZ1ALrL0qVL047Hjx8fRx11VLvufe9735t2vGzZsjbb/uUvf4m1a9e2HE+ePDkuuuiivY6x63p85513xvbt2/d4z67vZ3/2s5+N/v377/Ge2bNnx3nnnddy3NTU1Oq1wa4yOScADjzTpk2L2bNnR1ZW90VVPXPvsL/Pia4l/A102qJFi2LLli0tx1OnTo2TTjqpXfdefPHFacf33ntvF1YGAL3fGx+YIiJKS0ujurp6j/f87ne/Szvedb1ty3nnnRf5+fktx08++WRs2LChzfZ/+MMforGxseX4pJNOiqlTp+51nKysrFZvHO/tNUCm5gQAnfHkk0/G9773vZbj//mf/4mCgoJ97u+3v/1t2nF717+TTz45pkyZ0nJcXFwcjz32WJvtM/lcn6k5AUBH3HTTTWnHu/6itCtY1//Jug5Ad9i6dWva8YQJE9p978SJE9OOy8rK2my76/p30UUX7TUcFbEj+HbiiSe2HDc0NMQf//jHNtuvW7cunnnmmZbjgoKCOPfcc/c6TkTrNXnXmneVqTkBQHfxzP1P+/Oc6FrC30Cn/eEPf0g7PvXUU9v1MPh62zd6+OGHo6qqqstqA4DebsiQIa3O7WnnjKVLl6btSpKfnx/HHXdcu8batW2SJK3W+Tfa9dppp53WrnEiWr8GuO+++9psm8k5AcC+amhoiIsvvjiampoiYseOoe9617v2ub/Kysp49NFH0861d61NpVJxyimnpJ3b01qbqef6TM4JANpr/fr1ad+ydfjhh8ecOXO6dAzrejrrOgDdYfDgwWnHNTU17b5317bDhw9vs22m3hffdZzjjz8+baOTPTn++ONj4MCBLcdLly6N1157rd1jddecAKA7eOZOt7/Oia4n/A102nPPPZd23N4wVkTE2LFjY/LkyS3H9fX18fLLL3dRZQDQ+61fv77VuWHDhrXZftd1+eijj45+/fq1e7zjjz9+j/3t6VpHXgPMmzcv8vLyWo43bNiQ9oniPY3TnXMCgH31jW98I1588cWIiCgqKoobb7yxU/0tXrw4GhoaWo6nTJkSo0ePbvf9mVrTO/Jcn8k5AUB7/fnPf2758FbEjh2yupp1vTXrOgBd7fDDD087XrJkSbvDR0888UTa8dFHH73bdps2bYri4uKW47y8vDjiiCPaXWOm1vR+/fq1mkNbY2VyTgDQHTxzt7Y/zomuJ/wNdNqSJUvSjmfPnt2h+3dtv2t/AHAg+/vf/552PGnSpMjNzW2zfabW5YaGhrTduDs6Vl5eXkybNq1dY3mtAcD+7uWXX47rrruu5fj666/v0Buxu5PJ9S9TY1nTAdgfPfnkk2nHhx12WMvfn3322fjkJz8Zhx12WAwZMiQGDhwYkydPjlNPPTVuuOGG3X5ge3es6/s+DgC01/jx49PCSnV1de36YHZdXV1873vfSzt38cUX77btruvV9OnT9/h+/a52Xf+WLVsWjY2N7RorU2t6d84JALqDZ+59HyfTY9G1hL+BTqmpqYk1a9aknZswYUKH+ti1/dKlSztdFwD0Fbfeemva8emnn77H9ruuo921Lq9YsSLtDdwBAwbs8aswOzNWpuYEAPuiubk5Lr744qivr4+IiLe85S1xySWXdLrfrl7/Vq9eHbW1ta3aZfK5PlNzAoCO2DX8PXXq1KisrIyLL744jjjiiPiv//qveOGFF6KsrCxqampi9erV8cADD8RVV10VBx10UHzuc59L241rd6zrex/Hug5AV7j++usjK+ufMZj/+I//iNtvv73N9mVlZfG+970vLaR0xhlnxBlnnLHb9p1d/0aMGBH9+/dvOa6vr4+VK1d2y1iZWtM7MicA6A6eufc+zv4wJ7qe8DfQKSUlJZEkSctxTk5OjBw5skN9jBs3Lu148+bNXVIbAPR2f/zjH+PRRx9NO3fhhRfu8Z5d19Hx48d3aMxd1+UtW7a0a5xd79uXsdp6DZCpOQHAvrjxxhvjsccei4iI3NzcuOmmmyKVSnW6386uf6NGjYp+/fq1HDc3N0dpaWmrdpl8rs/UnACgI3b9VqusrKw44YQTWn0Ye3dqamriG9/4Rpx++ulRUVHRZjvremvWdQC6w/z58+O///u/W57LGxsb48ILL4yjjz46vvnNb8Y999wTf/7zn+NnP/tZfOITn4hp06bFfffd13L/qaeeGr/85S/b7L+z619ExNixY/fY5+t2fR+7s++Ld9eaHtH+OQFAd/DM3dr+OCe6Xr+9NwFoW2VlZdrxwIEDO/xL7vz8/D32CQAHoq1bt8all16adu7ss8+Oo48+eo/37bqO7rrO7s2u7RsaGqKuri7y8vK6dJzd3dPWa4BMzQkAOmrlypXxhS98oeX4s5/9bMyaNatL+u7s+pdKpWLAgAFpQbTdrbWZfK7P1JwAoL2am5tbhbY/+clPxrPPPhsRO9aed73rXXH66afH+PHjo6qqKp599tn4v//7v9iwYUPLPQ888EBceOGF8Zvf/Ga341jXW7OuA9BdLr/88pg5c2Z88pOfjMWLF0fEjm/62PXbPt5o6tSpcfXVV8cll1yStnP4rjL1vnhNTU00NTV1aqxMrekdGQsAuoNn7tb2xznR9ez8DXTKrv9gv/ErndprwIABe+wTAA40zc3N8YEPfCDWrVvXcm7w4MFx44037vXezq7Nu67Lu+uzK8bZ3VjtfbjtrjkBQEd97GMfi6qqqoiImDVrVnzuc5/rsr4ztdb2pjW9I2MBQHts3749bYeriIhnnnkmIiKGDRsWjzzySPzud7+Lyy67LN71rnfFeeedF9/85jdj6dKlccEFF6Tdd/fdd8f//u//7nYc63rnxgKAjnrrW98aTz75ZFx55ZWRnZ29x7YTJ06MK6+8Mi644II9Br8jem5N35exrOkAHCg8c+/7WF4H9G7C30Cn1NbWph3n5uZ2uI9dd92sqanpVE0A0NtdddVV8ac//Snt3I9//OOYMGHCXu/t7Nq8u92wd7c2Z/I1QKbmBAAdccstt8QDDzwQETt20bjpppv2aT1sS6bW2t60pndkLABoj7Z+IZmdnR1/+MMf4i1vecturxcUFMT//d//xWmnnZZ2/utf/3qrMHmEdb2zYwFAR/3oRz+KadOmxQ033NBqB+1drVmzJq644oqYPHly3HrrrXts21Nr+r6MZU0H4EDhmXvfx/I6oHcT/gY6ZddP/NTX13e4j7q6uj32CQAHkhtvvDH+8z//M+3c1VdfHeedd1677u/s2rzrury7PrtinN2N1dZrgEzNCQDaa+PGjXHllVe2HH/0ox9tMxy2rzK11vamNb0jYwFAe7S1jnz0ox+NY445Zo/3ZmVlxQ9/+MO0HUKXLl0ajzzyyF7Hsa53bCwAaK+GhoZ43/veF5dffnls3LgxIiKGDh0a//Ef/xFPPPFEbNu2Lerr62PDhg3xu9/9Lt797ndHKpWKiIitW7fGxRdfHFdddVWb/ffUmr4vY1nTAThQeObe97G8DujdhL+BTikoKEg73t2nkPdm10/87NonABwofvGLX8SnPvWptHMXXnhhfPOb32x3H51dm3f3Sdzdrc2ZfA2QqTkBQHt9/OMfj7KysoiIGD16dHzrW9/q8jEytdb2pjW9I2MBQHu0tY5ccskl7bp/6tSpccopp6Sd213427reubEAoL0uv/zy+M1vftNyfPTRR8fixYvj2muvjaOOOiqKiooiJycnxowZE2eccUbcfffdce+996aFlG644Ya47bbbdtt/T63p+zKWNR2AA4Vn7n0fy+uA3k34G+iUXf/Brq6u3u3XWu5JVVXVHvsEgAPBfffdFx/+8IfT1tH3vOc98ZOf/KRl55H22HUd3XWd3Ztd2/fr12+3n87t7Di7u6e9D7fdNScAaI8777wz7rnnnpbj73//+1FUVNTl43R2/UuSZJ/e3O3O5/pMzQkA2mvAgAGRnZ2ddq6wsDDmzp3b7j5OPPHEtOOnnnqqVRvremvWdQC62sMPPxy33HJLy/HIkSPjvvvui9GjR+/xvjPPPDP+53/+J+3cVVdd1a5NRbrrffHdvUbp7Pvi3bWmd2QsAOgOnrlb2x/nRNcT/gY6Zfjw4WmBtIaGhti8eXOH+li/fn3a8ciRI7ukNgDoLR566KE455xzorGxseXcqaeeGr/85S9bvcG7N7uuo+vWrevQ/buuyyNGjGjXOLvety9jtfUaIFNzAoD2eOPXP7/zne+Mc889t1vG6ez6t2nTprTXFllZWTF8+PBW7TL5XJ+pOQFAR+y6Pk2fPj2ystr/67OZM2emHe9uHbWut2ZdB6Cr3XjjjWnHn/rUp9r9XvCFF14YM2bMaDkuLS2Nu+++u1W7zq5/EREbNmzYY5+v27X2zr4v3l1rekT75wQA3cEzd2v745zoesLfQKcMGDAgJk6cmHZuzZo1Hepj1/azZs3qdF0A0Fs8/vjjceaZZ6Z9hdJxxx0X99xzT+Tm5na4v11/6dxd6/LUqVOjX79+Lcc1NTWxZcuWbhkrU3MCgPYoKytr+fsf/vCHSKVSe/05+eST0/pYvXp1qzbPPfdcWpuuXv8mTZq022++yORzfabmBAAdcfDBB6cdDxo0qEP379p+27ZtrdpY1/c+jnUdgM5IkiT+9re/pZ0744wz2n1/VlZWvPOd70w79+ijj7Zq19n1b/PmzWm/C8jNzY2pU6futm2m3hfP5JwAoDt45t77OPvDnOh6wt9Ap+36j/bLL7/cofuXLFmyx/4AoK964YUX4h3veEdUVla2nJs7d2788Y9/jPz8/H3qM1Prck5OTkybNm2fx6qrq4sVK1a0ayyvNQA4EGVy/cvUWNZ0APZHs2fPTjuuq6vr0P1vDDtFRAwcOLBVG+v6vo8DAO2xbdu22L59e9q5KVOmdKiPXdvv7tsud12vli9fHvX19e0eY9f1b9q0aWmbrOxprEyt6d05JwDoDp65932cTI9F1xL+Bjrt8MMPTztetGhRu+/duHFjrFq1quU4Jyen1ZvtANAXLV26NE499dS0HcEOPvjg+Mtf/hKDBw/e5353XZeffPLJtK902puFCxfusb89XevIa4Cnn3467RfqY8aMafMroDI5JwDYX8yZMydycnJajletWhUbN25s9/2ZWtM78lyfyTkBQHsdccQRacebNm3q0P27fh3ysGHDWrWxrrdmXQegK+3uw1sdDSC/cV2LiGhqamrVZvTo0TF69Oi0cZ9++ul2j5GpNb2xsTGeeOKJdo2VyTkBQHfwzN3a/jgnup7wN9Bp73rXu9KOH3jggUiSpF33/vWvf007Pvnkk6OgoKDLagOA/dHq1avjlFNOSfsF8ZQpU+L++++PESNGdKrvWbNmpe3IXVVV1e4HtKqqqvjHP/7RcpxKpVqt82+067X777+/3XXu2nZPX8GZyTkBwN789re/jfvvv79DPzfccENaH6NGjWrVZvr06WltCgsL44QTTkg71961NkmSeOCBB9LO7WmtzdRzfSbnBADt9c53vjOysv7567KVK1fG1q1b233/ruGoXb+aOcK6vivrOgBdbXcfvtqwYUOH+th1p++23qt/5zvfmXbcXe+L7zrOokWLoqqqql3jLFy4MKqrq1uOZ8yYETNmzGj3WN01JwDoDp650+2vc6LrCX8DnXbcccfF8OHDW45XrFgRDz/8cLvuveWWW9KOzzrrrK4sDQD2Oxs3boy3ve1tsW7dupZz48aNiwcffDDGjRvXJWOceeaZace7rrdtueOOO6KysrLl+Mgjj4yxY8e22f70009P2z3l4YcfjhUrVux1nCRJ4qc//Wnaub29BsjUnABgb0488cQ45ZRTOvQzb968tD769+/fqs3u3hDd1/XvoYceipUrV7Ycjxo1Ko455pg222fyuT5TcwKA9ho5cmQcf/zxaefuvvvudt3b2NgY99xzT9q5k046abdtrev/ZF0HoKvl5ubGmDFj0s797W9/61AfDz74YNrxGzckeaNd17/bbrutXQGp5cuXxyOPPNJynJOTE6effnqb7SdMmBBz585tOa6srIxf//rXex0novNrenfNCQC6i2fuf9qf50TXEv4GOi0rKysuvPDCtHPXXnvtXh8IH3zwwfj73//eclxYWBjnnntud5QIAPuFrVu3xqmnnhrLly9vOTdixIi4//77Y8qUKV02zkc+8pFIpVItx7/61a9iyZIle7yntrY2vvnNb6adu/jii/d4z9ChQ+Pss89uOU6SJL785S/vtb5bb7017eufJk2aFKeccsoe78nUnABgf3L++edHfn5+y/Gjjz66119eJ0kS1157bdq5iy66KG1H011l8rk+U3MCgI649NJL046//e1vR11d3V7vu/nmm6O4uLjleNCgQfH2t799t22t6ztY1wHoLm9729vSjr/3ve9FY2Nju+595JFH0r5Bcnf9ve7tb397jB8/vuV41apVcdttt+11jC9/+ctp6/F73/veGDx48B7v2fX97G9+85tRW1u7x3uWLFkSd9xxR8vx7l4b7CqTcwKA7uCZe4f9fU50sQSgC2zZsiUpKChIIqLl5xvf+Eab7detW5dMnjw5rf0XvvCFDFYMAJlVXl6eHHXUUWlrX1FRUfLss892y3jnnXde2lhHHXVUsn379t22bW5uTi699NK09lOnTk3q6+v3Os7ixYuTrKystHt/8Ytf7LF9UVFRWvuf/OQn+9WcAKCrPfTQQ2lr0qRJk9p97zXXXJN275QpU5L169e32f66665Laz948OCktLR0r+Nk8rk+U3MCgPZqampKDjnkkLT15sMf/nDS1NTU5j2PPfZYq7XzM5/5zB7Hsa5b1wHoPn/+85/T1piISC655JI9rudJkiSvvfZaMnbs2LT7DjrooKSxsbHNe374wx+mtR8yZEiyePHiNtv//Oc/T2ufnZ2dLF26dK9zqqurSyZOnJh272WXXZY0Nzfvtv327duTI488Mq39Bz7wgb2Ok8k5AcAbdea981155u4dc6LrCH8DXebrX/96qwfqyy+/PG3RaWpqSu65555WD6ljx45Ntm3b1nPFA0A3O+mkk1qtk1/5yleS+++/v8M/W7du3et4r732WjJw4MC08Q477LDkoYceSmu3dOnS5D3veU+r2n7961+3e24f+9jH0u7NyspKvvjFL6bVWV9fn9x2223JkCFD0toeeuihSUNDQ7vGyeScAKArdeYN7NLS0mT06NGt7v/tb3+b9svetWvXtvrgU0Qk3/rWt9o9Vqae6zM5JwBorwceeCBJpVJpa84pp5ySPPXUU2ntysrKku985zutfik6Y8aMpLy8fI9jWNet6wB0r5NPPrnVWjN//vzkgQceaPU+dElJSXLDDTckgwcPbnXPnXfeucdx6uvrkzlz5qTdM3To0OT2229PG6e0tDT5whe+0GoDlSuuuKLdc/rFL37Rqr73ve99yauvvprW7sEHH0wOPfTQtHYFBQXJihUr2jVOJucEwIFnwYIFu/299w033JC2nowaNarN35Hv6UNJSeKZu7fMia6TSpK97M8O0E7Nzc1x1llnxX333Zd2Pjs7OyZNmhSDBw+OlStXRllZWdr1AQMGxP333x/HH398BqsFgMxKpVJd1tdDDz0UJ5100l7b/epXv4oLLrig1VcyjRgxIiZOnBibN2+OdevWtbr+iU98Im688cZ211NdXR0nnnhiPPXUU2nnc3NzY8qUKZGXlxcrVqyIysrKtOvDhw+PhQsXxowZM9o9VqbmBABd6eGHH46TTz655XjSpEmxatWqdt//6KOPxtvf/vZWX+1cVFQUU6ZMibKyslizZk00NTWlXT/rrLPinnvuaffrkEw+12dqTgDQEddff3185jOfaXV+9OjRMX78+Kiqqorly5dHfX192vVhw4bFQw89FIcccshex7Cu7/ucAGBviouL47jjjouVK1e2ulZQUBBTpkyJAQMGRGlpaaxYsaLV+8gREZ/+9Kfjhhtu2OtYS5Ysifnz58fWrVtbjTNt2rSoqamJlStXRkNDQ9r1o48+Oh5++OEYMGBAu+d1xRVXxA9/+MO0c6lUKiZMmBAjRoyI1atXR0lJSdr1rKysuOOOO+J973tfu8fJ5JwAOLBMnjw5Vq9e3ak+PvzhD8dPf/rTPbbxzN075kQX6cHgOdAH1dTUJOeff36rTwK19TNs2LBWu3UCQF/U3rWxPT8dWTt/8YtfJAMGDGh331deeWWbXxm5J6Wlpclb3/rWdo8zefLk5IUXXujwOJmcEwB0la746soHH3wwGTp0aLvXvwsuuCCpra3t8DiZfK7P1JwAoCNuvPHGJCcnp93r08yZM1vtvLk31nXrOgDdZ82aNbv9Js69/eTk5CTf/OY3O/Re8nPPPZdMmjSp3WOccsop+7QrZlNTU/Lv//7v7R5n4MCByR133NHhcTI5JwAOLB1ZW9r6+fCHP9yusTxz94450XlZAdCF+vfvH7/85S/jrrvuisMPP7zNdvn5+XHFFVfEyy+/3K6dSwGAffMv//Iv8dJLL8UFF1wQOTk5bbY74YQT4uGHH45vf/vb+7Tj1tChQ+P++++Pm266KaZPn77Hdp/73OfixRdfbNeOaLuTqTkBwP7krW99a7z88stx+eWXx8CBA9tsN3fu3PjNb34TP//5zyMvL6/D42TyuT5TcwKAjvjEJz4RL7zwQpx33nl7fOacMmVKfP/7348XXnghDjrooA6NYV23rgPQfSZMmBAPPvhg/PrXv46TTjopsrL2HIsZPHhwXH755fHiiy/GNddc06H3kg877LB48cUX47Of/WwMGTKkzXYHHXRQ3HzzzfHXv/41ioqK2t3/67KysuI///M/429/+1u85S1vabNdbm5uvP/974+XXnopzj333A6PE5G5OQFAd/HM3TvmROelkmQ332MD0EWWLVsWjz/+eKxfvz7q6+ujqKgoDj744Dj++OOjf//+PV0eABxQysvLY8GCBfHaa69FRUVF9O/fPyZOnBjHH398jBs3rkvHevHFF+OZZ56JjRs3RlNTUwwbNize9KY3xTHHHLPHX553VCbnBAD7i5qamli0aFEsWbIkysrKIjc3N8aNGxfHHHPMHj+EtS8y9VyfyTkBQHuVl5fHokWL4rXXXovt27dHQUFBjBo1Ko444oiYOXNml4xhXQeA7lVRURFPPfVUrFixIsrKyqK2tjYGDRoUw4YNi0MPPTRmz56914B4ezQ0NMTjjz8eL730UpSWlkZ2dnaMGTMmjjjiiH3eCKUt69ati0WLFsWaNWuitrY2CgsL46CDDor58+fHoEGDumycTM4JALqDZ+7Okfvbvwl/AwAAAAAAAAAAAAD0Ap3/+CIAAAAAAAAAAAAAAN1O+BsAAAAAAAAAAAAAoBcQ/gYAAAAAAAAAAAAA6AWEvwEAAAAAAAAAAAAAegHhbwAAAAAAAAAAAACAXkD4GwAAAAAAAAAAAACgFxD+BgAAAAAAAAAAAADoBYS/AQAAAAAAAAAAAAB6AeFvAAAAAAAAAAAAAIBeQPgbAAAAAAAAAAAAAKAXEP4GAAAAAAAAAAAAAOgFhL8BAAAAAAAAAAAAAHoB4W8AAAAAAAAAAAAAgF5A+BsAAAAAAAAAAAAAoBcQ/gYAAAAAAAAAAAAA6AWEvwEAAAAAAAAAAAAAegHhbwAAAAAAAAAAAACAXkD4GwAAAAAAAAAAAACgFxD+BgAAAAAAAAAAAADoBYS/AQAAAAAAAAAAAAB6AeFvAAAAAAAAAAAAAIBeQPgbAAAAAAAAAAAAAKAXEP4GAAAAAAAAAAAAAOgFhL8BAAAAAAAAAAAAAHoB4W8AAAAAAAAAAAAAgF5A+BsAAAAAAAAAAAAAoBcQ/gYAAAAAAAAAAAAA6AWEvwEAAAAAAAAAAAAAegHhbwAAAAAAAAAAAACAXkD4GwAAAAAAAAAAAACgFxD+BgAAAAAAAAAAAADoBYS/AQAAAAAAAAAAAAB6AeFvAAAAAAAAAAAAAIBeQPgbAAAAAAAAAAAAAKAXEP4GAAAAAAAAAAAAAOgFhL8BAAAAAAAAAAAAAHoB4W8AAAAAAAAAAAAAgF5A+BsAAAAAAAAAAAAAoBcQ/gYAAAAAAAAAAAAA6AWEvwEAAAAAAAAAAAAAegHhbwAAAAAAAAAAAACAXkD4GwAAAAAAAAAAAACgFxD+BgAAAAAAAAAAAADoBYS/AQAAAAAAAAAAAAB6AeFvAAAAAAAAAAAAAIBeQPgbAAAAAAAAAAAAAKAXEP4GAAAAAAAAAAAAAOgFhL8BAAAAAAAAAAAAAHoB4W8AAAAAAAAAAAAAgF5A+BsAAAAAAAAAAAAAoBfo19MFQHcoKyuLRx55pOV4woQJkZeX14MVAQAAAAAAAAAAANAX1NXVxdq1a1uOTzzxxCgqKsrI2MLf9EmPPPJInH322T1dBgAAAAAAAAAAAAB93L333htnnXVWRsbKysgoAAAAAAAAAAAAAAB0ivA3AAAAAAAAAAAAAEAv0K+nC4DuMGHChLTje++9N6ZPn95D1RzYKisr44knnmg5Pvroo6OgoKAHKwIA9oU1HQD6Dus6APQN1nQA6Bus6QDQN1jTDzzLli2Ls88+u+V419xqdxL+pk/Ky8tLO54+fXrMmTOnh6o5sJWXl0dxcXHL8cEHHxyDBg3qwYoAgH1hTQeAvsO6DgB9gzUdAPoGazoA9A3WdHbNrXanrIyNBAAAAAAAAAAAAADAPhP+BgAAAAAAAAAAAADoBYS/AQAAAAAAAAAAAAB6AeFvAAAAAAAAAAAAAIBeQPgbAAAAAAAAAAAAAKAXOODC37W1tVFbW9vTZQAAAAAAAAAAAAAAdEi/ni6gO2zdujUWLFgQTz31VDz//POxatWqWLt2bZSXl0eSJBERkUqlYtCgQTFx4sSYPHlyHHrooXHkkUfG8ccfH8OGDevhGQAAAAAAAAAAAAAApOsz4e8VK1bEL37xi7jvvvvi6aefjubm5pZrrwe+3yhJkigrK4uysrJ48cUX4/e//31E7AiFz5s3L975znfGBRdcENOnT8/YHAAAAAAAAAAAAADofZqTiNWVES9uzYrXFq6JK9/xpp4uiT6qV4e/Gxoa4le/+lX88Ic/jMcff7zl/Bt3937jn3uTJEkkSRJPPfVUPPXUU3HttdfGUUcdFR//+Mfj/PPPj5ycnK6fBAAAAAAAAAAAAAC9Tn1jc/xjRWnc9+ya+MtL2VHesCOv+mL5xvj/3j4nsrLal1+FjuiV4e/q6uq48cYb4/vf/35s3rw5InYEt3cNeb9xx+/c3NzIz8+PgQMHRpIkUVNTE1VVVVFfX9/mOEmSxJNPPhkXXnhhXH311fHJT34yPvnJT0Z+fn73TAwAAAAAAAAAAACA/VZlXWM8vHRz/GXxpnj4lc1RUde488o/M6ybK+vj+XVlMXfikJ4pkj6tV4W/Gxsb48Ybb4zrr78+SkpKWgLfr4e8kySJQYMGxfz582PevHlxyCGHxKxZs2Ls2LExdOjQ3fZZWloaGzZsiFdeeSVefPHFePrpp2PhwoVRXl7e0mcqlYpNmzbFF77whfjud78bn/nMZ+ITn/iEncABAAAAAAAAAAAA+rgtFXXxwJJN8dfFxbFwWWnUNzXv9Z6/vrxJ+Jtu0WvC3/fdd198+tOfjmXLlqXt8p0kScyePTve/e53x1lnnRVHHHFEZGVltbvfYcOGxbBhw+KQQw6Jc845JyIimpub45lnnonf/va3cc8998TLL7/c0r6kpCSuuuqq+NGPfhTf/e53453vfGfXThQAAAAAAAAAAACAHrW6tCr+srg4/rp4Uzy9Zlvs3Ke43f66uDiu+X+zuqc4Dmi9Jvx95plnpu3yXVhYGBdccEF89KMfjSOOOKJLx8rKyoojjzwyjjzyyPjqV78azz77bNx8883xy1/+MrZv3x4REcuWLYuzzjorGhsb99IbAAAAAAAAAAAAAPuzJEli8Yby+Ovi4vjL4k2xdFNFp/pbvqUqlm2ujOkjC7qoQtih14S/I3b8P9a4cePi3/7t3+LSSy+NwsLCjIw7d+7c+MEPfhDf+ta34kc/+lF8//vfj/Xr17cE0QEAAAAAAAAAAADoXRqbmuOJVVvjr4s3xf0vb4r1ZTWd7jM7lcTx04bF6YeOixGFeV1QJaTrNeHv4cOHx+c///m4/PLLIzc3t0dqKCgoiCuvvDI++clPxg9+8IP4xje+0SN1AAAAAAAAAAAAANBxNfVN8ehrW+KvizfFg69sirLqhk73WZCXHTMKGuLQYUkcXJTEO06ZE4MGDeqCaqG1XhP+XrFiRRQU7B9b3+fm5sanPvWp+OhHP9rTpQAAAAAAAAAAAACwB1ur6uOhVzbHXxYXx6OvbYnahuZO9zmyMC9OnT0q3j5ndMwZkRMLHn2kCyqFves14e/9Jfj9RvtjTQAAAAAAAAAAAAAHoubmJNZuq44lG8vj5Q3l8fLG8liysSLWl9V0Sf9Th+fHaXNGx2lzRsXh44siKysVERHl5eVd0j+0R68JfwMAAAAAAAAAAABARERtQ1O8uqkiXt5QviPsvTPoXVnX2KXjHDZ+cJw2Z3S8fc6omD6ysEv7hn0h/A0AAAAAAAAAAADAfquksm6X3bzLY/mWqmhqTrp8rH5ZqTh26rA4bc6oOHX2qBgzeECXjwGdIfwNAAAAAAAAAAAAQI9rak5iVWlV2m7eL28oj80Vdd067oCc7Dhp5og4bc6oeOvMUTF4YE63jgedIfy9U2VlZTz++ONRUlISQ4YMicMPPzxGjhzZ02UBAAAAAAAAAAAA9DkVtQ2xtLgiXimuaNnN+5WNFVHT0JSR8Yfm58YpB4+M02aPjvkHDY/+OdkZGRc6q8+Fv+vq6mL9+vUtx6NHj46BAwe22b62tjauvPLK+MlPfhINDQ0t57OysuLMM8+MG2+8McaNG9etNQMAAAAAAAAAAAD0RY1NzbGqtDpeKd4R7n6luDxeKa6IddtqMl7L+CED4rTZo+Ptc0bFvElDol92VsZrgM7qc+Hv//mf/4mrrroqIiL69esXK1asaDP83djYGKeeemosWrQokiRJu9bU1BT33ntvLFq0KBYsWBDTpk3r9toBAAAAAAAAAAAAequSyrq0gPcrxeXx6qbKqG9szmgdqVTE5GH5MXvMoJg9dlAcPKYwDh4zKEYP6h+pVCqjtUBX63Ph79/85jctQe4zzzxzj7t2X3fddbFw4cJIpVKRSqXSAuCvH2/atCnOOuuseP755yM725b+AAAAAAAAAAAAwIGttqEplm2u3BHw3vh60LsiSirrMl5L/5ysmDV6UBy8M+g9e8ygmDW6MPLz+lxEFiKij4W/6+rq4umnn275VMZZZ53VZtvt27fHf/7nf6aFvufPnx/HH398lJSUxF133RXl5eWRJEksWbIkfvzjH8cVV1yRkXkAAAAAAAAAAAAA9LQkSWJ9Wc0uu3lXxMqSqmhqTvbeQRcbUZj3ht28dwS9pwzPj+wsu3lz4OhT4e/FixdHfX19ROzYufvkk09us+1dd90VFRUVLbt+f/7zn4+vfOUrLdc///nPx3HHHRebNm2KJEni5ptvFv4GAAAAAAAAAAAA+qSy6vp4pbgilhZXxNJNO/58tbgiKuoaM15LVipi2oiCtN28Dx4zKEYU5mW8Ftjf9Knw94oVK1r+XlRUFOPGjWuz7d133x0ROz6VMm7cuPjSl76Udn3y5Mnx1a9+NS655JKIiHjhhRdi48aNMWbMmG6oHAAAAAAAAAAAAKD71dQ3xbLNlfFKcXla0HtzRV2P1FOY1y9mji5MC3rPHF0Y/XOye6Qe2N/1qfD3xo0bI2LHrt9jx45ts11jY2P8/e9/j1Rqxzb/F1xwQWRnt/5H4txzz43LLrssmpqaIiLiueeeE/4GAAAAAAAAAAAA9nuNTc2xemt1LC2uiFd27uK9dFNFrCqtiiTJfD1ZqYipIwpi1s6g98xRhTFrTGGMKxrQkucE9q5Phb+rqqpa/l5YWNhmu+eeey4qKysjYkdQ/PTTT99tu8LCwpg8eXIsX748IiJWrVrVdcUCAAAAAAAAAAAAdFKSJLGpvK7VTt6vba6M+sbmHqlpeEFeHDymcGfAe1DMGl0Y00cW2M0bukCfCn8nb/goSkNDQ5vtFi5c2PL3nJycOPbYY9tsO2zYsJbwd3l5eRdUCQAAAAAAAAAAANBxZdX1sbS4Il7dXLljJ++dYe/tNW1nJrtTbr+smDGqIGaN3hHwnjV6UMwcXRgjCvN6pB44EPSp8PegQYMiYkcIvLi4uM12Dz30UETs2PX7yCOPjLy8tv+RaWpqavl70hPfcwAAAAAAAAAAAAAcUCrrGuO1TRXx6qaKWFpcGa/u/Pvmiroeq2n8kAEtAe9ZY3b8OXnYwOiXndVjNcGBqE+Fv8ePH9/y940bN8aWLVtixIgRaW1qamrigQceiFQqFRERJ5544h773LZtW8vfCwoKurBaAAAAAAAAAAAA4EBW29AUyza/Hu6u3Bn2roj1ZTU9VtPgATkxc3RhzBpdGDNHF8bMUYUxY3RhDOqf02M1Af/Up8Lfc+fOjYgdO3onSRI/+9nP4t///d/T2vziF7+I6urqlnYnn3xym/01NDTEunXrWoLiY8aM6abKAQAAAAAAAAAAgL6qoak5VpVUxdJNFfFqcUUs3VQRr22qjFWlVdGc9ExNef2yYsaowpgx6g1B79GFMbIwryU3Cex/+lT4e+LEiXHooYfGiy++GEmSxJe+9KWYN29enHDCCRER8fzzz8fnP//5lnD40KFD46STTmqzv5deeinq6+sjYkdQfPr06ZmYBgAAAAAAAAAAANBLldc2xBMrtsYrxeWxdFNlvFpcEStKKqOhqWdS3lmpiMnD82PW6DcGvQfFxKEDIztLyBt6mz4V/o6IuOKKK+Kyyy6LVCoVlZWVcfLJJ8esWbMiJycnXn755WhqaookSSKVSsXFF18c/fq1/Z/g/vvvb/l7Xl5ezJ49OxNTAAAAAAAAAAAAAHqJ5uYkXt5YHo+8uiUeWbolnl6zLZp6aDvv0YP6t+zgPXPUjj+njyyI/jnZPVIP0PX6XPj7kksuif/93/+NRYsWtezwvWTJkpbrr38VwZgxY+Izn/nMHvu66667Wu458sgjIycnp/sKBwAAAAAAAAAAAHqFsur6ePS1knhk6ZZ45NUtUVJZl9HxBw/IiZmjCuOgUQUtO3nPHFUYgwfKOUJf1+fC36lUKu67774488wzY8GCBWnnkySJJEli1KhR8dvf/jaKiora7Oe5556Lp556qiUsfuqpp3Z36QAAAAAAAAAAAMB+qKk5iRfXb4+Hl26OR17dEs+vLYtMbO6dn5sdB40qjBmjCmLGqH/u6D2iMK8l3wgcWPpc+DsioqioKB599NG4884746677opXX301ampqYuzYsXHqqafGZZddFkOGDNljHzfccENERCTJjn+dzzrrrG6vGwAAAAAAAAAAANg/bKmoi7+/tmNn70df3RLbqhu6bazcflkxfURBzBxdGDPeEPYeVzQgsrKEvIF/6pPh79edc845cc455+zTvT/84Q/jf/7nf1qOBw8e3FVlAQAAAAAAAAAAAPuZxqbmeG5tWTy8dEfg+8X127t8jOysVEwdnr8z4F0YM0fvCHlPHDow+mVndfl4QN/Tp8PfnVFYWNjTJQAAAAAAAAAAAADdqHh7bTz66o6w999f2xLltY1d1vfIwrw4bEJRyy7eM0cXxpTh+ZHXL7vLxgAOPMLfAAAAAAAAAAAAwAGhsak5nly1LR5+dXM8snRLvFJc0WV952Sn4shJQ+PEmSPipJkjYuaowkilUl3WP0CE8DcAAAAAAAAAAADQx20ur41fPbk2fvnEmti4vbbL+h1XNCBOmjkiTpwxIo6bPjwK8sQyge7lXxkAAAAAAAAAAACgz0mSJP6xojR+/tia+Mvi4mhsTjrdZ26/rDhmytA4ccaIOGnmyJg2It/u3kBGCX8DAAAAAAAAAAAAfcb2mob4zdPr4uePr47lW6o63d/kYQPjpJkj48QZI+LYqcNiQG52F1QJsG96Tfh76tSpPTp+KpWK5cuX92gNAAAAAAAAAAAAwO69uG57/Oyx1fHb59dHbUPzPvfTPycrjps2PE6cMSJOnDEiJg/P78IqATqn14S/V61aFalUKpKk81+7sC98LQMAAAAAAAAAAADsX2obmuL3z2+Inz2+Jp5fW7bP/UwfWRAnzRgRJ84cEUdNHhr9c+zuDeyfek34+3XtDWG/HhLvTGg7SZIeDZwDAAAAAAAAAAAAra0sqYqfP7Y67nx6XWyvaejw/bn9suLEGSPipJk7dvceP2RgN1QJ0PV6Tfh74sSJHQ5yb9++PcrKyloFuLOzs6OwsDDy8/OjqqoqKioqoqmpqeX66+MMGTIkBg0a1DUTAAAAAAAAAAAAAPZZY1NzPLBkc/zssdWxYFnJPvUxadjAeP8xE+OceRNiSH5uF1cI0P16Tfh71apVHWr/85//PP71X/+1Jfg9d+7c+PCHPxwnnXRSzJ49O/r1++fUGxsbY/HixfHII4/ET3/603juueda7rvuuuviggsu6OLZAAAAAAAAAAAAAO2xqbw2fvnEmvjVE2ujuLy2w/dnpSLedvCo+OCxk2L+9OGRldWxjWgB9ie9JvzdEd/5znfi6quvjoiIgoKC+OEPf7jHAHe/fv3isMMOi8MOOyw++clPxs9//vP4+Mc/Htu3b48PfvCDsXnz5vjUpz6VoeoBAAAAAAAAAADgwJYkSSxaXho/e2x1/PXlTdHUnHS4jxGFefEvR02I84+eGGOLBnRDlQCZ1+fC348++mhcffXVkSRJ5Ofnx8MPPxxz587tUB/vf//7Y/bs2XHCCSdEVVVVXHnllXHkkUfG/Pnzu6lqAAAAAAAAAAAAYHt1Q9z1zLr4+eOrY8WWqn3q481Th8UHjp0Up80ZFTnZWV1cIUDP6nPh78985jORJEmkUqn46le/2uHg9+vmzp0b1157bVx55ZXR3Nwc11xzTSxcuLCLqwUAAAAAAAAAAIADU2NTcyzfUhWLN2yPxRvKY/GG7fHc2rKobWjucF+F/fvFe48YHx84dmJMH1nYDdUC7B/6VPh7xYoV8dhjj0VERE5OTnzkIx/pVH8XX3xxfO5zn4v6+vp47LHHYsWKFTF16tSuKBUAAAAAAAAAAAAOGLUNTfFKcUUs3rA9XlpfHi9v2B6vFFdEXWPHg95v9KZxg+KDx06KMw4bGwNz+1QkEmC3+tS/dK8Hv1OpVEyZMiUGDRrUqf4GDx4cU6ZMiaVLl7b0L/wNAAAAAAAAAAAAbdte3RCLN26PlzeUt+zovXxLVTQ1J13Sf16/rDjjsLHxgWMnxWHjB0cqleqSfgF6gz4V/l6/fn3L3wsLu+ZrG97Yzxv7BwAAAAAAAAAAgANZkiSxqbwuFm/Y3hLyXryhPNZtq+mW8aYMz4/3HzMx3jdvfBQNzO2WMQD2d30q/P26JEli3bp1XdJXV/UDAAAAAAAAAAAAvVVzcxKrt1bH4g3b46X1O4LeL28oj9Kq+m4dNzsrFacePCo+cOykOG7asMjKsss3cGDrU+HvcePGtfx906ZN8dxzz8Xhhx++z/0988wzUVxc3PKVEGPHju1siQAAAAAAAAAAALDf21JRF8+vLYvn1pbF8+vK4vm1ZVFe25ix8UcNyovzj5oY/3L0xBg9uH/GxgXY3/Wp8PfcuXMjIlrC2tdcc0385S9/2ef+PvvZz0bEjp3EU6lUHHHEEZ0vEgAAAAAAAAAAAPYjVXWN8eL67fF8S9B7e6wvq8nY+P2yUnHQqMKYM3ZQzBk7KA4ZNzgOn1AU/bKzMlYDQG/Rp8LfBx98cBxyyCHx0ksvRZIk8cADD8Tll18eP/jBD1oC4e2RJElcccUVcf/997fcd8ghh8TBBx/cXaUDAAAAAAAAAABAt2tsao6lmyri+bX/DHu/uqkimpPMjD8gJztm7wx57/gZHAeNKoi8ftmZKQCgl+tT4e+IiOuuuy7OPPPMSKVSkSRJ3HTTTfHEE0/E17/+9TjttNP2GAJPkiT+8pe/xOc+97l4/vnnW/pIpVLxta99LYOz2L/U1tbGokWL4pVXXolt27ZFbm5ujB8/Po455piYOnVqT5cHAAAAAAAAAADAbiRJEuu21cRza8vi+bVl8dzasnhpw/aobWjOyPhDBubEnLGDY87YQTsD34NjyvD8yM5q/2auAKTrc+Hvd73rXXHJJZfEzTff3BLefvbZZ+P000+PUaNGxfz58+NNb3pTDBs2LAYOHBjV1dVRUlISL730UixYsCA2b94cSbLjI0yvB8U/8pGPxLve9a6enFaa9evXxxNPPBGPP/54PPHEE/HUU09FRUVFy/VJkybFqlWrOj3Oli1b4tprr42f/vSnUVVVtds28+bNiy9+8Ytx1llndXo8AAAAAAAAAAAA9t3Wqvp4ft2OoPeOXb23x9aq+oyMPXZw/5gzbnDLbt5zxg6KMYP773HDVgA6rs+FvyMifvSjH0VEtATAI3Z8gqm4uDh+85vfxG9+85vd3rdr6DtJkrj44ovjpptuykDVe7Zw4cL4zne+E48//nhs2LCh28d7+OGH45xzzomSkpI9tnv66afj7LPPjg996ENx8803R25ubrfXBgAAAAAAAAAAcCBrbk5i7bbqWLKxIl4pLo+lxRWxeEN5rNla3e1jp1IRU4fntwS8X/9zSL7sGEAm9MnwdyqVih//+Mdx8sknx6c+9anYvHlzq08PvR70fr39638mSRJJksSIESPiu9/9blxwwQUZrb0tTz75ZNxzzz0ZGWvBggVx+umnR01NTdr5oqKimDJlSmzbti3Wrl0bTU1NLdf+93//NyorK+Ouu+7ySS0AAAAAAAAAAIAusr2mIZYW7wh5vzHsXV3ftPebu8C4ogFx+ISiOGzC4DhsfFG8adzgyM/rk9FDgF6hT/8LfP7558e73vWu+PnPfx633XZbPPXUU9Hc3Nyq3etB8KysrDjyyCPjIx/5SLz//e+PgoKCTJe8TwoKCqKysrJL+tq2bVucd955acHvSZMmxfe///0488wzW4Ld69ati6997Wvx4x//uKXd3XffHd/97nfj//v//r8uqQUAAAAAAAAAAOBA0djUHCtLqmJJcUW8srE8Xtn554bttRmrYVD/fnHYhKIdYe/xRXHohMExsrB/xsYHYO/6dPg7Ykcw+tJLL41LL700qqqq4umnn46VK1dGWVlZVFZWRn5+fhQVFcXUqVNj3rx5kZ+f39Ml71FhYWHMmzcvjjrqqDj66KPjqKOOipUrV8bJJ5/cJf1/+9vfjg0bNrQcT5kyJRYsWBBjx45Nazd+/Pj40Y9+FBMnTozPf/7zLee/8pWvxEUXXRRDhgzpknoAAAAAAAAAAAD6mpLKunhlY/pu3q9troz6xtabm3aX3OysmD12UMuu3odPGBKThw1s2SAUgP1Tnw9/v1F+fn6ccMIJccIJJ/R0KR12xhlnxGmnnRazZs2KrKystGsrV67skjG2bNkS//Vf/5V27uabb24V/H6jz372s/GXv/wlHn300YiI2L59e9xwww1x3XXXdUlNAAAAAAAAAAAAvVWSJLG6tDqeWr3tn7t5F5dHSWV9xmuZNiI/Dp8wJA6fMDgOm1AUs0YPitx+WXu/EYD9ygEV/u7Npk2b1u1j/OpXv4rKysqW4xNOOCHe9ra37fGeVCoVX/rSl9La3XrrrfG1r33NJ8AAAAAAAAAAAIADzpaKuli0vCQWLSuNBctKYn1ZTcZrGFmYt3NH76I4fEJRHDJ+cAzqn5PxOgDoesLftPjtb3+bdnzxxRe3676TTz45pkyZ0rIDeXFxcTz22GPx5je/uctrBAAAAAAAAAAA2J9U1TXGEyu3xoJlJbFwWUm8UlyRsbFTqYhJQwfGzNGFMWv0oDh4TGEcNqEoxgwekLEaAMgs4W8iIqKysjIeffTRtHOnnXZau+5NpVJxyimnxM0339xy7r777hP+BgAAAAAAAAAA+pyGpuZ4fm1ZS9j72TVl0dicdPu4g/r3i1ljBsXBowtj1phBMWt0YcwYVRj5eWKAAAcS/+oTERGLFy+OhoaGluMpU6bE6NGj233/8ccfnxb+fu6557qyPAAAAAAAAAAAgB6RJEks3VQRC5eVxsJlJfH4itKoqm/qtvGys1IxbUR+zBo9KGaNKYyDd/45elD/SKVS3TYuAL1Dnw9/l5aWxoMPPhjPPPNMrF27NrZv3x41NTWRJB37pFUqlYoHH3ywm6rseUuWLEk7nj17dofu37X9rv0BAAAAAAAAAAD0FuvLamLhayWxYFlJLFpeGiWVdd0yzojCvJg1ujAO3rmT98zRhTF9ZEHk9cvulvEA6P36bPh77dq1cfXVV8c999yTtqP1vkiSpM9/Ymrp0qVpxxMmTOjQ/bu2X716ddTW1kb//v07XRsAAAAAAAAAAEB3Kquuj38sL40Fy0pi4bKSWFVa3aX9p1IRs0YPijljB7WEvWeOLozhBXldOg4AfV+fDH//9a9/jXPOOScqKytbdvju6+Htztq8eXPa8fjx4zt0/6hRo6Jfv37R2NgYERHNzc1RWloa48aN67IaAQAAAAAAAAAAOiNJkthSWRfLNlXGq5sq4rXNlfHCuu3x0obtsTNq1mUmDxsYx08fHvOnD483TxsWRQNzu3YAAA5IfS78vXjx4njPe94T1dU7PnmVSqUiSZKWEDi7V1lZmXacn5/foftTqVQMGDAgKioq2uxzX23evDm2bNnSoXuWLVuWdlxZWRnl5eVdUg8dU1VVtcdjAKB3sKYDQN9hXQeAvsGaDgB9gzUduk+SJFFS1RDLt1TF8pLqWFFSHcu2VMeK0urYXtPYLWMOHZgTx0wuimOnFMUxk4ti7OD+/7zYWBvl5bXdMi7Q86zpB56uysjuiz4X/r7yyiujurq6ZafvVCoV5557bpx33nlx+OGHx8iRI2PgwIE9XOX+Z9f/I+zfv38bLdvWXeHvH/zgB3Httdd2qo8nnngiiouLu6QeOueJJ57o6RIAgC5gTQeAvsO6DgB9gzUdAPoGazp0XJJElDdEFFenYmNNxKbqVGysScWm6ojqplS3jp2blcT0QUnMGJzEzMFJjBnYGKlUTcTWjbF0a8TSbh0d2J9Z0/u+NWvW9NjYfSr8vWnTpvjrX//astv34MGD4/e//33Mnz+/p0vb79XWpn+qLDe3418xkpeXl3ZcU1PTqZoAAAAAAAAAAAAidoS8t9dHFNekorhmR9i7uCYVxdURNd0c8n5dViqJyQURMwY3x4zBSUwqiOiXlZGhAaBFnwp/P/roo5EkSUTs2PH7e9/7nuB3O+2603d9fX2H+6irq9tjnwAAAAAAAAAAAHvy+k7eG6tTsbE6YlNNKjZWp2JTTeZC3m80ZuCOXb1nDE5i2qAk+mdnvAQASNOnwt8bN25s+Xt+fn5ccMEFPVhN71JQUJB2vOtO4O2x607fu/a5r6644oo455xzOnTPsmXL4uyzz245Pvroo+Pggw/uknromKqqqrSvsDj66KMjPz+/BysCAPaFNR0A+g7rOgD0DdZ0AOgbrOkcyJIkia1VDfHalqpYXlIdy7dUx/KS6lhWUh0VtY09VteYQXlx7JSiOGZyURwzqSiGFeT2WC1A72FNP/AsWbKkx8buU+HvqqqqiNix6/e0adMiJyenhyvqPXYNar/+37K9kiTptvD3yJEjY+TIkZ3qo6CgIAYNGtQl9dA5+fn5/rcAgD7Amg4AfYd1HQD6Bms6APQN1nT6qq1V9fHqpoo3/FTGa5sqYlt1Q4/WNSw/N6aPLIiDRhXEwWMGxfHThsekYQMjlcr8DuNA32JN7/u6KiO7L/pU+HvYsGEtf+/Xr09NrdvtGq5et25dh+7ftGlTNDb+8xN3WVlZMXz48C6pDQAAAAAAAAAA2P+VVdfHq5sq49VNFfHa6yHvzRVRUlnfo3UNL9gR8p4xqjAOGlkQB+38c1hBXo/WBQD7ok8lpA877LCI2LELdUfDywe6mTNnph2vWbOmQ/fv2n7SpEnRv3//TtcFAAAAAAAAAADsX5IkiVeKK+L5tWWxdFNFvLYz8L25oq5H6xpekBczRhXEQSMLYvqowpixM+g9ND+3R+sCgK7Up8LfRx99dIwfPz7WrVsXmzdvjsWLF8ecOXN6uqxeYdasWWnHL7/8cofuX7JkyR77AwAAAAAAAAAAeq9126pj0bLSWLCsJBYtL+nR3bxHFL4e8i6Mg17/c2RBDBHyBuAA0KfC36lUKq655pr4xCc+ERER3/rWt+L222/v4ap6hzlz5kROTk40NDRERMSqVati48aNMWbMmHbdv3DhwrTjww8/vKtLBAAAAAAAAAAAMmRbVX38Y0VpLFxWEguXlcSq0uqM1/DGkPeMUYUxY1RBTB9ZEEUDhbwBOHD1qfB3RMQVV1wR99xzT/ztb3+Ln/3sZ3HSSSfFRRdd1NNl7fcKCwvjhBNOiAcffLDl3P333x8f+tCH9npvkiTxwAMPpJ0744wzurxGAAAAAAAAAACge9Q2NMWTq7bu2Nl7WWm8tGF7JElmxh6WnxsHjSqImaMK46BR/wx6C3kDQGt9LvydSqXinnvuiXe+852xYMGCuOSSS+Kll16KL3zhCzFkyJCeLm+/duaZZ6aFv2+55ZZ2hb8feuihWLlyZcvxqFGj4phjjumWGgEAAAAAAAAAgM5rak7ixfXbY+GykljwWkk8vWZb1Dc2d+uYRQNzYsbIwh1B79GFO3f0LohhBXndOi4A9CV9Lvz9v//7vxER8aEPfSg2bNgQK1asiO9973vx4x//OE455ZQ48sgjY+TIkdG/f/8O992eIHRvdv7558fnPve5qKqqioiIRx99NP72t7/FW9/61jbvSZIkrr322rRzF110UWRlZXVrrQAAAAAAAAAAQPslSRLLt1TFouU7wt7/WFEaFbWN3TJWYf9+Lbt3z9i5k/dBowpiREFepFKpbhkTAA4UfS78feGFF6a9QEilUpEkSVRXV8fvf//7+P3vf7/Pfff18PfIkSPjX//1X+P6669vOffRj340FixYEGPHjt3tPd/4xjfi0UcfbTkePHhwXHXVVd1eKwAAAAAAAAAAsGeby2tj4fKSWPBaaSxcVhLF5bVd2n+/rFTMGTc4Zr4h5D1jVGGMGiTkDQDdpc+Fv1+XJEnLC4hdX0gkSdLufl4Pj+8PL0YWLlwYNTU1rc4///zzace1tbXxwAMP7LaPsWPHxuzZs9sc4+qrr47bb789iouLIyJi5cqVcdxxx8WNN94YZ5xxRst/h3Xr1sXXvva1+PGPf5x2/+c///kYOnRoh+YFAAAAAAAAAADsu4am5liztTpWbKmKFVsqY/mWynh2TVm8trmyy8eaNbow5k8fHscfNDyOnjw08vP6bAQNAPZLfXLlfT3c3ZGQ99762h+8//3vj9WrV++13aZNm+LUU0/d7bUPf/jD8dOf/rTNe4cOHRp33HFHvP3tb4/a2h2f9Fu9enWcddZZUVRUFFOmTImysrJYs2ZNNDU1pd171llnxZVXXtn+CQEAAAAAAAAAAO22rao+VpRUxvLNVbG8pDJWbKmK5VsqY01pdTQ2d0/OaVzRgJaw93HThsXwgrxuGQcAaJ8+F/6+7bbberqEXu+EE06IP/zhD3HOOefE1q1bW86XlZXFs88+u9t7Lrjggrj11lv3ix3SAQAAAAAAAACgt2poao61W6tj+Rt28V6xpSpWlFTF1qr6bh+/aGBOHDdtWBw/fXjMnz48Jg4dKBMEAPuRPhf+/vCHP9zTJfQJb33rW+Pll1+Oa6+9Nm6//faorq7ebbu5c+fGF77whXjPe96T4QoBAAAAAAAAAKD3atnFe+fu3ZnYxXt38vplxdFThraEvWePGRRZWcLeALC/6nPh775s1apVGR1v1KhR8YMf/CC+853vxKJFi2LJkiVRVlYWubm5MW7cuDjmmGNi+vTpGa0JAAAAAAAAAAB6i/rG5liztTpWbKmMFSU7dvLO5C7eu5OVijhkfFHMn75jd+8jJg6J/jnZPVILANBxwt/s1YABA+Jtb3tbvO1tb+vpUgAAAAAAAAAAYL+SJElsqazbEereUpUW9F67rSaaMriLd1umjciP46cPj+OnD49jpw6LwQNyerokAGAfCX8DAAAAAAAAAADsRU19U6wsqYqVJVWtdvKuqGvs6fIiIiKVihg/ZEBMHV4Q00YUxOyxg+L46cNizOABPV0aANBFhL8BAAAAAAAAAAAiork5iY3ltS2h7n+GvKtifVlNT5fXoiCvX0wdkR/TRhTE1OH5MW1kQUwdkR+Th+VH/5zsni4PAOhGB1T4+7XXXovHHnss1qxZE9u2bYuKioooLCyMIUOGxKRJk+LYY4+N6dOn93SZAAAAAAAAAABAN0mSJLZVN8TKkh0B75UlVbGqdEfAe1VpVdQ2NPd0iRGxYxfvcUUDdgS8Xw967/xzZGFepFKpni4RAOgBfT78XVxcHD/84Q/j5ptvjk2bNu21/ZgxY+KSSy6JSy+9NEaPHp2BCgEAAAAAAAAAgK5WXd8YK0t2hLtX7gx5r9h5vL2moafLa5Gfm71j5+7hrwe8C2LaSLt4AwC716fD3zfddFN8+tOfjurq6kiSJO3aGz/59sZrGzZsiK985Svxne98J77zne/EJZdckrF6AQAAAAAAAACA9qtvbI6126rTwt2rdga8i8tre7q8FlmpiPFDBsaU4fkxdUT+joC3XbwBgH3QZ8Pfl1xySdx6660twe7XXyC9frxrGHzXNpWVlXHZZZfFk08+GTfddFOGqgYAAAAAAAAAAN6oqTmJjdtrYlVJdawsfX0X78pYWVIVa7fVRFNz6xxQTxk8IGdHuHt4QUwdkR/Tdga9Jw4daBdvAKBL9Mnw9xe+8IW45ZZbImJHoDtJkkiSJMaPHx8nn3xyHHbYYTF8+PDIz8+PqqqqKCkpieeffz4efvjhWLt2bVoI/JZbbolRo0bFV7/61Z6cEgAAAAAAAAAA9FnNzUlsLK9t2bV7dWlVrCypjlWlVbFma3XUNzb3dIkt+mWlYuKwgTF1eMHOcPeOgPfU4fkxND/XLt4AQLfqc+HvxYsXx/XXX58W4D7kkEPi29/+dpx66ql7fXH117/+Na6++up44YUXWoLj119/fZx//vkxZ86cTEwBAAAAAAAAAAD6nObmJIpfD3iXVsXq0upYWVIVq0qqYvV+FvCOiBhekPfP3bt37uQ9dURBTBgyIPplZ/V0eQDAAarPhb+/+MUvRlNTU0vI+0Mf+lDccsstkZ3dvq9NOe200+KUU06Jiy++OG6//fZIpVLR1NQUX/rSl+Kuu+7qztIBAAAAAAAAAKBXa25OYlNF7c5Qd/XOHbyrYtXOsHfdfhbwzs/Njikj8mPK8IKYMjw/pg7Pj8nD82PK8PwYPCCnp8sDAGilT4W/a2tr489//nNL8Hv+/Pnx05/+tMP9ZGVlxW233RYrVqyIv//97xER8ac//Slqa2ujf//+XVkyAAAAAAAAAAD0WmtKq2PBspL4x4rSeLW4IlZvrYrahv0r4J2TnYpJw/Jbwt1T3vAzojCvJWsEANAb9Knw94IFC6K2tjYiIlKpVHzrW9/qVH/XX399HHfccRGxI1i+YMGCOOWUUzpdJwAAAAAAAAAA9EYllXWxaHlpLHytJBYuL4l122p6uqSIiEilIsYOHhBTR6SHu6cOL4ixRf2jX3ZWT5cIANAl+lT4e+3atS1/HzFiRBxzzDGd6u/YY4+NkSNHxubNm1v1DwAAAAAAAAAAfV1lXWM8sbI0Fi4rjYXLSuKV4ooerWd4Qe4bwt0FOwLeI/Jj4tCB0T8nu0drAwDIhD4V/n49pJ1KpWLChAld0ueECRNa+t2yZUuX9AkAAAAAAAAAAPuj+sbmeG5tWSxYVhKLlpXEc2vLorE5yWgNw/JzY/Lw/Jg8LD8mDxsYk3eGvScNGxiF/XMyWgsAwP6mT4W/+/fv3/L36urqLumzpuafX02Tl5fXJX0CAAAAAAAAAMD+oLk5iSXF5bFoWWksWFYST67aGtX1Td0+7rD83Jj0erB7WH5MavlzYAwS8AYAaFOfCn+PHDkyIiKSJIlVq1ZFdXV1DBw4cJ/7q66ujpUrV7bqHwAAAAAAAAAAeqs1pdWxYFlJLFxeEv9YXhpbq+q7ZZyhOwPeU4blx+SdO3fv2ME7PwYPEPAGANgXfSr8PXv27IiISKVSUVtbG3fccUdcdNFF+9zfHXfckbbz95w5czpdIwAAAAAAAAAAZFJJZV0sWl4aC1/bEfhet61m7zd1QCoVMXvMoDh++vCYM3ZQTN4Z9hbwBgDoen0q/H3YYYfF2LFjY+PGjZEkSVxzzTVx6qmnxvjx4zvc1/r16+Oaa66JVCoVSZLE2LFj49BDD+2GqgEAAAAAAAAAoGs0Nyfx2ubKeHr1tnh69bZ4Zs22WFlS1eXjTB42MI6bPjyOnzY83jxtWAzNz+3yMQAAaK1Phb8jIi688ML4+te/HqlUKkpKSmL+/Pnxm9/8JubNm9fuPp599tl473vfGyUlJRGxYyfxCy+8sJsqBgAAAAAAAACAfVNZ1xjPry1LC3tX1DZ2+TjDC3Lj+J1h7+OmD4vxQwZ2+RgAAOxdnwt/f+Yzn4mbb745SkpKIpVKxZo1a+LYY4+NCy64IC666KKYP39+9OvXetqNjY2xYMGC+OlPfxo///nPo6mpqWXX75EjR8ZnPvOZHpgNAAAAAAAAAADskCRJrNtWE8+s2dYS9l6ysTyak64fqyCvXxwzZWgcN314zJ8+PGaMKohUKtX1AwEA0CF9LvxdUFAQv/71r+Md73hH1NXVRSqViqampvjZz34WP/vZzyInJydmzZoVw4cPj/z8/KiqqorS0tJ45ZVXor6+PiJ2vFB+Pfjdv3//+PWvfx35+fk9PDMAAAAAAAAAAA4k9Y3NsXjD9pYdvZ9evS02ldd1y1g52ak4YuKQHbt7Tx8Wh44vipzsrG4ZCwCAfdfnwt8RESeeeGLceeed8cEPfjDKyspaPnWYJEnU19fHCy+8kPZJxCT558cfU6lUS/C7qKgofvazn8Vb3vKWjM8BAAAAAAAAAIADy9aq+nh83aYdYe/V2+L5dWVR19jcLWOlUhGzxwyK+dOHx3HTh8dRk4fEwNw+GSUCAOhT+uwrtne+853xwgsvxOWXXx5//OMfWwLeu/v6mTeGw1/f9fuMM86IH/zgBzFu3LiM1g0AAAAAAAAAQN9W19gU67fVxNL12+LvxalYU5mKVRWp2PyPx7t13MnDBsZx04fH8dOGx5unDYuh+bndOh4AAF2vz4a/IyLGjx8fv//97+PVV1+NW265JR555JF47rnnor6+vlXb3NzcmDt3bpx44olx8cUXx0EHHdQDFQMAAAAAAAAA0Ns1NSdRXF4ba7dW7/jZVhPrtlbH2m3VsXZrTWyqqI1/flF9drfU0C8rFXPGDY55E4fEkZOHxBETh8Towf27ZSwAADKnT4e/Xzdjxoy4/vrrIyKivr4+iouLY9u2bVFZWRkFBQUxZMiQGDNmTOTk5PRwpQAAAAAAAAAA7O+SJInSqvqWYPfardWxbmewe+226thQVhMNTcneO+pCQwbmxLxJQ+KISUPiyElD49Dxg6N/TvcEywEA6DkHRPj7jXJzc2PixIkxceLEni4FAAAAAAAAAID9VHltQ6zbGebeEe6u2Rn23hHyrmlo6tH6DhpZ8Iaw95CYMjw/UqlUj9YEAED3O+DC3wAAAAAAAAAA8Hq4e922HcHuHT/VLX+W1zb2dIktBuRkx+ETimLepCE7At8Th8Tggb7hHgDgQCT8DQAAAAAAAABAn9Obwt27Glc0II6YNCTmTSyKeZOGxsFjCqNfdlZPlwUA/P/s3XmcZFV9///3vbV2dfU2vcwwOzMDwgCyDIsCIoiAqCxqUNC4Bbdg8hMURYMixKjRfCPm+zW4fFGiCSprQAJ8RRSQRSGyGZgBnX2f6X2t/Z7fH1V1u6q6qrp6re7q19NHPe4559577udWD96p7vecBuYAwt8AAAAAAAAAAAAAAACYd+ZzuDtXo8+oNSitrDd6++vW6/Qjl+qQprpqlwUAAIA5qubC33/84x918cUXu/1bbrlFb3zjGyc8z6OPPqq/+qu/kiRZlqUHH3xQhx9++HSVCQAAAAAAAAAAAAAAgBKMMeodSWhPJsi9py/iBrzT7RENzpNwd1OdTysW1WlFS0grFoW0oqVOyxeFtKIlpEZPQr974rfusWetb1djI8FvAAAAlFZz4e/vfve72r59uyRp/fr1kwp+S9KZZ56pQCCgV199VZZl6fvf/77++Z//eRorBQAAAAAAAAAAAAAAWJgcx6hrKKbdfZFMwDuiPX3pVbv3ZALeI/FUtcusSNBn5wW7VywKaXlLKB34XhRSY9BX8tyBgYFZrBQAAAC1oObC3/fee6+k9Grdf/mXfzmluT7wgQ/o2muvlTFG//mf/0n4GwAAAAAAAAAAAAAAoAIpx+jAQHQ01N2TDnRnV/De0xdRPOlUu8yKeG1LS5vr8lbvXp4Jea9oCakt7JdlWdUuEwAAAAtETYW/N23apP3797v9iy66aErzXXTRRbr22mslSTt27NC2bdt06KGHTmlOAAAAAAAAAAAAAACAWpByjPb2RbSta1g7uoe1rWtE27uHtb1rWLt6R5RImWqXWJFsuHt5S/YVytsubgzKYxPuBgAAwNxQU+HvjRs3uu1wOKwjjzxySvMdeeSRCofDGhoakiS99NJLhL8BAAAAAAAAAAAAAMCC4ThGe/sj2t41om2ZYHc66D2sXT0RxVNzf/Vuj21paXNQy5tDBeHuOi1fFNLihoC8HrvaZQIAAAAVqanw9549eyRJlmVpxYoVU57PsiytXLnSDZXv3LlzynMCAAAAAAAAAAAAAADMJY5jtH8gqu1dw27Ae1vXiHZ0D2tHz4jiybkd8CbcDQAAgIWkpsLf2RW6JamxsXFa5mxoaHDbg4OD0zInAAAAAAAAAAAAAADAbDHGqD+S0L7+qPYPRLWvL+qu3r29e1g7ukcUm8MBb7/HToe7W0Ja1lynZZlgd7a9pDFIuBsAAAALRk2Fv3OD2r29vdMyZ19fn9v2+/3TMicAAAAAAAAAAAAAAMB0SDlG3UMxN9i9vz+qff1RHRiIal9/RPsz49HE3A131/k8YwLd2aD3ipY6tYUDsm2r2mUCAAAAc0JNhb/b2tokpf/F6q5du5RIJOTz+SY9Xzwe165du9x+e3v7lGsEAAAAAAAAAAAAAACoRDzp6MDAaKi7WLD74GBMScdUu9SyGgJeN9ydDXUvb0mHvJc112lRvV+WRbgbAAAAqERNhb/Xrl3rtiORiB577DG9+c1vnvR8jz32mEZGRtz+qlWrplQfAAAAAAAAAAAAAACAMUaDsaQO5KzWnRfyzmy7huLVLrVi7Q0BHdpar9VtIa1uq9fq1nqtag1peUtITXWTX7gPAAAAQL6aCn+feOKJampq0sDAgCTp61//+pTC3//4j//otuvr6/X6179+yjUCAAAAAAAAAAAAAIDalUw56hyKjQa6+6PaPxBz29mQ90g8Ve1SJ6wt7Nfq1nqtbqvXoTkB79Vt9QoHaiqCAgAAAMxZNfU3b9u29da3vlU/+9nPJEmPPvqobrzxRl111VUTnutb3/qWHnnkEffXCp133nny+fiXqAAAAAAAAAAAAAAALFTDsaT25YW689vp1bpjcky1K5281nq/G+g+NCfovao1pIYguQkAAACg2moq/C1J1157rW677TYZY2SM0Wc/+1n19vbqy1/+sjwez7jnp1IpXX/99fr6178uy7JkjJFt2/rSl740C9UDAAAAAAAAAAAAAIC5IOUY/enAoJ7b2atnd/Tq+Z192tY1XO2ypsy2pPaGgA5pqnNX717dFsoEvOvVVEfAGwAAAJjLai78vX79en3sYx/T9773PVmWJcdx9NWvflU/+9nPdMUVV+j888/XEUccMea8V155RQ888IC++93vauvWrTLGyLIsWZalyy+/XK997WurcDcAAAAAAAAAAAAAAGA29EcSemFXn57d0avndvTqhV19Goolq13WhPg9thY3BXRIY52WNAXTr8agDmkKanFTetseDsjrsatdKgAAAIBJqrnwtyT97//9v/Xyyy/r8ccfd1fv3rJli66++mpdffXVqq+vV1tbm8LhsIaGhtTV1aXh4fS/zjUm/buXsuedeeaZ+td//ddq3g4AAAAAAAAAAAAAAJhGxhht6RzWczvTQe/ndvbqzweHlIkMzEn1fo+WNAV1SFOdG+pekgl0L84EvBfV+2VZVrVLBQAAADCDajL87fV6dd999+lDH/qQ7rnnHveDTTbYPTQ0pKGhoaLn5h77F3/xF/rhD38or7cm3yYAAAAAAAAAAAAAABaE4VhSL+7uywS9+/Tczl71jSSqXZarLezX4sZ0oHtxQbA7224I+qpdJgAAAIA5oGZTzY2Njbr77rv1/e9/X1//+te1c+dOSSr7L1yNMTLGaPXq1friF7+ov/qrv5qtcgEAAAAAAAAAAAAAwDQwxmh3b0TP7ujVs5lVvTftG5BThVW9/V47Hd52Q92BdMg7E+pe3BhUR2NAAa9n9osDAAAAMC/VbPg76+Mf/7g+8pGP6O6779ZDDz2kxx9/XFu3blUymXSP8Xq9Wrdund7whjfoLW95iy666CLZtl3FqgEAAAAAAAAAAAAAQCUi8ZT+Z0+/nt+ZDno/u6NPXUOxGb/uovrsat0BLWkKjl25uzGo5pCv7CJ1AAAAADBRNR/+liSPx6NLLrlEl1xyiTs2ODiowcFBNTQ0qKGhoYrVAQAAAAAAAAAAAACASjiO0ZbOIT2/q08v7OrTCzv79OqBQaVmcFlvn8fS0cuadMLKFm1Y1aKjljZqSVOQ1boBAAAAVMWCCH8XQ+gbAAAAAAAAAAAAAIC5rXMwlg557+rVC7v69Mdd/RqMJcc/cQrawgFtWNWsDatadMLKFh29rElBH0FvAAAAAHPDgg1/AwAAAAAAAAAAAACAuSOaSOnlvf16fmdmVe9dfdrdG5nRa3psS0csaXCD3htWtWh5S50sy5rR6wIAAADAZBH+BgAAAAAAAAAAAAAAs8pxjLZ1D+uFnKD3pn0DSjpmRq/bHPLphJUtOmFls05Y1aJjlzerPkB0AgAAAMD8saA+wfT09GjTpk3q6elRf3+/HMfReeedp8WLF1e7NAAAAAAAAAAAAAAAalbPcFwv7OrVCzv79PyuPr24q08D0eSMX/fwxWFtWNWi4zOreq9pq2dVbwAAAADzWs2Hvw8ePKjvfOc7uuuuu/TKK6+M2f+rX/2qaPj7lltu0a5duyRJS5cu1Uc+8pEZrxUAAAAAAAAAAAAAgPnGGKO+kYT29EW0uzeiPX0R7emNaE/fiNvuHUnMeB0NAa+OXZFe0XvDqhYdt6JZTXW+Gb8uAAAAAMymmg5//9M//ZOuu+46xeNxGTP2V0OV+9e8Q0NDuv7662VZljwejy644AJWCAcAAAAAAAAAAAAALDgpx+jgYFR7x4S7R7cj8dSs1uSxLR2xpEHHrWjWcSuadfzKZq1pC8u2WdUbAAAAQG2ryfB3KpXSJZdconvvvVfGmDEhb8uyiobBc11++eX60pe+pIGBAaVSKf30pz/VVVddNZNlAwAAAAAAAAAAAAAw62LJlPb1Rd0w9+6Clbv390eVSJX/GftMW9Zc5wa9j1vZrKOXNqnO76lqTQAAAABQDTUZ/v7kJz+pe+65R9Jo0Pv444/Xueeeq5UrV+qTn/zkuHOEQiFdcMEFuvXWWyVJDzzwAOFvAAAAAAAAAAAAAMC8YozRQCSZDnb3RbQ389qd2e7pjahzKKZx1k+bVeGAV69d3jQa9l7RrI7GYLXLAgAAAIA5oebC30888YR+8IMfuKt9t7W16d/+7d90/vnnu8d88pOfHLMaeDEXX3yxbr31Vhlj9OSTTyoej8vv989Y7QAAAAAAAAAAAAAATEQy5ejgYMwNdu/uzYS63aB3VEOxZLXLLMm2pNcsadRxK5p1fGZV77XtYXns8X+mDwAAAAALUc2Fv6+77jpJ6X+93NjYqMcee0xHHHHEpOY65ZRT3HYsFtOrr76qY445ZlrqBAAAAAAAAAAAAABgPMOxpBvm3pOzWvfevqj29EW0fyCqlDOHlu0ex5LGYHo175XpFb2PWdak+kDNRRcAAAAAYMbU1Ceo3t5ePf744+6q3l/84hcnHfyWpOXLl6ulpUW9vb2SpFdeeYXwNwAAAAAAAAAAAABgyowxGogktW8gov39Ue3vj2pfZrt/INuPaCA6d1ftLsbnsbS0uU7Lsq+W0e2atrCWNAWrXSIAAAAAzGs1Ff5+4oknlEqlJEkej0cf+chHpjxnR0eHG/4+ePDglOcDAAAAAAAAAAAAANQ2xzHqHo67Ae5smNsNeGf6kUSq2qVOWL3fkxfoXtYccvvLW+rUHg7Itq1qlwkAAAAANaumwt979+6VJFmWpTVr1qi5uXnKczY1NbntwcHBKc8HAAAAAAAAAAAAAJjfRuJJbe0c1o7ukXS4O2+17qgODkaVSJlqlzkpi+r9RVftzoa7m+p87m/jBgAAAADMvpoKf/f09LjtRYsWTcucsVjMbft8vmmZEwAAAAAAAAAAAAAw9/UOx7W5c0ibD+a/9vRFql3apHhsS0sag26Ye2lzUMuaQ1raHNTyljotba5TyF9TMQIAAAAAqDk19altJlbpPnjwoNtua2ubljkBAAAAAAAAAAAAAHODMUb7+qOj4e5M2HvLwSF1D8erXd6EhAPe0VB3JsydXcV7aXOdFjcG5bFZtRsAAAAA5rOaCn+3t7dLSn8437FjhxzHkW3bk55v165d2rdvn9tfunTplGsEAAAAAAAAAAAAAMy+ZMrRjp4RN+S9JRP03nJwSMPxVLXLG5dlSR0NgbxA97KWOi1tyoS8W+rUGPTKsgh3AwAAAEAtq6nw97HHHuu2R0ZG9OSTT+oNb3jDpOe744473LbH49HrXve6KdUHAAAAAAAAAAAAAJhZ0URKWzrzA95/PjCk7d3DSqRMtcsryrKk9nBAhzQFtaQpqEOa0qt0H9IU1OLGoJY112lJU1B+7+QXPwMAAAAA1IaaCn8ffvjhOvTQQ7V9+3ZJ0re+9a1Jh78HBgZ04403uv8q+qSTTlJDQ8N0lQoAAAAAAAAAAAAAmIKBaMJdxTv3tat3RGYOZbx9HisvyJ0OeNfl9dsbAvJ5CHYDAAAAAMZXU+FvSfrABz6gG264QZL0i1/8Qj/+8Y/1wQ9+cEJzpFIpfeADH9CePXskSZZl6Yorrpj2WgEAAAAAAAAAAAAApRlj1DUUTwe7O4e0+cBgentwSAcGYtUuT36vreWZVbnTK3YHtaRxNNy9pCmoRSG/bNuqdqkAAAAAgBpRc+Hvq6++Wt/97nfV2dkpY4w+8pGP6ODBg/r0pz8tj8cz7vmvvPKKPv7xj+uJJ55wV/0+/PDD9d73vnemSwcAAAAAAAAAAACABclxjPb2R8au5N05pL6RRLXLU0PQq3UdYa1rD6e3mdfylpA8BLsBAAAAALOo5sLf9fX1uvnmm/WOd7xDjuMolUrp85//vG666SZddtll2rBhg6T0vxC3LEvPPvusenp6tHnzZv3mN7/Rb37zGxljZDK/B6yurk4//elP3SA4AAAAAAAAAAAAAGDiHMeoZySuzsGYdvaM5IW8t3QOaSSeqnaJ6mgI5IW7s2Hv9oYAPzMGAAAAAMwJNRf+lqS3v/3t+td//VddccUVktJB7x07dugb3/hG3nHGGH3+858fM5b90O7z+XTLLbfo+OOPn53CAQAAAAAAAAAAAGCeGYkn1TkYU+dgTAcz29F+VJ1D6XbXUFwpx1S7XFmWtKIllBfwXptpN9X5ql0eAAAAAABl1WT4W5I+9rGPae3atXr/+9+v/fv3u4Hu3HB3tp9lWZYsy5IxRosXL9Ydd9yh008/fdZrBwAAAAAAAAAAAIBqSjlG3cPFA93uayimgwNRDc+BFbuL8XtsHdpWr3Udo+Hude1hrWmvV9DnqXZ5AAAAAABMSs2GvyXp7LPP1qZNm/Td735X3/nOd7R3715J+YHvXMYYtbS06Morr9SnPvUpNTY2zma5AAAAAAAAAAAAADBrUo7Rrp4RbT44pD8fHNLmg0Pa3DmkPb0R9QzHNAcW6a5IyO9xg93rFme2HWGtXBSS12NXuzwAAAAAAKZVTYe/JampqUmf//zn9bnPfU4vvviiHn/8cW3atEnd3d3q6+tTKBRSW1ubDj30UJ111lk6+eST5fXW/NsCAAAAAAAAAAAAYIGIJVPa3jWiPx8cTAe8M6+tXcOKJ51ql1ex5pBPh2VX8O5oyGzDWtoUzPvtzwAAAAAA1LIFk3K2bVvHH3+8jj/++GqXAgAAAAAAAAAAAADTbjiW1JbOIf35QHoF72zIe2fPiFLzZRlvSUsag26wO/fVWu8n5A0AAAAAWPAWTPgbAAAAAAAAAAAAAGpB73Bcf85ZwfvPBwe15eCQ9vZHq11axSxLWrkopHXt+QHvtR1hNQZ91S4PAAAAAIA5i/A3AAAAAAAAAAAAAMwxjmO0py+iLZ1D2tI5rC2Zlby3HBxS93C82uWVVOfzqKMxoI6GgNobAmoPp7cdDcF0P/NqrffL67GrXS4AAAAAAPMO4W8AAAAAAAAAAAAAqJJoIqWtueHuTNh7W9eQogmn2uVJkmxLaguPBrfbwwF1NGaD3cFMuDu9rz7Aj6ABAAAAAJhJfPKW9Mwzz+jGG2/UE088oa6uLrW0tGjDhg362Mc+pgsuuKDa5QEAAAAAAAAAAACYx4wx6h6Oa8vBIW3uHNKWg8OZkPeQ9vRFZEy1K5SWt9RpXUdYh3WEtbY9rCVNQXe17kX1fnlsq9olAgAAAAAA1WD4+6GHHtI//MM/uP2bb75Zhx9+eMnjv/3tb+vqq6+WMUYm812V/fv364EHHtADDzygyy67TD/5yU9k2/zKMQAAAAAAAAAAAAClJVOOdvVGRlfwzlnJuz+SqHZ58tqWVrWGMiHvBq3rCGtdR1hr2usV8tfcj44BAAAAAKhJNfcJ/kc/+pGeeOIJWZalY445pmzw++GHH9ZnPvMZN/RtWfn/Wt0Yo5/97GdqaGjQd7/73RmtGwAAAAAAAAAAAMDcFEum1D0UV+dgTF1DsYJterxzKKbdvSNKpKq/jHfAa2tte1iHLQ5rXXs64H3Y4rBWLqqX38uiVwAAAAAAzGc1F/7+zW9+47YvvfTSssd++tOfljFGlmW5K38vXrxYAwMDikQi7vgPfvADvf/979epp5460+UDAAAAAAAAAAAAmAXJlKPu4dHg9thAd9QNds+FVbuLaQh6M6t4h91VvA/raNCy5jrZtjX+BAAAAAAAYN6pqfD31q1b1dXV5fbPP//8ksc+8sgjeumll9zVvk866ST99Kc/1dq1a5VIJPSd73xHV199tbv/xhtvJPwNAAAAAAAAAAAAzGGxZEo9w3F1DcbVNRxT12BM3cNxdWWD3UMxdQ3G1TkUU+9IXKb6i3RXZEljUGs76rW2Pay17aNB746GwJjfbgwAAAAAAGpbTYW///SnP7ltn8+no48+uuSxP//5zyVJxhj5/X7deeedWrFihXvuVVddpS1btuimm26SJN1///2KRCKqq6ubwTsAAAAAAAAAAAAAkGWM0VAsqa6huLqH0gHurqG4uoZi6i7Ydg3FNBBNVrvkSfN5LB3aNhrwzoa917SHFQ7U1I91AQAAAADAFNTUdwl27NghSbIsS6tWrZLH4yl57EMPPeT+K/gLL7zQDX7n+tSnPuWGv2OxmF588UW97nWvm4HKAQAAAAAAAAAAgIVlIJrQjq4Rbe8e1oGBqLtCd/dwfqg7lnSqXeq0aqrzaV1HWGvbc4PeYa1oqZPXY1e7PAAAAAAAMMfVVPh7cHDQbTc1NZU8bufOndqxY4cb/r744ouLHnfYYYepvb1dnZ2dkqRXXnmF8DcAAAAAAAAAAABQoWzAe1v3sHZ0Dae33SPa3jWs7uF4tcubMZYlLW+p07qccHc66F2vRfV+9+eUAAAAAAAAE1VT4e9YLOa2y636/dRTT0lK/5o4y7L0pje9qeSxK1ascMPfvb2901QpAAAAAAAAAAAAUBsGowltX0ABb5/HUls4oLZwQO0NAbWHA2pr8Ks9HFBHY1CHttXr0LZ6BX2lf14JAAAAAAAwWTUV/q6vr3fb/f39JY977LHH3PaaNWu0ZMmSksf6/X63PTIyMsUK54frr79eN9xww6TP/+AHP6h/+7d/m76CAAAAAAAAAAAAUFXZgPf27mFtr8GAt21JreFskDu9bW8IqC3sdwPe7Q3pV1Odj5W7AQAAAABA1dRU+Lu1tVVSekXv7du3K5lMyusde4u//OUvJUmWZemMM84oO2dfX5/bDoVC01csAAAAAAAAAAAAMEcYY9QzHNeOnhHt7E6HvHd2j2hHz/wNeHtsS4vq/WqtTwe4s9t0qDt/2xLyy2MT6AYAAAAAAHNfTYW/jz76aLcdi8X08MMP6y1veUveMb/73e+0fft291/jn3nmmWXn3L9/v9tetGjR9BULAAAAAAAAAAAAzCLHMdo3ENWOTLB7e/eIdvakV/De0T2ioViy2iWOK+iz1RYOZFbp9qu1PqC2Br871hZOt9vCATXX+WQT6AYAAAAAADWmpsLfxxxzjFpaWtTX1ydjjL74xS/qrLPOUiAQkCSlUil96UtfkpRevcDr9ercc88tOd/OnTvzVv5es2bNjNY/V/2v//W/dOyxx1Z8/NKlS2ewGgAAAAAAAAAAAJQSS6a0uzfirt69o3tEO3tGtKN7WLt6IoqnnGqXOIbfa2t5c53aGgJqzwS4WzMB7lY3zJ3ehvwed5EnAAAAAACAhaimwt9er1eXXXaZbrrpJlmWpeeff14nnHCCPvShD8nn8+n222/X008/7X5D6LzzztPixYtLzvfUU0/l9devXz+j9c9VGzZsGHeFdAAAAAAAAAAAAMysaCKl3pG4eocT2tPVp+e7LHXHpK6opVv3/lF7+uPa2x+RMdWudCy/19aqRSGtaq3XoW0hrW6r1+rWeq1uq9eSxqA8rNANAAAAAABQkZoKf0vSl770Jd16660aGBiQJG3atEmf//zn844xxsi2bV133XVl57rrrrvc9rp169Ta2jr9BQMAAAAAAAAAAGBBSTlGfSNx9UUS6suEuXtH4uobyWxLjMeShat2e3La/bN5C0VlA97pYHd+wPuQxqBsAt4AAAAAAABTVnPh78WLF+v222/XhRdeqFgsNubXvpnMUgd///d/r5NOOqnkPH19fXrwwQfd81n5GgAAAAAAAAAAALlSjtFAJOGGtfsjCfVHEuobSYwGuTNh7t6RbKA7roFostqlT1qxgPehrfVaRcAbAAAAAABgVtRc+FuSzjnnHP3hD3/Q5z73Of3qV79SMjn6DbTDDjtM119/vS677LKyc3zve9/TyMiIJMmyLF1wwQUzWjMAAAAAAAAAAACqI5pI5YS202Ht/khC/SMJ9UXSK2/3RRLpoHfO2OA8DnGXE/J7tHJRSKtaQ1rdWq+VrSEC3gAAAAAAAHNETYa/Jemoo47S/fffr4GBAW3btk2RSERLly7VypUrKzp/1apVuvHGG93+OeecM1OlAgAAAAAAAAAAYAqMMRqOpwPcA5lXfyShgWjSHUv3ExqIJN1+NsQdSzrVvoVZt6je7wa8V7XWa1VOuy3sH/PbdQEAAAAAADA31Gz4O6uxsVHHHnvshM8bb2VwAAAAAAAAAAAATA9jjGJJZzScHc0PcGcD3QPRzFgkmRPmTh+Tcky1b2NOsSzpkMagVraGtGpRvVa1ZbatIa1sDakx6Kt2iQAAAAAAAJiEmg9/Y3rEYjFt3bpV3d3d8vl8am1t1dKlSxUKhapdGgAAAAAAAAAAqLJkytFgNKnBaCa4HU2k25GEO5benw5uD8bG7k+kCG9PVJ3HqMEntQaNjlu7TIcd0pJZvTuk5S0hBX2eapcIAAAAAACAaUb4G+P65Cc/qa1btyoajeaNe71ebdiwQeeff76uuOIKtbe3V6lCAAAAAAAAAAAwXRzHqHckroODMXUOxnK2UXUNxTOB7fRq24OZUPdIPFXtsuc1v9dWS8in5jq/mkM+tYT8aqn3qTnkT4+H/Gqu86mlfrRvJSJ64rePuXOcddY6NTY2VvEuAAAAAAAAMBsIf2NcGzduLDqeTCb19NNP6+mnn9Y3vvENXX311fryl78sj2d6V5E4ePCgOjs7J3TO5s2b8/pDQ0MaGBiYzrJQoeHh4bJ9AAAwP/BMBwCgdvBcB4CFK5Z01DUUV9dwPL3NvoYT6hyKq3s4rs6huHqGE0o6rMI9GR5LaqzzqSnoVVOdV41Br5rqfGqu86q5zqemnG12vKnOpzqfLcuyKryKkZyYhqORvFGe6QAAzE98TgcAoDbwTF94hoaGqnZtwt+YFpFIRF/5ylf0+OOP67777lM4HJ62uW+66SbdcMMNU5rjmWee0f79+6epIkzFM888U+0SAADANOCZDgBA7eC5DgDzV8pI0aQUSUmRpDSSsjQYlwYS0kDcymylgYSlgbgUSVUaLobfNgp5lXnlttP9eq9Ul+nXZ/d7pIBHsqxk8UkTmdeAFFH6NZ0/ueCZDgBAbeCZDgBAbeCZXvt27txZtWvPm/D3e97zHn3ta1/T2rVrq12KpPTK0tdee61uu+22apcyIyzL0utf/3q97W1v08knn6wjjzxSixYtkm3b6u7u1nPPPaf/+q//0o9//GNFo1H3vEcffVSXXnqp7r333mlfARwAAAAAAAAAgFphjBRz0qHtbHg7mrLcfjQlRZJWuu0ek9+PO4S5y/HbRnWedEi7zivVedIh7XS7MNSdDnRn+1672tUDAAAAAAAAxc2b8Pcdd9yhe+65Rx/84Af12c9+VocddlhV6vjzn/+sb37zm/rJT36iZLLEyg3z3Lnnnqv3vve9Ovzww4vuX7p0qZYuXaq3v/3t+uIXv6hLL71UTz75pLv//vvv10033aS//du/na2SAQAAAAAAAACYNSlnNKAdzYSxoymrYMwabWeC29FUbtBbMiK8XY4t4wa3Qx6pLhPWHg10m0yIu3i4mwA3AAAAAAAAapFljDHVLqIStm3LstLfBLUsSxdddJE+8YlP6JxzzpmV6//qV7/STTfdpPvuu0/GGBljZFmWUqnUrFx/LotGo3rTm96k3/3ud+5YR0eHtm3bplAoNOX5Dx48qM7Ozgmds3nzZl188cVu//e//72OPPLIKdeCiRseHs77FRYnn3yy6uvrq1gRAACYDJ7pAADUDp7rABayRMrRUCyl4VhSw/FUuh1PaSiW1HAspcHMdjg+2h6KJd3jBqPp82JJp9q3Mi+E/B41BDwKB7xqCHrddjjgUWPQ67bT+0bb4YBXjUGv6nyjPxvCWDzTAQCoDTzTAQCoDTzTF55Nmzbpda97ndt/6aWXdNRRR83KtefNyt+XX365brnlFjmOI2OM7rnnHt1zzz1atWqV3ve+9+md73ynjj/++Gm95nPPPae7775bP/3pT7Vjxw5JUjYrb9u2Lr/88mm93nwVDAb1k5/8REceeaS7GvrBgwf10EMP5QWwJ6ujo0MdHR1TmiMcDquxsXHKtWDq6uvr+VoAAFADeKYDAFA7eK4DmOscx2g4nkyHsKPJTCh7tD0UzfRj+f1seyg2+ooT2p4wy5Ja6/1qbwiqvSGg9nBAi+p9agj61BD0qjGzbQj61Fg32g8HvPJ6WHp7NvFMBwCgNvBMBwCgNvBMr33hcLhq15434e//+3//rz7+8Y/ryiuv1FNPPeWGsLdv366vfe1r+trXvqZly5bprLPO0hvf+EadeOKJOvLII+Xz+SqaPx6Pa+PGjXr22Wf129/+Vo888oj27NkjaTTwnXXaaafp29/+tjZs2DC9NzmPrVu3ThdeeKHuvvtud2y6wt8AAAAAAAAAgIXl4GBUd/xht37xwl796eCg5sfvMJ1f/F5bHQ2BzCsd7O5oCKS3jQG1h4PqaAyotd5PiBsAAAAAAACYQ+ZN+FuSTjzxRD3xxBN64IEHdN111+m5555z9xljtHv3bv3Hf/yH/uM//kOS5PF4tGrVKi1fvlyHHHKIwuGw6urqZIxRNBrV4OCg9u3bp927d2vnzp1KpVJ580nK+3WCGzZs0N///d/r/PPPn6U7nl/OPvvsvPD3q6++WsVqAAAAAAAAAADzieMY/W5rt259eoceevmAkg6J78loDvlGQ9wFoe7cscagN+9nIAAAAAAAAADmh3kV/s5661vfqre+9a16+OGH9S//8i968MEHZYxxv0mZDW4nk0lt2bJFW7duLTtf4crelmXJsix3/G1ve5uuvPJKnX322TNwN7VjxYoVef3Ozs4qVQIAAAAAAAAAmC96huO689ld+unTO7W9e6Ta5VSVz2OpMehTY51PDUGvGoJeNQZ9OdtMu86Xt68p0w8HvKzSDQAAAAAAANS4eRn+znrzm9+sN7/5zdq5c6duvfVW/exnP9NLL73k7q90xYrC0LgxRkcddZTe+9736n3ve59Wrlw5/cXXIJ/Pl9dPJBJVqgQAAAAAAAAAMJcZY/Tf23t169M79OD/7Fc85VS7pCkL+myFAz41Br0KZ4LY6UD2aDC7sc47GuAuEuQOeG1W4wYAAAAAAABQ1rwOf2etXLlSX/jCF/SFL3xBO3fu1C9/+Us9/vjjevbZZ/Xqq6/Kccp/09i2bb3mNa/Rhg0b9IY3vEHnnXcege9J2L9/f16/vb29SpUAAAAAAAAAAOai/pGE7n5+t259eqc2HxyqdjmSpIDXHg1qB71qCPgy29GxcGasMRPiTh8/GuoOB73yseI2AAAAAAAAgFlQE+HvXCtXrtRHP/pRffSjH5UkxeNx7dy5U7t27VJ/f79GRtK/MjIUCqm5uVkrVqzQihUr5Pf7q1l2TXjiiSfy+itWrKhSJQAAAAAAAACAucIYo+d39emnT+/UfS/uVSw59VW+bUtuADsc8Ko+4FE46FNDYDSMXR9IB7jDOYHtvHYgfYzfS2gbAAAAAAAAwPxRc+HvQn6/X+vWrdO6deuqXUpN6+vr01133ZU3dvbZZ1epGgAAAAAAAABAtQ1GE7rnhb366dM7tWnfwKTmWNUa0ntPXqnXrWnNW5k76LNlWdY0VwwAAAAAAAAAc1/Nh78xO66++mr19fW5fb/fr/PPP796BQEAAAAAAAAAquKlPf269ekduveFvRqJpyZ8vse2dO76xXrfKat06tpW2TYhbwAAAAAAAADIIvyNPP/4j/+oc845Rxs2bKjo+GQyqWuuuUY//OEP88Y/8YlP6JBDDpmJEgEAAAAAAAAAc8xIPKn7XtyrW5/eqT/u7p/UHMua63TZySv07hNXqKMxOM0VAgAAAAAAAEBtIPyNPP/v//0/feELX9Cpp56qd7/73Tr77LN1xBFHyOvN/6PS39+vBx54QN/85jf1wgsv5O1bu3atrrvuulmsGgAAAAAAAABQDa/sH9BPn96p/3xujwZjyQmfb1vSm47o0PtOWaUzDm+Xh1W+AQAAAAAAAKAswt8o6qmnntJTTz0lSQoEAlq+fLmamprk8XjU3d2t7du3y3GcMectWbJEDz74oFpbW2e7ZAAAAAAAAADANEumHHUOxbS3L6p9/RHt74+67e3dI9q0b2BS8y5uDOg9J63UpSet0NLmummuGgAAAAAAAABqF+FvjCsWi2nLli3jHvfWt75Vt9xyizo6OmahKgAAAAAAAADAVKQco66hmPb2ZULd/VHt64to30Bm2x/VwcGYUo6ZlutZlvSGw9r1vlNW6uwjOuT12NMyLwAAAAAAAAAsJIS/kefaa6/VkUceqccff1yvvPKKUqlU2ePD4bDOP/98/c3f/I3OOOOMWaoSAAAAAAAAAFCO4xh1DcfyVurOC3j3R3VgIKrkNAW7y2kL+3XJiSt02UkrtbI1NOPXAwAAAAAAAIBaRvgbec455xydc845kqSRkRFt3LhR27dv1759+zQ0NCTHcdTc3KyWlhatX79exxxzjDweT5WrBgAAAAAAAICFI550dGAgHd7Ohrj39Ue1fyCq/f3p18HBqBKpmQ92l/P6Na163+tW6tz1S+T3sso3AAAAAAAAAEwHwt8oKRQK6cQTT9SJJ55Y7VIAAAAAAAAAYEEYjCZGw9z9YwPeBwai6hqKV7vMkppDPv3FCct12SkrtbY9XO1yAAAAAAAAAKDmEP4GAAAAAAAAAGCGpRyj7qGYDgzE0it0D0S1vz+i/f2xTLA7ogMDMQ3FktUudVJOWt2i956yUucffYiCPn5bJAAAAAAAAADMFMLfAAAAAAAAAABMQXq17nSIe39/Oth9MBvwHojp4EBUBwdjSjmm2qVOSZ3Po0OagzqkKahDmuq0tCmopc11OunQRazyDQAAAAAAAACzhPA3AAAAAAAAAABFJFKOOgfTK3UfyIS6c0PeBwbT48PxVLVLnbKA13ZD3Yc0B7W0qU5LmoJa2pwZawqqqc4ny7KqXSoAAAAAAAAALGiEvwEAAAAAAAAAC173UEwPbzqg3/65Szu6h7W/P6bu4ZjM/F6sW5Lk99ha0pResXtpcybUnQl6L8mMtYQIdgMAAAAAAADAfED4GwAAAAAAAACwIO3sHtFDG/froZcP6A87euTMw6B3vd+jJU3B9KuxTkuaAlrSVKcljUEtaQzqkOagWuv9BLsBAAAAAAAAoEYQ/gYAAAAAAAAALAjGGL28d0APbTygh17er1f2D1a7pLJa6/1a3JhesXtxU1CHNGa2Telg95KmoBqCvmqXCQAAAAAAAACYRYS/AQAAAAAAAAA1K5ly9Mz2Hj308gH9auMB7emLVLskeW1LHQ0BLWkK6pCmuryA95JMu6MxoIDXU+1SAQAAAAAAAABzDOFvAAAAAAAAAEBNicRT+u2fO/XQywf061cOqG8kMWvXbgn5tLgxqMWN6SD34qagFjcG0u3Mq7XeL9u2Zq0mAAAAAAAAAEDtIPwNAAAAAAAAAJj3eofjenjTAT208YAe/3OnoglnWucPeG0taQrmBLsDo+3Mit3tDQEFfazWDQAAAAAAAACYOQsm/G2M0fPPP69Nmzapp6dH/f39chxHH/jAB7R69epqlwcAAAAAAAAAmKBdPSP61cYDemjjfj2zrUeOmfqcq1tDOmf9Yh22uMFdvXtJY1CNdV5ZFqt1AwAAAAAAAACqq+bD3y+++KL++Z//Wffee6+GhobG7D/99NOLhr+/+c1v6pVXXpEkrVy5Utdff/0MVwoAAAAAAAAAKMcYo1f2D+qhl9OB75f3DkzLvK9d3qRz1y/WuUct0WEdYULeAAAAAAAAAIA5q2bD3/F4XFdddZW+973vSUr/UKBQuW/gL1myRJ///OdlWZYsy9KHPvQhVggHAAAAAAAAgBmSSDnqHoqrczCmzqFoept9DaW3u3sj2tcfnfK1vLal161p1blHLdabj1yspc1103AHAAAAAAAAAADMvJoMf4+MjOjss8/WM888I2PMmJC3ZVlFw+C53vve9+qzn/2sOjs7JUm33nqrrr322hmrGQAAAAAAAABqjTFG/ZHEmBB39nUwZ7xnOD6jtdT5PDrzNe0696jFetNrFqsp5JvR6wEAAAAAAAAAMBNqMvx92WWX6emnn3ZD35Zl6eKLL9a5556rFStW6O1vf/u4c3i9Xr3jHe/QD37wA0nSgw8+SPgbAAAAAAAAwIJnjNFAJKnOoZi6h2LqGoqrayjmvgpX7E6kyi/EMZMW1fv15iM7dN5RS3TaujYFfZ6q1QIAAAAAAAAAwHSoufD3fffdp/vuu88Nfh922GG66667dPTRR+cdV7gaeDEXXHCBfvCDH8gYo2eeeUaRSER1dfz6TwAAAAAAAAC1JeUY9Y5kQtyDuWHu/GB391Bc3UNxxVNOtUsuacWiOp23fonOPWqJNqxqkcce/3vBAAAAAAAAAADMFzUX/v7KV74iKb36zJIlS/Too4/qkEMOmdRcJ510kttOpVLatGmTTjjhhGmpEwAAAAAAAABmUiyZUs9wPB3mHo6pazAd5u4uEuzuGY7Lqd4C3VN21NJGnbt+ic49arGOWNJQ0eIfAAAAAAAAAADMRzUV/j5w4ICeffZZ9xv7X/nKVyYd/Jakjo4Otbe3q7OzU5L06quvEv4GAAAAAAAAUBXGGA1EkuoaTq/AnV6JOxPoHk6v2N2d2dc5FNNgNFntkmeMx7Z08upFOveoxTpn/WItbwlVuyQAAAAAAAAAAGZFTYW/n3zySRmTXp7G5/Pp0ksvnfKcbW1tbvi7q6tryvMBAAAAAAAAQJYxRl1DcR0YiGbC3JkgtxvujueNJ1LzeHnuCfDaltrCAbU3ZF457UOagjpx9SItqvdXu0wAAAAAAAAAAGZdTYW/9+/fL0myLEvr1q1TfX39lOdsbGx020NDQ1OeDwAAAAAAAMDC1jkY01NbuvTU5m49uaVLu3sj1S5p1rSEfEUD3el+0G031/lk21a1ywUAAAAAAAAAYM6pqfB3f3+/284NbU/F8PCw266rq5uWOQEAAAAAAAAsHEOxpJ7e2q0nN3frqS1demX/YLVLmlYNAa9aw361hQNqCwfU0Vgk2N0QUGt9QH6vXe1yAQAAAAAAAACY12oq/N3S0uK2c4PgU5FdTVySWltbp2VOAAAAAAAAALUrnnT0/M5ePbm5S09u6daLu/qUdEy1y5qQlpBPreGA2nJC3ekAd6bfMLov6PNUu1wAAAAAAAAAABaMmgp/L168WJJkjNG2bdsUj8fl9/snPd+f//xndXV1uf0VK1ZMuUYAAAAAAAAAtcVxjDbuG3DD3v+9rUeRRKraZeXx2JZaQn61hf1qbwhkAt2jwe7syt3tDQEtqvfL52GFbgAAAAAAAAAA5qKaCn+feOKJbjsej+s3v/mN3vKWt0x6vltvvdVt+/1+ve51r5tSfQAAAAAAAADmP2OMdnSP6MktXXpyc5d+t6VbvSOJWa+j3u9xV+fOXaW7tT7bH93XXOeTbVuzXiMAAAAAAAAAAJheNRX+XrFihdavX69NmzZJkr7xjW9MOvy9b98+/Z//839kWekfiJx++ukKBoPTVisAAAAAAACA+ePgYFS/29KdXt17c7f29EWm/Rq2JS2qzwa2s0Hu9Krc7ZnVud2wd31AdX7PtNcAAAAAAAAAAADmtpoKf0vSRz/6UV111VWyLEu//e1v9dWvflXXXnvthOYYHBzUX/zFX6i3t1eSZFmWrrzyyhmoFgAAAAAAAMBckkg52tsX0a6eiHb1jujV/YP63ZZuvXpgcNqvtbo1pFPXten0dW06cVWLWsMBeVidGwAAAAAAAAAAlFFz4e8rrrhC//Iv/6IdO3bIGKPrrrtOe/fu1de+9jU1NTWNe/4vf/lLXXnllfrTn/7krvp90kkn6W1ve9tMlw4AAAAAAABghjmO0cHBmHb1jmhXz4gb8t7VM6LdvRHt64/IMTNz7bZwQKeta9Vpa9t06rpWLW8JzcyFAAAAAAAAAABAzaq58LfP59PPfvYzvelNb1I0GpUxRt/73vf0k5/8RBdccIE2bNggSTLGyLIs3X///Xruuee0efNm/eY3v9GWLVvcfcYYLVq0SD/72c+qfFcAAAAAAAAAKmGMUX8kkRfqTm8j6YB3X0TxpDMrtYQDXr1uzSKdurZNpx/WpsM6wu6CEwAAAAAAAAAAAJNRc+FvSTrllFP085//XJdeeqmi0agkaXh4WLfddptuu+029zhjjL797W/n9SW5we+mpibdeeedOvTQQ2e1fgAAAAAAAADFOY5Rz0hcBwai2t8fzYS7I+52d8+IBmPJqtTm99g6YVVzZmXvNh27vElej12VWgAAAAAAAAAAQG2qyfC3JF1wwQV65plndOmll+rll1/OW1Ent50b+M6OG2N01FFH6a677tLhhx8+u4UDAAAAAAAAC9RwLKn9A1EdcF8x7e+P6uBgOuh9YCCmg4NRJVKm2qVKkixLOmppo05b16bT1rbppNWLVOf3VLssAAAAAAAAAABQw2o2/C1JRx11lF544QXdcccd+pd/+Rc988wzbti7UHb86KOP1mc/+1m9733vk22zKg8AAAAAAAAwVYmUo87BmPYPRHUws2L3gcGYDvRHc8LeMQ1VacXuiTi0rV6nrm3V6eva9Lo1rWqp91e7JAAAAAAAAAAAsIDUdPhbkjwejy699FJdeuml6unp0RNPPKFNmzapu7tbfX19CoVCamtr06GHHqqzzjpLS5curXbJAAAAAAAAwJxljFE04ah3JK7ekbj6RxLqHUmk25GEeofjbj8b6u4ejqnEmgxzWlvYr+UtIa1pr9fr1rTqtHVtWtZcV+2yAAAAAAAAAADAAlbz4e9cixYt0oUXXqgLL7yw2qUAAAAAAAAAVZd0pK6huA5EBkcD3Jlt70hC/ZG4eodzgt2Z8XjSqXbp06Ih4NXyRSGtaKnTitztopCWt9Qp5F9Q3z4FAAAAAAAAAADzAD+9AAAAAAAAABaAWDKlR17p1D3P7tB/b/VoKCnFUpb09NPVLm3G+D22lrfUafmikFYuqtOKllAm5B3SikV1aqrzybKsapcJAAAAAAAAAABQMcLfAAAAAAAAQI1KOUZPb+3WvS/s1QMv7dNgNJnZUxuBZ49taUljUMvdlbvToe5su6MhINuujXsFAAAAAAAAAACQCH8DAAAAAAAANcUYo5f3Duie5/fovj/u1YGBWLVLmpTmkE+LG4Ja3BTUksaAFjcG3deSxqAWNwbUGg7IQ7gbAAAAAAAAAAAsIIS/AQAAAAAAgBqwvWtY976wV/e+uEdbO4erXU5JAa+tJU3BssHujsaAgj5PtUsFAAAAAAAAAACYc2o+/J1KpfSHP/xBzz//vHbu3KmBgQFFIhEZYyY0j2VZ+uEPfzhDVQIAAAAAAAATd3Awqvv/uE/3vLBXL+7qq1odDUGvWkJ+tYR8aspsm+t8agsHMgHv0WB3Y51XlsVq3QAAAAAAAAAAAJNRs+Hv/v5+/cM//IP+/d//XZ2dnVOayxhD+BsAAAAAAABzwmA0oV++fED3vrBHT27ukjOxNQ7KCvk9aq7zqTnkV0u9T811fjWHfGoO+dQS8qs55FdznS+9L9NuqvPJ67GnrwgAAAAAAAAAAACUVJPh79/97nd6xzveoc7OzrwVvllRCAAAAAAAAPNRLJnSo6926hcv7NXDmw4olnQmPVdD0KujG+N6TbNRg9fozFNP1vKOFjXV+RT0eaaxagAAAAAAAAAAAEy3mgt/v/zyyzrvvPM0NDQkKR34zgbAc4PgAAAAAAAAwFzmOEa/39atX7ywVw/8zz4NRJOTnsvvtfXmIzt00XHLtOGQoJ58/DF332Ed9WpsDE5HyQAAAAAAAAAAAJhhNRf+vuKKKzQ0NOSu8m2M0bnnnquLLrpIxxxzjFpbWxUKhapcJQAAAAAAADCWMUYv7x3QvS/s0X0v7tP+geik57It6bR1bbrouGU676jFagj6JEkDAwPTVS4AAAAAAAAAAABmWU2Fv7ds2aLHH3/cXe27paVFd911l84888xqlwYAAAAAAIAFKJpIqW8koZ7huHpHMq/huHqGE26/Zzied8xIPDWlax67olkXHbtUbz/2EHU0sKI3AAAAAAAAAABALamp8PeTTz4pKb1CkmVZ+v73v0/wGwAAAAAAANMimkhlwtuJWQtyV2pNW70uOm6ZLjpuqVa31c/KNQEAAAAAAAAAADD7air8vX//frfd0tKid73rXVWsBgAAAAAAAHNRyjHqj6QD230jCfWNxNXrbtPt/pGE286ORxNOtUvP09EQ0IXHLtVFxy3T0csaZVlWtUsCAAAAAAAAAADADKup8LfH45EkWZalNWvW8AMvAAAAAACAGmaM0WAsqf6RhPojCfWNZAPd6dB28XB3QgPRhIypdvWT0xD06vyjl+ji45bplDWt8th8/wsAAAAAAAAAAGAhqanw98qVK912NBqtYiUAAAAAAACoVDzpqD+SUH8kHdbOBrn7Iul2/0jcbWf3Z18pZ56muCfA77V19hEduui4pTrzNR0K+jzVLgkAAAAAAAAAAABVUlPh71NPPVWWZckYo+3btyuZTMrrralbBAAAAAAAmDeGY0m9sn9AG/cNanfviPpHsqHuuPojSTfUPRJPVbvUOce2pFPXtunC45bqLUcvUWPQV+2SAAAAAAAAAAAAMAfUVDJ62bJlevOb36xf/epXGh4e1gMPPKALL7yw2mUBAAAAAADUNGOMDgzEtHFfvzbuHdCmfYPauG9A27uHZWp/Ye4JaQx6tajer5Z6v1pC6deiep+aQ/70eGZ7WEdYLfX+apcLAAAAAAAAAACAOaamwt+S9I1vfEOPPvqoksmkrrnmGp199tmqr6+vdlkAAAAAAAA1IZFytLVz2A16b9yXDnv3DMerXdqsywa5c4PbLSGfWurz+9mwd3OdT16PXe2yAQAAAAAAAAAAMI/VXPj7uOOO00033aSPfexj+tOf/qS3v/3tuu2229TR0VHt0gAAAAAAAOaVgWhCr+wb1Ma9/dq4Lx30/tP+IcVTTrVLm1Z+j63mkC/zygS4Q+lQd3PIpxZ3fDTcTZAbAAAAAAAAAAAA1VBz4W9JuvzyyxUKhfTRj35Ujz32mI466ih98pOf1Lve9S4dffTRsiyr2iUCAAAAAADMGcYY7emLaOPe9CreG/elw967eiLVLm1CLEtqDI6GtZvdELfPDW4XGw/5PXy/CAAAAAAAAAAAAPNCTYa/Jemyyy7TKaecogsuuECbNm3SV77yFX3lK1+Rz+fTokWLFAwGJzSfZVnasmXLDFULAAAAAAAwdSnHaCia1EA0of5IQoOZ9kBOezCa1EAkMdqOJrSze0QD0WS1y3f5vbaa63xqqkuvxt1U589sfWrOjDXWZYLcOcc1BH3y2IS4AQAAAAAAAAAAULtqNvz95JNP6tOf/rReeeUVWZYlY4wkKR6Pa//+/ROej9WfAAAAAADATDLGaCSe0mA0qcFoQoOx5Gi7MLCdF+xOHzMQTWooNncC3LnCAa+OWNKgxU1BN7ydDnL71RQaDW831/nVVOdT0GfzvRgAAAAAAAAAAACgiJoMf3/nO9/RVVddJcdxZIyRZVlT+oFhNjgOAAAAAABQTDzpaDCa0JAb2B4NbafHRsPcQ2P2Jd1znRr4FsSy5jodeUij1i9t1PpDGrT+kCYtb6mTzYrcAAAAAAAAAABguhkjpRJSKiYl45ltrMxYPPMqaDuJ4uNj2sX318ejOnuoX5ZJyjYpBV6xpFP/VnrDp6v9DqEG1Vz4+/7779enPvWpvNB3Nrzt9/vV1NSkUChU5SoBAAAAAMB84ThGBwdj2tY1rB3dw9rWPaztXcPa0T2irqGYBqJJxZNOtcucdT6PpXUdDVrvBr0bdeQhDWoO+atdGgAAAAAAAAAAmA6OUz4I7STKBKQnenw8E9COF2nnBrgLxlLxar9LkiSPpHDuQEJSfKg6xaDm1Vz4+8orr3SD38YYdXR06NOf/rQuvPBCHX744bJtu9olAgAAAACAOcYYowMD+QHvHV0j2t49rO3dw4omFl64O1dTnU9HZlbxzga913WE5ffyfRYAAAAAAAAAACasaKh6nJWp80LPlaxGXWq+CcxlUtV+p+a3ORJMR+2pqfD373//e23ZskWWlf41wq997Wv161//Wq2trVWuDAAAAAAAVFs24L09s3I3Ae/iVi4K5Qe9lzZqaVPQ/X4LAAAAAAAAAABzVio5fng6VWl4eqLtMvuTBUFsQtULQypR7QpQo2oq/P3cc89Jkrvy980330zwGwAAAACAGmaMUTThaDie1EgspeF4UsOxpIbjKe3ri2h794i2d6XD3Tu6RxRJLLxvptb7PWoI+tRY51Vj0KeGoFeNdZlt0KfGOp+a6nxa2x7WEYc0qDHoq3bJAAAAAAAAAIC5ypjMitGxnNWjc9uZFafdsHNhOxuIjo0e64ajc9uFoerCsVIrVbPQC+YQVv7GDKmp8Hd/f7/bXrZsmU488cQqVgMAAAAAAAqlHKP+SEJD0WReUHsku40nNRQbDXK723gqPR4fu88x1b6rmeOxLYUD3vzgdiawXbSdCXZnj20IeuX12NW+DQAAAAAAAABApYyZwArTsbH7c4PYuWHsou1i4e2coHbeOZm5HVYyBipG+BszpKbC3+3t7ZIky7K0dOnSKlcDAAAAAEDtchyjwVhSfSNx9Y0k1DsSV38kod7huHpHEul2Zl/fSFx9mX0D0WS1S581Ib9HDUGvwgGvGnLC2A0Bn8KZdjiQDm1n+w1BX2bMq3DQqzqfR5ZlVftWAAAAAAAAAKA2OE6JUHW8RIC6yDHJgmMLV6Qeb39hiLvwWoSrgRliSd6A5AlIHp/k8edss+1S46XGRtuRhKNXN2+VsbxyLK+OPOZYhZYeWe2bRo2qqfD3ihUr3PbAwEAVKwEAAAAAYO5yHKNoMqVIPKVIIqVoIqVI3FEkke4PRhPqHUmobzgT2h6Jqz8T8O6LJNSXCXenannJ7RJaQj6tbqvX6tb0a8WiOjWHfAoH8sPd9QEPK24DAAAAAAAAWBgKA9VO4erU2XaJ8YkeXyysXaqdLFgZ26Sq/W4Btc0TyASivWOD0napcHXusf5MQLugXXIsE+QuHPMWzhdIX2cGJQYGtKPvEbd/2OFnSY2NM3pNLFw1Ff4+/fTTFQ6HNTQ0pC1btmhgYECN/McDAAAAAJgHHMcolnQUTaQUTaYUTWTaidToeMJRLCe0HUmkFHUD3M6Ysex4Otw9OhZPOtW+3TmtJeTTqtZ6HZoNebeF3LB3U8hX7fIAAAAAAAAA1CLHKQhB5wSWnWT5ULRTcHwqWXBuIr/tHp/Tdq+RqPCc5Gi9hu85AzPK8mQCzCVWnra9OeHo3HB14ViRdrl5K2nbOcFt2yPx20yBWVFT4e/6+npdcskluuWWW5RMJvWTn/xEf/M3f1PtsgAAAAAANcZxjPoiCXUNxdQ1GFPnUExdQ3GNxJIFwW1H0WRKsYIAd2HAO5Z0CGTPstyA96rW0GjQm4A3AAAAAAAAMP+NCVKX2RY9rljwOqGxYeucc9x5CoPXOeeOCXfnzOckq/2uAQuP7R1dObpsyDlnLHdF6fGO9fjSq127AesKwtjF5rM91X6nAMwxNRX+lqSvfOUr+sUvfqGenh59+ctf1jnnnKPXvOY11S4LAAAAADDHFQt0dw6mQ91dbjv96h6KK+mYapeMMjy2paY6n1YuCml1a0ir27JB73qtbg2pOeSvdokAAAAAAADA3GaM5KRyws7J4sHnsvsKV5AuDEKPt69IqLpY8LowzE2QGpgDrEzoOZCzwrQ/PwjtrladDVUHiqxEXSQYPW4Au1w7Z4xVqgHMUzUX/l66dKl+8Ytf6G1ve5t6e3t11lln6cc//rHOOeecapcGAAAAAJhhyZSjeCq9inY8OdqOJR31DscJdM9RHttSvd+j+oA3/fJ7FPJ7VR/IbjNjOdtwdp/fq1DAo/rM8dm+32PL4pu2AAAAAAAAqCZjioSf84PN9mCfmoe3yjIp2SYlzw6vFPAVCT8XC0TnhqEL2rkrTBerodLANYA5zsoPVNu+nGB0oCAk7S8StvYXhLNz27kB7ZxVq72F8xULd/vTq2rzfXoAmBE1F/7euXOnli1bpttvv10f+9jHtH37dr3lLW/RqaeeqksuuUQbNmxQe3u7gsHghOdeuXLlDFQMAAAAAPODMUYpxyiRMoqnHCWyr2RBP+UonjT5/ZRRIlnQTznuWCwT0k6UCG67+3L35xyT3ZLdrh6Pbam5zqemkE8tIb+a63xqDvnVEvKpOZRuN2f2NdX51FTnUzhAUBsAAAAAAKAmGSMZJx0udlKSSWXaTk47szVOTjuVaady2hWOu/3MdZzkOGM55407lg1JZ0PVpcLSRcLa4whLemPuwOaZ+qIAmDDbm7NqdEB5K0iPWXm6kv0Fq08XXb26cLXqQInxTNv2ELAGgAWo5sLfq1evzvuBsWVZMsboqaee0lNPPTXpeS3LUjLJr4QBAAAAMD/EkikNx1IajiU1HE9qOJbUUCylkVhSQ7FkZjyzP7svnrMvltJwPKmReMoNVydSjgzh6pqXXYW7pd6fDmzX+TIB7tHwthvmrsuEuUM+NQS8sm2+wQwAAAAAAGpM0RBzheHkUoHnMcdmx4tcp+S8lY7lXr/EmFu/MzbwPCaYXeH9GKfaXzkAc5kbaPaWDj/bvhKB6BJB6KJB6krbxQLavnSwGgCAOajmwt9Zxhg3BJ7dGlIKAAAAwIJljJFjJMeY9MuRUm47vaJ1yhgZo3TbyezL9B2TM5Y5N+UYdzXsVGbcMdm2cedJ5m2d0X4qfzzlKH+/u3WKHJ9eOXsknnLD3dnA9nAsqUSKzz+1xuexFPR6FPB5FPJ7VOfzKOizFfR5VJfp1/k8Cvo9Cno9qvPbmWNG9wd9njFj7jyZvs9jV/tWAQAAAADAVBgzGsLNBndNJqzr7ssNAzujwd+KQsO58443lioeFDZOkbkL55jguBtSnkQgOjf8XDhGiBnAXGXnBqNzA8tFxm1vzj5vTrjamx+yLjzOnSvnOPf4gvmKhavzamGlagAApkvNhr8lwt4AAADAXGeM0a6eiP5nT79e2tuvl/b0a29fJB2qltygdW5w26ggyO2kA9vu8Zl9pmALzKTmkE9t4YCa6nzpILU3HbAOeG0FckLa6XFbAW+mn9kXyNuXc7xvdMzDqtoAAAAAgIUqG1h2ElIqkQnuJjPtRHqf205KqeRoOxs4zoZ5C1c1dvcnC4LBlZ5XEETOaxcGlp3iYWU3dFwQhs4NSBeGuHOPL9wHAJi4MSHl3EB04QrUJQLWeeM5gWg7NxxdELwutQJ2uXqycxKgBgBgwaq58PcHP/jBapcAAAAAoAjHMdrRM6L/2dOvl/f0pwPfe/o1EE1WuzSgqGyguy3sV3tDUG1hv9rCAbWHA2pvCKT3NfjVWh+Q38tq2QAAAACAOSI3CJ1KVNYeM5YNN+eGqUv1UzmB7MRoODo3pD1ev1iIu/CaAID5wS62WnSx0LO/yArSRVagLtkOlF5x2i61EnWZ4wlSAwCAeaTmwt+33HJLtUsAAAAAFjzHMdraNayX9/brf3ang94b9w5oMMYP6lBduYHutpwQN4FuAAAAAEBReStOx0dXlE5l+tngcmE775UzliwyVuzYZCznmBJzOsmCduY48SvQAGA+M5YtKzckXSpIPaZdJERtlwtTZ1eYzmkXrlxduLr1uKFuHyFqAACAWVBz4W8AAAAAsyvlGG3pHNJLOat5b9w7oOE4v2J2ofPalnweWz6PJb/XzrTTfZ/HViAz5vVY8ns98nts+b1WZpt5eTyZrZUzZqeP96bnChQemzueMxYOeAl0AwAAAMB0MiazMnNmpWeTyrRToys6F46Z7D6noJ9dTTonyFzpCtFjzi0zT9nVrgtWvk7F020AQPVZtmR5MqtKezJtT047M567dY/JHbMLji02VmrOcULVY/YXhKLHBK7T5w8MR/T4k7+TY3nkWF6d9aaz1djYWO13HAAAAHMY4W8AAAAAFUsZ6cCIdO8fD2hz9069tHdAG/cOKJIg6D0f2JZUH/AqHPCqPuBVvd+T3ua0wwGvQn6v6gOedDvgVdBry5cJXeeGt0cD3dbovkzw2mfbsm1WeAEAAAAwjznOaHA5bzvR8UqCz7nH54apnZLh6kB0WOv3bJNlHFlyFPz1w5LXk3983jnJnFeRMfdayfwaS4W4naRYYRoAJqmSELNlT/OYR7Lt0eu5++yxQens+e64N+fc8eouHC+2z1smvF3iuFpeTdoZUNJTV+0qAAAAMI8Q/gYAAABqjDFGsaSjSDylSCLziqcUzWlHEpl+PKVIwinoZ/Zn2iPxlIajcfUOeNQflxLGkv74p2rf5pxkW5LHtmRbljx2+uW1LXlsO7O15PVY8uTu9xTsL9im23b+Pk9mn2Wpzu9VOJAJcfszQe6cfjrAnQ5yB7y2rFr+IQkAAACA8ozJBHyLrNJsioSPxzt2TGh4Cqs/j3dMuVB0uUB1udpKBbezx81xAUmH5Q50VqkQAAuMVTponBfUtccGfvMCyeON2zkrPVcyh1UkMGyXCBEXqa3YnGPGytzvuMHmgrA1AAAAAEwB4W8AAABgjjLGaCSeUs9wfMyreziu3sy2Zzim3pGEBiIJjcRTiiZTMjOy8NbMhobXtNXr6GVNWr+0UY1Bn2xLsi0r/fMky3L7ltvPtiUr0889xipxTvp4uQHt7Hg6aK28vp0JWNuZ8XR7NHhtZcLe2XEAAAAAVWBMJrCbSId2U4nRMPGYdiJ9rDuekFIFwePJrOycrWHMPqeykHG5YyuprdRK04WBbuNU+6sFALPPDdzmrCqc7ReuSpwXCPbmvOwS5xSuqFwQVnaDyjnbvP25Qerc/XbB+bn77bHzWXaRcwr32WOvU3TfeMFnvgcGAAAAANVG+BsAAACYJY5j1B9JZALbua9YQZh7tB1L1t4P5i1LWtse1jHLmnTU0kYdkwl8NwR91S4NAAAAWBiMGQ1Cp3ID08X6yZzxwv5455U6tth4svz8TrJgzpy2k6z2OwoA85/tkzy+nK137Db7qqjvSc9Vtu/Nv17hXNl6bE9O2yt5vAVzjXMuYWUAAAAAQI0h/A0AAABMQTLlqGc4rs6hmLqG4uocjKlrKDZm2z0UV+9IXM6MrMg9d9mWdFhHg45e1qSjl6WD3kce0qj6AB9FAAAAMM+5q0dPMTCdSkipePrlJEfbefsyW6dwrNz+ePq67nhODSZV7XcPAGqbZUuegOTxp0PIuVtvIGcsO15srPDY7DaQDj97/AVBbX9+WNvjL962s/MUBK8JSAMAAAAAMG/Mm8TFm970pry+ZVn69a9/Pe5x06XU9QAAAFB7Uo5JB7ozwe2xYe642+4ZicsssEB3KV7b0mGLG3TMssZM2LtJRy5pVJ3fU+3SAAAAMJfkrTodLxOMLrcvUbrtBqjHma9YcNsNYFcQ4hYfBABgWlm5qzuXWOk5dwXpcVd8LlwhOneOzNZt54apvUX2lzjHDVL7CsLV8+ZHsAAAAAAAYB6aN995ePTRR2Vl/sW5McZtlztuupS7HgAAAGaPMUbxlKN4MvPKaccK+mP2j9mXctuD0aQ6c4LdPcOxBbdC90R5bUtHHNKgo5emQ97HLGvSa5Y0KOgj6A0AAFCSMenVok0qs3Xy204qHTw2ma3j5LSz+yZ7XMF+J1HQT472s2HoMfsTxY93w9LJEsHpgtA1q04DWMgsOx1ytr2ZELOnoO9NH5PX94wem9NPOlJ3b5+M5ZGxbLV1LJHPH8yfOxuIzpsnZ8y2C/q51/eOzmXZ6WCzW6udM6e3SH2V3F/BGD+LAwAAAAAAqMi8CX8DAACguowxGo6n1DMUV89IXL3DcY3EU0qk0iHrRMpRIukokTKj/VSmnyzou8cWOT5plHBG24Vhb0yNbUl1Po/q/B4FfZ6x7Zx+yD/aVyqhnVv/LL8tLa4zuvT8N6ptUXO1bwcAAOTKBoCzod+8YLFx91mD/aqLd8kyRpIju3ebFA8VnFfByz3W5IynihxjitdlnEzNxeoudp1U8TnL1Z03nj2v2PGl9uXWmXv9gvc1O5Z7/pivAYFnADVq3ADwZEPCpULD5eYvF54uUkd2PstTsC0xPpFj88a9OW172t76kYEB/f6RR9z+WWedJV9j47TNDwAAAAAAgLlpXoW/jTFF2+WOAwAAQHHRREq9I3H1DMfVO5xwA9092VdOv3ckfQzh6+ry2JZaQn611vvVUu9Ta31Ai+r9aqlPjzWHfKr3e8cEu7Oh7qDflt9jT+q32gwMDOiR4T+5fb93+n5YDQCYIGNGQ6oqbGeCuLlt4+ScN95xuf2cQK9yw72F4+X2OcXnGxO6LQwQVxIyLjVHwTFFazfj3FN2XCXGnfxziwaPUxpTy5jjCgLMZQPTRcLShaHoCjVIOjd3YOPU/kgCwLxmedKrGecGhCsJ91p25ceWHLeLBIOnEjwuCDTnBqKLzVE0TJ0931v6WPda3mkNMgMAAAAAAACozLwJfztOZT/AqvQ4AACAajPGKOkYpTKv3Ha67xT0x7ZLHZN0jIaiSTfc3ZMT4k6HveMajrPyYLWF/J50mDvs16J6vxaFMttwTjvzaq0PqCHolW3zK5ABYHR13qSUSqS3TiqzLeznvgrHxutXeI5bQ2J0vzuWLFJnpp1KakzNhXOlEvnBZvEP3gEAVWT7MkFpn+Tx5vQzQeBs2z2msJ85x/YWnF9kPnfeca6V1/elw8lu21twzcz+7FzTvAozAAAAAACoLmOMjIyMMXKUXuDDyMgxjjs+pl8wJindzpkr+z/HlJ4ze83swrXu/pzrZPcX1pJ7fLlrlLqmTJHrZfdn5p/yfefsd6+ZuZ/CeyhVb+H8ue9HsWu7teVcq9T9j/f+VPr+jbmXMl+/MffnOIpEI+5533rgW7r0iEv118f99ez9R4AFY96Ev7P+/u//XpJkWZbe//73a/Xq1dUtaAHZsmWLnnnmGe3evVvxeFwtLS064ogjdOqppyoYDFa7PAAAJKU/zMVTjiLxlIbjKUXiSY3EU5nXaDu9P6mIu290fzLlKGUkJxuqzvxFPt0eHXfM6NYxcgPYuePpbeacIuOoLbYlLaoPqC3sV3tDQO3hgNrcrV+L6gOZVbvT4e46v6faJQOoJY6THySuNFhcGHauNEydSo49t1hwOu8axcLV2Wvm7ivo5+3PzAcAwLxk5YedPYF03+OTPP70y/aOtt1xX0HbPxpgdo8tcl65AHbZ0HWRvu2RJvFbhAAAAAAA889Eg5O5QdJiQdFxw6BFwqju+ZMJklZyrSLXLhbszK2jbH8i91ki6Fo24Fvk/GLv0Xhfk2Jzlfo6TCRwnFtPsa9D4ftW7n1x5ykT/C0WdM6eC8wZMWkoMVTtKlCj5l34+/rrr3d/Tf1pp51G+HsW3HPPPfrKV76i5557ruj+cDisD33oQ/ryl7+stra2Wa4OAOY3Y4yG4ymlUtkPJZlxZT9QZ49Lf5jO7iw2bvLGR+fKPc4xkpP94Goyv6HemMxYtq2c/aPnOJkLOEXOyZ0n5UgpY/KC007uNhOgHg1TZ8dV4th02zHp/dFsUDuR0kgsHdaOJDLB7Vh6PEWqGtPIsqRFoXSYuy08GuxuCwfGbBfV++VhZW5gbjGZFaJLhp5z+yVC07n9cvuKBqJzVnkuGYYuEo4eL4DthqETo33x/AMA1DJrNNScDSS7QemcsHIlbTdcPc5c0xGYLrbqNAAAAIAFY7wgpyQNJ4YVNVF3X3+sX8locsZXhs09Jq9dJjxb7Drl5p1IILXsiq85+2ditdlS91bs/R8vOJv7XozZX2HQd7xAcrk/D2PuawrvOQBg6vj/VMyUeRf+ltJ/kbJYYWTGxWIxXX755br11lvLHjc0NKTvfOc7uu2223TnnXfqjDPOmKUKAWD+iCcd7ege1pbOYW3tGtKWg9ntkAairFwJTDevbcnvtdMvjy2fx1Yg28+M5bbr/J68EHfuyt2L6v3yevg14KhhRcPGibEB49zAcm5wulQwetxzk5WtQl1q1ee844vVzArRAIAFwvJkVmT2pAPGtp3eZsdtr2TZmXYmhJwdd1+e0dBy0f2FL09OmLnE8W64ulhQusS+kgFqQtMAAAAL0UyuOlsuKFnuGqVqKBYqLRnEnMgqtDlzu/dSGEItEwott3+8+idyr5XcX9n3YpzQa9laZMYEj4u9VxNdLbjkXOP8mSncPykPTPo/GwAAMMdk/x4HTLd5Gf4m+D3zHMfRe97zHt1777154x6PRytXrlRTU5O2bdum/v5+d19nZ6fOP/98Pfzww3r9618/2yUDQNUZY9Q9HNeWg0Pa2jWsrZ1D6bB355B29UZYjRo1x2tbCge9brja77Xl81jyZfseWz5vQT+731vQH+f83LB2wGvL7/GMhrdzwtwBb/pcVt/GtDJm/FWjC0PNbr9YILog+Dxmxelix403zzjh57zVqQvqnewPIAAAC5w1Gii27BIvKyeYXLDPHcts7dz9ntJzF55nWUXGssdZY8fz5rCKnFPiXrLhardWT/517cJ5CvcX3pcnp8bC8Hb2uJxQdXaOYqFuvlcKAADKKLri63hB0DKrrU4lHFtq1Vl3/gkELScbni0MeZa8doUrsmoaVp6dyvtQaRh2oisBl6ul0uBs2eMrfL8BAACA+Y6/12KmzMvwN2beP/3TP40Jfn/iE5/Ql770JS1dulRSOiB+77336sorr9TOnTslSSMjI3r3u9+tl156SU1NTbNeNwDMhlgypZ3dI9rSOawtnUPa2skq3qgNzSGfFoX8aqn3qyXk16J6n1rq/Wp1++l92WMag17+UR7yOU7p4PKYYHPuitC5geoSK0aXC0iPWYW6XHi6YA73mNxwdEE/84MoAMBkWDkB2oKgrTtWKnhrFfTH218seJw5ZkwdOcdJxfeNOSdnrrKB51Jh5dz5ioWaC88tEoAuGzYuEi52g9H544PDw/r908/IWLaMLJ162ulqaGjKv9+i95h7L/w9EACwcFQS9hwvPDtmxdlxVmLNC22W2D84NKhdyV3uD5Jf7HpRoUhoWsKz44U+xwvPFpt/vLBnqVBs2dBqYTi5xP5S18itp1xQtdh7M95xxf5MlPoaVPT1KhGszX0/iwWvAQAAAADVk/3cCUw3wt8Yo7u7W1/96lfzxr7+9a/r85//fN6Ybdt6xzveoZNPPlmnn366tm/fLknavXu3vvWtb+mGG26YrZIBYEqSKUfxlKN4Mv2KJdP9WMLRQDShbV3Deat57+wZEYt4Yy7x2JY8tiWvbcljWfJ40u2A16PW8GhwO/tyg905ge7mOp+8Hrvat1LbjEmHiHNXhnZXZy43VsExqdx9RQLQeaHqImHoCo6rT8Z09tCAbJOSbVLyv+KRTEGImg+uAOYT25vz8kywX2Kfx5cz5ssZyx6THcs9P7PfPbfg5fGNDSarWKjZymmrwuOy+1QkEF0iLC1VGJRmNeK5zPgHNBLYPtoPL5EaGqtXEIAFodzKsxWtdprtT3SF0XFCtaWuVWz+8YKrY4K6JcKxlayyW0lQuGQwuERwtlzYuFh9Ew6mTsMqvKW+NsW+1uP+mZlAeLbcn895FZ59vNoFAAAAAACmwrZsWdn/Wemtbdluu3DrHp8zbiv9c4KS+zNjedezco7JnCfJna/Y+aVqsDM/p8iel63H7WfmHm+eYtea6L2M2T/O+1eqhtz5S9VvWZZi0Zg2bdrk1nr0UUfrNYtfM6t/hrBwEP7GGN/85jc1ODjo9s844wxdc801JY9ftmyZbr75Zr35zW92x2688Ub9f//f/6fW1tYZrRXAwmGMUX8kob19Ue3ti2hvf0RdgzHFcsLabnA7mUoHuQsD3QXHZo9JkeReMAJeWyG/RyG/N7P1qK6gH/J75ffasi1LtpUOVtuW5Qasi43bmdC1bcltVzLusS15PaPBbdtK9722JY9t5wW53WPs3L4t29L8Xn3bmIKVmwtXi04V7M8NPRcZSyVUPCxdJDg9Zs6cfmE9Y+YvtmJ0qWNz5p7HPJLCuQPz+3YAFLJyA8ze/KByYfDZUyz87NOYMLSnxPmF542ZrzD8XCQonRe29hTf7/Gl72vMMV6CyQBqRiVhy0rDjpUeO9kVTcudL41dsdQNBZcJg45Xe7H5i9VS7PrZ/aWuV2zfVFeSLdfPfQ8m+7Uo9/6VDTJP058TAAAAAABmWskwZ5FgbGGQs/DYSuYpFZjNmzsn9Fou0FoqMFsuoFrJfeTWOqY2qbJ7KQweT+C9Kgzk5r4Xlb6nlQSdJ/I+lAwSl7p+wXs9Xjg4936L3UexQHR2HPPbwMCAgluCbv+s5WepsZGFVzAzCH8jj+M4uuWWW/LGrr/++nEfLmeffbbe8IY36PHH08tKDA4O6vbbb9df//Vfz1itAGpLNJHS/v50sHtPX0T7irRH4qlql7ngWZbc8HP6A0h+PxuKzgtG5wSnLUujAei8IPTYkLVljR23bUsBr636TFi7LiewHSpoZ0Pd9Tltjz0PPyzlhaMzYeJEYjRMnLvqcm5guWiYukw/L8ScmEQ/lVNjYV2FNaZGV5c2/HcNYI6zfWNXZM7tl9yXG4iuMKxcdH+JlaBLriSdc81ydeetKu2RbH77Axam6ViFtli41d1fZMXRUoHKwsBpuVDmpMK5Oe2RyIhejr/s1j+yY0SBYKCy1VQrWYF2kvdZ6r0cb/9E3utiq+xWtPprJasRTzBIW+kqteXOBQAAAABguk0kyFl2xdTpCJwWBCiLreBaGCQtFp6d6GqzpVaHLXZusVVxx9xniaBvuTnKhUknswqvG14tUWthuLVcgLbYNcoFaCu5h0r+HFVyvwAALBSEv5HnqaeeUmdnp9tfs2aNzjzzzIrOvfzyy93wtyTdc889hL8BSJIcx6hrKKY9fRHt7YtqX38k0x4NdncNxatd5qzw2JZWtNRpbXtYa9rrtbY9rEPb6tUQ9ElKh6uzn0nTH1Cz7dzFKdPj2W42hO2en+nlfrYdDW2nA9oq6FsFW9uy8s7JXn/OfmDOBqRzQ82paDpgnExI8ZzwdDaIXLQ/yeB03grSRULOJYPTFQS4CUcDmMvc4HGJMLTHNxpuLtouCC0XW+G5WGC5VAC63PHutXwF55e7B0+13+GqK7rqaJFQYm4IVCq+GmtuWHEiocyKVngtCIXmHp8NU04oiFqihnKBzpI1llrBNXt8kdBx0eNzV3zN7C92zcKxYnVnvwa516s0cFosDDuhYyv8epd7r4v92apohduCYwjP5niu2gUAAAAAQG0pDCNWGpAdNwRZYr7x5ioX/JzI6rMTDdCWuvdi51a68myplVKLXX+8FV8nEl4dM1+R+UvOMc79jwyP6Nk/PJv+s2NZOuWkUxQOh8t+LSq533Lvd6ng9Jz9WSAAAADyEP5Gnvvvvz+vf84551T8l/tzzjknr//oo49qeHhY9fX101YfgKmLJlIaiCaUSBklU0566zhKJI0SjqNkZjyeyrSd9DGJTD97TCJ7bspRwsnuH51vOJbS3r6I9vZHtL8/qkTKVPvWZ1Vj0Ku1HWGtaQtrbUe91rSFta6jXisX1cvvnSMrazopKRUfDRsns2Ho3GB0TiA5FR8bli52XGGgeUyoumCV6MKVoAuPzQ1NF86Zez0AqDJT7GV55Hh9MrZPxuOVsb1yctrG8sp4vHJs7+iY7ZGTCSQ7tifTTweXHcuTmSMdenYsO32elT7OWHZmvy1ZnvR+25O5lp3Z75Fjp9vuMZZHJjNmLDszZkl2dn/23Mx+Wem+ygR4K1ihVhp/FdnSAd346H7HyHHKB3wnHdAtEogttepsYYh1zP4i91V433n15cxXbH/eqsPjvcfZ41lRFgAAAAAwD+QGJysJnBZdFTR7bsEKppMKnOYEMEsGLgvDlmWCsyXPrWB104qCraWCn5UEZacQnC31nhX72uR+PYu9D+N9zYpdv5L3wtLEV7Yt+eeGlWcxTQb8A9rn3ef21zatVWNjYxUrAgAAwFxH+Bt5Xnjhhbz+qaeeWvG5S5cu1erVq7V9+3ZJUjwe18aNG3XSSSdNY4UAsowxGomn1DsSV+9wIr0diat3OK6ekYT6RuLqGY6rbySR2cbVMxJXNEGQZzrYchSwUzq02ad1rQGtXeTXoYsCWtXs08pmn5oDkpVKSE5MSg2mQ8p9CaknE6JOJXPC1GVC1GNWqS6zYvW4K1vH8/dpYQXyMbflhmWz/y/lWJKRNWafsbJ9a3RMkiylA6m581mj+3PncjLfe8873lLefO78Vm59Vl7fPdayCs4de+xo/Vbm2iq49ugc2ffEsYrUnjl+TP1592uNGcs9fvT+x7nXvPd7dG7lvK+jX68i9ee8N3lfq8LjLWs0TGxZmXOsTOhYMkpvHSv9qwscd79y+un7S8+V/3XJ/1oVfp1zX+morGPM2DEZyYy284O76TDtxCUzryKMpFTmBQAAAADAJFUU7CwROi0XkiwXxCy1v1wotWjQtExwtFTos9xqqJWsdFos4FosiFsskFyqtqJh2Mw9jxfyLdUvvHbee17k6znu9YsEnitdIbfYPUjFw7wTWaUXAAAAAACUNq/D35/5zGfU0tIyK9eyLEu//vWvZ+Va1bRp06a8/vr16yd0/vr1693wd3Y+wt9YaNKrQUopx8gxRinHKGWMHCe3LXcse0x6m16ZezTInRjT7hnO9EcSiifnU5DbyCNHXqWKv6wS43LktZLprZKjY0qP+az0mE+jW19mPp9S8io5OpYZ97vzjI77rKR7vFc5x1gp+a2UglZKPislr0nKY5KZSKSkkcxr12y9i/mBRal4QDY3xClZY0OmHkuO7KIB2dzjJxKQLRYwNVZBTRobHC0WlFXBfRYPcJa+5+kKyprc9y4vAGuNfj0KwrJjw8U5+wpCtOmvX35gtnxotUjA2Bqtp+zXLe9rl1P/mK+TNXr/xYLTVn792fmKBYQLv/7FjjXKBnqBcsZ53pkSbQAAAACYgvGClsWCjUXDrxUGMUvNNV7wc0xotOD4kkHYIoHXcYO1lazwWtgvV2uFgdLcOie6Qu5472Fu0HSigeVyXzNpNDxb9Oswzp+LcivtZucZHBzUb3/7W3fsrDPPUlNjU965AAAAAAAAqD3zNvxtjNEf//jHWbvWQvgGWSQS0c6dO/PGVqxYMaE5Co9/9dVXp1wXUJaTkozjvlLJpKKJhCKJhCKxuCKJuKLxhGKJuCLxlGKxuKLJhGKJhOKJhKKJhOKJlGLJhOKJpGLJhBKppOKJpOLJpBLJpBKpVGabUDKVkuTIOI6MMtd1UnIsI8s4MsZRNm5p58QuLSvdzqxrKsvKtK3RqOiYfTLpaK7luP3WzMu2HFn1Rso51pKRsdLXzY2gpq8t9xrpHzKMvZZ7jjU6nzL15cZHc8dzx4ylvDpGz0tf37Kc0SDtmEDw+GHg/CBv+o5SSq+XOhpULVgFWMXDwLnhWDeMOuZ4Zb4Ctox8JQO+pcKxxVb/Hb1efhg4r96cccKxAAAAAIBakhtizA2DlloltmxgM7Pq6mRXMy0W2Bxzfs41CkOspVYpLay72HGlVnwtFsItG7St5L0sUWNFIeRKVmbNfj1yArRjvs7lQsGF/WIh4XFWnS33taj0PgvfR0KzmC+S3qT8lt/te22vPLanihUBAAAAAABgNszb8DemX1dXl4wZXSrR5/Opo6NjQnMsW7Ysr3/w4MFpqQ0opn8kobNuPz5npdtp/IGML/NCCVbmBQAAAACYbZWGHUuFLmWkeCyu7KqkdXV18tiesqHJiYQuJRWvq0y4s9hcufdRan+x0G3ee1TBqqnZ92W8601HwHS8WsqtxjtubRW8n2PGpximLbnKbZHQNEFaAAAAAAAAAACmx7wOf+cGlTF1Q0NDef1QKDThH8rU19eXnXMyDh48qM7Ozgmds3nz5jF1DAwMTLkWTNzw8HDZ/lRE4iklNboKNAAAAADkGhO8VH4oszAQWXSszGqxbgizSIi12Cqiedeu5JhidWXahfdULPha7B6yx+Zeb7y6i703ldTtvl8l3p/CeQtXkC379SgR9C1bxzjh1qJj48xRtBar9J+J8WqutP6pGh4e1jPPPOP2Tz755DHf08ECZQq2FZ1ilFJqRsoBAJQ3k99/BwAAs4dnOgAAtYFn+sIzHfnYyZq34W/LsnT11Vdr/fr11S6lZhT+QQwGgxOeo66uruyck3HTTTfphhtumNIczzzzjPbv3z/lWjB1uT9cnirHSLbEjxcBAACmKC8UOk47LyxabH9BELXUcWPaheHUcu0iNZQ9zypd75jardL7xquj3PuSe4+F47bs4vOUmb9c3aVqL/f+VfK1KVtrBXWP92eq3Nzl3sPx6phzTIk2po0jp9olzDvT+VkdAABUD890AABqA890AABqA8/02rdz586qXXvehr8l6bzzztOb3vSmapdRM6LRaF7f7/dPeI5AIJDXj0QiU6oJKMeeo1kOAMDCMW7AskgoM3dfybZVfs4x7QrCkIXHlgpyljpuMmHM8eqdyNzjzj+F96DSesvN7bZLhJIn8j4U7Rf5M1FpDeO252pAFgAAAAAAAAAAAAAAjDGvw9+YXoUrfcfj8QnPEYvFys4JTDdW/gbmj4oCiGWClrntMfsmEJSdbCiy0vlzaylVc+7cpY4dr+6y78lEji3y/k3kPcluy61aO+71J3Cs25/ICsFF7rHcsZXWQWAWAAAAAAAAAAAAAAAAs43wN1zhcDivX7gSeCUKV/ounHMyrrjiCl1yySUTOmfz5s26+OKL3f7JJ5+sI488csq1YOKGh4fzfoXFySefrPr6+mmb/+/vklRD2TvLpG/HzvRtpfvZl+3GD7P7rPx9VnZfdtxy90mSbeWOZwOOo6FLO9uz0ivBZgOTo20rM0f6uGzbsuzRIKZlj85t2bJkZ/bbsi1bsrJ9T96YbXsy+zzufjuvnx/KzG5tKz9w6vatTBg1E/jMPc62Cu7NGl39Njf0aefeS8E2O3f2vRn3GKtgzoJjitWYex+5YduK7sWyS99Hsfcv5zrF7qPYezRmmz2+xLUBzG8z/UwHAACzh+c6AAC1gWc6AAC1gWc6AAC1gWf6wrNp06aqXZvwN1yFQe2RkREZYyYU2BseHi4752R0dHSoo6NjSnOEw2E1NjZOuRZMXX19/bR+LT7RcbmSjpHf65XP65Hf45XP65Xf55Xf61XAm9n6PPJ5fLLtbJDYzrSzL0u25ZWVE0S23ECyR7blkeWe65Vlj84h5Ydw06Hn0TBsXri2IPhbuAUAYL6Y7mc6AACoHp7rAADUBp7pAADUBp7pAADUBp7ptW868rGTRfgbrra2NlmWJWOMJCmRSOjgwYNavHhxxXPs2bMnrz/V0DYwno++7apqlwAAAAAAAAAAAAAAAAAAADAr7GoXgLmjrq5OK1euzBvbuXPnhOYoPP6II46Ycl0AAAAAAAAAAAAAAAAAAAAACH+jQGFYe+PGjRM6f9OmTWXnAwAAAAAAAAAAAAAAAAAAADA5hL+R57jjjsvrP/XUUxWfu2/fPm3fvt3t+3w+rV+/fpoqAwAAAAAAAAAAAAAAAAAAABY2wt/I8/a3vz2v//DDD8sYU9G5Dz30UF7/rLPOUjgcnrbaAAAAAAAAAAAAAAAAAAAAgIVsXoa/jTEVB5IxMaeeeqra2trc/tatW/Xoo49WdO4Pf/jDvP5FF100naUBAAAAAAAAAAAAAAAAAAAAC9q8C39v27bNfZ122mnVLqfm2LatD33oQ3ljN9xww7hh+1//+td6/PHH3X5DQ4Pe/e53z0SJAAAAAAAAAAAAAAAAAAAAwII078Lfq1atcl+BQKDa5dSka665RuFw2O0/9thj+sY3vlHy+D179ugjH/lI3tinPvWpvBXEAQAAAAAAAAAAAAAAAAAAAEzNvAt/Y+a1tbXp7/7u7/LGvvCFL+iKK67Q3r173THHcXTPPffo1FNP1fbt293xpUuX6jOf+cxslQsAAAAAAAAAAAAAAAAAAAAsCN5qF4C56ZprrtFTTz2l//qv/3LHvvvd7+oHP/iBVq1apaamJm3btk19fX1559XV1en2229Xc3Pz7BZcIBaL5fU3b95cpUowNDSknTt3uv1NmzblrSwPAADmB57pAADUDp7rAADUBp7pAADUBp7pAADUBp7pC09hLrUwtzqTCH+jKNu2dccdd+jDH/6wfv7zn7vjqVRKW7duLXpOa2ur7rzzTp122mmzVWZJu3btyutffPHF1SkEAAAAAAAAAAAAAAAAAAAANW3Xrl064YQTZuVa9qxcBfNSMBjUz372M91555067rjjSh5XX1+vK664Qhs3btSZZ545a/UBAAAAAAAAAAAAAAAAAAAACwkrf2Nc73rXu/Sud71Lmzdv1tNPP609e/YoHo+rublZRx55pE477TQFg8FqlwkAAAAAAAAAAAAAAAAAAADUNMsYY6pdBDDd+vr69Nhjj7n9FStWKBAIVLGihWvz5s26+OKL3f4999yjdevWVa8gAAAwKTzTAQCoHTzXAQCoDTzTAQCoDTzTAQCoDTzTF55YLKZdu3a5/Te+8Y1qbm6elWuz8jdqUnNzsy666KJql4Ei1q1bp6OOOqraZQAAgCnimQ4AQO3guQ4AQG3gmQ4AQG3gmQ4AQG3gmb4wnHDCCVW5rl2VqwIAAAAAAAAAAAAAAAAAAAAAJoTwNwAAAAAAAAAAAAAAAAAAAADMA4S/AQAAAAAAAAAAAAAAAAAAAGAeIPwNAAAAAAAAAAAAAAAAAAAAAPMA4W8AAAAAAAAAAAAAAAAAAAAAmAcIfwMAAAAAAAAAAAAAAAAAAADAPED4GwAAAAAAAAAAAAAAAAAAAADmAcLfAAAAAAAAAAAAAAAAAAAAADAPEP4GAAAAAAAAAAAAAAAAAAAAgHmA8DcAAAAAAAAAAAAAAAAAAAAAzAOEvwEAAAAAAAAAAAAAAAAAAABgHvBWuwAAta29vV1f/vKX8/oAAGD+4ZkOAEDt4LkOAEBt4JkOAEBt4JkOAEBt4JmO2WQZY0y1iwAAAAAAAAAAAAAAAAAAAAAAlGdXuwAAAAAAAAAAAAAAAAAAAAAAwPgIfwMAAAAAAAAAAAAAAAAAAADAPED4GwAAAAAAAAAAAAAAAAAAAADmAcLfAAAAAAAAAAAAAAAAAAAAADAPEP4GAAAAAAAAAAAAAAAAAAAAgHmA8DcAAAAAAAAAAAAAAAAAAAAAzAOEvwEAAAAAAAAAAAAAAAAAAABgHiD8DQAAAAAAAAAAAAAAAAAAAADzAOFvAAAAAAAAAAAAAAAAAAAAAJgHCH8DAAAAAAAAAAAAAAAAAAAAwDxA+BsAAAAAAAAAAAAAAAAAAAAA5gHC3wAAAAAAAAAAAAAAAAAAAAAwDxD+BgAAAAAAAAAAAAAAAAAAAIB5wFvtAgDUti1btuiZZ57R7t27FY/H1dLSoiOOOEKnnnqqgsFgtcsDAKBqjDHavn27/ud//ke7d+9WX1+fAoGAWlpadNhhh+mkk06a9mfl4OCgnnzySf3pT3/SwMCA6urqtGrVKp166qlaunTptF7r5Zdf1rPPPqt9+/YplUqptbVVRx99tE455RR5vdP3MWQ27wkAgLkiGo3qqaee0iuvvKLe3l75/X4tX75cp5xyitasWTOt15qtz/WzeU8AAEzUq6++qhdffFG7d+/WyMiI6urqtHjxYh1++OE69thjFQgEJj03z3UAAGZWLBbT888/r02bNqm3t1eRSESNjY3q6OjQCSecoHXr1smyrClfJ5lM6umnn9ZLL72k7u5ueTweHXLIIdqwYYOOOuqoabiTUXv27NHvfvc77dixw72fww8/XKeffrrC4fC0XWc27wkAgJnAZ+6pIfc3xxkAmAH/+Z//aU444QQjqegrHA6bv/mbvzGdnZ3VLhUAgFnT09NjfvSjH5l3v/vdpq2treRzUpLx+Xzm4osvNo8++uiUr7t161bzl3/5l8bv9xe9lmVZ5swzzzSPPfbYlK7jOI754Q9/aA4//PCS99Xa2mq++MUvmqGhoXlxTwAATNWll1465jm1atWqSc118OBB88lPftLU19eXfNZu2LDB3HPPPVOue7Y+18/mPQEAMBEDAwPmq1/9qjn00EPLfn73+/3m9NNPN9/+9rcnND/PdQAAZtYf/vAH8773vc8EAoGyz/Jly5aZ6667znR3d0/qOoODg+baa681ixYtKnmN17zmNeZHP/qRcRxnSvf06KOPmjPPPLPs30ve//73m23btk3pOrN5TwCAhWX37t3m7rvvNtdcc40566yzTENDw7R877wQn7nnxz1hagh/A5hW0WjUvO997yv7ATr31d7eTigLALAgXHHFFSWDyuO9PvCBD5j+/v5JXfe2224zoVCooutYlmWuueaaSX2ztre315xzzjkV39OaNWvMSy+9NKfvCQCAqfrFL35R9Pk0mW9gP/LII+P+47HCvz/EYrEJX2c2P9fP1j0BADBR9913n1m8ePGEPrsvXry44vl5rvNcBwDMnFQqZa655hpj2/aEn+UPPvjghK71xz/+cdx/KJb7Ou+880xfX9+E78lxHPPZz3624uvU19ebO++8c8LXmc17AgAsHE888YR5xzveYZYuXTruc2U6wt985p4f94Sps4wxRgAwDRzH0Tvf+U7de++9eeMej0crV65UU1OTtm3bpv7+/rz9oVBIDz/8sF7/+tfPZrkAAMyqE088Uc8+++yY8eyvSVy8eLESiYR27Ngx5lkpSSeffLJ+/etfT+hXNt5xxx269NJL5ThO3nh7e7tWrFihgwcPas+ePSr8SHDllVfqxhtvrPg6kUhEZ555pp555pm8cb/fr9WrVysQCGjr1q0aHh4eU8dTTz2ldevWzbl7AgBgqvr7+3XUUUdpz549Y/atWrVK27dvr3iuJ554Queee64ikUjeeHNzsw499FD19vZq165dSqVSefvf+c536s4776z411fP5uf62bonAAAm6sYbb9RnPvOZMZ8rg8Ggli5dqra2NkUiEe3bt09dXV3u/sWLF2v//v3jzs9zffL3BABAJT760Y/q5ptvHjMeCoW0du1a1dXVqbu7W1u3bh3zvPf7/brnnnt0/vnnj3udV199Vaeffnre3wckKRwOa82aNYpEItq+fbsSiUTe/te//vX6zW9+o2AwWPE9/e3f/q2+853v5I1ZlqXly5ervb1dO3fuHFOHx+PRHXfcoXe84x0VX2c27wkAsHB8+9vf1lVXXVXRsRP93nkhPnPPj3vCNKlm8hxAbfnHf/zHMf/C5xOf+ITZs2ePe0wqlTJ33323WblyZd5xy5cv518EAwBq2oYNG9znXnNzs7niiivM/fffbwYGBvKOSyaT5pFHHjFveMMbxjxX3/Wud1V8vc2bN4/5lU/HHnus+c1vfpN33CuvvGLe+c53jrnWXXfdVfG1PvGJT+Sda9u2+dKXvmR6enrcY2KxmLnllltMS0tL3rHHH3+8SSaTc+6eAACYqo9+9KN5K27lPpMmsnpJT0/PmBVRVq1aZe65556832yxa9cu8/GPf3zM8++f//mfK77WbH2un817AgBgIm6++eYxz53zzz/fPPjggyYajY45fs+ePebf//3fzbve9S6zYsWKcefnuc5zHQAws+64444xz5r169eb+++/3yQSibxjDx48aG644YYxv7Gzvb0973vbxSQSCXPMMcfknbdo0SLz4x//2MTjcfe47u5uc+21145Zhfxv//ZvK76n2267rejPCv70pz/lHffwww+b1772tXnHNTQ0mG3btlV0ndm8JwDAwnLjjTeWXDk6HA5P+nvnhfjMPT/uCdOH8DeAadHV1WUaGhry/o/961//esnjd+/ebVavXp13/HXXXTeLFQMAMLs2bNhgVq9ebW6++WYzMjIy7vHJZNJ87GMfG/MBqzDoXMpll12Wd95JJ51k+vv7ix7rOM6Ya61du3bMN8OL2bRpk/F4PHnn/vSnPy15/EsvvWSam5vzjv/Rj340p+4JAICpeuSRR4xlWUZK/6Oob37zm5P+BvYXvvCFvHMPPfTQvG+2FvrqV7+ad3xTU9O4P7Q2ZnY/18/WPQEAMBF//vOfTTAYdJ83Pp+v7OfbQpU8m3iu81wHAMyso48+Ou85c+KJJ5qhoaGy5/z61782Xq8377yvfe1rZc/5/ve/n3d8S0uLefnll0sef+utt+Yd7/V6x4S3i4nFYmOe0Z/4xCfywl65+vr6zIknnph3/Ac+8IFxrzOb9wQAWHiy4e+GhgZz5plnms9+9rPmjjvuMNu3bzePPPLItIW/+cw9P+4J04fwN4Bp8bnPfS7v/9DPOOOMkh86sx5++OG8cxoaGkxXV9csVQwAwOz6r//6LxOLxSZ0TjKZHPON2ve+973jnvfSSy/lrbrh9/vNxo0by54TiUTMYYcdlnetH/zgB+Ne693vfnfeOe9///vHPadwJbVVq1blrRxS7XsCAGAqRkZGzNq1a91nz6c+9alJfwP74MGDY1Y+efjhh8ue4ziOOeOMM/LO+bu/+7txrzVbn+tn854AAJiIs846K+9Zc/vtt0/r/DzXp3ZPAACMZ8uWLXnPF0nmmWeeqejcwpUyX//615c8NhaLmRUrVuQd/8Mf/nDca/zlX/7lhL/Xf9NNN+Wdc9hhh5lIJFL2nJdffjlvNXOPx2M2bdpU9pzZvCcAwMKzefNm8/LLL5tUKjVm33SFv/nMPT/uCdPLFgBMkeM4uuWWW/LGrr/+elmWVfa8s88+W294wxvc/uDgoG6//fYZqREAgGp729veJr/fP6FzPB6PPve5z+WN/fKXvxz3vB/96EdyHMftX3rppTryyCPLnhMMBvX5z38+b+zmm28ue05vb6/uvvtut29Zlq6//vpx6/vwhz+sVatWuf0dO3bo4YcfLnvObN0TAABT9aUvfUlbtmyRJK1cuVL/8A//MOm5fv7zn2toaMjtn3HGGTr77LPLnmNZlr785S/njf3oRz+SMabkObP5uX627gkAgIm499579cgjj7j9Sy65RJdccsm0XoPnehrPdQDATHn11Vfz+suXL9dJJ51U0bnvete78vqbN28ueewvf/lL7dq1y+2vXr1aH/7wh8e9RuHz+I477lB/f3/Zcwq/n/2FL3xBwWCw7Dnr16/Xe97zHrefSqXG/N2g0GzeEwBg4Vm7dq3Wr18v2565qCqfudPm+j1hehH+BjBlTz31lDo7O93+mjVrdOaZZ1Z07uWXX57Xv+eee6axMgAA5r/cD0yS1N3drZGRkbLn/OIXv8jrFz5vS3nPe96j+vp6t//f//3f2rt3b8nj77//fiWTSbd/5plnas2aNeNex7btMd84Hu/vALN1TwAATMV///d/69vf/rbb/9d//VeFw+FJz3fvvffm9St9/p111lk69NBD3f7+/fv1+9//vuTxs/m5frbuCQCAifjBD36Q1y/8Qel04Lk+iuc6AGAm9PT05PVXrFhR8bkrV67M6/f19ZU8tvD59+EPf3jccJSUDr698Y1vdPuJREIPPPBAyeN3796t5557zu2Hw2G9+93vHvc60thncmHNhWbrngAAmCl85h41l+8J/z97dx4fVX3vf/w9k33fJmwBQpIZFlFWBSQJagtdxK3WrbQWWne9Wu+tWttaf9pbr9p6q9Lebi61tO4LtkVrFUXJBFkEFASEmSQsYc1k35eZ8/sjYXSSAAk5mWTC6/l48Licc77n8/0eb8p3zuR9vsdchL8B9Nkbb7wRsL1gwYIe3QwebftF77//vurr600bGwAAoS4lJaXLvuOtnLFz586AVUni4uI0d+7cHvXVua1hGF3m+S/qfOwrX/lKj/qRun4GWLFixTHbBvOaAAA4Wa2trbrmmmvk9Xolta8YesEFF5x0vbq6Oq1evTpgX0/nWovFovnz5wfsO95cG6z7+mBeEwAAPbV///6At2xNmzZNkydPNrUP5vVAzOsAgP6QlJQUsN3Y2Njjczu3tdlsx2wbrO/FO/eTm5sbsNDJ8eTm5io2Nta/vXPnTrlcrh731V/XBABAf+CeO9BgvSaYj/A3gD77+OOPA7Z7GsaSpFGjRmncuHH+7ZaWFm3fvt2kkQEAEPr279/fZV9aWtox23eel2fNmqXw8PAe95ebm3vcesc71pvPADNnzlRUVJR/+8CBAwFPFB+vn/68JgAATtaDDz6orVu3SpKSk5O1dOnSPtXbtm2bWltb/dtZWVkaMWJEj88P1pzem/v6YF4TAAA99dZbb/kf3pLaV8gyG/N6V8zrAACzTZs2LWB7x44dPQ4frV+/PmB71qxZ3bY7fPiwDh065N+OiorSjBkzejzGYM3p4eHhXa7hWH0F85oAAOgP3HN3NRivCeYj/A2gz3bs2BGwfdppp/Xq/M7tO9cDAOBUVlBQELCdmZmpyMjIY7YP1rzc2toasBp3b/uKiopSTk5Oj/riswYAYLDbvn27HnjgAf/2ww8/3KsvYrsTzPkvWH0xpwMABqMNGzYEbE+dOtX/982bN+u2227T1KlTlZKSotjYWI0bN04LFizQI4880u0D291hXj/5fgAA6KnRo0cHhJWam5t79GB2c3OzHnvssYB911xzTbdtO89Xdrv9uN/Xd9Z5/nO73Wpra+tRX8Ga0/vzmgAA6A/cc598P8HuC+Yi/A2gTxobG7V3796AfWPGjOlVjc7td+7c2edxAQAwVDz99NMB2+eff/5x23eeR/trXi4uLg74AjcmJua4r8LsS1/BuiYAAE6Gz+fTNddco5aWFklSfn6+rrvuuj7XNXv+27Nnj5qamrq0C+Z9fbCuCQCA3ugc/s7OzlZdXZ2uueYazZgxQ7/5zW+0ZcsWVVVVqbGxUXv27NHKlSt15513yuFw6Cc/+UnAalzdYV4/cT/M6wAAMzz88MOyWj+Pwdx77736y1/+csz2VVVVuuyyywJCShdeeKEuvPDCbtv3df5LT09XdHS0f7ulpUUlJSX90lew5vTeXBMAAP2Be+4T9zMYrgnmI/wNoE88Ho8Mw/BvR0REaNiwYb2qkZGREbB95MgRU8YGAECoe/PNN7V69eqAfUuWLDnuOZ3n0dGjR/eqz87zcllZWY/66XzeyfR1rM8AwbomAABOxtKlS7V27VpJUmRkpP70pz/JYrH0uW5f57/hw4crPDzcv+3z+VReXt6lXTDv64N1TQAA9Ebnt1pZrVbNmzevy8PY3WlsbNSDDz6o888/X7W1tcdsx7zeFfM6AKA/5OXl6be//a3/vrytrU1LlizRrFmz9NBDD2n58uV666239Le//U233nqrcnJytGLFCv/5CxYs0PPPP3/M+n2d/yRp1KhRx615VOfvsfv6vXh/zelSz68JAID+wD13V4PxmmC+8BM3AYBjq6urC9iOjY3t9S+54+LijlsTAIBTUUVFhW644YaAfZdccolmzZp13PM6z6Od59kT6dy+tbVVzc3NioqKMrWf7s451meAYF0TAAC9VVJSonvuuce//eMf/1gTJ040pS0u1rYAAQAASURBVHZf5z+LxaKYmJiAIFp3c20w7+uDdU0AAPSUz+frEtq+7bbbtHnzZkntc88FF1yg888/X6NHj1Z9fb02b96sv/71rzpw4ID/nJUrV2rJkiV69dVXu+2Heb0r5nUAQH+56aabNGHCBN12223atm2bpPY3fXR+28cXZWdn66677tJ1110XsHJ4Z8H6XryxsVFer7dPfQVrTu9NXwAA9AfuubsajNcE87HyN4A+6fwP9hdf6dRTMTExx60JAMCpxufz6Tvf+Y5KS0v9+5KSkrR06dITntvXubnzvNxdTTP66a6vnt7c9tc1AQDQW9dff73q6+slSRMnTtRPfvIT02oHa64NpTm9N30BANAT1dXVAStcSdKmTZskSWlpafrggw/0j3/8QzfeeKMuuOACXXnllXrooYe0c+dOLVq0KOC81157TcuWLeu2H+b1vvUFAEBvfelLX9KGDRt0xx13KCws7Lhtx44dqzvuuEOLFi06bvBbGrg5/WT6Yk4HAJwquOc++b74HBDaCH8D6JOmpqaA7cjIyF7X6LzqZmNjY5/GBABAqLvzzjv1r3/9K2DfH//4R40ZM+aE5/Z1bu5uNezu5uZgfgYI1jUBANAbTz31lFauXCmpfRWNP/3pTyc1Hx5LsObaUJrTe9MXAAA9caxfSIaFhemNN95Qfn5+t8fj4+P117/+VV/5ylcC9v/P//xPlzC5xLze174AAOitP/zhD8rJydEjjzzSZQXtzvbu3aubb75Z48aN09NPP33ctgM1p59MX8zpAIBTBffcJ98XnwNCG+FvAH3S+YmflpaWXtdobm4+bk0AAE4lS5cu1a9//euAfXfddZeuvPLKHp3f17m587zcXU0z+umur2N9BgjWNQEA0FMHDx7UHXfc4d++9tprjxkOO1nBmmtDaU7vTV8AAPTEseaRa6+9VrNnzz7uuVarVb///e8DVgjduXOnPvjggxP2w7zeu74AAOip1tZWXXbZZbrpppt08OBBSVJqaqruvfderV+/XpWVlWppadGBAwf0j3/8Q9/4xjdksVgkSRUVFbrmmmt05513HrP+QM3pJ9MXczoA4FTBPffJ98XngNBG+BtAn8THxwdsd/cU8ol0fuKnc00AAE4Vzz33nG6//faAfUuWLNFDDz3U4xp9nZu7exK3u7k5mJ8BgnVNAAD01C233KKqqipJ0ogRI/TLX/7S9D6CNdeG0pzem74AAOiJY80j1113XY/Oz87O1vz58wP2dRf+Zl7vW18AAPTUTTfdpFdffdW/PWvWLG3btk3333+/zjrrLCUnJysiIkIjR47UhRdeqNdee02vv/56QEjpkUce0Z///Odu6w/UnH4yfTGnAwBOFdxzn3xffA4IbYS/AfRJ53+wGxoaun2t5fHU19cftyYAAKeCFStWaPHixQHz6KWXXqonn3zSv/JIT3SeRzvPsyfSuX14eHi3T+f2tZ/uzunpzW1/XRMAAD3x8ssva/ny5f7txx9/XMnJyab309f5zzCMk/pytz/v64N1TQAA9FRMTIzCwsIC9iUkJGj69Ok9rnHOOecEbH/00Udd2jCvd8W8DgAw2/vvv6+nnnrKvz1s2DCtWLFCI0aMOO55F110kf7v//4vYN+dd97Zo0VF+ut78e4+o/T1e/H+mtN70xcAAP2Be+6uBuM1wXyEvwH0ic1mCwiktba26siRI72qsX///oDtYcOGmTI2AABCxapVq3T55Zerra3Nv2/BggV6/vnnu3zBeyKd59HS0tJend95Xk5PT+9RP53PO5m+jvUZIFjXBABAT3zx9c8LFy7UFVdc0S/99HX+O3z4cMBnC6vVKpvN1qVdMO/rg3VNAAD0Ruf5yW63y2rt+a/PJkyYELDd3TzKvN4V8zoAwGxLly4N2L799tt7/F3wkiVLNH78eP92eXm5XnvttS7t+jr/SdKBAweOW/OozmPv6/fi/TWnSz2/JgAA+gP33F0NxmuC+Qh/A+iTmJgYjR07NmDf3r17e1Wjc/uJEyf2eVwAAISKdevW6aKLLgp4hdLcuXO1fPlyRUZG9rpe518699e8nJ2drfDwcP92Y2OjysrK+qWvYF0TAAA9UVVV5f/7G2+8IYvFcsI/5513XkCNPXv2dGnz8ccfB7Qxe/7LzMzs9s0XwbyvD9Y1AQDQG5MmTQrYTkxM7NX5ndtXVlZ2acO8fuJ+mNcBAH1hGIbee++9gH0XXnhhj8+3Wq1auHBhwL7Vq1d3adfX+e/IkSMBvwuIjIxUdnZ2t22D9b14MK8JAID+wD33ifsZDNcE8xH+BtBnnf/R3r59e6/O37Fjx3HrAQAwVG3ZskVf//rXVVdX5983ffp0vfnmm4qLizupmsGalyMiIpSTk3PSfTU3N6u4uLhHffFZAwBwKgrm/BesvpjTAQCD0WmnnRaw3dzc3Kvzvxh2kqTY2NgubZjXT74fAAB6orKyUtXV1QH7srKyelWjc/vu3nbZeb4qKipSS0tLj/voPP/l5OQELLJyvL6CNaf35zUBANAfuOc++X6C3RfMRfgbQJ9NmzYtYHvNmjU9PvfgwYPavXu3fzsiIqLLl+0AAAxFO3fu1IIFCwJWBJs0aZL+/e9/Kykp6aTrdp6XN2zYEPBKpxMpLCw8br3jHevNZ4CNGzcG/EJ95MiRx3wFVDCvCQCAwWLy5MmKiIjwb+/evVsHDx7s8fnBmtN7c18fzGsCAKCnZsyYEbB9+PDhXp3f+XXIaWlpXdowr3fFvA4AMFN3D2/1NoD8xXlNkrxeb5c2I0aM0IgRIwL63bhxY4/7CNac3tbWpvXr1/eor2BeEwAA/YF77q4G4zXBfIS/AfTZBRdcELC9cuVKGYbRo3PffvvtgO3zzjtP8fHxpo0NAIDBaM+ePZo/f37AL4izsrL0zjvvKD09vU+1J06cGLAid319fY9v0Orr6/Xhhx/6ty0WS5d5/os6H3vnnXd6PM7ObY/3Cs5gXhMAACfy97//Xe+8806v/jzyyCMBNYYPH96ljd1uD2iTkJCgefPmBezr6VxrGIZWrlwZsO94c22w7uuDeU0AAPTUwoULZbV+/uuykpISVVRU9Pj8zuGozq9mlpjXO2NeBwCYrbuHrw4cONCrGp1X+j7Wd/ULFy4M2O6v78U797NmzRrV19f3qJ/CwkI1NDT4t8ePH6/x48f3uK/+uiYAAPoD99yBBus1wXyEvwH02dy5c2Wz2fzbxcXFev/993t07lNPPRWwffHFF5s5NAAABp2DBw/qy1/+skpLS/37MjIy9O677yojI8OUPi666KKA7c7z7bG8+OKLqqur82+feeaZGjVq1DHbn3/++QGrp7z//vsqLi4+YT+GYeiZZ54J2HeizwDBuiYAAE7knHPO0fz583v1Z+bMmQE1oqOju7Tp7gvRk53/Vq1apZKSEv/28OHDNXv27GO2D+Z9fbCuCQCAnho2bJhyc3MD9r322ms9OretrU3Lly8P2Hfuued225Z5/XPM6wAAs0VGRmrkyJEB+957771e1Xj33XcDtr+4IMkXdZ7//vznP/coIFVUVKQPPvjAvx0REaHzzz//mO3HjBmj6dOn+7fr6ur00ksvnbAfqe9zen9dEwAA/YV77s8N5muCuQh/A+gzq9WqJUuWBOy7//77T3hD+O6776qgoMC/nZCQoCuuuKI/hggAwKBQUVGhBQsWqKioyL8vPT1d77zzjrKyskzr5/vf/74sFot/+4UXXtCOHTuOe05TU5MeeuihgH3XXHPNcc9JTU3VJZdc4t82DEP33XffCcf39NNPB7z+KTMzU/Pnzz/uOcG6JgAABpOrrrpKcXFx/u3Vq1ef8JfXhmHo/vvvD9j3ve99L2BF086CeV8frGsCAKA3brjhhoDtX/3qV2pubj7heU888YQOHTrk305MTNRXv/rVbtsyr7djXgcA9Jcvf/nLAduPPfaY2traenTuBx98EPAGye7qHfXVr35Vo0eP9m/v3r1bf/7zn0/Yx3333RcwH3/zm99UUlLScc/p/H32Qw89pKampuOes2PHDr344ov+7e4+G3QWzGsCAKA/cM/dbrBfE0xmAIAJysrKjPj4eEOS/8+DDz54zPalpaXGuHHjAtrfc889QRwxAADBVVNTY5x11lkBc19ycrKxefPmfunvyiuvDOjrrLPOMqqrq7tt6/P5jBtuuCGgfXZ2ttHS0nLCfrZt22ZYrdaAc5977rnjtk9OTg5o/+STTw6qawIAwGyrVq0KmJMyMzN7fO6PfvSjgHOzsrKM/fv3H7P9Aw88ENA+KSnJKC8vP2E/wbyvD9Y1AQDQU16v1zjjjDMC5pvFixcbXq/3mOesXbu2y9x59913H7cf5nXmdQBA/3nrrbcC5hhJxnXXXXfc+dwwDMPlchmjRo0KOM/hcBhtbW3HPOf3v/99QPuUlBRj27Ztx2z/7LPPBrQPCwszdu7cecJram5uNsaOHRtw7o033mj4fL5u21dXVxtnnnlmQPvvfOc7J+wnmNcEAMAX9eW788645w6Na4J5CH8DMM3//M//dLmhvummmwImHa/XayxfvrzLTeqoUaOMysrKgRs8AAD97Nxzz+0yT/785z833nnnnV7/qaioOGF/LpfLiI2NDehv6tSpxqpVqwLa7dy507j00ku7jO2ll17q8bVdf/31AedarVbjZz/7WcA4W1pajD//+c9GSkpKQNspU6YYra2tPeonmNcEAICZ+vIFdnl5uTFixIgu5//9738P+GXvvn37ujz4JMn45S9/2eO+gnVfH8xrAgCgp1auXGlYLJaAOWf+/PnGRx99FNCuqqrK+N///d8uvxQdP368UVNTc9w+mNeZ1wEA/eu8887rMtfk5eUZK1eu7PI9tMfjMR555BEjKSmpyzkvv/zycftpaWkxJk+eHHBOamqq8Ze//CWgn/LycuOee+7psoDKzTff3ONreu6557qM77LLLjN27doV0O7dd981pkyZEtAuPj7eKC4u7lE/wbwmAMCpx+l0dvt770ceeSRgPhk+fPgxf0d+vIeSDIN77lC5JpjHYhgnWJ8dAHrI5/Pp4osv1ooVKwL2h4WFKTMzU0lJSSopKVFVVVXA8ZiYGL3zzjvKzc0N4mgBAAgui8ViWq1Vq1bp3HPPPWG7F154QYsWLerySqb09HSNHTtWR44cUWlpaZfjt956q5YuXdrj8TQ0NOicc87RRx99FLA/MjJSWVlZioqKUnFxserq6gKO22w2FRYWavz48T3uK1jXBACAmd5//32dd955/u3MzEzt3r27x+evXr1aX/3qV7u82jk5OVlZWVmqqqrS3r175fV6A45ffPHFWr58eY8/hwTzvj5Y1wQAQG88/PDDuvvuu7vsHzFihEaPHq36+noVFRWppaUl4HhaWppWrVqlM84444R9MK+f/DUBAHAihw4d0ty5c1VSUtLlWHx8vLKyshQTE6Py8nIVFxd3+R5Zkn74wx/qkUceOWFfO3bsUF5enioqKrr0k5OTo8bGRpWUlKi1tTXg+KxZs/T+++8rJiamx9d188036/e//33APovFojFjxig9PV179uyRx+MJOG61WvXiiy/qsssu63E/wbwmAMCpZdy4cdqzZ0+faixevFjPPPPMcdtwzx0a1wSTDGDwHMAQ1NjYaFx11VVdngQ61p+0tLQuq3UCADAU9XRu7Mmf3sydzz33nBETE9Pj2nfccccxXxl5POXl5caXvvSlHvczbtw4Y8uWLb3uJ5jXBACAWcx4deW7775rpKam9nj+W7RokdHU1NTrfoJ5Xx+sawIAoDeWLl1qRERE9Hh+mjBhQpeVN0+EeZ15HQDQf/bu3dvtmzhP9CciIsJ46KGHevVd8scff2xkZmb2uI/58+ef1KqYXq/X+M///M8e9xMbG2u8+OKLve4nmNcEADi19GZuOdafxYsX96gv7rlD45rQd1YBgImio6P1/PPP65VXXtG0adOO2S4uLk4333yztm/f3qOVSwEAwMn51re+pU8//VSLFi1SRETEMdvNmzdP77//vn71q1+d1Ipbqampeuedd/SnP/1Jdrv9uO1+8pOfaOvWrT1aEa07wbomAAAGky996Uvavn27brrpJsXGxh6z3fTp0/Xqq6/q2WefVVRUVK/7CeZ9fbCuCQCA3rj11lu1ZcsWXXnllce958zKytLjjz+uLVu2yOFw9KoP5nXmdQBA/xkzZozeffddvfTSSzr33HNltR4/FpOUlKSbbrpJW7du1Y9+9KNefZc8depUbd26VT/+8Y+VkpJyzHYOh0NPPPGE3n77bSUnJ/e4/lFWq1W//vWv9d577yk/P/+Y7SIjI/Xtb39bn376qa644ope9yMF75oAAOgv3HOHxjWh7yyG0c17bADAJG63W+vWrdP+/fvV0tKi5ORkTZo0Sbm5uYqOjh7o4QEAcEqpqamR0+mUy+VSbW2toqOjNXbsWOXm5iojI8PUvrZu3apNmzbp4MGD8nq9SktL0+mnn67Zs2cf95fnvRXMawIAYLBobGzUmjVrtGPHDlVVVSkyMlIZGRmaPXv2cR/COhnBuq8P5jUBANBTNTU1WrNmjVwul6qrqxUfH6/hw4drxowZmjBhgil9MK8DANC/amtr9dFHH6m4uFhVVVVqampSYmKi0tLSNGXKFJ122mknDIj3RGtrq9atW6dPP/1U5eXlCgsL08iRIzVjxoyTXgjlWEpLS7VmzRrt3btXTU1NSkhIkMPhUF5enhITE03rJ5jXBABAf+Ceu2/I/Q1uhL8BAAAAAAAAAAAAAAAAAAAAIAT0/fFFAAAAAAAAAAAAAAAAAAAAAEC/I/wNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACEgfKAHAPSHqqoqffDBB/7tMWPGKCoqagBHBAAAAAAAAAAAAAAAAAAAgKGgublZ+/bt82+fc845Sk5ODkrfhL8xJH3wwQe65JJLBnoYAAAAAAAAAAAAAAAAAAAAGOJef/11XXzxxUHpyxqUXgAAAAAAAAAAAAAAAAAAAAAAfUL4GwAAAAAAAAAAAAAAAAAAAABCQPhADwDoD2PGjAnYfv3112W32wdoNKe2uro6rV+/3r89a9YsxcfHD+CIAADAyWBOBwBg6GBeBwBgaGBOBwBgaGBOBwBgaGBOP/W43W5dcskl/u3OudX+RPgbQ1JUVFTAtt1u1+TJkwdoNKe2mpoaHTp0yL89adIkJSYmDuCIAADAyWBOBwBg6GBeBwBgaGBOBwBgaGBOBwBgaGBOR+fcan+yBq0nAAAAAAAAAAAAAAAAAAAAAMBJI/wNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAAAQAgh/AwAAAAAAAAAAAAAAAAAAAEAIIPwNAAAAAAAAAAAAAAAAAAAAACGA8DcAAAAAAAAAAAAAAAAAAAAAhADC3wAAAAAAAAAAAAAAAAAAAEAftfkkd4307+1lAz0UDGHhAz0AAAAAAAAAAAAAAAAAAACA/lRa2SCny6MCl0cf76uSzzC08IyRuvNrExQVHjbQw0OIMgxDRWV1envLfq3YYZW7xqIWn0UpJUX65qxsWa2WgR4ihiDC3wAAAAAAAAAAAAAAAAAAYEipbWrV2uIKFbjK5HR5VOyp79LmSWeJdpfX609Xn0lIFz3mqWtWobv9QQKny6NDNU0dR6z+NpUNrdp+sEanZyQNzCAxpBH+BgAAAAAAAAAAAAAAAAAAIa3N69OW/dUq2OWR012mzXur1OYzTnjeyh1H9KSzWNfPywnCKBGKmlq92rC7wr9y/PaDNT06z+n2EP5GvyD8DQAAAAAAAAAAAAAAAAAAQs7e8gat7ljZu7DIo9qmtpOq88u3duqscamaPjbF5BEiFPl8hnYcqpHT5ZHT7dH6kgo1t/l6Xcfp8ujGc3ioAOYj/A0AAAAAAAAAAAAAAAAAAAa96sZWfVjUvvpygcujvRUNptRt8xm69fnNeuO2fCXFRJhSE6HlUHWTClxlcro9KnR75Klr6XPN9bsr1NTqVXREmAkjBD5H+BsAAAAAAAAAAAAAAAAAAAw6rV6fPtlXpdUujwpcZfpkX5V8Rv/0VVrZqJ+8tlW/XTRdFoulfzrBoFHf3KZ1JeUqcHnkdHnkOlJnav3YyDDNzUlTZUOLRibFmFobIPwNAAAAAAAAAAAAAAAAAAAGnGEY2l3eoAJXmQpcHn1YVK665jZT+8i2xanYU9/tsTe2HtTc9Wn69uxMU/vEwPP6DG3dXy1nx8/Wpr2VavWa9ySB1SKNjTM0IcnQhGSfFl+Qq7SUZNPqA19E+BsAAAAAAAAAAAAAAAAAAAyIivoWFbrbV192uj3aX9Voav30hCjl223KH29Trt2mYQnR+q+XPtZrm/Z32/7n/9yumZkpmjgi0dRxILgMw9DeigYVuDwqdHu0pqhc1Y2tpvaRmRarfIdNefZ0nT4sUhs/LPAfiwizmtoX8EWEvwEAAAAAAAAAAAAAAAAAQFA0tXr10e5KFbjL5HR5tO1Ajan1oyOsmpWVpnkOm/IcNk0YniCLxRLQ5r8vPl0f763qdgXw5jafbnl2k/55a55iI4lYhpKK+hatKWoPexe4PCqtNPdBgqSYCOXa05RnT1e+w6YxqbH+YzU15v4cA8fDv0wAAAAAAAAAAAAAAAAAAKBf+HyGth+skbNjde8NuyvU3OYztY/JoxKV72gP5M7MTFF0RNhx28dFhes3i6brG79bo5ZuxlJUVq//9/dt+tXlU00dJ8z1xQcJCt3tDxIYhnn1I8IsmjE2pX11b0e6zshIUpjVcuITgX5G+BsAAAAAAAAAAAAAAAAAAJhmf1WjnK4yOd3lWuP2qLy+xdT6IxKjOwK5NuXabbLFR/W6xuRRSbpn4STd+/dt3R5/eWOpcu02XTI9o6/DhUl8PkPbDrQ/SFDo9mj97opuw/t94RgWrzyHTfkOm2ZnpSkuipgtBh9+KgEAAAAAAAAAAAAAAAAAwEmraWrVh0XlKuxY3bvYU29q/djIMM3JTlOe3aZ5423KSY+XxdL3FZivnpOpQrdH/952uNvjP12+VVPHJCvLFtfnvnBy9lU0+FeNX1PkUWVDq6n1bfGRyrXblGe3Kd+RrhFJ0abWB/oD4W8AAAAAAAAAAAAAAAAAANBjrV6fNu+t6gjllumT0mp5fYZp9S0WaUpGUscKzOmaMTZFkeFW0+p/3o9Fv/zmVH26v0D7qxq7HK9v8eo/ntuk126eq6jwMNP7R1dVDS1aU1TuD3zvrWgwtX5UuFWzslLbV463p2viiARZrX1/kAAIJsLfAAAAAAAAAAAAAAAAAADgmAzDUFFZnQpc7YHctcXlqm/xmtrHmNQY5dnTle+w6ezsNKXERZpa/1iSYiO09FvTdcUfP+w2wL7tQI0efPMz3XfR5KCM51TT3ObVxt2V7WFvt0db91fLMO85Alks0umjkpRrtynfYdPMzBRFRxDkR2gj/A0AAAAAAAAAAAAAAAAAAAJ46ppV6Pb4A9+HappMrZ8YHa5cu015Dpvy7DZlpsWZWr83Zmam6I6vTNDDb33W7fFn1uzW3Jw0fWXyiCCPbOgxDEOfHaqV0+VRgduj9SXlamr1mdrH6JQY/8reZ+ekKTVIDxIAwUL4GwAAAAAAAAAAAAAAAACAU1xji1frd1fI6SqT012uHQdrTK0fEWbRzMwU5TvSlWu36YyMJIVZLab20Rc3zMvWmqL2sHt37nxliyZnJCkjOSbIIwt9R2qa2h8i6Fjdu6y22dT6STERyrWnta/ubU/X2LRYU+sDg80pF/5uamp/+ig6OnqARwIAAAAAAAAAAAAAAAAAwMDw+QxtP1jTEcot04bdlWppM3cF5okjEpRntynXYdPsrFTFRg7eyKLVatGvr5im85cWdBtOrm5s1Q+e36wXrp+j8DDrAIwwdDS0tGldSYWcHavG7zxca2r9yDCrzhyX4l81fvKowfUgAdDfBu+/pH1QUVEhp9Opjz76SJ988ol2796tffv2qaamRoZhSJIsFosSExM1duxYjRs3TlOmTNGZZ56p3NxcpaWlDfAVAAAAAAAAAAAAAAAAAABgrv1VjXK6ylTg8mhNUbkq6ltMrT88MUp59nTlO2yaa0/TsITQWqQ1PSFKj105Td95ap06ooYBPtpTqUdX7tKdX50Y/MENYl6foW0HqtsfJHB5tHFPpVq85j5IcNrIROU7bMq123TWuFTFRIaZWh8IJUMm/F1cXKznnntOK1as0MaNG+Xzff4Ph9HNv8KGYaiqqkpVVVXaunWr/vnPf0pqD4XPnDlTCxcu1KJFi2S324N2DQAAAAAAAAAAAAAAAAAAmKW2qVUfFpXL6W4P5RZ76k2tHxcZpjnZaf4VmO3D4mWxhPYKzLl2m245167frnJ3e/x37xfp7Gyb8hy2II9scCmtbJDT5VGBy6PCIo+qGlpNrZ+RHKM8e/t/57k5aUqLjzK1PhDKQjr83draqhdeeEG///3vtW7dOv/+L67u/cX/eyKGYcgwDH300Uf66KOPdP/99+uss87SLbfcoquuukoRERHmXwQAAAAAAAAAAAAAAAAAACZo9fr0yb6q9hWY3R59vK9KXl83S1ifpDCrRVNHJynPka48u03TxyYrIsxqWv3B4vb5Dq0rKdeG3ZVdjhmGdPuLH+tfP8hXesKpE0iuOfogQcfPVonJDxIkRIdrbk6a/2drXFpsyD9IAPSXkAx/NzQ0aOnSpXr88cd15MgRSe3B7c7/Q//iit+RkZGKi4tTbGysDMNQY2Oj6uvr1dJy7NdWGIahDRs2aMmSJbrrrrt022236bbbblNcXFz/XBgAAAAAAAAAAAAAAAAAAD1kGIaKPfX+FZjXFperrrnN1D6ybHH+FZjPzklTYvTQX0Q1PMyqx6+arvOXFnS7orWnrln/9dLH+sv3ZslqHZoB5TavT5+UVmn1rv55kCDcatGMsSntq8Y7bJqSkaTwIfggAdAfQir83dbWpqVLl+rhhx+Wx+PxB76PhrwNw1BiYqLy8vI0c+ZMnXHGGZo4caJGjRql1NTUbmuWl5frwIED+uyzz7R161Zt3LhRhYWFqqmp8de0WCw6fPiw7rnnHj366KO6++67deutt7ISOAAAAAAAAAAAAAAAAAAgqMrrmuV0e+R0eVTo9uhAdZOp9ZNjI5Rrt7UHvu02jUmNNbV+qBiVHKNfXTZV1y37qNvjBS6P/rC6SDefaw/yyPqHYRjaXd6gAldZ+4MEReWqNflBgpz0OOV3rOw9JydN8VEhFWEFBo2Q+V/OihUr9MMf/lButztglW/DMHTaaafpG9/4hi6++GLNmDFDVmvPn/5IS0tTWlqazjjjDF1++eWSJJ/Pp02bNunvf/+7li9fru3bt/vbezwe3XnnnfrDH/6gRx99VAsXLjT3QgEAAAAAAAAAAAAAAAAA6NDU6tWG3RX+1b23H6wxtX5kmFUzM9tXYJ7nSNfkUYlDdjXr3lpw2nB9L3ec/ly4u9vj//v2Ls3OStXMzO4Xpx3sKutbVFjk8f9s7a9qNLV+alykcu025TvaHyQYlRxjan3gVBUy4e+LLrooYJXvhIQELVq0SNdee61mzJhhal9Wq1VnnnmmzjzzTP33f/+3Nm/erCeeeELPP/+8qqurJUlut1sXX3yx2trMfbIFAAAAAAAAAAAAAAAAAHDq8vkMbT9YowKXR053mTbsrlRLm8/UPiaOSGhf2dth0+ysNMVEhplafyi5++sTtWF3hT7d3zV07/UZuu35j/XGbXlKjo0cgNH1TnObV5v2VKnAVSan26Ot+6vVEck0RWS4VWeNS/Gv7n3aSB4kAPpDyIS/pfZVvjMyMvSDH/xAN9xwgxISEoLS7/Tp0/W73/1Ov/zlL/WHP/xBjz/+uPbv3+8PogMAAAAAAAAAAAAAAAAAcLL2VzXK6SpTgcujNUXlqqhvMbX+sIQo5TnaV2DOtds0LCHa1PpDWVR4mH7zrRm6YGmB6lu8XY7vr2rUXa9s0R+vnimLZXAFnQ3DkOtInQpcHhW4yrSuuEKNrV2voS8mjUz0r+w9KytV0RE8SAD0t5AJf9tsNv30pz/VTTfdpMjIgXlCJj4+XnfccYduu+02/e53v9ODDz44IOMAAAAAAAAAAAAAAAAAAISumqZWfVhULqfLo0K3R8WeelPrx0aGaXZWqvIc6cp32OQYFj/ogsmhJMsWp/+59Az94IWPuz3+9vbDWvbhHi2eOy6o4+pOWW2zCt0erXaVqdDt0eGaZlPrD0+MUp493f8gQXpClKn1AZxYyIS/i4uLFR8fP9DDkCRFRkbq9ttv17XXXjvQQwEAAAAAAAAAAAAAAAAADHKtXp8+3lelApdHTleZPimtltdnmFbfapHOGJ2sfLtNeQ6bZoxNUWS41bT6kC6elqFCt0cvfVTa7fEH3tihmZkpOj0jKajjamr1an1JhZxuj1bvKtNnh2pNrR8TEaY52e0PEsxz2GTnQQJgwIVM+HuwBL+/aDCOCQAAAAAAAAAAAAAAAAAwsAzDUFFZnQo6VvZeW1yhuuY2U/vITItVnt2mfIdNZ2fblBQbYWp9dHXfRZO1aW+V3Efquhxr8fp06/Ob9c9b8xQf1X/RTJ/P0I5DNR0PEni0fneFWtp8ptW3WKQpGUnKc9iU70jnQQJgEAqZ8DcAAAAAAAAAAAAAAAAAAIPVkdomrXGX+wPfh2qaTK2fFBOhXHua8uzpynfYNCY11tT6OLHYyHD936IZuui3TjV3E7gu8dTr3tc/1a+vnGZqv4eqm1TgKpPT3R74Lq9vMbV+RnKM5o23Kc+errk5aUqJizS1PgBzEf4GAAAAAAAAAAAAAAAAAKCXGlratK6kQoUuj5xujz47VGtq/cgwq2ZmpnSswGzT5FFJCrNaTO0DvTdhRIL+34WT9ZPlW7s9/trm/Zprt+mymaNPuo+GljatK65QgcujAleZXN2sNN4XCVHhmpOTpnkOm/Ic6RqXFiuLhZ8tIFQQ/gYAAAAAAAAAAAAAAAAA4AS8PkNb91fL2bEC86Y9VWrxdl39uS8mjkhQnt2mPIdNs7JSFRtJxG8w+tasMSos8uiNLQe7Pf6z1z/VtDHJsg+L71E9n8/Qpweq/WHvjXsq1eo1TBtvmNWiaWOSlWe3ad54m6aOTlZ4mNW0+gCCi5kBAAAAAAAAAAAAAAAAAIBODMPQnvIGOd0eOV0erSnyqKapzdQ+hidGKc+ernyHTXPtaRqWEG1qffQPi8WiBy89Q1tKq7SvorHL8cZWr/7juU16/ZZcRUeEdVtjf1WjnK4yrXZ5tMbtUWVDq6ljzLLFKc/evmr8nJw0JUZHmFofwMA5pcPfXq9XH3/8sfbt26fm5mYNGzZM48ePV0ZGxkAPDQAAAAAAAAAAAAAAAAAQZJX1LSos8qjQ7VGBy6PSyq7B3r6IjQzTnOw0fyjXPixeFovF1D4QHInREfrNt2bost+vUZuv6yrdnx2q1S/e2K5fXHKGJKmuuU1ri8pV4CpTgduj4rJ6U8eTFBPhXzU+z27TmNRYU+sDGDyGTPi7ra1NHo9HMTExSkpKOm7bgwcP6uc//7meffZZ1dd3/Qf0jDPO0B133KHvfOc7/TVcAAAAAAAAAAAAAAAAAMAAa2r1auOeShW4PHK6y7TtQI2MrjnekxZmtWjq6CTl2W3Ktds0fWyKIsOt5nWAATVtTLJ+9LWJeuDNHd0e/9vavfIZkvtwnTbtrew2JH6yIsIsmjE2RfkOm/Id6To9I0lhVh4kAE4FIR3+rq+v1yOPPKKXXnpJO3fulNEx66alpWnBggX64Q9/qBkzZgSc8/bbb+uqq65SdXW1v31nW7Zs0eLFi/W3v/1Ny5cvV0xMTL9fCwAAAAAAAAAAAAAAAACgf/l8hrYfrFGh2yOn26P1JRVqbvOZ2ke2LU55jvaw99k5aUqMjjC1PgaXa/KytKbIo1U7y7o9/ty6vab1ZR8W3xH2tml2VpriokI6AgrgJIXs//I//fRTff3rX9eBAwe6hLg9Ho9eeOEFvfTSS3r00Uf1H//xH5KkdevW6aKLLlJLS4skHfd1GYZh6J133tEFF1ygd999t/8uBAAAAAAAAAAAAAAAAADQb/ZXNcrpKpPTXa41bo/K61tMrZ8aF6lcu035dptyHTZlJLPY6KnEarXof6+Ypq8/vlqHa5pNrZ0aF6k8u015HYHvkUn8bAEI0fD3/v379aUvfUkej0fSsUPcXq9Xt99+u6ZOnarc3FxdffXVamlpkcVikWEYx1z522Kx+Nu8//77evzxx/WDH/yg364HAAAAAAAAAAAAAAAAAGCOmqZWfVhULqfLo0K3R8WeelPrR4VbNSsr1R/KnTQiUVbrsRcixdCXGhepx6+arkVPrJWv+1hij0SGW3XWuBTl2dOV77DptJH8bAHoKiTD3z/4wQ/k8Xj8oe/jhbh9Pp/uu+8+3XDDDXK73f5Q99y5c3Xbbbdp7ty5GjZsmFpbW7V371699dZbevTRR1VaWupv+8ADD+iWW25ReHhI/ucCAAAAAAAAAAAAAAAAgCGrpc2nj/dVyekqU4Hbo0/2VfUpgNuZxSKdPipJeQ6b8uw2zcxMUXREmHkdYEiYk52m277s0GMrXb06b+KIBOU7bMpzpGvWuFTFRPKzBeD4Qi7NXFxcrNdee80fzJak73//+7rxxht12mmnyWKxaMeOHfrTn/6kJ554QhaLRatXr5bVavXX+MEPfqBHH300oG5kZKQmTZqkSZMmacmSJfr617+uDRs2SJLKy8v197//Xd/85jeDd6EAAAAAAAAAAAAAAAAAgC4Mw5DrSJ2cLo+cbo/WFperocVrah+jU2LaA7n2dM3NSVNKXKSp9TE03folh9YWl2ttccUx26QnRCm/Y9X4PLtNwxKjgzhCAENByIW/X3jhBUntE7jFYtEDDzygu+++O6DNjBkz9Ic//EEOh0N33nmnfD6f3nvvPUnSlClT9Otf//q4faSkpOiFF17QhAkT1NbWJkl67733CH8DAAAAAAAAAAAAAAAAwAA4UtMkp7s97F3o9uhwTbOp9ROjw5VrtynXblO+w6bMtDhT6+PUEGa16I/fOVM3PbtRa4rKJUlR4VbNzk5Tvt2m/PE2TRieIIvFMsAjBRDKQi78vX79ev/fc3Jy9KMf/eiYbf/rv/5LTz75pHbt2uUPi9944409+odz3LhxuuCCC7R8+XJZLBZt3LjRlPEDAAAAAAAAAAAAAAAAAI6voaVN64orVOBqD3vvPFxrav2IMItmZqYo35GuXLtNZ2QkKcxKIBd9lxQboeeum6Pdnno1t/mUmRar6IiwgR4WgCEk5MLfn376qSTJYrHo0ksvPW6Q22Kx6Bvf+IYeeugh/768vLwe95Wfn6/ly5fLMAyVlpae/KABAAAAAAAAAAAAAAAAAMfk9Rnaur9aTleZClwebdpbqVavYWofE0ckKM9uU57DpllZqYqNDLn4HELIOBurxwPoHyE3e1VUVPj/PmXKlBO2P/300wO2MzMze9zXuHHj/H+vrq7u8XkAAAAAAAAAAAAAAAAAgOPbW96gAneZnC6P1hSVq7qx1dT6wxOjlGdPV77Dprn2NA1LiDa1PgAAAyHkwt+1tbX+1b5TU1NP2L5zm/j4+B739cW2DQ0NPT4PAAAAAAAAAAAAAAAAABCouqFVa4o8KnB75HR5tLfC3ExWXGSYzs5JU67dpnyHTTnp8f6sGQAAQ0XIhb+9Xq9/Qo6MjDxh+4iIiJPui4kfAAAAAAAAAAAAAAAAAE5OS5tPG/dUqtDdHvjeWloln2Fe/TCrRdPGJCvPblOew6ZpY5IVEWY1rwMAAAahkAt/AwAAAAAAAAAAAAAAAAAGH8MwtOtwnQpcZXK6PVpXXKHGVq+pfWSnxynfblOeI12zs1OVGH3yi4MCABCKCH8DAAAAAAAAAAAAAAAAAE7KkZomOd0eOV0eOd0eHaltNrV+WlykcjtW9s6z2zQqOcbU+gAAhBrC3wAAAAAAAAAAAAAAAACAHmls8WpdSbmcLo8KXB7tPFxrav2ocKtmZaUqryPwPWlEoqxWi6l9AAAQygh/AwAAAAAAAAAAAAAAAAC65fMZ2n6wRgUujwpcZfpod6VavD5T+zg9I1F59nTl2W06c1yKoiPCTK0PAMBQEtLhb4uFJ7oAAAAAAAAAAAAAAAAAwEwHqxs7wt4eFbo9qqhvMbV+RnKMf2XvXLtNqXGRptYHAGAoC9nwt2EYmj9/fq/PCQvr3VNhFotFhmH06hwAAAAAAAAAAAAAAAAACBX1zW1aV1Ku1bs8cro9ch+pM7V+QlS45uSkKd9hU57dpixbHAt/AgBwkkI2/C3ppELZvT2HDxkAAAAAAAAAAAAAAAAAhhKvz9Cn+6tV4CpTgcujTXsr1eo1b4HMMKtF08ckK89hU77DpqmjkxUeZjWtPgAAp7KQDX8TygYAAAAAAAAAAAAAAACAntlX0SCn2yOnq3117+rGVlPrZ6fHKd9uU54jXXOyU5UQHWFqfQAA0C4kw98ns+I3AAAAAAAAAAAAAAAAAJwqapta9WFRuZxujwpcHpV46k2tnxIboTxHekfg26ZRyTGm1gcAAN0LufB3SUnJQA8BAAAAAAAAAAAAAAAAAAaVVq9PW0qrVOBqX917874qeX3mLbIZGWbVmeNSlO9IV77DptNGJspqtZhWHwAA9EzIhb8zMzMHeggAAAAAAAAAAAAAAAAAMKAMw1Cxp15OV/vK3muLy1XX3GZqHxOGJyjf0b6y9+ysNMVEhplaHwAA9F7Ihb8BAAAAAAAAAAAAAAAA4FRUXtcsp9ujQnf76t4HqptMrW+Lj2oPe9vbA9/DE6NNrQ8AAPqO8DcAAAAAAAAAAAAAAAAADEJNrV5t2F3hX917+8EaU+tHhVs1KytV+Q6b8h3pmjgiQRaLxdQ+AACAuQh/hyDDMLR7925t3bpVpaWlqqqqUlRUlFJSUuRwOHTWWWcpOtrcp+5qa2tVWFioXbt2qaamRjExMcrMzNTcuXM1atQoU/sCAAAAAAAAAAAAAAAATkU+n6HtB2tU4PLI6S7Tht2VamnzmdrHpJGJmtcR9j5zXIqiI8JMrQ8AAPoX4e8QUVlZqddff11vvfWW3nvvPXk8nmO2jYiI0MKFC3X77bfrnHPO6VO/JSUluvfee/XSSy+ppaWly3GLxaJzzjlH999/v+bNm9envgAAAAAAAAAAAAAAAIBTTWllg5wuj5xuj9YUlauivmtGpy+GJUQp35GufIdNuXab0hOiTK0PAACCi/B3CLjlllv05JNPdhu+7k5ra6tef/11vf766/rud7+r3/zmN0pMTOx1vy+99JK+973vqaGh4ZhtDMPQ+++/r3PPPVd33XWXHnzwQV79AgAAAAAAAAAAAAAAABxDTVOrPiwq9we+Szz1ptaPjQzT7KxU5TnSlWe3afzwePI8AAAMIYS/Q8C6deu6DX6HhYVp5MiRGj58uFpbW7Vnzx5VV1cHtFm2bJk+++wzvfvuu4qPj+9xny+//LK+9a1vyecLfG1Menq6xowZoyNHjmj//v0yDENSewj84YcfVnNzsx599NGTuEoAAAAAAAAAAAAAAABg6Gnz+vTxvioVuDwqcJXpk9JqeX2GafWtFmnK6GT/yt4zxqYoMtxqWn0AADC4hFz4+/vf//6A9GuxWPTUU08NSN9flJycrEWLFmnhwoXKz89XQkKC/5jX61VBQYHuvfdeFRQU+PevX79eS5Ys0SuvvNKjPoqKivS9730vIPg9depUPfroozrvvPP8+3bu3Kmf/OQneu211/z7HnvsMeXn5+vSSy/ty2UCAAAAAAAAAAAAAAAAIckwDO0ub5DTVabVLo/WFpWrtrnN1D4y02KVZ7cp32HT2dk2JcVGmFofAAAMXiEX/n7mmWeC/hoSwzAGPPw9btw43XPPPVq0aJFiYmK6bRMWFqZzzz1Xq1at0s0336w//elP/mOvvvqqVq1aFRDePpaf/exnqq///HUyZ511llauXKnExMSAdhMmTNArr7yiG2+8MaCvu+66SxdddJHCw0PuxwsAAAAAAAAAAAAAAADotaqGFq0pKvev7l1a2Whq/eTYCOXm2JTnsCnPbtOY1FhT6wMAgNARsulcw/j81SfBDoMH2/33368FCxYoMjKyR+3DwsL0u9/9Tps2bdJHH33k3//kk0+eMPy9bds2vfjii/7tyMhI/eUvf+kS/D7KYrHo8ccf16pVq+RyuSS1rxz+5z//Wdddd12PxgsAAAAAAAAAAAAAAACEkpY2nzbvrZTT7dFql0dbS6vkM058Xk9Fhll15rgUf9h78qgkhVmHdkYKAAD0TMiGv48Gvg3DCAiCD0ULFy7s9TlhYWG66667dMUVV/j3/fvf/z7heU8//bR8Pp9/+6qrrtKkSZOOe050dLTuvvtuXXPNNf59Tz75JOFvAAAAAAAAAAAAAAAADAmGYaiorF5OV5kKXB6tLS5XfYvX1D4mjkhQvsOmPEe6Zo1LVUxkmKn1AQDA0BCy4W/DMBQWFqYvfelLuuqqq5SamjrQQxp08vPzA7bLy8vV0NCg2Nhjv/blH//4R8D2FwPdx3PllVfqtttuU319vSRpw4YNOnDggEaNGtXLUQMAAAAAAAAAAAAAAAADr6K+RYVujwpcZXK6PDpQ3WRq/RGJ0cpz2JTvsGlujk3pCVGm1gcAAENTyIa/LRaLfD6fVq5cqdWrV2vhwoVavHixzj//fIWF8dSbJKWkpHTZV11dfczw986dO+V2u/3bcXFxmjt3bo/6Otr2nXfekdQezn/jjTdY/RsAAAAAAAAAAAAAAAAhobnNq417KuV0eVTg8ujTA9UyDPPqx0aGaU52mvLsNs0bb1NOerwsFot5HQAAgFNCyIW/v/vd7+q1115TXV2dpPYQeHNzs5YvX67ly5fLZrNp0aJFuvrqqzVjxowBHu3A2r9/f5d9aWlpx2z/8ccfB2zPmjVL4eE9/xHJzc31h7+7qwcAAAAAAAAAAAAAAAAMFoZhyH2kTqtd7at7ryuuUGOr17T6Fos0JSOpY3XvdM0Ym6LIcKtp9QEAwKkp5MLfzzzzjH73u9/plVde0bJly7Rq1SpJ7R/GJKmsrExLly7V0qVLddppp2nJkiX69re/rREjRgzksAdEQUFBwHZmZqYiIyOP2X7Hjh0B26eddlqv+uvcvnM9AAAAAAAAAAAAAAAAYCCV1zXL6W5f2bvAVabDNc2m1s9IjlF+R9h7bk6aUuKOndUBAAA4GSEX/pak2NhYffe739V3v/td7du3T8uWLdNf//pX7dq1y9/GMAxt375dd911l+6++27Nnz9fS5Ys0SWXXKKoqKgBHH3wPP300wHb559//nHb79y5M2B7zJgxveqvc/vO9QAAAAAAAAAAAAAAAIBgamr1auOeSn/Ye9uBGlPrx0eFa052muaNtynPblOWLU4Wi8XUPgAAAL4oJMPfXzRmzBj99Kc/1U9/+lOtXbtWzzzzjF566SVVVVXJMAxZLBZ5vV69/fbbevvtt5WQkKArrrhCV199tfLz8wd6+P3mzTff1OrVqwP2LVmy5LjnHDlyJGB79OjRveozIyMjYLusrKxX5wMAAAAAAAAAAAAAAAB9YRiGdh2uU4GrTAUuj9aVlKup1WdafatFmjomWfmOdOU7bJo2JlkRYVbT6gMAAJxIyIe/v2jOnDmaM2eOli5dqtdff13Lli3Tv//9b3m9Xn8QvKamRk899ZSeeuopjRs3TosXL9bVV1+trKysgR6+aSoqKnTDDTcE7Lvkkks0a9as455XV1cXsB0XF9erfju3b21tVXNzc59XWj9y5Eivg+Rutztgu66uTjU15j65iZ6pr68/7jYAAAgNzOkAAAwdzOsAAAwNzOkAAAwNzOnmKK9r0drdVVpTUqm1JVUqq2sxtf7o5GidnZWss7NSNGtcshKjP49cNdbXqdHU3gAAoYg5/dTTOXMbTBbDMIwB6z0IDh8+rL/97W9atmyZtm7dKkmyWCw6etlHX7OSm5urxYsX6/LLL1diYuKAjbevfD6fLrjgAv3rX//y70tKStLWrVs1ZsyY4547efJkbd++3b/9r3/9S1/72td63HdjY6NiY2MD9nk8HqWlpfW4Rnfuu+8+3X///X2qsXTpUo0dO7ZPNQAAAAAAAAAAAAAAADDwWn1ScY1Fn1VbtLPKov0NFlPrx4QZciQZmpBkaGKyIVu0qeUBAMAQsHfvXt12223+7U8//VSTJ08OSt9DauXv7gwfPlw//OEP9cMf/lAff/yxnnnmGT3//PP+laSPhsALCwtVWFioH/zgB/J4PIqODs1PbXfeeWdA8FuS/vjHP54w+C1JTU1NAduRkZG96ru7Fb4bG3m2EQAAAAAAAAAAAAAAACfPMKQDDdLOjrB3UY1FrYZ5gW+rDGUmSBOTfJqQbGhsvBRmbp4cAADANEM+/P1F06ZN02OPPaZHHnlEb775pv7yl7/ojTfeUGtrq6T2IHhjY6N8Pt8Aj/TkLF26VL/+9a8D9t1111268sore3R+58B7S0vvXoHT3Nx8wpoAAAAAAAAAAAAAAADAiVS3fB723lltUW2ruWlsW7ShiR0re9sTDcWcUikqAAAQyk7Jjy3h4eG66KKLdNppp2nEiBH6/e9/L4sltB/Xe+6553T77bcH7FuyZIkeeuihHteIj48P2O68EviJdLfKd+eaJ+Pmm2/W5Zdf3qtz3G63LrnkEv/2rFmzNGnSpD6PBb1XX1+v9evX+7dnzZqluLi4ARwRAAA4GczpAAAMHczrAAAMDczpAAAMDczpn2to8WrjvmqtLanShyWVcpc1mFo/ITpcszOTNDc7RXOyUjQ6mQUNAQDmYU4/9ezYsWPA+j7lwt/V1dV64YUXtGzZMq1du1aSQj74vWLFCi1evFiGYfj3XXrppXryySd7dW2dg9r19fW9Gkfn9uHh4aas/D1s2DANGzasTzXi4+OVmJjY57Gg7+Li4vj/BQAAQwBzOgAAQwfzOgAAQwNzOgAAQ8OpNKf7fIa2HajRaleZnC6PNu6pVIvXZ1r9MKtFM8YmK9+RrnyHTWdkJCk8zGpafQAAjudUmtNPVWYsjnyyTonwt9fr1b/+9S8tW7ZMK1asUHNzsyTJMAxZLBZ/aDojI0Pf+c53TAksB8uqVat0+eWXq62tzb9vwYIFev755xUWFtarWp0D1qWlpb06f//+/QHb6enpvTofAAAAAAAAAAAAAAAAQ1dpZYOcLo8K3B6tcXtU2dBqav0sW5zyHTblO9I1JztVCdERptYHAAAYDIZ0+Hvz5s1atmyZnn/+eZWVlUnqGviOiYnRN77xDS1evFhf/vKXQ2oV8HXr1umiiy5SU1OTf9/cuXO1fPlyRUZG9rrehAkTArb37t3bq/M7t584cWKvxwAAAAAAAAAAAAAAAIChobapVR8Wlcvp9sjp8qjY07u30J9IUkyEcu1pynekK89u05jUWFPrAwAADEZDLvx98OBB/e1vf9Nf//pXbdu2TZL8Qe8vOuecc7R48WJddtllA7r0+snasmWLvv71r6uurs6/b/r06XrzzTcVFxd3UjU7h7W3b9/eq/N37Nhx3HoAAAAAAAAAAAAAAAAYutq8Pn1SWq0CV5mcLo8276uS19c1t3Oywq0WzRibojyHTfPGp+uMjCSFWUNnoUcAAAAzDInwd1NTk1577TUtW7ZM7777rnw+n6TPV/k+yuFw6Oqrr9bVV1+tzMzMgRpun+3cuVMLFixQZWWlf9+kSZP073//W0lJSSddd9q0aQHbGzZsUFtbm8LDe/ZjUlhYeNx6AAAAAAAAAAAAAAAAGDoMw9Du8gY5XWUqcHn0YXG5apvaTO0jJz1O+Y505Ttsmp2dpvioIRF3AgAAOGkh/Wnogw8+0LJly/TKK6/4V8A+Gvg+utp3UlKSrrjiCi1evFhnn332QA7XFHv27NH8+fN15MgR/76srCy98847Sk9P71PtiRMnKicnR0VFRZKk+vp6rVmzRvPmzTvhufX19frwww/92xaLRRdccEGfxgMAAAAAAAAAAAAAAIDBpbK+RWuKyuV0l2n1Lo/2VzWaWj81LlK5dpvyHTbl2W0alRxjan0AAIBQF3Lhb5fLpWXLlulvf/ub9u7dK6lr4DssLExf+cpXtHjxYl100UWKiooayCGb5uDBg/ryl7+s0tJS/76MjAy9++67ysjIMKWPiy66SI8++qh/+6mnnupR+PvFF1/0B/Al6cwzz9SoUaNMGRMAAAAAAAAAAAAAAAAGRnObVxv3VMrp8sjp9mjr/mp1RHRMERlu1VnjUpTvSFee3abTRibKarWY1wEAAMAQE3Lh7wkTJgQEvY8yDENTp07Vd7/7XX3729/WsGHDBmiE/aOiokILFizwr8otSenp6XrnnXeUlZVlWj/f//739dhjj/n/+77wwgu6++67NWnSpGOe09TUpIceeihg3zXXXGPamAAAAAAAAAAAAAAAABAchmFo5+FaOV0eFbg8Wl9SocZWr6l9TByRoHyHTfmOdJ01LlUxkWGm1gcAABjKQi78fZTF0v6E3/Dhw7Vo0SItXrxYZ5xxxgCPqn/U1tbqa1/7mrZt2+bfl5ycrLfffvu4oeyTcfrpp+uKK67Qiy++KElqaWnR4sWLtXLlSiUmJnZpbxiGbr/9drlcLv++7Oxsff/73zd1XAAAAAAAAAAAAAAAAOgfR2qa5HS3h72dbo/KaptNrT8sIUp5DpvyHTbl2m0alhBtan0AAIBTSciGvw3DUFhYmGw2m9577z299957/dqfxWLRxo0b+7WPY7nooou0YcOGgH3/9V//JY/Ho5UrV/aq1syZM5WSknLcNr/4xS/0z3/+Uw0NDZKkDRs2aN68eXrsscd07rnn+tvt2rVLP/7xj/Xaa68FnP/QQw8pIiKiV+MCAAAAAAAAAAAAAABAcDS0tGldSYWcLo+cLo92Hq41tX5MRJhmZ6cqz27TvPHpcgyL9y/0CAAAgL4J2fC3JPl8Pv9q2IZh9EsfFotFhmEM6AfQ999/v8u+e++996RqrVq1KiDA3R273a6nnnpKixYt8v93/eSTT3TeeecpPT1dY8eO1ZEjR1RaWtrlv/utt96qyy+//KTGBgAAAAAAAAAAAAAAAPN5fYY+3V/dsbp3mTbtqVKL12dafYtFOiMjSXl2m/IcNs3MTFFUeJhp9QEAAPC5kA1/dw5j83Sgua666ioZhqFrrrlGjY2N/v1lZWUqKyvr9pw77rhDv/zlL4M1RAAAAAAAAAAAAAAAABzD3vIGOd0eOd1lWlNUrqqGVlPrj06JUb7Dpjx7uubmpCklLtLU+gAAAOheSIa/+2uVbwT61re+pdmzZ+tnP/uZXn75ZbW2dn8TMG/ePP385z/XOeecE+QRAgAAAAAAAAAAAAAAQJKqGlq0pqhcBS6PCt0e7a1oMLV+QlS4zs5JU77DpnxHujLTYlmsEQAAYACEXPi7pKRkoIcQdAMZds/Oztazzz6r3//+93I6nXK5XKqtrVV0dLTGjh2r3NxcZWRkDNj4AAAAAAAAAAAAAAAATkXNbV5t3FMpp8sjp9ujrfurZWbEJNxq0fSxycqzpyvPYdPU0UkKD7Oa1wEAAABOSsiFvzMzMwd6CKekxMREnX/++QM9DAAAAAAAAAAAAAAAgFOSz2fos0O1crrL5HSXa31JuZpafab2kZMep3xHuvLsNs3JSVN8VMhFiwAAAIY8PqEBAAAAAAAAAAAAAAAAg9DB6kYVuDxyujxaU+SRp67F1PqpcZHKtduU77Apz27TqOQYU+sDAADAfIS/AQAAAAAAAAAAAAAAgEGgrrlNa7cdUqHbowK3R8Vl9abWjwy3ata4VOV1hL1PG5koq9Viah8AAADoX4S/AQAAAAAAAAAAAAAAgAHQ6vWpqEbaWW3VrmqL9q79UF7D3D4mj0r0h73PGpeq6IgwczsAAABAUBH+BgAAAAAAAAAAAAAAAILAMAwVldXL6SqT0+3Rh0Xlqm8xN76TkRyjPLtNeQ6b5uakKS0+ytT6AAAAGFiEvwEAAAAAAAAAAAAAAIB+Ul7XrMKi8vbAt8ujA9VNptZPiA7X3Jy0jsB3usalxcpisZjaBwAAAAaPkAl/b9u2TZMnTx7oYQQYjGMCAAAAAAAAAAAAAADAwGlq9eqj3ZUqcLeHvbcdqDG1fkSYRdPHpijfblOuw6YpGUkKD7Oa2gcAAAAGr5AJf0+bNk3f+c53dN999ykzM3NAx7Jnzx7de++9eu6559Ta2jqgYwEAAAAAAAAAAAAAAMDA8fkMfXaoVk53mQpcHq0vqVBzm8/UPiYMT1Cu3aZ8h02zslIVFxUykR8AAACYLGQ+CXq9Xi1btkzPPvusrrzySt15552aMmVKUMfwySef6Fe/+pVefvlltbW1BbVvAAAAAAAAAAAAAAAADA6HqptU4CqT0+1RodsjT12LqfWHJUQpz2FTnr39z7DEaFPrAwAAIHSFTPg7MjJSLS0tamtr03PPPafnnntOc+bM0fXXX6/LLrtMcXFx/dJvfX29XnrpJT355JNau3atJMkwDElSdDQfrAEAAAAAAAAAAAAAAIa6+uY2rSspV4HLI6fLI9eROlPrR1oN2RMNXXCmXfPPGC3HsHhZLBZT+wAAAMDQEDLh723btuk///M/tWLFCn/4eu3atVq7dq1uuukmLViwQBdffLHOO+88ZWVl9amvkpISrVq1Sq+//rpWrlyp5uZmSZ+HviXpwgsv1KOPPtqnfgAAAAAAAAAAAAAAQN94fYa2lFapxFOvtPgozc1JU0SYdaCHhRB39OfK6fKowO3R5r2VavUaJz6xh6wWacroZM0am6CoqhKNi5fCrdJ5szKUmJhgWj8AAAAYekIm/J2Tk6N//OMfeuedd3TPPfdow4YNMgxDFotFTU1NWrFihVasWCFJysjI0JlnnqnTTz9dEyZM0OjRozVy5EjFx8crJiZGhmGoqalJtbW1OnjwoEpLS7Vz505t3bpVmzZt0v79+/39Hu3jaPD7rLPO0gMPPKD58+cPyH8HAAAAAAAAAAAAAABOdfsqGrTaVSany6NCt0c1TW3+Y1PHJOuZJWcpJS5yAEeIULSnvN6/sveaosCfKzOMTY1VnsOmfLtNc3NsSoqNUE1NjVatKjG1HwAAAAxtIRP+PmrBggVasGCB/vnPf+rhhx/WmjVr/MeOBrRLS0u1f/9+/f3vf+91/S+u7n309TmGYSg3N1d33323Fi5c2McrAAAAAAAAAAAAAAAAvVHd2KoPi8pV4CqT0+3RnvKGY7b9ZF+Vbnths/7yvVmyWi1BHCVCTVVDi9YUlbcHvt1l2lfRaGr9xOhw5dptHYHvdI1NizW1PgAAAE5NIRf+PurCCy/UhRdeqE2bNun//u//9Oqrr6qmpkbS56FtKTDMfTxHz/niKt8JCQm67LLLdNNNN2nmzJkmXwEAAAAAAAAAAAAAAOhOq9enj/dVdazCXKaP91XJ17Nf/0uSClwe/WF1kW4+195/g0TIaWnzaeOeShW6PSpwe7S1tHc/VycSEWbR9LEpyrfblD8+XWdkJCmMBxAAAABgspANfx81Y8YMPfXUU/rd736nN954QytWrNDbb7+tAwcOdGn7xVC4FBgMP/r3UaNG6Stf+YoWLlyoCy64QFFRUf17AQAAAAAAAAAAAAAAnOIMw1CJp15Ot0erd3m0trhcdc1tfar5v2/v0qxxqTpzXKpJo0SoMQxDriN1/ocI1pVUqKHFa2ofjmHx7St7O2yanZWmuKiQj+IAAABgkBsynzijoqJ06aWX6tJLL5UkFRcXa+PGjdqyZYtKSkq0b98+VVdXq6Gh/dVPsbGxSk5O1pgxYzRu3DhNmTJFM2fOVHZ29kBeBgAAAAAAAAAAAAAAp4TK+hYVFnnkdHlU4PJof1WjqfW9PkO3Pb9Zb9yWr5S4SFNrY/A6UtvUvrK3y6NCt0eHa5pNrW+Lj1Su3aY8u015DptGJsWYWh8AAAA4kSET/u4sOztb2dnZuvzyywd6KAAAAAAAAAAAAAAAnPKa27zatKdKBa4yOd0ebd1frS+8sLtfHKhu0p2vfKInvntml7eFY2hobPFqXUm5nC6PnG6PPjtUa2r9qHCrZmWlKt9hU549XRNHJMhq5WcJAAAAA2fIhr8BAAAAAAAAAAAAAMDAMQxDuw7Xyen2yOkq09riCjW2ek3tY/KoROU5bEqICtcjb+/qts3KHUf0dOFuXZOXZWrfGBg+n6FtB2q02lUmp8ujjXsq1eL1mdrH6RmJyrOnK99h08zMFEVHhJlaHwAAAOgLwt8AAAAAAAAAAAAAAMAUh6qb5HR7VOhuX4W5rLbZ1PojEqOV57Ap32FTrt0mW3yU/1hpZaNe2LCv2/Me+tcOnZmZoqljkk0dD4Jjf1WjnK4yFbjaf7YqG1pNrZ+RHKM8u015HT9XqXGRptYHAAAAzET4GwAAAAAAAAAAAAAAnJTaplatK65oX93b7ZH7SJ2p9WMjwzQnO015dpvmjbcpJz1eFoul27b/78LJ2ry3SjsP13Y51uo19B/Pb9KKW/OVFBNh6hhhvrrmNq0tKpfT7dFqV5mKy+pNrZ8QFa45OWn+wHe2Le6YP1cAAADAYEP4GwAAAAAAAAAAAAAA9Eir16eP91XJ2bEC8+Z9VfL6DNPqWyzSlIwk5TvSleewacbYFEWGW3t0bkxkmH67aLou+m2hGlu9XY7vq2jUj1/bov9bNIOg7yDj9RnaUlqlApdHTpdHm/ZWqs3En6swq0XTxyT7V42fOjpZ4WE9+7kCAAAABhvC3wAAAAAAAAAAAAAAoFuGYch9pE4FHWHvtcXlqm/pGqzui4zkGM0bb1O+I11zc9KUHBt50rUcwxP084sn685XtnR7/M2th/S3dXt19ZzMk+4D5thX0aDVrjL/gwQ1TW2m1s9Oj1O+3aY8R7rmZKcqIZoV3wEAADA0EP4GAAAAAAAAAAAAAAB+R2qa5HR75HS3h3IP1zSbWj8hKlxn56Qp39EezB2XFmvqStyXzRytD4vK9drm/d0e/+8V2zVjbLImj0oyrU+cWHVjqz4sKpfTXaYCl0d7yhtMrZ8aF6lcu035dptyHTZlJMeYWh8AAAAYLAh/AwAAAAAAAAAAAABwCqtrbtP6knL/6t67DteZWj/MatH0McnKtds0b7xNU0cnKzzMamofX2SxWPTfl5yuj0urVFxW3+V4S5tPtz63Wf+4NU/xUcQm+kur16dP9lVptcsjp6tMn5RWy+szTKsfGW7VWeNSlGdPV77DptNGJspqNe8hAgAAAGCw4i4GAAAAAAAAAAD0SGllg5wujwrcHm0oqVBzm0/TxiTr3gtPU056/EAPDwAA9FCb16ct+6vldHnkdHm0aW+l2kwM5UqSY1i8cu025dltmp2dqoToCFPrn0hcVLj+b9EMXfx/hWpp83U5Xuyp1z3Lt+rRK6eZuur4qcwwDJV46uV0e1Tg8mhtUblqm9tM7WPiiAT/ivGzxqUqJjLM1PoAAABAKCD8DQAAAAAAAAAAulXT1KoPi8rbg2Fuj0o8XVfO/GBXmZb8eb1W/tc5igonfAMAwGC1p7xeBS6PClxlWlNUrtomc0O56QlRyusIe+fabRqRFG1q/ZMxaWSi/t+Fp+mnyz/t9vjrHx/Q3BybrjhrTJBHNnRU1Leo0O3xf17cX9Voav30hCjl223Kc7T/bA1LHPifKwAAAGCgEf4GAAAAAAAAAACSpFavT5/sq1JBR3jn431V8vZgFdB9FY36++YDBKcAABhEqhpatKaovGNeL9O+CnNDubGRYZqdlao8R7ry7DaNHx4/KFfQXjRrrNYUleuNLQe7PX7vPz7VtLHJGj88IcgjC01NrV5t3FPp/7n6dH+NqfWjI6yalZWmeY72wPeE4QmD8ucKAAAAGEiEvwEAAAAAAAAAOEUZhqEST72cbo9W7/JobXG56ppPbhXQwiIP4W8AAAZQS5tPm/ZWyunyqMDt0dbSKvXgGa4eC7NaNHV0Uvvq3o50TRuTrMhwq3kd9BOLxaIHLz1DW0urtbeiocvxplafbnl2k/7xH3mKieQtJp35fIY+O1Qrp7tMBS6P1pdUqLnNZ2ofk0clKt+RrnyHTTMzUxQdwf8fAAAAgOMh/A0AAAAAAAAAwCmkor5FhW6PnB2re++vMmcV0HXFFTIMg5UZAQAIEsMw5D5S539jx9ricjW0eE3tIzs9rj3sbbdpTk6aEqMjTK0fLInREfrtoun65u/XqNXbNRHvOlKn+/6xTQ9fNmUARjf4HKpuUoGrTE63R4Vujzx1LabWH5EYrfyOlb3z7DalxUeZWh8AAAAY6gh/AwAAAAAAAAAwhDW3ebVxd6UK3B4VuMq07UCNDBNXAT3qUE2T9lY0KDMtzvziAABAkuSpa1ah29Me+HZ5dKimydT6tvhI5dpt/j8ZyTGm1h9IU0Yn68dfn6Sfr9je7fEXP9qns3PSdMn0jCCPbODVNbdpXXG5/0EC95E6U+vHRoZpTnaa8h025TtsykmP54FBAAAAoA8IfwMAAAAAAAAAMIQYhqHPDtXK6fKowO3R+pJyNbX6gtL3uuIKwt8AAJioqdWrDbsr5HR5tNrl0Y6DNabWjwq3alZWavsqzPZ0TRyRIKt16IZyv5c7Th8Wl+ud7Ye7Pf7T5Vs1ZXSSstPjgzyy4Grz+rRlf3X7m2BcHm3aW6k2n3lPB1ot0hmjk5Vvb1/de8bYFEWGW02rDwAAAJzqCH8DAAAAAAAAABDiDlU3qcBVpkK3R053uTx1zabWH54YpTx7uvId7auALn56vbZ3Ez5bW1KuK84aY2rfAACcSnw+QzsO1fhX9l6/u0ItbeY+xHV6RqJ/Xp+ZmaLoiDBT6w9mFotFv7psihYudWp/VWOX4/UtXt3y3GYtv3nukPvvsqe83v9zVVjkUW1Tm6n1x6bGKs9hU77dprk5NiXFRphaHwAAAMDnCH8DAAAAAAAAABBiaptata64Qk63R063R+4jdabWj4kI05zsVOU52oNhjmHxslg+XwV0dnZqt+HvdcUVpo4DAIBTwcHqxs9DuW6PyutbTK0/KilaeQ6b8hzpys1JU1p8lKn1Q01ybKSWfmu6rvjjh/J2s9r1joM1euCNHfrvS04fgNGZp7qhVWuK2leMd7rLtK+ia9i9LxKjw5XbsbJ3vj1dY9NiTa0PAAAA4NgIfwMAAAAAAAAAMMi1en36ZF+VCjpCYZv3VXUbVjpZFos0JSOpPbzjSNeMsSmKDLces/3srDT9uXB3l/37qxq1r6JBY1IJ/wAAcCz1zW1aW1zeHvjuh4e44iLDdHZOmvLsNuWPT1e2LS7gIS5IMzNTdOdXJ+ihf33W7fG/rt2js3PSdP4ZI4M8spPX0ubT5r2VKnB5VOD2aGtplUz8uKiIMItmjE1RfseDBGdkJCnMys8VAAAAMBCGXPj77LPP1g033KArr7xSMTExAz0cAAAAAAAAAAB6zTAMFZXVydkRCltbXKG65jZT+xiTGqM8e/vK3nNz0pQcG9njc2dlpR7z2LqSCsLfAAB8gddnaEtplZwdodxNeyrVZmIq12qRpo5JVn7HGzumjUlWRNixH+JCu+vzs7W2uFzv7yzr9viPXtmi00clDdoVrY9+Xly96+jnxXI1tHhN7WP88Hj/58VZWamKixpyERMAAAAgJA25T+br1q3T+vXrdfvtt2vRokW69tprNWPGjIEeFgAAAAAAAAAAx1VW26xCt8e/uvehmiZT6ydEh2tuTpo/GJaZFnfStVLjIjVheIJ2Hq7tcmxdcbkumzm6L0MFACDk7S1vUIG7TE6XR2uKylXd2Gpq/XFpscpz2JRnT9fZOWlKiokwtf6pwGq16H8vn6rzlxbocE1zl+O1zW269flNevnGucd9I0oweeo+/7zodJn/edEWH9W+srfdpjyHTcMTo02tDwAAAMAcQy78fVRNTY3++Mc/6o9//KOmTZum66+/XosWLVJCQsJADw0AAAAAAAAAADW0tGldSYUKO1b3/uxQ1yB1X4RbLZoxNqU9GOawaUpGksJNXAV0dnZq9+HvkgrT+gAAIFRUN7bqw6L2UG6By6O9FQ2m1k+KiVCuPc2/CjNv2TBHWnyUHr9quhY9sVbdLcb+SWm1fvnWZ7rngtOCPzhJTa1ebdhd0b5qvMuj7QdrTK0fHWHVrKw0zev4vDhheIIsFoupfQAAAAAw35AMfxuG4b8hMQxDmzdv1s0336w77rhDV1xxha699lqdffbZAzxKAAAAAAAAAMCpxOsztHV/tZyuMjndHm3aU6UWr8/UPhzD4pXnsCnfYdOsrDTFR/XfrwFmZ6Vp2Yd7uuzfW9Ggg9WNGpkU0299AwAw0FrafPp4X5WcrjKtdnm0pbSq2/DwyYoIa3+IK99hU74jXadnJCnMSii3P8zJTtPt88fr1+/s6vb4k84SzclO0/zThvf7WHw+QzsO1cjZ8XDg+pIKNbeZ93nRYpFOH5XU/nnRbtPMcSmKCg8zrT4AAACA4Bhy4e9PPvlEf/rTn/Tss8+qqqpKkmSxWGQYhurr6/XMM8/omWee0aRJk3T99dfr6quvVkpKysAOGgAAAAAAAAAwJO2raOhYAbRMa4rKVd3Yamr99IQo5dttyrW3r9Y4PDHa1PrHMysr9ZjH1hVX6JLpGUEbCwAA/c0wDO06XCen2yOnq0zrSirU0OI1tQ/HsHjlO9I7HuJKVVw/PsSFQLecZ9fa4nKtKSrv9vgPX/5Eb/4gXxnJ5j/cdqCqsX1lb7dHa9welde3mFo/IzlGeXab8sfbNDfHptS4SFPrAwAAAAi+IXe3eMYZZ+g3v/mNfvWrX+nll1/WU089pdWrV0tSwGrg27dv13/+53/q7rvv1qWXXqrrrrtO55xzzkAOHQAAAAAAAAAQ4qobW/VhkUcFHas17ilvMLV+bGSYZmelKs+Rrjy7TeOHx/u/+w629IQo5aTHqaisvsuxdSXlhL8BACHvUHWTnG6PCt3t83pZbbOp9W3xkcqz2/zz+oik4D3EhUBhVoseu3Kazl9aIE9d1/B1dWOrbnt+s164fo4iwqx96qu2qVVriyvkdJWpwO1RcTefpfoiPipcc7LTNG+8TXl2m7JscQP2eREAAABA/xhy4e+joqOjdfXVV+vqq6/Wrl279MQTT+ivf/2rjhw5Iunz1cCbmpr0/PPP6/nnn5fdbtd1112nxYsXKz09fYCvAAAAAAAAAAAw2LV6fdq8t8of3vlkX5V8hnn1rRZp6phk/+re08emKDK8b4EjM83OTus+/F1cMQCjAQCgb46Gco+Gvd1H6kytHxVu1aysVOU7bMqzp2viiARZrYRyB4thidF69Mpp+u7T62V083lu455KPfrOLt31tYm9qtvq9emTfVUdq8Z7tHlflbwmfmAMs1o0dXSSf9X4qWOS+xxQBwAAADC4Ddnw9xeNHz9ev/rVr/Tggw/q9ddf15NPPqmVK1fKMIyA1cBdLpd+9KMf6Z577tFFF12ka6+9Vl/5ylcGePQAAAAAAAAAgMHCMAwVldW1r+zt8mhtcbnqW7ym9pFti1Ou3aY8h01zstOUFBNhan0zzc5K1XPr9nbZX+yp15GaJg1LZAVTAMDg1er16eN9VSpwta/u/bHJoVxJmjwqUXkOm+Y50jUzM0XREWGm1oe58h3puuVcu367yt3t8d+9X6TZ2Wk6Z/yxF5MzDEPFnno5Xe1vg1lbXK665jZTx5lli+tYNd6ms3PSlBg9eD8vAgAAADDfKRH+Pio8PFyXXXaZLrvsMu3du1dPPvmknnnmGZWWlkr6fDXwlpYWvfrqq3r11Vc1duxYXXvttfre976nUaNGDfAVAAAAAAAAAACCrbyu2b9So9Pt0cHqJlPrp8ZFtoe97WnKtds0OiXW1Pr9aU522jGPrSup0IVT+V4dADB4GIYh15E6f9h7XT88xDUyKbp9ZW9HunJz0pQWH2VqffS/2+c7tK6kXBt2V3Z7/L9e/Fj/+kF+wENu5XXNKiwql9NVJqfLowMmf15Mjo1Qbk572DvPbtOY1ND5vAgAAADAfKdU+PuLxo4dq5///Oe677779K9//UtPPPGE3nzzTbW1tT9xa3S8x2nPnj269957df/99+vrX/+6rrvuOp1//vmyWnlNEgAAAAAAAAAMRU2tXn20u1IFrjIVuDzafrDG1PpR4VbNykr1r9Y4aUSirFaLqX0Ey/DEaI1Li9Xu8oYux9aVlBP+BgAMuEPVTSp0tz/A5XR7VFbbbGr9uMgwzclO8we+c9Lj/G+fRmgKD7Nq6bem6/zHC1TZ0NrleHl9i257YbNuOc/uX93b7M+LEWEWzcxMUb4jXfkOmyaPSlJYiH5eBAAAAGC+Uzb8fZTVatXChQu1cOFCHTp0SM8884yefPJJFRcX+2/KDcNQW1ubVqxYoRUrVigjI0M33nijrr/+etlstgG+AgAAAAAAAABAXxiGoc8O1frD3utLKtTc5jO1j9NGJip/vE359nSdOS5F0RFhptYfSLOz0roPfxdXDMBoAACnurrmNq0rLldBxxs73EfqTK0fZrVo+phk/wrMU8ckKyKMhcOGmpFJMfrfK6bq+8981O3xtcUVWlu83tQ+JwxPaP+5ctg0OytVsZGnfJwDAAAAwDFwt/AFI0aM0IwZMzR16lQVFxf793/xyWzDMFRaWqqf/exnevDBB3XrrbfqZz/7mWJiYgZiyAAAAAAAAACAk3Cktsm/UmN/rAI6Minav7J3rt0mW3yUqfUHkzk5qXrxo31d9ruO1MlT1zykrx0AMPDavD5t2V8tp8sjp8ujTXsr1eYzTO3DMSzeH/aelZWqhOgIU+tjcPrSxOG6Lj9LTxSU9Ev9YQlR/p+rPLtNwxKj+6UfAAAAAEMP4W9JBw4c0NNPP62nn35ae/bskdQ18P1FFotFhmGovr5eDz/8sF566SX94x//0GmnnRbUcQMAAAAAAAAAeqap1av1JRX+1b0/O1Rrav24yDDNyU5TnsOmfEe6ctLjAr5nHspmZ6Ud89j6kgqdf8bIII4GAHAq2FNe3/4Al8ujwiKPapvaTK0/LCEq4CGu4YRyT1l3fnWi1u+u1Cf7qvpcKyYiTHOyU5XnSFe+wybHsPhT5vMiAAAAAHOdsuFvn8+nN954Q0888YTeeusteb1ef8j7aLhbksaNG6frrrtO3/ve97Rjxw498cQTWr58uZqbm/3tiouLNX/+fG3dulVpacf+khsAAAAAAAAAEByGYWjHwVo53e1h73UlFWpp85lW32qRpo5JVr7dpjxHuqaPTVZEmNW0+qFkVHKMxqTGaF9FY5dj64rLCX8DAPqsqqFFa4rKO97YUdbtnNMXRx/iyrXblO+wyU4oFx0iw6367bem6/ylBb1+yMBqkc4YffTzok0zxqYoMvzU/LwIAAAAwFynXPh79+7devLJJ/XMM8/o4MGDktp/CWCxWPxhbqvVqgsuuEA33HCDvvrVr/pv7EeMGKHzzjtP5eXlevTRR/Xoo4+qqalJhmHo8OHDevTRR/WLX/xiIC8PAAAAAAAAAE5ZR2qa5HR7VOBq/+Opaza1fmZarPI6QmFnZ9uUFBthav1QNjsrTfsqSrvsX1dSMQCjAQCEupY2nzbtrZTT5VGB26OtpVXyGSc+r6fCrBZNH5Os3I5Q7rQxp+5DXDixMamx+tVlU3Tj3zadsO3Y1Nj2N8HYbZqbw+dFAAAAAP3jlAh/t7W1afny5XriiSf03nvvyTCMgFW+pfYAeEZGhq699lpdd911GjVq1DHrpaWl6Re/+IW+/e1va+7cuaqpqZFhGPrnP/9J+BsAAAAAAAAAgqSp1av1JRUqcLWv7v3ZoVpT6ydGh3esAJqufIdNY1JjTa0/lMzOStUrG7uGvz87VKvK+halxEUOwKgAAKHCMAy5j9R1PMBVpnUlFWpo8Zrah2NYfHvY227T7OxUJUQTykXPfe30kfrhgvH633d2BexPionQ3Jy0jsB3usam8XkRAAAAQP8b0uHvXbt26YknntCyZcvk8XgkdV3l22Kx6Gtf+5puuOEGXXDBBbJae/5E96RJk/Qf//EfeuCBByRJxcXF/XIdAAAAAAAAAID273d3HKz1h73X765QS5vPtPrhVotmZKYo325T/vh0nZGRpDCrxbT6Q9mc7LRjHlu/u0JfnTwiiKMBAISCstpmFXa8scPpLtPhGnPf2GGLj1K+w+YPfI9Iija1Pk49t37Zobl2mwpcZYqPCteZ41L5vAgAAABgQAy58Hdzc7NefvllPfHEE3I6nZLU7SrfI0aM0Pe//31dd911yszMPOn+ZsyY4f97Q0NDH0YOAAAAAAAAAOjscE1TeyjMVSanu1yeOnODYdnpcZrXsbL37Ow0xUcNua/Ng2J0SoxGJUXrQHVTl2Prigl/A0BfVNS3dISky7R5b5XafIZGp8Qo32FTnj1dk0Ym+H8POpgdfWOHsyPwveNgjan1oyOsmpWVpny7TXkOmyaOCI3/LggtMzNTNDMzZaCHAQAAAOAUN+S+xR4xYoRqatq/KOi8yrckffnLX9aNN96oiy++WOHhfb/8xMRESeKLAwAAAAAAAAAwQX1zm9aVlHcEvj1yHakztX5ybIRy7TZ/MGx0Sqyp9U9VFotFs7PTtHzz/i7H1pWUD8CIACB0Nbd5tXF3pQo6At/bDtSo41edfiWeehW4PJI+869wnWe3Kd9h07DEwbHCtc9naMehGv+cbvYbOywWafKoROU70pVvt2lGZoqiI8JMqw8AAAAAwGA15MLf1dXVAUFswzBks9m0ZMkSXX/99bLb7f3S79GgOQAAAAAAAACg57w+Q1v3V8vpKlOBy6NNeyvV6jVOfGIPhVstmpmZonyHTfmOdJ2ekaQwK9/l9ofZWandhr+3H6xRdWOrkmIiBmBUADD4GYahXYfrVNAxF64rKVdTa89D0p66Zi3fvN//b/DEEQntYXBHumaNS1VMZPAC0Yeqm/zXUej2qLy+xdT6Gckxyut4gCvXblNqXKSp9QEAAAAACAVDLvwtyb/Kd35+vm688UZ985vfVGRk/9z45+XlqaSkpF9qAwAAAAAAAMBQtLe8QQXuMjldHq0pKld1Y6up9bPT4zTPka58h02zs9MUHzUkvwofdGZnp3W73zCkj3ZX6MuThgd5RAAweB2pbVKh26OCXR453R4dqW02rfZnh2r12aFaPVFQoshwq2aNS+0Ig9s0aUSirCY+BPXFN3YUuDxym/zGjviocM3JTtO88e0rm2fZ4liQCwAAAABwyhty33gnJSVp8eLFuuGGGzRp0qR+7y8qKkqZmZn93g8AAAAAAAAAhKrqxlZ9WOTxB8P2VjSYWj85NkK5dpvmdaxympEcY2p99My4tFgNS4jqNsC4roTwN4BTW2OLV+t3V/jfdPHZodqg9NvS5pPT3R4w178kW3xkx8rZ7Q9JDU+M7lW9/n5jR5jVomljkpVntynfYdPUMcmKCLOaVh8AAAAAgKFgyIW/Dx48qOjo3n1JAQAAAAAAAAAwT0ubT5v3Vsrpbg97bymtks+8XJgiwiyaMTZF88a3B9cmj0pSmImrmOLkWCwWzc5O0z8/OdDl2Lri8gEYEQAMHJ/P0PaDNR1zYZk27K5US5tvoIclT12LXv/4gF7/uP3f6gnDE/yrgs/OSlNMZFiXc/ZVNKjA5ZHTXaZCt/lv7MiyxXUE0m06OydNidERptYHAAAAAGCoGVLh782bN+uvf/2rf3vRokU688wzB3BEAAAAAAAAADD0GYahorI6/8rea4vL1dDiNbUP+7B4/yqgs7PTFB81pL7eHjJmZ6V2G/7+9ECN6prb+P8bgCHtYHVje0ja5VGh26Py+hZT649JjVG+I13DEqK0pqhcm/ZUqq2PT1ftPFyrnYdr9aSzRJFhVp2VlaI8e7rGpMZobXG5nC6Pdpf3wxs7ctrn9Fy7TWNSY02tDwAAAADAUDekvmX94IMP9Nhjj8lisSgsLEw//vGPB3pIAAAAAAAAADAkldU2q7BjZe9Ct0eHappMrW+Lj1Su3eZfCXRkUoyp9dE/5mSndbvf6zP00e4KnTthWJBHBAD9p665TeuKyztWxfbIfaTO1PoJ0eHKzWmfB/MdNmWmxfmP3T4/sP8CV5mKyur71F+L16dCd7kK3ea+rSEizKKZmSnKd/DGDgAAAAAAzDCkwt9NTe2/XDAMQ6NHj1Z6evoAjwgAAAAAAAAAhobGFq/W766Q01WmApdHnx2qNbV+VLhVs7JSO1b3TtfEEQmyEgwLOTnpcbLFR8lT19zl2Npiwt8AQpvXZ2hLaZWcHW+62LS37ytvf1G41aLpY5OVZ09X/nibpmQkKTzMesz28VHh+vKk4frypOGSpP1Vjf552un2qKqh1bSx9db44fHt1+GwaXZ2qmIjh9SvpQEAAAAAGFBD6i57xIgRkiSLxaJhw/gCGQAAAAAAAABOltdnaNuBav/K3h/trlSL12dqH5NHJbavZmpP15njUhQdEWZqfQSfxWLR7KxUvbH1YJdj60rMXUkWAIJhb3mDCtxlKtjl0Zoij2qa2kytn50ep3y7TXmOdM3JTlVCdMRJ18pIjtGVZ43VlWeNDZjHC1xl2rinUq1e84LqndniIzve1pGuPLtNI5Ki+60vAAAAAABOdUMq/D1q1Cj/3ysqKgZwJAAAAAAAAAAQevZVNMjp9sjp8qiwyPwVQ0cmRXcEw2zKtdtki48ytT4Gh9nZ3Ye/t5ZWq6GljdVfAQxq1Q2tWlPkUUHHfLi3osHU+imxEZprt2meoz0onZEcY2r9o8KsFk0Znawpo5N1y3l21Te3aV1JuVbval8V3H2krk/1j76xI99hU56dN3YAAAAAABBMQ+ob1tzcXMXGxqqhoUElJSWqqKhQamrqQA8LAAAAAAAAAAal6sZWfVjkUYGrPQi2p9zcgFtcZJjOzknzrwSakx4ni4Vg2FA3Oyut2/1tPkMb91Qq35Ee5BEBwLG1en3avLdKBa4yFbg82lJaJZ+JC2RHhlk1MzNFeQ6b5jn+P3v3Hd/Wfd/7/30A7j0OKVnUIgnI8pQt2aIsAUocO3bSJrbT1NmRk9pNGydt0qQZHWnqJm1umvW7TZpmxyOzWY4bpzexk9gCqGVbnrIGwCFqC+DeIIDz+wMkxAFq8ZAgwdfz8cADwPme8zlfUgIPCbzP51TpimUlaQlJF+Zm6VVrl+hVa5dIko53DcofGAu5h9R5Hid8XX5JibxuU143V+wAAAAAACCdMir8XVhYqNtuu00/+tGPFI/H9dBDD+kDH/hAuqcFAAAAAAAAAPNCJBrXs22d8gfDsxJwcxjSuhVl8rpMeddU6ZoVZcp2OuzbARYEd3WRyguyUwYJdzd3EP4GkFaWZakp1Jc48SkQ1q7mdvVHYrbuY82SInndVfK4TTXUVszLKx4sK8vXm65foTddv0LxuKV9x3vkC4bkOxTW04c7NBKztLQkTx63KS9X7AAAAAAAYF6Zf+80zNAnPvEJ/fznP9fIyIjuu+8+/dEf/ZHcbne6pwUAAAAAAAAAc86yLAVOJwJujcFEwG3A5oDb6soCedymPK4q3VBfqdL8bFvrY+FxOAxtrK3Qb/admjK2u6U9DTMCsNi19w3LH0yEvf3BsE50D9la3yzKlddtjl7pwtSSkjxb6882h8PQVctLddXyUt37Spcs68yZYVyxAwAAAACA+Sfjwt+XXXaZvv3tb+uuu+5SV1eXXvnKV+qBBx7QzTffnO6pAQAAAAAAAMCsC/UOq3G0s7c/GNKpnmFb65cXZGuzy5TXlegCuqKiwNb6yAwNtZUpw9/PH+nW0EhMednONMwKwGIxNBLT062d8gVD8gfC2ne8x9b6edkObaytlHc07L12aXFGhaQz6WsBAAAAACATZVz4W5Le/va3q6KiQn/2Z3+mEydO6NZbb5XH49Fb3/pWbdy4UbW1tSopKZHTyZvLAAAAAAAAABa2oZGY9rR0yB8Ma/uhkA6c7LW1fk6WQ9evLtcWlymvq0pXLCuRw0EoDGfXUFeRcnkkFtfetk5trjfneEYAMpllWdp/olf+YEi+QFh7Wjo0HI3buo8ra0rkcVXJ6za1YVU5J7EAAAAAAIC0ybjw9+RAt2EYsixLfr9ffr//ousahqFoNDrT6QEAAAAAAADAjMTjll4+0SN/MCxfIKSnWjsVsTngdtklJfK6TXlcpq5fXaH8HAJuuDBrl5aoJC9LPUNT31ff3dxB+BvAjJ3qGUpc5SIQkj/YrnCfvVe6WFaaJ4/blMddpS31laosyrW1PgAAAAAAwMXKuPC3ZVnJx2OXJBu7Hz8GAAAAAAAAAAvF8a5B+QNh+YJh7QiG1d4fsbX+JaV58rhMedymNtebqiom4IaZcToMbayt0OP7T08Z293SnoYZAVjoBiJR7W7pkO9QWP5gSIdO9dlavzDHqRvqK+VxmfKuqVKdWZj8jBEAAAAAAGA+ybjwt3Sm27cdYe+xWgAAAAAAAAAwV/qGo9rV1J7s7t0U6re1/viAm8ddpfoqAm6wX0NtZcrw97NtXRqOxpSbRUd5ANOLxS3tO94tXyBxLNx7uEuRmH1XunAY0roVZfK6q+R1m7pmRZmynQ7b6gMAAAAAAMyWjAt/b926lQ8pAAAAAAAAACwo0VhcLxzrlj8Qlj8Q1t62TkXj9jWlcDoMrVteKg8BN8yhhrqKlMuHo3E9f6RbG2tTjwNYvI52DiSvdNEYDKtrYMTW+qsqCxKdvd1VuqG+UqX52bbWBwAAAAAAmAsZF/5+4okn0j0FAAAAAAAAADintvYB+YIh+Q6F1dgUVu9Q1Nb6tWbhaGdvUzfUV6okj4Ab5tbll5SoODdLvcNT/2/vbm4n/A1AvUMj2pm80kVYLWF7r3RRkpelLaNhb6/b1IqKAlvrAwAAAAAApEPGhb8BAAAAAAAAYD7qGQ24+QIh+QJhHW4fsLV+WUG2ttQnwt4eFwE3pF+W06HrVpfrDwdDU8Z2t3Tor9IwJwDpFY3F9fzRLvlGr3Tx7JEuxWy80kWWw9D6VeXyukx511TpqppSOR1cMRgAAAAAAGQWwt8AAAAAAAAAMAticUsvjAbcfIGQ9rbZG3DLdhrasKo82c30imUE3DD/NNRVpgx/P324Q5FoXDlZjjTMCsBcsSxLre0D8o+e+LSzqT3l1QBmwlVdJI/LlNdtqqGuUkW5fPwJAAAAAAAyG+9+AAAAAAAAAIBNjnYOJMPe/kBYPUP2BtzWLCmS110lj9tUQ22FCnJ4ixfzW0NtRcrlQyNxvXisSxtWpR4HsHB1DUTUGGyXP5gIfB/tHLS1fkVhjjyuxJUuvG5Tl5Tm21ofAAAAAABgvuOTAQAAAAAAAAC4SH3DUe1qapdvtKNpc7jf1vpmUa68bjMZcltSkmdrfWC2XVlTqoIcpwYisSlju5o7CH8DGSASjWtvW2fyxKcXjnXLsu9CF8rJcuj61YkrXXhcpi6/pEQOrnQBAAAAAAAWsUUX/o5Gozp16pQ6OzvV29ur4uJilZeXa8mSJcrKWnTfDgAAAAAAAAAXIBa39NKxbvkCIW0PhLX3cKeicfsSbnnZDm2srZR3NOy9dmmxDIOAGxaubKdDG1aVyxcITxnb3dKh992YhkkBmBHLshQ83aftgbD8gZB2t3SkPMFjJtYuLU6c/OSu0sbVFcrPcdpaHwAAAAAAYCFbFGnnpqYmfetb39L27dv17LPPanh4eMo6ubm5Wr9+vV7xilfonnvuUW1tbRpmCgAAAAAAAGC+Od41mAx7NwbD6hoYsbX+lTUl8riqtNVtav2qcuVlE3BDZtlUV5ky/P1Ma4eisbiynI40zArAhQj3DasxGNb2Q2H5gyGd6pn6WdtMVBXnyusy5V1jaovLVHUxV7oAAAAAAACYTkaHv0+ePKl7771XjzzyiKzR68tZ01xnbmhoSDt37tTOnTv12c9+VnfccYe+8pWvaOnSpXM5ZQAAAAAAACxCfcNR7Wpqlz8YVqh3WMsr8nX3llpVlxB8Sofx/x6+QEhNoX5b61cX58rrrtLW0YCbWZRra31gvmmorUi5vD8S00vHe3TNirK5nRCAcxoaiWlPS8fosTCs/Sd6bK2fl+1QQ22lvG5TXneV1iwp4koXAAAAAAAA5yljw9+PPfaY3va2t6mjoyMZ+DYMI/nG0fgQ+Pg3kyzLkmVZ+sUvfqEnn3xSP/zhD3XzzTfP7eQBAAAAAACQ0WJxSy8e65bvUEi+QFh72zoVjU9sWvDD3W36j7deq1deWp2mWS4e0VhcLxzrlj8Qln+af4+ZyM1yqKGuUlsJuGGRunp5mfKyHRoaiU8Z293cTvgbmAficUv7jvfIFwypMRjWU62dikSnvmYvlmFIVy4rlddtyuM2tWFVuXKzuNIFAAAAAADAxcjI8HdjY6PuuOMODQ4OSkqEu8dC3VlZWVq7dq1M01RhYaH6+/sVDod18OBBjYyMTFi/vb1dd9xxhx577DHdcMMN6fySAAAAAAAAsMAd7RyQP5DonukPhtU9OHLW9XuGovqz+5/Sx16zVu/ZWkdY2GaH2/vlCyQ6e+9oalfvUNTW+pddUpIMe1+3ulx52QTcsHjlZDm0fmW5djS1Txnb3dKhv3hFfRpmBeBIx4D8wcTvJTuCYXUOnP13kwtVU5afDHtvrjdVUZhja30AAAAAAIDFKuPC3/39/brzzjs1ODiYDHFL0lve8hb92Z/9mbZu3aqcnKlvLkUiEfl8Pn3729/Wj3/84+SHaQMDA7rzzjt16NAhFRQUzOnXMl8MDQ1px44dOnDggDo7O5WTk6Ply5eroaFBdXV16Z4eAAAAAADAvNQ3HNWupnb5AiH5gmE1h/ovuEbckj7zvwe073iPPvvGq5WfQ4D4YnUNRLSjqX00fB/SkY5BW+ubRbmJsPcaU1tcpqqL82ytDyx0DbWVKcPfT7V0KBa35HRwggsw27oHR7SzKXEiWmMwrNb2AVvrF+VmaVNdpbauMeVxmao1Czl5DQAAAAAAYBZkXPj73//933Xy5Mlk8LumpkY/+clPtGnTprNul5OTo5tuukk33XSTPvjBD+rOO+/U0aNHJUknTpzQ5z73OX3yk5+ciy/hnI4dO6Y9e/Zo9+7d2rNnj55++mn19vYmx1etWqXW1tYZ7ycUCum+++7T/fffr/7+1B9ObtiwQZ/4xCd0++23z3h/AAAAAAAAC1ksbumlY93yBULaHghr7+FOReOWLbUfef64mkJ9+sa261RTlm9LzUwXica1t61ztNt6SC8e65ZN/xySEp2MG2or5B3t7r12aTEBN+AsGuoqUi7vHY5q/4keXVlTOsczAjLf2LGwMZgIfL9wtMvWY6HTYWjd8lJ53FXa6ja1bkWZsp0O+3YAAAAAAACAlDIu/P2d73wnGfyurKxUY2OjVq5ceUE1Nm7cKJ/Pp+uuu07t7e2yLEvf+ta30hr+bmxs1Be+8AXt3r1bx48fn/X9PfHEE7rzzjsVDofPut4zzzyjO+64Q9u2bdM3v/nNlF3VAQAAAAAAMtWxrkH5DoUSHTSbwuoaGJm1fe073qPbvuzXV9++Xg11lbO2n4XKsiwFTvclOnsHQtrd0qGBSMzWfaxdWpwMe2+srVBeNp3YgfN1zYoy5WQ5FInGp4ztam4n/A3YwLIsHTzZK39w9o6FqysL5HGb8riqdEN9pUrzs22tDwAAAAAAgHPLqPD3Cy+8oGPHjskwDBmGoc9+9rMXHPwes3LlSn3mM5/Rn//5n0uSjh8/rhdeeEFXX321nVM+b0899ZR+8YtfzMm+/H6//uiP/kiDgxMvfVtWVqba2lp1dnbqyJEjisXOvGH44IMPqq+vTz/96U/pcAQAAAAAADJW/3BUu5rb5QuEtT0QUnMo9dXSLlZFYY46+iPTjrf3R/T2b+3WJ19/ud6xadWifx8m1Duc7GbqD4Z0qmfY1vpmUa48rkp53FXyuk0tKcmztT6wmORlO3XNijLtaemYMra7pUP3eOvSMCtg4euOSAe7DR3qMvTpF/co1Df97xEXo6wgW1vqTW1xmfK6Ta2oKLC1PgAAAAAAAC5cRoW/9+3bJynR2SA/P19vectbZlTvrW99q/76r/86GYLet29f2sLfZ1NUVKS+vj5banV2durNb37zhOD3qlWr9H//7//VbbfdlvxA8ejRo/r0pz+tr3/968n1fv7zn+tLX/qSPvShD9kyFwAAAAAAgHSLxS29dKxb/mBY2w+FtLetUyMxy7b6uVkObayt0FZ3lbxrTF26pFiPPH9cH/3pCxpO0R1XkqJxS5/45T7tO96j+26/QrlZi6f79GAkpj2tHfIHEt3WD5zstbV+XrZDG2sr5XWZ8rhNrV1avOgD9oCdNtVVpgx/72npUDxuyeHg9Qacy0Akqt3NHfIFwnry4Ck1hcd/1Dfz4HeO06HrVpePdvc2dcWyUjl5bQIAAAAAAMwrGRX+Pn36tCTJMAzV1taqoGBm3QcKCgpUW1url19+eUL9dCouLtaGDRt0/fXXa+PGjbr++uvV0tKiG2+80Zb6n/vc53T8+PHk89raWvn9fi1btmzCesuXL9fXvvY1rVy5Uv/wD/+QXP4v//Iveve7363y8nJb5gMAAAAAADDXjnUNyh8IaXsgrMZgWF0DI7bWX7u0WFvXJDpJX7+6QnnZE8Pbt19To/qqIr3nwad1vHto2jo/euqIAqf79F/vWK/q4szsSB2PW9p3vEe+YEj+QFhPt3YqEksdir8YhiFdsaxEHleVtrpNrV9VPuXfA4B9NtVW6D9SLO8eHNGBk726fFnJnM8JmO9icUsvHutOnvhk94loknTZJSXyuhPdvTeurlB+DsdCAAAAAACA+Syjwt9DQ2c+DMvPz7elZl7emQ/OhoftvWzshXj961+vW265RWvXrpXD4Zgw1tLSYss+QqGQvvzlL09Y9s1vfnNK8Hu8v/u7v9NvfvMbbd++XZLU3d2tz3/+8/rXf/1XW+YEAAAAAAAw2/qHo9rV3C5fICxfIKSmUL+t9c2iXG11m/KuSYSqzieofWVNqR75K4/u/d5e7Wmd2iV3zDOHO3Xblxv19Xdu0LoVZTbOOn2Odg7IHwjLFwxrRzCsTpvD9zVl+fKMdvbe4jJVUZhja30A07t2ZbmynUbK4OrulnbC38Cow+398o2ehLajqV3dg/YeCy8pzUseCzfXm6oqzrW1PgAAAAAAAGZXRoW/q6qqJEmWZamtrc2WmkeOHEk+Nk3TlpoXo76+ftb38aMf/Uh9fX3J51u3btVNN9101m0Mw9AnP/nJCet95zvf0ac//WkuiQsAAAAAAOalWNzSvuPd8gXC2n4oZHsHzZwshxpqK+R1m/K6q7R2afFFvU9iFuXqe/c06F9+tU/f2zX9e10ne4Z059d36jNvuEpv3LB8JlNPi56hEe1sapc/EJY/GFZL2N7wfVFuljbVVcrrToTc6sxC3rcC0iQ/x6l1y8v09OHOKWO7mzv07i21aZgVkH5dAxHtaEqciOYPhnSkY9DW+mPHQo+rUh53leqrOBYCAAAAAAAsZBkV/l6xYkXycTgc1u7du9XQ0HDR9Xbv3q1QKJR8vnLlyhnNb7775S9/OeH53XfffV7b3XjjjaqtrU12ID958qR27dqlG264wfY5AgAAAAAAXIzjXYPyB8LaHgipcRa6Sa9dWqyta6rkdZu6fnWF8rKdttTNyXLo03dcpSuWleqffvnStCH1SDSuD//kee073qO//6O1ynI6Uq43H4zE4nruSFci4BYI6fmj3YrF7QvfOx2G1i0vlded+PdYt6JM2fP4+wEsNg11FSnD33taO2RZFoFULArD0Zj2Hu6SPxiSPxDWC8e6Zdl3KJTTYejaFWXyuE15XBwLAQAAAAAAMk1Ghb89Ho/y8vI0PDwsSfr4xz+uP/zhDxdd7+/+7u+Sj3Nzc+XxeGY8x/mqr69P27dvn7DslltuOa9tDcPQzTffrG9+85vJZb/61a8IfwMAAAAAgLTpH45qd0u7th8KyxcIqSlkbzdpsygnGS72uExVl+TZWn+yt25cKXd1kf7ye3sV7huedr3vNLbo4KkefeWt61VemDOrczpflmWpKdQvfyAkfzCsXc0d6huO2rqPWrNQHleis/cN9ZUqycu2tT4A+zTUVuo//9A0ZXlHf0SB031as6Q4DbMCZpdlWTp0qk++0WPh7uYODY7EbN3HknxLr7p8mV51RY021VWomGMhAAAAAABAxsqo8Hd+fr5uueUWPfLII5Kk7du36+6779Y3v/lNORzn39HAsiz95V/+pZ544olkl5Fbb71VeXmz+yFeOu3bt08jI2c6XtXW1mrp0qXnvf2WLVsmhL+fe+45O6cHAAAAAABwVvG4pX3He7Q9EJIvENIzhzun7ZJ9MXKyHNq4uiIR9nabumxpiRyOue1Oe93qCv3PX23RXz70jJ4/2j3teo3Bdt32n359c9t1Wru0ZA5neEZ737D8wbD8gbD8wbBOdA/ZWr+8IFubXaa8LlNbXKZWVBTYWh/A7NmwqlxOh5Gy4//u5nbC38gYp3uGJhwLT/dOf/LWxagszNHGVaUqGz6pS0stledKN97oUklJeo79AAAAAAAAmDsZFf6WpPvuu0+/+tWvZFmWLMvS/fffr+eee06f//zndeONN55z+yeeeEIf+chHtHfvXhmGIcuy5HQ69S//8i9zMPv02b9//4Tnl19++QVtP3n9yfUAAAAAAADsdrRzQP5AWL5gWDuCYXUOjJx7owuwdmmxvG5TXneVNtZWKC/baWv9i3FJab5+/Bc36O9/8aJ+vvfYtOsd6RjUn3x1h75w5zq99qpLZn1eQyMxPXO4U9sDIfkDYe073mNr/RynQ9etLpfHbcrrqtIVy+Y+fA/AHoW5WbqqplTPHemaMrarpUPvvGH1nM8JsMNgJKY9rR3yHUp09z5wstfW+rlZDm2srUhe6eKypSXq6+vVH/5wwtb9AAAAAAAAYP7LuPD3unXr9OEPf1if+9znkuHtZ599VjfffLNWrlypG2+8UVdffbVM01RhYaH6+/vV3t6u559/Xk888YQOHz4sKdH92zAMGYahD3/4w7rqqqvS/JXNroMHD054vmLFigvafvL6hw8f1tDQUEZ3SwcAAAAAAHOrd2hEO5vak100m8P9ttY3i3LkcSXC3h63qSUl8/N9jbxsp75w5zpdsaxU//roy0rRPFeSNBCJ6b3f36u/epVLf3PzGlvD0pZl6dCpPvkCIW0PhLWnpV1DI3Hb6ktnwvced5U2rq5Qfk76w/cA7NFQV5Ey/L27uSP53jww38Xjll4+0SN/MCxfIKSnWjsVidp7LLxiWUnyxKfrVpfPixPRAAAAAAAAkH4ZF/6WpM9+9rM6efKkHnrooeSbxJZl6fDhw3rggQem3c6yEp+UjYW+LcvStm3b9H/+z/+Zk3mn0+nTpyc8X758+QVtv2TJEmVlZSkajUqS4vG42tvbVVNTY8vcQqHQBW0TDAYnPO/r61NPj70dp3B++vv7z/ocAAAsDBzTAQDpEI1beul4r3Y0d2pXa5dePNaj2DRB54uR4zS0fkWpbqgr1+baMrmrC+VIBg4j6umJ2LezWXDn1ZVaUXylPvLwAXUPRqdd78u/D+qFIx36zG2Xqig366KP6+19Ee1s7dLOlk7taulSqM/e7091UY421ZbphtpybVpdpsqinOTYyFC/RoZs3R2ANLp6SX7K5eG+Yb3Qekq1lQVzPCPg/JzqHdaulsSxcGdLl+1XHVlakqsbasu0aXWZGlaXqaLwzLEwMtivyODE9flbHQCAzMAxHQCAzMAxffHp6+tL274zMvwtSQ888ICuu+46ffzjH9fg4OCETiFjIe/xxgLfY+P5+fn693//d73vfe+bszmn0+T/hIWFhRe0vWEYys/PV2/vmcsY2vUf+6tf/aruu+++GdXYs2ePTp48act8MDN79uxJ9xQAAIANOKYDAGaDZUmhIelgt6GDXYYCPYaGYvZ2f72kwNKlpZbWllmqL7aU4wxLg2Edf1k6/rKtu5ozf71W+tYBp04MTv+9ejLQoTd8dYfuuTSm6kmZy+mO65GY1Nyb+Lc42G3o2IC9/xY5DkuuEkuXlllaW2ppSX5UhjEghY7rhQvrAwBggRmKSoacsjT158oPHtujzUtsPNMHmIHhmNTUY+jA6O8mJ89yrL0YeU5L7hJLa0oTx8PqvKgMo186fUzPnz739pPxtzoAAJmBYzoAAJmBY3rma2trS9u+Mzb8LUl/9Vd/pT/5kz/Rf/7nf+o73/nOlO7W440FwpcsWaK7775b733ve23pWr1QTA5q5+Vd+GWNZyv8DQAAAAAAMlffiHSo20gGvjsj9oaqirLPhL0vLbVUmnPubRYaM0/6m6ti+n7Qoec7HNOud2rQ0BdfdGqbO67Ly6cGKy1LOj6QCN8f6DLU3GNoxLLv38OQpVVF0qWlli4ti2tVkZQ1/XQBZLC8LGl5oXQkRfOjYI9B+BtpE7ekY/1Khr2bew3FbDwWOmRpVbF0aWlca8ssrSySnPb+6gMAAAAAAIBFIKPD35JUU1Ojf/u3f9O//du/6cCBA9q9e7cOHz6szs5O9fX1qaioSOXl5Vq1apU2bdqkSy+9NN1TTouhoYnXzc3JufBPQnNzcyc8HxwcnGZNAAAAAACwWEXjE7tJH+1Xys6vFyvLsFRXkugkfWmZpWUFkmMRhKpyndK718T122OWfn3EOe16gzFD3zjg0OtWxnXTMks9I2c6rR/sNtQ7Yu83y8xLhO4vLbXkLrVUkPHvRgI4X/Ullo70T/2ZE+wxZFmSsQh+dmN+6Bw+c+LToW5D/VF7//NV5yV+J7m0NNHlO49jIQAAAAAAAGZoUb3FtHbtWq1duzbd05iXJnf6jkQiF1xjeHj4rDUv1r333qs777zzgrYJBoO64447ks83btyoyy67zJb54ML09/dPuITFxo0bVVhYmMYZAQCAi8ExHQBwsSzLUiA0oF0tndrZ0qVn2ro1FI3buo811YXaVFumG2rLtX5FifKzpw8/Z7pXSbr1ULv+/pGD6o/EUq5jydD/tDnlO2mpy+ZO68V5WWpYVarNdeXaVFuu5WX2vD8EIPMYgXY98ZOXpyzvjhhyX7tJK8rz0zArLAYDkZieOtylnS1d2tnSqZZ2exvZlOZnadPqxO8lN9SW6ZLS2TsW8rc6AACZgWM6AACZgWP64rN///607XtRhb8xvaKiognPJ3cCPx+TO31PrnmxqqurVV1dPaMaRUVFKikpsWU+mJnCwkL+LQAAyAAc0wEAZ3O6d0j+QFj+QFi+YFih3uFzb3QBqotz5XGb8rpNbXGZqi4mYDze7deV6IqVpv78wWfUEu6fdj07gt9ZDkPrV5Yn/z2uXl4m52JotQ5gxl5xeb4M42VZ1tSxl04P64pVS+Z+UshIsbill451yxcIaXsgrGfbOjUSS/Ef7yJlOw1tWFUur7tKXrepK5aVpu1YyN/qAABkBo7pAABkBo7pmc+ujOzFIPwNSVP/E/b3T//BYCqWZc1a+BsAAAAAAMxvg5GY9rR2yB8IyRcI68DJXlvr52c71VBXkQxVuauLZBgEjM/GVV2sh9+3RX/9w2f15KGQrbXrzEJ53aa87iptqq9UUS5vMQK4cKX52bpsaYlePtEzZWx3c4fefP3KNMwKmeJo54B8oyei+YNhdQ+O2FrfXV0kj9vUVneVNtZWqJBjIQAAAAAAAOYQ70ZBkqZ01j569OgFbX/q1ClFo9Hkc4fDIdM0bZkbAAAAAACYX+JxSy+f6JE/GJYvENJTrZ2KROO21TcM6eqaUnncpjyuKq1fVabcLKdt9ReL0vxsfedd1+tzvzmorz3ZdNF1ygqytaU+0dnb4za1vLzAxlkCWMwa6ipSh79bOtIwGyxkvUMj2tXcId/oiWhnu/LFxagozJHHdeZYeElpvq31AQAAAAAAgAtB+BuSpEsvvXTC87a2tgvafvL6q1atUl4el1wGAAAAACBTnOweSgaqGoNhtfdHbK2/vDw/EahyVWlzfaXKC3Nsrb9YOR2GPv7atbp8WYk++tPnNTRy7pB+lsPQhlXlye7eV9aUyumg0zoA+zXUVuq7ja1Tlh/rGtSRjgGtqOBkE6QWjcX1wrFu+Q6F5Q+GtLetS7G4ZVv9HKdD19eWy+NKXHXk8ktK5OBYCAAAAAAAgHki48PfsVhMTz/9tJ599lm1tbWpp6dHg4ODsqwLexPQMAx9+9vfnqVZpt/atWsnPH/55ZcvaPv9+/eftR4AAAAAAFhYBiJR7W7u0PZASP5AWIHTfbbWL87N0g31lfKuqZLXZWpVZYEMg1DVbLlt3TLVmYX6i4ee0bGuwSnj9VWF8roTAbeGukoV5Wb824YA5oGNtRXTju1u6SD8jQna2geSv5c0NoXVOxQ990YX4NIlxcnO3g21lcrP4aojAAAAAAAAmJ8y9lOc7u5uffrTn9ZDDz2kUCg0o1qWZWV8+PuKK65Qdna2RkZGJEmtra06ceKELrnkkvPavrGxccLza665xu4pAgAAAACAWRSLW9p3vFu+QFi+QEjPHO7USMy+DppOh6FrV5TJ666Sx21q3fJSZTkdttXHuV1ZU6pff8Crb/zhoJ54sUUFTslVaundr71Ba5ZXpXt6ABahisIcXbqkWAdP9U4Z293crj/dsDwNs8J80T04op1N7ckrj7R1DNha3yzKkcdlJn83WVLC1UwBAAAAAACwMGRk+Hvnzp16wxveoFAoNKHDN92jpldcXKytW7fqd7/7XXLZY489pm3btp1zW8uy9Pjjj09Y9vrXv972OQIAAAAAAHsd7RyQPxCWLxhWYzCsroERW+vXmYWjHTSrtKmuQsV52bbWx4Urzc/WX3hWas1IU3LZ0pLcNM4IwGLXUFeROvzd0pGG2SxMQyMxPd3aKX8wrM7+iApyndpUV6kb6itVsoCOvSOxuJ470iVfICx/IKTnjnQpbt95aMrNcmhjbUXidxNXldYuLZbDwedGAAAAAAAAWHgyLvy9b98+3XrrrerrS1yK2DCMZAB8fBAcU912220Twt/f/va3zyv8/Yc//EEtLS3J50uWLFFDQ8OszBEAAAAAAFy83qFEB01/MCx/IKzmcL+t9csKsrXFZcrrMuVxm1peXmBrfQBA5mmordSDOw9PWd7WMaAT3YO6pDQ/DbOa3yzL0oGTvfIHwtoeCGlPS4eGo/EJ63y3sVUOQ1q3omz0uFyla1eWKXseXXXDsiw1hfpGw95h7WpuV38kZus+LrukRF63Ka/b1PWrK5SX7bS1PgAAAAAAAJAOGRf+vvfee9XX15fs8m1Zlm655Rbdfvvtuuqqq1RZWamCAj54TOUtb3mL/v7v/179/YkPfrdv367f//73etWrXjXtNpZl6b777puw7N3vfrccjvnzBjIAAAAAAItVNBbX80e75QuE5A+E9eyRLsVsbKGZ7TS0YVW5vO4qed2mrlhWKicdNAEAF2BjbcW0Y7ubO3THtTVzOJv563TvUOJqHYGw/MGwQr3D59wmbknPtnXp2bYu/cfvgyrMSXQE97hNeVymXNVFc37F1FDvsHY0hZOB75M9Q7bWry7Olcdtaqu7SltcpqqKuboFAAAAAAAAMk9Ghb+bmprk8/mS3b7Ly8v1s5/9TK985SvTPbUFobq6Wu9///v12c9+Nrnsnnvukd/v17Jly1Ju85nPfEbbt29PPi8tLdVHPvKRWZ8rAAAAAACYyrIsHW4fkC8Ylu9QSDub29U7FLV1H+7qomSoamNthQpzM+rtJQDAHKsqzlV9VaGaQlOvRrG7pX3Rhr+HRmLa09IhXyAkXyCsAyd7Z1yzPxLT7w6c1u8OnJYkLS3JS1yxw23OWlB6MBLTntYO+W38OsbLy3aoobZytLt3ldYsmftAOwAAAAAAADDXMurTucbGRkmJDzoNw9DXv/71jAp+NzY2anBwcMry559/fsLzoaEhPf744ylrLFu2TJdffvm0+/joRz+qBx54QCdPnpQktbS0aPPmzfqP//gPvf71r0++aXr06FF9+tOf1te//vUJ2//DP/yDKiqm79QCAAAAAADs1TUQ0Y6mdvkCYfkCIR3tnPrewUxUFuYkO4R63VVaWppna30AABrqKlOHv5s70jCb9IjHLe0/2ZPs7r2ntUORaHxW93myZ0g/23tUP9t7VJK0dmmxvG5THneVNq6uUH6O84JrxuKW9h3vTnb2fuZwpyIxe7+OK2tK5HFVaavb1PpV5crLvvB5AgAAAAAAAAtZRoW/xwLLklReXq43vvGNaZyN/d7+9rfr8OHD51zv1KlTevWrX51y7K677tL9998/7bYVFRX68Y9/rFtvvVVDQ4nLLR4+fFi33367ysrKVFtbq66uLrW1tSkWi03Y9vbbb9ff/u3fnv8XBAAAAAAALlgkGtfets7RcFhILxzrlmXZVz8ny6GNqyvkcSc6gV62tEQOBx00AQCzp6G2Qj/Y3TZleXO4X6d7hlRdkpknHp3qGUqevNUYDCvcF0nrfA6c7NWBk736pq9FOU6HNqwqT/4+cMWyUjmn+X3gSMeA/MFE2LuxKayugRFb57W0JG80lJ44Ga2yyP4O5QAAAAAAAMBCklHhb6cz0d3BMAzV1dVxab+LtHXrVj366KO688471dFxprNKV1eXnn322ZTbvO1tb9N3vvMdvucAAAAAANjMsiwFT/clOmgGw9rV3K6BSOzcG16AsU6fXneVNtZW0EETADCnNtVVTju2u6VDr1+3bA5nM3sGIzHtbjlztY5Dp/psrV+Sl6VrVpbr4MkeneoZnlGtSCyunc3t2tncrs/95qDKCrK1pT4RwL5+dfmE300Otw/Y9BUkFOQ4tamuUh6Xqa1rTNVXFfHZAwAAAAAAADBORoW/V65cmXw81rUaF+dVr3qVXn75Zd1333164IEHNDCQ+s3ba6+9Vv/4j/+oP/mTP5njGQIAAAAAkLna+4blD4YToapAWCd77H2fo7o4N9nJc4vLVHVxZnZUBQAsDEtK8lRrFqol3D9lbHdL+4INf8fjll4+0aPtgZD8gbCebu1UJBa3rX6Ww9D6leXJrthXLy+T02HMyoljXQMjevTFE3r0xRM2zf4MhyGtW1Emryvxe8m1K8uVk+WwfT8AAAAAAABApsio8PfmzZtlGIk3NltbWxWNRpWVlTlfYmtr65zub8mSJfrqV7+qL3zhC9qxY4f279+vrq4u5eTkqKamRg0NDXK5XHM6JwAAAAAAMtHQSExPt3bKFwzJdyisl0/02Fo/P9uphrqK0Q6aVXJX00ETADC/NNRWpAx/72ruSLH2/HW8a1D+QFi+YFiNwbA6+iO21q+rKpTXlbhax6b6ShXlTv0MxDAMuZcUy72kWH/mqVUkGtezbZ1qDCbm9fyRLsUtW6d1wVZXFsjjNuVxVemG+kqV5mend0IAAAAAAADAApI5yWhJNTU1uvnmm/XYY4+pv79fv/71r3Xbbbele1oLXn5+vm666SbddNNN6Z4KAAAAAAAZwbIsHTzVK9+hRAhrd3O7hqP2dQI1DOmqmlJ5XIlOoBtWlSs3y2lbfQAA7NZQV6EfPXVkyvLg6T6F+4ZlFuWmYVbn1jcc1e7mdvkCYfkCITWFpgbYZ6KsIFtbXKa2uk153FWqKcu/4Bo5WQ411FWqoa5SH7rlUnUPjmhnU7v8wURH8tb21Ff+tFN5QbY2u8xkd+8VFQWzvk8AAAAAAAAgU2VU+FuSPvvZz+qJJ55QNBrVxz72Md10000qLCxM97QAAAAAAMAid7p3SP5AONkNNNQ7bGv9mrJ8ed2JsPeWelPlhTm21gcAYDY11FZOO7anpUN/dNUlczib6cXill481i3foZB8wbD2Hu5U1MY22tlOQxtWlcvrrpLXbeqKZaVyOuy9WkdpfrZec+VSvebKpZKkIx0Dya7gO4JhdQ6MzHgfOVkOXb+6XB5X4uu4/JISOWz+OgAAAAAAAIDFKuPC39dcc42++tWv6j3veY8OHTqk173udfrxj3+s6urqdE8NAAAAAAAsIoORmPa0dsgfCMkXCOvAyV5b6xflZmlTXaW2rjHlcZmqNQtlGISqAAAL07KyfK2oyNeRjsEpY7ub29Ma/j7SMSBfICx/MKTGYLu6B2cejh7PVV0kr9uU122qobZShblz+9HNiooCvWXjSr1l40rF45b2He+RP5j4ep9q7VTkPK9OctklJYkT0Vymrl9dofwcrjoCAAAAAAAAzIaMC39L0t13362CggL9+Z//uZ588kldccUVet/73qc3vvGNuvLKK/kgFAAAAAAA2C4et/TyiURYyhe4sLDU+XA6DK1bXiqPu0pb3abWrShTttNhW30AANKtobZSRzqOTlm+u6VjTufRMzSinU3t8gVC8gfCam0fsLV+RWGOtrjMZOD7ktJ8W+vPhMNh6Krlpbpqeane+8p6DUZieqq1Y/T3m7D2n+hJrntJaZ48rtGrjrhMmUW5aZw5AAAAAAAAsHhkZPhbkt761reqoaFBr3/967V//3596lOf0qc+9SllZ2eroqJCeXl5F1TPMAw1NTXN0mwBAAAAAMBCdLJ7SL7Rzt6NwbDa+yO21l9dWSCP25THVaUb6itVmp9ta30AAOaThtoK/fSZqeHvAyd71dkfUXlhzqzsNxqL6/mjXdp+KCx/MKznjnQpFrdsq5/jdOi61eXyuE15XVW6YlmJHI6F0aQmP8eprWuqtHVNlSSpvW9Y7f0R5Wc7tbw8n2Y7AAAAAAAAQBpkbPi7sbFRH/rQh3TgwAEZhiHLSrxRG4lEdPLkyQuuxxuYAAAAAACgbziq3c3t8gfD8gfCCpzus7V+SV7WaCfQKnndplZUFNhaHwCA+WxTXeW0Y3taO3TrFUtt2Y9lWWptH5A/ENL2QFi7mtrVOxy1pfaYS5cUy+tOdMVuqK1Ufo7T1vrpUlmUq0o6fAMAAAAAAABplZHh76985Sv6m7/5G8XjcVmWJcMwZhTeHguOAwAAAACAxWWsE6g/0C5/MKRn27oUtbETaJbD0PqV5clw2NXLy+RcIJ1AAQCw2/LyfC0rzdPx7qEpY7ubZxb+7uyPqLEpcfKWLxDWsa7BmUx1CrMoN3E8dyWO6UtKLuzqowAAAAAAAABwvjIu/P3oo4/qAx/4wITQ91h4OycnR6WlpSoooGsWAAAAAACYyrIsNYf75Q+E5Q/OTifQuqpCbXVXyeMytam+UkW5Gff2DAAAF8UwDDXUVeoXzx6bMra7pf2Cag1HY3rmcGfymP7isW7Z2eclN8uhjbUViWO629TapcVcQRQAAAAAAADAnMi4Txc/+MEPJoPflmWpurpaH/rQh3TbbbdpzZo1cjgc6Z4iAAAAAACYR8J9w2oMJjqBNgbDKbuNzkRZQba2uExtdZvyuKtUU5Zva30AADJJQ21FyvD3yyd61D04otL87JTbWZalQ6f65AuE5A+Gtbu5Q4MjMVvndvklJfKuMeV1Vem61eXKy3baWh8AAAAAAAAAzkdGhb937dqlpqamZHeNq6++Wr/73e9UWVmZ5pkBAAAAAID5YjAS057WDvkDIfmD7dp/osfW+tlOQxtWlcvrrpLXbeqKZaVyOugECgDA+WioS/1+vmVJT7d26KbLliSXne4dUmMwLF8gcRLX6d5hW+eypCQ3eTzf4jJlFuXaWh8AAAAAAAAALkZGhb/37t0rScnO39/61rcIfgMAAAAAsMjF4pZeOtYt/2h372cOdyoSi9u6D3d1UTIc1lBXoYKcjHrLBQCAObO6skDVxbkpg9xPHgopy+mQ71Ciu/eBk7227js/26lNdRXyuKu01W3KVV2UbDYDAAAAAAAAAPNFRn0S2d3dnXxcU1Oj6667Lo2zAQAAAAAA6dLWPiBfMCR/IKwdTe3qHhyxtb5ZlCuPq1JbXKa87iotLc2ztT4AAIuVYRjaVFepR54/PmXswZ2H9eDOwzbuS7q6plQetymPq0rrV5UpN8tpW30AAAAAAAAAmA0ZFf6uqqqSlHhzeNmyZWmeDQAAAAAAmCtdAxHtaGqXLxCWPxjSkY5BW+vnZzvVUFchj8uUx23q0iXFdAIFAGCWNNRVpAx/22F5eb68o2HvzfWVKi/MmZX9AAAAAAAAAMBsyajw94oVK5KPe3p60jgTAAAAAAAwmyLRuPa2dcofCMsXDOvFo12KW/bVdxjSVcvL5HWZ2uIy6QQKAMAcaqittK1WcW6WbqivlNeduFrHqsoCTuACAAAAAAAAsKBlVPjb4/GoqKhIfX19ampqUk9Pj0pKStI9LQAAAAAAMEOWZSlwui/R2TsQ0u6WDg1EYrbuY3VlgTxuUx6XqRvqTJUWZNtaHwAAnJ/6qkKZRbkK9w1f8LZOh6FrV5TJ666Sx21q3fJSZTkdszBLAAAAAAAAAEiPjAp/FxYW6s4779R3v/tdRaNRPfjgg3r/+9+f7mkBAAAAAICLcLp3SI3BsHyBsBqDYZ3qufAA2NmUF2Rrs8tMdvdeUVFga30AAHBxDMPQVrepnz977LzWrzML5XWb8rirtKmuQsV5nMAFAAAAAAAAIHNlVPhbkj71qU/pkUceUUdHhz75yU/q1a9+tS699NJ0TwsAAAAAAJzDYCSm3S3t8gfC8gfDOnCy19b6OVkObVxdoS0uU163qcsvKZHDYdi6DwAAYI/3v8qlR54/rmjcmjJWXpCdPJ573FWqKctPwwwBAAAAAAAAID0yLvy9bNkyPfLII/rjP/5jdXZ26sYbb9QDDzygV7/61emeGgAAAAAAGCcet7TveI98wZB8h8J65nCnIrG4rfu4YlmJPG5TXleVrltdrrxsp631AQDA7KirKtLP792sz/3moEK9w6osykkEvl1VumIZJ3ABAAAAAAAAWLwyLvzd1tammpoa/fd//7fe8573qLW1Va95zWu0efNm3XnnndqwYYOqqqqUl5d3wbVXrlw5CzMGAAAAAGDxONo5IH8gLF8wrB3BsDoHRmytv6w0T57RLqBb6itVWZRra30AADB3rl5epofubkj3NAAAAAAAAABgXsm48Pfq1atlGGc6fhiGIcuytGPHDu3YseOi6xqGoWg0ascUAQAAAABYNHqHRrSruUO+QEj+QFjN4X5b6xflZmlTXaW8blMet6k6s3DC+wIAAAAAAAAAAAAAkEkyLvw9xrKs5Ie9Y/eWZaVzSgAAAAAAZLxoLK4XjnXLdygsfzCkZ9u6FI3b9/e402HomhVl8rhMed2m1q0oU7bTYVt9AAAAAAAAAAAAAJjPMjb8LRH2BgAAAABgLrS1D2j7aGfvxqaweofsvXJWrVkojyvR2fuG+kqV5GXbWh8AAAAAAAAAAAAAFoqMC3/fdddd6Z4CAAAAAAAZrXtwRDubwvIFEre2jgFb65cXZGuzy5R3NPC9vLzA1voAAAAAAAAAAAAAsFBlXPj7u9/9brqnAAAAAABARhmJxfXcka7RsHdIzx/pUtzGi23lOB26bnW5PG5TXleVrlhWIofDsG8HAAAAAAAAAAAAAJAhMi78DQAAAAAAZsayLLWE++UPhrX9UFi7mtvVNxy1dR9rlxbL6zblcVdp4+oK5ec4ba0PAAAAAAAAAAAAAJmI8DcAAACAszrdO6THXzqt/2lyaCgmrSqytGkkppJ0TwyArboGImoMtssXCMkXCOtY16Ct9auKc+V1mfKuMbXFZaq6OM/W+gAAAAAAAAAAAACwGBD+BgAAADDBYCSmPa0d8o8GQA+c7B0dcUiSnm2XTvzkZf3wPZvlcBjpmyiAGYlE49rb1ilfICR/IKwXjnXLsuyrn5ft0MbaSm11m/K4TV26pFiGwc8MAAAAAAAAAAAAAJgJwt8AAADAIhePW3r5RI+2jwZAn27tVCQWP+s2u1u79Itnj+mNG5bP0SwBzJRlWQqe7pMvEJYvENLulg4NRGK27uOKZSXyuqvkdZvasKpcedlOW+sDAAAAAAAAAAAAwGK3qMLfHR0d2r9/vzo6OtTd3a14PK5bb71VS5YsSffUAAAAgDl1rGsw2dl7R1O7OvojF1zjwV2HCX8D81x737D8wbB8gbD8gbBO9gzZWv+S0jx5XKa8a6q0pb5SlUW5ttYHAAAAAAAAAAAAAEyU8eHv06dP6ytf+Yp+9rOf6cCBA1PGH3vssZTh7+9+97s6cuSIJGnZsmW65557Zn2uAAAAwGzpHRrRruaOROA7GFZzqH/GNZ8/0qWXjnXryppSG2YIwA5DIzE93dopXzAk36GwXj7RY2v9ghynNtVVyus25XWbqq8qkmEYtu4DAAAAAAAAAAAAADC9jA5/f+5zn9M//dM/KRKJyLKsKeNn+4C6r69P//zP/yzDMOR0OvX617+eDuEAAABYMKKxuJ4/2i1/ICxfIKTnjnQpGp/6O/FMfW/XYf2fN15te10A58eyLB042St/IKztgZD2tHRoOBq3rb5hSFfXlMrrrpLHbWr9ynLlZDlsqw8AAAAAAAAAAAAAuDAZGf6OxWK688479ctf/lKWZU0JeRuGkTIMPt7dd9+tT3ziE+rp6VEsFtMPfvAD/c3f/M1sThsAAAC4aJZl6XD7gHzBsHyHQtrZ3K7eoeis7/eXzx3X3//xZSrJy571fQFION0zJF8gLH8wLF8grHDfsK31a8ryRzt7V2lzfaXKC3NsrQ8AAAAAAAAAAAAAuHgZGf5+3/vep4cffljSmaD3tddeq1tuuUUrV67U+973vnPWKCgo0Otf/3p9//vflyT9+te/JvwNAACAeaVrIKIdTe3yjXb3Pto5aGv9soJsbXGZ8rpMdfX26/881jxlncGRmH7+zFG9a0utrfsGcMZAJKrdLR3yB8LyB8I6eKrX1vpFuVm6ob5SW92mPO4qra4sOOuVsgAAAAAAAAAAAAAA6ZNx4W+/369vfOMbyQ+qTdPU/fffr9e+9rXJdd73vved1wfZd9xxh77//e/Lsiw1NjYqEokoJ4eOZwAAAEiPSDSuvW2d8gfC8gXDevFol+Jnv6DNBclxOrRhVbk8blNet6krlpXK6Uj83tze2aX/fKJJvSNTf4/+3u423bV5NWFRwCaxuKUXj3XLHwjJFwhrb1unRmL2vdidDkPrlpfK666S121q3YoyZTsdttUHAAAAAAAAAAAAAMyejAt//9M//ZOkxGXvS0pK9OSTT2rt2rUXVauhoSH5eHh4WAcPHtRVV11lyzwBAACAc7EsS02hvtHO3mHtam7XQCRm6z4uXVKcDHtvrK1QQU7qPxGynQ7dUG3pt8emBryDp/u0u6VDm+oqbZ0bsJgcbu+Xb7Sz946msHqGorbWX11ZII/blMdVpRvqK1Wan21rfQAAAAAAAAAAAADA3Mio8HdnZ6d8Pl+y4+A//uM/XnTwW5KWL1+u8vJydXZ2SpIOHDhA+BsAAACzqr1vWP5gIgDqD4Z1onvI1vpVxbnyuBJhb4/LVHVJ3nlvu3lJXI8dM2QpRffvXYcJfwMXoLM/oh1N7fIHQ/IHwzrSMWhr/ZK8LG1xmcnu3isqCmytDwAAAAAAAAAAAABIj4wKf/v9fsViiU6ITqdT99xzz4xrVldXJ8Pfp0+fnnE9AAAAYLyhkZieOdyp7YGQ/IGw9h3vsbV+XrZDDbWVibC329SlS4qTJ0teqPJc6YpySy91Tt3+N/tO6nTvkKqLzz9MDiwmw9GYnmntlC8YVmMwrBePdcuy7Kuf5TC0fmV58rV+9fIyOR0X91oHAAAAAAAAAAAAAMxfGRX+Pn78uCTJMAzV1dWprKxsxjVLS0uTj3t7e2dcDwAAAIubZVk6cLJX/kBYvmBYe1raNTQSt62+YUhXLiuVx23K6zK1flW58rKdttX3LLH0UufU5SMxS//91BG9/1Vu2/YFLGTx+OhrPRiSLxDWU60dtr7WJamuqlBb3VXyuExtqq9UUW5G/YkPAAAAAAAAAAAAAEghoz4Z7ujoSD6uqKiwpebw8HDycXZ2ti01AQAAsLic7hmSPxiWLxCWPxhWqHf43BtdgGWlefK6q+Rxm9riMlVRmGNr/fEuLbNUmWupfXhqR+Ef7jmi977SRbdhLFonugcTr/NAWDuawgr3RWytX1GYoy0uUx5XpTzuKtWU5dtaHwAAAAAAAAAAAAAw/2VU+Hs2unSfPn06+dg0TVtqAgAAILMNRKLa3dIh/2gI9OApe68gU5jj1A31lcnAd51ZKMOYm8C1w5C2LInrkbap3cSPdQ3qDwdO6+bLl8zJXIB06xuOandzu3yBsHyBkJpC/bbWz81yaGNthTyuxIkdl19SIgcnVwAAAAAAAAAAAADAopZR4e+qqipJkmVZOnz4sOLxuBwOx0XXO3LkiE6cOJF8vmzZshnPEQAAAJknFre073h3MgC693CXIrG4bfUdhrRuRZm8LlMed5WuXVmmbOfF/547Uw3Vlv73mKGRmDVl7Hu7DxP+RsaKxS29cLRL/kBYvmBYew93Khqf+jq4WIYhXbGsRB5XlTwuU9etLlde9tQTLQAAAAAAAAAAAAAAi1dGhb/XrVuXfDwwMKDGxkZ5vd6LrveTn/wk+djpdGrTpk0zmh8AAAAyx5GOAfmDic7ejU1hdQ2M2Fp/ZUWBPG5TW92mbqgzVVqQbWv9mSjKlm5Za+rRfaEpY08eCulIx4BWVBSkYWaA/Y50DGh7ICR/IKwdTe3qHrT3tV5Tli+vO9HZe4vLVEVhjq31AQAAAAAAAAAAAACZJaPC32vWrFFtba1aW1slSV/84hcvOvzd09OjL33pSzKMxCW1r7/+ehUXF9s1VQAAACwwPUMj2tnUnuj4GwiptX3A1voleVnaXG/K4zbldZtaVVloa327vXnDspThb8uSvr+7TR9/7do0zAqYue7B0dd6MCRfIKzDNr/Wi/OytLm+Uh53orv36sqC5N+dAAAAAAAAAAAAAACcS0aFvyVp27Ztuu+++yRJjzzyiB544AHdddddF1QjFotp27ZtOnbsmCTJMAzde++9ts8VAAAA89dILK7njnTJFwjLHwjp+aPdisUt2+pnOQxdu7JMXneVPG5TV9eUKsvpsK3+bFtXU6y1S4t14GTvlLH/fvqI/ubVbuVmOdMwM+DCTH6tP3ekSza+1JXtNHTtynJ5Xaa2LMDXOgAAAAAAAAAAAABgfsm48Pff/u3f6r/+678UCoVkWZbuuecenT59Wh/60IfkdJ47fHLgwAH9xV/8hfx+f7L72po1a/S2t71ttqcOAACANLIsS83h/tHO3mHtam5X33DU1n3UVRXK6zLldVdpU32linIX7q/jhmHoHZtW6R8ffmnKWEd/RP/vpZO6/ZqaNMwMODvLstQS7pdvFl/ra5YUyeOqksddqYbaShUu4Nc6AAAAAAAAAAAAAGB+ybhPoAsLC/Wtb31Lb3jDGxSPxxWLxfTxj39cX/3qV/XWt75VGzZskJT4wN8wDD3zzDPq6OhQMBjU73//e/3+97+XZVmyrESrt/z8fP3gBz/gMtwAsMiEeoe1s7ldp3uGVFdVqFeuqZbDwbEAyDQd/RH5g4luv/5AWMe7h2ytX16QrS0uU163KY+7SjVl+bbWT7c7rq3RZ369X/2R2JSxh3YeJvyNeaOzP6LGpnDy5I5jXYO21jeLcuVxVSY7+S8pybO1PgAAAAAAAAAAAAAAYzIu/C1Jr3vd6/Sf//mfuvfeeyUlgt6HDx/WZz/72QnrWZalj3/841OWjQW9s7Oz9d3vflfXXnvt3EwcAJA2g5GY9rR2yB8IyRcI68DJ3gnjr1hTpW/fdZ2ynI40zRCAHYZGYnrmcKd8gbD8wZD2He/R6Dl/tsjJcuj61eXyuKrkdZu6/JKSjD5xpCg3S29YX6Pv7WqbMvb04U4dONmjtUtL0jAzLHbD0Zj2Hu6SLxCSPxjWi8e6bX2t52Y5tLG2Ql53opP/2qXFnDAMAAAAAAAAAAAAAJgTGRn+lqT3vOc9qq+v1zvf+U6dPHky+UH8+HD32PMxhmHIMAxZlqUlS5boJz/5iTwez5zPHQAw++JxS/uO98gXTHT7fbq1U5FYfNr1nzwU0te3N+t9N7rmcJYAZsqyLB042Zvo9hsMa09Lu4ZGpn+tX4y1S4uTAdDrV1coP8dpa/357h2bVqUMf0vS93Yd1qfvuGqOZ4TFyLIsBU73yRcIyxcIaXdzhwZHpnakn4nLLymRd40pr6tK160uV1724nqtAwAAAAAAAAAAAADmh4wNf0vSTTfdpP379+u//uu/9JWvfEXHjx+XNDHwPZ5lWSovL9cHP/hBfeADH1BJCV0KASCTHO0cSAZAdwTD6hwYuaDtv/Zkk97RsEqlBdmzNEMAdjjVMyR/ICx/MCxfIKxw37Ct9ZeU5CY7e29xmaoqzrW1/kKzdmmJrltVrqcPd04Z+8XeY/r4ay9TUW5G/9mBNAn1Dqtx9HXuD4Z0qsfe1/rSkjx53GbytW4WLe7XOgAAAAAAAAAAAABgfsj4FEZpaak+/vGP66Mf/aief/55+Xw+7d+/X+3t7erq6lJBQYFM01Rtba1uvPFGbdy4UVlZGf9tAYBFoWdoRLua2kdDYWG1hPtnVK93KKqvbW/Sx16z1qYZArDDQCSq3S0d8h1KBEAPneqztX5BjlMNtRXyuBOBb3d10YQryUB65w2rUoa/+yMxPfzsMb1j06o0zAqZZmgkpqdaO+QPhLU9ENb+Ez221i/IcWpTXaU8LlNb15iqr+K1DgAAAAAAAAAAAACYfxZNytnhcOjaa6/Vtddem+6pAABmyUgsruePdCXD3s8d6VIsnvpqDxfru40tevfm1aouybO1LoDzF4tbeulY92hn75D2Hu5SJBa3rb5hSFcvL5PXZcrjNrV+Zblyshy21c9Er7lyqSoLc9TeH5ky9r1dh/X2hpWEaHHB4nFLB072yhcIyR8Ma09Lh4ajvNYBAAAAAAAAAAAAAIvbogl/AwAyj2VZag73yx8IyxcIa1dzu/qGo7O6z6GRuL7yh6D+5fYrZ3U/ACY60jEgfzAsfyCsxqawugZGbK2/vDxf3tHO3pvrK1VWkGNr/UyXm+XUndet0NeebJoyduBkr/a2dWrDqoo0zAwLzameIfkCiRM7GoNhhfumnlAwE4nXuimvu4rXOgAAAAAAAAAAAABgQSL8DQBYUDr7I2psCst3KBEMO949ZGv9soJsbXGZ2lJv6gu/PZiyi+0P97Tpz711WlFRYOu+AZzROzSinU3tyU7+LeF+W+sX52Vpc31lMvC9qrLQ1vqL0dsbVurr25tkpbjgwvd2tRH+Rkr9w1HtaekYfa2HdOhUn631i3OzdEN9ZTLwvaqygC70AAAAAAAAAAAAAIAFjfA3AGBei0Tj2tvWKV8gJF8grBePdacMFl6sHKdDG1aVy+M2tdVdpSuWlcjhSITChkZi+pdfvTxlm5GYpS89dkhffPM19k0EWOSisbheONYt36FEAHRvW5dicfte7FkOQ+tXJl7rHrepq2tKleV02FYf0oqKAr1iTZWeOBiaMvboCyf0j398mSqLctMwM8wn0VhcLx7rTly1IxjWs22dGonZ91p3Ogxdu6JMHrcpr9vUuuVlvNYBAAAAAAAAAAAAABmF8DcAYF6xLEtNoX75AiH5A2HtbG7XQCRm6z7WLi2Wx5UIgDbUVio/x5lyvbc1rNS3fM0pu4v/4rlj+stX1mvNkmJb5wYsJkc6BuQLJLr4NwbD6hmK2lq/vqpQXneVPC5Tm+orVZTLr76z7R0Nq1KGvyOxuH7yzFH95Svq0zArpJNlWWptH5A/GJY/ENKOpnb12vxarzUL5XElwt6b6itVkpdta30AAAAAAAAAAAAAAOYTEjAAgLTr7I+osSks36FECDRV2Homqotzkx1At7hMVRfnndd2edlOffDmNfroz16YMmZZ0ud/c1Df2HadrXMFMlnv0Ih2NrXLHwzLFwirJdxva/2KwhxtGQ2AelymlpXl21of53bj2mrVlOXrWNfglLEf7G7Te7x1yasrIHN19EfUGAyrcfS1nur/w0yU5mcnT+LyuEytqCiwtT4AAAAAAAAAAAAAAPPZggt/19XVpWW/hmGoqakpLfsGgEwTica1t61TvkBIvkBYLx7rlmXZVz8/26mGugp53VXyuk25q4tkGBcXNvyT9TX62vYmNYemhlR/+/IpPdvWqWtXls90ykBGisUtvXC0K9nde29bl2Jx+17sOVkObVxdkQyAXn5JCcHiNHM6DL114wp9/reHpoy1dQxoeyCkV15anYaZYTYNjcT0dGunfMFEF/99x3tsPa5nOw2tX1murWsSnfyvrCmVk9c6AAAAAAAAAAAAAGCRWnDh79bWVhmGIcvONMF5uNjQIABAsixLTaF++UfD3jub2zUQidlW3zCkq2tKRwOgVVq/qky5WU5bamc5Hfrwqy/V+36wN+X45397UN+/Z5Mt+wIywZGOgWTYuzEYVs9Q1Nb6l11SkuzsvbG2QnnZ9rzWYZ83Xb9C//d3AY3Epv6+/r1dbYS/M0A8bunlEz3yB8PyB8J6qrVDw9G4rftwVxfJ4za11V2ljbUVKsxdcH+6AgAAAAAAAAAAAAAwKxbsJ+hzGcae66A5AGSCzv6IGpvC8h1KhECPdw/ZWr+mLF9b1yQ6e2+ur1RZQY6t9cd77ZVLdWVNiV461jNlrDHYrsZgWFtc5qztH5jPeodGtKu5I9nJvyU8tUv+TFQX5yYDoFtcpqqKc22tD/tVF+fp1iuW6lcvnJgy9vsDp3Ssa1A1ZflpmBlm4mjngBqDYfkCYe1oaldHf8TW+mZRjjbXm/K6TXndVVpammdrfQAAAAAAAAAAAAAAMsWCDX9LhLIBYD6JROPa29YpXyAkfyCsF451y84f00W5WdpUV6mtaxKhsNWVBXN2IpDDYegjt67VXd/Zk3L8339zUA/XV3KVCCwK0VhcLxzrln+0u/ezbV2Kxu17sedlO9RQW5kMgK5ZUsRrawF6x6ZVKcPfcUv60Z42ffiWS9MwK1yInqER7Wxqlz8Qlj9o/4kdedkObaytlNdlaovL1NqlxXI4eK0DAAAAAAAAAAAAAHAuCzb8bVmWnE6nXvWqV2nbtm167WtfK6fTme5pAcCiYVmWmkL9yW6/u5rbNRCJ2VbfYUhXLy/TVrcp75oqXbOiTNlOh231L9RWt6mNtRXa09IxZez5I1367cundOsVS9MwM2D2HekY0PbREzsag2H1DEVtrX/5JSXyrkl0996wqlx52fxOt9A11FbIXV2kwOm+KWM/euqI/upVbuVkpe9nOqYaicX13JEu+QJh+QMhPX+0WzEbT+wwDOmqmlJ5XKY8blPrV/JaBwAAAAAAAAAAAADgYizY8LdhGIrH43r88cf1+OOPq7q6Wm9/+9u1bds2XX311emeHgBkpI7+iBqD4WR37+PdQ7bWrynL19Y1VfK6TW2ur1RZQY6t9WfCMAx97DWX6o3/tTPl+Od/c1A3X7ZETrqWIgP0DI1oR7Bd/mDi5I7D7QO21q8uzpXXXaWta0xtrjdVVZxra32kn2EYenvDSv3z/7w8ZSzUO6zfvnxSr7t6WRpmhjGWZak53J/s4r+ruUN9w/ae2LGiIl8e1/w8rgMAAAAAAAAAAAAAsFAtuPD3d77zHT344IN68sknZVlnOtGdOnVKX/rSl/SlL31JV111le666y697W1v05IlS9I4WwBY2CLRuJ453Jns7v3S8W5Z9jUBVWGOUzfUm9q6xpTXXaXVlQUyjPkbnt6wqkI3ra3W7w6cnjIWON2nh589pjduWJ6GmQEzE43F9fzRLm0/FJY/GNZzR7ps7fibm+VQQ11lopO/u0prlhTN69c67PEnG5brs//voAZHpl4V4nu7DhP+ToP2vmE1NrXLP0sncZXmZ2tzfaU8blNeV5VWVhbYWh8AAAAAAAAAAAAAACzA8Pe73vUuvetd71JbW5seeOABPfTQQwoGg8lxy7L04osv6m//9m/1sY99TLfccovuuusu3X777crJodMcAJyNZVlqCvVp+6FEF9DdLR0aiEwN7V0shyFdvbwsEQBdU6VrVpQp2+mwrf5c+NtbL00Z/pakLz1+SK9ft0w5WQvra8LiY1mWDrcPJE/s2NnUrl6bO/5efkmJvGsSAdDrVpcrL9tpa33MfyV52br9mmX60VNHpoztau5Q8HSvXNXFaZjZ4jE0EtPTrZ3yBRNh733He2ytn+N0aMOqcnncpjwuU1fWlHIFDAAAAAAAAAAAAAAAZtmCC3+PWblypT7xiU/oE5/4hHbs2KH7779fP/nJT9Td3S3LsmQYhqLRqP73f/9X//u//6vS0lK9+c1v1jvf+U5t3rw53dMHgHmjoz8ifzAs36GQ/MGwTtjcBbSmLF9b11TJ6za1ub5SZQUL+0Scyy4p0W3rlumR549PGTvaOagfPdWmbTesnvuJAefQPTCiHU1hbQ+E5Q+GdKRj0Nb6VcW58rpNbXVXaYvLVFVxrq31sTC9Y9OqlOFvSfrerjb9821XzPGMMls8bunAyV75g4kTO/a0dGg4Grd1H2uXFsvrNrXFZWpjbYUKchbsn5QAAAAAAAAAAAAAACxIGfFJ/ebNm7V582Z9+ctf1i9+8Qs9+OCDeuyxxxSLxWRZliSpq6tL3/jGN/SNb3xD9fX12rZtm975zndq1apVaZ49AMyt4WhMzxzulC+Q6O6973iPRn9U2qIoN0ub6iq1dU2iC2itWSjDyKwuoB969Rr9+sUTisanfuP+43dB/emG5YThkHYjsbiebetKdvd+4WiXUvyXvWi5WQ411FXK6zLlXWPq0iXFGfdax8xdWVOqa1aU6bkjXVPGfrb3qD76mkv5eTlDJ7uH5AskTuBqDIYV7ovYWn9JSa48rqpk4JsTOwAAAAAAAAAAAAAASK+MSlrk5ubqLW95i97ylrfo5MmTeuihh/Tggw9q3759yXUsy1IwGNQnP/lJ/fM//7O8Xq/uuusu/emf/qmKiorSOHsAmB2WZenQqb5kAHRPS4cGR2K21XcY0tXLy7TVbcq7pkrXrChTttNhW/35aLVZqDddv0I/2N02ZSzcN6z7d7Tq3le60jAzLGaWZakp1CdfIBEA3dnUrv6Ifa91Sbr8khJ51yS6e29YVa68bKet9ZGZ3rFpVcrwd+9QVP/z/HG9+fqVcz+pBax/OKpdze3yBcLyB8MKnu6ztX5BjlOb6irlcZnyuk25qos4sQMAAAAAAAAAAAAAgHkko8Lf4y1dulQf+chH9JGPfER79+7V/fffrx/96EcKh8PJdeLxuLZv367t27fr/e9/v97whjdo27ZtevWrX53GmQPAzJ3uGZI/GJZ/NBh2unfY1vrLy/PldVdpq9vU5npTpQXZttZfCP76VW797JmjGo7Gp4x97YkmvX3jqkX5fcHcCvUOqzEYTga+T/YM2Vp/SUmuvO4zHX/NIjr+4sK97upL9KlfvazuwZEpY9/b1Ub4+xxicUsvHO1KhL0DYe1t60x55YmLNXYSl9eduGLHtSvLlZOV2SdxAQAAAAAAAAAAAACwkGVs+Hu89evXa/369friF7+oRx99VA888IAeffRRjYyMyLISwYmBgQF9//vf1w9/+EP19PSooKAgzbMGgPM3EIlqd0tHIuwdCOvgqV5b6xflZumG+spEd293lVZVFiz6LqBLS/N01+bV+sb25iljPUNRfcPXpI/cujYNM0MmG4hEtWfstR4M68BJe1/r+dlObaqrkGf05A46/sIOedlO3blhub7lb5ky9uKxbj1/pEvrVpTN/cTmscPt/cmw946msHqGorbWX1lRII/blNe1eE/iAgAAAAAAAAAAAABgoVoU4e8xWVlZuv3223X77bervb1dP/zhD/XAAw/omWeekWEYsiwrGQYHgPksFre073i3fIGwfIGQ9h7uUiQ2tQP1xXIY0roVZcnu3utWlCnbSRfQyd77inr9YHeb+oanhvK+42/VXZtXq7o4Lw0zQ6aIxS29dKxb/uDsvNYNQ7pyWam8oyd2rF9Vptwsp231gTFv37QqZfhbkh7adXjRh7+7BiLa0dSeCHwHQzrSMWhr/ZK8LG1xmaOB7yqtrOREVwAAAAAAAAAAAAAAFqpFFf4eLycnR/n5+crPz0/3VADgvBzpGJA/mOgC2tgUVtfAiK31V1TkJ8PeN9TRBfR8lBfm6M+9dfrS44emjA2OxPSfvw/qvtuvTMPMsJAdbu9PvtZ3NLWre9De1/qy0jx53VXyuE1tcZmqKMyxtT6QSq1ZKI/LlD8YnjL2P88f1z/+8WUqK1g8/xcj0bieOdwpfzAkfyCsF451y85zULOdhtavLJfXbcrjrtJVNaVyOujiDwAAAAAAAAAAAABAJlhU4W/LsvSb3/xGDzzwgB555BENDQ1JkgyDIASA+ad7cEQ7m9qTwbDW9gFb6xfnZumG+kp51yQC36sqC22tv1jc7a3Vgztb1d4fmTL2gz1tusdbpxUVdFjF9LoGImoMticC37PQ8bcwx6lNdZWJ7t5rqlRnFvK7D9LiHZtWpgx/D0fj+ukzR3WPty4Ns5oblmUpcLovecWO3c0dGhyJ2bqPNUuK5HFVyes2tbG2QoW5i+pPPQAAAAAAAAAAAAAAFo1FkQh44YUX9OCDD+oHP/iBTp06JSkRwDAMQ9Zoi738/Hzdfvvtuuuuu1RQQEgPwNwbicX13JEu+QJh+QMhPX+0W7G4fW1AnQ5D164ok8dtyus2dfXyMmU7HbbVX6yKcrN0740ufepXL08ZG4lZ+v8eD+gLb1qXhpllnu6BER3vHlS206Fas3DBdrEdjsb0TGvnaNg7rBdt7vjrMKR1K8rkdSU6e1+7slw5WbzWkX43X7ZES0pydapneMrYD3a36W5PbUadmHC6d0iNwbB8gbAag+GUX/dMmEW58rgq5XFXyeMytbQ0z9b6AAAAAAAAAAAAAABgfsrY8Pfp06f1/e9/Xw8++KBeeOEFSUoGvcfzer3atm2b3vSmN6m4uHiupwlgEbMsSy3hfvmDYW0/FNau5nb1DUdt3UedWSiP25THZWpTfaVK8rJtrY+Etzes1Ld9zTrePTRl7BfPHtVfvqJO7iUcYy5UJBrX3rZO+QNh+YJhvXi0S2PnQ5QVZGuLy5TXlehmXVOWn97JnoVlWTp0qk++QEi+QFi7W9o1NBK3dR+1ZqE8LlMet6lNdZUqzee1jvkny+nQWzeu1P/3eGDKWHO4Xzua2rXFZaZhZvYYjMS0p7VDvkMh+YNhHTjZa2v9vGyHNtZWyjv6Wl+7tDijwvIAAAAAAAAAAAAAAOD8ZFT4OxKJ6OGHH9YDDzygxx57TLFY4lLqY12+x9TV1emd73yntm3bptra2nRNF8Ai1DUQUWOwPRkCPdY1aGv98rFArNuUxz2/A7GZJC/bqQ/c7NbHfvbilLG4JX3ht4f0tXduSMPMFhbLshQ83Zfofh9MnBAxEImlXLdrYESPvnBCj75wQpJUV1Wore4qeUfDz4W56f0VZ3zHX38grNO99nb8rSjM0eb6Snndie7ey8u5agkWhrdcv1Jf/n0w5ZUtvrfr8IIKf8fjlvYd75EvGJI/ENbTrZ2KxOw7scMwpCuXlSau2OEytX5VufKynbbVBwAAAAAAAAAAAAAAC1NGhL/9fr8efPBB/fSnP1V3d7ekM4HvsW7fxcXFetOb3qRt27bJ4/Gkc7oAFpGxzsW+QCIY9sKxbqW4CMFFy3E6dH1tuTyuROj18ktK5HDQBTQd3rh+ub7+ZLOaw/1Txv7fvpN6/kiX1q0om/uJzXPhvuEJIemTPVO7p5+P5lC/mkP9un9Hq7Kdhq5dWa6tblNed5WurCmVc5ZfF0MjMe1p6Rjt5B+yveNvTpZDG1dXJDv581rHQrW0NE+vvmyJ/t++k1PGfvvyKZ3qGdKSkrw0zOz8HOsalD8Q0vZAWDuCYXUOjNhav6YsP9nFf4vLVEVhjq31AQAAAAAAAAAAAADAwrdgw98tLS168MEH9dBDD6mlpUWSkkHvMQ6HQ69+9au1bds23XHHHcrLm79BEgCZwbIsNYX6tP3QuTsXX6y1S4uTnb03rq5Qfg5dQOeDLKdDH7pljd7/g2dTjn/uNwf1vXsa5nhW88/QSExPtXbIH0gEvl8+0WP7PkZilva0dGhPS4c+/9tDKhvriO8y5V1jT0f8eNzS/pM9ydD6ntYORaL2dfyVpCtrSkbnXaXrVtPxF5njHZtWpQx/x+KWfrTniD5wszsNs0qtd2hEu5o7kidxpTrBZyaKc7O0abSLv8dlqtYsnHDFIgAAAAAAAAAAAAAAgMkWXPj7m9/8ph588EHt2LFD0pnA9/iQxJVXXqlt27bpHe94h5YuXZqWeQJYPNr7huUPhpNh1ovtXDydJSW5yc7eW1ymqopzba0P+/zRlZfoimVN2nd8aqDZH0x0id3sMtMws/QZC0mPvT6eau3QsM0h6XPpGhjRoy+c0KMvnJAk1VUVaqs78ZpqqKtUUe75/Tp0snsoEQAdfb2390dsnWdNWX7ydU7HX2SyzfWVqjUL1ZIiSP3DPW163431ynI60jAzKRqL6/mjXckTO5490qVY3L5Ldjgdhq5ZUSaPy5TXbWrdijJlp+lrBQAAAAAAAAAAAAAAC9OCC3//xV/8hQzDmNLl2zRNve1tb9O2bdt07bXXpml2ABaDoZGYnjncKV8gLF8glDLoOxMFOU411FbIOxpOdVUX0QV0gXA4DH3k1kv1ru8+lXL8339zUL+or8z4f8/ZDknPVHOoX82hft2/o1VZDkPrV5Vrq9uU112lK2tK5XQk/n0GIlHtbu7Q9tGOv4HTfbbOozgvS5vrK+VxJTr5r64syPj/G4CU+Fn59oaV+vSj+6eMnewZ0uP7T+s1V87NCZyWZam1fUD+QEi+QFg7m9rVOxy1dR91ZqE8o529N9VXqiQv29b6AAAAAAAAAAAAwBSWlbhp3P3Ycl3ImCY+nm57y5Ks+DmW6TzXS3Wvc4yf7/2kr+ds657r6x3/vT7rOpMej98m+fhs6+k81zvbvqf7us7j+zG5fsp/v4v5/zC6/3P+25/j+zuj/xPnuf20c5z4PSuMRXVjf7+M0WUFrQXShndKnr8RYLcFF/4ez+l06sYbb9S2bdv02te+VtnZiSBFT4+9QcwxJSUls1IXwPxmWZYOneqTLxDS9kBYe1raNTRiX+diw5CurimV110lj9vU+pXlysmiC+hC9Yo1Vdq4ukJ7WjumjD13pEuPvXxKt1yRWVel6B+OandL++gJEWEFbQ5JF+VmaVNdpbxuU5vrK3WyZyi5r/0nZnbMj8Yt7Wnp0J6WDn3+t4dUVpCtG+oq1TkQ0TOHOzUSs85d5Dw5HYbWryxLdPJfY+rqmtK0dTcG0u1PNyzX535zMOWVAL6/+/Cshr87+yPa0dQufzCk7YfCOtY1aGv98oJsbRnt7O1xV6mmLN/W+gAAAAAAAAAAYBGKxyUrlgggxmOJx/HR5ymXxc5skxyLTrNsXN14dGKd5Hhs4v3kZVb8TEByys2a5nF8XKByuvWn20bTjyfrWWcZG79dimCndCYYqnGfGU8bzD3f55q4n7H9pwqYJh/rAtYdN38Ac84paULCdFhSfzg9k0HGW9Dh73g8rt/97nf63e9+N+v7MgxD0ai9XQAXmqamJu3Zs0dHjx5VJBJReXm51q5dq82bNysvLy/d0wNsFeodVmMwnOz4e7p32Nb6NWX52rrGlMdVpc31lSovzLG1PtLHMAx99DWX6k+/tjPl+Bd+e0g3XbYk2V16IYrHLb10vDvZ/X42QtLrlpcmu9+vW1Gm7HEhafeSYnndVZKk071DagyG5TsU1vZAWOG+mb1WuwZG9L8vnZxRjfHGOv563VXaVFehYjr+ApKksoIcvX7dMv30maNTxnyBsFrC/ao1C23Z13A0ccUOfyAsfzCsF491TzhBfqZynA5dt7o88Vp3VemKZSVyLOCf8QAAAAAAAAAASBoNCacIHFvx1EHk5GMrRQh5muXJbeLT1IpPejwp8Hy+21xwvcnbjQ8MT65jTbN8LFScavm4sVTzn7Islu7/DQCAizV2wgpgswUb/jaMRKDCsjO5gZQefvhhfepTn9LevXtTjhcVFeld73qXPvnJT8o0zTmeHWCPoZGYnm7tlC8Qki8Q1ssz7CY8WVFulm6oT3Qu9rqrtLqyIPlzDJnnutUVetXaav3+wOkpYwdP9eqR54/pDdcuT8PMLt6xrkH5R7vf7wiG1TkwYmv91ZUFyZD0DfWVKjnPkHR1cZ7ecO1yveHa5bIsSwdP9Y4GwUPa09KRsqvwbCoryNaW+rGOv6aWlxfM6f6BheQdm1alDH9L0g92H9Y//PHlF1V3/BU7fIGw9rR0aHDE3jdF1y4tlsdlyjt6tYf8HKet9QEAAAAAAAAAc8iyEt2NYxEpOizFRhKPY5Ezj+MjZ7oiJ2/xSc/Hd0ge93zK44l1cocHdeXRw3KMhn3zfvu/Upbj/Lotp1yWqrtzqpDx+CD35CA0YWMAAGxBvhWzZMGGv8fMRXhysQbMh4eHdffdd+v73//+Wdfr6+vTV77yFf34xz/WT3/6U23dunWOZghcvPHBsO2BsHY3t9saEj1X52Jkvg/fsiZl+FuSvvjYIf3xVcuUkzV//0/0D0e1q7ldvkAiSN0c6re1fml+tra4KuV1V8njMrWiYuYhacMwtHZpidYuLdGfb63T0EhMT7V2yB9IdAXfb/NJHZKU7TS0YVV58rV+xbLSBd3VHZhL65aX6sqaEr10bOpr8yfPHNWHb7lUednnF6o+3TMkfzCc7O5t9xU7qotzR09QMbXFZaq6mKveAAAAAAAAAMB5s6zREPVosDo6PC5cHZn4PDq2fHji4+R2o3XGPx4f0h67ny7EnXKbSFq/PbmS6scvaE/TRAAAwCxYnNlTzL4FF/5euXIl3XLnQDwe15vf/Gb98pe/nLDc6XRq5cqVKi0tVUtLi7q7u5NjoVBIr33ta/X444/rhhtumOspA+cU6h1WYzARZPUH7A+GjXUu9rgSnYtL88+vczEy0xXLSvX6dcv0P88fnzJ2pGNQP376iN65aVUaZpZaLG7ppWPdyRMinm3r1EjMvl9As52G1q8s19Y1ibD3lTWzH5LOy3aOhrKr9HeSTvcOqTEYlu9QWL5gWKGL/Bngri6Sx21qq7tKG2srVJi74H6dAuYFwzD0joZV+vjPX5wy1jUwokdfOKE3bkh9lYTBSEy7WxInqPgDYR081Wvr3PKznWqoq5DHZWrrmiq5q4v4GwQAAAAAgPMVjye6oyY7pUYTj+MjUix6pntqLHpmvbHnkzuUju9MOtYl1Yql7mY6tjw+uc6kda34pC6n8UldUONTb+e1fOyxNdrZ7Vz3mmZsmuXSmceObKl4iVR8iVSyLPV9btHc/rsDSL+x7tUTgtSTAtIpg9XnCGAnQ9fnCmCPnH3fAAAAi9EibTyM2bfg0kqtra3pnsKi8LnPfW5K8Psv//Iv9YlPfELLli2TlAiI//KXv9QHP/hBtbW1SZIGBgb0pje9SS+99JJKS0vnfN7AeEMjMT3d2ilfMCTfobBetrnrb0lelra4zGTHXzs6FyOzfOjVa/TrF08oFp/6i9yXfxfQn65frvyc8+tqOxuOdg7IHwjLFwirsSmsroERW+u7q4uSr4/5EJKuLs7TG65drjdcu1yWZengqV75DiVOCNnT0jFt9//KwpzREzsSr/elpXT8Bexy2zXL9K+/3q/eoeiUse/tPpwMf8filvYd706GvZ853KlIzL4rdhiGdHVNafIkrvWrypSblb6fzwAAAAAApA5Qj3UoTYSlHT2dKu8PyrBiclhRZbVIys2etP7EbRLPx41NrpsMZo97ntx2ZOL6E0Ld4/Zh2fc3O86i5+jZx3NLRsPgl0glNWceFy87c19YJTnm7xUqgXkrHksRhk4ReJ7w+Fxh6lTbp9omVbB63BidJQEAGMdIfBA47f251jmPOtI59mEkS0xcf3zdyWMzWW/SfKed3zRf15T1J9d3pKiXalmq78f5bns+/3bn8W87o+0nzcNwnNc2g8PDCgQCkgxZktZceqnyV24QMBsWXPgbs6+9vV3/+q//OmHZZz7zGX384x+fsMzhcOgNb3iDNm7cKI/HkwzmHz16VF/84hd13333zdWUAUmSZVk6dKov2bl4d3P7tGHOi5HlSHQu9rhNed2mrl5eNuudi7Gw1ZqFetN1y/XDPUemjJ3uHdb9O1r13lfWp9hydvQNR7WrqV2+QEi+QFjN4X5b65tFOckTIjwuc16HpA3D0NqlJVq7tER/vrXuzMkigZBawv1yOgytW1Emr9vUZUtL5OC1DsyKgpwsvXH9ct2/o3XK2LNtXfry7wI6cLJ3Vk5QWV6eL+9o2HtzfaXKC3NsrQ8AAIBFZHKHVyt+jsfxMx1/ko+tSY/PVudC9jW5i+ykfZzXfXz6LrQX3NlW594m1TqGMxFaLFspla2QsvPt/TdE5rOsRFBvfBg6Pi7YPCEkPfnxyHmskypsnaLG5PD15ND0+H1ZsXN+WUWSto5fEJytbyAWpOGexC18cPp1HFlS0dLRMPik7uGFZmKdlF3Wx3c+n65b++TtzlIn5TbTjE3uuD5lLD6xjuGQiqql0hVS6fLR2wqptEYqrCb8Pl+N/cxOBpzHBaAndKWefJsUkJ62g/XYz+YUoepptxnXwZqTXABkGkdW4u8uh3P0sSPx2Bh97nCeWTZ+3QnrjT5P3sYHO1PdxgUqp7uND10aRmI/k7eZss7ksWn2N2Vs3DLpTHh0vJTh24t8Pl2YdLpA7JR1J4+nCNQm95kiFHuugPLFBJyTc1KKZWeZ+7TbzuCeK/oiTUZ6etTS9Yfk89p1Nyq/pCSNM0ImI/yNKf793/9dvb29yedbt27Vxz72sWnXr6mp0be+9S3dfPPNyWVf+tKX9Nd//deqrKyc1bkCod5hNQYTnXv9gbBO9w7bWr/OLJTXnQizbqqvVFGaOxdj4fnrm9z62d5jiqQ4EeFrTzbpbQ0rVZqfPSv7jsUtvXC0K9nde29bp6IpupBfrJwshzaurkiEJxd4SDov25no+Os20z0VYNF5e8PKlOFvSfrCY4ds209xbpY2uyrlcVfJ6zK1qrJABm/8AACAhWgswDg+QDg+wJiiM+yZx9HEbSwMNfY8+Tg2bnz82KTnE8ajo91px9UeCx8n7+OTgs/xSeOTl09eP8V2Zw0c6xzj57v9dMHscXOis2J6FFZL5atGw+Djb6sTob7s+XtCekaJx88diL6gkHWqxym2nfBzbppwdqp9ApgqHk10ED9XF/FM5chOhMBLVyS6o08Ihy9PjOUWp3uW9rOsxL/9tIHnyHksTxWiHr88VWB7eNLP68n1CVcDi1IyfDs+PDwa8B0LFY8PGCeXT97GMWk9x8SxCbXPMuYYCxenWG988HjK8rFakwPM042l2v/kILVz3HaTl03+vozfdvL3dHxQO2vqPjgRCgCABYkUIyaIx+P67ne/O2HZP//zP58zGHPTTTfJ6/XK5/NJknp7e/Xf//3feu973ztrcwW+8NuD+vLv7W1lUpqfLY8rEQD1uEytqCiwtT4Wn0tK83XXDav0TV/LlLHuwRF9c3uz/vbWS23b35GOAfkCYfkCIe1oalf3oL2dctcuLU6eEHH96grl5zhtrQ9g8XEvKVZDbYV2t3TYWjfLYejalWXyuKrkcZtat7xUWU7ewAQAABcgFtWZS7WPhlXGLs0ejUwdS96PH0+xXnKdSdumCm9PF+QmcIzFrv904nb0qdTjRUsTYfApAfFViTBfVu7czvd8JDtTjw/SpQrFpQjITRuePo/u0il/ztjXmRoA5rX4iNTZmrhNJ6/0TBg8GRAf10W8+BLJmeIjd8s687N62s7T43/OD2viz/wU607uPj05lD1h/GzdrCPi90nABo6scbfxHZLHLUt5nzWus3Li8UjcUqi9U5bhkCWHliy9RNk5eaPjKUK8U8K740K8E7oyO8dtP6578/jlUwLEqQLSk0LRFxSkPsdymrQAAABcFMLfmGDHjh0KhULJ53V1dXrlK195XtvefffdyfC3JD388MOEvzGrXNVFM66R5TC0fmV5Isy6pkpX1ZTKuUA7F2P+eu8rXfrhniPqG45OGftOY4vu2rxaVcUX96Fj79CIdja1JwPfre0DM53uBGZR7mjYO3FCRHUJnbMA2O+dN6yyJfxdX1Uor7tKHpfJFTsAAFiI4rGJwehkOHpoYoh6fMA6OnTusPWE+6GzjE0KbtNtEFi4+k4mbkf3pBg0EkG98aHw8lWJQJ9hpA5Pn1en6rEwdYou1Kkepwp0E8QDgPlnqDtxO/VS6nHDkbgihTQxaM0VB4CL58hOnKznzJacuZIzR8rKGX08NpYzunxsvXHjyfXHbtnjHqdYnnLd3EnbZSdujuxxwWX7PlMe7OnRU3/4Q/L5jTfeqOySEtvqAwAAIPOQhsAEjz766ITnr371q8/Z9Xv8uuM98cQT6u/vV2FhoW3zA8bb4jIvars6szARZHVXaVNdhYrzsm2eWYaJx1NcPnp8Z6DJl5Aefynp0UtBW/HRx+Puxy4xnXwcn7R8bP34pBrjt1sYH4hVSHqwplN72zqnDsal4EM/V1Xd+f1/jluWQn3DOtY5qGNdgzrdOyzLsrRK0ippxkd2p8PQ0pI81ZTnq6YsXxWFOTJkSKeVuAFYtHIjw7ri6JEzz5/YLuXY0y3vtZalf81v0+DIhXWNy8t2allZ4udVTVn+mbB32+gNAOZCVo5UvlqqdEkV9VLxUjoWYcEwrJgc8ag02CkZA+fuXj2+K+GFdL6ODp0ZmxLuHrdufOoJswBgP0vqPZ64HdmV7skAGDO+I2myG2qKbqYTOpeOX5aiA+q024/vNjouwJdy+VgH1MnLxm03ebmM0b8JJt9r+uXTbjPNfaRP6jmR+FnWc0LqPSH1HJcG7b2yGs6DFU+ccAQsNI7JIeqciaHn8WMTwteTw9I5k4LaY9uerc5oren2yfsqALBoxK24hqJDGo4NJ29D0SFFYhENxYYUpzkDFpiBgQE1jTQln5eESlRr1Wp16er0TQoZi/A3JnjuuecmPN+8efN5b7ts2TKtXr1ara2tkqRIJKKXX35Z119/vY0zBM4wi3J1xbIS7Tvec9b1SvKy5HGbyU6gKyoK5miGF8Cypn5YPv7D8QkfoE/z4Xqq9ae7lHR0OEVge/ylXKNnAt/8Mm2L9ZLWT3fUvYBgtUPSktHbeklyznxuU/SN3o6ca0UAi0muJNf4BaFpVrwITklvly78rxNLUufoDQDmi+xCqbIuEQYfC4RXuqTKeqmgIt2zQ7ql+tvvrF2rz9Xx+jwC1SnGi6PDui06JGOsw+wL6f22ADh/UUnDhjHx5jA0ZBiKGBPvhw1DUYIzwKJiObJkpApAJwPVqcbGgtZZShmaTi53SEbWmdDztCFrx8Sw9YT9jhubEsp2pPvbt0BYiVtOgVRUL6l+4nAsMtqpumf0vmv0cdfE5daFnYAP4CKN/Xx1OBM/Q53TPB77eTvhNumkmLM+zpq0n7Gf32fZz5z8nhhN3OL9UlzSyBzscoEaHBrUoeFDyeddLV3Kz8tP44wA4Owsy1LUip41uD0cG9ZwdNxYbHRs3DYjcQ4OyGzf9X9X77jsHfrYxo+leyrIQIS/McH+/fsnPL/88ssvaPvLL788Gf4eq0f4G7PJ666aEv7Ochhav7J8tLu3qauXl8npmPQGxrQfuE8OSp+lW9l0H9Cf88P7FB/ic/k/AAAAIDOM9EsnX0zcJssvnxQKHw2GV9RJuUWzOq2h6JC6h7vVE+lJ3IYT92PLBqOD5y4ydjWeeEyyouOu7hOddJWe2Lj1xj+OT+ymOLmj49jj5DrjujOm6tR4ISbPZ2z+yWXR0aseRVPMP5riaxldf+x+uq871X26OCTljN6UO3oDMJ1zXWvMjmuRRScFtVPeHIn7sVA3YW4AF85SMnw3WXz0hsXBKalQUmFpumcCLHJxSZHR26TF/EzGmOfSPQEAAADMd4S/kTQ4OKi2trYJy1asWHFBNSavf/DgwRnPC5jWyKAOn36Prls5pGyHpVynoVynlGVYciiuQ4fjOtRqJT5ct0bv4/FE4CBd3awNSdmjNxniA3cAAABgYTMk5VqWcixLedPdx8eeDym3/UXlhF+Yuk5BtXIq6pRXXqfsSrcM050Ih5evTlz2WNJwbFg9Q93qGepQz0BIPYMd6h5qV89Qp3qGu84Eukf61DPSr57ogHqig+qJDytCZ7/ZZygRqJlwdR7H6C07HTMCAAAAAAAAAABABiL8jaRwOCzLOtOzJjs7W9XV1RdUo6amZsLz06dP2zI3YDq7rA5FC8+n45EhfuQBAAAAmN+OSF1HZHQ+odxAIhSea0mG4VCPQxqi2ysAAAAAAAAAAACw6JGERFJfX9+E5wUFBTIu8IPlwsLCs9a8GKdPn1YoFLqgbYLB4JR59PT0zHguuHD9/f1nfT4j6ereDQAAAACzyDIMDRmGhtI9EQAAAAAAAAAAAFy04cgwucUMZkc+9mIR/kbS5P+IeXl5F1wjPz//rDUvxle/+lXdd999M6qxZ88enTx5csZzwczt2bMn3VMAAAAAAAAAAAAAAAAAMI8Z4mqYWPiOHT2mP7T/Id3TwCxpa2tL274JfyNpaGhiT7GcnJwLrpGbmzvh+eDg4IzmBAAAAAAAAAA4P045la1sZRlZiRsfAQAAAAAAgFnglDP53kO2kX3m3shKvjdxzvvx2ykrOTb23GE40v1lAsC8xTu/SJrc6TsSiVxwjeHh4bPWBAAAAAAA6ZcXj6tk3K0wbsmOt9EtSZJDlmHIkiFLDskYvR9dw5AlWYl7Q/Hk48RY+lgyZBmO0VmMezx6bxmGpDOPz3xto8uTX2eK8dHnE+pMWn/89010tAHmhZl2lzrX9oZhTP2AdNwHnRM+8Bz9MHXyh6Djt+FDUQAAAAAAAABYHAh/I6moqGjC88mdwM/H5E7fk2tejHvvvVd33nnnBW0TDAZ1xx13JJ9v3LhRl1122YznggvX39+vPXv2JJ9v3LhRhYWFttU/+OgKxeNRyXDKcjglwykZDmn0cXKZY3T56GMruV6W5Egst8avN7YdH7hjFr14vFfPH+uRlUjIyOk0tLQ4R5eU5OmS0lyV5Wend4IAMM5IdEQnT55MPl+6dKmys/g5BQAxK6aR+IiGY8OKxCMajg0nHsciGo4PT1geiUWSz+2QE7eS4e3SeEwlcUvFcUsllqESw6kSZanEyFaJI0fFjlwVO/NUnFWgkqwC5WQXSFl5srJyE/fOPCkrV3LmyHLmSFm5iXvnWZaNX390mRzOmX1R8agUG5FiERmxiBQf93jSciM2IsWGpdjY44hkRSVH9ujcEjcrKzG/sa8hsWxs7qPzduZIxuL5+2+2/1YHAABzg2M6AACZgWM6AACZgWP64rN///607ZvwN5ImB7UHBgZkWZaMC/jws7+//6w1L0Z1dbWqq6tnVKOoqEglJSUzngtmrrCw0NZ/i4++9X9tqwWkw5GOAe073qPKohxdvbxUuVkzDMsAwCzp6enRH/7wh+TzG9ffyO9XAHCRLMs6ExSPngmLD8WGztxH+jXUfVTDPUdkRQZUnFOokpwyleSVqTS/UiV5FcrLK5Wy8qTs/DP3Tk7MwYWz+291AACQHhzTAQDIDBzTAQDIDBzTM58d+diLRfgbSaZpyjAMWaMtaEdGRnT69GktWbLkvGscO3ZswvOZhrYBINOtqCjQioqCdE8DAAAAc8gwDOU6c5XrzJVy0j0bAAAAAAAAAAAAAAuJI90TwPyRn5+vlStXTljW1tZ2QTUmr7927doZzwsAAAAAAAAAAAAAAAAAAAAA4W9MMjms/fLLL1/Q9vv37z9rPQAAAAAAAAAAAAAAAAAAAAAXh/A3JrjmmmsmPN+xY8d5b3vixAm1trYmn2dnZ+vyyy+3aWYAAAAAAAAAAAAAAAAAAADA4kb4GxO87nWvm/D88ccfl2VZ57Xtb3/72wnPb7zxRhUVFdk2NwAAAAAAAAAAAAAAAAAAAGAxI/yNCTZv3izTNJPPm5ub9cQTT5zXtt/+9rcnPL/99tvtnBoAAAAAAAAAAAAAAAAAAACwqBH+xgQOh0Pvete7Jiy77777ztn9+3e/+518Pl/yeXFxsd70pjfNxhQBAAAAAAAAAAAAAAAAAACARYnwN6b42Mc+pqKiouTzJ598Up/97GenXf/YsWO65557Jiz7wAc+MKGDOAAAAAAAAAAAAAAAAAAAAICZIfyNKUzT1N///d9PWPZ3f/d3uvfee3X8+PHksng8rocfflibN29Wa2trcvmyZcv04Q9/eK6mCwAAAAAAAAAAAAAAAAAAACwKWemeAOanj33sY9qxY4d+9atfJZf913/9l77xjW9o1apVKi0tVUtLi7q6uiZsl5+fr//+7/9WWVnZ3E54kuHh4QnPg8FgmmaCvr4+tbW1JZ/v379/Qmd5AACwMHBMBwAgc3BcBwAgM3BMBwAgM3BMBwAgM3BMX3wm51In51ZnE+FvpORwOPSTn/xE7373u/WjH/0ouTwWi6m5uTnlNpWVlfrpT3+qLVu2zNU0p3XkyJEJz++44470TAQAAAAAAAAAAAAAAAAAAAAZ7ciRI1q/fv2c7MsxJ3vBgpSXl6cf/vCH+ulPf6prrrlm2vUKCwt177336uWXX9YrX/nKOZsfAAAAAAAAAAAAAAAAAAAAsJjQ+Rvn9MY3vlFvfOMbFQwGtXv3bh07dkyRSERlZWW67LLLtGXLFuXl5aV7mgAAAAAAAAAAAAAAAAAAAEBGMyzLstI9CcBuXV1devLJJ5PPV6xYodzc3DTOaPEKBoO64447ks8ffvhhuVyu9E0IAABcFI7pAABkDo7rAABkBo7pAABkBo7pAABkBo7pi8/w8LCOHDmSfP6KV7xCZWVlc7JvOn8jI5WVlen2229P9zSQgsvl0hVXXJHuaQAAgBnimA4AQObguA4AQGbgmA4AQGbgmA4AQGbgmL44rF+/Pi37daRlrwAAAAAAAAAAAAAAAAAAAACAC0L4GwAAAAAAAAAAAAAAAAAAAAAWAMLfAAAAAAAAAAAAAAAAAAAAALAAEP4GAAAAAAAAAAAAAAAAAAAAgAWA8DcAAAAAAAAAAAAAAAAAAAAALACEvwEAAAAAAAAAAAAAAAAAAABgASD8DQAAAAAAAAAAAAAAAAAAAAALAOFvAAAAAAAAAAAAAAAAAAAAAFgACH8DAAAAAAAAAAAAAAAAAAAAwAJA+BsAAAAAAAAAAAAAAAAAAAAAFgDC3wAAAAAAAAAAAAAAAAAAAACwAGSlewIAMltVVZU++clPTngOAAAWHo7pAABkDo7rAABkBo7pAABkBo7pAABkBo7pmEuGZVlWuicBAAAAAAAAAAAAAAAAAAAAADg7R7onAAAAAAAAAAAAAAAAAAAAAAA4N8LfAAAAAAAAAAAAAAAAAAAAALAAEP4GAAAAAAAAAAAAAAAAAAAAgAWA8DcAAAAAAAAAAAAAAAAAAAAALACEvwEAAAAAAAAAAAAAAAAAAABgASD8DQAAAAAAAAAAAAAAAAAAAAALAOFvAAAAAAAAAAAAAAAAAAAAAFgACH8DAAAAAAAAAAAAAAAAAAAAwAJA+BsAAAAAAAAAAAAAAAAAAAAAFgDC3wAAAAAAAAAAAAAAAP9/e/cdHmWV////NakkJBBCCdJS6KCA1FVAQdoKFkQpiygoIsournwVVBYVC4qKguy6fkTEdV1RBClKEWmxgYBtaZEaAgQQCEmAJKTevz/8Mcs9k5nck5kMGXk+rivX5bnnnPM+J8u17zl33nMPAAAAAAQAir8BAAAAAAAAAAAAAAAAAAAAIABQ/A0AAAAAAAAAAAAAAAAAAAAAAYDibwAAAAAAAAAAAAAAAAAAAAAIACGXegEAft/279+vLVu26MiRIyooKFCNGjXUokULXXvttapSpcqlXh4AAJeMYRg6ePCgtm/friNHjigrK0vh4eGqUaOGmjZtqk6dOvk8V549e1bffvut9uzZozNnzigiIkLx8fG69tprVa9ePZ/G2rlzp3744QcdO3ZMxcXFqlmzpq688kp16dJFISG+O4b4c08AAFQW58+f18aNG/XLL78oMzNTYWFhatCggbp06aKkpCSfxvLXud6fewIAwFO7d+/Wf//7Xx05ckS5ubmKiIhQXFycmjVrprZt2yo8PLzcc5PXAQCoWPn5+frpp5+UkpKizMxM5eXlqVq1aqpTp47at2+vJk2ayGazeR2nqKhImzdv1o4dO5SRkaHg4GBdccUV6tChg1q3bu2DnfxPenq6Nm3apLS0NPt+mjVrpm7duikqKspncfy5JwAAKgJnbu9Q91fJGQBQAZYsWWK0b9/ekFTqT1RUlPGXv/zFOHny5KVeKgAAfnP69Glj3rx5xpAhQ4xatWq5zJOSjNDQUGPgwIFGcnKy13EPHDhgjBgxwggLCys1ls1mM3r06GF8+eWXXsUpKSkx3nnnHaNZs2Yu91WzZk1jypQpxrlz5wJiTwAAeGvYsGFOeSo+Pr5cc504ccL485//bFStWtVlru3QoYOxdOlSr9ftr3O9P/cEAIAnzpw5Y0ybNs1ITEx0e34PCwszunXrZsyaNcuj+cnrAABUrO+//9648847jfDwcLe5vH79+sZTTz1lZGRklCvO2bNnjb/97W9GbGysyxjNmzc35s2bZ5SUlHi1p+TkZKNHjx5u35fcddddRmpqqldx/LknAMDl5ciRI8bixYuNxx57zOjZs6cRHR3tk3vnjjhzB8ae4B2KvwH41Pnz540777zT7QH64p/atWtTlAUAuCyMGzfOZaFyWT933323kZ2dXa64CxYsMCIjIy3FsdlsxmOPPVaum7WZmZlGnz59LO8pKSnJ2LFjR6XeEwAA3vr0009LzU/luYG9YcOGMj885vj+IT8/3+M4/jzX+2tPAAB46rPPPjPi4uI8OrvHxcVZnp+8Tl4HAFSc4uJi47HHHjOCgoI8zuWrVq3yKNa2bdvK/KDYxT/9+vUzsrKyPN5TSUmJMXHiRMtxqlataixatMjjOP7cEwDg8vHNN98Yt912m1GvXr0y84ovir85cwfGnuA9m2EYhgDAB0pKSjRo0CAtW7bMdD04OFiNGjVS9erVlZqaquzsbNPrkZGRWrt2ra655hp/LhcAAL/q2LGjfvjhB6frF74mMS4uToWFhUpLS3PKlZLUuXNnrVu3zqOvbFy4cKGGDRumkpIS0/XatWurYcOGOnHihNLT0+V4JHj44Yc1c+ZMy3Hy8vLUo0cPbdmyxXQ9LCxMCQkJCg8P14EDB5STk+O0jo0bN6pJkyaVbk8AAHgrOztbrVu3Vnp6utNr8fHxOnjwoOW5vvnmG/Xt21d5eXmm6zExMUpMTFRmZqYOHz6s4uJi0+uDBg3SokWLLH99tT/P9f7aEwAAnpo5c6YeeeQRp3NllSpVVK9ePdWqVUt5eXk6duyYTp06ZX89Li5Ox48fL3N+8nr59wQAgBVjxozR3Llzna5HRkaqcePGioiIUEZGhg4cOOCU78PCwrR06VLdeOONZcbZvXu3unXrZno/IElRUVFKSkpSXl6eDh48qMLCQtPr11xzjdavX68qVapY3tP48eP1j3/8w3TNZrOpQYMGql27tg4dOuS0juDgYC1cuFC33Xab5Tj+3BMA4PIxa9YsTZgwwVJfT++dO+LMHRh7go9cyspzAL8v06dPd/qEzwMPPGCkp6fb+xQXFxuLFy82GjVqZOrXoEEDPhEMAPhd69Chgz3vxcTEGOPGjTNWrFhhnDlzxtSvqKjI2LBhg9G9e3envHr77bdbjrdv3z6nr3xq27atsX79elO/X375xRg0aJBTrE8++cRyrAceeMA0NigoyHjyySeN06dP2/vk5+cb7777rlGjRg1T36uvvtooKiqqdHsCAMBbY8aMMT1x6+Kc5MnTS06fPu30RJT4+Hhj6dKlpm+2OHz4sDF27Fin/Pfqq69ajuWvc70/9wQAgCfmzp3rlHduvPFGY9WqVcb58+ed+qenpxvvv/++cfvttxsNGzYsc37yOnkdAFCxFi5c6JRrWrVqZaxYscIoLCw09T1x4oTxzDPPOH1jZ+3atU33tktTWFhoXHXVVaZxsbGxxnvvvWcUFBTY+2VkZBh/+9vfnJ5CPn78eMt7WrBgQal/K9izZ4+p39q1a402bdqY+kVHRxupqamW4vhzTwCAy8vMmTNdPjk6Kiqq3PfOHXHmDow9wXco/gbgE6dOnTKio6NN/8f+4osvuux/5MgRIyEhwdT/qaee8uOKAQDwrw4dOhgJCQnG3Llzjdzc3DL7FxUVGffff7/TAcux0NmVP/3pT6ZxnTp1MrKzs0vtW1JS4hSrcePGTjfDS5OSkmIEBwebxs6fP99l/x07dhgxMTGm/vPmzatUewIAwFsbNmwwbDabIf32oaiXX3653Dewn3jiCdPYxMRE081WR9OmTTP1r169epl/tDYM/57r/bUnAAA8sXfvXqNKlSr2fBMaGur2fOvISm4ir5PXAQAV68orrzTlmY4dOxrnzp1zO2bdunVGSEiIadwLL7zgdsxbb71l6l+jRg1j586dLvt/8MEHpv4hISFOxdulyc/Pd8rRDzzwgKnY62JZWVlGx44dTf3vvvvuMuP4c08AgMvPheLv6Ohoo0ePHsbEiRONhQsXGgcPHjQ2bNjgs+JvztyBsSf4DsXfAHxi0qRJpv9Dv+6661weOi9Yu3ataUx0dLRx6tQpP60YAAD/Wr58uZGfn+/RmKKiIqcbtcOHDy9z3I4dO0xP3QgLCzN27drldkxeXp7RtGlTU6w5c+aUGWvIkCGmMXfddVeZYxyfpBYfH296csil3hMAAN7Izc01GjdubM89f/3rX8t9A/vEiRNOTz5Zu3at2zElJSXGddddZxozefLkMmP561zvzz0BAOCJnj17mnLNxx9/7NP5yeve7QkAgLLs37/flF8kGVu2bLE01vFJmddcc43Lvvn5+UbDhg1N/d95550yY4wYMcLje/3//Oc/TWOaNm1q5OXluR2zc+dO09PMg4ODjZSUFLdj/LknAMDlZ9++fcbOnTuN4uJip9d8VfzNmTsw9gTfChIAeKmkpETvvvuu6drUqVNls9ncjuvVq5e6d+9ub589e1Yff/xxhawRAIBLbcCAAQoLC/NoTHBwsCZNmmS6tnr16jLHzZs3TyUlJfb2sGHD1LJlS7djqlSposcff9x0be7cuW7HZGZmavHixfa2zWbT1KlTy1zfPffco/j4eHs7LS1Na9eudTvGX3sCAMBbTz75pPbv3y9JatSokZ5//vlyz/XRRx/p3Llz9vZ1112nXr16uR1js9n09NNPm67NmzdPhmG4HOPPc72/9gQAgCeWLVumDRs22NuDBw/W4MGDfRqDvP4b8joAoKLs3r3b1G7QoIE6depkaeztt99uau/bt89l39WrV+vw4cP2dkJCgu65554yYzjm44ULFyo7O9vtGMf72U888YSqVKnidkyrVq00dOhQe7u4uNjpvYEjf+4JAHD5ady4sVq1aqWgoIorVeXM/ZvKvif4FsXfALy2ceNGnTx50t5OSkpSjx49LI0dPXq0qb106VIfrgwAgMB38YFJkjIyMpSbm+t2zKeffmpqO+ZbV4YOHaqqVava21u3btXRo0dd9l+xYoWKiors7R49eigpKanMOEFBQU43jst6D+CvPQEA4I2tW7dq1qxZ9vYbb7yhqKiocs+3bNkyU9tq/uvZs6cSExPt7ePHj+u7775z2d+f53p/7QkAAE/MmTPH1Hb8Q6kvkNf/h7wOAKgIp0+fNrUbNmxoeWyjRo1M7aysLJd9HfPfPffcU2ZxlPRb4dv1119vbxcWFmrlypUu+x85ckQ//vijvR0VFaUhQ4aUGUdyzsmOa3bkrz0BAFBROHP/T2XeE3yL4m8AXluxYoWp3adPH0uHwQt9L5acnKycnByfrQ0AgEBXo0YNp2vunpyxe/du01NJqlatqmuvvdZSLMe+hmE45fmLOb7Wt29fS3Ek5/cAy5cvd9nXn3sCAKC8CgsLNXr0aBUXF0v67YmhN910U7nnO3funL766ivTNau51mazqXfv3qZr7nKtv871/twTAABWpaenm75lq127dmrdurVPY5DXzcjrAICKUL16dVM7Ly/P8ljHvrVq1XLZ11/3xR3jdO3a1fSgE3e6du2qyMhIe3v37t3au3ev5VgVtScAACoCZ26zyron+B7F3wC89vPPP5vaVouxJKlevXpKSEiwtwsKCrRr1y4frQwAgMCXnp7udK1mzZou+zvm5c6dOyskJMRyvK5du7qdz91rnrwH6NChg8LDw+3to0ePmj5R7C5ORe4JAIDyevHFF7V9+3ZJUkxMjGbPnu3VfDt37lRhYaG9nZiYqLp161oe76+c7sm53p97AgDAqs8//9z+4S3ptydk+Rp53Rl5HQDga+3atTO1U1JSLBcfbdmyxdTu3Llzqf1+/fVXHT9+3N4ODw9X+/btLa/RXzk9JCTEaQ+uYvlzTwAAVATO3M4q457gexR/A/BaSkqKqd2qVSuPxjv2d5wPAIDL2ddff21qx8fHKywszGV/f+XlwsJC09O4PY0VHh6uxo0bW4rFew0AQGW3a9cuTZs2zd5+6aWXPLoRWxp/5j9/xSKnAwAqo61bt5rabdu2tf/3Tz/9pIceekht27ZVjRo1FBkZqYSEBPXp00czZswo9QPbpSGvlz8OAABWNWjQwFSslJ+fb+mD2fn5+Zo1a5bp2ujRo0vt65ivmjRp4vZ+vSPH/Ldv3z4VFRVZiuWvnF6RewIAoCJw5i5/HH/Hgm9R/A3AK3l5eTp06JDpWsOGDT2aw7H/7t27vV4XAAC/F/PmzTO1+/fv77a/Yx6tqLx84MAB0w3ciIgIt1+F6U0sf+0JAIDyKCkp0ejRo1VQUCBJ6t69u8aMGeP1vL7Of2lpaTp//rxTP3+e6/21JwAAPOFY/J2UlKRz585p9OjRat++vf7+979r27ZtysrKUl5entLS0rR27VpNnDhRTZs21eTJk01P4yoNeb3sOOR1AIAvvPTSSwoK+l8ZzFNPPaX33nvPZf+srCzdcccdpiKlm2++WTfffHOp/b3Nf7Vr11aVKlXs7YKCAqWmplZILH/ldE/2BABAReDMXXacyrAn+B7F3wC8curUKRmGYW+HhoaqTp06Hs1Rv359U/vEiRM+WRsAAIFu5cqV+uqrr0zXRo0a5XaMYx5t0KCBRzEd8/LJkyctxXEcV55Yrt4D+GtPAACUx+zZs/Xdd99JksLCwjRnzhzZbDav5/U2/8XFxSkkJMTeLikpUUZGhlM/f57r/bUnAAA84fitVkFBQbruuuucPoxdmry8PL344ovq37+/zp4967Ifed0ZeR0AUBG6deumf/zjH/ZzeVFRkUaNGqXOnTtr+vTpWrJkiT7//HP95z//0fjx49W4cWMtX77cPr5Pnz768MMPXc7vbf6TpHr16rmd8wLH+9je3hevqJwuWd8TAAAVgTO3s8q4J/heSNldAMC1c+fOmdqRkZEe/5G7atWqbucEAOBydPr0aY0dO9Z0beDAgercubPbcY551DHPlsWxf2FhofLz8xUeHu7TOKWNcfUewF97AgDAU6mpqZoyZYq9/cQTT6hFixY+mdvb/Gez2RQREWEqRCst1/rzXO+vPQEAYFVJSYlT0fZDDz2kn376SdJvueemm25S//791aBBA+Xk5Oinn37S+++/r6NHj9rHrF27VqNGjdInn3xSahzyujPyOgCgojz44INq3ry5HnroIe3cuVPSb9/04fhN3In2AAArxklEQVRtHxdLSkrSpEmTNGbMGNOTwx356754Xl6eiouLvYrlr5zuSSwAACoCZ25nlXFP8D2e/A3AK47/h33xVzpZFRER4XZOAAAuNyUlJRoxYoSOHDliv1a9enXNnj27zLHe5mbHvFzanL6IU1osq4fbitoTAACeuv/++5WTkyNJatGihSZPnuyzuf2VawMpp3sSCwAAK7Kzs01PuJKkH3/8UZJUs2ZNffnll/r000/1wAMP6KabbtLQoUM1ffp07d69W8OHDzeNW7x4sf7973+XGoe87l0sAAA8dcMNN2jr1q169NFHFRwc7LZvo0aN9Oijj2r48OFuC7+lS5fTyxOLnA4AuFxw5i5/LN4HBDaKvwF45fz586Z2WFiYx3M4PnUzLy/PqzUBABDoJk6cqFWrVpmuvfXWW2rYsGGZY73NzaU9Dbu03OzP9wD+2hMAAJ545513tHbtWkm/PUVjzpw55cqHrvgr1wZSTvckFgAAVrj6g2RwcLBWrFih7t27l/p6VFSU3n//ffXt29d0/YUXXnAqJpfI697GAgDAU//3f/+nxo0ba8aMGU5P0HZ06NAhjRs3TgkJCZo3b57bvpcqp5cnFjkdAHC54Mxd/li8DwhsFH8D8IrjJ34KCgo8niM/P9/tnAAAXE5mz56t1157zXRt0qRJGjp0qKXx3uZmx7xc2py+iFNaLFfvAfy1JwAArDp27JgeffRRe/u+++5zWRxWXv7KtYGU0z2JBQCAFa7yyH333acuXbq4HRsUFKQ333zT9ITQ3bt368svvywzDnnds1gAAFhVWFioO+64Qw8++KCOHTsmSYqNjdVTTz2lLVu2KDMzUwUFBTp69Kg+/fRT3XbbbbLZbJKk06dPa/To0Zo4caLL+S9VTi9PLHI6AOBywZm7/LF4HxDYKP4G4JWoqChTu7RPIZfF8RM/jnMCAHC5mD9/vh5++GHTtVGjRmn69OmW5/A2N5f2SdzScrM/3wP4a08AAFj15z//WVlZWZKkunXr6uWXX/Z5DH/l2kDK6Z7EAgDACld5ZMyYMZbGJyUlqXfv3qZrpRV/k9e9iwUAgFUPPvigPvnkE3u7c+fO2rlzp5555hl16tRJMTExCg0N1RVXXKGbb75Zixcv1tKlS01FSjNmzNC7775b6vyXKqeXJxY5HQBwueDMXf5YvA8IbBR/A/CK4/9h5+bmlvq1lu7k5OS4nRMAgMvB8uXLNXLkSFMeHTRokObOnWt/8ogVjnnUMc+WxbF/SEhIqZ/O9TZOaWOsHm4rak8AAFixcOFCLVmyxN5+/fXXFRMT4/M43uY/wzDKdXO3Is/1/toTAABWRUREKDg42HQtOjpaV199teU5rr/+elP7+++/d+pDXndGXgcA+FpycrLeeecde7tOnTpavny56tat63bcLbfcojfeeMN0beLEiZYeKlJR98VLe4/i7X3xisrpnsQCAKAicOZ2Vhn3BN+j+BuAV2rVqmUqSCssLNSJEyc8miM9Pd3UrlOnjk/WBgBAoNiwYYMGDx6soqIi+7U+ffroww8/dLrBWxbHPHrkyBGPxjvm5dq1a1uK4ziuPLFcvQfw154AALDi4q9/HjBggIYMGVIhcbzNf7/++qvpvUVQUJBq1arl1M+f53p/7QkAAE845qcmTZooKMj6n8+aN29uapeWR8nrzsjrAABfmz17tqn98MMPW74XPGrUKDVr1szezsjI0OLFi536eZv/JOno0aNu57zAce3e3hevqJwuWd8TAAAVgTO3s8q4J/gexd8AvBIREaFGjRqZrh06dMijORz7t2jRwut1AQAQKDZv3qxbbrnF9BVK1157rZYsWaKwsDCP53P8o3NF5eWkpCSFhITY23l5eTp58mSFxPLXngAAsCIrK8v+3ytWrJDNZivzp2fPnqY50tLSnPr8/PPPpj6+zn/x8fGlfvOFP8/1/toTAACeaNmypaldrVo1j8Y79s/MzHTqQ14vOw55HQDgDcMwtH79etO1m2++2fL4oKAgDRgwwHTtq6++curnbf47ceKE6W8BYWFhSkpKKrWvv+6L+3NPAABUBM7cZcepDHuC71H8DcBrjv+nvWvXLo/Gp6SkuJ0PAIDfq23btunGG2/UuXPn7NeuvvpqrVy5UlWrVi3XnP7Ky6GhoWrcuHG5Y+Xn5+vAgQOWYvFeAwBwOfJn/vNXLHI6AKAyatWqlamdn5/v0fiLi50kKTIy0qkPeb38cQAAsCIzM1PZ2dmma4mJiR7N4di/tG+7dMxX+/fvV0FBgeUYjvmvcePGpoesuIvlr5xekXsCAKAicOYufxx/x4JvUfwNwGvt2rUztTdu3Gh57LFjx3Tw4EF7OzQ01OlmOwAAv0e7d+9Wnz59TE8Ea9mypVavXq3q1auXe17HvLx161bTVzqV5dtvv3U7n7vXPHkP8MMPP5j+oH7FFVe4/Aoof+4JAIDKonXr1goNDbW3Dx48qGPHjlke76+c7sm53p97AgDAqvbt25vav/76q0fjHb8OuWbNmk59yOvOyOsAAF8q7cNbnhYgX5zXJKm4uNipT926dVW3bl1T3B9++MFyDH/l9KKiIm3ZssVSLH/uCQCAisCZ21ll3BN8j+JvAF676aabTO21a9fKMAxLY7/44gtTu2fPnoqKivLZ2gAAqIzS0tLUu3dv0x+IExMTtWbNGtWuXduruVu0aGF6IndOTo7lA1pOTo42bdpkb9tsNqc8fzHH19asWWN5nY593X0Fpz/3BABAWZYtW6Y1a9Z49DNjxgzTHHFxcU59mjRpYuoTHR2t6667znTNaq41DENr1641XXOXa/11rvfnngAAsGrAgAEKCvrfn8tSU1N1+vRpy+Mdi6Mcv5pZIq87Iq8DAHyttA9fHT161KM5HJ/07epe/YABA0ztirov7hhn48aNysnJsRTn22+/VW5urr3drFkzNWvWzHKsitoTAAAVgTO3WWXdE3yP4m8AXrv22mtVq1Yte/vAgQNKTk62NPadd94xtW+99VZfLg0AgErn2LFj6tWrl44cOWK/Vr9+fa1bt07169f3SYxbbrnF1HbMt64sWLBA586ds7c7duyoevXquezfv39/09NTkpOTdeDAgTLjGIahf/3rX6ZrZb0H8NeeAAAoy/XXX6/evXt79NOhQwfTHFWqVHHqU9oN0fLmvw0bNig1NdXejouLU5cuXVz29+e53l97AgDAqjp16qhr166ma4sXL7Y0tqioSEuWLDFd69GjR6l9yev/Q14HAPhaWFiYrrjiCtO19evXezTHunXrTO2LH0hyMcf89+6771oqkNq/f7++/PJLezs0NFT9+/d32b9hw4a6+uqr7e1z587p448/LjOO5H1Or6g9AQBQUThz/09l3hN8i+JvAF4LCgrSqFGjTNeeeeaZMg+E69at09dff21vR0dHa8iQIRWxRAAAKoXTp0+rT58+2r9/v/1a7dq1tWbNGiUmJvoszr333iubzWZvf/TRR0pJSXE75vz585o+fbrp2ujRo92OiY2N1cCBA+1twzA0derUMtc3b94809c/xcfHq3fv3m7H+GtPAABUJsOGDVPVqlXt7a+++qrMP14bhqFnnnnGdO2ee+4xPdHUkT/P9f7aEwAAnhg7dqyp/corryg/P7/McW+//baOHz9ub1erVk39+vUrtS95/TfkdQBARenVq5epPWvWLBUVFVka++WXX5q+QbK0+S7o16+fGjRoYG8fPHhQ7777bpkxpk6dasrHt99+u6pXr+52jOP97OnTp+v8+fNux6SkpGjBggX2dmnvDRz5c08AAFQEzty/qex7go8ZAOADJ0+eNKKiogxJ9p8XX3zRZf8jR44YCQkJpv5Tpkzx44oBAPCvM2fOGJ06dTLlvpiYGOOnn36qkHhDhw41xerUqZORnZ1dat+SkhJj7Nixpv5JSUlGQUFBmXF27txpBAUFmcbOnz/fbf+YmBhT/7lz51aqPQEA4GsbNmww5aT4+HjLYx977DHT2MTERCM9Pd1l/2nTppn6V69e3cjIyCgzjj/P9f7aEwAAVhUXFxtXXXWVKd+MHDnSKC4udjnmu+++c8qdjz/+uNs45HXyOgCg4nz++eemHCPJGDNmjNt8bhiGsXfvXqNevXqmcU2bNjWKiopcjnnzzTdN/WvUqGHs3LnTZf8PPvjA1D84ONjYvXt3mXvKz883GjVqZBr7wAMPGCUlJaX2z87ONjp27GjqP2LEiDLj+HNPAABczJt75444cwfGnuA7FH8D8JkXXnjB6UD94IMPmpJOcXGxsWTJEqdDar169YzMzMxLt3gAACpYjx49nPLks88+a6xZs8bjn9OnT5cZb+/evUZkZKQpXtu2bY0NGzaY+u3evdsYNGiQ09o+/vhjy3u7//77TWODgoKMJ5980rTOgoIC49133zVq1Khh6tumTRujsLDQUhx/7gkAAF/y5gZ2RkaGUbduXafxy5YtM/2x9/Dhw04ffJJkvPzyy5Zj+etc7889AQBg1dq1aw2bzWbKOb179za+//57U7+srCzj1VdfdfqjaLNmzYwzZ864jUFeJ68DACpWz549nXJNt27djLVr1zrdhz516pQxY8YMo3r16k5jFi5c6DZOQUGB0bp1a9OY2NhY47333jPFycjIMKZMmeL0AJVx48ZZ3tP8+fOd1nfHHXcYe/bsMfVbt26d0aZNG1O/qKgo48CBA5bi+HNPAIDLzzfffFPq371nzJhhyidxcXEu/0bu7kNJhsGZO1D2BN+xGUYZz2cHAItKSkp06623avny5abrwcHBio+PV/Xq1ZWamqqsrCzT6xEREVqzZo26du3qx9UCAOBfNpvNZ3Nt2LBBPXr0KLPfRx99pOHDhzt9JVPt2rXVqFEjnThxQkeOHHF6ffz48Zo9e7bl9eTm5ur666/X999/b7oeFhamxMREhYeH68CBAzp37pzp9Vq1aunbb79Vs2bNLMfy154AAPCl5ORk9ezZ096Oj4/XwYMHLY//6quv1K9fP6evdo6JiVFiYqKysrJ06NAhFRcXm16/9dZbtWTJEsvvQ/x5rvfXngAA8MRLL72kxx9/3Ol63bp11aBBA+Xk5Gj//v0qKCgwvV6zZk1t2LBBV111VZkxyOvl3xMAAGU5fvy4rr32WqWmpjq9FhUVpcTEREVERCgjI0MHDhxwuo8sSY888ohmzJhRZqyUlBR169ZNp0+fdorTuHFj5eXlKTU1VYWFhabXO3furOTkZEVERFje17hx4/Tmm2+artlsNjVs2FC1a9dWWlqaTp06ZXo9KChICxYs0B133GE5jj/3BAC4vCQkJCgtLc2rOUaOHKl//etfbvtw5g6MPcFHLmHhOYDfoby8PGPYsGFOnwRy9VOzZk2np3UCAPB7ZDU3WvnxJHfOnz/fiIiIsDz3o48+6vIrI93JyMgwbrjhBstxEhISjG3btnkcx597AgDAV3zx1ZXr1q0zYmNjLee/4cOHG+fPn/c4jj/P9f7aEwAAnpg9e7YRGhpqOT81b97c6cmbZSGvk9cBABXn0KFDpX4TZ1k/oaGhxvTp0z26l/zzzz8b8fHxlmP07t27XE/FLC4uNiZMmGA5TmRkpLFgwQKP4/hzTwCAy4snucXVz8iRIy3F4swdGHuC94IEAD5UpUoVffjhh1q0aJHatWvnsl/VqlU1btw47dq1y9KTSwEAQPn86U9/0o4dOzR8+HCFhoa67HfdddcpOTlZr7zySrmeuBUbG6s1a9Zozpw5atKkidt+kydP1vbt2y09Ea00/toTAACVyQ033KBdu3bpwQcfVGRkpMt+V199tT755BN98MEHCg8P9ziOP8/1/toTAACeGD9+vLZt26ahQ4e6PXMmJibq9ddf17Zt29S0aVOPYpDXyesAgIrTsGFDrVu3Th9//LF69OihoCD3ZTHVq1fXgw8+qO3bt+uxxx7z6F5y27ZttX37dj3xxBOqUaOGy35NmzbV22+/rS+++EIxMTGW578gKChIr732mtavX6/u3bu77BcWFqY777xTO3bs0JAhQzyOI/lvTwAAVBTO3IGxJ3jPZhilfI8NAPjIvn37tHnzZqWnp6ugoEAxMTFq2bKlunbtqipVqlzq5QEAcFk5c+aMvvnmG+3du1dnz55VlSpV1KhRI3Xt2lX169f3aazt27frxx9/1LFjx1RcXKyaNWvqyiuvVJcuXdz+8dxT/twTAACVRV5enjZu3KiUlBRlZWUpLCxM9evXV5cuXdx+CKs8/HWu9+eeAACw6syZM9q4caP27t2r7OxsRUVFKS4uTu3bt1fz5s19EoO8DgBAxTp79qy+//57HThwQFlZWTp//ryqVaummjVrqk2bNmrVqlWZBeJWFBYWavPmzdqxY4cyMjIUHBysK664Qu3bty/3g1BcOXLkiDZu3KhDhw7p/Pnzio6OVtOmTdWtWzdVq1bNZ3H8uScAACoCZ27vUPdXuVH8DQAAAAAAAAAAAAAAAAAAAAABwPuPLwIAAAAAAAAAAAAAAAAAAAAAKhzF3wAAAAAAAAAAAAAAAAAAAAAQACj+BgAAAAAAAAAAAAAAAAAAAIAAQPE3AAAAAAAAAAAAAAAAAAAAAAQAir8BAAAAAAAAAAAAAAAAAAAAIABQ/A0AAAAAAAAAAAAAAAAAAAAAAYDibwAAAAAAAAAAAAAAAAAAAAAIABR/AwAAAAAAAAAAAAAAAAAAAEAAoPgbAAAAAAAAAAAAAAAAAAAAAAIAxd8AAAAAAAAAAAAAAAAAAAAAEAAo/gYAAAAAAAAAAAAAAAAAAACAAEDxNwAAAAAAAAAAAAAAAAAAAAAEAIq/AQAAAAAAAAAAAAAAAAAAACAAUPwNAAAAAAAAAAAAAAAAAAAAAAGA4m8AAAAAAAAAAAAAAAAAAAAACAAUfwMAAAAAAAAAAAAAAAAAAABAAKD4GwAAAAAAAAAAAAAAAAAAAAACAMXfAAAAAAAAAAAAAAAAAAAAABAAKP4GAAAAAAAAAAAAAAAAAAAAgABA8TcAAAAAAAAAAAAAAAAAAAAABACKvwEAAAAAAAAAAAAAAAAAAAAgAFD8DQAAAAAAAAAAAAAAAAAAAAABgOJvAAAAAAAAAAAAAAAAAAAAAAgAFH8DAAAAAAAAAAAAAAAAAAAAQACg+BsAAAAAAAAAAAAAAAAAAAAAAgDF3wAAAAAAAAAAAAAAAAAAAAAQACj+BgAAAAAAAAAAAAAAAAAAAIAAQPE3AAAAAAAAAKDSSEhIkM1ms/8kJydf6iUBAAAAAAAAAFBpUPwNAAAAAAAAAMAlMGrUKFOh+8GDBy/1kgAAAAAAAAAAlRzF3wAAAAAAAAAAAAAAAAAAAAAQACj+BgAAAAAAAAAAAAAAAAAAAIAAQPE3AAAAAAAAAAAAAAAAAAAAAAQAir8BAAAAAAAAAAAAAAAAAAAAIABQ/A0AAAAAAAAAAAAAAAAAAAAAASDkUi8AAAAAAAAAAICKcOLECX399ddKTU1VYWGhatWqpVatWukPf/iDgoODyz3v8ePH9eOPP+rgwYM6c+aMSkpKFBkZqTp16igpKUlXXnmloqKifLgTa0pKSrR3717t3LlTR48e1ZkzZxQeHq7Y2Fg1adJEnTt3Vnh4uM/jVtTv+QLDMLRt2zb98ssvOnnypLKzsxUZGam6deuqZcuWuuqqq8odp6CgQN99950OHjyokydPqqSkRLVr11bTpk29Xn9hYaG2b9+uHTt2KCMjQzk5OQoPD1d0dLTi4+PVrFkzNW7cuNzzAwAAAAAAALg8UfwNAAAAAAAAAAhICQkJSktLkyTFx8fr4MGDkqQ9e/bo8ccf17Jly1RSUuI0rmbNmpo8ebLGjx+v0NBQy/EWLlyomTNnatOmTW77BQcHq02bNho4cKD+8pe/KDY21v7av/71L91zzz2ljktMTHQ558X7u9jZs2e1ZMkSLV26VMnJycrMzHQ5R3h4uG6++WY98cQTat++vds9XMzfv+cLDh06pBdeeEFLlizRiRMnXParUaOG+vfvr7Fjx6p79+6W5t6xY4eeffZZrVq1SufOnSu1T0xMjEaMGKEnn3xSderUsbzu48eP6/nnn9f8+fPd/u8hSbVq1dINN9yge++9V/369bMcAwAAAAAAAMDly2YYhnGpFwEAAAAAAAAAgGQuNJakDRs2qEePHmX2vVCUvGjRIt19993Ky8srM9aAAQO0aNEiValSxW2//Px8/elPf9KSJUusb+T/t2bNGvXu3dvedlf87Y6r4u9atWopIyPDo7lsNpuef/55TZ482VJ/f/2eLzAMQ88++6xeeOEFFRQUWBpz8Vh3ioqKNGHCBP3zn/8stWC9NNHR0frwww81YMCAMvuuWbNGgwcPVnZ2tqW5L+jatau++eYbj8YAAAAAAAAAuDzx5G8AAAAAAAAAwO/CihUrNGzYMBUXF0uSQkNDlZiYqJiYGJ04ccKpeHrFihWaNGmSZs+e7Xbe++67r9TC7zp16qh+/foKDw/X2bNndeLECZ08edJn+7Hi/PnzprbNZlODBg1Uo0YNRUZG6uzZs0pNTVVubq69j2EY+tvf/qaioiI99dRTHsesqN+zJBUWFmr48OFatGiR02thYWFq2LChatasqZycHKWnpysrK8vyunNzczVo0CCtXr3a6bW6deuqbt26CgoK0pEjR0xPGj979qxuvfVWffjhhxo8eLDL+Xfu3Kmbb75Z+fn5puvh4eFKSEhQ9erVVVxcrOzsbKWlpamwsNDy2gEAAAAAAADgAp78DQAAAAAAAACoNMr75O+YmBjZbDZlZmaqQYMGevbZZ3XHHXcoOjra3n/v3r2aMGGCVqxYYb8WFBSkbdu2qXXr1qXG2Lp1qzp37mxvh4SEaOLEiRo7dqzi4+Od+v/666/6+uuv9dlnn2nhwoX69NNPTU/+PnbsmHbu3ClJeuWVV/TFF1/YX/vPf/6juLi4UtcRERGhrl27Ol2PiopSw4YNNXjwYN14441q27atIiMjTX1KSkq0ZcsWvf766/roo4/s14ODg7Vp0yZ16tSp1JgX+OP3fMGECRM0a9Ys07V27drpySefVL9+/VS1alXTa/v27dOKFSv0wQcfaOvWrW6f/D1y5Ej9+9//trejoqI0YcIE3XPPPUpMTDT1/e9//6vnn3/eVIQeHR2tH3/8UU2aNCl1/gEDBmjlypWmdT///PPq06ePwsLCTH0LCwu1Y8cOrVq1Sh999JGqVavGk78BAAAAAAAAWELxNwAAAAAAAACg0ihv8fcF7du31+eff67atWuXOqa4uFg33XSTPv/8c/u1hx9+WDNnziy1/6RJk/TKK6/Y2/PmzdM999xjaS8ZGRkqKSlxuZZRo0bpvffes7dTU1OVkJBgae4LkpOTXf5+SvPvf/9bI0eOtLeHDBmiBQsWuB3jj9+zJK1evVp//OMfTdceeughzZw5U0FBQW7XKEnr1q1Tr169Sn1twYIFGjZsmL3duHFjrV69Wo0bN3Y75yuvvKJJkybZ27fccouWLVvm1C87O1u1atVSUVGRJKlFixb68ccfFRERUea6JemXX35RixYtLPUFAAAAAAAAcHkr+24pAAAAAAAAAAABoFq1alq8eLHLgmTpt6ddOxYgr1q1ymX/PXv22P87KipKd999t+X11KxZ0+1afMGTwm9JuvvuuzVixAh7e/HixcrOzvZojor4PUvSM888Y2oPHz5cr7/+uqXCb0kuC78Nw9DUqVPt7cjISEuF35I0ceJEDR482N7+7LPPTP8mLkhNTbUXfku/FfZbLfyWROE3AAAAAAAAAMso/gYAAAAAAAAA/C488MADio+PL7NfixYt1KZNG3t77969OnfuXKl98/Ly7P8dFBRkuRC5Mru4+LuoqEhbt271aHxF/J63bNmiTZs22dvR0dF64403PFqXK6tXr9Yvv/xib//1r3+1VPh9wZQpU+z/bRiGlixZ4tTn4n8nkhQaGlqOlQIAAAAAAABA2QL/LjUAAAAAAAAAAJKGDh1quW+7du3s/11SUqL09PRS+9WrV8/+32fOnNFnn31W7vVVFomJiab2Tz/95NH4ivg9f/HFF6b23XffrZiYGI/W5crKlStN7bvuusuj8W3atFHdunXt7a+//tqpz8X/TiTp448/VmFhoUdxAAAAAAAAAMAKir8BAAAAAAAAAAEvNDRUbdu2tdy/Tp06pnZ2dnap/fr06WNq33nnnXr11VeVlZXl8RorUklJidavX69HHnlEvXv3Vnx8vGJiYhQcHCybzWb6ad68uWnsqVOnLMepqN+zY0F1//79Lccoy8VzV61aVS1atPB4joYNG9r/OyUlxen1+Ph4NW3a1N7evHmz/vjHP2rjxo0exwIAAAAAAAAAdyj+BgAAAAAAAAAEvNjYWAUHB1vuX7VqVVM7Ly+v1H6DBw9Wq1at7O1z587p0UcfVVxcnPr27avp06frm2++0fnz58u3cB9YtmyZmjRpol69eum1117TunXrdOjQIWVnZ6ukpKTM8Z4UslfU73n//v2mdseOHS3HKMvFxdo5OTkKCgpyKogv62fr1q32OU6fPl1qnKlTp5ra69evV9euXZWYmKgHHnhAH374odLS0ny2LwAAAAAAAACXJ4q/AQAAAAAAAAABr0qVKl6NNwyj1OuhoaH67LPPnJ6WXVBQoDVr1uiJJ55Q9+7dFRMTo549e2rWrFn69ddfvVqLJyZPnqyBAwcqNTW13HPk5+db7ltRv+eLC6ptNptq1arlVZwLcnJyPNqfFa6eXj58+HA999xzstlspusHDx7UW2+9peHDhyshIUEJCQm6//77tX79epe/DwAAAAAAAABwheJvAAAAAAAAAADcSEpK0o8//qhp06apfv36pfbJz89XcnKyJkyYoEaNGumhhx7S2bNnK3Rd7733nl588UXTtYiICPXt21dTpkzRnDlztHjxYq1YsUJr1qyx//znP/+p0HWVx8W/q8jISAUF+ebPF5481dwqdwXbU6ZM0aZNm9S/f3+XT0hPS0vT22+/rV69eqlNmzZas2aNz9cIAAAAAAAA4Pcr5FIvAAAAAAAAAACAyi4yMlKTJ0/W448/rm+//Vbr169XcnKyNm/erLy8PFPfgoIC/f3vf9cXX3yhr776SnXq1PH5egoKCvTYY4+Zrt177716+eWXVbNmTbdjd+/e7fP1eCs6OlqZmZmSpNzcXJWUlPikADwyMtLUjo2N1YIFC7ye150uXbpoxYoVOnbsmL744gslJyfrq6++0oEDB5z67tixQ/369dOrr76qCRMmVOi6AAAAAAAAAPw+UPwNAAAAAAAAAIBFQUFB6t69u7p3766nn35ahYWF+v777/X5559r/vz52rdvn73v7t27NWrUKK1cudLn60hOTtavv/5qb/ft21fvvPOOpbGnT5/2+Xq8FRsbay/+NgxDp06d8knRfExMjEJCQlRUVCRJysvLU+/evb2e14orrrhCI0eO1MiRIyVJR48e1fr167V06VJ9+umnKiwslPTbfh955BFdc801+sMf/uCXtQEAAAAAAAAIXL753kQAAAAAAAAAAC5DoaGhuuaaa/TMM89oz549euONN0xPrF61apVSUlJ8Hve7774ztceNG2d57M6dO329HK81bdrU1P7+++99Mq/NZlN8fLy9nZeXp6NHj/pkbk/Vq1dPI0aM0KJFi7R371516tTJ/pphGJo5c+YlWRcAAAAAAACAwELxNwAAAAAAAAAAPmCz2TRu3DgNHz7cdP2bb74ptf/FReLSbwXAVl381G9Jat68ueWx69evt9zXX7p3725q+/Jp6T179jS1K8P+4+PjNX/+fNM1V/9OAAAAAAAAAOBiFH8DAAAAAAAAAOBDXbt2NbVPnTpVar+qVaua2rm5uZZjOBaKFxQUWBr366+/avHixZbj+Msf//hHU/v9999XVlZWhcz9j3/8wyfzeqtJkyaKi4uzt139OwEAAAAAAACAi1H8DQAAAAAAAACADzkW8daoUaPUfrGxsaZ2amqq5Rh169Y1ta0+NXr8+PHKz8+3HMdf2rdvr27dutnbZ86c0Z///GefzD1w4EA1adLE3t68ebPefPNNn8ztjYKCAp05c8bedvXvBAAAAAAAAAAuRvE3AAAAAAAAAAAu3Hnnnfryyy8t98/MzNTcuXNN1zp06FBq39atW5vaixYtshzn2muvNbWnT59e5pOjp0yZooULF1qO4W9PP/20bDabvT1//nw9/PDDTk85d2XdunWlXg8ODtZzzz1nuvbXv/5Vb7/9tkfr27Nnj+6//36lp6c7vTZ//ny9+OKLyszMtDzfG2+8oby8PHvb1b8TAAAAAAAAALgYxd8AAAAAAAAAALiwYsUK9ejRQ61atdLTTz+tTZs2KTc316lfXl6eFi5cqC5duigtLc1+vW3bturUqVOpc/fo0UOhoaH29nvvvadBgwZp3rx5WrVqldauXWv/+fbbb01jr7/+esXHx9vbhw8fVteuXbVmzRpTsbRhGNq4caP69OmjadOmSZJatmxZvl9GBevdu7f+3//7f6Zrr7/+ujp06KDFixcrJyfHacy+ffv0+uuvq2PHjurdu7fLuYcNG6axY8fa24WFhbr//vvVq1cvLV++vNS5CwsL9d///lezZs1S9+7d1aJFC7399tsqLCx06nvixAlNnjxZDRo00B133KEPP/zQ9O/gYnv27NFf/vIXPfLII6brY8aMcbl+AAAAAAAAALgg5FIvAAAAAAAAAACAyi4lJUXPPvusnn32WQUHB6tBgwaKjY1VWFiYsrKydODAAaei4MjISM2bN8/lnHXq1NFdd91l6rNkyRItWbLEqW98fLwOHjxob4eGhuqVV17RkCFD7Nf27Nmjvn37qkaNGkpKSlJxcbEOHTqk06dP2/vExcXprbfe0nXXXVeeX0OFmz59ug4fPqyPP/7Yfu2nn37S7bffrrCwMMXHxys2NlY5OTk6cuSIsrKyLM/997//XZmZmaa5169fr/Xr1yskJMQ+d1FRkbKyspSenq6CggKP1p+bm6tPPvlEn3zyiSQpJiZGdevWVUxMjAoKCnT48GGdPHnSadywYcM0cOBAj2IBAAAAAAAAuDxR/A0AAAAAAAAAgAeKi4uVlpbm8snOklS/fn0tXLhQ7du3dzvXrFmzdPjwYa1Zs8bjdQwePFjTpk3TlClTTE/7zszM1A8//ODUv2HDhlq5cqWioqI8juUvISEh+uijj9SsWTO99NJLpoL6goIC7d27t9xzh4aGasGCBerQoYOmTp2qvLw8+2tFRUXav3+/9u/f73aOWrVqKSIiwnLMrKysMgvUx4wZo3/+85+W5wQAAAAAAABweQu61AsAAAAAAAAAAKCy2rJli15++WX16tVLVatWLbN/kyZN9Nxzz2n37t265ppryuwfHR2t1atX6/PPP9e9996rdu3aKTY2VqGhoZbWN3nyZK1YsUJt27Z12adatWp65JFHtH37dl155ZWW5r2UbDabnnvuOaWkpGjUqFGKiYlx2z8uLk733XeftmzZYmn+SZMmKTU1VY8++qgaNWpUZv+6detqxIgRWrx4sY4ePaq4uDinPmPHjtWyZct03333qUmTJmXOGR4erttuu01ff/215syZo5AQntUDAAAAAAAAwBqbcfHjQAAAAAAAAAAAQKmKi4uVkpKivXv3Kj09XWfPnpX0WwF3/fr11a5dOyUmJl6y9aWkpGjz5s06ceKEioqKVLNmTbVs2VJ/+MMfFBYWdsnW5a3i4mJt2bJF+/fv18mTJ5Wbm6uoqCjVr19frVu3VosWLWSz2co9/759+/Tzzz/r5MmTyszMVEhIiKpXr65GjRqpZcuWSkhI8HjOkydPateuXTpw4IBOnz6t3NxcRUZGqkaNGmrRooXatm1r6cMEAAAAAAAAAOCI4m8AAAAAAAAAAAAAAAAAAAAACABBl3oBAAAAAAAAAAAAAAAAAAAAAICyUfwNAAAAAAAAAAAAAAAAAAAAAAGA4m8AAAAAAAAAAAAAAAAAAAAACAAUfwMAAAAAAAAAAAAAAAAAAABAAKD4GwAAAAAAAAAAAAAAAAAAAAACAMXfAAAAAAAAAAAAAAAAAAAAABAAKP4GAAAAAAAAAAAAAAAAAAAAgABA8TcAAAAAAAAAAAAAAAAAAAAABACKvwEAAAAAAAAAAAAAAAAAAAAgAFD8DQAAAAAAAAAAAAAAAAAAAAABgOJvAAAAAAAAAAAAAAAAAAAAAAgAFH8DAAAAAAAAAAAAAAAAAAAAQACg+BsAAAAAAAAAAAAAAAAAAAAAAgDF3wAAAAAAAAAAAAAAAAAAAAAQACj+BgAAAAAAAAAAAAAAAAAAAIAAQPE3AAAAAAAAAAAAAAAAAAAAAAQAir8BAAAAAAAAAAAAAAAAAAAAIABQ/A0AAAAAAAAAAAAAAAAAAAAAAYDibwAAAAAAAAAAAAAAAAAAAAAIABR/AwAAAAAAAAAAAAAAAAAAAEAAoPgbAAAAAAAAAAAAAAAAAAAAAAIAxd8AAAAAAAAAAAAAAAAAAAAAEAAo/gYAAAAAAAAAAAAAAAAAAACAAEDxNwAAAAAAAAAAAAAAAAAAAAAEAIq/AQAAAAAAAAAAAAAAAAAAACAAUPwNAAAAAAAAAAAAAAAAAAAAAAGA4m8AAAAAAAAAAAAAAAAAAAAACAAUfwMAAAAAAAAAAAAAAAAAAABAAKD4GwAAAAAAAAAAAAAAAAAAAAACAMXfAAAAAAAAAAAAAAAAAAAAABAAKP4GAAAAAAAAAAAAAAAAAAAAgABA8TcAAAAAAAAAAAAAAAAAAAAABACKvwEAAAAAAAAAAAAAAAAAAAAgAFD8DQAAAAAAAAAAAAAAAAAAAAABgOJvAAAAAAAAAAAAAAAAAAAAAAgAFH8DAAAAAAAAAAAAAAAAAAAAQAD4/wDnqu3WpjeO7gAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 3000x1500 with 3 Axes>"
       ]
@@ -1050,18 +972,12 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-04T17:49:37.136108Z",
-     "iopub.status.busy": "2023-12-04T17:49:37.136009Z",
-     "iopub.status.idle": "2023-12-04T17:49:51.322412Z",
-     "shell.execute_reply": "2023-12-04T17:49:51.322119Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 3000x1500 with 3 Axes>"
       ]
@@ -1091,7 +1007,7 @@
     "                splitter=tree.splitter.QOSplitter()\n",
     "            )\n",
     "        ),\n",
-    "        \n",
+    "\n",
     "    }\n",
     ")"
    ]
@@ -1111,7 +1027,7 @@
    "hash": "b27b2a9f272874e098660428b28f5afa65c7850d82ff592660d49a141d883cd1"
   },
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('river')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1125,7 +1041,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/dev/recipes/on-hoeffding-trees/index.html b/dev/recipes/on-hoeffding-trees/index.html
index ade27bd2a6..50a5819e60 100644
--- a/dev/recipes/on-hoeffding-trees/index.html
+++ b/dev/recipes/on-hoeffding-trees/index.html
@@ -1585,12 +1585,12 @@ <h2 id="2-how-to-inspect-tree-models">2. How to inspect tree models?<a class="he
 
 This dataset contains features from web pages that are classified as phishing or not.
 
-    Name  Phishing                                                          
-    Task  Binary classification                                             
- Samples  1,250                                                             
-Features  9                                                                 
-  Sparse  False                                                             
-    Path  /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz
+    Name  Phishing                                                       
+    Task  Binary classification                                          
+ Samples  1,250                                                          
+Features  9                                                              
+  Sparse  False                                                          
+    Path  /Users/mastelini/Documents/river/river/datasets/phishing.csv.gz
 </code></pre></div>
 <p>We are going to train an instance of <code>HoeffdingTreeClassifier</code> using this dataset. As everything else in River, training an iDT is a piece of cake!</p>
 <div class="language-python highlight"><pre><span></span><code><span class="o">%%</span><span class="n">time</span>
@@ -1602,8 +1602,8 @@ <h2 id="2-how-to-inspect-tree-models">2. How to inspect tree models?<a class="he
 
 <span class="n">model</span>
 </code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>CPU times: user 37.6 ms, sys: 569 µs, total: 38.2 ms
-Wall time: 39.1 ms
+<div class="language-text highlight"><pre><span></span><code>CPU times: user 64.8 ms, sys: 2.75 ms, total: 67.6 ms
+Wall time: 68.1 ms
 </code></pre></div>
 <div><details class="river-component river-estimator"><summary class="river-summary"><pre class="river-estimator-name">HoeffdingTreeClassifier</pre></summary><code class="river-estimator-params">HoeffdingTreeClassifier (
   grace_period=50
@@ -1878,7 +1878,7 @@ <h2 id="3-advanced-gardening-with-river-grab-your-pruning-shears-and-lets-limit-
 
             <span class="c1"># Convert timedelta object into seconds</span>
             <span class="n">r_time</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;Time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">())</span>
-            <span class="c1"># Make sure the memory measurements are in MB</span>
+            <span class="c1"># Make sure the memory measurements are in MiB</span>
             <span class="n">raw_memory</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;Memory&quot;</span><span class="p">]</span>
             <span class="n">memory</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">raw_memory</span> <span class="o">*</span> <span class="mi">2</span><span class="o">**-</span><span class="mi">20</span><span class="p">)</span>
 
@@ -1888,7 +1888,7 @@ <h2 id="3-advanced-gardening-with-river-grab-your-pruning-shears-and-lets-limit-
 
     <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">metric_name</span><span class="p">)</span>
     <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Time (seconds)&#39;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Memory (MB)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Memory (MiB)&#39;</span><span class="p">)</span>
     <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Instances&#39;</span><span class="p">)</span>
 
     <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
@@ -1917,14 +1917,14 @@ <h2 id="3-advanced-gardening-with-river-grab-your-pruning-shears-and-lets-limit-
 </code></pre></div>
 <p><img alt="png" src="../on-hoeffding-trees_files/on-hoeffding-trees_19_0.png" /></p>
 <p>In our example we use the <code>EBSTSplitter</code>, which is going to discussed later. For now, is enough to know that it is a mechanism to evaluate split candidates in the trees.</p>
-<p>As we can see, our tree uses almost 10 MB to keep its structure. Let's say we wanted to limit our memory usage to 5 MB. How could we do that?</p>
+<p>As we can see, our tree uses almost 10 MiB to keep its structure. Let's say we wanted to limit our memory usage to 5 MiB. How could we do that?</p>
 <p>Note that we are using a illustration case here. In real applications, data may be unbounded, so the trees might grow indefinitely.</p>
 <p>HTs expose some parameters related to memory management. The user can refer to the documentation for more details on that matter. Here, we are going to focus on two parameters:</p>
 <ul>
-<li><code>max_size</code>: determines the maximum amount of memory (in MB) that the HT can use.</li>
+<li><code>max_size</code>: determines the maximum amount of memory (in MiB) that the HT can use.</li>
 <li><code>memory_estimate_period</code>: intervals after which the memory-management is triggered.</li>
 </ul>
-<p>We are going to limit our HTR to 5 MB and perform memory checks at intervals of 500 instances.</p>
+<p>We are going to limit our HTR to 5 MiB and perform memory checks at intervals of 500 instances.</p>
 <div class="language-python highlight"><pre><span></span><code><span class="n">plot_performance</span><span class="p">(</span>
     <span class="n">synth</span><span class="o">.</span><span class="n">Friedman</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">10_000</span><span class="p">),</span>
     <span class="n">metrics</span><span class="o">.</span><span class="n">MAE</span><span class="p">(),</span>
diff --git a/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg b/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg
index ebf8234dd6..0c51a7e9de 100644
--- a/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg
+++ b/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg
@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
-<!-- Generated by graphviz version 8.1.0 (20230707.0739)
+<!-- Generated by graphviz version 12.0.0 (20240704.0754)
  -->
 <!-- Pages: 1 -->
 <svg width="292pt" height="212pt"
@@ -26,9 +26,9 @@
 <!-- 0&#45;&gt;1 -->
 <g id="edge1" class="edge">
 <title>0&#45;&gt;1</title>
-<path fill="none" stroke="black" stroke-width="0.6" d="M71.7,-167.52C71.7,-167.52 71.7,-142.26 71.7,-142.26"/>
-<polygon fill="black" stroke="black" stroke-width="0.6" points="75.2,-142.26 71.7,-132.26 68.2,-142.26 75.2,-142.26"/>
-<text text-anchor="middle" x="58.95" y="-157.24" font-family="Times,serif" font-size="7.00">≤ 0.5455</text>
+<path fill="none" stroke="black" stroke-width="0.6" d="M71.7,-167.52C71.7,-167.52 71.7,-143.17 71.7,-143.17"/>
+<polygon fill="black" stroke="black" stroke-width="0.6" points="75.2,-143.17 71.7,-133.17 68.2,-143.17 75.2,-143.17"/>
+<text text-anchor="middle" x="58.95" y="-157.69" font-family="Times,serif" font-size="7.00">≤ 0.5455</text>
 </g>
 <!-- 2 -->
 <g id="node3" class="node">
@@ -39,9 +39,9 @@
 <!-- 0&#45;&gt;2 -->
 <g id="edge2" class="edge">
 <title>0&#45;&gt;2</title>
-<path fill="none" stroke="black" stroke-width="0.6" d="M155.26,-167.52C155.26,-167.52 155.26,-136.35 155.26,-136.35"/>
-<polygon fill="black" stroke="black" stroke-width="0.6" points="158.76,-136.35 155.26,-126.35 151.76,-136.35 158.76,-136.35"/>
-<text text-anchor="middle" x="142.51" y="-154.28" font-family="Times,serif" font-size="7.00">&gt; 0.5455</text>
+<path fill="none" stroke="black" stroke-width="0.6" d="M155.26,-167.52C155.26,-167.52 155.26,-137.26 155.26,-137.26"/>
+<polygon fill="black" stroke="black" stroke-width="0.6" points="158.76,-137.26 155.26,-127.26 151.76,-137.26 158.76,-137.26"/>
+<text text-anchor="middle" x="142.51" y="-154.74" font-family="Times,serif" font-size="7.00">&gt; 0.5455</text>
 </g>
 <!-- 3 -->
 <g id="node4" class="node">
@@ -55,9 +55,9 @@
 <!-- 2&#45;&gt;3 -->
 <g id="edge3" class="edge">
 <title>2&#45;&gt;3</title>
-<path fill="none" stroke="black" stroke-width="0.6" d="M142.26,-89.77C142.26,-89.77 142.26,-58.25 142.26,-58.25"/>
-<polygon fill="black" stroke="black" stroke-width="0.6" points="145.76,-58.25 142.26,-48.25 138.76,-58.25 145.76,-58.25"/>
-<text text-anchor="middle" x="129.51" y="-76.36" font-family="Times,serif" font-size="7.00">≤ 0.0909</text>
+<path fill="none" stroke="black" stroke-width="0.6" d="M142.26,-89.77C142.26,-89.77 142.26,-59.16 142.26,-59.16"/>
+<polygon fill="black" stroke="black" stroke-width="0.6" points="145.76,-59.16 142.26,-49.16 138.76,-59.16 145.76,-59.16"/>
+<text text-anchor="middle" x="129.51" y="-76.82" font-family="Times,serif" font-size="7.00">≤ 0.0909</text>
 </g>
 <!-- 4 -->
 <g id="node5" class="node">
@@ -71,9 +71,9 @@
 <!-- 2&#45;&gt;4 -->
 <g id="edge4" class="edge">
 <title>2&#45;&gt;4</title>
-<path fill="none" stroke="black" stroke-width="0.6" d="M194.64,-89.77C194.64,-89.77 194.64,-58.25 194.64,-58.25"/>
-<polygon fill="black" stroke="black" stroke-width="0.6" points="198.14,-58.25 194.64,-48.25 191.14,-58.25 198.14,-58.25"/>
-<text text-anchor="middle" x="181.89" y="-76.36" font-family="Times,serif" font-size="7.00">&gt; 0.0909</text>
+<path fill="none" stroke="black" stroke-width="0.6" d="M194.64,-89.77C194.64,-89.77 194.64,-59.16 194.64,-59.16"/>
+<polygon fill="black" stroke="black" stroke-width="0.6" points="198.14,-59.16 194.64,-49.16 191.14,-59.16 198.14,-59.16"/>
+<text text-anchor="middle" x="181.89" y="-76.82" font-family="Times,serif" font-size="7.00">&gt; 0.0909</text>
 </g>
 </g>
 </svg>
diff --git a/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_19_0.png b/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_19_0.png
index bd204c0a456d91c5917113d884cecdf5f4aa0afd..1cf566a2f358cd6af900a0a35a6aa91b69b8c2b8 100644
GIT binary patch
literal 180558
zcmd?RXIPWj7B(D@$KlM_22l`c3JM|uQUs)Xq^Pt}6jYkhk!B>)+l(_xk4o<d3Mx%{
z4;DzIgDAZykzQg5CG>CYpn~UpzdzsKFW2P&ljM2!UVGJh-RtrHLtB04ZwG$EU@$w+
zomIJr!EBGgV78V1=NI@t0_T_Cz>iZn)hjq%M@yW`joTI&tsA&oc8)kZ8`Fc%7Pp;j
z933P?PKlg2e$X0+yX7P+Dr*1Z10s&MtwbgNeYOxT^6Ra$SDi2z5kB;9i|bP*XUrB1
z=A6puORjMfy)LdF2YZ#iHr4#=Uw{97BAoeZz}9Wd>3{j9Uz<!Z(#uTG?4pjlcHK;L
z7{6)TomQ}jyV09c=xlBOQP1GQtwXzB{kf-b;(_vi{;a1h-HmZSc((aVTu}RX>Ftk8
zgi5m4?FZRfeI=o1`9o`9CEs7ZK6SOY?|-15U2Lkb!XK}TM`O1B_yhCczb%{_fBEhW
z7~1#?^W+anrj0)j{*!!a<1dWh-Uis|_t)9S*1Nv{j=?DZ_W#FQeleG2h@iRPaA=LS
zom~)-NYqr{;;StnBqWrZo6Gy~thp>wNeoxqx+t4dAJv#&($`m4RaLd?DHgkV^QJi0
zuNc{ixVX5ZN00g`dM&D)K7IPj7kc4A^gR#$hnoDM(YqLe$7#n(JBB>fh*Yz;w@*#M
zC`amDxw8Mrks}Y|<Kst<qR-=gUq4Uf*p6~Hr?7|!H8V3a_$Y?CB}E;-|K!a}Uj_#)
zzOBjk+nUD0>bE3$)iyK)!-v%I+zR4c+u&Nh&f4p$Vtu*BT-?}w^y#zAOs(jqy1KyR
z<m98rj|cVi^t`LBt^MP_n2~bW*N66Y^~;wp+jZp&N;&p%%6ZGZOiDVahUbod{5NB9
zdrw8L>kLlh<HwJ3;q}|MZ#S#*V}5w{uz1*Sn9`8)^7400P53Qae^JrW(mHNfd;$a%
z(+1K^DJ+zlo}Lzu+Jja$*BEgcT~Rsx#j|H;*@p)Q1*5kr-@}c!3D5Pq^v8RLzPq=R
zLp<z1=))o3G~W6$hqQwxJKMf}=)=DZ4h^aG6TEbXYU}DG!jPNT_~c30Fx>g#&~Mwf
zOR)TcvA%feQkZyO=^fkpC^0@(;cNM=c^qf~yu7!?xzI0|5%~ig94;|+4Gj&-doZ6e
zOYe+Fh-)9-ljA-+cBSCvW%;FvJh5A!UdhZfpGg6+ocR@<skP(8x*6ri3<V5by-L{e
zD<-<FhvxeB-p(BR?n0zgAJg?oN^1*E&oVOVl^F`x#n=CN-4uOAtXCFib8dZp<GM22
zZq{9BpL1ti&w^q8G2iW8neEaafBb&+>eUOnx-1$I$8C<qDy`DVpji@JyI~Q&J9NL9
zJzVbHgF`tK!jhV|w>O+8>Cq95<eVI_V{ih=$-J#pwAFVfz8m{U^6lHVlb<{}3`dhp
zASj|^XJ)R86m7aCjy@@__1)Q&zMPnt7->z@QBgtq!CT1I5L#o-Z-%z<;Kp|+)LdP0
zRno^vo%Z6zi)YWD*O(t>Tt)N8Rf-*YOI1}=K&d4qB~?^Zz!3QP{`VRd78a^{ZF7!X
zQc_ZCDk?@sM*s7f=3g921|!ZM-lN4+)N@-Sm9Hfr<kV19*nCmSxLx*r7lCL^OWA*+
zTTgzo5%fE}zPrG)Wn3;xibGLmebfTW`lyGuGao&Aq*wQAeggaq^U=4ek7}`O@50`n
zE2MW&DKTKPg(INc<s<!-K0)jf{E9P=cUR9>uB}`tccsd$8lsCU8(jE)43f{&(*wbQ
zTq(YFV|i4^C&1B@%prF3!!y^lrG6eEy^MD_dGr<Q<nK<HJxr6Iob9t>%jx>*PinnH
zlesLz)Idhsso&hcvh8C3+DiYkXV0u#as=XT{kN7LA~)st=T@evxpHm=_ldcv`#j#R
zX(e4<T~{i-z3LMb<faHqQ%6Nb9j~QlWCZEzD4ZN>h-JIWwA<mdJ7WQI-;WM=>^`^q
zh@9g5vy_kkB#j#EGL=NRJ-JM4!rNw*@QajxeM9f1c(+TM=;^WQ8@pL753p5Qhwnhw
z`KoPUvF=O3hpAS$&j!d%74wD24ETjD7&um(ylHDI0UiQgg+Fw4M@>zQzoXAmx1bYv
z6Y2+D9gCyP=p%NKp`++02Gg8?l#SbGnR--MI5^XwNDYg{1|2wgj9({#vlq;{V9Bn#
zP&mPLz~6QDqrO>ds#Z&pWB-0dPghQ-da;LziECNeCI$u^ItsIwj$h3^+?Hv;*4f!9
zcKwy_eg$`&W{jj@Zm#T<v82;*WAG`bp(-%@WP>6HG3(|endx_YDNmk!F*5O}d2|Gw
zNmXsQP)b&o$S*q&>}3(q4%)g~{%bE&|LHSl&eTUs2%Oi@FoQ)QD_vQd$9v6n3WUgx
zJ=0sDMKrd*m2;^VBdIt$JA>v+HiSn+5I;QEm54$@@y+_?seMHTqs&75N!%V8HgTH#
ze7vT!V;`vvj@}Ye<+Mg{-@T*d2?`!WpS5`(QhVj<>_}(cupD76LfBv+Kq$-4WOX_+
z$Bi}=-{vtpwr@DjN!t??4*%xO8wJ8DxPk$>{srwGmwvAXV<p~XlS*%F+f}P-7XmGQ
zq&?dtFw5ZL#XWTq!hwE%pWogmy??4<5I0)Zzv5u%y)dXnR_F1OM0RG0j~6pj(~OnM
zwDIh;h^%(0T<Z1nqfa{ea73DTyOqy%*)}A2&_CZN;eD3LQSxrL1M@0IpQj}RXo!>?
zPVk(rv25Qh^U1du);w^RXZaJiiHEvIq)4z(cDeXe#e5%9*tw|X<z@OOCZ+xQhFH~!
zye35tPdtvm-B$sct^#V1VNjH1la-Ni{H{Mzm4nG-2YrUkV=~Q$>Z7rr{$wIHNcZ>H
z)ySKAoL(MF&knI^O`Xi;ESCcV^z&yCAnx{|zcq?@mHy(zfPUrT<%!C*Rcen@(`3tR
zcEwy*hhyc+02o-u;4EJu+m-Cf)y4g%Zkr=}?n?16|1bEiIH$%_*tt$CO0GFKGebkf
zNSrL-lj^vQ*3f~}gbGk{!?>8K({-Yx``|U`Z`cUUGjD&TR9)6L+P5@S=GcR4)pA@K
zepXs4J5@{$VceC-;n<Vn@q{+p?C$Kzv&UcM-XpZfSD-wM{4PnuW2$6$;7h1Zh_FE+
z_y|2cJtU$FhK8K`_U*fH?OM!54CtQ~`LFFFewE9Eq8_-!v9U4N{-r*)?X1VGV>o1P
zUEjejei(Ut2r2%hrKPC_C{lUAN21_(ksm6nSS_ktnJgZAk?tHL3JC*fg*qtvaida6
z@bEEh5LU=R5{-Tw4#kHQSE+bP%lNr8T9c9zayzNm3l}cz;F6Vu7-LEusLsKXiKzwm
z?%gZ?`er-Xck3>{x4Y!*irQEE<_T<(M$VnuG_R$(U~~_ZJb<+pI`kf3W^Om}@!GNf
z#C5z0-1>~?bZBA_xRg6-1p@;E9UhblK2cFoy=-GHA^j}7Yw4Mp!P`a4PoR})m@Hl_
zb5qFAf}O{Ef4%<c)t~I=8NRf;b3G#YicF&ABN;*}K^9P2I0tGa0)|YEwo34aJ1g|L
zl7ovs{k8pKW69d;68SX~PfyCy^4I%HL6OGpg1o#T5Q?KDth6((=JA8tv4G}WL%(}}
zz00FAKh+!C*!l9tnK(EM!EG}`Jw9tIkT-$fc;!0s_<k~|udye!I!ejM+pgo~A#bo&
zQuE_8u_3nB)_yuZi>+dgedP-73Zu7|CX1xQ>vd#DpSX@aPcw!5Ld<2V#}(87U1W86
zSczopG5Havjbt?Cl-NC4kO-DWZ!02%i%Z{l^SA3vi+T!tG{I+e!F6FU68-K_P9+`{
z5ef10s}rr1hi%V~+%d#f?-M*igWQk~{=EaYQsOjB1l{6rET^hLMoLUmoP8d~m07v?
zF`F>CYjD&XB+M>hsRB2y?Rk8kJLa(@Qr4I8T#S0d*ExmoSWM5cD`IqieBUR<UOs_t
z#lw!*%3ZNxVPRE#c=<hi>LEQgULd)ESVzLC+fGj8dmIkdF^P&^?s2dOVM0@`MIA5i
zKysi+fXUioyWo)lzezdmwWhUY9Zs*A78I$}@7%egqEerzq?84=TRih#Beuc;{+}VP
zMODxVMN1_67KkBX3lPZZ@6;9dka&DNHREMZ4mBn6Fn7B&D6TEj$p5ezTT{o+MOFv0
zsaMT$b!UT{if_xy%OlUU>fl%A`;~}zp8grPSOpK~`gl3%U5fNimf77M9sAi#Jh8P7
zvlK9)fj$pPFo-bBj7Fo8SrX|goGv|B?1u~L)eu8;ow@qs1rj~Q2bs1k&W^L~R-|9o
zjFZXfB&&yTvLnHocIDe3$D$6I5=!H&6lu{>axM-;6CGPObmj!YDp!1&y}_`X9=uPP
zxyx%t3bAi-WTb7rg-^a^r5x$f_JM**o|3I%vK7%L@vLW-hOA`O%An%ln(uMa$vZ+T
zDjkZk9hmEObUaqPO6FPfgCgi%?M*kEwNH0<hoFc83fn*C#~0$4xco(c3huAeW@>Rf
z8!zg!PzM*PXr;p5)H96A3T?{UjXkP)R=?hlKR@Jn`O>Am@DUT!wUv213f?MJfue-{
zYoM!S@N~9F%B|XRnVpXo41!To1xYgLWV>Oj<UHJZD%X1T>)YKV(99l5Zh6;RJzn#@
za^boCH|bwLW9NHaNMVAyb4S<q=^HW!2M04VKMW7w2miZYL}SQtwgvAknE+0b4!`VW
zW~L1_1gdkp9Y)p~L`Z16U2`PT{0m|79Mn68uAl!w*~im*%2bmaQr#zag>BJK%*cC`
z=oLJhF@UWIB)FQk7K58+J$~h-`urlet#-Ssp6YByxC=qt3Z*vXrquzi5cXmf=A<hZ
zv})<#^zncF^;d5nq!EV+^lH)6dM!}uqG5|S^Mv^dR<3!pOp9emu_)6Wu@0qz7R#BA
zUPA8kQx)AFnhEl!w3Sw7SO@+6P<BJ6_^f%HCxXG`Ru40D|I3$UtNtV<aJ&uNky-6;
z6WqQ$RCitk&&@PY*zFM5<Td4V0YW;&XUfIK1u*CR=xijy_M^?Mp84(s?Mb(%yH9<2
zaONTrLZdacWGmCIAhbeZSt+R98cz+!g_VGbPv8teUgx7_m6Vhk<!SY-R*O{viVwvT
zPB~cyHfcrP-6eZ;uHR>kmCFN`kQ$cTKeggEJ}e<E?SfZyWHY>VIuP&m^)-_&aX7&P
zmKW@YoX?rv3i&qW>GMa8vhOQ6z4=vOVg_3)m{zqgoXCUfCNuDLS|rwe`>LCKR%ykM
zT}ZGoHqiI2F!UA4JW>{2Y4gf=iy&DmR;qyB197*=TAC)!1zDgVxiim-f~$`^<&^yR
z@naj$jvMz?vyCgRdE%j7X1H5ox_)+uMO{PzeHqsgIxiqCEe(}pEV0bp2U086@wQCc
z19%Q;k;S>m?tvw683om(sVh7BL)&SoH{RYmxkCGE+jib)&D4xE+H8I*N|YYee34s3
ziKv=*Q8-FA;1dIRi7R%fnulV6<y#F_n$J~7c1*KsN)RN2jTF**Agg>?O_Fk1hJ?*o
z){=CQUW_Hrjc_wFGx2!ds&!qM9`x`GF3cC8=%|c&<fqSDk(ve*2-5%j^N--Rl_DBs
zI;lZSJNEzXuVRW<1ZR<uO5-?iAUQWz+${Tkp_o-;T$GgkwX&soM)8>St;Pc>ddyEz
z=sjF65#Dmu)zu!jNx%u-)h;$gQl%Ywid)4XwGbqGuPjcsD8$HFk!&rm8I&u@d5g@?
zRCI<qNkIf7wfDtyx!lwl9>38vQOx22F(E86k_6c?*(q_!5WtI*TuZe=*;P~$Pya;a
zI1<H!U%$f&UsGRum6GbSwd-&}+K#spU5tC|g-@M&ZjzAo{J9&cy{pjPpjJj&dY;Q9
zU3hSKn9@S^SK)P^8DTakwDb4#gPbHjq!a5v;%YZAB{@NEQ%-6hv36cApB(jIX_;=i
zV-R+agM&ktIO?fTE{7^=u<K9(oe<C$5(tWe;zjX@U5!J?g$RK@KwqKwBz7jDRelF%
z<Q|2_?>n<J<#zf}NZQMnCoFml{Y#%ZA6KKePR=Elmb&9ZbofJ|if>EBf|8*FERoh2
z^?>ytvu3R4_+`qs^+1(=Nl#BIn8)+$>v->GQ=Od$B#&CeHMXA_kCC*w*n=f;VIka0
zHS{_)DNrF96V^U87`B6-k;Qz|Ao1%Yae7yQZ9laD%A5JZb_Y=ab>>Fs0RaJF@#(Q1
zMgUd`l6P`Qm5&?B8Pl|oUSzMNx?J>h&{#h`f9r+eQYZ%mRcXJn@K^b6**Zt}b`*%u
zzTf78Oc9BusNdV8wdY8r;U0l_+(mOME2|UiNH9i5dfy7v=2?`33X*5X+bdIU+o@E6
z>y8r_5fpSw%jx={AzG2IPHKVxZBb4hKF(_jZd{NI$;A{li>s-V8sj+DSK-mBRCy<D
zHUi4m74G4i8hmO&XW2nmd5}9^$uU(84C$qM2?-1Lrg#*n$eQf7Cqu9`uB|kKq}H!&
zs#Ivb7*PJ^*><4(&<r3EK_?w~YL#4RTgWgMwu+e|qi+y%(pC>Dv3xom%B|xT@;2|E
z#D_%7U`=a7NKJ}Mc9$++E}(Niefm^z8~OEJCeq6rZ(|e+lai7~6NQc)OU}y5s-Aa)
z5N+UTC%c13QQktIx<DINcXM+KZ0e_0xXD2=s=>ZCZnAchS|S_{N7}t#-2ANPVr!yy
zblKuqx~_LR0|a^!`wgaL7rQg|A&d$k4AjJRFkIiXuiQ=03B1WnS?xzXRJ-~>f@pxV
zgRB6lhM<$2H-VQI5UPDpyjwGZ6G|bAWLmYPd-J<J%11Nh;@dxdEO2d~fOOR+&1ks$
zZV{mkvVDa`noFH<Q9ivRR9jZiLf&g}M&a0K(WO%3-S)%pHAI5}sW};J;xSo(ivGyg
zD(I|ncfP>|=2fa}Mq%Ov@Z$rJFm<HZdQRG!Xt3+VN(rk4ADGR`D+ouJhj7=&Wyt0H
z{@KnZ6?}gO`yLcT^GL=Jas|m0&tW<EfM;i}1r<EwaSI31{Fh9A&8P?B4mOWhV*8gZ
z14A^5bUX)G1j*@HrN@v#chf8zV)o7Vc{m1AoFIixow!MWxQp`Kj{`J=V-Q3N^B@bE
zoT1P@>YH!@5_pDDAdZZE!@uA8J=aYm-nh@zCg}iKHYx&hT&V-B@oP}Q%1uEL7*+y>
z>XXY7g@q{hwNhH@EK(R6O-l?x8IK@1vYugHIk~b<veH}*zWUXlTVsh(d_W~3!v{cz
ziyJ<eLz-xl7S9T}hL#hVgvXoV6`Ui7%fY>9u$%a-%nDl2hdh_M`$JG|=IEiB8V3-D
znDazODs}8*IvKDA&+cAgB*Y%81h*mX{&Db`K^Ylqo*F~<m6mUy9Npv6L2q)f^Z+_5
zt3<ED&RM9AyBgaGa|Q3}>TC~GE>G7{;lqd4O=6_>`}U&@97>K%(`d28KsM13Dw#w=
zDO4b<aMvguA`lVc@m}3D?O-Hvi%BRTJD%_8O+YHn%bTi`$c>Ir9x`RYafk=hw>+yS
zQng|uE@G6vzS`<?ETQr;IJKTzZ+8khG4fh~Sh&6Ep8*NAi^+!S45cgBcZcp*^^`cp
z4$-Ek2O<m{+`5;^#Nfblq5LO<i|ji0fb9fAaV%;PIcF};uA{VqLxq4ArRCj2zW%wc
z=m(JN0sN88_wcM_g96+bj9R@4khzQJKY`G(v<7KXl|PHwU~Txr&`=ie0xp4+J1G4I
zoro8AL9hjA04qQtE|)G{nqZVo<zSRzU<a;{!E*rItFufeDWL+}e0jFn`5tI5&l;Vi
zJCs#AcC3uilX<}WJ#;Cz7eIMP$}H+TfMAyoA3i`;4nU@%bEgFs-3@%IDHNax#s_!<
zzfVX{l51WMY#X->`LK@5vcmMoZ10oav*{+)%L_E}=leV%fab{{tm(|X9sUim(xidv
zK$kAB(r3?(A~@(>LxWehZROeu1@GuJZXh_ggtXOPAYniFdofS?S8LF2KrV>@`RO(`
z)Yb-=tc>Z4K_u@b+#AoXR1A@yewS*x00jx7v;mZ8znH0tq?8nKday19ilouFyhd3e
zVh}qE;5rKq*@+Iisha+%R0PC`0)mGULd?D^A7ydh%XZ^(87SMkaP<fkLfJ4n17Sit
z(#tDF4o1r}X-Zlp?N_bh_d)y>gPeg;fKJLaLbQ|}WZKo$J^pwSd6Pg$h@X#A?gE}w
z4WJMQ5B(LBcpQQoeY1!m>uoRd51`?n5ni!_L&`3#0KzVkp4r?)Cx9TOx;65UzbY<u
zHleinQLco$M_6Ja50u}^L0oc$HVzFjCs97+I$6-}d@Oo5r_71f)%nVzXBci~EI42y
zD#5{;G@`SgJ@bS5P7F%LO5K`rXaJzZD&F~zzHm_5YDaHc?7PNB?dbkutQj&6>Oi3A
z)Ftu$UhUE7IpkN6#GXw2o0~ZY5=Ph6g<<kwq=~kmh0o{hCVuGalo4a4g&LiKHUr0C
zIEt_ZsKc(5;%wwW$Ox6HPF4+!5sn5(!6VH{V!_>-13E`oSVd~Rk=tPSI<ldxpSoW8
zDHAJ(6)67TcZoZtdwxgZ!Q7%^B{DlOFpvQXA@ss~m6k|xCQ;^0Aw*IkcoUyS8Xo}k
z_U2p^f*GsXOgOX4r_Xabciiy6t*ot5)<X16yiTHmz{pt1bZMAEOI-6rpa7E+{k1|V
zX&am2?|7wES5C0UGI`4{?;c_SxF8w1^cLt+D-=q>Cz%0C;<yw;4B_GcbdZOP+-q5B
zyW4O)+jk5QG%l~392gKV0N&+Rk8`(u4mf=oC{Ua|v>8#Kqo1w@)z2^+OC^9wSST>9
zPB5@zqYIFRLtlh}1deb5^0OX9!Mlu~IC8xU5A{K{M3X#Ax?W}gq(rDswU?C@;mQd9
zL#W|YzL}GH9yD*FAVWYt;lkCc?B7fZCC=#+jm8L4R$MS=K+4%BJV=ufq%2{n36Qc)
zk;Q7nl|s9#^)V-f+A^-PKwBpedSX?7|H@+e=I>v*=2or(`XRqE)0%Vh<BMm|D>!}k
zmum1=<fOa1ZiX&p8kUGB0Ahsf1;IklNk|5bn*&f`znrsO5a+3BqY8ZlyqGj7q-bbp
zq@cP6WGjmBf{8K;;bCMX8_H!<ii>4>FQ7ETT43G(`|72DhD?U8=Nukmy^h~dQxxAZ
z%4t735{y6pzqf_o)!;B4@hA{N>}!+cmc?14_TG9h>IgR%j~@LGMtOU9c(|&UmshJR
zz|PRnLZB4Fe+t@-1EH5{e&P^XRJ$HP>Zme3paW<PRA#NtKB`V#6)15-6|yD?EjDQz
zyhDDd&Rv7s0e?6U7KTt0wYAu>p8y@o0dU^x-<%t2w6SZV?&2$hUI-4@E}OKclipWt
z%2?2qzP-5q`j(ij|1F4Gh0azZ&5s4i@EvK$bHX1>LYi1T(U})Z4CU7fFs^X706lDG
zm8We<qS>Yodx=K}#l*z8cyVEpCcQx2@r#R#sZ^-!Kuw8&^zlKdxl3;1C2Fnx!tyVS
z@Zk6&rg_1w&qWJTPwe7onsl(s#D~I@Xupt>Jq=P1KCq98^Oo|x_kVz1+O}ob9KdtQ
zN&WWQZ{kt^!B}rkSb*;re54VeQ30NRJ}Y}GCcgrzHdQFbT3w+*(E?Z%bR7f1!*@Uc
zL^^W!*Dbyg(|5cuBg^s>|Be#f?zY_b?$TXPQ2+q^xmNw$xpS$EZr?ZIuqRvhRrdm<
zhM*WLS=4|1_G%6}8sl;!R?0r0$#X~|I)struD_Qr5in%bft(D=m;C&|$ymzlMDN9)
z)@y4txeZA|J)`{^<H7BrhN_&e&U+imo&oq!g`A{c$7(}mok-d7x9d6Gju@dB$ZstU
z{)+jkck<*(K{BKZDCM(37ajQzd^ZW@G$==%seX$91T2*Q7Bc(L`|+oFANnDo*4slb
zU*ZPFm!DDMLjP>X5wz^2ls_aXe-TzD9ZBGQd3jAcUf#fdysU2_!*Dk7=Wg7%QCpmx
z{3hf%=D}$|8!s3eCy47`y&Aoaf#QIkVN{R6Q^-i|Tj|3}Yi=`RABCL$yv}y4@B`6g
zI5Qx71#7Ii(vp(n7Hg7&WWl8hxOr_cG|X8=jWg_aVc7ow^+&ieXs=Ymk<fGNg`cyK
zK<k#XiIB_FzX&L?@7%eQnVI?!?|E2TVfMq0w7=Fm^xA)KU$ebYZ+L~3rtc6?<V{%^
ztsca_S=}nZhPsQ^InZN)@D>OgPAx&U3PsvpaCH}emrznt@-W<jX9vdG9t1izKA!U8
z#W5-SuDw8F=o`IX@ghAv1zN`IeejW^An6AHP%Vl1tS-YTjR$5!IkrRKuS>nRa|aSm
zIaZZ6oRrIKFf2A^)Mm1ND!j}Pz)-6q*W)JiQUE`cYRfW;eT~C37kq4MBSI@qoC~Rd
z@2*!7r;VzhJG2L)&z)9!jBS=bcuX+0iQ^B?R()w%i7|>TczL`5ow~0}iVLL=nyp96
zBDN~a<wDa3nul5onSAgj=A{s!u5}x70|55dq>3KUjut;6QaX|h0vpomRBiFZc1F0S
zB+Gug7P%qU&DhwO!PaO4yFow^B*45Nk5~o%XiYURa6jwC(_%^|kO?#w3<9Ubo>DrV
z6_}RjEz8Rbopsa2nNdWMcw_;_mjjllAe)w-FKGc_+CK1qne2fIF9Cci;;J-Y+C4$2
zE4V*g$;=~LrM--Ku*-g>o(Ur^8sD@8d4noMD{wfffWm!fYqOjf*TppN1MbSR>}))0
zxkHk&Z9A*vzWVX8u^5Fk0)a3Z1r7%#f-x}%`a`$)?t0Tc3$BS)PKkjph`al!(zG2=
zdlPWSberYbY##-Hf-8dt7pg_QnNiwTR!Ri_W(Jv3W|*IgEo4@i-VGJgd1jYq8~g1D
zl<4+k$LjP<Zzat2`L2QJ-I#pQQO~~YYX!#ug-8Z1Mmlrq)G27pnhZK&thWJJTh>z`
z&ua>aVMC)Ml$lZvJ^SGkId+}7(9}m*Z0AQ#jO<a9b|&A(CK|!^3c=G&jgoEMS7QX-
z5-p{I+<xz+YH0#B7&NT6J6k8WGkIY=c27%G(7R~`D>#UeW)OO7{=-*=_T9X^$$)G<
zjE#-0xq@kaS}_~V$=p)r;&=qnEPO>8_3z8(KzBoo8{ktvKUe`O0>CWlZXN5W@Y){Q
z(5T-oVERx09C8U<#!%h>j-?7MH{Iw=0M&puA^~Y>X(~9HsY{Zg$Bxym^B%gk$i=u^
zL>hz2NgL14|F97OvlfrCE^G3h%R{zmj&Br%&UUMgQiTmdAW(Q05=ePkXxI5@T1c8-
zD`u~P$DG|q56%nize^KVFMW`jFqFw2mlIFrI+<~q!i1SP&ei0JIhZHDQceMKLiy_A
zIBH%ai6M#wI5=pNL7O;@>(Z8_@DCq9q6S1P5miL3j?B-WKlg`Y>HuI3WkCQ=o1!*M
zMn;CrV3@Am^?zfy&Ze&UC}NuD<SAvTDEUj7*O#O`S?yn2@<AOvKfnEw)_<UeSiN16
z566k?%FwI(WMq`0pKW~Mk3X8v`@0sogVKyq)K9B$DkjTViX88@v7ei6poWK5tYIEF
z*i%8nyCQK)6jCq;n{9g*f*z`$T}Uw<egE`=zCIh0Agl;6i-3dz=VMd%Z%q6J;Dxl#
z2LXe}$%zlLd`R_ToW+`;%mLIAAuTE@6M$K@WSdlKR&B#v^UogNsysaG)_)RtV8$-~
zMmankQrI5J)y^hwRzOgrxBr{+>dS?d7~vSD%5vVq4<FQ<999=0o%9XK0KbBr$RQC?
z<q7+SVhO-ab*S>d7}dZg@3H(#dAPZ``FCIt*dNf>(^G?XzlzG?J^mxmS_OR+07wp^
z2#}D(yqD=g=7^@1<kj+F;%||PnfIwaiXij>z!BbEKj3PW8Z^bz?{n+5Hto*N8duAj
zdos?If3lxN-^ou!6+-U9r`LDQzJ7X*&_<x!_(R12Wp@3o^O%ti5XZ%YGz8>N^_Ho_
zI%dF%pX0c5D$KnBHdzejh3jBc{!VsFn`txgp`%7GmzUTr0Au9h=ih7>tvYm{b6TYJ
zblrsXGnbk1a5js}1#+1RtM1W^tO&OUq=lWX5g-NZ69NEcUk;qw2$Z)oBQ1QbEtn)-
zv?9>0R4lYsQ$QkGWa!*iJvej*MNvqXF%0{|Aez(UMLj)Ue*75QJfZkzTEW$)7VKyL
zjrJLiz5?YP5*Zvg7t#*Mr+^6&j50`9O!H&=&fFtl<ZY6DD3{SmR7AP&wr$&5Dt*=v
zyY2da(MG$3RGpG~W8_PH@>FT>?_ADtZ*AM(Lu#~y_nJGu3=fkOmnK3-_hXuE0FWg@
zS1y*w#O->0@7}%U>y-u9Kgn(axPGsBRU@Nb?sy64z7~IZq!B|zGDd8d!Ke!<Y6a&N
zt)B~~EKT0FA9|B3V>Zw%nYxhxjmYk8kb4a9(-al_-E*J4r7je1)P=NF{maGt`sw)&
zm+KT)`$-^hY$j`VX(NcWJiex^<Kwdb`&?@~&D2wjv<Ynu^5Z1yUHP`U&KD#_`!8~2
zXJlzQ9bmkJ{^Of<x`oraeOG^)%ye$-TKX9EpYhhXjeIT17D%IA1>A0(VmVKqbrxLR
zUibe0c)Ow|W$C1xl9i3BU8eon%~x;KfveAlaQ9YrE_B@^B0ZH%+qC25y$yiOn}wX>
z2ZmuQ;*>~m{Tef&RD5u8uHM!eN$Gz-|EII4kPx$a@L2mq*YWjShp`)9y)^4xxc}&?
zNt;OefRL^jln67z^tZjrO2ZkS>A2h?|0Zkb+y8?fnb6=E`TdvqQRb9iW^ST8&e~`*
z0CdJ@no9&@wsH}_Txg)&R7}U6k(A>9Nj$WRl1#EeDI`e~NzwEd{@ZM&(l*O}_$jPu
z|9y%sXGy2OWxJ(_Vd4;@|GmC|_{ii_%{OKyB3M}N?K@c}m7c$zrF>hStSDM?w0NmW
z9<AxPAm0AY&Y#wH3aza+tYsxOK0DJf$SN}8=bJ;H7k89ts#oAQnp3P%Y{|DA==;?L
z(A8@i&sSaGTRK_x4H{VAHE80WyR2$Qh60DOv$BF_V{LyrbsBKlWaDbej>f2~$X~dr
z&tN1nzUk|t0rI?cZ|@+<&s!nZ$iwsV;!h{&iLPv;Ul!AJ?FuWTjz1j{P@9v^#E63#
z(#ECQtHy29f4Z#`y6uzspb?H!SO0I)3<PN&nw?7EAm867RVL+h<Es1HZ<~c5IPriX
z<@+ZsaBG&siC1$E6@EPV<fpR&&NJ>xsQSFEv$uhfnS5mi?@x={4U2nwpvWch=tx5A
zL>KvgEP)1wBAXF6ks)*2oCe!FX7uw>--pFL)?#n<Y*~I99-i~~1^-W1XF^+#6@RfN
z(ry_Z@l@>B9}8C=Mhmlk8$dI2#d_!2=CK~zMENdfLA#Q+z*SlfTZtSG1jn~o!?+UR
zwx6}QKY{_C4DU8=S$y%+anR9mQ2ZWNKa3BzcAliR|7_l5;N!`bD=OS3e>&s^Y4qh6
z-zkZ(yq9V4W$hcaBdZOKN)@hWKD0^y<akJ+Qn=rA)j*(x(!xUtCdLncS{igVngu!K
z^G~?D)Y<%>mX`zC9sgekXT5SOb8H^8bt9f`J}$=952)j(+9#Hm!()?8HSA+re+UhO
zGX%M$i7`$)w8l?>6(x<|UrA5V%8@vC2#&Q(bY=YWo#==@A&iXn0QA8k;TWMhLR!iw
zDA&I(%MVATyKa?2(hk8#EDV0qQZwWd!uix$)%B7W3Zg+xI5zpyyNq|=ESJkzpS_W1
z`_p{^d%?-B??^YWHaY9D*iS0L4fk!0mVdF3yb#fK&-KOvapvA8U+RnYWVQS<c_ccr
z7wze1M@~k@6JH*eA!CZfi5EStvDs6HsDs^xL6ik?mP<<!Q^!hwQj{y`>Nd4eaMsfN
zd<ll;n~lB*t^7RS;%rdGBF@p--tze-Y~)opy7N=0=)gBurZ3e0&%(^m+nJ0IQkm>&
zd%XMQk)O7`3vFBW=B3KBhht1UJpae2529oKSY&0)S*)K39u|tu|Kr=bu?!cgBi!}-
z?9SkyT&Ni27x7&T7us*A%j4AF5!H9%yP7~g|2OF9bFKZx`xS2I{a;+@FGv-}BSN_-
z>@7u|0K@$Fs_zg|-#(~5W8;0W)x>8#dXby8e-atyD3=2A?K^{OTyM7g6dA48kh~o~
z_!^77Ofo=5|FiZlp<w3@{cXqt`zN~oSEegqx21nWrg2iP5_jfK#?fr;&4)dQg5I8x
z10OwGpwdD?mVZN$7`GOAY?AGOePz6@Aq%SVpG4Y)0%={8eCdKwL*h~4z<WgV&4!n6
z0w)rQok3nf-yt3E=4hws$Y5kJWaH}$eD4wQU0lX{dvC|A**doU9OA7jVdEkx9uCe>
z4itA5L~phbE%cs6?K5w1IFNt8xH<MyF!lo5Dh(4fixG`!m*yvrs&f2fnCowQG0lan
zR+K@sb^19pStp{a9|6c9j1legeh$W6C|uS>i>HG54a^#t_Hl5uZZ>Q#bo00r=bRKb
zm8yAe*OQRZ9N6TL@|BQWB$8a_Wz4O?@^wT-@ao1!k$uWPbOtjS1WIwlYqjG-nc>pv
z)@(JSGiE<sLlj*DH{rc?;N8ROhAd%`?maFFdGedxQx}R;8t0)>ZqOyF@wO~47U0Xd
z!@I}cb1*2@jJqYpL$oT**g7}H!o#Wbq_ZGktdzcx^3+BfCD!Y&W<{_KqpqivUg^?d
zsWtu24S5m7fax(w0S0`|_|Q9fbTf_*X&#317AkqR1v`VODV3EGLyU&-`gNj_s=Bf1
zaIvqr-?4R`6m`<C4iu6~mHz$PjUY$82gYJ#ob7Om8qPwRxV`eClCTHdZty0zD-Map
zLEgqF8Ob@Wj<U{zQ+3A%9!Iu4ImH0m7?)BWf)%T%IP38eu(cgVS!<sjZ@Xa7A4S}}
z8gNx#9d$`Fcw`-Ag?4A|^sLV0{<Ntbs!qN|^dUhrKB1Q7t~+an`#I8WTR9LSy#6}4
z6%z11)5_D^u*9mz4O#YG*VxwPv)^yN%2_423YX`?-#|1+{y0-dIM{-t@Tc{<!?q``
zE1#aHe`w*0HA=TVOFX$-_^cD7`eNM5cjls`&Rr<iqNE*-TbY_yjc_xH>n>q;y$Op`
z1f+bM1h9w$r~?`aF^Z@|(96B%P&<Iw?Lj<Bia<={Vd2+wO?&vKWNlNOzXou<Bggeu
zCO^W+nT_Vg!9P%hzU2c!Q3BnRQJ!{`yU`r*KEN~?sAAEvlLh3+8$Xry;5V)&CV?IP
zu?bfWK$6&FGg-ZlgPcv%BQ5Rj6WUtyU06_F=|%Ro0MGh{rw+hTsk@VCb&z!yX|tEr
z&DwHb`@MVYz<WTazrPMDl;T=Aue*SM5)J8fRL-+?ykr+_kZ2gQ*;Z_ltarWSFi~==
zeAjq$Rr=s^f6CJfgr`zJ*+N9}saFm`+#Pa_@^;pFwy{MYNpPG|o7K}E^eSl^0rw_q
z<fCv}XMtRTF>7GE{?pB3MG?&UPw$G-YO4_T&WUSYX5!voKmE1&xUJEwgz!vF%~F-I
zSE+Q_9A3YT*cvGP(|LD;)mcBDD(S?Io?Du_y)sr=aUeLjMv{NSqO3XKIv$_$@6LzZ
zotuI@6LOmYLWxjo+=hUXW|7|fnUSI5Nejy<GhAyqlU-d{dhe&}c%kczDDVZ4|6XB|
zDl5F%T3C#2d-T(qjo_e_-2tCK{4)fo<lG*th2wStBuEt|)%LQnk$_%iI?<8Cm_mEj
ze8_X<%gR8@w((?wSV?P_sMYZRJhznO;_O(&pXT2W5QCv4Ksh{z_yq_y1NMS|>Fd9?
zT`)0OD<sVW($Oe8o`DF&`=S455_9ptB*|o-Y<kD%w*zfy5tI3PmvwpC6{p<|?<}5X
zKf94VeVr1FQLoY`OotymcFeuo?AWnm7m3j4u?x(uB$td%_rm}T+5#(2B>=m0XqoSm
z?sGK*s!6{e>I*=xf`k9>kjX$2-e0NWrMQ%1_riS{Z#fTD!&~O(f(|sxZC2a9co7px
zpldN$){4V9B<}7MC%6RYFkrO8I7*)a%%QOY9Eh-$qe4P~gLRQ<=IPL}LtSvaj4RCO
z?!UDDcc~2$6i<h)XY|#(+nul3T5A&Lxv%^#ZFK9(FON1NFObf_@N7JC&0KgNS}%?q
z1vX=L@knwYBH-fRzklD6YoUP#D8xMGCJ?P0QSE_P_dB$Q@Q2RadlKgX)ShaXYlwUN
zrCBV_$Nb!Sep|_!5h!pTQu_@Z`qVrAUX=#bLK_~3_?QF1(tUoTt!X6a-m(DY8u%pf
z{nG%Jq5GpQsTi=t!wOKF1v*4gS?`)Z<C+>mvJ>+AMcT6$yVF~mqZaxtH&o!I0CdC-
z>XyFI?uGVhcM79pizbq`?vhD*zP}Q9NesQH4HL9yW|sYD9?!uk-EeJ>XjVsYYgc-@
z5Lu@QfbM7+$(7d|SM7T&Vj!smEp!R4$3_PL59M?pd5oI$sU-kEUT)#+Qv*=bF<x9s
zaXCFL#%PVIWE`kV+!?Z5F0!OA^5a6yugzg3X|f3zBf3Pu=-AKyi;GnCh=85`^KOih
zfx%T4X?eoo&Az1Xl&X_+E;N3_u+Z1B{7adMRuDQL*fRlPuhQG|LPmN|+r$R2qU@1k
zonQ%Vm*|kMg^&uM2|+<8)M>x>UYR6bUThWTqH-?J_G7_LYUq6v9mj(|*5>PSwnbiR
zs4<=c@v9L*Ejnbn(4llR6^$CUx>l~tAubFwIB>@km7Fg@|Db!Ki!z+yx6St0W<PPg
zNie49G4zKKE!J$APDN)(M_49sR_kyXMy0cD24qA#ZIuCx6dL%eqM+Zu=$<BBj7nrf
z*UqyIuJNNNygayB6my0!b%v9got;I!b&_yV&z{{1Gk>57QSoTIYfa`hOSA1Jc(4ZP
zVCXe~67iw|(_)xB90=Dpwz&rAc_7j?V5vp=KWly*528R(_)6hp_p}0%i3v}ohqIgK
z%*z{_t-l!x_{=W=$_ApLF>fG9sR5r(MMd}Za>MNfgYCo7Ujt2+c>z36te6yo+*ljt
zA?U!^;6c>bbSu;_&j21H169{z*N0XKoV+d3bT_mX2JK09^Zz`5OBV8RFX$}DUbFGW
z=}1=Pi&fibZ^H-=L+c_waaaL^+lgAJCtZiW!NmaVTFk5l3{l<y$&!rh6c$%tBmK1~
zqK<{(D<HbUs2NXMg8R+5J8f5^gUStf7T&23Y&W6*H#V%EuQ0M#6zEtkcY=Y5OakKJ
zH9=-d8{n}>#cQ?ICR*j>BJBDWiuOzL^NB2w>!{Hy9H!={e|fXzmE5QQw{;yiA;_o9
zH`R{x$|XSquNZOh!gRI$AD_8TTw88hL-fm@OGkairv{;yM+GS<A=(g<5u(zgp0@>k
zUwK9U+UmTR+w>Qi#gP=3u5+B$B`UlFTaWB~=r`mOdfG^wS^D_9kjviB7=wNc6}G;=
zx4~cpO~{}=hM4QuPcpQ62{O!n1fcfz?S<HFG(SIbh@1%clKv{#@BqvM1)^Ru@XjQn
zyvuB}D+-_go(K(p;L{^F>a#L#LJmz_HV61PWwaw&!U{3;Se=IIYp$D)bljT{pevsv
zi?kO>9Uh3xwj|xNx7mS^@by>ozx>U`JC~iys$N`9XHegf{ul$%&aZ=@>!$K#!f<tn
z-tgWHTHu!ds?>niI-^sHW^YnUfLtVpwD;)o<96X5XaJ}Ung*m2L@h|ALT`o(%~piV
zPm}-`?8$O%Fqo!lQ-Xp-)U%bP&Y-7Qa;T7ViDJ1UzIe6=+X8{$fbN!8-L!f}_lg@+
z`U`YuMh-AI0jTk2>^1w*waKHeRnmL(Y2*M*LY?hiaeD8Zw#yWF2~=njpF;Dk^!Cct
zeb6iuw3wFOJ_xxo;&~zxlqrl{V_|~FN@k%<8V!{qmKE?ya!jk|E>0jim%7s81w*(?
z=4~{KjrhEMA5WsM11_HV=)Oxf(9Qesz4W%F-2pGU{Q4Vg2zG=FPg~E=`0WE(fqg6$
zr3<#~zH>DaJQ!J}CXa8cxBa@lC4w#LXQ)vU!&}OV_<&ifVhDm{=mC)527rR$4`W`b
zMzwr}6ypRKHDQdzG|Ewe3T)enh$9D_>&9D)ZLJ{Z`13S0#B2FRSpcy##&&b=7no4G
z1*CBgGhNTcx#8qOf>`jTTr;NqY2XkV;X?#PL{38_x*izQ%7Kg^j0?<-tzG~cXYEEV
zMj6nCTwQFhBqF|<g8R%n^JOX8pSwMiMhnzc(3?Q-k9e)pOdT}0s-Cq9#-BUl<>FSK
z@jjPctKgK9BNk2El=H~d)!=+JPr;bp0Hg{Ch`V@P2d0pnP&CDRO_*VU8C3<HEAcoP
zFCe3&0P_UVy;4g~+O}1F{rncjJ!J_fIefdoLQbjlKE|5S*)t(F-(<9}T7fo@J5NcR
z-pcb$so}@y2aCWVyyBuY(4q?r`#@Us5p^X7!;lj=mF$`wgfBxwFzbx_&CC4@F*7l0
zFVEs&C{?Yx%@SWSSo5?cTi@7os-wk5d*itfFA-te{Jnw8_5>KsL@nQ7H24Spy#J}j
z5PC!b^sK{5V652k=uVvC*6pPvd$_Qd$I#?-6wexYmOeQ#XrsAdeP4NuO0Bu6iM{n`
z;;a~r6Ai^Wh5*ybLRL==$P+#ws!qu%Xix6omY2EfpG0GCsnqY8roK(KGM*E`FAOCP
zlg@}{o-;f4WADC`P$Dchh~I%=54jiOVtJ0~CI%p;6l0JQ+IF14xM0_gHiqU>FiQR`
zrl55koRJ~2qQ0%Gs`$`9)O4ra!6<%@LyQycS^2L#fS56%8~K=#QKbEDCHkkkSR%WX
zN|8hFMQ4gOe<&K^MHFLC`dgk55Cmi9%@wB1B%+*~ynv#K7`M(4Wq`@KwwS#Z0F#0h
zHX?Fza)g<uq6N<KpZZbvZq0*u=%F>imb+j!7~7KRG!T9{e01kVvWv4ZDzrx9kJ4yf
z@=Bhi*79^@Wzc;2u!5jmoVuN8yd&^Z7(O)LrWMWU6BZgAi9m#SR5Kzx4b!!Js6&i~
zzF{&;$sY)cIi0<oFqjV$N(Zybr)v?v2T`KVU0r)0N!ZK8h5Mt_^i1T82@_>sWPB+=
z(bDZ7NDI<UA%h$*S{cL0>nAW$i*mbGU=<DMCN5rLh#rv%A@f9&Z0ayL$ie*Znyd-I
z(`^~1bp2qAmkDu8Wq__o&d6)5HDB(HK_hYr3Ucc#2uj6#2(&E`-vp6aQQHv4vS3me
z$dG94E3~n=*}~}72gM6|mpRtvt!?tg$EDk=+@uv)eCYKLL6wUn(>D4xr7<F9DQ-^i
zcnU5US)DgUEOI0fRCtWQBtfnAF!)T!%g~S>YOn5>vb*95Y|D4IrX!w11M!GPL=}vR
z{@x@{(*#ioHAK{JhwZXe(p7srX$=p<2l4-TQm3!Y$zr8-ptNrGDaQ-)U$IuCA$z$s
z=Z$jCx9y2)PY__AsE^kJG)BZ#jDpccG{5!q)~e-b`QyijU}mg2p9Z)Q%!Z2ZtEfS9
z?+i<!lktohG48~(hfRBmvD6fvm7{0?RU&agv)1-sUB+MlLOOCMw0nF$iQwIAEG#_7
zz$vB@4sCLZzFGW02I?w<eUpIew4cq`^~Q0a;vy0pjM~W|=`b@3T1-gS4I^$hj7SQh
z2`+{^fl+o%kEt9p60sx4jGS(=aue}m-u$yY1z0K(q9)uxQbB$A3Xk~^^neqff@wyb
zc&z#lF3BGG%{$;XX$~=5d68k#9`&JtQdN;Y($ZOSww~rz3x954K&Yst{K?FxRs|^{
zJ9>hEJfs{9u67~}1)|bN<;fI|0OcLMM#p;(qQjaZ3h7X^6%n!x_9aTCPnVOGg+(=(
z3kMpW?xO6RC}B8Mx6KaKC$>@++KYig+H$WPH~G_R$o1haA$EDULzyt*W}^BH&4BXq
zf~uVVEDw|(8yg!GJh9EEnW|ylH}3NnW%cGXb%&lg|8rvbj(8((r=`}qfl{A{R51pR
zjA1g(mygC&Y5-d!2u;0HM^f-a;EXXQ?qSYV1@VMBp5s=5u5TeD)_h^d(Gl(<uYs>e
zr=pAM%)A%hjbllzb?-Nu;BSy+T>xQw&X@HEs|K=G1HXL$4Riy|@ML)7mbnj#EAB*L
zp2WQhv|NZ<MHCM~?zl?X;O84&v{AdIJeV;fi|R&{i2>W*A1IkpPzC^@kE{9gnZ>FZ
z8o!#cGtI|BAe7mpe5u;?_JymP-DG@tvKz-ZH233N`rN4R`0!QeVFEDV{tkCIX)8kR
z>7(kq{f$=2&)c6dGxY3sqQ0-y`pMOu4;ZqM1TrgqNV+n5biXpwo!a8p*lHiHmvyr*
zVit=(4Oy9W-V@#&S8!?oaB#gVQ4gzP3*o4!o6UU@BQsCzq}INul}z2RKv@pJ;dTva
zknkaSyLk%hW(Nm9V#j7B$OiL`_yFMo8_i8Fmz9Bh!CcY1cV3XaAHSA=lyCkQ+52F?
z`U%e0FWEg0W-__fIsqw%9{@$%5SzJ7PGL3U6l|`1F<<KB{UO^Vi&DE5Pk>Y)Z9%ht
zvjiweGUvC-rcGq1EYGCeS5cjvaCOHnh~T^P3q%M`{dL%Oi8`_zrC%w8vr{x!@JAf-
z4S7-L=oowU@cX$f%`v6JmZY7-T0tvJY!h=G&~PSoO3Sk(R?tciF>UAQj}-tVrH-wE
z4{7c-Ma(q=%2WxK^v``#Sc`D?^W{GmYzK<hypDTMe;v;$D102;`s02WuFFv0nO_5?
z1V)X_T!$GMDucjK9mP|*c~F&XPpU@d)2%2b$n9>UWjI@;Z#GEGboB7l@-j1=Z~BLN
z|CG=GM_N#@?ydt9MH@voMz9nb4iC?wC%nLP0UtwGApIOL<5{|blm~?fP108DhahhO
z9!Z(hN$G>$D;)#$L9?ehnORi(rmF=u2A)${Yy)#%5!Ehh<G%aliVtep{8+p1oQP2C
z7V*K<Scd>$Qm9qU88!4TUnz68N23M7Xr2X_D|?hyMs>_4y9(H~w>OLTI^~f85eo6~
z9*mN?)ANZno>u>8T4t~`nwse`tX>__C8fOqoZ$WqVcGljHnu86^tqC2ehxkV0zJMZ
zY!>LHwBaT^dXUAvU0erH$p_qgg$oAhfB%~A<H0uPw<hQD-yScj$F+Q2H#6~pkhDmW
zm{J0BkwSsw7)PJC^v0vUlAOkKRcX?|LO;%+n8Hj3cb;WK0D6AGZ@(2ribEh*Q9<M1
zcXuo9he06A9WWgTli*ENa8l7V9T?&11m<dLdy`H`y0+2@-~hOIET?APp~f#6hv;S}
z?$Z%Drg7%KKlCL@VQgBJRwN4tV+Qm%6sU^Fv&sgZglYrj3;I>-<XJ?=LwJ**ZYtv5
z47^Cs3IKkhh0Li}Sr%4D2IA&-;MpTNS%!JU<a6`(lqp!fj%mQo?QE*DqIin*J$t5o
z=KQ96UXXNDgUo6GhMIzaQgaZMd+ml!TAa6DZ4o2^)P|nzg3>oN7=A~t8O<jla$?wl
zPm}wGhN}nDxc2Y%ym6lMRBo~Au>1)hEzbE$e6<d@w?bi?R6cgo1}pxM><>mMBH|dM
zgF?@A8CIMxIVz$pYz_2TA{np5jpo?Rnv+hW=?UW7-AZwft1ym^{iiF)joWwQO}y>?
zJ7LseRzzz5AWcNQ0n!R3h(2C^83^jF56}aw)?e=qtI`xs0N_$QRXT3^@x^6E>4vJt
zckpBzctSy(08lftvO?r2<r3P83t!;(##^SoLr3=b%Pb_lrZ#1328$-1;4dAXTE)Hu
z_?F&dBw7{Sni!(5cRBUYk?%LcJP-u<L0MZ{JI=YwhMK0dBB+;nZ67Ld5IDx1Q&G;&
z?7X~976@&f)PKloB&EvRnsKrYw*nj8EYG>cxv$jIC*qr`d-id3OC8*}EHhm8BG6kA
zXA8z?AHqY(C>?$OC=9GMCj4GmMpqDXzV+Gx@2IJUq#id%6Bs~M%WA=Odr7IqEsRHK
zF|Iap9MZ)kzVrN{RCp?qf<wh@8@<kCbq|_+@LQX(+|_)63%Gx)iwiZ>?#?>0&l>gG
zrAzITb8%J?VI&ESTC@6B#@LoHW^C6X4|U^^M6)06-8V$D#dCLkthA^@*<>NoS5q)+
z*fTBx`Q08us3Sb`Y#_f?I}km21OW`}Y@fIg>*W-MojWargR@;R_k@Fw`&G&O>qXP;
z?RwQueWKWuR!O#0YRa5H#0=JLy-uwUHd>h0_5nj>h|pF{Ym_6SQF<6cQdN-*ukF7H
zXePGLZMd4<DvlX4B5fB<Up;-m()w4jw+>{Sdpj__WD+94W^di%W-DYnz_eS$Z*586
zCeDlgl1{&HM66f`FQvWx#~I}FtI{kHK95G6iu&Ab<(I#{Z2=A;^IB)cvK9m?KkzTo
zP&}7+K+z$-k1a`SQtp{ckK5<Z)cMHvs<l<}?3LoI7plXTKYk$1kl09Cc@b4M_b0g6
zcocZ&xV|u9f7~pI?+;0zz^i$d)%HcbtT6b<V+sbxOicvHrYs6Zi{u(*$U!oca+>M%
zmzwCH?GL0FAlt25am8y%QPjm!`bV=sv$;pptntuw3sO>CQ(LAkNDoALEs?6#@qT=2
zd()ObA#VBltC@g<W-3Y+kPv}btPpYh>K^8nOUHb4fj}E~>&>qWVZv}FcW;?XlgcIE
zpt{=gRXIyLgUseR_5)PBzm>;i@9v6x{sW<9O47WhMB8yQ^sncEB9_|1H}GWZFRI$k
zdfoRng8bZ-Na2`%7(v8#+ZR=XuViOl6O639bH5j6>rWt2$^cGb3Lb#H=GSIpF7f;m
z-qf5kW;UG>^oERTY*U;x<kvZ>!;J`nfLLT|-Or8v`~~Rs{%3fVvg;HXkK<u%F{yVn
zH<O^_HKq$`fOA(xIeMxRj0Ka>Kog99HvJ*%8<O(Gq^Wpp=I#LGDE{3E*H}?BIQC-v
z8g43JIuqMzVjN=2<EGH4kbJmN$<%f4{8$^&e<R|-Ex>KpHa4Os7*(SS!^1;vL*)-o
zM+1B_wkI3LlC`q7KR8zn4XjA*{Va(Ivf@*24ywzRLyxW)8}}wV3B4GHj8!DaM5*wo
zqUXqiV(VJJqoeJ4!7LjkRavq%H+q%^73L%)Ru`Il3KuFMO@s(kwBUsif2wZmj38~4
z3dzq*$3(5t2cqVQsh|tG{rg=M^W>*uD&)0gum7=JX_GSWs$wZ0Yx-RiFxS<M2-e7C
zc_CS{l=)S87){crg>(yPpR&FsUr{Wdszf--?Pc#3*r>(N$L(pOg>FKBMXw;gpP%Bm
zyEJ1>uKIDgSFw<3ENeNkHg-P}muudKd@9ghIK^^hj1BTm!L?*o#a%2uoSaytRGEtp
z=rm;qF%4yh1f~Z0=|HDiq09~UL&C}i<GJ<~fYdyFYPjo_&ji@lGKHOqbcDjiBCV7o
zIpuEYcIo2>%3QhqonY)r<$3aym41~z2pV-Sa`TqCHdNmJX&q}rq=-=r%r_aeeNt2y
z;Gl4DB+5BoTAqs@#I~|%nA%%3I5Zhlmhcni(iX6Fp&DfL5q11%;=TMI8*H9M{R@nd
z{^|K1`iBn>#c1gyB3@Bmgt%kkV=@hpBhKMoNjZ^XH|KJKvlB<!kM^!_7BGFlj_PUx
z@S2<lc6(7rqP(Xbi{<(>vBpc23udwJ3-BcARe7a0$G(-2ksr5Iu7mD+vxB+Ck%6kM
zzf_-D^THWoyS4zL+2_z?qp8+2TR$B&M`owe)HTcujLsZ*^utRl4+9lUxhym`e%~<H
zg*_4?3jB&E@2GsSpsZpqL$6P1amg0jv!=*8Pk0H8`B0m)8&rjOG>kT>hxWNxV@6Ii
zi9P^cyC>O6&!OkQ+DaK4d26LBucfG<Bl+(iYi&l<$mXe?ld(K&E^~j=TdnXxzrOv=
z0N2<fEmh49QA*Kk$tfq_PNVym4asP$fn;`>y?L2TCtHOCL9XVxW3oF1vI<Z&na^qX
z>V5N-dw>hjEpzC{t#G@b4#6Eoo{+0ZUw(S<IW>5GlC7d_cw#QD^qzUoOx2^P#T0fd
zIpYW-BiwuP<EzbBw8}$w>S{s)@!afqBUxc&;O16GoIM&LQ^O9LnE12!EP_3~k3miP
zO=97~gIKt(^(VMt7i^e_N(3B^Q_1;yVB^)&$FhoEBqwT%i4vESzMWmS@5T+cpc@JX
zP4o+SJFQj~k0-w+`&RsR4c9pFbP(Isl<0>lb;t|z-flb_b{Rc^Aink?-%|BTVl1c9
z>buus&yqYKd02)95K8Pum3(+nX?ftlzQ!L`Gtvbdi4k0G?jGvrGxw`fASE<WyrKDJ
z*rI5pyu{Hips8oUlXESUG*FZmNrLXRl=jA<GbRw@_3-5r^f%ZPGvX0amAb&LV3lNx
zVPV1UJtk|d%Td#B)ya_(tg3^BrG+~;eHAz~nD{>5zoxJa_es_Lib_Wit8IYR=9uM=
z*aqs}YbMkcCgb3O@b0b?8);NN4?0MI5ib8;<a7ICkLT+f*sslRSQGU@L!rZm)hrom
zp(iTU2da*zcZnSSA=Uh?P&EP^8wMCn7f^FhxN8lpBkSXZ14zau{mD+Y90$lQPI^_8
zL<44lDWwyN3U|zdc=SGOa^nYAkhmU!Lq$$1WdBvp?2H%Sj|oRtWgYKGO7gpSIEb^O
zm`?0BG3K~WX_Z#=s(&YAskw$`9?ouZF``fbVGg#=^`{^7b{?WfVQa*&uOA+1Q79V3
zlTb!Qa`cwcUhS->)3!UR0L6%d{|8*_I|CF2UmuPxX(hsv2~$<5iJS<*wP)mLeUm-4
zWP6)ki0F!g11AminZ}}#Tv|xj8M<NS-N1qJ<(ip{W;2n!p((#mU`7Ving<FWcx({j
zO4d<)j)W|}t{h0)UCBLYaI^b_;D#83<20Gz>95C>L<JPRKPX}wJ?@eEhcZJ<6bg?b
zD11FkSwYH;`mLVVmmlm`%0HntLt*DzoDU!<(7I$cw6+}aF<x?%bY8_SLX5~iGZ(-P
zb5JG?SM~yx+OeesNGD{Zd5%`1jxj>@O~K47g^`Ww=xR&z0S{L^h%X5We4->#jvp$0
zkOpUz^beeoX19wn`d9w4oY#A{BbiPTvgM2J$s4!J$O;^ebPR%jn$b;=Wi}^scW)n&
z+uO1<TN|jt+u|;|k%{!(163{G3T)--?56nrfo=NLGbC10qRAC@AW~7rVt^QU1<X0Z
z;aP~!<jb})5AC3lej&BFaoF)%+TX?(dn5o3s7zn_aRjeIV2PiM_IFn58`3^wse>$}
zDlMJk{$&7G#M+J-tSBN53}pIdQvtz(G=G(<2g%&v`?EomNBJjPrHQ+RE-C(K2YfOD
z=8Lc1iI+(pK>Wy|uB8SC$R!$cODp6Y279uhE%Apk3)L0@FDz3GVh22DD>mK_d*(N@
z_ljQVAG!In>V;;7qK3R5=iKZDaYR|@*hm6L;wejW&*f*2T(7T=MZt(3Yi4#p^>|T!
zjrMTEb09-G0mrMvVjs1RJ&;$3IcLpyo7nRwGH98*gaiWyjC<cd+R)sPb1*yDr99s=
zLI3pkq5p@wH;;#M|Nn=zI;l8HrG%s?OA?Y8O17e8->K|-c1D91RAh}%wrtt6@5ZT+
zeK#0OWgp9A9cB!_*QL+toX_`r-}k@w<G#f;OU!j$@8z|>p3jB9_TAVvChEu;G1`@0
zO<1Xn;$!Vi#b2O=%|fZzAhOQniz46vLNPX?cA@bGDYmqXuXb3&x}n$y)8z5LKbt1u
zGcpdsNcR^M@G-YncvhOrI5g_s#@yf=xyu^RYyYCwRKx!Kd;1r+^=1anC{LGXoHNim
zf)wYSKJB+U#~6O=<=dB&p97Wvl%lYq>i3#Lsx5b%`dmJ4Qa|;$%6PeE`1}$?rW;GT
z>we_V)h@ws2uF0hvHuW6)*Rf6kzMcAR@vM(tKo{T5$zoM^jsy_p{&rv^nBcJW6k*p
ztp4^pVD_Aw@8N&x=@Rhy7CAMmM?|=8aghMSp~$CSA{J+)$p~Rt`SD`1W*G@uFc(TZ
zGDq!~H05Pv;@Qd^v~P|?6)LNo)a(?c@5@vk8#gIxtol8V7T(Bi*wxt5itLu!ubWjr
zpwbA8PGjRshI$oZppt|86`K%$KpR_@sxe<;X>A~}NEmP*>nUPn>_Ca~(jV7l4!=dN
zgTf2J`|c{sgb69$-=BYV|Er5MQ)fVdfS-9ikX8TBNp_t>FgD0`=vTb_MM$yuSlh^l
zz5yQ6fF_O^DX2_4_QoFTcKX3q72Sg$KdX^DxRRB8ZZDIweT3)lF%R)VM&~AsPG6c)
z8}hR@b^kMrXHQ-B!ffYa>JJZ?Kg}{Nh|s|DfM{i7DWuC783ik^NO92<S;AM(zkG2P
zV=RM>1G`>#-0Sy67xF_5@Ral_&n#maYrfk%*RR8jjF7T8>@=(@;ce<-dL}&EDc6)G
z1Hzevwb3Pe00MDntBlfCW%7?$egz+jDCE<k;+ZaXzXip$Ohm3;$U;hTJ0CwCP2NQd
zGX#3i-Of_B*|5CIU6T0fl?tx?YRl3=J*!coK<+Cnw(>H7|BRW{uif{B-W~gsnyO6D
zWg-g=YhAxho&;3;#7>$RCg-+SmXw3fk)P+!v!NvA)619cW)2?BI+|>40(fS<nG?F>
zhH(7RAj^XMz}6DyWu$=n`eN%sY%sefGZNhb`Nl)~y$BP=VS)G<5JKOu$_LFJLw*<;
zU7$jgKxmArN|u=5%YF_$EA@W-McIzIP)6KrRCazMcaRA&s!yNZyF7msL}T6BeOW;-
zi~A>qla<FN*V~T$J=Q+Osp)%Y3bmm_Adp3sdDM@o&Q}eIcJhIR;1sF(1#B8Yb4w4t
zp}Rv?B)yU{uFcKGfi+5#HRl6L&Lrg^p{nYUq%-n#dBzg=yL8(}6KRF1GzN<fbjuM$
zw+PX*0MzKrW&421n3t1B78WyZcSPC5QbdHgM#nWG;B{l^==3<ubW}VvpKN;KmhSr^
z#sjp9;IE4Q9`334$cSi)AVt6s;&E8K{*?3d`;1gcLv2IGwhP(M#T1)HeaBghtqpEu
z4%r*K7)X!@$nMMAf>l8#kT7Bmm%DW#KA5dtJilNT{afp|tHP4L_Lk7Y&yD03q3I&{
z`SWt<xbo02ZPw9cgKb{_&h!WrNEY|ZzfqFo)7Lt(AE!}EoUCMIWP}XC=(j{)!S5rr
zOE>%g?;_VO+g17ZK(~TjOrwxF2)!*PQ>*zTUo|--V^M3W_oM&pIP930?hdlDe7e{a
z4Psdebb9C)B0q_iIj~{{v+RSJ4F`q%R$GF<W0s7yq3L4gE&DFCTY$_b`rq!wH5_zZ
z(3q%0KI?C3(tH!ylWHyom1}i%?p`{wl^-7%SC&?u<|*kZAz+#^tlnp3aK#x_JERrO
zm+1%=o|E!BHd)_Zjc=O#yk#KGuKJoCV!L`GzvC%LQ5HQ-p*UvI$(RteDd?iK_58Vo
z&0zJ+`u?VpV{Jy-)q;k4YR6wS6qjukm)Ak$gy(2Zrj;w^?sXFLY9wp>h^~Ube6;yL
zaoU8>2xRp@6%=Da{UER0!KovN*LNCe4y}hqirW3WJU?JcVOo!X9C#ZkVa-!g8`Eej
z=xMor#~Mc7cnS;G8{k>@^T)=yb<!67d>3ba!Rz+F#g)%0G||yag`i!VUU14Ct!Ly6
z;6%LSI+i1PqwC{a7vggJm~*oRm|PCp7S63-ut~RtLa@vAPek6jL8Vpe%S4RlMuq#Z
z&@Hm9K++qg$$E(4o#qzj&DQz*6f~}~$SIh>vN-@Q6TpDhBOxM2Mlh0oAPfA{;!F4Y
zwRP_Ib!4*!3i*wXgGFoHLFUi1phm{_I)aE!{vfZmu#qjhoG7~<`X=ew@0FR1I4bRy
zE>J9Z!~a-q;^%;viC#^+0AK6O2RiGK5Px9hZOlZ(p>}oR)yueUb3SXRuSJABl2grN
z?ae>L=y|NF>@a%M+f_zj*f5J2_ck`P`S0a<4QaBYaXycfBGf#Y7Zf9>G(%{0Z3_rH
z-Wj+&-r6FGmE4I3Jo+kHg>#(_DO|Ufc~70X)jvh|lCRd+)LSv{BTq%Z`zD@z>l@E{
zH29orpX}cGN7nLP>F3Gjom^e`xydEUXk^*saC&sKXIf=kaQ(x9$#-p&N`C9jRV0jL
zd~sZqYvHnmCy!5cQkgtnue_+>Z1T_?$8oiHm&N4<`VV!sMuu!|*ljLc!pirgcxgne
zscZaDbFo~^%=j`=5qnirgP`V;Zq##DGJ=fC=ga*0kIX=B^WGpQ+#=1XhJ3!B*XtT;
z6S&Z&jHMv?AN%I44qD19DlcY=@rDd{CdFC2|M}|9bP!iJ>p@E`Jqztg4_n?N3d*$I
z;X9Tyx~mD@)ftE4c@!&o_PcAa{meN1O--kUGq;{CN&L47iJb7R&29vq*YDJ~6$=N0
zvZJ_&Nmik{59z!ybct@(@=glKJtDUn<JsMP<h*nM?J>8oIXl8jw)XYw@!5+$%15r}
zHtuALpsnO#SW{>F`9UJ82+k)lrN~N34O+>0Lzc+5mDBufXd)-AJN#q<9ym>OA_+V@
zGcx~}>f0mYQD1S|!Pc74yE?&UYdsGScJR;v4|6lj@f^#Ef7GA!H4bH@o#t2MS@P7-
z_|f%#+EiE9DCew;sjjL~PRh!71~0?c{6F6RR!qprd}Z39AM9QrT;bJ6cOvNH)gs)o
zxKH4MBH=Uyhdu2jq9hA9PlY<B#J~2KDP0O2Ir73<OHZNA&dQNB_W(LPajAS!{SQ>x
zJJD=)HaY44zLVY8L7n)V=2`J9vN21EY{_aCKN@tlyFKPWqP=x18)KFH!EBUc>a{-%
z!mfUDs47mnojYgYG)MBB9cFA-OG-$4I(0oh?aHfvEKs;)u6^6%GpgP`+0jX+<Im3h
zDt`2pxRu46im|zjElRM+|9!ftl5JwtfY3}j6(#5*_d~WcAn)+)D+8suYAhs|@>RV^
z;k0rYML2~(x<FeN?)7>=w=Oase}CP3z=15ai#yh<iecKV9Sqh(+Fo#<ba^&vHS>b&
z^HlmZw4Sp1Tw>{fbjO%0LEF*W5fvGA<#QSfr%0h+cl(J>I8#MXPvp9Yy>7ah7OhBT
zhD<@GqHqPz2c7is<wYMGJdbMAZ_xuM_E~tE0G?Y7)AyCW7|jV>;r$?jZJ^4T`47B@
zw6w0o>8JKqBssXGuDZw%&KRW6&1Wqdbd)MF3eB_(sff7A7ulJQ)MMBbg?ONLSL3}-
z&tJo*(CCT`nVu9$oul{v$*IA)l<c-w_Qg|8`Ao|m@GV>=aq+#iSdhxO$cMf?U5}Uj
zdf9A;M^bLTG8m}D>hTYCsFXiV3^=P~CcM1)dQBomBjP|}TjPm?C{G=>Z*4Yz4;=ES
zWOL>oJlwrlY@5#aO+%+9^Z3)(w$nkW(vuB8J<ynhKixT=J=kY!N$8DCo%d>bO<S39
zVI@7aJ6@;vHCa7YxA*nBCr*)?!LyQn`kQI;e_eSX%dacHaGQ5&=9z4l^QZTgIn+c8
z>9-{A8<THYDhkgZks0ajPO|uPUe$W2le3tCn~7M&uVL=nQSfxigVGqbW3@3sc}MQ;
zYrj}ckV}TIq#$JUX}De=!pi%%rFVXJ%Fw5b?voeSyRn{>AnKYfbVxwe+W*2lvxtjq
zPxlelUJtL}?3W3mE8}Xi?U{|;`KJq(GxEQr-NIE5+hTESNfrMVCN(m$$X25Xe}Xf%
z?pVIWA7hxR5fj>PAK0<k`uwtptEIJ5$!ei*Q3Wa)WqW_llEKo=Q@dkrjd>sSn#|##
zn2IE;c{amqD7_kM^F3|hiOUJ{1e-jK>2QK#Ac5pecz!LtaaZLt<W~OvilS3^0nhKL
z7Vs&qL|pID+UxS8bK0Z+m7vvhkd?Bhb_HHKdVGHM<7#p-$@N*pLnTICBj(W3Bn2&*
z+XkWUIIB2gw`{tu-t5fENQmr?u(rgM!Gme<49`vw&ktYyw$0%zHBqOLE#q^(1;leC
zt-2mtI}9P2FZ^Lx(tGpZXVsZ4C?4`=>tM+0SYa9}3pIBiRgvCMggrgg6ISl1^#=w%
zeL|Tx0%uHe68ZZUkjFr+?J4)U?rx(fl=&n^=g8{`k^$EVx58Xbu@h)TH}!1T^{X;H
zz2W-G0^yV}e7I%Ea_d9=8P{UVq1MYdulV}TCj*!Gvw3l0<a`iU>l#PDjsKI(hKqNX
z%{P8|vCaaLacBTW>}hXL09ckiJ9qSScTWN0SSNt-=H9!gJ!=M&s-*3eo9)qc3%2%j
zNTYa=iuswi!`Ff{io~T}$5qX~;Hq1@Bd5i8zc4*d@$HF<<du2jf{Z88jP1vIBdxC6
zMj!2tw6eRMzF{bL{oexf9M_YHaBXYrOu&M?PlZCQZb-|dr>3TQHIW}l#(GRdmqP`+
z3)CbZ`4|D?q84tem%+1;p7AAZPtgr2fkVp4ax%f`LMPSCujB-E=et$8Rlp1RbHfz^
zhuACLo=Al;hlMrMe|sLQW~&%uVDR3z$TY@CFT=MeB}OV+X@A-qtIa~y2(riVf2)a$
zy$TjestBZ~GvrgxpLj)|{)q2r7i5LApsr3kp4^?bY_zpvl#!Kna#Rxr=w9hg!(cOY
zv~bd5jakyoD{=;*?q19&?yTX#;iTK*uGZdts>TX0k6XVcCVK~kp3uEvWpVK4+r?m<
z*6zZKBINZaS)uL6x+CG%v_Af;jtqw0Y<CNHI+SiDwuO|{k9I(*n6Po(f!YUxwj<RB
zb8~Y!myoK46-Z!B0nFoFD2-2yfvRkuoj*-pB};|`1<_|4;7^@Dz9BglMY1$f7#QqJ
zX`xgUmgVWaU3E)O@w8yL9F$tu`uv1AC)z<zaX2qVHGTpYX?fo^{iu?nUt2h5;u}kJ
znNg%hX^q6cmT8TQT>B$BNMH8?I;jhkV-cXxTm^ulCbUn7wnOojIR<dvdyy)x=1r(V
zRC%NC_yR#veIR@sK4$c}?M<i5r&pc5QZ(6AiC23&S#QHO-?9832`sMXX`=KUKQM4u
z&06`b;Yfd^Tk~X|x&Z-KsEV~}e3Ws1B{P+;YEJ8Rdaj$eMykZOv+sUepYZvqXJB#l
zLBW~}6ggP}WeU&v^V0=^FoaNKSm-*ZYv9tOQ4XnGp?Ws=Z#%LLDl`Fqj87dY1iko|
z67Q0=Zoe=q_F;^*_m#-@-qd;ZpAyF<N4on`_$d`kFT`antzp4qh2B$RLpkC{m$Vz&
zJ$T$Dvzz0OXp*dTqXi03*?eWW%|wp>x-2dUxU6odn9S11)NCM<7}6`@k^om7RRySm
zXsF;8q5x9Vg>9YBT%6FBG+w|{^hh(s8av87XSLr_&5CJzh<XRJ=UPw$U2GRrttrP8
znMGXXx~~L2M7Nt;qdl&<9C=)6YEU*(l*?D6xQ+k+pP9QT8EO@kjFkD^j~`udcm8}(
zbPCGHzRr3Ah>cu<4*Xiw4;y@ErDaMCm^i!)>29q1>kkd|cP81w>Eh_(?d@;oh!*<2
zey9|xi<0;rmE_kO0t@<SB>##hXw`)2@y*J6X!|Rugy|z*u(Q_xEi~!;lXvO^Ts0HU
z)yb&<jYg~M>gpyRg)zUE@dsJrs)d{{eT+(|8(-wZ!V3N|`b5xTNe*d$@UxZ0SxAfP
zq6?E!%-i!#y@FHIq^BaT=ib_77(tdm>{<dTxf(HW)qjvO_=*e&qz`=U6@~;-BeAG{
z%qvjvrMO%NNcusQGjU(D<kj$@QArA)RL)GCo4>NC9a+hyXNV8O88;Z~Eqct3brz4W
zXr~*CncC>FDO*{>dwe>PB3z_@S!OlrU`t*Ad^r!w03N>&0cS|~@Q6)szg1IVLa~x+
z>`WBQ*xVl^zc#SD?5%I@Ev-1c@Ix5=+C}~_!45HI|GF{#bipGl>zl`=EW@zFx}-kM
zOH9M^781u#Dx2*2>KS~@>6-^W46aQQ&#Eo3;Hl2I492_Sg{KXsroCviPFw-@%Opq5
zzt(+J*dH;UO>sE}9u$Bb4roUzQ|?*7gw7HUwd7prphT8#xr)?%C=<_!<wse~y~s!r
zPc4r7pj)1VyeJY+jf5AVd*@!H7jb9E2!?m=Ii$#3<p02mq4wp_Yn5^4^C#SxSZl4(
zwu<A|?G5i%U8)k`{b0z3Tv^NR-lkm*PT&G~pp1oH{##ra`zDC#Zvw1sMe#8{?Nith
zqvT#>O>!FRM5ex>MeMQU#X4p%ICCyu{l1*eKhIpW@};}?a5&S|Z!=(lVfJ=3seNBr
z^btL5A)x$b-&%fecs(leY(2A>oMT|<P5v&5Qm-IsVQgya#(82MKjLi}5R7HqkhA>y
ziuweDGd^EIbhV9#;%-^1eenKW_sEC?eVP$#Fl#mnlgXc_v$xGl*xMPYInP(!<OZ8^
zp2u8GqN_b*{OA~Suyo!t_=fjkIPNca+Gs?84M!KJ6|trfTV+6KyE>uf^1?_XMN(sT
zWxnlB!rx~Hxf04KEMJFEHlUz4CI<r&vEf56G}b}S2#I0l9aP7c6<3s(cXxCM#<YC8
z=<MdkPzsnnT(snk?|El#FDZ6y7_zF5MyL;%ix~%^-|rJ~tM49U)ljqQA3UPr<fe3c
zsG~V=Vm|7$Ls|l9$Jk4*lXJwt1LCLb^;AvUGcyhuSxswb^t!_Hgto{w<EF{HF=c_O
z;zz}2<3(MX@60vvgiF?2>jf6m2mVP#fy&*5k^K7@&Q7IvLyM?JH<{n~fK%lSetv$W
zYz%CNkbXBPn<7B7&xsy%HE@*>pxB?SLj<}a=v(5YykF}g!LUH(I)%_@-HhQ^jaB8F
zRn652FZEuYNw$!Fd8NN;Z1Z`zgaAQ7(EO3#Lri2S?v7vRtI$JXC%KD+M+;h*z4?xN
z@HH4K8uG4~D#m4*)^J9kUo}>$rfcymorp-qVYrF}*2jwiF!KM5_qSr>dU^w=7Ln*V
zsG?EyXLZ&7i(oF}^wr@c1}KA^eVc*i$+@1@jVVt9{;H<iOh>i-?M8+`X!5eqryJgy
zeS%fjmz~!nj;ogFbF9o_LS+^eZ|$|mxZ3-Omf2py_t&Fwx7jBZs<VC)uRXoX!YLMZ
z=nnDH{V>v{wa!NTG0`d#(=Bbf`jorUblk~z2X9?C+&C+GdP-7-S?49o`M8k1Z1xz7
ztyC{@-jKK>(%`UDu4?|cy{Yt%HBw^&Vx}Ar+*Y8k_OBu$91{V;Q)^pj(l8%%y_yy0
zmYn;MycRXvn13etOfYxRXQ8{(`cH>G*VMnd>9=sh!E{f1w$jBa>_haG`{$XI{rkfo
zyD3Si)T;{f6v?F|`gNT>Kh@w-dU}RqzrEG+W~K3TXrG$tBiBs_Qs>uj^xe-ZJvd3;
z^{vfo4FB>?YVQ6i9RCBEVnCW6ah$98?#JQ`cy>WIF<J&L5v@{N8~0-iEW5a3fpaB#
z?(m%}*MUWleLV91hw_Sw6#zpvD^|YpeK%B+AIXyiF~!$Dxr_F~3AgSeFEYhr?rNP$
z%u&!c2$iuC&zV<{Ht2X({%K(YGo2q=`u@cN<7R!$rx#<NX)(Np19)q_MKs^K4R4WM
z6Fl`8#7AG!!5fO3$J;i9yYM+>&tfq8oH|CE)u2!h#b>Kx<x{+ZZ#`o3unAkJz2?mT
zbAK)V_i;?#TS~;X?_=VJmBYgEpMZYM&;UU1w>Rtyq>fE6v!J@#5IP|_%b^*u42Y%V
z(`A{{0r+ndHciydsinu?qb2EZ&%YdQph1Ii`cP>ov^4MxK3|nnllx7Bt)eD+sFlA<
zueFh9WKzWD{2`}qwwLKBCJjAhzV#z7&yrSTdX6>@ycw`#s5SXo8_erae{P=HH()4)
zgU;PHVoxQ+7}O_78DEGhIyFhESS`?qw^W-tuUSnivnob33|(0893%B1YU=<|(qx$g
zmgK?Z_1{OYWs=$ruoYEixGf}QQBZPUzV;))G(U`HGS)2)N*6Ck`XIW_#{ptN=O7%U
z8;$*Vgf^x8WzEMs4kweq*|42{h&~6Z!-D4>Thp_yuJ0?Wca=JxC20?;2!B5ZIzo|I
z_HfW4`WGwd=MK3sy^!zeM3Hf;o;K@M>ABtIZN?46m9NC2BM;{a!YN&oCae96o)S7U
z4OBz?ogljS<ATGjftztvxo$FUZXTqsi^Vo_uD`v9nk&*^eCk%XX2UXjR6DxRTn+Zr
zH1uisO0X$2>TTN`QO|Agsv7CaAd2}vu=W=n3R2<wUixGzuT(`i^sex{<k6e_;T$W^
z9yo;eAG>{pKv&-rbKDn|(K~PQwF;D9oW%oYJGN0n;T54Ig*?4WSN@}l!?=&Al00qq
zR2GBP|D<~C(l_0SlS>11Yc_idnmlaxVA74JUE;H~uO#<AA1er7DOvr(M3SkW-Q>He
zDJ67iS!7f?k=fWJaEzIgyGY5NB_;9>**!mS#Mr!g>%hQo@x{gaWJ0YO3V#y-ELQLP
zL*iIJpp3o;Mn;Zx>K?YU2vZA<&q+3XSpDj`DHc3!BIm-h0nZ0kb2ok4)i`rXR?j@0
znoBp;Q+5<yT}BIk545bfSR^L*vX1qYL3f3Kjy*&5#+xU-5>dlHJ?yUxsoE<H<%cnp
zYu~cxCPin}l#Y)1*RhUXFqXR#`eekKH|oeYW&aI|VfoXo{-)*6JEv1dthtFj^9R3~
ze_AURj~bcW%yJ1fZOD%>8V);D_thb+=~?(jTnetsD5$}Zr+}>u73X4Suaia^;asXx
zW>fUqdNorz8daGebh9^Fuh)d&m}_2wYZ#8V(^@Sb%}L62I;3M;V5?Z26}>;qNOOWC
zbc}CrLo`EW`tgGsal^;|++Y)*85~ohm9$?iYoNV-X=L;n#dD5)X`S*T-P_;27psC6
zr%2QbF7{~>?jKd|y}{p*yL^EDaW@~sj`&#J2^;fRB8kh~g_$ngN>AcGlheuC!*UDH
zlA^+OxdMNrQ}doBxf8Azx?6ek-M=)mAVaL;4pce)-DLCCsLn|J7^$gYL*Fb?M=G<5
z(uuDc5#=<cR)gvwrgam!#LLvlZtA_nJoSYQsu(UWySwdK{jC<i`)OwF3e-RkbTFwk
z>V6Z^r7b2@JkzhOczkI!PVf0~VWm}KL{@z5Z2Vt?BOLSbD(B~~U^a&vs)HIfB&Vnb
z@l+ZW^m$WzMY)ER!Oa>IDU;Vc);f+YN<UiAr})YW$8KWmff~-0h`nzgz2TJK)UUZ{
zf4}KgWD6A&yD?<gN1NA)WhwBUG}*#N3@ImX*`7>N+8Vz(qG&Z#d{&t-IanM~-ez)K
z`7v>Ofzi<1xB*+u1860dzcYAi1HxuB2D&Q?5mFH#5Y2kdzpbniKEh@o@S-wu574il
zG!6t4iPwFY@s?j3rvvx#KkvLsjjLdDu;nKAz0tTZx$#Ws6BFOX>&L_C&U2N#9E{ma
z+47$Iqn=jafRdvj-f>S4Dl%eUaE0{vvTe<dCi+;s=B3hDuZJA0gLnLTJf9N6RL+du
z+`DiVGnakL#H5V<M5BjKiuTr9w4?NN@f`j8w9hkCK^^6g<G9VNl`PIOhIcENpy|;Q
z^JMbX++5@7SF@ODZ*hCqpOQ1x2Ke+#!|FaG_<BiLzN2}niJKdkxU8i`!eZ%+GtRil
zn73>;O{x9OBcGfvlMQ@|8*FBew<DSNc5;IF1kz^}fR0zSwzf7K0h)xBKI7nYC|3W^
z$lQDIkxwzjEB6-F5G?TX+EokhLiLSxI4dWD)=tbA%k+&Ye4Otv5~3{SgrZb!q&UB(
zM!u+)i)|^pv#L<Idt%4E#Q<@+1q<n+4w<~SJC$^&8T<U>cel~4%h8E@H$FUbGU-|1
zm)y{s+^km_(6)Q1>3^cpMqE*L+&2iD?>?e?tCycDm0e%2enRf&SgYPi=48Dsg?jqL
zlk3@_4wEW_-B@#exYsETw^g>L`Pl26)IV>Utp?8K?<Q^E;~qrP_ElplporaB<nm>+
zRiH{%06_mzz^8fH_shAw$eddriR0XZiF+VwDie5fZ~KkBXXy71F7k)yWDgU9>V)v9
zj#4mKS7hK-GH#Rr$%?}_F9=P_bWU>pk%ugyQT=m~ch6mxj6d~JwP2Ttf1A_i(S~at
zmxS$<7UZaHkp~iZc|+_>eY-tNgz8ntyY9O*G-CbGH)e}t>9FPzDw@%=OM`QSq5Ea4
zlev02XZ7oy?1hWkL}sq9e%>Qiz;*`LQ!mLoBe9a#rrJ7ppy8p@)^z_{w0XD!>UGF~
z>zrHQ$?z(vGJ((mVzPPCMIF=Fu{N7kn`6<$o4usQJ1t(J=fhE)a^*_Bq>f^z#^og%
z`C-04(oI)yu=fiIl(PSsX9^x<@&46zB+};dm{B->3_xy^7#@AwWo2RUb@r{0c@WUj
z?rVt_O8d{+piz5as<Y_H4`KI=TaS`l4U7Zd@2fu|!BBq)#I)>a(CK~hd8H8BZJ2G!
zGM@`DoeNmb%O&Y2Sx6*Wd_ti;Pg~27*4!0h5naH1$T+z)+TFX}JUBIOK&^R#X7mZ~
zGrN_&&>weQ!M|(h`<hAn(9F#oiiE3G^nq4Nr>R6kdnL{|C34!h*g8H_i4bL7yfcMe
z^`X>M=<o>cGwbwm`p)qPo;Me|y1KHnV;lMpRYn%3q|1v427{|B8O)EZk8FEeDVl35
zxSdM!dzP?%rnj^6J!oS~=;VyXKszoPnq-SCI^Lfy{sk9s&sjS%#Rr!J-!YfG^WJiv
zKkgMQ&UNIf@}ABvj^%FjC&8Cz-zi0Fe=?A)5;&o}cLWsMX$Sf5+K$Ixh|$#_e))xb
z<)|A|!)qt@w-(=#Xc8o?c;=ZK1YaJl;E;_tvI{*Lu|tuJDt^SKC~2?x=TJD^doVoI
zX5fnBaaFO=J5HJVJsCc}|8+sElSH@!`&#8#^P#Y@IMYuzTZ9vrtCjkf(N>pVz8mE#
z8Wz`+4I8S@Vrb~+2(xu-SqzC2lkOLi8++4MEl14SQI)iqCjUcOYAPg7u*Mj_m~4LL
z>xmXA()E4fle%mb!8%;zXtC22&mqr8Q>K*85%D`_J|*v!q-gyY=X<W<ZW|g~5Fe*(
z+0JHp<7fS)XS0r<LN_f(Hf@sTmQEBenzbps*+p~j{V5B{n=Y=dge@v|D-GDR%vUwE
zv}S;dBpkXi4ZlQg?^VciM4F~ZMn#-a&U-t0Ncl?iMYNiAMV!Z~N3CjnMeM_keLMmJ
z>;>ho0#9<6DS5Zgm|zra@{3u+H$RrT;i-=u^KHl{lZNN2B?k-q7I!2Jk!bzGC^uvk
zRl%lvN57sH`8~_@3imhmpt9h}xHn!Fr#!{_Xj4Z4b$Yn*(a+f7$<2=<>s5EWYhp<D
z#*=S~)YuD2q`&ORH}KA4;#3a1iKpL(i_4n0rJCDvaK@&w@h%4S=T<@#9Q~fUpI)bt
z7cLTroWeXm6Zw$INy*B)Rj}USjbc>u`aa`JBL%aJ#|w7t9p(sXjzUQrlPi}|wTHQu
zPAQTTBZs3|2Pvl)#&%t*jbuC0`R2G~=bcv<#g>VowkvzZ(AduF^cA9g_Z1ECts9TD
zLAPWkdlOA)HOL1(mU>Bz$B&k_DsKigOFSAI+mfh_l`I}@O@62Hxma~%>)puNehwD5
zZ=BYz+mwECbN}&L=to)vRh&z$mr6OuA9u0A|H~zYfa7QLtBKPidobC`@4b(Lv)=$&
zjQ9J_ud~zmzNT*8Tr5f>KZ;7g;>bDv463=0F**zZ$+#*VhER8*0+!{R$w=i~Pn0I1
za(HFd|9EF^V%SXjI1NKc^3#>4sApm8!^;bIWmm1yc&-l&NFMW1*i`_RS%KcsvQ?`0
z*2q>`YARPY^l@n8u0r#khMJmCOv^7n@*1PNT2ffVo-_8R6UxJejcI${fUol2{nZUW
z!G>$bu765}(hrJP%|sgqi3i3@;-X9}`W?uncX*QH>1`%43ehVZX0F%7J>#v_MFJSs
zxhkH8$C+}3ir}S*u67quF$(9tpIg80%ELTwSTbIe-R$3MC_eMl9)F8kO*k0gsm5?T
z!F&3+NItva^R&&OA9skK58T-iXOhP^?z@xB;wvaBEZ8qca#BrPQo4(>b`76RC=Xu|
zmsp}m%#d8jZWyu~dZMXls9xk`QrukydwX~8!*x=#?c+QG{4M*_-6?Ca@l>B%t2a)%
zwqo_>Z-`Bocg@WkaIt@Qs7j5VDK&OnO%GnW9nVl*D)-HKj%beY=|EA+?Y!Jmw?g%X
z#uuLoRpIjW836Q-KXe82b<<Ia*rqyPJsV#DR)V7gH&yg;MWwOQ#Pydltwmu?V{K;S
z5xzKb|LtXmSnVfiJlo>uJM&qS<Y#BZYVBH|y{3}SlCD!F*<0JZhAj1}glJ3I%;t@~
z$s(#&2`>jpO`SV7Hb@=$sb+VH3e;)Zjsp6!ptB{OY5G&BUXobk8D#<{+=Qi*VUkpw
zn>%M>G16Ljw3#id2Ic-ZRy6ARstBc`&IE11;FGYPyaE6R<8u^133zl3hqy%0OedJX
z_m@BbrNDb+Y**dkVB2(X7GHQjYJq~sDj}=BT06S@zT&gqJooOZCP&}7diARMP(DXN
z)#CHxkk{b5rFYTeczDct&WUqOif6Fz8%(dPL=599;@&mXRtc0fSAoyM_uYgo1D04#
z_YpT$wV?a@x7ySr_a%Dff11)ni%TWPp~4fnJuhFvqZCay1E0^&Ij>1!a!gkwFNTQU
zh~u^nc{;qxMJqjZu!oyg>F0!vrwy+QKi;J}x!cBdKKklV!AOb`Zn@R(&H1$9#p&bA
zodrIwE^af~M|s6=#3g?bzpd+_eYdM|LzY6S8<XEoH*V_!8k&bSd`+tDlPd5X@yOWF
z2)#GTT`H-RTQ#YOBtq~cyU+hY+-D4>+i!{^P8LYA-pcD{(Z0!EkYAE*5+z57jpkr=
zRGBZ+Bz|3e>Z^<<rF=RYByRF!Y_~>!Za96iNqju3?#;G`q3LZZ8A?`xErZs{1eI2o
zf+d4~mE7eIZ|A*BX7f|yshBrzO2lT`Dxp&cgr7>cI&@KlIxCc`q(sw4Kf5F>6gST@
zmT{)*MoQ3r96@DR5f2>Zn%V3s;5chu{gup?iseoxJ`RuDci!6X#q#X&ejyu^E$`PT
z{HJ>b9HmW%{@hJ-?~-F6)9owiDilF-KAXu%>2+*QX79d$)FZiKQOke)3o@97D{%S1
zMMJIu8ZHSfZPZGQ{GfyC^|cLuIkIQS8K<mZO#ff2&3W&r)8#*cl1h!4l#}12V_uku
zk@DVMie4GZduGhkbXAmcKXlgrn#mvo+C+-){jADur$!iag(`;7oe<((KhuLd{Nq29
zu>COVYpb`2@BOwXlp7U({i@IKa7Q9^$m;InJR7(yN)*5Oxbsr&p`Zu*<U2~I4V16E
zJRA66;oY24|GPPeB@i|&2B!~x$`oCW>mM~VS>#<Gr%2{x&FJV9DD<Tt=a}v8{!`QJ
z=!@=h{_}1|R+0M}P`hNw<^5_(S9C_&2K{#o3dxQ&%OuW5bCEB^qxN50{mf7%WNRX&
z5US8}F(i)lRH&2P{O2Rn(NHa>#(>=t?ESAXqoFzcJc8~{TJhLY-{L^k^@IUOEDx{Z
z%kHaz++|NSCiWg`xn{bQladuLUb4w{JdZ4Xzs8A-GF8B_(uFU5^a)@2^KrPBcFi<u
zBAHF{VKdEmL2z2D3rZ;j_4J07tM9P?{?`M<bKgx`*4jtPbcBSz9BPPW3S;nF&Z&Pl
zOnd7`&`p#~X9b&^D#Pn9YNm%9beP|#FYU|Sd3e{;L&jN*zo_5;9&ewU=R`wL4<$Vb
z%@;$cAN|bfhDZ<3sD59v_@r{WL!5GLOX$e*9i8@(ylOL%udQFrXqQIsXr3CG7JnH3
z^?aAP&xph8uXGdK`RqF%2F^UDF@9ZwEy;3hoFKMU+mR=EMf{~5i+v?mO%=Dctj<^1
zZ00SO-O=<MO{dHbU8dS`<6<j~9sFw+Wxilf)SW<zVgCIU@;#5=!zTENB-kcTb~e(l
zb1OMM+o;|-UABYNoF_xvbe@vh(yzz7dgjnV|Lw|r|1Pq4q}Rn$p1qHsg`;*|cSKR|
zT`P}s9?FyP!jJ0MHdVhVg)M9nI}<qfVJq@1IJ%*`@%d*I^VO7(N6DNn<8PQ*I%s+H
zk9%kS$DBeK%%Rw}!9HA{jjsMkf}85~6%+DcaadA2Uej^6XOkwEa2$OouemrX3^T99
z)DN-|GKK@qtBnz98Tt8+eD;S=A-Vtm`Z^FH{<`h!R#fHe1JVEb59Gzg>OcQ`s*pEJ
z|KEDgJz$9D-bH}CEvka%!P7;%!yv4m!Yb}+QY>oImjF;dr;EXd3EV?|1jz>}vL~cd
z_ae`K9pN2H`gu{Iw{O!~t#`Kw&d&{&broSC18_8r1G%w(eO(Kk9V}C;Kk20O7olLk
z=HY++*Y?wU(*JuTX&&YU3a0kGqS@Qo+nb6|<`kgphb8vxe0;jt!U|t#8QuykgUzs&
zrLus_@M!LJ2p%gz@R;V}ddCPHMYf;+I>I~3w*h=W+-<(uCLUU)Gk^n2n>zfYXZOhj
z0>LWQsTEZAUUHuEv??ob+QD=1q>Q&?r4S|k9WnhCJw@WC^@8;$)e6h!-uL`+Xa03d
z%y0hJN~59C|Nr89m9=&>J}**oa_$(dH)`qQVT2bG+i2mKgoUG30`mOV5#Es()UIPz
zo0a?5Q2csY$57Gt4=Kgp{~ai>UEuQf%U_Qd{{Q^mFvx_OFZmuzW>pYphiJfE;S(CS
zZ@&VlvDCxL5-NY)``8D@`n|zR|IZ~kQUr{pxp^9}*o#pCq)S6y-v8FETZQ-j+8GP5
znt-je`k}YTi;AuS?4p4J^WZ}-o<SPe1;F5Ds)(4}%Ha;rewDADJ8AA!?)U$U29zbF
za^yo%k%kx6$Z4{*2cb^pH>`A5pW*4I?C`DRrlFC;*S%s{A<yJyCMI4-p-@mr(E+G4
z&Oq8e1F$dZr~bV1&AN-Yi{>r@@&QyqF#t}Q(A%r&E6t&yshI(p64?KNkrxbjn)2oJ
zl_l74NKhNe$hEYzEV@eo0!?HPYE>KAen}6xt3cGIY*DaVUBIj^L~=sTGZwC_>EW^d
zp+^n5_=I-%Y{^Swe{%o1i{{49_8Sb~xQNv$5SB74D+f0|OKm*u$9h9xfsUr8w4<~0
z0U&AFIRSP@6rfL%@WHn(NyfLmgQV0%dRp8GhbkZn7ewG6UX2im1s9s~o1sFa86f^p
z@6jg#rvtcYTLAKBkca^WvKSyBZE$vRx&0g&#4LU14p|Q>4sdkor*xr9dIE@m-<{{>
zohf0E1ShNoM3lr2*JA{#kMuCezNGY|q|bwnP5fpZ?`3yY&yy(B3AnY(z)3{;n4^qk
zpt-hC37m}+hvV0t?c2R`*Xk0W9SS0VptqfzmUi@X_4ivMaAMIQw^#HKP?PPO8@<x{
ze1014<`Q7wG5@rW#8N7-LI8ors)E{{!_E^YPBa5zh=pC{f?R>)_$@UR6+s?i!{m=5
z@|RNXKsKm1*~>)l-@+}q@M4G26?1Q3%w_z!lct8*3ApW6SYEOLu~<C$wWYniR07%?
z;Lf4}nU<pmviD^G`^7MIdk|*40H`ZDM!JBLwgmxO%pvZh@yS~R(&p4QubFGZ-G&ve
za|sL|$2ScaU&#0wD07T3oa;vIpt(4J;CjXYqu+rUku6wUevdF@_(yZi;mo0ZP7r%O
zfiLzSYTJScG_$6GJs^!GFl3o}W2SDfUjm-E&(+-Z4$Xj8CpCr!dY@)0X+&xkP3Hm4
z49vy|*PgW`xg$6RkTw|xCIjW#-{A;{N8>J*^BD&+(k%bwa55HPXwYM!^7<4MTQKms
z<^r#s?NGS}QeZU-$dI}PX0HH~jdUcx4D=!|q>SGg0>Nq}b=s1{yg4dSn=f?^UoyFp
z66XAAA2(8o#oWsj0W65|z$15BENFDVNJ}dX03*`#@>F0DROc0a0Pj+BWOKE|Utjqg
zQzxvsWk`vZuTrR9{Q9MLA78r$>r%zd&CO6e&U@2?-=N%ia0u3raY|K{0i?2{V4Fm^
z*#N-u08kx;<RV1tgs1Eq3DSun*4{2IMMwE{lVMIkIjg;p`Q)+zXg*!Yz>HZ|?qB!1
zmN*ZS(}D2N>LJLW??`B2<^xs`u#Az!l*4Brp-ZU6YhHj^u^<jtJvEH?+NwcjG_-s+
zzX_>~8=n*4=kEa|2X$4|_kcNw&@K_}cVI_ykuo{<&7c0X+2#NPe06;++`yYxK^1bP
z2!G+-bIE|Hd;VxZSr8T&NxP7;zk3(2jCqzr4b%z(=bVFyG_{-`ECWKpt{_(N#ewfo
zs{}kt(}09F6*6;v3>M9)o3L#vHmBn^Efq}tDRGZ37-lL5dVXR)XV%DCv*tRJT{3|>
z?_7ICE6Sk4)wB^-($cr53^BmSUeVYREB+=8XeiYN0JZV$^f7>MZqd*(F!cglt!OBk
z(<*b9umkErYbaKFUFk&vtfIb<8L&OnhJ`id**uaxfac0z96&!<o!~Iuvr@B*;Xqjq
zZ%*kammFLCgwf`>krL3brYb@AAmHBx1Ff1v1d?gJEy2d_M%qnNqg@8$sGs!Ml%(<b
zI0{zTS$mmC;OdKl(d!cluWyYNZvjH!IGn)dr1$1z$iGB#7qI6>-~=#$)^Qnu>`=OL
zvXvKWjk~af1<5%9R~r=#l#B_uw2`zCS{kM{0C#<ZV_*ejm<b?Zj(t)4ASWM@S5T+X
z`gW)gKxkIHJd7UXMTz}*&Z$2Ml_)I;e4t>5g|J#qSy}(1X+@((76U3FGnZ`<1)R}X
z!qSpIf`ydh2z;Z0M2|L4{c%9%Ztl(2^H*2qKo&|Nf@nt#dTwB7=vDKNusI0Ah74@j
zCh+U}*h1t(NP5+YiGT6wm$qwc0!EDE4!r|3HEWJglNJLAgE2rG*|fa8d_Y%{E%+^R
zhi#q?5iA+=Z6lj=Bjv5)n`@)DJv}|+O<C(3GOdBS(8E5AWcu0fVBP3KX)B<pcvASc
zfJ8YO3_h)O;KKFP-`+~5fErVm&&PR|y_pOcWF9s4RtWd<!`KRoj?*}nk7a<ldQ@v2
zayi$f#xAFRB+@bw;y|X{22A6eeS4oqZ+@LzI=EJT1$UQx;g`T#KtMQiB&PsjPY7~l
zyW*ArJ)b!jH*=L}ZA2OHnM7#uoFj76(*<X6xx|tUz}0Zl^2o)#J;p>-JaedF2Af;@
zgHFqxPft(pErKhmKQ6VBu6$7U*O=N&CwR!p`s(C8@GXO?kQSI&bJ+i8uvW#bt~>qO
z0)1dqCn$>!K72a!W(Cy305PDY5b#DzThl*$xCn%id#nT@;nI{8ENOK*^y|mg7xkka
z_TO><M6P{l7(K&}`^xTx9jT<Q;&;Uzrt@vSPRlx$F$1Q;$-<od7fNefT1TiuBL~rU
z<`wMsALV=a%20I{QHXpnOHlx#XhKf*-l+GM_$FG!!osp7X9#i07N5y0LB>Ets*V#g
z+W2vdHaqh!4CvzafI{u1^NK986y#UP%M{KE#PC3{Aakna!#oyhkNG>>&7X@$Kx4`L
zEH7l5ft^5q93hKl(5**kJMVjY{R9)I&7~Et)kHe6-DeU|pNB<mRn(6P0uMOz&v)A@
zxF4t|O#sW+9=e`}^E&8amUJ->1`pQ-@zZ?$ekBN;7&csWE>N!L^I6v{N&bAyFa>q3
z4K{8Ndk65{m8e2S=v<fw;HuXnMD|`WwqSW=oUKO?{Jbd-z}74|S>Iq|1crfb^`mv4
z$=y${V}MiJZLR`fiuOvRfvv?LxpvPCpU;P&hO!o7;y0JmithdOq<Iw#o+1F?+R=7?
zTPtdU4B3<g^wr2SP(@{gaBX|l;K<_w-sw3xInApuvl&QsX;&PmVSvZaEnOXg^>C?f
z+4IhC)fxj>{ESfEdbt1t=HAT4QX6~E)hSZ=6r}fRKRS%c;Z_IBoLnGB!!SLF0M_Qr
z!R}wtTLr<`1tLfYzyH{UZorqCzCQDHE|XY6lq@PKVQ{}Q?>T0)_URbYfxWa6lVF7P
z0LWxi|D7|3JiJB6cn}F4VhdaG>T>%HQ@rsp-|-@Jr#qHAkQKK7BEpA007jab=faO#
zT|g-gX$3e=MFTv8)W*+qkSOwL5TMkCoe>cg%}h%(uW>>UPfLrPD5IPD?%#c(;-<+4
z3x6<B<>`hU_Xeqbo-NqCSSw3Au{PisgKn@b-oySJ!>XwCww}xT332ji!0B$YL4H>Q
zY{?x)L;yi`uw^JR(l2vN0d4uZf4IUe7i=x;vR-w_@4XJDL7KHV>2puVHS9=$7dy>e
z3#fd{nFDQpaD5QbCdE*uOy+-!7xf}{#j^?9oIU&0IW5*z*G&9qz%*cAVX)&YCr&Jb
z>@+I|5OfzXMGxo(zaBA~EEdz+zP<<FiqN?WkCd6k!3!E&A0m294j2|$^@JGiPHi&t
z1QL$!p&^u&Ew~8znd#{bc+vJLRu)k^=U0J1lZdzw4hk;gks@_tRMrrFQXfLS&e6j;
zzV+%SfKIOJ6^9@S$0DHiK{*Usgu<CKXXUS=X0i(C*U%uP&``duPw^X|)r(94!*{62
zK0&hd_vYjWgT=Ass%y83{Pf4q^eo8o8A6tiucn5^_yE1H^bJYTl3C!gr10x~APqRp
zh)pMl>+dQ$Fa{`cssNp7<_T5+8q6VoeVjHTd421wT}l?v%FKGm{pyt1#aerQ`0K(=
z@L-CMUeJ%~50Xnl151Q_x+!?zUT^`8cr86Wy#4y17v<UEJ0Z9kR!0nXtaGOij%BLv
z;ien{Sa{@*-IwqcZbVm9nZ#X`F=GA&7Uu$tIMU*9_9mFL<$!xC=UfH(Gv1>Crp3p=
zL;_fG`t=Rzm6gvPQVG9?X?>J^E6O88=~mD;k%1}FQU^FUbkiS_f*ft_fv588{Zy!D
z?<m56skf|S2UL~=z#MBP{^uW4QN}^X9a;AGjCfWJS9(t6=4{IxxZ9OSqYH5tiy;*7
zwc!5ey|`7VmAsm@<kU222xS(_M9=~S)}CO@EmEk|NC9;$Ye21jQXoS~&b3nsAW#pa
zU)Y2)X(e@gEVURx0&iYLsBZ69iQ(T!uLk&L1XQ!pTx3M{P|h4@u*9W;O<<9j23F(_
zSmo)!^cxWwY4!b+uWpf51Ol2S$<9OEPu+Zs+Xd?)O581ncY_IR@?{#2Glx3nN=D`f
z+yIDtRp)YvfL^)tv`_qwvBo(TQQLuJcL~Yla=M<8;BnaEcP%P4rGBF6435>&L%{Ds
z;#~!C*V%`22qE;xoKQV4n0QXRV-Q@e9da9)8;~3g6xz!dk8caoz*d<y9mTO!0ORwn
zJ5;f-ayl1e!no=koZ-woCtlqOLn_4$T>DKqtLH690r(PoEyT{z`bygM>vvNJ6V}1m
zfQd~3mTGmx&D@;9ZYqL<;0N&cOmx157>X+-#pDn$X8NmBr!WfvM$WH}_8ucs|C&hz
z(<rdW9*9En=Z4R<yuogmQo-n%E_np1R}%cb3Tb5ow0{{`d6U}~Ip<h_*8t?55Wd4g
z9r3Qhk#S&{1Pyjh*f5j9u+1HODSnV|`ano(64+ovc_i6`!!gX905{ITR`l}apj!}1
ziJC=<XJ!Dt)?uy!_(JzeaF@i#LWJrgp?R)^1X!TSXva43ka9AGCGqa|&P5jPGF+f+
z*cgf80!4a_`B{ucZpqXi;4O`<poQ>3qaVu3#B%lj5&Vwk>-n~N2pr|`u2IZBUuiO+
zp63X)Ubzt)8%vE$8flXUkMmq1nBO{Jb8p3Z+-481cWF5LW(u}gJ7BVB0CR-`qGVTq
zE<y>YHi`hM?LAcg2s>>8s{S$%FT5X_2`;pMos9Ta?k$Cbq&j)BAw_$I)Y*=hfYPHy
z=;{S`*yzK51<*v@0%mf3JeazlH}6$uW!cnN@1*g01md(Dyj}we#P#%J0sn1R+#7t`
z#Omj#4gBl@wXiOk{@5*76j$9kO|*X|jR=q=Qy{YdZj85=@Q4yB1TF`avQ-6&Y*Ris
zBR}YhR(fcUErQ#<{mZou?<#%P08cszIM;8h=Gy}vbb&L#ds~2yL71QK1z#)`8G;@l
z#ub``CL1}VTLl_J9!tV1`87^{fU&LWf70=JPT9nXY$L3ps-tW8;PE^85HHb2Y=8?D
zvzKtsr^@J73_Z^=sh$pHRx$7;5dGC)`TrvVm;r4{ab93__I!q4Msl(VG6XcD^$yDb
zZ>)+ihhTtQ0-5f;D7R#cn;mutCnBE|S4}uU*pJUEN9>{|#6`-isL%_8`@V!M>61m)
z$1)h=6fo3>1Te#d3rk8W<i3R^!<1=+sq5)wLKIaO3qawBJ2VAQ+?}ATKY)*}0ZODB
z8h8vQQGj?(o>ulAFh_y?mAN0B!)4%yFcPR*4$G;Szv>|ox1*^Q%n)$=Vl{IOQTM*;
z^B8)2pK?KBt~J&xq20A3e}vN_QsDc;zM?r2#UJx(utWMrV*B#}&^{(^)D_$egZCO3
zOK`jafZz7*PVxh8B#}lpBa9ozmfWvkzzHu{an?XC9u}75j-Y4Lb}O%@-NpQrA<S$K
zW>O-SK@c2u#95mL>uh?-9X!iTlJ}Mt0y%C#!LAnNj_<*pB~wFt_kesO|Ha^iNYN=k
z9Bg7aE`bA|(i!3!mLL$z0W+GGPcLh8^BUnj#5R3!1kUsTEF%{KvX33(Prj5(`dlq{
z=Fst<G0@_HV|5!$w1<l49L)BdSN{CO5i8YMEe;}cCMHQm*~n#u^dGAu6bZq>*p)Z0
znoCjoy`GxUX8iYUcul~@${Gf&a(Z|08p$x=#1li#h_)n8r_pBndwILNKz!r`cxH$|
znrY<C?T$lvp1BDaja`Vq+KwmM+G+@&K-TUG#9HLQMV2?!i9C~@k|O)Y5S^Zb-6VVE
z8vWngVPjL10Um>zmWUeU_IJ#MmWZ(V|5fnJVPp(z(MWk?wa4w);(zua=So={<_?5k
z!Vo1&fKyL=z^QzYbUzKsG#yz$?M6~g{ibgYPH%uwHnZHN>^3*UYjFoFm0u2C)Iosz
zPC@oq^XfkCcL|`BXTTlpMLG|`Pg{Q^2}?k6TmFLEPWbN6?&=!{s_XI)fL~$^vD#GX
z<8E1~-KNRykykzD&8fr&#zg7Z=B4@R>1ijrw#P{Sg!^sip#QtT*?>9=%P9?Jgja~%
zaCdCApp5$So?{beYsvVXY?dfYtWfu{V&iQ^^aV`mG!TI23aGKfNAjR#k__8G!WcL2
zsQNA*`L}(@5Bdv&m^qjuM~++t_ul`}tFAlZEk)GDCV|O_zlBFKh~ZmEC{YLxW#j1O
z4tWt!2|L=vks1y#_i#l-h_JGECY&<{>0#uJ4P35T&HG=%1MG)~(GZ{E9XuLP+uhf9
zCChqtps1<Hz{p67XWKdi(BuDA6D>Q;f_>UBXAVZFpNp3r)<8>3^nYp?1rz0`7^I62
zy1|&k(Q*R<^Z1gs6;Xd%JOqT$hGo*#>~}vPKA!OSs*&QG3lX*qRO8|s^kQF%hP0#<
zvGKu(X{-Q50RGk#usv88DNTqFrI%d$){Jy}u|4|vSEVB;(X*C-D!R*1h`(fx6O)U{
zB70g+fs(tHxS)#~aj*Q328Fn`u=nO8Ts7pS4$Pb=8-K&Ecln1aYz7_JbNi9v7lSTo
zB<u}A4S&#z{+OykhK%}*5eQ%BRT_dp#zbeiwMjKr{bgf^T3A~*Wo^y7VZ%*zo_b~L
zmvy?lLZWsNi2a$_3>F5svQti}!T;PUzftvIk%5km7-FiG6c<mxgtAw%L1eN<Td;1b
zfji!Lj`CF=g3(JrHx3GYdVmD3;aCKV<w##&-xT@LUqq5MxHy<d{%+5??N}_`D;(h|
zPKa%OpK(Bfn2$smHUz@IbN6Gh05-WJmzJhReWi}Ye(E#(=0{MMmncC%_CW>7prxbJ
zzr&#5F15IAvJ0Ya^rblpXWj$<%zuo-1T79qt#ps95d%%sb9$g=!nuOf>Rs>ue*Pv8
zKYt7F4XpRK6-Y*^W@l$x+1tC#=@nXZAXZv+ZuE60N<K(rUTAhL3mU9;0M9Mw`h&e0
znRlsFZ6tP$Xs}(mY3(leXCZGHGP!LILyyPO4M{g3zJVa5o*8~}X}oF&^iil6f@7Jj
zThu7>p#PtWSb|UPTflooY*fpPy-NU+ebBw?anQj@`)42uYF9WmK=YDgdo#ApaCf)9
z`~D4+OQyvow)j*?Z5;R?X1dr!l(9aZvO34oyI!Vt1J~P&OwTW6{kod%G>cuISVdcS
zvdrmFEwhQ{=om4l)XZUoW8kX`QwBD;eiV%a7Sy%2gH``nDu_!W1_pvY9wJ+zf@TVn
zo1hmRZFBX@RL951lX@>hR`flroNbAQ`A`YLA{2z6AXv%LQ3T6$;)G>oMTO^AfmWo^
z#e0k79c|l`zqm>NHPUky#2dS#xKk6o@VLeHiFd6)7G(-~yB7x#yc6ZXUn8{G2rO^i
z8maH!OM~RdupYTfxn~>*<OHJwqIcQ#KY#_i?cPKRwMnQI4^i?4Anw-F*$E5L>9b~8
zm%8FIrEJ?r>&D%<2R;#7uZW}kMIw>91%J_1gwiAyP4+H9Gyv=fPlXi;2vHKIryFe8
zVV7P7Q{eyFQxjYGa##eY@0wR3bF~bGM+c_|*|*l}bLJq8YA*bCFHOy}0`oRD%(q?a
zcM)Qxk#}NF%MbyxXniM^q*~u^9zQqGIu}e?0!oQnNG<ssSUPv$Pv%QD06qEpiND4n
zs?iCbzBjv`IcaM>e#>EwW%fTBhn4`4&RqZuESZ%H@s?hfSNASPX{F|h-ef2=(g(>`
zY&R6sA17$+hp5!me|hozAa#KkEL@+0h#`(xmf-poh*^}aW^3oYZ_8h%RBaCyh!F^r
z@!_fwFkNjNLRF`a@86YS(lp0H1P(HeJvo={hqJbG(sN7ehzYr@=LI}@h~yW-!wr!k
zEr+r|b$+E#wvDxyk8y;mx+T=FT~l9dYv9;$7Ho%+M@tC;yW^o8m<ph}tu|Wf>PRCl
za{$*)nu{ETg@qOX9zFo1jAlj#2C;138xBx*ZP86wZ#I|YY|6ic$8L-Z6d@9$ZJ+;V
zgwoA`f-suwXp6pV8?&7tFBX(Df=Df`?Fiy}#dKPMdwG?*+(j#qw(V5NJnWD?mJYRE
z1yqn(2gvtJ1m)nm3am#U+GB}R$SR}zohNXH&g6>Xtx>7X8=(p&?Mq(EN$i%86^Nc|
zY`le}VF(3$RIfu+Nty&^BxqJz434TRH0E%`Qb6xlnBiw<(3b4vc7I{FdC1*E$kMqD
zMLM8cuZ1ORXc<xSf-y>9`0k1er?9OfuE)bG65qriaMz9m<C<NK{thZ39;fmNBI%i+
z2~7e6g+vsEJt?O;F*9Sdh29RdG(*?7!MHtm2Rn<IAy}6JNw<^U8w)wrdFAB;2$Q0*
z>Im4sQyDYBv?2;Q9!#Xm$$A9PKW8!En84)@3+Hw;5+k(1yT*C~M-9DpHT<Q<CfIT(
zw6;}MmO({tj~2i7$9rEVhk<7*#|dHyCXkZpv0n$Q`dW?Z3?QTs6=i_1?cYf*UC736
zhu5hcwa1KXAOMM-gh}*nn9-BkT5n^}W}r$U19)X~tzN2}%zkqszv%+Y$6V36C<rU!
zfHep2GK@qwe=N9TCm(?e>Gr?k$iel-0M&nG3MG4*Q3z3=Wm@_Uz78kT+7{tH5#AT4
zh%NAO#Vmd$2K}tg{Exu=N>MI`pXj>`d@R$1yKu=Kpk5aZ-?hT&3yX`7<}T3CF!e*a
zYLVRGO~rYOKr6u7M=bPl@69H{LK6Y+E{7Z>v8Yf0UG`dKuUJH5pv_(&*A|KH`aiNq
z<RS4YuGn@-U~5qz$5k##1q?b;6vQaB`VpQZuA>15)8l?cPA-Ot7QKD@wxu~*XsOj3
z$<VRW69>zLh~If{P9PTNzq2AukVy_S)ya{Zh9xHo^m_T3`ryg0w?Trdl0g*-yLLfq
z?LF3I$})w<=Z8+2qp6{kGh}k!!mb~yDj9;L1Y%*TX=p^awSxU8dJolM(R~sU(>YG5
zx8It1LEC64!P3s|vgnYr+Az&U=ydD}Ax;l_5?icC>ZLXvmLN#nqcIN|z>B|fB4A&)
zfH?E}Pfo<9O_E|0A73!yinq<FHUn8t_2QCh|EyB?Qy23d0?~e^&8LjpcSYr&tnUC;
z&Lr@*yNgCf7c5}+LL#CQk@KMGt{Otl4d{jRAqb|rLvKH%OCIR-elW0xlm-${O&IqA
z_ysEfKJe&L0^8Mcb7S4g#zq~4^6)}tZnZ8LYV{#_sOwJxbM-bdSk~APA`bXzz}YT@
z&<s@Wmjx-uOB%rByhE%w4x9#J04DqyhbEl=KkR)4Ta{_lHI6kFjsb!aDu{HKfPkWO
zNq0ztfV4Cw1_II{B}j)Lhc0Dk>28s3i33vd?fYQheDC`Y-shUj!zkxGaqifA?X}j1
z@QL+(F%S=+l4o8$E&zgNnB4pUTov#>T<sD3L}t4(g>bdU*ES8!LdUPNkCe*%UQ?D|
zTDk}!e~6`(#@AQmfqtOq;E;>fS7Y`m9xcdPp*7spQwndQZ2a}Dy`{L40IWGYunA%8
zraOxCVCKW3_zFVBw!Xeph=fLpxfsMyIWrY{`It8qASMEWHvnO9#L}x~-2i+FfEz`6
zLK{+np+~3C7M27_c=al{K`2jd3~ra0MH#4uaal%!x&?tpAn?!k?#)R<E9pASOa76a
zx^f&U(gGqy-ps)WEt9v<81payUIGimb<s~RIN+TUq(04hM$`7zz^R*Ch&wR$k`)*U
z3hZqqHq_eXlj^XrXvxdTv5uH&JM`OaNx!<{3q{l=i1gg6v8Wb?8fulNC*YzXm2)pm
zc+hsiX5N#1Vf96dat7zn-Fscw)4x!TN$9i6%pYZ0z10cC06#9%37v`6Nd)slA17~q
zBL>o$y78q@$olP6=4CR}a~q%~m`=6bYq#~ugY0jNF1}*kUY6NrA8~?SQ}t-s)$No=
ze2equ+*{J_+5mbk_)h&`3^1zWF?;ARC!$JD!S?qJJ3xoA>glPel1ThJ00HfVmwDjN
zTv>@~bW^aRw*bsAy0o^=-~ezo{1@4RN<<`0Pjyf(3q3f}!GvEPG7HjoeW>A9@!D9p
z(Ams1rpE~Mpn3p6`WC{3?M4HYlVO7#v_>*CWZtm_wgSrM{~@x!`q)Jv`UhZ!O35gU
z{&<9m<SS+h#`Q8v;}A3mKwz|P`?XR;xS;k19Dfp~YZCy)W)KZr&$)UxJ3cvC9A&2i
zE=yAVu)LQ$xvxW%))sDAlEefJ!;+JdCUg5>EsjIUHlY6B<6yj8&=#oAF-L0X7F5!f
zm%j3L&>Dy}QQ!W(ybR^XNwmS+UzHm|rFVe*anuXOx)A=cg*_f_cY3nLn2NqWK=c=5
z_GCzbA^Skn+wZ{#oy_oqcz9X1wzd-xLbXE$Go_(HdjG>~s4j$l1;SglWIOc8oUrZ6
z7zn{Goje3%Zt83K@lGh34eSxPOTL3f4{HM50NP??K$Drov@|Ki{2BYyjQb_>eZ2oX
zcKV5fPfkoUp<jgEM;UexkdAaB;?F0RstI2qeBwZ;1MI=0j)W^I2KcTg%isk9UGY5?
zRaKLEden-?ug*6koZFbll^OsHt$RcJK|HN)YGDzyV`YOrisY*x<CclBG44!|>rs>7
zr$E#TZRvcWKZ-suh`iNy_9~P(Q3&B%efgg#gT!kT$Rp^F!$S;AC;s>w_@A=>`@8>p
z2LCq>aMR-d=EDE79#p8$9Kd^MVrJF~<<N-kNQejix-WP_9od-dKD^QZfq}{mI9c${
zvV$V;Osd<1F{0H-w{;U_dx2vU?|uWGEIU-V-$75<*ALo_&ZJWP@sIF7Wsd}R3t4vl
z`STYB`@J3?py5#?W(!tyGw3jP)ASgiLx?l>!$u=G_{=XGjsKMi9nNXq0(oqBcSC)f
zHwU`ZzwUdOIm74i%)Bdq-gXwY7{vAporON$DbPgxDFc)z^zQSMLq*=_I~krw67J`n
zKhfXFxy9S3cJEL7|7gC<bN`VipnLfrIq#Aa(T?0CO57xj_G~*h{QFNGxdp<)JrALX
z+zdRvlwfT))r`{b1&}|+!*frLjTNdS67w@q1<g|1=GqeG?$CAIz5`9h6a1xl#4s4l
z&5|7%-0u&?3sI>wXmY6wVkiJ^5Ee@0Q;_bw8s-kwo4o7Dspkdz81!F?hiq8B6q?mS
zg>s^b%sCZ&vn12Biq#-UVdw&<k3WyH8ypr7#xhkTQEHCens?WR?fkl-`%2<yciji%
z_|OdPw^PwApfuZC;QKw-Ih&}~n`bmBqZ+x^xqe8tet)XJVDrN<pxolBe<*qZ%9fws
zg@ryr#}&~r*tnt74<LJiEW!}i_V@QgsbZ@`&#M`Vp>fcT?gpw*plTKJzyZp{x$txU
zeG>SBA6ZPNo*Qe6sBG|D?8(VcWSV`~P8P^>yTvCbqt!Bl-=KOg_uhkVjIS*L$jv*K
zCp7r2k_*^d0f&MU_KsE(!3I4A9M%q?i=<F^b6<yt+6ryVIUr2s&*+A*9C|oS1HZBv
z#ZC}Y-9+gGC@NSxv*{%OyaRi-{y0PwTZbqv12U7a6TjPu>^utQcrO;PAvLi?7x~e2
zM)XO|YW!cLFC%KUHh3wlCFi>B=S|fU3NBhH?yD)iJ#+e!@ptr{Mge?;0Rx)O3Aj$g
zeOrf4$;p6JiJ(gHonEpL(_)}UD?+A$u6#Voy@ayHiUjAx2B{_xSB_6YDZC-T($usC
z*rge$y0pHMTbc$KnvJdH>EGWJ?kD-{%9N33@ADC#AM9;Jh8yCp@K8Y^5&!%oy8r^L
zV}k+bF@~gUea1*21z5|53eaUp9q~yac<0{gE!_&&a-QXYpi-cP$!*~XP;DgqfU*>a
zu6p5~i^4R{+T{0%%l;*h;WnM~Hgz|OTTP+!%kA$vq2$1Rt0`x8`fo6>bi6kGZ<|~|
zbbbIyu?v83>s9WXBANhoBqt_Pix#60x@5I@eiCx&c(<<)$tQRR*Srz6^ic7C#){4&
zYh@zE>CW%z@6H}P?CT)h)^e)--pi1=>A%ku9ts(OdyIn81fnNTLYWHaQB1<86tuL`
zf%ha0&HY~D&Vx{zTRs07=^o9k;IpePpPR#JhhFS+J=v};#w&R6h24GL|LH_;=Ls77
zQ_M94myAmwGYt&(@rhaAaw|<QUQu&hc^}1KFaP_A;C^D>H>7gCH{3ZJlO&-Y?!@|G
zF|EG8XYfk?UtRn@ZwstTH=n&r-V|1Jn+zF!fi1a&sjxUpmH%d6eU(HtU+=v;0C?{{
z$`lA*^7eFx5nQyK>c{~z;{L(3tE9Tb@-u|qUIt>Vb$i2Z5jTCRjh>==7<TJKn^&!e
zp#nC&GhiK3?<7cL4I4DqN*s}}^7b?f0SwUp%7?(kK{M@wx%pg2$ChSKN1y+pz<JXn
zG((b~*c}z0F%c7G9E~r@`Z$s_Td?R+L{``(wSRrbVRiR)>Vbn>GJ}*RxICuS!%J4F
zy84O(ct!i%e)jAHRjxdRZ>M5hNmeJ_U&$GN$A28zgR^WnL&O5b&OZ;LOqGw#$QQYL
zFbx?-8n`PPf-8>F%osL>bwbhAfSHLqvQSfg3flNQ@7IXdp)zPHN=cDGDGab56y)V&
zW4(FA0e7vi06FyTqwG;<54b(n_{aKNTFihcF5|56+^s%hfmr{It2tmkX$t0(h6)EZ
zj{3?l87*5y3k>rZlRdkUZPp2_H^_7p)zo?{*Y0wsqM-%3bMWoVfy>%=dOILFN<(WD
zBs9dWoc|fXlD4idrAr*$b921jH`iRvdF#k=NsYT$@`zbob{=EC279q2I2IG#Cn36T
zMaqr}MJdpJQ_BzN6&UDs>Gp7J;hqK<eW2utNv}_)92mt{p)(&&Mgi<=Q>dBf4cPC#
zQ}BUUs3eL7r*N>>?CCr)r)iksAe^0hmCr)>axT4zz2Y-MDVYP+XQY|H!1}G4ih4Zw
ztd1^$I`<+o&CBWNHRw*~d+249>q_@tZ(+AOhTI9*zM+7XMPEn2DPuX`n=1o->%6tx
zx9lAGByIjYIXK6`UM9pcJAL<H^=d-Ur0dIw6E}yYGsAlmhy(X@CXO7D|FF2$Do(Kc
zYGdDF^S%T16tA8Fvz<Uy$-4FdtT_pTS&;dycjT6g9u7gR+!2TltA8ByUDhTgB?Y|-
z&}`R%m!FuDUXXkq62d<YW;vLL;0e2Ip_gEvE0v1pn6v1iEHPD^otLhGUW}a_O#>Fe
zg0;##l_12ga(4R_@M4wO&jtpNymA?>4crPU26&;pyE~M)ep5nw-S0bAdz-@s_oFo5
z!VRdy$W#)c1rz7TyZh{w5s&n;)AH5b`+JidR_v!THh3^rmI*l=4s|RQ7X3{sLF~El
zH{dET_neU1xrrF9ik9+;>Z8|>>bRWCy|x61l70tJ!#Ru^Uc1!+1oqI+uazGLpa@PC
zRdt3@Bhh^$*S*ysSd&W333LkCka<Jd+U%&>e<nCHv!2(A{Ud+f#o`*~wMy3rr`$}l
z>t2$Kqdw9ld5pxg+VI^+H_Fe9?IDV>I>$Z;)PcAW<1t)mM1zm469HwPS{VTM399nn
zu0vQt6M?cu%#Di|#MFR`^k2Cp^H`75o1T!f5q!=8i{5vOx}?sXj`iOw-<fwT3{(Y1
z9dxv`>kQC@l?MPjG4UrYjnL+izfKYMCtyWD<>5W02|#<XohrK@DO3+fAHO+dZaUM#
z>&)w?yXdOflb0(5+gyc(T4{XnhNAM1)o-7oHnFU$fh?9_4?`k@fvvU9VR)oAdXk1$
z3148<foL09mkRu-E$GxMRcZj{Ic(RKyuH0|8DIMIgdvA7E<8HRdl9@&I|jVpt>k$6
zP0tQ}veq_IQC+BdlgzM`Qo>4nJ|p9AV_{mp$i83=XjSApXm+MB39m8<9%V*qYCyoz
z*Yo{_xr5fQguuwE-vlIF{`@}^xp#p7oEpBc7*6u%Sx-aj?k04)Y)_333;KzC`58%c
zYBPr7ew$kAFA{=HglTVpd|2jS?IGX2M8}3XjrmbwV;#sD+3wqJu<Ym8KDvGZanFJb
z#QdarXS4TYbocaxsWO2BM@0uj)w>Rl1?tGV;B5*9@ik@lSY;zQE;@>+m|xAj&Bf+p
zE(R-<?N_hEq^$2(%|2wyIbMBKy~nE<ZcPBnPM0o;&~jBy0K+5{i6Lqf#{i91l#!8%
z@2d@@+bGteCzJ=&mnwiC5%o>~1Bg&(-=&X1>w8l376#@@<|7*Wr338mS-Z|r$?UHS
z?g*xvSUO#3f*1b|&IewOB?C*5s}ta08vx}c&<5H9Nq-7p)IiE&;G?g%1eXLHdLt9g
zDFsrbiB%`dVr<Zx=8<dy5OhG6@fi78?mn2GgJoGyc4;dq_2A%J@RI%Xt-&8QnD(sD
zKpuU=$N18owMGhw$!*Fr@p}X#;TnSDV5Jd9Wew2yD=P;prE4U?@(h(?6s##-eC5jm
zk9-mczk!{-+p~O7nGMAPixTkX?0<51pBsLDQbQD5*&sSFF#>MLCJ;k))t^%+Gn@WQ
zOu%zb_;HZIV>tT(eN`m{zzMOYKO76zv=2o*->J3ns75c15-7oaT3BZ41%~_N5=>H$
zP6UUtjgtVtzPNU89dNr{hsSO3^#M=n(Z@Ah9N@7|!eqa+u6W|BsiC4Q^7>XJX#14x
zH<HDg__(!>-CZq-kMq7Y-l^U^{4n6=LbRp;4PSF2T~|~)-SoV?Si+#zi3r}1dagQ0
z04P2QijjtWt;%U?934`Bo?E9D0=Trx@x`RN;7gm5Ns(GSdjftkgW%t}*S~IT&NwqX
zEFr!P_J%8a78Rmhl;EOhA143J+Qr~eW2tel{!&swfm$Qqk{6h$^7p%MZ>>W-mkfFy
zp#Pac!Lk4vz{AeZ_L&2fQN(JzCHzpT%VNp3>lwh0{@}tZt0%$=I%}_ah*K>^Zfx|F
zhcY`-&Ydnyas)$Im~@}Avbbc)mK~q>OX3losdR1Pn}vFHCp>p|!EwZ(>;x%G|0jDz
z#dhQ0-Cl4f@({Zac^7(ADC!xhyd*sV=E&fko?6+UAMIi(VqIuJ;(a}`em)ap>=YW*
zK;79=V9k&w;|3n}!cR%?p6GtuS1<AgjxHb%?LMNZKW5&b<t9^t&I>O|Yev5}=!fv-
zlPTFaC{&&<#0V*mt;Ss+AqZt&YgxChPoa6$sduK(5s!ID>e+vRNdIRn!lXlaD@HH*
z2fB#oj`<kvuj4IWxXtBo!?bP_wXk>y(VoO~9mjiC>bk7w{d{;K+5c4%GfUe&(ZdCK
zVFiA5Tm8_wKmvl^)6XwG?=QN=kx)P>AgiUEj&oT$fG2<Qzk9@USaqOxL+zs)uKD?n
zAP78L+S0(#`Zky4zw{E&K*Lzr0F8h!Rf48ZpPXdbAi4z;^(?$s!gxe7-_rgRKnw24
zwim&?O`;>9oImDoX*s7vuM5|)S8i+-R%ZIhLw?Gzuq1PdYAKWXq9>Dt=b=k0-sA%O
zn2JQs@+^Aufuj6G^W9Gz*x&rn?4Yp6Y4PI-gtA~t3=!zD(9RqM<rA{5H_x9Unm^`u
zS>#gGEn<b6O|VG(g;yzh>%kaoT@WR)#&gT)s0JwpIQ<!yoRU|jOs|C1-R?$P7jXx4
zB-n}1l=Mus;k%6(x|bQw6MZsD1RGd`=jeJ<77Vo|ql-#dLwd+%rINIBq&o2N;<F%M
zXnk__oAkjJk(2*$Ri5VP`*YVP()MDR`n!Vd!a`+(f-fH(_dwSxjg=dxDlFza!0)U0
z{z7WgJxyBKNc`a7gGEO>4&xE|!Do&)MhVP#Cl?oM0=dpn*UG0V_WAR};FFpJoKN8H
zic>oAF56y((iC!_kNDkk8~5bAoW1q3b1726ryPT_pRfW<ZZVg_K-UTAt#P_AROsZ_
z9yN${QQVW4F8N}*7S<UPLxn{YeE|D_DikgOC^wv`i>NG(J-+?*c&P7Mvj}<G3#Jkk
zSk{{W)ThnJUFH0;kOyV*3`8#7UJ}@jh4sgvp>Q-vJ=+O8b*tph3ln6qd!$#aySUX~
zlMxT?T&9$$IZH$?HgZ6Vkcvs>wdJf&>odtGyE!Rd0o^`<`r6XaH>j$~^h48TzqrLk
zYgd@^<jnMQSSLHk&Zkg422pxZV6@&h&Y!TdVh^IEt%$Sd&e_dkItdej>$L0eX#NLs
zxaO8vcrcI_@)FR)kdJ4C?iMfli*#w0z_w<jm?D4)8db<`=lNjw;Ke_+>Zd?f%>`g>
ztM7G7RW%fqK1p4do68F-K`MI)feJiez|-E}pN4V~l*mKXVuZz%W7nDS@IDI?>A^s2
z9_wRX`_4Ff=NQdU$ZamG>N^r)>hfV{sI=vj)7PJS(7$M|*@HKXR(p5Q3<cvhGE_f%
zLdq(`3wymC2pOgerqlh0xeK5;hy@DOOKY<~$F>lG1ayAMm4|K+lh_FJo_D!I>Z=am
zs7r0%dO#5z#7izluRn)ObuXlVXoB0RZj5yVfXgc^y?A&eXrCX1S`HGzi2AwkNAWL(
zHhN7sAu4Ifw+bd;=1(!Xuf52*)BOf$oj$_yf1ZCYw3RTD5Y3pq(>~4Hi**tTALwaM
z7wx05Z`DYaa9xX8XtU>$)zUtp@GJ`N3OEX&NZYo$FN+=-3&JR;DA0oXJCd&gcfc@R
z=0A9-ur_qS$l#(Bp_TDb8R$7hXBrN1Cko+F=4hyNw<!H$B4ju1iu)j!!}Fkr*sKI)
zj!rjkRwQb$ZSZt={<$%dgg^|0E#!3)8a-y-Gteg=kh9M<hfs>C^Gg9ML!?%G1vC|6
zFV=VwAB;LQ1aJZg@z%@UXvZ)XVPI^>TBkJ^{5kO@Z^9gWn<N4KeI#D045S}(<~i*k
z`JqQgwWTlYB!l$+>fp9O6Dv(?S3?8Y9CLzH23Y6}Q4k54#;5fX*Ntol%t@iBdeEg9
zk+_{#=K>8bZ9feG9-L_S?p7YKnaa@*C1$d{DaLiXn4Ik=Rm*)ZWyzVH0xeub&&3C>
z!9HiLzjzbw;`P}i7wC8aS^tCVJpkt3ft1e&_=RG%`^up-Dtr}WG;m5(<Mi{Rh=Z5|
zfm9<e{{X%{4*g3M>wQ-Pz_^~?ouK?}Fl>hdD^J4;DOmTPjpO@_vHSL#mh?)lnrB0L
z+m{0UyrP0O?GOO3Jow?stpO>}?%xw46HpiSV-beHv>5={BoG5Thkjj?CDKFe>`CCn
zHNJL$Uo(c^*K6P9Jq?qKIuGy(Sh(r<=I~(ypgvx4@}^m=ftbW}t=&kGDQVz33Ljm_
z3XRPn)^<g?hgbnDO}5a8y)KKMSkD$z6jMONY3nRm;cx}$i-h^Eg;z81%Hjced*K9Z
zb+%5h?nJUXt+K*D=CemLpsY`>MyM5L-hz2%AWUUBMcYR`A_Tj2BE`6(!GlqePf2oU
zLo$TNe1WL*+S2<59tm9V2-GQq2Vam;LId~k(fSY;Az+(j;R|fug@P?qwiN*JH3h^G
z3iWRx+EW71mo=&MbxX{0qstf@k}f=VRR800nCW4UY&17*@jrJCvl1Z;0p^)PF~{0X
zvSK3(jCYuJ8pSx(!%+w@aaatuAmAyCVxSQ_PDyi$GuTgNWVXLh`8eNK51izovF=#N
zIX$GpnF7fty<o~86I@6CteSH+oaO3*bnJIaoA+Qg-z8Q1wI?X3KDp_YO$zSuU(Ha1
zon<sL?KT%g{9b-m<m|ch5(1k;$1z|(fb6t^jq>SHRjXu&3)7_D<cX%2EJ*F0Te@Wq
z5PLzi`5$xCBj)*KlH!6`DVY*~5{%1mNyBX}F+YVbOU6oevYw6UEzlaxuC0icl(SGA
zdT4b?0rsP3+;_0P6$$Vo>^fA}fo7KuB^eP!bs@RDeW=)WqA$lB==@gez^RLsartrz
zD%?!Ar~}CFLq`zW0bJec+;UokJBC;2gK$_W*FX$ou%|xturbVE2#{(F6s@nkr_Ik`
zov1%Y0=TfG+bJq|#6VC%$`(6d&mp}=M$zJPpeQB-H3pJypxz;7jvCOFk^KeoK`*TN
zeP;N{SJ$f<K?Ug4`7yUL=8OaNgi9kmIve&?7%3g#pev5Vq-OkGJ)S92Y7MD5jbvi9
zV-DDmM>tI-p?&E^|DPE3gfcWUSqcwd9^rkdw$b1Q9YtQ&+5r?cOxQM2{K5=EMlL~q
z7{kdY$zy%@;(441efOWBk;n(Yc;jP}lcjT={`2fpYxPoxt6qcnCY!ZXi9{nIux`q3
z>Vq9L>VZ#&M``_^Dv-zC)6c1vJ6$7s-X&11VRGf?XC`GuxcN=(NO)4@=CzeF&?mGN
znL6GPriC$}=kuS?`RGTYSZM5{9PLsbi4S2!ke+aEI)rMM8PeJ}g6o@V4eY`c6<G@i
z-~k@p^Pj7)KIv`XBNwSr@;+C7Hl{12{R$vJ=%lMmDV>A#>55I`#P30zfcNC~_5Jn0
zJi`Kcb)sEayB(yk?E?epvecA%PgX^v4OE?g99o(9C+UFdFC5CFd4CaB{A|0U!ZeMe
znGB?b(Nw1x1rkd%f(%}ozlBdPXTI=Y3<O_o$GG~Uk;p2ut2**9d*#=I#PF}b4(7*?
z^bT=kuNVV>YtO!FtZw5o&>c?lwkXYL20mI*bE>sgXbNMkS}e_*D6ROm<Uu=3HfD`s
ze&g@J;S@s^4MZWQ2tIZCkLlz)#x1<TQ{L0u7aNgwVb*xExuFd;!GlEUc}I=`8{P7E
z1MGU)q7x}chA>(948Y+bM3r_;E?etZp82Asw$@f~ZDr=F)c<Sz*VUpQ;-7%mJ#Uz^
zlL(b1d}xrbea>=X3`ZPvCo|o6z_0H4DYY%;J#990w1NJ3ZCR=W$wyfcCJ_<>AHNBq
zjXWS1hWl*lvTQ*ni`Y9rEFyiozU~A9M#B6*AGa{=Nf~1qbroLq{tJtw3|D))djM-y
z$qWx+fqr*&@BbRDGg36ZW=<R%2xD2y05-eT*<N@ulQN;VQ1gf7-ofumi0hjP)a6}=
zPDsnOMO<SWXX~H~v(rcGLT=}_FigC+tM<~5<|C%fXv$IzOs3ThK+|pw?PyREqVQj@
zYtEwY2lCUSPrFwU4=dA)2WsBeCowi>g{sCZsuHMGQ;&kD5`x5*U`yBUsH?3-LG#6&
zKdvLB2e2>u+{YV7UvXuxz$Om}#E751Y6}F)^=UNCAEC=vR3J%=%i1S##XweZc#wiQ
z5WmgO%>@C<<pPNG;@UT%KY|mYBZsPt-HmP(_6qg15_Zhx$j&=Cix&2NggPKf59FYk
zVv)%u=#Qna6d!1+C6zQLBjE<t)vpky_6(lVsgHmm0^Q!Qqj<ooS8~^L!_(1#hLLHP
zgARGki!nIlK4zVUAmKUhunYI%Y3NGU7iRmki1O4$8P8hcP(a*tY`uE-t%5?8hGxyv
z0#K?~>VJUtUNTpkAFVtFBmK7>PqT+YXHD_101nvTB~FHN+)uipT4YU|Gq-Bqe;k42
z6pY?8QE<dOiM|meD$2JRPX^l}*h1Ax-HP#fXWrfB`qdZ~T{!jMI|#YSaWhM2?)(|R
zBAlt;TSBQZVkl38C7OZvdB<2x7@Vdr%&$#3$;?&S?wR-|_*|cW%dbUY1q@X{ey2d*
z9YpC0GW9kYpow101@hbPgDC%zURxhQGa!m|q9uSuT%C>iWRF8T4n8^$Ut8I~Tow<<
z{BUs(q*pisS=82y5pX#l^TT*OQ>gm;)8N;m0hnA~Z?K2eAy4@HVEEyHDpE+_$e16D
z!0-7F?f^z;X)%g)j7NYG64E+>DT*yrTv3-bWH`0!pcI1^6$5?k>7Dl3&_zxut+zYg
zl@y?WX%7g74V8n9p;%$4xSu1X;D$}|Ls(+pbhlrFA$>^{h8OdGFDO=k9kIMvO9DTZ
zW_F-h9sFyMk~ke1!64qb9WYLlzzyny&fTug?`;YE96j~>cU?i*>CfE42!`wT<}A*6
zkD6n)Yp>dwUOXy0m&s6{deqdOid)MML)HePUom(chswcI_PBTs!YinE|BDq#ft<fG
zF(E+&mF=-GyH`9ir2b4GgfgLPmmROO2(7!GQ6UcnU@j>#1pKjUt#s^`YWaq59}Gho
zh;`bIV2|US>*#3`^}*9jAl75Oztop-=_T=cZ1iL_kBr7{3^$qy3LVo94#t|Maf^1m
zy#TAw+P~LVmjm$N2e9W=J7NtmAAy#LdS_{}Y3T^&Z6KYG64dZB!vRV0IAnq1m1~Pb
z`)Uss{!ud1@q%tqWbjNBr*U5nXK`_YQ05I?HY#YzNUXT0KAC=!O_U#6ZnoBKEjrEc
zH_lH?$c~Ra;3S62moJ0M9|Vqdg~jeG#-q9qX%A|*_AWtaerC3<vN%Gmv`{ncY9>pD
zGb9Q1QA%N_$N<Wbp<AlUN@cM`TL?1=#Ex#Dn}M3|uI~;nL~ti8h$xP5GLrEe=nmGf
zFrNyN_qbo1BJRRttp#DIa$4-T@^oT;dos<Ek81_Iy^I>Zx<86m(E_AHA)N7hN1b`u
zC;}aBe%YTiXr`{lpCTvn7J};fjE*-wtlgYY$$;ti1=k?=*JXHEF#w8=Nm4(zVsAP;
zfM5LnGBNF7J0Yzx@0ddHvbO?%h8ncG1Y=klV}wn3T*q}i{rUc~?Z$x!&l<%J6Y^4-
zS!e7(#GLKQBi~0H^v|EpJgt3$k3aF0`za6i(&57o%HKS#i)MP*z@1qA#=H9bXOF{0
zv?-4gZ>>C{^nKeZb~$1o#Z0?#Lqt)zprzY6X=-kMykLr_(PrxP_$EGE@MTTc)eJm^
zeXZ>6q++K1-Y!Oqn23jJgTx)ZnkU|}<{wI{n0_}^9lFLiT6MZ0kn5Y~DK#`(Wu-x!
z^z(Y*m3in(QtChtaYsSJwY2G@<K?5g<x?->sfq~8eeunfrzbjG5*A~P6isv=YAd~9
z8N#6V2HtKtP!EzbbEG^`a&|6U-`F6)_Z(f_P}I~+10;8vbpD<vA-#(19P93r#bfR?
zC^&0NPTP;{9i8S~8d_v<WeFYow{tjKH9N66qe9A!A@W4i5Zfk?wPB!3gV?!P^!Cb%
zxb-vE;97^H7XU{-(;igUgTu%-3+$4vM}edTDvIoT2$V{0T#N~%Q#S1{u$!8n4=x{S
zf?As$#95{wu^D9qspqdl@^<m7xDJOWm>jlLEP8aI^Ky}*<F#@!W-rxWg#WcB>nJby
z!sr`ELWe{GY|?n2@g&2d*T6UFEz0hmSKMZG*sxJ5@51_Q56z=yir~)EX$$<~X|M_D
zgvuI=E|x42^-om8h`R%qKHIdN78VtEdEhCP2sp3Y0cz39bsnJIptN$?KWg&^2uFOX
z=z+Bh>HvSnyw4AAffn!gA*uPyR_R>9MYT@&!bl&gm9nK`ct1`(qpXwj88o0f<j-Vr
z*!Whgj9YLqJr=!9{zKf~VcJ#|1c;{e;=*E#&RUbp>gTn&sv8$iAmT&2_V&7Rzg?&1
zDFy5Rk^&ciWe*nl1R!eq>NNOd+7wK@c>(d-`^I4{r^!p2;GTj!K(OF(p@Q$J*JrkS
z7YM9km%<9m_jHhV=6qMN>TM?P3@QnMLSpSpFEcORl!~Szo(#f9qA9t#&M(A!I1XxF
zdU+Uq$zEC&2kQ}l&=2d3-{O!}E4x8hykW!ZB&047A+-wtel!%)30?}fgX93o@DUFP
zk|DK|+@<4@&LCz*dUVX|+&H8YSw|B0L-`%Z77chKG7I;L^xXj!8f%D1`TpGvd9!uj
z$@w<W=(fTJQWsQ)-~4A+=u~z(JNLY!TR8dUmi(*^m2sP?uf*T|q(_&gU9G#*4AM{x
z5W+~yV_afbf3_dA7?*Sx5C>>*t--;zL)B=OYu^^A0kpKCptTIj+%rN`x&8Lxi0Y4=
z*e+TOMT0>3=EYYmswt(V+DOx)eUdc_>`QPg7pPM(eP0C(I=lpKW?DpG{nkPrk`UmS
zT#uPtbR|u9Ppwl%tov#_^JWN1J0?1Hh3+rLZ{~H3(>s{JWtL?tHD6l--$60BW1^=e
z@a5UTv#U0}`mR*+DzJ)h%{M`9+=;ed;wLe8I8!oQM(Lcu^|9*x^4>428yJ^0Yf?}^
z@qpyP7My;}z*vcAHoLV_e{0@loVTaM)!7Qlml}xv4xWscs_H?HfpT>uhC}#k#TJ?!
zIEw<vSoP#~k7^?4W_{t5p7rJNm&Isg=X?+0Iq-$x6eam>qL$ZcmMjWx-;S2zn;kY*
zdMToG1R@i-DTT1-$HFe3JOtegR=1yDSVAHVaB-5scLL*+Hz3J}3Zi)n_(Yx;a{_%1
zIM$3<=T~Np?6n_o9G?WTMF;qP7-Xu55A=c)RES0_WY@s<+E&e)hZSbwsqfld<eG>r
z90cV8*a=(`1BM)&>bth<@VP-Ee~|M3I?5(4v8fjK@JFBFS}`4cLRMU0Cm8x#buLT1
z?H*}G!-yx>ep?lP?IY)07rALUlhGck!$q)>u}VOvMEy!s)bD6{6TlX;x6&MX&2xbt
zA?+7%JTn8HC4^pMiPL<im&O~(SIt0{vgvd2m8g|lElt9N7h6Nu_?0KR6Li{Z;x$$<
z!86cUIkR{B*M-4(ZX4LdcicHXS#hjbEk9Gl-F!MF#9|ab{N{_Zt~{wMvJ<kA?(G||
z^&#zp1SkFW!!eMzFnCW-Porhi+qBrBXqXiFjVdd5KE)=yK`cgnV1EUZH#veuQ5ei8
z^_kRx?(QUn^*fF84qh4K*cBysypn8fdy@WC$zrDc#W|<tajD|7lKQnuKCOn$pG<QN
zm7JgVs&bH#c8qksR`a=|RQLT18rRH|3U``ZsQ<AbdZMPTUZr2((b4gx0G3L<EW=CB
zZ(LkQt54QEe2@ImHpj}FKslX+bfguoC~m_w)BO1qn7wnbRoT`;8Crc^v4Q8D+exLR
zyD+P#DH|=9hIyhM&Y=jcj9nq~noV$>B1zTlOcqr(GM_V5(g`+_9jP|}TfD^+!Pqbk
zD+ffqd-=CD=be<rnl>S+C#_rjc^Y^PS#i23me$vmB49q#3^&_8z{T19^r}@i1I5ST
z@?4A-F0{rIPVS0!6cTzEx7m3Owg9j(tQlS)S#$#8CpcP=WqwycX3>6YNTEHo+3z#x
zx|=C__wIpHCTTrw2{70uWX?Hb(%JaNIun<!EH|v#sj*T-cbxrkngsYVg>;R|nZc%h
z895-p4l{BgV-<c3vSrr>Q=UY#R0SZ*Fol9LovdxGkh~PgahFa$^J84vbz3^YH+WLK
z@8`i|#H0x*b<Lf1@buSnlPYbEKQ-Py5w~W?V{$DGqJ>k`xB$<Pmebe`lJ2DZd{yxB
z@ihr6fQE2rQ8WRS$-!bJFr7t)AjD5DK==X#Pp})ZCP_{|3hLKOOAog!JG#24!|UTE
zsVKy1N3Ex3XB8zR>O$Igf!1aEexgPqkE)kGEG``Jn{P0AKaqE(eNAU=Q;oFwWI(+s
z@#-`hgdve|EsSCaH7x=2l3M1Y*yZI3#5RR<z5#|Z`PZ`uQ{#uF3T(ne<Vu9rfr`T^
zO`!Zllv0j$)v5TPU5PG{bz%QuPX|}2&N(wO<o|5v;6UDn#HG}sA{S2_!^a1kb?$qx
zdq+Vm%FOZg6PO!M4LgF6eYuQ{91maPVnn;QtQ={~BOssjkck^_2wo?BY-P!Qb%;at
zn2E`I?aK}6EzfL{ZQDM1yw1`5u&82t$1T{W#>WV?&m?c$m^4XUEc8r%^Kc9~5g~&U
zbiGe>6QRgAf3J#nx!K1}0h!R+!D?4$c+T-isH89|wLva<3KiOG4c}K-j6t=b09Y1v
zcezd(t!^h+=W84+uDrLu9v~U%e(;rzRnvni{M5`0iN6!G6L8gN9Uw~A`RhM-RAw7g
zBjzfW#AK4lM1!R|-20L5HD|5n8_J|^JyYGLwl~p9``zRj)l2nP=n7QT^7H%Jb+bN)
zq6UdoOKwiamX=J@SUMi#xuvybuxpfq{X5;SChlmtEN6H-GBiYrtl)l&PFSRYnQ%Fy
zGX)e)0|Q!V>bcTM3gUkHVTLUFbJqFj-HXw@E;5|YZoJW-yUUI5bS$A&l@+x4vB|sg
z3y*~Wj<@%(`xbH0U@tB9#++Z%5oc8-)?FN$dc7vQ_VHt#C6mKVH0XVmn*r!|YIGim
zMaeH(R5+NK&zDzkHyMS+%OwQ>Qx5y3BxXzKXgT#090mle4UKv5lDWJ|M%6P%`pNV9
z>OqQ7$vGRTU9*TvuHlnsg|p|!k!PA07E9t{?#BhBl$~Fj`)fs$wC>D)sdnyFcO1?V
zAOxY%rAgp_mm*f)R;$34KeT;%c00u7UYSev*YoSLTuY#cl|jaGAe*Jip0i$9bGfwC
z&-<GEX+Lkse92f$0T?*jfjpZOH%yyAt*r7o3Y?L6Ake9bBO@s?W>;GbJa_r^-H!p=
zL2#Y%6p?EHJG=7D@4*WpyEnEs#0ysLpprZ-pjTj5-sLP;V6Fx<t;ouSzRus?Pb*m5
z7&8_vUw<t)EJeeV#!Ed#GfZ`~M{i&c(XpSOl#l?-)gbJ;|3#RDY(@=jmTpVukb<iK
zzVKy;ai(Dn?bpk-@GOskGLbM6ydt5v84$<}67yW_k3SZPZGOlz$X;)CB#eulJuxOm
zSHbL?3qN?dvm_mi9mhJYf#nWfD_$dVm*<aMg<Ckzx3ov$&4(Lm8Rn!-bhCS%@{6@I
zEJ&LK={4uFR+2u<x9w~^_Pqt|8lypV77Py0LdM{Z@tCm-MNwpxw5868R8mbY)u8#a
zg&kSU559IDUf%Y>!3<H#i&2dUpshn?7yH8EPk}*=bJylT#7f$>27s&rl|M}b1K|sZ
zRx9H;e}DgF%Yk*FJx!#|bhBGdj&&Yd-8~t(g%?@rQg1oxm8qU2BPXMdI$?W)61Rjh
zNESq|-NpA50@uqH@U*Da6u)@|^cCV`K&Cus_kFGk95d7zgU#9yLjLoJ55BJ=o^c0m
zT74Z`+p!0QBzIZgZ=b4Cb&a8LoPLMrXm;!x%4P6~EaVQ>XBvT6oiVf&M1Sy^Js7jq
ze6pXP1+Rs5`Zi8xMmLypSEmD4_7t}aWJq0Y&T5mJ%}l{m%AcjmSV+Y5qy^FX^x^Mp
zIqwAa)h9%QrGBkNE?tDQgdfNtvY~}8JgZ@k26ssMCB}e)w5$@mYb_5Mak^M@J9=|;
z#jUu6Ax?#s_d@3iuX)AiZ(l7&gw&(_xpB2aFG2xTscRrvNO*1iJQ5VAUrJDbiBgeO
z=X8@WC|D$juOJ_0JuQmgccEh~8uz}ZiJ*DC;a$i4(ec~}T|0cLN^Ak4kXqmArdw%>
zVantE1XLN&0yEQR8iRYB1Z3<m$vf?!87<*BGGRuPHIii>lyQ7tAm6^_r%&`G(q&N$
zSt_K~riubi?gkM7vQ>|cUPC$)utGUA4yYm08dtp){(V7$qt#=QnAM8x5*yiRUJA8R
zc;P7rbpoxC_cqq77@w3aMXd^U1D3RQNP}gyco0&7C)lm9ETfMqZlEin5>w04uetE}
z)1^s7oNJwITgOft5IsSr3dp3Ayj+~F5h|0ZNM8@mPJxtL3qI~`fH22`9LY6fJS08|
zOa|WS4}$Zlm@0jcRT{zXD`d8kY*ldJR-KV9NpFhToK1gsa<cDHo6dV8wHA`5%=gK5
zpHf}k6=5nF%9*J~*3lb1wo)dd*P!xAMKkz#ll=P-xiF^VZLn7IIg7k&@g`F!y-9Fs
zDgq?+r?@&TZyx!9L$9Hjv9YnMkxOZot`aEHez63qVDMA3?=#y4?s@r$vc_T`pI=kw
zEm!Njl8BAa#a6YPQWMcD%coP|Pf-)e-Zz@1emNVL!<BtV>WW^25e>9o)z{j0KkScH
zn@5CKD3_cULqb!e5(jM8+qAy<9*j!@pDK|VcOZR;k3G9_UFIsbjjYqy!lDCWuob75
zOs<m>$l6S8b3P9&_`x+&Y~5%3+i^77DoXba?oQKc){tlh%CGoSCBL63-YL4E+Qe9a
zbn`7M^JyOwb$5-OJYK1)ta?zwhs(45jRC_RQu)7w$az!L*d8zzK*a&#QcC&EYfDJ&
z!@aWRf);NP%m6r9F~TAQOOcpbv6G$Z!!h!Wt}2k+2W^q%+Kfx1YNfNwJo11?egiYu
zKCnhW_G~=B^oC%Na#U#LjK<yj;06F@8XCur9z{_EB!?q?;LR%tQj7Zgga7=#cpZ%y
z8c}|izH?H6n35n`hfMi37zNpJSI#HAv=~Ld$K0)Wr>O|ERA^#Az%Ei8sY?VbW+j%g
zfq?=3DUQnzLCky;oF4K}ITbkl1gpo79-V-iefRJcB!RXAG5caC3dMgM%zJ3Bit_)f
z``%dhp^PWTnw?6lHoRc;g$0l8QO=jZp$=XTAqzZiM;06>c%GTcY<=td7j6=KpPDfL
z4`A|=j^tOlebz4!-IA8@#q2^J+GV%~?#1#i#~hcs`}pU{P|y|{pY-8|aPPq{70Pc9
z)OZp-VNd8tDL=cV);7}lCEg{`#r~xTXUf~YQQQ(iC0cQy-qZ8r#>NI@C-?8)|I#&d
z*Q+MXGdV5I{CY+wpb<!ojvU4jzZCXCQym>aSkY0_bL2k&e!Yj%c10*ug$h&&4YJpJ
zg$f^xknTNXPX5$PggSk8@1dK1q1M6q%E#P{#5@<v!#35rJ_q;eGT?H1JP+0s$R@zy
zC2mvO!4go7Nn17h!Y$uUiMMh+1$JguTHx$8b@nnvZf_cLZZvvrC7dtud(-fEZWtE7
z{)Wb!Tr@HEpNr&&eU8@`Qiz2b=F{~HM774>e92GRak}XXRVg9CgHeuwMNSjklUGQG
zCl55UQf_asK>}l71R{_vIyDMx7v0WOvEp}LnYq=DHnSf`$mZ=NFHZZLm>AwvD6y5$
zIZ~&SK4x}g^`~=qcJLE(9PyrD$OhTp_oS^I<7Z&BrLj!1u4DGH-q|ZcZlJ6J3IH@<
zGJp53tlu)M7iJ3AmJofO&bwGEV*rpB?}A+`ZP^HOh^U#6@{M!$!pru`Dlp-k3hCaM
zZ!K5{+n3oN_Z!!a{E|a6;a6@*;}D82(}U&><V=JwZX%oLWiVtH^+QW~KWG7wv;1+V
zG_;U)!uqbhIX#0ERrZChlGD4EsQ_(l_P4o!D#q}>$#oDue1GwsJw8>PW{=y>&W@+x
z2hUBS1(j_NlDo*po7A#PAu<ENm7U5Ziv+4^9b1u6^@iB?3^&C=&`rREl57pAxZMmg
zH}*a4xQD;<NGl(jgiYaJ#@Z|2u$(_SJ)7<l?E-gO+^mpa*GrdDAuC_@c8oSy&2xYC
zAwS&vXm8y-SRV`yoB;H+BFzOgB!_jQI3Eo}-bgQ66i2F4U=%DoT>_(!1ZWt29c?<<
zpn?mHrzvNK%V8|tv?g^Nd1a8di=S6^zjjm-TOrqTC8Vk0!y`^R5$sBXi^JkE%@fC{
zSVtMH9!2-2kJ09;C2N?bTc6QpP-LwCyP*BEL#-jM{sXxh37~0=V%xC4#sF+I0U;~3
z{iVS?tJt3k#6_oKV0id9j>}V?yd@*!^QJiwMt!CPZ6wYSq7<xKYaMxij5l~;PCIto
z*WnmdJAt(E_}5vT>zYGx1?6G})`t0Y4Av?PVYf!*Y?`3{!HAi;AUbEKw3+c4H#4|(
z2)5<Z>kV5`yoxJ(f!`kNuW=B$gI-sZGONnQlYGO$eboU)vik28HyM6^F=cV`C(j=r
z-#dJ1&*H}t@gu&+t$OIgl;>M#&dOX6<S(Mjm$1`HpeKy&Gr$2Q%(_%Jr+C2p7o3a&
zaa*|h;CfI`Z||kq!+!J6Ou|^D<dMO`ORj-L<aqHq(farN1x~h<yt<Q~T6;%ADF$n@
z)gXHFV70@zoeC!UL&E-JZVY#p0lb$va@HkGb@o*tDiy+U6GC$S5w@r`FMjZbpTwOR
z*pbul(3`-gCZI<Dc~#dPPVgrZM41g1dRHi;ErXs=f;i<jOzKfX=U;C!>wT9+<$Gt2
z8}^3ZD5F{FUyR{|9Ao7pfR(CsO>}-d5M{VebK{Qb&ifXr6q_{sQk7EBPM~Sqe6zxJ
z=&lO<2eF-7X{lHH@0%+dA(Sv!Hr?>}-qVp$nuDE$7_maG+d3GdY{&0O$o?(+T~mz9
zwPLtC*W}eJhXQb40`p;iM&pvp`*u~(KQ}Gk@LN|`caKqbj8d{BSKu@XDoi~|C}esx
zFFX1J*F^=bS-XHjKHRg3kE8j6axxQT64ujlu2s8teK|MBZOcv~3Wj%`l23*2)Rr{I
zH%-%{-0lC|g~UQZFPeF^u<f6_$sN-H?bjV}p*9}XAGGO^H>};SJM5ldOl_jR=e(5x
zL{8a(`^sD=hDa{LQdy^{<R3~)5<*JLue^%W6oR;nQ1ES7_O+#w5`M5@S8{cAJ^c7O
z03&EO2h>|-t_645xIc8RLumJ1MUD5m)hv;yah1;Xh?qS4M<=Hye9Y`bM4J`tHPX$A
zT85<dkP0NIy$IVhJ)HM9@O0o{ek_?B?Ff2^lI^YeX(*{T7uZb;7j1hAZ0h=4TSAa3
zK!btEQ5|)1Y0K-}RiKW2RTnz-jH;bj+*pK~<xco<KL`A=@gzx$>r*iJc?vM%RgFo_
zRyN6YGc%_9HC7Jg@8m`y_x!1hzawQbig6&#l58beDAAqS$`}G6S;=~P24FU-X-X*4
zvgQB1oyc;A-jVKqH4?dZ<nbD;H>~-uO3y`5E0Ps@hik?Tk1I^)UExscIO*GJ$e_Lh
zpXuCIbZO!Xub+DJg0TgKs;%rI`)ZGscj<j~$pc+AuI6&!;K4vxe8HFaI<O|O_ks{E
z`s476yVHQ6Os)Q&4CP$>36vA{6Wq%2y0CaPRCTuFkd3Kr-)omV7q?;=!n%QTZn%N$
zk(Bo3;aJp0-}o{}ip6^fXa&G_5RnK0SX4#4<J>_GJ~AI5%JCtb#xD-m+y}cTOCDCu
z93ha>9bBH;N$I$Fu0N+_8)Zy+q$>)ADxR38v`s+2!cN!*7t-UdRrVSxOx&(Gj4C@7
zjAOtRTL*XGH&9y1z4vLHe>MMvX2a?T;>JKj7p7KrEVuyB28!1}2%|px^L`<rF1bgW
z0;j967|qBzZtP5bYc5OpZWsDKlbbS&P@1AtX29g2ihE1+14Y}WO>kfhqJ&rq1T&Bo
zX+eg`ABO>*-M?SBLnTcS{RbL^RK`jFK8w6IqmikeLtLEt+IobpfsJ9lgg<3B{gbnM
z89DYZWk6gImeR+Qj1jL>`^&l~b(9f^6L3ggBmkZYY?}b`%g!jd0fmChx>~~z)r=rN
z$F8nQ_`w#oyRB!LmYt{nnxJEh`r5R*(BaE$21ymVn3==A$B+1N?e7n})ulXbQ)}A2
zoqKI(IVX7vyEOvhyYDFZ>0{Jjt#mbQGvj1uKmBkF;L2EJzW(DV8-aq=fm6hG$EezH
zn_wL5{Mq(Z(OUgH(GYYTN-8(VH5P74i!XckkK<kCr$saqaxbec9J#QwOfcVv2nUF}
z_F{ezJA+{agy+J=hRj6t-sMAI6TxTw=OG&kMO*aY@Bg_tPy49a;fa3_Ogtm*ZLNzD
z;qSuk5DIxV{yj0#K}*`N_O{$;Uz^6LT$4GITPqvxBLv?jLd>YGt!<?NQX48WD*u`<
zT^6|zHvc>W*d<BR`pY}S{d4yCCHu!!M|2k(uIKT+t_~<vz;xu<wS96)+ze4wRO=xL
zK={ed0KhvK2jSCjvn$AWQ@GPpQqK8v{9eqxyiLLmSF}RJvXmie-FYw9(UM(p&U&?a
zK6^6pI#ij}Q*4)0Y1`Lck23BsY(D#<tO5uha^HpCf|QavxUS}QCLrao4z+^=^YbOh
z9B+00#u9YqA;xt2NCREtosW4RuWwa--#1Fe3{}T3RPInl`jkprpR!bQo26FsEbdmz
zLQtLv{4j+3v!ME?(`w<MYxVS1(C$}cwwt!|4lH=@j$<W_k|d?oH}rb?NZZFE6QNK=
z@nGbYete&S_cKV1#1a+_>aXRKc321RM1{=h=vxJYPiPpVjI%mMA*8@a^xEgo(a@X)
zpIEr$VHpsWnMwx*l-~ZXoXE?;tZ=4I2k9^8>W%EA%jC3!eW%)UzUO7-*R(#~6l_}~
zG5uQDcG`A!N`*f~{FvL%qzEtMF3gPJIOJ%LvLDJxu$05#BxjHk{(CL$RlT7daC4FO
z1<KbT>Z@hR_zeIc;7<X*o1?y#bI%A119Gv#fuBZdHNPaTX>xMx*ML0}ZE-B0<zfeu
z;-e*wJ>J6dCy;PT<V)<_b0QG|DSPQynAtM4lM;lY4TLDLVI-g?k%$<crM9}QXbb-9
ztw#Jy#K=IitKJ0~njv=uo`JW&2Rgx}SFZ;8PINR#4Y@om`iFL~?UW+-D;ft!&wmYa
z6KUSj(GN^8J*s|KSb1zu0-yKu?-=Tx0e&-iLPnuTD+4udL(FUi#$b6smXQSm%F2+f
zFmyd<q!~2$z<d<-JeZoEZhdm>+%ouBPCzZ`6$D-No60Ns{T5qa&XiI=`Ryp-QNtKm
zlXTfnL54DE?rXX-yv%OhGM{*t%yr>d{ISW5jG)54y>g?ODFfpQ#id`*@~`l|7+UnG
ztDOJ7GrcOsAPT=UxtIK#2d>!w@(GKB{1Ul|%;xlpd$UQ&1IWMi)K!R_zI)alJP}=a
zV9%h6zx!)`e*OpQdh2hJT#WhPN^}dIeT#Vg+63u~quOaHp3df|ubw3#=g+`i8OfgA
z?tQs43z<u3`60qa?P55<+R+uj7G)zNBNje>K2d&lsJ1>iS`O73;8nMQ)-euBs*WGc
ze($+)F5?R&#^S}zaZVI8*qic?rAG??8WjRC+gR?3&9<4+U**1rN`*j}NEwSRq!iTR
zq8FUAQ<>z)#V~lx&m^Bm=Tk;lRT85n(>O8*^2^Jy9Ar#l*WiC`5(cG}4rrqF7AgQs
zhxh$;N|o`~4aes}di-5Qy!e&Ny&@(xo;jLFHO>@bz%@908PW?!1qj9J)0RxNB9v{W
ziYlzkUWzdCl2-5Bczl@HDVPv|D<M5Tt|X6|TcBVUVMM*XDfjR1apWU|(llxshAP{r
z4O_KgxI*r))y0GLSwhy7&uID3-ppl-p#>KmzQI>~0%Wg8J}g{a;583ZjynFTF>T32
zIznXWuzG2o*4!fAUv7dWcyJM7H?Aq*?uGe2mBHcO+nK1-M>A>!Iw$w*9%Xf5;?V~(
z9H`v5O>^w@Un5(Y+3T@1*s8-B#s%EcaW<ojuGhKm5Z0$LJgRvSv$&bkG3~2M^m9YR
zBhZmM@SEMkycNfKr5MAO3Mm*?*weF0V(;))bGBUf<cYI`5%sqd2j&%u2a|ZB<gX>f
z7&qeYCy76+(Xv0GROpEMltJb{R6E~%S;?+oSg=COFv}^0?F|G4PO%hm9KAwnxfFa1
zdq<vJXm=t_Ub+G#ll7}!(=O8c`&az_L5p0ZYfdaTg0@+IeBhp4q$!Uq@woS>=s)G{
zt90hUsO9WYx(E))m-ASTCiM)<zU++Qi4wxPb-pXh(^G!hH?IIYW_Y{YoZpWjT`!p*
zy#}o$Y(Y6?T=AR$Jcw_Be<aNAbd=;Gu5qpAFI2lMJ4n<BJLdgCH)io#aWPw}q{X6V
zZOT>q_Isqs*ei<Db^%^yu8;FOlhmSw+Q~dqZ3%ZST5vxCIOpBBon5!4wV4{Af0qRU
ze>193JD_%ukx?MR%Gz-XUS3`t<r_HyUJBNDgE*L3Dihy+eap-Put{~y5w)G`UA(^t
zPY{6;Gn9t{l?fgULt<!Jgx~&+B>CKl^Npp-vze`SKZND-;;?KW%SS}AfU-aMT6P&#
zZz3N=a67EHUDn4e&MOGv|3R{Qf{;t0w7n8z@$<YMlZMQ%fHgFv8&NCN(?xpgl7212
z6NOQ$k%z-%p2>Xp?e}JrSGW~s?M|<Z3XQI<k{m9WwI%uSo#7}XhzQDE0-NNj<II_o
zulsR;z;n`i=+MQ%&7F9UPpi)znmbHFZ9Y#Ld>$4U$$y?*r<O%ZzIT0Ctx!+aa*l}J
zB6p?Ya_Rxk`ZrMgZ@)*9Oh!nQv2wbye2hcO^Do9i`bU*JU$DnRIRHSRp;S^rLISq{
z(2-W1P}M)BJw^FUVF)>pQr0V&++v`O5>y<f(SibSLD-iKkY%7&5<d=)Em0V_&PF%j
z<Za=-OEHTW-UA#V)sia5q{DAYgzZ)9AZa_naE-z2kg0C0jD3p1N7El~jgN&GuoQ|`
z6`4?w%rdB3ahgFOwrH(G(_L;7#FA+TvqHXA;O}-or&H8iMcfY=E>{d&<@e8775&=K
zJZfT@!f2n=c=7~VPCTdfXM;zO+n;=?{{HP~7gY(tBU7$fCy-_V9aI{CFy`Lrz9`0e
zR0v!gg4#{{r!z|-IeXDx3ozT}?P`}iv{F_?JF<qhp$;U7t1&FQli|ZB2#x|KgTV^F
z&|P%N<4bM$@b9CJApi!luOz-@fMPk^Z+*&YjVTLN_RbQGhv%#WPx~&n2FbyPABxF3
zL9YOD7sR?}z<ZI*M?LRu4D6I3R%G~(Wd!qEM1g51ZaznPA+4LNC7kyFri3jOia9Z$
z<Kkpsm{cHZ>*HaH*@<=WAXm84#1|?_ShX=_SOS6`F+bRHj|zbjbY05_;v|Lx^$1!`
z{IxoIEK0yi0mCPS_qtj?OCnl<%#2h*Utfu_+o!?FV2(faG;~c6cT?#L)z=e$Oq&n|
z2*5z{`C?d>I=gc?(*66|mwUhl1cbh4IuqYBmfO4Hc5Lb|xI3EfO^w$82_da^|B03-
zb5FBQk<dmd4mbM|?;+)<h`v-0FXb5!>MevD?+j5qNiB%CGSbol1LhS5!1W7=3*qMa
z`JgSIrxb&k3LQqe$HVsS-CL1aHB4N1`^VdW*;u1<F^vO=eDANuwUb^bZa&cxADQ^+
z(5C?2JA@1ti@}d-&v31cI&@!7|G5pFN`?>{A_Y2c|C%@?CM^c@C59_wu-N%Swe7hk
z3pB`Ub4~At!UsubTbq|=CT=vLKlF6Y2h}=IGd-$Rc6ij*!J9eQqBd?bQa<una1Cw`
zw$EBajHEH?k2ln&z^Oz?tR4aAhDzYA>~@Hz4i4)6t&;(i2Jcn-feP-osM%=F8Q#FZ
zF;kol14gT**##c&u$sWn^OntJU6BW}CC|ZAQ%5gl+c3xc00@OeL}5udrSzi0cdWw^
z6L%%C-00hj4~qv%&3?^3*?jo=EZ%q9qOL-aBM0abIe~vG&NKqtt^Hw2f|gWb3v94y
z?AZi%Ad$RIEP(E+tlCf|%j@`+qQwV`vc9L|1Nwy#cT7Z#i*Jz)b5caiqXG&s1Dml&
zonzUmw%@!wf-w8C^@U|&QQ%2!X38z+*<roH?EplH=p=}oIaj*v2D}e(Kj@o?Tz*PM
zh+qTARIvqt?YvDI>hQ^}^-kwL*n?G-==^*X%%l2QSFktaC<=V%=^xcBZTR6bc}2ew
zD>N;m-`1lQXzFQD^jN2}!F#ozc?wXo;@&(QgL)#ac^~)(Y1imybrrijH5ub|bD1^M
zZiN)0wA1dufdk7xZy{_H0P$8;n&Gb#J3c;TQk=;Ur<{9i0+Y5E%^=d4yxOKmn;zdH
zVD=m`R=uE5l6omI-%d-Fq4K-T50KBpHE{w(ZAbtIZ1{LwDGWMGL`xbfbNNDV%_%VA
zPiD*?@pDaIfWMao786P;;HxUKN+bSxeuWZS&IG90A!=NPMg74#E?W_OCF4ZCiJWP;
z+n}6%wl^eX>%u@5F#PKA&n3*mN_GLr7-|K!NoYNV=WPna!{=4Q-~bEY$$R{XO~f_^
z=QHYtM$k1b(EiF|6kxcv51gUfS1aUX-s_>{@F)P~Fg;`traZ<U1<psySTsbdA}$+r
zKQ7WMSnLEl`N_Zc*&@Zzvg5||y`Re>UVIQlz8S~tXa|Gll?KQqf&o3WPl-8DDj@{~
zh`$)?Ex(TP2IlT-JAL=p0xqlMPy5@Z>s=wRsL*b2XO`HM=^{Amnwiz10Ht)uqx8;`
zU$lhr5dNUyc3x5Cuy4inkRQvJpusLzBHA#Mu(Mu9ZKPs}X3R-*qH_I1agZEh0T@b~
zKS~GtNgN?;sg)jV<YeTBUmZX0cTmIhZ*Ei<jr$n`5$)=oE~pyEnt$84i1)REGB?Ye
zA~Saf$|MCUt{4o&Lp-L2vLYBI6IB~ohIR{%0~*OIT?Rz97k+F{%-CIVp&nH@8wZh0
z%4NB;giKGWDQa#jFWy%v=o=OHd(HX6+qlNF*2gprO4^D5LQ~29c&s{VbP`RQ^PrE#
z)?2#C1Idx8YE%Ro<UO>=8#;grmZOY6Ybzel0bxY&!Hc(T<WfljK}zoIfl+5xus2)R
ztaSPRk*Q;^(_9-Mev<v}LqhD4n8jE_rO9_xr87#9mj2j)J)X-2MX#aM2Kk^MQ)OGI
z_@btEqtrsEFJ1|72d9w_4r{2S>d*ZjPv;FKn<}c0F@=3P_;2i*$2YQvBh3NV7qYDh
zNX`oU1I1cW4mNUH0YEuLJ{_$cA337^V~N*suU!!{rSfkE9(61<0>EMD{2SekQ_w;y
z%-0O{qizChGYO37xgbl9H(A66{}_%QG#5f^OwF`4%&*?UU<tLZvY5|314KZpxxZ4R
z4nULY4jr*~MJ%6bf5I=I-weEDjL4bOB^z_{jKTgui-5`n@~1?Nnb*6j_~Va5vjvn*
zRxXDjAuE`4YfnP__!!jq5NRBVidUy6MD~7qPQO@(l@fpj0dHU3DYi%B?*Pk5NIwpK
z@h>^D_Fcho27q_Pc?VOiVr7wBB@hfmkF~GqXEv$)tP)gSSA8&sIu~I>8(oBvxi?yy
zkGkYyFgVjdM9fQuPE&|Yin9g+XQftD;x6oW6P!+vqrsU{175KV#N1opexC~FkCd~A
zgO59Q77n~c<q6a%wQGR#=VBA<TRW1Cai^9fqnnf+XcoYJ9KlbyiFGroNr}m`BUo);
zBf;T_XMVgPz9?%SB;@G4h%Sx<o2ZWz!h~HK*`<KnBZV`vKs+ym0&)O%9}%3{3_tlc
zj{En{2APD3Q=UBRS>BVW=ZTy}!AZj4eYYQ$vWD5-jzzV^CBFnZicNysLPRw0&j+Iu
zXUrN4VJOCMBbw7*Bs>4|V1$dh`yKH|H7QqDbU!Ry*XbKq5Gqrh5g+*MK=U~#(8c@b
zUPhgfwZGo82sOWkMkV~P<;$-_jV1x4h;s2JgaVReho2d-5}Wts-2xtT&$v^e13lMK
zp(nbhkqi2KC(5#+9IQt&_w#Bzil<-1XB#?;n&liP)Vcs;t@btLed~n!H)k_YDg<eX
zqT@`3Bc>|dG$VFoORd1?$FwSoQ(TE&^96Q^JjxKDaAmi>wK{$OAXyVQw!fGsH$LOv
zzjrF$#1#CSa1NTNODwPhI=asMyq_{=_-7I77>?e9m;wrNdUqRV(O#iv*1<|6?^G3d
zPoG@v@qAoGn%vF64wafjSR#9`8UI)!@#0@RKNNO6l*25j1dLOsffASn7lEU0BQrsy
zT0rgg@--}?#$j}mLN^8&G5G^qa}LwcSm1)j&t;y7EB|#;&OoBl8J>Vr)xU#Rt6B`w
z>X_q#XmcSz;VA5gy@DC|*p&iR)js>_JzS1IBRNR{f-~&oXiq{0MyN-gDB_I4RY0Es
zrN`GVRNQ6}^Q#)Z1quY9M?k(n>sfxQA?xS+<rQfCTYEKi)4KT+IUY-onmHV$4XX`$
zOIGe1_Hd1P?R9}mh>LU@f;LRktm;f+_x=p>KQ}4ZHn}(eu>g8cH9gpWq*n81Sw`o%
zgpm!B1f@Xjpfbm|W9IM4w1%g0-LCFSV=Ctk`ybz5<5}PbkVPV<Qt0P~gU8JH|JZx;
zXsp-wZTPNswVP<~B$a4VAw!W$hBS-HtW1f>6iMc>(JoUNGLwqTV@SpbnPr}$GS4AX
z#Cu$~RC_<a=Uwkw?_bYakF~$}iu?Y2uFrLy!*L$xaiTyszqEEk4?$aN+F0G&xa4a?
z%|n$&z9)D_9);pcp-r$sUpnkihT~1HiKtIxLI4?_n77T-pL_o5RMmbt332ui?QkP*
z$(8Pz8CMU-7_4CRBk7m1(2K`>OJ7E^P=lYVG@z7ibIA3IodalbLhIK3l$Nw(<)9N=
zx2Qcve^vMB)kQtackb-_#uM>DLGDjCE)v2RNvv8>dq;Ub?X){8^7Br(HMUn=Kx)Fa
zr0`-R|C6NVx;sjZYNt@6#CnTxXwztQJ3hI^G<8Sm=U>Mi`}Nn|Z>83wc!0M|jAMUV
zn?$0hEm1A&<@l~RPmOMpbD+^3)<p^@w){gOt-<nl{jttdb7V5yg4dtFx~|r4@#NXJ
z`dkFNpG2){JOfzhYw)7g(sk=ZHFrdE!hLHX#W<J{UKeF0a{FJIC_K@x<#dbyAchb?
z`S$$}*{!0+_85(Kf4fJiv+e$2>o|srrzbL<eJ%uVQ@jmF?t!aYGoM&e#L1B0AGU8P
z$D`74Dw-MGd@B9`r+yAFwUGsL-g}@ra)XOA9JeITZPJp3$hgh$%lrAKOR@SXQV7@(
z<&?X2W<tAEHZYwFH$DGQz_r*twe7ZCr%_j^w=AjOoTLYXMCD{k(LBoiWb4oSvD4m6
zcTQK#K*rbdDJixl&D2G&Ha`#+pmjP-JR0cwdXQ1JDcs_Z-xvDqiX5CD8=8mqu6))M
zJbx?Pte7h{cUyL>Tj|b4MC}a6VQ#Qcn^ZM75IWzy?u<ujnqezK?btW+T{0@c&lGQi
z{pXhZGg+kA9sQ+=y5o_=Zhu<Wb=hsHd6FQTwR0MXBQDZGVfLaK%D%bgw_C#Q*@=zX
zys$be)H82LRU?PG=Sc`MUJ|>NyN)rTRA6;-c-myxzF?D_*q#F_D;=klbFy0}zhnE8
z5T$57&QWdn74Nkc^PV~Gx*qvLkJI+*HKO5`b+2~%Ng42l1)Mr9`006efq06T&@MiQ
zAfQ#o8CHkWYC0vp#~R*-4&Gs*Q%pA+H5fqlnZGc~)UVS(Bf@zlT=+3w3*&H{w^wfz
zZNJ^h(mKuOUEqO-_Sx{fSaz#%Dh$NnPJwyzm{oQ3=YiNBy!YU$w^Ae=TUU}vP|D+#
zJzRagF2PlsO6~Kf)LBU(uSiG>@QRU6x{qwHq*1vCt;3Xm_$a?jr)fCK+~Wr(CK!I~
zL%VLrmU*cOuj@z{7sbhJ*&M)m@2$h9p}KOusG01HYkQ>!;`56oU<&<T#E$BiM4dG1
zP1E_Big3ev-eOVu8lSK9@eQF$MMJem_Tt6BBS8<Zr}$yIl~jp0&x;=nE|6fig=n+6
z7He&OEVnD5=J4(oWvk5ra=RY06<03LmG0rXLvYx;pS8W+V(KSE4C_ZO4HGLDHqx=E
z{qD7cmwsRwD4i=H(x=W#Car%&0Or*8y`R4m03DTcqD>8fP5<rMq~O~lpUGe9@#6$A
zl=7ar>$d)`+GOm+;Vle8bo9*sbS`QO$~Jql8s7$c6r34)J0Z04Zb4_2#lF<iXDNv)
zCDEfg`u?DU?fmTm^miOD7w|aUO)w}j8&!1j;_3cU?y;E_9w8w+l)+{NxB@5wDf6?D
zALo~w29FH@v69*GuQjEye51#z*PUTK<g}<Y)u=HleB_W!uW@*3r;N2>pM-x)m_?v;
zf^Isv4&#&cJAXViN|ssJ#1r$~^p%*s79<-XSHN5HSEkIKmFg~UMm`CBd+*W%pq)(R
z+1@=fHCPMU6Sw}pA8~`UGFH7YC%f(Fo8f>{@lC-VXUeb2N0mLIb)NFeHT)1eRBjWK
z@LAYr{%lYxm2zkKT3y9B-;>@ff_SbZ?TBQQBU~zVls%4>W<odWPEXKt!YNAH;6>**
z?E%W8%O|K*DgVEsxc(??X<jecsrs}j*y&JaQ3y56Iccbfl&mH>V#Lh9_ClZdK<tG=
z<-I@e)+-DsFd+qf@7SNodas!1w<6U)MgW<E1BWyC$I?bhE72p$Qpt<y>uf&|z_1@g
zDKEXx-H}OpE5OpZ79?$+anwBja2noznM=-VcJ2YhHR@b?D@H!uydI-0tH1P(;rttO
zN0HJxCct=*(mI|f>C1$4W8Ri1W3=O~%SU}0d%YSO>W6k1|JYn^A_&{_4=eRs?@6zI
zkEW!!P<GD#f7bU*Ri5^4=lnupxJP%bUKW+A!mIKg`I3xF1#=d*-Ti15#g%DXAJxt|
z4+K$ErAV0+0w$vn#ax@}(E=F&d&a$<&+pw^g7MUIlZ0))Tg*FZFDb$x-X0cZy@xQJ
z6S<h#3<e#v6)%b(TC!%ZSVdcVZ{1Y}sq%F8%_{9l5e;s%&PzW`Zbobw^zqB#l*VYg
zM$QP0*_Lbbs24`Tk}H6G+^&Q#id?B5qVcU+m2ORS^;)XH{m(+Ymlm1{6{k$^zDuT8
z!JS|2cVT^0>XCX(R4PlO@)A0X^}gy#-adz|96rXsJjgJ0nsZ_6{5n%SB7u29A<uPt
z4);q8r!r4(mW)~d=N}JRaiV)h=YG#_X!V2O#3UmLBt*f=1ykxh_n#Lkt1N?uy6Ysw
zq4Pyo((NMp>lCWG<ZriJm`*zK^Yz7r02R{SJ(4^0#co3D*t7Sa>-tT87T&Y8ZrHmi
zqzgXZM^IFu&}t}bG^_)^V%w{JIS!q#?T6ggCE4%$wr^Y4*7+nF)@Ly(lS8KE0232G
zvr`afH<{RcXa|`KO+J=p^PVwY`Ie*^H8<|Ow7?fVTJ26%a^kKv>{cj$ICwfE_WcVg
z*R@Mt%tMiW#m%3#&90kpB7r=)4rkh-SCWu)7-{m+M+RGm9;6os46a_?Wb8EeUReQc
z*FzdCo*`z9J=?x0?na6jI}*e;9@lVVADtV%{JnQH_;vK3X;zzEX8G+F`f&?HQkUIX
zI?NqSkLwAnDcCj&m~DnIYrD`<o*&xt!*XnA^)JyrD!-k1$KapZC1Vepn}kBO*c5AC
zr_n?x=nCjW4Qa`Js#a*NLHGhrsr!SIqo^6sy9oEk@-JT4yjSk0?xfvmQeLR3k>kK@
zFCy)`o%HhBGxD||lbeAgq4d2+gKx=%x1#pzP8Xu)?$WE_@9s#DW>Ey~aSC}EB^*+{
z>9z$@7TrTRr!}sxQE{tQ3r#5uuIUL{ZLS@f)}YR1!<4i$Y~m04pV}dIqNG31l{!zG
zD1TgHAU%ztWSBfr3#8F;f;@TVRBEQQt!*aq$dworC)+B)F?R(!9DpV@Yn`@!<Zkg)
zbo98%-FKGT3O>Jz;pUPz9R}6%VB)r;ZBf-sC85^~^b_5KIp_WkpT(_7bsp_Oqv?az
z=b*8HV)tvbfFy!?LexiOxEY!TCkc2>)e?cg8k>m$KSXdx$zZWGvAGZ{>kq&cL1qd7
zaC@lnLl~FO?t2B64sV90E#vks&L0nE{a6wI*bc#cB4EYfk_F&<j@5ho^wSguE?h`*
zW5316n@ewCS~dHlQ}1}seE(uETt&LF3FV8VY0sy*{ziu<neRm=UyzB8q>%yAe)KJ<
zltaXlyg;Fq{M;QLIFB@Ar)$Mr7cqEm5$c=4x=+ock~ElN;lVj{X>>%5o-nY*j2tXd
z{w0(lAwycf2s?XJ7;==aQRzb8@OIJnS~3{@_k;}o{A`GB2J{5$7br>Xxy-b3$tano
zf|~J~dAxg!%a<pSq&bgh7Z=(uEpYQzN+}Z1qCe_$aghC9fhT=-yGU~Suc(zmnUWzO
zC#h5tg#cx%p<IOo%`*kHi)oo@X+3HC_n#p{&8kVq;&BK1GkTxqZWJLmz+;FtoVb&3
zzp(w#lD`Yvim!X}vjLYREb$tpKLJP@n@Jzmw@>{2{gz&p0^C8wl*y<sGR3qTbmsuM
zC<WP-j|cU&W>MoJz*vtu^OV<a`l4fx=Qb_CYon0t`!AQMX|H-)x#6E+x(Dkz;$ANK
z%zF1;`u^4D)^*m4GHqopRXYW^K(p2YIh_~n<K#bp$Z^>_l|7leY<%Y42PK7+?V$CM
zn<dK(C-MT#&{~*1h<4BFtSmb);?P1x+7tXu8nl7;a+(&*y}(-7^KdR7DG)E3Po1`Y
zAEas)zMAfwix=&ViiV<=@8@*rkA@HNli($<SHh_*s3FMwl2WU2XlW<P$zf06T5{`V
zvwvPq)^@(ITOB#oRaIR-)PXO~*W?RHc=lO)E&v~C)UxrVb?aQzp8nq5B3tyX8)*3g
z`jPZUs{?{mUx7St8j6;CXWiu^3Nc4Tl9lOi{qie^?~88yk(_w-ps%j<4PhdW9vcxE
z`V=%Ypk^kJS<)7wnmQy1iwulZPAu^xhpc`{WNGHGx(m1s{LVjYAFlh^d|vvb`1(QV
zO^XacL#$~$v<-#i*@jr2NSMNb;T?Wu+8gN^O(TLyi`F?qrC#HA+<(sTz^V7%Ixi`l
z^+!R6_|<-gwYS<XUEOu3aQ}_tbmz_m9wt!FW748+zI@9t6v@10nHD$k&2@TFU*WL#
zeEP<q4mzgdH#JgW-S|42RJ4;JnH+cMsmu=ff#Ezy*h2&v<?8YtjnQl_>UB*)Y%a=R
zYu?Ifju=1GZan;GKKO@5N%xLwgVFf<$QevZ=|+o)BqT;a%tVilhEIn`lsdybBK~d|
z{x|h1#Mco<mc_HXo6UtJGA>;?h79tw#?Et3nsoNa;IY46y}D&BW7*QTw~w|SF|*Pd
zFsd;BWrV!KN#snoyc;^yuV<z*XBEL$dZtjsd*(+l^JJG>36Kgc6wg(yw0t>BkLDUG
z+`cbfU@7<r==trbMHBZwn>NVw8sX{7d?>Fu#A_N})A??2oPRA6u(;I>KXOuBY^)eM
zJ7>SX5T*vGnC&da?^($)ce?tp*ZLX7b^4t}j8D1-fBJH1!O*8SL#Ig2L;DZbuO^lK
zfJ&zkeP^POeb0)<hHM+p4+`@<2(l95YOtpl6EvNmn9T@H3(?7IlM;1SW?gf4D}L_K
z(lr+9il<^s@4S4y?RjnT-KsUBpg~J&hh5i2(R;;4E3}r>-Z~N3QDq@}y!L1G3Bj&V
zRG$b>3F-rJmMPjkB;+<G`f6YyNL;-dCBSsfJA&G$M_CiZ?nmp9`Tj39{nTtX9vOWG
zDc!`A<i*RqXx+JVzG{OXt8(SIt<tX@t?Cn6*P^5kw319bigPdrg@dS)6B)vG&Q;Xs
z?3gfcExs;j+R6E0a~A`e3<}n<B{Nj?Da6q8((${mJ3`gwI0Sy^JgA628zbdXu8fJN
z&#l%O<#3}v?)yIv=kgzE30M$RkrmQ8p4XC^@?RCs5>J~nHqn7E2rUA7z6m6JCo=Js
zH@TGE)Bo^K+ykSmi1S9Am}`hDCEeqmadUtb_XeDVMME%>tmyLs=Q>DwGU;KGyei<b
zc-D{c>>rcZyPEfv8rWxEsqIs6!BV%A9OJiEQAckO4iuv%^)KGLq=ix{K#t<Ib09}0
z?^;n!-qm6eNZoCBqsZekc-8j6BkwN?8yB6GwPZ}q{*?RVmGT15sO<-s7{>z2wB4$+
zJAc-;!RSrKYnS=#i#Tw0J6lY`dg{tb-vz;)>2z0=|E2zwEDyE}J-Rvc^2m^T1?ZD3
zF6C0~HII01gG^m);CmZEbK!3;lOnEwfL0iq71TZz#GSr-y&d(E9&k{>b;zjGo$JNx
zLR(1pgR=;#RJzE0F7P`uhXYgvo^+$FnC(Z&Q|C`=%NzBQC|O?Nb!{eMVh&_v)_0Ui
zBzQMeuQ?|WgxY6jlLmD;xLLt)y4vTgWqn0RCL;qSm=-1<rIrnR+qx?Ly30NJ7tcte
zLSVv=-=@B9mb-i$oGszmeFYlN>Q>MN$v~35Zi%T?9Ehj>QYZsL2=&cEyP=JNmj_Ry
zNV!==d-*GPw9suUZVqd3h*VE4`|WBE9|Q_JjWSCODdrI35fMuJdXv`bFo_&CY}hbg
z$z8)qcW&LSw%ZDdf4XN@ua`UE&1$N>Gzv}0SmRvP$@s@lHcUs)vq!0yvx{1RB{i90
zGXa7xe}$hS6Wpkx)yVd96q5A3(T#3;pZfcZFTqn+6^H^`4jb`ET=s#-HHR-<oGm}_
zSkCl7Sm~Q%DMd_g)K0-g!BK|`9BmAWgDtiX7j#xuH<)1{W~E{tRKqJyQH!vz+g!zN
zKoX>fWVm3>8RG_fNJjcWU;fM)C#C4yy=IyW_sH<*v(?nhBg9RqROY~=_lE^!|6a+w
z+=13<O1(Q<V0K1zD@WyS)6s4FmiU1&CM&!0{@`+#!3~2JTp#0$)IPan{d#lNaBn)1
z5e$x%yf~4)j%XS~H6r1Q?}Q9naA|4jZ50>JE^^a$TL9(w6tV$Byw>IMjxI@iRUXge
z_}|^G)<;Kx8(~L(A4=6zXlkoP4mjV@w(lEEbkPqkFm>&oER?Y-pQf^iGQR1wUq49Y
zIGtEaw|{4=!7TN<EIo=ZuNA#aclI)!Xvd<Dc=@eS*S7NP<<3w%FR=fvt;|csRVC1N
zS=c@U%Jh?tCl11Ie?o8+N}nufT%au(T0@|lYnd1A;@nL%_P5?V`DH>GA3!H-yEm=9
zz5U&srE9vgGsG`l%+NJ63_xGVIZgM%wnA(6wa<bJ1tY~$E}{j%N3qj`G_h$@?}7Ts
zPBes=Y-MBFJ(u-;La+nBLN!Q)2zo*od%MHki_~VB!z>%cpC<<I4B@bryxQ#)7WE>w
z-zl8kZ?~zr8>s!2pF<9ddJZ2yV}qWJ>z=>ehSye5A!a&-XD@8rufN;IiD)o}5gm6Z
znrK8*^DFMR(1OaZ(CKy35i<}vCPcTrrDCJ>MUcM#-nlBZTB5vP0ucr^4zLezGLg01
zwEXqRl+TJyMZOE8KbIB#x*3Y2J>g(M2-j7nPs;`!*=wj|Lc6`(XQWXBZ+Ng^7&x!G
z4p(u`ePE)IyW|{#*v6w{&F}_K8WC#<3Ul^Fu=N!Un!am|{hR5ROQy&;;-&461bu#m
z8YD=BO6#d=#dcvV{w1ug?{@j&KXG=ycWs?_4Nuyp)p=Nn8Hf+vNNQp^w+q}YyT%Z^
zBH$Hxn`pbD5W7Z|I{9w_EgI6mGM?mfW>%h1{S|CL*5(JJolqove0__QsJ>0+9!|=3
z57v8cw-vlxvL<-snxUWMfk&KrBl#=ookc<X%<wR5H#`p}P@8FeyHUfi$k37VR<&lA
zRRtst<X=i~pZb->K$0>7w**NS`uy9sZ)-$S-7<|o(uEfZ$QT~%NO-V==dPj6MzQ`3
zE@7?#vGy_t{FsI3K}d+n6~ol(hb<n1s|So4mN!~anmXe>?&N>B^f}Z&oT3-6(6wtG
z4aVci(t=9QH25ZusXck`!G0q|z{8s*<}9hWBu?d{Qr5?X_L(Cwj-QXV-jEwQ(sd}H
zl-|XG_7I;>G5xJisYlx1%bYlv&}1ra16`s79RVNd-uH0paE(<8t&&l-7gj!;^5aYC
z1F3$#wng@a%o`CS2!l9qzCJ~I^x=1GplH?|2pOljtj=!vWuK7j0!J!3E!_iXdfl0b
zOzr`f;I$k#XkA{c5E;HK7xy)Kv$=r3hv;T2837vIE#d>!eXp~V_%g39e|0&i=)iBg
z(Cq{mc`V&9devmsGrE|&p}q8=l)ra3sVUC2Eo2=)V*FkviDAQ_L-6vOLsr+Uw1fmC
zgjS2O9&P-x?2~CdqKQ;?36<XWey^vKnmgSu%~NfuZ>7oxb8l{Blwbp*X)h50fx4b?
zgZ7JV$j*l0y&~t%;m6f16&J2{J<w9Va=!P_(nRack}I9Y^QC%RLOcpP3Y9xvw#6&9
z88xiERxi^Yr;FlYf{xfBH+l2EI~Wo0^TTTiT7}S~9Sw2Z?-1;-!n<hW40i(@hyjUn
zJL%9PX*hU)?blOiEc!90#_4D-d4YQ%oAw9Ysp)5^dJT02Fe@Rl02tXLcxu8c{=!PM
z3rs5UP5PVQ^J0AC$2X}wnbM-+W~(*u(t~jjyV<w_^<7xApVvd7JUK!tnwT`m;y+B~
zqbsCb4+dqx{9W&cdqLtmc<5P(dwEEA_W3AaYtl-mfAV}UKjZ|j-Lf?ubgI;Rgt#YP
z+oSdo?OjX%ql&$;ac5Lc+6cWgFq05~i_uOf*ZA#FQXIn`(D=~qIg`NP!_eTfW7BG^
zkKO&pCEhSykM9)J8q<J=L2z33<PU5vIc&-5TE4Wlv`&I!)0Qj?4D+M2Ccc!v0&eZu
z7<Gg8H`_w-&?Tw%t#({*)-f<(A>S9|{*p&u%TnB8EZIO?-QWmeJd>i!+wWL0*ud~1
zaBveN8d@J>q4F;k+2{7lnfvjX-^>1N5+QJx@+$w5N4|dJTDD|Gjkt1--|s`504lR@
zcM3@kkkrWTfk%lqIrBK#!SN45_C9zKPreDhH>R=noCe()&9&#QtUc4=a-Y7L*&<ik
zJ3DUD$S6eSTWfad-Vec-6B)wC+HyXpX1CH*gN#n)064p<rj%*3+iaZ6y?#748WloK
z#!&I=RPu^=D8Q+KYf>K`J%7Dqq;cTO7x^Qw(%;lrSt>kQE?8Y;<a40k_l-zC=GjFs
zY>pYL6I<AB^TNsb^N$y#epwR<4~FluWKe@9g{Dns!`agmuL-!)y3qF1657A1(o2~X
zW2nQ?&q=J=mwh2SowVuyEDMs?Ql*`nd^4r$6XNRa2ZpCwnBFH3=96ygb1N?#cgq<I
z-rM8y_+FvqGiu$+K;sBlt3;HHG`^#k>I=T?o5WfASF}K(&@7~KjKwb$h$qz@Ok?+x
zG1wk_p3RN+-V0dNkvCt^K&QnPF;Q+ARv|ITTksDvox>K?$#sSt?dbP+9Bp^-okqG3
z^zUD3%g-F&?E$s0I{9TnRZ98OF;Ooot>(37bv8fdG)<AdRja8}U#!F7dm##d4}Ti#
z6Tr_@Gx?jNrq~%Cp%y^aZt>+ofhP=wXZbA$0%+X^eLw(M(+zkkN=wdRi%X7zS^XF{
zr=Gu@c^{v-zg)^DIrB|PpKHW-e$9*EDlqVzHtGIa=_0Xt^{(-#-%JQa^%k}8nk53k
zm<PcLWSi}vZaM0%%e5Z5A@?4JwyJz;vOi!fSLy;V6;d5Eh<bY9Dt$Fdg5!s(CF*LX
zl<<vgr_sC4_yWb1PK|AGojI*w?%ccN*wX-4)?;bus6UrvQa1=>iqC>ugs8)!0Hq*7
zNCZy_>PPnVTi~CiZ-SJgVRZWW^#{QtZwAfyBd{sW*j}7x<IxL{74Vl%i{(-|utSoe
z)1@R+SYUHNak=EQ#QJ-{jp!G(jS6+X<2L`)ZrX5S({BkpUYF)L-V#DEN_)7~eL7Tf
zcye4evQNDRy)NtghUHc}c@DBv9%SLE|Eu-b>sT%~>FW~*+&CEbPkTM;+-aZv>1fc<
zkx;+9kF90gGF_Whx^@XwU1+)_i!RD#jsOfdPM<}IIsO7i`!DW1x!1ZEpeSL@cYpzi
z4mu();D37=YOulaAr;<!74_2*ueefmFYa}dwx}}<yObZYBA=#T?8@+);Tt<Lxjae=
zwtrFH!zkwfC~0xinA+y1Lv%Ez9r^nCu~9~5W=)R+8?WZ7E1Rsa&H7n$!Q0xgM(Bwc
z8J+JbY&yA6_adaBD1Dr6C1L4YAx<CA^z=XreVeaVk)uf4nl%_tdZ1B#wy;ICdQ>$z
zr6t&|@dWqpHi~lKu*Nc<&g&_A1ZkEUwB|V}B<gq&$`k^6bZv5|q@CaCf5TOYQ$D3=
z5k)m~PBcR7RvXkCV2;`R+i9zJ9CFxF{mn0YeAV5&ai;C2q*gW0<syqk@w#4E^Ipl7
zaIQp&xFU#ShNSZA*Dk&S!%9N+(?{jf5lvcJwP5HQYNC}ydI8-(XRaAjDDH6Jf{$eO
zAY$FsQn&W~7*K)sOSI^!RC#fR$v*K*p-cK%-zJ)fwA8@_#dc$bc9Xg`(}o8k!zdvV
z)06xQ*ucezD{8Fru?5Y};}V1GbJO%*Wekax1Y;kA)}k>5(>5?4U=5i~RC}9NDAb<W
z<~$^h3jdJ(lR}_E89dUh{@AdxTm6}L-plij{5veNVM;CMwfo7n4_*{0e{)x3aLw2E
zXuRZ6gwFxAcT$&6HQlx5v`=h10B;rZY&E^9LVHqUn=#D_r*+Lnb${Cb>jI@kpdce9
zbc2tMFMr})`}J=?!7Rxf7nZ3gxd_b?pI|gjLC=VtZu#1l-hqJ{fNv2m0xV7w>Cc_7
zFH^D(6kawut$F>l#yx|C)ASt$;wH)CCY#(&b<&-?;?X3ySxDeOKmqHcLg%_bkZ`Sg
z*A`q#e5al8PCFT&3-Qe*@_nIf9skw4>UuTVhN;fR4IJBUwPJgInfO9U$zijv&dyAy
zKHT|Fee(6UJO1#`Ycb6j@&9Yf2x<liue(EhtD2g2vofEs#aKa7zzBo1jVql)?$<{y
zR>v^>QjVL!@5}22s_|$i0H5@9h!Ly=gtbp!DV{#yE6uOV=FgFqY;R~$w`Yo})4kBk
z^3Q*+Afj>?_Z%qlZlB?jwgGNA&FPE7)K_B_+o+%TPJiEJu0JySYm~X{OLj5oa;0_d
zV(i^j$`Rk_sN5>}$2)(%2&FOQn}+t)TOID&uK3+{QbGkZXnoYJM#si_HnvQhD9A9d
z?o}ijePUx5cGHvQ#VVphPxMWx?W15LcDF#=))=E)#bpX85{4j4pb+yt&K;Q3=t=uL
zpz|cy6&s`7<a?^}O{(9@qi``bw=4oXYNw!f@M3b+q@h!-B-+z{|9Ap&QQ&7nfn{f;
zS=UKqwxH7ZI-RMdqT(rhyCTSanuaZycM>!^VmsqPnC*#j6b1uu=C191Cb2t8!m4G<
zkn)r?5%^T*AA1As9u@!n%-fllyVp+f#x%1NWZ~a$swxE?io3|ONs}YX)Lg^Aqji93
z-uf$D9|mob(RZ>~RO$d@4VC2&DZI*8)ypYMp_;-{k)kFcKqscVIe^xgmiD3j=k*|@
zY;YX3u;mVrE8RW5>7#_KW#6`JqC=q*W>Tb6Kb|lLCC=aWwQGO+oV&y>A1#A{riMZW
z%>o2)TzX>wM^j}bWLloN2%Rox^y;{`uo=_=Z>6)|kzvTqcU*Dyx?&Wvx4tRbuvCW6
zQ<`6qzpPDMBR+UUuSSOd3Y+UYt<5TbK3KP?ZSpFc8#{#k92eo?dwzKOBwq!UA=<~a
zW}ANY?ftl%kX5%jr0Y=UQrRnv89if-B~}x0Ii%8$vFy-3QI})3yYE@p#_RRYbc`mq
z63fiXa88$dPu0*Np3mQ}efs{!>_ph^1%ut$`uo~mUI4C$&kqzmKd^b0u)Rp<PEu@V
z;xnn=eVxXspFYLjX{571Ls;0fA@T;x??EfYZuyaNtL2s`4vBTZ7*{L^Y=lbZL8Fe)
zsHiip?x%ylf*#v5(dxpSv;1a33GW&25O<z4-fW;Q_<7sfPVg!A4ft=Y$vq^@)&EC^
z=l7$^nWlq`LXjKIc6)!g@=)N>j$JCEE4Q`e&Yhj*jp;5|<(`>M52&YBZ@sc5w!evk
zF`z}EeHWu!i^8~>W94JpmLpxiiOpcaLiUlx0uCkCA@+Fp!O#)N+DNVRp^ZYnzsD|L
z9x6f;vi9ud<uNa}7KmqTGu@ggyjPDAsLk1q&I0jhh9za*A=guop2=$oFmLAaUe}!e
zwJlZw$r6I^UHTE}CW}AN$kMh98l!aK+4zXoywPNZbLE?VUx!2(8w4!SfhGy}vF17?
z(Y}7IJa_J0Vj&BYOGJ8l`sItdy1gP%aZ^K3T4gp?&YIEdS`JmBmke~Yw6ykve9X(8
zPfA16#7VS}`}q?~dG56E1EovzB9@~x^kzuMMOi;p_MXV8e>qVKx3TQ8@+RIhWMt|Z
zlc?_1G=1IOj}AtDhzA^Z^k|Mq)ccAGsl9_eJsaE_?5xdItK~i<s2QacWo-xl4XBqx
z3|rZkxT6b90nALNvn3z3@jOHS`oX^(1BLs@aSWdy1L$AS1c}-kS<Stp14boB-=mRx
zU|`@j{}s$`-qypw$jE5Sae$ZTpN_o0&TwOA;AwbEK8G#6v7Xk+P#a(A^ufg8mVW9&
zPQ{_MH)yYpOx@=6U8wKdCs!mQ_t;~vq0^h$$OT#M`<jW4qlC>*5lRZT;Joyxu%Od%
z6H{I+w@}&?-wA{9*?g*e>AlAO=1w2`&*(U*;z(}V8@>;fOacx{*4~PX)2R;Mqa0&o
zzomdbL|1KarT6v5?JlzTlw9K8$Y!qR+Nj{QF5fj@vrxk$GB4UwqfU21=xBwP_03~)
zQoOT;qCH$`sDE6E44g!2I{YOK%B~hvB~z-ht6i8&=l8GWXnU&ZmB$l7nck>mAJq9o
zQ^dUY{(b*gLDQ#ej9)jLeUWgP2^w><Yp!ipx?IfCzUbp)!Nn=E8+w`r+ph8qU%ED~
zFEaGucH3n6tG1Vpml*#2xGCc6*ZzeLW+FL7!rhc2ue~ONssX2iX)K&B?@cM&cXNna
zPQ=LfQladhim8#VMLx7jsmqO$Wv{P&ZFFdN@TON{NmGqGN9@Y#r4OfV-yJ>OwCgv0
z9I6E>3|70<!JsG<7;`zp(tc!7oaNGy548exr0xT)o~Lt<`}LRX6%He7&K?j!q%skz
z`zGlAiDxVqQw&MNbnmEh{kMUV@82{U6(;@UCjF2;48AN>^qTVLGq>+&wrMJ_xs>2|
z;S9o}ldaz&lS^sOA7P@{b>AdHx0Q?LWjreh#9U-yjYD2F5cK8zZm6|rFf47}ad3kZ
zbqAK0Ke>1Bo@CUgkx9Bg|2Uz%BJW;p@21LGOFwIw!@ABsf||XUn-;gHhPt85&6=U^
zIk%POo*q?)mcgNKK&yP%^Z`2D*q3>{sMLm1%BHFfm`lzw@_xzNe>rNbkvI+XEq6gl
z4H$?=pRR#{aOgf0VH=DeeHJ}e#rt8(@#S#)Hh8vvrj@qn2&3(ph63Dx(a)BO`QMXX
zBQnC;hr$7;cZ^cXuwB^)@J>h{pAk4tEMkuxB{k-Mb^a^cdh#bw4>(*vnt#zZ35SKw
z<SExl?HB$#0?YPm{v;mH|4pb8E<D!lEWapjFn1CPPo(;>sXPVF<y^rulo1(<D&~KA
z{;N!E{L+#$l>#3*8xI_SC>m6hFBD`zH&P>WHXVJM+UeK|xhXA!wI_v!9}VrAlVzFz
zYplRxHr;Y7G;HOfPh&Lt`s2MCV8$5_Pg><pqKXsKsGoW@Cg1eNjV`D|$P{FdURD<W
z_y5bHUroBMVukjV_O$7jkRg9TL@ld{*cbh{HO4BC0TlWe8~|M+F;~z$skj+hHPXPo
zcgW|1S`pNIKHsSoRNCvk+uNWnHb~s#uq&}?vo4X!sYz`vsDfox5uIjon(>H!Y{R9$
zWNbh1B&1%^$@jhF6LK&}{_P!*fnI;SwSr84O1hj0nUKEvgtaS|Btje#YKrb@gC?n?
z&`k>J`_RE`*|&#~6gB>pGLu!>^@6k^K~?L`9p$pUm*kJspWjVSghvP?LTv0x!iB31
zFGV3wUHH#KbJSP^paC3j|KC$f5f`}CQHd+6SVSxEgk%pyVO7Ot#3G$&0FM$^4Utg)
z*l)j{%-nY}G&#Iy5}J;ye!f~4D?%nzRK0&6ggh)foNbfM#3u_pHWo;t4-B7P39Uwq
zP39_9R8(xg!C!R#{nw;RIZMcW<xV0<3SYc}-aTeBs2!a1I=cUpopcx9fqsaZ&(nf~
z<}Q%8+0pkcX<6#uY=!7s*ikZ@B_k_K#?tRHU0>&bSJXm{tDO@r$&qLNQT)&*o<Gab
z*cP^CwCs!1NKFdXlm8v|px&d*_IXO;uba2~`xpED=TBMwI~f`Oa{4Eo{O7Mx+&BN~
z=$rrPu3xn3`5#dvephh%@zW=N{OU)a|HZ@0{=Z(JUOOixt4-?S#TYW1fraRdkIuWs
zd7beeW)ynimhQK)S1$}~oqwaBUp;c92B_t^pFbhrc-;L>aq7pv{QtY&|3elu-mvuq
z8PI}J8`5kZQmE$BbE!lUOUdaOL73taamc|@)^0XyT}3*<(8hh8-=8RzWZzvu$hR(&
z3A~Iny<`w58Myi8jR;g@N+8cna17LV^1mvO@uX1WdA{k8354x2y-^N?;Yuh(?DD<Z
zXVd5P&T=8;Nd?Q_%c7K0q@asKD1VGJ_C(1FC2vWX(2bS4XR10EtT8`_(~vmYTicsh
zt5ONv1etA{NoqCZM!^m14t?hATG=homS{e(h$10Wa9N6|+mjY$%sc@ZdGW#p`*?Y~
z>eOATapW#D<b}q#6<q%6iFq$1H8wDafi9mW=ITHipH!IDukB2|0Rv6C%h4bxl@>_e
zWv<|}F`-wyO%Ri8-%nhyzVU9$*{wS$jCROOXZN{a8l`jv(?y}cvguCSnJWpYsi{rS
zHj>!$?c29v^g-56zdI?U2KH|M=s~uEmyP`$&|=XNf;W1WF#zvWY&&6Z%e{#vpD6~i
zi{_Aa_92P~q0w$d?ZptN%vXA;^<HH#A}OgR4P%T;K+a_ySVplMx(+R07c(0y|J1`8
z5XkllkEfJ*tHhYblzqfK`2zjv3hfODEnBo>H~<|0Vt^Gp_Gb*OG6IKhYC5Im(={(L
zOmz~u=lGMF&J<e8;mK(X?cos~i*xZl`@$%&Y!aoC5tvmzOG``IZvIc#>e4{iEcWs7
zNmpr&&7t8TA{{)QMNT~dL_Kr_*7rka<^>_NK>Csa57owV?;Vd3*&{@G(Ht^u-lYAp
zdweRtjc4-1P{OyiS34ay!8VMsK04(^<bB5`Z;yPDx3(5sz=c4h5?+CQe8Yq0mR?Y@
z_Z<x?-=aMJW+^kTAXC`_<$UgyBcL@LdQNKy5rq;Aj_tJ@)CQHL0}XlX-^h(~ks&$b
z6HWZ?w26V%WA)!>7BmjbMxWZ@w$$4H{<MJVQ?j0MW4<lZd3t(!0-=GEkollBvksUN
z7+*kXhlW%M23D88JimMCcRyPYWNCX8VlrpG+03e5ogi&>dzLO)Lh~&E9@!jZLZ&aq
z&|{heTax+f-2HQ(sZ_#jy2&@RHsyNy`pttpS9ag(n2r=WIIv8;2>(h)=@a4fth5*U
zy5)aHqdd|Ehs?fWdAlJX*hy%LhmRkGrWW*^xSZ;Qza~BeZTa+zO6WZo{c|O3yFc{r
zEkHD-?O|tQw&>bI*oV+jWSPRW6Vg9G20Prip*7CDAo_I=8&Q2-YUOVxfQgcVXl;mM
zs~@~}fcHu%<3azk#4{t^IkpMlK^2Y?+W>V@F(U?4P1%zt^ZyywEDMNAN=jO!q6=9X
z!j}SnO>9hFR+dN%5xG~0(?M%cta-NFUg$XqrtLEGEVl~NdWFIqs78|a{|51?1D`u3
zJ&S@nIy8}CvNE~o<~whK$_;Bq3l2TfobS<PMziIEcv|nN&4;Lky3}D|cp>owO9Ws^
zr!wTswi%9(*A5T~!X89^9|n!d+;xCTDQzvwUE4+`c>#XFK#+MP{%1YYW=l;oq{{?G
z9F;h07qnDnP8Vf%ffZp>Ae6(+Yt$i5SpVti*2gx<Maa?$etjqeu^BvK>c)Qfob=>U
z1#`RVZhsfIF`av;txx<|O(`DzWFGm6>ELoST3<+b_UxINIG^u60+4z`?VjzRK8A!$
zSMQ;yDkx@HnGdC$mzC8}F>m+i05YOaLqqeJ(X-%kG3FZASV4M6@N>!(*g+F^G0B&5
z?)`O99V4$#dgIE)3k+dNd4wVK?DUF^`?#J}`>pSau;TS-G-DdP?%t6Q6BA=5zSo-<
zl`EkH@aNm=AP=2ae0lF$Q(|U4F<k8F^Nn?1u2jFdajv_!cjNm<8i+$X5b!7rpJ9Ru
z-9BM5+T4t@8528~T1DOeCZM{NJc+m)<SCeO5;5Hltg`lwzwiG=LR~LnAFtX><S_Fg
zlp0SJD#L-I*bSR^1=*hvuz$S!f|?Mh)|&kOoClL4B8t*58SI2rsy0MF3p(2^UC+KK
z!IY_|ZD~;S9w7x`@6*8#^6`v)pgNxO&p^-YFxTw$tZ_2$WngHW)&NL%fg$wF-67a}
z@-buQ_*4m?T;}p0Ht0GS4}FQ-lFB*{wUxD*=9l%jwFYnt!Fks`3kYDU931zYb{R{~
z7>!D2%7nc#B6ZpP7ORnxHRtyrY|y}9?GC?#M#TUK8y$1irUZrD3cQHI*vaW@iI$-j
z21FXdb;MbC#*Bw<dH+zI66gNU3S^$q(3Z&n#NRb96{1)=XRy)hdWYbI_%K!Vv~woL
z8L{_J&lKtS)@HcJ+CIzOi+Fo%i)|=T`$%bPf`0pBD@tlA;mot-L$T)jjB6wB&an}@
z`>%IGU8SPYAoJ>LrRdKi@AQ)gM>;=$*z_*Jp%ZHkqrO`)DY9+-4Jb-`KRR_euu}ll
zqmU9!_Je~kc^yJ!lVqUEbt?n}DS*J!nQYP1?8#`5P=Df&-0|(+;?8C^wxAnuEOS}4
zpPO5i33@{;`4=s^gQwC<k2SBsh~8C;OTOD=Qk@sZl|@a0XzdLMw*%@$hcrcuY~%tN
z$L|awc+6t5?iRbqHp$!zk#vn1VLcl1N2g1NroL;QJ6HCH>`KaEjY&8mqSSbsf2Ui4
z;{;_KRK_lwGDQf$i+0l5=UmDFhkr_7CzFfsigld3Q)-ZFCAR2Vw{2r)J=lrXf(e_l
z`t-_1WGZ5>J>93s+S10IFZL6*wHfmMib;Mm4~44+02sYDZx6#HLu>n!vRADcXU|x$
zRINUJD(LRv(1jHuJ{2O8F^grsHjFkb*4uTfmeZnttD1<U_QI!jH=ZZ2KC)}soz)As
zJbZZPtKT@S%2qeNHLl_f<0AqBE|!}83AJyQS!%|Y@<+(6J+yQG{@Sj_<d}LtR_T!Q
zN=0%lOY4UHkQRw8TC$S6(V?R(@M1{*l@_iK2N8vBIO)Q)uhDY*dIV|-I{Gu5fr1ua
zckSidwk_w&=;%I&S@clsj)uuJb)Oht3Vu~wz29RF#SPlp+PS66R;=LZt<X0xFt}rH
zzwS`}<IX=QSsu=e2i5M}y}SP4!Gntf*dvY@2}Q^|86BC7TSIAc^Yx8@H*4sN1Nw1%
zhg(>k`J$yXGJ!(Y!+8Y-1wL4`l(s+dSoT{@T*C`^XgLGxBU4fn@^OA?9Y%=xZ)$2v
z8$ts`I9l-O4&~?O9w+kX_39UGCN<)WN&|&skma;Fo!Q|A1Gm;;@`LsGhvAWtqK(YL
zHcG_fm^eq3!ki2bL!Y&$>-{s==h%zd-@bOsTSwv^C0SWn&0C=vs`D}HW^U;oZ*dEV
zKa?=}VvHR4fY+TlGHGM0$?`oWQ7t;|+nn-Um}7=o^F6ya^i6rj4lbgw9XWRFPW>zG
z%8;Ih#0Xg<GK4Q-=+U;54;;V4<lgxF_I_GiW@aYQQgqO5Yl(n@(iIjK7CgaO(>AxA
z=N>yah{YM*x_z6JB-)&eu3ulCX7H|TZjm8m8vXI3YJI)OMb_Pc)m&kHop*+h1|76p
zNG32$OiZL1s;WNuHa=)j-6ZO^!`o>chl0#KZ(Q<s<;hE;u7Cgi_YL4HYMr|;#J|<^
zvlZqn1jx75)O0*;Xso|+?Z%CYm#5+1-9g6PKhTnyxp2`Uj%f8OSL8pg&=4E^OV9W$
zDth+)jmf=`x-Hu{3#7yiVtBI526$Obb=~pY1&fyK+PrzQ0pq%LFNoUJdV`|DMRR`_
zjOW#)=Qryap>flCmbq_=LQ%w!7r*+t+S=NlloFqPJR5iJ*g?PE*VZ5n3dtPwilUy&
zyD^Q!bAM@JVa0HJk%Obl{rm5ul~afFn3$MIqYX#&wzW+$Bb|PM)Q9^Z>&mFDOb0ez
zP&wn_1;`9|HqtT))P>XD^i+?*C@_WKkdVh0^g(zsHSVcm{c_&$!=bHPw-)n1dzG3R
zait8M8y><n+>Q$e7e%|zrffNS?|h=O%ooAzieNDZ$HaE>{A=Rl<9W3!wzs#noqAo>
zedFyvf5}^?xx3Rr7@@gukM6iC<fKGyFYy+XYhu!i!2O$(b@RaE_4!~Nsh;nwgM;TC
z{U*b}*_xT6s;i=+qS^ZLC2vG$hYMcA=22YIw`jpx{&b7cy}DIWek$n}1tldM28@6G
z6_jI7tC%il?hm7wly}A0$Yaz`B4ZctQi-epLCfO28hR5^DO7^n2L}W9mlhXS-CVS^
za2F4S5}t{-r#FklYaP$)Y)MWv?%lC;>C%YWUWmWN<+Bf@xTc-#st9)Q^Y<Ugi;IiX
zl;>QReAYYwZ=`V*VZ`!Pt30+>M=L9mz@mC6J3HIm!^2}g&7Xfhba8R19=V!W>)AF%
zQKbHX2EsJ+b*`eG{AM2e#=hUGLztUX6{mhO!O+a?Rdud77Jz4?DT0dfSCuc$v2${A
z8uaz`MWQWI(f?W0nB~%?_6s{DsuYq0;C%F)%6b;d=HaP6B~SI!r%#%#A3uKlyQGXc
zaMlb^vKAVdxHnEsPQsjoy)#qnZ@h5ff~Inw*z3m$2?;nh`qc_)n1v@JyOf5p912qb
z`)9Cs6`yilS)pEJ{a#Kt4wAs(PqDLooetgTdN|&aYC2d$nvZEV@rR!E=f7@b<c4#*
zgqA6ijG-uZC|r?~VLRFjfSull@2Za6wELq+m#P=HtzW<10^SXFgI8NBC~k5*9u!f!
zsY7<t!-GyIof#!fOp;`E>vb}@%8V9LvT7zGZAN?REI8bmmzON?5)~7ZzH#Gay?RDQ
z1`Gd*nTdb?`A4dAG3D@?J<v%>7>d1=^T4Srv+Qwg#-{b_wa0QH46GZMJhQt*A&DO=
zRVbyeAHCy@&-3D9aZxE)*c&a@;~w8$X{2*8!3=hHcQ5{|H)(B0JuLC=ndJi9+!8Q+
zKD^ql{H6nuw8GO8Q)B(oYHCj_Dk^SFPZ*MXp2V)8MTfz+cp0x?UpbZ0ZcLtNdw=Ha
z_n5xE8P;HPqA63dzlCmnpqE!D=3^E_c4@c0d$i%;^~*s+!5=nm<ki}|Zk=bEp{8cY
z)2B}hH8>Re$5{^kc)E{k!&btU9S=H4;lUw9b>Ce5<jIrfmX@Af3}<|J_iPM2XcQuT
zeG%o!Yecds2)GT#>fn`S`~0S-^bnJaz0{nsJ|ra69Bw>Zmg<xEz`{i}0xSGvHw=py
z>CD>>*U}BF6cI1msG(RY7oT381Y%(G)}|&UmhIc={z+&bU+`lYXHu=8`W9x;lChYR
zflFLL!G{0{h>BSc9!z<6V17&2q?#I7Llww8up1i<0^z1%h-57w?e_B8rz1pM66^2J
zWc^igpFy({(wP!pVY4r)h#F(wwR)NuxIYKYbDd>z(Xj1)m4?BFBHc{SNa25N`RlQa
z3!m%|6&2mHF*PN{V`5z^sztd>IF@ub2AU3jKrE%{;<TRU#D~|$>?SHR)_k)<dZIHE
zk&(MKE?<6EU!Njg%c^%)a^8G<gof@SV6%B^f!j{@UAscXos}D`-(TKafRrdQG%8lG
z?lGc=9uhsUr=+ATUjCNAu4IOIMR2+{jNJx4JumF}@Tw{q@>2{ij#5)sAL)q1RskmM
zX@hZ3M`|y%aQy@8Ch-?q{X`NmS{6Wt7p%s1<=3gAGl%??Sh4j;JUT!!2IsEH`kvG=
z7(rN7cOB~HxPUT%Vrznhh{q6YT$dJ2miUaVV@%p{k3=P9We33>fbBdHTO1mAb#%|3
zJ?1Xv=H^5efWGsw$zW-w!8()T`13Df6C=N?Dykq#IecLff;~~VY*8r*iH9NL-cp+R
zN!3hB9%J8JBXP(soH~7aZ*LZhXsSuS>vq`1b3|7eV~fc65-r?0b+&NcMuS~jw;mxA
zXzSAmvWgV+Zgft@#n*`>;KCm8hYW8t;ct(PjpgR?>DGv!6aM~EKeaze&7nB>LE~VI
zCdZg1g)$MT;*)6RB4I5-?#;sDb$uP|*W3JeEgZZY98ogF*?bTjDdzbGcU!`Qw70CS
zw^!FtUweEY`?b}&B-6%W!CP}@`3t7MX=B`=E2r_$1bP}`bOLujh#LDC{<7BdG6yWf
z5y{NAFO!pr=w{W6bAd+!$G?0jhb@bMkMj}~QqOZ-c(?cODI4uta%ilP+GEah3Q5Km
z`o9@*aCWYN*w=PjTU+2ksdaS^8vf_Im|?fy%flmP<%kb87tG|D*@LmQ>G0>8E@VC~
zS;9!4iw)_AW;2xQ_7ti4XV<Xx{J4t>e&Zf@GLI~6D1E$H@p4dob^PzoXO~W33X+-X
zbjxwI&W8H>Zlr^Idi6KJF9Hg`pk!~Id07{WMh_1SImP|mKyj+Vq+e_o#XnaQ)Q~Vf
zrYA=Vi;HFS^dgZWK8;h)FTA{0)D^Dh1%jq-C|0u$PpnOBxmcpmH%_5^xscAQJH!<9
z?MXSK-FlRn6AUlO^DDZ}76wh{gFxqrIa0LW5b>*14N@>BBIWd`yPFJh_C09H9@AIM
zI5`(KD!{nL{=H@mi>Q#0kOc}^MOLd6``^fF$8Z%WAU?SJbj%yeR2Dvo2}m1wwI0^1
z-(BmIFzBgI9$z-$GeO9DpFvZU;{LmL?`k)^)FwfuPHi*?OyxeEs#DueIurZ^aNgF0
zr!Vkq74ZkgLHRME-)D4c)G*x7uB<2cg2VWq)l2VoP>S1T^CvJA@@{i>!hx``Fse}@
zm`nBB`)f+Z>J3}-x}n=u+-sxe1S30RUFSMI1cP~3WwBC&@p51F{-uq>#Os`HMJoh>
za7Xf(t-jpE6)AW0x|>|&UD+C*@r9#oJ_1}TFE3x;*yt<R(cK*er+UHK(dPR%Zzt2f
zI?2uVtTQ){SySvd$XpJLi;!)uYiv}+)p)qLoQoSd;j|$bVq3P@MK}<0`;N253^uH=
zoRylhn$#t%+Yng2BHVG_kpS7<&#qsD(9xUc%IO=9B>r+jW>K_#KjI+=MDSYE6GMf7
zeJu69e0+m2LFFcw4M#y{S>D^X?pii8)~Sf%<f7bco2w&bt1SOeBvDteVf)osds^mb
zUxSPkr5e`$46|=vwG+k}O-xL}5xm_zplHMV!}YYcWJ1??%b`QPwk0(fAcd_nwR_4&
zgc|!owCi#Ggz)U7OLYrZ3w#I4>$1%UwrmlB`qZw~@(t;duCS{Pf*l%@MqK@-{q2b@
zVl{!d1O$Ggx7b<_23bkMa5!Q>0A5RT>^#ur`_7Mqg<#aH%yA5q^)SQg*$Jn;sT?wx
zahM2~MAEAXvq@V?WtAxtBnx004Cf)Qd*I^2<(W7asGm?%Q){;6QcMyj!wdou(tB-c
zofv9etOmeFonE?T>S<u$#URsfvA_H!gwcxgh)T!%>FJ@)vOuoYIT;4#oA5vno9|h9
z6q+fX_3GH4FD){VT->8&)*q`wWWVCAhlzoJQHNvw;5t7)KRO16^IDNn8M?_N&{oy8
zl1olIt8rkbI~Rf@x`(sOZAcvPWX#OW;_?v-gft`@1QLD2;OEcV1Z%>icXD9fJN%=(
zu{eT2XJ=>Fuu0h%k4KM!-n}~&QS&(+jLg~C_6)6v#5xqUWhJj&i>*JfZQHYBBQd)J
zT(|GvpS*BS-;{hN4W6{|%(OX@GjBoq)vG0*s<*jt^_4zBMEC5Z3mXqlWzRl@ba3VS
zb^n%bQb=kTEE<8+jNK;$6;J*ol@;ywM~o<oZ}io@*TYzB&4|tbe&f>s9E&y{;^(KI
zScp=+5Ah_om(ud}p05cxv++BGM~8=pU-z_jv?M1RwCJ1ovWX$zmFXLLU>UuA@Gp~r
zChak?2^@yC=8EjpSldGxv2=S^m)CwFlRht;DvePq#r{#2Nuex~H6Fgu-IS70Kk>D@
zlEo)*wx)cvr<xrP=i=%qAxf6q^3;DKpXZj!516~UySYhk&dbj~3G<}c3#Wz~)JtrE
zsJ$-UK`u(8=tsb+BB3Gh<B@Qyz5O#`2$JDw!I3qZogO)tFl4u2QQN0alA0;lfonG*
z>b$*V)!wP#>xU29k?CzqR&8+asS^<q(e%%gmX?m1x%Sw{CmhlghYa-gdM{6-eI+&1
ziC@$<4wzJTNNo@0C_~mBk!UughKf%7F<ZE#s|o77dUdgf#U1E5iq!-J1*?+{E+*th
zez*;B16)lp6T3cbQZ=GEl0Fm`e)*K6ab>(LrW%na37R50JGqhyZGCm_TMG+Qjm}z=
zzs>`i$MMO$Z7$g|OUo?Oq^G3PlGu6i3Q9(&&zwor@rQDDH|(W(D@ZY6K(XawWJz3M
z7d3R@gOY~ek2NVzxN@#K=Ay6x<NEbvR-MAW@ram=y&GMa1Yhr##;z?q2rsvUhB4I3
z%PTD(q4!gS8WHe48u59dlFy3sCdXT!PJ{V$T2!=P<3(^_bedinJ{kf)h^O~sJd)Vs
zCr&6Rp)dg+ZhqgM`OI7?3f5G+{D}z5x5{9#bqVUPUcI9GhqX_vBI3ik+Q<vfLPJB-
z4Dl+&e{OBP`ZEvs+}|ICywF=P2`ViS9h!LJ(DBrC869lNEI&W?-IhQbddRIroi^;-
zYuN`dhwc3yxfqqJd>QdUg&sHta5Rasi{$|<Rb$f8a2}2x!o>5BhLFhgzy&uSU|~59
zq%!%Hv&^^e8EFKG`A@PA`-wVBYB3XRiBB%=E#Q|m7$Y&97Z4CYQvITB$6>bb7<HCv
z!&>uOeAQ}JU$ud^ywHPvEzYNAFR*w)h>e=J5;O-%Vs5GExpTD}4bm=+)jbz=d*Cmf
ztgnnnd53`>!@W6>2(e6K-(xu&8yh%Wi8v#w0hC#s+-woi^x?xu9%yFC6Gi&K>902M
zCgc;?CgE)4Wc@G_UE&Nhh!WHOjYQGB@ZG!E1A~y#kx)`nYJP<+vVs4GR#4nLGL)Tl
zb5Tj&VCHN_d_qE$Pb%6k6H>3zo1_)pquCUWI$ej8%cesi1StDVougf^VvRhau&5}a
z?%-tRHe383_kaMEKEE$tt^r>?XWp9Kyua<r{He4N)ezq4zHJd@`CrV;2C$F}uER6M
zeg_lYO?Y`R<g~Q3?4ChkF!oX&zz<G{Epfj#>DNixkAh}nUAm=dN~9?uC$FgLL|yr9
ztgK<>bx4a^$?Z8{x+E`xI*#Zy^z@o__4VE*-=Qh`z$pfdj|<a8NF)r;lcGqvnd(*A
zJ3Ft8Her-osh&R0yy0+L!PZt1h$NVCadB<ivgOTZ{-kN4EIF!m!miqL<j9fYv1j+!
z?A!qNbiA}aUPJ6=`Y7Q@n81S&ACh|>)vfxOZ3V9|Ea1V1-c>o=G|WPJy!uUbdMZG9
zoB$qd?X*JWOmhgh&K~3$4;*K4@g>;X{Z9Y<^QS{6Nzi$9Yl`&-K<-d&>*%205a{lH
z83Q`^X$}E#w$C-D0yXC&cv}iO^RJAC8=l?8Oy{#`SB2wxp5;~#ZN9u&TEge<=f|jy
z<FQ!C5B<4y2T&QxWkF?gac@he?M>^ih{N|0QP$7G7;>NRs`*on{i^-@_a}_<-{?|>
z&T`&v&aGRs8TRWd-E0CZYcQJf9mOC%UtJj5G>}y^%{4VOH<0rr`Qq%RggO9o8-ZrX
zMX6JtH2vw3V3Q+9ZhdNPl~$dBH7c<=Xa!ht;kt{Wdz-CB&P3G4|1x~Ag)32%PTGls
z@~#eas0&I+?T0|`qnkLc160%z_%>bYk|`m0gD0O?GH^f15~@N{OD2~u&S=Es$;csS
z6Kw=UwYXy%c|~rto42?2%d3qwm>Krk*vTMcGEdL{&YeG6vAK4YQ#yj|nc~0sf%0O%
zaFS@6=LH?LFaAu7#Y=;h=G~<^IXVA0I=<A|MD<?s4X+H<gEFt!PW303jQQZa(@!3n
z{LJaRcO?*H4?sTL>+juV`byD>#CCLI5>!A$xX-c)BtaZn_(~}m8FvKLB>|?!#?>$d
zFr{1@HBr56SKI4d*moXu0m<2!?;LsG!LcQ+>dTun!x7f$)I@rt{_+eqcww3Fx2|U-
z9IKvGN;8whT&+q>L@H)7y?$K^I~a)AT9|SGd<mT}2r*=U0@eC!L=cZiPbdKhlG>cU
zPfx?!?}_M)fkNt0dm4^w4!xk>v2Wgd`2PL-51gIl&YpFyA3^v)g5@Y1TeV-{=qv<*
zEHL_Wg}^{->ob@Gwtf3#VNPX%m(-8IX;<O`>Hhv(&2ZJ&_Xq+JoZiOCc~;PJOa>n}
zI|T#;=vJ@Zj0hW0#;(14<FiaFU8(il{^XXVS8{;eEXD_-U_IBKe_=k-aT-VymJW$h
zall|rlrL_A9e*5A){6&NfwH~Ym{-ANSCsW&8K1P~yY!;ip;^|5qzjpC1qNtd9_6RH
zSI&{~@miqZV#ba*!a7*g+&m;rI(};kMHbViEJCHDqQVh$j>N3B$B%yxlaEok$@z>z
zsXA6O;9oP%TG0G0hgO&&Iyzd8c?vJiIZW@JD|+e_HyHE$5w%0(<NI+2<Uh}yPWR;a
zS7$m=dUt}KQAp=%hQ!nf3k!?;wQ)bAQ8MLwC32#O@fg&F*L7{sSvI%9)sCG!xz3_i
zO3apV(<Y37G<$Ze<=kL>LjISLkpv4TYzt-ClVv#It>BZyT5Q6(ZN=TP4gI)N_JR!Q
zszBVOLLz-&bwu95Aier!ln0+ae}0N3?S>_kM11GqXk=<yi){HhqSf=&hHH`Bx!9ru
zwIS0s6DR=RL4<$nYZ0=LR0mbX8d>8%A{3LONq7X1TnWQacJb*Ky0ZdvO$=98y!68@
z5om#i@c?z$slLAc@jRG1QP2b;GG=Ei4<0(?X}0NC?jeXhO%0Dz+8?V4M=bf{k1nK;
z<tT7e0Rs3t>P<#j1}!C&0S+32j}u{vVrITi6_rL$RBV8qCW_&B`)c@9`KN;~0E#{-
zFPAtIAaH@;=6j<tAo&{#DPNKKg`4y@$T83E#k3ZZ=YIpfN)U#vt%&Gv;9TqC{1FE(
zmgampl|XV^e{7G(1>DP1TxcnDpWZ#%h5@?~X9EPnfLs!k8KeNo7?t$4&!45<cY}S(
zuJr1f2-@STU{5{#{C2Nfw+;!MI3RK<w;g8+Z$#nhpm-$q;;g?EP8l}H3tU#1<@iAE
zOm_Nns%4ikL8j9H*|qGP91kzAT|0N~+^1b}>_e(4*VvKRh)chqdRRp*gnj&06S1zY
zt_Wv?V41>kcywU%yd&RW<uJbI?AaRF2|Cz@w{MRHzjy(Ast_(86M$upL7m1Sx2xtI
zvQO;6MB7;#oXdAGTL__~;2C0GKN6EfqV*D)!SRzP*#!la5$EXC#|M*RN*cN9=QbSy
zzk`o%QmYmLIQpNDEb(rxuzxl2kzbHuhtvAcZ&HeZG5z;v+^8M>_h(rvfeifjgXjOg
zAX$?CyN1+_@qf1sbs7FoJt;J6{V96H-he+>^XB$af^s144kJbD=JZt5eA)T`7jC@=
z<!;B?zP19lhNP>$N%|kc0i<ff*PT9lw$!q@tn3`VXCLJs8X7`nzz5~RXH`{IN7P^6
z=zdBBXAtsmj*?E|e|>~=x?N&8aeSv_r5-3?(Cl+2uUuDGr>0)?wA>2je1ot}T4{iw
zuI%)kWoy2I7WxhVxlHeWIk%xcU0Ny&2<na@Dh-#Tpl7Iq(HDqucr4T42A2Q%XE}yi
z8GnAe4CRG3!TpfJ17y?%hy4h%Uql0PMa9LdsEI4w;dK*}=q$&7T`9VEhY5(ZX6M;9
zr>W827YOSb^{khduoFZWFXdhkM<@s&^yfytYr=XEAQUi-hx{?7HEW)ASH9>4Ri(&5
z5o2tcEe8b!C)?aKT*b5_6_d{bdJpU3{qOrNPXLUIfVV-jx4vGFh3+FTQ#j~e;M4CB
zB#<(4Hy~d2gjSM$K5%j>*|_wNKZw`bAZYRO!i!<VkUf>>gsb6>_4#IzmQ-U2igwQ{
zvj2QP*+rX5*t6h)_g#Lsdhz1L1*N5FHNtFc;o>#_xVY4UOs6$ghldzMN>vB1Af5XL
zD#J@i)QU<;Nx4!h1dFM{K|fy---PeS;vB}+6j?j{@7Wcf39|!><7<34c^`(TCJaHA
zu?qiNUbqBhNo+R7=2xhw4S)F(N*>8!bJen?lqcWRfn`6@q<%m589#~f1_|XUbbQ;K
zMrx}b<NYJTavj$BI_*5;bGbV02Qwii!9q#N5B%GwOoA5f=FT;E+I$<6wca1Wj*fbw
zckNU5;@sTacBjUd+RxyIWmQk0z$_5k2@df0xE6fop?*L289zD7c3xVsxlix)2EDDo
zIFKOhx72(0aEn~YK5y|k7#}GMM?%x@t3$q*v7z&kKYHoXr9ym=XZJ2YKfh;^6#X|M
zCVdep^<O70u~<S&Wz}7KdtWC|ZN+$nZLMZn|J&jb+PsT5<{sfR<dGt>PoVG&u-nt;
z&T$#5{Pz;Vmtc{Cc&?OsIRSK0csjXiBcJ>2+ee!Z?B6fHY}qmm*MB$Y;TRN_y}ABp
zxDQE&;!;yB?|1l$??`$5nvIl2)m5VKTDydVGL9J$LJ;3sQPKA}k%ilt-IXvSEKVKe
za>4)oQr3!V$Yxq5lItJN3<ZpG+RAa5jdvEA+djmDQk&EPL_TnEV0W{Uu)wzwT@X67
zTlN3)f-B#@f4}y%(A|#e|A(*dj*IF_|JE2K7I2LM0s^tnTcipIC<;giVdzDrcc~*?
zqo9I-QbfwodzCtL6a}Q$VdyAGo1qBIP=@wC*WHcg_x|4Z{*g7G>~7}XbIwz~<vAKZ
z=(YREif5*$M{z?J_2WrONuRw%RF84{qerdNBe1~1I?K%X2D_G=)e-pFTga!M2Afz+
zjQ*sVxw-inZ<QbaTi7DpN2RQK1vEXB+G~tZj=k?MWk=~2G*NwR9VwITgO$v3L*5J8
zG{E;H;|(wRQ?2bZ*BBsz&!#X|(-d(K2lTwTY_{42C8icE$A7*WT+<;2k%=8tI>*`p
zy^f?>_krUjgAj<eN02|mUw0-kYMtFo{wz4cvnR5NGLhz)SlLB)(?ZLt?cod;m$xO(
zfd>xgHTjte!S>Oeqaa&vE4TpwSY_dfKVP`;2yd*fzs6$G*`a`(B_s8xqBN%zSb6vS
z;q4FoZ{OH7Ji=~nJHMGc6ZBiUxj8*kT#VVAKMd-V+tlwk%apS8h-@FpcHn^S)7?5t
z&Roh^u3WOGNM!v>Np|*(n``Qc()kMu{^Lf_Q)UBl`9C(B#(0Cearf85$-}M}if*oj
zHf0iCcVXvd>|$+KsU5VN$FhBXhtfCe7v!_TNO%WPz#RDz`T>9e7iVX#e{T0{o=R=)
zJyf|TiEY%cT02DwC|B0dAFWZF47GpOgfw~BUWjPX4%COtqD}1q8M4&`DB___0PWaf
zSy@<27FK><?T4+FW62MwJ^eA##^_RQxtVB;az}r}+6G}=0Lv{Xoqj?P`E3TsZ-XGl
z10v>{6<`|Opm)lhbs8>hu^)xMEmr!6hniDsrG>%Ee%w(tzE+i^GhP41Nk?tvV3wtH
zF(-Qg0bPaQmAEkWIm$^<>jA=t)=!;W!I5zMbAE+Wd|q8$oxC`LUnq*=(KQgk|9JwM
zh}MN7(4KwQJN@|e*YrPih>)hrt*O1xnAZy-WoMTO)`eUj{wn?Y%7idyXefpz;|Q4)
z8>38ix>-xhS;AP<Q@0OPOr>ms$-KQ(@LnW%uMEg;7NUC~70ippT6QqX$;nNJfDR{X
zX{<Wh1{795o@+Me#3S+yWhPQ)>7Q_O@wMIa*&m=L&7SrpFV}TU-`Q7h(e^!QRqL&+
z;Op;%-om?<Jc$3asXa#7qLW%%Gr_2L@Zyk8McV_4mxpD5MbgsIWgr<4m`WujrQ5c)
zIhR_-#*6``LV%r}-Cf#q=Xg0dGLLmu+#7@Wfw6)XO5+nfKsmv^#s2dEH1oKG!qwnV
ztPGifT^_rzP{Hz#P)aKx#iPJlD3zl=z@n)WdiQc&-O~~i2HLzY=fNc!6ZC?JOR3Y@
z$t;vMalM(CCuzGfD6{;chw#`+*j<k)ggBZuXa>h#8x>XF4Ge`bGEyFHL_+c=u(w>2
zk|Q~lK&FBeET6Kzm<uSG?YoBuk&Qe1f`=aDl~SvpE>b|H6t^)5WOy#rG&#V?26V=N
z5(e}yiDK)ktG&oYK}(fC)k6<k1vv2Cy}m^TOs53UR^B1Ylesi?Wjqpm{eSqgs&@AW
zBnMrMvqkMVjK$4Y5+8q^CRn3tf>Ub~&h;9kN{9|b9^Nin9IU)I)s=2Z$WGiKL}M;{
z`Pxe!7X9|tmKox0PD(Fr>^`8g6i{ve*PK%0I2x$=?b!Pr-Ge3!5m8a;07_XekJnOw
z7@wY*$>>Q{K(3YRhA(eg$8jee@G~xuH@usI=3Imcrk>jY*ciZ)3U7CehKAoI1oY@S
zAd3!}oSeL*sHv$*fNY42&Vao<v&#(RtTm`MDWHaEpHat1pn%)w>jA#=@8=<bgI82h
zi|&c7lgjOJvk8ai1AO>R+ts&!;5>4N&<P4#5{a?d3~e9CYn^9vCk?&4t!FC!Wjy8Y
z?k>PrBSoI7FT@E*nh8&B+-Eas=N8>t{`9Ny{@xL(t4RSYbF7?JRNJ`*FeD|ZIQvY%
z3^3;uI0c{rVx321rF&fG#DIl3+@nhuL9gTEHxwl|7kHNw;5?{?`hW=poGz)E#ajg&
zI$>9@axZZ=fVSEgw4~UxXrPj*S*})e&$6q<XTg+!GvZjsu__Q_BnrFDX#mEc93ILl
zQvjbd_uqA`2H?3Vsi{#B5oV%j(p(SX$;g*E|M$0m;Js+n-afFMOGC^J(QIwu)5~4y
z3q5T>>t~?#6x%bXrg}`6=2xW;f1cnQcD-H5M~$!SR|#HGqw(-ssG_5wCuSgh@Zg3$
zJlMxZak_^_9uUdXUku(7&5KrI#4Ej=aOe5Py>AOO1<nn>^GY{>`Z7r{t6O#rv^>6m
z0wcpa5zeh-a<vUckU`-B$t680=?@XdQ9S@d!eKHaXnTP`1eh=~JUnG^P_MD6iSYUT
zV+4KyvZP}`=){dzuU>h<#7q)$*ECWBg9+gRz;kg4SUw1Qx`+o1${hElQ5ozSQp`gX
zI*xH2alqODY3;N5Qz?~n#PGDUya4ZLVZ`&p%Y%ODPx@7)#Qh8Xh8R-k&4#(&UT+lH
zyFqrodLAXfJ6uTpw99~YqQq0GR$BDtLGoCeaF`cMr`N_zXjQhWma1{7siu`ya7fq-
zbzFX;Q;(`~tZnOC(Hx>07D=4$D81w@(DVyiw4T98`x*q^eM2iEDK2gUB4frs?>vNn
zlnD~UPdz<FH=DaauQ^`p4Pq$?1Q`U<T-QX`Nm1$6t#0VN$j~>~Kxc)JWUYjLZVddV
z8lq<`wdq5ocXNo8<2xjXT+{`{y4BQ)pAN+}3ukt5<H9|UYswoK?w*<sh0|`k+f1bH
zhcjMp{awW+2cxwDvpS1iT*zCLTO9_zdJIFAbW_9%aYDeC_l)>TA4$T7y{2is%=nM6
zsP>*_UzcKG^+M-q;z0kG_f<vluJEZj1!!+u!<(QN2a9fmuU5T5VB<h0$8PB2+&)l?
zeHyEE)1{YyrW2wfB4Po}j}0hu2^$msctn!~_A(w04h6Vm1$b*joG88Z^|ZrSRayV6
zWj@6HA$vYW=<t%k-(gm})+zbFf_l2Z4%^29OZWF9a$QNjd_eAFThDk}r=Pp_#D~fh
zEpBbcf@D@~VmDL$Q=+U_`Sj6%Pwg!@I52iC33{rmBWhA{c!4QiP0Zp|KNE;5+>>s_
zs1-2}$DHgB4tSB##vT)O8pm`^W=5OVhTUO0_kzLAHTn_~smq&8>^xiRZ>J!VH+?VS
zDy&AUQoxy5P!$@pN5E~iSy1+PftLhPX=ZDt%1@VPfV<BT{4@x20+ntbz^es_>;?20
z(IAQpSjJ&M?tzfUAnes~<No{A4m=cm9TkyF{dCG*R9tK97eZ^j-+XLMvRkpMi-^_Q
z<=X1im;9D9x5CdYsxs2`-a75Ind@3I9F@_wlHAu4Tl|N%WN4y5#FSiuD$c>w9#_$+
zisUg|ZdxNb>s)wmF=-~~G3Bmd>5ECG0Ojys2-QUL<;DIIxkjT;-3=pJGP~ObyEQ`c
zLa(3ltlZNnTJcw4pDPzAx89oaog<xuDkbt5mvHnYR#sNk7(qF}Y{P?t9SR55R#v{-
zIgywTs-GytFj-M?CGKYs@|yne{VOKOVVpWjzs6F%6`K+3bAt(2q=_}%xhTx87(u_(
zw--U*&eGL-x%7d09^LYDR5g2<aPrf7d3S4G`sN@mY1OXQ9tD5q9xGxPH&3%mKp{4>
z#N_O$ybqZX<avoUSGom>?0TAnp07-Q3D*UKs+C*et`s*FTwQWiSgmk}E@SiSiQtvc
zb#|%l!b%pF?vw^UOMamkm8va9Q|c3|1H`ruWq0r`_ol*{GHrX7BC68ugqSR#VAt@1
z!ypYlFT{A~;_jaRY<dlt`8l9a2Y{9)fksW+n*YZ_9xw_i;bMrbBR!;Be9rf6bcgve
z1Z#P%r)52`DBP+W3j|(~i-yZPp0`{@6}a%SW9#tlqy>R(VKdJP$I2#Mv=NbGqRO#c
zQM-5MqLqln-K<b!XJ=VW754e{r4G^p`FhUuB&Q<dSW!_1?_i-rb0@8&*vh@v3ELvt
z^-EW?@y+F0ZGj0E;W9bpZ`SC^y_0|Lx_^6tTETDPdfJ`OM=g=Hx<2SHkB@4Kxx*<4
zUgsg$+rBC)5o77=z;CWPftx8ykUOn=2bpg5%pw>;@#kHBs#21!z|o#%z8t&aUGcks
zB`pJ*UqnlNw??;q;YAOrXtBTM(NpS@JbfMJEBVoM%ewZry`IlNF7CA=UL<b?-a<b3
z6P7)~Sc+ez`@VE^Z1#K`jo!jLAAMqALhNP!o5nkYH&WxszTAU-S=_fu)CezUmdw0c
zB09HM*FpZ|PCN06k@lh_hpwwAYhN|b$@zPhp^XpJ>8Vufl0zI94Jsuk|3|pvDjo6v
zIB)JC5uP;8N7TIr9n}Fg-Shvz%RdvCL)3dBp=)78`Ebnh>@vqBF^x|oQEiLiAMeI#
zeKibJGrfi9QEnh@?^WDpZvHb3^~I``d*{V3#A;$Ei(gfda$gP2WI9-N{l?sH7Yaxw
zt<eQFu(b@mR|})I&-UM-j!dmJPQLsjAKNvpKBl<qsuVT1w_Yao#9pL0nC6)<jj8z>
zid5vdUdNqbj7V!azG1W9a$Kh|%zEOMK*)*L*i2<{D;-gzbBpA!pLH02AJw=hGt8O#
zvYN4d{0t(*h9v}F!H<2J6!hs+XAg&67-338Au1-qaB>UsKjjYjF5eJSF21=`tBdZ8
z<ZF3|rps6sd>qe($oN;r8O@<r<8o~3nY#`BZ(1A8vd4CRFwJN5G9j;|bY|ER8Z;cF
zWAC7Rmg&f3%Am(;?yMuqM+%w&S68aB{`Bk&{$nU}AKRjp=F|MP4GRv8xa1;}vppHD
zXMUd|q*<C1F)}A@I%hbi>2CXAOe2O1Vx`M5M@rZE_rJDYuVy!49L-4HyL!LsovT0d
z)znX$XAZldL92j3DL?Tc*J$602wvBs3nXL({9<Bv6N3anM_h#(1i=SPxV8KIk5AaW
ze(X#y?0G%QLxID9R_0Pgvb?)}ULrJ+&<f__E8oPVE|=dVR`c}`PU)$i_NLfq#09!=
zrhItd7c*2zu^-d)t(bWxSi)xtw-t^l3WQpxUS^K_WG<+WnYECL#rG|TZS-W?T_o3+
zy~Z0Ai|N>1=$wNJ-OUhdSzN-i6`M8s<2Y0`>Um2>EG;r8HB*&E#%baB?W!`-*OCKf
z!b#)|GcN}fNhT|3K{Z@Z+sq6<7V!YiXtGhI!^lrVX7S2hl+W6cB?7+HaKceMBQJi^
zkV;_C_jc~dgr3N|*BK`oqc529q*=^Z512kBH&Sj{81}>tWxfv12yx6!cJRHVcyU-v
zr+-M>cYDKmw`T20cwheVlRdQgUwfe{8W#oV$Hr@892W;v$ibDj?rayi#@76yt<agK
z2TuS0R7q1y=I&|%&K_^^Q;!X7&7aD9-R<!UHtvt;D<L<e>>9k~kuMCR){K)cQ!10@
zep->{R$F{Yg{NUMFKD^DUs<RrAj`q5(516ow2G=9bwAZW;yyw4(JWHRWj-x^9UW(O
z*c{NCJ5rz@{P7X8p1>T#@Y*6ThxvmT)t1t;%jQm*3I%q_+uMg*hu)iD-8XQN0o!GK
z1f!xgB=wCJCQ~eC(=SZuP)oHD&g5XoPbZPz7bK1DxtG^+Np;~%VP;@ycxJHYWt(fh
z=vrYxJ#}iEbUAM|+ohSQpeq=Acb3fXUh;b4Q(-&uDQc$)BmrlqIxW`Fd{u;6p(*Ho
z$B5-v<IzXtF*pbRB3qku5zggFI37BQw8Thh-WMInbG~d7zfv%FGi39=S=qd`vDWlg
zMv5QNxwi6xEEmITfwcSRV!RMc>Fs3MD=w6$uYRM#u1a7|%JdsI{9IX@cW$>r%3x!|
z&g5VWd=I@sgPMyno~vnfyXy$ys>q=huOTlVSW{aI9T^WbtF#eFHY35CESCvq=D*KF
zR0^C$LW+jN6`z5eO}j%x+or*PL4WwkJ1+p6=||ZpLrOwR-jn#PVEslGv+bfa#`X6{
z#wG+b!c6eD`9pUSc8ej$A>W^G{Ing)2Nqb^)##Z7Wq@C_b8P537QNBuFcxcL*e~9(
zyIrC4q>-yRNNqkmGA1H7pjzGb%Tk2xM87h8L!^|$zYZkYD+M-gsjYh;wXBqIQe`}I
z>+C+Z#k?099Fe3l>|XU`Qsd-CywQ!s{ILFB-%Qf*oAN6Dth<$816EJ@5&mKq_zjnE
z`ngWy$)k>0HmM^yucGa?*mbVUyIH5j)U)!EW>?}mnuW8`56>+ua{nO&UxTQfBfAE6
z0z`C*xMqBOYQUI)OOZPcJ)9okFWmo1gmxGN=mZe(>jEvErmX9x+zY*aDU8Yh@tI-1
zA$9=77Aa5&4v_%M`}^U1o)>TATX|lIHBUN?lxGyYQZ{x@1S&EJiZ&)<M4lKG_K^A}
z=*NWnUrc|24-WCaty6?lFQPym8P`H00ND=rIj1XK;?EN|WR{=xK!p+&spFGecd7Eq
z@ci@Z`EKgV2gF||J?J+V%=W$`<$f|RQ%lh%7As;PV;@|e6R~TteJ`Bkb{-QE+{^d5
zxA7=6$GlY0kA@x9V#Nz;n~>ap{D%9Sn^MsV$LT(|u6X9EWK5U|Q=b}HD&8TSqd?8T
zGE`d6SI^Y(nRYoXz0zjCOHXN!gGgz97<F;(l}?)Vsx<?dS}Z~1(d@m5-m(k4Y7kQj
z9=5}G$kVbp)-eHGNiB?LP*UXDdHfODYx0m)V)pgN%B=T<goHr!&IzJW!NQ7BfMk5{
z*X?Wqp(O&HGRSh(p-<qO75eGiCw=vhOT<HRmN*-2kJ|ZfbT3IcfGwzw7-yNd{=KLV
zmZfD#G_dy}RR+gi@s5-Cu+F1bD1C*^3`U8gI3R#ow?ftmmasy7hp~p|JXZ6GIyvr4
zo|S}_^ZDd&?tG>9{Nw9}d{nAaPAj{&^LSQDKvVb!U@<(B)ql}*L}Cl}+{;Kk(i9i7
zG|PNx`<1_fCN`RFOjW{XM#oTmOkTsB%8?%_H&$Y+IJLhcfg&+!&!u+A{V#P^-JDCW
zd(bYg+r`cM+l6roRe!egU2oz<Lfo}<9j;>?$UFuZLt*6^@FjfpSd)NEIf!SOCERWS
zW)5cDIAoH>E&w=|jnletC(_y8^ac`Qq5(Vx1wB*icI4mN5cJHg0VLkqvJ0{k4pRId
zTrS?(!nyVl&1CzlEp~q|#HD&l?e0Bb^)2VW&6>|u^KC_`elY|${7lGeo$|-nc)otX
z8TJtad5=(l$+Q#mq;lHNwHZ$ny{{0bU%hASsnbk0o-TY{^0-zWZJ*M^9LEXeyz9Pn
zMx(bG-<}Ll=UiwAx#8O#k#Bi}OP>)?BFq1H$Zs}z^z_U4sv9efO^GTheHCG1#0beM
zBOH-sdI}<eiJ{lE;Ej*aw`XYbb)2J539Tz@6Dv~600^(i%|35S^4PfMJzcjH4~0bC
zOiTBFALlminr<J^JySqj$reK&Ju4sq*L(vU4wpkBw<Lj$t4G0d1*FReAO%4Z{Ez8#
zbJj?&4#cOYGpI|DZm#rR<1wa00KWfzW+2}L?K*o+WB<g|y}WyQ{C#H{<QhT`(V(@l
zXD<iu4jItwkRfEiCa=4iS)$B}oTAd=QicNj!-a>d?+KgPKN}X#amU%|dKg|fEo_lI
z9l9L7n`<Z}<?s8lX0#P6<a$d>pjM>ramER|)I^W?ZnR-2&rHqqQcv{OfIE(K`8)xi
zb6wn0-=%|4Y127oT*ga^%-Hp~w$dWEf9{f%IkBJ+YeRYCQB`Ir#ANw=2Y&GmGl#I*
z)^)aY3;h>p=hD8@j}7J%#t*ubVoR<EMs-Qm9y-PcbM%JdLg#OM-+XK+s6Rlf=mmSP
z<5&|WK3>?m`;Ws^RBh&hTvx9eA;UpI&IbS2K)hyyo)5@asDE0(=K<0h93Vc&r9iV;
zUkFA+PGF$m%AV=gC{X%lAr?+z-{HSM=z(j!=`<Yz0^@~HJwq)psjC10R90D-Rm}9i
zG$xEER0#!*-uFa&hmZu%E9!@KOOf}f(A-$3>4r+9wIjR1dn796V{lAt(bbwyvfVod
z00K5u{L7tOXxJGOlRLGPMnw_9`}+P;HuIUmcWvV`B)jrY50IjZcsi?@sys5c4+oW8
z0+=M^OP%uS@)`9O77{Ubb6XH!Uf<X3qH!?3D6ya``u_?ToiQlK>(XWYxpK9#0t@;q
zUP@YlhqEE;d@orvYL4ypW9^3oG2*W5h22-LT+xHsoSI<S&_|d9VURxu1O{?TOPeJ6
z?^RxE85+_@rVib|e?J+SjnNOl@I}DK5EYnbSXZKqDY#$wGfRH%!S0J!2F}<^XQ209
zbT_u23tma!r}0{XQ#+8aUwH->3khbv|K_ftShv6Kx&9gl4zQlKh>(UTxA?};N`;}0
zxsMzeUwf*R19k@;H_lG^BqtOovfhe-81|(eGML-*>zMwk;f#q{TvTB^Gph0UFD8Pu
zPf#7CbfiR!e6djT29BnPs}XMgnrU03W?_g;rZXmPZp-urt+Pbsr$Kc*Gm2EHUF+@I
zeA|8FDOs@Cm9!F@N_nt&%3}JAGut~_!jDj5VyvNxRxf-gd%VNdh1$1dt0JNPeAX|;
z@Y0+n0d%p2;K%GuKj{dLAF$<UKDywlxC+`upqtN}JC_H-GmIo!4S*S7`FV>!?I<CJ
zt#SW-UuG#U?QoF1mKRH%`mqHM@DBP50`%~Nn`9o2TKO;{*4uXRuW949^mw5N+iG!}
znTqwQB7g|>&*t37)CvlbiFuQuII+-8cJL*wW_{)!dcN^MHpfJ-w0<!3vo6v!6oxru
zzB|G)9k1Ua?DRQVSH29_cf-(bH~9gx@-5V?rF(xzy<^nE=2*5qGv9-(OqP9TbP@A@
z^wTCTQRm>gH}^vrA4rV+`5|_8IrV64)t}n0thDlj_I0tAUVD^3JS)XtyIP{!9uABj
z%UUkTlHFsObqF4SxTBpb3EKLHgyc#7!l+X34M)Z3f|o4PJ{8DkGPef}tbFJ>uBo8y
zi(?O_LUalNy1k+p9W?x*p`k#~?59tn#16<rUo=a1R+y*(%6vSnJh!ecH+K(hXcc!R
z#&n)>zsi7O%_`;XYgc*2fS*h66kX<{<vdVFgmMc>;*T)AHqlWlkT&tYd1Ktr3H8d8
zSxli;s_wC0MWJ^(k6?ZH(~`%50m8jEb`24<83n5(h_<5g^_Uf@%GAA9<m|0>$#$VR
z&aL9j(rks-td<bD9Z<MJfpR_gHN7J@WMsN;2sH<2n4A122}gNp+H}rU(MBKO>@<hd
zuutB~!ahm7veLze1y;uHaviXgmQycZ{wo)6J5lF5?w=^*KTh!c>6MN00W}(Uxd8wW
z!0RGGVP!&!8|0(itsL97*Ir^Be&>A>@fgEGn2`0~%S@z{+1|Ol2T;TK=5A8K{+(LU
zbD`Mlfx1q*B+rnY`>Eqbr;<<b9Gh}l7}d^ABoxL~2}iYPyC28qUVzYJ!pM-PtAIs0
zi>zIq?Gs*#=oq%Pu=3J_yQ1ipLVMn!@$<8o%XCwia|@*Y&pHO{daMx8>>i)UEnt!q
zi^^2~V%%os6m+{FJp|tMT@yFSk4I5)-za-=dD-bQ$auQ!jj#n|C$+*CwfDdLg<JdS
zJvp<5=zGbnPInDh7@_jHby@&PtRAX0t>QvNk=)q~aWooN+^rS6K60>;!|DtZD??(A
zWc_dk)9}_@z^V7Pw?}aDF$NyzrIeitu4FA^<=ft-g!a`Z#96+e*6gAqx5<c~v6DUN
zzct|8cTr~HwymXF^5vHKp(-NLAyzsA1D#}h6N8RG^Xc12@1^6u9i7JVh)e?1AgXG_
z-(qrUA<5s}E~5*tK=*)OP#Q&~X|}sY465!15blq9$W`jf%5Bw?c(|1CUJaB%LxVj%
zl#zui;^Nc0Xt3q~<8|Nae8(~5h#eCrxy3)ME@zCzFOW$u2Vw{NOxGLB918w`NP#rK
z&=isef`0SGk}Dcz7m+ef^&^9nU{b)+;%QEL1@DndhQGli<L$jGNbhRJd!|)6JhfKw
zB+0JeDOG7!cg<Z#*e8B|NtsJACC1M5y~jgZppM`9OM_Vkt=V<LhOb&_b4@$OV)`p#
zjr=OMjPe#Wwg<SpE#cdpvBAotV|&Wh_tgD;%PuR42e=6&)?N+*<Jkd?Y;Ei0t*AWD
zz7{5}9}9WSg7#E&Ud!=y8>oL!E02eT`*We&ab_dz7)KiLha!ywu$MQr=$3RhvnQ7?
zB_u@UV)w76C8C?=pk{-fDLF)Nx4Gq`aBZn|y$<VDGn5Z~y+o`y|4qPkAi`8Iz7Mgp
z>G@DAJRi4sESQf%-e?(44N|MfLOU+KYy3rL*mI{QgZDQSfN8mt$W8|$O4At4(e|D*
zNzp;qcw#`IO;(-uf#ZiSB@%8DG5$|M`ICNEx96uO^ngZGy4Cbt2Q8I|&%lw~pmwGK
zmw`;Q+W`IPD(&YN)_B})brkBQV0u~TZ?7{;rpG-1Kq4fFOS&zKjnHE5_Z(qR8Upyr
z<wn|(`s&V{3$6hD)dF_pS65Hfh>401iw>Tdn7HwH_{_wLr~VulLPld$i$)vk*p-#q
zdVxo+n(yn-p^e$byY`*j@W4xop_4w<L@v!QntpXPbqXprz5rjH?jJbQJJj%l&Ah<6
zdJ}|MUNA}>)QeCAL`<D0aUqj9B9c>#CVRZa_N9#<G}v{Mfpz$pBc9vPW!>#fBrUbx
z01X2dc2w3ZTzF<mHMWk|h(2G>>gKk*!LGK<*!Y;bhX-nHUqN*WjzZT`jXZi|RY26S
z>-o))tqFGK@?vGQ>b<jpuNNiLuP+z$Z5XCnoY_~821dY(R7Gd7l2}I=N~v=Q&zq4D
z%S{E=__(j*`+^9G=vbSPC-5#o*Z!dwW{Z3op3LDloGMv2oHM24Mxa?;=`TFFj_jxG
z*TJdOg1r9Y3H%6EMwx!Za?DX1h<{6u0%&;hkbghxU1vdF6!!HsTlsR6-NlFU`%bCr
zi8XgF*TE+6^+oIYx*1w|dRIm2v%IB7q94nUPM>@k${A1fF<!l|LS59Z?^Q{;azS{o
zxW8sZ-B9vV|H1T4^ET#*!j;@qi!gGJ)(wNU#-8yb3<D?Nv~HYnF1ji2QQ$Y|HClcY
z8pAV>kc0B~lLv*&%<`4wNATks#yI$zs-aKbV)~0Tl3I(a)MsWgxAw+w;&D!4>jQRh
zW?lzBZrqG`Zd0{Cf{EQEd&o6qMtbd{sMPGfRTG81wIX?iqoQjuiu+4USyAfiI7qzd
zCQP(;$YB*<VXTy-H9mlePso2>f}PJ?>=A9xTW#rV!9_D&5cyh5rVu|fe6Pu3FN@w>
zTV75E@X6uj-3}q%v{yd1w0_BLqxE4tkTshakXJF^I-L;Gj*-+w?vN8@7ZmKZ-vL_M
zz;|cI^`{<~^LgqD7ee<KrgrMnPGC`oR9VN=pecxQwWRGD?@W{Q-n7%Zb+MML@WgM}
zS@t#K>q40`g~`i>_*&mz$aC&|UuU$7e-G?Kl0o>NyLB?1Ih*UMwFrDOEV|C!Bat;)
zaNIU4k##QHd&XXOG=L;)R_sbGwPBYP%4XCr1S?WdAv%ZCuvQ6Y7Z|ldVf_`M4}1l=
zO$EE}GGpzOc@%D+nF)IKOBY75vwO8$i?J_T@q1DHElf$%+VN~4;8i2Pcg28Y?fJ|b
zl?=gJ@=sT`B~jXb#;MIJKiK=8<fy%JK`&K8_;rhR$uR?>=M`cI(+F|Ev4hk*xjRfB
zp>6M@6PgTC4_wL^hX`Q4)F*AsSC)A}OL5XLjMzbYN~!Zx`J0I9h}r~}VFLtsstcq&
zi$C+vP*?Sa5>ciA;YD+Kbo4rZs2uuwA^&ZmVsa+{e^w>aj{D*LP!nZ@IVfQLnJBt=
zOC+54mK<B+Gj({;I~OYA#0rZQrJT+wETDLBGs-DJ$w0W13C2|Z_qnEMAEMG78IUZ&
zm<TeCd}Cko4)yh%#Tjj+p<wp4v+KE0E9~EVbQj%t9aH}XWxtc1<|eiJ!Ev|yssVC-
zfZ7RY3P`L<YB?@jHhVJ#<$2-<<fW*75cT3eu`g`D!VrSb83rsgivZ7%8lTt!XF3$f
zFg!5);i%VC*J6+=cTL-N)WV`lY`^5SeTM$xk7&AU^Zm}~ULdQ;9|q=r=?X|t=?$Hl
z#o_85zpvu`=f-jzch1T`2WX<%RMpC*D&{A8KJV3o%UWH##p+m}y2hLfYctfNBKvzR
zUQ<o$B6U#iLD`02%617$g_4Ej2$7DDVjydwX0`7orBg-5@2NX`0qBI3SMq)pJrLIu
z4frXfP=1O}gUFc}{2xo5G?ax-J2$kz{=F%H^1qjhUsZ}7elJP>cw#>HH%Y1WwS$mQ
zB!$Vn;R;?hz~-Nc(S7M1<XPJ}e%7M;CalCU&E(~O0a~i}3NF_9`?=EuA_{~HdCk&2
z6p4r^0FQid?YLC8j0QkmqMzOpvKMF4@J*$Ce6VcD&ZEWX+531DBnybi+pLW1sWQ-|
zRV{UTIrsR<C01KI^FemFpPz_qxdi@D3%4U#O7N+@Jk1S3B!w@XXoxhtsorpUwpVz4
zssnjT0*I0vB6z8wwG;gqLozJI2)gi^;=U}I7g`0D4`8HrESx$}e{hNcl!q%s9m<zH
zWBR#LreV@;a#RkGg52-W8+S$b4#<WJKEcf1zn@R1VOwj_2iQVZ)@d|l($htt`L*xu
z3c0)Bd9<HrQYPJkbhen^Nvz%{*2H)uIoOd`9{XH{4)2+SNUqkfwkEa8!o0vlRe=}j
z>6lVSJC`s!;2fu|ocdlAov*6QEesLjm86>DpBqZtu$@pO@_R4<HPt7NO&{@Rb5URZ
z-rP~o0~AYYc=q|&C%D8g>96DeK=-ZnzvuNx03L76>w|{Dt{Q5kx1v?q%33ihZolv!
zMfMP(SNq1WXumhlNt>mK<|r-gHXELwana$CD9wy48YGr&og{UqiA$bApRetHvtV?i
zYp+WE6wt;1#<{rWNTdtf#E2t27{Il+hjez%Z2`*3lR8YGcu0$WqG9X7TKJZITkn1)
z#C14f+h+UduGM`W&nvnZJUftV(wk3y!Zosr5Qy^ANijk_4-Irs(gj+l^Zkkwn@p#a
zUwLx%yEyl^T^z9;GONY$B`u*?Gfi`k7;N2?8Jj2+mz&|#9%(W$Al(-zb1=#stSA(f
zw}DJV_MYQnke}!Gdm8|}g~-=kcZ%DXX0Y50dxUV&(?lKVq38>1N-zXvKEdQ*fOK^H
ztNcWC{99*vSo-L1Tt5xt*O)T8)zupuyiROV7bV^?DsFsG@8w8wyp2Y*dq9(wUk?<y
z@lu^{m$Yn0exJ{bd;6N~UkG5ZFwqnvJiAnYAf=%UDH7jSTzYo4y{+%OL>D;&c1-%b
zQ7wFTU6eUQU{JqG@Jd{Ga<l(v{!z$`A1j4}SAh5HZFuTsgMh6F{ux+dU?wS|>40t6
zXYCmB$?Hdh+U7t7<3Ef_wkM$OhL@-ILu2a3T#Q~b(n}7;kiI>fp-jp6;;JotM=t4q
z0E#4JY49e}ZEbHKX?<U?skX9Dn90Sduj8|5Lgu+o=2l8d&aEBA&lM!>(V2;zlg=eh
zm==op$z=7vSXfo>JXTp5=r_!gk94w=QblxA#^)A<w*vp*^Gu6ID2@GJHEj#j)~jm0
zOJoxz6YgsQ6=^nwI1Da54O092fWaROuK-364bW!JePR%~3g~t?6}F30`kgV?!>`6O
z3wwgJ5K#X@-lKg@7p*!HoW3k3Gnn1z=Sy-tZ1F6o6B0O}1}Na5RJZw58bzLU0wp|O
zm1<DBot8o4lzM8B2{jl%t<ZVNuJy0oVULA))QW@cECDl8#a*Gl!aS<EHR_pPFKtMl
zy&!864@s&2^XEX?m*8Q{YWW8ptZn!IhL2(vc-`>wLw4}voH*S3hMzsy_54F^R;$F#
z_$5{zZOaP^x*)ZaxOX2D_H1>!RMopCa-a0K-!q%yPb2ntR*N_pmS%+5=DwzOKz~v6
z(gE<;8v&0~^3EN{n!fz{#d=PD-^e}3D$B-!p2_@K@yyh8%9lkZ+Tk)k<*(uajqKS#
z3m*G)0$irri24G(H2O=J>YG2`=TsEm9{kCO@{nM$5B#0UzjC@G6oLCWX_FSMaHOr8
zPA-<V#az$^I<q6c_Pk=4HoIo$v`g0ZudNo81-8`09_^|)>?3?#%;)WOG?|3(m#uw`
zMMi7e+tz<&@Si|^9nwaco4HiGb7o)V+giAtZhsG6Z1Y6bX43j5jc6zIrTyl#tLv-a
zbmhJ;ncQ%!{&+CYKiqrQ$WgJc@;7u?dKXkR>5kuG%ZA!mm9;Ol!7}N{ii>N1Y0sjY
zGTze5v&GLr#-na<6XKJp<BMLPH)93!LI71ansMg?AVdVjA%5cw(5xMII*GLI1MbYF
zmLcx08kJ>E28M{VBXsJpX_)f*N5T-%ux%tFI2kY@2klMw7lC*6eEJ;D>i7__e}P}d
z_9uJLH5?iLzR$~9^NkvNhW?8(A!7o|_Q%giVEks9d?@PxQXuW^N|oN<Q0&n2)3DMK
z1SBjXK6!tqQ~_wF0caMuKV|A<y8=rUydK|gr~JX4U!?C|4T735cgq4>$<IM0X<TtX
zxnDeKwc_6_H=;c7Q!Y(7JDLOdUaNZhRxr|4)<_xmOC=R7EaX`FngWtEz#4=fe{<>Z
z02=>nJ>=^hC*2;KgV(aP2Q$s>f}7iNKGo!f{TzLbI*o2YTBWguhA(&WM(hIs@%~k2
zH;L+uIheHoP%pxHU4Kta-fR6pnOQb#ExIrH#6fdb*k?d~%o7sz*-<5vf>7PE^_3^u
zwQsHKMbAkCy&3k->pv3OiX5`W7B!Sx>}AHsYwr*c`TU*z85ieLtRg>sZwcuO;RD6f
z>P4@htMJ<pYytyg+C$2cs6H#s@V3G0aU*99<eN?R*8Rb%jtCM#=b~-z!dA*dK{{&O
z`--nM&2{5GDkU5ysUTCNeZz1dQVk?$pe4jxF#e?G$f|U4rS`4oGjQ7T(`aEBqMI7S
zEI>y4H@<-?HDiCcstJfJNJR$zTdXi5$~It75ycEBjJ@^(MH%tg&vBRsLM5)`#=w85
z2LFRjJXc}gTg>SM&eTm;-M_Z(YRx-W*aKzePI-=XO;{sj+5Mk1&Zjrtm5A6;kKUh7
zb_TgqQ(mgH#%&+~JgwtRw)j+5T4vC%uAGg|W0D!l8>trSE5TD4ljW|KhsP(RWA~nQ
zV}bARXC1BO_+13j$nO1#vZ%eQ2a-6E)9*a_Q847EI;~&jlyggL&7DLKIYh~krFx@Q
zqAH&e@4+|m@qo5XzVFrV5@6&A6<syT6fi%-<S)vmrM_RIyH+ko#PvAf%AHrxN`sy|
z@rZ!xSzk+Ild*@{Y(}g^O$eb%Qs0**YuIs#kh`J7+}qe5|4UcL_}O$2J-i+{OjY#U
z#>>X}jZQ!!KWVFd!Kk)mwL3jA%=tWlV2U+by+(EuysRp~H(ZFL>N>-;u{&VBd&<K=
zAne2dY+>%{!=gOv&GS)gcY~^5nwYETEgT*rlSO^Sva3fiJZVO!4=4pFxBiq<P-+lA
zk&W}g%Hb_+_`7^ozFrDezL%4a%gr3fgDDBZRc26Q<kT-MGjUS!NJK4z%<-bkXci*8
z>TS&XrHdoo!UlvoW2E_s{|oO3Tyb&l-{e<4?z;r_00=)g*F+HTy_QSq@r1Fi`Kzrc
znncB(P4)c&@NxvyjX&Z+Gz+#<>G<gM3u|qi8Rm0H#9tvU0z#zM$zZUoeLHqf3jLI2
z)6uF3oE3|qYW=kn3mRTJ?~f~h<~sHJ4XpopLydI%t3En6ARUL{Sx_DYn)ZksW9~~P
z^xJ*pMQ$rGl><?6fEu%(v`eMkpHDU6Bj~^A5=pD{XT?864-la5ZtW0(#{d71iuf^L
zj^@NxdmYZoa<E%($`UjtURyXEvi%?eA<?n}+%yF`YaDtQH<u)K%f)m8)9J-*({?!`
zglGKYNW;B{0zBri=78f<`#E6{cpFO8>6gjwE-us^Lub<8FBjkwsd!CaIH%WNJWYCs
zmW+C(S|t83C%_w|bbmiNPo6}cP&K71011QvE$i2*Y=$Y>{z`p}OXrDes)z~`;l1Dh
z#nvr6RAZx^Xk0h4%|KLrGWL?mUQ23}wpN-tB+|cdK5vWbF5&H!r=<hB?9k(yGVYqs
zc2?x~LO~L7IS_Kv4KDHtDJ&OCrwNGN<Z5ncG;FLK^`w9q0KsB?h*eSC0@^3(g-Xcd
ztv6z8=k>BpOv58zVRFTEZ(6S`>2;jNt3hyjt@(Q3sEqsdE>KsDpMI|E`=adBikHtg
z3t&4O;)8t`&tAj5tJnGa6%RaA>VUaM6V9@SCXB66uPTF6F+k$ns+Ss$yWo1j0;*J-
z2mi*qr26YC&n@yzu-#pLeiQmNDdTepPYR&j;|s1|D}fG34r2-=b<CKhORNJ`%(%KI
z0W@{*y;E;7imPzxe4pFcK009!gjp84cV=z0YeY_232%b@rXeW!EVNlw`QAd%5|up|
zg#%u-5>byRDZ}|Kqfk|Hh?$6~p0yt;b%f^_Y?BMAF$(hWlhcb&*z<JCKE8bLoeqqw
z{PAL0rC?#QM)YpauL6D!sIjoGo1{pt0tX82?doW`-etm?25>))2efj){HIHdf1(K?
zPd*b}UFWU3a!N!iY=(RW*8H(^%i)B$`l%=XNj7W}GeeOlYRf?{y8OJ1yUsYmAXdD*
z7PKhBzA@9D&;iA6ZC~dbKAjr_yQK%~@qG0Ljt;srUEpb)yz{+%%bW=5$UiGDiiW&H
zdJicGV9{c}Gxzr2iWv9@Vg`aQ4qIzKZGuamOgFV|GXbzpX!wu%38Pq5t@|+H+^CXO
zxRCH(U%k61TWa^9ocFzHf_Wj-rmbvFFc#D8XDOk!1j7(?Ot`ya-}Vsf7UTf)j}0XW
zan;qg&1v3Q{=)QN4*pCik*v;AbZEmoShF)|Mww2Gp;-5FPeY#*3Bn$!+T+W%ewT$5
z*070hDSDph-#o1_D7l$}&~>l7519D8vZ7^Uy|N!=mPl8WMbuSh#-T8oAHY*K7$%0q
zPbD>h{m#BDJ*0Rar9+DMvYbz&9W5qEsD)kwN=O04@LT|VVE-wz67xGdYz?nA!MB#$
zn_^LYQ>zM0gtGTB&);tgl0K??yUps3<Z%l+ja0m*lNmKcaOdih$B0mR+q*h9)Ub#C
zQTvF8EI2Hp?{*KJV~c*tkKx&oIFi(kvQ}7w%MCJR2f#9lcuBa;9e!+_Yb)jQ?1<!q
zIRsm9XKtXN4iYaoSzNhi+YcoD0kft-i`S2}IOJQr{!H4KHM@Ob^xo{?NQ|4)@&PI7
ztn|@%?ouBr)H`Tqz^eCHA}(D2bESA(*!+^op37T-x(RD@pP-D$&ot)`8Hjb;nGI*#
zVE&?a0`<@}gtGds-?4wDt#%ocNj-Ht6w(j0Vdad&1I;;P&9ZaiuJhzFzSb%{K1p;u
zR<z3a75&_=Tm8KPCrSM^i#=Xb8;_OLy7<>0FwrF95T2~)5udwh;x%z`BjbHz=+MA~
z)1k*w@G<zcK<It<QhdZ?Lt2W^;Bd2+>+*cTNZnw{*B1?IiI7no)z-bdtckg;^djm2
zn$+J@rR+Yz{31miG9`O>@LE8f7DmV_w8Lrl&e<bB%W^f2f_AvSeVU5gM?<VC5q0`p
zfHX}~Z%xF(-1)jvVu%RB9e1<n41~P`t+Pt9Gjpt$O;g3o8k)L?Z2Y0nyj3G#qT-z^
z(yh)u>H`sdR(B<$?1X08w{+%N<MBT(W&iMq1?3Z~Mm@dUnwt>WBk+=R-lG-A=Mk(J
z5!(Wyfx1{2K5_6sl=?r_F@*w7>Q<^uI2}B8tw>MI+RCbTaco!kN=34XA){lFvKJuO
z<CKfC!5i`Y2xih$@A$lvO;B1+i+%||@#?5f<A&4NwZafb(16QLC~~A_N6(D*q0oM1
zs`Nsx$3nKOJ5C{Q{N*5@QlyioAX0x}2I!eC(E2W=42u+`gAy{E-_M;}qKwf2LaWq9
z8w>MQ2LVNiH|D)ThgPc6sg^l3>bM)(Ug?hSy&nje{jpac27zuFJcWoB;LLEohzy6f
z<PX=BH<S5q2L{%1XA6eV?l{7?K12Y>tX#pV2zvA+4k~Q#cYI30_k_|6sGDf@+a<lt
zv_Vrc5{Bu6>OsPVy)j2*50@X4P%nMU#4%K776$^KUCJGG-+FQd!ktE5kPtInhvgY|
z`<nM1som7i>Q!=~VvJCP{#yu`=a;sXdi#{{OHS(azMF+=K+&JBt$0wc7O%`By4O8_
z=6jh_pMaFKLshAiZbI<oc+%>pasa6}gD>+FCv|mA)hguC9YZExS0-Q~AmD)Z_F>~x
z6c)$;FV7Xf{A2sG_p(J!tr&Q7nqbjR8CHQuTH4CY_fKj{a4`&dZHN5^vc@;76W1o|
zc`my9Qq}*yTTLCQ4XAJZ&dGwiXD#J^{;0X984^?4<BbmYqYL{R2YZgvlN%;?4g5LB
zzpF0^WWWf<-=)S29eUWyFqn~CaC~(jxsOZ2|FRaO;7>i{1u1>9w9||8DX*9riFo77
zhZ9DecWTxXZfR3Z42P$smGUBB@$OrZ;T$t8AXT98Cm1x)bVm=3#2_Y$z+bnmhn?<H
zeEp4zR<dKnkyFAq>>IwchtlY6<n7pzGLmt`F3||*Gn#n$y0272FtFG)^d@%gllt=N
zX^6pO@Z6d@$8|xCWB^bA1jhjgN5}b&O|0zGo1H7C+&f_kTBf+)>B;M0GD;llk=x(%
zcv|dotZN&WPW5%hywI>(gr}#dO-ZdF83|q(nTy93>m1Maq775X3WGMa!WRY!vfh!M
ziFcj7JQd%lv8B^fb}EDQz0Bk)EJN*_rrI?kw1FA@@TWQ4&4rk!kTHLR0lu*eAjALr
z=(hd&g4<ZQ2yk%|lixF-pI|d4Bzdoi)K!%{P23?=>e{>LeriQnniR;CH<Y@HN2Q9(
z<5Cgf6;${-^ZPOfZ0R5>4@f7_VvgiI_x9SRH?ZbcED@8N-!+u5{kx-KpbMM*TX-DR
z!7K|Yo7BQ=TuOLnQ(O9={$4QWiyC_;m2i3AQpE@_syUhO%B9<p#mT>ceF6&_R)W*r
zfw4zTU=28FpS?}_wY4SLp1;`Hy%^A8DjOvB0Mi2^#NZH_xbSR1)KX17nyf+Q<+&UX
z@gGmmcx&+GSh4%~cIQPL-)91mk?L1ZyMcTR>D$TbV{Ykn_NxsV5HIZ9n%Q-0J8?gW
zUK$6%6C)+zgdU&Bb}|-YGS^%C$d*o=X4^&<B)Dj;9Mhd|95-!j|7&EKQjsK#3IIPS
zGUnEx-meyhz!tzLEo4wfF}AQqci=~dmc=4i;wob08cDYpih|ajz@n#ql60KceOAs8
za!IeGKTEHeKWkr<Sa{vbgnR(c=W|sx8M}uKL3W?39!x1N$*s7zIMy>O0(yO#CN@J_
znR2buh|`<kc_rG_5`4KP4ou$j!|$qwfA=hS=i0W8xjos)n_sd@bAId)!M3SlwerTN
zq|bp><>36=o;}|&bPi>%neUbzygEy~Say{ZiO5_J8F)u~N^{npp5-SrHg1=RHdNtt
zyJiqk!oLpp3nT`gd+cL7A5G<DJHBpRyRv<qEz^pgss771eQHmqS{A0k+Z}|edD!52
zb!VrN4|X6}fQ8HDChGGc2t?#Xn60&3#tR6Ym~Ywgo?f_9NF3RU^pa9WC?%kSDPCGD
zGMNGTXik&I6NZ_fO6vuZuEI=#p#_9mixYq|B|eP|*W4i*0D353?9)?l6dibND;w;Y
zM^Cd`zyIs7l47MB@hEK97cWlm8C7V&<ST6$faea*$$1zPs12j4cmz@ZJsk%Q;QB+&
zW`&^;ropJSc-v1u>ND{CT#AS)XB~gLeNg~^ti8J!_yp4G<e!bkoK>z_cW)ui3lPa@
z-tk3`4Si4-^RUSQp@C?dkT_^Tc26ZcVb2MaR<O$emW(tPlu~<*y%v}rdw8^uCkcM)
zg3$-yalrqs$~+)HRme3V`Q*VB$+>h8koIL4fp8n8w-9h|%B(YUeyI>^2Yk4fjrm)^
zf?D~+46j7Q7Yu%MJ1lx1JMW!e{2CLHI~12f8gKoe)Ye*b*0i}pQ|l=ev8hwFb(cmM
zan%5i{`fJIEv5H9kU56-KL5_Q4TLH5@vK~=ZfK~s7ic<*ugvZ(b`f^}vL~B}=;G#T
zj4AgTL<pv#^d@;UGT>W!I&Xe&7{z)~ibr?f*jXdkBe5SEFT$j5<_V!ZPnwS=-CqNe
zJ<%D(X*b4<YF)j>%B&l#*dhx-s*(tG3}SBFU2PMHl|&c7So@3fy$+(6xIpm-H*g>$
z?hw%$Tq>^&hRJFlpVocEo&DeEJ6wcl-u(s{jm_Irq>3mP7fL~eWVAu1jtn-NuDZCE
zodybyD=IuE_3F|oQ5U*0Y=@nwNb9Vp!0egLPWpy;z@{Uo(<ct@gG?(r`pG=$aNF2L
z<^`3idebD9+5(I8ZAqBJa)H{uPs5Kq>UQ<Zz2~!`^MiT<FMiYUWg0)9=G!-HuybDk
z5PB8xt9`mxv<k}m(j|==q^8~{4Cl!%zjG1kOt-XcTD{LV>U$e7WpE_9sTUrQ5`9ak
z%^aeSu*S*#5*<S9zp2IZnMN1{1+io#Yb$HjzD2ar#=c)l-RAGTfB^>J+jFX{-E&-C
z$}DX^2aU4H+?u|4;L^TFCdjH)!DGYISEdg3D<ICm)z_Kx0Dwgc%UL?6)6Nq5Vt&Q?
zSa+w4StM8I6HJUWPw`t?5+9?UlBb(Er}cUa>dGUMR}NO%>f&EwInX|`Te;u1X)7u#
zZ5o~&MJ8szFt`l3ok|sy|AN6V^%^nf!qgug7;H0k{{uDR;*NZEck4LdzW;qH!tb6n
zs1ne5JZ`LO|7y#^Mqk{^YWh4fn!)d+R22S7?ga!c@zRPsQd?5+QEo;TII|uAyX?Ms
z6F~uGNK(*?M>SHjor-&tu|2cYe`sQ{7*HOtb|UI8DIwvkR$6m7OXEmGo15wXd}b5$
zz+xM2ecM=%VjK8Z(yV(_Vbzm>2GYeJ)?ez52Sq~m@eR8jD}SB~{6nD0OSpTM;%@cg
zNL`5z`14x4Wuib51b7Ql&D(vr+r~W0^SJ+oeJy3=AR;t@hHjToIbW?fMqc7$b}%vb
zOZUrXoI`4X8@q9BU$~EdRB*S>Ljgj=CtC68%eeF-FWdXM7ZSTZb>h;T%I$PXTY~xm
zVt549v+I^5LRW<bUyJg^zsiV>@4dQP>-$*C<?TLK6HCpV?9(^fwQ2K0uB4t{&c}m4
zkk5~%L`V_s=8)II?Al+z?_d9xIYLv>KLymZDPXC$-rLzyQc;P7d&k%r0$-sGsbO@+
zn9o{A6&Q(eFu3qoJKZ!FFRu#BpK4=*5y+;<WT1C1Y|9}wejW3@7JlT!*(6x+|8+DF
z{TbK26ceLTQ@aZVb=|X&U=X&p%trgNfArlVMI;}4n}xSdZ>ri>6A-`OG@~ib84V-9
zzkX%hpQsg<r56fbHsMxG=martAR@*kxC<P97}S(tT9RgNp4<Ea2T}yE$pF>kz5VN)
z%pt=TT(*l*4N`eOZJLjoXgHH-KW419L}bcpF0j+L={#RQbu>G+>VCZ_i@4oo4=4+~
zdYX)<mG8N4LZNX*h2u0I`Y`&f(ENn&As!3t`m+jiM6`-&&gbcMgvPrMynXuvuLYiP
zDns4TA#;HQdM>%92US<B$9?fR#-|H&O*k&_>qpQp^np1kcao~iCJWIiP7N1$hB3dW
zPU1Q)n=dW@H|8<xLCc`)j|@9;pFaqA8MuhbfhGW~eG6X<lbjxLR|S?gNXF7ngWVqo
z6F76g5{wLRMXnxMSm00ht`yya5voY9++XkSk6f-&SVzf$@!s5mKef(L8Xjr|b$YDX
z0I&%Fm~;uSm&`9Eq6iEF16z1gPw-X0iOGja5F&(BRlqjI)?IqE9vgCfYA(4+j0a}{
zg7K6I=@N%b*KPM(!s$zwWn$7U?~j1zhy~D8p9i-t0(6?VR51_+@YDg`Z}&aA0WJ>?
zng48P2=X(-2q$Z;>|>>HEm<J8zGhi0qNSl6QHg_01Yu$g&{;0-zjn2@)JBeY6Z5$R
zqtg)G`ZtU_#m2kTrEQa6Fx`1Zwu)uXFPh!A1oGe-2&T(OMdRSFX!WFug({P>wa0>}
z@5+qM1-&T23eE4;J)*2VuM-szjOO3HdR4O2y$H>9ORa0*$<T<~rdvz(cIHpgxnm!H
zlFq%MjZwah5V)(?fL|!bTn*lmthN6qaLLwLTLqY@FpzW*5(XeO>neC7wH3e~%8PLb
z-}y*ZdpL9^K-`y7TDQF<*#WYo<mBY=y?GbEt$H?O1Z?aT=M<QG)P-ZjU6V5nIp}J1
znEJrAj&}(#S(DuC2Y-b>`?u7+xrvZ-ZwJA-D)!B-Jm*8kC7g?M_vy!m^PzUTU#(63
zeCq~o#`hdQzF~;kG_wj{A}}N&UOd2Fz-X{;HcJKs(BKrE)ynIIfXb%AP<9dtPrc|9
zB-Mq)EbQt_OQ)c4kv8G%#T-y(;M}iME61Nc@R@aCOGMrNUTk0u)`V-TDz;j3h!~Tc
z?{0O{e@ORHFxG=brVzF@?Olsgi+=-kZd|kNL0IS=687B_X_}&HHaChL_uNIyj3mpx
z@ej#8vfzTS_BSdbP<NxwZT`Yjt3s#zk?L*Kb9}U0wkwHbK~?%7%u~~8JHrH7SO+Fk
z-^_2t;c<w$U|ZN<LFLuu@r2G;fB%zPcr=#q<{y9@;NKMk>gv=E`A}rhO?6$$B$lOr
z(Lql+;z0WhV>&>Dt<$Kh`&8YOP+GtH#vQX>2n{150=2>TS2dJO;8kdiZy2T@>nJR>
z>SBQ*^9nGzM9ZgU3uhaBywlj&*nNFLzp_Gmf$4apn%w@&cjw}O9~L>}Ee-V{>o%~1
zA~#eg*0cl>-P0+G<=W-kpoF*qXRx6xg5AN_w|=@}+}QILeI94Ut2DZC7B8EdZz<)r
z8m)O97)3RFE+C*#C*%b=;nQAQhB_3!%m|=wk5^LaJ877TaKoJ$ku^-!VBmKoj;lb<
z)=7FtEPR|vo=o{OH~7o)JZ%!k5mW9!D&W50ci8113hk2mg+37F48<UD8ENv_=U*_-
zc&dSa@hv)kyF`aoN4|Wzvl+2K0J^rDYbO*pag>eiqM_416sUXB8O2#uuBC9Jj9&-2
z`yrj^@(I5=w_=2I3!NIEt)+1o0Z6ATB1{*T*Eqr$lLUPhCm)Xjj7zc8e##z|qbChg
zvePn)J>mC)uyOq!xE(65EXB(*Q$U6Z`+^U0*v$CUgO_2MmZ8Tr!IGpDzlNBooLBrX
zSFE;n>o5Iq`|#42=yJ^Ki&G}R>J8wN#kAhu(^SAYO&ErBKFIp4+TImF#^E7Bw3RN_
z^fd;P4+AxbaA8Rga=Twt6q_QTJ|KvM;p=d*S;_q~+LpAtH-2%o2Oz%x#KIP|NB+p&
zH~l9>JiAPxzBe(bF$MoBU(@i<u;Lz<7h=6f*y#<!Fhf)pzvAQJ2X-J!KCCxn^Oo~F
zvg#-6w8P?vDyohHBo$C%Aj_j4(_(N!#jd5$MS%gO0Ge#&_(ae|i<R1e%WZes5OR*6
zWRgYl0PTmT!j=6}#l{gq%PdC^;N(=t*X*i-V}>L^M&Uk&>3^$(<2_Kf@NXl9DL{;X
zfJ5<v%mBD9UGAA*C~@CFz+h{y7kr{D9Yn-S5nL3021t*>Fq_N-K0>spYOs<4iJ;Xc
z2bdhLyIguY!B90CW@$XFn@EqMQln>>GVM|Y0p|H-ADQKU%q`G20(GexsNR&IJ&z=G
zsvtt3Z-pNyKE7$i`J3G!mII&nzoCvZnz{;cVF|eYfNffLQC!aEe^^Ywg}&YMS>KM$
zPO-Dj-AVrfL!lT&Y$M?g$Z41at}eZmCckqCtlZxp5o3Tqn43H2Z}|lM<#QF+f(`AE
z1TQal;{WQ6p889!3%(L~T|KIHhb?2vzdi(t?(KsbWat2t;Y;((NeV|wUuM!`6S@l1
z#@MKic-k4%8Ri&HLbF+fiM6%Q%zy!Gnyz99<(nB9V^$fQb;)A-tN|F*mDihEd&bRC
zufmbFW$ZOL?Y*gwIswBzcwJpxG~(oOsh_$F6RKy%`&8k-ODIdH;vM(30NuHK`*7Aa
z<p`k&3~;z?ABn#bD_<7q`W^3E)gX?WnoNP6t2rRc;)XidEHk;lWBOG^smK)u1`x^x
zumtGU5Q!$v9nBrwf1FdXCl_Mgzb)8RIUA*%;tJl92){J8-4S|0pyCR+vCuwcD4D%#
zg>m9nFd!kvb|A}DC%^A+{cBX+qe2Ahua&-)Dk<6n`pRLOFGB4QF%YzWw^&FXs-Otd
z+Fs)%Lw#h}8?mi{Oe{dgM~RTW17?%DSU}*E3Y6$$IoR0dn_2z);a1%;i#9sNluzgW
zvwpS@jRBOf_zp_UO3~>}7z1KKY~oj){$i9~zjx$-Q2-F^6HZbEz@u@peQ=?=kLK`}
z5-KfQTKFR1eVm^AFhJ`kH{9`{o<;=VmuG|PMG)<3*MJ-WkKrIMkn`QOW;1k*{|{kb
z8J1<*MT?S3i-aH`BA_58C5-_}2oeI)As~%(he$}ONJ~g}NOyxusC1`vzH|tj{pdLJ
zo$LHK@1Jq58I|{W?tAaG*IIjTJV5Emp(g@K-}==DG`mN7p5_OcWK#{3R;M~k+HU9O
zhLqX%zMnEjM!~t2F{r&Dzv-}4-4UblKj=**jtPuECrFPQv}c~=R`0Y1c*m5nR{r*x
zAnlx~M|wM8%37KjuIjM<f}jGIV#Iv;F1NfXD80x#o|Y`d!_GWnZ#-XJOq10kCsah;
zL5VhxKdED7ntkcWP9^9>z!64K{VBn7$ktp<(va5iZ-Tij&iLRcN65(D9NFj`!etmz
zw7eyl|Dzdt71fD-X8dH+pEs`L#@IOUxs}b9LyKu|7Oq5lQLLjWt=zGz2VwU5*W&*n
zTc9uJeb~L7a-u#kY7W{#OFi{>Cl-NrLH@x-l*z7x=TiDtx1RX0u;dAkQ%$5N5!Qaf
z+*i8AZo&+hhOet}cp(`UwN%c82>zT_D6u^Em$TY$`=2gZdhJK*VE?J9z9xfY4;Y(b
zq9<2pGD3O|O^5AQFD8O$gqben2G9<de$=|uryN|J!|u!rvt(*D>31wwbHTW`m|PbE
z2_$`x9})yI++}+#Vg@NC>`*L~kBzq87R7wxP-?rtruMd7v`i`giSHN#(pCF*S#8M+
zauiVDF+~TeZ(CUVO;p>TLT-Z4mdXM-5sfD>GBz2~%#YwWb~{NC+sgfrN$4?NgbvXE
zxD#m=E_-mrB3<#j`u$_NhN}LdhyfqplvHEj3lNS3W|`o)TJ#Y=Dmi$La`?jU!6}V6
zB<j|Jek|*c+$ivBdlKhuo0(>TikGIICQ%4nCt{cEBCtRFmZ%H`r|0Dg^hZ7jRk>1C
ziHuD&u_R7-0sjBQc&^h=havaN?05RM_%Jd{)emgjBd;NzP004%FKc+*jLSh|VC5B|
z7gRy`qZSym022r_XS7SIoF2gJ4&1=IC5!&Eg1V9l`D8%GML}ILe<ZDB@g(iL>)zTJ
z!l=x3#-{J@J4#{(wgaN!v0qVr^x?KVOzi&|2%gg4pA5$Tt8Z7{%*<M%6wqS#Kx{HM
z$7gki9lXcz=kIq>pk;jo#=))Nu%LF7D04-lU9-f^q+$V_B0MR%-=5?zPCw6wW}olD
zaj}T86PaH?Hop=m*&Uo<)*l5e&ZGEu3^CW{6}XU=Rn|d+gWK<VD?g?i2I1Z)eO^-x
z_8D%l{X^@U!hR-h%+VevLY6E(?I$gK7#T3NUJZbc&|EXEWdrQ6h83-Kc@ZMR55Nc3
zi=9ot(scL1M(UBQ@z_U4VF$N27R&t|Syf*(ZqN(DBrpCfsN@Tm@>oH@-d<X;r@TD+
zUC&JC&)kXZD(X|1V6!|ik%;{G=*Q?lLR#)PJyfu56n~_7G3(ECv~&YE#FGYdfX)#b
zVRzP4#abNe1;YH!>)fp|SGNG+Aw3LZXU$ZaS3!k%db;#m>BfcMaHxSI@1YThONtda
zR!3}?^LgwOdi$GiqT{mJdG9ZI_xZ7omx#RsSIuGzu{n2|{?J$n+I6|`%J08muMjeW
za2qyUFhjlmZZqI(elKiZLS7`_m~EJlP~OS;zmt{3W=Vh|0Z+?7)&>_tj1fgQ{1X|h
z#9AZFU6@`6&aSI`=~Bmix6$gQmI+#xeZ)TsykP76cfN;TX@sB6&<eVU7Mt^WL`dZf
z3@$iSkJ@d$_Vh#1bGe)o9Kl?_Lq6ykMlZ3QxKU#<%i0%5y|O)69|2SJ#A59u&3ZBb
z&YI@W=UXNp2+BTRi_j-K?ecbIBW-f9P<H?Q^5(3dcNW?kpF4g|4VjEMpZ-6iP+;82
z_0Q=MPnMAm0H6*7g5*jlTuRp2p}MqK=~hC2S^eAPH*`tS%+*jfZ(QB(q4aJ%M9|e-
zX0k32EmWwIw@<R}DQjL=MMlsB<Mcy+{SD{bXVs&ZcUDs@Drbx8?0N$RX2U<MtZsj{
zW3~0Dd#9c9SQ*fHqG-ZGHsPs2&bR)jwYMF|OGys7R*JPU?urizh9-bM7qMPxH3U{5
z9s`&g_99=cWjkYtd8`bU&MvCK*z38jxNO+Y!os)LZRk+pasV4(OMBxS#xn)is}*5p
zD{1)OSC#kyxDbe5QR4?(K!?~!+2u4DDQrX2pR-NNow4EE=ruU-wRU4)uPegBK<GAH
z_zf+5k3aDSn=A%H!kARA(%4*tws2h9rBuu+Ipo$ci&X3jTBaDxF2sfVA*NWV5^Ila
zyfKEF(aUz)6JldMf?}rhT??^wp$JSYq9A`DC@fMC4t;z&J$kTb_%YXhpUuFiOXcDp
z?FHMGH+6qc6Z91xJ@EgHFg*qt=7vgoW1x8$o8e+G=~PVF>Z>PC7zd6^&~B2ge`8#`
z-_D_XWJYk|H-^#8xcZGu`@So;$P+$~-7;i$SC}8^7|ID+wuDcYMW}<MOPL7+v{>p6
zX}S(i#!c<Ej6Bp=@Z5(=yr8Hw^FK?a<l3VJO@=`9{lP6TdFsV4*Fw~}%xLH7^crLk
zOIqkTd9mZKL%OGToqt;hCoF@N>1x7oai9wrne_#N1vFnK20e+ny9L8Ejgc7$vys~G
z-&rp%g|xTKMpi|+I5{oO#ai~mywyG!<{lse<1c@I9*68E$aI4^dtTVMLeNYS4C4JD
zknGIDkS+p?j4Ed=Xw<EIl63C^TCXh>1W11sj8g%R*DS##V%5;Ik{;_}5hIw@*fs;H
z8|Eh>zWKw2eEOIg&aczvE4epr<<GCBoa(RVH6`C3;Psp+qEeZrTdReQ-PbbZnTvZz
z6xSDT5MbWT{NOC7{v3BF;@#xh8kwAb)rEnvxOGC2>+j!CU-@G3?#My2Cgxduv=i^O
zs-ZELcn7bLN0IN_F@gwe>lehZQ!YNyZ92GojZ~>|qTw>(_KR5`jr;fS_hw<-sV`oW
zrC2ld3l}8WSpL#sxEyXmtW7J^5FuTmN=f<h?aA&y+3O_#+#gP=)si;Psmm)HJ52S}
zc9SZ-#(4*KmmdFG7dwCAa7BD(;*H0LFOBj0FV#;Za<WvWo8n%px(LO693c4oqZ_AA
zXpEN7-R82>tX)~|vR+cCQ`p%T9+u>BPA68u3EuoCt`=8LZio4hj!etTr<;|YEX-aA
zI{itcMLFd?|KPbL^N}e07}T5xs>L_Rd??v&VWGKIc#`|N%(T1};piR+9uc!mFQ(*{
zla*}^6+Q8X*#VcLqg80PPcN}Ww$H*=s>?8F)dyAqG{8QUSXL6^zaM#)tUQ(JnK8-z
zljoG&Icd0PR%?1eWla0@oOKuK;=VSAQ+77oLwg)p-!*u4C5TnbGnqgB@`~4@@P`9m
zLSnn?cEQZ99zwXw?3y)}%Ldu4ItE(}q?&dW1s80R^{kk4l@yL~JOppX(R9y!G~&7^
zT+PLa#&uY!cTnoM62elxtS>y+hH1mNv}-GADacjL-=#fbPDD&B&-BbVDb$|>b8UGx
zsNyZ>!<*0DUpnlDs|g)68OyBolLn??e3WO^Yw)F5k-q-kww6w$RF$S*fTe?Cu^n}&
zDk`(O_Nqeu2j%G;Yo<lLsfR}Wq0Nvg8Rg67Us5-RFOHa@9Z<qEocD8?Ver)&p;j>s
zJ0*-wc)r=_zY*UR#m7&?YgieZ|NaYx(C9aO{j<~suBMlHHJ-bNXJMsM(ymK3wg&R6
z(qz?(S;q1%6&qI=l&F?-V!2GGj#+N+6x_WZb+(pZE_d{j{PKnS&_FJe3zhlUMdmtr
z04gZpJZIiMpz(qkOe8LfM#NN9fp6ZtSx#tcZoUE|Ap)uGEJ#U72kSgecfP>@@pHJi
zxUBYYXz%BGg&x2^sA=IRAYYZfUjGum@biuAK2;VH&vakM=@L$N4xVZO#ne1`pI~z9
zjiw*AU!?C(WCXqzp;?>vZNn;W$_zwNugl#}kq*&fYpu?Bo)S4)U0hjJ!Z}`*U)4X6
z7@4J&*{E`q`p5wzkBz17xd~C4?Q#|g$u9yX@~N@T7Z2HT-c^T2#@)Euc#&eVPvivl
zus!74a1yaI3})t0rj>0<cL{quka1usVM8yR(R<b8S`LT4K~gBD;!C3bo{C2&wHL*S
zm%E<n!x@mw-e35FaXnQdlQ6NYid6X(%VgVk(<*y7Klvx_X9_ETfzo#2x*0<mL|40N
zDR4aX^J>Qgk#~@XkY!EWk#mL4_)js_GE_@BbBTXRTQ}-Q-hD|H#itaDx?L$^LcMLI
zx1P3-TW+9gZMyA!m9t4Y^DB0*tV~XN!tz)>kl!9(zo@nr0Q=bg`3&FH6{n6c3xO6l
zA<3QdM$$PrDp#6^Hq-K{;9x!0<`WuhBV|&ME}T=Pv~MGQ7!yQ*X227?KjC%J)#W-V
z$xCWfvknBAvhLT3SBc*$F+AB(t>vM4J8u&+8ZWOF=%*6*eVA(7VfvZLxj6}O`qT_H
zh2y5<(T%QGXsR&_W!6UB_&X&{r7C{YktCP)*Vj@EBmMZ^v~M)xSXSTeRyT_9VS+6H
zCWPCkr|TJC0`j8jII;ZR1;pg$o6T8%qVn0ZZvFM;j`<;9(G?uWf_xSU%whe5e#iS`
zbL)quDX%&@gY)t#x2&|w$Ovl`Mz#w1+FB#A=xb82m`iX(v5V}FhWj3C3$D1DQ`8PR
zT$;EnbtgINAYp!I{!<~}M>HEI%xSN2HgoGLeVcYpzFU;!MePiIa&Xx&70>EgTJ(O6
zBQBwi!-)w@!l}?8P*+!987U)#DO#p|TjySS78!jjUE=@yto{8noJ>mmwPg*ak5|uS
z<P7{`%zy25*)n*mE$0r+8@{d9j4L=43+CPsZ9g;THhg2cwK0;}Vc6uxr;sUb7Tkrr
zq*u4qCtu%*kLu24iG0s^HI6ah>WM&Xu)jH~Tp`(Ma|*8gVB}*@r!a3T?flZ{uIo>n
z9?>hCg|@Ay7hUT+8jhvAs^L{j)1Ny!H1;F1eWO`NPG)&3DAa*zDl+U+nFdDRw;oDj
z-Shi{vBv3=@aU5W>N{I8OcPcDJ0mwY*5+<sROy=-dy(^qh&b*0(yQm{T=!gmJr2z#
z_qXtm5JDX!FtSwY{ph_isLruH{C44s0rgyk_uE?dntKnP%Q1BX2Q_4FcN;FZn^2;5
zwIlOHPVsTQLfw{S7V?cBPxlCaQFXh9&wP&D_O=6O>!$*upzSitaCd(g>-KUEEm1ie
zf(P#860UCU9U=_>qzpy&_kV*sF|nP*>x1wp5~p+Xx7x4ug`Tsmuj=uh>TTH%>6$Sy
zf5{(q7PUm7eLwD7Lrn7G?~CmrBa1n%=0MC++M;jh!Pi0g^Nc!Ili-q<e@00v*%MtY
zCA^UIh|wSJD)8g>R|f7cn~_EM-Iz2_A4`0BJ>Zn~e1Q0iC*G~j=)#w@XcoQaeO;Qw
z<odCw$Ei#e(Qo48H_)Sx793hp%BF?aH?>~;DExuAzbCqf;WeBJHP0(JQT=1*nvPdC
zPC_t#S>ICJthJ8zH1czr3YV|t;QbyP*y*=Ftd+3;Yp3C&BuU4a`Y+u$>#~FmQCIww
z*WP3pz-yY>Gs4!ue`6W?Aler<LoPL3Z#vx%^~=h#n!7t)9Q*#|hc}yl;d6ejRmd(>
zW+U0?C6cS=Z6#;K5+G?~Fp(EPg&lvJ<nx|H;_aUgwW4P7`GnO+tz~`+3^n{qI=cQ0
zJuf*$22t@^g1nsHo9K_nIExafAfh(J=o=5lKE}zkk(0`z+Bm3fSGD1$wv^jwga79e
z&b6z#*(zBZoefH^g{QY*Wp0&7*Xv#dnO{FSv?!*6$t4_34KV$3x+#$$=PfDzZVB6Y
zNXJPPr@HuZNFCLg5VHLZueD(dZ+Kc6_lmgt+cr5c8(sMIEuP_Bz-5ljU0>6CO*%D3
z^z+#d32G?^^XaW(8rdk4OTk5#R4mjXOE6(MO?SS4EYbV=`IWaYU(FNys8Y{1QTS>p
zgU)fqis3TVldB>V4wqut=JcLE$MHQJ9_u*>WL_?`mA=fY*%7XkZ&lhHV1;@21MB)?
z_5DiO<xs|4o|+H;-D=zRw?Fm1v<u^&-55l3sOA}`C6hR0?e~VuJZ&0CS6lk|dS>ge
z{v}lhM%?fnDaj+1ewIQV{hL?6&0o1H<5izmxrfC~Y)6pZEyc;lOc}YZ6Ye|5ozc5*
zYk7-TyQ2jz0p4b*`P&(jB&AH$@egj-YQak-!OX!s%8ZAQx^eb)tmihE;jUio&M2$m
zDXqSRkxiIB)l(yxVOo7~_K~7@Hr-kqwzF8?4Eg&b{wV!18+6ps;?0~vn$WM6vD)o8
zOD0}>Yl?}n(&Ve$4}0&Lb>N|Xc#pl%wlwMp4Y*_S!meoN5(AIpN|hpVUboUQ8plT$
zLb{kB_y~ph_?w@*o^S5{P5>>6;xlUQwnqo$0)cSFT7I6mZ9an2q@{c27LgPG=aT^@
z!9M*Vh|+Q2-YU^fJ`eG?!gQ{9T4JKT#+#;kn+L}-XzSk33bP#km^@T)wrQ79gznP(
zgN^OS?9~El4iUYKNg?^wX~gTR{%w}&pRV<}s8Z>h3`qGWd5S6;T+cBT5eie&2#(H{
zovzhlL(ols8W!sh`!F_xNL|@y+<G=F*ZTbSY+I|pcolGRnU%yNKUl^L5)dKr$Sn4g
zuA9pN26V+a%fXq#G9FEVmd@5XY#|SudFwk)EGS5xg|hEyww^R!jiOxAmC#lZ>YfPx
zsIv4J%V|XK$n+j+$4kl8No-3>3(nQFm>%5;06`!;!-^?&rQ}KHBqU~@sb!|bOJEPq
zjD07%U6y==pX8F-msn9%RmCeO_xCCS1bTQ;JV)?%gukBsLkR!#Lv7!I*8QnWy7=#w
zO?(oacsm>WVuV7&6V!h10t@C;1qiN@8IQ_klA$1GK-Osa^cd6kXl!J&`u_b+VsT>R
zpf5&180UGOgJjz2hn7Oa?KBSvX)&%$i;g`hH=FcX4>3r-)Ax&Nd-<DzzV_WSbF$}~
zk83p5Y@HqLYA(>ON;Sg0e=M!~YjCU;qx~K!#q<cTVn6bRGxV)XcpD-^x9`rJp0bWc
z<O>&byV%g*jHY?XFl_NEBmC0R7bFPFtd5gz@ikAXN=X*&klIOg1O_x577h6W{HQNn
z?88|O&&octdmPClB8r73XmjsoG+EGVx)2S`KmqDptj_-q(}E@BRNNth4D-hy0a68R
zkF396d7s1#e_-otCF34X--99Dp>3k_p{QbOqcWzja}-mSp~NS<4_UJshxHdBqVr}^
zDRr(zK2ZyNIx%sKJg1mv;r%+}z7&_Gx?PF!(6;xACGMA>9k(~k$k=yLE04!>)XflF
zTc5wNC~_$Ei2mewfZ>-NFI9Wf#f?I~s`QaFXfD&|3#|0<tlO8l`<DP!3wGe<2kn{<
zguX^zZHoG^P5p<AqRmfSP3&@eZN9kts-+Ax41Jy2o^EVKcwwS~FVp>;r~7`?a&Ybo
zGP^M6UbMN&PM7*{_^WT|wnNhh$ypua91poNuZ$YK@Bam(huD|pZZy0M<}BAx#Y8KB
zfT=!njCv?)++MvDAQ*@B%+$~mfGJ0ARhaA}bmXspw@Bw`Q!VN)5byPEk0Civet5Hb
zT?Vf?s5WnPX(HI)Rxw#Bcs(R#Doj|Ca^2fAJCu5h5qtU!KFQ~r)sBr3F7$#E4T1>E
z;3iW1Y}pSoEG;Q=gfj=k<Bl(0?P>bd;<79iINIDS6`z;RT3_tPWQmCudGP0W#|2)&
z!C78J3QtE%m$UAoaeZbrQmoUl-kF2eoP^T%ynl>zQvSK*F%#>QfQ$j2CWiXiNf*oY
zT5vv>{r8%rh^u5#5f!r1EO!}BZ0~FiOWYhJ^8RcC2TJiuLG~K<>Y6R(clb_@MtW$K
zQ50|#9KJO^q-77@t`Nv|y@y9&62+(?pWZy0v5<XgF?D{QKdMYVJx&h3e9~8E)mX~n
zBgr<W*^-AWdA^}>msVJB6DQ<VzIo#7(64>>u6t_A;de+pq6|2thQ9BXFz?jEb>U#H
zry(AhMh%G4_?=|lNo_T92`ik{V~g<d(Rovro3fSBchy}u{B^kCg~w0RdsQtBGyG0v
zHrhH)*4}Krq(%bz)vJeoY>Zfr>$Mi;`DM6s;^Op~iCII3A3mvdXxU(;VOa;Wke~JR
zipR?j+H5Fls@6?xBPH^u;6_mMpHKH$`HRGMBG~Pvt;xmFsaoG4n0}pq#J-iq^3{|Q
zH4ebTo>@_$rY!D;@+*gXqThD8@IUtrW0=OT1hPhM7Cf`L9FV;fn=2aj0u#<<(7dnH
zjCQi?up5zD<n<>eIs6eJ`GjeS<ymLuB%B@f-e7A{4xQ9~Ln_2i{A7aqxOpDtZ_4lu
zw{4pCb+9RxO7chUu_k}YBc@%GX%1z&btNEXdCXj<;3hZ5J-5}hC0B^mNY*dUkHnob
z_m{}>4fIh6%;vP|`;xsAhOTSwd=%*O?mROm(=AFPuDpj!26qeS$>eZ-lNs2{ul)Yb
zNpm|p1+5X0etLaVO0K1Rmv+9+shkbFaEo@qz<XAgZsQY|X(sRMVc{{C2QUBRBqVfs
zOS9*-0#v$fU7@OQTw<VAruDU{dy$UflFr0eYAO1Fq@R;KXcV6f_6M%h3h|9*Yhpcz
zH{A=Y%A66fDi?rNne*NWW+v8NsS9F_v@X1<<V9E8op##2DsB|1FWbw|{8LHeRLvmn
zLc)4g&?9o2LR)7X--9vD!BW=D-mC6H@gal`jN_Y(^M&s(T$a9Zhn&Ri)t+rb)3L@>
z`<MN=){rPyDCfOjp6}etbiQ@w8^Km1OR}5#&#Qq5MRW3pqNyEDc!~97%kq1^Xk>8B
zIm{@*8$^WL_Fh+h3A<H8rOdvk-8z7+_&uZhLxe*yM<ak_PBs8{me_xwv>gqW^DRJ+
z*osyBx(yPY*~H_r@Pl;3Ui3dYMg5BKFTThf8XPQcWmUin<Jl_*|07uJZsJRo{5&W4
z=u53aAk)By)@R}>H$PBfVB@Yna_y{D@W!OlD-e|*j^#tI&LJjFTOjK3ycxU$zp8hu
z`M}Afu(ese2uNeQDhwh+&;h(}eZxVCIQz?eJ5sL2Eayx^Qt!CMc9=*=;?ta-Tuifb
zX#91<tAKBy^Yv#={@Exh(qQSK+0Op(KJMwGWMhQqajsII`dFI>B~!L+o{88VTUicS
zAa9GFgNE0f9$L+4<rOH2&>jSmtu{TD3xV9KYvRE4JAL&2SEm%5nDHXOU*FPK|3*s`
zIjMK?^c+(%y5|+NA}r@g1Y+`aTV}@I`6sjm#dh}f;gm^eQwPW^WgeVeGu@A&mftGg
zG<g<EBfPPY9IA(!Zn1(D%M!Q)2G5@9z%urI*e&#9a4@4!>%TKOBM>Ve@S73JsGiq1
zha&J}%GGM&23oG8s(WmvRW}5s!YSdQ%p_|{W|;R@kD8H_95bem*<;j<t(!TGXKe8g
zCaC&Xkf5W)5<>QlpR@j#nXdI&Rz?LLzxLVx_6n_IWghD()rmWIdfJ`WNI7_vy!d#G
zjn#t+F4?46E9uR#%YMefA2zH+`Jf0bRY>oRh>5kBxM%|`moO(<Xz}K^(z3Wq?k<LC
zSHI2b4E5jE$TU6?E)FwJjoM$N!`t}OF!jth+YaM)4D>Rb6&}k`OEUNqqLzLFIr-i~
zmSV}&56=JeeAp$TSf_KXox>RIO?NqZRrXZc=(`znF*=_xle+&xJ@^HNV#kG#ejBh^
ziV8N(nfB1IX;u3ZitM8;_vd;mWSaHZ{5_QNNAI7gc_+v|o4jjyNyzLG<UTH}qd|_F
zQk~5C=FLZNZQH-dNbb<^GFp}J{-Eg1&QZ~8XjRP-jr0aa?&SwH`z&+Cq@s3Crlkvd
z*38%X9DI$%1|sI;pa!3_^#8HHuB_bSUpAoKs6GFQ7<Jm?k!g%cYNuL#gKR{9bKP;`
zs{jN1e|LUXtSCnxx~A_kUDP}ArTF>2Z@lLP^K$t9>@!Ap{^-{_Ot>8j^S;0}>NH4x
zUsW)(;74#`lJDZ~K=F<V*~=@Fwc~6Mj6#7pEnG9RFM?1bukd;Fe-L40yIPj!KY<@j
zMTK4ic-;-HF1Qxn`_+K-fhYb0MK*m3y)>jXFO<-#or|HOph*1q@r+dJ1=z#Q;tm^o
z@F+OYzKI;&T&p{EkBW&Y{pth@TTrmaMGuy^-+nQTfhP9rR9ZPG$kyiEqksOstKGE{
z;gtVX9s-5?O3pW+K){E@4VASQ{2&~h-@mq-rzGE(Z!TSMy*YlF#W+*>A7U~U$lcyB
zNv*k^yULf+++SeG5u%#2^<C9x=L;&eUZ1YXHWEJQxne+MWAp**r-gn1t<hdnD{h5q
zB?AFNAAV<lg47@I;RE19_Z45z_CZ=d&u_-XaPpY?bw*BI*&R-x1~?9i`j3yF%jz2Z
zp}wS@Slw3!N6#aW!9N!jHJZx;>|ubGg>v<NCUtqw;%=8_5Nd5(2PpRE-fI>H<|dwm
zGz%dcP+OGIQD%zwIk@F~tOZ6k%QH4u64(CWpCDkH9dDsL_$$9*ALWSUF7nnZDa#qT
z22}dTM1xPN?w#`=CdsYdA_Y1W*11uumzq`3Y(t+vhU|yiUgTPiXJGbs3#<;}wEp?f
z(b;*trvjEs6~Gd1v%!KhF1sst+>ZPB48Zt!KT5!k8I1NbE&_yq*$_LKd9HjND0VJv
zvc!(YYpD7hQ0XBwgHZ1PX(-{gXEslhVw~n~IOWYzia@t>KJS)}%GGt@W&)8rP-oB7
zUY*r@wU+;V00IIaIn$%BtnV;tl(BzA!{u$3ifJAlN<RZ6*Z24?K-7MFrA(81Jx@FP
z`Tn2EHCfh9<*qyC+<l1|SHtL(LpbQobG)Lur>zsAsUI6CNWUNIYL=)qTc&fN#&<E4
z`i%+NW1*7len|T>d_ZsJU{vt@-~eyd#=wx-FT35P7CFKlJ8?&La)4!{?T-?bt(kgO
z4bE=-)RC*^>?cg~9j=QqPb|YPtrNJYP5ZDV@`ye8PHIGTesJa)GMRIY+cdJ(6#wM?
z;h=a8-=TX#oGzbhW*@&6;L5FLo?TrHhV^c`KwU?PdWgs-h~Ud#q{BJ8w6s(JMi{f9
zPnc+vD=R@FZiiQ{UBfszIk9^2;?6*xrmmqOmcG8ef0BP?rGS#M@>O<r!l9v|hcYtv
zZEd-`;spI*nf&s6jeUqI-4^Vy*E`sprQk9Q;;qI1i)2%pn3pI~Q(gCe*(M!$u21Yp
z<p_N6?R(J8Ay9utj$pU`iY1kArog3CsbHSp{O`41-Yo9^SSnzEo5ea7md{78^e2Ry
zwpaV^ShtQEvN@JBrst5!<?#mtdy3T71Y{0%?`Gm;_14ygF<BZf;#f~}C;o#mk(>)M
z!owdN$9yH_lw2xtNZH3>sc<DnKv%51^p4NBT?YNWk4o1WWE|}QI=owKprBpx?^#TR
zlo*`yGRtu9%Z==o6&@oF$tizvMep^+y<~n#%<w3xOTk>1RDhKuC}^wtNC&zL9vT8n
z)Y)gJ;N0A1K~W4`BUjc1TA;d)o_OXZHIixYwm%tfp7;za|E8(mx+hRhn|YDyjwM<Z
zn$32t1$~UaoF9T}dB3K~Dawr1275r)wsdI8JE8~q*GK+l(P6>d5K#(2`<tyH)PXQl
zUmxai-?m>F+%X?$oP_rS>zEs0)7bj{KA$XW-L7x^^#BSXM~-o)d1{$>p#vJrGyh&H
z?xgE_4`f8hEJMWah#zaN5U#vtuaDn93*~}t{}sYlJGQ>By>^-2DlAvN*YxFo`-3u_
zMVqUniUiKxtD1_r0#kNA7BhDzoLa$mzH+<QKIRo3-1wxPyk&Jg6X!I3GW5Y=)jfA?
zIz_k+z}Ob(xm2e}b^y$;7N$cBB`7c2vc)>CF)LW@`?x=+!(2O<?sT}WZB$gq{*h>l
z5%X$wq)mFo<Jrdw#7M?5G!AZ6yMtzkzg-_Y^GnAfg%E9FSvQ!Kov<aVDRbY<^43y#
zr$<7W`25Uc1<rBGdA~L+=i<R{=D`1HuT%wD5~L=|r}E#lw6ySV0S1t>8I|T?N`fvd
z^u3J~SbuSaI}9hc<9wjbDp%;aUJ;2EPN~i|tJ|I`R6Y_l*&veVm{^9pqJBd|t}I<S
zaDPXM5m-x+v6IdhCs91#shzE#QrS8>R6`xP%ymrc#D)|pJO^8g2BrbCy65q>OMl$;
z`+GBbFJQ>OzZIHk{z1@zK->L*0oF%`1_WeVwvO&kXZ0$_4!1Qno`v0OqFRnqnA@;A
z53?$*pX~pyHOXc|0$X~%9|f#S+!KqZtYj8J4e|SpWWsqqZ)uRaMIU{1%EY`osjXJs
zwc3*Z$!{o1Y#N$+ukngux#}f-<L5XMha(ex2cM)&3v~4HfP#_CT8H~ZtV@Su-z`MS
zSCM$ZW=-e9*5DQFYC&NNP1azBJ_o~0@h4GCFAmw!0OYrRJ*v8+?dP4gmsOrcBVE?4
zJL97vx#OkDS^)TzCQs8sJ?yE1_W_l@)pg0?^#^?9()VB8AewfyR7?*2t8G2RZ5IV5
z2u>1V;3;9dsY1Szu94XpchT_I`Zv1WeNQCWNRYCWaL=a2O4LP*q8sbo$*IVbrDunn
zUc0Kdj@K7=SyM}Bx1_VFHpX`y@5i@$DX=y+i-n~7IZpqUUn9!~oseqD$u%AxSTF1h
z_#-x@w_XQqfD23DsA~_GSj@&MNg<;(<Y`vDiFH{H>^S}K(^?<rc(_egRu++H8Rg=G
zU_^fX_wNOEmS}%(Scx<!TYn<k{a)@2h-?&}<p(m}U1Y}c3=m+b6@DMk=cmXi@EwBT
zCZa~la^;rOgP`dtPNn}wT`T=J$Gr_4w?OM&JmYAUTI{4B)1jXFcHfsH@$gQ$aTq4)
zYpi{_Me&{xYS_F>b07ocKlT|9?9@kk_5~cx?~Li9h^8F}%94vvho_5m%K&B%%+pTC
zuWz~%d^Mp$&30&U@Ghv{c_e?aa7~6c!ZPrIQ|{c2F^R)l)grBgMShMZc;i(=Q97$(
zZiTZ7G~;6z(ouUe3ikYIZUAcVHk+y7G8#G>)y1av;j5PN5WJnY25m+#iXL9{_?ohV
zEzLb)ex$Sk!5$%xX(1A<nU%JVIM9cyHLsPQ1ufaL&YM3%hOiZlT{gx7-+^n){Y$Zq
z%zm5g(Qe~#FWUOV1kms}Vp>nB0rTeN-xm9Ok5*%4F+1})xw-E<I^J$<YC3}kyFYKc
z?A~P4tb7IQE}t0~e5|g%%)_I?<Q)|il?Z#kt?cYz;W9QXaQ20Lh)wP7(y@F9CI9;~
z>=pIFy5n__gsp5v${~_uxOs4Pq<9d(Ga+Xrd0y(Qr&{yQn}h`KOYsyxUw@C6-Mc8j
zxcZqrziIkW;UIA=duDA_Aj8#0(syYA(vW8v9hL(=x|i<jJh<vE674ld_DU&J9FZF#
zSLhl)=a9g>tubLQl!~#Hl=N0jgLSI99&RYA-*GW4YS9v4@(I9XQk~FQ^_jvG4S>l<
z7tW&w9hgvKIkg4cixd;<2tODc)cU1ZL2?Jw7l1cELzKOVFwQ7e3F<~AP>YOa9_-y?
zJv%}BsA)G!=w>`w5UL@DJ&{OUep4JBO(%n?34kf8<8i)Ayuvy_(K05C$(=%Xkly@7
zWsRo9^gjL!OhCI*n0SG!R@lpUERMs{`pV^-L2H>zhw7$sWdjDl6BY2xeE-YSB!#4C
zR(sFR&SJ8&vkPt1qYqp7{C&a#{!7jbBI9wd)<e@I&(HHaaMm&;3K}U~(W091!zZ^c
zn0qb&Y6wGuXlCMho8+WI>j!5Ix9wVz(MQ*>Ka?E)s1OJ>2G;n*TmLNQ)K+Ijbp8|J
zaoSM)ZBLE<O+^#dpzT6#Zj33U0KY|f?x6D&Z|;4c-fj$6REkw|cJlGY*juGvPH6`P
zTRbNObE+>A&en{snO)!-`wAzGR(OzlZ^s_Ab&3=ol1+prB6M@PY-+S;I-oo0oO)h+
zRj%QbASLlmYQL(bsUd<2I8xOIs^T}yj9rcDXu&4pxRzc-LG{xUWG0a7%D+_h{w8o6
zP`wu<-SBowT0VUzf{ZebGJhUYtKo}k2tiQC=qI%t!LVS_Z_nt`bCfqZL9S^T-#-gC
zpInZhUx+i_B0nXIroF6{ezUrGaraEfoGOj|e9!8?<ngdV6;90GI+3^}#qAuXY5|?H
z0)-qSnjAi=d0GEB=7W%h*_AestT~eNSwIWa>HJ{}N&k(|)6(ivVi1gxR7ydz07{dK
zPGV%ICN@jvPV@W|1bVYQ3zy8IB}cxO_m93$iv=dqON)j+o$Q1SP|0kd)}8@)i^-iJ
ziwiaX(VbRU_Vb!y<<J#Nbu$mU>$L)JUY=5y2T45XakzZ$AiVr^S%2Ty-otUQ)J(O4
z^}|EfhEcFHkQv9v5Q4N|(<Df8R?RHnDDK1^!bAO!_7+F|99p<>XKGGQD=Z&pA$q_c
z87`)2lQ#`Zm4gAN$K`g(p<AUQxjypLPAO--=_3PCN0~B6gi&bpmbddhPdsvAV?Npp
zVc;7?N^^%zAJG3RI=@RFspD-Wlp{1KNcekPZE3yd{sx;~<BQ6@x?kpQgAft+KT;Y`
z|M{1mcg9O8ewqh@{P)ZDDtcYQ*ZbmebD0BZiDX`yFE9s5RnR=JZG<=n(LpuUHM<g7
zELsvjj72Ik78?XPfWe2Cnl-54nKMT#wqWTU2ag_5Z&o-gW$+dQe5mvbL<<+<L-_~W
zut1+!y)`<jg;-@;RYij~%9>Vq(&g=A6HSzjS=UYvZ})et1?n($?nzcA=A@kg%9cT{
zbFYN&9&r9{!a3^Ik*0ODVHY(E9TjixHk0$JYxg8Je#%Ap3jsr5g-Q9u1j1kIY$LPe
zyh}kS?wkzXtMJS_(#7o28jL#S3DmwvLmx9f(SVjR6ABfQY-in^wJs^mQ@0CYq?(+T
z)|2K;l|2)-29?5pv!J}G?ti4dfDnK|_Vin*6QnjWC2&V-qkcqX`+25fr7Qh=rCO}c
zT8bE=>QZ&h>va}#Un6~p%XC&u?+yb}bk(e$i{kl=&W^WdIH@5`z(}fl;`{trxJkS_
zJ5GI$7mcIM2ilHcHNy*AW59qk?8$An>;lg?elf7|>!F~CZ}@8wfUkE|<&0zpEa_`T
za|+ffybv+)bAE>Dr!+t-T($gOeMN_fb&-##9InS*4;XZ$BL@icF{Sd3ehskG?4jEY
zHcd6Ui361+@tQd@1xqNA!At6%EwOr*M>VyKZ24z(wW)oszX(6iE~p5&hC>Yd#BHSm
z9}%*J_&RWzdZY?>RRv1ho@;&4F}0=HWw<y!`$NRh#GeUoXMcZ7Zbyo=mOm;h?0>Ge
z+J*l)h#wi|RP<zjGJI&i8Kxx4`27sX=oW7!^BA$5qlPB%5kQ1`f8m$aO=_2E+-ulm
z?WR<yq48%RQMMu!@RX&;H{rM3;^8|8-2fVzR2Jcii5KbA`E9G!?#f`vYmm2YRO<QX
zvx<yH`vcwd@T8&&p|Bqjyt1Q`(_^e3Xt?ZA!$B14;cz~)ELu$SX@2v}L_#Aumy531
zn|o9a8BZl7c>uOIO#Rf8+(8GFFLV<_%ia*I8WX^ey&`&mhbn1#P;~)$5HadGVqe!o
zyfl14L<e8oxBz(v8O9m(YBP@db|{3T3Z5$lwFV}bP(5+<cmN)euwZdO@T1@If-C#n
zeoM_#hqgz4hvj-*DQLEwQ*O+E4_}EW-oHUn-cJee5$G1ERVeymrHM{Nv2lNkaS(%9
zMa-3mFz_}DTdvTmgJL$B4i=tcB}V2YZaEcf&zx>GAr#oxKuyI>$7&cK%@99IuFLHE
ztr?S!kUZ8J$tWCG)eaeGJJj3Nkz4qp?f^%yfTO#us^=bfr!LuCO@8}E+i66Nkc#9c
zp`uPfs5zDW!_%L71~2Wt5jK;EegM$n6vhK+BmVT@<~Xj(q?bsZ`KWFW+)1dL1LXFu
zC2KXlGGam4>&vf>oQ*9_YNJ2WkUheefGw#VuPa|geF&%$kQJ3Pg+n)~KVh$XP{s3o
z1WR4}QUGGG`V#+4$yjxk9;)G@T2UWV0|62?*_0X0do$N=<Qq4;2u`C$GH*)mXxXQ!
zf+c3L5BqN*^ymKHTl|6kOAA65fsjsVih0J-%u{fja-9&D{#bdv6Z96uYgEEn>o1Vt
z&GVq=?add=OA*pex1*F^oyy?Uceq=;`BF^MwUWU`uB-?42iep}sVrI`UQ&DRO`7B$
zHMIXGn>LAlS#jgjuFFc8CodiH3P*c_C1q+B+$7xtxsn}m;la^#?rv$K@u2^Qq`v2V
znhfAzPdm~oH4RY{EM^ES?b)`@&zY3JnUbjF<<W*|2>9#-t)XpP8(=n5;>+M($EfdC
zrG;v98gSO?(ut}zTT{=y8jf5)lJSuU&(B-@lzT&Px53ZhX#9#W{?c4u<{AqyerbWf
zxw@7o_>{}|KW~A*i;6wyd(GnR-)wQ;A>fY9Up8@xLn>>j(9BdQ>DsF~v@7*LE)&CL
zvo123?Xob?OZ0<ES>I5U4^$E`p)3xPXrswDZS4HZwVK_3#X>as{0A?-gAU@_w7Y_N
zDrj{nn7OlO-Mka8&<c!x_RCo%k{u5B;0u6s#wq!Obb?y)o`wackohA{7k{Q-mpe+v
zXpX%r-ip+EB(ZB99iO#to|`CT(^Ef-*j>6_dGek*<z$#PV+&qDZGFKD-qWn_%RB)8
z6kc~j=7F>IB}3pa@B7e4m<5>bx)7j2CgbpsA~Ng58_8&jFtW6Gbt=+}PPpgL^b((w
zlKo<asnSd`*i)A^c;SpTW}x};E-AS{Rmouq8YKfMRF&(-!LzH+{>v3_FaFPUtpZC%
ze_W?TbE)I|K5mm7zV65=Fi9d|a(ng@@506|L=qiZb-dj|aYIRbcY(JdfYCpGk{w!c
zN0`ysB6>&X400{%Ra|{^*+(^05J@=LnJF_{`ra+kf~C@d5#D~vPWnU{8956H7!`9?
zc^{socjK>hO(6$)at64ZvfLx!Ccb7-6+!@WHn&OulUixpPkmIH>P6*#)0}}wTu1kE
zJ}t_{<gKuRDmBWP<u+%1IgK-9<^kfePgfs}jO9E+D95H>E5*IK?aO~Siw1TkF4R=s
z{oqhhxI<Ajb9k*7!}?S2b;^epPO=CbvgA`+9UgRVe2Kr-%ag^^uN`Y~Dn=V=Z+GUx
z-_Z7!@qeFHgo2V!#oHgZqm`L6-2wGSJR<z@Mdw3o@Hq(Djdc=-db3>^znpIs4LPT(
zHG^VSFsr~8;p?lDQs+ixH+-U*X(0v=8r&JInd1ZFc*yzODX^IB^0#1&Jf}*W=UI|@
z24I}^NTq1kvni=Z=pruVM%lg<HeXE&t?uFNGG3I*TAu-+ZzWz88zaj2`3!oEIpP*8
zm6*TFh=Z8?;wh*5=-5|&gtc>+zQIN+qwo=uYh!$Cui2YF{?ro^9i?Iq+2}tA#7X%_
zjqbiZbsQ7IG4|Wjf`p<4`~T$NB>POTy8;@Y%s#&H{IPL7ugQOofG#<z67C-lz{@)&
z9EVtuBr%_K_8VzuwuFg(X#HuSS@>LvXoWRsIHPCx!9{Vi@;Bb8>eKRSV~K_1VsgHb
z4-O|Qrp7tG_pEKZELFonLtF~Ll^dwfSgMz?<e|y^vO1qlvyN_)91&NUuapU{Qh}2y
z!E-$LU}ggKIEsst2;9<l8GBZbMn^b#dVg10;1)42>o$ZtgK9t@<4jJVf^%|5MpnFN
z2VaUuWF$5S_$Srd3^@AUmu*itckP*%|1hYISJ9I)D#rh+NM$oJWq-@9tA|YgnMg9m
z_@mWcBE08~tOzAh!1-YK@dQdVcl2rd(##*iY>7T|-&PJG%&?abUHD@&$Z#NgZQGcZ
zO?CCZ)6xhAg5Bz;bz#ckopE09UY}(bEK*(zdKd#vj)t$zXqNeP7~z$R*H*v*Cse(J
z(JwTV-@<tF!5?Kf_~VQzIFAE-6uosUy8^X_vw;a1-0vm=b)JNFMdrSfxqi{kkctUv
zMb3z`ae^Q)4XdfAzc^UH{F0#FCW1{jS(g5oO}G%V$>!>t|G`2Q&58VfZ$Sx_0S~@v
zA?q11@xN1AH#Z6Zo$JtL2uu;VZSW$=AMEP{G@jeyYn^<oAYZftrnP_E6mX`&5-(0=
zmsLgi{&$A`wrfJLjP3~#aZZllt|R-ry<l{tK;Dsa6_O;_kDb>_MZa<KlR8c=&+Naj
z7cEzd9o?zO0@dre%7y;R(oN>pt9&oOgm0irN3m$>F))8^j4tw=qphbPz^Q5PxO4l*
zwh^t86WA95tyVVagt$6Lz_j7@+RLI2KU`foP&`(zP%uwS!HvC)&3{s#<?olYyIOgC
zp7!s*5u?dpEL;TZg~jW8fBc#ic<{$GY|&TiWDIbo1y}yV*Bl^x&1JvotV6?P#3`|9
zgxJo2O&V62Z*tF_HIuk8C(#Opy@JBATa5-%*5^DL8Gr+?(Tc%8nVx<)lUb53|1{m~
z6Ju^U<(`Kdo@mz^p~r(QL6J5nP7$#M%p1Wa;PXT%wLZ?cz1lSTB3_>QfDMbN9|QQf
z@Ywb2o#1dY^VE(ey!>p#A0<3ULbzk&<MY;?%8n%%EHZ5~i~CkaSHZYMx!j4rQ?7>d
z_nX>Dxwb_9MIg3n5ahu`(fS8cR_7E7$(*r7j%Cg&NH+*CG5U>w#o^lIKv;BVe>Nic
zL<Qu6;KN;P^XL-WKl<o$rc5x2;~6lpVj!t)v(Wr_&R<OoK+`YOikQ&Japs7YqrzkP
zQ0zMXkoAOxKsdVZLewA2`!U4o7qSXyE4VozY$XfP%Cj344kiheRrYs!1HDo6(KmBO
z-+Hp}1b6n&hY#q2Mc(xj(>b7)Tf)4BCoS}cCC0|J{s^I_cpchYe?)~T9bLqE{r9s-
z^WR;5$i$P?tLk<eKVj|BUCm@QeYd~P1(zx@B7xmvd1-w&fO=Ni>_(D3l|}iz(_d(1
z^{vZ*wqn59rM`U=GANjp^igxF7oYHo?ogv`&z_B^T?>wsIIMqIr!P~N-bMfU%h_dZ
z3jelRL7$H97)H~tP%JDgh3!uy*Kj072R`aGA+Gz-Q4iX({L_i;_uEv_l%G-Ss1rxC
zMC_j*Hb#v<KLduwhRm~f4mQ{&OWwbu)CBn6wVVZhU#}~t9F*N4>g%tfY=iJ^>sRRF
z7uJo(=0f{;@TK4ZT>_ZmEtPw4{u+}sJz_Fm5cA_*2>JWgAM*WYjWWNRO7;TWWAnH2
z5eNki8=XX^9((na<Srwvh7X^gU;D{Nn^?<Ooz8_+D#6jZEDmYvV42WwXSsI`d-H!n
zog#Ww@uM*F>qk=fz>v4pzPc;9<2ITL?DQFJ>@A3N89TtyOy|$%%ktKZxfz-#pO?!Q
z_70_42?0&pawz_dwihTOw-cT+E`FGKc^N6)E7d3`q?da`>^M2=!KVnURTG%k-nrY~
zCx-gp6KDV&`%m>i)pDlBD#rd2E*C8%+Dg`Yqo@pKW*}(@cAG&>)iL>8_T%rj&#;+c
zdG-&Y>|Od#w+9>2t2*kFy@!?*R8}uJj&qa#SdiqHZgI;##6)vIYK$j3{`uz1eCf&C
zv$v`_8mW*1-2?c122vh%Z7A3PrR{IM=Vu%~+m&YC`<z7q4x7zQYH&o}HBXk(uc>%T
z1LZ$ro_q7+0+$`yE}6vs3>3@GtHvK>>$;x>E3i(Nh_V6AKwN|c3mBIupCC40WojuO
z1*lUV;37^*;2J|%heS&B>AE6RPk&wcWyeOvQpS`B#dNnj^oTgld<#Sjb2PLIA<@TU
zy5=U|r;kaM`;m5)LF*fG3U2;In98s0yc=6SwYbpz1=J>?wgS&ZtR}^T_C{?G_k@mF
z$CvB>JgL=3{~0^?joeqo^oW($9@bAAHw+tx*akpFPaGE7600x&SU$f{OO6pTy_TcX
zd-kAhr^2&nV6Objc0)4>MA)r~wuMot=DDw&-IC3Yk#kHRmp`SkJu~hCvlJ9iM7whm
zNVCw$CvYf+79GuK*TpxDkI3whi|hPIQ+=<*ml#?_`d7lz$34qonutAqc^=z_{y4%S
zZkQizxZZ9WU`Z#2Ql~MWiR(w=yWMgH)u*9^4h&OUZjr>}?U+T_F2$djNx%UxA!{@Z
zEJ#L<B0yr!H#5P7?ub)u`=`bWzrW{ZfijLDCPB#QLb_~RqiKGPFSN`|!>}`ne65<|
zF|vPxjbs5|EgFRAtLZtrr97sg>GZ|vT+Ze8##OBk=YK}@=}om#th}}qpg54L8Owzp
z3D3Hj8tfkely`_YKn4Ue>eMmyIiO<=i*`(K24h(!U0<W259rCP>l560PD@goy%fH`
zZVrzsATXTsAF#R1_aKF#70cT7Sk>~E(J=$4G8N`yP?a;#ausC5t7h3(eMI?`a#<f$
ztb>$Qq6wdy*_m!#le?X-k_F|d3<4qmO(W)iyseU&?6#DYmr@7?9vzgHestOsCFTML
z@9aF=WygPuv28g4YVNwUq@=SGu3PxE2Xj>03qQ~iSBPQPhjTVIwbB3h98yfrGcSQp
z5HWe0=8%d{#_dEF9L&;vpWps0`yiDJfq5ds;r=mL;jPZlB=yVaE}G%4jkip{NaOcm
zJ>#5E5c^(1C(r0-ifd#_z^|#XPnPVUR2>S5aMLlCUHv^Akm4Zdj)hZPt6GIjeGh0i
z01Ls#85nu*_1bITgbCNz30O$bT^+8i=7s8~us1F1|Kflejy8EQWH6R$3VG!(Uh(m2
zeQwx$(9I)4Ftz|HDO~IZK<=Q~T+K-@O6ikEE~MUsfve4ROehsqt_;Oe=^?@j&I>a>
z@~3|@4si)joQIM4#g=9E4QN%|n*TP_LayDcZdv1lP3uY|Cwk0{MQ}M|mhB&^S<Me}
zHvDzaG2shbYY@J*XLiZ!(h-w)YOK@HcY{zg-~@WNC;d6Ud8x@5>?;l|_75r<8PhKV
z2|^sF$d>8;DTxJY!k0BZ@+=Phh1>RJtr04|Iy+O`u9bY}?1GmJmq7+>XqF?KdV8|w
z=$1p9^9X-9L(zD%eYpV8p?$MS?Hf$A$|}{XAT#MOwIkTH3=$Clw^vZ8hzv$MwjB}f
z6*U3b9(Nq;J&X-y9nI3RFZNOSG_+{Kqa{5e3B0zR0A>;h<nTkErCD4LX~V9Z*j@M3
zj|@a=25|3DhO*$)Cm)ZY*3X>)n-e+uuPH^|D5dAlRv<b1^oV6$3wzzKm!Ce}LQ{!6
zB^4*`>+4f3GQ@`_HShX*F`=WCBFB}&HXQ?l)UmX`kMZF@o+!QDi94fER&W512k$Pa
zF1@5nMqB1{em!Kg58rN9Wgm+Ug}stI{{S*lshNI?4T3lw9WK<0#$c7q=_Vs>pTDgq
zliv?J?H(UmUqbp(5b>&j6AL>mhVUR$9HFy-$HdML-Dv_Tr9`At)vn3s=93F-&p3JB
z%uIl>dQgn;GyieCz>PaZ8{k#}KZ=f)QgO<W26RP1ZT#xSSmjd31V!%&fGubQLHa@3
zeV*)3J*VtXXzzDK_3$8lMJ*@UVPDhjZ5koZ<GiSKJ|k~zhaO=b3@s3sCELzhaU)$X
z&Q)7A*Zzj`t2b<&YovYddVLc;y<AaIp;~3n>Wfc>C0~7LvLCG2H8VZEak!k{G&FQ6
zROIkF0gX^bXQ^H5bW0!|PNU}OUNec@^CL!RJcW;)6o88RW`S+R>>=Y9Z(-+Qi8-{8
z>a><IaV!?m?{CSF!JSZBLZ}Jd*$@nwgBAajHwUYm@xZ!4gswQLP7p`~<%D7nMVBLz
z#Q%(q#M8*vS#OW3iQ<+eodqwW>ee%D*Apwb!I(=XIr$lidY}i${Ms&PwQ@EM_?BJo
zb}Hx#j6H)-M;j);&bQbm=qZ%*?Rbrb^rC8atze?#lLsNccA20E$sCKLxRtIv14c}U
z8kaqcV3_74AvSJj=z{_7to!%lzcsx77CPBr88Xv;9Laf@jQ;NKoUijOB<S$g3B98G
zmtaZ~OzeRMNzPlh8udekecNbU@FXQARZCzi<>4aJMKZFp`z~LdG&QN+k5>79?T$cO
zC8_~7l6yZbGHmGTl5H>Z{F6H##vGsXiK?T+;x{OvG@aRE-hjro0&7Uni|f7*u*;jq
z8#C}LtCy^-iRZ*yt`rqZ=;!{ijTU;X;T&&XXI{3z52TT0YlS`lu;6r4uD&B9{eN_m
zmL22NUTY|;|1lgJ#IjB0Z#fbDa829>J;Bh;<9ssu3g`(}s#iLOQwIK%N<O~l2pIa&
zh$Eebt?yc(Pi6a{w^l~U)+e=4B>n(}mqle!E7sXIj>pnS3jUDX)U^z*Pq%pL1L<8i
zIv%r|1$H$7uVS@GORma0ucUTEHZ@!-i+ag4(BLDT2LFEvlx<=A{#M%l!NHU_p@^8h
zJ@3h{Ex8~nzO_T!aH{kVOiWBOqNfLh8k(A@NpC_uFiZ`b#r}DO8Bcw}?Hm^eY#gmS
zjV0-}sKS-_CX3u6IUQ$3s&|i^G`_N##xb61zjz|7b$l#DbF}t8<&Be$3hw=y+Z2K6
zm`x*Hc+j;_Zob;hin^X_@%)RuWFVk$(YVj`3!wy3=c8jOKNng_5%E@YN6~F58$!~-
zBymG2^O06}rsiHN;QL54Bg+5EKz$l$>A|dHYANQ%ayf$OW$zo5_vT)r32}Z#ubQA}
zeZw!$g>^7^b+e^`V!;3%+WtONQOvBKI~hFuO61!iX$rtmW_>Csm>s{havodnZo!PX
zN_~YQ=xzJ95`@2QSJB@4R~@_Wkf|xdZ5a?BPlFgOM?FrDKX-IAFZE>?8g^1XGcrO)
zPT;+J=!n&2XL+EhvlAc5E%C0KlCbdE_<*dq2G+Y1ja4~BYD`?J`=_tM{826$YGhHS
zx=i@vmm3ofN%9POO+FgH*C2(Nqjdo`BZlBzx$#>A(Sb?+c1E8HUDc~o-ekV5#mf&x
zH9-{mUiZ<@@2HY)NrRy4nMQ>{LiZ9D5+eKvA9|cfRo;MZd1yzO(m>pNoL&Tu4$d4O
zDk;h=^leGV6(?tPNZH<>AAF+oa&!~QXlSrgrj@+cdvF>0-D$zP=Tn_}!3!+_dhE=g
z$z1DmxDxGP-`RB^mhWRF(t$f-y>AZ$V`j>Xu*ELsYt_Fncf@^L3IP<lRuT6S$c0GH
z0m$qhvrg7dMBFn%S>|NUi;<IfldZk~i{>(HjLYt9Q9*2+8grOvfyh_rc4VKUR&su%
z)ZAOaX$SMODSM33ALF&^r)TFFWk2{E#XDhzF+O@J)4zsQH4zS82mNM$;y>+&@CTo0
zgWm}(xqO$(YgPVnMc;l(IaLS}T6w~<Fs8XeKo$pd34>V0&=(3iBPd6ps8txi%y7hH
z{w0B0B6+D_vc&@^T1Pn}sHAI_-6dTR&;e#FFt=pAxWI#UTa^OA96^Nk&nD%iG0rR)
z(@(c7)}Y#a^bi`mBp6(FH=xZQ<X=QM1tLh-GqO{7ClM@G5U!QY<lflQ1s2v`hRoHn
z(h$#T&xtQ@)Oe)cZIIW`i0Bvj!Kju5c<<2F{Yfe{cYnsfD1cP={B~Vi8^gajrZVeZ
z4K97y7Y?V*XNco8IyxF2f@fqTxo0-}2kltvJSFBr@I%TZh)~ne(7=Bad-0+$qUsOF
zmeiF5tp?!c*4s4lgV4U(iH|sE_75^oz?<RmVE!v>e$!X#a}))7>_}&LMdD`2E}&IV
z#jinsXhF)Giri>A5sz{vcgG~lFuE;MVB=AZR_h}c31IRUz`vu?E&SkJAm>Tgz5OMz
zfYvt^NKCENhrGi{m%?%{RG|K$c1YxdBfV2~dQg*|c8j2#NTX1z=c@PHpMqR9+ULX$
zS_&k(4cTYKkkept!-}&TMLhLf=No=vd2B<Alu%^KK)pIWYA3no$G4`#(h@9y3RE+J
ztN8>5pa1Q<%D}Yqe?9l;g{|#<s20B0xHzPwrD>fW23x8HB>AtbnB7z@Kx0wPsc&bm
z3&O-Ds~?P#=Kf<mkYB#-TtR_2S+tV)-`v2dhsN2BUFeRdSVTwj-5<URED|`)^v9xa
zD)BwU)gR!=LM?xh0a}{mo|`$RO92ze%Xb*7+{*Xv|LL(W$x!R&N5&ug=IZZjD;x*X
zy8(4gqY!8*?CvayFESAs<Pl2v#`b1+-n%U4n<JE=&?fM13H@?sA)Hdsk)Spk!+#)6
z51rkFLcHIprFq+?^pvAp-4JF(&Xq2nddUZL$EM!LTkRqLsVfy<`tVP_^mh;;M^cJU
zD@yEsZ}ew_vy889>)rLIhgux<e;E4;s4BOp+hc)B8K4LfqJm0^Qj#Jf($Zamba#Uy
zU4jA%lF}(kH&P<q4I<s$^{)@~UcB#rZ@e?^fHUqGa`xHZj<x2Rb1q<#=;gh$+>OpI
z<6xAzM}3lGO+)U;wjG`{;#%(OQ?T3LUEkQ-p7;0nKlZc3XwxT6=s|i~TGfw0$^#mD
zYn;&OvuAZ$iw{G?1U1T3gVHal#_nts)b7*sDhHC%f<c0c)l7y;$YQP3K9cOQg}G!^
zxB5%UQRW4g*HkipRKTQ_7e^QdAfe5>jVmYB>I6!9*hJYe!<)ag^7Vor^gKbp4&pv5
zt9B3a97of;c&+)Y<%7*TzP<wbfP^2>XL|G;VdIbA&<X3Ttv@SDi}?+%b|hy%OF12y
zijXlX_iAM<88P-hoO5a-ZpbNmKWfE)1_~%l)=_-ob1RQ70VnZ2E=|ye`V$dEJqbo0
zW?A$)7I?pPVX#f}nTW`=(f|oDpO!*SHdc!nC;r%c;#(z`ZlI%ZaB$d`>+V}E)li%#
zA@P$;mb!N5&e_$~)y`=#adA40I{e4HyyaD{wUmeDk>(;0haQmKK)K<6u7aWM?NDHI
zv6aR9;3`sJLUM!?Pq|Q_+den6I|gC2>&3*_09yg-w5o4D85=5^WW-a$p0dttMvTK}
z5tNnB+_`EOYen74ItC}4a1QH4Q2_=P&PsL}<n1AtS(NQ$10IF1R~vD3ES6Zbc@a@8
z50Aj!2SSiiy?~OYM)_SCTe7!jPS20N0zBl<ae^5Si{Jd~Hm*e5un#9KprBWPY&H>B
zvnrjGuJrxOoZht3_ebCt5(<&cmi1D`%YUwetgPusYa1IDsdObD2Hg%)U}l%LGcBCr
z$|b%UT%-Q}-ix#0wsQ}>NjUem%uIQCdA+YbxN!64%?<D_SzN3p`IMH19epc0G7?4N
zxI-62Egx4O`un<_Rgg1gKuTXS{2nn|hz<a-e@ntvRu(_te<*yuUUiC<j3(ze=T)Kd
zDz7|(^2kn!j@ubqPYK{!d~4V6Qi)N9_!3erV4cdk4P{`MVb!-oYs(*AAha!F!U^NX
z@u(otbcvzq6Nwup`!9eOx$oT_az&-=9jV~-oLOs<X<IP}k%v+NVoXS<MEo8=Ug5K?
z@cj;Vmvr^Hfz~TP1}t4{drsAV!LNle|II(-GGvD4gbFZ1C<FN)^YExbUyH6BLz2g$
zh1xa`j2QIhfanh1)dY=f%b37kH?U<RBqXGg&&Kp)TRM$~Mey3w^DGeFfmUt#J<{6V
zo*fpKsF>Jsdwct?uCCIWnlt3&AIYB_pbBkbP&KeKy?ZKRzJ%)bjJ}!;gF9<cbrHI;
z^GB)$DDEsaRG^TP4Z^mEsxNlgiI$)It8q8TXfnEd@dU`pUQ0WcG>xpSTi$rC_vZRl
zY9F}~JF|7+xF8h+33|ALZ?-p1OnX|AqeXgoa8X)5(XWL1x-xqUAvl>Jdd6SNcj)IX
zT63Ti(vAT>dNWK71d2f}*>x^d|DYf6#%#SusEO_N?K8<zsoxNzp<lnAqjH34FP}Vi
zkzBCUw2|En2&@mu-*U{}x_kHB{#IwnXS+=k)I%K|o#Z}jeVfVH;5Q*5p85Itp~Y$O
z@oq>!@vFkaYO(UNs-|XqEcW5y)l-7-=fSi9azm)H&J4eLaPfrdi=HoB3WWE$3)y-u
zH)5?<Wlyl#InrYVf2p0He2;MT98J!p$mdF<5J^7lTd37EQ}+UjfC!J2IXem}8wFaR
z2&__>eR9W`T;N}Yr^qy<{+TbFg0Zp-*-kosZ1@JGl28TzpHIXdjUn^R`QY>rwbeC@
zgZ%LKkA8N5$2vPZ)j)2A<Ve0(9~?&s3Da7dnN`eme_gD*-&oq;znj+BnJk??P14!c
z*2g`bf18FTttK6o)<*!=C`7_5kn_7k-cYqUn&(aJDD?ohs_HdQPXjD0`thaFS|)oX
zNl6#av-PcC63iu}rJ1&oqLn$OT^&WfDPES(hLs~rhw}GeiFs9hZ<mYqP1QvhkxAS<
zJYiM#a~-z~O}$sdem)nz>F^-Tnf*-VGd@BpOq9{rd*61>aNz&cZci#IzF&1BUQFOw
zX9eyCQO0JM5ewd@9ew%HzBYPM+zjfp(<C#(JlaB^s%$`&q=}aIeC?q${zT2jj$(CI
z#qjRCQX2fZZo<K<^%d#yrcv*hvvwllZqF=^Y0=7jnE3SN%Tk&zA^{W{Hnjuql!kx(
zi&*26?r5KAkn<u{5-^x1rKC)rHhiiSD&sI*{ppF%x8+7ozsc5kZjt$c`^&cLbCq2g
zYUvTYv10wpbXnRU6@(bIMsb$)dBthHfx;^q8q@`k7mHTR>NO+m(U_QWE=?UCbrS>b
zWJbl0AFAhL2Kt0hEc`fiUM-Br+gyrUWb2Uv^%Ot55wo)E@TucE5;^m-H17DbH=cj=
zBG}Szn=V#3MtDD@c%i%HVMX8E9sJ3a7t2Jv#zdcKBwb&dXzy=uT+(5ih;?-~reu1;
zlKlBn*PCnHGkBAh_}Nd$h?m658^tu0qyNPyz>XrMu1>IU=?7&er=N!hmywZCXqa;R
zU<frWZHe{=1yNj8U0pAVaI518sAy+rCpTKi<l58A3Ro$?)S$h%0!>`FRa8h*Qd6(J
zt$8LU_6qDwRcnK&5x`?=YA^h$<<HV%b?@m;5HcC)1=jq|tcbYyVAMjc8~3!M_zg|Z
z<5(wW5?5s1I~kXkOjmY_S)A&pN;u|b7857V8vW(PH^FBWg?h4ch7@@|-oD^3ZaTfZ
zw1jp{{;PU!<8p_`c$<lYj*4Tr6H)g)`Xq)Dj_1)7WNnH*4i;oA-)nE11<&}r)I8DV
zPJ8iuAPqNHs(~VM2mSl9d=l?GR@&aggu*0Z!3D&I4eTB{@bU38f8@DhUU`+7nJM)k
zn-LOa?E9JxuOlLq-}Z)m{P=TZ<TV^GD$wZRJ)IhwFEP7~{dPyH{9?Pb@(>u?86*e&
zK0Zzb5-`7^P$_65O;e~a^GoJZ_<()N7;B`I6#e{(S;?nVm*2?HL_Rt5TE^pac8hSj
z$uX<%@-BX#M$Z}Ro^V?}wUT{`t**B?r-8KS!p3z;-#+qJdqR`C$xjm82sE}*`|z_U
zUn)M{Fc%+s`qF7u_Fh|(LD8ERuEA%0_3i$1NL=A63>xX+l9G~o&!Bq=xlvD|q3INa
zVMFYOjjZ#<;p%k9`hfvrZEfwlblM**_s(Kqh)PJ9@op;=+g$;F{Z39!kia<-XC~z5
z695*Uzh_<I03{xsq)Md1mB6q-7csFGZA!T~m#u!|l9WJF#8o}FoGPobs!Vg5khz3`
z!LU~mBIAsC^_>kTOB(0S*gvQy&3Tsu9BS4#zMKtXTt6^)(xjfq+$}>t!Imml#H7Qn
zzi8zc8+X1WtfsPN<(>?|w5nYs5jk2fSJmP=JV|}m#zmNH7Icm=h}zBV><Z^U{u&;p
zQz>=&<6@Ru-;fS)6095*b@iB8Mi#8=*RStD^l%yz^R-ywNh1dQV%^TP&{qTN-{9-7
zt*xz_Vcx%gUq(hI^C_p<yr7WK<~$r7ZD-*7olZ#6t>7&P_`GJt5~J{CRzv?4U@f3B
z?*z2+p=sQ$&zzfQO@8PYb_Xd)ICIhl{RGXU+{BC_KzT8^Qr9DstqZV<Ah{3AI55#W
zg||vDy{dj30x_3h-py}Ml?Tv>#N>5+s%<eaeumv<q_z<}2q^7*5lzPf7y``n@bIwx
zxQd2GT>J90UenIb=JE#AL(&{MOveO4mdFdfZP*+f9Pb(R3CONrPj^hc`G;>g+YH_*
zR6Y1!JDw~)S%^>_6A`N0sp^%I&h_5|Vh3o>7u$O73mNRIi(DYTeRas7bxoWAVSGKe
zJq|=}9%nHu;HeW6{q&W7vi+^?nWnN%9?zjTrxFBLe5-%TE1#_4hW~fB!Mps2nuEJ+
zRtuyYn<E!bze43H_#z!s3Sx!Tpn}14s{Z1|i&<S*2N#?F8X_@lu9BJYe0!;47BQ&!
zhIiU%WW*`xE{IH^{+GePG9RRmfSo**NPVB{+-t_kqd~uR;oj;L8n92_c_NB_75;PN
zuFTo8B9`3$kJ3k6L5|nqiT(ShTu=jA{q$6tu*XH7^E5OxpT7=^4MX74z8cr)<>{H(
zcII%r7GN>=+b1Aej{jWeGn_weOBMR6karC|GdX`}`dyA5t4SU{A<YPBO1wbwy1yJy
zokW|N{pD<WLkgEzAO(Ja@alnR`BS@ht+j)}qvm!sfaj0L-@%4TneYUbzKY>rWp?EA
z{nMJeBlA9ex{i8y?qpYQuWnvw1CO%ueq19&g@)pM2hX8xv0^L;ESl(PGz2hy31b>p
zKmul@axO=*OCJ9~rs(5oV6qKJ9sZmqj;^?~)C0#NF_(0lm|p<dHb;sH^o$!p|1d|p
z(-8Q40~k_8kJK=5VD2$eql9|jI&W*_C5BE`a0qK@Qt>`(qy>D0qpznFlX^{kH8qo4
z)v>WT5tp7&({ggcARh=FvZ7d=L(pz=biefmV-v2M*F2`xwO?DtyQv7CbIwiG2Rk3A
zut8~zGDi<dE0<CiHzvyFs2v8xLCX&IVz)u@TWILa@6))h_ylk*s&Imo_K3HK@_|bS
zDDaEpcOf-bBAG@P=MJ;)J~}ve_a3K)Vl`%~1r<bJ6BmaDXW=bsYL~36tnA(?T!^ep
zTF2AU(&VOIAN;!n{TUG|r1bo~n2g$e8<Dw|ks5^h*PH}96qOcDx8{WWRWc{=tb?lO
z6=!=r`4PKO1|OLWcH)wVhWv507eL`^<qR*IK-q$n1ry94FHJlP`R2(&)qT|s>^=jg
z@SxHR+#dI@FaEokW?u^SV)gkZCf<${3PnRbOKwLFfy_S9@n=#}t^i67u4FZT;?+$g
zJOGZOJ0xXf-5hsqutoNa<ix(SRdgRGR3>-@rZZ3tPy=l-)V3P+b6`zBQT#L&-f+#6
z45`UY>=VDiy$60ocWJ&KGx-piVPW{_TGh@vDe$}O8trwW0o&SqjP_)IJra;1u+4+Q
zplZ2*^Obx5P`Z%MkDfMTgnq>sm#CaxzC4CfsR_9GR9LvfgqLU#Db9WQ@?|;#)b1(}
zh4<(lK9mH!0~?kP9&{u`6+r?muX2|u(4UukIY$|(o|BeWx;lhK%i2nw6C2$2bJ^Z<
zKc5RYH5$7{1c+nf0he{#R!$A^e3~7$|GcTp-BjJrKg<CK210GS$noGc5MeF*bxkiU
zQ!bV=Rns|gW5Na{n65s_qS6Rpm92YxOex#J?*8+`oES>4IGC6fmV@@_T<4IPK7aj*
zN@F;Ck3q7c)qK&q5i~EUSp~(uVeuw8bf+l@Ee5o`@e^Dz)cwQ<f5*S$#$}0LPzX_j
ztmw{-lg7O0U22`v<%o^eL=Cj&F>}+5p#{3%uk79up#8kw@nmJu0K=PoqNimD_WkDd
zLJlO-10^Md4vrQmkZGb&T*8+&mh)b2{M<A6Ovlh<<-HO>P`9;*3&0+gNiDrdOE3ca
zL1v}`u%9vGuxFJG5XS9xwP;gp3Yid)_$kg`hjm-V#188Sl1wrh4q&3AqmPV^s_two
z;&a$B7w>K9B5F_RG=-P^?fC-2IlO>zJdEJ5zYCkk4qB19dw9GQv(|Nr2E|&*?i%6N
z+N{_9-g*g>{_@&d6*O1sO*-_Lh?bHBB0J<cbdz7pnXg}S*9A>$I`O0n$QDW~f5c$1
z+_;Tq)%`PyBTdLaJc$Ps9j<cJ{45=tb(HV7HqRq8rTiyZRa>9K8@jf~j1XVUS4PV#
zhQLHPVXu%eUw<ArYlJ7yl$YfI)wRwM2PqwBJ=5XXpS{XQQ?((2J(PUn_A{N_SturO
zG!a2D!S~EHMZ-Lzzoz4H=Y)2_%dzjHOQ9T)XGY)p_1*tEv&qPJMH1<AugSSPiJCLi
zs*Ps4=ul2kP<~NRP|?;t12<eiz=fFjc<$8V)p|XEFZ2eBi5M9f_51G~tOZdoRtrEG
z87oLrbLEig(;pKl)tEuF{^qcd`?pcH7tcOZXg8|OJ_0=S?otAovJ8=<1DW!*V19wm
znIIQem4tLC#0y-sr^1j#WWJ!9LIZ2e&{Xhn90H;RVyZO|M(hm=pAf<=N5mC0n}Jw#
z-1J5}I&%mja|CmRfX3fuDZeKmELa%5zLIcYwun)6x@Vu~_Wa3Kp{eO<|A2t#41<;!
zzKtO_5*0(kK{Um~eUcRpWeGqp-M*W`uHD0ycFry8zf#fuLQxR&GhD+^DSGs_S9<Pg
zMHeYAvm8PHin%Cm#(hQ-aDR!8j8+=9UV)H^Xv_tPFrF%>W$ZoaSI8%Y(hhJA$q~8S
zQcsn}G=RjaV@YohobM>mzYTnw$j$;8cz?GZltg%Cg_f&Np|0B_sIVKL#gCNJmc4op
z2l)G^zi-oI<b#EE+~uItLCUOBns*Hc*dQ+cwJ=ov^yyQ;>~yjR>gq5rQV3U+?5)yb
z5;CUv<R2zdIz-X8$d<rhOW&s)?+lj$H~35v4SPX>mCn6a7{H&`zz+nGfLU!e1h_zq
zEo1Uq&D+ZYZ5%>1WM4cL;5%5GiUI1%(?*%O)5N3qj={69eyzfbENRfr>Go;1Rw{t4
zFd}yYo(=2D-s19mphjCC#0z&4-BcYcWEj~`LM7MIoba!kEMd82GIH~?YI&N--FVjf
z(0;(1i1iZcnYg$!^g4s}cpY#yNl8h#H6~hOF;Y@e&f(&oba`|vZ>J&d>*4fFMrZ!g
zLF{(G@`!I0FzpOreuEDnV9;4NxXvLw@L>*)*F9D8v}pq%D}ay|k=y-tzm;I3cRbIC
zdQUhtH0yE9Dkg$Z{Y#Lw(6_3(bBF{Ms!f$USQ+k($y(XPNlG5TSN=RFcnOv;*oZMq
z1>Ui<C&g2qEAd7PlpB7aU!NvyAJNh8kq*qABd=z~X84*fPoQV^KXOxf{`?g<KSYW|
zaD;gM&cG7UVhIMX3YLu{P7{d_+;tC;Ep+GH*}&wiGXwb3Zlk@wLi>ur60W$<@d|)A
zH;D`pLWeNcD`yu`foy9<LrYz~y-pQmqwV(xzW!nJ;_K?6>~Hw}K=~OkH$v#iX5m-t
z6%m-sjE+vrBm404e?OCix9~K6vwZz=<68v~$J&~|Fr^=)2Da-XDmVytyQ@z={`F;v
zwok}bphL=-{ov+6G|@yrJ@8nBrW_cUbb0d9DP2KMP#yqHht)OdmW-lV!3?rO!YymL
zLEX^5kFG!5deLo3rB?TKiyJ?4#6JW~70n1D{!S>`P=K9!Ji%`RR8y}VtZ{V~8`c8P
zl)~pMf-dhpLH4Y`4IqYXY(x<wDCQ+ScYizo$KseE6#+A<M!$tuQQ?KB3XUQS0I_)g
zk&NWiloVnY3@@ohQgCyM5L)$w=oG|~13l*-7c+X16vltNg+saeuoJ-XlI=m)Mc`Au
zEfo2eHHXLAs)xX1MJ%`oYD-}oAle$4U%s3Ahd(>@=C6IgLivY*dK#&QfaBO8F~7@_
zYeo}7x~d?x^j`StCP=Q8QC{)_oCU%C=c?eH0ExjrUc2|*d;N>Rx&(;a{^C--dSQq!
zu=i^DMQ651ir_3!%lbd!Mz9`+8a3o3BEuYtf-7`j%`rDWdz`DuXYd~%o`XPaN~XuW
zNg+`Jfvn;F<a+VqW?g-~>Wder;EtV`%$g4Ujmc_&(KX_8drv^upxXj$D8ODBn76OM
zJbM__RNx`l`6$>)>`+^=_*+{cPq0r^(H{7QEsI#3n;j^qRbcsHAb+2wlz86NwKUJm
zn9zb;QCdLFZu2QE+dGNZK-2b@Qs`G#gt8|{u}%}-FK3+l&m_LJ2(A+5<12&uxA8>N
zwU3;R#H*`0H2wx!TJB0kHmVy7LsoF-4^2)cx(FYxd^34q1O(}Ol)GZ&5$N9M*ZB;B
zAtdfX08-!#+7Z?DM%E^S5Et?N1l$*VKi7=FGK}l<i*q^RSMYv1A^T~2G9|Rw03rg!
z=k{7fhN9p;G1MW5(y=OmjfD`xK&XpCDP_k&srxik<#h764`%t#k10$9D(2)Qp)KAZ
zW7O|CDO#BCy`8ncFAn?>UlLB=;c7p`cH}e`77)ekj`8wVXX_#5RCo8M6tG002!9^J
z^%o32mYIe^&Swz<Un9jmx{B&cz!O!GaAF@xrKc-IT=T$uvuAiRw_dz7G*>oM1@Wxi
z?9SrnjM)Z<a-+|8zkx^Gt#%zm2CM02TlVcK>)Va_ATUZn`0l`@g`n(giXi5>uJ%I3
zlcRqhAZl8dxt4)Vpf*<b0Wy*zVw%e*hqgkKna~;x?aC!_c>w88NVs*C&FUNyZB=<+
zY0a)btYc2TdG|+D-(t(r;a^(`HhTDJ9aV_X4QfMZ0&jybmgl_dtD}7~KseeelT2}O
zq3$ki;k_T1tI(D%O~33RfnW+Qp#?Cs2gL`O_sS^#O!=ALpx*DZrftoPL(5@hLiDw(
zYjk$@rP6lN%`8+NP$-TcKdz#yiy<y94#@GR++2JQ4-YLpy~4!en3$6QEOiyxG9j$I
z`rpS^+ZdexNK1nQzr4VF9=|ZK00lE!Ad204WXm$_!9~1aNnbKOJo*PBpnM}S*qVv~
zgiDZ?8G}~g&+|Y09UVGp)>H_v6%N}1N=H{W9lh$93QW>_c3Vb-o^|4cv;3N_mR9Ld
zx!WSp5{96tkjpVz2q_@)F9HGr;Bsk+%Q-C0JeB`%MFY4`3!vN6^J{Pxqt+QX^AIT3
zNY?_x_<MAvwFF=-QTqNNI(TjetVt*G>{zi8fyd%wb7zE@97PN~DL<eKgSvuX`me_h
zYmf!N{gVB??eC3^CB*kEEi9_Q?BOoJTN>;4@7^t)X#PZJv!YeF)}w3n^CkM5urMst
z_uATWAUitocwNe>6go6sYzg@if`(iepm*X+TF<^;38IFHacwLV{*0Q}tD2V^qmR)L
z%gbL$Xf@*;{&JpBMMSuGz>-pfv|HxGfKAi_Lq;AL`Cakx>K+a7<)o`Pj}UxdA)5d;
z_1Ei07(|T7u&1w?0{(E7-3Hgdz`)YX+hKQW2!gAL)_5F#etuF<GAR4i|GpZ{WgvKi
zhzBZR6t}M$kOmKd@tBpv>&uv{BTV|Huz|pyW3lBH3Br2{QM;1+UXna77&IG5yabj#
z*D~GiYhW!seDk()b@nJ9|J#Te(DA|D<)qrHt2fsFG2Hk)WJ_U9=doZK#Ye)z*eAKb
z`dcQ?WR%FQF5TGFwA>SqasetRD3pn*sqtjXO*62uyD3k8u&i40y#sU*gKQ{SgF&{H
zteW+E#EZ7R1H5#>P5hK9yP^@t^4ShzfogDwr%$4HD?b*g1hyfXUlyuAP^~O~t^$Mk
zyNqvChpJ@Ml@r@@O9WAf6$exGW*H!B5kLMB$Q=ef=J+hzb9Q=qR8GlnH@tm(=+vsb
zZCM7&aX7uDm8?$zPSNSt_-8tCkv0fK!j<uGNv2Earp*7_@Z<C=Bi?bu@c<|V?^z$5
zD+#On1|q&^GP1PR`{I+a%z;XG@;JI`fcs+!Wc4NAt^aPQg7~jo%iq2|Lcx43tgO8D
z@Tg%YYOJU@86F<)?&Vc5xp6qVv;BFHr@@+ey~EG9ipbZ11_w6QVA%~3g_XHCEyC}d
zrL&f?r6K~M<J{zXrh6U1!{FO^NCf|KiIwI^#D~1qj)TvJ+TAn>tgW2ybUxnRX^slY
zwDvu}^J|)<f0du^(PK9MDhRyRAP6a6A#Q-k?Mid^D54Pvr5%grinac-HS2vAEU>_s
z{<*o~Wun8=^Pi7y?neN&urMOjLtGwp4UO#YbYH)GNpsZG?fp`A_Fz3kXOnr7SyX_Y
z5+o`TTE*^CN9p*ZsXd|JDlZ_e9H!)_ey)pu0NjKek%rB0?1P{_!%F8a;H}qAKcckY
z|7$maJv8#!$;s2JeqLT)#!Dl7EAxZ<X?~XQ4i^tkalfy=el~pT`r)c{g)IRPF9dI3
z9cNo0e~nD=7|5KY=4Ld&+XO69mBAvFkX3CNk*f>GE9dDK{grmQhW%&NiHZYu!=e8D
z9R8InQN?>xX2q5R*67>o^Kb$~`_}QTYFAfR{Cn!(+1C$==WS9FI_E(q1KyV&3EI!J
zQ<#`qzy;&Pf{`-QclDl7%rx3yprKF*PL6@h+XOie0T7MmN-S0y{=*QF*G?5VJiPz;
zng0QhSRYvR27SzWJ(*`vMi7j!t+t6HWlwOB9~p*Q5Udc1p)-1J@yyo7;;^c^dfOfQ
z!$+Bw`IjF~KXgNQ2i*`RI+nxFX$llNI$A<gm~-I5`9?|12hw2tIYV^&yp?h5|2&Mg
ztpg}aAc(3A=mv0xfr2`OLDy*L+bKK9gz+Dk#8mC#TxE80+}|E#ww${PhsrHaD5_em
z_gnTx+8(YITXDtoit2*53*Z}{z*D~pPKHQ+1!jKoC3&`pIurcxbqcWl`SyozbX?(N
zEwG&TWg4`*NMg58wi>yyxjc@rMxa%^>d1HhiH=l4J$-#u9Uaf9DV=xk-u(oQ9kbC-
z)V+K6Pzqr>G^dXPRTa9mo^g2;H7fr**eonsJOzzo{$QjB%?LO;aG)8%F1_DhAvK_+
zXTn5CQxx(pY;JBMm?P=w<4(zCi{9RtDT)Pa+1nn!+KnAMkB<RH0tAg}+xenS^54&(
z-v<FV2d;YQH*W?^732ZsUGU{OO@#9PpATWV7={yaqO>PVw>e&fq!5~?C#0qp5i_Ij
zVZR%kkjvfau>eQELbzkBpqk&4qIo#2tWkhRj!FPX5bpHjx2F#1Q~ZDcL;AnmukLe?
z!D;5i0sU)X!N_iNG#DW_iOR@W_4eovnL<d8EHJAfm&;%**e8{WY>j53JL%ewi^NXZ
zVqDkHF%bbzgO9bj$GOfmK|a4Sa!)_UtP}RNamqudJdU4g#?zHyZC?Onq@!56(C{J%
zoM#|Swv0!I7C6S!jDMF%9zPC~DmW8z!s+YR`|yn*+kN@^^+}{if91-RT$9nWNl8gi
z`9Y(kq=W`lYNbxc7S`80EozZ9+U5-uOsYWwKSkrsXI_9ApF-^6!L>#|$Ab5_v1k7U
zHz@s}oZiNshZOhQhDvp))JU^Ap1IGVtCRn+^Cl>#)F^l2U1%?%8Z|n{5}yAA4-xYk
z;>12m?6^8%dEmuWHVaOi|9NIT5f*s#C87@>q9EkU>_<Zm&;C>3hzR~jL&N<w3HlO>
z`!>+oLnDpF193lPx()4(T;*oOLS`r#kma|1!9dBFBBv0$ao-LbNYfc0MN>!-%xsJL
zWAFz`bReJz^?FX9DlFz8mRHJfR;V&E6?hP!;B8G9?Jp|!DMRX<Q#E?-aQz~paxiqB
z`_C{FzUt;2=B7FkG#Y_JVc)gD7pO0r9N-+VFL@40JX=#Q-aR5rCdxw}Z9j{f{kTE<
z@kM7`n1jS#ffe!dwR0j9wJ-F$V(Fo*Ad`u-T*5GvzZjwHqAaZObMYmyey0q&q?n{1
zs3E}Rn^(*pT2?`P+SFQ~6v%pbRR6oTw+Ig_bZz8GATt61X$P^0+bx|m+GOnt#|~b#
z-R`SYI2bR}ovo*tZi|{(JQ#bd_(-r}wtR^xNnb*>a_uDep>RFUJzivX<8BHFxYw-E
z4wOBvy>6rFo4^7icQtbf%bODk>Et<zkpZ9L4@73Ra{Sc4BRY6D)pe*)kCw_gK<BE+
zN}}g{*VzM=JozGss=%9JVd<7K`1i<1acd(Z*OG>j3%JQWlG%#@e^cYZeP31Zf<R0X
zgQ~Wu2V))sTw+BbW+Ow40<0ah&cyrn3B1z`9bB={9}P-V(2oV|GyZVR)LM?nNq^tP
z3_iaf9s}6E!^~eQ_;q$}k{NOLo)ak>^Rh<TYXLMy-4<n5Fnsp27UK3l&qn2VUGyYk
z=Np!B3bET1FEDo@Jx$#ftI@v7zhh~6!GSr%LRxhbY%&m#1Cd?ZnG!vN`WFJbOK*!e
zMT+UB<S^VXeRZ1)Fvmn8N1FdEEcd-c#Z!nCa>K_5&Y(TIe*gYO=${1#m&>gI*+);#
zowS21Ahqo#@=Xw1aTdf@{ALt%y2==+dxn#(f%cR(Q9GOFYbKhXqvKg1Gpg>rdk8A2
zEQ#8jKxHMjN)T0FXFQbcqDm`gmNcfp1Ag&`;W#+nA?R6Yf7MF3ZM!;f0YM9jm^QEz
zX_i&t>)F4!1x6E>rc%^c{%@N4E59fB&o6(CSu!mpB_-b%wLwEbHG3o~+RRQQfXj1;
zsmO_fA;;Kbo?#%M#%w_k2%!AoOT}$%Y(2bF%w}{2>&F-c=YNc>KctLOaDti{p2JJ1
zMet+S?%Nh}LNOT|g;bF^x)QAiA(}=cD~0!fUWK{(>ZMYxTh2et3Is4}Elo2j9ILE;
z`EWLTid^fM*B9f$gJ<Uo-Fb4%l8GM43Z0PB6DWls8~Ts*et;XM?Dpb+?(i6KBw5|r
zFn;ag5|xkuOvO=!CrAnMX-*sct;NZ3^%RZ(fF74U?+XV#L9hi7kt^)?fVmtU*r|ZL
zrmDI2X8oM9Y&_D#2MnitK=g9bA}e>i*GR}t>A+L>RmJBWaMnjm>e7O$I!hQ4qlnkc
zzYsMjL0IR^9+aX$+~tlnZu*%`@TEg|$9l`2`GGI;VJE70=Yp6((1ef&?IO{WlJ6Lo
z#1kl2)r6+(s*Bu<v#`vcWVE#SCu;(6Jetoc!h<G&hDW?sWzW(l=kNb{n!kVhXHS?l
zu`x1U0^oyJQWDQ8d1y&jb(xTR9GRjRMf3|8NFnS?dYeuPV1NL0EmkpezP~cCHJ%Wk
zdfUMviIY(&m!J#U*>QZfg_aoN9GuV+0};$aON`uYPGem{i38~@iLi^<Bbfn^w}7Kl
zRjx`kc@C=#{!lHi&CuTKM^s%2o7-<eu!cy}|8YB_%T0zJ9e*{8!ZIlhfGYLWlBH2!
zqu66wLWYLGK9(0naa=9{xGj!eFPh7*_Py~n25p(~@vsqM^KeY5LxwocDJM+!zeQ8v
z4qAfPcJG%LZFEx7)YgiLC@vCCB2@XaF2|uTbC-^;*(4a5i!f=?7wq5>=~jQ)^kzFc
ztE>umFhd=JUY_FgaeyJzK(&lWaiP&oNGhcDAWg(WOjZw#5b5Oy-$8v5)JFuevEb|W
z3I)w~UglJ7GhKm;#Q)e@*DQmAiTkeKec3J|W#|ib9BRayzEaDHx)MWQi)uF3-ei6*
zbauHGB#2S=`b}K<QWUXHMbMc9>HZ$1-xz}3!66s0J*|a!Dsg;t6<H^bN)t`ZD266`
z$i;u6NHLfHKUvKAN%}CXkocIGpOA%j#=n=Bl~n~Q7e3wd*V*#Or8Kny;vA%hq^JI3
zLIR@7J(`T=2v-+}f&Yfg`2j3oW+Og#(9e=VK0;i|t6R50<+=NHP(r$XiuJED#g*((
zgLXE00r){UQ#K4#gZ)`vF<ZLcSR+WQ512nV?K7+RhQVsn%Cw&cT-|+uO0y7fcXR8h
z2_4d4;tAh@OD-|VPhXg~^1i40+2%oLr8lG*qFY9V43wb>C94wQS?;2^97}c3(W}7q
z0hi9rcDGeCXg+t)K?pow{_~m(gDe89{?GoL!ZihXU_H~|U3H{kNz91=j5~-@GQKta
zX%Y$eR3b52<<y*X^yCq*5k12%RW;3EAaBf^hYvmCz=W2aG`OGTfgFf?5Z{EZa3^SU
z;=_(VjdXxit|OkY=2hKvJZqF$s@#b44cvnl1}y5icGYg*e7{hmim2IXy%${l_flWZ
z{Q}lM1aJWBU$DJf4c)D5@?{}joVrDpU4_f%(w?IyK$%vRgD$?fq@~PPJ#`u}r7rV*
zykmFoMD$qHqEQZA8K^`tS7!^~4*myAO4x-g(%QjcZ`Cg~CB+E*V6`(2xB*G9;(*_Q
z*bo9n5wAOYBpJ;?E1G|Y_{8}xl;OHuyrkz{Ru&VjF%4>HnZgHPkZKB2Y6vw)SM`H2
zD50)_7T+RpGt3tDMvySh%jDI5w^73zZ_glBZeRlgC@=_%v>ZZgfTRHnR&5#Z9t?~!
z2vSkfv_BLq$qCQ*4vR{C0&ioG3hS!vAeqR-AeW314QiQU<L4p?Eo0uU*KY9np4Pm|
zV$OViT%H;Cn(ANm8MhJ=I%U!UyX-TfEoA$VQ-g8xKabws49Orzmy)?Kxz7C@9c?EY
zI>0sZS*yi7>1gkuX;<b5XYNsOp%8%}C^4L3GaS6ue`%<_Vh~xYgk6X5GNONsohX4q
z5#6lUb-*Yn_85MkBgw8KI*q{D+TM^W&lfXE1*OdN150>eDi<fc!*ffZMGW{pKj9Pl
zx(b#}@AIZ^d;=iU|Cj-yFcyyE!>Gks{#w4&RDe^vZj@09dl!sl*Qk4^0(B6-zxnV9
zTj^>89FtlwC{n`&vqLZ_8bvr!1!aTHh5u(jsYAp<NN;IsTCiuVs2FyM6)_u_Dom4L
zQUNQEo%YAMxwpYF6uLS=yGzOBwVS7p$C;UayUWRHmwO)(=Q(u=8Ymq4tRfwybQ^+C
zfHd5)+>ty}7-Bo<#-=*1TYf{t+m9{68A7H2U;Tglidxy-MhC&D2TUSvr5BmoYz@4A
zgg9Szk-FKkxTT3E@*p}fh^eFFxMDK~`VgiRCdh00NZ&3#m;Fx&s#Ai14_Zy6z!ww6
z^$!onjukt@&K{<Zc&Q|CgERuF*GO@JKberegK(;5;fIzo+x<2SG#kWL>kh_sdj$C#
z0!tY1Pz9k;eTTrA572KBan!eGOOa0y&;~pktQI-r){VaPCE$MyU`{N20Cq>e!aR;m
zJb&JFoai`YDOR?xIDu(KpJxwtS(u9%(5Io0ry*V+X>I}(@rjQne5(j64H}h#6_Z>Z
zlD-{%MR#;s+>T@&0op#mb|_R*a&m@#FC(=ja@Vvyh{2N{Zk`v0sD>6qkvNMPl?|P)
z2m-24aIZuZr;`ThR{#X?bL5s+Oc<Nq4y779fV8oR5$D6;_v(SGj%=)mw=kBX;G987
zKJ*OQgJ!<aN>$#=r7ryg*w^$cQib*f!9h*CMv*hInHdGG8{h)H3#%A}xJV;~{uGXl
zC4VK%5C6F|MBSpka+#t47QMN$n(nH*w;Zt_nV&CLON7J}<_ci;RNNKpy%=%z8w5oJ
z)2TvO3ya+Si>>0jKUH;m(1@UK+JO_@=WU-MC=na+ZeuTHmm%K|`W0ne3M%nOdW8B>
zt`*Z=g=@vXFaqooJF7QTdy~1F8^4-=TLM#_7g1+VKWgnQ0i$W4<ENU;Wq*c_2S^1P
zO!(=AOM}lK?gJUqtE%|+Zk(pbtR8*k3vb^J(Ljt6l<imXP^6$95RVh&f)qEX0C`MP
zRt+)1&wq@&fs@${`djS;(<BqKuZj#Yd1XW{pUx4us_S8TSuG{J9et3kgHVw=G=fGS
zK$?AyE+V9@ayZMmpnVEZ1=y{qSnjOVp{%+Y1_x2Jx_XZKw}2Rc!Ch1kIz<x*2%z7Z
zrJ;%ZC;p0PA-Z!sShw|w#AQU?f*l^X!4*mhhPi4*-?}e^?lGK%etrN0rvnA%=t6?`
zI`S+L?p9NY@kq@Z5l5oq5I;6Dx~pU}d9x3wNvF@8Y0gtY9;2wOm{PC%VGAVar2>UA
zh>Q?>Zh!*^d~hr(z;h1T7M|g}T4c_$iu4q)gq{LISedOqiIA43Ag8PBL>F5eeOb$-
zh}l&6__x2HKCnl0KXQV5TvTq-P>?<^ZLkWs%R{^4HjUjr>Z8V}lnjVh_`mH3g_Uyz
zjy@T9ZzclaI5r9-vv;@F=lv4J<H<NUh{52xvL#j^Dm@)XS63JR3Ik{2Qp8EFhfq17
zG)Mk~WpbM6DfSPg3mSyyc+MlS7YNv)4eFtjo$6~Ggd=u+w<9d5KcEhC6``Jdvtb!d
zJ`sFALi!ANS3=8`0>#&(hUy6v)ktVx<aQA#oG<HI2u`xfLJ~iRG$CAepb}c4(|k@k
zpkSF%Gm99&!|hh*v(lS;fAaqu)`>aM!_%|_^%|>TA0}wBjN&>+>M30<%1BR73KU3R
zzv|l)@Rjm!f~*i}zy&To-%qO8xnGCk;q0BJzHp@T9pYgkLUp7*9hwE!U=$VI7eGWK
z->aW_8b@9nbT~kBMxmh6x>!9)^^u>p=!LxM3wDH7c}m>uQzv0NL(&3Rs3OkYKOBAY
z4T0%^gqc+U0dWK$7L5K`aM8lt<IZEsc8jN!9CytV+I8HcPjj7{yWM9@9fL$oJ)Myf
z!@b<MH}nQyz_#fuA%<=NfnavEEo11)BL4>J)4&S-ZK+J(ArD?Cb1{eb%9VYh38`14
zD%x77$?YxhlgN+vBKIhmSDv2HiE^0>vABUyqaI)X6muZV_ro$my5^}ymA~!&Pi*5Z
z=M1=LjzPbtG;HrUr_HR$T;Lf}QB$ive;$2c_c3Ack&m|Wcf^`!55^^k^<hA?E}>-y
zonqacav<4Oj7mODiU3jvFb&HZ`aTD;ep+i7Y`zMHH7e~xney3?v;+I0ETR#sZL9L^
z|4yF*e$4M&>(4KL1B8tO)-@LF>NK8YGqbacRfL8rJ(=1I%gfG!<gFH7Fq39(h+7r(
zSwY&qsm&II4&SY7#MW?S*`@FjPCjhrzTY`uU(G8UyGyb!Y;*J4$>T5IJeoiKil6sk
z?3rj%FV9?y8C{oCWv_n8J?mUbIVG*g^;rAtQ*^Fp4;6GyQ%7-Y@sY+f?ruAieC_*i
z<367T`5C)`bZI4#27Bf4kJEKqMjGUuADgA8#Uv_Twpu(VJH9&gQ)+4;H*WiF`HNwX
z0Urj#<?}?1-!lqwjQ6kXex7{eszfAy?1SRQlTRNUueRu7TnMz_XjHXxN)Ha7>6clz
z@CZ(4N@qg-`zj4gZjsX81>1(60?Tx&APfqUY=eF|(2j`v5HQ?fzK=pZyX2}m5l_v#
z+TzD*w)D}SPDj-Dh5GdF3+SP$e4k0v(ps)?_dR)CZ`!BHZfv!pBC7ln-B;lg?N47i
z3=n_E4t{&ik)((@(uMd#cH;N#x0mN6BF03|;w&v|DcOpAY#r1zY&YB%X2ly!H5Mvj
zNr;{5{3-wT-yxz<cl4t+<Ag4IO3;LB?R9HLh^k|w9^#oVtXLN@TZT&A2)kuIn5Y|I
zu3KQEBgWc>^+7S*H%?RgTi>i);c%|_qQ2U<)vY&DI#Tf=q4&#2Q<Rf?rh0CPC0j;h
z7Fgc7FKtM}-x4D)XMH31<IeHj!5{WpMwP^)mt=^lcwINNcbvxZ^Mo>IozsqvNjjfj
zUJJ@=2HQmj{rUM@Q^}Nf36aU#JS8Qx%OEQrG$SRY#MNE(nSg-PGE{@ji276{Hm(eo
zeGv_hA@xA4>4Qz|VeTFKl|CUMvucKnrL|ufJex99mx|0xG5S~LrpUYs;!`;zRi&#F
zgamWtmpAMOZ~KH5N!p62v*2+Sa(gRH^tKJhr=B=6ED4Li?50AE`j7w`+!PS5N*lq^
z6Pd|KBG~#~Rz7Q3%T;`lDaWG&=KJ=lo^OBAYE3-Dc4Ij~OImnxYoXo@b3hc`*BqVY
zGWz&m+u13pXHLi`SjafpUM$DE8KvP)JQE)2NHeHr*?2c*(KJVUZKA<oQ-!-{Q=zC>
zC0;|1Z9>A%Ii$?S{;96Hx1W(U&5^O8f}LT?h%a7L+h)IBo_Cx9W^1uwbYwb}hi^+q
z(KE$$c;mhM!y=a4z<j2(MC(w$iN#6BOIOx=UOk4sUa_AaDqq%A$O>z$@3a-Loc_vC
zbI0h{F+XgKrdnsYB+KPnIO=Yc%@f2}$QNQUBd?ts2;!a$$=-9C%V+F(v3$if3i+FX
z5f&38c?xce4g*RyF`C@$ZP&T>I0k5HkIv&y6LD>f`U11b=r?c9g|S)+gVs#)$rINk
z&<8hbA454^pz3iDyf|jinc=PHmNB4oseh1xOJqeS=3xm&W%vpC&Sq~c^p)vwaf?NY
z#?h(SN<JIwX9B_Fijt{<{O!39dOfKosOxIUe5P>IOZuB+uI&Y`-6o|u0b3@7`LoO1
zwvL+r`rwXr{1!Zf@8|YS8qI&US^I<*2$jsR3`y){ojD(3CP2xMa7An`@7R%9rm<AQ
z3wvn%%GsI0Y~mT8n1e$x^chJ$+i)6%!X4ML2_oLm`9!?4g%j^t*IV^^I=(u8!lhV9
zpJyyUJws+?DKcnjH5<2ketK|CcRI=}cvMj`HoU$=t#!XGftHb3Qhk6dKV*!pMV1y@
zPuTwfTQp4ks)5u>;^vj^&5%MP&Ah5c%Fk27fwt!51<4=p+{3|HY7foH>R8!MkNtl2
z$Vf~1V67?yC|Qr<wI_&j2hgj2I|))^Nuwty)K9TfQr!D{46^nEn>=2YS1s{Lrsh9M
zqh&SW;8==%W#MrY=W`CZ1_#X}|8-1m0w&AQ6sb=N*3yzv$x+%RB+uq^q$jKEd9PNX
zZ`tT7>}!N<Kh?}iyF9md@}+<vewV_rVxE$|jXgi^o;N1>g2Q{(KfTfROnnA6tmD5d
zr(h4eDM(7!?sX_~r~CR>imkZG92q!DC0*=t2(X8B2MVo0m(>auXl-9LMW|)J#6-6q
zN1*~NuGTcY@$1z21t%sA#h|L)U{F@p%H&-BbUw4SZJ@%;X4k5Z1QXlm@3x^hag)7R
z=(X=mC9}6TWc&AOSddMArQ4Teemb<HC7{H-)n4)9$gpB!#M00`yo={!cx@S}*7kC{
zd*4a_%zd^K?LEVWoP(}*g;K13VdnTG^4Fj4=dBYIncB*?pI)@IAwD{Ig`eWhXWosJ
zzC4j6pOXfhcvg1kqruO|*DS=;A0r&bqCL?Rr88Juq|=d-=;Y)i=BHTVV9%tEi7FkP
zw=*ankk6NoNad4yd6B}4*X`8AC>M2mf%Usq)011)y5CC2Y<^C2dKT_iHL{wkc_mOr
zThX(oKO{-(`m`^fC2f5QzE()o9<s)_vXbg2>l2LDEd`oVgxs>`)#Lh$Vum$WM|*Jl
zrR$jR71Z%v9R@4}FFN+64PSYHJ-)K|1q3E%Y-YBeoLyL@F`Ya5z2HDr)FQroS@X`D
zFEUj3Fu8%_-wp-KTDG*x=b{e9!*3tLzU}@IBjFSJRmiPD{ek!Dm#3dtDyI#Z=%w5p
zCnHQ&w25PtgmksL8zc&BCuOZ|hR4IUs`d5x*$pJWSTyqO+BY!Xyet|c|Bf{+F@4-X
zCkf6ZCQUTv>cMJxS{$83R?hvR@7r+nw>Wh)ClN`lp!530yqK)a>&>KZ-Wz-wo#~gF
z)@h&}OP<AD`w{Nee_u2SZ7yS%uehpGGM8*PVwEp?goEcLgIJ@+bYR4V)#0)0!j(Dh
zX>m2&a><fKpYJtlXUNbmj$M-bIv!)PlxneF(rfj!Jk7Vc-T3^A@K0Y@!agMPEni$M
zkK-1uWb!@RJk2eaXg@Q$E)zL5-EvE87mlbMYTnto7h{(u+BaEO%G@pDrjcEz6dIj#
znPSq;;AQ!PM<?u7c7%i&qSfrJt&IaX{~a&NiBiJjk*UWw6S$vWwd-hARXN+(b4B0#
zLgYyJG%`IWyf`Z3iMi)nzZ~hjaX*jKEuYv4r)F;!couRRG%)V2$XpJtle~iO8YLTw
zJq-ge8?y^9w{j$*WgkfPpPLUM<&3BurYjxQw%Z?r{9W<`53bLC=4gVeC37N;57|w=
zUhH_?x|Gx&8|MDRjP0DXPiIbqBuiyQi}0Wy8Ro-oiK$1+1@pxP(c*BfcxJ&SLpB(p
zSOYVjqkXF!oXZ&P$;2ZqY{V1ck+P1&QcE?01-7u0+JmRprUz&iX~Mpe-H51b%Y7if
z3Maa7rKL80qBMGrgo66#(EBHTq{9UnKGiCd-XKD?wC4raV#kE=mdYkE5(h|8aHz>K
zu{}%2Hb@K?(#>O*KQcJf)4;-}9WUDAgb7>}D)*QAINn4#p6!Zb^4gvBQsXSm)Mt)D
zg?TP;2~c}g{#vrzu`gV0si&qlnc5<o7m@uUa6+zAR05B^o|$PdNyMQ$c|CYu)S$Rs
z)$Z}<h$<YdxHA3e{Lw|UzR@{h<B+_?Hvzs8Sp_+;)mVg-(w2EXS}DB`=E=_yC`7&h
zmqXyzDCUvzJq&#E0u6=wEL$)hv&W$+?;r1WStH}ArGmI}V}F}O`*FMe5b2yMhV~Lk
zqWVFbwz+}4k9I{YoTB?XwUP$)Wkif|A3Ko1gLU<z_#$_2XKR;3y##w#WBrF?Y2(wg
zvC@XxiBbDGY2UXSa6javU5-OGN052BLfBtt*4d~gxH%@wG}6fHMcQyeu^-?K-nb)?
zj-?&DmeJn-v3uW}q)CDDtyrM!W4bB7P(LjzH+*vg{K*WvAJ^F_MIh#ETUkZ_=;`N&
z*#VJWlDKnH21g__(JwMH2pWa=<xNFDM<7eqV{Q(Y!jM*Y>F63-IC-}I7%KM_L=gYm
zVkMpkB2_k|#Zlkj%ql;=qCQ_0l1ZBJeS3J^p!FL?{*S@x-cQznuB!N3+O?uZE5q1f
zY{A2K2ReJB-3pkRCc?4se#%>So_bgH-a#l!y_)WY`Zxr`1+wpyh}#}D54|*KIQmRb
z!O<`ou=`+{?%J<OrS{wYNZg>oy3Q~e6y%wDerBxBlPSscqsE@`vjX3Uukf@V$8pGq
zrSRG?ra0AIWwqKWRQgyTL>VB=j5kO}^9W`!(KYK)|HmsoW&N-1*{sFI5pZhCdl-wh
z?;wi}j`3TvR3D#XA6XbbwI#Egc0k?AFDTdsg8abnaGK=yBtpYNQF!5n5ui|kat6hc
z`*K;*`wAJnd7Dyhiwv)J=^Y-LV(Abn4poL1L-=KGM2WTCnNK#qZ@Os!|3B5H-I?v9
z?BWteO2`)UOrh#dO({I?wg?N29&!z=0+uIAO1PcBmgwO?s_(OUCXXH4+y7N2#=o;_
z>H}fuE&9@&<6n*ja!wMJXX}CMx>|-#GT}zz&;;!zXXj|)rvCnD8Cb}V0IPX-gNK)Q
z8g4>UTL|$bo)C%&>(0Qz5~kxwW+m#Xs3@Yg#VEe2!pWpLR_MEU9)jgG4Sg0pOIp^(
zq+o}>C=p&I#bY13d+qKDscGo9`+mA2TshcBE;o!$FJ1r45pD<xDnnYYxV{0Y>m0j6
z)%Q*GIzv0K+u=x_OxkLvyvurIYEe%m6Pk9Q4$_943*hkscY<X}%tfijFxGgFixfYe
zXMFGNl|O-wJu+g7D#g3GYZp$wUnt(%Nn&(|BTD15QbBBBnoz>fSltf|!9fkV{Ndbt
zUzTU*sJo37YA^UDOMmt31tW}q<L)&P`I(BxC#M48CjZ1oy-tBHaRE!mRSSaqkep3-
z?2jDj20mo%$+GY4ii+;AK-~WCE8#3|$YoqyEr=OHSS`~q%Rf`z%8{YsxfHHEHDEj6
z4y><0fB&dguZ}~jP$3ca)px9|XsAfByO`XZ`VS6f@9ntB)v9f4+sPqfEc$%S6Pw#s
zwxs1>60c09YZ~fBLz;2f+VPiPZ+o6<%yB#R64%OJKkaXYvn(Ouvv)gsYooXGtEOv|
zeDEfutw{_^6{E{ftUg;u!wZ9x>))5>vq?V;yAgk0dd-Hb^8R_M`1w;w$txfRoCd<C
znT$FO9~T!F0kctLYLQCWamzSN)WcXKNHKl3o;wqV?!}Aqj1diA&;03;W|Jf`%&(~K
z(Cn`JygkW4#(y0<yrG4ca?Of9;?tz8&Cg^XBnX1o*YM)SG!l~8Xgd2)BxgPHifl=2
zqX<ct{rV)rZpmX7clrDGr@PyO`%;6&U{?h?KllFI<2+Z`tUe=-vT*PyJMKz?P21A)
zvZ*}8JreJBjwf>{zfHL<V0oLhaq2DyRg<*ZHbZ;U8r{;8NnyOyB}hsjmx@JmTuJua
z>g(Q5?TMG~X&1Ls|17_J>odWYjiTp(T%o|Y$r4eO+s5`pH(}^RgRa`Bq6@L%k&&WM
z(m@beAw^pL0L=1dSvu{RI_-BD^m{S|)f7$Z8L|gWO-z8<79*7^mv=)1!U3;+;)8rM
z(2Bl1af3_$69a5}9@mY62lv;tU`oytO;3jo;&k=bQh%O;;Ao{?nU!TVD)UR1@eOV@
z4a4>^XKmO)U1a>U{e5C^ruGHk{57In{#a^yPSY`OAS~-sVuGuM!UwOqn~5NQNKz`w
z2g1j7zMec&3YRgIQ*Bi@#K%Qe!Nm%}Vk46SE|sv<+sjhhij0NUDaJJ#2f7|(<Pfz^
z)+rR&0wmD(Dz&<;#GEL&;X|N&q-F4Ii)Qj$Nfq-Qr_M7NO#)0gO<2@A1@wOp$S4?6
z+hTAd#KXmX8c<kNWDW(rK-)UGES>Iy^QV>_F0{b1=-Fy|)j12I+V2+l)59;Ccl$4_
zY^?g=PK?jKoav)w`z}JeqMtNgJSaA@EA7w|H7BHVr(%#n#8!$tR;TZyK9bg|rc{#k
z+bezy&&%+r9XonYp->_TnRp_+zE}Nk$WmRpdE>?{Hnze(HCGpxZU;`=HNCv*6DSFG
z2=d^tXaZcxY)T<5jnBR5Z4qIzv4j>+yp#@a>sMri)C0zSi5tmNZquvRQrj{u7~t%)
z&dExSpJ%+<R<)}+GY8+HW4qQuSFc24G-SA<`)binEB0>grn~rJ{j@|Y5^f3!TIG$M
zI{M_CJfA^c4Vp0hZFVxUZ#UJ(Mn<|E)>l`}Yu=-rlGLjGF?~K1NkUT8okrbdBj}Yw
z(s=^rNCxl0XhEsY#I*Ts8(j#<YHJQNLWsem&v#b*=&k+r5|h2Lwl-QCEvL=NF-`Pk
z`{#oF%@Fnep0x0gHkw`Zvnf*C_Zf-XrU4Z5pWRr*URmuJ+@kMb4;vmCo|T;1JUUKP
z<h_JUm$5l!do~7!_BC7Zst~9;iIQ*_H(g#GXzAb56EgAV`MxQ|O_JJ|Op<f8nG!Gv
z-nVjiZ(>ceN%%UuAn4hAmM^|&FW=Eo5h{NxNzN(sezH~lltgQSu<@3i(3k$A<H=Er
zrrFw6&YeG=8y9yuh)l`)e8_^(bVh`qmcNmlVHMJGPmj|kHh-NRs0437#H{N>7;f^p
zs%q`;?S7F<U0S-cofrwXSsTdA^=Bt0CQQapqY`$cZp7;B{9ZUSWv#LVtCeV2T8g~1
z*jj0FfeFmpNJ^&&DcxmpDrFfn87Bxj#EL?Btw{@h4A%_!tU%_$&mO8~X=wjLyS~Rv
zCMP?6-00&>;c@%S=YAYYzjM0p=a4`(Z!0HBJn50eUsIWof{ZqlQD3hlg40o{WN#}u
z-(ps7Y!UpiwYU4>F6&gX)z4dkrD>3;`E_;4k9DOr=##HA#wNr|sr)Qa|2*a7=NN7R
z;#Bi5{2{aG95m2MIk(SWP+uUV<&+N5Zd!HRzU;_u=VkIF%dfXwPHkRdt?DVFSY3xy
z+3sFz;sahJa2;%uP<>hHcjm7_N(y!Rowoc6{f#L(I0h{4Ot8)uijQRgOPBt|4_w$x
z@Spia6Bh_%()jUE&pc#OUE_8g9u^HMNyd>ado_%W=<f8*5-css>2n6meZve3wqca_
z6Yh<%E$;d$;cx9Vp#vDi<s#<Kn|QKtcQ|#Jx5cWe`>zX!*S5=hz4a`#B)>1+Sl5?w
zK4NbU{=p}?Yg>C!(<?pHskiR^wabmV6Jc!z&_loTE3Vhd@6v-#)*iUBvBaMuBOhMp
z^=$*;%nN+i8`@A{OB06Co|)lrkjioVBCMuqDEY*qxO@YpT;2N<j@kT8XJrQMJ?s}_
z%*3sD{PI*J6Vs{2yP?{1*TdSAl-{G?OX;J!<fTN=_NaJ(C*tvb#NayMLi_esrFSu5
zIXo>Ikd2++XC5)K163EhDYsg^V_|Zdua}YRz+V&m>;saAL3Qj2Tp$6J&;8*7o-f{B
z84EQ`ii|8x++($zYg_G9P6y+-W{VK(;VK^naB*VN#6~T|z-lWhV&>Z&P)^0Vt$DSo
z6LYoG?RwATI$hCfu<t5S)n&j{^H~5bl8udJ^(`P9eLf4{xO{KaE@NjEGWwdq{K?|9
zGfRtUZN2%tfSN_4=ikJ&1{BlSG&`wO=hmot+}zHN<?d`jJ!H<)Mt#CqI$F(d`&TAb
zeMZL1hp~=+nV(Ce2!?WlnOP>ne(i%n-smOSR4F+6CtNNoHSv<z$CT`h(jpj23<ib2
z9Mz>=U|zG>Z)L8{bblJH4c3K*=GhmUZ{EIp_Zc`ez6F5kOgtQanzP|=x3)j(^K*Qd
zl-MEaZ0j^-Vvv-HOusP+r?8p)c0K{CZYZR1)EV>sD|-Fw?N9pQlDueW5XGYUa<pMT
z8rhb)&;9lc+m%M~@Ue$=UAJ_9m0p_aOk&)Yqa&W}ermeM7G16_Yq8Eq!r#fgzsU`F
zV$?^w<njzM2+z|T@AsQXD*iVBcntvn4c`eg%q9q-6X1Xm$bTkL?6~hx1f`QW=JLBM
z-!N#q6boc9$EK%~k+51?(L7M|0y-+U9qv`3kcb2lMk13t#l{luN+d1(vuf98=Cb^J
znne68o5ytnR_9t2e>70$Em$?L4a<Gqj@h%|z6f`mUY^JWTA_@>%B7O$n>xpeM?=<4
zE>Dc7dcZBbtE>;%Hn^q5m!#UW`^<I<o-cDq6a4#LzLUkT3?m2Gm=giLR%F3kzCg7v
z(N{b_Wx1yg8TS6L4iA>1r>AF2-G}?_u>zO<#<5XG)Uc~`TUCo#9Nii|Hk#tk&kx5|
z+cLE50unbaZpW3Io)b(wHSlm(zP%Yq_<349E>zdoQ8!U?&Nj_(;!I}A%O#o@Ews?Y
zwpA_(ML$~Yf%*f9d{LXJV&WFPpICZaR;He&PU3bIFPd%(HFbL;R}A63qnQiJ=|?I*
zZis*wSf##u|2_dka55k<cnQ8IWHky34!6tiN)*|cvu}>PwfW^iA(1M_AQFdxGIAby
z`(bN)CRMIr{hj!tBEb^~cBt_#Uzu6CN_U<sgeA%Ok^yU+$8t91s-CdNVlTF))`DA!
z&WO3O&6aD`S@U1-jNy5f$CZbS{CaMUtN*K>I#Z*OZ*(NsPHdN_c;FIyU(w4a>v{<&
zWkV*-zYZo;pm^YI`rw$)pYIkee8aGr%Nx(MUe-ueDv|?{A&dFcyOx#~TwL532zD#0
ztCKYw!>773KH1qNwOu%d;`v^mGo_roHWfj+cB|9l`H;H`rw>V!pmT2rgZ;A0S2%_M
zGW-PC&+1zA4g1wz2fXDxa=w7}H+w~lJP^XQzkW&Gmh)ov`S|MN47R%GWUp4V8eMH{
zYiQguh<+taBhN%sA_4us)11sNrD|(!J(-l8)*>mbDA}BdLiOo!|Iqdc%aSY*Z+w8q
znR&Rv+MRcQuv=!Kl#A#SVqG(n<1?UEvhDAEVc1V|L~Dg9wCyXOP!U@~EPS@!I-4RD
z&R6t&3ayR^w?MmlU%&rPd*2-u_1SfcF~-FDMroqJ8yii)0@5K-(a<|c7Z4SYCcT@2
zMi7aJB1ltVU_clVhh9_!6i}oNQbnXQAYG|<Kd4Fc`|i5;uDjM<_gm|m_m4LrF!P(=
z?|IJIXYYN^GfV1Nia8FUcDq<m;92lb-kv31nLTPbi%39!@3`3e-k7MOg|j-N-(7x4
zXns>h{JBaO8$jpWP}fF6OF>YOjx*M@oC`|WIIz$9>PLBxfsoFn^Hb?q&I3@AwDmTL
zm*Z##4!`8S2sn1*6uF!91Iq>ZKdU!cl70EbR<h^{{!`{Nxm4n5?ikE7T%&p0M@;i9
zExToGY;>x&(DZdfwza)lbhasIdC@)cD1o1@_81s+n{|^EuB1w}8tz)(-~2llS!nHM
zVF?LVC*9mkeqU&oQkRRWPwA`wXCP&XuO-Y*N)C#dBJh{v**upO*$F(!rqlR%9ce-8
zY4z)3<!MA~vabCu>9tImVjukn;fA~V$v5`R#`V4Z(QogVEh%jw#?)P&7~0SkBd~W`
zH$yf73#B1ZD`8E^pQV=6KG!ckmyjqacb~1S6FbqU&y<X^$j`zm_nU9=FO*NXYQ$Jg
z%Xy>xL-B(YYB3Hj3TvyV5OU(=W$#PPn2)kOHOuMcrKg(V1R-;vKK>j4l!{J(8bBw1
zog|yquJvD3do1fTVW`l1LD(@mzmj>Hik8;ovzE}~pKZOhZzp}j)SJN~h*mYHJXQ(n
zR@s|uf4^+fF&&h-!}F?)V*$MLg#j*Wi&E0dp0`tQ_0hw|n}WJOTSzSY>VfQ0eA~C{
zJ}F=iR)lhu_Gj+0_yTrE1<H5hS%as%-7h%nADo$PW)*IA*WLI{mL}v0p$38H9Kqei
zCJ|dpE|fg5`lr||l*nFV5jE+=r#9<XyfEq#2DB5wX&(6>K3Ewa3_I%+P!vv-M=t^e
zb#Crpk;Ern+ga6?UMh1bBBf*W;+Bww6IZ<VQ>hUv>AI?i{=Bi*wf(Qch(GtJ849S+
zHRSZu<R=hM+L~@<na$Xo?{So}<<N-?{^FbG=;8v*-#mTfsKG&B{-YP$KjhYR>J=hS
zKi@48h~ld=UDa)ZyNMJ=DxEv(q|%Y5r>y8J`JO3u-!G-zC_2IZ{61EmH>rg6KjNc5
zY>6X@{`nF~VGZoru;LYqW;pkey<#V(^zG%Z_k@i?NrEDl)HCOG^a^-QP?e^8)3+rG
zUt2#qU4ztK!%a$;R9pYmilIB{_26EtyV;*GTDHcm;vf1vvc}&LRJ;k&Vr*)vk)Wi@
z+m56-b`*_G^*lDf;DYho_uh{E4t`lX4Oi%w^FF3-WRJ()4d1SIJzwoeZKle_4zHm*
z4)dOA3$+<VW;s;qqhEbZ!#uQr_&Ugb=4?7Z>g;P*_?&;0`sa6Ji5Dc_4hu;?RSzDZ
z26%uVf(o}emveKWxy~rt0b@UwojvkV*L3Yuy;gEmG}zT6Nm9=5;Ksmr=I{6lb$;XW
z_UIP@Azo^6c?MMMeej$JM<x)Xua&;et1D3f^qgM|?)H%gJ!ANJxx*5Fx<K&E7<@=6
zSj=G}(YF4hy&#sN_F4y}=M4zb?OXKW9;?C7M4`QUEKd0wPr6JrO(=*D1g}+Ri<F@0
zsJZa;-+`3f6jZfCpsC|Xz(|M5I1lsA{ym-R%B8$S3^aUa%|oK|%mn!MRc3yD<Ej!h
z&!R$fh9Nq;g+0>8FrnD}661BTTheC&CJW11<y!|<KRXh_l?YTPBqe1iT8KeU`%j<B
zbL2b+A4LkmyL<NR;o5!dEUF!9YisMO3QiW2YFlwJzxid8-ad)`t-g;jMUj@h&`yi&
zk!v<g8w}*w%-AZen#nVq0L4=?xwuR8?$N^81P9M)j(L}t4&84!!cTm+AGMN--mZ?V
zGY2x}f|uX8@#Sv(u{7nyf$`u0291V012xRlDDy-J8LElrr&b$i>7rCoUCqGsCvo3*
zP7Ud%#&CSyZN&dywpspTE>lS^wMD@&X-{UB;5Hd(n5akT@2C!W#m2w-7H}_a9A;YA
zm@-OjUe>jV$GdT#9|g%<Sa>{QrZC&6m?_g;&y}#cbga%_A->&_b|&wr{|6cNY3bN!
z>x(7cQa9+pxQg3Ay%OQVE^*6HZ0acelp79V5sW~wwIzRAa4M^))bYjn-by#kPjT)l
z^PX%lpa(22ta`qMB|0L;Al`$Wk2>O$O8=qzV#zxM%b8=3^+*On9#)n!PqSS%bL^1J
z{A}zxRMW1U#nXo<=K{uV#Kz<rChb#MV875-F3MUN8pAaCXSwj4?K4jRuDUw)KAZo-
z!UC7PZw~veqw&A3Tt5&Z(XJWHF08r-r8p*!MeVlul|ao{IiG=!#f5C}%?3XiE<bB!
zHyx|nF|9v1-|Z7_a7^+dud|w;@Vv3d+Ed)ncC^!TeZ;7Vr5f%GI2hh^b$i}{gGZZ|
zZD89|_FI<Y^&Ec4s1PQ!i>G5muPr(qlJWDOZb&gGocQv7K5!v^nM7MgdK#wQ>KkV}
zG@z`V?7JPBayW#G!U>+<-2c7ebh{6Y{0_my=$@s<UtIQ}J+jI~Zl6o$Vc(hjFpU$^
z7ag~--QwjbQm3`mDqH+gBvQHUMYS=Jepbi!rL1#q;4X`kic30K+;OWjZjK^H<&b~k
zk^x7uI%}ozuG$!e8UMABjy(0;F9Y@2+4s7xUnOGE$<XZ>>LUf?mwKw>XgKOf8=YDP
zW@fR>`)&BAL3GB}KxQBua{Ry5pRY)XA}ZG(#{$6XI9wG~-M{8w=A!H!gI>M?s6!>5
zqzJ9~s2}o9`|%_@>n6i{6GPMxns5_!;!eSat^f`Bw4#yuTXq|5PfZSAQGZtRmzZ>E
zb@S)R^v7<k?p4o3$3{nM;(z*04A$ouX*cD%Bn?J{xj$85;oU)m#&Z@;tE!jj3g?H$
zHQWde!}IMsudvPrxc;i<K6nBIuY?m&9-TJhRxJP!BXQ@Z(r?BBB72*jMVh|qwyt4v
z4}h2FFWj%7mAN2mV(Dx4OQ-JT+~Uv(p6MSmWIx|9PN9Ovr~CyJ6cko`^Nrx{-FI*4
zEYnnn@d0R5koM})O2tN|OyZ4p<1{!0>VIq7_-3Tb^x-CHKH7s64TF(7xMzg;IzSn3
zZPMtPk*E_k(5w1xm`FdpQ(1YWn1Vn)tA65|*B!`8@SI)a5xVwyI$}vTuwMOaZOMyU
ziop|@vTF+`M8)&x*Ai9<y!iT+G~wCKh3})-k&uviJfg>5{uWX8AfB*=$DjmtI!uI;
z=T;|Uf_R&YV#}uNC<71u3B*xfyNwQxeWqB?XR;=!utM0$WP5<tUuvch9J8O_jU4r>
zLSh~Ftb<{55h5Z(c_?p$>&?DafRNoH(Py`Cawh-u+c*6PeEOQ2nu?Uw@vKBupImXF
zw7uhsw|dC}UA;u+@hNB}t5%Oq9&^70dXZrq95&IN$2z|la7me!cheoE?EAV&-&`Kn
zNN4@Zw{Q|7A6F9;o)eCBd45S-rl_i4Zo~MwltVYYFprlI_<jgj&A7(xFM}#X8|Zch
zAzP%hOU|v;Idv*IsqT*U)NorWhC~j&xOuSR*)zS#frf_rHaa9EE?c&YErusu^Tt%1
zmV0d1#{0!>PMRtkL+w7;(p(cI<$U@tbrFvlJZ~}=CnvM|I){mna&W~vZ=ZkX-?P!&
zcc^Z-`0zjWiK*&=<9ByrPcy`mwU%vyW#ykQ4?-_NYlzx+@>0><m<oI?hgPgu5w_sr
z{a*3A@4m|$OwT9e6G}J(-8{XrXepP83L)AlP#KEt>tS|I*1Ap1Jc@_)({-*nzt5*y
za|?TJ)dl&~F!QEvM_H_FL@Hm=Jk7t!^(bwfEp<j*u%~6U_P%@KqvcGayOY;-M*ptY
z8s6=`SZZ1Gr~9NUQR|5%1Gb$Cq1~)Qy=k*~J5RY*e={~s1xN7D?*P}^X+hJQ?=cEs
z0+XQoXGe++yuI@h7TF@qWlLqn#B_1@>o7DBC4WSaYIenwr%!7Nr`uC8D3bCx2lKQ0
zaXQnBi<|aM%ujcuB8c)-{3|EkBJE#(K8tj61<-i4;1StTws>#wcLz_8t8hFXopuW$
zpz_GreWSH%9Zo+I;rZo|@A=W2%jCIQXps)ZM8RYW_{zs7aOHc#>P%LxRdW@SGj_iG
zaMdWy)fhIdf4-b9D{Jkz9vyv$Ry3mw4Tv6g<;sqy`nvv?BO)SZCi-s!2-?1V^>Ed<
z-zNW|o4Osr9cRXw;`+r+r>Ca$(N4z3&Q5vr=D4v%@%9)==L3GTQv}1gwYf38q^(Ul
z3kr5|HU4mzA(5JB5YT*6YEe$x($_s!&vC9m$7RvyC=uD+CsXsqi{+>%#ZfHT|18y|
z>$}+KCT8)YOGAqO<YHUO_pfPOKP-G9xv^lT{3>Cw#fdr%EPrzwW-~o(!N9~JV{YVr
z2ZlA+Vm?A1MhWQUyE9ik%}ntbe<r~Us^azT`yqr^!K|jH1}?_ikUb4F<liT(+amdI
zCFy{L0*nZv*tcW}?Ale;<+YrIEsuA4dGwEAOCv^fkAH7QX5M5MJ+CWj#(S7TNnw9A
zSnGRcZ1n^p%FL;5o+WPVHne*1>jQ1F6^UN}dX}6U28!wnIDX3(7rl2)iR-UkkN{#m
zUw=oaJhGExy?QnURaN%YQ+1-mY{L4R2WiFW;AookkBgW}?pe5I>`(*u5gQCi`Tm%U
zQHU=ZtIWZ#%{VMh?|bS1>*4bEo`%X7#n<#J6mt{9n$5$;+wL8gH1!9sXry-dq)xWq
zYg-pwCCuI*``p=RE_;|Y@-u>n<*Xp}fQDFccy@NS_tX&eQ%x{g=(NEZW<}#3oX8Mm
zT$XlvVKg+L562H=xx<uAG*6Dr=D_WKQ!h^rwB=_ro5KZkCNP9M6#&8E85_S6>iyWl
z461{JjsXI74Q08_j<TfKXEl9}`}=1oblf-g6a%?r)p5$nZkw@3AK3~uyYseVX=x4n
zcNVCQf1XBJbicVudC_VMJ?x&f5r%rT^unbD{OK_(%l6EcJ^-<Mx9JT@=Ucn3g=zdg
z+>japkszfZ)j%ced8}M2sA6>jPg~Bch;B%5Pc8~bzCwXJR>HrXn)!0a;^Np?40Xo7
zLts8l!wRNKkO#d*Paf`GgAx}$O?$?LOsREU^L_=yo{>khEAWkp{`_XL#NSI{ZTOR8
zb==yLw@uYokKxHnJHP%tE#}WZ<1tlUvH^%`M4aAR71{Nu>g?F)*gYbb(uW+|8Xy;*
zAMJHnJuxtMXzfCufxTS#@a~15jjjzZ+TK1KkolClb^Q5SyCp)|DfFBF@Ej`-Budqo
zgq2*$GVN3r*c*}Xe!TZav){~w^UR*%x};EQ;hAn_>k}sgB_*4z-!JRdOEo$xzokY!
zo328<q^^>}sgK{GNEPcV5zouli6%yt19Q(7zh*}POecP0W$hF2bG=Vp`QFxKv4=Pq
zjx5Z75%=GQW4F@Gi{(or9VUQX8TG^U)GKv<w{PFZGENE)XI0X6a7cF_YPz|OTRLa=
z!3W~r`_mG41PoF<B8D5m<LVs~URb^Z+`?-H>wE$8x>;Uspu;IG<FIf~v4e!vAdE$n
z%M(4f$?1h>y4xOiu&Mkr+Tov*bp`Lgn6vzTU+XsX{?<DHSWssq9NSwbinS~)w+8kk
z-~V;)gBt{Xk&MhN+wq$d;NK`G2Y;b0z6cS%#mceiPPGA_Q1S0yO$Ng>FK*)`<Ys45
zr$_zbf$K`M8J$`;{j{Q@7DFpl8J3oo%HoGR-z6j@oatSWV;tPK`x#f8p*c;ZhJd<A
zLbrjZh~Z-V({#|w4AT8#tqTeRG3wwPk8q2*5!Vyu=H@sqCRNv}KFyd>udb%%rsQxo
z-^<5PEXAe3;k2PK@e#k~e$IA<Bgw6B_Y(hf=zK2!!Ow5a*@}<@<CXQfF1@PE`1pSt
z?z*NO^8J=ACa<qsUyh1O!C2Jr6civnLt{^#JmL2c;lEK;Rkfkzs?uFWZ#yR|VMq3x
z4E7PdftRh?lj|oYYj3x8iM<{;h3j}X0?J#Fs_Y}r!ot6K%7=v|ea#T`P?Jqf*v2_?
z86D<0h9cjXhc(KavTE@>+rR#$Ik4xe<;zvJWbTrm>DyK~QLEC;WrbuTjp3%bF2C5_
zF)uE<rQ`HuYk&WV+PURsMt)UW4_;Dmt=ZL@V)5PkpeL<~=K9Pt0b#H*oYAwW2E6Pp
z@n6sK;OZ}Oyl^)yC)X08Ff}Vwf@dS>>QC<I%a<=HSvdHCT8=R*5_C*_s<^sQ^YT{(
ziN;-Nd3h8uD_ZutIMKW&*~g3*Y0mnldL^0y#b1AZ8(Ci6`|0L;>y)?O5sZ__vyG20
z>G7UlQuy~D^3HSnMW35~s`Dq&Zlgs5HGfm#)NdNI1)H+J+W5u0B!A5>R;(KM`|sDA
z12rT)4tH*>S{-W0e)~vhBQTsw1E_5|F1R<++A^$;;3TCxt-0~1_wL)LW}b`5#78h)
z(%{%(>pJ6FxL@d=y-L>IY{_=NeJh@#H`xfAYQBlc(!=1Hm4pA{n<?MF9?3$}2u&4i
zI-9gYU{3_~kz!|WbzFo6C!%TT#NCL<NJFd#Ef6$h$(dOF{`>FAFd=k5Uy)AMiuK}j
zvc8)4Zs~is>Hv|HlPk!_IIa6nKW0sOa+~ma+Nv0ChmD&y*$4_kU)RB*J=8Ku-*<PZ
zrtM!je{pMB^p=y0!ojo+H1iHuCQQbOT(_Nkfeb5SIKKGhvlXFO&qq5Sz2%m14^Iie
z_Pfya#ZF?$k<~iozsas8D~(zR(Ku-qkyuwiKp-oNMx#YZI1b;!wC&Gdx+kzq%f4B|
zdCXSSG*9=Og=B*VPKKel<B_m!*zo#Z>*v3Z6=+8O0{R3ICX9Vx!7<7`lc?jTusG+U
z<MSf^r*zfN^gsOlk2?-y5mi-Is^GYey{0~xdbB0hw&?WBy-+)v8`rH}+kkn$t(Uic
z@mXh;|G<}S#W^-KbQT!<;3;{d1uZB<0k@QJGj(*F^zz7cuk@FHSb0N3k&k6M=;FnC
z9C{;;ebz!p>uMkFF_(lx^sU>scPN3P8}E5mw^2j(Cj4PdmWbSwKwmzxBP!i*-du0?
z0ZNEh{kKx@^O$|}5@FY`pPJc?pqL|jw|6;<(|fDpl`O9+9>o5&4J&zP=dV(oo}KQD
zGV;jeJZItQde}3%!L|XVFy2=oGTq0=M`-_keG3Z<Wp#CZBcrH(|6|AEukA9t1>(bJ
zx3Le9Xv3?AzX?HM`1}>W71PR&{PxY-u_j+Lo*Un>mEmUMWS4r(LQrF@UbX7JV~70w
z``$Qr#U63n`WL^hSS?ua1^)&Ht{3Yg@Y`>{eF2tWl0bDp!_qSBFFT0e%^6nby(juQ
zGv+@3Y&>!v_LAKfgVeu@%|2PpGmj}#(s%$EE6B#flaBrwDz2Mg>b?n8Xja7VWGu6D
z=V5R^Y}ae9+JD;JaxjyL<J8C38<0yK9+ErP9XikRDS&<0xgr5W(*~62=q=o`o-Z1Q
z+Y9p_m6XWsFAgm~aUii4%S3Q|y{e~Y4vq=at+9#cT)TO{E*K?2X)L-|Czb*+c0B^|
z-lfN6eGJj^)mXWIK=&z-dV$ZFdKUY>NNq4-?I51jFJ3!YAu)aNQYGTg7q9C?uK41$
zd&z_Q`45jBxcNC*jQ{-L_X94U{}!IocW`2T`P%rudx5@!(#u%%RN!|JM^&k_8HvgN
zQ)0rh{ct}%EL(E(*&bSQp3|yP`ttKw1lL1P`VT+V%>DoB%l^N38W%_;iMM@;jz1ik
zOFMXeoq&Xd0kF0K0(Apbhi}E#U+cPhW0x46v*6#cLy>TZHxHf<zjkeBKtO;V&CC?d
zf!gQSahnuH0CDIOPmepZIT1tW2qlGp0Fq4G)%CtgZ*!4f{=&Pi_1uWtAQ4iuZl44E
zr`o0G9+sC+_j%d*RD2nW=1c37C+z^ZVcJU~?mci@xEu|JiA%_a4I50PBa+(JzkL&h
zR7Fon*lyomV2BT|1xjWEyMs}?;ts7xVMx4OTpSQtexhR~OZ@9MZ{GCb6e(SFoZ=mC
z-?_7k(B3egXrtV$ziV+20P0NjmAE)sN5G;fj=Qac!0?92OhX<>Mv-h=b1QJQ{+Pn~
z^}LOk@_HX`hZb){+B+hqdHmbAKeitCJfp_K;>ks8s5~$ISnx3m3tv^NTnqMO%n#Hj
zaR~2SHAi^G)uSb~YN%haXxc=}`!!#ec1ziFx@q^ey?gI9`RRsr4i4I_;gM}ZJHK^q
zZ(FuwoO5rC2baIUzv6T2jH4Hk?l@7!2{L5$aij<;w-9i-9Qo;7;<lWy$m}EZ@{Y!@
zff&~C%C|Z!28DzO*Bzx&sb$+jWi7v6xsoG)Wwv8S`}KV%)pN(E>Sve5nrsA^2)}&U
zLN!Y4=^vq?qN8rbadB}q@IG{5+OBZJyP;V;`sk3&5?mgw&gVNvM$BK!lX|fWLz%_U
zA*4s~LQQ5jH$M^}f!5@pnA=KMc9x+MUYpurlrElTl3R<`>LVAb4bkx=vQ0`V*W|;^
zGI2#EY=yJy%a8yaCx%|uVbZhPQ=hTGQKLfJJKv=ryeV~d1J=T3eDIMAqS2!BRKv`j
zE6i9}IL~3UZl7iFE$(@oIa&L&hK6We!M+n!yMO)l*XXEn%M4CDY~RTC$8xZRk|Bjj
zjPO7IJa!t$L7N7zmq(<8quJ$IeZ%&8vkc3?lJ$4l{LL<3xuP@RCHvn0Y!bPLsq?6T
z59{jcQq9JXC@M<H$<N|{5GdeTAa7sL+1;%-;OF1@C@?X~4n4B#6z;nBS3?0fm5`Wt
z)mshE$P+bnOe`M?H8Q$TMJ|+&fp@mxECUmD(byrk^p8uI+;e(!ZoP<R4Cq;@ISB__
zaGqm{{}#FX;JGz5t+_60jKab~Jv3bf1_la1RO;{Vr_92fY?S9pP0t^eCN_^B6VHHC
z83f^QH{XBM?uXmAZiPogB+buGw=fgK>H#r@jkE6Pnrq=mC)KsQ@+P=6*Fq$=pbHnY
zKuWITmA~^ybQw7!TP<>LX}=fFe5t>EWPO@>al30<b+v}Gv-22c85a4?hDS!)M3jTe
zAc#gedne2hJLKd{g^ljNfX|FMTv}EpxMN3Ye+HiG?adB3KN%VtQU*IaJJD^yKx>@q
z+gCrq8+Cnn{ieN#u-6Lf%)6LRr<cpp@m41G72yKj7+20T3;pKnulc2=jc}H21xhk3
zAU8}@ZJEdR20*Op#Im$D$J*_7!=bl>nbq={`8@EFAZy}SNlhGqR*Q=rid~J9)orlj
z2U@aeC0-a7U5|xb=dwa+)py@%9XRkSzGyg($|xw1t(ymtsa_c&YVhNaXY1^|hce11
z&?ay_9bP{YeE{3*QG5)Iy@luscaU49aJJ;f2fDowhsk(j=0kLlF!QhV>!5jAZ<s|e
z`{@<F7C-I2o9B7<;zpki7U1XKjeBoh4^+%*Kr@q*cU!}fbBm6RnI(Ge{Q1qO8}>s0
zaXfzf`1RNDc!#hF>2sRqyVF}d91AD)lX_b+PlmQJj?!)F%8nNK<_b6f!j+MO1$$<2
z+(&fmAo;Ax$+5A$Wud%|B+{^7xpKwI7iVZ52AiywK@3@!Q9Jc71q*V9I;HM`NL#A1
zi+2SYIGF0kFNB1MW1zX!%+65gT3qYp-^I5Ty+dcXjNIShH?dlNw#vbr+}sSO&I8+I
zWLiWPfY{0y<^lGt-h*j}RaGyRjJ}+HIlYX9?IhmMeO42gGz77FvzM0_rK!GNw<|Hi
z?B1+<9kxVNyQru#<er)oJt3XqKmPc(3uixz0f*T;<B9HFz#%m)G3g6UR-sablui3i
zu)d!@kNXn&`kIKT?R|%~W>yX^F5B&Vd@|xoJMDoYlwg6WLqizqu(z1coRij&S{C&R
zmFsf;T0EAJ7cVsH6q;$p3*H%{$RT@QzkaPc3dh>5mHW__aK;9vz3;)<`C=QF;hMS2
zdV6~}6aQFSaOxw)hSK+y<_nesjGG5nZj+NMd~!6k!B)ZmD0ZlQyQng9O(&CSKOlo#
zWZ9K%-*V#j^XG58#y2sdF5UM#M>8JXJ-X|hYv*r<i|@)6D&{CtIJQvL4~NL9IX+<D
zZ&SNn$M@*b>m>?jHlI7$vHop6NqUJGHoqB4K-T;d1|$11qcl(|(<5Ufb7VP-pgQ7^
zn$HUmtoAqrDE+ZK{(T=#iBz38^I_6A%+3v(FYYbHqHAj^^7F|U?eHBB5oj+jFHhGj
z;Fpq;a-W?(xL)qKAWyz7;JY)T%8l1>koP<BXrzC~a#qE3m)QEXYsC?KEo5hhGRv?L
zLO=ysl?Jhi2#NL5y&H~hZ2b+GPm%b&R2PqWU?_JJ#|;i))4ck6J~6R+Nw2Yl^LxqN
zeUJIH7jZnU_T<y*>TI9o9ym9C-7FCMGuN^6$U!)>=hBAR<JQ)jfG_qwn|#U_CmC(m
zbk~9+@6qNKQ6e9x;T?=+8e^tiWA_!y1McT>3Xag^9%S*!jXvIJ?XM#PyY}##t048S
zHI>`axo`~YzR688?srOZWhed5E(K$uNhF^89DbPoD8e1*SL}<oikF21W7q7nebXR=
z=8@vqsUuH=GHPjs-iC|UuXF0mVU+Q((<Rt~GwTBs-a$=O^+OUqj*Gq8baHlzNzrLW
z*baI8`0<9>C1hY?iz=aPirEr^?w5R@biq$It6YUe^A8kJIN`t|Z@=^FLqH-MX0c0E
z$d0GuuL#}wTo@<EYQCHt=`aTxNF3+_H0h1?dG2bDLt)p=sv!*{rZ@6HipD<Dw~RY`
zmW8<PD0c9q8eZ@tpvN%t#LH&rli-G2uXo5zx881QI%@0#$2f9{%>}=u5Zr2wbEKMq
z1-DZ%5S~cum9mZ59sa2(fnGM_2I|0ps61qaepA5jIyzud#qNuivz}w~EE;J@tp=p?
zNB_=I3%m8)(z-yCZv9(^W;yPX)p<Z$JB(_IhzIlZLuyfWyLT$J5afuZ6^F1<0^&?_
zGcrvczSx%MTAs96DHlp@U7gC1&O5m=RJZ*9{&)Oid7PTeU}kqX*rC&?NzReK>I$CN
zEA6#hVkES5cXt<QA2mxg*L=zNdKy*A*%)Zb1CK9k%yLJ(C-t9h2N`=@9}7WiHA<yd
zV;=p%JCuka5evxNGR|wfC$c$ky6bhT|15LWx^-D}iPKH-a8k2wRtQ+|?e6VuX_oVz
z(8Sx7coh^B47F>zPi%Rpv$O{H<5w}|htat3wq!jB2JN2Fp64-kwlevm@cZhFj*ezc
zVPQ`|mtosH@>k;PzoH?(hrW8zxq+=|dhxs;FUu>NB#pTJbSq0sxVn)3b!3Ev)0&zd
z`E=pQb=_@J&eQyvQRw4gEF&tafwSBA_b-5J!rYQ8Y1|t&+_ES+Cl!6<UI%S;ugP_S
zXMR+CMW0#J?Gb<IhpOV(NgR<%amT4((ca&C?wQgSGDz3Osg;#ya6`@*z)G5Lw<D(g
za(MW0=}kLeZ3cutW1Idc1LC&a`W)R(ABk3{-*m=*o~r)d9|_yiN-mcqaGX-BLA(U3
zR;}7@D%tOfA;{J|BY-h8(1leQ(QUs8vj=-m58LPI*YXTek@uJWtQ$##;>b;y9i#iF
zI%}nxrk^z-XT-9zv!Ad%X>BdlmhE&J`7k3R)|*!NRu)qj_H);3hXL^l@$)}vR99B6
z9d65yiLEpxkD<;p%|YJ~3joHxvoYjwGfGxwplKK@H+LEiSu&PEg@aSkoU~5hnioh`
z_DF-;QCh~)o{gXL^YZd0^$y~)FLYgXrHNI4-<<6ziekrj79t@<LnJ~hRE8kA)WU_C
z!R9=-)a?SYvL++Ni~hM~EvRwE#>P%F-@WsMrJ<#NA+~u?W&C5fNu2|1&sra*#G(H7
zjQEtF4}sSjwuI7(xqiLRbx+;Q<e(Z8z8CgQ9vFg3z)s`T^t1u$l<4O*I<tkLp`mSD
zK{qPNvS0e5w7u!y4J)GdmH`t;amOZ5X()E`?HmO$HA;G-Y|G2aAg<7wJT;gtB<<>y
zsmMeW7Z<~t@~YlMmSY)<t{%Sjiv*=Br<oG%p0{-!pR<=<nZ1?NV@ZP<RB`kE)4S;{
z_ie0Z=Yf3dyFwMxabQcb_v8Q`w6=*!G!Y-kc7C${Do3DS@z6NPid6_=``8q~HAzk`
z8w2A(P!h!s%fs<<M&R^4NO#xXNt}JOY9s_ylon=F=(u><7JKv}$-NabOO5d3(WqXr
zw1<`S^{+wlJC?`3aidPko>k?1J9b=^5k=|3s*|KHPM@6|G_dsT^lLPoodWJtbsYNU
zn{TeYYl!QPtX$fcSMTVj4i`?Fb@%irhY>9a5^!;ASDv?dYjHsFz06GJ>%V}%5SxgY
z#?L>$0JrXnQv*{Fhi7^f7OB9{INwDLfVLTb1IUp=95u*IwV>eCk^<QR{W?Zv$@BE^
z0gLyhd$L$N3SnIf1{>>)Q5;yA$INO`Rn!0*R}Z2t_@d}Y`_ar3YACRqnn6E<G;^;s
ziESQH$;4ig4Pgvi>ZPu+`_)J5)Vc?6-aNem?Xhuca#AnNhz@N6!B~w602T4(8xTzn
z4}V?pVUuAY>siJ<nmt|UjVZP#JN;%6{nVf7<d_@w2o6jAx>Vf=6=FR54C3f^B_x!Y
zH&-+;@S{S`!E3q{5c~bRcTLTxL4Z~61&_Z$ooGbrMBR7BMF@CME+q2TU*y;!B%}iD
zl!q0YlzFn6eRFw{W9U-oGR<?X!8s?#eAJM~X<D}rQ<2M~WWDa4sE$pG3s^8*d|w?f
z0USq#83W-X?)3aDRuG^khm611^kJo-OP55PtAbY*OPIA!H@DF{RGyQ%O#?D+VMWf)
zoQTk_f3X;ZAt^;RqZmqDDDlKe*%z!py!jw*HbSmHmA4-U9=Z)RWjyu|oPPOe_U^X8
zE*v!+e&xzGY~_gOI=XSMW4C$QqHfPoK6B<CYUz`9p_}#{hHu2}X;XI_2Vxa;4K+o3
zMdrg;Uxf!tOGyt)$@CZ0h6?GRgmr2I3vCePV62P78B9qyyqnOJGc$uUw>M9|H;x1N
zu&qzlBEUQ-`n+EU52+!y%Fewlv8_$Gz!Na%Y~N92S{vvnAuzX=FG3j$&!t)}3G1`N
zb*r}p!krv$Gsu$es7jq#KbxL^?)uUTJuIrL7Oi~y4Roc1VE%c$2YNvy;6#i^I~B?I
zWje~nelVdPP1yxL9-V&q$$L!qXlS`e%|2>e3YB_1p0AccO;iqx+HaqN)RcGnv<CB`
zo()a(nF;R90T?CvzZOv<nPoc_YYPIx)t+ZJlFsvZ|4f=du!hosrG*3pl(rqdxO?)3
zxZO1}WM*rxMTckyd5c=&Q3a3L7kM)Fa(1@Ig$%e`82hSaZ{vW@a5UTMwfdW1%O$Ml
zE4sH=&ughi-2_AK{9EGT_cc0&1G00^Al09l9?Gn?McGZUTbyAOvu@njb=T2rdia+5
zdlB1_sW&cPe*hwcAfdbVYa`T-zQY2qxUa!puH)mRhj6?P`a`;AEAJklG;t(#H_%N_
zW`FqMhouKsvR2}HdNB^}aSt5aFzfHti+g@`VPtVZ11RGK4n?*0*z0M5qby#4*&u2t
zP|!6_W35>~!yZe}=4TlL-I2k)-$twlt9hMu;f*Ga*rArgRU+W5ir}VzOi|SC)yrK+
z2i8#syvo;^ugv#A_6g>yV;g4`H*l{0ehH6N4B{iadm>MsW+Hmhnhs>%8>{CrxjHpd
zTeSkrGHZ4<nJSpq6J@77G}zspNL&Z5c{V^==)moE;+dMSeMWu}M}+ogwK&wFzxfUd
zOyw|eH4|v#wIw<oSQ1oOWWfSmX=Jya2}Xa#_I|MFc?j2&xZmtD{))_L>&Rg6*(`|7
zCq-hnnaC3)0ZPu&mSa-s<dIbYXOiDuN96yNST+II#Y{Jg+lKGdh#i{X>~6~so_Xx3
z<&(`9_hHk8Gr&sSx_@6xNgH>6GBjYWq}#>Qc73|C&xd8s7SwWb7&$d*2LAPAkCAqY
zog!OQ=17F22-0>Zc2px=0LSb+hMKfmvK<*JqJ;_2qS5ARQdCrwqLXxq99;$2OBevk
ztt2EU6->)hyxe6_|DP|oz{NfCSPhrWI}O8#URn;JuU120J^>>`TJBe`17|^cyqFUk
zPqMb3Ts=~9iS^R-=%~P$&V%llq-6Q$MR(m8NMHP+pgL>;__C&eHIs0BMMSkuyI;-j
zi+c+@2cLhUJ9dDoXm7{YLL(t*K#%6R4No&=3XzCu*)+|}0BDxA!4YUPSjzTH>^y`@
ze{#1}hCJYpA<gPyrapvZl4dP69iekf&LtL%(DUtzmo_h<Bbj<-;tva#9u$%794bGT
z?Vt^Q$RCy{;kcw-lfgIGGe|l|Fm5Rv9sIe|ZwkONalqWOBi%5QqBG7qY;Y|%$mh~4
zDj{omBK+67qlSR+u{*m8Uvd;?Y)bDI45x=Wb^o8)!*kKL>F&*fg6PG4##eT#sWNX(
z8Gb@Q-k@>Rm~i#h_D@F;4>j&VOEckd?igkcy{^cYP_BigMS08ktw*^L(}nIH%;~W4
z{{Vrm{7==!h+x9(;%Ju4e!H7Q-FumNvJ#-EzKd<)AU%JIiL*SG(jM|Vae5f{jFD=a
zKNJhJWd!cih*C@EcXgQTRL6<)b+Uusj|42*1vyU^ac<sh{6@m>?+OUBk|L?5NYdJq
zqFVxbsy5|wPWhFOlM<-46ebk&qE=V?R}X$spPn8Y)5g71WB9-C=E%<(8x8?kODPrw
zYbEf*4>zA2)hJ{P6Hfe<O@Y3LtcEqJ>tIAi_f?C(6(FT*Pim;D?sAqTx!E}Dv=+ol
zx8YW)7J6!Wo-&F8O(ez0vWfa4COYxJjfH!q1BWqNva6}No+5~9i=Jo9C+baxCed)1
zjKYWFuCA_5oiR6(-9aWGxbFeY7KeZ~MnVK?F*U~Pt}Yegi?~&^>Hb@@ERPX_siftO
zbQWeHms1W}Dw8}_xCD=rK;#aMdwPrHtPkvA8M<%Z(pYbn@19oYhaul7;0=sh7!1bn
zT2Ea6AtfcH0k~WGAKwExW`nX8!?c9Pa{{NCNFCRUC>589<L&XNa-Km{@3#SSqv!iW
z-@+6rFlCpvR7D8pvkM!65@9srK*Sm_hB3n<)WRaMk(Emoc#bk4v?>#`^&?UOI%((9
zAwHO{nJto=@?$xh#5(!Iy;^n|^SoXq?Vo=7#Z(oW3|2Jzx&rb^b%;yF)$qpy>cm9-
zXV4p&a5o(%l?j&YjvmPO9JSQRM_~(=(b>=e{JA2QjKN5Ip<00MB@m%uJ5+QtPdqyl
z(gtH^ol;k1WTbkrWMx%VJ;a66_Q9o=)3UF$14m%CeB3X)y8PP2)!R!{y|uW<un8G0
zrmFxd@S4p<fVKSVK+;i`2M!)=05Gc)KRJD@WcPg}zB+o?)G>`h-d9U2NDy>zL<D1T
z;m$QU2;o_lX)AWh(Ceg8uQg4twt`IJsjbEPl?=R`)G~hGU%i}17N;?`xZa)N!wl>-
z#B+mXAttI9<X!{FFncDpXe#{o>eb!pAD5^^>Ya~pRUKQG6#>U?q$OJe#ieV?2sjf5
z+x1*xPp(oCH@b2199kA9Yau0_W(+i@rQ#?HDyAojM0TMKct3shNO@`G9;<UG@lM)d
za%cm=g!_xki~T7ZP4v@EsE}o(qRV%$A(Jxpo4R}~!Q=1bd5omD7y3jDB;hwGdvM;(
zkY$kfmHT&QU*pKQ@aX8%cHr~-qwLZJ_P1Zb>iB>Fv0q`QIax<X!jNi^P*5-jrrmEk
zh=p`HV`z4<q-Z_M+wDY)qUw~p+M{bXk;@AHHOf@t$`q57x9#sBh)C)=(Xl1)UKLA7
zz1V*~C?xd4g&MRHTHD28b|6MU#FTg?jEzhW6HLwa3u9nWC-Ng#!7VB(ih`oTl?hVx
z%)0|l#2;7aO;6+I;z~hDpSw*F)2V;Q!%M~31VYm=ji7|mk3Zc2|84&1SR92+gZU$C
zicmI7?9*{*X=87meyF=H_#x|y0Tvs#>|Uc5Em>pXPwFSXM7`6e@1QI<wd5t(>1@g|
zoEy4CeFPvdoevm-vz~Ob^f!Ziu#&z&->p%^yA=OlDHCd#I*&&t$j4W1QH*S<OS)?5
zsd}jti~!1ba&&5bH7VxVZi08DOd*w`2=+~yNykl^u)m=g;!y+9gcOr6n_=dd0Vvt>
zd=HqgIy1k)v~0m$yY4)e5A4_$hx!4Pn##}>G{A!WZE9BhME{UF*#9R80<BM2&TfsC
zbbf>}%X_xJ^v7V_NlWOB(#Ic{mPWoV582%N@ZvI-WE1rAUj$aTK2?U~+=ZgnwbGa1
zFq?GzloKM0Vwq8zeisw~u~MK9PLyZsdS9Znhw5X!MCoqmDk&*Z8zuZ&B6*Ur_bf|S
z)tRwv-aP2qfwkUVFM;6!FvmD$j3V3ZEj=F?xCy)BjASU^Co1<U$iUlBA}RBIuzT!B
zF&)Gl2Q~DPnI>BB_P_<?j3rvP3>SF1Ibi#y>5hPa3qFNVkid3?^!1(6DF!6pgwwuC
zr{1F`Q5%I;A(7?SVRm{Bxy~vGw%`4bJ+`C^;OQj8heaJB1n<$b#;sj-b#)=Bw4!F#
z0H}=8(dAc2IyLO}(kR+(mTC|p{!k9ELGum<GCUg&oB-B1-K>dAyok)%QYaa6>C%4S
zDjnyi%ULuZ5=J;X6bA9iG6Xl}d6$_9oAcw$_QIBLSFPInj6qS`Oh{r%4isCfCx8Cn
zo=X^4bsF=Rl{~xuk}4|pNk}l>VF6r3Ep@{ZO0zw1tJDWJ|14j4uNo8UbrTr9tU(1t
z!aCmb>v?2PO?L_@l_@NYu21S6`%BckhX`0=nQ?t80l{E1roxDoCMG6sax9+DYU9<$
zzY{(wTyo_HRqr*?A02oHY7>Jq@Q3-+SsJmjHU*Q7#)m<`o5lgpU6a&+;Sq^xwNBh8
zZQ_{=5|CAEj<fs92aso*`^8Z_J`*W!amatxM7N6&aKH{e3=Uk)8O22zxCzLU<UYUB
zh7C+$Y-DIDjMFXz1qn_$j_vT1tk~_DW~SHp_4HlNbHDBDTV|4hdK0NTA<lp1sVf<f
zk&%&s)To1s6=oFG>E+9pll_mp|MIt&Ac=D^H8vuIj>S0HS5soqN+JR3tR}x_x(F3S
zNlc_r1_0^Yr+Iyb_7EOGz1L-7&dY72-IUxW)9LYX9beyqx%qi$Qu3hMp1UgS-K$M>
zeMsIz6DCV+K<r)EPzib7cqt@APgyyrZk_~-HVn~rH+SZSa|Q$$?sz|_4nas>ZM<Tq
z{zU!HoEYEucl2;97(Hj^*|{Qr5D!%!`2YeVwa{l;5DUSTtZgIcv&6IJv~Ftt9yoxr
zW0LMe5Y7h@U}Mks!Ci&2XaJiLr07Hh{Fj9i!b%82_uIV(h0nX}*ts(Wjf!O6LYpk%
zh`}VmHGefC;$&V2gi*-T+Q0nri#m-sgl5PNVxaVa`waP6joDFb4&$a+8*o}J-2bjE
z_SsS&G`|OmV5-3b1>~0HpfH;R7|egn`C;3v6}<a|`$x7vuU@q#I{ffP#nO3D042lW
zS_J4k$1x{!;U^qp_S;8&&&k<0(Nrxvu9{(f*)WVOZlu@3x^aZ7O+a)4ld?;Bf4C=z
z)^7r*H0D-!P83OE7zWg~{QB{~Q71vrYvC?Unx^eki)xDD8u<33V7&!%te!XuyG?Y#
zU!WQ*o2xsbu{d44C~j$G6%%_Kg_X5*O(y1Ukg@gAv3wv@AwwOe_Kp&hMNYEVth%!T
zip*>smygPP_YM~RpP-XcG~&`x%bG#75#BEXnwVG~!Cgj6JG0?0%gq<YIPl-pKn(mz
zqd8_cgR`ouxGFS>baAc!&|PdJ(oaG<{m>|R)Up*!8r^MBKS}+xP|y-<t>-j9$*@C0
z@y7X$5RP?Ew<47$^7_B~iD*|?qeNBz6i*Y&d3;oH+iE~cn7y`ZzzReghYzvnL!Qx1
z0k6CyMQ($83(8m>zEl!S6;CQt-_%B7P=Oq18A9jA1}wbSE>vhk)Ut2O)5B4&S|Brn
zL+t94FU!wQ=m?t?9EGN>9vVP8FGwX1P;UaFP<a1y$mPT+k!9aPLB_u7&GXa+`#pZ^
z+YhD&>pd5@7L~7L3Cj3iSj}Lb+JP)}A9JJ-MvZ|c^>D{Y11AtsDgjC{b_gtlm$?Kq
zjZ8uY=YpiH7iXC0yyk6HS;DFD_bQ>EL$JS!tch9%se+)aCBk6t-$xBS2O!))+hZ|!
z`sB$A{2twB+95G1Nof1_Lzv!&rob4qG`tSOtwY|F#AT2MLJ}CTw*_QmjA2?N)4_pU
z?Zn~Cw%C)m5A^p02rSU~_C!{}1S~L#+}PmhRV#B@dl7q#fF?oc#t#jh4m@`bKA^)S
z^1`IrbI)rD%oFyr`yjVv!%zQ-sVrfRm->YT7leR5velP{uK&NhYyW+L|F`bH|MNSj
zxdWl}sbxl!KWYZU8i7wXNT-)%=);HCS>+Rm&y-s;7+e>mj=iw-ati!^Vseyci~0D0
zz>wGA-3Njr-Qy5yK%nSC&T(VX85J!p`Bhy!GVZFFou!Rzi_mbKZiI-Kfb0me;3w>S
z-JgH{W3|ESE$I7#N5Bp`wDg%bf8aOYjEv9_Yr)ZI5_>hIpUPf0Mwc(<E|J*iKHN%$
zD$EZf4~l*W9$65PDc}#Udc|VqBsma(jD*I#NqnRp5+f|Q$*u2uySr^j1j@3%L6R@1
z@AtE_Lg=OgLa}Yl$$-41*)#g_hCg8Y``MYe!)QS~bV}N&JG8a6w=1DGuU#!#5D(F%
z!j|4VnTqPX>=Jh>3Fsj6)T<?6U4l7D5g#bD=Q&_dnsIg`yu_rHSrF6X+;QykLN-hX
zv0G<R3vtNwBo5}+1FDK+)EzMwqX?}BiVNZ{>F6*g5)5)&JzB^&Fq{N56rOW%OFI2X
zE1ZfV5=DJ|y=7O50WBRGct2_YU07>lxO@=LyfIpf4zCMPQTmhXs2$q8dSkd0{0nRw
zQo3E+k*MkbsZ&6JUVT@5e!Jq~!vl~480L#J1_T+YSASZ6>6UxelqC0U|Mcvg-qhYY
zp+wLQ(WrM%SuUFZ*e9bM1cihuJ55l%Gsq9lo;|yv>&%%m6A{I8il|WC{eM{O0OYRE
z_i!}MPdG3h#%geiXVWH7(&{*%D!B9(j@{4$SL7zxt`j<r?&Lm5v5h$Kf`UhzG-J}7
zy&XA$xR)m2P-HXQVjd1#j9Q$$DL&GJGauM;EtGteMCu`aT5uZC!Ov(<e)=8;uMW9<
z{0<MS9ib|dc6d|?+m-Ox2sDG2SzAYkxr&a~1R9~ciA;K-s~x#lkv)=;qNn0E*mxC7
zl5}=K&O%$_BkMuu!|v``RQrshy08lnk0}<Ww9nKXU^h2lRpW~opo_Xc9hGDu3Wmhk
zI@8e1?NI-1;3AoU*^W7kZzX^{kF}}^Wt({1kp7a!LGTuwTeAth%Iq)OQnA*N;vid#
za!b2Das73MtQhollkX4xMA&@}Y?&R%x{9Yy_n1yYh=tnu=+PrxP0jd;30Hy;u~F4A
zChEg$_z@(T53jR=H}CL8oj-rR(lVnDMj5Q9STjh0ZSF1htJbcimx2#6CPPq(*DJpV
z+&#8%GA>K;d4@&FH&Cu>8mG`{4s%;7#57IpD)&+JGc!;dO+tRMg`Wyd53&7+#sRx*
zvD?EEQ1j^obBJcO%~l62+=dx;>-e{8*68A?G1Jfg{yRVJ2fmdac)|u?8>mW=mMAZX
z?-wvhqr-nTjhTlA!p1b?B-FvIQoV|?p3$O9ivtB}R63ci0S1jh3iem8j@1>)`AnU{
zL!SYlyAQat0Ud}04#R--vKiQ%{H1HP+%!K*Vb*+%e=3ABMEP|ZD-5nEB`|xt&de>$
z&)Pw~q!fVV(VOhAI|4<L(gaT)fsU&=MCwTigZ<@v>!ZK??5e*}LA)}*-OcxkpzG>T
zB;P^cVt<*0$#+DWB2{z#6r>+p_zD%v6sB8Uyg{4WGqE`aVtt04ZG};ghc(y=?~eck
z>4)3wJ{?XuNIJdsO&q8V?lqMs@#C_t4j0Wj82i9{6a{1qugs{VN-dU<Yn_@zWV#$Y
z&1C)vv{B_MtuS=6QZRg!&C9Xu;wG{k(%RyGS`Lfk&nQ6novX-0!aa#j^TWm14Y%gH
zJ7af|U=Iy^I~B2mfI29T+&#idn1rVbBX&&Cr_q@PXXb{5J}xm9Jrj7C#ZXFG2FE3?
zPV(dsaW~6xvd9=kJiRy110x6mh;)nw0Ge`DDVwqmCfkykfRG1m?5d;0Rq(-yTqWGt
zJEU!kGWEyz+a}Q2D5ZB?gxa*#1(VM}#2Cw<B%p3}QSye=Ku(e{o-OAM5Sy29)<PAU
z_QWLb?GIm_#LU&dCLP5;`rAjJGbA_h;vl@pZk!Czopkv08c~uj4Z}Eo8ub{{1KTet
zI94cAW%aZ1@o_p9lJ=xWc~^n|yf?!9^Q8_N44XTeQV^7Rs%9&*HMg$OA9KzyRk!#Q
zs_(20Odmi?E7MYBe&kS?ub*G#p8_B6k*8Th00V6TA6_dRis#*vtPsiVX4}wd3gd}l
z874lr_*Vn|lCf$%bArLB8+2x|dUojrc*s7-hT2bu3y%8s?OW|`z33II*{?Ulf@VOS
zK?ki7VI%0Ew(L1v`UCjFCSaQu5EZ5Aj(_~f9~I;OLK;qn<IwMs!lN(I^ir=;V`5{?
zj+?-Sqso*S&N)cP2^96pF}GzfLy*)Iq>=?O2j|bxKtvMcJ@(+ziMj-(jXE7y_%x*D
zLi2fgAC<O1#dGmqcSM9+22~h12w@x?x@S*T$Ht8tnZxjZ5Qv>Mh0vRR!y!)zNL+Xl
z((KafmD1mhAR=}sFfmrhU0NQd<@)Cp6@q&m$Pp0j1-WT{kQU^|tM-xkcxxqE<DvT6
zfl|Pz2NC1fBD=xI5P(5AT^QqTgU%(Tzp{o#M1!qcQ?Z!h_^@~MG>mn&gw0j2?tGFM
z*a_wv)xtfYB9Dno1icncsP`ggVgWUxnu!?~k&BI&mqx^9GG+sz!ZZu&-_pxz#otXM
z5p*QRGm{c)Za*1B;+a8XHdB3B^|NOU$c<trsyLzW>Y-#Cc$t~h?l*19jg)PY8(v1b
zr@)8q;Ny$%f*Em3l2zNJq`6T6G7so$!*nl^imIwz4KvYNt<)iuu`h&Z1zW+-=f%P{
z956p<&V}<?fo52t0<Yxp{1MkIItAy=hnL3R2=eS+BJESuZivCv38Fsx2^#sg5c+|n
z08-XrOPQd&a<emoVI&dErCS{xa#ovD$y(CWCI9}-M%--`NKr&TIITcD6s=JPh9)yd
z5y+obR*DBS^FOm;J{P1)`eIXH7*h=N3WK4iBTye0P!2H;*_(=NYjp4Ug-VxC71T*G
zedW^?>slkxa={`=NSS#+7b6&q3ENK8GveiLB7kr_Zn6G#Y49WhW-?%+NHre}RoE{D
z{mlgF%y;oFYLbvMbR#_c&6sZm0fi@@M_=9^gPE@|D()!l!N*YC4;}jbMAdbzVP};y
z&%_xdkw|+_B(sZ}-MK1cFo2mVHN?ej3GXbSlC{Y8i1i*2$nN<R3>E*r!7Lnap{4<-
zk%<~=*LVMfyxg4QeA|EF{Yf$j1RkS|(w#eY*Z}Gq-97e8<v7|b5-;&8IM{XI788jz
zX21uuWkj!z8FoR|0YpDnn9d_u`$!TmFFyiA5te}7kx7uRtzFW$fqL_h?cBg*s)S(_
z<1I4I6Pzf?%2?IgO^=SCFD{K3%R!>19f7%S>2)^PWPBdYt{+|x9y1sQ3{#I7azr4}
zc0q33?B6~dxSB%{UC-U)3&7u$Dfj?lhV%Vq-3^j-9B^~@(j^>P`@)=IQUQ-+&%||c
zOPzVV!zw}_y*9%I<LZ83gqjg(rCs}u<)7p{M^TN`{=w@baLc&|SJB5SIC}#o_YfXA
zBVyn%H3z`IJQ~5Cpu!Op664ly(-T&Yc#||M0^zd%s78M?J(bF=(0PSBYGZ3~ZW7_e
z78#3TS32+UX(0_C`nzuDsfDhhGYbQgBI(7u(F`Q$;eF|mVn0}Q^+_53xg0>s?g`o0
zGbEih6P*uHuyyYgV9%>C=gL$h1O%OX^x)Z%Pph^S=Z$>2gR%)u51j}>b20djKX#K0
z4q6K>NHF~+Stv|3SpmE`d-G`6fXv8q?YFc$dHi_a<s&@KPn%#m)yMD;dnQ@mm^PcC
zQYI3LC2oEEcvJKL<oj4Vm2-QX0`H+-9w+5;m^cFZjR3!bFT#x*NS%k>Mn+Bp$U>Eg
z5Gf=3y$^O{6wIuU5u1PiZX=vlr<oru68B{=%NA6W&=|p8<oKhdp3ehddu^y=EufHz
zKZjf1w`sWjzD?Z(cH|(iq<ZKy><^?eXcQSPhWfDda^m|tAoBkPjV|>_C=UtUJ5{g8
zW8e4By&rdX#x5oolb+kd1^C+GUWna6_~6S{(|t7u5j?Y|$TXuL4xT$TP){|=ak@$D
z^|SBc;!*R<hqy*;!?P&823m77(cEb%SOs|qDMb%?j6^pjZD^YnQ^#@)fp90Yi|3!Y
zGC{-X;1;2aVi(Lt&69xHp}-k(rbq>tI^mIFwH|#bL`zDGLF)<$r6iFMHR(+g*~tZ7
znIvwLFpleOAkhp6F`sl0gmBB~<LTGgXSY${TDY+wlS2x-2~>syFih+>Ee6LO{EYYG
zU1}{&y7lRfVW6D~4y`~OyMym+2r~vj=@7}@^!6s9Tp&_;I*SxP|Ia9=gx$#q_kMKU
z>@7uemociZEEv$2$nP|wuoGa1#gu7Eo|{8X=QpHq!HgWx-!ITR;)>v6tg5RU4v{W^
z3UR~Qi?h`M;+(YS+jw&JQVX3p%7~A0Zf=fs^X634?6_Ox;b7*Oi}z3f&UYnByv@+Z
z!NHhBl@SsHj~%WRi{vOaRAR*<9y9Tq5y|G~FtnEvAsgsH2tk_61V?fmnjvjKSo|Ul
zf58A8-teg(^wm~m6vA$2@ad~)gR~V#^-cJ@#~Dk>Q`S=xQ^Ew?cu-6<z%?p{bK2_!
zdJy7ku!k%-csFjm4U~k=xP*EaFI0h{Me|d}q%V)~{Zvl{TnSc^$aoW$8xZw5_-kpn
zEV7{8MIpVy2GanHsImpEaZCJsb>y{cJ$E@5P}wmsQ~AvVd`3&JSIRiz?BhSPu;|qw
z;}I*-L98BaC#Sn5?;ws3Ts1J$f+LFbOJVxX&6_Y8UnY5B9uGnfSdi`e?|&fF`PgP;
zJ~Dd@?pD^#I+&s>H$PEF7-6(2_SfgIfk^5jZ-(9uXcLJ5gd4ihZRJW%>;?o=4_Rz=
zsU~e&p!6>ysgLX~LL4G<ZOF<;d?uW#`yR5+VGB=1^3XsH<SI!){YxfClj*pG4fpkx
z-1l+#&yS^dIU{(iKLu76<up_`aQc$SPKsKrLU_S<nV}_*RA+FQvt2XC_4zN(dqC+C
z#N8w%Ft(&C;gMi@c{C<S16_>nE;DkUao=50Qe%8UGKgW)=?EvFAg0juj80=jHE~Z9
zLk#lTHu$39CnTK##Nz_kqb%2hKfiBPz79J@gE%h{bqFbi8Z{Lm&bE#cqQm=XiLrRx
z{tF5J^~jFB)~oqMx&8G+7D0}`L)l+H{jb@v{5S9Q*BAM3TAJL7|Lvad|B`obPT}tR
WcguVfwU?>^B}Mf^=?9Me`ab|5`Iz_s

literal 180906
zcmd?Rhg*}`);=6x&*+)KGKhdu9TZSO5a~6{k)l!*QIW2I0@4f}siULPQBismq*wsy
z9dsxWkS-vhX+o100s;Y&@LM|;^gZwO{R3aF!x1LQes)>)UiaETXLL2U|Hkzj27}pt
z@`T!142C5RgZZWO-&^3nT)S!72>-~ts$X=~ce?88aoPC_M(48Yb$cgQds~Zr?pK^$
zY@Hk>#pJ|}ite*<b-nJQATI9k{R3i7&er16c}34*kYBH#Fm%CS#P*|~O`hpkcg!XX
z=A_!6=R6ap2fY%y-Kwy2%dzZR_FR3hx1@8X?w2~jUnF#N$SwUAMvjjN&i!4EvXU;g
zdF4k|pKn{v7B!aA5)ozfxW4(^J>EOMynFV<%p?r+kBt2F=T+CS1jD2@JV}YTu;LTl
z8@t)%MriIrtlHs6r+$H{e0$Nfbc;8C`vd-MFFzRf!{7HbI(RnT4Uh^R+xQFfL{Dn#
z#-IDbQsp=P!r1O^fwg^mok+9U@$GjEMwLq)w&DA$^Zy@@9J!*%TylHfle14x2u0r4
z7AoNV1w%+oNJx0o*chZHaDV0?`a~h_%QmZqJkQAq*NK;@A8UzEd-m+`*{A1<_is|I
zh)Pc8AFS|*=<MvA=*f;JGWT64>con;0{XrxOJA9b^}|Px)ZtZ}hEcuyC_0)`*?TE;
z^G>BRAJKsZXy|`AJ0mqWH@CORSnRcH*YaUDfq^jS9tGE|eCD8G4<cvKkQmi9eFFo|
z5RM~fk8KUOVAULTB+vCz=gXb!=*_VE(ceB*VBeSj9-dAx(bd)cJkhcLl$KW7%a=!;
zou_YY!n7QT)z)xua5#7FTwT-=<JA28V-CJBmfo2&zi-~M?akT$py3F?YE4m0Ayt)p
zmYp5eS74pl!p_0bFwv2=U%YLI6Larmf3ZV|dI(4Qj71Wxjnohp0^3(_g}z?sUcBvp
z&@XY_#SZ<Q4Y08q;XF^Rc3=$8n3(YF*9bik?`?@^N}#96*R3ufl)KpxN{ve#^)6nF
zNteQ?;&3?E*#UV6N0^`CDWnss#*e>9vD9;A4=M6cm3Pzw494l;ks~_XPK3fkJueK|
zU^fj<{R@5AZrr1rIgJ*fN2k<b(FBv;9Mgh_TQHu-VeA)XRpovO+Cro}JYUE^|NOJ(
z=q1%{JaR`@m&yDv<B&s0du(*3zdkYE>R4H?iCGd|470C)$p@=@U>C>_5(*A+GoG%@
z(-DRRSJe+cYWlK;M%?SSnYw8svz@(VCiFK<Uvy-_GD9CuFqlT8O~B+IMnyHGs)q;&
z5VO+4=Na4Kc>&*4*j4T6xx>Xy!+iZEP7%C{?sWm1Hd}P0s^5=~=O*DJ+T4}k)0ljF
zayAS41-|L-R2RtFQQ|aw;=nFFGc!IBgPhR7z}qaG8t2cSkLp6Jx##fBe6(+3-@RKc
zS;g0)J>}2ED+{I-?&`;4Ux+87ZEnXM`f7qyw_bn)tFNzTWzBP&>In?Im}j9bvmcGs
z{@XXBp}Muiy2EF%+)dBeIKJfsyoa%h{l1RtFmza8;In7XPNbiO9kYZpFw{lDr7E-W
zm}poQY`Q?IpYTFlT%5fII=Fq!-%MD_Ev*B)7S$Kd9cE``ZL~TTe;+;N&9Sdm%x06v
z08Y~vCwc!w9k0j0{h1R|X>6yJBxV*JrU?BDOfxtn|9_gidpPgeC0}BYaP^FxPO^&f
zjM4Es+iZ&yR2TLhvpPYj>HgG}S@OPUY2a~{AfLBiu3@pgVOp!m!GnQtfcPxEG~#A{
zs;bX&#c-IQR#fFoE>mp^cYk}>n|rXF_oX;XQIpc6jJdnmy51Vb;vNYLH5%TZrU^FU
ziXtCc%)77l023g3D*Y^F9J?;x5YEfpro3!YOhO+}Ra5(Bz%Ur8%=NXJ^^haR#Zr^+
z^>yV19em)EYQxpO%id<=t%=LtZwqa^mg128+RiOg_cm6NRJ}GE(h@J*o9obDe56%r
z;cbkhweDx~a1HAxv_mPQ-ww1rCzKz{a7bRu@u(8Hvu$susJyPq%7|}{sprSrIQn8Q
zzMj*Zr)H(aX>wfFabQocnYROtHjZ_Q+J+`N=RRun#n56fu4?G`y=TjW!<Cob7p^Fh
zD<z*je_rqq`Ff1|#+n<On?po$D~~zkfJsX78jBSbL?RF{IG@jpK8nE@MP?h9G=%Pz
zL~4-dGTu4?W+`-!gg`i!K|LrcYG1rIO(+scx-t1QM%Ky9ez08n#^(vvTU&RABUuOz
z4j!T`K7an46l<Lnl&%%su(~+4UshIj_46mT{hHySL2SYry1M;?Ay=2C`zW-9HavU)
zSx)$e6VK~mvTg6N5nglePo3$M0hhM=XXl@%W@HGVciDur1M@8L)e9fIvz|W>8Bg|C
z;W%Qnd#X1#0_1)IJjFI%1*xIM91rQ{;Sy~X{-J6#C1)3xw49tAKSx-QgxCCtXa-tx
zO4`?5NjWZ0sbeEi@cg;_nP;DZ@)cawF~QZd<H%LZ8g6cjX5((3It8n#_2Lcd4Dz~0
zw3zA865V6^J!`Ltiw=#*SrZ7WuIj09pWzx$^e|&e>4OIkgv|ZXB3hjMB>8;iUT?|e
zXYF`ykSn)5;6jQYrd|C!+5KFvmlxz;)Vx|rKw!&GJ|3{acCHytzEj4y#cqS%2!8MB
zJUN&iw~{+-pv0A?hpUwH2Bz%8t34%P&_KxDOneu+DXUYtvYxXYn*6+2+Q*$FDk;Lr
zwOAH}PZHO6CAz-rG<NXA>K}J`cA+U(uG7g*^F_;r;KNFn2zI2uw(<pn$jW+Z>*?vC
zcO&d_%7pIh;3bTBTg|V`wYq_WxUn?77q&V#xgXSwgAK{Ds#^bG4srtEgBoG0SuOG5
zVf{`uRj(g-)y>|dmekN^Vvb#DQfYp@rhnlA2RcBq|N7dR=wD95)yj_LpEBgAA8wIu
zbDG(7F8a?$Cf9-MBPDsy_Kh#@cjvt5R7q*rqvGQUYx1}0RL*}~Uarsz!cBdnsgmFJ
zz)P*prA?VrILXsu0Eg$*DQ8Icny}&d{b?#%eXOjx)vMQhK5XU>Z1ZO<Eb{AmD&|zo
zmdj0~+#(kDJ9;<QXg+%M2nPzyd}@!elS8NSu@xU174PaDU?sIqqJ>?ORs3S5+aFiW
z{lkJ={gjCh=T#IC5D*p?hArYk@dg-(I&d+azoishZ3zw$D?iBioQakGqyi713@um7
zj<qG5V|&4CAoqu&g3ffUw2X`lKZlk$Y24(~%!bdOx-6&P=dD#S9vPVJ?KSlnG+Pi9
z6qN9s`+$V~N^?~G(oFwUFSvRR>ttW|0iT5yN?wHTi<51_kz$o||CExe*Cz4l>FE;J
zKfHPadJcwC&CAP+Ze(g|YT`qw$G!REZk?VcZ_%)w|G46C(DR(~Yq=|Y$~WphyuO7z
zY?PQOm!iikxHE32N(qp+u95LG5Dg_$>x7B0?E`k%)@@0+4}Wdd`18*{Ul<k$UC1^%
zUvt6Kl$X=o2WJoVuKIJjcv0N=@r7qEUJOj#+UmQu)N+;!?c;l$FA=tl^tGu6c~_@$
z?D00ss~+UtUS)nCN4Z;7)?oFPnkba2ri3}w{laPTJ8bO>bMvYjZb%(%E3B}e+mBP;
z?2oAXl$ks<vF5YBHXJe99IdHV*C?FKGrTsmY(b+^7N_E6ox<ats^zN}vz_X5Oe@xT
z?ICPYnZF-V$0A<&`^w77TIJI5oyvs<jXv;HN-aO*C^EfoT3W&a2h&Hl^7|aEo`009
zW@u=rHZ7*=KdGM_Od;X%r8p87-RIcoYy~+SZLeCLZDt~efvMYh9^cO$ixtYf-QD#t
z@a}c`@H#IjdrtD&v^5sFmjPq5HJEm|lMjj8)<u)g%ZfI<PB+(1QYvHcAvqlOU9LB%
zS?zvbXseHOT1RY?xl4<*t>6O0G1Rx#Ds0haH*9&plGx(K%qr#l=5F!Vy?d%Zg&mpe
z&h?WHS6Q8qn=K_7)h|5uLOynKa<X%5h(>4n<@`UtZGVsbAiJ>7dLG*UB<keDJ@3;F
z_DdKczgl<ikK;rzB4HnE?~;rBUTxZ1H&Zgq2m6IvXp!x0JDsRdbW(jm_S9IkiStR?
ze00qVaeDP*%?LgUc6|XWVcYpcZe=7g4Sb7$z*uV{H+H4*G;zLJ+-a^HEwNEB`bkTo
zbJHP8`|+LXmoHx?_<$h!OdRJAjy$a*FLXMI2OJI@!7rcToQ|5^_>``JP1g2Gv+^5#
zWY7T0UC?{O(a|x%t;Y~I_j)UyRg*_=-7Q|;<?7ocWyKwe(=WH=#zU;xsU@z{pUtQu
z`#LGjz1r;kXf-4_m`LJZ_pDqSB!_!cO_}Ch>wZ>fieDawL(yH^r<!Y4MZB-c7xXKe
zkd!BF?qe+}Bi=FikbXv;CI}9sF%WifCxcpBg5NUqS7{jqs})Ef5K7!;x%nGxc3!iy
zTV73h6C+{CwHVLz&?$mnBd_+yqhn(Y^yT5<OO3RaoEpYz>Dp|?6iW`+!r}dpD(LEx
zJnmwrU)F*%q1KxTF32h_y|*N3VpsKiK=z&`S5O+bNzLXAXL{?1lRGk5<!)1x_^mwG
zRcW&xHT}=67*|2{DVbRMp&csJ4=6QJQ>&!SH&Y_~Y5RxhvmUamD^^XQBGp^8#++oX
zz00BwjH__=n*00G2lhuO<+r7-KrBP-HZafTUnUP@LB^{~2IT$bKD=hi=C8sD?N4aP
ze;kNx3=Bk>9O%jQWSHz41YA7gm2>~0eb|Z(3=EWa8LLZ+P~j3z_Hn+DeA=aKI=?yJ
zdAP@FZD|HasprH?wkbu*)YR0Fk!85|tDo#)^b3ZUuvKO5FO5fjT3T8pWwO_4dMSt!
z*v{c6-o9Y6Y+s62XKIO+9e_N?5Gy7k5@OqxA?-PRq-5ZMO!eyI^R&`Z&zj{u;8J})
zvY4NUk5_P;^lNLrL-rY03{#i|J-<$%LR2*{o>wyHZcLzZ4^uT<r+OS}mgN{!66r-j
z>$P)9fq`BNWA(#~rINx>6p^I0ttYRqB)@51Tdw}I2HYdJl9+zm-3+hK9h%EGlxTCc
zII!3C*4VW^^k~|cY|Tl<aTasxe|$hk-=4KVYop43ja3y2m75!#M(1jGp5~5z^hkPj
zpxSq>;b~?PA<NRm*_l|m(5e+5uj0FEhIarbnXPSl?%eM21Q*>jNnaX?|1>ui;(;&~
zS^XW#pWBi9<aR@rv+Go!)8cC7%2aGJY*%i-I;ZKo2}^#jk^6;|mGR`^377o>$b~~z
zlgm$@l5Tpp@Yq&Ye)M4#pLGIG!vtbZN<lJRRK|gD&fnhF)|Li_D<qjVBHYKqlxC;Y
zFDaVp-JY1ix5Mq3r;`gQJ=w<GzOxWJ4QIiByAA<<MwoSLLRqgw8+P62U3YhPM1;a*
z)&!x*E{!bVJo09b-1SRzPjGs9J}dJY$;rR!c=^sRETlbo^2Dz)x7T`Qel*XIzPd^Q
zQ=1BgC9Jw_q%o4)cRHzlE<-6<b{m&e7Nc!pye;C~;YY<ujpJPjd=Mi+(&IFReB}!(
z$3rO0SXmgyTdOR@+G`^77jWZiM&@ec9fco01tDB#1GqZ-&t(v@IDF3A&RYX%epPs1
z!EP1Dfl{Ht{Q|_7wL7%9b1&wlHul<_glP(Nny2|zsd)PxiC-l=eE9GQ`{&`NO0R{^
z2H(}s5HEB*!GnsM`}={vMZVCk?QRq^z<qc~-)OJ~i=;rIxRTC~!cWY-z4Qu?VBu;7
z!F~xs6=P8GisFBscRuavU%xS3R`A*Q6NKH7Ii^NNT=MerkkIWHNUf~&$+axD?|UFK
z+x^@ia<-#g-rG#F--x)_w_w-zu6HcVrKzcD$$KC}CqB++hTaFMzAL2fehH1^$GfIi
zJdVa{H^7OJK*z&|K};pwCf{X2>Jk_Txy2FW0b{_F5?X1@2_E?h89cuq9nZGed-oN^
zEqm`pM>9yDAlfeR&4!dCJ#1lNVR)&?=&YVz7sF9_SJ1>jnG1;HegOf<s;he?yjy3=
zNH;8}y>tPiiOlWjnO<?To3vx=pys+A=l|U^bBH?knj4}nPitpq=hVVNDU@e0l@W4-
z2>sK_MGSJ|Ket(sO%Fpl*|(jU)O>{~R5_;0jGc}PPBst<_6!<%{7=q{7xtYL$m-o^
z2Mn>ZgrX@k{IvYC#-XZ1OG=r5d+1ba7#CPSEq|J;qeiOffobJIrnX8!Hn4Z=k+^5(
zHGZ}v$R6T)>w?%&Fd78rs=#lSu||%lp^b{;8unUSN$f>?^fi^c_ltMb>)veR<>K5T
z5Z;nXce^|ssX>Uh2Ew+&#OK^M%)mI?k^thWBAIxbEo=``1F7<>?LVYa@<h2NO2^kh
zfNQ-79jj3X4I-RS(0{2FKPSP+w!32<@W49IVSbNcUl01gm1t9r%tUvik+HF>U#7up
z`VHJ{r=S$bq4IL=os#q6VS%2IMRYYluI*KkXY>v-zs@Hl$XnxOh8FBNSdqdMQl|R~
z;uGxJT$U=wxWQhYTOXGt7Y^;=ma(^S&WVz;fB&)&r*9rVKR<s^M8xi0o_Dn)pexrp
ztGM>V?4l@%WFO&~;rFza1ro$B=f9)|-w+nV>kuwB0IJeChEm27w`}D$<lBbbbD}<v
zM4r|FWk~}UMZtG9unFr9OvQi$elp{!WY_cJeYLN58adCVBen1*Zk<851Q2CApRtLF
z$@(}%+-f>HI%#A{_@D-V{I8IJ>h_=anH?z01M97kTr+OBa3MoG_JIrolKj327j6+W
zTZv1r0;umYRUXMK&p7!j&Xh2NQ`L;Gp86hpr0{87?qS$zo$3a=+$s*2v9~AV<E3mn
z6`Q)x_T-xL`>*v6bGg|ciw#Bjwu56^u7R2wpe>dt^dx}-Qeoi!32ksN{eEQ19a*xg
znZAD4glU1f*XW}v_e7`dLo^e+YuDmFFO$O+XKg;`q0=OIPQH7pbNG|&g^Z+~N5&y&
ziU721l`@rE!-p_{{Fr}(zy*Zseu?7(UX+eJ(CTbh9`^UI{M@OfQ{K>9y;M|40b^qU
z2o+h*SZigqw`M5Iw8g1<W$a?<^~-e6R{@(SNz0X)uPw!^sl-BxER3u%bFCU|G4<1F
z|2UbFal84eC^#jQ=G|Tr$tg|M!(Sobu?$Kf#jV$rlbxM?O_>bQcIZ|<&y<eky;wgV
zLeAKgD_0<9_etv*|2qAE%{$&t4KJVs5KQw|q);>Ss|UFNQLlVB_=Qy*96xfBp%8sa
zP0|oF!FOZgaba1rSj7uNNtqI`f=oMJqBPcLog60aztU{$l1!hr227#kH3_@g5upKa
z%oP6lh3w#hHWfvHJ(j&9Kv+E?AK-+jbJ0bU2d9?c+IezKX&gm_1Zo4fleS2$>`0SH
zZYY-i>9muVc5&snGC4@Xvf*7sFTk7NZ(EILWzfdR!S8WCxgZNN&dv;avQ;%i0C<Q+
zyPo_HrRk9f2^|%HlG3H%jgB|35PGXQe);8>MUsKJue2W^R#zlgk*(nU2G$w6Kv1Cu
znKs?C@qr+&j#aY`rL7^H;zLu^L`9DV^Yzu<7$q-f6OTbRTRTou*ABBQ6FpWCh_SLF
zlkM5&J;VkFXhB#7aGAI^yzZwRdrUAJqvAgOzAAX!=>kLyvco<u1Qgiq0<{W-I0*rd
z$+=+dhu0a)IO@l{cu@SQahBhHBZ3rqHq#Ke4CqWx1cEm7Gqh27chn(z3DCecl{NbW
zztvBRxg8K056=_&?nOqjA!E4z{)u;O$|wYy&cmZ5w(Haf6zZ?dl;HpVmCd62+4=hb
zT!XY+N+Sa@QzcSOpK;1;F?H)ck9()5S?{|%$Y~5tZ7QL>yc{7)+KCF%y2^|10Gzj>
zL0}mQu&fn*rQLbUwmrY|uf4~2jzJo)P{GW~@3DLJeJQx8;@wb2kmxbA7lAP6&YxFT
ztAk{j`0RO3Fr>PN5da10Xjp5aA|BNlUc7k0D3t}xK8qrqW>=cf%A`RJm+YXc{@X-F
zS@5A8T^TwdV9)Epe5IzQ3Pgz-=J(EafhlJ~Lw<e?$my^Q5|)U<NTU@$glK_!2}3Qw
z5>87Ga%TW^AoU0?bU~pV$|%4u%q+_fc#^_!fLU~NHXCFcv0Ep)U4V$9?&alL0r1hm
z>>?HmV=as031DI*AXG1f*ml{QuC4g-Z#LrO^W1PJzlHck7Zh@%lSgqK0^q<eU(B~U
zIXzHTz=%O0l7s`H@M6)m^RV?rxxPn``2tmr=$dt#EgOfdLl?rSgXr6=6&?xZfO`?P
z?l9_Kcs%MeV`m=H?^uz|SbLoy??OZf(fVrcdXw`pB+Ck1UqO+3=lLP#w?x>KO?ygJ
z4|)Ph;bJAN5$3*hICgwwqz(mQo)9c|xobFu!EO*g-sKNRy4D6kgiMk@W5pBT&gH|g
z%QOA5;NokfpJDh|aWh4|4d5C`2M-=R5kH)NMGNIK5+1VyRj0-PEXS?Wm&rx9=Bu?6
z<Rl!+Mx!8o*NGPw7molmLc+{%WfVTQ)_+L3Kc{lOi3(A9Y%!asS{<A$5psY~q@URj
z=__siDmkUZ;1ai7e)@za4x~rp<jIo+AF%KUbwP*$+GtU;%2L3!N*o856lCL(E3tZW
zPi~#I&^4A*wL}n2XQI3NzoLr!ro!dNVvyBEK#hbSjryk1Yl`1|dAd(htb8KX6B1e?
z${74SXpTpaXd~pPS-eK6+<k`dLBh1$HAqXmO3KW8rbuowD=p5IF78JOk#HKSlp|Lz
zEP2rb2hD5Rd}m#dfAoYr5^^LH*A8{q)=)Wcu*+U=Q4))g3Um6Tfj9fGaoY3TYK0_E
zVo-^XE_^Bo83IZd<g~nlQ#V<}0?}%Vdd;}gG{OjE3Q8wm@ANx5TRxR@vBKSf$B+7P
zO<9=f@)vXcmFMRf^pGHP$k9C~9^d`uo(|pzSiNrt6LTosDJ?B60qH<t8AyFm?5?BP
zcocq?jVFabq=ScpNCnvG)TvXc2v<Vt3yI7!A+IdEugK0o37`bD-uJ~0#;Aq^D3xW?
z!$U3kir!FtQlfwELZ!L)FE1Y-85v;~S>9#oN#KZe^r#hXOGqRr5MMMv=7DM<9gwcb
z&A-`Cg5b)&Ah+#-pcWB16qtu5#1WsTdc}pbV}8HOcf%%RzzM9D1jH2=Yy5l>a)ct&
z(o(q{fF<maysEv8Uxmh7bs^SIrO<49m8Q5+;X%M!Ch-VwgxnYbjHxLp`;ZYs;CV(G
z3i#VkeGL{{P^sBS_)9hzH<{>Uz|04bS^=V3@ioi(`se>=0a-}Af72!Zrsn1^;Pp<Z
zs{O{oQt%~E@I49V5Dq1b&W14VwBq7oze-4Tz!{^+^i5k^1Z4io<sRgu`oGb$c#WZa
z1!a=%2w^~B0C?P6QIjg4Jw%GY@*(R~Y>7VA{%?4PGLaT>RdEaaQ8Mi34PfTG2ydtW
zKnBEMEZ1m)+OUgUr~}w7KjwG%PJpa8gn&d}vbTA|Y;<IvB2zZdXfU48w_*tM&wF21
zv}FfRjEo<A2?a8Sr)mNBv>baIyGTN{15{gt725ndw6H0KZufL~4g3?#olI50s5XOp
zP~j*lDq18#=|SW0qu{?bZ`JVg^W$-Qr`J*Cy(}v9FAP_6+ai2*!EadN2pnCj!qkgZ
zN>U;rD#J$ixdBu~W%!_O#N+(t4|q0s5Q`ZCj0bw92IWi?-5z=Et%51^KeFg&uCLDq
z0iqYacN50Q3?QUbRA{IKp8<do0{g*q1>V}BfNX3mY_zK7N*Vg<5IN|5g?@jh^RXMU
zM(KcI%kBzNBUrw<_eWp&53B{Npj5A|%Yc<3FBv_vhq+12|5Fu7wHf-$0g5jw5WiT%
zX?%aRHyw=gxD;*f=k>y<=&*o5x$CC~qf+1BhJx*5o1s4uK!|MS7a#}u$L1jP8JnHX
z&P$B@mQZ2+&o0b|mIsX^W-dc%=*&<JnYSI>Cy5v$p0ob(<G9Ll8M00J2`D2ASW0=E
z>IL;*4pb~I9$&*9est}n57Rq4osl93AS-;a(i5kqhP)1f;zfmT`f7nhTIgT-B41$-
zPaF^c3+~_uND(SVS6+q!xUD7szfi&SlK=dY|G#G%0eL10V^>bnsbD}2U1tc=-hZWR
zGWO`H>k5%+2))g1(Qq>?@0pMFTiR+x+G$*-y(aoP*JIFO8K)r=><}cz5UL1=1YrUo
zk$4gY5zqbf1MnG4Av-Mc1Xu(gB3p7Ggd~B1)b_h7yRDPFu14v<u9<m}6SCmwD`qAp
z5>ak-*kLB<HaBTAX{*&Cln}P>h9cBykvM~M=hQ$ZcXM!Dm0-uXnxHB^M0Oc1=g*z9
zI)p0F7zcI8+3v{?12Sg`Nee5h-r2LeWCyMvk7E0?y!Dr|y!8AVxv0Vc>&d@|Vt-G?
z;E<5621so=#7y>pX+>c*FDeM2Qk|^}h`pYnVQjh@8orR-U5dO9c@HZ1Q&Vg;V|6MG
zGFwCKe{GMK%J&fMd%2uD<zw-&;e?R>{m95S|3MI3${`4xQ=dFJ07hkM9Ks1A6eEJ5
zM4$$M5UfT+H{ACqCZ$X;vM~z?3y?S0Lj<}+XjHQBSYZ-qmNYvmH-qp~f=>GgKr9Y8
z%HM3dfe5r~ewuPQQgbm}G0CjBmo~=RO_`i@z)b|q#_mKiFV%<8=fz(0(;_7>J}Tzg
z50pxTG-6ULMZl?V=caM!p`|UIhDVfsyu-~j;DLcRZ{CbiaJzP3S6?6d4f^V$4&&o!
zQcWukM97`+CKbZR%28?}SC<HO-i1w1Kbl}SLw0GlxSq$`vS}Zi(W){(=1sMB<?u52
z07&0b!B+O=ZpTOsLU3;h_4fM!sC~Erf(>*QgqDE40k**KcpgS|ia5TCX=ixVkYfPu
zNa|h_i7Ro@iWY-hh{U}}LREmUjx<f{BW<Vzg`&9O4(Y3{3485&a~v|%D)l9;TkV(Z
zcFe@Fu@ukJpBM1^;S58w%m${HH)ED^1;NGz^rWW`kr%!k3o`G;E1NK`t{`1{P*H;@
zG34Aea+sNb0FY*&o+l_E0Bb<CsU+W}z6q!g2!Z=)srzp7UR=x*c)n~P<uoo<^|^EP
zbap78vO+auG1pq2b$WPW_UXl!Zse8p{{8OV>aI;KK0>hLA7-AyX@_ykY5@4Yo1I-9
zk{3W9G;~{s_>XPH#37rHV$TvsQ=oe&0EAfBc5{Urs5Ve0g9O1}PE@vc=Lj+)3dhtJ
z+^3<|7N*JXD<$B*e&#yly}*mPH+t@+f11`=O6C@ozazB;Ek5?B@eoMa{(F!GDO3TM
z4^L%<1Qg}>fJB5?9ovfvJ!%51RW}yTs%#CodJmW)aC(j7V6jC6QW+)pkj$DTtv^Ki
z(|&*D(;3C0t1WS*fJaZ_QOzph`ai!KY8UpS2o>r1yC*KFq>MZs-IG`sBysg^%vD_t
z%Nc;QW(*urqzU%4^TjeaQd~cA+=3JySJiX4d|TileGRE8Iwqj%3K0oZj(zoT@4I((
z=8XAhRKEjDlJ)FaFj!B`wj^aqXMfDZ3D_T$A1#uAu5=VY(5~>O@JUox0*|$A&(VE=
z@pEkDbv*+qkW3eDQ$R`M_hW=|tvK@-Ga0~8Py~GifDrVXLMQuUEB<1npH5amF^`99
z7<gh0@LZCD#KcJ`JBC#9Medkn^Y_Gcn=Q174Nop^4G^_?y6l5VS$y+(jgG=Bm{muG
z2N_ku?T0Gm0TtQ};o$uf2aZK;#wf%=K?;}vFZg?VdNiO!26}-64VCU7QnGj(BjLvZ
z1}-LfYP0IvIKA2u!M_*Cxi&*P2L0FR6o3=*{Adt(N<rwC&-w46JB?{KWG_mYS3f-#
z<wRh;<-K|U2EY_vIevFX*!kxd)RAWe5`YY_AW)tUR13A`*}=4|tb<V8f7H6^1Mf3X
zOo+i&DX_y(SQP@(V+mF_4>CSDDo|m5G!`(>fDu%&UW|&?0jXZzt}CO_z3VHXlC6OV
zJP5J<Fyqx5Xtrh&pLg9dA(x3)I1pa_wV8D9_>LfyF?{7MfsF44bUP1#HhcW$0C&VN
zV3d5QmS}|}L@`4@Qx_#fuuGwkImSrYKG^Av#6QJmh_7np?rnfE%!hh*{!g^Hd3CRs
zc-7~#D7+>g<(1OS&ABbH^or<trMtMQ=c?%Y-^ckS<yfAg36DKEalm4r6vxU+fh16)
z#*dx~$BfhhIT+AD#I`$?o}aKALnwyQRH20zNYZYgAeot&f!GQ{yvKL@_HA%BfDn)Y
zoJa#ev@aNOn@~itqtGZ)W6ANQ4dX3Q2P|bMZ6HDP2?|f+nz^qm03L-B8i@FqyYIa`
z+ZVnCGlPgvqQ=E9{FEReTD4f=MuZFhbp}E<4#pzSd**Zpk4=$K*d#ADx!N;#W#)Vl
zg*bl%_xWX#;R&Yg&jc+_(~Jm5u@cC#WqmMv9w;hOnaRm(kZitEJO|mdy!`r5sUQRh
zNFo7b(F5Al=B+zIH*Ma6bcREp8EJDJiYqz3Q-ylz-Me>EpAry+1eoas>@1jVpp+=v
z;zRdHq#DluX~`>}B~`wAq#=B=9PRZ$3iH5cP6*sb^gtlIh1Kp*6z#`hQU*~Lq6B%>
zA_;1O-y%UUDi;pI!vHjNoR|0V`sB}!W`^osF6uW+()WoAhw~1J@FkWw<`(T`k!cvs
zy!yv(*pV+U)vfmm){kTItvj>Kwzt;g->|bMm_7RVL$JnsD^<SznGRQ9#p{)Ghl!2q
z+>!|!v0jSh8L6#CMSoP4*QM~OZ_jDD_+<fKMsMG>yXCJ7PRy~fdDoAFnr_S72)=FB
zzr6isXnSjJZdUM$oyf5t$EHIjYgA=<cR92&LG6~EB!~T*>rAkeIeGwi;e^0I#7)`%
z=u|fr!hoom6K8UbGih?pXKZiTofaL4`EeyP$*>Y3ZLaR;uUBH5W$>+&<v-147-l1L
zxibr9W6&!^>PwIRX*Rwv8^YgtTU{ggOn>77^}KZaPS@9MgntU1g2z<j0h9_PX@0(T
z{VUgi`F@HQV7m?tzwYN2ixVGux&Oi=^4p&lq6NY!3Bp;`RwO)q?F*WOzS(Y%rj+J{
z%iJ@VYm9c_@DG$>DNc2l6rH}_oUzkl1AB_x8h=_%GWzP3&Np{L#`@j68>Sx_|8#D?
zXJIuDe?5LGP2aC}xE!?n-cNfK344{{+SC@7c6Z7CI>eaY2!&`~El|q0ae)aBexNm9
z8O<xo7pk>IZ+v+X_+er#u}aSjZY?yPtJ4pWy(-6yIGJy&t^q^2P`=jNBW!cI$F=JY
z4L7&%)kRAB<<-uBjLbn76yPM-<Wd>S*LT2v?$t#G^Xn(7(I+~Y`<XjbF2dpTKIx~)
zhoFPz#ikmJ9N;p8gR;C@`|Z1MP?G4N-c=pM?%=-I`GEk@?J@2jBsvke)`S-)_j}9A
ztxrGD$$Oq5_S52aqQ%8YJ^Prtk`rg8RAaC8lNj)$(cRt$%<2ss%6$zgCu{Z7;-P9(
zXy{cYPsw4eLQ<sq1x;tp@52l_l|r;`Ijp~f`itRWk)NKu6`gp$L5GrnMZ-T~d$l+6
z_I5wCCxpKQ6D>W-Vn`8R{_$U5ZRuwU3t>;A)#0DsR6%Qkpd8kOqT;5>?4M3#3CZtJ
zN9_%@KOJj_Tazx9O8ocNMGn6SNxKszQ@TX`Uk8f5KJ(9$`&HZt{<xK>toP?5e>^US
zcsMRG(97!`l`^rx)*MbvKWaxYsFDq*#_|+LRRy~Y$iv4wKM8t22)g?pKnVSHzqd3J
zmxlj-+j1Uhb?$qUz0I;SKN*o?q<gaA{2d~@g6{MpHRwCKAuA3sFg3@|(?)wNcTNm_
zAdpyOeiG&WbU00sNR7p7*BP<(+-c=9u|KU0X;F?g({}Q9yHSY2M}FE@#EYG2V!d@L
zP3uobrosA22_2YU-#s!~msk6LEiE3c+<HC>R6O^^5LbM$!Id9Htsm)`B23Qly2_(q
zPv`rlKlBMemc(P@v1NIB*DY@j>Hp)*K%8bk#k{5E*6aQW1E2nrF?2vmlFn3;n#ZY!
z$8xiO-c@xNJsKR3<h=8)|3er#K;BFzdk$g4c}xgM1%I?IPUy=eLJ`QiyhsP?db!L`
z(zXM>JQ#<B)yvKFSdwwR{C~f#FcLtV=txg_U4-ZS*=rOZN25DC@50dn&9>(#=biaM
z0WX=OEp=e}fd9)<7bC;hD->q$sksmsxR>Q8WBof?)Rg|fA&}Lc4{_g!(I8E#wP;xI
zgCK<1G8@ZxJ%T?O<3;3Js!k&RU@-kGtWTumCn=kPs#T#k+nt9RnY24Kga2E#jj(7#
zrXD79<F`%q{qNoM29u)jH}j;h6)%*EFZBFmkESwgcDVjKQbXEA>U4%cl1brDnr#G>
zk(A?mhAOmp+N<>c+XeT4ZchBZE2vJzL)zcs<9`|I8zA7k4b{P8&r4>5>$!`qf0WND
zP#)JkWUe6*kda*F>i=b?Ex?f8?LcOlpJ}Fl`Podf!=5QTWSYwLfzJOj(+J)KUhQS8
zTJ;4}>Bg8MdzYWgv@ROm`Ma4G=5YMUCHI_!g`Jsqb|F&lGtKn>a`w!xOWInIw~UYH
zW(W56Uif)eLG@haghQ{D+CUXy(A+EpxFWBAh^n$9z%qQ~x!5VcW7d4&1A&c2w&8Ox
zxC&;Wj%k2Yryv}5J&_!H%qQ8dmoZwD^;GNX(I5Qim-nrC2w0(QVh-0G8t?Gp$OX-k
z2`JV4@=95R@J!(egti7rUUMqF{__#Hk+;6ZMB2W5#t1pIS_S({Co9(jH*=ea<h}?>
zW>{Xn!z9>#`Ap#sv|a8iwp-Q5X#-n&XK!3J6dii0T>HZ~`4D1Hv;&`#Cpkgs#p@!1
z)#2*3xyo(A8&2vC@-wv+uAMI@3<jRQF47#&ju7UAofr5zU&t_~k-S!>-mIif+HRe3
zp_kWGa=0~~+w#L~tfEupRn-#QsDI($0KvSN8?)BLo{##-#GSqj)dwf=JFC2WQ0e^J
zTgzpPG}~Ow`kc6JLO;xgPu(TJKlbPo%>;=Y|66w4B?kC5KI4R+h5>gyCuFYZ;JSN6
znwCu|7LINR)_L?}G;+@ijF;1~mzsJeKD72Nz0al|P^xNLn}1x?TM*XwwbmJ-fo7&G
z+$P?hvxJy3SC=2R*eWVj{uN(2$5{Op^4_AhMZ0yH(SgXPKTTLvePLVHix;85ZhMCf
z=pi3x+tLujsD~XeEs|!;bw2xPvlDh0OJSklFvSmr9Vn-epNKNvO142x!~_4ssqp|a
z%Srawy+6yCyCkt@vi^E_O!JDH!%v&7YgWA3su}7+Jgf*|KYaM`k~a<bC~Rb}Ht)%{
zr`ZlhucaQHl9u0p&lJC%%$~_JAe&KD4C#hj|1)9+KDsXY^xj9et-c>I=E(EfSsT8*
zilaljpPqZh*yOs++@Botrp<xsUH@)xh^5;~>yHbu@!~DF+3^y4+}kaPC4v`1Hi)}?
z)^ismxi=(z-)3kbSt7ZR`Szx(uYg^jo1gTwt&jR?AVWB*wT>zmKc7zz_0~*kmfCVr
zBhRHD#4ierQv1L8W91HNEPVpk6e7|@dMs~u>_??S{S#9EF>};4!8OGA=9sU)p}jw^
zAN~3V@jC+UHRZmpQr2m0zV3?GMdwEqNj<{RSAW#+X3$NWn@$N5Le4JWB<a(6>Un<s
zIX}(V4`!@$G~cO&I?-{9%j7v=zx)jP;(veUbA6<ydWo*r_ME}>f0`h5@A>I7W@wyu
z5N7y%9Nf=sqLnU?Ltn6CdUYn3iFr6FD^it$;xoEm`_v$z;y_pjyt1A?4I&bWsFoDU
zEh`4i8Ej=`WqsklIsEeren*gZk||%a%=ua9p6P6%lo<5Z+gh$&`GZMNVvhVuG1F!!
z9DwGU5Cpn0TQ{)&e(!pEsz)iPzYY~3s^@NtBObiu!(D`oo0SRFRd0M<x<^g>hgA>$
zSWbP7<?w%$_=h&+h`^`55J+t@sNMz8kdxP2m~dx<aH8wkwA`BUve@~0aX$&*hj&kq
zpk&G{s}J0m%BG;wVNUtvGiaYW67xHuSbxVXH-5*|-Zs&`!d}9>&LdoJS2O9s$=dvT
zOxhvyfgch-%`w}Opx9zNFCT$rct*CKgx0WA>2a>QS_pkYH3bB3K-&_6i9{~EIMI9o
z$}@o3xIta3KD%TnuD-CnTyPiDD3L<BlLPNCyoTo@Zc`ZhcZMdJvPkl8B!3@x-!!@h
z*xNOQ4+2+<(Q$$2`m0Tdz+5-zG0cm)%TTu%BIf{v8;V*{(1FFvI0Qe{&t&qQTB~M?
z`-w~aPHktMZMb)+q{yi#UA}b7jvc@ck|yVm&gPY3@8gg$0QS=&w7r3uVL96L2oXI{
zrPqQw_whcov}u_E6%f&`p~px#TxtGIVG+=C1RgY6We6OP|2(=J8Z7op83^)Z8GGI(
zQ}rLMTcsqhB<>yIy_`7<#Xsp!Q~)<h$CJE?0BSuIZ^#8EKN1Q#U>6t(2o$pTmb(SF
zY42slQv0X8vjz28dl@T5rBfuf{&!BtHzYuz7;Ib(G({BfK0eO{g;D5dQbE=H)iQu+
z55?8``4Fj(DbL3*)IFO|^v+fWOikP0_qg`nL1C80jd9|{g!?zy_}<ycpCmH`uz%D8
z)&7n{SXst$RoZfyA9CPv<yJqM#Y*4cvFR&N3bZ9bqs{cc{*oCBJ-&wj`*r&EoFkCq
z`z;Ox|EPhU-n&~QU*zP7^|KD0g{mOXb<$hHV76MeDGne<xY5|HWEVO<LM$-*k@Q~e
zjcj*r>0q4o8(_*X+1Ch^?bjRXbSN3*K<s}02msH8bQ5Cg)!K`BWL_Q^@~``Ad(I<+
zp1RZPc}IVA&~ai=ttAuyh~5Cr0BjyZUe@R&`*BNC&{`4N<})U_NCJG4>c382R~NYd
z#^SJzw-|ON#gqC-I`6qqSueq~O*Et+Ty9^(_JfT+8+lTI##t$+t5E$xtonLr%wzSZ
zPniP{E(0Y2`VASaeijbVK&Z*SivZS{1u#-5KzypZwNqtJZq<?z-3U1Nh;+jwiQC;-
zoa)8j+0Gs3+KM__8c<^-)On<>MdL0(<BOBLP^y*yF*R*#ozCf;_jcKkGqO1Oip`dO
zc)5pAaFFE_CIweH&IEq>FbZ49E0}f(&$g`rOKoeu_(FhVUkqXI-*91xE15wAdeo`r
z2h&BAoc#imEMra&oAsZbJFqJVQ1j+f4T$u><R~18K<7lT&8+vRSsRoCu6&$wZ3%RE
ze66ZDLPcngl+77Q!JAQf=Q$}e(Jq#DZngYkhYp<p#(9sr?d3brnfvYK@DGq|hzrT<
zK6EOoaUI_wf539NByC0UrQ={ZqUV#~SY8Zor?rgELj(uP3A+ikk9z29+Y7qmD6^m3
zdspT2V=A=d4GRdmOq}@5Wk196%&}R+hy5RJc)1DqF^n92F58>BwzOTB`}SZ=0R4=V
zQz~H9o<MjY0re^nk+XmYSLvlsm5pvjgsgY^r^8%Y<%NN*zl@449FelEPCz_F)Ix6I
zgzNtqaPN1JfbzJ#=1m#9|Ky&lEEn7>?$Bg=-It!DHXtP~tKxm#)OCJvqhxHOzU!%M
z$&<cJn7i04kj$yjn8r3bI?8&#+IxkY1Zzab8!?V3P{zk1X#Sz+I<RQ>BCCc#RQ`1;
zo(iod?7*CUVJ?2?P!P<#9#~#+GfAik4`6wKTcKOI-q1SnJl1ci&)O1-0WDEM8GX>&
z^EN@gz)uPI852;2;9x6q>@I4g4$yNOB(<7>t)h3U0^f#JlKE|cOYF&JRhh3sql|Ig
zhD!aGb4fcfGs(+q;kHASUbwds_;(SAP$@TZ@AxryfA5Lv+lW&FeN*g6w?r?#+$Yf4
zJnA%5MGMP^GAi^o<Uc5Rs^Brp10C3bfEd^9wN79)UnBGqkMpmdLEIw1#i64Y6@Q>q
zEz)u!h71kIP$(YfHBEzx9iYtvqF$ckPyr$?zK<{#Q&3R3(d<HTHuSnRRS@MAmb>ny
zM<b?bYMA9fTX}XQ)KqNND~C6$;y*x5=!-I-V&NX(_{4Ru*-6b_1{+41GGA9<GTxLt
zucJ;UW;b7{w->O~iHHP^s`G@}hB@fyMzT^5<%p?qaINfE>g%2PF~1S2#cq4y*3>oY
zv#Qq^cd#=>Zj>VI+c2yA8GrE&kte9AFZg>wuNe_hsTuS&yw`XlXN1bC4e<2SXD%e2
zN*91e%^~O!X{cHr;-2cs4ngXP2-%{UPAX7k3q}kghglHXH@bUlG*4CRt{Y62Q`qv*
zeP~>w#{2RWpYe}gGsIgW-=$r(zXx0!@I!HfZoT!;$ZOI0<Roq?y95!@xd*CzpvZ=+
z-7Z@P&5$H0lDZ%P>?m8PW=V7C^QZUvcVm|W1JTLOk2VAGgK)RHK9QJonuoWloMUz&
z#yH7@3yKPIx1N4~BxWfJ+VuP8A>L8s17<Pcm$vnw!+Ka*AL<-+LKDku(}Ws;Ky#2}
zCRDpo11kvmvt(o7Ic?)pk_&m7;=8Bwxz@lUC5ZCzX`5Po4q;2g%IjgX2x99Bjv>ML
z0nF*^xEwMTp}cgFo;++KMuxfiP;2?a7g`m?QBRhrki97s_X>D75yEcmJQ&_Drf|5m
zX0Z%#IQzL6Cl%@`S8@XXy4U+ndv!OinfU!aTu9Idbco4yA0S8Llh<kEs2qoggS_Qd
zW>E8i;-_x>6xipePSrKnclY=xAravb2&|uadaf?of591?t?!-44GD0aOZBR&@sRX~
zCPospj7C7OrgE|4VBE|_!^P+tnjmr*r5@y=jvD`UQK6elwoMZ%6n|@ej-WdgV|wx0
z^_jC?f0$jYF8jEF7-Ni1+!c$f(aZSKs!u}X;M0EdksZ0_HPlyIRelEsA3rL|H6N0+
z1MWGl`@BJVOCt0h-;eBiy#7oz#B2ED)&}WyuQw{a9^D5>mL+Y$UUtP-jxz6DU><9G
zhuiz5IYeeft&dRD&6pWVJe7{vV|<<;{-Q$L(H_L@M9)P8IMED;@?5{g=ZM55lvn`K
z2(@jU)^_pbr9bH=P{a25q-B0>*7H?-g8%eA>inU!rLt-V0|%4q{ckT;+iGsn-`F!=
z!@Eb~k|8=`HbFrllLReV>O3xQC2SQFG^fQ9qobqo(15fHF=qcbey5Jzz=Rd~Rhcx|
zCd9Cdk;7S_?m$W!Km8zLC2sMsKAnD6!k<B7Hs7Y)+7XPxatFGW`RZ97U_ZwOZ|Ch9
z*X3g6_wK#to8y#~6^tk+Jr4bk%FcWjb9CDUhzPnl0Cnk*B9m!WsQcZqhQ5S?I|~RR
zS?`-En8RHHK4kCV5He7(B%rwkaTZBX2Wz=XqsKwN2iRpv0fFDZ?Y!E&GZf+}L|THh
z1J&5!pDk^u?-hwlo0ZALOCTzlcs0DvbF<c=eXKvfoln2m%i84n)LUwca7569)SKIc
zH=-wn2Rka#ypBV=*52WjRu2nA%3H2kS8jz62nFo~-SIDu6}9h))zIdV2lrttB)2)w
zH#~)sE@~hL8su*1cr&SlR^{zcZ$m;tNWkd#8t_}2xgSZ&{kPRAU0s3d^}leq`Izc8
zrLjdfb_zxDsya%ge-M7&Fb>>in0<oJd?VuTGMRzEJ-~r_WwN&pA528eo6yBD<)IQ0
z=sDjgT)<v6lnb#`DR5^<z$FZaB#h3N{G|p<xy4W0hqwZ$C$KAt?cQEgR#;bYR^w4F
z#H&Pj;ncl+=4Uxm0hNBek$$UcK7dopHievs2sEnj5b<g`;1%yeR610fL~T)lfrZDG
zxJMTnBZcvsw(|Urn!lJP1zIDll$N2x5Y^ZzsOb~Je{|+(9+2?_LZGG&+$HE}JA!f{
z==%slZ7q7YT~kKgpqykZmhk)d5Y4_a|C;eFyb{FYKA%d&@!Q9FSj*&9c5k3FQsU|h
zLVINFFPZ>@h)Kk?j8Nu4lcYgIA);ifsjcdcqXZR^W|^`L@jD-yDp2R;5t-9k$paY1
zJg)73;@bb*dWAOhs=q5+-*14D7ld2VX{&n>rpTo<ChpRsw%ocxnJH*k-M%3?ss&Au
ze-&Q?!Y8x24k;dDjliC0s-eq$L=vD`|6-wyo)7e6ghS7*6l(tz72Zk+29$?13$_rN
zs%92q%#}+7{HV*lk}(^(_Qbz;w+ufPGcB*ft1RVt0(ISjLab-D>cd40yFexdEOI+n
zpdI1CXq~FD^QDvHrE6@Onh|2ZfPh&?90`xN7HzzQC{tfvDOLJXP8w*x5$!t9lR}J9
z@x6igxE)1yy{nz_ulglYPxqFREH%}mZw~nKEzH~<uXjooZ~k!*Wf<g-NFkkIU|t{T
z$uZ?d_idog1z=v}^Rn2aLF6A4FP`xGFJ5bmVtUbVTTo4PzRVB4FJS3ZN9AqQt}j;l
z;U?l-Tf!X#xEASNE5%*y8oaWV1_Pe6toI8d+Fl&cXH8yT)S`S&4U65rQGzQ}3?ADn
z1>^<bM2qC}Znmk_ReoY_0{XRPlAEK|An2jYzpPTCH2DDFu?;ssU;{i)W(<L1vaNdx
z+7_YYf~)5@50Z5B{AYIT+eTqt^#iclzQxq%pO@6>hpYe`(2Ce8_l=W6w8%G9fc|m+
z@ekVKeGl2VW6*>2Vn7zEOvL(-0%xZb-<2kBjrypj$<pOAb|<faVRK{7F7x5vk-?-+
zpWL4cW$0~!8`4C02AQfk+$jQi1uAD&IYELoK@V6Gfib1Y+}x;0d%VD)W;Czct&ut!
zDo9!5G-$X&mOrQ>b@|fB7}alMFey<;SM!hDKVdb!seJ&@BT6JlzPW<ex!q$k>&B}M
zGu_PaYrT}x=;_{=vF*UdcL}LG%%vg%(Hy)mg?|qusQG*xxlf76)ee_zSaf<PpPSvs
zF|O?^qvE#Af_RZEKUh;jT&4ZiH^sm|1NaBmc~ZC@HOXft``tjTV2T{7k=qX*2~PGM
zc~d%3+Jb92o?Aa~-gw!&)!f{adp)5>MFsayJC%HBf2!T(yZ*-Cj|B6d4ka**KwU-x
zLqZMU(0*12-NzjuL@bDL(igt9-AZ%&C)S;e<qD#QI9y#qn^}B+MsG0+Z_q19;_TwY
z)7x@GbCYhI@wrY5c++^&>fn*@PiW_YyGm4xnOO_mz!CVq>iG(H1YC7fkNEv^#K8ST
z=!cC!2}T~IU!s}6*u*6ys3j<0zNF;d7et9=yQ}%<5Tj<$X~2x#oK8=mwk0d?m5g?5
zRXz3nAt^<ZFVz>AmoEUPzX6gA5^9xT-LLT^8*D|4qKBi2d*2mwJ%Suuj6p*Ee2|8T
z3U09}&$;bI>F(0NW2=?qhrDfSG!hq@o;l!_#FK;8tC#B!_M7zm^XvDZAfP9!mZN3(
zC{hwg6f9rg{DsLiNB6a$TYT~=?D<$xzj%bw{0U?ZnXwSu2*G<AOLvvA+$`1jicg)g
z0O8u2n6eNmLS(rxj(O-*u4zW#cQRzK?&rFTum5LbY0?P)l2wHEehY9}dC(%jOvIRN
zfFsk4UFmSsR{B|#y#Vtlj7?YW4~;M`cIZkrMR$udH*Vq@kGZT&dfdj#-|PROeWJVQ
zQVkrO5DD3`L)Zj96ucwA`^ko^=IeA+ZUVLtu2p`#u68Gu2SxGdS{k@V<bk_3z$hry
zL${IC-rX62I^_}e0P$$s*=qp}jV&kZ?nZF!jxcq%@VPcWcks>Ny7BAP;Af@Nj6PgX
zTXOJtw8M|bw+{-c%ZQ?i15Oqm+DJ*z@)*(Pw^YzkVBJ<|39b&j-hLHemZ|~siJ2Ke
zHgx5Xgeu8(>LI_mqOYs=+WZr_nR|X=3f8Sn*_7AuNkMIl$@+^gb5d`!ZFsjnAW#1b
zb-4{(uic(^V{wuSR|D)pdEdzBsCwNjZ!16$=Pq1era?%*;P<QY60aEBXZr*l75sf@
zlMm7?wbzLtCgbvUL}hFeE9an!WRg*#1ccBmnH!sU>|_Vc0o}&246SFByI9&E7jn&b
z5ziNaWdZ`AcWQrk3bv2&fN7?!xZv>~!~Qm(r_Z)$xn$H-|A7)rT*LO$4g6*%dm&-w
z(EathR9M|iT~Ry-SBe3(I2?Fy){4KqQjCg^40I9R5sVkZN{A&&i2~;}Oo3b^-A`<;
zN#u>JUcWf-nM!<LUXXD+a$`r1975<+Dwr13-iR*yU^d3(LDm~cU3MJ9IGExxX-rJG
zjBRVZ2VW9rmzi;I)<TbCKl`w^mEQn0;x=wcZ!?KR=Hh~8;iBtC-{+Z9b|_Ak*+vAU
z?_ku80!lvHc?Bfs3$h|0fYNe^QP+t7u!x7I3XGmgao{FE{^j%fZ_jHT4W(m`dx;R&
zg()~`x|t3z-sP7re7b^LeM~1kV7pWQakG|@jOh30A>z)mB`|@RVID#R05%Q^%o2Z8
z(+9U0{LX1|{m%qo$9BL?N80#*+31nEIz$-rD<slKgPw*Xr$vM0MM`8QE3FO_Av&ou
zAA1p$cIRP3z3{e>Nc`up0iK#o8#`wtslKoUxM_GGasG~w2km&7BVbkph{F}pHJ711
z1;<z!$0JJ(48)cTCIO*unp=X_m8Juz5jeW#SZ51vflZ6p;WfqW%Xo3c*rF$&)50cK
zj6xyZZP*&Y^LrwTv++@h@2i`0KNMHf2=`^|h4w%Lbo)a{wy_7fXHBDw7H0ak0yv{6
zNiw<q`WF=1T1$$lUI4czcjICk`ngzn*?VAhq?1Z#5lMR{)0edn?Kr?{pfY!gHbKtC
zd*Qc*vxM{R0=7r-^=scBnsP|Nr^5$@66mG_1c4)#GjmJ-0Io5*M*z7BB3z&ltedEC
zW&dH-=aQ8kz0_8JUM_R_E9V9lrh`kTVz1=cYPzMh4xTk+lV!N*(dH?F6mw0UJ)Fr$
zr8Jgidj1L8FtQC3V9wDsE~o>^6H?aw#9@TR>G{8fHX#z~SVB2A+zb`{C;{W}XIsu~
z%I!$a24UfyA=v7|!;W4SC!tK5rZ;{8Jd|u(biGrOcnI6hN#5v$qoU#5Ly|LV8{2x%
zkhj=|(RkYL7!=gaDx#MsyG58c#-Ni!1-q!uO@%s%^Dd-;pj*UR2y;IFGDRBOt3`?x
zjOX%YK>fH5{k*1!K=tA{=`C)nnxCUCi_k`7#nlNwfm0OR!G>~07O~QOx>)*W)Qb~{
zBKM*<bRhT}kCbDw)ghzN<r<VlKW2tS(vE%>Cuv@-aamd!Oa9JkmBm+$eLl$+kc1<|
zz~w3^fXl)vCVUUZRJR<8(^LaeHcGwG1t4kl<VPnCAZB|#d2_o_sPap1+UJD*M1Ngw
zUJs=zQ9t@Xa8}l}f#uhu@rOw@{)-koDlZS@$XROhNI%hJ$=v8k5fE_B=iS#o^!^Xo
zN2jhp0L03Brc=I}F(g6xyq>gs7=7hpXIaO1waEoO{_;<;r6W1xmXtd)M`Ct;m!`m%
zX<k*bzl|R`*~qgG#cHM0zc1rDaL0*1Dd2!@Zgz-&t2zI;F3%ow&*iL%!R(i}yT0q5
zD*hd8=&dc=n@*JR1l$=N9jo)_b62I|OBoU_pT<g8f}mHaa&b*LS1dLds${JGKDhHf
z&hvBdxW{_u+Aj~cexFi`y6gL^L-|*l^LaM~yr**uhV%7Eq935Q@#*uM?^>=o!_i2m
z?FvLxqBL6;;?Q^$9%`%X{5;sB&MrOy8`aRb<ES0~75AOxS*~9H>n60BkVxWS+M3j4
z0xHV-`(b5u*FS}N+A~&9q%U=y*+6}kfDaXL!P;ur5jO>JUaq*j&<;4Ov{u8Hj@~}g
z;f9nB2$T&jI5>R#`}@?}tAXdyevMbL(prf+Ob$*?a`D`Da0Wt(B(usxt(BBUD$!TG
zzUj!LBMGP>>&uIPI?#-YAj+yF&uu30o5%7PRC;Ld_Gq$u=e4u4QbBk1BZ-m-SxQe+
z+>c&o2Kap#hyPqQb2K?-d+{yYAXGX6+j*~Iz2xO&xs`Te-T|TYxxCWRAVIzJ#?gO0
z`SwIiJEEesXFS}^UdL|D!?R9p8lkq;?yz|$Kl(@-cp78%jo2GX3@L9npsN1(NW@XY
zICMhHgsUyntV>o|%aOZ!F+w8|VGllxUh#TEtoLEyB0c@V%c~-Km}<O?qv}c8>H|w+
z4~uYJpo%HCTPv_JNwxaPVIkxM_K+Tf!XE-H8*k=CctcOYF_HPYWPu08yIk|~n4grx
zpY~Z<1hQ8|t1<t2OpS`JA|yo$wikIMvZOZKez#gFiv?-&z=?+Xbb=!8jCRd*9hKO)
zKP_vYN6q+?v#b%7`7(^D@zeM|S!FhGp`1Vl{ORjJz(^VVbT)?5T*>R4b`7nL`ljQS
zfUIyw*1}b>exB%oMW2Lu5}s5YD`H`wW6C1@T{i;|Z6?6|`RNi}O5(bL6l1BLjW{CV
z%jG*n+^$Hp^N-=io3CG_)%c5r&NoeRXm3Okh!BIB846SxBu24B1T%wD?)IvVaR-RE
zv9z_Da<ks+p-%Kq-6#^idFY4bLK!`!7EYs$O0phemrd|i3!UyN>KH$Np|_s*w0KA(
z)>Bkv?K!9AY*Y4+HXvUdA`Z;HNZ^7|lJD~VZp}x)@|nN<!%yB4r&M9(lGdt|A;3@q
zlnLyfocqPLQXsC#QBsB=7dDcU{w`FAxq;z53kEYIbe0Xu)Zo0EEhiy^n;Ve6uPr@G
zvyQS4Mn8JK6%B&m!<TveF6|y!QUBxn7==5za2Qul(w2vH>uYcBQ>df*#8lE9HDKFl
zBv)J-Tq>n`xDfv)uS@z#_~Psb4ZCih{JsVIB!T<z!F<co!C)u)_#c$dy}H4<3C9lK
za64l2xooQVeEp(-B0t`DnIAWpY#st!1mmNJHXbbsYWV)RZS+m*K!s*Q<hsjQpJn>p
zhL=O8T)wlwOD`R5OqW<$i>T)$dml10tt$FuBWzP`e$(s@J$u;-qvH)IF1stdE!6+5
zIlsQOXt-fBTv|B*JRm+#FE=G1WW>l7YVydWcyF8OmcFw%yQ}twThP$pZ_vSCI4Aji
zLIEW}p1>^a7ZzqkToPURQm4vN3#|bq%agRl?xOprU`J!MWbtp)<GaQXVW!>8)Z!vp
zOro%N7C$#+YI!18tfQ#Ce`CbH^u{e1o$Dhhoz!|Sl4V~5FRnRKi^v!^&^Ygq-`Lw{
zcAneCz#**cdGTyfPf6*Vx!~s<i5JzOL(*HK+`}se62?3?i05-rXuBN{I&b39^O%wz
zsaX$&(TIv9Lk{D^c5$NwwB_Xv=PA<<KAW~LIPa3kvhJv98aMTb?f?$U<EjcZa?Wj#
zqPS7U_f;d_JXsY*5b63F^_fCDBZjL=k1->N;d6Zg%g=N&EP&yaXH^>>=rz^VQ|{Ij
zQoA*(PVe{)<`1w)Q}z8ELq;}6MEv=HtL<g`WLvRUulraLQ>bgt?MTs*QFXxD-do`h
zH3d)Tze7LJh1sT4_kME)zRsV$O?ca(FaT0RN59KhiUV`7hY;I$?VRDy44dI}x!@Qk
z%;_*t#j?^cgXRSxz0iL7Ym;Xwox$^-w(4Fq=Y|J?fqS}3?2r@J<|(i6@l!oH9%{8Q
z><kb|132!TI6!>)mmu!_s|STEEsD=9Kf#Eewc!`bahlL4s2l|WV)Osw?ycjZY`eBm
z>_V_XDFX$SQV^t7P*75kmTnMGx&|-+0c8ZFOQb_Oq*0{1g#kvSV`e0VaTsE+3!mHj
zdEejn{k?yCb32sYb6wYYo~ze74t~{p4%+#5c{$ZfL{DX5m>~rO!(B8Svy4)QFTYLn
zS$*vXYXJ%tIZtN)Iz2;s;ea=OfUf<IQsK<kJf5Np$uKyn8cuj~(dJ4#H(Bj8N`>5h
zswp1^HnUt^Op&Ad`ZYUeGx~sp>jGCi?d(xQ+688EsUWAV&EMrkdLyaK_CWUK8uWfq
zFFZT}qlNNX7!7T@7XHeIB3Nb_{)IH|BpZwh=E>>eNB!Rl2m0&abypG*>zf9O_mNfE
zeqBprEjzJR{crOJ)AM3_szO_EypbS}zNh-8m00M5I%v+$Guv2ohRklOX0c$LZgJ^g
z^R%$#+|Sv%XykR?!0EKd!j3xRDI<82_@FcJtyo=b)TGYe`!3{yUZBo<(1kKHSj6M%
z$oVGGm-t4Z;TMcyF0PgNBdwC~<`+#kM;I+ja28>dzKhD&+$Q@YDs3%?S9`l*M|%_5
zk#}B{crc!JPSgO7GYoK=g;D<Q>pM~8B7qYVtaDD{)jz(UgFYF9$*@?&D{}TAPa)lJ
zh-W;AFEOw)&Vt5lPiJ!#Nl7sN0gEqX48Oi(zNY`w9BF6(&BBk__<Q?})X1_oK-7vX
zImCH&`|4={@l6hs32_VO;(C7DHOs*%XJcY9_^MHp9!O%%FvsR~gAVq{(WB&2znc?Y
z_-kWtm&otBAq$bKf&Xj2ZnYq%ktW4-;OkARC$Q#Es$akU{!oR1`x84_W9v#>b>T-5
z$Nt_MEtSYAhpKA@_?RUkrF`el`>b&IFTQjGS$qI@-wi#pmJsupl(PK2^ET)_rV{#$
zR7%$yb&wZSdtt;0oT&}9MR!vBG@?Cl9?(fupD!1R<p1tTO5T6y@rGrEz|=ttZWs(?
zSQY=f%ANMG%D19mMhZ@oIpPO!#hwU~O)&s(w_IkrMWEqOOUXCrXliq;UTTi|-LOtv
zM3d)XPVcM$TRo#9qZBoVU+or=ez>49d;0H<o=yT?RFMph*_#CnCj>>OSOi@aB_*?R
zzMtggz#Qw~#%?$}^!2C7A?pk2$_Q;$=7N^y`RNA%OXIl$Cp&0a)Q?@fyQj@|8**&>
zjaQ-3n2Jv$-a7W@3N*^h`mRFE5euFyvWmIxb|Tg8GVoRA(Lx41qE&j9-z;nzywc(s
z`ZHVb-Q*7=b<15F{)uO~QwQ;XJ%`R9aOtQB>eDZVZ#p`6Ag|Z2;V9?nq3*(PhSRKj
ze|}q@ZA-gJ<uv5`4Vvni7Jb82K?rZ_$iET&a1+{#&-l__@w>1$?3-72JEE3}Re#rl
z={Xqp&IRxhu9fLNdxKYFoMz&^dHy_<!cKN@W3+`l^~qt*R&Cpd4HpOb5kxpEK7SPX
znLc6%72<_aqPyOG2xj=|4=*s+#V$qT;{LunE)?GV-BDD4aNX(gwFY<rl4xMY(Ds6c
zS@}(e+XRNIzHeB~7=qhJ(z*~$Ag`lGgQO*?2_rY)?vL#w;!YugP{SN+sEGbJlj8MP
zksxz#RE_?~nHzqsgS_C(h0E5>TVDM+nZM**`a}jf!iNTI(2tg_T$!P4`;mw->{`RQ
zeF{A_*#JXbTUX-g>OKgFLT4`^r3$vu{Y@=>2nCx!rRfbhWJdQ}&ybh<#LR8D0(1*a
zWe=a>4h`MP^wMEwxRRkcu?eT!K<1Acc{xbKKKn>lgawd4_l9kjzt&ND4_NFU@7n&=
zAVx5EM%8KI0DbMn^DdC60k_2|yj^X9E3N~&T{A2~IHM9Fs>*K@PJFW5?tFzG7hAx?
zy?2G(|F)=5YG~I!BD?>N<hu{wnF`qdv)~_)xdp1BH{e_Edf*gZ(!uBiXxqKL7srW3
zC(E5gbssWwhg<p8F*q9WsohnvYSk*>occ6aJfJCm{-Y<U{aTXRL+ZJ%i-@p)5e+bS
z+QD`0v+2VVCx2HP{&S66^NEAIsS3@(T)-419(h5z{^f=92J`Jv5e4}oI%@;j)7;^q
z4WP|6n!;u8W?jLa&TM8FJzVKj@|Avg99y@%cFYz1k^OQr8nE`gd;h-mHhxv1@QCnq
z1B_-MukBJ4vIyhJp!`6VI5ew8oB7r1n>mFO`6HVx`VXH}$%7G%INd7aoT8u5A$qEF
zAB=sV%1**8{V}0`-n;WUcr{**+)$4A5O)_mmWN1GHa|TdoUUHrV2Y^ds{({gtN^od
z^i@OUFvr`TwZ+;+*PQzLBg!r2bu1w<VF0HOu??>FmtW&!0a7vTtiSLyVRM;bORJFL
zS?<t`eq8tOW&|Nuq`4}<2AdIJaLfGRw_lY5PLKNT4Td$V=b1Z^-N9m*-(ATVGMbXu
zYJsj_jz6Y<G;RF2-)n1c7i2u(fe?W!#R(c1MgqD6X3g#HmZRsD-ZA*S3jX_S_N<^M
zsVJSvJlyw4R|7$bkThjLMd-V!ystE;TM;-oV;t>NKV(&&|IQh!ls+PiSZxO5yydL|
zr)hm#l|7>ko+YdJInI$;{DQF75eapyzTB&st7mV&H~x5m{W=E?%`#2EPOet!7c~C0
zZ_-|WXn%da{DnX3?jzUpbIwWT+Xu@F&8{d__USla#VsmTDpN8uUCADCsqq<h_)<~~
zCU#{uV{ODvsxYV4B<<_CqPOt!q=WD=hwQ9r>+&nZ@-8EeGUX2x^50_4mDJud`wvw5
zpUsP)+K-F{GEzmiDH?`Ky;WgmD)_)}<HmEWC!40)v5?Z&JDM8XG%Y+t=^Z|;$Y&j<
z*Sqi18G*@VsflH&dngXyHfi7XtHC=Qaxg(URw(^gwE%_+&BwY4f<4W~B}X6pn;w7*
zK6w5C2rFvnf8E3hrR6E@yP-N|6;S^ZW}_^mq~fE7HiiaA!>JP}MTv<DU!*H`U*HNf
z8-ApA_G5NUXM#icNQ!q4@0aB|;z`X%guah!2UR|kd68M`zaR86tjHPhZ`Cg`*U>JW
z>I<Epc$tNf?RjLWz+SoLa^5}8MJ&c^)#zwoU6G3}nx8P=gyBzYHazfwJ3FWI=iVHX
zM=nP6Sug%-LFBF9oVpD>6kgB1@lfATb?H@~?0R;{a=DnYJ|_FS;*-KB(R1YBtFN5M
zwX=O+%Xc2FYdaQmD`){nV`1Z@$Zc(2((9(}$e96ezh<<*^4!ro@w#eU=pLPqU4N57
z$W@NhZeQg<Qp#|0lx=ue+o#io+9K8&0*or*;>Sfu7vm{A{cgEBIclqxk17`4WaHht
z_Vrq2LAeZJ0iWNCp*FX+Dm^7fve2<AHR*qEZc-%0n|T}!TUyFkeC+MNK?q!n-gA4~
zW$*AuCtN`*t1zcd1wO@^ab)B59#u^ooUOVgiAqRYyYrB^J8|w%T`z-wn!pKVoBS#P
zdN~nV(>ULci`N88b{!Q6RX<QQf+aZCzDrU2d?Rxx@wCQ{mB<^$vt{*H&@ql*XOJiR
z=Og0G_El@X>X)SV&ntZzxREg~X|O1JQ}n8r^??kw65{R)L%2hLMV#YE*||q3WQ<qN
z+*3iC;&#V1vosUS?{ltC?K9`tvPPeAl*ah|oWN1V*|3*9e}xsf(4`$Oka{isartt&
z8<Sjp@6jB&cv`8V%1;VfB$7G2cQKyO*S~=pDy}X`%1nw*=^Z+*WT_x+gPW^<+xG6v
z6;X>U+f3L5^U3xJQcZf)_0$xX<5)EEHMIQExI;!a?wvk;o#?+k>To^1{kxH_DxU23
z<%~k^szo&MDpu*}207g7p6)NppD1q7;;M!jS}Uw(d-SnOvrf(&4u@O$%i7TfYu-o7
zQ?jqRLdI6+^1qGq<;e7<E4sRTl$&#j?2*6qKjhWW2azDEOS_}nI){gB+dgp)_)BDA
z%61;LYB})1W(1qbg!8yNZeqSyL^khp`f63~5any|nonhXi@tJbL&5{JawxOZ&z4x%
zI2F^&rgNDh@}*4tU+3UG|9;HSAqSC4*16AEo-<xRu4P)TpjXP^8E)Yh+skRxP}gXx
zsP=6f?qFIS-6I8d&5uXXIycX#Y1yb-RgUcv^Xajk*wc44xI>g**@}~2rBwEYb5d=Y
zL`D|hft0^lUgQEt|16ogAda6Ck-jy5j}j6b0nEwXxa<#_?mLR?_w;p#y2|zvy$$a&
zJ1j~XqzJ8jott4`&$5;gFSqPzx#Bcfls1})s06;sI{^&GI_Ts{&Eti1rk#JE2fkrH
zxP2%h@{eV@2s<bm8dqcFdf?tOPU$MTmF*mgrjBP*y{ph$^!vp@$xAl$qS4}vJ>}))
zL_FSFrcz(Jz_PhJVe=6^>e)**;z?`O+yAZBBD}VbHPtR2Y6*~}43nD<jc=aU&Wt=a
z6M9{hdi6u#Xs-M7*Fqcm4&>u#F;7X5E-_E_X9bOH*Xw1KE%4N7rMw@eH_Nd3pSp3K
zAAJsT%6oT&pqJ;GCmnMS{`cvwalYH>?$0Bq41F@Lfc4fpJj@Z_!B&F#1jWQzXvXRW
zqMEDd2IV<@&@Hkzee~1HhSTS4Q)!|#ZWvLVMe-h$ai11b@a=?~S-UWIaAJVGuJ5of
z@$Sv&uk#1)68pL0M~5a05N)Dk<>I<$h3ufymUDGrT{Fj|&v_yj^MF_HzZX{eyqQ?K
zgQMddT(<`*;8LPcoCX1A$o<qa+Y8;s>CkkknHuM6aa0QYZl0p=Pkb_3VluewEIdYj
zkwE#+(cB9;w~`Ad7|}H}_xg^@igbu*#yjo1Fn9Q_abRL-?|4dYFJ4W#Tz7u7#M<sV
zbz7Wjic8Vt>@RXf*|%l*?uygm^?!qQ5XP*a&WI!g&VLpVa39`eWLBj>1)+i2O%_@m
zBLMlSV-~@DS5djK;va*Y4;tT*6wMr&BAtO9I2LsD+##txhUVQ}<;!<wBcHlT+1wH)
z6=!m?6D8m5jBoq#@tLwP7^^P)K)iS(R!4WXe|>04@^=M)-Z9soE~OTgT&M3nYxoc8
z&h~QgoIc7IpM+q-dq_+<mTSY@#Un$*M3__u)XN0u?U#wx1u_m-Km{za9uWehr4yEo
z@PE=vfQH@{+Ef!&+L5aAaxG|94re7Nb`K{@S?1^tl~t=@R!J7~n_@UKf-<Y>x^Xv&
z^a|8WlA4qKlvSp^FrPr_v9gP=b+oci_c06Nia(<1{%Z^M&chb&0k8=(z_k{F-E)BE
zu}nZNujD)xFbhqs1qi;*Jks4-wmBKD+YfyLS%5T`c0vM{yym77{+*ae`2)7e;l)d5
z0xZ=`Z)s_{6*+j?Tz@s1%1LnpPvOD!{y4=)gd+R-$}qe~iJB{8lq$Zv&=&NnHhiZf
zSE^hDn%Y@AL(V$$vO*TKt}_08gE{w-#(&)>)qCdK%lDr^w*nAvL>HR0ok8GHG4<9Z
zmVmdRS?y5?5M2*O0Bda4idK9*7r-2ILa(Zw+Fr(pTa1p;r{qZ%WA-`nYKg{LQo7&s
z*U0_-I#!l4SudEmH_y+Qi+)LSeen>tK))$wEbk^{FZO9TlsZj#dFQ;JmAqWe)8qfu
z;R@y&xt+PGXq$c@d6}>+_30b6E}4fKaW~A>WFufJ+Mdw(cLZVZs(Ib#s+~dXpZmV2
zC~QrUFC`YYDfwHZ^q{M<R=lI*@4kKpt}e5Ebg#b^V(2=pb@jee9+fdg>CE=891I7;
z%Bve1X^YB#jQ?r;-$sYtQ<_g!3BbLkUX$`LxH2d;+C#ZEbsm|AxkH1TKjj#V{=n_G
zq2O*^x6!g;&xpl&fF8Frm5O&{jVG9qEK9gQs9z4h!;%17B$|*`TtYlNSUha3;L`zO
z+5G6ph-RG5n36Z<TrH3P)Bho485hiG0-%&TsAO$p6OJXQ0bhsTJqYo<(m#I1b#`m0
zXbNRsvC(2ZHNdQUYg=H(2HG7hkBA=e3)TDPJx1FQJm$Lf&T51v8Xi(*_F3Anv0CX>
zqM2GLCc)NpsmnNgz*sG^CCI(7m{`W-CzPs9Q;zd$u#QUkn)mrm*XOTY3k~1BCKXjt
ze5b-kOd9ohjY=>Rg{S@UN8Z(%gw(=s6hto-!<qk<$VraKa{B(HY2z5h$i{BaYWK?D
zcoD?x+>5E)2!1O~9c=(=**1Ug-}hMetPAB#4Y({9N2a82RGz~<A!)swVe-Ju{mT3#
z@c6}1^jk%~?2)Qlx6!k<-zVhucC~cxa428kmq?HnIm)UaUf(^b$Z_;k{C@b=No(IL
z!!#<cj2TNeRmSd%<gDC$H0F3|p2=^`sI+^$)nc4ph5g@*50#f6n0gLJ!f31-^6?@F
zk%d}AdLP6=Nn4qT*i+<!ag%s>!)dLri!Q8#<zcFLR(n;h&_3r;rZ-_fI3BRYJl3x0
zVYy6Hte54m&bWHyp_-okwE9$`>5FMFQ~7shLYYm48g<6xclYxeo0dHub8NIWba41r
z=xTmTbuyj;@UM8zNx-CUnY0Zx<AcAhnNj)7BxHZDFBc<a_>8>sC}KobF7BL3p9aGg
zE7&2t;Wz!vj#^>o_?@%Il;Mx1g4v^n%XUK{S5t*KcGz3S@+!AmnDSEB$tkB?u9M>>
zhpG>X$Ek!5W*ZGY63Nyta{5lK5-zBsGW7_w!;PkCc~$#=ZLZg4Z&(|AB}qpHK|f;e
zl7^e|>VEp&DoSSzWQLSW@@tTD*muE#a47Q=TT=08?vSg*Y|e&gzb(%(mRjB#=kSAB
z1Quhx%J0agz=dL|KbSIW&raWH!u8GKxLmXyPw&nA{I9!GBPr3zXpZnF5Mio8YmN&b
zs9&sh4Gm>%P)KOActGM)Z?3GYoWtX}CUSXpqEM)pVLPa5CTe2DZTasLt<y(w?^A>?
zx=^f4qf*w#B8Di(zN`#41XjHs+K3eLys6r|OtrV0!9PRbL?37XNs8bTpDUs?DsGG!
zS7gLo8Eu7ewu6VkRT8vgE|}Nec+&C734UHnFTw2pyz8ZY)jldc3%&NgpO*});-g&M
z++>ET^`kBcIQt3-2?1x)^&0{Lsq@ej#VEQqleh;r4P;I!)_}47kO`R?>IF)GgM8;<
zIrZER)g_I-?mZsldfvzNf)YEIx1kcd2|J@gVUx(2ep`+%F_W-MPc0kY*Ip8&ZZcP1
zE3LgWc)02Pi2#|!n1+ZXXS|_b`;Oz`wLB{Lcg;+}1Ls18TU+f?BeP_zItm>ISx@Qz
zyQ7L0FqYE`UHp^wWN6Un1B_jr!cC1XM*oKKH9$DFJP-*&fDD^)jHMw5R5}<F_d8=*
z+E3lFcuZG-@h#ie>GU5i=hL{nfBF=0!Oi-}-j|P#r12Jh63jdAa_MHA`FQKWpwuDE
zo3DEu<$BIYqob8tTRmGp9jc?=W1z%yWsjlKSra2s1(Cgp_fi-`&%YXC^G-8Q6F%=C
zrF5f~Qc81Dv7?u*IC*PidYvVY?icpo3?<bGhTC>CLzX#xEStaf$&5hPf?->%aEasv
zB*arROaEo*GMZVl`E_o5xn9lZ+~CDQ+=UUli5(keBByUDc<ix#F*94^n$IYm%@bd#
zz*B!^;dtNky}fp)uN!w9PMChxbsTNt8oK3yITJh*c2jk(CCoK$*t%sX?Duu!5~5oC
zAUDaGf3x)8fU@xpIEv-aVnT%0tqvvVwi%d(rW5**O~gzB(6q~iAyb&96N0Aa?n#*B
zlJ{kDRDUkzGd4Fr-vcAtF<dOrz`NvHd6DrbD9A&t<eC{!gP`!Lp30&UB3x^)5xqao
z9j@!83rrU{(UwU{ovY20ELVI~ITrCE==bY2`By%NPYa^`PEH+1^Bc!8Xjp;D1`B(4
ze0=%EOTTaL6Vfi<HIyh~WI13HeGZO0*l19cG|}f$+uqg2ImZo6<({6!(YR&2<;;}p
zcZ@z~YN54s)7n>VlB>Kq#xb;fp=r9m?9rfkN$48pdipi&E-IcIJkx(aldoYFfVIHt
ziuu;=w$xk-p;{O0a#1oKde{453X<Oh#m~o=mXM(Ts39#aO%b5Lp5J@b1<$+=%!v^@
zBbJ|c`_~_P?hqNQ-}0*d)43&CGxtK-p7-L6A>2nEhKrxqJ)6blDXINj`XkqoAW*?-
zXJd#WUBzrCIBQNPp7@z^A~5n`gO6-I-CIb_0QbQFPBF5DD`(rpwFJ>*gqJfdl`UMc
zOJ)Y)wz3_vvK2h03xTV(ly&U6l94zMe8kT$j#F+_R4IF>-2eG}NqmZuo&eBnNgK!c
z;y(f?@&SNZzk9u{^`HaHPGkc-cHE0se>zsY$8#}{v{fspvh?Yf%HV2DQQFG<A5n>U
zpUYl8B(IOzvx_n*Dfsl4?|OLG;F)*g;y1(oW$D(!Uelq-S;q(=@O4MNq?Ns_EA{$f
z(s=BF6Wr$|7Hy4l#7BCfl?LTtX~Q&1MT^?31ttmyyB$^+R7QKQ)@hXp<06H8IeX$U
z`XLzea5nOcBkS4^)Hcp0BEVE{PQI8@?t}&?hf$a5%V*l$rOPe)u0VfXb?}Ha_ZZ^+
zf|nIrYpwigAXziVQZ~>gD&^xd-lGrDQ3vY`{J+WG{n%gQ3X!J`dlYqvRPQ8hCNFt-
zS|RJ(=qzo^G&(I+`kcud&u7X9@BM^7bC-#FK3`0vt$nqvP0zo^-{0Fg5;&2_yEr`F
zn$dDFBRcPO6p7xJHOFv}G{hY0B2;f(<8w};zx;Y*W3|l7AeG|N!S7Xy@B3sq*-0;i
z4D0&ECz?n3M=K5U8DAlrMfo=z?<l}TG;^5_MVP2!7Wkhjw`URT<|uKL8ONcC`&;e{
z`Iyh_dKEk&;#l?m4v}S%#e%@P<69;MDUt4RX$5TD?9aX*bGjp~ee-AGpwYOgh<TES
z__v>a@tz!HVOApLGSRJ(C#yPy`tpX`KjY<>cEKvP!jwr8Kmcle(c{r}`g(wE2HK!Q
zTW;{5+g?PfgLOP_>~j^k?%M{gMWm~ISN^h`ed}jlPyGHgN^oYL)1_T=VxH%-bh1>6
zpFjU5cln4P{CWG6T_b?z307$Cn!cWVx&Glz;ey&ZGTolfjK#A1u_o#}$$qw1V&a{x
z75W(j$2JzgHwiKeKe>R8PBBRT;BfsBg8=W?kfe71YL?@mL*C~sOj~Q-(yq-EgI%vE
zJ<kaq#i?@z+`FZ{YsP0cuUi=|X8mx!&K_gIdn%ux`J<4*_B%)fwIEY*)|xDjsDr@E
z-dO6GKuI^?5A~!?Y_iK0f%A~pO1c@QLf0|KCd|$hpoPI$-CM&SwoVmAk$ifqt6S~d
z^5ycabQs)zx`<OShb6iu)IT-}hf9t>yKCqdP@g|<_)Ixyq8L(N(&CgP3>%BGL!L9F
zUOqlwCuj|yVeDAbF&QxS%(-{fg;Q?oMMTWgGS1V|9W<#J>L%-grqFd#$cD{DBH{8f
z;nG(789z^_Qo1`yt1=r}y#BUhGINwz3Wb%(dw*-RapUyLvT*%cLG$KT9GNMo?5wm4
z-RX<mTn|?XfvtPk(l>tk-4Ul;YG<R!v!)Bb`yK)qoFqO9o^`c1Rz9CgJ}<QemO@f1
zRg@@PwuK!StR``G6byYYnDQE)-chY9C;A{R`cY{un}Wt?EcS3ZmYnB2yi_h)t-U2a
z@cD8}#e0ud4{KHrQ_W(whA5Y~)78dq{p4h$k%|FZHtfPyQy72#363=kTRAF)t6HHY
zVQscUsJ+!rtG{%pp#26hbZDxP4D)mORULs@s3(ut|5%%QejmsumH;YX9{P|!gU8Y1
z&=Sr)091)?E|sIl0=G}9SGQoN%J;;)ZyB>=S$(}d=%yS+`N4eAQ)=uM+TNn&a_k!-
zc@}Oux44;lWqgSFFPscde%$@^$FVK7#JR(vuB`M|)2U3@&u|kJeXPz8SL%O!Fco%u
z$CRYpW#3(mOF=*CogN5KpV0OkBR;=_lPGfd{&rolLbSBaq!B0k_@>%aVP>m&cmw<i
zb&X4RVq<A}9J$u3t-rs1+r{IlI>Hx!9^jR;0I)N?pnzA~>ASw~Ebv+>0iwkLsqMI@
z<{RWc9x0275ok`8WF3rlX7<xid-pa;?S<Ul?}i&WtkLSC{N*Dx4nbLc#&&91a#n{V
z4VHWpmwXMIa?)M|`5kXY>R*?F)rTL+rtn2Y|2W~NFU>4<-Yaf@()6qT5dD?b=HL8-
zRvKuZYgt0kL8Y}XIW15AUO0=I3%uO@!%0%Qh&OaF3`Gj9*a$7f-1!;9>vT4E_`n72
z_F>|kriRwoYxx*sCbsG9)5TfS!9i-KR`J#GwuJhi3QH-x$85Y|dk3Gg&5D-zaPhtN
zkz-j-_Y^&}<u@cNUw>&W3wRY-CJ``cE!i*p$_TrsF|0zuC#Wn$vY*6C;W|BQHGCKe
zm~M&hk5bqn?6}Yg)N|SW{r&bMz;8rc=~2k6s;YW*;g82`_{D^GqIhsJ?kr*b+fQ(J
z({_$UtbG-{y`sp+Bu!|^4luF_NawEfJpVbaQpx@O#Zh=~uV)>6$+uWjZW--N`gv_^
zh2fEcPnPm+%ks9)=if4=&NTD4NveyS>Ui~SJpI>a^DA)~3O+3|=N$5GUuleI&{Xgy
zDPv`PMYjU?2kzjSl72iB^!Zn<et`z&`*o)WP;*E>ba3xyMOG3Q_3WyklYZ_bqwq74
zXvS4pa3C%+Tk#Ku5Yw$*EyA7fsK~xUGd%mCsi|oXL;LBarGnZ5ZmUnA<ys!v`MLgM
zoT!e0yAsNrdPTJTnxKb>^%cedwyOM{Gh=2cTn}yFr;GD#ve9N7rJUJ{j$Wdc4y0a-
zUwO`(SMYgRC#NpteKvSz*Lm`XGPIJxhqr!|5?@iFh>ua!gI8m0jZ8-$oa26D-9eiv
z{G{J1-OF_?o9g;?r&H2m;n$t6H&*uCn#<ljNWna5DyO1X;yCuu((?K@8)<Hz;zq5h
zuomkOUg9LrL_TIB_q46hF^5Q8laSU_sj*i7@;C3(={r*EtuCk3{*JV@DA&Ji+F}(_
zdR%gKgf-YH__b-VDV{5+s4~7>e#MEg$u)JKmhBOKEl(%k8AAIJvcs&Qk=3|0+_c_D
z$Q}7P*xQ={=I#*Sl9y#yRq4Q(NFjiHtA7pJelDsLXplm~f|l{Za98$Plc-1@SN3CS
zJZF^IgZG#De$4x3$hs&(-;^yQmNIlWKS%t8s+MWV$0h}p%E`(6!3MnB`%Oi)2czwx
zc}ns5EhbYEV+&eUE$@?K*52LJZg288@~nxbBv_ZlJSKAyZ|8ANzr^-tph{UzF!-GC
zi#&MO;GhYrqx27kK}w+6()mS2KApLQiQMG)4I4|D0}anNjj;F6p~Ahh`p5-@+;^2{
zs@s%Q%VLdhcwX>K-teehFEzg4MCojp>A2;j*Z7SrmEZB4B#@stQN`Aab(7Y>SY3D$
z?}~0(D(LnSA0HN$Fv9nK_!fIM*iaf9d0`$K*c11;u`blzekg?Q?agC!l{az2&@2A*
z2gs6=sHo^#^%iN%0w`=|%c7#r04Ba?x4tiv>OTvp$L^z_q;}}4(_1-~k&zL|b)m{C
zY$vY)X6~8jcK@>{MY#J8>UT}}lfD@J5{Wpu*6~Tkro~m(yI+}(26E;Xy;#s%wIm3%
zgB{XUh73$hREj}<PIVl>k?g215YLqaEsvQjy=?g+HY1C=WkLRB_imhe^^4`|YOinZ
zAl=^YzTd4|*537*uy5>eo7<&PD;;=wUSe=TjJH7{Hv0N>ki*#RVZM`ok>D*m-IgBy
za(3RO)m81KEM{WaV`!?sVLauc<h!-bn?(i&*4h~{41A57HD%RhW+ICOUdA?&6#UW=
z|N3qx2k8<&<8Z^-)dcBPkAC5M(te(X?F%YQ%9(6Kybh7mezet0g1CZtr3TweXv31!
zc5K*sNz^)(*v4)5D~<Xxo*&}L9xJ-xYXZScLp(;*Hbs(luT|&@RFzsq<%b`wVejyr
zQe%yCZmqJrw_2UKP;Qu1EHw^JZ@;jy<anfKL6>s{`=Qb^`J2|#dkng<SPu^yqb*A7
z22%)ya*?v-Hs|C|{Q0r9IoL<3T6nLHhEh6~Tf<7ZfiHPw<laXOC+4QAg8O{dSHji@
z>+<oBf)9Q$wh5SEyznrbjAG_(d+yoMz`GS^kdp2^T&930tR)zE+9+33^0%sueB<+{
zUo6CDUf3vTT7TSQ_?Atr_HFcR*~0YVgrMepKIUQxTgQ$5jymH#WKtAecJrvoigLI`
zxJq=v3rqYnidN|jBH^mTqV}8{G$mci`Fy#~(64&S%fQa05-&fJa8@7&pNpAjcERTk
zPS~^RyiDDSVfsO{XJh2c*$olX>F29?6{`i)72Ki3GZ!g4-wqG(M-C(mV)oH2V5sNk
zqZUYSeU#%jbOz0^(R?A?u1Yzm*5NgK!DW=)_s{RBYQBRixE~9HDXu^c!{GopFdO1f
z^D+lC^9w@#;Ey6Dij%g^h^hlKa{J<oZ^`r6pmnPD`6*7dTMu4o%Uu+Ex<~O$dZ&w;
zQ%Ri0*5mV!xo0o1Xe30<%_-3mydrkJ$}5mKxyz@L?bfgJ4qf-J*t5ua<3s4!jP@-S
zNc`@~s`20(6f2kWbO;-#R-|j26)um#=(3tzA|xG0l2B)3K6`ORaF{YTV4ADpCX^+9
z?j%L*3{pZHEy-wqyLwohM@%!l#>Mf+sD6pYDQ@XKD>y-8EuG4?vR}8a|BAO5&ow0(
z%7k-nm~v9T!$?hCD^R?ZA-DSVSfrk($=Ct6Rqt^PrzBaNz^{)d{e&*ei^Mo;HJ#$z
zap{a0F(L`Uw67>bX0rF$X8}+RlDAG|_@}8|uQ~T9&%OukCcH+<KWpv6MKe8idcL-1
zCGhiTBy+_?x6;i<Y=_^sZVG_IkYGAs>f56{MwI4=+yA*C>o#Thh{x6!_sgfyS5GO3
zn&2-TMYrVZ{*=<Iym05|-u;n!0YzfR_3nQfj^rX0c+RoKsQ2k8&9(DA$a)%&qI=WJ
z#Picn%UHd6R@lsl5U&z0B)>XxBOl#`jlCH>k_3qUR5guBcpXLAl*qCOFYzDuu5eSu
zR|M!x$-5TEc)5R+v(~=CW1~=M(pE;%9&Rn|iOs^W&SOcXINqEcN$PwV+174r2Hxex
zRoMic+{Y|YqSv_D8@}D@{5K+`;+dkZeTO({=5!?GxSO`SxJq{r?S&&XC`JROfgxJq
z7}Z^j+-^l%>irpOkqt4pl*o!hcRWxcaRs`nBd@CkE{tCI68HEyZ_!+85L7thHA8vJ
z#oV+qMtQDZ4Ig?E87em36tL%~AU1P>U@YRW*+h75tYyo>s}a5-+BO>JR`PNa|Nh<g
zFjRun(lv)S>YK4Am+%>#9eXZyske_X?oA7*%W`slZF>?#Oxpj_7XSWG*RvOb9iG|h
z#;@J!>OQ<jdFOWac>9pUr_P*}2l%LpOq}7IpQ-nl@;R~@$Fq``kRFytvl2Xx-0znP
zN%(Ch61Ts~)GC0rcEt~uUYx$cdz;c5yRR4P%}{Dwu;qH$F8qbFYV=d~tP!_WR{RgW
z2Sq&$MrRx$V83hNm**YQaa=d$YnSur>QfHuN-FR&-&o3G=q-1*v0V<SUi)K)|J>3N
z6yTm*p8!+L>fJLk@(K8+dnQA#(kiQ{^zC4}dh#P}!%u(7asPVWgq5cp+0G(eTn^LE
zhhIMsIuc)i$`xib%D7HR;<CT?UcFCzQG^`fZe3tJ_rxb@#ZJQKy$AH13^v>+hL&1N
zOa&eubnx^3bsRlwCFtZ#pN5+I>eH*e`}$SNbXE;Zhi<7jNq&c)WB<4j_W9Xm?beRN
zhfiK?ZaYI95LvXF_1t>SC3Su8kp7X{!8-b%JJS*ZgVrMNM+)Eg7DDIpZ9G3r*~zc5
zti{*CO&k{@&c;jPI`r(KXZ_+zEbG!HXA)jA$j*1abtBaW@x=s>FS`;Y|NEqYx5?UM
z4dsHt$w=Az?s~_hy@v|8#iU1q&N<wc=5tAqo@?LJ)qD3s({%dUg79fcWrxMj=es2L
zi#<!Y|I;Po(|gZn(rX`mvV42*9wJ-uP6uhL_U9Qi1zcV$@BDhK89n*<P0-2qjw;0q
z0u>5+EFnh)?cUtjE<*ovG~nwSLm&M{YgVqkU!J$nETpQYD!l3qJ&=XY9p&SFPI`oz
zOMS1YEnalcGlioi<asee{VihtW9K7n2ZvJe&PU!ha6aEKQ&q&e=DmolQoghL?B_G(
zqb<)0&?gmGcqzX(-d@yZX~`OmFOxpsC+1M%6=8lXhWT8TE^;vb`%HjnCrRmfuJg9Z
z{t}7vXcZ9%(cgT85=vqwm{vPaxp<rvJ8rM=its7nx4NpjSg>;76yw$4lTqQU(LtY`
zYfM--+fFy->$B%@J!}^V*PmF3H)o;oF0`8OH*(u9O23$$ytv__zXS!N|9X1()+<=X
zJHzUyf!AZiuVEPu=E@PVF3!l<A7%Jt@PX+6{KxQ9&F?$^a~F|+p?CZLrQeOQ6RErs
zUfnq-;{ut~i)Rk7rk9kcK_Xi=T)=853E)SL1@4Dk-h-s>AZho1O5<G?;Ej$>XM!EM
zh6hvGq9g_h*$2G;ScQL9;DaPgO(!P?3T2pW4EpyU|L6DreAT;i_J6M}6;&>eY!9D|
z=zcvOv3LapgrrQw3IDis1b-~h%mR~d8~|m27<)2Ox(7zF2Lfjy^0^eTyn`xYJ%RwA
z&uoAH=QI2!HARs<3E|CcfqH%h@PGLCS0|c@?%0=>l5!6K<+zAEJYGNmG6U1_+(aJO
zEqX(w9s!kzzg3Ap6$+#_A%sw91{1BWYIOYb5dVDjdgiy?<?_$Z+h1#5{D1IwUmwoF
zI;ulTod~QtwMvjj2%)tTgU>9>aJrRuA>aS`jQr-o7aqAD4&vGW@!-f0<G!DI|E)#+
z^Ve^<|F;~&-<<mY^X~$cXsK$x<vYx)Rs!Ogg|)TnO~!rzN3#bKzd4|4de<<1nfMFJ
z6(!K}r>q<TnPny@c(qI~H{l#DhYHPwfZom?5K~UMbKz!Zy8yr@ts2@JplYWI>{CG2
zo&)d$w*mC_ikU8hfORhrr>2C3ovJgq27D>A0JZiG0L7dI8NJ;GAGX@<q|&Q6wD;vu
zuKvqW&n*=6!8A|WnzzRY3IQK`5l|bxSXId)?ece#r=mJB1R#M#K!Jg5f`&)6F5nSZ
zJbBXlWgklxP#41OvHZ%5B<-MLj|C|c7<HC_!iAElY3k1BUJ&Q?c6NRM>3<*-#u21X
zpNMBW^L8#dT&Ejo?lV_c9S3oaZ83t%fW%ZtBfkD?XUzS2{75;mdW%v$1mJaR?leFs
z5(`)?8NfQDE?y_X22GN02j&tbXrAr7wnLAe8MX3oIV~~p`b8o|<>5p9&06vGm!-gi
zqC|TV5;KX3iM17^QMb0`4smP@;A?m-r?K|T+DC!13M4NSbOe@RVF)-9;kK0vZ|Sqq
z_0f+2Odp<j07QF8JdSQ?Ly(51;AusExWH0I0#j+oAO<6RuKfU&SDx*dH{Dd5E-<5u
ze=sa6E{@V&>kgOA84&)#=nkkJ(a^Q?J*`3e?^-H3IhDAyaF#?IqT^y*aj0&YysjWL
z%eo9~vYW31J1S_YZp~HzVbv5+Bc1NSInoMs0C5p!9XN%pi%Uva6vZLxo_Z@VY6clB
zn(qiGgu$pAq0&TznWaxql(JAT1qh3KH=-vAn|MMi4pV~8h&PA>wR^d(t!)Zmi41f+
zH;7X}vegERRWB;o;sXZej*rJE-URWZzCuqO6;Ez8-HRHF*-+8dRj7epj2^xm0Os2v
z(ItTb;>&p6^0G3TG%Rq-)^Kohv#5&0iUF(P#*Ruq%;2DTwq}vV_vu2juJg;t!1}=S
zZmJq}GD6NwsBk!IV*&VN9*u{-+iqp=_r7xhpBh2fcVa#UI++R)QZ^Gq!|~TT%L4rT
zG?d@1lvWB<$6wbOOaVce&CrwLqaDkhfbw>)cL2dqo7iJHsLKN6gR=1*0zd7b;yMLH
zn!*bn6*@U&K;?Y<`Ol-Lm`4GX=nFE8nRmzBu<U<lCl75C6{_*{)33f>2UD|$YU6do
zxDaXBj`({Vtu=>pEQd3n3LH3A*ebw?)DFV|WTR%e<EPCjo6VGn+C5ZykAY~^a0M!-
zlYObyhX%iYPlHA+Kxmq1OAfXNj#+cyFuPE8cM<?U(}2uWanX5LNmo}lr_^pbqo_y)
zc%D@8GG0iN8B{A9aw!LP@6^mQ@CK+dW0}g9$?g|>4ox_g7g=HWiA3V$Tf<tP!<zX<
zO`nB90_q>AsfB~QJjIPx3fo`+J*y8mcNPp0$gnj)+RM0gRxdp#J-rJuoPC3X>0tXV
zp9;_@Mh-O~o%#Bp7zhScEQ(p62tITIya~<5viO$W==?);Ln5HGngg@!!QDG8%*_E#
zMa$I<8opNXBf=AlktEu6*em)^P`7ym%cY=$a&aj=#jTSCeOE;#C6>U5<l=eg^J6YJ
z_l9e~8^pzo!~rp{|2|u+5n1bYFd%6oM`O><9l)e_`Iw)2@m3pl{Vj9?opuq4(~s`~
zV3TBo@;1Nsv`+$Hey2fR4jhwK2Hz3u0c0%^u(?_|HgK-ISW&>hIv~E$BYrp&Cyfp1
zsIw&cJ(H*3l`LDjiymuTjw`OX>J12Iwst^VHOrr;-s(OPB-IMYbQ(i#k-WZ{fjg*_
zhakp}jiUpU)>eR?>IR@Pwl<`FvfPZ%B3rk56k$YNo*Gd=%_-O7ZNOo2Daq3;eiBIr
zU~%=K%B@Yx6x1!F2L=GGMP#80xm-&Xe{|bAa8jE%+S?~#z|&f;qTqQ`<)Tf@%iYPg
zyl379mLpm?>oIiJh4t0sPiF*W#dUXe1(lp{f&FZQmcLe8HZ<&9WVp7Y#%RUw5rT))
zcht%G4X<r^_O=JdXrfI9LGNi3Umv42;h}G}tac+=FC*Hs4Y}PZz;p2x5nG1;s6N;;
zyZ`!9;bI1G5>wFpWPb1bQ5j5szv?oyg0+{Io1;*blN(E$FebA6`a)ptf|{0=<tCu4
z3e=80Ez@>$D|Z=xD=_sb+XIjU4^v_g1V9gQ&))!}X<=UCh|^rIW!cP4);2ayE(K6P
zeWEgAX3kCmY9;;Cy9?k`?($F&qs!@Is~6ON0^MbuIe?X0ke}bZBtrq~!jy}$vnwdo
z`?6Dpf}|SmG6G7(cnj=}6_<K$O$F44Wvj8`MikhUO<?##jRs^6D-v&QuG(z%=VQSa
zv~+ZEAY%BL(bL2tfC8%!XrdID_gw0+F40;=c4^;}E#T#if`b>f3MPT@SqKrOW+>#!
z)$+(*ex~2=RJ9zt%R`xxsk@P3jP*W5r#b4?<^bwieF&O3XvybF%ukVE{bNC&dar;@
zh+syuiLwaBw%^vJn?BP%TvvgpaQ0YQ)Qm)!;7o*J;59clw|yg~pbS!K$8r})pKMT;
zohT1Lh|n5Rh~{qtl&5ho970T{IV8*&Sn<Kx^G<@|=sF7Ec(lG64!VoQy8!B{`j986
zUWWWX3T;s7_HuLc13SpWAR5n7?DvnCCg3%c0tM=eJ#8+Z#ICg;Fnswz(8~jGSRBH1
z`%`0Vo4vUCl>y$&axfJZ{fVXJ)$Z3jsOWW(Dm?FrgKy2^&G@27d+|{>EPaLvpu3B*
z*2ip1TVbheI#FQFT>wu`W33E8dVLq>p_iy+VcE%(<RpO)Dep#f5NBri`!z&69P+5f
zJ_gEBSNXLD&-Dga(ek3A8#a@n0Ln&FrXa#BpddJTqD2hwcxvDM*NHB)oqW*pcm-+f
z`OsiG$}%FjgNo-2aHSQsiUzkL+^Xu7sq7`PfakKnuHwVqcaZbViSu0RiKiKCKxmzh
zdU`wss1%J=L2#E^Z=L0t5d&waFss|T1OqJ9{D;dFu62OjegfR**95GG&FT&EDy+N@
zMlEZi3}TMg2g*^syTFW|2VpJfMJ-2T-|N%A#5+MLWfqVK<-^@vU6I=}k*(iPRpab5
z6=@27(_1r^@{w%lplnwn0fg}3QY_%OW;%<+ap^jbc?$t%^4^h#@x_^|x@7-kJoNRR
zwvTUbmlp+^g4uk1`Zb`)7M%Dkwngp$c_Ys*1u_X2LeO-Zbh#f{0M)A1q8xL8g$+&u
zy)}#7#9?WEt0T@|9|jZ66>r;oSqN9y1J~f8MvXIIb9+a?A-FDQSA+5=d)&%H{FFPc
zfJsvXr^B8M7{on*K&#d%7C|~50e)L@uFJBCj0@uYfzI|VBBY8DFK-8iyNXeOTU!Wd
zMD{MwzPbj=K-`F>)Q*VgVPM|7O=?1+*AZb6Vea@q;m|I7gn;~%F$UBvZCW>~Y%2f^
zfX5Zt&ngW*F{JaJEOPzoEY9|Z2@(oR055l&LLw-H({XAh0Z=xzB^C%vzbScun|tsk
zFrHtkUZ9f+?j0TF0>PgVs|E^t&dFi2^I5pc^9miCvUNbzHUs}?SFtB3?UQfs^-RM1
zfctt^V%4x<aA{PLdCun5wzwLO0p4^MpkQv;llO`Leh!9+=C?CI+J8<K8FqOFsGXWX
zvR78&M9|ro<n}zBi@JuTWk)tZQQ8TMDoKu^j9HxHA(-+Ic2W6%0=H5r4(w7cDOSKH
zE_q>@y!T18uz&}29I{-0a`EB|d!%)|jL}bW1zbCinpN<=Tmi+aXVTyQU@U;OORQd)
z>=-CDY**Pf!Y7<R#a)Z*IAG`tLMx&mMovnuu;|OsuiP(N)V2zKYDP{@j-KHzD!qJg
zuQhc>^fH}*BXThRy8pm@AHXZ|WL4Q>qEaGBP5}63g`h+F_MzWz34A@SAaYF)^T%YI
z@{_a&Fn#t@0rwwd*}L{@m@@rZgu?fq&OjgGeGEPcJqqF@N&?>*AOH1Po53UJbYc>Q
z^XdRF`E-S}Q>d#`D5_g|Dr#z4&ChQ^*Y8wuEaF^S1rT-Iti{>loIe^Tgnfwt*7Gae
z+zR0WP@tv-PyRdVZ6C6vPbySP&g4wL%Ddaicq>i->)xZpY@I6;4CI5!dk&pSY8iW~
z4q%}=&+qMpPQ()6JgfjJ&$1nu>2^#5lT!>M4R}n#z=F3=bW{&>Z%N=Q*a^-OOe_~+
zu%N+qa!GIF%_MRmvyxHQOeC1PuOuh;76IUX$YcZcDuBS6T>|gu2Ef+p6LhmmJy^jH
zT}J%S_u`w2=-ER}N#73Yc-L3Nqg`026lDRo47LO+0s6j?+ddlk^LXHV$w(E7QV7t!
z_7KR-Ll|;ql|JAG7E$DO^l3TUt6o=cNpv4yjNvJUX^Te0BFVUb4ZbiWUV_3eLb9O-
zv9emt5JQBwOrFU_<xSxL>{P?pJqcAhV$G>j;jE5YEZGl5OIKJEdoV{yE!fS~5~-D`
zC!YR-+@~_Rr(CIOBv(Kh)SJOSkrDR<Q@sMmSOb3QfiUxtj!rhLD^l$&+gQa=D$^>&
zTPgcSX~B;1n6@*@{JwnR;Nkh5OAx|A{{tng3?wlLTU=^42nm+G8`Njj@WY>tTTb0g
z?9wNyWRX2M5H)HrUbOa{q`XkY;|R}|yMOo2p;EilWNTnnz94kZngVpGXxGOu&v6or
zNKUeQ{b^I`Au*>orc4(%@D#%Y99x>3_f`_8&DO5`5w%6<$H2DW_jN3n716l;5Go4I
zjh}MW8O}5Geep$4r~I$q`p)*{6fi<4+1TW8l};RE0<Q5jfE!k*ga=$AyMzA<5u`5|
zyiACNRe{j;9e~yEZL!eb&>v+fV}S!)qfP;qX3l8N<H=+Qu(A=f=Uv4lvill<23j2u
zMFAOgCmTTDd0^0v)>%0@;SJ;W!@-OL>#*1*)Je-KmZtxlW|p$}#i#Q0S`b937Zw#Y
z@wM3x&e~@{hn{3Zgs59_^6=1*=DR#l=Yt!7^F9FKs7-qL@1FttM%bL<VqvP{^;&Ce
z35kFe&+@P(3BRk_yT*{94o>c^b0eUx*IINp&)>H#eT<l2WBU3uwD6EiUS8fq8s7`r
zfeqspHZ~oWYON5mJ0Kyu5?;IN34b&K5LR)ho^pKuZhHX#o`aTSMFoWpFI)s^`5L0f
za7iKZ-3Z=yvTl!5;zMos#X2Df2kbFcC9@z=eaK+U1}rKXxI?bMNmN%5S(J3$f?ktZ
zwd%D#uLc&}F#CPTI-FUFxLitji(ai<&5I%=kHv$B^zXrEln~g#6S}gxs@V*2)P+sG
z;dvsBt1K1OyPU*C3Gi<tlkou9*!8S|NnpmKbomKhMCASODvWz4C`wzGlz-S*De1W7
zdErl>_#UF#`IKnu2jOrFIdc%`aW>{j33H$ENq{IsChb$T;OD<l<$JKhbVs(9!mG28
z+=P;WH4x7i*uzOQ1B-{Wj563%By4RGw)*2l-MT4R+U2T1QJlvN#IPO|{n3sF#G&&B
z(vLg7jJB_<)+f0wL4O1va}sE(ti4|hV2rZ>l-2_W)k5Q_s9N5rCGGB={&fb4bD8SP
z8`5hKVSNYXZX*(Ry#CZxkdnp?;}}#x#@LmsR~c3!K>n|Q!%+~wkrfpk-Qxf$hB9b=
z^&NI9v~$8&j9{-F>QeAT9VU6>)VJNvC6ebB3GO?Dl?g(*G9ctW=(u3e_T{*FHp_io
z*!w#W8yfH7x{sKfi}#@m$Q9ZuS1{O3Rq&un+lA&iv;m~93}{jc!SH|5D5%fJLSEn_
zGRCv9T3aDr21o$xNoe<p3rI!O9#Cu>txIT!9Dx9!Y%0nUCRn0wOH3)vPEmHv29TMl
zsB|*XDcqa)B1E>8z}}Z(`Jl%Zui2^{I=p*l;u$y!AtT_XW+G8aSQ!$qM1kpiM+tPq
z<849-n^Sm$&9{Go(IXz<%vnG_Irvzn^DxZB?Bi3QNm94_B^fm@)eYl6-aMVY5io~@
zm`Hgs@&rufJbE;PL_EYUv`4Sf7C6{fxj%DVBcO}9si+Rl0-kCR5}+ap0~_OCkKxL_
z;0mOJI-G$ZfT_?b{r~(Rjg_|Sj5O%q^a+Y)orXK9yxssTzcLnZ`Z*lH$Badwe)VzU
zL#4t_qj*Au_e^T_U!korD=I9UiL%VTA71^tj+=&_Pgt=o>LpwEWAR{QbzeQ7ibOaz
zkaXL=kpYu}S-_}FFm~txjX4j&Btt&S)J70-4@f`6V<=dYegHU6fP~(CB`d2>+k;Io
z(xeJ$Hc*wT3xkP>L0Fq$6Te)`!+M{IiK%QNSjHY#G(h9LuGB`_*G-3{Z7Wn+<aF2H
z^0+|wr3VBT`j<&g$;*niIgHJsm!L5C8KNWyVxQXflz_nquwB%O)tgP@H?x5P1J5;u
z-Cu{qO!wviGd&n06IOFzMOtfjs#5+X=6_?KLK2u!6j<H;yV`ieVP-6MRkzSA2;lbY
zWwbqS+f8kBZ9fviw%`h$M9+5nHj=Gu6ToHx)U_(aX0}4H0FR!60FKTCNilc9uT2h!
zjsWa<8KH`@ChGBj+pZ1c1rmi)={>Etwzjqxd7gLwG#H1utOCk+AHbi7=xi9>w4ELt
zc5!Ae#WB%HRjchl7sqd|p2i=-<ZVCPIK*|miD<DXt}TdOe4$)$aJ0j0`fd*&?aX0p
zp1b&mZMB>L^hQk(TF!wS7Xf^@KFc^b|3&Y0QN+Fq{#FVZ@vZAtz;&BT72hx~8-_$E
z_(gYkO<L*gtC5~pghv97l8zfL_tf3fnWP~Kg<@PtC_?wS|2ZZ6I+@c9=s#YPR~;~7
zaE=v2k_j?@N0|&Ep%G>)At7<Q9$X}&JdX`mq&MFGUgB)of7G+eC_}YGQ><xpbhK2V
zC%!^kSNDG#ml`u8>|P4wF?s>@Rs7P%8%Let5}Wu*8=C00l-Zut>7uJslpWife(<u>
zCM2Nzo)nLHwmLSR^Hp!&Xe;>VIqhMlQ?nVG)QP!RU6?QlZ~!Rs)D|2L=L!jzrJ=Wu
z*6))<-BzhxTi*}o{a?%kqCNorz6VA*I2=Gu8N068Vpu@SB{GBLht&P2Sr}uhm4+m{
zfkPwhG1tIrbU5WUQN0ZV&OmVOm~{BA=U%H|N5McNc}4oe@g3nFo8U||^Sn7%t(yJV
zT(BNOtfip}@>FyfzjqwnbVodv5G(d?w1mWL9Fvf}K8lOSR6$o|&f(?F&JD!%ulF~P
za)r2$!y*3nH#VQj>Nb-em=-lpcY^`8W#l=ZSXmXhSN*Upg+$j!56r3g%GJMqU;^bd
z?LS`cux=x`bEB_UvEZP6XaLtf!<B+70kj&=$1smGTQ=DeJ`Vv)WrYr52UCIiaxo01
zLNG=9IS0HnNLnAx<la05U~Y*K%LaLC2HE?lWF-L~wCKG=Lg(w%4jP5~N-)@_!!Wh6
zi?EN%t7~py0T5HeldZ?TdT+aDn|SId;c%;wGL^BX&fifJ?Qa9<xQjWC@v7_AMG+d7
z5N{aHAd9nj9sbat$mEY34D{>z1Wb1~u*0Vye$G_o4k&Lt+O{W}Ma}sJgU|(*0uj&y
zFlYPRhlgD#pmOtuQ!LoI;c$FtV0eO&-n{1w`iX<(Ye$TrT@6O_B;tM36OgnfBvkB6
z)6?&Ba&p#=R;$}>NRQI8Pa&=ryA+ed`{W86Y!z&co8=4y1^)_LH}<a4g~P(89WKEq
zAOsx(;9?07x7-y9q-hVAqYc_fU1pECf$$)A?aKDsK^)Q{zzktm01|cq6meK`1*Ebf
zQv|b6Ti^t0G+$NAesuTl-7kogNfsJ5(8PMI&4$s2(s4ab1B_x#NX{k-ttR*~TWFY9
z<mA|V=WYk}vw|2TB)E6++-KA>vI<?Mv~Dq_R{P2)&9)N|yO2clJanQmcMaf5u-1b2
z3=Iu+qM-<>J_NAGbMKYq^Lh_E(eTSDL-<|PdzgP8-QUcgEs}RIX>rdgMo}E0iILiE
zYdBX0Z<TRzYU44IGB=nboX-Vmdox2{dNqP}WpfKTqL<NqZn)A7B<zMEuiCk37>Iq3
zh4>qZ%$OiKSc|vJLb3(m)|d4;i?Y4pM8fo#r(X|50jz#m1>~ZU3PbG>2{c-Ias@y*
z-=R57<#ix6Ir%PtVn=OzFwkcTKyCAUZmSC~R-OSHkydl*^5QMX7w#aguBNm!FxtHV
z7vhE<g}41?ZT^cE(1n@EaUMjQN39KZc$lO!{1dhBn**KN2g@Y(F$YxgkS#U3A!Hz>
zTHrpdz0PA8odr)kjti;<fF+G)`LCFr{qW(#LJ%KvIsh1WWA5tPdx@@4kKuHH+RjV~
z=d#CMBqbX0m@C|*JqCmqR`j0z8-PaFXdXP2Vbl`KK!6IB{+?{$X~}@pBbT<LP^{4S
z8{WN7#Gw?;3Gm+9cpR?J#$!4993|+UI2OGf*dBxfMc)A;4nudhm_dm`q3*kq({h^t
zE&0w<$O5hb`Be;;8f-CS&{zV_?WE#4Xa9fcL(TO-=m8sH1cE$Jy;;O>HN-a~_C-&z
zWW+H#86Pae1&JgGD`Elad^R7}?oT2KnJ#SM{DhPQA(lsVqP9Z<+1+$H&I#K|6Sy!f
z8E{6T%0K~fIv`OG=heKaD}OcpY<>$@!TT*pvTB3x)3^Z{_johHXyvS}ib^OjH=v1_
zFc<OXNL+jn+4ki14T7g`mzljfMGT4pVdYk<P8ILbs0G6)UQ_w9;ojaPh+1qK#j40{
zHkwm&NNPGGJDb@)ewfr~Q;y@`^YY7W(qN%^2$EE`FRiX-u-(=Mi1uN7L$doye|^UX
z7RVI|T)lEd_Wo>+mQAI@c1~pq$|M{(8<?y<A4pH-wU}I9juC-07N?yd#QWrW2Aenc
zJ&|-#at=5W{R;pQU19|ZNbd55>oOE@-XlTvHF^Mdq1n(A{+22d)^qCv4oU$WI0E=#
z&$gqf7Dy3u+Vw$-)3=g<RH6o*K!r|0T4di7&wsMR(O~N&R+ayTU=KJ>@uF}3+~493
z6HauIdFoGUP@52jOtgK{B{!?>#re9#BP)0YK?~+wMzbyhKNORFwt!sE`j3~itx$&1
zSQSRnm}Y!JKg1B<nG=T;5P(bOS<Na89CCBwpiqU7{+;W9@I7*Qs;ywb4f4j(i_R6;
zRVZ6cU29-8G~WW5a%}Y2F6L4Xn2r7|lQ1A9zZos;n(FAGS%`4HUQhUDaBUd^zc#mL
zLYDm;1@K}2av#-)Z>m}9K?3N_mg@BYGAJ|j@65zR!^TZWJnt%~s2J|X0)}rZv<7Mo
zSwU7x1Wv(n{VI|v{XV^l1T0}FEB<J5Q4Xr@di&FxPevi7HU(yn!wPw>Xdrb^-}sY#
z@#(5a9~m9BZU@kLo2>>G&*7!9U)kE_OI%gKMjq3`*wc=>b*|zckowQ91c=u9`KW8J
zvmsh}3W{kK=F`jMR)Pnq+=V3xDR`JTIgvh~cnG{;yC0ZBn$Kmq6DdZ)Tk;ctE3%k>
z874pkGBY!mS6#qo*@t6eq>1)r@%U_v$ksA&-jpWCL#28dBF<(lswqgaFs=%Ycy4XD
zj8l+20VHBQiwYr29ZMdg_59wQ{qf`1;bJRH?%HL<#=Jua_hv|TaFyzj>jQjH+58>3
zu16;oc?oJE%}@)WPZ9ifW`!yx%UL8FXsqV_*y3_~F;Ezf7uN;*IaGxz2VdLR4eI=H
z9&6^YkW{<N2rLid0s8cNg!|!e78%s}pTlfG4x=Bk_e<8iP`b^5bCC{(Mo2umU~(XQ
zN74wj%xElO0H#ASz*Xy0B7l?fbnFgfnLz)_fzuI&1M0N_q`3f6yx<aSdr<5?fNZV`
zhEO&fJf$44`T7JPGwPT(WTB{!a%O|HAn!tU7sCygDnpGeA4Qz&L)5MB2I>C4X;dUc
zA%^C-U(E4<umMy<AXv&mXo>Ky)o)oXD--&pTdf0_-{0QBezE+9wc|MhnLi}E`2j-y
zX{*e%v`)y`si>-^z=psra}p}e@2^dJZki(Tfy8PNB-fB;fw`HP#(7iVmO+w0uzY}4
zVN8MKLMrkg(7#YCmIIYOrEQsV@X<Ged==zZ2f!-?c2<)?>w^D>y)O^PatpuKTq>PY
zDG4Q1W)&$RrIK0Zd1#QAP$Cghk_J*TMKaG*rj)sakj#{^GE+IGgy>uQZPf3&zQ4Y|
ze%JS2=St@s?=$RYKYQQzy4Stdn^BlD_12>F|8VO2Q%pr+>LUcuRZAZChTR-RzJ97|
z=l2rwHl=5`_c$-v_|Ia0tg%LI^atlT;<<GtZl^_1B#0WtE(5lEI)nM&oYE2!PQ5P4
zLl(U-=U}&CQ*(0#$+HVTodmw#O;XhN-``XBjQuT9XR%)xo11@p*i(e6wf|GY)6emi
z{H#BbkUKyj`t0kGMq&+-Y1*sgKTe1kktt_;UO5eFi`4nm_6cHree`ec-f2|%0st{?
zIJW^|%jBHI-gP&NUS6DxzQ}lPFF=`Y2<Z#_IC+Ho@wRon17SD$7}X7al*v<<&@6m6
zbbb_Qzsw4;(c8oebxyNguD%}J00}O6HRC;uc^KAToXWfykJnoRCSZ4J`<NcKaz@_|
zBLh!B7AFbr%k>Qn{n*kQP-03%z_YCB)7AABRmAfyfxdZCh2Y_ao4&k|5)T7vsHmZP
z#<i@xe9@^))J?^{{#Q5&T$3}inT*u0@&=r3<3~v+7wHrp&FCkcJsZ9aZ6E=)4a!y?
zF~F<|WKDS)v_}KAlv-@u>k~Co_U~+dTMe4FsUFEmF{0K$O+0(2`#^2!Lqb&zm`jbc
z;uf4E2qM`DdlfHE-bGe;)$!|UZ1BY2izEcrs6ecE{i@5VZ%6Qidz<699)bDUK1p%4
zk-!y!!4JAYTJY2tngW_fr{07iN0uf;fL%=L*sb8CBtTy7;q#)AEPz#B)PUxS8MSYN
z!+)3T?d>~<&I?+$iIQIl5W8@0-xRstQ8<LfBm7FylmpZ=S>+}k(c!t*scJ=wR2*3&
z3(l`yJU`v2aG`lqx-{5ye15VdKpQIAu(#LMa8a1%FW4I;Z*zo(<Yb7dRgWAAEiOJp
zP*o9lJ<`@JLcl~5SzB8hj-^8l=<VIg9u)xwRfGiCR7bJ&X(IVx-tI2W1l*24chrlF
z0XPBW5IX68c@LzyaJuuXewPG=h0Q=_AV~4$ry<Cm$ph*G5UGqd=BM8mO;RA(z-)DG
z)uLIXWGa8mxu};v_Lm;xPfD2_*Z*ev*#Ez`|K0S!|Nngd-#=eMxh$ezwlD*WAEAE$
z{_qB`3SfgZis5D`5>r>%p9{$MwdIU+h~0Its&s%7qyoX@AaqT8K%(`vwRhXZFU~js
zqV&iLhIYyTkx+tB(hfiXW6j;Y;hqhRd7p(6^>gj(rf0L%p~>)gS*>7&@`|Hzc0)yL
z$H3*$AWI~W+Z`s&`{!2#JAT$8_#IIoQ=h5?&u$WIwM~BILUB%iA4^Uy!9;&9IC(cf
zeyk1~9fNyMFDdP8{O4g^BlNJ4oZx2b5aI#`sc8nzY!Cx}H`%tA6>crt_4fhYH$1{v
zsngTbFW5sMWgy#gWnVaej9`yad|h>QDENJPu$a;S$Ak}=Q+Xb;>T4(z-MZ}YOGx_1
zKo&gTSCw;xfTBl>X!YP@Xpg8G^t1><?_02Eh9DzmsM%A?ujJWaz?xE(C-7WG;njIK
zv&HJ>iEkGt{4O3jdGh4b?`_V1JS4-T-^t<EVvVtn)4J{#8Lg5$k*1Y4Qz<@`0C?{n
zUnK%(=*+QwI`(a5S{n#7wYM8MN<AeyXy;q;D2geVGxh-NYHf%ols?TYsY0BrptJZ!
zH^;;CLq~b_vIO~$>mMe33trX{6@}d=s%}4F2RY5sp<}&o7N`~)x;Ov0KI&iPc+tTU
zOR^W`JOJ@mGulD8%sBn-nYA7AXj05mu0k<z_J^SzU6uP0g_A4ewV~GVGm?!MM5ae;
zd2LALji6AoKZak~OvIO%w>&-q4QD<5$EQP$fqH$ICciB^0a%@<u4?u-P>rP_uZC;p
zUzO+^6+I(;$a!V`U>D1s*{t{M-@mW$824&g3cQp1hDiz$5D*YHrdZ{$vMI+dEfNg<
z76O+#mE2;cUFd=!CY+`D(s|ULtijgzj`%Gi|NVLlMTr4qIxwWLF)19x6tJmx56s!a
zrq{1u-*tM|YL7!4(x?X`ucn4O#gD-1{r6)Ug4-kGe@fJm;~9SWF?PPGjRVZpGtduA
zNRR_pb{@-!1SDki2-dOG>bG0fk3j)RYJ%N+J?;2;Q8w{n%>@-p-w{3wKq0MuqaVNX
z>&LMu1X>^zn2~mbmaxy{-f^ox%3UoMnUPG3=N}S^+D{4?AdqVO&qEx%8#qXMCbopO
z(7!Bv_un<oe`#G8igjPgf}<VL69EkiicGzzhD!T+?)kKf`P^%x1?yHEN=i;v#F}K4
z<p8#5>A;xp=>xcH9;#>z<YInF$9$KO`9nZvm59j$?I+nvti7cWUU{bcO7ES+E-MXz
zN}SZx#6(F_vM}ZrYC)lZyDz&h3hx=SpRhwe#e)CKp_jXs4YI_X*&sOB+gk;4YX}Jm
zEpx|wF(p73;<K@}Ef<uR_A+9?L2Cf#rz3lE1aY?kfJ8VLDMt{ia)`UUFQ1mGZ)_9}
zo0hL-O8Xye63#=Gx(h2Z&6}f$0uHA171T<EiaLfyAE`bT4WtC1%#VMlVQ_Z)w1V;`
zXD4Kx1EijTJUIP$saIH#BxhF=Lt9IWXxQ}l$L45o-1wSL?UWA|&)o<*&FqiBj7MOI
z9s9~^5D?9#|8v`0hmwPLQl)-|=+v35QIs-4`CO2*$$nr1NN?cvG0%FxyipE4@VrpB
za@~9V%=_DtbSd7L9=pS)&o+~lh{f|{UB_>Iqwi-F*!CJ-Liz2<@Enx~a8paf36Ty^
z3e?I~3>2bOu+88Wd@&JY(OJDaNI^Dm=JQ2TkH!9BMoO`u<YVkK+*Nq8Bv**lDLWwv
zJCyz{N<MI*7_x_1ALr7<<J<wL@Kd=2AUz}k__!B^Vqp#InOZ>v-jR0|M5GA~{}wPS
zQduTv$8IzwW(?{lsW1W>GZ@Snxka8qE-E}|Jy6(CE3gcyxgsIO6B@B&7q>w*D7-?k
z>bpSaFYK}wp@+q#G*)AfiL3E;yJdU>ykNq)!LIX;E6b)?SBc8v)-`@vn`*Kz*jw*c
zQ;T(TkxpXYH$uv0@R9zOfHxzeuw8l6;ye(_9FdFF!M_|Cid3ob`kAl+Oscr}_}YJ^
zm_kuPSM*x#Su%$7XaLDhBhX`0t=8<sM0ph45}mNs^nsaL1zcR~^(x*wU{eNxNT;1?
zp_lcEB&R*l+GHIU`T@TaCdk$=41cYMw~Yd#vgRFuQf_+5+NuXh62o>%*-aPf>r6E?
zG`F94G4iUpX_P;$W}U7fk2kxC6T!mO`I8e8$m|>~;&(cNYKH2*snj>8L_j9D2G6tC
z;M<tN7+}lZEqf=f!%N#BHa=d~D;?Au?33EnCg~xLel>4S2AeMlAO#9$^(qHg?g8Yu
zkktYXI?4SAIdRrUK<xlTa6i&UNp@avcxoNvV0-d?3P02O&c*kX>;zW&Rc@#jOg8(l
z=oV)@`@XZBXX=YSyh$7L{Px8&>qBa}s+N|Px04^g0VE=`WZPjVP2H!FU4IL0&9;q}
z9K2fuh%08j#bZ)e@QUaOzNin8Aa~;9w(K!L{ogpVRC=7~tV;txayOgN?o$Vgfzp)y
zWv=78Hktfrtvwcp=Z*s80EVItrEgZhc)@Q4Kz|7xp%?pSDl{YcTI~lCIo6eEQcO&$
z_I@qg+Y6S%xvWn&4ZeoypMSRiMTYoxM~mDsbur*oeO5L$8A-XHo3?G>{_7gfp9qL@
zn7O8%b-AgT`LO7@1!*kvAm+<-RA~5FbH~zN$+Y;JO<SUTz2;L_BXF6Ot%FLC>ci=@
z0mkG-VV|cU&BgW4;?DYfyqCUK0t|KVPM#7gPS8qAx9+Q%GxZo3JPPn%7m$_@JLxOf
za{u&YFwzvr<hpIdZV_75(vm>nlUh%xu$9k%uM&<oP|8|=_T|#7ItX>`T>kq@+--Xn
zz0k?JOna~+lEw5vYu;BTE~Ud9O9DLoZreRN>PdwnpwqhTcs3~66tKUhEwX>YR|Sq{
zW@cjq=K_xBk5rtUf~{LS*FFQ_#-D(m{}@*Q!6iXwSU0oeuXn(mE$ZIlJHoaL(=+3P
zeO_eGYigud-nDaPe`jF|6r=lD4ZXOftC)(;mT^Y+!lb?qC}i}lTeqxpD|-m_rg~5u
zKv6Xcp@I5COtWv)Fz3ee_e89=VLBJ0*F*+;bDs8vdZ<L&pI`LcVuxUOS@;{F69H-+
zNA#ZPwerdbbo)j<kxvw#K3)-*%sVEqtrY6DbIC;P#TjY|&483UhNq^dAL-;dJh8~?
z+Z=?Zh=VMwvrk>{)?w!K_5%-=-Oc<=X`a!C^e8u@u&h|lzp=x+1mmN4*0C!(+6kW2
zes3okDRoZA_~~2VCjc<T^pZ}mnp6C`!$9l0ppX!KMm|s!5`av?GBSSo`kf}VQS#Wd
zK;=SI^N2KM@oJ?;#DHKj>+@obM^Sytx~fNy4%`;K;4}O=?{uu2Fwc|tJCzT7m)xyP
z;;emX>InfC<rS+KY9f{ejDP-28mp9bbONyjM1j$xj2c)ZOT%L&;H9++d*q32ZLHg0
z=ZT7WV93tAv6AktaK;4vts>mwoHxs2Q_8}jxm%I&+2oMgrd<!XaMFCn-2z8E214w%
zuC77pqqb|^4&#`d9EXC>C`mQyp7lVe$8vDf9qU|_#IZsp61ohqCYIW5f9<mp+vQcD
zJ#S8tzfzjEVcf<|Ml45EAv?4<?znS~$hwV+eqsSydQpA!e7ip&Gojyl;|#Olx2G$d
zLI{)3tMl}(bb!BeT)I)sg%^6w)#dAt2SlCF;aO9QB2N?sxfcsk6>8-r4j*QXDtZ?8
zLDlsY-9?3v9K~kLrBydDm%P|B3yqh!pHyeB^d89Jd!u=s>G1l-eIqCX>|$ozi<;*m
zJ&&)xIu@kwZfl{yafYiLryg((V&_BqD4G{?IkH>DyE_)mKAAs{z2p(|a;<v~KShp>
z=^UN68SjXI^s~0AL1@vKK2<*)!rAvfsRcE16mC2~=l=Zh@&LE}tXQec`o7iS1S}8D
zQMb>7m#x;^?kz#^RX}tPiHbgyok>HPTx}g5`6yJZG&S_?P8@ml*LWL-UBi3S$Xxe|
zF8|SHm(Ey2J6Vl~b%l4?)v}niz3JHvbg8Zc(98B~e!Bm;M5udn$V9QndU`rH4i0s8
zgC|y9&gLLuu#Sz6MriEFA1Yoy#{X@YzwaYiqZD?%n@@s(C`vyzttEdWb^?*lJEEXs
z+Eh!gdXxXQ)2TjMQ$-Dl3l4KW4ld!;pDZfnkcLb-&%R+Y9I?qtj*)@&rI$7_WbPZm
zV~z|aDJ=pJu+c#!VV6eshJTwZ7S1zzlZUu+dLVx$o-cP)S-J)QdLlSd=qNn9?9g8w
z+e*5K-+DYvaBAMX5bdnFz7i8q_npdex$(SM`i5xiONwH$!UO%toV@)@qc5m+AGNUx
z@h&#6%0bYtd{k%z1-a#~`Ov;d6s0H2yaCC-u&Df2WQA;PV7Q!LgJ;cM_FSYHe}g#%
zR}2H9K964=ZOqLz{WPkh!KNs_a8ru%in9{6+`8BQv6FQ5jeuW_U|Jblpa*V!$fRXq
z#nj|vO(i!s_o7qd02<<uzbkL@b^>p4v^gm?Re9+eo~njK9S#y&xvu{!uaUSkpKY5t
z<zs!6uSITtrqtlz$A&3hF^J0g*_*heuB&Nj8kW0_8-?zCymzB_wv3o44V$2aOLx@0
zCqk0!E7v`2#Z32i`eZFKc5$Zi+q#G5C?|@Tw6(VCIUo6J31MQW6|AqJms?b(;@R9h
zx%y_H<c5#~q6&d)<TJ09`U$F<><iLhiO#JnoTs$0W2cF}ni>b^kgPQ_;dzgAP{faT
zZrV*)0WB8e&gREbUh=Z0y8Qj#9xL5&%XFme!E@R!-qt2>hYxgzWX00wK%UxHtyJad
zFt&lzH}1+SRunL-$HYPBSVVAUdb$Y^0F-tz(@A?B`uCQx4V4p40rRMTP*6}`55YW0
zE$z>b^J>q)rux3dC|?!~p3dNw4Yt-_gdQ}!u-SboK<#7o%l_>Z2}368uR8EINbt&v
z$<c6-!Gn67pSNagi=aG}Gdt54JX2cJ4a0M2Oe<#UkhiGh=7CM778VL_H77woJ;e6c
zigDM663{;?RaZl=bdx{EH_7?4^@5B{?wF|0;^uA5Y!KV6+16x!6<!~LX-(m<s|fI+
zy{&OUFn;rp=73G)4j-DT;4|w5gVB>9j>75yAf-DX2H;%@MF6agE}$FieTkWEHRvGq
zrweOrXdp<)JQmXKqqd95*L!g!KXxOA=)ZRP&5QAfnXkn~?)i69d##UMOEbNyti~oy
z7We0}BCq=BEud`gtAEEq-NWX@@T5%Uhn@R7X%3ylH0J0-!L$xsMZQ9Ty5{D1D4P?;
zt`E3OfKH+{c{bMyo?y627~H#=<^{h3Wg*Dv6B-{8G#$PF*GyyBVt@J0aZS;fH3mmH
zhSwNKz=r}JEHRPXn#1)k(qIU|yggjm?Nv-&=#Dt+-j~mhB{r({htBbzTWnMH74`Lc
zkS>>^%E6jzHGw6wrdcKPls}U7ykVc@W%fiJers+ea#XhH+TG{o{q=ATLjSnQzObpS
zAv)@jxVhzE?dNGnQ`5oPPp_Qq_g5>gI3s~|%vy8%VW-zG55zd^3-a?Ro#XxTF<k)q
zDo7H8D%c|un9Al-0<Z^qczBS$95oe{9dFOHIgC(~%Ra+N=&~2v_x?F0k?OmD`C2Mq
zxBYNlZfo*K^ITKYk$77sw^EMMfz&PPoWWX=g<hU+FJn}F+!k2Rap&(4G2xR&5=pS6
zG4Kicu~*a>e4Erj>m}l1S+Z~9sU)GJVY$6K7EV3o_c9#P2>-_XJ=f1ZH`C)bCBYOf
zJ5!tCPTDScb4N^3Y}u^quBL~)ikCdeuFkpF+BlLf@JJ-gbB-VLv7%X3@OVW)^nD<j
zBFGu(OxhtY>^6xl{5Bst>DHQR&OXCi8b|~|$DKFN@Kt~3b)|`!2THMSyV#+i=g#G6
ztbRc-ElMsePAtHnr`~7>x6SEekvX51tg4;wEUNNeSQ91PShv|nEb#t!aS04k>heC_
z{(NpxeG@bO)?#(|(&2<UN#@&ByA)WlR40&^8BtD#RGR6^%9Hip6nj^lbLv|b2}sNT
z4>1RZ+`~Lc(_*34CJqMb&N&%dVsiDaTiFU|_lMGz2}F?hAnvn?vCTc>26hdOWWL&j
zfak{~BX#Jq6YC@wID4`FC3R7-dhPWhXy_}O*&a?5yIytPY`f@#CEjjzZEL+KzV_*b
zDt%{6s;!gP$WaXd*3{Y6#@`WEx9ha}XVW6*r&NjlKW^L5i}v8LzW=_=JTTmZ(Dl}q
zxn0*MKj-~-Wy$T6+-I9I46}sSZRKygERwvYe#`!@41GVq!DIi-F?_b^?+cs1YH>;G
zv-eM5%5=W5flbiYJIX6;>U7Tqb~pM&Yiw3bRLFyS$yFI)BP$vPHe?+9+UYT?Tcd!B
zLTk|1df6YBg}*K<?pa|bm2DvZ(Xr_b)e!@}OzGi;5H=50UXdIEIl{BiK3dN@V<jdM
z0=B;~zfw9U<h9I7K}%Cpph(+gt%H{*>CavbyaOl!P3@~{Zqeljz@8?&HJO<iZ%DR*
z-lgmLj88~xV;W;+h9~Rc!y=?g?2j7U<6e$X{e5&|-DqiED0_w2m7Si~9xE|vGWUHH
z#aFOpcGTc+fAoyX1&2$=uZgbPY^f+VNq_CRjM(#e-REA{`7gU6jCGieTM%YG)$XRA
zj9jh5S+b03^J$nuyuN;ZfTpKwk{#GL5$@X=ok5QvPzF@Y$HvBDqobu#G>zZOkL>`d
z3-Akl_g@uFl?T`fXb)fp*UUV)t$pD#xm6Ye0p`u^^?e%bJ5$yiwrN@}q-w8f(5EhW
z0x>$R$Ck@o*Olzl?j2KI`eH6kaVFB$z<_{5bu*ABkDPh8VTP4?y8*HuzO3F&r1p(^
ztxw^d-5w@_aUSc1`+@z|l6nS-Vfv;mo^-82b3jzV$=T7Qzucyj#|Y<4KPoJEv4eG3
zHAZxdnm4o<jqEZvjggyQygshwdQWUgLKd8dPVJoANh472F*S9>VLZFN9+}V<BIZ!F
z0*(Cp3g6SYNs=BV(1)n~N`%%l7Rm1@jbWS2&Y#)?O@)4B*?cv<v$v#<g9cmU99y3r
zKi!f=i{;|@!yMs=TO>&m*#yS|Jd5k}FXBZ_U($JP32xM|SM4%dU`12#9}<`eG~Xnn
zT~h%8;lNlwQkDAQu5u}B_ezKE9okvev1prFjfd2SgtwXimVm}6?uVAon$lZ+pr4KD
zOxFKf=P(1O($iX#qt4}HXd+qK<_zsJ4=@<0&&jgljp1(0!7EqiWA-ZDN=P-U%f@qH
z>qZ*G%Z!a_<r;az^i&{jG#PF0Y;^acc?|M&(Gi!H^rV}ysN;+t<QnXO`7IUIo^=5Q
znmgoGm>rFTGT=Iv%RgA%<LPaGZBax`M-^Uhyrx_7a{>%>k`~nFn#NeCt=vr$(u-);
z0hRk7%|s$Wlh4-X3B+~iS(S2-Mv~QZZey4#2k|SlVFz#0Zd09gMu|C)QEzx~JD_QM
zyccocfc4qA^a*MTb#2jcd)Gay*ptUhpN?8p#PgC>#<VO5?=ekB84{Z4{oNqipfUwS
zn6Z8cI33C%`#~1l?db5Ax|EW(c`mE@>!u`+?CkBAhR@~Cy`vfHw)1FhSrGgqV)AMd
zpJbc}&={z{oMlxZo1~=wjwSOBx*LYwo3+eHpMMVwRu=3w{(L6Y6Af;Ee74eE+!g1S
zY1>=GYi@5Oo4qr`+Ry&5i&fKd*mJ2FbREH3J?8T%K}s=8H1`>=U!i*cySX$<0ti|k
z91Vy9y>+Qo!hYu2ae;tnwWhA6kS(RqcW3UyNcrT!5320OzV@1Nqq`=Ty9=v6Q8Rca
zEuN(YZx|znNESXCnuEfK7Lgh&>}?0zp6vY0fI}BK<d8ZW@F97{FQljH<3F?K{B9(A
zfI{rwxjeto#cyt=0S~LuGw1K`tiZ54={;E^rzqZFCuFPrgfXsVc-O~+8_;^QsuZKo
zk2&R4^YlMByyz>6jn&QBFi6TA5zjYlyK2-+`WB}*&OR4O7jKr*mm9RyL^-yGhK&x`
zyb;QO6~Caie3hq7tW+}IuvC&L(>Tu)!refG-=9FoxnO!Cv?0Yd(y;=_(Ck1a>o$26
zWTugzul0>Of#P)qT$hI_jDI>rp)O@A_Vk*5Q^OrP--}gEdyE~`?3lE7wVEyW+Tzk>
zqu+RK_&6!_U=r(kk=2Q4Hf~=giL?`K{S8q5Pq1_x&>{L4BE1_H<3FQFRkK{UB?KiK
zZpUSX0;87SK3g6Xon1TffBmfbaa&85?4t7amM#?@i#6$<6E<BUrfq_}FSJ5)qFU6d
zoKO+`&mAj=*2$nqB7LzJS|T<3hDb-*$@^!nGOk~5QzN~FDzgJJ47x4Ru}tI*$Y|I@
zBBrjXiK|xb>$bbJkr&@BHK@XI)XHjS?PtzsI9A?-J4=LPE9=Jry10xz{!YY$OZePm
z=ZX^ArjrnpU*#v)c>U&p^?R?r0399us*ZV}^{;E(cUF25%}?yoeGi{_YbH7%u_Em?
zW012N&o;0qKte)&#c?q=1x){VAHeU=g{JcbgTslXRp&(xx81eNOWqUlIq&w%XM9Qz
zN;=Z}zPQ?mP)e<X9v{!53p&CXSi#V&0C$i%>&N$<#fTEj0nEU<dJTg_qN#jdwCLsJ
z(k&Uh8FRBJ0fbb?Y}*owRC%?{?2Jx8eSft-JbNgX8Um}>6&Jox;r1FFT+X>>L&y9H
znp0QPPg%%|Xy!TSd$iD9kS@kh9kOUyRH4F`^WXj;n+7ey0*l2BDJJJl#=Rdpub-{j
z+k!qSNG?>b&HHbGWA~2WBOvalxXlhl7X0fONY-qf3h~@0wb8<X+36I;%x5WIm#J~`
zCXGkAbd~VOso300tCPez7nwYpU%?R@7Pg->DU#AR*%#2uR{Qy5bJxfOG8*pW#&}d{
zc(v0Hlky1|*0Fz`lC7a(eT`1GgZ+*PVpEpof>eQ9(J^ulGtb^L(m}@Rem?exX=p+u
zHmg>N2BMvt5av*5j;xcM^-em^TeG-i^wR^r+>G*QVXx5P?pG4AZuR`h<w@VtVidOU
z-AlSl7m(z_$aS3IP^=pY^Th>MmJ^rE-=Bg5t8NAo>n+PqqkR>|!EG&lwcU<lH;TYb
z;H@~385>%bhyaN(z6DFI37i<EDubg=oHgrAZ<Y~5#ZhVgU+9LR`-hLT{S|uRx%YHq
zzXrAH!TU^h`<m<uC9I{()mVhojTe;0i}v*(2&?zrqZJ8^_pi9sxf(F9sp&xN)P)P9
zjgDgW7cD*8D4%NDKE3rHH{XG<diT?zvg4<7%EBj)$K<>k@TxfEjKeIW`cD47HyQ@y
zk{Qw5F%=iGSA`DeB8_-7?MPel{#aXn#ceu^gt|Lt&8$0H7<mJA!a0`H!?ogAn=rT;
z+8Fqp++01v3nU&`5M3cvmrD{7QIuP~@S-yT5%ZX!%pa#hJJP<B2AYB6V<rG4Mmr3~
zKgYm4qlv*-?Sge@4o-1*vF}Bba~Vie=%+A~R@Ghka`PB!=fv!46l4*umDk<-H4%ac
zJf5DlA~|1?*cG)x`|hRT1SLB&#h93m80$TF9Xw#Hsj#SgeVph)W;|1gGGtiah~hFq
z)Z5zHM$5Y97tNY5?pF{)(jCZ%uT5tDICI=TN9ST|V?3|~j&SLN9S_%uoHF*gbzdj^
z#90j2!P?x^H!lv&0g2v7cTokzF=EbEifKUbOL>!_MS!u$>Di$%=geu<Y>~MDw9GWz
z46;S$^_6tf<s%RF@-TPOv$OXZ#<a2ilE7tm|Ff3|woJpV$Vr_@H|gMbw1$B2GD#GQ
z@(9<2Y<{E^O>DS`+1~F@2m@A$&tEQ={`YwADlWx^rOi{%B3Z(aF5HZ|VDq2540Dra
z?23xmhSc3cabsCBVla7f{&yV!Ak;fBHj^s$^dn$*f_jo)3);6Tz<}RJ2VHd@ye4y7
zOE=mz$aH3lOvBR91ooCuTKz|iUjkx(>On7Z{E!b;zF3#_+!>Fba%Z~ulwtjQPImCh
zz29<Pb{e>13x&voIP2Lz4{bPi>A_>BLklusj0S)p!0|eLsV&MR_}8|ikg%aM-t3BS
z6YRge&Ig86k#zTt#7Z)9lY3ILyC2EIh%LvT+=#EQ@90^qF<3*rPZJNT-=C}RS4h>)
z8CE@?0~^NHASV<ekuNZ6aC`Mw?KuVKwEb>^n>DsVY<@jg>-J^g0N}9V#{nQ3ME|h2
zW`zSN(pDl*CTB%STO*Sso~O>8`1I=d?H0c;{k;GUuNA&eUym&(Ns@5~xAz8sEF%Ls
zsm+VMalpeJyG-<fPdj}&?cPk>8-IW9?iX08BCDaqi`|9%(3|sdh=WDPVc*!5ut=8f
zhp(4WkSur0ao66bbm8z(Pio7AGItK;gw{xg;0-ri+_Cd%F1(+ly(j^I&G?2)ejgl9
z`a1W@n`Q>KIPkuY(ylKY=X_LfeUW{6m9fyUcOU(y1RA^d+vvH1we&WVP!7E|G;$`0
z<(!B-7vMJ>QQgce9?c&ykZ#JUE1Y%l#XgTIaeH`du5uW>P#P)Eu3vXb;Y!(E?mL;E
z=hLGURC6a5W{?)<_qyxYT{_|Fs5J2*z{=;zhe%S^E{NEP8G@OC0(2uP;vCYXPd1!X
zpEy|P|GWz&(Wi&yE@YF^BOr>9&*wPQIocb85(a4FGPXpRh;lC+Z8VQu|KH*|dp%g-
zL;+$E%mx~g#?sT^8(-GJgA5)9JL!0=ei4M$PWmE=pcWN`MB_#%0PB%R7OLOyTdKLU
zm(SRnl0lWJhWZQRqdS)_D@@q9EMyZ`{I#;M11^kr<{xn><=(n*S@z@kBaCwT^JFCI
zu3fEgo_~1tzt46RU;i*KO~8!5RxGzMuhH&XOn-mUJ)?$C;`OcVl{WP@4|;Y-znTd0
zzges2Seg9uMy1$;g@u?D<1Ia_&KvBC%{v_@NXM~V-OpZgnWSl)ReWxP#+pr*$-Ft*
zW;w%8xJ0s$4h!_CjVhX92;0$$GzRQQiBt+nREb8D0dU5Z&<b7OHS2|*LLn&Y>MDCj
zjl+6uXU1HWBPHw%w#LpJR=#(iVsp=}!e~dGatG&?7iJEo+zVec-wYl0HBHu~3#>nZ
zhyQl5s&&;@Qh8_ZBWNLZ0#urII+Db7h0}lYNW>`usxnL`**#0nzUavtt&!c5t^4yX
z$<LSjq9}0}*h3rb_uFUei5L@O&23l}kka~sBetPO)l8mMpiR4j(_F`lHmEevh2@R;
zRqCk?Y1MFQG+x~`a{ne_%5W7rkfJP&sOKkvM)vh#rTlEISU1z7F<&zt3@J^RICNhr
zo94T~dO*KJ&CJH`T2ADy@vIN(eq<%RiK9BWQvxCjCZ4~Yc%A1Wv_`dk1h3ZbPr-xY
z{awe5h8v9~Vp@#+O=E}R5*wl$7?ZW*bMY_Uk9h0#7l_df@4#1fh1^&X;JKi51&5MT
z$k|yVzY59WgX;Y;U+X^&S#+_{ujWcdrIf$7I>*#Az=fq^sePU9$0JpDG#aBc)H;0U
z()GREp{Opvke)?`@8xNFX2E<kcqj19I%=96pJklct|Sk~cSrq(A2y6%5oD6pQTA*#
zj)^VPqxJN=+*#jcCf=AAE)L0<{Ezp#!|;890Wz3q-$<|L_<f^TPp+2r$lG{4&#<#q
z4YZX?kG$v*Sk@gKxv=o|nue)*^$uRwlAZ-6mpfhmv2!fo&93nGwa53JUDT>)SGC@P
z>Y>ugw#2-Rxw?DiT3ZWqec-gKgx&TXVwZ-uNeO+)h|ATEZ;H!EP1NSrc7bzZbOxdB
z8E&5)`xLm}t_<v7$Gfo1R%*<6o2JFV5M8e|<m-w5a2eDq#E>VIwsiK%RlQRrCdla~
zx?5fOx^yPPWsY0<)}_ZhP4+Rb*REev7x#!U(PSyt;qPB(nEiFlqenx&R<u^{_ZvRy
zHhjJ*K;j8IZ^Ag`gigjSv*d=dN6&655v%6zfOYEyFM{SYMjU4eq4dRzYv`mw0<Ws0
z6HoLLm<P|hL0jRJ%{~17ug}>Blr{6ZiZ<f%j^sYs6DG_{=Pg@v**;i3L(Na0yD3iX
zBZn#pCSJyb*cliE6DG$r=`4e1q+>ycj<&XS5xqw5+2E=BAkhggr~h+VeyNB0pT<_L
z#dii#5BhX%=eShH3EKnPZqDmsw$W#<RgF9rFi*QiHIk!3O(d(7KiKS0(Mg=knX==U
zlQML@vd4%n4tRxt`#$bi7B2YUu_eN?Nr?4D4LbDfp)S*ChCABS|0lK8KM^ma<<?l?
zsii@s!h?L)ob5Fm)C7Gp(jR@+PWBblfXKb`ElN_2SGV&T;RV#4k;cvB(AW8n<Ypl6
zV8nSqd=1Zrz`=E*?IZrc+{m&#iM7puc)!-F3jG>D>>e+DO1tX!1gJJ1*3~OKW8=wQ
zC$v(d=d`ZQIVt*-T}o~09rZX7PD?pc`=w#<!M|r1@b!@QYF`x_KxuU$O`y`QFq<KD
z1~4c|>nfo=$A^aQLr8~i5f>L1BJV*23IN8$^PDF9u{cP)lI~KU%s=5s!za3Jb)QZv
zlPmFHuM=CT(RkXZS<Fkxa%<?<w%l(~dO98&C9CIk9n&jYE4*O!MkVre<zjElT%SU_
zBx!KgU$e#_!_;R6EkOE^UR9t>Cw_%tT21@ciuPV4kG!sRmig1?NSX>2%p&WSKcBfX
z2Y?R=LA5n3!LRQ*G@9(cr@Mf?^rm*am`KzU8QFk&MZA}+aK-`|`sur0P}+hlfCjC%
z0i+eP&_Z+~vw21T^x%c|DQhsWNU64w5NrTd>+(ehTIOlNb@xA2-#^1!0bab{fm3Xg
z?H>!9()TE@*{XPAMBOjb&o@QFP9;Uw?ySVkz`1y>6Zr@%STLV{y`bXeyF`DeQu)Q#
z^jd{lPjJj6#o-`Ri>DinLUtb{7W~)&zNpA-z6*NnT`fqQ{rVJmmv;_h+tM6(w1U$5
z`QPcmTSl%c*Tt7NmFI*=l?bkV)``*eWTg)6O9q_JAkEmrt2BbW6tZtq&F$i0BcusV
z1jpx~-~WMZl|VKfpWFO7l5&y9H%I%4NR8$1xtd`y^-%u<(-G02w5$ZL?vU)NdpVT-
zAvwE4Hd05^@KF5g5Ia<BD1=y+3H3E8>pa_>4rb7UlrLVybNmwri&_2d_#yreOx0N*
z3(2V2M_y3HmP?TzbMk8%a4=0CiwkdvUN9!kT)Vnzo4i`w=YO}7?;D5TSdJa#+44dq
zDXEf(`)N?@wQjp~>5}yBEx!f_BYcO!J!-Usxp1=Z^Zt_I<+(;((YrNRMt9Ze52#4#
zu^w*IA5fDDQe?If&!|7&Wvg-OIhnx)t7Bic&lP1+q%e7wZ0Wjx{Xm%Dx$M@vRtkR{
z+gY-DM?h89M@j8W#{*>e2P96FCHL7hjnm%ARAkV&&?iIYMu=|FLV~AL83<rXL2een
zzMEqn+BQLd7_<RO^y}jsd%LW=4lnTd;r)BA9<?{>xz=F5LA}!CeSxL;GP=M}wU71M
z4J&yDWpLd=?Hq*yQJq=j+s4rt4=P*-k_jF!X`7#(2>v!<6g+ro5zE#AlRPwgvL(UV
z^4I63-gRpQv2f}#fw!{K<jkIx8kHvdr|)>no$XH=Jkw<T{;PISYK%fOPh9O1<_uUj
zs@U!$-#;sZn}Q17dEo4XV*u2Qn1MoGJ^b_M&ywOAii(~|xu3PyU1dcD7^bl>+_a#L
z`;&&Ac98uZ=faYb{hdQgjE1jVJ=#Y}j4tb_;#@jlZM6-qYZ=;nZsyu%<a7eIs;7Y7
z5g_G<x*{VJQyp0OQ4p3m0IG|4@7Wy0R9WDc3hmmpHMWX%8Zl8|*o{A;f&w}4DXt!)
zd}NgO&GJ%QcE{H9IlY20Z(>SHS)$6CHT%1^@F&!!jKm$!T<6<Brh(z7nBI^Hlpdl<
zUqSWTw;{_4mC)$WHgwCjcUAD0D>Go1>XKG=rdzp|tbrA#D3KCKErJk_pXeslRpda9
zf~fv1u>BKHiIUwERGXx<Kl{+CerEZ&lj#C{2^<+_f}yHAEp6_d5b4hmIboEmprsnG
zvZmh5C|B|7zK3K>cNXFjF=1Ief0R^aAxfvh<%2R8v?M2yw8o+BG#YebQi@<?Wb7QD
zqQ6-rC@2_#A`Mp%pMw}k0+F|$lyp?1Nq1|0yX8>OAEV=3=u;WUcrB{9up_+V-B8NL
ziPJphHun};%c)4<@pI>D$K|T~QI~fP#9V$6;~*9hQQ*;7MMoLDeUTSq%>tro`qK5G
zWRrR^*DS!8a*E*)=W%@%m2h%t_6Uc?K8};;`SU|3zx6@2nHcLwrdvLjJC8jb&Qnr;
zlVkVxuIg?3eA$r!N0Bp5i5Q0HyK5U_Uk#>a@sMwxm!;o;3qWM;LMYW^et0_eXrq)h
z8g_IPt9!n2Qd4*Y#To;Yk)xZMnxZl+ex49NPaD3`)ek%;HLBbbqu<X|U#`d2B=Qf|
zG8bb8WfdPCb0PhV#>qPM0ksQ>nY+g0GK#qo3>=`Q&>_c*zpklmYa_9>F3~3@ik*;o
zP#|qTHGR4H{dg9A&}FZ}uA#5zG|@!}x;!ytll}SmAdAGpFZ&Sf)8V8exed(9o9^P|
znV56|Ye&83c79wnw~Jgu-TBUQuM_zI#Y54u-W}vjEy(CtV7HT2770>E0CeckAv8PC
zFP=~0D)5)U_P)veXYhg7d%)HeH+oI~6PGp5;Bt;czIgX?3l%f@ZHM($+V12eHpJWQ
z^MnsAHJP9(NDnx(Z{#}UEr<*R9I$otXn$Teej-8(XcA|rl?@7aRkCZmwsZ85cX^<1
zL;XLe;}eU+kr5#c!~dyx{W>1cZ)czp#e;lZiDGO$D-F>+DfpnFTrWIQp^M+vgUkDc
zoY`{yXuU)smW+ngBDb+=b?h6qt7icQL`kF~JI1j6{NqXt^AT2EWRDaEuj2Lk>Z@%e
z#7{RD*xA*XG@hLv(xyS{mDzB<URjKVCwXZ12?lE_WyZPm(5&n5j!3i1eY1gcL7DKV
zYeuMW*gH-8vgD6euhUb7VperG_M0;ci5NL~G@0hiM@0%7!(7b$i-gAyaR#66@mAa5
zHYj_s4BNr%NNc*t;HxXySjlcpRJV`&`~gc9xC=7K(?9FXbrqPv1wR^<@~H@{wVKA}
z7F2aaWtmT-sg|q~S@V*pHN)5WH|PS|vHB1_LUzi;aF-kXcE1QPUG$(Ih2CPk`h*%0
zLc-6xnw)<Oj}Q0Va<gWMgw<RZ!-W$(9RFQ@K)F_3(x*8zvm%+(hHGzL?xC?c<LP=-
z|Mk-*SXE}wlmLS%)^BNhr_$I&8o>#TBV`|~>?Ds~@+Tf_%v>ud^p8BH{8LQZ)^nWg
zuh%vNW{1o44A-i=RouQ6w3f%UO!2-!csS!+kgF*1)Me)Maju;vd1%}M6z<cF{^WQ6
z1`VPGtq!3czcgv<g(yoBq&1kU(fIu9<FD`p$`0=$vZPk_uk(mM$$o@sOPxwfRBG+o
zts81ln_ohGM2T$;N5VUr;>i}y{{4LaaDC3qB1A($I*_=~H~nvr<(t$K=<SEvl~)A7
zfK`Hjgd(cm);p8il(vbAdnws&lW0S@ioMV=G$PfmwuZ51ftYrKa^wl#eTgQC4b9z+
zF&Ry4g&{g5<Dq%E8FAHMU*8?l-vyE~n|QDhL%`mPPMig8Z{FBFM?dwi&s!z-n%!gW
z#q~nl+b%gAy`8hy%Zp-uE9d!%%szjF<{9&ECUfxyu2oHJKVd^5Uvs7?rR*C?dDY>y
zdf7q*Bc$u%4ExG^-e`FL^%2<YTZVn)AErST>D2~9NBu=ZLp7WFSB`^{tG8yeLBq<r
zBOXq0Q;(OWZbX_9M_syCNxoxaW4mkhs=B(m=5^P51I<4+DLQ%V&+|*&N(wt&4egA?
zS5|83y?26ZHPVz~$*v-cuH|zw>m#XS%_WC$U_B!viE)q>$ljAXq!2ywu+mQ<8j1#L
z0Z`guIYV6vzF)BO8;<)gz4v#nn%8-(wN&zY8J(A+YLBgt%~o=u@1vY`>9Pn$NJB%H
zfa^qo#F}#dF#9Of*!qxoh<_M?Cfiv=Qjrr7a=8^F{%ahN0@2?%nV!N=X1#guax+PT
z1I>%VXt$)P`<<(bYoW`ix35R+gv{e(hIj^jiFt;8{gmGJWrh9QskgF26}LjX6(CZ}
z%|s{bl{c1X$$X^hD=|ipIfnWiQTvwCW&3@~kkk`r?LIQxFq5=={149{@BDIGS1+7S
z!p2F1^ML+huM73+1KcQQ<hp%Jd`tZv(_x$i(k9~nU0Tsgv_jbX2%mryy`V-idAo9K
z#kc=fOv3&^XVR|;J517j1*dnv!zbrA$Ju=wt{(MwIA)-^-kz-~XHOW39Vi#JEsF)h
z(Ys%N*)31k(q^+<)5M{&p19Y0`>5mD?R;Y<ku?7|LbzcgICKG25}V2K;|B<N;KRA8
zIqkvaT|_OGD7^T>pNtZKMw2XXccViIs`2Afp!%YFxM?Q?_c=~}>#gmt*B>!>Y?bBR
zyeu{{OH>qLHTQxt9aB-OJ#=4fzxVJZC|B<rA>w32v@-A8^kBKbvaS(3qCH(jxUobL
zg0Q|w-$QO&=9!K|Vo=bBNG26bqmmsE+Y*_44AYr4E!0Kw`_;TvdS0tFcGatYowNx_
z?pN>e$$zs!t?4~q*SWw@yM2nB5^efAy)x8jKT&ZcP7FmH-v(;GOTirkc~9731~7dW
z9v3#G=OZ$y6T&R@LXEoNW=T&DA@c<%{`<m#z*Chlc5yw@k>f-^hAJe&ZTZJivXS%7
zt5{)g<hfesJvP^^=d-wJ7J8sKb&_ePBro>3#xG@AcRX#j1g{-^vF|n&RTH?W=1|r0
z?sP9{Iy&K}7lk>mde!JK<BB_Izk+<OW1j8%l#9b#E{2iTQ97xj_CM0VmnNlhNepWg
zV{8S1kt%0zp8w*4<PwkK#HDu>RX^HbU-9vMwUqo=BQzhWkJ{NPw(N?BS)$b8<JrNc
z+z2v0k(?ZL+3;1OeX=35?#&xcis<1yero#~ukK?df)C8R=bt&6RrWi`aMt2qUD@mU
z%E-LeBL=sO*ha5Ni5ffFT}WwO#$5llvBo#V_f>q&Fn&BzrRZ0swdadbC!>Hy#kG(L
ze`?$E%ah{aL`oFWtWJ@n6$oq+m7RgS%pIWZqoFo;s&%k|XkwG5xzsmbvi`XJT`O}#
zuOLrq`1XmRVS}x^N$sa=Hw+HwoiTM(;5A#~o8`@Kw#4*|iUdn^Ykv*(p8dBOt0JTL
za4uUM0ee3>h)pQGE3a=|c|9gBP6kWt8udRO=neyMOVa@f)qxwK!#2%s^*aI{W+mkA
zI(7Ay#WE8uS+!V|iB#W^fVdWGPU^SbF0ObSGLiR>!0%%?7y7;G&T!H=e6eL)f=#p3
z=(ATw9?y5E`>92-zU5n38h<0G%!MFNnJ#=?)Jv3e)%sVkB(K$~oR>81t(cl7QS=zO
z)M47+OTW^;VX+DNExVN$`wlXYpi0Jg*tcG##}*?U-oWVDt#+YK`6I8pi>o=Fy60U9
z>Xt=QJBy|G(rVrv?-|>Y(|u#bNe_p+M|HD{dsJmJG<fS-GE^_rX?N5EPT&=KP$R->
zMZG$%O84afD^{!^njDNQEb3cVD~ks=f(sVk-EI71lcD~iGsO>y#f`{}J@*l@#p;Di
z=hg_t?S)F?=cli|^)h$&Hl+F*hAy?s!0|gmN!T}`yW?}^_viGIvZG&ka%RQ}aw+Y#
zL~j<^%%_ZR5H=eVccBJgP#s~HPE1Uk#1z`mm7yN?ZR+B;WOT@UaS_kU$grXP9)D0T
z<}{kX>r6z$Tkk}ib5n8MXm{QrXOEZi#a*$N4$Hiun5Z~ehZ~S=S9)o`by=85Mx#Ac
zlf4a~BZ-e(^14-m=tKW@q1C!pUM9MBdqwVErZ?@2)}o_?G@xU(gxv_y#N4tcI2nz1
z(9FrIKSBL6^$#Uh2^Q%S<&qu8_6?^h*~#?jws|o!@00|(X)}CTJ17VBc-|N_#{zR{
zUg{SN?XfP1`jz+LvB$7n_^|C>)Dw-676Et1g_T>fATnSB$!;ztI&S<5oDA+I<xTd-
z^87)>m6>Fd&TU)OfGi6Ig_^)@wkBo;&O4=AdfMxP%ad01H1<chW+2@RYTKEANL-`n
zo7Ot&ndL6s_;%p!QiCs-iC2;|o(vJsBygA@<P01^N7?muHw30$?fvFJWUag+wgfwP
z{n3o;EDhrGBpYXLgWh8A{Ji*wt;`BqdQIZkZbc|Yi3<9fB(0;^nJFn`SM;Y+Pb#s1
zWZY<Tv_@#{m*p=kC<tE8<egV&3aZ&=@a5Us&;3?;{A=~B?|gzT5^4uT9so}QeNigv
zd#J}Ce>lIX5R}J;0Bk))Wx`C%X!#-T9jbiNBLfaaN_IH6YIHc`*F|IF>eR0YjFSQm
zl)AiBzc_DZSVrYE)<nv#7|^x6IMpoMp~@8rH6PLmcUO7K?6Ux5m0>P37+5ke_Tcu_
zqm+1A7|K^D9V<x0OlEG^PrSR9Uc^^;k(j#Q_x;=W8iB6d-Fq=l_@@UZ&;a@Fp=38P
zEJ%WMn1{RjVK3Ac5C4&=6zC(}X*?aquysY>m>z55;1UY^gOn`6byw?<dc~F{c85q+
z?8^*@6ODw!c6d~X&X^O(^ddggW!AY+0|6uo!CU>xrKwBa$h}IYq1^zbWcH2B%*?Qf
zx666UM3R~n_PJ6Bki$I(ZxnE<pBGK;S?;DoTb&_KWLtHFy`=Qm)s_8AIWoAtRR>M>
zqbjmm!8kfMNhCj>M?!zG$Avmw?U5Mqw_ddtfuEbGBUaF&g3AC&LfUItVHJ8<BhmKF
zCVmp_w-gv2-1_3Mqk>RyfO*Q79#1{h`tGy|1i?s1U=74ME+yqmnQ8CfU}Hf+0c-9_
z&_7R%01iLBOZ47}cmF&;nlk+g74{DWJ2v3)d-j0YceYU1p}R+3g)PN>auerAy6C#K
z3KUy*y_PjlNlr&x?==|^udCgr9N9H8S6g*Z?W~WEhZ>*I7d@-pbgMTia{nvKy^Ysr
zE{(C*lP@pf>6i4*Zm;mc@FTLx2Dp`X@7~osPD-*+esxbKQsx-A|C~MdrQ6PTcsM4V
zOw*c%wow)M>x-x&#L(`tMo|Q+MvC^!2{3{F(grq>5=dUwp_N^l{@r0PpkMW`M3O1^
z_8xRmKa`BH&^SLairPCOuX4jUvf5iPV5gvR%dM!$YSlA=zE)92HnCn2BAK!0@?+`9
zKXohuYF#g7ZphwiRSp(x*|J==>9|b_jkDGX90Ox>X(%Uny~v(Dytz&YKcmB@&R@KE
zkphKb0bIEG%IjC)e6*9qKcu|?xIzNo@AUPo#P%(&HOgDfw|e_NTU8t?cSdI)>#$gT
z_R>qkpCz`beiEklzmWUAa9hBmjNw58BdNcW%x-uh=hUNlj%qDB1F$x>w6vtOH8t5k
z7kV`xW!j)YhiGwV8WZu-Y7lxMHoFA^S)rk<q+NUx{lqJ#;RvO^{T@&5Ac>MW{B*<M
zb972ZB#=74$?P(;c+sgwd)=+}>i&C6<_xD~q<fcX7&|){Fp2{Imd{ueL6^^IP&Cu^
zy@vkwKL)sasjp4pCnyfybPoDwHAD!c4a>=(&HW1FyGE<UttX+;m$;)+^lWg?l-tEG
zi}cP`2zjx)6^7#C5F^MR8S^oM=r~9yRq#8YJAVpYb)+SQ@J6_GzC~Z0+%i3e*6fJ-
zy7u;Dpwp7}mA<?>MA^}&8`ZII;m`|{*OuQUn4+2Rfm@g`h$p6<mU3fzSGjZ&X-(RO
zO1NltUyXI@J~<L~&SP`NS@zHe)e#FfQI;R5?e~nR+raBCdHq9#o@LfLFEITxPU%aO
z>&(>_=MOm+IFHu;FxCLBrTINjdYHoF58Qd`bqGP@VO{WD0FaXHROB=lV8u$pMcZ@k
z!)?{YHwL*)oCF{dPPAH-H%>zZ`N0_LS!q%wSTXq;DtclN7ZDkGxnm3=SzbEPGSnk7
z@ebnc5vX$4ML_M3O+;$rKS}}&_r2{;cYbx+`wcfDu;@SX9)0ex!vt?Ry{pmz@bCs{
zO0$Gq^P*$Gmg+x0^?$UyxK4;kW3$E`$3(h-a*JA&g0+kFfr+VJus(Ix$2W7KTGttE
zy-hSUZtm^GzH|C~Tjlag5(PB_8dRJZU45j-+bhBV(wUP`Mos{yr`FB-7AiM{hY5iX
zFWS~>;!h`?Y_s#f^P)sHCkzjRGmzKXmn<^LXrfQO^`5$Nkw5$$_g{Z`{<cHNKkV&*
zz-~SDKiYS{zKeNHbnDiuFLH}EKCu;5(*LL?>mg+$?ssZuVZZWs6oX6Q;G&9Tkg`cP
zFCzm3@+lIjgp-)2Pu9Sz)nf14DMFhfVX$_Zv3zXc1SZqybw!7xak9ZFM1+1R-(v|9
zoy+`bWNYlEx-(fGS?&h+HJ3Xh`MS0(FRD@ZvpszOoMgp5Pn6+Z1?)Vec%Kg4>O?KR
zerm~`@2?9}5`d+tX{S~YwI-ofk>DVnjDaE4cCs*u!^gx73$GXL8t%D;G`I`t1N&4B
z8c#A@B~3`n5%Xc~CE9hPP|SECD1XoR-M}Qg^PE1X1^evpd7YW|N+h1gBo9I{OMk~+
z1@QV*VH<KsJc5-t9noNq#9@B(G>QP{a`xpOUuSH$C-RMMtow2wIA{!K?-4A<xQzgp
zW6k?xa-I%uKJdNLCg2LRicfk2H0wr5-Ha%paL|iyY{ohRbo6s-2SPjyb!KCeW=u<;
zbw7%?wZ!R0%(c=G3il^QE?t^)lk5A#!Gm?C1ki3tZDN*=%V*fgzus->(_zGdeY#E?
zPCk^sP+?g#m>U12DZD#w=<Ruxs&KnimpeVwEbQ(tnmKpu`|OZQ1iL}D2H}?$GnADk
z>!QUY^YZeP&ja3ZI3->zu<Uyo1a1206g!N1K~s98_$sJXr`m14n-YU1|NF=J4dhP_
zs%Vj&YJ)=DUN0`!;-&kBhhBBYe#o2K=@S{*6#j_Oa+@eN*vdcw3p)GNrz@}RRs5a|
zNbF$8xDYXF@{UtQ7XVEXT@~rBcf;2aL4fx{3^^Pj?4*kK@|D%ebX;eu9enx@`uw~I
znwLDs-VI?0T&mL-hQ~hR_NtK(jJ)*XN`{F{sa)5h7hB@Sc4vSzaB%q?=K5a#ThrQ_
zgMN(%*IH4J0A!+=a9^{sv`O8A7#|)Yno|!h&e##1KuFIsGBf)?1W;1aWepshpyn(|
z8Vs3kt+TmFZ-ffUiIH2k=>B*@4*@&hh)8vP_~pTzVG|?sjR6gY5dy(WT1&=3HKxDZ
zxg2QmK@TV1xbgS~6bZg(wKQ^f*WG*e2pSphuB@_twq%Q@URlBp5s^5eMSkjILfWg;
z7{;GB?Oci^<pz5i<E>~RCRxfZ-|E9WdiU4rv!$d6*lEwJ_O96>Dw>D5uIj5u`}x~B
zPYo9P3QyMScHOBk)+7rN`S9#ybLsXMAwBJfq@-le`jtmQPoy&I^7sFZKj}8!c}3Z!
zzu0Ys;(3qf<6;}Gp9Fxe?k5%rI@99E^>1y<d}-sGIE3uvf^5pT68+m_Zixin{|V2d
zn0Oh&1d%|ZW)z4Msd}=$qf!ZC4I-FDYS!fqd*KHLVaF3(GQU8_U0d(z>gy~Dvp=Sx
zY59S6pkgNG>QPRQ=i3=?Mf$wxOyE1CjhNh&*1nF(I{r!ALv#F1wM!j=B*Mz&bNo;q
zz*gXdVzDp4?qmHBN8?Fg`F>M77VI0*>L*N(F(mG4#3-34J@;XFYp|_$o%a5w$U^Hn
ziXEgMDjSdVQ4>jZB#dWhC`3^bem#MgHg}F60+NM`JZw37b?lI(X5ds9FlBYWon595
z3PKuob*q_{x@Xr5qH4=D8nf-KZ~4A0-zOc7T&~tyK?7*%5;YmL9~}ZsAucho9y%{$
zkTF&#l+pSw`D{4JQC&jVGn*5!ivi;!GLt4IWZzQ~&mUiuMnrW~6vZN;G26%YJrly6
zzMKPKlm9-C4d;fXK7&j*bo81chM#;eO|Xh*eXA~6pw^NlYeqsxHPSEUTSCdC(v$Gb
zkccpsZelnyiajz{FdtYR5g5u8oq_aF5^2@}l2j|mAq@=_Qp6e~BL#Z=W1<=Q!Z<Kc
zk3~7w8*<|I1sznh5X+g0ib}1Z>Msz+IQtY|vboE)+&peU7Or;shQp%AY$$FVZ_jzV
zqNz&v#Ji!Z&N?1NHo@^>-<W<t5;>7KW{wXK`;dkNEw(~|bl8SXUwsLUSBfKC1^E`d
zTwb`f*)kxCc$38g6M-RMqj!VelBE0@dkE^t5f&?<K<e^kws*ig0V$g3biKi}Pus|Y
zaXFP=>xto`>rtsqulKNQ{!xpFOuKNg&@}S&?o)B&*{TC)W5tzTH_-Qhbt1c5t?0k=
z1-!{OUv+$+UjEahG=Xi#yBPjXrB0sHHGX;Qr1keuY`7haA2BgkUOzyYCaoOAfG)_s
zKUPIAFA~~eOhn+^D*|P9(r-Z?Ff5<Ks$&q0f{H+DW&k72kkX|#PbjVaem)u+rkr=0
z+R1~o6h}qPft#&mnJbEU5=#Xlw1Z_c#H0JCE$SzO;zm3dm6Q~(>Jj&IOx}Fx`&SEe
zNtT7=g>w4zL&3esTH1zR6YXN{d~Edd3gDfo1*^eIYe-FT-!1J$BK-i5KcHr2&!@-7
z8WvS-LApg)f)FC!qV>D(=6?zM*{9Po0~hv#=fSEvW3PBPdw(e<v5+F19`Qt`T<5a(
z>$mOPg8GBDoV_+>7HopwH%c0&SR|U3Z8=W_AwsW>HgX-7DbmW~MZ3W&3s$QTT7u&7
z*4%L-M*~p+8|cEycoFjxj+IO7a@#m_eudZ0_euoiZI*9I2Y`hO(az^i5I*OMdX}OY
z^FB|>avc|BQF<*4Gahqp^efz%X!Bz@&#EEOfT|UzG&*jeJpg#*V)?O)&?oZA@-cv)
zP~`220|fSy-8X_98_hlk2hW%6i3<7gUSoT{$Lc4XVZKrOgOdIS)jaNm!`t^H+N9T?
zN42*r5!IOGaf!t`lT$yq$j*J_DI8{3W&{Hzzv~RT^^t&_>~kr;$(LmkT*7?z<u+nN
zT~Of6PJtoQ@3w@3`*#4)+1VcW@$ktHNy`J*<t&;^PrqKqYe8#raNxY(a2M}BwW<5H
z>OaQD*@a}as$6P}gUNvt5bxoj&nvDLSQcd0eb3VFl$4Ydr-}IX?c1f1M*jGy-g0Oo
z1P>IU({aAQN!sSbc}GrIb*I-K={jff<_cdI9;!&x?v0rpiQhi`I8lfdI1nv^<9@}V
zO1-?EP@n0M=^z%_Sg|gYAJgO0AY>KKhx>n?hGqcG(b&fWH(x1T+T5Fdj?*s!>i~E2
zW$icGiS%Ub64j-GGDkhqe|*LG0VYgiW1}pW!{ID=RzMzcH`4{bDIzZA#g4X}2je*>
z^Lv<>nRz`N#oIgSe>EToxb7d4V^zxA6IS7PM>%u2`n++I86|$m^n?zpocJ0(OIrJ=
z+#l1DMkO9K9d3;z9uJjRZz4^ei(&x(o^E1V0@a}-{a4RS_C*hl-^Y>|c}7pK_0JP6
zS=o2L?L%-?r~|l|Q=9siZi&ua10H7mR7My`lbDEa)D!WH>L>LUIT7wDKF;fsaYnSR
z6B8tC%_w>hPq&~m#p|rHTfHI>{6bRbh;rz_lfrjZ2%7RTMGc@LsU<an0XCHx_yyzo
zZv`KDrJ~u!fEtO2ul2i`8lzhOwV^9H5m0Lo*lOGhGN1Y*HM4`;RPz!zVw|&&e)ImZ
z4IAE2`w;DdwpHR|Mc%YBfn~X%tPr;en>Q!?fV%V(2O-k%t}Hx4dMF^lZfTc?Ez7q8
zGFsx1V?SR<=QoukPJA;|_2mKU@RE^GP3<Q+3pj_x9y9Ia4b)KkpsJtfW4@1<lH^5p
zqMz)4dQ$WAvIbz-4{-?%=r*vEhzwzx-kWWJ`OyXi2&`%F2@4u4YG8$1ghs63ua|#@
z$^{Ak${bHWyEUry-SwwKo%*c9$2VKr#9GFNzluK=7*7_Xl2_b0z{ForGPU%6Z0*jp
z8rAzo01Au2Z8q!e<)uKHWLnEh{EG%}5v$`UEQ}Mpl_WAF(ZlErai1tN{Z;GQA;o^L
z`qtCOk5&|=9}|dTjx^UTOi4%Pl^(^Fc`Ha`nYXY8r8I{N1hL!gdes+yLgPwLxn^pM
zumwmFr-U(L{z^^fPwmucvL%vfM3<)orj64NI!F@xe&{hN-4pd~7407rhlVDGsao|B
z&w)Hgfh1<hZ6BF#Py|HIKI`;aJ-VwHWYv#e-CXq(dcROIL*LjhC?=6EX`(xb-H8;n
z4{QpBVKQ|2zlF5rLwcqan(~noAWQ8~i~O}JEPby0=ICuv@lb9%R#kBZc`dy*R&+*-
zM8*EIdBcE`6B6G5@H6w~Pzm{%)ltvf7YBGQS?EICz*bB+F6ux3^9`fnQW3Z0y8qHd
zAfU!jFfr6b+ojt3Y8dC3>)obp42_>d{TFW9!7XROpq$$tNn!&v3!#NA+I4UF7nJSk
zclgnvL0+AQuiR;WmHwUY@%vOzOn3xYWGMn(HA*~*p-265{$b}o<#vp6VkD}bV?#Pu
zrX)F<s>h-i>*F`)%(W0wd!p83TWW+E!AFjWPY%a#GRNC%?zYVQSBl2z88_VUsAMZm
zz)@EEweDnLX_q>klM?q2i{;|ST{dqL`M!hjV8bl<%}jpicU}ZuTH@QbiZiWWao2M}
z`AO}@A;&YjPa!L*y=<RPSkVvLl3kaJ^P2t_{{&JwsCymwejPOa>c7{2e>Sgs_aCh&
z7W_w=velPsVQDQR%X&}O7vU6+?Yra4gRJ?WxH#Ylig&K!()B0=EzH-xO!}0mAHz+u
zm%E89ejKrO5t-}_Pkj7UAEbjYLZVn9`k!-s<d2im2yh}7!}`x}<I!If#AY9Bri!qf
z^KRgqppq5ODZ+I}dTPV$FQ)9V!-|S)2dDnSuM@ou(OfBbk?^k24AxJ$>O_QuSVpIW
zEh8=XlSHO5_-p4DywARMKhIfk{90rxX)N%|I`otIWGHyTM(Zx$+jGEal_d)jMe=1o
zX0+{h!<}I{t=I0jQ)rQvCHLbH{zE)V0%VnU2G$eO6IrIRU>iR+QF)Otb6=(FP7$*i
zB1|WS`3U+j=~3%7{5ik<*MS<_yzrQPY+6<9*T`2#%=?#e@0}Ou(`<gV|0^F!KoCr~
z4Jtbp=+WL4p0A7GuwJR9=S)-LuQ0rOJ_eb<Oo;ZxQ91_vPs*gEab*pUW(cu(pCtPK
zC{!3JCr?0HuOBLVt^}8$K%CZ#)?huC^v*ze9AY9u|3}0?<6OG(#>r8uaT8X}w%u`H
zX3@(WPv9HiUC=^@SKIaW`2sOkfxHsYx9T2^oPi$30zU>siGl(7MhNK8e5~w?i%+V-
z_Ah&Bka^F!USdW~Y-^B&$dguIBu?Y~#5EHDXQ#EuivF*C*4maiLsUp*8dfKnB)^J-
zzC+FV#||&E{||fb9gp=M{}1alk5s3V(GU%l5R!yMiwfB@E6K=~k)2b?Dd7;wN(<S0
zuNFec-ei|mvWb4r_a)A_zxVyP|G59S|M+@1j&mK?b$zb)=ly=o=kxh$8|S3ZN07km
zo7}r-f%$0whC#_Ew?=V|Uw)0i)7a$)C5)~%nnZ2LP+&F(L{8~uyJ9dy*1aRNNB{CF
z`z*L<5e|j!0o|8`mzcx2mC{&;u^+<{J($2ULWFJ(vXHfe`gRT_sd~UFNIwFoG6=sw
zd;))SnI{rc<l5RaA=4V2oGcG~<DjF@<;`8&Ws!%*#ybreky?HDXV1mUWkmxBxE=f0
zea{K4CDT$N-pY&ZQTg_%vNJEap9{9DnCCR;=48a;>c235;n&1RC>dg%y|<{V%ss10
z&&xde#|Fpa%u_E{k70z&0GZE#GNg1dZ&}sqI-Q*(#(dGJoExHFJbSGD(e~1HZ~g*b
zew@Gq$$SSZj4M9>s&(-oB$(+u`9|0^P{^3Ml;OzxDBznBGr+~ajv86L2Hcf9U(9{m
z@~aId^3EDRpY-y*uH6U6_ozL`k^y{(DYGC9K2B5_2kPUwhC~{q_B*VqIbU6KmyVqj
z?oN{Pcli4J_hPP}c`~M5;m`w66c5?D){=S<Qh3%q`DPfO{2SXB)>|cBUtY+j)(iC1
zFJ^V`Iq5d>G9MzXHbl#{WS)ezjZJ_4{6PK~8KOdz>L5krThTW;>x^<a6zr6Us2s9N
zQzA`xt90=$$m?+^2-iIU!RGT?a@x>~XIuM#%kV3+)##!xp=uK^SXs6Yn5De9j81AY
z)xzhwm0&F9jiWt-DM9<0yd=ozMr2Mz*c%0!Om$B7(nT-*UXDVqh^)^#;~rA-<^n`V
z%sGx7sE6sc?)<(Li>kD*Jian&!f;xhZcbL@=fYmR9Vi<roUP7DX2g;qa#@Rc*h|KQ
zqro-kX4}UeeXi_qvU)4qwMLT*mGm4yuPq?*tk@UBGD-I>bW2dKi&3{ax8yb*ic-!J
z8yCNJHr~d|@EZSNlX{UT3eiO{?-@8Um0NwAwL{Ye7DFoTjY~e|W$^v8ePc6mGW%ub
z#p_?#&=f=r&A7whcl{*hG~J-K53aUxTHI3WZ!L8_FDe0!-&Dg;8I4^w{1@LBPgegU
z5j`_&2b?a{YCcmmIT6dTs`S@|z8s#<w*Bw7jq$DsYC1?;9o@P!x8-n@a`u_=nYaYG
z#_uWW*8u@$Z;k@J2kfBe)TgO<v6DSN-`^zw!@n07v=EXB9jBF+S3XZ0D*~k8%(&sT
zv)-3&E*PrF(D*Sc(>#bb*iB*4kY_wRhS`ZfpUNi2bHDmHOW6EbabN*fJuMizHGR<{
zxct1d|J8HA;rCBXY>hSKx>cf_rWD<DhQbjdPvv&lxU}|?k*Gv;sAT;{e7EBc9wsKQ
z=iw3CrssKAs0+sBiWQ~Tf6DL#5P!y{`De$MT*W`Uup7stazj=W_;|w*wVX=yUG?m9
zm923*`EQjA;a98+X6en3reGn8tQ7`Kh7JqHJrj$qW!iFEdjTy+E=lF?(t<qxU@fFc
zbJu@m(c~H*hEv@mR^|OZ8v@W&_*VDW6C<Dji+xckyUw}M_!bDd3ttVJmhQ^K9z+EM
zd$`pz+K44Ct|J3I7>U+Gx2Gte0lgs*X?z<HX+O^|thY;=jjRT9*fJRxL3Vhxi()&L
zG?EUfgOmo0OuhL@kPNLi6d`|5Rz9fS#(YjyqtiD?tFPK(s8%qqA<|LQNXGdH^B;@%
z#yn%nchM%Y1z!n7;jZ?u59^9|Ze9IMcl>Yk9NYaXiNAEE1k>Myqy6i#EPZSA-FO#$
zM|DTIR)~5i`7b7GFJn$FwU#-+dwg9SgU}GYgROVNQ;kx7+wo;9(}w#+)dJBHA+&s_
z3|ZHD`nwhGD_?HvM)M{i>+;V<qUvQ?isHCQby+B+T820Fm5TVA$`sTW*NIi*GE0By
z!A%6}Uia#u>}{FA9y`5Mw4H8+!>aj>)oN?}h73~B(;ToYHr*|>=&g>2?Os~7S*RtW
z)gpH3ipSgPuAL(WJ6VMJcXA8Uo;TtSlw%7@&l=pVG+-Gej5^%o-{{R)Zx<GU^fEBM
z%s=YW(ks4<!DPLKD@UTgQ0u)DQKXgl{+3-hEi1DcvxmNKdD-`m8NhIYekgk;R(i-<
z6ZFHvH{AtN-FUTrN9c<3MVBsGXe#<8`Kya!;<}d9dw8e|4sAV8`%CAuk(2z6{EzAk
zIOH3o*8JtQm#3uXe6&QU++pDeK|`nh7o$<n%9R;sYh-~$j8!&uYwiDiar3>T;YJx5
zuv;K-i9{){kWdT;ccMo_I#+8MI8zo2+*GTJdCwy_^(Z{#s=P~s+S9+RYm4jeuH!N}
zzisx4uvGFl`!)TI618VVJo~M8&HGlA-eiiX-Ln{xxu{+gTUn-M*F)!o`DkQdZnyz&
zR{cCQUrj<!j1u5OQ-;MemMa+Okk#`*Ej&YRVF53iO6ZRgOZ8y>1qqnd2l<%9pSv&n
zCVAyiaoO<tF1M1dl*pYQnZAe%*Lmk`W-+75fL7lE#GJf@i>j&Ein8j@DL|_jI{lb!
z_gDCIH6>zhPFt5Kl!Mp@pdT;-z`=?2S^j*}-%rb{nhbdvN?cb^TZM|roGe%a?Ed?X
zbDl$FZtd*fznrsnbqm5q>i9&9$Bu)RWq*2^EUhYs^2qhXKa0A^81EF*R>6PbXlCB-
z6Mho!*uzfRb1VeRk~Kdw5D}yI^R4+{RBu*V?U&S~elU5amO<)*Mq5g9^SzVijvOq*
z*P_e6$elXKt+{o!HEL~!LXXXdKYngC6ps19RY1ls5=W+ng^9e~eESJ1QD+H`hB(p<
zPM=P#7IH(`fl&PjqtE9#(%>Znmf(h3QH8-%+FhXyVM4y|R5aRr&7nIqrH(F);(#Tc
z=KQatiHZ3c6@3aHx_ryT%>RCCQ-W1TC!fp=^*>3G%0y<rF;+b@X)xpF=T%ljffS+i
zgDZg$Fb_fhA0uOZYkuM?6_R9xo*h9!-r~cD<8uA(+p>(B-J1>u%kbP!&&qiNDKj8;
zJysXF?;H`-vM3W_+RMe*r{H4RwXkUTIjexgp9s~<bwu0sl?748s9`p%n{^y={!CsB
zy=qF(z6n*@x91`$JO||4EYpB{1D&<LpjLpkzPv+4TZpl~{K?d<;qHlFzuZ-o?H5+e
z+#WR{px9*@RXD(QFW1#B^;*SY<twdSDa=#dOYhFl3Q%@?u0uMSmacL~p5Q`;C@>pz
zrvXh^M?(XcjY-q#MYC{7i7iNe^6b;!SeWl#>HDhkLWo#<xyF)DlBVffF35bT{w^45
z=)|e$;I2L5w5nKx+MW?nqLTj-4i&_6;}74j<<EvlR{OGmjX`LMXB^9K-@YyP!O`6O
z>z@@rBNoXpa~^pbABHR+FOQ>cT}^c2FQ!IRMZXFT0Rb3a;IZt0DI2My7@Ww};#~d8
zV(~+{1cal6gR&T@#t~HH^c=uD`rMm2Jf2m4+8FvXrFlrR6pf8vkVXXLHU?DR0yaq^
zwnCMjg?U@Ll7nvY7A6117>df}906^}uqteorumckjtYm;R)^fSB|4osD@;6ytR#?+
zJ*v<!af?@7`^mRDLoZL$ts8v2tl4177if(OS6lrUmztWI8aDFw!?gG20UyqDK>jaL
z=sw1xI@#Lct2x9}JM-u-&7ogWaX)*rvSf|l(RK3UOp(P#{k~yqQ=UGPI&*|x&~LVt
zZnkpg;zCe`pD375eF563T!)th88IUm@}RUwaB!5Qz`*EolpPfm^vw8Ko4@#U-drwT
z>XY0&q9K$*!VcF?1z(WqtNw0(-D05nhG1xai(TxG_SB>;u3hE)P94vkl6Ne<mtG#q
zqM<0uhgk@Q)Bz6i43)|`F!;do%ICGoRJSiW+GcTje8JXSG#&6_S*uZ!eJaf4`sJBZ
zJ-2tli}2=`6Bgrv7n2NW*3M<|laWmWEq${n5VdiKXZ|f~Nug+x@feVnBU5h9Re=TT
z+;CzPdDmS2kI5gGH@oHsql0{fP(hX07cNrI?X+OT6BTg)orvcxs+x5q++PMMDK!lk
zM>sO5$*m|ZI@Kc#Zd12KQ$*}i<WKc}9S#a4AmTDkVjf2->@EgxULV-jK6M+to|#jE
zXE50!^m}nA0f{5uyNw=l)czMgU%(Fv4M>=I8QZU^IPyQJLs7`PiXVJYGwVijXrj^#
zV-2J0?$#L|FdQ?|1n$^l;%^i2P?+%d(ZQ#T4Ioe<G&{~8X?+fu*k_VMgH5nuY911I
zKdPXH5e8nje^x-K<G13OkE=!aYr_^Iq>Da#sxnU+2nE)eI|}U(PV(9@eO4&e+g2<y
z=ZbaX?BV>l&1_~|D@rM$YhTI&jf}E%kCSfpVr&NKnpy+Vz!o5hVI9OJ)uWoQi4+4b
zK-r!JjuzAoqQ#0q)K5uSNB@zX7%<`oh9IYh6ln%^5YrKjg)Ok-eksH2kBRSXkAFY#
zx@&iVlivrE?`Ko$?XA@vQc)is$N<sacx_Z+-%myx<x(z&Qh*J_?~ao;O%^5q`AY8H
za}KO)Qa~a@ZmTR17?2dNQC$wqIt`6jXpO62f_Y%w%))!?C4;JKzUx`#X|X5xo_cGM
zm2hAf(GBlbX(QGLN-P1XS!4T&X2F0$9>VBU=cPmfk+=YxFfat9TZ(>Agal{i>G=Hl
zEXaKh1@S-H%*m{F<h(paAp=J&^b|7~CCxe(HsGP-eE!P(0kxlQU9{c5gNcio;bc!&
zQP~)0*Zn7{O^29R&EhLd>K^wmH;pb`j6bNTJl~;$DN#j|X0WRVh}5jee3ytJWRt=!
zPCqKFQ;t)QBz}3Od!Gn2#P~vb7M@IyhzcxziYx)LECGuAvJGN#J)`D4(dHu$k9nQH
zd{wDkOf+eWTu6HuYFeqn2n`BMEKE+^)PlA`?eV451@Nj5Al6Bcf(HSWk<a_)%^QGl
zJP3pWATJM3&yv2;NU-0r##LMNgGnJbT4EvX<-fcn*#Qz-ow<+(&7TY>J3HRmstEa%
zT+kT4*J0fx@oHpwZvPzT%41TE#)W@K|1B%_{^TJx17TSWjSwPcq=9CblK;ZTGjD*c
zh1`j9_NkxguYIh80<bf+VT!y#J8Ng){IeDMEF<f_a100wGi|;G+=D`H`+@jUQ|rcs
zJA$7t?;X^++57Vb$zEqsUE_PrZR~=y_EAz@e$WFpOzq6tS0giWDlMwiKK>`W3+z(;
z|KY%2DlA60+@c8KbCu8|r(d8E8i_+SS<xXRYD7SBXuCtEc-I|}MrG_gD)#ul(X;-%
zbP-g07ZV`vUr&zQtNXHbKtXfOgKJ(asIet9n27D<7`A51IAh@GXd)yeY>`ho7LI>E
z{l3f3T<+yEko81;wut(uy1F-?<#`s@aZB@n4M+Py+VrgVza#Hq^O8Ri|14pgU!li>
z^^-+ZiM&d4eNLhBn!jc>Y>*(1oBaK<2_I0`?z<{52XGjf*7F>d4$PB9<1--K?rh&d
zno~eie4$lb06?N@=&#I6_M0N>L8jl^5Ah0HFg?n6@Q&55Oik$r(#P$sv@)sbLX1qo
zh#%IvuXb0%GN|VM{f2sOTQ>4;a6LJLX}fXP=jyN50>-*A1c0BPS=9So*i9lnRiFm)
zO^Bu>9*HT-eufMR6lGfJz8KDt$7S)OR}>wfwh>*2LQ1T0w9<#Jf7&uCOPPYzW!GW6
z2Hr5wC#+hvszlI#bi9mm5$}(I4NAmI63sXiG&g&Z<kjXKh>aH%X?&UMHDeV`YNJLn
zV>lPnL_CW{(e}f4qQpR8{RX^jk-u>=uMLu>387eBkhnsRfC{nsi(DH+U18gw=+N+P
z68YU=eHIE8;lHtR-7eYo%fAl1OF9V&iP#V(($wTaU@#YRP^-xp7{zF%NT7~|Ln}HL
zuvss?*X%n)N-2d{5ZZKHI^B8QSk9q@npus4<8~gN>^2-`>+Sz_!RybV-JDKFufu}X
z6y&6(DUpeDQX+F!i<AVn9U*1xzVBFl&l>SQl0`t{;iPTS?{0ur@3CjUMoS2BEK5IC
zs~O>GIL2_Jr@*u4K<%R{k-()e2@UBe1UC7gqp;9rJ%mCx^Y@RdZ@s5j-vk85)u0zt
zUb^VWE6l{ie17ntmp(1r<5p?*2j!|7Gn$T^*-=94q16Qy2wD!)Xd$VU$2?yLrf2lp
zJg7J(g`zV>#dvAsyw@b$!hr#H4~$*0hM=pO%0j@IERSk}KFbrdj!hu3j-6xRC?v!b
z_!|dBbHd^H=nbX=CXrQDb|(PSQckT`rS|qRS<d7};6#77ov4nBLaza%oc#%T5$(>q
z@xc`H_Hhl%Apu|uuSi_qDWlm?m}0U+aQ83W4&Ts|veA?&?Nz+;8C|!dTra{#qZ9_N
zEj9eerN5`fRfmEBc_mnK?|~Rf57Z?<hxZZZTJ&g2vc)=i&PPWd6#^drf?&M3gxcL#
zpa=EVPhU<S@v2(zWaM=&Mxp8b9o1exs5zgPCm;^KAip=Vi3^T^+XPL8?2~uo{&eI}
z$x9W9BAmx>>(%^<p6HI=xX>gQ7B+eVSL=s9VVFx$#X__|UYP1)mg1n6VRKUxMcjk5
zE-ocLeB}7;+=3ydB>69Wk@$HlC_S+yzVzA1J+Vz!Ag-}atg4Am<&#XF$&T>Td-bg|
z-O73r{3s-&2U&gytS@i=$pfOilZ!a`o0jLY<|dlQL0)7FDx+v6K+AIvNch&Ols_6&
z-s{h`vfxy~<0%724*o~7{N9xYLa~*d_WZIc8}?A@2c;qg&i)#Qh5e&)rD~_B;|=4Z
zxhFg;_6^66WFe+o(2k+J8r5;vXz(bS+a~xZle-4>MWPWp4V+@0`HLt+K;Zr7?Yc;h
z_$ARws^hd6gg81*+q~v~W`5O?>73raTP16keN(a5v)N`AJ7J)GQwxmYTaPp34+|?_
z5k4q1e6=BFsWXFj<rbxcFAFH(P*O!E98IXQxaNIz<@$t9Lmh@0es-a3t_BHCGq%7I
zHaVIbU$(5@urI0Luc;|pd|2;F8bc0e0%2V{Fn6Ja_Cuw|A5{}C#PYun-~I7$;lojW
za-Y|GtMwP!w_XEaNoErk3Tb8&6&iwKNA?BgX>r#GW7RYwS`y8?xImuxvZJK?+ugm%
z1%KIUZ#%Cm-gTBIyJ%jr<Kbl`@0zK+DViZx;Q)BX?O@Gv-6};l^lH=RpU_jv8#m;`
zkpI9W&Im5xcS(hWa3txQO2VqIAi>%GZTiy<N*$|uU+9t4qdp3KGU_2vz*)zT=G1X{
zKZ`IuBjtb|?Rvk~zJ#KBP{?*9|Ht295!HG%*ztVKZ6SlAl!Qb5A;&}0_2)diuN=h?
zNj|a_jE4V1mR$0>o4mRQi|^j!&QL`|X`1-tF6*dFS~f15{btmxALFL9hW<WNREK0l
ze;l5{xcCob233;Fw2Dki&sBohT(Z6wCrbOykDrkX`)UjIpb&6o`pi!vajM;;#OvQ0
zzBN?7R#V?%U~8yX?6kV>=+=0NWnQOjwrMQ3L&-{2$3MN<tE^$Ixc{;#M34gm$A~~T
zgSz=24>(kJ1(oSp-ywg-t<y6a-Wd@5v^eHzWG8z7oNQ&BOj3=XW1q~uxNaW|1i8T!
zaYHlW2Gbdf0fi3(GJg6eFXn9C#k|2M8{6LVQ*$PHou6M1O<{R{K=_2&q%|vb%xwzF
zBU1V3#3^WLnOW$Pzq%L;rndoGv<h4F+5Ie!_Iv2){4vs*h7t?Ev>x4geZ}upF9fu&
zpBNb#Ki22k1#_Lb-6q=XA1ml`g@Aq-v>au9#J87y!2qL^lsHQ-81--4y%Bh26}AZ5
ztOSp+q`P6scO%wzC7N)AC<^(>8xLkA*(oJjnU-7AUps0{yny*p+Re>bAr7&Ay#=Xk
z>B%WvpZ2gWmO^p8FQ2@Cq7Ex9n+Gvh1RX|k0WOW4aAle_&M4LHsd6>H_LQHiU76;#
z^yYm$n||rq^twAzfSy|9FT;3wm|2WKkaq}Cwid2uI$@!&YS8k<=3P&Mss`WBbtC_>
zl!E)j6wN*7Fb6X=wYHNY`m0jFdp^w4ZqFfnyHeTms5R{65eVb4+ryLjI<IqHflsSR
z#3GktvnI_gFOOuq>VCBlIw);I!vX<I<g?AJp;IW=#B%;qdWP{9QtE3~R=$ND<_)hY
z3aOIexFzS|j6Zo8fYhA*X8)eaHMhK6ON#2=xhOwi^E$P*zvy+>e9{g(VC<!iZh!ZW
z8}jfo#08w2u0-trZ4K*I4?{BI)$I+2Y%2K^Oa5^O_893zQq&Oe7VC_3v`!y&>s0c;
zJ;zP6A1(00J`UF+oc0UcJ7uJlm*8i3vR*!}6>xbc%&)5@qD*KwlK$RIbp8zcLOPSM
zAyGkT+w88e<4|_^lW$y<mV_!M<>p6?sWY}y+cLVwHb05#Y|v7VXNy}cR9@x$gQ>_q
z#HP{sc5V7UoDvm>62(;vr8SR{VXsI=$Phehpcy|wpCOf!xLz#hDf?BU#9=p-Hnbjc
z_PD+3N-6%rwj$Sw)8WcC^P}CC0^Qe|dn^TLSxx6Qr7cUhjNg%2s+~OZfZXGsU!dq9
z5<@aG_su`QX0^!F3GmLyG~f?J<BA|B7cedF4a2Xn1>B$Ml#_1x(Db9_Y3!+UCc!r~
zN;GVOayYXc!~CD|PD*cf(6E^f);VGB=4k5nJuWdJ<CJiE?q&|In8wdr|FM&)*i=A<
zIC}JGZCzdXYGy~7(78eVR3Oa=0<4}4hb8h?B;$rOcF&0I`stR@zKYNrcgd|Dc%CFs
zAct?&f+|(~6m=gph~f^-P`V(^P}jMsUQV^={hfTyu?sd@t2?hYbXzy|;M=09+k*aN
zJFy)p#a#)d$~3kPAq6ipns<g;3ZZx_-O$Ij;*FD3HlvN8>pyOVOJ)Sj7s~Cv+V`$a
zA0m3;1UZahKttN0_1rYN$(GS;cjL139zf+xhL`lsvfrG8&FtR*^!_>Y9K$+P#1j?b
zXlcE|l1jQIqf3QZ;J+n+*Q`<x!>MG_N70z(j)OY%c3#&B?>ueFzFq&>A^ydCgnz$$
zPW(6qB}x1-nizYfJmxnN{OvdgKsT&0D6IWlE8n>{zXLIaHBio(e(qiN<F*I{WGOLw
zRBX%EM&;H?oL#C2<Ef8Nf^waCI)Oaqx6l6w@ZYr3x^xnKv=X{>(tW&)?5m1*7hhud
zc2b$pAl{6m=#6@%fkNe<s(^S8I*ko>iqskU@8cI6+jt=;ytu9|6Pf!gt)@ZTnBE-_
z1yO*34O9_~hN~jRP*8bu#M(bboGRSEoa>>jpiN7(QYw~g>u*k~dCS=&(YG=+2dadW
zSNr)RSrnV)3!om+5_se#4W#r}?b=U5(jpm2KuG{)0uLtx4O-ul@}Y}U9aozr4;aPP
z-A&ti!03s@Vo#L3+DrB0JR}G`bQjIDJNhY3+)8VFHx#Bg8F0uj$#X!;?Csr4>07>K
z58}LjW6*L<bb6hUVeJvWB{PLD?z*a?RAl*q5bNF%Vp7`wxudDVf2A}F6`srNl3(Ms
zp`hec&&`9b_f?|-?1%V3p$k|nXvPT{QfJj}HfR52%_c*0&&#c#^k%oI+xgqtjNcBw
z=}3?}8u3w&T<PK$_il>hBlO*R&KFdx?x_+esLD*T*cGD4;U^CPy`h^HL##IYnD2(g
zaypb`4)PsV%B-aYet+{0GYF$bmOLx`)rc^W(oSt~|Hr-tXUd(DQL~(b4$>V-DSSst
zqR;C`Zpet0)QE!8h$Mc-aWkuNneMw?v?(C?Qe+HtnQGII#KIc~-h4Fj!Pd{gk%=Sb
z@aop63Be7~&HvoAnA<^VD=V;8^ZJ$d_`UD;E1`eg9%-yujQ1Lxy0GiMJkKGfHO$-g
z6-{`!7DyEJOkR-CyGqW8nOzg4O_3yh=XAqQN7HOaPFR%Jb$v;tUpR-^<EC4?W+#GY
zHP+i{*pN=E*+Mq-bJ_TJQ2<?cQ9t1`W4m9<e*dsv3Ek`Z0Ao!Yna}EZ+!Z~3#Sbc~
z!|d{Gb5z&d(BF6KP0=r1y4oYJo77y3Ex>kmqd&YV<waYB>pEvZ#-P51z*l+yZ^JeX
zpZ@8Usi+2T?m01va&WS->E_!&eYG=w%_#n+RFBn8b_elsyA*OK1+|&oO+3HVqF09F
zI!kjq!g58n{?`1`IM;RaCoxQI^Iqi1Mov<geD#m7m)c1z2J=W49-I^jzmlg*WzcDH
zU-vqB?L#E-GXjgmm*Gvf*~FRDSdpl+Y*29JKZy-R!sdpL)lYA`0Qr`ZfVyYQ)zRd|
z<}u|RxK{$W^v0ejToCqYYilf!9$+40X1M}^U<xsL5qaDMY1}Tb@(R}XxXMh|q1U&r
z(6_wG!*Op?GtIQR4Ze}aqKeKwAfA`-D*nOaGkn`H*XJK!7X^XeIda}Iv}<m=+wMt%
zSW9a*t;USFjEXtvq!V>~)4XZD!@RtbmcWeD-nt(k_t0_R7SyH8)$N>l6K*wYv#)x+
z9sYA?Xtk!WO>?Q|NH*iQtO*s%<{oP}x<)_)dy<+zo&DFLRM-Ke*!1^T){Ms*y38*|
zDu`2HWBC~C#>C(uHjc7`G^M8MJf)r6ckD|*IQ25BLyCL-mC4==k>4fnNLPf9f5$2>
z<xyaQnd6TJp?F)i=<<#XFxsQdj0!<t_$D~y>R-y~Rm~Y+t<(Zn(^xur)Yu_jU?{9>
zt<k17Eyu>s|K2h0_dl>kshvQjD0pfjoklvkNx`b@p%nwtKgOiT;phEgn}=)w7u7-v
zgAu*iC!fe%X$NARP%H@X(jNU<f!00Csn%mko4D?1fijM;cF=oCmC{~`fiRsznNB%o
zo%YkSg*UsWPo5RJFK@a&ee*Q|V<5$@Hm!pS5WX|9sBcW)4J8y0C6sn?7`K+ccJ`{)
zY*G$nnDpEJC4sJfQk~rxZr*ss%YQ^#pn)%wxY<ilJ9ze+r+s~Po9G9}yWi)a5~aCw
zl!%iOtr%$jp<E<(9kbf4Y890uffu99RZjGBk_2W-+1h$MrG6dNp>xOsecl`JKB_c8
z&D!<?@@%A{FcqKGH5-E?CF2~`ZRrZ}o0Wx0#mIcy5x&Ct!z<KDW(b)ePs24YO1d8Y
zMw?#znRP{>gk|=5PPc>F>Mis)A^ajcBS1+umo}u{He9*fljZC?V<Qhi7|+bHYn!eS
zQ9H2$xy3KG(U^}iZJZ2JAd^oC8I}cTw*h8ML9!Z%O_x$Grn(r3>_KMNGnri)Ydjck
zcHxSj*k*0s#T9q{p&KY?mvh-qLOJd?Rx=vk8$CqslT_9IWu>U}Xi9JP-g+NVm!3ao
zBD%}F>ie-Tc0@3-DP1TJh(RRIUgg5GgH=sL5$MP0^?9%Ufq{q=B`R<z?c`lK+w9~6
zjPq)WkA`gj@-d{m$9+~Z%u*mMdDBscKEHChwxiDK7AD5mEwp&<v%40bD&Xu+1n}j@
zp^rNtS^~pRdZQa%HBC+b*8Xox%R_#-SePti;2C+pqR+bBY0@v2sE@3snGD6~45Hr`
zOO#oBXz!e+C=ilao)IxhRCP!eMdeVIZOwjDeDC$0*E!HLJR>ilJL<Z_mQ$ZTO=mPm
zAg^pl=Rhb|?mji4a7U2?uVl;nk_J8%e&e3Tjij%c#i)oF&P7b-!Zqm~xY3*308r&&
zZf==CW=!lB#O~kFpNqq6w;zoPAU|18SU!GD(20BF&676Mw(sT^PUFiHQL`~-@$)-Z
zzU}^dM8{nzrD~3L!8Udp4xT2inK-j=;t+tHVYmsVfR`~}-W;VuwX_o9+rVlYM25^7
z6pB!I^`M^cNW&c>@m6r@AbPInqBlI`1Qc^2iTu>SE+8zWJBk9;bk2Txxi*elX`>)@
zal9WbSYab<Mn6%?v0a~j(knJdZ`wj<cApJZd-85uKCQvQ5O%AH@<Dw~!L+IQ_qPO2
zSPXiVYTs`r-(1Eae0GdBnIR5w4Cy*X1#_Hk=kqS{w;ZCUZPz!?`3kB2TW|k$FfZQT
zWgzUH3sDf=QBc^%AGaf4jB;JeU<lb!3Y?D+p`g<7jYVlqPAXa`unZi2iqAJwGjH=>
zSva}I<IH^o*RCedr%+(fA0N879;Uw5HUvJNnAw!(?v!l>LsJd1jM;;H0(Xrz6*({s
z*t;iIo4%8${?lu9aZw@X(I;k0>qvfi`D!<=3bmOh#(9p@-%i>8Kx-3ppTtq9+bGPs
zX_?hI8KtoYmRE&rqkGkpK;+In>cb2}<qvB{fcQtC4xFAKGtY%3Rz$iqTYPBW^{<aj
zbv$gRfv^Nh_W(#fh_$w|VyqIF5OT)A<TL6F<~xwUkZ#N>y){9yYi@X1FT2)ca#gS}
z`-az$rPL?d;ot<6vZ$Yo$X#qvr11u`>zGt_ZTjEJ2b2$yf)63FZvFa_;1BDYAdnFm
z`T4K%O5{&`U%q?^^=)#Q`!!>_LT=BmNcZd+sqI5t{fi<W7wwE_-J9L|SGC?d)8u09
zBM^DK+OXHuBrFrYj7ucXu~j>{*i`=D4o%z4eX9z$cl}|J?cv$+ul1vnbiI4d${CzI
zB@>gO6m_U(&GjD14|rgtx@RkfdpHS)(sX_JCBQlVdVZe}<J{aFP%Qk<Cyk+X7ccm_
z<Lk!y8*etb7fc)#k+ZpP+IzqF$`R@l7G9?mb<`$rB^EtlOLqz_S|PMvfNj^i5U#wt
zK`FkR#?T)yhzgy??Xws90*3uI+g>OL99d-ViyS#GW;1zK;q(Vm!=LGouC|FiwNAa_
zw8Ol}gQ6~hZcE$Twz)^6<}H{qLez`AJ~1LEm)Fv2$K@0!nzm-0JJw#l?39m_Tty}q
zU%<D9J?R=(8~2<l@R!Z=V`;vXsC_oAs)6Hrg>ah)-vgFxS^oM4o6d%~JC@0g?elHv
zMS<0NB}V=^>x=vT3g$akGC2EVP5ATEBZsx$T?0{ILN|$A{o`+6bD%e1N)gm(S(FBu
zYx&UFc;)jr`z`~|Vt>iKrM;Z~1AkR?*kqUX-Z|a*r{OQ_Z|jTvP~^E>qU$fTNz*i2
zl`k}-ms9OXX|GhXE!960Dsr!S*!CKr#8tcJJ)46Kw{%3co1>=!TWY)IFY?-X?KN5)
z#eT0lnB2P4=zjUcsrJP3$iHteRk)Jqin4WYJ`lYza1zx(RNlB|MM<xN4Zp}{ykh%k
zuv-_IhYBg2%702o5Z*NOBy8TMe*6jnl%)6;{vKa>D8C+vfwr0C%12m&_s;+G4aA9j
z1-*pRP{ArcHmOedYD)eGjRv<+dleD*{%wn|LyvV58=Ncb_))xrsDst%zBMa;K?Zb2
zeeGVi_Ah<cVXk|r{-54NlI)TOj$FZTNf5tPUkKeVevzO514pO2TAoj&%H>~q1D3@l
z;E%`C96jUq1U;RUT3%j$O0eir5tl~R*q;OcD3MZDP-c2fV8tbeRd3p%xdOuiwA_S)
z@P8JUDtQwz<|bwiYK@M_;8)LK=#A!3L*g^uYoJBKCb1~~J50c~@d3@cKzQuQ2EQ`G
zh(%9O=F1!*IP-}>5uJ9;ioLP_ESyo^($5`NZiDGzQIH4WZBH`^g2QOMX%Bt$HZ{I{
zkR|pdFH*6Vu95B@o}`!DXax!wye&j4Ru3A^1Nk5Zey!sb%sdYBavIc;m)*7Wl!aeh
zsJj!gs3c>_X!G_T^OJNQVtdY29GeU#3b7g>>&^=0flUxP{a(MME)Uuf53I#PO(PYe
zjPrC$>D}4>V1bSeF_^vE_>5(C{Fab_ci^U@9CHYT{7;7_MPJXRLUKp}5rXsM;U<0_
zhy~3w=g+Cl4Yki>Y&01S2Xz_iH|vg|dZ_3FZ2CB<ViA64>HH}SGmGN7K3!U^Ie8jm
z8g)m3gL@Zv@;{%8%H7J^`t#dO(!ps1s#|&6^PC*62${7Btz{2rC5qCq8re!<0Q``>
zkPX2KA`@lw>c+~bxVSou);cUGcws^qQ*TIZ2~Cb_wMDYV(z{6DMYR$x{^92zddQeB
zhzZlliB5LrJU&Bg%A?XAs34^|<wc+u34Qm3w(+=Y=amDNp8oR}wp&1@Ua{WzP?+==
z^Pqn+fT_4`ea_H_BvK=!YFArR!<$=>>snbUOFAK8c4yAGwV0;6(v_L0r5Qs4MnfS`
z1Vo_=Ikt(`D=|Q6|EqsU7cH+q6TElT;^FEG2R$|}F0Sf3QkQt3@WY2@9@y{}jb%e4
zhqMKWCdUlgzK<V0VnQbZZ*BkwK*IAqUPU?(mBw?dFj5YVu<bQQ+^1eDppGuZ$C1DH
z51pBX_gg2lD5|m@ke8>r^?MD3&j6zT@OtrokKn~nW2Hr2*58Ztg5z&q)zYK7e`C#;
z9xM6xTnozMzW*I}i$__P(7Q`drINY5gz{c`@G;F2a(n5q<6D++%1e*A@cd5~$cT?}
z5vtY3HDyQI)%nnBN!~zigPzEvNf^?FWm)iqI3jLzG}XN4qurNYbnzDjjD;ap=YM~(
z{bw8p`M>|~FYW*LYxh50Ab$)e^jMRu2b4h2k!o#ig-{z&Wq>e*HSA<b*NBP+%7}CT
zLBJlLiqD-0%*xKjA!dU-0}2v^tStx)(0y~NTaj`A_ap_mCM6J8G3^cvB`acVq3|ac
zKMqAxLWv<1|Ern7NS0Es_K4@s4Vn4BIcS$}BiVOs8I{au2;<kqX+%Rp&=6QQPJaoE
zxUdKBDvr>6<>C)(gs!IiC3ho`q0b2$9T!am`|Wcv^r;TAJY*O~>5vWcM5Y~x*%w1_
zD*ZL>&%uU%z?c(2Xm7lF3~y=Gd1s#?2(+Ya7k|G@Zye~}6Uck(KYaKLnv)kUTu8AW
zHv~=e((l8U)ghMe4Ah1ZIBH?DMCDuR=n(J9U;a3kU2fF|U~IKVd#x9Zz}qcvSGe5#
z`sK&`mtN#12I5V9?7yvfczs;?y6$=!8WjyTtfh{vZ3B?T*A1Kpb(jx9leg`g09&z@
zU`Q_v&Sm73$!8o}Vd4U=Pv~FQ%TkYX(nLf#3TD-jP|${UXGsZ%9)Zi!v@G`7V`is?
zmS!)8VO|t_R=LGO8bO60P*RuU`v^m9{eU{c&0ZqfR{do%zvqxGWP^G!G2Z^IwRC;o
zvT8f>gm80yV`qrGax*|+s=a+ygVWy*otglm+_&NLccDp?n##e7J~Mhh^!xMAHrbQ2
z!DdEAhs?4*@3k~A_>>U0>CWl*pi#EPL`OT68ZkNyCiY?s@To(g70i{#j^QKqevSyu
zYY$Zw9*>j`02)-}>A=E$4A0JP{$9Z4tgQ)wlwRPsw3Q&+RigjRq4U~!fQ9r!A=rUI
zWu5?DGMaOElA-TTJix)fXaWlt4n11sJYCkA;{<tML*shV&AN3plkLl~X~)&p+uv}%
zhwq(85PrLT%a(%`VDsM0i@-iAQHS1xXghA3d=IAL_6Ucnxs5)VAh5pW^UKqYU5`5(
zCg<iDf=_-iYxq~t4<~@n4g%TRZS3fEOUuy{p`m@k6;2TQ$4<5zO05s%3mW<>bd(HW
z-KjBvsVB!iil_ul3BswALAm)<UWB8tnR5@jmcXZhsX6D-4mW1u?u7-w#Dh%m{YD8M
zs1cHDFZIhWuPB?=aMEkpwAUX#Y(G_l8>j_dj@`TMQL8Db8?wnJ+V2>CXVRXh@kHi@
zaehT#H8|O^MEAH_l@oichY%Upa^)n{&otDZ<U4W%dKL!Qybi}C_IxHPZiEASYUXYU
zkvoEJ#~mnJG}=RDNKjCa2%*764NbGZB!Et6Uvxc=zkim+3Y32%0RMCca4bpNrFp0>
z&7_$YiA$6YX}sjvS`@UBRoz2NeWl<oUH|9LjYjR*g(*5ko;QW-AsRV`kp$A1)nS|)
z9xg@zdpMF%2So|JWa4`FZstmiYjgad)paZc=e!;kfxD6D#Sp!dwt-1mucBv2=unGf
z^*;LoLoJ{o6E=tY0&8;g<Fwr{^E{qn{+vJ?h;)`i?5v|O5qyGmz`TVjT<ye+>Prj&
zs64}5i@fLB%K_u2lylH~y3klb?SrV%sz&zAe($pe$j3`A^XSgDPnTco572+)jg;w=
z!^}Ybyb;kkWmiML38o1`6$<+LeVWz{LpI08KAa+AU5+2>1IitKp*rr92M7|GaV`8p
zXT$iR>0=|=Gw(C@NiJiTAEsh!Zl7yy9~$R!6$Y|32zqB>I@nFq?pohp2LqPVXn~ji
z9<4+5GC0kCyuZpaF+98wYG~|baQLrj+}`LGLHuD9<*C?dXxTZ$_YD`I-Tk4y8T?gE
zEgfr^!JrDEQOQP&ZAVObMZS4^4O+)SS*iPRJfQ^V4d(I%mF1uiSVmM54@N4K<X}qI
zdrV_U(vjxxSaB$*OsR$30Yxn5-6YeCn?e=cs*dT}+S<l@azH7Bn;9#cWcKY-e?N~1
z__agh>fbR5&+<ckU|hxW@X<#&p^`WO6`B<kl_f{`g7=qsGV4e1VS5QpG7uM>*jQO<
z@p5#ur7v>>tjwtm$5QGo4Iri8yGdF|TaPCoNoRI;woJ_|*E-(ph>5W=$83(H7Ei#v
zOvvFRO``r9Kp-Y%R)QLWrUnM@a=;6j?2ntT2ZC=krG)%a_{V&L-Dq!8*CQkfqtMG^
zKN=SmRnn5@G<!lrU+@b~_(AAZ7a>c74!zr4!1qT}l=Bz93bD#8FF(HlY?9yHnOW#<
z*QqhJx3-#as%In&P6e4}UD?XMit2GvjAm|=dWQKEI|TR-M{K{l5Y4Ai=xF~KY`tcs
zP}@{A(*9iecSDyICqMl`Rpr;_y`Ely<5Piie6Bj(Y1xF_hObSpZse*5($f9qIks#6
zEgx~|HIdIN?0$UwJs>6Eugh{%&UKd#R0QaU<{r5{c<4}t-9)Zvn>w9T6ld{)^p;DN
zarzaH#KgtL6BG54GtX)PH_Y~Jr=7*FUAt%?apIoV?mTO9X9S7Q?fCe3(eIcbq!f$U
z^v88o!7S0{384A>`lc%Q&V{<yU(BJcaUo?LHFeSyRa%08Wv|NH*{La&IiF_k!C5<(
zT=G0oLC2|tj^19S1SWx_NBw8=GPu?1IP0A1oT!f1{`%{$i7Gek$3AI4kBIPZ(t~-p
zegFP_(QGm-x`q#P?-E5FVM{y`5)yvz%AfAt=5D)hU&O9Eodw0^<(x^*?V$M37wh<o
zOG)vOm)Ur|X!|vlp`u5S)c+{Qu&FFJH`n9D%~e}qbA3l({I(oCc(4%75;l&?;K}~O
zI+(D=61beT4*$z!q}2YU%d$OrjX4GoFQYfXNLY`zZ%-ff*i7ZpuZz7VW@BR$(A3@2
zLpM!zt;Bb~QnTyr+ZPn(%`GerzJBvY!sLEHz?GCM)rC*vA%*eQQ0x9a=`;h&*`ahb
z6ZwoPwTl-Yi;nw5J%aJ`>-auBDh4GJ>bmFTv?Ynu2wV0_dY7<caMS)&4^*&M(QLk0
z*$c#Ga+80q@P>rGV6bY=-dw#QO~u_A$Cf%*c))p@tg#*AG5eZJL*ADM8xr4yr@2Nu
zYC_*~PobW^{x*D)<UdKCR4WWPRT|Ir>tCTxqJpao_wWt~P&EunNZ@v78^W{$vD2qd
zN16<hxl5eZ-S0N;=HPh$uA+kL+Xc}*fuowadhdVrD>^|X^Jk-R9m>b17Q~eJ7GGvd
zZT;}%RBitCA2+&EQh4X!1^sx$sHmc{59$=YD<}w)G=b90I3g3ZV5~)ZOd33W#bv&r
zwVr0^8LeEs+P|r=tn56@(7l383@h<(>TSuiypUob`zo>cauvEv2G)+qg#CY6zI?5x
zj*d>MURj`AbAyo-6_vsRQprB!#pK6w*sM(*L)G&JjhueWO^QoPbMZ5AUv#;>e)|Q(
zZTE|xV^qbf&KB(Gy>Gb1jddz2mJc4rXJsS$W~nG--RL6XF(|F-xK6yvy1r~cnCdax
zX)&=2jH_3yc!6`nmzoQ+alq4yQ9$m}Am){YPfbnT=z{p&r?~9lq^2_+sWT9Ymc#Li
zG@-sOM%sQQWo5B7eA@-BK7b9OU>+PAs@0wMgxjGU=0qaZuvQ%Z>#v)qdct0R)114}
zFT78I>G!f_w26qd_pq?gGNv0h%H3SGE-7*dw~WKnpF>sdkKN0u9#3SCmHDuQ9CJ_t
zu7WGI4Cb7}b8URlo=nIAbbtP=YMx`+Cl$hP@$~H!9lH`M7gzj8+YgUV7&az_)jBgW
zGTwE3Y&^vDN)spLG0$L2raUPBQm;lph2g^Amgco#RStNXXfUkaz!dZN@#7m^@)1&3
zK+fM&*gQH)<xqwXaCfYDU#JQr!~E6MT?xZKJgk|iE}ES?caFY@Y`aVoC>kB`#DSK$
zcOZwa4O)&$_hX;0y<@d}^?DBe^0Km800|}TiP#K2oEU7BHy`Zj2`j$8Pi^Q7Tb3pR
zlcn}VAdZUSACvvpqsuK%_Ux==JiZaEg#>;kVaJT~X1{P1Y8uYXjF-H7_aY$?c|mh6
z@{@O&RZ-W-ZBq~%85>gw6?QW2k2a6d-Yh!2oKtZsb>nWah8i!0F(z#s={<!_mSh*D
zw6v&082bG5^t2DBf*_P126eT#jvdp0S4`p8AJ}S)(9*bp+ubrO_SM9U&`V-r9zSIi
zac;R(pBNpNC61@Wd=OTdJdlWdVz8{JsHl_w(4p$yG*_K%yaqCtVseFzxLTx92RT5U
z>Kks>p7-tRS5|({d-rNO-nf|zM<?MZyX1~&w#r$DY+_@$(@S-?A>AyrATjoihEGk(
z75P+y%BLUs_w73aPfW+lLq%nn-&N$rAfTM@JokIo+W6*N`*9zvcG=uG=8@G5_)T0;
z9p0FA?F*-}a{Z02P5Y!&gfi7Hi1?8c&enwa6j8VlXMG%<oN#n<u4-^`a7bomX09wc
zapHtwOGaYZ&`@ffe-Cap*{yfKA#|q~I=ffn#Ye?Tl}YzYt*faTsuBF@S}<<hsBOtp
z?YaFZho{4MzoIe5ibq)Y*F@gXfqa+sKwVky!6u{JYgezb@woZSBoSMdaHK7oL2-NE
ztrF3v0xobsyEr_LT78lUpK$G3tA&cBuQ4kB^hHqHy=q~x{~%^Q*qEBeiTaHsP4x8l
zM=8Z#XsltF+(qRQ$R;GDZrS^OKfm+bczx+uhq1+ACTCMLSDii8@$)G-2ku>*8d<d8
zC);uz#=pMV5G~=)8KssUCnO|P^Hg;0I%Z*Y%f9jh6HmT%CJbviL)AdxmWvCOdw0a(
zpm7k>j*9Z~ItZ8RSeQ1YTq!7|*DM9kQltMGl`Ea*HduqFIXOZ>GfDOKS^?M=FWYS6
zou}s-vbiW{S>@gP_X_Ij!37B51N}a4b?w5emeOO0zvE~_ohAYbMm|+OlknimDOak?
z+D=EOJMt<nJKG^r?fQcVu7zU;TY1=|iMOY_`>fB*<d=BlcM&c1v7f9D^6@FDs;Y{1
z4o)xnTee_>wj^DNu!smFTx@?`Y!LVl$FMG1aFR-Tdf}HS_H!#4ZDa^1@W^3Uz}-;0
zK)W@)Z38iRNyNxqHJ>`_r`P*FU<QO9iF?vgQgtMG^d93iZQ)1S9oI1REzV=zHa_2=
zAhlN(Qc|8zpFLv_<8p|d)D6I)ohWp$9+*>b6L@mf+hG06lb0?%1^I{{4pixxm`91#
zItW&~cOB9%S9sR|w>G2%f)HNzLU1;eHYH5tZ9~?rS;HzH>RLZ^>(+{(nf)!pS7+Eh
z|Fl=TfPt2VDZ0;+nUV1c98_<3p-Jn~Y|feK>Ehzz$d3)id;^1x$^TlIoKk;e0&uG`
z-GGT+*3;`p3Ra87_j~X_=@R3Bvuw;P6_o?B`%p+F{-Di?s3<M14!PDAshMm4IkjH1
z8s)Ze9D;uQQ-=`rIkq4f=d^OjZ3A;0!s58M1OC@ce4j(l<AHsdJN0!w&9;grcB5Q>
z<Isgg8Gjnp6)ck(9OFin0fKOZ8l!`)t?#tEg|u{qTs(SJci}v$08Z4mFztMM2dXj3
zkwet+$*%R<M$|=py}fKlkE)@3NY^B7VIrP&CZV201+2&(0}twmy&8e6Ni^!G%I{LK
z7=e;|GqbbRSW@ZISN(0XFYyJrv-~Ln(ZHtcYw^$58dWzW>KwKoGg!G|1?%qJryM4S
zu`W(wuotu`s-RF2DWEwpB@d)A*UdduL7K1Ffl-7qK+uihFtNjGRJpdEr~YDoH4;sX
ze&q0VD3t2RZp`I&aH@M05D@wO`}eEky7A+A98SyNB65_uq6Eu-^>e@5OT|Guuj}}k
zUNP<JwQF4CfoN3uAigk3Zw*H}NEbXhGE$jiHyV?6;7gN+Z7b(+AYG-jg;V29VHz=*
z$3-*2@A32Y-g(ZjJuerVIg>g&Ew}DtU`m~T7#Hl^NpbPJqANMiH6`nNu|zu0%@C?G
zE;F2r_QPA$5|(1Uro(w}Z|^`J-MgaM7+rOx<F#wS#G5RwygU+gow`j^SsrqfNP3o3
zRwmXoU@q+dTwjgvetGfpJpNIpx%t6)D(J6$%(b_h>128`v7?gF#Wf~|!(E4g!JkL#
znR)7m_wV0d^?)I58U{eJDoQb$qudE`oMBx|Ky6x&Mz(wOH;tAilM06w3#UE-3S&e9
zwaS%^i7+K~WAE=ttlz4BziuXJY$ZNA>t_i`1noqmY;JavYr-0y8#DWqw;sH7t6-YM
z&}Tcd-ezgFQ9cCnFz*YghGChOee{tunkq0!Opaeqz^j~u8B&c*R7`EtlG2Jnh4iat
zu8@<3myE+-<m`6rbGv%=YKf!A;NT!-kIwU<jt|v)HTpi1E0Gx*e5qXjx<7taUKisr
z{!2!4pY)>|oKRJBM=}ZGp@B{2VCt&+=c$yfAhS{Yg8p#ctm*i0Yj#cA`t|E)u%iw{
z1z{wfAqJ8t&1$!Dsc8{=3<uq6{$`#1$3IqEed|7VP7dCV7a=n#IHF`B4-+I6%>M+^
z-&d-hpW_LT^m+dLIScXWi2W7r)W=7S&tILnl)IYBWwO8Lv%ABsrkkfNspxZZayUHQ
z9jY1wUrVp2%iBvumGQCJEWZ}jFGGuAAa!a`9nu>;T#{|WF!|QZ!YKXNmyMLbv19<#
z+#}#|_nd?2sw2T;hf{;my0~*jPO38q4|e&clq0b{#+QnDWnpM)=~={OoVKi+`!-H)
zA84Sf$I$Xg|Cpg1uSvajZFP)la!s<n^z_V3Nn@kx`9R*v42!N5gUVe8RFmHB`g~=$
zC36OvLGHQEim{Xa)eHm2puwihC9T!6yM_sQOoGgm{|EwT_tw|Ryj!+x3DbntIa}(_
zRbLY+f1s`rjTR~3aJgPMUvhsvVBBHd5cqmTN=m9gc%^!q^IapIfo;ttB<AWNxzZKX
zJy#YHvxW$PJty>AocnEU^}5x0eoM~A;@UNTjEpQ0z$gIgp{CkiBN|5j51qCcs4RME
zDMTOw^%q~=xX~r%v6a|M#1<BjLxo<<@ssV>+dEr;anO~Q^(~o+$<3Jc|K?n$sX9Pe
zLE=d@6|937!XXY`gAXrvWga+kL|MN)cz^X_Pi7HQi<TF%&#1R-qoPVJKwd{g5!pzr
z+w!&J&3NmzcDm`0^?|Pk1`~CPN^0`HX0;}0pZc#m5#nrJ<?#OD!9XFqt6^jEZPp`W
zpR4I78L=G=5i)!8J2*2f>}GH3crApO4{qKZ(n^M)V|gdQr4Ow0S&`1V9D`S?=$t~)
zD0O<Y2l0YD%CiUfg&ihhkRm9s#N`-0V<8cFjgh8;?O@MbOm0%0L-7QT$v;dY*^@1;
zt+hFJmhP4o78bf$VjWX3>HZGtR8%$eD2mokjdUGIbM3E=@Pej12gSa`e`TokOEo3g
z0`BL^qJV$^@v~=N_~tF_)ZGFCnIH3&V^ntDFTOlp@$uEo192O-ZBt{>Lp(}<kS>=K
zBt!b_YPVw|8x~y3*;lV#(H9|v+b=)+dISfI{r5LnX1UBHZ`TE@5_%MF1(_CIg^ew3
zuU?(dDZH1+k5ONRyDWM%wp9cR=#0$WaCdjdxIR^568feuhJ0#YA_S30y5n<-($2dw
zGBIg}Pi)+{F$_9xF6^~h*XP1ypNZ{1b}aX!I<^rh|J&KyYt|heN;L)GJR1B$|5jxi
zo3y7-pBneOJAjQauUBDRsp*thx17rG>H6)0DvZ$l^pBhiE0*`^?&y$Zxo!R9+w3x`
z$2l<K5x%~(?v{vfDGS?av+6+3gYC!c6$AtX_$Fm-O^j=!{6xJUR{h_FbF>zK!KpHu
zwPw}!rop%=NJ>h+N=#0+^xp7s8CAybqOe?3UpwgON^jh}ISxxwTvXJT6t8C6+6Gp9
z0iR+W03BhA(K$w*n~dynyubIncuQRgC8@|7KubF~Bm|l6rAx%r1vNJt#d4MQEt?3&
z(WDCl-jlv4LOPTacSh}0eY{rwS(?S*W&iNbq@=w^wX%B8$vHbadp|7n*cOf3LO(a$
zmfPGbJeb<n*VA(abxXc+-HQLq&J(@2{b*gMEwVQZ(hE<^|1qCkFoNCE)6=7<t%J04
z?_Cd%+63)49C-@ssi~_FpOF8$s*&w9J3;SPc!-C`*xB-dxAzl~AK#NdHeBYR%Y-Ie
zM?Hk)Oh)<F7ClJod2tM56kqm`>9%-OBU}WbcWTCMB*xwkLGMy~YwO@(ghC8`pRvYG
zfGJQ9<^ZU9RrA1NBEqBhezO4o_qo&yR#quKK0d~#TKtVsU-@c(A8^*D#BglGI};GU
z_#)b7f9O9p{o~<59S%?Sf#iwTbH=S%im;7*<H_ishd?OpV+PJCWanbboD}PRCMIf8
zaY#6Kk%ZG}W=s|tIJM`qr%zLy=j?-;I8_o}cG|wa^(&nKZ~%tEDDxP8`tU^1W>Dpo
z00LXm+Qu{<x~d{_QYl&aG-~<2j~;27w>&$u0kM3_+o{TDXVetTRr8&5v6EvabT;44
zo9bNaZdo_H;9fZEU;eTDuyI2I&)ny>-0XtGEiEl~-_lO*{M-}gPB1uDR#vYPLqkLD
zCx<mI<t&|CR*$WR<w%-YjyBD^@H9I+JJOjh$V45CZd+;Ak0TUgg~<8Ex%+$R`yle@
z#iKz=^Ylgj^a_~@9jPN2x|_bgqaU#`qNP)0dnb!|F=<?KOx7!_f!({&H8y5N3O!1*
z-X-~M%4FGIQ0FNBP_urtiERVPUCqtSKOlC#Vqpqha;5e)aaa`0I%VU>T<N;*;`{76
zf5M)vLA78jl6g<2V;9WhwBOt&oRJ$hZm`9K617*j@s&j=^5r5s6gRo==NE=Zt*o;J
zwGeR=`FPD^=Y#nZKQ=&t7u|EEqDd(GsTyCQ+r0S&UdpejkW$^)n}H^N52Yl6S%lGA
zHsJaqu2l^tzyl7pM0&lyOF@*#`#aIiC@C*bYZ`_dtc{e9p!OW7iG1e0`wTr6@MmR+
zX1icb=M+AotfuCp^Cm}(8!jd`>si!i8+5<(rbhO{?|J7=B`K7pdZB_{c_}sziB(Tm
zm%L%n>rn6fi6*0xk`lJm^QTYWa-QmNyV2D-yp1|}(N1c~wD#Q+zf5)f;P1cxPJ_tq
zl-#ROqU-T?3Q_A8+}ngdLl5fQ-hPIL>iD=%V4&gHU}N<qld<vf!xO`;YWwF%Tt@FS
zjz`a)JGY;&&X{i-La<O#M<5J5f`b{O=We;WZh@1dH^I1#X1Olt(H!VRIZ2CDd|h&5
zFvjRoEHw>HO6E}hk2y2<G+52Tk**?%d!dK)Jw<c8yu2(X234cy8kPz|KB1vzR{b@_
zmyQJrT1R=~P+tnd>7r9r3k5Do4&j_Wss;)kF!QyS=DaVH@F*;9{I>RHq)>c;d?tpK
zafsQ(G&MEvm<WIWddhtYUVW>!w)URF>lnL4KSBzXmoGoB70@Ns&7dRc1`eq9Z9_G?
zVG5@G8A^flBZNI#>r5$S8Y@1k=w92EYhU(o6WeO+Oz|-wuxRae&%Io}L4|`A6`w-n
zb?!L=IF*qW2(hBJ38Cs_49_oGSBa)@u}ZGJb-)jnJ$qD#z96i)?dIktdR-&S>LARO
z{zir^Tdt_1Iw@K>(d8x5B$ST1j`dGtk!p@0R#UroXy3k~Dg5Sv4|3tH&2UTOyRx#>
zNM=%0p>klYaBD(o@$l9ors~h<NX;#e(bLO|>)Tg97Q`}b37K3DPD~exsg{w&Nz!>J
zx!3*hm4*cELYyd$PK2KxWZ>sJhDfaQVL5quiJ_a;p{T^kSg+c^q<(pOKl0f`lpc14
zn(W)VcPp&zVMj;DyD8Ji)5M4N;p#rY;fMyIeY6F7s$rrc<a+epKa_n=^&S((4Mv0W
zuswElWp~`(cJGwideyfuaCM(rGLuDv{r&wnqGTa2Lh()hO^8|&+mkCoj@Dm_9T^>E
zA2~W*#pKCD(-vLti(0@#nm6L>?5UJFk{e@w-L#i9aOnFm%LONvm6@4Y^voG{0fEL&
zK((*MV(K28N@|zEGO8Dvl6&P{HAq`=E3d&{wHaUXe`v~wOYTo9Q$UtLVx<rPOEGvg
z_HQ#JF-IS#QRopuU89dL{{ZgTzrc=xfnnv!mBpB1D=RCD;&8fcE(~H*lCFDGnu(F6
z7NyoWkpKg~j!7uvBR`PJ=oRp#w}Tig=E<mf+1*G>OA9ZtUqC<=aal=4MY^pMoB%Pd
zWt00Rz6-sin44XsenO_|+zmEE5$%^>e({Ar#J*(1Qg@Cc7$%<swXyrPC>L^FNBPmW
z2sWlBLYgc9gI!rEV6`VsARn%8OxCBPqf_iozH-hUNCe(%!|KsaqNh*4(8_m4p@6MQ
z8Z880B-o!m8T9n_UdU;SR_2P;%GVqo9_FhUAUfpGl`*g=hvq9xo*}@yDF669d}uI|
zg$)X`vzjDTe3Lq4gPF^s*Gh}9FwRIaL{L%7aYQuoV2*P97R!@+Uc6?xMs@f&>PHqh
zh75L3UpyDV<Yg{7U0Q@~-myjz2d<YSr8%R(e>*!nyR6=s403PTmCQ?a(`}k9mn=nK
zv$(M~@jMG}C)3DwDX1LYWGm1H$#xpS$iBV({1khg|E^{Q9x;2=fg;@KOf9ziF2z%^
zixwm!BjeJw1E~{5jg5`AXs(xxO^=~fckkuP<3Y2VA^W1MIQG7x;s}|w7&JTb^(!|q
zvOnM2TvoL%#AhC#vNk*qImV&hTX%bQP_eoO1qJ2J4j-Mvc_o^&;`c_!#%gc~UVQy(
z9>f;+3zbWW1xD(!^71NR|9K<qyJN9@70n0S)p6oA{1rE%$7fHxnDBJ>Cel`@$smK}
zSFdMGh`gj`L5ar=Z|ppEn-xV%LR~=-QvBXPQ)=`))XV#W%<{aWqm2S)6Z;VGkz9=K
zGttu_aWVRcku{T1*c<jR<f!OfkA$t%?f-e+4<k~7@MC>HhT7LWK5?`Aql~?w2aGgP
z3fRtXejMn+{L-;$D}PQ6tB)_25x@nipr&^$VEHQ1xQK$M)xaPjKCK_SR<B-7GW;2s
zv>J4Fe(zFOS0~H~Hhgl#JDipxK69l0qKJwIv8)e@x$ia>t)>z;N9Twj4vM^(m64Mp
zb);itjKQ5DoscLuV6iy80|TRAxd&iN#U&)}W6g1!<H(=1x96Xn6*T|of_}g=ybYU(
zh!%F4^4YV0k|?L2VOW<EwaT>W>gf@3@ZCTEl(~8}2^ZJ><x4!uyZ$pf8hR3dSy>2{
z_Z&W~Ncd4GvHSV>?1CGGy^q8rhR7{k$6jS^KpXO$ghVxV7cCuKSa<hjU0vNssSv>^
zt^7Q}Yr`=lpExg;E{f7Kk%0g3L54J4;DrpUp5K8<w`p`$7r#2#a1b<gMPe69N_^7O
z(@}eUhGG+WCAvB<67rJTey-!YQ!xT(mvmN_kYy^YKXxxoT3LM5<$^1+r2l-mtVDn4
zKaWw-|4%Q864ZbH?&2T+`+``L|6Rj{jq$(RhO!PS|9eu%I{bfc99~66t)`lVMXW-k
zZ2k4)iIAh$q&PV_ksZl=Y0Ire(>jWYX<;ue{d6&OIX%i|^L@&Kly*tI3vER*{Vpsl
zj4p=>x!uEu53f$%|Ld>E2x&e89Uy~hapJ+&>}&PNTjLQ|`erW*lP|sMVSmg7c^0+W
zA`ED1_cq7T^TbK*lL}@%oVM%tGE`CGNdfoHojZI5?HKDZ1Gqv@XQy18GrKq(4B`nH
z^OhLZLz!7wN*%~t|Mlfk;ujJUO92D?v3zw2ChwTe$yer3leRihLzmDZ(@9CE2>=m`
zw)%Qy_;5Iv;LfftKjcVXn_j!F^?URvF7*1m>fZasoLwAcL^0T+^KBH<e>n{@x{zIY
zici@PE3!I!d_|D0ot<JsDk*ItKq%f-EQHWi;sJ-OoSYAFqX-QP-oE{WuqN3c8@1$I
z1oL!!KUE+9{uqam8!5hPZdOCBqlyep17G4CBH$U|;n@af5ZnBSAT1%r)hkzCf#(h6
z*1jPc_6==P;JeNQl7w)UAZSr4j7}K&`*jz7d$A=r3UU8al%lS@{c|Jf)j-?Rat_5~
z_UB$`@qt_6K;I`kHzrT1j_{Egt_UWNn6zZb`uO_zJSA<5bH~%uk;$ksa;YYrMjbZp
zY2^;|=+Ufa{XcxYcUV+c7dL8RBC!Bb#6peI1O${WFrcCmdL4RE=|htaLp3TW2uK%@
z-Wg!%y{HHXIP_sC0@7g+kTM`3aM$sDllb26yZ8JP^E|`MIcM*^_FBKPw)iqoWlyvx
zA-^CVwvh%nm=!_Cb&d>p1JQaS9}G18zWG}^S!{BlR=}+N+WN#9!^;TIoC1hq^jIrE
z(fEyxjlZUTI()<m(45SQt8q$L5ZYg!1hK6UAqh2-_H#`=jK4k;bSBeB_<s0#$S<;0
z5_!a-jMh$slJ~(aaGeMifyqj3w|MOEp$F!72~>wZwZD#sOk=4POPgz@VrTAL#PV0X
z)k1IPnO5MqOLIp7W1~NPTI+!~0IpES72It7e-C>KmaV7m3=}RTfb{q}L(W}&n^nOX
zDG&8esdyzA@{TbA2>w5pKRN8yy8fmAgVo>NT(WVj0|i15!#~IN{r3<38DR10U%Ghl
z?PL{{dgd4t@rEyy|M^z<pMHDxc+yQE%)SdfcoBrSxVRWWGq$S!kI$Y^L8A_7)dAP5
ziwJ3=a_a%JdiCm6RBSAlykG)B0`T!~dwaZQwjc*H$2dd&7>vM6nORveUYDz+5FUf&
zFwkZXMc_c_;V=purp)_&i2pOV7@2`xUQH|(8@;KT=E<k^HL$&}uT*JCnKKe_bcEZT
z6cHeSkpu}B(TfZ)Te_j#;xUuD)Chy9Mfn{P0_Okk5%V*D8XS!4-Dl1#bJq%bD?edx
zZ*SfJMUGc4>6ag$7L(>(>LQGmNaQ|$2OS3Em$H)vHxv~eZSMFPLdmR66*74&Ei7Ve
z{yvQ5``=$jw~J+ja4GLBy?i9Nt{jokvK(Oln;<TP_WT@t;7PcZ@p1aXyGfCenzJ@$
z0BvIl9h6UxXvr;%4wp3({+ZFhh-m=q_T)L0d2D$2z2ccWaCAMQ1$x2;c8}=)>-$^W
zmq^Ns)nQhjjyn;&o-L@nqgs{T;$oY_6<DF=gD*Mj0$?qR?h0NBFnaW`%(L0@7f|#!
zJ5C&%tGHzNrtXV~fDTal{XoMw88t!aij=l5R#U!7p-|fga9B0H|Nmv&zBOi=&5{p%
zC374qTH7vVT5Sgv{bDDDRZ$JI&E~E@JTrf{Wc&}n(@(&f4G3x<uQ#MY4@>ggo?y*;
z{`~o=i@}!X?qqWObLheM_ZUsMa)NKj%TRXQ5Rb2nPBQaV*Vj%;GGWMClpahw+^WIj
z_X0jhWTGe7q`swZ3S*Yk&e{YdT^r!0H^op81G=Dg0dVdgE*?#-n>Fwqd3<u6#@f4k
zts24;dxV;)_%m4VTLwf&=fY1X;qCUHK$Zl6sHNhe!NK2`1R`8<cIN%xCE?#55uJdi
zC9x6%7Xc9%H!QS?qh1IuxT^2?DI<w><i?bQ7^oTYQo_G~Db4q_1N4sp6QEA9xju+S
z4{09p@0Ay+97yM~B*{ow9&Qrlh@)Nklsynqm*2vXVZJ!AvPs_jRq}N73*(cr;AP3V
zngf%`6%J<uM;=sU(d@|H0B|f)%_uJce{|o`8wd{c#_vBC8U9X^kujM=9c*Q`2S*bo
z50h&aXO3QA_}CEcwDw`gMKfw_cdDqwNl{g0+?!pU)KDtFMT^O#qAHI_C^fx(f)&<+
zembm$>7}Ith_AY@44^<(w)-31M+7BrTmC2FMd>sQb?f-GSj{Ie+A0XTy9qBOKQgeU
zm>w3X@?n#Y2#$QTf32!qTSin(_Q1VR_}aDw!N5zGZG0$)I`C9G3ibhiawx<y%(t(r
zXIepsT$Bs2%i?KfW}^q6=jO7ZR@9jX?a9BFe+NBQ>Nd%z=ysO?wI<9Q;#cDvl(Myn
zeJsYztGf1d!L70Uq`+C#rPMHomBK7(du{w#ug$tF-wN4-Zx&%HnPx@<NXdoy`4qt>
z7b<zZq@<)@TR}qB4^mcj3}e$3VVi-=!On=5k%{T+)gNE62G%V}J1}`5smi%sj1i4u
zGQ8JY;v}*ddnM$c%VJ3;=DtWnVPNU(*x+rMk6z`&Up-?PY(SJ&Vrp98>z%U9*sY;L
z!-*rAz&r|Qs5U+>D#t4@Zo3Xx<{`{6!KXI4z`ib+9PGq4SJ$pI7yvs`hwes1VDR?O
zFoAG)9_iaucJ&8mn1B2QUNIAg0_hBU0sH{!xcfp4ZAIR}qTf8JJ5un(sZRB3=*ic9
zm`mhy0rifz;&WTimWeDD!bU6IBNy4NLvcz2ku3siF0Q?$>5s}MC-%uhX@$hE&tG}h
z*GU;p9hBbon(Q3<2wJb}%Ad4|hcAWY=N~uds;br-FUhJw5;Z{~q2O2J;D~N^)Ygsx
z{8RI@2%yAyP?bjwwgNg|Akg?t19spDcEHYXs>#v_Vgx&+&I39hD9T~mHux?h-UUG#
z4v-o1_#a(vIYw~eLHD|%EIo{4^EMr~Ud_QGis55nsznH``uYwFTKHzag`;Jc&1{KU
zL6}kzcSAvP?5q+wBZ;p)cD=W-xYD<3i(#*B?xnHO`BdjQXT=a&nYg?~nU8r1UZSK%
zyd9SbZLAXo9#M^1YxmYVXcFwk>hwT6)4RX7OHW6a4*e2gt2-lg5=K)iTZI{su>w-2
z28k3A$Rg14=>P<RpFDqhWx&7zZEKrPtm?~FwE>c`xk1#GtmOl)L5_+79efgk=Y=p`
zEBS+4283aBoJuc1#0_jp)T>wdRaN3Mb8`k|iIBG;?J?mPiGQd5AxyMTt{&FfvnuAm
zwWPnwHT>xC&gPLln+G<dl0}(@iA+n#i@4@#;Zwt{us*<|?UpozsY@t*#KJtF-M8~=
z+fYyH&6ZD}?)FZz5QL=$Rq+Q{wegkb0wQBqGFIH)Eo>EOV%=Pd3Rh-LvM^-$WukJq
zl5kj1P%pGa|9*h$*7B%e!a;-HW&2u=rYp!T0~mwAR*m1*f-{aEoktRU;PHgb+fM_o
zHf-;l!w+Jv6i~O$!$zp3p`r0+8~$Wzw6=0k6wtXIkovZSfX?opODDy<G!<Xjj9GRF
zSLo<0%~f_w(sL-3&S>@uTg*IqflSJ}!4Y<<e5fL)G`^T!ORh6x<g`qg4_m!>LwY<B
z_bPA46=k$BOi6S#4LhW4!j$(YyH_DZCLuhX%WoLZ>fUVA`R;e1&kCa)VQp(w1bKSn
zf1aM51$Re-1}IVcvy>U@!9a{ONMBoD=Zol2=G4sf<A+04ElR&n3Pb`@Xe4O_Iv#N2
z<D(J`cC<yy2`?_lo@QYw1lQ7a78Z_iHTR`qG_+dtqkzHjJ-8b}*?t(!6t`ei3A`q^
zUh|njoC*z-VowHb#rzZVd!k%%COkY>Wh?mx3j-TQa4_c4S72N%`${SA!ggKPxkez?
zI!sPwApve%(@RS~Vmq^jYM^f|aL2mk<z3GzUHr#JHVOIkF%V7pp>miQ^GaYh(h@@(
z_k06MJF@!5e71}bIKKA@`|4X_nSf6^6jB#eRaHGbJl;dYH46(KkeLDB&Vi7gK#1fb
zS_xH|y)qW);)9%KySA~B|LU3ENjf@H(mrU%HUP+;hbQfaGLrt}$y69+pfBH#kDJtc
zZ8covZTNRXylxj6AB~G_)59#zHLDB|Unk3TVK##vJW2dBn*G;-t!}&e!^q%mW2>}Q
z=l5ga!<D`=8BhFL-W&c@{iX4<OsXqY*Zi}ScCNd`hfYrUqUFD<Q^yekd_U6Tb#$1u
z6tXBkM}*RF?xi~%M@i%tNEt09>`zb6r_}CN_P^{@-slyW(5J`d^?#KrO@)0PJ)JAe
z_j35rICykZ?&Ox-S_iQxpr5KpL-td2bjKq)1URFBl9X^IBd|UhxL){Y2JHDb5b5WE
zG^m&)1vxUJZUXf`8Cc<AI1K~oT@POVzidF|mniL{FJ$qd8`K5`NI!d8)W!=?5G;vq
z<xMXy7pBSvBK$NO_4lD%upqAg8U%ABHWspwN0w2RqW#??Xj&S~?Fa{Yd8MuaUfzpk
zRx6Id>%|cJI@^CE?4dNKYJ`bPI#hXzLgRVoGx(=9@}(Xql=>d+iz~BD82;Fex(@5b
z#>Cqx*KuLUGoGSY<vZr1RLT{QW3ygEf8~C^D3xj^re#uXDF|h$=}nq#h!-l3ln^e^
zY-8g!qGNUU@i1^N2$o8ZgLs@ArHA>(3L^oxCoDYJrGiZGON~78!X^#g)yUb#%-5XW
zrD3b@og;UrKH^l4&w!FR2asK-2Ss}Xdys{r!M_K|s5~h8)7jf>MQys0P>AXPL^Jtc
zzI^%XE0{YF6)ae{!!|ZY9Be=WL@%lSPqYB>pq@&SUb1~qua_T_wsv9lC-0m3qVf&s
z*S4Y?O@$uy7MX(15J6;Cq_dLUSvBVlv5vaArnI=)^SGats|^v^v<<M}r|t`oJu2E<
zxxPN|O8x@5CoSoNg)D?d(qf}N{yMYUMKsB$3SGH@Da@9ufKv3Mx@+g$^ulO)VEJ9m
zvNhXF=_aiN`L5^31_-Exwl}FeTEDV>EDT37pB21hT9_yk+TOR#Ij)gA9szgN(zWE%
zwh`e$UaS`=DZr;vv%6f2DC3VUb%0a{h22mKV?iOE`fUJ&L4XcKD8<deaXZ2ViZA7t
zS8jl2fPPr)hc^*z?bWZRY;r1(&9YbV`Gq!;??sg?%+BQDVY*nuEH^)9#hyNygUoBZ
z7hO!Z4vk`6Q~qM`(C++$!+dULu`fg&yAsCM-YH}d;_mL&pWZY)&${>5^tVRxR~i~l
z<%Lm0Hp!g@BASY_zHFLl+;k<wp|W+MviqMcL+$17I!PiE*_5{>SD01#>yLfCO>(7>
zAgdsDkCaEqxp{tk#Y}nk)GdCHXwoHclsJ%X862`JM9#k%mVN2ncxxvzeX69;6=E^>
z&aG{2kpS#Tg`L+2+%WV>X2!1o|0e^_qvq{hH|8-#jern$k<{t$tN$1I^VhKC?7f<W
zNV`nVI1`tvZSIhpZonOT%Nf5BELq=ZZ~raC$H6%+@`FHhL>z8AAzj2{89Ul=x7L?d
zGPBNm@!2Qy)w!NhhP`D@!rtBsW%=2rzZk74Q-@u~Uu*v=KO393GLu&==GGIoWt3jw
zYpjiT7iE)E-7hl{^Qlt~&+g^7p~<NZ<+3D?J%N@9Z;c?`ue6=dby!(0sI;%i8;1MJ
zL(2;IIJ-O4JNnF>GCgA(S}}#dRqfddrwCv~NP+Fv0620YXfnA$wO1SV25L62D`kKL
zBd>A{t4PftT(o=c@8^d9UOHYn)J-cnB|J@_?6fKTx-e-NKkYRyEZ;ys0oyvm{=gBR
zlB){avq6fV#$CkKw|vVD4v*<(AAEDvJ!(BPQ)7G|TfuVRm~6l8k`T&irW@iwp2||S
z|GvZUq!b4JC(nlT5Nq$^<yj@JA2NP2GUMvH=mIhNWczyys-0nevGIIWu{+jZ4H!4P
zzQTdj{3<k9Zx4e4Y)0cwS)txF6hI-7UZR>^XIz7Q_G0(ATzEJ-I@2MUt&&nf_gHIb
zMb*{i%QRzX0e{2-V&w`Hnhr(n$LCasP)*Qvx-Mzj8NI77c|SsUXH+?UK;TbWT01sX
z5|2D<PETl}B>rsd6C35nlnlLvo944NlA;5X^{uWYC*-rZG)jcGwq&vHOZSWzdv8^m
zFy|!kk-;H4)Iav$kWoQS(GYYlXpxk7DO_Okf~ER-@^MQ!s!_?5=oUBoyjb6(B>cT>
zg^(^G#vKpSDBmH!n?kLw{qIA4MZ1oRv@Q=i$2WZSry16L)8LnI1nYV8slM=+{ONFX
zq4l0V#`44R#kutl0S$u;>6UlO@qej98WN=(U8kAPk4&#n(;u??Pu?m?vjwQM?S!-_
zOWEen680+O-VZtnG+Z$ZauF*tZ3MB!QbG^bl5o#_b{3Ydt5dhEto~xsPkb}v>73TU
zO5*S%%MD6}F76=g@~+56$3EST`N7e0h;(+OUoaXvexA`0Cs-T>(S2uGyOXNS5_aYX
zKI%Agx@tU#ln@QWi7WezQhJs~%a=c$Cm5c|`<n4VV@fr{Y+)boZu6ivAYMfJ7Gco<
zpAt$I*!B)JJ^*3U($iAzhSW-&Md(sWabiM$(D>~p$*<f+4T1N&k_6U<D@RNz-7f~D
zrXeTDLoI9nFu*xk19x5xe8{{fkz|NXTfQAxv$I&;^9D1&l!xs3kRSyq^2ASj<Oa((
z$PSD3=C3jn^I7B@IELsWGZunn={<)l+CLl~7VYn#li(@ZACHbpWMW1tu#nG#y6Q(4
zb}YPJ`3}?L`}3s|T2FdPC?lcBhL_F7sPYi9GYpGt@_V4zBvn@mQTe|nq(?hUH68so
zvQM7!rmA$vAQl_}N>^^9C*ab|bb?}t$=SG+0tbig#v_#j^@j!6XVYgvfEm2SLV87i
zQX(PTRAkI7V!28}P4&5LwuF7B+qqO9afz23VGTF1_w;nbNgAbVNSKNr&VI5-1y#1%
z5zq?%`(D%tum0@p&o_CyVK8W&FliwArT_D@3*^3<8*i%LEHN&`Yg6o0*7l}8owvId
zVWsZFc$Pi6tH%aML*7kX6>Sz<b0Z#~lEAoRw`6Kt*bj9pc7-jaaLJYR3GAv`O;ntF
z!lGL4X?XtH_727%zhU?c4ytOG5lF5aUAh$;A1~Zeh*2h072+C`80qql7#E6e8Jw~2
z3<Y>aQhA<?hissH=KYYyRO$K$tTW{T-<X%7fa(eLaYf%O^*!cmk%i|KX{-#iB9vDz
z?7MEXXQ+^wJ{eRW#$6P1q3up+lsmKgN84Y7o|+}{^=sOnnvfQn^dbMlgXTS}^Daf!
zOr<t!@AfP2@8M;K^l6|5|Cro$Hqx8RjY5KXGwjV?+`ni(7bgO%T)%9q$~ftI1peMR
z{j#~L+5G2f5!BIf%u2n?q_S>0w&C{L6VY*_Moc2rmO`V0Y}a?gGUstJrqD`<D=JcF
zr|GBI;gg9O%smwxTM8N-0S8{<snI_Btd*GgOnf$FK)^4tzoVlm`bCTp##`%tfPG@4
z(NwK4F?dQcFDX0NsO4ElBtS`9B<3DD#!w!yaV))$fDRW54imO-%(`8mQnm?&cYzKS
zxrrhdTz4kc#TeJ5x;Xrz1$_m~V~0jS-2d++xGqw}wfrhefCl|eeuPJV%c1YOfa-7R
z!AFv`O$|ghdUg7+*4qaW-d(ziYsz0G+uWfV5Xqae=zEEYgq%Rw$1$g3<YnDx+qj%F
z=aVR$U@15^(FAW-w2%qm99@30TkGPYonw%qgx?V9@`vWO8?@B+1zw6C;;A6=;|y8p
z)p|56(r?hn+WS_<)T|;RvJRl8r4X6yuB=ThMj40Q6Q|8fCe;2)GC74=?^=LNTiDr_
zYTM{SqfVA_-_2zE=NiUWWj+J!0fr-3`sMnOR!Papc+e1WOG_JrN(9rN6AoG*UO&Hs
zJeD7UZw`fP$vBdqWDXRnZAHev=~CAliNKeKEPcGi8Mzeg4^VQ8t99N(bEP#S_b<<r
zs($97HHm_k4_n+VR)?|z!O}`xWKvB<qWu!HZmORQREaJt{@OLQCW>}auTqebbLowc
z=-%_FjLWoKT3N74v5wwyqmVQA#XiRPLC(CoDj!#~{HS23t6)^P$WC_m#3}7W6+*9K
zM^pJEJ`&a$el9hZ)1^PfEriF@jHqbJDi*TXs|9fYqF(1q=i#%-`MCJ)VZXaDs-ihD
z&t@TuQ&3Wh0yz!{z7#@Go@*J#*aZ$B5b%Iy^3oa>aFY%2v>?4)arqNJYz=A_0r#xh
z8m8h)FW!8%a7}wknR6i&cbM|-y<6VYtoP8Kas0HTseI0+p8T3}M`vGf<Kx9SRI9Mp
z{cQcj->aq9o<rVSS}nLf2nCThhmM8YgDci*wRaf$3&STPI~PZo9e3_o)<p>BAAB%G
zN)LhOiz!zV+czKFG(Wz&)iX)8GY`Q9yGMGY(Ds4vswkhNs=H5vEPN~{*GGTQ;l-tk
z<F@mQcE$0#1{u92>-jB`lJPF%{@OdF7Xwbt6%O=!<oo)KDfj6J9M=0;m-Z|b#3aL@
zm)dJlB4>{j)SsY(BZkTB=e&Ra=lKJ#Un)VYfe2BN%EEQg!c&t1{-DL#1P5)<S>5oy
z=eI|R%~DCsgQ`s*D7_ef|1-OOXb$Ax%cT4#>3MVN4kVrb6N&Db{svEUkulPngL}gx
zj}!1kO{@s1k8Pv_8q!3L;u87*dRd5lP#}KSz<l=g4hz=$=F?G}jTmk&)z4(aIE=<7
z9ZjXO08cwi?^XEDBT_ydt9%?Kon}|fqLQQZcM2`Bvrzt@6q=(Yo_YbcGl}>Yg>j~m
z@7sFbgt9$h-*dgJ*LOCM{Cf3t!lV}!u@CAI%;@{t2s!QgCtr8x<6|hndx?!a8&OD>
z<6pz_Jy7`fqJwX(gYE@%#R>>v8gV7*<;xsIf+w^8HGK>k2J9`G97M1qgen+PbyPZ^
zaJeXYO%7Z+fr$7Jj}=JpcpWbO8*L6nT?Al}H6JNoZgF#s+lq{J5am@vt#BiF>kD#$
zLHJunG3+Mtf+rRdmK=9PHlmUkne!KGwsmmFhRmZKM9aQ(ovYv8y^uMg8@K)NVZp|y
zPk99&K0{ja%xF)f7}xk#HG{mkRIFdJRRz`5*>_qkO-G}1AI9Pk05#G}xX(G5cK&_b
z#wpXY`{!}<?@rYF*;HZ@uA7WxSj><K;}^>x800JD{A!&Fu{MkJ*Z4{Lh2{Ml&7ARb
z3DiEt9Cw6T+;aYtUKO^}5yDty3K31$)vM|nZAYg-WQ63H#7xDr2!01ScP&_`%mm}i
z&fR%NPXN&8#s@?)28VW%!{u0V%YdHz=TdtW5_oFkTd!Jk63RjI+uxCn=!lyR*W=vo
zgbX!MCHB=cue_y@)5bg1T?*8qgrsw66}}>P{nh=iX38c`*Hc?H?SdM!%zf&muHGtO
z^hB?{Z_kqLJAQPg>p2Dw`Oc-~&z`Qwp%st806r>^jt9~#to}|{19p1XN1x8I=3N!=
z3eYIWfHKsdgfUK5=#FlN5fy)+A+UF<P{?{nw7fb~M`&+%cyo>NAa}Zlc=^$Y2yA3Y
zK?)FDhPNcADDR6z*Jj^<&Q*<|was!9>9Gco2j8i;!MmpLGDH(7aGVSoG|~g0tv)un
zyRndt%&Y&%s5JET<KMo0y8(+35r(DI-#drU%xi1;GEe?JeGQCM;*s&0em=Ew?%owm
zS?uex6se77BNSyn%HB<0uu%(nuGI&AD9OC~k9IkO_5X#h^co*?a|n@W*e*Br=Nl3X
z8S<X7+7@%S)iq=Xk2T~5H`Hglmn*{GGmBX&SGAkaQro3B6fpL`XI~GNGD_T7T^e;=
z@sG)DK~j*TD-Hvp^X!r#pW9D)iOOoqjOW4~k>GANL+UB(d!%{dqC}*b@d+xb$Slfn
zp3U-&&r*9uLSvl>HCI%qKu19A1whXS|FVuK--nA$1AF`Wz}c9J9H9sv-e{LpiETi(
zDWKauV>kuVrUz_@BA#nzVBCZ@3%7x6I7UM=fSmsec0f2o-uTy55MLr)x|XDM{+V8f
zUPp64?CdDG%CIwggzsQ6gXm!?7r8G|RqpF&>wl!&#w_q;j4%z=s8~_-fYenrsLl04
zy2Ka}fd5FB5gv9)?s3C!q0?xDZyWKuDLei#1pu_X$02=uAA51t1JYO3B5RIBRmn>7
z1g1Zd?+T$ASR}beXmZQ1&vnj`2ewC$eAJD}XelN!7QZ#XGVrug3^SGb3vP885<jQ{
z_tw;8C|2rSG;I0Rg^LU+H7w0Kd<Sp?Yik5I7V!LBU&s^g`35mMHsi0LDSX{p5Yp|4
z1%-Xz)Fcb)SH$=%y)(~N53OtdgRUZuCH7nh`_P-QQei0_q?tSWyDMO-*7}mQa^LIU
z7j%fH>7gli{KlIz^+lJJtcM2NM;mI)QAn+s#5AO0FRFE_lFHA--N)OD*2m3~Rs=U^
zFCxv&CfCI;k%G*G!k1+9JqgaMvM?iMY5gj7j*4nq?5`a5*&w?n=6o=0wkHo6nkX;w
zjh~8e=HM0^?~asN0z8N%sybeJUQN>$6`!NNV&9v9qve+D!uGBg-IXcuyq~hn+^x_V
zijEJ^VymhZCvV~o2m35}IA!@#OPbG}L`uCvg_SRE!0vq+ht4j&pxR$KWeWU<BgjHk
zddHtFfXbm!S@lFEm_8vcN3S-vhP$tf8FhyLSPy7nv<r<5&txHwV$!OS-$Gh<=}7=s
z!hOrI0I0>KD{o5}d&g88WhB%N7(VKEh7L5Q)1s2{7^QbK!+aZcvNcLRUwIn=*pJQF
za1+C7=N&tVS5_ipP0i|ka#S|kvU~lgqC)07zZn?z<u#m<8Pwrp^oU{Dn%GIlen~({
zB%AI{(C8`v4ml<Bxi3n!l9%T~6UJMURm{i9%bk!sEW6(@z<TS=xP2|B8b1g7j9(3R
z%^!^rXHq0`dcSX0@)3}KLdOXmOm7wZD?>i=vV9hWTuWyYSRAn~7v%Q_uU07jkn?CU
z!BM=BX)$ch9`Pv_xl?f(-GIcm)pys8#!;`cF4~~mMw``DLv6E7Sea9#3&<n=7Hti5
z3~RGkpQ8PzCF^Uvo{w+ap(0@HY<mr0>{<ibKd?!BMGzj>F8>&(G(qQby8hRi!K|Nc
zD03^MBF*@3tI6p77qZItL>YJN<z4}EcO!U{rIKM~{%&vmVSNnzsYsfsspRvbP3!5U
ztfM+2e4FKat8K%~UY*}#$0fb!1|bE79WPuyNPrANXrZT$IL(R6zPxdh5Kq!RHEL?)
z%ZU7t>ihHE@3NTh>~be3k1zKCc#B(IV)R;Uq%};}!7pAmp*;(~HdzK`h{N)7!Iu3(
z9zu0ab<IsLhM2I4OfH4!)A<hbM`P)FODKc1yTa!CZnrt~@gF{~EEcM+%%`d@rwBT`
z&P`3^3mo()0<vk0F`Qr3QugpGr5P;3y^g*-X_jaI)e4tn<Z_)mu3CGlR%D9^6vF~k
z<G>b{H2r3S2RQxmYB^vNOX8=1%k4!k7@0u3y*HXeh+3S%M7*V=qhq##KLq8sUq@K_
zVf9{8aWig^9zB=2wJ)A#%gAF<D=A4Xp0IP#8UN!N=~K`UKt%|a-Tmp#j@6#JPubOW
zO!Jq)(m7W^)EEXUQY}IkQ?#k=4jRU_ElN{Y+*%eWXsYP=XiJf?wl^8pdyh8WcZ;q)
zxL{q8^}x-gn>X@ie_m=fQUJ<#@xM|b41};c5GBQoGHqnz2i;!~4ez)mjTw3AB9!cS
zz|vXokBO8IIh!@PazfX{wN^&YVuIX4>WPiYhhdrMPM43rRpkcg_~%70?Kkh?!7ig0
zBuU@CHCDDbITr6nRg{*ln{S~<E6@M1X#1J`SWvhg2pS*9X@{cBq%RMu+9aLJ@uDir
z>jCIz?SW(O6x*=%5==$A))vw7p$C_4u#J!9=xE=$#MgoS)!g7o@}EQss~vHpITdil
zt>5x-GqhWMrRjgk3*sLtaYimLk87;2%wJGF+j?rM#EF1VrC7hwleJoX_@fBIIpJW}
z&Bd4w6jEbhLL;&Zz}Txqc>c<pn^%4B&t!*Z#VdE=c@$pPa{Mq}woxyW3g9-ZdXlD~
z%%M2;L5Yib9P(^XM7<QtvNONAJnSv5jxY@qLJOHKb8*xN-Wa^egz&4`i?zQRy8Uc}
zyklb@_>queZtDS3lU`RS2G3Zl8CNZOj^T55GV6SLZR5wxskS(dmc4O@a3nW|>CUc`
z9YNP;Q))Wbx!7zz0g2CW*4G$nl!({;r#odBwKld|Cu*y{>vnr}F7qxu<8d;}K_t_r
z;daQ~gPkCJXgwZYefeWM;OO@N^^Jy#Q~NkS0)!W+A^`U?wPmD>w1IxhGf6n2zx}5l
zfobH%nEk-w7)O@nVs7$5Zw7582Dp>B9oVfJ1Cn}+Dn+gKXRQ(<J~+&?q?Cuz_A8@u
zKNu$bB9@!Zaqt;eNbpe%F#zDfKB3VF2)resBFsfVNyEgLh+is2hoj7v$<qi7L(G%g
zfy|@r`v~fIqu18~iLtkQl_&?0xY}uOME!e3UHt!Dne`>24DK-FWp;kG-iv(XqKLN;
z9D31JFC(o-H$I+*aG*lgdfHQAP(C0pd$)0{Ad-7v%0O(bcWcYI0HAGkz$gr|9x`MB
zgx63zhk?SstiLMp>YVWEBR&al34#h6PQTVPQh2$ZfORF__>MfXc2}e#xyimr27%sS
zdvvVDgs4SbXBgzO6wG(Ey-jGu^A<cr%>St1Q_h_q^2C~(KW=HL)bpB*C}`QOl3au|
zY+-J9_fnO7Vms?y5$*8~G~Sq`*RemOI-0K)-!MNn+A3_ee8i{bYTn><`$z9m=^YdE
z*>b8b-TEY4<f9~pAr)TMWjtif0*j~BnyRizl1CHpy)Y$W7|td~<$}~G4-_b$g#~vz
z-bY&$NaZieeAA>-)l<!Lm|n`;%693Cb*E_E^--04{`=W#`14R3{nNX{x&4<5q#pD(
z9ELe{DPx#DKEc-W`T2i`=8)u9)aR&x#_}7<w$C*hhKd;cjKus3eD0^%FAxoBXuJ=6
z)Uyw<?rlAk#?EEo`rnUWMOGonB<P(_wHY7Hp@jQ!MXo%T?|tc+un`{;9Y<AelS2%n
z?w6sej0x5(mk+tlAq0v?GhN1dcSW7*n6(4cf2(d2`;g-CPoH03$zVRpNf?^TIaro7
z7a4oQK01{B1X<>$5@HwJvx7bk*Lpsa!LN(SwOz@_-++6M&eZ?hV}33G#H`x0qM@PY
zBU!Cxf{yDy6wa4=ik;^ulO+!do{8IOdPDgTkGguxM_(joI-F<-p_;CC$3~Y*roM6c
z=^5J~Z57|1pqW@GF}f3zSNe-S-N~C<NMm(pF3lBalqkgBGn5NCX06owncmhgHtSf<
zVxiZWW$pV|x8kc7vH*v^4CTzQnH{77W_O5|{T4?O$?q+*3p=sG(}m_s&#PyN{oJp!
zBjk?QkiDwOJIg=qxOo|dlYm9S0K!;k-CPKjM+!y@?CbKe(T9u&MC+cxGWz>E%>^s4
zOH^(#Q6$hV@8uG#z`%gA0n}mrc4DsW?cPEy<3g34t@0(2YB?c-F-svbuk1=W-(sJ;
zqzbP0OY?1f{Zg^!=FGpMrheST&R9qBfT+{gFqn%74W1i(waibW6WZ7>7}d%?vj5p^
zW_D4=kLf%9*JX=p`#1tcw9$x4<t9xO8?+x{JeX(HvQ4XI=Nn{#CQBgy5+q*idG*VK
zzwa0FX!H<s0s?L;D&MK^_phL)tUV4rmcOFc#)eA-4iEDt5mnB=_S|@%!%+R%c_4FQ
z53v&B`sFX|Ygq(vKo-3B?K&%JXKze{ReuoJAJbX8-VephBA2VwL)uLBSUv9u#RhQm
zFVrcoBAf2I>0iX0Py>#z6XwL4@YsMdhYXn@&qJ2V@6O-m$iSJ);(5-&tgq@td!}vA
zXbFOj>+dV2E~0C09)URaIz19589i$;vsE+<eYx@>^0x{)k5t-ZG!!t*2N(3jS8hJ-
zEnRQ#A46V<$%u+(&WGn39@P>HQzKsR4JSde=JL!DLEJ1S_^GW%)P!2y)E4g$#d5b?
zFfqnE9EQ9!ugbWkfmJY$|2^%Y9i#Tzdo>pKEm|JY9>WM=)qu-Q{48}5G7}yZ;jmEt
z?IS<v*!|n@o(nj$bgl3jFI!X;0?@-+ar_S16p*W~jof>zLvxnvJL$s-$TDN&9WsO+
zTZ~wPq9!3lP{?)~vcHY1%#c>ZT3kMgPO6?xd&)@5F+c1sN-k{R+xP<cN8z1=*>ys|
znf>$WVtz7aeUHv}hH|2=6h*Tzebfj1-0j?j_9=jLh^g|-Pm-1R)`tDInEC*h&P~cz
zq<;?c*su+1C{T+R@znnCnho!TzLM{QBw&~&(M;2u%MXV8bXw<64bhTCyoPKJVubqQ
zVEd&}LjnHdefy@|%`N3QE2*(S^ZPOK2&I3vUnUw77QN;l;<qkw2vxqT0v&{^*lnBD
zF*>Y;mX{HUXhMTF6gj|~wqfm8e|o|q5dYZlRT6|P$__5i)!a=N0O6WizZXCm`h}Aq
z(SS9W520WAzc<YUO7b5rt&04Q-h@3q4bY^RX+~7X;vy|VR&rpERE(%*c7-(r={8Qx
zejd05Kb>V)U>%xdN9IX#R4>dzofi7?9>#nt#p{cHDPu^0L<HHy7NtMtL5>|QQ*|Ff
zgPvrj#(&vA16y(Zs}rLG5+>`}E*IPCjxdZ==YAtso!K3Z^=sjg6d3=Wkn7a&6#Nge
zZf@}7B7j)g1M6d2r?ntLE?`nz{G~9&0_D1P6R)Yxg^$~Y6gwl6G5bz5O1S^j4Qi?K
zugN}?;k-vXzLu{}q;GunS#3jZMtS*UO7@)!9odyhx`(_G_s;Fi;3Kh~o>ixMOYAUx
ztzG$!1j5>b8Z~A{kuVTTW^G!hius0IUemv+UlFq;nab!TjQcAby4Ca2|Cj>!>o5Gc
z0vbF~v<QH7ctqoBt;7RIC$*Ir3J!ZNClg6xp_+BES(!z`s#`1cshn*!YAqwb=0*wT
zhrkbBu#^i_IB%eIKLF9s0-9tO<S;&H&?i@>)jKg7EyyxRiZUip#)jG2Kk(_-d%u~r
z0z}-O3hFrjQLRp9Xy5eDg=PoLVx6xC!7N}P5}B2;R)5;;?GGM``0$Wd>1kt_Z^HpY
zy>lNLV<wxqRt6iN@yS%Ah|X#{N-q)fxz78$K6Grz3g$>)aJt3wqXSTK<SJfIJjQ%H
zFBBgb%(?a3rOk4Pv}M~*?P&ivlp`_Zd7Jh0PyX>i(5UL4%U^%$pSi1A&b?2Lp#bpB
zRc=3xZeu&FW(0JG@@F#s9`m=2{TjCTMs9`b?K`=4u&PK)$K9-MQOOX__1%Ca5@mD3
zAdDT;5}|gR!(#BXOi6mO@i~$@kfI$#fxPV6og>y06}sn}zn$wGKV7hI_jy;XKd$U@
zfuzSBsG1NgeR#)nq};FHUXHGpGBj&thr(*}k-e<$>-ZsgRSSAq?1Zf1CNrs(G0o+R
zR{S@gQ4v$6qC2h_4WjH}e<x_D_|H=vQk%;B5Avy~^no`YK(MT^qXQX*`y%||N3YL(
zadPd2Y#ZN(inrcedr*M#Xu%|mH>uj}rT#K1mXgVGu3v$L-(0a-{?86j53!YbtT-l2
z?!gb*c*{{Qe$vVERVyvC`jk8I#&-wx%_W>9%B0Yp+Gp)1zuDQ@f!yInva4KFq-6|y
zeaTu%i@SB+dDBP4_ag<pt^UKj-{E4Jys!aQw95XIWIQ0Y1;I+)Vk1++miM0y_rC~a
zKz4Re7ww&Qt@!kDby^Aag}_2!7+2TaEz7BS^V9WfXzR3Xqzf?50?AC!8cv%)_x_-9
zNtrwskU9gT8!#?Fy4h(a7ywNFUj?TE3$uUxrJRS^XFd*TF483Rz=!DVGTS{sAhfdQ
zc<W%)N2x31=)g-A<$USa_mN`W@|CfPt6Hv}r*`Y~^xfXkJp=MYYC*@V525i3K%oaK
z!vS)?CZthzs1te14+`WCbj)Q^=erI{_PEMm!%botQqjaxwf8)vs(`c7X}QSTnW-H6
zzN5eyBGVdj3i8ASyXoagtGScISyg7+Z;f@&aDtWq^fVtk(+~Rxsb{(;fr<OcXz~fM
z6G*eGf3BQg@1GJvAZ7%Y(e&PJ8H*R<zBPJy5m4eo{YM^+h-oAbP$=6m=jtmriv*q4
z6hmafBM^{P$K=;$a`P%P{l^TIV#f(C4H2KVpO!)tE3BDNKQCfBGgL67DhsX6+s*SQ
zb#;t>_AWcnRR|}YS&=N{lB>{^bs!yE*|Nn}K(*YRhRW0#QtfE(TUTt_SWlXY7rJ`u
z!oD=U^qGEX5RL%(WT?2ASj3QEDY^mBkbcF=t@4j;_tqzFAu5flmR$xRi4D{KhOd|q
zv9Tm<X*}BH9`Aap%*F*P*ncRFqc|l6OjsSB_WBJltE(8V64?Z1CMI)Q1ImesJ_hfH
zdfw2m{i0@2C2@_s|FA$f7uSdkUGQd1k~gpXNP48O#TX~T-?OleRovZr{QR?2ef`c0
z>2Zd1@6vlSJQ4W7(j-Rue8w88_YZ@5beN#otTi&+E1PFL`MA3B@fFSF)d@S^d>K&E
zgw;+`$ax5_RbD*`U_B!59v$NPGc3%CSAc{G4-LR%<=2WEk^$ITHm*_xb;AO@@R^lG
zWBK$lWeDFE_}@qxj1e;eK#$}mZ*>gyWE|Dm)|s3?lro79j^4ZP1gf|*@`!ij@5>Og
z7!GgkR9?=z6A6=YE?@eN6aU~odYv$<)2i$+>Jn{VUsDb9C#!oZH?t*y+Ik|n7}0W|
zuBIh2cvM>r2uw)CMVM>)A)LB%`Xe-)ym>Joi~-~Fr)mB2Vj+~(w~cqP2Kg*Z{R)tL
z%q(-QX~ky=NT(PT&++5xhh9jJoGOT>QXxNaWOlLp(=;d~?Yf@3<E1w8mp-Jr4?AI#
zZoT){=lw4!L$>q44}RzVcuh;{TrLcKTV+L_@<zI(Nbcj4KVP58IrD5b;NE*Ukp*eZ
z2g=ZVEM+k32U6fm<>NBj`VzsL!FD;Em*w{xmJz;{dgYT2gy;tx3oN>-iU&igGD|^_
zeck5aX3OsX3hHeU;4}#&htc<nAX}V=z}_LL)&H|fC{0bN>T#%~)Thfco--i|1VA=?
zhe$HXk=dR$VOGE}&J3xUkH>2QmG>#(yMi1^94#nP?LNx=hDar9Nx0xs(OhA~WXQxE
z6=|}gA`(}<jkP1*;2U-Zm86>n{*T@1^V(V^-r#&_8efWzd?e2%AzJtnLKg=P9vEsN
z5D7zn=pJAJph3quWoXg>eI;VjO}CS4ewgn4N<Y9>7+K1nTnOnv+sh_9^1dlk-<yLb
zCC1U^%EGKML4+sc*_fW!pT`fLmvcWW5w`E7!FpIRj*j9p4PSjW$UI9NQLc|i#E8)V
z)=|HL%}k79#Yq)dqC<Ny^~oC5$eurlS!k|qF!0wG-RS==l(b)Hd%bv(P8du9-@mLc
zIeCG%2L>8@&pJnCuqwD|n&l_R1&a{z{b%|K1to#K3rVuseInaaGOn<c5vm62FYR|l
z!Ix}GyiuxWQDPE^mJFE`%`MY$)O+hAsGaFh7Le+z-=;l(h{rIx0oMRW#3K|6^gdOr
zdGR0#JekeGXKkR39kg+Kn57h#1_D_laDm|3Q-1({Kj-HY*=nf<hzK1zUZpZabK(&D
zwuks4OjTD!{4CZ{?_~uG%1e)QzP~WlIBQ2pL9LabCq}W&0u4`aoty9lSw<QTs|Q{q
zk!aN{Gt5BAgFObf_N=ek-*F@^qdxM@=)F~=cyhD(J$<h)O1VoV>mPO+fn<bTk`Q+;
zZA@#S@gkzaIo|QMOyXOa@~Yi_WpGn-au`Bqg5d?pZ%rJ+qVI83?n0?h#yMG*R9#We
zX)-H&mSY=$0l@1moJ+nbqv99h)R)ktzS|M7mkx~B(9km`Bsk1B;N2EWb5TUR@}pyn
zLWN8L7iMDp*p9vCMDJ7$LVKcA=<bYoX}dvbeyo!*-~veS!C?!HRC0ksLb{b8p4mhR
z^AY7ApyU*b_V=BAujnTjwvq_|8t!7n7b%O93)#Dc7uuNoN6RVe17hx51A&!pk&XCT
z!V;v*<R*jKg>^}<2(wTP1VB(|Pfl#S)}CcEJ(GYWG1Ok)aWh2VcOL|P-Wh&3BC6Se
zV@U-;`ux;LE$&%Z0&$TEShob}p)4d{&v@R8(@LLh?+~+rl}PPng8*-Z@C6t%$pVim
zzwp4{k#u$K-S2#!MR<wrUEO-^wQWOG>AXJzLxYsnc$0}=BI#8wfCatvCQB7S11hr#
z?0JlfVD|z#L7Eu>HP^t-+$U|0ijFp+#pNfG<$UBd7LB*ji_@M9^;hKh!b~fF8<dMa
z$N-u9e5m9y(zxt2_&eZ?OFgi3mOlm2*2KgF7S6{Yf{_?u)1e9Lqmt56oK^qdaT+q;
zAPYEz__$=|JwQ{l6Vjp`DvYyM0+xT$-~D=hw7=BziFWZX{v}u9lo*J)&jzHV)j%J>
z1}LVc6O&S}4CIVUbdQAdH0k1$zAfKeh$pUh=cp(L`Pg-YmmZT0*TC#TWfT7uxUDiN
zM3kkyyEvZp(AUq!pM_!gPN7QZZDTR`?xEsh<h!>vXd}|87gxG!!>r#YEK-^}#pCRB
z$JL!bhe&2S9t@=of#P>8AQ^vSi2(!VEK1j#f*FnWm3DrDclmU(50d?P)=KErFN6FK
z#IdCo0<=0<H{-b3KaBp2IdCzJ3K)5Ld1dx<N;ba!#pQ9{9OG~j$Ze9CdQ{-Ic~%+p
zsx9`Wf)j`7BeiB%wJY9j*!q<7X?O8Sr+{gLdrEL$MZH+id5yAMV^zNzn;x1KS2mF$
zAD-QZ!|GQ3tOY1SCejxL>_3ZOvK@xSzYK@sY=1TM;A#6hE`1KD`klS4aS?Epz&{b9
zl?d&7^PGOLhWR^5xGmQ$>s2Xk`IH-Q+2ybd0d&#OxP?JVVF*;i_a4I$Lbbu^h#STx
z`CLv<BEwnFdPV=Rm_1xF&<TGCFrMOi7`(i!<5WThEn0JuDxsbD0qRN^h$k$S#tYSm
zG-0$CeH<7QJ_Zl{kS!2z0R*to(YTWf?TK)!ZFA0)l>?UU>97MW5US!<bH+ePDq`C9
zlhIbHUGzJ^L&$sFXWbmFjK^V@VPaN71b+xT2dc4hhGYj(;RC<uT^(j|y%)|_grozG
zjxIgD3bSo0s_3N44uWwTALsAFg1r6g>YVeM>)f1Vxd5!MV(khr%>cb?_2r;|w61uQ
zva~6V+HCTr_Op59ON}yLALr&7V5I^>(g<VOr!G@L#<MKTS35FdwV5X-n6)G#)d>eP
zbJNpSa1N0^oPB|~x@YE0zTUR~E{AxdnFwyDZfH>I3cZ7PFXc=#^7czX!>?mUk8+FC
zP*bC!trPCSEuD(~el5iF{mJ9Yr#E?;flE}-7|Ko;PB_ciLt5`vf$Q32h~JmqaFiE%
zAtjXGbuJq-a@LKj9-#9A2NMFC)MWd0XwZ29O*jas7nCo&*VuF*+wJ{A<132x<L4dt
z#@j?;wI_nk_I4*oipsXfWua78?tS9rPY>Bkrpa4ZPQDScD+r=0#vWpw%p|v~Buxee
z5l#Qn^S(tK4Ub3%cE<F_E%!+vKjsvWiy&wN-x~<3D)zjU>$9FDo$Jvavya_cckP*N
zdKO<TtOW?0Xfv%nNv*UcFzV%C5~#?<d{H?L?(f=SE-U<tZyIKK*@BsH&rg1YBX?>x
z!(pXbA2MfAlgczuTlQ=;Q$gC@R?B|Edi>AQ7I&>uYz6AF|Jt><h+Msnw-lSf+*78r
zO2>K%>JKl8^9&lG=eo6b(Ze7jfTlZ0cRtsh4Qn508jcJY4U@Rq@8Hyd4&NeRUxen6
ziXf^ZA|nPJ%AETCO}jn?MZg^+x^Km;L^W;uh8j^kj=Mg=#%lKt0ItT*h9pfRgiRfD
zE~<`b6J73(1b~3Kt?lL^wNV$^{Osk2lsteIIF=V!0k!nBZ%7rWY?)uM8Q}$~3aU|9
z3wGH-7Xpnkc|ICo{5*?>8HR*MkK_UF?0-rVLIM3fq-J&Boi|}(x2(7KBSA_fM@_xS
zt8-8k_VV2c<V!%`JbapU(nJmqHa>v0=zIMdMXAfkJ^MQzvQhh`%TK}6n%RPf>T;k`
zY(Diu!FTS~)hSAA)6A19(naoC#0-Qe3y??JgZX1gTH96$qn%`LP-HYd>RkR;!l2mP
zRX$!F#5V~K@Lu2XvkX*>1L@DOR=?5uYG?f|Hq~L(2#Ds{#p5M%PBJs#QpIYeQk@W!
zpdu29;AF6FINT-`-u3s5*?rt6`|;!BVt39azaIx{Bw_%9qeE^%%L=d=<J#~2n*rwh
z<krG&-8MEQ;l{67Z!pluhf2T|W-Fp;J1jUjKG=#!r2s|I-`}dNi4`L$qY`vHPra>v
z&QuTTbl*XG5FO2Ehy8LWMfChwYH>W#S;t$$BAnDP#KI+JDTe^@B?au`3K4*!Kt-hO
zzG<zVN}DFK;YJ(*(vz{4%BtnmVsv&GmnCL6wpRFGScBL0a*p_r=e8)9yJ7_3bjSj5
zH|$G$>+bl#)u|s~Ijgs5Wf5oVm&8CGNRi@qcQ5b_dl{B;J<iF+d&Nb}NN(?1b!NP9
z;_8qTe~B3Jq7xhjB7C@lLgb~BaU-)1B!p)Y^&Oibcbx$ZBYK{ju*0F0ty#eJ5fqy?
z18`nUF5>nB`*KYddQX9ACfd^B<2A&~_a!n{wAr2J3tFXt;urgRM(}(9UxKuj3DQ8b
z+9|W)+nOTMY8y^Sh~WQrN;pmlX<ijbrCLfdLmO?f#thmY|GP86zs)B`*fY$}8hG}#
zj_QS-uuC7oAB}Pr!Tn;a>w=(Sr}7AL`CK2wCF6x55nrz2RD&9PSj2Gz&IYdA1y8A~
z8rV*>r$AKvA8w!n$8z+L&M^p6t#bxx;12!*rh8-gQFCRL!u;v2yodyc!p*H06@yse
z)wi;Tk7mjUKj_JM(Sq<IBAdD2CW~StFD-qx(y(u!M76am#B_CtErzdbSvNn7<Y*Cw
zA*6-(7eigolRy4;vNMVF!8}!;)p}%^Dzo|@g|=2de^}H9Lts&<D1=S%p$OvQlC2f+
zv^5K4ZK>x59cWh?yO2C9V<zY>7{V%J3}>9EO|^tjpb6?4(iq{tmp}T4Iwd|@XPN@v
zilikb4#C8Yt8W;38`R5p`2`W|$4)$tc7VCOI>^X=|4I8?$7Zq`cnE6t$k&xW4{NdT
zNAFTr!-cG|_KrDjx44E*k6(NJeS!b(m#n}HmS2%JFs5z{kYyEkXUCcufVV)Pp(cs1
z=(q2KYIHrZ-mzJt=oiw-k$6aHHpWq{2m=h($MOzwUJc_0JrlcNBA8%Yzbe%fVrFr*
zI~Cmk_T-KFWQYL!Dq?B=7yy5Jl&;2gBlmjY)bxO|%C4?hc0}8}+<yx(4UFJu7XZN&
zG?4Eu=V<c=y?p$&3a5w}@DOZt5_CWYln*=7S;oj0F$LVo_9WUZqk`8vN%X^h|8=3&
zl?U1l@4Y?)0ufUD(?0CZ@McY>&iXO@eUp*BdLeYxslEc4=Qa_k4ZZ;_!eTZqt>i5h
z`f}9}6=79*`5vUh337-y9GnpGPUZ&zv^K*0Z*WkRiSZ9%Y(Z+C<x{P=r!Ut1y!Z_i
zL|Cn-qfnuPDIV>TW=G@*rUOR@l7Q*x+?fZ8tY$C%dU@Wb_GfWGEO*s3?U<QHDL+Gv
z6{^4;y2bZOzF?<6bm-VBBs5WD1j=f_rGydiC%Cvb{sBO-a;A)~_Ny}hbbn#3FZ%`{
zHn@;a^(x?{nBiwge&4NeS%?m{frZ}<`iFlGQyl>D=B3ZKFmI|yi|5KDH(&YMdMOF0
z&g)LdH$}ez`h5z_c{P7KZLYyj9mHVGtQf0n#UWL@?FmHCb}_RH@TUNYVERzLjz9m?
z(LlYKrs#J-(dxxAamxpo%7JJ#^>3hvx>4eJ-6-tXg1bS#9s;C*=-n=eb+{oM(IBnl
zU(1KII<qCqV#XTq+`Y2hm$OPIChn;-0;{zM$lb8hG{}ON_M}Ae?gi<4=&Zf=SM0C4
zwf^m!Hz>6f;Gn#II8Hq}(PyHi>B<S1HRlgTgkFQg6=`Mq@gtAl@gK@A5!pL#V^1)F
z2BuLC1t0HSNB|Uhqc#Ef_*ujhMZ~8SOmt<KTXzQi#y9Go_O>uCCNW!}q6N6ItK^ZI
z2K&aECjq~TMMS2{Ru|R+*a&KEw6{hUP;_80cz*Q_Mk{c|^QS|X+7f;At=?o=%?v#d
z=qsqkBhL3voao(@SD>k?;#U?aGLo&|Hpn<*9~^zp{41DqNDlY1DUe7SjT0J&&-|eb
zY3|4Iixf1s^RUOI8%-cM@dMre?vaWpoF<Xo%z)05M;UPk6%IO!HY1voYmpMUMpNW)
z%5ZUR*uIw^t52hlG5!E!kOoMQ-u~E6e3b6rcR4z00jz5psaZ^VQmc@^=_hrq^pBys
zXJNbMTdtxg1AM*|u+-NqG`;Cq#Sr)Q9hWcA`n=a)l$rwCjQUlZRmmGhAiOVWIj}5U
zuKGI}>x@M$4_`(CPN7U5Eor1eC^{S2<$6>iZ<4M10w~ck*FT*S;q37S>m-sKA>AT5
z*bO~`vL85AgVG)`qC$5W#BWR7oc$MN@b@_%kP6EiL?-tDNpl?{U1jVA^t#M;{3uGe
ztQ_c)dzH6RA=P#`i2W39!SP16=EaphJZORd)W7CA5P6m#nL`y!*KnND_VFOIM2I%(
z`i>YaKOPQ60Y2Kk_QTn>K@RHUvWPwAK$N<pAKDJ+q;X0w15@=ZCTgu)k6DZhCB&mE
zwq?s1h$)l(6SS=az^N8%ezn?(ID8D1q>u5|xD%T6&AQR|<9YlLNSg-mSaxB>v&#ZH
zEaI6Unq7Dv&9n3!TmXq1l4SgcuNNz7x&#NHMdIB_ECb(^)kRUSKP^t=TgsJ_LPl{U
z2!+>x>dJI?4*5K9sp;g}b>?iu3~ti0gD|9?{Bcay-3OX|r9}jVJX4gw8_0|nuB_D8
ze4sUrDVL}R4e7!B$W178c~y0|g4ZjJm~+CJ85hAG2re9tg*}Gevj(PRYp>-|G8K=&
zM|YEdjzg4L1v)$2q<7u~>N));gAI_yn8~Ta{{CJ+%b%lD^||7cr93sDv1WP04w?7K
zlPB^TH#D|0kI*pXzzC8980}xg>NML={kvhMSV%%4h)5$vPFnJOj<-exCNnpDN>M6Z
zp~nWmq!%48brPD0fT=@&v}8X`k<Oyie0sjsXmtncl~0In=2$ROh#52V`Ez|=JYW5S
zvdgZ>L-I-8%6`w;nS}B$ATrtZ$^g8*U~^(}K85N?dGFyz`fY@2toA14V-hu%*A)@P
zZS%}8s$?>L0m$@}AUBiZ+p!i6pxyS>|7!Vs?AzXp-cm)sr}lD65qBDE<@<K17Q$2Q
zrYAJYtsPe7=O}>tLobU<&LaQ@E5|h&(cv+93#JlL+YcLpHe28HK_#Np+BxKw?BP>+
z2qjkI6TP%-irX<R#5y~jMTp1qiZ_vcuz9h8r4wtfRb)TUqL$gVw#*-@f2BMd`k|!1
zQ&@vqrp9&O&BuL(D0)yU?ZE>sz(7o82yvlSbc={6EmE!9YFUw-!vIMeL6Z-SI9xNj
z$F1*Kg0isN-HU?6ap4QMfCjZ=P_%=}OR+Ro&T7ba#O=GZ1F^%r*z}E-5=?O+^7lyb
z@+u;yg+ep6a<?V@IKtVa{6^jSZ)tD{@H`m2NlTUEg|A<~&ST*JI|Vu9j{)Zt;>DvZ
z$2YZqC{Nk|6G2K~U`5YkTs(PHB#^S6WXh986Y~TS2p*7XU0pk{SYKNzS6i~?jda+;
zBvl>o(ivTqx6UT~sjX{7Yl)JS&=qspmAsc#m>s;#IH<6-;gAdY_6D&+7wqk(uJ&hV
zW*PM>4j#uW$)til+!ZE4yhn87?Pytf#Kp6K>~4?k(3?ADd3c0aN6Q91gZ|Ij%wV7d
zi6o+nnmJhEya0TZ1>%ImXmQsHBSJHx*q`Y)<L75)CZL^=S?;Q*g)BmQtnG18CclO<
zjJ?@!i>==Nr-_Gl;gV#_X-g2X0xJ1DsiC4iYXieCC&?w5tkBjAj=(aW{t9L*1lWb~
za#6<ZL`#(f5o^tT099*j7&)1ZPUVXBmi}HskM8}Ir(9B(jf<3S#AVx?6v^%?coc1X
ziE|kh^{Yj+UPOuu_o_aC3mfpR;~THld~%ny20g^Wc>3^SOcKFPrK&Lt?Ni`g)hIoB
z8swWr`wsW|CT@jzTc)#|^Dvjb8}A^xv9zgMziN;{kkmwUbu0|Mcb55e2FB_0xFXdJ
zpMBwh^9YXc8CIzwClK`Z2}mtCU9J8$`8zZ>N3`+?W==A6Rz}mDbvu3LOxBARFaDYW
za|^UYDn2IXVy>vHOoTQ;$3l@~?<8Cmd?To#xs%f0{o_D1alUX`#LrSG0wFhKvla0r
z?ma1P)Rf+@zl-2k)F1F~cmx^ivlKG-G&`7@yli7vULI2dXD05>nEA*+!<D8hV6)_|
zuexlQtx;-=5vrE+PdZPZkIiIHGCb|RSqD`Xj@7N1ciY=Ou61sSoUvF-HD7@{3`Tdf
zvtMvOX~fh{!Pw!`Oxk%<38Y-&Ek7o8$WR{c>OjF=_=3^yR3!0r2iixU7z_J5ns%Y2
zAPniL&~!T1Wi0FKy*Iu<g;4$4Pbp$C{K>7AbD{cnYLHZ-g=QTfw4q!h+8lL?;MoII
z`uBq%AFE>VZ3|gM0p>%+z(s`;t}0(<9@HpQVXxQDf^>aU!Y|=wSnRh?7Ek?{eVjqY
z>*&tEHjIG%0GktJ-2dy-??9tvq!->UtJ}~HS9oa!|HtKYq|DA^$Y+4vd^9dkCm0)#
z;=Tn!%V3{G%6_{R@md*Lzvmlt3I4v(?(f(%H#f`k#2YTp&*#7?epY}=GsyVX85-eQ
zH4*(Ea{1?h-0ZDN2HEOn4Y1=lA5(`2;-NzlvEk_?ZRUR3+DW^hE!<SzSuj<XUkC}#
zX9L`1XJQOTv6<(tFX6elDxb+^doBR!?*QJ2$B38)`r8<3mp6RPSVxo&`$bpv(g7Ss
zL{RmAk%vCYL7yO`w+##9n0Cj3L@s{+c@OAfL@QOn83+5)M)SZqNIAJtBUp23@NU7@
z4dkw|G|O!|QxIScm1E}7Hf~fvE`<B~<u^#{y5C~8d#a)0`~R@@-Qifj@B2h3*=1%#
zC}d{KrpVr96S7zKN)aNG>{ZB~nc17NvNs`nmzDXuUhnGtd4G@N_x#b((eOOa{kre#
zI<NCQuj^h`e={tZFcd`>gvcoJt0dP+GQP-sR1{85MP{#TI*&N}EQ9A734`C9U;PjD
z-uEfA{R3?f-v@MRN0w{PPcS15U;x~eENFjzI4n1R7Y%i_6k3lg86FF;SgSccJ43gU
zZTcHjTSUI%y9=o!?F;&ftLj^h4nq8@h~EdEE6;BK51%UPovf#mcnG_EoOK6j$?tkd
zoKAG}gUEFXNVppZRspR~Y*LWsvjT-*RA2M*VVKn}lwXGqmLYI-5j_W_#BV?4)dbb!
z9#6r@o8Yt`!IzqpqG32ST;jTf%sPU(a@mjlem__k=LL}eCD@?`rz*ZbbEJ$L=ta=!
zvATRg#VuD<6p~uwW4C<{MD$y<%KW-+@MdF;D6Q>9`AzO{Fddq!WiTKE%aAr-V5pic
zRGg$0YEL)I?|D@r>D#7&BQb8|Ez4_AO^&;i<I0B)_kO<l4$L%?3?@$=>Ny)l#uukT
zu6qYU?_*eBq?cJA3Q^~z-BY%Y^q++)WGE4@lug&F-Vh&*zKd)Ca09w-2B<hl*0tt$
zC>uEFobaqUUHv2by~uRwID~PWKo35ewg*e#`{I8Jeqe85@Bl*X<3(eeyYcQ@<j;Vi
z1cc*5xBViO@pv4lB{?3teM>_uBdIUl@P9b`@o$vc#pelrt@y_rH#}N)q@|WnnZ$Z{
zxG_~ZA7L%vnSL-CyVu6Jn&0Dsxcd-JALQ&pPDkq;g|mW&JSYA0ni+pIfWuOocW#z=
zmSD}9fABe9Z+lqAI#BNGT+=w%R?k`P*j$PRzjBVaqWKV;p+I6BxF$Z|Ik&TxX)su7
zXvXx8DJG=x!IblNm<&~f53H6sj3YsAhJXn-YU)km+^T~nXNE!RV8cM@+uuVoQ`2R8
zy8HiB%k&y(&|Nl!=vqh+%uVtt>%n-svLgH7Ctk8b{|^28F#^P<_OB~Z*kptG+AVE_
ze4}=59aW1}c;k*fG}viRIly0E-W)^OD+3c0M1wwG_s0REl}#Qi>uu?Vj&!K^7$r;%
zYyqpH=coLC>;W*~0Cw0w+;;+$%+UO|+|-)LV*(Zzu@?E=9w+1}*d%_pC|$1U#Ww7)
zOPLt+9$L}0<e$_n0XOMxqQdC(_~DPbDr;pkL|6_TajoNp&R&EaIJc_r&20}q?MJ<T
z+AtT-F=d=wUtUf8YF7CA{9HHgZS`fjhfy}CuJI{^T(TkdF3u%48+1O}IvuvFn#UD=
zf9l+t0CNzG`vnm6t!sH8kp;*On6u0wDTa~D(?dO=U`JV-F(I=t5q1|0t4-|RA#lQz
z;OAHO4tD~R0!-A>bQj4aRX+(skJ8HhzYhWAsyhF~AwU67`o0iXlf=sPeckhp8)H-g
zYT(^DlBdin*7`rWKzf;?4%q$NdL!q`&+DK68ZHs%%6rdM@q-{WSgriLyVm!G-=skY
z=jw9!`rydo=K4$or^C+57p#*<9~$SSUaeLjE(aK<(4t<!5;SV{xM>F6Yu7AyNGho)
z>u~-<{`!a(Ou|r7)!+X@MuYNB6yDt4gxQkYhT6ppX+o@oHhccJ+evJQ*CQ{i?`Spb
zM%xQYz3Rz?olVPeOWA@2o?Z1JN-Lc+P2a<*j!aQXNVle}*n{pA5qg5hwwQ5oNo(5i
z2>^XSV__L?O8(miUh3%N4$h4pkI-dx3YP@V*tCDFL3hid!|ryuw_gK`2RIy;YecN3
zV^;ubbLZ(R#k!;$)zon!RM@<k(>OE*{JtoW?GjT{OPX`7+~2SkM<=}ePfY<{;I~<c
zhW9LK6)feO%^@vh3Fe!>hy35(Q99;%8yDq9fD_EA05$=5-Tw3gXa+)PGK=r)rgug#
zDC35D5v?^YkPC6naF~IWk!8$LCkHp7?*vU7JV#;=PlsjViP%6V)b)D4GqP3Ytle?-
zsvL;u;fQ+{5*$*dK;dWWRE=0Bev{|L9T3Yz*EK-%E@tpU7@xRWJ~hnHjJM>;K=&^a
zQ-4*gnnUO^0iIH8l6aA@B!o@69I51Re)&Gq+0WG)wSL-%^!(#m<k=_2uRw3|Vbd2g
z%27AdnoozX5qHoik;FiTI$DmGj+}cUn2tVL--|NCj9bYm3X6wU%yOGU*LXlDd$o(S
z5i*r?E24KeJ|?bIjEdNgH#DRnwlvqmWWBT9ceT@G`WDrO_oi*a`fcB5?5zDJBc#8(
zMLC&eUt6azA36wKYYZd@Cq$gy0g1T$!Fgz%`{$LZ1L(L5%yv-3e#|0QGKX<B$XpOa
zt9SC^^_lF=@PEj?^Urk7wPiKgC_sAY;A#QBF$Ju`(1UwwO|EgE1-%w8T(<h_%W$Bk
zBdw|0EA0FD11>O%jV~x4zS<W+5js*`>tw!=!%rk}dDd3@jhe}0jUEZ4h=fK0ixz!G
zr2QCzw01M!ANtDmv87vuT5v%MJd0a>{PmjmPe(Q3I}U<><zda{7)5lh{igL$qSv#X
zgTOb`L-)V5_riRT!%eDj=NNOn9tm)?JQf}AsH?MrcfwBJD|Fj0%k;Y>|LaK=1I$ta
zQx7srX{6#KLrv(?w+xatmq(a!7A!EI2f^<0rw59OvZdD1b@*oj|AaQ&t-Ohb(RWbe
z=vCDgBWV#S3%ArBW^E&V$={(bd9EA!k}pGF@)QtvqX`}Dy*W9w08>7$$qgz)TWL6T
zFnPw8=}AqEU57tUK(worYoU=OH9%o}uO%}NT--bJU`6`TqqPeoDL^a`hsi8&jLFLT
zFi=MH5ndMBx68f5)F7D;x{|b=nQgM7rB)2HK24r{RaHe1juZ7wZ$kXp4)KcK%7>a#
zl$O<v54SF7m)u5uR#nwHdB?Y6W22h7Vk?U9BT+j;{P*KpQO=%DA&SxKf{SM%mw(>%
zFg|^3OS<&s_E>yU=;g3iTaBHktOu?x7rpO9qJ7BTU43UHzL1U~;A;IwWC8a{e_XOU
zMkQzGMf-+ad=i~6IrY<vKf)iX;D~src2X{-mx|%TWmYRxk5n#fP|x}XIbi0F#RV>5
zpU)f0LI2j48aqO+{j@wC)9~ZOjC;E(KKij6171!UV~Th)rRftBLLvS>ik_+Ye%3mD
z+3SRIZv+i03UGu+6Q6OneH=K&C36y{_8<!09(&}|N%;+9`ON#SmF3dlXe0jp#%Jup
zuU+0PU!2H&m9M~awt+62Lt*>CET!PNCd0wZ={2f{Klhus-&1Lf{Ftyie!_xHy{;gZ
z7+1HfV4~alRuONmmmta2Ft=cc<<S*%RG-AICZ=$g^fxl}uCAGwdTaH9ssyFw3MXH>
zIy=`o`-xy+@b6c6)F+*BKbJ8(!+|L|YT#-8(;6+{Ymp4T%2VVz%4o_m;Y)akL2_SW
zLS%%}C7`mgPixazn~}CpKpm&+;D-UR5$hM4a1@98S1@if84QiSntXx%=}KV8ODRvW
z$jn1S;UO(y3ETqQS`YuoBC4o>4*N5Fm)w!KK#7|l%Dr;8Y86}^El}3vaK?(7_6EmW
z#F^8t6A}<8_ZXX5JXPFShHHxRwJuWzgnS&+SPjl0-`-(cWdG7b++m9TUgo{{Bekor
z0>n<WzEm1oWY+=;Fo*|ZmsSmCN<^XO@mc$lRzgqK<I>t}G2iw~KIC~ZH_qQ<$+b{j
z+U=dud|uUhsmA%HUKzu|Zjo5UVinPi0_#fiqXC1vEJ;*Y9$f1uM`D`JPQDuhQ=wXA
z^r+)=59{tniqn4^e@-^y@E*n1|Gc5N!J-11B6F5B@?lJ0dQ2^?TKGm1IH2=8O(kXp
z!d<4{?%DS_Y;SMB?wB8*h8e^XN=izM&I85j>gtAPrzgYHR9vQ)Iy)<8@AttF;Q{i~
zPDa>=rw2ciN?y@m9sIPSnj&^HRAWM=B3}uLZar!o5G8>t)iXs0etG5!COK(GeBE_Q
z!`fU8!F*Bq^Bg{blle0mgN_g4EMYPg=3|M8K~%&6=M60ijiW1cUDPGGghygSb{Rij
zTgXm-1%D@xEvZG#nCI$w6mh?hmH~D7Lmp=G>F54dQdlk01>@9)?!dszs+s9u0}<Rz
zZx1#MFltAVUr75OR)Gk7?;6&*xX2Zsh7$I^miP!XcERTP!yP+$l`QmQdj^tCD@AdW
zn#19{V=2k;=QX6z>FUFKaC9G7*mLV0oSY-PSisu2`;ogf#oU*)zau|F(6Z`8hF+_h
zANN!As?^~0ylub-LovHj&OSRcUW>;Xv%y~G2YX7DnTxM1UDD%tzvfLQ^B(-HHCtJ`
zbSs+v@6Lujlp%XFzqOG^j$n1pC^V9w;kDp2DF*M*aD&(LN&8J2-m5(~+A57lM<SKb
zxm^-#^OfH>Qkyw!x5esQ+A3^DlMrH;*+9{aEy=?R3e4<(6gg<ro6dJt;g+Snhm1T?
z(#ft6&%r#5al#j}3K6W3utygY?SwVAyv8z37=&Ko@|xmQuz`9(x{~tidp=|LQNxNb
z9Pc}^&%^f9-lasgsJRQM2yu;JDu(`e=9{03R&^bTK4Uvhk6dkX*-UqwT4%fL`Cd;P
z>YBt!`t_n3X!wv+^S4kBF&wV<rr&_&b;PVR^n7<m!}@+RM6%6;7*izHeS}!|`0OjQ
zlaRiLs+G4hDd!i9_evMB>~0UflqxGX4ymJb_6Tc?KI-4<65GAD;Uz=Q7pL)JapOnB
z99_-3g4ADi@wgw}B474V^l(sia_{hk@Q2=JOetkXUlRiVeF;c;MGz)$-fwXU;CzQ4
zj=?)P+>jIDZa78b(*E!hJ5%^Q_JMH>i5lzI`pRMY3XkUY`#)7oG+O)H&OaHtlF(gw
zPEgfrLa?3E5%bfJa|8tDrPBU{(^uQ;qcQs=&zm71p^-6QtMf6n5O<|9>(}5&UA<uc
z-g4ZIGwpY&WE#@#y**tjO^K6Ky0Mq>aChrNY84Ki*n;Dwr*?U9%8r_X1uwcu_5SlS
zI8>6lzVW35LqfMv1ZQ6sKDmGHWj49ZDxY3is}>nm$hE1V^@;bcJPd~iOB=N@M6)mb
zdyA|qCAvlY_npMQ4~UM$IqPU8-B!HI;GbM$ZdTsQdg+OYtD88R-@aq(th_xe;FN_|
zs<=O%^M`MnKYu~cb98yelx0!>F$TR^hv~BVrlGapm6-<8jSfLo<^862{QujiP8G<8
zFS&{9rrA76&YK%XM66(WbeqNuTcTRvGj&hcZp+Yp|AS>$V%B-}ms53Vl?2qAG7Th0
zsvHkC0yCuD=)MM(r_$4|JFw(k!cLugtvpn=B@-d;4;zi<uyGK3C(%o+TKRpm3ntfA
z%jxagFR?l(RzDXSh_eatHB$=ce%l}-mUefwbjRPISPSL8%Vp2>lXD_Bgrm5Qi=l(E
za_zD>Y)x8gT6m=VBLsBER@<L9F6Z(`o&V6Tow&SwZ+k5!$0epW%0EV;I<M%&KU~X;
z4=s6J{>rWgCyMJBsj*bWf_R1Gde`_6x`>DXmNx4Zqfdc?+9q#@W~Vk4r}ivgl&7d&
zIGBILGc_F^vLfw2=6)ykvf`_~*8bEQH#4S{GSb@+6aTzYa8<P)gqdg)7T(Ffr&beC
zN&tz4qj7jIwA=C>a}y+Y2>v0EhYpsth|5}uvuo%#xw~Xh<JpL57F1BFNN0Vc84nr;
zME678X=y7}KMcOoD!oOZya+wie6c2_6`KJOF;i_9g3~eTyL0C@zuD2IUJX~CW#m@4
z=uoe1=xcYIcfihUy$8MybK`TtDjfJ=z%5AXV`68VH!}4hVF?Ls`IPWdV>RHHma&v;
zp~jS<wBp^DW~~PX?zW88rTn-MYHf9yBa8sSbeb(iR?-AXJ}VSfAf4<Wre2qAsGNJC
zRIc#oXUn-YMWzO3B9EEchP>(X%83=nvXas*vnlP@u1Hkp7SW31+G<CP3o|Nf*8g`;
znJ0*6H7d3hZ};Xu8sND(IApofm?=psCc=Av$FW!vw)C+#6Q)RVr?kELs_L73<qP#=
z#t%k_wF6uF(KxtA!igRSWj1B0odYQKeBb%{`)sijKkh6^6m(Jlq2z)x9hQ3EG{S13
z2E2W|?TS?F9V6X*>&7p6K_jlLifT2*{@0?quo_2DJaAm3k>fWh_AY`9rnTQV<%<Y*
zDPw&9vG&)Cbsh$HEk161u0sLylj(h%A?btd_oyRe`hN8BL`rIdc1?Tlr-rl$SOZ8T
z3I<wY$(D>{wD3&>Drh(ba7i4QALbR<sC~=p+J+R#s^N#3T96|Z->uo7S%0@mrEpIx
zeat`*MT=tfnu7%j?IFMA$xjSrkEOTo+&n#nVD`4a+(3ESt2_TWh?pb}%v%f7MfYyx
z5~_=Iwvl(3Jw9#B%*zr^qF4>ikoGXv^yKgsNb*;Wjh0&1R+$Vm^SJ{--AzlF4r^m*
zjCVUEurkB%8B;acm0)ZO))M{xo|a2Qxoi^nPer&)+H-AAXkDFB&)qM({;j~#4GBHM
z*43hHQL^;k3X0vdO79R{aq6;IbxI=?5@;l13)%G#r!q4~K4G})pMWp^?oO@n>(Ck*
z`q#2g?S1!RM7~hK=}nwWc??-6;(I9RhGp=y!ooKL*_*i`yX^EMs6@}NlDdDek=n2d
zty{!v+01a<+Hl^*&YSy@gTFnf+)1(YLX+{ZX(DsCis0h2r-|DwT9x!v{*gF&LwZ~;
z@0j)CzfXTi;tkwT%c#ECUv8^E0gflFkB=_EEgpiY{;Ob=Eppv9I))1bAKPf0(Gve}
zNrO}$Oo!~dFw8_p@Z(F$p?&1&ZGudXwc1~#+Wf>sMQ(jPFsp_#TuI3v00}-oWSlOh
z(jzL_n=J=ncr&9M!mbW)Np_rEwJm7-LgJ*)|H0GmE^`=lHQpn!Rlu!B7vZ`p1dX#>
zp05cbHBb+}i$;yvx#)ObX{-}?d4%z4E;6Xbz-Hv|{9)NW1QBHNC43<)uV8GRBxI)h
zzU9JhSx$vywampLp5C@bb6+jKTZP5ipNts}1zbJTNT{fn2S@Yp&ELu~HPk$x;NWOS
zwY9yk;=#)zhT_d1`<&8V%GFU8#dOcsZN+seQlPr@CY48otmJNQ$JAwb2dv$~%DgtQ
zfk+v_xir}igm6XA^MV4ljk+^$9T<At<Wb?eO$+m;gb#lGnt_Q+FJwQQK12O?4Z|5C
z)wsWf_46O>83?=y&K9gn9B05f$D`ZcHiCyYyGXPmu|Rmi4gk%XE;~8bq4UNqGql9c
zt-a1EzF)0;k_s1|=0v_9^NPF7<XIwkK0<^)#Ms<~@+&>T?sb5PcXbF_eV*V?Q-8jE
zg)^CQ_L&!3SnDL3&b5;^$85E8oolO4kv~bmCm~qG7)ow$g*T-=^b|(t$a(Qlc{;7x
zL+}mze1B7l_`<@AN>>k4_ES#>EQtl=)Jsa<iFp+yEL>fMcj{`ye!Mm~eZHu=>;l`F
z8^PJH4Rn>4XJ<}BrXMa&Q^~a*gy9BVjHOtT-9CKe(<~&CaLsY0NWdiJQ&JLDTwL5c
zr5jtT1$DD<w_*#}g}0lD6b;}KC3c>n&Rf!!mX<{zE_uRzvwZfm=qNfqVx4f^B;6u{
zseZpyG9Ux;VsdWH``*5NGat7FF0&>~-?wi}$wDpUS`{eJH;o!JQhstZH1LuRCSRPP
ziQm<eNfz#j2jejm%d^|zdajQu7@eE17`6<dmzIC+x{=dTy{SlBT|h*efzddmR`<M;
zK4pVx-+?m9ide+3tF0T7!#wGl$)rUh)IL+m{g^Izc2bYjSX5qGKFnjwF>em`(W!sI
z^+}6I0+(8Fmt^<M+Q;DnvF%HVjd^$FU9Kbkxi0u{95aqLfA0oe?)DCH?PO|S>aa(%
z5qGjOZ8gds`arTy_jD%>3*BnIp|8ynEF=1+9hKVnWWDe@3s$~{^m?y=a`fO$v7azi
zxq7GXeqd10C#oG`SSJ`<?)&TKqA^^=0pt5IT0_Xt6cu{}1vE`f8TK}(!{CCdwb5eC
zSFc{7*fll#gHwABZk?=6{fYnY?wkyT&7?*8PlXm=X8lR)DH>FrZqKNpghq`m`N`mw
z)32gc%PsBB$wImr5b(lX^zINCsxC`0L>gGw-VNC>30B-spT#8T_e*8FY+$Q$K`-%p
zm>b$;$>GnTNR!CHS*yJ(==|~Tqif7F6dLP@xL;jd3uG3THplT?lVcbDN|laLnK()8
z^by%d)ZNV)9;#oz=AQBoifX=GY&)DSn$i2(PjHqXP~Vg-PqGyDA=>yyM!?qhd$vLz
z(RhI4EP9l62A?8ug1byfO;L}hRg2(So02Vs^6u|VJW#y!B@N;bZ~Cr%<HuXukfA0l
zYJOLNa4277&I!hl8OUhSFktXM86A!f98=9e@2aV(DRTR1x;9lq0Un}eSUf0{t`lA$
zCnv`zB6=PZLyGdWxR^^^LgHF<M=#tC3-_kh_x2JnFfg#+zU@bY_4x5)Q70$vi`dxV
zFby6i>o>Qy&Q5%CZ13r*^PaL?tRUv3W;a05$kz#i%e$Of1APC*=I(`kE7G}RQGuC1
zf8`CS^D-4ax_|CeAUYs&reSH$qL%}wtf6R!lO@E0pJn=uj2Nz#&>egQ;uqu_xhJHn
ztKYhAq6RK4hO}pDC3GP5bETNCRImNPsuh*Si;in9Jex7n9#7YPJY*TW90ryA@bqxo
zU`mHq!g;a!sQdm}R1r`6R$5m#c?Tj5Wee#}x$3W=39-0|6=MoSx{+b04l0t>nLc6D
zW=XzQw|>F$tXj10iKCD+eTlbk?Sq@&f=|pxo^ViiG=3+YvmG`{RG~LG*=F%9WIhki
zzSIj!&h3Ayf-&qvffcm1zYmFDn{;;kNTP_hh}70P^zPIZdzY`3SF+GuA7?TW%WXXm
z^3IU73@TB{@a7Gn7NLTHh;31~w6`Gg&W;or50~p=KVO`mza}C=4Y#3N{M<g{FFhW^
zE`k^dcRIU*<(dO#)(d)~|9(H@SL;-j3kT~@aHl4d{IW528>%d4A*G%UDnnU=bC$S-
z=h)Pp?5d;DAE>TPv|S!oc+A~R{6dR|4$ByAje8<?0i7>yrXb_RY#7%kS2GVnrqy`u
zyt)`|-$J&~Zw>FMwAiP@BC<C{Q^kWeaZQnRd3-!PaBNAvi21_RpaESZ#{5?se@-rO
zFi*>joyDm9_)2^vATT_Np81B3R#HcCT@L1EGUs%R#IHA<(mG1z;azBX5<U~D+uEjw
z(f2=RmzfeK8cMizX?qe)lkF?p)a9__mqTn{7xnVnnk*HKRbHb_UBvcF{dLAh^|C9c
zIbOLau&dy*)5eT2j}{ecK*p<W$~zdpBkYP>E5?X%W;ssnMDhz;ybq}lMh_>kwEM<e
zuIA7_s~W&ZO9@T6(!rXpm>i0P64=TIFzUVtu6|cjS4XYKVGB9o;+u-IJUVuC|CE(=
z)3hh)6@(|c>(|o~ssED@-7lP14+Mnm#@%0ND~10wS@AoP#vlySR2MjA$O&p>q4$be
zqI?aP-8#6(Cgc*07qU|_YqBgz8!5}~@{WQ5eav{U?7EfEu=@=)dKAl0Q$ua5Yg2Jl
zexKJbUtE_7{Sa+v_~a_5tnD3cefaw@6GKa@Ys3mTq6klN#K#LH$bu+Ft<dWi=|2AR
z+HaIKf?Zs$&pg-t3>*e7@MH#1Wj`_L!(ECJc=?CInTiTzc3MeR<-TLMK6d`FXaB=&
zl?0hm=EFMSa=Kcq0G0bi2G!JnZofff*K!fUws1y68Hrgk>@5+^JNBmkXOUC#o0Jq^
zXfo`HyNZ~xOkEBzUY<|c<AT!LhjF(N(8;xkADb#EQI7|i4`G^G8=lDV(}LQ=i*Wmb
zl$_ip=k+mvK)&%7XD3GeA`(l(r<>ou`@!`L2B6r-@K}>0chn!D{EZ`-<EmNcmo<6o
zrv^||YPaNQ2YHybw*QoVG2au(O}OP<m&1Zb>;&*Sn&!8PxXc4s!3dCz5Odw8`UL9K
zD>gwuy)4ZeWY2U9B$fUK0}FnY2C%+;vKEjB^DUbD>k^u7h-kCK6QH|TDr~4{<f;Bd
zeOFRRz;^77O40q@X!H_)wYA{JQ7U&^-ziBHTa}a6sm5C-R1d=7BE}-pvh>54(P^@?
z3r_ygQ9?YGUN>6?wMef7s6L368r-+%X#0X2Q)rsW*pw4-Owii{5opeivv~J(CGM?O
z5jO!)R4Np5m?%N$F}z=K3KVt%K7~6^`#$}>43&_ZiH?7a&{)-KJ)P$Wx{+gEZL(eh
zgrLwZd}q_@;H%1(B}|XKU)9zMn|3Dz#d4XQL*eA)wA)*I0Ql}gY-}uCD=%eYLcg*C
z*XzKIn1zLfPjhmZ;d<$>RW7!Eetz{G9XVXo7=M$h6J3|=l_9FGM!dV6%4&lsba%+o
zF$8BJAXFzZOunJPf&>)uL3>tNYm~+!dGs3VRvCB0;*pB-aGF)=o~txJgO59wV#rQo
zv9YINCZlConYP++T0JDj?WP$y<!j>Dbh_0XqtuS#I#nkrAq{#%BYVeKo<7%+nq5l5
zaWx_vFCEyIwt%fQQfeWNFA!(rj;fv1nIfM~D=yAXQqHC?lz)Hx5Z=gv6!@z~E@MxF
zcjNrji+|G&kKg<t<tDJCkRLHHT(xF5lr#bBkKEcI{u(jBdvklVRxOv9gX1cS9L|%i
zmFqrGgbckW8uDD1-##LGzP<m+6G>k`#?m!bTZD*xFjaHo$U;tSqSgl)elJt66O=np
zIU;<P@K~fnziv5D7KK7Op~~K6iNn{GN&4vZoOXWvcorQ^vd|gZ4-o#(0xRP^;W@fU
zn`#uUrsj3x<s>hA?8bxVlhh^Q!;3A%H-Hu~ANsXF+}S82P5wvzz)YU}_DH-MZT8f^
zkPZhW23WEC<;})sp7uFM+|1`aKZ#JrvtBkB4WXHsxAkeOIUAp9Hi8oh@f5IJ@nIDt
z)zd<rWqy~~f&TE><zC^KsqqWJye)=44(2(#uP$B@o=AL_iTw-eT>u~PxGQQ$^u!w1
zIGKnS9hf!_wB%*42v0}9Sb~2+%+N;aQzdAtbUjkG-WDn6_O7dO-?;4%+W$b)PGI$7
zl#NXNj&Nv&;J_6x-CH)5%q-^7UNV}=sz37sJroiVMo$&Zz-)}4Xb2FAsegrm)l<r;
z@z{`Qg?Z76SMiRdD_;sB8c|U#pi+|g=>NgcU7TEBTiz7XOmKa@GR}I1cF}?I#o-7|
zZkTEY&UKoVmz9$LBM39O)P}g{5-DX4*3}epoZvgyu`ZsAiob5OA*$I)@g3u?V-xPm
zn-ckA6@Ti`N3;D1oVD?y<wsdsy|;a=r7tAhxcb8XBA}9ky;G$%d6rh~_(4p<pZNoG
z;iYEmH``HzGcX%J&vw^gU^A~4J#jqe+)NUby<f)N`f|(Q(so(xoTH06;NQQb!Ut`_
z0fmn}H}YDl2N{ttU<;*Do_WuO#WQW|xwG^I>d$^E&1#PvC3J<@n<+s>Q9ZKnuF(k#
z7*1+!rtzrezLTk2EAlV`id95eAR&vwb1H9!^|-9g^9@q?NVFR5im{!%eCoNn#4jzj
z84fdMw$G|0bb(02hFaX_C&6oml3o5mVAea#+BqgP1&LF%NS6)OCD|eF8yKoxCA^p;
zxhcq6Pj%l6z2QOAw8tN?akj8&fZ>B#+->tbv*SNB?2<mP&O82|xq&j0Zq^Y?Ctobc
zw|~{%_H?45BznHKU5}4<=|{=zqfx5wPlil@IimegA+2_@q*n%;C?LMH_m<_Kqz!xP
z<zN8U*IcVRo|V7z(RLr7h|uj$w90Z0ZjJY9GZ}NE2ah#5E#e0P=69c@x!Q!_C6Ugb
z_o7rldvou4%ypM$JAG}>kAmkdLrt=9aPB7dw80su=7C6qSs})>{-B4VYBsp=k)zPX
z0rb{_*wHf!_UTqhYQ<MSTFuU|GnofG6I@zJ=IkuU^hj5O@4oPu8rr{WX+d%2K<oSB
zRTP$xC)=%{o?Wo_S7Y*>7`-}oZe3Yr@@ZYqym8aA&ZD=3Wmy4lXUP$L3DPmbjc?#O
zwP5LMx4XkY8y@8w>RKM4ooy-*>?bh&=H95+^CF3c4yzs*{VZ;hJV~(Q<gJG%Du?B;
zRF50<ClO}Wus>G#3;TBPmOpSK*=3GwzTE552if|v5n8L@TjFdUszU4olrvt5hPge_
zbCo&F|KKe`puCu~r$Ar358C0I39$ijbcs7;NR<<Nl1M;yB_JX;T05`6QpIS#OwvjS
zKstegUxjwi3N{MscMTc{0Ndwh83seQN*jKVIk=9H1`FR+Gz4wXzyBQUtl-S$A$6RK
z@N8##xZDoDi+Fi7x82^9g)&s3KB?JAc^6^1k)X19mb_(^TkxDW9o+ySDPM**WwGo|
zQ`_1qHiCcvWMU{P)oh)JjTAomS_oTf->q$nj2wL~!RGU}a(e-#pSx7o*5z=v25#7b
z)`@NQu>fd^Pb2PGN&~ut`Q{p@XFarc9nm^*?HeZF=i8N5?luxz_$)f+(|p9~uS2NN
zUy#SG<Zn-mCiaHQY%kd<%HcRS|A;}idUdPXunPE<CW$*+eXb&>rxJgbd%%A?lJZaZ
zp`}H^>5}y<6~URl+9?{LGHBB#&zg_uZd0R4e*D++D|`Y4g*-%EOEU0(tDxD|K@}>)
zxn+9))y~Sh7Jn9TwQMS0l12oyiEQs#Zxv;3Q!lTpfFN2{BTfv>b6q;dvR{~b7IKlT
zDn61>5(y*&bpWZ7`aJ|4Eb7R86--AC29%Dy1xPkG?unatt?Dxadx-F9xo)mBhm{YX
z$VE`*KFDeeyUV%#IY~`<#@vhYW>QrLW>{GD?!d7gZu-I8=LSIQ)H!AJvmfI&!@kd2
zbpBd!&GEJ6`2VM!yfO{iaD%4-S0JO#<SqLKeo_k*QCOT6w~>l>XUVmyUp(sDg42I#
zx2%<ju7<rbS~osa%=$kZ9Bs1dZlRq84aiF?Dm_`a3m6AKroccTd-GHvKH%V)?4_NO
zM$pFyzCW58UH=P6dMW=Ca@?z-;zQ($(Rrto6-M;2)Jn15^sL1}oQub<w~0ikx|;8a
zXd}u(s>i4p!@7VWXQeWzeY60&WV@bhTOL-#7@J74fA4l7u8x4XF*<!7$cpW$?(4_y
zvR;APNF<F^^5bQ|G;j{YPCGlOBtl2!h`w0F!hi}?J#9DM1aCWKf!x}8$ELD?c@L&6
zwP$Y6C)lsGQz0w4=?EpSewJnH#HPZxoTEcyIbLk^<j<8*&GKC*(G1R}UN#G!=X&<d
zCeJ%ORFbw~4WA?gyRgYaFk(ACa5Jo~d%@5}oC8SDFb|B1sjZXVIA9h;u6}OLu*T{T
z5g8&03)^~@6sk7BfBG4eSVV=}5aqp2qHf$2t0S4%qe+o@Rt~tFFG?@8>Uh2I@k1IC
zky0)({K8*Gs8Ma#Q*xyz99ICVZ%qfC>K^htr6u2zJ{)y+ruZ9T2D<+zccf6|q-CRa
z7!l$-W+|!F<U|6q{#qZ;EB&&j@#Hqku@@_~ll+Rri{Y!lnZAFkQt^;%_OxLGN&Upq
z<w>{Y$3E1`1pQlW41{HI|0~q(JL_s0*rWvmLXQFF&%f6`kS)`q^i)=tOK;Nk0)o7y
z{H~tjnzpkRw4}&y^SxDJT1oH?H3hBxt_W|(o-5X!HweL3S;e;CS7BuMgr)DkrR@Xl
z>D=Zyk;I0oVs~SW!26iaS!9|P@mm|A*WtWqs?{WB2_wbU%?4cSq%AAMv;g(tT9K65
z*GeOY`*%&exTZp0)*PM?uxeJ({5Nfhz=kq<5OWoTf)r{AOLzVlBdCq4c|KD^9kAWD
ztaDZN))j94qc}v;OnCV6Zh*(kXqBfyO3!_17pBw_45Unc99QB0*i+$mIR$~mx=j=3
z-#Fi<oQ;0$NlQV!CMWHbDsEkqFZYb`uKOJFQ{FLGoqB^+Bo_;>73zrX{ZSzh-(Q7%
z)DDn7yS49t=&+3eRVwVM1w<e**|+uQ%M|Z1MjC7{@O{_ICsC&!YP$gXP<{7+sPUa$
z$KvL6vloUZK~|m>Kx|mlruRJ3Zy4waH86U8XOAK3o$`AX4_tu$#3B1l5)yab(ODi{
zH?17Sx3#qLF}$up-Cb|yp=$ffO(_$-`gl?5?cnX%pm05FTpdd^*lOE4!ti-6b9-q+
z==&>ped}fr+IJ~#UNCF%@zH6|6CEMByY)>8kA>9d=dXeRo|(jTnS;5Iio)ue(ZRyw
zM{eUyKa7BG;h-NOP<=+Jtfc(U>40tiU6)}0>R|5Wt*(YAum$mK<+ks->MyeX;vO0$
zkBdAw6cs>J3Wg7di)zO39psk$F#{@o@<;wHElbFs|DlM%@sQfQpg!A`snxuo`7)dO
zySYDPz=M(4sgJFlq9k}YKM^tB?sm;35z3C%sWhYB2os7cOjdjm0XSwq@Mmp;Fke&{
zf1Dt27r2yc9-Q(8gC#hq4U)3*F4d4UuoP{80B~HKBSYn01_Zbn0V5UFa@`;&?&gVX
zB+X%w+@+6g+WpVJJu)1j%Ig#Phh8G3mfKwS8|Kk#+z!!J6!QkIST>u2fS(?v&p9Ea
zz}%dE5KJyjpF2BJR-Pwz6>*A(PO%cN4>gr;hGjK`0^v!Z%}QMIuz>I>_ub;cU6pI#
zvS@~)5#=coUk1x0b+RlqJQT9f%P-|vzKrm{mPEZ8Y}R)04a&DuzQkb4ZK=nEMKNh0
z_OhMW6Nn_YH}n<Ku)143r^_-yqHioMNt}F?puzTzC|}%v7nb@z3rnZQeS0od_a;Io
zFcF~IF&p0jJ)pkxbT*?3l!%b6QUo2r?riE|EJjX{S|Ap^o+DI8P`x)eL`VWqe{qCS
zW&VVmr&Qs+n&IR&vR%na<z-8E-8pYAgzdE+_fGN3)otF`GsQS?(DONFEV&Hl>{?az
zMp@Xrm0=*bH%LhyPlr;f+y{HpRzDru_KH3Uf!EB?wIu-lDNyE9cP^*yVk@u2zdbPW
zVX75;>udVqkWrV&i}KjTh;4#*gpw2I{PKF|rLA8R-VoW&L)nyrgAuL9JNs$68wtC&
znbi2`Ia#LLTCOtFJrlf@dkdJJ&NhrDPb($mVLy42i(ByRu3y&3n%oCIS?+8WYlG|?
z5Ge-@W0G}f0vmDgcncjYX?aS2+&NNYjrxbBbNTh3g{{QJ==wKMXcPJ6M8rqi1l^T!
zO3m~FcUemg(LcChe9@_tvymbKZ41z|74YH7p=Q@u^}X6R<37eodzSHD$-y)E_F=o$
z)is9kiOCox%eu|~V4kpTlV@&`=ZmZ0Nh05ns|oim8~HVR&(n*BQlI+X-3XNv$vI0f
zs8LaD1d+7<YP=W5cFpDT9m<<X2)2^nEjqMYxV6fo&r%;*$>8awk0rMQm%bH?d_spM
zySDF8lX+-fLs<kVX{}4ZRJjYD{+g>jdaYoLFWX+md*!a?P4IlBgj!~U^$ZxLYOy%%
zj`B#5A*rse_+kdQ>*zLLj(7^%dfo;d7Sx^&u(wf>5Cx#&eN(c?@J;o9HRDRL_hou_
znHm6!Y)$CavJb2toWJ)s$4R<~3+nqb{DpQFn4|NhTGcnHFjZxx29E?|jHD{Ag5hm_
z=(%tHC4|TX6uRihT)6R%r2jH*3mav(OBP51zgK2Ii#SufJW`|7hj{0nCw@No^@s<Z
zAen)J=nhL9aUjU*DH+o)=xDlRdgPk{Ti)I9J_>4DWp&_3k_JCgZ!VfZnooo*eP9AP
zmY^zH?nIPpM<uL7%+cfpKyxToC68yHD4U=~wKN-RTZL!3yI9h(#9wOz&I;lRmlq`&
zq7RdKB^J3lUd6p0e8g|hZ0MB$KnrC};0BA2p!meC?fv8Tfda{4<>id;bJKTQGy$`-
zjJ|ma756MaInov5ez8Q0f`9b=wwaW;!c%UvJGq4*@I%h&;`v8KN7gRw+NHR^20rET
z{!AsK-S?L~YZXt@9-^=hM9#F>*Md;BkpANSb;2E&Vo4v~`;rPc&6^nxTOFUTURLK0
z925CeL0(+P9}1P}*5P*)t;vQGCyQ91l{|}3I*bViG1yT>X;Bw&o-`qQ+9qurm2!$o
zk-|9F;}MM!SU23{#DgPFx-LCI{`Uls`zp6$n<paf2=`(0q`Mz%CP%+hDy<&7##2XF
zTr2#-DeRJ8RjECuiz}VK3L^7v&*WboxSgH6mCGDqa0<o5omNcHa0F*YyT-6AsmMWr
zmI4JD)jx7JDWYJzkf-s79l!K}v!~Xn^Suv*h=J3O#>2Cg4M&9PbM^f22>0if!I=FM
z8MzD2_3^6bz8RShh)r6AA#(9p<g?N?&i4yV=TQo8_q9-{o?~ZhmdYWf`HO7{O0WDU
zVCG#_WNT|&(n^aSf=onW5Jqq4OG%zk%strVXzaB;@Yr@u=0+<RE9_|JK4yCHCVXzn
z_cQuUrjcH>VFUL_N`^O@=+oZ@rD?>VREh`Qj%BX|_U!0e$manH?cMta=XGPzp_Z5d
zh3_FZS^U#LZ`MN2p|<1Xo|Jr<Q6qV0H%h2TE?H7jVSOrggT)(cnSdg%jh3}eR_z3z
z%@T1pwRKB2B?d8qFGpr0kf62<@jrR<y}9rq?sid<+{<67H5@)AE*Sw}qRaASok}2l
z3eOzD(E*YZzhEm90MIZ13kvkUTMBQK5u&3C&gQd<<5#hVU^o6v)tQ2Z|D$y({eQ$s
z!vP6|qB}U-<=<-gtDRYSI`~-}3h5Xy1!+HJlCOrE%>Wh$Qios7U0w<x<v3|DB<w;?
zaa>?Ba91jh%++peclloObfdwMr%j*!uPKQ<&bvUy6r)m$kl1gS@rdqy;YIJgboSoG
zj^;6ox5>A>Jv`Z|^gPI;E#n{N-pny?^r^iQx=DrA!cHBJyPB8%o(wQt>mku7xDH&)
zc!ru9%~8Q2nOjPf^lH~>YvC;l;t>K-i!agXLrdi!qbi&B`h|{ZR)p}~*+K!%sR8_3
z-*xgWOKZU$$mpT6y%;ORl0g1c#sM?$#B(ZZqQeP-3AlL%1GZ4>d!`US5XaYk*`~yQ
z_m|UEJu#X;VJ?ML{sUp*j2!FKRFdD9L(|e-&MfpW-2G;3V=u#8Db#VO3bn`xLNpnF
z%^`}vr!9Fl1%z58Bd2>iqlaDkSsy%iT(YwL<ow<KFNHS*aLG(&)29{x>r09=d&j)W
z*+1e@DV$F{!sl~?>R|i21KV)9mg;y~vxKF>OjFenZEjeE^br9%D&oa*lRF27W2tp8
zdr;`b4`35Z>l!x}*F7f#)gFr{siUb%@Qsp&BlmY(%j~fBo2Wp3W@-E9&=X#0JwIj)
zE@#+tL2P>;vUIoJg4ypE(D_QXmMj;rpMhHr(<#SI{^At`umVTj`!&}a^^I@-WiYoi
zr8eDppV7bj-^2JqGwI)b*CET23rYe@_q<wQ_U5K3xM~})>;|STd_$ZDMrpi_M*_yb
zh9n`&ezm9pm2h`P4$OAV+F)Vg$UIVrwq`@DT}9le@vJ4MMK!J9wFcv`q1Drx7lL#a
zu^F#8c)F=yV)_WZYHrLXwv3BBmvpu+^u>~Vdy8H1)lT>Wjo_B}Sv$T`IklRzGvcp)
z6)rVWIEWDk@)2-)khevdP~EGvL>bm=dkVl@F2vP}$#o<+2S7R{iSCcqY=36pQhj?0
z4?Z>J*Q|wv&KnS7_|l>V0<-Hnrg85#J^N;1k~RY-+dnS>%>RU>`2p^mTR<Qj+g&G;
zjeJ)&HK9aOpt#rOBd&XGo%WiTAQzgaa5*(FT@ADH+wE#xPE~~;Q58=-alvznC?^uO
zx0Jw(>&}FUFcnS`tNpX6qL7xs?YVV~q~!=F(E!PEQ*d01BwY<*98PT64*8)ybZhd>
z(>E*9)dEGDRksBGdsZ**!YSH$<Z7Pqn<0KMoV3LfAzS%K=~RI)j$*do1Nt__He4+o
zk8hFi>ZGb&KzKK>-+dbls$pjwDD61^gKd~+X5BgntXoN$?wDL?uv;2|WM$p>iwII|
z<a6|AC^;Wt4dv?wA09^8V#@vn4|jSt{ER}*mhb^9USoPB3`WJhTA_<jiaW)={w4AX
zaP1z6^tTW8-e3Eb&m5*szpI2CEz#6Yu(9HgsWy|S87&OA6|xH`sI_}O4WKWpVLw+O
z|HG_3q2xuZFYAynCyMHGO6|NOC}Z?~{UnTwbUD3hcRTMAVx9q4WuUu4EdJxLNo?sE
z@P+6KKbq>t*(nk4bZjc3)xpK4+l}~we7)T|V}jFp<%F)<3CN)Qz}3G13+!qW8hB-p
zbC0v%oD_Z6@J)gX{<XgWVnZ?KpXmpLa06r`OnnT5Igc|%ZB!Cq6c?<z^>=Yr(0^Uo
z)%wU!RYmktYS3ep94wYs!E1XPL}jw2FI$a#`khOwep3JJ-rT`MY5$9xn{utd@)cIc
zdtS291dbYb(1y6wSo|VZ)F|mnS7Wd+?Yk_$!6wQ)>K_iDDgJa~V4M;F@OAp5&D<v;
zoqX%&=>5HbaJ_N1^KUhHjpm2YCG~IMt8W6xrw5Qv{dju8F?<iY4k~O51=Dw(o9|Ys
zZv70hD((4D^eZxEch}x;->FeGwPt41V2GouOxZ!1n^?MS7VN;vyP#7*W(cKWbo^r(
z{0HpZDG_+=Ew8SArS@b`+Y`pnEsdA6AvZyG#`EH(r>7@QlK%~~=31a6fO&O$dqZu2
zmw9k#6)~oQe#MXISCf{6r2(%vhVF3UYa3%1S=~PefL}XLk?pq{F!hfjVrZE?Etcpk
z4D~ZtE!KS=#&?<7VgtgU=YHG$j0)+w-_AwSMH-wd;6q%4a^$r8yY|q!A}gXOY|lxc
zN&4vK{LeEf+q~mL3C|gfIS}7%nZGLNJ_jTX{EaaXMl7RO!y;l=7^Cj$-vA2#+@<aw
zMHUp4hBFySF*P9Ynwo^{qw6RiJuG~hkuT%hvzI<|)I_EfsO9^};@$IMhF`{dBsE+Y
z|9<`6#IJ{%UgyOsyjR7<@a;~I-Gokd`d#NEHSOR&?K#*&O)x-I;M4uTTd2>kcr+TJ
zpVLw-ly~V)tcpCP@h9?Lf#Yd09B8?r@e8To^zGfa3=Jpu*Po0+Ck)77&os|S-uAzE
zm&`B^WutcdToGHAq*_fu;swCc#_uvBk<cmv+FVb_wr^gL@KoSS{~yGP=s=FLAH^GM
z?YE0oXpxt?cLLdnaMt2B{+U$bled6<&Mzg($vQa?o``)$47vrCYrtSg8h$wR7i#P4
zuxF{cYNH|TY7i*6LG|q-7uqE5(w-?a)D*d@3jJVS0in0q|KZ;NA$fLveGTKG<MynS
z(9cdTc3$298Xd229oleFeDr~#A*1!NQc_`0zV8nF!bfXdI-h7a_Fnw?&jb9~6mjr&
zm*%<i6b%`&Gc6k_eDoW03&Mgg#*W9X%murhZ1p#xRXVjFCxd*sJ+wmuK6^gkd^5q3
zs$>8rGE;A-%l5w_<7^9wZr@ZpSP>;}_3>0vYo7p967uL)f6|e1X0WbqpynTJoZ^!%
zO&!jkhV(t`JUk?Q^D_J4gdVt9ff$!NI}Xfz{mDO^bt>^n2#zAIVS6<uBDR1%*+ZYo
zA6)ysNud?@<@uvm3ymykU*wJjmqYcPS|Z()cv|=DrMu9l(@Rt*%OUVnqF-@%yIJ82
zf6Abi_z(GF?(|iNkB>Cj=am2A#fw}0iXw*J+rxR$yM1$1yNL)1xBBk0HH?mu!8W4C
zr4ky6wf<9o^w#O%;IyTj{g{j?KfErkvDOe!^`_t~QSk}1t&F(x;2Dbu1wwt3ofj?p
z6r5iMJsqj+dFB1P!y)d3pNjcUlSB}IBs-Uq4_Z^OTO`)eEY{R$WT?9o=Is$N1rxp2
zfgaqs8p_X)A8TagA*r%TpWGintyIAEpprpw7lSNs5JNHLF8jn@oo>O?@hT(Nmx4M)
zkS_w68^KUG8$cIpu^EbjQY@iU=+bcG*z74q+T`&D)_?%r9m;B<_auAOw=WW%TbJ6@
z1Z)Jj-hMa$bNg$YDqoCBcG2{OE3#K=MG}1NsVSSg4~&5G5KyVU-HkV{f7RDen1_7%
zFCh?^$*k-%FM~}=O8O11E^LFjrD1R1Ue494AzvLXxGVE{FiUbcJ%RW3?Qd}9Xp?Q-
z2?jMawb1Fooa<HtrlF<f_~)=ceuGRifZ^apF9J5=i)Fmc$R05ITI9<!;WJ5z1C-|R
zC2uUiU)~fH@_)FeIO|;O*Ru4`b|MHiGy`CmfS0QqbdN+ic27s)sN|G%&HUO^xypK2
z0WDwU<@B>X#cnApl%1Cln;>X~9^V_$S8AbBEwVqw7ussNd0CC8L`!w@UHyqjQlr`a
zj@0W2{wS-^Ha&v=Gt~NjweZO#YgOKFrq+H4`UV1;Ws)##r|)6!5!F4`7T@nGuH)o$
z?+ue;2B*oS-PawLS9H3sz`?&(R5&klddAZ@jL~7*K4ad`wGkrz_xN6yj;0u#TU-o)
z$!OFt<qr&4Xbf^Ob|=riiew~Yq^Hl84(mM!55-39k<HpzX%5%;A0}bN9$Y2tw!1Pc
zBX-;AuYYk<ugOYp%va)Wgp|lj6cZv<!FNSGILYzQCuXjNpOQ@0`ubNL7;WZKtBz*N
z+c1%;%Gyi47+vJUmo4>_%dsZ9s~*Y-ZhykMak%vqP7)|p#!YvM7Uz{g^)zTtsHCSB
zRJHPSzj3@~dvY?JrZlsx-)(r@hAHLW39VTNo6UNN#o|S0mO2t>f3}1lSlNhx6aR64
z?ed3Gq{FQtXU}M`tXc5zr0`=tnXPn-(a><4q<^Qi{O<PXgf|j5Uv&Pv-A+H;m%D8n
z6dH<)n50IF&FB~zF{gdb?jTsHx;lp>GGCtG*21C@29hHNtBOyb5ERkWgeP)|kugKT
z8~x8xPpZPA=x-=C@nZJ(=305lI_O3ahJ)8{c?R{8*PasQoBM?jU>zhez91PLyxWDm
zTu|)kjw3|0hd28FaJ@yT@CTKbu{k}nwGA7t_6gK1uXY78`^O_xbYA%@+w(=z+Z=l7
zYl0H~ny&q`4wOhv!gn1dj(>eu5-Quctzf2WUL;K~y8iPV3gQYiF?6VSKvC)vKOn!{
zo`$tL=LbYi#cOR}$nix$s9t8Wcj?ZwQ2GWgs)JKIj8@FCxf#PG((mT<EBlg=!k5KS
zHLT=CAINtEUp<qdMctrY3yo&ndG_gje&*?4s2%7Y$I9DVO>bjkqg7@__VMG#LX!?+
zKYxEQh6vqhR;+WNuRA;Q5VPrB!o$PsjNyRNf<q~5pf2W*AUqJ;C!1Zg;Y#excsVs)
zxp#EEWCg7E4Bie{&>W>o!CAMUBzrTZd(pCjtB<Ee9mjQH*wOWIm$~vaN1&08ciKfg
zISNE17HE-1nod&cHE;9Ft%u`Td72mNAs}(AoSYzEFYfZ#e&qw*h;!gpYJ`?gWCxQk
z8363_>7oAent%5Obed_MEzyMFmDJ^(tat5f@7A0YExbp)6x(mT3O$L6o67;I1=IeE
zn?HP=D2O?-&<)HyIX1wDo$l$34q{Kr`v-0vhdX`b6Gta9)dy|om9PDzVdRn&xpN7Q
zC>iI!9XJ+J(TR_z2(#2)!Z<D?u#VkeVrqn&q_-jL(b3V(9hP8=;e%oOkI$7)P;f3Q
zBdHN)rlL|%#Q$&eofQ;*r0OWUZs4KQySpE|vTER*iA{BN78Q?TPGN0B8deMOlE&Qk
zFE2fVN<3tz*#A|Kx1fvkm~<9l;i)UJ@JJ-B#f%@wG`~`-w|7XXp&fW&sJ}}mFJ$ZF
z`Fqe2L+In@z$o?A!b|=6Ip#8kS?EM?HU209+r_el5_6CKim~#O-vA=uZ?o4AH-C2q
zpbKkj%SDK-U$z0wDY;G;iGqi-+kAh{%g~~J>sBeA2NW$aU$GU#wi}{?&ucl}#=hXM
zp0cmVlG~W$quW?D9>H$=ngUv?yuErKAg7G-UK=lFUvHXPo#+?n6=GA|YB~Ydlx~%{
zIOgB|cspDBeAn6Mh6Ysd@7+xn_Ie#2ZhvxeR(%6Z|37~GD1<Tk)hB!7t{a~gk>8V&
zlRHay{5j1iD8R4*Vh7IubEb8ry@N4yn;`bau3naqWs_haa1GK5Zl(2gU+`rDqE<Nk
z6`nIg>LG4=p5_3z@n{sfCr`P)p!40a1r@#Xw5hZB@%lHjy#u4Up~Gs%*eqqHyHW($
ztROL5(hcM1h;bV-tWx&Fx>Zrr2e*>6e29^*B)f;A8S?0Cxw&n9T^B|O^wVldpF**%
z;n%^@86ZI)_~emF?#tIrLu%AuII}J-3k&84@F=m-xqOyJhO}sd3=gU-^awsf4m)6R
z3-7DCntJ1YpkvOgqAkY|t^wV}bKu}Z{_zVWmh&iJkVC{kzL=lgH~wZ3Nh7DKcWz>0
zsNTohcaIO|!rHsLG3*>Gj+4Rk1n1h?`qq@!zOL0sA!<CYt+auGLBL+#h5Jz~4RA5D
zt;ZQAwI{yiaK3fji{DS@ULNq?u0J=mlvSzr8KaGS9Bx)yg@DJ#1t`hougj@LZjr8B
zOd3xfyS~o20^TX#MQ_0{)A_ETlr9o%{HdM`F2BahU2+CKXOqWm&vZY^6%5A_lZ@#c
z)4~H`5X`H^8ghLb3L*a^sCZ8+1An@$BwL1>y)_<NybI*z`2xA6^Mb{p7nW|mWy-x1
z<sasfF;TLVPXD)`^wr+8THK6jB3-)hdx(i^O>Vd7vFe0;Pg6QHv^c4-fvAJ~fbo`2
zYLXP#3EpK~!Ek?g4GbgDIoS$ZU1^ZUrZ+{L-T=DK!1G9xJvI0Rc$0RH^ytaN68XQY
zJ3!|Y{O*(NhsL2i?IQPG3mDUdCi~%s`@7?Zudkwjxp50td=3W7N*fu`Y82?b%FMiR
zn}<iQprgILzQTSEpMnD0(sH%iQ|tFn9C)q<^!-Vq4734;X3}hkn7kb{`W%H`UB^WR
z@NKJJ3D0%h!sgr)q=6Y|RHwFLN%?mroI?`d&z|K$!J!zI-<nY#lT8ja%_MfNVaoI^
zy^l-Vf7)`PEyDaCsRqZP$r7Qp2ke6x+-ZF<xpHgjc!iHwX?V=;EB6k#SE%7XMRY~S
zEPO?fw~Xnk4c_H^V&DIjzzh!;H;9&a5~UtXp8^B`us;{Iy3?+#z#+#4=L<M$B*aY6
zU>1Pf>bf6q`ax6oTcV^aM<{q^Ufo1>HB4ZGL|_buF)ebSq@&2`&(Hv<l-^U3{Ew2J
z3}y8i<Y8vU{+rPo8^uvH*$iZ_zgcathp=njpFZ=MF}b-ph^RoZ!NJb19{M+w^z=f*
zL{3TJR%s4sGN8da_cAzmd%KO=xaH-Q#9v##H*qE=zF!UzS@b_X1ReZ+4GU1+mR)(E
zt?d=4O~}9i>0hJbV+#E;;N~A15oZ~H^OP|B{mdsc-R-gKKHyi=^_lKBt}eP&hM#rI
zFYqEW#nx2lbyyDD=%lQl|Lc<`Qs3M(c!_X!_T|tn`Q#P!9|pd9YH*((_p)qZ!99}Q
zu569{EK8Ny=ejq+CCI9mIDieZ$(YlMr-?2X*b)8o6@#>&7#pXAAzg_z<#e#CgV=fM
zspvedmDNU3z^TW&axKp=E#tWd<|pGRQQoUE%uR8r55S4%Fy2P-uaaD59Q}9OO5Rb7
z#*SmYd{I(L$`CFr9x1n_Mi;9vbmzC*Z7hd|3@VQ`4g{FnIXG0#Z|COa6&bZ$obcGJ
zAr*LXBRe}A))fYP_pLu$p!VECJ=|HQtE?>zqxfBiPk!qB_z*Exh2ORpn<xXr#7l6?
zY=MMv0{!HS1LsXK%BdMT{%5DiKDBtP6^ieY6JNHx+P>ADI~8-%RYd$e4Qxkr&<ZSm
z$7U+JL?1c;Z1H|~0H~BuJ;Xpf!T%ZqBAi77c5K}_4W0!*Jx_qPwO-^2hE_%EcEP$7
zD#SvK7}#e9>IL;^;KTFi-l%_>cL8k7D%xz}DEoNMZ5>4J4HK`$9;B3k?X|IMEVFo=
z#v&Z7F(40uMl1!P-CH2aU!>=IN}0?}3m=e?t3e7cK6PRIpEs+LOPAqs(wjHW26Mof
zQt5Pqxyl{&bh^I-XkwVX>c^PuN!Xt*|6GmfboP;`C`yvo-h*77PlPCt;NFL~^~Q>e
zi?h4$EP=)GpPic7W(nQ?5bO~%LEPJw=M}B)oCu9-`l3?H<9D*{iB#7MnCG}~V5iAx
zy6e*LTw-$k_Gl5(>wJcd!}}5ohhpUm7|Qg8ggS{T^ZZ^&fDrc$9}mK&Q$LRwir%>E
zg>gMnR4>ZdCB3Yll=qpN8s+WVZ{%H37rFM83I=n4D<E^G``#sOqC3^RRe1l8u&)5i
zGV7xK6a|A)K%~S%kd_Vs5fxFor8}ilkP-<+Ktx38?v!p70TBt|1EjmV8}5EV9cKP}
zpZh)YATy)*#yR`!z4lsbBUB0^Wdfx8)9KVOCaVESI&+yn0fL${zB9qgObBj6>VHrW
z_@v-jvQ(ycYb;yj87vinzHn+j_eM343lZ6@OGIrYWOuJs)xc#8*;<o#Tog&CdOrR>
zYv2)d-b-~K1?|=PQlkJ8V9B{HkD-*p)MoqpUBO=d80>W}P+*#EJ%vo6Vt22&Sh+$`
z<Y1j!m-xlK(?lfv?_Qi96#v5$P{$c;IBD_u!!#Z81bGL2qGXdvc((<B0-Yuh;0wJu
zkWpje;VyhS@51{nt{`IB{$?5Z=~(RmMi#c4e`u*X2J0ZH%;Jqc4<IC`$wR=<g14Fx
zJ-Jj(OoN<KkGEXcSxD~H4}`$aj{_4E5ck+FP!83Y`Qudsc#E*R)8ubEwIeNZ)rGtE
zkDpTmo51Zw`DQQ$88ApDfkASnXuKK`Rab8fxLukJbC$S+c7IPgJ_wbty?GKALawRL
z79|(k+N%=Th0e(t#<3prgD?kQ*>1t}!^Oo-N-5@vpMiyVL<7z!`^n@)X6<V1@$v2r
zp&P8M7^q(HfP+8NsVZWFA#W=7HR1H}m`PQ7ob1Q<@Mjk*Zg|r{hwW44DU6$GlAt_~
zl+YO}kST&X`f73TZ;KR8x+@527#ahbrb&)Yj*rBy$eW0L?Yk^gDL#scbg@nf;>I|l
zGCc1o-1)XJAJW2Lx(D}|ux$Ih!u?UG-$M%F__Om}6Xc~Od?%qdoVUUQZGMUYX!CD=
zxi68b{%&#s(w1w-xHYVqc!7E30f=DA4ml^SETlm^AnK{4BiIJ>jg@ykk8l&;fs`>U
z37;2*^h5^@1<jc`4VJVoU5=R*1o%=ViVr$BR}xEc7mf#+=Q7`+B(_p({{uo_ZpKe9
zk5PE^=t-rI`?HIms;e>a_?Gbz*mZq<-C(%1{r1f7aBBA;`Rx2uGI?pim6NTP-3t-b
zftB6H7fFdWqx9IKOS`;3DI0r6d>DvN&$lele;rYHG0On7aVX5b0#^$EXCDb3wnfcG
z*s0PSzLP;K^+s#KpdWl(NDX|L)7ocRe3V%df+?WUbE((^RSufF72j;=X&IyjwkKO%
zC6v9y!gFrSiuO*cSFtjX)xu6=6`uz>#09bek4HPI&^IC6e##;|6HW*Xu7%EOMcDbi
zcV#e`(KVx+`EMpMfjIqj)MxNq5Afy?ZkhJ>%<Q-Y{u!yYK|PaWoW`+#!VxTyq^_39
zC~nuOmO9~A=iL$`98Trv)jx}fh`86l{&d}3M^Dda_}A}bW(jWf@_|hKPAlGW)|Zzj
zxVNXpx~wliT><(REuc0wjHmzr|LE5lg<~Ee%;W69XV`XN!K#S(amJ}(*Yq6H2}ITK
z;^8nwTzD>&tji#-TLJZNUd_k(aq~uAe!M*YPH1uW9yknP`W^X%D56DIYr^mq?iXo9
z)qP}Zt86B*>=yWn0nA7(b?FzKPG?W2GS6)Vo`>Ex^i_xK9Z2Ayr8oNNn=w5Z6RKa6
zq;CC(d%J%tA<?aZR`9vG-tTU|SWA5dA3thgX<}-svZqINXzXCk#LT8ANV)mj#%s)E
zi+a~OD3eYMLc#-18kWL#zW}d<6yUaZ3DHYhGGHM*M(%Qi;aH(ra8Wd)TI96<-t&BQ
zgJ0hj@q7}G4^{~<<`(V%kK;YQ0iFV#c>1&`Xz&zn-vIDQSX0+M$q5lj`PPD(;%B?L
z9a;)>pz{E}L!XY2PU~!dib}d|B;SjwUxAABo8%aL=~*l)xP2Uq#edf~K;SW}@vVYO
zkXHd>XKhQ?^FUks3W#<fhr(4=RaI10uB@#+dGB7SqQw4FuueQC_{p#EGClOfVrb!L
zhd%l~cY>r~uBU|j2hRNBb2a-_M#{%!vfl85%&%uI`uTA~UkaM;C^DSqp8Cr+w}Gfn
zexI?-P}wQxEQ0X#T)~*5@s=oH+GlI~)kNG`kx6q))f^@ou*6>d*k-PeespiWQ)_Ce
zZGA|T5whN9#?wmlzSd{B+3|BWd6yi+|3*7+@B60>Oq#a0H<lDDoOf0V#_B`8nwpx#
zr<0}}M?dJpDeLCxNmhx=!^3mp<jK!4N@iiXMWV8;O<tSt?c29exyz$Xw~U*cn@#OY
z5)v@w<m6^L&gBW3n3$xR0pKgO_9=4Xj!Ce>PuBvwgaPwS=-Kpg+*9PL#kyoV>iY4B
z+-3KP0_`w=l4=L6A!$K&XFgTe9uZwhZ<4%S0nm*izY~Lx9wm1rC54o|d5mo6ra4P2
zIV6Fh`4r*|l}t6w_~#yH23X0|g#^*22?Y6@`3*-!do<QnOn@R}*qrkw*Ef=4mg{@Y
z?UpkOOU@3iRMqw#{ae~aiY~O2^$!*r8TJT&)?8rnU3d<Fc}i-lga`szEUa_#BbHLg
z^@9@yrAzOcC_6iQxP$YHizf=~R)j10H--u56|+!KM6i6o1~W`joLhFnzYD>xT>t3k
zOKML>h)P%5lwa5CDE(}ya9ZuqH@C7fPV_r?0f1c|pJsZip&vn5PrE{3Zxes?T2T^n
zr<AK-E(YmiL0tm#G{u*(RcB>6`-=5dU$~xRr0pI-Ta;a(eXF4@U+3aaI&s22C-ZDV
zF-6NmiD0$kkH5W+PEzaEnLwQ;(sNAFs0?jcQ5?bfG)jJ*@vV=`7jtGp^Mc^By?UgN
zUmaQmNj~3eYd=HA;IcDQSjctk_;H(G8+o=d8+plH5X!5nJT5KaN~^2;kJJSZfr0O@
zkGiKtMMZf^eiLHTgH|gpnr45yXxhK`tgtPd{40Aq#<h|Z=3bIQNyg97m#=>`Hhqe{
zJ4*XwVRF4`-W=<Qe%&#h*6!^a=`#1{?Ggy_Hj^XM#0oO=H;fe5@4P6?$|}lm{SZOB
zoPGA<nYHv=J#X`l%x6WOnvkQtqg(r0x$FDwbIGo?d&<^VrsD2$9>mXsuV|ihoDg0v
zE))t*zUk>{o}$o<hfuP9dwuCWj}0yO!F#*9q7)xJ5~K}^DJ>-*YX}ztm7_BZa+?}(
zMsb<<qW~qj@VGNq<VxUg@?P=B5Iqf<e8R*BcQGwij{R7aj4pV0+`=aAR#LjFq%zgV
zRJY*=Cyj{Ceq%Vk{@zABQ~D%xyYh=Q#@Z-CQ`3HxBEm}w$G$P-En=FehFnA?KA`&b
zmf>#t?Hy|Mqere#922#W`+Un-T{EVk-vpEXg#6&+566@(<B}d1FHBwkChhU)LU_ga
zKS>_EIwk5e#{}szf6R8~oesPv_Uz7Ow}JvrX=!Pml6b~3kS1qkXS+p4Ms{|Vmfq~P
zYUJaBEE#8RZ#t)O9{x2?S%Qj+iiUx~q=nzm(C`EvUd8U}3j%6<eEcR;KB+F~b`8Qr
zC~z6$GQarB*Oxc@3T%qzW8gAo^x*+MzEk#j%DKinx@ACjmOa|d`-w!t-`qW$Kd{hb
zVP)X)Qzx#(DwEX&2XBYV$F6O^&kqG~KkZk5XHJnEa9_#AvJ5^l9i_0R&do@>;@sQL
zNtZpLbwgZ7?o+oU7QxZSH_$Cd&zdONz@dV!Uod+Lp*O-!U<r`yVu9V#xesRv2yVdK
zuqm^es;WygG)a{WnRyC2UN~f!fFz<@TQ9M)5(G1;dj<u`=@+!dV^i?RTBrN?oCfa)
z_mbJ-(>4B-6}!7cbn^W_g(*D(0;Ecob@cV^Y6vF9L`609^x7DAn!;H*I7Y{ytVqJ@
zZ+v?BBCwMX2`JCh%cM=<$S9I9dUbaIAZ;VTB!>N^_iY{cgcKPnTi6hk^DDWI3@nO|
zPt$_X$D{kyRgmO?aR_9asw`Z}V1EIemW7^ZY#P}~{WI?;r}|q($^iD`)oIRHvro1O
zS$|@N+5e)1e&J5Z1%&)Odu*>7ad#tGXN`pG*&1rxiun9B&I(K5MM;evH8Tf<RK@eC
zTk+DGJDV%v-BN4%8XB%PQ*DC6V|(qC5U4fUGgQNW5$)HTQe!E`()~a-dB54A34pQ;
z21VM5;Gww<Arh7f45V4bm&0iq)T8EdnN0s9qupbfJu9B6eY*&RnFoab_uCVi-Ag?u
zuM)io#mrzpQfg>hEVQ~=1NaN1wXC%fDUj7*{nIeq-<o``ZJ6~(G=9e1ZE9)3K+&rf
z6DB7oBbPfiG-Sos?}GE2!Ge~V0p%XF;s!$92cH!VIAvm5%#r;tY@9(JBUBoNBdb8O
zzAcpVv2wFWy6buSZt-*OK5}78ixr$MaXq95=%+F9&K|%aJ4K0t*d*Y#>{*_}%jEMd
zrCTwY>Ph#_nhkJUe`+f5>jQ~3Fj)?N;&6aKZ~-6RmC||r2!wYcBBJwr4s5V^S~^T@
z9JOow$_8eJhlj-{pYI3JBr8H$^ib`J{`|Ov{%iGUe(xzm?N^bM6wkqo5Q|p^)1s@z
zg?jZ_j)$gC7ib+8Mt0eJ*PyE2y&33J!hh3MpkcW8qd0)mqXy@yo$XOj=XpH%I{4f#
zPBLY_GC=gz&O9o=>RfVqd!PuSa-4o@aTO7N{P()Wta(a~*dPNDA-oL)2wxD?)Vy>}
zJj{Eb(o1``yXZV@=9_$cntFPeA4J<a(rw8yGBP+zRh5)FMZyn+t~T@l1|!KLwD8gF
z$&qi<6R%XU#yZUp(=eSM2bs7SGnnChKf!%L$*Q<&2KdD_U~>q5I4$2cWPDQ3kWhFb
zK1Xvh@-5Hzc5O~pM124Jk(v9M>JRp2Wo1M@0Nkv>(<zYO0$l+pnx!Tn?IQU@NZ9|?
zkS-lJWRLeIW59iQNPgtp&#nmjb+1|gCO~+qDm=g4WMXo%ciz@2$F^@V^xzfnh1C^3
zL&Q4gUY;JrUCaIqKl-N0EB0;@-&tKRZ|4S}(Ymy7cs%%IOs0*{YF~>`&Db_<UZX-Z
zjB~iUw=6;fYa7R8FHYB8*;jM}VERzI%yvp54LEm*rVEU(JTDGvf;SYRt_P;fu@`YW
z{1Jy&&K>lMflZUjdJR#NF)?T64xM)q&x3;ar19!5{jSc%)q_MI?IZ^(*B$^ZXJi4i
z%m6XWISIV4%e{WsgSJt3TaH>V#}fia(X`X{MgfAv4`!cHo3YG59N!Ks4(HYtpP{xb
zS+IbXve5UU%)Aj*V2ks$Z?p6$LgQY1e+QHu&_TcN`UInA9#8^sqe)5s+loQh95Fx|
zav%M)Cvn5fUt=RrTWV^TkD<4<H7}6oAmwc)7lb%}iIT41T1?vL==@@Z$kk6y?%fOD
zzp62F>fjf|J|dYy1ZDOEYp0<uHCH$VJ>*ZV-^cSC0cUg7y|Dn+U<is2UTdQOpmxSS
zqDgveke45mK@uQk0XL8P&a?K+py`t?r<6dT%^}3fH}cm~_&@!`h8F+$Ttd-bUf;o6
zp87c%wec<B3O7ZDyPT}7223I2EH#4yJsgd*zjOlhfQ0)mEaf|3DK>dckG0V$&<gbC
zkM!TEjpu~)R2~tJ06SSDRQ;hOh&=6Xl2Oh+xMvb5ZBq7^2FnRnF9<OSai?hYVu3bH
zUa=XtL?J9&1YZVh^H<1ZL2`Q=8`wJ_0u~++u-u(;Lb(P+U?Uhrbo7O$0FkbAJmjJO
z{Y+j!I7<DW+!{=yYn}k#Mp8=3V#kD!k54q>X3-Mq!EZDYMx=|N%Izb+uRwMJ69wW1
z#1IFhE3DqB$05IwzjI9ci!hcl_H#<ehJmRC)*W}3Z!rClfWm3}kAyO#qVc{27Fdk<
zcU;Oq+`6!oPJG$xQrBNXm`ljT?tZr0U9kbqnokTk0?N~+ID?l5tj*_v9s?BN!TT@a
z&zE8**rhEqj$`3=%+A~P;7H(|qCi7vKa<x!4;|y~`CX*g_4D%!F*txql7uKgZ3f1N
z-)J)l<OIND3<|f;pSy2WhD#}Pfe%Zo$S$z|p89kJn~8l&Qp?Y}Vdx}n57I8gdNfi(
zPY53!*<O7I+8&r%_tX=W#YNWu8w>Hm)G*UJ`L6owl{HJJ)S5IP(@eZO23B>%poiAX
z3eMZSw;LZ<d;}J<R*T0`sg-~1RJxur{(U9LH<ORfv&|*gPXBV_?6t=b^1`j^vFr2A
z)~@-|*GI|2!|D8;j*c$dq>H0;t=F;8eBcXCS1CC5*e?v?PKO*U(H$_}(%9#h(lB~Y
z8R>dMOZwG9K?`8nB}g+_jCj$4F>x@#1WelyE}r_2slLH-s8ykH0@kZ{9*M<vTER{$
zbu+cMi4pkWYke2}keRXRLZy?*Jmd`E8Gz&avMq6pnpj~dVon@{^aM~YUNo|B2Sd1E
z8E$(s;j<}Zga?Lj9A8d@6{K6h1$Eqi-RR{Nd)@3%Ufb#OeiXczsHXOIFG!Dm)Yo4M
ztVLP^X=(IHvYhJkw$LS5o$1mv=~*>2I*4?gw*b9?dc3sa3Q4OkAgskKCnL^Jcz_NG
zL2!;xG|#+wKxJ<yZzj4keIn#7=bjiDv_%G-d~Pt#S10!X+Z~}UTrt#*X&~q~@C4Zw
zNDdxb`6EnN5K5le3Cq?pK5AP{oZ6$0co~Y&S1$pL{u{FdsH~LJe3+8%fO;2bwBD1t
zz{5m5{DR#Ll`nuD=%LSwId;g}ML<IDOGqdvbHY5}v`0CZwzgrt!u$83Dx(7SD6v`=
zH!`JVcWz10rC|qGwnqqdXMAbzB?KPY=Nd<8rS5VA8FI`gkHGhl*!L6_B2UZ2jf)kj
zx3)krHwS8dwg<608iVKKyUJdzA|;Xm@Df6z_e0K_E3s+$t((Qys1ThoPNG#dFlK<q
zk!hA{DBC~Oa~*oW1hj%XMGPQNA00-40NPEKwkWnn@LvZCG#+q~#seWS@zD1mx}=N@
zPcXTmXJF7YGsB|@hdgywQAg+M(W6Jr*XK2_Qc(#e%jPSYX=y#H4Wu6G(2uNwaLJo=
zK;Vm6mFsDGg=ZJ90U}T-L6tv{{3fr^Wlj8o)DHrkWN@OqmR7N9$^Y^lv$W*ONwBqm
z*Okc8?^GXm5esoRraoP1J7WWMdfQU2vxssRB!yD#=*_+f65Txe{u-J0gu|{NGsh2B
ze{tZ+cyaP6*oMqkzeOpMOyvo0{p$|V7&b+0whj(F<$Jr1<UH0V#u_7qwY7)D*f%#f
z$NMS-?)j2>)Ld}H2FxtH$#4v2#N4d*r^G`grKSqqzI_zf7m|vK-XS5RNwTzxii)3_
zo6owrxt+kp^%SjEuH2`cNwr^v222f+Cgg;K3OyhYdk9J{gj84gMzEXlA=qr%>8(@)
zTTjqHQ}c5pwIxtc$)##6^O?VXBoRDMrqS1+x+?$}VFc-j?|gX!V*Lr!<fI;b;iqS8
zsm}>r*Bd#8Qs*UrOj`SO@Je|8!++f*pGxq_5Mm;tfr-S(>e2epOWSjk*-g!IIw%|*
z94_Z=E<Z{EKQKrn9vB!{AM|1B@9%F4U@*pka^|_G=Vdmw+#KbDW7)`#d&CCO^6hJ$
z);@ApK{9rmFAb6?o9Ezl0>)KXZRGXE={txaB&cfuNyWHQ=J({o*U#Ok$od(tB8(oy
zPT`GDs&YCp0SjkS(BMXPIl!mH%hqu-t+a<W(U~(V8mgBK${p8C5$-IMR4}zM`2N*)
zGvnRHPa6VeHV0%ESy@6L`FZy_Z{fg^JoRhzfOAxQKInm~EUlFKuUaq?Oydtj2&plk
zQB^ieqO0^7db;q<Tw?tD`L8qLY9E?TgU>^Nx>R4n4k*>NK<o|BCp-Gll&8J&mC=m>
zMHe`r=fnkVVh8j6SQCO7fD&8T`aLO}_aDDLh;cCyqEqgp!u}WT+S_wYPGzdmzk}<=
z7zPAuer?Um*B1xMSt(^@pN}6|u2NIS78PxHZZ0lbKKJs{UYqSX&!Tf*pa1tyOqT`V
z9y;YfH8}&mlVFPRWyihe#@DV*&%0%v{dewh0~7gDdK{Qz6ry+$>4FcHdtpyNb~&zm
zJ$ALbK*U)&Gs8F^#5=*(C0JZtHkvLFX+$7QOyJdjfur}%+?0l%D=3EEpC690>5Ki>
zvO%u^&$2W>|D4t64?!oG;QWaTg%tDl!vVs~mc#eQCnkDw)n#M|a4Gp;A=Qe@_Cn2y
zPr8kb&3jgTTz~^KbiSJZcELi?{B8Y5<%>Mo+36a331+w__-(Ht1X(aUtwi4{AmBX?
zT83;R<+B_S1jN-ghs8SR4(Re~=?Um>JMmYv7XO7a`?ei!>wg5f6PDGc2C$S7uUd-@
zxq>u!{UDwlj9+kO{@FemZ2Kp7I5;X{{*a?b1)QY71S-D3VrOex{WDg00Sp_px(h8B
zJbxUyB_V+a<yKW@#qLY6UaPFC8rjST{Izs%cZ*^L|KP{vc0!l^{ce>MSa{eA0lgN;
zd<gvvGqV#(Dep66%=R-L0V4feqNO=7HGy8I3tbY*ffvBVzoFCXP9Jv8z7ACcY-Em`
z+{fnXXJc(HExDYsC_u#4+F<E;?j9%8G+5xd)i>hS0Gkc_!*4R{!Bz>lXShOC3mx<Y
zhi~1Toush%JVHa2L;Y@;_unGte|kPTI+})w$qPKafabXXqmT@fj1P|E8HInWPS^YD
zW6>!l(pn(zMjC6E)Y_n7#an&+mymEm2m23^yI^4Uva(~EE@pD8<dFvKkfY@FY@Ni0
zln}_b=QU^$Z4;ywC=`b3bwnCm<LM|UA)MOYrA{6A0L(t^L<4xW>a*#=kK7|D-~yt#
ziiu>^krvS}_(x)iEWrIrok_L$Im|P?fAuW<Q@S-<VuJfYeFzLYfs9Iq^bt`}QBf!u
z@N^r7XLjZ29^6qnHFS&jYmj45gY>Lg010IX^vUG)ILu~IWV0mWn94Fcw$p@3078I&
z&-(8DYj0^+4DDGlC`$RcQES-d9&ZJ^FQi6>QmxXT_Xs2jj+RVj?rg*zWe~-jI|cke
zgr5R^?5Sz_f3GL3=FWYaUf0x1CxnEAkd0zzU0P%{Hn=esDU58NA=clPEPNth1qm#z
z%AdK5w!C_px)_L&QsZhoBJ@W3wa`g<eSemIybKza4>@YCRK%#s?)-3WKqe177VYGm
z7FP{~x-DVVc6yrQUzg!s%mx5t@Y}ch03=dw>*!bu*RZm%JWEWZK7018DHkBi@R71>
zZ%X^xj@7)d;%3cuUCwCAcSUD>&h$mBp)UT7`{bymG(v=r2)*}PuLDWos{FF}8$fxJ
zm)s8iP>2+u=VVpPWZ7e1P>}dV_i~u<;2o^UzhCpjjonA#h-u$5GcfqYSi{E5T)4Ye
zU0rR<ePD?dz`+Zq2e3^JlJZBhuE3fYFfjElrL$RI(ZO{V2=@UCz?eo7bxDtQj%uKr
zUhSrsd<OdN%AU@`Rwo2@=L<n>!=o}pl2^$QLJ?;*mc*;YXXWBuUw#`c{*7vJ5r6?f
zd*BG_*~N=MDpmFS5E|;Zv&X>^W_ysB^(Y_>)vs5zAor76`75gK+8c7ZXK(XH09n{3
z*&ax}2J(PAYm%^oz=LRE2XK<roGQeEA7(vBCl7lT=X5CGgQ+s2Rj8pDFm@kd9iGoJ
z<SYGa%k%rP3MAku0Tj}{r!Ft=8AvUm)f6RI;8Y6V#pLELZ*~FE(+*9!|5O7wq`~gN
zfrV^t2J9Z(0sr)qeMWuHVW6l4eAC(&b<Ckxr?+bN8ODkfzEyAo#!)s{c)<B5#dq>(
zmOv39xOflf=|11WD$y2CI*sb@p{QM-d0SXjWt*^X76U4xO9A)MbGQCJw1aO7#Na9`
zz3{x;+>VfvksU!nROA82RhO<B$01+Nj6HRAbxQdE6+;&ej^A97{S<e|TxuA-fDF4h
zbIB{REP32&**5zVmK8ld&@m9Z4v06ys>#G<E)63*h)NUBmI^Tpd|9lQw{`#OCwHNn
zSsKqMe_&hRjr95VQuj8E%*`PyB!b;0`kwj7vx^{<KvFn={!JQLS_^`u3=j)S@NZnj
zCFerDxN{U?0TNJu0he8&@PiO)lLms@cP1i;s{|028GNl5cBgw0lN*y$ANtI(wfR7b
zLx#1-iz#sP{7Z0YGwH05iatxkf@jN7gLq+psOCQ~<DEMc_l>DTFg^hx?+gKf72o4>
z7$2%qXnq9Y-hf|itnD5K7H)sV-VRekI4>4lmmvPdz}l?G9UpPW4-kj@Ifq(zDVS1Q
z+tg?x5YTzWXRDPgUkrSJ5Q@N$6H)sio&84EB48DF|FjTWwgHEhGV?wSyKixCY$yJD
zSNwi(U!fcP1PGe*U}8B0%-4zwgaR(RH-T}D#mUL}ii8!j%yG^0pDYD36O~f?Gg!Ff
z2&33Fb->5C<%hcShVC&eEW5=k2r3V5^sF%U0**ZLRqEiEDQh{Lp8@rNc@)ix5)oib
z%f^6@&_;ovfNYu<9&#r94Tu#X{P$J8Qi20i_;1LN(g%=*8-)aXgrU2+GIc3HC0q-_
zI#_lufY77IQa}(N)$j001^?L1`A-=3fB^8snH_|GF$3wOtW9%F{;trM_dFh$lpR)W
zUYT*lJpvjE7@Xq?HaUn%@3!?#vM%k@(UK?AXUAYyj0{-5TFjYw&HtCRe(>PN+j990
za8Uv~%LHJ4y8y1?vE}8v4*lfod$%?OR8I%1cbOU(ctbLQ*LZVD)N4g_f776Q#r57^
zftI=<6?@EN&1M&}UV!Jz*8Qjs;1kH3@h3Gs-WVQP-;rfR=4`~Ic#=&57w-q<h;L%e
z`lnk8ZP&|SxXj_tPhaq?BS%Fz&T{_<<0gQ&L4}Y-0cP+1xT>_?-P?Ns*#M)Bk$C$0
zV}3>lYamG#ak~d|DR9IE^&&9;z#9(HP?#wub$x(oHuHe&{-B)kg`f@|P2iNv19kCN
zcOkxhZT;pBhW}oKfb|D>O+b^@ZcUL>%rhc-^X85D;;;xbGWILQ<Yc!OFHS<GI50cw
z_t_mQRv}X@yoY7~Mt+*efVlGs2Qj#K-M*`u)9LU*_L-NLLx0tNrq&>^hS;p1wEg;4
zqo%>s<3F2TDLT9s<IMKX&cNJUUx&#7aBO(z|0~S<9nbg#gK>Vl5!eXZh@Ap^3mouZ
zcD+rNlnye1&&Gi}H|#)iBQ1hu{qM8{5|bmpTLgEMw9K?_fbF%g^Ih<#fSEvu4}-Fd
z(;*??pTF;STLC=*({N4&q4+2a^$RR(PSH+Y3cBB~VcOJ@-W>o@h*9<3Cs_s8R11B(
zmK`Elv^Err;AnPEZcbPG6|76_rR<;P`oDkZpI^{2Hx5#Y@PxF4Gm+CGpZyB8&>Xbj
zBSOmLx8&a4QJSM{oO1>ISD<7Bt~bh6L*J_OMir${S&2M;Vf3RH04NNFVi+1oe@CGj
z?j602$@%$5T43muCm~|!S@#75k&^9kvdWo&#OG)~MUeRMsc$?{LI*qU@7KxXV%a8@
zWZA(VA6U5{rdV29O3KN3Y;HQ7c-E9K1WDXB0CS|1Z;;oCmO3<P$7yM0ZBB&#b2(Iu
z%wI81gTcuUs02y~6aT_y&&YZyL)BkANtHlSMueXLS)BN!<RM=#&QgI2@n^?fs0TAQ
zDlY{+;7Kl-9s8}@S#a?oK$HAAf(N{x{JrXfZ<<RP&#+~trG0%#M3?2f<M8&~yWXGK
z#{@O@O$3q%{xP*w>drXp3JnVI-NyGxIIiVjCVc;DC$TBm_ikH)T6qT4%I<^jY!ERN
zxV&bHPkVsR*QJ&(QMMv-V8(G3rg;o77&nZ_55P19@P*04;GK$~oeEi)1urpMS}d)$
zADtnS#w%<8#*@yRv5`=sWD|f|9WGf?Ny(vd*b<W-d6vcR5JpW;=u2>>xt%N>nZb~5
z`gN%yyatrJh;K94vQRX|a{+4#$={(LD76B<XNV2<fwKYP0z^6^K@ZJBCUDf*_0y5%
z?B$_%sqgAKdjt4$^3@I$O{!~GP8^M48rB|ZL%{uH1l)%dVrb+f%qd9PY1#Qtz)W(4
zFpdnZK>9xbj-V7ON7}!PK70Su1zEdsM7CALnzf_$h+ykUAE0sXmuofQtUZlLFx@gl
zF(_WjfSj<VL<YK2*Y4<yn4N|9PojRI>YxYEfP+^vqJUuNFcgijtTCuq57<~|km~7k
zLvbPqLg?XU7hYLPtq6+iDH5G|1x?cslf$$F<k^j%10Z}CI3=+O38S0KmV)@qnqQ6~
zl9|t+KWF9VdqQ)T+HRkqSn$R!9vAer8WLIy6cmXf$zQ;^4(U+*_WTy7cnOaPa6`DQ
zV}x>feDp3u*{(oO%;w9$2kTP&-u5D)xi*=<5LjB*sX8+-gaS1D{J3H3ZoQ4rKX^OX
z=EUds9#&i+jM+ci{?D%rEl_d*b_MnKQ>T|j@$pm~rrq99#oE~#??2{yh*Rlb<NcKM
zcHqi>ktC*qS!r#ch28hd2JbF#HaiBm+Nx1ekX73NOeaBXK~s9$8;%`y&3zLhKlR-x
zWWJe_7_nOdhzZnYxfI1^>>yZT$Ugy=vZ*^<z|SV~NpJLbjlgwpO6LX=F$lSVDoUN=
zY?O5coTQ$+7rBAX_CGsM8p(a3Vm)rojt|`bc^InBqJt#J=;LAk>wgF0ZhydV_5~bC
zBza}5<>$R*>gETz4#={k@3*@_$F_BaWc{%jEon%rL=4A2@Di#YbRXb0^0L!OLPrHr
z)D1yn%YK(p{rqHYXZ)Va_v@LJ`}3<*Xj96A%dV@DOu2FHl|OIx3{6Z=e24CbA8hZC
zE8$qs$?z6n_D7;e!CK~Jid`NHz;u?cCR(!tH~+H_yp>|}1eaoBVy5;Aa&x}|2+WgB
zhCBo5#F&heh%O37o7`;$iLN%dVu7{f^E;O8Kehp@M25j3EvX=zqSl=$aDC2WI7!wx
zstxXT!0DV_zoB9UQxp(K;1oUD4ffy036NZ5RG5K1is;;sH@L?F<F&ALCp73Nf*scr
zpUPsRKLHk`b4h!~`<U2McKW^~Ny>L%d;i;phkDZ3n7KyABlD~>Zn+D1KY`;*&-{Bw
zp2P8h7Tq@W{i?lX9A^Z{P;9_)%s(sg&#!j3;k~kSv_^RVH6%E=ePw?C<ToT1#Gglu
zk|EEBy3S}S{%pV7B`}Tuj0eUkoF;Q48ZQw;2Vb!EeDL)~pik<JvCtvJ=#TT-R%Pd%
zy?n~Lk<M$SWejJ)`xw~zf5EG!!MRk%clQ)`Zqck>B^-h^{4F!dY{eevzV?rMNWd+z
z=1msJxO<(ww|*mMVmgY>Dy|goo*!2g(C2eiJ9qT<V_7a`+oiATo_a)ZxL5k);q=c<
zq6Qb~7W8E4kx^EMtlo?YS^u+`W4hpFTXAw0Od<8|_*e1$5F-y_y93MAbP39eYri_c
z?rV5_M(d|h_`jVTyOPOI+@7l0g^>jy%p1xR*)7y}rHhkZ7|;j!NPTQ^e)YhEO_>RF
zU3g2N>)O$X$=p?#gqb=p^UfWVMp`Qa*lcvlF3eyrXmK^25ShfKU0D{DXl2#3B@fVA
zrnrLuH6#6;jwzsig75K%n+8|S?TF51+?UKO-54r@3Xyln2fTS`iYsOlioug+_F%MA
z@&@T~6d*kZW^4`E<NpGoVA_v71fz|nXd$Q`p>GbjIH>8OVq^1WkkoO728nIiOgMcP
z9w?B7?DrD?7^QvWIQEI*)p8c?WpMvj0gDS@_{ycXDS)&F;yZZe`2e@@TX=nI#Fh9$
zV8INU(>_#DEcZ`6F8}i8ssPDk?Y<i~c;k4$ehK)wxj`Bt2Yo1(UH)KyD7w!Nn)Dcz
zJ)qJ6)3wqvgt9wXxrmrq95~ZxbU#q+6~%i$n(!s^!^d+4OdVma1LgTKN7s6QYzIx`
z=ZJ5)bPtVDBtm*W(32Pz^8WYb^HMvrX<=Cbj0j306e=$-?^nKwGPT|QmOz<bEILNO
z&;J}O%V1=T#MF#;8I%7PNUu8yS@sg0P2c@m{=p0>xuk5=#%<H%U&^_jA%AU8ljV3Z
zm|-H`z~2+S06Y}KvCaa#1*RgISOMAyMAC(>A@_w^m(4HLcbxjU(&I3#6e2%8bWyoA
z!Em%5V7tPtt|9v!kbqrU0$j=ai76M19mpqTsnAO*bJd`3Iafd=$KDT?dq)00!-wCt
z4@9#kF8s(GgMxU6LUf^BMhF?q^B>d^0HomPR&A_4P@|D^nWIj59L+3SC9U?OI1SyC
z22-R|PZFk0d5l;BfLAVzllxm=nFKFQeTzfP<PZtmUpJfwDCit~fw^%=Y1rbxd;4+`
zsdZnl>?<&|39yS-Ll;$U@gU|?S*+}ZLp4Vb?&H8t*p2EwTv(+0#z|<M)mTs~L2kaw
zTT8iwSq#i^0EXQ5RezNqB$LoyyN67`nmL~D#-G*rsYBVlH1s6bIs|;Tz$9mh;`n^Y
zGZ^iM*xoHU85+nYq1z6tGFZ}8hyLARfdlI@G^E#Nx`@@CR)`SZd0bqaMrW=eD9Qvh
z^!3|2(vb+ba{fV(D}XZd`+i~vVH6pM1&T~ad(wKgvRPR_jah9C2*40gaA|^`R(jiS
z(?GCSDnj_W2;c=?i!lu&!1F<9w@cZE{%vMjrb#}!ti!)uQ33qv*w>FjFMWROg9@N#
zFEjDtu2kGsQFI0jy_B0B1@^!Nt*I4dCqei30<y>6TrnzCfK>MS)`VsOU!GbQ#UE#}
z{M{AX`a+Z=efXJUFyrrktDHCzB?>kyS7AygLYNj{4}AcO`F=s5at?gZkN6D7-o@a$
zOo@1;vk|ju2fUJipV{7ZnJ}{KSKTQvpgIt0!oK79wL5s?sU0(h`m}<8=H`7R62G{h
z7d-jEiAb!mA4-<$B|Qn#6wQcF-{IrKM)*B2%t^AR$suY_S}nd)a+6&xU!ihQsBG>g
z{T|pdBjjeNszdWtf24lR1PFdlV~v;JpW}L_;n(j_4R5KGCpl0<HB5BwIY>lFXaAcb
z-p|=D{jJ6ts-gtlnVmMRuU@;RwXry&SpJmT+QvpxSNAvuhC2s^#N~jCe0-`xJxIK>
zaYBa8YUs1T_$kkeNtp#4p!q|i?kro)HhH*i^03_GY$JwI0B}%Q{EAd@sHq_baVnH~
zFnX{+$ZN1R?kqCCHt-oa67;v7)Ks!I<e@?K*T6h{^<*7l2LK~{A~rWve7gF&0^kN#
zvsg{K=Egd~bMF7?+0{q2`{O^s>#~tt@cmold=7-C&(YtKmOcyU#PhxQa2R4GDJzQ}
z9kMvZ19v4=2e{PVX*u_#wh$<#{o2YikAMizk`i`K5Qncfp?({2)1;L?8O-NFOsLry
zP@t0ISy{UaHS*KtNElc-xt}1)OSg<_gI;UjCfwj6y#P!q1I4MPx^%5r7w`xai`dUZ
z_O@j9G<F4m3;@AXt+<K))q&_bk8G?%A1SQn&8Q7x^`~yZY7Qr9gH8c3csZb3YF-$h
z^-UlWm$1Imrh+-J1vu_rOX=<V43-|xfC-d5(C`(pk-2Jzh$Ru<OR!LWemj2GP5_3Z
z0z*>m!>`i^j#ymC)X15QGtfWS2b(<gf;yeR%o2=MkSq?ZsHg^{U+`)GRaM@v768AH
zeF=7Vq}&zPTKC<tQq_4MoKfdRe8HSP3hWY0o=Se}^XNbM<9;2r0dm!aFQ=Kh@tG^m
z5w^FqxEB?1zk2oRz!@hdBmMB0Ftpc)KR#EaqeoyCEC$bGGHoY{(i>DL)~ynRodolT
z%XLkVfu!}=_tX`9{EV}3khSHIi<huw`=y!TB<K*jwZiWl1xxJT_4tzJ69u{}rOa)3
z&^a`+*oqnF0vFD!Ku{u`i!YlNr>TCkpK=k)3H(tI5d2@z5HvSZa6yY%4e!J0XDD7?
zUWCN1si`@?wB#<E;`ABu#E8ilR#YNEnwzm{^0GV6Z#ly5Jcv8CV_=~?=ueHMJp@@K
z2(loHY%d_0s@)eeKp)XMmzjRg3>l%Y`?={{Ws{{oYud{b_socWaAzj6&qM$b5B&B+
zLi2Tl+7Vf{y<w@k*Qzxy*zsgTTiTX7mwa%KlSx?{X{9F>`2KInGO40O#wGxx$eLSP
z0Glj3?gR~}Y`Zx%P+-_uA_z^~$rZ5&<YIL08GeM9X5T^cu~S{9*yGQjk)@58$#4d2
zgpo0XF$3O3TZlUjEDwt#wun8t74!{;ANzsnsiv7;Q^!nR$NDWtWREN?TY??zC3MeO
za2W*#Aw{oGhUiNe(Tbe#z<~n-7?`9#3r-iovb!e!Uu!84By_9tBhvwwZHgP>Z2uDi
zV!Gn<PG&%uJMwAe#VDfU-3PXNUc*$*Vq}Vf-6<ndA{5Gz)oFWs{Nj3NsI8@4k3X<}
z3R<}B?Htdhkp5=<{22oo(Y8C8^bXvkkP)rWv9YtIv-@#N!SlwhEixplBOTFkBOY*`
ztWP*ijHOeufbHV!Y~8d{7265uEHeAyQgV`#><*Z-(?=qMcC`O5Cl6mP2ZJmKzjpQ-
z8X8XE;LLnAg*|?s;IBii9A{}oVagf2bZ%d0x!O*c=j4mfB;D4rrUx~ocM2YKT7kr{
zP4R=R>%5I&rUdRM5c`4Gak*jF&s<AngbvuG@<!)`%Awf@diJM-WF$GjBw(PUc!P7Y
z67jo#aU0>mHnXLRnRaw`9z|W<tLb&}5`Gj9PW|<9oF}ecUB0^P=NctVc<j!xW3o+m
zkE=Ka=S7{6&!U@DlCLU{Inf;Fy?pu9QCj@lVz<3e*I%oRKG~bseMcS0BBFQg<Wqt6
zAY-eDG5)b`>W|#4fsPG|^eO5U&Q$ssD3r=|2095d`i}TL(_c)LG(4l`M(=tbl?zMY
zJf7(oZV)2j-UunSF5p*>OggH3Ltx(OIp*egvRgV^%)={>(h1vRbn@JvvJZr`IXrD^
zyF6D>sNv;M;4N8yixN+NRlrlzVD(*P$VVFST9TspYGsDz@w%9m%v7Z$PnVGq9MnG_
z$qy-yqo2gy^PTR4W<&Aw`zVy8>Uq!T4Ht@^KBot*S@;ss>k{qP4D1sz!;)0R;U|ks
zTk!2^r}-u5^0tn7GUBYDKgpjuy=rA;%N5z(6d$c;${*)@vKDtuJYj4ykBXOF*M954
zH_^^e>w5%artM}6enmv_PVONgET!Q*Y}httTK`-Q3MHo!#K1P!k(2n$-90EGBExBG
zbz*s<B}K~pGAjy|lKMQ5#6hpuHoeEHd4hj~Lr9{rBjbaedUb85t~CvnOk;yj>teWw
zconD5iMC<Q8EeBpW=?(2rGP4}pKmp1*yh9YzB3J?w<pN(#y1&pT8k_g{72q~D@3iy
zPqzviuU^lLS`OTH-zi;LcYRkp(oFMYVGHxn_1$XwE*`}tC@4sJL+km~>jDDmkVMK+
z=^sO(zF(oPxHU)L6??=i^SVsH#-q@+#PKaLte)2$8`m>GCG4g3ChnT0TcmvJZtO~_
z!OBI)csWSv_?<GmKoV2JXeK?(SNS?3B1d)OIEVl6M<azd86m~@=F+>TWCJ$(aaVG;
zf6}ofII`UxJ#;;&z0B|DLLN4QnO{;?R>kQEMEDH$W2tXgkD^ez3w6DLr0c6nVygMd
zMH?eb%uY`h#KRvnT4?Hxf!sZieY_{nL3cr1cZBFIX<vyh1p|eh$Lw5-XnC*Prsk!T
z7g(8(Gd%N-zAPoSTh1+4j2v&<@Q-Y0+v$7!^H-xwyW8*&=^?UqTy*f>bi&#_8+jcZ
zmx9s54@|O0cH(6WG>43Lw>MHA<s<;t1f3{B$)H-C`>_SCF`mofnlPTrnwM!rzTg5i
z>jPgie9|bxSSI<*(Sf_Y5_R8O9{Si8^VBchdvGtKy0J3_rhCej_$LOFQa-AbpzQ8`
z6zM;cQmyHB;Tt~jOO!fO8CR}q_FA7(v9hpqjtak-TYVF8#nq&_(9}HhuAkCFPNnn?
z<KOi*pLCBD$K0g|9)tb(_hZgKpD}&xfo1=vBkTrEw;;Nf&4RBL)wOGrlc|~V!2Ds?
z`g+1<qWLb&>kFuG>5!m&++CPW#)66J|Du;e5>me4s<va8*q}2OOgd+hH}0n<wk+AF
zcWQY>KF3=_b!9c=vV_2%X5FP8*ei_2q@#Z5RH{m5HshsBWw|us*FQYj->~mze{L%5
zojbL#ws6a{5P!vxDB8HQy*0)J(ofydbK@SAcH;sCwiX>vmGR;yC%C)vX3Rrm7J@mF
z(`zd`Qy<;Qs<ZriQ}7KJrQF2rrSWltNA%|$yR@>uF=MBzSEzx17}HweIiI^58}Y;y
zM^LD-stY15Emrwcjqb_fRqEt&4`n<H6zYmEts4Iv5Oj!=U@gnDwhM5Ih$s>Bp`4`>
zFk@1PQ%RrUcD^Uk*pmSpU%blhv7!v}r%Eu{d%{RA^05}C%g^ydzv@2f<tR{fp=Ggf
zXtImv<87<3hK82Vhqe=`eXDiM5B%{?FSfS0D;YzqNf<kS`08ntf*9EZ>X@+|Z+z?6
zV-HKd2A66Y3<l205ACLw)X(ZzX*uHJEdPA&mTn^y?D<iN!iR6JJm$sdu7x5jD)QN?
z$Zt*5#)B)Lg6dTR9P-`<DMz_v{)+W)|4P^Qg3{hQvLK?cIR2jMS*py-&b_X@==6z9
z;Y_1s;&an9i@U5#S~ibbn2&G1+nMmzJg=0slBA}XB}CmY%+)$Z*+Xi)-O4~jI<7pr
zarmkkWm{vKRAREoQLZV~%-9Ss_!7oO=bExxI>c9sq8|#06(-~vVG*fMPmImP*H(5J
zqN&MVmhR0FIkLxTJKgm-9z-`c`FNz^s#r*Ys+aV~u$LA2+D>BRXr(jk+wf%j4VHDK
zHeo4MUErQEpF@ri)p7+1+{bbWHwYy344O-4luE|pWERpVJq^Ck+fJU9D-m5fe1%aj
zi9`HJSQ9?Vz<<5V^*$_Ff2_u+s^MzlVCdar#*10D#knHyWfpouJ*SGraoD!}D{<E>
z7fnLgnB>)a+gf);sK-jQ4Lck(1=G%%@Q%tpwN{K$BX1Kg3u(8o(z~@#T9yWJzRc5Q
z-sh!5<9Ke~0%>oun^GIjb(^%X?W0b)JZcc(QoEljCoi~_tC6;6dQ`7^zw-~yyE$=q
zTdK^bSVsdp$3?B<Itt}#H9XqIW};dO_bc4-B%auDb4zhWvTw9FR-a(QAvpb))t9I?
zrE_*Fi8)IYv*{g9w@(*Pv_cTAW7D$<RM%(;u9$6C7MPR1XDXPyP_8E5(^Zl@JJTb(
z%dZAugr?)J?`@kpDyhxQf*stoMHA0!4Lu1YcB<D>^VT_i)B5bB;V4tseu14hTACsj
z7g3g<PWl}4Ew9<(hby~q+X{|To5!N}+h9i-N=O&WER-p?{qzy)N|u`WWLZyqVLFi-
zE5GeIcNe_(FaEsNPkeY%2Igck7E+4^@Dh-HP3SnBE3*JV!bDZ{3_4CcE0h)E`{+Wd
z@?_#KZ61qjH3PkKFRq8=ZM?o`Ycgy;r{mzKrgUCmn3{|*UeLfbq9i|kv*1OVdBfpL
zi-NW^>g{FjD+)|ZFI#@pdxgF9Pa8-a^UQ5+otgZub$$1(mVP%nFF?<PKZyZLnqrnJ
zy!IzNur{mq{PZSrIWZ;qh+f5y5BGYq)IFsfp4w++Uc)ttjV5l*oNpvE_afcp4`D0x
zy|B6J57|jBm$;L%Fb(}Li8neqB%8Bq?hd*ZDgluOlJ5&zW4aux#s|vliN@<%$PQnm
zLiAZ!0)(}CtOvo7OFa2^aQroUW3ye3fpB^(#Ou6#>Rkvn?fov9GH&(Rlbdzw1U9N;
zn3GM+XsRW-YY<xZGOsvT2!@F#@J4K_ysx_KJK3fvfo%{U;x;yxJj>;RJs~N<%1MFl
z<fEpTjn^BU7^&i2srL5kv>48X7cV~?`tW5lRm!^rY_mjp8{<MQ?ZcNT33LM#`82;X
zq&JV-tH0_b4)SuOC10=XN>z0aVQh@IDI+Z|(|^UUMlfgI$Q*YPmO}3i--vE{dEyi5
zupdovMO}HOnc8PerIgxJqv6jbTMa3hHej3M_*BIZd*-*e7PQlF_Kljmw2caVXUdwW
zNf-8cDkGv9?Z)q5q(SF9-h98!XnW7;&{esnq;;MPp{Ay$V`LPst*w3V@L^!RUW@Ay
z3_=o;M;*yWP?9FPBj?6@h#Z$|L+nR*rKU*+=hCS&Oyi|`NK5jXw&0EA^=Dq;dGl_@
z+-z&0WplIrj_=Hwq5O>=D&EXndymQ_DAPI>bQVlBVdEla6=@frQm>jkp3aFvaZ&r%
zvvWP7GQl&9i@V(d{#@)0E8%2DU)a+L&rOegUp{oHD9QVIv}_U&9yEguP9l&~1$pge
ztqUuxCz>yxt{2$bwxYOcFZo8c!r7_EqK3k>kqG5FsaMr7<e;aPh8wTLO(d`!v+3X<
zX?*wjdI>k{^>2;Jjd&)BycM0Y!#kYLz3FZ<Q`bLwZdq9b**J<DK~#Le^<hAEyVYcB
z^1~$)Spl|B^&gJRT1m`Z3t1b}tm566+f@;pI%c?-wOX8J_@0dAa3s2=*_zO_z0$7U
z&f0i0w!2$}#B=Cm>bZvtiAf2GiBm;m5rI`&FjG0$6Px5q?HCHRT@h>e$iaCRcZbmN
zNAmjn`HPUCggbEH*;(o7?R4EqaS6T8&|}d(pynW6uC{LB)mu@NBWy+MAII7e@{w75
zDePuh5$t5O^PTce%1ghFw?;-51Yg55PLyilcpL36hqq#q!RyE#kjY8CarjE@9|UEh
zX=rGgY}!>5!5~MPo0}VV?IA6*z|?A&nSw7VyUuw}FlA)}q|7ev6zW#q8;Fb$q9@lz
zjS^q}a4Ji4C^WNTa4rn4@%ho{Fx#1bYJP!svG<FKaGs^Y8!uMNh<d&8slL@z$gRR@
zS=REEtj*>Ulr}T#uCvrUiFeE~$C*QaO8!Nsg#OuVp)kJ7Ur)n=b?DvCXVI#sTfks-
zV^AHkU<*Ma;;ES=lPC`4{nB2Sy`9SvoJ{o0%#uQ$*x3cd3`)ZdZ;zmo7I(Q`$6T7*
ziK#t3^fo|Q(&(wbp62F60GuBc9@{?Gmsg)-MjwHHd6K;r`Jq4+j)=^f7A^m{iDJBV
z<L2%ep1^Q*qo?iTN=nP-OuiMxK}VfZ>#>?p;-ThZ#;x;AyM4AgI08=QR{BmB$_nhb
zr<XPK4qY~iEA@c_W4dyIIFNa>chHOT^X5(qRm7xR=23j3z}>Z*lwH!S^qdGaD-X~-
zzc~`D{x&p}4tRG3Be<wra&O-SCAJ4oqdCcNzr9#9x_<MQLLg?r&pe7L-n>4CFD86i
zY>suBGdFFk2YNpb)0Y)!WnLg_`goSb>h0l_XK+lFu1Q!zwMB{3+LR_W&uF{!h+PuF
zVB(lperq9#%ZCi8{QgNKljEij-@`?@h1*N|5l`;Dfn{KXPKpL=rBu08`4Q5oHxC)J
zT_OVmV^vFRlc54*tR;XHrhyr|es9Vf08Sz57ORK81&VRXj4ztCF_cpDZne^`E-!pE
z`aZu}yj!_)w|CE=o*!pQR=xi6?VDj8dr(qRW!EfsZ&UEt@^mHhUtuy$f|E~v=3?&&
zuhW(D;yAhicU#)3ramQ$t~|bw;h1TVu!>_WZ)<e;k|l{4*(5|neu5k1OkyGon9uIM
zJNgElr6~l|ria(BUw;!9&TA(P0lDzv*U0<raZkEd>Uw3ws|u18gy4{nNvzK64tum=
z^CBhQ#o9Q{jPrtdVX%UR!I!X^5R3NsAj?x*pNB>Hcm?F?Y_D+>_1(Oz80eMPLM8U4
zJB&nf1$zp8tCENPwdQ%bQ5k~6S2kZjt87qi+QUOcNO=EEX=$l-4TYVo{SR;N6OPeO
zq~BOosiRP`3LQ_5Z<@FAI~5v5TAqF!UyZflFrluHhkjodGd!caa?6uwX}C3g*=qVm
z_;>T0(N5a8KRW-iROGZa9M67gxnvTO&!{%X*cUrp+U(N1%P-G|E-0EE@3;fy+TIvW
zA;G_ELpcekr3Ea9z2ho;#6#E&W5BWFl>eg~J+ZAd{f4mQd3(4fIpei3<Om9~r)^1I
zcbl2qkBOOjuA!_};URAPAzX!d)%EMC-?o?-Udr^zpGhGao?2DmxOR04vXflu>pjWq
z{0vj$9*~_R*+xNja`?HpE+&8U7=;69bq>r9XWrbfvb3D;+ge{Q)DeYp{tXi2N-v5p
z?ePrCKCM(b;-G1utlB}_T>qh{Cd-F(e5(&{qJ3QkckK<U<;wTLaEBF7T+>j2!--3#
za;18sYS%rC&bE<b`iK8=l$;Vg0K1xrArTRg)w#;bN>!F?05J$cMY!yXwcIJeYjcga
z0&Q=Wde=_1_YR&~hsu$1NC2Do=+F6F!!t{CGs~1Sb*mrPeI<rvQ(1-6%HGLtro>zK
zzbOf_XER(*AK#Me@Pb!;=%b;aOBW6{rdLEDz-_I~1=s5Zwi+?4*<;~dKm!A0W(KNb
z1ukKD9M5LFT9%i)XEvuaQXuJ$F*aVDw>3ILGT8g|W}IQTMo&vgGNinT-?jkuNbta0
zdE>9JK`9(&ytcsV;LyOa6Y(A%W6izj5SFy?PWGfn4$Oxx1YY(Tx%{S55K64hv9hwR
z)G<{g0oiZlf*Fi3UY#q0RXY(MEKehVy0wA?_|d&5ceW&SlZ123dmJ9Uy?lF{jyIj<
zVPh?lI=2x|5$Lc!@XsU~k3HF*+lHe``dur#P(To`IMYt1erqXfHS)W4VZoMtF~4eX
zlw*d0QZzS<)zZrBVp&K&huVht_9vn3GWOj``Ay-1>cx){>H_~tpHX8Ku%~)s?w+pt
z`jzI!4XHQFpoj=6Y1#%nD1^r*r7S4}*3_w!+ydej$5C8!k`EFhLMw#o2K~A+O}JmD
zFgc5$AvjIGdl;K?S1=3Wu;JDXzP%w_3f}M;*+?=b(>Mr0(&-n~IrSB|x1CCt2b*k3
zcRH#eV=a4JfSH$>crOp5j(r#E)%{!}QSWcAF=e!Yc^KwEp<Z7#)}@ww11}WHR1!eY
zmd2*w0n;y?KYsi;DW5|wP=Dwv*SD5`0%5kXHTftiC9CvkD8Evf#7>`*HZ6<5l&E-l
zL^x$?^jv03B$f<cqy`DOXaicHVqJC3Ij1W5^3u70&mm75b&*XlW&#LPa6wY+C5kEH
zCrrGJ2~fFpQl93TGEjOqyXz2Wx@Q?Eb9eN{fZ*obOz?PkD36p5B&z>>qLfmbJx2Zc
zR^I~*s*|k<r@(CC5N_G-<`gXrjd{u*aGq^IaXeW=>GBp*nkp?RpK}asMrayNeOy!$
zEeF*#<N1=Q<;B79@89X3?EP|H&qeAyfUv|2KRmhcy@|VMgQKj%GILWxH9e?A{4MF+
zoOWv<hO*&+P#K!NI3J58jr1ChX*&L1j6jwyRGkbjY@WsnN-ds#jI5BIUX>YHN_D2x
z3lr@sfHlNN)K}KMW&P(4;5*s7cNMhc&Hx}6!eJ8PiATv8TA%Rj*-^fY;lRy)cf4hP
zmu(@{V(WmbB%YTbovsh%q~qp(<hVB53Jk(rFkjqyG5&@SY^Nq#66>_GWk)}vP#0c@
zd`ML|H;1;Xprn@(sC-@CVSY=%L?8iK=QiPFvYX}Emv-L*1fuE~*c|?Hu8a0IL?C%z
zBdlWXoD}NIU)7<!NETVr@+sZh_RpT|xT*5lxk}(zr{%@F2w+(ln;0IFqn~45#+4SX
z^~L>r(0}hruDD6%4JO`&H1H@_2XFjH2;tw?=K3-f)SX%h_0M}oJY5v=GS>IOaC-W5
z84BZ6NSY!<$WfBlu`8RV8NAqarNSU)*6}qYzf$4jSz@aHWi1J3nT!vhWO@@}!>?2j
zxG9H&(vr4ZP0WRlH!dAB*Eel>aQ!>m<Qtm$&ZqZXC>~f9+OaVx%LLE1#eFs*pN-cY
zooJ=vU74xI*xXx73Zl9BO9C>NHppDmoBq3f6KT~A7)9)QY=Yt4q$rz}$<_E#<Pckh
zE8lm{cY3$Kq#!$b`RWxKn5eBe`V#(QxTyKCG7<gd<*=E~*RQ8P-r$6DCd967a#igH
zl8heTEOwd7?ZB?@ek#et)SHa{Q7S!DmUgbEjZbN4vp`bvB(;q?O=;RWF_K=j_VNPp
z{L81R1@@=}D|y)^0gs{rziZ9eNO(M&P!9(0jvn*5TRifHhL0mE-UxHOkmqm_g+e8r
zO$!Ql&DmU?Nd>>7vgPK7DGHepWl7!4*jw#I*p-q721yYvyH5ad^|@??;_}xWBO3Rm
zDnW2&aiR}i>rbXUqD}HvU+|MQaePFtKMjrcm#<0f11R$9>if!wp$8>?;Xl9mJGZV=
z=5x#GI7`J!*1JS`UBfjM3fSO=5T2afqy4f}pEWKK00%g?$_XZlpLAmShAsrd?lzfh
zZ3^3R7_ZWt`FCQCGFMBLCo*I=ZbJu3@F!D8*xz>9SqTOMcjQLGd2Aklqsl34Y*~Bz
zLM<&V1d#<B7_r{oR-+dvR~Dr_I+4a7KCN!UqKSiE)@hj{?n-nqo)uRoVwIWNUWV7K
z{3unVcu-55Jr|JsxMycY4-W0yjq3Y*K%Q$a2MP2Ii+r2bCeIdqcsxs9lkWDC3S<TC
z{>;lxlYf}JKpf+u+Irh3pQ07uFx<6Wp7$xd<=d(i(|qyWN0pPck3(kkGgZk55U$15
z6plIYfjRVXwM=~{_e(xi4(>!6hS|{a5}|HUaXa%UGi?_N0oG$f%TG%$$%Ji^2(^br
zp`C8(ebu(BDs!3Lss3%#GHQag9=(v_O$3)wuk;7iTaS&14Uf&pWw3|k-JG4fy;hk_
zMO+-oUMhoufOM13*X0a)msGO=C8TJ+2hlkG+ZLT&xVcdn0?M+wccqw$xhNzYE^Q#s
zP|Blc!$PSPo_g$AzR50Swp*$^!Mzb*y=DK%dX7lUMm<t*aqW8f#hahnANu1MBoBm>
z89;HOR52&}VrX=@Yq`3ygdHF^2MYt<6@znXqj}^a_03BH)ke^R0fg>37L=&AlWKlN
zjIs&^Ft9-Ru0d5p9Lt)CBHb+}T$4op^vm6^Ne(?X2$&{>AsDC>-wIqnmx{V_cjgTf
z2bqk|CJ;kx0DwAi%s`1+v9H_W#ps^Roj~I1iLW2R&JGPp=!}mVl3AP7Y=&?p<y|&<
z>pW@W>?Wq3CV^IdXh+9d5QVl^H%q-R-XdzJF166o!o&u}9I_&&*1~0NBkGg+`PI(#
z*gPTNYf3CS`gG&Tzg}n|vlL|QNsHq8V-v7{mRHQSbkE1vcpcv`8tiW)wUxqnME{PZ
zQ9oOeZ8-*WvW51x*-ojl`c4Rq^7W+#s7mB#MVn(4@|x0v@7C2REte|e#*IUMN@RyF
z(%Mj~uUvt%{Ut6Q?G(%W9%(BsTF5DMDlgJDh_s6#AwL1zIB_)K(uHr>|9Vy_jHl|4
zpyb{`=3p@;RaU4WoJ{Q#$v*j}np7Yv$Ov;&)_xa&E~GFn1~`|G&#na4v69yIhp?x7
zYeTpYr0yfo6o82{Yt1Z6|EIn8jEXYbx<;$pW7};!1_YIG45)yBC_%Cb6(koyi3*}*
zP?BW34YUMlQ9#LpfMfy56sU+O2uP9~1PMiyoZ*|B*6!B(zW2`^_Y33RI^&FCBUU~2
zJbSM-*PL^$J=W_JJY(DOL$rHm-;F7w$~M8Rm5+3?+&j<PUP{^>Fk5tfKq5rv)4T?F
z9S6m2+O1hm7V_U*(Y4VvH@hg;I5~zd+}d5*n3(1$FT&6oYyLZ^sU#kyG_8=+2yz0m
zErTH0mO0o!>?teb<&&6rW3*<mjd4CgQ(v4fEK+Syrt;JB%ObvaHG8!-dsz2HDmjeB
z`o(Th9M-d+D=jKPlp6|D3N=+!)A^O&;>9RbiL@P~_J;+#!FOw1A#!tokWuAh<*u|d
z`x-JVbR*xbq3k^?@A9jJ220YU+Mud=UPATMVO`$&IM-EP1FYN@ym5^qBPklzqwGps
z-=`<)=scrJnhLAv<(@xPCB*zb9&DtB+pe?(-DA9KERybY2i;g(ORvuKTW~xnM5CE{
z+p>s8ctn`c(^>g?w`%94P`_D8*Epk<SCwwuxY1w<(wywZb+HDoqcqV~9PUVHr6~X(
zmKY~L(G5J+>?l=bY%el8L)#;8xy2R|fhDKQ;l@)W7xudSlqsCL_{+M9s=%G1#-9q;
z2wWbt9gvpc<!gDkWAvP)g}8(2s%8hH`<fn|+fh&^Bw6G?PO19#ad9T$TFOT*e9VaU
zX!zl|wyEH;gF<Z8B_4L10td@o<~sz21G4C0AzSNE!W*H4@6FHZdi9W*q-LvyL;WgF
z*WXjbUClJdO*YcI-nrXJ-`mgakm*klwVmI9lA6wutk+wr^FjclBC-YMwS%^Qa{-i2
zb@r8|>?@!D5Vnupw&-rts;GGm$ZBujRc(|stwCKQV1Gt5Em6IoV2eo6#e-u5Lo3F5
z+k-`d!I0#FofUNusK937c%Lznv7e1MZZIo6<`yynql;&aPhE%nq?wB7VP369d1no&
zOul2^etdECNZi<%-Q6tkVTrjTGG46W8fdAz@9(c}5WSqDDu2;DkI3GkiE@g031dP%
zrY)i2FBWy88?#UL-CY2~tIqB+aN5U~SvUa+u!?Pka<F^;p(QFd$<O9kQ{Wk4{-4*p
zKbfHu-+8%cynk*fD+zumDj||;_=*{R^=rJ2EZ<yJm~rd|1~)_}!9ckZKklj&gOW|_
z*FOSRb2j3nx0>5p7EV!dq<+65mRchG97>zo(G|X_Znd1If7o(OZ&D!hKh@TTk|tzu
zHrq4ENlrUkGc-v-<=2gcBOql1_CHNC@2ON&GwJLc@XSltSwl0*Y|+h9XG^G$&i!$8
zCcCiu1Jvkhv6Nnhl5bWo#WGQbo1aYI8>>;*HNpt-1D=Zv9W0VAS^#)87`0cE=j`aU
z<=_cu_R1G7TwuSrisIXG%GfVtkx%Wkp0~PTkbZQkO{<V5(Gna!Hl!o!T~WGK#<%(y
z{<?3PP2Yx}8w?EA+Y{hfFfb^8x<yd;I+z|ly=JKCaMg$1JsDF}_qD64-+OE|?2#Jh
zN_)fh%`K!{QZ;@wjp=~`w*6;V4p|;XZ(C&E!i>p4b55;G>;Bbdjp=fvw7>w4q`1Uv
z8U5Fu6>uDP_Gvo1ul*rR3=n@t&pEsWBJIPh<w%JRXW6B*mZxv%_|Px$D6T~(V%9;Z
zMqHiWH{aX;^a%G`LdytAs+HIgT4vv$v`i$SWggD*Cv1%OPcqT1H~RJ(6?q`mANJWQ
zWF9YlZ?_SoTa66!U{d4MJW)F4)4wOUySv+DxV<o&9u=sPVXoZ_0$Kw!vq97vig{}1
zVfS0PlODTyJ*{Hj<N~a7Z?Vhr^UTqr1-SWd4GpxYYAl)P>CLD$EFIjKcq;3Z{c=G<
z?4a5iP`R5GnxX2w&M-!5-FWPWZ)q7MVLR3E_j6j!Id-)uuHsRU8)4LY_DpY_LE1ua
z)OjW_1;A%BaHN8i<4rTHIEohI4-50NT*6kACz;RQiOD@km8*5l)VX|80YW%*B)i25
zY2DTqITU>EzpSfNCxZM*BFN_}&+Z8+uI?!p>|L-(?>VE9&gFM*(orwvCp4BNEj**v
z+p~wJ@JJJ7>I+HxsZYBzeb2XdlV`7Lx=(p|xq?u$^}Ws98jr89Sl5d+s@j-tCdUU~
z2)tDe7+hNO%!oQpuR5vgG<*}jD=rQb%3d~zFx}qAvPVi)1M)szTUab$b~i{q6~XIK
zIdU@TJ*o$kUO`sn;YV0{jq39g-w%lEe=<nBzrV6|v$<$&o<(9MXz60!y|lf#yI^T?
z(EFgfbvRTvZT2cX-+QOY6ulr&v~M(7yXMfor$?>6-BFa<C!nCZV>L4pSzYHYla(1L
zXjZvwomkk@r~9d%kVOInOssMIUz~Y!b~eVara^j;DQ2PsWegq9KH;2lq~qdTB$YU6
zGLV$4hWZ{sm7$hWLiIY1&N=fNS#F!3I;>!0&aBa!xg~3PK<<9|>x(q`Kpf*?@Qcp^
zk6$Qa{7hb!Osj>6ig9kuj``G+o_>-In#&q%4}Kp2t`)0tuPiOE$+Egc15+RZ`VQ<&
z1lZ)ajg6X>tx|Vxo!@)#Ahp$L)C)68pS*kbPNs~B;_e3%ONL8JBq*zzq~T~8uXPin
zAAn2^p*2+}D?O<ieHNp8EOn5csGekz^!|DKs;cq^`+m>F#n0+qp=qid@|*HbSB7+4
zvhFC#rw^ai^!0hDZtPTaOF&@p*ARz`r?xGhwHFomHvOYoGuCYG9vDb?bj;1T=lvrQ
zo8J56G)3p>PXC5n`&95~v>s+kY`8#<0@0%zT#K`W3RWBIlCn{$IbfkPpxijE=XIi2
zO}?BqE&FQpMN^S8#-UpR*Y<X^6pej)uC}!z@@~9Z(Z-_GE33w!jgJjOuWFQ84Bqy)
zU{w<L`|!8R^>B@%8B%X&Utb)ixGsl*%a)U(82-2lTJ?*y+;tAsF;if7#$KikR7b1c
zINg=R@+nT;q9<%UqfqrLC~~894>xpB4~+6JxS7qkRBe}I{UD#vSM#)0GwY!Ay)Lub
zNB8$dcd<GsIg5(}5Fag8-?#4DOAw2O1h);u_i4W#FhSKAp6nYO%q^XA=FoP=Lu|W(
z%hb+1ZMC=RwLx9y>e`qj<kw&MFlcVcp_1J<vKwv-tL0M;Sz@7pJ0>r#i;ox194EO3
zi;VrX#g|l>+#E6PZSFPRRi5+g>MUP$7YW=R1r0Ikqaul?zD<3UtB2FL_*=7W;__yC
zx#U76v@zk-f6T5T3)T2}CZET+S8Ff?72#S5C)OB5tqNmCsLmenXvH)xeuJx`Hik?Z
z^&F~i(vKNAI1CD!iQk9lpYfo9FpdEyZ)FPb0^bex)EvJqQe6d%sgarRiTYuc=`8|T
z6l&QI3FIaDt4>as$2vL+A-MN>bi&H<kJq(X6_2B%k00-H9xW9@L`-^S)2Ak#!sr_s
z2d<J3X(#S5$Ncf{$<2m8T81XMG$+;VjAoZU*19Ma!Ru8S0X5=Qz5@FtHNi_Mqhq)!
z<26aQQ-l|auPBPXQQw$ol+Y(C^tmVcV0GM<ut|%-`1LtHW55jqlD=m@^5NiuDk9HI
z2cKiS@bf<|3x5<#fYqwItIHT#)ePG55hj!g-H(A*otFfRDvxKhIK-XsW=(kT;6bqr
z<24WF(<L7QC5KWn)*PD|sh!t&Hvb@GOVf$#qSd2@>uv47Xd(o&NCtPPga&2{R!+qc
zn4J`u%B3^pw_ao9)Psdohfs+#X$o1>=?A>mv-ZbHUvKPbxHBF%=lWfQR?Ov8U(z6)
z$7|FP()=)Pd)GV;eedV!+P;1JR^cm;$<>93JE~pGrfBsSE*FJ()@C^mz$rd8z=l<R
zgh-R&1=l&Z=TxG+uTCA+OLMy9LmlScd5k=LZD#Gp(ucxDtwpE3$4nkQVib6Pa5UXy
zAmpC>c<PzQfxJfXMM=gG)@%Ac*?)WY+MdX43fQx2S5I70US+MWYr>5i|5|#i25A~K
z`L3=68W!AIb1y&N)!^~U=U${nfzWu`^DHbZi4Kllf0mXN$6Z=<Sc?-ckZikf`nM>a
z=7@N)qNCF<e{{+nJC)xxbE%wZ`s!KnKhD>`y0L;D=W1I8)703@-+UA<T2Pa25fNYB
z%fx>FcTlxW-dDqL0q5Q<T_7lVcHFuqz2rK5s7=wJb^H7@&6RNU2p4<#Xhoj$>@%Ev
z$0A~VDl021RyEE~Bh5H+usMhN%3G#t$ARPOX}7D(Meq1C`2#4RLN*y>b0bLG#$xl}
zqtzbiKo`5;wL}FMl^tSeO|rQ`d>UInthBbe$e@oR_sKDF?TVB;EjnL-<$$Zjk1I#U
zEAtbLWBB}~-sY^_{2g$-HWF8TbTnruPjj<J;m0u|NkZY3A&8a`Bpf-TRZUq|<1lm^
z1N2I@tXsSG0|4AOpeCnS?iCNa%O8zkyz@S(8y_ipr!Q+dStKYxRnKYe?P{-p^dkj4
zu}RS@(v*V#ExFEMCgWMW7%lHYG$BTBx_B|f5C+lCOmmH)Car~kV}EzTZLHE;9@~t|
z{0bT_k3HkL6Ps{Xdt0nd>9u0{@8V?U5!8^OB_$=NooB`>dMY9WFb-*YYU-4ae5k~L
z-!!zzkA9cqVuOO1n_XrHX1W3`WIRtRDAW?az?Op-4L`)6&7K=65x>9vvm_DJPp0mt
zr8;>_Xa|NlX}(o$vRV`i?-aeq52;~mO#<PcZVxjUFx}d0Djo}e*ZbQCg_k^ce``1o
zb#n|p#G0=Ww<*0jp7V{;N4d62)i>tqvwBmFJf|!_UfscoEM&Qn(a~lJ1=aJZ_12iN
zV%(BzAJwO$qhkzjpz(+J7%cY4$jJJk;6s;>q5!H!&0$?{;BPNtaQN4}7PvS}pp+jb
z1`?EK?5{)LmenE&3BVKxB2(V1uh#ms&I)-0VqVao&EB96vuyoPeVSNImQ8;84)3(B
zCAfDc=d<R_?EP~J&f2GRT<2}vc8od>W}U=<5Je}aTpU5Pj(yJ_6U-_mmngm)hyufN
z`5D5Ng^(}q>^cdVVP<0GnlTQ61$Y6sjttp2Tz;`OvO9Bd4LjL<PmKw&52$NTXtDhC
zgs!IA>mWhDc_|L4+&(%=U_fI&4?h-S<$VsSY^@FklQ>U(bb}u{Z$6;LxCX=G)6mW@
zb@F6m5*s#^1#R3fx^lVug)|Lo{o2<W3rSqo@byK$Zd^^TviXHL6u4V#pS`r;slWdq
z{>V^bjfv!dvOJ+02qTx`s{M$sIik~m_St3Vn+t+>q&(-q9*d^T;pJ!N2Bgsn7f==?
ztZ#t|rW39Mr{v)^TlwEVGpedi(v_S|!x@CVkK9<5&zyOnX|-Ht<L3Top%d2CP`ixV
zn+6&J@9XHzfx*n44CL;uO1T4*hCE~dKcl?rcUrWWDtYIe;V|m(nxo73vH9WRREqj8
zE>dCxQ1L0q%ErH=mph^)4Ru|}qBmeBmAdg|*Va5|QUN4r=cxHucfH%<46j5shL<$^
zaVc*07yP9P9%7@0?5ngKC-0O|83_ZY&cuzqk~V3qxhYwL{WVaA*<Bx8KhFeb%X%<a
zZt@17`~tlFNnQWNb)^}Tul8*Urf7wKGCr+h0&{3B#_;qH;Dn_oc0(=lIP-C$QEmW}
zva<Id3~P6yB|z&tNIb(c&*G`ZPPQR(Nxb!uCN0`=t?dd7+x#sL9LsRYR`Px~RR#G<
z-s@Oe+lvsg+TGuM`;pJ>uOA&9t$TTMmD~Wz9Dj#^>8#{##8h9Lo1x{mt&J0RwKp|c
zdpuNAB+h)zqH@RN?#R9<Z`Z1b*2PFFU3j8TczU^m-8Ac(tBzhD2E@<y9@@UpLQLi^
znbA3LRw+Fa_;w{wR0Npw9a(2_o`Ku|LbqshhRghn`<**#dV1lzID~}MG51sv1JSh~
zY;W5Nz5pC+bVzAm&p>3(`1H(ihC0s;vvW^7|L`khsiiK{<lx+Q3bw$iyp3hFhwVtw
z0+%h}NxH97_l@obOCXDqp$n^rhsVLRMpR2t3uX<^k{JU+S-g*0sTjO)X@TbMb<55?
z40KH`l)%%T#07mXHA7h3rK9230I~RB9t)-D-yXs~{m++*88$v;@UE|KBx1~iL;*xj
zS^Z6STGLF_Z;t*b_Q-5LAi&wej)W9}Ij#klt!%s=B&|w%=LCJfb1jdw9UmR7#&l3b
zO4>k2M+AB`4tQ0B9a%;d<=}{UX^(>e=r|+Jpf?pQHB?b_)I}?VCR+0B4MoS(hDO!5
ze|$)*rYSMfvUVI<mF_>(Y7u=)VC>!Vi^dvzPGz0lQe1lFvJZ-3r5dAhlog!?F6$`I
zZ~iLIh7h~VQ376M$%)xjR0ju#$TEdT)Aq5SCF3y>iV(qG>o;vuX@Af;{9*-L57e)u
z_e|S_%&RegmosRUmOf@n`UW43nQ=R%WVSD0i-oFky49lO&tnIy&F;(jAK%sYe8Nja
z(SZ?jR7`5llMiS3uRpxt*JZw%v{U56EtN6>O&%8g{PWLyFUy9B7%5e~pSrVL!{t?Y
z=(@AM7MpY}TLhY-9#sh5oVvRI$UmP846B?su)I}lZPQ~0`ua4x_~z#3WC!Bo<5zb6
zOGVHkjz8?tqq6hM1ANcbl<~*WU07U??F_7|4v$ij-tzrJUb876G<s#|xe$yF?Bc`l
z78&MLvB8`n2`?tTa9QVx!R9y=i}j6wD%8FY30y&|iudf?Sul2Y8~KT%q9Xf66^Wgz
zkC{wH8crwPlPN7$Pl`(8f9(8tE;k|44nFoDE%W+eWDdL9KZ#Birn=D|D4F}8!X|II
zpr4<=zp$`SwRetO1$s2(243z75{>x$OV`b!ecrIUNF6_}V(8h{b`B&<#LKMb0>AzC
zTkL?V_lsZ?W6SQIV)<p1YsQa6hVTK;h5zdh$PBm5*l{R(QcQJ<P3_{vsMQ5~tC+TI
z*}{J3mv3(?rETswo(-p%7+CDWma>u(D%uMkzCPqM^)yDi;@+EYgACT%I&#?<f?J(=
zS-&ccP?1KSFNYH}(qb)jzI$&<tOV+C{hO-_?E34Jq82V+zRZ5-yYlzT1@wn|@El01
zP0}5*ZUx}t<m0PonHU~c0?(jzf6I68do9+Jgu1pDxOWC6esA}$j-FYV8!eBrMF-9%
zX6F8srtkg*MMWEzU6gG%ln7MkC<-MdPA%a|>`-*5i?Q)K7LjUS7&^awJ$bp8U()+*
zf|?hL=m+Zo>=E8^l~J6_Vgjvl!uHgu-%&=<?`-=1->DpF@eQUlnaoWM&y>3^M2wAD
zOIP;R#Eqj8>jgUAcVGdzI=kbJsBA5(%hdU*w>=?#_wMN%<D{Ytp98<{ii8RB`LD|+
zY_+t4us*o>c2X#9Ysq>FTC5xTYbBjOhi%t3SLEV)o>S|fW@glfW?XRjO*c1_mp6V1
z*nj@F`39<jI0<Bi?XPYVps?%h#Z)77kvIT2$mRduTbq=GF6QbE`_Mkz%&3W{=L<OZ
ztpD~lP{Mqe;U0x6Y@*Id&Aar5%t6c`UxS0@tp_lCTm_MW5LPyWO=r>CxK+X_Gv<xh
zfBv)7_ksCg;z4}hz2CKBthnXRjnj<5$`ImEwszIZm7Cbuk|Y<V2x%APh&R%BW_TCp
zsC;}O`+vE=%QqM7_bitGF=P~<Z#eSl?F{mdM=sl@O^^BXU-w`Vv+D8VH!j`dI$>-a
zi<3>wEQE}!Pr10b_&%t(X$F#QtDxxvnz-Gd7R257kqrnB<}X4JIj6nnS-(b3#-Apf
z5Zqk*CmEvvn_-}R-;&qv&b4Xl*52f4nz-P`*c%w1s)IU=Mgqo~ZGt^`ti`3Js!+5+
zlP0i=JG^Oa%}=S~|7FdZ8c?@fqH8uqz&mCPzRw^UU#rQJMCwttS#b*G&(Gso{q-^P
zGUJXXKFi|Z&p(XI^t6Mk;@j6=pR|;3aW(mS6>g$Ve)p2y3l;Ud*MlY3^|wFV|A@HS
zzkQuM`bhZOpCIhqAin%>U)O$tuJ-+FBa#2Ud+q+OE-;r%^ao1KX^7EdD5nA%u;S+m
z{`+V0Wc8Od@ZEc(SKthJr|({eYyPKviT^|Ha>;*NdY3G%*JYHjV$kQeZ{Kc6Hw#?L
zBJ#MpS_P+W#i%7{pe)PEwjJmlp^d?DLTtIuFtIvV!dc^oAAWFmcUOi+pb7!Nzl>qq
zwm5i&`cRF+428Mj4+slznkVacOG~0-!^gJ*aM0HnT)Jd}gLE3QtfG;3K7nhzg)>3n
zo>jt8OIjlS(K_ose+r;bEYo15rDF_dC1}$74<E*WW|KN`B38;{b8lU81i(PCxZo@h
zk1rhiCovz`t-ZbdzK>5h#_JQU0xk2sO<Aenbas0bDu1*`v6*(h;3u=zJZ+RzJ`mRs
zp`&@Uv!8(7<~j3r5yOPf<S)#`Vd|%at^#aZ)o|A=U$G*`lvN=SNmQXD%b`%w?Ti-_
zrR|2WRTmSstKcq%X$EFyQ!=XYHDEuds46Upo}WHl=twP>IEZv%gxS}OIo>h{oSd9I
zfiboq3&noU9UdNj&7IMtH#IY}S=hf&kI!Lp&@5humVI=z&Q!1ilOP2U50(0}U%q_l
zi8tO#r_*cn<S3NP5;>vfPfuN?q@;XJ;z9X!z&b4iyx`t^J;$1^_bym8<E6c0?>J@Y
zqSZy5XP)t4K>yC&yQ5xp&*$akohT0x_rjRr9NCW4p=A_BdqJ~?3r$X=rME&ub#Ko)
zUH@r~CoImd583ppWOd<-dXi)5wxyZW^4Q+-<JR07>OAofH9P0CC^(<DV5BC`FkeYR
zMvL*TUAt5amu}XzR4k$k??S{InP5t<fKz7Yu3fL#lio7Ewi@qE-krG?p+n|=aIhKq
zV0MYi5|eTA!6KWk{y;g$^xAo{S>060+DI3;KoEpnvA__9cX|THMt3Ej!Qn;29VO#C
z%w=TUD?oxp*H%B4-l#u2F`(+Obc4Vddo*PfkA_cRsQ+o_Wttz07+~W!2r0UkwBo8U
z`JD!gST;O<;)EYv*!9AN$5pr!<X&)|JaXh$J|w#W<1|0}P~F_CmmhXDWZRg;Sey}^
zG<kmQ2P7EdhqV_{>pdYtHPP*H<2@1JeeM5TBS=%Q1Z}~fSRtfZ-QJK6_oG%Jaq8Xz
zFVgniI?wduwH<i8+VFP;2w8;Cv#|<^)P_aMxqbT)92OQei}Csz>81~Pa{3P_M6IG6
z_Pqj=yvb-+>Dt<Imwiw#ALGpVXReDbZZs92Y@5C);1hrPPEKM@4m8h}77Q%4rr}<q
zj=77emDV`9fWc+u=QlgO4PmI2FTaOMr-kNdf-D}@hvk6U%@1dpwRd#*-o5+rk0P*m
zHJE6^nE}^t6FWDz9JWUd&NyNj#FFCxN~0%ZU_&$!D9@ff8`X#T+wQk-E8$^aO8pbq
z2&?A%5_9Bn5a*Q{{ao>BhilOv{zbVKCl@TDipiZ%3PzP&U0rLEbagR~zqq)#5;UO=
zMr+Q_zj<?tMbtLFdbobFBp;v7*;^($R1H>vIdR2LKgHsR?Ug_O{2t^2ki*dDGtX;l
z)zQ#j1K@Nz_!3IaUUWdylYiTVJsSt{67Q{yq7-*ac9Iu&GvQ3~(S?Nt`6N0Dpla}7
zm$EM=pII`($508!WBH~4eA>ZFWz|UQ!B{OOKT;0+w7ATgd2JIai>XV=+pWt~UK3Xo
z(Dsw>+0M0L#*VOhGPiJ8my1s_xvgVMFWR(e6I1G?azQ*)Q+JcPr`v}_exhi-&&I}V
zWyBy~FXCf8lWF<r^30eK4#{xIOTN-6L#BtWogGAf%+Qz<Y?d}tOmR3mWGk0i{Qkm$
zrmU(=w9_(o&EV+vppZU{M&%n%(@1#zSo$7`G>)?J@^^*-R=1@Y*BmIc?X7<Mro24q
z3a<XS_F(R)e3j9;?m<-pVR3e0;rfrq{c!9%Ij|~9VdT)&&taG0loajvu*jQ^s<N{G
zD{c^LXh9x7SVeuPy(dtG9h<`{@IpXt$b{Y13nxeTib%}jf0VEhjB2eq&t_Z|<&|vt
z@#s!o-ttX>_wQd2TbvtR8)~i*EzeTb*D3Y3q~sv%E*8D&IksjUkazg6SXm8RMmRnY
z;xc38R#k%`!bhTO`5~q3SX_9DJVR{)>*tAJ@}M23QG0?CjP4pH>EVzGR&!y<?<~yB
zT7$KpRv|c#pV`=S9WX^o#D3)Q`VAZOW+LYyIWTT>?Rmd_r-(?yrp0#$%dhU&M~L=A
z;Ag6+sCbR#<q0`Zxnp#6)C<Sgm3(}w`Z2t>eXfITasPN9y29`J`KeuqFTH5>AP!4#
zEw2wFI}cCU=aG>E<H&*z$-|8q7DB+ia>EFWXMHt*&<0cRR15TynJQZ?-Z|jVp5poN
z2_SQW-fbH<>O1_9{*;3n^`ISC^lUF%)|!IUuW_joLc?lok6D*b9Uav6(e{@?^*MEu
z^^-qPe4~*Mqr`haMhikCKHZ<{G<~I@v9YmwDAHetGr1hN{(^fFSoQVWwrQHq3AH-u
zyH8F`*zVoLD}xnMu;7ek`oO)Y%r_vwSj%a+Flq{0&tJrQPwl@b&$G%l2e{q1!Gv%w
zNF-l<K=l*YK4g9RGX#uk(H!D`89eex_}UKf;a3}@bU*m(y4WWx2$qv*6{CRS=rVTk
zI)l}e!!;@AUi`Qd1IT5ak#$*gp0fxTtHjKwTeO;sOc&JF)`CMndD|I7)I4!qM_?Dx
z>`w=qe0p;?N&<S2NsRN#(6P-73<e^!$0@kDYoa*GcPkX=v?1wrl5>_>y4CVBGFJ}S
z8`P&5uumk{o4y7tG4DNi{^bqj?vd)gA40V76cUL;vr1<Slb~~~ohIgS#yaJiJC2tR
zH5$5m0UhWSF?lx?rgQsKO5D9l-@fJBDqwuUeG$p-bZ`fR3y#s&brMr;%c!Ba%54aC
z>=U=Wyaa+P2Rl0rUKJMl)-8pIHZ5#f0X=<vF9bg~$3#y4?myRJ5hrEutBa|0)G9*S
z;E}sw7?<g<h2IMnm68?-WL9!4OU9?4(cUCv`2IiBGc$fM-pC1Lz*bcnG|>AEL%<Q}
zLQKuD$foa3gV_E`0zu3H=l;soM#%-L>4o0Ezo`_7wk7?{J_me=OzBX1qd8SY<vxvu
zEWxdr9{)-rWNB3%Fsj@&(3qj%Ln?VWoQ2h-k5p?Xqo9zH!J9vKWgLI`N`hDrC1&I4
z4VOZl(Fy84(DAl*nOGVEvX&7jdX20rku-7i^Mg8h8EN%&Xwl1=jiR{4cA&w)Sje<a
z5zEvjb_l`DVyV8Zt0P)2ip>dIYwKs`IRoj+-FtJ@%Y%fe$am}$9CKTzu{GFTT^BdZ
zVmF?^=}-|gZAHbl&zX<*6A*LFO2Di^1$^J#n%+F+Ga+${39(7P3?4(3vf}@qZYAYh
zx5QbW$M6Ah96Xr%N@BFTTqteTP*Gvl@at_5A}?6rw2L7a22LdpuVUm;K}4p(VqyVl
z@^Q6&yjonrs^UoT6Q8RIB6Y)L18s}^`E=Sm{t?%qLzzt-0KDbZwzj(cRngus1@{lY
zm_i4-;u3863vE;aE0`KHE_B3l;dZS<#C%@mbMKzq0Ai?cbH?S4*HN~>70JmrCto+3
z=Ok3?anXm4W^*NGz9jriRk)OgyO*SO&mZZg^?DX7R;(afC9-U%@L=stE1q{$MEbA;
zpS9*)V@CqoBv5QwK1CF!=D02{$cz6Xw#_jSK#c=@mO<7J6w5p`toaqB`N$8|rJ4BC
zFWe6c`;W~~OLI)t%A&2eK7Kybrgd{j+oncUzI3Os#ECOc&mBol{&Pe5`TIvVTI6e^
zJHux|f^Pk}?L{XmFGx&8GCpl9+jUrjz&)XK4Qa=xe*t-4z@b118$$RfqY%~>SuvZp
zW$7kB<uNk8WMF`sU}<{w>Q#J>Ljvuyn7G5(ewJ!w9~9Jk_wRq-(t-<x>e4Z$HAoh6
z$ZIQ`)Y$CZnT$Y@%yUS6QR%5SwU^pmi5k)fd9fi`-^~L}sBLq0KtOILAQ%c?Gqz;p
z*!1nTu&{{k?nCgq-QjDUw9k7z^79T!NlC|9oaYo}$K%iMElR>p91RK?sfc!Q@bjw-
z<;|MkeC^@orK+P7LSxyuF#_mIq`8&q=zR8T&EgyCGV^xf!~y!y##}8g=hpS)Yu@9@
z?%6>VMbe<_MFC0#G!RAfz#w3(h;9D^+CX=AH2OwFgCn9R9C2-K0-J>VNl?BPJ*?HN
zq^h;C$pdL^E$SQD0f6=UF|Hq{UUvLIxz-Mi<3sfwcXywpB#Y$XkcNy=pq*$>e;Xcr
z9qgLwn5?V&exy7}#Ib9SkaOx9lk#SaAL;~gyYHElQYbCd!BJ0lTo&i;s5wh5PK(gE
z#K+zrBPEt`O>87EH|}U0R+0lGI(H|ky;9v|i6qp>#P?_+qz+S&TBsKPDS!L60xQET
z+ZLW6#x0sxc}a4+?YeAl{O6WTaNV8V-J$~i_=0v{YpLn(;V~^sHt+=xE*Kcl0pp@f
zpuTceG2QI28)#7dm<yn;=mdLI)Pz8-7FEybqw@#tSz9P^^qbAsu3g(8Ig`4>%6<vZ
z`@oTe>AH#NEtXbJ(xF*nZob-8E%hM}bdRYM^b)j3cio@cIP&GqF1Y}HB?pIW^Za>R
z!ecEiOy;M-GZ7gT#mU8W8vmvixMK`?Db9Yhi!f{SXHUbQ4*5$g?cQb`46r$yu*el@
zkAu9&kbE|83k?W}-DaNoLS>Bq!kd4u*>d3Vn<@7}uce76`T4Zqz`!`5myCCvG4px*
z2sW#_ny51)rg~n^l9$|rATfL8Tj%Q<B_t%=fBzliEhM%5Y4?15w0zLv-<^ZH*Qhi=
zAj$;Ki91>0qjNf{NrQmY&w0-6uYQT{;%G_|vtpQJdO^a$bKe$Bu7vPp;j;@Jw9c4V
zm~v(x18YY3tR5-|oU1orhNd5cMd4TWBd8UT8Y$|Wa~!(p4?tqiD%keg!v~cD{(QDp
z%gPIKAJvq3HgD$yPG5{3@2{s0(I!LYg`E*FF%^@z!05i}Iu${}R>+@{be#W?#0oG+
zA~%NaJw+Q%K4i;@M!lk>o|e7R<9)Tfm*1tX%oo(b{IKS<*3)H4f7aO95wV%OGyU!8
z+bKM|Q1wRH!c_BcaX;&ldI2U%>B^OqLF~mUl=HV@G&9WEC%AIUH@CQ*rXG^u<yAnb
z2h?Fj7n2(2807oy&*}rEDWOCs2nve?tC4k~-+j;nsSYGYRCs#$#5sjL*0vZh6m6WS
zpdKBbgMe9yPUY?NA(|25Ty|H038@tfE?nqA@%Wy&F(Cfd&F<4om&GHg0WeYgB-THB
zr;dQNk1dWvs_+bqzdyE)Lr_qyvNhCPd1|A4Sq*Ac0-&--%S7e&gfn)<)HbB1q^Lk+
zc$6iU*6MG-1J{gKcki9p_JB5>Aj7rKAAjcUu#yZfK8tHzqVwOG3I>{Ib#8`};*Cqf
z#nJ!=WbxAjniaI1&w1L_4-y|lKgkMm*6T}L`aYza2#Vk?X;Ai+H-6cuC72#P(boH{
zcImUUwsUirTL0+3%d9>{%4gH&%~23f$>5>v?hr@*tDny`U+wU6K%w&t1OWDDakT|P
zF)<mr2e~=t>sjHJ$*AktN0KNaK2fKSf{T+c9ZAihKe-64Z%;OOyo)Su94a};NN7(n
z^`tjiVkZu{f{lE>OH{P6dSZN>dz9&>!m85Z;#ftP_k)HA0_E1p1>qdwEujqCuFTT6
zdqU0gXvh(t{<6p!B<9Dc064Oht<hJVu*N$Z_IPx}h-Zonj2k>$g%!Pb(E9-#1sbTh
z9L9T9Kg~m9!BIuE*}buGvbDMnbauCwHn*Mg!<W9r;&izy>P;#Q*kJ9R>x{M-XoNE=
zolzox02or!Gc_2P?0o0}&X|It{sIr{&LBxpfajE_f{!8R7_bu1`-<-e%JW5X#D_y)
zLWXQD0dwRY%8z5^YhP{UF_u+-N3GT>`~oYUJORzon_R@2nlY5waYqlgp@iyaY17bG
zasX;uqGFST6&j;^K#_4ICqegjV{<VSX|_hit3h+Hi5W#$GQ*<L+K$C9)MR;`oKego
z?x5nMf-*~d^xeUR%*zqr+R#oPhb3!>XG=(X_zQM%ps$ZmLAc+;*qQP3d16+{t;;CS
zt;XHo73$v@U4aZMyChkKx6ri$UJfyHiUA2uHKLz%9Gt>~L}(>yNx38LRr)H{Lp;4K
z`BDSTAx@r4dr46Vt$6v$R4ThPXptI|BwE*quvpp(^=Mk&K61-f77iS=Yq5`}&Ih8^
z8cOoRL<UvEvOL}7Ea0u1DALs>=RRMjH795a6~2Dmi&U?U@6E7vD<>Np8y2bph8$G%
z*QW}A+ox7kZL`RIR4%!g`(~KSB%ilji)dw^wP-u6hM7Ljp?IO(dztTvHeQdppUZu~
zrQAP+FZ0LdeP_a;BRrU>C(K1vlw4}4OS|XGS<{csnfy09Xf&FK6QiY6H%F{@s)A`V
zfx?`6sE+~;Ub;h@2T8G)pTD|uRK;4Ce|3Zw?^O2a*NFPd&p-bGJWtm^c?6s1r!gjV
zt;;uKXhv1L-IykvPxjA(S0su(S;>wVx_lJDQW5arbiW<S#$J>zC>^k}!HpGaC#5lt
zPeq0&rRCtum1t2YSGSheu&5Rayr7X)xPg_`oYNehpnnK@EbG^Q^l3u5tQ)VDrM&Zm
zry>IOyvE|f{5aC3NbYCNfS0rVU|5T*^AS5xsDGD9!giF+GxZ<2{*w_fr}@L^@Y(j^
zjv_g4VPNBz1qCeaV$)w39xjHKe8RTI)l5sKrkU1PR`VE#8noKI%3DYI{21G)w%P?p
z>jk+u+v8AS<BIT`H0R!G4pO^pPYidA*u?^~-7CIw&LBrAT@$-n7hVh#{bwu$u-%Uv
zstD#vt`>(LDMs<FEXn{%d1+5zZQaxJ*Ar<I8dXI{0cW*ws^49pqK8FXb8g0V1C7Y|
zcQZ0FXu-&88~4a(8t{;$cg_8joLrh~KJYR$a^-wj%JsME<MyoOb6uE@=6GE*tEZx(
zayNAB<6l)!4Ayd#?NTVrw{!yWb!_#l3mw~V8KtQZBf`!kzqB@c^T&4i$qbEzt^9_6
z&>L`HnA<tCg?6*EKU~Ob1L+NW$fhlQ2>#+(U%q5yIhEnVzb3=qGhe&@GXIZn1k~p@
zjUh{uQ5h)x2WqjD9s>_>?A`lBOW(n`mFT8ewk6`~Kx1I?f&)B?c3_H%_dh@$hBfOO
zcYpeHKV~tjkm|FEiK*w`1$nv)_UPDrf*KN5tA~l`8ebB3rJN5Kex#YgNdtMy#^KIO
zM}54%oZGAW{QnJxJCxZI?jbvXye`^22?an#R`yv2`fQnXu0gXrN|ML;P;0BT-dTK+
zv<q)H5R$Q}V{=OeCYIP$6{+}k0c&tv&h;7e?c{rjid)Y0MiPjzWHyt|Kb3<tvqm+b
zqX}Ek5_ed|d#zm+5a;d1pA(|)xvGfWps6t%=w{jZRj&NjkRR||z5td@<GWK*Hj9Sl
zbW1CP;yosdEvAa@g{s^@Aq!iFwF(21kUL16q*~cu0Ym)F&T2ac01!0+M2LQGsxubG
zC2EdBsKm;(Yi-Y(LxDR<KxV$nysWFro!hr7a5k1INUfD#&9SOnRni@h;pHWTOWwSB
z5BAL1LUZ3{x1t}qIh`!%m67t@XAh-6_GTU@q}JXfpiLwE)L@3dZCQO#)FQ9=O+LKa
zv~k0Ra4=>~)}{60b(o8$nf}f?;jwrf71R;A;LKvoZ@!SWFjQ{*%P+rx@UhKy{iqlB
zl8wrpJjD2VX%B|C{L2=*_<)zv)W1gCbr#vMHe&`7Ya3RTr@}}EXmd!Q97I6)yLYmV
z1aOi~T4j}G^5uq<#`Xe|koxjH&Cw3Bb7LcP3=+Pm&Nw`ZN_B*rJyCWI%nIFD0=<nH
zNkj(7{{a6Bw{D&m5Uu4~=2{)cgc$9sl00P~_0bS+Zh^LkLNetud6+;=`oH{1xhf%%
zS8b2JY(`zD^Vw<*`u|$mt|Qq{WZ^Q)Kj4gcf`$CI^7HeL$tv;s_}S@dftkP&fc*m?
zlC`hAWiNc~_!qRr;9*5?R&m+di{&8$Dy=wrvGn1=BI^V=w4`3Ad@0KBIXOG4Ch3S*
zr7>`+VIP254{YdWeR3u7rDF%?s12&TKqt}cGly|w$Ht<flek*B<Y5E?(oWky0NI~d
z%}|)DI_2Bd;(0z<EvCL08k@U2a~<+1^O<j)1-IKqI!n@UdYYr?u3g8Gn5|E`V=b-1
z;E!H_Wt<FsDIYde7EFCq;p5}u3G5DCoQo#S0rLPQ96UUUuO#a0>j^ZItwkX-v0=74
zQCky!;M9yY1@D*4a4tlvq3=w<5gp{FZJ1q>KU7wbeiTR-{R0r1@6QyO7wjK{n<6l=
z*|yOv>fV`Eqo2T`ky1O+8qhMZ0qZuKxMl_>iP;zQ-2{n${O6uu+o4}Sxk#4Rlhqh{
zBaqe#x}4N{HFhAmq7)kU8Rw?{w3;alB40xz1jZSB4(K(Ed^$K#L!@+Y0B7|=Unbqn
zA1UT4E-T|nR)7oQ;=2duEf~kYFhE3mn`w=m31~a<6J^s!7^qHIT0Y(Wc47Qmeiw6#
zkFUE(7|1K&*CR0LB8tX`+)Sp_Hyvp85^6CFh{I1$XlQ8MO$N@-1WJ<|Mrz;#6MZ^(
zkD%a(ry2=Sc~#L0=m5pP+$0I!iIZe}F@w%x-zXX<CucD|59g%{jeFg`&4&RMTBdWv
zpE-<M$q(H{wO1^}N&NXz6{d<wQJ#2^ffEtH7i6eK;lwoZF7w0(%$7=dW&efR?SLue
zz<{KWr|3{_>ML5y{?+;r=}tf<m79l9Wd5nUo3XJQ-Ut4|+E~?+-@UxJ1sBkJ$5-fR
zZ@(qSc7W4H4rR{?k{O+cvbtFdE~YYU3H9^4_$SY+X`U5XZ7)VLO_1u{Z`txf2h1;F
z4ms5duobl?GA!&lPPe^NaIXqN&^C+*^7R*k#u|@}vUav-6>sjjeV9V}P8X{*vMl3L
zIqOakH2k`lQsCvwr5?~uXh1Dg9BZ@4UXV{3NX!{L@KvsmweRZc+DgotN^xr3R8zDK
zdgkVeeWCO}-*C@;LLfl|s>!wU-KBw-W;#CObTyV1C-%2jfro-ZO8$;_2*;~E3iTT9
z;X*H&HavT~$sfda?sQk+TGvx%h+NQE=o`AvwO(pnK~V{jaGp8Te@xtQf*%@Y*XM=_
zxb(0ft`<DOfYoE64-a1QJEUN4o>+lnEvTVTuD6l?JfjfQ$XkFHtPfGT@@5CE9OSSz
z?A{FB+Gf7rcBsY7T@#Mxibuy*P_?im*F%}A7F#0Ay5R`XzUz9)l>~A}Ie^V%*L%G5
zQl(ojAc3ar&U7o>FC%=N_4)D=Sac9LHJ+aPbx9G#W>vwwA@*In_6i8pSWCiMbK0Lr
z72!+l0XJIY@ue@<pW#wZRDrYQG?A2s$b=C$?}xF}=sQGDge**I!;cHs*GW7f$cyfD
z;NU@<LI!n1r13vxzuUL(ev$nUg0tCJSrhSnrMNYoSvo8sBZ>mfz4Ik_$l~M|;co$)
zP6fu{F-x?IfDl7al97|E`E}U}RIlf<K~O4^ph_>=^VbIOB}56CE3&p0X`d+loI7_O
zgH%h!ou<1WN2?`j#bAf{gA{a`2IqBHidocF78I=d(WAc=&`__DNjA6z2(ikbehN&g
ze8zerynFHN_Sz(3FaseYuo~Bn@`GytOD}Wklp+{9x=V(6^E2ZbPV_Myq9x)kDPT_C
zBYFU4vY?<;L1Z0h&DY(d5W?RKoEE^Rf6F}c<4@7uf67Z-A|e{d>M%)?HcTWTCB4;O
zwZAi;)`o3;Q(Sz&9GtwH-6n=fhRvJ1EQ|NT)U)H#r2M74;*<>d@SEX#v~=jl`Reg?
z-f#B3g>>o3m6Tgaw-1kq@WLoGzb~?}omF9xHIHsy(-ZBS^s3o*xBxx-`}+@+|4qHq
z5%UG$AqZ;8hu}iDG&VH)8a%+9Nx2vs8R-HXy~g`1N7Y6{cvIL(LX|LfhkD{haTu;V
zQIiXcE1Ww=iXBh#=AZ{G)y_@?x98#H&W>s=)aI-vznPp}PGQG+-nFO<;~;L+LELf*
z^6cGv3b`N@OybAt+i@Ub#21FWT@CRtp_|;trMY|MfG>pz$w}tnbc(ExM=;RHwvLfl
zn9@gdjDMB+%f{V?pn&Cr>(n+a<@4UQVy>iWh@IBplQ{S9e_EG`7)nQp`yK+;PD#o9
zZ*BrnZlONgU{Co?X$kD!ePSqg^iH~Yb1VR(ztP$WXYf;pI}M?Iqst(1D1T80f|g&6
z#uXzukY_gd5aPump7n=1p@Sef)6E*AqC*^H$Yl#A0)EYWx~DJ($%YHjpFUikqLDz#
zcrx_{2`#Sf5a_gKcorJ+oO59BINN2vtDMvk#VDDHvB9r!zxxz38)ffni0s60!U9_f
zxPg^x)~I3&)I^+IH$6M6ZtDQQ6EnQOA3!2MLl_MfwmJb}hcp(z1o!BtHCr>S6Cu4D
zfkYB<7<+<DLbw^+Q92<4hSv=me1XLV1_nmR6z(1#RJh*&VQK)k2>xNuTOThbHtuk3
ziA#<Pf@O!uC6E|#0$>z&7O~SnK{u{Xi9FqP4pA~}1pM6zI55dhL)nhjy97Mkz5Djb
zBAzJXFJ!n+*qy-c5;Uy~gId&w=4$Xg$~1@ZYB=1luzgYvI_9>PT#txl+qIO`R0iNy
zJe#;n<sq?35w}YIRBu1?gFjhq^Lq{97ruYpbI*_5@cj=hb^ez*)&93PdHsLn0>d<Z
zhgYRgFWHrNk5i~$P2O#}Qhys3Ro?-f%1Mm_YWfC$T}5p_60z{R*Z)hF#xI{D#?8SY
z4XCM#G=?3r3HcS$O;6=%USAAy$SM+t@&h?9{MD=D#7l=B78rj({42r*2N*9(jAxeY
zWze=8QZIQ!UAFo3W<B{$xLRY8CBP^|0VcggcX{V%c3-y>Ca%D#6lXiw6a%=j7segD
zA!*<y&Qy|N9YY|F5J>=~c{MC$FfoxNC}7c&`*OOwv(pHHkwi7b-$+l-)u@L#P?snv
zDUm7=b`Xuh;V(AJ=L~<ZxF9>^vNQ<I(#nvWH*DO<E}dgH^nnaeLNJB<Sa@vJs#R5>
zoyRa*thlJCh(3K0RwdFS1o+R*2Z8f6f(z87g0bhz);&Rln{Y%$c#}xQJ7$snH;Hy-
z^@-L3q48fTV=fIARfmU8Sw==CS$}GJ+6YXGLvKtpi9Z`^EqN3cgUU(l(E+&&IC2tW
z3=3bAQJ18)B)S0+-(!+9ELsy8b*>)qVB}Rpx5QmO_?WUg{WSTZk;9IED0mW$5&ZTe
z7truad$q{wL!KZ>u8VWsk^<+RQ=+@St|#?mrd#J#Y`-ox%KfgU@Qcz!Cx@v|+ql2}
z#~x%+qp_X}Sdutx5($}xqGx1sl9!JM-o?eKB1!Vix*10n;Y*1H4O-vG&Jf`LIP>Tz
z%<Hho#>(nrat0*(vm1BIT5`5fCA(8m8v~Aq;S?Zg<QMsI;S39D2p<CGO^2L6zgQu)
zxrh-E@;C~z1Y{Ex<9^T*WDY-w9$|rrv(Q-IBli?nfAtA#{kril>uxvg<|@YCs6cbV
zmh5_qS{GoCq%dV=pJR_E&~h~XYNJ9AFbVhNCegvnh&MY%(Z@ih|6_fMz}Y~oXV9hm
z6N<K=NR3Jd>Oy6T!k&BiPe1j-NlIWE`V_=5<XwDztg5)YoD2u1b_q9+TlqakRe%Ch
zhPcQwyFr&hDd#{I0+S+wJ_V$v;4)FP-_>f{1^ewg+isbJy+9T_SV=-Th#Z;6M{E_b
zenJ}SNs~w2u)e;&0F=4TPoEwn&SG%`-~p7V;0wZHNDOtIot?tLqF<hB<|>?W{^ifH
zH%CGYS;0AD7#^510hk<kb3gw^;Ns02H|jbj`|Bfst&Bdtyg6Y40S%fInegBd)B*Zi
z9Wb=t46?2``UkTCLm3ztI{)oDH+7jbsKEdqLAC=X9|p?vOCr%@tFud!dfNpw(=3fB
zlbH6l?|?V_uj4%x1s@$yXCNs*Ucd8%VcYXxiBkLHvSo672qC1O2PI?#r0O!4W^63t
zXN7rJ<5SQj`}S_94>-GT3=tqOQQNvg3e!z}NNEoJzlsK!WQ^-4jiO_-L;3z!!>1qc
z>63vf^^G_!7F9(MHl!f7;{(0LnLb{u#d?*`tjO-5qEJFzl)57c3k&LpH}@w$y}d`;
zli_Cx(1RlV%z4%pYR(y)rWXu7=*8`?IiGX+U+EwHEx9+lE)g*nRMI_|yQmjJVW>gd
z!5Q*{`G%d|V&RAyo3|{Ir3ZgWMuy~U|3N}o$R~8-_fzUk3%d?rxC$AhFky?pi_jFj
zbv7QWj$w9`qgY+nITFyft`THQHOMEK{*K$Bsk^w(rX0s#rO_LXtrLHYk0^%w^FhQe
zLRo>+;2vqg?^;*M3Dq~kgH+?gewvsPHxxNRNFv*yu8~k$06E?N_6M-Sjs*d7+J`hZ
z<KCcl39HU!H|j162374#P5!c|+GHa0XI6gskHZ4$5hvDw3eTx)zeFFR=B{EC<<-f}
zteZk5)R<5DjzOpem+jfNFaDL5)}wtG;mhwZX4rhCgnI&VT&gcjYEgVm!`M0?s#>a9
z0m)nJyIC-jnw>%Of;omC;zV^vQ55SjJFKJ~ASsYo;5Z9w3jciIA+GJ)U+7%mnV;YN
z<0X%G?0=2D46FRRY2eUMGe{*^zBvVmp``cyqw6#ppewpp7`Fua`Wh^@7^jJ7k7@Y^
zYZ``xh9;4NKxF5ehpk#bth&sPt1mHK0Oc+ZM>xxHAD>xu%RA1()@U31W-cB%sUcrz
z^)WE_mwy+YY`wh;4bAs=Qor1z&$+#eu|i+|gA$nukuLZS4Xks2Yon<kV0Px4OcXnf
z`I4|a<IMP?E8Xg<udrSlAa0#}IFr-S4em)YumN(LnxUZ~xdu3EWCt*1F->+})Ve1O
z@vxy0N!#Ynk0%U9aY+eR6&R4Em*SRxB)(ddJm{V~8Qp<ne`N=rJ$tsJV`g&5!WdIV
zN*yqW0X{p8F6L&^fm7(t;4oHk60TQC;-W?I3{?_}d{h$1h=7?8F?$q+P2*NPTDi`p
z0=0+4Pa2i6h@#>G(%~FsLMTjeWn*OQwv^=Ll!4_aBLhyWFiZs?h@N*8Qqa<?P5SRn
zi!u{9wqe-uuNuar&nR0pHMP5!i2wu?zr<A%t(MG@R8`lGgnlc9>V#@*YdgKc?pI{)
z7;I@m;I=sQag&WedZ0Bl0!#%pW9Cbg`G*oiM?r<k4&V@-I=ZlomHGLrCr;c#_&ql4
zY^0mNxRB!vbLyuZ-S+UL$qs-}eS(9K;S{bApKOYNiAoLCwOxJomn;7UQq<1P&vR9`
z?)Ti0gsI0pSfbX4mW!dc6f{T;5%6_r4&)w^)_0gr62Qi(@AXf40GkFFhNv4iZd~!c
zd+%O{b$pIXS@&i_xPz=#Hw;OI3|{<9;?gEN&RNvhWAIX9{_casJvgZbLUSd4wuyx$
z0VlpRWB$8FvYu2jvAQh1K5zQFvt@5I<Qqd&W&sBb+@bI&ULYP^*a{+%24aZx-Sfze
zv>s=4jl&$$i~kF1uY!4*+-ofJUht70uxryGJAvM<UKnp(T&PX3_btkjj3e!u35nBH
zC(g>7*r`buegvGWMnZ@+Nz96>yVpu4nkq<4A4h0X1%dVq6E*z1zmDA~8^K9DU#^()
zqzs8c07rf=O?JgakSZ1{N@klNrqrqVaR}m`k_0o3ePM$782HoDUIJ)>jFP7}NG?t*
z&?Xv8R<2sbl{}QFoud_^Acc&#mz`b82Q3*Kfu&tOdZV~tDmcgnP$(yE7ID92_ty$1
z@BtBP27)cAtP{?ro`6)aHW^;oS};gsNHZvPFPoP_rC)lv|D#`r=N!gcpo1Go?KH6>
z0zV_*?gmOw2*80`XudM>ZiXd{7*C*5WQlnr9MNH?nwTIrSV3~WIwlX45Knn@xN&Ww
z881Jg3Wy$S>0e#Yss;CpZ)hm5G%<)nwBZy>Jc!CZ8kxZmXQTT(CK@?5Yyi5ky2?Y7
z07ax-lO`)pveg*)Ye${trB&~Qk8bJp{A}X1lk}f|rclm7dW$IG$&n=zG|0gOr2kgy
z4U_GK9t_yLBlfPd147hULL+8dn3~4NRmD6-+6ZjGmb~(ooG1jeJ%OF-cZXTH-6y$(
zK*1$YYVu*<Kz-^-SS!<753W82PBP(s?w60%s{1=AJuihmu+-NIqlUFJR4%F4p_+g2
z%xjzW$6F~J^nZ+aBhqY#uP0yRAOoY6teJYqkF7AuQgb-tNpu0dDWuIc*a|n!(kpZ0
zUpbm`^%T)i1~6A^^A<*!Cyg&%yeJ}m@?-cIN{i?jGI0&=cUuQmg)A6E<OZ;6HPGH?
zzGap+*<$rJlL4aNL9h-DjfL}n^=#Qe@%-{xlyw|h`?Y}XcXW{FW{lA%2!;3-a%Ptl
z3S59{eX*^_#6l`D=?T1FRSQ_%XM;#6{+{Bdb^C$m-nFzq2bP%oe9Yfddp(viX@H?=
zH|0ajSbACR`NuwXBLnO(Bga-m0&5!F=t1p1z~sBhU|s~$E|ms!j)@@y3#IAWcls(T
zyb3vVIUI0}KE@m}fvz<KkGe+Ouv%b~c1s7x%OIU}KH2c{^3tZ-J)%tD33@64_VYkz
z2E6PfrV#X6K*QYRK5R!dXvb9@fOxZ5iYXZ$vvyxg+QH+0*Q=ex%sD>TtO+He0&d_q
zUDtWlhYugFy$rH5K-emZC+pJ&P?@bRH29Eu!Cc?fM^Ba}kxD0^sm2gf1JQvt^XkN-
zdA2?uQyUnmt+1CKCF^Yh3_GEvNFY*qV8(RoLy~S1<_VY--^bYiVB7qM+&*3eqf?c?
zFwR?*2EWEhOot|}3(_(2{t_8t2D_a#`c&ee;uYR;23DeD6{ZtAqBSsjj74i6=<OqS
z^-*Uo^>mT2jVK<2jY|kO3^(llH#!C$gCKkYTUYkSTieDUlmslWU{L#DvqRgjD+ucw
zkl1S99bDM;H7F0ZYuA5Ws1(IubXn7HGU|}J=s$P}1gMiFn3R-+ih%*#yy7)xCZ;DO
zrquBvYg>ca3m{I82(&pJdUfJYo}08GSD@1~@$Sx(4M_tnG);AF8(-pvBr_et!VZ&M
z2osw4?gIyu*__Aj^5oBrjLb1L4H5?&?&dgb8;wcx!$oY_ga}3Xf|iRKXmg}|fsL1x
zFRZf&`5AEK$m%DdF$Jw2f54sK+z(|ECdA%98%w#V3wY3^?_&(&&nX{t84%U_=B-<t
zh}>irK9ULZOf$eKI$X~@?E1QX)Rzt``g6fE!Y1$@&(lLZS;Gg(Dh!2mENDOWJL4z?
zNE<LXTBC=fOgNn{O7>wm-ZDU*j~p3+*u>Jl4W$P08o=%R&dS~$7m4^KO2+HKN*Wt;
zRT5X?IML5BS%(EjSag_a!(knh$LgGLB&s?L_uwDY+nLbtgAN``hqCJGYGsd)pl;<!
zr7G|J^${jsNcczgx+7@b^aRjaj$}Ic<6?%9qN>QLeTakfloB2;0SOeir~UR-Aml%G
zT^J3`1{@@=CjiGozO5h~o?-9Uz;06a03}loiQyo83BMBkIzbizmp5-J9KKq9R1LVR
zeoSmzw#1;GJ%&ok+IWtPfQFh*jBK#XcPMud5d&hH6r$V*tPY$DME|!HSaA~=<4J#a
zHB1f4MPs9*Rd|3z+a=D+k%_K%|M~M-P<#vYf*kLE{l1jFsVH%<YK<WGk<b8}<xcvL
z-Y^Pwd=nmJSy|c0PDVz?Xfzw|1)+r7iDXPh6xcwuE3U20s2qtUO;=`3nMz>dtMKqh
zuO!G_Qi~&sVS^>0@1R(G0etj0YJ*;=r!vyg#0x`)?7a5c76(5IEN(ob5BA6hy$2Uh
zI<wJOWN`5!7<onHZ)Lp664Y(U{PI00gD3uuP+x!c{FTPvyYcG}DY2XX4!D2)@V{&c
z{yUHT^?Uz&^(_DY7l^SO|Bt%9|Cipu=Zk43MN1*_`dort`J+Q$!zJa~`oE|zEIp=*
czW@pOzC0;ghPgVTm++Rfl>G6;qvwA6A1BlK82|tP

diff --git a/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_21_0.png b/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_21_0.png
index 8391525c4e7c4e46b86c623ce1aa7d5124964951..a7a92e6dd62b692beb06d5eb11d3de1fcd05737f 100644
GIT binary patch
literal 193069
zcmeFZXINBO(>B_^j{1y_BBCH!K}A47f|5Z;1e7EqL81~Q3D|&54(ccw1_c3$0+NGB
z&Z7dY<eY<c3rK96ocU_Cj^Oit=g0YR{++(A8B}`jy+W<3yY9Mc`&?6&+rO7#F9w6z
ze??yAItH^Z8H3qX`k&qKFIdJ?B={j}e_6|3&E}51!);p=jM8oUdv|T@@0uGP!<pFH
zncG+k@{01FJ#);=-u|AQ7$2Y2&oA)W*qZVQ{U%=kH~IaZ{7pLyhL;`vx5M$3G!C-^
zgSjGe@rGm6<bYFnhsl8Sx2A{y<|oX*^-!x;?W$7z54N!WjBwtGhRlp)b+=jpySjRz
zTz8ESUxkrp&sV|~e0?A6`w#Ewy#kYOsLsM?VtM^`Bk{vA!TNg9+%o;HbDDNTiw;gr
z_o8gXY^I;dpL+HY2J-L6Q|yDh!@qw)KVL`<+i&~(xS}ZY)?Yov)RgyZ{WY#Jqz#7u
z?<Ym@|G$*77XR;-=?wS4_eEs5E)e1G>+746k|OVL7-M#go}NB9IGEZ;hHXF<-Pr8q
z_x1L?b236vR#jE?Zf_pv-o1O{i;6@Bf-%Qly?OH{-UuJqlB$;cwL8bi*kn2M7`iYu
z7;U?~7y2_Mh3fI+$2s<6t*1FTub}4$+1l9|{o}bKI3glq`ZW5AkX`K<`gtMunwHj4
zCnqO(Rxtdk`D>ne+o>mi&>s>N6&(xwAM_Q$q^JYvyFyOBW0==2Jv}}A_FtRL+LDw?
z641XlD{?09M1N4yhUpa!xr<-DdNr+n^vf5S(Z*<YVPWAnxw-7{QTlzyv2#y98i$02
zR(EFV7|o6}sKV1(H`Q<91DTD!Hor8#`X9K@1-r1&X>>yj=DzBU8+mH{l5msV`xy?y
zLMf$Uj!D|u+QwV;6|^MC1)b*Q4Sc+hP8N&B4zE1t(TPt?JORIM4L<~zdydp<TtxS|
z@T|bREs2X56~$C-kMZ`_$keWWefvGmdGYW4jOP<MsnM4jqHV>{&*sD<hY#PNcbM+K
z$<K`0U>6q`FR>rHZemTW2{6Bkrh36H`Nx{v{AeuT>*tqVKRhy$#PJ74=z6rcLzci`
zg_}#+Y@>M2{fSPYlV};^ifgZaKZl-@>Z+<aZMrxdj^Qch0<j}aJ)Mgv!$xgWv3#eD
zT*KIsZ`pP9?5zuvxlK}oPQSs$Kke;B#(3=*J^kR>Ll~*F5>w*BR9}L6x<=0J_fNWC
z-@bJC@ZqX(ext+f$lg+D{ynW`w_#yuVWeOO38qakIc6>K-v8NkAHIYcucG}E{e)@1
zGd3}~Y;JCzVNfYG=&`vDCWXG~xH?tX#=IZC;d$z(v(OGde(6copbLo^_5>`{aDKdl
znmWgkOn|G*DL&+a+N<x2gu(DA%F4=q85yZT7f@42p0|Qi;M-kbrE|CY_3;LZDVnAI
z>ucg3kArRJS#)s0$FS}>58T*wc}6IivW}_Oe|RWQTCQMunPlZ?Ha4!hG0UzjCDRjV
z9xu-RocG(_0xL#p>XRo=Qd6ty>P9t+pf3pB{%-s*<%cH5uHU%d!(hCZDxXqzzy0}l
zE5J0ToY5;0I^-iWj4vLIiJX>i=na-*xjDS5R5$xM)<qyf(GJ-d=7re5&qII1{OeFX
zX>Y@kQ^$E_O|uIM3W_;QGws~{hc_`$oY#FWfwfwJuRLV>Yx5Vkwb)9q&2!!PQWZ3=
zmt6lidN!_BQo5pVGfN8Dxy#~oPLtGXRcdk!gK+=7OK0@U9D^$9(4(5WPk%po|9ITE
zaYN?f#fxWj3eLnhjYKR}^SM*YF=1tx?)|fa->Aml9<ShI*CbhZH3B!*`f4mNM1z%2
z?2=x{^XIbe?(S!Fi_UJ4gFLE^XzEg5dh&-)lgGwvj$yU0Jsz3+PxJE}K*nISwnWCl
z&t&nv&GjdqeOwxq-lS)j_4ketG))qx_=@%`8YL8;fVm*HHm+IeyOT=db*(Nq8syOi
zohLFKzrOS$lrIjZ>!zoqcyF#`Z~DG{v<FYdv&N1W@zi?m*vS^5m?XYisl7pXhzTqv
zttAJ~;&L~X?6x>SC+V`#KjmcAo7XeN-y|_xuWSc<oa4T}5`NB7L*m;%J9Dtan%aCG
zor0<>9MOmEMjHbL%U!yTNup&FlKQd#dGZOe0lw@(5^s9*eP}pp?sw_Y2@kp2>AEiu
zhh827t5|wV<$e%7*4Wt5?z)-3Yyd+(ev}1UMa_TfA5Z6x*m{2ar0rC?!Ma7iBQXH`
zb{AdM=33L{jmFW@QG9-v{)8R_dZgK#wJ?AM@JW$n3-*S+7ve3SYvq~+ALtjR@64%`
z>Nyhcwz_~Fa$Bta?8T~YvN2ss3VizXsn9EA_MRoG-_71r<zDp>O&Min<<Mf&Shw>^
z=AI)bxVgEr%4TZ0>@HuvOrELZOaAWA+LeCpg3<HHNJem%S#NZl_4Nl?^emTWhWT9<
zCbdd&R%lPe?vEcXcb@lU4-{c@o*!3Fj<W2`@afWb;d#!h$DmVi*9Vrvtgpao?rRG>
z^2KPw!TofNvCNw+O!fqah0&#JW(P8h<775B)`{S}vKCiELPDzQBLt0l-k6}>InBl8
zdw^LCeZi>8o0{M8+b8ErQ>D<bFim$>2nlr6)zvNKE{jJ+?%iU7MQ#DtC^lsD@##r^
zyODaWVp|hru-W?MhA;|#!zyp*rM)S9ax6~9#&_<>f^`k+>g((Kvr0<^q@u;X)%5QH
zv7VdiE36ffmaeQBeGY>nd2FtROE}pgC*8sFO#Xyndx|P{I8?LdBS)-m`duoirCVz&
zU6q4V%5@Mm`E9>^5E%|pBj^mRXh39rWouO%!igPp8dY~0{O7?=2d9RO^;LZ9D~0rW
zw>52tf%}?KF4M(1u%k;I+3rWsKb*PwCb3pDTI_z>b!CGRd-Kp$_xW^#08X_eS3=jK
zDqrQAZU4RZ`^m6!5c*qqgBT$OMB#eP$@AGB<yTeLO{G_F>blK$bi}}TaZcrAA|4On
zT8h69o=fbt%npE+IC0*(Uu%jVqGJG`+n55j;arz1VbMcVA8fj4u2ms@?XhoAFL-z2
z3(G7Y^p*UI^YhtCk>^p^(JHjo8`iaQuR*JK!RTr5k2u`iee&5yE}{z8X^(capJ1f{
zB=^PA(lbdOj_W<Srg*xFZ;wTmzdSPn6DcjnK{$3DQ!wxqY!opnte*3x!i~GH&vy~i
z(zATQs4``jC+gmq)S-POx5aMQCmTAuah*Bi-=-|iBeg!Jf;S41T<}2``VuNvI<g6C
zvrRUy6<O~O-0wn>o6smu>Nnw+WQ$KpISnVJqT6A}4Sef#N{3`t@yLs#;<!m(zcFfR
zbimY9B+pf>PuY;>dQV9%%$fdyW+!wW_v1tjIBu>lru)NTDDJakahc2!nXMNzT3wh@
zAF;EwHT?9?1BAD_-<T+T86LiT^XARa*+8-J6vxkx>Ea>2mzHhpqSHM{CB61dc~;VS
z&RfcDMJ8P%Gye7Kv&g0=y0V9?2A{xepNmWv5s}mD<1~T=0%x6}KRvs#O2Yrn5p@to
z+Gn15$?HH%vPvvMcXtQNL{Z>|sicIhcFKT#M|o^4V;2V;NO_&GTVLCfRla=rQZ?R@
zZZupQ8fpnnkm7>iDuT<<>Yz1gb~biXBCBLPEqbKXe(cct>S7RqK+t+)d}Y{jrm%7y
z9p{7t2K4hUQ&P;5D>JS=i{(eF(|q&?sQZVbJI`DV_B^?^KSeb`iX7!MJ2G`ckyY4Q
zcWu#Q(}Brtu0{RoC_ql~KRc<aLN)d2Ar=RCd*95xQ@vQRSiZDQ@-Ud1Hvr2mB2}z%
z;b8@_Jtkq*)pQ0-FrcBaC74Ui+*lhCJiOl?R@ZyU^TZ%&DHO=Oy^F?@A_-&3v01vX
z%d5DXAy#ck4IFNaY=lSe=DZBLB1~iGHFdF<{h37H*p}zz2_-2<m-J1zg6n)LGT*M|
zz?BqhTgyp)#JYCu0PN9+`udD0K5lM5;Xy}s7N;-4MT>(j4kd$$SDqP8cIQx2Q@%OU
z7_C#h@Od#+4B<K41{{{u>xxZ{je!ch#V2@6zQ%XkRIaBnl>$IQBQ%`(@-ej3+yG7)
zw)?hMb+YvOnBL&(t1x}v*tIVl>LUk4zx-XaJQ9h5XQ()q?#Pj9h=~Wo4BVX%2Fj{f
zvAFzI2;!V}2zbMYzJaErXdlcneq;o+sTpN#8aB!UjURb#l9V&qxdqt=_!qiSqz0Uy
z%4B-yPEA<F+$%j60}TyY8V==*>xz{t<LSg&?(B>+|M9rtb%2@hZXch2llww0|Lu2w
zX%*eOEhDq`naac4kGJIX0jZ@=(T&TFZ(U(3$g@oes5Qk%67$-W{m`kjE*^dkiA&0x
z!BPj(*MU+88JW;l7nv%68Pou5x`*=}M&BE*WEKuG3@tZ0;Iz?V(WppZgR)tvu^sA~
z)fqQ)x$uxz@SD_zh)iVUrb$Xc;F1kuVTQmIn4VR_rSCKx$~Ez5_kQ1tk9J?KYJ5Cv
z6eyac(WCD?_Nr*QWP%GnP;%doBi2;_uF{(#KiWeTC=D|}(-B#n%uR@oXREEPB`^*x
z$(F%lcf_cvs#4SG*{hmFNU{-fW7qFW%zR)Ej*iwF2K3oi(^avMFS7DAf#}U1=pD+f
zsnL@usXkI4p*GCC(}F#;bWk`CQZBCSAaC!_@4fKe)a<PxC`G}H%YILr&4M*-2C0ee
z%dRCxdH-qSi#_P#U>-SrH2=BcDdKY-YoAD)isIU}uF8%1Zp-X)oYUYXD#^oOI=JB_
zf*)UC?8aPjX}LkHlp6s;k_(f4^Tp6%GF;@8{KiqH%7s*}x$Fe4$fz~(N@@9qR|#%P
zn`ksqSLH*K&Xd&T6~HK5W8<;f$7NT!IzYnRRNO3@oSZxghx3w*A9!7*&2LSDkbAMf
zDb=HGlOC@ZZ1NYi8>vzf9ym-*jersOHVvIXkC6z|v`K~`*j&d?5BA`=l00spIR0P{
zi&=B_`ZHbF0`p=Uqc`C3iatHuM;Hz>$P(~?@bV*jYX6ZvVR)|J7Z(l-3zGvpS~lOQ
zLvFrARsC?+zO)Qh_odIFCZF|+;a|C>Z6H#hfOoFZ{+}Its!#xNT>9+A&PHgFV-1*@
znL#JjwqMS{r0K&4Z-4)qa=~>#RDHL)Ho&Pd!PV(ZL5#w@yk+0s?JhO2DqEc{C1-oA
zr`x*1Tx!q>WQg4y+FTzZPZ(^L%-mmDtlTW`wOxsJ7`$n+_AGYogqZ!;*L@Q~%;M*j
zMTbKizXZ!!MESgk1S~`Wl)<sFthsj@eD|<89y@=x`<ltR_|UR43dyOy6f2ll{%!@H
zw5jLCg-uONC_YViz+MgF@|`fe6`brl4KLJ1rN$DU#lvFf*WT~P;?<wG6<QB4WLIvi
zD2rQp*pDtCpb8O>7EIeVbNU&V7mKsmAM^r=Z?iV%+Eg^@VTXsTU%Pa4K2wppY2w-_
zI@&(ImZeu(11{MR?3J3D-)&{iA_)19;Zc;LV+mCZL&Sc&CUs?-V+g?p1Wol9+vx%l
z!G1g$CUlF9;$9pBT)2;*KVjr^rw2;${`7(b2QVcL+BSB!JN4nzAC2GGu92@hDa}C~
zt6ds!7#bK04+_(-TpQ-YBWS*B*RK2ugG-EpcN8D#m1bm|L5H{&3khG;y?4Lw$5r;O
zI7XCZWqU2pj%qr&U6w&P3dBWCQ5I2KV}zw@x>%*%T{8tcyhdX^q@I5K0>(-jlIDqE
ztcBoYw6V4f!~=YU_Wi)^-+u4RHekiGdblbAoGQe-m&t?Q4U=OFPU*mpN-Y+1b#)hE
ziOP$j>vL^JmFo-XOf~~0=Mdul3Rs27p&v{3Ggza{4}xp=`|Og9KbS;bzs)L#-9l;U
zup^|?EfsF7$gtej=9|GoX;-qaxPE(EJ6l&*X9QWPdRDRV$ERFGYIfOnE+cDe>q&_0
z`D?W)D!Hajfxpw7s+t+DYh&JphQ5^)rj|rU?%TJI2utVBXkB#NdG4#K)8t4)B=O5L
zl|W61k~G=Hkj^_d6+@tn9s%xz5*Fc~)Fb#fIekoHoK8^^NSGlT@FJ%a!w(h?I$LJw
zl@<)<cE|vg!Z_K>$~WyX*Ob98Up?`eSUT)5)tleh)&I<{yF6&qm6MMz2ssWEauJ{f
z3~_^H6%bVwKc1=W^w2sQGRA`aU*4+qr9*Nj|N6}DV}UJfbJOi&1Jo870BsQ}J_?)Y
zI%R3#hZ5UC*VzVd?`|MJq9AsYJ9TU>m*S?<BrA{mzB+Q`NJk?TgV1Sxm#O?B@BqYx
zKARx$xzqNF0EQGJ&+{ZHMbg4?-DIXSaK-9+tk1W+yyOK7!3_qzA2;>x-8(K~O`fH*
zjD<dxPN8+CfHgRQ%<SUyF2Q+jaB?We26ikU#|U2|IvPcTEM$1X`18BR*p|z5)k7N#
zg>$>net3#q`;Psz6Eo`lyGs?O6r%E&5O#x;a^`^8*SK2dlCU#34@s}~G!c>_t<x5w
zg6JM07h8kuOKkY4kXbWt7orys_E`JoCG8J`=8pl;9}Z`*K1x)uz-CB)xEoN&(v*z{
zql)yp{o3l#rnP;kKglO3Y*JdkUAYj&AOLGzconH-V`Gz!DNR90!qNmb9COMb*20#7
zF-8mInAZ5y(*W)>h_Y|J5^9IC6N!x_;{A<c3bQyC|AT2MeGOm+k9<2ECHLWetHJ@=
zp|ufV#Quph4`u2UauFdB#-n|1=0~JlO#mYu@LGN-{9aX1FoIN$+^(ily}mfa3V<^d
z&#U$NSfk`(zaznigF!;`(gY+Mq2!YL6RjbM&Jd2%uPaM``TNrNI^Ql}cr@rC@$Fh+
zU_5^}C0VGw6RnoWXp|iRn4iq3Qb<6Ef6*v=*VoqzouW_+lFxqT7~#>dddQ|~u~)8K
z(FHT<U=hFf=^rj)(DQd{oIt}N{8of84V*N;&Be_P17qX0q3qvcTxUKy!t%5|&6ENn
zio3Va#z0j6^{tBuy^@kgvb};x8Jl2jUE^yHA3oH|F}&<18~@xL!id~NXC^m(cC;yI
zl~gGm;DhY`R?|=D`vC%vWma)gZ9hDa5zc(yST`K(8}!nP{dW6bWS#<cNZ0n`N$f(8
zsb)khzmaS{-XpJyg*sUA6yq5kJrjrsD47?{0*)g&#yL%&q#8z&<I<-B+?)M|aaG5r
z{0O74bq0Bvuk72O<)-ntm4zurgUyw$^m;uw0Em`Nl0l<-J^F(lyCtkFwfsc{I1X<7
zt5>hc5DQ(Ci7RtsG?0;}F!5>Uokq(MV%#4MxqLh&=j|o-=38o-=-#-|ek)@9J!Bi#
zOkDe{3Ogp|CuY`{91A`^<B$X9ul&nO3S^a<?)uu=3{(=|E(A113z%FjAAPyNTRQp1
z45-Rs$F~o6k)=0hrOg!)HHmTgfrKzP_F(xc75ABETy|<niatlUH74yRO~UjvOx6ZS
z?!xD72K(cdEeJ%DxyER5O&8rTUcEGbIApD8NC=1l0eorL5oSV$*qu6<We`TfLwrk{
zjHv)BDq3G|{<EdQs&*(D@idTcluFvbu^@k74{CQ|v9QT5U+~?>TNGE@Ki_3AlrmI|
zMu$mD_;EDO-5XC$u7PCBK$I8UsKFY6PMmphn9rlQ7E3B$)Q<uK&#~x8V;D+@Bx1T^
zb-D<+*mS+knXpPGv^xRWg}~D-!FubOSOZ(o!Lr&iNli^9Id?69^z1-!aWOfX<O*q0
zVa0qV1BnH#AtY!+!_In@tdR3sQEb)7eYwt{=3JMqT6!xPjE*%-4|lT+Y`P<h72*=V
z>(aMcGD_$ORX(vr3acndhCiNbyzqV%p!!k3&!I$!4ChunHqiV&5r5c4-0rkK$#uwp
zyHo9BA|mL4;226!OI8lGL(GEzQGPa*20Fr2)5<r%9Eh=-Yo@xc<Z6^|ihw;6A+~2O
zbc55B2n3Xj@*4t5*C9m_1?~qYW(_!${OJ)@&22AcS~v{pQPN<AM1Xgh3%wK)#?(|Z
z5R$a%rPi>D!w!Q^rV_vi@ZTSAuMGn4nA2;S*sJ2P(zeu|ETvA_h1^ClZe_{&c1LnG
zT|!w|SuGik48Lh()Y3;4_d^KYb7z;?hIL6TbQ`7n!w&a~kjiF}GYLS)sdmw*F-lZC
z+5@tXD%f=clW8<!fY?GTfMOwYZBrhFd<qY#BjYm;l|YnM?mobD6j2I>5Ij2W)WZ>%
z2JSOh{sh-(4A_9k+PC*cy?N&9zTbdN!*=O8R3j7u`E_t;=n+6!J;Plp<3J|~L%=NK
zvV$)~4^XOPX4l`e{-~wBK*R?-BwDcu`LeO00**Cs`%4Wr?rOtD5X`IJQ%N(y94t(h
z9ifzhc$zA(3L2E)=$AXAxQVE51VI6KW#5khexq>a1#nR<g*HQ9!3jgurcl1X2PY5Y
z!ekg>#YcIwdjP8-mz%~k`|Ujy9-dRymV*-iR|;XBVh(O~ke30zJRK;#u@o+Bqd&Zc
z=D&oO-k@d{v-bv1Cl0vyV5G23AY4%cj2>4IBMNufZr|SS!K=zKA8C?Y)RmC|&bcat
zQ_T>VFmYya_q8R6e7(xEC?PX~X;4tNtu<^Aw)3XxWdMW#A^9MIVW6f4(3k@(qs8W%
zw;c44tRPwo@j!XCk_gY}mEr<`Pm@C=iGG;~ilk2UC~tKHWN!#tkzc~PpXepMcDvtc
zG&*#%A3TaM%H@{`bd}*F#^_*N6a1IoVW@>YX5C!EtB*joC0+gEoFz^S_wKLX$>ZtS
z8l{(<fB`RCU+6;|BZ?xBSaBTk>4x0PGAgsIOagH*fYqH*P^Uek^;(U;TqRZtnY9Q6
zJ}I&`z^@5q@h8GKunr#t1CWP~?%WG74{{A3pv7y}mS@usw^<^~9hh3Y>wvQ9B1l-E
zJXToi3WkPi4FKe6U<^K&{b<#76iLl5-z$&0_ajCHav7EM$v75jROxY?%I`89EOS!#
z9Yc`D!CCT|J}NEPHHw1wffOk+Nr<unE<f<{wSB-8<4`_fTL!QV@fB&3xNo1{@&m`a
zgfb+N@stEM4vtZp>}YTuUaanpx^@lcfYU9kyFLziJ0Yxc%>*$$Es%5!++h(gmPhe9
z=hoYY=)5?%xFU7|;+CcaP%h;BY&RZ+qL7&yo0^gq`t8TtQ`ICO(<TFvhsWZX3;Rfm
zh{xlQUcLfZie#)~KLEoc*<~|VC6+!tLJ<`Y&YBh6YH+A}sc=fPkok4ANLk<ny3u@G
zT%o*3p(g+~aCBzeq}ju4-)!Af49?UDqDbu0s04V^B>-I&ZGoFY2M|&V?dqJ!u4Dmn
zQHy~FVW$fXC!r)MFmN2L7-m5RqF{Zrcwue-hhTO_w+GLkLwVvfN@);mKuir7BMWfJ
zLDmB$h=?>?`u6!8SER=c081zX&AIpa9U`g^)6&XT?b$U8WMI|BJ<Nx9?m2MRn=iQq
z&Z$lrr2b@>oE#V)cqchj{REZ~L7%UEg*Mu=nYzUS`fwx=9gChRARs^~`+}!FD&^|<
z2aAM=fWU%l?8|CDIy`vDLqIqBwzjr@?MT=3XOR>;Ao9u6d5z0#Z<>q#2ym7#HW}A%
zx#lhLmxG@@qj~>izh6d1h6vGLu$3BRf1anZa5~PE6~MS;0C$XSSC#9SI0tnr3Qv!i
z_Ht{xX4rg0VYV_Gg(1KJ=5GFa0%3Y62(;hoq9_NJiX?}uzt?`g-*Ds7|5pkF<Zc=M
zL^Q*<cl$ySC=}Puw+OAF04AZoj0Ga5|AT2hF+4gd2b`mf4A6V*5Xn%97$uO1*S8rg
z6XHNP=9bD~Iy$`X5pFI#5g!!P-`^iHARRo{$N1u<9IKlU;s@;UXhLxhpwwFf)|_4+
zz$7XF`B@uKLJYC6{Ndsb)}h6Id=(;a^x4_ifZal6707&Y06Cy?Dj0{o49Y;_VkCix
zB0uJHJ#MmCC*Ko|ZvEq9R`4UFfB$<*Zif9H&nF1kYtl=@Rs6;u{uX+5oXrrxD>}io
zWT^R~;t@h*?ZQni7&Cf?K0#@a5P$>iou<Mlb%2|sgU||<R7jAt@-5U+t+&#BJsuKv
zs4{W|DJ{;jV9bsNObwBDc&>~(4G9bn4_9+0M_m`)a7-?E$(h`CCLiEYt>cHkkLPS|
ztR{2rr=~`Ap>vj<Hv+w=SXrR-qiDp2iK}oL+<Bp{xjSYV@F_o((enmaF)z~Kz)%cP
z1{UNpY#NGRi_-&?us<s~0K<;EEmYuv--8*C9hIaOcd%g8mc2@n&8377$n9rP&CMQ<
z%3(h~tums~+CbRj2|!cHJ5YH2@4t$pR#i^c0L;lWv{WP9%1kjxHa0ePqtVT(n}DjH
zoWL|YsxRK1D?_^<cl>m+8aIMs8F2L2*Z?1ejZmCuo_$AXn}UKu+uy%o7_xyo=OP*l
z(1XWNGDlA`1BFZ~+TCh^Q={HXpTgWV_3o>`p!k;N){}>P5NHl9hP-3$uN|JU77**z
zYk;RWzl!F8gapa~cZVt@d4W``)f3v7sNCQ_AUW6kz1r>R4Ti`?gz8Msep<{%0w7aV
z-!ZWUXs{(kP!7-q2Sussdw=c9Hb5kgxiXp;hV?6uKA|X|f<UTuk=c_r8=we8v4dch
zp>`jQuTu`E=k8}9xe#*C1{%5_s<WX)N_LfJGbH8q;1f;8nQj(A+N>aYCq_cQ%3_e}
zdTF9KDt<TrgHm9?q^j4iUl-N9cI_adHtnbTML}wSgcAY+lN~!Q%mN#l0G3t&tQW!+
zkjY1HywQN{2DBirI4QmtsV`zvV1kX(n`@_mq44%@Q<0Wb78^?xN`9CYWAK_c#>GLe
z{zF~R$>lz-(bfCr-=RIv1*oX**tuH<N`Ilzw-v5lH3YUJ2RGF#l(f^c2CW&;O<~g%
zK3&MDU@w7Y0M*$LJ0T+jyiGr@&kB}Hywqc4rf4zL1mY?+wIJ=t_bTe&76=uA+M%17
z8H2kGzB=b_&l@h15XBDX8*D7PUjgvq$O1O6mK-2FcoZ`Dt43@ogGDa~(xPKaZS4*p
zUJjZ>t99K~>sr&tkKaW(5OyF))F78L#93Hk#3tn^{4{8T4{ck!22^gFMbrUdEsw?;
z6uZ;_+k@D9MLVvPLF!9se90WosQLJM4|1?Hi&vw()zf|q@+XAzQG|k|(jRONsz_c>
znK3-Fpv(bAl|pUFUA|n+5i@(+ToT+n8yh$<MhOn|(1vHWVjU$@1$_j796sLOQBhH-
zMB3~A0NBbBtx9*d%*Ky(b?E|wCHF014PHqZsVYD`Tdl(C+1Fi>%?`{oY<NzH?u>CU
zRcC6c)GHs@hw09L;ySn!6CkJNJmN6f%`b+VJPLA#yKw!<&MZAf$m3dy?MA<LX0B9D
z9>6Hw6o}}zqAnK$gs7^o&jRWakk=$YNK)@)oCO!vZKpJbT$CgcT$B~CdYOxw%BCC&
z&(HXvLQ5!-b^WVc4kRH-JWW%DLoC3k-H*;fZc;1vj*{fbc&Yjq=hF4FyDHUXGkpA`
zM`B$}@B3S=yO$5{!=yy4xTC?UsX2~h-^@LQKm`QkAXv!{O-=CQe@at#UU0o;W&n#}
zBI*X@WeXI&5wp(~WWE=<uxW|e9dXOEWj6HB`3zz@S4sU}601wU#`|$?;_4waqVPp8
zXx>gwMMh@e+%I9-W*iblkmqVn0scf*Mh3h(WYVbGhx|hMOH5oaV$@9d%R%WwP4;L~
zMw^wwGi;w$1f-iGAS-HWNMBG13Znb7ksGgFE{UrfC<l2v+T`jr!<XYuT7us}iW(p+
z@+`Y}+1QY41P(0#;-OITPR}`Do4kP@ssSdFoh=>!c<8Qqfb)spW4DqzvPZ`Z{c<tH
zqSlg8#S>^F1*3Q>3IVb*mfSTS1sNQwl5^ofapD67lP)^}3sewRA3S7SAI=|I`9~Jt
zVx}X{NcKQM+geRj4FBzi(jFKyvs6b|_OY%TvCHc#^M0Vg0K-Jqfy~(38#pwiJV4_Y
z5EN{qr^Z~52V4$@0l|zJRBiNt5?dfyLF}%K3=^b&El|fewOqCbW09P?AuffvW*4)_
z(*|i^Walb(vf5bW(AphPOUcN{fSQ1UZk>0o%LTDW#>4;am&ih@n*cYT5Aj|>sK<Fz
zkX|~&wBDf9F?soB-Fdmv1!gwE_Xox?e_!2ChQ~0(Ce74K18_xJA6P>=G!J0+WFgR@
zZ3LE?8mMM@2N{ffs=vQKlFy;-hLgp{2Jng+3pEg#+qZ9r8b#KEnWr!|xnj&VBuF|>
zKxC0Tzfn6TBUAN=O6n-onePoR30E$o>y!8nPCj6NV(%xt;WBEwGBWjw-=<^aXpgY)
z{7cw(Pub(Z$pDgNXF~}?3v4hPG#x-Xa_}hdy(l1qf=7sBm**1@6|lkJk;D)cp+S04
zn@{C8=JQv8J~Fs&kPN~Q5TUcYOriA<d<@^cP#^w4kBx(y{c0^2PCK&VtO3Pjw2P?r
zjYI7;5X#=waIbhUUUgq5O!HHaYSbX8k5W>AY&xK^LyWANG6x${l5t6Utda4HU!|pZ
z0bL}|>H`fX@-^<!c=!Q}r2Du#Ok#0%W#NUllk@5K%@QMNo+KVe5Vkq|#33IP{7{mK
zh)k5QL-GuvR#jX3#k3=)`4hN0b`A~>;4W!VrUr#FdpYFA<LE(L^CC3oQi-OHa%}Eh
zwYz+rLFYvy%;gbO6Ou*s6o4K`llNWnh-wk^V+SzTVrveJX-v6Zak^ZuUBX{Yfdp^~
ze6$aUC(}?<2T84%WeycAA-W$_KY6zOP#i#I@H8etfN{<r_ngb$)Mrkx>znMpkvBWx
zRP%10-#vEv`=h_#s(FnQ-3nl7_PprLo9YHqu#bVLGFrj*mgVCI`Kn|udq4bW)}Xo7
z%DXk86I%$$j9vgRa$H3Kb8BYr^0MV#Be&aF-?s1fMgPzHqju~<Zf?4*vrLtO-((C$
zy+!$T^TCj|2X{<KyjDgRx(Yh7lnhe-mGi;xzyEg3TW>OE!%*KHZ0K+@N~gYm%r?-q
z=Kam%bz|wmB=5@;g|9@Ag7Ew2!Ys%qg%rG~rfKPY#=GvE(T(jr`|mG7E$C@0<}wMi
zp1-lc&1EEHwU>K%HM-Xr|DmF|Bdg&<or)DDm{TtCq`e@@Y*s+oh&8C{%bGh~-abPi
z8#KdnIJ5cTxy))4@`&%l_9xqhI)a8e@wa<)f)egdzGWNTNb)a31@)_tj_6H#Y<C=Q
zM^<_G59KH2+1Fyx#a5GlTX!^8R>U9M>kyLk%f+C86tW~f!DZtr%Gl_$&kTkWD5&!L
zWE;Y4#hNpw+H&3ONzG}!V9;H^KAUnmgh0yVdi~4Rep%!S^l%%^;W=2|>hXp?wJ4-!
z|Ne<{0-5BC@|;WQe93Y$iUwwj_W%A0<GB(C!*Y=nF9>`o*{fN)U>Ng5@`%22!3}C7
z+JEUyQ8xC*jT67zb{S5J_Fvf2)5)VYJU(Dh|Ciss-;E2%@R!NA<Qp0Obv^*t^8PcW
zZBl)FT}-p|yxz9N5J}A+&>BrP!%{YBzd4QzdH(CQP~yltTFRA7I-Tj=_1x+zmLja9
zOv-cQGME6r?84Y#W^GgT((~+m(%aAC3%X(ZxatqJ_1^FI+|R@*ZGWsadaOx5dTeFB
zt=-*Tji<kCkr!-HygzeqdGE5Z0AaYA@%r{vUPfybvO}*=Hn#sfIwRq+|NCliOh?5N
zb@D9*XF7kK(@U6>h>@3ufwDmk*5yp!Yo1?*y$20@dG1qIscvL&SnRtKKerImfW}3z
zw;JMF7)m#SThnrVmx5BhGWk1Pd6_{xPbYttkEi$G@R47S=L(v%dH>(j9gQ}F`V2PN
z{ZZS8trQD4XA59y7PWnown;7W-^&Rd1aX%wqNC}JcH5`oz~{_!DsN@{&2vyc;pyzA
zpkHvNheJbtIs9#KqVlwiylxmM^YN(EWSnID#fHqG13^1u-PysPcgi+RFEZ!suX}kO
zElMqNdV3r)aaOXw*sc}m{dju2k0an+nRK@E$h7=+J4sRiJ87vokXaGMLQTFt_~h`Z
zZJwYPtxBfNXaWCgol=XgB))o;|8+6B=0K|Z;vBux1Utx6x1aU4M3|PN++=)I1x^8b
zM!WLdFMD|k4O`sxUe^ct8<pqXZo6B&-`Y(fRq#=MU8zbqrXmMx*v;((fQ5`G^Re$P
z0QonW|MqW^!1Cox*sc^jopCg}eDRn2UWDIKWA9Zqww&k4?ad$CH~Y(4kHKjMoS)&p
z)E<`wo>i+z>X$*Y4BLBdiWtflT8s|4df46VQGM}?o%q>`{o~=!sO=_NPH}I&fB)}a
z|1s4f<!o=JKJ~x+mp06D0~L+Bk9Uw_vbIH+Zy(`TQihW#e)x6S_^I#u)wzwqv0L)i
zn}S{BH+uP<egD3iIPe6Y*q!a~GQW4mrQocbx0}a5SzsQn_0*-^lIyR^)}RaH*Bu16
z_>G_V1dij->&z2_dbuZmS&iMW8bS1euYuA$^?xx;%CF74xVR5YcNK#(clyD;QU=xm
zE1gnPL@~^KW24E_JipjhjfyC990SD@@bRX5i`Je0<;V~r=v)g3cC$;kG?DSYE%W=;
z5%fMw;4A(+Mt#G=I7-jr%obr2_47r~TXA36Zq_y64sX%b>_<38fSycXE8XTuTti@o
zdFM#I=U2WIq~fNkCAMkZJ%b>|bU%j91z596mzBB4%TV`Ji=s$lD6@_u!#9?GGjdQ>
z_V8$1&?<O&di#np@WG0zd>eZ^0PK#tm9ze@hr$pB9vi1n8rAKQ9SGAZy0*=6f4YRG
z#gCJRX&GpTu>QA;ivkP68BzSYO^+(A-4LJrcF-Vu6=p3gJ5OTD1Q&;01`P8HXi!FX
zZxIt0todW|`c&`SWBB{e4>kPdoB$3T3j>`B&VnOhzwQ=<xobPWZzhjQujG%K5L|u?
z24!LFm>t-Xucw@ES?Pqk9ij;VcH4?!k)b^B_m^?K6FUu)E_GIrSX-$*%&`sB>7v`1
zwSHcAD8|jU?K6}!ciOuR(+TmS@!7+vC->}hRxd4Zo((KAs(Jd$JtWXQKF<&r119Ss
zR9D6poIZb-M;XiZbC#Yp7r`iLciUUi8AxS#<Xh_a)e#Cz>5@b!-7=K#T_D1lxu!5A
z<!oke77AzF;0|w4^+e$>-&uip(0)A(CyM>}*YS?5Q1uhUFclv8?T?xRr{BGEl$rn7
z!gza0Z+`MfrcaN8@GrM<hTBX&=<JEWj*;KviZ^CC1_OZNeJAsC#F#MvOs~C^MN?EJ
zD>d;JUHUru2URM{Pf-YxA75*ggPo@`F+M@?Z8|VCyb_?&5cKqo@8MHh3oOqHb}5GZ
zo`|j1q6MIh)4r>wyb8aJ7+g?F0&7f>jq!;zgVl!IeMWVGt?j$F-DM}bOK+hjUc~Cg
zkVV(|BcU~<WS*<r@6rzQPT4agjnm3&%-WC}j~0AhmG*Fo^E-J*8S&rRu$%L7n#rBm
z&qWtIvW|qQS7pcO|4rC7?^KxgH5FbKX_1orCOD>_yEJRc)FZbW*0K8`ZH%e$@$rbH
z9^TWuly4JyWJvT*tVP$;kKT+i;jF-ER;O;Q<#i<gdGYkp0nx;nSyUm!LdS+Xfef`O
zs0)2pZv~L%q1ynJbLgm17aM3VEh1>;y?nf;nH|cDPS9o#6|^D0@A$cV9{c4ri@E$3
zL~VW@(q{P?2s3YQs#*f2o?nr*%TD~vrM73$5!BKWw}*<p4vKbU4SlOxJ1U&#VtnVs
z^{6e5H}1AcqWA^1XY%b?*Ip=|;+FP&{<(YIxh8yvUYefH>w>czQlsm?*t{cJ$ukge
zj>Ixn)Xhi6-aL5uWWEarMWRKSxn~V1`r>vl<vCbK3B7U1x3nxgjAw2OOWbbr@4=~=
z9SdWvw4nro)%W}S3{hG|;e5X@pb!)6{qELm%f4{?B~Od4g3u7WO^h2~{)4RrX87Ay
z?845K?1U#W_|BUhS?l*ZeLv_Fp4)D$3{TK^YONw^hQuu7^DV*46_Hbyx7$baSvbiI
z-HGq_$WSn-<ZNSaIE0w(!)cdB!|4Qf#w&fho3|_5gD?Q)2+r>c4$6?OZNhD?f0|`q
z^xe5)pN5-iII@epq?5kghT`1e2qs>Tm1WCXn*64hwK<czOI24RX8RGG=LWMeD}xq5
zLlEwv<n7;T7*y8mfdE7WV0O7cf1rot?%cUklJMuVdrP0PuyYlXgr4OIoVHFy&gp3v
z6Q?L?+%1NuTlfqKOw}dknlGWsD-sMKWuU5t#)1#tVZ5EQQEVb(snMZ=28wVtzSsY3
zn}1KU7%)ng;ph9M(X!Ib`gYX$G#h)1XVELSwl?8@VpN5f`?p7|s8>;%91cvoIz8+C
zcGYtg8~7%rP}Jm*V>?{Sz*-5Si>3Fh8wWw9A*YM2K7usOm)U~N{`sppZ~s)!@kWU}
zgSGpeGf?>Ty-Hjdoh8V{GRHhI-uAGBsER#6`=SD4=O`*#<%@9u)hBSXVWF0@6L<+k
zU_hyq6B^&xq@|^SLexL00}F@h<ES4C5q_=Vv<#IbW^o}FHzN}O*Q7jLmx2N@>iw$R
zP@`4b-&-f-Rukof+=z^RO9O3ea%wpsL#cjDr+*Y`NjA{TPz?-~A?SG!JsK|Qf<yIH
z5V{^jQV9@0YKSg^#=*aIx`bQl<4g0eEidou&P9L^3Jn1tw2^;oyQz*Vi<Uo!$z7Gc
zKIOv^<GncKA$>|EhRI;LR?`q_&B4JrvvrA}fyF}KL=99Wh<{%?kosnU$hs*4)4hI#
z{zc+G{C?kQW^MJ-g%6DBgv>?Z<n8Bf!1xuM_trzW-&jF(m>EcdoAiS$0#&0lpO0x~
z>ofCJEE)PU3TGW>`!lyWOI^ctv)hdzx9OPPJ0B}ew(j@%d|PQ;kJ3Cx*%r^|N}}bo
z6`%%568|$n2+GAr&sp54kC803od!9Ky0X`Rh}(^!BD<QWT1D0q%9eb5g9n@VwCLof
z`ULlX+}c_TK6WsBmfch=x+YPvv9U=fn(d8E?o<Uym6CY7Y#a^ULqatZ&u&G|e`3^>
zll7q@nNTYNDo)9ZaXt}eGvt`0QY`Z=_jOqX>=`YK-R|Q-XI1_?)J0Sr{!$46ae0TP
zL8gGPMSF_Ss|QIZDNc<UoSIII6CVO3zdTA+*`^9<GnY0ND_NmXsN@i$Vm}(~P=i`e
z@#w&S?)OxHPn@}EoK3RGa9lZfPy;O{j~5jp+xOUl4-R(<7g#20xk35`qv`(QvA|p%
z*aing1cwrV+*|^^7afFyfYi9L;<=^U<f=Z)_{0pu<ou7D`b?&*oqUiGL~lPC=Mtg@
z|AYF1kq8>8$3Pjw1<?e005!6bbHA>GZlW6Gv=qr3Dz~9NL?Y6OL+K^imIqipWNT2V
zwHJ&3^I2!1U7rZ4Z6_gUbI>VlF8=Av(%YdyfAJ<sp*Mo%pSS#dvj}H$a!Zt`9RYRQ
zfk;0aH4b`v*Ir-odOR2OyuIx+QmG;dWvrtj)R}0MMUGn+eSU)UcN3ED;m8^S(+kI#
zk1xB{5vcnd;5%3RFY`dfVu}VY6D3$;D)DW6rn{{CZZgWhh%O${G+iF<tRzLug@j=z
zDh$sfQz?v)N6aFoS-^2*=Y?5ReEqkwp^}4oW03j~t?+Obk(DPA#3|ss0cxYN2)6YK
zNA*q9=+MRwrT2k_$3s)k)NalsIoE4{qxO#p`vBC41Dcm{PU`vFP|+9*Y(CB0AxKTd
z*N}mminYo1n_}#$`nb-0J!&sz!GBZ|NWqw8O!FG0jQc5q3eylf>Q?s}T5$6Q4MZh(
zfp4cs^r3UDO0Y?S1`5UM(DY|^ToSf{qAC)>8oGYoXW$L)C5hjuVGRVZelRQ>e(V0C
zBIvj?f*SHz;BQKu-7y~>&8-AFt+U-7=1etBWbjeOOg-Y|q_nN0^$iPDYjacssBd_S
zzdS{)CyG%b{4vhssi&ch2ROoFg%z(rsg<uto5uvM%m-EMQ9O`URjfq427a>uZlKZu
z9`pzqI%Wh>jS*4s!#SYP3qpFaJ54dsHC4@dCMGgb(isxHCQ~J@8y{Nz9eFBOW+;up
zlo4GhIq{Tr;9aDc4%E<*ZUuE?;Xx`yN3Dv6VKNsKD19ak+8>fR?lGehI<zja*p!iF
zhjmet2b81&p``0vjQ`hk8t>O~Cf^7&fC?_sc9eg)-E_CFAhTBIAv%+&5M3h^(AOWT
zLwLA4<jef(NJo~b$qB+Dja=33owjO>e_*5@{6Fp499VTtvebNPExFI8k{RTN9<|f4
zp>M>`89_0^8+1M*Ns1ANkemy|TsJu5Jhc8)IrR()-;t8)DC)#X&#n+__&8w=TSkWF
zk*ecfOozBNGQ7P}zf2918h06ZxRv*V>Wl?7<csMgfQM-Toq_YmOY3G`S<%wZRL&L{
z;fvPmda$1*?c&P}M=LMg&<OS?t*;L(#rjDfStWIF?*DzqtwjmajyRKV^?N)=nxF`x
zz5Bn4AoS+tE;NhKH&b(&PVX=@iUwx84s#<s0FE)cf4?N*kzOm*Fo>wr2O1PSe9-9u
zN6rkj93{Jzl{R3`qkR{^!(350Y9<@lTWH$<?bS|PaYcU7na1*o4y~M=(~q~I;I!l@
zeFZe#iL6py&?Ln!)tgGuAP4~6dBwHLW7Mo6t<W_=Q#gRr$<WT{+E{M#sDbl%1e%kt
zn6B-fg--088OtDVB)UMY58!2;M2zMJu{|h@si=4@a%w=78|7ZNxqceGXS4~5b+&UY
za`=6GWe1@P<0`#mu>6h2{h<A+K)n~56*HknL5KB%CRrJZ*Z{Onq8=5|LTF+5I#lVw
zpmuTg-pAlaMpMt3u}yUyn~zc5oM7qD6j-V6sFl}1Hrh%|<*9gTv>i8=N4cfp9G633
z5Q}<f&@)6PU%zh+e~tav*r@FAn;9*VJkKw$vwK)#CCHsGEP|ZuE?;z=<;$-AawUOX
zye-o3=PWR($#1n3d}QKhW|1UcdULr+tK9ki+CpKaRZI08r~sf%!zi>kM$#pTbH9)@
zFc?L{{*MT+AL!wn>Sy<L;)6EG8pGn^TX#%+YP-U+bgyd;HBBQ$Wf5$QA+%181%_$_
zNpCoY{&^RPN*qXopoVEsnV75(mjK+^m;RnN$jPj6728%xn|Stn%L}^gKYJhx8GHXf
zIB@RZAwWqZOA*CFSr>H=5<$Kl2+|&*WW`KSec{zL_0&zK6Q3#j`v1Ylmtupe5+SoS
z2zr-Fj4YV@8P@tKTn|Stn)&{@AX=`0^w>yjMX7<|euonVFC$<Il9-W4y+kdjA_R&s
z;?U-X14CQqAaqu-AR$I(V-GA(6kJS(@TcBr0);cwP`MJgXH70!Ig>b@&CF12mFwVC
z-?8b+!4g|4w#{@sp93JHMeZ2&a~U?gI7bF0oD?_%74@Dga8lx(p*Fb&=1S>Rfrfhm
zNI<cuA$rVTpB?opAP00neDZbC@N*pkBx)%*x+M_5Ah|qLyw5+`&qxab<IFc3<s>&g
zq&d4${e;$BKIi}zuSpfq+0(IkROJ+V#iF!LRh{4YL=pOb7X_3M6#H%mv!F~JwZg$$
zJZkhD29BUMhcbY4)T;R@6%ceN?=q-_{~Lb!5R8S^rz%RgTPg+NQ3mSwmytnIBJi%L
z9R|HJ0UB>LGPI~6rR<1Vh4$-n$WU(7Jf^k0vDrv2%{}1bHyzD<<YRP~LM=BT-sKK{
zsKVXx57Get{Enabh9_-x<Jf&6c-6@gtPu+cZv2vC-NjH?F#~mLD09cNkH%2<a-3}t
zCcdfT^@INQY7~5hII<(4VvTxi%QjXfD5_GxT4_8<NdI6B?MTRG+1TIL?Y%x64hd^h
zWzmEK*Xc97e%XVf+=D|mLf+l@xl$Jt5-;njz@cNZtLG<!4D%!sB_MzfYp21P>Y;Um
z55zL4xoY|~qC??b50|s&py7}*Ymt#~BajXbklsXrLI5pbo09NlP|COx8HZA4&h1F$
z$0}AXZRD&@+Pdl8#wwQBX`mX&FC#$OpaxZ>@O-$B*BdnU$7uQVnNUwV1%88+!`qu{
z*EH3>2_&5ZeJ1xQ<*1|3sB{O35r>PBIPAR_RZtyYr38AV2Q=iM*9Fvt@dZKS8<JeY
zDj!EuIV596jDze+T-@jNF4aqhdY=P}NTI_WS$z7HJQcXbbnND?SYIg4a{hP?2wJJU
z-)hJ9-))Y&hy`hE4Jcq4o1`~g`=L*Oj3m$iL)hZS1IM{Fv;1|ei%%l)?NJHGdk99L
zkph-9sI@aSHm*W~PU!sNtQ5ZH0suBk%1P!6-SaB7+0Gj-^%Hgb2s(?Ev>p<sH;hf3
ziCc+QoVz(kQuANG(-5Em2nph<m>v32!4^bJfKOf}#wea*hiyU{Avh*G3D7C6XlEP8
z`JrCEDwBrFmw#u;<V?8fXtp0Wi#s=DQG1{LG(-%~16yYXq}o7&bL6CrK*x#H(n7oc
zIC||S?Yg7tI^YUrJHxnCGpNdVybuyw80&El!>?H_tYYf~Nw2m!yyiovlaqVUaYyeH
zI0Nk>sO@Sr#>E=cUV~XR|FBH7pBJCr=nelmkUGyBv^-_!geBD}sL^dXHM0dElY*OE
zM+C7bivVzmMBqp?3JI~i!<o3>#|KiC`e*a`TT+!4;2vX}jeg04`qA6X$}R~oe?Gi!
z=#<in!{A2(PAWV{68LA+@)u}?@=U#oi0KIl>KgOX<Ig^N+em+tt=BC(UcHrcd5*}7
zmIs46-e3*X>&gy)#Q88t#wn~VWISRwqO*B-?YagvUB96CNOsTD-dkHIDi`?K=6dEr
zkL;r95QzQyMF|l5XFIfBRPt<hpd--P61Pia^|d>B!05@ety=>`(zVRZK%L|YI`OM@
zYT;WJV8#n#)HqIhA+u}R(~)r0V2jL)g_~VnN58(zfr1cX1}wV#>bMA<sr!g8MM==|
zRcOFe>Cq&A;t;3u?>~npq+UCg{~E$M{5zu4oehSs#YsU8v1no6s@z5=!Eq!yzf@v+
zU8#>avDn~)y;NGQ9y=ucWX>%1UC7VdTu=*z#wsOM*pHq)7u;&0_&F4F|DON&kW(RE
z*&v*iqkn}|v_W==zb9wDw{^?flp-L)UIlV!8Ixj@nJY*P%nl6X-&s1j(EY~Oc+Gs}
zsmS;U9f4#w805QGI_&2IJ;y079a2*yfk7`w2nWSB>Z+l<c1jC#?s?yNjt%F}$pjC5
zJ!;`~OZU56D_^2py69-}R4lH7)3NyyaNstNwj`_hKHcI`l^(-@U@<@~NR7+{`jw&k
zTar*`uwkqCLWol~kSPM>9p|1#1bgRFP0+<k#%cDi&AE`nBI*ns7!#Uq7H~>!meON8
z!`-nx*!S^vrBUsFZRLOr<^J}2%&tyD<U0y|s4}jvT~@qU(K`%K9~Kt%JRku#N)C-c
zSZJ=i@0wt-l^vm1O}5Ht^W6CZ%+0W*Ajey5(q)m`u+Z*booPKiq?fP-ugs_s)d}fB
z0Gu=4=g?S)WNAo{U+awCnKkIljbv4*<$#E=1-t{{EM!@&AxGmzpuVfNHbr<(6b3)R
z!mK^Du+-3}K&+J;t-ewjO`Xj<5dQG5pSy6uC<!>GmH7!eO?@YmGT?boL_iJONI*_a
zzIYK{nE^^n1WCdnO`C&mxe9WCO%-|_QDZlzG*tcsVc(v4pHW^(0U(r5zY(M<l^h-D
z&>_r8moW{weVW%QHIdtUeQSRLf^D6d-B#LS1EGry559l^()J*fC<_QkEfv6f$sk)*
z1!*V>T992=ORY}QK-Y{OLc<Loci=MJ(leG?eBKjNN$#g=NNwe^qY^VUX(HpAZ3HU5
zGrqvi?^fO_kqDiy9h*-CIT7k{MqP<0tHTllgelEk!{LBXK-ENf<G`o4kBp$t1HEvd
z8#IeAuJ3ex^LC&Q5Zi$OX(a|`=+)2dBNhTV%NG+xIT7InxAgWN+BUhzw$3||tX>z_
zV}c~3)ssEBl%`pGu!UNFP|=d1abFxIl;~?w(1d}~q(n~sf3iURD}t+xA=fX~;1`1i
zIJ8Yk8!b5^9y(DjUFnbt<r*zNi9ek9+t1x=1vcsdv+alDSZK~eswo13pK8jYCqb13
zFT99u2h0KT>1u<GnJ^J@6-`2lybq08l(wm9O<@oVgIcAul$6VbY=-FBdT~o)xIyK?
zLW%NY1z{n=_x}9(lw;8$ZEEP9LC|r}SB0p3l$#(GEI-gm-b=SM3BbsYV*>PmPb4nz
z1^HI$-qV6Qu6rRq446Nwrr~P-!m@)@jr!ClO^$HNZ#l2T7Z9ot8iPjkc;Hgu6>_if
zfk#_;VwM_<mOhR?k@M2HSt{vn@^+zU@?|mqM$#y;sf`ZbNqDn;9j==!TKzeG8IFd>
z`h)=`#opW?ZK7TzHo_)OX^)xj8$m(BL+m4ptun^GV5Mqs@+ch57%XGYa#<TFn(BFs
zZT!4W>{p@k2S)MLF`l2(Z~hyCS2rRcQLsjRpGav)&3d>4NKycF-rk5Apzs&j@P?ii
zKo9mcySmv2#PfU&qTO#rxoK4{^*}%?CfN*5iR$1&syAKSRVkhk3Oz+;{c%T|Gxhei
zZduX(i=yQ_GIR=gVcROJpqZ8iy?DVp<xIRsI|zFX;pGn48hTS1fLPa=Y2pT%TeIyX
zY(G+y3wyW;VVl|q84E)v7oy-G6oE$B@mA-I2v&x)w6gRTdtpeKQ-0si$+dF~&pCq@
z`w+@5`r%bs$QY59RAz06cZ`(*;uA7ae)HBu^pd3#!v6?`L$Ohjsf|yWsYTq{>CVOe
z)!H{H-OHrW$wW4KR01H93pMbdwg|`$Vr2p`Q~HeH;hzsQD+_6xFoQl!EU*Ywz@!{T
z0&nk4?nI9w9cP3Y{RJDnuRJ?d4O-+jdb(pKV)+e$>6K^M8pRE7!J-iy?=`NfUe{1f
zurtukJFXA^EldpXBi|$CaDfD>9kWkWKOc8N?{qTh0YVCB6%maMuT?t17XQY3<0UYU
zBG4y@UX_6bP6Y{R+o+Emn?e=LE)TNj(lq~SRY=(Ls&YO)KOXT~3;SvD^uj6!7DsmX
z`MHNh_xj^R+}a0QPI2TupxM&1xcmuu6s2(tJ(90{?z+sH!Euy~vj~E66Gb^7Aq;$=
zYwy)bjtfTZ<(JZnCYRiZjB*Ej_T1qKl2$P|WLYqo(o)QQ-a05&N{r7sy5wwy^O9${
zz2oNBe%|EmkIR1<rN0j~z(dH7#$6$SUNg#Vp$F}l2<S`OLJq{4n+<Vs^BxsGeTrG?
z*v4XmIvGz#TmA07j-~x{jnzr=L@KG8RbT5c4StfD&Y$tHn{A)aJ;R@C7<Ve9ZSfk^
zUKGR69!KDdBgWAX+O*_ks-V@4;fp&oa@_rU;(rnqlr>~g(ZqG^lN7ms?LOHH9y4hY
z2@AK-I#V=J{R)VMI>MKoi@tt%DqlC3Wzj8HFor5Cfc6PMngl?<R>4~m#1@D>8E;8D
z|2{xZ?;~=jG6q}kE@`yT8h~z%Z+GbG{MtX`Ym6%1j!W~Q{I|6hiyYTJiQK&v1_)q(
z)RyLJNo06V^4sk(evsja{(fOr!1+Sh(yS+M&ONK!iW`dr$1ee)s@nRwvH3vPv!eeJ
zRvZFZJ**v>;Bf6s-irTHTtMBwcq7Bs@`)A0)6yKnOh{^@^7k;1I?l@^e71MkxO1r=
z6dBcF)*hS{>s?(&;JtP$Zq3v+B)ZM=B}l^e5;2_ZrD~B&<k1B`8>OQRiw(N8PzM#7
zDWvWC+P~>$r@m%6_yQT)Qcduq1U>E`iJ!Y-CjY{jg8S^{FlNE%kk}|6Jv17`e00;%
z$^@nquT}EWtZ)?7e@QV-ewz{HY`o&uT2W$YHpX|B6l8zisreuhePS%qKrM@$mE*!5
zxtb{hsS~mbQ#TT3J7J}VJz^PSH>dm^om!sk-UdE-palS<q<lw~4Z;Y?aV}D>YMqXS
z79IK)CsM}XA=CA-(4wy~Jvl-iIy;kpMlR3ipu6!r#SLW$a1?$nP10wkB|Q}XnCN*a
zJ1V)}*ekNo#aDG7Sc>yDd{&>fU~01fNOGFb_%rrOvTQDN7j5{l$6J<04X_w!$M{P(
zX&sUqoOA7;1wh4}Er0Rq&pDcb_;H8X`8NWBggUSJpiw??3R<xhaM%p*yK#d-+Ra`+
z>RaR(dF|o-?Ewn)IA9u-@tr;71R5`VJ1HoriKRJRcJxbTAH(Rtyah^t%L)r#KcM;P
zzAwx|!f+wC1FE^eVI~l4=vZ0y%#Gbhu-jN4ToEA)w-Gsl%8oD=&~A^iW>RQ`3!oo4
z6y!eZU9>#6Gr<(EQ7Va)po`^iBm&nu^1|0hmr(`v;kFgYgCc7)n=uF!+Ev|%%L+mM
zBqm8AgdM20AX9PQs9F=5?%E85e2NBW?Oj)XV)r<6s7%LQHjjLEWJnwr>$uU%PW0Qe
znA!r?d$_ub6iGwFQF#c|e*ORa{@)j&gl%o5{t+SAhJ`v|)gh}zR50wWf77P5tnSLE
z$Q(l>)t!_^Im#AzCZYYNor){BBfiaMWvLWj{q(i3xWPII2p^Ugv+hjG>E`71O(%BR
z3{J)E`?iKwg$#T~l#p$9_{usNyZrn6ctC&yF>3n3{vKr&0)dtDL)f<i*)>rnk=Os;
z3N=VBgjotg3XqS%mk4NOa%<-N{AC&%BUh$NMCKRys_8vkMU!~6=zuW2{b*|k*+HC#
zVQoX6`Ln{FD&i0eospM)I@u}r`CWfIlJhw!mp=ccAz{tSH=Qo;Oxm*R8ki?^{Wy{W
zv7}`q0?LIh4)k^`l{skI_}FaD&r^~iKEbf+z}A`VP9fkC7@F%t5J$;Tk?2+276q~I
z!wz^q$UmLRH>7n~+I?y2s0(qHylU=;GO(SeemW%2IoL%{h7TWWz0veihgexRNt2fh
zH_OGq5v^rbV~J|B#I{ODUianpH7x_Je$OovI3@|}8%HZ3c!*paTn;_{=gv<r_lAqO
zUh86r)dBOd2BA;Q;F7NH@`pakrfz$1JLMI%X1=gHNPR?(a4W%2)CpIEYQ;Bs=%!AB
z9m-z^HJ?|n=RU_@VRyj`2Qo3OE~G6hPyRfa%}0A+$;Q8aa*sBeNo?VtUyb!?lbuJ3
z;DAOr#5D;v8NM{u(JTg$GZtkf_C`Cm^cS8_;G|*Zje$ZWr-Kis<BdItL_xjArZDx<
zk!-)V&2P3*8(v|A;$=%vV4lAPuk!x!N%6P|l6*{q5^%BDj?u-SO)(2IMJIFf{`J;Y
zsK4dOj#iY|tCfkF;D8Rk<j>s7vwP8u^-)Q_-&@IExh|Y&uzdSnP29it!^ocldow$x
z*E?K=&^zx&ecaMS#xQwwOac0wn9qqhY`l0EUYP_hHcdX?!=K_(dbGdXZFuv*^q|zr
zeRD?9n}dJ+*RJmFgrc<WS!)1cj+>5i^Xin4N?5bXuV{qk4f4v&Lq|8Ki=oT){xWl<
z)`F|I*WWjZ7uKaFXa6*2j8p`eR-$}*M!g8Jxtxf+#F&#M0mx@UW@zMu>i|U1GmS&E
zezS$u5K`4|PV}F5Nq)ByH$4?^5LOTzYF>E%=Oo92(9N{K<10(VIYga}*M#PZ<L;Y(
z>ch{M7pL=6Q-tpD^&QR)vq}?)jgIi%(Ce$-;U-oMT_1?3H@sDdgXSZK6O`s(%557!
zK8#7c0)%=Fa1)>dCLk>d!VbWr$q4j(Tjb?YksU0_3@)^`M4{f>`|IFKb$|NqlTzni
zD=?i;vHW9fP)a}`q~oWTN})%4@!NrZabfq(P}Q6h9y*F{rdHso&(dZNG5B@b_(4{z
z_VhLgaU`T1?xX+y4{?7171jEN55t%k#Icby5L7}^kW^8+q+v+u7?6&k6a_&9K|&B1
zR8pj2kQfkAP*OUE0hJPlP--Lw-h1OY9?$RpzH7Z}ec$@NSq>`Vo@YP%e(t`n>)JIp
z$EPgjyFEf7eNk_=qw?MNA(qpc`oH7yKqF}43Y4{gTX6njy<0m1qAQ0%orB2LpovZA
zXwR|yVa6trqt~zRm@TS?o!Ag|LW2n(fE9ld<ATWl4Bc8=>lD<l_#q%+<o&&^R4QSx
z2|_m^iCBSiI$}g9gB)L_mOd9RSL7H|*!lgW*!N5Bs22sVUkw!>o;~{j5pLvt|32<g
z0{_c^iN>PU57o)fwRNoU!@nCA0yCf|k18=sM?ZldGGq(`#WJzbmwud7cC_KSJK?7D
zMvq>}Y|z)$uj{!#HZo?SUS$${9w;`N!(7OdYQ9ZrZ-K9${mY&E-sB<k49?$&VC*5H
z=y!edvJ$kUQ?($KZb&uAMbhPte6Kaic{OvECo-K0cO4!YBBMxuQ8TLa()p=p1;&Ot
z2Vk&)0eSy$xaMJXif;NT6SC_v)E=QXDB^NpY~5@A?R<C`4LehE0#Zxn!^SMgdn7Fi
za~+&dmUMpW$&4rEaB;(;lS4iMt})#*D13>;age^S{-%r9l~IR}hu;5!7q5S|qrz8R
zkL}Z$3QrsDiM8Jw|49@aip%?Z3*9}G`^w~y_qA)#CG+0CY2VH9Xj*>PAN5F^bWDtH
z*NW7UkN3TEaPO+LQ8ycvlwe8<5ruDot+Dr({EY?xsyAx^|NGMNk|2YrVYo~B{2n%C
z4A5N<BjvC$B07iPHNR&v@9CiBx9-pAyJ0{sf^nMYbu=QT>yU#zaG?E#Opd6VX*`4A
z=b+?n@=P}*NB$7+#O-kD&xcA|Tf^08rx>w}UBah=Wfk<VSCtl>u;T4!5Ip7-4h@m@
z(B$n?-dpkZtC#QW6!Hd1>=WaX-OS9AvND|zO;vLowXp1J`ghe?CeZbEITybBE^EW+
zrq(7eSkD8NV@H0pPVpeS0sAe}Y*56Hn?9Qpk3mDTY+H-TG30&VKyH!1SFrKs&2Q-U
zkwO;dm}gKcHG;o)<l*g$$Eegf^%IWs7dbhG!*f68Z>0`1Bj%tP8=tS{D2d2!^hEoV
z>pJVdrxwRu=w~=^0IGa_`JN4;ZkOX3BvV}xcYG0=K8<I8>&nYB@C>Pz&YwPgU~Q(G
zf0>YDp5*!N4KhFv8;aNrSnoMwqYC~wC#bb!Vs+U^K=W<ATaU%VP#*wfj=)II2qNq9
zo2`Y&h!t{@2I>D<d!P}tUQ#h?fuvu7j1cI?k!G1Skix_k@vq(KOq-f|!wma-j>-z?
zOOsJ}Z#jvLo++>OTtGRGdYo|J^r~BU(3)RC@#;y6>kH4!i%YKmZp(S_;Hb{^=4g_B
zuHVMIpP(P)f`Q9pvGvoIh?G41F%|vrM^9r5#3$0|rRe!EFSPfSDqPix+55?!y3(&r
zCK%cqW~csHDwR95cT%MpW#ams7r&<<Z<yo9r%4Ry-B2&DTm?7jc~)B0(5x<Tc5010
zsi&_eon|u8$JP7CH6W~{qY@HBy9gjB8hOK*CqRl0>Eq8>@$~=1zFp1ed!3sGSJ5y&
zkbmLA{oeRvTCGb{ePVQz*fs21p9!Q5yIqX%$3-g<+%X}aKc7E@o8XipIvHH6Fx_~^
z@ZLD5FiWPesihPMg70$<85~lpD&;sO)o0{q)3)4RV^8?jn{Kp7>R6V>mOaF~5UyFt
zJ~y8G{iHs1A=03t3Qy{!q}aPhtSn!r>sH}OZCcM&kBpq9Uyls1#wk?hSSifn|8lr0
z{A52v!J(w}?K}7E9ZQnEy64QXh`}4tkLr!&<s<IitrR-(NQpP?lWW>v$~1I8-?$Yx
z)6C+t>>uD-=OTFq`eQ9VRu06DwyiA<^fn7FG)uI}<5*{=#M?@CpYpN9gcuJE<;9nk
zkxE&T_ca_mV=tH}Z%3Tv%_R`E3);r`gi333Y)7gRqYfEh+n?+!)U8Z#{%vrOC;FyO
zP87ZozW1SjWUC?H6>HunCltasqX!D|Q5T%%K5-ESmKr1I(6)Q&ln8})(|LGm{o95O
zN7}=kja8f$?dnCbRzev;(+`b>R|f@Xtc?COyl{2C)dNUf%sjf&Y%1%cyO^CzveYuY
zAiDy(Hp^I{?bXISjt-A2mU(6~X{9^Yc6;_dotxa^SKRU)KdCaAP9{J6mnGSJtBV=i
zryrAES|;x7{cVDg#fQ_~?c7H7#>_maEtlMkO(i$0wdAG98w?bbi67B{@{w+tr_BOi
zOWqLHROs0frlQ1OO}AF6tg~y;IlExB8M*X|21(_AFDvouvQ1`;27Ki@`#E~2UQG6P
z6M}Bwi%ts{uX2fN++H$04l7m?O6jVnY(o#So$V7*zvZW4RLcB67mGRm#f^$8keODh
zY{+kPCw}e2P=ELEKyR*yr>89nB~L&FO<Kz5*4n&Zy<~ceXlCd0Lga{n)1s~HW3^&N
zzr^-$-J?EtNXYH}eg#u+U2~hLvdjW!x7|kCz#YHa@`ePI%)rpmE?kfL#gb|~Pnr>n
z*_mjaT|V5`5AC4t&3u^C^E2^$H^%5Cxk5{KMm1M=A{Nivty`%*CSb|1bKpN)OA~$z
z%<!9&^O&SxdvI)buX$*ihv!|_tE6nIwPjov*I^Z{)Q0TOs~J14pvC@TUgUSo%&wS{
zdP_<u>P<GvSDVXOsL&d5K4|wJnV>t3>@HRC?Itrj*%xYpjDEY;xSKSTP7H{T_}I&f
z4_qA>VC-7PJu=wdzHjk$K)aU)b;W71Pb|kfoP$rU5LH)llIAfkaVbMCDjt<wc(=43
zN!9K&(wc;7dGa`)(SO7{P~#-%1S)l_V6&|9`WNGjkw;q@bPH;7I5TE)Pl8n2q3qRB
z23H4q7`u9UKHuM;NB^!UZC2ip80FPIF)!M3ClI3&t0}C_tHLzVJ@~`F1-)$VQLLuT
zE4SC@w@yGFjA_;$WKt!ZuT+~C@2Z$QbSXX0)WlOwnP8w<{K^Tv*UsMlY^B3q8no3y
zJNrSk^9Lhe3X9$n9p+23r8RD0n(IZbu5e)@f}DeE)Xo_C*TaUZhj6}~gx_Asx10zf
z5$3Io*_vvX4CBtAo%Y*eQ1WUUel_UfpNx^?D)KD_DNKe=%NPp=*?T_!mIUE{hqG+$
zeEt2~cSs;ED3r#tB_o!T?ssoa?P=&b9+rKYStS<wnS;&uUHf|E8|6D;n5BJV_I?Vn
z&yHY>8tbasE8PFdXIwbF+8{&0=06+Fz`eD=kZm;${Z6+eO}L}9tVIv$L|XM?cFf#x
zzq`LF`O6dT9GYXAH;p8n@g=!N7tr=)=zN!i#K&~6+U(|SU5rXOdUfqJ{9-wJwQ~k$
zveYC0l*quSP(&iB91Q6+Rs16p*Hcci#48(2&4%WjX4b}>&A6;)cw1iiw*1xGDP7^y
z@ceu*tpXjW*@$;X9&(0EHU?*K#&uN?jSR{vbJ{4h#=7TU8SCbrThdO{!tTpg%ggB)
zUA23d|Kr0XU(=vHeA4e%s>{}+O=VZ^R8Q_r7t2=?WTdA^fMC|Tnc~HnOe3=^IrB`}
zqxMy022HP3_>?#YTt*q(2|Lp$j>f>!mepSkf3K($5_(pz`IiAU##l8kUni*dOdVh2
zfYa}ffj^kO{tMBUZ|;BfNOTb=+I=sl_EVOKLQccQS$dn1+kqEnm3TyE=+82!(3m@|
z#e=RR_P-z|OZwQNFj)7Oh8Y~a>360MkzRIvbIlnd!KcI;MJPGtkSj)Sa{L=i!e@n>
zZb<{*)FqY-E0Hzf%Km)vxI-DY5=Kn3ILB#_?Gs^5^Y+q1>g{&>Wx0BW!&CG$o}%sy
z3?5|GqA*5kE=k2RR?RMQ9lGh6QyXu<7Ef0s@>63i$B53#`p*M@S^w8PJ+Wj6=IG6|
z;*h4?w<1*IOocd0hH@WHeqNnzP1(1!{JG+^v%B2af%c=UTbg9IlT#`_Zy-JKe5PGz
zQ{wq}JHaNqOuL+pwN53@;?jRJ;;?OxwjR*u0-@H4p6B`_B{n^xgk@9qJQZpmYlC6m
z6RAWN$$`$}I!dYY#kus_ptY;>?k~Gvr<?p~7cW!Vz5Ex(@iqRUb!BImt!DhFr^3UO
zgC_sh_+YnWCAQ@IG99)d+dQL(^dx&}*P74Gy)!e@?XwXD9+~kNvATio_7{>YW;3&U
zxENGoYC7xAGAqF^nv;VmJ`c<qJ4(t-;0%=}iVtD^T5OlIGa7|`S^gbCfobRWTYE*d
zJG*i0Q!@t^%+u<ilYU%<&sV?T9znBZe6nO1XRZLbYCT$@B$wwE8f})VSJQ@bx{20y
z((|h_aeb;uQt>aNsr#SfqyFoBUJhjlepX=@KlRcmQ92vfCKj5O9(S4jh~C^cg^<2F
z(6Tq=5P21<q<L7V-zT{2v1H?<8=2DIcavbOJEM57=c!<=J9hkHuZ^8dsq*7bzhgDn
zqc`&4OYA7(R)U7TZYHOyX8^eH^2G}o)<`*8TG~VrrxbwCVb_LN#Z&n47s1Ghz$qTG
z%W+#@yiHnucd$Rz$I%9V`miRd;1&vV_GRlW<({+2Pw;}wol4+{bl^M)9+910({84c
zhYYdeL;1%a8f%v`ADQ~Mo(315KcFgidwJOd!L(Y1Cfrv@vE0tqcCsw5X{-YZ0J+dF
zremGtEPEUSd2_yHxJ07f#HV)^wZk+|<PD~1thbYHlu~2rW@er}slePslnhsrfd~0r
zckIy7`|m=2*wGoWr*y4K8X44NHsOeTX&>FY5mOy5i?y<fQ!#E~n9wYJ`uWe@xqRt>
zYDf#x<wAr)SeWdKpqFe9-Y){Mz&9ZBX-B%<C=&r2nP4YE%8^EfZ;`M+Akd;%%!dY!
z=(z2Z1wI@FLrfNt**Tm;Opa~5BU5yl%kKAHgmE;AperpED#hLobRX!MQpw%5#@XdF
z{cH)ABjh7o{E@RJr0gnO^Y<%V_SBAxNLv!N)-9y&8C1k<p)?)#{w5$OJrxrdN4xv~
zcwq7kipiBwoL5+(Y`C~14RdF{^3isC8@*hpfU}dA$4eRe7;85ZwH!>l!qL*x+WO=o
z>mHlVjnub=*FrH81%5^cW=LT+EpfOzek%TIsy4)&<0|-*%N0{HmHxjM1O8DR%NMuB
zoa$<0Xv6A79ua{2s|X7V_xpujyNckAFGA;>Hu8reLhmd67tkP}g+RidsQ?ofxX(4A
z<66E@yS?pXc4x6#w*m*x%q)%l$Oh<3dYjJMmp$;3hq*A)sO<+zcb@WbMqz5Jcjt34
z8DIxKx3pdx{_L7lOVS7u(O#}#5ukZ7?f0hwW60i<`Qi<5PAowSuRg3#*WKM6>&{FU
z0<9Czq3w7IfE#-NS}}o020T(bfch{1LY4BZl^tjmqE$i5-^0gOnOC7r>`z4RdpWVQ
z2UNI?r?uMk)dAM9Y{{-?x}I({dTld8!mUsD<wh<n1@{yyVa4&ou|(rBmzarDe{2=i
z`M_IZ4vxUi1>I`Oj%672B^J661cQ4LS~YtB@9hcEAG#I~ZCR33p;bw{MU310wCt8f
zY@xn0;FT)QWH9_%Iu)IJ^8I6-dp-m%Mm0M)=F1+mQrG6(#pmCWc!hrs+RbCSrpa05
z8D!zsiMS>+x{-eyRbfFfN58oBA;mx$sroOsz6CQmeu6&e7~=BJk;wQW6#RrJ&5DF7
zi)5*)oLX)%I<o%pKs#&4a?c}N$<EST;jCQn!0c)5<S=@n2mUggQ#+feewMK(FXDUZ
zROCW=H+*z<J$iq>$QbNMg=JzrADQ)!eJ}<4b>m=WvPWX>;K0C3^|S2xy{Kob;4_%M
zauHVn{c5jTY_ife6?!ImfOw!axVsFO7N2`%#%4cPqP$L8TgXcu25l7IOWL*Fc@uoJ
z5Wo2C?tN;nQC!rEF`6$=p7&v%uV8uUamnJp&hh2Rzf|QEgoK0+JCLTQ*%ALTu*l_-
za*GW(?Jv|ciqR&gY;D!7^#}$s;L0lVpzid$uuE0kA(bCgP+F@zJuu92e33u+q_+{B
zx1{RIAf0xlW|MM1$)j<?Ics1>{@wtQZbc$HU8E+j;aq;x$+Au@p%g84<X1ti+)ah+
z2S0Xo;4b7dE1gf2@4X<ZrU9-<#!0Z_%o!)+>mGFqjS10S5+r#O3@0nHXR`fxLr%(h
z*n(p-Ch&0al8Sdy#=5p@w@$g1=9rd>kN%ho&4}j~dDyS*4QzjSOHA=*Z=TUE0wWU>
z*QZ19{ZuEq&QD#t^D{1bxlavp5UMT229x*YQWp#6m+gFRM=#sW8un81u;wXS?JRP-
zwY~;lqB>vVr`w92R)?L%o89#AA_XaIrgNSscRS(;`)Am-e_jkv;efFNc4$q1qo7su
zBf?CcI^q@uKI2!B0ar$-Fi&xluX64w_hSVmqNH=_z2|oOGp>@*&#El>;`S9~mVwJ!
zRIi%C68$_h%^>JT>FyUHzI3o{Xw_T9Ip>Uc{1uOyhI0WKs#XEGJSG1~O}fsrUt<4o
zFV(@kpA7}%&ITPR-~u_{h1{~K*OQEA;O)5^$6E=n>~;rF44m4yWN-Y5F4pjDVTKz@
zC!X&hf=YRAZs*~#$it}i^2}e_N}oUNn2jEF7k7C(bMK00K&)n|HiRY_@bDFsz&a{$
zm8O(%>+4L_KNxbE^Ql>K#p%bTU+aiI37&{*H-`8!$0K`)q~TX8Ub<MJHRs+m;qifQ
zPuIU5&l^2iQI_j#FsqgYjYs`BBVfabokV<nzB{5U8H^NG;Z@0?msn*3JNopiO-T6k
z*S8x=o?6r$UzW52B}*OlUr&go@Er@|I2^`Nq86p-X{nx+@ysWmi^UkcIg*AE$D0(o
zl3s6O|I+S!l>1Ou^N+iK1c;_?Cz2wbmz0)HhH>Q;pv5=W7fOM8Cc3mRVAG!kIyvMU
z90p&v>(K4oJ~cIUULVWnR%?3ejQ|xD1BGEusqTzzv~J?4yV9XSO3wpMp3;z;*~eca
z&+WM#ZT-F;)E$0z^1CL_ZI`9LDsWlX|E%5gy~Ut`hMnx@^(NAlGY1dSX@XS8*Tskz
zyCu3L1P`iewz?&+`|)NbNJ<sA#iAO_K8y%y$2+#!9t@*iefxwSQXLIm@b$}Wem1JW
z!pI*R-w*HEd48bUOuxA_MCP$FhdXq0e*;8lO%IfjQSy-22rcvs<yy@6Lf20(KqeV>
zT>`jKIu)!G{+c8bp2^00X@s$-$y+5nI;9}fzUWhuKQoDLU&IY9HS^2R&xp|~@oKs}
ziEesim@z7J#@d4zEr@SR7>d_#n-xie*O@cir}0`I36&|tIAxa3N}-}LDH2M9``i*c
zyVBjq^w!;UO2(O^?ut^@cEpKBu8El(7kP_69)wmS;BmPFTC+tHv<P+s8nQNSIpGpZ
z$n+{Hzie0MnMnXIw^ay2f=8;b_`N=B4yd(CHSb&G3!I(XW(RL!1{-M;$$6E{UDLg?
zA9qyNVe;KO6nD+nCFmMXDKJc0+M(12KlF3nI~45mb<!_X%P-vc%eARB0%MfW2c{!5
zW4i~=<aa9a7%g9}6sK&yGg}<;idqR6X<m^Ym5<+B^{>z}(C*0`c?5hTEUQE0YHNZ=
z9j(YW=(NvMk3<$1^Vn9NRc=aHCm~pCOc~Eugj(3~V+Zx<W1aJ_rA-4R9n*rQ@wb;#
zl2#={ySRTauy7yWSD$WbD0afO;MsBz|3j(m)3Q?vJMoq|rZowg4ay-z%?nC&f@Mkm
zOQpHk)r@wg0+EMr{dP|MKK6lik!Jcm-&KV}_$L4huTxHX^~TsnXZd1>k~_2yeK<?e
zx<618P(#No%9myiNS9d)ZPL;x|Jm{hp@5C`gJc?+12nr!VZz118yalqSA2Ic!^LHp
z>M^E2uYFb6b;^0y>AlF;Q+s#4*y}!l-hb`uKG{S4OPAT7UF%S!zDIi;Xl&$VeG)41
zH56)bsg#ng?nAn|q8Y6zuf^(|;Shv)J7U7!g4r_>wLa?h#3eD<Fymy1?V@8_e=iE>
z<oP7{Wl~0!W5`P)owrf8JC+bt%u|};jaZd(<%GKLmv%8MY@-ro<^xE(TVAHk)KsR^
z=2g-Xxh6Hno8Rd-AMxZHRqDvGxNG$fSdY8We1w_D3^m`chj6B=0IQb@la&~~JCo5u
z{_9>3*`%wpTz^vhyL)=#vgFS0!nQS#cTxrNNBm$tn>()PX?h=&oY9vjVsH7~?>^tk
zDk$cbh1{HK?jMTcEem-*Mbq#Oy=WeE=3HFCVI@5cZv7~!w6{~LD-x6Ggp=3?<7J-?
zr7}&&%yVURF$CMjSR+P1$GPH^I%Zs(oUA~-FBMh2R6T}@ijF>f1!7pgaB>KB&9F)P
z^4?7IPda&q*)X4^{=-Y65)CAOt3Xx96H<4hHf8IX%QA-8X4OvU1hw(lelNbj{y44z
z^L1*ET=#*-y|BozcZb62ABS;pUmsC;e^7xSkXSrT^!Q$J_e(>by3jND6?b`Mp!sf>
z5rA<tnKeF!$XAS}yt%8xjM2r?D%SH!zy6ruTCw1V%(C>UiI$6V^1DWo`^_~hBb!b}
zb}H8}&%fT+OW(^UiOBobE13R^8%-lfRX)t_=}JmU_M<bCT*6rWf0~sOXZI~{rEmD+
z{xw5FahWY%r?U0L-m!+)Yd-tA_-b}$($p6wafX&`e3r>izAALge2Y!wkve((Q0#1D
z>TA~>)2f0jhHZOs<O41u(atkO%SJwSnT~->T;s?OizWe@<JtFUj`z<5pF}ZKw^1ft
zoRSA!)0={9<+ATY1w>R`6j<X{h_n1y#IBTW*`es9`SPj&-f`?zQ<1}xz*>xMTM>Jv
zZJ*P@4{JWUmNh*QcYHEFs_d+LU&MVTI(yDspwjVNLd-!OE6GynD5JJ)>+E53fr;2I
zOr1h&3unY2lV*p=)5W~ZDY4*53|OqN%rzL(=~T+`O!$VWw3W|Wd?=F5<V=IP<hP?m
zKBm0K>p3}5V7St&h>VF_k>V5>o>(LcM|p<lI(nco95q5MriSAaHp*iwmGM3SyjG1?
ztJM0Amlj-`gfwb66J+Pw@|w$&BAUsy8<Y>k))smE=J26OELmf9q9m4)T7a~SU75r#
z6m<|>8$vhQPG^7KEN>DT`>??+KQprSD=8OPc>k6d-4sCl_reg9|E}%$&TtmM30wt>
zd=~5Uy?A&_-uMotTV=$-!GwS*(a=Qln>kOD1_S^l)_0~8KKo$!tR*=|hYm!XJE+t$
zkaD6gV&8*$O|=8xbk;w|qFvr*R(iygsGS}4c(TjP!?U$zM}lc390{}ir5Qbf+vEi~
z7ws6bO)dP5I{AvW^$k{Jn0+wIz*$~>t<a<)VMf0&zT0>IovH-$-FvoM8^>ug(6uFT
zdSWg2hmd=k=pzVI`uHW;)lNa0)105}_T!eN&?k$fUGYCG1Xy^>=HHIt%@)2?%_;{<
zE*uV3HWgbC3RO-eCi_3X=9o2*O!or<(wiT(K8VN3FG<JA?`qOqi=ITseVzOv6jwc~
zCs4|hQqpl{akRi>!RB+LcZKXYX|{Q7sIMuTiB^C_8Dj5j>vd1PP}bchQWsq$y~Aab
z!m(Bnda4~`er2|1t+_3F=qdNK^kdn?Y1(ruSC*@uYBOgfxQ-|~5eKovauMf#M@RWh
z8a3jlN2p9PN63&*<LrZmn*-&7!b7OIMddbvOU2+amaH=@J>eQKG(J2U)x6@3U2`SM
zN9xjb#?-|yPdc9$)=AGJDm%F*;z|9wj+81f)nZQvC8AajrtWrn7p-{0(~kloY@<V7
zRv#N#Gpn8+nvWFMlEJIgN;yu%qDATsN6h$9R38OshM%4z#e2J`jVnl%Z*s8s4f?tl
zJT3GgIY0iD-Z4J_2i*pc&JA!52?GQ|F=4z7n-Am`h7e00nf^737HiEAZ1>XeWwwhg
z5KBCYY`Z&iYBKPaP*ij9y)^=>3j5&)sIXB-B_}l&YtSjpe=#YAEZz*YxHuTUcWx;T
zL$Z47U%1F0f=Q*%DzQ1W<eDn3F`1G&$j!BIZMTx7qE3Z)P;^O}yo7LYXh894+3}C8
zU-Z&3cMKCAEq|coN4p9RRO4ewyX%F@PP^|gE^T^tKK?Xbndqw<Yn<PjM<8icvb<E3
zcIUwO`k+e1-8D_%%OMlT&qHQ3w>p0TDxPJ8INl!MQN@6@vNT|GV;Tno-;)5rpziAG
zx(l<l9dG&`B@+3MJZJb=B-oR&W2W6LRZ^sjwN1a?W%-Ot*;sQRliFf2kF|*vM;Bxr
zAy@s(#;{iYx#@A0@Q61Yi0Z7IR`HUQ@rzdb;S)5~xVvY;dO`$U{4*yKjlWF}pQc%#
zO1@{J;qlbPYP_twEJm}p@lwVuEC0GjH0f3qYO#W9>IHLx9kIo0U2=Zu9(|3MdZUfn
zNI~6d&uYwFNY?pk%cR*k`xi+*<!YZlNbP+VNl7q>sG3W+WpuH?_Nf2#6B1N2zs2C)
zu<XB?Y^919klc2H97^9qrRMY@U|z;ivVSA@NQ#W2<dtXS&7f(M$4R7FA_rl^zb0cc
zlW)wiNviauW=(#kM8a~W_>)U(^Vr?7-imimQ)2G>V+*3Bj>0{@`F=7fLTaYJSZ>a{
z4O0S5+S53ga{mULuwB4(alY?YjaS_<;V+6jB)N}7a2yU_-rW}z_4-cx+Y^|xjB3<G
zP4}ylBX8U9qT^QeJZum6Bbkl*)N36YSL0%`+|;OIP_r%PR6Co8e82LwIGmMs4Z4%y
z*{L`_zSDX+_^4ndaq=v;xUwLsSNNUFl-FrY=Y0yt<L2(s!K`Sa_2Dj3ZYSoTjK<l+
z9jg&qHp8}hc7xsNtBy*%A?M=j<S%rRR5cytoHqBYfFqbH4H0{4ZmtSUFG5fI(;`CR
z)2B=C4yoC|5aB<E;4*}}VEJFRk{II_P;s%e*$pn+r$GbQIHuwwXWFk7c^>s4G|y+F
zv;CI!ziBRM6n|_$Hw{v<7e-sfu(rR7GJX9{s$cNMF@h&+D|&ak-!+rxl@oiFLRUDE
zm)RJcbm^pLX2P1q?#kOKt)^ph%%A(3HMc=jEnurZsoUx!Q0l0!aSGouWgajt+N3B+
zyx>a5g>PwYt;a6hy;qg6Mk|BM<Q|oZN+m1oI`4H;Znkr~z?v)el<BC{jW;oPhqQs2
zvg)X8?3CI#N6<Q_Q(vRE+84g?YV0So<uwbsx($m5)hUmn7Vyo(c}eCA*uzo%lAmg#
zgD6al--fkJ!jm$yM-c(>=&QEIn@kMOd5&X}S45)M((bQwmX1noJGB{ne62<3JV_Ir
z35Gq8h;j4uByCb^HZ$ExJ(gwNy}hIh0V@wc%b5J_{ZMHIn=!bE(?TRVn(gMa`$B^9
zuBp?7X{UbNJ@uojh%>G^Pwwl=+T*ZyE<GcmiyK2?wYRQe$YmCrz9uHaCo((rzuR~n
zkvKoJ5%R8??bv?SOV|bK_tX4-NjYT?{aKBq?=}ofY(6U`M`kBTzg^zAa<N#=JHSo2
zIG|YtEvT}_=6)zHU7kBTMXEYOW}WF=;JzkWdW#*~{hDzJdrjFpr2_<N;~e-n`qviD
ziL5_ghhUM4@2#HBl!8aQ=of!e_D0%r>y)35m|WT<l558xm!VRgR4x)XDsibcLDwUV
zV{c*Xf}_Sq$@nI-QQvW^!$Ut$q&wZ@Tdi8DmDeqpX%d&4@^N*X{;+>xf|@)tH-?Ek
z{fV9`{kka2MVN!iGxqwji+~GQi4VA;r1StLcKOp>c?@5xL+Uzx`tQWk?vFyW+6mxU
zGe;|m<oN39#)He5z;nUKCmhnYN*)_^Dppn-gG@VpZiZeV3O!<TgQVB4;tKss+j{R>
zrZ@KMf8J*uq;!;-!1P=t!y4N}2{PVrFvca|9Re0fl#$fVsI`Wgb@MYJw#%(zaU!mI
zb{U0T<weC2=}q^Rd^TS{S)*<o>>u$Vv?aXfUXW8F(0Jh+7F#2%u*Z4G^fl!T@){Dh
zme{06@&-806Ba3nj|Nh_U9L_k?CGzVoB`kH+}_YZ9|f3#7hnqa7+;U-k4m}Gf+ON;
zB4;wrh18wxo8_a`kJC`QCoi3TGR|M|8aqDx%&Q^Fk1ChePEHprU(_XDS7iy?kKprO
z>hM#@2i;KFlF%s~UsuxYInFYn-1h04vWcq|R#3b+i4wVh3yAmLJSu>WTchuZ)BYJC
zAo3~2J4|+#%)U_C=1UpDxr{3?EiW~b&!m(DL<(&7tX58MR5R}IO9=OmXrz?ICv4-}
z94?>?+qcqg^WIER5u6)#U;e2@FCfiEV`=qRdFf=aG2eo@YU<mPmQ(#?Z;efn&dq?%
z%?ZuTkjH)-67uY>*ocm`l9-LDdT>Ys=2~d*B4QSf%~O=J=xTkgVv}Wk>Bo~yWkn4J
z8bXYn8#}0rhMPi18k$C&3I0;n@m6di4i^5rN4r`mHN9(s;({J$xX(o?pO0eA`MQ@&
z_=WB2s^Ey$vSoU8Yq|UCK-|iv#w|hp4dqn7yGF`&Ql!j;W8oib%+OiEK93ESGb*bR
zOc$KoChg;T-#SZAc;2!2QkW3nxw1*O&;%mSpdTF<qntMDAE8wt_~P2^gcu$>m-V@L
z`<k`>JwYOwoN$MNS0BbEuuNIg$w<C?<TGqP;p-nmnSOyKf8B*;TUucyZX{@q3nmS=
zIV91^<R!IX4e1^;?F{4xuhjtoD!GF;p-k!0Pa47qa#!>jr7+T8@X7Gv3m$82Hqo4H
zHSJ5YI;^>Bb->g{K*Vy9Z3net{&%aAmBH{}Da$+4Ka%(`63*YB77C1YJ@(U3BkygP
zCPnF;s^no-${<@dPWrTiQn~xKz3XDpJX}}waQSV2Em@DRx0SPq?Ul2QsuxW&IQhR3
z<Mu5ntsK(r>+KZTo`!Xs;M?DeF}Zih&?1HI(cUkW%=nXQ$|gpW8)J^1V>_4jM}Ew;
zwyL3nQ*Z5fzEvV!$hPvMglnD8+#wl^)3q`u65ZXB3xjK29c5%{;kw1FkJ4eRM;_b=
z*{U3G-63T4KG2*##GLJ>=7W{*JfxPzIl$xsr!@QclB?CQnw3?*+OpKWQ>AMC`eec4
z1y0xa$>SU722Ko!zPG^LsARKYsT~%xuhz?pXRLAQQ)z8x;%ajn&CM(Q`iq^tDa3fX
zXL{aagZY{!Yq^VixyOifv4qX4eDB%OxN^1k1x|CB{%j`k>-k0-y&SGdX5zdP_Hn!u
ztfu|dHRZ!ii;o#QhRK7Rg6z4AFyp+za+X3-9f&xmdpGy<@U>-)navCbHS#0MB)wm<
z%q5e6rlNftHKWG%8@<_|vyXXiI!$j5PfvW5NIpl-rrSHqw~gv^L9EX{!{;4DhB}w$
zZ}ai5+OXbtH&&XxV@Y)Nzp9#l?@Ztg9&)Hqsdk>CC-s|?MJ{)y%4?SfFJn&GF7-4y
zmh~!)4nKM1T6}v(_HNQs&95IV<{qA(eB<}FC<e}$qv|(FUiUnU)pG~#sfB#=E4l`8
zq{}JWb8)(=Xr=Q}uBu!U_NSMI45Rzw9xkF9`-kO>#)dvbFD$8YbQb5$r-6#4J2qDN
z{$L2#+sT5DRabuJX<&J0IqUdF`Pf4g)GL@PDk@4`#uWAAVq!{umUL>5EpQ!G%djp`
z$uN;Y!IOjn>7sXZ7>CA5U-Q6B{!yoIbD=bTEKe(kg~AtWPRnGROt6}=-EQTBvXU&m
zP@2M<Nfz%QU`UKEPqG8BRuzTKDhKxx4sCOBdQu?$@U(03_3?At%heb^)U=;MaZc=}
z<?{1KU7E<C;hIy}snc3LxEgBVbjfu=X5)~MgIdoAZ$-H;hr~MC>yumr=Je9bj!E#p
z$j=P<u(9*NQn6oZrag0WtsV39VV%}q@u(G_38LuZ556<5DFSBsSpVp|mH`!esbUUE
z2k;GURki<qIPhD@xw=Qp(Ju|@Qlz%UL{T{I3^6eu{5ZtK;DX(FRMMe#>zlR7^0~kI
z7t*lXqXHaO73fz)o?IJIz+8%I9<n&tRQ`p%^1?zGYLe-Oi=$3xW+PsN@9<FHWznNq
z%pCmnw@xFaj6YtNk6U7oI)A!EY`_gAtjyFdDW@8FkKOS}NcEp>UR2<6I!C|_ax$^I
zIMNh26}R0rDUhsG!7=TQ_Fg)8$?yDhyx*q!$lH*Ee647aC}qjXQ|@(F=L8aXCZ^RE
z{b@DEENYz@B!4=Zz20gP`{RSDEFlsKcaYlrPB#_lk)V!|V6ltj>>!0EEm_!QP{4WR
zZihpgzGJSu;r+qvK;H~w3D=ElPBu~rt~;(+Nk)|vsd32bO)fRl?wNdgQlP;=XO@<B
z@+r%!p(W+B(s9)hdbr4Yeh)LT0)MHo3wIQ+DknvJft>oEYk)t8Gz-y|tH?Me#Lg<d
zKjXT6W~J5_lPakVDUZ5G%5{&P4Y{Agx11exvgoBv2)4*7GoHm=PDO2&`IN>;&6d1>
zAnb4GmtHL-s{E_0|L5fu-w4rqD~Cv&KJkoTc}<%%%Om1t^wLB8eD6u-qpz?3?mqqJ
z|ELRXH6Z-+*R9_JpZ^~_M@4lQiZ}oHDfmmO%~r3+f4_bGf1fw}GZMc2_otvKq5Xg4
z5cz?yafAly%U_|_9L9jWS0{_}!=9bn2Y?v_SLILffqL=2WoV#?VH^PmHH7;AHgT7^
z-t6F?;Q^1o;Yo>k108Aw*x2%w<^E>@f&2P_H%3+T%iW?<>$u!A|MSmOZ=Qe)_rKp%
z7pg1b&gn>|Qp|-81Oz??CxIRi@|H+}bpek@7ZBNCc@X}pL<E6U3}$Q;A&Uux=pE)#
z(6Pw&pLMU*X|f+|hk4(kF<?@ZSz21Mv>(>T#RUqy%+K!yHY?ppM^h>Y(6%!0la5fk
zGJE%*V#A+TrhGwIXS4JOR__h|?Ecughw$V7Uw>5pr(je4|45n=i(&NdmY0pAV-HZO
z3Zo#;5zZ4%rlMkIMY7TiYYcOco0i}k-vM11{6jAe(liylQuO?lW&7ii@>ReQ{NwMx
z{%E@R|B(d$|8HCVUmvpn+a+43i$e~dP-6QS!sZvyV{@MI;mc>wt^nt08W6)B-fFOc
z6nVH_oTV*H9s2+n0VEGp-QC<ijQq%gp9FxaP6f_nt@*-|%1RXQAH4#~NFK;UWmi-f
zKn3;_5Uo82_=Nb9T!<zo)8mah02B+Xd{<vzmS^=svKSDlj=9Cn<|<yw-%AzPo|>5{
z2M|>~2xe4iSy|JJhduBBAwMOIS)Pu@Y3H@=Z1h$Eeb50gO^XBe`L+OXy6@iAmw9<T
z094}!jH%!H%p+qPMlOxI3W7MO>jM%g&@YAoHC{gS^4zO4gQA1o`p+L?U+x|f@|-n1
z9(==1{D+qzG_h2{$b3#ukIp4#I+a8rh5g5P0sDnT`wrJ60Iu-@g*OEYWAkzN6rdo!
z`rz1wxZ$}A`r>0%kV$HMNG82M`9M@rkBUzj$-9%_Xcn+9t<0}cdZa0x()!eN%vo-_
zAgufY0|T#+5^)9aC%wwZ$nbBtp%B+JRs?7ZzNGBTl=BtDmZu^pgz|}}j6k$T1)J#s
z6I5Y}|LWxP0R@i7JwO^NNV<PI;v2aF3hTKLp*QKA_>194?@p>Ww<<=XmG;#a92c}=
z?kHcL8+P&o!q!q>DU+I!?{Dwz&qbc^H~)B&;B6OUC{L$%baz`4U7C163W|684$pa1
z<^lmD3Z4Nu#LXYkxaUdlkC~K6%|}J*u|`_IDf9<eoF3hpRqdS8(xpcOle4op$BIb~
z`@z@F6x@#p&#Jl#h=gAOMo^b9(BNwJKy0vk9Y1Eyw)UhHkR+cEoSP8dM)jx=q>1z_
zgtZ8etYqzhc1lM@MMXjCaa+UMQoFOtSz0hvig}rt4XYpIqy#=Q%?#?gx(Yr6QWfr|
z3P6Eeqt#%WUiuLkFgP_mEs^GdwDlMm=X<KpFa(<sy+$5}EIxC>U@A)i%jt?qCtzIU
z!h?+XX1|@vRFnd!p>wX&Vlnh@b@L3pKQRr@2bA5vc1p%EGYPvlhLi|aEFIx(F9q&a
zzBKasAr57R#g*d=$3f%70<F+1XiaMuP;vcWPQk(;{mIfLsFkuU%9aiGt__i;0OiO0
zLoWcw<N`IP|EHOszEULM^MlmMmcVc~6{UEOA14K|t)~im&yNO0TuR@Qx!~{TW_U=k
zp@6&TAXSXKKtdN%I|JayE*$a8EgPVxe3_He3G?h-{r$Ftxh0>{oA3AGNk(($tvx}s
zZm3H9xCW3bO1lVxkvU0xW_NBDJ^_){;fqu3cz*rC;^<>Fy2<tgK(Va>)X9Tq9xD)*
z$H%RpKCQAwo`X(}%9u^u7dThl0KFvj&ExHt0;D+!u%G&Hl|)BCtJH)pJitQG;bY+b
z)&Pvo!oY-hz!Q!nO(sC;u>i&}fII5eR)hH2fYJz(>IE>(7YM-HzK}ZH446E?Qi+4q
zbtzctGvFX^0P@ylXu*1u%EoWp3g_E>>wLrK=LJxD19-fo4Yak>L5t*MXDfWUr3Z=t
z5-Um`n{eJs01z)I1<Swr2~i&!uh-|B+@WRZm;>1dpD1V@0h`qgJw0%mj;%|5_NZJ!
zy5uO(jCO_4UtkslcwY`{epjN`{c8XyGxwJ0D*#eFfAm5Bat&;02LR>hf)b=%O$564
zw#gb`F?1l%)O`Y$%<jR#R{)%MY_SeHmH-&0ms<K~?UgdkRuHYr(bb!jITADsuBT4X
zEeWppR++G|^xi#~V<Eu$rq7j>TXuBtq1#xjt^v>?H})v7AM62y2@B{4AnG`Qz8mbP
zp%Q%sIU_!C-k#oG6rk#qE&(lYE%ZPXbw6kr>1mARLK@q0O$RBTLZpPgEhF@a-@S67
z=GwDLGTF4tA(Lx1i4_2lvv0r$uzskCJ-~ivE8ZidLgF%62}266xd06E3b1*4?QCoc
zx+OP$j^uhtNPW$No&4gqG8Rv;VrgQ@4;D~TLZMK#%@;tS7XTaa0fMFs@P3&Y0a)}d
z^AXRQHR6@Be-(2h;8Gg#ao}MK$`E)7eW*t6TJoG3u%rM=NQu<qGkSeU+KPYs=Onpr
zQpm@`ukSy~U9j;zL|U&<uUW@)bA!@@bV__@?*{QCSdaUKzQ5T6$hc^609$qL3D_hD
zpxSMOQ2C{$-4($6yU)sbb8tdR<eU8}z_Jo1hEE`-2!#i}cD)8>*YI3p6_tmG?zvLB
z^^i`C00J(gL(w(E0Uxj*Cq^i%-(GXXu!7rqrn95tW_MRtpZ|vd5^8v=Yy)_hbAj%7
zU@BlK$Arfm`pFsekMbJYz;WoT0Jz{>^|NlDR)2U6t^$mv=65S@>H9=%z<NVK35zEH
z+3F`JC;RoNa6I0HZb=Z#{4BFn{mP_f$VHSHXFdmYaeoq&&U?TEEbFcTy(!xTx{)sl
zGJ>|pgUuAOnJU0i&N0tZe?i#Y(G9JM!cEK1(A6l%^7lG5d3m#`0SslY=KVR;^FaDi
zwTArH&^a$(o)<q_ZkV@i_}Bzf^XvCd+MshK>lI>jan9c$S6;IH_M5;4gvmUvbFzem
z3>4n3Z_NDx@MvI4L`X;v&Mv8J1E6BKP=1iJ{fl=_2@t&)>JTbp)Msv8Ik^ybKq-Pf
zSs!7Ow)v&hTrA&CLbD~J?_Yt|8>#@n!(2F129O})))!4ClU|G@s{4Rv8q-WGGHAN?
z1x#8dd2L+C9MFz%=5}^=uj1n31eO@60;9$X8(51^MSXY_5!x`ai|r?`N%eYaLoRI9
z#mf7H85{!rye)EXNL^2r_kshO2<|gAn)LhuZxPEvVWi)5YQt#TSrPMk8UoF^*lMuR
z!AfTj7XcgfW0v@h73}89Bk4VWC>l^pa{-`G8NLI=l5H}q2WMWrn0|q!B-h1{LmfHL
zsakQoMs`dg0Gd7=ySN^{^4<vS=6hgMe~ey_vdD$PlzPK>8^>y%Cq8Gm0?;B7FQOB3
zHxO#?Al#4kNPo8v0Wv?~cMEYVAgt37WoG^U1U#aCs~}HN<{rScqL#==0^@x-4`Lt#
z*!p4^R<EtrxYy}jBPEu%x32CmRm^3u1^}6CPgsU_1fyUx8m~dj&;n~RfNl1>czTv0
z%cE6d1L2%(1u9A?{GVhQxh3ib{V{zw<akp{&H^^j4S|H_xC@~CTd>jrZ_Ho5e7OjG
zy|uU*K-Gn1A^pyPM&Dm##<05D3x;dMZ~ig13qUiA#sHYv7M{}%R!v0GOhx?wAf_D(
zFzgC|H{2@%h+8sJy-xlMOuM$<c34^LRO^KWrfiGkr;#yP8|ZZ)K*tNKeq8q)|Jb^6
zsv&a2*$8{K0WeGBd;vgx3b;VIZJEMX&Ouj3pdzB9b0yrTw36LA6oSN-W!P39<yHaM
zmOn`f?q{ITTn;DUI<AbYP{M|3Wn_s$9?oxVZQU06aWiTCfyw%FxV_yHRsaTH0a&EP
zW7kgI_+BYfz2*3ngDAY%wg2b`p^yt#o7R?MqWo1DSP|KL2X@UJsH+{km#6y;iYz{+
zM&PH5njFRu*Xge04se^B{9Ml{<mTpr@416@0jl>yI<0JxYpwv`-!!pc*>s`m`yG$D
zA)*rnA&w7g80nvfLm>plD+p(vXjM!7iF%FY8L|a$Zh6C<FM?H)Ms0kd4>*fxqzMdQ
z<f6xFBOWTn^LB${Rx}2<z*FTDDWo$0OVWqV_JWR3KaaqK=O%47^l6wj&%H-Z_+1?C
zBFY;?Gf2t<yak+#oSa;Z`$9v5n*aP`YFZnG?C-?m)=5!34LA8I;5kiiX*Y!L!n(=7
z#^jihTl*D4)V_x4b*Txitq<8-Wilrmoxis(=$2SuG{O~N%5?!a4Aq?&1>Ic{_io?r
zpfe;U2zHHRWMpb_x2^Ud4-UP21lyJgC|{Q9=&{~X?wrr$K*N=_A@GgpUb19=_q^M{
zig`6$vE>Z~%Wxm<1QN!KYo|`06V92sG?C=`VPgNz?FjQVnqb;sR>$WC*u4WdS|PfB
zUDpC|%L$z=5#qC=i05$z9mGEz0d0Bedm#wHfr4dSEL?GanwYqni^^*wED$cia?yvX
zXhyp<)na=|EpZ|aeW_)`{+Lf_+O4~FtTHKM%I>}UMMjk6vO*O&nYek=I@4$&?|BOZ
zs~Z&|`@#pXQI)!&Xj)$ljG{*z>>V7Y%ltQ9<89hcT}p1fQsz08ok{0aeS4X9%=0b)
zz-~3j0Cgu0#z)k}>yQA3CdA2)ghE0pb_MW>Du4kYOl%m?vkyObUo-|9#rEsF&>*R5
z!x4(Q)S<J8yR5!1U}FJWERv#-t^Zc>A%D$ZkYPw2L~b7C4RGy2beBOD)$16S*xbM_
zA^e&kGFDCCuW=^bdYihYm&ZRm>2}mmCIB6~fOxkeL;1%TL;tx~#3bn$7ns&QifGHn
z20-%SQw@S{j+=+GKk8ArmTQvdEDp|*l^~lP3a;up-h5mOwa&YpilGH`u?iAH^v@$$
zu_ygf3Gprw56A2tKq`c90O5qd&4W6BY7_u&j~nzEB2k*Qj7(6KalTb+QZ^XoNgTnE
zv>>}!%{R{GBMu>6Blub9+u8E$0$ER6Pe<KHyeb0D2oUT1dRo}p#=$Co9^N)T78wb7
z7%d4_HZ9Z`<3h+kw+0s(bKJirwa+2yLl0LIae!WJD<dZ7PoZVHc$jzr$rO_ZT_baS
zd-Sbo^`rxmSn`}P5Y^)7@8sm<Q2Vmm@}pz7B7ss1f$S#UO67JNt!TFa+ssKgF&9>=
z(pj0x01ZARCFPFKzdCI+q|+Mw$;&5Fk^cg_e`jMsdQ&sMW}`w(OzbdSNGY0dF;}nf
zDqxJ`fmSv1g5RVP?)XP&FAezafn+qszJnKo%IIgRU76CYd5OTp&6fFt&6NUeLb#*B
z_VW$(;%$20)V9_45s@t|EuiNb<6g99;T-w}xY4op&1{W`$bdLYdx@X^lE~)^$+xox
zP?6+rYK$}DjjxYnlTLG0P7rvnGXzFZVRbjv8$B{ikKD-7$t^-xLJ&bM!4Z&@3;?!|
z2AbK*=%ITt%L5u$VsNW}tFFu0L}<hG0j<ui;8=!2v=L3++10A|5f33)dek}D9QXKR
zngI&6e=2qX!cD|%rbllI@plR#p#H}OEr#O3=|)Zh;13sf5s5@6$@=xHTgqRr%BygT
zH+(HciZzndp>&}eA{aODo~Hmlum`+rYf9VZdL-hT+qNcM@&Us1q6W}R%E*|`nq)xX
z$P0Fr6bbJ)u2!P~QpVF0Gj+n_N<lS$N9*@wzY3fE6s0%bMYa3ro}CVs`Mu(!(rIhH
z)!W)&nrC0>|8^yBi)>=EUTF9d^f4a>ZVrcD8~x#>IG}3Mut*laT8_BQ8Usa^4-go0
z>n6lib03Y8iyIw;-GHglii&}w!6bWT-U;jNV->Il#ghrjNAEV~mya{z=Nq}AHAXr>
z>%Ay$6;lE{P;EYjF0TBl=LjU?03PGH`~*DNZ^$M9TW+7Vc=*aaL{4H|fUaedWzH)k
z53U2Ama4{-!ub_7&5w{UaX<*cD<CF93+96|pO}H@31G{!aE-#_^yxgmk`}+VfzMSe
z@cG#0_f|i*9#d^yU3xE7sBpzAb1Z?^q9yJ*+w2Gjq7(3coj;jBV5wYan5k~n5A#^H
zvoCPE8}chyMv!yC7DT6=j+D?aS?!2d^q6&dSogxVicJv(3FVyP;?9dr@$7h5Qgm8<
zy{sf<v2zs+Uq0Na259^UULZOl0#W+_*biWdaTK40>_Eo_7$K4(m^EL>gEcG801ZbO
zQe+2IZFf-dJ9?6({3`M)XT^9-s(s&>R%n&;Z>kU6gbvj}$jOwvpW9=;1Kj%Sq{+#`
zSfV5OQCpNWm#ru>?@KjEZn-085D8TJaTNI4U1Hlw=i^Xl?rBmSN*4BrU+}#9OK#@@
zeP0<I#YZuTJfnbFERj}NBt6C$BG>699fJdPsuK{f=qAd%3KlXI3C`LmU{Gs78kV3>
zxfe%4$eFM78QpBYfZMtcPU)3U#04QZR_2=Sp7#Yx?p}oc1R{+o@Y9YLIHuRH%h)~u
zdqxGCy7Oqw>2O+bPY&~J&ZgN2iH*8mI~BDht3Jpv{BJijYuAtW5yX`d50eS*>*9W;
z-NSQxeu<q~TZ&}GYz3)k3|iDXDZ9I%4Wa%^yU@^<NQ89{Gb~K11sHH*edhxk5;L;d
zHh(tYj%y)?5|F}IQ)l?b>2r}AICJ#DB;0F3Ud7aTq`4?s$Z=5A4<e*)i151rPryd#
zC9%?z1d%9S>Srx|BOi@|1`73*w{lv*2<VR~DK#4A8_!A*zlCb9_krz$;RE%%A>|V`
zPF_QgRnKNfJz1}OJdd144RB7Sj4(5$I5a@yXFKATdaht!^c7a-SB9-IRyM8ta{_-(
zTTACeM9?X8ybC?FlsxB~If{<4XJ%CaJ30WaydLEtf4qtRN@@gF5`$}%SWqV5s^3n6
zPhYwVyir5<Zfzu9E*`u7;HWLJ!o6Z;{HZ&{aM>_Y#v8Bnm_wz5TI%~Y#6|Xj<Lz)I
z8y=qug4l?i$nSa*a2CxES;sePOE(9;dfqL(4L%ccuITPv^^-DoJAq6uxI$Wb5{zLd
zr__(V1W`=wu4CWe_F7vD1Q=FE95zIK`Bs*aRjsj}jK>I|UTBsde!sO9aUkK;5<q`j
zH$k?6UdzP9WZol;g^8z0ER8-i<hO=ZH!^~5Wix_~rsj*0NNN9169`Pd!x+FX7N*jf
z`=5lI-sN({b6f3i^N-^JOJE!+x%>n`!$m(kBRwqs;N}hBkcSkMsBHOK%4v?|`Q)aw
z6I&iX+~F^~azW@IIE7FW4}6Zy*KKz$)Ki8W5?jY54+!?T;G)&+e&8y<(kpjK=dJpF
z_5H|?X&hqc0ma?Dmij&QW9e4l*>RO`n202R2@A60+<Yx#n^QA<U@Hf7n2UUXh(Ijz
zLZZ~nHnXqLdAQmSH!QB)simo@c?IwCJLH{j$SZf9XcVH}%q;h;8eze?n3!|_bEC-N
zz|8}Ey7;%YCkW6bi}Sk!{Vw+QIjlb4p55^Q0~%d|A<u4WONbgK|GG;P#`V9==O4ET
zdeI*7snKv!;Wp<<Y~G_$*U9@~3gj!4fqKC^x5pTEBS~-IAkl-7x(^dgn<LA&9ukVJ
z+aSztj;s4pfJ^~^h2Bz`I^dmqQ0N1G2GP`senT>#I9G%wCAAg!N$heorwW|`gVaw-
zJMDK$G8QoL35{T!WW=Ou{4p5J6_)z-pt3qRL=38NmXrAblpHhxWc2nGHmAf4BoZXx
zV7li>7ng_cAWR|5IMINuh6T+QJC;j1UD$Fh|Hn@VU1$63RJ}6(D8i)1pAtB+gX;4w
zB)drhWnHr!#vlnl8rbp?j9nO32ytt5c}Rf60S@7%Q5*o8Tyz?e$in?Jf?`<ZA8sJG
zUcnok2*|LCNr^a|Q83Og18LlNR<?uc^0|_-GQ>RK(2#}R^3bYNt|m&SAao<`Hg2EL
zqmhwt`9c+5LG}M9FE3gYrfM9<0IryRwNDbWjHDm?i<-vH|NMkh&Dx=2Py*f&q1gtg
z9(v(m<Ut33jFi*HYYK#&eXLk_J}6;K2Tm~A46FOgc|pCxA>cI@fKY293Fnb*;$vin
zD8r@Puzw!#OH0AWyTWFY_rw1z<cqi5&U=6lU8^%x<}?gWbby7sxw$#=c(xoGmnqVC
z@w44&t__k3itGn>xx;bG3O&R*FyIHDw3{efUcc@okq4<(nwPm-0qR)&+S1k2KmPXJ
zLIL2mLC3i8<~{Bv#;!t0`I|V5r7OiZ&XMC^Ie{gUg>Ko-1nU&rHJL?-7#AVQlT}_`
zp5=;&DeibnQ0?c`;pSaISCB%63C@8*wsvk?`8-(?5SN2yt^J9&S??Ka<vSRF-#se@
zK&VgR9V=%p5-vdoTwUE74l7O!&bk<wZ8TxGI2|f39lVmh|HfpufKR4L2(x*(%(Cey
z$SLjW&-Gh68zaSM>eqb{&(4KJ8ZQLHKtIX6BA8Aliv$+8NfC5dw*oYMyie&JSm=N^
zT;|BWdqrbNtwoO6`CTf3aVx(y=vCt=qVc!ti54A;(d_E#is5k@s?t{jot7Fw0i|Ze
zg#~XkeVP%*!&{-|w3tG!lG}1)o(fooA(O}|2?FWt=p?Aa;9NGm2C>61H@I)-_WpTj
zG0uW)Ga0q{s<^eN<=_H@inb6FP@@})xGC!s(q(AzzQ2uW7=my74X}urJM4k}0q3fv
zt$n0x68e8-SeT%ykPV}?7ERy_e}Z1yK_|*@jdd8@?|e-)$!^>XcsTGlp#ViMy<UJO
zK$c+jRnwDU#O$j(!bt^_Q-ofFq^?++E0V6mxy)YUE}w5e=$Sm#Pv#<c)WF8nia7NM
zjfX!;4YK*r67Gv*-m`PDa+orkIwSu+a-|MQtZQwb>%}}AwSW8lL=qi>M9~FRIXi*}
zgtTmn4@VxWNo6BJU%v~aOA)>M<=ZLP!mD7mq38gqo{N@c!XhFh*rZP&V~Rbkz{7Oz
zcyJWY=D0{r`>#wE6!8H29kdVx^cO$#kJLZx1oepJ0dWECM53M<2FPJ^Oji#CmPk-f
z=cT;mKDU33R^p9nn`nyD%{6fUB>f2f!&9f~e%#Ds?0Ooyr`Px25DTFy#LXi)*0Tpg
znHOIJo1YTs@Rm4xd;29QHsrTe{F6n3p64NRu7J(8VO<Cky9%L*Uo!JC#V`57h~FFr
zo1vPV$m2w;&;}zucZKj1l3tEX`O=W5&d$qod}BT@%~=C%Tiu<Vf}i<;mZ8@P%1jRB
z`NJ&pzr*Ov@J0K#e1%l0*Fd|Qu2B4yG?tfgo&0i!S3&4XAi}lH*@UU@@^H!BRHhb_
zkjF<L3<!OCCMHgZ_g~jB1SK{HdlKN}7s1&4+N-BfIO58C{Z=Oca!@)#jFL9uNM2C6
zvxEgR>_?{6tEsLY(%D3kb~lhj+NE5>@{WtfEkO12sR3pVAL7idTM{h+&K(v}Ht6yo
z*B^!vp&H`z$)j;4;qtG51>lFbvg%9^q<(~|A<@CGIryICcbu2@oBVtm2+kf-MLj<O
z^kT(mnrI6n|CO;F5#%^aeWw}<4hmNm1ZOxL#b1AT)ifZ46}WCEC?|k=>~*3b-GR7O
zx{x~X=j*QIpF0?;2Z_Qm77SAR04&qCRmhC@fGpYMY(ku|m^!niMj2k+{I>=uiPQk6
z5Z}3$X)Y(PqVXaXRE<aw0`htZio@sU;PH5uWNNC^w@|6l;EUKG)KIp%!D&{Y%Hi>8
z8Oet?k(LGq!~sS-7v}5-rqF!_wOhrJ)nzyzj-Ps$6WV<1Jzm)+_5eB55tKAU((ZG3
zvH>*h;wq5(!I1~=cXz99RjFOhjR$>)DzQCeXm&M7L;eyceRd8g4?kz{DD)s1_CM<(
zZ^Mpoao6!VjI<-F84p=VoDT~NOAk^vxg%Z03ciSru5NE2`tO}aoPb^C?O)$N$OYYc
z*tePp)d(V>*y|V>8P)Pb<!&=@=Gu!Jq^uVaMJ$5ga+7K{*J{u+B{-Y`5-slMh$x4&
z6}6uVLOFD06Ui8$obXF=xkGTeZCQYD2j}BoGduuU^VGi!u_RdA<bu--(0x5cze?aU
zyIK<v&-N|&eA8FTs!cgZOp2L4b;1NIRq%-f<td<)=!8m>6=C-OV(-19qR67|Q5?st
zpdyL^FaRn^lqgwIa*mQgB}<S@6T|?hLr`+gsfkLIjG!POsi8qYKvY6ENudD&fp;#T
zgY$iFy+3~cy!F<rH4AZOx^LC3s(a7bXYYNic{u@J$K&y)K*cAHj=q@4fkHf`yg)px
zZ3RB%+u&ipxHs|k2q3)4aC>Z8F_jBVR{7BX=s}$xEF>tNLefZw$&VT@R&8UKqF@jA
zyE1?F&~5@atha&-fl2SA18R!I;w(UG0gL>7k*dN<C<P+AfgkVZ$7r$<NUsfy)bp&+
z>QgJ`;w`U2%Rau*FR+dz&qEuhZT<t<K(K)otD~(g9twA0>HJ|1Z4sCsgWfxq`KA8F
zRMAE-3y65U{t~1RT!22qmbo5>bQD&;e5r`Yu26|`Yvrb%p;$&hT4#T`3+i`g0<*Af
z2~`uDB9e(jvx34xGk7_+#mY<LQ1y@q5Es9R7FtB${=(C#)(XIdEdqpKuY7y+V($v1
z9R#2NDB0W3#^sD5LkRzSKX3JN*RXQ}?Fh{n?DF72@^XSlRvE3-vj7=8wXVN7@4_zI
zrl4|f$7S?7kB&fZG7o#FwGsk@+G~9QkBdwv$hAVXZ6sgoNH3QIcVLeZ$2JJ=I6u6!
zWzUKC$%1DlR#itwaMV^0((7fR65Er2a(AL#<Vq0-VC`d|R-@afVK>Ad^55?^U4J$U
zF!_C$JK7bl#b}-Ux@m<e24s^Zw2^D{<ru&e;;-K)?WkEVMn%DWm|u9*@vvN*Ez}-x
z9dZH(5o(?I2x#sR*UJmS9}3gq)@!E?+yM8av*Xvz=|aUle)xI>q2AtW_>nL*`$XRL
zrh>)yjlwz9Np7gB2D^jQ8dcj{A@=XG5(FR`0%}bZ(#9D%04J^n6n5X~3LGXh0W^T&
zGX^k_+?Jq6Xt@q{l~YXuP*UYZp-~C64nsxmEn@)E>EWR0e=#k5a8YTk1d{IrMVBm|
z0as;p9sA%iU8~Aj=uPG%2$uGXU^K$uU-!pkEI^4e6#xevNX5Q;OsT>OolN)h(+<q?
zY!??qFsBM$Z_Eue10^7R4s`0}XVu8PmU7veJ~caA4h@48<%r!Ny6CeAFzT8`4#G&F
zjyqZe^`#j*=tDw4vJK+c*bcXqz3rT{@Dj+Qt`qivUCwH?OgN04p<8&2_%8pjm6*Jn
zkQgcjOp59LdWDY9%W(F4=1H=1@lcN<qTB|U0f&6WRQ2?7ApaPHkO;AZl1o7aWMgM<
zPk{BK4SF$VO-@?Ezc+=qnh(-TF6dAaP&iwA+-ekAiu_OR&yJsGmKD}^QR;m_HBigZ
z&6*wS0_KD$1U}Htunnd_;M2a_-U-^jLu@dM$<i&?MB5K+ltc4NTn5#DM_X6#!qoBi
zD+a(zxdjTHa3W}}g+(q8AlHue_QakZO)hS3V%ROewg=nxkRQ{h^SQy2G7@`F7ZmTR
zkPU(n3$zVLf)IZ2*;x-l3*gHIm{~Ibj&-1hosPw}!PMqEeZPr{=b6#JL-fzjuAiX!
z->8jhKUu^6OE&YUK_%w@9RE8r@_!|5{lAO6`Tw8d`knkgqrm?Ev-tnPS&RarDf<an
zdq_|RH*N&VOJLm>tiJz$UVV>2swdZgwCs0h0pcA6H#fKLp0iPY7g0*r2@9_tA_4z-
zP|Y9~69i$*S}sQ2n*W33>Z9?$F@J5<O14&6haqCZPx!nEB=c0nee~z|?=*oXn%&b-
zTlqS_#Qfn5xLlh3-&1SpVt)HqG`Dn+^HdY40|jXPvjl)iiAu2OJFyuV>$%tnXp@2L
z`2Wb@ezu#Vpr(n%{13N8bkQCc*UcLLn1KI75;}RQHAY_EAA`X#_3iuPi{UTS%5dMR
ztWC_#+YxOIwrRzj^0^3zCexvPOd4ERh@oV_-Wml9i>ws+*ldK=qa6i-1`zV9`uh4(
zRs4@TLakFP!|Mi-V98X)5(&0!M1@rX9>!$j-#A+^?E%0VShb_jjI<S%`lP@g?+BZA
z8sH*Y$j1yo(ng2>o~sD<Mo0(ljKv=6o-&NL(CVsRx?L!HtuN6U1GRL-!dQlMtP@Nj
zV*6tF`7x8z`YeqBh8h^4D9nOi3UN5nsuS3|Nr<L`IHO-}ily%TYiPbcH6pvlr-*GC
z{CuRvX1VhGg6laJj^~0ThD@4dwPwSUr=GQNq<^WQ9uym@%vLy;#6S5;6k*f(_g38{
zvHH&LI|UtU?TUI?V_VMVbTK#PLaa-*cRQ7@*b;(M%Aqh^f^1Tdhf9Qcb{jNGJQLf(
zBXDH<f9}lQtH!)3JPr=Tf@m5HD~-fJFFsH`gGVbeKdGRU&whHmcG}^Pcd#OroPXUH
zR81$L0xByjYqto7@sXuQ2<R#cVDCmsOo-j{{h*N)6bCf`oEV#lf)E#qY(*<T2^~W>
z0bJpd0C`3!SGVmkuXt$vOlZ_w$%Qtj4sOr_ymdrq{<4wMW_LF@->Mg5PZ;to9o6|B
z#q3V7vfM?!AdoXf50VfrvuXlD*jHxtR#l-|o!=Ayr*xR*MjVCvw}*UFWixiwyUxDj
zY`PL#1Q`ZAMSPG}+5;~{Dj=XfN!NYBP!RqFN>*m!wi%5T<>!wA9@_=14$40Fr3t>J
z@jPjl`2S2A@13`llI7*Xaa|5}qXp5?7z=Uley|L7!Z9s=%$XQJSMjilOiY${8G85?
zGzI8Fy<n&@bgVsgB_GKo0tyWyQV^pw8lb?%`FpF52&yQ7Hm@XrqckCR4@g4sAuL78
z0<;%Qd;dGUz>Sb(@P;c7iP^VYKtrQhTB<Dg^6QIyuy?Gn=y=qg+;Oi83DJRz6>I;N
zN<9t8&@2<Q$N>Z3fTStc5&p$`dG`bqA|qBQ_c^E({c%WO%goi~wQ1thdcbfNS;f5V
zh*7qrc-wL8u?hHBgP~VV|5?!xBc27jYM;AcNNvOS-oT2|J5`ZV&dSUT2c5o?09BB(
zEGj+i_HAhyRmRr%31H&$yaF&B)g&mt{c%vc3%-2BcNK<9G^3c?-q<VJ7WK{9$c0mf
zC*@jhF6Bx2<LX)4C|%QcPqf=rCYuToR-=m*012g<I8s{y;|@Sj`qTYha_K2ao6^3L
zxgPUbsJNBjbT%v1U`UYzH=iVN)9f5=>eIn?#jt_N9Tu$Sctg_cfI?G0)p=2Q%>I1`
z9}Rz&Zk-HPIdLEn1;^UOMYz5i10$GVaVd7eE{Sm{yZ>=4cH$}92j=a@n);$Kw(?Am
zS33JW5}w>nF6BN4re;%7N*T9O@nawCPP$3C6@Uk*T^E^T;<^9Gx`4#uWvJ29-W?dd
zOEB+a|KW7IuI)_|>|FP3vizboQ<1&tD^vT3zOSLC2d~w=`=9EXL^YP@9VuJytB?s&
ziVd_U`zd7NbdGb@JR1f!JvgKCIhaNJMXTjF%49yTBfwdBoLaMC-h4402U>Djz<L<t
za!<r+a`L@b>p;(O1<vQ0cJqrv;Km1HsQD87Cwpu*U%{Pk#atD&iH>sEcK+0k`l(Z$
z|Frqvduw`ML<F(_H8nLmT3h2_@sNWiw>fr2o5z9QYcov*u!Z_6toJ1rsZ`BRCUnB@
zVA}?6fUh5GZdC*i#Oq6A^Sr84_Sn~B&1|+0M`jBrEb&X)>EC$`sl~1@?F(uQ+mkAO
z`T)Rk%vv$LcN5l9Mi&gmuFt-U0AQXPOXt#$?ZA%!QUwvHJ*{BS;8oD_pEJ32-%ta*
zmAjugTYVCs60zDR)`6o@mFBzCf7FlWXOiJ(8|nz23NdgT2UBlNS&Otn&z7-t9UJ~8
zDMw1`wpwl8W#ALXL*rhIWI#Ki12`y4`%>$^+y%T^1_T%(hTT(1z)MCY&V0y$1R)m(
zK6Uep`Tq!{^ywcn1b`XSot$hE^F0?LmAdh{^bmcOHm6)%pKw|aQ|e)vZ2SXBdxBxs
zu-`nT(2l2^!_@=|p&$C(CddGboOf)2wdi94T5Y`y-m&l15)o8}0!l2ZutU#fbExpw
z^xymQ`aP5+>A{$Dr};g{+aCJPRzC#tXrD4N_00x^_b6m-3pC3+McNi<j{K#)3{*<Y
ztHL-^stF(jLDj!G@3+7d?Xv0vx;o&$19_gfBNuct(e4e~1qQl`ci{01jk)<CKU-r2
zKuumJ<WJ*t2o!+7pP$2=z{+|Bxqq9RW4aG^6c)Coh1HiG(k2(3sqOl3brf9H)3bY2
z3E5k`eJ@hU=@nT<C$yy{b-W(|THi#`<W>rSxXH=M@k|0wpIzX}#J~z`>1s-(B@WdP
zVbiJ4Wr62H=fCls$aPp)Lqs|b3k-n8`7qd~o9{b+;y6D?G=&(L+lk9&xBrYf+6ETj
zjT4;AeO#YjaKrTjYri?WtE<Z#)Emp4Zlm$u^vUiw<ijsr*F)sYC1c=WAzpPC<mzma
z2$Fyb7cB^3OsO&Cf8KzTu+*04#nSCGlQus`b<Jv5I{(2OyLqo*Q;9FGv|I~)X49!5
z&<Ws}Z3B30*LJCxUd<9mx1y13+6QaB2uJ{ZvXN}8fU`~I?C0;VkY}Uey>keQA@@#<
z>j(eOB*Y1Ma*b%20FY4df2D}jLEv7cPaiMBnZ4%MO{DT6h49y01XE5^X^a>Ntt5SH
zSf)@cW2HEpNW`)w*P?(~dOeGv*>o{p<%?osOBOg1rbvD|LETiV!YfF<*Qm?f%xp1h
z9zZvy{W~S3wsp5aIYnp;sNg2R(?E1zdZdF&9%K1)HmL7{TiI*7u(Hxv5$BYT(M5t`
z`{K_Ydi<t?Qd(`%Myl4}7_wpn59*%xxeeicA<n)t#qC!}9X=yMEn!d7|KfdoaaOoL
zH**}r(zzNf3drUihrS^j0^Q0W-C|bnhYw-<QXTE>gX55$@1`TJBS2C*{>Ifl!Ivs8
z1OmuI=igAlsL_<%Q<G1ULoaV3#dOT+b1D*~k8-nl8|(=+t{GMZP5E+v2M&2bhT%_C
zRl&uV7Z$21F(p-in!<ey_75c`vrZDAxQev?sRJcXu1%c`Jicc5vT1BlN_l!JKZZko
z3>}mcIuzoR#Ci>iIvn)rbiaH^Hpz&^|Lc2eTHk-dVmm(Bo2muMb40pE;KOve7dXq?
zGLHENdR+D};DW~z$WBe!Q@|eqffRWA<YmbLB^p5UTw(1N;4N%v>F)23(V1UbW*BGP
zG(P+qd`lzbv!pca#+aLuvu_SQeWN@U4n~7GwN$VPq7NC@=^-W&4QswS8_e8zKyo_Y
zp>kPy3(4+>tAiYMdGlDh-8|WeTDbmY<TM);ai9+hd~1}l)rFQ`BI*JU^xkd^dROON
zf!=@$h}*l*!Yd&o_-E`F#|d`=S+c_W*clR6Sm>2sz>sEPamwB+fmpvQ;B5U<{~NkR
zq8!KF2pX;B-Lo39LhulTZ=tDr%n6f#_|q5bP9*a5JBdOO-&}7g0t5zW?efAp0Z8Ts
z3J1#uudX`Z<EhXkl;lzYIwp|oNx?%30yr%{<oK}Z&=;vsN_OiL?CCiu<=G04nnf1Y
zMxI53z?MKq-u|p*LP0iITF75i$X~h1U7p)Xh@<?3-j`Dz<oX>)l?Ux4k+{kpiZnpk
z+4k2K0M%6x^BHhb#Pfvmi$(}|&f9exhSn3tSEau}rTKQ3Agw<Hv<iOGo6ZA%MQ2j3
zBS`q<EYYj^uqS{#&dU6ZhpybheH*Y+1s8PiWp}kq1tZx7!R7}O-S%LDz!WMudZH?f
zYq**KT%BU%AZ;>B#CIRXL09zHzSxEPLZ&!+6o^xQ(?;&ib7AOdWeYX`!T0A%MLh#8
zAvm9_@PZ{Uas^h0pX$8X78ck~&P*OLn*MKg#T}_JIM%J;B&$jc)^x1b8&dI2b&RZR
zV5qFa_M}I>aE7)Gv`6mSkWnXHJR--lR}=;E&4TmVXj3elK#q1SvSI93P|Y0h)+NCf
zfSS`GbG*nO2ECx~yej?ADP4h6f|zyUb=uXoN7XN1J$U}eLQwd2Oern$U8>D%d|&8<
zOD84!O3<ca&5A_v*&P`TL+?Udx|&qFC!TfTkMqiJd#{5v<hGMbJ3-c9@!qqY0r8W7
zxQRmq_#L!PBB<a4h$^FeOeUh-G%)9=h>F&DjJ%pC?1g2&_*oLb+EyQy{>&=RQ8eau
z&^$4h7x$mD^D0QqJ3C9<X%3Fg(66JQC{O&gNditMD-D)archB;Id1_rW%vo*0tDE+
z1)51h=wEUe-(!-{4JHp)FKz1gC~iEJ(+gJ-o+oVY-cEmwoGh^~Y;UTuw$q?z31oj1
zgb#>dwg^>>FxD<SJ~$nif}0Kx0~D+FH}^kVm~5t<XxbA`NB(*S1FaK|yG3-5;=$Y=
zx;->Tl0Q^09=|`6ZujZq7pdSrawD1@WP6*!$WxCbLQ;X#+4Pn0YZ6#9ZaZ^#8!iXT
z{h>?8(j|kLs80+1&9vK2f0uGBzEivK5p39Hy<1_BAZU3rs#@NqLzvt`w$XKD)7LQ=
zYlMJHIiEy~glD~rQv9?&C(R}K(5-~!`zDixcI7c0-@$AO7pkJ@TfBvoqVmlZErzuV
zDkJu)B`#!S5~u$YS1XtqO)Kzm(mqY8{`^|v>T455g&2i%0eQ5to(-ae{mv<t=V&-7
z#J)&r23MrFCAW=qzE2^IkND~~nM|9iCCaxSFHg^4^dKjrUic3xT}2I@E+F6!RV#NB
zxnO0kE`2gxpYc%)G$S}jU%#V<lRg&RE3_=&*}TCFv<%GZ;FlP??g^O-U}4QaWZPS$
zUP?1Pl<CqZcUdM87rKK?MDc$NV;9GtPzhsX^z+3Ta~GyD?9Z|b6=SJ+R@>zSF3g&N
zW*MB=3aLP1IwuzX27iEmzq>rsv&p*=m-b%$N=8O9g!BanHacx{`)XQ&c)-;2=ZZTj
zPao3?X8r}ltixHDF^~R_?WzPe-<@8kxRwMXR0bTsxq|aCjQ7<hEO?R$NDTdHyHZh|
zAHvLBrkPRdNxOedW#N9Z2S88-;T-xhwer4%5$+-;=uA_hWe6_V;9?#TMII@iiMo3*
z$!2DBel1~=m1)7qeLF$sF-^@iNb-5@^@iir+|8>)A%?1)`~S>Ti%T#!HOpVBX7mfi
zsanyrHqTinmvY`#WmATH3;wAfF&B-`^Dce}(Yv5p_k}au=@6O6f!i<#sq(QxU-eEy
zsRQ5ErnsLS1Vd1Y0G5O@JGBa^ehxnA=ikmT0XE`kg@uKjM>ai~@T+K$geK;ey0Mu9
zsfc_un1)7*zLBRp#Xv#`EJr6D8X|QU1?0d9c|j0<mK)p!+gG-E%e-yx`^YbczlBS?
zXb$I41jW>(#Y(7Xk>poN^4<E2i2jAlT|pf41Xlf%sd@9bFW+B5**_KPXqr%WY?*~P
z%E-TTC`1dDdk^aUxzdm<tc>Lyj7aF|ICF@(ySR^B`E2u@R!NRJ$ekLRwfh}gKVq4V
za0G58^LTw6<~RPwq+?~qam!a%0syF-g#w;al}9loFF>WtfO522m$t1hbT-!`08uv+
z=4%#2V2F{}1=K&S6tJz`bnu>dLn%3Nhr--eS|pO<?*f8|0`!q-*IN3E!NS-fP(GEe
zEwkO<1SkC7GN?<C@zyP1L;p=}TWOf7Ss=yp(fT1kX)Pn|kTfJChNZ;F4^Sv4fS72M
z)Gn_M+-->3Q_-FTDskEr-9N^r1`58CIeR)v9fY3kj<=G_v9j6&Mz&J@M!t1e<q>WI
z=}d%uPZ=Zh3SxCW4yWyQ)OL_`0h+gi?YEb~DDbxiyidrq@PY`+9+*30fXOI>It-wz
zG(?zu1BLfZ2m4{tn}a>LgX0{J`tg)>orU{E`HalUo5MnPC(-d_>B@Ou&a?o~l4L03
z+0J9tp9vIDxd4CzNmzNe^t2fyxd1kI1(1%@(SL$Oq7$^#(rv1n27wn$=8~zYqw$SM
zim#7ap}~vm$+W&`1C8X^zsvO~%>?<}bo1nd6EyxA;l{omVbl8pA6}+b9wf=I_p$E<
z3|bl>lIzhxAdt&5YC`({+>x_?E{#5{&}IY@@HHdy$7u}Z0?=nf8siMB&4k6N>!0hN
zlG-LX?WZ&E@_l)%a?-8esS>~o2|XBT5oLf#Zkyc&Y_YFV#Uu5C!kbUu<2iG9e#cS2
z*`t?)pXK!*d<?Gsin%^{F%+F;-`%5Q$P;4)%%jq^{iGa$kE}0pizy075g~}U5MUcj
z{I}yZ`qHO?yP}Hz#&JaLNHCM!(W3Pin4ro~3kn^i99a%i(#z^@5^6G=CPW90<vZyH
z!Um>Zg0?-;ODG;rk*Uv~>MfGbsaQ-01mKmZ&_Lsb%1k8Yp}WTcQt_Lmf3%c2DGq+}
zT99^6nsDFlKlXU;sEE}bF>%#T0(!E+%6XB?5$!>{<6A7(8cxBsneM>B%m*3l@~_A@
z=}y>BsL^(yC;8pT(&r<U=gxCcmKsnai-f0BU(ccPDbOm%P!|tSZVv$;1Bvonr|TmG
zT^!b`z`v)`;{fzWQqW?LIC9CKgwEu;p)b^=x1^eyU=r2yB9&gxbQAV3W-`V`{G7Z~
zh~|>z7(4~2tUQ|N-w8OMp+*SU=EWmeP8fhdFOqb$p#};`k;mh0e||thxxhDjkhu74
zyr)3gyfd>6Ssw#OWE4nLuqZk@tOB(x9~71ey_c;&d7vg2h`9$Z=6GH+cp%IM=eS-U
ziFVX&o7jxG>ED)OWVL^E%S^TfCQ`<;QETe~8i@<2v)ak@Nr^I?4y{8ISInJn0tGTz
z@)l{(%R&up;bc@7;l>5nHL@0s0+bqTKn?FHQo|r)4M5@}F@Q7h>q|g>fpnN_?^2hp
z3#$ux4Lix#$~DnPU2znemEeICq$9Zca|XcZ`4>g=P-Z6pHC07gy3u1+7eio(zPGZl
z2&g`&9ZIBuU;JTdGfwXD`8uVNBg-s?IHBelNCP1*pKCvVsk6#NeA>bKi=5>PWP3%;
zNX7WC(2N6`n2~bpr`mpK`a@F!&W!rh2qyL9(%rBTe)cK0UPM|Hc<i)+1)|Qay#fK`
zDDWY1NOTV{dO0YPRc_r%1o_t0_-6o|<pY%h1;mKVlS#6DaOujNyc<!O4^$mY&Kek`
za0KFdXmXZujI)?=v^@$=EnM8($`MrtG^Z0EUW!6j+0v;al|MZHy6-{j^l1yN;O~yr
zFvslGu!=#`E$N)8@m6yvNHokjSeQ3HA<uHlvx#O7#N;#{=CsOwnZpC{L-u!*s4mvC
z9I>$So(O=rJv1ry=hw3VLHp9Au^MgjlYJRG=v);L4i#HB<c(u(#3A(||5RDt-f|&3
z5N_NTiK8Dev$E<!yu3XrEnyee3LCi`IB6U(17_j2i3%?h<nv~MMScol@Qa~f`WQg^
zW9bz|owcAt3IgEqP`Pk3_Fkz-)%t#scg7iz8rg7<Vpi)(xfYCWa>}uyV{Wv?(SJ9?
zBk9!(LGp30cw{UAO<=Iu<#U=71C^Ef+*3091(k*K@>!q~g)J`D0P$ieNM&?8E9hi^
zvx&0CKsi3ie-5*PY9Tra06i06lrdahMO;Y}xbW_2L=Fbds>$@%`a%q(-*{9-0QUhM
z0HKr}mMWKfD+!2kdF<FRDcm3EHwm!T3F1!FB@$y{?5*3^77x`IDulfih~A`Ddf1i4
zSg8dKdZsdJqxZ$}K*yKGzG+^X;7R1dY2#CGjj3e_)lL8^vp5PT?0{oOMDOgSaF(>t
zUFnf2ShQ)zE_S4np8LCAbFZxYs59Ax6c^>d<HAFOqx0?g18<8VrwPcv`>ATnTB{?A
zUeE%m?w4tqeS5)O(O-Dwm0Ff2gCv-ObXXMirGQXAnci)4RZb*lPG1EIAn&}|^m^Ok
zJN5H%eaD{FaCVH2?F-^r5+rmt@7X%~rOfg&&3kJ|-`F*M3Dza_K<q@W2PL1MG9TKz
zeHPSM8@xA#llz}RM1~$`(ETCB=g5p|Nu%2uW?J+e*o$KKMNVo70daa3n8?Oex4=eX
z&B>@7$d=**KWce)8)a+`aH~FfChp4W>%*>>Zm>ghg$l`~43c2#oKih3RCEjgEhEuk
zK(#LAMW%KFxO+*~h8`x_`{6}8xt5`s1v^mKjX^Y$W2Y{bgd9ZFDnP@$-ToskSsIqg
z%<;XGK$yjYVSK71IEXMzoq<pPI;u>L$kRh77F2IAXHO{9@0c^&Cswc^qF^;Krgqvi
zI9Z;}`+0PC8IShl%O<B74ZoS<p}TwyxC`(NK5X8qyfn+d)w?bOOZ``9%&EPQ+y>#9
z-4>8TQ~$w(vA!|-grVb@Pr4ut3+!GU$0W8jL1ASa!{`K<wrXY$^zjsz&uahqT^DN+
z$>_|AFG-q@Exv|qug6f(i<Cpp=#c{M<&CN1vA-3xE3k+bc(&`g&El0DHV5T#ET5kw
zL4$#U9UOzxVLhURS$!3Y%VSvtzIe!T`z8Y$4MyKRDd2O^gc^iq&X*KCG;Gu(Z&z4z
zYyB9JWlk+N#0Tr8$3;*^(@O%YuPFEk%MJFVFEe8!v40Hp5dcD(_>S=*=mW;8-ygFp
zW+bThfujBF8|J<VQSQ?2)LU>5KVa=)wDMv3bk*QSMS>-)1E1Kh^3nt(fn4=N15gN#
z<GwG<t_x0SfoDb4$Mfrk!Ji&6H?GnUG~V}$D%yM9?AB=F#|_T-kuryA;=_8b!oRx(
zo>=p)PChx`M_-vjaYeHnU07mjCqR>`8^iGo*ASo@iUCp|5`~-B`O&~@q{4e5gzJqX
zEySL{$U2dttJc4=3>q!0aw@d%+C|Cuj$p?`E8iy=CBujeSD>gDl<B|e-1JG(u(Qs7
zcsN8uh;yD~89^1hhc96#Jt3`9GDfBR<t{PI)E<`Rb_3EAdl_noa6FzHzxQf)9RCoH
zf_}t~zxncp;>cPE9F%5!M)~Ca7r|~qAhiHc1~{lhK+@-<yv@KngwOg@)ib~DS9cS$
zfCCSBzG}eAr`Eq8zTN^rnLg=O*$%Q#->N_|v);=Ef%%6X(|2@Jsza5z*&5SLIb~aZ
zrhJ_Eg!!_VF^8}U7p?cPGPYjxsIFuUSzQ{zVlj?d)y8~7^#mgE>0vzMhS%HjAXa6e
zPn3(XhJf@LcChUMlziJ*f^KYZ`sUE8XlDAc#lC_XDmlb=P6Bc}$TMI$7K1mDRq~#~
zF?cVrcJMe?vH$lRZ^ar)3hp|8DD=VI^M`nBI+OKNPK@=1Ix}3SJrjFMS}66aW3rx{
zv=DS1lyVPcsFWtyVEWEqZhDm@F<7sel*NkcyhMrwQNeo4nio)Acv>htTulH(yB(Pf
zLF23QR8mDO%*U0)2Y?-M$FQhZ@T;wp0Ie4IVI9Gr-E=$I1rACQX1PUMCOPtxdlf#O
zzTHn()P4N)eDcHLim13^R=dfdGP(M%jS${>)O0}23$aNNm>Q0QbRNO`!0f$IN8=a;
zLWm2{aBEQ4PXN>);)$a7NXqm#D<94)80l?Z5Npov4^*4tAiWn-j&7^DaAks9IWHph
zui<izs1dJbj=TutP939U7nEiC&_ZxtVKpGM43zu&<`7+V^;Bf!ga^_sb6>%MmZ|yP
zh6N5Rh1iM=VPQl-1sUvsLaq7~P2SxvVkpR3<HK-5kQqSQ;mt_Bw}*CDE~xt#Tr2)}
zG7EL&O2vh^CEX8M!r8q!CO7X0xb=@Xq-FPDQIka5kXbw%my`0?KHX3vZm7g8HC+et
z2$wg1v{9)@nDB?%NV;mqZ4E4s0=#QrD<SlnbftkJV8_GPr)aD+I$wCU*MV~6`<J>S
z_T-fVh+P9`m^?VM@FNDcsoINwbZ6P&(Z(DjL43k9Pw2;M6&OitC6~5eNHtAcYv`E_
z^^aLNp}aBdME7z9X~nGQqg)E-1%?fi#ibfi>~yKO86<`C<K2Ne^9!z;6|T<PTsTU;
zdRGD?_Hxpfd2ktU5{O}ijT>OL0Mh0pf3L3ix%7du7owFvD=na(pwN#k3^$KSHR(>;
z8yhpXWTyT#6mDHC_;_+26-T=lcm2FKHz^>Oo!dPBW4@zb72k(<!9`E9(4X4#>=BFp
z&Uc6R2fw{*O(tG-rQ^yJ71g#2k549&H}mq^(kO^sD?7dQaW(s=$C-?OKN-KucS;K&
z3C+HOoRJkpJif0xP*=`zc4Qe7-Q^sUDknIi{;pB9q;df@q7#!-^lV=$af5IRm44+?
z+dn_8^<NWCmVv)nT7Tu#EJR7*DR<OnGmiM_UFJZ0#*WVX(ITr!6@g&EVYlsG@0YF)
zE#IG6X&!z!%)l0mIg#7FGaRM=lo~J!*z)YY<(-%&)bltJ%BgV+VEEuiTn6!@_rE>d
z;eXy+Dbv}J*W@xg6RJgO2x(4quk1BwuRF{&dh4HKoY;?|?@qw~RrTzU>H`<>HVL3_
z=R|f||70B11E=;2sqI)5CI(5MiMLJml_GbsGniEf<^@bDtOu(H**24WzRq4##(XW9
zz<dfmJTdA=GrM#))P;nlkiid0N2(0k`igkMP-Kh=3A<B~xE`@`!N3x_9H)R_MjoVe
zMJ1Vc<MZ=Xpb@4Q(vDR3(5-yi0S>Rh-flUj5f~71?a0yrythVobBICNS6hB+*;V!*
z_nBI`w@TrO?yI?`!6l3NrhHbq(I*^wcdDQ?SN}9~x-hhSjR)fOOLp+2tF$uU`qBbR
z2&aiV2w`a2mXH|SqOYWYJqIM}P&vJnS+FUqV^=iHONlY>&yeq%Pgm=o6&L(rHm10(
zUB@AE1@ryT^8Bi1ytvTn7PlX7>i=#>AM|;guoPH}5FA9nN--sdR{l($tXy@^kUqwP
z)XT68R6~3ApNTQBRESp8amu3j|6!*P<O7XH%q8~<(3EW))T-s*gC@1gRgITFUDuhZ
zGS@74`6aDiPkp#^-$+wugjH15y$ZYgawg~K;-%2=C+iICx70MT?_GwfC|+D>`T*iP
zL%x{R?>4W4TsG3v(q5F5UCe^A-I~t;WU_}lg=WHAP?ZFHq=Y}YfCOrafTRNky1KgA
zIc^i?XLOl=of7={MU^QKx*f+CDqOWF|NilEkOKT=PQkrDQo)5cUZzukxGZnEdz9Fo
zGWLLyv0l=9jkKJYHd;-n{PFezy`;LXb212hlOe$vMO*@~mx)EmFCGMJDq331{&lt;
zE_(0~)aXnqI)j}UC}`%i?k!Ce?Pvm|Fb4WPZk<#FKDqPEFb#<oyw<vAtF}^@fI2-O
z3BowDh3TQiqI*j`=*hXXS2O@QyDrxV|9pr>+KX8Qd$7!ghK9sN?M;_;=;4h^iG^DR
zr`_t@?vt`5Y(7j)<R35jmW0tMaE&<3x!uccjZ46L@cI$CN`MshY%x3ECE3R*dp%K6
z5Y%-j@bP!H%BUfrd=Y#)3Tt$W9H(@lDmv|vqXY8r#f6r8?yd7`YHDjf6u{BUaS~vY
zMEy>YeF4<VP%8CWUjf3XhTE4O_PYS#ZVb9w@=jB1%4Y$KZ;BxLG2&?_$L_?8PGMrE
zT>Fa-OC^byS41ckt;_{sOMV84;X`QM62XfF@ESQd<tgwEI+Y~Ep4mU&^PIS+XsT5F
zB0q?`NeC){d;}!Q>IP8DCc^9pd~(Tt^?CO)DB>d(R--n}(ie5ka5XKfo0YB3E+*eJ
z#d+iH=HA(EPc(cTWaGA&Pgwl+Byh;)LnhnW224+7%3FTH)t&Pcsb+$!4Rz|&1R?|C
z!U=w>+6o<Lca&K|-?9>bdP(Z{X4Mn5Pi-3PAw#={{q+Fi&Ut$te>!&Sep;z>0q;+^
z*IcYM;2;fBo*jdSjPNyOcSa+6`aPBp>3}!m(o|8&qXXc?b}4-xIZuGATo$5%Y33RE
zLbng#5DwWCBeo703|)t%^aLJ+IKK}{Z_@~(Z!-RXWEw}*o@hreO5)D5_{%1&j+4Hr
zS!~YNRF-r~ZfXPyITKaz!Oj~ON}di+HxgLj84g7U1RRWpG!+3DnQWVKdiG`p+);b4
zz+#aFq4=4PnwRKA<kOe~RG5@M4meHb_Npio)hxobxK=<c+mYDwv*+vd!KQ5H;69@p
zI#7iGI6|$>b8VT~INPexC#jc+Prp_VM?cq&D}%2xrpIPmPio(wI7=mU$k=hlZ9&@s
zAL4BDdIsnEsqD_`UFRptURR*-0M*sIAmiNlBJ{`ZoAo|?Gc-+mPR&T%jD6v3nZ+EX
zLLdIJ<g4N>YNg<oWV1>wuH;GAZ)KH@&I7MfI#^>PB@l+>@x)tr7n=W#gZ;|H?pji{
z7kBxqc%7zF7yMB<rjQ6;gBc;AP&b7SWmei8Cm-p;wI;VPr1lxv7o*Ry8~x!Ut^eTN
zZQ1hfo~)6sBErI~g=Xd1+xC)Y;I?nK=bhRU(G{|fJX(E;V6%1wHHKH031+~@1Pi*A
zB+y3LJ32BS+Kq*sg^G$Qg3mr(PH<KQS?&=pZ8}kE?$;(t7s>~+(ICn5y;MFanQVkG
zm@?JS64fDFY{QgV!9cRvz16aYDJ5UwuEDbEv4AGH8wiphlR0;tPHrI2*dMq^v`G5n
zJvZ6s@?q>O6i!ih=h&r>Bb=tVB*nCi)&}T99)q_cQaY!0p^L&8xJCAxOsI9{m!xma
z>)ormsGUANBs#pZ#dzS8b8t)6!O%Wpz}g9WD|8H!K((umbdf-THH>(;nq)z}e2*XV
z$sDk09C#;LEPvV|+zPDFt;p>nQ4QtOzmB@oA@4_rWN9$&qLI4V11UO!ccqChjw*R}
zB}?f|j_wJb_g(5V|7oeZ(6>8?mteUzt=E%GY6xia0G4ko)q@c?15(98g~6;_4mjs6
z0w|h;iZvy$my2V~evJfw`n{jaPK(hSNI~-&%o;6~l2_wSQQD;7$<x(cc0pSgrWQA%
zmRxByu2djgq^A2hDEh@^^aV0Et{;`FgvG!Pwr9zPZOyXnL8cKApt}ZnG7IaC7b1|X
zfp5UmYo|dn*otIBh-ZyV6%Hv?0z+B_;&s5iYa0AFt(Re+l)^K0%?X`*j@#TP$yy8=
z^tv=BDMfRos_jV$%4nEkd(_$}V8^|-WSuBZ*ppfb*M!7dJGQ--2NElI@Gb{}siMXW
z;V=Y3uI)I$GCTBO0ztKCUq_C3oI;y`BK^PqVQeSvJ&UKRrzv&$jB!@>UU~IWC*BEj
zV{-6rk6q7QJfFE-R&#`MhL=eFi}5|^`olgvWs6z?KSkwmnUwukD}gg>4>M73Swz>i
z)B?1&Xb6%@h9k^Lu-^gW&G6DuFhCl>A#L<?mQ4gZh1=kC!TVfIogUjPp`^3W>Wo$f
ziL23r6?X?PSHG(`Tr&$-QcTF3F(6UAPYbAXzFjH4cKx3us8o4@kTG^|W#r!O*CgZ}
zgv5Y~Q@8TE?Z9x;9F&U4T8*9nkaILJumosZtV7n?1folT-A4S929R}wN={2|OI-T9
zv1XxaA?}5<Qa$P2qb7<vOOE{8W}eM4${Beh9cD&Zt1kw)*B-Yjg+r=f0z<@u<y`<h
zz}w&$aZh3inKMH2P@JUw4gE(xaNY9OKD#~?`dNkP&cyaRv6=MK$folLD5-N|Cna)M
zPe`7po`~IX>wI_$KA28Bxh0vyxNCI0xPZ@1&9^~1N-cEl2?-N<3JD)%wAHUtQpBM1
zVZ*aIe$bz(YH4LaQFM83ZBo`p^P$1YsKJFjXbS??(6+?$_TtJ*e>csXBK^cTa$?7+
z$s_2n3$uoNF6;MooAO_|y`z3>7xzL8WeQinf}PXLmv?W>-MMpzWxTh)Ux>wJ(?dhf
z5<a;ne5533M`H0^y>DgwmHm{m=ff!CcO;(oq%_TtrsL8L)nPVLJMtI7-jFs!dhN3!
zO3yvp1!Go(t;G}D47xcE|3g&^2>*?_f8PO*CuyvQx{mjj6j-X~@ST>NC3!X_Vy~VQ
z40wM-XE^Nhi=*k^id7)4#F+-8(+`o&3F<W{D&bta@;POdK@y1IBm+z%ATv<pJO&1M
zg2T%|NCbI9$-v6`{Ggv6qK&s@Ek01<#<Kb_x3r3M2RgGlvj<HtamN&obE6fGRID=O
zIP34mrn>kseR|$kCw;nDRqgXL18TL<wH&bO!1;9RMG(x<OL`fcgC`Mrc0mtW!ma)(
z_?5QfvfIkf1<~wHsmN1BD}m%9&4(B_ts+=aJ0r8Mpy_HncGuEdR#hl8{paMCQ?{W@
z%aC7H%raF<yYm>>qSF=m&IDtDs1F=*a==)k5#n)>M<UH}icGjohs(srVr5`fCGM?x
zW*UK<_5m5^{7V(3W$Q=PkDC!GnxzRC!GQzIvle|lFL>zN^GkB$?_R!>4!x5H(OReC
z*%i$1%BG5mUYPx;IZ*t@u&CtD+UIM1AUTr8A@e5CYnDQ^3JPycaO$9S8NbM?TlVAe
zyc3jZkyEuc{FeZm=nqibBOXYn{&fO?Ulv~DOLTOc+q*My2mXNE4V}(YFxT74ny(m4
zyNwjL7E4ntoGj@wOwCwZWXVJo>;_4ZmL-y)V_l600m)IXc-S<fp)C)>r%A}e3At6(
zyj-n=lvWdbHQYf)JN9}9oeE>%c{|yhtSmgR-*3N8{dYI@XzQYt8ifhKByt@QTW$2C
zW#G_|HoMDzC;i*-gP|b?xou@~+oWt}MBD`xKC3vpmsf}KyF_t67+$*&pIlgWmzLcH
zp&bH}*MrmhZECMe(18iSu(d2IN-h;yUV%1x!Byz`=>zr-4rX9oD8(2UV54`X>xj&}
zlK>(ef1O!iS-Ji1?6Cg~AY^t<5%5~Q4>5UMX*`;x1z8+?3G;A!%qG7o!86Hpfj|*P
zK^`^IOq=R<&3yf$YEmJj3n0Cwa92S5ETLJf{CVoVhgZ1EOe0`W3}jB*m-jihbMCoS
z!ww52SoR?eJ#sh^Uj~QAUq_WShpaMx$t{ez3p<_IRA*@^CRoN_Tiq_SFNN}0L%x%l
z=VAG*)_Pg0bk-T456f$x^RRz<DT+;!ZsDCc2{xM1DbV)J0wRCpuhAWgJ%#rNXDhM^
zM|7RyCo7gLxi~p}%=&(f2eFj))$c+5-EGRFPggqU;`pkc94I#qZc*Kta3MxB9>yfN
zuP?ZNE9L`Z4{fi=)}$OPqSjPKkPHyAiuyri^l?fvv@sb8%aPYO@`;0Dd*g$r|K9db
zA8zNS^bZ_ovb%QA+(qGdZ}qWn*Ksw?0A-I!*-v-uX0>eaY)e&hrue|QzDFFYOdwC4
z4+jga$+8(aNf@OAIK5!t2PWDqfJ;4nx*Mr$5SFr*)2?6Fr+4EMzDG*pD+|iCrp8AV
zIm3^(xh!fsu$ra6L*H*=a7$!};b#m&Wyr|B$~?AS6ail$Fog$W;M|f!>)(QY@+QIC
z5_3aJuq~*kW><M>-q|B!R{PA<<z3%Q4cyRB8hz{zr`;5;!dU8?J-YU}PgRWCyqk7c
zu|U&^r>VKSYXf$RDZ@WR!tuJf@R2S|g5<Va*aK6Kgr3!zwm;c7r1^omHNo{{dG>n_
zv-$O%<O|*A;sx(t^>%1W!F<7fyc{B2*AA)2As@yaj~bPi4M;bn>g_s-)AP34zVdsH
zGv~iJA9vk%3ym;VMXt1on7ejvRlJ)upjD5aHAU<`+fIB!zBg>-!O+&NBjw*oLW46O
zFUK5n*)nrRb<qAWU&4hudre*kc{G&EWi+mDKi5;>X$LWpA9%Y5RR8?tOP<(20yVad
zf#tpdq+M%8n(`*NU9TGc-CL{=L98JVta>hHkH7cl^PZmxKc6#2xqLC5B@gqSp6S0~
z8N-#eLVu>kl!r5?Li?X%TNgwyBC^4{U{P&vx^@w9@2DVi0|W^5y`wjUqJP~Xnb0KA
z(J6zP`#AR<*d2b$d1`K)U;7aH!G1A31#1zlm^DDDB+nAQ=ULt2)sd~$!%tpsA*mSs
zMc?RuMLAi0&&(B)r|g!dNNq;{c1wGWwU6un#Z+Rw2sOh~=es6;TCL7mdFOY5(>WHP
zbqzpnAfjg7bgMp5Cvj+SO_38?SalW>wP*$(;SGJI4QajQA_B&|DqdWS+vdAPb*WD+
zbUouciG5d10CcV=&sg5T9rAbz(@F$fgWx}p-V~e|dP?jwKnEz4B>4Mn^0;|K+K4nf
zXIb8f%MvcKBlueMJ*DSR3*}1r0X@7zd8xh)`yUL2+qO-LoYV3AC;RlWH<IFO_zD?&
z3IuL$;5+B}a$pMS{=D-C;3ufusmQ$<Bq2Cv+jKelbUM$IbdR@EE%!wWN*T+dy^=gK
zJ~u$yr0o3{h2k!*liHIf1m^ivb#Zi;9D6L(Lf6Yu?Bgh4g6<2F0S44&ckY~+c88b-
zi^cB8SXo%mOaqp@aa1`OMWi^`)lFX^X;Zr?YkeT*`h~X`ws<x!k;@C+(f$0+yE)pk
zYhGu*Wc<?J8sbLLhusLm!1A1$V*23V;C+oT$;ru{ch4O<7P4uczMl@{f=Fh{gB=q(
zH^-RCCLM5_gTAsiUP+!!K$mzo?R{WmDCWe%86LI^cDz#*k)h5fY+7HVv=?B(4=B#m
ze^ypn3Ng~&?GHU)HV*yq^)H#CxeAKadp??*NWW`kwJJ+cIL&c-{?cA-MHxLE(}1u-
z%%14^mc}{fCh-xQwJ-I!n4DHkfb0Y|BZgOQOAg6O=fTkTRzLK(G+UaR@N|}yZF*+@
z?^{zF(eEU6!NR1=7O>i%i`=udJ64X}{qcan<}JJD_*PBsEG6j_)zPn5DyEqeOH(sz
zqmZ!|!nrSl&k3{7t(ReIybj0+>dFR??9hZ$GfW;#S0!eDKT%}2uA3XHv~W-1ZO7(`
zcvc-L<0gYjaX<m2Q`AfdBq`QtHMAoPIehS5Uc-tYem{=x@a_78!J(lUMMdfXByfPR
zk{Bc*8UTxrn@}t`#8K=m7F$0h_*u0(JQG_~=s4Cr%q>`InZsS^>Ag(5@BTsb?F{Wq
z1$QvXMx8$l^N!ZIZ;%3fWUo`n6?NZP1mFHlZ(58*00{|8ft`X21>?)a{xQ9kAw3dM
zSl+SQ2($15vt`I`I{3lwF{y=5Kgn0KxYI?~SwQgIXF+^Q?`^*dn@)ip#xq1Nqa(mH
z*lcIe-m#6~lv)q2icJ!|eyGPvc*=ROwkXJPlLhD4#m=G%hSKG4dI!SlZ`<`+6cM2A
z9J3bk<-#d~J=Nvi_<ut_(L4_7c68hB*ha487c1AxINX@C@7Wj4$q<*^&ScvM`%(Hw
zY1@q?g~SZ&41}{JFc(up#&-Zo=XPfzXMPaU(BcqUj2c;hNnQ7p8*+F7?tT_3?gIt<
zxhb$(MWLXBB(4y9{t<JLvqo~wHaEvyJz4JTIQ3$DEH|7l`+Z08o)q)9-vbNcE}rzC
z`PLM-K4<KAVU5)ZKuRH5;7-H<&p~z6y~26MXwB<&(CauvPAcI42zxL%>q1xvK^BVm
z15}&_z@%eM;8QgH<@X&Rd-x{cXjXqm{73}Yi_cn^Q80KJ4oDi(M!Rc^1iy@qP_=9@
z4Q%Gik~B0ltjXP=%stgo^lGOe_eK$i4pi1LKfl+(ge0wea^^ju#(P5Olgkd>r7q~-
zfnt%b)`y0K8GjGyWAOZ%rbYatxw%^WVX3q%p6oe=s}OUb?D`;K{KdzSH(oZ;{6%sj
z@3Zhf7cY5UJq0>pKZ9<>goJ}qx-BKUv7v#4XcfW>_44S^mIt&oV6kRo2L$q9Y6dQ@
zYiz8kmB}fwjDHbg8L|}9be<SBmvh;L{ck>fa~AUGcl4v@IHU9s3M@~<@qB0LByyN%
zeBQaO561rekOgmf3)1_#4Rge{Azd}%Iichkyp|K}_;g;?LT46;YEjTQ8&BaT0cgg{
zJKT|;|0Wpv0`*3++ismNpx7NbzpZuY;iyIYtIya$f|EO^nN%O2G31)zTB%eV8LFRi
z{V^L=v~%u&O@-2z_GQDupaQ<9rUtBscAsCz#9&1|toA#v^>~?<)94I9?wrEp2dk)9
zN3TG%A4t>Qp8R?#nxE&0gfKQ<o4faQrNKtdK}}A1I?DazBi!$0hA~Cr^U3=&)>kLu
zlOU+F|2k4n8g;xZtj1O<n@X#m9BgZDKlhQ3>u;${HmK{OgYx~OeW}0Sskm3OZ<HW=
z3)SuGL5W;X`*C%sDwtflWp7%Of*Zkg+nU)=e$>ngQ$14|skgd5auwfCJ77_49M|Io
zgIZ&?O7b1d*`8In%jd|n6VtuKvAyvTmx|1!`_h?bOw5Ai>7sP9JbkHhd-rBEZj4Va
z`@$Ln+zdd6VFgc)iJ>*j(?GRcXabYBkprVaP9)RxDQ3YL3KxB?9@XkOqZVq{0a!vh
zvuy@KY?X@48a}2o&!#z4X#$X|+x2L%fOh@tYOx8c=heUq&z+P=w`SoAhIjKW$iTB^
zOAP^a)hV<ZV3R~CrZLoG4&<yyvgp%0U>_~Cy9U>#KKS9NkA_a?71agp^m@n3Q+vX?
zLNJiGtI6u^FYjPQ(G7Nez^WJLiPnp#eH5Wn%S!c9c#yNl)E!QrhxgtZfJzF;O^h|{
zw~Km_QXFUSJGj*kf_NUXCDy;&R)Oy0XN|)}NcEB9gGy9&h-$vVN7!=C+h_DlHN^IL
z=Cxbq<*RKBw;PbVmfM$*Bp-1tL6E+Db=1q?1be1XD@gO#xQxV!3020xHq;r!4bnQ=
z`rLn?pv>ER8I|MXZLwO5HKTx%y_wSboIiEsQqFf56&<3I?>+1N(QG3dgT+%B@-BnR
z+yfAN-<4dxLP9AD(vmAH2UdLZyl}#SBBv{3&xJBX(~F}=ju?#pg5B0WkP-4#@3MbH
zX+|*{+bg=u;?ABi-%iW7%H0Qw9WAh0sqfQp)wLePM!|=FjR-j^u9R%xTQ9D)E$3SR
z424k7J$*Q+FU&%>9_=IA)>j525Y2kYURn5=G+XgCG%_qFh<|(g(s{74x>@l1arZe(
zkWqsGtmo=h(UE%XQGqB0@qx=x3hWWb3QAJcoD1wUlL3P1IN#2US0M-{4D8#ODC!7E
zlLx@&8r5x8)znhp)s{r&sVU{qcP*%z_ql{gjn_UtFrV^HmRp}K0A*gXL`jU5M+^4g
zDUToGqx<l^j5#96R8t4bS=yXq*v<?aba04lMn{s3Kb~P@C`u(jtV&Bu3(WB8S4aK<
zCfrciLqY(jVZ$L+6_vEoQmp`_bN$y<C~h?Ak_Li)dift<GPu%;9RDtO$tuA3U}P!x
zC~g;ILnx+6RW~E#OQ|mY7)G>ZIP_W00R#hWGlJ`H(@Q#(>clR*H})zswg3rSRDWkD
z_t)k1{}Fy>-i9~9@hz#-FRs6zwgYAKj>i5637Pn0nrDrc?QqXR<Z@bb395X>=i`+8
z+c%OVcy&eKYM_-6gCuOH(2fXbchu7{LqkJrvE{V;?;#Sg`~fU~w++D8_wr*ps3bMv
zb#vBnV1?A#a6OcPTHx2aJy~pPkS#<%2jBu=`A|2|*3h`j;=*|FN%aRp(w^TdEOqH;
ztWSS0KBH*&6;fQGy;v5TxiAFVYXucc)g_(u^Y-ROS$d5r>Za~);ZY8ec!mv7kas(T
zNpw%xMqoBP?}D@2i}8*16LKFF06v>049W`x!}cUj?KE4Se1_9#55U~<0)%bq2wzTp
z3xf1VS^#|MzT?GUzwxEim*GouKnX&RlBeFn^N~#rM*MTl$B>OURQ>nNEG{md>Ov*-
zwZr-(gm?M?tC~nb!2}*#06<F;B<;lmcZ=D4spy&`y`hpc5-ZDA@)r$k6kOgiQ}M!?
zP<66ID_AIWe2tU}-{Y|>3nFCb)pX7WEE2(&;$2LCHW@r@lGm=N7^3bu5XL6@3u#jK
zPB<VeN;G{v<ABe~?8VA(A35irGJR?$spK>OuYp9ry*uM5UZwEF1enUU&-@iylR`;s
z%#XVm=edxG#WB~Mv|9r?fC%>uB0nMUv`58N&}j!Gp$_YphpjPIRyp9=kX=oo70RZQ
zj>F*wnSQUjKDKSQHCbsC3+%-53(h#^e{G3=o`fHw2{Dl1F#oC9O5OUP*5feOcFxjl
z6}GyU_n2ANC(Gjry37F7ql*ZAqC)AR5!rGm;v}OAuy}zuEPyD>gr<p7D5Hkxk7vAK
z<IcdF${OCBUcjumGsf!ZevyIp<3%N9iAn&>R=e(Z8w~Q0i&BDV{`a{)^c*b+$gCga
z72urC22m{-Qi{Rzve=+rak{k6{lk2YUZpOoMu8rmc(sevoo77ulsTK4@OuOI*anb2
zf9b8Bo}+D@b1yXc5<T}X`c0<grr@b)1tG7ZJl;?M=mQ|y2+}~ygyF_Aan1&6%fRnh
z1k^AcS|{CVFv^44HEOJ72abSazyw5&g$$A@rXUF|Us)W3R%AF(Ws#uM>KK&3Lm|R#
z`Y0bEEhR<D&|6O5uk!oF`J(JdkDYb7+S7BOt<&#Cp~JO;f-{*DIu`+2%xL5{f_Lb0
zp!A+oBv0QhO=;g7Ex$N!1bSp*>TMuxBqY);r>y{gO9yQ2qLZ}uyFuc;R@l)oj!1#V
zgDK0Miw{QSQkBsLe3(^}lh5<lBJFW%9k&DCxcbxttoS<RnnfAnrI^oZp$;kna*<4B
z*&hdO$*D_K2Ai6WGnYr`=`ljQJwUs$E|8tcI#&Y-A`1HvT@4|Krod6~tXio0<~&g5
zZ$J%e5d@VWTR{jT2p29AfA0|<X962;7ZeL-&O6Kv*Nh6n*y`oq^YO|1KHGNB#81B-
zoL+u7vi#-Xk^5^%HavY_q}#mUnxkx+6+sNWR+StT7si4A9{oP@BnntNnV6XDK%74&
zt+yBe1PHOb-{GzGPKd0VN}-nFO6^wD{|t7>Wc$k#UdQ0N6ersAnPOAAesC7xQi(Y2
zcLJa68zxSgY`kA_Kd9^>v1n*)v~QQ?A%MS23Gz+N0=X05ABY0VP94-WrfP3rfP9Zo
zmkKB$1=c8%R&T7oGmvw(_lWK)hv}V~Xz*<9njor58(-T&5a##{vV)yLCYS=N%EVI&
zr@i}(WDPbx#O@^%cBrt)s2%*L@_}KOA-(ZPlIdTCTlf67n9g7l-<e%dafjp(k2_j#
ze|-R-F{e-+&_ita7@;=Q?i1m9gyc$A{u0ORA2_)YU65IyqudlNh<O3n$H;-c;(#0~
z*BkS$*VMi_TSmcO;-SFy#I7?JvsKsl-pCA0)X2lp_H?)D(gpmlwfY3Jd-d-?l^n7N
zoL1JA34$-h?$CLA{;54lT1UBsgHoh&hPy&d`@ddlvO3pZRzC!BWh49E4C#$W(jx&n
z3;mk0LjXsWm!C-!t{8~NNAi&X=)I%?>jHTrz-$?y=DU`2q5bEgO;fmb`XyK}a2p@g
zuRa;YW(p=E$czUWbbxzQT0#QqN!n>Fyrh9RGio5sTtBKFUzAnu<x2$OkxmA;!)S0*
z^lk>6TByB5vZeR%oV{p^cbhRjq+#fVh7K5JuN^Wbup-<8b7m67>Ng6{1?Ux_44C0k
zT-Pcj`yJ4{?PNQrVMKBB1!!s2hH_n``}?KVerNP$N6*GLd3@gQ!&qQso2q6p-kR<^
z(bGngSCaHFLS^`9>%MZC8OBg^sq0VfaM%34I<lp6kZhwKr-=9jX)}bP0=L<hkwON#
zj^eML>to-(da{>O7PVnuVr9jHpEO5;{jZmvT?roi{yP($L|K}}FSX>$hT;k^tLcmI
zDx|wYd0dAtyH;;!VoRiU3R7h-aeCuS$;1)837lYD4O~InPBYz;<2uBANmkj*;1FLb
zh?B7Zq`bV5`NAE^7L;QbgFtv8gd6%j?(Gy)Gh3Hy89asNco^1HI`wq@`?S8w3wLx0
zh=_m)7!9sJ1b8O395ZlJO4#0)dRvcfj9&Ev73hFYm6n*8$X*HM6LQQ1M89!hLs0as
zW)iI4p%u_#Fi6690hfa2u);XF4GK=nfVcdsA*RlQl5KezRy2dV(0sO*e1iCdVr&KW
zaK>O~m7x~z#u<Z|j~{~RMml#!Zk%}48x<HCEuQQSe9=hs1tX)%u~dV-|J`EkMd&w|
zRa;wI?Si_Qm?IxEXnM#X33ls2&|g$aN=ob-<*zxkb^<WGw-oF)Y1^QD2%=ZfsY&8<
zQ}O{psVIkNzC@+emh?74UOHD^r~IdNFprGPS^}b(0iaOP*R$`HjpkEzC|!>05`skA
zlP*EeuI;*~{-OwKDYJW!bnSFJh+ec(!K`(s%Et0VR;3Sdl#bv<i;2hMj^m?#=L2bK
z=}Vx!y4;;}T`*d~O(S2qm}Ad+?|zHT`*p)!<Urld`)YjktFp46ab3u+7q%8p>qXG0
z*i?-nX;K6P^F>9{0nk2J{`Y&!eh0vEab9dl^W(xo@%-d730X7-1$Nh9c~U$b4U9yd
zzol#pNE&#)iC1Q-KeW$aHgFT*>o7x@dHVS|9|}B|8`NO}Y=lWbUkBCZ+?5;)s3$){
z;kq{#_Y#EqmhI35)&2ccB{4mN1R09!kKG$FhR5A(o){(Rim`=|ISDqO{^xs=06S64
zs2Rx}+ghhOM$#z0g(=8sefNvG<Pi@w(vp@DW*;ycBFFS0M1dNKn!sM3Z?u3-{a$Do
z^pttC``7aOmL-y_0IrOu(0;6GqOYgpaA&9AW4>xhY2wSHI0&2MVl}ebjk{3I1_~Jg
zn!g@^w>c0xfjA++hNewRQ!_{|b;x>r^#Jl;mqxJ?bf3HXPVnJteS`5?jCanj-J|N&
z5!sa4xjEYMww|o6B72m|L3Li*+$@s?9v3sa+DtPyeX#<-&85eFouteOcx;5>f!+ND
zo(kk4M7P-Ly<KMp<Z5YvkbL7x2CB#Ksrioxz(<k2>s!Awt3O*-GeN6+;yZL+n>};m
z7isxM4W}%;E+bb*d*SZcm%0SkM}**SQ!9zaT|3(mYW_CUELtc-Q#r%c``(#fmwP4-
zF86GVQvz(uAPNG<G{~_IN85L5fVWpbk__Wo(i~t@dCi*l1af*KFVQnMAB$@yKmtSE
zwpf=A&6er7-~0-b-!g_boApihUojvFmfw{&!$MktDUff^aE;LY(oQmqHBwpXxIqZ6
zGc8K`jj7G4stnqB*Tk<0_F%byX@&v_7W}?75VswwuBdTqIq)4lvk72%TLIx4dC36u
z>Dk^i0^M;FW`DgBne9jAMtwW=Px-9O`NaVo2g_H-msTI5_BU5df$)*5In$<^1?tJP
zBWd_wccJPfdba4@W+DeVpgUDsf4{Dz5(F<OG`rFjmIA3s3UVNS<{}6V?4IA>hjYO5
z!S!?7uR+ChxXsOOY2C;f0owq${aG~64^-y#8Z>&h*w*E{?jRBHLQvuL99|>Pp_%s)
zhwTZ`eWBG9bo?xlcyjG#CEeG^l=G5{z?#j1#gf)8R-Zi$e5)Nj=VP6R!_TPws^{vD
z!t2W!t_U%JpTyOT_~KC@@x4EPjU?a+J>4|Q=bqEP)Ews)h3&~yQCTF9D8+S#?pmz&
z@be}yvS0hRLKHB4*d&o5IB>;M5P@f{`;%SC>XE%j#EjHua8G_db3xYl;Nl-l4`q5-
z)UB_XZ{K)=#xFp!z#OnA$WaWOw9f7xoN2ZOvQL2@Y!opKno7oi^Zx_2(eE9*qd%&|
zZ}T0RE_pgF{_WQTctWLZ<+dx3DInWQ?da{z0P-yKFh+(TP&gGQt$;`eLGn!Rp&`X(
zrR0qjWWC7vww?HN`0CK>pT+zUD(dvAVUEDme7^%H`u+KsrO+V`^JU1v6?P`Iq|LP-
z_;uOrgH(Wt%4=wZgY@6JV~Stfc=ib+fELIEetbNrod9hxg24-AXcm*jw()j(Y=cR+
z63ChaCR1Bd$r;Ezm6zT_2AH&aMxP~F2KaNNM77WP=2vbxpL$%8_VOx~Q0VUjg^Ydp
zkQ*<J=sXy+Ujam25jr{KgZ(_xqUu<`-iB=ZpefKT#Wk`zOpTb7JSQN0cBU56p)0wG
z;xvQT*z9O&Vz11cY1@CUtzMZq|JC`px5~_1#x_`#tp7@dV59?bbp|krlOAIPJ58j*
z)^(dzMGU4Az}HF8)<N2#)a}O908rrP=H?PHEszLpZ-Ul77#A1sJeCv567)9jfTcmb
z>I8BO_KV-%UvNi-x`F<TE-e=l#@xT(>Yp^;GozQ+a-W}?9~9C(l^g7oM8g?_i+AWy
z{_}_`FHMN-#a}HTE@s>wvjEN0gk$C@m3dDTl2#tIUu1!87mV`BQFtydw2<`Eef7nJ
z8_LumTcyL%f}=Ga3cOy}(|f(aDM(WXZaTGrz|<D9Flwh^|9gUxrnWipvJ1DY-oGeG
zGPuPf6U@g@smx>_G0neNCzPu4<syDGP<zf&vJXd#b@uPLV?PM$l5e1!(!cpBoB}8}
zUz~+6qLu@gd({NqlR)%>ZiFI;>VSIeG)42PL*WnVF7P~b0CPVc*_w7DyE7*NU8e!4
zp{AkMh``Hx^TJVKBT!|xi@s}W`>r*GRy0!7i|_N7vp$yRjxf#hPaD{#P7ltk>@OK;
ztDJl(UHfCf>DS}$<MM?uYK7<C49_C+hX4gfGf|0NGLqW@hZy3}i0K1S$qU`RRA{Gh
z75W|teHw#h|Nmm|&7*o;+xOwH$u_l#Ei`OX2#J!E3Q-!Rk>;p0r&(z-l_8~+RGKu;
zvoyC+R4UDLN%O2(qxZNzVcUCs-#^~9-amf7wZ7J~*iZ85zOVbb&+9yo^Ei%^V6^af
zyg>N$84yN}qsHB=Vvl!-Qt{f)$A3Gju={(2;-aebXlPF_?Xhhx&wO%aPprW)ljaY$
z5{2DtW8F>0fnU;Vx7Qa);5jY+Qe#5B5mtSc{&(LJW~ecUl%1HYabS#rXkQ(4X_OJ+
zMC)A9n*xH&Zgc0|7y(1f8&trVf2{z2S+KHL$V|fd#)bxf0(LUP^{^mYZ0#c*e?kUe
zb|_pMz=-6)_}?zQ)c3aG3%I*!3FxT>W0-h!|K`<&I-t&+mS-lc_0A8~wM0)|Gm#k@
zBs?+hO?!>^qV@dZZRF?%A-j^uRl}dG0^e?rFJ`@6(}kDdzt5UXeF4e*;0I6{l?hR@
zYKY&EJ6u$X+ph*YFFXJ|b24Lh0`zzlL_~Ve8{c5+^N|p@F-uMg$N`7AosN6=$n|jy
z<~?oNT`J4%_tayx2S2jB+e<&ci-Mei%(ZHGGdvX?6Qln=Rqg9n8@`2OOQ-_hk9^;H
z{257?r2oDsqU?4Dxd`EvJMRKFIl{^^exa!%HtzlVJLUaFP28H+p2+;<%_BuOwKQEo
z_Wios-8Jwy)y9MVQv`}S0hjo_b|;4StjKLO>YaZF6I9ZiBlb!9ocn>lz!Iz<E+@4Z
zLOKBtQYrA-?ForyZ9i`-{P*X{xEq3j19}seiW__E(cfW*xu$FS=L?dFD=<!_3L^b~
z-Ei#(-P_^vQ8qX<mQt4>`rUnkD_`~ZSlqd!YZ^kmnQY-Q9z7nwJT<LDCA=?0hKU?%
z`+*4{Jtmu+j4YTT69h0?TLntNeRtj>!5YUrM0s+|Rf7#r>T*JpPetdKyo3_rWA`S{
zGe04OeHHt8b29R`iWC(Dx$2_{nL0k0QaO$(ev-tfq=n{qDIvYze%%YYMnb~(Vo+@y
z5M#@A1;4fhHT0OgY5e!BSYDR7gM{!RXSs$sHsmBJ2bj*Na%PL5Vf>xZNM42Z53ha)
zGY*;%ij5>XWEq&HLnZ=jR3XTW#lT0*DG(*2;XQWM4_c7apD2tv3ZH=UAS)%cqmpBj
z>$^>>?qWilAu$Q~uBw=P!Vy9H5t!5j@%aUE|My<o&m*CrWq!=chI^05;*03eMtfd!
z)hTkMGAz5*?FTGjg)`62-$RdF*{N^GmE1pv^_FFQ%$F^d`jd)Dx$ve}tHP(INn@LI
zcZi5-BL3S<{0vrm59ng2Msw7xu=U}bNv6sR45b-eHA63kPEdWGOjaP)nq(<=e$jdG
zE=RdfI@05;=i)U{T0xg1odLiS3^us%6t3@_YWP?Vm9<pcnTEb3rw?o)R!@ov=i;yI
ziteP01K<B8m83?2CfbphG#3rwwmcStOe>uDC_vMT7wupcUqb(M(7-lb3<n`Y&Nj&_
z1(CtepigaG`26rA&V&^ua5+2>^_|0z&+2=0lSoK42&#0VU9Y=9VtO&(uEr7SKc~l=
zm{Th)qLn1)#s3+Z&>jwt{;FP2ZcV{ZP~Bv#?vH&j`pJZ#yJgHEF<e<y6=G!5zHO%9
zSPmg0&O@zL3q9jFtcvx}(J0cQnJ;EWHVAQ@3c)JS(usq0N5ILM!xyuyCjzZtDe+^O
z)K0L8OOMEaSfzA5>lkQBQ;${$jcS{mtUGaR_<pFzQ5t*K%iX5+vYn`<yQRSKZ{OqD
zhc`&!MCxg@d*;3pSvcjF^UkwVjrYUCm<gwz`0ef%F4U01;x**hW<u(U$rfZBojiPC
zVZ97Kj7M?4eBHXU@bQ7M5U2Ja(742i?{9<i(-@tu*B!P?#^%!h;5-x)P8~a?>{axb
zjY(UrE~}_p%Cj|8B;ZC~T_{UHCfg*}pq$tDz7w3)32D<dU5OPk25G2PQZhVVSit*w
zFF1)^V7g%+-!xyom{+USJqtsH8G*1CD$hAot>-)NAS_f{;X!Va!rNjR!`9L+&m5(?
zEc*c|^9A$QT|aJCEo=YGHEx$u{)Kja-OeIUg{<o%>y-l}!p$*kx-VyTXj{qxiP2B?
zoM*hhVex0Ny{_<%@v(Fo?|vFLS03wf?Q-5GN6pkrCF<-lox{T_DucuQx-G|s?PH6&
z<?cUR<oS~y_>601_?5@7Z~j(}@$YMifoH(iw|mS=k9TWhw2cYZif_>saE~G2P%FfH
zc=?UiS`UKzQVDV*_XF9*16FM>bSo`QEwQx<857-4S+f&k72Pxn`jQ02^ZF0`_JR6t
z@w}ld%^LsNCYJri@7b6QxxQ0S&>4_i8Q$(doL1m!aL&ESFb$;N7o(#l2q&PIMumo+
zt=G56oq}tlFlH_dBR4(T@cm%_w9;J2ea2MOOQo?UHdQfm93FF_$zj!+YwwplMo)Ov
zb>-<~>6^CQD&b3^=A^kE`=R~u8^h!bn;)y~@ZuyFgOlgET`AD9y-}IMk#~_GDR6`p
z3i@8I(Ekp3W-w0}A%m4om-hHJyaxCt305<DIv|9BI1O$w&f!{zgmmdn6?yfh&76nK
zc{GyC%YOhQRCyxE``(k329qw)&68EgwaY%@Ensh(45k<naFY=*+|YsvMt%=|^kOKh
z-n2;C)7k2IO^z1`r8-Kb3207Vv$DD$%&$LG=vSOQ-=3paG#Y>7`Q(MzpKTK=GS+j&
zCGe-Am)Iu^g%>$Lh|3FtFlK3^u@0FV8i?8;`H?oIX4=?lfyE_>9xsqa9xQ3}a>az8
z9)&tCd4hMx6uO543IV#09y42sTQO9O=C9_6jLQ1s<WweuNu9w?6Z;=4O66>Dr_xvw
zucPSG1LZh5e#)=h(cjqCc$&ihRm_~w(v$RUcypx0Ej^%EozgW#^RuGYQ=w|(WB0?v
zTK=w{gr7=KQc{xJzMr2?)xPQngHFvEs<3f6JxL?OvgM>%2&L4P#92I%REQhUgRH91
z*~A?bq-6N+lTk^(c!A)+cODWxjjr{l>)n-;NBm^RhFX+_#s^Pj*Kd{4lQ%mvT#qut
zU1yU5H|dwk>dM8ca{3;AG4(TKo*elJ+Few$JTFETOiK^U_qBb8cy4%zT@@Ple^=|N
zTbQ!ypsz|K7+BSR?A=m6`=jbcCjQe5+c^+)Sg>f&{jt6w#Kv@vM)$mt&6(l;lx^HP
zB7bcb43LXNt+Vk<hTypW&!u=)_wyEn_W2&IhV+8>InjZ>Y&oN@#5FqRe^FOPH)=97
zB_Z+jL!YGzM^aC(-nwj*YcSZv=;x=L{COK2&ur*D_4nvaA;rBa7l&8wp};4-DcgMH
zRpUT;J*c~?2QokL$h~grbWf{)E(Fyd5{m`E$#P(=L1z>gg6?o5L%Oq}Z)NxE&UDk$
ze$&Rds&n9x6jE1VSzm{p<a1UDf=a{e><^HeD(~4(V7*<0Sucvmr|Hl|J~kT_Iw5z6
zm*l)`NWX^=Jt@^C?gw0wi^_lgypd}cfA}n`MS{n$*egZdmJvL?(;dZeXzBY;>$>p;
zG}L5i0=87=D9Mk9tvaZ?rsu10s0m?L{y^K%sy_Oor%uO-wkOkpvgIYWQTML+t%SNX
zlc-MIDHuGjt_bhCgZdw+%YBzBaP1CU6;S-M-Hmpu`eF6gz2LFpy_W%^f`O&Pi%Xv~
znz$pw5F|O{1m*f24j%%UGM4?tS~S`cj)l~eYO`wl(|Dgx3AAE*^Ee8!h1uq)eGJ#W
zHiMuZp_OOt{cigkwiwneKX0FIJ=jJ>pF07X_(@b|cV4%Bv5SwtnE2VDZ5-BLvn1l|
z0!dUDW8xZD%CDmQQ#MW<Q`y<S$3hD~HRYIMcmCY)@LHYrmITt#?|ig1pjc(4CkQSp
z?v_$JevGzZJwCu0UHs2V9gt6H8`n4zi9E1)2I=X6eN&><whU3C)gi)9%+QPiA`H+3
zrm8;c`YAs$uxI?O)clfh-k{U+<Ng<m`YHtPlxfO)w#Mq($5mS4&#k$lvUX{@)W4NR
z&l^rRot+K-f&GB0aAsqtQ5{q-IHTxel{4kSbcQycKDMUaL4D#T5zIm>ttx%vM&_<w
zXeFwMkoNM?#be4j<T^{RxPiLCd&^V9;0tH&F*Mf^kZUtvx9Yk4FfAX`E0xblips1%
z*HkhE3II$@^y-H;<lr6ReL?JxAWA!rL}LaJzI7ch=1%cVCc5@w{G{hD<ng&Xzq4El
zvh-`JUdBb#3=dU1X3zV5ROH@K#5gnD=eh-r8x|XMF3qDV`lZZ!7jBMi%~b?oLZKae
zFIsUGlItBSF~?h&D4T(x6XPc`edG?U1f5xPPIDDa2e_G4*;6g3c*rm`jG*)M@NhoY
zym$vf``t>l@gzZe2%fT-=*w2l)@^ByrVzT8-wjN!ja(HV>Z#(jRK<(McD7VG>dSGI
zm4%&u>HmP`zaw?ID9d-Dd8}IzB24Cw5KDq94P?~eU1R1aq|qtCH{GEgx!6Vcq<!b}
zKCracVu*(B>V2MD<}G?0w~I%Q>R9-xu3Zw(Vq3c4VAb|qap#ZjB`%i&1-E8e#-q%c
zz`lThu$@p{phb`V0pfG&*Wt9L+?(^ONy!&~oK{6c7JcChqF1Zj70^%Qwv+O9iAU-j
z>Lc6jMKIRy)GzXnuE>Lag4yAk-db^r&)_^s=9_l#f<CmmZQRAhW&7}AF!~5XfK-dK
zV}2iRAca>+y}Kb;MKtmq+T0>XGviXO@!3g5E>)?GsQp10z^1ez5+<bQL@q_hgwRI{
zv?RyG#IRv}ra;RgIxI#Xro-~ID1ygF3O)59ZK)g9pB2)0HlH?Xsg9Q1!5einou0EK
zYNG$E%~zYr5D>Pdyu-g3EK<uUw|(sZXcR4#mC|%HYdmSr?%J@=87+H9&GVGMWn0il
z3bVdUm>H`C0nLp+GVLDHW=YE_Q#j(68lvJ$9>ON>z$pgl{n<FRpH}@7!~<V}xt{&v
z#S1n@IvJ)1Ifeu7B-8q?jB`lb>0hKRt$1<U<eikvJx}{h7{$AI{HB(dO7}eq*YJ^(
zzo7=swCNsCT@~AO(m(}=rb~=P`hGE9G0MeJeq(-&jtx3-!bP^4lW-gL6k~=C+{G#v
zb4a2{-^>VPc9Tbx@6Fo^e%3+1^<XpAIA(9p&fOET_J%-1&6W!m&W@!tAyw_~sXrF4
z?rzt$F%$TCj8Lhi6Q_$yNg-?!5C|<ycN0!$&$Fo;D702zfszKMpFh~yuqXmMe#I4Q
zd!uvxQ{#6(zG-OsQ|`!<)J47r;~%GySfH?n%JXLf7}o}kJJt$=UeHRD#^&GEOQs!k
zU7G=KXTtv5@VA3=NtXE!HjQ3C@kVYx<!0zIO0S*XHp=jE_I1y;DaTC`A+fB35#sTa
zIxP-=%z!2Pi)w-L4}@YU43@#v+CDP51|0$TEDPCo^R$*Dq}j;axN~~@JjT(aW@Mt3
zhbAj(G^U%nl<Xp}4Ke-Nj@U;A>R7ebwY-o#t=tpKIvn9?MeifkHaP6qA{m$dq`1R!
zbF=F&k4KE{?37{|deRaomA^#+8!3bq4@@_>I(p?G`Hn)PEn)Z@M4|Bn#2ALDMv&eN
z9LXkN_ofjdN_h-{J^9jGD9TK1L+QTzyOEjfqazh63tgcf4$p}*v+szgredl+sd3{M
zZs=h)m2Tn-Cvhmtw)^IlaOCdTpK@OR#38DkFVNftNrZ-X{4tq|p5JZ^Sc76d4c9mV
z$`cIasYic?7M&w{i~F)i3bql`f0%q&Ven}B7axcRV`8cVXm~Y7!YS{rnm!l>6ALYp
ztV)dHEOeq1uHaty&nZAi`j&2u_Y{P4Xf?<CNKJjZHthh)2vP7In#;GKJR-_(ClR7|
z_j*W6l#`BYUs5qEX6bnC0&Saj?#ml8@n>}{xz2?zPPe<<AyGqRGmja?HRHXNLaB?C
zw}K>`ORc$SLasS41r8YvNiMinwmUX!X)Acjn=*UqSSUxj1a#C6WJrm}jusm3{3Se7
zy^A9J7UEQ;OZWr?3B^IQwoile+6lJDEop{6VX%~mf5&)34A?1S^J~fup}P%Mq^iYa
zk6Z1_{`+Yb`!=3eRIXUBv(kg)Lf{>Zm}~l6Xn%qT`U~}zYA^TMZLne_UY{ucDnJyA
z0!Q4w8rx&F`nc^xYyjps&NS5Kgb~yN@Nw{^#l!T91UMS4IqA~m4!2D@#;|q=ve>dR
zsLFHybx$SglDVCy71Y2#dMa31TEPoWmAfIj?#Gf=5_8AzgCqan=q$j5zgOxGp{Ryc
ztaFvg4}<JNmixx1+#ZAF@x}hD1G<;>KtivZtM~C(=+9If{RY)?r%GzFJt*c@tQqh7
zq$tmq&XhCFX`)AuXS{A3PZgtxa}n>np%bOH^o#nEAv0ti^}1gs@NK9=ZKOkO=*fkN
zj3cgFm+ppdP|!aIdEP~ipzjH#S_0n8@Rs^eA(h4;G)kGq_*A@jH?#7e`<Qhi_s{q7
zo?abs=EwEleEsFcq>}rdrRV)dtP2bWD|W^Yeyp^wkG;rZDmLq3MW3N@;Wn#sQv`G*
z8gynH|HMOw`_A8|Z_vIS@_)v7!`6w>*47CONLL-2H;?5$#MFd}ZZ&Vo;<kD|sUElC
zpRLWN^4$(1@E$xf=GB{3ct;!7#WvKjR-Sli>%)>g)1NOrK9VnuoPJBoj$c;uY*5de
zUT-l$GJB1T96)D2io(4R>$<SO*}}F4V3+MqncbA1^{$jde4>EJ^sClObd|b=w6xMz
zJQ*I4V)Nk+v*nfW<+J&$p`7vRLbzDGM~T)N)6Rb`(c!WbvQKcuDb6wrt>0$t;rYo_
zp%SLEVX-Q4jzTREcTNggF~T+m4avEqba3_qT7|pW>(w)-3dNTwXM|i};?+qK;Vo5f
z{E0dt{c1w`N3l-5!ZiB~&bid)=Z>x6&Z69Dj9ZZg%qprVg7**0%XUt)cDF4&wi4PE
z<RW<*g1?9w`9aJ6_C3dXDt1eAB)>G18UE3!#trOUM+iC~qL|@`+E2euv}k$7?9#qv
zxBDn}M;~p*9IbXhcBQvvw*ZP3v~NG2U=m~+Oqh1NR=38FH(V~@VnqZEqv}p#-y!_V
zk{HJzP!g)$>$Yn_q8U0ql=)=D#0ESZ(%FOQYrZTc{uL|*JS(cX5w}4rx|Rz*2@HR5
zxUTV^dMhR8#kULa>daerLFJ@~Q`L59)xWGe)q^w0rHI$GY_!~_j8J(QnI8CfSCSdo
zi|4(=dXG|#YT9Dz;<0XjZB~sITG9P<qLcCYGq-!P6FuC$QCzq_09keO!HC*DPdWNR
z_I2LPt@>q!@~#mHI@+%X_SH_8!cM7U5C8UOloO%-nT`)=)U~t72VNZu0m^o(yu;3~
z^v!*6^IZ1vc`2iOh?-+PDtFy{^0|U-&mCb@z5j|YdoL5l3WZj1;X)9cmfHW6qr9(N
zXm#jXZok7=>eu)hv<c?xtc6<VJo(b1=2<~KB0_mTngID(<>^F4*_@_VZ_lp$(OAVp
zV4FdLW|-D(H*HAgsP*C5-|`;hQ}gKX+Cn5Zj<d%8kL-gX;y2!`5>NO8#|bDs&C?0#
z(SLBok7mjannq@DZ04f5uAJ2Pm3=XIQl71vK4G4KTdz*<`tD#1C2?cstmLTvvu|~f
zQ|jT?-{<GEx8MDozxgzzd?YgUni6edMK{Zb{aF%Vu@E7=n<~w9ztJDwB8!`5FDq6;
zHOD5MqO~H{0_f|!xgf(t(e@X3<oq!c2E&;{_ZAjf9$UZDS=rp36rJ@pQpBG1Tnl80
zXwA@b46hER{Uzw2<MX-~!`F#X#02c6PGNY{dH(qn){LGVi)vQ~<3k{*K*JR_nZP-?
zR_}`9IpAsyb$>EG@%zZYDX=77x^s8>&QIo>3eu<ZlB3yJ$G9k-Mn8c=<YC5<lq3l%
zaEc2p@O)ySPTXnv0?HQaa5bfKSCe!HAN+Z%+jj+hqD(<W1GCo+j_5cUbUKI{9u<AD
z$n#YmuVnmH7e$a2*1v?ZXWHNhz;^9)GrD9m@M4MvJgCY9`ql&tkzr4PG={9B+J|!?
z?7F@8*7Z5$q#ukqIJllj@AZ6Ty2@j?bG53U>Z14|G=%8Vcu)Z=G8Pw~@_SwoMHNP*
z-B#Hu1mw2Hgh|*J+E9iSzT;moCIgbq%E(rd9EXw0>5&x3%h7&#Y1WYuVASNxW7PkE
z<;l^5v57CMV(X_rO%G&%XscxqStR=FIYh<A2-SwNJ8SNoiJwW)J~G0ix~VCK*pyo~
z89RY#!m@vV=g4&zUH&(01QT5!ML}3h(EZ=h3>d_Z+L?A)Ep=8-ne_=xgQ>BbQS*i+
zrPLp1Z}lYW&I54$*IP#AcmZ@+QghjM?pG~`+y*i;G4|T^Y*1u^hAWiK)LcVWo8Ea*
z&Aq4M!LEwCrX1e&R}ThjAK_Iy7<_I=tmNfuD|tN=Uj|Pn;{Ah6%skXb*>{`#*MHyJ
z`i)SePzL{j=e8e&dJgd|j)z08S*a$FS2XXd7ecnU)<)5;Tb~nUTDdIWO7*_(cRM5K
zronoSk4>y4w}!t=B9Na`jCD#IS^&@|d}Mw59r=<A&j0$7JNzDp5KHKDIpd*+)~*Vj
zx?8Nw>IL#G7!wC6M`&K+z1{5An7jhMw25CK^>4+q0$sgJ+Q$CwPn6MWve?aJfJql}
z*N;Zm#CuPFnU2YjVvui_j(n$MZbjJ>i@&L@A^K>HY*$?w29HMAe^Azu<R~DJ7i%S7
zQr>~@bl3^Sc|*neaw5jiyOGw?z$h~|^!NyE<k{)HI4;DaCp+JCFPs({Z`YlpM8bda
z16WtMp<?<5>UagZVR$~fdPAN_R1jm~s3?|)`^l!e#-)rky3?|?$`?NY@GxOPYi?Q#
z!hgFijIAzBU%`5TS!Htkt7z7*H_(H4xPX4)IjJDi8Nx!n)r-H*qw_d-HN{ZgC}+R*
zP+iTI)Qp(J#cUMuaGF3UW4H*f%UqiWKR@fXS3@oK*&o_*@L!z2@XH6ETu<WR`Sa)1
zo6tAKP^F8DJg|LJUP6j+0)Y>L(aWw|J^dCFEZ&6*$_@8SkL!<O^hx^QXgd8Ul-c$l
zoA~~^=FdM&dwMFJ{x^zy+4moi_e>s)O27_8Uk#=~+AGpLQoD=>PEA74lzduL`oXZY
zm(<l1Wr}*efn%0LrihXSBr2Y-=tnaKsMkE{uA!$^G3Dv7Jl<j6*qPE8v+#o|NBJSa
zVF+CSk?tBp0CAVnTFgJV@x0Crd9hgK3kJgvAV7#ZXpr5IP#ouSJ?D+(*vs{q_R!G@
zclz7hYhsNrcH`-O)0v)lC9mJYlWR7f;nk2icZYF(M^Ymaz&*b{U@kcl;o)a8XO|1#
zc1|4I;CO-Haft$-vJHX8JUDcJ^Q~^mj~=24X3C<4C^&PFQ-DC&>e45nh9+vBE!B4Q
z`o9(a5+A7w+_8IRZTOTCvohdb;VBV5K?nlG*>F*wL-)k`#Xf`KA({Z1fm=DrJYi-v
zDIsx%os0+d*KBm54fiPSXNltfH6T)L41FPby~+3;1Io~uLokW%RcxJkif%?ET8}N1
z$nJ0fTs`Y6I)iHpQufnTBDJ;n?Fv9Po?svnv8>YY=-I>Rp@(aS*?~gcMJycS52J;}
z$DDT<JY{Q`elPUrE{GEDOBD<-NDCe_sT}ab7E_tL1mO_&hDzGnI5nlr(Iw+xsp4m*
z^6<M7SAgU9)XyRCO}q4LBC3o5?p%7Xk$x>LsMP3SiI<(4DvJY=n4?g{2++WQ>c|&D
z2DZZMgLvD@N=xr_?e57u%z5<aqm%i#$UHSD6LvnY8uC|qZ_^z}Wo_8RC<3n_b(q+2
zTj_@Wd31RSOb?#GK&z@f<)1b)xj*N}5T%^=<rD||xeGIMIK*RNR1Dg|@h;o7ZzQ?>
z7+pyBknI$(f{QG{*G|Mr?uf<js7@po=v~51#?F8-(19Nc>?mVyC+vxIBlcc^lf#p_
zb-TGOql$JOoe{I06WA}KqGD)%e{oG0f2Z4x<aW{Pu*H&7yt2q0UuP=?EI8PEKl+&e
z)<5>P9#iw@-!o~;yZJ=fpMTrmlABS|jTw)MtcVUPQu-s?h%fTPPN!yOVaOG$S05VK
zn`QJ#b?fq9e#ps@{SDVL&d`y_PL05x8onn^`R`{IQ*Tp~<MpN;toHI9&N=>0GfhW?
z&#c9gL(aPTVw>z0&HEy}649dn0AE;TF4EQo$ET=|R6lBmkyULH)|F?AQ|6ry8dIKT
zn~75sgW{RFs8utiag8|3SOnjv3j%7_y2rv;;`>B+lj?gGP4K>7pF_|-gl&*fjLd(>
zm*}>GQ{8q(A?UhD&~=68at%Ixe;U83aaURbvAg)7betL8P)#i&!}O)2Y`5-vS4hSv
zF#E5UdD0CUc5ZxJfq#yq6_>x<Ubodxo!WbqP#ACy`#Qnvqo8ut9;x}^``vM(5`lSE
zOkT}$E1#cd9j19c#l&c(Ja?7%`vp?|h!J`~7F56;XU7(29b{lYrl1y#fu0Th4b(He
zzgOqaU0WkCnn^!;gMKt~wk(v<iP>w)^2G2q`Dvx3ddPQwsoANh);*+Lq02e$C$f6-
zGEjvR;&DNC(CI&T{aF^iq`%P`r!E>(A60tQbo{zM&2z6I+7nZme&o}+2k(^p8dxMf
zDD5q^$0*>QmY&O5ZO!VCqhoYYk!Od9)&urC#A0Im<9->w^rk1nDlPo+lbM~y4H|TJ
zihlhRhuf4c8`~*hQU->@{kK|}&a~ESk!99XP;N;uZdfbMdgy{}UhvGQ(x)sw$rpu7
zT(^R&M&w;b{`EP=uH@G8xZF!MNXaCsi7DFe3Y$*&^P98}EUCRx7?Tk!Y@oYZ?}nv}
z)_RSP(ep#Lx4QjLQ)OeXq?R4@edN;p>+0v7+jgvN91gWNJ~pwffdazRDckTB{Qw{#
zwaXBE+&7ae6qCCz(|Y`gqUcrX=j^j3wA0f;areWMW}QArZ^<7!HD<QnoMVd1fMtS9
zw$I}Iu<`rhQU-E*&UP^hsXyo8p)RpCbRs<kreEJ2Du;p#T=TG$@lfyJR`ChDM90*Z
z(Pk2X)}+JdCt_Cth&`@>*a{{|q@ixmdvCAL`@Q54ZFyFM_8ldkovD`Tl=cqimucC&
zch656Kjj8Dh8xq8;+yXGL=8c}s(sDM+15r^U0a%~gVEm-(R>gtFnuj~ixgrByPoLk
zQ{$IXLkZecR${M0sY`#X6K(Y?5gP9>tGtn*IHVyxv=72Zn~D4$r9#nYz&h%Vge^XD
zgWws@`&^Qb@;+(@1zKTyilXu_us_nH!&*&uDwFF65>#+m4-{d@hjvI$vtPEgrMzoP
zV|_3xNI1y|Ln+U$Yvbcd6EI~i5TMYB83I2#bU!Ys{Vf<#Q_~<RA0#SPGU|fEpn*eg
zwa-a%u7a(p3(tLcQ^Dl-<MZRD7@lQfbpj{PGAc1iMDv3lA2WGy2gplwud->G>jal-
zrs0zDzv#D`^7kuQ(<<XOgp2Q<KI;00)wMXz@uYCvPHW}Jt@}kf5@=8VdahHkakR5O
zw}V#(dThcKI^lkTT(%oeU_=dNf;zG;`Y+~il0ffGp%wlbFqYsg0`0rtp;!$F)N%*B
zFL2numC`x;WXstN^X;eN_n7LH%fLK7Pj7zgjJ3Y4=>zq1pg9;iDdTrmr-Sy4Cx&m>
ze)OsIU+u*+IJ#z~XC?!2bE};>-NP8J%;@yU;rNe*qWmfPDDzr|NB`r7&64llV<0ib
zHu2MK8phEK*jtb)iZT<Oj3bAvpkS};N~#1AT-kSdm)lzh<0JeKeDTxlF<CO&LAUxb
z)9=VR)6t2^4N@Ycev@<|!>jfQO-J`$Y+*WKS+{0E@n3sA?+!cbo8CLwf-0Q8)va?S
ztv&bm_|v5rrZr+NYzV+uI8V#+^77uT1-@hc_wvn@K|YvRa<d%GRkq)RwsH<{dnes~
zNZXDJDaBZSF-LBW63A3~=x}J&%yfoq`}Zx=yBTkhc=vub+<qo}{#I|mx4G^KVkQZE
z(%odqB%}tXYgO&D6%-YB3zuul5W3Ac21f%~x2fu1;V@E)De`^=I2j;<a`$dJ?#aJr
zMNFs5hd`^#d9Q|f3vUjKUqt|Vz^!k$7yJ{ykCH@ldo(^rj9iqp=XuU;_Dg=j&G8&u
zHno`zgeNCUw@#N@u81X5lhK!L=Fl$DO711l*3*NEVepuQ<l^k2PjhkZ+(X0M`N?vY
zEmQ?5XSk(%xLejsY+TxwZuRXIJB0GPA~mcpFV7cJNNtU!-8RGhqPtX@MdT~<Kkaz(
z{OPprR3-+H-ZN$g+?|GWgxG+O1lAto;OK#yU?k>nHob1Z^q0_Y^SR&BqN1V%VP=qU
zno}=o&r_+ZyWCcLbua5;z-bOZrqE9SRmbl0bg0}*$}CS0_dBa7b|wBh?V)=12ca~f
z#XdWnYyYtU6n=}kp(NM<yGZcQn-Rvf`^Hsw$KUUCr5L&i#mG_73NY)jcoxEN$dmOs
zb$sZNFfdgRd<)H9etTYu@?ozhrzhR3r>etDzROy?wqyyl>h(GU!n#Ze`kEjVeA9m5
zSn?Rd7Qa^kT&7n88ZXd!p&FRX9!e_iPoc3sANelwQmP%G7&-f&ydcVJ@+^T|Gx$#t
z+1c4_0f(=UQ7a#IS`WjtL3$+!n8<o1xEP-OCFQLjq<9?;-!76lde`>`xm-J~T<)<U
zQjArsW%H4;XCj)W-8bR7wUBDYdF)uBgR2!N>LLN8n;vBoHwWEVCb4-mI{7&34^GRj
zTU~}ug9-M(FH!+0mxABeCc&Wp3*~K9;B=AaepfEGS0<fBr^blOD{vgX0HeCTWo*{a
z(>IK@jplr^8ansq(jJC3rIoyN;+K?J9n0HZcg?pq9o1x{ZZrAusf&NMnXQ>595SCn
zsyTD_S{32=s8bmhNj9E`7;`EH6=GVg8QuQhnEmxt(?;~=BE@q0zw$8@nD)>6y|46+
zk(yrD898?}eW>HMs_0d*yR^N_R(vG59}@$$8l8KwA!7EEM)m#9tC2l+=haSS7Z@IL
zZe9naFzY`gJIcj?oe}q@mzep=Ubt|W1=dmmW!R4qO~phrd%FwWJ)WqkGT&qIe@F~B
z;`lFks9z(q*G&%61hoUTYqOxaIDa5iEoJx|dPn={&+pgsDe?(B5~k7hu0F=&sJM5S
zM`65o*s5Bs<a9mL_=)U8->fb2xN`kVH9X<Qw&!-6278D|fMWGRj^y<?S}XG$Q7cdW
zV~57LXy{lG%5Js)Yw;Wo>ohr5idR-)%oVKcd{&;yyLce1rl#hU9tugJ)OR_xMs*45
z@6DG8?FTLNLHOz=kY+Hkz@X%vdtV`S+3iFbK4*V;w1ut_zD9prDsAUx4yHfY;!7=e
z4zKo|x?8qP#cSZ@XZx^!{a7kyf8%Wu@4oc+^gPy?q&D@F2nRdPRoB(g(ecz6{8`l_
z*K&DOZ{ry+E7N~($yH{~z2^#F;&FY!pvqTx9+igu!>4o3?=iV*H}G=!n{`{NZO#6y
zxKrw8a7vjF`d9u)m1ok<{hW{O+@qk?PH%2^sl-o3@D$A2<OmE|9l)@RsvkW1N;~uM
z$;ox;f6`|XwZ^oh*@PqHx1SOQoq`gHrLuSb)}dm*{n-+pTn1<*n$)d1KHx~tDPW;E
zG%%nztXQ1upIO7)eX3@4`;vIH-RlJh8(F193h+O{UJHmN9(90(Pvy+;gK55~y;w#i
zId{F~yx1GJW`+k?7HI#-I7t8}SIzgGz0zvGg_p7H!teztDU<iSuK9#L4})aeb2nnZ
zRDslXi^)McKFB*5US_{^3^AUwzuJDQnN7COV@@Kn%6psFI{M5IDW^y&uMX35oI4Nd
z1iZE2w5MMpEyV(YT|s=C4HPL3i4U*xQBmc)b|amqT~=m;oQA1v_I^-mYIVh|Yd=Rf
zF||i#+1PjFfgqGN9G2-yLfKA@gNz)m`PzG&-MSfSzn$ka(RbXJ8Fb3YARxV&`S_Yb
z`@p<(cEVf~*=?ay<>N+xpMQQi7~em)XVv7OhrbxJtF`Ny9Z&(@bYqihGZP5QTDNIk
zK(IaB<jiT0d8{^Q9PPecvbOV9+p_MvskYs-S$j6`I<{JUbbe_U^-BM#!5`xM@1M@5
zurfS2Z+MEvXSh*#{@g(0KHF9-9`WRY&T%}VC0VDES=pp;_uc6v&0-FWF4<mt>&oW)
zXI9wmevhxfGs{tPScW-TQRT*TV0`16$fkD5OZTO3ZXk_1mN70FmCust?}AR=m)4w~
z7|og35I$#^slN-29Inedry)(Cx<dVZ8ULf$L3clNYR_|*Tei15a9TqJ!S&#p;jRtG
zJN)0h_#XuiIqLJR9sNw)D&Y7OWwR~^wtW42r?6<)<h|BTjdfIc+S`W6!=v-P-nF=+
z7Ij9{RDoYdvI6KFcV_Zzyi<8W5>8+srTc{K+UF?}85=7OBt$6d9fTv)B~Dves9mbe
z2?_bvDiKTS@Oz1t`4T!F2z#rlsyMV`$rO~yx~ZAtrd?~&f9WYKF8>vnVXjFsdS(OL
zLZOL;uC6YDp?_tW__jbUawM$Y%u3Ip3Sr#(TuV%%L#trl2SfvGeh83$Etbd!(-o65
z%@tNlx4X*tW@E%EPrR5Sis0K_WPTu#JXQbu-ACawu+^*0tdavq&ZFRDWX_m}u(=C*
zv2rMmuAdpsh8x+6l0!LPKerwDrBg(?k32fgq1U~NX@&>s!~HCLsyjC_fT@nu*ZN}v
zk>S?muK9xb7CNBb-nG)viR2r(f8P1bzhDdUIR$~nn<WCc&2`(ZWx_{N+QTqwL~we3
z;q_domr8{ntj0g~V*^H(*>thruA$(+eR=;*x3FBVkoR&6s=#ewq3qi<KfZC~kx>uS
z{*^F-Sfy95FGr@_L4F?h^y$;r1cS=KI6W7+bjq##@>>CwQ}uE+{S0s!QngC9&QIBY
zvs531NoP4&U(t{TB5zc}P0O;;(U0d~=mGKWUG>}2V8t(d8}7dioQAdyY5`loe#kgi
zkKyT_VwhP1Ed1SfjnV%UNpxC24CHt>xWUzwS|hoB9D_ak;Od{UFvkBaIl;eBBL$j?
zWjVlyMRFG=;>{a$!XL?bQ>Vhh`_veGm)`oE{^cGh|M^hAHAfjjqS1)vGEQB~*RR?k
zBqF+p+T4XyOg+5*u^;j$=p4Br-IE}y--zn}?n1SJKvV8@{TLnmiVqfkdLU3*PgKjN
z!U%Ks6Jb!AiDZU6{1c4uM)_AIiEBuBtnL%0W4=D`+UgazNrPq8SV4hcdb8o`{jae~
zW`(=GHg=K6qE-4_NBI<XBP^PJ70hAW*xNjIOyK(bZp`ko$gT~LAKXx1Am+q3PpVZ7
zo$of=7zzhs?A5xyBiB|!AW$_oJr>J~K<fP0zjaIhI!)-YE=ihGFE3eK{qM4!EdTd_
z2+H5n9r-~{{pZ^qw*R0V{_{1e|Kq;V^rA9^Z9#B(C-vwG;5{de+pK<F*hS&n_e~k2
z_AE?CLHR%N4Nt}fjgMdMbMZgb=KgP3g8%=ok+P_t(_kV==&;aaNgnZ*IeYdlaq0%V
zT--i#k>DX$o>GkC%VChcnJBR^;s(CHWH!f9y{AuEmAMDXDkS%YzystGcwIT@@<6}!
z0wIQs@<9`XspbY^&JAmL*Q7#6fUyTqffFw3N6c*6;dJHeLc|fDfZwScM*d=S)q^(z
z;TwaU27({)D<kbjpTk!Q5?b_9?3mdo-`SR;{$@q?{A6Q8;uR-QdU0Zr;Sp$U>0vAu
z1o-c=j*)XY1_F)oE?=W43`~Vp=!5*r=u%2UV+q=+2GQa;s;`;m`>ysSQ<fkcDKbpc
z!-Q1GXA(hx;$IUG(n{vO!dXC@m_(GZ!;C8SV&ZALT>H8qcFN_H7X;KgIUe}TrM@(8
z<urt8dN9Z=3=3JtXqVfHc1EuD%d|8_G6v>~Ae;)PF{u2*+}k~T>T>e(OykE{3eC9v
zx<C-Vj05VSK+Z7A>JMZZ>rrLa+9kv2tU<<p)dv|(#p9tTC0ZqL;V65~dt>qxzdUFe
zRv170tnJ!$B^ze&k;pvTm#ysjeN~a~Qsm)TI1TCa-{aFZ)VAvmsbtmJz_QaxX-a!u
zOpSvdyxTXSX=$}{rItBYBm(&6AKMQh=j(c~Y5xbpJQGUgr!3<{gI>Hh8H^;J)@b|w
zs;z*)Z7VRjeEj_U-kAkR?$rQUr$kFrv-8xjF+Sgyf408tgFYuuNEtDP^`Uq%e9P=4
z)+y<j&b+<(DhGe%x2wZBZ-OHx`CaTSDpw2d4RUI6+ab%nw@xzgugho84p<x1#Z83E
zXHV*FJ}bF#qsNZGy?f8<_8i>q@!M%_oyU*;2TF%&Sknz(YCO^B+-{ZYzo*PxtJHgp
z!7%CS)vg;Dr~4@;BxKi>p{Dc#a5pub=?=e+kPded@-OsyJ3o_dEdFJUd*zbrN-8S;
z#<1zn%qm})J+A>DJ9T6sj>Z_5!6P2RI@k?E4)!BQjucf7VHl;_Z`9P8)h>k^N5B<~
zG~pfRwHggMtXs7=Ek07*m$LxpbkxMrB~+5$b_>&2oowZ;81JBF&8oU-v^!U^qr2NH
zoxzBY_Dc=E=aVopBaW>{b!EbCEC@c@nr>@1V>4qzb+S6~;sMGvKP%}vGBuIAFr(+)
zfW0mhxvY6%!4xMBUATlF%h=INv2s_iSq;-@H*B~(2kok+;XB{j-yk5MD}XECBfYrv
z0&S2PBW+#wxL2mD@*j))-Tw*<oByD?ey>`$I<;0M8jcus15+a-7ce_`BO_znojbp2
zYG~Ba)2v%pL(*ooJSro{)l?_pA9bDygvffsZ}pul+m7mnyncO}f!pkT+#$~pyZM=3
zoPm2QE4<-oSpIdST{Bg|U{Xj>5dIh<g@uLOCLQ-gS~yQ2&9DZ0smXB9v19UYoM`0D
z%u-71?CtGgRVcD#$&v@IuJRTZFO}(0?8ss`PE<2xF|qztse1AZ$4%Z2U4-9&`Ik?p
z(#ld{fuw9Rl3!F(l3LHSeti(A!Tr#Xa@%}OIPmAgk`Xe>hkxN~aHFX6jWKVBv^ca?
z&Xb=%j<srRc3rVzMHFxZ-H(AcO-xPA2kH)ohK2pfWK<R5=EB6U(l^~ZR%|a$MRol(
zL<!@aUaAd)SQa@1f=wGYhNPr$<iC5@Xv_3vYN{3|wmIB`JR2mGl%B7kVbmY9J>`B{
zK%g4qTRXeD`ab=Ao8`cPl;eC@&v_JDtJbVBoAn_>q;1E`g*a!$n4kC@GlG!$+-_#(
zhWxhn_F&}Q#5&kGlfg&@ud4evoZb7&g_z8v^)Chd+{6h{ADpQr$Fh*>Sp>TcWlyQJ
zzjxc2uZaaXY&oP94Dm8&CqBCLy%yEU*qThs;m>x5ckFom3QgBXFd^CI?d{Fk`QX6=
zX9WFi0*-4E`z_;<Po*_B=Gx~rk1Ft5kFzbzjh**3K_PmWjHJ$d3CF)ZX^q54)1c3%
zM5`S)bPR&OhsD_8+mK(e_tnJeNl(Hi?ifD#`$J?F_t9YQuWOx+PWn*c?(c7SY>c>x
zJpwPZF=ngD*Dvbr$WRmex3yYzGo&?+*03+3+_?WJS@{WJFJ%kw5%7n%T3K0Hbh^?X
zzx?6Z;Ww|~0pT1G5g}D}KHxm&??xzK2PXDE%rNV{d;k$|FIIh@@AK#SS#zVs7W*Rw
z1qB;Uuv>;CBz)|tiS-qS0JU$6s{MTG1xZOsCDwxnOTJ=ej?S@%vtayoOdg#FUuRwG
zse$Iq+OQ_P5YAw0H+n=PlDIv7{7H_dB@1!4lx(W!rCAJHw<-)>9o=o5YiFoLd&0HD
zjEeDzkB=_o<nOu!7Qo1`RN&jUuTZ->%2RwALm>WsxE_^U_V`e9Bl+40?sV(%<Htjv
z9%5lRLIy-+`X;*HuPbfBWE0MbnaM#t4Gj&h1A3_ngAH&kiftA!7rK1q%33BSCf^KX
zj%EsWwQ<UVXU^OapC%p_3d-0yo;o;+9TBra*q3FHSZ%_;l9+ty_rYwSp)1Hh7-RTy
z^^2UGoV;c(H?d{SisdPiFcLn{m{zuO&6)#459j9Qlv*}>bx7o%{l3hgPx*`US0O6w
z+`qqA(+@A&n=ca+sgYASF4AMhuqPTl;uX{45^?N@cjnFx@%WDB<m6ZscDi7jG)}Z(
z`^1aCu5X3!c~MVgxU2YdqhV%jl|_~n-nqHv`i^mK^FBI6iDS&?{q^A=^N#EKm%v06
zc&@v{O@CS9v5qI_8*E<DFmPA(mh@i2rb<QvBEhip=+SCX$^suyNO}Lhtn84q--{PJ
zGO2UO%=5?zOfuAD&%BqLo4cNtwmnJ0&@iqX$+_=KXi24X6)}}8Inop--BX3|4rddX
zbftK24~>dPvQp&!<jIv3Fx_9N&VXojVTig7QO<%r>wx0R51I_o)w*kNF=3@-l%%d=
zi#7A761}BVdD4Q;bn2BfuvBXd?hY@DuOrdz0`)rjgkJkyTguDJ4XY0x_w8|5vh4ZO
zlCrX65LW0H!52QYiWB}qPmkmZe8sKm_oW!Mw!AMXF~4*b1vO{#-?Lx8(j_J)vi4se
z^P!^pnmIo^wGD4NCxSrp;qyNhi^b^@)u$zL(XvEBRzC&)0QN+BjAi-R%{uQNZ_jTG
zKOaytP@lYiw;&Z&RAzawP;qN(u9&%vjm<b&;Q`3RT18@~plT}AP5slz6)_Ty0v4C{
zu(3%Jo?b}~TyH8g{U9>d#krf`$oxbYd5cu!NHa(oy7ft#Ba=Y_PQ)w3%RI{*Pgy1m
zzC<cmfXBO1waZUq%j%|=O`rw7fB*jI>o<Bn24(cTc&Jo!*jtP!hzurSJ1&m{Oy{3d
zbRT?4KaO@5KTL~<`d%42AV!1ReBaiPlT`Np!-vWBhM@7|)#;aOhDM=O>jp+eOB64_
zGX)35wGoSGxmYupJbE+;7A{yrzFhSF{k2Qs9j2pXUf9i>H(C45dMnNXuM-$~nSVKP
zc1Y6w7}*24dUyd5W_Bc}k4kCdbPdi>rDdTm?4>$!8Kim<Vc~r(hLvHD9BG(_<eKl(
z?rG3RM%jAX4bOaRe!fe@9k!(&PoA_VmBM{aKUq16-=Yf>BPGwBJ2x^}@MxR)c+Z9K
z&drBZVz3WB54OE=ibjqR`tk~=?;ztzuStf#$y<6-lT5sBvHv#zc@Sst#h{>|PQF&m
zMbdxSJh)BBnT{Nbn~j+G?UZ0pZ>iAYTlO1e?XMSk?331iDVpOv@!QPYx1cg=Dw(_E
z<7U2t5fOXS`d@07zt);X<dP_UI>0mx@wOCgN(g(QB_#d(TUZYqsKUekoMIy-<qk`9
zDH8jljN1!58(cJ6uX;JB?=m8>NA~9I#J9-O+B-$3Ot`6BFCwolnT)s)+?1hnLUD2!
z#+g9_1NyG{E2vJ2%cmJCXWGVTtt3y)+jjS^U1ye2uQQ8|b=O?`CSjKGi{)-ta^4%J
z?yR>Lx|pKvR+&Tzzbpdk>OssB&-Xse-_iRaCQ4q(_-uNam#1eac*gYurfE!^-eOyi
zn}n3`$<HUG`TtINO}^aODdbWYV^{+^ulZm@b+Ez5?#36CRrvDdn(ts!lTup$(*uox
zr<R3lW=~{gYw+A&u{O#3Q@-b^+bi~QalN|E!^$dw2@$55u|*j;2C^Q_nk!aM_()ov
zlc4^z9(h7?y~VLDV(vbi22n8W-(<9T<Ho(r%mo6JJwVbKLsbky@_Txy-e{`7_B_I)
zVcIe6kAHb}o0`WQXkBkTH5BXM;P81cSje@(*~Ow-LX+eIo^3cvYEhW%7GA%8{R~>8
zEM_^Fffb#QkZ?T{Iks-otHj2M?CMspPHzXYTdB@pOwte@$z$*Ra6sK(Fu=%_kBRD&
z0m_J!dY4C!%z2iuaC0|kWvb@dtt<B9GCqxg(}!Bttz0P?uae#5Qm$y56CWSne3VPC
zn37?p8Rr_tbF0v@(2)JUgOjsHD^t{qRWnL8$)*r4D^%UEi#_B!h+?LwwY62p%4nkT
z?%lhb$=+V6xFageSgnbD`IU7k6_;23+qXTK%O^iFnG2235#EpHZ+&fp(;8mnQd2$G
z<1}p2PHx8ifBx2jxhrG>#6(ZI0B%zD+O-5Y@`qjs7VJf2vf%oodHu}XXd1BYa1NuE
zCT4XOe*V_jQ{B_k)7Hr64JO5(b79Jw`}`%OmP$F)dm}Zzy|VvihiW#x`f1wCToF0U
z@f0UU)7l4LN*>2!lvGboPZtprOR3+yX_IW2smB4P&70-hUQtm+9K<xQ{l0_Xnm1`q
zyU*)uPBe~;jBwK2x_#@G605L++snoG%m=CV+xxV#l9GQ3Z#|-M4~dXW+n9BR(FwDj
z$D^a814ULpv(tUv&s&f(V(fy>=ePHf#C!40eGwpqH&T8n98l{RxI+M==^*ddZOzHX
zMBPiiTMFre$_`~SAmlk5o5TF@r}cBW8=oV;I7Kp>%2E8=in_!r1{*@-vPMP7kmhy8
z@+tPt&dxGwYHA}Er%v76uwesB=s}~2Y0dQSYf#Y`xeo8GfC;z5DokVYRHw^tw6n9*
zMYG7ev^?e#yYFB}hlcB0^6E}u5s<*ha?YavYvE-sGMNl`_mH&j?%6>PkBw;!`!2<w
zFgQNz@+p>_gOz+=KP`)ewXu80@NoP->9F@2M`XWdZ5rH$!eW=IYU^i%=;|mW*qJ6R
zHq49vMp`6}S&P&6s9BF>=BX~14kXp&B|yd$R~deu=b+Hyo|fE%>^p{-@_zNtiHJA{
z3^x-5;&4oY6F_UO64nCa*erb*-yww;6sOLM&ojY-k18OOeeR$8Vm^4+QG<r6&f{e)
z(>7Tz${P1amw7eiet)}fRFB~8wh=wFlqwF-s9v9%n%Zmnto6Bb_livc9p32e?Im{3
z`jccpMqh1wK(Y1iS?suLIwDUG9XKFkXP2{d>CzJoF2UTTIXkcS<GB73f|?5*MAQ7n
zvJ+#@$ay;4OyyIIIK0JHty+~N>}Xm=^H2Gbpb%>z+=)^&fjC9=ishQ+Z<6nUjg>s^
z&eP8?7C@+Of%6t_>v3&kKkSrV9CWofojVe+KW>aqQ#nMLz|AT0$~zarq9CA*@Krgf
zed#_$t*JVdg2Y*jlB$nc3%6Le`1|_qf0!>7^fIfu+bi_oVvt*0hG)kaPTuvL`?4@w
zacKybYGNe;kgZrZweP8-A$nO-BRL49wop)p*2HSJDkrsCm`R*ZX^%K)X>@1*;;m;6
z=P1_K*Sm*E_;L5b>UE+nJ-eDFKy=>rQo~ZJz!#|e^tGnlLy&x(bH{qdd+wL-L(Wh`
zfNWyzMfzwhux857&xdcdVN&EVR6VvR=+`v{mBn9#Q~mXZyPH)aAQkNchvTuUt7~!6
zL4}l$aF-3jB)vk-%Na7}wsuwxB=kGXPCQ7-x4h$X6RG>Ar(RxHlLv=~Rl7_*o_lf}
zJC=6(Fb?lZGUN<Zd2~q*4sN9j7cNllZ%~`lSjaZ*Snl`7>Onv-<UH=BAO1X|S>$0d
z;aPe;nu*VAW;Q8hIn~Lt$FW;b=b;GGL;AJO)ydJ3JUQa0WiXiw!y1dip$)cnXB#PR
z%<KR=f~K5N?)=N<H66QACibBqlt=b5j)W}zfTHaGd=qjeW>Tk4o$@Yo3kVn<eGcms
zd-C>W14iPUC8J!(3>m}f_<ud1tF7x7W7q4%EAPgk0NQBOipH7*ZB<dR;{2ENs#eNg
zU@5x9WD3y`2Fxt5_M;NCo#_qdWREDw&)0zoP3TL30M(Z-vBf<A7Cd6$k%L)XfC~c;
zvv8DryuS)2xStggA|*S^-n}1@Z%)7#s2?@2TcL*guF&vsJ;Xqt6IMx;WAd^As{Su~
zaI$=|%iduy!VskO7ukMy9UU9=dA5ox`?v=`5%I^_`24VGml)}1EXE?D=QIomeqoyy
zd?DXu#?Z)4Xc2w+ozQi$>vjv1!;<LDT+`ZO?Y=7Y4Hs8{kg};W#?_lc$6axvF-0fX
zZKqJf0Cs^yfNCP;hOm5ism7&8EOPDUi9<>@>dU^ywD=u6cA&fo$1BtW)UXP3ypBSW
z;d>?$kw_7lx9RnTsJ^k{%0F!ZS2xAWhaAk=S+!<Y__BxuccvB;f9t$9oA2h~Y3w`>
zW*u?I7JtU}cUV9+HpB#?-JfL%pIva;6xzMw*j+q3Gs8`<qf~A|Uu`@k_$mnyhr<IO
z_Ny*vugPc4gGVHU41v)o{Dp0>I$fvrIVJBO>ECHd$;U|T^(U(%rG<z+Xk#}Wr~b2&
zNwhTvijWHh<Au212sDtxyszr>pyS$Yx)}IPR;$>W49w<R_<F_H7RkPa-Vx6t>z1J&
z9<j3lY4UObs!z!paSqiE%D&S(-MO7SsiWE-^O(2CB_v3qQqtsphZWU-3B$1-w>q4q
z`$$)dh=b3bJ)1uZG%WN|vQ|CsURLo!JI;OkBE=>|CmY?|+zeBtlT@r4OnxN4XXkD2
z4`Ju%HN7(StdKZ6TR^)<qRwWR&9{WWI?Bg?qHX(j&&HwPE7Z66R;>iGoxw|niNx{g
zNwTL}CSm$er_YaFW5|Cc2hegg>XgreIDya{s*O`RJ1ZoNh#FCB>Mwg_@&TVwd<>te
z$u|<c|9zLsmoJYbkjv}_TH0bW7xzvc^FA3$wy+OA1c8Jutd$;ha~ZWROms%4rVfow
zi{v!L#n@#cwI7|BIM`BJQX&gPJ2a(}9F>7DutjC(T8T)xP=jUS>f3wkIlH-#I4FCg
z{H*sFF>9?$(iC-yesKL~64Bd~o?K#a@#01AKCc7zqaDJTN5-c^(*y`SA_mbnqE<@1
zCB__ND7Wf!>W%l5bIu|?)s&BWF@MFxB&mdF1%c#rDht#v8SxrTbrH1Epef#~MZ)|Q
zvkW7_P0tk4e0lcl*^z2jWRF(Z%DT}EJQh7f$t)))H<E)lSv7ZV^x^5#r;E%bko}R=
z0S9h+V^LX|yp2tkqQMAh(I~bIY|F0xbDo##lTu}<=w{zRKn+K#zuidk+_jvVy3rGv
zjXe*+;0mAY@pN-jPmV|Gr^zi;IO-y|{a}3n1J|{aSarj+3rY6+sK`n>j>I*LVh?%~
z2m^Vl%7R5!^Ph`_*^8@X{+x{f7tJ$=5ZTPWeER#d0Y5)~rqd)9Rh6Kzu`%#ikCG4C
zsB3EwYQw&K`C<}~rjeU%+%D+bnJj`f_SPTCIhdRJW1n+`pD;^!HI9-+y2jemkQ$8}
zc#uZJcp?HvY`Su*BF?g(C5KA3b-jK7m+=d1Xx2;0xpubX*(`3t4(%_OF42a3YZ(@H
zN1?gD@|E$GqlQg&Va>>;GMgQdpaQ)>#LTRIO3CSs+U=TE%(K2fk+<-DQ%}xDalOT{
zO|H4s{#&!w4Q!WHYu8R()JI@^NODIK$g=JkHfN-wc@y>i{rgpZnZQao*?q2Fy_#vu
z?u8Tr3%tq5SByDxc!|CIXdQ(5%`V9rm*ho*tO>plaSx!eHf%gJXRH7T7?hSrT-cd;
z1T}B+J_5U}1@xg6kU5{APNx8W%KsEy1Xj>rXpMe_tOp@JQ#n_|M6R$idH2kmJLO&M
z(*9CMz6oy+dnnHhYBpptzJrJ_DoK7I%CaCd###{PujkL7e;&j=MF=>pGfb1c^!<4o
zMrZt}OD=!PyNNJxYQzE?4z(th1&4BjzUvnDf+s<EUZuw^2A&al;qKkLHw{DP;0+oq
z4JdUVb{Z&Q=m%%x?%69>?Y_2ABiq|#GzIlN-cxgh1teT)-n+G6*8s@+P%6V4HV)ev
zGhf?K+r{HWay`oAN+7Yt<CU3~M!u%Mtyt0EX*{l$nq}J#w8Hc8W67*$tlPver8@f2
z6Zn1YHqNTRQ@8aeN#KdiKsW?=>tB+C>YwL;XuvmqWN=hQV^dS|@bK23Bv>+nDU<!2
zdh-0#{fi568sE6UEKYMzSx3(mh-~RkquJ@{UR*DrM}YAN^mZc_a&k|#%KVjed8G1Z
z=P0#n)Y7T`Z_<A8zkA*$sHHlmuQ{G=<G>C0*obBu&w)!g4hHHbqgtF6K*pk-{{RnW
zPY4d_s)!5R*FK#b$pLx|9-a&`(8-HDWW2f<v-HR$l#lUsbr-$NQR<9?U0gTdi~V2B
zjZsXM$Xt}O4Cov&4lz`!eYP(;UjJzWlBQ5W7y8~L7lPP3nC7FOuxY~vsjO@`ph>Ad
z-sTvDc4NI(D}!Z|I(xsK@IN&InVjJ}nATz~ikdI9W)}S?v$?M$p9d2X*a#J9U2#Qs
zz$p?3SvD)1xs^<U?ir7lDy=V0DZN5V^Sn?uPrth;EAi-fNO_0RAMk7_0HH{uQdQNm
z*?biZjU@#gA_WDIXx9F>1qFRDoZZ`EG2R@sivn4<6wJ?Tsh;<d6kbF1<uMANog5rh
zk7ee@V7I|@Fu=n@BSp8mC~OXDx$gXXOQ<4Lj<B;o!YuQK@kcnW3xL?{56#|Y`0DSl
zSbTNsmMvS>6)VKbQ2_FNttb$K0|NuAEG&jw44#!}EkeJS_yFsN&5=%QEY^+6h>Ttr
zx39+Okk-$K!RVakpnMq619i}uhH14bXXl$YZyu$jq)0W6bOCH9c#XeW;zegfhx+le
zw9H~`01zv=0UM?D%Lmxa=y(l&?5^xyMio_xGD?5uFW~|-MwBL}9M$OngRleU(3Oq{
zokQ@#1-D9W{EJBO=?wG!t-vBJ6c&){C+{0>Z@=<3Ki|_@98X9PO(*?6I8S6dR_mHy
z<lDLP%_0ztkyWAJf#6rsJIXplKikbWy<)!3NYI8p^sSOJM{+>x(dYi_VgBV0zQ@Xj
zv|i#-72uGFW`#{~RNa@hH-j_f-)3{au_MwcbOnr$k4rUTPEx>ndV2jz6<0=H((-8Z
zc=+%kJEg5rhgV{7GL=YnIn6<G9zp-2@$Dr^Ni{KF&XNc~+2Zv;|F;3hFidTy0J@4S
zqko-1&p;bVk@7t0rDaL-q5aexcu=?iX6(Hd|A4==z(Ta1i81>wXm`AKD?pZ|Uqqnq
z>p;>6RI)63KK}jw!w0ka6}Egx4Fh!_p<@|$XzPjV35kg5nca;r;VJ+v(BO`fQ)4yU
zW?hOcP<1(@=h#pciF|ql^>Aa#lc!I$PN15!pncZ_P|#4d63F@B=WRR|F^QJLD!wN6
zb0bdW**7eGO#l@Nl9PDwJ8WDXEze_9OOVmV@uQEn9yOU!X{mN#dGpC`UMZdVx_=>?
zh<7(5RZ*U9sxEpj3P9jW?9h5h1^(C8)+Tc9+((30eHZHG%XM%(Y~|2?yg7I|RiHKC
zTSlH*guSCSU%yBd+X9RsQ-%hf<~{%fflc7?{5k2?X0{Wb7E+$1wIfR`Pf&k@nU2i9
z^9u_Lx;U~>I&WlRijRpo;M=}-E5}E05UPR%s7bSkq$me5`uyX)^*bM=f&3MDo0=v=
z*&d)oMu)rTz|&Tjd-o*!Yhsbh?nCiZbUCq(o{3${8HsZDq~*qc@<bR`=8}Qkje|yb
zs-!Cigz|fq!TAm^Lp-)kG+w39m!|?$7br;@+J$jQ>R7gK|9j}7wsr)Z6JrAd|H3eW
zeSCZ^L(?g{cyX{;Bch5L?C0c^!?np-TW2WqAcvC$ssr>z=Hf+d(=6a`WQ6>!Teq5W
z;t-rv$xBqi_oOfcpzK*e!M=|{0wUt#smBdDeJ9?~@M{m$B^sttHshxl<Z!+C`%T>M
z?)*+m&Lc0)SCpnNquky5BUHK(fHbcl99jfFeLi0mui9$JDUHN!r{%1KrCm`)8o=+$
zm8j!qn}`uyWhjrbu&5|IYu1<3X^mjOc_L$SIkNb}^0u8wywgLDovLX0vca4Y)ZRxB
z;ES4Ejs^U_h--LYj$YoEu`$!k;l8@W;N}k@p+RNg1Yq@h`}T~}{*b4W?cLp1N$XP6
zS}@z7kuw;0g<;xZlaBW$CG@(8L_$oqRf86{Zr^^yWcM}b!{SEeias#!k{s|mE%U4D
zKd1V;3y%J6`e7**&FVF4s)*Scj;bi+c@o8U@7(DjHL{qPvm$%uQnYQ1;fAV<4jI{f
zsM5Mqny_A}m-AO;!=tp*$0`Q-nL>tHBry|3v5<aXY9P5xx4YEOuw+g5z3;~nffJrR
zqa$u4tJbZvP#8j-AP7qc0+Ixp*rW^pqsS^k7cqn+#sK!OS;b>=wB9`Wi{&VSs}R|b
zq@G%Ar*Q*Cn8Af}fM2qfiBCx@x!>bC&=c}Q8q!V4*ptX3yum1SoGid2B&`W#Y32%X
zh_*90<;JmB_9Olejs<99XFwX#hb+J9@pggdPmwIt!u<(cVhbIVNt%X2qb;QIv}tj3
z5JmvC-tr|5)Pt?Ls;J05B1e>wlnlIgZzTqIUIOSX1(Z<M3B<YuW_EVzX!-aWT)WxK
zxXyBP&{6J?Hy!U{SNThFalmSNfHo#wJP@Utn?p>lNRrbjI+OH0Nk;36nE!0rwhet$
ziNe-aRq%eK(UwtO_t$<JBVO7sl#1hbw5Am@En+7J8^fKQ*Pzso0@O<|QErQY9yd;s
zEk-DirWIl-i3|9!eTNR6S65djKRCm3IF400!^0<U$OZ6&L0!Trz`jxn3O=ZMt3rfn
zk$^-u(dKbI@$d*CiRIzL7jU3Qp|#tPW)%PTZR-684t%(>xaUZjx8G6et?4qHK>zvW
zuqyiNA7cOcBAV?&=y?3+SKw<v%K!Nql?>hg^qD)4GyL}(CDj2F{P&A2&dAsP^XueY
zw|{14{`k8HHq>oD{?Cs;HwpglMgCZe|6R!+D}l!B|L)13i}C;A#j%OzrV2droZDOk
z!w?R%xIB0ub@JrNO@jaBqRPb0{XfjThgXwX+c%0b%2*k75Cy?eQ4|CO4M;~35$V#w
zfJpDsAyh{J6$PdDBE3t8&{Pzpmk=UdItfi60Z9ma*UilHeD8bC`2$YYa*b=$knDT!
ztNhBfx4b!|ysJFCfKGY!;>AyW8dp}@U459oTnMyM?$Q6}InNeCM!Uxdn$-3u4cPGg
zzYenX)&H;4J!631!w`9nho{xt3oeU6QXh*PqXJwY_`t=*Wf!~r6ErIU$Kd@`(|~VF
zaZ7;&oig*ke_xcizPxJ)#d_43s@R!9Y(l{o^Y3tjhziOq0?(`cVG*&hIg`zytPUV_
z*Mk_WbqYoS{k%a1Z-g}yP*V)nQvdt+_tr(rBRdxG{r_F*|NI7Z{Qqwe5qYrx_m!d8
z)=G$1oFEhG{|wkqC3W5^K-C)s=N|pnqH_6vMpd<ri<#4NGdc6FHzhaPLk<&()#8!p
zM?GIY732xu{O3)7G_-5|&u~Axo_MohUfPJ(@P*vqIZ4p{dZr`)@9cj+f!F^`1Ngqq
z^uupx-ia!vGvX44mV)H(3wEu%WgenJ|NQljNYnJ6mkjF9#J7d<^}0pjq5=GEi=w{z
z%lsqr8UK0f+5efJa$_UCG+c&s*j>aLz7?z*ZLt(lR;(h={5vxI2iyO8<JI|26YTu_
z8~E8+bn~_JW(x-$^Q3vb-AO&*FKNB=KT?gF@>Z3o0(1K?Twka@gOyjACQT=s@SFpc
zEb{s&`M<v6M>6UF=5eQ+9(pRn$j#8i$4${8tJ!ZbEd63lh}P|Y^Pddye|`A3#;2$r
zo4$2r-IV-U0>!$J*+If6I*FhwLU9-`8@}%B&a>%~l-2kfgXYuo!CKY(qW(Q~!~g5)
z$-xj!cE$C%g0YE*Q8hk~c<WFVHQqj*wWGAz=|p^q?}x?`Zs&uPzEE+~Q~a5apuDxY
zlC?(J+Cr1)Bq3K%$Crh4(>F)cO|@Kgk?*F)GnRYwBN{Nv!8)(Xqkt2F#EJL91=(F5
z?Csk>0)m3_pr`_{BNCC9`1$Sdgq%YD<?icAr!|Va^*5SoFZ;gDi8;hFt65;_ukaSl
zS^=()I?EyRIrGv6ym`;gkf$B5#GOvej;B?UUA{B;-;dC-?CHZFpt^kBV>ZpovOE^%
zao=5KEIT9gXn2Fb+8>DZA-g`;&ItpWSVWlt^KlST7x|bV<GWvUxrIQ`fhrOYMYAi&
zP%RO3@$ljNDS$Bk^V;}RKOcEieBa&r%&M~l=F`1`%3VG3-P^F3M|Z@1e$Dy@H5;M-
z%)4dBo~`xl?ZM_l4A~F0^HKxELr5JL9mSk@2*$CpR-*cB&bQNQL{^-3pYD%(>*xfi
z<s0Uq1IB~eGT7oS-0b^f%p&lzUM7$V04?L7P^63po#nZCQ`e;Fbq`<V@OuDm%A5rL
z^Wk75(Yd1T_e>V{_>%7@RN7*cdU;JcaeS;*Nt;{a!GkuQ4@=xQTFS(u3@m%nN-M28
zB^jsY8?V+dsG;p7VhVLwzgl`dxSM09v!r}!w?308*XB#QZDwIH4|kxc1s&yZ0Tn0_
zkPZX|cl=!4V(Jw0dV3?ef#&7ecr60JRp2$O0C9&<oQO2KfGc{=PW<-+`tP)M3iN&{
z62U4dZ3xDbw~t6FDl0{ENuMnpWhZFtsSh!t*(pn8sOp@#>|P6p%J~|~V59SUK0NeU
z?Z;TIg5mgyCx1&LZ<x)W>oP-zp3et(UKqz(83Cfp@YlXk#;1U3(1H&O>K<-jyEWmK
z1a;_Iq0a)jIY?n)$WI0+E=Y4dX@jK}p-2G^AX)tS;YKyU3pjnq>UFEFya+SL>HYH-
z!1P`e?0>g_8qIX-PtrQEXE4sA)#|jLQK?jYPV`+@tEqL*A(+>T?1^aBrkfJm$<pgq
z9?gNIeu>)rbBqi;x9+N>O8s(9uGe!{3y3PY7?=F5oiKVr5AIG<x%kh_cNPt^79#Nd
z1W?xH0o4ODcH^JB09%2E>mu0%A3q0{Dw9(Z17g1-rC9->72(5|J5Aq5MEic*n_1AK
zz6#CC$RNVd!5#pfvH;XXBO-yU|J+#yEBE1@lbBJZ4@<#!lUS9u-)OYC;32A|TFrhl
zsh#J03d*joKkr{qEfv=`MUlTT8<hVVKX;J4c4e)44?~i=nkoJ@C5Zg#EG^=lf$q{;
z{$Tv=-N@%}-L^vYjm!tKH14M3V@G&eu^r}U$FD4Ll3o=LG&Dera2~-4Nbm(?PvA1H
zA6SC+4v#R^5D%_gyeJ35So`4$>MNz2?PdUj!U9+#GLf-{2Kl1h!F#(K>23OjEGO5-
z+4+Z;GK2O}t((4VPv3`4R63AeS+tz`L8jSRgX3OHKYPQ{6#{%_7L!wfQ43;)!D9wF
zIw!wYMaC?d2<O;;n0)vW5`YB9dE{L^lp?vZ01;8|jb;C^{|MV0INKgPe*7L0x+B!?
zxpU_@9j=3B3qS%q$j@9r&<magML>A2hL`~H$>{+2;=iwQgWJ{>O+FPWD>(xyZ8v_W
z5H{SI)s~}NdHb}(fBseHMDbJ!RW<Tb%iGA%&OX8*Gq7+^EQ38WpHMeu-~XxXOeN{{
z!tlKYX)(<cOqFgP5@P4jqWk2M4)6X5iOl<9xQjWUMx%myH!>zB8<E1mCSS205~%(8
zH^PH}f}%Um?!Q%5irWjxC?s-b!O|s0S+Q|u4{RJ(i_SFWv~pJJGRMBJS#vj1Z67Fn
zozQEsu#Z8ry+@-OLk_aTlG0GGRPMRwsSq-a<3w{B>Jz%<?Jmfo@?~X%rVn1#wtL~e
z4758C?{->Y2_TCqvVm3?^W!PgpQldgvYfjYwwIBS@{y5w?&DKN42*8hz50KhfqhpE
zPN-UMMNsZ{w7TnOYSABcR6C>$-U-B%E$S3WslNHV_Y9|aQf$^N>%yv~7lZ#!ORGTd
zP{UWNFGIXH9OjQzl5QM4)ICJl=>E*aWb<ff|5sX#@DUCAAAUiu+DpnS_iA@PnicZV
z;HIxDmoI1Y#JOEY6rGY3O|77Rj<r7<k7SLEqKS&-;<lz<!CkE&SUH}7LB<^HX4{e0
zZ?00Zo>@(<H)x}KtWS<_x|<*Sw2h%rIaNY;YU$a&qrQEUi+g44v`vexX9DjW@v{k#
z(yuq#l<R*m|J&x~T+cg>vaOfnY(q8G?dIVfj<8YBEwbOtuX*)PZ;Ov`(E3l?+-E9n
ztJKFE{5Uewh>NIne$cNexhUk#yr!cmSSW?A28*WBEbP^v2B&{XQa={wD{mzCn|RU@
z!im7L(7xw;_$U;t?9otZ5r-^ld2UwIxK?GWAVc4uyc?Nw<zI_mi8~G!tX>MgQRp7Q
z*--V2EwFm~4Cmmv2WyoB%$HxOcJz;v&&c5q@$0lB>!zlcYdZ69`H_-&{jluvHfL6r
z@1R~n48MX`NOC_aIlre!JXFgV{!)t27A(dR-L16k3we(nX@01Ns$>0_s;6qyuV;5&
z_6%oRJ8$pQ*~-$~LkyZS!z(!KVxFb)FP_20FXT^)Y?dpsvgGCB5hNjW9u{^ozgW5p
z2V@8Sx9*Y1|MnP<lV0p&0xOS)MApamwECg<BGC))0-j%k3jDcV&r53ahJn`9dxwT>
z4O!)yw4&RLwWU>#ZyQ2K8&b2pJ1zw^?+ixgo}TCE%0CK>COr49&Y_&P?b7>s!%BZR
z5Uy$B?3eRSpP=O^;;vN=y~;n4QmE2eGbD^Gh{uWl)P(*_(|`U+TsjRfWoBbW6y5ae
zo)J->G$E&hb$^_O<a8Kvex>hFn#Igg#Q5X(fk??XQmKI#Z+BO-*Cz$isKjk%^!HKX
z3c>d!etYVw0Q69^{K5Ech^;p!7Fpt|&#5oHMisJ76zLR7sVZ&@-nk%#ifEbg`1qDr
zR{Um?q|f5h8!Y+>MgP>FT8N@&_w`Y>w`A3Il9io2n`Bx!pFbS`o>yAGK(hHduOO7n
zxmib6tgtiTDAIbb1KZjP8=rveS$&Qv7Jc&14Fk%`DkFc}$ajJJ%VXUdzEWCi)=N*1
z+2Jqnd>${}I-kc$H)d~n!=Uzx?<F^CW)hF_-R3u&ijw;HeT<L~l@R__OQY-jKE7##
zr&5n%!l%gjTj%XWT}p;$_x+H9*_n$!ulUrj`i~l5bp+)-L*SJCc<tVQINwx0<V?@&
z)7Q&@e}=wM7swM3;-!kY%%uQVIuA;dh|o}Xi`0L<qRe7gOjOzd-22G4ap-j{Q4~8f
zS&o^fE@9t$;qJdU%HU73fO9A*;%BjvUpmB`+{=LzAtj~S-PjBZVYxrC7TA~XtxPo!
zueg-tq_?z`YDaE2OKe4H6lVRR#!yD9IN9i*tfx=vFN7>hgrzd?3>P)ub<O^J4zPZt
zHEM`n=vh!!uqRaK%4;asr>DIq?epRaG~^@kd>NA;*tecMp0}Qn?H5nwv|{qFW4xlL
z|6virgxeh^oYSifzn`;Gb<RG_=%GAMmb-B9)g-m@rpN%p(}BlCTz?=g`x7iU0MS|j
z`$+2kv~8>l8ers#D-x*3qrW=MTx$QdjCtv`v4jVh4^-nuh~kY3xIW7E`?Vs&f9_Ub
zunt1Y3%StB-t5NLrm^Py+j&}n;bvpbhxjm0X0#XK>v0PZxz0M{FMa^bG#0vn8pRD#
zqx6Qj#P(*chn=ce2D8IL#y7D&h9RAjG?j#YSW6zFX<?gFXecR#Nx)Vo<0a9e{N-VM
zX`1-_=4Q<q&K>AWjx3+bfd#=po0gq)Ra^GD$eCV)hObwy7kw)bt25}wJHy}hX7-Ds
z(&A7b(2RU<M2+2Tr$_&U^$eFEVzjO~5$ma6MbAEIE1sJuFLWkK5L(M*w_FR&=SY4K
zy$wAVsbG*Rs{xD_h;un2GIILoFMtrpKpOK3t+qR9K@)<7Yo^OU;Xd))CA`IS?aA`O
zHmHK<fCL4NXQ>O^Zm7BX?;Un2!g}Q*avMH11LKF?ECQgiAT9btM(U&oHH_ur_>Z$o
z8J#YQNsFaE+S<%zpD;QNMV*@2K|XCsI!=XN_LZ_wIzNd+QJ*!CTRQ6cva}=x<Ytrb
zv<I$Z?(yjL*-PQ%2^O>qEv4V^b-qg_*&)?Tkh{#u;nj#B9DEaLTb#hosR#Ss?UA9B
zY<4Tu{aWK+6bmD2Z)Rg|VP;A?u?w|Lk-~pjSq9w;Y9ZEynDU@_jgH*ToAu}a@$g-&
zPWDCdP@C<F3;L|dGcHa~8+bqHQjku{?eUJaAc9t;V6#uopF_0tn2DF%2*7<hP(4AD
zKgYuhoTW&4>EzUT_1zjIN+QtO(d347a;aI@cEX4Qm}dbp2i{VSwjrJr@NZ#pInUD|
z++~*hhW`XEK9Gr&Mcbe2kcVOG$w~F~eez*Y#6>c#-v|}}-<-$d+*fz!cQ6y}%UV0=
z3jZeKQf6|bx0umnRz5)|?}Y6rhgQaN+6h-$nPv1#;-_sX>O{X3o?j3xwx+UJP&~qN
zY+?52FDQ=c))n)k)Ast4F2MH>pS2~pCKtN$3QBf&<u}W_cK6B$5m)V%c~GLRy0m*)
z&D(<((sRGd5LXZK>6gsMEF~7}`52-zx%~!H%bA81m85Mtwr|jD>6X*`fL4BTG<P)?
z*RVOm?gQ5&hG}}IX$#$=+fI1e+U6U2EuxT1SE*qAw{3MN^(laWUlWuNTDrQ~!1*9W
z_?0VHzD`4rK>^jebxYVq!}|OG1g$<UkS48ZtzaezlKgG1)(_=z%X5&RpMIt3i~QbG
z%e3EWHDQtJN^s0_WzL!<4XP66I*KJ-v_dmEgE>If`sDm|Rd=PK>=+X>b`LEm&HaKO
zmr;IRsdKaw3HVL3Babf+-`(H`x;CArnyZA%S7kJ~$#~C>&vk$54WJGm>a!uZ;OJv^
zjviY>l$UJXB_YTn=9(3=QSBSg)zJ+SgwTg&s5QYgH+t7}T+}t|e%udPdVP}e43E9_
z;`oEo#gKx*_*|P#ho#7y{?3VCtu1}L<KTF{H*)827oF=y7hQdY{~2<Gg>%Y-;V%XT
z89R-P79!BenAH|;yRddTosM#vy5|aZ6L8Lep$DO&fZ{bh6<EKust9&Zy{ji<l`cU9
zQycSMdjm4L>7QGO?HZAR!ym(qNpJ@?3F=7199Ajt?^Jq}|5xj|NIAi{`t_7HC)To@
ztS*Pe$)8_ME&Yq6s;i@=^c-azb~tBU?iSYG&f`of*MhS%*P<xaO32c``uukWnb-wY
zse%JWc75+Os;8xzKQGFc&+2AM)n!x;MwaY-nKgY<mpsQjRAySacFQcRuiPpoQ)SNP
z`sD&Fw$|VT+ROKP#|Y-j_zElQB0eoKLv1yWp>J<Ib7J42LvB`;@w__axo7jE3rV@-
z9PHXort5(f4nMd=CFQj9&Vjqe+@7h^3~tCFyi)>L;m#rVGx(S7-l0j-u)Q62PG&&?
z(3Lg|1Ke)vXjwT?VJK>3Lc+s@gi+9w(Se+^1{aQUHE^y7YmKmJkUkJ|xdMV=B7z@S
z@vq~X^sTKMo~x$N&d!56oM%)TxHw4|Jre1n_0JB?&VeI>v0WFYm*8BT2IrQP$xBt7
z2Y*r=^_xtEh)<6J7HSm(>xZ9S@Yc}&YO~RE*vG>}0Dovjz<mpfBLkP)*d;0^t<9JJ
zBO#x)XdxHsu|ZF+MyqO{7CpAL9_u<+NPD9N3TDr3xB;EI<Wr|G6rRw0QQ?BPNFP+X
zMqpTrZdNqGTEo@~F`pXXQ0(CETkt4dntK%aQbL{qrD?5QKl;lx)OR>xN_$gAx|H)w
zk4ku8g`;PYPHdp->psH#(Sb$PHh#7C;&$sgCnhzPqnYM5pDNPyKhR%HXe=<fMaUJM
zJcj+CAzRmAoBS$&z4Wkm&G_4<b^Ah2^<ym7kdRX@W%uSpMP1c9PxQN<iP)MT422<P
z3~+Pu{`Hqk7+?+jz*+-IdsA6yr-q5s<D+>dd2Y^<8T~I(U>*E=_%>9x*uF1h@%`n!
zL6cqX;(Epa2<ZRne&lMa*=$ygqTyVogYwGT<a%Ki0lsB|quB*)s?gUGj;!PIYXrKL
z=?0dab&M`14VR;5+xb7tZ7)~M83mhYnigZkOxe9cEJ$&ogs8X}-`SLY>Q=whtS0o`
zkh_SBj*<zSV@dY?nVLelJ$({#C-dqJz0@v@8jia?rd~v|TMd{rl79p&=3RyF#&4-%
zw5_7ur(Z4dwXKH%udzy$P&Nop5j<Pe^RrTCuFlM>Eiq<PSm+Lt^|iv~EtlxekbjM`
z%Jwk~ha2F@3$=-n+Z+I%{&=Vc=N=RUkuK&@*-*db0jQ?Z27-Ql9YfEI*9`@Z-XpGr
z>XCj>y6{4evjwt~w$p<D>_Y1_lhf5~8v|l?_wKptEycEp5d(O!5uR5lQ54x58{1FW
z(5+WLAc!hfm0-Qyf5A));Gog<X`>a)lm3$|Rrflr)CF{<M?}=>^!q?%N!^^I*woB-
zKnD3SPi5(6^}9HiDIjurvy7fUmh|B~zn+OS`k%g7Fg{(U$pwE%$L*c!Y1%gFF*KA|
zn<q8^#wuOol^hG+vq$FQkjpi+j&Iprf$ds@EcD?du{_sjfB5)VNc;HOoJAF}F9iIK
zKD54}HP-LS!3k7Cxt{MkxwVQ*#Qq$1mrpi+bJGEWgTB8st2^;Gud2B-+oj1dAL%b?
z4kV@q59Z0ycj$C!Z(}cbr>PH1OKGuj#iPEikZ}D6Gw)|Txrvzi{J;xZ*0&0F<{rco
zS36Q*AW}I*8m;qM@k^HQ>|MnCd!k3ylWsy_lbhEKT$>UxDphbW8k5yY9?KtIeE!=n
zQd)ecPNmj3#<_)4RQH^IScH)&{sPn?A?b%Dy*#-$#bQy+ka-@Jvte8v^Xv7)f0R7j
zm>G`~OD#G-|J!#Cb^`Z(blh`d{j%`<6YSd5&$;I=Z?+$mBT<)Hk*hQQy(_MG`>i3>
zv+`=En2EwUNa}MlhHO2>sm+50JgN_S)uB+fTz+}A#>Devc>Rz=KPqs|?-0XH*7Sr<
z0suHYss_XHSCG^m#BW<v6IfQ*8{5lqdQj?t&3e|r;+WH1y2>m|u1bm#%bk6iZ_4t$
zeJanXU++Krpq029ntkXH89zM|X9*AoiGWRNdm3~LK0<>a8VV5QqoECJG(!kSFWCb2
zMTx@+^mj%_)>pTF`!y!?0)`^;lzW5O)~;e6@4Rzsa;(kV+#HMkx3VtRfne9<i|sdi
zjCs)znmN)_c^m3rNM9r3`30F{J*wlk3Qrt_HA^wbv{%zViHqIKkQ-EcJD4-Byc}Y}
zA#qgShP%;^Yl8IrwYUJAo@cTy#@vLKF+kT*zO5LaO`X~fQq8n+F&oBA{dH1uLmT`k
z+)o+0$Jj@<cQO2lvBldXj*`=}lQ$38_+{#i-7U2~<nOHU?rSd5=D5qaIPb8EQUlbC
z)X9}`0H<rLqoHeW6uT=nY-EZmTle?r6;nI0DDJ4=)jpE?)L|>fJ$i{x=P|~=NH47h
z)e~XhWi#n~fzsqUf=aRTsnq^b{Xydby0`pYqlw<89JN7C&@5y(SmyLjAvkQTl0|(V
zh*Bc-rEY$Ai@ho>y&=CvrO{Y-+||{IZO;D$#uPULsKV@HvA1IRqc9GCu%(<`I-PQE
zk%OBlg3x{Ro&K*MkDxk&=tx;1VpkjSgRv3gC1?EOtkzP)vFp&dQa>L@ueGq*z2H85
zKx<Z0<*9u<TftZH(CVx15GIwY%Paq;D;f=pNPJf{8ts>dfb~~+@R@UMeik-<Px`gZ
zrlXfR)ZBdEEyWh=Y3M{tP13fQ_UAZ<e{d?-KqnHeJ`4>Z{UHC6&pS$lH)p-D*Z<Xj
z&phAj9xK)jSBrA1Ki)emEo7-W5DUzY-7<Xf!COo?H|?%*2$Kg?NfXnS49kLtWf9n=
zqUb$}RHqfk;-d?QkWgZyZCGHLLka>_DtEo<YZlq){fcIfw)v|ySjSE4L&GE{*!>Ld
z{x&Df0pioP>)%3EV{&x^|E0TR<a|n(QkH1;!3Q7KPT7w9b!TnqY&L8lo%@IRNK7_M
zbVgUR&`ka|j*f4*?_K@Yh4G~fd$Ujbj(94Gc-N~K99O+k^YMMb%Pg09ot{DOIa95m
zK>f=^(~u_L3Y8uv96HL&Ta&5IO<81<TD5~|wG~F<()_$Q6(G}jh2WFRH!}s#7W8jV
z;p~%*!a^*jtcGXj5zM&FDcZ#@{p=EFN}%ymGqIiO323?TS!U><=GB7*RG-Ruzq5ea
zhh1MoI6OAmfY?_~ULP&WU(#ohCfe7>^hKcC&xMbZt&fCaZ}hb=5CNX$sSW#GZ3KZQ
zd|cl1`#MBk@@7F7-Z~}u{ycdi5?!`E5PZYrvEQ4;k%z8(N0R@*y~{jy3)BAfM?LGw
zg$IVI8Mo~q7AEB7D*uO`Q^P}(8xNFMz`mMkl8<j!_V09;F&RL;+zh`l{!RCxeEOAy
zSzlFgD$jNCeWw-LF<KbyJ6dpCalrft<C7)54~s826Uw#Zw1=2;{{~DXzD{{yIzg1}
zawyVlcW~R3ON+KyokpVVo5jK)eruzpm+;MA^nDK6pXcRFc=j3CGMjdJn=;F0^~%l~
znq^?&w#OT{v#yD?9$nqSRPxN|K_?+`bLAf<!bCXYEE7{~1nv)FjQ%9P3zcdP`~zvm
z4h7xAeDcG&&YqP``s=^0NnU%8t~OUG<mn8X3s;%pIfoj8JRW%gs;p7EqzdEMFAdeM
zksKfXdIz!I%v&!wIR<X=CY>tW$9%Y#<C%V*F-O-?p<3Yovhqeb?zi=Dw{3Wp!8AU9
zXE~zHC4@VJSGi2;*IdB=)UA&<$|bUq8X<ptXmIB&K*FA6SpoLO0m8G=iaAY{R9p#;
zz^U0@tmNb5EP(pmwYFW{-+3rBKAb!iI%MW5<UgaPA7Y3;lSe38oEiD3#tWwg(7ciS
zg}t-aZwLOO&EX6%UZ$4@XI!75W=Hp6JdIzQ+uw81=ZD>kFkDfxS>*QV#Hm@+%ysFF
zgzjh8D|wpbwxrC%4eO{9Bj1bU0w$$d%d*er&(rQ$Q3s#7H4A^qYP)t=TWr7D;uKW$
z6zsEIvT#bQ!H5c|@on;IOd;EZ>Hbxc{gaygLa|d%)F`cWSFBH*Cec$Em|u#<<>cg@
zD0@OvXNhoR@O_&cS~x$VBto`Ih4ZIH-8gA<wshN7Sp>a7)DLQwMtjPbbUI{KY$<~_
zPd_mBUwM++yxA<P)dV5_zOvHUF_HVpV}nCBFNuC4RX&V--cLTjB0B5w1LCjaTcz(6
z5?${t>F?aO(epBH;XLFD1me7~{@nfV+Xa-#87>5L1iTgS8R~e_9!>=%Y2J?n`64t<
z8A<hf>I;pWeabK067eZEY>D-BYtAfC=tdhkIK{>oa(~o;7SzDzIIfGWyQ0344n-A$
z>_n&SVVDJ2qht7O_PB!pl7=0jz5doEuWC4`-XJA3l>iWJI`-T)sYe2EK`k$hTK2=p
z=!I12$Er|U_0O*|Z}+MigtbP*j6#j0iXK#$PpSs5^@#FFT#%QY3>vr)R|wWY%?J4c
zA+;$6=1qg}pTYXC|HwSl@-m<T_;qDo(dX$-C<Gv=;aUhS3wfh|X#G>&nP7xn^_Fz+
zCfV;;r>!1L<R0F0v(g)aV^UHLZhRpoM1q1lnqfYzW3QGS2}eH2Zvl70xgzh|*LyE3
z-kpnc-;se}U;AL<6yWkRqta@8q<uA4D_4FfptnC++r3Z?=VH;z$2flCudBV*G%XVf
zI}xcrXI&;9dr}<Gs+|w2`*$|Rjxpqh;jJyl*VEW2OZO$k9kNtRu^WlAtM+dw&6ABT
zJ3rl^hsv3{%$r>L$!LB8SH=28^g>q3z3crPec#-5tYIsZt;-D5+qgJz7Vf-W&S^+u
zOQi)?YTfzZcTtWss1fb<pze=myCTWAnfr!9Frq+M`R7j4uOub>g85%%FefP5Y+`cK
zt^#h^-0oUji_ZU7sBuQAqu<U*4jx{!)#*|NDgiZ;+mW9GH4tVeFp1QE^<feH`23CH
zB@go?-yxreu%v7D{tOaJZ4DC?(!_Tmyg~<0H%JMrTMO#ygiZdkt_LYWm%#S|*oFLU
z6gw>)qv8In-tcEw;UffKVCIVlhRDrHrdiVuN>^QR(d^_eUpZJ3-)2rb7BZz%Ucc&Q
zt)!@%CNii7d8@qmeO-qZcy2I0!Oq>*#~WL?Jn3Ps#*l3by)XcQbrgdHh>3m~*~0J6
zVK#FMQT88uJ{)~V{SoTpMq4{rFzE6#eSf8c+CKF?OQKQl@`?zYz4>!@DzznliI*y1
zs}xO?&-Cj1==T?#+z<!Gh6;iP%CCe)sQa)t?_LlS*7W$09DM;w>ufgsdC`$;hdW{f
z)7o7P;c{akJczolM(*NV*(>?lvTGHbJ%2A+m`_y~J4EnH7CDxCs55GE1ARp*EE2iG
zSM$*-7P?U>Y;p8mH>;_y-|kxo#kvsQ3X^)dln^0ExM+yc)OJ1Xw-%zz0YpL3x$HRT
z_A7!da33PDxlwD<n0vG>Cp3DQ061x6-coPOiMY}$3X{~Zd#_%#UAf!~5sswFs_>&X
zR?ljTgP`%$cQp2$FjD0V_sb_2N~E~^hB}K`wmn9XmoIwVmmHh3S0;B>aT*3UU`e?1
z!wODf*TIL$h5!?g|14#1V~1Qx0l(&<vv)y2`(=Z*lab3k5)X}^*0;9vmWWq4IS927
zv!mR&5_>^fp<g>0)28Kd=D^BEPgT7-*5r{*dG<1fl92nvuE;#Dj`*RmE2l4iZyklk
zS%-10<au!zv@Wz<IVX%>Skun-`MUg<GqulmsjfbkfRlNgo5t}_PiWUV;Nf}u+EHRD
zr?ZbDJ8|ciZypp4Ds6Xuqgj!j*ONv{3Ry4k#y<s~Xk7LgiWuMVO43}~`Nyj&(`goc
zYoEV4o<K=*b%gvCb1}IynhTNs<gI7UQAax{pCi$l+#jgzk1b9GZm%^<)it#H3b!}J
zdn?GB&735yX@%%8`*@Dqqq{SG%aukyRwKY7A|2sSI1OxwXm)uL`-q--sry)SOvfe2
zw27D-q1}yR4qDjYrxt}0*WMR!p2hdfujt787zEnPWTv^%LK9l?yIk7j^?m;C3zCld
z4)l{*8G_uyg(oJK(`te$@L$dqu&}A|Jvh%3P!y^sJ1B3x(rMz>ti7m=mzLMLrRL8F
zJ$uyGq;6RHL64uNvGXFqzaz=o>fzYs0If8yiZGa~we8yBiCvKP%jtfc+(jM<yz^#}
zBLk?xQD&f7Kepw1dw@~R?7on+LA}KFl<PLTLa&rdyq8NzpJEMcA^0Qhh0t1S#T|Y%
z4W#GyY8Y7%60Fm^^VS#Z@TgoxNjlGS<dLKZwzSb?VBg6C$+x8G7yhIxOTVn%B$pK4
z6r?ZD*sAoJ?QANt=eE552skIGi6FcA*}GUEtHoJU^-K&^IQc=z?WtU7=jbKH(-s$h
z>%4!jqrW^kpARKSukJ}io>K0OVy`E(>(44M5rX5*1l!(tlNy`Vi;4G?c8Uzu$5lcj
z&H5iRTrO%PiXu1_dUczp@&u0V(;j!Ts$5%A^!CoFf%?1LnaQZKXOjaxlfYIKzkVnn
z&-h&|At?=x2kwWT0K&0faej<}6hr}>zRYR$$p|W4dG~vw8^y3*mh3^zhh?d=wTV0>
zH~lyPcvI8RB#wLs*FkQH=WZcn{d((sFy}9Ocsydh#_<}DdTROPZx}z~UEaT6^8zG7
zKl<@Js1+VY%@1=a8IASm9BJGOIP6!74H_Cl-_~#CXP>VQ6`lYXDvjsG!E7nFvmM=h
zM)ut;XJ#48!&GLhw^-+0H{J?k7AB4!{8<pk!UQQSFIaIWf46-d^4AVaXD~WjCtaUa
zNPY6=;2AD4TPc?Z$psQAOn1xjUcXg;W~U&L^Aab5-5Ofi5S$-j$IQK2Qm#7{>)dhv
z?;EtFL*&-?r+jLpiu+dKyi*tYN7=sjJ31NJX^DJSRsQ=3M_aLKztP#`%;LIuom-j=
z*3W=7^C8dr`c#g;ZHx`#0JLNtgCbl=6U)}SqjphVx9;%bA4KwZBQFh9p+f{~+MZ6O
zq~aSh&+Erxvhg$xt$tB1pY}^g!+q(fy-#Q7we24-))8tfOl6Syj{%<>8e{q5>EcY4
zg@1p8Wv<P-ACAjXj@ORt*p0(#7oZJr8LNyhm*D?uR(*P&nOL!h&rC>=oy$5IX{_nS
z@xCLj!)DdD7D@A^2AI4~(QE94vCYjGSM{vGxEjifkz%uAZqs76RpU##eGd?35YnUg
znOgNQ<9o?PuLw}>VU{5?4s*31I<_5(x1P%gsG)RVWp~9?2!Q^EuK#^ux$mY@xBxL5
zp7Q*MQ7^>}Yu%uGiG=n|+izMe)~GTkqgU_<bKQfnq~5-oo%IhJw53VRFIoY=4%-2M
zB#<PyBTT6~rAOQgOG?6z^ySH+k<tNYV+r6DZfP#B;$5m?$j{+!sdi83Ts-v64O8nM
zvJ?sEa9_^)IN?^OSR>qjBHBf*CNM<jT@EqsIa7{on%bh|J2Rls>i-PGjiYGcrR2t@
zJM^;Y7rmv2S|2v3q!ihc2)8Sb*t&Ky3msV9=t1;hK<J4{Y(qLiz--h}Y4=NVu=<zt
z;u=E!kwT($4O8XZyT0dZd7-&86)g;p7X+)CXy5CDnHKvHP2^yhN2^)2jTZ{XPH>BE
zC>jud>iHd-T5%;6Kw`URWV!K0u64*h*N<n;m|rgvCb{^k$C8jBZ6c+g*37?@q98ly
zVfG{lmVCfdoI-DEge6yo0N`~qvDjncM?cl1>p(%MYURJGq@l98)K3S=d2xE<i3(l(
z-F4T6d8t2kChF5|E+EY-p7}`j=Wo_U4p3wTOE0^L;>=24{H5HGRx~J*Ti@bOShp_h
zRQ~L;Tp90rnXh?9H#y1`HC-W<%Q|5s*Q4cU*Lb+V=~|%x)Wh&7vmpR3Gwn-hYT+%j
zY{JS?O~3j^^SVadNKy8Ybk)mpb^~)I+n`jhu@jZ#lXF<}ZbmELhq>s)W-`6$j-7-@
z$-`MoEooi%!`20GoOroXX40>{nd?)FP9%IE=Q;pkwXWpy-LqrEc?ZwKX8#!yx7M*M
zlkwNTRQd6}!kBF@iJoImh%H|F12VD}VA}KRRy|_6=g$?S2MJgg$;($rXX%_gHJJKz
zuXjhQKuI#k`o>7Jf{J`W={YH7ccn4i%vh0~!jF&f2&|S|+=8)q$SjA?tL0Jx_Ay*~
zsT&IRgs2rz<pJu?hAWN537P)`7)ICaL^nR)qPOT#H^L+_E3Ji`FNaPb72X;v`=9O;
zWgXjNZprYNv&b{w?RP`ktHSkS{UdZ*r6kqcthUW5G5)y~mBR;rIiK3K{=Hfu2H^*#
zsLsj2AMt?t&wh?Q19D=H8zU+=-p#mEp4;|?s?ORd=wvT9K+P#_r_f7lTPH%fKSaTz
z4iUnm-THpG9qA;}L{!=O1s@m{s(ctqerJ%qsE`GRX#_SKYM}7KX{zz0amSe3ovgK1
z;Xi)mZx_s4!Dhl{JEyUU`%eV$)>uO6VJ2S1jjmmbX==J?bEi2_3sG>$OapeC0oPw3
zlh!NjhQPyjRlyo&XJW{6so<k>#O)%1T7QW*+&j@QI`_m}Q^z+{G<EW{=T-J#ePckY
z;>aDBqT3W`amtv*ID6FNoV7=OMn@L^$bm9oA%uR?`Qgr)k=WUr*(mL0MLY~E&@@b?
zKYCLNSb-)0ebLt=6(5j4xuB`rJarygI_R;Q>gJsggm4!2syS&dO)7g`G?2cl`*pQ>
zK-!EW9O>Udk7$K%e;65Gv9dWzdav^4MwJhqSzA#mF)P0QdkVc}{kqS6i{pglgn9*$
z4Tn^}kfr=beuh?>nUK+vVoDpQm)~<8u^N*}9v)V7-Ak{9q!qxlNlZ?wnS)=Nu9s&c
z)TDD1-lm#VuP1u};q8(bu62gtBMZnoObL#46t9E{jgpZ<0)PxjRq1K`;zfcRsEcGf
zE!C&K=0RgVI!zgG6OM-7Ow`Fvt~`t1=m{!cyLY#`^VkoYe&C#3fXEOl(yI!(knamY
zjo72kK_*S;f~xl62LndVf-ZjrNXQpfxGE3Ug8mKz2JSYr-{<EC;-q}AvwNN1gIab6
zgc7^+M86%QT}-{016Q{0-Z2Prxwfw5Vb4E~AX$921k@=bqIPNTnZ;C<+=abLfYLN~
zdC$p?PZg5=Eq6E!-AoQ$t`yx8zsU>2j^xo^16lDq7{QWOSI6kPN5gTz)AFo;hZ_;u
zw4n(Zg}kJ<(f&PuFRR}ULTCZB_rSj>^sbwIde-ccnswCL)2ndOB{wIJg4`h$kdMs$
zse3qImZyBn4YIJqhmEW&)+J9boAh%7DEN(ksf*LswaZ-pWOzQno1zliUlJDeSHJxs
z_Zux*)Ld2cU2GP6gYL5SgF$!p<K^ozaibgr+1c=iT30}Z<ht=%dGPHI%#X?Px;Ww+
zo0_&U0_Zr@@Ph%ODk=ht?)w<d=s|WT_`Srhb8Vb3h=0yRdiQS86<sv5N=e;u<m&E{
z2QsyY#qkPKlq&Y5Nig0P1r8RpKNKIbG2fu0Qgy(zL1eDRq!_l?=n#6OH1_i|8y&-z
zMMeW+7N7i|URQBTx{om-Qik<Lp+>{SyRj|%;c(rjR1k&dAxcqX{LL2^1sDm^iwHsY
zFz&Cqy4xW9e2w!Ai2JEk2IvOn+;*JTj!e1z9}_=+L5mlC!0ThRJ&E+&$i~P?5DS1z
z5YgtLc!0@*)#kFuGv48xl-Oi}!7EJ(-I8Nf|03a2fUNzLye>ZSjsFdK=z^?kF?-7x
z=>Fz<H*`eL{PsY5Q|{yl43>cR+*5C^TwUNEdmMKW=13Cpd0iTfVj<ykp%yd5&3!d(
zFzZPS!=nS3YN1kT#-(&~n+1z`@@Q`zPuWk&rf^6amJ1d(ips*ibD!NXvs6y5OoGtj
z=OaP16F6s=DNO`sc~UqC(G#Ien^g6d-5z7^d`RB*^t9mfdt7BdH}v-x@`J;6%fCk5
zXyY1Uk(1tkT>5bj<My<Ei`S5~q4GY#2dm;N%pEBzC<)tBWZfneLGy}sPQ5?CXn@DU
zvaV7D=Zy#n+!lwVyen4yagph;?>o(gKYdbxK`1b;GZm&83@$JqJoo^-Gx%y}i+%eC
z`FHAJY&!U}Z-XU!^zd#CU>yl;=7<;y@^_+(e&((026JVs3_3n=KI)LZ*wt_+v1Bf-
zzI!M0QlnXy`a!;Edb2Yv%*DK^&~t<N0U;CRaDa#aMnJ+enlvmHdI<5UCDKdQVwPxb
zr3W8!@f+dOa-?ckylbWgsi}K_$sEPl*t!SouQGgc1C=KeUO$kY<vX03zxCefYaA&3
z(;b9u^lVFTLbrUSsHF9Jw$Wna>cMRGiK5EF<#F;agu{OnzrlS*K`b^6l(o1|WeGxr
za@`~J*-)Qd>^bZ0$MFL-q2_ags{Pi<!Ou^fEmbHo8d8Rhs(#5r50za!7$~~)p!}Rv
zNANpfq@b<D0Te<C+t8`r8eMb$1SkqTT7AmY@5*e0cc1!7MV>M1xR#$bIpvafu3)rF
z+&iMv`kv$IOOm1j+N`zfK>&BouZq9FK3n;sJD%XGLzb2&0j!lHuX(3n=jZ^GfB;(t
zG@1mQl<n#myV8h%b`=3&-_B<(L{5R-zIezTuC=Sy>eM(>uA?>TOS7ZQjc;ANr2s=a
z3cgsTgLeQ8!$vQU9tEo~+$oj{vs+-0qiz@?%!e^_$SrPKV1D4T8msrgu62qd1F3YZ
z!Mg+zl|8ZbN;g!)HbKFsn}FT3>`&jeCVU9tva&6j!OT}mg7HPQmu5b44&77dv@Ed4
z0shC_*)az`)E+N@OQ};)h6fMjSC_g1MO571Jk;gO?sZio2<QB#mO$0P_j`E=f7{6e
z^1oQ3o7m9ZP^jZvCkj@RgsTDo_q+HcUxxO(o-?@=-ILLNo(`%?efH#B_hyRbn05$L
zz|AlXwe*_$(e!er1a4Qrhb;#==m|%K9SHyl#a&Cus5f-~Hoh`Dv;1I8o6>jVeD>|d
zs`)3F5aeVzRZIuE?u`hGTy}cfv)-6>gZd0lot^8%R{I6(Cj(7@AO-afBD^&pH?PJZ
zJ8BAHDRaD^aXneeo8(3v6>^avY%kl&0!p=ruZ3@;HUcOvF}Ps@Wu{f@^pJerZ+6zC
zw}Wm#0yO1MqA7*-CXxZ;<bD&~N-7uP53rDVa`{4*T1{wD|FXiR0##vNzA>(xg=eQf
zKihHv_=Ih&GC)kYaB@?|#y^Xnm_Fvlp`%|xzrP>Rl=%%ELpw{2<w04}p?<N2h$#9;
z8XwEIOoX(0-gmvPvOH2#36oe2@y7SUe0S*XbkJe%&NHOKG{74$R50EN6zmun#tl;t
z1Yr2VebD8Er3V~hMJ_Txq+dFd`(YdS$1Rw;J+<F<diFTPOpFf)Dx&M$=oDy1zS2HF
zqq{&lqWVr6*R1v&Aifg!jbpu=eGJi?5=7$5P%9B_PKXl;5}Ub*iIcLkBcTbw?U55N
zRS4wmiqjpL>9|tjhomj5pf=MPcA6Z~NXI>VK;bcU3<#M1d<9lZ63L^EK4chuz4)w3
z7rv^CKpZuOjsERuhHt&;G%K;wik-!$+jGJzk-<p0p)?s&nQvg7zvMwJ>7?5D<wEhX
z7?Rk@+Y4Cl{LoAK2(>cj#j~6NEHRiO7WZzpObL|S<PkKT1wwC9Ox=jt!0_$hr1%C!
zzb{GM4b5&kjbcw#+CGmaC<js6hZA|61XI!=-qmC4im)P~KMfycNA0FiqvVh6L<U>H
zh~r8{6~Jbe7lGTf2i!0nBvP63@@|j*vfBp(F^<fi%86w^j;N=i&o^E>)vcpwHp4=C
zWL<GIb2?#jtk3phdgA677}j?9wCY(7?uVSaLVxd~KxoQ$QhW9Dku=>Ia1<!{8YheR
zU*Q8d*sn-b&8TDoZi(Su%fP;742ag;jKAp~l__|*@q+YHg-*QW&eodn5HhtG4ND7C
zca)Hu**ofCcw^y%$4EQvKX{ps*GR;-cob;maG;f+Y%s)sA(z@$r)1zTz@U@wypW0f
z2cs!OFdC>0DC?9F8*dPppa6&hW!V`{nq(8Def49tIp}Sbw|iV4Q$d410+3N#vu*E)
z?2z@RXV3a_BAz}BJm-U*@Ne?2z3bQv<1C}0Qa8x5e-azR$vKs^e2KE53vat`lQu_Z
z$Fve!C<xeXWy6fgmz0Mmdc*hC{1-C6^5+}Qz58;{vZXw=W1l0)2a-rh8C^fCCHUo{
z>vy67I2;Z?N6-ZTif-nG+HB?kUE<K4K+943vwKQGTsi2f$+1W<Ux`JHdtdyGp*#pY
zbik$X)4Gf>=1VGoCCR(lXJ6bog~S~$!4by5icpuTojd0B>q*~Moo@Whgo+U~nW+Io
zp`ejnC$0<(xsGJn;AxQ=r0w2i#4LgF=+~~RaVeUymqb|B?Wh<-pq3b2#(q}luY7sZ
zfu-$zLmYVh#YF_|?0X&E-Lyi|wHnxmS*w&Z4EdcwEk0jWm>5|gH5k?8FXtg2*`B~(
zoy@faPOIZM^bn}K-lwGnSIXYL4PvgT2lJAfQ{l*X_J~9u^O@ZNQZRo5#(Nc(=^}i4
zits(r#G?3yhs&$`9zz4%5$Ez$zt4T^_(rYyI0=9Lw!s;$rOqLzI|xVPx;OS6(`;r(
zP_HY25dW}Vje?3NQ_Dw?;4E-go5D}*)vXEn&TKsZ5*RDJ&D^uC<jXfVl#Rkuq7(9u
zKj}|DS3p^qPHdA@G@ZR<tpD@JU&u;i=D%`}{p~Cy1BB5+aFdtM42HNGlOf?0Xrw{)
zABdw;Zf~JX9!tC0u^ATXqKTl7->J&38`Z-S##j6V2GYPVku7_nt0=ofT2yhD&EgO1
zLmI)?FX?|CTQI5>kocy$94c+Z>S~{kWP!VdYh6_G!rqbVCZ&bTBM$`(@mi^SwAM*!
z`n+YjlrB$kl$S<Wn-lN@yLimVAAZr(YF=dplUt1rNSv2yhy@7CIt$)|4_p`C!CFx6
z<jAl6zNxvI%e@4$1O(+vqpWl?{fO*K^Q=m$t1X3KWAySJUW#!DA00Zt75r?ECGW<i
zQp~6*KlF)AxDwZED0%n~DAx;Qtp?zDsar>RG2U)%166>#Z8kggF7dO<k?C2DHKuKy
z{U9;ZDO$=z+zA0EX$`p8o|o7{cV~+Ac=e}N28MgRBJ5!|=0C=MG96pEp69ovfF8uH
z{3ha`rP*>~5C#T$mYtC>0d24F5X`jFNEFK$OX-IJSIAwfnX1W#wGQB#{>O?cvyTf3
z+a}=dWbRksgw!d2-&iX=bUzWjPyvpxi;nRl+Y5VxXY3^35A83@eN^VZLC3DeY_Bv5
zv;3)-7<J&tPrOAXx&O!dk=j#wlqV4RkA|xi1d`=#OPVMJ=oV_WXJ)=w3c=S=GH;mi
z!XW8ouy~X>@R<N&N;>OVp!;&(AxydgcVgG^hmVR$Mh#!A^%f4)T`vrRzE)dbyWT#Q
ztw5JGFXhJXN4nb;=)4z2xAK7|!zVko9My@t41o5#<w~n<#%B`P&tFDQWVzy4=Og2^
zev~PNX8-QvY#|-MB+7Z!9n=w|q1+_sb<uenSy)?#&Ka%()qA*_NldN-Z}auz{n{XJ
z!ThM<L&R`q)onGXe)uyX+C|&vn~HWMrYlk%+iMO!-rx_Gxd=$hTwB9&a!)cX(!lb{
zs1bmtjbeAK?_>o69#nFF|DPNxT|Gk$%~l{mv?sjGWlaaceR|JGT!fVtdlmQ9Zq+)2
zKG&)TUAc8RfISZZa52pSXGPjhr5AKq7t_sZ>drsqMk4MJpR^kl?#`$;zpr9wDD|4a
zRIG)iflrxt!-!zx+ig*Y=#x@6@6`k8!r%dyq+n(@>Zq2Uw^yWsje9Rhf1Rtr8gruS
zj$O_H{}{JY%#R+YpRZv&>yWJxGIpH60kZ*>ov82Sg30aIyF7Sq!22*{Yk|Up2%Ijj
z6yEbOrj7b34n^EsX?PgjUr=nQH&s+-OZ)NxP`2Iz*0TI36P!QzfWCv)j6NT&8-4wh
z$k9W6W#ZIa=kP&MntagRRBM32zy<;MM+NO>3-M+aK*~~e^qR@jAG<Ivrc9FF>f)j6
z4s?A24(VjTuooe=Mh<-Lf?nS?iu1OT@6dI>M+-|V`n&i47AKs+_+vo6BOcOdzn{@g
z2d}y{n}nE=K(<Kf%Se>yHC!lG8rhoOpCSHh+ff_zpK-phA0kU$DNg>BLH34E-BJpo
zD_un&Rsymh55)dih@s`e1#yoX1KSTH6JoQ(GadSt(mI*$-RlK+JEf~lJpAhKLDaI>
zPvR1erZ{a?&pzLFfO2P<43252+0+4e(lih6yUugFQCzt1MaU6+xs`5)Z<GZ%NGDln
zD4&j7EV>CBEq<(!1VP}IWx!OX=`X;T!h`ERdgRQdEP3v9USNtpZRcOpNYu*@MA)D*
z)B!X7Hy^{n%Y+^-f7i5OmwIcFO*3AL%{mP17+C<SXg$W#XT9Jn!?$`1SUVa_1q{~5
zK_7lddbzi9{={MkkY=vUWtQ|OUH<yCqx&L5vr68&%zRyf$vw8~N6y^rUZf)9;0H$Q
zeQFNtIQ(OQKFT2(nFr03b8O5gXu^G~y!?l*pWOG0I=7$uWj4UgxeI2jYTCP9VACA_
zYvBo;II1wsllgEyo>@!zQaJz8;P>(3hV?8S-iB{&$M&!QBR7i1R)QXmTVOHU@3pPb
z6~-dcZ&V^k-)~I!kLDV+9?r;G3nqX5djv5V$o*!uF3HFv34uc!1RGl{=mgFk+vz=a
zE^pg4wUKTPwrY)%((+JlVSViP26*FA@Z2q(>eCPAWXms9M*Gzp93h6YFd;&nvgI#C
zp|6HGME-3fr8h4mIAR(GQ?i5M*P)lhC%LM@Pl9O7FyFxAZW8qC))mkDY!Su9QbK);
z@nejFwk$y3_mSEU@mTp5(Nno+YzZLa1#j$JaaCvg1ZPpI10NwmYsLOzCXE*3y3sAH
zkMexJ#MT@5#YNy$e`;zYec#DE;h$SsgRS5A`dITOPzwdqSc@j}BI&%f>!+=cJ+@R*
z433iOQw2y40ieQS3>Eg$y%DvFN+>w56GF^x8WkU6@b5*;=~F>=M^E6ofWPH&4UkMZ
zf9j-xGR%VFm69^Fva(uTThoA%{P;2}jeSfthH)brh+8xvVMlWK_X7viVOH62;TVk6
z1C=;y@&$2mgCOR!{5Vl05~Ng!1Bn)2{}AZ6P9v$ZH-~$mT+cSveH7d2qp@?63XegL
zgNxj$>!-#z*e_sH<*$PX=K(<@ZN!}5Z~HH*IIQHB-@;EEyayukp+$kqCDwq!BC>GM
za7KheO|}ts(+A*+Hm9yPOZm_@<d8jJ=jhf!sz%&@CAx%U#m+nUL%qlc@I)_5FiP2Y
zk6lL6wDdaH5Xbey#GbPiM1tSpD;v<}r5_#<a(&ijT6Ji!57&3Vhvm51y3U`A?D(Eq
z8WRyp#YEpqQBbohp;yMOC4Ka`l}~l&_FpuF(`VBw68~`9Zybha`q|Irw9TDRJ802D
z3@bZ#P8m^4JiZ~*5s_$=9vZbw=xaDWob+z^9nfTSnEoJbuM!qIkUq*cZ|%{1@6FtI
zwL~_;l{E&$2lu2|u#Pgs8QsNuAB(lnko0wwm&30uxhD=5+TgC?nH_5}qrgS6^|Uyy
zisd!|vF%g0p5i+gHuc2yg6ir9Di5r(U@CYIhhUr5@X7j*N0u9z<zh6g>atr^KVKi?
zU?(B^leW%Dx8{;VSE&sFf>d(UD1vPNco=Q)s0WN7{i`<#FK0(FA+H95r^wde*yTk0
z@2jh}7Ey53jn?vL9dhXt5t%Ut_wr<X{ezX&qLhP0Yds?|4X$qKJi*HP?+~b}D}sFT
z&`TJ~tBCsb45g}P*VoMNmFqund2KTuQ0ag~Hc_P%n%%~M#Bc2yX+oE38Arq7B8!lQ
z-Y!TLl$$=wGnmCe`_(n$Vi^T7>D)rL=ld_Cc*mB$JJf`RCm1%K3QPq9;$FJ-sBHuk
z_&X=RS`NlN<8P}I%FMIp=x*f|RCw^Lo59D?z@cLx0xbAT+HR-A6cBF>6bpE+;W{lf
z2?WV|Gmo7I3`aC!Kmy1eBw&wX>*Zs+>cE^hBIf!`WmeQE(O9j9N?om}Ke#Hevz*&d
zqzBR`1#n>YnUq#mD@C*kl#oEiO?6f+8D$%iebZ>uypbpeBOZ#+R@5ljzy5t47Y!U=
zvNomm)ag#GBe&^zkj^tbSkUp%Kdb5_rgjXz!tA0vg(~3XB7rOAJ*Jx<`2IvMNM#B|
zqQB%g>ZaUvdDRDI8dNr#F(67v4WNfOL)t~{p>UyLduN$=Dku~?N&Ul{DJL~K|1lI>
zZdarMQY(1fyc`tU`qct*xc)QbUpw5}Qs(8q9QI=7_kZ$Jyv?j0{y`?_8S47=>nsK}
zO;5Dh1s?Wj@lHNCGpe|aov(^oRxp=2z>uOO<a7x70QPcFL}i&N36Xo}j>gG67UMbH
zLd46!(78rBjBg$MVEhaj{S4RHk0Guy<W9WS=H@kln6Pj=ixw-G10CD7d!51*Ic#=!
zP|!^`80vs*mS&-ZhUZItRU!2?c@3nXC?u*w26Tx@urFpq9Izn7Ra<gqjJzmyQL$2W
zDEo^Gp>04~IQJPR_5(WBVo!bx#C90HkgnIWuIRp9&8@djPJ5FU_RX&lyvBXep!Cn`
zP=KyKPvuP`d3he2<F)o8_h`oj5cCLa)wWF^F9TB^6*Y3BK6!(?^%`8GLJ+x+fZols
zw$7Uqv-xgEWKnB{+h&IQzJ>!RHU~I5V*2F(oJv#yX*CqPY!=o<Mb3)l3^ly!CwkAm
zB+3qoATp<w?|;3jFCY=4gMxP5Np)9R%my0PGY|j~fs=7y3C5(;NigF9wBCPjQxx;t
zoRGSk9^z}wewqYW{Ry&z#u!kFfxpN+FZO4jejH*>0z#T4@ke4q+GMOJqA4zY51dAG
zI{YLnpsv_@t~>yDe(-YEI(ITmKxm4z3W!-hB1-|O<c;?xvP*5b-$7&z+{L6uDNO7M
z8d%7?r|tXBcQ7Fx^i!JNxjud*e!sJ&U}?<(Uqktp!-VJjPUqjFToU)B%5BS2ob{k$
zl5}&FuPgcH*tuI!EZYTYSw0eANLfdkO|TpA&gf?1grqLXo)|Q6Rc78u-rO@S=R_4f
z57*)q)vmrmE>GcmSf!Sln&k5+Au`gelmiCf!Zif>8mkG@;BLx~h$wk;Y80+$Ltl-J
z?UeRV-z+IDMcc#OC(dW-l4?>=LAns!#Um5Uq@9U+iEIILYb)q!iw+(0O#0z(OQfD}
zMtr3GJS*uw$G6&#1i2U_2e#jQV{JA+epqtL<h77fBDWY7c)69;bv9hxJGdTl@#|dY
z5m|jgPrfCMp0)OhJX_ev_C3=+wo4(|NOJ0={tX+Py0SsC>6*xKD9;Ig6)GAIOPNa@
zpL4H$epL#<@>mh=hC|I-`sGpu%=D|H*jkM{ky1OsKf^O$FXtACa1qlYgEUQ1XQFJ*
zp3c7_;--~yFd~*%ySx#2=>6ZW%At2vc6^dIt!l*j*0i3f?L6oz7ZsFcZXYc;!&=(R
zY-OOcY=~3eS3SV<M5@QLTEgAY=7^=9{LV(3RH93dh3V8uQmURoxv6|uQgwRb=e;)l
zyvKprHE5eaIfwfQ4UV~5oyiStcGaRY@OE{KN2G1n%TNB*;i4uig)*Rpre_<AYJ2Rp
z&I6#|r<U~A+(Gmbw-LK-3>SI?Zr(dRe4FWkQyEnTtIcsU=R#JiG!B#cwR@1!O5f`*
z<sIrpZRJMB8|ikEEP6*e#(W>16qyK1|EA_QB3+iz<l?|(RQi%uV*O*Qsgvo}cE#j}
zCdy+LKDeSvNT*bo1HZ+=9TB;_ZLMfQvE|HE6c<s0SoJEGJ^hmL9+l(W!4ZjC>kZON
zD|!8>Sv)S`&ACD6&)Z}4Cz!%fy%GavW`}9|M<Dbf(oTC~E&BO6oQkn24Flb1jsnst
z<L)uS@ub!<zM?qF6y?lg(3U^?$nb_!QXdK-4H$l@Rb(g$lS*KksTJ5gkg>1za3|&T
zG<`YsGjctE@0JIj?uU<Q*R!Ly<lwqETE671TNR4k7*aGXTve*4O`zdns`DMV>H#J>
zbG~FsI>xd1qXR}?WvkCEKken{dM6dKqHdnLcKXfx+lNO^MyA@aYkQ@47#d%?m{it~
ztklBO9kixR+@jjo`|RiWKTN%KSe0qGJ&b~MgLFs;64Koz9nwfQvPq>I=|-fxyQRB9
zl#uS0Zt4D>?VR(SAOFoYmkzV{``)qEy4RXZP=j8=XvD1DA5;Av_?=Gr7FjNQ?C)lj
zMRLhuSec4%boAf1mrqgb3CHgrDXan)vPQ2eC`)TE17zf)lhQBAZ{BGv@jIlF@sH-*
zs&n(Y1ZtsLZh6;m<@1qqm?dQEEPQyGqU|LFt1ah#AYj?=O%9|!4^M?|A=&)e<`Aj#
zloF(oFDTFR)Sn5-{%UyA<4e}bL+JQW*uwu|6Y_V$i;zZP&P3zOlw0=Mp0<;dKW_Ph
zRX%OE^AE<|YNqirSo6NL&w{hIr$@NubBSd-^B<;!TFpJL=&GdIFH`HNmM-dCLaX*J
z@d|5AOfGH&dz&cu__S|ce5=t6Ut#=ExQqtrU%`2wTozQzG?&*GYHo(##NG1g*L(4q
zy80rs6RbWPUP_tfPJ_J_8QGCoW5pC)ho5<{$uu8y7lq@uK1#NG&wd^{{QGDwj?Y;K
zk(eNPkrpv!z@S$-|Lhn-ww{}3eYX<)g7}i2%|z*?gjx$hP%ZaFp8A0Nyw^CjxY^yp
zIV&MwM|*a*LK%cGg~=?Rfmv?e<^8-SF9rSv*~%SLc-*?;HP5|Zay^rB%p4MA$PZAK
zv{BpAnf<prYG2NdiMxb?j&`k;OZhD7caN=tdnL9@W8B5#X$nF5W4iiy#h~dpa3?Cw
zvyp-3p*T$_a2Jas4E)OHhT0XSa!moV`)q*KDgwv3nH0_^kN2&vr^a63j@ErAJ6Hg3
zVg+aQ_25d!`JBBhGGbLa78UnQ&%_FJOcz1>eBKed`+*62LMMo*xuND=7g?(Zuygpm
z-V=mOO8vE5$d;0qRDFacZ{?f(E$84x);*$+_?6@LBFi~^pHg+tVU-y~ah>slr!AL)
zRF3+K<m5u-hYzThCSfuiuao1CUd1%#m5Id`9Gs2`kDOTxucP7ZCsQw!TaLt9k1~la
z<Pu%#42$K_Hh-wSPm&@2HvFAPrNKRjDf~NqRZ4k3*$&mx_(%?hGrwX$2(Q`nys5Z9
z5$~f*)GwCZa6zCDU>MocA1y&TEdP7az0PR#IKGr#rtQ%E+fTd#XG`g2(z#5;2OK`F
zsT!h3viNrz+2?&^<fKENALdx9veiu%v^IOTg?`d48bAm>zTq8xO#J9m5c<b43m2&{
zpB#sT7L^}+&tj(pPvatzgT&i{J2_8MR3-P7I8_SK&jx7^_7Uomfi#XLdBtMH8dS88
zraR+X8iDZ-@ocSms*P(WE;e=rGI*uE-EQ%@os>4RoR*ucHBs9M4|Xh*qrX=rgWx&W
zk4w2M)uo7LfBa6tx$I!a>&fFqG8dffyT~$v5jl)YdhLa`zwvq1sxdSBu32j`;EUqP
zcY3e4tiaPax%K{VkR^g%Ns*k#K5M}&6&N%_V~)B}m?wePc-U3P?OrMHSIY787d$Ig
z{#<bG=l7kNE;ZAxrPkBU+yKg*`_8XQUnciDYGQ<IKX3!(evivhSG&qFe9$y4zw8^<
zeXXc?1hpczz-e~&Wm{)f)$i1tC|r&%wBeM`b@6(9oJuV|%B4I!4vEEL*Qg2TAeYdV
zQrO6`@6Pm?2M_vIkc@I#&g<749hwSSd{YW58=tw;9eq47VRN*QDjg}+@rr%rH9THq
z9wf<G0|X=a5FDCFYx9r0SOXmfXBa6{LPI%0^(YpQQM%anMH7wea6)a5>4v>}=jDe7
z_T;>-NHizJhMk26A-+G9Mho<S`?b)>idIjjEIB@oWhi#D&=)I+`+LK}_cKR%7!_Ol
zz0qNJ8g-$?$*&hYvC6OB3Ybp!s~kCuu>Jb|o5tc>$FINguR6DfT%K$%Pzi$^88yQm
zj?4B=3l+X=7;0S9jFehU#lI~ZTJ=ERAu*czo_pEhaDx|%{XNgwNF7rREg^#$yff3y
zI5e9)T1{mf$`nCCoYi~xw?UflPC2(tIf3d66AI|GY;&2G(-pB6LMtFTz-q;ZHu8rH
zE3ddejQEucuZ!kPZ&SJUP_hZZ)7|sUL(>ZfQA5M5_M0tYs0%Teq`&2Xys-zcY&C;#
zSa4htIF=1N4<=78LBHg0+l-Y^zifcqdL{22taPkwtrP$^?LCl%BDF4hV`^csz=$+n
zX&?-oaN2;^A}7*=;A+O~#s_z!X+^WAtH+U;v((_-SmI`S>a5SU3&c(%<JXn-d;$rp
zIQgkSF&gLmOmqA@%K49I*7*$gd9$W3HhX*NR($I7D^d!R+#S;FD+ZNf;SX{{5q#ww
z#uEE%>Cr+f{6Ljj^00erx=GRZ+9L5zz=M5AJAFlsS%11>Uf5$8>`A9h)$=hKN44_c
zuJDO);T`P4<b|;*=ZaytdT^oLL<V?6tX^oudMj3xR@&`TtUM_$+|E2YDnv#pXRitH
zu8%XtxF428^*NPv*MS>mT@Ni)4cebwQdxQI>{yB`$A!$cdteM3DQ7I#lTzBxpKEbq
ztNi`%{)#!;wvwTR^RKaK5mMU4RTEllowI0~-pwC@*pYpH>6mQ!GvqY&kJWoEkx~~o
zTM}mYjogOiKBb6)f+dmjlJ1vx7rU*L<-)GAr5Jfu7hhs!2k93i%(gj^rcChbH8K;h
z$RbhVEvouTitlC^2D*;*6<w{s-1_rQbczMQpug7ShZh~yeEReG9@=*!qGgEYioe(%
zEyxR|Ocj5>;{*c@My|~FjEA{Lj|A%1$;7*BSbu<H)reL*w}NVpiMpZF<BL2m(<-5s
z>QqYc2hd82XJ=>WZ924D`}pr(F93Tu38#A{U{mTirLJiSO(ngfXXh2<Z#iIYm7P9S
z`|J+vDW-tYJaRe}2Zx%G(e`wBU<wm8b-<Gn7*N)Bj-Pq(e^ay^A4>l@<dBi%Et={M
z&U@|hX_oad@e0m#s6Se*)3um{JHOt)VO;{|In1qZfbd)4-aHscT$&kO#53QtxevSj
z<<~6mt)S_qMzfs}`L&^P5LG_wqB3eQ)h5Q=wR<LL2b{H_+OVF{-M>jKhmCw3p}*UD
z=H35z#!~=+BZ9g0xWQdIZ2`i~&Bwg}A)?s|aCuoODAZcqP;qTv_cy%EdBB-Wu$(aU
z+eLKDVIAXE0NDeb#tWwJb``uG$zTiDYoA&@yDc8WYHrkVfYPPdDx|_!BX4W>x_DVr
z97bX4>>O<wX-~H^Z~(ZU{`0M4#(@^Nfy!K(*YUqj{eqBocr4|3kw`*aD5oMX`=!GA
z=P~gs8R!Y;CfK<$*O&Qcz^SJ@5Vh;M>u<=)yhlTct+3Kknz^u|>S4ikU{qyTSE=<>
z`d&|+5m%Fz0Bl=QJY{J0#>zv7)Ol7>H`&215B0fe@af4$JzAq*b=1#>o>KxPv*p#;
z5Zeqm9xo)mtNZ;HzgBG`Bx{_(mGJ6XeY^jeQ?0#b$#mZWvrogt4@;0}<>U`6Gp7cW
zp!al(S|4<&3$7qo*_I7gs^F%jXXE^;&cE=l%db-HR!c5aaUmh0=96v|PB6&=`oFS7
zyd(jd3X}Thypd-B<LPIv>0cLM8j(Eu>CX?W#D815@4U6uy3Eq0daYuMv^@D_MSFW|
zd8OaI7rkJH4)e!dmR(kGi07vB&2isXBc2Qvd{FD(E>?%ifBtlNY@m>V(tLNp4V$lZ
zc>zjY-Sg-CdeR!W@~N~0ZA-G5D>QJP<}E)8Dq3AGbc8t50KW*JS+WEm-qOVdO&`BZ
zyBF|DfdDOZqKnmfvArP8e=^G=K)2ych{Jkv9!9nAEreMN#OHhE^10uBSNx|m*dV`e
zQUW{uTIL-|MX&wzEI9uFd;hOOqSsaFD>)T-!U9^AjA3JCJ3Z*X(tgrS0s``4!wYt@
z)wU{;iZK=3SM7isCcX9mYZAO7OP6!?ZJO4P0T!IdC~Eug6zB*ZWzkB|&0RxOP2}Pe
zkD%03!<&91u_`K{7lU4YACXQ^GmJjZW5*c)TxLauLeE8Kh%;ZjYL@z)N*yp808*1T
zK+rV255If)r5t>E3E(<RQTL=Zo0O9Dk?Dx<TQSfP0NXKe*OZ<LoRh3D^7aOXWo~V*
zK5f37$Jk+100IWbfh-NEM)&!{b&H=Mpa}s=>YSA3P04Nm6gN{j-WTnf&tTLlDm7>U
zRL1M=>@p43{@Vkf(YdU|EAA@lE9tY#0y%Ubyc5KJgS-}c=tcxWQGSCEHYRfN_P9)t
z8){aQ{>}JFthyCg(toV+x~|$lIIoB+n*cc&$bJ979L~Ni@!gb@DapjahW`aV=6@9f
z(rm0W+$283rX%hOc$zu>dhu4~xn%yG`=MxCt9`@7hg)hoaMgchw^J-PTI!8dxBV?9
zop|@dgd(qY?McHDt+ZmLarFQj^h`<{FZIBg6x`Ep0KG^T*5MS7!253;-~nj9-QeU)
zR^6!@$rZL%1-QfoM0tkllSlV%tJo5&O)eKH-)^mZx`+Xk;Yl}$T(2mmLH(F7Y#dwX
z?o;y5b_uv$HJ1su+r{a(6zJ5Yec+s3(S%-40?Z6bp7gK{u%9_DOnw}0ckv|(&=@r>
z2U?Dc(VHmY(b2uxqNswEIZcJ`(iQo%B0^xIF=`3GJsl|Qy&Lv$YpvdjOj0Jc{sY);
z>^6<*;i^|caL}my9-M%yPxD@fzZ`H}K|bL;lh1-Dm=+q}tp;Zjj>9*@Z3M$jyCSj)
zP*}!ICwfOqQa^b=Rero^Bog>XX%PJ`5c1J{qn?>?VahhRcJ+XUWC>_ok+;C#blIR@
z>FLHK^VetS5x)obd0dCxRHZqh+OFN2O~X<FaNs2jqS)wIDFa*pr`@suG|Ce)Ut0|I
zJ+%SZMD#snT=)0Zi67oDI^KUP762hv#aUTTf<+@EBh%U9QRKh*Kkxje*!=Sc*Dg39
zVD|L{R8Asd19e_rD3Ao?3;7u9cxmYFH{6}=T;f~4cDn@+N;zWev{8K&H8o&Q04}q~
z!-ZQE<?Xa+MI4R@rHz{rB&Lzx+tG%$bsP&FZGN=Z@RUseMhWVP58KQ@mN87TJFh={
zzP-@{QVo{EaDe@#*#joPddY9@c)Sl%fp0}#CHF3nd-ZHAC&Z{0>HPaeq5%#&)WY<N
zbx95Yg#hMl>3;r8sB#$8*P{CW+#1?k&1TmB@B@)a4b*umedKKM%ta~r`<`z``Zgb{
zm*K)~io*&F_JnfrEl?KPKYfX_2A8Q<pE*RPfH=J4K%gMzxZ<B7Ap>ihjcJIv`~lQ|
zNOd!GHwy0d=BtQ~IP?g48cmFZOlQ~7>}^^M(7qgVk&Hvl6W<+|P0});$MLeZf#VDs
zG|O_Rx5`f&FODi3*vE?11R(!$vi{@x{7WQ)n?4FmT?{yo0LU{i&sqU4edLR-LZQ1w
zTqWn+pKi~0`MmEN0k2<y!rWTr`ZQ;H0kWB(0j1F3MzA~-fY2%_uuv{1n!T=kc$#ZD
zE-D%+M_V1mb&qFi3we=;sB%Cg$5`Dv>k+;CP~bM<aUA+~TFY3zEVQ~`5exU_OEr-c
zTv2yo^O37ya(y(1AW<WN)thoBpz5r#Fc*ij7;#&Em|qC51dTRS!mqQlvn2|ew!A+;
zF6@0@A&dYI|F`EQh=9Vvs`H?&#524cWqnhr95#)*yXBi{9#sFsxqYI0iQ;n1MlbFX
zB_Wh3H&6D9o0Xzb`H#tYzQ>V-*+)}yxJK>WqMG_+Q6Jj60UWmQPi>7V1534b$=nPl
znJd^V=f9|ClfQ!NXi0b;qhp7M)4n{zF-uE9L7S7^UXi+On5?W;cn8gaoW}2F{teu}
ztXOQ`;bOCeNmyAAp;jA-Qu|=+Xh|{Pv|ibZ<dU%$Hqb7UF6dSI?>gnFAPJ=s1bor!
zt*p!(rSIylD}0}i>4Q-y=l5#$tWD;6bySXMpX_MOZ_1&^$kIBiI%>!Jo$EqKM!4eU
zxg$il;!nI$|Id#Ck8%Rv!-%|vZEUFrz9}zNZO=XK6dL7lDytIx{_wC;mz!uOl5bh(
z%CB0gw)S3yEMpNfy6Wm`<3f03!_i$iH*;WN8TB~oLTa+q|FoIxAVQ>EwK4JbO1s)}
z9i=5Sq{v3)jk>Fm+;fdg=?Sd{S6>VvS00$}8um6(^U-()G)53e7;tJf0#2>ca&kSu
z&^a8$ZLg?PAwFl)ZS;M*-xjuaaOeSHLsdWutftvexr;IoMcT4Isvwwr5A36w_eVh_
z*2Hb~Qw6D@yJPIR#J`_OZPFG`Sn%0qsfZWtiuy?1vk2l!eqKVDc-A@GkL%yUS0s3K
zbwlzep2NGvm{Nfanc?II2CY5yhqHkCZ11S+3uQ$w8%WPR>ATUxmanBfiu0AN<Mgdf
z4+3P@2Q1!#@ksdzMqfqs4yyNy+pmq>-|+DkWo9`SR(CPE8hx}FJJbC8^JMmxr<}%v
zE11}9DydljIdtxI4(BDF<ZSC3Z10f-zJrLp2&B*bvxcX;>n<rPGbnaZmsaXMvZ$LX
z_dJ5Q*<|ZDH@Pavr<45?Me6lKqcZ8uyILz`tvS_4V~@+%#wwVFCNYYRwQOGse^3?|
z;Ra0<A2L){^acCq^@dbNuU@}Ad>j$RUJvtUM(_0VUS`3Z+YBn=JwLWCymS?Hi|oJJ
zIPBY8RPnr?d3XM}^J=RitgoVG=6Kg(yp!oti=CO|E|b_dxlO>xeDXRyBxElna6;8C
zj;b<l#ws*qKAb)fJGwl&hrF<cW4etkhe4lktPW_CaxX<Ig>IUgnxN_F1HdTFe!Ulo
zQM(pVL!+T!9r39{DPN_y7kC7*f&e`pK0afRcFF_72j%L^alraH3krnZ06YfSVqPVy
z`A|~JW2@OOJYcHGdEGcZu3|m+eY3&@cud-iFNgg5(T?pIyh-Yy!Ba;*P5qGjoNv9w
zlHx7ds}!u=>`T{j+0LLOM^YDYo4Mjzv51F99;R)}_UV%y7(2#vte?b9!<{|TsCwy>
z2&qqs)*US`_r#Rnp$XmYW8eCGMzm7Y7mrLZnz3glzOQe@*BC~Va!Kr0!0`F|MbVYI
zSN;4xwDg76_rt}*{fg#qnks>GzjEy93e{@+2geCg@!xf8nld<yN4oSoN=Y%8L~=%>
zpY((GOnY;f8AqJZJA;(w)&r}t&VD<7#p%gGrBI`|+owLm#-49l4#Rm(L3+Fiky)p1
z06Yawi%2m?h<?6$7B#HbXFXc(JTH*VdY**f8nuk}P1;Ux@?P<1eSCC@FXEMOU+8{s
zD%u<KD+NEQLH*rJPAv~MG?Nbgplk!OuJIna9}G74h7t%UR08uW#0UlizMx95X4QC=
z&dP+G8#ESPGjUcIr)4NgOp?WE(6QWY6C89T(P~r0z>r5_bgn-W0*|h=hx<##4pmcA
zYVX@!+CP8(7y|EU0^ZNmdE+{V(|U+ZbaY)m;~7{%V&V1m^=i7yvM2-sk*i;_*7rPp
z2TJ(TV@-B81(5!a<yB%45QJ1!RW0~1F);MdW_pgVZV27{!HG*uTzPZ={|ONFnNrIQ
zB7Z3<bEemS=L<0EIv`IG6);AE_h&}c@4{GH+sD9TtT>q1WIX%hnq~GR*Zn6xoQVXf
z+^SZ|T1lq()eWiVCwn2bt;q`!K{x!xf@JY3AvwsWPeP7%CAhMl9;<A(UY>};r0pf2
z#>Ug1>y=kI>+^6ihe;@*ap!Z!NK;?id%{q*^^rgA7t7zfUhfGCKD9OK>xK@3ul!vP
z<x6}k9BXe5?B&ZXzWwmY8aB>O3mkumQDNR99DTy&X4o-}0(dQcW)fSjb$+LNa4v%J
z*jjYv>#k6atYpU*xw6j2Io-xNkD-LRH(Bz;|9&5LH}`QRk-^QZQF*%ES*YN0O;(@I
zyey(WmRONWDqOE0w29NPYZz;voap{^zL1pE;%pKl{k<o2GDV2+nq#0!AS}MaDX2mL
z9X>F2)=Cm$=AS^xe$Uh={)My}VoRXd`WHr@bS^SeuV|20i>OEZkGqXE5&m;*tU9L`
zPaXTFjwRQ}p0LSqD+*{`llCs8&XSWnZ+-Avp9KX4C2?3{183O4p&@xskVlP(h}gI<
zR`m)|T0x-?TmVFZMBQv~S)<kT`L(R<xgBu{NQ*}V(XZMwGt<-SAl<7IJRiq=?Ep}A
zJeXD9FnNtVsP}XSk~4`s+n}+Q`W?WH7pq*@^O^-@AaArpfiQ9pw087=eOp<l_E?Mw
zube;s6Xnr08PoOW&n$o3G360piq$Cj1O*%Ni0+vF>@`wB<kaY&WgaVZJEY=pJnCvC
z6BsVtBg&(11Ws;o4UH-ve#)2a?2=no)hhY~C)$~D#!K+G&&ac#nJ|B<PFt`dJoiPy
z2x{LY?QNEi#oZlxD$&n;ExugZ*27^5E1|jA0Psd0?>p7TC~JL@kyE0)kZk%G*%C>|
zB}{~bp>ygj({Stfz2{7ve}@0@0MnqArOY7}QG#EK$mHQ3aW<Jabed5(rDP(IB%V{2
zQP%_6Cj)0?N{s#)w88UwwK`7YkEQq0fT5bBu&@CVxXG#IK#6vI)a-j`@jXkWZs-W>
zHm9r^kNnXMtEYo6Y(o>7s4P+Db<)HFPOv^f2;JI|uB)7Tc1LB~{Kn}nx3YHr?@!##
zPN(ij%L`7=+JpprZ>$v8D$jVek4>El$&RL7pVp2-ylfELQ}O&n^+?XQ=E>?5j3*{I
zU%Uhj{o)r>NpEjH?I$5Qfh&mdtDdkqBZ8Mjy!^70<Jwa#W>Qj8tzgBMmX;Rr^c2Vd
zX_m8db4x<Zh|j@?d%7`D4b1m>`1x~7O4j1{n`<pb$<7bvaJ+8My8xsZdA2>8TVMY;
zT)e(+Y`xfs1@dhGEx~-mx4p69528SGKim=vdGdht0q46><iO`3`tB8!BvVsUTNKjQ
z*N>`fzeNToUBP0f9T;U)Qc=CDUH+;0|CJ6(ulntKCxLn)#f|!995KDq-QHI}&JVWE
z1Kw*A3{DwPYPtMPiE7EN&ymgURZUggSn${6n0A{>Hd?^(`u$5vU{yk4U}X-QX?l+q
z?kW8ZnIH9`-XKFyS*A&qs}~8Iod)EB07FOBQ}8u`kI)1L#w|am<?77ONtn{DfUf?b
z_qAI$1~`~5<u~>49UAYWK(F|!{O0lDq@KN}GWycb3gKd6t!Ps~a28)!5u>Z9Gm2oP
z=S2r&NN0`syd+CoRB$=r^ax6DV6|@TR;-}REJ5t@L0jDYQ~hvoY#(hh(GTitZ*4@H
zfMzZaVF}~3a)njf+(#E@HQAnL`s2c~@xD&VGb|?);;s%-lp17mee9_thL<G-YLg!u
zepkH3f_uh~^Bpu7pQ>|dWJn$2Zm`F^;njrK=`?|lRx;5}mK^0F>Q0VOntmy^{W@gf
zE?w`vMx^?HD(hZqF2U)Aav4uT7_nL83f&Ose$g1`U0?9wSEC2+4BZ0|eClN^x?3lM
zCzv<mvKqhJFw;{|LMkh^5{<f}X}^AB7Fz5X*NVD8p=8ObxxI3cxRtn<EzLkdNAK4(
z@CqCix<duA60{(X*ng?TWd&Up#n1QWPuU{1+op%>P#jw5i~-{t6N9Czr)Okl_99t6
z$j|S2UVeV@;nLtBlF!2hzO_y2aS%vPwi*MMCwT=0zy`0R<OBo{t^?QVz=j5HI8hOB
z{@1qJ5gm=80X%}Nz5Z1itliQji^`2%g~IGf`x<vhBk?jhepaIDM418_E7vSxN5;=G
zmm^czXH44(-7{l3FZgn&=__lIQD*unC~~Du^qB2*?s=%-rVG}yKR~pxL*c+!S-t0~
z-R2M+d#3y$Hy|`y{wlKWS-v{5r^70n2C32neJdOcX?euyxuJnPqEnzKyW-MW6=%=i
z57}UlB;u^QsN|iVEUEf<NOyngO5PZ5P3rycvz-6?=1+oJ`1Cbj(new+mVFhA?f6VV
z0Xt<Dl_Xs4p`|>SJFndo3CznvB<oT#abR2@q8+N(SUZ21-|FKg%s>|*o<wy<Z4>KP
zedEc3ZaCl0#qt`^#!(pdNTU^PJVXcTkF3qH60G^7YmzT~o=Dm}VNqr>x_{0Yv2cAP
zKgMC+PFNo%4b={IK71Q-a{I%UfcRjs0MAZ&VXSZLc~pWVWF?kdab&>%QSt3>^$C??
zIFgpnu&Krq)yHBL{cBh$1cUhm%S%Y${Ty2uP&|g0e}6~8#jA(udu_$dT2$&E#Seyn
zEk~hJf*PMFQPHMrKRyo>s<L$aC|lCEPP2q#ZB}(_=X{@=NI*Evm&{D^w6wGn5cyc&
zjPWovHPz@|xAs;PG?q~HQjM;zY_}CtK0dy65Vb?V@5~4SLT#bJrxGA>V}1Y*0Raq}
zq*V>uFFySnJ|G75nS<SD9W)<N3r0g62lzn}lNn3f$dI&B-I^ne7P%9eUG!naM$~)(
z7>+g1ei_=?!Xf;sC+u$hs^|B@$~Z=^%#FKcP;lwwsPTveH+f!ZcpJJtuAS2dB1<uE
zn`jXu((HVbeZ6`$@!@)>Qm#>%hODzawmg}FjC>Mlf@;wT?=SL2Ro6C-{|VR&Afxzk
zGloZd1ow|xnKY^JNohJ^mcMd2y$zcAKCC9bz^dZphJ2f$ZYDR%;JW@KiHKJosoL^G
z;x){uEg!6$3GDero$49t6;neVGP{Pv#%KNHkCA~fBn;aD6uPEilra_W+!sn7P-D_`
zg1{D2wQ=m+rm0)pY2ia_<aN%8Nc1^vpb~d=%;}RB7Xz;3-+JQr%%v55+z3n?+a|@@
zZ@J?B>qMjIr-6a2g><ox-=wK`j;*5Ke!B3nAeR{leuY@hWTL$7{^@mn+RxstSya=P
zmJJT^po8-niWM<P!aE1N_Q{aJaGg!*?&?N#%fAp-k}AYwOH-5x(8;u*#;(2fX~|VZ
zt}+~^i_-`{*4i{T@w+&qS~apN=otw)QYu9+#K*^nCZlMzIKR!w$$=3$7UpOJ(x08_
za^fg5!7#Z*CedOwNEtUb_W;;5PyWOz<$%G3Zx0*9(gT4RDoBcB@O-$K`7tu0p|gE(
z5Mhz&g*Pg2hKh>DyBYCs37Jn^*<e3iPk{T_Y#|8U)JYBtuQD}Zb;~gi%p=($G<tTC
zqupS`?OrTWH<0}JDb;xVbZ(h?@lEL$iRVK{o1vOg&NixRQP7F)yno_NC055WMGQ=3
z<WA6wht~7Q?HnNC8hb`6k7i$!^T_qvgZU3z+A`ucTlnc#)LDoU^LWWJD4<dZ*M^FA
zN=bE{$CG2CBht)eorONM#ezPZ9VFp114hHGCwKZhpD9}4*fk_`-}lsJ!r(JNUmkSx
z)(Yv{jV~2Z*Ve|E8B3m$hc`s|$l=rJ-9dpdh|V>(-)7{Qi#pHam4Cd!#ZUwHBc07R
zSUX3?Q`D6*lem<veJ(eVGH<Qx_273Nk^Fpx6)o2Mg_+kSldn6bT=>$`qG`QD2QVhY
z7Z#tZiegrs9XPdi-1+q{=^x>)nfbf)J4@ly9<fQ5i$9I*_3Q!=CFXN3iO}WN+uu`d
z%5T(p9O*DpjoD9>R5JSaKc_Tmr5bvMp!5efcK?bJWgce+b=63pYu4-1z0IN8GVtwh
zEUcJE=|Scmunu=;%efV)BZZvTcMU|t)&E?*%+3>F)W@f74kw3kbmkZU|0nj=xv<ei
zhY%;1_;}gZP-PfMa>WCTmYCB;ISQOpsaxQa!SwO^x*s9CdFQNFhxZ#0?G%<R9{e1J
zo}S)*t@{ONH*2>S`)?Q+{AKg~>gqT_ZUl_5urM4PoCr-zL+Yeo0TzNb3kOH9&*K?+
zE0}CT!o$G@)pL-R1OtPPj*f_qE-5XIm^WVU^bDjGf*)yO&|W%!>5M|sm>RMw@%724
z%BSG$EhOEG&<YdPXV#-9J|q$DX5SUe!+?3e!G*@yOYQd1#1E!SC?@EB9OB7dCsn#a
zqm2nyCXf(z_0(E)YSG;*5>#0T$9})q{fCV%$k9QKSf0n86~9jQ<sUZv<FUM~GNQ~w
zh3<k`)JUK0ed*VowTLdQEVXjs2-W1|JN~uAn~<kF*v`SY=dF(eyq;_nCmBx<e5oG-
zk{L|i>wfTgCXo&XNi9Kc5)GPikMCG@3LEZ?tLE>}mY3Z`4{ySVUIVP5E1|FL_u$&2
zSZJby9IJ_0Z9hw1Za&uZXtZx773|kM1Kl!(RdbUZXQOwHvd?;U-RXH7aOLXLrQ{(S
z{x)~aTWh<Kg{OzLWGo-4j`3q1<W9FPCDP>SewQqRLQxsL7sM($QodCqgavbcXo^q_
z2k~E&`gW%Cj^{yJl$UG3+)z%J=I@kz>~v7*7gVa}X)F}IFJqSL-P?XqC^r_~+TYF%
zQL53!@hyZ#hDaNzO-`=2_-j_(e?-Rp`P5b%DofW(C7jXQA6ULWKlmlQ$<^9R01~&}
zb#BOQRhB^vZ>5TUl{H;B6)>TcDHI9P8;!y369~*gcrX0b*4Wq&gxM2-ps9W^p<X=t
z`4$(`xnCZHW`K+;M+PF9e3gK}Knh02vV0wif4yfM1?;w`d&k=x(*k^*#FePWC0CgW
z#~k!kRLf6iIrDT4`%3yf5r|hB7Z*GJshgax_ld4!Sv^M%Ge$;?1Hl`x+J{#f*=lQ%
zhoN9{tf;QYjb}|5-5U(8bT>}wh@H3pJB<9oc)~U(G5VT%^0EwI5zJ$|*PeFRvL(F@
zAKFxtED%~b`^g_iZckkdC}UP1f?>a^tA*ldPCePqV<#dh@v{rC7OCcD3JX2Zm{&%O
zHKN&oKU0=MJVMGVT{(QX5t#ZCI^ukvKjUV=O{%7Z?+~0usUVd<-sK!I!!wy49fP=h
zFWLYV91VqX&D_DRQgm{WvdtRoR!@TO)R1Z2_fr4vX?r@HuxXGfygO`*B80D^Wknc$
zO7Uf;eNngfBGPQC?%Lhw-w?HTM-eN_($GMgLAZ{BV!~9BJ6-6XOz~WA>Nv@ThnL*p
z&ULq)?k)I=XFVwJK|y{e-0_jB`xkt4K(n!_h?F;awg%PWFUiACfhwK5=al*_6gke~
zm~aQYnf@*=dLquB;^$MK*xq+W<b8aJEFCF1QkPj*4;+V+-k|FivCF6FhFDUtJle#N
z@`R@N=|2s(c|Y(?R~kfvP-zNQRxICJ%T-u-)Xsys8djrjxTMtmr!f70-!>(rZ8}}P
z$ESf7Qs(ygJuP%?EJYzECjP-vTuAKA=UKPhl((qTn}<i)oysj(>jd77vzat8ccZ}m
zW`%0PDgZx}QZ)m7x<FMoyK{qh-Tppoi@5hog@zVuDJ@CkVczo2wiSY^I3YbG?qcL2
zJ*kH{4I+_`yIM{f+8C!=qTU8xMt$PqOjS;_{{YDiAAzXSn@xz7&;vh`Emr{n&Y!Z2
zv;I6h<D(LSxY8RB^}@u4w;Ch6NWjv*okIZ*#?>~2*%nNddXt=W+YYiNELNN4NPG2L
zl9BF5Nt0qj2Bf8*D1s|{%5759%dU@UcPkO?bm~x+zkQZd*3ySf#am_#uwBx5;Acg!
zJ8t#erxcCyA)f6cvtSROV$y~8O-Gtz`6>(0^el}Al(<)jvoT*Ip{UBInJ|7ZxU8mE
zFO4<5R4}4eDfF@M08>^`-1D~7?ldqZDX<b=KRD8aaWMXw=&kcd0$Y=@Cx&L@!sl%A
zIw3uhw}zICnk_s+T;*S9Y(t9l8dD5{8l0)ucQ%R9rt!egwfFZ;Stfb*jQHovw2mlR
z?zHJfY3Ifcoc0VNRH~-OR$(d#V_OUCdv*1|6`v<ktC@;E5M7_8vNT_xWh?7WYG7b6
zl*Wt0ZZQ%NjL`6s_TSf@v(>QYN)N4)Cc`!2o~J{G25l&m5G(fp{sginWKOG|YNoxE
z5#T%5rw)BHAHf6_SWN=B3f7i}!eu)OpH*X~j5$YmGot4fF4_Pj)S!WP^)%cl#BGQJ
zWjm>X29J)iYnqP=Htpo5*D#6TFJ*}kE7w;e5etudC!xaSCNKQYr&}L8H10ggiuIT)
zdhc48jT2MKYo}Ph%CK})a%XsE#p)~lf#O>wylbx4!5eQdkuusEUq54-?nk_MFI;}8
zNL#VkdCyt+z8Dju;Of{q+KgTNf*vdqp%Ro`3nur<YQ>!+0oHY#blg8#Ez}vg7$bhW
ze>)PEanouQLI*IeO)9KzS(UjykAGX<6{Q4?`*RU_$g?1q<Dwqhot280aT{R8xTd8u
zD0K&@8?-2iKaj*E=|zCqS6R1M2>14Gm+^-gwkL-W=C}s-d!>=LeZ&i+R)TXYpf$*m
z{q5Q5jPkUTx9RpBxrDd`7ONCtrfP(Ew_k!`;|WxH<C(erS=`vD>+>8BcXR-N_1vek
za1N2Sd0FcWAJbAo85no>`PJ3@R`+e=iAFgztU8-bP<k#`UxGfH^Qrl|<E8o=78^I*
z>EURRuc=OFoUYN%nQc*p-049o;w)Y&N}8T0_9CUr#bJoQbM0@}dV;H+54G<v7wo|N
z0DICQ;=WYt$iToLpTdCz+Jt1Kew+4pz5gi=78)c9T^=pWpf~`wL4b2sQ;L)mgPT}Y
z;CJ6MgvrJ8SqC8mx7X@?soq|o(v8{(Ie2Ircw2HVFL+^f3|Op9gnl`3neG_G_j_fy
z8-wfFy8Uh5w}U40K*ldV^jU$_&J*SRPTX^WW}%9%T{9*0y@2gt=dEEwLC&ESWg8yB
z(jsg?XSfUMN#d7S#cO@yc)3E_n!{@ERrp2k;Iv=d`=s8Ew1vNUA;tLSAS0=n5qYZa
zLi{^t9X+Eiaj-DiF&c&r``9)w7z^K7DHVG~@c~X|^=_8}wLrC$Dqa>moUh{4=<Rtv
zcr~8sYOsR6rKhCH2<?&dX@9Xi^=pc4Sr9-c`qDD0CfN=YC%-LP%dgFM!GW=HKRsNQ
z&XlJu5EOPOOuq8u@Pd=*(W}DbRji@1kDUPTkE_cZ6(0+RvQ+Y@-Cw!yT->fN)M!vm
zRa&_#TJ(b(<Vta`-Ji_L_+koJ;Z&rU;tll;m;o%KS&2`K&8hLVG=2wni)!%ZEw&;)
z+_UZPVbye8u>2#YR&76?Ry+@eSvVI-F|O<_&!e~`ZW$Nlym$nU<`pLC)lr$Sl~Vz?
z^(c_&f8}h|aW>HruQp}eANH{<1HWMEH=5oYs9@V%nPb<<rW(u`t%CgqmBI#CmZk56
z6%5)v387U4Oqekl8R%Kwuk>hQjX<t-lAs4ye*_^c6_xkbSN}aAOSh+)rPr>buOm5`
zF^<Cq;BR+|a<@2h6NBgN-2o*6COd(pp=X`|`qaFOJzdIXfWR)}S0ox9xD1@mijWK3
zh|sppO;-UR%&H@^=eV%u=!O?8{TL`Fuz<6&?H);)CNE?^&0HCQpiU9qKf(1$iRppu
zn{yggkAxyocG8yo*2P${1-+2GF)7@|S+BFs54n7A$wnUfa1bmGXHPBhAbvXbWu|$t
z5sG#S7Fp0Ij`p;`B5S9WI-R-N`Xt6g_Vk*JQ}%Rq!QO`!46+EQo2{0L<+0hiVdNG8
zhTvlxqsyRGMLq!w6X4)NK8l$BDn@N>y<p_nq(pG6A5dZ!YuZ4pqX$#n^;@Qz<<p`#
zKfIkFblUBW;lu5ig3Y_6Wubak3u_n0&qW=LOuwgythRzpw!%Kf%@i4wUCBA}fv2Kv
z(M<|;ed!<lpcYbA)@qjyod2!w<2QL&+y6K=w1l;rp-b~l*udjJNYh8N2(VGEUU5h6
zbQNlb1Idim5rdt9`>(IgN&4YcB9QI-C+)cd+#P?ntcXe|Pz<Sh6@AZ<4DCI@Zc9U!
zmvF2E)a)8)!932JK^hlq#S$DldfIy79~ewd&eB-Yj}gm8>L)(9KF+WcRU(0rDwLaS
zykk^d;;Q6b&#qpxV-rJ&KKO0nUc4{@U|Euo7as`;iFT3r{}hhnc79hvFxyXS)tIVr
zGwy7c=SHQhz!-QmE+kR5Re2{Fi97O<k?%}ctGfM_Ejc+am_W0w0f67hVHX3msAS3`
zN?=@}506LJgMVN3QIBNA6xXtBfBoYZz!aWrMM>1*E4+B->U~D2MKA~e8*e=ftN@*r
zGv-UnN4FhAh;Kf-2j!xi56!o)?tl>iJDIviwU3=KlRm+8{)=YFhhpxZrz0a0B(f^P
zq&F+_sHU#QUor5`&uph~x};t>_tteYU7IR>n?Iww*@X5BtL=A-FIiR`9{DmB?X?e|
z2lM6nrva}06@h&Uaoy*FpwIULw5YL(k`#0eYBs~mC{3;xR}R;vVuo$Rp{3D<>*eYA
z9PRx-U}!x#lTmn!E<#VfD-$br#HogXDSF3H!{-wYXWM>XysOL1i+i_<#}S^rSsenS
z!6IAe1jKrexxW0YbUV;w`X|BIk||)i{lNpODdjko`l>I!m5p)ExuR<{!HMK;<jL(D
z;Ry7DB5ng%Wrv-WIYm%Oc^gn?!WqWKe&)^T>tMq57A*A^%sSq?9r2amq8%S(DZ1dx
z$)h_A%o<u8afsXK`-Z=aK+A*p=o?QU6Drc?aAmhP<E@aiMr20kLrB6@rRblTUtRWY
z!1Ax<uV9}<|EOF13-+zDu)d#jf@3YQda4cOb>*LuT~8a}B3llbcJ>2jq?|S^upp}E
z#^m3d$>{-vFQ3YV25mYbA|fa*@BZV|50hUZUf*__dDcCB?{DUox-+sRXC`&ZCk7ZE
zJLS@8SLQ`w-*%ju(Nmu;>hW^*putsdJh7|$1mWTvuvL)4=#DS|SC|Ya747Q%aT^aQ
zIO7*%KhQ3GRn2h=AB58GfW#S!{KLZiZDVfOVsd(_1ddUkJlN@s>YQd(PqnnW6cnTB
z>)Y&H{+rp<wQXLblPb;$Wedj+vU3+TVnwB&Z{N*Fes-O&i+)P@f7<aB8YYbEn<^@O
z;yJXD>0$ik-}wXZ*Q@i9$ksyf*_WPbvK0Cc4<tgVt_+T)!(6o+;<_ih0k3}KwHAxn
z_}3LvOpjm;fL~o?Ygo39Q)ivc$izGZ05-7p`+}QS!4({hv!4K1B}7F2+ZqKXG(|Np
zh|Y1WI1m7tGQmr=!pQl^R*HFRzq|%>CWepI@c+2lezYhTPY0l!7H=UfSccy@mEW@L
zey5^iiIX{9_XjrK)u8r*zk0Q>N3+h|fHqMU3}5&eL<cAiRBeq;ij!rxF7^0=E*v-T
zG=WW=H&%G+lZIBFcwGH?8?yRyf`Y*S#sj9MCbCH$Ez6tX^W@@9xPps(t9dD;mN}{@
z3zMC=ln@_4%76v&bh#oNZP}n0XKU`+al|1MNqq(*XA!g>YM^KWzRtRfcx14$NA-RG
zAjm4xbC<RCcmL@)r;sk`5%B1(Y`Q*px7eI7+pYuNB0e`|BRn`bus>N9B@u9~`e2|$
z8S{#Xsp5ly|35?{M4MR@^W<jP)YD}rtiRc1;MT;PS!szQ-z3((dF`xi5aN$urKE2)
zZr==G&=zsclc`73NFAauA^pae0kbX?_~gRaMH3=dV1jHt*AerdXwM+{<BuQY{N?-!
zHU$6;SwO49T~Aw5URj}T^th*Gt&2Oi&aD^D@KURlN%2sj3iyrH^|(xk8Jcw$o0Si~
z7pA?}{ByjvjKZrmxgs?{x|{I-uM#1P*oS!ij-P5fbxiKZeid#u_}j<=sOea6ku>?;
zOCIlc52JU}*Yd9ODcy7PaLf-0S*I?SaV*?mxa&Mz@sO|y%*|&7m_}znreYXVk&Zj{
zi|K+OErCvA@&_j*x#+x)o+G?%b~Xz`nfQ=$=oDjY<5!k*8_t!Zf=r-jCke1Dt-k|q
zW2B^y<-skSz*>UzaU}970-)pQzbr0#{ebp|3j6)j(eIZ8Kl5GPtL$<!JLpIN#O~9O
zuGxZ;#0Q4f(@O%MYd&ob2MdYY+}wC1KK>Lc-Ue^!d;8Dcv4ImWL`4?sQfD#Kqn5TS
z!kMfsrIn)5ldzAXVxB3oa88E1qQr~@y3G}}F}cu}W75ZWj$%nn(gW_RFO?trU8VSA
zYI+mc73W@psi%pd*M>TAEqIs0)qKM8-bw#~No5K;_BkL6k05rf+D0jicW^-;)2WqF
z%Q81m-1SMO1G^j{vFBS$EwcGy#_Q$8Mt%kPlvkpZF(R07k1RO<h?tl+;K4b~puTs!
zF6nQ^AVY`3QlJ2NfDaT@;s}5#)0$;AYWp&{<?sBlf8stt`B`#$u*J^f-t_oqt6#QF
zISMee#?H(b_ZF_2Nt_##{PF({K*OTLC<GCIQ~;EPY2vT9m!3NH3#Si&741q7)KlYo
z+Cz`L@H%)`ehqDIvFA%#gn=)UTKMX_2F-z=J@msRu%EXo-7qzac*Si+ZHha^f8}Cq
zn~Git)LH%f{LHUt%HNl|%bD<Ikk1pcQwq)>n3}*Q;|j}T^ntR1`Ox&_X~0H}{W0y5
zoqZhEF8XvE?K7lC;BW{kLZD&h4;?SjuHz&>ZVK@E_7^j~<i=%a0>iP}L7e}r{Y9NS
z)-u<%pit@ZX^=Pk&N6PC`bJL&(;7Et=FDU@7iT9!Pm8gNQp4TtRTEf)f$Syh-M(U|
z;_D}41GYbGNMz)*yBP+B)zE%FQh_q-blI$N0hk&swx1XnFP#4z8$c4_9qToT6@{HQ
z;UV!QcekyofCK<@2zX>FF1nL__Ca5l64Pbn9f}N>3V{jmC?W6tR3rAK^v3L-mm)_a
zA28G_WQ`H=11id;eYKRyYJPKV0;dP>4aU+lf!@iJzYZbejuT2iPEAj~QHS>R6$O+o
z|Ht7DUw5xbPS8a4P0%WllywU6*Uo*Od`}Ug{->Pyy+U;Lxq1i4NBVyfm1qp>1e>Uf
z;URr8NY5@zl@L9tUF2y;<#x2p+g?@T4dOHnYcpftQCj%y5{*z$C~|;A#N;6*cE)~H
zg$&g2koI=xtEpT*&_JMb)-}KK16K^ReOFnVE5S8=2%m<I$GGbBRVsgubw%CK0Grlt
z7kv>_Q#bffyY$h5Ev(8=fzdY3^3l74>pKJ9<=NInn#KW;p#ccu`aJaqL%E-SVD(Wd
zMA9u^{rcdT+B)>tBtcaLP}szJG_U0&v~*4{7TpkA8-ev*i<1@=<H=t4eA{5+%EsG(
znUQcRvID1R_{^cc7w~RLO>EkZ*dN8S7iT1$Z6?Bfi@ZX?Q1x$u@yiJ#(n=+&7tyqd
zR(WlxYRv~#EUe20YD6b5PM|SI?;sP`6x`Uc6qUlZXX5z{_=Mu4#d?0HMG9i0gU(#v
zW^i-Wkgi|f5(zEE28=^4!UK<}|F0LFnd=V8=N*GqH1Id~qEZ}Jlm6@14-xO9(Ief3
zL^17YauJDhCaTY@o&8&Wh3+m4qN{c<f%`Np$<LrgAt>wMt{b5>spVX*EbUB+1x<t&
zsbj)DYZ=iW0L$&iHh;6mLbPSV?dS@{1hna&W-6YXyaeznNZ!JFPc?KfZ{nV3T)!qW
zP}=>X{6(RG|D>Z_Fqx*=$~JvF$g!VYT-$trj#AI^RB36L^ED8#L4S%^LCag<XEwYD
z<rpI>hP!qY!z4p;n$k0Q`{wtQwqwrU=|=h1bHhZB6vYY^BrQ;{!MN=A{mUSMXobeL
ztu`X4(a&JoT5V1*M^I)d*dB5e!BjA$MHoqEWb109#yu8F6dCVU^BiS5tvjD&c%s8k
zx4g<)Ly1Qlf_@=OjSap&wAb;-V}USped<ZoY(zR_hxs*z2P>hx(25z!9Ty1-5ic(R
z6>Q2eCDU#{i!d$tMv9j|iNahO{IFtgk$`TtZj3L5Nt*rs66I?|BRSXfgLMRJ2eQ;U
zyh{jh0LIKI9Myz(p+UA&Zk|~f`tDyBeyiMJ9hv!)7X%xQH8;YqOf1S!K3gWAsdrh+
zz68i!`TT~TJp)I6_@JZB5`ez%T|9E;M=kaooxXS8VYo;Y1>ahhUYR68A+B*)C7X!g
z*~*S9XM+GR?S5zzNWH)KqSLYpB?PEV@vPKWel6e~N1fSyBkc^)ZedX(CLorc^4=2u
zmR9M+(^f0{#m3@$H3c6jer4jKDl;fINd$XAgGROul@FL>OY&%&Coa`0cVw@)$}vH~
zbX>v3Ql`=cI1cDIpVlE?G%kRW($&k9I|9wwcG5`_R1_sz_%0EezAaTkh!sTrdB!{e
zMj#u(hWq5o>81CBC*#ZGgXXO2tv;jl(?75PXIPlYSv#LpJC$+#Z@`sk&QU>vY9prK
z>EV>e$O1?i!Nl5r!k}#2f}Kima{2mAc|~76`-dd5vBp$#AlN<WPJkn61z_M|+H!`g
zjVU+5&!>j;+VUo>_T+h3P&<3S*=)yXmbi8IgN^$>HZB^Y-(>(+kCb?7zoW0<rSWMq
z4=x8wlb3ZByO1|EW<UJA!VO?Zp;9y1Qb;u|tCWp`tT(TDWmp$r;S}&{HNkH8zeLU_
zw(o!!P8dB3#k}fQP^Eg99P9#wnK^(e^^X_9<5VsrK=&WmfbrBEGL#yxE!aP@h*%l1
z6Lu=7`zpSEG|LI>a?q(8Y})#zw-&+I>&ad1;InCAZ%m~UO2-V@_EGp<qE8dQ8ZIMx
zcqI6`&I{o6tJY=_2kRy7+c~NPiXHzxiQQ#t>j;?{$PB&%6>kRdJYkK8n+Q`Z*S)U?
zl%jyBcey&B5stMl&t@Ptsm<pd(?U@e(151Xq;b&tR|cntFy+Yhf<VlXXQtTsQv{kR
zSPlDxWo|A;NWm}zTAh{WebbP!bx2bXkUlAAZ+5Tn%e?LcpC-%eAyU|uS?>K~`kZ>o
z#Tx&4t6J9LKuowVPyJXi`t<+DNawULEy>WtLn%@L{$6~%D_3#}nl`77J`tv_+akw0
zPWorAM>FhzD@^(w&{On(D4UzBrU2L1mL280-#KfJ7#J1HRR1>WoP>Pv%y=#ET1O}g
zzkAl`#Jayv*X8==_lMy0-QeKo{Ob=(_)x|+;dEb7zo*vTrQFWDw{WF5wxhQct!#0y
zTRKXK2F^GjD0C5EjF@X?;oz0ROy|+3EzCnc)5z@Z5!5{akV*3=QymzXQ$HCAQMDa=
zjD<P7D1`y=bY;H}GZ2ZF_Dv)^@B=#y$8FG8U7EfbRex5W3pyaJ5Vsk=DZ^&eazM7)
z4zwcmw2RQrM+Pr~ZyjwPz9o!rLmQ#SC{l3CTiTSm*Wfoe*$~#;Cl&s50WVvMd*#WV
zm7fllH2xNBtW;;!Qumvu6&N@75W5;CILo(^@Q%F<RL$$T9ZZ&G9=kyLZ2X><)ahNG
z51?gpip0iNO&P(~AuNy5h1qeZ!y=UzO^uC2*=93kn@JX*v*akyoGSa;@T?dW;E&j0
zlb&R%7LH%p6C(DeSJ4#}%&e<WAP{xe-G-VR&PqFe1c1Zhcr-7~SOI#uV`O{vFg^6g
z+p$*ZrV*e`Y!zrlDN`VRBK%rZzJF|0$(82KRf7m#(mg$XI-rV<3eZs<A2qXVFzM=W
zs%7i{Qw9E+UmLVR2h2LnHT`-m@7hP0-}`;)KUzNwrXz$%R|2_hI#B+AZ?M+;m<-fT
zm;T$_(%4O}j&?Qrt`?J)9#C#dHY_OxpM$|4%D)z`U3DZ~f|ZT9HRyK!M}^j8CKzmm
zwK;%X;Xh&ew>bJ|mDYl#tc#3^vL}a=WJLbsHp$grP-D9ntIq9Z+Z+-<%YNFMR|s%}
zEYq~x5?x98=t1oHf()eOfQTI~!`U3|(%cN<3##2(_A86l<_l~8>?<hSzO(7s%3t3y
z{Um4=Pw#oM{rcWh_w2~dJc!McGan&nS!#AjnyF_zIXkJHlE`9GUhKK8g19?i2^rG~
zk^O1X7&Y|u!V9@G`oF-_%uBh>8SW0f58|KCLU`A`i=6K(E^|YGGue!{4HQOeQ27~A
zrQ7Mx^jqieJzzKbJ=m2)$ZzFEc6b~Kh=y6*Qg5so=t)6VW<yj9<+$+Kt$TJ+4m?0A
zaxPnovr!q$FC>l?o%xO;q+NQuckvx$-|J@k#?#?@TQX4OBHCFCM+e9sKF?ZqHirUq
z=ND5C$4Q}rOIh!cT@RxjAfqVHPcq9bj9$eW>qdJla8l-0J%|1wafn~-|I@R!MoT0P
z=X{wiJ1cGn)SufQ{6vd*d>Zu)o$G_{JJ3p^p|`Lz6C>^WCH;_9u-jjCJoHFh3FCAL
z#pEBtLfIArx;e8AZ+NUWlS+L9OZPrii;;DeL+sIWiWq6=%gORprusno;ZCQse3ZWO
z(Hk8s{59@w%V$<zK0C;OE|W1C_96e<bKkCibx$Nwj@7gKKaT?wo%(bA`EOZV0?Gp-
zG+kE{Vk~9pcA~;m-LSu+@`{#9ybcGgh<bMW;*syS8{qEKlNMI@<)iHfz+Y!|u6FV)
z{qXb(oR*ws$mJ|>OgAZO_zwV?1pu>J1q=8O-bQd-KK==EC21@3{^mwHgHEHx%a5v;
zb|m~8Vop7<*JPB`%7HA?ln=2&*Z<iQu7iEDnP(`Sf8|rn1tdXd8UvK9EfOY6&>}QO
z^^D~k_rs(!#O(`{hJ7uE3jd(Fov~smQlTc8N4%+D2$Qv!TwMOms4oBjC^^s-ta8ml
zq^Yr6`78MU%9egQiF(0^jdXGeF3+1kiBC=oaM!XUGeGv*$g4RLOfFjO0~*5-WJBit
z5bZ89QQ)41$S|~42BsapGjc-{dbq>p>N78TxWBNI{7jeOo`r*Vd1X6xwF@>d<x&+W
zt^MzrxUaM@1kZ$8)+I^@7z4CEF}_$KX^fx`Ea`H`fhEi!T@QEjYD;lsWmeYtV`Bji
z2qe>&>eKuUS7J=`R&m#_X-JVM!?%go4UHT<WSgxJg3LpYG!fXvflfr~v+aj}>JkF-
z_4(&|SXfJkK$2a+6fNm(*)uV2rh>F}dD+V@?wM*cqX-U<00<bEEgyD_UQA8{^G|`T
zP`;M|Zx5CEwa+G&Rc{@#J|-<K)QpWPOwP0ZZ;?1uELRBxjkhx@z<P{G9PEk1s9msn
zgjx1AtbJKb=q`cS`v<%}q;C03tANp1xOPbHbM+J_VME?u*=dahFBep&KfCIYnu7p?
z*AMS%rwjA1R_lyXv{i|jJY;Tv1_I7!%S4Bj&XiKJ-h_B%yn%7Vn?#wkfc^$+dSK*-
z2?gv1F3#TOo3*5q37N4<le=VVEh1vTsXku4ENF{U^>ZsVCQjpf0vbgD?%u?%B`$gT
zc*CSj956nWx*TQZd8*COU$`5IEooGfKRL<RG>Va;fDM@NARLT~MsF-!WpKa_4C+8H
z^VkgZ_W@WtRNC-3qVaZ#C`KP>?p-b9U9S1GR#RaUl~wXP0bA$lWXgR#o7&*$Au&9%
z)Q<~PlArX3J$?aZH)w+MeF>a(n8E;%JY^S&Wlx#FyEs9Cn+tQ5y)ylbqGI*6wk7<j
z3{9j?_Ebf2rr1@{w|Kq-fWaQDcYuhy?xF5F@85M{GR8QAMg{L!JrJ~W8-DLXWBUKG
z_0?fju35YwARyf(A!4DFNC;9=igZhdNvo8!AYCG&bV|2?beDxlcPpKel6Sp$&di<X
z-u=hSJkOb<dw<^>YyE1Oru&#bm~6-2STAXXOPp|dx=xx^O5qBU2h;`v)W`9Ih^%Ae
z^;0<>S7}PCH$B(18UCu8tY5znBSJrAt)3IkiM3yDYovdmyGhnOXS9aFoExu2iVcwJ
z)f!QoyQU+RQx&EU-pi&qoj1EotN0HoY%dM6jRA#+1$)DGupY`OzdrCWF`~g`v`-s*
zaI6Hn{N@CI>%H;J`kK;ExEwcC`c{|o0y9+1;;s5Xb~&a*B5}bt4c}sOtFSTt;od}1
zZFu3lR94GlFKMb%H!A-S+MmBS1$vJ0dv=$N+}W-(D_Qx;-j0}F%1W0mo67AtY$G?b
zE4bFO`-cox1O88cdAhqac0|2tCmoHo-#I%40~?0)Cc#5bzowGAY}{fw|MKass`F}M
z?XHc}T{{j%J&rmSk%Kr#=m=L9<)vJl!ZbHk@wr7Bu#h$|w|r+A-&P<~-4jRzp78Ka
z|7#$Egq;yiQJA3*6)~y>na%9e13I7H+1{F!mmwSNcSAaWk}QWJo_gJs1D>*&L+=Bc
zjdh=!m&T_=-gU?4Yv4ORmAnB03@Uv*18oDZ(8CHpL#6v>o6+AjTO7@U+$+b{f@Tdi
zwl$a)t*;0_J1ajw7mzuz>{MjrlfiLny;!Bv{2r9wR&O*KL;3{$GQ9OZan=V#bX>gB
zcUs<|0B(Ym3S)WYgBKEQOHpTPUVUZT*;rxgh=#xDWwZ`ee9e^GJ0Y;-1-gFT*-G5G
zW{<HmK7k=0Xj7Z+=06Av*8gu$I)YXvsF_f~U)*ww6QFwI*#-0T#_t<ru;yXgs|e$`
zO3Di{G0b9>%LjC_;~>1+K8}jys4t3kJ^<8nzW&Btil+R0Z>EAf+-Rw0U%qn_4t(R%
z*40j;_eJs>jc?)4B&&=0nv<{t5<5xFz$4)Jte6?r&$R^{bq8HFU_+6CA-PQTV57;J
z#Nb7QzeGxD%rI?u$ho2Kl9>|(CERaK=Yo_smYWj1w^$p7PQGn<T0+3Pb$)tQU+@4V
zoB%Go=f{A#clnHOgMN2?R%ZgO?$*Tyv`N~4UQ8@g#k?FoJ&;$;$9^Th{(>{7F%*Bn
zfo2P!`gOEOB%vl*eNp)0aQV?XkdWQoSyJib>~Bs`=ZCfi+!g7lEjn15g2!1}CJ*I}
z^l-O{Q<Xe>{V?=ifTu^&t-TfKsGc49O#L(yyCj;^^U;s=aE>%@XQPaSvkob&oiD(~
z)$uI7^At~&vn->%q?GN~KRG7Qy5~N@&8XO3ZYKnl>XWao=Ut$Vg>RHxDS}x?v-dhl
zIcs(Q$9J=pBd7?K4iy_hkMyWqiT$y699>ut`hI&%Dpw=WMO8^&{$*{g`<<%di>{0R
zyjwm$xl46QzWIPMojh<|;>pja0pE^pMXnPN=Z=%_y87LDw2E6mk{9vJDY=xSvFzx4
zgd8^%0L+IP?k7e!C)vS$h3f#^e&HFWOo3~@kW2I4(ZRw&esQ3RX8NXx#p+Dg!RqFV
zuiEx((x#-9#TC?C)*^l5&vc?_#{q|(O-ZS<r@24?qBNhm%gHJ#DeAiGqsxH>*Z6<T
z-%JOTy{kH!EB1tkIW7hI|0og@u7135^6I6N4xkLYI0TUUN-zaW_`H2`un=;oh&-hN
zpHu+5_w)$eyyH-m@n^{q?u<aM@9Dou$;e7B0NrX9N^pWSM#t}r+pfTpZXXoANWC^U
zb~`yx`N;B?0B+UVE3siq-Bzi|bX=GroY~;u+pR>Nlc`}mF{q}^CqP*3x@ZHUCaC*E
zK`N9RFV^2izalma;J2p20KZD5>0=8I(rseD+g-~KNig7%^6+$10lGAC|6y;?2w4Xz
z^2r#P$m71)2$9W=0-*6tn=JPU%^)ZfcOJPUjuH~w{n5?<KXY_A?4r&(@SDmPaiEL%
zs^9k-N1_hNMuCq>4u$4Q=>ql>0Ug$2WkC@^L0aAi-|BR&t8qw3Lf<}`_z0Z*M6keU
z9hg!Ql^;1^9&c$sKdZZTH>g0hWV6=}#}p_87*~P`>Q<L`i&E_)Zu+<Owt}Iy=20-*
z)?g1gbCXS8qV9dX6>5pI%oltm#P%0hp9W-i$5)1jR@1pfGG!GJgteT!3g7UujIBe1
zJ8bVe*HgFm^`Pa~<`I^k5dap~q3$<OG!7b9%-+n`*5>cKQB$`(4Z<U>#n(txo^0m8
zCsHgSd0Nr{uAPF?am~7=4sNl{i4BJPe1dUq6e1$c>9%|V0E$E9fqJq$8m-5hv<SCm
z?4;v$eqJZua@!3`*S5CCUP{HcEuSuB9$DL|%Slv{q$eoWA~bbUPqR>SbD2L44p?TU
z*};k$Yv4<`2p0&UKT2h-51!Ydn$8#XUOU4Hs*979UwkeI=+b^&`@NbMNxe**$#f-}
z&5QsL#V+<(ra4Qm$7D>7KgIwsJQ%5dk&#QVUMwMFe6YY@7_5Eorc^hjA~e(Bz){XR
z6FG|HcJ1>6KPzWO^9QO$pf{K-5o^MwKCs<khfZ>5MRJ5C>=p~&iO~@|GBIW)D%T+O
z6E32c@JP0exa06Jbbf{efj1RAA`=S>fl<5<CqbDfv;q@LFyW<(Lq=br+ksPt`k=zy
zzkGVK?px;D<(DH3W@PRX!7<gS#-Id_!!#VBN?mx#(hJvcyV`<(g6%K1e&eJqH*eD{
z%fphHJSz2TwDQahi$RP^Tb5z?bT9IH``7LnB2419Gae7&qDiP&{A`zrT0Jt438*=X
z=h#JH+$fqO3^C9=+OC4L1myYxwDRuoXkw^0O(1KD;YTx*4C_$gQCwuol+__j22cn(
zTEj+C?rft9I42hkm<0DbS-veSG#d$lIywYOb1BDc!*A5Nztk5$X22f`ooS%2yI_(A
z_XylV&m@IR!MCof58fkCZb7SWV#Tb~5iRFUUMrPuP9@Jwvl!&XMc5Q{i{xaNfAo)+
z$^x_jE+>nDbMUM&5L|7*u+ZryF!hY3lfPhQJi#i7#d+^tVsQZ0b^luelX+Ao({gR~
z8;5aAg(e>lak>`ZW^BDcaU$t)cv}Jx^H9<&XJS`Xuix~l-vBU<H&W5om%9A2>7z$@
z5~Rwyx`8lIBvCe$X|^k!1X+WAKW)=>9}E5c`)Q6&$tf_ENr0M~-)b1o`zEFYsnNZr
zn7qGz*C5izI3BeJ(wjr&W7_}Dw{XVgt2f<c^EcWB)081>fw)~9LGa$hs-)8~OV9J3
zWvIo92A~tMPFmq!;D~08DH~ay8aNYIxSm;qXVc-fw=I28UHH_V_DV2ObYZ0rnDkzT
zGUHQ<7^bKjcMJ+P=~q7QTkB@{MSIlvBzCuN%{>M@-#`KN3<Wm7X8@f76qjnwRx93m
zcsCpClhWRRxE8hJh%=80V$EgZEeI6Zy3s`f9uv^(`?L+Q058@<ap}XX08b@tbHoy$
zx9a%Fh2kZ;hYMo)VyADftU~&a_AzeoHqo@4%-yRlhZo5}^2b=_{ho-J@4GlU#7%0c
zIoXhLsPNi+xY{D1=2SNsvcP8?&$#;QCyP|I5G@Giyh5v=6WHI9ayYfE>Q1D4E^(Ak
z0&F;>;W~aGO;Oh5V3K!F%*J}#M%c60&Wo5_zzdTlUtl2Z=Z&2nGLWO6*Et(5x7yat
z1eT!A4F&QN6ON~{f*^x!7uh}yPExx)A5C?2O0a0G_M3(p&;Ce4mYkDB^w?Zn`Fl>e
z-QDV^g>3PLK3X;&9_*>;>l0x(jND=nak^Hs|NHBn6XRb*ZfkM~>)qO)ntQos3uzgl
z#7_Yl-?yVD&&kMa-tS4bxtc0o+yYh!P!*8=e2`mIqf-f_9d^0SnKNpYma`(33Tp=&
zq&vdq%qOj_Muc=GLtC9q|8%Ikl#9{vu~Li?0S&<|ia~L6^I>OmQ@qv1%&g|h%J~%?
zjN#}Tf({ylZu-Ln`f*ptKvC#zt^|$94Q#du4B((zp$BOLDJ(zWYz0Rq2Gvu2r`TV;
z(LMFBYkCFAIPQ=bHg}%AM6~=xIX&F#I*9#=yeb5*J#&Cn``%w$Q2!hTs$R-;7h<tT
zwZYA~*0QxOn4poYQ4NmMQTnMqvkR$#|MX24&2TJ4Q}xA}%Ql}c7QPUL`YLqPR|mlO
ziIF%Ud_-P9`|_h>kk)=0*4z91s>N?c53T$d?r%C=30An=ByK=)Ci#KwZ!^s4+z{#1
zo0oKWfwvh`Sm-db*L_~*d-<p8B{*A7%0=wfUwOCwMT(j0FQ7$B_M@;|hs~@+hb?v)
zY@3Y2_C_%0OZ&q;d=pdCGk7PUwF2i|fq5V2;$R5@#yD6iK=q#NyuCcznMwc#zK%ju
z*~plf#-=80PnN{2tWyw)S_aDLtGv8nZ{ECoHuL?As3_CjyLU?sOPBfPJ?FqC>Ueap
z&2#V5tG7G+P=mmygdHly?1Oh}P#6WfErMDgw#o_X?;Whz_G4t^OO`p0wZ;{b&Zg2}
z=ydC#x0xOF!ymN$3db4SzWvAg3ZTDtX2a^J69n@@_K&=XL1hY(H*(Sh1KYmm&@TXR
z764oqM_1y>^*yJgvVY($m)jZ~5AuTQQx2-vKF70lUu^cOP6HH9xJ0%RA1}-vkdc?_
zPcMsaS%X%b=R(e(!<v7fMz0Z?Fg4ZG9#ilZt`5KY9!WhxjCD8Ag7P-30Eg9LK5s8?
zGJsN!HI9$gS40rQ4cHIdq-mO)2p6I4#t+<eE*&8K2oNRi6jmbCu6uJiCl~f?KQtrA
zGNvoq-xhidNsr6<VPtc{08;=tv!D8)w6kAb5d=lk&lZ0ZUD0NMe21L_pNT`ood;$l
zv|n`l_y)mIgj(3{7RQTk23Jm-v4oF9)`H`Y+CMAK<Rvm3rSJGXzz|~sTerXb*#&TI
zo5l)0ncOO`z<B=P!TAp#J`}j_ZOMoo#dURn+k43r`F=0wS9z+W$c)Tri60-%-J<X?
z+PBY7RZ7k`77S*@B#WN^L}x89MY#!m4+xoh%7Qb4qs}0sNJ7#5qKzND!LZ5q+zlw>
zS}Z~1`T3&QxA|XB_SVa4=6@Q6a0EJ=n#v2g`76raPCWs&DRkoBXCK|~>|FA9g1!Mb
z%Yx~jAW&bwlf4FIF?~WKh=j%3@uyA@Wwo|^BL$U?pJHxp-ONf;>;r;y!!MWWV}>Yt
zYVw4j&j6SYQ6>dfdupY*;rlN|NU=)1FXgajyVob2zEr!O8wSLy<OZM1uEVjBqO7)y
zTTFI;`;1uGz^svZH1&Bn6hFTd2i6-g?}PNh&m^zV{qilW|L$~j_cbt<sV|)~p}hKh
z@n*!$fd@2|WGY|((ak;{o_MM*F6+W~uB?Zcgbr+zU>t6qs%<hqy`$Eue7+g%R@_bb
zmRvRGE0{Ux74vx^XCsJ7gnn&pZ3hw_3+)y(-@F+foLv4+K;q!j#+T{*El#bI<H{9o
zup`{q>NmwBBC?#!_xRhUE|FtnGB1_CO!ZZhGG`+n&y<K;o%wTGucH8x9+5uM|3P%J
z0Vo3&6HI~ny;a--qz(hMOmbkjba;)uvxx-@7*t3~0;%+bBggk1pTT=t)az=xeG3>F
znfJI4q?|zUtP>7c$+KcHxh%6aU+Qq&qulIp9JhFilMl@gh{n~vI{g^CZRvQ0rxN?F
z^q|7EKFueoCk&VGv{Yo6C+nsk4vps#BNleYCi*<oI0obdNP{7BJK`pngH{DI;^yxS
zU5{;N@5ma38Wx?(fT*<%N4bT4eUF$hY>dhyD&+*~iI8{9i(jOD;hhrU0Tkd|C6n1O
z@XZ8XhM1C+Ef=x=m&TNZ$!jM+wqjqOg4^y@X{V7>+!?nJWQ7dP3+$q85_of`&{pQ+
zNjpo!gc(2jJ6sKOb~kU{EO`1$ZXagA_E$R#pag|#=WSNu%ERVt_x!iDyk!R4M{#j+
zJQn@jLG*m5NJKVBgMxyhw+a4gd(BLNzy|!t<X5432R-zlJ7MC0Tllo%+BInHahVQ;
zW&e1?Wf5{|9`q~U_OH`J_aSY*_$|2Li7~bdBb|J$p_1qi{MP(Y@U$ckwfg+IKG}sY
zK}a#{w{diV<gMl<xSHa7Ll<VueSs^kOAf~I%y>++`K(E}k~2fZ;bJ)kr@|^ddxQrh
zR-m)z;>G-qd7e1Nw!(XFK=k|3+LIXZ?B!5el1OJ_kE`pPfi{3Y?Q4Wq-(*qsZEaUp
zqQP(dSNUH}`oD-PD5!O)j5C9yWU?|-g!@}NSu5w5M^xBpFaVdQfbb{mZG~O&OAxcA
z_G~(s`72z$Cm81!{jCOMocr$tFTR;%wr|<CsZXcyVhR5k92a`h{z+Z?vj%Q=g)3Q<
zn^^NcPcK1F;E!1)s?g0+eJZ_==jzo42*~Dv5JC(ve4m<}oV3|mybF$S*0X1^nwy(5
zGBPkQBBP@l1_$Y_tgKcyHl&P<SZ+#6PAn~ryf!>}e7&e-pNBSL^f_j>(tH12u*gB!
z2e)VwRb8(CAR<_C4;a@vtsj=eCL4HcGcSkW9;~<q?6&=;M@oSvi?vI3#E=A3cJZ~E
zeT*N_KeZgo!C<`z(^vP026c8~G%0n+%mENl7Xc3JXPV6{?5T%u7Tja#mwf>2u`bay
zf4y8b5ZN#`pt`iu<MkOQ@_EAC#>*hqs=~8m`1JM_W#Mo}BDs-T!?4Z)_AwGuH}stl
zX!B28xp@wpLaCN7yGYLcGXBgw6Q-e_sd4b1b19Pl*~4AX?HK_B*K$&z`L!&<aiq^j
zuyhw;9p3(u5C3)vsS=^ti!{HCwt>IWTly0E^C}92cFnS2v5bPPAR4z=;AAC;$_MG-
z-&;@4mQ!AS=$n+EuTNkv+wC-aU+`e8s=6AUYA`tX!S!~0{7Wr)0wNL2I8!Lfu3s$k
zmw7g`KEG|V;-Rm!+4D5OiH}Ji;H>)Zj^?&g=@~R{G)#8`9_f9#M3p=D=zb6p!C8a>
zWXzmzyQhlp<50UTjnjP%_+)L-><el6KdPS9eFZ_(8peuKWe1r*-So_R{#d32l^|b6
zjCE()8bd$|m5yp&f{RK=fhOXwfmw-U%R2%;{QwqZ2{rVyaE<gwDZ4xTJXVxv%z=-$
zrLvOKXfJBlFh&S5Am%}0C+#QRuE3A#zSeNVvfHO?Du3jq>bc_b#EWEey~{;?YoM-H
zHY+O^;2S1d2_^6-4nC{_=}xm@`$i)`*k^JRPg^m{gB(;}5F;>yOh+14{_)3FHkEvJ
zka?!y62Ii}BZ|2s!p&Zg@<_onJ;PcoJa|frUO-x;wwS(4OX|#Zt^IQS90l0_Y{Qn<
z<3a{GLB$V1Pl&%*TwTxp@5=9VUs88mOh{#Lua_!2bnA%pkB}KIE9E)pox_;1mLr`@
zl&;u6>S5RDCk+pQn>nRS6|i-uz5dBup2OM2M@gFtP;-SH`W*|W#olQI!9{%~NVl(n
z7mU7qq68~TH6F<UK4a~m0a^Q*sHojP#vt=FidcvZtt(apqOX0J*U_so@LcB96k7K4
z8w>zuZgoE&pWbT@iHpBSE?e?P8ni-I6X?Po&++@|a4f&L^LNFR0*Q85w3*XlRH4TX
z92zv;E9wH$t^luJvyIEL3fKj-f9e4wUJX{gW|!B`yB@g=UXd7To=C+dFW4qvKxzV<
zwKUWYv@R{)WQ1v5B6E|EFiFD35@t@7)UF|SP{$Z!E5i^)$*DA9UAig2)pqVnV0$4V
zFIW3LQqQns3fe8Dg;bV{&EZgiD(fVd)$Jebk)m*>?IkdKTwq?k&y8MSv|eI172&V>
zvxdMM2rc`-k45J3;b~s@vCo{GCr1h`#Ud4@wnM}J0f_PHa*cNH067P?rVe_b!I5xK
zS*%q?WIIl++i>``vh|1c+8za!qw$Gl+O!7&PKr;c(l|nIej!?$Su5ac2JwhZA^&6b
ze5u?h(4%U$6G_S#pJS&inO!uh4{Wk+hngHN0+T---A*&F1K2V&Psq@J&@4Zvv9sD9
z*l(Y9*raYpcwhC({7P^u?w`8uSMN9g-_#LicIJf)n+ch_cGb^RF5%F2Hz$FHKNd6w
z0IxIycB+!9%%{C_?{SbrtDW`6!w1l#CAV<D{)%kzw0sg9{a^!JS?B>WO~rg`;jeoG
zn<(b~Ke}`;sRn0ascvp4O#dP-;G#i7lHhvVV;*FyDnDjXWLOw-N)Aw_S8iGcmnyHc
zNM#1O+S|BpTKVk{yp3tnplI1+3%fOLWgY-hGI+hN&dO~k&R{6xk?XOm9&Kl*X?ow6
z&6|ADe%K)tc_^-hyXcxHX5dmlaQuQTUqVs$vNU)Qe&eoK$GVFqPs@SLY3BXsne|2$
z;NK70sH>UT1AGEjgAmzr=Ao;mh0Ymlg*jE$WcL9;(2w@hu1D5D{vG^z-50C6B#&2a
z_xDS0*l$$M5!xqIa_X3}y)zleiKqQBa9Cs5-Y@P0^kJiod%BF0$~H*-x_^Q5K8G@Q
zyHKDJz0cxnH7f>?!&b?(%=ou+<J<fm$MJ^c)7G*uHH1k_fsKUd&?<k**gQZ0Ja6Mx
zgilaoB#jO)*N=w}+t%+kU(C+)E)P2#$;!!5nasxe3|20UDf=xuq3e-T5Sc|fpCrm6
z17EiLbVHSehva4`1Wt^bxm|~UzEE?C)yxjqKCr3A^=YqnW5pquoG^_-C1rMuiuvyg
z>x@E7$RKTKEyXiwbFeNT2HI6<fN##c4;JiG_p1P*y-0IDv{UN_$OR-;RJrSR+^t*$
zXUC1|0jN?@dv*I9?fi^iFc014_j99f#Qz*gO7U82j)O2`t?$q2-1xqJ@qi0Z|DcdN
z{#ShWzrq)OFYDwluc(!U9V>Uasg9FaX!%yrl$O;vsE&aIO|I90Z3IzUFTopCckBB$
zFZx$>lb9G;BE*;MHv5Z@)rb=R`a^mVG}lf%my;6Wm%(BD&q16KmzAdhBKgTeD48Aw
zgN+>821zdFp$BtGg=*b=I9-)Oca07_QO*;eT?}*wgCOPu5vW`$HcjBUGN&tymz5qQ
z$|ZHq|B=D5Z8A@(sBa#8pQ(Eeo}I{)sWoQ>!5#I0!<SvwZ~Mj%AWZ?NkKB1l5ux#v
zHOy7J;z!fp(S^{>EAYB;Y~U1F(V#O_;ArZ$*`}Ti@Y4yTl2uzpJ&ieNtm%|Q$FtG*
z&;8H$XUtN)hyub+%YC8A<>7r}+%s(R(wXn5_h)caa$X2%PI3%MF&TIprysxa%V1mp
zZe?2L>6^^EEXBWHQ)b=iD|U)kB%XG34ss7)X_A0vxO)fW{taKLE8F#ttAs=xp^6xg
z(?8!58XXPu^ZdPwx$6#Jcj_0JJ`afKRWFqm!`4oIOEvvC1@7TEo&FOeq$=iXT@%?T
z00q7|T?bVl?#^t+Syr>a_Z%Y_$MqN4oF)LQfUi6zOp9UoTxefo<vC|JpO#_mnj-)n
zyka*!FwR(Le?lwh8PDsl+y#NvgU{Hno&IuF;I%23cXk${++eN3hL8*!ofp%JZ@VH>
z?lV#tt*fbW4vLoME(Rrpal*=TZQpMFc8qaCA_JG7er-E@mXy6pY6v*Od&eui-Z2oS
zae*qg-j=n(nlSyZT)pHha3$XiDFPux)2~;X_G_V)o8V0mTN?RVwGqlqSzO1Iq`d<q
zD!uQ<mZJa=DXW<xAVbS@Pr!d-v)YWQeauf71i&q67Rp*>*~5gEbA`QH>&URv2nt@>
z2yCRMvFs}ZQA%i+B<Pbb3h6}6CLX)JmR>%$Cl#TIX_=do+7LSiLq6|VSpVbQkr1;6
zrIwT>-3s2{=UGAldS?)|pKQ(dvyQU@g|s2HRHAR?j<lsd^x%gvK9}Z2hI0$8B(QVj
ziEM`%$yD}uGXg~j?+q&9Z7w|WAEVxGpb7V!Fftwvii1BhtOm9*&Bz?Pko5lg<7eP0
z_mp+J@uJkhKwLWbL;Y^%gjN61Fh&sGES~?%u1>g4Zq@cjGx!^bg(}{M4Q2&_RECRp
zTA-;h&vACpbNIG2m70<V%h&YZ?Z*hGS97cekDnY_EsLe4)8$B}S+m^A7JAaBt(10+
z!snNXw;dV=pHCEAdjMDOEt)GqL_daF^Xi2zQ(z%gioIwLjfjZu16*R*aK{-UuoT8<
zq~<Qh8USAcH2LZefmY~cGwPkVgn2D=DE&!Vai2<DgWoq-V#Bpr+1-SqXv(PMj~tcA
zYeXESO-CSVft$BeQWmZ>XBO+ZlgA~SU&iwY^-J6{Gqg?VH2%H)k#r`I|F&~ocRr7Y
z18#123l9>-hm=VW%1|LRXP6)*WjW%vtSA^8o4*w+bwZxVp*66v!cS2Kj)vr|%do@T
z<G*G)>{~qG0cyk8tly>dijrRW3!rMx{*rm_RRY)qN}E(8*=gt(@3jy?Z<#N`CZ8C>
z9}HG@epOwCyr)$mA{AlO$c(v?T@FbN=)J{O2MH<Q77&1}KdB{%-GDs}A4_d$vy0q9
zBs_gS+t^S0K<&d;A$WH-uh7}Z(##KX;&dvASd;A+CP--@9FZLuj$u16BcE6iJN*(v
zE1P;A<>YMWzc1bOk1t`ze9~u?oRIGtUK7uPCm5ybk-u#yQ~Xv<(rY*eMH<>Jd}{sy
ze{HwZ%g6oYoS*@QFtxb?2G&k6EQ9WkreIep`cW{DH~c(!uv_f<Xvs<#{<HH`AoCIx
zU~nEn>&p>@T3?~-vks{I(uoh#^HB(7UF-c=<X0X|!f=+QQ^ot;`IGg70kD5rS;=P>
zjEEU{CE@XO3)4iX{<t?`3sUxdIm{Z-S3&*7F`%ODR`#LswX0tojfcBQp%WJ^_q;ND
zZtuVn{0{-_=n#e_oFM|%xT^BAWDCi8$35b^a4$#<llF~+mC*pSB>Jc@s6)d+cRBgV
zgp>cHU<h4+YV>7jbvd!{SxCFg0k@8*YZ(JE<Ck(*d3IyJKzNSV!sX84Z5P+aH^qVm
zSG73CBF`V5<I_JUYeo}-b~adZnFW9=Kwcq~u2G2#KP&kyIk>y-KJ0$k)wO)&N=;V6
z2|avYX<TDVBw+_V=%=mIHtHaB4#GYC1{wa<CJaV;elU|aBnt+vF_{n4HighefHB3k
zpYHhUla*+}mT8;=ltEcVjX<rjW$Ys*Hs%rAuc-yBcTWHXT4)`Jrkg~f1d5-8_%ws)
zH(#cizV8JBa8Q~i=zIR2*NLR#VUp=)x(I5x0Vis3*h!oJRK>4}>BVZSL}gXmoxx6`
zN1c$|mSa#&9}%&swf*<6S8@H5iqduEOu0vz2eD&lfD8M#OFG}#s)@oo#P>^*WklY=
zE#!WN#<W%}q&(Y{flA(;t%(X1=8)NYd=IF^fc!RPt4#GAi|7H=@n@3XCaU8*KEC4#
zjl~OjPw*UHg|8x%)Hks)JNxKcf*3Pk+f9%y76K8cZ>|0k{<o}uMW`Ep`4E2VI|oJX
z6A-6N%buOE`pWV3pMUNkB&=OG*e$+P4w|LhcnA~$RiBq{jffA9xzdEigTmf?q@Ax7
z9VAFlI&~A2XSvL>uovF1z_n(4_V?j={u1TZ5z??c=*S$c*6H@$gzgl#0NMxAPN<VI
zAoeOlq-(*(Dem=a@+<lklmL$dj@R6odPhZvT@>fl@qv_4{rOT}!_WIFAQ$J<K{aAq
zpi_Mb)^Wj${yJUNOex!}hqhVM$4<QbXMH@sV@w@w^-q%*-6?h(sfk-$T}Y`ec%s7A
zAZ?HUe`p3DV0W`~9TH3Bk6`@_UIWJWA%9%`Ul|+7mL6_5fs73<x{;1On+g`Z($a1U
zY|M0}gcf*+#19`nM9=hsQoO|k@!yA~qX~C0@;`MmOxD|4!wGEAP4K<<JX)j2pILV#
zCuR~0S9$q`{(kioENKYqGyN$byVlROR_3U`r|fj}eXD<0n+qzLdS$fdN}ijnIDl(8
zsp8||-W67}10*sAq0rwV9D?uKiy9$?uo`W^&UG6n2xy9ToW$3@dsl)K8>3Wn4-{62
zVuXbVXtwnB^2l17z}Wb7TMWlA&+)1Zv${{IY4p1@yi8O1j7}HG@?)r_8$N&0hMoWS
zGs4jTu>kr>l%xrVwR<jd5gv^A1CFcNXB|x>Z*cy4RGacUk#lS3U~Lpd#wIZlrb1Dn
zvd2QL2&Tpf7tKPnJf}7Fvzq|zomy$q>!1g5qh=evtf8SWI=XEG4Sc(>PMla>Djj1L
z7G^N-&5?kV?UhrRSfy#1hGjOGh;`G`)8jU6Av`VWQg&|WJe(463;J((o|3;y?G1i`
zWIhDz!G=G51-&)&Nq*bY^Z5kWlFZtr(<u`nBnWI^v|GQ7Z&a^c<D7|jd@w9oaouQ-
z!nMH6O7P~e@HGuojZ5WvLNOUh_oW*m$T_;72uO9uLhLSNaQPn4G<(efJ(3ee6Jj7b
zPlHE6#wSos3_u;Y1iwY`uC<FDjl<e?obaYGvbHv~j&{<}paNFc50}Xi11#_L5Xa^N
z5y8~U@n}ulDgr104sY>Pa-E8ZJyoy`m*2#nw7#XRGSoRz><a#Jp63I%?@{^V9+&4+
z4Y$C~Jy*bc{QFp0HzC`DRVvNNN;o_i1tu58v_r#F-rIg1_^-<N`9P|oMj#GHq~gfo
zU6yb@jiNlmG*2_z423}V17yu9<|Oug-NYLPr80=BI4e?4kcDv;2iWY!2{|kWDTy6W
zMny&KKsq*HyahvQ-7B$aZhwB?uL2emrJcXO9bVwDGWymc2K}69WtUwAkgfz98#1*#
zi)nvEI5S@a>4MOFl8KBC1io_EHr{Y(T^?+x;JXeHT)yvGjip}`nsa_#RjeZ9c`VH_
zq8VeyR|^jK_$Ck&b<n#%yknt9i?90(ID7Or_}y{`tTtizoq_g?ZiYeMPTxIYW61q1
zbkS6@btp8;+Cu*)om?BGO^I%`KC7DpUvSyT$dvD<G<ou+LuUGoNGoF(sJ-S<yH=8;
zNu!v5_T5*MXaw>J+)I}(A?^>B+Ps2Ver9HUZ>}znNfVyq^XC%>+hel!_Vxr0N@$I2
z&qI52<M$LkiiG@Jet&1mmRiR38Rbk-fNn*JOr~y6&EhRc-_l(%rYO3@ZFiTF*t{$;
zF%(HaHmEv2mHnwrv`tm@?6YS}O7=P19Ww~&=JE#g-c_Au9ns=imo2nO{ed|D&*_n2
z95%Ym^!n8;4;4(t!kGcLoyQL@?#I!|p|zexasKML^5QIN5ainq6Or+y!aem(pV7-y
z%L?jw`mbwWJ@Gbdv}yy<>n$0C*E>7{NR{vk3kL_0u)}gwBo9Q6e6~t}kPxU^Kvm*6
z6)Z45H|Jky+G^65r>~r;&e4Az9SD3a`Gs^~bil?tqF+{4PpX4vUvNY&EyP@>$3Q>3
zp#h%rxQP{kZLi~#zEeEU?i0#o&-?yZ|6;Z}Qyxn_o3$kO<x=w-ILsk0Ql13tV324B
z8BjlrAEE~fpTW85d-#{bTU;N2o(XuooxOp>9XBUD$cf}vL;SK-u2w+|AL0G^6r;kw
zuvD}h2se>8T|Ho@y|5$`6ABZl#O#t~dN>yo6<&5N+)7nN6a_Hyl1@%SqC4YQj?T_l
zWqh}*t)iVQEjbXm0l&4OtBZo!WfjwEnAA`M{Z&#N)(|rRkex*};Kbl<{ia9f)qm}v
zrYx;3YYW8=LR4Y<^8;dgq|Jp~g*O_3O0q|M{F1(mC6+WF?r`59xJ~({&#(8TlP$y$
zP6XF{K3+j^ZC!?%Yl2lxWT8jS<4OJZ{%t*X<SrEw2KV#d-}w&U_7{2c4sZPx%o(xb
z{bsEoc@-IadFYp43_)Su=y}W5VdA^0)O)c(b-@Eg_`pzd31#IU`mGB$=K<oMM;my>
z{fK~zi>qpD&^#k2$HLzp?WS8^B^SC7HcOv#8Jd%v2p+*F8e%j|W<^=mA)ri!tP?a8
z!s|QzN>dL*<iRt-FXnIP7fd=&J>jdBm(?%Pht<vlgCMLlFG`s=h*+W+q$@%H9>1&y
zy<dkkr=DkGg9P2vwVecMHxk3KB#-O#K|G4P*tBsvcoG&TD9PqhC*^P;FI-}nZ>{+p
z6z%<G+%R}xSU<pT<4z1gI^4HY5UK42(YFx8{=#zT;|OsIR_Td3yCW<c=V+(r&#xl}
z7t#yI8@vfPdPLDmJ%Ay<svtX%%3ZijCHGCADiEZ<ZT%WkkBhPn#z5!SZXpy5@e*zQ
zcc;({2Cxt$t0r9sg;bwfY`mKo%(p<PNCDTdyUtcy7C0HuLg2%HHstJR5XqAl-qGy^
zS2M9GB=w)HdcS}{2i#Ek=q5t&D-^_cEPPSu+O5kOkYf#bDFN(e+@{jkj^}2eQvb0E
zG$v1YxX9)b_{kKtGSv|ijpA{Xl$5IeOvk{SiAqNCe1a5bN5}EM=MO*eX+e)mYH4*p
zevsB3-JFLLi}vb0yi=!6>HNNPd@Epl4Shczryp!6s13UjoqDhvd2M?f2XoA5Qpi1M
z-0##)_isFy%~^wR8PmgPZS_m)I${sZ_0ChuEiKHsPj*?On4dT5JpsLtM(C^Ytijj}
zVH3xn5@M7I0HQ}Le((uECfoUK+r}iClapQk6060$=6}l}opjI|l$rX>=$ojOFekz;
zntAY?2w>ip@*I;6dfBhK^|s4brKNVWG<>_dIy(#QeI_rftTY`idxDroGjnTi{`s`j
zJFsaPg{e`Ta-KnT0K_X(!2+kf_J|bbXL~@JJ6h1({QNZsk;B7!E`NT#<Jhu0{+g?e
zVp^+*2{e4YJ9d;~@i{<<PHsMdRG9QkU2{rHtr8_dJ1P+_Jd{^yJo33X&ANaL(WG#C
zDeDiFCH@KiK#%N_^03O0j>Q}uV{rK&2oISyFUE88y!p>9oV?f9)pb(Vfc|A<AMVs*
zOyla>OYyRy=#RF#=={iyPA$Z&xSm<-PTgiB6gGMp=odIL%WSHpiqoKl=I5Gkj!Odz
zS4o8pI*2}640swh`Xs5PEA9aC8WtDl>MSQ@W3F0JRKz>#vaX<4X;0+7zlQ5W!t5sO
z;&wq-SGQ_=)LGDOp7II@hqaROSoyPb&e|(zHB>Z~LcT3Hpy<uPs^$X{zIq6tPv!%U
z)CK+~(}y?h)k-s{;Bi<RQ2HZtAMg;t?b~BZ{s69~W)5|K0rFD2c$w1dn!GqOs8=O3
z6Ssz^`-7&<auos;FV!JAW7?Tx82U;K-6>P7e=q6`JCMRdsI!k=@4VGrRDR6mf~L8o
z)wB!D0-X>4q?UTl!F45V4%wLn&sDgV`K6_t@_Y2=ZYwIb+~KOmhA3S5sH-@*#HT&v
z>@L%)(Fnb}#YW3xg8k8Q$OkD|p^|-BR8*wdK9;L1aO2O<@KVX;OP4q(c2NqYSvzpg
z@Ah6Jbyct(j4Npb#Pt>M=C&rJ=Qk-6gz}(YLa7f-2?O0rX7Agw>}QS)dzLM&i9u%a
z&ex%y*UObpbOq?^NniK8Yj&0NOW<RHDI#7sGesSVF<{HDO+<3e8fVu`C%nJ$lTwRM
zE70<b|78Jg$cclb!el<A!I=so{r8ce(!_6;(_~^}qv6zO_L)vKh&_w^u*mlM*;grZ
z@M@z2!6BA4SyN&ZI!IN0{a_B=k4Arf{}Qxcq{SE}4(-F~#F6j_52t_v-q)LerlGNM
zD&CNbjqT^wQGP~7S?G6ku4fd&PoHsytgJI_^$UMfOWKFr-`+k4VqzAEFvtm&)7qnr
zz^E!XhY9wt@W$bo50)jr?_`4^<V{N;E@y8kCsWzL{>tPFkl=xxwGrkpq`L<yg&f^e
zBBv}l5zjPydudr*;qmss0;J|VzNi6Nq5zUPy6a&;)Op8O74O;oU2`Li6F{j^peYft
zOCOz3XmowFT1H+Echcw>{+Rp{rsVVNyXNv}oBZ<;BA?C_jU~q5dF)L~@qQ#xiM2C0
zc9LCQmkF7ElKGfErlG6hpT25#bk0r?g{Gf7S9)IIlqZJ2)Cqs7r^1P%j;lsj460}$
z6Wd$Q?Z_}7HT4|EHRn2C^a_>%MG_r+hJS~|e6)Jn%NL8ABiXG?k3C|1q;SOeV)(?&
zR3>XaCWLdHhIY%rdniW7;5HRlry6=()&g<i;6q;_kFh)7%pIxkzr08C%;fg9Ux)X)
zFGe^%(SG;9`KaPRo;v=BPr{VjRp!0$C|wt$>Rc|<jW$eK3e56|P3OVWF)zRcJDitV
zc@X}_S9)PmIps{GD0|}BnrL!DEGvh9Y?#Ln#th2-`l0XgmOuFTBPMKFU0(zFp7}M!
zCRg0$RbFWv9Rgy)BMMO;SRv*V_vj)gXU#&pCre##FEv-q4(sdJuM4clL~s25bXI{}
z{%9Mqpk!fIAYTFxFK|!qKskOMJBujoC|}0&NVJ;>VC+XO)kjuJO<zpsu@<*4r<a{l
zd;6F;&3Q1|WIfp+xqd^4V9$99Tw(O)HDb0@P~;ch9j!fNO7KUiaJ2FXFxFuuWXVmd
zUyl`&m9g75yNlp5X@t&?pVc36a5<+<I&?r$3*7il^f8!qe0!mt>(Rfl0i*__!%Zm@
zK&W``M@<`5fUumO1K}k)%+>L+#QR(qM`3a+ddHzRe&Z1m5yku}+1*+S?Cze%XGd3n
zitGOueq(SE{ky+kfJD}sSdTi_8M3m#zp>gc-X^it@+d@OsmR&60qn91L*FH?xYz7I
zLV|42`M8HGupNO8++fs6bAu1lNWze-N;E5GFmuUx>x6wOf%2*O^z`kmLi5+6Ljlzg
zkpTTMrQDkJ4x0G^$IiW2p?@mB<97;Doe%Yp0I(SXGA(HpX`&p3m<!I*QiGqX?!JI`
z`$|Rol1|q^$7vX=@Uq%F482=3dM8H3(hnaRpj%m+O$klbEwySpbdr&gf&A}BTYbm*
zyU<OpwuG!}Ge>ahoQ=RRR3wJMO*FX)S`dW$Cn*kA_;^wE=VUvS(Siss*vyu76+AFl
zirdnAIuJMR^fwskg?;)8Lur<)&P!p!d*u7FlG-k9pUpE=vQ`hvJobn)tb=a1Lc~><
zb`daz227*U8+#9oF>F^@Sdt13{~*hA+$kJD-<Px7KTZN5b37UfHolt)gikrKz51V5
z-YNI;-R|c(3>)^P^FBM*=K(#fP?IsnNxKqwQ!{L*`d6MJHKMRB+Mu{h?!BGA?NtjR
zOB8k<hke``-g1D^DPX`z=ZWM5k=j!CD;b=wm5rZRHIA<Dr?<mEBhd2DIoxWc_Ze3w
zftCJ^AC&Sd5Cj)YaF&5lKA*QRouMZBbPi8!70-Drt^<2&IifQ&J~_fsy++&zpC#h1
zo$OkW63SRf0rnNZz~%DRkN5fpEN%>lbGw?A35z1k{=XmkdWdO_m0G?7!3pzaG1FkY
zzr~dyZv0%~PU+3=#0uxH(VlZ|j2sBB01&G?Yi{Z1MpwF$=}3iL>Cp`sHU*PLif~XB
z$(o#okiGj0cOiI%GB@nbISBS3fzYzG8Uu4<cWc^DY>wtLI33X|B>K`YpC><fv3C}K
z{xKj2cF!rn6ye`{ttV>>fJ2Du;!_+LrBA>ItP8RXB{<E8g}Q;7S^Ma-LV<D28WL{x
z%<pyg=y;@fpLu#`!4Pj4PARY_F!UJe7NG-e$mL{oFF7KShfMcZoav7XZM5CsQ}S=H
zdDJWY6+K(CA-wb%z!zP+`7Hcyw?aX6>s;w_y6eU*jG&(%-dcrb#GB*%iu@qIUj1vC
z^R2d+*a;{tQGJ~;H)kn$usYGWGoPK0ToB=(SK)tsv^^G4*d{>f&SiPKZY90>!b65n
z$CE^0P7>c|^Y3$0IFjXr$besnIrlX*Eg4>yfUG$)b`50WK88J|tIDA>wc@fcDS=y*
zR_@3R(<a;?#;l2J4VHFyFDL_jz~Ivd!gA$2eR0?34$7+1z2uf?fY4jQNp{Revei!n
z5!g0$P65HIUuNJ5yfg&Mr$S8EIo+wNprb-E5TuM~>u?J;;6^;-zaMOj3D$`X_!lxV
zGQBgCMq6602>}=gZ=i5*akKKI5Gc?fBOPFR$hB7n3A?)CeDG6weQNJm@--tTP-679
z1$snezBoP!n1UJ7GbA-MO5Ml^JTbJiwEBmJN^KDODrGBX7Qud_^iL3g!Qkml<&})N
z5^3-dwzBARHqz*)H83lz5uI+Smw5J%AjQ$-%T+jv|Dp~9_uyoQ%B9dkGoxkvkKxR-
z)g7)U8F?D~J1$~}&r0fzc0rTG&klc}-mCE0Z5K5Qr<8<zjvYRJEG~XhdBgtp!2j9Y
z6<ibX{AQtjzz5#u>a^tDzMZ6Qycc!l#(v|IpuIyum{UPL@^}aEltma{H%08<q~FHX
z_~!lvzLQ<Ru@qW~qAg-SYY8{~oF>a{*04GlhT&a_&bs71WO)0Q#@nCtKuD4i=`4<c
zTgI4FUohaW_WU&ZbWL7nj5hoQcnk?reSX@ge$O>>a?5n&7AcG3Vj^6e^neD($b#k2
z-tDOQ#uXg~Uv_Ut@dIE=kz$bE&>Z+z(4)7DY|fcj28oK=o3&elZ0Sq1Pvg4fXP;Ty
z{f7&8JMa%N3eZ8wFo2lV|9&dCYKW%`uBy1_vkM_8d^iR;isK1hZZ4$Ie|mN4_{<yb
zO`V49>1)={q;$$&YxR!*DAL`2+bHc0nhBk=)kNvS!op*RN8aAKQ$)u*tw#%x!f=Mk
z!ef`MDb^(z&*p;}7voSG3mbjM-i+iEA9i<X%frd?wuj2u>OH~^Fh)V`o$LgRIB3p+
z-b|?65gdn%z2As@qmtx{r){2!9*f@;mh@Y}QHGLM2S)D&=R>}-7({Qn8oDYHKT~tA
z0*uK$j^;?Xqc=w+%BSy8FuiaZj>pyCtAB}!<F5z*(cy}CABw1WrkJ#`yE#pZCRN4f
zV1$#9U;FksbHc5xa6_HoQv34=&>74e3fArVQAD~8UEv*5D3>!VJBDnW5=32%%*;+n
zkiLqJ{^Ei9PnFB+|H8;pYq{$XxCx0<r(Iopl=`jA&SD8ow$deWzwxi78ij1ybpCi5
zg{7kt^Z%d}r}*UWA|@&tIMyE>&0dh-ofF55NpQCOQ5%fLi(G$p<x7?|PB;8~5Cg6C
z9Db!w*c&N4j2voRsBu0$y@F>Rh7Ys?@ABRsT`!2R1k+D6mxrjpdtL*V|Gm%5geD9a
zyO~0jYM=MHa=XGCFmC^|5Lvvz1{fhBP(~}+tsojJOlKsLV?B5s>wG!VEk*uD<Y-q)
zW3F>GlBF=bFP*UE<>d{c<2{LysG3<|HBvzhYBTuIr$FA~q}`K#cNKs$%`P^=FPi;j
zn%(%_1A-}$q}S)^T4|$uTzfqr?nxDz{zXyJksoiI0t0o&uEFR>Z4oZQX?oAewlm!+
zSiM|+9;t;p3oY-d0KS5@{}KwWb8%h`Xqnju3-GtaUju9hbf53j(kv!#!Lfcu)%uOV
z=Y`qqU$Uwwgzc|j0}QD>D#xeKKgV6SfuDk6zG{YzXr*@RNQM?fyLUEf@r-x5!vv}u
zCaMeqb?RN!<$Xtjp(ltYjFP8e7$Gdt$D_f*!(AsKq;z3_^eRx5u<sc$4)?~68UXUZ
zScTB(so(e8x)_fUEV2J!*uSOC0iDqn5Ia3#FwYghSNcN#bb*;7Z4gn@82L(Vq;J$^
zsKlkOfmU030R@48@Ycwz|0)fMC!8@r3<>$kU&QqQLh{DyfY{F5s>ZQm`6nor18uB3
z56}CS`^WEXst?7*A42ZT+A+3J1*0V6T|EA0$D28g=;s)ju&6n6ehfRanr4rjp$AWh
z!<d<nQS+vz%Ka3kv7uIKaK^JvZ`Q9C3aII-DI0W+FMpI$n7e6@4={<$3!jm)vsD%a
zW_ioL@z6WJ28yK8Zowzwver%3u*8fEJS<$|Ch!*NR&b!xrZe$_j6V)cN4xdGk$a?J
zN0VEmJLNveOpqxrx_pA0SKVEO7{f%BKn2`~qKQQ!n%|c3DTvyW2*9P<Uz|Y$vTG^x
zYq{Uw$$7M&$2NZL!}6zkzPE!MR3B#T2QULrC)Lf8VSutm#D)$k$M=N~;9!JsNLg9N
ze>Xkaq0WFKvo<yAES2|P8}aSZV8=wI{`8kega^Oz_@&ZQRFk@Q*TDXf=2L0hUUX1k
zY|I9icYU{VNc#mGNp_W^NC<XB$OMHej?^T1Q6NGXPw`=QIX@pV=^5sZ1SO=DWh^`o
zOe2`ziy*VpjEqkXcI|HLjC;{?8)2X+k4>$uQ_v(UvzNGs-$X02Ej>An8W6t8{s@L-
zK5&EYe_%NQYP{J3-X~<RC4|LJP;c8{j=#tw))wNWY+<FiV4E8lj>FFp@X`w|1bxO#
z?Bnz)d^^ju?;f9x=hF*k-f5c07K{x-7sa)GZff22GyMWg)A5BVICkBCXZHSmG$mTb
z@$<gY&1oYq<<8B%_XW)<jLvGtN;vXD6YXa2uQ~hA!*tXZcOM4{G@o{2M;x-usuo-d
zVoF+Gh#!r(F+u#^cDHaC|M!Pfrh~s9QEC9@3NP2PW2DUA&-Qk{d)McC4!$m+j)d+X
zkZha9{z7Lr188;zb=KzIp=Z$Tcpr2#rR>x^Fpm<l_KZN*Ve0^4P?f0&oZiJD10@3e
zIr_BsV~@dyjwo&Lvf`!n&4D+?ka@`)RZ#lFtJ3^H!NXLRsU-242TSI*%Cc3J3!Z~>
zG;laVVqW20LqnQ>A&8{Q@Jtx{7$KF+_J5zrLsSg`?E89MZULt{MitU<(I6O*>Z@pu
z?Br;oT+g9a0#H-j<m|fPwF#*OF!_Z95@0sJLty>tF7r$wsEF9e!^jq-wrBMo@TBGY
zbvh=LBTIk#e5Qb8J5W+F21<~8KCyK)-(4+Hwmw|FABxV<XvN17CtxZU^r@vhyiGnh
zVNNvi`4m4N1P2GQcGPz#)(;{(k(-X+D40DR2cwMFQ>xL3mqJAt&<XPYXpu-_6j(w_
z)vvNtE_wU;r^}B=y4c}o%Q|!N(aIk|E*jJ=|NXq@h1Dj`slogyM1iWd)B&Nvt9Lf|
z<ag)TPP%Q8Kp3;RI`;^0<2$4@i!L9y+?;a+WN_>)xZy97c}gdmK#3josjmV?Nq&s`
z+*v`wY6<C`#yVKXu2j?)*~{iP&UrY}9=OFL<pumk5W?_kwXlG_wgY)}5S1o2GaXa)
zgK|GE7tkiI<Drg0rxyLJ(>Fbm^lHcCDhDD_d%yaDSIv18Vt$Y<hgICwsOmZ-JF7YC
zWs<Mzx}<&IePjmhHK-a3H*5kP1xuYkainUEBao~9f1L&;4(ENYVwYtxGf|C5p}}F~
z?(vl&poBZKcyQo&5V}t9tCMMJZTu32Ag*aK)qyBW+PU|AWgr!}!)|ZvgLx@z=nL-o
zutVhe1|M;XIt(N*qHzYW@e5+`gnz%a5)X%SkXISuCGT4i#=bB^8{2k;Iu~ipKq5W+
z(<rI&`?5ad3LsS!TEtp9u6^YQdT=p$rP)Qd@uH{%9|ov46;pLU0cO8f2H*g5y04Lq
zfNb1WS62!ajL>}b8fDcX^2@?QDcku7=0yI#rV0&*;Q#CQlO5FBC^5t;a%DN(0AY|P
z%W&5LU8<Zo3<-iC<SjDuOnK06Zr0(HZArthXgn>|_%K^zQvB&|{5`IplMS1%oo0}?
z2qx(iJhgAvbWjS|xC808K*a>L=wmX?&U-LFoL#XRhU4uvy!)g5b)`F{$JR7fG|L3W
zX0tu$t1VD#?dZe<b5ld>sGe`!NG-@9`?sW+VVLr{(-m)UVZ1LB!?+i;dD5jPy0wVk
zw)AhR8=~kwUOMRl9=;|DFQNL8UiF!3aAC_Y&-p+^pZbcPjsy;bGn36(X0O*Q!yKyr
z8X?#*Yhq6aK_&VhvIH$hzghQ&Y6}?Hc))1%>`oR8%E{bxJqvQB2`7B$0a~_r?|$OQ
zwAzs{`;<x!Qaiuv)gok*RwM5vo<Lonpg`ErzY;2uL^&TP=6Hz&iYT7U^j$`Ge|mC<
zTvFJ|Jz!JsY{MK=dI-JvAQ#Z`rMR9fh~NY>yd-$~0s;bz?%jJy%qUdX*GB_N6JQRk
z=J-(|v8)Q?At7+$2^xrejLLHcy-bgbA{I6m0;~}#>d?1VDYt1EK3%gi&bR|IE))=v
zq0v)IyJK!41G!Sr7aEM&6Em1zl`;n}M@>{GR>}$C`TtP$=#oyZ+}tRD8ESZ&*6l1p
zC5iPmPSb4xLoYUd4qpu}iME9~We^h#&GpN0jrCti`>`Br8(1|vE`~6LXl1#|17s}Y
z7M7hBBFaOQpz^;i)|yx)17=rH?IWkb>o^SHzAyW9>DINy2ozcl@gq(fmxQS)m_b0w
zoQRN++pPQIjlIPpxFtOlch@&Inqbz`@}D)B4%Sfj0xKpD^;DggkxZ$ERc^}r0vZ&o
zM?1jURerm60D|Og1aLtCq`NYVlDMbQtWDZt@rQ$IS=dz{Hm`hax*yOBFV96F7uUxD
zy@vmOblyf}Vpa(1WH8>Kg75BcmgYwTG2q+f3;k|F3;_3TZ|=gd`pA*56sDeW#ukY&
zw>txIca6Ye9{ZI&&mr$`R7haSdP9t@nQ)0^$0Zz)fKpS@d(Jf;#I2&?TCY%Pu*A?f
zC$!Z>p=8t?K$Vf1*^y<Cq-+GdZ~sS2h6ioY-qy7T)NkLuZ5uuZJ2lJ+Vel~V93~)2
z0|sw})1@!W9jQ340H+-TQm`1y-sFemnPPFRK^6F!(8bgyd?(L^5i_d?#iWGm4xRrY
zu8$A<Nm%4k4RZq4M@Mkp;d1?&fSw)(z>u6ujaFIEd0NO{A8GV3h`?Tg=0c#eTfFmq
z@RP0ImT;VnF$D|)R=3lill(3BjC5XBes@mko%z{w;+rmGpq#fOx9dF*NHlFgTwI+^
z#dm30*=(Y;ulKIPP-3!>kPzjIlx6ly^Z|h@2IZwNp+&F479Y_k<>lphIv|<p)P=EW
z5S}Kfl6nG?w&{GV9>8!O)EQh7X!4r-i><%b9S@U%^p^=RLd3p?;7tD3v;|ASYLL&s
zG)W`bn;#zq1E`Sjzz1rjV+G!SU-GRY@aN`;JkK3P;Q>4A!*&oP>RbE$S7LF+R=i>3
zmnID8(oFOUc)?`^p`MHCzrTl2qePtJsV?g)D>WW&HTE_|6Rr=)9VWjJebc1~WS2gW
zCbol{unE!(5V^eTo+}h<0Fes7`3+Y+rvS?}Oym}aMLa$`i&w69RC~?b+#JmXNRbJ?
z2|%Hd@mj5hr(7yI<S#TcSXaGBkqsRcWul<)y8di>ymZX1>vkwSKA4l|n!-duN!cww
z0`T~O5>zCR>F?ldR|{Sp6nAANsP$36cQIK`UZ2U^9EU2I4<i5i3%JP@HCy1U(>V))
zNBBv3JIg;JoMyg1j9`JR0~8VVBT5kI%LL-;Vc$rb0|kRZZ?S}i7XJ5q@bPvY@QZie
zf42jb8ytE_o&>(OllFzaVS4uM?LDU<pt%9!QU$|R-~@giOn&rd>5AK3(vC?9r%okb
z&R@oByMpR{0k~cNIz5ph(KhDrLfO%;`JQa;LKB>j)7L!#YfP!As4xhIF|^?F<Tk3q
zxT0Nv0b_|deq;&){e0!_2W*%_>E-Xgo0)tyiV$@hV3%IqnF=)>_;A0p-EyRY3LP7X
z5?;~OTBfF^gmhQm4;TW$lEH&U<sOfB-CuDPhY>Ei!sH#Tt;EY8^#(@Y9K+1gl<;5D
z=HSdQZS=$w)CRHQU{rUP0_yeLg4+OG+`JHL03Zj1V6Qot<UI;5d;#%m-r&fKL+ICk
zKb;|961ribz(<_Y`Bv%#ns!oVL_<ho<C$70L1Ei}<LA{&P&&N~Gi26a;1&h_?q)|H
zclNvV<(RY8XtcEGo-jTx=dZ^(43H4gejdptH>`7~OHC93HxFeF<<zej4_6YEfVc;U
z0${<rS$(9Tp)v9EbNCJb@Z%7^f=_Xt<-&#Z)LLweIy6oE{d+bL2O3_Bbh`kveVN^M
zFh!iVsup$vs4homOT$;Mwm+9osrGgRfM+$}=zStLj%6`0vhvOSXs@|{glt}O-4~ZX
zrp+#yW^WBMrxz)wD#MKMO%%nnPt#U!3nDtV_(pUV{O2=Q%B{3EH#ay7eH#G50TlT+
zJiuefbLpExzSo=`NE6mNz4WEp-FIfr>0Q3qXDs_ZA6~>N__=8Rv~`^HnwxSi8(+U&
zk3^=K9qewr%R{9kn3$OTHLk+y>{Qqi7#L6k++uUE&U`w<BS8wl#A++EI|~a7#@_Zy
z-}99tez;<2>FN3Bd0^EX{b_e!7TVe#a~HUe$ne~of6=$c2cV_NU5m=l2(ZqS9=ubj
z67u=0B5U?7fIvX9$3P>~Fxqg-H%>(tV%im!)?R1n?8o*04ul-u|JC6^1k|YLu_E^0
zXAtH_Q0r?`0peyD4dYeazS)W_iphtfvyXaK#T8w7OG9a#oNSey^Hh_X?&yBMnM!Uu
zOf?q>^CzoH&XSS19Fjn3hLiN7_SC;~x8IYud4nJ3O;swxqfamwoX2WdP+fEMl=N-3
z*FzMYzn)&NuwOa_7+qvkR8m#dX#uNYaaC1v%2Unyn>aW)v;tPAx0Z%{4XT|=e!jC+
zf}uzbeF+4oPARLXV5+HgQP0BxdaD%TW>CQfYQRT1U%7HylHR)zZa(W)d1%?yvwu}y
zH_z>+D<kOPV3OMl;s?RHj<V`#9xvR$5LKqKGMpAk2>H^FU*uV3s$MLFVK*UcGl9L4
z;5T_-KzmbU+4{>r?up|k6~-y$ls(q7HI^e!$bB{OEYtFYXFNEHEU_^4pt);5f6{oT
zPgXfKLu~QN`CQU$#rMW;hWXU9%WoTg)5j{-!n}B1$TsX@7?e}UhlFgvf(j<J+1bsC
zhX$UbfnnD~3l#SCNXhtUpb&fa?w#pmlRrq0su-{0BK?ZdXw~!CvD(_fOF#Me_ynCc
zE^6lK`9s+<cIaD?oP2`ff}+&Zr%$s9>>Os5-G3Q8dGaKCPY52=a`WFDwBY1`T}(|o
zxSV)n-Ix)EJ^%YrFvUEUer|!40r);P-D5;M4IzaJuG$tz%%6`FhRqL*1DeC~-0+_f
z_N{rtq`8IMuT7WsZ9pI=X8h0OkK;S-))>e*Oq6MyPs?SA#kK+o{>q9NU@%Wi{Y7K8
zcO~M!ZC|^-6Xp}H1eDqvC_5j9i@bwyF%f7M><0TbC*@oU1?Y6AcvS?d4wCAQr_}}#
z4UW3wy24&I0baxfB3G@<q9idqpw>mxrqEL66G!^--=8lez^O804_fhNoYqk0RtW9l
zzW0f=q}~<-&1HpIoQ1}XCu46sC(p~vD{a@Wbz@9$+s(|Yd3ioOB0^&&GB?*gn+~d5
zoua?^pWK3x5C+(%WUX#%@Y?s-pZoR3<jaSP!GFhq#<Uv=@de*Dwo0CH6NyM_RJ6*3
z6q?~49^f;M;Sz4o|HZlNYNCg+p!9+W(>n3<64uPRF28TFD00wwf)^jgbgIP=ru9X^
za0(FJJ2dBl78o+UlzY!L#oWCeaU$W+6(2?a=uRGnR_}#`W_>vlJB;6m7eDptmH4cJ
z&>4@57mc(mAkIP6!d$m<AuFe1t0HeN!U_vh+P3*5-bK}$5!B*N<Z>)$UTj?5-o_RC
zp!hmU{`Kc?Y|llQLvQ>ay50mH>h1p@pXye(6t`6oDyfjAvagj&_MPm>zLPD(pj8oy
zWZ(C7vW%Ungd)2!j4dgQVT2hn4FA`3Klk45{r>**xQ}j+(#)CjKIe6wujljmdL7P0
zzs~9@5BHdh7kxXcH<_|kdMby)iCkm)oau_~(eZl)hGWxJ#!RwUV@?f0;lWY%Y~QE@
z)OC#MuIz0=9K*;dieZ^e^!DDy#zrbcbzZm^#t47o#*HpCb|b$5wZ_NIt?<~w4(wV;
za`n_q?o7=B35_)>H^w+vizb@0swYWhK^veXNssH$lxsH&i$U8f0bl<;h*WiF$vZ|9
zwmd|q%SxDL7z(O{S8rvnvw>o?sA_F^a(7i6DLT&B3Zs#tuO>1T9<Y0vJC?a+nvEl$
z>Gl<jtU&F%Y9X<#nQ;q_uiuD?^L>*CUrl!mvcL2^+v||>8sUnPD#6w?J(S7<ht~gI
zio5rI@82BhSKQu|wYW@qZp+O?K;Dg|(9~o-2c@xR(YfXCR@)UzMJDjuYDixyyz~{c
zo*H`Kx%&O&=gU@W2dhaD(~;p?Y|T22j_tyG=;fq`qd(juHg0s5_?gt^9+#C$&k!UL
zo+Tbb=sp-CCpLgbuXN;{+UY1W6cy*Nv$r!eG_<!(2z#zkVue}?2+RR6IdcK-C}48y
z?uNxy{-g`8NMucoCo%oQha1!xoC>SCe+iO0lT}qE0T(;jef{vl==H68$}0Q~H@6{D
z#l$!|`kXI1@9f=K8KIf*<c)Htz(A~a#d3y&bWf?Mce7LiSHP)qm;4B_>hW0S;Iaxo
z+S7}VXx}12BBBeKY?(s5)(TH0yrpE{lZ%(;eF2-}f3Mt?^*UL0Q(=1DK=u|St+hkH
z6I<<x-jHzbA75g7oW?U0ZvSJbfU+LwGq^r7>#N~k-9D*<*1~x0yx`dw*gsvYAvVaN
z8o^BMbrko)v#>Dte;bKEpt>{2;m(j7ts_aAC&~CFeSKZT=2NVZ>HI5Wmlx2B@ev8X
za}2eT6tMb@HPrIJX;rt@;nmeuWp(vpiiwR)cEbkEF^p5z2r4=H^7RT5wGSXt$5o_a
z^A;{fz0|Rjjg>36%*>9MFX-04QWSh~v78)4ea^dKYwYl<S=~J69DBq$6{lWZ5Atlz
zar4T6@60oKj(%6$I%^jzPp6#8>RVkOTy3gxwuuzukaNapTzwufsiv+QGyPl&z4NcB
zLBMse8}2o)O|NE6g(!bQdXCTY@rp`2HDo@BV_SX8HX`m*#%%j_P*#w;@&?RHSlFP$
zEk&aWmz3zt^S%A{Z7<H%HjC`~-ha`+3%cSkcjq>gsQbOUh!7ERNf1=l(1`JL!Bc+x
zxV!zg-}+{d06jt?BEWxKd#-q%3{921@TNY7$JizBWbWU;-$YmI+w%Mv8r)*Kt_`c%
zoc`Z02b)RD%9@_h(06V51SJoK<sNtLcusygs`%=WLu-3GW^hN)lGw419u$*ze(C^?
zoql32VTMYQ$~`M@z)c9Ap>nD5oj35mxG_lBHpBK&aIC^fMLNSzZY-_rQGd$)$N?$U
z`OyI-p6$L;I-lQv(VXw|tQS{e>py>GjGX&}jXpI(lyalIJbB;$nAYv>j0s&^;vo2b
zq6zLAzc(5mpdya>UHGorE=17X2o}U8%Qr~)b>dBYL^F9lrit{JJ15#$cfltMn`1*7
zN%6K{Osz6%cmJ%LJZk8!$-kMq+Uy`dua5tu=O>!)6KaI(xq0m4C;S@rSU*ecY-hr%
zpP!xom1oT>u6eiQg${k2%0G)`4*f0h3AMq!^<&Ar(mt;y=SJf8?VBocbl;XG!uO>m
z{xuJav^9D1dnYkRFkmXHsR>9QW@F0-Qz5!zih0+r?r$;X#chJMs<KFmroVq3WDGB3
zVsaRJxIJDp9<G7bN1K_7WtW%hvdFK$ju&;*Cnb?LXCJW0b@k=xIC?Q4F^7D>@a02q
z_kk11#B?h{y!=dCCecqu*QsLTMj=_Py(ZhfCSZ*kc%om>f|KydaDlx|o-C7gc|-Bg
z=L+;)6VhixqWs5XzH^w3l@Z>)LiP-G`;i&*!sRW6c^ybD*BjCGvb^Wnk+utLrfd^G
zMvmVJ%?g}f)HO<`Zn8W?vey)#8p3C9^y~{ZKp-wPzVY)%AlT&k{ovP~{r)1?@#P}K
zrESU4$!niu6n0D0n|I&CVO_jmP!vLf`?C|QKG5dVH$L@R<@V*)%@@eiPb@8rI84SK
zF@5mT^b|c7NuD2e`Zix8qY;&MuYFJ06JD(+tEpnevNxfXdvObLOyZd27sA5_L)S6T
zyM;&Tv4zB}5HAl;)@<j#ebMr-nvxhjjA+p5NFW^bV>~z{EUXA%N+8|K)E&Cx?B1R4
zCV1-9RX9p!`tqX4bxZ0HRt;*FI;5kcqj`wKfoNz<;_%`52?sz{N&UMJb~hu-RO73M
z6q>|FPc82ecl4JoYn7MeKi}F_q1<3+<`i2_e5v#xVd+WP;&RFhIn`y|v@oHqp1UrR
zI>K#ymohI%Tpqf`+tb<g@qWl=f=LWHO(k8$x^({HsoJmWo406M>~AT;Tnd3i?fe^Z
zU$^}gA?%*-&)7pxw%5O--((*0itRM2VoWD+PTy$Ur?3CuOJ#X3YF9|c=|h7*OlMP8
z?;c?zyz6Qb_@pnc&nK2ZS>Q@WRvG8j{h${;s@reBs>M<yWm8(cZ!{&&D%a;>8oy<G
z$oGO8c|<C5*gKErfmnkDW(0|QQE?^oyG)W<#VUHeZ^DsZ@O#aHW4sr-pIW=mF0^pr
zr@J#q{^SJLhH7YjWL=EiK%$@#+u_=wsc+wiSutk$1#q+d-mZ?`(`x9Gv7^#Z-Z{s=
zqn9U7CpJwprFGjhsb^&QTX@_03+J9qy?v_K)Tf3Ac4ap{RXv>-__DgkgSh=PhXkpr
zy5peICc$1hcJzHq7o7no{+b#QtJ6^;*<pqo$<dn)lA`#`l%9NMnV9%2T#FjgJv1jk
zmHL`L)+I7+i<XUSZ-K5sbKIe6fiPEdKG!I!QNWFFtW{HoqLtfvRgo5DIj^z?1Fo#4
zc)<fJA!o6TCBp&McPPr@h)Z>}T|htsm_zk$-aM;RGo2n<e!D4z&3rymMX<Xynx980
zARwUi)2EjyDSpL*(4!7l<vu}~+7y8^r>oGg%<9fp?-$IJ^`%ZnSZvmF*=h+hknb*j
z&!sb^Qz)LHLWc(|p-&#0<Q*$RKkzKg3|D&;IbLr6HaTe|v;WkI6PNGZ6TM5`7qr8+
zX>^Zl=~+V_8<LPxG5K&7Ycct_1`CPw8LQ9PLlNbL{kGm=Hq$fQAY>-$Nn%d!-^S#V
zmzufw$Lmw3g!3kl#-4KZ!)~yw2VzX;H6{<*zsL3$lt%_K7uO$G7BcHC=@(7@#M_hE
zSwXyYy#Xh8-Xb9W{PNbOht*SS+Ytzpu$3POgouCP3jE4kxcX+(zl{Dcd3AnNxzpD-
zmO^}(9KF#oD7>d=AR*01|5#6&DhV6NH=pjUM(yhAcVIPK*z{Nu<HWVe&!IgNm3j)@
zM{s8Z`^eR7Y>mPc%SGP$>h&P?M1fL2lg?KskZw0f*XcgN%}*iV!pW_F`}S=^zm{;0
z83LxDcXH(pK^6CPejdG#`}#C2#TTdOi)}*j67K2Q8YyN)Z^|r=)U1(fF?YVTmfl*i
zjxn9AcuN7oQXfdF+H1n}$Lua$;0PENixpI2k^7Nq=D%<)`qeAtNH`iX?i1H}PMzwb
z5wx_lfa}i3iQd0||KR@p+9adT68G-iTNmGw%H@3weIYhLC*>8A+DsOEK#cSpO7+%n
zB&v8@^OY!g7CAWou0JQ6x5i4=P7Aw5(_<~~lD0(dR}EIs^{Hps_2RL&L$>wDGkl50
zHPQ+~qtoa=<mcJ&9x}C;qNZFbj;gvI;!LfMI&kPf^D80(F%bIi*|+=T!f~W9dabnl
zoMipG>iuPDQ`n&#^yY;d4Q9#KE#e$<MKM^F{^rJ<x#S$bHceTtYqops*j+H4FVo%{
zc}zdkpDU`0%Q-=ebKUG(VcA=Zn{S^f7-{L8UUl`hAGxPm+u~_=II~5Fl&K=ESnajJ
z>~^?+t@*^+5153zuukn@JuiO${`{j=q{Wf=i?-pAjN7g-&+r=*>%ysK_<BfqkMG^P
z(_EWB6gdpcF7oRac7e^j5Du+U%f?_9e4X#oS0d5gGLs}~-*XkZp;bs!*miQV>TN^h
zvcRpw*=v)GS-|p~&_@sMQ;o^2`i|IJzPKY`p<m?C!I=K4{>PuEM$U+=W`x1y<EhX3
zYk%t<zimlt(nrc*t8_Q%bh|LLp9sHsMotm#o=&y?+Iz*{95!BV`K@=$UvY%(Xw=8d
zz)`n*@3!)>_|MA)=x-V|XheE`*DWTmdPcddce2$!AeN}nsc2)Q{X+c%ryaeW_(hu9
z2=2YIfW6@=5^h2CZXeRLnmR<PM*B>+`UGO6V>u5@qQzawY4colHyUe1Am>1+f31+t
zQPGazl%;&&j+llzKq00W*TK?jjgR;0n3yC!X6DEzkJR#?Jb48c7~m_t>(?I#Y|?#-
zOschmS-4(#x~zCceLMx@Rpj)&%4*_vFLb^zol3%!2##pI%a<<?mcUi>VD4*(jEIO}
z;x99Iaw>%44$Xc!-_kFYuxX!XqARnBXr=&zJXnua?<MFH1ee9v+_3?p(*Y$$W%3#C
z7Rt8s&;nY{n~oJ&hs_ktzamSe`N<Due$gfcj)_x3x=G?IuSubZOQvi+=e4!oHYm#*
z-y9#lzn8ZL6IkG2ev#**hNW$xPDq#{Z$;RizPgW3Lw37&o;mmTjHkL>+rDN$gE6Ik
zo-*c_-AvJ2^MYqIwjR(odD<!P@ZrxzI|c30#s?<Od7Gk3U;QCp@2}u9LMLV`cM1^_
zvsI+75ogGI*LHo$vQc0X%buokt(?7B`Jwpy_&qsV8_%3bMId#fV+$gDqJP{|2?<BL
z*ZL246tDo+V`dljD8O9yD{`T%yb>)E{kFKty>vP97}!a)ftha;ua`j!)I@kT9Wpoz
zJml<6xPP58;xoo8EZp@dS}&rq@>?Y}BsA1icNgMKk^mtwUy*e~(0CqqW{*?RR54L|
z&Q;L7A5(pWULT!g?!Gr3dm>Bgac|&&yJ}!uZPR2hxA=;Ep26&J8fUJfSu$r$o`wqQ
z^_{>+9spALR|k%Ixmav+H-}q&CB7n(UcR!Hg{!LhZg`wI!*^kz^1A5X=U=KtRfS@C
z!&rcc{H%=GxJlk%SGs_`w`ogDn}QE>2wql!z}JUe<mf86<6AiixrF|bc(yKb!_|CF
z6H?*6ljRn2hwd)D=tYuANrdEA&IX&8xHQxna>GvVb;*F7CgR%GOAjW;XSU8RbSrWZ
zVGg2GQc{`(r+cdZK(2Cqyzr7dHI)-cCz&($KGS^q9lzz-ZZMj9(I}go3q92ywsc^)
zckkWnWnqo;S+DvQS_-)+tKL?Gn*rmoz2Lh~j1qi?))F?2gdEWeg^pG3ERuenr;u}}
z0M$-&`dDFEgATbx6WHaBjJ_o`{0Z_K{m%9=&DCJnXh}~#1K8G>qpI_LGB;B+PQVWP
z-z&%?u8b8upAEUF=rN&@f!Zv68L6upX5D$sZz)U3_1U3Dk9O)J1*Ne+hMV2I$zROO
zGgAb*xS0a*d$|fr_+!aLi=Kz~iZC+^X-K=9JMR+_pLfh$lQ4}WV>bilMrsW-<W@5Q
zc?#U0{+1-qdgxGAPD+3gG_u8^Rz{}2h7@@J_-+9`s#S?ZqIJuci3yA+u?NXID|qf)
zA23hn!ke0nSPpGLu%-AaDN=s04p~{RzB)DH<<3r@%kL1~ot<LK-&gadRlJowov~r>
zbynu~HSgRvv44Wn^TA~iGH~%;sgs)Yi3EKlxj?|4@2p_aBHNnAq#dOkBlXJk7DglH
z@QHY7Nv-kAzr;E>^{da(W>v+;m=@E%6WS_du0nA|s-|kJrT0Y^3bkh_!_R2MX|@t|
zG@d$iIr!5zho&r>_8-(BL0`-0hT%j9eC!tIchr$>4;b9ep||tvh&RrC1?XK(@83_S
zY_8{Q07vZrh4BC)>zTRa3PBp-KXXPALX$&J=2Ih#L)QEESG{Hjx^X<l@W1n>NMe?<
z8hz{v+kpca-`ZmMPiHT3n2!ujr34n&)zy_&pwVb(1pW102m(R=8s!yCU1^ebT3&7I
z@<v)+4K~^ih*HJ#NloP*Tt#2VSn9@Yo%{`{x(9VKr1q612dnPMK98~Il?&H`c5O4p
z-wj5-Iof>cy#xL2r`;`OOQF=>;sZ6Xi*O}tJ)JYK-@F!`(r|LD?;p-YJ>9jNDR;1#
z^{+*Ru&XMb8ND(#Z<pp;$x%@13KNW+X;o(%Zg3Ifp5wq@T`})bpFg*j(X)i6zgA?e
zh|tWLJmGZ#dvTrCq9nW7u0AJkKi1oop+Kj6!kKzrtDIc#U`GsVLUYPZ!yNS_0N-Z}
zVzjuS#*_^T^C1~G_da*wg0@PGpdKWOO#B6L6D<)W*TA)erV_wBXOE{YDz-w(6(fn|
z=v&$xu`cUWXe5x{stifbs!hOViGJMM+j-dGTXGaze^#8rq`M+x-#ry_p4XK3b*oEm
zDD{@^+vYVF;vztq#Y=ltq-MUW-p;OF__%lFn0$e;ZDDU-;>iL$c^^AaA*#7l%Y%PM
z@h-P|H&b!OcGZ!Lc71XX&O1dRQYs1gzHhZRfAd0ll0I27uZY+1P{*O@cxu;_G3H0Z
z<;A7t!d|Ik0&qwO2@Ym)`6RzCz(&LblOgVqkM^i<$WcjVTT;*44-7r7NrS7%X8_XS
zf#himfAr`P5w@8&7!Ww5@SvYrU5Gmu00smr%qCd0D8n9|L<+GN7e4|2#K6eNXz)Co
zl(3Q-4o7c8u!S7Oa^_x$-T3gG8TRy?kLqTbqu+G~VG|>n;(Eh<K!$8%*0WD`<I&tL
z+6ZeUPi#2yZRd~<x1avT)im6iIxo*f^t%O9#m?zh#EJ*R`iXaxk5?fy{PlWWqy&w&
z*>DOCX0Nb*%*#tXItHFSi;9B%)Ow?SUkGw_GP0D8>Ezw7_kS2gtrr62r;&soKMO1(
z(vN7i*UvF}<Sk{~G~Rx&k(GqzmfEkn6CrNk>Z6WE7uPQvIS;&k`(?m<lV*@4<)sV+
zXhA;)OnVup)#4=zE8e}kLZ9H;?1CIE%Mo3#FFtxVWB-8zhd4~#4O)S`vF}KfL_&@h
zo#b47aSqso+|hbJ=|VxoB|0UrV3`y>?=v*J^!{6tXXo<@<`n7ooyG=cT~|WIa`YrB
zZ)?=_bRIm*_r7y{six<QBMWife7<~eYD0xUL^pD*T|=Y_hLKqdH2OJ;DZ}r=cxj|3
z;ns5;_JeHV9`&V*Aw_0rbJFQ}l;ZvS{=V$L{WWJn`HVQUlzqx!l*TO;kbUW?^E*+o
z6Y6SPQHf?Mq$ihTebiWG7rrekcXr-HX(~uxv)8Qs{fvQ)JfsT~9TUj$A*(FwZ$qNl
zYFAT}Yby!_$$uEO;I<%+J<@#~1-Pm7T?urFsJM<dD!KjW@t&jFwfFg6L`7v(drUcy
zhpQtqzkheTcI_H3umQjhN_o%5swc}B5(gliFl4Y`jgDIpeL|aNB^qdhg`!#8;+aCF
z2Sa^^djVwXv8fA*veWK(7$rUYVrsqYx>FPr!ZE|e*?*aZ*>OHI1ko~m_&|Pf_X&b8
zMfH&flX%w5TcDZYaH;4!7nV?BX1iOt(2T!CO{cxCK4t4K$!#hdvPD#DUZTbMxDk5>
zt=Yl%PE7zoZ`EZY;@l5Yc^Q$L7F#-4It{)>9IzFoVr$EhW4yJrowU2~0dt}jx9a@s
z_yX@pA>PO?pvCfEzIc)L^{Z8pIo1ql;sTLaGKo|mKIX5pG}RdsymPZ`Yf$rSlC@vK
zo@{v^wHT?r*(y`sH(U<vyGev-pksC9dQ!*yxr+g^n%2Vf?tQ=YY6Zf>HYP4u4sHhq
zIErxRYq?9&$ym8u(<&Q{8*n=P{d|$_Ru_oT?6v#=UV4^VIuA}0(G|70`NqzJ2T6}y
zeTX4H(Q<$S`bg1T&nMX?eXkgqw*r+fNz2G6AG|gWu+XLMe0>7THv^h|I0$8*j#9d4
z+fkUoM?wSL%>X7iP=m|Cqmd+`wJJWlQK(m-Uo%%R<O!`#&PquUb^+uVj<z_0bR?S5
zNmD4XH3(e>BVwsuNqEZ(ah@JyH|uGQ<lR%u$*p=lZ&j(T2h66t`m#0)hs}v}`d}37
z;}KTzMAI+bKTPo4*M$U?^tGN=zpJhcAQU{vxfA`9FRns8gdoW;@7h^&?!aFYfN%=|
z6oRB@ljDW`6tW;e3owB#MQoN6NHU4zDI4sHNzkFru=siM<#ZLFH_cEGp%RK`hHC6N
z40a-NRj4NularE)fV|gA@n2NRs`WMqwm2dPRSZkU_C>~(#r^&p8ym>a_jXd=Y-70*
z8O|Y{4FdV}hZo#ZUNbKj7Z<-yOfVwESw22xI*rQxoEh=l(*@->*CI>nY^95>(U)jU
z0GJb_@*`bl4`hf35Hhqqny79Yk6%K0T;S%EJf&H~&w1H*GQY#CK|xj%e`Q;pmv!Z&
zQ61Ipk;hPPsdG37;k@?dQGD<}tUrS7<q3`q&$S?Zn%&&Ea;N-CyE@N%aXqJA#2Ewc
zj(J`~Bsn385Fcr6uz7mRD3Ved&ekiWp3IqDBfyN%oEnx(Y=8%98c3%~`7UN&>mpd)
z-k0wvU{tOddRV~3bO+*1`_ac~fdMgvL@Z}Hp~WiFtUj&wIyKbmwzanI>l8ua!K|!W
zKDoVLYdPR-0JJhQKi3!U&?da6zIL6ZzHF8yfu4-b4n4gm#7A52(g+()sG`TMMxaqj
zkvLYl{0hwq+TdR=5g9;_hS<5zt_Dit?x{PwRVd%ri{b<Eh@8juKAF~}2hnR3diX@C
zt%e>;l&9mSKERWmkGB8lL)}9la=yEHZK;?YuGWb$^^WW5?QQdxk?({q-*kBYmh*cQ
zHh*}>wBM=;Rzgl>bE>msr-(PV)@^UHK`r@F-xU|z9C<vtZtoiXmcn}7n>#jq2hVvq
zlcINXW4Q6s;&#g0BGm8y@M5WN7grYgfMstTDqH-%3fVt3u)s;4KD=*FV;61PQ8syg
zC4)^zOv8|ixv*@Smk!Er^H#lZDLZ^!yITyY&TqcnKvGiDI?c0DSOgljnEH%()qK9s
zbdZy?I0%84LH>paYVL3nqYR5nQ$N<UaveCG#FUMdt;s1S9i4dJdd_tC#J>|mfs$yZ
z{Cqix*|5^{*S~-M#xB?es9rsPw;MAHOVo907>*=Q&MTe&A4SqS)LVoNX8LRT5q(CL
zv6`|M(lg9f1AMZk#>{FA9t<hw`n3rMcRC+=tUs%S#~RGyvw`a1#GiX_pkY2}h+lqE
zF;I9I(dAa<EP;X^NHoZ}tuMq`9eErtVxKOAUDsB4yvN<k9=I%|O<5XNU^ETjc;WH_
zMVL5pUe?QN#qNy0{)?ELU>EX`_NJNE`M!Et0b}V1n*vAATqeG<6bLEyN~Jc16%7jY
zA5YaIEq%gfNbwvq_n=oJkU~p2vKNL1tjFUyyODmnEWZ2TJpbS868CvuH};}|q+;sM
zi~NcFz_m{=715UOF&+9m5u1Apvbo5)7RuWY6akRJ$V8p2TD(YA*apC=t*8;Pir*1#
zh)Ol*b*SKo?08h7dOAvO{ksAKja1#!E=xPZ>RfhIb!AbQNH<1S%ftpG7rj}A6kWYA
zlLpJ6Z<AaKe=V%!Iy(H|mK0mL&4~au!ui&jRgz!RAHR$;V*(B<;|xE(0rcXV6<|<k
zQ$8Ot$vLkxb(bQ;rz6D;X72eK=R>zlhye1k{w2v(bq5#=>9=9AYWMXY3#IbnK<zNF
z2vY$-$DZn2G)wdKVmMWbmKT;qh!2eYNFhu{5+xUgASZLUl<-}-6NTYX15Bh|{6P*L
zi!F9EPtuXuaBjjPa+GsKN1Wn9tb2!Gtp;H=fA(X5IMqH++~xEyXHGNk=}+cD;j=c>
z^&C?8G%!RCo^!+R_Ww*|kj{^ijmk|y(VOKQDlvR*GdgNeHh)9Y|LU!<$(@Kkd6$Gy
z==}cjRGjK*jD7F*>{jiw2*ewEW#x7n>3)$>xn#Qo7ncSL>aH<Y*fe4IY4o72C$`n+
zki3p&I+qV8nv%#L*Zz2;;%=>+q0fK={AkpMmx`+?{$lgf=q<%je~jq{)|Qs7*LBv1
zEi6#Fw#Af#u<0F>vvdBRT%+qQ{QPzN^6L%Hr55Z!wvOZ@Z*w7j^o@DJHH8OebVE3T
z`k!80j+i^dntgO)ZDwufPEt=t`z+^9gx!(h<)cTz<6#{rc=oKD1rGv|TGdw=Jn4#y
zbZ&v=y|CC-K18#-(Q@{Tz74jmR@C4F-b_kIQ+s@PN{QFy(04&mcI0B4vr*MY8Z*_5
z^K`v@MU3G5q#Wc59m4i*?51P<*A>fl_nqrA(0UtCEV%2hRgG|C5;DQ7v9nmu*m*bA
zjX?4syZTK`jcW`)dA)>ayIaJPdKpzgetjo2f92rf+c|E}`RCrsPQ5SN?Y5-u^8h^;
zoU?!KQ2_Rt(8tc#x7xWHcIeG(JT|gFRu7}FjWqI?1~blhUq5-%0l9Zifs{$9%(=cC
z(%s_7Prll)eCaWWrulkRBra}!2QkXPyD^MiD3xQTURI6vxnGWL^pR_9hWl=oMZLSv
z(FJXP4<0eBY!_5HdIYCX*>O{-G52nO;hmaEo-8}3&8po~3`S-%U=S@NmYtv5XHw)V
zL?WoJFx%&FmRN`u9z%3Jzgty<a0H541a5Fp(spqvZdfBrvl{#clBCW}?fgH<%$1ep
zzFFyE=|34aeTK^Z<-IbWfwn_`<5DGIPxjhf<aFcEF998a;A0yQyP~tBvIM!ofJ|>b
zCYCOfCu}J|pNV`8I}~5L@nqz!CoD9VX^xC67V}INSCWb_2ST$M)X7K+5Q42kL&lL5
z@bR7%7sodN^U!to;K7)gWDRjgV(|g}HMr5xp@V}i^15hkC#2L6-oWypizJ7t<?-cB
zR$@8wU<K`zAIo<QkaxgDpksow$3_ieJWu|!?GT9EVRJ=K&8frDn=TCYm8E?we{b*w
zQDR0|&3ckKmD-<i!JxDkY3*MxBNA|lzsTS738YIpa|zp}U*ADoQuy3IPJs)7W*|3t
zeu}}eK#Lj-oR+30hd@g*zwDkzrddS=j5?!6_AXL;T6|9D1}3;ci1y1^)AN^!JC!7%
zldI>aAypmk0djgxAo;MsV8AB_E_qiL2Ec~gfcPHr<-cFlFmz1LC`Yf3oLO|sw#qCU
z(&VObf%V6J@dAFK4|tW8J@y4S4U_ahSkonH+m7i8B2w?UliX@fsU=EiG5kU6bUMZX
z$LBRi(1UJubrr5-f{m}2NJbDbTvo4T#Z$VkCtRMpWiEu0Ie!^tEDqo#+}%0CZq>+l
zn_QqmNL0a#-73mn^R@}p#*PX7wI6<d4$BXJGaYCMnAK$Yd!PunBIfq{)V@PW4yYss
zo}4WWr_Fs-p+LHJ@aQZlXa*)IE~bF#b&Ejda5_|Qkm4PSweH+0HM={$z5e?<rp4uD
zeXyW@o0>8xB0*@I1G`vw{&qy_Mk_yG_j^fp3~U>m+=&1)nx}T4m+R5Vx_8IU_Kp3_
z0nMG23fx>)$(~s$0-CyRR5Rl~UB=S@dR%|DWaC;85Qpsz+?RJPQ0GJc88iZcuSQ*R
zEE@38#`b;Y{nDKl5bv+W9!6w66UfymmCKCi(!_<j(Lj>P#%@s2gnGY9)zdnI;Y7&D
zqymO%f*Jj>s;3oWen(uYkI}iAE<NR>3GloC^=@k&mH2*R=J0`;_pQfVJ+TAIjYJ@W
z8OWL(I@IsG7tW9P#gC^GxVHYbiX>)8ujOg*|B|aCf@T)J3lpgcHKawKfNo;l?b_z@
zr@!}&C=eX1I*+X0^yQO8AS$ZpGYcRhJAx8SsB*V7JUe1_0rvwAab3@f)SCxvcn0@m
zi|?g$4=X*T%7Kamd|bZQ-Ah?hhn;Tl`n78rEQTsG)<38dZ((MwZg2esQz9;fi83wp
zwuzf)T4NzegxEA{M8k)Q=DVJQAG;J*P3<76ygJtn`{8V(aO=BoQ;oOf<m7-*)w^`*
z+uI8W#I2Nz+nZ`o`jHjKK9&;B$aE<JPn>VLbZhF6elTDHfWtUP`Pn=-@|!#pX9-WE
zT?<|Zc6a{2pSb=R(O;e&TSQwehJn%IV?2QrdiW!|#??;Rv`_ASyZIIb^Em9)U`}N2
zxme)N;pR399=oB3H}fl!@|-@-!qSkm9dXH-p@>u%k}v|ILk=lVj+?h944cm-h(5bT
z^A)GM<cS9o3Lw=m?U{1Dkr(BLRJV9aPOQJI6kIs3$ps>S#+hZ;Kj6&yXH>(Dq1?53
z_NkSJomBNK-1GJiJ#=in=G<2)s<E^KA@OSHaWB5eIETKr5`$td06x~J!M_@$TT_#x
zut6G$!zaTMs?DS$l1m6VhELFw>gu{fAC8~VpFF1Z696f<MQZ3uR9dZR$@)06KKkJc
zUYxS+O^~`h_|wMIdx`6(AM9$dgp=7n<3h;Ds=}UBM|xgWJo@<S_*+3DBB*A(!Z^Zp
zw9YWn>d2J$#ia9=I~Y&@*eUKDO-tJW=IqMfslDsG+t{qY8`Vv}%`a=F>C_(>C_nSs
zw{$7gYv>chPxa(DS9?O@4T$D{zcg)CPcuN)r6e#wA)$@pV2b7K+kKU;=B)bL5J4}K
z3<^<1TQ8}~54fzp*1W_kVMz^UA6p7{_FBGM7Y%GhV8Va*ylvef4i2aE`zU@{afn0;
zlOJ07p`ifY(NXyoPVU9q2AaQex|A4;_lLy4Kbi5H&%4cv1>eL2ldm+@kaviKWA2Ls
z0udGm5@0ngb<}V<7_y_U^K*GvlOFXIli`X{RDuNE>7EM=A~196=3H;-Q!K*^#YhG9
zC)pSpNQs6D-S*qY?x~6_8%KQiMGWENx~I#F=JxV>1ytD|=Iu#|1F1f_f1_u{IJzXN
z<~oQaB_-B@avsgdR3?~lfc9oak%izw<j;A1HS(poNj^SsI0KNqVA-IX`=e!P_v}lS
z*+qbj8Pe*i&tAKOpa?+vfkC>wZH}K$PJW@2Yn+W{{J_4MoK>A2zo6ltpL!gU3Hm!G
zAze+)=YXUk)b7gNhDhuX-lImJ7i_<Ydfl_M6sb)aU>(vmV%a*QShgriYs5&oUBEk}
z{CIMyJ>|ls^A;QGg`cQjxVPfu8|<^D8VHfu_rLTwm@j=D^mt1F1=eWp0Pv~s@wr}C
zZ(-2CoCGik@&`E9K_RSxu68_1JUl!!kXcMk_90TwpHJ8;yGB)SH}v>mgB23bnHgU-
z*3)+IR<{s+j+D}1$e?M@WC~D(>pMUOy(-E=^|)<Kwp6|Htv1JYij~xo)KcJ8M7g-V
z#+oKICu@NUrVV6oFjm5sckG?aRK^ti{bG}=+?%7)!z5If3M`sZk4L9$4sZ`uRVTrU
zbxVw$HuPnVW;J3_OAB0D(;AU?4GmEq7D`QyjLYffD%Mf90fWI@#(fc2<@EF`Y_>p<
z7EkwIfj}9ZZ2bCs6Pa|ZQ?M)6o>VS0f)uXTyBjpAaWr)x`xiZfF%*$=E*$DmancWj
zRz_EsW|ADrNFkUR2c2V0O%Eedx9|g+{nF+fA7d4G+%U3X%-v)qphD>pPnMUiA(cZa
zx&Q{FXY`7C3z;+dmhn~7@3Xu9DM^Wz+Nx$v1q{Wcaf^8;$<N#AH(b{+F4&fHr)b&!
zz`k5~Kxd^sVVf~q|DF{1O;ByVf2gLo`_~WqKo_xldw+ZU1?vz7o(0FI2^4?yF;s9l
z6zwdK`vCzsmOi+A3U|2)5kxEObDA!BgmvtGRn8V>{wlHfBbRFrfb|cZL%nZtdY$*8
zB-x}<_2DVJuL(ISp?{#H^04tcXplawh}M`F;zkx(U6i}IxuuLa65x4Ps#d}V8v1Yv
zB5&1y_s{EUFNu0^AYO=5wRQ$sxly>>rk$<Guq?Y7FB+%H$II(}^(i7~M|x&oAy)8*
zDMO$Anfl1O*-L|DiCqlg8#_UdsXya2mAE(F-Oqgck1CtEk(lI+l3qM7{a;GT@z?+}
zNa4Ng_obV*bU=)Cv&`pY>%R<y4+B~!GGz}D(i#-Xj-$kQwIKK~7eU1S<u&{?7|;z+
zU$EP`^u_mN=*uGzi3TPP{SQ)HF&vQUc9k!nC^0Go5<|IJ#&F7*m&q#}uyNK@Pg}zN
zj#+tc7#3I2ZzEi<=oBlL)R%G#$pcc5K63p>+*W$&iq)fuVD)bfpUN?CSSQ8rbR4z%
z6)(V0*cy=g)MNM_cW*(sJxt$UMoy`{rc%LGMrgseDU`X)=K$e#N(p5oipz<j@xSjx
zUXmqhX1l<F1I9&W^kDo~OIr~ef>cr~Zz&<i$0SxRE;dy&&@~6Am%?Bb0fb^@X#S1+
zLYEnjS=PBI+=lgLz1jau$Yacmh=0G1m&<2h0DQc_0lA|PlJXT)#Wgs?Zq7|W0FFCA
zbGSA1d+3Dh?MB8XYi_@Z?)8n{*;RS$V^7sl67}Qzhf-r%{!`sf378w&*nTyUwea7z
z4-Qc4OPIjn96b{6xX>R`J1wN4j{jKpC<GcBHBgR?TtFqM69&>Y?#F88tXKXkC?kBv
z`-$a)Wwy07koIiT(=^HVlN-rA<fjf1?ej$5-~n1*AS1{331h}h>Fk*9KicoZKbHKe
z2!bv?=fHs(r<K{cyg@dDCc(L<&hZYDbM%8ZgzJ?vz|#SA`+0Uq!1Wk1z^vzxbl=L8
zy}d0yr3$jYtQ6p;ni_FnR{HGp69hh;yM=s?j*)A)_|^dq+VJ)V)YX?;LUvbh%4o=W
zU$0a(&!LoU7cCx)7f@7poPFj6&;J48kt4wH`$TW$XDkK<--CdZs9S#BtXc-<b`I8T
zjnfQi=9gF3k3ZCRB1HK9hxzHHc%-oG0A7VlLsfIEUu~|&23*|&RgOXF3iQI|LBErV
za0>M<IJIo=6aQ(vjsigW-XR4tVv(ig0ATPu2kQ1F1qUku`LB-@6yP_HtL}%6nMUH>
zEeF>4-$(pHwZb@pnuFb>5UJfC0NN#ah}lsaboAFA{_rLCD$#M$#Y&js5s-_Q=Gh>A
zZs@NalZY$m?(OFj;Kv%_i&X(rho!`2CSD=ErKE9ii=zC^SA7qtqKgw~<u8Ntke2(0
zL~x1H`wzbV?+L*B4uH<}6L8qVPU|8-C-Sw2+wVG(zh0$2*;~+k46-H>031z<zZuPW
zNN{UpY`7LdB8{_SRx+xUSE%e%)^^d#!^ts7*G(6af^hxg_(wnN?OM*(uCX|<77rfS
z50C#B>;rC7ZhhBAB50&E=x|OM+uSf<Bw~z)wMi20?{L;z5W`)A_ivBL@hd3>Uw}Bx
zHl0x&wI3&Nx?JDmTJWkPoO_tCa!yT+wpRSKy`$H6T^9^`bErY5#!@|UF|=rBFC(+(
z8YU<<nElr4Xz7W)SxejN!j%?Rxe|qC2^{<7xkFT_L}Kb~!D>GmxQRkR(^SY}d>$<^
zG}|(pcD`dNVCdD>);^`w-Q5keqjnO0H^Pl&aCPcF(O9RnaCsLCqz@mT&J4&5obb2~
z-+jY$n}|y@a!xLwqDQW!lu2Z!5fd++hb4%YxsF11>(JW5u6Ofx1!1a{DV{WPzW(c+
zp({3UQtYvJh&IUk>fa6;&G$0+_?G`Xt>d_iq+W&z7E)Gap?XSWWK@*(k;hFD0>Jhn
zH&&-ay=HVrfc>3)&5Q`Us&DoJg4d{xu<XeX1}PfuCc^Yie3H1N7Kjvu%aXnony`j-
z7E3PbnYlKRJ=Hcocni}RRA<Dkn#Q|+$mwi3fudxteo5s`9|pPn>r0|@uGbMtO>4#<
zvs1oCY2?3t{iP%8bW~oeqbj7<V9W)Z4*Y$*w2zDFOPDe49FI^>(laXIRXn)q8ITam
z-%U<xD<}Zbby8hfH)24z0CnL`sAn6+i}1pxzQej!-LCZvyPmFtY-|7ueWYGCKe+Dq
zZ0WwaZN|I<=r0d!pu*zs$Naaf%^2lpnM#e<AS<Da>h6~8Y5ddR1_hrnjOzgC&^@R4
z4&GB~_wT=ViQ-Lk`Zvh6^E0JZh@epGuLl_}g7T$YD#u6ThctVznmMo^)y=^?+;NxG
z`*hdLQ2tY#oS|<MoZ_Zg(si4!C#uLBo>3txMQESDm`pQPqlYw7y)lx(V@7_Rv+prw
zt?XX2^lNJg#k&4Fjffx6A^l#>3tWMLBca^hx;6uv2HAm9R`Vr<zm+x&U%`7WS?<o)
z8>r<$TbNQTeK`T#jGzhW*kZOJOeVe#UKx?HOj=I<P(nBZAGdV+44<IP8(z`vYrdom
z=>sAQ8GDU<j+gbwfmX>_m+jRVDYOa~J|&~lxmeC0{-Mh45SKs=ok_u)A7_ck$6x9D
z)D^B}v@rb#M_Y$MF1GUB*1HU$<Cj;oJ4rx31+{Fb1kS<T%*_2zfpP9W5!a0k+$@!6
zJ$%@qGg+27Zy(~b9LUage;&#V3*PF%sqMX;Okh=+-L;EtSni!eO5(fyVNef&S-VJ~
z_LuWP)#d&Pc+5}1Q~-?ff8sQPeON3pfJU*W(@1ClKJXuMGR_v97ddR^XzR9DU{g^t
zgv4<q{Oke5=hNY~tnlexgg^#**B{?P^M_z2=H((xmk9*36xW!6g7kHxxhMnO{9R>H
z3{415j{`d@`kC-v49nlIFE@4<c6D>8TU1t2fo&%vBO_wplWAmrk&iEbwiBE}m<CP|
zY>&178J0f31Vrk8HI)H+V}%15N(9iHa&<-M>z?*D7!8Z3hGKV!@pmoil54X=RZss1
ztsqv$E5<2MRiGRRL;x&~3@Gb{4-*k7pY62mOfI5Q5vdIq685&623~z&wrV(%vZ=M=
zU!%ku=UXgTa9vwt%AVmRO0=losCaQNUY<v*U4fo>GtNuSjFYxfRPUv?au~<yhi`JG
ze9}w$MZ5m_orl92Y}DzC7t3Zl)e^+KK8P|gF|}>X&dtp|ciegjW%)Bb;9!sovjZ#A
z;rzr~*>XS+B*h0;y{?&&jC_#;h(O0k|9?4x&z<y>4^T-r4RLj{vJ~=k@*+^@2F5+J
zY#w7>6^y+5zu&)-;kLv#2~IIQSb^bBp16Gw-I6ND$dIb-62Sgj_iXs&$OY#l?@a63
z)Y2Jg@RLD6``b_Ejs=<ml2hcwBqtlL(mEQ&RaY+ipEwp|44gI6$}02JN8vpja{f1M
z%BuD8DoRRlm3d1m0b)L;h7-{z4%}2kz(Ot178t^mr&$T3gb1uL9sGqmm=iyBGmasV
z+BXg<f&y1W;H$oSFnkd-o19=J^l+CcEa<{zU#q)2Wo7EN{XYi)meSUZM5rPe3s@`C
z#~W9<Sq!Gg%F0H^#@;L-ZMlTf271|?)d`p0Zm+j23rdCi#eT<$G}SY@`Y5nIIZ(>I
zq};Q+98#brPSod4py*zT`)zB{sd0-QSjoMaOcMg(393B@Vtjh&F%>>q*1s-na3*&`
zM#>@O3H2=ggMuZl^;G>Zv=<Z#)zg=U{@i9Wcy!AB2M;*(_z+c?FWP0CiC8^zS6PRZ
z*nc+g%<IybA-^w$&8o&ZQMPap`@Mh6Krn|6Ys%jzP62Hx>*2-Po)VdRo_^cQaZ`f6
zBE&kAe8Sn(l<nUG@^&8rmNtw_-t4i(2jM-arFL-|6qx6u!g_kN8GmeVZx7)x(sRCk
zMXE*@;ikfn7ld6y!F8-ZSK@muivb4oH{J7@`3f5Te}Fr%7dPoc4@qEeAZ3w+G$Fq+
zi1~%Bf*}Qs3`-?$z$f#8{0_basBwe<gQQdOdPYVL_#Y#+J|spRLEz@vSgC2RwC8jx
zES!G#Z-~!G$WUus$u=nLL={eMV>Ee&Ypic(_5STL^19sv{aJN%=>EA1ZUJKkg8gIy
z&@G@SVS3^z{m>VKzz6@>l73E1pDiTGEPM9!f#3b>`1lDhM!`hLLYr>~d~s580L`8O
zT_SZWxM@(IKxtw%pN=*#>wdD2yARrd7n#-9IIVce%geVXNgJGw`WRig9pP1+TBQpC
z!%)R1={4cZn-(1p@^p^I8Te|f<*y0o-1mjNDNcP6(nZ0;q@RYDqy}|3`SjOLRoLfC
z`kXrZ0pA9S*uNfJki5s*!7-eHc@bkqYM^L}Cy6PY3pN(IftG)b;raozpfBMq_k=t*
zR|JVhfdSgAgZCK1Bol_?gDO=jSWYe{oB{TpF`R~?&bjyva%}SAvWWdce@67Okf5mN
z7|`wHShJe^TI%uIjDHG9;b>+c|K=Y*0l1OrzlMfLJy|&_Nw0%?>uIRc*9CNUD({?I
zfSiB8#?(49amq(qJ`n0XY#5y5^)R+?rBDrJ`$32gml+`jy3WouByr;DF6+0RN{fq>
zu7nKa3<DsAgY;VyIBEl;CoWCYxQyrqDI?Uo$bp^2W>o#(*cXBD(S*CiBR!$ShEGz`
zaC3c19+!SCv=o6{y+reUY;pG7IaTO&j{_OS6RypvNq0iH#n+|pACa_upg%8djnR0L
z5(C+<dHqDZuEZ3GyC6N{2R7XGTN3s%eBpQyiJ9{T;~5o*@)wmPAHm@cN1OxgN5kUM
zFCy#DsR@$)e#dJIZXcAlok{W6j*#D|V%XIgXDg#x1KjJSrKJb|_#^enli!<KS{$dl
z(v)x9cphU=UvmwS`y%ljkX`UWKXV2^M|8mGz%YOxz>IBBO)i-;zHCx63$;f9x!&f?
z$0zD&a=U==%IqzHezeA;W5+kv7^At1g&r?AP>!yv$X~?upM!g2+Sm5AA~-mhQS=J!
z0;+2cBHXI~f7o1IUs;A%u$Xw#T!;nrqist8J=w5M0fNGauZ4fuO!21~d3F1q7Is}t
zCqUi|*9BAqw6zqB%B=rq$6vo^#f9jzwA~%@KL+>vB?g0QX8@Q1KI05}I&)3P-nMK^
zzF{P5(-<>GCJ{;<=9Sde8Tgrj>FFi^p|}ukikWMO)a2F<{(i{YF`_(eKlyZ&9FG=9
z!cK!V8ibaJXVbZgk9}Sb&V!SWRR1kdca0WUaoeZnbYQvGBOZ8;c*>K0KTG7tp{T5X
zoE1Ok%IE4k7Q{?iZ}l}cY0D%aVU6bw9GWdy_2J1!mCfwz-JIP}86xHJOAXF0_4m~O
zG&bzXAmGhwNDs69OSP_LG2VV9q<bg>`62Y*sVM^C68aKm<l<4uNAT|T-kh?Q7>!U(
z`F5;h5b%(aRvop1E(Nw|^6j!tY`|!aLBp77Q4ozqI-CUhBnXeRSja>B*u|md_g_2m
z&BzPNwjktJUG+3LPtjj3VGuyA#>-m=0kz5@wUWfzOlo>D0)FA|G27y4W2WBQ`(pkq
zE75;Ch#Z4cgh~h-`9`$J_pblfTO;07*O^HACWtwkTtI)c>H(V9^ny5|PnR~hmBFU3
z^>`F6+Yi3~iI*$vFD3N}3zkIt*U3=c#|&8s1j^aApJ2FNzj%#&wcyd+p!SQ<z246A
zVfLwV#T>~AG;yT==frwbUnP<lFx_<mPPtm|0k2b0PgA*rXy4w)ZDkNHfD$l-90msp
znuTg?U{e2ueAhy7g&?io&X>LtNC6EvMCB7QE7G(RONeyvC_JThWWcD?wD(aU`rk1&
zSJ8w`UA{AJT)}qd_?Fa)aGP(AvlK6>moIEF8VtaxPDUS2^I}((pQo7#e48NVf$*EY
zpPzT>QGzz4Q~e3zE6>3cbISg0^TCALVwIlvOcT`q2L{yatOC#cNfBm5w32MnPdxXN
z^L6Il1{fn95`D>%3&eb~03*dgd59NOl`jT1Vn>m>JXw#S$G?ublQK>xv|^}kl92#K
z#rXV7ZiWYTN%rgEad0U?6;YmwVD(k7N8=9}n4Ex~I|eb^Q#k?N`rZ|JcFRNACYb>$
z%Fn?uxvX`WVT=coQN3_B@VcjSv3(^KXPV-o4W7OpIiU1<)1H~k$sn62F=Tdvenj~6
ze<%n<5VBR^9T7I2O_?Fs6MIXo+YUkz09bGJU<d|~KA4(v1|idd&3&=m_z>5=%$>m)
zA1_JFooU@-2KLJs+^)ao>A+bC=cU2b=Z=7G_Rby50)_$iH9|Z;wQ4a4srQz$Q^*+|
z{1|iQKbgcO*~#(NkGAdcS)oD=H+!l_dOJ50CETxokXpfrb_@|6$dP_1Vl^@3Lx|go
zu_M<*ox!k|J8D*8;JQ(+_}HSVrjSk2?E(-{gUs1Jz{^6RnJK5LysO*VjrMBp%Uk4d
zfiq3qjndQO;~f98Bf!LKc2<id%E3inbf{~Ty8AsfTRkZsZhbOL6yKvTROyP(h>*)4
z7`SN)b+txSZfTHnn&_TK_*lV69J;+x)smRmfuEZ_FgI`umA)w-8?gQi5}wM~KiZRq
z&DFM{5&`RjzDJsnK5+1><EDZnC$=twj4cnCzw>W71kvGcFF~(^z6Nyqd>|fN%@`!r
z0qjyrKqExz9zG|)%i9fIbI|84n{$1aCTpgW{TNKQ!K=M*_8xhhh7GJ&V3fv4tT1fQ
zO*|i|r&Ak*=s!^?rR<UZ8pZBte&^}aeCOWO!*ilKkLaeT&!GRn=5L!}IC;n%J;kj7
z{|+Ci0d62{r|dl@p$V#cFZ8()^&y*>LNkKJ?-B;t7DoVl8X6kLi#f;dVBstTZD1NO
zSKoMJu&Ie8!eAgiGOHS_Xvrxcsbz2Y#0>51+mvwEhx`Pu1eL<0?iMsF?Z8Jn?n;-G
z&XAzp20T36`|C-aW+T_4gd8T`=l^>=mrOls-DtiI4GnA;ZJsfFDOs7B(}1(|pdEk!
z4Q2rx;>7ByWF0UFi+WCLw#Qvm1Xf5epDn128seDlbA9Dy@JD0{II)OvP106kwYHZ~
z^H0kdI#E(<38(5jzUHZ1@__k?05XTFJla?_cT!DB_o`+S$LOXzm~DM6>X`EXC6gc!
zULPTC2<)%(^^k>fZ9)U87H;>;#jbx7f({>*)0SchR8UOppxF)7VOP7<&WQ<_*7RLF
zw9PG>glwJ$hU8=K^(*u;sv@N?ea?kda}2fq%q6ZuJ?BnH;j+>b*MDFp&J1cXXv-UK
z8TD3}(B0ZsNZIkgWliiX_hvW%e|@Dg_n|X0GGQSVz;%2`Qc-0&+^B@2bSoN_T0Mo@
zFY6;yS8{xPeQC30og%KIGK{t)&|E>VnC+5jZH-@fos252ZCHS>WdEfU?D=a=Hf3hm
zyx{iZwPmNcq%)wZ!oa2&*81&)?0C_%l&$_VBl7sj%*nI>My^>b_-{agkhokwAX$F_
z+KJVWYe?mG-6uI@k-Ct$B!l^%Tj@ev$0PO5VB8iF<`^Hxw9RKEZdVP`+ofv>y%hZR
zpX%Rj>qB!g@kOA@fM0>diJd{K&N%-UT8ao##S%Ac!C|ntvJ!*EVhdEZe7@aSE7A)B
zRBKBcO&;!Gl+E8c<*HW_x^)KHf_MQTCzWL2gy~H69%uNmR@d%Im4Gkd*H=({LFLc_
zH2UBQT-V}4QC>8xPO=|5c<=^oteA*kL#HLBi&*-bt8LuP*sa3Hk}5Rlt;&;SLnpl&
z6?-rn?kyLaoa74aIR3U*|2NT3-N^Nk&T$>8P>YbG<%0jp6Q^fj52{;hXJ?G3;A=1_
zfNvbSgB*e4EFWO)+}xWGCgbVMFXCm>Qr4Y+s|y_Vz4AXzP(9E7&ki59kbr9i-}pZt
zW|q$?odYjYS*Kht<N<}ynng*9^{SBT_s?(H<OAve`e%Lqe9Jaw*PHe|PONE?K9pV#
z4>4#8vxuOTe)SxoemqSdtCI6#`+ZZEM`gmM7(aM_e*x;T=d;44zq_XVZw6;Vz|s&J
z5>kG5;r7o3SmT+etkSt%WiNx3<Rjs;)!0Y8hnyK73}t4f;7}6BVb!WY?YMlv##K|@
z;@jKr(SGU<i!JP;a}^DXEAIZn{%tRM8Q|Ww=#!9AjAuwsU^DgKcITg;t|?z_DuII5
zF${T`3gPLjw6t;0=2pmbR9Jws%hk#d30S{t3iopspTnmydWS$~imYs~$K~FD@T&zS
zO(JfeFF{>Jb)g`ri@r-X(CAJN_?XB{Fz)9;1HqY*+RCPw?FgT~N-!c{IDZ*B`52B=
zujA#_i5-&cA|6*jRFoj6G^+(`kbo}Kf3OfK@)Pp&|Az=cXkx3cNzkE@)6d~T!>4KZ
zJ%s<O@|}&13XF2&9}5$rK!wIU#&9?<d}%^Lf&^olRPNlleY^d;hr1Z9hYs%BH(p)?
zdz{K3b(4HXp$fq$3uRQ{lm~W~8!R&4gX-$*ll0j&P3ynJPnRjt87SX+iq(K%J9lr7
zS}f*r8RVVE#|WECRg7FN#TwP(t;9B(6}KT<Q3(jqL6+;+?ExGCf3?V7Nfga<FQ?4S
zIRd6veq(`!L&7cf%Gr9jkp?-3!M}`*OrLL9Q@3n>W-t7f0;N7s7@>|g53I-CsyX%T
z>(^<x(9R%6(3Ar4QX;G3<;#Oqs2+=uU5;h^06dc!DB;8fZQ16uylwdM$}0<+lXjr!
zuGbsaLNXQN7!IP2!@8WeD)EmMAn)s44kvQ|_s9?0@jmP6hgx!t3S24s!s5u`Gzz43
zvd|_R$i*lqnazfhT3u*5=3|i+ZY!1`ilo>vSm(nnKtvtl;z9!|;S&+*`!f77>WmkZ
z*V@5%^)fOB>4Z`E43~4=Jhrm3VgLsc4yto@oBuHY@5}frMnY@ve974>bQcb!lHDwn
z5`t~-crTl+WiU1W6o(9}Tc-dY5NnIY?bLbW|5A}bj~kt}UnD0dvx$N73px>et_r-T
z9vpSRQN>BEx?^A}O+gvKG?moOHyA61AM1uf>T>J0!`3!74&CY3U_tPcqJUu2%DB!e
zEnVB;kRWVvpHZRLzr3{6D?MMA0q<xJtqI_^&lpCxs>)Gh+sVugh!E3oGiYI14MGv@
zM^{rc;81*y#>ruGtn7!MQCnO2^ZPM)jt^ZA2bEgOpb^I6{Ye^rz-)2ZGa>N)zmYq_
zp}h^aJrO1s<G*@Gz`UWZV+z<cQ0nuRd<#E*_|OH-d#v1Np~Fl+bXl}_{{$<au}Yx5
z4}%Vv=}J?G%!EE#_0)djcqy;^M#nWMsiVN+iNVy@*Hhs(=9%<R!P05gBS&(BxdLuN
zO@2RA^y-=Ty95(AH&(De%cz(+X1)^;iVkDvpzc<wZvg6)11etk$U!ZLs5cGy)099M
zu&37nDenp1fL5UjPE~BaA5;Csnlu01w_TsBu1WP+T~LbL)96Ns;ID^U2&Md%oebAt
zBs~W-WGpa&s;bYRxs!$aETCEjtpMW&6~?7I07|;~e}nY&OQ=w>=Gw(hnThW;K{Z{1
zd^Z?c=i#&f{k2&s@9UO1XeeZC-8MT_H0Y!3^GONZ@7Qd`@=>1uAZrBM*-GUb@v7NU
zo9?1zgM~ma86WclX{?}du17@(2O*`60PH#L{&uEgMnlkvF#6?7-F{|Z)+{4r^}kiY
z^ELr%j`f_VWB*x6$-y+t7B3z-kY024K$$HTCs!Mhf)nWN2YbZjtSN&{cNooJDv)|O
zM-%)-BiD$T6GH#z%(=IC=i65>bi3?3;*$+;JPrLrtmnj>33gyi^bP~vGd%z(#nR<3
zO`%>;^90p^&{vI+E(`4=x9{9(ASoyzaTDw@Ww6>UwTu}Bln}sTzP#HevRQnUkzQ(F
zSmK{sQbdL~`&X$mJcO$$-bS0&z)Ji)mccsaI(;Yu?@B(Q5z$PY|Cb<(K-ifuGD^?s
z=lnT7PkB^c-7JR+dl#Qefj_>FAD)9-7Wlw*d>em9v0n_zNorf5GI;o8;s!j-nY^6j
zJ2y;&E|xp(PX#xP-Jgl5fqe>iY($X+BVbN)r-cKHXq#^-CyfF5o=4LEkJOPmz61p}
zVeN3iumA!4HRXOzo}T4Kl`f|Jf_!{E#iq5#v|}ue5GtIF!yMT~?4Dm+gM0w~ggI}o
z+bekcpH#$Eqs@0M<IiNmk}LF6&T!=Y+1ca2RLrG}%W!oLD5nAhxPm&b|HmS|%^aQ0
zD0Z%^+nhVh0Xqj$k=m9@5Lp2fTF*gaT>#q>nPP9^Amt@4@ji9nIt2abAQw(HDIc*B
zMtMU0nYX3S|9S4+#}mmv%Mzjdt*K;SV8FpN!+#SjR(&<at;IScHGJ$7xdeo5=<em-
zI^L%<eKJ+oCtQ{Ozw*8_EXuR%mc&GjQH&Zv5ipjBQbh&nm?$EmB1o?aDrKZekq${z
zP?`lqx&f8m89=HHP^us>bP%Kn3`p;A);(g1-+RuVbN+nanfJQ#nm7#e+-2{z*Is*X
zk6LI-e_H!rK5pGMd>ouIa&8w)&CSiBvQ_D46BHC=p`#CtdyVRX7_4yogiv3s@Y9WG
zx<#V&?DUl}by+F%C_MkKU}$(4{GT^wO!~-(|Mb(LU1!F>j>qX2PSQBjm2}31)3HXy
z6z(1sT<57p15KGpzw#MYgjgPvj1I)A1e;!Y|MQ0zf;!f;T#iPY+`dIg-@cY#ef8Ey
zg@mNp=&^%3ik3=5+lmJ>>Z4yV8BM7V;h*fE|Gb4uw(APzrbYG!|3zOql+AR?)a-0i
z?!}W&tV}U?_%{TlnsJn!KmYszuAez(h0kq2zxRFX2W++C$6Q6j#6k?_OLzVe94PdL
zOD0bBF;rAcOwC%(n)oe;%2T?RF}?Yi(u#kIBrpB>ATwNp<%c;={?NSJdv@<mHkzH9
zQl4c)T*X2M$keR04il#>&(d@Yur}{eDqOL}Kdd17iN?XAV3qO}+y3qMnGWo}`ZhFr
z&g?xlqBj5NxpRM(Q|kQ4&o7e}_z+g{sBfbgzj(LtK5Lm$rIt5AU-D{hNU<56KwBE#
z#;z+onX=O@uS)gO9ydS9AQfD=?^cXOSJ)xz!%N}XxicEgiLeSncY}i|!^du2z530!
z%a>;-M+7}_y6@*CA-nmB%+0lHPoLPN_Z&I!-0P`$cBZD!tNpK)k3u<VK!t)x%Z(Cu
z>gdP_gqJ63>o1Gqo_7mzsAx^w4=AcpNntILo2oR7)Kxlm%;Rw1ZwB8iUtZU=xNE-O
zxP^&HM9Wzhi|sc5AAiXTvb`lQ9;F@Jhc>G$zl?Qs=s${zQtM|!_J)Ptcs(OQhf7F^
z+DMCzYy4tunX;sxe>g>&1K~1y8erRE>V!%rhmh$_=)dz2Ws;idXOxtX5E?OHSK0FA
zPfyC?l9#N=xB_8+!tm?YuZ5G;&Ln73@1V1whWNpQYA|paj&q3D{R0Bji2YYatChnh
zUxaQ<?F%cT#5Iu2=4%9MN^u-OcE=htcxpywW+s|5cHGKFb~gLUkZxzA%Pjq%r!BZ6
z)W5JR{pYWgm_K9UIRUx}3$A0}irFll6L9JXr<4t;Bsd;S>6+EGDi>mo`MW6R)59H_
zKZIvm&deT-cC4ur1m9flI-XMmwZPj5=N229XzBqSM>>^-j#;SOXnj&*NgN#}K=-E$
zf4M$`-WJ(+(c8xeu$iTp+_SF152XPDH259OI_6K&0lTIao~AZQuOQVHCPEh<&RtC0
zJ6hZDQMh*PiiJvsIbqEgAMWP3BV_Pv1&QISINBwRajFT`=xAd?b=A}iLLn>g&9fC-
z)9ZiB+lNe4M{yWO<KnQdt9rVgeb+|P07`XMO?8$KV_gzod@>Uoi+~#In@#9{*$doL
z_f|n#$94cNsQUH}*|D<yl_(b0ymKD30|c7A_$@W~?<F%wWo7$s%W}rtZR9VQyX?J5
z@K@>ivAa8Cva-}kJ*O^y?@1Jm!`R<>2(xxFTuMhla@-G?v9t(Z%0GLoTE72bMjw(h
z=|)3MnUymB?YSW#?4FApTwGjQPI5RkHLjm~Y7t@IzyIugX1eUs^a)d){hQI~&gSSb
zF;<BJPyu6bpqlKHV`Iu#qaTrk`D4XeZXTY}j$8!q_xN>UfB9p%im3bR^QlHj>M6gX
z*fHjEGdRHmXi&}}VVC^8*|aeoNM6jM-3>kQcq=kZn^Z9iSmpfr^Zr&fRI-<_9o9Dg
zmSQ4eDaEKzbu^C*ecL^1(Rd=c{oT7iacWG;?hCRM%gf8#v_JjIv?2A;uX`mWB(zBz
zPMv3CjYf6xfvWN9e{xDXsIymAR+6?37@3f`>KQ5fF~NiZvoDi4utQE)-|D=otW+57
zE`N(M=s)pRO*+A+#K`5?(o-otR<cI5vHoZ)MM|BxQSfb2(A#T7P8#V$qHkaj`X8>7
z!9L(kn{^_F=)FeXR$X1KtgQSnIeD+mV6(;_%h%jNx0}+weZl1<z9D%n*dF;isCB4G
zGgKHKY|-N69Gv8EkB{etVTg_A*s_I353_nyuaag9Xs>#KUg*ghr;}rw$g>irj=f(J
zGWlf`DO4hC{nwwJJC)Vdqy7B+QYR2)$KxABl@E2L2cCWeM)gV8;s-2Z4d%0=(NksZ
zqk2B%_{iGFmAb7#D02n@lMd>knW7K>``F+hp+(!L<K!(n)S9OwoH=W1|K)XKioUWk
z>Px%DWa14y;TNw5b^jgSn5(VB<W{r|(rO^#5E<Cf1HAwHg|YY+J^rr8{i|w;@z^!y
zVB)@50Px<6>!9=e;#I@;?%(P*{QaSX|50zWf0B)3+lDk4;vEt}Zrr#rM(*w|(moR|
zWjCY&Y5^;l+?v2^V~{~L35<e*VsSU7XJ%sYMw7PuJhX@F`A8+hSc!Bq9ZruBr)Xbb
zg4;_R6E3ZL1=XtO6zq~5V(Bx+#xbbgsf7fA&>6bAK8|r6WsYAnJ&@YHd-of(Moo8|
zG$Vq4kd`K}H#3t%NeGKXtoaR{ceRsuKD|9L&=A??x~LNq6H`)JdKab<Kld1ndo=i`
zk@4|_VX6ker9}c*71%5pID0YYne>s-(T(fZM?^>OCRZKz?C$IHvEve`wjjpBcA)-G
zFR#`14i2$0o-BQDqkPe|zRyj3Iy^o7K!Q$=D)0e+PB`K9muf{`bX#^yeg#o-u3i#H
zs>k@%ub6D!!>{dgm>e`c7CXkZd-toUQoRLz^dPUUee^X`yD-`idP7gJ8G|?WVbDQ*
zV&t9Gfb9mbEk$TNNs%0a6Zf+0EZU66_pbnK@Y;6x`X)Go*jkB;X$|*9tsYnXd3Z(K
z<VH?TGl|i%{YOuoI+d5j#l+NCeP7UkJN_K8TvepT(i@EuD_5>wou17+`QdG`daj*i
zLceXD;nx+pcsqMnS>S#$NB%rU!8JLx4Rv-JXoL4F0`UPnl?Hg+uDRu)*#R>se_qgN
zv~JC<8#Wk_={T6CeAK&J)NvwyIJ02B+^8`<rdAYvR*cGmgi7}Yms7K&5FoO_Ii*Zz
zcV#r0?PmrJ7H1z~uK<C6Kv0kguV(ryyN(-NUTFEl_Uzd+=IImp60J!zU&r4Kpt^3i
zz*vBygv?fxQSXUxS9In2?%G#OXEzn0VJSuEm`-gaz`ysvzyMphxa)$`j-P&dnrIKu
zHH>Q5n%={0Xi>EV>|o|>80@?O=DfW~7=oNWdkh#rIor1O{U0AU@~A3L7iNwm@vZv)
z`#4G8{n8sO>d0muTSUgm8(gMJzoaV!Ap`kR6#S3rsSPxK-dtgJ&oN0ldO{OzciPk_
ziJ*#Are!jv7qx90E*w==RizHSz9hVb&~z=M0LE;3=s-#BVH}jRo35i>w@D|o6bB<B
zBjdEK*w>xvnRl4a;u7(J*z7P<`%lO|9&EPwgS4{t>;%CD#Bs*RC>jeGJuq;|a}<aK
z#AV>})tlPOF5`&vz%$iC-^0x=y_i)(C|E!koYchRd&iZ2{q^^I0(!a$vWEQHS*O7?
z-fp;*A;P(Na}4r$aD6;{d?&lwgeAt_9CnzcXVSyCq^0$+ZHh`tcM2`HZrw^XYtcj?
zM9{=#D=VIw6y(_8-Ye*XWI6rN#RP#kHy>ZZ^CzkanjZjLZS^Rim)QjMuj01x`M6)Z
zwns`zHwvBFj~~B@738aEvv{#)2IS6w%ge04SDoZJIql!yUNt0(zTZD`a3p|<L}_b(
zeTo6Po+vj=lfE$6b`{-lp@Qy7^sCr$0tjvY#j<D%2?VOx2!9xYef#!Vw?F-DRniLR
z)(FK;x&y0Jpz9PTVYOM3Sa%}GI|gf{h!(J<fCmwX=jgKKtKXp7mlsAaXXpd!Lus=^
z1?HAz=l8`xL3-26i@-+ins-P_0~GaP2QwWx4O`I@=FS+nz}|Q6TQlO!ZKUrC_)hkZ
zcZRi}O*~Pw%*YNv(tlOkQPQ*2J3T!;RSE*=BB*=3NC@MC(ENRHIn?$E3gJ>0`t{Y-
z?^_}X#D)q{L11`;DzNV_F_Pw*pxwvzRsDnB2V0FMon|NKaUbmx0J%0vFV3aO6hLvd
z1&8MFwI}&=Bl0;t<hSt^IoR24B8n^Tva+&NDRkN@f|d3L+EyQ<L-&k5?y$u&9?}Vt
zuum;aG;q~TicOQ3zj*QDwNO2@b-MsSSl<z|ZO@)PMQ9hIa?aEVk~?}>sYdvBBBNnU
zOcs8d_g_?2znhrVv8ofxbr67)drU!Ie!tZ8`wg8%Tg(ptr^V$i-5)9ozqa~0zP{>5
zo+-$^MP_ByN05H@f;N3Ms}z*Hy}Z(=%5wLiZmQ^8R_(KbiN<dq7m=2ZNW=S&m%e(n
zcVQy4K&8HL9@~Sf-CY^3_hIO#iNO|16*_KSIEui8ZwB1h7EJU?r+nI-9->xMz0%5z
z{?NmQBVEHTc`nTx*nNS7+(pq$XB|8HrD5MWQ0$ket|lh2RoONa&xJ~<+4v1S=tq->
z=3~W1E!kA$nOE-+5J)OnfVU{}+jE9HcQen{ml@q?O`o!f1|+uw6moSww4%?oaTF6i
z{^@W5jM5s&7CciBq*EJR;r)(chh-Mw0G`ys)|?IibUuf2=)+=@*AhvkZPC`N(&IH}
zSPVKlJNcy2`G(MTIkk5y8=GdAK+&2PPq6a+jFp#On0i4vlWTb*bzzz>z+V|=<0%v_
zv=v4h{f5)j-Df7~l=tu7*R!XASAJ1iTB_eHy*RB9Y(ScdcrGBBBWOF=Y)oB5FRhDJ
zZKb7gzh>p`!!g-d_Ys77({_sqL!`5h0!s7E_->WXFZpD@(F5PY!NMY05TiCaQ;IqF
zV_S{b#jIbEbNb@>2l&FRliw8Y2w{GDj?;C{($lqBAiH;aocXCrEf}uC`$(j9B4j@m
zyNQWuas;S>4ZpOO4UxOjyJc+%rd37Fuv9ELo>Nm(n@BVux=UKR%?N0tP#c-|xXX!P
z>@~cepc_;y`xzHmVtO<NZgun#6p{G8t7|hhfKMv`-R_^Ux&8nns(a2o0N1#I9qDIp
zPyXyz`eh_e?9#57Iq@`**pkTA9AY*49AtdL;iSqSy&Upc8At}0rz{-XhcIM-P0QtA
z)mO`wtJAgA)s0>j6={xzi&|auxANL@FtM@VFp?%~d5hA`+gcIXWQtw{Bg>NW%+bMt
zjZh86j2knR25Y@p{L0_~SVEo<D`k;ThP60-P~saV=Ceq!fF$=cAZ-WW&B`qUbRA-#
zCD5*ug0E%aRX@!o1Q0QpnBB)c4U&rDg`_28jSX-f4E#G`BGG<IyN(lr0I@5Bm#n-H
z?}VYpl&1mgWU^>Cj<aUEk)lQuI(SX@geRruiw|ZU24di*5J4t%FKMDh8Vsv2Luw*W
zbuE9VqoZRC_PTCLGu(CI35L#;G_vYg-{#L7Qwlb~_Y89!6!kQKs{{$DiU-~Qm0=4E
zqt6I>^H2~0UZ9iCYlN#l+;g^E+MOIyZuAvRG0MEupoA7aX_NtC*{WJI&BJT+@hFiv
z3nh(cg;mFKrCG6yy^GdQ2n=!YrtbSA;djXufp<w0eGIaJP13<yBL}wiImLa1fiUJY
zkOiuJB+qJZSEF<F-eiAI)~w{8W&~IK0LG>YxvJ7eIDo2K+YS$~iCcCY@gyuehdL=J
zZwqhJmQzAL0&TV~`Jfut984U|fO6EAGvVper>PU@^=UoUs~9&Qz^f5~gONIckEtOZ
zxMvu^;Nw@;DoN`?Y=CrX{LwD77x~6L6qw;f)zha>dnZj|QMA(OI$Hp#V{yP!Cm<w&
zn-z<j51kHlOM_!&k`080L?&lWz<}d7p+#3hq$S=83^cCt@%E-VUeeYMkr4+cVA3|y
z-$@qe!XbGv*8H5Q4$sm?n9mucIpZf3PM)mAfZx(aH0op%GEw#)J+_fHFd`Jt6HhSo
zM!SLq=|Lxj=q*>@ekJnq&d>6^jRZ3FpX{8@cg+BegkI%h@nDJAvf(q7MJ`g@Z-J&(
zAIM*iKpiNxQAlk%|N1#)JC2WrU6r6vo+sKryqUBve||zM&N-A|y_#!quB5L9yxyG(
zdMs(%OPg|l2xSKOEMA|69OmE?yRb@S9rqJ#>i=>qSAn=aqk_i=8qOfrw&`V$pT5Uf
zg&)}1OsXnyzZ@@3=Yv30ofYdJofsKi&353G{vWJb=qvir&CM-!0*t~K@;3F7YC7aB
zRus+J$PYRwM2j<?mXTDLxc+SC9*{#gX8aWNdo@PT%^bU$$?duT0W>M7e~z0kO=|k?
zyYFxoQXTU_y)@*8C0Nzkd%uZN*or9V9u+EmlVQ4%)@V%^G3cziF4GrG+wyhp{K&%c
zn9PSzr8PaViXyyG<M8k>(Y&;DhEiMAdT>h!eDL}BVy(2F^Y96=O!@nfDcx!>gVr)x
zWDrIq{qEhnbml{FI2!32*01NLU+H-H9i~L&#?#U34^oyUoyfF0CwQe*lV?r&86SrS
z9lzN;9Cn%z02UsgbUkg6Lyi^7cUl~wffA@AN?sN^Oy=w2;$jn5!-OUr8J3(twJZFf
zI;f^ib%{^x_`YU}t^`v7_u~)~KMx8<wSQSliHMb{b)7aMZ?~C?XmHI!2L|z*w};sv
z-Rf#0W!I9X-+-tlaGBQuLP?XTYJmu~>ws*dAbxfO@uaUjv5|$Pas1IJqO&~AXJ9Gb
zbt**N7^hhS*8b80=Yw1vY)lYRR#MWZX;)~D_Ky&!CyW5WiPNz1TJ|p@9ETDJI7CZ4
zmSO@jY`jP2CvjZ*5z`v+!VZ3#m}5F2P*71<o9ztp%5&}S^YinjQg_mYtwb4K^Z>Gf
z;LeD);m!fOk7vR7J*lJFj5XR#eLP3_*WRA8%5NBVS>!mp9;Bmw=rc)v1R|mtw8wY^
zPqT-IN1pVVbec-O$EWqUnA7nDy(yCh2V<JDL*s(_Xj~Y><gF%hcL|9F)@Sxep^qbZ
zuR#y@_kR>@mi>DBZkuu;S}z*B#H72L(u&cE0Uzm4g-pa68Wgd>QVoiW4Y3V|o%0U0
zqggK`{~UxtJ5BjN<bIZ;{|?kKZ65;kTv9}Yq3jDbC`n2q`JlRxE*kwVmqMeE!nCDO
zU}+=en@=`-qSbN87y#nVPN07j`CvRzOdTzO-dG>6u^;j%e`@)~G+=D*F{^F_lG!AX
zd^Gp=B`Tk+t7KSJrta8ZfN35FXWL)GlT?FbYr=#<d6EY)`m0!V?fz_aT|{a27*2H%
z((TA%kKuj2h?=W08s&k4^t>|icm>Go#gUNU(i~FNLguX!akM1*%x+R~DV$`lJ6%~!
znYDM+NtnJfPuzDJaNYJRxm-0INwGOhZl<s>EOZzGlf~_lX!6}HgzS$D>Pxfx6LWZd
zyxB#SaI1xj{q+n>lU}IoX)8d`1QY`5z@}Hi-#cZ3?HTccZacuKp01P_J)toRN?8eu
z&3>8QnCK0eC^!z5b6F*I=L}tTwKGiWy0VK_*G^9_TPyJZYkW*Blb=W>voW}-HuZu$
z%qeMmIfGgU_>ZboK_W7}`rU>xSVC2?wG<Z_LhPwQujg2kVjwH|@#(TgSdNEQ3QBtx
z5BN({-ahswz8;6PLBD5wBxKy)5fKc41Vtzyt!}Qb?Aw2?gDp86U5mpDqmgL?T|-0W
zqd{n3bf@7`9Y$IB1qEr^@*X%q6lc2r29zAz0KH6}oX0!5V=nPKwymTQblQIG%;OV^
zXnD00m2{^ZW<Er5*qRz9ycjpmORCU@aaXmq%_!v8yF)}ka!Ph~SuiS(&|vi_N)$6m
zR)fr9O3EF%N$48jI!!OFbfeo)Yjhs(Iai+4g3l0X{5e27g1AXs2Y;ZM@_gjP9U#jS
zvK>$lerEmJhT?LvTfZDU)MP(3(37@u%`z}^R5;VKRcLz_Ga*zmbUY|LTrUpKXVlB-
zI3UQFT2f44ZedZ8s%S<A<;sPe8)_N0;-f;pFF9DyD7dV<J~W$cBapI6)7U$$?%hm!
ztC}GgLX*-)0VrG~sfbgc;K`~Ug%c$^M_MO(c9Ieeo~tcC*UdFnV9_Jz+ZN};$%!TA
z?m6@OE)LPlM~Lx9x*EL~E;bDG#|jnfvLflnr?caYWrdWh*MGARbJOHmq(21vY@&xs
zKE^41x|oX}B>F^NA4#kf&`r8d=f5|IvV<>0o?}tRP>6Jo%7q+?&;!*o6`Jn-ZyoeP
zpMW~%qfp8o%H6FgaCHqf0Np%>X5Pj6D{A4yL9MH(RJ;h%%flNj{NSWptlyp_B3cZ}
z6_iuLl8i$K2@#4MiQE+xZ~Jgc#*(ci$`e4ooqP80KZ6m3L?Ll;d?rzIl(LVN(BMG-
z<=VMNjW)skL{lF;_{r1uLfhlS<J0C`{sow}CSr&Vy$-#9y7T&+yubGsaynXQ(ey^0
zhRmBhS~;Q9pD!M%b1f+;xw8|OUppypBIrerMV**-c-<^H=!Z--v<!ABgk7Rd5y?b-
z@;R1b?(d`0x@l(EKK3p^(kfUzjk;5qM~9RIse%U8u7H`=q-z%1jatLr8bJj_%7JB0
z_W^&tL3qrQmmgO*7gSaVC(JZNqwHr*TF18h`3EMGggNO}V*jLcYwnReHx2!ct1)yW
z4x{f{^h5Jd^n3sWODc!zfTBw%Ujeh)BPUn5=Hms%+D-P{TMUM_yvru$(uDHv`L!9+
zzpWASuf*ripR-FTn9`2E*lRMj%~IztV}dqhT^OWc$qSuFp2K+REE_T<DKd_oMcx}{
zl0V}F@N6bffLx%1)N}@gLjXp(B?%=LKx0AYGLTX{(0JleqLg{kfY1q3Y4fBwp#m|h
zCD!s4$Iy2+XfxoZ>FME-yz=EnR#qNK$vn*Qlb263l9iDWeMk@>TJfs(0t&0j$;maO
zAVW&Q45f`yyRok}!pPQYb^<5ezJ1%a{EUnC$?nu8^Lfa0etKPtnvRV>1e7XVqcygs
z5|$lT3;WR@bS)Bg|Mss`l2;k|n7pWFg#m8qMrs*Fsh=McccL<4{>|aF4fC5vVKi&N
z8>ZR<rL*v9D*vfxN|Tina~O**r-Z5HPc*u;>Z(!l&o2@qM!4C(pK=spG{EP=6y(5^
z``Nl?uYm2YvqA5?)A8t}OxCTiGnVkyaMs5`!Pe&MPV1S`YO`CQ%4GPz(a}+xk@v5O
zONNIyWP0Jk1^;>b*LQw~YD;B-gpnO=gnxTu*k6~x;>?~WoB=MpI_l=%fe{&_;Ax9d
ziPWNiAy+7VS2=%138w?lfC%y5CCdSTffUDF6g!b{&(oj))Xp^*s?OH8Z{PB{6t|-V
zFpj=*BTXYbQ=CD^03WGI`ieJi=wpV7WL8|*`VlLtLUBURxbl8HqdL&aNi}WDYb1vA
z0Zk`Q(s*AlTSl=y2^`qk+Db+adiQW+qyYf_K#(VGI}?-o<?hDb_$NfCF@6$QlDCl&
zuP^o_`|rdMI^|y2kx_ftk*j1f0AjQbbx?2AM8$m93kuu#3c}ErP7ksH+e$?j=S!eQ
z3cL4ZbYHFu(hK<Py1Pe39QkY3;GVHZAVJMSlvef7kAwPyZe63PsVV2V`vkH+N0-J?
z@Yx>iZ+?>IGB+%H?fUh)scgU*coB68sW@&@4`1HjAf^^dTM5LLI#uY5`yMZ4RvJh)
z%=rzKqGs>t7(iVM^3R~yr$Mq+qu<#^YJ&xX%wI`n))<9De}z*mohl|Fk^Ae`y1APq
z{-{uH2&jzRa%*8Un@bKc&Bhr>#kHZ2%)EaEHq*B9UA$1i!-q$3O>E1%7`ciDy8_+Y
z;;l=2Vu=7qD?I?7=f&Ouwpn0AjdT(S!ce-`0<Sq+19>vyA=WB{3z|?Pf&8Y}&>=t6
z7%&Qh%40J43G5H@5gGBO`vSRhnWxf5dXO15xf0qn+vxCP?0!OZH%<ZOlL-|j^SQ%C
zTUcmF)M{LlLe3+tcLO_nJRw0k?&tL<pW8&}<U&EM{^Yd3mVjJYAl1pZERufSzyIyv
zaMufsuC3vGDcu3Zi~yx1U&0nJ2I{4`Qy=PFR#-rwt&!f}N1`!QOdiEZb=&e$G8Ys^
zFunyT3!C0I&jz3L^({ept;;mWUt6|;CfY{s+y7p`fXqfHw&?@3dMYL+CJia~?{9)m
zQX3*C=D<!&$)Oqr==mlDO1tJWxCu}!Dv*lRE@1S_qe6Z`j4mRlIX0wt?`o7l6nCiq
z6`&W9-0Zm**H!xA=A>9?VASH?M5V4(rDO{yBE1eGk(Y9iQg;G8NM>VO;Ai^nDy;1q
zgq$7qpmM<sDJez3UX*myg9_lLmj*3L6D(&qIy|h1qsQw*Dr#!8i$=Oi`5IkYJ-RAO
zl6=8PXYf3vx-PmfQq3r$PvA+)tu|Q%HIJ(q7axjH<J<39ltq1&XN3w93HVoQpPyOG
z8vRUWaB^}I3|*K4fTlWKWH~+3H3NE~v=JE<67uW#kuHm14q}MOMz`b(#W+nk6-ubw
z6u%TkGCjce@$;a9cpz--zW@90U*`d@G9oe~s}nfWP*=Z7%|KIRhG5bhr-;S3ktFxP
zgIu)oB5=xWMjs-9RjXI?QWk;xRTh44M(4CpvD%J$uoLG}`YHAmb0?lM0Ci+D22`@p
zi8dEjM8zjq$~oHywcB-MiXz5&sT%9m%~>&G{mUqb)l6@*@8LjxvR=p*n1w}QB7VT3
zM+d1Eqy{f&%p&0sB<X1DZzZufH_FmK4mTc+eqo4nTu-43W>WNn2T!~2BML4LmoA9c
zY4}VLiEJ67Ra#Cu{>mZ9^z;{u2QdET+iy4DDRCk3d))k)GiSWP5onFMqLD<1x*+<q
zk!;z~0+)H-x*{a=4FKM4_O)SR9$z=9g1;Ras$;B|)^LgOc=JujI)SxT5v>IFSOaJG
zi2`nr=H)GmSS0hw7`Nat+^TxK8umZcae$~|1%;)gT8ZmzT>MP@nn1XLBk)(EWF<);
z5Pm#`r3LDuqe!RL3c}OGB`1P)zbg!6Z3wiSRe@;gY|3)EeFP<0<sDr80*ZfWFQj8V
zEfJm`JbwImybmV3EErT3AeUQ&FPbp12++qnLpV#=*_e?qq{h5Sq9TPL`g6j;JoNC;
zP}4Db6D?v9VFa!eF{y5OCZmB)_WvM$|0FYGRP8#Qk*-(gJw(1T`o5~XD<>LGOSI~s
zZCs1iDr+|Hd-z;z#@BW3g?hHNG1V2>(YTNy)Ew{q`s=TGyZfsm-Ba`nS9!YN>j;`<
zy}Q1O%BP<EbkH#v&DRRmv9O&(u8TP`p>Qw%$ZoXe<tS=vU!qAc5}hHBgtuNbJb!)@
z>~zxJ1<WAMn7Vcu&wow8RBj&>OhP$5m;cI&p2NAi4tb5bFFD|snzO1R^i);v4HNV;
zRY-xc_QIfo`j9`C$abX06Ovg-YN-ekE^eoM=f0Xfoj8Yke?5Gd$4di+a^?0YmGeTP
zOw-F4sZug|Nu%uU?c3)_`gWtR&l8{<dVUlH@~Y88kAuzjeM)KIHZP?#a5PsivnMa5
zG+aqN;Bi;l0un6h@j@6dnl|HTuca5gZ#4*#0LR}u16N1hbRPWb0bAL{w8Wf2F#-%3
zl6_nu9{9^XNS=YMQ$>cLrF$eFVDjd{FQ?E|ooP}ZGBw)-9801?R+*TE-9jK%zsk1&
zDKKRqC#01j0?>RnhwNt(L1lvuAjhnS3t1@urYyzIPEJ2=-6{Yk3DpjVNEqI;WBc|o
z_<<O#lx<}fBU0ADEb|yZxO-F#gMNg+Xpme0TxS@7cf(CU=@@FOhGq9*7mYwu1J6++
zj>(_t<H^yB{2ND&07dtqDkR<lQ|G#gSO7^%Vo{-F&<MflpaHmAKl_$dS#$-(rJUgx
z#a!mHh`)hjQx#$T@yCZ4r8_-4%Y$LnSmWrA)9E`UM_%o4z!t`az$j`E&I0F>r|d1H
z#)Nai;&JoyXNZK~y7d6?yQHnHZE%*fH5exWR>%F<=arxvgrT*8FUM4uk`4mmkgZB0
zOwZs_7MzAe6Ni*jmMSd^87d*e3ie=UKn3Z+@0h!vpOtW1&W)VMru`RBTr=m>nEh_;
z+N9**JrEn+&Z9(0O4n)-#WzD5>r6+Etfntg+!VzEd-<QdU!T(h28WsguxC=)$p9gn
z*@stFVHDFAFgjF6Q+9FNctx<$bo-FMC}(?60Yr(ALra8^QnfxXojZP-crltN>T4b;
z982gc1`f09@>h!;-#iZM=#6l{L9#gc+zXR*D<jyUVNjV(k_v|oxe2;1%q{)y_T0XB
z@j4f?f_dAV?vABgw>#6*XQX}$ypA)DPG3V>RILl?eu-5?dYM1R))vj)Y&wx#tHQTA
z04@NCO^bd76q9jRi9cz!K}jD~hkqj8jPO(h*)$9h1t4@P^v2DJ`xxX@pC_py2CY~v
zIoTZP=Ef8^PpWCjpaAqL3UqRI{%7ugH=&+L*-KTVY?D(G8!B92`P2Z4&yb|6(26A`
z02oI`wT61~A!N&MySvkmokEfxwMLwT{Dsc(2vsU_5atWrqZ6aT9F8O1meQC6Xc7d6
z=K_r<ykCR!{3v*{u{tgD4;X1whl7<c0nAVZ;X^D!AfuXS#o930%dI4iS+!(d1vel4
zxpLLlOe-3ZEU8P<O9EAn<Gu$1vQY6Wbq{{|<9$z0Pah_=J`E3n6bHRD>QJRe#x9|D
zP66x+8^rRcsACtSVWp_xZt$y4z(kV??3rdQ48EY?VbUi)dHfe}OmO;<@S%uwz!}Q|
zclEUu6qF0xs=fL*WP}F13L+drx|5nRG93)kKnP(Huz_Odnd_V|YP26AHkvL+GP8?3
zfQ|?h>uwC`dH3#JA51o+vq~4-S~x*R{|7k^a@^hr-BNZ538|1A4zls+*7^XsM5=k4
zF1e$aPRTCpOTaIKBc<PxiQoF4v~39!de8ovRrFc)b-)36fzSW)`Twa$MZPGCfLM(3
z|KN>NuJjg13%{;u>2~o$hrTvixl;M5Tg300XFqBmFVOrUq3)h$%8%>bC|bsI-Bfy{
zzVYMf&EMCU$eM<l@Ok{?ae$q2Tt?=pzRK>BUk-iqt)IoMBX@4iE%f?wsIIS1{_0X*
zVqj>)Y|E^3n4{jLz|=<w6+gWmp8V(YxTSyhPl@0y;eQF<s&|DjfBrcr-x~k*)W?GV
zB_uT7@K62RPo@at{(k*;{iRdkaF%heK*Z;QU$M%PWz;#lEov-1-ACyMu8RwDzs#Xe
z+t$I`j0c_y`j<fP|7Imr`z^pgk=NoH6i{!1g1{RSkYo^99x!VbIR&gI0wq=?G94>&
z2hpizJpzrJ_%3A7Wd!aPy0k<9*I71O78ioLW&`_`RAhc92>)F@-^rd=BXuXN$5xm&
zPsnSIKtz^q{B~CbAW1aXUh0rza@eR@S6A0C)GaR|%Vk9<eG{oNB5^uEspCaW9BbQL
z_rbMx;m8l$!CmSjYDs}+V>b_vCt3xFof3I>HN8h*&z>8wp$Az6blFLFIEb-!fOFY^
z@X4dOxmg5~txkeW+s7IAFZPe6qjls6G;jI<H|y5j4i4S|Glq6w$l(_vq*w=H`UV~(
zZ;%2Lxq9=yoo3k|9=|FpJ7Hh&-G*H+QN^kcf@*dk4&*B`@=^|Cz3afu8bA-RQ)>2Y
z_`;8fa8<5NBJY}AEi^gj;)p_ZN63jpF)z6A{(i$#KO*~+eH7li1Ff8HAW+(mRZ6cM
zN{&Lqv@5glbe&M4_#%~DT;#olhfV<nsf`SX;Mu2cy=YT>oFbr`D*_0~0#d}6B=nz$
zL9f<}e<LA66^L2Co&_+<`Y%1!2?Am{FHCxP7&T{EI!)%Zc_SSn#-G*xyB`W=@QUW&
zjIHQ$cl>mhMK(>W{#EwK92h=dG-$q3ke74#E!bEu<jjsLDpt8nW+NADfEs(tm+NE)
z(#(MshVj$~5DSFNuud;ttPJBol1IK~c2(4n{DpU8c!px=KvW-5k=tQ>rSq?D`OU1Y
zwQ5bs&6Oa}0oUmIBP(l^b!FJ(pggKIR<n!j1>K&qYT5WkV<1TAAo%(7r#j{*D#9ei
z9VP}gqtf-HF}(#!1a^eSCjNtrEskbB^~qkit%6oV2D4c8k$J^72L^7&0rSB!)g!pj
zeK&|{k((s!Eao8lY3a$PX<MDV_G}Ev=p)dLM}iSafj`At4K4vyH|8sF<Cl9r6x*?5
z2Q&`rz@m7?$H&)WW8<N3Kq#|8lALGgG4~;9(y3N3qvAg)p<XGvJpwh(@KhXF!sh-o
zoNQTycaad8fM54fN6FcxhSZ<Co}k-ZCrAkI!2O1U<2YI65%>q*<vj%s{>~}LVVxc4
zKDtX2`KAo&LwIytK?_>G{yE?!qN}@!G|Ym8u4<z!hmG<;Bb6t^tc4A}>;^;xeugW^
zK8vV_d@{RF7vq;nib{%$i(@|`zz&foLdMmzls|`;IPhLQTw<~VK;P?WCN<IPhA{Cy
z<Z{WLje{u`@1BbwwAf&S$g%^ygnc1fIbvous5`$!_>u?Wj3Vf73{d7VjK}#oG-LtO
z-;1FD*uGsKI#fY?r<aD1KwUZjk;HS+zPf}hfEKO&4v=a@;tEy)OH8#+=Ch(ZoClWp
zVHhzg$!C8-V7m*<`;(XBKmtX;<Q-o7E21qbTbK*2T1}zK6U0S)NCDpPLd`J`IpEP~
z^;K<%DbgO;Y@(H2|2->zbI<D!_+6fcVfMQeiM9%WeG|E{>Cj2UOhPi`gHf3~uf0dz
z-w(v@5h65e0b~w45TSgKBkq(X_zGDf@pX_teq0}=;eyUHV|GAj9hlQ>jWK$7S*R}o
zgpbnwhhBWhfe>vEYEst$P+MP{BwLgZF=#O!YdsAr*t$*NRIbe<a_|BC*wqJI#|>^a
z)-S&lcZY=#!4=i$c4>!H*@kg=j|f=oJqdYDz+v3ZQXB0-CPe5JxZIclOX3UJ$Ixil
z;o*=S+b>pxYzBlmS51XbMigB6Mj?}$`W~-!>wr@)TjMF7S7Mg&R-^%r*8pHSm4%4z
z_km8vIT7Ie6}cl33(wZN`~bVy5N8fHpb8ggHy@w3hX>pN(Hlm<(#?j7Z|{JD){`LI
z8b-UQ)1ecKQAuPim_7<wzrQV81jEF==7j8APx?oJ6%Rp+=^8I0uw6d{|FISK?WBDe
zz98M|Uf*f|vkpg&;Lt~cJG<e49(XC}JkFh9xi`$}$V>d5U}Y%s#EZN(-&I1tS?)ED
z0|Eky0mJV`r=_MUt$Hkbq!|nbIZa0-vGfnyUYfz?AOGg<j)l>+Yn9eKmc9C^JkFwi
zX`c>#n^>?g^QyeO#as`bD-z%5h6Cc{VGvkq^;*ixes+4ekA8nt>s@fX{uWg$21vH3
zWG|kT=mC}X5=U~q4az_|5P~OEYx@U6z(w+HH)arN$nCkG-vmNw!w|%B2B7#I#|Ngx
zZ+O73NVv%{Oc!(B;)3)z>M~F-^G7?E^t4<_9zoB!0`Pfwc$NAqWrD>!ThX|J4+T^X
zPe+|Uomm$o^kS_SpV?KwY#fh8FtMefp+O}mZXY?Yu}FiJaJOJ7X9-Ok5e9{ruB79_
zsqNdtTo<NyqZYgxxi!4pKf6aKIp{P|b+)Z*0S0krElh1f@({vxh1jI5j+=H<@7xJT
zr4lxL7Ue^p2(~sIFgk7BU&n_K8B#@Yp!3am$tB%ACAQ~7EAs9Kx}2HotQIb}lOpZo
z_P+BCR&>NlGFa%i+@tug4MSl6vuQn`b}gTO$F;L>dbFqb8msOm6csu6Pci_Kh~E-Y
zY?lWa8v%H-XOb4DCJ=+7-Pq(Lko%UX@8;@*N0dPGmkemfjje}TC5cwg4&-<zio8UU
zpT#|c#4-YLT@}K?sd96d4df;`;i<?O1Hxz;npgR*acg`}?&YqMhcjbu6%@!f-hUz=
zCL8|0NcXq-`8-Gy@2eZYTWlPJpPbxT3sFTC>>m`JHrg0NC}`H4rJi>2B-L4ftgc(%
zPfMs*?=l?cLmO9Qe6G><@853#$l@3(tgEffj^pY>@BqS<_1HtpR{Nep@PG!e&c@|R
z8xarQfGXxCu0OM#H|*x)W&)+cejD=g@*@6Sb9NvD-;vw7Kv~&##t}6<1}LdU^6a)X
zY0NPUFhmM_B4InK@F7_!3fnF%T~OV_IDr<?OIP9PHfsXyaJ4(o`o|MWFSYtKphN;*
zh+ptQ`XmeTH3OWr6*h?&vMa`E*Z$i>3f9jX6X}a_(mQW(g?RuZ%AgNT8GQDz1B5AS
z`?Oc+R~q{J`@;$;SL(SdcPURP(|Jxy;keyDXs%0Oj(^zR0_?3F_A(OHUR5}`_rb$b
zD;WUgR;;Ew8YPm~RQBtVlA9=Dcu8;@^0=A^Pm5s?^%v%*6>(u}w6LbW5+lb@JHSKK
z(xjOtwek%9Yr#~Zz@0>*p%_NyPX-rz!hQ%kV?Fxl!#IesKw<o_ZXE(?MPO3Cuf~Zh
z5<~SJOl9~)_Uz?44MXx|PIJS>g79f%WpK}UhEW7KP@7{n+yVddeFv1=K1dI6)*uy*
z7x(t1Bj7IQ%_bB9fngOGvMea7BY_fHA%RAf#ZfN~abak8Z}$?dK_mhJLAXkja07_8
zQ;<(M(t;$?6ejoHug(iTXr4G>U+oVu*UBhj0m)c#zCRZLbE2`we)sMjJ#s`*QIXtD
zy3LtkZ*!*|sH$Nl4$#8`uwB;0#U&TWhB#}2|BuY|%rdhb*j*vo6GCEuwXZKRzIl#H
z<zn%A#vhMq!8(LkRfrNbIpHOV!8<87?1)@r3P}pJO@g637HbYl)o(~A^}L*o@7;_I
zr$n~JxIp6_ccBG`6=wEi4T#kgm-!hJjht8?(eC>MzoMD9AgT&D4MQxCcBBD+z%ACC
z3uO3{DTk$4&@Jf!J^~-VB?3|@Ihr1Jan<bP>J-^It_LBpk_sn%Jw!e{ysMOd5R&_@
z*27q2emNVPFkx3*sGn=~t__6)_T!?Q%vNm}Vg&5%XV)(Q$E0KcDy~{Ii8sDRE{%LF
zu-Tc`&eC5d5RCW@HYJ}sd?M(;9#}Mgj5669voIRI$PW3j=x)m5;FW^7dp2)L(FCzR
z5s3p=7^cf&?@~bgRINVXa1rohe6j$JQ;aUvM5V-$1U)gQ#%t(c@%BJD9XKN8ejI|J
zj;6K++lW?>gL4Ch(sB2wjtDkz8sus)iPyIc4K`|1ZN(q}G=L$!k)7HGf&%2svz?#M
z*(-FnTemn>EUh#suzR<M^xV6(a<o(8^z>DZ3Zh+p4;_Hx_Y93Dm^<^txO{xJV$yMh
z)CUv%wr9vPx&<AOi1mnuW_k-cl=SOHy;SRls3`HH1Hva_T;ynGPrB}r%Nlo}jhqI^
zpoqJok2fI2+WFfGP7jpj`q3PryNN_<OHpk?oMCN5?tixCKfEq2^@I&QNFq;i8w?O&
zDXObysp;YDp18kvTZLGEJh7!64&cZ?0Y5tV0dS`q#*ygcfRxZ!0?J(i#HO%l_v}bD
zr`+TM)H8bTySsPw!PXx|sA2%UkleXJoGd%=e4TJ=zW9X#+qVOmUpc$B^CtittvP6L
z6taZ@*F@;)Z}kcKz+uT7)JV~nAvp9D>Un$dzDJbFzFHoX%iM|v5M(?#nD`>S#TnWv
z8b5+lXgI_n?&Uz|hZ_Vep~=c-h|?U$;Tr@oU3xv{(|QotjTk9lOuU{n2J8U!?gb0<
zvrYjXfhUyF{ipqGj7=5;#xuDk1>-yyfg06ET&M`2PoyS@K92y)5x>l^K^rwIU>kuV
zW2=RN4;ZgUzVd=nCHjDT0w3Mo-9^X)Zm_{!_F@;^heG9%msH?y8NjMYSQ$oakB8}i
zg47Gi4K9ZuVN*6_Pwbp}#M?w~6<fR^t$b3T<Fo@B-asRL&!$jU&5nikLGb<R_3Ime
zFE4?2f5hdGv!}~)+V4aa$DKN8Y<&S5h=tOpql~s53buF%WpR$<4!DA#&&2?`VbsAL
zIdTNks>tO?O-n;fhe%zb&K7V5js5w$c@8qr$Ht~JW?&w&fnJdueYY1zi1$!Q6m(pn
z$j8G72Z@{?Mtn^rI{}B{4c(xcy-%2=V-b!wD>4Kp#rX0N2a&|mU@bYs_k~S0Q9)Z!
z;>f{`;RiP%UgAF`G5m6BJjpfm$0-4RejhO2TR%2J_9y5vH${#~2cnZ3&|AGU1mBqg
z<e>iKa+eg1xMk84xl0m)^wc({c`8kpRB)-6U{W|<%28~8`!f>gkb)_KOc1@ay38Wm
zw*k^=Uqr~##3;{C95j0K69M%Q-FuKw9<_0skPAVC8(8f`5CVc7bPPEBND>Qv9^&QQ
zt$0ikT#hrElNKefUU?v4TMQb3xEACV^C}l$t%@+2V8iz;yvK~^pZ&3-7~r4-wRN5b
zzLK`6!V41e#Q33?!0-BS9aUg+@*HsNNlb8qlK5m7OkG-=P(P>_>6t2DeaNs#wcL>e
z&5B)&hh+Bk8j<vsX54!w<{MCs6yggQ09~gbb|7K&kvXoj*sRfB7!5>XGs)$d@Fn-9
zU0skS6GTi+Lgp$QNCq>7Q|E4)NP-t0R4)90EW8)+oD7Z*se2AOXwDfGa`||=X_GKQ
zTxPp@B%NLsU}7LTM4bWqf-gqxh>rBRiN_NrWE07fj|34GiWD`G3-l782fDevgzCa|
zsKV_fi+X&qp79XvXTRVjg_u9FDP&9Wnw$LM@Bc^emY<&O{|AQl|NcqmrQdLcZ9lum
SIDmYXoXn{siNBux{eJ;Qx#O|`

literal 190612
zcmd?RXIPVIyEYn^OMOQL2OA<yQ9-Fg@5NUT1pyHRq$-GjG$Xw;`YN5Mj3PA*2#7R6
zsx(I-Q93HUjfoJX8d?H`@LhLsMAzEivH$FUo8xdCOrGa%S3R%ux+Bi(YqM=XxE+JR
zu$?`lc>#mjk%+-;sruJe_?OB35)SZ}qNkRbr;+<LPoFC{?J;^+Ja4$Td%8H;9`d%o
z>EYz=CM~WY{+s9_M^Dci9!e4tuHU~P?tarjLbj+h7hdwqjWd@#Fc|S8=+7qKOm%O}
zCJg4R=1F7U<jFz5$DQ_r>R;Ou_T6<g)BLp;dy-?{KBGee@w0|ajhQk=A7-5l@<pY~
z8=Ivjillt<Ma34fGmi-iJ`L3sh<x>2qIl=Tt5+@*2J<%|9M9FQ*IE-s7G4%qCa=`i
zH3i&#6S!r_!$9DnPUe5XO#b;{9+BvVDSZENytKFFhu_a+II@5L45M*S3ugcQ=l$`-
z&EJ2)+&kUDv+-M~OyuasZ<r^BGTSzOJrt9!xbYjt>Hm*I8r_>%-vVWkhdNqP3DH-j
z5+;sg+9zjcX9XlABz8w=7G$7rwBJ3kzRmgi!Ve=G8yn54yj}Aw%Eg~Odv=re7ff+M
zMTLTnu5LyEZQAg{g>+wZU>b2cPcGyb7o1{^A!Dqir6p?i{Lr)9To2wY81v}#^z;KV
zj^{nUyg$}$h`!@^Wc?6AO)}Ec1z;3`gma@x+c6~yetv$EE<L~5vraxy)4E{&%w_)b
zKoJx8{Hhd}y}iAmq2Ya)Jp7bw@?Jk_^J-(^ebU#O!^g+ROO{}~v7wrPKOQ|gedWrP
z&m+ASn?uVa63(60)6+w1=_;^FqAn@e)`y=tx?54_U+^Xki}U|%QNPipOO+QR3Q9_3
z?(XDD*W9L2Wo&85lcMYs8@#&QQ0+HoJNVLT!MU@uv&e65^!SMr@2oV?Y|K^rSJ2-W
zv7x?F=MMzf2AxsNuN)DY0`Q{U@T=3Db64IwLjm-qzU}Mcoz@UABc9K}F<9keDzz<i
z4W`sl6F@7eq^T<--AF6?wpaVb;OH)1yx3>y<mBY`8l$mef$2bgBxXSxiMNS;C}MhA
zDxq1AwJ+wSw=CB+fPN^U5qsy(9mO;Mg%Rss!N<U4Mw=7%ytvj98Oy7DdX2eiNB{b%
zBgZJ4=h%Ov;Yy1#SnATAZZDij8ClBJ!}u1SYa=X7bZc8#S-A{VD_(0$<=rEAE<A=?
ze)JG8T1@`^keGEv4wZB4Ew-2R7;QFte)**F#fuGDdP$_5?9>Z#Xhvx#|53j*w(<5q
zPl4P#efl(fX^zqz--7oWO^8;PMAN_w>HeeOeQAeJ-K|dznC9YD@(fQ*Onj^!68y2V
z6ZTUp&$3dgn~J{g`04wLV}!-UWB<CnO&bK|<HwKn%L@}n1Z=6K21R{jVl{NL|1mSJ
zW+zXcoFD5rA|R0On0ijogKGzTA#}g~_pvM7248Av*25ao3kt-`TzW6qD4}(Pp4%8s
zL|8cT)~#D-GA|e!8SUZVfHP9m-@+Ox>z|RPG=hVJH8qdMy$cV2T2Rnx&$3?T3g1lJ
zA))=EX1_*grZcAoJla~kp2$swABjD)68M2xy)u!ilX){4?Mi5u#y>NI-!R|JUdf5u
zTiJB_OBFv@z%ZUOL#kWZ4(=uL`d9{1l!qfI_z<=3jz2pC)@JG=_6W)Kmk!mP3h=0A
zJ1BE}Ds^V~PKbp&Y-UlDcol1(vs1jy8DrY&ks#hvUnwjLe)Ht|$f4ds3Finey(Vlw
zy}m>J9A`klg)qnmq_3TuEFTV*bb9~9lGqgK(WWY&f1v8}U$-o^8*^y!kFGetNoaz0
zHz!C7F_*jR2q3h4Z|<^1tu5i#A{wGNDN1~3O)}x%tTlre%_VKipi{9;V&$Kewzm^N
zR(vOmh@iF{lJ<>%SQ3%Mg-!{-^+FGV2<WS?elf6HTcsH!<>ux_l{$A5CJO8LcB#=X
zH>avr1&hJV<LekB2b4S=c|sV}-q`+vov;VUcG<P-4EmB|ioB=#w(<tRFj+A(RWr3c
zUfovZ0oKG=-tvIvI0LoHPD?K@uRQZo;Vk`>gS6q0ki1JTvVvV<CD`|8g&O9+e8_Mg
zu4Oy33(ctV&-L@yNb+t!xvhaio98}uDq6ZV+_|tu&aIkCsF*Bn8rN-mcU7u(sl}a8
zMC9Z4^9xouUZS~;*R!1+uY9ffF*2QTpA|+Q+m%%=_O=Df71S}8EB?N<gJcl0Y_u8(
zn`-INA{*?_Iw!HjI$oCQr&;M9-Hm;BEWZ9&yyXMAH3GML{al+mKEqhUqNCD-QZZAz
zLVEs&T{o$2Wx6Wk<x3^)95m<4E8ishkbrm{WA?bPuuf9Icuw}sKejsRZr{G06e&`0
zCMh>BFPhWZ_po-v`ZL>avg`mD!CUX&zgM9DefxvAq=bk_|DVoc<|W6i{l8?I71?U#
zSyjn_?CcL(o{x~UBWU8)g1m((vm;I9ho;sMTXyWPzstrA&fxUfvuEe02bGcCJaRfZ
zyfsC6=A&w2qpn2FUhn|a#eR1pSU%goU;U2ly5u0c6a>!3uJMl(MK?aZHY>e;(b&Y~
zbiB9~`hp|~3_9_+;TL~`dl*;WC1P3-4gRdhKHkdq>)(G#!X_UV7a#f((2Q4C&pMZ|
zTQ^=j1`N&Ehin6$z^{LyHwe=I<tVJpgVp>@p&&cBYI*6>rD%S2wXpSF>;JZEf2(^B
z2hmEfa4NUk@(T)LZPu2%u@m{_ge3Rc1nOf=0S-9Y^Oo3#ec0J|Lh{1>n|G=Fs=l_=
zR$jIFCTMRokD`>m>cU6*`}o?us{Zp&ZK@^;sz@IA6-&b0)?IwnEzayb3crE9xF<(r
zIuH6$Qn1sN6vAjr5;<Fa)oB3!K{ABNDDi-eeq<tJ17^4{NPVTZjbP<7xbQpwa6rx1
z*E^^g{Hw=P{l{~j=H(5HV!?hy8U&K|Q13_t9?th<-sWc<DtI~mEShq3cZ938JLsV#
zmQ=s1xeRo=Vl>g2^eK!#R@S-es!G<ACr8zoR2;F>)MoW}{F<xZr#qaa2fE^CS4P8W
zeLllM<ni3XSY#$*YUT6_?TE1*FC5y;DsS4aTH4hTkN|0vjf^Ov{{f%AH^*4mm2uLZ
zOiPrq*O{!L4N*%AgItr2&G{6rz1Nj!IFX&aTL>R`)1|M(sj9o+*|SLWZfcG}(DkZg
zbKyST?ZFG3rsPJE!e~=#zs!?MNsX`X>>&SrdspmC$l8j<!{($5VD4bG`0zKo)K&O`
z<~w$YCTLe1Ei=Cugd7BO8T`WDzYr<uVxMzi1vyr^?2;5N9JVSNbVXuuC}5^yb+Mn@
z3zu71t3-(9t!jDTr&-VKUd@4QXW)p4P49TAp4;SD_W`t~w_B9gqpDXSqL+$~x>m!9
zy3vxg`GaJ@X3h6h`+4*Qg16eqr=oz=gImreytugdX@0(*^32nW*hmL_kerg|*z=YN
zn;U;^p|+pe<tjT|<ufzZU1+nHlT-VfD>TVBFCDCIu}9Wu084C`?0r$*Xm;`9z8#$6
zE)Q1F*!l{e{sVSSzRY~$eWvdGM7P9C@2P_m-G#V2J2(lTQ;DBz*$hohIngc*1kna9
z@)st1rNM`C!XPZggXbo?LEp%?c8G_wam&?r*D)8ShA3g`j4PIYBM&WFXy8(}vhy4O
zPl!Zux%;z3$WqhTU=>Y2@T$xm2>9}AscDC8?9k>MGW~9FATZ;WsAJsh8#uX*cm8(@
zSV(R-E@BKf=kRh@IcY2dr!R8P)Y!Owl{U<8YKhg#Gb<7h5J0vV=@}Vj;zgJ+NRktq
z2IIYq1@~?VrU%=h#o6%c_s!)>!3~l7sNiB7GH_}Kcd2}RWimnJ4+&IR`W(l>fqla!
zUyoFAxfG7sJt?yrLZsmK?85>>C)Dd!uqnaIU$9Ff@x-_Hk4$tsU%zhq`ai$)*R*cS
z0X_equk4!pU<o3fgTstbc`}&RC<cSEywum%N2k@T@$OP*+!zR$sj;w{N%m-sD6%7-
z)~uJWTWWc%7UaL$SB1|$_tIx370U!|(HBYJ+`pgDz-hw+R`$x|i#9bUA{aq=w~9}9
zD;ByeCVVPu!FP~A5@JAx1CwaM%q0prV0gv4Z{Vq61_{i4G?h&3p1Egr=@J)mRF>Yo
z_C@{+;}zr2a{oy5a3R%4z&XdumDdzXqI0;XyPia8B$-&mN0)ayxNDOapShQ_8{Wgm
zPaIGlLFa31YTAvPo0}t(`Pb-jxWfGW80l7pBl%Z@J1?3x>@&4Av{?))6t{X=Pl1i3
zztvXTt<C3m6?uIDf#A8M;rctA)(5un`TlJ}sjd?ZMuw51Myqta)(4)Jya!L$Wa%Y-
zb<*MfIl&KID~nUyK`V<f;b%miYimMCAPAvNNsF_48|ZqH+shNR%bkU8rPgL<2ZU06
zN$fW20wkpQ5YTqvq9P+n1y$3L(rqfzWa^eJTd-gM+D80q8$S;i2K#C{DavW$)ZLvC
z5MXN7ca2c(VimItds9Q0OBJB_6T}+-g-8$&6?QfH-_gWfyh<{xCEVBM<8nyxGJ$9g
zR5V5+m>NX|J7KSaLL*q8naw#|IBV_b%=hl!pT%o&;lgf8ijOr9*c#+-%wBpr^@)%7
z&dxSTWKVq_KF~W5Q(M{D)s<DFCLnNk7e61ky|?R`LXX<FOhGRoVD-@fTAag8-i(%j
z>Ea_9A-D&Y#Gb=oF@qOh;2UD(haStVArtQ1?^^DfN&}lmfIS;0a$0#B2Cl6P4^OWm
zRR(ReA%$*ce=2cn7_5QCzPcj<5Y=0)1yfp6$RM7*(+k6$V5PV6_Zzn=kfWoI&1F5m
z){;mckqAkUqvhJHUI&9r6A=}S#*zuL+9lz>4)Sw8A*yq;vn1G`-fQm|HORL4g1(UN
za=V3xhkyByP5jR<d!+Uc;sa;jl?)D`vjxdqGl4f9RJd_@AY^T|Ww|Kip00T1Q6ZY^
zSvj|X1DuvOPF91%a+))>3G#~Fje<UnH{Za3_AL88!%B$EyYekK3+tHvDiC={LXuUo
z@P=JEua;gVCO_zDw7PQa*fHesp8k~pp+lX*$R9?eKg0MU!^1&3k3vu&psubyRY9A6
zlo}t;>E|aF&`ozPsLbju2!f47Da<&LOo)kU3t`T+c-IjgD|%dGfC@b2$0M6S{}d)q
zXSfGVSWxHiYt>AzWe78m*>x~tm8Z{ehs*>FM}bPX(3ZjX#TXf=%JK!%KW%kL|0H<s
z@pD#0`A2*_@nen=nYl89>&!LH&JS70wTVF<xi=E!rx<0;Z}GQWzNE4+=ia^bTAWsU
z<p*w@@|^6!CK2<kYm~qYnyagQc@mcn*?CN(7r1~<&K7yt#KPL;aTDdsbw1h)$Y3Mm
zhdgM;bevci&&@MCx#jYD3)WzD(yok$g@Kv9)178Gb*6709ZZkg$0gN(#pv7YO2<)f
z#KF;~O0y)81@)87)`B6yPR0_r*lLNDl+;|up>wgSi#;GUkisGzB9o#d+z6Uz1?@8C
zi(NwK|1kHCRZSM_o=cQD5e&-=y8KQ>!1n!<v-fo}$B8o%;qvvNn>N2ENISF+j6bGv
znxU|s2(o=(qATBdel@XwtRo|7s;{)PlnSr!!XcUa&mMipvK85`m8eh3EB?M<SKeFO
zaBzU*i-y#=3#T2+!v)cl<JScuj(>RUoZhDdhtvpT;nGv?o42typB){&iEM*~drjt5
z7N(K=Eq2Fn6kYFprog0=?U1!sU0lul3W<^^KqeVOYRetCEGxT~L|KbLN*tbX6<O*9
zG9<<Xh`M)IO}}*J@fpBUK~xk3^%D15KnD32?&TM7^cB4YHGxaBJta;?tG<Obi;>8@
z_pctDSS<_#@Iroh;p?l-yFTJ=z!yV2aHeXMu{8IA0A|6_m;f>M*l=CQ5p}6Vh=^+T
zA{#3TarQdA@;%9R{2(>;)Y8z91Kz)$(ZQR$`KtiOPyml-2LTknZwttqyk9Hi_wKT$
zU`BCj%Z`(!-PTTXfN-!fJfGURlmqf^mH?;_knKY%1&%lU-3wXGZopEe`er0P)zP~1
zj=d#Jd|IvQp+Vr$(LHeAqo<Y@5|jhE7(|NokQ8#-EWeLWhm4=uBR?D{9jiF{c>j{o
zFGwc-Ui~MBNK;&x>N=cSHTl+(=s36RJ#la1Uor0?WjUaD^C}9DTa{<36{ai5B$#DX
zcz8>aJn>65o)782=lI7H<fRtF-Fkv?T$C_K9}25ybp|{?=%F;S9zvILrL1Sqgh6-@
z>=DwBMmo!i4#4|xT7glXP42&7X4d1<UGVhj8Se5YgUv^Z=lpvruy%bFPo6x1Rdr1y
zWIY5c0e}q-oH(JU$c_xh9!til*D}4N99okrz+90*nLONXqnzxO_cv?hYJM`<dVZ#Z
z{%^r4?U|t(a)&m5%w~3lv|!34ARxqD!C!BKf9IA;ont<j>8@QYr9&3}(nt}~pyHO&
z2M-=pFlL*`{liS}bEztdsbG2@D`0KUB}g5Ub013fc7fECF&keO4OR+IEeu)BZJ1)#
z92v5C91`+Hsar=!#}*>P1u}AJ+qZj9<1a7Ie~FU|4mn#5(Izad#OZ^+y)7}CPgM!U
zPYPg4#wp;df2YHkWgl$h(LtpteuD_xrx#|1)lo#Eu*R61pt#j8jnaqLR)-;DZ8ss(
z#ydq^2g==tQR<qgeC_kdNJ9mdY&%ea#g05QMPOX@{Ciz${T_pQe&*M(<02xt>fT*>
zmssh{@EVhLt*vvwe}3$~oHgVMu@A0x<(d|jq%T2ulUV(?jk$TEoVbL9`@F14q4l7q
zlVct(cs>KW*y})qoWVlf2j#xM2qpizw6vsn4k167{zviOaM1SM$pO=qk~crUUB<$$
zp#+)>$E)IdqmN#*WyVDq{2DkAQIrBbRt@lGz_M&UkF3%Pnfw48AoH(ZESsqow5$mZ
zbAkwP+I7RKL<jQ1S0?X_L-XT~r*qxllA<^z+#1iwyx$c<e=`Pf*D5`~d?-?#@oAUh
zITp;tf9@NgLrNKZjjVJ@C4`9b`^7CQI*X82_t8PL-MU=Z?w>hI0&5rhqE$)6H5jZV
z7cRv8u&MxH?6MYRDHqS5|22%)+iX<<Vvs!sDjiQ~Jqc~va@M=Ryq)Vmwz|-5g99y;
zp)(+vD0?T}p#Ucw5cp|_&6y;yQxgb+J<fr4De$*%s1zLsh=<}Lc<5y;oEGD2nEJqY
z1p-0CIgX4IJt(a)NR&jl%3;NlPGkU+qlsRifv%7o_qrq`zJ~3;CkJAYlr+DrJh$W<
zQU(TA_WJefiATEwV)#K}Rw2)>SYxhm2TntJ+WFbfu^J4TOk`9PYgZ$Y?f<q{{r{k8
zYiKXlLtS`gA8hi_cn^BYl>wQeSXZIVaNg2E(rUx(^k9{PN`W?x&xs5QCU23Ibz{hn
zq6NsnI1za<#<WkJbMeGXGliUXn_Z{BGZ0`xi<;QP*N{cW%hiGh1BBYPilPU;3OQ}T
zwo~M(vVJ%0Ut2hzw7}?IV#`9Pl!Ivx{-zeM0+PM^oQeQm>B0dFMJB2<sV2mr<qvp$
zWw*u2UMwj>h)`_bbTF_GtsOEA^72GsTLsEJl~RKid$-!SgLA_|;E~w^D9}kRT#)->
zc=$H{z~_I_-rWJ)Paij}OO&H!N(ANnRps9e7$?})@|J~^)j%Z4_v0a|@?Lsp5WE}3
zOc~qjbsvdFpbdgR<`H%k656OwZ^F89!@(<45Zp<t-Js2*^pDFSx#RDhGIj1DEB%JX
z#>VQwkSUroX4KIF`-HxUg)i=0RU&%SV&7Yo3P5hB>j|zocp73~kc`vy0OoK(@Yw3R
zx=+NU-VUd_AH|<2*=$v%4Iu3y*sU#=*C9lVV`cJ%0ZJRGuXjjj+S`wq5M2RdMpj2*
zzWtOPz>EuXD>HQ=JU8DS<|+)T=g`)KpktK|7K#NbU;*c>2jj~Jko41#Bo)7#1m~;$
zka4Zj+r!k1)e9gwLkWPs@>I!V_!OYyOkNuc3s$BXh+?xpPlXX6bt!6b<_F}@i42jS
zAmk)%4PZyO4OFO_eH1dll|$nUz^v-QQ^d>pfCVEQ4&&L)f&i_mymLJuinIl2orU#K
ziTwPxx9y6>Gv~95oIYelbD<ceyl&MM;FRS`nA;4t=So;ZeZ-y*I#UlHKEy(DKtPEJ
zf}Q|OhQxkYPCdBTfi~!aYYUq8aPsK34#>A+mCGEl|F}wwd7?qkxDge>H4vTeV}Z|D
z&~+k8N=nEGzVIFUGmKR7K|fbwcOk%U1z=<<mdA55Obe|$vJLQK5O%usJU`sn)MVHB
z>|#u-G+_73&Z86x5q6jlVy1AYr<j$ySv4ogyXRWv>+0x`Ag=8hm4zsi7mSH_pX4sp
z`SU7pS{A&@H~xNwkpB!1`O?>~U;9E(MW&N+IQb#pj9@1CBxlA#VMq*w!<tZg$bjV*
zfk8n}xbzlhGAkezbD8Ud1o1MD*GEI@{09SVO-(3Rfr%0UD^-vm%c~w7crIB1o4bU>
zwCLtYBYmkY#9EyOsT2`{Ixc|TBrm@T9qWDJps9)KJwqU?(ZdQ~PZ9t~Ftzf!h%zCA
zDk6$yP-Of2=540|s(|%LfFHFCX-1CN7tlGoFYhyBUh=oFTrNb@ybE>iuw{HGtqYvA
z<8Q)BA<dvm`~&Z-ASdTjw=!5#IlMaUQv?BCeyr;3+aoB{;__jKQX7s-`da-TCybD+
zaOsXhJ!Y9UGyFI5-3Yw&1?UO^nJ9_L*BOOuE$Wv&g7x6@)V`WS;Pt^o*{|Fa{qwsx
zA>unBe08Te05jEKV-YCM3Vd}&*|0M;G&h<@nJNPQNJv${Y?LfP0d2>A(eQYiU`6ZT
zrIE+VK5m2ueqavElxV936{JWylU9c>djVk9cxNsPl!b$X^qvV>fqibbbfv6gdzu2M
z?1#~U%F#rgOcn;;{11Y28+j*ya81;Q0Mi<{@rDh&DqyeYQj}zw!)q%c*pG(VT84%l
z+pLu#<6>8ye9n@^ygZkrEgv9fCm00J#wje1Wn>s->-V>)fMg=H(H4aTpfIj9I>Hvf
zqT+nPjFPJ7TB9I<Is{Py2UNNGRpsLmeuh(-J)<($ER8@gSK3?+s7?c|mR3_iNexi@
zS~2L|FJ>J`Z2%Lqjg<GIH4<Phc~sO)OT(%kT14m!f}&-+E%!i>K=H;wtQNq6CE!<~
zM-n!C+jwrg@@}gHB!vWN&&n$s3fNPk4Rba=-<y<ekHWMM))wH;udBdXzWJ{&yCF}1
zIPohI8BG5Ls1*UeY>>naQEimO>-V)6O-u+;614}wpT$B<P6R|Z1AJ@;L_Ix#McP80
zj!ihW&J@t+=43@_RK`QrA~G^E+&5cvsL-ZP;qTDR2+?k#ek$=zl5>qjGV<V<ZhCdK
z3Ye#K=k5YY->-k8IC`>nWm>VN9BQ(U<y_5C9Sx!=UQnG5D4QTyaVNi;%3XHegMNO>
zwesyy^&|lr6>Z=2NDD#%D02uQP^8DbZZ+|1Mp|ame6^^ENYu-hFPEvwiXOI2kHjRe
zzP*QxOEj;t+}523?oB;K>n_<H(|>EdsM}Kj5v{`b(~QLVM2OK?IK8B;W}J1o7<vvR
zPL;7mdPvs6M9B6ppl}}G8mF=e)(fUS#Jc0wtB&Z+7>%SS|CHNUzy5#4AJ98luvx<c
zSwIyK`~@;|bwwMC#j@`~V?WP8)uSUAja*}EYd%Ox7szlRPjhl2;0U#$R`JKfhoGNY
z?0Zlu@bx6r9Z)H9fehfBHWYHZrVAbQljb3TAOLijIKje;Wqt>3ef-;%e;3%ni#@<M
zWbJ#WtE+nibVkml=T})-*&;BPC@WciZ~Fmcl9F#Wp14$HW8F2)iY?J5z^>$`{j@8I
zP#O*$ZLbSiE1?2%$O9Qcli@*xw1CFt^Wz})SMr%oexr%eFrp@J3oXk%rb;o&F@{p^
z$mbzUdm=a^d=!A1%dm{7jI1m{z!dj^jK)GVBnyvDw#{MM2_;X|KWxV6HF?yoo&doB
z(4#b%!3=isFp|mEg95ES_bzTZ(ZxZZVV=w@Fb8BRm#JSry~zL}MG7OcugwqRSY`g+
zqZY=<GQg+C1#H%EMmfM!2&?!D!LUO%y&E|wn9W#!xjPg-2l13mX)#V`L$bDE5+6}i
zW@d<T<<)sZF>>T)>9%N1f%}VcV8JYjtYX>v4kd=BE5<AWwoCw28NYr4>z`;y8_HxG
zLOjWD!ys{b@FKAb^*$=$K2b+FCm6>ns+(px*a<&uJh>Y^f{OYFM<6AS$<&QMt*M!<
zpK>!9r5>S+hm1O^VIkYjV$w$Z*Ir0I)L9~kqG!A#(-qm|ptw>2fu#T`C1jv#wo$|*
z8NojA*!?q_AZ&dDVApQ$*@`iLN>k?!GTM)xRMzcC-r5U>))I;W3+lGiCL88W6efKu
zKL;419LX9O9IL|@H)#aK>txzOWa;FwZ{I#e9kdt?i$5LpZ=+(Aq|fv~c=&<eE^Al@
zOqD9?BMH(7c#oI}C_w_>Xwb_O>Mik52QeVhp<L){%}QNDQ!WB?q9pJ`mR=O#u{ptv
zkTqHesxS_0x#ikpX~k*8nHO=^!$iyQq_w3#W2iC~y<6r!C_gGLige_|NdhE_7!Wv+
z`{R&%RjULzW7-#w0l*5H<TcUN0E~)q;RI7tQ*H3nvG0-UwVNDIockS;2@+$j4F{$t
zJe>YnNS#Y4#q&a}(O)5A)<#@wGo|*_f#i$S#@AkkR`Jo{;e08Pq$J|>K0m(*9w9W7
z#D5Fi4?lT(s}>aW0MT7u0!zuNRYGYY><gUM8NoOtot8xSGA51fdCe|<NXwTEtC`Ca
z92~I62La3Qh~0DYW${IB^4*Z;NKQ(AjzogCW?ru`uXMco+bbwcNXzj80t`6EYuz_7
zJ$Jzsqo^Ff3kon_;KwyI<o=bUp}@J83J7$@iQP6KLCDUAMO=0cW1r@@vy|W?hPO0H
z9g%@=Yj^~dyI30=|AA;c=>W^SmtO^*z<WZ(fAHJU`(BluNQ^Ut$O($R>QJo#3It@{
zR0x&Sh&h*?k7;d@yskluc5Z87v86)Go`N%B*<iIm-${tH3@t5_C(dB*d4bnL)zyXZ
z&LSxO0sDgK?ITqPpiX!=6gW!OkSb{iZmDF~X!4wEktZS_#=!wL4~4>`E~Zfcz!9EZ
z%suF(4_n;l3qWcSsC-tm))6S-ec4P@p18-fvTbC@TC^A>)d`y9U*c@oQk%UMlc?*V
zPT3SX;3U?zRA5zg8o(yI)?`J9!lO~mOw{V7%<SBp9e6zN>s!+FbTThM&4AlWA4CMq
zm6pK$r^CI~X@94L2)pEfHMvYId4#M|3`KF6=`v6unN2g5aS)lKwBcrgW&#$-GeDOp
zq74O5MBBv52X;fK*r{)2H~ujPoxh+?eQTnU*Y!B-!sh}4AL3t#mA<Q3DGF)e8@ynV
zzX{Wlk%OPZXf$=a$Ik)I7Xjo6E#OeW;!ne0VisjRQ#=?ZB0M2u+tAQ}UW$aBomWXy
zvnix?Ge(*Cf)CRt-nLZc-jS}QSX1X3+}nr>3Tsy401Nn7Ld*uL$)6WO36EV$X!`7)
zU+&BW+h4K`abpmn2i7dn;yO@K6ihV1IJRAPcFrg+mJ&#3D)!a{_^2<jOxUg`j{1p!
zgR27m{3{0{D7n=vbb&TDVuk6Kmm<QiSV^yCP$#{Z4-ikKFACH$JRCV?NMC@yf(pNy
zdU`Rh6){s6U~%Bo%wAm61MN9}{P<Ie#JZIMq*<Y^CxDw_9t*?}W%0_-50<H0I4ysL
z<8Qi>4%8HC0~J!#VnYhJ$OdqJ=IW4bctYG!9TC@Y9>N!$6%O7EZCtEAM2}xoO+2mk
z7p~b&o6JSYAG7@!MmSv_dq>Y-9rHJDuP<y~WyeAM9141}Q~ia(Dx}-g1sC~JNua)l
zknN$+1_I2tZEfWNA{bX|m^P%}$c4hR#{d}IyKf(=5ke4n2#~V$ii!#*z3Xf8Hq5~S
zS!Mzs#_>Sg(xL-IU2rbxiVI9y$eKJVJ8*CyHUpmVS*Vh2o*xWmEJTYlACW8MfyB@?
zU2`%ItzfB;D0yU-6vh=fniKxGs8~sVpwJ<1Xcy*fo?<Ezoc4pMbMQb6pf{4KK!pNM
zLM(|yLUaZstSG+(M_C#BFU%!VAf%zMB9{xC421WyN?V}oHX-Mn(RL`e{v%6;5Ja-(
z%)vngNV)#hE>&Z7W`jiVCvCy^&vs6Co>+e99i2HL=Uyt8lcxgHa%aDQ3~X-~B=HK6
zy^s-B9v+VJBrqsYLC^#ZLli7voMCk^j%px50RD`JBx-}lLx0hx2g#^#4U)l=jbtHB
z_Ph*~0L5$O-!IeD0X8ROt1aa4Wc^M6nu_E#vo`viu5OumlCeqZZB6*TM!`Ikol!9X
zB`FBo78Vh4eTLDf>H?8K;5QU4L;)t+x1JLJs^uXdz#`_&tWCLx<PP_yOR`WjKNN*S
zxV}j_f&JjM=I7R2QKLIdO$j8~haGj=XV+I+*$z(Vfb4Z+9>@s{A&`UkH403#HV8Ry
z%O=M;Ff$oXpB_hNy1GKcK@##=iRJP0@1-lYUyJ7Ia!zuNYicA%cWf?xj6A;MFHj+X
z60JC*RRMnq{s~r<`v4)3dYPsa(t)9JXF@5ZvGD~fLSTK4Nn0q+P=s@OdjtmxrkBy`
zH&xn=$P_vJGhmakz%9w-m4>WH@ZB~xSvQ=u<lGdSMT@$P3E#V`!n3ox70)2%+4@iW
z%j1c_M<Ww)?(>RUwtSJmgQ=;PB=USPzJECk<}Bj2+FhML+HKNRpXBZy&awJ(<j}r<
zp2lcQtWVZyK8?@t5B!pD|C8cb(d><g+c9Nt%kOfTc80+`GS34DrSE=Z;j*PWvX>~@
z(Z(KPgU1=NMVVRRO-|q6jWOrf@<?mpk{0#osD38LB_Uy%#PVz3zLAfEZ5^HLF0}BR
zI)4v4RjD{~NuL$<v%b*243N63ez3IwJAa~|>D)W}ABXPGaBO8)CWRjXY_zjZ45+Bx
z_tS=YRHZmu97HYno#ih&D6)N@ipB(*=~=rld-25V3mk22Rw+NtBnoCC$ToRt_o|Na
zHA^I*|NG-(yCV*iYmZvB2#Nf_QVu+X)_4%DF|Z@C(X8a9)K6Qm3w_*Y(gD@42kXer
z`lQOvcJMg9@b?ss4Dy*s#JXPSefHC~tD<dpos{#I4GEqef2x~R{G$YUotN3xS5g#k
zRXnjUH!ngy-u3OxgWp#h+66>D#}DjWACE+CmkP-VC~&#>=)?DqF(vGgqZ&zFfBvS<
z&ex+#?)&^dj+_^b?LNs@Bh_kP@m#L#<45}+_p%ANl_h?xy)^9>wClSi`_r+_0!HdO
zR9od0_5Q)@Xy>^9A^amoF!gFiwI+{Ri36eZtj9N8fi=A$1DH4`Vdum{EHlQQ+qNZZ
z{HI;%g_(I@8F+35G&zQ4)eq-T9S=t+zH<zYFu=39Le44n2S)g-0GQJhp~rRdI&&a-
z7Fe7=&G_c}r&nOT5?1Ak`wI%1f85PSKvQ*;XXo#e-W}1*S`Rm?(!iQ=+7`5)nCX;A
z+j_S_sRBKdA9(3c(N2lj-Cbye>d8Q-Vf?wDHxhDL__*9q#-^@P@S3O<{zs`3i-Q#v
z@Tfcu*vom9`CcB(_D_-}2$E&><@{s*u$|-gv99lVzCpKWTA}}f4|xw(X%|M%f!c8=
z<xc!0KO+zi_N5fP0d2{hcp&NZSK?3G?1#450bgsC5pebj=)C>Uia864Ii+t6iaAUu
z7>yhKR{CP?2m0uJkL%u+;lO}Xf4Jv0{<M=?u#@p+7iDnaz97}2f<M?9)|fd!b~w=U
z;wZWAiDK?gGqy)F&X?@(=?r}T|8Rn6*scl~Ha>rN_YM=&Yd;ArlzB@m!q}#`A~Z)q
z;!N!2cd{mn{-d!2nFfo@V_HQvb#7eUf)5}5WI3A9!_IbL|F<)Q%)7+7UpUd!B`4;K
zZr9JRW<8s+)f>ltC!ubyzPL!+Puqdiv|DNKDDm)Z|Erb(Za>OwyBd<pp&lfab;XXZ
z#wOiAEoyrGV`T80$1g~FF@HM3x2hljgWN;e&hul&e}Hr(|76wIKW=K$t^af?02#c0
z$}VU}O4Py7a!7*X;i3$iTXW<JKl|}04wZw(3fMM3Z`PU%52sN2IybsB9Bo*i(WZ|{
zR+wgk^8X|oQ($Iag@8|zCNsdz{0}v}rwqfcNqU=s%(@p2@1ARv|4Cb>;fxu?E2sRm
ztOe$f&6>QsAwF*pYk7pOjhS3Hurg(xh<wQp&O&1a&fzN`nWcv$&_j#(pA>umtf?<f
zvj2FZsc|R4S-0ya0dRwHtBFs4&+I7rP5plv%eQPWFzvYbTU^KOfUA~2!BZuBU=lOZ
ztQ8fCn{y{UyZ&Pwa0sz*ha8&aB1dJtavm#%=>2G<ui{~|Rwf6tb?3(|;RG*S^84Q(
ze;zqT`ub;rk1SqB`&*F_ouDInv($kq?a}n5ynXwMifwg&dKPS@##Xz#;0(1j&xyat
z-RPZZhm3Ob)%L;?yPaP;Pq5VRrzuy#i_5M|@}VIuP52;f{Lv;A!z3<q6LvOP%GlIi
z(QURWmHgiyvvc=J&+bm>7Hm1u=Ej1;(3CNuq8DVg_1rDs-EOzW%tU&a-}*m4reoIi
zkW}4iX!P?LPQn?|F`$R;dC&fr9zKF23=G}8%@Y@UXNjvjHOc5F|K5wlV^iezSyKC9
zRWRY=VA5H0LjUOlI~zK6KsnEpuw=@a9~E=4^e5-RKs#awI}$Zo`p)=VLb=k9OHM?m
z=sj8L96#1ssX*@g;GX}J=#7Ghc+Lx<HGzF10Xy}3{G)mZS}(Dnne{2OSHl1YFEgyG
zt|k{2=n`Vqk{|x)CR)HdrESvhDR-ZrzL1Qa`V=A&#iiTrpvbE8vfiOF2gcJeG*)Yg
zx?o2e7+v1ArX~%L=6)8sxju{x#A=~imp`AZBJ$NJJ6HyQeSWMEaPiTP-+^EzbgS^&
zwkUUe1x>Cygh4A$7k4t<XyHm*fUR@<PsZXjFTfe#mAWD$=}jWK#Vn+2eWbK6AYw8I
zkZDqv3QU@iXL^{p6cjYs3aoz){|(K8ZD~KVLX(JD>kTQ7b{3H{Zs3ih{IsYKNM1I@
z^Oc+K9Tk!)SoJ3jnAH>sH?i32>#s@#gZhg7K>LfMt<3hcXlL%+>*RQYczgMEd>(yC
zV-I@GNgK86W}aVA6k5y4Nnp(C{d5E}a0K4xY5H2!I&Jo*3X`OB4Qy+^B0mjv7lzVT
zpnW}AWKqt})y-rs&-&VT^oa^>ppZR&U=Ni4Jar}5@+QN&-*WBK@HM~<8h>1Q3KEDo
zebqFZoiqKi<UZXU-30Yv%ljiJVfyx?jMPkM1ayBEH(ArNFu@-pa((>4rFFUi>r2m3
zSe5vyLe)D08!+EkAy*h^#Bk~({WKkH<fxc2z}M&SYBfwY$E8{i($_Obr3wdSu6VoT
zJ!^40^v{TxP~InwL;uP+9rG*%((gWR+;BoMeTPK#udFH_Bvg{uKR)G+*?F}?t;#C~
z$6o=d!fZ?@cA{?7{s&2X6$@xVXxC?8e)vGA3;+#JpY}{sC91#v8<heUC?C7z+~mo|
z!kuH$8g4;z6{_T-FHPys<SIM+W|*=PY93>uV}qrq*dJ_|ewk$67VZ=G&&Z6^jfF3+
zZ7m6^f>d{oz}{L>R+%LptaiowCm~J+<F{vp#BpjpxKY=$-~j<#6WS0z%;yK_I}?3>
z{#G0Kk&mnUK~DGt{^#>P2xpMS6*ziovz-R+LA3LpYX#8&3AWzccunYCqzqfNw)kOt
ze+h8)SPcuob3q0d^@D^Sgf^tKgh0=_+M=~-6h7J%t1%as`+pL@-#~0yj^000Kf=#d
z+p>UXkn^8D%l}DX?jj989mf8Ji@0+b#D*lBL<ULzY3~=2-n{_THT`@{s&IAoi(*l*
zho1N5jjePEgLO-DcU9>o2fq!CcAgM+CUx(jT>J3^x51*`-YVpOq5a3+5eLgEyIF?W
zOE-@nx7fu3swwR^FPfSnuH8OVjYLp6poOSG2mrnehqhG2%Q*lr<Y$V5c-w!ibyhit
z(HRwosZrUZE~O?`NzvSEiXy>_Ih%|5H-fc8o3%XNv-qD^UjGLb`m2E_4am>R??j)8
zF0oc92qNCZK?I~dy72Fb`K4XEB8Ckv?H%FHEi(@o>&(P5d;Th#aT~mO?}xXhq%W~A
zkIL6^0X)6-$`17o%Bexo>R{k14WA@tv(ndX3uR(9HK<W7P?Lrkf^K!Ii!7oR;vs7a
zbVUF1=fb$9<*zo%*KqWqH_~P1x|ssO47vq&W+`m+5W?@)e>9Y|=6LS`NDs8E7&S_-
z1K#zcBG`X0#~H0okh^<^Z`!<7v)pYEwa5UVg<uge3&061y*e)<tP6D|cs$;3UM9`{
zh2Q5~D*v2`Yu|O0JyQ#Nb4hOkrXSqC`p<9}%-TahBk~RHyeIXK?p|=uZ*Z%c+K*pj
zP{`2X5Cfbr#7=BZlode)dx@I)3#hFV)d|ao{GCw+(TOqyRdGaK7(K3761Zi_k2<wy
zacFcI#}^_p{-APg>z?nUr3D$uxIcjkXlJ?0%F6H>AA-LT9Y}31p{*XsQAePz5`PvU
zM?j(t1jD0xI_BWRvc<ufVEG5{u9gX$%JrC3t0Ek}{nEiQX=(AUFTFGKM`gHIaHxBe
zdEDZ2BN{ODxxE6>yI_`4P~R=DJ+OwlhCaWGvjZHwg@ePq26}OZm)?G*)5Y2xEbFNF
z7o?0ME2qO=Gem_ppa$QtbiX5kz^-ob`p<qRsKH`UD;JPyjMe2mM$c(^&@mZXJ#o0{
zZh54f7WrZpb;qWYs}I%s*0=K<-FQ*>+a@ne-`zh?eb`YnGBTp5^I!SIOybPGEq)Qv
z&gCjb`<>+gm)P*kuax(;IPC@``7C$*qer8H|0)r<?e7)Y7U+1HP#XEHkKXyg{>RmS
zW!IWBNdqo)H;3AJKsSMQvd$lZQcr$UJnrCrtH>Qv1%oT3TV@shMn5X$@x*dRL=g=n
za}S3@NlvxfP_3QUXMh7>uv~ti5)4T~g`G$6R#%1tg+zbNaZ0+N^J69nY8{O~7&VO6
z-2QARHM_n1lBEFbRlAQ*4XIH!3;~k@%Y+HDeor8?5TS}}sovVu6lJqIZVE)Mn~%4f
z$IlUV#;RE<L!RTZ+)tfpm?-9Ev7TAN<46O#ODHPx`ObG}lM$gGsQv-~Z65)Ar;t)y
z0wj(}vl2(c)e5Mvp(Ya43XGWDsG71|3j7t+GeL$DU#pg}W2w^q5u$u-zqEM34S&Ld
zF2AoBALI3>j{GOTHNE>TuAz%M9V%gnCBdR-L5q<cwE77Oq}N_%x>Yi2?#x+Mj6AX+
zan{irw(-RQBMm6X;ry#(+C@NTllQy(|5o+T<WbIZo-EQm+kELFxUl4h+~jIyo9N->
zN@(e!pWKTn5d~Pd-P^Bqvqt}j;x3G9l7lLy#42@estt%N9{wR(bD|c|REL^Mu&}k=
zGh3NMH~k=Zf@Wxlz5&H1J&Dt2bw9-4FP}jCWYn7E^yq<Ky|O(mx8UXV>;g$3?3vXD
z`t3Pp>!1fPbvK9klZ*gC+tz1{XlDtDp<g)%7A=#j9HeG%(oV5sE?22Qg>`BcRfd~9
zpjsi<<_~3B6-s}tR@DBHd1mD3rSn!+yoiD^08}YNAU+L@g+Bd}8L@ayz}OKt${`th
z8da;Ih5W&r&unwhETYI{P}GS)5JlvoaHtQBDD5uM35d_K#lOE=T`4|SFjAQRE~-vE
zT*39c)b7U7?<b`p-8gXV$yr2$6|bd}P$d{q(h;##)78}#+D4ZLn})qWc8IR!gZrD}
zC2&YKkVDvSC{_0*D`{N!E9@5l%yv-IruNw38kQ({eqLqoJoQE3cSN0HS3HVZ7<mNm
zc8S~%r_VpXIzN4{Z&b2PtS#!7rjq9GZZdTA;h7pAXIJk&VEB=sX*mK|mddDMn!~Uz
zV5zEMmKHlKmeSqXIU@K~A4ZHqgcmCGrBwwNK*0i)wvLO5xjqPBQ8Mz)JI5@co^gGZ
zj@TDKGLb*achDVbtxoFccF?)ZYTOpQ-utMaf49T%>df(B;}<P<1*>>f^5<3W7F%J=
z+h%l1;;dpiIy%?6Jl4_Gt|kBvFL3M>|1<u!$C5`-DL!wo?vS(3^*5-ljwoHM14I-o
zsf2SPadj5Q4KgpF$}YW!sJ0(9&VUFB6W{p^J-_mX1YD$O!;I4nzzTp`Be+CXI~9mP
z_fZ<Oe)&K@XFzrVdVCCuxpAMbP(bqD6%x0hUk8Gie`1&2ZK5HC;=(QcH9T5BUGuVm
zBVcM^h7IF*{r{o~t~Nlw_3&S-P9Caxh^T*-zMt-N0Oy$A?4Y&>^{698idEy0q23x5
zAWx{x-pAFWCO}l+Mcr+Z-jh8%+3rfv>C^X2CZyZSXGCyk3CGHGmsoJX;z`5D__r0-
z5Yji)g&fP0xD-Elc|+`~(j14*AhHgtQ4pHS=s=L=fnrxco=u&a>o#ab+&+rxSJ3&X
z2%UBNajJ`E$xzto0NS`E!Cj7jrH44K4%ALy)igkhqQ~QZnb%3a@oz9tuUwPs7Rj9;
zRaa#-yzWdYe!N~kK!ZE(0&|!qs|ds*GK2^*;89IP4ZweUoC;%)F$h`yKuu^16of+a
zBXWJO5tOG9MFQ9;-ECqvwJOk;_xO!*`*O-njXJZ+KZOfcR+^(HAO<2QD`ydGYJGhH
zQ%D~1+*qeYCWQBGDs$&nS3rdkts6D=uy#0%&)<=KPnjriuPS3PuCFQTDn}o=_v|h=
zNW^1<K8-gj%oI3LiESarmG-R^Z(Qy%SzT?p&CwMqB&#c^Cx*xuAc|OesJ*DSKz9@f
z3Pr$YEU82VE^Vi@sikS{yTrB0x_-ItY>7+!oNLU7PjhsyNp4gt+D%R*&V>5-NJIA)
z1u<%I&?O)Z+ztGMnc@OaFU25?TJ?>$@{XttS{eO~qeKiS0Q4_H+62Ij+~yuTI21;@
zuZ=8}3`c!mJ4X569o6@T#BK=gj(|EMYHzO?OOHx7z^h^VoU$9Bl(rYhIEwlSRzQx7
z6)CKia-Bvtw^~I)!cnJT%M!KUgFUil#j|%*GQ`jREMp%3;-|xmlKGwc6PQFC;&Gyq
zdzsyh^@^%5bQQ50;!rW3)uRAymW4c7uGOgh1em;CPd$JZw+A)yu-e<jLBMO_EAn0{
zhXh`FACInT<K~5;1*~l?;F{aO%IjN`cQ!K4J|3;P(+ben!fHAiX^JNTJ97eYX8$OA
z0}di{4)HFK#Y8-G)F)rNGC0F(V{RH_n?=n+r*V9|NCX&@4(d^$Rl{694qzgvT^8;Q
zEcV<~sp+}Lza4g6ov>N+M;8S2MzT${7Fa3Q4^m=cp$6c}H@CV*JFOx~o)XRaVQvqA
zrQ{fcT2m2o*wV8@OA<9!%(W`B0=i?mk2E#?zXU>+oYh+s49!@mE0fiTfyln`)N*I2
z+au=|9u9@=NQln0fNljvUc|-{wXRkW78aID1$N|mL%j>(j<=K9Oe=Q9VOQ*nVEJRu
zuE$yALp{i*a%~tpywo7+e*?#J6qPd9f6S$o6K9@4eey^!{S7|>br?hB?7-4*AQYFR
zv;t`gU0Hw%c!&`Mp?ke)?d(GyA@|2n%SE?XR09u}#o7egg!H54Dp+p$z)fA$!hJuU
zV!x&LM*Ay1YVM2E2-fuXa+MOV2rzH9zQ4qM>Yr&~FlAatDk30VMr{@(pgjkPf|jFt
z{sCog7xPby!S{aPe^D~Dpo|Ah518zx9TTfmTu3zBj-ZBmaKHjjc;mo^pu`Jxprq&J
ziPq8^B(j3eY0N2m<E_YjMzsFX<-1~~)Lt+X{S)#IKk8oJzRyY;79Sy#V_i&u8iRrU
z(2(LY#07zgji_bCZ*av%^s3uZAas%!^}AIy0N0!&8z1bC8tgB%sRq)&qKpqUsRD-s
zu{;b-O!i0Z7mGyYW=L_+?J<bki8|H6&B#8z$1YR&f}Qu*CV7{LBz$5Vc6DmOf;6C<
z)qt<NVdgnu_V`C5<_s7`GrCTo+tNc1k>shs2dhUDM2-%4C?^Eo`zYB!2M|k`QU9r=
z=U6*p$CJ>F4ZzD6ny3U8w=L?`s#)w4nV@YwI5R<<<Pd=gtYnL|R;W2cS2;5^o|HOw
zS{tPi*&ETnc@naC2C*u$ZxIXCc4>A5yE0Wyq*KXo`v}~Vv=7M(VnU!!B>2Q;vTlv3
zsLx*F2^nC4s+T5Djn9lLpEQ8^bOL^j*Xy0}{<!aP2i(At?#GV0pIF3oP=P(DJp;Kq
zN#MJk$s9TnQd{S#2H^#d&{p%?w;0v*kxSbMn^twp{hEhuZ1}oW7O$;#bAOjBbi;{<
zOMBW$7P)-1HH91XOQDNBpa`D#-`gkeJxIS*K^{0|d!|soUuCp_!ddU0niAh&{QVUg
z&KZtFw*o;uAf&4}f{3%wFky;_-6-(q-Qj4Dik!$yRe7Em8kOu;r<yPp9v-`NZlj1@
zA_z#-dANWBwWUTNE;RJPp&JESy^f|m{4$(zbXtXWZ_Lv2d5awi=v9;hF77w_VPo#3
z`|XeiJ~$nEJ(4C4Xo$$9+mzE=9M~gYGn&=8{*+6Ilo+DjtggKp^yr5T6-xoUgImrU
zXHETJFkRQO&^Z2*K44{(;bO1#<NMdY=hL`p^&8>_&=&Q+&ACW~QOKEg+e>aF`0W>v
zo;XVZGd6iZ9zdIcN)L~l?rE+F-6#qSx;P-|7fe_fe|eQL`=yvZA)-sJ;^*ZPiXGbn
zLeO;L`>H}8fnVZSzcU8G112-MlfO*np@ep2?5o+OqGNnX%tbkz1)wjMj1^O;$fROB
z->ndy4_OsIW)T-yr{s8I7R3@eM|WQe*z70_4zYr2w1NsLZnU0v_PON$B&9}8Fc+^G
zyX3t4_qQ7w;K{UOhBCt^A!ZhF$LQr$98wPsXgw);t!J?b0o{!0$|dZ_m^Q+xLUF3>
zFW=>%?>@__!+hZq4Wgko?><Vhhki0Jx$oog;HEKb?@Lcz83-8qllrPDio7gztszmb
zli!!|Eb<wB8E+k>e(us<bp^lS9-nfzOFA1N(dS42Y2e}{WI3QoQxX6m6mbSZ&rPgH
ztD?~9Se}FlUrax=?S+5X#r3PuWWPMZ{s!h=-#X*w@MhL&M;Bp@aq|Mg?8w~2X)Y-F
z_5Ok`16u#NYGqxpxkM0PNNsfIhrm#8Cu6W2HMgLSV3g~Up_@7!K_^AF4Us(ATY75U
zj@_Z&j-$m8YgNQg?;0-}B**212UwdO&>p0_FtFq4Py4NvzulSwqc}d?+rs?`qR1Gq
zib|txcz)EWfJiQ`szAC#7um#H-aI=GEqD4#U!EZDI$~jJM{GKH4L|sjfEQmr&mcu_
zPWOo?P4U%TGx7?g6c!SRZV5b4UrM_3pMTbZ(U3!t5^_h-^uhrYpV+f;f(?k8g(yYg
zK8>Rs9H_Sz+BS&DN+Ws`tH&K^mJ}(uSKB~but?s&=*EUesi_>@KD)bL)nWO;0=38@
z71zWUDAp)%Vt@G9_Yv>0MUE<e`TAxT4&gn>4nRK~KPzFbuJ%#O2SEa%K&=`qzXh$P
zlaO{{!E++N>XFQI=tXP87HSG^Cr+q#@e@!j3b&@rQJ^4lUp?i3k6tQ&sn@$}`Wuje
zjJ(#I-~}K_p+u9hIun9}8-|F`0>_UocIcCY0pSt~)<prR`3E(EAj^bkR@yn6%3M((
z(B_vxmd7g|2XfTFG<BcFb@Db`Etk;h%?qfIv*VWyWgJa+9NGz(-JW=Be<#EWrGme2
zh_9W`L_H{dl5o8bgk>SDq!Mi=^av}bLJK_4v8``K!3z9xWV!x7?$NkHo*M1F`&`zF
zs9m#>3aee5WRZ|S*++%L5AOW&eJ?eP07kn8XnW*8X$5j%$#9d4A+!K;%uL<@8fZFj
z{(>PMBSr|N9)R{DMg5)6OHqv{#*P;Kiw!?NAi<?4DU7=AM-7JZ`q9HG%JR>g<64q8
ziXV<TkjY<wS7s;Q)M54Ppe&_DewYW{U!z(5!tO@Ba)U?cBNvUf2NhE<wh>eC%15`-
z#!CjtD03m=>h=i@%oqB)EEhl3U1r^ywZ4e_-@#R(Kn#p}21NgrvH$h_RO~>mj|#OE
zi{NlA;%mkfpE{H!0>A7kBy^w&8eBSHaech_-iGb!F@y;|(8+{8kkoJ#^Pv+!w`D+Y
zY-O;#M`%|T7$$KwTQik~7Ap4r+gdgyhrh-f>C>mAZ6_Dn>c#dn*}i#{cujx9LQRGv
z3pD|3b3WRDhXM3yhJ=HbYocfvj4OzZfPj+s<N>rN2osC}60;>?i(|>4jxwRJ_251S
zUUNQmdWd`d<Ua250R1JxGy1zdFG!CZMhV&KGKX6Er4{^rrtaM-**MQr#}nmmBd`cL
zYgTLk3*q2c$eg7e1Wf?1r-<&h5q|0U<-ycrjNbUyKWj+tHe8&v>gT_?`QP+5HtvMW
zWFT>-_B&7-QsVa46SWtjc0ek2ck;dB4yO%4-35ZGiEeH|m-irG0!bmT>7&BermdL;
zhvA+z$S8S{RYq6Qm|{02YF!DoPFDB6kzBpz1}zK2k#*I@Pfk)Ub{``%oubGF5@NA?
z7N_>u98i~8oU!SvzOpfz>jjx%uq=9!sB4A~!aTFO;2=##xg~R2d=z@EBT&kNZB?Yy
zLkqPB7v~`n?=Kg4<b$uTReXJOid5d#8l!D(vES=men|0~mCOqqxHdCB^e2q%3zv<G
ztz&e5hH0Pt<oB(_v>0>*ZbO$iLDx=d1rT}dn2aUXR{8+q+JLUtVtE`;mJZN+OU*5B
zb8417Bk(D~8i8aeD3wu0iTEtB*cMqYHYM5}zg#l>u^R^SsznYXXrG>sfWYaR{ziE~
z6ArI&CYV-*4vqw<5iWe~QPXhMhiPbpB%z3<EpSvC_1PtV{%=Po?-}de$br!tI|Axe
zB}BukQf+>PO3FdcY3?SudQ&F-I-^AUY#<ZXAGd9ak9YfAlA`4I1(vu+j>g=8%WQz!
z+;g2aYhB~taLFNs<wg;6QBh}G`|QG)@m?r3w6QkP;KP2RYg-Er*q!oY16t0)<j63G
zdI`bi*tQrh9{f`KtFf-4=Lvhh2dWw?PaJD+1Kp7f6vSxcJoQzm^~Xg6vnMM6l)F}G
zgF604UScZGUobvzvp-}W2=++sfJ?xpR32#OHSiU83EL!XPyMos4N7eQ><%i)c-Vel
z1mc1CoCh8$;_0FrsIJwS0%i&D?zOsI8fE)NDGfyJDL#JX=Qb+R&i0IJr0T~nFYDcI
z9W=J$EN41f4=DNt2Tr~U#(oM5gMcRGrybe_o(A<yXlg?76uUdL>@XNu!+@EC+EX>o
z)zvrj82<3S-#oN#x8~>QHsEc{ckimMKC1T8W|-dfOMvzo-?5hD4dy|goYq{*SGP~q
ziYH!do@zO{IiTJZKgHo;VqEXOCM&@g?B(=Puzaobw}+8n==!N`@}~7$?6wgbwArIt
z8~8S=-_MW8d_8#ORp_QmC*n8%T0|_cB4y(T)N8Smso0e#OH^V`RC@7Cw<^#qLGq&r
z4|1D?ScEQaSXvExe(i1BT<Jz+e(=<K><8B(V5VB#GI`07af=ZW{Ia6cL07H}EHQ4G
zy~y#eqC9lZi*#QdS*Tc9x`S;vyHbAz_&kTjz8^#hn_neBGHizC7xvny`Qsil@-s-|
zMvE~JI=Qs_x!2YlZe<KcVLNIh5C)Z~xZ%)Cz@cMGgn6T8$;ofFHq=8PV3Um1K#Y{z
ztt&)s5mFC+RU~+xmQZv>S5G?od)ge@rPsj|YLatET|!?m&4w{0LMAPLSd6kxi7E@w
z_p*`-UMcj$erDT4rWx%&Z6)>a&Xe!+WsyGHpN&wCk>*crt(T7H;Xqbk?}+<aN0~pw
zJG4K!s2xf8D8xxSvtiWpfm(!d{dYY39cuob#n|jxMKyHX5J7;OrO&$>k#1FGH&kEb
zPEtJleXtTfw89gs0e{r51^F8@meSwYYStBi3whoA5IfMewhtvtd4+jSqZ^S`JK{IC
z3&!vb9D#1f*cfg#NhyDy6GPKfh>8c)oPv3%9~?-joclFht)zBrg!5_kKr~2eX#~!d
z(5`9zHp`ouflE)X7+OAGB@Xp9CeoQl-0LIie8mM@%@clz1=<nYu6@)?SUlVvxDLqA
zQPo#wy3#yG^9Bx3h7`qGYdo(1k)P-I<0OmGLvE8pHAjQ}Mt`TqOYrRnHic`u;vR?m
zNV2=XctPOR(|Lo&gVme98}qa~z@bSSRCK(I^Bs!{Beod~t1o*_Js%`bJjkh7xpPO7
zdDnpi=CfgR{H5rVjq@IW;&Yl6S{adjKsB}YYGB~Pm}>f|PmBB!++`mLr6EaNc7-8I
z%$bB@GhjOIIQxA}76;0)TPJcmX#zrW)~<ls!x1cGjaPi)e_`8<!^iTgs!5xexHye&
zgh=<S;Ou_o2&(dm#^ZxER6MDluz!cX-<tBG=$Tyb%4GF{fK_SgV3;*dD_0~*{^<8Z
zzZwCOQce7Q^Gl*lov-ge;OOs&vB9R4{Zfvr4qtpcwi;_2xWF_e=Z|pzs1Gs{K(O@1
zLx><WQ)i(P^p1nt0+H5(bjH>c)A(}@N7k(OLoj5S9MWCl>-Py_j*aA{<rJ{9$`JD-
zaA{1{mUK)TSrGY(8+;tpj}{{25}UeT@yk;?B(YtMK#Oa6y!ZQj6Cc5RU9@Kkcu4_5
zQ+wj-yo>{<9;>F8JbC^XML(rZ5RTtGdA{i^wP)VmFy2_<J9Q}yQEfxFjt}J=uk~(D
zr83HKks|tiLU5+-e|rp@l^w2~4>Kh{#;bDE46OEEEcN<vTyI21l(}%$@FWTi7!$=u
z$U@mM(T8i4Wa$G-BT1zGkRVkxM%=nYWIgM<c5Y+6%INB5@AJ?N7C3&7N&PHOAd2Dn
z_8nRFa{XF~$UH2OC<svsW`3SDtIB!F755z&)F6ZGzS3`O)2-KDoZ!X<JGl&a42{v-
z@A0vz`B>TOF-SRzSUQMBE`N~pkab<<j}O1u`x#Q$l6{a%t5s9Psh?DRB24$^$5^B$
za05rfvAr_Md7KpcZjL;}$ytwGnUDAsR8`+@r9^BWO0m1#_5i4S?xXYHxBsgR$XWZ&
z(`;jW<w1*oKQ65n0O=a={agy0ea@9uo%qt2F||-mb}y{6Qit4gY&hd{3Ta6^Wqilj
zDW5Mqs!IE0DPi9QUPCz(S~Xi73)QSh4%z&_+NgWC(FZ-|NZ`e_FS-^RcAv=-00wgO
zom>&}m-6@BI=b6G>=>)^j6GcC8M#SSC;gCm;V<eNA#&dy5X-l>n5sHp*H72S<NA(F
zC#$F<HvvK8^t@O1#jg4$U=hKetQO8uA=}Gs`U_j;53ilb*wvfq0U%n%2ikWmCe0NI
zjc8w{F4off^$_*Wk-LN-3GrxU!4@2#9(uueSnwbD0|)Fy<WJW}=1WahI#U#`p2sP8
z-e^;}q2c6tyxDzrBX}yYhW)#@*-CjYp+S3R6p9tLS)23JK~sWRvA2iGbj`DLVUvr-
z4WElUIV6Q5XXi+v%uMXqJ)cNQ#@0ut&)#3t`hFOWZ6$9phr$qJwl|C|Qd)ag`2V5q
zJHwhvyLM3r9UF{bL8^@=pj7Fgq5^_|G(oyZCxCPU0jyZ)O0QC-_aI$FdhbFgBAq~x
z5->pEtj)~$`hMTf^W$8HYr-&$X74=vdDgmDyYE*8nze<LRu2g2cldHVjJb97PD9qL
z!0ZFG%JsnS=bi{HHFLaqQhvjCs6vNPEXj;f?4vAnRm(bFTC4iceJ;G}qoXLKfbNDz
zEzp0!w>f`b`W|$l0Gu)=r2Fx?^wxzJTdE-~jZK<1zOQ+TT0@f|tpt1P4e~Njj%Faa
zy`sz0=`Z~522)F|&R)On+aB85VX8CXNjY6#nZptMuqa9jh@MGxb6JOG9PXifS*?U+
z(_aZ+%TY-AUc`zYM_zqit#59ed*B+E4XeTYXy%(s)AX?T3895Mzcy0?Cp>#|6J!Xb
zD_I0V)-3d00w|Qi9X)QX3eKtQEnCvoFhAPFN$>5DG{6@aBw3omF??@pJPeaGKo!w*
zs$G_va12s&Ho@+{-Zzi|+4L{qQ3gO+!w+6}q9Es==TQ?HUS_XWPFmYQ+Eo?7`dqP2
zLj`ZP-M@RR0PqsPT^PT74?9)Hr@R11g1+W20A8Xdq<*c%L+CN0DBK6<6Ih?<5c{3y
zzWN#>8Ns>WG*>Iqi*SD1JBkriVkvC~v{qbJkrSS<La$3QuGF%ma08ngAQ#_Gc;B;6
z-M`FKOCqUB$TUVy=>NKeAT&Qyn5QnU&>)>Y4*=TNx)~pU0_ST{F5$X&qzruXG!(<4
zCiE5hZ2VcR!s-kR^yrTrs}MU?d-mzbz=Fr>H)g(a+x~`jHHly5)SMGq>bze<i-*D&
zeN5@@OB8k?6yo6qZ*#@wd{ybauzqn=i|OAW=GZZ4$9T~5iNLoZEgl;H8}Rt43E{?y
zUw3LQ0e5;P5qwGw+Qr|fLcBhenu!iVP_O_V%1a)1xb=!l<Ov#gH5l64^iLo!n7V|9
zva35?bdn!r8Tnx^!(0xsm;VF$1`_fpDFo$^k=F{b+IamK1WVnx@g=w@gjg1%98tvv
zZ*Lv!4lvXh#&(e2@t0=jmT7iNqPJi>z(fJ^VwMy?_50m^jf6*+TZ05RWi0A6sZ53L
zH*RnnRI#SC7%*LK)1mD?nfWR|gaICRqxu)TLVrE-T6jApd*cgW`0>DNlHb2Q9v+W>
zJd9XcNao$K-=&>F*28-ZVdOUF$CNs~RHK?XmCD$0`P?~8z_&=^u8?wZUTM27pBSVl
z)LMa@K)B9$fZC~>H*N$d-Gnh$3r3N6cVGGr$+g8%p4+dta(-1JKlyi5?xl#l)81D5
z=<`b{<aO*=9>4^B?T=TGU4{%VdtKH))mzlaR?-jYNIs*fCWs$QZU7T7>w`rpy$3zs
z1U^ec3*`Oxujb-^HB4WH$bKJ%c6Z6gGr!!;%a`ecrQwwi$>OyuB&+X}&#jD&HBfJd
zGA>2D77j(dL|*O&x-dVBY`8t-9UX~Lul24Ot#_jr)_(<Q<_gGmi{^yRa+&?am&j{J
zayiESXG^g#oZ3_#E!qrW4dDSY<l*X{MQ}a%+;ZaBQd{<P&E~dQ|H@7|aj~;Ad-~t4
zcC`!|ODPKJ5F6E*qvJc%&(p>rE&?gJ7Ov4%rCE>dNF)N|)-Ee)Bp`6cH<{r<UDwe^
z?S~f$Xnn>5R2*dGrE<YrgT!C${^mZ&EuQ3p=xf)3f)kBMJOCRl-zf?i3*s}`j&wB{
z8w@W>fCP3*nHi)?T$fu<EKNi$&ZG4M!(JjgK0Y=ETv9O7rMrL4b(&l`EOrAp)Vha%
zU1t}VQrr(g5-^+xR^K4o<Jd9C)_pIk#gB*FwAy|3-o!s3Perno8KC%2*z)AOEDpT~
zorG`|#A57`6tDfn%({5I6VU!Axa8FU8u9nPRyzwpSsm$4vnq5j&UONM9XkfSno}XR
z8_KgDsPcR}y%zm}!m`+^46yo?;GA~~Tt=53^&HsV87<Hl)N^XCtGLeb{wtl_2j?em
zZM`YZzK?#ZAor(iWA&q|#3VcUkK#1J<20Z3=+D|UQmW1z<ox65V~Y2}IJyJtHrjgk
zv#_3?Ue+;sPFc>ZB%+7JL=36LY-;7Se4lIqy4?(mU0p4~ScV9nkWy~*j%B!!vr=NB
zt+^!r%X9BO>6=$OVA&KeQu!p`RNHr7P$>RzXj|w7r?p$eDSzh*Y?dVL)x~)~KR1KO
z($AK2hwzhGC`w#<`K8vzXYJ(3{=g&g?Q!4xdoJcMQeB_<ck}H2yU8aWwte_&I@gfc
z<hNTf_I8O$tC`co5#LBwINYj@+In~kdL%B}P7&|yVmVA`dxAP)<1;1nQz)l8r$o&o
za`an&IcFo$qF3Ma`Y)@ByfyFCpKyr0_bJ);tz6`Zyq|p7U!e^>a_n|1y0YR<4$8J4
zkHht7Jkf67>|(ex6H}pwHnY7~jlEs0=D^_5cxK~x)s%8x`!l6K|C4Hmx4p{u7t+Tc
zsIE5Km*yil*CUf(5NkU)npIRLUYM0Ei_<|DW);ce?xTB#m@er^%XI#XKf6j&>k^1(
z-#5FG`m<N=zu)i^@9rDUJgqUB{br#d=6YYF8fz$$vXIt6TI(~M6Dq09kW6y@{O*1I
zKA#Q@bLv#jrXcG#4Zrp*u{fQg5a!;Eq4YGzjJ`14>@3F&HjODK(Pq0{nupyLM!wbU
zdxCU5)l96HI(}WXWV%vOYN#U^+d)_h#3q|w9UXZ<o#2zO#J{RF_v(w<p4HEPp}jnq
z%`UnU?xK>#Pv2NC(NK#xnvCmH8zUYnemc<o%c;Y=dLQ&hp5fJFB{5X3s2^4~Ppb^0
z_-<{M)eMew86QrtMa_0%L*ob02_`d-cF^Y@5pR4S9PSj)Bo8Sm?8#B^%t5|uoM`O7
z`W#nDaN<-MQ`MLgD(3#?)VTPlaqLX)FQbcmmUsKEL2|^KCXZV|E#snKQTla->E2V;
zi!-k*trS#rbe;@$MModRC^*<xV4|OQ9H8-IC?ei?LlfOj9hWLINAwIhDY)P9N00Z(
zJ4emYcPI-!{5r45+uyu_>~+KF4@)IZg*lqC?b$qE`$lLx`$CyfcPQJ7uN)|1EgCr%
zDROpoY>ukFhsj?)W?%ndSH1gRXZk;<?O~EAy8T9V&#EX<N;Klce{CJQE4xpD_G(sp
zqOk`vQ@8S;7zH-(k+(+^gb(x%FmPean&ZV8yQ9R#SEt&jbV}+cJDzZEEB^4fd-0`|
z?Q3QIumZ)3|2mp=(tp3DF1%%}gT#G_)NDf?9o_^Uo@*$pgO<v&IbzrEI<4O5?W4Wx
zuB^`V{==Yqf+h9M9)>&b-me>T(J1`0@x!u)W^)BoN5W{Olq)Y;A8A;ey!Q}UkAJ_s
zkHg^Wrwf|X>mS+{JA<XJ&ydiGOV&Ti*%Q{(IVvQN%Rld2=~KH#B>eaiB_tWkI~W{E
zBrg@O4`5p}vwC1<cr1^YYw9JN_Gns~qLNekH9u4@2AOD^%nAK_4dmY{Vt*iW*=OX_
z?Zh?4oGtG=)#k3fsiTeXM$2*}WRHZG*6=x{>Z9;M)^5)3{F$-5#*Pna3gL+8Eu58H
zDJE0m$}jmK7eU{7j{NJ^MiEw3z>-U_A|?2A=kFVrmoxAQ{P(91A7iNQ9CQ_8{l>J!
z=gW8K=J*`JFOBQOb?O%d&BJ5X<`w6U_pbHDj|}%`rDWS3Ykz1rrgct$7VTw;vAL6z
z*3+W?LxSnL@`0{Y4Yx^qvD;s*e*Z*<KM_Mj5kJq}9Naz*|8(^j&G#RBZP~qTgBt43
z^4lyU5-KmDhDNhuP-i3fTLVW%-bN2&$gyow#p2u+_nmH<nyzy0$(bWF_mYOviEirC
zEASAMDmNt3S49*&{xdg<ToySU6pDsIAvQb_YN@OabJmmZ<#eOgT-ar1?hK=aPuv(C
zWK`$AoAL+x_GdYFF?3OCqKh1nYly0sB$4)4sg6^c3c56NB<|>k{!NK=VwuLIQ)7N6
zpVWUEvXKjz?mi~oS#@+Yda;GR!o}1tOM+h6Tt}hfq}1rwcSn-eZ_%2}Eqp>JzA~ch
zW^J6zRmV5Cz1AOh$VmU3Ja?!#ol7@8-7ce)pLjoLP%|QH{E=wg4`1XX{QJdwY}Yh?
z+EcUOXx`bgYsGigN37k>8Nma8cO+ie$--Ez*tzgUVcGd4$%Ts7U+T^NtdP!ocGtn>
zM(c3Uv4uq^QS0gz$G;?LRof3{h|}Ih&As!ulvb@XLHqkwxH|C9mD<O01<OWK2Z&NU
z(vImUopVM#G`{98?i{4fPMzHHDlXlW*Z@B}?)PN9F{9{v%lV&!JJ+|@@~K2W+<%j@
zzwiTJ{LSvYh>M&%wEorUKlxzzOgCi_(?UV#U1OA%*OXCrF~>hv4`&u*$DNH~GFq^t
zwt#-gXO$Th!Ug4J759e6v@FE+_umS2#;#kMx#z@sq&8wv;H*zeDSIVdpYhw1!`*yQ
z?RO$8iVl`td+>?FO;4%S;)jLO)0|PUn2OGM#hHc1h>msVwR9f3yE@A13zCif{wjsf
zBrnILWaon0MtiEg$to}XY(gEceAN7|afoTJM$_0C@84~&%B4SA`OX;oVr_=gfyl!g
zS|}}TZNvKU8hUy$!0KfPo!^hrf$hLW7#6p#0(r>P_V|vc=?5}Ty<zV;_S*FGbXN6C
z#0Du)cD}7%9m;>`CSi2jk*yZ&UpIJs;^3OIj@xtGdrGyvlxJ5<j!L=dwJkgT?#{kZ
zT<>6{gslH6)P?cTw)hr`QSw5<!s|Ny3(k1x&gp>`vP7WO&w5q?`NZ&OT_qp-z4}6b
zl*^3N(<K6ySg8JR-@WS^@IO6i<VBltq=Q`%b5ER`_cQ4$LnqE1s()B#bgM!*vIFNX
zMqG<k(kU^K7qGl2<n`JBzyTl{bj<huXms4gp=TYx@1xg`<3+72e>}fD160H67idna
zlFbFveONrTfMzRK3MfhORXdwi8hVmHxMN^1a9nE64;Dzd_faUdS~wF!doa=PLQ0$Z
zk8*)`=!H8z$P%@wt5k5&sw;;&Jx`ePW38KMIdu9wvRUrA%eC7S+7@tIpq-viUHK_w
zdt2;TtZuB%;=N>_zy8y50Txc>n4nK9u<Lv^YZ#h!w1hHY<P}t8Lgxo83YX&VPo)!s
zG$5d18e<iDpE>^e^Idu~vQsI2YE(6z>u8*?kq@aEsVXQ?3y{vcpJilW&=BmkaeZOt
zrR6vnG0QnkUYg6EshdW=g=LBN%EZyc{#G7JT&jjUz_hp{@u^Q{W>0sL^)LCsyZ-vJ
z4@$XO1<m+H_LH|`pu1HJ$kl;;CuHDcKs^z7&~GA*{>TrkYTHm3pG7FUfoRLX{}N(O
zkLXfaSSAY=b(xc16$mJQ<f}6WcX#EA6T9@9i`97Lgq4+@xpD_*3MR&A0e=Kv%acWk
zqt7Uh?Elt1w7&{mLwfb%-I&otbeyb{XYsCal}NV};QD>#L;#%0K8B98S&&8OX$s-e
z&>mRN3{^^1g8_$bU^;XHI?p+1QfjbvhrWtJG|>`TWy{s|z$SkZd8i$_>w!?oN|H&P
z*eoHs#{C@vXIC#R5N3E34Axzq7gcd(h81IvYPC;4N^hH%-`@^4esd_|DjIW!Npbz*
zli4Dv;vps3S8cyCnXr8Eu75_>Or=EGbiF>oHL>LSLVa}BaqfWh&&T@i3qln@0v53h
zJ*;mLSVpm-vA90-lSCBr^sLB|p!2ur-X=WxYXcj~TuYmxSkVfIub1{9>?ZSuP4#;p
z?VrpJoIx9Z%BV|^4)DGQm*;M#uMzn5(BKC;`X^JlqQH{qQxVWT+XKxJIfpNOJfpS-
z{qH9Vbl^{jT>V*biAg9!Q?@m|C`9(bL6q%LOC5y(h&Rlw8~~&R<Hd1kK`Q<kGMu@z
z0^yV_9?7SDPU1X5KNrSUR+rzE8<viaUwHSs^{G2@&EYtr;qpMo*23bhvT}fjltBa<
zPPljDmvQ@l9E3(L4~>GknU3<sXUr|-HYh4fSsioTmS<AxogX_#+VmF|=9vm@l5r7-
z;@#o1o)E2e3`oDIyX>aB?5+#$bn=nt?f*j0hWuyD{S%<p&BcMAnK^?{;(7B;KN2mB
z=ZmNGd;A2|>3Y2kCo?$Mm52h=Wzn|3)1`2YgFX%rqQ`r4U7U`4VTIF=h_3D-C9V?^
zkZ-$Ff!1A~%-XCN=!am#(>7mes&_p2vIcuB@9Qr#hw8r{s;4<5OZ)l@?IBq;eBO<b
zHCIdbb48M|gCq55!ID_s;AV@)1-pjX+~DT6iF}+z<D^|f$VC@1#sU7+kc;g$$}BZ8
z=}{7z%dWe?+D7BvM|HhfO^xRYAvUJ|zeP!m;eSRZa)5G`hDu*Yhsujs<S}d8q{=uj
zY(?U}h4q-@PAZ41|0x-M1R!crMUgBISnnstiq>xQjL`BZ*VoGl+JIS4KQCC)X2mVa
z6beh(NS&k88v=F}uGj09qY=@sZdk!z(u%kLOY^sDD~kNldiOY@Ct;0;j>C}paZWW`
zxV(2Yyp3u9CgO(Gs0d|B+_5UM9O>BDm-nCEE!uxRZPJ=fl0>helb*@N$hGjwP_dTw
z)`Dbb&z%GBk1^FO6waS@%+Yy#C@j6ak$ClDIP;HE>uW^s{$y|A)0*q<G4`TNwUWP5
zEpWm85Z&H<1P%NHeeKY-$xnnGMZv)w*8Tp4_r0Tg76aE`eW9^jlz-8oQ|9n_ruv~_
z1uNZME8a}9USj}O{)&|tAjVr+)e}b#C629#2pFwieXi4cB3qHJBK_QH{hVyO#sx<X
zBVU?Z{B;jQY3my3ID;DW+ApnZNEh1>oPA8vQo6G4$H~G`v*a31Un<einY|k$O}am3
z%H{&M^3PXz!=I7HJwSIZJp-glc_Dg0gEcve<eF7XnZsn%GPnebN*~A(&*we6uUfq_
zMX;v8c-_;2FwBzh+T1I>JZhke+FUZ|BSc_APS2Z%L(VRJWK4&U5$w+Mj^RcXG+VR{
zby|ON6W6fP-noCT3z0O{UxM`?3bEKZASEgYiGi*RG3fq&(|1cv?JXq6q%$!<OlGdL
zNFZpMnVEHNJR3W>|LMMLh`wLFc~mK~6*`gQY_9K=oYdrrhv3ih=&c?`-*i|+2N%l{
z@q{VE8$6<uK_5AX4^m)W!dlNAq86uHbiSXSW(RA-lhb@Qp!sZZcQ9@?kbSy$+`eHl
zR~4sbqU58ScF!eU2|W;V$zd>Hc0~kME_hVt6H`OI2sOp0iQ3)oby|P_?DcJyK)7VJ
z8Orx!b<ZuBe4POX*Dg?_saMnnwkGI^PlTqcQ&}-cH#-cXMcs1p1*$VWU^V7c4S?nF
zUTUS|j1D~ah?7zIlFzv61sjuQHIuQSd{=^}W}By~?e*K?W6$lFLW7!vho~AS1BW!1
zdne^WwAsu=b=vj&8_q&LoaLbLBsb1r^K!xd?6AOuTXTN$2agF#sfco)k5gDKdxsVp
zFmY+iS6H63UJ+>?M!@_(UvGQY7I03$@@v)yofH}8{Y4TUE(j!w-a^{^3!b;I&H`ch
zqq{P@^Gz?>^OFvlE!Sk{Z2J0mE<WN8I(17~PKoO@P5V*b)SB;Ye2vxt!xde1azW2}
z7{adSt!5;vNEElIa->A_bCDto0?uYion`e+CVc2&x)XL}0YBsXaNnHgJ$<d=7~LF4
z(;b5(+uVjNgW2(3Bi0?3|HRN&MUM&AzZ`;@jEy#zN@K1vXlru<VXx3MS>%J%-52A)
zUv`nLdubxCZ?>FR_x4gNHI|yuQMsKh?qFhllFCs2^M3d7P_#hvY)5GY_m!4`@w-x;
z&yxLL_8bd(L9fK(%lh_UtoVT=$!0S6ww_j|*(hm7QM^*~!!Xq3zIVy%v&K=00c7rD
zzgapqhGsee=k97F)lz8gAwpNw?UGTkQwAP+z?gVR+xW-5Nf-cJ0CF&`P3Yyj3q3hD
zreq+{>oD?R(wvAj`tYPW9vS3n*?^IY%T-?ab#KAV<?^=eGd@qF@IjnW%|#@eAg5cs
zYTstXO9QNm%R(+X5=Ilb7ko?_Z&$FHHDC`lb?3B2FJO9`F9+1oB98C;aW!|2^tO?Q
zIFZ~E#!y`)1sk7``_2s!`bsE;urel1=O}?<e;MXf*0_4ks`|9s0cujkYEl8+`csdZ
zrk%ewF<uyc<Nt^mJm8z%?|ylI|GfRFz>|!w0CK|SOen}`BKmWUgJ!oJjx>XWOFGh8
zgbgIYv_LB01P8_IQ^A5xbLud~=cHc$=O261VHi|3fcL&<Ra_33?6=3qB~&ETnuZ@6
zy@nhA^Jeb|tp-HnYq1|7t7EzN>R}VkJ`^W&tmpuOotuW8j>~f!9ao?uhNh7b7ncL~
zjO2Md1Jo*4GwQ(<m+8+wYQ)<&j5{{Kd5&=_nwU)-db={GR3(<h=YIKN`2*n3GhE*N
zZf~6V_s?-#dmmrb#<83GpVg^A7qv<6c+@@^`0Ep(pjBWnRaHtY@Ri)%iSMFtvmNie
z@2Tw_F<Lm~$KG@Lbvgu$$;InQHFI}wem{8g!NHqqIV61N$GajL%y+8R)P(h<X_S3C
z%_t^MzOFm*x{mec?LJd6yODbw!!d(X_9DRr*y)xDf^$j}W20C^eorPLVqk3ZVzX#$
zvr!{_47_s#-o6Rnq~k0);lv)M=fCeV-L94+vg^G&&%#Z+E!CcK0-1@7Vqx*i1JHf=
z0cK$2m;+M(xogFT;Chel$)SI7T7`wvSdq<+`IU(b0VNrv*dZxN>yAFT)?z&`+4701
zOiluV`a`$APZFBq8AF5jIJ#W)9x>pRO!W_WX*J61qZ@ib;r>dGriN=pK6hjAg=ST;
z(pc^SejK+#grPg(3a6R7jrN?@06FW4^M<ud;|Z=EIx^#^Tm(Du^m$g_Ail=s8<y1#
z97gXhDqm_2@2OnFD4L3v1;C*VlR05y23^;$J#CJBuoItjv=jKGC4a$}rjEnLm*${J
z=$-4Y84_JTHzYL$H>*knr5pQxe>H*roFr}Er$%H94JmcI;O_9UIjB7C0!HRlx6!1v
zLRdhn-x)TiOL;f8t<I6?7t17!-jxTj?!2OITfHMVzMe+)s)zK2rjeoLdzynn_~*%I
zjj`_MrZ>+NNRmtBz5E1l>MB!m<d!B(>m_!t{jANwxOZu9hwD`>hmB0u^4>{mZtPE*
z=oShdm7bkk^^+f8vQo92-3f?@>&3=(ro~OZdZ;~RR;o%|_UhxBp7vm=f21|A)2<}D
zY|3law&NaF6Z|v|k330;C%=IsR<W%hh9X0OAIi$a)v&Hh56IB+892Il*|>f8Pth?q
z=TAIht#MesP23(EmSFN@R~5*X?dhY{(BTb^@T=evVsN(?6&H8sWmFS6VSS*9)yUoq
z!$KwN{HWhQVeQ~^U8?3Qw3us#lGC^L_|yao(5klX*JSdy<`okDHs`o{SD4na=+Ng}
zC7+Oky=?Y-n_rcGLzM?m*rMYU$wL_XIkTs@ra!O_`-bW`Xz5yqO}nlwD*jm>U>qKe
zKBz{xya4-#U-@Fg(PXeQ7e7W)(Qq*wDW<HYrF^`cCuQp*_A{C)DRu8m1aE2cS>-l0
z7JK~1X#e?j4T;l70&4vd(i~pZ`8`;A_(PJ3E+A#@LUZsmLvuM>0N<X<9^<ApQZYs{
z^9jH5oW-euoDdl@WUWc%YwNR0qaEF0t2>R`l`gcewxZ6{kl(RaZ68dWZ>3F(x=}~#
zliYHLbv8;l+D%!ov0_i{Y=hJu)(V?s|F<QaXUPIOjd7$|hKyxfSGm!0!%5#0vr#OJ
zp~>bK67<S~>y_oum&)%dh2gdxq{srLadDFa3<C4QRuAg}qA{60UxO<jeYRkI`ZGH^
zf5J6{$3e_q%DxY^Ir&bMd%)(TTJ_Nf15rmdD)X+Y67Kw2vY9;R+hb=d+Q5S2;UbDQ
zSGm;&xj$7PEK-ndm055R4r({n+y&if)lJK~FVe-?$CY-ER@(SHjrzN28vY0D6Bq=s
z?g0DL)7KZu38eKks}LlmrKF_%bL8V!hafqTQEr(@zkOt$RFGyS_1!He@7cMfkLU89
zeNH2q4{^}E!OcBe@uyWdu&8QOCU-F@><EFCAJ;WjZqEJQ{G1LuP5b@|ig&VLa^HMm
z|H4h;$bNQrdD^W+oc{>eH$mom)T24$w>GTKEeV~zOv6U{<9IFW&F0~#%84Nrtcx<K
z^s#)ik^Bjp)Qdw(8l`WATQ#OK?|o##E}TeP7-=b03t{%TsH>#m8l7$@sxjNxmtn`=
z+@*X{ZZtlSYGT*kdUu{T_xD8Muy<evp%my*`AvWvw;#kcvg_*V_}6w@BD`O*dtD(e
zU6eF77i3S0rTQT2U>fE(*&@QAut58U9X`?K1+(`>J|l09e$H2x>>=3+zK3*3teo!f
zSUCFgQVSZ6N_fN=BwV}?o;6B}m2<PXZ9mbH5EpwwT7bc`jhjT}!;j-P!L)q1te{HB
z{MjT`eOBDq)BY?5pN%<*nS959TU)iizmfLGY-YKMc7E5SC@xcda`lRQ+GO;`ZOy{0
z)Xg9PWv^}8vx?GO<b}67RqgExZPRQO283xgol(y%$C#mo5|rplrRG|W%W@jcEc+hf
zdXduv#_J(IIrb3mycx|A<&A?!B3AI@oJX2OU2y9m^UB5?KZmAzl)WnL<IV(alH}qX
zwx=)b$kPt&aLpNeZPEraYOcMaEm7976Ps7Q*I=zsWRT0VNEgAbC$du6H}nt*{(TQV
znVRX2m2_XNruI}3cOkUAYmF9F1C^0yrft#Mb+Nm7DDS)pJFR6A)yF>{anLI#eZI&?
zS<~2Gxp#IJRV`<)<04YTL!-t)Q`~xv#c#TWk%heh39%I&51wcJe#<S$g5qs-&FxV`
z71#6Ejjj$eC$v``t6gw>*tF9BR$0|<!W!>2JgLZ16I5Wkd>(UNW@_q~i7n$@!cq1J
zl;$-_+?@-em*1<%Ef%I;-wf@1H>{VwaK&DnDUDhowkN+iLP9z~z|8^4R|md{7d-?a
zg(b*pg2IJbN@iZ(T@a4@2=glj|9+NzF}PWkJIOLaAp}0rO1{QiH$fZgv#VPwr4=n4
zGyz=n0*C5pU#DP`&0Lk+=3*m@BqjHGw9ONiRx=5&=DC0DYu&bq(vS*O5eXgIO_G0l
z%ng3!arr8>Ds<ux)H>N7K?SIXkAVALxV^tikIF|PZE`3cjTRM-irpdH@KTnRv%&9F
z_<m;Rp<|^{9ilsJ94vhNH1t-O4O|E~txU(Ns{E&1SUs)V<|Na3Jz<xV<e}5+DCO#g
z2-4{7UcA+<ogr;f%&a!aA-2?TvSoEqj;XmaR!>RlVEoR)>}hgAa~cEfa_qC9ot`5;
zA!S*M?50{xqA3gVr$ZMwPd2%yUoYj@u{b=ik&>%a{r1|1aL0PPN@eQuq~|aX*XHVr
z)%+SGul(!VZ%<~HFP;WblS)EFl>Tz;pGIW^wH97ox6#o4I|&KXD=^Br;kC24V=-Ln
z6p=qTc%KZ6-hBwX{b#u;d?mqUIf+g>$uEbN3Clpnwq{|>oZpq%zxHiTXY{0&N8rN2
z=ZOQ81Qo5abs^4BxoxAI>}-92UEl651gi$1+c6UE1mj_iAN4hg!_gZ}qoEFrM7N>x
z`Wou7yUUHzNrO9ABe!L;l3MIHs57XVw-2bx5?IU~wr3*Clt0S_MYlh)72IqhQ+)dJ
zAd1A#?ijdPzwP+(T+qhATdLwD87{U(Atv*?+enIzm+A{`W4yCSXyE5b!clMC=VzT7
zCmoYM&o#1(eYCgPyEHMv_`ONTMQq~HWL9c%*`alpNeRm8+1x6FP%m1UxrO5LD8_kV
z2@WrvZNAKT(%Pv$mx;<LoAzfr2dDR4&HD>drZCfx1(GKNnrk~`ubsHasHh^4^fO|@
zgAs?Ew;o^F-w6$GsgKcACDBwQiCS?}GqY69oRKJPgUT&AU@JP6^LU>McWlESB=<z}
z5XE12KDc;C?M18Q4DecA?1*fL3TnMz=3dY`XX=Zosx*D7z{C~HJ1{V~%*?siqSN-w
zuDhTxxk*=PliP+AklEgHGE`e>+elDSKxad1;PmHwfkI$oplNQInVLz|XTi-21Zrbf
zTIR<CE0oZt`IU1?+gg^s!OCakF2wQwr7Rh7hWze{)@P2`2Dh$j8wdACEDW%p{L)?D
z=yvhCL{PLv`=nz-$c98|bZE4HG_fV)yqP=8B$m7|85$xHu&TzGkhl7lIiDrSQF}w4
zI*ncAVR35H15uNu`K5%VXHel1@AcfE?!A=C>XLq$&{M){rcyZJC^q4&6|v%y+|=zQ
z*Is#lt^F<`(@wN`Hm9t4tYPcD*b4L@cw$A2H{cx==j2Z<t<nnkF03#&lj*<Eyk@0&
zofMk5^eo|3^rWl34)+SN#gpeiQ`!3Zz|7*}nx?dSQqA7RoPhHylHuDM7pnRO7L*k1
z#ST~1qv{*T1HrT(8(y4Pwmp8kwP~iIVvM5~zuGF(G$NN2JvsDEW&=;tPV$nD-oZzt
zhED7l1TfrGtAEM5XxKpVY#32ADGNGFmVDPR@*|K}bn`Z)NsyfSoz3-T4!7;@6s3^<
zae_c$5b3lQFV%-pZ&^0F;B>1Cwc`hC)y5UMg=~_gg-Iq6)Ni-k=!FNwFd@l1IS;E$
zXLH>3?N~2=3Er0CD9>xzmV8KEd!(J09oI4<5+;`FI)&F0!LAFhO^*%*Zx*n|HiVw?
z3`nWVjb6NGgV0mbv3x(6`0<D`itb!<zCq5a(>}K`Cnc|bskM0b?ac$^3gIhVpEm7W
z<g2Pe-D6^QD&@9?90~3+R9uUa=gr!P#U@@moMdK2g1cDBVr|kW&gi74q5WBNswkMO
zNROWfk=D7F{zXVK8(4%Knf}Q=v%Ok9x@Y^KppMjww)_hLHNmp}_AZpPDjQR;x6cn>
zeZ*?Zp%a5_dtMt3nK+s>yPd1TH<N2-N$&y0d=RF;XUm<fav@gNpY8Z-wlJAAqCO6-
z``&z6UOa**r(2OX;im}a`jZOX`Pwd*`Gg5>1^ekgM!O%cZnH7ii56UQk!`!vj+JM#
zXY*``b3KQ7dv#-KY}NWFC9O^pJF{GCPu!@FBa6d6V)jrYwd&@DIOf2ZpJy=ov#3j{
zAuBi9%Nc5EG4D=q(uxZR`?2RM?`^H<EYHZ0b$3@4zNmmCa1%eK)YF|qQoR3uaeTAW
zvTq2+amu&lud&AR9;(lWa-yrG<R<2v8Qa$CTOEFZvFVoZIB^G0du=S4mdz}#XQ_){
zSysh4bgSh`bERYzcS&rdlXL-3YRknV;Ym*~1WP78SnligopmphXZpmk7@9J)J&mb!
zFzaf;s6MYKkK4d_=;t>Nl$OLrt$MC445S`<IH=Gof56sS`Dz0xa#D)&thO?xvTB2M
zvwP(ADfgrsC52myKJ(LO7B_hoXBp>=S#ay>b8)NF(&P%%PF3(qNwWu)(5LAO>kjNX
z+8YOHXV0ih1nK%mSwkXJgqQ9-5)p?#_BvvcSheC@o|T#{{lOPg{qXOC<v(k|n+=Is
z59Tlr=It*>6>rqhu`XYqIr#QSden_8fw6MO`<jPOwjcabPxI=bw^Z!Ws?_H12RUQq
z9@?<UU!ER$_E!M=ap8Q*$?p2s>@;cYf)C@q8+xV7q$|xeQa#Nw^A9Spv6m87U1I~&
z!WI=*X_Qn|Dk>Zku1J2kd9a2S5@F%<ALFQv{W9#n(%GEf_2>WG*-a?4M2AkaNU-iP
zPJ<#9D7`3AZY0wxi)w^ydnYX2SUzuh{LaI$^J&XkLja4u_MMNw1dnaXp?B2XJe!iv
z434zZ(j;xi1qW8$362g8*^;u?vQLV=IF<`J?cno8twyY2O#aa(Tx7CYV~|?E#UwL3
zDKZ)(y;*qp?fP-wXLm=`_#c+-NodD7*u=~A%a1As@y<r^=9`d|Ddi@t7hOi5VMcGv
z-Fkjw&i~mHcD)G+p~N4rPP0wFcs`Hb_*o(>b6lc2A%I<Tm6-IrvgrVNu-0&Y2H)4S
z%hK(yR{KS!9il29*V1kFzhf7nprHC=xA^|=9;NV>ugkxX%1V+@(zsXJJk0dXJFxQX
z8TY5x*W{1PM$y>YUYC<*JsVWUW9H5sgh`Mw6(t{kF*MmjOA9s|!i<Ea`J-n^!Y?Om
z6l{cF*Dd!s*zn8eK-s2N&4qPhBtB4TaVV9Cf+OaSoh9D7-EM|o*9J#?JAz$AWPh9=
zhQGNj+?M)%>b|)qo#Ni+gOk(ye{7=jy~&*W(ozpi*a)xLd2Xcbd$skcDywC?@W~DR
z3fJrV3T;QFhJNOpzGf)0!}eS&E~hb79WMIUn`xBtLt7iUFyjYj%>46i?H12}yYBJl
zfZ+MHH}{D<O5aU3n+{06J~!ZvzTVkm#eG5xGHhvfX#2V?S4(H|gy(Z&*CspOB*23I
zJ!T-ZTls+8sCA&+Y6YWkqj6H)nZTWAd)_)oML4`<xY<1&J}2u5Di%vI6U?I(H4k^Y
z-+sF=1x2vE*|bZOyLYN$_xp7%B??IfrS?m*YGa$O=Yq-yt~a(P)&z62+E(VOHdD!B
zxk(hyR_*PjW!qZmsbn{_8kY$K8_As^hcsjP)EIlk2q^&(6~=GUznA!s%Xwc9HEEnI
zoUp$Q373+x#M?L37aFtg!6WWJ&G;ANB;mjA3Hc904>02Y^S8PGuYUI^q-@_Otv`Kg
zutd-`4ozbZOo9)N8m*KpsD$h!<=)5qYcP3z{P4I0<QPx=_MO34a`!h|kJ+VnBI5#>
ztf29LbKz@V4b~GTG(%I*|BnxO_1OH^JQ?5x<kWWe4=zF~IREt({@4H9weY{Kxs81}
zUD&-JSlQb4cPmTBXvayqW6SkzPrExB;yyk~L^LN`HbCnt6Bx=ua%7<LYEC!3ha$ZE
zx9>xA_`iQcEiq=NCGI@mkQoOup6OXxJldPta4+vvP^c^zR*klI*+&MlxgdX$?=RtJ
z2<i6JxlvHCUH<L6a2<O0H>7<vAivhshfTER*^RJ&|2TE5yImf?|B8Zw;s3|q-767?
zEU%Tdbq|OZ3*i9Qhco`RwtuhluNA&)8Om4pu8j7-KR8mDskuw|U;Cb-?$rN}OY(nn
z&wno`uF&Ew)GAmPhDy|6pcO>8#6V%w3C7*ifzWVhcprtKE_6$P@ap{nQ$J_IP}<s>
zvs;BesCLhOdrI90#GLn5mkKg66hP@VdK=_c5vK}d0FmM@*)Y_5qcxl_@dFBkPn19+
zB^{J$6I~^;U%c2~v0ky*3(CD`!ZKXL*pnsLq1=-(GNLaDD$huB2_(VT0_qdB3ruH$
z^04Cx<>A+ZnrYc&1ve!y10YeuusRiGYYA;dbF;H|O-z!3t11O3^lr$qU<%9i7heXn
zLM`v=?DHC<3aBdGukx&bHlX8&o~!HWWr0MGWn(bweK=syvEU@0r=@tg@@E2&I%b51
zo)nEyx)Ud*2XtYX$niTDEf4>rY_-2?TLXthHAlCDBx?eQ7HdDg8gIVvZaErCz$da>
zWqowV{xG62^hGpEP2uftsm8H9zWQlvTzWfkpvudur0fvIRqY}yHWGA3n-MuRQ_$cf
z`^N<P;ufwz87bRsE-m%eFab0fTS2&nb9~8l!@4=2yTE#Ye+3xw0@7n{oHOzS!jVY$
zq)DSImgeTXh~_C2D`Wqf{)6Ibo*s}`QI7Wk<%_IiCX}gGo?rH;{z=~UtRDe|+&+n|
zA2o>tmw(sM7izGnC7|lr3UoII%Inr$C8mH%@k%j>SLXV+?=B$HA~FT6j!8@Cihcfz
z0%mQ3NUzNu1%>99gR^sU{05|^dx{tgrV3R)ExE`C6c=Ig7ibd2QXxQS;)bB$)2PPw
z_TtV6hzXjg|Gi#cOB_<&K()2?sPK>>P<7aVgwN~cN_VO3R`xp4_ab0{IZ9zStblft
z{inn8LjiSlw?6Abi*_H#{tno3pmPHd61tScMB^I^+Z+8=SES-><rcI;O%SQ@V-?l7
zzCKgUOf9|%hx@sPkF|mI>Z26wvDXWTK5pE2uiVHF7;GoT3ujO6NFAf{@flX)%pof{
z?d~p^wHFsa4Hg>djVc4%bVI#arWw?g-f2oO3kwg~J$)p?Si?LD)nI9=4A9y}TXit>
z+Dl6TlS9<n+L}?E+PNTJXc8C>#4T--r?xj2S)9d@E&l280;CCq5K6ul-_rv7nXFzP
z6PzCOco0qdx}Lv|ZqL=A3p59TFUk!)5hQCp3B5y5QN9ZmXCO;VfaWWlb6k6>Muz4r
zu#I?emK~<JI>4h>s*b1+U31$A3IfhXH9`7bXw^DYr!0?D-igqpbp*c@0t{Jkmr7^O
zvC<sWaWghf1h&X5q>MROq(BNg1bkI~2=N+KNsJ0d5^!+LCWq<ghqy~HgFcb6e$G80
zp3~%|<DRJp@uwW{aDHVDdnw|-iiwFif>yFQ6qzY;K;M)B2c3$f)h@M}DYg2Zn)CRf
zfhH>}D<Q9K4<LK704|Q#_2bwDINz}ZV4gZFJy`axe>+(Wc`{>VqvY)ENrUa=Z-WI1
z3D<#MwF4w<;m8)6wKGG{o*5P%#vMctc=e|*@w_zrrCW2*?9~ekrmaF)Ar;z<T?c*B
z^yK85aKD#+GJP6fngJGyK3S-~T*D3I8a-8#B0a<{{uzAy&p!$ctM@*?eik)cs?llQ
zgC)a~Rw3Pi8tUptNYNn5ev#b1vlWollF!{5DWu@%=Qmi1ala-Vt&|E<w^A68=4G(|
z_pyRncenJ;iZpc^!EV&0y}pR_MTGshwx}9`f-O$RrX=}WTSD{Ys-gM8A}%#{=71;=
z+U{tH5UAI*g=T`Gm?IP--xxs`IwCI@i|s>{s;7Wutzve5zOeUA?E_QL#hZGkro>5S
zg~-xUms9{sItg;Cz&3kDS&EdDH3G$1V`wBSe}Y9+t<M%aktV}%Yfd7JQwBRkgFEmP
zQj|m(uegk2iqkSO3VQX*UGrYmJ{Tt;<_il{!#!7Iv<P%U(GeQ=b#xA!T19{bcLRDr
zl=2Ut&hRM#nW;l{H9H$QJM{KC$)#(TCq?&A@ZN{bYF4ea8WiC1Wee!u1S={kD2Xl$
zgC=m~x7$5{Gfx=Uf0&U+=1ZjT?gB=T%|mr@fRu)|KnGYD)?TwXEv<3LvHK3CGB3*7
zy0C(MB+OjU5@^iiaGpdWamq}3GXm!IBG5cMJX2f-q}$c=bBJzUVEZgsWv4hV<Da<E
zZPScm%ifC)ApScr2hw2?kdQc%7NZ&q7y!{p-i$dW{@0(rKQZebx>3r$4%e5C2_8@p
zY;A2#Nl$NDli3DZZPO|uNK{&Y0XECn>CN#hdM3G_B7O@L!n6czM<fuD)X3P_*lYOu
z2Yf5kJ3pu?PZ^Tl&uwoFkCJ0hRpb8RZH|tPtJ*-jb-b_$l$M_ecWmYZfE{-fLu~@z
zbp^T4pv*ZdEi0?2w+f$KR@V!QsA3IK=#@~D)0}QHePEv!j#Z73L3h0oy9jm}W!pAY
z1~wW+w6e6^Pp&EEORiYEjQl;``>>YTX&*i`_+GOqcK>Ac%|qsN4juk9pv>g;k@d>{
zD{CNM*yHHpLjAEF6yJ<=FNLA1Y=MD!A7N#jW#_AARd#k8P$FfIZU7ZcKvre*<i$T<
zud^~zo$|#&HnW81MrcMw#W3<NnmgTb+pF<^8*pAW2Em;4bbe@U6aY&XQv!F=0z!4I
z(C7H(<i9p8AYRlvp#<D7N_t3HlGx*U`F6Kv)_oIPDy+DmuyELidKl6eg*F4r&tpGw
z*I++V+7hyLOYA$y;B&2_PJ9_(3P;FJKVh!F4;20R?I{X144C67)~TBE)YmoACbY<2
zTW^dbqX<JK*oZq#7{q|9LBpT+M>``^?u}HhjZ|39;hr9KUan>kh}=KK9wZkIQn9_*
zcIX$KyoS5jTja&reh>=p_bWY0P+O!{TTiu~ae=-7<ZRHrx)XI}ppH>~3QQt>S0$+c
zQ@KN4Tr9SM(*!#k+q*NX>?LN-5Vz5j`hqO{gk|g70W1b-ai%@=eE*Oc<j}F&gE2~}
z+Et!p@6BDjzzc=7d0@VbaFx){&C<H&u<Q4XtVRByxEUyS0pYO5H6b*c&H0&o);GT$
zkp7^iB+6Pa0<!X>TT{ZuU9y0e5`mCNjXA(fYf~gfNzaQ+#O4ENvF;!vjVhoP*0OQ)
zlwMK|mALgM3=mPSf*N0^rZenes*lP#I#CE)PD`nY_6E35Df1mEpsBQkRg54)ZO)=M
zU1J|XEq&L@Zxao6G{6rAUucaGklk5g+2Q+c3>ctF)y2idl-#4H+zSn*)qB=q&IX~X
zw>CjQfG;1Jpw?uyqZ$JTFp&sMFXmvyB8Z55;B=gq=Utp(rt$;Ct#$#Lv&I*gwsygz
z6;8q+CJxspY%}!Lb{Ia8;%=bS4vq*ROgw-(@8w$w+pVXwlkb}pY4NGotb&2+&6~B}
zn~a`6$4Y;EJ}iJ=axMS1{@i01S6;9f`}|xCkk6!@uO4Hl)k@NmrxG0l<VB9xBJK>d
z_RRWDacjkc-#cnCs&J{~3w9g&#ZTp#LAgt*<dk}FPghq0oclfNB#`~H(rIUTy6FIY
zN==LKHw`>VCJQ4KWuqHaurE%$spZEb4Zna`_oM2^?2eZYNPJzS{M*OPPKFuu0o!S)
z_72#-oU3PezmFrz?O5V_e;?qC?uYX(fCm@X3(^)Hwq#ZP&9FIS6J`OoVGTBRr@rrj
zVP^a8s@=A-%0OWNQOk_D0Fxz_2++e5KNaw5ca@In$v0jdwn&W$)eg9oxKYEh4R04C
z?lM=@uiu<`VDBD1z@JaC=TG=|lQ3=fb|3V+k5}61+ozx9UF%@HdKa<I#aTH(Vk~#+
z&1`82u;_47KCMn59)%Vn(C5sKw~mjH1f^)hD52WLbvytKMpYe>lOG?Yu~+sMie;n2
z=S7dd`cL<2IN*b}!@HKamd5XvQzL5_2XlbJ?KU*JPutAzr|a<WFnTJ&%-TeT$(^UU
zYc@5`hKs>`p6A!e{0ag#3kQws86A8POrC(P*0QY!e993L8A*>ra~!8%t|U37Dn@j{
zbt>XwN89AtV;)BNnw$Zl-e73{HN`;8fQEDBGt{Sr?8xNLMJ2<olI*@r5JkQI?k7Bw
zn?)@&!77j74p1NF$3r{>%Hz&K=>5<iSO|*IPx4>9c;Shk4(P3C@haE7)E|fwpG|tZ
zUt;7c?!}6GHBdis<@85sk5;%BFA%0451wvD&W~0lSQJaZ5Wz`MN*ji5w?wfKU>QB`
zcS`_|l)kSWU=o}lD+tw5PY+hT104U!d<$$!(~txktJ&(sD|H*!5!b1Tkgr<f@%=e0
z_8)cuS{94f))J-<xX9zy2*Xj@>*$L6`41ZdH?`6qjZE5~h?em}h5Q?z9QLbs`1}<T
zJn<YrSZu2!4JX2<$jg(wH4b%xGBAagu|y!wZW>=&)u~c9b`qp7YUP(2f-FZ4h+K*!
zVSkeMQSf$_gZx7&_#*-Q=tf`h3t}D0;I=4pXgW`wE5)Z9OUuodaeV<&8#67-Kqr`;
zkyCje3CVzrc4CDj2u?IhEAKjjnd5{5*PrKkPAouLkRA?y{_}Wn0)aLsseNinL8aQ-
z1fe2~b6Tm-1;Baqr<7Oa7T+Tfc70v@K*lmdeSAv`aqaLNNewtoC!O6B?R%f&co+0O
zqD&Cb(Z{!pxBZ%H)I;7&VaQ(w{?-}~WPNqs#FhHKJo*e_+YxK`?&AD>`%m>x{8O@a
zO;s&ofRP;vY|Oz*qLat<pqDRE$|kSA1`K0badC0H{=cDuucl{*NA(lRIRc@NHB2`F
zD1&E^p$*`{wN0z2(1ZFmgpA$bb{vPD`gbE{@Hr;9c_WNbCQCUJWN<!dk@|`&S$)nk
zWRSq5rX#19+-D1ECU&W%gE~KF6IsX~q(IAbP1~!`_yIt7gi{#VoDf=_ev7>=)J8r1
zPY8Oz)=VOrwvGzsx)S?_hPaItO@|kp5su;-u{uDTC_Hf7-kgOp@7qq6?H>mup)ntu
z55pd=pD@eyeGqIP&1l`qL+@;pWh~jfs)7o8NB=clV<2qN4_4`n{IaV=DIlGP+I=4a
z#0Qt`peAo$q8mZHaqfL+Xy_Aka{0gTgrvM`FTyBB>`^;xL$#;?k4CtPbSDOQ^I<#8
z6*C)S&cX^@O^XJGxQ)0SQd|qso%>`ru+V2hJBU8e6u)6c&zyx(7_TlMhdd0BPviba
zyZ6kP)y|e}F3rr2%g_|=86Z8#cQ+W+z4^ZJbuWYH$HL~Uu~3_W3=$&Ter~mLeS9gJ
z*ID=Jy*=KKom^aSeHf|H+#M~#`sM)%HVO(kOBAZtBuvkgOB_&;)7DZw^+$v4uaVMg
zRBTnAg7g|9jG_H_9!qh|LifMxamxyfeCbjhg8<%OqbtmD=Dp9!$*+k_)I80qkd5H^
z*4O`4=a=^beuzVRzuz;!6zO}Bo#Y#rcKeBe_wqfgIVatbIT(!S-yx6gSb&;H1O_lt
zGCUP6YKb3L;}&@PPW|mBfl2fIIt1CJ!023LGb|?Gm>M}DJ#e8Pe2pWS*pG)`sHpX2
zY>xV3s?YVawc|^7i!{<7d5WL_V_wvaIz=A<#=XXOisBZ}j*kl=G4n+_viFVmQg~0W
z<Vi!Xs|6%fBJ#neB4+9-0UocIS0(`;UTdD3gMEQs64%@{Pvjcut*6lDdSSLhhPoGl
zQ3*_C>c)#V=*NA!VF2rVZ)c}bRFFb2GGrpJDkJkW)^o#x4C$d>@POF`ZDI;QqWIpX
zWb!2HYwk6!d)b8x>yS^H1C0hZ<P!&ELu1tIv3)E*y+IwrL=WWR?ZsmD^~DI7`DcTO
zNETqkPk|-sm5)3xp!nMMQBZ;OT|haHr*JqUBoF9{AbEf*tsg(V#<y%nb@ci5#5u4P
zZ##kaI2C~x*nFg!I}$WK;y3-B%L!JYi<NbF)opzh#1k3{B1sk~-)jU13wR6xDmhQj
zvZ<Dy=W@<(sm9_kg?G^0Vn8H>=ZMR9YT`^?Es^}|&zT*Fc_8e?G-z)7*?J_N`FM%1
zF95syNkGdz2(0J_;3h3HH2vWLHoG5E8EMG85QvS?=h~Fik%9`5Hgu7|M<}Uf?9X9C
zdl(@x>{(tEe0}3wjnpj>M&=*k6cmgzApYZwnHC>~&VE3B2;%W6+%d1mZ{?Nd7@tgx
zsT`bJTz|4_&CVBr9YmHdf{3ull3%plO8>71HTrcKnmhE_qwPL>Z;CNekzs0}u+_fQ
zsAj(;dA0i~wnj_3919S{52n+UJ-*YHet*q{MC9_S7)(XwMsLm>0Ap3alR)?3yf~6I
z3R%Dn&~9LB&c7g^m6cUG5v+h%Kcf4Vz2C}t3#9wl?#%e0#{_Drta<_}CZ9e~?%jh!
z8@Sw{X4_Fp+=BgRccu)Qubo<j`oA`T-o}Gr`(_>)sXF+Km4rhCtMW^e|JbElbB7In
zJ{iQ}Sml8-G_aDqV0)^<Pt~u2*20%X2?mUO)xC_H;SR-;$Oy)R-2Jd--@T6LJWoeB
z7QFQLbRRuOBIez0?Hs`5(9Lgy0MS%%)+NI>NGcE!Tp=Rc6Oswo=W)Ovt2qq14jASt
z-SI8CL8SATvEkzt7B`(O8(vWu59PHaNte5$(HkCLrmsG!{^ni(2gSFmM2Obs1mZ}(
zC~L{Z@)buw;-;pzPQTvD6Y`p8QuM@xHTO=(d`?8A=3suEHMt=O+0kHL`0=BgBkSrq
zIy$S)x5Sz0jc=`*{?PgtAo?{m-G;=ajy4R%oxH3sF<~4Hl?5#Ep1xahJk>vjmQw;u
z@djAaNbNW=r*tr*cbObJuI(eZK&LSt<dQ10kO2XPt}pDyL{GFqRk80Hc{PuIdiPL+
zr*(V3<f=p`4$#?m5OOot*NS0NK!>6clQE=#utZ3#o=k?x9L0wa(e?pTWsVkiC@1L0
zJokj*os;F965(5UPGDnd2Bp0+ZLF;sRmDl0eo0x|o|idxLt2`WfI2B~OGtd@D=!WP
zb$tP6wy9S$W@irBQcB`&w*`aUGg`P@kJMTk$LKcVMn+a**T42{((9bYALi@ZM{yf*
zSH)Qdz`-u#Nlik=0U>J}tPRs(olmKyv{Y;hjT0^aY<K@;oHDtNDO^kq=oPtql>Cn;
z{RNf`dq6)$$gGV~X8>S@kCX4#1~g*NeAuJ}u(Jm5J2zj@j+GRf1Nhq`LoInBpk82p
zd~#<Aomn?WdH7_*q`gU>%=<uO_Js`<vJ2*40a9;S1}0W_?NFha`sK-HRQ*>hO6OV(
zBy(#tQ@@UnK>~Nv<Fo89oPji7nd(3X6?5M<iHI8<JT%AKM?FFX#jp6UzI^EAMs}15
zvQ2%;t7^tNVCu($%N-E)p*cTiMQcmTeDcxi@g;MBDwsZ=-p$$FzzA`T)wt8t;1pyK
z#6Ph^;RU4dK1DklMEn5>W(tY}!GP2hSV#@BJ;TE>oH~#*LZ*ru7S{u2!jtJUhHZ)w
zX!srbc`Ww|V7tFDqIZ|SPqO73K4uEuyGTfeCEAHdWRe|Z)Pn&7B6l884r~Xvpzd{t
ziHQlDPg<F##?z6Ao9GR*Z91$)Hydz|o-GcNX1k>OvE$0ImaC``%V?HeG_8)DGx21b
z!^TgM?84uexLtZ@D{y_dz$^gfjU%;P%7!UzJN8l7#RK?z3Ta@Ys9NZ!rRs(pcnWl!
z9%B*q$^_A+FjPQ3eG5W2|7~gi;Fajn!}=Ja299M4s8mN56LW4O#RlJdFcI-=$i~GK
z17(mwz?FgPmwV}?qan0K0BB;~!$un12IzgfDDv7)Okkj=M?T`N&-N{<v|(nj`6}v?
z`8Ek+eU<6>r&L{l_a9oV_ueyLvS&!9pEk}5N5)0Y7HKpg;R1ZX%Yw}92aSR51U?@=
zv%ngkp_vuTXDYY34W=5Kb=ja&7pXm9pgNNP8z%~+QXFwCk<;yQIS`rXLaH)RZ0)@d
zvt^Wp1l<uT(%wq|F<?~5%5*qC^GTlcy=^S~IgbW%OOANQxZG|{<w*be0Eq<T*IF#_
z&@&e3Aeqedp}@TJoK8%7`o$O^QN`xJ_phj<%er6Gq<N*|t4{jAOYjJ`TGLp=b{Yy$
z5uhoi<!#dYjIDZ21z#cwDS9S%^tT$6xhx`(+>Cc|QF0q{7u{GX&BfH|;uYZh=7Rf7
zi-97DlBA?$1QDs^_%PZx1i~2ZKZ{b#kn`_GMcCY-ZTPk+W@V}s?kn|0;Ubg+)NmV6
zI#p}Q2D^TCexN`WNyQYwz@Xv)fL|mNZa&o#ank}3@W5tG2UL?uz?hvyAP`zMx{?)y
zK)1jX9|!0ZJD`U|yQq<Jw+?4tzKzixvVqQkVb!U3yXQ9>=Eafb<&9h^Br}NP14Mpk
zZk4!yPOiQNG;k1T7(oPSG^BJjQ|$Tc6xa2nsX2AYvkx>OxtFsG3~y?oRzy9}1j0Ri
zW_ooS+z1@zbd_KRuQRN<2DKO}JRQ=ui;{HddZ$Y={*DNzC4K9KYS$`Mgv|Qj1h&B>
zq9?vRR#FFag4!}6CxZu=-5S3jm&h_*kK8c_Yj$fv-HND@HTWi#<5WH}$T(BhJa{_#
z)hA0ZUD9E&y&Tqp2Ip}J#5M9}&QVZ|YeIqn&dz(tf0;DVGcXW=tKAB?j&E8`JH%~9
zb2m3-?m)!Sg&2#W)Z*fM2wu;c4_3Hz+AeNm1C0#-MK~GzB6%$8!78Nawe~dwBFs6s
za~8X}U3@Y=06D4)Rikm*Ua*JullOh(yCppA_%>_4AdZ}g((Lp2PdEXxIuR%`l4d_a
z<#S+okzJbfYB%l$yV&O=y)`C<=kH_RHUG0uS+?R)WJt@aai3)9s#KOH7cRVt#4uf6
zfK*5>$o_<AkCr-R5J0)_8tx}#z-$;25nz~+l41(M(CC3G&|T}n;l#g5LBQRQ%=xu|
zL%ck}g`~X^G|ld}?(mt8k3!|b=K7*A6#C?vJUmvSM(2yS+hW99A%$Q~v@Kt%qgkV)
z@LtBx&kNQwxkRHRGPAOTa;mmB!V6s>g)}%QM))|cC@U))=S2JJ8IonUCIV0~JQvgu
zzW}M(HW5ZDTqS^^MQ<V`@lixT4sDN+I+bq`>@{572Ut-mw^0V0kJY>R;#MeDZe)3Z
z+6!*3zo#c0VzO&E&dDOLv^FHi`Lcd|3UYCUe%IoF-$_w|dC?^f!QzGzWRRH?F%<tN
zPY(ci`yqqNg$O7OIDX<Ap)(%Atm_FF*B!xmLh&LOa<yc_3X)g)UrFTdSwR?Opj(Xw
zuMh!%9W{!SK0rN23h(0R7!Cq1gL7VUYH{?1e`n{Po^IVFkUn0Cqnm#A{X8TnK2$FG
z*&f%mGk8>c0=oxNMN!~$KYTmqT<3c_1t6tI%mEncheM;u>%5JtB1gGF%;rPxgvB&>
zM{?pzBoFMGzto0#0W>D+RDilmIH>yM#M{ETuzlfS1DX3BLtOuuUAPyy->ZlyK=Udb
z3KO`V-rj*ZW{?hHqxS=Iv}@muEd*^{iwg_GpHhi^Fx&y1qAzO)#zD93_>d!OqsJz0
zl=-)M2tAl;{!o*Fy7vguEQjJL7QL{rFjcjUuR64yi*7jU6W-R=wg-}*yCuaD@cxJE
z;-H*7ka@o-{TmrSHD&66&ngBt75M}Mm8Qx)IH-M0y*%%q`+wMb%djf9u5B1~Z&5@r
z5tJ}c5CLgvMHE50OG>1rOPVbLTP2hfBqbNANJuO|5s;DwX=zxLw3L2hE<kZV@AvEd
z^&ZEwj{8tIH|vUd&1=pv&T*dSn3C7hGIs?__>N9!GsFtfg6@0T%MjuXLnQ=e2-8>}
z0uAP&hES(81Ftp9Eh^ifbj{O;SfC(AEr+jG5%{o<KoxRo-h2$L^QJywT&8LzZss&&
ziS)Yg5a{6{y!D~D_rrmJ+zWDgu;;!7cuq37{@5UVI0b`ggrRTg?KSJtR8_^G9S1-*
zx&2O1j$Ba<QOwp&*Tky98h9$ZH`8_T669)GUu6|wR|<#80JoJ5OY*}hDBuL6E~aOe
zlai8dB}LkW!+jhFC!@heKtYEW9djJ!^-;E-D;>sm6VVGuc!r*x$y3Bfe~eI0kO#cp
zLEi<gsT1I|XUk{>SpaF(yRUEBq1+gL)vXv3gB+JNyYfNHt|fFM(8i$QncL;xuLD3>
z#R;hK#NWdxe}IJ{L7OERmb{Bc9)HQb!oh(+YhHk82~9l!tU}qZ=wwf!GBk&L1w~o8
z*Gz}@8N#BNl9r|*F8&mPpgZ4wyrtc!DOrDt`b-FQA?g7tJ3`@~Ec-2#C{fX|BkLx<
z=m<+{JJcc)pi#t`XnaBlxQur$TTK>+)6#uTE-quhkBrUCa!u{IDOb^Mg;FqKvm`uB
z2y09>U{bPFgD9)`vF!HKzLa;fxb-}kb%;befXMUu$g8K!4Da?}O8f!6d`NL?7Xh~n
zv277$b)`i`GgPG76_k`nR=%`rI+>RE*HenZU96N63bG8@p)y#n<3zeno;-?7w9}3a
z9*#o05hgQ}X70f|@ZxBO6>`$<cTA2l(?PpJZ+?9&kO3~f5U^p{T2D~%G+O!s)nwU}
z-Aky5>Z!~Kl>mOC506jSY!Gb;jo=adpnH8DR(PnKuQ-oDac}W8tX-(q6)~F#mj_~0
zVdJMJ^N%g1=&8L4Vm_g|2GVmb_qmmzE>o@(D;usFP(O7<ku1j=1MGfLC2NHx&Cmc4
zxrgNUOuJ5?U<Snv3kY;N7k)mixL6Y@N?<7_VFFr6sUzACb6_@{sT*hu-P)-L1;6dW
zBJ>k7U<hPa)AK;fCjfm}3LtJ7T6EmOA@FHaRMtp9!85F_{op8oaNgzOWdWEH>by|L
z@^0O$D}bW+Y*dMd*>oK(P1(To;Gx`82@$)oP5E(ULqLrSpjDRFA~kfDq)7)SoiTB(
z)c`=xR@3c)Krw;#K-APSH8<DpQ>C;u%o31N$3ed~In+O*WRaiRUvQ;L+88SR1z#T?
z@apkXC_oVvfc^OO_KnZ@V21#K%#Bl5%vu0+N{0R{F!z&lbCtZQ>|Q_{7+R=^2NgO2
z@bHb7k8crzCmci}AXf;5!RLU~DAJ}$EF6>tvR#NIgdm?O2)<??+&OsgpyP~FU+PgQ
zZk^JrA4rw)Wwu#{V?^VS8@5B>(AwL}3^X6uC2$Zck$>C!`Z@hC(0^=lvIP}rAOTi}
zJb~!1-N=e2gi-YsLcwktc_0OI(*zFJ1U>Oy0aGCrAXh9F3zB3dfSmkzar9J>et38_
zdY?V%ivw7(1tvjjQ`6a`X3ThVk_nJ>&PAmqTm^*`{zOw<d8rV`K=BwoB3nqRQlYiu
ze>grL|0l<jK%0HFiykkk+Q~NyB*GhP?feme`@#SH+5e6K@DTrZ9{j&K2EM7?R0I{*
zotz2*(jKo}g*+2tm6|XAr5S6FLG%laU!SX9oyu-TuwB;D{%~D6$JOy9TSQDQgfh$@
zpKG#+p2rK#V{W%a#oc52AM445zy42zPekPO@dP`Pt~MIi?>ex3B}-t1?CtY@c~C%|
zS*C(|7lHd-o!?Fg|M5$>`t6sOg}_xBzBbS!%gs4ra~A<(8;97FUf#T+qXUC=TGnqP
z<(p(d%l{i7A;hTs|8sDkBQ~~QK<-Zc1^$l7m;X-`>;Jo-`Nsm}4>j=rIj#gOj2Tw;
z+OJHY$^%`oQXyWxX<odNh^*-#j86u_Y6ZDifSxMs8*LFh)~HBH1v!9Pfei+2f1Df~
zK`VGw^3KOq>KC)J?|yHQDx_wa2ua04RR)@tg_(|9I56w_K;#LVTL#*(?Mv1o<L?n{
zfD*73kU0$WVKRoMqUZStcODS_Ey4IcIs2TU(JdLZFDILvQd4Vs&P&|=tInGqwlC$9
z)nL>G6zyZRL8G75qSkoHb$tN*17ER)>Pwkp+raf@=l%l)&_Q;{uJEbbZ=?Rrlf5_u
z?saN~slq09c4O%pRF2k8FYR&-;!4DGb0)C!iNSBYUrg=`Cw&D$IWTVui-D+%!C=JX
z*l43@=<6e^x(8<g|En3DuOg2FvLHmuqO?usd==!_^|_jY?*mV5Q7H;~C$<5?#Z=OF
zm5c5_6Tsc_40wz6OiN*N-fEc0_0QGS0eQs1N!tFgqxYWULi4U4@3Nu-6h1f~85xmy
z$4^Cnr6ReYKn(($ECjkxISf5VRAm5dh+Es+mmO+>xeIu%I;6*b$e#y_ZHg#qfRKf~
zGMiC+ZE30*iu}o_kJ2h+y2;R1`U%D7|GZ=u{*j^0$Xm8CuKR3<4JHaU2dB*l9)It9
zJ(>?k$kqR<Tl<DUd@bN9W?K%5bWILdkfRs_6+9AAf&sdM=VkrmbCv)Dwlp-v0H`Jh
zY?or%i(n!I+>2&}vLP!EL@h{kwYGi>)gN8}Zm%}MG|@obcI5)H|Ctv)Vc`2{zhslJ
zdw9yx#K61P6sN?`TC3nd(`!nU0%|$WF4ad-yFK@XFZUyCt4>o>Q859g8E!uMXAuNU
zaxJ-(B;Fy+8Yz%`;{eX;TZHDt_}~!%^C@w0!U5=hBH<RGynfU4ziU(ZVPCpz9rMt(
zNnC?EA78e<av|5`lpKfPs*Hoy-TiR~oeFIhCOcVq1g|v|(NvHlx%tlp>7Vh?Mpy#k
zsq;qm(Di&3<mfTxZfjjo&UTFl#~CMx$CNKDko<YS?r%ax$-yzY^`uX7I^C-_Z3WZu
z;V3W@!(_G0eO`KY_RS5kRWgfHOkrIz0l5}m&l>i6UzATA17_fpjCTV>4*XGB3umAn
z*J+a33X?D$;ee|nS4vbvT>m^1sM#yvOceqB-xOGi(&>!V>-Scl0|T$UpN<Y{M|}m?
z<XfLpTAdOV=o_;Xt=C03smJ%wa9;=d`|vdN1=fcnE?<fq-h$Oke&HE>VSFUqWC1|h
z;%<%q%q;ReP*3INEg6b~sohJD&Irez&Lb^9GG5;uJi{sYFODeGFG4ZyoIx8dS(TI5
zO5YcmYGyIo+SwT|;+Te~&yt*m*~g-JeI&m^H~K%far+{e=(dtIjf|L?nvxi!Q>Zy+
zC*-Rac3vO6yLT>@=z#`UuNvch_CBXI%Dy!Q17EtDtm+=lYe)V^MCx<y@uSA#3{BGx
znwF3dZ7!K!%9f=z>YIZ{=_RA&#_e!e_SCoR!SyMP>w8PUr+b*Z<bN#QG4YOZU>@d`
zllN9xeg4A`xGm#+FI4xGAD%Cu<aHLw@e5al+dc0weBjg`t0N4Re?bl6KerVZB1)d)
z{mK}JYlVRd=Pm2(i(KnW-RRMonjuaFOE-Ia@47)NFb9^6tdDan0dq!woU>Wi>Iab$
zk}#$7dCjuZ)4StD8j6Su3JVvZT9cBWuZlztz@z~eqZTXs1Am?}<{CFy`;QC@Hhwdx
znFe#(dms0GX=QIO3Wj_XB+J8lD|=Fw&+i#r<oRb#Em|EMq8WOmRLR~343-KtI3Gh>
z{#0kM>eaCr4}6B$*VUB@o!gawC7blB(J-;Q-(n2j(6*_(z6=D78Abm&O-lc%`RzJ-
z*7$%3n83)IjButbwK$rvyaNAK-s_F;N%{R$LO4=0<mOMVtFe@4I>@hw%g;0ixnE(Q
zf7d5$NJ&mCG%x!d2wa)Kd65bjvdYhfLbI#K2q+y8Z342Y%es^H6CVdH1t8{dgl^_2
zuQLXsoNn(9BRMoUEN>`u!MYm5L@w#|QU3jGoAwQ`&~t6qef*ep;=6Vt7<aloxjN4Z
zChLFuy4GbZgI7~Xp;PIjTQDG~vMK?syWiGRTX4-mGfC*eybRDW?6$eD9UNWyid|5^
zgR~sg<3oi1nE-@ru#Y;v>TIHHX&Z^j&HXE|hDiwK6tbx15ahW&xPe)Ax_@TQ;UGq4
z<yf>c_1)K{Dg$m`_94{=Y{y}h7f@%zjkc+=u?G9%c`EhNb->iyfEyMMFk6q?Pw<qS
z3}O{Q5U;=6x-J3^&hz}TV%nS=GMZEZvZh92URBLPa|>1m=4stMr>qq<Lq`$}&VyI|
z!<Gqn4UC<qt?Ws;wGVU9#6lGrQfSfJpGAwESLR$NdeMn+xawN{AHkW<H-9df{|xEC
z{}aJTP*<eW^>LjGjK&)a@0gI<N5&_8^*x5_1j0J1@^b!y`)0CVuDF5ii^C{tawZBU
zDk;WmVsqQw+k?ZtpD6tfbMO&BP!Xyb>FKRd^H@t?gR=GQ+kzkNLgiz%v%OfqOAx}F
z4dBfdGGiCy#F~JRkN`9YLoj;PZLr>Pa`>kVV+FbAD@)tu3N!&yuOw2L3SeX$r1kCQ
zLbT(%HJFI}o1Y)0TsAh&QTF}kNsEawwmKEur@@6Zzwm(ev3=qG4d6mTd)|OOnpIb%
zWby8s7l5mj+?&o;&9_R3kTMX$f*t5J#55k5eMdVE8#LZEbH`<rlK)$Al*lXa{kLfg
z#vID}pugAX--cEmlQ2CXa`*NNtLVm0pf$!h<NdDZofKxk?>bs-*<nuqbf%2$`M3Ui
z2#Fq!oI6Imm%RoyqK!7ELEkG-&1AYrar-JX-d;_Ed_Mu+4ye~i2E>Dafh_awj`PDm
zC3Zhoxvqh2tgY?Wz>Xh!pwNS5wM}eifBW<nyKR=OE;w-x3kI{Y$`}^)&WM@K)9o38
zg~8Z`T2c8BC1JxAIQ>0|rKLDSj!ttdKt;@NC+!LsA+XBqD*%WjEXDz>5Dz>4ZYGCL
ze{CFIyHk?%$9(Cof)BpPAnx%3w)a%sXUeF9&kXhqqic{HaQ`mzT|lbt<p6XxaIG`{
zW+LSwEx()k`XB*Vpw1tLh&CjMk{C!uUFKM*769c^zu0D6&H>x&I#Dq=TWe+Z><{00
zC=jq!(4V{l?!g{C{f5`EjqkveTSC=5kujrJ<$AwiF8`Hd)K9<$);IME6{U3WaPi&m
zumpmczmcm{3_k_)GKTf*`5PoL@#x{FslH{aaax*e{IRjt`sIsZDo~=cfmc&Yx&$>e
z(^B2soE*K7J^x%g4B+w3&x<i1Rxppt!FNdrqyf`Z@Sc6!|4qV>pN^Kg#;fvZ?LTUE
z;4e0RGN6AJ*1xLZz0iYU)<lg{$BJN(5bj+Kiq-K$5)h!F!bSrs8+GBj0q_LDSXGNU
zK*uGOPetN7^Q{f^-Cqd0tdjq3EP}rj7R~a3{Zr#(;O8C{am|4R*w?2n*`HwjiyBN%
zPdZ}=^6R<yR)kuQTXF9T+(Z5o-7usNHXuMS0^E+RV$4KqEgS`_2yP6BztZO{kHk`J
zLJLqG19d`L-(Wfz?g5ogjxDIzu<?~##GjFJ%*TiQDpJq*Po3>+?2H4)_wpBZMT`+7
z;$V&kcC9~JZdr9)ENM?jd1w{78)LfCm0HC7VTj-Y|HF|u3*<HoWXw=KRm4t&GJ`FU
zNUFj08ulI#GKI2=gy@Y!vF>?I>(8V$uqv!)bi!()U^apKkH!H6*Ph?FyWbNvW6&k?
z8Z@4GOj|a@gkf<oVUy_Xrn+daOcxB!eaJ?YAz;_cwVQ1FG!^@$u=?#Ann}nn*J<*f
z*%YEs@Y-j5YQaqq4@;ycRFxF2{#>bFgxarxKC>2dZPj;jEDaNMDJcG4ewpM!+S*LX
z)wlIfmmRh%tjrkPIXeg}2qvJP!r><j#~x$ojwnmhMiKveYXGLmu_qPQ)o*Pb<!Z(Y
zsvJAm^v}|q%xjkO?83km22-z@upOKJyx)6tZ{^$m*=~aTnsNH&i@QqWZ@^x~Skr_I
z4<SWVM*=2jF?LSZ2gwSd<Z}b+2^-X-us$^a))=rq5%Hqa3jYjT1Eu(Npp!>iuOG4w
zZY}=YTWgh=7N`7Pw5VHyDwHxrq2KPp)jK4L?;__4BB5!4Lcditm^q(4zHd1}^OD>d
z%jn9mgZ_nUn3s;WM~MmD_x}^KLf_ifhJ&oJ5eP>~FeX|MKKFqriUSfKM5RS*C+sbI
z`71z)#vsOa!Qo05RKeLtHvX7P?vH87{q!CVIPp2nhm>`;H=-!2ob;J$IhNTQwFyb;
zP2w$~JGwVhl(4M%%&dYoe;&W;Weqy@5j(Z8{qnmAP8({{qD%&?6xR<=twH5arVm)`
z>=T@voaMNkuIo^my!3nH_`EXady)JxhW(hlymID}-J?MEfTZ2S8vQEyg_>5dSV72O
zWX2#PnDjmqI$^*{0xL*xnGiiC0gD~HQ+r_59-jVF@V;>AC<$rhG2$-af%g^?8kqL=
zxoP+t+c9jjvWso}d(0gY@wYgJK%F@+g%?G=ncxi%XSw+G`~V>VJ&nkjKPJ<Qdthzg
z_`%Zl)?J*~-$hPmXIo4pdiviv%EiuT)0(**q%$C!*<Z=N_V+nfiVywg<P`gxy7xwp
zw5`qW9u|bA4$oi~*`L@&Fn07mk@O39$Fr>1q8!P3bK|y4>Rs8HZ?7CB_lNCby7$qb
zC9lFbSaVwHYr2?)I`=Asbp<olbRe_KQM>9HNhYU=6q}@_#dkx?yvM&4+*4J`mnJ1}
z*MS1*AD4ghnmI>Cdv4x%=1aAOc1b2j-CKos_a{=!_*afMv>0cSq+~Q?hZ9jLXy~Y$
z?R#+STR}jqWnN%d`7VM_{|&h-VnrEa^XzgbRuo>Zw@#VXDn!XmSknm)I`q$a7#D?Q
zIhJ^becT0Z?~wVZyw(;{?aE^m2Wbd{3C$lD1yV_!Y&&J|9|ie0;pQ7_)YZTzv!*9K
zVAdgG&PfCEk(6bpETOrGZ{LMDtFnml-gfu=O`zR$4stozgCX;wkdLaYwm9*}_QbMs
z{)ouT<W!T^mAtXcm*I;8AxgH371cfIIv5+i<H@34WqUM3*EN%NYv%3ly_erNAs5^p
zy#E<tQO1WkqR@5f&<GMMd6=Jcf7KDPw3OQOJ`)Ugf5)1b)^GR6_3J_yE(y&x+9j(f
zYP7y|zsij=6kGc$U3YR?+Vg`;)V=Rrqn3%9a37I#2@~-XP63M>Jh7G~l@~*sWt)D0
zopI9{xDy`h5n?`$-p0mQpnN@i{jPZ&nbxnnmH>AaDhtQ_4QLDk&5|-Qq#&a#hcegB
zkDh@~W5`I1W@l4MiJ6pF4S2vm?UnNAi(6(gY_Ociae(`B2^|Gx{lTC$0cOR5NGlc-
zaQq-Yi?UKEh`z@40kvth(rvv`;*&E<ziSNy<B+%$#4VvU!5bW)VuP`kk_K`FDjq5W
zT|q>@1V!n<$Yq@C*m*Ss^q#$jcTDVT$AZ&M9c|TLYIgM|wrR(YO@R}<&4-73Bj3Fi
zn$h|aBb=yo_%t9}(89tw2&&#c#M`ypu(TH;A4ePwcOA>nngm#A@v?vnP_Fi%tNd`H
zI0gi+8^D3aLW$T0n4dw(%YR&eh%tCnXPTKbr*^3=R_Ai1_q4_ub|1r1f!poiNIM;5
zl8IyAC|a$KFv96|ePHe62PR<w=+E?gp7+h*3$5i(PXN^{dVPq_7PO5Mpj}dAA#VX-
zCKVwgkQ`9QY5rlNK%Q~q!&pzE88$1O^Ek74b8bi!t5MB_mEm|1Z{$0RC%t(wLSSA0
zylb315NNUltHQ9Ape?ML{9H}e0*D}I%1FYJm^6!25U&N1mqCNuV^Ma{5m4YK-9S;@
z@eyu4`gq)zYRb{p<fUduTOz#I5O;H3FwKt)A*;`5UL)fg`{;QPn)fJahFVK>!m>!t
z0P!WHcluc10SD0Lb*0z4Ok0D*1PKxlKOV@#j=(fQzrv*QI)+^1kKLW+UjeE!*sL5T
zcJ&qLU~ydf1>ByF7~E3pn*94Y-O;wH(KFTXm5LLtd=#-ZpSnR08+}y^CTtw9CP<UP
zg)b8b7We$5|IFX%{Z#m4L$q&5M(S|dE}~~AsiA}IYDsU;%|R^$YCG*znsk~|7k~VH
zkk?Yy!|$bXW(v{riyU}(GQ@w?;Z?<9!=Ma*MajO*onQF}TH25YKwV;nCfJh)_RoGE
zveL(10+)5H@qUqe75We7c0+vhbhO2IyxWl}QaLHkpq=6C`4qT~3uLZN6z6q)?54X_
zgLIEiz#H@XQ+h++2Q@zeC-g9iPu3oTl95#uRq*1q9`_3_EDYDs!X#Kv<lHlh4Edbi
ze5N^N$VO#&8uq@@<U^46K}N410({jZU}gw|R;l)1|7F0SjAnYVS!qL;Ux2iT+!Qv7
z-ye6&GV(asy4DIm7*G4fKODez_E9NWE8l=Cz?fF~8y4MY_9qRj(HZ|Yj8iR~D42#X
zPYV3ot9yV|?*8hWq2P5OeTBqJu&jHXGHU>SBiF#73kc!@g4R7dvly9Fv+gZK{^4;_
zp<>Pv6=qb#bDXR%x-jDC=@|se^ys8wY$m=a#5j+4^CFlim>PU!V7yRoG=F4mors`J
zP(cN=iU7x@35gL9t`_!f1zOhGplB1tkdyYHT*m>|@>D9Ay6^bxA@b4<3oCzFc&@5F
zW5`6Dg>R38wXzC63{Y1%kof)W`#A?&HJ0<Iy>-VNPQ%?*J%l&7+j+i@cWv^mo@$5>
zE0Ucop!Oc8`o4Q1f%!pl?kg2zpu1{DvKlO?lEQRVLK1#{_y0-Xue#&x1ln2b70Kt0
z)p=FOpP3z*{#WfGU`W*=c{aBoWUT2KW*xRsF(!V%nbVnC;ja8N@$YOKGi*pLMp3v_
zeR>)mna=^3lKsdA9?0Y=XaNa}`HmD$?4P-XA$4r7qKi(03O^Q18J^XTA_*$;0JNJ@
z6M)NydVT<_NB~>x0(v=lfz(;;ptke+UTFKUBAjF1tt+XuixT(bwubEZEp<(0b!iEv
z_pe`B%1@j5@kCc*P~xxTzzMy~e8coI6P1+j<+iTz*UCXqz_JhG{sqtri3YIgSYKRu
zx(58_#nL^bWB(9Rk6D+jyXUitfT-#*&>Z}ZES%hV{-dhUIN^$Y_Qc}Ul%vb7m%U#a
zGnS$ZjZ@Dh>D%)ZDBCQ$O55$7i#WZ~qVR7K>v3Ar2akneSdJYiQS}8hXBD*HX~Q+4
z6X7Nqvhwos7x<$>NP?3llC*qT*(#F3u@h;qfq+pRu=K~4KB&d7vykVaQ6;mo#Dy)6
z(>V3=f~!0KS6kHg=;AJEHh$`F#<x$OqLnricYScj?>#B_0J*<93(@Ea4V&kmc_$dr
z22!v#UAPEE5PDEvw}^XxrrLzN(P*y@(Otzr2@HpN!TkDYQyNc`7zhtTAECC-c7LCn
zwX!!6g%pK}Hj3ogXoj>Tld@yP@H8`g0lGdE{k!`gcjm%LHVwNDJ)*rHj*G3zIeIl@
zIeB@hg@x*_C0dXmAg2kG?yYS6u(GH4btd?S-m%B$7nY(vOC+x^Qkix(o@B7kyI;h7
z%SwNiA@k&(s9MIsgS-3z&ADeln>oh--R!c`HV*;)6rFv(Z@8iq5(%kldXDcru+N?f
za_A6K=T)7TXEZe}b@T4yfaQehiKZZi%*6PIg9mwb%?)|e9c5m2ig;8B&x_>RfBe7>
z1A2kFEF2-G(<(d%%V$ed<Q?;Azhq7Wb!Bd@#0^W8riI&aM!_PAX5(YRCk*w46}hfy
z4l6<+LX>AMu?7cqe+)|RotO3O2>7Un?Ee>#kg9L*1Vp2VpL@5q^5wpteJ`>9LG}Zw
zNV0lN>p+KXxYl69(|Uoxj^g!?9mq;vmlTf2A`8;|9mgw;zvm+hPWdMGH_`<T=lmUb
zW;R#bN<gFcWUbl6KCmaAa)tfT?}cn2ad17Rd7vXFTv7RAO%|IiFCSmc7hyP*#Dzca
zo8`=n!TmW25BtacbG2`zLz5>Dot&k3O^-&27yL)Wg9^LOalOrDJH-$!pIVlrocdvk
z(LN83W8n^Y&<*e*a0HK>1+=(>sReFqOosj#Ftk-FK>`g7sgLA~JL_&S>vv}#0dT8e
z!JmSMPdmaY)jedp9K~6nO&#Px0{VcVz^Z%UD1#P%q0;sOPe56^FZ{%NKPo=FybBoJ
zavZz`m2?!k_x;&-W<=yO;PU7v!^RjDWm$yp+QnfmpxOrYlXDyeKIJ#^Vj45c-^$0%
z@#HwxXEWqSfEyzMTt1yP6ub?xplDk_fMFu@v0L*9S?(Bl7Fo};vkIsAp>dxK)JIa5
z0Q|*Jd}<yZhn8RpWzJvM;_s=-ZG!IyUHwn)9@on}xx1?U;x7MQYnfnUmnbXy6L388
zLjXvt<e5c+MFs!j!h-1Sy|uoqVWm4~&9!{mBNvVynb~)<h0KS{tI~S+@c5hsqwI8s
z|D_y3dp3?JqNnX^0>=9uw0@PoY>?NgR1g-75aYnTaPW6aohGf#&4FR>AwWUmW+aQU
zt!1q=1o`9KinHGI`5pJg4SUu?Q;1T}{D57$Ymyj#8{)`#M;U@!r*2Ksr^;M*QW&*Q
z?VVA?)~By3xI+OlbJ<~^A1onX-ERWS3aD)o0x-f<Yr&Uc0nAIJpf+ihU$`^vCSi%h
z$_jkPNul#V?4vO~9V8Llp0MhE%U00;<a*S-f4id(K5kE{Zt8*_cVYYghS^U*458~j
zq|Icj01Ww_YnN%>VYZ#`!}%P21Mg`E%a_y+x_Q?dALJLZGX$F@p}thB-Bs=n80a-8
ztPMh}%ZA`A{LK2N(3dNS02BlHg0z0e%(BUFb+F##!9Pai(OJul>GYn0QBPAxYXd-V
zo9QxZp6~Z;Uy<v#h~_iziQ{!+yP*jU1)B!pWk6Rt1oP+mdu<sxE3SFZ11A7o3eCT@
z<DlAXU0+%foHV;L2sL{!pL<oRI@41u5<Yx2YaJ;{G&orw{ZB*#iolR+iY68ainUoa
z<w=Kk9O9T3ZAe3OUvH{b(QC=e_sytU-|$O$Gf`@WhRb%7QD;ij^G0dd^HAO7#n&v4
z?%zLtn0Iw7?z|YRjNSQfYq97;w?BbZer?nnxZ$bQ#TvfWZYt`fA0*<drLpph_LcRG
zvCXN_7AB;AFi1~Tw*eoS2Ofq|Lq<AK$Y(?@S)ia*gL%AZ%(fG>l#rQy)Y$B1(sWt#
zOGfLd@O!o2{2Nt<TW?m@wcUh+*LGG9*rQdUbe9J_U)7KVplk~v7A);JxR>N2UT<)|
z^7fU&-`cN?pz`ICIoX1}Y}TH!)bOvL<-mu%E;twKoPlpfV3vJz`V9P@`yRMR*>}J-
zUIaD4I0PasNG%HpqXbeD*PvMO*N5dZh3>+K5v7fRa#P3qQ<~*R%A$**nO}l5C7*h<
za~~zsePSP>%V^Ljp^cFLLp@cbKC=OCEe>eIttk4*F=@v^lQXc=(#S8|IWep0pR)iy
zXsBfX!9La)M{J^Hkh$40@GaLAso&VU%G!)pI_Vd2{?r1fH^g%%{2UZRu@z`P^J&L{
zcAkx7gh1z;K(0EdEE5S#8^p#)l!{Dw^5lsQ%^Lic%^UDP`u%xwn(DMU?)Kl6@<MYi
z`I(XUK$=5vJ*;{SRV=&9*%zvgjzHzo<cSsXjwK-9hXxJ!3VNzPuR7UNQ>$u>I;~q9
zYXvH_UFZ>WjEgPo2sg8nIdt-dUf}y(RL?1HP(<WE9B?mVjw5lTig2QmA-qA=xa;Gt
z{L+8Z?3#&oA0%E7xjY^pYSVVxSZr$ahiouTA*nq+AW*7AtKK?lel3!Bk*{x$Ahq1e
zkh+Ma8M&84vjRUgJ}(8cEaMiFqdT`f?Yjp_pUn_Ir+YF;wMWeM=A6!))!(0%^)!XA
zx!GDhW!B`-I8HtK=niO*2nc8&j_B!x>$6%2@;?w<==^b#C}pN#=TbzDeZUZxA)58h
zmgJQEO?$7(!nlii)<=(??mHFQlQ{1ml~}@?e^XrT`Vhod1OzG&e3KviyNZ-nrZ_VP
z{bcJ?+N+nvvsf|LQ|bL-pwKMx`J;lfV&!tGz*V_i*9tItyuL>6125y<W~nu35?TBU
zNy=W}T|aBxxd5~1-9v|w$N9xW(;$*AIUMs})o@9+Hv@2w&vPDM&F>%{pIkY(ni{BX
zQ_-!~ao^@LnrZlj!UKhMN}=1A1b>&thg1)>92Fla8+;RAaL)7V_iv{i&ZzXBl9qh@
z?fdi>Vhq<;C1KB7i$Zz0OZ?&LGz}}aB;|F3+i`Q~DB6EAtG>E_l&4rcRyJ^Pk$8f?
z_c#?%;OOXvhf!(`m9hxywP_`n2KM{Wq(N5{hWr)RG_1auDuv?b;+pRtagg`BJD(M_
z_!FX3j2<{Ycu~X`t{~0m#u!ztrZM-9ofuOQP5O*<=DHxiOs`D8?7>}D=vw5D{edDT
z(l>Loww-GC%1ACachR=e&a2Yf`;(Kd*sw%uY9zU4lE&h3v(~@6Qw|g;($vm8RWFSz
zh*8hkwR;F1jKuVhu;<0uRl;V+B!RR%Z&U1>pjJI!zL#fOr<u8_EmE1sF7iy<9}Pxd
zbKiI`Ej>5{O34QB{Go%`7AW7+P^f^l9y|>o{NY_03gon(zLQ$BBe|rEZ|aC>v%8U<
z1!w5`pw`iw<g3A&TElRv>{*HnbL-yjl!IhTo~G^B96pT(^ysT-u%WX_T>Pu9CK)&=
zh{XK^5Fib3eEBlEg80W7`6DM?W}j-cWP>U&<~sWI%}?q3PaKk_6VhW-xvzW8W}2(v
zudcSt8@vX#MuZ>D*cGN|h<pnbPFSRJy`Ui_4QSu1k+PIO^tvmJ86TRHsKQBypbgd=
zq+w>D_OIcn3uq_S9o3Q~bpukp>1xH5;#kB{Sp_GhB$JGM{SP?EhqpfxnvM5FX3X}R
z=&7ri=Z)rNh&JJ}>}vG#Fo}nbWnSFx1Lw90trZpwy>CN}X0E%(ZlQU$cZeW@?DWXb
z(h!A^*4ZD7nsJl@&o%@>p?Cr%Qv5=SJ5rSk9``@D5Jo<CPA)pcsMSu<Xy)3=ufxFa
zclTP+g|8>cXzf$mX5>s8(!)Opj@boqXyd;taqzFY$}#YvXoaUpf02gzills^kufM}
zQ1)pv`KB@jDBh5`03`s~uYeeN0m?-{p~li${D7))N^GnUatjO2+wp|yJtVl2dLP^t
zX#7AXp28O{XU>R;GQ8c>7*r4^=JNX7D-{O5j2v<=1sa=FnB9Ku2PXtIkJ1Y$P1#pX
zz4kW|K*Q%=<qwNX(a#@ih&X@y!vHb}LH2XT<4p-`>rbcPr&36w6NAoAE>{BoZ3Aje
z6QCy?wcN4jyKmeJPlOqbdwkHdG7#rCyqq93xBjO1L1&hl$sT5^_G3-2Mi{8t6%yhM
z6jDA6W~YIs6&8#4-f;eIyDrme{kP@UR*()KBuz4QYd<#hmh(rWQW292xiz3Yxr&8Y
z@2x6wokIExfvAMvkEZm@<=`sl@P@e(Me4m=-h}f_YB_PK_CRIff!MiTmD^zvMz$U2
z-cfQ<31H<aITerRQJk3(d>Mz2NM2uoO%JickYUKvt1m!LIF7X0j-?Y{?sI`oMYt1u
zWkG3kLCg{4*}<Z;(7v-;75~PdyMXsbHSE4r|N8~zGqX0VjJ;Qmo%SECAAg!~OiVwG
z*-1gabu}j$bG!0*K`;$Z`gLaSI$Re1d-8}Kg1oMdJbpwAw}-3*D4HFS1DKS+qJr4U
zp*2)41Rq+Td@1}l@PBZBd69_u=Y|MYICl2Sw~WDro{F^rqs0n29F2s!#?rgwIUaG|
zU>X>0hUl8DQ+X2rBmq((P0Ds~42=(zI@#=qD15kyiPnw|p1UqP4*%kOQ^#GyB3fE%
zag4zi{*r{_en~-9VM?I_{SkJ;2S`#s5mvuUwYeiN%5LIZZr+LC-L%g_H8COf+B+R`
zz*rb+!XZ8@w=FCvsLmQO?1rvopl2@>+4zA60$_C2iXZ+yrG#;Q1je^V7TZeJ#y6#t
zvwV<@&wsf>?xci4I<uwRzLkiSr9y?Hu!MY-^X*FycNfpul{Z486zl1Ge2#lcDHLC_
zcZa<<Ze;eslR;4UhHw%R5<b<<>(U6}#6M>I*<!jKbuVIjdXs`pb+A2AeP<v0wCYK6
zm(*m0UlU~19IW@Qv_C<cx&Pg<x%{E*?Ip^6Lj)jn7^?GPiAE_oIf@{u0GrE}36L{^
zJEhUb)!(<GouF_bNwJG?!A?C|iR}9DOQ-5)ACs*8j+E#YhOIH3qY9s9<Z|j0I%Cv1
zU6om71hbb5#su-Tq!$Gc=rw_h2(6fx4j?FF&u>vTBHKWJM=TUS<KyF2K6oCeyrY}H
zKJ#;<#1<Bh>E#s=bPcNfP9fZ9?}jJlE*u=$Gi<7pet)KUt!r;k^-vn~l>3UtlCw;A
z^vnh7lVp@Mu0@?Qa-U`tg4@rBdiV|E?}7LXB%;W16?CRZuoaewd1+Q4CLYS%3PzbE
z57MDdfdhVPfDSpEq&%D%F}NyXagC}_<^Rk-FOgdFdn{!1(bAmjodeqte!zQ^rVXoc
zSt1r4rYX#i%I4pH)1j*LCagHH2I)ODT~7u6Ns%9TIHGIOs;E4!-nSY;TqXk?YNV_d
zfao8{1hcN0Z$LkU+<>5<9uhS3%oTpuSHArBiAfa4?@OI3c;#5gl$Lkk{hV#&;jod9
zZ93#oh015=n6}1Tc+E~*%r07NJ#F2gM_%!fj0k<Z^bC-R$r04KiZomxdaGd}hD~=I
zk(H9JZWwKV16vNd79PrTKMxzQQ9$S5J6FLMXAw#{mtNM96^6C9w@(0zD)+0|@9U8`
z?(!x`$)4D7ap_+rbD9R+)SgxCTMC%_W_L18@+f$%)MP5T6pb8%^Wtm1Hz<wUR-2vU
zz|V+M@bh;j2FwdFn<L$RtlYj3$VM#OabKwfN*fDLwbrkL`w5yG(3E+2c@1pOTAG{V
zAW5PRu6FB*FbXe!hLn6z=V2)6GoOw8{Vs5m-?L8SvaNigGq+AATY2s7^^B|OAL4G-
zDe`7uZa)nd=5+)MA?CG5cQ#3=K9}LKAX%S^zrfN9<Wk1~_Jk!FbsUljau>0H`<>V7
z*C$!)<tbx%a%LW+>Uh2fnCr0iFaI73?YRjW2@}aP62*z<`Y_H@e*YdBpPiBu{5Y6B
z{(?eLttE&wgr@yYLKy@0sFf-Hdn~j2!b9JqPMPQ|s~|L0M_G9}%gQ(Rm1q4z3$bP+
zC+=AMgX};-u<dev;&;H!vNB|q*y%V}PBNUDJ1RV^UgmLBtulhQgpAXhsHZyB=7z8g
zNulX=lg%q^J7cJM0XdH$F(5J_c~3~wf>cdF!B)p87=O%X4i$e&#?%BQG#w3>#a85J
z$CUiNlr~Vz6q-<iXde7=K$<BF22sv}kf!|}a6(UrLTR7NK43{GexTO>;C6rPhPE2t
z-T<{`)|ecs!{zcF`_q|*tjTr{uV}BlkyK1s;HAsnycL%GLmZdiB9odZr|IXkFyOW}
zHpvhRHX17YbL|?$f+$Ff`0Oz%9CjJ%4rt3d^7uxTrFU5@l-eeM-O-LbcM1x4pECT8
zBQ8iW8!!aaP7*&MB_Z`qYCR#1DX~bq9L4OnM%Y{(rlfqCXUdC=Qc3m*qd8`FYU$KV
z{Oy#+K8;!72D2$pL@EJwx;4S43cR)KocIH)@Z@TsS|)<({1((-;*gfFV`?c&6ZIm8
zOqFmUYH^}Hv^K6WdGFr6)v9D9*BHsVCICR9EP4PALM*nkNF9M2;E6T}PdpM9u}a8(
zQgkR!^zP^bZI*P{_%_P?7D@G6w73N+b`P$d8?rToRP0vEn(YIu)-8_joP`y1p=Df?
zX=&huqsO_Mp!%r+#5?jQ>Q{uN!Axyo6}lLcd3bmrtU3pjen`R!qBcGCHBg~9gI_Rb
zA}8{Pf*}sVob_pi>VlaqY%$sq-?pv{y3@AUvRoaNl)pV1|3Ku4LBe~VWA)@KE?igr
z4CXRVXw{@VU^J16B<bY}OIg0{D=6>lD&OqVWK)76G7#~4{;Y**QMAh`eoE=Y0fHAY
zg!6A9AeyAAU%_Oir`I)29ObAqqwtCaugROp9|`$@8zXTm(8%iaQAq>jpiCPjKbL<l
zfIMH~@r_sCi-CZmibyEnom-V<&-DuqC~dhLF2@i``VXnT_maqX(IFBV*2@v>!5;?J
zYD}(h_C%8gOE13+pJwN*qvJ7MbG?jz6H0%aHS{|SZu)mzK!M%{3Rk-|N0`&df8epS
zGjfhgWiDB_LiEn^c8HmbK#S<I3Vg=3#|*zQ_drqpbEZ$oJIO-tjyGuoOO-=3syX(=
zu|6#?iB<=NF&VRz#><k+VJTK(dw+h}JTo`DL^DS^^MLjuywErx3p0PF0rxQ%&|C<9
zwM8~m8+Co_zVXg<vj)*FcK&B(s?c6}j%MZGA7BMd<_|gq6Q~3M*r@xTNw;3O0*FOC
z_?-CXlle$7l3iMP;s=JTvaTh4AD#f&FMsC~R-7I!G79fJDP=euvI~pVyJnt4YA9@9
zM42GvK=Lw{$X_^E)0+VcUG^xA(`-yM(d_$+Bkwa^W}x}uy#$oxu8Ery*=<Y}dY^*$
zcxYf9yWHChn>gL%stpI&R|1g;g!BRNU{T}4aF1EcAj%=J+bj=US1*;Z;A=?8ZKzsh
z{5#matpcMx11|njV3laKcUd&NVgaw04dH%Mi;G<)O#a%v3pAoW`D8)feKVtdFa_j~
z)jJRnBr-?MLzt>TxEdjCVD0GE<=$&J)%x<~+Cs&MAY}T3D*ZzSc@!ygyo%%2Db*7S
zpGFycG>9PVn&VL%wjN&Y{m?tAP&)NY$i#}ElzY&?ar2n+TBi@aMz2%lR{*<$f@ZG&
zP<C5X)>WH;3Q!AEUiaB%4%jglwFng(?gSuTYA~*oK?2(+mDR_8V=V5rC>>5smp0E`
z8uzWkIu;6~p#)NO@WVuQYSRRx+3~W9V9liKKR&oVFzlXDC>?%QC!)Q{QkGzX_}vK9
z;eh_rf4&TY8`gCD#+xU`L)6Wl(}H)1D)3^OfB{*NJ#vE&YJlPoN1%L~^ZIw)jS_PE
zoCI?j^ZV1Sr_x;<<8q`Yc+as(re5lDGxL^I6da}AGbkKcX}?_1rIT0SjGw3OVgMh~
zg9l3rHwh?Wue$ws(e@Ig?yy<EYD}gbhX>6!_HYE^;PdlPP<hJmYbPlE@o6M}Iqz0B
zu7?_{x-ZmTBp{>>n!+pe+opV&knya<#8f_ycCw?csi!($>9u>1noei47~l?0WP-QV
z^UQ*2DZE(EY73n7(n-$G&nMS5H!(RiAAYoM+$N;9c|7|j9zMWdr?$OBb4$w9VSz3)
z!!q~SI$2>%doOj~1gF9~+mG2qipL)g3e{8Z9_+my(=9BsIi}iK=sa-8=P}TJfYD^H
z$NK^9Baegh>K=pLt1Me4I9Deh!5<#|K`a*kR_#r!dVS_}%YjsT!^&%Naw}!sUT3Hp
z548-0Jd=8%nqJOyrf#2u9=S+I=4STpK8uJcUfv(pR#&UD^tmaS{WO@BRofy&!GQI0
zSZG``->(yp+%cey4Q;c*EvND>;#SQLCcf1x@v1axwv<#Xmsxo=@{9rd*`;I)uO<Xm
zR2(S%E?xY&_z+AVZYMQc@!tCRUX>wO&dJHi()G3N9x%6b1hWcpzety*4zk~AzJ(#H
zq`V35Sx47a5`mIL`I&6*8dqOHa_DM_jV#4;Lb4xa$Go{NRhT~6zWG2)!8*-32x#5%
zl%ymiI7ng}5$_MTh-?(V`){BqAwf@KQ8N3Kd6DIfTT8(C?<@G7{3O1+cOmtv>0~Pm
z+?Z!;Wamk0#;#7UGj*w?#<R80(D~vM)FfLqDZFHJx1tnnE5Pz@gTx6nT!2lKRt%ZF
zRUYH`IqLRfbl}U(@&uTaQ&{`PrRzc}j}Mw7qpR+Lwf7Yi(J=D3PUW=Y*#Z7Xeoadf
zRe{EMO|`Cs6G}4;G;OvQL#jjcH2j+rbqy4Zs%o8+*T2I!yTtwE(%O75l<SbL1k~v_
zR|+n%A|K)aTD<lQImVS8yh8a+?dRO(pRYnS9Wh^a4S-!rV6#yjp*`|fOh-m8sE&&7
z>kZ3zE^-;?p-lKGKRz?l4yHtLGtQ%Y$UHz?^Cbv0kVD1(v|q2RwlSn16MS)wuC_^<
zDp+CjoW+X<el8k?I>u6-m=(oDLEE;wku1wB`INbDE^mqk-7mrmOWq9*cF5Hq5DR!O
zPzw`qHXrxnn|{_~);GHLwj*u2XkN+%$ekhh9Ea<rBx=L(mqLj>Qfxq9SXwc&Javc>
zWnl1uK<?=1*wWdVa)<fKfD7UuAZa5oryg&@TWF-TZ;KjOug)j-I=^$SBQc$R9IDp?
z2@eg=E6t?O#0-VQ0uDqa7x@cVML%u6egx;B3gZD=!Wc5uLt?{f7UE$m9v85+X+c&V
z5Tn@gLWd4pxDmexec_xY3$^U^W6<HCy~6jDUU(vL*)tt-<WGqzZz>j7oI)fn<mi-n
zxkN3$@pq9xFfin^bTMGdu=#QKkY)>19K9yTxAzE+T-30#;oa4%GnAr+PHxw$YCm~h
zjblyvoM}CBWemVz-s1e*B!!ZD_Uv)Q#4*-v0x{Y|FeycTj{aPy!2+bDmV00I&7~Ev
zl(qq)KPpoM_01?lTb<4I5p+P1tI!UY;AG@xUC^%gW2)~*U3FI@Y#Xm&k28TT70AXQ
zt7B5Lp7gKX=yn(F-Inda&RCE`Nby(MRe~9k@+%<>k1FSP&H4R^wY*N{dCkF`ku$^g
zi>a5?3kn9W8iUr=%0<|;&<CGngEkN!;)D3`HgeK{@yceuLG3GY+*ibYt+#*lN4eIz
zC%8ii@ldw?xTFe24^Ztfh&`zsRLe0A24{Yh9p8+Tg{K0%SZJ_d1fFiF-ldn<#gzUl
z0Mahn#A-C2KjJ+HwF9r+Lwtn+_6u|^$Hf?enWRt5?K!@{Z8LIsk=MW#`#C~6^LBJ|
z0Pp6NvgAO}@A2db(FSPdK&iKI4oyoXuy~A*6q@USg$jT~Sa=G+%VP10+x(TIiqo$1
zE6(6O{20WBcWp0j9tZx^3$7Xgd>?ZuKw<RtAqN9m*)OA`8qebcC`@(_IW@}-Hh0|O
z8jDz2<keBiiLiUwdVX_R(0&35k%-p%z==hOJv7nQz0hr4Zv+nqr5J2s60?+$Vjwdq
zANJ1;;I1i)TAyTfeE<4Oy%R(Es!BqTUhtWWG6&}KKSTc>a!{tl0C?$nIaz@I3-LHC
zE1#pHNki+?Nhfp0w-#|0CBTJi&GII)YM7hB6jq6!?D36b@IT_c$BFkcrQy?Kbbs2g
z?@N!>)w!&sr%TgJ{!5QweWi}?8g?#*nvtKQ`eNStCCf{en%atO4hQ$6Lmbfrf7Sk4
z_$n?0))`6Ox&e#GxNjT0q$Up<a9I@}N3%@SJZ~L5PA!>QJ9CPmJ6x$>RySDr7ByKA
z3Ip)fE67@2?a<t5H&KYj=VURZT~T*h)K`!0H9K(0?wQS{ye?v>4Hzdc7^-;|9y+~j
zsKzQ2b(L&^JDqupNAl&|4pI5*Afs^O3Ev60D@R@H=-$rmq#VApC?Gk7X_`=QY0#Lw
zCYXNLz`|Kg&sFoNMU5GI+V+9Sk3Jk3j0gJ+uzplBH_u#MTMMokw&`^k#l*(G$2DyZ
z0{qDsTzhY^w`~_TKVRA0Y`Xd7%QBqb1*X(W9d>}sp$?(sqN8<UA*j}S$;`Vai+6Lp
z+#la)DDnY5$0AsJT|;Iqpz>uOSVSE3!h_*g0WUQ+HxH!+&m5$BU4Pe(hgQ&8Sq{sE
zMHIXe!<OWf9AcPhE>vF&t{$q`=-ibj3<V+M146T_8fAm{G78bkovg-xww6PBp@t#}
z1We<=O;VX5I3WM!j3tMUfEzajxR?WC4$EW5lpSIl*a-egd;*oY3{vPq6kxJV8{XD<
ze$R5kkF3Je<)^k|_e7ZLVDmtTF_YQ;@o!$&SVgC;XH!`=hekX5yN#(&R!17*83{mr
zg-z1>Moc%0PtV?Z??SYu0ujJSTl*S9VZf~$?R7o|9h+Z<KhHxokvod|@UhF&=lDE^
zv2Wt7oPI7=zO&@H&#Uj4K$iC?``!8YLG|k?2j8V%+=41ze1N^QF|>H$yuj?oqXBka
z)hC$L;(<`542?0s*tj^mo)ho{aE05Al!5{Auw;7uE^?8d=!n29M#^HFsoVI}KcEob
zj+hqSc!tOvdV%5-fR~cxDHw@~=lGfLA5R`XZpAmL6uMPLk#K+rUVCM37}Y%POy1R0
zS5Jj<QxaNopN={Mi}7URu(Lh#e?{kpofq|xlEVan_4>QkUoT>!AXLbD`I7B(A1zLY
zyzVucX?N;cJua=6&TN`TEX@1rjLM`^F+P~--B{H)usLAvb7!G0i4^Yf>)zSNj~_=e
zdK<K_8K(zI@nX28E(EGUs}67x8_R0J1HI^rBnRPv3d$5GXw77gmJPeiX1D51J@3<N
zhgcA95$;s9l2aK2wT1%5%@Yy8d5oby5`JUyKq?Jh+_QXIXpAKc@Cp1M0r3Ev?U4RC
z9TSURysEyoDCJbr+jFF?P<=Irli$!*)rs@m5R(l<y?2d&gwmNpU$f!OGnC!~`AQJ9
zQOdZ_^)n~Hu22K!|7c~AJ{+9YuorOlk;ScR0sP>Zs6me7ntN|gQzjZQDJPXvg}Ue!
z54`WCFlC?0{G!izPsTd;SLTj(LV=Wp#`LYXfm;Qlg9Ipzi3JdsHGuC0Y(ShNoR=<L
zdO9dd%eLbj@5?zPA-z-UJKkE6@uJO!cr0?sHGQ01hL`URW)4CS+g*9ad-#NhR!%z@
zxdR;h>Jrc91+x5&;QZd+-hp?(k7qQw5Lh+vWjsFa*UKfZ$QIZ-67`PDMkMDAU*f??
zSyTE)HRq~oAGLhVJ{9h!;^`MAi&^njgat2&96{<sc3<e?w<k;BQ7C<!^q(G#jEpR|
zG&MC<ZTp2vwXsObLs68=rkv=^oS|iR?<r-o-KT<1d2u8$$LQ)S;J#2O^5$Z0-;z2|
zJxD9Dbx3XSvhbJF(1?U-^bxg5Vh#<g(?7L{T;GuAm1XajFWe(1w-^1=@8Sm5#uHQ>
zk<P-(5>QhXHxAbvZe?X>u&woBpz2b<#3d;+jP-XZY<*+$DkK%CTt`+3@IQJ1kl&+h
z<}$cR9Iu3D4ds-%7r0+Vso}uSBiT9TGNxF|oW+XP-c$A7)rGHslyQmu`Batt;W?fI
z|I9H^sqYJI|GOuob?bZD#whUuH_c{9SRt%r5L*p(TM3mVtRvjJ*JcL03@e>j6ZC6~
z(PmM}ko$EvuKOzG%TGI~o8K`N;H&KV;E}~5t~q4eX}hp+8*XMyNigoVWNYuh4Efds
zjJX5D^&VB+zz<S9U;&sfN>>5XUC4(DjeFZC{kH|cW680dP!a>IzW_R!RBK+^p1j@#
zHe5+l{Wn<oi}SA}F6qS1{d1!NcSKxaO(R@>w7tkH#xGJfK{uR%GmrVScaOfU(bhLf
zb|I_)V!@V;<vwtWWQO_zvcm@TZ;NvA+;PAOU5ed6w1;<w02=EFKHpl@a0FJP%0ip%
z^C!mN;7whOY;=BB)bQMyUZR+jKY8+=p}3$=kA8AOXHti)YaQmmP@ZFne*4~IbA@Tl
zTfs=_8K88L1C0~!76yjcK{dlcb^5ko(l~*zKSZVmxKunqqB%&1+!21<3c_)KD<_a?
z6}WN%P@osSLYjls%tVF_bBiI{*EH$<S@e1;B=wGK*DSjlj=!|gu@WP4q(mHt$xi2I
z{4@;9srj9kLp?~gmJ5OlGzd{gXc|%?CnAZsoS;H6=mQXw_WUR+zL*y~e0&otlixg!
zT#e(w1x8Y$fBT%2v{2{gvMUD&U(wUWo<q;p=sdBoG0{|0WYOo$4%=O=d+%`YAGvps
z049pq4Izc_tqEfx17Y1>t?wUBAm1<-sIDr4^X06^K)=vj?8>1smV0<k448LOJ5918
z+RstVU)gw|7<UxIUsd*Y3_tNjjL*2HC%A`ZoXTr0ccz*r)k;QM2ssKrXNGaQIz-r|
zZlwnV?tm$1LaSw$T%}CSbOd}suNqSD&EpbF=%4F{O%wVOfd76b(n_QxC%^b)wLQIG
zD=-_R<j^S{q1VE?T<cmrsuX%5v`mRO;0l~mVM<;+N4kzv5a5Mj>)DB|c#1%tCi+nb
zN|4QpSM>2l`49q8Au<*Ui~vIglK|LJp>BdGWs$aRs=ToC4qJT7+#X(*90=YG+2WwE
zE&yznILtbCcuVe&N2r9;>G$v6X4M+ww1e+j?@aYj&}Yt`sDvQqK9_Fa)2#!P9uui{
zpoAhacmat*(IBhf*!H2T&xTb&(n9KEe=D&3kb`Tnb@x%p6uuBShQPPknm`1iDDxO9
zzm_j8c_w<@%=PAWw&PTml)QZm%7G)}X}#UuN$@7zl_^RhzfezDa}HHBr)6Cl(qZjA
zmKy`$eXMGZE6*fXs`%C#<L*KBo7qCb3ONMCzBO0J+GqsFnkHU424}z9v+~2@3vpdK
zsi6?7LKJC8`(NEkq1#0jdDPcp>qD{&h)0?led&4=1v&R!@_>VsD&Cp;te%HaB8oH!
zBKl8LuhliS^LEN3`@*GPgAPdpx%Hr(61vNR4<l0LpxI~xZsRH%cya)oK>LYk{M%J)
z6Z2RThddbc*9GxCSrkoaYHm<9ZAmYR{lHYl^6T9uKunU#uRt4UzPizT_4p`sZEy$c
z#JtM+66g*vfih5|a0p%k0K$re@JXbef8GcAlx%=+UnuMq%bnX3PX4J;xLV=4S8!L0
z-lPQ!tBIQD*?CfH0%#o~xDb72|2xG&dn1ug>Ogch*;hUBN_=bMB#_`jSApyR;r5uz
zSZu&BaE9~V52(OQK$CW3R_$Vw6J&sk!hhTb8V_)7e!gqlF5kX52_wj))tB&VJgfHS
zXTYikrCQ%U#eL5T6$a~POvEczu=VAc=eC~0SM4&oLfvlwm6KIM31CIDexq8s7{FJ6
z+up=Ez-tU35eGN73jYu2Hl~8eh_>Lm=x4vObpUq{iL;Ri`czvI3jOg-2#q%V#413p
zB$=c`9)l1jY^-d^JS)nzD5{=-6?4)_XzQz`LQo5$N3}0tuavK>j7FDKT5CZoQ@}r+
z%9%%?p}|A6R2oBXBmm;@)-!;Fz4^Whzaaigszf3S^YHX@S!LST&;P_ju55ac6qh53
zbu`uW4+y){kum(5g17j5JSmlCDmzJLvuyJvJ-Tb#;RIqe11hRvIcdWSj#0O!X1jl`
z*Z&L(`Z?@gGu(tMw?Q3yyw3?jbwg-CQ~>6*v|>nax0jF*Y{M#Nw~s_{^x{kQ)}!Jn
z)~7KUqOKD@v@H~3s3K>nOsm?)vXY_SzOoowHes{ctrp3jB(m#D$uA7W=VMZ39FljT
zEz{V@=s6%tgZW^zap#TtisuPU72+_`<>AY2`Q#kZ7XlH}#|Eqo{e`#p19z+2`dOY0
z-(~)FHh55UNp0UVQd+naxFEWs;@g?>N%iTi6yEK)g)$R}Kwo~_eya!wVIzry-1x~L
z_?HSqwYRk;0&Vkz#|YR<&zjZqLC_99VIXg07Ha~m=vTg4N~;^5*y%*LSTUlBeUp#i
zcOwmh8zUb_E0;g%u-iCWiyBbEc#pJ>kBtXsbrGl5Y%fHV_fWXS7RKne+*apY{^WST
zT!}Lau`u$eUwU8$$`goq+uM~BZ*RX-alsG}8FyWS9WGzl&1_0unKpmsiqz|loFC4w
z*T3;@jwa2|?Fg#EOFH~k)y(pV%8iW;C;l0TRWAA&fJrA9L`==z39V72tQ?$WRl?IG
zVKQ>d?0KAxGUY2*mdESUv5rYtEsyau;MU3XTaGPUf4#ntpJSY|q^hwETqAHp@HR}0
zfVs$OFop}Rx;JFuvtGG^NaO*q1tMwFlx>{__Z`YL=XJTQ8Bb6V2vwn`D=NgnCHeW%
zAcnXXuB5D#f=|rT%TkKenvOJ~=LF?!G-liDE`fv&R4T@k<NO%>0;}Sq+<rK?K!t~i
zXB4=#aR4dlk*|=h!J<@)&FBD>ol+UR-7;0SUs1M>2qjaRhEwS)e#59x;rCDc`J7Ec
zrHiNfqL4q%1NetMp4cP{ez#g!b>)674-JUFf^3*!sRc_Gh%3NtMA0D9HUvsdv=?92
z;eg>A7&P-_(1P0r)R5Db*xcOQ0KXB`ys3S4>j`QzK*)@HoS1C~P)Y6*&KJN5DAYUv
zrdhi!-~Gaa{wPmL{lb&6R5&KO{X)HHfr%0Ta2txPpx`R)rDF)BcSPleE<(yx<2E>O
zRBc7(Jd^san<#~j{{%?tU)?L#e*e%ieOu8?!j)*m5MG<i9snsl&;4i_WgY%(psupj
z&8C*K#5*hsF@;ey1%#cUYE{9k68Qr&Z(#Jp_CuqrMFB7Ld`tAEaa956G9Vs|I1p8X
znWvINvg1A%ayoO;m;m+MWj|<*fk5236PwU6Xd7fMyp#t}@ho?7;WdY|k#FNg02v<)
zPeJJXQv)ybuE276K4<%UEQjHILMVa)=7z?DeotU-oGWN~;c3Uy_r2nZ&4>MTla`Sd
z+-WEx`i^Y)t-Fo4wpjPakQ}z2635Vm%h@wxrbXoh-UK<ms`~Io;mz6uX-1xmp2}Ns
z%wuf<A<qz;a8Eynj7#a0QJ$&>XdpK~N4+4@MNMfzOzicepXP@U+67^XKgmSi5(VJH
zZ41t}leWId=@@X$09x3rP`3}0x~)T4#8~mj_rK3}d?jXkOCI-lc+VhI2nr7DnwuW`
zeA<CFtYi6A;HM%lcYqd5AmJ9*_ksxURO6lPRL)&^KTs`Ipi>lhG#VWwCN>0s{3Io%
z0u+)#l(#&w0Swjx*e9OOgX-5$8MAfb*SC7064CV~6%ykd-P6x!p#QD?*%{x2pgPH{
z+`%|VC2j7@E3zuM!l)|>hPLW$FET6`fK1~6GNpnhu7vBwF1GwzDXaS|%Q42E%@x!e
zM!NojV?JscP4AKqzC%#Vd8Sr1e)jmdlb43AzN9f9*t!aLvEM9W%Co?$#1><e-MU#g
z9}Wxo3)=#N8NF}<o>+BAqj8tIJ=^)AWXeWdhWiz`a)TUo`}`ME=)zk}UfS6)YUSz9
z5!Os`3Fqtz#d$>OoSBPUi~aV%w3I2c#>_T<>-0b?{KXm8elkXZQxU9sIBfSa_GF1T
zC(7HITfb`ngrumkFNl=%x$@ebtd1=n863gA_4g_-1d(QwiKom;G}n^xl;5XE8hm4x
z$;@AsBuf)cy6I>U2aJy9csh7iiapqZ7=R=T+{B*m;GQk+GNInr1b!-M%r^tFp@k}H
z0fY)DY0O*jk^A3SFijrZ#^>xe7n6Lkt1!#}Gem2z)Sis?Gf9zi@ie$$0VG+uoWApU
zWrED@+p#1;;2t>714yeEpe@80>Ss{Al#_{+@p<unq=MhC^{sriUjQKSPMAbR!1j(8
zqy@zaEynaH6*=r1g{ror;+QLHFHU>_94JV_dL3tknH?Vj`!)xu9H}UcIN%Zu+Ouwt
ztA)&I!(zfP2@yDa46=Iyi$><AE$O+`ryRGRYkl}^5I8{RQNEW*?2#F~b+Z@FHFF#n
z(?8~yaNzy7c*j?m9>Pxt)H#eybszV?p9|D)O@8x4dpi~+pa+H06{M|4G}7u}D~LW(
zwIS-k!2X=<>@nmPjyHt{7fL<s2(o}eT}4+RM!>)ZE?)if;q8F?Trc4WxGc@>999&_
zlK<RL1IP==jGhOm%FAY*|K$hv>*ZYQ?o;BID5Wn3o~pDP)N6ortm^H2PqlQoM%?&c
zco_g1gi^sn9DTlSKbPIwPn3MWsR6yK)ZW~^$@;Aa>R9IRyzzdIV9X2z<<EGg6Udiu
zfOB?e^LTgWqC6%DHAX`=I&M&!LPB68=*R+?WsP?PG&@>FerJ$Hhb~{(d@bECGDp?{
zJvY@2&wK(d-0DEWU~J6yhFJ^#1hFWJzROVGjO0%hNvb`gvb}K-hyXj7ni0;78ginF
zV3&D`hB@?nn&pIYJ%~Q>{4%7l5ew)sQwG0uXgwDTZB`|K5e2=nVYhfL3f%(@!E&-Q
zOYbT4orlO(ye$wGx+j&;oGA@;;6)%O-I(9jl~zHp#mVwQ3B&^5v<to{IvY~@jcQHM
z@X|#oaYrd}guN+P*w229vEH6Bv(4vOJuYB-R+S0?cTmt7x>4iHp#pH&)poZHWu!W0
zlYC1~L__o#2!p828aRA+PdFCZF<v1S_R={9yZ<0fJIZ{abm&Amz4dOqmg0RW4_Ngm
zS^Z2|Il+pI{cO{rm1}32tch$WE+<i($%Lsb|7BmucEIF5ch0al8rZ^=$UYMC+Gc3(
zoQ9gjI|6X@B$M?2WADA=x!(WqVeQl6bQ(&=sjSFI5)z#@S!HBX_K55~Ivr_<GRu~b
zT_M>@C1ho0R`yB=+4uGOXgKHh{p0@YzW@1s9z7c3JzwMbyq?!}J%>)@9mrl;BUN1k
z0?HU6;gZ%9HO<XxbDzH7KVNaD<nceYxv!FYRbWRz>_6Kk9Zt-;o3=lew`rplT)yq9
ze5ZAM;}G4`Oa-~E^~=@POoA^O+tT;s#}Zty>15>$)&2tq`ujEKvq;mC1oRim2YOFj
z)rmHoz_}Kg%^GcHsqZOm%i)P33=1$)qu9W`uwE8CIFHcOp?#{bkHNtvO6t(+-C=vb
zw$!@T9o`vritniHu=AzHTRvKL>1|0y;rsGwp50h75}AbLq|h`q%jCyfar*<=SOm-l
zNoRGUVGutH5&$Y_#1^)#EyYfRTO2$gm>Zmxlk>%n4~xFUJqv_KDs~BdOO^DChY@X6
zZ%@iaCK{d<Rs15Mu&`n^XGv;Esi_?3vu^^2m_?n`I!kSLR501@?f`+N`>cD><F+=p
zAE7A~ukr?do?Y<UK%tEEZcq&T9!C!-GT*5qk_qr}wg*qAp>p)xs;=(|>pw+?a@Kn{
z<?UnFppb_5Ffz>rRp_!U&_c=WQA4ctV}6-9&PU8O2MsCCoiGju&daW^qF&n|ICwSQ
z$({OBs}p-pN%x-RFV)>2%lWrDts0566p_xZ{l%cC=l{7|$Q={VH)`=Zg_H&PAlL|L
zS&4@%8rLg0gvNgnR_JVzl69gvZ;|EsW7tu70vTO+-UWXd@%q;`4W1_@C4~6Zsu!Ja
zF!ODMrWe6cw`m4P+m}q^m*z{`f4m<mU)mpF!iPV#D7<AOT}LI(rYZ6$CZ3LS`tB?0
zbh!GU&?!c5g_L`9%oZ-eBkODbQdqy%^W0R(nk<GV?|-}b^I9bL|9mLo>_KmPTE;K!
zP-3iW8g%aSig`Wgz!)n9{y(XM1c1+B`X=(g{IkSwQ_4k5F%YzwXN&&4Fjm?hcjfj9
zVNicIg-z@~IBDA1Z*z=ax~zmn9Q!xIFRK@CNgc`Xymqu2wd(NL<;$0cvx%5;;lq09
z$AJo#_g`lnvvE{1qs{WquVz$azPGL3xIB5Z{~uJM!tS90nmAm{>uH|L`s0VGs5O4x
zkU3b~Ko{K<pN<*o?oi9iWD`xUw`yEZL=C6)6iEbj6Gf%f&x*!^e*XMjm7i}`_jWm{
z0h7%3X{%kNs(pBClR$(#>w)C@<S&AhKxkirHExDsU({K~SZB4ApVbz(xSyfWqg4x0
zI4a5d=S#ge_R-NHOWiDm7$fLWR#)AsG@^Cbe~vt3fCq2%_OT93{koi~AD8ptwr|-z
zNX%|78A(2(lw<klnTDZt{xmVE0d*>!mPTjf{K3$L$iUz0V*J_YdP760|8Wp4i8nfy
zMD4*K9(OQkkLe-kUlxK@uK|&;eM!l7%pPTxGld*!4>Lh&{vlf6TbJekbI532_#tvH
zcz}v%?M5kt-89~|>sP$7-N-t0D^^GP#g}ib^oqU39wos?RBF_sqhijLw5O^53bv_!
z_9EEGcCVNNTOA^I>>;g6E@J#`@0~?R48IJ}982IWu$J~#CrD)Z%OreNT6Bm<a5Y|-
z&%6+Z$IT(`Zr?rjY9w;FRViL@==x@_qUu-mQN?i^>D#u)w#dq*%aEab#`4t7pm?j7
zX%VDszld%;?f#=YXp?X-s0M%~QG`Wu#)lb*9&1(o?e@egIYRLjdi*z|2k_h4RW0!}
z4maAfUgYGxNXl|kRS=C`SNJGP?dNmXLMpfL*yulfit`k0*icK3YIQ8lrpOHk$KX;{
zZ~W)cwzC%ta*HbMoR7O`Y`;|6|3p=t`{zM;cOWlXh7BAXe$t-?I;Xy&p>y1U)ub2!
z=N6Uk_@Cf02prSLU#?XzS+yo|<Qg^uzE~xFi=Ps<)g2QkYZ_fi-VixLm#=EGy@ePn
zEPUKPf-+upa408=D91=!J`g)j<^af!9TG5@3mtMgX<V0hhnZ|skiye<FTfN9@_Ior
zD4eFpXh;0cQL(B-tjCkXzgStl_2K)ASB+-eEA1+i8r@D?yHiajpC2&_KTVxo*5~u<
zw18Ge`A$WjCCx&<5^J`zbAg%Je4dAFUy+^}1d85vbq5kHq`p;?3(@vKpb}qHZPRE=
zasA-6j%+{MV>M!TpJt`ap6w+&En?>A+ZEA9`IbsrIsB3uPzRjoUZ4Y#d(}FEYG9O|
zNHX!uLqDr5$!huy><89qXFMPMC<<vpKXE@q5sz7%Mr;P{O*;e36Sh&F5`7K;WW}fM
zV@2y*9ITIew;IxL`Zi7L(`<azo)-6dNb$|ko_}7i4X%j3-G)TLMa&A|--AU?J-p(m
z5=L<*KRc2FJEgr^P|}hjF=;pk5zssIK)UXn{1KP}+Bo+Ri63(>q{}SdjnkOLA)A^{
zP1ocU_}b&W-QjC@9Z=TH)k%@Bhu&?d{@W;Sn6~iKtIB5;(S3(qhOFPSrTrR4RP97N
zM*3)y{xQ%SAl6YMYy@;0dFV50GiZ@_kZ1y25L=b<OXb3R3W=v*eLq8B7UgvnboiIv
zQ-O*vq2KW<c>ou+Q+A?!sn*n=W(+?0DBa(}7gS_VN+(p}TA=D=>-e>dQL)kxjSAYB
zlTs1UL=w$jKe5}XRRQ98#5|--FC9Iv7+hzDb>k??U@A!sK%AB`YUFln*SAbnUu7J7
zdA8j_DtJd({8;}r!@n4+WY7HOzp7S#(6{C3kl5Xy)mAFrF;b6ZIB3&TcA#yK4QD=(
zl#>j6fe+wd2?waSgoM*RY4EuDzST&cVVGT=)Bu`@N?wxKUekJ`hnH}I%|vN(a+9z>
z!R6c2yqgwDqbOv)cW4vNQO(aH4p9jKlJC}|OB&b8vh3{aqMQX-{w_VT4w6GFn)KA&
z7(3M1z`!}Z|8G-|E~&L!vVCdXOA?%tpJ@I_qLFRX5T2*7urLud86fx>nV!|wNCmG{
z(Cb8$_V={Llc$=0|Lr&IB#j9}`0dYB^lnWNK!<1&2tZI(;xZV~6zQv1-;<WPU)WkY
zscZl88*Mp16G=FYA=&Z++vR5a$0fs$uf4Ld**EHttE;^5^asMhv%IupkLadNJgdEn
zY!3w}&TFZ>4-wii224McH&rF}ZCsV1rBV8$$47`FB>4v>52uMrn2GrviIg=L5wQ~8
za#Z~tOB}kb?lGoBac((tb}HZtt7PoDPh)e8l*7Xeo^0jzj(wsSsPy9eKC=1X;I(m<
zM&z#`(|lLuEzH85SP)5T<_9z9bj%vl#N*^8xC4!A`km2N2nr|e^5CGL*J`^c-|TT8
z=VG!tk!E_5pNV7W?W)p)yHIgy;w-%FWt3QhGgy^`(2=`wIl$CH`4P%*k5=X<(MO{#
z`lccro+t0yebUO@%OMwF*fT+!Of{k-E|On>+T_&nWS9mZ^YeJofnbN#Il}2#=Gc-@
zfmmc{aka&%2@0LtXPF-V+(W4j3z6{|6T=E;v%{#b1u&BmW)yXhSzDeuWD0YdSoXw)
z(a5+H;z>nL%0;b8#U@urGbF3=Tfop(LA#}$M`O;}TQ@e-g$iUS#=cEH<Ge!bCmsyu
zUI>Bk_SdUL5yytB=X+>ezg@`#C9MxsZ=s0&kbBI1;+Z=_xEut`LjMErqYHqXBzK%7
zB$|FNhSTtC3W9RlaVT%5BKF<4K1iK>I1?on@ZRBCgQb=u{IvC7fuw|8-BlMK*QjrO
zebJNDBo{%nG^Ax+EecEk2uQmv5Hh6NkU7gMD`!?r%zw7cuO>MiL`0jj?}cA_v3d&P
z1ku)opj%b|d>!2Vi(%x#hGR4_(5o&lS~WWT5o#>KXeAC~>t4rB+xKAK@9t>ZMn^Ll
z^2&Km$%SboQyvuk7wznGo<9k8=nuOO%h3lbVM<YnK=6L2m_cx*w5cF=4um#DYHU9E
zA~+*xi3j?N!)ao-O<GOCHg1#VJ}Eh@B+y{H=nkt>mPp>SC<-wh_}bH%9%?jrom1_%
z0FQRV^~~=GOQqTMuZ*J}2bS}v?0DEk7as57{^sbMlO1*lpVITGS@MgX7@(xR!VsNO
zx_o7!I|Vef%;L~Hdy9-*V?+aqPxdi}eQ0~^Qml4%uZ{+q*=f#C)uU2T_fH+=E(^le
zF~4&#l!^^=boJ2CXcFo@czei*UDn0U`O(%r;q|}EA9W92>`nWK2JAt!+2XDw)?jFR
zs5X?3!}BC~czbx1dUyrL#K9+pIHlD?+QRzu66JDjs;a^(ht3UES`OLv$ZU7?E0>;A
z?8JRqZeJR7U=L1r^l&wY$nuh(8$Okm9|DbHl23!0)=wPJNQXhvN1kV8sqKEN8NmJT
z2+P>HYhU=Cve)(Ilu48LIB#Ekx1x2A?a)vqB(Djj@1qV!9-WkpnE7%wqH6>DuchD3
z9PBqw5RHl4MjhR9`vwPVp$jJ43mkmd>8a5JBn}$#RK2oy%KD+vuV0@?I89j>>HkLN
zGGwy4pPa=qK{)L(r=GRA4|7U&^6iQ^iSW}q!sYfIX+2_VvGhj`3eSPJ3M1UNug{2}
z*2KLh={wRF%_lT#uI(M>n12*LTSr6hbUafpH9R1%@Y~IElWI_pY{uwRxZCYbt1;TC
znebJ()4Z`L=~pg8Rd9L7!rou7X*0N0C0X2uCg!Mf(_-ZA=;*yQR<xs^dUIziNRC$4
zKmT{Si7iXp=bRokcR#7N|A9;5@~ADo$H{k(2=7{bC_i4VBB`$uJCTI0rMAZCBMI+v
z^~wlKTIuTRsqvj@O(n9se~m>gzVEiJ1%K_lQx10ESzG2fpolGZ{Ib?g<1fLEpB(g0
zE5!(n8<!dX*3-PA+UoO*l}NFCe$qB@v`g2%--z3BZ2tvXC)BHe1r`H_3zE0x&mY?D
z=`miVqt|S-cY)oi+7J=FHpWmf$+bE8JCI1O=`INLfoBAQYf%u5M4J%Oh#8d$5~$d_
zC3Rq?!ZScndV%Zw`ZUq%y?J;b3}XNMu~%nb*^7$gS+;FF%@N9Vbb9!V#ruoG7fo-K
zs|!l$SEQVCuGCiM`gP}S#qJUI+rCar^oP&&hJR(6A7+AGF<rGvjNu;?jM`1SfL{v2
z3in5LD!Ic>208jlb~!u(dt@0YTe5(@`XwjV<t@~q);hW6>J1Sz6$x^cpw}_~I!6IB
z2ymW6gw+Q_>ocfdiFaER^6p8bP(XRrfGj}4_eF~$ete!b)qb$(qg~e4Elhn!T2be`
zhd|pwxn@<1N>;>Q-CQ9tzWViVIv@Qi*x^iCKE%cZlR1w#Sb=~oK6z<Uw!z7+hcZND
z8KL~0b=R_+*OK^X=MqBWHMH&)P1*FC^Aw8GJGSNl;rwz{L8)$TgUZMI;&oDoS5^JG
z0k>Hqw<Vomjz?8pn6RkkCoQ7-2tG{O{sjfmO*)C*Od$0Qw<{+_{HvzQACj!f^yD#Y
z%C(!txBT5{c-+@RL(55qUf}5dC4olEOly}n$8&kcQg${Ka4MA4cm6u#hd)VePQaug
z0*0`gv+sHw*+F#9!qB%SaZ@4?)?6scZ?zm;)@gk3K8Z2L^PW+6@?2Z<jNxEmyGx)E
zst{A^?tf-xe^a?w5G|3C`pu)_*9oZnwxc*seDYX2v+r)d8cDj7|3e`^YZ20D*`dci
zypxR#8`5ox{EDS!k7WYZA}L23QVzNObMGE~`(oO$-z6i5JG++U*N#v1=k<umhmc5a
zDgu3yXgu6&$ywdfF(c}}Xo*KSwIDgPJ>mZr+Vhk>CRaxy%{6xjes0?KCk<AM@uB9c
zU7~@Zi&t&8D}UfxpEQlCcP%g*S5v@XL1yr91`*H>dG=i?e<;ds#ZH(<RXVTxM#h5+
zv2%CJN7u(;+g8xf-@&iZV6$;&$`?W1)Zx@0wL@Ikhf1Q;LD}hM`>~zp&$Ez=m32W1
zGU$8-YV2b`cvRZ9)m|;15e)!?T;opZdAT>;P{&70gv%+wLEUNci(O}BLqFXXj*9%Q
z*y>*p4;7Uoi9LyF1Hv4zV1c7cvhMP@;u=EELd|{MN-;P~8ye)J=SQSk_C`MuD(xE)
zycksfdkx5PFo;lSXIgVJd(+6!I&a4in^L166*T;`7RRn_ha{?FW$Ga-iXt>?bOv&O
z4U`qkWK|Xb1PY8eb>y0p^Evr`+m4CBT=P!J{yxBV;K4y1D|=FM?UomvOZK}vp-3e7
z6Jp2fQXt{Xp^4@w#QVx9iV$<$a?xoMwC`M~_Fwv~xJ&Z*#%<X5=}tY^EUvRjT&HI5
zZFU>Bc|=ceL^|D{w(Z?pI#<zCqFhX@V)u7Eya&S3pulP4MWcmk7Xrj9@C}tl(iEQe
z#hu5XCChs1C7Wm@v2}+AXMte!;m_Np_ijY<(Vf!SwEODIYPzj^2G&LnhqmA8ehG6U
zGwpwR6*RCi8?>fHcp1F0-5svVbi2qfBa5Fr>F=LM;kc<P()KF6f;h&UNXZ$wv4vah
zA(2LBp<qEcBE`Jl;%)H*S{%h&UVMD4<8qTV)qPgOE82$fCd=r@VU$>4Ul3zB#&Amm
zbgDvx0QIuJ`N^#9`!ePBH)HH#tcA*LFN`o(o>Mb-C!NX&5nTm}Pjt05ZGm4rk%U9K
zThuNTF@}K+P0Z^!6`cA*B_PkU??XNnCw}TpTq(Ehm7Fqu!JB7T{=}8ufk*Apm%kY7
zfDwn@&B)<z%maUlh7o?>B!7nJ?Y6$9K~29`hGe;M<Svd#sP4uSxjZtt_!Os3PNJcK
zx<%PQ{H{bam`~;f{Z%U%aPkJjqQgf3wAM6k<XM^PapdE$yv;G$oUP0<`u=4MF9%<+
z-K{n?(7UDSaiF8H!*b2@5h3d(iwS*{hrN*yDrY~e602N)pgBT~n~ji;Uf=!u)6pH)
z<PK&1PU(-$JzbJy(qCON4u_z%#uRl$%%<?tWFhOzp$sK!RV~wHq=BQ1P>#6pTLCkA
zsH`JpV`q?uxL4n$S@BApJCK8uvvYKEPcor!+zr7>6Oi{;k_k=s72<W#2sBfG*bW^+
zX?~5j9M}H53+G0VxG=Q-UUPmk<9idQo8$+7d>RgtBpz7;?a?l1h|qghbF$<dnN~*V
zIFE_4k^uPYuE+8@*q+V@P;!%@G#doB(jY4}>R=8L>J^8|KZ*93ZxV>*IPsPN@c7Nq
zh@!vrEq<H;fAZ=P&WDWalrPr!KMUX3E){`e#@82Xc^<~Zy#7=|zO^5O45^mK@D<9H
zF}p}@3{Qk7Le+imUf*id4Qv~fh48iwzb^84En_KSSo3Vqz2I9pXhKTOV0kz%<Wk>H
ziIepswD);%;pFHlMmx9Lq!dZ5d}W)VWld$HZg6_MrL0+o2OM3JxQ)HlY=W_>J8?Uj
z4_OL#SikSIZR)fY+VsV9oyW9#bkW~oGp*ferF#1k^A-gXAaWAg3`jYzhQIb!r|T5|
zu{MDO#IH5M($xMlvhMR;0h^+lMuzHD2dC1rT9T9h(J8rIBVbx@7C#>U_+2oKdp3Ra
zU*@q<{QRGvz`}b|k!by+`cNC8fv4sP3)%X@?PzfjHZ=M~k#w#GeM`*u<3A4DV>?Jb
zbrvfsvJyi%TeaY<DActR(%E8kSTllc{qub@pag(}0s(7}OYLBQpG2cu_7BD8!odqZ
zFQlw7pX{`>3SLOyt8UA{hno7->jaCi7_8G5`-;6)v$bzTKiQ~J-8|*C<p<uHfUJi-
zJ85Q#&BC!3ejgw-EGs~aoWK8uPJu=sn$d0DDtr96`vO4$71H7+$9DJf4tF%~!ByYK
zDz_&g3EAAr5?Y|$&7K?zQz1Xe;FP1f-N9Ps1J`tDpkheFI;_!@h8;u;r${6mZb$zy
z3?e`Y6wInqpBftvDAAksZpS7Z@o9?{Yny-BZLhnEk;K>|?|1HSh4#RoiiL&i_N7CD
z_6&&97-4|}kR|@kX=qQwVE%H$!YOtmHncwOJ6ZXSd&dZuvpLv#9Cyl<)w_PH6^->W
zi>n*v!o%M54*dB}+fnUb*$Z)by85zcD?j8w8TqqGqI1LE3Gl5T<RjwhWC(pKW#xAh
z=k_lK_S496IB(yvJTT{r-*&9^p@|#L=1-_g>GkK<oq8Hq&bfbDN`0!TT0vRa(As|S
z)87gKm3AelZ=`TdC`Gh+Usj;mcOQ(HACj$^SO$}3Gzfd);6!aVh>nKl4+$Z@(+7J_
z3}2sj?Ec%QjJ0^9=AK8IyOdU}ExI{6lSxQq8INSZ?$N88+Mn<`u#NU9b~pvQ5T6H^
zFE7dc`mXMiBarfVwZ)g?2QSFIchQYm{Pt_!b(rCsITa1SlZ(=*hmjTEJqtf9E^f=P
z+W|qVCE5}$k-Ie4Y5(pxx&zU-NYtsFNwBSYrQL1&et!aCQ@j6eFNHleyDsH+o9@5w
z{SiDA`FK%f7G!D+?%H&U0rOGvJmALDZ4s^S|EQZc>8&TSAhUkA`^9k+t6UjlxtRWB
z+OjiTxxfCcN_gK=)2!QzcWYwAr+efPVrnf5a3H^$E=XVws!kC_45&%U>Wa#CAp&K;
zP4c7H6pGP$|2Vnlo~u5pQ9IRwlAQ0qjy6v=|DR@oZY@1blC;@=`PnRqwi#cI9bI3*
z6sW8}jrK}A#3-UtJoE{}**y}A!W1kzL?-mpzL0)orJQnZev;;pNiWaUk^|O7iQN5e
z-_*x_xH}4ucI-PM>h2`{cgNxva#xm;H}dwf>{CXG8^6Y0$)UU(v6Vv;Q8k{myFG+x
zn;AVga>{4A%n6^*z5Y%cZTCTMnS?J7-<`fqNilvL(|0YUa_O01LR?)?$E`ZNGwJek
zNpnW=2S76Ya>CkZH8?|p-*&L3>1CHyejHm;d(KY&qq*PJ!inD*X&mU`SGZAy?Z@%R
zpS&8B3d;vKls>H2F{g2bRMuTmQ=ML!zCT3v<ot(gx9#@$r)M=g2dQ`0BYm-Y>nVY1
zoy6MhoE_$kNk6Gelw&?xw{U^K%jl+Yi9F<TzJh9Q&C@ShJ0wLPUi|ov@X9|*x?tEu
z@87G?dLd$_VVl3O(TQjd5$RtvPs)K<H&b9`kFAJox1TobfAr;BrJbs)OB=0>s=A(X
z%}O=ln94Z(yn?X9iD+az|NZ%e3h1iKcv3)=w1g9$*OWVp_;WFO(|WO;J=lFca`c_D
zdwbjk+Y#74jB)v^R}NNI6~e@<T0Z)<gf}TeEspP~K12YJJs>)A$J_7i`oc=MN<69<
zEzy;!$Ed3-)v=V&yZSWo?9p~B={OU`;;-fPHerFBAsM4%HIFRM-cu9(Gw<{@r%Ot1
zBL4Rro_~4#Mew4^C8gt{@~wWT!Nw1l>g|$WJou>mC@dvd-`x!5k*aRirhWUd+rP9m
z8SC8Za};et%tXfeO;24e4%~oje$&!V%ET5jgf2%q8`U-Z;>eLkv@p}qE`J=RJb#<&
z_cpi7R}TL9@AG(4M4%sTlS(gg%7itSM{r!&MlB)Eo~IA?Ape%4vEPp=_nITcXesxK
zo0o^rJElZ!Ww&jF`6VS`q`Wiyrp5DH(9mVWplpVrK@p5Cp;tcJl3MdpPfx-Tj2ymq
zPsQ-;873>*D;?DdIYEpg@sIiP!|?}E%^mx37|Nfz$Wjl$@JG1<Pk3W2ThN~6C8Loe
z3OG<48|$2a$pm5vx&#pxhGLIZC9w()py(xWFW$?k3YLSOLQA-hq!7>p-<m|d@({y|
zf1!CXd+@&WT-QgKi3&aPdAVZetqnJpcRniaMtHEtB>B(~O~O|L2mh3<+$m$(x~ba~
z+rINB7B13F9Arlg;xfnQ>GY*9ty-F(JOxWiIwb*U+=zWbC#k#nv@VR{IHI0gDL&RY
zA5y{*R?ZRjHhiPZkO)s$Nk9&M<G6P(r)|$Cn+9BbhegAvcC3`ChkEjh;n3jROBybp
zt%Yt}HkUcC8A5Z*b~VY<-mku~c$uSO2Mjx{1@~7ImxoP{&R-!WLKojZIS$AjIWlqS
zBKwfN2qO{2c<uv}?6fZG%?tjG(P0J)G|$~*FMaxMMb|KtF?lnO{55jrPTB3QniXlN
zOJJVwKh)8(ZJljn#}nj=G&DD|8Ek|GSI!Uq>vesI0FJ@*+YNETHX~U`E+pdF&WF(Z
zB|ahf9fNIiB-l`^lc}kAw_J%!bhuD_K~^N6XZ>xO6(!l)yQ5jnX@{#UpjSn`2<L?3
z#aaBp@2cWXP=G~6W}MYc7d@)IeTpAYcy6GYJ09Xeo8(nu-!22&-7q^<pI_$zCiUw0
z->G5(@CGi4B<Zlm)>H<R!Ky@AF3#kspp2e{gsw+ue!s}U5hXrfIXhmao!+t3<zDSk
zo2?G==RdyR5H|zWDxksJ)!_I1YcZg@D~~J4u*eLVgWbeg8PQ3dV)N{lWOSBPP*gMm
z<rD>Gp1iu++Gu!hXjX{L=ST~6`VoT$)=*$8Fo<la(#hkri`2HW<!{@Ob0FaCgzUr@
z1BolWM>)S9YBgH+`A9_0k&{vtwj5?_CV?4Uq@V{sANeDm<z<gmNn-^dE2I<(!-$JU
zCMJG<G*MzGtYo43i1uNu#6DoECRQ{Wlvf#lfB%gd2dF$_u__9Not4=k(z;Qqb6H1~
zj<}W+yPD|jH%jUwJEGzm6Wl#I5Bl+#y!9yQ=M<sDV?WOf=${J>kt5M0nlJ}Xdl83#
zI@Ac&EZ*v+lD2YH*V)1z0`OO-*bG7b)I*vkTh*)EO34>f#qd`1`<DlmbXdT<Dps;w
z9-On7OCrAeAk6lA+u<kK?e|v4wtU}vpY~$O<ge*pxw|*l7kdfv1eAbdG%KTH#JjE-
zIYPh1o9-txP4pww+i4gkvIa(k21hbI7%YW1T{>8!)-DGE$JoCcsTcSisifZpkB_!9
z%zx}I7X#J47si5(?UMxRSVkif0TNrR-9W43Q0@Gb=KN{!^xIW9Daq31=bl;jSHp=p
zG$%WXI5JPSDhV^KVcM{%&C)=`my5piUWv~@SQ@f4qgif(zftTX<6?MIhKUJd@lYe0
z$O_x8d~--T7@Nz~PuBFpgLMR*g9YP>5suqF+UObEEn4VCc-oMBt|JOEABZi!k_Oaf
zQ0^5aDN?nALXIlV+RMWf0t)Tl?UP&z>wG4T$pMvC4D{r?)ZlqUs~Ow@PO<Uq(@Q&Q
z0o*N{eB7qXw&!0v4HdB+_Cr%Uca-dJjG~=<Kb$?9QzkTEHuowblbOiFmzQ1UEA5nw
zwsim8_CB=z>!b1yh1pkPZH0Jz%XaS{7*pa?ednBWFe`PJz3^$a;o_Hu2VgJMVX>}h
zKp>OeCX-$ub8U*y;E9jcj>As2#gmg<J?4H^On5j7AFa_UbJ6gkqB^zHAg_($&AVxh
z)SGnR7uarjchoo5l2OH{nPt`M;z1qAt**l7iX9TJ9vu_YrHV|vVUYAO-#g<fl0*t!
zN2j0r5sO&wZtvqhhS8~TUy(2OvvAKf3@Yh*>tkQ;_arkn+?e`c*gmOFi%E0VK$<JC
z3)Z*%C!a=9;As4Kpv~$OF00=vY}ZxTZlIY|AIY@2`Z9Ff>2R~&E~<Y&!@GjZ%O<WE
z1~~w0VjECVwDEN&MlxM6_wIAa&p;EDefs6{#>NTEyZt2$YvUD!KmN0^L}|w(v+Gs@
zOzW5!w|K{%VB~x|?U8%JYx}DEFWO}<7O1bgac-4bJVW0fjBh+jjMp+0COC1wU;T@F
zMtN$~ARL~#05>X@QTVseS5DLJQIcI3M3Z!~lD>T~l_oESVcGQy*F*@`{l^}itv*fX
zeVfi#422fHK3WuduqgQueOrrTF1O=e5k`2Bf{&i3{;$_T^?Jo$qww36%P$-L@F4=r
zxP%v`216xXc33`|9`CJRF;EIx4kT0wrHB8jY?oCpz_3$If9@1(7GBEj(Q>;`&$m$T
z&H0RC!vi+knmQhBw`d&g795<+Y!Mi_Tc~K~-NLGW{#IncvqC+ksj}byeR|0i<k><J
zJp6h)&9m%@=P@7$Fv|{E2`gx7x*j%`3>VGSf!19)pwTTiZbWExkaucCQDl~us7M&i
zo$hQ08is`RpGCSzxJvu)Lwh1+nk6LAUgG6etLvFyz^}qLG3m6bc{4nXhOEn~dIkpM
z&z-AZlB+*EHMXm7D?XN7TSJxhJguKpnhbsVB>6yX!@9wD>z(x3%rjbK0c6tw#dZmd
zUpw9SBJcH(JYk?~yUuOX_oI8uzF+V&T$S%0dp%8>4JQ^_GE%w4-RwrRB)7f7VZr3g
zBg~@XQqkWLLN{z?nWBK<eb}F)F{DvXe~YMDV8l~@x6TeAQf*b$-YykcMsRQ(l<Q9y
znfR122^`QVF%Hb3F7CeVM4kF5kp3#(c;rlL%)87Ta!+&fL<iE<y9>+B8<-tG(mR^(
z)6G~PPSa!~WXfqW&E?-_;e0l51Z31z_nV208?XIhce>Z`3?vsjQv<!1UhmlIpeWej
z`Mp)=u3wV!(I)$J*Oe=20<XV~4z|x1s|i@@D>ZA-ZRKGIEI%~9Y@Ol10vBY98!&Fu
zj8~1yb66J`Gy4|jCa?DNXg8LWn$Tan!=kvKx6^iD;MnIquBn#)ers;K!415D*6YPi
z_0-+d+V#|3*~vn5*kD3opz&mxL&elszxD7dTH0>XWf^VC#JW^?g&!*=c5n(yiwqw_
zo1Q00u8Qv!5!0<Q$_XC@W*{umy#!7@6hFxH;l#x@deQZ42445okA8#<KgO_x*K<~5
z*Or@*X;r}w^PIEu?A^NGmM?{X7$bw7CcmvBAFnoseZc|me;rnA%zu~1!7T!(Q@q5Z
zp?hMM50j3CE3*AzW0RJwbRE$;x5ZFDxqG+kn9j$+F^4$^OA+QEwZSd*3bBzlyNcBQ
zGpO;s$FZB(tiPI~lHTXmn(r{ymrD|de|@wFcbf*c!!MOkdY89|!B(x9sOqL?TL+sm
z65v5AXe%6w#zueL<M_{;Lzf=J`LEC*^+uCnY^a{K(8f<2!@nIXo`Dv#Z75=vn27L+
zfy=+?x8)^@-?_~{&+c{|Kr4@mipq`PropFKA*;>7r-CK<Q^7VE{J?FobJk%9Wk@By
zeCQ-fl<9u{<icOvR%0J$AtHsWzIbewm=B`aLHQ^f6Qd1t#&s)+#g~zY#G;Z0<tKT|
z=@n_+io3DtzXOruD5>SWBUY;v<IpyzQPYaHEb9bOc(Di4PorD4+PiJe_gQ?4eI_xW
zxZ;=lY`S#<QI%py3TT9v4;TEO29B>N^ZC|K_s@#t$$ItC$L$!fCmy0Rx7b{}SRbi`
z-YojTQzyQb@VI0V&s3AnpB*$CBYMq_`DeuU64VIm7yXRMK*)4lFrZcCeiFRDKNQ)O
zaGM~~HnstWr>nF7pynXu6wu&g{OGe`idEa(oQPhOJ>S~B&!J@>cV>r=&+W|m{#w%T
z7N6D01ddw#ISk|1B2%c-4vjZx6Yj)mFa1<yBDY0rNO_p%*(b%sP?XHl2M5zuCvIB&
z!}$B*%xy7-L5j4Oiw6nWfhePU(RjFZupgH5;nU6CRv2p!;U0P6X44g@(aveuM+=&v
zOBx2lqx(mje$HKzNx;F;Se@i+CjT~SC3dJkm!n9wdy?^ZM1u2QJj4>oO=ah4zexV)
zN*!rgLi!C8+6i%pg_kskzeHSoyaco*^%%RX-bLEh_gL^V@Q|WaR$n=;1-_UH^kcQU
zm~`d^i`Hjaim|0Y*cjF>b&+nG^+imsIlA?%241rQ=!Y;j0%Y`GAHn6&cKz!s<R8RX
z_rH^o%J09;&+(1$Ao>^lLw;Bmy8`)h@h4*W4?_E|uO43gg985RYebnqQ1gF%Sbh*}
z7kyRYe_h}uC3P(RaL|ypZ8kqyj4P8|zi4e+u+TMXS0--;vtVNxuT*tf)_;d9RY3=+
zI*ZP=@YjM4OzHoA_5Z(n{r}`;P$#Sf$&#pZ2#5Bm$@}~A9MQ7u4@k2`gpKhcjNY}K
zOQ?9`P<z0dGg~n~<4wNnlHeY6`ouR9{wZO8z7G#ae-7dgQ<tO~3w+o<SA%H1NQYBx
zZEfY0F}U>gBSWh}=PLg)Jq0<rs3<RC3ARuxA9}BmSv$Tv6U(Uzbik2HyYOmFl5*+^
z@Yw*p1nt$nN{WkRVTZC4>w_MK&$5k#VO)BUE<mBStyO4Iu*tg!YWCX7$_Qc=4QCqv
zra{KBMv;DOrgp_TXR&6~^wlQ7FgEu2^S|H~i<z%MCJtsc{AOP}g-WtU)oVCPK`;~s
zV?&*cChLat9)=L;U>2U=_E{&E7{C*~vVd8uT244Q&DUy~bASHM-ek>6uIDcMjESf~
z#~PYHSgAaH=ciKU`9RCHlY3{HarjTIxt6xs(D${qy4KKoD#d4H)Xx;lxLk1QlMClF
zHa4F4HsGft94b8uD+2-WPdoQ&+P1&G)ulOGt9cKk$c%c(D@iliGOSa&lJ&)%7G2nz
z49a2n<2`{#sSRV~Bt3q*L+qbdQ@L4WW5GNrl7ZdrM7pBBe(Yp4k8|<Y=~+joac+&A
z1}FF`zgC+#Haj3jdFV=zsNN-cbeVFh-r$(NQ(&nsN|3!>6Z!MA%T&V2MzV1T&BuAg
zM(<VJHO+8LshB{yA(+@#y|1p`7vFzu=Y~<R5%(q}Buv=d+T|tqf^vgh7MXins=-4u
z3A69I=QwNTUxDgJ7dGwJR-xB(*r=#5m@dr0=WnlBRxj%)gm-*bo@*(K+O};L3udC9
z_-6#uAYuTWA|~%uIO(N`Y0<Qyknx-25|sB4Yqc;Qt(tC3&KZ$N&Chq0uXlz*Q!>0F
zL$iMGy22b})+ff6m}zQ;XIk~Xo6U<wUKxyc-1;C7r!E;pWoFPc{9M#z!88`>#%`wM
z!Kt>5#X2KyDf{k-P0Q3AT0$jDEpuqh?|o(ErNd%v2l)91CZ<IC?dz6Nu__7#?<ZGX
zt(VI1Wpox4??VyB#HxLI=4krICctv~R|`Kb%ZpXx)^`~+)@&N;hXv1Qeg53o5>5S`
zL#wGS8K|NShCOKFchOT|+NsWOmqB?&*Q%mJkxuKX`K6W|zSjZ!OK3G~d}TzHEg)M@
zfT#-)3feFDg7bS?18IT-n&(-U?2eVx$;99?E%sp{4n8O8nWjqKT)nz7y4nzK!1gak
z<$OrkN!s+T`a0%M-0TUpuG1pZryaFMFxSsws5~`oiC1@(e+KBr8c*u;gf*-ptU<e=
zaQlQlTV(QEc1dQon3BEtv69I#24WA|oN39{LLQbW>Z4@i!eN9zr3VCTK_%+*gbI-|
zlEx;_pFj6$hi?}+jsa6sQ|QbA&oI@IDfotn6U3}_C}lkC4W@V>Wr{lv<(?x#zLPTp
zy~B2>gg$I+R9R0=Z3Jl}p$O--V#<4Yc{wLnq`G`1>xY55W!Zj*50_V+pvRL5f`e*~
zMha$hntA){4fE?iSY%QJw{RtC>$Zm5bOMt3+>ZPWq(8L5_`rK`&`5&(uGgx#KQ_eT
zhKAGdaeI+43>`Rp=hxG?e7WMI4L&#e*u*C!$f>F}T@waJk=O;PTNJZQJ5xq=LF6E4
zoq49uK3<&_o;WP_b2Rg%@^w;c^-U7L+6}JCR<NTDf`JED7(BaR?%j1j&(hMe*d8vc
zA5wgmP<ezvPf8o^jwjXy>aD&gh{Rn51&536p$c~DjShT^?yuS}Q^%o2Z5irM(#&^x
zc}|^Zz`MY2RkCQsbABrWt?#bnF~nX&6Y1#x+~_mWXd*BQ>w|r81-{u03;$8HcI1N{
zTyZAsdieI;d;1`pGCzWhlXv(2E-<sr`p-}W2S>+~R>p>ghI_;9K3(@Q`(j{bc7U{F
z@Umlc8FFlE8ZwrushHbnFN1EiwLr3XKPg*bXa-k^PAA?w#e5<pAGTVfB=~%baHz(_
z$f&XV$6RWqv<{w3)q1{kv^O?fpc*eA6DDb0#FqZkc944dBt$p1g<1(;gt0&s%TPjc
zvVbcJsrTaK<LxwHYh+)_mEqZwOd5$#OgqbfWBbO`wdj#)OP0?l@thdFtL?R0h-d4%
zAoyZ8ru0j8V7y);L;UeBn?HZwI@bL;`;-3YmG&@M7lbH&QfR-iZQaV#b24)%505ie
zv_VUYAoi`#s2RnS7a?mu$i)rNh{sH#%kAA^JE!n&XkAC)Ui;!2Q-RHTg`r~DKb#rK
z+bAvgd|+s(Z7?%a&GcnD%uDxuTK2Vt*rOX}G@WjpF4df~@b1nBK&pqAT|6l^SuDd^
zOPNtTxnF$@w_e@dIsa$>LCC@%ihY^GRM8k|#cJ*EB$%CG<}w=0(tS=*@$)EcTa3`-
z?<*ItB9qgX%a}MfD*lYE&a4l=Y@04t!5KD2_Qj1u#E`>&975xlo+qLlohtxuX$jMN
z`AiVydt<+&3rsrvLzvdMzlB$it9Q2;Fm9IagY;e-{u*u(hpR(a6SmBY;@3=`T@p0m
zR(|rJV+MJQMR@(aH;zlG3ixeTmR)%sfpuSh&UO6rL6I9q=I!6IdqaH=KU|G9_@<l2
zisQLUZ#-nz8X8nd>2yeHIJoBip9;IzY&qPUn{l}3cZW5-&bBVDUv>pb*&GPgN!{%v
zR^i)iu2=q6XjgIAz4x}|d$Fx-(?+oT`muJACydjw_ZXDt+P@NHd&Y=(lzVg_wL-=D
z*+hSR#b>ErmeK&O)J8Tz!G>!$Z(7RTTtda}NCy0qCr@sq$(Wj&UiQe2I_b@Am4C=_
zT<O-DPwnm6$BrKtYG&KDtI(h^Elxq*msN@*E&0x_lPZIj$RIXl$;?%??cB91>4d=U
zeoNf7|GVnyTQDQ;9y^W|R_QU5T{z6q3m)#lxh<c0zq+s&sfB(zdD^lD>YuYFY`cZ9
zZk}DXiaM>&6$7LFU3+zX;6MmM9IrwT8r3l&A5W=Z0ikrWxhhe|@d3jO6Gg><Gx;vg
z2_wF2GTdoid$nJ0nmqV7)D16;!m+i|ZQBNz_kFf)N-g_fSXkKWJm+cp&+X;p@_4z+
z-EwV*Gzv`wXdAlXBZMZpHn)e!3X3eK;?{bs^cCF<l#?96nnbCd{__EY%0I^jn+})>
zv9KJ+(BJJZu#`%caXo{uyr`(?@y4R+YB@5yYqP}WXHqL?V<hkGy!px}!wF=Zcn~|O
zCx%BytC5K7r+<cu-TGMt*ysFatvNZmaDpKnt}4d~QS64N=UY`#mzigX<+?60*@mKm
zmOM_RMPEL`^YhxZ-*N%Fy%L4xYN{X9${Ecmnquqe_}_dKW%U&&EVtUM-EuK+c-Li5
zKO1drQ}13*by%EB?U^h#o(fgQy%+vD;kotWJK>&wp8F}QsGiALS*6ul<T{Miq#K`$
z9iN3*z%bpZr*cQOE(Gi{uU@|F;KI!xMsEk(Nb8QFC}NEA-~qG8bcZW7@w6(fsHpfT
z3K#r$56?u6pgV=-<$m{>W_xJ>$7d0`0bJ#@xAW7W4{#j4Su!mYX@zv=!yoVqkTWqc
zX|;$taf@H8fGT#J;ecsK?Ff1wsWtFXQE|kUnmuIREh?(f+uKV$0bE%#F<^A!`M4+D
zehCO{?Mo5y)gkm64A2Cn!@|;XG+@=btq~_~(QQ@%#sB1Gk1}_4+GqV3K9{0N4@+d&
zC933#H*eY`KhTb6Qfc)5{oJJw1(%H)9-VI;K}ZCL?3LM_E%rl0$rvLM4_KvwbkalS
zOBK&Wq|7r-IZ(M}Gcz+Qan+}2w)Hyi+PO0^VB^M(g8^l2Y^`{E?pKo^+`ODMH#^<U
z)!ot2gQzf7b*`h>xAEyk)m95x+1n?)=#>o)oqg^i>>503S&sq#LAp73UdCRO&5s&6
zqS^&S+KQz|udXxi(;Ghfz7#e%N3hdy8w0lSKa*}$yC%_+|H(=6)*kN0b3ia3C}-Wt
zW4!gm<-B=|$qdqyM!I&_<{WONG=n=WPd<xa15;yRV>^kL;rresz{<R)8b;uor|sh6
z;)s`Zt+4*I21eoYE2~tKENdpR`diyVDdQ$Y*t*2%>SJmK;fGMMa3@{KYo&8OL{*?3
zFCTXyJ><@vk6-g9ORu`Szvt>t?(UuJ?`_G>sMXrz_4RX$>XIc(>`SYvs)X&nL{wY*
z*O7bWZVp!EOzHmT-O);Vx6=a+j|=e%ZU-{g#>t%|qgb`Pyj)RU-tVT$N~*)1R%jT!
zwExJ_qk{nzZRc$J>!dU^G^Fe6vu({hiY(KT*y~q>9<&WeNil03!AMAIHMNnKF$^P9
zW(58A3@1Lf_&h*cnm&Y#yQ_xBKer@=goNaDf_@nf0}E-qWzjtLi8;z6lK6xy<&C4g
zWR~Flz=W!L+GCnDRF^z(?@C-lO&JxvvZx~lCkA9&_v@u1%Img5kd&jVsi|q*S5w?8
zkd+WVF*3q&iF&-xf-Gd@UTNhSD^^E)03!%&$S@19wMbA*x{>BOH!|tH3TQLbuZ>1R
ze0*oIgxHj3C!~}Y7eA)c&3R}&IW-lJI8D0t)z~Vki2K)X+;~S!Npd<-ii$l&V|vkC
z-?b+>tEYVl)#g`FASDGf;rJ76_`RMWNysb{@M_om_M7aLmFxL+-ux~VB3RY6w&Q6f
z96zArlen*u>!7&fVPfK*SdA?eB_;3B`cEg_wE3C2Kxx-dVGzIJr!PXTE|b$Qk9tt=
zK5(EuE~~k=BCzYno#ivy*1Nw0By#w=u8vO6%j+8z`};609%y&)FflNwf3m?^eoyKi
z-z%d@bbhl`5_N4;QE?u_hLOlPg@FBF$d)u_>rBf0TygW&ecbNug9lZ8YBJ0;rS9@Z
zzAUn~cD0-NX0EHJw}rjC+|iY?7_#__8MHn;vkOu)BU~&-h%aJ6xxvQt)7P(GFM0E(
za=5*S($fb?q7il~4ZDvk5~>PKB2##B7%_tn8zLRC?f5vHuT+Ml&dhm!cEYBVjg^&X
z$R4i|BW5V1TzfHq$1q$e>cslV$;nPGeSQ7@o~o$mvw_)DllMa~#6G{swf{Kg=V5uI
z7gCUjm!Cd;(%E{V^+`rXh7nfh2ii4Bs`W;bGn1p-hGgzmAy$e}NKjNx+J4X`vCw&B
zA_+wz&a<JD(|rvMO0j*#`I_c^Pi)mckfo<!GkV2rBxr<BM|*pexG!t54xj(GXDov>
zg@uKkot^#}CXbaiF)?XIgd7~2ZL!93>nA_AEmuTDq$Wi(e<_bglh2(ykwoN0fcUd#
z&kh7crSM$KW5mdGp88saYG#1#d{}UB0s@A3gx;jp!hY8|r&+*+L88uCq;|jGv{?yp
zK}nPAo7+2r5Z<-42d2sRJrK6;6BOxUnGAOg{A%xDI5z4tiUq?h`;wW4ti1@EA7jdk
zn^bPvSYQ-Ou?`%cdm_TiTlsPC5~_mZt5>fk?t6)KEq3j<`s$K`3uo~xCZ}6pbnt}x
z`bJ?$V)$pJF{(~CWOH94YozSilirNA2$lUZX0U)Kzyy_B$VPnnsBbZ|tE+2*TX0fd
zeJCcfFxEPW7Z)CH`t6z+toUL3oBiPCY}KRlg;s#2<1pqWPM@yE?AU*?kv~+_S=cNb
zo3kMnqA6hI7we8{YW_|ZxKKvB4@>p~;P4imdWdy{v-eXX>!iLwrW&@ZuLz}#Ui`CK
zao7FsZY+BUGWdeaSmI07XPwUU;oYe}JHYc$By6Ju&90zj?2l)dS_5TtJNMv9J&FK=
z)8@Zq+^6R$9VT)wBU!?qV+zgX*0f|in6;kEYpmO=e#Sc~D<pT4D~YlNea@ZGh>ngH
z_fbhRh@{o9d#+n#bFNMG&eXU2H7lqNo3pU6DBpU+BHr}$qA#=i)vHGER-3|ZX@zlZ
zjD3x6zmZ?7%k;(LCr=uG?Wt0n#$G6c2=+>uLr?!{MHDNNNvl0WSPnaYx!Q^C_L0f4
zWB&m5FucWhsPX(8HI8hTJVEO!Fn^MDu&Cv8C$HyNh0RK0&%S-tM(>M?V&4?K5wkEj
zapJ`FM#LMslwK5wl`~W(po2?rnVoX~tom-Qv5vj%>6Edp#=^GDe!bY2jdXeN;r9Bn
z*YMqg3uE5n&k}}`PB~MaW5lxo=QvzW98$jo$Vde;R;^fJh<u;4wMeSVhmp*Rzx)4i
zb{6j^69yJZoMgOiWRu>*%V+1pCLKc5Qx8|Jr!y$=+f7{>pdh9hlGTH{(mB4*Wj8fx
z>sR_OLu||vFfSD~Ffg!_X%{DFDH){C-Z{fCU<$og6YGe54|#@i(}8LE<!$Ghgkw1S
z9xDXq_AjL@NUyBB-flaDbkx4Iy~sObyD%rW?O5*SIBy;a)NMBf=H}_nd>ig-&CM=!
zJ|rUY6|Y9+mIWOHLlX2}TU2PAovScrBA6{KEUb{Y^;}-XSP2sZW^tI))sqDZ4JO5D
zxz%KCY@SMoizWdSJ5Fi}lB~YWyLasP3%hzXypgmgzUkIh(8h94*(3D!bR7Lu|0?~j
zPVg8GUth6eg)&!@!>%me$u|BHSF|li)*&tK+P%e0(@in>$<z-OJt01AtZKHRsV}9)
zeqj}gcm@K*4i;8c2_K`{_-Mp{^_|xLenVqi8(AeU8O<Z^GW_!4xnjS`in88?am6x>
zT(GA*+_hZwGp0G&ehIRuo^7Lmps;grFsU)!xPPiYg(xu7knbjZsm4AshRFN9h>P;r
z=&QJ!E{s%{SlU~2zFC`JlodkcD0^X2ET_7<`l9r3?<p0PphE4Xi0?2r?!cj%J0Wij
zetva`yMhn!-PreYT^Z`^)E$-j=u@X{%%pGTa+>Tq>|Vr+2Tj%NOPO3}9wQ>?8*z|m
zWTLxzCeh=0^p6;jd1iJNlfp2a8}_Nt-&;*>ot>V@P-%Uld-^ip*Rd5<;2bueN2xAI
z#NMLS0^Zf>230$8hov7~4m4l7B04`~nP5$<X62TJ`_+wFsn&C`IEQJd{2!|Y8H-!C
zY|-0nRa#tJg%&6`tvuyorIoqr6P2r3E-s^r*hx?_1n`l~&CNO1h!U%iKd%+p#4S0O
zH-<R#c%zuBraCsQ|7<4_g52SBjy;u;bV3$7aW!)9=Dr|=QLLw9WMo{kb}grD`Onpp
z!!^A2f%ryyJHh!{RV)fg0T;)Te%$6qL{g7cT+lZu)a9y(&43bdPBs%ll#cJ@ludr%
ze4iQ1YU9VfJjHF}KB6)_NiG@|b0h|*{am(|!+Rn0JA8d|dYW_R&KE{4*Eh1=u=1Sg
zR82EDLx~yg5IBuIx7wOBNK4*f1MO^Ym+NTnoFB<bo9e?rD=sbNG!xpj>*(XhkI9Z)
z4y5<fDM`t<(?eJ)N_F@)Pwl((;c$XBy_ieR;d^*#O8(~7u3NW31aXzdwskt2^8VwH
zW2ta?{PWKy!+CUZK#ml#&e{3nSS4PlS{d)USy+@S_9vs7A3HJe<x6E{<k6%$((dbU
zrqB5G?uzh?DeC7%MJI33?SG<Nr(RLrLXw_mYbUzg>*W~BzXyJA=9e&T-mLIoc4Dxp
z`->f7adD6|qKVxlwnzJZVVi-6Z7$PNh~-}i$7D~TcZM03v1bj|y1Kf(Z|>|`2m+}-
z57M+K&^n7C95kqn_ph~3R8+h(o-cBYMI1Zqijwbv-Hm=oktHM~o~YDg*W1ak%hg^r
z)fEvPHIr!Kz<^0?|C=^80n_FNqd&Kvw9ZTj=P4uM2HCP)4t-8u8-0aDs~9O)YDcoH
z`$JkMFv4(+N+hzey}arxZeaKQsHpvjDHG1TMTs{K8Lyl|y!`HQ^U;Y~WMxl-O5%CZ
zV9nZ2<Ydzg3-w0;yApUv%e9NPeVn!=KJQdO{)ZJHG{b42VQ;9A<?G(rmf9lP&L5cv
zLjPi;NsLj9=z007$jC^_9g>)w{o+#%Moy!K$&oy1pSMUi`H&Tz#d}HYgP<&-8Vgi-
zMOj%bqV<sYQj*9lic2Ud7IsC&QlJpN=eJ4^W<$P`(0O^;Dh_t`H@ak!-4{guy^@B}
zWbC*l6ddW$LDV9*Jyv8|b>F@AR6Ei!>9zvw$%dey*q5^U#vN{R&75gMf1p0AgQJ1g
z{0wmiD=;SS6Z{unRe2DOVXnfkOGE_25jmxj|7vP##eA39#5$p-3A(G12tt(qoqDYU
zvcSkVKOH}auv6mHsY?#?Gm!~1)rzOTj!jaQoac3=nz1tB-0${|cx#ysZJQ_KGvIW;
zy~CUXsXV2n@yGTl!)XmT%xFOHe&ZOuvLGS#bA|*{K=hiKKH?zFeh3KPvtuZrZ`!gY
zAj7HS%?4zgFXEipWWr3+MV!7}Iw!E6%5BX@i~>4-gWC&13!p?S+xhY^Tn?-;ho!O-
zSNG7B!Fz`spTb3LG$7lm_dY_z1};}bUCOsUKS3RfLCE4kr31)gHF0uDb(?Lz?=TOi
zCWcawdD8aAT6UK!7JV5V<&=G0x2)#VxCg<?tD;Vh1|a0E&2_Y0l$kRl#*S;vbyQ8l
zfD4nGhLVFU<%<0)*KO5G-QzqvPiAp5nVzVQJyn@NG(y|E6|sFFr1Mu|)VN*4zhR>h
z+MFZo^zD&ivf90j0qjVy;4e2N^nwKKA}{+=%upcb(vf4QScbYOyT&E9&t>zmBH<e)
zRuNe3<HwJYh@VxG906dj$vr=V$C3tuM^5L54_deAg`V&S{e}{}|LxlmJC0uM0f=MY
zZ&dRz5^ksQNW!@j&{6&KonoC5)i4wS5SVsf3V-`nGIw^oNwJ@RXP3f};S=lfx2gi`
zj-sk)6ge2or+cF|?cAL^cj!dy9u+z_Q=)%;!dzIoY}shQxg-^Z9@-D!Si{pYRmB!0
zIMrJvLE%3Wg&JTL`sp)ZR_^BHOsJgAo9x;iddN}0Oo*BJ7%|E#d-8RRmNLo=D<l3=
zEM;>3dIu0oXrnEnOGV^W!mTXi`w03#nZh`rt$W>YUqXt?SQUDnh$%!%Y+k^KV2i+s
zIPs$>N)^>;Rx%oJQJtrwn*zdN*~x#HgNJ#22^IGr+qWApU$J7F&xi|(cwE_?GGQWy
z{05Z;@iPehJ6>;DMkPD$<Li4XSv^~2%mca9{-<Vb8XRL-fJ-2+RL`ap7Z(>ORoSzp
z9lab<IogmX?mBodyV3!%hq0ZVozmbz?bqu(I+2DODa-+f<2pxh4nwl5Dll!{wypPT
z9(*VA6UUKxMIyP}o!yDFdtSvq<!tng4al|(4t>s<u%+ZAf9R#YH)wjAq`*YDwA=8n
z&!XD}OxP>KMbW^Wx<4`{mH=$^2rkao4xQp-mAw1mk@tahg@OP?&csSHB+SAQ%BZ8O
ztFfgUg~2oq%_Nom+C#0mHl_2ZQ7Cqzm+-Bgo}MEWcuS?%u3dZ7IDyjffjwJpmW|*}
zPk?fnSmM2HUreO~1#jCF!(7oaifHLV$VN49vW}DT*V^VwVqXfDDKgv!i3H>;%@3?k
zr9*{Z1Ar_^9!A=DdC9Vsn|cmvf~!uN1s<OhKXr;dE$_-I8anJOieu?!ZLLb=31#;5
z7_<|=<jSXj{{@{*0n(?`<{^f7O$wW}<=9`^vxoBb0dvt)c#1&pgtWAm+}N{G5^hPc
z*nePPVlu12{}#-xrR7eWdLhWl8dGrkoR237Eqk+?t%rulxQJlJ)~t6|k~P7_072zz
z{422pVJIE(@VLyQh{a5gnT_pW;+4EGlF+&UGu~4<4>4kq5;#YfyX(!(&0iyyyv3mq
zzo|hVusC&pae29bN`~phbQ$E`w{WD^_^@x^?AlT9<luc%id3QJs}Pz2MID}oN7k11
zg3HSkI{^gnMBzphJOBRsZ_iyPy#y7!$=Xg;$m@yNr(uTL`P0kmiq4XDcF$Udniz67
z>27TfKIdoocmKV0QMZS?mqh%A_jQ$qDH%zta?-@tukt!N4}hQD2fog+cW;$Zc7A?7
zHi>wwX4#L^@jedu7I!?+CXL0Htz5^R)(ohZ-{EV|Qn@qOhK_F7utC^$E(Z&-veq=n
z3HL#Ff7v_!AT~C^b$*W6oYp!pF){5oX^_p!%M-TfxP~P;>8XdiyCFalESvYL7f=^=
zDN?V*U5*Cq+O^9X`*7@pCg4DrWCks*)*y=rY+>XtT(~bCBDf><y-B+Bw=W;j<}|Om
zp5^B(I6IUx>V-8db7{XZQUol0yScceJZP8{k+0N_S60fB8-X<rR~q%y)TeOlJ+I8i
z2bA#+P9v%3dc?)fehTOD!&5+&!Mfwkouw7wEWL5DVejw)*CrXa<vI6N$9n(q#~*8I
zs-Kx_6SxU`VhOOa#?~a&0|X(Ro12pkuP50aPyWn1qfjs%us16p5M~f_QNv(RY(nnE
z?>KHk%!{S#DJ6i2zpktlcA2re?q!Y%YzQccx)cD$-tyJlidT2oQu)iDIC0e+0R9%c
zWyP&B1_lO~H=IOrcvi4UQ+QO)Z_Wy}&#SoZ>LOw*Hz*z6d&<cv#~j(Tkh%<TH%|<l
z&6$jph$s5hW;we#WWy(L=b@izaPJp&fNbHvf5xNaO@0|4x5=s?^5e@@N7o+Jlp!c&
z3Ub<pkb7y5m=GMcgT2N5ZeI_=+O60Er0e(Eefe<V)_|cv@GbBwU-UXGUAoj0U^ead
zQy=d&8<=fI)fAEBLPf~SgR0=w%*+faU?X#t=|i~JVu1+M0ghAXz|~pPlK>8;wQ2UC
zz~KATlNhQ2z_nT|@KCGK0P`z$j?Kn?kFfk3W5obi=tBU*N6o0^M6_hv9^8K;n2Q3&
zl%$ddi)YT9A(iM&zyGe4p;qL@Fa{Y$KUgPzfNt;fn|J5tQ(|}@;wqhHT+dAw^pDPl
z2)@PuMj>!i2a;!ar!Qh*Nj-<lz>{<}L`MlaUM)8f5&9w%GNmARKAdekR0m!`%FaVq
zOxyBuZH8Ktkb-I;A4^qDM(2PjEMRGf`4o~=YJ1x?KVbtWF4L~_vl&z4gT@&l-rnBC
z&57JM=KCItLq5|;FsN)lEv&0WI$H(0J};r76}IZRjX?S#GBg5QVmnemOj!$X8i=94
z^(az-Pk`Nxkq{CH90*)TcXuo*IAZ|E0jFCVs*`Jdda=n02*SNGJ%BgysBclA0-ldI
zODGt;y+2Sqe77N{frO-_AFd9VS;FU*?1N5A5(R&)nAjN8K{a3yv6IE#ux8DgvsjW4
zggpQ}oc(|r$R|}Z4~kc+Pfe--!us94`w)SK`#=A%^YhDNWst+BxMqFS!Cj~nz@NA#
z?2seiMIIR$nc8G^)lgw;j&Uun@emRFvk2X*5zqSFyBCKb2S}Pc@WeBxPF*YQGyj2n
z2d>L}apN+!p#OXdB#Jeo{!0X<lIQpzR5-=|L4`~FA5=J{?@m06&-Cbj@Yl3{+F}0s
z&Vn@v;{Nk-TM#?``>UM)_X43O{J&2SqvHRLK|F;2_md>2Bx}iGL!dJ?CH|bF0igc!
zXll0cnQZv?1f%+p1K^)hZk`xw)5L0OtvU-u!tNxs%(Dkt?X;B-{FnBA={f{928c;a
z6DkTXE^@N`eq;_v*#C19Dk@G5-4?0{e$+_XjO@{|*XZ2?@oniVk?3KEk!!WcFm2g{
z`tVgQXc3Z+h|!=7d3@3DC$Jrpuef#!Vfi=B8GfD9IWyVt?nEWMzfVd^O05NW5j6l#
z_>9(LMYbk2!cK&*9L&rQ)~RCQQ*#HT=~hC3%^$ztLI_Lw`{d~3=(573^#6kuT=LUF
zdeQy-|F$IEIH!Ez*LAq*Jl;BG-fk|XZYvq5oJm*1r^cgEfA(^oQ#5Lv<TKG8_luM+
z``V&x%c1FN?y@Eq4zm`|TWnTe)NbfW_Utf|VdbEh)={}R4j-c!hEaZ;{it%L-XRg~
zY=t*7K4nGRJvTc&N+T2~{|BW0hj%micgd*}Pn9>iG{lRIho!4_`R3CY)fXR|*#G@t
zzb{(sBP5yiyL!rZZ%^+#oLFc9dmT2tAz3}I_flNn$JyfT$JBNQmuiM<>X~3~^-J+H
z9~<~{XQ$`Rnr>dWiZ5b|w}%75;URHneCv!S;vVcAJYe0jH%VygIf<n2isi(`SKQ3Z
zx<h?eo<_>D?z1=3=l6*X%AY;qbyM{_cYV{bMLg_FR|+4;(M^syFnG&ksIrQ@JSQer
zDfe*)3M1j-B{d2816)b0-%nz{`0Atg^jSSQ;hd|X@54735U1IqX*tv%U4Ex_td(nU
zFd#U8*3YHMIfg$7Jj|%HL^U*;uPdY6S@ypUW6*ScZI<;rhgZ>hO<dK_PDFII)}P)G
zxy0vzOPNtmdDtDB(tKXmjB>Unnxth^C2WPfX?yxYuDa$aCTUK}4Qi<0ub2~MPjxRK
zVS(ESsf+*k$V${PcvasuJHR2duio^YV){}3?!2&gg;y0uv!nG-<)a=Q4xVO?>F#+S
zZoBsnZYudd9c9>h$IT8H&CsZ(?PERT<LBd_aQ!s3L3zO4Cl~3-aT|Vma^oNDD_n=f
zVzcv`62F?Hw(y1Sj5q4aJ+z6gLscs{-#fOI>Agl6BWv(<@-?s8w^GYDE{VuF7#I?#
z@7fr5K0LW9t8iGLR8qzIWf|o)y1iKZ64gwqlB=^OJd#RE(i^y{v&A3A-0Dx}m}|8u
zweoxz<FS(eY4+@`S7(G7!xis8T}mZ!62mln##(k6TXJOx7h9*nA2*`R4TU#^f6jYN
zCcoRp#m}9aU*FP`@&BRfz2m9u|M+p;C3gdNOSZeE?6S9_NOty4_7)B~R^^T&R95!h
z9D5zB5{m3`tV5B#!a3v|hu`a}&-eHJ{mwsmJcx6y>wUf6uh(<Elj}_D7|1U_e&A~n
z*~MUv6Nub|$B`!;3aU|sBNMSH&cjh2UL7lG{P6e~@&?a2OXWX3qrVW8O~cyl^r$X|
z#!{sYJQUzr5dQxz*7LhAt6DPTEq;j(|Eb=lh$hwn<$%c)b+&==0R@f-2LW?|ar0GY
znSfj7#nL88bAM5pIL*9yOncy0oRU<bZ5t&NR=Hkq_|LD57C0wqmjZp^*RL7)cmKDH
zVQk<iEiyUHj4aY&Q8$hq!i8i?ejcx|GanGiXDSM$WIo5Qa_~u|HReWzda6=AS!NAW
z8K4YQI}B#gj`<fnPHkVNZ|7-udi1Ch`3Pyhj;Dr|yt;DvvK@5S$js}tHjxjA9Tz93
z;+HR91Yd<I+yeLj>25Dxlm}&rX1&+yV7AJ6r1u6e+1l?<r9pmK-d6x6#{*YzIG#vx
zUa=+(g+}qpY*kL}vL&YR!l$vTQn{#*nG;T(H(1c_+v<FNwKW5mAjI_^KbMSB>b}3`
zREb;;Yemo^FaH^aADOj-D}OC(SGZTL{-Q>?-tI=O!p&Vx{-8Of2-dz=OhT2w4msno
zx(uBMlOv61`J0n07@@OA25n?m0T@QNzm*CNIx7*+1x=t}jnoo^AC3RtXC-a3!r&x@
z94C{|^!>jU_effYf_54)KCjOvZ>6PG(XO|BDKjm)_r7%SmCBGzLS+rmHw8>^<s&F6
zs-!5(>V<aNc4D77P0YKLnL%0Iy+-0lS`N0#*dOI@8Q)*tMcq*($@u47zMFMgl@h!6
zv;f5cG(89C^L&_~kn@?5L+)T?DRis*V&(qJ<D~^-xdAV7Bg}kPMG)d#22JRNRP5-e
znF%$}oBZ$fKm#+HkvqS}D{Qqtjp8GIdTn#%c)a=jnu2CCmdUZTZC5>xzTivLN^odX
zZLkHS5d%sc=U02LWS@Cf=kr8oOO`KMY_P-j=t7Dt?YVC=*(u@9Az$>_UU;BDBn%5<
zq(j;cgnk%c0F41DVFsxM%xHGrgPSUHaF}rcus1O0X9Dq92#n_D_I5S5rQ-om;*LOr
zhQ4^wiyXlN$hmjY#sBl3skq~dOe)w@%1pPaQ_GFptlmyG<QH=i9Dnp<U6%~>an<?O
z9*GurHM&Ogqg9WZ{Y9le?klNW6Iib`6F8D_=~4GAEym>>=86Vkpt-~~yWo(l&oWu0
z(4t~5Wk5Mpqsv_xH2m=1vl==Irr|6WgnLHL20VNIJd{u?!T3%2>0pa%0L>zE7OaaQ
zP?G`2C=bFTEkF~T*@b|B11ew`glAR0J1)rTf}vR#DCNl;YMfEs$x^wH&#251@GBjl
z8VMP&DZ)qh8n64a)unApMn$LNuSP*R%v3)$WFqJeiK*Aj1<Sjzt|Yx;$$uPcUgd@;
zbG&jY`Odb4Z3qCh*umtW9_)siN#WP0^FN(z+;@zgWa*onMIC-0v*72R!^!`0c%t~K
zwdI6Vq7m*`pNfL<C|g~Fx%T)ikCxZNtf4$H`@jEuXo2dpIS`rj!)Z&|AU2wP-kcqF
z>n8}bkT`&TfXqQ1b`|(YxdpVHT^`86kk22^ctifJRha>Dd@Lew1Ou7Aj*gDmZf&i&
z3`l=KB2Ys39>U=OR2D`?>(9+J0V_nWK2PLArZr7;jN_ZjsFy`KkuIxg=%K$lYT@~K
zulrWWL)c}>##z?ug9{Uf2ED&1HWTCG>^t~KKaYJO)7#a)&^O1Wtj>RJIEuNSyFq6h
zv^}O!&!>uHGfMQ>_0ZL$`Tidj{D@yg_&hz@v70|j52halmEOFgL*M)IKf+(*dh9R$
zB;co+T#HKEh1k3YB4{5sw^GGO_F_;k6#}BbU)<T!qF{>k)B=zfVw3lj5NQ{jxReRn
z0B$ZW<+q-o(WK>&HL0ts<1hZ7r&IrEMQd;8i5<JPHjrc0ur{Z$$Ql>+D0!Dlg+k(A
zo;ZJ&^>Q~?VkKGUEnaYcG9|&&)|OZ$bibI#bm*c<4XUstKJlTGOJKmH3o5iIr}W>c
z`>rRbbT}Ep(y67?7x8i#C{c-leE+>h8I<{fBDTi-67?_f^Q{XwUey7yq-&)CLr=wF
z_i0I!M6P&k&{oUy`gazLpfAFJK++WY`vuVByZL;ch>c*AT7s`NRaIXh-$HJ33D|3p
z*G(<!xp&0(gkoy>xwy+&){b9)geR2GUF%7M5AnYHsx9mbL^_4J4^3tXqL@Tu_^Afr
z%9x7<<0&#%WciGHdd9m%{-!$nVR+^k^Zu@pjD&5j_63h9Wo)*G3|}Dn2|LHc*F`w{
zo#?7JFh{B#<Xx3YTp>I{{&rerv~sUBFRv={v>>EuZfbxLxHVi{6+pj<sAAY;{R|L2
z`NxkRCno>5kLWnPTpVTIT2<tGDLG%7H2QFXct;EEu%>Q3CoYDEi*ZeEJ5_BsJhf!#
zsYkw7rw%=#@2jtGo=VV?i&OmBh2?B|#p<}mTdr}6$pi4%;Kw+r@?K2euOJX6l1cM%
z5$4=jlSsuO;&O|>@T1$lLj}|NKO}uc>0;uN>3{0dOo#eOBk$f#WZpa*l@7qwwQJY%
zcnkR70|SPfiqICxVLq((XG^|G?Jjvk!CP%KD`<X|rh7B7W;L2FMP&%*?Wvbsu%A5w
z@3(^{thG0iC31OLS1CF=C3C;aabdAAvGA6`14FScdmj2vOX(anTp~Qst^NGRT#W-~
zs@8&+t9^SJWv^+vLv$??&}Xf#qY%wA2P?Glmd2UjunUaU+`)Pw=RQ;M4^?t1G0u^Z
zA#c9KIsHQMM{B`RpMmycx0=kBSPj)!B00~FR6}RZ&e&T!(th+?(yu{YO4WKp#MP|)
zJH`Ld+MnZ>bL}a2f!q$*q&RPFZJrivHt}%+co}(h<StZjVI4a-b91zMTzB#>n;tAO
z`v>@5CEA@eIu6Zgn&0ORk>&WhdO~OSfX_0+s$^zNUl}^k%GVT+)nlsemXLFi#smsX
z@Km&x8t0ga1a>h_DV$2TW|<Cn=q6MQU1F2a7wYP4o7tuOIw=uHd3v7_77~2Ko5qpm
zz0K#>`X&dc<)obRr`L<ykBSVvV}G-`;ofy*laer0$|OAHE`7`1u<kg4FUo*MGe6sQ
z-obuQf^Q_?ULk!O*G{=(V@kL@5sx=LmIy_1PYyS+`{$EU>Awh9niiQZM8W!hPE9vG
zP%bhz9!?6t-(U)+d}F4fI;ZPy6S21MQi04%(M2YBDd)mTie+;PmA7HJ!O6zmaoI-k
zz{nO+QOv2l)U|cpxeFeP)5P|W%(2e)fo;_1oXleh4Rd*kFoeu%`#Hp>Q0#N}cV{JY
z<>$=UIq?Ja>3Xt4H+Km33*~<vp_VxkYehP((zK}_y<2wY4dY5_QUiNxm8t2ZK~B5P
zYVYQPK_UETvE1nr`Z)G$nTXLpTCRcgzqw3&F;d2suu#O-(GC6c8TRSI_4UOZ*pCR)
z&t$wh=ZsD^Zg!fj%En}gxj41lHKclSZ^r3<PFp~YGPx&5oOh=+v}Ft>sLU}nb}Ri>
zlZ%7tci6hn54Y(kWjiq3tuE>IMVFbL(~B6J%+Pz0g2l#9lKh*yR@W&^#W1?$d5z9b
zA4*ew#}{nlBALFk|4I*rFm3Wl`*Rp%c`AzBvyCOdOmdcaA@f(|3Y2iR7ei^W-?Q%v
zxLtuWZ^tc9SGke7_{LCeb5rqrQ8?D2uH>bBpW<)#T`nN)AA|+`xqWUQE1~;ZfA0Ik
zj%u?_<S$}N?OK8HkU5{j^E|`HrE7`RL*EzX<(y^mgXZrOhS-vI>q7&%D&c?cNVxIe
zvCMVqU%X8ApPGdYbbtth$$3l3LIp}e_PF@fB#keF|E<^9&40NZGK;Zi!_X_CG@oef
zk_(*urW8qzQ$Eg;v6>Hzt4A~1ZP$AVQsc3=I>E}l`a*fI^Tf8@4p)2XMx5~R6N8Q|
z#)Yoh<Ch}pLqkH8{S$-jKSVsMcmjQ)egW!m%uxDPzESN=G{c&B@tI?bcP8a<4;Qkj
z>qW?uDVsm9$H<?6I>Swe>jHB1_e)0}xM<3iKD5u%nb$tKFA7MD`(_2k#6XLUZDQdP
zPKlJ~800{=JmMp$j0?mnCPpxv{10TGT29iZON^UL<~?C44=Beo?+&3UwHvi3m6Dyg
zq~Lmp`8Q#DU+@0cL`!U4P42WtT3{Jf<T0iujim5dPsLmLmy!NMma_F#7~x@K$WClB
zScYD*XI!DjWWc0ZZn9=jVJ?o~Xj7j8Ls%%6;$4Q?)5$8`gEfVbb|blkBEN!SNQ8BT
zR%leqcSooxm8)4NjKN8*c_XIh>Vwt?qKQ?O?R2oUUE|?V0a8#0a`-_SfiHJ~rxAH^
z=b2R85%@Y)EMd$VSSDU;T`SvIoVn+G1nY2wywtuSLrA@&l~Db?7H|0}+s2U~#zN>I
z77OPnWvOy1e*KF|hc(PQx$jJc_qA~+uHpSrn=rGaW~FhB%S+)b<DYxRz3yTwi)0xK
zX>wh)@9AOif-uQ8dMP-DFP>U#i#?}7SvrKCB@Lsu=S@Xx0&8RyBdcSXkJi^&P<>N`
zo!v0elCYLa{xc68*qN#bm``;qQ9tx{J8Ng|^KZ30LFT66-R`e_9KPKS-y^h*0>#Z3
z&No;58g?ZMXIa-4P?Ku6-^1rTcen0j!u;GEH$T5RFmiBeS7=MQZFf>1as&g=;98)Z
zp{}lR9@j$--Ujka+o<ag&~fuh^c&~l7NP3J63RSVY<;n@KgISji;vG(s&sFAr@}#9
zS@_s+aYh`&N*T+?&MHQ<&-dR20T)s)XGH@tSXDz0odZMGS+}{!qv?mR&4HFkp}S%`
zM(=S@NzBSnzq(v_Bl+AZX3gWpe0Fm=XVCT^*yGtQM?LdvB#bUZF!wg(7?1rp4kz>u
zyv?gHsxJ9f^{VxC4hLbdVzp~?*8T9!D+tbTU{;&kYsk%S{||GTzN?+7P3o4&Gyfs1
z`it!pb^cNamUzI&gDX1MSB5+xFg%v5OiVW}TvJYeD$6{KsoqXj?#r>HwvDfUos#Lj
zKT=Y2Fp9aX25m%MZ--j1G?1r2_6>rS?kpQyvY5z2XnBKAGG%}T{T`4o<zu$sYQ;jB
zTTFDcl{a@QP>hQZSKklZHzMBNCD-qCSt5&$ib_ZNnEfd84#mb<?#)8!gk?~|(#8k1
zWOjc)D;&vznLUe~>pE2TgSUQm&PgJF!6To9R<%VMi|CxgRNq8lWz+;r<-$aUGQjv#
z3KHD)&p$u9TcAI49WUlzxSh<!m{osyNwpkv;1n*)Jap@@akSaDo`*MJG4+ugW-UTy
z9rQN_pzscrd)>{i$`*!+yb<Lg<$Pcl1KZXKr^rr0`wx%k!yR<?+!&~$#m@Ja6DQx=
zbxnAF{C+|+?w616!uO|Y!s+IhcF{4biDG}YPIFj(5;kECYz-49b-dtRs<of4bZh70
zn?$vJ4-0#K?B~YdQ1M5R21`?-{b^2D4SKehYD03eM$R4&p1@ic&QOs-MHHF6)rM=7
zSK85<{292e!Hhd6kTasjz()auJp^W3FouSO4FLH*2+||}?NM7FveW;hiKow;v1SYg
z1umWgFfP!C0fAj~vgd!%(&uie%41!29g<sK*M%sP*43N|Fz2d=d=*$^)KY7f?N>6r
zSSZ0;1FNY;)-<zARB;d_4@K6q3B-{Vw&%fLX{z$_<IiAugg@)QJ$s5d&4-@QFDEtR
zQELv{(%No7g{%qsCIii-@bmgM5r*_V6MA-^8`SXs@IB7+etrVR=}RN}u9FkPYul)K
z>B=4XK8!V0nl$YFU6X$D`i;%<LZXuA1)H(DwXw2&=Je)6m0uO+-VT=7(<c5O<U*|G
z=rxXr*6hXNsh#{Dsler7Wfox%ZPhaQIVdi)PG$<oW@~K5Tm<7&-qS9}xyJW>d5W*s
zIC@9c_6549GEJl^24EY|@v>$YmP0K?LplsMEZ`282PUA}t6&e@dxV_+#t?P~0xUwx
zD=N0C&5!5IAH0b^FKQ0yXR;8YA}FQ*dnHpvyK3b|5qku1i+7z8@PbWd>?C(pTl{3>
zFYP$o$?btx$7DQxcm58Ug^t;CJc|~Pky<<x1^VHdtXHgf5$mV`yw4Z{J>6MYmXhRo
z-;RD}8pxrc#@eTT(3l_IAhQ~hM5{phRT6}qCwJXb6%fDZ{irp^Y7ss+VSKHEt`k~L
zjKmI6=keXv6w@CG!&ROtw&V}{h9NSZ7i8MQfiJ*~d2lO>1=gt_kbG*iQ*Ga*A0oLp
z>8oKY!j|;u-4sqF4@i_J{N?XNGpWz^3gzMWq}-A*Sd{%G?mv!dyKiJ30<q(gWOB4&
zktHfLuK*WPGn{k&=wf|+z5U}5EHy?=z{l;T<(-jT)saBaJK8w#n<H}{H&&27K@P%U
zV7XQ>`%er<ezAJn`mhhle@_h{DT9TngJ-HxUjU#o6%fU1+Sud(8C?n9$3{q`L98E>
z`>;QQ*1W{sBTVi&-JfgDjlf>QOUp#b)VJ0(sxMOGaw69;jz&1{NB**K17KYfhr68F
zf>+%6g3XLdm0)u%E;8|Wxl9cLp6<)mc0cr&tG>UOVG>JW?pJl498@$*scsGxj90}r
z`ew~d;Iyj>#p&EjpqDPyz}fZWXFaSTzlFZXbKtyV)g{h8i@Hbt%u9tgs1-KP4e{V=
z?&AygMQg6izazA9ggnltz4<%}CX=}HD}BV&VlIM*DfU<Y&nSh2#h!Mu{e!TC?5Xz`
zH#asld)m@PYXHTVpBtuHi?gh@`>?Hz-LDW~7O@YCRZRA9@Ork#jQe{~TOq-INa(Q0
z`iDtug>6v7D2jDsYkP3H#dsXQ|Gob(pd?y^qsXFmvt|~S18EI#>$c~vH-m@|ur5yU
zBymIiMi_Q*^@3+ap&fXv#YESpFH&t_>&}7Zzzzg11^Oi^etv#u85#3{8d`#U#$JGE
zh9Fp@VRK_+15u5R_xz7qn8jt7=8AZA*koIca-^ksos**wZVI5BHSyV~sbN0(inZ1J
zgbxwTgrq77#TxaoG><Q>EUhPQwnisA_9=DJ<;@!h=C>@X5cV4I9iQj2&qRj<)!?~x
ze)+6Ff2#|V27T0IV|Fq-)1a`co{^HIqRv1Dv}19}5Aav63Ro?qnqvkg_-@>B*}39K
z-?}&lkKhk#@}Ud#Q3TS@VYH6{j}Q9n<bLSIksjkSN|kYqfp4?PGnE^8GjZTtYbb@H
zGkQAn>3T^oG``8lGw56lwN+9VN|b0sVp3~XwOTrZpaUN<X`c6dJFUIie3n+n3Px$t
z!`2H#f7?3t)J6qg^(*Ih8AXEpMF5-SLuW<C%8Z&L$oGrhj2?sQX+CPExZ&+@f7y@P
zP;9ky-b8|0L$fc&zSj?YSZ|}nrhma5$OrqC9)hRby!iwa{Rr9sMoE1nAP`D9RQ&yY
zva9lafQbnoXgI`+GQmEFWHU50G;FflnT`GL9FboLS}y2OU*5$~Z=DcA<2V`AILIqq
zt25RsvH9>bM*FsI$ZU1$-9cnUFF$(r;C;-$LhVK59B1TaEUDhlIX&PZ>@jdm?UzN^
zR|ZA)U|(nP(<Olcb?UZnSDJsG{Wg>Dy1tb0#&qfY;s^{gr0*#%=m$3~LuSj}`OtH@
zrf7?RKHJ@Q6%vZb03WSN7KouHJ$BeMHAUI>9K9pyb*Hc5QC=I-#m<}jY&CacgqiAE
zicIcX&rbbi!iq+H^y^qtyIs-{g}9n*N?0{D4Lc`^stHhYrs<+pF!Fez)umO(8m=+`
zBdk_W&HiwG;O%9nQ|3kV+r<7C-9*C7uQj8Cy?+~pi(<wGi>C)WBO>;E4IH804!AH|
z1G3VKB)^vtkhrGV7Y(-nOp`tbc#v&T;4tWN<Lxh3Q+2>AaD^i9E&v=#QmCgRX}1o;
z{>kgw(F{*OZ0YQu@{_lK!1B%OILBs13D!d;#z9L79_AoMnKmz2&1ag<jo;bsb;e(0
zjSW-#(q9}Uihy3(W9(GcJeS6cj>$RoZ7z)bHIB?lKSY!|(sXO@!+d7st?tFzPj_Oo
zugKTe?!i_<Ye7rrfWiSu>4d*XP6afBtg*>u8~-T=RGH<TUX4@@c-i`J0WY_`48#)`
z?!yV@D)~L(oqaRrk&MTyCD&4#a_SPE(~M5Ey_lowsIYuwkM^ANq9uEVS0~@)t!#*l
z3t?v&)InDKhvJVa%}@FJ|K-&@HRMb)t<Qe#vFYDB!jPfS^5${)K|mM=9r!%JAkc(K
zqPzL#!pvfNq4D$OuyHB-EtMeR<csYk{N=CNLVbe;)45lJg)8bQQzv$~EB<yyn#Y9>
zVY?>V9(LuoU#$8eTxjLGu!~^4=YX2n?DN(u`5LLR>bN1WRw|ztFalkz;a5=8BCj;T
z&o_(-g8(x9?%4MGu(1J*I5pkB8u0!6<^mefuo*Y{XdC<Zjs@z)T8a`r?%~k^W_?PY
z)SCMMTP&sICsvWtC|W#M_kC`Xdl>xh`K0AFnfl+R?{SmfQfeu^+2xy7Gd5IcG)>y?
z%ZUx}sjb0ZCtQ)z;9MlZLlPd;9QBp8f<3;<)V#=TVyXsnCWD%us8=X?q1D0T&h-3h
zen^RfgU5JZt#C5W^xX_9&9UxWt0iRKL?$!qdHdXLrjdqV=w&oCPTF(IxsGR^j<cmC
zJRdb_COt^fHTx^?Zn+0vLNg@`X;od4D{pmfCwu(r4-DvP2_jA<j@fO61&J(n^J6j2
ztM@5ncA$NXhO7OZA78xC*t@I(gj`3KYS7%VV+Slu>CkaMk98f$2_JF?CFcN21fl`f
zo1@;oO`3#NkGx`3jPx*V{>^u`s*~Lf)*{&7koLCKtxu~m;$@J6ozDEBiLMjcT=^Qc
zv9wQwkGZdCG;d_t%sQ+@cNqm<p0ucQs<;!`GxnjA^O^{BxMB=<7Q<V~tg*|Q%>@Ag
zX>k|jog<r!Si>ET*zX55qWsB{be&gzW4kQG-A#q^A1uYvvLY&(&7*0p_1)sqZD|Z5
zB7X?gl25CcYiQvj7w_?4d0Ahuejt$dMvoma(EE9gtN3>}y-<}UJt8hkO46}o1QU9a
z)>DB;MX5PQ#0e7#k6M!umx~Lnrr`furFUFqZusVICvlH;mLE>Q^;SN@6WV9|QNBBO
zYiN&rn_Wx?*v8VEtj|NYeAlVKcnr`V&0$!tDR1At6$|wtE^Tu-n7(aR)qFkE$GZ}v
zrlH}Dmiu!lb=V)}8)8=zX^nJ0N6%=O<5f%OU03__<H8C&tWd~iAURmFR60?8<|W#p
zTYX3P_|5!#Fsdd;i8&0E{rB;N${6%Y9Q-l{9mOAGuEAucWb2Hpag31U6@xk4MtvcD
z>8UKgt3p~yoNQfHkXc<*Oam*Sok0P;-eZT%IdF9@D~ELFF0=^@JB+`)9fEqbI&?E?
zZ6)KTxq!ZC<3u6UqR&gvm^e!>P0Z6%MaRN&<}8*h3RvgI*>=)%UCR9#x8x@+L-?$#
zR=M(JAB#otg$U*AA8AbZZ*-Nc$L8rLa3cD=(o8>L^O?-j?(h_PC(;fO2zRu(xd1(W
zxVQ;f+uG}EIZbMg(O;%e^Pe5CFd`?8!!|_m{cdK%1XoM{KS6(Y8_~oq{-geW#{>Uf
zTs7SjJHtcjy8P!--kZDahh0l5*J__$rI-J<Xwh#hRyFh)Q{8;mv*zbNa$QX&A!dfP
z`W2$>BYZo!{jE^tyZV$R42v4W6W=S@^S{LsTULEGKlac=7N$cPIamqCbjE9;NQX^s
ziIqRxo>{N7Kx8rM?5rTP2_t=8y|+SDzVRLU{aUp$uI<D3R5H64gEepq`}^U<IahLY
zQoLhLQHV0qM&R-F7WTsR@(ZW6?zEo~JYF>Q87ZlEfiO(1Ngn*)?m0jvI_fEu;Cqc|
zTe#Q$p4+9~Mdy21i6r`ObSxO)r<xxzRUW?vjOv)%<y#02XHM?P&tqCC{g(qjTJh9n
zojtwx-*%yfy3Pt*WpAo)57&j_Z>sK&bghe!D&(nfq2ec8Eget&;eY-_>FdbD^XD_J
zu^5+}YyH;KxQ2wyIsNJzb!|T;>Lu28$)+`%VejG3({ir#{*QdG0*x{e&c~Bf5|=xP
ztt*BdGDAOb)X}@+*A=aKVN6d9%e%vyQjhuXtOE!RyM=@9)!RGUj#<N~f@oBQ6ZUm%
z@eNqI>x&s`mjsl9FUW{^hXe!`r9Q)yYQ=v?=bjM~-mX`ZbmJegsLL&J0d0Zkqj91Q
z?&wg}uQ#&j$8BCAm-acrG;khUUQ*I}Pa<;>sOe}GuQ{c$9@wsoRPQ9d(tS6l1gN0}
zHl^lpx!%V>TnMFZr)ZKaWE5gKkTh9h`1o~7UC#5PHARM8seuGXy%w8Zzs`*#+19gW
zk18Ie`E_<z*=?*1ki3X}5Ag$)dFP6dRy~*w^<#O~@HiiDlfzS4^x7B$dOH^|rvqE9
z=%d0V>HjoO(<dR_*<{S*^*>O3+TiRvKJ?=`Q5hNUTm)9_I9!GeB#Cz6-OGy-k4Ccu
z0{^aM?&`)USi<nFnCT2Uy8iX<6$m@AMBn}+;jr_yd4I+CL~!d9o}ti%E#~b-X1;LA
z+@N{wDz|of+5@LzS@VFqHc|qP(J?^v;J3SuhCF=*q(xU_bMD7yP6w3s%T^0=&5AL4
zXC6I=*~(BXu&mqSH{HDBVxJ4!&(gN0dDlO36!CQ5mu3aW={JUw$Y`?-1UP>&x9!Aj
zCG1P(76bE==gj$K&jW%pO#91FLqDBbC6X;E!dbeHvropW+&{2^BHD9<W$gXRnQK=O
zHg0P+WN-)HO#HNxe1|Tc9tP`yb`)+sk~s8&pV(;%xTmq?kT%?3Cl{p8p;4i$t-AwD
zbPY0Pk>~o!ac(JGMu@G`fNTAUU=?%ov)O}16M}Z<G@JGSDdpTNy~8A7Q*Vt_bPamK
z{oKFD&6#7bnzHENOdFX;CaQN0o^|CXJ>}^#CwDJSa#VBk;2u`V0-)O{mnpId)8DC7
zGsWTxFWD>F$Zkck)n19s*KXCQSYOS@&=xGOCXSEtiTSFW6i7-N8Rm$<y(k%8c5=_b
z3m%e9Nsz8CRP@YWZ;LhD+9vdNwiqoAj7xIA&$YXi6<eiH`7M3YLA%DU+v%Ij_YY0S
zx2h=9U2T^@OcISjTGsX-;mnjS#no5Jo(`!{r;$2Ap?C)3`|DDqWw>qM@4W?7ex=E(
z0jQ!x<HLKAAQ=F7D6(q|v*t?41|)wN04}g^5;u`v5vp$hX>HAAX+XGWqWSvtcZ+@&
zv1*{udXhPYIR;X+m0b`?*Aw#pWX5TK0VA=H&NNP#pY#x|$%P$ye_I=VMl-;GH+XW_
zvKawlBaRBV7)ssVVqeK_6L~g)t38d)LGdLn^R=};;d8q}w(K#)%lQ}n9vQ0Ho6{WS
z%(6!lH9lYOaPi!#P>#nEmuV#*Pwp@D5es%5xBX>?phbKuT&r8_vg#oBgA9{fs0vE<
z$DzCZ0#X{w7_P+VxAq5DT1)>m?`o>a#rNsrS4rEb-xrf`5XkZz>4xSer(nGP73bc`
z9m}cf6nIqdKL3+`cV2!q`ks*-MAEbr^hka6%)(4QLQjFD!p<Z$c-HHarT*5>>-nw&
z*AvI}Bz6zz+_K@B@o(HLh3+)lg=`aqp#Cc+<Q>!9=hVAP`4_q>_jhlhm*=V-?hDnP
zHC{${IZ}@&8@4~Z3owxBnoSRE_ii!nXM_dTU9Z8{@c5fq0oDcdt`YOBxTd5(WkZm*
z_mK4!SjboBS9;@k=%jdYKpNUi;S|1K2yk(TcN9KBf8WaPNtg4snd~9Vkub01VFjUT
zu6C2hQ*p7*!_5Jc#lE@Dd{Ga!L5mQcP-&(#-M<q1@+58V3K?AxfLd<ei3_f}Pg$yN
zDc`UkMX$}{XR!xU@T^yerS_KRd32zD%2VA-wP1vjc1lLlqrN3hi1|6S1GHCz)n>cQ
z#~Og1VUV3L##PdJ8?1rYjWpNIe|(eZuHDHU!Fi!tj)yKqLswd$n941E>q|WP(Y_1q
z{)22_7C~h*nZg+kT?0=lTOM@o+NCf{)RgZn+Rjs#Ae@8pH4Y&{nmfpP0Hr9T37jZM
zfz=D>Ye^>EnuU|S7LUO?zF*xjl_gK<sE95~>Jm}^NF<#@fuydJoWdBeCPHG#ZXRK9
zZ6`8Kq;-W(Du8>7e^WLk<&LlMC?tn0)2tv@p6V<oGK~W68@li@S2;p9E&~*mdB!bE
zE_O_svX`xYEbQAGS2ewUBksDQskA=PkCBfEwuM0gRp{nlL9%=54E!XwjFkQ%+1cqb
zj&GtTs#E(xcqX3o;eBkj)_f)96Lq7%hZL>Lta#B9_l;wVNb@G?bD_m0UeV%d!j1G0
zhta=p9N9%Lw!C2zhWaAf<2~Bskx)OIr6|d`+rY~hobG+_Y8b~uKVMaU%kGIR<IpWo
z>%}<FU~Ym+Q;b1rvA(JuT46>l-Kg~?BbURsJ_JmX98_)9j8M1N_sXW0Tt8a9HDe#U
z=e^_d{(+L0aP=ACxGXiLGoH@lV=hj#Pdct~T~abHkuKtLjN}$-uRH%$m2)wN4u!~(
zsLg8Y`w7ktft+t*Xda3H2g70<#Pek|OSKDsI&;+cA?sv~xVvsx0y8c*9W6pvER1{j
zw8;!T8$6749n(u3p8~2CV{fVgtN?xjf~=nO>$)fkgY7Rrb$Z3p(fs#q;7))ZaV>ey
z1nRpzOGMnmuqT_dIuC;t{i+Z?E9fxua?l$#ZY%h9p)sz!PL>Ckp-b~lkxf4~wK`?+
zOzYqA=BMKliZ@`)-P-cv0?MH(ymS7sUjoQHCoK#z?YsW%!uEK<|L2Dq-LxOR>Jqq$
zYm_`?9-vrP2`q4fTV#{TqmiYlMFT%9{8=;Tk<(Ze#k>=dVJxMr7~aNfz3&M(nw08j
z<INw!5->k#Lnd&d%jp6rJpiHSFwOWPLM$G&(tKf^ofvt4=fKA<*M6XnQ?Y*edSiJx
zCS+(GKMPonMXthJkkQ~Evaiu(N}9mVsa$Ysr4XvOlZ!Fx{jROT6Cn@=^My&~ZcMrA
z0V~o^R-r#DVYRkAo8E`Ng!Ih`g4Km+v15gvxQF^R$8j*yIJ>BHe;qPzUiJ9B_V1b3
zF^=Vxn;Phe!cYx?C}U-oA>FBR0?Nnf>Gi>Wil9FGfC_`qhZY#(gr6-8#S%J|RExlD
zE%@D289G_bp?8=&2xN$!J%e46aY@!SuK>dD33mo~IaF@RIQNCaaaw|wpOM#7XO)cZ
zB@SmNn!cwUHVC0)vF{?mZ2Mz-?Udx{M7;%-YeHhJcKGj){jZk?Nh)Cj{#!-#yA<d?
zQnmRUs2xea){dXx_B+yW^{CiD=mMBiqf8zc8&w->bm7wtcebN*l}2TotKeqUKB%#A
z2`#I`JKZ2`nA22E-z-817uuG;@Dm?-R34(Mk$oP~CL6e@M*a=lU!vdvg)0=f_dqXp
zV42RT&-|mXQAT}`EXJ;OEoc}31GwB2V8&#Pyfi_@nLJsdVkJUZ9%EP<d%`rjxr{3c
z&TukWFP360VXe3`nKEnRbW2AQ9_WOMD~cY~pTryVA7ud?8NSAlKJI%vmK=9hrNc^a
zafrpparL`+qCnr*@ql|kr|ARyYTXNxk|+IIbKY!s*kT&8<YtlMNx7rG=&YoIeHxpc
zEFmG0ApR%=BBV<AO|f<mnSzF6IFVyA?U0zK{_L=e7cUaP{>pRpYCE4ZvX4CB(L#G6
zbM`3oYVDodd;t-oy-8iwL8dpY%b+0u3}^ED`3#UbA*DLzq7}a~!d<v9xp<62v%?ej
z=IJBqch&pvQc~x|tpY&Lgq~>=B|Y-<ZWOM}#_w;wo!MAq-WZZ>Y`#2}eaVSKIxa)Y
zT#61-d(LFUpFxYC6O7~Erf`HW{mUWs9Vw)C{RNj=9ZCwJIY>#hm9q+{OTO_EyEZeF
zP_}Wq&L`G<^wZ_6Wq?$9K>zqvq>GsP{Tz=~#Iz&xL-M-w!RXm5BahQ*shn<{R8ZMY
z85-g!?nnA~HbGZ*_M$J|sJ;oZ?`grB%)km}Q5@2ZkYC>35j`5C=!Ln{yegY1#p$Zk
zyc4@T&`f_09vv9*kVjasI&64vnOjCM&pw+T*r8_7Kh+iF<5vX)CNft{Qb1Dsq$5*R
zNmvd|urt)7r+N7Bp)ffAou``c7Il!UxCA+*!w>*a<@Q}3P->hSAHwTD;ZZOeZhgHQ
zd}&+hY!US4_A}V!ryMohoax`9tA#{UdMOl^&M+;E<}m~UK0mjrhiMJCc7yk`l6ISy
zZarETkGn2bo1<21<I2T2soL?m50ocMLzjzA8d2A_187GCTAZ#^+FkdIj6~Mx0sXfN
z;p^jf{S$|KXDYl$SgH3;hh6X|?OZ_AI3Os_Q|?MTU4;V9KYZ6Kvy16(etYwh(16<9
z`hp{m0ZoOzWO+1D7{?OktkJXoq2CVFUkb%b|D9c|#XP8HB;k7M2?N7X>g!Q&EQe2Q
zc`ujv&l9@&R+H%PnQj0=FRtY$o=bcvck{=rMZeU6Q*NLaX1*;py-4K$Y%uSxP~S}V
z`gD1ztCd{2_-;$+!eH@INiN1E2V|3*54A5yD(PvbMdl(Z+v;LzH#0o*>E4j7h)+2V
z+jESRhSjX;`(ZoshLoj!Y?b4Gik_I<gbVs>CqvJWMEe_@ucE|7i{Do(%xe?7mM)>~
z9z+S+!Q}sB8L)0?!E*Ok|LTq=$pxB$8+!12OGxoBnM^L!%2Whtt|Xd}Cn$8V!%2c@
z2oVTJ0jO2o!ou$*Ok-eld7Thb=-V{D^0RXc+HB+zs48O1xPiY!99}1%h?Yo>&TrmH
z#SIl7dzrQgf)~(g=S3b!E|i*NGCw8luelVM%e+_P^GG!dZ$ThzPnM@aF!27Jkl9Z{
z&sro>@Utnb{i>04+~OZiwv0X){nQljFC{zym}J^bn1IiHD@=$QLVw4`0`Zcinv)Td
zww-jZ|J@gsn0GFLil5a_>533jT#j$>7{Qi?Xn@Q8=bf6O5kT_O3Y*a{H`iSsUxwvh
ztrpdcXqz>4aF-MqkPoU38umB(xEr^6yqK+Qw~vau&OSAe5%(|bdNOtev}V8}UL`s^
z_zr?{G(F(H2#3P?@)H4V&eF^p5Kw3e)egPWV@JVmRSuB<J4#ykUYKC#x%2lfx^8_e
z>*CfQZpo;6-4FUAx=)MojvZ-Qd7AFL{oRkFa@3}>`L4bVMV)ljh{Cv-s??yuHA~vk
zH@{Jz+I{S^ASYJcx8N!|HnG1rMT1fxOU7*afKR4&6JT@J^N`o41qXk4WBq_My;mmE
zKT>8{EI$_z5b!&v&En$`>LVr%f2%{BS(b7XQYU{<chj>==OKq@H1BPy<X8VjBQZy~
z;w7-;`mQney=QnqP$3{aSOV^N?B(!<`{wHcG7mf)hM@;JBdnDD2Grrted<MOUTeA6
z-_{!t^Y+rJx+jUMNUh05=JzSDRK8N@FOwA@C(&y?-uBn*sBIv};HN7ztg$}Z99_h3
zROCzTyJ(-KT}R`kf;kc5kECSD7y~!a*D45oF&ozi7b}S~!mi}!#aAN;1MaOlnfK$X
zNxd~zG%sg;Z7t{DscE@_ouAk3r(bMiw=;+Cec?9HC)AxqmWx>}#=30zS}4eAV2{jy
zF0~TU?bjgv14hxTljK8twS!LK<bW_m@5o+|8*{%8DG>^_T#IFtG5`FP0|+EUPzO?t
zb7IveXO*M4gGJjJ8f@Hz3r|1c(Z7$5kTnhfF~1bX$>YO3ef1GwRpgr$nWt*BUSfPP
zI{y>yc}Oa6<QpEhW?(ddV_jQwZ0vhpUha}wO7D8anLVFy`J9Z^WS(N0%QAC%qSVmr
z&evdgCOoy<u?em-)Z<@S!_3I2lBUK&N2bb?(1%pjhVjrydao5h2tzbV=(4g(EN>r#
z6Qul(BSE5d!0cSXeIB?%Fn7ztV(5Q;K_NQIW8=hA#43Pfq=1JXKjwlw$a$&&S9;>2
z)y4uY_PN9(6Jk;?2s4AWhIK)4TIy9L;%fXsKunM0)d1k>A}3GqhI1ZviFzBK2~$VT
z5X}s0Y(2)A4{2G9m45P_(1-L|A-e!cE^YAJ6snoZ2j#H>FB1Y>R+PNWjmP$ODo~R@
z=UaNYo-L4LUoX@TeA(YK&e~jC4$wPh-z^3a%Li8%nl6;p!0cP5EjoHKz`P%(JI=_}
z>9<s<Mu$UaV(>7cl}#LPHuS%(59$Z@aCsGzxJz|5VF&>T(_Wcz8?IAfYI5j~n&Z*s
z#X6~~xz{s2TTCn(b`<Ea#0!ED9C(^R-wc>+*!~uSvLnpD+T9nr3$SScdWg|}<KFP)
zPrY*^&z5&m)%4(Q7aTe3+C~jvy&lGJV3-4!8_41Qt9)b@kXldQ$iHQzOOIU`I=x_P
z+ji==UVY#v9ST2jF<PYM`q|W21iX`Ej?1y!u$N?r!@v{=?jEmERZ;dkvehMVA;Hzf
z#m4n_5M5itd$SkkF^>7o+sPHX&o-cPRavbY9z6gJWatkkuJ<aN+-Pt5vrM-<416Hx
zwm3EwiBq_+)ky>+BoaqLfcTdm#lDboUJ21r_Z=@Gw_3+WnMnfc(ZLDmNTqR(VrdBi
zW^;-J9e0B0=BzF_DUMS6z1EC*xI<TsdScDQ9T7|#lDjWU{V}<?nRfC#<emrZMn`9h
zl}Fat72lrIPy`+@1|vYAd6D=^>4c+L<uKqxXBS6c$U<~=w7}mYQnnL|+X~w74$yXO
zqI~1}`X!USM!I{>Gy-C5YQ6X=XDsi8mgZEBMS`c&`CW4Dn)N$)8ixAwJV=(VAL2Al
zDj;w@W&g4H^ItLQ4}{yD`YbD|$NhF^5hPWuuCNd-axCgH#Z5y@&D2}f^`LBO?iFtw
zLQKjvo8&5$I1~W%85?(XDox_8WA(!9b6=3BylQdBGwa}>Y+bm0Qsh{f%ygs0-NcA1
zc6STi#A^P&>GO~=m|<Nu)BE7X@XP-^bGL8AXC+Dw51k20%nv}B*>L9{wRjqu$NpgJ
z-~e#QBV9aYp9m`r|L*pU2(yK2;K~ln)gN8=xc>+NJP|EP8y%`nmiHO(Ip{eKJ_NoO
zt!dtNj3x=wS_X4cJyBuP_}m-fD}9d1YN0!)yG!s$^bvi*&lQMCW2}<PRpR<}O-P1Q
zO(!EUgQTA=^f9rqoXQYWk_=fuxv_kBp>sUH?>2+-q6F5mLvdBoK9GAH0WaV6Dug?p
zMNq{MlI~Eo%ehrq-JgseH7SGI`eNSpd9-eE!1N1yb#NxDcT^(2m3^6_n6)*0F96iW
zK=2J^YUOpEdKz~-c_2z(Odv?_5C-%plY7poO@{u17Ion8P^|He<n@xl!D0rxAYrAa
zzVhN&TWM2VJa`5X<qG+dRGzKn@~MwFu<zC88%Mvfl(Uj8^`Z5b$f@YGHMt(Iir$*5
zMm8Ui<{WpDi+Puh3wYU2zBFE6X{L<8Yj5Iz!)sohzkGcifN;_~=m8rOHdjLdCe0jB
zs=EEU_yz>^E!H%Z)9-!!#mj@3ud9TqEsoY9csHf-prOTGVSIQPey2VuUhZ3xra71N
zh|>IhkeH3TT~0}p(CoMmhAi+RLUkO>vU!cYtrw>u!4&J(S$2f7Q{zu(YtmVw5p`WA
z(DXzVrbVR$GgxBy4#+UA2NZzG1WyKO<?@vN_?mPOuF4)4a6@PRz+&?@jlf5&JRs~C
zSAC@#RHP;ut@}%cJEsqSkuVU;xLUcPx&tG<?EZ3EN^|VqNviiEph9$9sZ752S&-{O
z=jA|+#e%2R=Ks=|fUka&7s;dc0d>f6I=X6YQsA?oAd!ck{#Hwt6mi8vD)t{o;LTH)
zb8Hbi#F$4%!21Hd7pXRr;|jEFM+!lWwzhBz42zSx4~#)47Jx??U8*1SR!%(RZRjno
zzN0q2<W>okF1^2>BMl`}%~`Cv$gegBRoj)eyI;Wt<tyj2=Erw=@1VW|=Wf4*we;$I
zr`j(iRx|J!b7g(pA^6u~eVHk7rtJ*c@=>&)$4cZ;PuLkUoAGHIfd1zfCU*9WYeOUC
z@R8m)diH_U8VLJ|Ag<gbawogF5^r)e`l=Y?g-~H#t8%VV`4@^Eu!IUh1i!X%8tA`9
zzOY3Fnykk9M65`Wi-+(3zfru{^1<5@5xrtH^p>YujO=xAr2j1V&%`BUgCoc5SAWLk
z@Z|wb0kaE2f$t?aqXw^Jgfgu2{mSTS%iOT;z4)yIntWt<UEeR8a?p$U*IWSffjNan
zgE|LuB4^pB=eMibpuR{ouh&Ruhsq$_@E(e9u-TB7G=IMrQODid8i@w0NK}R%N*)vi
z*CJN0@*`rRF%L<@_UZn!sd_)}l=Kf{%+BG*MC8Rh^D_T6f2eR}76!3ESBF9{<6L4n
z(`~=Gl!>jglo2V>eY_9cy;3#122wKhHm#>(ilo-lBlTGannUUX+>U*FySc$F-0>V@
z4UPHCk!0_6DI|Dr7Q%$;veb~u;CAKi-Ok|!S5{ygbT$T7$nb?gU6ViC^IxO;Z|%lN
zGlHzyHBa1aK)H)JB0?@~IUJgVCb0b+lVDud2RcP2)9DAE7HQG|KQ>lAi2S2kd|>4p
z_up568}X{l1DEbM%Z%x-E=#aKAGn?cg3uwv$3njxY43O8!q)QGaR2!lZ|*9fW%R55
z#x1O!@~V-ObFJyl9Z6-&xK{4og?>QNgTH%|LxbWLNr9hhGD|`4kHscPq=x`8;cXx)
zGhQ@5w>sQ7P|ZErwy>(70UIiIHw-a3a7GdOdt(vkUw-adnu?Wf;&P_?RUWQPPbF<?
z%<XY?lV0TQ01RgQ{A+gDrKAH8{ijfT;;_xTalrxQMrQy{1S62(_`T~`O?jhgGm3Rd
zuwQBa;%lF-2j(yvRFhWAnm_=|d132*KZs`<RtQstbdo7=XsiWbWBSOJ>p3&;h#EM*
zOrVfv#?X7uj1}f?HKkx-pJk96Q)-r;Py*`nA3^VQQB)xqp=q65eruFJu%~<hyRX&I
zQv(=DqYy&|*RsY^=Q+f{UVA@!QFlgWXF{8kn4rq3mLgrJy2M`sPano~I1>-XzRpF8
z@6MWYRfEU8dd|Xc==|!ZT$|svX8;TSkUF4a$qOJ-<wh*7&*YynU2@z^$fT}D%o530
zqXyFhhNeZC+)lJyCxA%;;j}|$FDe`sCa$@tH~yz(2y&W|ZDPSlQihN6prP6VE)Rz9
zv#C&ScYW%GZ9A3gtwB&va?_!~_cp2LSEg%)|BDFF{G0Y(%Tma%TQqS{J4ibp8|fmw
zVTK{Wy7BSB+OEW%FLBJT3YqqlmTi1rePU4pf!q`P^y5y;G<SWiV-z%Jca|GD`(rs9
z@p{1Kk|gUAshE!33AFWhcD|8EV-};o0p;5nll9rjN2~J|J=tQqs?VGUf8N|*w^}=q
zAqoMJEAcVA_Zg1ku6*3?6k$*pbLYJkWHc67M*C#zp>{c;n}bXo&?Dm{c2{MRivZdq
zj&3RxFn=rF?{YvCKudsh70{4svrOY9Z2W7s-!&No+~qRdHs5SCf$F}tj59bx647}=
zZ!y%*urgn){${9h(N6yN#J2=QHr&c<s<5mO>xlhw<g!z_quDm^@j%$Wf=;4ng$N5>
zjc3`H_TcdFwJ(K<_8V(Lp=w;e1Xpq_kCA-Fxq!?vLuLWZK1wzD6?<Zpse}!==i0%D
zuigwvI6CsYrA7;}gAm~g##I;ki{{7W;%d@-;N*)4R*4J<F!S;C<$(wv#0+}#iN}R!
zZz0pFwtwJYCtKmcgS3kmS0d3-=Acx8RH)5}H?grf@7}$;$o5<PgAp#{GQ?8GX%Rx%
zB_2JrC*tvJ>%x`_@Z=UkcKoKSLrG~89ThNdmF$)4%enB1^Zl9~!<7^O7^P+Kx+Kv^
zF8~Zvv9N{2&YV9Rm(Y4}Nrw?&dfU0R!G_h`mDr8V>j>qyG*+F>@{R}`;0*LyfM22A
zyVarE@(e$6Su@aI54RK05R3Si?Y9xms~0%L(IhNooiZ<ibaiif*bqE<5~zu#CzRJd
zr6qBQNQv!ID7y_}utGG*xA<C!(|dSPB?s(fei0RK4SdlJnTv#JRPm2>ZS1ACI59+g
z0L?~pyk(#;<sUf#e_wr^yi!bi3|~e<s>4z8QbYKI!M02|TlsCr4P#+v$|L_;!jpkp
zF2<nQ1BJOg-^3#$*jLg6bU^^Ejm#;4Ux!LtK5u<!_q|&iy$-4kb$9+F%NB1HQQ^I3
z6cP}I{gL|NR%8=_-Gi^4{~8~U{?k|LrY<XTiAC$ftOL*3a#_34KaK+$;qVVb+R^l<
z4&aJI{`rR?@49(dwW~p<${W_eb)IF(I^O?&`7F;`6cZM{u0p2?4hN(k*F9{q_4XgC
zZynbmC?F-VrgKiHNd<dHuP@pcdqz+xpX<CgbDRdf$CZdDlwJjTBviKJYh{xUoL6uk
z?8GRDu3Q4qqYlJw-7BW;>l@At8Y(d-EZRU=iz_M#Qvd!0sXS=kjaCicx5=bSuHg`j
z%wtaj4W1^5a_`6nWHR4c8W&atcIe#5_A+Jw4U?g+N8e}LCp?Hs><!a65hNvlWYiii
ztB+f$kFVB&0==tm3Ut?wQxf4z9YP5mmw9l!<|Q3F^{LR*zm$+7E~}F>4SanQ2;lD#
z$%-NW70Gs~z<wjsp8)z1VgI0+^A9#zhO(O`8|sRoD#v*OEkQ#g+y}y&(TV-G>1zmG
z@wqpn>+d(<FoFGLq@k_$@~0k^fi{#r`E=8W7l@~p`qL6p5-PNp>Dr}urTklt6-%8h
z0r%9kM~&xN5yMu;M;W?iyCN`ZA>(Au_YZ51jNEWWZWaux#IW$5Z?sCR2b4o@PWbQQ
zjigSq5P|sfAr^O`TR|XN1iApHkHgIhW{?f$z;K}bBDH45i!F2@v3VV6=P&9wAE1;F
z!Gwci^L<W|SfRyMlfbwWc;Po1&g!&%Wl(_lijwtVc0R&bRo-8IFi|k)1s`x+bNy$#
zS<}bhb!1nPn!9^B=KU|V`1&SOXGH4nNPljR4e1V~K*L#__}lJ9b*>M5O$)!(WcJ2g
z89ho>TK@>JWt@!(o5HA^Zw$6Dc-~!d782V;abV?ffOzpSI0hEZWM)G`&JpkbkHtlj
zz?<&Wn*Ggcv&IY!oHpdZ7N+^^Llif>_>uUN@utzVma=UnMdyFeH>}L+@AbjGd9`@T
zUg$_H2$Ls@Z(wUU2Dmf*UV^Bhc3sRdF8ocMfVXYW-9~r+Wx-=EvHEHbNAoQV(z;H?
z#jRYiGh?!vHxl;UIz}nouSJ+vz(`3urNb_?CUKCya3`{R0yDLfRAt--b|Le~#iFNt
z4%jm5^VGi{FM><TK{L!vW@~^S)H<viV$I_qRFZ2MKkH+qrH}U1)Giixo3wet3tb4L
z!1RK)qoZ@6t}g;OBE&xv@*y6=Jbt61`Nr8Jt4$`W-1`Z#nEIR|nF1gy^{RtTU5NaY
zy@-gV5u44^L*dD?C82;1HIi*B5uVMW4&)B7>tc@6TT~i~=lW(Yi3ZI9sLXXmNa@4#
z|MU(SNF~2ZKiIF<0I%006@Mtq3H8LRITsl&0s1Jca9$1!>JA`kcQ6vVwl1}c*aHEZ
zC3;^F*fGPPlK*LZY^p+ZIIU;Y6>`RCh)&BcE%LX?#u)W)Y?=>~@|UlD5H&)iU@axz
zyf*tq=k*KivlsY6`rE_Td*wHw6Pn_2?`;eH3=|GrnFS7xV!i6RZ;hQ_)vo?_%y74T
z)d0*t1Q_xMi--J4HLHg%^Di2;WQ;Q#|LD9scg=eCqMZw4u(XT}3K9?(;Q$t0;*#pr
zJ99`;+}}z)u!D|<1hIW^{LYX5ncN(s=35+$29;V)PODJ|?^;p&dp@mgW2w)EViiJ)
z<gB!{wPTW2_Qrp?6+<3F2#^$M(;2BWp=&1hV%rXCjX@&@1b@+5iK}9bRvMSmmoE~n
zm_kR>z~?mN1(SOp1pW;)@K(Jv9`YqfAYW6R6~_0Vik&5$jG4u;)Y^jdHm{Px%E7Z@
zRYlW`y;%vcgDnM>NuEj6{9!-1INwPFPWtJ_d17n9jASyQ+08>$J{Td{)=#=(9F6Pc
z9<SaTtz(d>c+ecnQOaSq4FZ#^myGf~*qR0XyK!|9sR8U$S|tZwnt>8_>kIrS!iw&x
zdwevhx(9Wbmx^km2*`NL3`D|%?J60<>E7Q3GP7(GiJy}i$NWC-ciz;cGz!~$vfMe6
zXtKX9T<-r)a&Q~Bbz*1vK@o*Y1Jl|I^r)TTAXB4{g2zya-d@~xM<zT5CA_kEcYIkx
z4*QB073{dWa0OcPNYs6+`u9O$s;hPj&c-n8Lh+dQJ%5%h3fs3=D<)x0Q;+XY6@_Wp
zL{QFW@r#uj=CN~(+$`f7HS!m~<w$4V`@MFWM$dyUSd>+{H~7*q^zsg+fNi=^HoWkQ
z)7=wZDJXH4wpoYgzn0$YzhNHB$&psYtWRlq$}sEnO*c|EOPQbYPYL7@lS+A=9&fDf
z_yOZ_uSfK}&%qjPfy3Rf>v!~a^sP{VLnpV(wvzabxkOV|&jtIE%FoP!Brp@xWX59Y
zLEe&E9sKGm*%5XK5OPt2lM3s+5=HUGY|W)(C9!wJL`pxm+?ETx;-<?`Q>&`TM0a8B
z`**Ex#VQbliX71eXHv(3Ne+9Tsj<GOk+f8ZMviN*u+>1m38CTI5ZtKSh(`H5etZq0
zG=MoC$hZ?mfc$qJp#jo~;dF%%hwmSs^HT!1Ao+3;&bRC`W`{E&AW&O3%Mv+}3XTN(
zVw?g5W(s;K|8&jjkiPj467im4=?7^Zp)Vf%;74ksHO~zdl5mIiqxkLS+kfwf5j1%!
z3Vzk*bjxWZ5R6}~lH19~?}p09V#F#KJ5m+C^#8Ea$etfN82>5E#60jRL)~Jpd(1=l
z4#_TZ_4+Qp>+8}#9S8NHpRQ+_9^>ST*yyc0Ne*N&<5Zxp5iPVRWnr0@mte5JR~eE4
zT6XNrjK#tv1A|EM{eXZFT7|K*j{QCD($Ut{`#Veq;~C%I#JY>9Q9^|{yX0)h==UvF
z3;ESHLmt<+@5UOE(5bm}xg&D<H%r2B*HK}z!OCxb7-h9ym9E@-r5wz9ps`K7?&+Zr
zh92aVYnHn5<_(XU#JBXr$`MNWOHd_@Sqwit(7VRn^4e^2DL*>tSs)1d4LlXHJf0eU
zyH~yQOnEE9N4#OZ*+flFK}@W4q(LmQCpzP_S}68G{|Bomciy%7+Mip(!daL*6bGiw
zd_QxXzFnfFT0+3*ZMlwSsX6ZGm#X(-Ebh|0RW<V2o}RL?z-8|GB%Va&TjZHLhb<k6
zzJE!J-zqV?#v9Eo+qu2|S&jB9vu3^fL@va#wL87BVcqVH6>q0UVe)PJs)!x9Ev0A-
zm@yanlsLMKCo60t0he2?CUtt=-jZ5x&;}brr^<MuDklW>+e7#`5{(O)5Rlbthkw-0
z2JO1Gq3BRH1~R*m6d$l(Rn#>?@^XQ8ZUh`bm4igGAer58_-0~_Kb-qTf<tc!pD#t$
z7OCyFtiZ8y8p6LwMv<Ox-dF^B$|CEhw*Rt~a_VZ;!R)ikL!^V(J}<)L!MRtFlP;&u
zT+T?nZ0gYskzv*@13pnUY#Xmf+twGZ?56~`)m$%Be!;?`cj(Sh<qwm=U2Y?oBWG@H
z9shVei=xcQC;L79UFFASuFoQ>B(LV&6gx*f1L_koaIsu7WzMetE4;jHvCtH2Bf@VR
zcmOSMV@^8f1sjS_zXJJ;+VP*UM{#9&9`Cf)ntm+mclM?n38UjYzZd9yLp8yxS6ib-
zsB^qc?oN&&$ssj2Lh|o;8XqQfUezd{1(N{1{o})7M!G`wqzm*{Y{p&nV_UQ0Lu6uJ
zTlb-eJYF|N;ekOn=c?yQ?ayMdYpou9uFXMtjH;aZ$}iGaH>d-j(p}k7p%y<K$JHe_
zmo9TY$+xq;y`@)f$#Af_+G3|QO;ax4l~2Lnk*PyeB_v8M^q#Q&I(1Zw?TIVjEd&Rz
zPV|Zpd5>@M$8c-Pcd<42)d}=v$yU{_^ZY+Vy>(cX-S<3<iXvUo4HANMx1<V4cXxL;
zNH-!P-Q6I~p&RM$?gr_8_jx|=_5JamUIv_d-)pZmGiwHU&e%*c<tyEY>u!lZ&iA9j
zb%O#M`f&GYwwr}U;Tu;83Vij8Dencwpa<s;5u&1GrKzg`7H~z%Mu{c4XPkN<Xe$e=
z(M+6u%#%(nsxlcd2PdL&zo_`%G(iv<AQ1=%ih<*=DA*@Z0u|@KRA^w2ZZxYuvkPq1
zmwvc;gBx?=kI}#+s8q$5Hce+i;}hY6TIzrVoL&(&GQsiM&jqH5#aj=(|0Cp;Jo7Hh
z03<X2g!4Ja3Azn0?3r^lWhH1k@6n!fBbA)&D{sP_BSy`4v7sje5Sjg!AYvPlzC}D@
zs>0IL_|I7k35I2}cms4DF`MC!-75EdsbK5<XOhKu&SRRy2z_@?gX(X!r0Nwrus}aC
z`l|G=PTzxlruX<R6?XG-cgfCt@>H()En=c1jv$G(jebmP{?SNC6&@8mkK8Fphg7*^
z`D@q5H@h%OVGaJ_GBYfZXLB3nor(^@#V5YK%s7+3Yl0E6!#R>9$99A)oc*JH;>O64
zq=r#H%?S1H9muIJ*G)KKEatYuS*@CCIwt^zlP?>teW*K~(Rd}YeS7OFL0^n_XCe}=
zeQY|7NM!G1kfSzKq`GNl|E(`a%PBm~#%5ZO0E2~b1bj4&FKVBMuBq+OSIu$$<mg0s
z?8aH8C3$sK^^u*w5ehmsH=m{>8p4;4|8X0aXT%O3@<NMF|DS}5-miJ2zzNOKE(5L?
z_{tzHO(*=|{wtmsWy%0e<7hk|=5+!NPPD*|3>virHO~U1{AL^wc0j$5!7}mXqQeL@
zFbC>T1+|fUnQf{f4j$|s@GP1H(K1`x+7Gq6ApAuAk6|eI1LE<M=<nWvFhZXI2a`>b
zcD`rk@B=WkO@^RTgjJ7#8>-}K#Suj0(QDK?e)W)b1iSYC&(RWB^{)dIK&s0~heL2-
zVOFq_S;-CXWZcm%Qycp9t+6z+2(FRK=;_&yfO;=&4}ws_X*mAjIjGU-lfQf+P6ahk
z4dVFlXXNIu6aKbPqx+H`X<3RdlzUz$hvz2t-gN1(`Sr9(!Z!r-Ng;lGWrsV}i}T~E
z6U!n24L2|o>d;&<4;v*^lX6JuM3~dtjQo2vasoRt#7R{uaIvvRzu~XW$7kwl<&t^Y
ztu58Ib~H|!(w-^jw`;LX_#E%suJ)HRir#}(S><|UKHH%1gL5<v_HJo^U7bQF6KDP)
zBdg-LjmOAAs}G@knE$)OCWEy7`Lc$f+QPk-o`$yH8=>xd`82tP3xb@2jHt~X3W&(2
z`1?S*kySM_^0_N-FW;sXi^eAAYkMS5feXb>Ii>I4K_(>nC6fH>XP%;}{;QK~#+hny
z-?Y}oN<bbsnQ4swv9H;4{i#eOF}mLg&yy}+{i1t*8h%>AAHPW9k(R*<M`A>pXzJG`
zHDZb2&+SryNB3X%_uu~wGa()E#buFjrjP%5v}3_x9@qkE5_bAdM~|6CBX_}0ey`in
zBFW!DKFvyd9A!H$)NTjiPyAbZ?ULjG>xH@oz?qzkWo{}$jGFkrxtq93B3q+Vg?c|p
zRNJhyu#~nA`ftY<>$J8&1Dl&~*2Cv3^!%X}2w3*hFf+>q>46r%-;%=u_y|DH7L;eO
z-~965jRicW)Hque+0Dj9p$R--yEVS9jLm-=oFy><@h2}$EVgpBsyy@Qk8_>R85yL3
z0lzjfqsy3pbNGV?(Di-=8DY~=z4>Mj(GaJe_W0pa^I%gTbIZjBNb~nn|EZ4;At)(y
zH$TkVhaKFn8mIqV{^5_LTQ%QNU}4vrR)z|_Rjogr<KKBYQop|YUEHg(wY*yU9YGzf
zbh5-xM5A0FDXVzlGxq)L#d(<v7l#tbM}FvrQ~jnZK{VxlbF0iQm~;OsN(Zw^gDoJD
z2~IPO2mlyO!Pr0@imdiAob78gJl<FgjTzD}Tiv@H$92P9-fuj<7jd^87C#cO5;P<R
z{xe|Bpui4!h7XP*RKp{vz-FM)-F;XZ_}AFfEe?dE6eyd9yx}Ov)jJo1NFVgidBh#*
zN`#GmBKbzVphUE<;-u3l`$21ehZh!(j&1bwnb~XqBFKpG?mS9&>u(s^aOm*1UoNNa
z(dV7+&;Xz)pl?d-xd{2C@&VRY<UMk^oK`1<?HAZ$0n1AR*&`O+PpVe@c0$JCN53pZ
zFJbuaqqp(rYn>aKodc}=7ba`)*klGc4s5f;mhhFGe6nf30~b#ynwV$BRi;H98`k6l
z$d{#MeqpdQLCcr(F!VIc?=|-pJ_f4FvH8e=iy}G1a^ToF*@*JY0P?hWl7AJRvDO}}
zy4{mG_}IvR9rmg_j#9lkq({%KouZG{c5w?TY91H6nIMrw!ek_E3iu>IBlh|`H}oBl
zH`a%20T@9glLnPF6o6<)Xv6IU;^+QYIu}B7WTxyLtB+z#!YF)x+;8^#apnbb1CIb`
zm>1AQ#BG{tLWQhC`LTy1xKnx7A;wZPOTa)VQHpA5tkaqFJ@EMQIpo$Tx_lLX$QxUd
zETE%e+UE(Rk6s=oIKtcAs|Q<!FNVOqr;YC<jm=AjSwGW{xCfR|Qt|(_O8_w{Rji{y
zFpv6`q2Q&Zq0i(x)C1+Rh@#BAND{j06a|b$!LAp?Ncfc(n}aZ_@OEw&7>ogsP+(kf
zlqf7iZL|*na>x5k<oP{PO$E9x8L-!<#sG}Spo%Y&UY8lfnVKKXFJ6Gbi8!G}Nh6xN
zzRs?j$zlQ?x<1_b!_9vb=JDYZqwu!v2Y@3TM;0wC)}EQ(J}<nGPyhx_PgOJ$Gu}<}
zg+Chl-$(At>-Y%re=_jk{22Gct)1taIaoMAMKR6dzP_HUjSt#|Laf`;PFPpZtYTB;
z$K~-swUgu2_hzaPo`6BR42GB~tW^V#HeV$;{iVgEupxcs>L=SKDCVX043Qdns4fOq
z9)Kt=j_Hr8j~j|FCZ~pgoerUqg;eyHTi3bcR~!yruVGY5HqOi%xBfGqBJ<Wx5@-QG
z3im3?*3$wHn6*IQjXba(n*_mXY+iSckATmf2j&^tc%4j+7rSyGaoxflfQ^&DBOja<
zEqrEu(Oh?hY>*GAJWyF#2Kj0UV7w`NDJiL#=op`LY<l%y1^<y;vT{UG-==_CYB$9{
zW^iGuILqwXQ6&z>rmlLgC(7ya>f}Do*dS>d;O`c|E5X_fu1s?P2-bk3%-jP}Q`i6L
z!9UGr!a}uJ;g$jMBUC2lz#6QE`iH79lqdj;lR&2#(0OX=EutQH0O(9$H5NX?(~3U<
zHi8<IgAJ@kByPZeHATwy>Dg3af%3>X5D{3cu)1YmrPT`l1TY*_gi)5aX2jY25SO8z
z5e?m~2eON~?W+}MUhfyB(pZ8+O?AQKA_FUqO~k-WYoWYS_@U`?v1y58z;IjD@!BPs
zedj67%|~Ht=>H^;)#W-$3dj3dqofOr$S{;+&3XkxBYr?zDlsyw<SWzg?2c~x0r<5&
z9OXknA*^`wXMtz?qXVTei_V2#w*XCe*#un@Z3ML7S9#zW(xOegXrp8+piR6##ia<g
zVHqo|rrK5cy}mgRc&yn|uTNZ_*Tw>N;%jQt54p|9Z&YSq25grm@-rJbONLk85&k>B
zQUrW=_3don%LNV+NJeEd!Ed2rFyWd#&lxT;yT$4&9IogDyuLM`ueUZ=gLn$GcEsf_
zMwwbmf?NVHC4V?tY*2ts+R(h(ii(P+BXBb@A(d)Dl%cUQP?yd@DWE1(Vq0FGUh_{$
zA&e*D1zEYkSjcSvTu4xsW8$6!#Cw2<NVfrS1-<q=gU(2{lw6JiN_Eoqge*2Xtwni}
z4mzL__Iqq{QlMVY`$yl)Nb^_d6qTm1&?4G=@j96(Xz-OMA9NeuQ6@deQ%FM?EV2Hm
z%RhP!mdr|M&-nNr6oh8M>*5v206Q>q2GzqWDFC{6doh%%Rr0bDe7NjJmMX!l`g{T&
zpEIlA>LoSJe?wPgz-#UrQV!Nw{C_kgBbjPX99vRHK?}s(_2tKSpzv9mrxzZ1>hx{{
zw#O<i(#tss0ge!(zkUE@bDe7!2BLY5>B&L(CHTbb$ytIiz8QVfg)^w+m&O2Khh|+f
z2bW-VTRL#C818O2)u(pq0nQ^Hm@8rn+Q=N&(S~{tSw@>T0Jq2WR8O*XiP+z9#SI$z
zP*acn>t%bdhnaTr;6eY#CN|)#Q0i`+LA5j|#*ZKPkMniDqlKwY4{oRK6*o-%PfK1q
zq3o9BRVG92@}ySp##U-FhhGF(w5Hw-ARu`cJi_rpg?wm+;}Tdo2PPK%kPuEJ5s-}f
zM>T~6ub_$7WBM+<4WC_;lhO}_vjCfg5+Fpa8{T^a;T{$>i)C88)&<a~r)dYA2**le
z2?YuF!G;Gw=@qbit^)@d@is_hIlQvZ83|qu|7F*>03K@4JM<6N&2S0S6~obo5!oCM
z7$__0lX)-i&v6ZI*8)XTLj$`?Ew~M#gymh!eH~Z@RK?@I(y6W2><uXb>kSa7t4S34
zlqGaqXc8<vLrw4~mVr<Yy1`A&_{-|Ym8V3Wb#k>3Fq@mDSI7@bH(ps#h_rxhs5EkY
zYQ#3j-S*UNf?5PN)&&En7n*M6hT&I_1Qwno_Y|+vWctU6VmW!5tQ~-xEA;ieT>JA1
zWgzmHIJ3OR(OA^%SCKKhztYgKWF<j#52S(p)7%${kul)NwtF`+ZF+F#M=`p+qxZAt
z$~<FB26?og4E^8fs)ziqGGN+Z@)`-4HqZ<Ym40abh%EwKk`D^Gd8aO02QCI>Ak}B4
z!ZFbkG#_Qb)^sWI2<o_Gr@B&th5j!RErMB;q~2U(u>NuD{sJ3D)_s~V;y(lK51mgi
zodA5_%fI(16hcp1c#&J$@n(qe?BBm};8mIdBB-HJ;>D9Tccp4oJ?3*$6hWv29E0X=
zAPPqMB)RnvfV?L^Ie<?J%qKB)uPgn&#j&?0!1HJ?*rV}7*T^GS{xFxL6J0v=EUeVM
z|3GQdXubWB6ae8HEh||h)4blHnOe=X>Nm1BHAS6$gk_FSbPJh}V4u_JJ;&5gX~RG0
z`q<KZ_Wtv-ee<<RHBj}W&X<do>OyaS;5HEN&R+TWr)>+M=4IF7)m0FFKnQN+S<6Y_
zd9|c}`#V{xV>8-*e&w^XuLR)|=Fmy0*YS;x7r+G`;L@;b<VA3%U<;2<F{20E`GE)O
z`^LPijbFwqZyX~ZtrZe=UIB+wZOw|$vC2ets>h-2Ll-N+hizaOk;=E&)ivQTs<C11
zt&oGFHCP?y2Cv)cBNo)%E#-E<0c={SKbYz?yNg)V4QmF`X+Vu(XB#*)+(@UvGxU+}
z%0KALg_}HS-hwVpK`U@BZ-UWKTFbZ2i>28z(6RVH7nxsrxy_+=L;Zvfm)xxwdInHo
ze8YBtjiM9STR|!R3j7v8Z*ZwtO6zTHi6>i+@W{pjTw{S^w8s7VxD<5u2VeoCs5;ve
zntTlca%$B%fsaA_^#9WQMpAiI3JU{$&Juq9%mTN$dMSZ`XT9XYe{QE+YLk10&@I~T
zd?VlkUn=DEtvNs0Z-B<oyFVeV_A|T82#3W}E!dMprY!~q?eToTDbTy&F?za&4LAfA
z;dO{8s1WE=gIQTJ3wNridNYYW3HX_)eA6tP@s5<&EPC`_vqu(wb#Ycobg_`_#a^%E
z8k1&+aw4lDgJNdv;sNBq&_DqDq2tusGw9mXH}!G~J2U42(IDlfQ+uNc=By-Q{MjzO
zMBoLN<fVf;Rl0wYr`!+)+zd}1s#3vTvsvBX@<e~q=_9rg5BG*|@m3N&prlcTW?eh<
z8sdX%#tr<FmFG`B$v!#S;F>!;4kx@fdPh+Jrw9w(1o&M(y*HQc{Gq-5@sScZ@Za4`
zqa=CRpypM7PTZXtyc#G3M5xmKi@U?G1AnK0e-8fTrTe9aO#@U7Cav8^^Utos&l6g8
z>AkYjp>FYju40jVA@Ol`WWideat{|o6%-t9TZfMM3tB7H`kPeq<15WXZ$OIOGolGJ
z-f-o2ydD}dK7X!snHiHyu_9<IkF#7{dhPe#l#feHJwq%EBBHTxZL_o#xQaqgPd_Rj
zra&45`&L>r1Y+XSB4~P(F*j!^{Acd{%n9M}_e2wu<A86@F5B+MaG*AK{Q97<dUzP|
znb-{$L`>BQ(7^k_2%)X+?}ve-LM$$Rv?0D#H6joW;7*NaWtw-m?B<G9Y1t~}+HX-%
zS}sq<$K^dQ#y%qp9D}PtSZ(fkg$cyW`lqMsR85PELcem#*IC1u48(GJl1DR_IaK)t
zagE(aFLwoesAm)Lik4ieG&Fi4=wF8p6eV6zdAcB}{9%*P_Hq3}?a9ds(}R#0MSGx1
zNFOme(<yP((tO70s38SM`u62j%1Bq=Nc8IMkx7hpfmT9$f5+Ps!C6euKI3lO)pKw+
zozC*7p6TkGl??@#Z>~nBp7Gohr6=h`6$++(`V+L(>UAhp>Gah#8HuruGkR$PuX*v~
z)bV*GYxi%;HQQQC$g|8FmJcn7=%kHBh%lm*+Z$&?jp;w+l#0732xRnn6OqQ4G2l<z
z{rM|xed|!@C|-7WPQncp$I+%pe;Bo67o{&Q$a({qA!38pTey6A1Oh@E(%99^_Eg)|
zL^9l&-ndxbf+?hbH7XGP{2T-vR!K+AZwIo4uqpLv!6vocWH(D*uE#r7A?Cs^rmqrV
zuWxzvx>HC~T+MK`n^#a<qPFQ}nVk&V$<x2;<FZkco6jge#fI#73n0b?Nmg}2cy)&!
zrfrnP-sfGO*jnrX-$2zmYa$Mt<%pD&<fRH&AzxAQ1}Fcn*;!nW?A!(-SeY$nnVKJD
zp8?vW+ZBTT771z6+|$a6UfXr;b%|!fzo;Wr{tL8})KqqXt5%(75a!7YG-{F2ZXi1C
z_-%y`$P~jB@Z<tH+&Rt<U@zqiKwQg}N2xB$W3DJ*tBagG>uTN89o@JrL|9HNayP*{
z)v{9G8cpim-5I(jS-ie`2ycskZRv80#awQPRNrS;>dQ=wtf0e)fi_mb?#xDIz5?oa
ziPL3&erC!(t>8E(DT>DwTlH_U+m7aBf5PeJzP2Q44luM6wCc18&e+#@M?@CB90+pm
z8I(oE#anzdL<|-6yQaPpRTm00rLIPf`EXjxYH?O!Zm*4(#~3XtE|<HDs{_YssY!?c
z=O6%g`Te_87-Qa5`{Y(k--p7+n&v8hJPvvUE1Xuk2qeRh1cCsw0SY_jD%GQeW}H8#
zf6I5y+Pl(t8tP=VwZd}`WY%|;&Q^%*3t`j09r;%_YpFL8XFcz48=^=WysAleBF8)*
zE3dj^Xv}#Y&c#}bK2K!&0kmuJtc~1Gi_(jJq`LQoj-IDGVx=lsc$3}4<$|lh(pSdx
znnZG)9lUeG3~?FC-^};=rU-8us#nrael^(x;cd`S{&>=4O_MaL3ZfK(ZRe~&*&(q6
zJww;R8;dCFsfDx@mEi+6QPHCxq;GFLSCl+!o{M_yHty?JH~O3PNG%-rvkPC1MMD<J
zSy)*s^*Y~_=(G|8yE2%J+JoWMc7JA<V@;_fHh5#>yT?rLku-i+AXpzQ*AbLTWO)su
z+(dvSDrx-6t&W_cVu0Y&%_|TpiVhOo9C}*-DY6ElN-bjUjv*%k5ro_rK=<|<W^!_J
z74U7{a3r3-Kmpm=wYkkw3&<7_h1muYK?9PKo?H940NsKF>=(t%DgHZ4<J5MAfeFnl
z2;FftSq5($Yel+JNShC4`S`nDtq?cm)p~RU^%=eyDjb6qYMX&8TdQg<HzhPNv-Bk~
ze*KR-kAmWn=xi-b^0F53WyOg;hjD;=wXo9B=dVV{E%QED#nF_7$j$`@)-=68g#;1=
z+aPl)4NSiVPB#ueN!=Xd{uJd2ZLPY0$!96W)G<=2HlnrTF8#q+RFu59Ucym|liI;j
z^&M?v<C_{35)amgf3|`p(Ng02)_Cp>>iT0)g)FKA=f_Co3TGiEDleaxA8z-3#A|!<
zI_4F><9v3q7*h|Uv#8K@Leh`zh6vMb%6oEBz;tTt?!*<4$rr+sEpy!53oAE!$-%po
z%Azlo9{!YQs=Hq&DyH#t+2vWrpUD<!VFWW>#Eg$%bi(^1o(qUN-bM}F1Jn439Ak2-
zT(6<Ep)ZV#g=q2Ks@J9j3Nij-Ay$z&w~<oK&4wnGw?N`_PT7{z;ov*mm0m2&S4*Qy
z4Ua)*U!Oyq*x-D3b37MU!ZD>nL*^0VB-%6}3<asWju7s(G}nz6&s61(Uvuc(aoEaB
zO1eN&qaO&i(|dk;lr6M&ZYw&_*6y8|!2wK358y%8_V(t~w>aj2A->(ijU9|{W+oX(
zJ=ZlegHMzSYHH$pPf59SH*d4ExfuY~WH4)M>0Lye0h#+274@^eKDlr5Gc}-B-fO$>
zQQq9#d_seL|Nec(pMS$;qF{-p19-csmoWTyV#vMhcyeJJl*O_|MfyB5PKJKCIXD(s
zSu-g2;4UwrdrSq0KZUh^FIkrW(sLBE!Xo8RoVxVR^85+*sPkIZ?!OH}r+Pbrse6Vt
zLDE-w^=S&qATbq@lgBWlfcveVuZ~Rq#-kwP(%7RKCdI1{+n47(BK}^wM&tHc|4P1L
zsj@TTqI&xf25w#Qjv~A~z1g0)*~_%Ou&uG2cF-B3ZNCVi+58>mBlDp3u|iLereyAe
zEMvn$!s^!8k_IFKdrQ|k;1W@U>FgdeYh;Q9MJKql@MSpMZS@!ATcfFjDq4h{i76Ll
zI2g=8By<PA(R8Bh!nnXN*ddvG?5scHh4JZ51p>JeZ9N5so)0ry?&*3KN-wpaE(tf~
z;qzc!BCvwNi94NJrc-qx|GF`L>GY1}!*7(Ub_x-mx?*_MNpGZM{c;Xd;$%hNfBWjq
zYI@~`utJ|S)zNE3^H;Cyk$$PrB|8#=o2s*sTVFnd;EhX-Nw2Iq*41_pD6?Yv){h5l
z%%Mn0nCLY)H4<29E8f_xDdV0an`4=&XM=qVDSZ-Fe#@RkT%I>fU}g+X_j$qt;oC4z
zf|}Yh-HYSn7z%k(?ckn5CnjD_+TtiKDx%eF^=@vr1F@AoAfj?@b#*lBl8c9D9aJe?
zPHV#T^>u5j@`8f;DYd88yNf;l6`yCKtE($|bpntXf{rd{!JDY*>sFj4Cr?StLNY!@
zEI5cTd9d1jXe(1AbgmxfJ2dve)!sE;xuEFr>oA>2r2K163(RJ30X8LXHTK_S(N-0R
zx&?K}-U(QJwq|s=dnW-Ws8g-Sjt}a@60{b&)`p`9N;O(L6XB;lCujH&w3XpcRP7gN
zsC+!tRl~pAt|~b@QLA1udl<bl|8+T`fW<EHQIo)*XyC4MZoFP?*p0agw@O)*th_|s
zQObr>vP1(7K@wA7r@+Z=K1qMc4^2wTTi9`Y72=~AP*eCa_T^I2<)T`5TtW>wf=ZR7
zSZ@MBf2Fba+n4;M$(M_w-_W<STlVCfN)b%t5yh@!pSY5hLsY!^qaF6d@Ozt22h?ab
z<s(iGin9MY%Amm>a>Yiuxaf>?F&{hoH_mPhRX%|SX|=W=l4Sj(oFBBf_RU9A5?JJz
z|7>OIY8!YY<iwDjQ>1&hci(9?%8hSsfV16}1H&0{w1GgN9ZSlOc4$-zPEhF<0s**x
z)TF;+4MuKS-Dyi0MPfwT2$WtuEgWg%Ej4#15u()uC?h9<0SXJmP=Z^9;K$FOFEhOB
zHK}6t!16=_uM0y@6cM6GXKi!NK3#c}-|-S;1H{GFJMLlpk{DvO9vU2EGM}X3yW7pi
z1wb@d0|SaBbRwci#+I9xI^MUGEG#UOQ4V%??XfZfz?#$bCBkdLg#Vq3d+g+2(GB2S
z<Yjt{S6HLmSS!OIZQXg1mH1Hej-l`*XDd`#nwmJfG$Mje+x0fyVb<pZ0nDnAg`W)7
zwfK)9O8bQ*(8Q=#P`&wZyM^ET4Z*R~1JhlYZ0Ed?J>tSak$|mSxyW|c9));!$Xacl
z=3{vewFjetf?}9*Dq}9jYC8-kAM+s2F$WdB&Z<wq?d-ZgoX1*X`ugPY2yIzM2YV&7
zb|m00FHx?a?};VUut-?JUGlsjR#vSdg~d*p{p8*;(*2Td=Z(SDUmC@!X&tq9Fxs=;
z*vdU`aF1vB)=DNH><2Z`bI+V6rWL<CA&RV?j5pDtps_2tm>_~<GbQ~uM551RR5Kb&
zX>v8TmBBznkp-518S?h!=|wFo(~qyrdGQVdI0Xjr*6%iIh|WT!i&iexh*and9!|Uk
z7Y|PQj{S13K`qm@D3ayfJMW8I<iKLXfqlXJ_4O=9s^8KMi`Ys&d)Khk_cJd|X68}k
zrBwQ)28k>P=An_lRi)Wwtxls{L{=ESD*m|xE4mXO9HY_3Mk~Vx*hkF4&~;9r*2Z6I
z9(p}IP;61@s`W8SYXFmoA`XUxZ??FzghuS>bMc!YFyy#@E@+A(eY?8^;oKff^ar^r
zwXSeBP*56HJOcLx3=Jb=FUZLL@#|Lr2ul6+`?qkeeEZ+O2-DNkAf#RdG&p3j>__q2
zKCR`Exw15;59cErKpE8CA4`pngA;ssn9SKnL`XQAFGB(g3;T(7D^mZz>&E;!3-g@W
z#;Ll8(mmM4rK^the&4H)5GF?+-Z;p8HFXK+;Aj9m%N9g6+^MR^*GP_Qmg;2K<>6w=
zXU{JEl7H!X597vWD`={<9BKHS>2Xl4xIWCo*AAawJ9T&Z^D?K5g}Fo2+Q&Lhqjn5Z
zK?91*X5%YQsVgakAaHxYg1`n_XoItE<j>i2*Ey2w^a>eV;y-?=1eK#|DnOpWr%1fw
zi9fIdD@0{AKVqtBPG(#lAzT@>^15An8q8_1#x8iK)aKUER>N5w=C?H$d~Oondx<z-
zqhx74P<#TGjS{ZZoj(|thIB-(ON=o~V~c}t_gyN{;PJku=+IZHIMS|x?di?_1u?44
zHTauaVWph@XP2XvkVW~kIPpn2_%=)p=}u4*zxd>17s=Lvnt9sYQmpF0_x0%H?wRGs
z*9yZQsK1eqkT74bKx7w4<0P0yXHa;DCv6B_HYKF#6enH5-8bvaf~=u0Z_A(WEdpzW
z+xH%KdI%j9L#Fy-{$%PO%Lr%6D*?i2z6$-Xapy}PH?u=R@PC3yu+CYlZKclXSsA`<
z9<#qbNh=J?!GC`6{vWvC26c%13q;TQOW&}-x1>ztWV@Y2&XYFZd@X^@fvolrJXT{}
z5I4RH@|WpUrv3pc1MS_rfQ*ccX$CQhLd7tUm<tOBSLgiUzbRO>biM16@1rWFe;EGR
zkK5*_s2g|pPs#i9eB7f%>4(h`f0OQrvZg;%v6?Q!@@}x49?IC6L#&l1X_$SS*lPLT
zMOjN*)n!kE%KBi_>o#fr)*Kh_%h~NO0Z){@7ln#`=-yGYXo>PREA5hV1w~4=q5aII
zYlikOw4WdOhVyYO*q6sT&={kSqren$VQk+$LD5M1p`4I+W3pVi{#l-5dCuzv7%x_K
z4br6;+M1NcXbLe<6NB$(^|zX89YjKS($>7AZfx}#`RK+UTZ@zf#Viaeoy=TgsiV70
z-suhhHLcMSKX9E*(A~%yRaSfPy?`uJVBdcm%)K`P!XwbBteAJ0f<*{YwzNO_#M^It
zn?j0oH1F(h?MDjj9-6A|$?YZfW$G18GRldTaSx)UuToA$tVS{zuFQcYXM@p~A;y`3
zYh2nmUrcznS;ADm)^(c<@wsX$cYaNJrVnk%2KV(HJ-_HGVxP_{n7{isko}oz5)h-_
zVl|Z(B@zZ#4Ekq`)1h{YEV=~3*0f-^VuK2I?NhDtzS;4=Mb<%s?4=oomZac9e#KlL
z-V7#sa{>1=1)8@n4O#FG4-aiu+g=cI*=C51P{(|+{kPeV%@6nAb%9=k+FQP*-#(99
zqj$)Y?ZLA)VCJ=;S@&KN^Hs#rQI>PV^v2A&Ej<Jlycrsqe`-JHVt`{#cO#MGV4J%B
zSA9?$FY*ZW(m%NLX=!&#v=HO8Jsk(#j!kZZ%fmsWBmpyE`B=H}Lj*qC_{dkQ(WdGm
z>=^jlrus*cIm)=3(~*H$@1my@aVI20jE}ghk-f)fQ{ZF92!$n)*$=$%vrm!upvY@{
zS^3^|AyHqtK7P74QhKoWVQP$KqT+AwCmr68=|r#wuV01atWdpf%*-gkKY5b#iNk@R
zDJ<$vQ5u_lvgP8l`bOY%7~B6kp_ND7V8#bJCab<f_>7n>PL%vbr9+hGPGzddmZS7-
zyY@cB{^zlxBHh{L_u>$)qzon5iP=v<-&>q#Jv3(87N%%k^Ho2+R~|wXe;#%o(y|VR
z{8aLr@tzQ{J+U+km^r@g^x{vsF#ba-XMlWx^(^IbBY|QZ=(b;hKLn=yvx$B-%gC={
z65ejHQF<0dZ2>K13AnpK$HP`G<bqC8;OA~%=9u;3s9J8pMb@$fBTBePeAOA~z`p#o
zxbgbrs4uWx7S^+W+s?$vGAiU0siLkFep{T49;-;o)3f*`iv8RleYbyPWYT$WC>1qM
zA`oofz;OES&<l~rfcZlEJ?a1EiBiJmB!BQCi1E_Gv^_SboZU_eSTPuCn>Z)L&x6oQ
z%Mht{0t14Ttt)Hr2--|M%2)M`nVSx!&PG?Iao2VVCh+l#n;NlYY$7|;ZV1#PM?MsG
zl*Gy1+-BNxRmaGxxjE6N&Dw8AQGT4-+cu1fJNH{NH!8{g`-MPHpq7dASMd>FGw0JA
z|Lx}@xuS|ISFq;bf|ZNk=(u7ni{sJs<eRST(!$2_gG^r<t;fd&sqhxdytnDhgKx3)
zTlId5^MgABc3J3D{omfvva1_!Wzq;FT$cP=y)#mh%{Zlv;Le$;;}QDO4sZ`ex*rL6
z)8m9scV}sK21F2-VXM}W{#xSvT7Ju|(KaYsc2&dt&z;zRyVP<e@MF`H2j4j2RBO-k
z%v-_F;2R;)ER8tpUmm!*%D7wcaH8WW(L(b>JE=YM(F7lU`ISz0kq-uYS&L#XxDB$t
z3%EL(nlzvM_%ewG-hdl!ja6ae$Ek#<U7=CULJV6lu5x_N*L}{lq^Y^Vdt?lS#T}hP
znGoS)YJwOftIPe^Z53_;VBAO^R^}h^{*iIfNrqe;<<+B9oa&Znd1{UZ=on~l<cFrZ
zt2Wz|^XJXEa__3)AJ@J`(2h7E3MV(o6UVfShP$jhBYR7}|A~Do^1nKsQ`)IwUs3V>
z$8Eg;!%Y@)@ZgJ#{N!BIoxLOK30dt@t=WIp@P57|s5-ryB+{$t77cxL#XbF-FF$JI
zgn#Bt71jo!V5tY{;(D*a)Soqm^I75F0O<eO5C)1h3jS<6WEWt>`f!cu_C2J5zVmj?
z8!7RCEKrjcpY-Wo7+djMegDcbA>?=Xm=19_FNDz?i^Zjiqvzk;cU4|N)pC(-C(9wE
zN_TCh!N|$r0>zi7yLk%r1yl+45DFE0CLgDWo*%cT$5A(W93tD3S53A$DpJVyI7{h2
zb)vXIzpE%Wv08bh)z{I)&WsOsAJe91tGOb`9L7ED6W<AGPn%zhRUQ}@f2E`8&8h*R
zd<@DU@2NPBS$Pm%7{D*}YRR9-V^-&4v?^UQ>UIri&3}P%fuAqmC4zYe-W({BS0RI{
zcN+r>JWd{Y7uH9p->e1RH!>SaAN7#Nz<2eQ?_`R9@U|+Zgg&qH)^gp|U^ph9Z%9|#
zeddmUVCq1FVzr;s2*SWD%-opI3Vx35&~KB)%0+1L>IDn>AtCCjN~IV80t(VgCpN$E
z`o)v4fWT}a_Acx%K?_wjU3VNi7?#xy`WD+4(KO<NSi%1-T-^E!2UaNG2S85HFMRt`
zdk$zTRucqG9V)xAlpyI_u<dIawu~88rUVelk(79NdTT+`oA8<Lbujf8Hm}tmSW=05
zdPDE;i~Xuc@X#Y3w-e^0^yceRl>A;>=sRL>+k}NDcH=%DF?X4mKH=kBT#JK`ErB^F
zvrhc3SO<?3l7nC}j^LybLHw1mqzwSg`rBbNnnMg$woRFuVfjHW?JfCm205(?$>!E$
zbM1s+U=7eLYT5o{P6^&*A!obX`A5(v8;tP`VOEG=+IG0GzS5;J=}Va>2H2+y^+nqk
zWV%8yo%w;fC7Vh1>B&R$4e`9K{KkFXi`1hE>&z#ob#e^MZG&QG4@>)&o*f?c)_rxX
z7C}cM3ISd}FC2Cm*w@z1N8W~Cs1H5dnS!2ou9HQ{+Xu2C2G;ei`5WLnfmYXs>MTfE
zGbQxmAH<S#wLCX7#Ny=Ro&yhXWpFnf8Bt_xPUx8$5re%agTdLnE?#+R-BBWQc)tKt
zvYiLyK@FnF7bL`O@%QEA*<T(_keKD+!Q1@`oV1Qgoa1&@3P{INPfj2$)p&E6^mZ0z
zyTMPcnCOuCXy`%T56`~2DIX{(F}tbC?lY2v;Z*TTs3gaDZB)9_lwbenjf1kdg=h4Z
zsa^=ZWXHT{EC#t%_5M<8S6A0GAOHVub&!_JXuP(DoG)9NxiHZM!|BOZh+~*k=3YZ~
zeQn<ZUcL<3Z=4~Q)IXW&XPdh_egQmEwRZ2_I0Bo?+5ls5k3q7G!OR>|6MqXF;b5eC
z4zbmvJuNb$wP6048+i<j4C;wr_KZC`{rJ(Dq6FFRGQrR|2mi}5ikUS!Zq?eeUp>de
zEQPQ(5tlfrC7<pLzNo;_7|Pd-%r&2>y*-s(DXeZTDv{Ig9)tCCI89RK<c3+*_W9hz
z(^LKpj_}T<wiANsz8ZM6113hAqOOjb%GpLXB&x9IR>vlAXZ*I}3B=F6F-(VZhpe7|
zXkO@s{kUJh2^f#0Wt?;6&!AO%v~ZD<e?h$Dh#)wKQc_nm^n3SR)5QU|X%Y@+#|XNP
z%*jC6^JHFtd+|x5np$>8!-X)cd(n!_5FHB{>hpq~c5%NQ4BEDVze~^cClfdaz?X!=
zf+jQCJQo2>&{I(#3QM{VmE>ltX{2m;&XhpIFa;Wh(LeqTnzZd5FP2>`6Bwy`<sn#`
zX0I><rH;aAKbLKiif+2NIRG@K^LK*cgC`3zH{~~txZ$b4xD$R$%-nrM(!WbRmVyf?
z`*Sd0-PAW$w^H2ivtlniDACEutqk8OLnp7(UhX5F1Py$G)dmj;`Cu<LKv~v;zEDKm
zMCeOJhjjIbWmd4WS`PoLQC5`m=cda>%hzKmiw#8QV1)OtOD%*phiavFU*{)cImtr*
zD;ZyWOfhqkF*buxi4R|!@xmv*@{Re9q~Nz$`S3-!*$amy+>SOE?R(k~fiM>Aivx<-
zv*EIE{LcC3OBGM-ER6HByI0--X;s=;3W%e9LGtW`CrH>*W|tBw90a8xzW7iN9-Rd4
z4N!<d{79OU!t-lW+f^p+_J=kD=>bY*)aw>0sh&*m_iSho1_@g&E%_omF_RF~*rMjp
zhU*UGCaxk4I>jlTE1kiPWL`#6%2v(dGl8o-fY<tr<?b#d6}A(98KlQ~<acIhek)_e
z?|)+QMwm8kb;KbN`uawamBHONu4jL31}%bfd*7#^5N@G_ytNt+a1A$AJbn~NoV=g*
z=!koMzkZ~J!$$1kNrL0a*Wl9Z%M;Ubux1Xg5Qt=C-VqL3SEQu6ibW8rdfhAbU{9-6
zeCBM7(8Q7lnNE)f)A2IV3Z@b}?SsAgBR9f;1rtEWYCppJmuI`$YT=-05BFHKrhYbW
z+3x@A)Zt}$HqG^{T%KHz`(aZ)0L&}TxrgreQJO>TRoBi|R}RxOO0zCxhy>a4rz16s
zR74^Sak>HN6nTNJy1FJ1XQ$@KFR{8d1T{0JAbsRhA;!fvJ{h{4f#OV2#lLh>z>Uz-
zns=%ALzX5<U7DDR2rQ+rV0=p@JDDiR5%vQ2S55#(@y}+K59w;V5jhzrZbtX2h$dwU
zcm&k4b!>O*o$7<yizhut^}m+!CA7BjHSYcDAzivab7|sjF@ZGgv9nH!SOK4B4pAyj
zi~st2#z%V;aE~Ni+;sfI=c{2yfvmHf9S5ZG9M~&?hKaf^rKctQ(q?`cdhqGC-(*70
zhpc_D#owqxNMRMcxxNQ1veZP+d2wKZ-*dGQMokUgv7vtZQee&lZ-sc0FbCQ-6uGtU
zc2tNbvy88NJ^D5nkir7WwU|lpRda+1U@Dg9@yf3}GNKBFgQ$b<^|b|Unl<cJt@(Xy
zRNn8zt&N=TLl7RZ(gRViHT_zPDbwcN#W!+dKL}a7KDS?0{e?q^Ng%)jEF_xL(;C;$
zf0Hs2w_A!lmQvP1+^qt($mx$>VjIjR0RRfI<DcKU|6=}(WGD~oKDXKzcUPVn1MjBF
zQSAVZ?9&IOm!RES&E}7n)%?)%SC%LgHUVot)a2v{LDIlqJXI2$QgAM1OBMBI=A>#2
z{{1atHV}JPOOI?5H|#kF5ITpM*N51JC#{5o$Y=YH-mV$(4#nO*<m0*RT1QiFhDLI6
z$XZM8lzk7ZbFqFINIKF2CWfQIqoFSKL*5&Rfxd6@u!z3s9ch>3qiFMZk-FyKM{!gY
zNg@?dQAZa5JavO3*?UQNdHv~WV5R$|P4tPJP3|`t4FyXzjG#UAm@K+A?S9(s>MiOD
zH#=OykB$yL1r<&BRm$RPo{e{TurRCoZdI`j`ShThd13<d>VIEE%OpE2XRfc7)J*_j
zbmUBk@~@h`dzImetAvX0&%Gs;Hqk`0+HdkE$86kaOD;<Mdh3YC_&?~6w|WmY5hPS_
z4K=2&r0lJP;nO|q_R66>b^zIdw)uxo=goguS-Tno$r2tq;SI41b*}6j91%*sA)P*W
zl}@NN0`6nSM<9G<*fb3WQ11wUP}uj1%y|xSa`6>?`PSTf>ZsZFRVvZE$(NFnB5Y{4
zh4mS;Mq5Y^y!B0Njl6_Tj@iO9;B6L29*4sdiwTjWSLEI9rGU>GiRVEYmiI8Wu?~T@
zaA)@$ec14_r8m^*CKF+3slUq4RKwxiHvsE_=a|DmKdIq0`T&p&^Ix>i`!>c<Ed6iP
zc~<$v^VGx?#!1A<cQ71(6Uf)3>fTYf&k!%{&^c2x@7?^Rs3$J^9-M(-NaWYpkttjN
zTt&+D-?)w#{ZeEntdIXnsXDrX7>#-!*dVv@@><ktiB#vZm<j%+?S(l?&!wH!ze-Su
zq0N+mA`6OE|JV(;P=qi;cLA|<0`}o|CK7@c&McJD{A9{^_8*EY4s`NEqz6q#Q$pq=
z2*l!CJ?gG7h3O+UU5}T`643WQ%-4f{1s3$12H@NJDTWWDIr5<s5X+#!emDHP=UbT$
z!D~R{G>{|Uuyl)Tmnrwm{&_EDk{%o47^&j*-xU*5AhLiL|GgLgZQ=<0br<i(g3k|Y
z=Y;%uC<<TOowsy(<`ed<`^F}k9hc?gP&Y^`8oABj+R?f~#QLidJ(O{AQ0Wa}$aSsD
z!uZE@ykLMFG)N8CsMnb#I|fsmTmwJNi1fWC)OB_ZA~I9?5VCE(PM--qrz)bHxGW@7
zo$$#x!OVus;@k-YQwL}YpFc`X{w`E}3CO~r%yhyIm*6rBES8gFOJJJYdDdOsa>QY%
z-K$ClsM$~T>Y*zkWABI)XGRcDoPB4+_h%Qaq$<4iXg)@Cd0er*WAQO}#R(cL)Ovef
zsRU_mQV%`fTOSBg*m$Qhy&Ac)!F-kMQ;m?AOwbQXzR#iR^mCc;Ho!dOJ^A@zt{olF
zh+@?gL&3{Ne|3A{e1<3HdI~XcJ*smBqXtl(+-#hEcQuM20dE+Y+gxIY$ooadO$qDR
zC^s;2=^R7CpxtJcQze&c!x`B*LzJ=Wk++~|GaHhQ^g;Nkbgie2C{bFbW{?1R`O;Wu
zlAr+^`kp_2F2X>xW-os_@i>p60QV>|TpE$z+?qD>^1WRROV2dr+UZzDk~}D;c~7%{
z(!c$C)|&gO(6>00MCM@r43%v_zp_vc>R9(&^#{yo*aHT4F3&rX4IX*BkSj8m2t{87
z6J-Bn_fBUkEKpu=NMAo)95Ino%jVwYZiz<p)|e&%=og^R%5JRGd>mbBO-~6yleD@#
z60Hl4Z9rc26h<XG)oV~`K!sCxj8J^p#FNJ>kTi)HWS`ix;pEKJJO7%DNhu`S<0M*g
z7CUx+Gl_tXvYZf5m72xYB`d>kK_9bS9&@qqMeuXY!iGGeok-4tx5m5JU(t3e#GO@w
z-J#CpEvbPqgf`LVy~CgHDY`Ts|4kjm_&1;fj3+2jZv(qUldX*aw0<*W+61AEJc$OE
z54yN`Y){UAiwzn#Lf2Xz)H0nj<C7^P#FpX2shE(Q>HuqRV=~TsIME5<VEB2@3;dPd
z*Z1jo5!~tlZ1^H%@#~Pp7r46?)I3zB882BW$YYciSx!g>ip)V{g@}7U#N3t3&KJ`T
z26|}!^qW#|ci(WCoE^Oe%fGSUd5+(n{qw;AC4dv56TP9<$2%WanDi3}c_U9UuE8rj
zD)3(1cRrrZ#QIn0`f>sSH<IU#oCnT&?$e!!l)QJ9_R4#5b89fL>R-fvK3gV<2jjn`
zttX&QEPc2LNo0<f3aB`we~W|H7zqG>qP;nr@do?R)IXBSTh4En`skV)vL?ik$sAk=
zz^OOT1(oB!FP<DgB(D{gny@Xm>CCqKIf5a8RhWPBSdy-wZH;a&QegWsMINd_@a8FO
zplAdo2~bPx=Y?Ln?i#C=!Nru&Qz3(V^72*U_H!_}t(yKFxB;%mX$A+&B96AH=KLjK
zf>78c&Zt8hG6|s^5fCJlrc*aBmY^|nzC~!bF-pYo;ST5DsX2IQ&ncb%PPfCQ_s0*4
zWg~6c7t8oN(JDPRl@4~iOrUXaTv@?c@K!mm_Ol0l%{!`R+m}1lyJfU7FcTt+cBd=S
zJoak65Y73ie!`Vuq*0!XtJ5FSu0}`@R)t=ST{F;Sz7{Uq8VMDB>3OOv^AUe{q9YD{
zFA6AcKFM6tWdq^WUVYNlmV`2V9({Bdrp7So=WuxY)WdI;W_C0!3DY<z+w@g0!$Ge-
zU;;;`*a_Q|(_}vw5;9q|-7`zz^9oF@5m?Xf&DrPN#0%iA1o>e%?i>F-!lU~NwOpCk
z?fg<))6d(Eo41!JI$g@Nz<uZ!8q~sy1au1il4INdF7xjNDnV;N9fQ7WHkAI29KD^q
zyvogJ3c2j<SJ6rQKVfudfXoNnVPNrGE^-tn=dX=OxQB}Kv8T5emg)|uaEKI^+Jr%6
zQR|oz69}|Sm%W%@hAmoFQX6uWpnpq}vuj1r4PfU3KW*#Ppm>B%@gjF<bhJ(6gn3Rh
z`~N?W4&2-t;-@WR=QA6&IQ~G@KJpc6ENnlP4S}VfN&jL!S?Szkq<c8?Y}$kd2a{Gy
zb+fhR4Q`&ytNYR)l4o7EI)|Q?Fz$`&9Ind%k7oLJPNC}X5xt1YQID~#q#X>1s!ks(
z*BVLof-~`d$Kgzr#}c(9E~~K37Gn7Ja;CQnswCG|*Kk~=w0;M&r<nXq4aiyRi&*Y8
zZF*(6z2Bf$YUC<9fvMtr3W_Q=6Mzay98Zjp?k|{}%^DOfNL=V1YD)x{eJXG)3C6zW
z^W!ge6NBm2#&&U|)AQ`1S&Mhkf&v`|>1f{<u&CCyDPRhWpSaUk54r325VcZEh`!VP
zWGVl`JT6IvA?r?}*!=AH7!mhzOHP!MV-agvmZ96>+POPj21&S8dVQ%UIlNLhNa@^9
zj2aiF?J|XBP(9P_HI0?gJ7wLoM%9mMJ{WlFj?%{fSUp0$hSIYg<6Wy|BQlfP0WN>A
zQUSwcF5PSe=!u(4&5sL?2#*4@(a~3$;^y9B*~4arq}^a7fc`|z{2VY<oqT$FUFnip
zL9ni>LY52`5!fflAt8>rpdSg#hOgg=6}jPO5Gn5E!(9z~HG<OK^t9}<wAi`4h?a~U
zoRu6j70wby%Y51U<^3ibp|8ysz>Nb*TG!UcGY<{$EWzw9<rt@%b(N>&x9xRz(GE67
zV^S9D2`1^uxViBhc80~$1-#TD@BTMtjDNRcE(Gd9E>m@{i#4*;)Aa&tB9<>ECdMIS
z{8KJ`0*;h$AKZ{#5Xy^`)@xRVH!6qn<)+*IghEw@I|i>tMZ#g+IH?gI?!E}}`RJ&O
zF?ptj;ZrWpy1khA{h7QHoyZr%VSs6E!GOy%_i1QmArMSny%djY#hKyaBI0+vYn`FZ
zqSq>PN~(VZumS#-<cZ=0DmwTmTwOQ5Do%yL`5g`+S6Z2I1>JLV9NZROMqVg9vwE+)
z6Nk~7J8BLs$rZv{3MK}52=E60!gAs!rOd!2udO%MeJwut9$Xbl12}WygDB@+04T@;
z_rlm~ndsedG97}@eIQ{7n;|*HTq+0WzO&>Ll!WOl3UQ>K>{ULx0tK<lB!rPt+64pl
z1(>mz4kttaOA(?@Pu06TG4$d-jX1(cgx5k7udpEz|8J$cH<LRg)JGcvK)gA+?4$>Q
zKUXWGe@{nHHck+c%PR~3XC#^32>saDf>uL$XdNhC!G=6o5;h#Ee-=_>S0?-h1wBFA
z_s%(eXtF*lnMaFg=xkfnN3@xbkVJYv5Hzq4O`j&;Zi~M;b&-3<CrIte;6~pIb}$pG
ze=hbp9X<|6UOstYE&X<>mW?p|+ukdW^KFY`JqzQIq43Vt6MZtCZE-tFXnka-Z!8V(
zXDIBt2>B98w9tG6)wpt{(BrY>M`eSfBaw>%^c4Q(<&4H7X^}vJwciv}U(Yp|#Eu9;
zGuwgAB167%;^qHj5FT~hJg-M=1dEvspMaQ_*RyVVZY|Bc4hZI$)z5RzfP_Hmb`5r#
zee_Y2A#Szi{_*s&#8=#@;pae(tKk=uhqEr2i^~dgTTe%Mf#du2x8S2NwQbljn6YPm
zS}>?j%jQlHV4Zw*fF0B}ubAnyqz~w)wJGK$vFmshI%a_SmYMFRj6pkhP8aPqDBS(P
zed@&2Ssr~MB7^)L!yE!o({w)BVhkXqs0Br*F}D9U;dF_a?_7cKPgpTNRC24^Pf9}Q
z>iiarLn5@(93`(j+#FpZ_VzSw?!&Vi*DCc|fPgOMDRE_m06@zP%g*5PR~|v!hNU;3
zac@xT===0o+Fj7aN2D^rq4odZ%+lr7dR6?jUVyxFbG|+1D6Jxd1jf9WrhgllUgerW
zA&`v=m73-JvC}(eEewP<Bv6j?Z_V4%MR3YI((waw{dwD086e~sH75R$+2SRrw92P!
zdws9e1nut08~VpF@R_+u>bWbA+W`m-XrV(ld(!QzbH7|>NIWLHQbqh7mcf7WUw+#|
zbq1<uS4{(x6CUFr`zGgB5@=x}Ti*AnTcWp6cnyTB6I|#V0AZf^oo+hLA9uJeA!S2a
zaBN@_20+kNL$k@<4a^#&(tq%pnX&+_pPZi72eF>J3w4Zl*C)|67Bg8TC3Yl+_}L{T
zNX07U;y}@dj)vwtl+2l(m-ohxv~9G)fH7L~zu9$i*(yY;N3u0#f@}LsujOzWfwZj?
ze@RRjc0per^sfRi6(_>jJ)TbQ?99aQqfhkB0<2I24ccK&sC>$Kc@s8vg74Pi#+LME
zKYea3QpW-WHF~T!SCqxvJ|jb!U`zm@E7+aaV!u+lTBHarr;b*si6Do+xhp^}nfmat
zy!|jv?#qx(eR31k)>MsZ<J0?TLYZ-{G97A-hX-51dbK*~AA#!HGAfc>Y6Y!mEu1ZD
z3_nZhn=4)d9a3cIO1kOk^|w^Os^8ofw!4A@!?0HNU5d7yll5-BGnAjXC>XQn!oDZU
z=n=~0m1^O~XuG+K0=%mb2Ve}DKfaE&R?*iAE}{YeuEyC+l>V<QgNSeN9Ycd0Jd%i+
zys}dG^>b6meG6CE#tZ?<)<7=CN1{W2uMwo4<|paWC%mcgE*Jvrz^w4>#NCAYNnxlS
z;ZM>0n`jkHHUAlJ-e~w0qRzm<Ajlf8I_~;?;)_!JdTMh?klT)$1j_DxD#;=Y%+B6d
zQ6WVj4nk;6k^WqRT`aUT+&lcPJZdkhAU<fVs?ZB<(`aJ9H92J7AI;|Sq*VT#&NjP1
zzIO@@{jn7Q=@q))kZ3LNy+@cb%jr<~SaA{GxQZ-yfWh_~QiZo%L65j-L+(^UEguF>
z%}t3M^}&kuZW2EF?d%FtB5#<c2DSOfW~tpc;7|E3ClzLEEN}vR7EHv%#mQM%Fi-^_
zn1BN3J6Idm+a1k3y~5ThQz8fr4drq@C3QZW{qA*tm07fJ>;kh^TdRf2cM2o${BRE8
zLH%Dpv{&v#{quC*<_y1=kG%;o4R_eO6oxeB3lr}kCgZcZV1kaUw*Qg|E07i)P1k}+
z4XU#sL8WGMJtWgQf@&v*#*sCt;&cJqiF9!6i&L8}CMEy9=L||0-GQ>ZX{+-QZ}(M$
zR2cZnHb+gqmoZ7a6&kqxXG?>hkWQ@<Vdj90BBP>7ygI66offe8f$7BiGdWvSRg$!o
zrdA(^Ine%kF|}a<MG|7zn~wX*+OO|1A}(AMA^+Mk2j&V&<UnzmMu;<026Ax{6l&eu
zm;?2tRTyiAsk_$uN8Be%SU}nUTs8s!aBMyoZI2%OvR~*pfpBvf`b*7%y3+X!N5E(Q
zqJ6<qhHDQegm&87K;Y^z^LmkHn&Bh%u|arU?|pWazZ;^;3>QZpf~DrWpuzzeQla;r
z=*gPCP!)%}cg6g5tkX&~r%Mk7Bxfz^7%(l=i-;o;hc6wZvEPp;wT8d8{DuXH%$DK}
zpsuN(S%GI2;YZ)@)a90we2cMXin6F#XlH4IL!>Ms>No*@G}S*(hW?lnja*4<rFQ&)
z11Qe^Yu(teFaLJCJW9y&&)vN+RI^Hq9ttIbI+f1<mU4pjho2lH6kpkW4?q{+!bR3}
zhktN8NtI%-olF*Df)rtw2jJA?3eud>F)?2Wp@C;DtELtTd{y+oav=!DR8fI_{W|k=
zH1Y0y4IR)BgVh!!R#sMA0s;{<SVd*!tehO!#Kgoel9HWU1Mzmpi`*$ZP6%MFv~F0#
zC93g<o2@O=ve&g15{W<)dkgaaK)9S&KnlJ-l#0U^<p!V}RSv}_;7H3>7LB<4yg@E8
z>6ooSlITfi>J4CoXe)s!-98Q47ioBNSg5GSux6~mGM<M+72pn@PW{pm?7@&NoU_3L
z1K{P;v9y6%ho?8%L>s}3*E;Q$7D>DaceBa*V9DcVHr(X|q5<d<o2bjrKvn4-Js`_V
z1q3@#+Irw;g*d&W7ke=*V691;D}*=dse|v=L-ksk>=uQ9x=@~gjj>o6syISA+6D^L
z!hCu78+ryJk|#H{1K;0ZDFQrOze1s7`em`;kdOij_;WduAX*^y=pI~uW3%e=e#SxK
z?9S%wW=FkDd4uTxKvSTopswTO?8@`$-0pYVSJ4C(bHS7cGWxfNZM+@Zg|}NbD>gfJ
zyY^AaJNs+q-><1IL>W1Yw`o!fL}DZ;!L%MsWHPCvV;Ta9WHt_P)wlmaa5z}c!Y3yQ
z;K+dJ=EC`}n*V8pV~&aHe+33lrwiA}^BGzf{a6E@w@h{EDph4q^SGc<g5unW(_N&r
za{nJ$-yP3&`?jyho=G-QD3TG$&Q{6F%3i6+-aE?5PC`aSWv?VNn-67YWsk_nO7{Al
zm+t#{zTfBfdEKx3{-ZlS@Aq|G=XIXPc^t>NPpB6#EeLf~2uY~4oTN6ic#J({-_HJ|
z$3nTn`$m&nmd9m#3_srJtZWAt6<%=L{xw`i$?UVm5&^nmD<cCMF>Y=W*ausSC1Z5n
zdqWj3`VFEeaE|ToZel`ts)G5<nue#ful>E)R-bG1jW1yLlP5kWqrWZFO1jxFerZ1L
zhqX0V1UqUj2}Zb2RN1(Jgo#nJ$0eTb3nDijNiSU4i^+d>KULLH3kL-ML4nMR&t)uC
zFZ-w`l6CX412i~MSx}mucos1=IZg@$VSvG4@+FdkPHYKiI$oF0zE-PUdjISO+m_P}
zTfoJF19>i4oHpwkq(RhRGr4-k^hl$Q#_n|vHT4TsUZ0_Y!lU#w>9#hQ&)g?kFZ2VH
zrqMEm-jipzGx7Cy=JVqS4QPLR+EUOE8Y701JYrW`wK*h9;87WG*}8H8A-Fhs5g5KS
z0+=>Uoho5F5())M)wEM|3_LM63SL)Jr_hX^un5fcSkU4^V8_R+#L#|V5lL8Yj%7fA
zKvg0weh3aE*pGna(QBzm2W=qOeosLvki@I#M2+iQXsgkQ;;@@5UMv23Lk)0(M$bJ8
zZU}Juo<a7KBa=+|Bt`LaZxGn5G{w+KVt`v3T++1uC{1p`b>oWQa|9*<y@G2{HKX*y
z>C4)(h@1#{XO)?ehsg;f?730p)?AsLCVb@*>GTiL`ISGD-?p*I#2)z`Y~vLc7RF2Z
zO0>2e`d%m<y7!C7|8+03bEKe<5C+`X$tfux1HGKz{4XTmgM-4<QlgoT&$(m3Z7W<h
zJ&n5E-}E|OXH>o~{#sEV8I%uLsqbAUS74OIQ_ZlHFNfr&^WD>Qdd#wB$E^3u$Lm^X
zV5{v<6D$AlQ0dk7EhgSx*5KThy#qrXaUPv?6HV-dQ)3;(vQg>9v+?n7kBfa+J!dK4
zbn4h6BjtA{OIi2BSsWeD3PYx8(p-rRBuT!UinKv*U!!J!^<KO4S!{PtYmT>Yr>~B?
zVv1qb=<-tVXBCpV?9E-ES;)%vNP~817CId+M&{BOQW>DDi2uCIxA(1nYpK)uzra$X
zMErz)1Z2K%d}G&5e(Ad_AN|cfjA^U3!YYI>-GJ8RHP^bTd$^Fd*#@?TEOlQqT6`&u
z^5QuP1G~LOPms%;0o_1HlyG#!3C~O;z8UC4j2bv{q;4$aZ_xw(oPMHWYB#~pgMvTb
zEH%n(df1q_yOTWo?T6Dv+VW>NJ`spLI58dnY41r>6jY>+522ri|0o5gZ2`!(#?xP7
zo4<Ui>ySJQb6=aO{r<ho@DW2PEo=3D$KrgGsHmvfXr=oWjJ1l2jSZmU(mDfk7~+gh
z{w;4RqVH$`lA&^jrNqZI)<`+stl?)+;i0J(mxk6DK-m*O>v+Jh1~+kmTU2BOr)8pu
zX#Ck>1@CKOEa}A3*kvJ@rMWN&IupiuL!l9%$|<T7E8Lu`g*ewgi-DA`^3YfX$WOl6
zPw2y__;Zq|!<O1lr<y?zv-a}P<ZE?G+|xM65cZs2()ghA{4dM9VE{$QRJVs8GO6wI
zcB^=Iij{n%1GYX?#J8w=Z+I0h69)P?C@w5{8Zfj0c#f~tO5z{+`3{k`A{<#0)>jm!
z&wlq-NiO1}`rOr-7`PpTv~sZ|5h!j0ngSRTuah&stzD%AG?T0gipJi26)t_IIXGSj
z6s<xB8*-m`M*Mp>r)Dn3WYm9vzvO+OY`J;;T#fh$LjnA8M3o&G6Z1^AwjW<=1bYK?
zXY|P*4g81@2l9o}6f}#Es0+IU09me#K1uNqW@&?7@pKWkePp1f$jxj^I0^#Leux4B
zE`pI>=KBP&zrJM7gsTTT2n)+YSa7tnlblRq>kO0EiWJs0Y3bw2DPkc|tFrR(QDkOj
zp6BG0cHeHJWn*V&FZl4mzoJ64va+%>Tbl<57q<=*<ag{Zsy_3&?YI4RHcg$<E2k;3
zhgSx%bNg>?Bw}j=5sixucyN!HXVy3S)a0y(K=6^Is9OEwHH{S|wwFXPS)dbDbw)Z7
zbYXzzh9lYdeqwQiTEJx^&&=BN5kQtt1MfLCb`M}#QRA5XCgX@oN<a9y1T<D1pcLfV
zAC$09Xu}o%JOAg(T8lt9W*II+O~=O*HS{fvgTQE2ZGM-Lmpf+gJAdoq7O7qD9qzv@
zSP=WMnOjn{AGbNElo0>G^&YRHXo`)3BAPT{X8>z=!Imd0qqdR0amBT;$6GD;ZK`gF
zEtz5c@3RCMHpY!CSW(y6;-SByLb?e}tsx`7cQKzqut*Ax$LMBT<6<snb9I8#q!32y
zy-iX(zYg`uS`X???M3V91`Pa2qzqYXBLOlC<L$>I6BgFpUwSFj7=^6NuHK=UkhkAo
zVClNPoyyfsK_M6fdG+eni*Iz=rAdr4Awrm)g}cp_5dgxDbr9C1B`O0nGM{J~3st<W
zGjWa*v4G*@4aZM4h*xn%^P@(YthI9DR>RWN6gJq&D1jw@@lBSaRhzf#Cj7!DF1^-z
zFk}s4M!*VI4;oTvvZh|IQt#%Chk9$n{py({X%XZ(_x`mDYUV@56>XsYu6GNIj0^5a
ztV>!uT>;9h(Z)9lMB#eDe63EY_q+K$)vV&`OVH37yA8}^+O+T-&py<ld3s%vK|!l<
zXZdia`G=m#(Bv07vC4^a5eDDH_>6Ug@Vo>+^C*!FHQZ&flzJcNK>X`r(7vV5B_tHP
zyKms6j!Ec|t}MIE1VuwJ9Z%d1$Wf@7sxj0r*sUdB%R#j513*xQB_@kROM&f&Yw+u`
zGx!6ZEJUcD_#?e2!G-5}X9{v;fjs@5)6Vt+n81KHC@%)xLoRbaW`uD0)+~W!+{XI;
znQji!)AC660p0gBEw-AW_EY@gq`?%<8vjsUX|;Xtmc+}t2ympnSOZ=c>d2cld%>*^
zL1--3LTIN|^-_Jw<s<DGNTp>K;{nv;eEK{Uv*KDt%U{B8(wsEw@^JkhvSX_H+E>1H
zg+*W$+MLohJ(#BvJ9K1t%2iz5HLL*}w&-&lJ#j*{=o>tlnQ-$nF(BX#(lVol{nhtw
z!h@2(IyvSYg;qOB-M!l$*^8efa}u$kBv!G7w%tFizZ`)l52{6g3$XM#?D`n9Ew8x*
z$1o*m65ysP3HzfxM#UwZ&(QDKs9kuURG3ZEZ6p0^*x^QeAIq9&x$t;?I_`w;zt`Fg
zbgfOHYfZy+N=wEv^x4`MRKEhTDpT`_*xB*^Pt&J&6N^C6#eg_wfjY28KeU5(M&6<F
zauuGBdnq&rWksN@7{7b(X*;O}aRTLBAvup#@emLMfJiDv4sgGQsB-#L3$924&+PJu
z1w0CLf2&fusLectg?0+X4Y&&*sL{aBv0TYx{adJJ#wKY8(o(4Dy575ePwydJgkf7_
z5M*Vl)VmfrONVg(s%#iGmEh%vMno$<+T{4tAi$0N%_)73g=uSMT(FBMVkXc5m^4C5
z3lndrzR)QE#S-*E#4E-1{k!=lV~rQ%&zr}?F@(mWhi83pXqqtKS4}{>JD;!RYc>bD
zssNJ=>oHJPF%sdqm1Kt2_52QWb$z1YKsxVJNv7ZO*p?`%NGr2$r`g<VW0MLJT+4MN
zgs$|cX2vb)v?7295me5IBuR~#cO3}3(HeAvZFc?H{4LEpX$^2wBBrpKfnIguuJ3iW
znj||OF~QW(!Ir$mTRcKsId{kbH*J(G0T|X_dwH1@4j!Yn0$2l#`G2<VvB_5>yqc^Y
z3@uiXyx`!$A|TSP{OYhLZ@4_8!{3a@6F%R^zRW?>{C3O1lDYZa$vv0%2bL7`e_V=y
zzwm}}vWQ;oc0ro(-*?>`^IR_wt`)(+)_sGEyw%mzbmdLYp)U+@(P}r(J+<!Ek+u_3
zH9?+qqzS%P3#nyhTx(mp@i#-Ad?BdT<YDk;y_@%3NN42TJiW+}+I~awGS1qL=Yn-}
zrLqy372?VV<gCUy5KkXzashMt*aC0#0}yX1eq<h4%+zwVjYSJ^vXE{1?vsrUyGsE_
zHa6s_ZzlPQra20}YBUonxm-8_sEYk{5|G8;q=sLb5Tu;FuU$--fajrW26*yHlg=S2
zl?MKwk4^LLwY<A3_Z-RSDIwMO&wsypw;39Sis6??XHu7GO?#A<&azr&Sr=wPPQ?sb
z-nPKjh332JURU0cvE!BsUi}nVK?U2A#8|=cZ;zl{Mq2s8dxd`l%YcMIGz4`ab{Hfz
zKYgCcs2i3wP1!^!H%4;_RV0$F6jv;Wz?<{*)t7DPl07m%Xn8vtPcyI*aIX8;I^G^d
zoUR+@a1F%vNC8Ge+pJsoA;RoYJ|pO7@V;J2Rv+@#Wu(7-0#X)ffWoF-L$HkBR;LX{
zyj@Lrcis*+hh+JH0qN-uBSQz?+{L3U(|}%jikoRXPaG#3D{HsRF{VBco~fqg95%uG
zWgbbosQV^sCJt^dgb%l0T^y|+c*(iaJ-g&qkZ)D-Yd$Yrm053Z|Ie8mi5E|Rm4Ux(
z9!lVCuw51nuMQh@GiOw+epNp=uw{NcBio~ol&tTJIvx-w@X!wubVU3bd=Ie??o5f4
z9wLWK96d8&EzT{N1QZiV8=EvhQp=9?tmxCl-TwhnV8<H`GBck?atmyvnsXEOYYne#
zt%XQ!&F0@kqNb@6S%2yXTXksz65cSmJ#gCeFlhhChJSo?)R`hXA|$94lz&ARym_ng
ztzz1xTtDz>9)-9LN1-Sqt#B!WU!C7;D~rF*+sUsxHSLmZG!PAS2wv?2NH6;*UOfo;
z@M6{Ut^hZ4`Bznj%UGEg0j{u>aFJ9v09Xjc`0A~#rj^?>&nHtcI2Ft1GYfvIKh_`}
zR{P4Y)!g~DjgTXTXnrwNskwgG$FQ6yixz?t6dhBL{HW@Tu6)r4Xo#zovLkF3b_(vP
zB<}Fl-?K}(2DtDe5>NiJPP=m7hqDX|<h?&`>C8n1XS9Rb7`30n%^+tCO%L4t6dL8u
z2rqt<`4>T%l7G0`1=n9hTSDeGfc2<7{+AKKidEB_a}t=C(l(iH&H4{5s-cw!EgcLb
zqsSCPv88`_#LG;BkkqK9Rw2gYjZu1&vk-_2ZI>z^31VVoG0ksJqCBo?!{tFw;q}Wh
zWns16;=F<b*nkzd9d_JIHS_<RQ|RSFo^$Iv!ArWC2UbsQlxIqv(%z0%Ii9#h_L3P$
z_Wiz*nBrgRxlOwR36s8LTPF9lgV>WJp-u0^)ryg~?iAzXWA;Du@w=V0rH?SuQbTO-
zwe&I~<Ej+T!}BQB{bY9+^75XP6Q18QMhvxb8v<HQ|2PzNTT99I*yI3=eYk%*-@f&B
zeZ@GiAVjhhOArB)b_{z0yfNlq$!c(E()8BTLfCekW@F{%`<EJ4>Xn8Al`d<^M^Fa8
zP8aNJCF{FdB-w*G?U}+53|-1T8XUYwAN>iT6JD2OrvglWp?J%#5q*#z?JTeAFeh9T
zk!Oz`&wk*IbexYdXDzc|kS;x`drIf!JL~b;o9vHEEi~I!k6#e{f`}hdYgTe6pZ6bU
z4=;ThucqS%!r+D_#{}JQA+=_3TB*{xT0>7LT;T{up_uB;=%=j><NeJv0Qr!t?ox!c
z)roVUJ)buVfu1mxMvp+{$xy(dct-n4K&W>n_EVcGqFv8LrsTcg<&Ofrx~8oW0GsGo
zp0kQzn+!h!;9Sh9#XMAYi4*#x70;Dc#}O?pH{xo_-l`Q9g4YZm#^J_RVDNS_@RMzP
z==0dVN*TZaJ!T9rG3|=3*x2orgV1mJ>shcD`F2$qUdFubPq-_l)d{MSYXNN}?id@Q
z;)$5Yt@nLg25l5ES6gi_;bJmi4c$MxpWI`kE#<#;e9`~5X7LR#&lS2rZx5Y?sBIAc
z6doqjBFqPh7`Vmauc=ry%O3AjBk`*GCPaNX6cyt~jl95Cwv)!dL_lczZT}Hke&7J5
zTD*z6*@YPahnv|3uaVO-vWs<JeBp+Y^fl^tVdm#Zt-wUg{PTo5Uh)(mxs;Z22MZN)
zTl$<7>KskX>QI2|hejh6(mFg}!)_W<yhlBIgi3l$A@2K?!09tENiAK|2eM0?uXqqQ
zUuG%>0_jRy7}hY?$rtA=ZO@$~00RgDpesk;RzJZ(bXMo3fEeu)MMht0qEcPjv!4r6
z;aqH=#2F%@WA>eOxB~auv5>O6y2%~=wZsUetZsL?l@$j^L-$qY8wN1b0kegI(W>SK
zFF?q;-EW1XTmgJICNcE{8JZ7#s<DT?$ku8b4B>X`lX8ZK>*;#p_Wcb18=nj@5a{@y
zxyCwyBgERR+z^dZxr>ntH#4N`CAo>`+N~xh+w<bzqtGLjtin@=lASE_e-(?bJ!);O
zp?ABwvBd<(hoEEMOUwGvstbT{6XVklp-UqqU1^OqtY5X)xOfi<%DONYfJg?8L-qR%
zi5eVgdKP9`kHRxki@(0Uf+`89GN6H!RkCdQ2B()bT>SRh*VVo}BJkjNq4ksR^7E;d
ziK_Ua4kBEB7hYIU%LVDr)<7&xJNf&M1Tj@?R8X9ca1ahR`xn~gb3kjo%*5195a^J{
zgw~Y%?ADmo<Y6p?_yys25R>z!o(5-d?;LbGdzJot*2IVUHFDVAP8x8woGTOmX6HS}
zl5cS}gh2+tvWAo!mWFmQGBYI${ohG_#Rc)9{H9|_Owx`3w0}G|nOYTC!#{l@ta!~2
zz`+D<(xq-i-}P#RY|WGU7s@Z6(}LxJ0?T;oUJ!srk^}uzcqz1D@M~$%4z=U}ccY*;
z4mY#y#T6=Im7Q`yM8R|DL3%q*0W?TtD+P7Tkz@@q^%cBFP#HHTbo=--cS1b6LQaU}
z1qKhMLZOO&JJ8%AuU1R@H)T`9a|Bcru%0(>KWI6%0?aAfO5h(YwKgIv1n>1T!BMl#
zaBKS_5G3R<gvSf!by$JynELTXrE-A&Q$QB@-e$`b^+Im029RM*_rSHqb8m~bg=`%N
z{U3Q#7r{cgy`8Z#e3z;W01}d8_<!!f>w>MCu}Jv)1GM%0)Y*E5sAJ=UAnDIljV%Rb
z={T?=&SsZp)fT=dq&1}JB`J~?XQ3kSLU+9&$88y5P_~2GF=MnU{;gLoT^YF=W_`LU
z8w_=*!p#sx3TQFq7a$UleEREs#5#XXnVs5rgRqTd4f;=s;^saTepMS=3z<?tCmkKv
zxy9lrQ+5j2SO}%-Q35GOptJU&=(?95PKf(7vJwEP5xnaX-e*uS9t^AxVEv6X7cR>P
zdz9*Aj8G>;<xg2Q+>O(ij1K20blSfTXdW;lYsOT_njkAb`rj4P8}i=Ba?E#la<O*v
zKotifD**s3?|G5IpJ!bE_X?grV;b|D&s9_4hQO1O<++(h%=hp<=|GWR7JW=2et-yC
zc{Sg=GrA&$_@hjFD~*Z&6x;gE2M3Gfd7?PUlO1bZ_dDjQo3KLmloEkMz2ChcW93rn
zAN+>?=U_i|)OkJZwTZmZ{P;}IvQyqoFgvnRA`JBPpz%$r=o)v^yL|#FQQ&pdHh$5;
zqe7V7$OQy9i9wT=HefLKl7cK_5Lg)fTISkvIMfWE0%j2hl8I(;vRc}@#X)BSEipE*
zaxoxcr4xOEkWj#GYE(x*u&fP{EIAZaY(GCEl*^1L9P{ohvt!@$PH>%NosY-e9;}DH
zKbuVKY2iY+lD5{PeYWna+k$1;6-N}S-bPA$!+H2{GYb^;c~h1LM(Q@S;QliKf*aKw
z6U}@PK%5Ge0Sg=&I&qSqA|A3twS~@)occ;9D_&A|9a7>PW8QbBHDWGh7m;T@Ch>BB
z2=t3iI<yqULjUHJ#g0INk1r9So9+d5{pOA+P#()k68Z~y`D-BQF<Y-RzN&kll<nnt
zbDhhQG!w}^uB~TTQ!C7Jq68ll<8x7BR|>u1aJje;S;f@HtxAh7*z7Yr^onv0-{B{h
zbK_piRS#GuGr)W{VyGqHZeO;r4g*g1%G~o&fN5r5OQTA`9@>UZF1)uZctoP599he+
zlj9(m3etq~S(*@>EjM*rB?6azle@A#O#c&<BN(e+3>i0jX^?RsM266P!AF*15YZkO
z!a|PCS0pBKnj>WG4eQqrloV=fG;=>Q)^&i_Pdo4TxP!PhOqHy;K^<Nwq)B&ZXn}{P
zzF2jh5hR`I)F#6vm=$nt0-gH<2Ci7FP+T{&<=c;63Uo!F1%DUpEO-x!oZ}$r9s|~Z
zB-3?r<O>13negj=gZbx`WY5XxKc1PGn9wP*AmQZX{FgSBdgbS<?6UZ3cHCIONuhoW
zH2Af@?bcm;&&*5)4R7>X5ZB|<3;B{yo`Wxqfi{ivK<CQy;NX{-f~AO5gX^~az?Bze
zW_$!nJM&EyMD4xQqql#rIHSP}-@kM6XFia4_a!fx!XuEsQX3xMf}|5R*MZx*k7WG$
zAKZq_ut;iVPu#MNxR-8dT_F6D<*ZLaF-HlVUAS*_wpCJRX)<xPyeyfOX1_uyDM<FY
z%HbpxRRBzH6Wjg~6VGRc3x3K^9zA;0cPaGf2q-QyRy}*nwqPpgVn+|#5ncpIS;~`E
zTcMnavw=>|*Ob&H-kv1*%XR4+yh}mhj1O!6#A9gki6WjloJ2#0UNDasK64ij)sDx>
z=gmT?Ed5Cn<YE<Dc!JV%AA}}pljDFII`nYgg{j4Tnj}KI`5h|UUJ(E8N+jq<*W4jn
zUl#+GX$%o>Dn)h|h1xC){&B-Yai#_3a{zo?XWKHhWESfjk_(EZQ1k3(&wG7}ntBSu
z>?;dh+2h#Qt5cDZ(7=6L{1t6Vs0MH1@!T`B$7WfBf^Hpn<_cgp0ZJ~gtAP83Vs7B}
z+47zt_E@a79ruZvA{k!c=42N1^~>2BzT%#YF2PkJ*lzQYfEU}*tFN@ZiZhe*w2#xA
zY0~;Jl<C1h0Rtu`d~c7ow!t3}RFbyTF+F?BxxtEuz<4(tu{ShpMy#iWId90}QZ3Rq
zwbj5>@5dO|Z3wDhx99dFT48*0(jUA<CL2O1bM;wZX`%E#81UsIy4jyBi?pRkR>*&+
z8VYmSo^r@ZRz@^@-4X~2H1P2YG);!;uqyfbm|ryE<{B`;-<_j{`rOI&gK7njx_RvL
z_od%!#qfX?<97!XYB3s}<7TZS)1dZId3c8bEUGNpw20tusof;p&>HI%L&rBYLLQRd
zFA<IdG_kBA^6>o=X}v*4TSUrrHZ?*z|Mm%cdwZQv4{0xQbHn;zft(kZK+j`iO<=4I
z4X-hl$Ddh0@Pa2iae~B1>1ZKXE%Ohmq5^`hB*~&)tpr;$3G=P_6SNhbCn?mhah*QY
z-WHE_<5CSNGtiiwG#(rTJR1NIBE<3+2a`}$cwH;83`%<kSHXa!sl|M8i6COdu>oCL
z5C-y^q+t=PZvfTl_4f83*5}&&^QCmGmfVocB9d}jS7xdiI3J+oZFAiiKoQ+QL%cT6
zjb9=8)dzH7O{`Pl!VRW`P731;rS2wkbpb?f^O@<$<nm6D#l*@)eeRtr@Ri_8wJ!?$
zVw2fk4WWB?M#KW_2qoO2j`j>ZsHG1WTGXq~1yF$zg>5$<Kq0kXzXRFK9?^wwUkK0@
zZwEFfH129ZjcxZqz_PmxDFbzH2P-NdFR9DARUE9TpKgJ@15o%2xv$)E3X^7UME*}e
zqei=4A+29(O+;Q^K3?4O3ivHQxfyq9BH><&-{;TXpD+E4jg1|sbice*_LIqJaggej
z*KYy`2M66UkE3NseX))yQ$ihAOU+4^H3TrYda1bB{dLPrJx#&d?M#UUom@KSi&Erv
zE9zIk5rAP$8#AtC`KB*2neIn~w+cy9MoN1Loi=(K#e&I;ZN_&^U%MDNb&urJ$jI)X
z?+(r+13ouoVDC3ZLh1#r$D^&2aSNx-w*TEtYOlRj3}RS(vL51f_|dFK9b&WW026LB
zD_81>>|9+j;5l00x;)x<G4UumWym2_IA~?9i2qTwc>YZxVb!^tzzq)~B*@O$EU5#5
zuL+V|6#kJSe3FiWml<ULL(HU@pcs!@^>fbwZ<p=SQhW!(&#YI?)b+20)he{IH4vG8
zWns>#i!adO0_zNp$$cR_MlE?+M$JT`m;KRSr{e`4oZR^J{qd70IF&Fp%+A?4w|}~_
zyqpdOWnePY(#97T!(GR`sbMqa>Z3Xp@Sc&~MI{k%XP~=5!m@^nEFg5c*#G;ssgDHI
z8mTi8GmzPUBLYalX-?;bH-7)fn%g1VxoJ`3g|3gMPYTgn7)&L%1(;cdjXG66vm4Aq
z-x2|ak}~XpTa5|%Nl!AZo|8l$R%`If84?6%b8CQEM9Ij=D%bn;6mQ(9Yfl#CbNQvy
zZ2m!S_k`4=Gx&<i%1tnq43~$8CyZW*6kL$}Z!`)S!s~$-w6W(QLIMI$Y!X_>iwxu8
zfUB4xED?R2SdrdJRIvIuY;>?j^nV1Ft1C4^hsIsyI!QPCC5$C9Ld6lWZGoEK7=_2h
z+i)^BuYz?M7{M%{llegK1{Kqo16@VC#nT8r$POV-0kHZT$`HDWLti8O=P6Jsj4=_6
zir25#dM<y)gDw!3kZ4lBvv7WHZeyN71+UHURX2C{iO-v(GB!3gT-qu}U;b_KE0y;X
zbJq6YPH+=%dmPB3M-?<W0?7mv1TV3#Y1AFM3IY`5+c;JepMOyMY@Nei!eIAxPZW4u
zOh@~mw8JlDzQPNol*M=;pD}rO2C^G=J|p10sol)V)12tKm5zv1sb*m$D7C`b7eRMH
zd2gQoOo^j|gY>m)SYWU0joB!#5w=@n)za0aJJ_r~tQ{Jn2fIUlPR@DP+kZ_$x~YNh
zxH=4Yw!o8CW~llATxdcNF#60r&rM(fl2qmhb!-$Ejnt&9CD82mN+Q1EC38z=Ugp7G
zCQ&%pFSl9nSBz`WA`MCcJ-}N$r)MeDv@<DVBqLAL<oo;ETU-)1ox(sY4A3OvTh)IF
z4z_t#;&QBw`|MflY^~h7EY0kwq$FZ2*C$+04&U<4mnX%=#!l3eNjAVyUD;i(uJ7nj
zNq0tYrW!`wAY1x@Cb!Zlewq-cwEIMsn#U)J&ViwX0k`0}SDAMnMkv?*C3hBjicCSX
z|ENlYMM%}6m@y0Gy7O-R({lCm@Gc|9#&`5=;Uc48qvqxn&*M>{CwMymt);a>K^#eh
zQAc)(_?7LMa}t@)H;)Goe#p{gWBP7KxXW`lG=gtm$eig1Tb2JQxWp`}r5wSFWu(Ft
z4%u;t?|$l;l~m{RiJoCUii@K@&0`q0z4mizvJquHH4yusW@TmRO`SY>lGkg?E=kmt
z^wX!LW9T=(<;k*)1?>Ytb&7HjcozU-twiJ!q~Eu3dJaiuZI{a*(Kp5x2wbTFf*({Z
z_Y#S4jXEwg$}A{PShlylu9W7iX+BF$DRWV`?8C+b8FPM=4@|{#1a40P5MaGjhJFov
z78No|xPkeVS0Y~Wkr3n_y3F_7TosVDoA}7Z#3C{D#;&kISyS+_pc1#=;YL?ump+Dy
zLd`KVM{V*A2?rum&C)=H*LMkD>NSV9Giz$!xA%`{u3x{dQ)qgkFXzr<a4mzg^8##2
z^lI(lXu(o4!_CnMN5_-PZUTHWVQLgB4-XkQDhdOI(h;a|(&RYickfQDbx2}h#ND@-
zMku|Y*hg+OAQm0%DxBzL{AmxAV&r{A=srY{m@oVySjfe&bbT3GlgQr%+H0%TyA>q~
zk5-C*9bvV~Er(-g^0cn6G%az_{Q5~KdbwUJCvX3bD=R_%f5V(8)WAnkypZ?#C;6=r
z;`mLwyGPqQBR`1hhdP0!=DKz)xD<h&G9w(>9JK7jTy>BGDi|2Df^2nm0nj>%;=E5W
z&D+}GQS7rb$ASE0lSN&d;Q1rN!ou_nzWODaI8swnS03yvpooa}#O_jsiuHX=U*GBF
z$~96`Gcywt6ML@LRWN$pz~>HHvvaBN5`bEkVQ_k-V@+tKx#>1?)B*#klHadH0b<c}
zzDNPjLl*mzC}3yiuP~|dthr(O+Z!R!yn>d*hShp?2g$9JiV`lA*`d!{YS24?V@D1+
z>-LXPR4_9?0{uM7RN=LMm1PO4ZuU$AD1lL1Mz-w@^z|DdUk_{r0V!CjJ%=Quo5-5|
zzR&a*DzVLBN4TLHKR)<Y;Ctwkp}~n&N_8eq@f?ky<tap){qe&bJ+Ym=eb@XJ92X3D
zb9-<Vq(nt^@*Cl!b$*Zw7E)_kjc53!_N2d8h2B8{htukh=tCy^bH{9#)tUM8izN7I
z^M74U?>AYyDfE=j+x;AXKXRCd;kQ1Uv7Q$kK~j#}<_^-y)JqG2kSs4`pco9F)WCo%
zsoC3r)Fa8|mc0x4o&gO4#aMUp$$8eLzQxPpwuKD71d}yncA2K_D2D;sgz>jY-ajrS
z-3BJIvUJUQ6<D|6_^y0Ykpy1Aap3)uVGR@J^aOl``z<Dh^7KWWBdg$V--=p(65->Q
zuEUV)-xV20*VvL4nD`zMO&z2jn~@o`*G@VY>PU#<gPk~OmQ@m}c!7%fK+wg}2`p-k
zOC${t@juw7a@YM95M~NWxe))~bm3Dc9lqG{mtjd6x|V3TC~QtmEVv!2v~?tE%e!JG
zr6feM{%+!6MNtIS^Kr^e#dnD(BQSh+>EWBSqdy*eZ_Gn4%Wx~FL>%~`z+?gx`awWe
za7N)@%Q3+_MOXN00_EN$J-I3CAqQotva<5|OP6r%E~{ROR)k)(O+Z@^34LG?lLtOb
zV|c9ZvuX4+ps|hUjx&o3dfk?c$k3*Q6I^x(i+x(VjtW>hz)k9VRDDNhd;p|IPMv8?
zYrsV5Ij_0p<Y3!;cc&1)3zBKnimJnfCibVMxBT~gsH;SsNSGJ9_A1?-m0T~<9rY^F
zhM9W>RCv2W0cm-)X~rP{u$@%yiTOnGfRwXD9pNFe);9|Rce(klgSP`^@v(QB#X1Ed
zxLwyLSsbC`cjXe{w7EWOx9Co^E(O>^J7KC}8FO>FQ@l6s=vR5s`X25Ju`4GHTx3~0
zOgRE>bP?^<BLAU2bY0D*C1!FX<R55SuTl5m0AFfwHp)GDtlfM(B=??bnuegGX1MF6
zT>Q)0_nG<e9)Yvu`pn^*-0hi~UR;?OTd{G>|6I7-GG)ZsGk9s)Y1`F@FD4HR+qV{4
zq>#Vi--8-%Y@PKe|8c^gcUK5WKCzN)ZuXBccB_}tGoOGwZODMG?m4#o2kYlkFZ~g>
z8^0#@r&A{Wsy*u|Lb=c+tC~m06qRzSdx(!-n!K7jJi^mD$oabXB2EXwRsH7Lxjzgs
z8jnCo1kTU+7sH5kvoyj%83oYm<kx-Rwj@$6h2-*OLdaoeAA`#X^1!MTCai!;=b7N9
zcc)GJzMOxqapjVLp<!-AUMaB>(dzv-Uk1~8HVkU1cLgMEN11*`0ucaVk4K-3kaFQW
zsEx=1HxN)AXujOdH9)D4S|Sycx}aEG|Ae`ZpLbGi;hP1Ut>|SLcuYbg1?6To*8R8r
zjBPMgV0CLM@~ngeQxF+*6I8Z(7tKkr0s{kA{`{$2KUyuPbWn+3)|R&f+P@y1$-*<c
zhq?^7dbd|M0}z`YnJ>|G`BjvGOLE6SRX4a>?c;e)0__E0Yu+&8hOrliU?dSFlq9Q}
z`=RWnbtf=WL9QokKH7nyv%sC##u=nb98`Z-Ec$<FCur+IO8XvwxxXAur#Pshdu*y-
z7(CtkV2PsXU?)5lxQKxj!_n#qekOf+&%y#`uvT5$Tmoz5Bh8$p6jI-V7;|l|G1UxF
z;efLZR&8A)-;e9Pcv9Qms_L`{3kf~Rp1DRrKn=Q#;=tg}D%I@V$?>$1&%FC%%44AR
zFag7h`11|6rAv_huq`M<E6den4e64K`03bCY4!%x2h-;ypA2M@y?mv7p`kbD*$d9X
z5ptYkNTHBV=QfIzu&u<?Pjt`$|N8;1-M~%nP6lIbM^<kQ4a3K<gTV0Wyhzi;Sd{xc
zyvrk0^0GZR9`pi|-a7(@KHGQ2V2r!l&RjYLJ^j#8%}t?3)XbGz2g)%0<{k$AG0-J5
zePS>9v@P<|Y@wU?s%~&l8;|*E<WB&h{$;TRQtUxDscthU?}(Zb$+6z_+;~&;Q_%JR
zavzST{UXQg3FdrI*htAae+_Cgdj_D}O%Snz;b{#RDVm&*V{KJq6S1)(u6Sw13jQO*
zj>vyxuW}o_B$aku9sWwq&~X9S=^|Kh+ZFR`K{Ae@M(@<N8>7fnv|NB8`4`Ib_tQ(H
z*6M@EreN`E;*5MkY^%0$Q&N5n1D!;+XY|JlEiipJb!mir*<;bhEDU^F#%;{hZH+iQ
zFF!OmNlJb5Y!?O$Tl^96&_MtDMZxT|(0%{^KtdbKO+5GS#`|^T@$?}<ZIK3F>no94
zA~{yIo*F>16`XW;>5ea+-DdzPNR8fCLs2^H(V6**mxm4_jJ?H^Qhy?p$Z?MBZR~U$
zoA+X_e?}7voQ}f6-)E&L_62EH#mk~J;phXjL*ctUG@!$^6}^p0(;g>S$^4v3S{x{P
zq5df-DY2b?iVk#!@HALk?EF^LZwH4?1VbLFhWp79`d2?(ZUp9)U6IyU<1wtfyK$z2
zzZHkiTOJeu>CDZl|3uCnhZ?c~K%H^+KbmmMf9tQOOFSJ=3r!OzB@Hk}%y}w*{qC(8
ztJ97k#e}FURR$;(y31WGuulCJmAvspID;(aXPl<lVPlR(Zjm@_TgaD#gVRXFY~TN>
zm5pv8I69Mm|J7rU<B7I;$9bDBb~)L%$#5uJbtv97-WZ)&`G_bigQTBHUWy6}AH#wk
zBm)D3ZW$fg@s`Tq+XA#-oIH!TrxB}QzYYri)UQ~h?SEY~>c44;MWsaP9jRHMJ9+(R
zDG{CsM2C<gJkip_C~RRWtC!ES{Eq3`B=0~xrz|E3>`ilGyUu6buPWFsv_fbv9hD%Q
zN<NqtvL05&8`XPA)77u_=#7TA_F8UXW}DVMPnb7?`khA<jl0k)2G#l0uH|OsU&`Xq
z0ZrZDZWz*Cbb%)K+;eHv!gXy14>0J@>Gyyc5V-BY_h0F>Q%MX~WS+C`9Byo!?i*WM
zD{DA{=sfKqJpgI#q=$0CEX^xlnVI$%U@o`-y;n1crd#)QOfLcx?zXR(U(eoe`QDs}
z8*Jl{q8Bd7ewd{JqD+4JX5;>6eOXM#)K61CkcArGV`sV&d6T=Ll`0zSr9mFvE9}6A
z2Z`<L5AxpH4p`vOM+Diuf;D&&6W@it+q%>mNWP-(^mrXR{G<dhA!w@*YsD!Hgl}Lq
zW%_WrN!<4Dt@8J3*`GJJWgpBqFB0;dGqvxTwr&6|MRci?j2D}2q%VDX*$_f<tRqKp
z+jcjX$tWo)A^tZs$Og6{U~JH@H5EG4KF0~l;PoqS2+7PuoR$xlr~=a9r;8+y;TE2a
z2L^-E{wmHhfu2rssE@fs+fB?go*q51tpyIGAwSuZ)3RfP!5}K=aoL;McnIG>pA4w%
z&SFo-#v(OnZvsz`j4Ud$gvjM7|Bm!Of%@mn4LMq+_W8ga(ea^fjMYOP4bEfv2QEwt
zX9ZQ1w=7`W%=Lc!N%GG@MgJ*Q0g9y*fCeuy=RW1@$EvfP;Va3xqrJ&yN$^+1|Blam
z>48=1`i)Up*&RjO$20HWzo!v(xrlY`ow~{X&U&u?(T^Jm2Lh1NKHtgf?vo=8-i)m_
zf!T@V{P|O0?%#H^uCuL0-$_YcjqUv<Nk3=?!`py$ZZv8)g-HdoG+?d|{MrU^M@1?X
z@b0Kb$Nkf(Lmx0Cg1MrKKb{LnJoFh?Km8&{G{h2sDLicb)w$;qzzI86^Obm|P}A4X
ze5geuLt|bv#TDltKEN$HcHq~YLq}X*(Eh(da<MJo>6Rt0l({?Is?&3u&*z{UL3&)&
z*KC#}h<s&Z%x${gQPpMKQ|WEB!{MU2kMK<~^QCp$=HUP+S}g1TT)9t*b+|ir_*!*X
z|Mzs8&{#unwzgi`(V?+?3a$}o3tZTSEsz&6Gf;Vsc!N&F&FX7=D>-=0)Q&YuJ1J?|
zQ@uZ+3CX6D*gA|;XB#hj)nS?#xHL?da0>hn8}KDD^1T<EfDWq{_rxk`!iO#U6i8gJ
zY1zN)!;FCW*UkP{08Q_uPQAS*gnIwx>LFT~=ZcRApoO<^dyR`cAu8S3doculbim@h
zLbmsQEd-ab?eh;uZYlp`^@_f3>IN6Of-67B2dvS`iR8U^hwVJ@*6ZSs8zHu&NHC0K
zP%znUQ=S36GVq$*TsIrF@}A8q18+6HO6;WehX(pYcW~f^czsn5Wh>T+0904FE=R<2
zX-l(4zb+^smkA`Ut@7U0t+<GOqvaDMv4-6O#;vJl<-#Mx2}21Z-q3^vw|@xRZ%Pv1
z&BlY18X|gtu_OtT%Kt%V9gp+nc&<HZ1#9M)#&F4(4h0)MfyottXbO`2o2|{Vb|V1z
zfDxXd_SDrEI?)l4>>FTaWBBuci8Lxy%)%<it+V`dxv($`sbq4Gm@ra2(~K&`Q2u8i
z@c!*u_$WmBJwU~#RT0@`qP@l7-^NK|RxKQOT{5W#m_n(^U$vQ4WHs=t@5}CRr2nws
zSj+wn<pHxOGReZ_AmP%6$q-Z?j4)jUJlaivwkO*;ImudBurGbyG$dvcqmkTO!G;Mk
z0sj8S&&Pi0yA*@IL62g41+9;AKe-R$mjlhGfKO9kcl)nC_55+M99axl*}^#7bM5J&
zuI?F{St~t#m$%lPpm_=HcJQ_0{g_=p^8DhLwn3bs7lHLd?HW!>>W0GT1Zt2ZPaTTI
zh1{iA0Zw2C{(22E@gIb;2n%?Iy!z+vAO75?KfhCgEvKLgICFyay_K&QR)2<rC(CbP
zztVL2f6M=)+wg6IKD*qbpyEMqKI?P$Q-HR}vuPlANJcgefacZo2_^$ZDXf&;?TcTs
zt(;X^qDx_@i<0ema~s>I5F5ttA3+{u@P^`A%%fV&OAilrqmz?Kz&Zsru_O<Tzkj$3
zbGZVOlNnDDHs%%%?g6%)yZ`Pef_Va42MKjS43cUF%rvOCRdq_P!CpF2trN=uj2mi$
zrK)527=k$1b>)=ggLws|UI()0?!wT2c?Zo;8W5@rDYT>l^RMvkbsCZHp+RkMf_San
z&#C{O4{CFL)z*-fsFJ4jmL3PIm2j;_bjm~E0$3tj7F2*u73_-B98BdlDweUFV#VCx
z<bL006d7Eza0>|g{l|_Pd<AuL@sL>z(d%wIvW#NH@d3$9=kh)_qF8{tm*D&=j8@pX
z-!XkUD<Z=LWeae~`T=DX{hnz0)fAB#yt@_<-@fFa2k2t!8-mkE(&6GHh^#2J;D<!j
zv*M2FR2{EQ!bhkyBfv2ccnxQUF<MSa_IJ6Fvwh1Cf4w0v9}}zE76AW-Q{G9@en7W8
z0mN2=qP~kwU3K_SA3#A_HXDy(J2VnOdShd~D}D(((pH$e{Kpxr)`gz|CCmI!HNhN>
z`s+U?Tl(UADnwlLpm?1K;l5XJyZ#5x*~f`7l(@w!KXA~||L0?A<o~%>NShO6A4f#t
zv;;8t@7PvCzh>!hhZ)U#vV51-g3$w$B#ykFf@T4-gDscx8a_he(Q$#2@!Q;(@(d|r
z+=xPV$?=+)j*k!q{qWQmj@%UxCly$;bt;LZz?~&W?}-1K+<O;vSiOMR_mJcBAP4YR
zHQ-Pvz}S#T8<7CXjxlmZTOdpTCOzAKM~G!j1SY(o!0m&yO-LvJCP&Ea{4Y^ndWLAn
za|-CeFQ>(eAd!F=T)Yc2;p`1V*~o{o^kOL6LsXP`uSA)|l5%j6AKj8i|5DrS>K~!Q
z<zL@)G(EZItRL<nH!Y~+^FsvJ+ld@zAq33I_ub8k3R&{>@nratWx4!GmNoY-l{QLx
z&)vsQ*cFL4L7_GtLHCQ1mBmDz=AEylh=UO1R?MPZbc0u%6);)2c|bP;e$>Yu&yEDN
zF3@nnWG~7mP>eRzeigRFJlEtxG#F@)v)-te<DqPXxk14tJJSSuFM+`b?k%PpU+yPS
z64=~}(=eIb<WSYWgMG_UkecjI6PV%(EqE?pkXk}S{^DJqr?=$Dw7*)2##Ln`CKYT-
zznG;t`g!qzOFRFOHOrcUTnvV%G|WA(L*}J~yrRQ5e+u@NK~*b=p)A}t7XjQV+-DD@
zMDb%|M8JzKT@F2KsjZ^imVZ>T4NZ9pD747Zjezy*pWtRut!D>3$g&-(awvsSh4EGt
z0*R#4tg2Pqk+4;_98pSHS);YJxJB5TztLHpep1s~us#|{N87wo|LYoDb9B3xvdD0>
zd(Hzg*+x8#NGDQ;xs;&XyPJwYEL0eQSPh(AvogBe!h&#J>xed;tv3i#pwPtMENl#U
zvh$UC>1vE$oV;sLw4a;M(@*cgY7pWp$QT}W@d4MW?;^gG9hc;P<(bw*s~^<p1tPDv
z0^vu*`d6kp!Bq!AJjnDXR;bp)Qv~>6`<TAwf{{zo_wd&Ah9rTY<ftu|?cEBJhOqgG
zw`-a~nPx2)vugfKM=O5eqGtToxM~5S5b(BUwXA-dYk+kWB)x&D$@ke0Eig&nCkKcK
z;P|#|forTmmM$;ZH>?@~?G;^d(2s!j3-lvrn!lBIMH*cBco#6V_c#@qBZlA?3-qy%
z&BA&zFmiy*7O1GeV>26(K4M?}8{%e{LPl&$)$~Ik!A<T_1v?1v5$4@ns|UKz`z{to
z7^6}tkVVgGL>CC|xqvQ1=%^Sl^9Zh(oq7c~1ehNsYKW@I@Dp<J=EC>YR&ac10o9ZU
z7GSIJOJ?)XSO&QDenX6__j*`h;lr!6eg;?)V4Mb&h1F}e)WCbPIfvPidxQT_I-`Ou
zfu7bF9MPY6STURW?4`83xtM<qXgvmxelMOr^H6f^bmE}t&)#=m=-7}~F?Jj<-DeJA
zsIvI~bl{HSN(J=~HlKV>-TJK0Z7jL38J73$DJGVeF^4{(3ew7V(wLZGCdu+W-0>Ah
znF1#}4xN`Y!4ckIu>rMC_DGzej^E~C=|F}f<%6_K<|#QW=A&&b3~<cqUcg$-WBui)
zrLv2@GJFD}8Ypcj7{RKF8ze7&A=19E4#>nfeh`mr!`a#{2L#Xb)B&lH72p@thXV?(
zoCIe7W4ae9w!q#&Y^09G5exXnCi=|lQU_-Jpi1QNv477E)(ocL5f#oTGSAL|<2Kv>
z-$A(|N9ok;{QW!^BPI1UytbP#w&eChPMB-hP!maN3hw5ov@OSO>9fR`nm(dOE-XV#
zgc#_oM}4J>$c6Vje&mj18+HRcwH%A{9%r!3!Hc{tFpR0B<tIbUSP|mC>zHwUQk{tJ
zEbB=*Du`1sLIK^u2*&2JVo1>NkL1{F>N&IeZWB$CdtgrkL253qfSp&Xxc~+$;<^@6
zGp@A;UxFD{)XBX9g3U5cV=C6A+3s@1CyDx-#xnv#<A7XpWWfC2v3H2-S8i3yEvL&T
z+N_>){;G}-K<4$GqPvK@0DU1Q9I0J10Nqf#>*~g&IMSm-z<TJ!i0!q=dsbu5=No7f
zQs1iY`LIQQLXtk~Z31Abg8p@&@+vXVi;hr^@;~{x=4ZiZhLwwp=){Q==Pz9FD=QPu
z$<3XZoy9M!yyg1<g+-4=ryIu*G!YW0d;+dev+ZBkXlQ3`;e5i40z!x<`s}q$8P7#G
z?j+J6TGm#Dz(Jk16<XQtq<<op#Yo{YxILSG?`#*zO%KXGQ7)<n^BC+udrKxO)l=42
zTw}NX-ag-!i`qZ^4U@9cDYG=dp#xdW;9H{i{~4Z12sS>{1~xzU7@~Ce5~ZhmqJrH?
z52U;R!LJS_I02#InqQ!1xZvEJWe9M?dEdufCZ2Zn&0hgiQ~NCk(rzNI`Nf-H@D5^{
zhF!9!t`Z!Uil87sYMk8H2aY)ruZj?89v}$$V^8hJGJ%cth5ka*MnVQ*2b^@3SMr*g
znxFXqg2l(juYqZ2h4;Hy-8?-@q~D>hx%dsd=Uert!k=-%u$j)I(GEkP<OT}{H=uc<
zi$w@8?+q|`5mA3^e`Ml;)!^W?;8O^8K&M^R(-(1h_4$|#_Jz}6Q2#o8r~Nedbs#}o
zM$v-<*qhO+*3=tl^aYCnqm!Zb?O4rT(}R~ZvK&ucu?wM_(IjWG;#k-T?f(D$^h7^#
z8wy?uz4ZkH)oZbY7HE%Ll3I|(V{{V7Xxkl1*v-3p$w;vU)5=()1&q>IpzDc*H<z_!
zGe@e0MX1s|u9`&!YWjvWyq$?NgKNH+l6HQpVwgBVF99J9j4Fu)FULylu_MKP<0HQi
z{KLFj*5I5*b+8Gc<WL?58rIfIJsF?Ro}1TNn~<G@L-*YlT`+opZt7LnC5G<qZdDx}
zd>I)T9X-8zm{C|;Tg%JOZ@I{iVk*{f<#|EK1ioZJ1`YbTnv%=dFW-s!<$<e<64`3a
zAM(}d%|SvCw-9)bfMJfFRh<8-x54V@t2kAI?5q-7y@!_Y1;Obaq?Gp?&QS$N6bfn@
zPfe;iIfAPF->U)ss~b`Dj&ImHEc&I9Bm;f2PNy-_ffIrU^vXg4al^aQSIW=`Q6(aK
z4A6QZZVGK*C7A=}H`ve>O7P!VJODEr5Cf3-2SiJS_Fh|zyuz{uBRd8P5S?khbWpZ)
zZ@K!gDU9}V)T>v%T>8*zvT5_G5V!N$eRvg|Rq&7d?5}GxNO;jC3fVRRMao&Pk`B)2
zK%p5PBFbRnk)9C}(ss)6^E(DV<uS)Aual4Q$oj5(8yXraDr+6SSE9!wV`_+Ek$IVq
zO6YDPgqs4ha+*$h=IkEB>S7occuPPK*lGVF>j#wKg3FiEMyslM%vth35`Ma+Q>7LM
z<Dhs!KCH|h8q~VjdJ1_<fa)DlVE*^xtN|J;#Wt1v5^?tH`(JXv?FmTvr+vheBcQYR
zMkCYqcV;*s3-RANC@FW!>tx>~;twL<ug=(q#S=TQlPfD0SS6BrZgLc>4#hpwj+mo(
zDKmp~oG^p&;;pNGe!(J_K7$@e)P;Bo6R9BA*k&|)GUSKWBmGNpX?i6#1w}=VGczxI
z`SK+yF){RO91oab%|xuaySvj!`iLDI?AksF3Mv`wFM9Z$KS!tVcyDj7Zg_rXW^Dwc
zXn`4qv^C)}o{{gq;Khprj37%3c3Sw)QBeqb1t2r9^?EKCw!kDL!5hbbwt#^dDZo^B
zfsu{8uqDBw^JzpOi1lY0-^~81`M2G5^qS`6gX2higf%7aGrdxK0`7MJ+p9sby0tF$
z>uPd@_^Ob=l~~JQn3n{m$F*Bkc`J^eO4C>}E`f8&YFEQ3z-gH-hkXMY!~Nd~WQLk8
z%X>Va_s<I*;Uh*q$e9{;0^Sq|9OWNZpt}OR5)+t@lmU|$sc7&XJ^1$Cq}KlgOyMIx
z^>pW^x`wtkn$)LL=D_yJX2fIR)-88;VRn_2FfQ%98h{7Wjcz7qWSlQ%aZFIcy?5^(
zOms{If3cR97Eu0|o3)lZFWAj>v;7*YX11`fDCbN7BEd31F_islQCd33K(Vq_q4UY*
z`r;tqyCD7tHZNxvpP7fqROkb5<1=u*_AK<boVMv}Qsl}QqcGNc$O)SGzU@Xu>r<LN
z#_eFRaZ_F-p5Al~tP(!_^mIaL%|D;+WMmdUq<OqRke!M)fC@|<u>s$?B@qNJ>AMi0
zq>Z$}Ju*OPuzb@IX7rlclydZ?C~8$4zFYjc(trCg4Jo_Yb*RKa;;jVu+^Ee9oulp~
zmN?l<Ul^rb3T{}rFjFO)3sD-L1Oz<rN|Jt4Q(#ih3*(uO7`|fe{1B+Hu)xY_3mS8)
zI}7;>lQ%oT2!rslMQaT(fO(-Y8J~FQyD9TdJp}AaCSmAqeOH=-t#xEXL{p{v+6oXq
z7T(G$C@4G}D0rh5IR`WWB25sMI29GGn1OtMB{oTK2ss5DE^S&hW)0@UVJZMit1L_K
zrbgPZpSL|^K(hA$jbuYJ%$6%)_*iU?deU`nStGIqtqdi76L&#G=q&s5xBH3ieTlz>
zhkuT5bj0>RLLC_N4tMX|;u#xte$#@9y~#!czMxY(<)*T(3Q&u=-nJ<6G|>=Zs7!kN
zeDAl%E>xIkz`#n2`Al3!RrVb=7y?Wu^c3=3kX?c!#>FtiMLPaR?0%JHZ<Zd5qn@4~
z0~}r^CZ=!^c|}G12qy8TuV0_}F+D9sjsqt$RrB!-ge_z&1*|k;u#3uOJR8%(3&X7$
z#N5cyW%7c@$j54IV{yoSWn7AwQRHKzl^{QV9Rx<bJu$fD2G4Wm*CuJI1>dZ={HF{C
z2N2JjMgm<=FZ093T<7#dWH#Chk#B0Jbo^TjaYBp>`-!rN(0%QdWobFrfzydu+EQ!&
z&>De$JdB{|;%f&h9e7`cF7HKh!v5oCm}LhGurn(3ze1|JE@BoyiVEy!zW3>uL4BM!
z9nW1W%Yp&fp2<91j=R2K%MFYKI~5@XMs4{W&Wchnx~_f-@-Nk_p5dH5ZB+E5Y;%^>
zBN1Q3bK_$7w{ORWy>9=R2x7hh7$By;9vkini6_34+`xUj>bdc-sCSSCnr`fujH<$b
z+58loY&c?CRz_UMSk9lHjacN;%DJC;60XZX>S+D*!g8pAOR}Sr8o^~bhD6TKoFTkI
zZr*tB_6}Uyx{hEkbIoX-QX1TAfqA2ap~{Q{B@``}Kv1i}-!|-jfoMHhpc-GAUp4&1
z?&eU+Ln<zcQ!UZW>{$2t4}4>=_&mL~>lw1-YriXeDz<+%?@aeiIVwSz26K~UZ<Xa?
z*o`jmMqj8^242qmtTpF(XlrA^eFuagUhBb+6(Xb1;9kae>1Q<Rhno#MK)Ndf(`?Q!
z1{4(6hr0{;)h~+>Hv3si6}}{e+SON=n0CaVv63K1C6bf1KX^p$MtCz&p)YijfDbfD
zf<|inmkjZEx*vHip1`U@HlX%`>(jBQTY2u1#nZzo`+pil1%h4wl{d+*2e=7J+_!--
zXO%vN1BE$~q!OYyDx}NZ?1>9vzP3VcA;bCL_gR-%q07N6pPT<dK4e-U&uj&Qx4Kxn
zkddKg$-q2r5&$!;keeEXnmF_oJKEcE?TQl1N=k&3l$I_vn3$R#x3yjNz}Rx(k}|kq
zs@H!1Ry8yny#Y3W6m!ak52zq9o%9dv<iN_n8UHyTz^K{lvlw3O7X;vbf(nTPMuE~3
zR7<z`hdYk-vCs2#J`BCpYcJ^f<wDu^?o4<1$u7R7E70FrI-=(P&*iPQu(0%y)bXtQ
z<Eb#>w@4`ixZ2s@(<W|aIg&f2dQz+pDmM233PI)v4OLG-iEIW`+L6=F^it$eQMaFB
z$9;h8yVug$=^W4`J~h5Ig{H&`vf|E=)7}MbH$Yln;Hjg71IO-ud<Ivvo^SPM%Iy(I
z>dMzQv$8&|Pg+}B8`$cpsdX9Pt)Ojt$L0Z*g}m&9!wa|BHJ2gf_o!fq;>Q{}=h=R5
z5{RK{*(PNL;LQN^YBHb5%ogHlex=!kKuyfag~ib8oZvL9?({+Eo4WxF=R<@7m;;+?
z2IKS4<Oltnmp|8#o5H`Jt`YD`1(&E0;3VajsBj)bgL}3l@=8mBC|ilwuW<I6Lw*B;
z3h22GGkuo{unc$B8T>1AOzx(3J}>~f4RjggAm-v=f#4aYiQvItli80E5y0>J^*Jga
z@~}K-)o09?<WF(^X3_DX=?+|=7IqDdMZp+kESoBP5L5_!<-2|Ruv#WHIoY6y;kGu!
zfCcNK;$rK78z>t&oBoy%U?KZr3g+WSy^{DKpX9;5g-VG5V5b1WN%9Ul#5ufVm4Z;9
zmwSSD3<w|n5)Imn=z0y(*UbTu0~`X#RI(T}e-L2jzuk}jEe>|3fk3LNmfP@YKtrSN
z`~);G(MZ@&B}5Dt!{{Wg(Qp20&bw-Nfj*>ceR&<Q-w7B8fW`}mWKP4-b&0*TlN$%=
zG3C&|fmRs|M8%h>5T1SeB7zl67?_vJo*;I<WD`tELB4nJqumb^gENl7NpN*_tzO?8
z9@dwYlVcGO_}GF8efm^dS-Cye*VFA|?spR~6Z||imXea9dfrLs+a)0(>IV-V06!}*
zAmGG9YwL-bnZEaN9nc5fkKMPOT`LdlOpmwb%Orj&)ZJc6ELOIT2CfZ*{+h4vRsMd@
zMIcP2l~hno9xRUARRCFV6`l-~xX$Wd!&4KhZF>yda;D4x?hmaWA2@cY0r7PH`KS$<
zor#$YFFbH_|I=n6L0D$9R5VLu7C`(;X(<4DERSB#y!aV$nU<#a8!br1igjQxk|qe_
zsDP^5>o&FyO|!{-4!pX))`CXMx$|HfHw6P#>{c9<>mMK9HOs0=lMywuh{q$I{%|@c
zA~<8ebI6qn8<+S|baYoP?>8V^xM^cN=ibHH?2mvy6Z}bO_SD=Uu!@(qHX)sJb{PHj
z>*3b+cJ1gW6F7G^LRs2XVr%5)=2p|*P70|r26)D=H8`Es0fjI2MMi4YWiwG|#>$Sf
zdi4j)tDm;7Gg9Bb`(owlBN`LIK{snI0p%b_uYbMAEz2H34U@-lADq=?V0s?3XWqah
zitn|=djC?7k5aVME7w@!TuOl-c1?x;BHcK5IS6Yz(q8A3fA7$PEe4J$rp*|<LKc3x
zDR1rZna>5F%*B&3U`_dmM#83lN~xXAkFaFqN4oYcULyt{8~oG6D=4?kp!l<q98g%u
zCm+E;a8x8kFbE@g4-5<zoWBY@|GDmWSDj|bfye;=ptcnaZE^DJW@g_#zq1Mv!-dx1
zMkXTcDkpd1l3`_ri;<6ygwO7xkW#V;6}VV7PSgiEs{=Y+Yy_7HYEYaMcp`zi84J&N
zFEaf|bMe*8C9HmR-8S>&5crk7L%N37bGn#Gzfm--BCs{{yLjgiZ4>T1&p1S2tJSr1
zYAs^~hyRQXjhLIf9|-$Oq~Y1K|2=v@+#5lo)G(tOi&bbO(}AZ~QS!~qlaSw@kdY7$
zRynMKyks%aX$iu~D<?b2oX;&lBBHOxOr@xhP4s`8v#jVpjPq0SFPliYx;LyUCZ0;v
z8cwWRrOKM>B0oBK;{NZML|apE=q#z5vFgjIh34K$3&$&CuzV$vIOu#M;|7WryZN3A
zisw$^`(q_21(u9@+{Eh@irm}tx@b`TC&C)lU5bi|T$*o30SDoOMA!?6O>UDR952@g
zzJLGT+|uO}LWx!CvN*_ic!`@^4n$0jFXX~iwY1)^j>*bmMS%lM0DT8bZh^X!5{D?I
ztkUwavl?lUX)FwFRLz{3k_%q9HTavhhd!E@S&!P@D_R-M<LdnL-P0$Yo{4e%>9c1i
zlY8Z?J4PzoBWEo_$2xkK4SiPBPv_nHQ8%h&-@l$2W2l3E18Z8w4JW00o|Jo6+n58T
zsh);5V&DK5-`f)d0Yi_^s&fiZc)X{q#GTD;BbLI-7AAYH4DCP_AtQ3f)J>>@o!N*S
zXD>$eQ3u(%N!<uHnY6&l!plqtq=cn<^g`P-jB<B0x%=eO&8#&<zR*+5GB4UjD&V=-
zwGGmYi%VK6Y>AAm!VK$A^V@9xDf#uQbwoVlpq#6sI)#S?jY1HOK>B*>v&XtWbP1Ji
z-BQ!i3aqRYYiMkofTaMIG_Kv+iK;xJpN<{kL14o6y0<E&Ni|agH5*rdzVaO%w0Q6!
z1pMY}M_^8S`@-}z>c<X)I;Irp=3N!8wtz#`*VP@Lo}O-QZmwCK!u&txzB;U`tbO~K
zj7W?@2m*r=3JQXBswfIbNY?@B4y7AKF%VEf1f-<9K{$kpbjP8)>rjV~{+{i;?~L;M
zzU%t^`R&W=%o%26uf6tKPu%zYJS*FI%@hvJ7IhF(1`r84N`K(7P|wBUtEjDpK@U)p
zs^<!!_tMgmaRMlul8Po&4h?;;f2+bc$lX_~`XzQx^1~3%S4W4}XI<K{>@6#rnd+aD
zqtXUPB5DF2nms*x_<Q-#MNK8jv5dpip$byPT12j7neGd7M`$I994z+yS&D*rJ%-LI
z`iK^xrH=}YCO$hyP4yq5X_wArgN9Zqc>mGP^}xY<o@ej<K_9cB6?ZA=W%rj`N~cU%
z3hcTDT^3U@tmw0U-aJG`{g>*ElvS-GF~<&{hzlty7yWT6l;L_PLtv<cBOEF`HPY|0
zz(3@J;V-%##eW2aLMieOF@)uh->Y4`NZ>;4{0Y4|NFWfzcJ&^Y*+ytUHQTF@TDd0=
zR<`AdpArkVq}<RdVb=KM>XSG6SwfUCG&pkchp0UMnomZm6+JsDx)*vQN7FhEKl!%)
z{;Ebtj)7E2<6^5|m{c3JRY{Cqwcku`vj{7Z54Cp;WYTW1OL#Id*Mi(d;zV@6zhpVX
z8PfE8l$|J4stq6;`DA(<3Fo==!j5tek7EP`6oM+T;>6gb2Vzj{*A8`0TvmfZzEs?j
z&~z`LX&2*9E397XvY{LyV8@a#gSxTyHsGpTUZYgvJO~VqQx#`EYccA&=hGOyS*M?C
z`KpjFx5HGY@=>7SB0i@tQTxrE-4me|$4=jh`@=(wImc(;-$E%zP31NP-<rn@=A5S8
z5{Z1^k|^|zUGayHo#G^b+534V1#DHT=S}2EROh}fXAX+AYU|%M^fe#-;ZmFUxXI#a
z!Ft@-A-uY~cZP3%l4W#^1CL8EC6DM^%eK)9rnS_m*6r`bA1mMP37E8PylAlJO=V?a
z>uw=lJK1q5@3hYmPoKAAsZrF(WNi(5k&Z7eYJV2od>(`U<iRlKSyWzLzPG#RVb+^x
zVp-yovk3s=+5%=#oyG;yb#DEdw~|pVz$-F>@bU4OFYoLNI(NAnUxu{D0zyNRRkWPq
zbLx(u6fZL8#*-HIl7?1y{}C!B|GB*5vv@5#Dos#Fo|fN+)#E%XGz73KJ&aa}ddsg_
zp)auU<Xr%RQFz{B8h%o;DPRvRStIlwga`k*ZUj&1y|=Vb{VPzC#5z-!!^}-`#_FZv
zWK`eRo!~Vsk{1m}N{EPGvbF@`2s<u+le_=c7VXw&rMdq>zw@i@3-63|BC%fk-nc?u
zr<q*g_3~4@w;mkw7U#a)B9{I~=#d)+*Un7^_9%S;pR~4IO=ngYbXihHhArPiQ87r|
zpEliPq3A9L2M1eH3%_C0$K*asj5Nhw;yM1uaS8WB<tei^xImClqUAX9{F{BcdcN)G
z_Y<LwWcydd9zV{J`N;Pm!3=Q3*e6E_$O}|5G?OPA!&N?$2g!<ydn;#Zp}QB_;(eYz
z{ozhEtQ+FUBBf_&-nN`u#Iwznc_!^dh5BW#FZQ!GBc1xf*$<6gO=;1!D0bz&NwZGJ
zeof22yV~3Af?F?IY~%BtQyaQN7E_!wE#PrMSVd*mC74jsk)<fcE>5eg%;dRQe^fv+
z$90~$+r_vj-t^hya~>7ysov$)9y4c9D7Ff`5DJB*m_t6P&4jI+2&6X5R-z;rBU8@}
zr)O5%DdPSb(w$l%yt1V`@IYGCUCu;plcaHC%%Ckxrj@EsxS)HxkG!I#J2X6>i6i~H
z8FdF?%X}-2Y*^2uJ!{r{@fA^NwsDj)?LE66VKskh`!13tQup4j?VAq`6^Q1{YSMNs
zuo}v+94IC$C^YZ+1Y5q?e2*pg)5}+{T2nL2QdG3vlmzYPTObMx`tTvCJyDibSQrhc
zb&6VU!szH|N4f?tw}tw$-0M`gjRA+^F+ahyTa&y4wt-C2VdL4z_Fwu+u(krnl}1P3
z2(Se&oQ}pQ!%+~)OWp6@y?gy8P4(V2K0Z}$(~czTtJZ#io9wxuD2hs%+qexU4Keh1
zp0!(RJxLja#ChfUOTH0LS9@h$?3ge=j_eg`$eK<w`m$(RDe+GD@5V!4vY9T$jrEZs
zLNb+3yE1X&WZB$%R|mCryyAT9<EDedkDMLV-n&}0!4QT05UMfn-O$%X+MXC&oFsm1
z@(`Sn_vQEZ!KUo5)hAiLD22@!t1KiEjR?-<4PlrSEwsPXpnuLgW|4;mt09W(_^J>}
z(rhEvXDr;7nXGGetal#&HOp^yW6w%J<J)#GfsDaS>=5Z({FC(ak-5cu;Sq_X<cpno
zWCTH0kIc*aTTpbqivw~FwpM@b`|y1v{a7uay$j|NW4Fgb##JttYuMP4rgq&SWJD3l
zuCUQJy@I0lH`vcY5R6Sz%LSaN$43_|DmF%pARXSzQa99VMx5p%Kq<1j1hjLTk4i@H
zh3n<Y_H|#Aro&b)$V^UcZabPh+@_S?zn<M)G~ps|JFS>7K(IAyIcl}NTDQ@MTX*{8
zaecr}O^ib6=vm&+3g)kF;?qhFyJ41-ydAC%$!uf%qOeP^Snb+mYD)AprKK^sHqXD!
z|JQt=mW12~p<AGw#=ws#GL}p9-*+nB>up)KKR^%hsHbj3wXqIabybNallTvDm%WcH
zNhs>A@(%9h6}WPPozy&@puAgdbhBBRX?Df%-E8~(=#MH(pUM^}2UzPR6}06Xu(uz+
zwI^aY&~s7CLA$dh=E@<|7ygC?Hq=y_dcGuYzqMCRZth8cyTw5?ezGwz(`q@>c=<Lo
zV^+oiQ}a3bG|Drv*KH@wTPBN+(@I<Pew45M6@B-?VVP{6=0-J_r2E{;%=_DRR>}1E
z#7qmBn)8-jiE44*ymlppcrhRM=>{Lozwwrazr5HVoghMkeg2E><i;jweOSoGyHDqy
zEHG|JG~@meC`v;T%KMtAb6ikh#^=$%dBV^r-e~k2OJiya{LV!i-piF=TkMnyCalAb
zyR9VdaT~>o(c7@+N>pGvyWl*MRy`G7gr#Q_Z|Of`-kT=@y~9<Uw}!Oa+sfQr-1c`j
z2nh)v>lqk~jp>!XoSK*@LLQO1*J)m}rhm{&xB7Efm`I9`X%NS14-1px)mW)Ls%ZYX
z0LdL&XS<E%Heu)87c}<ULN4f&zB9qYpI)Q-TuT~?i}dcln&P3A#5U;})pHFNT%5W!
zdHGy{*d%(?%%nF0eRf{OpnUwqKd+tS^ZsjbQz|m~(W3THC-suu&thZG2)1Xi)EaZu
z1THhLCm(Qz-Z#jYh(DCB$lFo=-G;f{X7zhlxB(>=zl&=>SLAFuXo%i#rBe@a?2R$Y
zut2N$&=xCZ4tA?Q;FjET09VKMbkec_>gm>SeJY8v`=yRNR)d<NyQ_h>#KZ<v)p}?I
z?Hf{PPMsP_kVK&(4YymbQ}JX6wHSG+ll7`O-kS<=;1&&gpFZTEtvQslJQ4sDL1Kjc
zc8Rr^l<(|0C_nUU>)AGZ+GBQp2<uuu!bKdFC!cuT!ee?l;8|~&y^+yxvBUaKN$G0S
zaKQ>^h%hCa#FH|ut?FRkwYiI>+$CmiPA<Wsk1438V;zp?=R569SyXcyS;!YvZ`?jc
zE*6z}Uopzzy-8Jy*JFmeLyTqb%Zf5zh?M1foxbptwfr8+6N=skE^%-?K-_#lVBksr
z%Ia#f)=zho*y17r17o?(I#p?WeDi6@52G}N3TP$TvtUQo!HzU@y((P_JJOz<wuMK$
zY>;u^6hEO}EtaAbszd2))yh_iF%B7UXD2p~7cAzrmVV&rMJ0YT)y6A~F*3YRM1p_w
zSiE00|7Gb4<_yJ@h__D7;v-?4Ik|`3;Nao^zHQAb>XpxC2M3AkZGC$fi09<B>uDF#
zJv4j1n}k!^S)I+`u;)4EGSZhWdB4`WPIIAvZ8^yD$26(1u!qmU#!{0{%19#Vi~O$n
zM*|%RTB;Kif1oZY1xu=&J$u#|*PE|i?p{hlE0lEIsOb_bYiffwg)V0(m($9)WJ((c
z04G>770sBqxN^}a<Y&&rGD^L_#KctP8ywtBq3s&e(9rO$ASx;f`bmsby@qx05Z>f~
zfJty$j=AVHdn;9qz=dhWkrpH4X#cIkl-Og`erE*>jr$(O$4w>KXK6p5T+;W}nI5=<
zSzXj#@5XJ+`lcf<ltSrPP$L)#b(`oPUE>i=4eg^AwZ#R}=<qH}p`esLrp&gUdDn0^
zXKs?N@24Wh7sn=ooDcnX#^mh!9h~%bOK_j<&kJgp4zY&coy|@SPky9$FTP{Nx;$Y^
zuetWLS#8bL7O`C3>3R%Xa?8TZLXazuM&^E&nD)M1cg&uM787jI=<O}8QH@uJzEmi_
zimC(D$a!-jWUa?2Y6e(nGOGG(M1=&6vJnFDAon$094vi~sPgiF$FHpJ+sg|}6tJKF
z5a~R7IaN;uZ5Ip;PzDrFp}w3kM;?MXv+bN<3>@Ip^jzeKYs8tY^KF+4zDGN0J&AFe
zBQ?99c{H(M%!H_-1@V3!?{WD()%pQi<;nTpwoLjY&2|>y#m*yr7Lv`E_iXgmd&PLX
zCOv3`(gm3^J9|!N2rlL@o!S~-_j}gx&&06Fmkk=0UjLka+%->g^L+p>kA(ueqU@?w
zh7?t2Mu+;2TgmEPi+%CPP<^n*!^`%2nO@0Ti#esm`>(5l)nDXJkg2ZDyguUWvAl44
z2+vw;AbnxAf9-0}pQkiyv4>D~6=R|+tB_u%09oh^kxKB#k5?+L)s=V^G4s2ur)89F
znOY1K=P#+Ms(x_a+ce^~8thNDq2aT>!4z0K+YE7}`MRj{YEmR*UlAf`R|*Li8%?)0
ztbpr}McJs(`%J=s-;*VsN6BH+MIF@(gHLlVV)--!7p%2vGy{qmJCx1SJx@2g6|Kt!
z@6miCuei3l<u52n99r&VLw##<$?-|qU~!p6t<K7jLsZL1N1ASR7h`1GOZi)qA-p~e
zVEF%g<-Z}pws(;?8)H+?MU#3LwC^knvp{UGl`Fu#dapczr`aP*zmSJ9zBrEDNVRM#
zZp&i{qowtEAm*);c{)n^e5*wJ!u)+G$`JZ+chR`aZcZ664_P>!BB1Hmhh~EBDW-Ds
z-qe;YVcceEe!KXn{f^o|=6uKHQBGfMw%%O`IoeI~C&tI8D=D-)FpFiW)Rv!b@(#J?
z0&kiyJw2`KwgHhh%<~M?1<R|ehhd8d=v?J!1Z=I<(J0Rp1N5lL0Ea>T073Pj^u^Wk
zzK?WUT;bAG{T#}RS9+D^`!fLcv%aK+n-jFc+490n)qTT-tya5-yEQP);BH#jDZFrI
zOC!2e{xw3E+iWdHx0+Zi48tQqs<pY1L2O3yv90EEYVbRahc+e1{u<K|tZ5Ga*E^%)
z-U*K?F>~D*vK0FCap&-wB~|jxh}oiv-MCiav9)?X{oNw=`0gTCpBN9<?3+WvEecP{
zBB}9$AH(8R$SvD9!u|O>hMN1*T(-H|K1-eHzC?&hK{cbFF!<7mYQa2$g9~VSo}5uU
z%R)?f?MAvrp)y!AGD^znNtSv;&`?+U32)EmPEeTvo;M53XeA-bO=ss~B+mixN;1H-
zD<i$8<{pwLtEs68z_LR!n$di(<HT)HO+U68hC(S-^d3pHy?Z&PnKkSVgrrJZ)A;u?
zYpa8!XqK7qrZP>Jq~4>A#1$<*Ll*5F6R><a!s9l)^V&V_IWky@lA`Uibxco_rJ<|a
zeImkh7v3(Em%M8*3{=KRwMMjeUTSY@4o_9TJrg@ZGd_n~)*e6l-vx-GC@xl_K@X((
zVd~%Zn70Xf^`#7Lm(Q4=7@{m&)c4lfb;oGYcJ~rvdqN^qqF<Hc+N<l=F9|k}ws7xi
zU|SWscvMQmlvwcuD2nU;#9@mA7?tw<J(m%-N$_VKNXl{ft7&&gK)|P<pwXx>3`j_#
zwA|K|grOAnIUNxSrKx%Tgxk8ssd#_y!psiYr(MLvE%DM9^308vN$(&LYGhHy^Sd6s
zeW$>JZ(0@1wu_#zSYPH0U)_6a54&vHis_J0Xsv!5*l}m4k#*gv02w2b%E7Zvq0&^`
z7QQ(v=KCcxLn(eD=R37Tw0<#D!$qGe4zI3!QFdPPA!dx+cb&M2ZoMMu7Ivq&;2Jd;
z!~k5a^D@Rlz>%dr#tb~_%qF#Ziptna$qW+IusLmbv1*xXQJl@mK)5>~B)4ix0`A<o
zGvh@n((>iW5i&Bei56}E7o7ns$hR62LAD>V`6-e`Hxi7;6}Rc}?pI@1mzV1LYIkmV
z30A&$6cT3EKFLdyzPia8SgAW5dQ1bXiU(hwFujeU8n0avGhJqYN2cpoHBGTiuPG!{
zW*S|L|JoBz7QGu7%7KSdy90M8E#)Ph{Z6v7^y~$mLnfl?zV@74jZjUOlq|YkQ&h<D
zGRPT|J_*?iHUktywpVJ{7*@PF89t!ui>LG+$tj}ZGL}o&Jbd`@>X##=(@zL!-!jk$
zJ1Yu0EOdPJBCuN-moP9itn2Dhfj6zFuOBDiu#h%weApaiovi_;O5CHkYC%N@-Cpb`
zLOEO9G8NnytH@fTlF0ekCt~|Cj-EK@)a?yha-W#QNvqa0ECIY#p77PyrFB@6XbR=y
zd@AXGX|KV`SmdQ+wYS8NaACT%AmLafwq%92@c%5~q+424Es}_-Xw93~osvnIntqkZ
zI~Uj|SZr{yrJ>)|ZMB|Oddsc+@kw)|BW$*M+myns<)IhMeyBXQLwVMXXWz78&$Pir
zgxzOiWMIfrD4>Q^SjBGcwEPiN_xkq287lmY`owsj``eNXX>_S*T!fTZ6u7v7ZV~ao
z{ci2~{;(s7`5U*3VU1wYXpOQ|iG-Ke3>&pvY7FV<*aJmq2XCt6r;UB9^Q`0enSI%~
z953+c?DGQf<iC#*J$r(9wN=oo*Yro660An2zDJE};c;Ih(1qFR&N5|&8Im2}hc@QE
zt5w@8T4@z}Yn^YAkD%^I8J|U=GKRBHyxCftqu3J`#QaFiL!mrfc8lkpiHDr#q-$a8
zouJ3#y}O-0@3qOO_MIVv@oiz5F!VOs?74d`W_NE*Ch<6P`^niRjbW(!JLtt1iR_!3
zssB4P)R%L`yo6odTFX3u)8LdD6Z93GX_7;Wkl1t0ahIZWYnS8&9Grbf#M$JAl$(Qx
zEW92$Uq^GoSeK0jFrl18Vgw}T<=oxNFj9w6sC}y$KYibbGUXzB02uQ9TQQbIZal9D
zA7Hw(z^~>nWUZXv5ny*<&7ySHZxU}rhv?_}XBMhO^$$K!DR}u<$Kb}q3TL>jRJAk7
z8cj!b<X~}pdP#%p$<zM~Ii=vmHn&o)06k-5eZ$WH>{5Ba;G&F3V7f~AP{u;${wLD+
z^UCVVTFG4>?zd&Jt>v$d3>NPO94|2<Kw;lhA8*vt)wP`nR*?+g<m6n1!^AK}7f`6b
zB%})Hy3kSSxzwEH@p{6g+%>x~vKpAMsQb;WZ8T6#kubk<^2*j!2=(gPRt3-Y59e_1
zN4J^ROvq=x)uFm5JCav3cd2{%m96^wt8k)L|BM)Aw1i)8+ZOAq?W#(!^q{$q^$5Y)
z&3=3#V=$ZS6pqFF#)mESi5N_h^Vy=ew{*592P2(v37siq<8vM0L?XZHc})ha0#II4
zU41uTwOh~s^2OMd@^x5Z-Rt;Hz2a@WU0ptYX8LQy?2I(&2{)~-gp9+kC6YoL6TM<E
zaB~>A^K`whde+FxMiKWxL+P^EKsK|}BgnvCrIRJm`kM9$i9+C9>a1rLDm*+z{Lkoz
zuQbkfv|QW`>lNDn8Zgl7cq#uIeqO{~nY3{*deDl!{f!a<&hQZ1S=#y3LQ9j_%!P%F
z&b3>tngpmXHcqxJCBSKIHGAw`l<3Vj3(3GN2rx3c>@CCsCVp4cl0RYj`kuVj6P?`V
zEVgXt-2@)mGh0N<8?E^kv+o@(XPVAPiUi_DU!M<DdD!ZuZQfEyZSkUE*R26bbDX)E
z^!;XW3-wg*9e36(A2}O5WcA;4`e)~%_}Zr<mx~!<#-&47u6omkmEH_3+MJ+r8H-UI
zSnE+=b6ezX)bLO}M&8Hod2)0619yQ+2^;`Xc--#wM5zGk^B<13Et&xc&<Wc^^U$F~
zTWej~Sb*D+II_3IDVL}s`xpd$iVYBM%npWnTpntwn6Ma_oeO>vS(v-@wkeyDmOYCy
zY+EJ3C-pMgvh2=Avu5V=aR1)<Gs9!?$rmw6cVn%=B!0e|S#`=ePFrEqDayrm8P78}
zk{UK$dFZMEr&H&z8&(ax|KPcPW$v9)DWUTJIP!`yz`4NpOt06bat)77Y?;hGdXY)I
zjPG=FqcYjl3z<tc(@63z_pj(Wj7lj8P@yNi4)mEFu2-o`+oKdg7eln1CD=<>16*BQ
z6;qY3Zf<Xfm<R>d#vw?*d_FuPI}(U_Gz>o6IMd*uc{*{*YN?^cDDiWc<jX(W%Fdjt
zY(lJ>3!B+AhuzeqhV5cQd7g1Nj$rGCHoG)Uj~d#-^)XG7-vX}v9(crARrP||XIrcn
zzBdmr{*zyzg4qyc3DVvFAWJH=9b}E4IsnpX<N&|j7E!z9IHQ#=C4^4cG$YtFRcv+~
zaF;kg@)-5`p3<x6&6SDzHCSs3fQCY897}(#5a?5(!5AyvG4eFi5!9FVtKHVBuyX%$
z849p7@;~mpUQAAlDFX*Vyl^(P%cJ|jd_TBV_0s*IyUz-2R!-V3zA4XhbjfKHxml(a
z#*^YBz?(Dy+ot)?Q=@mkod0K=P(&QU>E`LX<dpR|#(l^8Hw?r@?X^nfd!-LafwkLm
z$3Ji@vGqq1$^`ycv0d(kwxh%|n%alTv?elWP;zTdt}Qzdinoc83N!drUZ}XH&l$>c
zR$q`#9vG1IMvbUA54WOL%Np1F&5pu$7B>FGp-~PBFDOgb+M7;07(|t>zwPsy>~T6)
zJcYMYFUCJ>6#42txMizoWcLPvr2e@m0z;1HwSi-^<+_ULE()CCcOIQ<L^3_F=XH&R
z#)Df7yNg|Jy&gF~cs=^g(9t4qHR%aXZ+y<eUU?Jc$<h$(HF=VPtpfrgW3p?abZ0l#
z)|8BmlaTyWlM!I@=6*W~XZ;27o^97obO_V*@?N8mh(4RdJNmui616yT3qh^<eq_%;
z;8lz>E6c$X+IAVut<JIa(c}LVwUVYAxPzPPvehy8XV@r|?4LgonnG-PpmOjJk-g?V
zKeMf<S2>j4Ogjac-)|HPH_mmol)6!+c%&AuaJsl3N1=uVXlc0it3~%7P;&ATi2<0s
zH6^;Aj3h(SVO;Ne8WIywDC+|DuJdhr+EzK#oODB&7}}YxJjrJb&D4MA)Ao^fkhQOY
zsVXW-m@heu__y*!5tCe5Vh<}fp5j!%M2j-6{X0_6yEB8=H1(XT%tjZtrG)nTc{S>V
z3}0;N6fdrb&$e^rNS5xCqO55ktj^Rdk_S(ERr3oFO9(oW1njkC1`*10)~s{Z3wbm4
z+x;F+7k6{50$DcR_Fzn&T@c#=s3Ebt2uA(bsYQsj@J6gCI+n~u!F>G$@lxwlEGEUS
zR1U1lze8HOO}b&00$FQ^pPmsbmrQ2WNO7U;MLXY2rHR390@UaC)s`prz!*pbyxAX;
zv1^NyKmzKd+kk#u;nv?9Ci6|zha9=26hRwHPd8iKmu!b$rKv1sXj!)Kv7pzoM(9e*
z-Ro6<{>#{*P#?#paPb>23Ia1#Hg!Ez3e#e(7Up7_#T}FipY<xOlzN~%&790z;NZpd
zr9~i;W;>DtsZ$T3qMgI{rVjl?phWiP`)-pewZ7eS0=v;G#Ou;XAJXymU0~)rg-+v%
zG6kOP4x-i829ALa7Vls3N7S$t;<#c}c<tbbfS$PU-fH7ku@E>1*+DVfStnTKJn*Q?
z-O*0BZ$D$G)#tn{Akf>0MWN)}q|s&C&1~JKh0WmgW}mQ9VUj-8A=`j$IUitUt6iOm
zaSFxRof-bkV!b-kbdiUG`dWfDD!Em53O>_R!Rjafo%d){D(pqA0qV|zzUr>}G!1}>
zb6&X24bbB{_*lN*0nj7m3@n4{s=d;&X-F&kRRN{NcEt^C%sjU$?7`u?v5mG?QQ~YV
zNh`$J*{M#ZhLOnbF~LXHHnqR3P0iz3&TAAI`3i_{<Lqy9AJkyZgCycgtTlz*MPP+}
zF<du2ggWnL_IImi=N}%Bqen#-FTo5nM+%djKMzPtp2MQnH;BLadYH=>u=OVk`*8RU
z?nXP=MQ^s08tx1ev<kiZAYhNBlXCG5PHg&R>JZN4_b^}hE(={Y5|~_H|7#IZa<8Ye
zP;Pdz4(I?m4T!24Zy%lc%1UKcITkWkYZk_u<6VxCI*L*}LQBm>N=VoSlnH~x=H_OY
zh+A<u>tR&%){o{KVQ1V=#M=fC?^3R>cgTulJTpTwtSHc<MWX8;729U+CpLw=4^8Z2
zPH*!TOXk_Wz2{L=@_+$ypnon;z0nFUE;CJDoa3_T?n>sMbCT7W=f}v0CeEWgS5~^C
zix^5*+oV#^m;#2GqN&Jspk@X%PeJ}ZbhmuIGhDP+TOq2v?78^H8;1vXGI-;FX!!|e
zu+^I2;|wQfJ8N0<IW9LEMhS55aBpWwbzHm9ZGwk@_`eq$tzmBM0GP&<%l;%`ptzn3
zw<>!8CQ~L#BwfU9%NXF|1-6qY>r;{f5y`@}M#14$j~p(Ot?Z4WowujZ0DN;r$BVBg
zl>u943ZPr#Y*2~IkB|8hIjV06n;!{WQ~?aQ?R|xj`Fb+-Y$iLbMOR0;=TrX%L&#8!
zR_q-Pt%IIf4mR5s5PsayU=zX5AfL}Kc38}~>2*9Al15Nqye%*H2(8Z0E_Y{CBtj)w
zwsGQD8=hX>G8{W4F;!+i8%(W{>{4OPtD?*U`Fm+4W(_cU`|Z-ldaMJz+)a0siv0hR
zyF;d3*+e7L*tSy2j7@!XyQo@LqPZ1}xJh+(F3<M&XUB{?yDd|+1+)6`|DCU&|GZ(a
zJRR)>c#1yf?ruIUQ=o*mj&bE{GWBfTJ4Myp;H{dQ9mHRuJWc0q#WO6!OC&Y?cS<j=
zA$c*sf$yG;83DTILY*&`zv&uL=`LOdJaA^C2vgu{FTlik^_BRWY1Dny(`$70mkOhQ
zu&w2Goo8`q+0l3QvyAvGDd^uv655pt%bZrj=U2-DgKO4@iS)Zh6uY!I<u&E4qc}|p
zUixEQ4R%SQ6LPZ0C$At;`PQTQ2fT_uXULaMiZrzRkxjS{3phe<FIQ}#K`kBm52v|D
z5luB$@gJC*CXKtF1>1{Fu9+R1^?CFQpn=SWV*$%4u8`LR^<b5+T%O^l5_uF~KzEgv
zQ<Qy3cX}{%ZVgv;ku5FF9O5K1(fO8r_k21|kL~vbRQ;Dbp9een(eo?_7Jm7(Nlg-B
zk~V(5he}{X7AGT9*u$K$Ui8+fH0<CFd8`~L9yZfB^p7k+`b80X#>mEYE-tv&uHRnu
zN6BHI7=m)uOjn;fyakz4PiM~?QF?1_Sz;2d3|Zy+_b?zZaW!^9QkeQVE7O%5FBEcL
zM}XaUD{gNk_rro^{k&9tCoq%w5W+T!ERWm_<1Pq?h)^ZT@aMb#?}zm<*e?K(W0B3n
zcA?fNm)mue1(%(B(e44c7?O<zYd<u8`(mEZ67K)QK0G0v@8Qq;^TtIS&+~bN)}gHd
zL-^irZ57g&$|fA2y>d8dVzPAUHH_YQ>H4;;6RdWBF}52<J$q?Gk58gHFb#^th7J^v
zb~E$2Giiu1r(IE~*%7LEOB;%jxUNEz_N=b1N|$4G{%JbAI4#3K0HgLkkpgR&q#)eW
zo9DV7CT5>044@=^R5$5QYLQ~>7jOW1%p~<c!j7QWoU7ZKo;Pen)MWKA(_gp;F}9Jz
zN9HMu&Q(!Tz9NBYwJY9{T3L6Z((mW>RQH*;-L2@IE9PlsOKZplkU1&R_nVZ^Mx*z|
zy1J{EmNmgH5=Sg0l0@dva9RO^sHTUx%=zs1RqUDaJenUh{K=kyTS|;+CwZ3{a;xUW
zd%HWtSMByEeCPc(;JLz9w?&LX)L4Mn4aD>G4JZv%*T?x=;iC7_=Zdp(Qgo~nyHEQ^
zHP&{KYWR^0x&ue;?V2MG*=^7HT(yWl8>#xFPtm)}q=J^>mrL{Sdk5PJC?NO#ezw{^
zjwkf~R+p0WT63B7yx;c9H{8}xrb`4mLmFr}f46)I?KFPC3;aVQ_Dl0)w*Yv_DNYGU
zRTsZ}r6uvZO|H)lhABbcc=}nez(-R-{lOAT;>PoR()nJ$uwlpulEO*`&^Mewz$6K+
zox7415<x4+<Wu>{eRu0J3rpQVJh){~u|!UUlxK`6D8IP&h6C;M{5jp{PuV4<_nq<8
z5j>f0K*iS87f$c7QVFI{2Al=#h0`InY;P{BQKU@EaCdQ?q`2gqS=?2i*r~eN#M_n6
zU$VEv?kNaZbBYmv^RgY*_x6_7OpZ0_;i)z~hZc+SUq}hNSr)9y@yj$p=9K~Z0$CZE
z&bxGOanN*K5|W!s_U_;?7>_q5%0#4yy=cOTQ_;|*L*0s^b2=C&yK58B$0Cm=+33wo
ziaO7?Z(}aJ0hu9c?CkxPHg6oragcG}Azib$5Ll}?QtVU5o;5wfPtEO7aVp%+we)8$
z0AXs|3|@s-OrZKbC09rsdlzlnRhl`I?(e|@?o8E_l<9i6);6az2=lWA1%4kdnG%!n
zUZ;lD4A5LkmJrNVT(Te3@?-~13Ze$O&lCs;wc75kb>-suQJQh*2i`eXeaIj@nFsUy
zLF>fIGUr^8RRPE|M%Z0~j5t<}%M)aX!>1@zS6f1;*Kp!}^fsbaxlDf3aZ=&8`78!!
z)0_sV5T6(x(DwQ#013EK+;}eV-xhimTyBaK&yW8W89|V@778oC1ju;Xc4^)jkT$8v
z$ShljT#PA&0;yDSUn&5$pwcmn-^S1&h!C}8y)6@GHhmP3-A;~}F#ZG$5FFqarU!38
zFjZ$6wa?a<3akVBvX}hCyT6f&ENae;4GyyvnursjA+3O6Gt!ENMw;>J50<%q5TTX^
zYUh{h#@fj8O+56#R>4m-hv-oL4Xxq&3cK|Dg%kWm_qt<Rv>rZ;&M4p8`nJ9?<e?qP
zcmyTX5;+bwOV7ysMMGbe_Y9r`O-7|!khPSK9$TGkw9sG7l?~kM&YUXmQ6I?yV(<=W
zgZjVl!{v*DsYYW<gpGC_#WCo-9k;smxd;E1ApAJ<Yaoxr#Q=uvz(`u2oj3!zDf~H0
zOPc}0XVZaVJ7-*emu<7~#=XbpDtO@GBm=+=K{g&79<C7@4<oIK9M7c^&@qT#xF`JC
zCT)<WU)fMlsx8rjX*PLNLq9u+-8?efs<rg`UK^N^p7}G=bMeUz_R1UH&l>7u0lLUA
zQR*7m16;HNq(GMrS~AlrNJ*PTw$tKlMwZ^b{c{(};bw=*J&LMk0d^W0f_B|`AVx^+
z_ye_M4x*Top;7k>X*eA^{W`#XB|?f45)zTO*`P$Q)~yn*C|8x`B;)?1s#!HLs*na;
zB!KI{$9=LqkzdcnlSaI7eon3&(g7}96C2?=K4ToX>tC)fYeLYQeQVWZcW-;~IVKcj
z5ACMVq`9a%RV6bL`PMy{t@q-3-BFZd7EN{2jz>-ZdZ8+x%iUS(Uh%nD9o&Qkv$=>7
zI)FAZ_rc~d@)uPNO{^^gQR(fYxLTg=quWkqThnZ_r?pcXxGU7VeyE$u3AR{9Nu4$|
z>y3yYyM17BaZhC5#Eb6zK$bKtY{OhNy{b3KOCKN|e(oVFQ$W)Rf(^GmzmKhmGi0f-
zuPMo~k6Rcj&e(I&xV!1tLlWK$I2oy1(F3RWXN|dkXX|;kQzydRZtaQc4|4duExP^z
zK$Ka17Zp6XB5nhHqhnjA4=y3d^>%9BT?FE-6gqOBB_~%N;ea~pll$(V10?h(KrP+Z
zAW1a&5f>~F5o(kxYp{54pzux*Q~#hu*<e9PuN5zC>yg|}R^IaQEGK)dCsi`qsoD-b
zCqlPf=5jgE*cf-|a5p*oisB9x$1JPtu~MoRW^FVPXNTt|yi<O8K`7K0JLDBXzWJ@Z
zD`ny%jXJCl!hJZ3cr1wH5Gv9@K|aM>Rx?>q5V~;S?hOC}#Wd6^;cowJQq=r!&=S@M
zb~&y8uy0s_I06UYdqE=Bc)nd4r(5w+iE~dINRC|>Rf0+^Ne~0VS@o1Y0i(s>LH7&A
zfs84oyiU4X>4w3)NaV+lnm;y{maG%soIov|rzVM?1w_x5M{vk6O3SwbO+~(d<-4V@
zIG@(q-pRt68U0<vr|>6e-M()*cgcf}-C%OktRs(Gy=YPK)h;9W#k+`CQRax@*^bs_
zGf~x$+H(peNi<A`9c6l)Y-r>+D}+j6^&e=3cI3AL^eMehpE+ZYt;)hR2#v#SSEpOQ
z+1uLMKCe20a(rJ-64{9hoPW{44j}S-nq&+wQ5Ui;Yr57;>g^+2x@XMnIEC+COa!TG
z7m-k5AU{36ZVG3htcm;Uz|*q{K>BCZS70$Rbp87EeAmqtLSJ5^rjOssU7Vbfp|YBc
ziWD`>--+<-wW)SI)%G<zpf(G;_9?brY;wx(Rc8E5EQaNK^LoguowDsX=hAG&)D0wZ
zCt1O!foA1GvA99dzuG5c#yuHGNy2zd+HNl|FC#o~90=D7`}HFDZNwK#HzL79!5@V6
z0!v~POo+0M<tBjGtR45PA=8{Td4I6;ru*Q+iO{Gnj*J8mx^^Y*9g7!@xhsU`ktaz-
zC^)T@U;G|vp`std(g!H{4JADwdz9W~o0C7K#s;*pAi2CK{-y;awg!~m3?@52mYdkf
zP$@(e*SL&u^lQ#|ihQ>bBLsAMb@|)^TdJn=f>bMwf->vHE_=T16!HgK&FM4)kOyR1
zn13+z!zE<hh~h+3oEl9;lEA=ABA1RK&-~Ao<O-0O$B&U0WarLZ4y<K>`xrI=jM%Bc
zaw&`EQb`L`q}zf@Wv<Vv{Azz=i9{e1m58e&qY4QsvKI2F9|JrP=me5P)XA$WBVa_v
zAv*T1_Z21!Y?=3!=Y^966EhwpfEa0C*=}_?9U-RG>d~nm<d8rx-^-O|mTq&O9xwjx
zK%kAv7*VUmTzLBQDVIqbLujKR=rzdBo}EUVbF8@U>Uf~+ET~-r;=V=Hb){=07THYA
z0vz9^q=JrDPFJ4+*L10$Y+z_e8>C1<wfc{zEDm{UP<qEqmGz_~*OQ(V3<(QO966(6
zS`nid(xpV}F)xz*07!J!W>`|aJa%g%S?epyD-H1(5KnCBYVzKB_M-<!p)>}ul+Nue
zAC}*9=^IEc9bdFiJhzHSqv5@QG=mY^Tdo09qq=sTNDM~gn5{P@HX$M+5+-1m(pzY?
z820VZ7cW%^<<+w8Tvw*a>zTT5UuGGeCmH28o96c@UsDy`vr{nl46$q<U40rwc=LQa
zF<yOjePXcMx%~)RL5IZ!DG-fdAbQ?)K+Xf1RtP8i<!JtS#mJXo-N~L4CHuUP;Dh&(
zef|11%-qtC&A&D`MW-CtM9V8SH}xJoc%Yu6|LQm??R<d?3gwUuK`ggN?f`&ofNoK7
zIOT)V;L%M3gF<tooT?z;9J@120U4Eg`A6qGrZ`!v5tTXySQ3{@KjXpIY59O#-Z3EK
z^C+RkP#GEm*Ob}LbPY--b}BZ}=4(XHpFf{@PNCHTPzyHKNY){U7-eaYP3gQdCmoi*
z^|qo*f1KQSyn|akdrF^CS=qjPo-gB3d)MVInVOvh5)Nl0YYY@Jh_Ywczjb$?mKS!m
zQy|8jPraLInG&`CD0H@q#MZ&&w;A#D2`a>#yyVl;A}74}W6T+Z`}vQ?#1S9eTjqw5
zmzNI;3rqR(<;L?DFIYuHwA{8<AB3=>$8qR*%TuV2314)lTZ_V1vDy*_Wjw5zHymU%
zVqaYpm}z4=6)cDDxzcN|IcAim@RQ7nhIDhQ(PG?yrznea>cr|==Z`_(e)KPhEHbo!
z6xEg;q1>J{s3}hcq3{CRF_e(}lggF~8y?fa|B#GW5uW?0>rpJrt}<nJ^hD69`vZeF
zL2i_iCNH(t4(y-Rq&DQ9VuIDPpx><5ydwq(-mjM<RW1e~Nj?>Ki2oV~<xFc_M_<Z>
zaU`T7vesN}%O`gn+zMnwt%Q}&#lMI4eRu5^i6GJ)LC+nC0a0ISAyvJ>9e{+mpnMDW
zy=%S>WR+PRO@y6V;2%ydp=*O6-bi+P6Tw038?_u#HsqSeDOoUsk-lp?l(tq^*i8~T
zG<a1elgRwe??DO*#e5N(gko?16Q!CHd_#yn0U-K#NZ5rY4R~hKZ(4B2DWo;d0iCxH
zm9KS@r+bibAe7vkFAW%@=);2a_;&-;fAeO)l#cwIjW0cr^`TJFqmS`di#<BE5t1z2
z4QNuXwWg-o_i~tSeh~6z4s@swYB4pc+GTy8J9*_NX}EdJ+j2p**(9HW+sZ9qZ*4-n
zr}l%HE$}dI7B&am8A|cAJ@l?*VPh`8F=hlQKf#vs4CtvPi9(}{Km`G+6eJL66d@OL
zc3co|)KYj%S^B9=JtWTKJ!~%rlf%CPm&m|bLNy$?WbfUxt}g)u49Ehz`hL{#eb9aa
z(^I|*N~mzRr@JNGICzSVXyxQFMCaz<c5>4(lxulomvNngXeK)o7VMW6i-FNPw95nS
z4B@!yvR;M?*h-Ou#A}dX9o)Q0`j*6DbU%f465geXI*fls{}C-v^D_v|Y|n{c2C4z*
z#k>dT4sd}LV?dw@J4xHXJ=dZP3PgkOt2<ZgShJ>^XqOFAFbvvhB1zTdgXV?JKuFGL
zt8kAP@%_3)0oobvjNWw0AR;an=paD_(+g(a&`Zq3E@EVXDji)+M>qoz5peojafTuS
zIwVW<V#HXigL2iPj{O?fdfSSJSmhg~S0GI$$GFi#s!m&+!*OsnHNRhcI(hn#HOg91
zd7<d-CY=l9PG&z`<{)<hJ-BMo6i4WN1RjP*SzT|&n6JA`Y~$*#W=#)@_|^$k8+p*S
zTt8XRi^I1BORo+G`_>Eb{Qe}DME(B=qxPnUQ<u>0z&3N}KHJ(@gnwgq2swtLWxmd<
zjqKuo=o)|)uq;d+mu&_?bc#$MNaxMhoj~o3OyF16^Fwy7y(_!Udwmw5{B~73GvL7g
zKc1=)o(fh6xz8OR3ieJo{RW(SvW8Wu5`-3V;XD@oXj+uxvo{}Ik!TgHNls6qVYELI
zx?GKo05iInQBPG@U7to#o=ZdI;jwIqNHv#~q@{?%Ww+d-5M`oeRCR`Bf{Qcn6_wK8
zpEz2#Vd0}I)CLvL=hQPq^kH{)qM%rF4vwBt*j~qBGth#_DKrA^JD&xmj-d)An`^rw
z6mT%_$2LtTyDeo9ye!421k^rlmQns83s?X!-|A5EY~TN3fk^wAGwFY2FyZc=f<@fM
zB9F)K?E;A1TkcT~HAcsvJf&u~M28Y#=4su3=u}o!%@)sPw14Ec2?`INOL%?g%UYA+
z)QsQd)ch?^+*>EyU)qTG1llmEL*24G?g^rfowraG87Xu_q_K?Njr$n$Owh``7~qdA
zD=4ah`m5g~W|YHYvzDC_km$_88Evg_X4NX3R%qPtfg|O?n5OEAs~m@@s5UgM_3|d6
zNfEG{od$7g>uWOBel;GHV)6AJCn```LUdcu!z|SOlSN6eyQL}L-om!F)_K(e=iQi?
z&KGS&ZvL)v#i4s=EZ$|c%70yKvKc*eOV62+FIDmPmmw!ZdkV@?;7kK<oo`1$Wq%*S
zp$@Ey_SM`?%}{ThnrlSy2??g(o}HM*riO%ZnS6o{`f9IUy|T<WkNU`Zl~uM-Rx5pt
zK*3`D6%jwgvgaqlARH*PO@k~sFR(|Zdj}s(u!a-^WK(WET($%PS_Y$6#upvGFn?-s
zT=HKw9~syUCr3^2aiXH4#54kmLmvC3aHL%v@1UdDRgHqYSv^W`qylUz(nW@tnny88
z#Er|Mmn%Lo5tD3+TCg7>=~YrwH;lB5Dv_injR#ZkGa7rKpEXB_`2r%zt&J>_m7G8x
zg#OOh-Jo-}T4uxOAtZ$F=eQ?<tpDmEf6mebk_ex~CM(>9el9PeA|Wl`tZTMxdqDxT
zpXpnwSIBqQ`>d#ho%0|wjD-Lg?L&U`u|g%Pf20e|Y&+(ma*0v6dLUl0X6B71QgHx~
z)8B+k&luZ-ThCl>O-`Zv5;72Bn21(E#blp<WVT?H8E<|kKr$cERKLHgBu~Kf%jI5>
z&>+25W+5{U<F$%}?i-h^tgIxa?D>GQhW<Wb9D3ehLNeiqCtekzFJyb&Zt{q6gu)yu
zo=5RAAJfBh@CxUOs6<SH3uv=xn*2_jY?*%)l62GY?Mc>L+$;p*<$VHmU8&|$uWK1f
zLCg$g0ObJ(0$S9B(!|rrDJK^f<OGu~Kv3+)YfiaArZC%`9Sa<(Kw)l%4&}m?Q=UF)
z-8S+F>D=$?!(h_2Y)c&eSst+Y0Zp}@pCrgDR@?%L_6&J6l7sB_J^^CFR)x#I?Yp2r
zZF66?L8X2GBJDcv;FmsL_xq&Wa8m4v-hA%r=0^7CpV6M4D2I^4Un-HLXIi$4WojS7
zV4Q?ZR|_B&JNL1FgeS&L8UOem-1}%gtx*JtMYl67U#Cx~0YC>5rx*aa>W8ZL+H`p&
z7D+5qH3T|s9{j2*fSX6BrI@$$UnAOuK1x(P<{yxE4-J@1Ku!YS&5@+NE97FCSNb#3
zN}ir>UKD&66LUs+)>!?$4=ZbU(CT@lUSZ&^lP2y)Bdx=$S?p>9>=$9G=K#f^&rZ)N
zjXLE@I9uHl9O#ZbkQJgL)9Q{j@>=x9LAj_ZP;$lVy=H3@A=)ggbDA$Zt`VK{ruXph
z`0vg+d_-6<=tT4mNPrM9g81T7^YtDNPBAeNwd9KA$a6dZFvgCY0mC|0;k8=%+440>
zD%DVL%hi~I1pnDj4@Ip~$Nr`$w2t&veop56ed32NEw*7kYeQ+KVZ(=*nf638sL}?g
zrp^bk6p*^cfI5_wf#EyW;IOq<4b|S3Za*VJL)kMYhICI2C*LRD9G685YIeTRR0T6o
z(yaM&j<t4D^0yq{8v4#9BvI|bLKqd`PKtj{y4n#AbqN`IMoP>b+WxC%DLiZ6`Va09
zYfsSY+1S`{d+fO)rMc>P#_s^NWr#TWA^d*AH}?}KP87qI<<|ZF2Nd04QZ-SQXCd=d
z8!MhEkSK6CE-Mfb4{yu#C&AdatAn3C|98#+JM`<ofpy^dc<FQWCiH3e`S1(<MUqld
zfvXOmcXrq&>FMdIgdDSwS%?&NDbCeI(Ov+1eZAVg9bqz|e2H<LAE9~9G~vt#{}QUH
zB-_J%?>UbV%!0?i{kDv<Px_q_*8w+%O5uTEVR5Kj8;Ul&jH28UpkPc&UMJ~iR)Exm
ze1Cb0iqATFXZM8m;VflP+2&T+V=V$zOzKAhDkkr?=bWsVJaOtPTZWvZNO(GAw<i9O
zdRM%(5e&WSy=3!??gx3<pI>NrO+sZ|2edcJhenbT9xv*pnG|%QAb~c4TNUF9`V<po
zXbL+{M9=<lauxfgb-;EzNU1j{NLe>(%;+(f{x?bF4f|d|Q}D>b9d%%Gp@22qjq1*E
zv&rs^gMp-{1~_W>F0=zkEXevwg@lAO;pCRCm!~dvKt&v{5<zqdA;@EmVllIbTON@!
zedQ+ev9T4lH@A(s3im6*<khf%W_`2n?`>j}@bC(^Uuws9{ho@W9{R&`QNY<>@`wk4
z#jv(GqzzejKpZ$f#X47{a5d=BAxCe3=s>aKBQArEwR@E)HE#nbRRvR8$D3?4Hce(?
z_x?T5k;VW<5CyY1R`am&*P@P}+ePSE_0h(w<Ua4OsC@#xj^dUk#9g3T2o@nlZ4iQT
z5pXc$4#->Gh817A$#Xt#e%@GZtKefi^`h|r4+UiwHGju#2;3IV`Y03BcBX5ySV>7q
zc_2;zC{70Lb~42LD;paQ8fYNe;q<yTZ(`~osEm<-oL(M)GpHzi@onGTlNpd`h{z+j
zO?#=*`&Axy@7NkfddsROH{W$o0{kVh(QW5`fw#13vKvD5h2M@i8Tx|YV2X!|tbj31
z6<x@hf2FeyXDQCbhiJ7yYeJ>b&EesPzUTSn0q6htt)c?0tfHkADe`0ZNGY_1fEpX?
zS8iY_5<Pat=$wb25R3zun$zo<^9N+V9q-UwhFeruhXUnwqdFRdh@(EsNBPend!9uo
z@ghAw;Q@UZM*-`>z;^giKNVM|cKP0&Qfhk{89zjedG5MlH!dVHGQ(eF>t3!=b7y~%
zjUBY4QFgWfPuk{r=t!RXt}|vgARu5B%JiYzh?;uV!za4Bx-6wU(L#q^OSv}vny3Wb
z(%pyLa=wcg<>2k+;*^lW%igX`wS^+Tcmr5&!M)m&HLkFvQx0%7z{USnc)XGHJl}BB
z%s`!PwsFl{dgKn+M-b#=Gqts2Vq%b(6#DZKHb?M1x&@oM3u1D+TE-AXbqi==INP4+
zzrHYFW+(L5UpK&p<)~gk{sK}A+YFT`G5|dwF`{PioPZoTl#+$<eF|LIGLLW{u=@l9
z&c)kpk7NJV_N6LK^7|zin1FOZ;B5ei(m)y=#_^jH3R$;Hnm}V)XCr+cUO#VGEZerE
z0Tl>D=j{->+Cjy)8xV3Sp!U#-f-r-aR;ZiBbHN<?)#MR|Zf|eTflRs!sW39AC%5P;
zkY<*RM5>BdJv{cmxx?WVozN$OC7tcdIVb_+(z$&XJ$R+fFlrU@jSnSAAu&sPH`pq!
zjwf$78?pxu5a1}3Kh(<-0|2vH$C{~1;{s&T>d^k4N^5KD^Xdk}T+5PW@pRSfTf|gc
zmxLmNf_~z)@}}FHQ&DShbm`dwH&<7{xI|~Z63{6=eE4u=87kC~LX<5)<|f9*6dZRT
zmczEiOR>iDd{IMcL5KPTlB|3#g}ZS+K51JWgx#{SdM&0$D<Mq5DVukO6Vc%#SgQKX
zGJ}kBKR5nAA76t12*Z2@{Z2A0KJo%k&B_a}ckA(NSAYL*48l3(YG@TA93RI=?s?V6
zDAK7a1qm$X+N9X?d-ikc*;tjgVXU~-Vw4dcgyGc{g446_?abF}>sVVN+Lg^VBLnTL
zAM@#pB>a!<AjkM8mjeed)xcRp>xsS1DFe>Xc`b)ZGL<BiOs(fQ7}0D!d%3Nt1+;AK
zgxz?u@8->$&Tt61Wa&cdT{<N>IXN8{(9j@=mx9?57B5iW@6!%ybroco<;sH8F*YH+
z)e0jM<hZxA|Du8Pr+#1=@Y;j&5Y!E_n{a-Sa++$)8Rk8bG$sayBskES1TE;iYMi>h
zvx$#@;}jl#J)F7@J)p==pO&AsL!2m=@mnm1QP*)rays^05z1bfu0mhPeVB(nakO`t
z_B83IjzQqy(Mud!_$DLf+(CbGgG>zWO3tQfj9mcoJ{c3L-Mw+E{{2-2I59gOYmZe@
z;dixY?S+_LFtOw4=ck1sYEV~_3Ob+e`9NS8KhMkoW-=BK7uOz&NML9Lf(`J{l<}X}
z`}0e5$_3*EU1}clEK_)>ff2LV*jS{d|C>G7W$0iv8B`1eXM%Ki9a;k#qNMdw^I8V!
zP|73|TOYGx&mE-gQ08?=0tV|p;2Ov%JHVykyc;wsKZ4mkc7PfKsTt`_8;U-7D@vF<
zKI8_)WybrT#)4IJbw6=lzy9+r!tN}q=&G))ynO9iR#wEjcVPEP_+ddSl{7Gw+DTCp
z&zyyK2<4w3&VJBl0uWyF-Tz_UmEZtf3h05?2EkmqUu9J<N#6T7<9#?XRrzy9K-Fb4
zG$VQ}trw-j87_@fAy9QzE|aQjCfVIjGJLZjelY!2G&$Oco*u6a<RpsC!i|l^Lc1SM
z-&1GK=waauOM6L+JJrdsGDEAAkl%S7ekX|Z2L`WZ-0#Xyrbub7wpB8YmSt2rfQ$}P
z4KA<^>jp$dT4G6vhyc!KX{UshY+rpP^V8Vbi0V_pN*cd55m88c53Zgso;C;#-9rWn
z5}iNGrV*~-xomLn<hKh_sMGd|Ea?@r-ZzaK6h(t#K}bvqJ#`3mV@4G|lgfU64yw-m
z{heK;sc!T=GE*#DbbUiZ<qdjAj~?x&$t@8oD%jtd5_KMVPQk)e1slw6zMCVl!|Sz1
zsdGL?%377eYUUm~_dQMi?y)}~ef73X4@t4}*joU}IFOr4CJ~_KHmDFFg}ynJKR>I~
z|NgGM3kJC*9p5J-^KAwkL3(dlS(yPX9LL<85)nM{F(Od7i04bheI}EKAMdE#){qpU
zqcES2oIFN2t|xe4@lw%K%|J)d6U~i{SMikm{QNL&?6Mie*1Uld#bsr8ZVr08^XV%`
z>I5+4XPC66`(7a&X1@IUFG(_ddAC^3+B!=KGtD;Ycd1y)v6Z8~KG2L>)V<_5F_q#k
zy<0H0GoPS$i{O7|AFE<(JJWUtUYBKBk}F$??NI1>9RIr*^(P{x4|u;Lg8yNAtN}6=
zmDx>4Kw9wgvosrMXyp6PekTi^n<v6Ba;?L5hK3156(Hw-><H~Si7<KssRf_#e4lbm
zecrr*@-+SDRdC#mGIS{VC{N15cIi@5-QETiWtz&fe?ubt;p~(Zm}%6!9^5c`OBeZc
z)=U5Ud*<H9;L5A)xf(|0HPE10<9Qu)y@cROGl7GIPQ)CWVT|hQ<b%fGK-A@!9*8<o
z!|Jg3FM$h(+T20``=#NpoW2k8JfIZ_i)IJ|w?%J9iW@XI<Me;9N@avSu*6Yc^1+X&
zwl1xKJ*l(Z1r7k`r=kUQg@k|namn0BUmto3w2sUHqZAw(s@6?HNGNY^o~H1BA4M9J
z*(l@4Se5x)aUfx_8~*a-TY<m7KO{^eLB&94g1%f~p%+@%T4F8R6MW$(|Bv4^50F`|
zDZ5sQs<#fGEe<3*=GX;@SDt!#v8ES*Z9?p88B#Z*VET)~_2=ptc7_Q_gLsvYl$70k
zv9C}TEVIpYFGLO+`2<9i!wqe=6~w%%`BM#{v8$`A@|l8A*FD(~s*s%jJ8lH3e43||
zY=%7H+<@|>>amwB-AcxUv8(0WM#@li1Q{4^L$7grQw!+V`T&lJI@t$Bz0T0jU8-~e
z`iUumJJ7)ZeQetL={Y#M-xoL31whY+;p}Q!1#4?-oo#p;EVRzj$=wGUmK*5MRK1^F
z&zhzg0S;F7IN!s+3x(8qrXatW+rkwNnXj`auLE#a)ZujCH=`WcKsRa40W;7QC@Yc-
z^`kjU-DhNlVqmzlGl@~kwzhc;wYu5U5J9v;1w|74oP6Ea&#$w5Z!J?Y83!z9Cqin%
zLEUNrK_MZDAWNG;m`>=s4)w{GB<gBvTA+Qv+V?ZEm)O}em$u9@io{hkAKi)XP$q}n
zu8b3QS%>B+_onQt%a=SVz)u!#WdRGERQ!vv`yVFn4nFe^K0CM(a*>g?bM!NsFI^EF
z2)bOQ`2iPynT<{6KY^{hf<k%|!O^3LYIzn`Te1ykKT_8rzQRH*2rm_oUbdp~)3~AX
z^6q|!Z$Wedp{9bpeF1h8vX@y{REp4kFxf`2;MhrWV_2*UYzJSF^=n8z5G0B%ym>w+
z^kJnrk6F`4ekuXmB<wPDn*(u_>|9qSJ2_`2=(fvXQ9z|4*(oxz_;{(16bMq4t8;U5
z@}P1azNf3!Acbh^(9-Dc!H@zZc0@OAcp=5=f<`@D4hxz93N{&*Lkx-3ibIBFV|@3q
z(MunncChydyL$<01q{olr;SUYtX4hWG#E}0N6Y_PR{8gm@T@c7B&x7>IK^iK3VY;#
z1Cx>lZAO9mOm@Og!pyzc+1(vb4yn{_g|hrSAR`%>nWX|QT)c>2=62I9Hw|;A+_dPq
zxs_nyndu1f@F=^xyIZDBj}{m;(-NQO%gPc4WCQ`e-HyQqO4Rw+#vQP9<v+%u8vu0o
zdycDT!`z1h&)LF<`V6keD1zl&soAdxZ#1-9=vRY4z~139$;p!^+aZ$z%F^7pBk7%b
zf%(h=;6*nWAO64Z09`LzzFOG03@UdV+!Uz73qbB2xUz1w901c%sE3Fw^uYCn@a8XK
z{=fgDC)v{jAshUh^=RWkD}qu+#I*;mv_GNk(t%5r{(m{Z%D#bPsH8pU)3Mrb)2s$|
z>&DubUmW`HZk<%+`Z;f%&zL_B+z#bAbq#|#_(PPZ(f@WB|KHEk|9XI6F2OJNVH`4{
z>05eS9K*!qWD4LO0Dpu+O#&MrpbjAfD9^hbKZ}i9Akqki9I&H95nvYtATjO#oDFK7
z`PDW2d{3@A)b2wORXl`)h-(IaIy+d3`4k%)A<zW^gnY{Z0i@a;q;N>R2W-oDAZ_aU
z`kefkGfJ2tFjN9Fg`_pWolFBxwgR<n&?&Pc-%JIH$ocHpRD`!NE0N%!{!e>f9!_=M
z{%@X{rst`qCS^&YNmAAb*)>gSP)Rss9SjO3`%W{@6j_@J*`=}_9I|8yeJf0|M2<Ct
ztj8LTo!{$C%{<TV`u>(bzSs4;e%JN&*R$j}=X2ig`+nW8*XzFT8@MahSjSH28h{iu
z)bB*}!A!LjM4XRPQ$n*X=`$eRj6qy9(M)KFSPZ-%!1sCFxt&nxpoO4tJ_!#JTbuxN
zA}S@L0ps27-oCw$Pzh*O$D7bb4voDk;4AMnm$kAqrNMvT0DGqfX16uMH-QOc@|SsZ
zJ7KQQe4gW8eoH8QAKEqcW1Q9zIj6y&eiWS40En~i@}Au#a5m<uYbxFl1d!yQLt06k
zNIlYEaxQ1a-rmB?%WIB6T=CfAwV=W{xW^!j^Q)PdnGvxU#UO;~v5xjRWFaBR&~h_z
zENZZYP3L95HzHL#Suh=pbS`MG0iQuTf=pnB0UmrGmyoP%D;WtIj9?mVV~1AEU_^D@
zf-9dNVhR}?#7TSlF<?6{uifmfuIA?E!PcDQNw_j1E_pS1V333z#F>hWC!N-qE8J?-
zh4$?rP^C>js;VApRzPf(A`@~RfWnj>xY(APDsEA=^MzTq_Fx<yr6qaaW8;EPx_pYW
zeOFgD)$~?*{*7htp+m3cpl}3N@_1Gc0^Mw9W$n4!+!5H*`qcW}j+lQU3MId;t(Atn
zMJe9a!m)y>;Cn|luoPTGPUy5eSd^;_Y4tEDo+_zvoVsB6M*R8#7Aj!arDSrrX~ZjC
zo*r#)?)Fss@yEmYwyiz&vtO}X%kc(8;m_3VbN>%d3>JChb2Vddd<T-YMO<ZA-AZN=
zPGC$o*b1q{#Kfv;FJ26rn3zkSAqS~h@ZiD2Jf0)sFKneF+`U(!8C6L(qX<=7PH4OC
z3nItrVNz1~e4H?Q;N%~Cp)v{@=g!>{cO82tQMdM~4d21{vaJ^xOcM7&-N+_pyX3(7
z2d+fc|BmG<E4YQ|D@CfC*@d&@#`^jm;YEccCAG1+SC<Tlna9Tz0W^#-bhs(>dIQU|
zQAnB(of%DR@;)m<d=C;631E5o@}(IgdA~D?0QGR;LvNdPYa@@GLbV!JZk8>_vwi#h
zmKHsvItErJ2ri^dg!(T1K#X@Urhc}tNJYV|k79~$Z(?F1b@9zHWIQ;h;?b-hKquRe
zNVe-8gQL$%U0q!Zm&18(qT6f$gASeEKl{S3asiWWoxlSe5)yhn?1oW6J-xloFymsj
z&5yYClmWY<p#wFs^3B~erb}N=8FlTo-0%jbX}-(UmYie^`^58MgS|ODU6=$5&|Sg`
z_$nTuH!4g{$;NvgL)fo<<dSzu$92~paf3A*%25i`sz(nWUhFFMF8A4pt3wA3LND`O
zJwMP|=$P!m9Jw?j|LcC-S)uQ~`<_gVx%wXcXCKpRO&nhFFF^Rg*wOI`#SY)q<%Piq
zq3I#;a$r%e28IXAxlR0(EeGekq*X0<hg)y(*3aK*r|+saA-lHDh+vey%A~Ea)Yw1H
z_SfE(zcxI88!N_ql?bk`T?P!TX*`fqFZLHm4>!~7=rrRJ4==f0ELZD?04}@rIoa7Q
zgK)m}3a;L_@2}oP^+AZO0ImzhKV&no!OjOuzkzndoorf>HDJ{w$R@p+c9@y%VWek^
zX27B6;Fd}L+^~L5tF6LR^^pohn@x*AADmjrXQdF;FVUaC>~s=CBPu%(w`!$bM)atA
zo{JhjsCt}Jg?}9|+QiDiVO1Tk+%>#g0YUQTch1d5<xMYKJKZb03k_#R#>pj4NF)#6
zRs=@xvPtlOoEQRKzQC*G)iO7dQx^n$-Rd74EaHqPf;e)GQ4@J2xn<#PqRM9T?&sG}
zg1px0`tafBUf9qMGQUZ}L#EJfM(h_8tGkFPQ*`G%Yr2He^0XG;(W6hYOw`l@CL6Uh
z*xHSVW6X6f&ay7fsK;|lvCHV=;U{jBW)IFpOH-DQA3q)hzE@r{REV4r#J;-zkQNN%
z?DOd;IT%+%tx)`rd0>*OQFjD1xWI@}ls}kwj!H!@-?@D|2!*xYj4!j*%7}D<j5TAF
z(*yM6RXkL^6NF)D=NAVC26iLm2r*eGlG>MFV<Sv@CgWXU&hO#j6f={+^7FQt0NCuF
zUzF?z>_3Y0lAZ})Yf{aMLB6f{^XJdg7c?Rz${(zqc>};<Q;EneL+>^|?!+!EhqCv}
zC@S=fb|V(g^w?^WxU94K=FOY)U31b~nw1udhmrVFQZNo__sYu3b(>o#F*~f@Cvsn)
z=`k*VJ;`iN(-ETPCF83;iD^k4Hqc4TFbvwjXcl!%TS-4^qU>Rl9LqE2jHV3f#kzaq
zFVf1MJaNrVa8q?~a0tj~CuUq7|B>UEH+&NPE$IuPoZ^X%?uPD2E}Ruz-%r7n`v5_r
zf-h9T411F*WpInKorcaQZ>VvXrg6=FH2^2svr9bQ)0$M?F-$f{t?_G^Viz;Ebw2as
zkN)-;aKoX47VAYa4fP~ws>sY&b#-FQo-xED^^z+HDU{-0=N1;Yfc<kk&rm4g^#%6k
z)WQR&@0}YqMAFPJ>W5E2Y$htPxU9Uq{KBYh&lnEmvq*4t!Q}a8s-S-SsnBEsuP&M|
z^cw8{sIhsrUXZ$2N#k%7DGD3^<s2_e51Hq!c)@jhc29g>sTnfGG53*<;%4ib-+xXw
zF2Rh&z%iyf^n;d>=sS1JUshD;&%`UZJES_kb7{Y9;S>5^d38RNE)~4{sPyB<k4KDi
zQ1?)c+De{24Fc?FFR2J4*~c_y@#_%?3}lN}d9AF_#o8QqdVzrU)*O~+|3KDAIvZ>4
zJOWxByM-Tnc97A1<l^U}exZt$Ej>PO{S~Iq8G)b&DiV1sg@rl9Mwt;pET)f)k0SG4
z5DtIN1OlKxb$2Xoo`ZA%4*rRpG(5e<%`r;e`jC>GP1}E#7{Z?s%PYmGEG<BV>!X-d
zjm*+ACbgaeM~R)Lv&55M734}Y!i?cqcIjNtW_Uw5);M<~`*JoH8gXl3a(sHonMeth
z8>TH!D?VpYL2Im=Lcw>*lniDjytCH8PZ`R7c|0ud)iCQQ*SZ;zm6SV!Z6s8rm7t`G
z_V&fXC1i?}td4~5zwB@yh<#M6=@cAy#3}^2e#BU7+Id1C&h0o(zYFaWxP=>AcAakl
z>vWL0cwBQ0K<-a9eQ|@(4%PitfiFJV`h+BR?GW6E8s;oXUl4>6vtn=EO2CJccN)dg
zgp4*c{kWQetJ2osP&%i29-#&YY19}{p-V3>F!Mk1x9pNQ0Y2^eC`K|3!Y3lVC}vo2
z4#D~QC|(BH<bI=jwWVV=N3{B4$uk}DT_3@e*^}7Y<72vrgh&C;QCMjJl}e|*ekKd(
zBp@_Y%oh91PGjL#Zfn<Hj}6u!%4nipzTB5K%Uy&K&2)f1M0ElA^r9I!wks_*D#|*?
zu3l=gR(@Pq%85UAbEA<<tHt$E%#bHTPNf$G`1yw*RSMc}+_=#Oyx5~!J~or&z$U}H
zYuQ$}r*+9L&PyYO8x@;O`Sa_WGun_6oCi%Oy}CBT8FU(phR#yI!5D-T0Q~Zh*7jC&
zkUW&)q^~w?AcE-D8l7C4DQ9`p%4Rl2SHk*BhH5X({t9KQ*3x7R57*uX6d~<gaM(8#
z8fnmq$BG<6iE!BEvsg2H?|}^s9m-+4#+RI{JKGp(R=#V$#%4b^VM?{*v}w+aC1r1;
ztR5qSP?LuIl9G+!V#E3-@>CXY`1!SuFw&ae2G+iPaB3U1MsH*Y1wBS!Y;Gr$w8lD$
z_ry^jlQ^r}-TE#nxpRjgD1Mv)(-&V;^B;UCrWz0uLP2-)ruM?Z!nXX&(RX0TN$?;-
zYPLBji2T;FLH&+L>Kt(7$SasYW*7;v8aKS)BQ^*>&eGD-ruI`I+}R6GIVJ9bQ=}jX
zZ#MQk1K}rpKXF$hBc{J`0|lmoYicSFM>BLj`#BHYj7bZ3rfZ+#7cA$tqbta~+pqGW
zqr8XgZX0iVf-`k5;-l+$g0b1^7loSaG981s)o(6`@G57LiDLQ7OG}LyW9(<+M3);M
z9W@%^v90MN8Tpxxo3vQ%j`4ql`4Ir!@;}KLD9&xb-WBV}gG&OvExlN5ol*Pxbxxbb
z({x8t&Ee*3ttlVpg*0x7;Ol`gO`F>7Apb=g7l_;xYW_jDrkraVONt;wU32;s{FCmk
zW|USNdEAbn=+eP}Wl&@(NTVrsU8Nq2qfgYASlm_h7_XH<0kdh+j1{ThYNH<eftS7R
z)vNl&@iIGSTQs-uGewGS5M(*VSW;m#+CioxV)KMwzXM&)B$C`G>V&QFio2mA$J$k9
z3l9r>giN51$*#d7Z#h*K$B~7fJL8$gA{3RUdDloaw~U%NUCv0)^8g^Eh%I~#31M0P
zO+a;D8hKW`5O9f}4Ms@yJdl{A%Sg+nP{zp2Va$?UgEEC5Tf4|zKBmI<O_ad?{Rh!P
ziA8P{Vi+6uT{VEQ^Srfn8Uj9<`&}N$!T9|Yk0#*344NhMM|mi$O$!YJrr^4@!-?h2
za;hsk3Z)Yz-e)1TW(5hhs4v_g{kaBVb4hC<>!PmH8EhIO&HtwIn4JUMPrF-am{%Lo
zM-CWUT#b5s<&Lqn@Auj!dH0VG(cNjgI5&4tLgMjha{zz6ygPU9#1b-$UXMOS+S*-f
zLC6|4*22ReT2Q}m5hY&6{`r0P0At;_Lfd8I_v;TtX=&nz3`|71{ei)t{$s}hY~0du
zdl!)DhdzamOs1%<fiOGp^-jh>6)r2mj%+&9yGx)Di(-C*8ShpxRpe@8peZBA8Y!$Y
zORw=spDACvypK>Q5=^}*zH4e~ijc~rJVA^q0-ul!(~l<j*1|EwODWryb10XMx@QoQ
z$n0+05R-Z-&D$smMnl|<>zm7j7CbyGGNx|#em)miUb;gt)OE;+<yq<Uv=h|@B6MVz
zFs52OmOp@!c%U&=3$%K1dI&hXa~Q0i?P*k>mp9mE5S6%06o)%^_+XJtuGMOg%J&hw
zN+)+y6A!-`O0iLk4|bf8;o`|bM)fmqFso$m-e-dEDGhCmKc9nXwqC%7m%MK8zsN8t
zGj1rH86?hEsb<3m9y7y-`1pPRRk~&{f~3FGI0tZ*I5|u(u<_<pRue}hRH&<u$T)w1
zSo)XS6cT_E)Z}I}4ycs1emD*am*jb=HK1M}=s;%-p?X0%n)1dlQ-9#K1Ulpi{a_#G
zrjepBa&c?I^MI9!(g|uhH%xp#xt_+^89OsV_5(mmxn7AG&@-rB!#ysmG&2%yoK?-`
z%;RqNJ4|;!ArbRh0N2qgF(~-<tCAfQfQ&=A*+zLx$|I>#VMja~V@%Se$UCSKUkbk}
z;c>{OQc_wMEshmCcQ`j+f+xcRmWDiZeSX8M^lrEaU$s75R$}%%O$z}v8;~FkVb9jj
zu?!|&VJzxw9W^NP@m5@#c!7SWf{t5-A4;7|6&#5+C2FY<9pNkLb2Nnq&^;v!JC3ON
zG@4yx<-sDyixdjwI;4;UIij;qBkbz!S;Ph0xqG+4a2}fxO}(wJ_~A@jSQ$sb8hrML
z9AY!sQ+~U4bVA(p`EL#xq4@>YqZw*8UKjfDI!|p&Qio7`qqjB<_k}-h+t#g3#2iM=
zEUFrwLy4s?Tp*g=r0XO~$O7Nh#duPD5{jdx_Pq)*cEEnEi_{_sKvwMCf$M7}+T_mi
ztUSnS>SJB`_}BC=k&4(T(|1lH_gL`o2?$h<-2>G!Laty5o*|`@z~^svNxZzz5Gpd}
zFrun+z1=l+S27}_YqOy<p!nTp<klG&Y&h~OgLau8aiSP!>U&ZfxJ<$eSy&E=8Q8qW
z5=ugw6*ouKST4_&t>(4$IU$RaDQfDmetD&~<h$ICM^C~KX&)X08oK=uoE+&LrxSG*
zbvx)lVvSwE0wE`?)~NbfLiP4pj7-L#C-E*@wzRlu{$n-aT?_7^`dulU>qzj}OAaA4
ztBf{&?|ns*D$19%op20AEx|W6Dz8j#qnhI?G@=w9o8Tm0(t`F?RYsnW@2A^mjLMA$
zp@Ux)S+gDhacElB@L!K$ep7h1d8M3__wnPuW<LF>Mm6W(P;-GHB#pbkW=}p0z9>Ok
zS40`)VESgUrwQnF5)u|Z4P#oG!IMk`To2;9iBk40)tMWDs?>xqq0Zf9#PD+OCpx8J
zHPY#-;nJ0JieRkInX34@Sy@_!&#}cbp?kLM*}-xxkdAUGS$?eGh4tl<p`>V#a%hm#
z<kob$EK<Y_PrB%foqnv!hYai8)ru<W{kp0M!&GGsB?Vq%W79<_P}p{S@9jY9pvOhd
zlZD1xGOIra*X`eayB9np)f{E2bysQ03v)xjJjy7*2|MG3z6rODBFi&2uxv><(H!}F
ze0(!l0hx*~$AwnhePcIij5FBdiCe+lHuvzcv4+^tY<NIvobd(5+DZR6xNvRL=55=a
zY%0RaphL2<g{RH2c(^5P&#qlVciT{NR)vcirJ)o)q^w*vp@)PDB%E$0wkaZMur2ED
zUF{@BU?v?U{MH(+>7tL-7L3`yt!0Ql`)*xx6xeyNZ(e;=tyMoWF*n&uDYLm(V~IvD
zj(^Q1PBrieV!m!=mxm3AM4jS?d0}~_GK4%3@`4YVGD^H{Tgz@JAbm*TNoU1k{w>KN
zdSwBbH*Y(^FI{XNr<==M|Bz2V%ps7!7gUJm7`Qq6Th$M5xQ|`m@YPo-Q1mF3n3}Ga
zBp$%D1D3hYz`YkVfJStsp!;hoErf8^NK67ICS4Mty3;j`Dt+n>R@M(dTP?M4Z(x(P
zpoQrAC;UAsN`Gz^(E?W^YypeGl_=)>K@u}^2-H-nHQv_S)1$U@W1H@u1w3BbAkU^T
zMH7#WsqDSrl&zFL^Qi6xNdwXt=1gdvJ;e|^_mp<pN;BGQXnAs|c+1!ry7f}xAa#co
zzXCl6Li}z^vkonJoe=*wc)*f;aN$cYAT*02A3#Z$$}iEA`1sP>1H_vNt@e5)SUnmF
z6k`-NeOzyKZ1O-RAdu;e6@m!J#b8~({+?ODHp60`B5TnmH<n{M5F9sCVFX0UUUv8d
zzv?f){8GM<)fC~&gF{Woby#+D<k)$6O~{iK8){b$%rr&0K;}@qaNKFM1D2kPdE0|Z
z2&U2P|MczP*LCZzSAGA6`<hlRw7;|x)M<V+Ma*>GihSL|oSZzw{F@?%-~^&xOaro{
z6yL+}@z47VUMpUy&+|a4qL^6dMD@LnXx~%US@?L#I16F}^=Lns4x1?|ZH>WjOT&Gs
zs;o@T@&;Q8a<Q?SVU$-9(T5Uj4eAjQEzKM4q!LCZ)6_(iAx7!j){*(ODoTT2Wfhl8
z>D7!o578-W=1sinpkVcCit(rX2CmZOFo&52&QM-5f)G%o_;@O#@l-&Y2ZC8l&;MH7
zkEH;!>8QYPv=+^dM!~+Bhe`A*Cr)Tz96=R!VN^2i_usoGzBGix20}#L^L*HH@|h+n
zgN4KvU}#le$}A0OT>BUoMD((bp?N~^Z9MW)yLAxuK%04UNm>6)2ar8MNEy2LDJ&#D
zoC_>MHD@*%|0xOh+t5lVz^Zn65Rgqk0XfVqB;Kbcniz;=H*u7cGkX2D(h|B8euv+}
zX``b7ZIaw@%4n5`V#%Naw$Gl!Tl`^4*re1D{S((mi6(<S=B;%)7d}C@D>Yi5tCT@p
zHHaXa+ld<z>IZj`3-(r1M!)#-YndEL^ymKu%sm_+lz9#g#qR8T_c%r1LV#QUY-BG+
zuxD9w82$5dq@rg&c4lsF$5ff~Q21+BnhhO;>0_cGlios1QH!lSyam{*W?1jiCme8)
zN`zP}jj)JZaT;l<w?^aDs+qT%#}|1|8QrM3@}O5dcfqQqB9~F?oHC*KWh0V!wNGFs
zGd3oeI*%ZGfxSSTQWJf&ZdeZs1k^`f>#rVNi&!AA(y%m$QVEz7ArYMY^D9;=^LliW
zVai~XjPtOz-><s38FYe+$O*3pSTxV^MU{_xY0`35L!+5=1`R8mFK?b-%Nm9^t)|M{
zKPujLKsI{66JsQZKU9v1?Q(Y)o)zHr*XCV}f}ewdv_L`)WRW?UvO{n*Xo}6DI7`Gc
z0P4ZDO#w~aw{d`MeJ9H~h#j%R2DYaeh%yFycB2EQ!v>3+A7PdVdp-uFV{@E|wYOH%
z>Fk9zYC(aFk$Yd53(cMY<@c{hYgIx*!gHow*)nB#_j~xH!RcnnxeUa*>W^0G-~IE^
zH5$2gSkQgof(7K(8g?dLcx3TS?I``umH8~I;qm!yC21{;bS_hwC=h9+4$MTNHpwW}
z%y~Fj3n(2AY@*wuboKo;P-gnW+%PmerE?Jg3bmwhOiIOMqc46VEH?J?Y!nDMvghjh
z=xra9gH_~ukYaM37P|Qb%UK7k#p08ZX}P66H-H`8Ijzc2r!Kxp_(f+mIo_x3IvXOV
z4@M%0L*%s0<H6R^U3d$$yYt|l%%pUIsDg8Ib6FSlh1q*CVE6HG{H<F*edzBGm#c%|
z`3-1z=TY#aqs(oaH|Gp1gNX|zAV9DkXDp*B1y9qD<~@>KnVuOO8hVuYo^n=&Xiv@P
z%J?iqWpn2FGjsSuKX?GWhhp;vlfg`}1f=DuWnWcTdDC0;y}cV#_#{;k=WRyQ9@Pry
z3Zt2mH%vL<43S0&m62%!x%T(?c)O{n)s@*Y$};l!CEJc7y5%aI-Fh>#A3sXCz=$xc
zi1h`W#939uga<`hm>m?v+lGc9z>5nrW$0AEomz`Emjpi+d1@*|>jr!NI<*}~Qybmp
z*4Z1!P|s9ae?e3;<U8Gw@1)_|knvu~T@1i*;%_`=$Bb-8y>R-15x&Ca>!3WS#oD2B
z?j+<Kx)=BaDS!@Zx*eWy#hIfW+UfZqK^wX~VfKun=UL{oy7fm|r4Luv)JPHL6eZf*
zG&>aKLEyps`t8>kmPo;mzq*3Z(?lFIYZlYXdN%w`FQhQG#W!L5dqMCyx&5gqr}(3W
z-;G@s)_aORRtW_IXX($VpE)Dd2_DkSE*&K5OBu)hpbk({>ts8Uy%|FT^lUJGlf!d)
z0Zz;c<mt=5-MGPCj9mq?E(5Rd_CRD^az29<Y(W~z%Z=9XQ19%87fCe#)oY-kJ*CN*
zJ;_cT4>!0X4(;56Zujao|4Dq3^aY4jyAc>2FRqKs_#%CmCQscQzh8#F8;Z?ogr^#8
zqVhgib=XasCJ6HV$gY!z7VEw|whQfaWVHfn>DV=EJecxBpR`1<7W8?jWg_~yKO)Uu
zHO+%89S@IRn;y1j>5xD+It$0uMM!()r2&E%NrIE03&$s1{Q<Eckrqh-DQBnw;a#+N
zL;?%TnuZH#G#bRdzJY;cgq%Z)iXFpVZf*rIy=5W;$S6b*^6^?#u5GKt4Eme0_Hb|{
zf4On1jMp*(M-o68+hQ9dE9)>?iG<*}*|?2I`}^;Iq0b`|T*T7Wt*gd8e-_-t2SjF5
zh`Bt)fkd4ahkBI^&_ubSA4@Dr1hC@%MaXUZtQX2)n+ut;2z`h{o`ah^eQwSTaPS~M
zzX~3)5OX1C|1!g5wE<tC(Xb)(8(n;}C_hrorX)d1pyVc90&9XHDJzg#w2mFS_F`7b
zXUT1?zLK-VR3>b$_xJxhTmr*543vQy%N!_G83tDh%e&V_6iJAxX!vB~!Xmh_H@TPI
zjxYfdi0oY*{GBTcT^l5dIADlb{ISO~$v6+?F5DXc-ns`phW?0uoSuvlN)m1dZk|Pa
z2l?|ekxXom<|KBw7gKogXD#p=Mh>i>{|hmbY;jP5#SzW>COn7;$tN#gTs>9rC%Hvh
z3~vDYy|AI6<1BELN1tn5v9m!boAuJ}fXFER-_S{<hKM1lK{SeTLNVA{XAgET+{UVo
zNSgQkcpTi~piY}3(h@D5V5tjDyA6ED3VOE!NPd#Jg;}mjz)%9LrjGTcIuP${nVT_u
z6t^Jna=WyjiwS&kwPZT><<`e4XjO{9U#{^Xc!ktCSV~JZEdGt2rlSsU(|}P}z|eff
z%W+;GRk7E%^}f+NwVpLH>keuDCaf;h37!pKf893HOjdwl$0=#&1bbVh5c*eILVE=b
z=zy>mF7fI>UNdNAxQM3#Z;O3o|4$iKAn^()NY1412vO)fT7|+}ZIvx|aB-y(-iG-7
ziS;0<IRTiP3P8ZPDG+xKAM`SlMBD>sV^WBt5XP^qp}ujgwoPO!jET>@3d}8!TkEjy
zfqXa^6%yeLk8tOC<xCQl(7K#lYc3JQT3Wo3CFbJ)*2{)Aa~6&goR2{aKY0TV+?hFY
z0mH<pxLm~Vh9;aCxn@LTWK-WERQm|d=V0%^0~NwJa{5^GgvC0OtYu+gap_Z|0?P0{
zFoHl{YyW2Bv#J+5@JbN3j$jWmn|`+0b(itZjNDvFqOA)GszJdyho=`Su@SkPCDRq}
zf~+*eEz=@|X-<v~*>Rd_WI)`b_$Jl9c_0tVjmCdsz>#x0IBO}Azn`C<ydB8!3HWvN
zGbC07@1}xOOw)XD<L!E|CzYP=mcr+0zx*dAHnno)lfdKu{*&@wZcqEawq5<d^3VPE
z8^jU#KR6ImK0>eDi|LGWR@g)|Ci?l@ToVlb>tyNt0ah&I_zV_sTy<6bbgr&h`~USX
zKA*M!dIP1~zjzlFpxZz8SzUG|)f&h=IG=TKrO4J%{I@XAchHQ87}x+T?*voH1z~o?
zLqQ3N^PrWH6~e&M#kvWEWTOeL#Pdo1m5wyj6@$3f@X%=ivyc!=$a)xo&c;7^yb@c7
z;Yt7}4P;Uku=ArwkI)ix4vA6|wl_Mgv<`K5qNAx{Lb}7)*CS`&^BbfV7&aRV?0Yvg
z$3qrGc(g(KR8v*eM5Tymyb~81Kp3RX3FH^y*||^8?)||(@5!}5HBhQhS`MlPzzd8|
zvtJeYqXFt_CS1&am}T!Q&P2BK`1ne|87k=O%F4>_d6?TDfJ)TxU71hr@PQI%4fj1-
z{8K=(YQSM4sDS=&Ng_1!8*{*R;aI(SeEc}#3Iy~iNtk<y8VZ<~FM9)(J>d)$CtvOu
zeslky9i9cZsH?Gc@(4*}^AbLj26_vn7ok~bk?IqpsYKCD`ucu0SsROedzxep1*%l_
zksa*pNra8BQ<w^Z-FQ#tB5*p{O(*5pf4X>8asMGS>XL+xQJ9YqyJZzNetIBxuVV<<
ziw>2~XY1T{jvG<@m*8b1OR`eE>oz6;E>pMjoh0i38w)c_nEEhV=@P+w5s3a`OO`2_
za?pG9<0`v|{zDWRkFzd|UAz*Sk>C!nh8nv{G91DzD5!M!BpU9oWG=WWVPaItTu-0}
z@+W3WGTumW7~5#TLB!)R_}wx_`X3VKHo1+$lO%SipqEp<Iq>3yFBB1}xu0FfK=Oh4
zEmXqjvl}?X?9^vh@iUdnFa!~2Iq5A|1wV@lRTr!%wmAlF$m0+-Ou_I~ZC;<b`yKWm
zyS2>zP{8p81%MO!wl&8Ji|W!*O3IA-aQ}{ykB&jM1QEjPlnO`^tLRA~20BYiOH*Eu
zYj6>P#WcaVO54bR^mJb<)vDL8-@<z$1*@Lf0GWIM42QXuLeOpew((MsvR*iY`5jJ*
zzS6Lo=Q^9M6K=(QZ9JU))E6S<U+j38AQLEh>{wDz_NBQug<|<P`BJW({Evb&76;q^
zzWML0kuSfnx79q#8q@p3&Kp7Ft%o&ty}x=z;)B77!+(FU;kOX>!Ii^t6PY404eT+-
zx(y%R)fILPl+;B}>CVrX;)`YLB-<>>RzkQEdcp9XiAnH*m3)r*q-+mzeGm?aPT?x7
zg1ymQOP1V<9+KHUC|Oi+M|*zVEb0$6q7v7|4E9fKnh$5LFQAp`KC%7<tc=GF-Ry@D
zfq9hMv1qFvi;OwtU2NZBI4g`dz1ph)4CXjD*;l)tH|patf`wi6Cnu-D6OAv@rE}$C
zNyF*s>2BC#Y>sz^^3vqKFOW*MLtm9%)=%Ffm<W(H)Gu%L!{=w5Zc?VSZyz8LCxU=_
z$!ztJx)TT;Lb1ebRS#&ji2;XhK%iKu3_cZy$zoe`BBI#ukTwTp?R!o@6g-f0I+7Kg
zK57}Tk%+;3%7*@ejOjRD+1+*Xd@pW%69`URpb6aEPFQp5dI4TT;S4&^*^d1&!iEKh
zS0?<FU*b#Fa8H~^XJHS@v9omYSD}HILk!?S{ds1(bh2XiBt#a0zm3D&6N1WH1u;jk
zV-<CRhp>L`LGf7#PTY9QA$X?(9<QsadQembp&YElyX*l?=bE_Ys6S`*$sIpe=9={V
zRH)O~J6V89*7)TSE1{Bp0N}}BJ1;)`Pq+1pQD2#(l{JhB?-hAY_tKKjM6-j*^VE;;
z>snoLje(1$>H-GGaYIwnprcj^mBt*4yxCK*PHXl}rB*|^+=F<jM#t7c$Lcu6#k<Wh
z!2{ge;nZp<fH=TvgRp3KI!f{oPL+nN`H7c!+??oKNuMskl{&S$vaB_O-g7P#gu$>)
zP2cszf<19eVE8?)Y$n659NtQyX$-I5EBbWeIZ7+N6DL4NyhQj<#J89nE5EM?rr`k4
zq`B+6J<}M|Wu}1l7$2mO1wu)=zc$vuVotW_g%$?ojXcHnM8QQw<GUUmaAkAKT>YtS
z3gc*|@8&e#82+uAZy&-oCqS}ITAaM=6>f}W(D!UCcMS1&8@%twI)iozGYmnxbiJA{
zvFa95pL@`*BL{Q_K74q{CK2jS&gKI&Sf_fxmvnRd<teP)iQ5!;%4_b_S3@_d`Hru*
zR;FluN;qC=9q^~&B>T3N;xmxejWiUYj$U}3FlIO$@Wu!UNqan2WaMQp`i!(>>lM3P
z&da0)4m<^0tCwf3lXeBw!|7PLJbgzU3tZ$$Ol5vFjKm|Baclk*LVK8pk+CrkPCoUG
z<Hy<k`}WDpFK#)o_J~q;udbH0uJx>4<C+6e6YpXN3gbLYBSvOBj$m5ucw(TaT8R>T
zsls|$?}G*w3ofGaz!2@3W~#Hdk-~t*>u~;I-20PPu3X9MPpe8)31pqQ{G@}D!H{<v
z{Fc=IbeNCQ*Z+18B)?!-8LT_jQcXhUr^bzmRIq4!$RMNQvI{wuHy4o7!<aK;9rBu8
z4zEQfu?|ZG<zSvwx;hN*{;)W5VqE3)FkJ}28P?6Cpz1haC%PGrjG|ya3Np5#RKrMd
zVD%fjF8F9Kb@e)(?JUNv(?oOhsG=xbW{nIojmQm>yfVmz$J!{7hRK=j!5pOw=KA<6
z>*!BFD_$R8HJl|Sxr<O2?xm=!sR?3v4b8+54z<(;2yFhar|d=2<3V5lP52sK;+=3K
zDEQXePUs)@U0H(R#%Ow^^@z{>Ym*9c4aT2Noy7s$ixY3(#-Q$qqEQE);_{#HhSlkj
zcj#MLSxz5tBY#v^r+-_j+);->qUAXPBW%MWIdz&G?CdAOWIaF-v4_jC5&;kaY)>8&
z+AMw=_}9B#o+UR~DNRLk8JPf?40*V?xZt}?cz)<TFjY2YvCg&D`LKha@qhR3T><cf
z8JO{GfD^8o^Lqf&g77da(70IV77C6$sl!0GZvMlB3wrMi5p1Q3>P~@8G|Z@r2uV9@
z!F%|pV?n!*ih?I$Wq@#TQ2>w;|LNH^Qs;t&H~>166~-kUOwz#5wYL4AI&z%f-&PmB
ziBDgSAoT#JDvU{T3<Aw|5@*4&RARo^Zn$F7-WDcl#PeXd(~o8uB$NX<aY|WhE6jhm
zLwxY?HJd^dP?&)&xvp`@(5mNyRoI<7J0c_Vrs{a;vKfj-$(#H_xugV1K!;_<JWQZ$
zyd|3t9R{g`h)Q5Zz5|_P%s8^v`WPe3FkE}HEG+TbF!75!eE4H-NW+&4##uFtH?F`C
z2Qxr0&GYAP4~mG01cM`00FS^<h6YH2_-q;gJ_(Lr`XF#K4<arfME831Fbo}{b-;0&
zQKzLvqCQ#y{QYI1Ig+SLdESq|bW;w(EO8+BdTaE-SsMcRuLASKIgro-(FXbw5pyRY
z0^Te_H?Kgs%^Rx7)Z4OKI{oax+Iv?j`IHY5La^=;MkK{VtZNpBumIo|2n;1cvcnO}
zYJVWmhaMOtO4>z2K$X%LxFNClBUC>eR`OnWIqoxCoSWHE=Icw|FfK!C0*QVj?N4Zw
zzb%lw9u~%i!x!l95B6>l%z5zanaTmB*+NciqTGU^4*T|@eBXobeiEyi#7Uwi@4<hY
z@;Fe@1IXw0a8t#EwXU5Djfl2+<^5}oUe|IhtgKEwxzf=C{wDwgN;wqt9Rl{~UX_ss
zW5$>zd|Fh>IF`EP(u`p5xE!iw9%1BiA<+jzCaf*G&{F(IU7ZG<pPxS)YKVSoagk`z
zU}6Er;r8N}0s*Q|;c{w?hebqG!i8P?%8<N?X9Dq`Zhy=Vh%H3!e~I|d$^bRv@Mu|J
zV*{VkmfgvW4{wn+M9Y>w=F&u<{1?yK!X$VqF|$VNJ+UL6Shk;Ag6|EJ`-5KU>vX}<
zgo7W&y5634E%X;KC<4RaGIiRTVf8-Y4aqBae6sBRG|Z^EnZ$@+9V>rybTLNc=)0>w
zB(5xv`|^Tjb$&cc^O>RQw?0WiVBjE?0bR!%+7W)N)joCIDSf?i^zb`;CbdOSimWrZ
z$8Sqdp>kt&E=o3C9XzERKrXd+e9j|KWs`kkzOp*J3T}seCjQCWSk;Ml6-@f9h!Rq1
z=Q9<JXLkVdYcIw<q!y(S?GgQgXa2F?es@fy7#@!vG2eOhwq;<v$@Z=l>Fg%7YN~nS
zvQ(W|Ml;<b?|3oqA@!Rcu%IR@ZudmtCl1{scicWdm`70#f#duPqFWFA_q3}Xo;t=L
z@70<ai0|;csiy;(dJ~Jjj!h{!3b(%d!3<v=TW7Sm`ALP3fADF7t7iJZnI4Q|sBJ_C
zzlEQKq|f&tTm-NT^-5boPk@jaOo7>`PRX^qUW#A1cf{%}Stbp+D5TK?$N2%I*|0Hh
zoTHP7n`v=M-X31F#a+Qzs$34gcP|#vISvoNU#e=jNT>Nok!p4Z77(&Jlf>2&hl`;T
zpmL)RqH;pp1t2M;H($$b4?++#gnWQ<1)2!VgkkI^#vNCnIa$as2LbS&goT=+GhL@1
z^W+6*$1v!A%N{vNorth7@~IOMpeK`+ms1-`2pkMP+DRNoxZH|~Xu~3*jFe_?7V#J>
zK_NWQp;AcqMmciCj;?>+0VQ_GXYK`W1@7JshHHnjZrIdX(bRaNsSx}g`B6&6_4*c1
zA{dY3B+_FdUgY<P2oA9Hq3FbE$-iuHoy7q{{po1wj4H)wDI4}D9WDlmR)8?#cAZZ*
zmEhP<!cvetJTfwJPsZNt9U2f)^YF!~V1`p7)LMJk-Cs`jR`$lacHRVKp6o$VsD{Gi
z39mjO6WKeJ=LTTN{}yIj(heq_#{|T#oGqO#Nde0GFfecmDGeSHnNGl{gD{+Wj+5w%
zp-HTsBs+o#MYqu-rV^0jP7aPrbO9zpM$@0-m37!3hjQHw<5l?f?E`{96a99CF-?7a
zF7np^wlOOM;%xmByDsaFwRK;6jXuG)tL)s$J_@jMYWCfP9|f^9NY=6c1ZrZ9!e$Wi
z#Kug%ee1e<%r)Xv_VEfry0H&%hsSYg)XtashUlS{z)Fee45XJfx86qdM4=IY^E5RX
zXtK0#d#4zZ1}=5{WG8&0;-1WrK}dNd?~_)<abP83uqa%Jn~Hrr9L8>@Am#Utda`qH
z#7l4S#W1dNG&(VI*Ux`SX0;<YQu5=~Knfd@gGjD}<S>!D401LQ!|=+%c9KAxJZc9_
zDQr|ENwS0MJNr{kFoz?406NNN%%2S5-i6J8L11Pmt&Vtrl{iGb@vBQ#Ufr8bezfUw
zgcp$yuf+kV<PW0x)Rik^hZaFz^PsoKiv0PE<x@eN+etw|P?P?XE8cA@APQar%X1+P
z1>>%qBDLByIr9B=@x0_0;o2C>^^C)RoQxg!5};%n?$S`2|M_PNS&g;M+Z6d-eFD6?
z>+=d4(?~&zV$2M$lZ{)9#^rM-yg1qIK(>*O1KQrRw!b~g^au1_$f3V#nUoh~G9~UO
zFQ}C>jWm<hm>M0e%23`=7?EBAgP23KvPXPIyR;@znBbzQLR)}8GR%=qU!>(pIN%g9
z^=UW4AIXRuhF8DOg=>SnChlA+GY)|v7+;J@Hc(p&(kVAiBCIgvG60)?OE55F8|-EH
zu_hggfBpO;asZu@fBh&Ke+p{r|I?5E*6_R+Mwh?->9w!^t-G4_zkl)n(}(lh>fi2)
XW=wJaAuvt8T~(E{Co+%!^y~isPQ!tf

diff --git a/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_23_0.png b/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_23_0.png
index 49e61dbbb8c46c2be81f0293efe4952762b1e3f5..a3a11821692bcf8646c549696c9c60b87d6de70e 100644
GIT binary patch
literal 189983
zcmeFZcU06@7dDEoZ#0Rb!G?6jf{1|h4kiwwA|PG5(mNT-(80v0bVLwTnt}q-dmD;T
zIszgc22h3$0}dUAfxFM35%b<}-L>wzf8JRuOC!UtoU_ZbpZ)CPe_dID{%7W&>FDU_
zuUy8c(b4U3q@()*|Kl$Bm%0<iL-2=$v-}Nbbq8~2*ISNebV|3Jf46mTwzV=ohBI??
zvU0E&;*;Pzf9{y2v-9sxlKlL3|9pYZ!O?<W_`jD6;UYi%ep$zfj*jmH`frEZOF10f
z4m!Fkm|r#AVy64u-6BTH<vz}yJU;!)FTY@p?9joy`g7<%DL?9_X-#2YY5idLN~=$|
zFR?ILrm&##RcB|Wu1(sxOqW->%La$^ACx}(@waZ*pQQeq+dRA9H!0}av73V7uf2`5
z`R2=7<|}5o>Q536%g&z+yLLJp?)vp}p=Q`r>g)f&pReOCg|U45|6^|tm2CUA;I;q%
zhjjnnEy7$b8(QkyG75rEf*KkcQqt4k;SSN!;nXE0B*MbO5Bg&``&7|a3KeM!e_JyA
z+UdmNV)4#&or5-AnZXYqKD1T%58ciEt;Ja4nKNhnMD2PpdFD-r@UUyQJ-E<r_v}ME
z_<F|T?LB${-;MP%yu3~5E=#|!uCBJ0doEk{<eC*GqT6Vw4kM#~)@j$Bef;M=dlGYV
z1<?Z><K%R7bWG6BGHf8g&o7jRg_F?t>81@Mig(bZgr4Q$NzBeZFKXNUfOH1^fZ>*c
zJ^IsH6<%$5&{yuNqM~}p*w~mtw5VOevuDScMXV1=NGvhJ-Rw2Av@YrB=%gFqMd8K5
zmo}C5OMiZS`Q!net3SduKIfF#?nW28xvZ+MU#6s*1i$aGF#i0^PPz*<!uAAl-F!=i
zSSi<#5~s16-U6GnQMjAQ{Ag2ax=!Bo5C4T<_4`qe11-aK8*~T3=9U)ip<Q&xY!3Ki
z7RFmo%+1fMhh!O+ODg)K8*N<NT5jFEMdkYp40gTws_5Hk@a=r7w$mfs$(xmj;d{qe
zw%jA-aJ{ME)vH(4LJl7~WXiRdj?Q+ZZPTdKtTFPjpxM=P2BpGh&z{Xah+<4Pal~xP
z<M8)k&+S4)tlQ7*xdIpKfU63JU9KXOIHAR47PDtXFIC}9@i@*2zxhBFx3xW5FL)fs
zhF2C3($V!q&1@=mfi;+!nW?I&9dH_J4jzm5mDS2JjJ@j&zw|n;q4LES7xs)9X^g79
z&*5_veQjuH=nOZvKckVC>J#Mhbw}lCTL$YK+6D`E`E+=WRa??yIbWZvPw+2Zoa`gR
zw{FYa<`jcJ;gelEF;MQgFww?^*8lL~!!+xHOM+G2D09OW648A<>AqbyB0ip@ztknr
zyy>Yte9(q-avtH+f82i)zWJK{*RSEF+hk;TxXRFzbeMr*sxu?d&+jmg7Us&8E21X<
zzGi2xSz}#&J-TUku9+g(Qcc4no;<caaK-K>ZpWSQCwx5f2?T=WhZk2cn21M@<T2f}
zTctP!AcKa_Yj%FFF`}<zMoNmGpWlZMA8c#TotEgne8IG1eRcjs*kz4!1Gi6iT|6qW
z#G01`2SPE{#Y66xr_Wkje3DmX<-!NWo{;>$x`mdRYhb_(4B&z48|YcCYyaPd(z@cA
zetcK&zE@JEojJAa(Sk;d4*hsnm6EY!SMH1%UO73r-XaGMaMKW>X7Lj~i)lvHU?8rm
zPCFjZGa9;mvRGq+dv6H$ar_6phw0s|?YR(%UQ<<7^<A8d)W&LyZ_R$+mAB&^Y1&@o
zK%*e}z$0c-hUJSBAI9Om^4MPey}A%c&sX;Ojjck$5nJk;NR`Uh$Z_aWe7`*RWSFTS
zQ>e_+4OX|0e~@cf989J5p)b%~SNIah=$`RWmx?NbMQl3TElK2Bj(EX}`DQ7T+4ALq
zPwq<5qWzzqTEu(fqD@}De3|1o(tw<U;5l2K<FPoITCzCRRm+_mbD%9rDNs39vg4Td
z+YWedu*05^9qkXFZcMLZLfK^|&(ov3wi|8j%xinQ_s@RCd(AvJelfvwd8Xi@$#7j*
zM{_W{jAUlz#@bOK^K0uwj?ENFLk*2Xa+|9y#L;Mn%tw3ao?MTXlR}$((>3x-Jfp+p
zTefn}l+8Db&()n!4+-UvlZ9B47lAw%)`I7YH7sHb4Gs03nnbv30|Eny1zm=XYRE+)
znq>}uac{a!>~v1;C3|~&4fwF}oI4j}5+ZRr@YT|}M~@x}m^DPWE%&>cbfjvwmbuT5
zx4%+n^<HRg%`vGB8tW;>np}&Ql`(IQVM^1=2xy8HQvg3mG{EBoe*gG-94w?psN+#E
zi&)KzE8)hi3G!$;Ey1_;vWlM%5g+mc<Li@gB@b1TC)2WO-rU`jn2^9JiJLxp@!np4
z!*UPrQT^)bYVMM!!d3^^B%S>>sFd0<+2xWJFRD(Sc{P5vf~7H%H;|r16uqjj!+^A;
z>kO-m=Tgwled5RF=GMcNMS1m$&nGFzGV^Gq`@x=L;0e>%O?p;JveP;<4Op@&)~wIz
z6{b(-5Fmz$4?nm#bbIH%I?E%Wo@k}E!oqEjmk?xfx(q$56*#;#T+&lgQnY4sZvOq}
zI0ToDl{6!&H38RW*C#Xf{62fY=33U~RSD5sZ~iL&_~)KN&jHtp`PdcM-WwuuvfgW=
zW`D2FkJb86NZKOoG9J!BEMk0SO^_Rh5(4?IdWL4SQ1k8kORK@=i=xQ~VIRm*mFuT6
z@iV0mvB;wHka3m8KmE1R<lu8~H@n--;LMUVh*pP}2~H}~i|y(HE|cwN`1!MNcC{gF
z#1D%6!4AHZzSbnAXp&(OSRZ<DJDS<lgmM^>+)Zjjg7@&3W!J2xIeogzm_)D`D04?i
z_eQSimGzZcxe&?>o?q0SP~7B>7I$!SV;S4Poop+#+$=R)wOqMLbzADOIPv6~y^x%3
zcXsjWXte8u&C(2hwnoKgF5GQxBH08s%BstGh9FU~){#}yp>4#*tDB!fE>OIBbqKED
zy-srQ4U}*iwRN=v-y+tX2o1_>mgK1*2RpoY=e_2%=KsT<Beq5BaWj-44RB4`n%I{w
zcCLK9v;RZpm6HbqEZ;vX9(`&v)UK}5KeM<G((_|kZ;y^*$WtCm(<SOp0C*f?lftF1
z$y)Ul+QGJ;gPdCAIgl7C%UrqI<S+z}m9aE-r7P2b7{HV1*T{!I2N|wRy~H}TkbM~$
z1U@ikE6<};*q9>K>+fdaGS$fosa{pY2UZW~);!-LSJ{*kE0YBa%QQ$XNb1Zo;^2z;
z{Wq^Ey}_$`Lm?|GQ*#S#Dz%^PbFiz(QJrJW+`$VIRN`gHC`uaoQWr^)#V0r;`Hk4+
zR_j%Up556OAU5cg*X-Ew2&|A7`9L+PXfUp@ij6_QqB%w!ww<WryQbbi?z6}9?%L1J
z+9ExFt@nT{riv%4?0^EhyXmz!>5`f!59>|6HYtOlf9%{{b0Sn)&CXwn)42ZODe`>$
zru5RF@8)A+E4ARv%HKb|5r?HE?v`8mdtYNZ14Fc!LkPt3<9y`{AJ}BsQ`j8J$d?vK
zePWr#Ljl%`1O^T{E#zp)u{hlrkI7dkFI#toK!h`s23Lq}UWAQG@`{gfYL@8Z#riI^
zDhPP3e5O75NR-jQ4AJ7#o4e%lS#SPbdyigEtQ~2dD@Ks2JMv4mkWtguXHC=2QkE!O
z9`Nj1^A3@kDNbD;Yl-Iotk6HR=KPYxcDO`4{YF?WW%~1{x2}R_=8$2kAz(jLlbn73
z{341xaP?mxR;lP1!R0LC+|7GqGi)xFe)t{iF41*%0NIiF!uyLRmDHucn3#4WGQUpl
z$q<>P?#BWqmtu!g_vBf$goG<ga%*MiLhg5WcW+Hri5H*FewzT#!EF;>Z>o>2K1^=l
z$--c&WR?jBjW*Z164TSqadJKtb6}@ZmP3ENv&$||XtgGog$R}q3Xy60ygI;8@Wb;y
z)F~qcz)B7JcpS<2<Lf_Kvy3VcENCFijWiPfI;h-ztKJu&qk!XZ-D3$S^9FKEOw4#^
zhTdLg;WKFOy(vrmuh=T4q9LDE1Nb36(l+#erJh`aU>ib7Io89i3LL?o-rlc;xUNP6
zz45Z%sTH#DHhnR!1Z=IQ-n!i8`uqtou_9})u~=6v%76Zv^zv9<REc_9B<i^|4FUYb
z(90;ps}gaSPok`b9e;km=GWb9`AtMyK(N^1FohbxXt=X|OVix6czQebQ4#SJ@*_Ia
z21WkzLjcIQYj@EzYfLRO2^eG6sk6RW%$1+t?BT{cJJ<o{w`%-*4<ldDn>TL|Ea;t=
zHCSL5Fs|Yoqppw1akR)%Y%v(Z?{DeJ?}S=3n)=<xVml)H0lt^MwJ~z-chQtzg5=I$
zu}Ph+g(uSxF#`PQI_T|+HYmr3y5_f1eAH{PL%UXPgB)tG@wkvnOiWB!np{P*%VJ_k
zAcSaJb;3aQt&8hZMw<cg)M*RNyNv)$R~ob{4mgxAYW2nvW=p3syUX02(r@H&Hp8-|
z5|O{_yL4!Fj7|(z`to|4zT&G`8M=YT*&RA`s0v)Q?I^^4_`61v$`ip;H9<@V0fHD}
zFbEtjS1gwogN>WKdn^Fp3|S?%-=(X%o-1CzKnM%4R2qB}TYoC53QCC+oW>&!j{q^P
zbNW#F2JOY#;y2gF>~L4vMQv~B!z!Si(DF7EvnL#di#j0x&+;Hwk2XaUd5l)Cmcc*f
zz*1^>QhV|&w1fKq%T=zA@DpK82}!r(uAMfSA(W5_PA%04nxxMDkzwGy*r93E2$4=s
zEByuo14kv@Fyw;tC1N_CksVq)6L_P(=>!6#e)09sZ;23;gR^|8Q>_4nB^$HTl2qc^
zjW*_@Pzj`2dyk&w2udp^RelWIKWsoouL3;A(xvaD)G_AW5W!9EpY;)guUFbSkpOp9
z190O|ky+HhVBW<_iJew@dI&1VG}S`Dj4vZ{DN7`lJG=KQ;|rk<RhA;<M+mM#zGZ}1
zwtpx+ulcHb`lG0*TBjBnM!B`t(4mZ?fdIeIN}~#R>)NTTN_GG(nW`0}SEO1Qu+`Ty
zziri8={#8}L1t4QW(}{9CfiG|43BhH%C#@g`fhqgzcEtMESk5vs9|WxUb#}uuNDGW
zksoCt-O8>`DMtm2G`N$1`MbyYHbs1<MRt9ns3ZaFPM6F|lAlo-bh2=sZ0DAAnLNah
zqa@){_ux26!Uh#f;I)A2tKcc0E;+piq)<Gb5Qr*W1m3hrAJ*Sr++Pg^MI!h;-fPww
z9O{tM=sSNv5q7Qyka>v!76TD9Zx$y8xefVqadHmDx(@g!$9bHs_<Wb&)~OkgDd!N;
zpu4CO;>1x2M^lm)oz$(3_0=4(j#>yyzwBoH{k7;kEGsPytS!%SaCk4MVldZYB}FRW
zg)d>T*u5;G=h%JM$#y^gT~bnl1yrB_d!f$m3K2yCnPO{-YGS_o5q?8fwB70<2m#lG
zu;G;iDq!o->Y#h?Ws?++Jf{Pt355F)l!n=TRzBq~7jJG*(gsjLQ?@?W*jnT;m~R6F
z#KLrs5M+3!D1FB(?_wl`%oN7_`~U(zn{+C2sV}|~ep+Ys=_QONd84h+w&%b>F6AKq
zgIqN&K1+G<+&&D0Yn?@cF_|7CBO};miLn<E#aZP`y$DNus5qYh1u}Ut%U4EOa^l5N
zSuZzD)zwHnd;dWG3h{js!w>A5nZS`X!=#3YY=t(u!xh93DK(t`;D9}0lRCH=JNKU$
zejjom7I1WOKM9C8eiUBNg<?EsO31Kv@>R8RiuM(&qboD6m3oO@ed>K%(X#(boupG_
z+aGy!X^JtA%_Q`5L&w`h#Xj?%kepyh?Sg#_x%R}GV6=2@q=GKBkj-fexwLSK%lP}_
zXp`EKRYLvj6ub2LJnUCKe`-zDOgk!tv&DGn4_6F7RK&R%F3<F%T$dI+?lLsj$e&eM
zx8Z;5?Y(Lpi&)KqJ+8odAj?H7({3FZOO+O}`fzdvLgRRAqC&b}5g#Wf>|k_ZodE~9
z4WFGq<shlIAaK@u){}gPWBu7=cMy|Mf?Z!BcF=p-&Q*I1Wu2e6Vy+k5p@7kvOt!y5
z0V>JBcfLt1Il@g2fg5BU`KJa{oM#7QW-2!~jtZJyJ{ex`=eN>Txp5dJU@9;+(TnlZ
zaT`}ECL7Wxrr-6hfx)|w0Z2-nR*7RpCO14{wi%z!4#l9`CUrTj23ZI~v{33H+G00{
z*ZF<2D<4x677;|wmrC%RP&5jFOsegjCHwjHPV$)Cro_v?jAAof16u0quk>j8+5{fq
zy}BB>XXmcHew5it-}_QF5Lm@gJvI)Jz9Ud(<MVyFDu6T_PXgPwP~${LWriy)?cpxA
z03Z-`P?08Mu01|~ykarKK^`_B?dLb~E6uJI2N58NHS6_A9txFRJM;2&aY4qOo)edX
z{1{GL(g-&4n0T2wH`#gQ+>KuVYbbeUttA1@!ZwL?K{X3y!bk*J&!JqCF$|{*5LZ2*
z4;45^+S&=K3T%hD;@pPzb;SZW>tn@F=Qbt)K$VhS&TmszWC8=|v+6n(USCWq>{AP&
zj*3;jQvG`caQHQMTUhxBPEuzfUx0RzklV$Gs3^u03L!x(4rKzTm1B-JM)8O8>J?%j
z0Gzv#eGDx(N{+crWAg)^v$X0%=W6UEg83bkrl%r-M$}1d5!q#cwGfGApo#?!Ag8Gm
z!gy7WMZAHCVUfK-0lvtA6f8F2w_k2O+gjSV%C8#|cUxxpL{hZcMng5d7$N)Zfn~0z
zA~un^JD30bVUICbl8B_MC$4KNYFoUlqgCM#cGPnCu8TTw#d|phLA<dqok+S-V6EBF
z3k5-yl30I)+S&9ydW^XzWCsi=ZKKVxdIdr@Rke4gGZcGxv@?TXh3%XMlCu^kJUh71
zi$iosyJWK;EGL4TARjn+tTnU8gKPGbVq_KKbsi^~H%m^XGiOXe4Lm$Hmdu(q5y-kv
za{O5Y8LHg2%xC~@UI2hKiJ1oYnRen5svsLyz-E;3_VM0JS9H`3;_eNjsO>&KTAkM-
z%Q8rvaV3r?hK4|1ltQ!{K)Yc0?HVSF7zPNe+VEwmzUkLKjl4xwP|!Smxxk?ZFL~{W
z;7e;SsMaumig1(62$ytFSj6npm*jUQDugOC0e|3gziYrlY{t2(yZ4}qwCbV{6po0Y
zkSg2gEZh+1<TP|2c@!%t7u!$9?gu-*{)JP#o$g7lW`x3T=7V05eSa=nr52mV_;UeB
zKE+^Plk0}HtWMX4M@O}Siw4{&2Vsq+phEXvTkwY@E??<Gwe~W^PzS;&5C${Iz5CcC
zUj%i5_w`}b5b<V@Cl#VxM+3o-mn@+$J$b<2Za}u&^LMbBSo1XcJG<ypTT2mf2F~A6
zteSWU`|*$6o#O<RsD-X9qj+3Dm}!_GI|N+#Rp$N^oI|jT$~|^#P(XzMiDuWy1{q6p
zHVZ=8z5N{Q5LbMmYUo%oTx<fEVMLmFAKzICg^@Vccww@GXJjSRXCW~kPqAnKaHBR;
zDI~`z+gf1L6+wnT_aT!7Le(^cEKMTtNTN`~8IoKS6%~o#3K`5rwmpK%a@2)<8z4l(
z=eiE>+Q)iSLPBb}<qr|6IId=9g;w*V(0?Uy>VR`Gfw;}olaUXUGRioq<SPGzh$=B_
z>O4itaOE0UqdH5uSFQ{NYR)bL=l}}k2G5hJH_44XeQkj6Bgg>tY^0Pi7~9@_?#zm{
z$?@(S6L{FuP=7EwR8n#0j4GsoB(iZ1hWF^aQrO!LYzT9f9l-bw3WRPYcZT1TS^`rx
zYzl!qh#=qIvC}$+mootfMwEq9u|RtQ_BD=n(Kc|=yqbhaZt!F_#M(f;$7NqK68Tuv
zP8Y!KOgok3A&%5rJ-D${6oGg*!!qC(bgt&x>8t_|q)hqKpHv>}HCNBwb)($FrC^yw
z#9E_+0;K}B%fM9=zXTMdVo4`tr-CNp+CdJVm0MSjQ~kN(x+g-3ZE|CU%h>h^S_HV@
z@nsd6K2O9}@ti$tyg1o`%Cf2=h`V(yEm^vhzjt#~)rFl9Qmg9sNS=mLz8V%>eH0Ly
zgTcVST3M8lk3>UbV_J=n`8z*=0xu2pI3NL<Mzv%Fvi%hp5HN(O3Ksj~<G`)sRwd&{
z2-kW#TD%RsnrJM(eOG3`vJs_i%xmo_0|QDJfCzV@EF3{Z-4S2l{jd`+qiPVbWeLnf
z47P>|3rV8AXV0$OiEfk7U?|m-SUpFdSmZ{WHU=uLz^92s>h}Hn2gVoEE3!~qLzxX6
zpqVs~5Y}DhYs8c<)z-osu8hWyPHH80Dh)kKyKf;?!~R@-%79-^4Uf7d5JUu=$KM~7
zbhgz0{qG;hz`<6emI28#T@2Pltf0)KwXve?gDO}}BC7xQ$*q6(^F!%W9d~G;w9Xq5
zh|0>!sBQrmKhz{P7zD(EAZKC!bI+;y?i@tpfbEw@m>85g`=jK)V>jzBu;mVj$9yVk
z$1EYS*^_1V=C7ZywQR1Yk~8)u0BE?0DzjJh55~))o{&|S_oqEZeiLoUwJp9I^TqJn
zMJ29PwWmJA|HYHx--HkYjp_v&$-=!U>Wm({Xuu=QdL4Kcb6!P*vfs@;@rtg%)}UfO
zm9g<!v?%WYl#GbzQ11i-s}5I@)e#v0k1+1ezSUtU*2YoW{P{Z;LMJLJ`k+CtXd`ZK
zUFzR=cGm)qW=f86yxNfin=6OFulVY4_|$a8#ryk$*Vj=iE7&hov3qk~Zu1}mM^vd?
zE8cZ>958)HF<O40N>ka&;(a$(Q#qGA$52lJ3|!A*)m04Eyb@K|skE?xs-LQovAAfp
zUgfd2A3Si%fJD@4AguRJ*m$kQMDprs;c<Ymf-5%`v#`Wqu^Lq1@Ru*BA+$^eI_?rw
zjt#w!c(iLE)vzNRjLa`AIvU^BGnvR|Q2HWBJ5&G3%aaUEPhW2M)*aT?CqMCBS{iRP
z!Ox>)v6{B~;OT4KNp;=;XB7Y$cDS{tsP^%S_^uHVq+JK-L-23!vu;ajvXJz(*@8c>
zzOwWFu)Y#}z=}o~JjyDYYY-<_9h^#k4UUpWD-sb+;pbN$&ZPkH1U1cG9*wcrCIO=6
zds#=V+@w^h>gYUC!$9J_V^i?oVmuy-6ZIM>D~*Uyo@TU`K)sL|c-b>ZMNU~eL${!<
z>&)4+y)z<hR**SjaJ^R8UaMqzOx4=rlnD?j<P~CQEh1>3<Mv9I0t8hJ!hywWlWDG!
zdfxDAi4o+^yCc`YQw<u5)svMG<sw-=hLY~!`lwi%A>Hq=61AwZt!t#T$PFqzKL31@
zis58Pyc7&KmZM^Ytv<YHmJ2b+;gXv{OnzY@m9*7dt4o15+6)BPqY(@w9up!aVh82{
zTBmvVLfM9BakOP<`!s9Y4wn?kF7?`E(F%7+Udr4b?Y!-9(~Dh3SsuPnvIGE7z}Ji^
z$IF)Uagqc|U8W)hec<t2C)c6#wDS4w{xk^qVhBQbCOhR@cbr8vO)&r}#Li<|<r!bT
z+k_e<&{VLbtt%rGa#x(7{a}T6<U`DK(V!1E5qKEMN(->Np&+5=6y_|TJK}uSCQ`|W
zWCG0Y+Tk|-{<#!kM2dw&!LB=eYyb7sX+1%@6osXJ*TIz<v5GLqX#lHc5HmV^WaJD!
z_-w3>5m6Nm>E{v#1L0&Zhpboh{Jni_%+LX5AY=Vq=7#IZZ}#xpF83B3=i*w_?!&8>
zI8+B5hMYIqkyay>11y>B%g-ez@bhE8YqMdi7`Ta1h8|YQvix9cR~te;?A*OCeQl##
z6WG5{K*=JduCs<NwySd^9g$8&8>OOuWlf-rUITHeUhQG`s0AU}H?rdqlsU;kMFW3$
zPA#`(C|Mv6gXV;0utiH88!W|I5Vw$x1qY2DrhoF4t90tn)Ru=ZmR5rj6s@_FM?nvT
z`n{=SR8Y6bZF+TpCGQU@wCl5XGK8MET0>U6Y!+Y`0pQVfTl!G)22_pB7(Ag@$Tt`n
z8QHl~*5^<u19hw#agp|>i_qLO+f4#`yb5a48^n1CUrP~Wfm&8|o|3t_xgpqh6$vd3
zjR+j6)2M50LxdOFJWv29v8Dww`m}3|HOCTP@7$lx-10<-D-IH`WaTWdr|P&no2b}A
zSqhrK#egr<Ld5KPg=yH<V7}_0$~7k7gM2e%vzhXRwo2A}U(^@R;voHd8v}pEUF);j
zXw=!W4<F1d67c-ueXZa@ulXi7DEU%rAl~a2jM*EMc}>1bR>O6(NxOX(z5A*6^Mu+e
zyt_4(z2^fon^Kp7U^Oh@Sz9^>RBR@|@7Pim=sBt3C<C#w_wi?e9qFoADu^&a@B=zo
z)1L|4>Pd)0igwIU@!juxB%Uw35ZdCT(u_(m#M`on+NKojEqDbmssgD0OTc?}<iLe_
zM#ip-BI1sl$dg&jiAurb<!dJoU?JUV1Ot3K01jm&4ZW5e=ZO!g#N>D%5AdYaOI_>W
zIV^A)O`;riLWSI^=g?s|zW3DxksX}=+3~MFaNp<i9S)3-k57l&+QI}G7QVfQTF7S;
z1%wn(Plh4`1AHZV@yknn4z<v-`Ow^v1bwgMpsBg}hgJ$~E&zp4`$G=$NuNO_bkxL0
zBGj!lRt9t-;v@i5s7Z7kaOWC*_aslftDDULD#lh21f-(6@Gx?|LJDWV-93yoPb}h|
z<c>n|7Z@kwX`hD!!VUqEOSf*_3KlAd9u65=N1DNVe*QT@A(Y*!%br>eTqF?yQ##oj
z*MUIrV-Xvz;4Vf+#@Zhk^?!km!#mNgo;3?Y(HM9H?)bN;OUDQVQ^aqJA&7`yArpyf
zQ(0ML33%44Oe#<SLklY2(H8U#A{B<vI8`%@$cB;!z0(kMEYrv$oU$7{dq4m{R*nr<
zHrNWG%z|GVbC$>(1NlnNjC3QGMY8V^zBCsHSEnWLIM<a=_qFj;OG`h0jIV~Y$^tf+
z%8WXu8&j1VJoexzZgcfqL`bV?9?{b7j<KdGJn?+a1CEWn0syCOWZ(R?fsE)#yQ2Oh
zsN_d$%gG7(qey*O^3z`jQwOr38xIASBr;1kpumT~@|9Ml`Sc2HyJVm3GDlB{;1hT4
z#>R4GEnu9exuJW0evtgD(N&><2>|X(lg$Atc7k)8l2sOBsf!4Xib%H*A~E`S1=>P~
zW-B*+dJQTeK{JUBxS0eU72q`UfraZh5?8eG^pcnUwM}Sjv(dVM)P*)~qc$i|Z&Koe
zn1P(eCm2=wVBh@2RfS+T+Bb;3NmREDfEYD^=7-`uXRbVvLQHLLBVXDh>3Df)3L}=e
z71Btm31k;tT2DPZe*H!7o3(($JVUUafr@-3CsETCkl`(G_Qaaye&0>1x?z=o``mEE
z0UwAhv_f(*2f0V!#eEVltjlbBa>dui6N%984CpfQl`#`rP_ZQx3m_U7@v#XGgB3vX
z``P`xj8Dk?5?OTyLZWCC$^&{DjSKz}nwy*J=ZESFX?Sofv>ChB%27e}!w*07O;3AO
zF(_#0wTG;zv{GIr$6b{e06#!Ia~l~84CekmwxK`w@cBVBEuZYZxL@Ybewl^$qBeM!
zsU!1aEm`5LF0j4IeyGs{yorCR2_PT8pAMcz{rv=BT63U?c`W8&7!et%m9dJN0SMje
zdy)!7D!R;~42~dJg8H5e3@B8rOsM!i#gU-!B0?4;ybOKyd=*F*CiSFi{?Nxtoa{yo
z5!9&5cNml{b)Gl?2IP;*e<<G6n?c`WMF7-=h<e*)cGMLHuCBI`zf#}D5LP)6%!z@4
z3|*aSpcsnWKh<ndW;qaFpH@DD6bq>61}fz|Cnw@S5o{(RC@pFO&dmmis?WapX=-|U
z5Gq>Zyq9|X{7|($4Uhs^00<mZH8k>@0vTjc!RLnhl~DW@JRHfJ1cp%>`ejhJSX2QU
z!^vq391l4!o_Y=l)_8enVj-dl2?%af`glXn2XWPM^WNyjh@R>we(~bPBo#Fi9364?
z4oML9Y7mAMpLh{I1Tj3&FLbl_4e;K^!2HsL2p?W5LXTV$N>&rNSB`1@LrKq^>zIBS
z8p2{0l-iQiw5tOF6S{}5_}It|gGW73@eDmS3F#SiQ^Iv97nuXY?o#SG3G_Whd}D<;
zHNDp33?*ju$G`6sNyxg+5ZnMn553(lca0kx4`r+XZn`_RLS=TK91!V@-w|G2ucR`3
zG=M!~ZD?p(W~p`b+%&u!4ap$qi$36rTJR8Q{n1)WdAA^u1O(VJ6kb8%Q3JG9A$K0T
z`*0YoT?p2aR;~<U2Abyq%^BN#9Rd79i19=XJm|I0LbDaHcnCCvg`oQr_35uZF=(W^
z0eec!e!H*FJToQb%m8>HdU|7s>Q;&bkPLuD<*F}BQ>OD0fe^PEyA;Y^uHLm)1vMTE
z^6nU@K*BDcR2xON6S8Oyf*^AO)onLGCIyXTK)|jO10t$=|4Q`ilu!G8kGi{Wt`Y+f
zl^_kC?c$i_e0LfaDYLnma!$9<mI<}Fkv4(ck>w+TC|64ecEZ-}Day0aKY_c_j9H{h
z|6qn*Q6|n{bAw8RdL;xHa*HU%c|?l7dn&?%G!QGO@rNo&SbkgeH9{M(IxE1>v!PZT
zYCQM#^LhY>=5Lc(a@rgq8q{I^GcLQK>l@Cc5`s1xb$BCYNt3KD?WzJ$KR<@RKaF@b
zeIRf*Q9KD^lM-!)bcRGM$j}^}oLt>zGHH?v6zYHnO1l(_cBV?Za;?LSLMk!=+gx0_
z`(LDxK-hz3z(AAZR%9Zhqgjw+uLCn52oO5fr&a0KUw=jQNFS>lxK1JjXDOGA3=xp9
zAZFbS`mIAy&;<Ya=bvnGZQyV;ZkXmQGd=P0r?+r5y(Xtll<x^da^99f>O@q~j&wsf
z#|5gLx&VHjdkXYv1*mTgsPi<a9Zb;5yxk{t&SQBdmgMFc1dUA8@jnMLnSg)*8leLi
z4KdlQUZ4NQd-bC<1$oG%IYuJKxH_Oa-%5RWWJDe*eZYaSb;s{xpv7Y|yYrRFR999g
znEf%tP9shjepQ|`%nx1y?e^ZI=Z~!|k@}7qow*L3_pHhdk8`^DXAl@e+5*^43<?#X
zf}r)gA@~%U%v^^)c-dm7o&Z#6P|5{BPfY=EGE!%vjt}&tF2u@><}MbiEN`aXy?Yl(
zK~<4BIbR>Z&&N@<VF_J*^d{KXJ9f8w96?K^a)uTiuJfR>G$o5xoZenL!_|kXv#*~e
z;V<0L);};MZE?w4|M%e5|N9@h_w2jyHXr2<;1fHF6{Gl&F817Mq>RhueR9oQ3jWr9
z1KtB@cI{v>+wNaL1eF5<AJFuKLOxja)rYTkH$94or~>jCKs0cy&`36&-UuK}KSZ6a
zsEUP`wzcS4C&OU%-DXP0a@jy+Xl**(os$E-om4DDniHVg169w_Q>RWLW!8;6bEP8(
z(PKPWf|8-uDmgYXhcaS1t&A@8<%9X_tFjXP!-X286Qx&=;xkaIf#mW#;zt#&x{)#r
z@y>weKUzX*dF0>@x`KKq89I7-&^RDLFg)CC<`UG#EGsLsF7sNQJHy8p0@k4b50(h6
zP1O1XS?sfcIZsczZg2Ov!TCc+j?msLm7Gk;aZw73HuwN57l8#$Y=ZCRhR)TusH}%_
zPLl|oeR-%4av%Rlm!cFmwZrlZR4~SnLP4cRM<?<Dh;z_zr7$}HAIixI|9%kit3R|r
z`1gr_ylq>(^BMd6m-~*jQM*ec!&~t=>dinEfM$)`!Eh0=;9oxU@tm$Qi@+(@pmKwB
z^y|OF@~~YW&b$vL1S6_L3r)~ZrXylv?0h>kfXBFd67;fX`!}Obhu5DwiHvV$zx<5H
zcxqZ^0JIV?7!35r^I~XfXY&t-OBx#*avuPnr{=x3sTf}Wf{_hvmQHe=K}#8EI?2#q
zkpZ+^+Y-M%%%!7@n7`{n5c#275aVbIJ-MErzy|bI7dE3j{yvU=%5lcA>jZK>0h1aH
zDpL_6Kd{}m?wd&<<?w|jPznPdpRPkx>D)>8KobPSh|_em1x8NiDiW=J<_I7x)}gqK
z`ptfRu&y--ho9p@kJc>#t9pegDl!rYPac7{)dhe)6A62PcaI>_oalo0gD4W6*EHe{
zfk9Z-Bu3kpazkr8to*`*OJFYhwQc>b<OL3ONTdzzA7f?ZjTUaeorB9yh9QkER8m(}
z&Ef6H0C@hNmD_nr{`mZSVIXUBBak%oquimAsXqWnuJm?Q903lHfdLELGEFlyqVYK=
zH=x07oODW{B>T*1#v}5rHJj_XP7P%p0ttfyI@ds-biq4(Oa7vI<Hk{dWtz1>_L~4N
zfuiNPq@8p(RkU?<m>?du;6QXuciYb|dr?*9GqilpqV($Lhcxj>ega#z85R}pXC$|H
zOM0&PLj+Q9y?B#w?j<bdGg%vu!+?J^X7)jR;DI~kxRHmD=wwS551#4T>HYcd-Q-52
zja+Ll>SC7yfRDV#KdPNN;C~c&qkO0E!)Q_>rH0FF2AD}(g&lOafu8KY<uCQ*+NG-6
zP0<4khTt-A{SuI~cZX{Nr;F<9GrUx{C?e_!10k?@F8NP8`iTxcb{~rg(OQd)?xP$T
zg9q4jMe2V$i?#(1Ok~X;yV;K-%L3I-Eua*HgEgzcH&S@OijXsc4WUQ`^6s8b60~&(
z9+HKD0aa*iL2w;VHR~*ZE=K_A;r#r7uBVYUJev<z5y}Oe<vJMy*))O-q?QSFZLxqU
zFg6={b)*ovv=*22iWDk=5=e~LDb+qnrMs=+Le<|-_vAe=@0LKKqn0)j072729_0!Y
z1HgzHab@Y;5NCnu*9?{dDTcedkJ)ZI{LYs79yHgGo9md|{0b^Q$ZeXW1_LP6KsB&I
z-E%vCeSn<~XB0QKEDhbYcav0V;Tc7M?u>}z(-YZ<-&~C!2WgE~E&=EdEVSx<l58MK
zo<bcWMgNubq-#&qrn;uzpA?hr%_Z%jXZNwYBy1#6-Z{IQP9uvCqS!HYqzFWr6<{P<
z)kHvgpklQFfxW}ufBw)D0z1dhGQX$?lo9I81N&n<@!=)Fx&-KKpthW?!p>(qK7S3V
zRHTkgTW+bJ>|eeKH8!?`M+8zdi`@FB{dqAz7G|kTnf4MnG8Hj`RG7w~vrUA&3nDhq
z<3e14aRFn9P(AcNfHFM;TxfET^i+6tB`cy3s6~KGc}z5c`_40t7Js8k7rGky7!2UG
zV#w$y`!v3~N%3HO;b4=+qg17!C&&u1?)I)U*h7>PA=DzU@8@>|+QFzkgb9wkn0<hC
zu0U9T2!h%}kR%m=E)=%u1a%#3V))3`M8Prsz8y=#V0D2YlE}4|M#>V9LmfFNuaE07
zLoQBgmjJ*(e#!6sik7Ixp<Bqym6!yeE@F<06?Am^i=m$i%-%=N9hI6YNV(QFmT5aE
zb&sR7L<_cLUEws^lJHU-iFvkyVvoTf@fXZAurV+IL4#;MOF}W3J&$q=jS=XV<XR(%
zvCOE+?%VybW2<XC;B!D1p_Ux1WCCRNFzBoI!{`C{rX>X0AXW)M7(t03rO>sj`#9{U
z!}GVyd&V&dnVT*`s%KgP0k9AfF*7iLsx=643_bLR7*Nj*8mV4MHmJX%3q4?DT_8L(
zoH7d4%HZ5Jt@q39IbD1cp}jhKh~<PA6%1=~sDzzPaN@zmeg4o&MY>sMQm+V9b4L*U
zhs+I}+PE=NW=us62(~Kd^rY5+#OG&2y6ouB(u3!r%}q<)fVR=zq1q8K6ey09Ea`-g
zeSLm>rS0av3t>`skg5aGC8;%_`R71@&hw9(T=b+4KD#i=Vo6#;S%To4qmv*9$l7o3
z;sS;ba4po9K$^8ennA6y2tYvaAOInxVFfv#T${IejuQfZgeem>5k3g3Wzv<oJ8$>@
zftH(#iwkTr5g8X~&}f4XS80<%HLG^2y9|Kx)0~Y5_7ka=EClGM%brt;0_7@i6gQEB
zPmPdghFk<v^rFwbK*9L<+KYGnP^Kb&2u;qQhCDFq&A9xav)tTQxZOa4Z52vk1?gDB
zyw_XQ?i+`i6@~BkMwHM6ufh_*-svrF5JTk*7biDjX|n8QPbejbzzJ^zP28;AH=+BB
z63a-<Xl{VrD5M|Sq)4_pI3L;_WO)FOQT2<UF*w8-9v%o_K{O?zPF|8V0rkwmDQNhs
zdftor2tq8PVIeLlAkyI3vzt*H{j@xor#8IHIvs0hzjq+Z?pjQNV|2-tyYkpoKWg3s
zv<3aYB7PBMo(P%QD%7U{6i=RRcW+l!_y=n7#*HBu(;bFH(#(<1%X?qSY%IMI$$9lc
z`mGiG<jaTow3MU8d*~A8w)q}e0hErEOu5!bh=}@WPjZt00Tw*mZ*6>8M1CeW!1UUa
zO38c66AwONzFq<bAwxM`cd<FW6Oi(Tig_|B1<G2~p6LzvuT+LZZDfx2{Q8Z8SI*HT
z^?89`I#9IVwmt?>|MtC8TwDwant43SkYe{HqvziGSUx^f@<*&_ewU$DXp4TuaV`5Z
z^}jQJ{VCn;dkSGJ-BuS<d%~nR0hHiI(0*=xx*6o{Zzhxal60oKGTnPiuH?}Y?w6n9
zFz}0~E_#q2Ev*c{NdQWPNg08tt<U>xC`<6BmRUHfP=14=Q=^CN8os;jDTrv@6(*rF
zoijD<iV91XjQ$u*>HRUd)Oc%!{OHARuOR#jOkv@!OqmB8Ul{AO8asXXh?zJ|3q!lt
zg|`WH^wYV=Y1=ayqp7ND#pV9>Ip}Urq6e|Hgq5e)Yao`Nni8a8(5){#`*a;H-2)RX
z30T85juYYLS?|7kOc+b+HdA6Asrg9lO^S`yxDoM5_@8Uj(ckA3`}|}tcsYq&bR<1^
z=)>H1Ykw5hzQq*n`Ue1BP8(1E_ou_Rl`|5Q@-D$^G5+hdp4HKNotBXB7f*d^P{P%u
zwbgc_-TgA`+RD3oTI$0aH+$A@6y%)TZjh65^7bEv(lYDV7|5_7OxF;6-TJ&g56cm+
z6IIxhVAE&*zc-~O*U`5J9PgNJpf*hZdsDAhg3A^dcW+Dt5)?b_Lw-mL5Zb<&KF}4g
zt`3t~3czqu31Eot?tR<ElrdNg-PR;`AJ$@(sjukm%VVj*Qx%wBM%Gd(X#pYm^miBD
zK;GQ+-a3FVB^p(|@bddT-1_PB{yvNk>>*G1&w<}<G401n;g?FVH<m@=jd{KfsKLF_
z_@#Bn14GgWc3;?@ZZ)m2?fbhJ&M9_1?cSnw{F0lMyV<qa%G2MiGnc&mGcC4km9#UK
zoo7e{p1VBXJ+=>eY)yM*&XCCx?cS35yYkyz<hmSMO?%o)s~DBaYB`*3+HQMmu*WGb
z7t^>MxxyK;nsgq0_hg~)WK*Suc1I|MR!?#EYPVGHwS8Gk_@iAefN&v0@#KMynFbl1
zuQPt|g9~s?mW!#}j{N!ttb4Q%TWohEDmYTRf1<~XCW$coT_{+xh3mz3r^yq?D^=29
z^M2kI3U13p!^2qITe6N?T1mSrud4IpJ11HLCrS}QH_hVa3)~->(emAE(tdj1#erqD
zWWx8|wX^5G3lv+wtTgXra=qx^yXScx)?b;vztW(Ws6Ltz!fST;`vnH8a<RUgwIDz9
zaIV44=I`(PFNFgPQqtx6SZ8bM6+;Mb|K1+-+h@fftS(-w<XJZTI#da(cym99@!Cs{
zQ<C`|RrGTCIuA#E7m=1=XI<<q(Tz<7hYCuzN7W^1w3Jg7+Ii#p?}bT(7r$-4a7!Y(
zFmSLau;T1L*#7&6rd2d+jjv1<Ka#=MynQ97!Q2FgQsTfn85pvfb9lu5F^FsnFm%BL
z??w<fnKYvP)Bf&(aqz%QLiyTj1|>$k=K>lUOt)K;DHz)Wfs4y3M#F{$uY}F{1|Mv<
z#U6NO({~UcFC`K%|6}G4WZ->kgd1A||1ju#5gYwt8}&5(D{Mg!b8p(04SYBkusuT7
z?UT1ZmUwSEzM`(YB%O0WH(NCC)<5q=mkSxFE@eloo<d`d`~QYA9PA&{Bha@fED(4|
zgp=D>a{qI9WWsyo_wj{pcIkckk=x(s2}D6t!UKpmkLvxp#xlRN$afIs1T!rtqph!8
z!eJ@*d>3wx&7)WP;(1rLd0saZ4m))O@}F`p6COnyPxLlzx77zauu5929X46kW2gHb
zq3PhQ4{e}Ur}ep6_;{Wx?A$i0?)8i^EWlG*;*)*#yC&#bzgs<4SUsD2GnLxCM0pm)
zp|^)ww#UU|PVk`VOl5<HP^Dj%60^d67eP!zV9!N(na8SMXWD&eR^rpxmVR(B33@>b
zRm~)_7&zXCxrXoVych1Awsq&~mxsyC2?bB+zhQ$1$7#RaU35%WxLHw3>{a%e?NL<_
z0zzHtr4k7&CbaNh@fyz!SDw1JfFg}lU}w!eUbF41czqeY6N|1=7(PLe$>E(r?Dk!C
zQGpAW8nWqP?nef<H7orq(v+f2H{c9Q!D-KE{|g78pSACyc|csAqk5UOw*A))qPr=E
ze0I+l2&Jvk3<nFC?%E!Vc_B<0cH=d}#S;|z@||XRbr?gH^`35re=aE4i)BhbS1E9+
zbRT_thC8e7<~EpCitfR3ALqcr#hUHfTOyw(RvVke|L%9rf1cxZDHwG-k9K^wn4{AH
ztFeuRH`6zYO3uBd4cKpe%{m?;b=Jb<d(LKwbj9A1f@(8tW0eFGjb7XO%5@5?p~sli
ziRDo9)e@^Q`*>mg$jttj?N3_(ha;r8O6J=hFyiPuLDIkCZbn#{PTx!8$+j+&VgQ?5
zzOb0+iNPO%)s|W59D1B%>wOQ%)32X)cC;|ot>XNdM@xKrOCVORC7YXzGkpC5-31cF
zt;r02j?Pma`8IL5nV0*uYXBmCcPBP<r;amXT$!>t4h8d-9*&nZv`!1rsGqGXJwvfz
zv3HCz%XX)<;EL4p#=}MOc>c@n@BO(4V&I}KF#H^G8q`dJm^AfPM1;2K`EO72+5(w@
zCtM=9WqDAFL>Ll@CWdq!G2QmWuPtDwgn!(&3%}h;=izJIzEK8n8Q~vg`ilIVcDr7Y
zDzbOdAxP((-@Z|}es;ZKJ|K~yz|$<>u{h{@X(5ERA2rHx900zWzq_D%RsaF%fjm5+
zZ{}LOkr$C;1g#R<1Td<CL3BA|ZF=lZZ;>r4BS$}M8NeCB%DeBjtXl-w{Op1)O#oD7
zKqTF|KSjF^k_I#blQj5>0;2lZ<{wSFy6!LfNf{X(h0s#?gfi9_NVK`Q9Jq7I>d{@(
zfA)_~L^}&N9I{gUpj-%MH9J_cDK-QD=k0#ooaQQTf17P}?v_D<0%a7uzgd?l+?>&D
z`wBOMO)$qmfFM&3QnaKt)3_`!W-kra{xPiEYha(250u4k&Wh*jMdv%UMP-Kimg9xD
zudp=)Ps<1Jn(HlBI%XTZ-?njFy5cUsYrB2MaEiUZNk7>ssysa?Kq5RW_@C{*&;*u6
z-`l~p<WGo!<R@2t{peIcqhJi;9k)I=Yr&y*WhhXNU}rw^S&ent@dZX3o&C;r*&wGd
zm(2!^vo*~*wKWHOyZD^b!Aop^-a?38mY>lEMzQt_(Kc{>>B)Yf?RP@1+yCbAEs8t^
zML3y-rH79zoDH`7oCZIllAM9lK?E8)Y66Ln;24o#p!i0WeYZ_kx$*1UJ5bxIuBoXB
zr;*b2RIE9W=h6&^oX0rTZuDWj-&9--ldBi$^SP7X!a7X0x*kDtVA^cmaDBK5sCHBh
z4J-N=p$!@W>P<129G}I20#7L70COBaNAD^7Xkq@*OhSGWfd@G8M?E|y-5aF52Z&kT
z`W(|N=XKm%_T6*^wSzf+>nssj=qdswtP&r6MOz;$4=?bdW(tJ%j7VjIM%T%2IX2G2
ztadyG!ui|3-k%>C8Lir{Jk7#5NS@7y2w`l#cNK@Pt0z;xJ-?}T`8j~v_0TcxRgM?J
z4pqfU0#Snzd_s8TG<h}rv{C}FM@T*Zr^W;&BqZD@{{7a~3SbJ0LC0u<G>3h5MP>rL
z?MAg6*{-a;8A{g-vPpp=vNsB4+1*{Q#Q4-z=v@9*Uw`J1=-Kz6xp`VN_VUTp0p^FG
zQ{buqr2y#22n@jHp`l}>b2NdGK{N)Bb}L=C;4B&xhw+j24-=r9WCt}ulbU17;Fc*<
zQ)4%q+VYGQb-puY+q8khB{0f+X)yN_h|B$|0~u>Up~ws;m-wK8Ge^flsv0_Jg?4}V
z3mQ`3cB1K5;7A(vu;;%rwPz@KeR>^YH5QrP&+1xMcx=0QszLAUb>~uqK@#+)v(=~i
zfPsccVzDqgSefco0nJ;~mxhVDbUx1C?znc0QL6Gd(*5Qd?2NL>$&BW;I`?N-erTH8
zzWLAM4wpbzHkS0`q83b77{jR(Xc<yXK)rr__@4EG<sfG>8$o2o&AYF^sLO#nG}`Ft
zK{@GHMn@k06Ab7u=)5J7F3=oAV4)8g`S8Nq-+>28boa6EEx~3rhgRD_8T-v&Kbiy(
z=<9$WRI7nje$P+;p~rwX5g(d(B*pUZW(BdtKY6r08FHu(hxkFa*;b+ZIrM`ZDpt)K
za$q9bjGJDV)#M(Dk)$W8EVcYzN^yva*oSX-y1Ad<u@c1Jv@vni-nCWed5-juk$eWa
zA~Qg*?`4-35A=U_%Q7;aageP?ili^dgr%wrZGYZloZ!)daEi(qG_`e9&c_Qi&ik;|
zrt2$YY>!!#X0Q`TML@FQ1Z4HOq(AQwr!OuC_nmGEmf#Vy6IzBs-_5<VLA=zJvfZDT
zG(0h3cwi&h;gF1@V)pt-@c{nJUT>OfE!%f@9O>aeos(9BXgg?LAr(C_Oo81Wy5`TU
z`G65ehI$kxp+ysZyAuIOTyEvJ%9XXhURjO#E)KsFPpCBvi!V~x4Ex4U9J_;?;)K>8
zbr!t{hz4ZPNhN-MssCB#8X;xu>F;rb3|GqmQ)wi0Oad0hKWnn!jFuI`n5=f%@+NdH
z(fqKKi!umP8r(n%g!&@ALDkC8>u<x=V)o7uPVVa|(kv@?>#LoB`B!3qXHv~(Y%iDN
zwDbN8GQ&U}qsGUMo695a+y3g)k_Qr@^a0-@9!I1UM#={Cj2+6IdQn9%w+ZkYNHD~d
z)AWAu>`msw%o0=Sd9*pi#RH)5m!E^^x+G+PJUR#3#JX3jEJWa35?h6os1RO1H&5&u
zho{OlBPPAhbicMzO>@yhbkqN7S)|LR%t>!i=G*>?uLR|z`Qip%J`8mMk?w}JPoRnc
zLl4VsTk0Gk0sT%BnT*9aDI<*rdcbq1vr543_(7CXf%*sp7$<H8vAM0npSzMh8~yI|
zY1K%jIeloZ%ogU-9pfT)EB6e(;`kbr>AK5bItYpWg3e4p{fAZ{X2s$9L!e6wfG!Oz
zgC^d=7c_4Sv^Fm4^Mp#zT}C6Ki%C^f%HU>2swGTfql2|TDRBs$apOg@F$x3{S}VhQ
z<dOCeXnz9XvgQH;!um#&hD^M3sr=S*(sEn_hL2KkNQAoec_w=Dp`L?5KOG{jPhWfd
z3lcAZWy)hTNEOs8SkEak*UK4j&*}eW{s1wR)((}LEmL<%ht4gn%lhlDe_K>+z*#7V
zU_6d6am{G$LsV)FQgkd#b<GM{-0DJIjS&@b<c-jIh^xjd_IWCbRdvlgJ)+lewyjyL
zL8;29MdJKElGcNsn{?Od|4%zc>vw>Vj}?yB0B=czMn4nY*RW=0feFc5*kRlZ?I0Wq
zw1+ku(7Ev<I~LXn6qQI22*FTh2zsAQj&r?eztBM?CJ?A|K$ZZFB{<-Oc6tD`Oy`7*
zDpB(S)Gsa0!AOk)0s>j5Bi0Jg<MNg%G`XCnMUI+hu73QJdSZfOUsrB~=1HQ|{${`3
z6<CdhiEhC?Kkc|lk?z|0dZuw+nr^y6x@Fs6XE#tQq=PbMx;P_s9>kGoz$OtU3a}u3
zr%j?m4^$IxtKd~e!GZKL#`(h)H17nBWFBJ(OK42zTymBH^eFq19?QaEEYpAe(f|5$
z1>2BzGKU6f{T}ZK_ER@^j&1+w-lT8gVd>CfW?ebFEp5^nsQVD4RYik!sH@hn56<mi
zEZz-gMxaR$6KEEaVdSV9^sp?@;cPNrmvn(u)b?IyU;w2`Fo?`e1sH^FEug%paP^wC
z9*fH8JHR-wVU7razcs$v7#!(^8gPZ8V9`kDf(A&3Z~!eNJ0Yux&z8*(t<SfFf{flw
z0&5O30}Kd+&@n@rwaE0L3D#(Q*0Q_8TR-bYp}itO$m~|{DiQ(&a$O7l$%utDc-~P6
zT7h3b-3A?LoXbf5zPJG%jK<5*FswK<d5HiAT8!VUC4;IS8l?27UWt$Il+&{BKG4#%
za)$I2)#J1M%l7LNF&8<xxWaJXnDW=nb@jO0DvBpJ)E~0Kkd*XNw@D7z9TMc`*G3g1
zbq-k7)`=t*-1O73(6Sm*8QT|h2Y*NdKT5W09EwtTSEqmG>yy&mEYg|{ATVt91XJL+
znP8B7%fk3H3+SmDap~!v>*QLPr_`by!VoLz(qPQl?JeBY65PixeQwF7tE^X~Na-n~
zudkW?F7<C28=vN(J-qQEB?daZ06vhA7L7VPPj?H@M*gACmH_9EG;#|$fHjjr#Cvk2
z8>vDiBo=2Bf27yy8(kIHNcg)oQ=VwuwLU~<B4@cqnV&~8kgZR)RH`=bM;M_MY?h@=
zLuV7)(A>6lqTCS7SA__*NDD22o}n0K%7<XOsM)yJzzH3!5$#Y>qCS=m=hi99EVd)%
zaYqic7-_TaedL>=T`pPX0y7D3698Ql*lbWLT!%WKT-|2EII+dlIw}kr$j*Sm=0=5=
zI~>#C>wh9doYU<749r&bqaG#7fapLObR^UIxPn{&Qldq-+Ds$$B^uO1r|p2I(F6_3
zTDcxoIUSDh{0dx`9cg37;yn4xHt>T~dsgT7zdi||_XOoYQAlNs+?hE?AO{-HKE&3u
zZH6#4c3OxteO(s1Y<yiK?zS_uSr4NX*LSQxB>>}dXvCfXqh&~=;dhzWg&!L2NWEA*
zQ#yre1vE}T8|G?S5fOxRhE5s7U{Divt1Wj7Y4wmq0z4Or%8R{D5Xsm;EbV=o_=bIK
ze1Jskve@T`%(i!x{uIsi$t{idFk^ovF)5>CbGAOh&WqLxGtTH>pA|Geg&LNiYZStQ
zB<vCZbT!<h^67A->qlKUkg%RbT}2prbdL=0(zmj*dKWFmKLE*&CWXDTU-q)zkUjV*
z(l8)7Y+e1UWg|(;&l@%$E3k|H-l?%hITa@_4a%PCJn}F5u{>}05olU8KMf1PgZv!E
z^K8&r4=>+Sue`#;C<dG*rc>aAWG`33a}Uc@quDI<(x?><OI+}F5p)OW03R{J?-!9C
z45s6r&S7`BOnor%s7X12*OW3^X{|Xb<YR5iomO`f=~1>m&z?8JJ{h3a?@>5y4Gq7-
zpf?MY0>O~mgpg#BAxzDA;|0O{oj`3B*!V$||Gi*#B?QFb!aO~c#VJNmnFOX~RY-yo
z!DMN=2T6X=@ia)42=dE(81B_AvS&l0W8e=uQ$EwT2g57Ntr~CM$njB>mhqPF6;WqQ
z_ve}M=CYS`xH&xP_WXyhk4TqoLZ4e#UH~V~R3W)A%{2KdR*aDv6(u^Py~MnjX)lM4
zZ!w&1M}!0Jf?#-~Mq(_6_Ox(9#}1wPI5?FPSu~tAW;(KQp=o&ZBoa;?7~7-<KQ6tg
z)GMN*WW?SjD=CvHQ&4Fz|4$gX&DH?Xu2&em=<)emzoDUElqkeUADeJ;LVG>wP$?8d
zXxaIFR~yC0pWPX9il^vDhVNW7fNl|ax%K{~mZhdN1EEg^KLATs!gTH1%L!}XHNz+e
zn_%_|&1)EXk*gmISt!%a7lY#k(ea{w-AV0KP;psE2)zI0kZ@xt*@%smO;<D%*R-h8
z6y#e`Ab#i7KdW{7o@LQFM~4h(Nz;zag4+2Y8g|r&vk#%P3`|uMd)?e!q?C8mwe_QA
zk9bzEh_>8$Uc)Elg5O=myole?fdnWxgjPs^SpozOL^Fp_9~PSIpntc6k{?iMc>WCY
zNYdu2s^OC$2LgH4&9}aEFW0wXm^nZOqLe?<uEW^i5M)r=>^?eV1dcytew^r05I7jK
zSYkaU_DHKro;VOBpB5<e7+ULJK9*;e>R<vC`5!7GL9l6gTUn~uX?^^KPe*eW;rf9@
zhxh|&Vvn|m<@di#lY#$fF@Bd#9ZI{nl!o)MZ%sNXw9(Y9uN$^4Az$zY3hgr!5=#$;
zQ>yECN^0UqGw#|Dhoq8f0v7cIAAh3|Qd;1!#G5uU9t(91-hk8J!CV;6@-F6zm>A6Z
zQhJMg>%E6=CIgfCHil0O4%JdeU#>LQQHcr1De{T>S&855Nk*sqtYb^OoE)qqK!BhD
zv9Qag3qG%BlCLBZie1{DV>@}XYSIt0v}N{a=rU|BSG_BCm>eozYTpoep^e|0j5Lf}
zpUFUvl2f*vVRhXs7woM1^Hqa3CqIt2m)7ID4*TQ_G*c)n<R<(;z)Kr3i!-z%YiYlC
zJ-)q>(-i}h#Jf89ML4v_3jLyGhiR?v=a1y&$ZEeq<)CQ8b3v$+^5WZeP6o1x(dQil
zjShe@be4z!h}Bo#Fjo8ms+XRSAFiJ$U$$pj_9oU9ug4XcbOr?X8Q`JiV(+&0w%B(v
zVWs{R)x<4+AJf&JzP<vTsXF3I4)`NB1f?68xha8h$%|-O3SIu_`I{HuK+@^!gCQA1
z<i`8eE81hVv+>bvEQ$?9c%O~bp<>JFXzcn9&)QHGiDRYtc9G9t{B!jSqENQK?nE<9
zG>?VTd`jfp^+4bDb@b*W65g-CNlJ`x8e*NJ?roAyMD#(Edy9UInbM~>rXDnlNRIXF
z*Q%;x;i{R9i#-fbeNz1RKX-V*3yIhp4!NsA<BI?!0--h&g=+JWU_4|HG^v43q(QSQ
zXd;>bGYG1>x=&wL(MfeeJEoZ2xVx4*C@#3>f+G2;kw@hFV-~gOW<qUI5CGm?xcbk%
zj)hO6Z~=!&2GfN6aK0g=s{jzH0xsu>;*jGOj)aLh7${~!aT*~VbTXYAK#n{nmYeu}
zQv;&x)XoPhJyz62&G0g|OrtV$Cw;HlN@4=mp{6WBiRtn;UcvM#?2H2`B{3ub4ThrJ
zgobfrVD2X@d>%UuECmw3g4T+bvfz{>(9g4>W1fsbcc-JgFFT`*5OdVUblHvEU_xxX
zk9*ZB!S7uxD7%S|BHl~3xP-NGXXE;2&VtVe7)#`kQi@gvB7zAjmTDN~<$&YiRCJKm
z5Y8>6F>)<&GUx;d0l@r7Sskgi^F@(%U=5b~WarQ)hkgD#D1`%`)<7>&JW(wnFjsVt
z=)WAPNp<;F1U$QCeO`Vh!vz*8vve}m4GvvEhoz7+9K3nZL~(@3*LfnO_C$w6&`tw@
z;U9BufeRkHUAg`4^RXP*bY1&Z^?iElzAhwC;lod56n`w_R$XbmxA)|?SDXCp4y>ge
z6j@}X-UMcb878Z)d!dtcK<w?e;&vZ;yi6c!r`0!TJ`l9u&5p*mHA*D>R~kMU5UV$3
zMVsXkY)&4KlC^+Feas3)duTK6F#|Zll<diWjO2P6vlkc%sOZpKH|=z?8>P-x>*P@1
zAmEeaF_@vvB2LIzq9^`is~tVMN)F^_I~3|;W6AoV;O70QMxJo&RHGBIc(CkHXjxuW
zi}*j6w!DCghG+~hjHVa^CIsZN-B!DC_H6roh$+RN-t7Reys@v#H~;y`3a42qvXQU5
zNL+jl=UJY^rT>J$XB7x`^gaalm`IKfArotB(O?KV1sJn#A!dL3-bs)lS+Mn<QXU@^
z@L8TtfBd<3y<1g|LW0<ALrlIjTdr3n7BazPv`&z1J>y#BRsOB)5q6`m_yi-l`NeY$
zJc&@9V_}%F7N*1)k@VItw6Pzo7O6Fni@>Q<2WeCW*RGV3P9>IV6%`UksFCcsU9Jv#
ziB9IA;~m7$_KPpwi!vq*yXTbTCxobfvmvRpUuWv5NO^1E+J6|$!U5L`M_0WhslY7l
zjUZ-`v$V<pRLV&v|7m~f6RTS-$0xzLu{iT#WrQd+lt_O{O4_2nw~5@QAm=Anas6mw
z=rns`=;RR~F@+wW(TuH6ci1lVVLmuE0ZxGyhxt(2F@G8wUS)4>0VH5JRSPl!4AT%a
zWLjg0ca2*5w{@c~pB&1+BUOv_ma2+3JQLK%)f9-x(yBQtNSYAOq7VCeyDP^?725<c
z6&g8P_Y41D#JzP?RBInLjD-hOIEo4?p;AgCA`L1E5`q#UjgEi<1Cm37i3)<G(kjv=
zF?1=y&`9Ud9WxTc(0sqmbB^&m-#_pAzO~-H7Kq~Pz3=+fecji!>*ddZ@|<d2J57eL
z^DgjEAg6M_LD~BBkj&_;s>7fqG%5mW&D%yA(!dMDJ6rQZt{<<URh^<YWht;)pUF5j
zH=hzLES{d1eHy9Ja}-^WLA<x?@H^(F<R_)K{%sxbVc+2Q?nSI8947pR6t0n<UnFp$
zJs|t!^p)9MW&p;M?ezkQ+&xyVi|w%gc#gx>{-LsN(FMy;Q8=aT;lze1x`aV$5RW|g
zefMq_iz2%>%as{YD5H)G_AI~7Atb25UKb$y`?s5ha0-0$sOfom<)c|Wg3<<9-z((o
z9k9l{3yz>5ReQSa8yZ`ntmq9sG2H%kyi%rL=TPhsXd5{ce1j0wpv%eTC*Csm2>DLp
zrAJNqem3of2x7DYl=vV@&!{K#K(WF*-F1{3={IC&4IuaL-KPPpriZEYTqevF4=*V!
z3^jDne~W8=bv$ydvZ!qizM9h2lk*lkqpX`BF|qQC#QW0y^vh*Y$p*lKfSjl*_oDTK
zoPPU&@qM6b006FGX^9G<1GZ$u^Z72u&Ek_3V5@3lC==yU5oyKq0+|t-;fm&V1f+;L
z@6zYnpN&3>?Cv!%W?P+q5TuLyH6e@|)vdgH5xWSL3LJEh;W$~5?GTi64aUhksy?;X
zAFmMcBEL#MfafP{k%#g~qhcBNNn^(&3m@8BuM^9Oi0eK=f1ryWOqp)BWp~0K?O+8E
zVl&iZx9PWqmMA|Lnl+yh;S>F+TeRGdt7K^q6J2$%&>pt+z7SG*K-GgmHqGUfJL#Jb
zl1VUe{0yvtnIC+Y$7%m-tt%(WqE7RRa3b?gawYPP;L^wPp&uLF?aRFgg0%z2^0Q$n
zgNUS*(1qY-?%(!Rw1E)13<*7x^bXv>LA17?*m4R0mw#^=eCF7ma2rNfUpMeVF+bcO
zf{uJ$pHa)BL^VS2VLxmM+5Q4wINbUt;_Ep`v2+IZeP5Qo7C&#2-uZW~a>NO+v1~Qm
zffABdqt65KrXD>IghXL(n4kG?FZ=WrLf_Acbn%Kx4xBh~TO;Dnxw|Zl-yLczABOPO
zJMOiOxm}-&QhSAVZqd>HqP5i9*#&vO*{L!DfFV4&BKL(=`jt9fELwdsJoL(cP5x4r
zoUk|%>M?h}y{1Dva%Eh=ytvA*;0%uOh*0$0Z;_NqW9X7F*!W^SH>^(cqBv}v_#*<~
z8i;eXp0P4`wHD8Zz<nqKe%i*1<p9yShz%PZuh8-$(DMVSgr2LN-~FKQ&Oz8P2>PZ;
ztL)uk8OCVUU>Nx*=w~ax|A4K~&$6ju=U=fzwem-85CoCnu45vC9FpRlW$DCQ;zZWX
zaJ+8)Tj=2V{3<l?_;g>I(>fX==#F>gF@Sc3$PFnr<v}#t5Bu;ITxZjVpZ<Ch0%H8!
z0x%GI#NxX<tv+hXaRTr4Hbkp3+K|U*u+-@+^D=oTAw}8b<bN1ikq>VZn9l^jMr{Pf
zG1qZ22{k+f_=K3=Z#0c7_hl9IoVE(ByRQch*RLcCy*c#rc~gM%qmT}~^^b{cbSJ+P
z-SjLrw`=%g=!)X<e78@WZGZ6M1^@vrrcV4Hwd{rmv<wi3|EDdx_ZEg5%Lib!ai%_A
zgMz8fyyXezrZXZL<+jq@Hx6m_ZCScbj#Ud<x7S?b_|2ntx&i9phN+J5gTu(andBNC
zHMG2IDXmGY<!HKhQ?BYu{!J$}$+^DFgwL|S@MMLjphu?k$PV?xRxvcM3o99!-6V2S
zn#73w+AMGJq+5(zg)go9Td&^KG0?`H{pptw(9w>ncK}(c7=BCZZ5ZWll#PxujIo7H
z;KN8h>svfn!64yeV8NG~F#Kz7A;6v;RSEx&91@jImoItC<-a^0nQMm+$y;ioy<0`%
z{}vq;%>?`JQ=(^oj$SJhyldu1`xd#mm_XVWrX?=Qj2>lu!qQfxxaQ^AtgP)1rbk5_
zWI=bTdHs6X_m(giZ#37i=FA=^?C(oDPTmyEH1s+iotyDVa#P&ea)0q=#d5z_zc=K*
z(*nQEGd`quo?XIsf9scLIjX8J$fF|R7+s~%I#YzKy#>;<?X^eT9FBa4y(^2S{AK@i
z0QGyZxH%V<yj(8k_03z0`GlcZk@?7Y`%GdIfSPEFi`i6`@wI{`>#dIFgJI$-zjE$E
zfHZhIC-xZB4{xc+oH1D0{INX^BxQ1ekV^!0b-klLy)r+yggL#=08)TVn!ul{zN`Tt
z2=5Jg0Hw4%sIYN&p4Qi!$kZ><ecoJS*w!$-kc%%5tr+3ZGU7P3VSH0N@%zuZ^2H70
zouAzJ&LhLelHsAe`Ni70{_Nf>xLeoFTnX6~(?cgPB}Z^umUkt85G!upx~c7@@N-5O
z;kKm=9OSari<Y~;Zw#%o7F3p7@xLrBI?p+l&Xv}bZ+pw~6*tjC#L{)FT-2g8rzy+L
z+pk~zydS|u*pK72&9o>&Yx|$e6&PDQDH8$s@4E6i&3f!T-`vPr?a&zg__w{a?Hv=n
zvC}!<ScyE>#X_!Kv3t<M<D6r)xmAnbDjmU>IL)+uwAOe1l}OEefoXUDJHV-|YdqbC
zMxKaQC}rKMGz6!iK9=4$In5S$aGw@E?rU4c+>jTy`WW<9P^Jr+lSu4|doVIO*4pm%
ztB08Zujq-t3>Eu9H}P*jq?v}m&J2&y@5?=VkC-G}VJv9>B@8KP)y%E;Mu6eyy1-6k
zp4GkLT^V)vXNM(3g&d<Oj4Ut6eVsvnz;{xqqr%W2j83&Emxn75>k$o^6U-@8olb^z
zc)o9H*tb*q`w+KTIS9CHX_s6dD0|F${o7v@?=Jyr)F`*jBf5QVJbk~<%GL0pbpFPd
zNC*b`^82R9%6XFRP&FB{QzF0Lix-bgUJrdHpod>DyHpIWVP8XJd^PRvPt#Dw4A>h_
zya9O&*XM?lS7TY)@@TYZQcQLh*C#$RtHFO6yg|H6vjrdl)aNI&?-=M_p)TKjr91P+
zH6ue-?^>@w<x9HZP7Mi$o`(<ZTGp%?^@#E6Z*O&rNB6Vnnre}<yMyi0$DbIRN-u=2
zxQ=Bv%NIH~n#NI*UemH9H%8m%MLCXp_Mr()V`I!{>F+VKsl~7SD(vu1;?5}N<<t%2
zE8tU@rxrK8a+890z#t*Uq`hW316TBGe-r5)&$&k@MJNW|2x1E^vlvL@G><BYb~m+$
zQ#*KNuUtGgP@I@Aj~`rqM~anT>|nJLnfA{OXIE%DVi5WH{UuIbi&h2ixQTaN_VWj4
zeuXB;eHFa7?>n~8sne^GT5-KC%?9P8<NPtyh&(oCQRRY>k5=~QoFlyaM(V8G<!mv$
zLqRRLKjz57iN(IFuf-n>0_%Ugfjj?~Ul2s(ec$o?1;F<2w6&S78tNb7!l;WeH)G6o
z4bOi_<?Vu7?>dFHBs7JDFh4`7-0UX4=2#pl<aM6MA5~B*Gc{i+8u7ss_0M0$)u(Ee
zjh|K2Meh0Y@@!@W*lyWhnyXUcX17v(!!)P4FpM!$k#5cy4?lz6zPRz6>`AOX(<^p4
z88?jR*hFZ3s<Vl0GFvqfi?y&UR#B|)EVeKxPFmkT{qwHK4+1yeEbpXvf6bz&IpR52
z{ey(&Db_dz#fH9(d0z*q+*r*<OLR-Jg!tj>KJM*=`@K!Jx;=}%?H3dRI&v1Hy6koG
zAHKT9_wFMdz4A@{V5`>IKkiOZNqGsnsq#i_e~fyFa@$#@IK_ak8BY!HT_2D)?&3{#
zc9JhVJ=e6iaM-sgSwdoQs(}W-^zEp2h~4ty@l?Hc`|Z>9M6b4(?~1YfwGn|l<Obtz
zIDdNPgJ)w5na`}%g<g1yn62UOcsZRo)oQD&LxQ&~&I5AsjBg1&f^yC-hPi!x9jVb<
z4+_~HTtaDbw7NBam8*|bJ6oG6hyRr@BKI~LK?d`N+z|sm6}>kR!fRIS6^B&Lbrxea
z8_^G1ynDJ2zw(vt<W=a=alxiywJ^omN&)AKb1rk!UoabeOqr_JnGoBOWZbhGk)`@|
zy+4fHSHO2!GUryE)%RB`2MWxB537pml*TJH#+l5x-!Ts_V4eGzjV<N=xaETO+azM8
z0zR#BMXS7%xys2K0sXAf)Rbj5y3l#`0dgMl*EJyb<^+YOb=`D^eQ&QblSr<?+vtqj
zRJi-)efK`Et)!xz@92hiyP&Q<NRAPS!=6|&pM4H1k*id`c3gPk+aC4oyY5eX!*%1s
zJk!sh|9I`P55BNwDCl0`w#VGLaYo#2Ec>brV@&;CXJj@=MLW+-Q`5($_T9g%iZ*Lf
z57%K+$YE!?P%KwFE^;4ccgo<t<H`55Z{6(vnSCLz?kP)!Ob`x%o}6qu=hq=B=}nw{
z5{8uwvu4B=WU76Io0q0u*VVmz^+By*bI)1<X{p2#ztT&i&>ZyMuy(B7^eY|~je2mj
zA^)$=fS-)cu%*JXyuwr4+<R6tOd?j1MO#olptYB&eV=X*X}$dx+*sd=I2|BZ@gSY|
zaNEB|gxz$%jE%|bkVf+=$uX70d+mQN^eS1TpA{^1u3sEylu$>$0$RIHz@!MwvX?2v
zUbmW=(&#;_k|{CBP@G?=WJMq}H*RI?2b>2>Ry)paI%^<UdI?vTrjD$0mp|TF;O0Sq
zc@O-pt~J`S<{OblW)jm6Xg%?Ix{Zm++0s1RptC*hEhd|r(e_olfs*PK+G4CB@)a<G
z<z<pezEwNM{sfQo_$_@;?LVg;qZ%^x9==GPcd?%jNECOf)=*G8PndX(k3=ShL1);0
zhg@<c+_zWI8Lbs>(9@Q<<!Tbn_CV+Qv!f!K=I*HAG2vSeHiL{z@-GZ<>-7Bj<_3Qw
z0Chh1^wc8@fmz0e)|GdUp|W9Z@LO4RC`oja=9e6bvr2V+&l-f+;|~gB<QL<4Q}^`5
z?cN9ziB()Uk$m1m5uQLPRC`_cNZrWK@CJE|(dE;~u?0$F?U17?2h0Xr-*4WnU{p|3
zWlQUDaF~dlokh%uL08j<hLWNB?#N``!<xEhlC&@mhb^GN=dBbqIVPmi1rt^hN4rSD
z^UgDZlb|K`+vRf<nTG;*5eJfaFG)rFoMym?sGP3OWoNM?GwyKrzOH_uv5g5Z+%N%)
zi_;5@!aH)#7k)TrdVx5i+_x{DQW&Ecyi>aI9r-st2Y3YKWtcD&K4EBGopY%=N^zRg
zAL+&w1H{+vI@_DdbM$UZ`O?_S53x_H_X>9Rbti+zbe`8IU#JZ*Y12g7dQ>^J`EjMu
zyr6J`lX}t!`CQ;ncaOBdGvn&^16~#=MmLD2D3~&~vneQw%7uryEP@d)4x+|biT%x`
z)bHc(fLVM1{w=C|q|ZdRdFHty@BRJA6UIa+u>wbyqF|9o)1I>WtzRf8V&sv#2R+5*
z*e&}UXOfPQ^T@Y}VoQ`d+-6=Vo{eP>3X_ufG>b0oZflRf|Mjer3=`|D#dJ$Uj<nQ@
zrMG!FF6TV%$b4G!<HSq67W>UqWMhk-7X4vg_R}Gqf8)Y|OF;Q7eLa~5^Oln_#!Y|l
z-seKBR*$X=HikF9C<d#A_jlA@4S%Q{C*xOYTZXMS*G1N~(Dcrv^!dcJ=5#QWQ`~L@
z68xcFyCXWDXZr-{&*r>eN7jI+H4dd4rf--CGeO)b!zEl$ZnjtuZGPZ^JeNjsF)y~a
zJ$V=2KXXxxz<@?mJup|g-?^sYh!)mZQNLt`sVd|%%`Z8JH5k>B4OR`7_T#BNUY_+w
zdj#6IpF>rV$#b1*RhWIEW8Ue&o*r5MJcsPj`XPLKGCK9ttR*oJRr7sv$CV-9CHrUC
zbZ*Iy8$~&>Q=VFFN2=woJBtn2rfWHBGW|&6AVuKE)a}cXxuWHBuAgN5*&z7g%-+Xv
z?$}6;>XxJZNSzLoSXPdaE?d-h=zg?0)dO8~o240;QQh5#nxh(|L#+?bGb{HBPEEW{
zj1jqzG<-WmW47^mk!^XO@Db^H^OWZ12W`kSU5L}Dp3d;L`y+Ou*lw1r+_R1t_CzC_
zl7~DWBGJ`1$T{tAa<EGryZU)g%}z3Q`E=1e6I8>nr4g>VRu#DWr9M-CqI0fToQ_~1
zo+`I5?pEU0>9d!te(y5R>Su_D@%#kdJhsIge8KfEIS+aKL-So+%pEQe(Y(do9l~O=
zeJdZS<WG<>=EIwoTKO=uz-Am(RiZ3<tCHRx5O<t6>Zk^I0^vXI{pd{^{GH))*?7~S
zkQvP4uWv9`=hKj0={w%+n`3|_SGG2tP&YNyU<w_5C+-|WeU;95Vxtol5g35(=0nZp
zR<js%xV~X1wL|>Qz816Wdpg)DE;r*T-rvVUz<t{YGCbod)sk;WY4aB{_eweOT4HKO
z>3LDhBcgBLeXa>6b$PD!$kIM0-?szGudHA>!m6Cic)eF|w8e}on@#u$dA;(zNt>3L
zgIlag+url+6K{>Rji&^Ef6@(cB>16F{6@sKZHJq$4$ji6icV4E+WNw29B{34FOxYL
zwZJ|ST5X-B7OE^%+c9+vk)3~m;RP4iBowZ@FGtf6U9b48z!c1k2|TM~ubOdin5I^u
zVrl&T;y*_I6%sU4l!=a$I>Qbvnk#<y_>L2(OyCc>p(>h2aF-pZ{*1vSS$LS<4yReX
zFRz=fOtovkKQAVg_Y^Fl1GnQp)V!seX`ph7-#O4?wsXF-Zt8af?>YB+`~IiOGI=yt
zij(GDgwea#o}s9a4B~oy7p|~gs=NJ=T9LB4D>$jvz7s90Qu-T~<nEBT4$AX|C2B&`
zyTWdsxFP#J{W&cD<bX%Zi#Betss?`gLqflI-1ZcvF#3B(w7h&{QcF|Tl-_8@Q^-<I
z6&K2!AoR8NA~DV3mlDQPXf?3M_@;O~lUsR`;fWt{kNc`UF|xs>Czti3xm5YJ9HmgM
zf5eUNW{?F!Vf4+FSyw5p?S6IkaV@8>aFf1xOn9Nt4k9MpB|AS~UdrDw)L9}g?V+Cj
zU|vq{5{*kVS1IR_WtBap$?Hi}j+(lYO455>LrL9r1*+I!1I<;*Odj+7jlVKuXcFBH
z#As3+ehsT2JwtKfc=BQiYrJXAYr*c0BP!1e$>jSjkC@S7-(!j+@668r#T|Br$kWH#
zcH(-1i1@m8sjZWR5_X3bN2k8JzcbCU-i{x3CyOo&1kYY+I}|dt5zxW><X1Hf9(1u9
ziA^ZV-evJ92~BeeXQipd^P+9gwu~_$H?FuyeC=-W-!D&`{L9?R)X+>SB_!%vsI!49
z6)vGzByOp%^DpvZO+Zttr=4M~Gm~KQC8c;x^XhCf-l$utQ`GL*--G*i*RCOP(fbt0
zO0vT|<JxxH_4cm1Pd`Ob%}(z(S2aC1QXJ2EY`(80RG;vgL$PP>oIs}CNgk;s%W!6w
z+<Gk9ME|0a+7|+QOA^*j=XHp3a(mGGQsv^Gbso5TnbCg+t9ReBq>g}a!XEV}8<rxK
z30eFh2b?e*rb~T22X8?Tk#j?XsrV*hBIv^-9uc=YbN_TTMptiC{jodmgQWI=$+qfo
zjqfh-&=BL`#9X-f&+BBBzDXqmLV6Eq-RA%$jP>=UfdkeFA3j_H3}hSZ)h1j#d4pgH
zbqXKz5aYy>8t`JJkl{(($Af)c>PzUB%^0!e8o)Cv@?jP$^}Z07ZfJlbeA)16x%=`>
zDHgMqg`Qp^M=dQ!Y~I;~3MV1QMsz|@2kPygZ{kJsxYzi5Utby!OBy098H<2YNs5g%
zE-R3ZR)sx1-?V(#Zvf&&_8F+9m5m(!;dCAb8OGYHSe0imAx7lLI!)}(zT&O{jJ%Zw
zelV}#E4-PZGECxva>0s2Ci@%521r+i{s|u^xyVjDkYB~_dHrPsWfqJC8J`6cv`ITI
z|EkSHM_s-ArVt=fU5!(~!l`xsIMc0-+dOf|TY`8(>E=}WrsCEnaVxF6`-^EP><nMD
zUXjcRGvk6hTkzwCk03RU(cTL+Ma@}`hh*>Zcg&r?Bnuf|aa2yEtkNqnpvC@f4%zkL
zF<RO$HvfQTSpPd}jOqb1chtE>DX-8!0ui~VCowMpC1$(vJWq<*5Ab7&KoZY=)cXOD
z(oN0SHTo`tu$>+d6XpQe`=F+l*4w07Xo2W87`SHQO~`T|U>`jZmy;a4Z>3bo?7Db%
zmY9`Ys;V}-A8qb;f8i05{aZ^&y}14LzLL^4a4rEfc{xsKCEMd%_#MQ=FUnOt@>LLS
z{k+Paox~h#>*$ys^5$;`WL8Xzio|tcVc}5p%PW^vrB^;73W!r6<y9Ai2&@$VVA2ja
zh2O>??{!8n9d}DVR#hnJ(=koCfNW>d!KBpG;9-VY!bD5lSk%n$MZZ3Le`?ypqE8z>
z5_11!vwo$+G)^uym-`@PgU~rR?v_@<sR=C<*w*)k>PhQKi!O@Yraxa1+_uf>Dt*d*
zM|r+$mCPT}2QU_Jf6nCk;)anEO$g;7LWoiTS}^B&04m4~=aEzZ=*p-07JQfhO;=8A
zzIxJmh@Nk;2=c01`ZPU?dT~LReBajAudklUSp*}m*w^?Jm0i3Y43fT`B<+;eRZoHi
z-Wv0PixffaBC^}98lC7|xA?nP;oy4Q%kBWYm1aQeIls11o<y8;Tm%)Ni~ObQnFwY~
zc?DrUN8($GjZ7XT@JwO1=)PBb#~0+-FIAk|LA<^f3RGabvB|}%l|hN+X9wRw)#c9Y
z<bGP0=rGR?rscGG@P_e7$0T!aSAM3buO}ydhuqZQasO|9WE26W;4Un)h8N^HN6CWC
z)oFX+NH?z81O=g^S-S9FMX$7Kt|D1D)CL{~ldsZsH#Z~ov+nle%{d`Rh-Kzter>H#
zQO2A6(B5l!iUx-*#$u`pLfD1JD2vQ9FA>*AAqn8A<@)jqQ;H<nM=lYlhJc*eHZ?aV
zCn+~qOH)(RcnUdL$q}en`mZOX&LPPKUYhKENN$Ix<t~{ScKk4H<P$-C3W-X93K^78
z$nWhpn03{YH+JnUG3P=uT2ipPrl#WvD(?5JLE!T4e9jlopFe+`+0dYZ1gI1Ywwmtg
zz7g8VtfuWpEvCIWvWs~?p?Sfh+Ccu4(&r+<sZu3jcz*D{s_AW)=T0;bHzra0J$6q|
zT4#nQ99eYH7$tMv_IGaJ<?oD28?I;M9LUnodVB(P7~^x=THgQm<))`G*Xk6~aCEP}
zF<ot`6&tNS$C+iwhLOS=N)A?`bmzNsikOuQdICC7wWhlBojI<|-1$=aV;Lj0$>FXl
zOsx7LeNp0au#CGsRk1swlXe+6S(r%u=9(A_{<2a18zdL_&X`+R@W`q{%REz%F04yv
zJ19gg+c$XovP8GDdKQM$bfFJI7Qmm*en{wciLp^WCN4z@;cJtDJX3MTN2Zp{Pw!=p
zIjHQ-=BzE%(y@Q0CH!b4=E8syFK4tlch)r4smGi<u<=qU?uf&0uV8fgRF%{vNsxs|
zBZ2g5Es^{-5y=P3zVDRwqX{>NDUa(|EW}&;K!RyD{W?hsQem!M{LxpjQCWVA&KOaH
zp{z43s83N<Bhvj**Yu?P^OhW#dpC1pm48SuEjOgOXzCAY+83mp)(tVlK@Q+@7w-P=
zwSN~fm4LAJVJeV+b{S70d!9EKbI^%kA!NVv3m>qGz7P6Ph8nXU-s#95#Comndo179
zO{WRv3SKFH1rcxIOh#5%U9Da8fvuSDxWq-LfJa2TPKg1{>(BU)>{Jbj-!%wn_#SS_
za~xeIC?6leP-EN~tmTO4<ZKuErJWmYWF{IPB^@QG_W5h_DwpBZpq88Qe12Lpr(eDD
z$iHt2mZj<7NE8LL>yJxPfL2M%1bXRDl95PiP*X+33a4_se;~X=VWV*|l6#N=OO~^L
zFA?Cjcx6>z_4y;~GgIE|t@KJ07crlD^^fIrduow1-5~|kcuVb681h8AO>0$CYgAn}
zDJ6vB7tn{ohkJKEE<Y#dg1-C8Xj&$)1z&_yEj&6W;3%g!sB^qn>H=CZ^Wyk;d3}BT
ztJ8FH9EYh76wts*yimE?G^Z5C6lAiO`_r)_yV|`gnXu14no(7>Hq&XEpGj(OwNWD2
zsA>%6sTa^utTr9-P3C6o@XZ+{q7;y*QEO6DzbqLHz+-p6bpu^#KlW(Ol^Js9e0)c7
zj%yBksYpBzDMELQe0x4FDiBxOHHIB!zeuzHg*7vUXSMeq-#5Rvw6v(VFY%)GiyUW(
zXT&ujtmX)%VrXVTfp{?hhQlN<mQCF&@gA$3cmQ<Ah*!di#w1`KJCZ05eO$I|>~q-q
zQ(D4d1-7@WR7y8F^O@_<db6n+C!+nI#=Zzl3gn{YWQ!Sks>gfG30-ZfU?8#bqbs08
zQ3@95xvo(SeLr@w07h1JHCj%hSiVJmZN!K>mFZ_Uw)kt~UN5-^1gV{4xzZ0WhK*$L
z7e*EG>NEeiHd2a=?|&|zSYR@n0EkLMN-0nzivyRiQoFQ}jUNh*!bEa^<5fV261|Uu
z(mhO;b0NX|-%)beDy<btJ2-aK9cQh0eeMI%;=9DLRR`q@KC@Wc%bkf29S8+D#U-mE
zYqdv&ho@I2yp`Ojl-%Z8os)PXxTb4cvjS=pGoK#w;`F*h#mV!GPR;yEv%r&$93QS?
zH37O+g3SIX9-;a85_-v7t846qF!29g!v`-BtfvsB|J>3Iv{o0Q#DVbQ|Hn{!W*BK#
zYiGFSXw25)&pBB}-P5g>$4)cKA9WXzdnE=jnJ5!wBb76`B31deWCR^eV^Bpb!<`>g
z<^HiZ^r$P?pT!PRmMz4ZCN(}ST!>Z|YBS|~!SE>!cW(aElw(SfyoWJsiVQRVw|`#;
zy2OD8RX3urSNRY?O5>&HUaFoUE7^ih=mf0)H^P)2ZD>o&cEF~~Wj6v+*$z%2+-nD`
zpv?p);jR*nJ?B15J-IR--I$WDm#_B4=(CR$M7b5;Zqpt{;z`bc8)pt_VF<S8KSVv$
zlWKbEeA=3HrML0Jwaa+_JTT^81?5B7yF%D|0#aJDQX`CexOy$&tnt@OcVA<+XQ!#`
z7ZD?()jOEF_Mr@=VBp7_j)tN|Kx0lYZ*2;_<w9qOYkrSSP0QM{TCKESDwyeDX<bcs
za4LRD&9u?-!HJphZwBFZE4NV*dnt_O@BZ;!W)Y6yUqYk@g{I+HX`HY0>O=sx(WiH~
zZWpcOdsVk7^|ZgqUNY-<@NlL9b!aH$6QI~007Y7ixSYK`*38`8yOq`FmH@-Z^bU$L
zw#wVIJ}Rhu34zO%3?w-^GvezcsKhsvyfe>_5lLF=yKN>_*xPi1okIExn@`XSiB~~|
ztYTb6IghuEA@yo;^#JCXNPYxg@{fdWqNe6Ekz>^9KavW*#!#oryQlL;<3jpTwZwX-
zrS5sB6y>hlZexN61sh<)!RgQMs_t!VZN}*6lVs6MBsc?%`zzdVXeI~;Prym1SV?Bp
zV=mkI{~7w)Otnio>0{^jd&r=CU{J&<|MJZB`UuwXu#)JjN~VfKa**fW|9X>WBcl80
z^;FJj;|>(n<Do!(Ho}=heo4INb7q$%L)MvfOEf<BjAv;2Q@M^)mP#cG<OL_bmGI8z
z$1K_SMTWiX<$IUdJDw4k_0EOINxf%{d!f79f!mc?({Z-7oHZJE+V$sJ@w~}eC8dJM
zW{AV#ufsH6LdI~GC=-rTO0R5eo?F@mp9>t+4vC$jPEV3&pkSdwDRX>}q;bU^@;xo~
zh<M|CT0`HvZkLuzHsqAS$%jtzhRReXf6Ls1)?{3{{EDLnAABc+$M`b-h=J~rl88~e
z{6^+aDh`D&<*_TC>=O1fC8YYKl$j7??MyW^d^J|vkEU?01*(f^8iuakYH%l@5(heW
z<+d1l1ofA;X8QXNUz3xoJhO6*SE91&j5fzHvB_BuED!53<B-5L4zjw6w)hOo4=3u)
zja`WY#T|j0S`}B`xE~O$JTu53mPmSvx~FCwaE@NbQH|cXGlb*Yvm=uW-V(lteErev
z{MRyO=laP_*F0tqaje*$^50xFsR?MDdur`AOXq77@|s5}Q(Y#;Cc4FNtIlR>+#k)^
zp}%&f*pI!ofnXJHAOnWCAndPn&VQDE;9f9917LnS_DcWQL0VkAR|Chf<3Qi?#`YrK
z-oY_43NeoT8W_MOhv7l_6tG&`KLt0w)OX)x?8lPEL}3hnh_=e!vcmGY541FgLj~1D
z$IigEh26IZi41i2?N6w$DBZM&Ze<-FRnkoB4YW_!)K2yEmL){eF<hs-%G+_FRxxLq
zCQN6(lkoY0v!O!uNpr5cu!la}MPUzZWIs<DpB)rc6g~dYfG`&@D=Kzpa<QcHNK}Jm
zEgq+7<%xc|=q~mFZy`ar5UHU%xf$0ZaeGpGrC3bphl>T-`Abfn#b&<H3TG*w3fJw3
zhdx6pt6kRVoRnu8E%<p@ChWsXxG$~p?XLC;&Nn)x4t`QKc)b1k+gPI6n-cFw>`HSQ
zhIhr>Ua<#ks91S(25fjsRC{Ly*hkcfRYpXJQ*o+`=!ZB(jusJY4Atpg8ng2sp6d#6
zo%9&)9gRr8zU1Us>uxpQK>I|ne3`6hKqIs)81cB{-u(2f+fn$aD*i^7*PJ#b{LRyo
z`|KGx@eWHL83PN>$ZzOX1s02PYDP)>-W>bLgk$4pgYGFb`&8N?6*~HrXy1|3>OSc$
z)7)<x8htP)cwIYvX3zCW@14!`)OmcA*W3=w78PvhTNr7FoF4q#hqsE|)c=HUB!-lJ
zWM3dgY=$k87bG#tFF05Em56UQj|DQ?W};Lbx@=x6tc)^CSjp7Z_-N*jPG)d#=+qZa
zaBbMzp+&l8YZ3#`DruhIu&8C^ZwXm*%qIz<4?K)moEFIK?@C84_0IDjR<O!V-Q;IJ
zHbs8ASkmXd6=_?WX;`>e>b8oVJoYIK&2Bgi0Y6yd9qFdch9@D-hX~@{l{bZTKq{U9
zhAE-ASW4XibbhA%Jl12Trlx8-|5Mmg#AWidQMx7_ScWo~S?5~CA>V>>>#IXl6+?1O
zS1$Xv&I~jm2C6thLf6qf@>+w9h3Cf5%X@jeAvBqzX`BIfa_#=2x#+~s7z4YP#Z6B?
z-Oew3B6zs*DTBAY_0`EwvY{1)0=r}G_ynLz&)*>&66~sAih05I;IxO74PR$>L98pj
z?9>-x#*rWF{2EY9N?MHNGpW^1Q%2WHTWH~X(Ua#&I~tzWNr`<}RDLhXB-z+w@Jhn;
z)2_|QeGNX<eQl@Q?r5(*&g$J~l2W%dPY{i<h_)qO3*=)+G1$zQ9}O|}JxdnMG8XIE
zEBd^;^!urJIG_B=PG4xky{2&B6!|WtYD2}os0i;{9D%+2TAJy(P57_a8Ge4}F&8Fi
za=V|^pkohHAhG{%q)V#~j_}3I_T*02fyBA3t!<pe;MZr)AZ*jt+Nx0*wH-VO2*Um}
z?8}W3<Q^@~yt2mK<cW%`KQFSI`L(I3?CaQ^SoqFqV=8g|E&KF1A5I9zoATCW#?fF6
zdhc>mo-=E;>CUIf)4tJPXXC#zdt4sFr`VuqS=$ggi#H!{JRhZ}^4v5FLm`-tIZ%C|
zc?^%<J&Mv!OgERo#8>wnFnTZ1bB}2EMq;fbXuUhWW5Kkgkv4w3LrZ3Dr2l4R^?~)b
zD=MeEx|Y@Zxf4Ak{LrR^fPuN&X@%_j?dAVcs>$~*tD54n*P>Q!h}_X^m~3}V%1z|S
zBf`Kyb?;o6O)-rghW%Q<Ju8_Y?t~e9h`(9x(67y^ALU^h)hl7?^Q_LC*H(m;T!Cz~
zlzoA`odb@AwXOA$r2&J?tqywi7KqMln3RjdKYo%zt~Q5v1+C=X)x2CAN`FnX^2m&e
zjMr)FXCJ9Z5NuCxbkrX3B3~JL^^M94v7cVzIJRA5bk29(_E#~Sjgkl!k>eR;V85r<
z&syZi7qWPjEpx=#>8Re{Z?YOM((m5i5N_63wKdc-?=;iOdJHOS0UvDBIZefmTp>H9
z6wUXphD?hZxD0e{dY>IUBfL;lEj`W`&*yqgIy0+fyuCgomQ(!Uh-lX_0<Th9=%kQe
z?+=gFkI@nPJkK@WYM5EtsC!JDG?h_ZJ^iG0&!Otj`K;O}B-DHDB`GobeItDz%4&F(
zQjhdH-SI?kFv%+RKR8t^S@w(%j-BQi67$z+0j7}m#lUX^|KOzC7g@p{_RxLm^L(or
zJS5${kX!gb5i1*ueE@ZsZr~V?OG!*r0EYk1NBQ(_|1;@4W&LqE8Yg}v!rpSX;A_Fq
zP#g+!Wk(x_vXKIXRL;Y%Ph6$*y(`v}Me1$zzhd)z;r?m=h)K!8Ga*6?j$aiTb)v!^
z^(q7#n@D0B9hhswizB+$p`!r;uzZf*KPW{?Y4BY+Q=_L~(21px*vCI|xr3r<)?KK}
zm-Fc<@71E6Qtoy3OI*=UN7s&HM<8o*u~;dp-Y2@AxKCi+K*IK>SARH{Qrp6~-ppX5
zJ~^&N^1=susM)}R${-LdH$1lbx8mXKrfJR8^fU=1XeU9-lLytf{SFkbaG5D}%4&4a
zh8Zdt<*9IILaHdg#X!}<@v{G-ZCy1Vj_DaH5uMTN?0Y&RV$^-f`x_(aDr#y!$=F18
zeg&#ix&hU7@9z0j*JTG8+}gBItZB#z3Vm?aLn@u%7`*BdF@0emaVx+hCGgxS*Z(|I
z+1*#6VW&(Nai@ROOPP(8cR?b$E*@EVv;tW!7w9aH)a{R!n06BXWT{=VyFYbNc`5N2
z0nIJdQ+zI~C7z76eZ1IJP*Z1SW$Npn`qr{GCrc>ZwEKEvb5!WFekoa(9FL8<&L|0W
zw}Ps}7KHfT{W@BT29g4u`V^8O>|m01<pdYKE3@Z(RJWN}xO-$hJdXeHwT^l>jsaR_
z_uctQGjMjg1<rmAaIJZHEbql;XWxhA#VXzoBn7SCzu&0amfxYEV9d1WQF}If_CYvg
z%y1%B-^7hHMNZ1e$+<ThZ<VO~H-pyFt-_TZ)>;fj(df{gftmng`sY9fjHzm`)n@*}
z!ahW0TK3*m^ujf5l?iFdi_6lCO7|?OA~$6}pnM`)Q)6B$;4Q_m5heE7)v4hHuk8qe
zJ~zkmCOtYfEr)xzX2V-NS~K)LuGJPBXX6?+Sanpi8(3=Rw<dxO8t@k)WImEGK6X-m
zsS%^CEaPf!*pwNT6zZwG&D-zNqGzMoDs1_3uEEk<qD$;rrt@{Vu-U6k&RQF7_#WMR
z6w{0oBFKa|HE;Pkd|_z{fVekztxHCikijcfd5>`AP0{qlDZX7VdkYyp)ih%`uBDn1
zG~N_+vV~+t^&M4WGc<*?$1v%7G)_e-esw5gBr7o2mK<jS+x<CGZr<cY_r3^}qx$#=
zcD9a%ZNbVs$_>p?nwIa8SLaqw#^^3CO6TMJgH{aAagbej^9i3yqMQ3M`TgFL(bq$X
z#++-m+-^RLWMiJFq83>~>4Q}on#uG_l7pMh--fC?RL_r_2fO&(ZrHTO-LHzaDPI;7
z)p%WJ-VnH!RYn*Wo{ZAu+ag77bls(AY(eRE?X#eE4>9F0s@__$iVxU3DIUVs(_oMt
z$d|=q?A~6f-k+OML?rbR`xnU%OM4CxqIb<jL^E0AMv9)#8yY4?w~3{vKbjho9&dlm
z$@t9K#hBZPAl!CorKr!n6l1f6wlzICW93t|5gOgsm>H11^%NC=(wGt(Jd+i{D-@c+
zJ$SECB)8YPRaKvrd)z$ez>|A8yHU%=q7;Zm(vOV>#rnNnE4)l_pX?|p<ms{W!1-(D
zsX9xM$1iU#&#s30Yh$*U2aXf>c1EpP5k#lP%>#3#G$B#)l+~J-y7#dcUnV+S?ZVRE
z??Q5~U67_zEZuAGk!EUqd1@tkAy!6J*~3BWbA)@Nm9Z>&(%4WXx=1Wq!D(j3RC6Wz
zs`2za8H}RULU&hVOmj5)lW|`l_jr~$ZX@5G0FK63TB*iOCfX&15G5^4KD!{c446Wu
zgMTG88+Mnn41B)?yC}>qGZ~LF5k8*1_9;Np{{Rlx$4n!RA1)g0kl+JK2bc~p8Wu6@
z8u+CeUCnpdNpW$Dn!i)$X7j!r0YMUdUgH7hamug5D)R(Ew~VKN)myzwb@gtwcbiQu
z?Dp&d-H4HA=3VkR?xNUKe)~H}>D+T(v6S{jNFl3NM8y4BbN=bRWR>=LC*=a~j7!_K
z_3g(43~)zJ&b`_ID@Nr-n7Oj}$zyX&f&2Z&8#kk(j>uq^maa5d3|r$A&@JzA)x{D|
z(4&3zr(8Jw*3pxN&LjPg*vM%oCo>mQW<m>Vuc;9t@7vS}WsyfyO_!$PC8c&5Emb`u
zE#!_6dJOSje6<7?-1;oYli1#_iI&(Ep($=t8Se3&jf*QsdQ&T_!~La+mgiqaNHDX3
zNwtWss?&>hmSPC(7`Ip>bS>k1=A+ID5et`UC2+kR>V(M4^M)8gVeiP>14ZevBNkiv
z{j{MSPE#~=8*YAVHq)G$TPpeeBB8{r)>8dVyh1PL={3thH4N9yoV0nuy}Qk8<89YH
z>RbB5^9>7U%ul(P?nU*|zyHK9ZLNoUvo?NZO3cY?fBb-@*rW#(&phY8{z#^({Cj(c
z_n+SL^ln7ej%U>34^Vg|*a?)JoQceeco<Pv!I5G2m!!T7=U-7LRvYzILt>{78$jUv
zrnDFe{UBDF>5~y8(0r39CRg%Gv4TlH<4UIC+*1W!&NVu3iC3{Zr$fbkV{Z4J2@&Vl
zaysqej;d!EE_n0B?WXMf<>&YEdHrJRp6zGuvEh&BcmB-lBt$Q9FFN*Kf(-fc-i|#q
zlbKig_Q;CWuB;uf=iwy2o7_h?M8#?Ae~GXM7RI8V;7*hm?5Z8(tN*7R+kY^;G1i`U
zYQ~&l*qeK{s-<mIoosTJX-XXRAO@-?wcVVD<v9c_sh<qxV?Ki2Rg!ha-^+=u36cm*
zuI{n&L@|){DJct4k8B8^1-|2{=gEzR4H$vBxJOIYYFS(H#P&DtDvdv)rTcp&q(<5W
zcg+N@MdYt}DHx3Sm(*U8<nLv{Q1kD+X2=jw<np3rOFHp~$5+8u9BT9?Y>W_Qe4Lwk
zMxW8@eEw#)w;+X&R7tR4$o6jWuQrH|bU{T`>e|<13?CNtcYUPdJVK$GCVy_n4~;XD
zmWQv4z5@KkcSA+WuB_TFUc$b;&1>(9_U-ANY#EW3Q{h@Bh*rOGHKBCD<$ay=wKlA~
zaa${NAIqW2s(U6l)tty{UO#O4-aORXv3}3Pwx`}gHTP{PuNzOGq~9jrjn6iO(t+-@
z-hx|qoMh}<==BywhCK)s%=xu-B=MCW!WFl-;c~xT7I>#&x1YHu#?xvYuTO#EWSUSJ
z+1+U?J1}X?Y09zkl(m8hC+y-X=^u4WI=#2LLAu{69~E?M(J)FTuz1o+OlaEef~(ak
z)sTcQ_0Hv&5hXZv^jMU<Bt|#MkM7du)q@uWx6uK=J(A+gPGq?sTG{vzs_~a($V2K~
z1Bn-%d5+vu?Rd!^@WHG$z=B(BtH7Au^lmN>ITIaQ(`<i^#M>oC`ZUr}AJE#;5+ftE
zplnjAt}1plD_%HgoaHaC-P@Z<e>?{8yCWHFep2pgM12Qk(vdDwrCI5^L7^+t*H5_Q
zKS8qaK}8<~YVzk5#PIt{`_J#r2>#EbC~n-{hTZ(};rHMEYi7W2FTVVr-k|V2gkZS;
z>tY|{#q1Rjyn6az$10RiYg*~9si~^^0T+tDcK_!)Pm+?8YdcwCheg?kL_wi!;Ir_*
z-@T&N*UjCf9a0XCR9@=Jw~aU704d5dsz?$3_sjEiC;SplqZ?!{%>-b#Q|#Y=|K`f?
z#`-_MQEWI@`OXHjlTKA$-V2c<uUiH2CE)nKS;@@Q4NQ%n5Iw{!giOo4I)Hr?@ik|-
zDxG0FgTE3~0RHRyimWQgurx$U2u+(tSU-nAS*g7w_a5&ZwZ!!F4k#5H5-g1<ltOr#
z;ApTu0q`xJf?sdb0vMq}^nbs5+(+npy1H{}+t2;p5aegZkN;mr1%Xok*Y$sI+AM?=
z?xK)Ohm^^1t-C{S8URM~Mk)VWuKv%z$R}!7GN=<&{_FKA%07JK!|eU<e;v4ipqc;c
z`oGuI|F;$L|NB_<*<dKjCeVFF+Mt<Tgg|ocpHF)JoRSIjw8ddt^~hhlYw&QSsgeoh
z16tZ~K=ULET7qh@0zeoBGL!eQN^hq?u$pI;p&O4+1<__5C`0=e6cm6ETO#bqPLBkt
zA~N)^y4yiSjs(ib4?yM<&ik5KShTwIEigv{AKSdMH~HxMpz+-lWsXN^yhu=xnE*7x
zEf<%<b@x)>pNRpnSOt&-=Q7icb>1?pv4wLQ3`jMs!@%V_@<LWt=|CMM?6zvh<j{FX
z92gNbZr-&1jx&MD@qQ7`IT<*O9znwv#_a4YEH!mBIu&e8+-U7vWUT_CtabL=ur-2r
zaRySyw(f2&KU_?xkf|Ba#37qy-VZsnVXM16dUKHz-CrVQEA7SP@D7N#GywTRpBkwa
z1sLksvbFi9Q(B^z7%1r4fh@oj42&fkX)Pfw?SjWzu7^1dqv#Dn+D?ia4rnxb40etL
z==JUVL&fc<Jcr}ih8I@qK<?QHiCk~y78=(cU3jVL`gM6@)4eB?rxxf|Ya~HUHO35Q
z&A0$OX|0UV)Ll_pC{zrhl6%+L*_q>;*ba&t7A$L>IO@t&=zf;gjIi+VdQ+J-ZMyxG
zdl_A{Qf}Wt6oPJ&3pI#aN=~pid~N}FCm+P>>wrRYubUr{yjBnNiDo5hc%tvbGGc9m
z`hWuNzBFtRh68`$B^<dc%QPH`t>ds*();d70^tG>{k^RZI;&1;fe^!q0I`dgHKVD<
z_#)8Wg=!|>I<Q(;!zr_(kOi^$O{_XHgpA!V<BWlO%)qw%fXF*gGz$+43;XCg7Uf4C
zr5lbwj<r0ZI>Sg44t37qCnZkxZs&!C4}wq-V>3dxu|pOoD}{ifYU~^wm^BJPFf?6R
zQ?u=JcLq-_LO-~dw8ISM5O6Toe{OsL+(ly`hR4_e^Xy{A2536ZVv5Ay(FOxwugJ)=
zJ1eECK+=0KSG|V36)O%q0IvoSuvHz1bjmMox`RGaxwJG&3MEDHKKszir}=}=PxzpF
zSebzWY^~ofAr;}%s@nI(h6RyAy)G}0d(l{J&1j3a?>JBxX(Vu;0E)24-I10TL^)5L
zUHCxjRt55_=2g~l;C1`ns`}Vop1WPiz)C^)d{}h#hnGxB|N59nnhi`w*i24LOH<L!
zwbV2<jW>7E)C>mNlw_H;EE#TN%nk|$dKJ;Z-N4kMv}bfums%xST0i5qG7z`&Gsv<(
z03K3jC>YDxxs(G(m=ZEUMnrv+i@=fsyM-o5ia8MbQr8V}=>U9l8<*%m<{;tb<_6SY
z!BHNYPFnf>#14?ug+=v&S2yR(Vd^7`i280*0J~BpetXVEMn)nF1q<cD$7s-b?f`r7
z+$|+O%30Ag(mrH$byaVaL?Y?rJ^aw!(}T4gS7ARy)v-hi@Vf#a0QGX49#<>b;%`H4
zw!eR+0ka4MawP-BrYHaY4))``{geiL95Ehd4<1xoL`%CBfU2d)$lS4M+RX?+4BdXK
z?+}WR^gNzVO5)-*D7p<C17>J+8*JE6yYKz6wo$OPxkh?K>uOZxfY=UoRjD?T#A7yC
z!R9bGz$~=DJ0JG0(CgWnY1~xylU_~O`Kau3<0O!AMQjesY?f=9Xon!?C@ziz1gB%(
zvoQO~WAOHGr58WY!XP77VBk9mh=8;_fGJugrGRy#PKt$C%ULP*p{geI^j-lQP#3oS
z5-bR8(a*YyT`+Mfr~L)UeJ<&tz~-6U7a?YwmwA-p;WZtg7c!AdNlzEi(zg#@fm@n_
zQPm?2&E;w5T1JY^X17pR_LMGrmohlGfL<<F9#b<Ypqm0i?oF-Y+<x~SSHLy$-Jb?7
zq+<@c$m7fndvfY!pdDdQFH#1g=evlEbSRj6Ej-Bbg*r+~6B?)}Jnt6HWY+2l>5cl{
zWDRBdl$x4qROt`4Bph&v{q}obMM_}8KpwluNOC<F%-Wq?%_LYblCWUprUACd&)_Zn
z;}PvU1R%k|NJHP+U+j{*LfqQ4TA=rsqc<V+=F2iiXGCQ%Q`~Td&eQs`Yztafq5T^K
z498LL{HY5fBH<^jiZTXhBCcP*p4RvclCuinj$<6|wgR%+u~LwjS3ZBdzSSI!7Dmj@
zK9jv$z_}|jV+#hry^Yw<(C0MHq*aVH?+8O=oix+q;wmkG`||c|&^$~PuN<Q5xWul8
z2|ay%x9`#U<HK$%-$XeNvs|(?+6mNrf&eN^WBJ&>Tq#1CQfl78mu2*2r`85y@7QMm
zdp=waWw|<74t3z9ES+6kyi!>FaP|G`_5Fv7s;g;vX-D|C=K1y4B7x>8E-Ndmzrc6+
za5few3m?-atYzIPryQPh)LVnMjNH}k0S@Q7n_F@6CQz0LrfrV^tnx^(Vb`knMU{|J
zt(X?QY7Ezh==In5^zy!wcHA!kDbH%n(zG<oDjA*=>fQesEV*eQ8x($Y@uU#tifhTG
z{$1lI8XPNbH$vEWrkGqbE|r!s4$|uaQc_Y+2n$e?Wk~Os?j}xy!rl8dgfPyb&FcPr
z0gxMJ`Me2oqx-bn(mQnj{ziaM^(;efdfpJV)(vhg@cv>z!>>o$0^4g=<)Daokxx&0
zq1#NL$}(0kohF2=imn`VI80XkrpLWS{Lu{KPlq6=Q9cQFvUu8S9B6x_2Tqh^!zdLj
zB_<{c^78VsBhdcga)1;V0VCOqrI)zjJuFcOJW-ZF23hX{6D+LbF@!GMF1n;dI?HZS
zO-nZy@6huGt#8+KHO)<WrCOCHUv+cF7;F%xzV0Rgi51?#Ak`05cnhJL`Zw_XNPhwd
zX^)V13h-ckcVol}LgZ3l9?kDIsU(~8Yd)KkrrOfx#zCCZ{-$5!ZJ7eN*?^HnUs0Fv
z*tK&IGp&DKs*lc?Z`_<|q)a&hjBbz;=#xBFTcT;YDmeisa&YVQz+Rc<N9NHGDTS4k
zlw2Zfm%105hgkdJP{4IBFd@CYbRd7Y*Pd=@1r#A)a_;WQcQTX~Y@k9tIVmD=iR}<o
zOKa=M>Y!M!_9az`+4CLlGRt3REAs$etxSW^9_gd*qfo<d*4E2q)}NS14>m*~%zdrc
zeh~BAo7&&rJ{D&EEt4k;oXDvj;6^doTwh7c0XP_(kB6~4+%pb>_I0A?K_<Hek9Ygd
z!qx9Qow|qRF%1sH<c7MfK<)gAyQ9v(o>6WZ*aR`Jr4Y&PrXHgdndR+{ky;*jk|t;h
ziq_?Q<PToVHnTmgt>=yK{hLd<X$1g^WoYlr0bEF(e#xg1rc2`D;*@w(&=pS0dG4ZB
zrwXK8X&Y&!o2!?m-y-XjK{?~!nUis6U)j?iNL*O<Xjdo+D7}z+UU@ee0!sT-@W2~a
zX8`9J*a70oV>(u7w5F965#}^FdHd9Hzw%uxz+9SSB=An#18CN=O&g<jOm7h|rRtK*
z0Ljt8Dnf91Jd>#>ht%P*(UF$<!CmCcwkuNRFZ%HqiG)nr5hB#&f`Zlee&q1}^-8RC
z@7zO}AK=mMn7DzIfHP1a?@clM)kir!mbR%LKqk%Ye8PvM4S|%W+5>ZQ+tfxdp`B|D
z;UbOLzNRKYBmBDHT>92(y0Lcq8#E7iF31SwOr(x_Pk!0U!uWX;WEmIdYxzyvlarGX
zr0jt%Uky=}ak7|UB4RGo2Hl+69i=&a4m|EH+dT(Vs}0x^U_rlAd{C*Qo&*8dS#a2?
zfJ#-qb>P5(J;vaxceVHDS;xSv3(}^ge1gRKUTwGB^-;=SmZP~ndbCX=g;N|peeFNM
z>^Sgf$8-oX9%n)TjIP9AtO0oI06XZ%DTEb$Vi;f%69C?S7<>RTgXK{IQTe!Ji_aOk
zKLgB4;GrZToMl<=K*Ku?V0%6@=n>299!X42woN6INC#Z{*9(BRnd!4-qO@wG)p(G)
ziuHAebMMqu#I6EJH<ScIkW*mbm>hIb_AqOMb`opxmD6H(X5gq1#|te(w>B6PieNU_
zT2q3_R+S<ja}#Lab+1lsPYrm9eKp5VoY<QZEn|Sdunf{MowJcPHD{kNTYW~euHcOz
z+D}SIs7fJrW#R0f5sYSiMv-`g2XO_9Kni*-0HOr@>C{}`DMzr9T0BQ<Q-Fc?2B-iq
zEN#XfAC{lrEZxpy?t{{+N0nX#JG*Ht7r+@WZ?{zx;&=UNp!H&KcsKz$J|-33BeUOm
zjZEwS4byYez*VhN0(=o`x09^<rb$O5$}ggWxBGri-&^|Qe&bIwAWMCB5-i7nj?7d-
zj9j*d`^Ne(2o7&U;LY7&Wdpu@S37^3BNK!&S@-<1Q(wKJhIq*FHhNCtFAAfNPS60m
zmg^$M?WHUAR=yMEO`AbBl02HaK64bFFNdDd8$3*9fdD>qRLj8WD)cKBQWa6*^XC(i
zk`xhWJA|xuwY<5DE{_~M7^Z1Un85WTI`(PrZ?YXa)?U#GS`~;p*EdG=xl8RAaGybU
zd;hAKN6EUNH3-K8#H!pZSz~zp_P=@46RY@E^c@RiBt5~fLv&THc>zP{J;=mbx(PbO
z@_2qIp%h9G^2)L9u+V6AWISA%lINU3i8hT^lbf4+Z!vo0emB>D0!ZKC39t;B=Z^=o
zMFP`&0Yo%%ZoPN?ydWUf5@qB|(E7Pdxn&R<@6*U6hL5z(kJgePSRp!t&U0H+(+7AN
zNci-D3AxR_Lq8tLca;oR1I#H?HU-*g@idSQ7^S!_g!c!bO=GwXH>R{mhFc`VUG2n)
ziSP2#^9QxcCIDyuUK%X+lCDQK>aMza;Lb-EO{9*$HUn~MRP(LG$`${JfAYr<J;!MO
z8Dt6_9Ua)@Oj&c42Fh^s72G>1mmKqApvgSr2I|+D&Y($6g4{c=hA)34;1ao-#c&)L
zML%9xz)gb!&;I1d^$nW1)JOlCvi(4@m>dO|M1=r|=0P`@15W$9^)~r>wZyBw?U=4l
zk?@UaZ*PC>9P7Gem;@HxD0`lG&A2B=MP_61v3L4fOGih%mFw^caiH~Y!{eolr2d`Q
zVj##sV%eHw2_?h90zF`Cet9cRFA+GZwc!XxG-xEqOn7XLdo1QRx~X{6^S6VGf79gw
zD|Hnpr^N>5<Rv&1n<37plrquT9Pf|WkaSA{DYa!JS3CQor0Bb&B&QD(@w+BPkS<gO
z&5M(g%)WWNprL&7gc-W-9u|w81QUq~)_?eAk50bL==-%%zx0)1;*I%f++C!q2q%pW
zV<R`0j!j^ms71nOVzBnpx7<d#4XaZ;=DZDC5ia~7l%IoSHjfgd4rvtH9w@7`{abIR
z5MOivXHC7c6Il#MDM6unZUT&7TbF5cs+jE<Bt(<JS_Vx>)Nc;7lA&%$ZU!LhD^O8T
zEu9r;r@sr8h@fWpPl@HUGJ42;z}@qob<*-(m#`f{IPAgmIqN%g8K)g9UI?CAScI7e
z0^>Kann~8~Y`{WrgSv>H|2%Ue70T-v<i#hqyif$R@AG7k&i;<tN#V(cvtERP!DXn2
z)C2*laT4&eOg8;ZzFjNB_8vGMIf)IdaIeU_ZR6?Fj)bf5-U<P}qC<Z{++)bqouMSW
zr1D8{`)Ln9i%Ur8XDKQ)L#)rEfRt-MP&W;6<po2pxb5KZ%2W)?MuH`XjG%k-Zi32n
zhIuLIu4_Rk(YS{8%5b+sUV1+=>}e1vT!LCnD!AMC_T%;8@j!5T3qCUU{*+~+>+t%Q
zY^16wsxeoxKF~8neq0TVCjvX3mfz_p=qIww3C^1q&rDCB(2NEQLkjSHi*Ia_OEJM4
z5VN*J$mP|G$woPNWNDB^Oy17_JHAK^qyY;zI4B}^2%ND*!kqQV*yApo%?vUDIY`R5
zYh&>H@Z1EZbS+XvVm8}7S4&H48lm&H2OaDD8YHx$-(AiQX!M*t6c+^%`txQ8BkjTX
z9j>#8w8Hr^K^-DF8}b7@WM5C(c{@Zs1keb?lt#0NUuHRHx!B0&Bt*RwD_S*IVww)J
zupWB3-&vW0;v^m-tW~54!Bq$cU`S1V?sm7={2b~USkl)dOa!|p(|<1Aho6@S_+*3=
zHfuAq(bc5vM#mSfwoVEE%4Pp-5DEWs{JzCXovAcZCVTMR4c*fYX|-$zPvi*^OJvlf
z?@9QOSk3oQcs9MLxVu^<FZ~htWYr*54`#a_dhY7B)dDuY<l<ueru!N|v)*Lov8L-b
zA<GhAtR3(P!SFjz#ufm_I;Et0e&x!O2M9#|VZxBx%9q><;-0`=Na_T2E{9YyMi|yy
zdQi0t2V`0WzyYq_p=bL#c?Q@Eo1pw6IfL?Xs3Q<-+BYj_)HW?|AckhZ$PH3aO-L-*
z9zdwM+MZjYxp+bG_t{8!T$c|L(>t@J{||d_85ZT*h70S$SSrF&L{S8niioI$gh-00
zq;yE5NSA<wv;i2DNC*<rJ#;&OA|N2$GlX;wNDN52uX_|@?R^}3|JXmi?>oLZ-dOLe
z^UU+y&mGrwUgve5uvv%S9|^%^Z9MGuBF2y&3+Z&)fDQVcb`{umfO;|sr*$jpC|p@_
zSauSu3%jInZ0++-8&GJSK(Xs~FZ06TgX>cULX%5hhmVX>#-9g}zAWn5((ul{gQbV0
zC=m99)|iEbMQOh4Q7NVaM;2ZInqC7D(N^F)M_PHOAA^m}28^W*^cQJ-)@Hd}*XSf%
zAZNnQv|(b*5=xytR(DvZq8RNF(qtV61gJLEb;hwz0l<|e{9%H<q1UBEN{y<ybyrd-
z*8~#A0ab&RfxtmC1aZuA!OFu1Vf|{ZvlWN-Ol)o=?;)#=2PiTjgb2~}IEYO)g6{f`
z9)Mo!Tfnl$S)~iks*9;luT{c6vf*NJm@qo7%T@0idmf5D50-}AD+hmsO>W?9Qgr1G
zKt+%co#aK4n&u>&&}Su}W9!vedzyLz@KR)<O-mt9zwXHTTyW{RuZ=cL%Y%~;l|uBf
zVc!EWeyZ)<65p%59V<O=6cLR3?jzP_4)y4PB)Op!4j#0?^_xq_%#l1Z6A;MVPLx>Q
zC?)+(!(m@lsB*k8qx{yO4#K1bCfL%<z|4|=5YBc0Y<rYDR=7GeIHnr>SPO^tBX~E2
z*xs((16$2@;&pUvfKod;HeC8dSMRUf^qz12(tx7k&(8OwX6>bE_|Thtla(b8a-i0M
z5LxJ6t@@bm3f?DPMcNP(--CG*R?j!X-$8Jf7F@mqA*M5gP$vboHja5#8Nbznz4@({
zg+4YtU1lf6QJR-|5-9vU>gD6(^J#9zWr<HUh-*j}rHZ<yeI?j8^`K-wk*qK^dl!PS
z_1>uH06<#NSG?N6lW1HmJuQu{X=9m)$K%mE{T@WT>GWcESZET})I;;s6I*JA=Y<wI
z4LuqD*{j0XCZDJ9f-t4WzplHSSIbH4D~em;$D&XZ9gj|9ww2|Zf$-WfKMM$ZpDaSv
zto%(C*_O|;^DC3-lVr!fx&cOf0kEF)E#NqisKaJ2TXn9@o-n_h1K7kP^RxS(8p0oz
z8$a(8hH#%?e=|9c24YL93OP}_VKv{YCr;voF2*v5LpZ+O=11g5fna)cR_K9Z|IsG)
zxydEFrJ6N%QyA`N*JH}fPr6EJytU6Xi4J@R;Texm(2tvjo&JoCIt?v)Jp*3{eeKeO
zz(AqA*A6f~T%MV_e$VJUwbNG+dy2XP%JFL1`ral7F_4eSBTE%P?Vw|-?pV6EegF0j
zf{(}CLW>Ng!`pXx-a~eeAnvRtdd>iXPhsyEOG4)d+n|tkKC&TLwGVI!*iiYV)+M<r
zeTUXv&!G9z1QoPG6X$Om>+q1@Y~Oo(d!uLE!`@D|rBcoHkbyU+R2W~oxcrG1Oo<%2
zmE?7=*vs3dAs^zLf~~3^R;>WEokCo@`Br`eEhs5Nwh%?_9T?5Cx}P!@oxwIlV-ngv
zm2GTnnpSpfDZ85GylP9@)r5us<B>&-K)}MU+f0EQ$9paXTiAUTU&qQa#R{#D%Y@j}
zX6>&0rm>8tclp|XYl)+DS|>l{9LiPL<WWq&01&+Im?a!j1wQOxS@4h-mY8K5pe#)Q
zyG%3FPIsDkHQ{niH1!^NV>e4GI-K>FMvr#cE;gtlm|0yNpRv?H$u-=EyLmgcp$ef$
z?i(x^95~H9@p&OcwsbjiHqyHO5{8($!Y@!74vF9OC|Sds1pf7fmI(r(F7+5H#!O|D
zd-Sy5L?IVcNUZW_9e%9I0aP1mO;X^PRL?VQf<28p)fqu2MePV#xeazPZo710GC1_D
z_9g&OqgSKf5RoQIfB!1n0gD9fXt-U3iGb@-`i}t3okisQb{W9HA*eJdTQP`q&NH#~
zucCL9f~z0Sf(q8gywJB6CGOUl9VB@F`h*upeIuaQm!hix(9@{;d)r^zZB|zO=5(sc
z5n~9V^AI9*Jx|8Ils=N-@-r0lMw>3mP6N><RF1ny?>h-QyLDiiT5fV*(PpX-NLA=1
z>-dx@n0Itd?5It;!(lp>p>VG1LnNF$<-0GU%A2pyhCQ0<<Dd#opRobaOfDGVd~$#&
z*JXWDo3hJ?bH&nybcp<!=-<NULEyh%rOW$bFVE2HOf)di27N~#8y|li3QJ8l@9hJH
z7r<|CfLUSFNZo4=Jpfb@!#VeE|NH3N`EeOnwIDsccI0ZPg0j+kk#~7gA!zSXvK@~L
zrYLHFg3k9|CU5o?=x$3mzG_!RXIEhE&FQ+~gy!p`X$4ZIzz-D#!FUqD0zPOSYh>B%
z*P&V%lQM;X4bALlBWH(KzkQFpZI*|Fi`yYd`lbZ71s~Uu7`q_=C?N8HQ^NwvGLz4u
z>%1Qw00F&AB};|vr2xP%)B94I^i>^m?Y0s9xZxAL2oaz*7|m3tT7aa05H!tum<~p6
zj7MtdE@-b+XhSq&-sn6hv=cSFK~-^Qj{*3;AcQzjb!KMC-HC*|q0>-&a6OFeI=S1f
zK>(NHi;8e!HT)&B-WKFHU17!QgAhGtMH@<N5}mqG3iZTbSghe0iz+`rY%yPRHr-3y
z@!0y9sHWNPc<(C+o4b`<p|HSs#G9h49;%<-W_XM<I-`XrJ32awC;FgOYLJv%n7~xC
z_Lc+4^4MYtSg<@Y=>tEtQ1CmUy!kEuwYgPWdP)ihyh)L~pVg`BCtrxY()GDi$+ZJT
zBNv`ty<O%E>0Q^u*@ZpYU*Erhtb+sgE{RD91|OMpzqo_qC*dl4U9bC)lhr}8rLlTm
zv#9mM;Oy3=Wq>QjlKUDPbs#Mf(pg7vGiHU}x5^Y8i9T9CyLv*u_w9X*Gs1ued8A8;
ze0+R$yH1CFPW}ct$L${=Fu6YGpK$ua<&}<7tuOkEFiiK>-2g`(ft7OB{t=H!`?;3h
zUab-cm1P;h5saAz@`lK7UHofb%1^ogLwavlFL=V4Bqt|p5C9ps)CT|`;{XvYCyKiE
ztSka-?a(>8c+qwKA_ih!?r&dxzl}{8a30}8lR-*E<Wb6WW);23?aa;$+!@m57<7F;
z%F4tfC4^n~`nwIZQ);;S^%Pngyt{V8|5H9-?Y~zubb*6gnRTv%=iW$&K`3M^O-ku8
zG39Ic(slKB$5<)(rr28A+PDk>%0lQ)vpUwn28tOINeQ6<Y9DlR>1l4hjlxU?NGmcL
zH0^r04EdT2TaSW^A;vjgD&#g)GB#@4q~VTG1y`Q!X^oPKCfAg85m+hhSy_7F>+}Jp
z3m?V-JnNoZ0*@?BBG6#!prYl`CQUlEpuU#eR~p%8ReK&Ys|PhovW+z7j6FVH{m#o*
zFP%ORsrTuh`2;c=6=+)pz$twvy?vR|^sGHvzmOxMJ#J!b?5<gQYHBMarl|n7#XSab
z5FMwCb18;Zz%dS2PE+H$1Ix1STU*5;ZH>Fd(NO*ZyBQe>NZ<%jdAHc$JZ)VBK$~Yz
zB)n3jo8bWQs^+jusW!v}ir22W=b3gzM?}zfEJC<B0Y^~#El)b`sqcZ5R~*`;;WVsI
z1xphsQkl^%Za~!YxECr&hq@0ImXv6r%RqJjWT2?4C;D+N;O&OCT-wE%Q@_B95_q2z
zF!iqpFGD!YX;6RE{mOB)#lo29Vly-4;9*;!j>K^X-q=o8QVi$3x;T!GAqaFq1PCgD
z&;tUB4T|te;GX5bEN3$~Z_Gz_oX_vRa7P{X$VMoQBf|AV+k>oHsR$kFDN@S3`6<^K
z)mW!8tAO}$X=)y&g$v;Q+DIxH)`zFSm?0bqiX0e1p4p^I=}MsB+zVgw&8lG@<dE<e
zoE59Swk8P1eOqhzb~J}#vvf#vV&`2ysG)wU5?suDc-C--11C54!B0WEP;gYBJ93x)
z<Y5KaTBH@wuj?YGWU^i@OE%atMjGH+o;~%bUWH822ENy8yaI1`kIX_m%B~PEb$HR<
z9^TmNyFC2x$3H`Jl%iIS&hK^n#0#r(kACUV-9PHz@FJRK!nr&hzB&n7LjlWojAINi
zjkZsbLav==VQ?_)<l@tV(k7`6I%Ny1_!Fw1ZPveW*Dth_FQ6yoO3kw;)jBG40ph3n
zd)oqd2FgPSo{t3kw@z+<B#aa@;7;;AxU9kMjR{k_R$JN%r&JR1r4x*_D04v78=y_F
zufuwp6t1%ZBg+E<aLY(%BL_^KgeMWe(F|2Z_Ego6E1O3a!~@m{ATEOJ5kgun9W2Dg
z<4`eeLd7otjJyiMH;dA2YLTnmqYEgI2H0cl&5YzB7L7#I5sQ$*+<W8A`*PoXFSVr=
zT8~b^$Tp?BuA4wiAdx3v9}ow{dw^5pdhG}I_5e5G<O=x;_;$9yJw?H<JvTNqq{--Q
zj*}#UiQH8%_V5F!+0%KA6KWC!3s-eijYb&7*2(2{7iS27J4hn{>D32<SR5ZzUnShX
zvnKe1*w?%5c9cEN0ux<CX~97OHo7ShLD#5h3?dh~fB#{!TLMa1EF5Ow9;e^{CjuaP
zX_x<f12rNP+%!#fWk1KYquT@5GF89|luo>m1+Gc&e8kciiij3`yH6_BAeitis-1&T
zUCdru?!=fF5m@&Sp|y*j4(A-(uDmnmXCdG^Fij%vj~vEA#eZUIN)bAP<7FZ-K*O1Y
zh?P|W)+ONjT6T8%Q1iQH)|GXQj*f1(XE{i<D8WzrfH^0fO{<)U2iEf>R7ao>zXgo~
zq=WbQ=~0ef8_X&+RxP_D3#zsPkWJt+bv@trXBZnB=Rm12++iXKE_Ggw@8{VcBZ9em
z8vvUB@V>U3;$D$F@T-6R#~+_0=@<U_cmF?55&OT*^8CNfVf+7Y@c(H<-v3v=;!o<+
z6gsyd7HR~*G(t6!&kh3=#~Q>q{^O(b71)Hf)7>GeLW5o$irU)RS&O@@2HzlRUeV`=
zdupI5;m^+-CLr9ogu8AmpE7A+QI-7jPvL)w|1YNS(A(kZEsXJSk`%Siz}L@%+Jh8U
z7?MKZV=aGLfx^BL^ng{;L*?%_sxACivElpw1G){k9PvXMcZKx@gpTon80X*{MMcGe
z_TY1>(O_6x1lNQ;8JJYTszT7m2}!^D|L&Tih+y4y%Iv?-x#jZzf<H?VGyL<3|IZob
zRJ<SQC_Eu|L!BP~`OxgM0QrajfTl_37ep^aFx)bN=n)6hZs1DY@p{1kvIme-qF5D`
zEg>05p_1ki1`oAoPzSDcg074|KcBf-9(5xyBHXSw2}0$|;CtwQw5r-v(lg)sux=~V
z13|M6DZZ>gl|V?m+HDDtgL?p*j3H4nV8cn+Qv7+zEw644_#6-y>wml;Z7o){Gi8|A
zIXr!kG0Bo@$OpjqzHjT=>rftRg;IL(nTBv1zNPz2l{ANS7vPE$5OB9bdY?!o!!L(0
z17fDHvy@<D`q$I{Ih)-4Eum@q=9MU<Vq{-$eMR8KD~A)+W`+2d__@i`k-ATzAL%UX
zWFae71{B1H`Glp6^(d{D78gk}E0ARia9zA8DdCYZPlhk#uv}etfyfJjqH3BL2y!8i
zz82H870+Hd2Ib+#Q^6Y15KC&IG8U#}#d!OpcbS*|Q00U{C>Yw2ZY|P=Z8F`$hwY}l
zQSiP4?k>tu`U~$-6^nCGVNZ;jKq4<x(AKl+IL1_X+MXW+CUHuL^4Qkg?7zz*fec?s
z)7IWT(W)TRh6Wl~U}H~C`j7~LVD9ST+l9j=3pg$!w($2*Maw~4Mrb_;-v5OvsN<o+
zIuc?-l*T4-MB5ITN~Erb`JeB|?be0L#JJPRB#F-PrfHuHlaa~pe{47AuI_*Kp|)qc
z)NQ?8RzhFO$X3=yh0>uKTd3d1T{#A}cFmrG3&+vghO&L3DV~1Ol9raXt+O)`GV|+D
z^@?Hu4k&hQu6Cu)!X*a69vQ%9mE&C`C7+YZSg4|8S29l*4metcfZTuXYgg*UBOOSA
z?C9Td5sv;1=a1WXo_76MIQkEdXn>6B!)_7;wnzvKJ%Xg>R!|KHf~iBf=ka+q?{}RZ
z1GZNq3YW)FbrJ|RR0zOV1Oh1oU|2VJ0k5R?ov-`PI-oc|`#46E;EaTFM-*lG=z4oQ
zs96BiO@_iFyGPCbp&<Wk3n+7{yDr(eCIHZ(0o1QkMs`rJSb0b3^2Wk>QXQN&U1Cvq
z4AG|@qvC&71zQ)5E=W1meM$@`H}lNNjS~f>FltG^m?;@12|rrhgmc2vC{=`9APe($
zP6D({tnLXz!de8jdN$xiIEhGA2&4&!JND<GZ8=FT$ulwaJSm$s9@A=VfJ7PX^##`7
z7RSh_Q$B8W{=1Gvb9_FL_p=h{jJ*S0wT%U<IXTUKaRGx`!c|4uj?(zuz6ZXDxw@>J
z3FR=8D3sa$@+OQm><e;$*Io)``Mk7hQ^(aY#u2$JRapGfR^$79Q0xOqn~MEOWj0Q3
zvbK4jlujR2u;7%NR$xkYYfx^C+^e>8J)$-1bOJwZ3B?Nbk}$!Pjqeq1Br|Q_QdCNc
z<ZL)}?Yz+z^ws>3y{#Jto1XRfw8mg2(pUTKqqFwx1J%UD^J21&q{ErndesEYbyMF{
zQiU>6OvYqd=S`E9bsHlOy{~5hHB6=9C5A}rhaE~Acj#Cj7#)2Gm;KL6XF5&Nv;p->
zkkd=@bi8!9Y+5L>r|Xac!*swZr$psUCRb^u#PiUHYCO4W&*OI})Cz;5v!3e7$gQ+I
zt^ZS#50wNz=o;;3CD#$XJ+Z5*El?#Q_4QsOJ9pjP*9NQ@s~&e=;{vhTJqe6txbZiO
z1M$Xn$>{-AjH!DMkR-5wd(<tm`$3mBLMnrIxtpqbN2=qA*|&sDDOJ1ryk5iT?t}|b
zZAvQ_juVDH;o8)AK)%#l7~CO-r|Iv0d=(%(4Y&i!f`B$G2bd6KmG-cJ)4F^9d~Z7M
zNgL=1s%Wsgpsv|<+w(-%+=DBf<5^<eXO_mL)adc@pdSe`v2&M*i75+7Q8fw6-d&Bh
zpnM%vF@?Ka=so$b9kld71&w182tl&IM0X>W_h(?Ri)u&}=~>0?5Gz=hK>I|c`8G(f
zs&anx#BV-rfI6r_T?93#BPt-vEX&ZpTLbRnFs7xpi?9o%Z;~9Ub)lt&gO?LjxZC31
zcZ~mIt1s}mE`ZuJW#P=&(m(Y(7++b^4Q&88nm&msmi8YaI^;rDyY*2w@G=k%40+ng
zmltmXsjvrVa}EG)Ex`6_G&@kC+iSOZAK(Am<F=O^XMZAVs4P>ctOc`F5ABT|>5ef;
zY_`m@bk9C;5U(^{WsD8qWs)#)o$8a5CIz%cHawyGl9YELmtHLRK1a4*2gs46Ea(T3
z3Ca}8Y`we-25AZ2#>-uLk)WjYs6O5qh)Cmr2qFETbG6hX(&;mOrP`yBt|eNI1g`u1
zYZ*u*;@5|~4i2Q7NStIP`spVt7^M=!ytfx&yw1HiBN=qMytB*Mss^McD)&q9NZAQ!
z*_%mnZA}OL!ZyW)L+@>OroVhJWg5<`U$KMjuR-pQbWg7WXP#Bh<qg`!$=V51Q>`S?
zF}GE8>VTJfbWqU29%$zPp%s7j>j(mZb_87nN}Rex`Rm5upURL-cE6%5Zy0TsPvN7E
z{)cR~8qRW}&Y7nF@+q7??bmZiGGO%pJNeegv`{x0E^4i|ZEvfGk;-}3F!;daZq>e!
z7_N}Os04bByKa*)g;>%KX#rgnviH~_FC1WG^cmAopG=#cp8jOojmXbL1n-0p&_CLM
zNu>$YuP#)ML8k@DP2#uPW7=8;`motsC+?o3O`QhKs`@=@2_o%<$EO~Sf>cgd`bo=#
z?u4gue}6SqQn@42(MCOfIP+zL`(%kC4<Bs}2J@w6Cb!m^c?;VK7&e5Ie6j-CGL&Ql
zbWj~cMSgCP8m0YUb;{MTxP8yTG3||E?R#B%!Q%c}uIo00o?;aa0OcM<{P9N1qTbQO
zkoD|C`ts@~V(RJ+xmd1PIEo1~yHkSwh_QOA^0|E}I_90c#J~0RKh%3<w7f&7a}mo{
z(=DFKFhsq@cImN0rrprzaU?^FpS}a!YeNbH-$C?r&bLZIb|(5cOEa~K24IKxpFfA7
zCGbCekK}L*K@o<`iFD0*ZyRmVcFl5o+d~(b(*DVNpLuV-*sHm7{XWqu=&Un+SDw;z
z%T~8tX5BjqMWST&3*+PU%N){6R+6E(%5X}g4=PW#r9=cdAV?43NB<lUJsz<6LmwM_
zKEP^6q)Ab%SOpKl>bOvHGYboXk_>~^Gz+~yl4X;BH_lutDqO0fM4dFM_%Gjj7zVE$
ze$W2(7p^T{tnd!ZysFPt-Ok)PDFF4h5WUET1PJ4>WbJ0Ark}PI78eygYy30Wpy?af
z1&Z4sQEX}Bu8TBNi?K3zbrD?XZZiv$T}iJq7KK5B62wIln`bP~t1M%yN2OFR+zjuT
zmWyl93?esmq)z?9^!e1iy<1-P6zsL?Xm6L0*ip0ujTkyuD4Bs<PNg%%9f{G=x66v2
zUR^v$Unp$(+av@cmG6EZIT>cRaEe=}kFj@Hs~8uiM4DlnJ~ZyVaeHfIIJwel*4By3
zyc&esJvysd4<;X)fzqJ)Kd&>mw&-wHn)x{b(J*8f>$o%l)fyOOtG01Kp8$x~Q>}jR
z1xk7}3$LKdD`Xhf>xsob2R-ZdF43sgbDa<(TV2|(B4dm_9i~A!9B?YXxEvQHPXB(<
zk#I+c_cUj?OFgCfC}wB#@cJhYx{qg08t&af(|~>*VjO>e4l`Qa1)*>ALMyAK-yBuK
z5z7UO#fCVb75c><*c6C_^|%C4p|AfgnhHvSAs?uGv~ax`yG6SM{kPKFGdP3^HyOiv
zsKnv8RNxs2D8`>sQ5kO`c|@c(g8)95UZA5VP|!lSk@s*`9w~$1-m*oH!~7BQJ8eOU
z4QkD+q;UqeaSMu+SH=@0{m~BUyj-hYzYUup6%viQu<YML1BwEWeB>`IAiI0{us`Ae
zw_S-4aZ=43Z4|K}bi`O(1cMyqX%_E2u!SPK5n)|fP(sepF=nIgrAR(L#)?qHSF$iL
z>i*D@#jI&rK@IA`&|sj3b;w1`G9cd|h!r~iE>wQZI)`{fa}GNN!;uGVwg&VQ#rW_h
zA4vFPj_5oz9Dh32w71S-EX^4iLtPv2>`le_D{A()Jxr=r%{{~&{}88;lJkZ=@r-aO
zi2T|e+PdY{fl3>{)vQ{UmIl`iYlQxVgydf|?ey!4*l^c@ww)Dl8D|^IM15GF1W$+E
zedr|E*J*R~_n$6%oRM}a*0T?n?%9h=IFz4X`^su5krS5V2$wz?t~$fkDxQ$k_Ba>p
zmfYc-%{wPa$^XR-u}4rEiu|*@*h8T=$psjpi6td2WrJ^5CQ?v2griggN^bh8zw2B=
z|LRfLt&#DJq4R~e>-8HER(3`oK+>6OYozgCa6EvRm%S3zmt^jQPwN^5bs&1+d<3{B
zbMUC@h>VN~pkt6pG^0}iPE=7K7D4?x2zoAK&SDC#0N?u&{a}!~`HO(HQ&Rr*P*`W5
zx%m4dyUiXyVh7Ge$^tnqrSvW)rQpC(3zCpk@;Ay7CU#pa?)xa%p=b{afB$=A$iqJU
zsM`o-HY}jM^$3SVAEQd3Xlu|uIWh69?fmc8-ZCg_#}i-Gcj=91`%FiXcOC3GqRyXS
zTLZ~^gYwR#e{63|TzZuqw%1_wK<XD1le>2)Jm!D{;$_9-T~@x=p<rED{KqAAytwS#
ziC;X{9Sql9){U%FqIVgzR)wh%IzmLHJ5)twIzY6V`SVFHfDE1u`u(T(`IVDHj3qWR
zMS<1aq64>gm008B6MfW9SU0fOb%8wGX5k*4P`)rdp1H;nGHk2=gtctbmwcMy$pjp1
zL_M{-x}v~UXDocZM}jAwsY=M)99z$8T!j7&D*Hit`+8w%>5|+cem#JjWkMles)`*0
z89se_s&TTIw~gxQA;MQsWEWEBS2FPl#c;u6coki4@_`3nBRPBh`udgtC9qMuxOA`R
zA-<Sz<yo&Z<S;1|7R{=n4UNg=uR%468VlcZ>t~kqbA;maupxJO);TzZ3R9hby9`L6
zT=UV*iHj3WA9|eE2WDW8bt_}BMqJct)~)wo7|5}9O$U^~)}a|Pw=%K7Qy;NVVpR<~
zrCT`?oE~*ENGZtncL>5w<SG00^8#|%lC!ip7aIf5qVidGGdplK2oKzCag486I`4)S
zm^OF`K_;EB_o6H)Q2H{unR%TtvHxQQzB2V<hqT7Tz%ku6cgaX)<Wg}r)nR;N9z)xX
zx4baM$d3pO6WG=1_A<iF8N$s8SoFT#=a~gXY2a+j{ZqMU&!3!>B*#Xh$aFv+$_da`
zqwiQ04!v#wCmaL<(r+I(k*fyUseNvXm_<Q?@uZU7u7|inaGwEn?uoC3#OkWI4q(=E
zVG?%SS9c+C>V+=BCllWW4wW|cZ4mnOuXH0qRe~V#465~r(%A&O?VU1$cH%VcQY~P2
z6d>M-0t5)g`lZmfB@TsT%NGrQY`d?JYXCR}xpEUU3D}9<!Q6@A7SbYmpbQR9bv_YF
z32qGv?Se4i2SgwfJvnRfZPB6#Xro~B*VeWIwR;{z8vG=20#%fNh?)QlG_}&N6|4(g
z4KB*i?y)j-S}18`L|@aoj_cF}baemv$O`(PR2)zbL8OHr0smz_(=zRI*Qzi^CfqsG
z%8nx@{(Z_|GlyA2T8E|eB)F`|O`F5Lcs+$3&;_Uv_n-n+OB18RmWV33FdDQ??5J6I
z5&<L-+hc&Ygb2TA&xM}pR`v<5U%wBE4oCWLo}l_DE$EqBEKvA1AzKD4G@dbrS=nW4
z)8m`>aTsf4o3Vzf0jN>a1K)J>r|A%$W_9Y{LpoB{YXRYSBGPSyUIPNl&`&`FfCsI4
zV~dQC=1}h+nO=pimmGLm2+-Yv$E~Z6?uj5bni(Hckd(>k?m3#*MGOpST%4VsAV#R#
zfV_WA5A0}eS%>q5r=jz83<%$W3~;7715b&*ZI%VQAdgCm@|;K7vKs_b;IA0Ms;d}d
z{`~>jZaYJw<cG~anF<anIDA)v0A0V-eI9N>gO+LE23;etw;AD7G@1?=nh*1k0PDfl
zw`YX8+7ttdpTl(qYarB05a|xmGeXsAU=Oy<LU5CFxBm(-ui34g{#56n*#q#+$n=xN
zdkaP~20Ya5)97t7r}iX(u5MxRr4w5C<|#!sM`>r~Sb;csYuaSageVCx+_{5SE-VHS
zbGBPv_<xsB9$O2z&b3SW)0S4ge`Gr3!R+!D8pgg7lf6btqB-Ze9@*Y><nupHSnt_)
z>Zrmr#s~cII8y}ucI%JUCx$ykXwmw4zJUdFxd|?97s`Ge;v?po%x8a|zEaYaAU<B1
zlb<cK%3F@ZhlB|`S{U3!|J>7?f~BLs7OwbanV4UL@81V$N*yM>HFZUzPFg^}EkVCC
zLOyPsiy92gtn7el26mYc9YH=)e-0&-C_YC8<wt_OfLEnL*}po=zw~MYoRvA6zi$U+
z+Xi^9Lp<t2hAVtW5bHiXAU0^NJdxD4AtlE5?Y^x@+>vCbkSV0ATVnw!K=65WM)44B
zdW(Yo9qY5cuKZ(z&d^N|5b)NxIqTec?GmSj@IS6!;CI(c&GAH%n=L%pYEJ&ysoiEX
zGhR%gH1IkEm-&N`-;uKBg@AB!!=2#gBauBp7OJG31Umr98bxOtL|d!Syx9s=h8Psh
zRnnws7AvFTvdfo8$GpaBF6(-sU<_5jL)LvRjnE(!YTY)Q-{<53Ii8@#lKKqfw<UBs
z3|o3uH7}hb73R%;pEGtUlxn(cmtHS^D!QzHkPwpOwivb|aU%`mXU6dwUSrvv3oy9;
zsEKGC2r4i2R3VB1hJ6dzxu-+bdy;oDHG9Y2dk`f<+n1d7N^|f6p!0e@w^V~R`o68Y
zp5ov0`^_v=(Ztl3+_c(3z+@~gQ8fbIs4Qxc+O0_!$Xt5*(raY@6!#DZ?q*34frWJC
z!0tmKFv$DS8MPsxkPkHvO8v8nU{2JNfmnMeeMbrtT-@A1Kaha<93Y@3R4+zN=|En&
zz<57Sk9PqGm9A=|5sv1UWPZOXo#y8ssEntd8M><6T;*t(c3XteIsrMdCLMLUnURB8
z8O(a#)Nl=qqFJ2XRl7GkVO*kaY3wXKH7+W&@`6F{eYgCa@h8MEL6ibOJz8d5mbaSg
zdZAqddREO&js3n4g3XK!4{sw?_2cZ<){k&KFIpXL5~!RGnu*VK2I;r%;EZ8eex(Y=
zPG!vx=w_08Em}VZ*YlYK9+{Y|=~05=l)5}9_|Z%?$OYH|sB^VwB-Es}yIbR~`)%Hw
za7?5NcjZSK)VoeX!x5LKx#ag$T<QRv1V^4RmUs0OW*a8F?@G!Fig;obeS|8g;boXO
zGWFq@@Xt1ENUX&)r0y{^wsGgr<4b66Ym2jqvb!+3jEC3a)_-J4csj?|c}kZTP&eXp
z?X@e2kA=z^HI}<A(q2;7;PcX`pbmo=>_k{H8gPciP5-W2bGfU6$_H#VjHfA53eQH5
zOp93+@<oqeo>0UFQQi|hV8<wYMcu#8<TP1V?zE2WoLtgjo2^_s*0Xlqf(Lrz#V7^Q
zF)*bN({K{nf>f9e0LFuj1-F%C;3q(OvU|s)WYF1(zV<xlcExU}Us&1w?h~RT1qMrW
zVxC#nWVn#wJ7UFL2f`0<7$!gr68d7dK|^9%qZh8#O4B{QgRva1@NnZHA89BD>mKj6
z8ikAVLP~A}Nmy|4+21GnwOd|tG7J$R=Zg#Z!gia0{}^@sww%|+?<aOw)%Ns6XPr+<
zVx;>dy(23jLB9$7FbrC^D-K6ySRR6_l)3kr!U+HTRK6Od$t7Oj^S*1D!}!>k4|tF<
z+1WkE)XX$_;oQI$$p76G!1q+mm_3O}MOVN842h3y*>dVnp_|YDUK{kGSh!<QTfZZx
zr+*6G`2cJIcF*g<83K%4TTJ{POc~`;hicJ&47eeAzGIuy@H9H#*rrJyZ$Iyc<^f$A
zwrD1>XQhYMae<V}7)aThf|m@lzwlJ3DG%S_PxU);CsUlV&ZTtsAO|W&hCk0Pk+CW7
z@dw``v3$PSDVQ0d4MDp;1MTb=|6Jj|`uA&gv77IiU!Omid*XcbLHx6454Z1&GtLxB
zAv6rg?<1a;SKJo6O?B^nS^49CsUDxa6XJ|rb#QU*=Cjk!I&E!GA0E;%KU?6N-OV@Z
zWNzjd9->k*KcXcthofINGDvS5Jj&r&C#%_*w(>^Z=H1$E9MN62xL|Lg(x6?f`N>8U
zuvIoc-d$K+Tug-CukqXttrqBkXzlAux94YR;V%*rxI^OsOTCaibpOvmfn~WPNlH3}
z+ivVduH!;?B}H7;r4&(KCf}C$l{X0)&+^oX!ZPfg7}l#eqHun)6HC|MXnS+ZVmp3i
z8d%Tc;Is4=k%!}2#^p3?Id5#l{(cto{M*8rYpW7Te;+JzN4>EV-rg7mnzT1nayJDW
z4cn$x-b>(Y$`>D*?-W4C5Zk)|=Z`QqWhO&SFwm`e2J^P=BK>c7`zFAa_+jBAPQc*~
zf!=K9na?<uTqJq6`1ymyFG@L{^}{Nyi$$=uNn7P)fXzp&%en&=3Ox$v4!#VaEV$8f
zZb#2=*V|H+EELgRb5R|da%?AE?G}7i99W8P*zA0oJt<<|wtX(epyR8iRj1i0d)Po>
zMr37C6j}gjvf_5;!0v93TVv}pKdi5iYJ-xtHfv;q!EC^9KcN#Xrf22QpC&DkWt$(-
z(;216X!ogu*GSDBBd8bJE0C|e_jo?@Tj`A&<WAytQaSIq9rP?jxU7s1Fn2@Ctpju=
z1L^ugis<j#A=MM3PzPtbQW)8Mm-!VfVr6rcQ`*RGUkoo?)oy~bE{!tit^~Rfg&SQS
z-KCHO>xIV0u1R#GF1e=#zpd4>efT!2mKc*IGzoIj&N_VR*i}GRbjLCVBWM#^AF6rs
zwYO3}`hu4hTncD=70#u7>6CDwAC&(6rfR&6LMMC^+HjGw>aJHDR|kzJWfF6RqSC_m
zrJ0TMjhNugJbJCKq`_s2l+PLZb?CA7d`7qU<^n}wOw(HFV=i(LAW^>!lTN6h`t1(g
zW^aeaJLnqa9vqm$uJ6hvRy}W8)XAWeF_3iVt7y4Wv=T60G%%#!w!P;R7D9k6TgX*l
z93elq?CWR3v;TIfGP#;zDdGqD<;%san7jv9*&EHzf54@!?~1!nXc>}_^gbp<IwXE6
z{KAdXoZG5~BhbZU^V}D}y6fDTZ>*-!0Ae=FiT>XopU+|+<PYZ(PqzyCDXw0=LLl3f
zyO*Jr-A@sJS+ISY{B&TAk4&sHn@g}}a9C?By0AfA(u+F%o4jbHxFsc+icE9V^N2v7
zYX^k2sI3=fBNBa4_WjKVuPh*-ex&s<{j+TRo?DAw1{XITzpwUci=hvw^Q#w(lAhtS
z31!jfWJ<3!CyJq4i_gf~Te!Ep5t+TXe)V+qV{jg%?Q2AJ*5?VoB^^3e)uaKLpb<+J
z2$QYK5G#(!2(?|kEU)<@R;p#f$hR(f#1BnYjJ-kj#3TBYD$;O@>m9X{Y`$@^Cl+!0
zp()}mQfx5-#e|jDrOUJ_Rlohln<+Y`MtYnK{D5NB<tyR+u@?$=sTor0a>RQ0iQb^5
z=Tfq7yV>8gz?_a&#Qc$m%D$upw|ZvM56?!9RJ>7}G&rLfBM%$I`~?{;3ZMX}3jDr7
z44ShwnjaHOjQK~GatDq;Ry|Nyz28vMzg$1@_>i1#g-#+tPS-uZpii-1$*k;@XOBKH
z5e?Se4>i0)8xU(Ti=#%u9J?7U<OPV93K5pLHjq>v0Kt(H9NuaNFRchU-UH>NKnD%<
z-5(zx)pLPz20WJ*o-nj2by7r3GMS#1d~;r0x|sd;DS}h1zyXtsD)x$0cWcc#Qt7GD
zw<MBNMWzu|!GnFl`_(O)3pW%1okdwWJxKWJmT({c4Ghysjk|EkhWg4B=Ho{($37jt
z&>A%X^J*pgaO1xV+y0pwQfZ~QfBoo1B?rYxyV~oq0JLn;+08uZ=jdk?TJgrwzEj|9
z_lqOs@0R|s3>9U5&Cy16gYI72)a?v9@nqJZJcn-feyh&K=UKEO^jwPet^eFh$$THP
z@<voExMxBRz5F+(fzBTVXpE2D$Z#$&`E7Nvg^_lEC2jWNf>7J!uNpF(N!<+V#Oyjh
z<G-e_#D-JmK3}C4siM|8{X7wUuZ$_s=A$#n5~?H5naz*RLl}zwJJ);M|1LJ^Ozx@}
z{MhKi%O^f*I!s2uU3MW(!7)g5C+MVV9{Xfr@|Ss+*)|;dG8;X5kI+MUn+SN$=Ev=V
zznDQA-HjwZe?>b6uAE~xl2Yt38Q!r0H6y9?_oen3qFt=)gHrn+18!A&$9lp$?)j{Z
z3PVzaVKU7fyK5d-2-9K`FYZ)Myr^>TFjCQM40qi&hsV7Sx=hmNW88V=H~z-uYI4jg
zC`@{HE?#{(@icx9UU9bRkW4LST7-iW58*m{W$(rM-ix@!;$<5_bhlErPNSrW|NYi^
z-e}Y-^1P5+5}dV>ITsi4F=PF`aThi-;C51Ko38KKazEoW%%dG$Hs$py_Td-EmA@l5
zL;H}u>5os@0iDqJ6V+Z0F-F7>b7%FI4xX4=<JRO?;rEFPsJroEIBQ96+<Ihi>QQQ#
zChgrMmNDhr2GYtXKv!_vW%%Bs8(AqL13d7dyau@nuwGG&nw*Tf;_pE_vJnU;Df=Ri
zu^*pP2BGoMNmKhibSB*{fXd>T@bFN%#W!wv_7m?hdyX<S+NLmm_Q&7K|GtLG7%oO#
znFhX;^HOnN8>pPPCSkGb8R-_r#?1&dK^kk&<$ct1*RRQ@W12(BurTyG&B03ZwN_tG
z!c|@aqF9i1Csb)PlXndbPKCaAxltCU=IWwOuuH0p^v%Jt449%DyuAF1?f4QLU}U7$
zUI-41eAZM3JZ>ET#5f-oKkB@1x>&k=!x^A*L~`OWpZ_&K%M4+^&zY-J7AV9hx#GIc
zTJJuWs5x$Z@X?f0SU6=COvtts&6Dp5epP2)*YT`fBi%HU2-S(95};=hQENJl5v2$I
zQPrwP&uR(d{-<M9kzXHgpjIc6dXACt=0HLxxP#;B0D4B{k}j}HUsMyQPwXU5stNW@
zPgz^0&Q78Hk~lkBKE+%rDP3GQkxBY7nnQZWtoi_LE1D@;Wf&ebZQ+FDAMu%i%B6x_
zctF2*cxjc|`#MT+K=w!z`dpe(+w8U;q>6Cl$Pr{vslFWa>rQkY3piT}EbFI4*k>!5
zsIR2bkG|=qP#3JEC{P@xCC~0jUgX<@@3+j8q}EdYL;?{%>%f9URbt46BlfW`0_{2$
zy<WtadNxWqg4!)W-{(G}tyTQJhH_B|T~k0q$Gb}X*Dt$~;UGk@=*S&^3c=c_rt_eS
zE5yTb4ROY$)*q6)luB+@mE5{zTXrhF#ig^ah4il6PAaq?UpiF!^_pIG*8t^Th|`4n
zXS47G(FG!@N>K3mFT{6Wsg`j$QO*`nr%#MybrwfjeU(=Ce*JuB1191X8va0wya{5@
z`l(_}kNyF&DdtE?C701B|2Tf>{-VP-X8VsGY2E2W!O=_g^s&n-zVD#x-3*A{kQjjA
z#AK%_HzGDb2kC1>A{TFlVsO&c6UU(tT=U|$sV#KAhh0GKNq%29$BO;x;}tg+fh6%-
z=|niR(u^Y|g?&Z1Y2`?P&K4?hh{>R|r+ngtAH!jfC=1ZQh$-rYj6MeRKqP8J2jCCe
zLA#M{0$MOPKRVic#W5B3jy(|%<g{l#c)USB5FF8(NOk1lsaM~X$Zw`WXaYe#^%4|M
zP@9p@gaeYxgUSv>DVcr5UzhuAIAj&SGH7IT-5(5I=ka0^bY#1*Uu}cAb9@AQBl>t~
zEG`<EJHOecKpG%7EBU<ROFDPWXgu^ky^`}csei>DW8YI>U+=F0@Ys(}*^pmt$i(x6
zHm-B^)8wOM1dN!xX2oD^(aAG<R{GTjHyaN$>z(GB@P8S4&xvmK`*6Heh2fz0O(;0_
zyh6gN&YoYZ+pf6hR3U)zKR#}Afz>0ew2~=&p^7pLx!Pd@H2S`JVa`0yLq#eA^9?#y
zs@6A1f4A(29<3F0!%6x}wY2)s@qzr_(EPH`vcq@r!oHc6&1*tx-~(H=EB`TlJ5l)w
zpZHcf@q>9u>-9?&Ef=av>jYCPT6WJa4lvS*zC{P~ma+gv7y;0<JSFaNi|K_JG~LoS
zBh`ze^F(@wv8a7#tyn>Kqm|>=u0uoD!pY_LG4XV$ybNclysh1xPM3ZsJBB!nm;@XN
zSqD=?HFwXv(vtJaH)ub)dP13Wb2?T`c)wNXB2oq&MKyJG*acAEdfLs*D-dxCO_&h6
zvPF^j3VAui3C3U)$JaaS!~O~ic|Sg{aPlExE4P=o6kp_}xlQT#L4M*XIbrF#LXA`-
z+p1U?e~f%^cx$5M!HY)MxqiF_8hF3?44SU%#1&je31c~1D{L~Tf2H5^7-FLOd_96h
zYe=n-Kr54Xai4)W$i<8CSLK0`vW1gcTEF$s4fLWc&Xua!S{!ekKAa~+W9CV9IHfA@
zs(VKg&JE15^Cv{;!}+mS4zZfIE-(+6k#791jvfe%0Fbo;O`fd4jjLuO2k(lp)a?3Z
z;O7aigC?FgG^;Us>VC`jtG<I`OOj*<@{BX(R@d+@hTblaZ+pFcM*k!9$HlR_F4tDA
z5sxi@6kQlR_O0L^+0O-_(=2IVJ@GuQP5tKf;)_Ab+NNJ^zgatHF2rh`U%k6yaH{By
zdhUa@7&u%t%hGUfApH3K_(Pem%||-uFG7eE5~%U7jwLUb<$m4P;IhbplptQVjJK{W
z&jSbgEaYCGgbQ9KW<Z}#Qk0N)33Pk+1P@vvu-CbRC~d_I$ytpw3~1Kmrkza|z*-Uy
zMo;$R`ybe9bEe(3GR6d8S-&5LTVz>amkx>rnx|;#+Au2-YBo2aR(pY5M1EtZ0{jG+
zVqc%_{=6&<N94)rni>goODzCq3QW)Z90aI3!})++unpG2;o^ex?xmdd&v9U=-QG>n
zwoL=lbVY()3ioIE8#;!ixEc8yw@Pbf{9DYk9qxReY2sA>!cEk&6iXow?v3f04Qb@b
zS&1trn-(q?nR(Y%0EWO;h|d!W;$Q{!&aoQ}-<E}tK$!1WB7mm4Jfy`%x?IgQ?Q?NL
zriAv1^O_F#P4e^XuBFi%eYzi(ciUc*>h7q>)RjcdLAyhXJ?=O0r+i;3Ws@GH>;vcV
zE9T;;Ob$V~C8+}oj?jhodsxSybe}Ds)asIMQ>ei^9H2nrR-;M|^qT6t`ygr#a=nOZ
z+ot>df#5&OZa=-J?22njX;Uv+XbxgY!{gI$x%$~&N&dpCL}(E0P%3#})3r4?*%>nc
zZLXz`@7H#J-vM<L8M|eKp)ooEKpRp20ExKP_V)PRUM-Ld+Wyy8NPVDVUR*k%WkCGr
z8*me;9g)HoCTO!#zb6P0rj(dpW;>)lvcm0EB2ei7l#$GAnzC-Lv>cv%#x%u=<r0BA
zrRw{VW0Is|8KSQrwlV@T0%{F!i~0EwTRdUc$g{cjynpdno~_BhFEZ~OOmD$3M=Asg
z1}Cq%r?DQ>_U&%4NFig{-a~QN<5u<C@85k{_n4ya*WFolMEY+SXnJb(U^Q7S2=XSb
z(XkyD$&;G-E)=nNbZ+#I&f(uV`?hE`U;6(2yZ8S7?dx(MN2G|A@)OS(+UTk+?CkcA
z`QptSiu+{MyZy9!G@2djEyyQ&tW9!F&C}{%X`U~At-bpF$E7&euKo(EJ4KmaD#&mi
z)3YO;R&CVcC^1lBC>1IoJY@{zpu9(|UOf_`Jl>X7n5)K{e_hfyT}iW1Gs}c@cx{1W
zfzGNF0v6Oq`K$`Lp99H0ET*I$T(lRE?5ICOIoks4xyI0z9Csv}0ai2AW#eJVUJ926
z^_0ZILRG=YhKZ)1L(IqzyWI`FHpsyELYXlTMdo;Vb^){49@l*Tth4la0l{NttgqEm
zRJjCUfX6|k2Yr~*0FXir=*FP-;~xus+4KtFmkOO`!FWPDOddTN55#|%eBHOEzw0zz
zI4VV;=wtuW4^QSPJVlU(J0i@Z+Ar*p2XFjLn`B)El2VIe#BD_7%kz>3BmnbYbFpJc
z!Wc><N!3rhpsXQejC*ypIV^QM6<{;YFueA@`(2`3=fc@Zzg6J<ybRF3wu)NpMQzd$
z-xHKyi{T_iDjCYhmpt~;QrHW=tR~UFLGmXW&GY<jtLG&Pajezm`N0bt&|n9hc>Kxf
zKy0dgGAPCn{oug^c^~(ff4r|#-iJ+&ky{WfJ72y1iVQ(xukxmp*k8wH<~ZCoB3@Of
zSns{5*3%TV9$_dkdvDpJ%M<X1Yd3_$?`y=>^J3&r%TQJmGN>bcuf9Jh4by|deP;Cg
z#UdS`k0x&EqErj#`S8#GB0*k=NX2VByHS@eG^;`kfVh?*xAeRNpQZO4h=!W}s=n<%
zkBNm`cn2Lwl<M-ovIpnMSA4&HrGzHH?5jU<Ew`sE;V+|&&iKEGJ}lCdbD&P_Mb3d*
znr8=mJcWX5ZcI}r(!Px!Q5rgCEfVjfhaLJ3+m;P=G&e`Pu2s37qfb}Q$8@s#dS<Q9
zHhxh`CVNnpc*0y<Ekox4Xt-sDX#+~4cRWA9x4Msru^UH~DA4PP0`g;GQIQ(=>l-~i
zJr0E2j_+hK2!W%oo{(EB0w&ft)WaaZ6i}WE{I&il7!4%oER%!;4rq<$Uq4<SbV7lt
zM({vnxnkp)?cwsd_|{!fPH#2G^BjIuO9Ea(7`!yqb@I$H{)SiVx~&}?<RU<_Jhx*2
zI*nsWMly@Oyx&6vQobw@#rQtY!w!xD5XZh%Eu>h5==4aGojTGL)$x%!(y@*HW0fX9
z&I?HQx9eFo<JUcU$JiJ2)1&S>7*}?ANNwMJ%eFDZH-}96%+S%@d5>5Wwu)}NE!QW0
z_`Yaf{awR7M6Yp4-Tz9ItT^T{`HttS{dWr(I`|Jt2D1nZbRd1|2Eo--#GG1(vyds`
z+o<b?0m>BSmYrfEMnUzU8FYWF6}7WL2_3O*D+b>wRowE1a~V7k5BN6l43a}D?4HvQ
zzQuho08YcpLy_}a+^h~r>g76^_(tHn(+=e?GWmZ}6KH;Q7q_<!_U2hWK=YGuql(^r
zNfYl2*DPI1n@0occ#58cH{6H0&MNqGdcuq}9AB@6$Dr4QHkqKIP&lqDoP%qX{7rmz
zf+9C6U!jR`A;gOz393race3#0)yho(uu_2erPu*_BB%CDvpcwyMt<W5^2mp;q&}?y
zYx*rZfv{j2%uPF5Ch-fa`NaV>L#g!RiI+^T3%;i)nBQt!23eP6uKVD-QR6+MWage;
zF<#lCkN$IyYkxg(i(6vF`xWQqDf{0EjYkUp_ju1IwA}yHAsHu+y7OlXKnJ6h!zTeX
zB|kfhX+-7OfkJur?(c635UhzV{Gkp7xRKTs?zFDnm@rt`C%aJk3widMxzbLJWFjfB
zVEoLyt;^lHRQ>qOSC6jm-v|1Q*+*vp&<`yQ&K78a7!Ok5t8&63$+yg#srLLpI+v?6
zaEH>c>(!6g`}6vH5`LanRCPf<84+rIn1uHHaqph>L5G>$=?ZNw!O2e2O3NC_%&Gp6
z^d~!X&VHVUgK&!bJqcU@6h{m4hDeZh1ijA2vNAV@ain6LcxBpkIoS2gG_)^aCpBk|
z5WNP?Kv3+Y4={tyhoS-q=_9o*-Uua`kc;HoT$i-!dB@~v|5$U_dPH|>AXBn!!zn<s
z-=^kPe&A5=_ivQlk%Mr%PQN8CSSuOXzxwAGw=qMXOJ8#WNb<y#w3n{qOUFS;PII<l
z9BC0-@rIuYx8tAUv*r!YGyGW)lEqx;3Tro=Ir9NeFs2prS_u`-7a2XD*m3Ro5u5tA
zyvsd$t7m^?QabOMCL^;@?*ODLp74HQXCD<@x<rJoEhIuYWxIN-RPc4(1R?+;k>bN1
z#{q=M?pml|b+J4e&x!@1f_+t_L+59bQ*({+wSpaIT!$TTJc&qfFjpvExgDcT9?$)`
z-TnEIdri^Tu_n^q8WhT{KQaZ0B*0MN+?&unw)zSf#xoG+4_jSwCZ=e&Ass1nu%Uo@
zq#?4juLDKQ5L33Ip+Ep{yipAsIV8=r<^{qt_Emi6>@UXTI}j)KRY8+P#;<{43xXKF
z{w{*Vi%yg;&HJudqMS=An{z2#mC$m{-F`J7L@5jJ)vQ7D<DIyWLTaS@(TL0?pu&7m
zrLDKfCIyLZ^nfG=$ataF@qho3W!NtVeU8ARXQ)26)@t=-61*8Oz?}b<Ge<a@T~Z>5
z$Py>(Q4heu8{cCP_z|BVCCkPBPZ0AARCpH|%EgRa+@VMF0srIo?U`2n@cnnkW~+{M
zAd7O*W6n8W%gf)1@$G-gwEyYRFD9O)i%1IGrq6k)x}Rw^-{~?seL=|KSrtpLBpNgu
zo6>5vEwCWOuL>*v%63jQtJ-+HL|HDiG=wXZnL9-Y{iBr}^@0b={enKFHeY)_H@o=h
zNMzf|AB#z$`Eg>XNR%HE4Vql-`&Lo{>V+u)Ci+tpo_K5&3UDt-_k<x#f1LotHG6MI
zF}SsF(bQ*^4I!!)U09J~j6n@}fJyZ|nUDPX;dCoVyB$2aId$0dLgJGi*k2EKr_L(g
zBWDYdO1+pw&!f>j{kEKexeh}o2Xbf~I%ac=?B!xuZr)5^c~dp<;`>;+ow2y`xcd=_
z6^5`m(p@5C3N#!NVVazIS$#lz@GcLh-LytHh}aZ;9e8dt9H>&GpRLM66f0dPSP<P=
zorK}^XoH6%wbO<Qxo)_ebzfRqG6C(4mCB1HQAm68WCS1d;@UuKoX=qE_g{zoLSt4@
zxu?k~poi)PwaC<f%N;{J-3{jO^N;khCCf&$OqQS8pch;puAS~zW%Rwi17G$Ck%cU~
zi(reGbBIv!Y!vha;9n2ly@7wZx6l<Lc?3wBvKXofwkDv!0y~mlkGTr44v}1m21|Zl
z2T)KZpt_}a<3^pm?`Gn7SyfCw3lQtw{WdOvQ%(Wl@-oLer=L*}su({ecg4vP?8e_N
z6VX@(h0C7xZTV4jf}5ha{ocKM=Y<!4O_PZ;zQh&TOy1JA1HmSwY^dVwyv`%FS=B20
z3fT%&aShjdx0;PVNj&R+ZX0e{r<l)Qo+sqenmgmZ*T<Dsdy@ASC?2|(!tz<!jLlVp
z_HGI!(Plbd`u%UwC~w~H=f9<%`8l2_TwlcJLSq9KUJ%&3zs%|qN&jpA-`nn0p9&sB
z!nwqVrMCuQ@tb?C&eSOmRczbArWNeETB+UT$)!aTU6Y%$3q#t%tA+zpaLn1#T7CzX
zwy<^mW96HTCL|~^`^SRZG$=|KLfj2L4oa|uS%MYx^x^;%N8C5$9Fv7#&i4HAj@_P~
z4--SzMnBOL;go}oI~gripW~3@ZwaUWj_%|$A3C{Q@{J#{w5`mKKKY)ELLKwsEfLft
zhEz;}88R_5qrwv0a_x|2EYL+$MEPzDM#?0F+lHdzREp^4(e>sIZ+Z0qGoE($S+kij
zA60>N_FV;ZXdtQXQsODn!XWXbR&KUEJ7oEh?dNYD4cSF)Wq0r17+>GA4~Vzjg5A=n
zbTkqKQ^-8JQJMos_$hG|q5+4fi9M23#DWKOtFGs~L74VF6hk1o8x}kNYeY2MIh=7g
zd2#g7)R9tXaSBdeUuG&c|7whLPphiduKV!<*ti*`KCYt-0f`C)O2pj>UHukPA(D1?
z-_xu2K$H?Tb2PnOmWQc~hkq7UQlX-Os=<^DP;`~8x-9>_=$ezpkxX=41&r(_Z%mzE
zveWXv#g#M4Hf3?RD~TwfBCPi8zOTylJxkeg`9AEBd~iSk*#HT=F}83nt@bD1qS;Aa
z>{$#3D(Ik+BO@b&WxfJ_9~c?Xf78A6V>UpC1JE7ZUmmt0XK2-WlD#vZuKzJ^WxfiE
zoAO;@r#NtC9wY7BIg8t&?m%YVc2KziGfPQOS<%SC|GoKkrT77*cLjy9R%mx;He$jF
zB2)py%Z*V-0MKIZcbb5R4SIfu&kuGoptFnNXLF>K#H?ao!9!a|9@Qtgo}M^ys7f4=
zF-W7&{Yc0#5>^lcKvS(%BkEe+@$gQXfGVSWht0w@4F{yK=~`GdRBd>cau<4KCV&i=
ziNu-*Ops3jeN*J+w@4?bjEwRl=MCiDA`vHVRj8rM&G%zYZ;A8&bO&Y69ASzoJh0um
z<W6Cn^I!m?lje3oac0Q!fG*)$%Uu6rm|E#k+t+O|KPG5d0n{Z|A-oO<_(tU$B`b!6
z?BRrVmFnPy)`;h;)pz7yU$j6XEsFj>QEMKx5L0Q`Cd056udp*4mrv*t3#wv|579r%
z+k+oh>OHCUW^-#{EQZv>(b17~?7F^kgu~^iH6+FWd8&Xp4LB~G5(nAp6F<k&tI+4}
zm2|>X3ob~@%#aQym0<U@H_S(685Q2Dwy!ycxQ~X#+@JFt&i#0LCUpq%&=7W+ZZrtt
z2)51(F~{hLT$7WN&yU-I!%kxQCgf0dcidE(c9Fu+Ofp@|WW2@XJQmkOo9~e7pK(|o
z5dF&CzED@^dfXkVeylNiWcPW)-OOW_Y62&I{FD?E{L~C|sQCkj2U)z-)YQmnXGcdz
z7aXJT6m{ODOqaECJLM8O)EtPF(=MktE~QlQ8bi1`@yML(F~2eS@JYgopXgrqb*Q;>
zxdfA5$dBW*EEXb2I~f@n%Tnl0XXub(d|Ba~@G?+%^Hqzo1XdgAPP%E)YM411q%ST6
zE*ay6eRY{CDI8j5`{hKA14$?>eNWM_ZD-o|>Nb2eJ`7$%)otPwsF4o1fUlG|X0t~4
zDvWo>{t81!v%_h5gdE!BJws$uxc7^fSAh=Wgvna}Nj{XiueVCoT~x#??r#)|<P)q4
z!%XvZzrFNE-dZLmCbI>Fh4+5}<&2*8%qjV}@}RUlGmh9$w_+N@nPd9JipS6qBKL99
z?-tD<_m3@Li;igkBnZ%&cxXvC^Ocsv_$hS0LxO->7Au^KdobDcye5kCn;R>m2VA^;
zqX4%;WlTWa7OhSSx}}O{UBN0Z<8<k8)wu09qMQN*&b4Fa3h(6aGx!m$l_{cv$J_h!
zoZv|gDz75`3y39Ob<~QIoP12ZZ$;VC-5>G6-|1v1#%#tk=j2&lESB&~zwTwn$|uyP
zHlN`*Yg6+{qHU+Zpy1}@dgBB+&UtazEW=9xyOs1u-94zs@+p$=I@-pS)kPAeMmRs3
zKjuZ_@%|0FH)k<ciaVjhZplPcCCBsxf$T86-Y@K>XP6Rz8gcW-Uvj&k+<trsRK}t~
zP!?$;1sH{{AkPZWn{XcCh<wpwE;Q9)0kQS5Z(pl&A$}e(CiQ*+&i*cwp6<tG?s`FX
zu~CpFD)Y8bMJCNcMTS$4D0Y%u+ibNpB#>86KXBrq`7hpx6dUYOkSmb{DB4uC(==ET
zOu19==EXOVClT`TL7|~P17sC69$L;sB~aN!60r^-z(xecXxN`a;?IDA8y0QfT3$t)
zZ*lne%erw&d{ORPU3=E^d;{gxdlR1|!g<e@;ufEJr{-;f$TV0$*G`QCfh?WWCULRn
z-pg1xy1^f+2g;}?wV=ptUQEbauitweFP)%(j!5ZC!vV90k8VD)7_5BPPF@;|{G^M(
ztYEkwZj04*aF-5Oic`?fz-=BTI#a-WJ1>r`ukIli3J_<@z~OMuEEm7XI)1T-ISW{P
zyisyglrRG5kqD<{<mR}t-C^r>8SqvXz}{LLBtXpKecU&cncp~hAu;KF3g`tKIm>U1
z`C{x4s6wHP->k5BJ=(Po2s}u44|!LBF$bn(K77UyG)?`lSK`YdoCd3^9;f-b6{Ptl
z%fuNmxH&O*H+O|;_2SY3EpjH3_L@!5#@H9vjGpu8+W9Pp_ALJ#4?)+;;>Qqd@o9g&
ztMF7(bo3QeOc8`2coOoF4isC%4<{EFs{?icxC$ggsO{Vav8JI@(yyF7s`uv(xEsci
z)oi>OarG8F7Uin&^a(etjmboA*~VQg&05URc`dfNsM%gfeXQxy&n78_L67upBGcwX
ze)!IQ_36dASzA}9d$V~RMjiL}Ob~lthUTn&aJaAen8YE0zw1`5d&OfDgK(Sks~K8o
zZf8k5C-;_M-DT6e=Ae1~dl?V@?yI3+7`5jCas8ZpRtywd2;<L9Ec!DOSTS{GxArnz
zh$$**(Cb*<bL3u^xHqo>{f|N!ARNa3Zk|GJXU+i`=diS~Odn8uEVMO>9p~{)9=M$s
z4nX-7l7Km$rz9R8dK`t?*gb_E&`E~`El@)vY}|@UN^xLjF@|zs7ATdZRQ=e0+$^aj
zsg+x%B@d{y(QDjrv~bl(bKa$AHB#_tfb|1Dc)!JFS}EImd<iz9G^C8rLjMhkzreOd
zv9e;}x{5Cv4pK*%KF7D`o<P<}1D_I<P45)^vUPnSK`5|JpLV|M$12lFg)|ZIB=UtY
zc^i+_R;i57f9QI&3h-<k$BL${W(M0ai%1S%`_0$pCJgKS8`2z^XiI?=EzLU~D{{RX
z0da!B(LP9KUe0!%-y$>~tbv3<{{>M+=vp@MNu8Yi<Eglv`*HYSS^Q-U$JM!8w(8$L
z7e01~K9f?#56qLOJqKGSn_q@5(Eq3q!XQ#40(yY@`3qbD2&JFPjRdKBctY}WCd|hk
zu#kN4U~9$T?7%Y8y(W3yK~3F<_!YeX_}smmBxU|V0_V7-daXE3C~EqGcMhdKTW`q}
zT^;Z0W3}IYC<bcjkV=A~Op3h|q$5WcauIHyW8PB)JWr$%=mJuXlOQ#%!W-U>R96s2
zP%&Bx7F$2*JT!;mjg0LqG)(M>Mi5K1=PekO_iiO|cKM9CMU4Nx->NaLWF4o{!i)`7
z9&eh$xk>Y3p8Xo)7+E-mP#WEB#~ZHp)_zu48xq#_KL5GWe=G|%kN|DmVGjKRrHf!s
zkdtTLlZ=|@{THB-lZoE9c9Q7RzZw0pQD5^vjLj^TF;SoM6`Gk)P+>44-W2@+>8jfC
zW^>E}+HdLt>A6&fd|^+p!kPUd2$jX6`v8AgRQ)!DcF-(5Wr=?+Isi4yRsjfPy53!m
zgkzaKx*-%yFWrF7#mQ9mS3maEGQ}6QAvoOOYEgXgT#8v~8@)25Qd#SVJ>Zz1$qrrA
z$ssi5NTf5rtsiJm+uZicI+^i)eBk=Fbe5T!Vx<VA6A0af*<WkjQ=lWoEu9a1!xU9i
z66))v#hbx_^XIxgiU3hpmTh?8yezC>1R?5Asq$w~S_v~w{*s*Zo@0vrfZ0XG|H0gQ
z$78+!;o~~Z)1Xd@7HJ5TsECX@DN;fi*&}2VLYXaTAtGf&*?X_dl958iZIA5i$jJU(
zuUqSUzK`$Y@%!ud_}!0l%5k{w_xtsJjpy@vUf1<Z@37iz_v&W`O~aJ~m|2q5dA9P)
zbNM!e@ZLMcsdy9oo31Xk?}rWOJr-*?_}^vy5w>z|0L9PPU=!Q%`fR1lC@FzVt;g@r
zwOG;?)OxA$I4!T&e3e>#g39b8voG&Bsj@c@?i&Egz&v7!Ao`e@BUIgBN(p{3ROLKx
zc4U20Cj%$>VZ2ICZ}X=NG(E8zElkPW722=Lot={@)jKw_`{`Ski5U?lF^8S~CC}M1
z)A#y)%P1KW%b5Q?=#?0~`laR!4Gea4oUdzwZ6RRg&8{;=_OO%Pfk*g7Xz*$|OI&Sr
z_4$VJ3A>O@>*s&-Xzi(siCB@vMRh_$&-F4dHLMe7n%%Z@f0}Th%thHAU}lG}iaNPQ
z4ZR!WtY@C5?ci6ham%LWvM6L-F*7^zRH6rUX5#KD*vf;3jLgYeMW`{dh?8DK_)PdK
zHpIaxvN8V0)$Vx&dPiNrhgjSx^VUiex?S2nZAZ?Blq)_+ZRQU;x2rr{)~T9*_U6yK
zyOv0}7=)RG0}p*1-yS?e$`3>#(qWakQ@qwlo;VKg>^R?V#kZN%Ea+^qA3uH!jz&35
z+$DZ8_M|uBkTRb6yTAXwp_IB^O=KN6lHw>3ppD9IoU>(gF8y#L^r7Ta#ix$%!#_oD
zR5x4mGYq4-W{)VY{}SH_T0kBp;mykY4?xH*F~B{r_4G#dpV)q8!wj@Q{gw(KTzGQw
zeHm55yjoL4%<U^Ce$4aD&-8sZ-qB6Q(UWa&*5B`DC}nE*C4HY}?Y%nZPqHoSOX|bk
zM_0D$ufsY02gjK8A%gXt-C!WRAYCL@tz3E1gH*l{a2dZbl&AnZs)9`2VnYH)7R&&%
zj|>eB9a;F}=wQ@DdT%@RO>^v7j$L$i+G;~F8AWWpJ0K>omWbLDi#Tb6oI&(g1)(3)
z1Bd5lprj^xdLPKm5q<tC)<;t_8_p%guPi6lSpJ~FFU^)zR}Xk*H~hA>dLJZf?=Suc
zVM6bLasyiAkJF`}Sw&oc*<`TW#k<3xZmAaIIj!QLZaKBm$f4f$4TvvALnMt>mguMb
zK^cCcO+{$|>U=?V|A-o#z;yjA>mznyYvNHwz-t_D*G1IYP?majA>qjmU=k!D*E)P)
zegbiQ)UgOXoG+d-RzwkgNSCtoW<R;2rL6{oQXF2Tib6eSBUYvJ1l2`#%L$i;qXM7p
zbGAc9CPd7{xv^)>uic6M>=@8I^iMcR{Dqe+S&|If{em_#Sf3dp=>ugA79yXB4|MAg
z*mH#jvh$xG*hh%Lx5ocY!V$aa4uhM(YjGWmwc70cnvr&87mzJS3&nie6?b3y=SfTB
z>5SNqZn@JjDs^JAmp1Lf52i8Kh9{vBISNYs05g_)7=TKBz;91%xKMiky&XD|S)$4w
z^do!+vrEFd<tF5k!_Hsj39{j;9Q#kYN|Uz+z~s+2T!%n%_R32}U2FQ*KG*AZ@r`Gf
zr3vJVlT)im=%SVH^TLN>IIj$1N5V5cMRlGsZQ*lt{e7(x>u>#t#I8AA*r304=V;hU
zXpO3mz7*M#-`e-;l7QFK^y_1R4~r6{p7^^ZEp_X?X~N$JV#80A5AA1vzC|8ix9g74
z&SwtU8JX&)ecjc)nKqO)Ok>+$x|k{+YGC6xy1h~1%&&3!eCOBEs};EK@h4x~Nu<_x
z=`(l-1bE88)yxEpFr~w}+t124iMgz6dR-;cP&L_@Si3Sx>?ayQ^Mq^9-x;JK<tfq{
zoJ9D_FtI}re}a_6!S`bMiQVNbs$`ZjoOw3QQ9nURol1_<y>Pr<*jsg;BQJKVmV9x0
z_?1;iIeY%rPd|fs8n&IMj}4Hdm9*&?g*W$c;s|^hQ&@rfc9X-D@jUt8aZ*NS|Ax~S
z=Vwmdn|~0mnv{Rws5c$xt{#gGr)c#TaRqP8h3qG7<1xSnx8nkZl3!ST=>Oxh;;-$*
zM8*3MG9}eYjWp~bkUZpS_slZJiQ6uT8wisf`-Z;HbmEVVZt8BxegLh3jK;Z$9O_@b
z*nh^biwf|Lv@h%r6dPqBTrQqP-(FeS7su3HQwF28?%$tFgyxd;<68~Hf}WUGqRNA*
zg|ddM8|;VCQZR%LO@XNW7>H4L@VFhRZKLw0M1XhF)K}a?@w<yJwy{-*8l*q{;D%^i
zu*npP-UVuD_bTJMc<ZAXW)D)MvS`74r>rg!gV0JLQ@EWyI=cU?ao%3<oAW$xGJqN{
zl6JJu5_TEY@)-Stq5-#OFv<=h!6y%TBqh%N46N)bz<rG(<SK&HuHU1u6*bV~)l#T|
z#%tari7ROsRZW_;kR<(+be7N;^wdquL0y4#01vT$H2=JwtVWIPcGZFzt9deTjQ>aO
zTwUyP1r;2ZFZhc+bu~5^dAgiGWL;N43=+1^@t+_8&9za?z@6R66_OQ*u2alAPBo&z
zE2;k9E5|6p%)b|XxStXUhd4@inPfPjQ;h<}==#y4YH{a7^*P*6qTO{=N{SqsjmyI$
zFtN7pPOms%_yP+;BO&{>s`=ZUZ6mJp4@{a9m)n~;HQN4C9;{(sC7TOcR&jW!ASph7
z6e>rbHp=3*LtjJVC?er1?#$m}hZ1icmhnK+%@Ms)B1l`f8F<-b;tUXf8mjmpcIoy$
zl!G9cG=Qb1b5>K%Zj(*7S@OB=b?wXS+fQ;<;vQJ^xp98n0#!Q9L)DDCDI|NWe97D2
zFXcJSL~*FTqcIiSz0P3eRna|M^FK=dJgKwVoQ~L~lI{}WC{hs@6_Rw|17p8WyU!*X
zHq$g5JVUl>k{67kRrX23Ynb^u|0Zmw0aV5r4Ssyot2A0UX@z3h&P*%B9P~yEFZ%Vp
z)LzU}6v<C-c2E2}V=1wRMq31i5mH71IRakLVN{Pz;2spvc)Z?q&`ykkva##gl^2g;
zj|x4%GTLaY`H@EE(R<A2s54`wyh7Q}&txZzgvV{TEO7Z#yj*HCcg(p1k#|#nucN*a
zkiATBs=Ne^$+FGm&?%2|5uWJjmke*}(ed%!>+g$u*a-)azV-9nWJEZ<-<8aB%W1gE
zkZU>2$%i>)#-1rRGCI%azJW5jQsl6`d<v&pLwLuos_yiSxkkKy+^46cqI1I(@M9mC
z*LIfq8~#kn;}s4*{TNy54~Bcxv*ya@3LFBFy|u=-%Y0;F?lT<fesN+|B%RHvgY5HT
zi$CLYQKy&>Iy8E)q107usvc-cw}Lnyny#2FYY6e!<7q>9EPzGOlay3nri(M|F_KOR
z$Q}8$V_qBj_ouV=X{WXNBwx3d63p#)(YGG_?4Tm_QC57g%e>|?%HFCr=Yq<7n**1}
zB?VXiT2eHwi+&z*g<$>)(d*JFE822Z4!DIC*)0ip7i%FEUA+_h<c77q9oFBpxPRS%
z!u@82`DgL@35gwEQ$t2m^<yzJ6UN+C11`J$-Ye`^{?Pwn@rq5ChgwDb55!3_g*AHo
zpvB>ht}SHwfg0p3!1_gg5;Jr2xs_n9I-g1Z{V3?Hcy*P84B(6vv4Vm3i$}p3LS!>p
z`U`eII<WS~<>%3_6Tetl$58$3SYTrv1M|J)74_Pzx}#5=<u+Zy_`5&~_^8ZJW-q}6
zPyV`#Jk6hnqr|b6<4Op8Q^s~x@;;PS)~r{$lRQ3Buuf7tn(13O<LyfNVLc_U^A%Bc
zKB$&|YBRiCXGTe^U-=84z?-@*|9Lr!h}iE1J9#%@>18R-1(4^Nq1dxdTr&EkvPU1U
z2oAwBQSY5evG8m7NJPTx?cO&jzo$1B|LPy}!}BiS&9amGne_)VeXJah#VnjSDd6=M
zF&Q_?Zai`;3u-Gk>_w*J)Oc<Sn%Ddd>!cqqE}oz7@qoMHAcf&H##9=jxPB)}^8KQP
z6K!Av@Dp{Mf4R<iix6u>``gb~#nRbmr)~J1=)y35@ee`JwX=6v4;bveRkOv3Ez`JW
zRwG*I?D>yO?a7-+`~aX%9|ujurzm}|35(KoYED_S8<sr(i4Ov5*zq$RrQQ0tBwTAN
z>MiESJsTl3RG$0bu;XchP374GD<e2RGp)Hu7aQ3q3n0Kr4CH8|=+K{lCN5{?k5PWI
z-<)4YD8UnftL3dnPR1`7jjY$__uqN}Wl`H>32Id$ZKH@9k9@jVbYvr71(D+I-XKvu
z`8h)1upj%ef>^VT9~RhCCfT>vfNpWUs=H?*hQ{o4q2__g)x+T|o^DA%0I<zgDzqR>
z3j4a*{r8PYyZ*SbqqW>;WIoEutF{Ca+Tm>(D$$eO2w$%&gRo%vuKn_ED&|aK#tNR<
zj=_zL!{Mx-A2(i95aRfCN!PTF{<x&V6P&t{`<}@|x)lrF#z$H1N~Q;i&H<-`E#%z<
zSs@!<l}=f09)-ZozBuT%hJ#%HZO!M{r2i9E(Xj6R@%W_o|A1an$G=b>tM&KXC1o#A
zRj~AGK}x!#75S-4R^&s_<SRA-PrBq@k!P)De@YDJuW=7Ai--vkMfWTHx1>w@x3;+o
zl=Tcze~|ikOs$V6UlenjwYJ5-Vs3!Y<lW@V9lWzwYe5ZxDh`R#b`MY897-j{y&w4i
z`TezTpaoZluOU^xL^|@F+>zM)4dA$dM)8b`shtR_UmiQiv4dvl)r?Zq_$;p-8q;Kl
z!E2xZRr&O1PgayMjTU^8ce9kB%B<y3h8IpB<Ym5Cy?D6DVf@zQ?*tY?F^yx>>-T$|
z>L$<;Vd99F#1&`n%jcq<97Vq}((VG~2jsOtf6GhekLJzSQBO(9v`Gn#uNyNIn4*?1
zdmp0Y(R1_J%_Vg=i%`EJg{oiBGjhfkCBnp=njG;dFjagSnxP(V%viV}f1rglvhA@5
znDRr<x1HkT+&0Ob8RPcdKnCU7-N+-4s{*XX^h(E=VroAIS=vr|l`&0D4GeuYtCQVy
ziGeA$l_<u1@`^PCK3i=bX14nYi$XKTk8<%8=~IUut6r`9G@z|TL!1!Ofe>V&4@eK2
z^40Rexao<Ct0LMms=7yfem-6c9=m?rETx~P-ZnWyC^~=Iw%a2D6maEb3BAkDj(t69
zdnLKw+Iz<Bk2vIWxaX+pzsz8v3xP9IaA%4bCX+rm1@{@;t~9zwCsHjWh}kd#Ettj7
zyA^ee+7xNe7s^Y{^0`-qrpEL3ZTYYXQCO~k4Xl(lB~AI)tgh5AlG7JWG0pW#j+)`j
z*qr)j^hM*DiMj_!?<S<=Mw+XF*9^AJWmIRO&c8neK!h~!gjI>skLoPjE)*w>w?1EG
zyL8yCYFbNoyyiqbt?zRZm=^XK_?xMu0Ej&0C0zZa@ehLl)|Y@B&}ncKL$>~WJ-qKS
z-S}G{2cvftRA~`ONirVE;N;CO!Mt1En4KvH_HV0esyQ6kq+la4*lSjY(hY~dgGve*
z-!U>5<x9Fa>Y0CJ=H#Itt!1xoj?Y2)8XO~!_REJY5%aoE`k*Io!Nbg`D8tHX>ND05
zT(#p<UFF8A+u1V(2$>A-vw4-eOk|5NIg@v;aEq$yPKruN&p5{EZ2W%k4>OiWa!?$h
zwn<3egdqgd_F44<Xlg#b-M#on5janQ>x@jy%-z=I^*8GuKdVVE{jl1+ccd-Sva`=F
zT_{eN&9{^>ZR+=I%hM(Tc1K4?Gsi2}gf@QEOHLmgwK;8mhTId)splaAp$ttuA6A%J
z_DBiX;&^iFoq=-6a?(uB=}M;PfynIrD_{SPGHDohqlApo_quUsvBn_o^pdn4;GuiA
z`%vF+^L;GCBPN4YD|`h;Un~39HLVkxX7D=sp^YF`>v^0n*F_un3um4h{kJ^5@efaH
z=|UE3_!b9G2Rv%ZvCl*dslr*!tv(3Lb9wZY8oC00W6-lT<U_7)Cn5qXQM~`er717t
z##0x~wH%re4evFg)z_2>Pxn_VFsP?q5A_d?6if~&bd_m!;PCoH%zIEW`u-$t9E=3|
zaaB%l0tufjrN(1<Eyn>yXN5#Dnhp-QxYcI+S&3Nm-rRnp-{}mC+;=}ME2-aaoR_e>
z{-G_dOey`+b0A!7;xFN_<I3M|Vr)#Ao5`I^ORuW|;k<q5>#ljmLI^IMH^u2&%UGu*
z>^R&$DJHd#|9y2cPV5(qa2}&8?uhrAR{Q-C*AfULa|h9?Poglg7jgjL>}sT!EosTh
zOyL1bYZe5zbJ^M1Wa3;VeTrTslXtil5e`#|p0v!7*UDRna|A-4(>608IJid{9dbSx
z^GSKSsN|2&x=SdKgy#r?(_E~-fOQD~Ok{U;aGuQ>ONm42tu<bK4wggRoxY}Ucg~iZ
zUOY~$&!Dw2iWZ}epv1Whzlc%WrO5^s!VPKwpKDdR<r{_JQ}H-JA<TM<deDi|br7wb
zw)$gM9i3o<KEL~N&#R@(mH5OkNEs^*j-p@+zHzq{*`7!O>oM&3vJNW#p*L$u#+eMx
zH0eeOZkXW^f5k!ciiiPg^+T&Q6)<7bt@Vo-IC*YMOE5J!S=+-{%$x?PuhQwbm)jyd
z8skd3Ds4L{q#%YO%VChGc4q#LCumR*w?r)!4Ie{iku@rl*3SRjy|sgVqe~A_*@@5z
z$=dm&f{p&hLahqtxLadE(<NOROVO-VWggA-bk=57UYQ7-%JTNwo@KjMbS<AkT(Vu<
z>(cf9nVOZ|{s%a~h_&&zxZp48BhQjq%9y$7XL%IQrsQ0$Gd!rs0HEa~VX~*diey9K
zhkfbJgLH0bOUr#jBcLgu3gS>;E402uo@m|^>{1Jd9vt58#0O1E(a{(29%IFn<f%6O
zcb(Cz(k&%5Dg8PYhH@R&ioAd9L4kr2ci<|tey>t60Xs<F@(9x1sv4)ly_T_@CFT3n
zvJ|LIqDZpJ>uW4nL>=tS+vinw8zdv6R_j_jJ?gm^JlwTydpWhJ)a2H9QSWVZ<b|y4
zK<*uzqD+2oP#_<2{U9HrpBK47N<~EwCF;B(+M)Ig49EksMFI(xF%KvY{?K-SH<mgt
z^}04}X0_w#hBaCsp?w_Shz<4UxLqG1F;P(KTp`=?ad>Sn$-4IXD@XmwcEs>p+s_M`
zcndL4Dp+^^3kpT?HuPlNo|xkVJSrG8pmz?5+tA1CW`}dxACnPB?)<cNA9$(WjiD8~
z=k+IkJh5=PVlD;CUpODaYi(Ae`IDN>A1OnV!pqefU2VP3^b#4N=#=TX8(g{R-5?P?
zwo616F<Ah%g!Itz-!u?+C`%h7>8_5r>aPR8lxH||BK5a;lID`pcNgk$B(WdW0sDkx
zC@Gf_iJ<8oX3g<+7y4gyO+u{#QKeg$j^r$7Cb(M>!iboA{~CDh&~l~5A*Fd|ri<L~
z>;{SgvH45Y+2dXoX{vCians&DhQIoA6ix{x9E;vkn#<}}tRR%CbAw5Y`*kc`hVzNK
zn^F3iattGEgz!hT@FAl{URCpX<TdWcZ#xe{T^biZJbFjWNG~@oehVWk0@e{iS+n_q
zG!fja!NS<rymO2V%yzN7pC6N$Q)T{LsDHW^Bj`-3jfV8#h(6jA5=S$z-&=>$-v>=k
zHKR;Ky5a?~lN?JzG(tjh;axjzp@+rvTD`NHLze1$vK_;>NbUCUd%0Vi`9ZVJd1J2J
z3s%TP>H<$&E|AjfWg7CUKH>K}nW1@N$9lZD5gtdRT3rR}wmB*INd~=qtnWibeDmnF
zd_w=}_IrIddC5QeqeTY_*aZ9-8;*R)uuI+|8SHhkE81a`PANvbaPS;>f(E$)xw+**
zyj=8#Qz~m)>(BmicZCwQbDh(qT_tHMVrgaNs&Ez7BrB+1DqKv7{K#EIKWfje5x=-j
zT$`SKvzW}p%*hL1CZ>6rFF3E*txLD#(<xoIfqfGa7|Ed3I+?dm)SAdm`A<33fgqVB
zDbV~|dVmQ@kIDoQ&U3!}vJH|y@rf(8T%AlzmShRWtMTe`JS*dPT*CIOghT$y8=2ZQ
zT|p7gYhE&HyYZE+FE2W$7}Z=A{Mn*buEQdtWbnPIJ*V7+U*vGtIK*U>?~Mg)nb4LH
z|Ail#1?ru$rXcV$ec8==Vw-zwqzQBgkUdtyw=M$B+r{Cx2%5%vDMI0ru*?4FIhz$F
zCG^qrLU(j)gar3%&Ly?o5@tu-%V=$d9fhC1Rh;1sy%kvW9E=n%C&tV=@8&r819aWK
z(LddLC-GON4HcUhf)i7;D#ZDI!ED_C%5bE_M3quf58y|~3>{UZ;!LaI0H9y$%A{j4
zP4OMNkhX!SD7!5=+ATZB?I-%L1Ji>ZkrSRM)N=D5ktWTlKosDCCW@bPSO+4(2wYtP
z?vM?DF$DHRLMb_Cz(_(l0Hy}I#}2GyZ{M&%(|I}gH_I|V9TKc6E2=@>_1)W<I!^O{
z<W-_>XpN>sX1TENa~xuD)NDD3|CRR!41%h`Egy(qLBuE|irX)Yp47?LJU~Bu7)7_`
z%3(K{oR2Yu9cy~k|J7pR+rLt?hSQwRM!XmPd>EIEP(Mzs@2AF<u%?%fB9GoxnpYPg
zpUp@;-q^-Ib8Ms_hAAR=rO0sR=Hbl#<>_1S&oBeiE6u6=px#%~w%>D#^xLL>Ri*J3
z8cz{e!TnpY0WDA0OOjK|Ggq56$~*0HZ(48NNME6qrI?Jo+rb;v`rhR_-ql{sw1w6A
zAf{KfN#_}RH_KgVIxu`SprY(l4Sx6MOXD>OtGYjhP^n5pkXWL<-<+?+itG^@4PXnz
z)HOVZmZsO*_b`YuqV}cW2Cq}o<xU@axq0}o@o>iG35&Al-)?G-6`#z!lyj-9%RGMg
z7ZDKelDELqc!Q8}-9aMbA>AS9T|^0%O)7`;l}rT=kH<_`v(Mx<zf|03(tVEO(WX(J
zu0u;KTV9B&W`&<!;dA47_0KCNf45;?m{0>iqM0E@4RmuPsU^%YVwlJg%V>dkWcG@Z
zeS+uMW!f)J@*<`qjCF@KiYx1ba%Lky$%86IH97oj5oNgf2SPgk35CkQJc%)wCDE=C
zQcjai@7`>r#uNl0B+HpM^>T%o*S*lh9+%Kc;7t3Tox|CnH!k|E2*-tu*2K3Nr~aec
z_Ev0m*f|y6d8uY)cJKF%s_Efp890rkMT!qZLIU~wrE!^B(j<x&aKlNPW1@Q^Sara3
z`>s(2%u^6QxE;p`ee5w<{&jV0D9Zhs1a4#4v^?>C{p)*m-O*RSuW(&}%I&zHbQoaR
zp6Q8Ikze`uKK&yCprM)CJ%y-)F!8>;KTuxT;jJD^xMTyFgH@;vLFyIJ(_?T|P%O<S
zP?M2J0T|C<M6bQw;e8+l<>@oVn#LxZ*o^X#Z^*Igo(Z&PgR=$09=#9H8;v}>YB`+2
z`o^*2H{wbTde-BVA&3II(ck6)RN#o6wb#!U+HrWDLr?It>TYsb7>!F?u`ltatkSb!
zscLFqs!}%AJb_}gm_RUu_!u_w0hU#M8#6ZZMv46qw$bEi5(_&2K|3*`pabkqB?B?a
z-4SBEj$7C0xfhmL_i4?Bd#UyLb(QPob@{+`7c;t;B1Oyx@UE9j9&pqeO>3R_{X_sf
z1S3KRbbl-WTsqN34L4a*-*ucr>sdrd1v2>jKo#J?R#svu8GD%x=tS#b&QWqE-mzKt
zkOa>}aDiN?u+5OZlDXY>NBv&_Ha<Y(FQT+XP|qpafCL<V1%$tJ8L^Qbl*fs+-yab)
zN#+cksi-~?Bsg+7LToQW2EG)3Yy24esdktnb(IL&M{!VmI$vYsA32gC+hGy;=}`J0
zN1s4PG3#g%CAV55O(?EO>dhaAFH8%-S(qUns_4kRcL@g(P>sNRFd{Uxu=OoISo##i
z?|2Y4%hy<02gfsSsT*`9=2&iFNx<N4RG8>V8XvX%*P8o%>@dIF*&2C>>KLtr+s#TY
z3cOrqtFd+a%RiD0%$pGG;Rxr?c--4<PN-=+S}__=0&(`%XTGQdC~(GjVO`C}s?@SO
zFbSd3bObIS7e1W861;ZP!ovyN!4oe3md1|0)9>0>@W&IP`TPY$U_=t?4FSByR5b-v
z2$<)Nuq-9Q^5`bzZ?DEeQx_PU(Cv0FQxvLO6E)0lc|lA1+a?jbZTQ+vH+`yC%jT)3
z&W%#Rrk`)VR-*PX8$@no7l+@e5!E}c<~*;ifv$BjqajRyY$sdg-K^d;(E4`rEL!o=
zDxT4}dU3=4mH9uQz#s3#br+*8q*eH!brwOD6DbL9g``AAIeoQZ*Uf&g3=1mA4By5p
zayYh7T?M6xx@U52-i@O1e?GKpLjV@Wk=@cSTSR~Nc(P@$JeVGA^ve|qD<d!Xhd0V@
zkIJ%vaNcBEr}|9WcNSj8VU5x*&1ejB8b_=F^bF9evYV)aadt3Gcq`HWr2o8Q^7qlR
zz(mk#6&hI=Qme(jN(h1!!ZlN-e$cIdPV2xVCE@_F<O)H$N0rvG<KTgT3}s8{k?OUl
z-yV$dufBMaGW&d4CZjf8VeNJIDF_m8zq<F6s7d|ICs%M#J`P4hZe`LnyApZGKKNcM
zC0uZl6dXEEWfaFt@B$9zrPs7R;oRB>3oli7>Pk4z7josW;b`&pnw9#Jsz+D&9P?>r
ztc>0mK<;sgO5|83;){}t+vnYke%xYS`}rysYxt>9sn*a{%b1$Rl?jcj%s{g75!{Su
zCJeboC8}g3wl6$4A$Bmgnc;NAA>Q;O=Jm`{$~gvO?pSjvf$ZTEE-ZVEmJ_)K3<ulI
z>SD<6{(i-);0!@U5RMl<=)|i|<#rP77Qhe%A~=H%IS8Cjbd;B5a_b6@6GGd0?k9{0
z(M}-YXgMyraLE0mrRg5kJBiNyVig6_nGN#qgyqRBsft^CW<tZ(0ev%kj)BUCoJjSL
zoCwxa4-pT#2@IsK8FHiA(-A2dp&V)D#A-6)cn@I&wj*%~(19MuBf5mO-4`Bik<_M_
z4CayMJF=SjFzv2S`D%}z9{hLAeVNVXsm1lM_8Wewp!45PA$|&U`_CjXUZyiSQ;}@G
z1B3VcGH;jyN0b6bke+#fF}~OzbaH<XzN5zxq_96qZ@&!C{0sfba5giauD&<Jc>7>x
zVcf)-fA{LGB!l_Q_XZe{I$nQnAT)uCkT2|AgEW?FgXOKYllfI909VFVBm_AxOObi%
zM+uTT3IETO1>OcXsZ;##bd<ol$`?08{+%w{2KSOL=YE)@75{!jWZA$vZd^=wcukZT
zzP}Ux{4c@|C3inv)j#<yR(QxXMKtVnuPmVDx?|Uf=}1uD^P>Wen=cFeK{g?8$hH<k
z(d;-kBkzX)5@q*2kdhj5zT8N!`oN|-*ti%Vj@<pl3LS7{y~;yQ!G0>|)r@<+furz;
z*s;uZ^LT?lfDq{8*8mE^bYcyKH3G%dnp_ak)V|!t9woN%Jd#+BfFwpOw+D%;kz;hv
zHreDLDX5V2{Lc)OSVwZHz#S;b38iu8_Ke*(wPt@-2H`rl1;5eqtA5L`ZeDJ)dHGfU
ziI`ovtJVit42;`3l}7~Dqau&|irm+)R|zAjKk;3}0cUejB=cF*hu9<iU(S{#TQS8|
zaOT!?2E1blc*lu6Y!$-TmwoxIMhKHNS;pdz3VbmuZcA3468-gN<O>NKa&@2Ypu*qO
zP!Xrg+kU<YCqCy}OI^x$RG*3m6W?`NwWo24ahEV19(-7CTyDDJ`PgR*wJ=qyO;H;E
ziD#ZLl3G+A3siwKX3B2Q)S5$e52Z%gZ{_bX+<2QH%`pl!f$LC|wNnjT^ytsP0Z~7-
zo_r4)2UMc*tX>%VpN(mxgfZ}1b9lUeFj>dxd`7oChH}qo)nUhfZ}!|6_($T;upCvJ
zM>|=@0izvitpQpF`k}MEYQ*L~2i)ai9Phh)KBdNnr&^1qq^veJ?R|i*kfE2W_S9x`
z*h~5!%2h*LLvXQ@uGZ#<e@28f*R;<TXCt}=Ym%M$(W(LF9?KB(aPVKap6>Xh*x%=>
zudI6<WSj|w%+<!@MHO0B%^N6cs_F4(cdzIDlB_5P2V5<yeg7HvC*ca~*P`h2{@@w-
zo!;ARVA}tn^L!mF$-z&)*uB15IM`Yu?!&#stCSML|J}vAXhKZpKW}h}@v&1wXMEy&
z!Q(ns;axb#SI+0q?#D&lwMAQ6d9#}{TFv$cp?rta{a#p7n{ZNv$(Lw;o|l)1gjYH)
zbopR<`G4M&X6@6REY_UfySsZ4xJ!YCk#JdlNOpG3X&nF=!}YTALzm@0YRkK83%{Xd
z_^a=0{4xQLlMMNNdIr-XeOhZkm>8J0ArIL?GFk@H%?5kXAFn6zdz9UkJ#vP<;!b*?
zl!g8>np><J%;a4c!a@Q2Js}}P9g4lQE$B+*YPB%u3w_wKu=o@Vv^|AJ>_qQgmW=4D
z{O&AD7}~16)puR~-FqZKQnxJD{hemgFLBxYM?Rt7vnpfoWiKb8yV`*6ex`V@J{(At
zk}kSf4K~HdazJj%Q5tYW9S4C|QCotIAPt)`b*7v<EwyLvi>rQD{7mZgTa@jOLjvoc
z^IJNUMy$}2j8RYOo%WI0^r25FH;Xfq()OR(k{7I_U%a;?Kl9ZSTMk>$$t<C{?u9EK
z96G=Sf>mHPacGH1O||k1TL^Uosxr_#_%|lZ+~`*o;zptuCAQ}@sy|OD!le=446XX5
zlZC=K1M9BcFW3BIFo9!S>tGp|bt{5DjtN>62%(9ovz;pX6cYZs(8vc7Dj|J5t4pZu
zAPdcdxa-H2_Bu)1vemdoV(w={!2qA4?T<ke%UrF;z?8C}H1?YzVsYYwNFv`qUik`4
z=2AKtDw2+)=N&`P3dHEmRmi)*-;IPar9RE{Jc=zt8Q^{s4wKDTtu8!MvJJDumRx%=
ze-xoaas>~PBM4t8w5*ZRrI+fsT^6<?ZtsBZZ8ha|QAfcT*!A?J$*<qcE3&{%1i8$D
zG#Lae`1<;5-Y;c?Qc=SOh%%B4XK?t(F#UIHymV_>#@-PkhMhXmK-hFt{V6<AJcbj-
zNeLI-0TdAlP^HJ?xpOnb1s83g3++v4I(7mF@{`!@^_Kl9{wZVkD#f3jZU-`6ThzKw
zb-%XAS5wXsz4*oOTlITpI?-AzgYG@MwFcjD?Fgvgj3b+_+MW0<1;!d&C1WcE&aX`J
zFOViXZh!oliQHcl`uPEUw1Fk<#7j7hLoQ(XCL<1}9i+M|>X%e&^b#GVEDJa2>cO&y
zG`QcPk7gZ{Fhx2<ntd_Fxi3)Ifv{DIh+A4Gj7=pnZI_O>L1`Y%WOj@jz8+m31ai<X
zH&wNoP@%o*e{;$yrh+Bz-DA3>6>+2KeNOhySq0S{X>b0M#-qG+qS)A-m><HNw4ja5
znU`qoMK?K#X0qL+4-{wm=NqjV)b_7Mj?-c&e?91qrYV%CQg_BX+xi-_6E1`|B03Xl
zz13z}$CFeS_LN7sG<r;5-bLy_q`i+^e?>A!Tmx+;W&3}&ewt9eV<s!w&H0J9<MuKX
z)K#5T3y1`*n)G`>Po1+K^t8^@b_1j<?V*cFe=)+B4Q0DQ23-a>+qUJq4#>Olvj9on
zexbkRm6d8<Y00113X<kT(PHE4M9vR{v!IRNqEio6%VHxbphJpqBm4ys((~aa88uE_
z4nv=k&gFC!vb?^{o#vtYGwh%3?$b>SJy#p;(e39^tK#7MmIE_I@k;x+bHJw505V+y
zb`LP5DegSLUvN&5BocFC2U(CEmxd3o-`@X}XZE>5W(hdNa=#fe)VZ*hfhBG6vn#E4
z*A(rRkLGLj(mEflz}<R<5-E32Gzb<KJALUG=@5uQqTSzr8j4Af(h1#(VLT|}L1M=9
z)dIQ$YqjZCB%P_~vA)@2bi3VxzumI7!=kpsvNKKC8#Ia>`#0azyo0Xfu(W?fWWMnR
zQ>{^S<bPrmnkRlGtnITfrHg_9-z>vhlhFn`vce9vrNhKt^v%$xkkfA84SjumEgQ^=
z^Z`zJ1Y`Szg8vKr=J&PS%KHN1bL`2;)jCCDg>~Q?jqR*lBgrOqO{f#sWL5`VYrA<(
zyH)#mqol9O*z1G}`71eJRaz`#U)gtkNGRtQd|g&lJylrCnc075&g=)@_2&j8Bd2%^
z8F~+#WEs<J6}K>&G^Cro8G`?#TTd?or;5VLjHPj6#V$h#oyCsV!tk|D`A(`b2;Kcs
zY=p1W84T~L(B*NVa#wk>KY>ZEb|!9EADk+ZKIbtp83+)@^PkuNK6<J2dTg)%xD}ye
z_5Rid2u@skHcsU@z-=k$*$O^S=9s3~=G?}obSYE+P-oylb`YR_ID~xEp1LL7e{RK6
zXA=FjSp3O<#~SJ#UY0a{A9*(}PKeG#v(5wPfo(|8%fe*+3enB55b)II{j5Ro7fPpO
zRMC^KDptNeyM?y5_H|R#&P0vqLmIS-#jTImaPGp-A^Pz*BeTvL^G&y(E1P7TfUF_e
z#*>@j?s3u0Q+C1|3O_peI^@nhtQJ(FI)e$mJZTL|%Ty^BUXlEl`d)ne!r~?tRd-<G
z{B^6^RT*1&;c$mPL2zizn09dJ<@95&V?I2%C(GfDW#8?{8BU(_B*K4d^<mCdYWGHQ
zw_e*NG!mP&W)y)PN9T75{xEKWrH@@1vWBZe{6|I~q`uk`lei3Y+81cuiFi$Slu^s1
zc`i?gsr;;Ze(xyLu#Z@8lkFhI8)D6~s#>T3q7Jr+!Xtecenaftqmu$F#m79<EcntI
zWxY;En)^oXF<L13*5o9wIPdm3t}%X2;T^RrEdtu?1CD&<58LY)5Hvkbzx}DlC#RwX
zBkP#}*q(|w0@&~9#Ag^bU3+`~f6bRb<wQB9_Ev)opnOKNKJyIM$&Dsywr|V?nV2aO
zc`dR`Gjl%cYhCPuRlAuUq<ovY`O3=c^ziW9-Tyw2JbDpf{<1OE>#%2ly4~Y#J4@W(
zo=RTzf}gL7gdXE`0YUFZzg_f}OP%L){%8K>tv)tjwtm?n(anRI$Hv`jtQQa1$6ZZ^
z0-2RKmWYk=kaqSU(Z~r3w>>z3mZUe%mR{Fla1j-o{@a<D;ydK#YF7g;sxzpM4P8#9
zq&qbjtjv&Oxb#*(&8trsp?_ZCWD-V1!TG;q07JOpRttxo+H|v^knvsZOuAE|)i7=)
z5-eA{x2<{yZHf(<VhGiwy4tO0BSM?!(t5JhN4vkz%zDgrt?{^AJ{5J!N(gDx;gDIl
zW6^Z{kH^)Ij~L!$d{aPaPl!Mnp0e0+)Wga26ti?e^Y;gQOvbnVyL^z`+O08nI}leL
zh3LbT4}Nauveh-?qu;v9nMVm&{-0MN9d_8URdHw*c}Z41*Q~8jL=BVkKd+-#x3{O}
z!M3ApO%YP`;WduNhK5l<3C;>yLFbo#N%Jo2e-a)1HW$+#x{Nb~oiFdk9osb3H15jh
zfUw8yyW<k7TUZ-$ioSj{Q2VD_#jlTKw58o`nuFd!tqxjRK~9ez#6cAq37aofRaK8*
zs2ugN{S)~V-Bb$Iucr+H0dt9qzXTaS-iga<kE**Z<bMUu_5G>lo@Q`|x{P*hZhYe?
zU{AzHnIFNMkp7hSi(Y9+4?50G6<s*~>oR_Pp;i3pp)E%y4*PY>7Tqf%RuaT)`eu#;
zy8jZH`r+0F(f|+5^;~No{O@lU-p=n?5E@xmr!-S>WaiOqcff4Km4OxN-)~?EU50N2
z@oMz&eAh7o3q?_a-&TJ5bk5Eg=nqk)x=uCR#G1I%hURJt2mju|{?FY9-<#clg1dfq
zMv$O!p$(Js|5%{ECTE&sW18C*!`u2ko#{<$P9Q!dt+G(foYkeqr)bJal+z?WRabAm
z)0M3j_v~uthHOYjnTqtqZ2A(4E>q;rD_S}J&y59oGTD!uRRvY8&FV;fdgI?y3AIL4
zUI04KS-qKnlAb&$lxY!dqOzxyVIC5)<~Id@{_U=PWNA-*Qq=a5SZNXVicOb5^VJX4
z@V0A2gDs+8fLE2U$uEP1pI5;qlz$K>It!OJ5#+-wDOtCiDdZfxoV>ijs~d|+GPBpM
zNNe=U35dUK;COP6IBq?}G`RF4!Wh3Z@2yFc9fU02coYak@NbK{|6@P{Y>-1(_19r1
z>GHFXRaE3R8Ml5=?|<nyeqDfN)-5M7@f;aiiq_|?_!e;(R}{j2#kk$BYJOv<qseP8
z`0tJ{wF=$1B6Y@76|HoC-r3K;pS<`<$o=^pJZ-<9l<42z%j4VrQ{VmTo7c=l=PUTX
zzJo8?9z6VSLV)iTZumjh{o@B89Qd=v!2JJU)cvV&`u&4kW&dw4&_Y3o=GsRnlPkQD
z*{vzUjSPSOzfW7Hwo)#@bT+@NLVtedlT$|W|GPDz{IKf(i+lM0)gbdnGtuOIF-Aq3
zG_^-p7g!q+ZT`f!E<+SZhpz~d{w7qyvw&6K2h#Qp3Y@68I4P4fWjOmDh7y@{N%o#T
zaBwqiFKqojfwdI^G5XbPHAEAqk?+e{SXhV;5>`<*QZC{_#p^3~=8VhweQMDVV9C&m
zb>}Q*EFWJ)b2I}Dj%`3iO3|-ig*{&hOnzYnMx1tdPzLsBGR(@EW2XKjyAfJ~6S*~k
z7Zmoe;*5gNRH^SFDbg~a!DO6U-M8EZ^HP2P(@N-5Q_R{#@s1o!ZoBE1N1*;h)(e)`
zZb<d*1EA5tk~;y!LI#FoP!;+3_#|t;{RhR!@I`~##h_b_AT#?{z<OX$PQcY~o%%=`
zm&2Gcv;0cVE_}+uW^XrI)s{t?e?5sOU8cT)!^X37%hxr_7QN4%o60=|=R~U8%sW_r
zJb@>b+k282Uo<xE|M$p|oloJ8OgxO<!_w4cG6!0r1IVuD>Mm!fEX_iS&^g@kt`t^~
znzG|>JIp)n#~XFad=Cu<4o0DZl?68T`;$BNvox&*<HicSwSn;rn0rQRy_6RdbB0Bd
z!>5s{nWZy*s)^cnC%y!^x!Md2P0l(BHscq9c(}MU9AHAoqg(#WzIbp5e8mj(0r*yr
zo-$`u1E_=8j2h#vZz9Z-54){0=_V$GQQ3wM*5E;O@p9`HZn;#G1w*q^w9~xV)CLLu
zK}T2&J<U)tksTx_EV+ETiDo(B=@vR)oDj?u`@(NR3ur=9F`0aG;jmYVSwj)T8Ct5r
zrQi{eF?fIQ-L690WBSlGtd-v8&UIwuIo+0F-|7}O80;M50HxmwZ4<W~JS^PM_Odtj
zz|_=~TXznf(8$AX7~g*pzODHLUh%!$Gc+k_Y1JwT7Yf$oh9MVyf@i$Dm&~<@na)GG
z{GYoh3DeROuvu)>oYSnynX}*aRDx*~*EcmcWFv+6^)<Y4O?VF;+)NoCM;~Z0JA2{Y
z2?SXW;*59=@ewhhr56Qy%EvM9hQkL&3pbf=9%H_-%8^K)FXAXXn;WLWsp~Lc5g^by
z^?k<S+j(?tINPjCMqi_c_+eZfrJVN#eBwOg$9|%?bi8A2o6z&iD=QA3S@47lCPGbU
zNZ$>Y;!S2vCTT=r8~J^%uhH6$o-#W(G#>isnBH~ib8OFVH7yRlxjbB9HT?>egx%rQ
zV{&o*HvOyOZ43Uow{7{Cb;oT6SKF|$tU7l3uZZA*vOzk><Gh*Tyv8zDgqZ1==yc2Z
zErhqT=<32JXK4Jqgv5h$GBPoB+1c4C=<SD2PvuWr{-TlIJJLfrjdhSbG2Oa*vF}0s
z7g<??1qB7Q(a$|9!=#vDwUH<rcgXLEV-B(JSKPBcs(j&Jf6>#^2cwNB2Mf!;PPwi}
z#%i=CKi{O={A}0m-KRxvulxwt>1g5cuV1D0^uh&&gw}1@67&AOgfAZo=fU__HdE^_
zn!pz!p`nplMXsk#o{ZOD@%P_njf|r6r^wk(8g=UE=qTE&#i<Fwbmm-Fc|i40Taga0
zZ%iHG3C#fGpv|4EecClwc9@lhhT}DeoPnrxzwT_Pc=zr!_9~emQ5R1yuMn9qNp^Pj
zh`?;|N34hKCokWf(v8_S#l3(=TAt|LbEbz+`<7!^C}4$BB4otgzI}Vo)irg3_H#?i
zbDY-tmEmW1mRWrNs!4(x`Q|k{-okDGU5(Gl%Rj!LrRC-AtxIW&Q$Io)9jmFS(K1(5
zM#zkQ6k}Gjzlh!H#fcS*7cF8F7H(pt1vQzVri2`k|Bdh46B-)tIH(<a;gx9}ZmJ)b
z!BBYhYAGM9Y<%SVZA+zXOmikeOCNC)J%RhfhteO4jCeP#Sigts#?6~ClPP*1Z|p)7
zB<~RHXX~S#(cJO?k@RW}wHAmfDf!6dPEXru;k$)-*B2UB)nm7>ZJB_1myw!tB}{O&
zu$1LzXtx~JZa_%IYcYeKkhJBaU^y$eSelt}6Z%G5Lx#m-dvkO12iTnJ!qszg<eG7;
zY?_#upbX&h!}Z7*$~i@yt`7esyOiI0!0$`}E_JmQ=_6(u$mcMfZiX&P_0i6tqCCsD
z;P0JoehP*3(W~}27<dgohMx_3fEKj*#bsrw-2o3DYP^4XpeTK!)7K?1Fz}!8a;0)5
zI+{y4IKk?pkF>P36vF%31#>hi2OAs@BoXU;4_#WaXpw^I+O%E!_LYBuvj!!7$*PTv
z%??vB?!LatANKL{*It1)lX2utDgVQi7A>Dcrq3E}SBklAPUuO)Y6!xyCO<PhJ^kRx
z6GrDwvrfzoPE%$mFWuMqBTjs0SDMXL_Rg@7`+2K-@v6$L;^N}B0|Ejl`#zFKSv<Q-
z(vRzyh)B$2E({<gA28Bf>v@jL6UFQw0y$+PkQTNCM+b)-GkBiX`nA(bDsh$=r!`*W
ziA7&GQ3Hd>#+>!{3ogc1#VFUS1g7rVzyH%EOUsmvSeXsmrqHXmIsAOwGr2j^u~%se
z_W5<jc(AwMYVQxuWx8SmYWF+4vl9UQQZ~XC{B+ccK&Na_W94V?I-QkZ#8@UYXI(`m
zy_%RC6HiuF*7B;Y*4E<}%CE1m(9XUPqZ_l7hEF|A(m%TDnOe5Z2D}Pf6%m`apg}gz
z0Hibftlq=0#)OjVFf&<rec{r3eR!_T%A=$nE&e8T*REYf7aJYm78w0c3a6Kc#|7*x
z?;_{mOfJ_dSSE-?%E@XzH8HUP`vdngn;v$p+35*UEcpcZb}c{M-kFtkar0UZrn?PC
zR}BLjQ=S>#d{9r?eU}{rnufeKcz^+S_w;NJzO#TPoXKHs#(Mj%U173X!{5Fcb3T*w
z<4bsBHZ?w&7HNQ4qPbb{N_-8sqN1Xxg2EGuiaMI!+_i9FKGJ0H9$pfPtxsN4-W@x3
zESkeYc9aZ@JEuyz1~J9a*h`e`WHjLk#p`uKK3d^yALIRk3oADsEH-#ga?@6ovcq%M
zWRgg$i8#}ypwSazrsa)C4DBae{{~{pXVv$F;<Np24Gix^v{syt?yHVdcel5|G71db
z8Z`?)1r9S;l(&N_$Cn9Dw6Bfg*3{G#m6w-K(4yVUry-})02#`i2M_jGJve~wR@cJA
z+1jv(Z`T^7>PLkiH<_P{<_|F!S6@u3UAAml{A5SBp4<mK7bO?cb%ldpiH63&Ily7^
zt-&hent08-TC*@CQ*tp&HZMf;tmwi~Y&<oyU1k1Bk$e~6tAdT#i_fjrh?fjYr&6M}
zcnTlcS?ktH37(CoUA%PZrta=;ot$t%i%tpO`+=?sm>rb#yu7>%`<@;CHr=d9UDk-P
ziVgZRVA#ZAM_V<S$c)LVlY-zYiYE-aDXp7os?k$Q&9!USwoT2B)tk!Oi-?G9QL4wj
zw7h9}=gg?+Lr+i73q>PeKIR*6iZ$N@&SkCSVv7;J-#_@CoEv=WyoyWb_eolY1>tMR
zq4MU<n;ZS|F-rQy4_Rnk{P*kJql}@CEe97o;SJ)*uoz>8_5%1UaGiikBsz6PT>got
zT$(rR?Ch4OrkOU!KH4R<zSa?&XGwYaz8a?e+IN&v3@~>&IXNdmHqsnb6mMSTXDLBL
zBOBabpGI757uQ22m0*V*A}bfFH;O_1$I(k)K0G%-Jo#!21Dt~1Gux&FevOVjFa`Pg
zeQ0&;RQ^h7?ogEt&9x*y0qZgY0XDYcmV{TYUdh+vxH>VmH;Z)^&0f$T22%ywg5lTC
zuNK!|y?T{A8TT0}KfdRrMUv6f<fOs$_o1C-+iPPhoU}OKlgDr^>5|%PT92JX_~sqv
z3whpP&x}dMcGvykZcRasH>u<8KX7389u(~jqTmu;Z}-^8M@f4w-<b{;MUfiyMMYvW
zQ{&cpv9Ik{<;N?|?UPIIkBo{n*JFA+micNhcTP=PU6+aMLp%5FladLQaIZCbyn{GL
z5k@{n<H3WKP_38s91yEkd-OJV&2~`*d-;aC-so?R)ZgpCArW|FZaPUL6C|#lnglJU
zvR6u`E-6ejpZCnp&dS$g?b5pKZAd#CBzj`ZvTAhX^|zIbTzQc-;$}-$GHx|B9lX(x
z>+SvWF^INYj(XPzE~%NsykuI-K}R-`1lw_wC}Xj;;X5LLW-MQ|YHv+hX({E^&8559
zsI%hMtwp}&8+nZswC7&;S_TN)w|91SezjY;WF?!GV?l~nXXd{^ayq)Z731xP+r^`H
zUB2%dwx6uWd&7te^_p|+J%X`$k<KL@b<LP$p5ET);~TL7FJ7|buwav-p`Cu&oc~o!
z8P6gV0#-1UdCW$hlKZI8Yn*`W-|so@UB2Gk-JNz9El`nCgG{LaVXG;BehbkssQ}KJ
zv5^s3HzuLYt{)Y9vrt1Q<)-KHCOZO+?&quqc}qc7S*Cw7u5a9i^TI$tP%uljZN>P$
ztRqOI4QD1t<?C5kScboTWlPLWs_G?&CP!_E|GSC`89W;+J<gn)c>X>&m(um1%DTJ8
zp*JS&l%-iElWweYna_P5CK_oOqvo7OgG}^HT0FO9&6?+^X+_{aA`u8=WoNJ3xG~aw
zyI2o6mnl=%QiY;Nbpn>sTpQN=_`*r<#aT&7R}ng`uJ(34EEV~HGf(#ym^8mORDcBZ
zUUr$fLJJQ!2gfOd611fq(Uv)6WXXMH^?l>B<&>o*H?|e=<j~NNe8A$xi@gk(ou-(D
z?a~{}u=Cd2Bg~es4-#|ZOm%D-O-Zck{qjXG>VBkUPbK?UihIY?chn8(M7+WtA8ZHa
z78X0j7cQl(%pPx29+;6Iv0-Fj2<z)J@^vT^{CPGyt<6wQF$lrr#l@ZwvKjf{%<K@J
zZ9C4fbLR<6$b0wiSAb?Uf^?95&mM_#p>aRC#d~ZOdQU%d#>|ygBs|-U^lAT>!|+}2
zsW;8to9KAz)V)HNO}bthq<3dGvYVv(+$_TjM~_}3$8ajr>2+(@%CKAT8~X1n#i8<f
zrJuLApp+%TK<dNY%FYg}D|#a&XHh_5L+Zp(M~O+r)Kq3kz(^?XRZKjNJSb_>Xt@1t
zfh&7fPAZ;&ti^h@xV3B7Zt)5Gnx6PxuF`s;E;Vw{#B{Ag?~omD^YcY;Fgi)F%4Ccy
zH;u+4LdG!du1Qtcb4pS&wY%#*RxA0P4!Q3xve*YV9@KkSnqNJpt)tV8lF|pPJ+Eia
z)U=OO*(gVk;^}3;CyIo=>-=)Ny1EQ;Jv|j+ZcTRI&g@l5mnO6^Shs{l$b>~&_Et?I
zeppfHwweFjk?F4@?o;~)eb~eix$m|LU|5$ktJ>7t?Uhn?+Lat3WqDUKVtTGfgbSMN
zv|b=h*}s2(bSk3u)Arlzii*N&?Ej*<mJ11*B=N$e=n}6MOgfpq^w+(HEu(2WoTj=u
zI@ovZ5_|US*}B7S+1w}W4_%rMh`71AC($QBE`FvjsXP+s^18&q;aF$ZN8*@D38%Iu
zrH%bQ-W2w%_g&P7Wx&2|+p$mTz0|IOAA`gQu2=H@z3lPhw=ldx<Scoh2qGY>)U#-H
zhlzp#!RXOc1yoMTMvy2)V(TWvIvn;}yW`T#=^YLfh2XRafghg|HKIN-_}b4>NC?ya
zVp4|6{M-OU&$qCvBQ>&(`}wf6`=;;(%je9qMtH<rBa!4JOabP0Oi<YK5&**G);D)=
zluysh@PL)~PtjxRQqC;3`g+|H@3l-h#iY1)_ab;_i-Yb}>gm&`9>HV%^}WO_Cf;7a
zus=o|`&K6o+Gvt8MxGKU?HPB7qh?8<mr=~>?(0jLK9GKif3Icz0{@xHmV9R(v)1EL
zX2?!HAaLq0%U4p*fsb4Gvi#etD)9EB2ueyQl$4qroN+dAzQxbKx$r1GBvQHS-F}8%
zL7LCz1y`@ucD3663)cA|>4n<5y2{43SFU*4Fgr!5UDVO>A}t^cq5$=rLYrD=if(}8
zY{rcnH%@I<ZsMs|^D?kCOg)ghiH~}3ZrJnn3t*T&8+i_guDYkb{hoVCRZTw0h29t8
zOji;azR5W@HWr-pgeA0__0k%V)nTwmSx9Ge0$t%u9x-wzRC!zDhdEyB2H1z<6z(Nk
zU4)UUf}LF!$+etr--d)Zha=TgF6Nug)BBc8G?y}3v4vR-Wd~Yqr9Pu0?2nLz^w_tz
zx6@|=g4jyb?>ItpD7vV3B(q99`TXJgaBQtpwV~})^qs4HrN7py4%y6v&}M9LJ-`$x
zcX3snj_8y$)|jD?rz;R>au^unso7Q{e7R8cGClnZoD5SUKV3z#8l#$-`sONjrb7%^
zzwX}NVc0vmiCz+`4-a$W!4Jd<6{k)&+PjtDV0`;%7xmfVX!Crl?XiQbCY<aaWu%1U
z+GnE-{P(?6iAFD(ck8-3bOP=VE>2lUj@i$>y@n~qgZfP#^bY4jzX~s)NpC3i7dY=G
zH1>p|i-k~y#IyUWa7pIHdfP-wlTZKq!6nqBy`%?S`Q))Z?qKKQk~Ynqv2rSJ`QO{a
zYS^-|IZ|%73nTaIo&&ab8=^-$d~x%`SH8YlI{I#3F1^uscZJwe+RYzGGpD9(+bAS3
z=GNBd;ecd`Emt47KUx~P%iZ`0s%{iXWC#B5w<G=CyA5qU`f_Fy_^>nz20%7@SBu9L
zi``K%CXRUV0yB7bqlC$oEh}fen&atn>A5(Y8^cuJ31DM!eWoE#-B{M6u2zkJb49A|
z`zOy4C&+yl>`<j!mCshOxVq%$nZtxf><$Q=qU+$`(9@KinLovd+=ViRzK=$e@m2K~
zNM@D~J>@}QfH(Sal=NbcJFlUkk<b>KJ8MJQ%*Y1_jaJIgu3H}w-P=8uljb>YW1|!E
zjZBjNi4+16PRWaFBaS=_d1S;G!Z3}L#&ol`<#$)nT=OwOR!PiOJp%&Nzt$GJZy$y3
zzeeTqUh$UE(KmP3hHO;7l1;m8*(JSauiHxYpFjH}BK9l@fm6(Ziw_FjnAO?8d0Go0
zOL#!BUd6z`z++HZ92#(-e9oW5&YtI6c%shu@|v5OO^?aLN;^8WKPKkq=E#NIBB~!O
zsrN00#h5$tz)n}BK6><sbXV|o=ulm|v%CH6qk@5NFJ32j&)=m-8;N*H>=$+TXY3~(
zCb<O}i?aQCW@hzBd-?&yYE$Nro*q4u2Ie_w5GfzW(9@ioyB?`I9i7{`!MLxbEhS2E
zIRAGZI+S*!KRSBPPF7a=5+r-rc1pJG+!^vYmtmh8Y#wD>1D=aGEA?;Z<SYv<2ObhL
zDT{dGpMNfluUhvzn%Hvqa(rDZLcEAVh5^Uf>8rlN(W5)d^8sjt>%o7N;4cBlk1c84
ztj)|mqEl70Fn-$qOzv>(UBCw^FP#Yng{-EwzZVadnne3u|2FK=6+v5ct3(95qzL`&
z;;Tpv&bhdebP?4!6_c@n#wa{y_U+q)J}2Soh!lEwXo$T+*gr*$L=iZ9b)N0UVfgXm
zN7~W?>(w+gewj>%4LPf>V1U*zF`1<JKYAn+IpodCF7A<*mUf02nOylonxl&TM;z7A
z|4%-ESV7B9u{AU|)$d*rrwL+<jEPBXz6HJ7THJ%NoL)$JhF?nAd6FhmBN}@q-Eyar
zf5|mp?yV{{u*LGEbrZ7e@dSvrA{9Nsj5!?^(p=)_xBDJ1>bhAUs1ma4NeJ*e1_#wU
zXAsU{pEDVK8RTKqke*O8v#wg+XK)DtFJBHdf&sEk-L0LPAihLC5!BGwxFS_cON(rw
z&P4)-F8fL+a;e$$Fv1pR${32^r+9L{_TEBAHN6Vwl&1BDLNtMiwYGdw$#36y$69)5
zL$5r!Xvq=|F0Ky-P1p|)3pyuNO#oIS!TR$IKN8vhK9Z^FI8l95#t<aJb3{bTd$01v
z6rsysq$egP<rR!*XexJ@nVD_mzHp1;(tswQ2H;XHU$%^`ItMtqsT{k&fddsSkAi`f
z8aHKSXw+-(uZ^Db=4eQ@n=p57+tZMiUM+W+;D&55$T#KC>unz;03lU1TB7?LOz6g#
zZi*7dPC_Czimq#HHs%>lLHU4nh+=+WZ(!tza&kI0m)*O2_apo?H#4d8MMQj!a@kom
z`I;J~XbiSQV*Ncv%)R>(gQbh(`zPG;^<I`j*<S?_zn{B!F;wIm9s{&sDr_|-^{FxF
z?$`TO#fz+QG9*=zy@*n%LxN25JiyrHsX;+O#5l#du*`?&oq=s%O~SejkSy~Q;z&AZ
z_x+MTpV^DgNw6Iwt!qdRL#Ov<D9bAwGpIS=-*4I^sH3fo-5!WFi4qg%@El_z9|6q}
z#YEm&riGq^6P=r)%1=$2e2ykaLGlthrfB4zo;^|smt)3SIy!VJikeQ8B*r?E*2NMM
z60fWtv7R9Z=$~o&L6Zgrq=E61MmRfYT}sQT8+nZICV&EsInkqzT4Ync>Fn%`D9&KC
z7Ct4XQrx_DpI0(gSMqE>=T!ZM95c_`LxHN0TKD4mfq{XILN=G(XKvoOQHhM=MnAF)
zxSB{IbN6!ek0aNcr^vT&PT5kJbw2rdT~KfW&meWO4@y|}A#Ed!ic+qF{i{?!VPvD-
zzybt6hMd#e#at^Bw2GDsuUNM11IjZL2yK@8c0myL7yv@eECA+p8#XBRJwd{g4RDp3
zjfK>3lQ4jo62iykHqlr7(>8^8CR)vf{-1wdVBqKl3aMF?r4@3+5(L^Bwv8_>cQ$G8
zt`v#O8oo!-h4KE8=*WBPJAdU0mcRv-jdM{@DtY^s-K+uZ33Y$8HPy%jyhdo6{bM>Y
zb1Sge@}AzNV>U?<0xI9p)g^Ecb@UW@(_6`Dx%Y|I836YS4FO(Wd15GZ*CGq2uDsz0
z!p%L7j&m1^=yPaiAwlC15Xh|IMh*>BSM615_TUO9ezHO?Eh&39cdpUxZ?88r(po!M
zSvTh7<lGLvafI}gr2;6Lw{G878l6Mz-|>bDDt`L(=>g&g<(Rt!OW(F_8;4E~B3y-1
zN~X2xb{-yzi8_El+|Z2t49*E1;02HQ%8~c4#4#6@V9`~!H`OuQW;A-z5#TnZhXCY8
zm}jX<K>*b@GfhFPsNiB;n;1gSeC;{x6{V56c)9!esSuF#M;^)qOpl6VZ#<~~X-B>h
zF9ZKYP!o82yCGwmTPHwgC`3jt<1weILCV0%{R7BpFH~R2=X!v2cq7(E(nf~$>o2x<
zbusXFl8~@R9n*#&h$qJ8&<-hPGV|rjjm~t;oI_6tw*UFFMpObRGH50!^o>cKIm3f!
zMTas6h?32WiHg@0u$S|UzvkN)x1s3PJuz(J`B}4*y}Y}U=!CxqQubW|19y)~`iIU%
z35dH3f!0THUZJ6h1R5t|R8@{C&|HXVd3vS`1%;hu)+1le=EHx-AP1SnMlwP@)`(f#
z(BCleZa<PB^Y1m7yTVZkNIDDQbfmFP7pKb*VCYRKU8VKZhn!~E4z&OIRq7rr8#=5a
zD=8_-``5yS#)We*E|x!}?Z$k><eT)P9c_U7tgdkC`p*eacTP9B?lQ7IdTX$S?88_!
zv{4R#+zR0ws%!Br5nDT|CrSwx@l{C#qcH`Z$J<+uC8!?_9J+JT^4vL(Aza-?z*M|i
z=pLiqCzz3uA&Mg~rtqNNhrI8AHDfx(aXBc3I@Z#ky^2P4n}tKM(bl~=WYWObhQLaG
zMm|<W|8n=Ig2;flxVSZ2w)B6Phsk-3YBa`YClIR|rsQA(1Q*-(7YJR_X7ux}_S8C7
zaA1Sz-=h@^WZ{)~0^17(v8l;u20}>8vG&2iIMgsgf`b)Os!_}$%|Nd$Sm?*NmxhLN
z^R0GdynPH>#{hz7PiTfsYWUr?yXvxH*|u&yinI(NVKjhr%?e@59$9256)jf)iw6QR
zGsMlw2Ot>>2cS6&RO<5OWRil{ZQ?Nq2I(jgqeffaLEUus$6!Oo$-TFR_D4;xtvW_I
z3~5qbXG$?vmJH@H3Is2{f4}my^kUQ)9)Us2YbRRAeP*2#xk&^<A4VZIZG<?9@CGbh
zvLtdoyCz$lr?0Oer72!hgxm+VK2Eb=D6`q*i2mqcvpN!bUSlkFps*o5l@USh?b;{A
z#OhTX9|sdHEHRk6YjHwUw5McuTqPcgQKx?6%ap{#MEbRBg9u7CCTu^chh>Dc+BLsu
z^n(apH(5oK@3r@{v$36AuwVgzAQ?;pxy<|b@8i58xfQ>l8;LG1U0tzim7I!-O2!yB
z7Oy@0bqdB>=4%@Y>sZ|vvIQZsY=aw4%GDd8;YW~FCr3Q~n<lU0%}1UJ_ah;SGQi{w
zk);*2*dIaacOpkEfe%H=!g$Sm6j%=)tgOLq6wyVkQb{XvwDP#27s5dt_@BSp@F5A`
zjt#LA)y1EPJb93ptgWrDlp0jW`kxtn=ka4^??z2>z?bLrWE&a;XW>)`dAS59lgaiS
zJKnVLi#}jH8`|TEt>_9ej4jWKuwI{Fi~WDtd(W_@wys^+z1`}zvMqpufQ6zUAkq{B
zR0O1VLdQmL(xqELKtP%(y_W=tNC`!P3P_VqC<y_j69^~}N+R%$h0pW6?>XnauJ7mh
zbJh>H?kkYB=9+WNd))Uu#{Abe3pr;S*@x0a;TeM`A>&1~n~1O-y_Z1(r9)n~47d#g
zNKj#A>F3Wbc_=#vo>5Bg3bakpD?rXt0j;JVGKz`$dGq_V*PrgTg#q^guD?ZZ0uBE}
znlRwkM)!kvBxfK1VcLL1n9^dt<ola~w?=pcn3*)7Q(8!*>ZSlNAM}F>F#}<zf#u0g
zY5cc3++q*Y0O?8uxwh7vw=K*+ap*2eg_Dgycgaq8T9$(F&ZgI|Uq|d{pyu!$JaOr!
zkWfSJ`!xn*7Rj0+e^`d_9ZO+z?=Lbda~_eEVa$~P5gHJ+@%i)T<&mvT6#QOY2>&OC
zw-g|5`M^F{2GvgoPhNi46u`;JS%sVu7q}GwG8Ddd=~lZm<P}NJmlhLT)bcL#*1cOA
zKCwewMNcmlIsX92>50inG^DbW&h)1U<1H+VMIQp2(=8QM)gCy+*d2msc5ZGf$lb7Z
z*)HCGgzSLnZlFzQLSEGkpb7vi0hS6tU}fO71Xp;yY`6Euyds$GfS7q<6(H0m+#JBI
zO8N@bWNxQ{<@f($>0iHw0|_K%0BNbHtH(j0N&t%hr;JY?B0gN&1KZG!_V|>PA`WTK
zb5c^q5Jy|u+YJCCY=7@ew*rW9JiNZrXFw)^*+AIbV8kUQT>v3m9Bcr<u%2@hiOop5
z>~aG^mB6aJFDU^mko)f4MMQ2#!_Pc#8$(e2#}k)@g}D~@+3!B`@3S}l?f=^GvAJdI
z#hL&7^Iv~S)4TP5IN)~hosIfG+-^xPZ~ksw_uqcQgG>L@*ZjX<hz!yH8^i61^8cpI
z_AvZkTq$lC$__12Ksz?uITplkz$OKsUu@skfBU0xVtjmh?dRAo5^O>S5TcVL<U)qd
z#gy6iQ-au?@+kkU`{nkJJ=#}n({(1CL%Pp)sq(TT1nU3wdc^%dM2DFnwA7AXHMk1{
zQW%S%4n#EcIkx-v;Z)Uf$6X|U`f?N&>&&bOJU#yrByyG^wD<r1=dTGqP{okys!yKZ
ze+{%Ko{?;+@9F8WtA;>~2&K#lPfJV1erHqwvyc$^|Mxqu?we{fkYLSi-~GQn29VMJ
zLo^KkzZMOV0sMV^)UI4-X&XL?sU(d~Oy2G8TnVR*>r=<)RcijL1g`f2sN370PgA^{
zm{J&AJCaQ;p4u-f?aC`3teI=ymo@C_A)<fB{XdU=ZAJ_IFaI#X{N^ujv#sxn81DJ7
zzLOfgx%L_8Q~bRKR$)*;j+Q#`?;p}?)&BP=5r~?k$XSc96@h+-^Y1$tt&w@om>>!V
z@nc9-difQX%_lk7%jk(c9ZijO`jYO>MSNI0K2~(i@4JL*Iu(#Me*a!{_K;r~Y=?jN
z<9P7(cozAD75Qhc)!6gsvYZ+(4wZ#8+?{4P{`7HE&bs8*7M;~waMl0)<eSB>EHZMu
zA41Rmr>LPUN>v|<M7FPEo)R)9N4KgQ(&3pjvaM#D*M09FSL9I)BJ#?vKXKY}#h4qG
z)}9Js=vX+t=t}Rg)&J*r+mN#T{XkFcaQLrBRe|A=`9ao5R&G5{8;A>Tr(nX16I?A@
zRbCuG?#`zD_2ruN#?zJ)gS-zc?jP??3_RH_xivjjKfQe~dF$_&zS5UI+P00j++ZBh
zL0%)(|0!+YMY*esh`tBphdT&=h74_gct@VZq+?|N{;)&mU}VcJZpk}0@L0SftxR%7
zQ8<h(%#3?6&;HhCujjR-%vh}0nS8wxOvheH%2U@?ri|oh^C)dk?Q3l~xzqY`)XPN*
zhxe+qw&Uy)EBRF}y<P5za>Sg2$@PfRkd6NL+i+bH^a=PbE3?#!2PKMZszzTOh@dYO
zU$^V8?{G@JT9U3bsd!FM8vFjU!a>XO;|XP_75l#$PCYoXs^I*~<rd3+?4#|k{g?u8
zr)}^g#$L`8YcDsHHkdf(5M3cmcW4ty)@bu|eTF>i(f>TFj&;v_4^kj*no;<(Va0xY
zHhp1X22)Lc-qK3e=Te^&682)m<3>BoI^FiKG+(^+p}`{KsHoWYr8Xbh>eH5X0sLsS
z3=Ao)wuVpOG?*FSvf~YF=*+pN>@(N+H_hAex%nFH(F$Ky+-0GDhpzb9%;bMgLw^(+
z<nG%a-+}&Gw6;_=pLvjY!V5nTU}my5i&HAn8!<SzUv|I!Xl`I6Rd4*w$ZAS=Bq<VQ
zSdjPjD~tS=8Ec`oXrH~(UODUN-eM#28Pc$sr{R_L$<PDa4*W-KQ|@-<vc?{I^227C
zAzNg$LbWD8U#&ZndSI=%`P3yhf%7-uH6+v(qIW*fgQOlb(6oU8Lk_tBQy`W>LyhHL
zUt4Q}mN<ZF!L_0P@L|Q(t5?CK^!vV#O*O)*_}Ra+MVvJ-)7i9xh2C*6d3<8tuE$fg
zgN1l3E=!@Bic$$!$L*wWnrc#ahH11bP?<d>%rrMXpbq~$&$CiEHjlP0vdx7Nr#xQp
zv>W^+D&ZfI+S=`9GAcgN@;dGx$d}<Fpi%}i;-ai+JrQHZZfgY>Cj9mq8>ASGIK1qV
z!`h)l{(xZAt3{BA0z<e7;<*QxN0}2@isSNwYlyKS+{4XH7jkEDo6b{a;8<Xja?b<R
zpa06g9_I2H$&n1yrq{I*=WyZj)Wk4lVRP0ZY3$!o&G+Av8t-44k(2c7OF5b!s@zg)
z+tG}hj;cINr$+s;xRO^M|5@wPVjIhbm;XyyLzkPChxGoc&d}dk<Kf2oTx>atTgG4A
zxA3aFft9pZww$Sl-k4AMt3Zy`eUaM$_z{Jd;t&)-bTQrUnXILw*kI7PX{B2*&(W(A
zV4ieuC&*r?vT9%8`7eP%-3t^n?duJWb?<8K=&VhWv;7wr7fT^oL{1Lj3Rp`AJBYaF
zoFQ_66>=gt|LX6zwL>eb#CFi<2gB_5Gz<5WS^iniNg<){gfNzA?mfXckAC%3hxaCA
zX}BHpP3ogz&i=BmE}4^;U3twk+eR{F_;gg~W~txN)=FjlRDb#L(ZyBT7m09x5Br5v
z)lBSJrcFK8_K`Dh?TILx-_b;m_3f2);}5H`lhn)RqvA=U%k~7Vqdt4iOI*?IL7gnK
z)og9=Uo{z{ef-upu@?l=G(`+c$Pnm#ptHS}rB9!Qd`7K%BxH~`CjXQzqx-(2Z!?+I
zEMXjYQgBWos@ODGL$r`g63p{t7@<WPt?*^t?~VqKIHbqgXFn}?QXG6ViZ`mtfAm{>
zt?21)NgZ0K)^!V%k;{2%PNzO1ucUe=X>Ij|yUVXmW`~{-SAzN0(a+iQHBQ^}D1HCz
zNLBi9Rb|){wudCgK=+$(SQNRZZxjvYP%#HZs+L0k5ywoeb)5S>SKr<_9(p+?Bek5X
ze`_U4FDH5Fj?!O!wC;)&L3dRw%PwcUN=JK7!9fxD4{l~_f__|mo{L}DtKYQ&madkr
zzU>(=ur$kp7o!HqTPg7J>MyP+1l?(BYf}e)N1w4mB@`HmB8eNYD~l^Dub~%DON)-8
zVvt74)qJ3;Ayt)^@>$MXTq>;H!4{MbjvS<gxDQx$K%dl{iBjT>!r?X&|J7SB@3@>p
zSbtdUJSwGfI(Ug^=5-4A(GMoKxG&8Y1)L_jUr-L@ynGM0vHqOVE1s8PEU|c=hS#o9
zNKxt0m0RfAd{%mdLHDVesqEr#H%UE}h}$$n7pKP0a}2168W0T*w8DbffMWG|K|Sy7
zEwb04`NS3D<56keu#Ha-zPv=OYir0l%#mPoW8u_6JN8p3*4ZUTIbkmt8Z;A1VeqAT
zOH<b`jz$mb+RjUDP*ADWqi=L(RdU2H6NGo@pOF*=$H7agLxe&UjDs9fdI-b>iU1)t
z&>K^elXf6I7*~51LcL)o6oi}ydL~Wg$`zyS118tL?W}>LP!g_syDD&i7J^)xwMl*@
z=IbNZ+sJGd4sw#LG3Hf5re3CpbsStBUEql1VSFWKg|7g0L*=!)uT_x|Q(!H4%P5+0
zxyENY9J=^0ku#YDB~`y$*P?d^iFYSTdTG1u5wIPR&Wpp3uwk<fkoXsC=cLllqssMy
zb39Zzyd3Wy4;qaY&cjCr|C)`RX9LdzEDB1JdTSq_eT40N`NN6H_+M56zQ%jjkf9<E
z)I9(5*VQm8Bk~VH8cZbs0S5F<2ggz;>d^~csQ60_uoARl^p($ef%IAiB&#xi-e>CQ
zXa*>gGsx*?VEi5*)5W*k0`h@EuEzPS&KE+_g$^u2I@D;0LFY-t*kn~=1Pxrl6pom<
zWH**|p}MJT=wB7*k7|)S+&Mz+En17c%Mf3rb@Jg>^-^CSY>~;W#Z_Ak_nD2!ja(>c
zkSJdLmM>OQ%&micN!6RL)G9laq(+EYTIFBVYu2yWxJq9BT=u;x8x^J_bMD*hOpcP$
zvzg}#3`gPtc^~_k)Ba15ve}{45=Q=O1?CnfjkJm#9dK4YOF2U5u}@ErAj73)#$!D`
zT~gL4X04j^en}<uBNc*{eYF_j+3<DBVbhWCOLMzrITHpX4ND~I2TFOe?$7j-`NWH?
z!*m(}lEBPTKC=oem*u6mARe*-v#P}DeG{<*fy!iT>qcK&vw`w`Hc6)>ja1n}Xj*y$
zv7a6}deri`;`O^%kedLd+Sz)@TV@S#n>w!WzF&Tk;4j)>vJ9bjq<CL*#PkUr3r%x^
zhO?l|ecT4A&4tom6;(8I%<zueXK%nmbQ^;9Hq4}Q&$3EnnLQe=$ai}i_{E9Jk%g74
zKPPp*C&#hv`%TA}D&3DH!dprg68J^=EVhhJ=5v)Bp@>@^tIC#><FmD1G=4Wrf8>U)
zP28${g%R#km`zkKu6ku}!d%TwemXnTH+-F~C;P3ROzUeW_l1uJ2{l*x=1t0EjgDrs
zS4+lY_D{t?mGz>I<pTHK0|$B$Cmt}yHQ=z1OQ?5ro=4hSfS5}EQOA@l^&8Y&bCmS5
z|KPN7*DXYO2)FEz?!CH-=>#Y|&(*8)l(_rY;O26Nulwk)=lfDw<LpO5^vt%pcDI=_
z>)$=%WhN|H<Ex7Dm#Ors4EQo+X12U6P7QMi9E-eVbn@0YtYCR#N>AR2Sw{eN#)rKP
zEQ)Z2MceQ%kIi<_I{`oXVmH3bIJMx40h)Ur-gMPe%WLD!wNrwAHjj9h4+*&)!Ex`R
z`!XAONK1+6wjN3nTEfFBu|xCcH*Fw>QevqsP|l;y-El$j@$sAiYj+SEBGjkmbsHih
z37@S(Lc=EYcGTV!*2>B^%iSK|tHwfKsbGk+qj$R*W7T%cFKM1fTYBCviBp6BZmX_M
z9Wuid^!;)2e)(~_jee|EkraQ)PUW6Dl4Rekq=!LFl{xdJEdobkp7kv_8&ul4r(Eu|
z`WDGo=U%lq_EjYs1K&7o;d#@dAo8{LXNylJs!aJxxBO`b@)wf6ZQqo{CAURdTaKZB
zv5;}Tk@ulUHxXqRY?~X{QwJA1Z*n>@`;x~uuNR6tGh7CxYqlP$$HI(jGD(33W|rA1
z^Ext65vZS1?|)ucSa|#I9KZ9_YvIhPe%En+<(2i^^NRQs`ggp5I|O2t+VlNQ{klDT
z?z-ANbD^4DzO4~@b_dSY)-RtV(+CFs??!LI1laT#qJ4T+M0eG}?o%Co%?&<eYb9p0
zI7YG@lK@}sGB&&DJQ4+HTu!}Hvnm>J0EAyydcK?2`H34&6MMEdjCaw=)_sBf3ih2O
z46Qvim_uXKNvB_T>*SyTd&-F%=glsqOex92AKXsL`y9tJVYQc#kO8!h<ThAKg5e>Y
zm<ZY)r5##As;jBpZ<zY+7eadPGCU5VbGOtv(uUIyCLMd*TdRInorA^TJ_DeP%hPvk
zB-s2it&94<<c%L;m@^OGY~8wYOVZO>`#jU$<$aE67-@-OlIN)3>q&<Q%#SM=WBg!7
zBuZ%}C{mL0<L$CexXjyxLcRdv*|43=z1yf{B0k2s50`jX{0Eib<EWe!Ez03rQ67cO
zHUA)CYM;u6wymq{Eij*8weqvEnLR_2HS#a$M!M4HY5be^y!Li{Jdw8Oyl45NS9NSk
zvW3IQ&l+Q!VSiL!e(lmow}hi1JI&U}eQ`~0irP0=>cFS?Ec%ULkX6BDv^0JQUB8@k
zm(jQC+5eSPHM=D3i!Qck(EnaOHDga~B{T#?uPI?NRcL4mMTqyjF3t~cwD8N@(0lSd
z&pj;QSjbq5zBO$RW_*jNi5M*PAbg3U@P=nv{nB(8#kYEPP@7t|Ig?(+XnDQp>Qy*?
zYpYN8U6G>uyRCQ2<pXX+7PofWV)zYu;{WDvwCsL1vx?34w9&0}9f~)GOx1im9F!0Y
zojd$bY3L_{f~YgKh(0{;Ns1A5qfJLvTjsbvD0}rLC9@h&L^Bzj)K(7U$&}t>H#qjx
z!@u7uPUm^`yS_j0Rb^hU$mOy7z3J}iZCEy|<3aGI2-7EKnGdJx+}(n4o6ebUgZN@U
zbBFqJY&S4La9^c<B??PkC;Q*6_DxqlXSiP?6bJXt`c%#|V~BPeqIK?D-}=yF+T#88
zp@l6qye%P7$WIC4(1(y8hfBPbi=i<yle)?;le@-01bCY^!y(fvT{>+y(0*ikA$#?U
z7lAx7_l|_WB4xydjp#1@en%tXKs=H-DNRv@ihbUs<|dlg=9d)B62C&J0k!M!9RxD%
zue3@m$4Y#e256??ebB@5a$G`oZs0J6rWB(xM{4SqcOLFlIQu#AoIroUPQD)ZHMJ^r
zxsry?-pa$qR~^$RjE8!P1IRr-FcL92uz_FALgvMEmRfM*I)J!&0Ta8HIKc^v#?89<
z_TIA<cskB0PrWhPk%+J}o6qz-?B*?YMZsjg%*y;P4dC0W-vRLv=dS=1toaRN@!n`#
zltCB-{h%`E4BtKF7vB5RL(dY~9DSGmiR*9>5%X_2OqR8`hfgObGG<rd)5;K;#aEWP
z{ht~SI<~fLgT|zj+UqBR(t$^)qCfRG#N%SoBcc^3D{<@W=PjRuo*Yale?MO)Z&vLF
z>V3QNlsC0XEa+i`S-AGr=t9uufR{?YnnkWs;1Kn}5ibrel+4A7vOYN(JqR7;#@sFz
z%Rf3L6w0;ZAQ>|ivQto)2gRrDt*8=m!VQ!<#>K>Bg4YjemcRzSIzUWPl~o39nA^wz
z!bb3PZG6Aa!+9a9>Mg<Y)MjpVc9tAiz~hv=t~%}!z7)y|k;T{CHzIeS)aZaMG4e2d
ztn0enwTaugSAZ#N3>b36{fW90!w9Afc2Os049fcH2y7m43_<AxId-oLS8Le7+aRRd
zlKEd0EVo-uQh@fdZN~TTtk9NRdukg!dk>NmTJOK#TqWleW6i6JV>nZBjq?Ngz!cTa
zW_oOW%dkk^Zb9-6PE|KhOd$YWHVr5&xY4_+aymlb#xkT|`+h7H)c@UD6gV41VbklF
z_ehXiNj2ylJWyskH~Z@&JZe&&F^{XYm6aXX*bos9@Qyy%`#=~}1lx-qaI21VFn7<a
zPo?%R&-7SIY|;32>((&nR;W!Vy;HXvsT4q@PXs+4<h(kNkDx8MwAg<YFT|#7dOL^c
zSFo@9*nKG^i>0Zb<x?>5;dBzBPnUyb$E~Kj#4&P8%{U?@saC0km-b#+PoaSXi^W!M
zzC7_C|MPO1k~K0#VTvA3l&q%Esj-=yM#X0{Fj%D06G6+9T*1p!vOoIN`)(tT>6a<%
zmKGuUojkVJ5`%oKO;O?)RBn?j#P#&TuBqj*l`&5!)Lwe$6V!{s+UezUA@_-fs`k=(
zo9JPSfO@A4H|-%a^{PV6cbuF=MRkggzWF2Ok3bYer8~MW%yCFK2A%dVUwJcJlWos@
z6ZX+>R|&jP4CtfL30~O(FdbD>5!V3J@)bRLv_n}3C>X>>DrkZKaLOADXGY}P)obhE
zZ*+%Yn+4R>2~K~*=6`jo){;hc0Q%@H|LQA6_4o}F$=+p?>il908vgn@<?9DO(E8fU
zmOEFg=gP5C?gjvKr&AsmCY>CdsB;im{*KN}C|=Cl+~WCg#=~hI8_Bn#^+4Wv>aVG^
zy){pl315yY@DcGXKGY}#V4};J#P5%f&&t(uUAXj}u46tg<m0KghwQEJ!t`X``>jP9
zM;YT`=%Hi{oUN$I0rid3H^OocW>J7y-?8J^CkK(zU*Bx~t0b!Q_6`*Z@rExDZbTl~
zue)@Y^K+q75YD;4Q~j6w+DoeUc*cvrWF*e02~tp4dth{SFgF89_$n)y)903^?0Qyr
zPY;5y|18gXLPczMz`7H_Bh0Pbadqh>xcGQy7XF$K^B3N%T&sj*<6%VSE5;5fC@APR
ziy*&l+xFM|mTh%r@<>dDI`2&J;X4VU+0Nm&M)5Z8Q#pX(zMUbB{2F`Z89!=u{-hU>
zxL)l<^o#(2l9VAiJvq4nERn}WHM$xTmV&SHkD@yNI($AX``ONdKTR@YJld9#0OhqG
zoy*g++fHXoD>%A>r};ORc-ol>RZY#+DWP?wdZJplwvn<{9B{J<)#B!7hb=IB@T7?c
zM{r@vRryM8g+6!R0p?c?R{_Mt${NGTMkG3!;2Syt1W8{x1J5#rUJM<A;_p82#Pcfe
zB9!jubF)zEj(GX97hLm2NIPUM|K%wCEl6cBfm@q)VAce(=ol9l7TOq`6A>v+vjCm}
z>8l`UR@K|A1Z6*PX}Mh(^it{g^#L_=Wqq3z%|C(j#FXm?t1e<iz7r4o4m(YJLjaoG
zT=%H$PW}GYntd6VWC=x{nJz9bf%9i`2Lb}jonTe5n)T%8$D9#J1dycN@rn-A({PSs
z;EjY$+-lYrPkZxSQG+e*qO%A!Fhc9%;q9yjC_V7vA`+vAOjRxRXQ|(_s0p}dEhFKK
zBd>@DAEm8V*~nb3-wgLb?zJK*%e<+_%Vcbs_GREmN4&7*CFOok+Z?6f;C%IaBPuW7
zEr!nw=`9FKXRNugIuHYf0!)#q_bAnRdrfMsqs5}6K@vv_0Q7H^gOL#I7U@5JJb0^y
zX!3|H6fF61caGDcDBH5w5QKcl3VSPD?!j3>CJ^>YtGAHCad7{~G~5LW0<lYgV;pJ}
zDwUBtgiSUd*%={Kz02$b6nRy8&IqwkQUANp+}(+9v$8{J1y@Vd89&{O=(QFrl(7(*
zeMJ`O4#4q>qBsd(^?6X==es{!aFP2mjSapFf%OGg7OKZmZX&h3c4_L4S<iW`6sHx8
zu5>Hx%o|I&WGPqP%zC_I;u>Y+C%HGjGO%*;L%Oe<W4Ll36KM{$8Q?W|hqkW}3xKyp
ze48xp)q`m%X7B$pV1Nbt)s!@Qq@gnBUK%IYpaJ#I4gT{|Q?70vT|1we%mNg0Ai6FJ
zA`JYNBzhWm7f?X&ef@-{nukB{Cd>Gie^WFBqBwv-l?2a43)%t-i@!v0V8HfpUPMG2
zDRbg(hQc?n%ILx8$Q$5;o`r+t5O;HqMzZmbb$At7Fr&+(BYyYLOPdT)1r*|Twp93Z
z*+2kq%<}Y)ivZDiQSk|>Lqo9TZMw<x$%q6-l2>>b8nZy<!#e(lPszYHNxh>lQ*N$r
zavHF^1~5t1N;vFd{Z#k#iSBDCFNX{{rlew9X%|^lH;Kj>h>vqG3;fot!eq_d)I<$9
z_3_zj2Yh0btGsRtY`EPw@vcOD-P(#Tn$3H$cC1=|p^ZgecS_ObXQ=?8GahY+ylf#Z
zrS`1ZrLr!%hu4jeDZA$fokt8KBOOfUgPk#%iM|s3FXNQldE3hQ*MZ_*{`ReJadooL
z7C4@j^BcY*0-eBH^K6)f_(Ivfgz`}IF{tbV&sB5J5_Ufw{75m)_q(%{V?xzN@x8!(
zI_N+4g?*RV{a+%K_(-&Wt~>SXK^|Tg$wgZ4sxF;UeAv-m0fZ9CRfqIMA2H$g8zZ$`
zdgJ+Ge2RL$4nQAFpI}!}8(je`VePft+1l?_pshT_3ZymT%$B-e1;0u@>tfQ$sAE9f
zkI9k>R$p_$n@m&KkfQ$_7FZsdKE(XGUT{O7wmC-f1-WfgRx{UfHdT%ql^q*`oSy-N
z%JHCdN0BM9i9~7@<*5hVvOh$J$A!s`J#7~gt-qkT#@-TuAAk|c@EOi$?M==7b8W@#
z2mgk=R2f;I+a+GuQyhT5AT`&$KLW9y0tI|AF3r^_=*?giJXJN5lgPb6n^*&Lr6m;p
zBUjo=Q?ul3w#6#QI$q~x_-Y5X_sM7_tlpMHc+#<Cw;HMP7~@@Q1KU_Ls<Jv(zoq}A
zMLR4$t_yM*(pHZ8fWdeQRXuFKV;gk$P!crMlaHA-*mHctkm!F6I;ys{^zhkdCb6h0
zYT$<rkL26uVEgCCK-s8;tm8F+RDTokg4l<^)=_AQQmljcm0KCICSC<PlSpR$2yIn-
z+_-A1)QRpM^Cb{(B*2vH@vs0BrYz}hpMz{6D`bBF-~l{}#xg^Fe1^2Jys8b@oz0pA
zH|fj(an<#*Lxvh7xrEmiz^fHE5cuumV*^sMG^_v4w0A;gRmXm<f(Mj?^S3Uk#ooYU
zO_|h`oB_hAVMX+9R-;lWA?dt7VW6W`x$8=5WNxBtRblYep*ag7>DO=aHTy0LpTO}-
z15!oO#uEaOE$7lJ2F5qj6Sl6&?UG#f)OZj#-SypZ!tCb63|8_B|7jkK&XnW<`7M&0
zPThz}W=Fq5yy0LWWVcr8lx#`o`7;)pIF)c9?Ir;`J&2+ru^HwDzj#oz;e{a2`jnob
z!k?iprsyKb04+Ad8w8z+?dPU3qJb*Z=U%>A9#M_qU1cH=bAxQ?g=FU80;h>x#%*g4
zt7f=%8WSC*15azpIY>L<0Q9EuVul2kd2Xq1Fg$9~)6;oYnD*{1COUPdJ+5;zBT5Q}
zqoDl?@>Q+<qcyq9fwgmj#rwR@T4=U?C^IQ6{A_0Uu%yc8JMlryJDXbaN=LuqY!0Vj
z@B65Y7V;~Jlj_~~md?b+MwvLs$pLRATBI6Y1V8gAHFuu9S7lq{*v?}tm6nIsisCM<
zc8SjT%;0tcr#_gz3F#_DPSV|ACs(9ONMlNT(SBt1gkH7ub?k!#i56^Te91A5jqW2Q
zm=o5=rQSQMddLVkROff*)#)+7K=*97Zq{<kwwkb=?|bN9CZZTJ(@>j^euprefLOfD
z4`<Ldu3BK6cxn3ytg{($cc!Y~zY#ML0#7DpthTO0+<KIdry}5LZE4AD`uFP3^Xt3c
z;BX$6oL*iGtZs2*-PnR~<@)*uyX^zxc9;eLWC7i81G1hXfKhu&ZS!~{&AkU9mvUDc
zxgAb9W`h$1trPTwcx|1xGp(-%?;`BK`xMGVRc#d6&BAgI73f2dAxpoDV`Gkb@!{0y
zvqLEb1AiVpab?}dUpY&$&2T|$n+j~JC`*)08K}#LXb9pWI+D*bUh}YiYe&|^^v~3l
zCY>8RKSPgs^Q(LLm#G6-vx>Pb3xve$c{yt7Hd)u}VBPJuOpm^G@S$7Wu(3~`p7PN3
zgOq7PF-O_Yw|5dGuFky&%85JB*^BI=@Pb?}YJ0`_oxrE&!?3mK74fT&<73apspkd!
zMvz((E(n4g<ONd%%0Vth*~jmuNgKWz%f|4&a=waE2OD&FVrY@xhfSL!&B@P`G}`*8
z7EWDX<COGuA{cVJpV@8h+&DJWS-ln^9t#VOll`z2uI*x$WIQMOl{fShclOn=Tl2MF
zZm6=Ekbd>+>cxVnO+UX^2wt-6H!|6^MhQ=>J~oJ-hfreXThKj*<o|qA3tiK1XTgPj
zHwyY+y~tnw*Td%u-*U?|6{Cz%6%3+;R2r>3Xx|;lW%Q8KRV5aAlw&`?%}yW*Cd>u7
z>=|IqW1Y({Blz-B%a{RSo3Mga<SIxGQ~?pt<Sz644mnNarO<Ks59kN11|1}MKms+8
zzG2BL;HuOD%CV_2M%BKRDWwb4A?=puAMP^Ny(?#uUCit(ndd(P2iuKezjrM8jcrdk
zJO#3f;_g^(NqR1;Rhj>yJ(5t0H+^gp4I@ff7vXRRGKY|%^@V1ZXlw8O9+VSj+(wOM
zFhL*-PcOGK{O*=(IW<!v-Y8}-|I&3)6+csZcK*Ym6g6E~xy;)~I6X?3z2SuK<5cep
zWU92;S`Taf5>abrbKCGNSuU?^cd4zRV0#Zb6P`?cn&Uq1)OFLvU4p}2DhT!Z1Yx@P
zIlU5*C;<t7{?!r%=S`QUboBe3M6urGy=3NZQ!~$+(6ufyK*6nyZKGVOo?SUmynr}7
z@}4xIgXgXS20UVP_qmrc1%Y##bLnZj%S!fn-I*^E@|jFnsS0e@s7BI>{H-#)j*w7x
zwNYwR4EC)+Bn33aL;Bx5Jw#@>Hsop3FD}Ah#@iLOtbkHNWKPX!(zTUvM0_jvp0Q}M
z=PBZuml)?j&*my`u>aDMDHZ?L1YPV_UJvt93U%w`_EYfIT|5>On@<QnS~mH^$T9p%
zF1TR08O)hf7m;lU&DdS0lRLQ?$Lb*HI+A7pIm+<-l^=+tRVCD>;_rk=a2(KtXM0ez
z_9v?~cb|3hvPJo+6mOWSb>BNnq<l|z9B*mE{wXBHi)^$-8xLycCcd^Cb7jIvoht}Q
zU3!YS3wX(%GW0ixY>fHck-!6g6Sl(bgf(%kv$Rgsb4hJ1WDR$wu0H}EYnhZ1Q<YwQ
zFRl(2$<|6rw|y8B1^58@d-QULG_Xlzc|ImAP4z&!l{z#m-|t`XY2LnZJ5G34)2MRv
z*x8&9fY4#Tg)nfdxS6%3G!kefA5V8*LZMaj59Cq!QJyJAiJ38FlbuF|SEBPOE~V%`
ztgJ>jbh79>g5}a9Hw9bm5fhjh{XoqOl){Y{fhWiA{=*pl7hqLNyZfI(X)Tb6&T9;K
z*}1;oyJ2PjkEPz1kfI09Dr$rlSnYK-K*i95DYN0l5{~OE^1{EWEaZlK8!VUD?jy(3
z^*yYiGp<jJn}kwlrzUc|zIYJClm7(tL4zhS%X8B!RqH)%KvJwPy;Jl!bV20PM$=pO
zn&sI)93wP5ZHB)Dr1%7=?J*lEGb-DH#&b<d&O^OMx`8i%Z``zrM#LSZpyzS6TOg3n
zlr=>0$bQv{hD?NI?rM7TPUF}1(7OT{kJ^fHwL)<B5Q01GmWs!Zbohtqj*!f}U@ytq
zOt<?sru|sk+??}dxAFcn8cpw(UTpw#ES4cZW*4{#*iS3kE~W{(;5tC(oAd!$s17qA
zZEddBatsH4N9bR;%EAZl+I$H;OMl=IOJ+BXXY0&K&(WMNg0(pH7fN16_I+W%h_6b>
zOnKZZ+|AS;Bsw}sHu4Mf-#^dlO8YA8OPhKu%Ckac(bMD_?%Q~e73&ktdR#PfJTH5X
z1_@F3Y}+R<!^?{!7sKw7P(1nqKYZMNmAU(QWz`$^)kj+D?3}!{Y<gGdRYh&3;=D${
zz2Qikn~P^D%*1P`QoHFNviG7d(fYW8=Qaa{RWOROKBH()WEWRydAEU+1(}hT#>BI&
z`{h}}?g;d@28Jn>K&%T1)o8h}Dr?e<%>)LtTd>BsiIhpq8t*l9WMkOr<z5pUwDyg_
zO+5^i;8Hc%k~nq_hD6+?Y^~1*c<QfjbtxiH-ldZN<&7He2_AC8Z=9d^IQo+7@^s{3
z;&FYy3G*1`1c?i3Jwd_Egy&iH(Y-X+{-sRm>0<9{YWbG6OBV32rkT;!s`jex?|~XL
zfn+a?YJEwW>7Z?4qhe_skQp%<nBf$BPV#G|l&C?CODn;c40qLzQ|rE48zoTA7XWAu
zD;(1MR*dxQu5XbI;o24_Nf}a1eZ!&lWncwDUubqcj`>#@l1IXj;ix<xX#5G4i+K>S
z7qk|uxy6@%cHTSt+1)<%c4c+dbmxqie+7R_&PZ{+S~S80J~AF%tK4tw#;t9CD4CFR
zueqf~6x9?!lKk2ARK89u$4$4=MZ+|mldb!U(-|DM{|DYsH*hd$jp>KoobTo~v@oV+
zl`0SFsr}xy%DT$mxcS_l7fLSE6{qVQ^t&qNe?t<?1>dro?1GEQ2{Vn<pTr~n?Ky%X
zGD-mW(X8105bpU=AmJc!H<O^ve_ps;*-X>_BA#7RbH6R~Z`@yaIXNLNSF=xXW>A}h
zTNz3D8Z1^x8&^tli&JwqJsc~FX1hxjEmoIZy$Fp*eW!^`lIPCyjpS@N_(ULYU}Tuw
zIA6zSRB44Ew$!34R$d=Au9&%Z#1(?DU|_&>2SNK>tUs4va-EbLK_<mlK#s<~+}fb*
zvGw7LtM>F`DX+A4xh}Q=C1Z|ZN`_8XA)!)<+IJW$9-_VYl=R_0f;cPEtD*aZfWyd|
z2}E<YA&G0Ch!IPH)iVHF;if;>i`WWVj#jEBzBEe-CF+g+R{Y9Bfb#G&D;<XLLJqBs
zr+{1yez>BXO^>8wX4E?nf+zzTY~uH7$SSoXP7wr{*?ICkRr>#!O<WF`f5MrFGl9qw
zKj3vUnAgYGlvVu4&j&Z|i+5z!k?cMd{bklvR=BuA`(j8Q%6vY#aJdL_2Pr*eBA7s`
zu@3nNfj(<A5$`2%)8;H!jklY%%q7XsPK=;DZvXdED!ReJ%zjp&qgwXwt*M=Rp^?jM
zA-H)4GWBfh{gA0kgStCdDv3cUos|;ndjHhgf%ux^YGzOVpl2h@jWnN-7jjt5zLPxw
z7GRs;SHY48lyR#E^FKFzyI|e@zU<OsfdQ8+20V=|rEanYEW9zjWePF}ZCTS~1^wJ7
zI}e%p4VUuD-8MOT^=fH2`KFUH{UJ@n|4M#*%vpemd6)HlvI8Me*T1sOf$q2#9_Mpf
zQ}gRcCUaEz-r(ned$VD=)eL!;Ilp}_{<9}mz=X2o(Iw$%6_(D*LhaXFU5yD#4*ewF
zw&1Ht8QjH)O)>NHxW^>Px?!(|Ft2}07K9!CHkHrVQ`WafAY!0&V$#GN=tmxdtF?I+
zm>|SKxzBzS^-0^H2E@>wzG=6F5}tM^27EZr+Qy&eOuhv(ffCLWCFL$f{=UKF#GVH9
z2#WD>l&hkm#Nq`!6=+6Gq*2;h6V#L;<lvOZ0eSY!Xj*f^IY+724!Dl6T23uonh#~*
zCAzr`*e_U38Y51jg!fN#Ib=KdmHnN)V7;ZKQ`zhRYl$z@V!5-kv(Nv0(D3zfG|hJK
zn*R-U8<PU6NrA!56vPF~ld^!IZWItoP;hL4nplRfg-g5}e>C66W?q9sE?)n4ss$DS
zGqOmhaumS{Lf;HdhR=haP)=Wns8~Yg1-V-wZ{HcraaPwQT8r}@ap%ULM;JlkD}Z>a
z-C}yLuO4v7zhW{9soTAt$y=`1W2%r1b9><$@U_W-G3-o4YX9alu|$cm(=zliI4Eqn
zhn}f?XhHOYt(lBu)zhK__TySxe-yIk>!0OP;Ikx_ZoCsTk33*sCy(T=gco4V1w$jq
zj<G9AAP!+64FdkG?AxbJ*TP*)-$#Yv-pVeXxn3gKF!SR?BiG6Wjd+$#wr+`Q@<d64
zeiquAcRnxhzQ!bcxjLNiq1k`32N1+I`}-(VTY9+WH)H$Y#Ki;hBaY$ym@Vl%(~|4~
zJVzpdkP8MJ;Pm+vP>glRjGx=r(YED)TCfV6F>{`Sd<QtP0LD=racbk=!1wcFSv-A5
zZo>qtpggRQI{lvbsj){rGV(yr(*D(WyCg70>J~ZV=Wnk~hVX<>Wi>)qB6O2VM{(ib
zsUXGHyOZue^i5dz1)Y>M6XevUs(*f@p&^Te{kscO%T;O%R6?fZo^~6x_?$xv2>BRy
z7MxPf)tZ!^vPPho>b|>xR%Qc6Cz#~7cog?oztF^kjrSg6{ZtY79<Ial6FXM)_)fT3
zso6bba_x}o*cW<)+4ccxhlhg!YL5m{_7|g8hK~kTW9IJ8+<j+YZ#kDr45ZcWh7FOl
z45lVye-@~#9vB!v?*)<xF1@#3ll)|G4^jij{_pGYM;W_k8|>>QCNwe}71#;qwRYCA
z4Ho{Ll=K$k70=O!k#t8tX=?d$@c@Kx1LqBwy!zmszh*0D3lpVh6EwJsUpG86zYZMf
zm-PGI+nld?B|h_W!kWJ8eS+#Od9E7qBGo7+wb=TOR&^#O$IRvp`PN_34@@Ije5N|&
zTiQ!DBt@)MFKqji_0LIn6r_5d?4PpFjC-Z?%LE;|xY8|ORdw$uNTuj+(^`WUSmb9j
zqnD{UhJ~_z20=TWFY_abo;Wf>5VM@F)5gCfdZA^&co7_2UhIpv5y|T4Q=CQef$R6-
z@h9ZZc!|?$w>CO>L$*z+J4{m|Qk46jjx6vOw-|zITH~T?P429zj5$;N3$RFytG|+&
zsDBN)erlooi>)8_l!w!Ii(OrD8aj#*DG9Yo7$&~g131mr-3hYki3tSE*s~@(K1md-
zjGMYIS<c$Jcq5^g)?54K*(^~=-SU!I3U+~$T$v9fMHen{>?G&dyj?d3>X{Ak-9^Ic
z&vX61HizfgNM)5^Bz5X3=b<OT?bXg5nS*G7Ha7N5!IO^UVkPR&oj;2+Wq`4=A5*JD
zsUuaPwA}L?W(K|K-cx(Q7@j-L-N+ds_!Y(-Ua9S^>MzK03!jZ{vN#ISM62*mzd2d%
zjn?*~KJE|S{JF7S4Wx(o{*WPp7BmOt^!xD%edxDnskd|gS&iw+U5vOmYoT(B#4mXI
z@(L$6tJHPO4hynmUYx8khh>k|@$T9HGWI#sN_?0qP5rZ3pqO@K2Pre_B^WR&-(&&&
zBGk!~n#$O?G}T@7A0DOc%g1uKOgR$400AC<Rdo(dUJFejlB{ITQ_qH68;cuuA^wf-
zw&+}}6Dl3siT@p`l_9umY;l^n@h`yb_1;%LafR-4@1>P<gszWgK>nANLxouXz4@La
zrHimMJiwpL)+>>gaYKRUg;ZiXvb^AtTfPczR=toZWLux6Xgo#+M&<^kH5pA3PXS7j
z>5x-lZosg8&ujbgY0#BvkbQ0!EnFLPQA%U;q2%YrzIt^^89JP3TUiaJocm1|i6aRW
z_SZrhXbTp1N7$>xd+a4WUo$nQYAk{B4u*!?tNO#R=BFE}jpD`5514Nne1KeZ*u>K?
z6&og0?7ZaTAvV_M1YrE*-Y7FpV7;t{5IZ5sfmYH}>mk44>!4I-`}OH@R)j%@h$Ln;
z>61M*pgAxD1q&VF^J(*+Xaatl8f_Z{nTnJ%qq+`6mN}Kqp8it4?{IKP+7ZM7SUa0R
z8(Vo;4UHL35QVNW9dyhDfn*uBx|*8hcjN#hKSNvx=JQ3&hcEHJfUJd(UPAZOEs(g$
z6X)}xnH!<{k&LcIB3y_h$w9C}>P}_^B8WA-qQFQYGV#Hd^&DPUZ`PwKZsO2=lnrUV
z8x8UvYt3@Est@$m#9Zb_;?a(8YCtpjOf$@+9$YFM2;c;gV21uceeUSjaAiKs*mrb1
zb|KH#df^>ub0sy{T|M`soyXWkiSxQWkZ%qjJ*0Peen<~H(rX0ftK@$MC$hb3r0BG$
zKa82Z4fu3R#DG-t`Ky0w<Q>PSgEit~<xW07s?A!Ox1_yu^&drUE+nk5ofyv2*{VfU
zUh>WPmq~ttp|?6=N$L?1Z+AQ<ujB;Xfhq$3R}0_p;9*jjG8-&lBo*XHTxazQ04JgG
zv8c4O)|VW<)bm($HexcD>-DMp+@7(TEzeEcNCBn@CsFTB`imUB*6-2}?o7l=y-WN@
z5mcC%RX~cc`KfSu6uZZP_<r##-Rj=U&E{Hr0Q^t&z@Q1;%^YQ`XN#&&Qc&7+Z72ei
z(z-)^a?{04snf|)9%wMKnl)(u&IPQItUrbBtr>!SdB{#nDRp>QH#vd9N@9w$zW?wj
z^6G$lFSAvxe3gsayqtym;*tNP$w6?y<A(4BqQxjCZ?>YEGBH~xjz|3y(h!#ig!Y%!
zLukrHyJvT(i8i0)H_9HMp0ryu^`w=Di&eSl=6*Qh50WyyWAs*X>(??{hLdWN(?Y8}
zazH$@B{&}a-}9=dG9|tK{&2G#aS~K2?I+7sl!3RpxMx12-+8XUCDpN9dJ{3SmYawU
zN9aC3qnSNJQZF_xl}_&^^z-EVr==o1_<&QJe&bULB|hwD%@Z-V32h$BffTHN$$5j-
zLv}oJ!kijF|BK7Val2}!TpgVJ6IWAj=exnL;-NMk04Hy_&kcwu>pXbyZgVMh%ck)V
zLq8drfjh2Iqpi!Q3*`>Yv>p`US*hXw5HehuEkKCIJVrFIgWAdlgeSrYxUmY}<-IzQ
z-{|`P3|d@rU8G^3GfHh%n*nY~7GmDwJWvoEHC`NeVA^Q4X`s)X+yD&CnC~uxY;+)k
zYoH$>8&bM8;{q?MmjycI*nhp^|3OM+Hj+2a&H;8G^*Qze-`(Yialc`O3RyXB<q%R1
zLn@tkdr+qKL2bZ?g?*geZW+MMfu9q$kh^7qE2jC4)sW$PH1a0&3hVhBOMF|Hzl3ne
z;J5Au`{ZD0w&fI}7y8_OF#DUlSw?e5F?53N#I9u%&$XS!HC%kFcBl=-Tn>F{LFZ+4
zfQEOCi?oTH-^E(-Pj&N+5~L(=NRPI&k~#<tSzFaPHS;G1-W!{bRGb8Xq)%R+IQ^7a
zX|B8$^n(E3ah1xd%UcTqi+Ty1n*s3|F_%>5d{-a6_Oa5F_O8;dIZ)Ay)Ml+$-SfoU
zXdgbgT_$wx1PEwA!B8^wwSn-{K^McS7S9jNgSW50lZz?1abqNJ#n|)5OERv|0k}L1
zPaYd6$jJ`fmF>kZu38JE&O&+pth=#iexoF0;g0@KAk@A~APC4l<|-TEK>an_Bd)Dd
zvSd&#+}~*9x~1h^>6Q#TiC&=o>n5s=tEU2w3u+n9uVZeWlB4$aOvySHMH>EBo7w-w
ztOM*3yoklg)0x+T|6NhMY{Gz9%`LV9&v~vP6(ESt39Ua-Cjn1Dhs6MtiHGr3c-cUi
zB3z26z;g?9dSoydW-vkJ&U0DQj}mzD9^-<_?F+!lL@Y%pXzL#7yy8^UZ}DD~Dw~qg
zAAi%%L~RCrQF3LeS7xV)-QAIY)_q>fwxy@wf>XH2*CFeI$^{xv6x%Sqm=b^H%E<_2
zsK4N1XxeMvd?KO5NAkb*p4-fL&0eQgD}1Iqw%+vEk0sZBMC7UjdP4C0StX>94QiC6
z<e)zFkq$cg<~(vn#tjC+9h%q#fxm)J84Z2Gt=f}}8W7n4Vb4L@qu*DL9X1Kbs!x1_
zpbh#nC%fs{nWS%8{IHAoOwc0|e$hOusS85V1zpLB{2#H4Tbqc77puU4ydb?Ek1Ds#
zT5a^wAFJX{|FfYIqGNT)khhh*oTFS3(0BUR_28?-Rs9xs)oe_i1}C3h{+C^nKl(PF
zn#@dc3brJF^wo`=M6eIAg5|c-@ozDbn=g7vV^y#15B=f+<Q<f$Z2V7D+Qc4B0cq@7
z@Jp(Tm$<FkR8B&#oF16NQ8!}q4)QYD<Zl&4Y8#|L!3${H2?KnFPDMi7oByLRSTVkk
z>M|3tywVSUq?!9t%brl-kO<Ysk00wJ$3w%F=Kvb=GWcHXvy<yTs=ih0&aA5w^Dum4
zYNn<UddN~F%j}*R*!mzImfNZVdSh2TL)(1j6vtK<1$x<!>SU?cZ;ZA}FjA_jk`aTP
z&@a<JcmTv)L$sW=$_t6*mA~tVxDf{9*mpmxB3V=B>?<aB<E%YD9CNGY4e+tCu?IUy
z>1Vt>*gH0+z5s#YU*p<V9?}3WINe3&X>Tr%^Jo73VDl0_t-s%sXFAYVgTL~bWF&Jo
z5Iar_>=`8b?w>2{v3ZXpgLzjD_CXbogyY`xM#RHj=D-vh{&qzcsT`^N$r{#>ab7p)
zoa8F@=o{!wM1ce03P<y{$K#qMG4&OCPwj}U<mn&V%oG3|w&N_oOlg@5o3a*_DIK92
zJVxFO__2C%(N3K0BQB<ZL2B#ytv`Xg3p5;pc%FH7{U5!G;He^aN#2A?$fg{`18Kl1
zg~3ScB*FUqrZebm<zIc?fU7V<$Hx<2HwL^>Pn|r`#*pzPZt<J#><$Z=i&<q>MOW=`
z8<3rr-X~u3uK1ajlBtnYn+EJS05q-9rwt7=%sLhbx=GM@>^gN_IrekSP7~V|D+W{8
zoOF~5Qp}jQa%;TkhNyLWFUa%U%1};1hvvKYFI}nK*F9Kb9rxyqAzDORTibvHP3@s~
z2Bztq);^@QBOJ&5C;{m>Z5R36S7Wv;Tf0RteV@@PiwA8boJy$gh*_et)psaVIbVBa
zRS;4}+8laPL$_7&!mxKwm`#-|e)ZYtxJUO`czc8qVbiaLeQ7HoC(Ig9^hl>F$4sUM
z_x1kj>Z&ArM83r<FXo%O(yGS%I6qD_-92vXdP<wCD1?w-dNR0?=4_;?OW%`^dRGe=
z6FNBy2!4_FmFUYbYh(_TCFKZ0U7W1(;e22E9)04kvg^j4_}5%2-3yF%o2j4gQ!RX|
zDs9{wcaVuCZ>9dCt3<!QB&zo-bAhmP)(>7+w9hVvy_;I7Iw@uCes;FFYk(F|Z{Z%B
zi!o7A+SyBq2kIXD+EB>52wih1(46U$++w`Bk1}%Lx_T}6wQQjM(JrKqbZB^ZVW%N9
z3A3z$CchKJMySbQK6%pg`tQJ_RXuK$WpUTEblstuIXg(KY?^*M?3}E==!8iYZncV{
z_Ie|j=KQyay-i7I<w-C4lV#drRI<?BqA*kSAg8~JZEmNf^XHx3(aScXP-)|vv0>5P
z;nj*7lhQZ@sd8Gvl_a}vXBQ!<sF}48C6mCgqW$IP25?7qmW(|JRZhKGMRatNJVCE@
zvJzi(by6dk;T1yFL~NBZlo=g~vCgp@e~G?}NSFPC#jI=KRiy8(-65@gSQjB<kM0$}
zLfhopO&OTA$M|Gr&7p;!z`$^AV6Bwr+;r)Bev_F3G<(87;hu+hyWJ#r^u@mx)2;T`
zc&Sn2(QpS%R{5hO!`{Nv9Yg*1orJn8UbFc7bq3t{$kk}pTa#`?yV@$*v2rseAk<U4
zD0?jxvpXe-7bh-I->iK2nxnhrQPrrBA@+`n=`UJgzo|S<4f9t4VQ$*@L&r3^C2103
z67?A?oBB{rUF{R(Bk|tGI@<B8caXANW5x95%Int6JBD?yMlWSiLxe(aNt_q0o!gS9
zRIyXKnzR;ngeCaSzTXNA3ndJZHfg1zgMrM34{~oKl;IaxJbyw;&<!mMxs{>mF=Cb4
zuDAW25NLHBN|KzZ&J?P;Ewnj*|2Bub7fuwjmtboDynHjrN37Ha^`2_>`DB=K*r=&k
zrQ_fcS+}<CCx(saYOf;au6hX;Z?|3l<o$CRuk$K0eaw^d>!43xmdN$!;0^1`FphTF
zu_tFDj|vfe`ncsIEGOvI>}(tk+NnF_h4hn}eC1y#?12|(Ih7=HsX?JS=AehOySJ=E
za;zZrmYA(lbl8!S)=k!l?|1W5cgol7Jn(#--Id3iJud5c<VGNdr7o+7HR}}T0|26g
z<2aY3Iq0i|G#}nyzEC;EiFD@KcQzVn<cqYf&t^Ny%$&{cRFQrg>BEi`GwxE_tz-$-
z<Nq{ph>X<yuVxFqSAjO|=xdw_lW$a=ljgsAO*s5ejO1WonM$Mi6*k7YhUFv?kC!|%
zsK6BD*~Kh48*i5!sg6(MW996QDtX(`PKBEIu{vW)#BkgFjQSH=={oEweX3!RM86bE
ziSs4ed(6m!@AhT<sb%IO%am5$@)gdWG10JD8UD+1N_yoLIx-kDKi_}DYuUC)YMwCl
zOH%S%thpJRC62lZMT4tV=8H>JSG)PU-!GPmT~e-Hki(Gqx?4VQvBtP7*vN#>^cvQY
z#L;E_(uPx*c<@%Pt~l!z4N9bwge#r~c}|!f-0|&l-+HSiG_i<lx5&0W3B4x0;=bBn
zdW&?U<$ufg-#hrm)w|>)wsmNTH9-u9enAxIntc6m`Uf~>VT}O`nFU%Wq80D@L`*t`
zqky!L!u{wMlredAYHu~-G4EPGY#b<-KM>#q;_U;+ig^m_=MEh~JzUW;(82VMyA0N?
zVbjBza-5ELmb|rRe?h5h@7Biqey6)@*$MraOeQ{06yyY!`%hjg{<2Ez@)fuj`#RaV
zM<Q_bggV=CPorNN9bohV{2}GTYlbRy%1ik~o6sTfh(Bg~wB)@*+zd)^xo{2k^w3O|
z_OBi`FM)pqUe2EQ<08}-7L5O`y1@1H*D~W1H<*H;iqCR#CajH*)-s^c&h%9oBw{G5
zrw7=7U|EpKKDp=U5_P7tw8)7vY})T-pTp}>!+)_Q;jssvQs!)o^3}{D`f<9+M%J>!
zX=(4;`vdTJeFi-O%pXW=!=wOh(#4njs^cke{1P;(HGC%nZL0_vRL6|Vb_i0jp4rs(
z8O@b3*c&TwwqqJdi1A5^&hx1BDnx2P`eDT64}u0<Fq<9eypW!9nw!*T1>33JX2ek`
zKVEW<AXK7R6h(F=%Y+41$O7ZHbwM@{Fnj#vksr`<1H41%DXRA5n?H_WBZ4tbGt5~=
z>~|r1N>b|q#}LIlj4P{2(qy?O;;2vH`W+t2Ut8ma6W!o8p#YC59LDVl-b5t;+dxPo
zWt0khEL%ZostD%NwCK?&{1Yv4KI5_wylXJqnlZlbek)iW=V$F4(xWb8oM*2kGqnd5
zq9Z(Rd%b=y!g4FM-;ec{d+bpoR_-ebMW~g;J<&tY(9GsH&#ZpZwZvWoYWLd#!YIeJ
zpr5OIwH@)D45&M1{<R9tb;SaHt?|Fy)|s@9fW)u|su3~?<=BCZrH?0SaKB!pROa|I
z-wdK4{3;G;t<8<J#kmikX*!6@9V(Vktt$8P3=!W1El7iHYu&1v$^}-7!;Yc&E{^!5
z{9yb@_D;UA+V9hpyWKq~Tn|7M?Zd1Oo=6w{U7Xk?s0_RVfp~KsKo*TS`qcBNKM2AY
z+4O^AXt$p2JWwyk?B^8iOY|+rNSTEnZ#X~E5T7G3P-#cbVlHze^brujCJYg5f=+MK
z#{Lhlg$KSFxp)9d_pp_u7L-2;I*&j}8bO_$h=JZDt`k>}5pF&dW%L`K(ad2!<_}VC
zUIbd<%xZ(MYS0}+9*=$r=-Rbw3yid*m;9AqhJ}@!V1=U|pjl^}D0IvKGi0_-$>Q=r
z<Y8m~%f(HyARFc&H(l!gli5SN+N`OqABcMUJ2!8H_;Ya*jiJCLA~7jGN>-44I)}5Z
z>~=i0L&-Bm!C5%6k9N?papQN<1-9F5F^n8|+f3MBtk^eP`4!Z(ybkw67$R_-y|-of
zFPuj-0k*Z?l$JF_>jb-iv2WJ&!p&&0dvLrlS>)9o6?hn)(P&ZHN_U;{hlSX>dpFp9
zW}y=R(BY58h3+hZ(;n#qa75?*?@0?JuNX9V(ErD1UH>M{5ycvVR7i)D6KByst`UAd
zDExNr@#1V}Z@?KZ9}T=lQiqP#PG>$P7_@J1?4p{W{NL2fOx$7MA{jaq+I2o+*F;9>
z5!!8Bt!h58MjqT9DszlA&FV4$v{2m9xEEQA8ecn~j!?V5i_SExEP>$_>NIVE{^3&y
zqKt!-3v~)7vaLzHme{M&1rk*Ceb_CO%TpjDk#-39!DA+)0ef%<JNF!|BA}mXr{z@q
zPRO7`&PM521?Tahs(l$~Lxg+1y?z*3CM<g>*ppJpMV5OsVIcG-c7q==4D<}7dW%u$
z!h?6AWFSU8iGpy*CZd8(+6s)JSUCq(1mvC-Lhfz8uVW)$(Nf>A|3XU*MdQhpm9<WD
z5X3wjb9TrLUkIWU`lne$IquQS-u9iK3z5-*!sTMaQaDiy&KJ?R_vK|5c!tG%mTfAl
z7P<Mhr;;~P7=;LuP)!z0&xO_vh<j&vV`JRB&Z0Q1+o)<Q77A@pCwTPK<{@y(R%Zs^
zV546?e@RdB5Ac>yD4s~~4^L8Hh>P}{S|i)a?yM`m?%(u&GIxXh=P0VI=jl@u-F23{
zGj$+li3SQMtF@KeG6L+AAbUf9!TX<vAP1^1WsIQBQ{?iz&G1Av=5v0_K`^z(K&@x$
z9t|*lHkMtH8vO2(;v|HS;Yg-J@u@`Jebz2==0ix)KIaU}Jskt3mXisH#)p(wV@_Ra
zgb79E^^oy!i)2A6Dave3-S1w|BCmN@3S_0@M(2)AU4D2;9p!bP;0cnBsThEV7P^1O
zvs^Gtd-<|f;}pPuXvbRm;QI~aw2W{z@os34=m9+u(Qs&&{t3J1YoJZSd2=YVi0{f=
zSB$2t(K~XM@=@qM;bq{DSd>ryL|A~26JMd&W^=I))Zt>CNHTb}yf&{EOeuQhJ!{e;
zk0%O(+*1cSyE}0DG>7!9p-yG<`Kx&WmDM%QFD4<$2iubaG1^rv!M|5QNmsq)60};W
zfmBrXLt!=IeKSh`^TDG;Oc;`jR=0u-4Ekuchtd)8QvWe17n_4^5Cr6zHn~IR!I`VZ
zcyTo@hdIjHop~>m%J=>a;@@^l72s*bq@Dla4{hcq_trd5sX#jx`pOT27rggN9=9dY
z`(xnD;~wJ^-ime7dRUwzvSeeF5ryC=Bgn?4dPk*p!47e~;Ez=+M=Sq7s=hm(>;4Oy
zh>+}r5JFaDmYIy~y^=kWoxKTJSxK@&M)oXwW{Z%$SN0}*_MA`m?|ELYr~mF(ci-{(
zzTf9u=en+Qj^dbJBz*hq`(WKZgPj9?Fijv0Rzy5QdR5kj^5`#|(rBbZLP8QAIb;H5
zk^}7)^2vhFDrUTEuAo^iNzB4H$yoQW#KZ%A*q0EC48T;TfmeJu)Vl4)Kr}RlqQ(v`
z2>1Q=Yv4`NvJxSUHevc*(4?Oqs*&;wgPcIG&SojqC<)yENZSD0l1|BmppNc-=V$~p
zgW|7K-Ia!txz1gTacchuL(Qf~Z;WTOCkWJD9I1)qCx7@YKl8`^63{|g0FuWJyBfib
z&cht#p?U6xc(MR)eDKhRzZ~k=VaSI`?`o<DyRn}9hL)O}?Z@|GNu)a}fG_VkR-by^
zi-Wb5-6-pWl-4Nc34Jg(o|f6%vyII4IdPLvpGk(dfq5u7Zfvrh?Tbvc&ve;@UX3s9
zNg-{qk9jl}BbD7fJb>ihLh1ry3W_{UeJ^$BMJ!tbTh0wC!E9tx2~0YJDFq*p0jQro
zePVu#%&+(&LCE)dZEa`OG5q4C81z_z;b^V59V16m>Vo!65ZWaaK2;PP`;8mI2&eG)
zs#Iw0UV_iMjCr<NF5H;!QQQxmpD=DfZO!gJ!)glM#9E@yK1~St3t`{{jAT`<v9^r3
z%RH?>Qn|JSzMX_qu{SoSCeh8(lR|#$=D>*a<VIikv0y4>nwu_+VTZwq`w$D7xT@NF
zs*49ty!7-_NqvNr)}WyC^-l0|q3(|FuNY>q)90@8$!<y<7ioL*fwf!9Ztw@eMK>hY
zV4*CZo{0kCqM~0ce+9JG?%Kc+1gYk4U0y7&Fqa(UyN`Em2FWEZCWN4+15SuS1fY!X
z57d)8ymDaSo6z6$J$}cPcgUVHWaA3V4Ss*AaQ+~%TN`$vF(`b>W{^)<sXyBqfK^<1
zoX8li53ldqpbQNmt-NiDyv*fq#Pjl+5?{`VU62g7C{inI6hioRJ_Gyp>Rc5;CtEX%
zm9iIftFn(gT`&J@qip29ix-1fD`v9va_+OOJz{(~_z)16-a+jY<<<-|&(BN^X#Qv2
z+E#h&<Y3l-Uz0)+xPMXpkn!n!<^F~sGTIB*$EV=3VmU+1SR0&Vkqpiw?0AO=yiVX}
z&F`p(v&i-k4|1dW|2OnW-)5)?hCXF|7-3B~^;n&HUTv{k>Wc_}?`aG4CgDb$$|tE0
zE^e^%wXFOyU)DI1uX~56s4Ka6!{(D_iDh?aQwM!w5twJ~8pkWhC8z&4JyJ4|FDlWp
zOoj}#v!D`FJ{v+dN^V}R&x|5LVdZaZUAtBi<YwPw)6M|eXT4YC?*~{)W_XJ;UJjX<
z3S;7-W?_YmEfzo<R>OoFWJr4M4Wz{~;9U#uXMSi(uB+H67JlZC|F7Stm%#b}q(b|X
zzgQpSFV<Hsc6E79);FEoHy$OY@%??6f%vv|D(5_rpy1r72cEh!nPG%wGzPcPT5*Gq
z28svyvFi$gN5D%1P0~f`V%?PmiQq7P1Y^5Q`@l*(j(NKJYt=Ea92wj*Pd5%Dz{;>`
zzp>9)_0>|gA6$N=`>zavrfe#zVM*xN)uxmMhjBINBH%7E=6a$3Bo}Frh7oxztbn&?
z;{Z8mQ1Yk4j2lbnq<q$SY^9#H<iwNeQrvd%GL@fdX!$Jcn<I1!3Y-SM?72A+v<ICh
zy=<ddDby?aIU^`ARH`6SroUG}@#RS)mnrk@9nFKyHl*h?upH-(9YvX*st^0gh+V~V
zF98q~z?X;AsNi<cD+AYh2dtHvZ;u>-d5ca<Kgp2flmHD{hnr_-FJSpGvT^`f)Pihi
zh8g?603tYcu~lT-OpRY?14wwi7Qt+VlAAb)<L2(D_XfThE6lskN(Av$9#VRVIYobA
z35c57)J5ef*Di<JZXt%A5l8I&cX7%*!&K5pIjH&1YpB^fp$kJI2HrW}&1-x><bKs0
zu}OOgw%XJ{!!+g1%%?d`xN06idbk|o^Z|&SC{1d3mj&HitI%tadx!J6<&Bgf%TPL}
zeXBsR3te3~go2I?zosgtuUO-iiHr9Qf%OB>F!#<GRd5it(nO0V$&b`-wwD=b`ZeQO
zk!Xx`$wPN<;V#grCGkF2pK9$1I>`$l(ROR13Y7K#D?bPHdVmYvd)ny<jtl*Y6_zaq
ziwt1n@NLQrgo>@zOTv!8&#niE(3Chlw6tQ9Snki5gZQ9xvn)~|&>%4+gfaOStZPh#
zQON5^2E-FN|J>5jU{kLzM>t+-m(FW5nf;X7OAYpy{`r$;0VMfj^~&RmFp49T+|cH%
z0jHTyly7D0m8=?Iyw*W0OpRgwUeO^O8Tm4gvShi8XX(?_A!zaGJDE--&`F^4p!oz5
z-o}4R0qTSyhM;DwM^AWUOL$!-&pm|hQKUsK{K7T;XF%ut(0_9uEDc5%cRTGr^H!!l
zcc?2_cn7awJr0h6yg%Yw@EH!q-%Aw;sGN&g#UfD_(b0<;yju^~2PgFrTisi!36m|M
zcGc2DDOx;!*nbv+NEc_*(Fqk7mGD5JK1Av@MAfY}HYjsXlv2-IUo0do!W|i;rUgCh
z<Y@+k66Mz^7eUi9bO%q8y<ymi$hKgrkc8m=b3)Pq{Z>qdL#$!^T-$lho74Hh;#;c7
zoXOW~Ig<4D`pCS^1XyP<?RBJ!3@Ad@%p$wbYik?yB4LaY$iXHhA1)7`KwP;e^1c#P
zjZgM4sUJoS!Q`&0QE#OG4`wh#7n$v5{RBB9!R@(Acyh_K&6m9W4NoCKmsoG_;uOKD
zN-S+vEB=oeSxejMPh>Pgychv%$&Li<9ffkP!n4?(?w9Dj{Hi$i_wT{z<^|j7N+FEE
z`*hdlGsEt#<3P7i!a8(Y@mkkJX1xU27ko13Z$E~tLXFT$a8ZfW2>Kre3qw+psRu;;
z$N4Wkpr6XnnW&219K8GFlOJ<IZdbbtk_3ye1fu)EBtW$nSR;?#5^9aeR_t4(K6l61
z#dm*DxY@6^=aO7}rlbSRwBB=1r2*Sby-cs=L^fb@q>%>;^Dys?=#)=l<l3SQ^K%zi
z9_^)@aego>???)Qwt&Yxs>VcB?67)n0Pt-Qz%p2$xHh0DQlL&65OkeP<sCYBI+UUZ
zGq+7pEO?=G_vpH@9?jGK7I72*z1^n)UM4zH(su74o}BeB&^0`gq;DNlr&<Bl6L&AW
z3_Y@m9M;t4koyZu>!2%-$v3h59O#w+_vN#BcMa<R^+8Xh17ELuZRP9>h5E>pk^^;)
z-BmSvv{_GN?A8MLpDCDi$x|3QVDtm}hV#N7oSGaws8Mg)G>6%ZjD;3ipnxx|%^rOY
zMfj}Z!|<}TU(XWDp<mNFSmrkyqG-N`R*WTh7`A+^%xhMX@rQH3{Q;6ApAnGqZ@MB=
zx1}Eew_A^LLRr(RSmDu0dB||6bIKqaXzMVPAn3>qN=)xDw<kEXgdgIPRWmE0-udMm
z?nE(Efl4taao2u1feqxU)I;CH2|%s6GT{mf3i9$p$e4dfh?X#`?!DA42-7Gi=WKB7
zGcRtXmO1A#=yvuF$aC4OXM&;NgL?!ED28jr9Nju@gEnVSE)Qmzih(>?I(Qc7U==Bp
z1?N%O>Q4Zu?7aKK$h0k2E}P@eM&P8-400E2!5efmgtmH#JdA%9=gkozEO#46%oC3q
zO^fR=$*UJF3&EuPcJ&K<vQj=mpB#I?e{_E(l}I=vsqzULsK*e?AsI^q;%BLJ#zT2b
zmel)m=o6bA;(3|k^LvyAz1i|BBpDPQ+?sUyR8(vQ?*fGr`18YI5@n0cSp(QusUvTE
zi1p~R-H?IFQ-3!f<)pYI|AHxsrV(Fq8e-dyu=`!%C`woijB0KbGAzbh7ggLAjE70P
zP(C<Y=+IV-Sq;NztyBa@Aj`HCdh`{u$pdL%^^;OvwcFkjvSV*yaZx?yzb3xij=6!=
zYTKS0tugODQ4Ei2rp%0~f~TuXa0CR$Qo6%Wyp*!#6PM66FjVqPHvs0??cF9pbWnE4
zvAgxEe-2*_u?hKe)7I`|ZIp?Eb!q$34aS7EjTU6<jb?b$fKKn><}p~=#RTKGEA#ZT
z!5gz77zzif5DW^rrB#t&n*(@?*duO4R;51Y5^|D1!<I&Yp&E)}<5ypH8%_i*JGUf_
zfWQ4qt_nCfGl-ViUk(S}x~SZ&1fR<ns%;oowqDIWaQtsvS!7Ud=W4xZLD|bA>6)Qm
zNGHN-g0Z`&$@9G4Jpc$I2^q*!{o`-;!LqzyW%C-l3-sMTP?~qg$kAU9R8zr}nVnlo
z;|;%nF1hZ^&DkH^Q-kT)eh5yG46oX|1soIde33DVM!)q*{c#v=k(Cw5Ms3Jt8L(2&
zffE}!t+6?+P)p{dKmfJb<>N2f8WrCT-_|I^{_q~go%F-}3G@9hT<D4LqPKy5SgKR+
z*qefb4WnyvF`HUOftQbLE7TXWpy#?TTC;43uDL0fwJ;Z-z(-H9XC~b^B%c1zTCXSq
zJSL&16%44jC`zSKbMk|uy{cvd)(tqGFt*eM?<G?#wnDWu^xcM$JU!T4(u!w`P(RuR
zaq@39ug;X;n)*vtfYZp~z0B>L0()5D7#hm#Yv_?W_1t`LVY|%~G;$<+aIG*4(m8jg
z>^FP;?e8;!lREq3xJle|p={0*BGCSgki;-N2zu#tk;oYUQ*2B3rhX~cJz28^X9fAx
zmmC=Ru{ELldYl3Lz#z*<Wm$_re95*LI{mo`^2RBsRmku>WCZDZ?DysDJgz$iupCNy
zzAZeu$aah+8=(mPi=#CVM+g2FM-{`Y@`3_ObBf>Owt4G8R^pr5UDOz>>2=>xOVGQx
z_zV+X90Go7lEWpg0q=_SuBhqqn1+wL%NRjf_I3e9HJ^*SRnJElGPMtn=w#g3`EN3I
z4S+_-FusX1D6a{T?hB~#kP#sA&*fAyd8uUkzZbv8j_)#h!0oeahdAEeb2(6w6AgZj
zNTwVTP3OO=A@jru3J&sH!&2*!;~H0IZh9y@PwPdF7>O`D^zrzD{rh%OJ~YT$ZW5f~
zNs?rNnZstSPY0|%O!*=D^y1f~Vcck{&Fa__Bu|-l@jT~tvUxM9BR}r(18x7y(9ap!
z|C-cGmAWi;UhsX+#D*rRhK;Wm=d(Xb{nDb~aSch7)!@V@B^6x7x%u*NV+`37!TPbm
zhJ*BymgMHmOo8oMRhMbEEZD)4%dCSu;Kw{s_B5IU-jfAR^piXL!1{rQ+Fa|5frLs7
zCVs-WiE^OK32HWHY{NBS>Mk-nibU8&xhK<XJwD~g!ij){limGvxz0kbA;LX~_mzrC
z4>H_X@x1hDP;W-;C?7aorpZSn{3kTznkZpX-mB*+241E7oi+zQA0^H+gFgac4$db~
zgNWXo3r=x6xl+ilQ8O!6bb4`hKts$1gS~7&%mo-Ma%zxCqV}Ob(R5sNfq8w%bSAgi
zQel0MpKF`X{zlfF-H6Q99znV6k#pa!<9m;SbvM-oRk>P<v;{l(Vl<LO$T^aVfB>B!
z$B)>Y*FEU*74-M`{T|yw@$p`NXZ>b3r0L@0?{06qZ8=1GZ2r00^r3F%py1;>nb&iu
zhjCNAlt<|NO^+4FMb-N^r*Bce@!vhUS75G27D}{wIYB6Ssxmp#MK;hp#nI9JaeGmG
z^Umnl6GsnodAdK$YqM77`OpRge+CEQ?;OE~m}2h>cUW6YsjjY$a(TmcW~0g4+&l!f
zy&1tWuUp)~e4Zi5{~o*&Xz50xRVj>3M$aR^wY+T*xJ@0g@+(48G4fCINhx6&O(vgn
z%jek8tDYMI-V!+?#(O($JuLUkMPZJ503ptZ{`W$9vDu+keqo-D7%lI+STng;!xsbl
zulQ<(9^Crarc)jl9IV~`qyO5xJOA$r7B^;N%=x`2T@4(~yz;9U*v#uYtJ_$k1O?-$
zDq(6@ep1f*lD4CNAO2Y^KVntzN|ECF?zh&Q45OHmZ%p(LV^zXXg*Q5<rgPU8M+ssA
zHkF@AbrijcOk&czMfCd2{gHVMH!ZD7dZDr}W#2O`FTtzkt@1Nn$Baa1WJ~);-;KrU
zCeEw;#(UZZQEbETn(`)$w@!5~YV#imP2$db2sBRyAKeKakFLJM_bQ!*&wo-}Ts%Xz
zlzc%!POb%ri4wbcNss|ilyGgkOgJZDTas3>0V?dby)Gu!J^6|O)@9MsruEEKuvEF8
z#CBzyD{)uNR3=9pZ~qkL;My21YU0d6*Y95Q-_=ifENWWU>FVTvMV41$ek3dCyHta5
zwUNe9*Y4%CmQ~HKub)Tczwm^+T1~laXmg@}WYC>QHS)B%syFUjC81lg(T%(NqjdTf
z<>2Mict~*eSsdRlRql=g;@51;oI8IisS5b@{dGc86td>l#X_=kV=GqTE~j~2ZB-P}
zYqIg!9X~8T#hg{^C+jOI!lO6h@}Jyfs4B*$=*cklP#dOfpcwhXyt}{jGT)<AzsAv*
zkv8p6Rc{~|*KDA8%aQzTZbFKqmw*5lnjn7*#U6&MbNu#3(oondKC8&iCngrsd67;f
z+)e&-HgPwXhEu~af~y5&iM%Sc$>?hGHB4EBEBucB!ugZRddbr<1sl5ro=osz`Mv)2
zOG)s2r`Z*jMa|C6e*S{}-!YZaWQw^`&+ke(b6df}UL*wF)!Yi@W7z({Xn(2mi$E>j
zEcT^BVVn|-U+}3*9Bo;JlW+EKjYP<>H-<iE=bK2<N&1jx!8-1Wx=u~;D<{_UBg+RZ
zH&!xzY8mnLw3GXjs;89(s|(4+oDY6Ib`lpKjKTd=5>Qmf^d2Wqz0F%V#dlfqtm0Vc
zaP;5_>l8&Oag;-d-B*sL21ay4m0+&Bgx#K7Ct2`#Z!H*yaxvxVRw*))&U<QOow|*^
zO#NCYno9hNI~8SJAuC50b!o}lcB}ergWm0F!)Q$S=@#ufEq0rKs9A;leCbSkD&jVl
zF#BUp*p=gFc+o2V6WXZRbWHcT%Ob_nm1O^@dg7xa`!={>*Z-6Zjb1RNZQbt}d&1o@
z^*#^}j;nY=#?YN3c|O~F0{r|&Ta#7D2}S10F6REb@xfA>>cc$0TMJ_CID{L1T>K2*
zFSc^l55pcjN1yW*E_Yp>raC_FMeo~Xi;BNBYaN>7r8}|c9*#|4FB}(4@-$k!dA@>c
zFgmR$h@>Yo*d>BLQN@qXMYqvxK&<rUl^P1^t1+(T6Q(*n%(l+@&sEVpj~}y7j&wak
ziy<ITcDWNJ#o_arae9Ixi&H~w=(UK^9>=4>FFzbih(~7qFYF%TkL;TzC0;jCVZFWO
zPoM5-X!QAF;<7h#w>^Ga-G36LcteSnS5WwIGl`S;B2W0JGYt_%PiBQ{lb)nB?(iq6
zMy{h{msju`W+*(-qt~ERY)h7X^p5Yy^n&`y6l;m=8}_ZuRdh1{2LATm#@nHRXGSLk
z2e=rgZqJO}D=FHJoCB)}YO;yUwTr)1tz67k-o~hcGbmN@@^8d_8^>8#C0P9JTQdMN
zyW^d?8+FOzw4!QiYC(Gb@N~0TYO2y?%&s31C@;rqtA#U;=s#8wRgXS95r~^R3!*En
zmfKXN^CXDZ_A?&Ak!b&0w~p;zrQOuqxKUUbt)>(}TA|kzv$5pgRj0$Y)9}n|*4*-K
z`*4!x#E;$BY*yE?_}{C_-Y)8i3``%cct4EyW52A9z^M8<cF}(1+=Qv>-Z=bI32g{@
z&YWzMxG5L$cgF1;=Hng9M7I(!9%<Z`ZaZRB(cag?Kr!~ay?fYl$93Fdo2yp@cfUl!
z$Cg4oGgpCJAWG{^_P)v+4{giTuRY7!dRM*qlLB=~zbTK5ew_)KU=w-&2D52$*1~iW
z72Yc9mMA_xe4&{3_jY$h&%Gb3N*)%sr_I&hS}|@uE8^ced2lHOV~?pz+$zeew8`ch
zIW*;P9Q<Q%Zc7so;BOewR14K*V5lG1$C0gxn;=<CiqCHn<+B<y0R;ul?6!fDN3_&k
zd~{!ZS0osWql%~!MDgCP(F-=?dzzl6FumY?bl})T$|Y9j&85d2Gn@C!i{o;gs3R|1
zm8{5IX?9h<@AzPqhJ*Lz#h(tpI|j{Tj-LC^J-;<l<)FJ-WUwbwQ=*^=F`*coOmfKm
z-Qp%3dh=W4FBhs_d1f4b+SydDYH056ANE*+7`^e0dJTqCS05R6Ic&S7Gcp1Kr)xNL
zUUHl3Z@ZT{)!JwWWL~iqUGY`$3BpDf{azoZ1Mehg-=!|X6}(%**D65d|KMxTk|vdT
z2&v?m@m7LEnlZ$nmeclA%gVGKu|lQak9CBHlN6dCMR9mq4)QSX&RsstcfD7eluJiT
z>qIPriuwCb@gFVh`=%eu*0kc){C=d~*LpaKDcbY9QLJC+l#{7Gku)$g-c+iy$gjws
z>9qQ}Sm%v3aekW7F%`#}J1AIgB-f5g`(8;PvEsKQZO27#P^M?F4E$F{4L>yg5Gbp{
zM^7NiIX*se<*p#yl{H4&`6=Yhzc<wnX|rqjur+_X_X&-P=M!)G=`k;ol-_IBLvA?C
zYdf8Xx5}pKZI^9~!-`%UioO|1!ee9&KPWkj@{T**WFpq&3#K|NvW}g#GJV5=H?@(F
zqlSIFDn~ASF|Qq3trmf0-|}?8y|Hmp>c2zGYx_Fu<vR)#-cQo)L3sNZC@sqxvvoox
zY&SL%$kygg`YyFNM%RZISarEOMs7kHw-c6kdOx(g9#9#l{I;I|^yJC&w|Q&3dna;M
zf{D|=AN$CjJJaK3y?@Km(pxyyBP}lO!%|@Lon1M8)bT#rYAa6x|FD*R;Mm2~?y-_>
z<+DUbrGUYxyM$Oxg85E|^;pU-OE0XiJBSIX4d?M*4e2s`mk3AFF=*I-^7<75__G=z
z89-?EJ~T>QJM@*J<^C(q2IKQ3Gd^9cr-aH!N+a3dDFliPMp`!*j<~o6n>D|k8dw#l
z2Qph$U1O)H<t)S$KDkI(Ic8UWc5Bu+bal>#jemw(6Tw`!J`8j7)@(zDt>t36wIq3(
zHowKopjCJ0%C`oQP>d=I{A#_zmZk&JV#^=+l@lw&Vv9fDlh=N|;OxodruO9G>u>gm
zb~n<FW0`4(D@Nz1%Zfi|9*#qsC$Ui-7v4(eS#m0l&6_N5>zp<&j~7@qS9a$9z46Hr
z<6Y^ygo1qI)O$X`#FYBZ_XtFUI}0aK@T1iO0bLl|%0;X!mn`i&m$mpc>fOsT|L$|x
z9=f%+m8^AsdCsxY<;ZtFqy8i2H9E@Qm5}P2qWG_vzFmiyZLBY6lc(QcEswj?VZi>A
zVg<`m)&EBB``~PkXc_uzj=ZEvI^?m>a{~S$+ZJ=4j--Ty-0m(Sc+P#+GI<omFU5#y
z2QF_kUszh9BvWkSy{56D?T?uJqs^)VD|^Ql0nwUT`VHYd{XWxI`{Sb2e2#-!JoDr1
zPT7^d-3sqIKYTt}bnaa)wV|#OM@3fekWPz3_hE+9V^?ikNLf{5Ud@8}yNf@S1izi6
zt+*<yqfz}`6)YFwxpmXx%3Q+XM2t>tIUPofQ!b^`rMdWPAz`;-zE5o7JW3E5`ev5!
z{t)BZk8t1PSPM;mSHaOp`TIr45yR1jV-i)+=@`w=X6!yPQ)c6*gxGeEzFkk<(jYIA
zCFSmjIli#Cj|2gUD3O$<m9D$DW+upRFg#L=YDnNLLJ$y0|3(n|JoT%OiYN&Zof!j9
zO2rNaZ+}tw4slk?iJL&v!Gz<(7602;!%Q6L(iDm^MI(e_?ThEE_>COY^lcO84UPR6
zgU$>jD=EVEO(~UV2neR`LrEe3uI8X<4v7tv5_zIFii(QXddYCcgD+EbQd_v5r)^)e
zL;D_!S=ih-iT~}i3e6xmF_?S(i8j_v+b~IGb+70%+`WqCKaUKHx%<Ljm+-6Pp#M!G
zc#GAga3`x)!L+teIfOnjt>g>>GLPCtwKq)xI=fBHo1gE;Ulj4sey2)N#4}f~=n0{@
zFU4Nixa`9a9HHM^)HDG92EvH;=7JVnBFwYj#j84!-_DRQP=F;GH`JT7Z3q#J<mkZO
zFDV}NZ7#W5n>OL^avq-Vc}EA|!(O>aeM#D`lLIhy@&Hw<Si?(mi<)XRYc98xr6KJ`
zVvni8URBr&k>$5`9DFTeXo3&SUJKm*xqY6`7%fxm2zCuEH_83czm#$<3revHeSLM`
z-m`od^`R&2!egW<`MmbGLPjYa<;J4a&Q!?tTH%rk4a>V}<;cMQwr{JW1W7|_Sgn5t
zCx!?^C68w>^?KepH61xZt-w6m`o!_9Y!bVJL}tZqj2H@tepGE-d*qZy*&OdjT0V?X
zW92{W+0sJifBspKZ}YvR_;?9DC))N#{aZ<i0n{vsX0M$!FW;mkn(VS1)Q=*>f$=Q8
z!GRU;v2%A0i!^s<;!`X9+Hvgx!8uaSP|8a>xUtiDF-_m5e&dEFG(oJR$JtNH6fiVO
zz&5>mCT>J9Jw+6Xmvz`L!@-~PnXuQYKG@Q&o860lbmV4=Jf3f2SqKI*q$5JJY{uq%
z@2%rQDf_&LXlZ&^*LRFih@r?DPbKFMf10~=UYWNEkd22k9#lv&YvKF|(2hkt2QDEx
zY9HAbEf;>u2K^t_c8rG;0Up}MjRpV|2e2^Q*P0nFYOQu2>uW-2D&W*mXK!cp(P@t5
zIx9Ur9V~?qTXX+~ack7w43d%TsqG-Hcv#9xt&?A=a$VW%SN6XoisX!V;$4rdZZax^
z$kt{rj*gL!L}4MprFS}>33lkRYtj}}l6&8%u4$r!N9{y?{of;Za;RZ9H9iPYE1So~
zrvGRa)--Jv)C91~KL$|MAiTD}mz|amL*0*y>-!nU`;F7CpNkS9fQuMX4ZB`kK0Y_|
zsa-kl2_1s~?*HsU@SU1c2x+-q)6m9J?CHj)MM%+$1K$+?XNi`8YLGx<5;HujV-d0<
zoY-sny}gH6@%o<mo|CpLKKi*m4bJvfh=g@|+7GYr1JmQ9<Hv&B-@mCk->0WdD>PH^
ziM%P_QEgsa#5SEcNrW1qOQ!_*dguJ5k6`r{e2S4E!jjXuYq*+U$l%vM)c?^q9&KLv
zUUzzJ9(O+@j&RNIs(+m}iL=i<d~TA&pf!x4UU`Tu4=NfkX=|;tb@6g?V_AxYm~oI(
zqhY?svdqnl?&<K5fLal!4+^D0jjF}6QNs9;6gn;?rLof?qWuiQu<gw-2|CYT179o^
zHQ+Qmp++pdlcrY+_M{2zDlqvkWnTGLTZ>O=St&fyn;;n2@P4%D<hr#Sdc4|qsq-Qt
zm|xCLUFf6LTMddk^*_e?ujMTT(Y18Ta0NKMZVz&w*RjC5(XtpC(eO>+W;}hFmKW{S
z1AEix<@YkL>FF+Z&TsjwR5V+b`X&>@Kb-d^$%@ieW2^2BYG`e4ZbLshbgW)q=gG>|
zi$F@3E!juiTN5)~7yxB_vkJ_EMgScRs@M!OR5Jb|z?^TLIkL4c<7}HC^5-bbQjJ|7
zee^X*=*c82LG%~$7sB|lk)xmf+$V8lDa>tOav;<4;iU|)zJ^k*MQ%8KNAc&@GAf0t
zzXtWTf#Dm{HuTx%nHI(y_3*(xQYgpGHOl%VoWil8`1_t~%mk>_a;RkDzKLPqD}C3o
zT`o&aHRX|DsOgLO7%%~$tSp=QyD(ne6Odv2tcLwCM(0hq*?=8K{bTN(k=u+obO?ho
zb}>0;U^s}XPDr3^Nw(TGYiav-{Y0JF*zOTY-0iirfPbanR?8heIQhza>e^GUB`DIA
zMn)YU?_{b<w;rDtq1W2^C}C<!iccxSFLbjo@`>}a0o{!zp@QA1T8@WtE?bysgdynq
za(Jz8Y!!l?xAJ0HO3tNJzAf8TJZRH|o9gjYf`tez-z$TfO3M!0lqZ~d-Va6a?;gF?
zbM9+(Q~dWGo#^v!_u7Qm*t@yUO2%CTJV14I|3Hlfs4OFzifSQ;x5GM{u~n7MtK?-R
zdl;?>d7&Jajq{@Sc@|FEeAf+nqoMPjnV2)ALJ^D))Q$bx_v@#L-~)e8s}h4^c+ig~
z5ALqP3E$Z}xqg>4SK%idzwaMAt^?F?(;Qie$X#Cq*he`-ttg?azAko(@>vP{c&2gv
zcxI!1Q-{)ARaAn~`(e!6ZZj^WXY37S7l0dDmOdD7bBU2{3@MxP;Eqzo1jsLQ4l2B`
z>EZAZfENzw^L6=qDlaa+$qrSyU`Nu#Idd?QP7}WW?`hxFytn)F<H}98Gmjeec9mc4
z-PlJ*&Rd^5gR=$3Nu&KWy+cH;)7Sd9D-wYfl+D_Ild3mz?cuY8mHCM(!?^lC+~gz$
z+xt9RO}N`~Ygk;(5hoVg)@E<Fm!Hn2@|a1i=UJ=G?xyq@A90BRF*sQ`auXd@Bw41p
zy`i1&%xvDo@J*4~Q6mcVgmZx})T}~CzV;GI+Iea<O*yf>z4(TY5xuc{TwFHByhq6Q
z#>%3FZ{8TJssiqhAmzF59Wxv@HD{L(GN^!CiVxM873fJSPF;<?$f&M4wEBtat;`L2
zcU4oYNbgQBB**;~%UC%k^`SR-Vr!;Vkvi|<f}H1rtbrY1JU+aYbF?a$ighS9`9OW;
zHoW_%Qo5Ya*m;pz0Fte^@3k<#TF@LcUaf!VDZnS!Jfy68-|V9yRB#)0p9VH$w2Yv<
zcmPkr0n@c9k@7Gk*b(aXfE5d$-GAXcD&m*SI{{wan=l7!u<grGe`^AQW%xU@d~_`T
zpnm&OBHU@&N-bA^-er0$kkHuci$@W%vUM-!%B9C0)EO#i8+#F<9Q17k?6z9p<abil
z8WoF5E-dFBp7<ZP!Yy|n{0A1VZLKd3`o>KCnwh|W1c`7?p%J8N!}BFhq&r=|MK^EW
zV}~+>WrBU)z*F?wS;P&;@5@|I44Y91p=5X%BJOAEaB6fB&;3dIe&Cl7;TMvX#s+)|
z!FM5;PYPXXRno{fo+mbkXzeOh2Ig7Rthqlbe`-3{uA0+bNJZN=FpR#3>24%hDZnbf
zFYx(^L0p5-7W|GgMN2thF+<$H_ZkRy`q1hG7RGfYp2?#P!(pga&B@M==JWaf7xH#o
zfBV&*7w*T0uGO8DcTn^%ohVtBU6N<qs#Vq8nKUS6+kB5bud&6&)`&2W4m4wPF#k%O
z52Y)!pdcRgc!UX9eqP4eEwP7S%_iUkP3Xac7Bb0GUE{Pz1j?GE8??fPrNv*VuEsbe
zdBf^GTH3YqQo@C;fh;^Q%>}_~AH3w`aouWmAtB1`+PxdRw$ov4Z8h6zkKxv2tUK#>
zwyhE6ShO>~c{pEY=Nnggp7AoSrdrfDfFi9JfsuS`aY5bUg9qw^Kx?J2&;2UPY_8_b
z#DJ%z6?@~65a%dPR1SR_YN8MMSTj|M(5<vS`YP&`^}xY2a#n#4_xP#cR4<q7w5$cu
z-HD#o{Uyv<hTi+vlb~wRNq#GfJ$m-w%ztMTzvw>c7SN!DU5^#t0^ptvF%%xZFJak+
zAwY|8@mPB^g$pz^MPGGmR9pbhlIM#=2d4CzrF|u7oK6hH9G#D9Ouj`ni?XWg?Q9VO
zmuybz3F;cTdQG<t#Qsm-R4bb=#^#(Hc_q{jv>a$jfQn76PF;=hw?RHbBL|-#Pd{mL
zdFDMfmiEIv^Nu3*i<(P1UOdgOohQpPA(I>58~$i<_GHDBpB_VT^A_zd`3)kp`*Hc^
zITAyouNWvN(m9{=FwHXn_RD-zZ8ZH@@iLK~cI{1q_6ehK#d}d31bg!5@ZQf=x`zk|
zu=d;d^86$i%u24fvF!S7wQhU(`1r43*Zq?mtBd!~khqp4|1X25UVhX39Q{Z*h3X34
zb0SZn8oWW%c@^wDggBuo%~-@I=o85x)|m6PRpg=geZ#)|d$L)e<0?Mz>;j8f17fp=
z#%a#f@X3VZ`iVq@`xVn^wi5Mj&qo5kVbPAK&jOfVds_WG1*#t;^dBCXCE(h5j3*eh
ztNJ6wdSE56L;PfLmw>9xgF02q^LJc1P|ADe^sW}h#FC!g=y{46<;*SC8SJe9@L%od
zH<w(NTtbRhrlYYzdG(FA@qLtcnz0{BZOt<C2N~~*1|}#Cs5e5Je<|V_FP1Fa45|d&
zgrMArdQR*V!CO}ppR1zq@jJ|H7hawWK$I($of<45UyuRlMl^rvay#;`W2!v7c*p&`
zR!z*p3J0f*p6=NTIuS{uLDHus<uOSIsNdVCiMQphw^i}A%?_;6T(=`4a8dzQ`xOTt
zF=Z%eyvaXq##}r8kF|lp>%c&ajzxQ`=~}WxKHD~!xLg8OX}J_(%FN76O8cp`H4CeW
za$IOkY6M{!bynlJqa&DW1A<<*B}<OY_of5oG~G&+@BLcTGrf_OLnJRv@G#FU<xnp3
zmQV*$N=}Zqw7!mGHi?THMcgHxtzsih#I2XRX4kenAXY7(#R+T|&{5Y1>IK3vPLhfS
zXly<nTWC-pk0+YvR8yqp^=1a$smZ^oy!tyez$t5B77`PJF`c%~-}2Y)?9qRcX0maf
zfgkmF$5&5>zz;qbQ4QZHfv>_+<wwmNPNb&3yQJAPqs&)r&({$%3K_91OT~_-<^UDX
zOqNbiumi{8w5GYU<ue+yReV6R7XnW~y<#?oT6)y&0R?DerAqNz$P@2O|1s)Lqq?}~
zs%rCQU#R3&CEo2JxytKh1hW>jcWNYgYJE1`Y2e0uPKlLIw9JZ7eKrydP!=KM7u30h
z=1RXHvGRj(;mTs?<5y2}vMpQKNxv4)$&vTJRo6dCpo^AyWm`<Qgty;vNB_CP9R}Jp
zS|C=G-YI2&6}lhv-;EM35G05U4{r@+uMdV%>{Hk(DMB7M&3cntRTYH-K&&(B{J?aJ
z$ATEvM{cia!vfHU_6yg%FAg+s3%cST92}s$dV16$9nCP8S5#k}w~h~>ZI!d?Nr)Nm
zQ$$U1)L}-gj~^hVDtB@A_W}fU-z*05O#O<T{(1cC>B5h<mTYIXcxDP9CY_TdV&;hZ
zg{1cAkVi^&0nwK`&x912FLUD?#IMwP+TyX2txjxGeLS=;YZ(qe+wCv!l48mY^sk~p
zzUA+z`d_i;rlJJ`w^qA$F3w-c>Dpn()BoOPls#%|0%V<jg;LwGCmri5q5%Nm$L(Ov
z^ig3eB%WYvYU>F3x$~lAY#kT%zA|Z>%HhAZy}@WW_NiVwd&3sO-l*q&G{`3D#b4`(
z+JoG*=K5%+D~n>IdpsjNJd8M!b$mn}&!cDN?Q9jFYB@IluwQK*I^KH8=`3!Rs@(Ud
zXCV`VQ&4aeClc=<Fe%>Er9Cl&(7-Q<a7I5mB*JIc?25PE!*?kTPEw+#l*36f&4J%m
z3)brKPzp8LT#e%R2to1!t}w<fK;?S_)CC}I-WgCbyWFCE&_F1XB;DMzVl`M=oOro;
zo9+68tOm>%M|-cM>9^?!37mKudu~2tzPa$?>Ax_Fs*PoTc6w?Adk-JJnnjs*UuXLy
z;zQ+k=P_;6=fc7put&1F-V2%m3kwV3ay~B7tNWGy6tA+Xs&Q<L?8S>09K5`NcdljU
z<%yY^(t|B5DkdfXw&Unj*<XcGo*g|s^-fcXBb8*ow?>Mnk$sh6WL%OIKG%_$OuqMH
zd*l`WGq0=x&25~P9h4!Cay910*l$lBn4U0Y+*HpCW4Vgl?ULpA)K?V`l*vW{6Ak;=
zFXdjJ|BVg01o_9x-5v!<&oUiK<s&s<Tr35QiqBLOqR--iaakYp;^6O47K>R;uBRG)
zc>YHU7&OCt_JJzQv+@^#$GcJ9t_H2<eUJTBP?E=Phi@w4O1}FRFuupl{Nea_FM2+I
za{mU8$HN5UlgWDif%s^60$bC7LGfo_2L3BlJdEvkCSUp-KYHoS^Zs}e``ne8q(QIK
zTwU`8%CBe{j`|8ND$3sh_58<E4s!3%Kyo0(Eh1LunO*k}arH1NzR5fgk>Mwk|L}bn
zpv%un<$~X>4KsqC2D#4n(dZSdeWkLZ|K26{SUYBI`OG1Q{$adYMs)N-P2z_p=6vBO
zKT74XmrA8uJa>NmxE=cR*yu?x*S@KW8Xyd)0f>X;FhYJXx1VyEyE=AE@qA08XgmhJ
z{&>|<JNigCk#n&3#vX>df#bV$1-W_}+O&w&Ej?2iLy|Re5{`b-6vwCV8)aZ=>!!PJ
zjF#LIaArN+nhJ&83&dH9DfK+gTvkc&V{dF&ZA{nI3@ta*tsizPV1RPb@s7q7M)N%|
zY<g33js*`b*l}h{c1+8GQ+@OJ4ZayG`Bu8~WRny%%W{D6T1A%9MD}=hW@#pYqV?OR
z!&d(?@ANz%0yWFq4SeLbC?_l_NL<}jxo^~QUtz$|zPKhgxM>nQWYiak(|un8sVRo=
zf|`4M4RvQFvvoC39>p0i*1lrZqHwcp`nj1)LG0=|<#v9$kN+^3l|AK|ioaQfPRo~{
zUxCPjyD^<aoPTgc3Yfw|A4tI7>G??bdo?m5^w|kIX(VZc52ir{>sf$esraRw3H_yM
zT4dFQ5Qon@M&M8dJ~tiG0RG*|NPd{)sKfoa8f51e2M$jklcor|0A0!w!7x`;dCs7T
zeadJ_<R$pvC*~feTX~K{)xzHzdmoCddX^(45dr7L;rF4yH94_f^=VryTRXaff^jlx
zv@4~5{itDYyBD68EaOp&Ds3Jf3g2Z<`+=ihXv|i<&C1vQHH>plVIqp&%sgG+Vy2st
z?r{Wj5kVNP-`n-}h=PDb-}BRL@t>4ImFmb7&#sAifdgNbwmMuvkSv+ooDd9LZ(#YW
z=h<cjY^=v*X0{PrXt>A>`qfm!$QM~vcUE3i^(a#@B_b=?9eZwWZu6YzUue(F8(VaB
z@(3H>eaz_KDJ+yRh8ZCA>F4g)K7L=&24A77FQB{eGzX^-`@ccc#t*P`i%FXYu&U+8
zP4>6CA}ElBZms?@th*WP4N7_wqYK`2S2jwl6!f*oT^hbj3gppvJ0t1W)w-ReZ}-z5
ze!<Iut5&+)I}upi4Z1&C;jSm>CQnW(;Zci>QV@yhjF<h3X_VlQm<Ka~6tmK1XWf|!
zZ*pCGkyx|Sm?`WAp}}boH>J%hJ)n6kg2^Sw+d_f2?-WoXx*X@Y1Sg3x(9O8D&m`sn
zgw~oG<C2pfsJi#%Sg&2_$sh(M&*YOFv&B#eqUCc>VnQ|deg8h=a||u)m2LBoj(z)>
zlCEcq4}oa$@Zd*=Z!ALQ>U;AfU+`lX+};9y4Q5oRc)|b<BwY8J8tK9Uee)HNBBuFJ
zD7YN87#%bd6tLAjuSxJqPdov>C);+h6AJ}@!^-!nztDq6rF*r`L<GubJ70MMO6uiG
z4V86luKs8LQ0FIDsApnjy)JUTgQk(M<$G~<WDNUUk73czyXf@l^FtBayVF#E>Q5SU
zb#z{B*Bu!(y}^omXh(Y;7gyjA^A3WlY3PS=wMuDZHpOsdjlDcYMABL*a~T7wXYF6a
zk8e#+q<{U@K7|cIqr7UVoZq<)zt_5I)Jn^7ahLdK@T;Jj@Q^@ey0v5oAjb<wmOz{q
zjCqkp?zFPZyFX^w)HaOTGSCpg%eXmYu=rr;4nB&`kNfRVh0fIkHT8Y|$5{f?kv_H;
zSpDpG;v>VI;zdgypnfzNqGfhAguGoZmt~oaWl#|VkABa_K;cM!EXym7LX_w1Ccnjv
z4D$rGuYB*II&`%P<_kP=>Dl^$_=6xDE~pRYge6HcwOz7Q>Av7hQem1k@D$d#EpO9j
z!BOD=7Kz^k!f{hG>AIRhZq6B`OMe}oIc{{JjXYrJAW-q<TT=pK!J%GHBT!#b9i+Ax
zpCf>><7%4PS>T`sy_>hDL7cX+pQ-K@|H&;2ay2ny&_8$EhXf}_LQyX>k$f_5L4`;l
zIICX@g!LF=X6yA7Tuq8eX$1=e7A=9`ni2w)^s$+P&d+<QWT9Q=?mW`)i=;(ls)}wU
z!cX)7c2~<K^5UYrd;i`qJNsTsOUoS|9ugRia*v(;sk}Tc%J-HQbPWxSS1v9=v9YA!
zZo{Xdx<O4%ZGCwQ$+p^b2&1#~vP?EqhW<OB!)ZDH-J{bB-^v8lh;K~4limVVAW<B8
z71HT*U5Dq_{9ebjtL&VljC{6gL_5Z)BK$i>9i+ZDwi%qAj+998-J}Gu6Z_q^VnR&9
ziBx_}UOGH#WKM51+YcVQ+wpieBrx({npp|!&5-%W<=dCBFihIay`o02+pQJpi5fNJ
zz=^`O-8o1VhZoEX+pPcKzr3|;tE~0)5tdubsJk!`5a_9;nX6dejlvF0<EXX*0j19_
z|FY9<;%OXI!n0Mlrc(2JIOuHNPVuu?D5KXpEdWlh2US{j+*l)f>H_tAt=;3dWF=4M
z8eeJNS}XIZm{!un2?Jsy)iRedp=eRvXeLHS?#3p$sJpHz5RBbXWKe_ZI?{@%0%x<k
zb4L+@@`p<WmgI{TreU#Lo4$C5tsxN&6^u8<&aDh>T1av)b3T8)@w~^>XrRiGSZx?s
zCNLcR+}!r9LKB=bF&{)F0fN(vXu*nxd?CViNskwyGoP*#yZnYp3$yd{{$yM)Ue+Lz
zN}9sl{7liDqkYNv?thTzOd~~8k)ibuP->Mh?@FvlGT?O<ucYD3w`T1NgS;{z2O$Vw
z2h!Yn$j+y2Y2L($An<g;Ny$`vYNRmqnXAKydY^)Eg#*}}a!&}LcF#%e%ubj3Lg@ME
z8=ie<&#e8&HNst#;>{---<C@r@O<|%3p7&03p%_Xg(U9L?!rlS8}&`4d>|bUr3i&n
z2*xuVG>3VN^yTiCC-+)8@hv6lu!a`{m5IR2FVZ7e&lIlPwPiZ2rbU-Jbgl1fM%}ID
zysv~4GnG^=c@-ET!=YUhlaIlQR+{Y-ASA#Q{UCh*SFF=UMw8DJ#U1hmjMslDD<gL6
zK7Af(<Z)ySIf>EoV8%txLZPzf5Wf?=-_9`yH89pURGZ61RJ`CKdDq2-vyA1=#Dmdr
zXQrWWzV3(6gy&_UI6k`0Pu6BdVDg}5<7U=waC`zQ$XIV}gzVNO379;=098c~P;uJC
zyaNkx)jDJW@Wo3rRnZYg4cMNXb$>SDF{%zlD8koDo3qxhYU%ljKhurVoUd*MVap5U
zqrP$^R5`};L}>gQb7pY*Z(0XiD7$DrfO6w^#d46-ar3jZt+Z;r2p8veqo<C0zEhWa
zyvK*B#WzwmL=%jy?liAmC2*ac7_iXvd)fJzngXZ^_S<ZuG~w_G1QLjU`<Vv4f2W->
z9^<jxY9;6q=@Y6M4e}Iezii2YC&OX;LRxvrDpuec_+jQtXr^zC43x-XSkMz<K~)p5
z+okZB=4<WH+I&tN!PfNssb4&)pee|k4U{aIFk?R-E8_zIDJywm2<&GEH}T}^s!<45
zU6KT2W)`u!v6&<9i$jMIDK_002%K6*G{ebqtsyOG8q_n*eYZg<G*EeYhJI$Mf+d_h
zOc5QMLaeL2mBnQqH$Fn*HCeEAs-2{+n||fY*nW2)p1UPG{_Z3(T0Ak4*e8Sle^RrX
z0@4-_+o%NST~nVQOG?XUot(=;$kN68Pkzzu7}~I}t)GAUAJ*U%LMQw&CG=@y-7wsR
z5p%v<S5u<B_shrGXt+C@A&o9hln<T~Ved?S3`NMpsu_Hd=N~lqXcbJPYg_!7!DA5t
z2)n|=OMFURN8xJn7-9H_CquK{n|BFk<CVQ#y-R&!uQxlo+*p!4o{PE$W}EA|Yl+@o
z_0r6!h=DWf>4`Zjw-|iD>Zfowxzf*f`q|e2%<&6l{BVE>U<gmFT(`sO&>@E(gOh1%
zd-Dyvp-U)-DITK(#1kUmxmC$5mG13c);|R4A!P~2GT{9hM!rDITVNv+>YnZ>5+o06
zadwiXyFZ*f5PDzkYG;K4A(e%E?c!94Hgn&}ka3PT+x(sJQ%7EJ!I<&P5kFsHUM=n>
zQaZGaiHoDO*gSeDJ=n%QMe$$rIqNkdJXTF68W@<6ccE7Nl3H1vI@B)k?3Us5W4xbR
z+o%Fu3b%kb%)H7UFGgFnJAI~TS4Hw4KRwLR0a}6hpu`6?K7+o%dxKrAu_0yL!|g|V
zbOuJhgsD-N509XgJv>QM14acqN{frCaaz^fnf5&jiq5&_RM7-#v~yMRoom3`a0>tB
z65VH**t~h;s4;KeeOBcKS!R(SU3D+$^~UzuI94a`g0kfSI)Nm8Y~UkEK3KaoX<};=
zTx538XdohhtNwHxf5pUi(c$cOiPN$K8q>cNKJx9%;B{odi?^Wp(kAhKiNyIxsUYrd
z7Y<F`E3ASJk<^pcTN|Bhn)mF0P64-MNb?A`xPWYhI52|1WzYN87Z&sKyc~GMp9ivx
z!S>>9V)dLrMugQ%J@yQ{VL$l9RKANTuXT-x>G%=nw;|n;r|6^e;B{NswjK~2Dho^T
zC!<<fXQ<9qc7DbN(Xpl3zm{8Hi!nx5BlN(mzj#qcGEeM+5bKq`A|qlPgtC-UZ4UP@
zImK&{!ljTx6Jp?a%NSKlUcRvn8ve`z%r%I(xOov-pEGb#x4^ln#nFKSvfzgEoC{Pe
z0z_y+t`iBg5+*#Kq5K3%Gygx)?A8Ke@?9fbz@~@FmhJS;waUH&M<`?2^dhS1!JRRk
z(vP!S6;t`1t$tzEa>5uXX&*E0F0N_kLX^_1Xb-+G;3<F*orzTLEP_;Xrv5ozIy>uM
zZpV1BtS=n@E<*PdELiQa5zol2%_Jz=3~mHVNB5S1;CCwhp6jM&`8GyC*|8=~64`D?
zsMqwaJjV1CrX%K^V!elWy4F^IWFRt{-V{|jClp7Ju*s+u*Ktv#SaRFSapDol%CGa3
zSGV`45?=HZU5=U*0Z=l_`lFtaYLTzc51n0+_&bSeusZz^tk+;SwQ&qoV9IX-fClUu
zPWao8m`C)42A=F@2ptVRMS<d`VK>7t5g_UVbxa7G_=2K2=q4%KFsgUWibByvA3I7i
zL0oHwZ&8r^(-0KBQVu@&jnHgyoo2Xs2Aj`dde3gk{on{6ryYJUU~0ry_Q!zrtr$EG
zAi;pCWL>X0eE&<_csmIm-k&GMT~kw*cWKkMSUOt4_evR?UR})snLjG?f0ya1=lL1W
zCx!bO0+-`nzW>;-5uX0oxf(aaxczQvFjD1)jqoxTyNVGzi*ONtY&*5CmPB$3sGLu{
zO{34Mo>9!7oe(hN+3PikT=QvNOwSZ8lO5?@=Gy&*qL}3VPBvpbtzUK*;-Cd%#AF6z
z%xsLE5CU1HzD#l58qn>gUI+knAz#R8`Tg7}Cl>NDk+{2S<%UdfdQ`UILL4u>qUDrT
z6EU-i>=b|Z?Ga>}-#Ql3iA8;&Lvx)~8GG7hIX^GR{gz0z6bR-*FipvsvZiNXfHpQe
z)_0k}zQJ4aXc75&p=Pso4k(;h1tsU^(p%8p=PYf9?XH$b)r4a~$=sglK*nqw5OV-W
zzF@-9%qAQ_XM=us>v(id<tO@St<OvmHv@szCIyfDug-=3KG6uU9AP!+Dkt629m#Ha
z-k{f7{q)SKYFiF`1Q38*DxSW7_grrz*LW&F`~moR;a!4auo%&a2>Pb5oXfvo-Wtjj
zwX6Cs$Q|w|&?Cr<v0~lmCfHVUojp~uaUe*o7O&SlOPqy*#MO24Q815)3`=2S1DGX1
ziP5H95=>Bf4Z4h#^RvlRs90Yp==YfxCjms+zbE2(Y3A1%cL!yAGo+P06w|2+(?onz
zK8Lt?%@w=;m?d>g!OUj34Kao{@w+~==-0KCzXpXpLA}uXH($pkuOC&k%N}9e0Y|~a
z7v$_j^v6o?{=6qrTR#7`Q!D++cLDkQ<33^!2Qk50OM&qOHamTd97W~QUkGbOzA$Eb
zddP6!g{|Os<#1g5^I}TcEJNkk8sDZ%KTJ>>&7BW}(>gGNrg&q|H}vM3O}N?iZu?<2
z>tI(5v+)ebX_Kf_9DEiNEysnK8P%xIX}O9o<;efAXwQU;40aPn;vTdePxt(al?D>z
zqlb%%;-^ZzH=XW7XS(f$qzFD_xcF<~kdy?DEU3X)1wIAm_h&M``8xo72jH&&k1LdS
z=F#mSjxjd&{LbTF_&AbUS}nT&2r(5YKQ;bm!2sSpK{sMVg35k|c*P__k$Pilj%G3S
z(%IEi=MFeB!G@S>c@?P$d&`PLWA99A@t=h@5GHJvm%XgS(<2)}bC(x<<Kdaj2M6N6
zY!I-ovQhQP9}1S|Y81*YYFf`CeXMKV5QRuU{Hm`YLR|Sv_s<7;03{=Q4Om@7)1TxV
zd`5@Q<#YoGgXXVd(%prZ{kNfJQx@6)X_pch<>O}=WHdG!&?STRoK{^xY~=s>Vs+@s
zz&CX}jQu+R$ydS!rRwbo6svPlje1B8Xw`jPAiL>s(Zu`>$dV<JCVt+}<mr%Lv}as8
zL7+QPxUBgbRMb(jBsOwa+qZJejb3%1K<cx7<EFLx^RD+(9J*R}eZ1O&7k-`nb#}M5
zZ1H5Hd(P^#Q;Cb0?RgPLJj_|KTZ<#_*fFT20Mi1N2<G06{CB~@)p<UJYwk6WJgy&p
z8!+I3&@9gPzW^moqB&#?*}e>~BwubHvFL=ek=D7s!2@x7E>dggiy`W4p8rVSL^mP8
z6mGX<5PbE<yq`ISj7^ZO$^sLlBxfTUL`&yIHl2VU1~zki%D*cWu3=dAZ*pivsE95G
zax!RZiw_8~SrWHgUwmdnoW~Hm@RHmfDpdP6B2mbK2~5w)REt4A+bSZ4RZM#23~^(<
zlmpF^v)~=+8FI2N3S0LDO*5^wT-kI3T6e&R_J)S?mc@^v?B?F6c<}xB*1oOQptLl5
z5e)p!e5%5D-Dyr#{pjs@-vo?+&d~%SCNSs{X)sLXab*C)h3<)bp_p;R31<Rq>?1V6
zot-=E1&-0SAwa7c0RaD3q_gDa{_}ga54c;_7uRHfN%+Eo3Tgk4zN*e9+yf5Xr-819
zVCHmw0!FACiv~8=pwR%-hU~D`2mb(-c2frSzmW6KZLt(;QS(8X{yVr5f$i?ok_YDs
zSbao|MCt!Ecw9ww_3o0`@N&K<Xe}?%8>OKZ4t|(-@AStO$|kMDN-Xrmqmzg(&4Fn~
zblhX7A^P||mbG1^0CU$j2<?43IP`-==6u_Lwq7U!mL7WQd#JL)D3LonT`C~)6A+XQ
zLc<2D$fw{>e?A7dK{7)uo!@9D`(`Erqp>GCz*x;bL-Dy>Y^TP<X!mTddEDtefVzj#
zDq&#n+10cXUg&&L?|SZ^SoYv1?ongdH5U+I#tZJtf8*h5F&3)@@6G#8Ez`Q&ERkP8
zudZpocngQdS<(z#0_IXoDs>fux2Zi;k!~IE_FCkZh(3o{21tXC;l2RVX-xwp+kqyX
zzTPfLoS`!0t$}PSy1_szT#?q=PPQhEMERSM`vJO)s~L(Q`zAu;!@F-+6&P|9ZiZU6
zdO#~HfwD*cX3alN<nCJfOyQiI43yg=+juQXKHV<39DM!lxhHdRU(bniCHSS)2Y-~f
z8gn}f^W18~a1k~U`Y+mxOC;6)u)GeQl&@o6&!>`}f@3o&q_J|_r(J^jUBRQMx6dvn
zhXQ|wR7Y=o)fQ}6q`jZP*YM#4jM1z~{i@5<@Ban4Wdk_$s!%|H0Wxuty``Iwi*E8k
zRU{58zai-hU2d1xjOF4l-ePDWCdq+SZU{%qg+;1*6TwM+&qiH&fv9kGIH&vx#uRpj
zz)HvXA^l{)m4G$Hw?py;lHRrlf>FJ8-8r5eR8??OfRDZUrRMR{b-d#Cbg^2FrWq;~
z^X2%T1c0Z(Xggbz{v&Pv@3h<1vlI#pG@+>rAyRbIr$wZ{^Uy{JqSdE=&qL44&(-Y+
z`t<YJ4iaLZmz(``2hpn>{)3#Gv(e6QYts~7m^t1ARZ?9*_|f21#qwtVq4GZt9POlB
zPj&}0U-s=n#HlUe!hkL_v=H$tqWHzXUvmD-h=mQ%?ScT6&m(P^W!^VlAR%)26YQ-<
z3Z}+-*ZgPreVGnKP9#39OLH-s54wc~`FP&VHDkIPTj*=zs!Jj<Y^rqw9G`6ehpO)Y
z=X!7dw-T}<v#bWP$q3=d%nDh_7Rn|oJ9|q)2-$mQ&$0@U>^-w*_Ws`==bYzv{@?3d
zr|Wu7kMa4u$9=!nO|Nc*RvHy3oUv!q0uoRyybSbjX+-b_i0`Q-UZpyp{30%gZ~sQ@
zx>$^QX-Dyvxd7CMh^}dFW8SCw+4NFGJvs5*Q1;hqR{fWG;ZU<|9VTHj8QO<lkT#%_
zhMs_mxuTX7>Ky~w=aRV|49KlYI`1cS+2sr9xxJZIow~<o!`(Rfzp*H3+p`ygZYb=|
zEYwDtS$VHBBZD?4;nDVcJ(}V}DkH$|P1d*IvbMRb1_Vr+PEp(j5gaI4G0b{dgRj+H
z=-}yg1zvErDGGBSR-G7nEwSbVi(J@^LwrT{(Io6ygbe!4xfS*h*eFqeI{=++>uAtw
z-B90-dyrIIkBjJk(iCHA3f8JP&klB#c`u~M_^k`GaQcN{6*a7^5Zd$Z+FlYqTcZS#
zx0%CaA{(iOFdy!O!>S(aERc>p)}?Q0Kb+R^5Wr)I44Q)L3o5coV96zqa8>Qn+;w<C
zbjl3yi5f+2PRwkiNWpYRted85J-|SFEprC5!**KjEM`yS0Z1!;o<@UtJYst4E+yrR
zz&Q`1n4B97wySU-@Q-D7VCaIorT7m}s5+ewQ&VD}o}N#ELd^fNi-CejxLV;rMldMv
z4y;YB9@O$Gg9_=^qYvcEDNiOFx-+Au8a(bynaey14v&!We5r5gv`+2eG!$p22lgxR
zA5Zx1suWy%`$5-Nry!aVVL8DBc;TiB5D1*faYm>M=PG5pv37CLA`ti&zp;GK=YBYW
zfY(i6`Rv&`UkwM6ZAtpBsWXL<<8{NlII=f-F?&np#`<aD>TQ>1`l&cmW)UbTe8GQZ
zr=(1LjWxXpr`xhBV2G53>JV%NwVc9lwI3nhJ4@^-v^TIi1V#hPnO*)*@{0fc|0J(*
z52Q4ecz6?J+rWYWUIynf8csiFWjS1%FHPPLK;HyVkKnB*VnWdT5P&k|Z?NG<vbgIX
zy#P{B0smW)>wfnwu|KpxmikumL}fz^@y=+aa(n?Lh>@W_0xmVIT?71?i2O8z0rQ31
z&8GBDu9N*jjE~YRrdymu-$VP+lW5imXk1G1^TH6g%ym^>{}Y3R_qXkG*yDfO(q{kZ
zM=>Z#WFFM<Qw|&DbmXD&RgbVF5o0=DA76Tr$`ENVyjuUE_T>GGaohBt<^e7r8mOW{
z@48MEo#9`jKU-zj{5&c*B>kG2AxO~RAk5S*e3X`ppmWb^3WEH&ElpsDvK#vtTv$~Q
z5a8xb3+p0|uU8DwHm9w&Qf4<+!&*Qm{gA%yo(dDd00)|=ydUMQRDW(eAY}#LPhwDI
z=o$SwtYNJf0PRiDkrNKUi7KsNx%irLwO*h<wkrMR6InQ`h{EWvl`jJy>vinHKV6(W
zfwf(<{%P5+3B<QH+{uK$%W{Zm6d1@5@QfPQvG<`7pBmQ5a-;o?YPFv^v(P^xKNfV|
z6QW=^yjm7oFBe1|HedNJ|0sl!t>Vy_;bM!0{<F`gQYUBPwF-82kI^~%CKbF%%0lvE
z=@1Xq5@_fhcwe-qTorb**ZS1%J-tU8C9{cW8p~%sgSh%KF=u)>Gx%^vPe;V7Q%VZ1
zf>^ty>qY;a-t)U(dnr5ScYI~Zp0*+up@4w6a)9IzC+Xb7e(WItfRHK)s!~)G5XKEr
z#s&gF{*6@~kCso02^F-f24}R7akt96Kclas+mT8haXk<mApqZK4p#;M#z#~3Ct$B+
zRT*&hqMcfVy6XNrpY50F;9nY(CIJ3S?+b0A<+e~C0IKKIGy#JST5u?LpWP3^vGbG{
z<P&_+4#*!ABtBrk)U+S_Kzq6NJ}-)p_Rx>_08XpFEsdjfyrPzW8(y`k)!)+irAoLO
z54Kd|Mz%n|<D~3gBjsQDbXw$z^EB*3){#l5%Rv64r<^70KR`0CANo?iAcbzA-I-tb
ze47&Bj*(;@!k4noO|j3))`&`ET53)vZ%3eXhx}>UPCZQ?Y>6)T+d#bw%6ca&+sB>f
zba3JXix5v4y!1&RE8SY?fAB5pVQ!&5Kvw*xW1KY&QWe-M<;|T~j(GynULo%&ql$s<
z-|UkX*|GjLj^Bop?g=7DgO-RnHOJ4N@7-%AZmLDf;1AcA92I@5&5V5nK{|4aOSLRk
z46O9RV>IFCU8@H!s$}<MichSSa|Nvl;686?#rzZwV%=+S1NMb^C9sNDv@NLpMTjAN
zd)(0$tTR_%QvVI83arYg!M$;JBJeVb*GJaPC2u+b53!fVB8~`F9{nwZe*YEdqXFF`
zOq<8lba6SY|Lf#64?#a3PE@gOO35bR3qGcY(@!4Rqt}ZhhD8ph6>(y$+nZ^dyh<4b
zi~Yg!ls&MXxXG~}ee0^|&iHZ({0^3MDm*TB<+4-L$(oG^JHv^)YT0$G*A}<Hu=Fs#
zOYmnKCP+l5ClbScF8C;sfK5<`d=_cW^0?@+z|KQdWa{n%=a(iNNnrIv9EQrlDuPuP
zEDw|odXheZh)+l*c^$DWEWXjHJYOMxp_1kPa`I||@8u>f&Pwn4Oeru|fll>)ghpS^
zEYR#T=`QGm(?-2?_VA4Cee`&krBwR9Bkq4bGh#e-WG9ZtDqOC*j7RAs5lpH-koS~>
z*O(6zewk$*aPAeqXu1TDsCNyxE)U$@?}y+Cw4oR5(_g;DR)H3CuS2}e|8R6Vp5IO1
zD5dI-0ToNg=IRR%*FVQRQJkVEU@{J-3chS+@pK_;hX}5l(3Bi!6h3N8DKc3O1C3iO
z+>!Q~Y_~z%!){*Z12+a9f9E7V@So_=_T#Bslsn?p%RjJ1@4r*@tZuD|S24HD^1s|P
z4zG#eKycZT@1-_!1_0kFsVo}RJ_P!?fbUFV0r64uSBRB*Y1^W|$>j+rdR??hE0C^`
zDS2c7*oWBZc{jL^ABXNh!yA|~Yjx&K(jX#|H;%Re+gU}K69Cq0YZw#nu^yY9tW2Qb
z?)}EFhG*i6meB7(AKH<vchSOg%Vcf@eH3)Whj^p=9}^OapMNJ3my$B8LALG9&=)F<
zh=YIbeXs~hq>Sh9S6`<rdtY1Lrda-7WUE7dL)<GFDQCu0PkAV3>X$$mvsr$RgsSF)
z5@Pq|(dxkykuuf;rRBLu^JztnB=$f;uY!CZKX~y$Jk`={rNDcACK_x%Yp{rSUU*zc
zFarXxz&D}6^{enl+rC|KBSWwA>{xs%q6Npbsdx)5HteP>s`Ht`70NtGt=ICmqN!w>
z-#*5l1!GM<j*UOun8%0H2iv~WQ0%o5?u&pR?FtRgevUE}Dl^i*v~|S@F>X|;dA^)X
zXsxB`Dr0J<S~d<$qFxEtB_%KbJ6eDME65ZpM+5lYhji!nStGr7-}sW9)OtJ<6H}3C
zKU1D&8IIf0&X=#gS2!KlwLZje`_UjPRG#LPG`#fgGR-HjKM?vIIo4mk(-qx1bUwy`
z*#0ZWaPB{&M4TB=<qvAg`#Ez#$^}+<KCv#*5maINTI>uTC`{*5U!^__1U6Iu6s*_B
zH|lgkDtMIf>>UnC?2%XqR!!1jkjf!B5a1F0ccCS;e4Q3X(Aki8ainWj(7rSinsm}E
z1`4mMy_No4+OBHi2<Y~XT`^(8TbC1OI=C-Ea4tD(PomN{4Qv2(EmZw@L26Wj&E2HH
z%<6+L$b&sjFO9tk%(JZ@`%NU;8U&+59woC`#E~aMSIx{u(-D4q#?)b(*#U9?p&@vA
zk0}1h@vWOFY~UD118WV_=&Wr=nIazYd`tvr=14K72Y#S{i*sv6g5_xm229D|f+Sgp
z-E%_o#>D$&8+CEPnn0TZwPuEcs|*bp6cx{2T`Ic=`wH6Pj5V%x>&KsNN$wW4Mfg07
zD*oE;jF^h7#%TJc?XRi#&o2Gwk@q`5rLD@syhLITrN;7RT-Nr{nhe_+SmxVJDdQpR
z*GF(7@mgLn_7&v&iTAxm6p4GydJn2}T+w#qsd<sQ10AFU`W88dBo)<^*(zQ!8*6W*
z1{27T<%eOeeP7Y=%}B@$W!{6t5Qq|a@s|0S{c7EpFyMy>fL{~HsAPNbwvg4>r)C=L
z_ff?_5qoS@d*xrbEXrgtJlXK@?i3__Xt>|U#y-qn;i3e4CxY~mV}cerD;RLlW;hU#
z=l6_n7@<5DEthtl9CgI7&mM0yv}MT0E2^nsqP#`i?Wq0z{TdaPR4^&PAL3G3v?>X=
z7rOn+cxu|<!7D8wUKy*^+7yQe@K%!Ku1t$X;v>K9M>zqo9Dm{}-+Ru4(8LvSMi!%D
z|5|opP4_QzX1)ssD_sSCuD?KGP_<+&5bRL!#*YsI=m5+>sEtqpH)2>tUa%VIW;n{i
zuejH}f7RxYJ=Z$7c<2ppue+cnS=lr;LTL5syuR;Z2v=0`w>}~$RhNINc0uil?8p3t
zdp5U|QDpj`(VKoxBa*|m{{nJRtQTSnFVN0}gt5Q^_$jSg)M(XJ^KQA$x8{#$)bfd7
zc{kWkzv1e_jSUXO{AzcfOrAulwQ%qb){=n}NL%@B>4C}n&Ucz1zWe%BTDOS=c^-~;
z`W;Z^m&R-O>JR6y&&|zEK^E`V&FLmOEWUN3@riUnHa2|r`h%O0RL*HN@!*D_gIv53
zf((>BQc@KnwW#!T=Ftw#liw*eMXnX_7Uj8Y7V|Y3iZu%+@PWy#h`pexpbqgnGzj+2
z5b~iQzig3n)^-p8y{G4bJt>6yS=4(6v%dO>VjKCPN`d|X8b4@3>iAY;53WA7nv?N+
z8iiU1f&rF*#sCx-ze+(*kh65mI$s=sKF_*;j68vUL-=@>64S$${0g5qxEG=?ROTN<
zeiBRRQU|h|?CJ*`vuVUrPUT}Ss|Oq(R5$?>8W;w}0D{1|oVnn#pW0Tb(yfaMmk*uH
zp`4UB5lUhV-D?JBQCqDj*mtDZtSbN67ZBgV3S2~v!H|#;4v#a}&Lk0<!9v|O2!<}!
zZ^FEG?HbBKjjW@#HdVR#uovsI^2Y9Ng2%##w;`Py#pO6`MH2C3^if|l8+R%Bf6ox;
zg1{DX6DmhPyr?W${~hacQnYQri^II4+KLbKUWC~ta+1ZLmV-~2F1fy*5{RaGWV%}n
z27Fp9G@6X7p4SOpG(51T$_-;ktui@)l%rZ6I&uQM&8ZCn<dI%2`vB!TSsUh5OEKyQ
zTlzDKRWpA&_i{iY^SDSORm0G24l66D=obfgm^Mw!soldmn`5u_oAI;(xdm#fnc`#}
zkkMDKI4}BLKB@+RK=k6IheX1S^I@`Z@Z3y{sDZW>STT^!JzaB?w1?+5JM3}-HOs3(
zdw8q%m>>-<;`k*dk)UP7!j^gCwC*rb8XiJiaxYa?3kC+p)ONRsl$si8xy7g-<N@ug
z9Zc=9jYGn=(^}c!)cSfDgf7j%#Ij$;!^klue`M|PpesEZ7Qp@iEii1odJ^N2RT08E
zHSCIO;JBFi`6<KD*$gsT45mSc=~uaHG$T5C66AcFOs!0owXaIMyRf;>>aqUs;(4*L
zWRTylfrN$cXCxh>(L(^Dncpt`j=#_TG}<#h8)19aTvm&N?0Ixy0iOkr`o3ktiqLFS
z9XRQq6pniXI`=%FCIKw|uJ_CMaCe(&mev8n2LU(2f&U1+hI_n<H8D&znnSohq$joN
z?u_N`oF^g%$w+<q(zl7qw5w)qnYp$8z8(@%ZdH&A2?-&7gx<`@Qj(IGP||tZuME&J
zGhb<()ULixNm*prh7Y<|bQn_QG!w|MxtJRMwWUR(Lj)Cgn00VfADoHfz~!b#8)?vk
zMdRc0hu&Rk7}B1A!S4%uyf*6v22ittPnMRNy6TAL;KE=G9vA3Mm`Fo6>vGLN^`t-T
z$9S8Bo2nV#j;jrv!+JA(4B{~y!m%0{BA3~e#<VZ07=<P<7MnSEy-`5}9$N$ijRZ#}
z3Vca1NXr(G^=t){5S*g;_sMlXaXGLS5`iw6BR$+KxLX}O$@LxlL$<=iF%s(@pM6Tx
zuO{A@_lD>QeS@DbY4{v)bR06AEI*4Nq`cip{~D(v<0XKoKOcM~!se)txd+gDCT}Cs
zQBYy<KbtZSGEGfC7?WXDLG%yfsxeMp8`INoAbFAI<;#~tHID2^%v7wLgP9f~25883
zcI;{)zj^b|pRe!lJ}I)A6lRQC>s116QC2QoWLt~X+Xt)R7&qh@n{{mtoi=OkRld!r
zf5u&=EvH5?F=P+ORuLduiKJh<swi(qfydqUaT(&OO!<^zFWWn@{^B7j&|Epgg!?`d
zT|-sRIl$QPvqR!Ro}2RXr`pTZf7(}yrrN%d+6V4`=J~Rhi8-rlh)zE9cMG?irgP)^
zCx=y!%fxi*+^6G;h**fR1^Dov=S|*DZVOSozLjok@>|2+bock#86WWW#nV~hy|(>8
zfOg}DcpKz`J-aazE#nEhcBYWbZ+6uGEAq8dEZK*ip44&BH4ugVaIHrcyeYf(^4)qB
zrWPM;;!V(G-n?}SoyKkFDoDUk#uk6NmC9CVv9*ELp2Mj9n$zpo)2ZRY50#W6qH`HQ
zCx?gFdjiG#_gnV&T1}JV3i7jZsDpXMv+?vb;};G4PprwWe-m%Zo&_XFU$QcOQ3DdY
zlFsNRP_yrXBD=r%`GfNH2TUt-ru&i7p6ac;jen}#tb}l>7LD#yZw$YmF{bHL9xR1>
z1nfXsJ7q^n#`~|@th({UKKPeixag5R8}_TWg^2j!M~OD1$h{>XYnPXofok?RW2XK*
zp%I-~!EnV_u8*5h)7}D<1Jt;$nn2AB2BuparAdOgJJo%+Boj=aVw57-vE1HrR>lU^
z#q_4^t*!n%;Z4Um94lB-D18d1rf-=elr{3FO_9Z2vs~i%6f)}xNlE>F{`@IeK8b6G
z&i9r3>C>l9pjn<?TMP9#Kc39W&+nJ4f-fMTKMFZ2_v6_F9C&J=GUTBw$XYQ~VJv?B
zgxj{GMNqIKjR;(txDZQ1SrB6Byj%a*hwDL(Q6tbGf&!aWSRJ%VNT(I<(SVu$$z9~0
zm{49K%^OhtjQ99zYrm}l=@iI(Sqr=cDxgp~F<ZHVU|PS+Lgm-Z%uGEY$YUz<vY1}Z
z{62)rAs>Ml2#fnG$GE2)R9uSe4Of4vF<tto!1VRiGq#j-DgRB70o|TcFF$fd0+o`o
z>j)3$$xj@O8NuCNY5I{=!f9A<9VJ|i%8)j4CK;9b{+~b6uDn9mVCzz;P$6~K+=3pa
z5D!h!jP2nGB46W9?GWne{tiz1GP7cbHO;ilNvxD{_a~1C*X6p#R)1-0Ylk9Aei)Y3
zlqlpvc;iN156mG7_`C*UoQb1d)5zl+0Ud3IVs$*=gWa1xbAmvUEGoo-f58(<>^$C^
z_q#YZPFQv9P9W`OsO&jb+H&Hjvxbi)nk-}1sOqK6abw+}@XMva_fPURl}aO2zd|m3
ziJMWi(4Hw9YdUeYe5^k~_HJuKxwfRk({19%8`yS@nFM}~6Jv@<4j1x?$T;Btps)Gi
zdbt$LrtSi}l%rH+GJB-KsIOcaSw%jbcU1)SKkz<yfL~YKc_QcnogV!7Qu;=a^MQ~;
zj-Ho+j`EF6kMKhRik4k*cb{**;%F*+(ErE^WH5kJlLjFx+%i+vZcdFU>feKmot?cF
z<^V{`%GQvKfI-=dwz0RDn8y8p4yJ!tFaD(KOcc6~iHQm6g?5=~X?mR>I2qa42Ij+I
zA*Lw-WTsI|hl7U$VhyU0?m<Nf?NN3zuHtn&0H+W+&3*K30e#4mI2a0cWUBtC8n$K{
z&04<%iEQ-jBEx`zI(Z7YYjcTnH{*?I2oDZEtc}|Lo|Z!2EuZkLPU}s$M|j(6zezlv
z&GnUz&1v+y%_c9bf7;uC>MSVm#B}L}vrpi7Lud;N9Gm_9k}n=6J~#hN8g72+)4{b_
zVFaOft>7cKdrS~M2-nh=?}O`KY9gJvkh-i7?F$<sjI@n&FGtS%J{<vjsrApFxgnt+
zb7df3%+iw0^LnyHptvpb2i7PVPMaBV7R}FCkY8HUpD_CUJ9esM=tCPD&ibQeX4l<*
zbubEf(_($=?rwu26~5;u3nC*YTgZA%5iMwj@RSI3rNPzu1!Y-9on9<R;=@xJ;L+}4
zX^NM(6D9z`MqBU0O1b<nj-Qb;z_vNfwQZ-&CY?qgnTlV&O7BiKgKxgJ^q7f~WCnFL
z9-$y}_b)vhkTcmpkwx7y${A%heh)HCkc}#w_9TjHL6Nd8qWjR*a+@z$Y&?Inzf<vG
zDE{RqF679DbC=ZdI&tUnLRhHDJxE|I&%kuz7{ddKZhE0{BQl`{uAEAN6g1}WJCh^E
zfVlwQI}PefP?7yKK*)uJ$8dQ$+<a!_qGj2VkBe}uN>a;qFA1u&!nA7~^mmv0@4Fss
z=39=USh_iZo)c_V7sbY0<YT1RSUWVgu-HmEKe<dGv_ptVKphekbe*Odd^jD5P2c_s
zaxENw{zbZ$tB7;oxef&a0v)VXwVxo(gH^~wZ>`$xs<J>~wI2v?A=eb81hHq<jR2#9
z7D15$VX*o9iMHl_8U*Q}P<QgsH7_Ml!P`wcFn5*?J{Q_8x<=pwZH9P8lpbh;NfZ3-
zl4!Az5Gwe_Vs&wY!ST<Pu@fnN`y-rHeV!~!CH&UN=ZDiCwFlTsbB8xV3~m$YRW=AA
zH_9Td6#3#-&2y7m;F%e56XM_<LWQ>twpuU!v%Mm2<83_Hh2>Zdq#NI~mB&53+MVxf
zTKgi7m1T_t`fqNhNA`I-b(AZEg;GjAp}%L(fqez)6(({trAP#G!1|L;XKgry9>K>X
z@iiv0Q&DK|+({R6$A_<>U9#p%k3jzYoFLGrn6vbTkQNBGyj6ibC;)vbUNucw8m3IQ
zXWc<03$ug4KSAL8*U@Y?H!K5Gu#8+&?Kk|M@dA!R?$^TL^aPUxTHUy2y<BlAwq6Fq
zCz*ng!)X<RtKMVU;s9(;3{iN4IDx08FeV?nKCb_=n^4|G?h<mgE#ZK21JQ#bj^6`v
zGTB=QPZxs4SNU}N?1>3Ogf!v$O8{T4Tf{ts&9`(Z8`~EBFOaNWptyb0b^_nDb#=pa
z-48G2su#9?k7Pjo>+^AWWF#@Q%PQxC!@0=KogMS--LGH2TApsi#l_WReFVaGUmI#1
z5KE~FvPGbHNuy60V*5fZHk_Xs1<>;3^UD2Dpc#g`fo4!LL(=@V^e4c+SknGM>+aO4
zn+@;`1PCj(S~5c4-QV3o$xMyoUst7{Yx-8k6ZTn8OOEJz<RgiE0kWkGxKf=QIwYP)
z@tz7|pu9SAf&NgGgQst)(C+V*(kDs^4iU&B$+A?;8kc&^^yLGs6zEaIU_rcnq`Gkz
zaU0n?4rE|j7jr)hlfIV$_*Mwc^IKFGi$dVEZxYTK5)u*=l^QGb#>Jl0Vy7(wxmd1C
z&d!eKsTvw%AB-ID-By4Hs7eCb+^tr|p7pONdM3u6|8R3mhEcn=@Iv*nM8w)ycRPIL
zfH?<5$P^GE;XXiwl;R)UYbO9S;;pVyA(sPyT3xrvl9lMVj;DFyH$oygEfTS8ZhgzI
zKGoE@18wCqDtv}<5R)T;FG=UIC{-m(lXOVR<dGg$D~6zHmH_<l@cAVHMnoq=*S9ZH
zF=+&!AV&=ll(p&?4J?{y7pdvrfIXFbruW$I-@hGwaxRH_CL~Z%zn;5_pfB$3?qH{m
zPX-lLN<kJJqVGa`QuI}(Br_AO7W|S6{dp!P$^3JitJ;Mj&=yvFa!0I@Q)yt=f{-@L
z=GJFI_q|07IlSFXaDq75tQhrdWjq{%$_z>B*~Tn)+a*U?)K5R7%!~|%j$%bSA&I*X
zN>6r+nh?l_1boZx6T0wg4j+PP1UUj!6@>#Z!5t$a=y=}1=7R5n1YexiSvVCUe<O<o
z>T~Nw%{T3P2Y+W)X3Z|yy}jO;zdqXJhu1XI61+9#P4Km`@sn5qkKLkTT1JM$H$;hV
z*K&MpU_)1@vUUI*8)su1DGEo5zYqu#b5>u`ve7{L#XNel@in!9$wgN-vJF>DLsL)H
z?IwM5+Fom;KD8&ul?F|*cH$=Q%MQrlRe(Qyb)X@)M&A%?hyt52=gDb>BU7O0de^+2
z@>QK7XY5N+GsI?=TyWK_L39_iu7x#!uUI>O2u$M0P4X}AKwMMHeHT<qCaHQVa<hmf
zgsUUxJVQor5;qGgYq8!JG~`Z%2~1S(LSkZKPR<kBG2-xl)Gpt3#pF%=p(rQ}xxNGh
zN5^&6=5JG9ycGm%I#eZC4p$qpMgIbjHj_{4miluFUK9u@(h#m-AIp>H)}k24gBXGI
zNEiJ-6%2G)R$*?kaNE>-+^^L>Pjt};x?J^`p47%DR^cJ!HG&@wG;-O)lea)#1FUQ7
z;4@_X{ryS7CrYFL#){QgRx5)9YF5Hx+8d-*Ps!22zup`6GDTM0BC}U?eQZpNoPy$I
z4KDn5HadaSYgXr!xegCN3wRzovJytP2;M6sLWxl|(hpf}EaqQ5_FCw;fbc$OXuAwZ
zZvrNB%HA&!jDe^KJj|W+`|;#6A>En&<t}ZzaCBt;QLR{bKS-Bfi8<krvF0pfsteIg
zAbYwao{{b0R{0XbPr=dfU4iEBS8?%gs&4>OgQZ%9k5Q#{<`pxL%_$1GpjbREoHN)+
z|M;w%0Rr+iTS6}G_9}s!)sIYwQOVeKn*H#uTb}O2C}ntI$t_5LpFp8kyTF4oPAJ;a
z7b_EDQPsK#{3Fg1-aLVnpdF~5V&5A?86rA~s-80{Wh;|?I4(}?=(X1l(A~7Rh6vez
zkch9X|F#h-I+tOChKtFN2zzt6@-G(Z>0Y|=Vsw^E(kKBuw$R{zqpiRfB##M?snC^t
zDsKp$W%6$qMciMyeE+Ce{y8DtKm&y|-w&O$+cmT-eMy<1pRCDo=7(%4eSjrD%CUHz
z(1aeR+-u)#KDsh=zFOp9B_k<rky<LcZrBz!4Gj_vpnlJ}C?=d{@hoEC>-OD+3tyvz
zlk$*cH)R%fyxN0n_$IQ3JzJb#!cs)n!Ll_MHTdQGyIhm8bz5i(PIhxfwsZ<eAq;E-
zAN%0l{7@{#%#STmq)QeokH5Z*7~~NYO@ENL2i;j9e%jO$xTf!EH#=$O`t=7h%Clpb
zH^K3}Ih+02cpnd{Ip{(8(Jx~AkfQ@`Chz}~5{;{~t50b>q;t^i`U2|vKAs@H!<xJ4
zMU)UaWdJ4fk89H6^1p^@5f~5!I8MZpJ~5%wf#9z83Lv%!ny{-}N_g}IwE3wHuAqq%
z9wK89aNrQ80mN;>K2%#m-v|)a04S19WFr7;Qu`6Exa6vk(7Sp@{g(L>V%q!@CyD!~
ztYu~YvgvJ4!NyTd?r&}aRpdD7D6#K}FWE@|D2lj8a)*CRE+Qr*5KzA@y!xM|Nl6N>
zuWBK>!gt*|0n!YRhuf8L&9FQD#)&%*8OhDuM|{7JXYYOxY(ziedU;t@TAVe#RMe7z
zfdK^&*f-u{ZT8$ik}bCbV%P}sXwx<mpBjDld!pu$=z(!RZrZut^u>G8^#-JP1B*0>
z6~x=Xhf|+$9aEiot81Z;|Ht*k>?jX(^t!ywrX`rN5M^%*!WAjd1ceTY=OD2)4URgZ
z_TDMqPZ7f#xOYQVmeP{j|9&GE|6X+XV(K+k7K)QbqTUq4{E{dFADDoc()MC7*Mg8I
zrAXP;XfGOxvVag{sF3rim?2c~=P+$WFl1SsYbz_Kw8+4h63AQ(QLZf&R9kb(ioYZL
zVdM(YqwUhRHVpnCOo*Z;`RDvRl}_eF%!c2%caxqX@qeQA<&=2=)g{7~q#O|Gtdn+p
z48l>&s<J$=iSO?<Lob*ID{B99YYD#_|92$g_@9q-u=D*`F(%aaUdtMP*G*}vko-L4
zcK5Oi+X8J}WyW~d@i?F(KL<&a3k3d3QxzB`^8f=o%vgUzizO{DZ}b6}x-1eLre#Ek
zPkAke?lDTyy?c!yeB$^OQ?}@8=uIV2!N>R>*yN#kftnx-Xz<SDsz<g!SKwq95HpXH
z!q%<$I0(!eDTWNh!2m*zuR|CZba?&d0ncut0>TyyT+Kzp_3zS2GXg2aK^C@I+i$fO
zm6SJAu2Yq&Q_HrV>LxXQU=N>-Z+c{bZ*zSqxJ^6~C&!vr_}sGnc8bF%SUgXjJdsO$
zK!GF^BWO7*LSOgsLtLkm=@V|8K9=NEhA6!jv1#ja^VilKeLrx(&75NB(PF<rup7hG
zu{ltXYue^grHQ0@S7dGTE13cOOFT{;r4uxQ>FbwE2U@))ErARc5#KbleiB?u6VTP-
zh2u3c&!QzW^bQw3|M!=GKgVT|oU1)2kO4`HAxt@Bk{IVb2&GH9(aO-EYqo9nHX)V{
z$w<(a#hl$=@E6nWU4!&)I;@)=R|z9p7hl$RX4IbUjSf}4VxYW!z1VI^IfO=p`t0m%
zge@3Oj7(M_JRt|VY<5EqM{V@GIu-C|0EHeLx@NQw0j~d+`TaOoh=$^@-NLKuaO=m>
z^vrZEi05;vK!&Jjax+X1Kv+_1iE(u(*U##<aS$)1!=(P1!-1_a%}{*s<1moi{qM7e
z<xt$1*ujtRWynPn_5>hS-Qw3nulO%B5CubqgOG}aWtu<8g7jr*)Nyx;eOJqO_D<^(
zibW)MU(f&U<bwFDRz05Xq`-^PN05;b3-N3NdEr@7!WD9ZpGf3%hqyI_B6aN-EMvQ0
z-QY>JV-s6D0P!d=t-~3w!m({3vNdLa`=&r@UjOuFSHb_<X9fW_FiWoi*P&Tfi@ZGy
z!Do#iT|<)X41v)gpBs1!NVJpjEAA2*v-q*c;6(=cLy=P@d)jhF%>_jSM<{$E1~9Uh
zzG*8y4F^3!88bQY)v{<>tbi`YmVxb92)qf7tI@C-hLz1J8unqV?b0omp(e%0wLN2y
z9WLQL))-na28x`Q;UJtqol5qn_6iNfL;8}r`FVq}N*i(kJI49WSnspb<EGKk>!hTl
zZ@&*My#w`K#s8fWH6(wZ5~FB7VyA1s5ld*Nn(i+_ip4nFx6>pDVo%Db-#(0=^@iL`
zHRx9yZ$-&)G!H6#zOn;1535RF9i*fUe3biQ;&J=0iOxJHb!J1B_+GN<!~x12(5wCL
zqsgox4;ju-K5%p<$=d}it|Ofuu)|>E61Ua2kMD*Q9S#s)M2cx!TRjE>otFlE?YC**
zRn)!v4t0t~tIw6M8kDpX&KUR4#|31qL34K6ZCwt>IH;6g|GBM@_+WT`Tjn;=x8Hwi
zSi@oMnp{)q)8F!A<YrV68@OmlqPD;4rA_VyK8D>7+Tyl1m9A06-ZqC2sriXFsit%o
zV&qr4({ibj1_nvO@wa|yiS}9GZ4ndLw#};mYl#YZVwzbwD@eklEXX~3F#6+foO2gs
z-V%1<xE4)+M41)xzuy=_%G)Q#2m!}q%*#^;&p8qkJt7IkqLTp~VqQx=@G_1U=SfeB
z;i2ERX*tkQD6(i`LIuMvF`g}YW6-Vd8vG))KgzBujY0|kjPT68r+QduXa2mw4Zcg3
zpc&x%p!K~LFs6ut0{L`En^>B@rLAlBca@u-MG3-Iz~O3E%oeMWMQJvuS~gp!&AQd|
zdBS4dRiTekEM8+`+0OPVh?`#&#jS%i`l`;s;k%K+v3Nv&<?@hl9&cxE1vr_1KS~T&
zAuho%Du=EO8bySsNO&q~gNN9Gz}o^z0A+^{!0wBzSn!oinc6#6g~Qn#u=%;w*RH?6
z?L4Gog|~{}rgmztsO8iK+t}LGvHI6+NPjg~S63WBbokb-w<gjHH1QeQ1w2eYQHii=
z_Z*yIOhJdoC-yA!>g}q0s3y7y8gh|?W?J724Md^Oqx*6~5@ZbC)(C+HAIva5kSP>Y
zk2%o=VXBjzYr%<Z(Q$YGyCO#<AdKck#RzhEW?e!u6(B<Zc`vc=XLys^AWjdKB1|Q?
zbQU(hra^~Dz^R`(iW)(BIsj6$!4%0*Ki@P>g#G7FK-0C308;V@7YjD6zOe2P>m~>b
zTyqctwZo2m#X~#E*!H0>m99{zj#>`>;sFa);>5Llhm1VHMZkaVD1;uJRpxgztS|dK
zqkRwhd(Es2vv$_PmhES3bX@sq!)}?rRWsgq>-0>sdUebGv(BW&f!<x*oBL`Cez~Ea
zfdS&$KiJxU&pg;Q;AL8#vk>ryN}Jxl+Q62E%%}RPgNCK83c+Ozf!JA1o%HshLJ-x>
z457?Z2XM<}_FjSs%l!`6R0@O`Y3b?H?NQ9%+S?yKeM$i-3HlZmFbLISXJwE;Qc`k+
z4I+E&6d@cC0^n7l=%^t9-(&8$$XGa@0}-CBl+pW?${cEF!Qn}1gSPzs$Hz}54^IH0
zAfGeK{Sp*lNuEOgE#awhFvu@JGW;Srr(jl3OIT;ZodOP2`E?Tm+9nui0O<n%f5{FC
z-yw+a%~!;!Ow<h|<YWFp3d}?<I3;V?O$=8K<(lU|=bJUCpoOeXdxBE{;tSAsjD9&3
zC`SSF4q2jGC*3hBt#dM{h>s4e?1HuUH;>}sRo3ME`lWnwv{HC58!lpfu&IBIBzq$|
ztFSN}o0xvemrSQI;OZ@I4REq8l|Yyo(#nM5h}w;T!ZLT>*YR7(X7Lw%Bua~E^b+cQ
zyL=QtGe=M3M#}$2#?%FcSvyI>arfY85w8FqJJSI<*ycx^Vfz)N>J&6)HZbU|W%oh7
z1?hND!Gnn}<IR7|#jI4P(O8f`05_5j?aBpOHU1HNHRdv$h@b+3;uYD|-SS^xKYm8s
z8Dib$t#4^v(&rjE2$u6Lu<qYVO_MZQUD&1o46%W2v^iXwjPqN*KJXRX2u<tf=SR9W
zIW!cY>v2lbaCXE@?S7!|y8VN%sE9D>6g2bWVt|c9NJ8?b5*<4&0F)fJzY452)fd)L
z)SAn`Rm(eibEc=|9&^Q3_W}sZ*>IhJ#A_O%pEXU<JU30lpc!zk)(GhOBMereDs=KW
z%lywNrVna<-z<0@!&kH6C7KAB4PlHj)aL(wmeBwSb-w=;iArhLaJ3v2SSNz<8tOX!
zYY^M-Kqt$6GDZX%Z6tOWQpR9p33|4Q3kAAR>@_56`-gVU(|~HpJ4l|o?Ec=*hmhYu
z>^(;5us+7$Ls%h4?_FAY?*<iB5B%Q1slJbyq`2|4!Ck08yT(t<AOA~d$oLuJ=7MS%
z8gsd|gPKT}6o`xS*Vermp(pqD7C0!@lwEEXW7&rWu@S#u$;xY*2~~Qb7|@+k2I=ED
zhg7|XAG3I1e+OQ!iVOzqO0y38CrGm2hl2Q9LbVQ(=~+T{Ho?>dne&gt{-I|sPTdH4
z48h>bRfx)aSq)+ZZ`4PJBQhJXS-+pTlEIf7lY7ZnqSDZoMjgDn6;=_B(~uL1_>gcU
z;;_!_1n{o-A2*IkJ1*Z@ZciIw`y^OeQu6iP9oZs}bNBbr(KqkiBPL_hYW&?1qok!3
za!V>qZ>UJ$@dhykMW7V*YjMF##89nR2*<o(jX-)N$x8i1%<7FWJcn!JI-^;QOLHms
z4fXU;1OMIdE(<1k94nG*<%RHQQAFf`nJ{Iv!<142eIhLf37)FZZ3R}ql((5`{blH6
zB26nHfbIW&l*q1KOal?FAZjnC=eL)8X^)QI`r8orsP`(lylJ<W3&6UL1Qz_QW(a7x
zo(h7aT{_Dvw}e>8=K@c=@mlIl{PoXh*%NSV&^;sSaUbLEl2YYnMH+7N??Se^scu3w
zK+<ed?(V`c4_WEyQ|wo-UTHjg*3{E;)!DiFIQ2CU#Q7ByUje$%H)o+fd-wf0Ilt}g
zup9hdcVwfdK)2PvQw{=#8~nByK*9V{`3`&(I+%es*QxuBEG%)rk`IPv(VI&LT{Tb(
z6>;@QJ(W*wA9_=XHb%@yy|XR{aWF05lq{PCOX}CZXrAGzff+@F{W^rV^w6rWQG0M@
zLq0M~nWE}~4Ga@O;@8u>a_A9$IaBgr29z*AV{8;y{(WqKh^b#&HD{}?1Nt=YmnL$W
zR>H6*MJ4|Yh|%q>!tLW_LO}r+vb$?q!$_;B__!!E=tzSK*S1ziuKD(BFo+nai8w-o
z+ILrN&;7+G2E->DB+xIZZl;g{s6e5@)+<7Lhw69-wOjoB6iB?})@+*%NC5i=a~-~O
zb90MpXpqCS_&10QaAIQSp44akrC;etxEU@kuIA?kIr=7;qE1Lf6?%n?ZEU38_y;9o
zVW^IPvy^3mJXF&6fcvrFJu&cj6ciKubcS??rD;!4eS?q)Kp`<BcIk8~8paJqM3ef2
zxKx$~Sa29P5yu>m1!=Qf;!c{g5bx4H1Pv^hGt{rBE(t?J#Sc088=+!#(pYB>(2qgv
z7^C0esM)_T@x>wY%a2-t^sO|C=v<GvmgVbAT-$~B{RY2_pF{lDlXOl+a09=SS3+b8
z8u8^U-Qs;Du;A~zveH0ollARn!XRFUF-M3x0)7FM=o@y01jc^OV0^p+)2fsynDg@5
zkeCGS#wS;zuG+H$*^&?w;}5Gz55fTn&(0&?(e4Fe{{?VM*GGQudnR2|F!r?trWh8x
z9@^gLf9bKb4L8u|Bl1`|KOpx=;BVBHt-)w-^X?KTcK!Q1NHl6WaeSk4B4beg`ukJ{
z&rn)VH2~t!|6gHVxf-b*1~O90{LDIlX=f6~i%r|a##pwd(c=J^fnxAvRor_l;^mzz
zGZpKM9T6nxE|f|gkT%QGV|?)9L3Yj8FIuIArT=L`(_X``34vJ>rb=ZO*Ab*3LrTI3
zeQG5pn4G`D>ME0OLiv=U5C*onEr?{tQ<F-2?L|w=n~4UG4m>ctG7@TZR@iQ_7@#}k
z1TBN#@u=89;Pr7Nv=_LWPX$V4Tm29{@PT9+U#LJ$t}j_Y;_We{o_-K?WRsDRvGlFD
zL-ai#rGM?s`ly&H#48eFtd5jjX$zxjie`PrVcbdngLF~>;7Ery4>B^c)Qk*Fdin%2
zQjnLGO+cok4d?%z>7nyDM;=Y+tNOH&DWb_o`p8`divfb1r?W$qQbdSR0GZQE#}rH=
zE^7s=^@Rt054`kBOX=h@ZNE1ytKUEMGpP{2Rhm}*{S>Y(ooYUmVE<!>WmU}oRBc)I
zULSw7c#l;@L7}$`4pbz`2kwL38MY=QWfFA29K2Jdip$O*gDE;Tz=vobI=Y6n^RUw!
zogB<xsR(?kzIJsMQ8D*XKf5OeX+3X18l}?8C1Z+UqZvCk{jU1r;{ja&R3`}dW<SYH
zDc_U)27|=La9*q|L1Z!qp@V}12M>>gy**D*P|(}AZ#`RETLt&n+1OfP;n_?zqT*6=
zOUH{S$HU>6YtGEhZUQ|_R`4j~rGs6)Kdd{*x~&$Knlf+E3RVFWb-}<|{Jm8HqW!F>
zikKDt?@|C+2%B0f$U?G=kbzfUr3pyvA=C|OU0hpqMW#!6{IOo$XVRXvvGLKMxC7rl
z!B-(35hcI>JhsowAbo|bgD1lf*qrQZs>w!INCf(2eO)<xpXlEeR(HXPiD>yahH}MB
zZ~gl0T!rR8dj-jd1)pWPlX8;?gjXU-Je{ixVH^axY*o(tkq{ueWnR5_uWuua9x_s7
zV@|39?R=pg%+1Z^G;GD~8yJ|td5+$=I$XLnWEidwQ@=PkI28E{l9N#x6h1b>cyZ7C
zXB899S0T9d3xsf5pC0Whka>1>b*T*-o0;YM0zfDI1i2w55;e+ZL07za)|aSxTJ(T^
zNC{w1P?dp}#=2tZ!A8Z#M({sZ!(-f56q@?|ckAyS-603jqlkg36;q-N)31jT)#*@L
zgXEJ!idaRr^z5b9V=09F%9B%RFheN4e?CfsWEa;dP#%y}FR+h04VK1w{*M8W<G!){
z08;P2Knwwd42))T-TVO*Imk-jSrON;FMP_&D3)J{koKgjcR;eiKp|Z1n&}K3J~HVF
zl+sLwFlP@D`@+yl(`2rcb<xa8dy(VSdoT}p9LJ`nx_Snd(NOK{dw{mwqMX4*K}Saq
zp%$V<GK)bFIW<4;V8+77N0#JqR+ZuI<M3*xIZ*Iyw72)kS~ub@)^(SlLI%pDK6I7L
z0-uXo*A@TdM27|zu(&G<I?%4KTN1taHLy(qqIbY=z+4S%?-Os5t7Sh3J+=x))k`{*
z)OtTno7%N4&D|V3-wiYt8-v6k=u(Iw9CKw1PvhVE68`-4k=A63mJg+QNl|?rFcL)4
zMeqKqM3v6cE!L^D2#m(7;omI^{{(PnKq48^IUu^MwBHPnyR-vJwz@UBt@cXl`@17T
zLkG<$@RUY=i;eNIQcqv8cf(M>@o2!DQ+zx`!EsKf#$hd=up&R7RUtv(5&=QDT0*MF
z`PqG&84UPA6t%Q);nIS;5fR5wso1w^z~84j7ji#wVpRpInkpX}1t>28SRkVsjCr&<
zE~al?!u`wIC<ih#dpi2j)Js*Z)NX`yBN8JhAy$y{eoeREQJBEKwAmwLZ-z^>?=J2x
zARAZEtm&)BVfMg)wZzCBb;jaVYa&=5L#uz@2s>mP7+dP@*cJE5+5Gp&Fa5|8qGyp3
zt{g%8!qxH)=5dfnWY7CocUvINgh-*>R#gpwfhc|lPoM3PpH3RS0Lp6bDNI@f{lw-W
z<5DfMCuc79Bt%&}Og5i?^NA7;kPp|ELtxq!;`J+UI0kPrIoay%YP21wlYl?|)KC#h
z3i*decD&!Hh>5)+8Wp6c@Q>@!Qf5BB&Ey;?CBXwjj%bxpXc-tTb8>QGVPn7Seka&z
z)o|)J+ZH}qycQuv-J`$>pzZX;C~<Hwg^_97WI`YDRh{bXX(#iykuTmJ!&s4aGfMrJ
zDTTf{n#j9;Y!O0KCyI*VV2lZv%niw-aYH{=dv|a(T9<KwwkjVXVp1$g=DLu69TYDx
z`?B;{;mWHPWE@|gqDcb`h5g?r0uzL`7&RJT@hOHYGt5E2_KKG$Ox)0LxL+*MEn<nx
zs{#W!GWuq*BMk+SKN^7aFUk!9^=4i?q?5dqqni*b9SPGHo~D;cGv$dJxRZc|{jG+g
zZ}W)^5OpJV3W0Nyk$Lbm!7-dYYBz4#^-ifiMbr`n$?iL?I|ddKHmpe`PH{1xfFX_-
zilLz)sI86*y7*vZ@`L@cS0MSTvp?%1oMXFt_Y0^$)`xiQmsNH)Cf(VCE?v6B$YSms
zd*}hzZ}n_LZcdJ(QnH|In;jom8063RuG&*Kh%Xq(rY*<(j?dV&`I<({oZ$R}YVKuF
zp?Enn3@&W=7`7#3zkS6W2v~{u0@{X$pb<$_@#@aR{HpRRASh^ny-FO8$Zeg;$zHS?
zaj!9k0<y(EGbt&7XpNAHeQl|y`Oq|l?4~<y3ya9DuVG2J;q}$tfWyz8(LEDnoy*k+
z!f=fA*>AbkyKI*6e9gYyzouR)!*kRrK?n|8ww9boipqCShI0(v*5o#u2^~YOl=#Ds
zFtW@v<{HG!H--vq*uy;ktv_PeSdKksI6ZxJ5H|UZDPPQ12~Ro5?5(oh?D?-CGbIKx
ztF8gfMBdg9W22~%&VpaRf8A`gU&-mw7-Y-b*r{bnBVRP~Eqw_ykRUqkYU~H^zohQ{
zAj{BqP1<nJ$;tWb?3RFMO9s;WO3tPqfY;K~(>vM=6B83Vxwzb7V_Of~Ug1ht4upa0
zprKwF;FXn?WqDSfr(IZT(1OFFUA<G#brssR+6P<1_tRcSL`00Ry$KkhO-Iw9W<aN{
zmkW=H^`!HAdf%B_)=ujg25J%4+vGgavh{q1S$!?ST;H6y3Ra1r;6rFgo(TQUk`8N)
zc#cOZ(f3fkZZLjO8YQcXyqht<X>T$dF4xIdPp}&6?e5|)<eXQyMdk*2UP8s=U=M1F
zxMf3`h1ish4ibQP%vzZ_egK`xV9Wkt!K0@vXPtMli*PREQ@zpI4!^a&euQS7F7DM(
zlKw4+)Wa_?48D-d_<OA4Y|qcMqzrw{Y0jh4SD&ODd5asV>8&N++T^v8{VgtUrh;~<
z>@W9xjMqFwbAxEF%ZRQGpyDLkH>|Mg-zNS!sMd&b`A%&o6Wz-!aaR?tnbA`jZL!Kg
z3=;{RW0a{`-PBF#zm9#D1&Qms9o;A>C^YNpyD+O(md_IB4b08Q*cQ0Cjs(k;bqkNz
z_H<tVCOJ7f-f%R3R%S{vHa=da0{hth{U6M!wiT{2T^W|0OAiLbdZzi%M;_ipb$)i`
zeng?Bi)$=~?H#*<^?QdzA5CvIk5B3ZCWjfk>JGF^YVbXp9AEiOgv+%Y*D%^2+W0t7
z`SyFhbFX`yj{@%|$}Fx7nf-?*xVnU|PP}EpgflI<+1LNsupsxEr)&$+Nz@$)ON*6<
z`P3O`tyOn>dNn=MF8PPqC_$-@I;B{C?J<T!LnD1zlJljv3`9e01?U(&c<Up<7-(oL
zmv$=12n}vE{`DY6?&b+AhEp82bfNO8V6_U1s6MOSq_B;`%_9$i>vQE56_>3IU%%ax
zsWCA&o;aF>dU4IRE3CwPn9KF>cZ{RC3y2Om%?4OlRpEZ1k|@9!p#P@W!+0hkL4S4g
zg(q4xCM8=QiSsZPJeBG?1v*h%Uksr<JBIslYS<_5a)j%a^qKTB!b>FhcmwPrd{L(i
zZqjB-?#3KAS7CTpPu=z%qtyGsLlLwbf97w-Ld?;o9;i54uy2A0c>evYb@UvxmTDFo
zYYq1kRmPQXZ2SGeq%BeODn0yVob>u6vf?ll#qpC-2ih3l>p1i?QkmOoS;8W9%1jbE
zp+pMw`AojmoW3ZxRi&-7rM=946TScZXTMVDJ~{T{#wT%~1Qau|P~Cdjx6+=R1u`dZ
zKgGF|(SAw3ao70R_T6O&I~k_kU(?PE4hpgcFbS8O<I|4?LC5tQI9IM%omTk!w`une
zMj+!%$aI{h!z{ud{J|0i=@@?11RyXRhOHrLRFUuB`#^m@akK$!%`Ha8tOVxN=$wLr
zdP8h~gum(g?499Gq*MdtnXJd-hnlL1BW@Gq3*oW)U6gq_yM9dXHIiIOFWkdiPaH!i
zf5U!OAyMO*5;0?cNXH!zHu_=F@JFiK^z$!Pc4^Nvk9BC$rN10~Gml!!F7qSjv_(nN
zev$EY+UF+As0$U(%|eliEA~8XnNQHC4PN%mxx<r=Yu(s^KeWjv7eFjj%?4YC=Imup
zWwdU7iMBznaQ9!oWl%Gp$P~{iQ-?uzYfrnmlgDFO7Ru!}SOl`tBL^pKjiuKfpSr5r
zu_#!#d{jcU5MZ|_0BiPQr|k)au8;R%kfL&L))r+au2CiXH|i%3Z(N-*otXEyvQu%@
z%QH1MH9|`zmCGSn!;ioi&&T`q;1AVJxBBD2@bJOqU3=bdLZD^>`Al+sJvGWE0JyW?
zB5qj-PhPjGwOR21!->J-&tyk!L#d$(D;mVfBzkZhIsEx$FVtCa_%4w)*4Cch-c8yG
zO`V;%z?QA4TpecxviHo5(4Gj=zYQ1e?9#2nOs%bh08fQ23B%`~&=GNzxk$815CKi!
zSxh?gfQ6O<!C#rS2rJ+gi2{4~lyT~Gdw^zP)&7x^kT!JJ={%v|vZHB-(ziVnpyL6<
zBepJ8EljTk8A~6QE@Hnw;@TZQKonV*8hR1)k+y5nKbP60{jNhPDV{9@+2CaV<ZI(c
z7qkn^qIe6s-sM>-7`a=ze?T|3y`E$-L1r2@Wk?awk*`Q`%vz38_;?Z9M0U2O9^aly
zCfyT)l80y_K|490TvVcCIOtHu<p=SKCpS7_)-I-p)IF&Bd?M;i_g}!sZpp*hbX~Sw
zb1N0BA?IvDhy@;as5<fm4)yNNPPyE{L69O87jv%H?dw2;bQ#6^<iO^^*}<$voik6`
zujtDhfY^cZ%w}h?=NW2=&1~zJZ_@h$TU3Ca-81g=)M9HWvj8v9P>Io%=Unx}gfK^e
z>=$@Ztnx;->Z#Q<1ky7H(0{V>@P30<DFMPB!6B1>S!xoT-T!jak{&SoCEILq!w4O4
zgwUH7*I^*l%hSo(@tlH~*>sDTEvdYtKn@f=2(duMEQx#8L6-LaRsSgkw})EL0+h$8
zW1gXk5^C<UR6val1fjW5QXn*_gqp){*HPB-9GYRWG+pzLoZZX05KLd>1e1eDZIz7j
zl<21XiL;%|4EC|j-`$51oGqQotHIw}tki8IbbD;3EwV8-KlAt&o`!xZpFBVJ&~>@@
zfpXb=thgf<s4`vZ$}s3JqM+g8LW1oyV#!}XM^zw|sXH9arX2(mLqsnOKVYJo{*^9!
zFcZwqVcM5v94_L#!-!l`;P@8183Dk@x9427=V!;1S@rf^34;EhT1_oj-+1caeVL#Y
z;A}m;MzRM$w$T^X;s^6d2K%v~f**ho+My=J!fm-Z3Yd-r<LPnIm%HsagmrVj^Z@7!
z6FL6Jk7omKBM_aPOnv06aSOC(7tAcABBcW8BLz$#jORebK8%uICxgsOWa?^fw5-<u
zYG0@ZMltw<MP^wFEyNXGQ)%VLZKx!C&<zAYyBIu!GC3{k1eQVKXb6yRB6*mdzhD^(
zQ>?lzLL#1~P!5ejUWUoKzFw&y9EPB$C^|oH8s-0;zd=2D4PHE`+BO|I&TBf#QxP&^
zSpV5&sGyP2$Q$cQ7K$3ZpBf>m7(t;}RWkU!P}0;<L3m@guJ$r*1sG*2O3I)Z9cOH4
zNDuRA_gaF<H~~9ya&pq^%Tj_?;tfbc<>cj+z}_)4W1v;<N&#e<gtM~%%2*sK=!rLf
z$+9OqZcJ#NeP6!@EeZg|G^(nq73I5Yqht-Id)MXUDIU<>TsB?)jRSC}Zwk{EMGj!H
z(AavX`mX0w_gq7CaN8xV!`J$!7QY<0KH$?7q?t;En8~hn5RM=sdN39wWPtQQ7H_u{
zm|{p<_@q;?s6s4ZDFA#5Xc<$XasPgbtksy(qsEYXe-LHpKc7W5Zjz5--PwxQ=L*2u
z_fN{SZU0opBBUc-TlNAU4oo7im0x(VRdz9cJ-EU{)vZ7io}AykB_}o6ZkI=;u%Dw=
zZJ`@(f#KFYmYw-VdF<YWO$l!-K*H@b^5L(?^cMa(*{KNq1knbJq_I9F>vl}AO7Mui
z3-;xBvx_K?xzYmI-hHanK_hD*>MSZ7aCqA1yL8f!f*5B2fCmbqDc>^+R6>s*x559G
zws(K}e2}J!VhW5RfVXhKElM2PH?EA0jg0`zGP37qKS@EDE^>ZC93364nFlPnfEO*6
z)54$z1R*`C>2`^9xjEJ=`AVWJwfKv|e^hwNrPDiJ7i^x4#lkCy7ti}h);e6<cL#5`
zq6uPibIl7aRA>e2aJ^kEpq(BC=N({en@OizDca{&`zJ7JQ)Glj`7VRWf1sD1@nY@)
ze~4-;kx^~RgRB>F;#H@W1}jWG4+zJFIlPLh>{h)zZmV*fxk-W<z96t0EBaAZF#tLg
zSGP@ppqys3_?Iw|h9Cs*K&y-y7JM;32{LRJqMjp^lwYYY+#86QPNcm&HraulY^esl
z=zM97Li^BPevnHxyDdVj(kf#{2~%Q;VHD25L!&kJ(lkskFBUB*;}`r^MLTeL_r=P_
z_XFoob(a>Un9OZx4-b|yli-(lGpOr9RaRE!XxCXwF;qYPV{`9^4YY`^d&4G9&~Njo
zy^>AK%rrzCIgE^zWk}C*8UKc7U0oe1Gvo$@m27EY0v)L$D4f|KNKH8f$HvGYI3vJP
ztzfqb;{oRJ`o_8)O1EE&raLXX`d7bTwB3<#)n1aWAew`(Pb~eL{MRRsBmgXj0MbSz
zfOIb_4~AxQzJ;i(WS9w5`u`u|>$EU~4aO}#K}BIKeA&F-F${!!;K5iBuQq6Bf`9-;
z9`%vS=nEqreaKCurdai5KU222rlK|Oshh$Df%NR5gA!DbEdi8B^VtWj)rNP$eJRT|
z0!hh<*8u0zgBdXE*mjHWwBu@WU!x0cjrTn7`F__-Ax-b>ugBzz_FY}eFv92nLJELe
zd1<@r6Z!oP{474{f%TZVsq1OIwFbh+tEC@!Ehy_x_nzfx*X)IDZi|fLItmI1%xtyM
zw81>6OX}+C@=+r<tE#HVg`BxdO3v`pDgjRWnJgxt&J3V+ILtHCsAd4A-v@sZ5R(Jw
zRVMPUXwf~Y{X>TL*v49*7^n_LwqQ_bYJ=m#Dq#FXz^Gszjlg{Q`U~x7?Sle;(j3zS
zgnk(z?4M)8L;-_F2prqAKOdd`@%y*NM*g(8I%oc-K|HYn0h3ztW01gi$TGDM-v4;E
z4r63<h*7^Bf=LRV$c0oGl54zRlFKKCm5*=atLb2%BWMC)<P841D;sRJ7U1g@CvbsG
ztyXZYe4ucMW4u}B1)~^2aMIjv_?^iRW|<%Y1|%E(p^*hLlnXG+;sslLxY>DXZgQi*
z6xx%k@xh=L=T@Y%mr7Xf@7+Bgs@d3N%-uPkO^V%_xbo&U2HLN*+t<>uad^{)2(Ky*
zVrH#Wo9WgR812?n7t|FfYcWfFLBo<(BJ}ztNasT;E$!VGyRd&*BqcKT5xvE)iNclk
zvX2X&kBdIs>+Td<csZ)p{a`yJ@%21~#bmuUP+&b!q1(?iW0R1QG65v4udgp7C)dZn
z@G-Tt%QWu$U4DLkN`Bk4z173hm}e>~?@3uT)6&wSx3W-CJoB4gOT_WslmD%tgCcq9
za53~z_Ma{Dv+xwk9-dbHoBPY-!vas03sCsRYJccNCWj9i-_NI+tVnnk+5V=L>puT4
z_&cqgTE9u6?lu>0O%mD}i?w2p^EMr3ADQ6Fg#FAN%wS|`9F>niMfv;VIgs}3<E;1d
z5*!(Blcq0};be=m*~N9U+{wCL&Y$rt-}hD|U~ldJYDUF&Hfa&AsqQ3}&@5UiYTcXn
zY@cs6;Q8z{s^nHFpq;z_eDEa;J3*wML(ngyIt2!+%g4Oq-Pe^o&bTY<CUnalr{mXu
z{xknasG6%R;X$YTg5y)879&#D(TMcd4b;Ehtv$sbR7yB(pt>1x9WUeQ9ouuI7&h&`
zblI3z{M1$)92~o?7BaJr=*h>=@$m5a!2w+e)eEn|a}-ZMWo65rzSx%nI*a!ydODiY
z_!!a|>4ye?MDT<}j2rA6aUEM3!1=FPyz+r<y(dF-LAT-DhdRDt_@!InfuazM&5U#w
z!+62y{q7c(P6g$!ZkJMfrF{)PskylBL6hZVcE0RB<z2j;B+NTY$+9)pf6)JVd?+tC
z#z*?PiA9g;{g&d~w<!BdLr?1BE!zj=y#*H=a(h*ci$&5_fBbgg$xy$MRK`oppb)+6
z(QxBuXVU(Po~2TL+ZJ~`cjc2bwiSv@_*vt2^p#$a_lVY0!do>yGxzv3F)_=qJ05+j
zdS}(|$EECS+yBShdxtfdZEv7aXJjnESg=q>MGzEFq)2B(suFr9AX23F-Yo-)0s<le
z(vcQ=@2DtMq=X&<Dm4j3N<c!p>vN8tqrd0=eQ*BY%ro<xeA)Y3dzJTn*V>P?V6Sbq
z9fDT~Q)jr(aTQQ>6D^`PYsEF~i<X827_Z^{w7RZ_p1dsAu(|Z8qpV?@QXuNTS$T}l
z$S|!)T|)y5Q#<L?E)*%&<b=2D{IM<fzXq)0Ih$5gt#4Is+_k=0H~bGbe^;}~XKHGm
z9&KEUpFo2y&LZFAYsOu6*}c@6Jg`cN^)_eHp8S~;49SBxjagdx%CQDz8=g_uCwMD#
zDevFRc?U#qanT1%%$f8{@h+rbH-wVpU4NL}R5s>$(p_H(Emgy1_E#4J_%N|O=z*0-
zI+@Rt_ddwqei!tWv?OUqAy(h0skRGVF7utfL@Rc_`q1fnSRuCeIC-g;Hlqc>n(B|D
zg3q=#+V;E(JH$x1j6Uuju9hXG(rh<p&or#YRv_`$qkEj)4fVDayu?HJ8RMmk2W6~I
zR#8b6GF0#JQ!IQJ@^GFk_YS_4({tN+{<qTO(5%dJyL{N4Yle!=+h_A7s55D0j5feo
zGYZ-25_a!K31r3E@UOI1??ICb=8}>h?$Pg0(|K<ZCFdI2zvEwIyhe`?7a9~kdcBP&
zPROOMct3NJ@4S@|F;tN&gx2aLvS}A%Z!kV(&gScx9kVp3+VDJcZDQnM)XVDeae=&H
z3}t*I^`7ra|AZz*@#BHe@0OPQDfR}3o86JzvJGd$`OE1V>G{Ot1z<80<3pQtvDI#t
z@_yx#8bSA#OGb6egRjkVWWB%DR}mjR)7VGUuh_Yk*K{#H-9=2!vUkX-x>7#(%^ro(
zriIUqTY}ZMnL4&8PcVe3d+TsUDudj^tnas^v&iokSQxlRwT69a$=)I^NA{-K74nLo
zzxukW=IE3QRsXqEW>=a?L<=oSde8b<33-+GW1mYU&z{l@4<}AFJs7!tZQw-k!x2t&
zG#AeqovkR+&TmW2(+*nta``xFbCau1!z$18gv%?fdt$OzZcL2&%%;;#9^>NeEjW}~
zTsE$7$9~;1f!`UeHI(n+n>8BuBIo(d`X}Fw2sOfa_EhfhwRteW&;>kNXDw96x3VGr
zw9Sdu?mLV_f^uUH64p8u7RF&hW-mT=C2*X^ChM{lh$QRTtcppj<Z^RF3H-%nnC9I?
zjASheIk;jKc&=Oh2Gg_IixCE*e%uveN2=&2Ui55^(OGR51ecCm{+-aI6mqcZxseki
zP7Do0sH2mT3SM6K=+j7-rYKD8MJ4ZI{&2phG}emS<BP&c;>yEf2egviv$1;jcgYh9
z>)x?ih88=|Gv49D`h8BX%?h5RdfElys5aCl8}$QX>&%XQ$zc&_vi6*0K6az2<`SdN
zn7w3fgFk4|qV;oZJ+!RFBqZ{Kee3k67L>o6MwcrFFJ-h)Fr3-rjPI0^c+Y|9I;($#
z+H7N)nYGu8-^nyqG&imBn!Td8%Q|bFU{yi<nKx{9Emu<Lbuw*UXEbt`T={T4O9^}-
zraLo_S42#^`)}sd-v(YVFr^>Dq?LIZJi`z=eO#VCbGph!yK<Jk@nY$DtZKUlGfp_3
zUdO8|>HMv`egf6EUo@@SV_EI)ne)ezI8~F6#c7?ccz3J2+KpTa+IVv_vvX2X#<H@q
z{LB)~Y5O^%6k#-q(H>d7vbd)GC`<p4_6u>MmzA3jums@y5I@fGlDqo%q;uryxs+20
zsiiHiL$^NX$y2&sV#s#+lns}a!Oadd*{3;})apd=>JHUDFFL4`V7j|(*)j!#G39VJ
z$P3!8GmF`-OD8$-`#e|n_SDoNTr8B>VfpUD1LY!497@&`rti9Zpu&or;_LQcfA5Tu
zovW60&9V63;v@ge*roVMfBYl+(n1Am-+b=Xv+^`TSIzQt6su&7cieT^ww~sX@h@AC
zjNuz|+r7)XDSPl(9bd~Wng`k@!-ZyW03uhYJrdq)RO&{g{Af0R-iV)})Vz4cq*ypn
z!QUyN>A>Q0&&%7*);9{uNl8a}=`SZqrCC`XdpH7Rk2jKB1LMph=AMf-JsH%U^>73g
z_^jjNK?zZZ5`VEL<kF5PQ=fB!MUzq%)@I)hoKJXCZbdvbZT%f=X9rREahm?u*6Bd(
z-m~(eHztg##A-fMio_V6JtY?;ZIV`w24B9qRkt$y_9(5AyMNA=?{*a^wqnDUFM^{H
zY`c9v)SOfpMt7u&R>pgyS%NyPRmXTLr+?bfD8gdPm&(;Q#ChFjL`rB-C+dS@*Zu8%
z-;UEC-Erw0qC1hzpY7z4^ZX%%Hm7g!7&av|wu<TmLZ-3Um&WuHZTihFE?c$Y0;vX%
zn^r?`T_4nzR&7O8(9-SQ6Gy57s6<YT|N5k;`|R@$tNy&n-w&Zmk9o<O4?Cqxv!w2h
zql7jh@f7D4izai@DRh->9T_>HHhyyStUd>h82`2Hn1h)9^tJg&=SLYI3=2$u4^xp`
z6Nq1FazlJ_>{uX;^({x|bV=Q4dyg$olZWoCJh_xmSG(Cx$!r5j{ppKEo8i@O_Y&t?
z{i6T;w2;yAeTPsv198sJg51K?V+Q}SlTyE_BYeB@?=80pJzrM5^ufliVLuV-mrPf`
zib;^Yv{=j~9nELjTRzZYe`#syFE0klZR!`ZyrGJI6GD9Z+RRHE?;7#`?#){~V_r89
z3yH1?Kfk7_moUC85btU<eJ+&9l6eSYdh{c10%z9UCP)lOF(|j~mOYM1fAa>X$jT9w
zeL5EJ_>^HU%JP{cl?$WdU#2y=v(sccTo_ip_NK(l-Xi?sH&^Xj)@S9XbTx~WiO>;x
zNH9O^FE+|Udc-wj2yn&(DYap4j5~|#2BoiG58f6R%BZ21(-+{FrLSK~mbowfo+H@d
zOYVr@h~2}H^Byb%b0xT^I+~P@wRZ$lO2D{}lML<mP3n4ZgCi<~mYh|yy@thLV&8T7
zoL?g?r0I9w{y49yY27_yOizL!MR#NdJ5Dz5pY$8HUJ{w><KeB`b^5pTl!ViL61=(w
zbd&hLj-9zD+oHuKpBq`&2DKpTqplp$uU~%tY(ZQb8!#RA{D%6@>L*5gO&)}MY$0mF
z`{Z%QV2*^7m;9)%NZ#Wc1qTP?`RN}<I8GlVH*Dc@l$NoiBUDtf$c8Op7n~_bnS`Is
zMPv@8H()%q&&X=I1TTjO*iBJG%vRcv$UK#<x$fy*DN@`dSZt8ncx|3?lJhA>H}!#j
zV!fn~a*(8v&nv4fLBDZ9D{1l-@aeM2;hEI8!PbQBBkpckceVlLdr^$yK|_#7wA6O=
zB_8`S5Ru^4{AI#o&7Af7t_41WKyA13#zX9>S87YX{c7An!ll}%tDA7{il3!HY!7;D
z=+`j$K++&dZ!Se4tn|P#?y1WW!Fq?<t6PdUWgGcpZ^TJn(5vV~mtaeDY3sJejU)u~
z<=>U9jB}nx6O8IO4P?!qc|V$*EkDJGJCBZ`Sf>e{@}~&#encw^MfiXYO~2@t>^rUz
zs9~r8FbdcX7B<n*qEuDD?O+=^3A2UsQc|<p6+*A8CuW80!*Zs+d2rLZu;b4}bJvzG
zLe`TVLTB$6{*-ZuN53<mhA>}kq9A44+0*AYZX-TL-4gM(tsf5%j_eha)Km#0kLIvi
z?XA3}>YNVynzUwteSgcYusK^VFUV-qKiJ5)JhRPx*C?wuF>bWF(8u=P+aJk5VBGS`
zMRI=%%PgH`RC?6QV;zPi#ufA`_V-)j_#j?MZ9LVU6-9h5dAe?h`vO-%pRSCkEQ#uZ
zxABaz^m0@^JTmi{wTSfR$>h3bmEk_W;`u-@d0h2Qz>>S%hivcofks<(I58i{%rCEh
zuult9yF(EY>e7v~J-wErg=J3|tpr0y?(~hU+?D*hh_-5K_T>R==?JN`V@z^GSy62l
z=`h}vZO!yV5Nm9WUW56Rh~4tUAghw{K^Id^W%=eT>9X)II^N}5kCtQ&!uzj8Y*_ji
zWqi1y5&LfOVZB=33gk)*kSmeKuDcImq_nKN3uv7C3j93lI`!Z%)Fs*VLaw4U#Cz$D
z@xl!v_QR#WDXiZE9STL7BK3OK)q2J_qlek-SGU{zs;wL&ee1qHF6Ul<XRco-W^mq}
z1s7fuMwAZQ+BcoQ|I2DuboQLU<jAU07{^0~&_>9#?WWzjwv26yvFCXzNclTk`VKB-
zv=qc=ix#+~@8yu^9*oIEC7W9nKOB({W6u8SqO1Enzjmoj?(trfYEs(;ZGxnjGMp)_
ztw&C8D)`sT^$L1zU1^-}O6Oy6<0X`z+&JzQf2=^N&Eru_RcF5ayZmu(-jYgR8RKs?
zAK5K8`K?J^Ot7MZVNB}alYWmSk&hvxiz&!YZ&lxD*327ms~tU3wTzA?_Maq|S{S3F
z=Ym{AdIR$38qbV=KW(VEb5aiUkQvl%msw_FFsrTVNH!hSsb~xK!~3YO%OSQ(zi7D<
zu_0O-XY(Ra$-?(qET{Lt=3K+X4x|5OFHYT8S*s#eS85p(HldTSqoE<0Xpw+B^?8TW
zK|b456QyD<|ES;WNR@;*n$r!*=4ZO@?EDF);zcFr=^H5D8aqkj9OByCY(@Rjw7s-)
zQP|N;u*AZlJaN++i=+0wy(l4BUBh(7I!?~j<wXld)1sRs(d_0Qb0Vh6%Xj;jT}z9b
z{?6dcJPD8D+`otCpO|31J)+lUg6H)9^*e+!Vgq<X8fP@IX=gi?p!bhfd5xE2Bx_~*
z^{9<4x*wZipz%Su7YRsD@5$U7-F6DkpF2KAl3EsdZjjFA!HNsPG?zLLI87dp8SuIh
zr6{_SYl)F6tlP^e-)74ZOs5=NkqHTvVh`>>d7oD<y88~pL;P0M8<9!2Jf$e*3ADo3
zt(Bh7pQ&3-kTEa%md^)DZdwI|QgeqB@@_W_<;(Mu`=!<TMn2V^Mr>sgU4Mz`$ZVQ$
z{`H88+vIw+==A!BCi^LC>hszBHPx~PC5B29iDecI|9am0{)^T2(qxHm+4O`0<X=p5
z6FJG1xkmL>4P2L1gtym@v+%k4de^hZD)&I{WO<nL@{hF(97x$7tu7C^JL=;2VBpS+
z(>x=-*G#?N30^e|oi<af>Yr`z4+kgG412IP*y_zBHaMUGmFqyd%i-t~hg2QLcT?tu
zxA%Fw5$%ITPHF91qV#^8P)HaMQIZjpM{1ipr+<s9=ZJ5SaQr9@R&X*X1v(6(V`AEI
zC9OdRP>!!1Xq+i)Rq+Y^K3>l?Eu3DQ*cxr~Bf<Dwos4putS^Szh8EquC&_NLg}o+q
z6~THAH<Ywo3n8hui4&K2kGu`Ps!z!nLZqF%dD&{#l0hT6$7SW~CJ&mt!G53gu!!L0
zP4n^Ysf5#jcIeXP4aKJ&A{dID8jEEsXX~3Q5z5MXzs(+W88C$v(p$;uWE;ZN?(Z7}
ziVT4(ifs9456_Q7@$IXYuAM?WR2KtmUsr$QI@M|5Qr@7KA$fKBN{3TcD9V980e?yT
zOpUCKYr&OupQ!V}8GNsrH3Ar)rU&p0>zRv1DUybS(oZ|pc=%dkrQWe!No(CMcfgnD
zfowblrau!WTBGzU9aBIuh#NH7g|a5<{{7HCzR2k1q;@dn)2>nKM8AvcbTd=wmcpGz
zA^HzP=@T>3ln{EmR|;5*pFQoolHwnTryYA>e+g3AlKo1hi#HR!#nyLL5J%gg<l?=|
z#4wQt_)%MPlspNQKXa5N3o;{?^Mz78K9lp~ZvPq@;=a+Wntv%mmfO7{yrR|Vo>G=b
z^L>v{p=Wq|RmQ2MebeO2b0jq96}ADdgfs};K`uF^hSw|DaBBwPmXrD8ZIZ;VuPTPG
zL=B3j<A`tjCHJK)oi1YN=t8_~4~yRLlL#9Z3GNno88KNa%V`B#;?b91Pzz1_{<QoI
zai@Y^VrO$%zuY#qt-alCXKRgo_W-Y|G%7p<6*!eqI+BCG{Sr!yqDF7}62$`L8)}))
z|EiOyDrl-L@A-5_N2xup+OEmFqrv4#>OIEkQu0Zug@N02_DY?V_R@DbR#Y5KuXQwj
z%f>Hadv7JRG<cHL;5?n3Jf@sbHEQxE+m-s;8iKY3MRo3q0b`~`hxIbk80YE&7L?ai
zs!v<@Ws9z^$W|qKPE_vqnFiriDA5D*rhbHN>G61PBO3V59->B-!PV0qPO-A>?q)=C
zXmFXCfp)Y?T3+_x&9RX5{@1M$`_B|H@C1fu220jYGItOhRiV()G)t}k$CGUnpM1<u
zNlMi?&h<1fZgmu({K@?W4t+p+!d=uc#3&U>HCI<x)8M_RtUH;<ZTp5-m@_E>4W-x*
zb0<zIDn0lz0Pv_~+lCD@+^ob7kk{=DE5IjO@VL6MYnT#Y14yPJXC4DLEcDF{OYZg)
zce8K)1G4q^`Sy$DY1PxICARr+pqr=ciZ;rp*d!z~YSfQ3lEpNW-!V<FNBHPyr-V%%
zADC%B1bawW9+LB;;8VEq6qWr0Vo@91Z^y7ri$9K9GEpInuIEAiCH>mDEDS-#r-VYj
zW0<L8X5R*R7SvCdcm`c{A3;^x+Cx<wU8V5bNUPrm^roi^9ibFP#}2y{Sr9`*!?wGu
zH~mP}Sz<=KePlyJgMHQHF&^W3Lqd(mz+A;x{`rDhbqVj4o2GjLIp%S0G%MXI8wvf!
z5k1<%bk+_oc8-|}60TM{5_LD@8zsrBNnNJP6*EKUKje3+VqMQwC_sv-QI$69ta$0Q
zq_>}X2tKMkKI#zR7Bh#lgOqym9+6C2G%=x!LuO}VG7wjkKgU);-peke@Iv~!@Hvq;
zR)icvvzx4vSWHu^gjSBh?c9N3zYh&rXB5Hj692*av^C?><edJyFII6v-|!6LO7@5Y
z8tvwCtxN;bfi_HY+0Ahio?&mz-_K4``0WRTUNa7JsUv<@@Y0QXBVY7hlw*D?r}Oom
zvH>IPu04A#L4q(`ZWo{EP2!TE^lB_EEvb8YR{Hv3qy$w(w-nYTd~*C(#8Aoe{t{2H
z!PB<x`T_3vKP6n&hv*ufy5K&t+ethv;A45gcwUjMphI=7@}{zA=BFsHM*w92{P%An
zyn2!REfLNc^6wpfJj0kLKP8%PP)f<#23Hzc$_vPuZ`nKI6|Q|L-o6I`A}s6Uc&h^N
zQt9-dVoh~4FNZT*i^xkRNnNtzV<}|u^L1Y?#CG_uAO7xgl3yw)AJl;2hc4;;7NP&l
zuuHBgd;!y`Vp8-okg9bOC*~H)nbrPwB1Bc3*;6N&uJIH}gFd1oCLr)zr|Yph7wDHM
z1#FRx%ltOnk-*eM#HoU=&~2h6f-$_AYZTS>rFegcpj(r7w0<rf`ONNG%G88r=9su(
z><#a!kdm?u-4r2<DG}Rch&3eUBk9|UpD4#X%!5p3K2^n2;-gNm6I+W2(UE`l-@H_M
z)b8pg0w@BUTY)vGopU|C29>XJd~^o?qw%QDw7b45P3fBG)!#{$P@6Zh&uZ0#w_kkB
zZOrHJ{(WE4b`84u!1Wj9m*Uein<yVAe82BR;4y1bA)Wq)@y0d##wVleP6dfI#kbE~
zo7Am(m(%A{jF^aD&E$inkT#M)B81Pa$T!@P6*WzJT?!AOlKbO)Pp@GM>T_xq>J7B9
zWv8>#c^?kmj(oKXMVtAnM@8RI!>cxMH!o6_x(uQ;1gTP3W{!i;3}}^;%8Zs5PUKcv
zH|6Ao`wtfYk06@Y@UK_3d{SkQ3S~STkAk6?G}GVD#)*QdFkC^?QJ`k(0Hk=|6uw;=
zL#q_l8!R<fQR6SraLp%-fm=kh#iZdZK8zGL#H0zSfoOR{MX*uAp!~GQ!|V#rA6~Vq
z^q|yv{oC(;vuf_%T~p16qa)2nBrs7*2tXg$tWM5=d!@`xHHtdr*tg;j?HWY@VL8`F
z-GS+j>psHkrD7=Q%iZZ7Pk%pfY$m^EzF6_2WYmMXxw*V2PySrr*bsJpiVAE?q<Ulh
z6{S9vZhwNCmcS9lG+7_{oC)z@X4u2u-BW3K@r^n`)HFddNwkYd94#wDa#{F4;i!<m
zdE2_Kc_);-WE$d;<Kr7O_aNL76!<ik#GLr2Z_~JG^?AXfPiSKr{U{evxfa{VT)O)4
zN=P~(R9yBSEdUSeJ~;gtVsl-9pHu>YH=Q-@_wObktzil^gPSf5VXM`_-R<Qu?5J^-
z3cb;AU*09X+@SvX_U^mN!7Fo9XGD#3^)naWY}UCth3jJaFQfNFzL_MN<Yy}ELVd+V
zp(nQ6jknb!-rAiAN=-|n3^Z(OfkIDh`a^=bP>INHX^mwfAC18H?F|j1&dSp-^XyLy
z*2|f$+Og#WpVB4cA|#SK=6lhY-nU4N4}+^gtJr-_lc|!8lROVuE&nmoyM8Ow^rK!r
z#mD=uK^Yx#V6iVnd;>R(Rr*|C9#EUl^knc!%C0=6!h#s@cfr;&2s+b77^*f=RhJ#I
z7F^l%z*G8{>a@aQnOU;v?PoH-I@Vk6`@RodjE1lzKK}|08ka}2B#XS(u9Z)%`xlq3
zyH4b4%OIZoZfJ0+<MN?Ql|Xl`2T3u!B-S)FiE)8K)BCjwqkB;Tg^;Lj!7ytZKm!=|
zc}e)J_JU0D=%G);`|?q!HT)B&tMmW_lx_ov;61feViRpE;Us;xoVg8`D|nKQJiYC$
zKp4O3+TKMc$XZkUng1ICHyah!kU$1%C_YM^fu+|o{El@#)Xs<n%1eo;tQK7OMk0Kg
za7nI*l*08n{Dx86XoFlRim>S;sXmhUtO9>A%}HH&UzF8}`u&RzgTkM`uN&3n>gn~|
zt^EnR#CG&nRQ`eMZGy|wTh)G^&UKwWz-B?2JMf<(CNpWShP!1z<GA~yK644YrK&SK
zP4zC+NKPw<bLPvJit6tkjx5w~@G6d=y6&4Z9JL|6r*N0)vh>UU=1I<L1tx4Mi^3cp
zI-j2)e_-W0bzd;0)4xRd_Pf21uVv|Xnw$vQmb_hp_(i+r-pYAW)Sud95&fD&7>vq9
z*#4z%rYQBvalMTIVH7hHutGbVh4^`I6U8e3_V*giSttUv+dEIWGu!zE(6TF?Sns7V
zuJ7xVJ}*#jT#EqQ=oj22*o~W7)%J`pG&99_5m!uXZPq3&-{qSkj7~WlR~F7Q@+gqI
zz!(XTW`!7gL?Sz@QK--iSm*TQR-*Jkxt#&Xk}3|R?Luk31W!l2jB^zjTRamY-oa7l
zjtTjEb&Dhr4y9dzaZJvog8bf3wYO@#v8M>@i+ej7OrIP+v)fv*Szc;x;J6i1!6!r|
z+fdK;THC%5N|vdFTuDn;-N;-FIO5S4Pg9@YL`%{zUQN2My6)u#1;;I&H8@Hu?c2<A
z67I!qmon(dSw6ht*DYK+l1jfX7_!>VF}%hAy=_6v@r7cnn2~NOUk|?<A@D6@sMU!e
zcV?chkc4K+I4rw{PyXy=7=1KWFP9bQbGbBt<{=3uy|_t-lJ>I%bV5xnQ$+(}SIDDB
zHWQX8l$I9>>hOxOMXsU9l`mQOqM0&V;##L5`*u*7$6`kf=f24C6lC3~@GV6fquJ7>
zlj7`LY*sDE*#&cK>#lMQt0vwn#q)w;C(C`sC0s}K)btA%9i!(GPvB&-AAYhA*>C9w
z|Djik%dp*p72cN_;STpkoN|xGxOeYYW#J#IEEOL}!W%j8jFG>fQYr#Qu=}<SPpEag
zmLB@&tbWx-rT>VRA(*g+_MP#26V`2=?K0CIq5bB!E4m9yQYWXCmxNHsRtWyGxVXp=
z@S(HQ;jIEJZ+#_ZPu$|Ne<npIqZ9`;#Q^!m?TQ24%Q~yQ#**uOCR-oU{-NSC8%UvK
zb&TZMRaN_lj7}wusy;_uHl|tZ08)QvR+XEK)62PsX7fjEK%E<RB`~V<PLR+y@qvdH
zj3)j@BrF^zNUQybN68}l4t(jqzr9EW6U7j=XEmr6%O;q9-0EMQJ<izvxz6qoOy-da
zVYb|c&5LX*B_9?J=c!5HXEu4em)7iyxtvib0m8NU*Y{}7gQnu2fBqRRxgEZ=yv)GN
zJc3k(+c!bzAR+Eif}<wRU0n%p_h~V6=83K5XrfqQd4iW#K7{zuksI*Nnmg+z74wYU
z*~3NOuu5QPRS>N5TZ+aNLbsHs@%}3TwsFElsP*$-PXA#u`~OhemDia((ZX034ri7|
zcts<;{1olcLqFbGTv~jvYuDqXRWWh#xlnT-<d6;fY^KAzx`DVGL{pH{yZzh3@De<$
zwj|Y6uTuz0>l>j-AAvtLwJH|VmX;38ucuo3eR)m8&Br$&&*qmdHeZ=m)VLe98KCz&
zusJSuN<2{ZM%)?oc2Upqtz(=oy0&IKB8v|{3TJ(sj%=)7tAWA)Mtn10gl-11J0Ph<
zkLmLAi*he-LwbthA6S>#ub8Zc3M2=QmAXA>7*4m;4PJP1ymdSm<uP-ZDg6P;A=B7w
zJq^m}t+gh9>*G@kLH}hux?Yq5+V_0aiON3*7{fD}b;d9651TVQ?IpiJXxAPj-<GwK
z4t7K%@#Feb6J0X5iUj2;XlvOO!zEUqyUz}Gw;bAqO2>s?iIcpnEWgY$X66;A>}_`o
z@iB}7bSm^E+b(u&XYY%taMx}`8JCeQ@8GCFR+1l;-pHf?D%bnff6*=ENEji&={TlW
zA|urovT{FXd{qeD6o8}{AmsN}J94jyu)LwX>%GJ7^aBNbm7P89=Bls5rZ{rS-v|+G
zBsMJB=6Qeg<=G~CK~myDyKq?furMll$ht>N*U<3ARA*9wTACd3?ZYFClD?Ian_px?
znMI0=WcCDV&$Sj*SOTt(CbL!L#_x4~;CTH4;78uKm5edlRPNw#jHsS<HxFALt`<*o
z{_R+M#yp_U-qx>$GELWP*fId=(q6bUUiz0YAHvH5`we0Sb+hDf@%I@`bO$Wmg4pFr
z*fBbhJT|--dZ#vrlXLRNQj(PtOz#3TT6)Z(Jtt-nIGLC1_pL26&(RW@UzXs5zQppQ
zlHXbNJRE_h7Hfc1B)3*H2;Ss8B^8x{cgKx$@9d!=k+22Y*48<xsi_lWgXJi(BZm(&
z{&fpwxh}bWR_1cR2S@z?Af1&(gu6ozDMC~u8jt%RF_4aoD^VeK?%<Qx9A<Q!G(0XM
zb)ZtzJL@a6N4`6Y+fn|WvX8pDI4kyK4HxpNEiDQ___o&Z*!U7wJq>ge|6<UIA4e!h
z7$KdMCeV++NF`JfxYVcJAFDno(4i!tP?k+dUB9t9XBR04nF3I2a6L(xH-)4hZo?m|
zX=G%?c51gYHWC=>@b`qzrS-RK{sg96K8<}7Gjc{dpguXuNUylvWNRs*d8Koqoc#KF
zbx!4bK<N9=Z<u8_mXESp)Lr6|?fzSEC-qO1fG}uqsLQ`6AOs9|K4P8Dw)D!(uVUDK
zIJJ+}^>dAV!k2Q}GJSW{o`h!tj*ya)pu*|EDfgRz<9%>hJj$XG8wVRNa!2m&+$=D;
zBKbE1s_P<BMA=P25jCq~8=c-F?NBGerB!dlY9BW15m1#JRnGEZAT>{(rN5!fx1hRg
zUqAfjQsT`-oKqs}fQ;YaH;mjjP?pa2Y>Bx>b63mEHq^Ylo&(vGdieAvlh}nH`zas;
zDX;86Ev2N5Mf?=rHqyKNKp#K@jKd;Ib)RJ>CtpVu??UD2lqgI*QyAGB*rcg%-?suf
zsyO4%g`+sz9-AW?M~{=Hr&?@dJ2<v%eEGnpt>->)yADnjByDp%x)=ZMGDlKW*5cM@
zChlt}%i_Hr-zv&bmc2iIL|j$xTgN)lqVU8W_h5dN7wWE03W7Fw-p<`&Idhb>Ko~r=
z8`WhdK)f~&w=~oj$)HdQ-k-5NT9T_X5$)CBNB$6&n=pJ*5HlLvqho4}$=>|zTtm%?
ztlTK0$DN6BvuXMA<5^%Piy-ilJHBQKSRo44H6aM!OE38~8NN4AUo6fzH#F8OgnkXD
zcj<4S$3j`u^EMsqf*>*eUo+R@<KmYtFvQCTP#5@ePCMY`YzqsF8hEuv6UB>vB-h`b
z+Vus@QWzzmgQx&F8-{b1c9LYpb8a(jjrG+{kSDuaBKmIVLudkC5l4l$wX#yls?4wG
zH>lTLz3`dWdp>@yubi?fK<;SG4%xrtBxCkJtAT%1U>4htqS%fW&`@S-c^DK&B1@ru
zC9JK>oeX-^EF-*24<Y$Y|0gUsTSuYM4R)3^p+p89yPC{qit57Gt|0n9e){6To$zb+
zs4V$-`YB?4r?6;l{0{yULJ<nfYl;E1iqzq`cMk_sKwE|^9g6i4X1$K*H#j5$%vIdN
z54aCov{gN191|W5{coKA_6TX%kly$gAZP+?G5V>P<ZJy%c=<Iz(oQvHSx+w(lv{3n
zbD1mzWuOHJC)DQFy?kaMnch$Tr5sBhlQaxB?9V%t`UCO#_PwR<iJ>ggc9RnmmT-?k
zTWhPDkx^o<HkP+~=JdO_Zw1^fQTi!R-+H`9<6e)Xa^v-Ai64fn;sTm)mW3a5)NmDC
zUK~CMx=AQlNxklIzUWk@GdVC8eml;u9i^O5WJDawWLKPa3+rj%TzM8lGiLSk52!RQ
zQJVPB*4Bf-18|MhReXg*1=hIKG6Kj-urg^7JU?N7_wF5WD&E2Vi|(#c-cG*~L>gJs
zE7VYFh~$kf_DJ5CL9y|KE%0_7waBEvQKa-%vVuL@KhE)YeMs9+AHb9$(Y&8whMWzA
zY$(eQl;_(%Bs19PpS-UHB=+$YxFsj2v{W<5WUx;sT2oh7H>;T~zz~EraE%TlD1|va
z_GAe!`%7eb=*(l)k}!JzR6{(a^rvgab||0WqJD$D4GZ`mZ7t{`U^Y^af!ThVY9WOW
z*{XvYuZiwQy!qFFVwuDONxBz~VVvGEcz?V@qp7ZbOJ|`iyHJ?m8g=*HJ@XSm7mhu$
z=u9O1nX=pRA4IL^m*n88&-QUnoaoivpGIAa#Lynn4h38aaf9XzgFA`Q>?#0I8^^S9
z_5b^n?|*xvIFefY;NlUR`H?FAgEFwRAd@VWB*|*%X`u|;6o`l?)bPU$vQSou>eO1t
zPj7+_o|HZx;Zm$w(V<%q>5Ofpss`}!d|mz%uydm8;{d*)mkz>gR#|^yR=?^PJbe~H
zF5NEbCO}p`;<xLEbN}}It~ZGPh(51F0+jSws~L>_Q8zP7hi<WC3VDD$bUUdJ8phbG
z+_~2(oyOVWms$4ZXmRuK*tK-+Ds`MZhMj+&ka4d{mX6%PL6jO;RoiGm)p^^696qy|
z5Jss`3F!Nf_B$<ub(y45d<y6RUi629kPQ?xuBO+%@4&zRTk4C1l5TmnXjjp7(R1e%
z;J&zlvoVJ1*rL4$PpB?VM@Al=8!SpQy7gO^sKiExd){c&T<P}qqZ&(v%yWwB4;sEX
zP~gt#@QD}7W}b!jbFDqyXt-JIzk_%uNSuupQ;;ARQ-AT7H*Popq@~J>D=7@;wKZQf
zZGCB^$6k-&En_W!OBJB45qr*ItiDe2&2L@%JBFTQuW8S_=u1`Wys&2(em$i>{rs;j
za@;6-1JpDx#iT})ogbCezsW^pF3Ry$M&`#FP5dC?8I5}i&|dStnV;94yo%84Fm%qm
zNZ0H{(0hv#d$`+8H2fqB3ubXTH#b)_oF<tv<a<#CFqT#;KhWVw1WXRmsT9^{`Q*-5
zCr9Dxk@Q=t!KD+oTj{$fQ;E6X2V%OZddMvqU&3MfpC6j13@oLj>&z4ORx|!=nD@1x
zmNsws`KeQ<7y@{q9dp?er5`i<<}g7B9B&&-QXC6u;iX5$N_0a-ChijQP~O>N<E7hk
zY-`r@LHFFk%0$CE$8t&^8)j0%Ly7tRbB#>m|D{{MJzO6PNJ~Ogl*Z!pmoHRBjtgAt
zTN8SGd<EVok4TeM?TZl1&-G$Oi%H!OJ7Pni+{7|B+X@kl2UfH*XQVUj=67zT37Jpz
zagFA@|E>;i$Z)9TpjEIeT>sg$rH`N|>>uDtVDcCDHuK4odVHq4Em;!vDlt-X9q#%&
zug+owDx+)~bf9jhe0W;Ff7Q*`*+a2P3r<z$_&;aq$mCmF+sqO|0spa_TecptH!{4F
zn10^M&3wQY<Yi?QBOzr2WxRQrK3QNC5>HncnG)h`XlR(ww5QAaJz_UHb6__aU^gdw
zQshj_!w+@^aAdU1vKv!XjfyflZ)jL~5&YM-x)IAshy|GVQ=ue(m%M|rQglI#d|k)S
zp&7+&0~K(cHznumGBnecTKDCMhOWVx)!H0tY?PUP;etjUEov+IJd>t=aH6!{CjKS_
zw@&?pn|`3RLQ0EFiVe<XWn#d<T0H;EzpR&0GbnnO=DsU>7xQA)e_pY4A6TphAcWCR
zpK3-huNMStVf#%dr>Ds;V~oIqAWo#ge+#b6&@=`InNob<x-0%Mh1<!Q+dT_TY2piR
zsfFRBn||ajI!0Xk`jOoh+b<BQf3NrEW!9tyStfqxgnlVRkX1%suq^)b(_aaKbjKhv
z<3{Z1#*1)Zkm3cJqY9f$!DTDQ^2^fDvpzLB5t&ZJWe#{`q4b}Bi<qOy-@R0yjmA;7
zdE-nk$g&d~iZiR5e;2b`o~iB7k4e-Zi&*3h6@(A4Lb{|p=Bdp1M3ubwi(z#jKGSEu
zO1`_h8)6#xY~eBA66kgQUGKuN7%I35%yd#fJzP<P4Z&4HXLRnvBb-8x6R5bF+sEt&
zfaQYKc`EAA&L7p|b3qb=IW9kjLm%e~>)+q9B)u14`QY+G(<s`FDB=yG2Z*2JGL(tR
zc?K1!pSJ=v-wmYKp541w=4%$(Y=zp*_*-uk&D^13RdseQX7!#q<p(VVk(y3Dt5gb^
zvOb+eu9{B7!A!?2Rtay*$+l>QOz#aU)p~Vyi2cIutezOFSnq&7$xMo=Pcw1qlp;m+
zHeOY#&L0B^Sy+G+h{+LJUdwQ5__!WB@n1$6q&onfeCL#LY1)^5cCLZL%eJ$q92bP4
z1WpRKLd2a-Ko5^L1T-WDY<tDK19<|!xUj~Z!wTQtex1$t%N^<paXj(WE`jSv^7fif
zSR`)&!~va{?vlUj|KtaC9%%ZM!{593-d%Nw_42m-z=33=ISCDboPr{W3<P?kHv+>|
z^2!#Ss&@<f<;?aPMoC6&JT`Es(;D+zOSpCG7JK&TJBv3`S7Vbdb2=o6q-?w|d;a_c
zk6!ULn4#iODu}YAem7&T3ejBp<+UE5LnfyQj-0wGqMT|+sPDw`b^tIKHI6o#y8_NI
zmG)#66wOQoj2}LD;Plm>U!5vjoGg&r7{}MYe`DvMyQa(vmvLIJ#(8P=WRS@@0Rc@F
z@x}W9C8Pi*BrG?GihIcaNDGKaNc~O+Q!JuDxp4Gd@P@D@Go%bEkLU8A-dD}E0|%!J
z3Oo;eDVq*|zuMJ69ChE;XTT5dSKt4|eUMs8iuPnZ1j0k&%d0)rfB~=I9U83Og)ksL
zsko#hIzIk9Bp$MIa@<^8ig25*&d&DMBylMB*&oRf;w6sp{-f~_1fXHW8uV-iw-y&y
zx*)w0r1nKag>_5Ep@^~C&pmExOj38>q4I~5-$feD^HG21a5lLYctUmd)5nszgTH;q
zRtO9E4+(BkG!5RJ{QFma9u2l!C0J6q#|waeC+Y*)Wob4@d98ke+j&V))0JU6CA2bt
zR>d91?d5(oy;87*NP~sWD?{+juVa^mPNNQ_(`d!AYw+hsN>kp4vOpo5p$vxfrCk|K
zu7P^gMQ6qK>5*JScOBc_>8{@Z(u?`6O<`cZe@JXsIX$mO{zsSf{N`BD{^LI_1=TBE
zA|AnnBFVU$AaV}$Svqv>vvlYLsS^~@@*fTLxJ%K>o-3VHO~f5(JZ>+VI`C{>d+On7
zMC2s;!8EM~>#*@9Xy4DNF{<|jsH62?{>KzjYH$6IDb$GY`yh0`J2O`oNn!}FvQ7Wn
z(OaCL-ErG<m=PMte)^~)&=pF0xmXOV3LLxL>_yUS>_7OG<x!9g&|`+Rpj(J=BElsR
zfCJG>vU6`Xtw$<Uu4W~kz+*s%yX*AN2OU)=3w2QpLQanx8N3veMC1Bp)<ap)d493a
zvgx&7isi8lh@pjP(ay%wynoPt=@-+W0<_yWDXI3i<TtcW9JBDtcaAicT;g1SF2J<+
zfP$3X?o5=6WwHzdZU=!mjr40vJ`d+z?k8f!_MX4+s^-ucY}HRMvIJgaB&V(iw3Qq$
zn16}CovDTI|8D~oZN$&k5Ey6Odu<-nO5bElpZdMxZ{6kS(quRqLv%>m&H#OUSyiG}
zs)C2_iwt^)Sw5BUDW}Q+c)^J3`dcdg<NYeH{YD0W*OL4x%-hbY&sfgO_lCzyj-VPY
zm`eG-A5{(I(;Gcz{8e)_(jD(X|BE@ib`tA;o4oE&Q6rK%%vGW52I`-GGo#?4^Z|~H
zbCM%t9T9yaV{(Zvmus$x<u&|ErOh-w_}_0vp`?`sA3|3I|G`EcDU`Lvt)1UONkdIm
zTVm%|t)SvY3RCf&x&Nfn02o=`bWVqoQKDohJ%)2dJ2(qfQw?qfR31KOuKi#i|9yt_
z8*m>_(40;_o}M$C%bf#oAE#^U*_f8bMg-7J#&J$HIBKcPwQ~2ap81^)KLRDa+vEM3
z04v*veGps@5KH&ao`r-c|KC&H^nk=WIB=b9%sFhHI<|MKR#-j~!Tz=vNTW-O6KkA5
zFHIQ}r7$5Bd!txCPruf)2%5igXJ#xh9t5{Ie)AA-2x)_#cI72P!aABbqLT)=N^L5C
z4HaGzqOAhzI{krklH7x?DrmlP+nHuc==ZQWl6$*ghAXOdqA80cf1|Kq+k;%y#(M#z
z(z$;I6g1KO;y^1~MxIACK$#}GP`k{YAQ`>!zOy?`o&r7b*@y!p)KnOG6VI|pc$Z$1
z`CY&6{vQDdPV^qVlt4dqjEGR&7Dd&<h1?Sf+&H_-{n)Jcp9>{>$F4glqob2NmL*?{
zp6&`}xBq$Jaj|mB6ZM~hm{uC;r7Xwl{k-8e*{3gF*lIYO2okazPDfG`;6<uui`!Qq
z8_X^(EsZ$6SHA)pOjaQ4ebK*DDs0esVFVhOUVv<xKxy4;KJmg?*Fi|J;KoeaSH}Qh
z`TxRO-yYu8@^IufJ3IRfGD{5fMrF>EN(2~@yq}KlnX725K_sZNC>_FMmT&Iw3keNH
zipElaE}ITASw=&$`!~p3MDlik8mZS!A~ro>_Oa?l^WdG{TWv`kPk5P<kT!DSzeK~g
zC%#&UyLNhwjh6X}Doipfhe5QdLnXJ#Akuy|<STmPTk7ZR;zC))JJ`VW8l;h`l0<*1
z)!bM^L&O;xeVVA&^yz}xY<s)A3FTgnA2{sZzjrfMjrOgWd|-sB##?P6?I^=uEQDVl
z9GJ%u)8ZEt9v;qd<9YHw2ac0q?56kj`gC8Nk4L-^RKRQV1n=3R=H4Pxd0I9}!=0^#
z04-hJK?SuvdH0ekf{`vR@B+aU0GW{bDU9K1r3k;3hjz;LD$xN=7L%nRCFxKgOLhjy
z?9ZQaq!wg**VIHB^;zhHo6<||38Dn2W|oi#4+^bJ%k(KQq?-gHCv{ufT(}rF7xaUI
z>TjjnkF(7e=x}DVG|pZ*MRhbPvQJ8Y@}bY}65_f*7ObhOK3^!=(yRfdr3Gqz{H>pn
zACx1akJ(XEQVKz4Yp{sAD!5F&?}nm(g6Jj6g}ZnVLZ3z^+6o3{S5HJ<cO~^^%Y~h=
ztb&dk5;%@4dB#9Tssz;a@f7JoOA@Sf7@FO1LVPMSd30)b>>vJDJNRwM$=n^hfE=r+
zsD!eJX&d{mzocUkP1iB<l7)Gak?c{#m3^;+Oav?*NMjvAgoGK*%macXV7NeKfW`95
z6(fUc189CDm2T%B5GCHtzeLOW%L4+>$JxPNdinUYcXq14wPv9#`@2X8yyx?7h}11K
zXcZ4?r~aVFM`({Vf)J59@HqiZ=9@X3SHnBgdvp1hK-8;QqC6pVP-e`3BQL*hRUdgr
zi1-e4@!aU~y&ok3-uAX}HvWgkQ!JOSOjpni0e+4SY>W__RY>{a!YxC)HWedwQ3nL;
z%!QU~evUgR0T$Q}AGqeSdOFdk7l`UmMnR<{e`+?2DP>PU&%=}J>+9n#n%q6cYj`do
zU<cWJmf;{OvFst&l4ImWXkonudcN$^5s<4Va`O!I#*(9r4d)9-$M7=9OMj50^M49R
zRW@-ZF_ey(xjFJ)p%XN`)M;E!F*B<CJQKg!1E{f5VchZgbLUzJCcE=St60t;tTCY1
zd35A~IjI!RhSAi_<^;@i71X|ffgz3_jj#1|<8ur9<nhZAExnPl(aI`&>O%q+DmK+e
z$9N*&-HLD(^}?A^x89C{0az|gv?A_1GBT3!ydi35jCK#+X8E<RU&J$Fq5o9pNz$R?
zsr$B$kOVp*gpokxlhCgsQIN(fQKOtN_48RmS)NmdJ%uU<S=b6xKQPW!rDvfPe;fUY
zZvXzCuZ!b?a5pk7t2m6H%k%JAo$Vejvx$z4&4!8iz`$k$1jKm7G1=G8k4a2CSs&8u
zoyyu^cXI|yQ;Wn#q3{p*RP?34p}7Iu$BcBnxQy9Ck_wHh^K~=1`k_DigZ}+pAL*1&
zNBDrhMt+U}G`xWnK_!wqvb^(Rd<W7nk8C%L3M#8ipQ>`1CS_+Ps_W~=HBw)_k|sDd
z(jq?R=*g4BshPkyM2>}I+Eb8e8|hl>7+V)v55K2SNgrN^?8_etXpiMYLwvC+W(*_a
z|5+zS@ACt?SLBySg6jJ1Ig`th$EcUQOwd6!s2LM|mlWp+H3PhNYGBR_by{jCdk+nr
z<)=6QfazU*r1TjkBX*}NM!CW)G%@AR&!8#mY8}+Mr2f2k|II~tVYxqg(S}(`2%w&b
zKAZyrWN0h`jNHJGwd5NaH9&rw%^ysuH?ly|V3S>=Wg#bjes8ZZjDisx<#>A>5MY3{
zD)06HNOHcK%$sN2V8kSB-z%{;k~TOvDEGM3L^n?x<I*7*CusAm!;PMv{_NSa*&vSH
zzi(erwitBN@m5V7-de$_%pYXPiUzeF(4WGY6ljn}D(jdgsNLy1hSED|mLW>aha{3%
zkAE)x(V_J3?@G@80tM`cFJHcNefiXgSPaIw^(^QpBiSdX#Z-oKAAS7zaqr0r4g>+{
zh!^gh8ipaTl$JB$aC0xZhiJ9{GYpA*0qc+W`T5xuGtmi%<&}9YThv^0NY0LRMW+g}
zyvJpd`C0k{pkRZk1l^FXW0|MZl2*y>rT5Te2j*TQ5L@SI<o>)>sQDhV!&Q0O*k>2?
zQem>Tz*htrhie^aB~`SbfTM&5Y&K2}mvl;EX;?j9O~nn__D{wb=L1Y-^X21fU`b=x
z%`GfQ#yixQM9<IrJpMV@8`5g_tz}@k`|6hWUjrub(9hfrY!V4CIbyN}?S9^%Awf@{
zcycs9n0HQ}?b9!Tr1GDtZ)u^oQl!ZV`wc5w<WgQORaI3rR#Dfi`Gy_;%sF5;upVQ6
z15?A-`RdYVXJ_|5I+VqScu?T{Q}6mR_s8fRL=dnuUhmx9<d;GO{j}rS^1^m%LTOj~
zutzi|CcD}F6Mk_pmFbG+l5saO_U>5i2i8RL>&HFuHZUI5vuqKmvl;o#6lANClcm9}
zTHSm5M{mX8pT>1gn2=g=<{dF95Zd4^@^zeIUw+ny##fw0-oWCU!@+*F67RiTikE+L
z`g!XEGuogsQBhE^<Ztbphh)bZ>0eD9Ns*zUWfFS-vK}D|v~zXU)Dp!!@*$FIkDc00
zo9eLPh)AXp!Biu4jBbL|?G;bijFvFxk6n4IV^DVL%mC3-C6t^G9X_;2{>yZJd${zT
zf2)&~rWf_y7jz3>Wr#aZD8w8YhAt#44I>cA!<|6cfq{YEkLWEG251#}r|x-CJ?C}>
zQ<C!Ak;EI688*;ompOie`3a(363~49^RiI1Z6kxbO6A}h46L%($5(fF*FqmX(g0fe
zeA~;HFNdq$Y$Rm&RyHH@#j~e+t+t|!?fTM|%dg}uKuzB)=V250UnRxAUp;c~+Pqw$
z9bPEjxx!xXx<cMa{gS7&#`HKb1%#B`qAkrPepP*{+3!e&ya?EdR*DYK1c&r9ormg5
zR+w-vNliTsP1b{kkHW(Xx=%#o7l0A?;IwQ07|SeHm@~5t-$*f*NwkO>eU1pQ#`Zow
z`1AUbKSi|6Su}^U#h9F6Fo)Z?W##1uEFYKK;fJ3BQcOB4^opQ<_|3}1;5=_dfUB!8
z#fEJ(@X)W87JS>3rfc}<v17wex3;$6<yi~JNlQydoAzsJYd;Hvj3!O8#$%!U(J3jZ
z2DDT1D!dpy^D3^I((740>cca*(nhUvCKvb8Cies5V{o^cmh}f82^1=eaRcB^p5s6L
z^(AWzFppH9j=*<t@3762$p+(>Ha9nOm`gH@0=C>F7T+FmV>F!`E)xZ7NNAGY(yMW|
zGYwWfv!^!Q?Qhm$gO6XY)6VTaNV_POZr)B;CBlzF#br#D4?v1DP}I2x)#d#2-wI!l
zQ-q2(RH@x|_y@FOc5S1hqk^oB&CSgjhMJn1na=~8rmUXX&)TXzs4&qv{?TeCqvXQU
zs^$GxKkqh*@sEci_S);e&G+>5FovtCtE*2wmv%Wi%cDGJt#-e2p>^bxQII{^TCGxr
zf#sJ?jyu%}%9^r$2M?BV=}4yF8KPA_=8Ib>D<?@};64DiQ0nYxodKjw*mG+OW5&SL
z<Y_Qx)kSft42!=>3E0`*|HmJeZy#7MEJYl%6MtUdj7=GxyUQ@sSiprs>Gv1`C1U&Y
zy8qpY3Q2HJHofbuPh3md6bHaxgChf+mp9xAvJ>v++-#^b5wo#+43YVF8kzyCgS|;`
z>MS;P<O|aWTcCw>4=Ru&Ph^3DLM2<zE~8MThCl!1F~+z!lG{eIN-D0x=vpB~yt%n~
zTU%RB_X&WnOSuLF_x%D{q5^#o%Y{5vNkO5o>RCjDTG}P<iUk;rmoWZ|3r2mEWOo+6
z+xBq4pu#>+ZO0B@t|jtIR6_-3gu=P(s_}4$UxGl3nfHOae_m#0>O8Hht?h-;?%pB4
zoE=o4*pn8*AG|fQ{P9aL-4J5)bw4P7ed3oOFfyDtq3cle%R59_dLq9B*br`h{^HbH
zj|C$hgL3eWFcu+E*$YM>Qcg1dVlyb=zanZ5J@2qUk{+P}h)WNxRVIy!%^rSpr~k>^
zm`?w~AEHpS4`4yg&?5v2y28|2xJ3f#9YW*T#649JgubJ!X1=<8%FN-{2X@>;j<Y`C
zoFuqbu1AJmLepvPH4U4loLmrc7Z4EMijfde5UK&b;yRoT2D|akpI0gxwmRmVlEAKS
zX^cX7ct&=&BKL(0rR!@&h`;08j{-esXdNqzGkE0pfB)7~vR~}zi%6BH#DauoMDz?d
zq%+^jp@6Njf72Nb4sLe#Yv6u0?CtZF>yna^kZDSP-`iVtN#9_3ssjfV8l#>6FDpux
z>_`yPfs0z*@V*um9lwcLl;|b-t;Rw{d((w*tH=`ChlUGH?qLplT)TE3DvH4@Q2+k&
zAqBy9PLau22%);jz<Tes`MFXINcb{TlJdbotYKiC{i9!)MU?c`Wx%fm-2ePam?zYf
z!Ay4+*nfx}Krw0!<YNRayZ&0IQia?-8K8bI^IcyGarX7C_4V~-yp-B+2gNm`u)U$I
z7qH<<XtaH8AfQ-vw+d)b$&<H|{_kH0KbXhu3V|wQ$!j_xv#U$BqGN$r9{VwJVq(H#
zk<HK2GOH<!1wUx=>fywMnH#LUs1a65d;kCawYVuLDq59Rb%YB2w`=l0n^UUJ?)*z-
zhzg8H2E_dGv6lld^p{WNNx*J@`Fyna|LhB=h5!rCs>#YaC$TzcDr63FBIiN~3crSf
zfB#J*;YN_jiBOi`epvvjlmmg0fBon)9j^M-8NBL1CdmBq5k3PT`7fW5#~|JL<>Pqy
z|NmbI0ShHfm>VjQw;9OSvara2%a&j$Lk2IV+7e83i9)fP1u>VU^T9?{xONEv>tLMh
z<kVDKbF)0mAOKJ$3Ya>b1s}`5|GuN0r~fiR%!3;y${=nZW~>1c#c=$%PJxTvP_Zm|
zpkUxrV=%}Dj>2Wr2%+~XaM#jGZi4~JBYz+G_ngW~U93()|LPnb8P^6#2o{1Q<HehS
zqA)qQz@>`3J(ggz8<i{xepseqYc-z#@?|$tFW36|Iu{3r96VdQz^HCwd|ZQEG(_Ho
za%=z%hTUM{-w+^iz-dA%kpA)GO^Ai(&z_ZSZEb}p>6VyTTWg3+iJR((k4{M7fuanU
zEjd7SNZ=wRawj|>!u2BKEw?t-baFFYrav%>dlv5AvxjWQiu`bt$H)3w`<C;z90ySy
z{VzmZ9zT70R3@@938L+InArS9NU!<_Q$SQV<LvDG80Msnosjacg<<eS*PW+ko?c!j
zAt|^F*WhSRdx1N#xe+JO`ek+1{Yngo9pFkFiwnl}wdiH=wc~YbqnAZJ=4V|oFt4=<
z@KjsK>4Y8$?n0@KA<nYG;VcJ7zcr{ZE{p@xD7<_3?icfQu$CscD2vQoQ8T|osN#UZ
z1Mrf@zBWs3o3NiftK}8GFgIs+kX3xp6Y69e0ZAmao38*UZ@ikEe;Hg|4$RVlBtsma
z067>jo;SsH?%c)g?X?D`PoF-ar>~H6OTI0vP(RO29|wcF9-A}<X|3y<Lk7}R@3#SM
z3StAlp)3jT#zOfTHhU@og$@O4V1irWDNBhy<|8q};esjtPg-aNWFUSuxeZ`+COgP%
z0ic6bNZ*YrwFY|4BhJ;3&<VKx<J#I<{f>=R&ZHA?6E1HDbQe8_QLYWz4#|mEoZi4y
zVCn5Pq^^{EMNrFf)FCUzZ>}%1%<GhSX?Vf#us;7wxl81S2yW9k{$V4)BM&T)`lV0D
z_$1s(Az0lI{_vrbm+z|gsyAdZz>|-|h<V}n7A~P{YYz468FDUmBb7zNe0R4Nr&AyC
z@<V8_spY`-S&#TE)bVy6`SZ`4zJS3BCN?7$TWWM(CtrZKVbd9I3dkS!%aA-=RmJDP
zYIAk#5(J+=q)SZ7^xfVu=va={dI=3kh>Jgc@+29EP6Tp+kwZchSS7+5e2`c(^njLh
z>8P#>2V=k&M(3%q^81_KzRk+by|J;jP=S{@n+>_7ezBQiD67L)z|*Y&;|9_}Xdo>$
z^(Y%#0oVo-;i8Shb|(1(x}l<-SQ3X3P%uyJd4E2}#)W{#8^D8O08(C^Ct#5T^<5DZ
zjAuDHg{!`q8h{DCkUqfIbmjA2x}-)V5_!WBj2#)|!p+THT*b=Bn3JB)o^ZwKDx$%A
zcdID7;d97&1l6k9oVo2Wx>I8V<Fa~#VVJLCzy^M7c3#5I&kwvsW=o3#0|NsQQZyKn
z=PB!V<Hln^)79s`&Qi*ZkDyRxZtXl3GX#$@NRJj43gC3L0bn{bG&Ee}Q9!UkIXE~V
z*+h(?vtrpY;wfORa=BONUy66`+(AqN5Xz^CiTQBVa6HweQ$h#s9=->V8O8{g;4zvF
znL1ow3yKi9(B&ut!^_8y57t}}7Z*pE3F1;apNH4CL*M0Hx4G0_4Y$8wtPOB$Bj?BK
zYrS&!QF1SpwCw5~f$zP&*6|!vE{xzkaxG1ZbRQ#TuK>BQ6(kj6^?C<TmeDZ#@BC^b
z<N&6iAZWV#`+>)so4)bx0Ejd}o*a5gCTMW30x;&6?aOncb@+KF1yIlM`+xmJ6YmaE
zN8$X+&--ht>c4(I5HlSo>XxHyTry-UzSMF?4vj|3#)J18zeg{5@y(kzw!^DLT=O|T
zJ_l#SgVqzA+j)dI4Y!=ab6Sai!30cq&M<Zk4o2{9WnR1AK?3|3Z(q}4FSXgQvmq5T
z?L4s6fSh1yOdYJ*-KQIrZnuC?5teguD#C2Q7Bj-2@QW~2MCpvAWc~2s5E=2n^N74>
zXikby?RFVNdN-d&M-P>Dw6*ag=uHi$qnxblP%Y=Yf}UQi6U?o}uN<JK7e@5_X+Q#{
zV%*)!7uOng2=lIA#1?9ClY)Lrt(<gHrf?Z{rg9Aoe`bRAw^A#(mBVZnU(xx}YXqE|
zG<g*WEfI-s5lE~17pioc{JfTRkC50Nj!0j2hQa4BzrSTFW(H6ua;YB#z3zHHpZL{q
zNop3;85n<I1(%)+4p%rhAb0yT!7I$MGd41^9|4)ZyRb-f)U$-D)nOYuh<gK_E$!_t
zgZ<WJ!ySFVuG9!;u?&F}0eqqf&I1@dG~QvfcIXdU+A=HfcMHPV-%byV3%3SbUq*E6
zpUi;m<)kuyjdjT0ONsGMpPDW2wKU%W&ulv!eE+`Y#_M8}%C+q=4D!<Q-RFkQzf!5%
zvw#0T?R|Gt)K}NFPl~3fBq~t>A;t<Qs5FsgLxT#42pDN9ouLRg^r9g`qF_OaFf>KF
z3?QHk)dU3@%7~PK8H!5pO7HXSi<;zn*Zb%9$GhJ3xt_J2EJ66q{oQ-cK6~$TF5LAx
zt)J_4F}<G~u}~RaEJi+U98kg(av9?P8^|&(UsY0DTbs}(#ZuHZi^0WL6$xIL`;uu^
z@k8REMOB1ZZ7aSm@jdr8wn@I12XeXWA7_1nX*9~%AeWsmk1(4OwHUr$pZLk;<jGsR
zZ5nsAL+6pfcwDJXN=hwf@-A5wZgF%nZdGjfZRY4taxVj#L6gZAEBde8&^P8c9DrBE
z9sBy5Z}!{}Q5j13{Xa`A1&S7CC~=stdc+GF++eFBLd1ofd0f9><CJps$&)9U7@#gT
zi=($bY9IAcct2@}ccz2Z=Z=Ldx;f@}iNzR&-o0z-wCAWvItkj4c0}V)JMeqdKuy)@
zmkR+gGNAFpF9og+Azmnhb^|@ol=+m#PUifBA?SX5B6e9kax>BRK4lNDmIiEYTMWl9
zGiTj4#n9h|_@WTahvb@_sK+}+MR&h#Z@1+XPqup0SMRoZ^+3Sq2)V<6JJt&fqy=PQ
z7K2K$F>=okUcL=lJDAEt28dQ*Rb(IAG+lt%D?e}MSVz}loLJ#|*e*6a<IIjDm^WJ`
zsi~*=<pu~C>ctr9TzCb2v$1AVV1aEXUScY^_3Yeux=&7Tdtdqb#X7VYe5hWN^0sYd
zUC+M1Q*Mf<PUN`3<9i$HBE|me>bJCK5g=#mNnjP5xcA#{U&O`6#x~fC6^}5*o$Y_k
zyHFmqMRxvziAk#GXkFAKZ|E!@l`7*hWI!wIMgt0cQogaDQjEN5O#zuM>!E>gXfQN8
zKiS3|3%qvCk>k8DH!W+6AT<1{^|8m~(6vNQwZxdOf@m~-vlsv%8pFU#tP7(;Y-bRv
zy>uSCbdyIq=Jjd6_?`dZvibS(zIqIiJEO{gr;{Y0<6ic8y<2G`1g`7`?{k(;f?#k~
zRu+I>ve;~8j7nRAft!ePR|JKw1qw@bI?UL+=H4Oy$j8PR=dNH+i8D+5R%l}fV)xp}
z_LmCUXEStLD4%~w@naQ|#XW>m-d>DT{LS*^564|mmu89P=ZUO@I;y6o#^1T==#3s&
z;+#3Hf3FP@Zn?oC9s`-~F}{ThE(737TmT_6MZq%VH_L6@_-8^Sjn<!S+o|*(zcaYp
zG{yM_l2c#7Xm#rLu#gZdETjh4!rUBo<h1)j>((V<Q|l?*G%eQDyTRB)9&d@uo1!@v
zbvJd(b*Qqco*^UHF!3;#dXEg8;Hr?ZB7~SDw8cxJ_%s<-AOLZ^ag8De`B+u{#~=4Y
z|F~pDXWFhP$>8#MNscYB&-J`g44S9-rWY@8tn?Sb_RJ|=UC6R|YCl|84Z4IoV)`Fd
zNTiOt;)l*)#I-G*kvAD}Vr$^K-G6nR)$st0@DVt^ef_8J4D5Si%tDVzUBK0T>dFas
z{l(?LocB!8wfy{y<|mF^r13=CmT_<QPPe5i1WcV8)pYRxn;`{W8^N+<Q`iz=-PTEn
zh_6>RhjFb_C<jY6-WIgn>ODWMM|+g|_35BBKm#ZS^nx+-AO~TAFg%w-`|$+MY&5S$
zK29$%CRAE<bhHFZ&hllfSXpWc?oC*MyxNH>K#MqrtK=@FMkH<|xy#E9IR@Z<tJ*14
z+0czc_60scJPZj5(Fwj26tqTAu;0<gW$?WWgZE*(UME`q+lpUa^=c>g33?PN5jdzY
zHq^pRC;(#;w%VT{ChZ}p_$AKdTJGDMj*2mrm6eaFsX53WaTy%NvpG{P%7&5Td0FA2
zv0b_-{p%I}<oGutRj4ySdCBw{GrT(AgM2B3p}R2{mr8Y_wLiNsf1;nj6zxZpeFTl+
zr1ZQpC|LR*bVK2v)vGMLzj<v0z2L@iaz{5fbbI&E8IA4#SyJ^JSmT2BP<^W2^B6gs
z##j~JGz*+r7=S<rAQRS<f)Nt#Fk5c$KEVBcVuk}&b-y9rN%hv6qz4b4#Y>um1qYLJ
z@|fXVt1c&RqwmIdTtkCC;<dmO8~y`PP1nthaomYPiLrn2@g1_gg<V)z{SF`xEJyqn
z-L$Es>nw_gz|y2<vPG#kzQyYsLJWB#YV6({uY>njEL-PY2$qc|r!x<V*we<;u*;E6
zA_bprst(a4P?By@gWOlM^GWlmZijvkm1bBm!SW7r7^9yuqu4e_m!6jguzT|`QYAPj
z=sr<#IJpjS0OmR<`%t@4-oJgGN8)#~_BJJq0+8KyPW~?=<9S8w-OGL>a0FG@EGSVs
zVd<~;7xC`N_5y`P&+8MTeGvzj3<ZNpR&v=?&46pyg3{CNRT*gW0MSLc#c0l9u}y(y
zb3dQ#tY_7(Y52|W5LpBv6>kH-^QQ?xxd!g|xZUt6Qnb;pk`mU4syZ1&R+5O%Tum2e
zg<SJVvlvXeL726cw&<LnY0NZ{sM2N_+jcolaGoyae`Snp6Q2Qs-l7J6+Z`<!AoBYZ
z?F=VN;Qe%Sch?Puv<4aN|5_Q9+i==aaJ7!Jagp<bq@<r&%Ciq83dc_IuUWI#w#>->
zCWxii5<qYb=%P96zyr3R3)<x#z>=|nV-w|_x_aQVtgj50qIO5Qq#)JKAoGdO+ypAe
z<7d{s@6X$I>=c~iAtupU)APf5MKP@>XA3%J@E$KN8>$$Tc`kGBjjL|k7yC`bK{txd
z&d#!@e0#t_i<N<&DXu6lud#j7Vor)GhrYIAOsW<eP7j%E>pN{{j6em1GCeg_*RObE
zX0M0sYdV2ZdRY5fRKNyW-ssM)$5?Xr_4)MKd+>9EbyWW>Jg$)acl1ViHMlB+><J2z
zLy*Ss(J({jaIy+H6t>1T&FDJR*+k=pktFFp3uH01WQAPMb;+WOd-*;cdv%(ux<wQ<
zbb<VsiqM@w8Y&@iBX{H%l90TJ9rY3llg7v%!|?o8O-V5^m<5t+4Td<~ls&Lo4n6f^
z6ei?ul@`+ISQ>GKSuDhxy1Pja+=Y%j^6cI!>m2J>57d~Il@D`&Sheatf&H8!YD<>*
zF#AU7!<%kGRh@k+%ny-5H7{?xL7zI(p{8~UBiXT+`RciiLmUrUt+4SO1`bbJL4hJo
zJASk1&YkK(gl@sj;S^+SQX~*Q+z=YH`w*i$8&_njNA9gjXhRlF1_XZb2FHO)!$F<7
zi2AjkBv;7Sn{WfB7<VfSRnZ<pHR!udJ@GbFz&<9uiU<Xl8?Mcca9Gd#e+|8Yz_uPb
z@?%jC4-d9$05j3(2Y&u~QGCL)DJYHg+B`=CafL>!`*B#l>L=3rqX&C}t&e&<RxlRK
zT`UQo<J%Ge9FOnwI`$kJ&oFhqX1&W`VU^X*^l}5#jG<Y4D^Vh6pZoK(73=prJMVSw
zT&#KJ-Fx3k7QF150p2wUTPKmpW>Y0l_LtD?i;_tiMtg0xLQ~!&6m&$9L4nH#V_<B?
zEyW9xA_`V)&fB7b38(jYv1UIa5iJ$-(M~`aQfd;6z5()%ZV{^y#_{kTF`VJU?ga#K
zNcJ(D9AM7o#O>}k5kBf9|7m;b`}2u3VCA&6&>cgI+Dfet&7WxP$}VG@;<2nNnpz!E
zJyAe`=}gGbs@&TH<uH<|In%^HRMOaJ5N|<-yoJ2&;^MVBdWP{ZTa%JF=)t>p7s(9t
zvyhK8&;bPN2Qfd5cC*-s))IHhH>;r$uSQ+2<pI`_-8&VC;AAc_;kINy<++#JVE0?<
zfeJJPzFhvr(xsf>4PLY-`$CFEwcSqO4doNd8d3hMa!3SVDNROJnAA~O&SO=&|9(Xc
z1e7;78>kUm*TOW*r(ss;-`j7eg$Is8^V?HA;*ojw`}eYJp)HPCY_=_VB^Nl^R%Cjq
z7?Xq;@LprjR%<317kh(o$JD(re3=B?ejv3v;6U3|0Y;whRY?#2$};<hUPZD_L<T!6
zK2$+bS)2at*`=!(SIlEaB+KCUlJ}f5(IFVA6GAa&OG22S7caQe=Etvz(_fMaLJp7o
z#s3a2!(cAnap}~kGA$83`(1+8gp(+>i1A85)H@wata9Sg(%G%u$^G7z>yD(gUbKDj
zku1u4H#&O9-`~F~CpPxSaTM2`yQ>w36=@@wtv$fWU~^5N0pyl|3mZetq!S#6X*J_)
zV{uv8$4qaGtf`@-7n(Z_;%n62bGQB6Ra$ELLq9}LPxY?PmOR~%EBS=IhW#;@96oY{
zv-viVim14F>JbwmP%jBWNGg&93pVdG%sUO{B8R|VI(p$3<8lj)UY}f0Y;JDekmS=j
zzfVkz8Sf@GnObE~ZS?W1m5PW&DO%TU4-`=;I?#m=={nY<MPmy<e}T;BJlO2U8~zY&
zLna?WjtH-!`xH<cez9l@-Y}RpFh0>piu`v>@$|-E-bh$+?ws%la#<`amwE)Wk7G}A
za#H)Fx2q9A?pP@$R%XPWtQEA52{RyM&OcF+yu_y<asVP_*>Ijfq3)b8%)6BAgflPg
zLKxb+yG?9_C8aT?u_|2ZC~}~TaQcXU$%-6flKg?R_&cGu6JNuDk02tjxw-k8isYG$
z{V;%YVn8@@12TPh-ubeNh5_14D<8nt2IYHy{hcc6b0~T=$BP#)2F~c&`GAMcmmL_d
zXO*DJPO?Yu2cSU)L)LxFGHjMsWe(dLvx@`8$&yW{dt88+US;tWo{SJpC69;jRtf({
z-etOXbV3HAKlz;ZM<icBkZMTcLC8-L;zI;ACtf-j6-mpeLmJ$;WW*xqTWgtw{0Rz#
ze=4Y~1}C&I5iv}n>N(}RB}<y~Me-;T6Ru14JJqD=%h4QBy#&ticB`oc&CSmbViy9F
zA=>mKVq<G$F+vf^hz#p8HpXOnG)60VoIZQDZ$v0$@)=?zM=1MLJxN%CjvvuXPE1*g
z)4lVAm0b<I|5z0KI;XP;u}#<A-CeZ_<;PGJ^vSb#u<Dk;l(LBzse_!wE|r4^4_YhP
z0m>kUWV$tLQK1j$bh_%)*LURn3HHQRdCIOfA+WY=6T=C=PFe@U?{NC{ROOYF4DlPB
z8O8Y%QxVO)eDdWwRaiaMr+aGRfR?8n{sRB)Y}?pBm<a($AR<oEqFy?D>ZV83W^lFn
zvYDBgu=VDIyWo;<3Dz6BR}mrk*>tLv#3#zW*+|=9wZKY33OS(Hrzv=?q|uI&T?T!N
zMQ$)2fd2I9Q}^pDAc>lyk3?Yih2N@{F|fjC(O(Q!x=-2}$=61Msq8lLph8ZKpa{qW
zr?p`6cAn*l=wP6n<?RXwT|52ycCkg;-P#)bF8;Yzpf?F=wB6|B8Q^T*wvC$&lH?WI
z1{Mo10l#~MI59}I9;W`yy9K~tX+AnEVnH}z5Oxxi>5+A(r)x@Ad3c#EmR}dgI#Yl|
z>MaA9+b$}2-1NbfXtJG%EQKSAL|4H@bA}NXaDeW^Ey1d=>{SyNa)pyj-A9~eD^m}g
zw)nD9MV>Y_HC1)PW+P?iaj!OyVI|rKU}?V;`Z6K{(O$DS+Y;gBc@DO*H7Es)w6HC#
zBsa3r96P-l!*S2^VPJCpPDlV^5)#!`vtXO+x-JEoRfJmMC)ZbF^76uL8;@g<I+SxI
zpKvhhU}4>`*oOnXQO4Ppz8fFUp|tm?GdV;4FgVteAH@VmPjsQ$iJ=0X%VN239VUkr
z0@5$KIM{Bzr`kqF91$eSx*!{u2t77S=WT$f@pMB$VIgeFf;0+P-$Q|=XQ0xnPv7(p
zn$tqr4zBc~D8q`3*)H;QBfI-J=;`Fz2|yLn-J{CL$T&&dSUICVQ`@>1vqkQ=lbyvB
zCW=>r8Mcyk*xWZuX%sX=9nO(6IUc&B<{H1a7!x{&fQq;0q2Im&c^`V`P8=#lYcj%%
zjjfbzS@RQ|fc0EOpPAYi(>>v0OdeJx8%79FVQbftHn^<CLQJP=o1&t@qKQNhQ!Pb&
z3OG}(K0Ghl(M?c(tdVMUCLG9uSTczn90>c`2+_j_msKKm{jn`NgW(LQ!HE<*8?G?S
zYsmqu>Fa}eF-x@g-Cy6LZz}n58@Sduunwn#eSLkYm}M_-Q(@#IK-MoxBKP%YG|=G1
z53#^uGZMx{&0T-ox>2go4i$uO=3-2w)S1`5d?``wAQGN5#ifH2z6vh0DRE@>HGveU
z-9S*Hd?suzzuD3BwPH?$93rKHCZ&&Z@bleEqy<qhN<m~>p~kO5Dwf)b@bK^>!2Grx
zyt)#*fOSv|;Z|zvVW-DbMXwVOu*NX-dk4Dm!3^1S_2F-ChlO=#q}5x?*yRt((k1|t
zcA=O|jj{lC_iM6w+^YZ!tr*we&ySPsGuh%oBj>=<k~C5`#jRSs8Wt0nGq_{w$Byk;
zDXiF&J&%H$k2Zm>V<Lj(jOKo-KPAAuE%Mg`2MB?8KJ!fE#54-E!HHV<7-N}kjUU+X
zao{G-fI?7!z8xc;snoh_xe3I8BN%C;UW|L@*Ms=im@NQ(Bs*C0*Ixl36!+Sy;PPJD
z1f~bT6`+t}sI8{<$L(9U&eghr5A&<HrZy@|O0s&mPtw(A)$VN7QCJk(_UrWhBdRSx
z?w1BE9t~)z8bGImjrX7WJAN>!j+A$n``y98p%l`nfTZh^B?9F*ECUm)Z84s&&X6eO
z1Hm#S${x??hasg^o<w~h>~*aj-cBjMp27LG+{75~z7dXH;kYiX$ULjbwd3|to3}f5
z?vx>Z#!WkRR3_MBn+Jl&2<vhnui5fvs++dFf&%w<MCg7M`&LC7pDCZv-gA@jTh&TI
zRcQWOZrEW|(1`1KWM^Og{{5x^kU&k@6;)L-3&Xqx)=)_WfQBF`p{oD<$b*v<Kd6Fg
zRu&yLHj@kEAlU-f8hh4!1#+r?6mo=)ye3TDHQ>u=%3$hCk<cxrG>O!JHhS)c`+IOR
zK^R-wW5OnXij@B*=p2?Egk$x>*g#Wlf+4DnOfVK7Kg}72V|)pVWlX~PJdR*#uf?E_
z*P9o+awY90pzleoKO)ne__^gN5Yc#psf2^;$^&l12IEJW2gY7hPL7?>3g`oa`K#Ci
zzRl})E?J`P3j*;;CRRIzNU7W*AKq-tR*gu@$=v&JKy2B(8I{3toC)V~#LGldnS`~=
zQ~QDm2nDvN?#ct14t*1HcpOr9N=C-Z1m!(@PNnLmox&$FjILND6*gj%oj@#4-@Xmr
z9gzB+3n~j!ab$!?e%Vhr8>E#Ix7;ybyjWE6M1ZckD#P1H`d9re;?;wKN+*WhA$QaR
z@T4a~GbF(cOlMB=&EJ222{nrf?oCf&nTOXPq2g?X<Q)$q$<!g75A@-j1K4a<QBg6o
zu%I9@I1j5Ff7P}yAy<fdRO_pA5G-JOKSOk?(Mto3T97ND(8R!jX441NGadP)oW){w
zQb}oNU+~_qN(K!zQ&X<^Mo2I7<hm{OLb!>sweP3W^O&6<rJ@FCKcojo!^FU6etc%w
zho^5QI8;F^i#W*=*!_pl{+-Yw*RS4sB4c)GU{=iUzkiiqy9Y9cx<QMS;dF9&^giqp
zynj|_<}jmMIPs?5!(-S=4yc{;6*UpA0s{r*8eA7OLtKj{a?{NKDd&4(;h_=ly$){=
z$K~ecmi`k+=;S??09AD=K~k@Nq%i+z-@rJ@?vr={7pF|C`n(~12cYSKeq{In4?N6~
z3m5KJI0dZReP1_G`z(ZNkh9Ki!svr-$AIPJ_liV@h^3~M8?_H~<NIB#BtCpTpUTf(
z4nR)oLyJxW*ls%P4hptwzxys0o#>SQhBS#NAsE8?*km#<eBzY1&;Ee$>&USKnM5!F
z{Nq>8Mmh2e3)9J1aKtuU#EXzY(#UPg$u5uAej)TR7gek9JZ)R@bZQgS?;w)YI(yBG
z)!<a#`z6b~ysRwo-%Bu5FscC_NQ-UYq#iOE55P484KNMsn1TXNCc?KcnlM&INXk&T
z{~#l@;9x#s?o^+=1ED!W))xQcJRo)eEYW0N{c)PC?JMN=q>PMRiGu(?4@d_=n-8z>
z;Gsi?!o1~KO%KVG-I+K3BGRTWzDpbgN}#q-*;Wt+NYW1MzTNx;iL&|X>_}JU);f_p
z=l8k}Ex&AT2LAhOmU$quLlWu1@bgOn$P|#ay!D|6W66>qMd5~l_+N*s@lKyS`J=J1
zF^(>%>H~{dshY6S@S2g*{?VBo=$bltQ&>s*+|$cqDjjsukG>d?b_BlxWI>%1hrW&T
zp!@mK<w+yo>gK$mqaY>znY<S)7MslA0<(@pB8stmfJq>tYkska(vfr`R;kzCUw--J
z*(Y{UMA%0G54~M{@7^;2(|yp8P`T>bK6)aKW0egboNa5tFObzo-n~1C6$Ghzs=wr9
zWO-#}9ezv5?c4Wo!!CXt6jBZ1h{2?p|LTYl{_p%#sk3@({;Sh!iOj+O@%FR-`3AZF
zl~0%g#P5MYCf8yR<Yk<H{$#V0ru<_>e78)l`J%7-E7YX!CjOrsf0kGNyY2V?+`dP~
zMSS{`b{q@<Y`^!A)|27L0f|CdjjH9lh1^_S>)e|GtG9!b1xAKxC9K{$1kn(4jO)>c
zG2T;i2o{y`+*NE?y<m?&PHvKdvKbr`BPk*xa@F5oTweYcaAwegGz(eWlRhkqb$<5{
z7_E_LAP(I{cqaNzW8)c8goDO%PU{8*T+dSBJw%f@YV;FORkXCUfE~4?+9km)lAcYp
z_8{-}L#!Qn|6U79Kd?zX!^vWia%lf(AHAIV&uZ4C5(x=;HR!>!XNO_JJ|5&e)SL_m
zupc4wPQ7(b|5}jF{=)7*6L5lzWe`0KzeO!+pCMg>OlJUE5^_od1rsq*^ALa-Kdws{
z_MxIW**5_3spC?m3Vrw{?irP~s1_oQ9>8|u;)}fT29Smjgv9a;H*E-IYLg33DF%yu
z&m!9G2pU1P@EU5J;ZV#Qdv%=3wS8iRo?D5)LVQxC>5e-y7zYNTr6)rjI^b6IhzCc7
z-40_qibR=L$BXZk(0~=8<l!8`^ZA)fmm<k)zE{)d6>jzF7!msTu1~JsTCyvkbmb>6
z`^)V4-BxFU{swFlCw_fTvZ|<dLK6OP7)JsH;_)I3&=;xmSR8$6B(H*I%q_abv#+_Y
zN@`wJM#|x#l)cA0ZUh9JMvW0z3X3ASaBdtNKclYI@WU$yiaAB_JjxlvzR|KG9Z?J?
zh0LCY>Z9fS2xAkf&6%n8n=-h7-kK|MHv4Bn4q|s02+Rh+jBSM`Lz%u$zF_*>zul|*
zea}KfN9FkA_2)mS;UAy_A_!R$yrWszSA}5QiJ0%+8KRdWG>JuYzWZ#$h7D=ROvDk2
zWPsH1D$}H_&sK@Pr~=SVY$XZ{-o(h{q#jJ7*f@zu7&_UzF$xt$t`5{Dj8)Apb&{=X
z%i4@zWCfft12t~24{4YmhBbh$C`GqZFjAd{B3I!MvyXaA&%oho(d>{yejYYEdRw7!
ziAzb<c75^g*Iz#)mUJRak<qP*?e_4V#vQ$>i^8djigT1>-)01yfOHE=tQJ-Z9bMfn
z$4_VY<l4x;&CyA^wwfI)K}w^=3>yzQT!HMKyuOrFGAyXZ8-)?EfaRR~^Ve$>aHeSX
zI}J#%q53^4<RsYe_|4ET+yWv#Z}2fU814-A0vIe@;&m0%cEr5^i6c~eimLu-CTang
z@XczH1@C?>f?4QVUbvKn80v}hLNL=3NYOG5L+SIfvVQn88eqS=Z&c`B-qzNZ5Lj<r
z570eMz$>8*#OP$g0qQa`)kvt?%}-$=nw5*^97o~KHRwm1zpz`!_lB2hRMB#hk<2ah
zctR$q0gLq#8E9m2@}rT6*O-<nJX9y~#-G>~ygvelqA7bGFmDXj6z=BWWnX?t^d*=e
zR+fp#SO-JSv|K$|tErL(ZiC6?auaOf>w>fAAsmxO$dj!R5~}>$QZBIk@Bh4PT`!CI
zHn5G5!E!L@IX0GIFc2&WwNKi-^&JL@CP79M_zw=}4=Wdfso?K~k=9tQ)iv%orM$X2
z6qT?*1)-kFY^D1J_HD{|&#m#9tTRBWwZ$Swm1lZ383N<;ccLQfC(!uOO)SioUWfv<
z5Ql7>23bA}S>(5bS9OV*1V2E7Vh?U_xp-z0K2tI&fWMOnN%($kl)tG$qi|Nd^qOqZ
z(-M|44}g8xf-QlLU4$Xb3J%*$?R}+cWo3n#$f>e+YMdgk{JcxOZ%^rej2}tKeQoDI
zji`uS3G_tB@_p(Op&Bs<4duh9AY3_cyiuz0`>b5Pybg9OgB#m^`sqINOy42(pxiB1
z%wW+7X{%GJ)7Wfe-fCc}<?qE7YGk~E4HNeo*A?^T?Zfb$^6GEVj}02gRkaBlCVsP>
z8WF;&uzlzS7h@yRd{$e)K$rg0ANu>E9t>Nrda@ZerhRx&>Fxjc*jHF1alqL9@UbDe
z3OdVzb!~(ITM|lbkyf6^RGDX0s6AJ25?Rea6*EpGoFtz^b4nOYr>g>_t}2g7dFkj!
z4}9y`MOGg1K%9vo7-7fNnk^K;?C|^GSrNiBDWhdR+k!eeJ9VyO_W$Ca*z?jnm&c#L
zqdG^1ZP4z>Mctw|*JPd32V=-eF3iI^bMipl#$Mw(Y3&p-)W9}OL0Ow5EuP<$#{ztK
zwoL7W6Izy*mUswrVsGjMHOfx%A#|LepuUz7&Zi+wWkKQg148Jc)k!9Tbn8#~47S+X
z)OSuVdpCR~)T$=!^r5Dc*Am2f_5@f4XW9weT5p`WY^|?Apq4?Tozq>V?^Z?r_QIff
zSM^S&`Gv=?g|2D)Yt}D~;`3!Bo?yS*vFFTp2UBl2XYc1phVs6)h<7~3Es2cLbX-NB
zH7M>cVwJm<g`w48o!GA*x-gc9_x1I`9G*O|fb17m2XD&6fmw-ycYg)V;HXq=aB03h
zF)`sC<FFkit;)`wI}t)O(EJkZ=jT_~&|r=MZ=!3y*^*wXp^(<m1iO4_A?u_Ui?{v;
zETq>fTH8;qj8t;$cyZ$Hu6u1#8jA0qy^Yb=*Y_xFd-?K~wuXHC8C*yrw$|{HmJu%h
z^d4T^y}KH}_|Q19ZV#18U9arv+B6T8vm;qSL4l9sf&nWX2p5tKM0|IG<e6AhqztR*
zi~FkAM<4=rp_iBaW`pN<#9MA<V^ax&YQ$G$?nf|%2CZ<I7R*m@n~HsS8)Jqww|gZO
zb)(n|t|Qo{$wP>F<xhk&7YlUu6#g~n?>-Mpx@2`DQqF-%MrWx0vA)pI&=XPnWVS_J
zI#o2+DcOzDT`zD{mkl~CZlmDiw-&oOE^l<I(>l7MXBsM!FI`VhuN&m$F!eeW{~1A}
zjYbW7Zc|rRPfkm#`f{zoEzt88z+9j2JML2%r64C|QC$fjwbG*$iVAaRL(1^<(~Fnx
zoqT~Gq7v)1(1y;U+3{&+4Lj{^mU$HuzOGZ-*BonTb8QbKUg2;Vif-vmkr+fK3)n^x
zHF`&97kB)+6CYoGfn=2DNcnWT2=6Xte3(NFT86$23r$_RK{$L|qM(E;^onQdiD~ta
zI~~=p%99pn{c;ZMEK%#F%uS&@FmULz(7^QYOy*BjRaMW~lHTvHZ&YHx+50X(B{>=F
zpYJI9n|X$9fx$3;QM8=B5$K2z1JC!*y;=>5;H2O2%cIRej1}9I-~S;kC&xFlesK|3
zo~Azd*jDAAnvk#+%0d-d%?1kqzVKPfF)T6*!~QNgJBN-rQ>mS}zk~@s^3GZ#*Nw=`
z!egVC@VEMHGrHv*IPjw&`YV-TH+v}On^#2~P+{%g0N<Szg>pOy_eBvg#&=a0Jvmu4
zxxcS(1YGk<*C7viI5o2_Jij)FC3-=AI;MnnAws_Z#Pk?1g4u6+z(R!vu(=7gqS{;0
z-;xEMdcEpHi_dE>du$>rYwKg$+S+KGiy0akD!`-wH_SD7bgB2%L*wG2M)EiY)OsB^
zgxIC?La6I25x=gF2dtMG$%>hO_|U>-kR#L6y*?=~FYm&w{KUj<7^f!`yu{Pi);1+2
z<q=x_H>+7#Sm019zh-n!^&C}+z+F#!VD>NzGKG3VmJ9O<`xVjQ)H^h!F*3|ju&9na
z5x!>&yf;Rw)l11!duA-J=3V*FaLiLpNlle9V+MOp=C@S=2RMPs4r|^#-*HDXAt#6W
za&o?9*jw)ur0{Vwm02)Ad=m~!2ZFUVM3WIB>kowMZ(s8Y9`uJeuwdmM9|&(T7MX1<
zb3`vlH!wUCw16Wjm^E$vy}jjpK2s%|93kT`S@PN^=En~`W4zjLt{#NWK2sSXlWIDT
zY^rF<yK-f?qwRdF;|5njm!L>zj+L1T7bJ<ZeY~`cOh}!t+x2N2rIV+4a-ItaP&*9s
zvQ5*{+ZJaQJ>CEHhqkqMS9Zbp8RU*MxNJ~R-qBR8rKUz}LYTLHW~i@Ete*ij%wl?^
z(`;B<lmsTV`Oz4jBFuA#_RJntnW>4b+>lgST3W|uukA1L{xFj1^DQ3`-qU4&`7Cuj
z8Yn=0S%mi$<c_UvHVpOZZctt`Hxe{rDRI^gc!_m_i(@7#k0)Q42ff-MZamG5t&Em;
zG8YgOygrVRDkFDd=1fnT^BqRGXhmP&g%<YkVi|fa_G5H(ZDU}*5pkJ?wX`)fgvKn4
zC2paBCsno_UT+bpbxH*`9<$-ma!v0?4~!rPZM45cP9Q*`?u2lNtk1&Sns2UND8yFu
z1Xndoopm*a7l+|X=Mzy@{Pu?(+kctmu!}+gTE_Z4!9rEx*|Wfx8x6x8AWdQchr?k;
zyI^-DLYY4^Ij*pHc-L(|U`O8Ol2#WKuD3zussK8=lOup#xdrgPaAqHNhE3C>Nc_I0
z0yifIFBp~NF|TvwzFB-AO~n07f>}r51X0G%H^x|wa>dz!tmkYLe5XeSER;vXrKP3U
z^n+|R1%$A&vy=E*d2(#51CXa0hm^#$t!kjRU0sb!zoRL8xfi_;v=4sRg0&>BZ+K#&
z5$7k}%^~PAMynN^0G%z+G~rcUgdpPtvW~wWC$=(LNr40?d+$-2Z#Dj++IV3k3>v~-
z1n+Q+1Obg7gugZ_w1;4U!7jAb8hbYy&%Iqc`t)<Y;JKA6vthDtMk6mto#itq(YW0o
z2DL7|-}ke~;zBK1sYjtupm;Qpo&b{ZH1@vvt**Y_4Dy?my*-OWoG(2o*4B=2360A;
z!8yo4^UE=J!0y|mJ)_WFP(WY=F5xu@V11r|#;mZSvG}(f9A7DTL!kAFp~iDWa0x>v
zuoThNCa{M=`wo>Ss?7J6ObhrmBZI<S5<1tXS1d*+1uAoG(Yvc)f`3}EVnuRlY6Y^+
zD5z5<90xO2Q9nKZRYQaBp8WXuEx-oJDJf#rt!Ss_aJgikAOw&1&*62uO}+6Sg%q+)
z&6+|%roId4kdT@x4mP43_}5*@r8hpz92&qpXEB7v8hlJqbDE84N&?<*?hdR_+k;V%
zJM<s#fMc$~qZ-Xi4zR%ob#--l$Fv6>mnerzT?*Y1LHfB|4rjz)W$vCH5BlWYZPBhj
zl?geWD4t9SM#!9ltE3X6Wg>>XhCQ~tnWEF_5A~l6Wmu3L4FaGNtc4TIMh~#3r&<98
zvmlp134EhuA>=dnULL5TqBX0qaQ@c$Q8;d7iD$MVdNIhnG%mR@_5n=B7Nc`;G4GJw
z^Wyd<zNWUu#=E#|%d{#&CVY<>bK@*-%4k#sH9#_gBcOA4r4;Lgp`jtdZ^gbg7?nu&
zA@Gb9O@buZKnpYM;x6PP*I%4&J~(*q7JlA?oE#ZFL#htewk@j|ScuTv_~nKTb7a#t
zWm{>JdCCETaznS~u@%a3LZac`vp{l4PEQ}MCwrZERI31f?;x}ts-Ao4uN=(s8giN*
zwD+N**p$=J87f))V163+x}Wy%U);8@z05Jp$I8~W3IY8Q-Z+5@Y6}}mg%zA_suf2o
zBQL#DJ38Q*mX`KQj<pW1O->tV$(KVSHo(EWi$f>5=}5rd3*7=qV7JWR+;7S_e+X~+
z_Kh1igq$jOuO~leZ+igX<HoB8-(+;B8y72+2A3>YMzeEr;<4jIre7T0W7nK}B69C;
zOe!6otOP05fkp*=S-a-DhWXC%adDeuWMtOMSf2(BaSM`W$T;5zredk@$8@v!arR$b
zq44V8Gqk2+zyEi)Zqlxgac%LtFMqT8|9|m+@gijKK7Sq`a&z)qCun6$mZ+&}9Ht*U
H_v`-wlHKp(

literal 192254
zcmd?RXIPZy)&@%APR!O=umDmO>>vcCSB)SFC?L|Cf^-1~q&H)t(ovD#6r@Xsp@UHv
zRGPpbNL2<Hr8*$JowWw*_kHKz`FG~J_KrF;@4WBxta7h=-RtpHJ*Ti~1M3C`28K;%
zPRpHVVAvSN!0=1iZ@<F7j4}zB!+*rD$!lLzw==uue97LF;q0YrSFhMzyJBg)+sV}4
z!P3t57{3_*AAGwlu3fw8AR!=N^YsJ#cJ}51!nuVRaFPFBJ+0%wz`)Og|NF@$N!E$s
zCkBQyawjgh#Eka1#<ZFC$ga#r?qYGerLpxtml+x5f00+)N~j|j*q>24QFqiPf1La>
zyGYkHMaRtFZ0P4=%`i(V{?vm$egii){`QPNZ_}d0$o%rVrtRxD|M8?={MfUpd6)Xc
z5}9^$*M|B?%7G!rtdP_DLtenWe0g18H8RXv_vLT!U)7?6QQ!Uj&Z%bhZ|`0g@*Dd0
zH-<-QLhHZ%b$4Kb__x0?Sng<mr~C3cooKQ7%byt-j<d?cLVSI-{QvPMlXth%*XN?v
zzK|EG>FIvy>FN3kKQTP`>t$V?e|dTN>Al-Zf_{cYx}&kS>SVSTmye2E>DbDvl@*|o
zp($@^X_>|I6GP7JgM54e2?+@nt;uJFtY0hBaLX({ZiA0Uj%>wc&f&3c&k8JYpq|pv
z(aB1Lf4FVkT}=4&zL`gtcqMY=AbzNDZLhCO*o}1-s_E;qYiMZD?|_l<Zg@Czh5M|g
z2<|uQ<8poazF!|xJG!3X0YBWiUw?nUcA1k6UYmh|yn&Z)M5^ZSY%UWOvk8wM^Vzsl
zNP&rGqeav%T-XuFD(qJ-HF5|HP7=PJP+l&jnW?)Io~+)*&h71A>rZjteXjN=T<dzL
z+MN-+C_~N>HhB8<=j!l%Y4@2613xkBw%)LDW1_r2Q*)A1c!adO(_`Jj!@^pcnoMk>
zwgF}hQG@)u;hV>g<dC=HB^+PB4CQSl1+s~1!kzCn-{vd#Y4pu8R#Dp+qGyfYPI1Nm
zz^7E**K9X6U&QFpQSJn|XGO64feYMseMD?JAK7$GR_=t)J=mj4cYp`y6!(Xy>FP!i
zGgDI!ie6%1xOhyjH{7<f;83r}RD(A0iq77U7v2BhW~N^HrDV+$A}?rY*c4`}C7u#L
z4exv)mJ6KSyG_u96c8cf=?44ASL!&zw7f9wpL{lY=-?js@{{rWH6L?4pPb-o80X#i
zGsEuLqdnnSI{Euy8I7Oa+9c??@X6)fO=e?QZSm8%PpZe3Og}r{MbRVr#ecx{n5Kpr
z{3j<TwJY2T^U!Tovk!go`_bg5PoJVi3R}Of9(|LqZaPJy)G8c}tap@xPhO8r{^H9y
z(QY%NyTCAl;2Q^ddHvtyn=63TJk~Ecs`*&^?uZL~o<Z{Lml$$Ubl)~#i_QYe(Xxrk
z=2W#r{7#{D`zg8V8^3I{kHNdVNd0;v<U6MVS>@H#)a2wI8&*hga}PDebAeIcPQ%MO
z#eIo4$2VE^lsdwP49Z0Ay}0~XuZaJ3woyFg0)9+V;mfZ-_*K}lWiM<wEWA9t<mB#$
zg$;?}PCjY;8p={n_{wolPEGM}C*<WF<>m%&blCM@_{_zW&o}n!L4B;GC|qZl@^quX
z<F=7NPHBlTw?u{DthSM#N*sC|+mn_ngSlj7+V8RkT#<D)KgoUfG~0oY7wr4c<vWD`
z&nP-v!71%V?%0qoDbb!=%NZ?V#KtA<_UUxlP*_H}?MNblLTY|^@^*(^)pCFRv8Di{
zrG92X_nA?H6+?!Br#G1mi}jiwwi`GbWL2RNGgxf>9D!MVV51KnI^-L9%$y{<LJOXI
zq2jd-cdf~D+#&bkzc=)2GxCXig^lo7?RSS`j~M+<01FzD*d}~&in;g7{Ld_S!3Xcu
z*W$g!6If*_TJ?4UI4Wr*yDDx(3_rkdT>6WzGEBe7_Ff**F0nT)oRNl)wvuN1yuC*Y
z+YKe9vMgJZCA+C5)VMAyJqSpAdPKhcDsk*vWS6c!*3RV?E&|UL8!Kov9ww(XL)5Z*
zlwIZa={+OteY@aw*7H5oMwMx>Or}=+)hBT$*S7fLf!|q$EW(4O7v3krZg2|Uqx`e|
zT*pv1SXzH_w5^}NK&9BqWO&shV)*_0)lFVA`NhjKUDkWo9Ty=FI2^!>sd~D8_Rr%D
z*3Aj>-QKIqf|sA)DHsFC^v%o5V^2aiW?&fJxR%4N|C;{b!S1Y*p{N2Ih)+IRMxMe7
zzVwe=Ur6{I<QY<>YdPJo3{a^GrlzJ=dISRDcP<&J+i5d=;4Xr$lLIc)SZCw;$-(Aw
z*Qxj2B@XNk^@7c5>M4(@Iz_ey<*O@;=-Aqo9<Eoqiv@LyY&pidih}=Ix1Qv+I2Jyw
zTxbd|AV}!1*3P+j0v8lNvTpt60G?o}quYF|or0I&Ck7k0JUS!jy|R@0_;J9wSc#fu
z1uhOSgx)cIxb@R@ZI8}`yzj_0`83fdyIE%LZI8@qz^-HcvCh4|5MygP3#}93S))Yl
zI1Ng#`5IQZ)%>)ced{m3tjOJ0nxZxZA5u@QzPC@wc<^OtGXyWP2_JLQ#xkq6bPYkv
zmPgtJ7HY$}-C)CFpI)7$Twk~OxfN5eJ1o`pD5{Y<eaTpJAUK^Wa~d=L(2|s9vv1!%
zVYfinsUi7bPU-WMY_I7I!fcPT-E4#cm#0wWT#Z1o4V$FX75%IK{?a8q?%ua(xHRru
zr4-l)+m`t<z=7Hr=sDXXD$FTtbs+%U)F!?;QK43z+2B5nHJYm|X}qVb7FN3`nx+mL
zL93Nr5wtC*k>+5nh*2tgw-H*-9H_yc75n&PU4~<$fiReX%ln77`#+?_2f_2o$;;P>
z%#M-M)b-syB++6gJPts7%kP<*f!sRO5M9(Z10lUSLePZh+Fdc*%f@Wza;b32t_~bF
z!YNkVVMq=h&G_BZo3yIc#VT5R)r#=zOueEuLWSG3pPpTX@Hxq`g3O^_*yiHl^o)M+
z8vn~jOWKxlE<Nj?>9Xrfihm<HQNF$1(A`FAs{U9mI9I^pwGS=(Di_+aNugSXyN>7<
zq|THmDJfB2>)8d$uFf|JTEBigHr`cW*~)VC^641QnK!fsJFjClwsjL9Qt+;O?7fov
zj<tk_hSqSc&eaxAHA+(c-Xin7SOs$6iNAlX5pLJto)Yi1;MC-dL11yBDxQ^@nOPy*
zu)^tZ>%#O%qI$}?w7w9cO&2La-ai4N7i^Hx>VnqNuvV4dnUMW9Q}d7Xifjq<gW<KN
z;RZ}MZ{Cy)%_y}RCTsyyIBxdU563$5_{tJPW1KZ-$9odZo8r=F8wJX9o|(jXugqr>
z$3_XU5{@qy{7TviF>ZW#6w=?-x8g*)=URP0HrzbUb$^gpzmLmMq`B3Ry6q}bjGl`#
zfg@RE{D$shg=1m$JlsqIhU~b0?AKnMppn?@$hujblfxO=q<`0QvCnopsKB-4svqm*
z_kU_t3GQ26nYHYis9YRpk#e~zwK$r;OKFIaar=H{pFJmURr6+-pC40(P?c3WoX#S4
z^`g{Fhe;P-q=FnD<X(t5@$&I9p2q#vzHJh#OY>8dJ0~~$axK4CkmTz+C5PT^2>HAC
zQ;U-GoXZ2V@|A^=TyX!|!EmF>27IRAQ1r81<}r-00d9|ts=T5XgII+R+ekT$cEmPL
z&sMEUkIfErJ2ds!zVHA)5;U&%5nKM$N`XuCj#JE6mgZ@Hb1m)u^>-H4%-X@m$zp^F
zsh*$H(x#4Eo7jv24Ow>0bXsS#Ry7i8Il>n_#=A=}-;9mNr(LK_Oqd5xZ_%9KfUBQN
zt`CA7gCV;`k;WnE6o7&0`cLZ;z;!Jb?K*PyG)jy-4&eQ}{U7C|A#ibwTFig{y2<a^
zW=X!89_PO2bxNxt|31CBk<wuj90)etrkK_Fhr>|4*x-G242+D7YO1PRaqFIKk(DaF
z_Te7U?r3B{oj{e;e&vX*jEq~PKdE{wj~E5Ro%r}z^p?9~82fb-YeCz~@sZb={sJEF
zHs;z7zT_|ixP}RiB;Y+?2S7(L+PbgO^LKW!KfrF8&qfKIiPkH3A;es3IV0)8_!QtG
z<`JI{ttlxsO#sd@XgoOaxADif|KiR}kT-mAY=Ey)mS_8d|GRyE_5DNY#xtXD5E71P
zw@pGO<UJL@!U#`urmMG@KzZL)WH(GzS5?hvgQa!u7BPyrx-w5y@bdDq@>pG-wVV27
zTUXxT@U&i7yQnBG!sK%vlchPDW!H8<tPoi?OOED<y?^jG(JiJSLXhXy@4L1gGpp}N
zyV8}PA~lXE!#?w3Z5chLsu~3X7CFqz+e+NTDwImfc37g-)*3kXgtAtBe7eyxBgW1a
zGTpAaI*j1}<lpy{Ig=<vz637sC0bd`?6LTHWhrn2Mn=L^gJ^AjlT36}89aEA4#}>w
z;KSx7PfDO@vfqr+{7bF=^o%UOzc))96gJY;W!)k(cTwMKelQmt(`W8cvBzMj7Rd-w
zu)#_c@qnzX>^YhF0lH}xQ-b{Cq}^>b_>7>8kXo6e&|2eHgfxEZ$+>jWW4_KPNVL!6
z7)5bzi`2*C=p<o$I@_r6-d<LVXAZ>f(9$Q=h)ltTSKX@2yVf^H5;cabgOvs%q5$v?
zKR>_oQy~Xbf)E7sLQ?SYL5$0_^maY%<FhTbHOxlr;It4?tBN58P{`^kXER6jb#!)l
zuP*jc?kVy6V$CI2oo8CtPnBJDU0P_b>a|UlU3#}gFH=bRN=FWU&*b@?J!umokp24K
z-R26)$SOTN-G2uP4pMm4(x3|@FB7PTdWYuC8)G6_o!`IOV_W?0me|PSeB$Bf01>f>
z31AcD2bkvl$ZNfbGpty$R~SAipHALhZW-6VUV@obHDo*J&09T%EW`;_UzgR+;r(Z$
zsu>m7=|O?+2VqC_UhCd6m<9{24X^x^)KN?@vFgIKfqO&=mX#U%<yWi=Z*zHxA%G#z
zdQJ|!pe;{$%dX7T%Gx@P10t;TJrKvDBD;J|POd&qT5|un7#659GBk<5Y-D;Ail}ZC
z!yw616e?5m7{V!C9j`vm*+7hFgqb(yDblYGA3kgh)h1!OO{+HAu5uS+{8FXI2PGpv
zh`C3gfXfKAbGKUf9QN0OkdWBUP>oy*$DuaaG@V#IQQsR&rL!boNL}8<!+SN`iJC!~
z!EjyrPIMBzmKSRNTF<FXY47xqpYrj6<&xpAoa+x*v^8Y0>mu3|STtkx5HFEcDnE53
zyX2UJ^Ei8~q;rr@N4k{zr)C9!i_%W>-TCHCL2}%T&b_YYq=^%kE?v^U)-2y#<~$(=
zL0mmUUlyQc?v*!r?_WK9#&0AUf~gSlLW2A3cvnxhX<ZmCG}~ieBP3!m$i<YjlsG>h
zpQ1i5$vCft4|AjDvSn?809~%hdF1&m58+K5h`IV_gU3&jmms|6!h+O5Zri$^{pu6V
zslV562?DsVXJK}NODUW`(8mV@KUHS7qywDdcQz5eIFHE}nH%cn=jREH;#9fYn>kuI
zox#S8x?L!NSYQRX%D^2XaA+Yy&3$+rY%kMq5fFh7`oTG|cmJ?RT(iK;#LW#Q8)LYE
zlg`pq{A#^RVAb-NU0Tz|jT<p<1cA}XQYxV~a+@CBiP%JmzhrN+N_-rV+exk(aNwPV
zUK_(3wtz6tc_lO6b-VN=42tC)0u`UmmqRk6yR+BIRD5j_T$nGViwGVm22rI|aj4zM
z+f(16L3o!@aWlBr9C#LIN{qc?0E-~E#-mC~kYqnw-`pv7xAzbAIF4D7Q!g}2^@r?y
znV~0C1$nqd^G#o`)zIpc>}tr&&kwPxp}qZki<$nBa)7HCVps%?<ziq{X@KiaRUc^D
z2V3Iq0e+pWSvi*Wp;j6LW#n3Y_v$U5ZpEQn%ab+wwH-Xl#rATetwfzHlPiO_WtT2x
z@eFjMbNBlT1R>P7Syb^}Xw@w-tPSEw^j=+|fww7CzfSttU?U?6dDjBMP`CR;1taZD
z^AZG+H}42g)cHXCDcYaV?l<8zTh_(b>1_o&O7|8`K>-1QV6@*x>Dg}9hP?c~6!dKP
z0yB^v0c{yt3Ubx3i_y6jhRj%bn!H$2$6A0M$r8Di4;I=00&NE1H8*0kTH2$Ls{py@
zK<u^iR=+HPh1Otpf+)3XwhD@$Rz=c!E|06-j_n4!lt}Tz0FRf(?7aOGgJmS3%9E4B
zP%H>3=zVKSljfp#>B3UVJW+$#aM^q&b3`-SkfU$D(YYUBrtji-nZSk(_NxbS6gXY?
z_QFGx=P+!pF88hWydz-AP_?=|L3qCB_9MTyRV&lk$FdHhN6SF161!mFIdE5@e~b6r
z$xi?O5KX5-XQn16TY5t;I&5U(JMCWU_tUyxtNT`Fia4^A4Yjpdu%5&WKW%&;|CQD=
z;pJT9-^)3Rd-+CfHjI^Cs9n4b`MapCR$WDUr%7#4pq=+(7d&L}g}>(2`xbj=`yb~w
zrWYzlXMZXO&|Gk1a7aWT023bpvSbR0QX8xiYAq^i*ISOEnPlB240KSSbyi8T?xGh2
zF-5kj`Jw1SM$42!n=X+|Z5gO^*pt&L6N4lUqv>&sJ1=}lu`3+QtK;{FnrrN1$FW8+
z9-Ae=;-=jKk;)`MKe5Cyt*Ql$!VRxZ-unHF`xT&z3KAyZPV(%mO{W)9(Tu@WiwDE>
z`jf&9{c8M~YXL>k`5w447hJhI$g7%tO&H88r(!1t4my@%`7rI8Z`RPyTs0?O7%UBl
zp@q{rSb9eLf~KZzp;5^Gv;8p+jb{S20Jk@^O^D7ejAYlsB_ddH37UbM30UlP>aww%
z*k%v8e<J7dN*d(yq|A#lxFSbQ6$^uU;%+UhAxP`bjKlClfZ2qMU|4fupm25;^*HJA
z8C6OZ+C_`l^b@;mm3XCJ-)h)MVT^x#t}+Q&qZCbI;M}7>DqSr--sG7IIu4UFgSsa5
z^$auk#ozt2J;9*NsjYJskn$Ne0qvXzOxwny8npg2D!zINYu($H>D1BurmWY1DvOMS
zY`ev5^c;=;A-p=VJ}Hn2VV_V1;H|bLNlBez+fzD5rp#m*En9WYL11ev?gLYeB{mT!
z$p;X@7&pd<(*Qiz0FGW38;US7&aRm81A+^Qt7{{#k6?6nfug5>tO&x+FN>1n#XMll
z-h&614}=LAaRSIV7pMYN8}P9W=VB?3Le43AR>JSvRHj}cr>a>}pduXo*d3j17WaWY
z+I=`J<$S#JF!>N}F-M%YL$o&sy`1Psqa3sJS*wssPp@$t7FP{DXf?p%mwjr6%3Z%U
zTkST7leTiaj5L4QXr6A>>yPiA@c^<C?)4a)RIxE+nSjUsG}a}G$p+ZOj2Qx>qr3bR
z0Pn}0i%>BL+LS{`j|vJggHVOlHI~OakUD_uuK+wD&)w5krgH~}>!2DhjLO^(BulF9
z;vjGtfU4Cgu)*})n`u(y!T4{Ku=UY#w4#(i3_HGZ3A*h*KDdl6nj>B#nMFVaU&&ZL
z2lNq{nxT~)#K?1s?$rkc1hQuehojJPqBE@R(lD5|C<(|<^j5SIjXbB@cJ*=W(F6Pu
z;j|p<YB+bH$hN121mLHxxDR;@phwOG#=DP~XqUUtM4I$~1V%#1sv9%PQ|8J$guYN-
zt>g;HnkRp);}Ni<u9R!{WSf`^Rnx=&af3%|wC1bpXCsg91H67Ww9U}p&#(X0$t`CC
zji9;*)<n6>d}`bLP==)4ifeM{D<jwoG8H*m@a(qJL!CLMt{(0{ki>}q&Nn$*>+9n)
zJ<?u_RX7x>bOP#BFeisy#k3}$VYwLa*{YciKw773=CwWA%pq~Xy${_>1~E&g|K4<W
z{c57O8Ek?Q+ZL8T9(2Cp<_5rI0yTn4AQbc_FYfKr8*{GNsrAS-3c?fkvUqc@NiF78
z$eQCmz#yH{&}chnP$gptHO>-5+jCtr<7E>h;0!_%<@CbSvdbr51_-?G=&c2`66F-M
zlUMV~lpBvikg}&CpkKh-Pef)t`+X0zG&%yFvzsnnA!CHljPM4_by^tdm~^#uH&SqP
z?Z(E@<=bnt>~ZOWLB&xZ5OSfEjQHcypB{@{eL)b5Q)ZSSIrl?F^;}WSWh#^ev4=xF
zS@}$KUpWMT5|1g0A(RZMsi~&}Q-G=VXFBq-*v^0q+?0U~QAG11KTw%iXg7%Vu_q-Z
zm3hSVh>PesO}iVh$>!r02SRyO)k^K1j5yM?i?KOHB_0E00>F36E-mOXK-Fi|52h6e
zjD}ceksTLs1ZGmeNfplAxTP-vNYCW$lfNvx;(!-nPx3|5Eim4}eZmD;Cgw+(5whSl
zHsg|J^$`U$z*xi*J7QJP@LCo9Jm?0;1U>OgD^XvcHGUe(W6xm{{HkO+EBsiRj;pI{
zGX!qLT)%Tj2=N!cy)jpBE)#M%yeUTfSS2mct{OO!cHIWdKDAh%lIMUa^5F>*<L2hZ
zoLaCmQdA&{%Y8H#n=(LJ<vNXZ0@@SBoX(g#y@49dRT~tGln!ijvQ3wHyeHrgM#7Vy
z*2}eL8PK~WK>R$fTbx)B!i<P{yNkdU;5hO+DX>Vttwe$-Wd<#vU9*b-SlK43R=t+s
z5B`V~GOQIm&Yxceo{SM_A=!kJw>J|SgxjkT(-qj;dphH71ICYJ^`3ioTfFW(o51Ls
z-r;VjD1%LcrA4NV<G>_IP-Jo3X171x=>;)`HsQT0DctMa=V<~cs7nf0Gxi<598g_c
z%P1CCH%v|$#}gauIo8=*LETLN52&?Fi4E(VfuBBtI9P0&tZqG43U?F;R;B>FvYf~H
zZL3g6u`|ZsZ<H<U^IDV-=QlV{0a#iMF+iR7f$ZwiR9eFPQ0?I0U=5J&de-x7QMFv&
zQlwz6c-EpTZ;oOiL~^CZhHD;fx?FcdRx$;cP)iCp^~5J}qEs>fs;=%LsG?{ovdf2T
zO9sPOL~Zp0px{O$MphTw2sV8Pyqv2Rdt5#wh4;h0DghTfagA7N5$`zzISrDS!Wr%;
zVWp}qB}<ca&Wj~QKy-D*ashAIS#~M_IF*cqd9YU&xYIP+^SeAndZoRSBM5Al1`f!+
z@g?h7L%Ol$s1jdG7@bL<!`bmzx8XDb-EtL|pK-1Hfv7~6w*$I6d`OI;Z_3F*5a0#A
zf)!Xe%bG#ujC)+p-2_Ydsnyxl6(eYvB<)PvB0g}3^~fQSYZz8L=K%MrBc^VXa00Ff
z08nu5Amth`HIkk|_wL;ri#rwqOkyqaR9F^5dj^_RSU#%A%wI-tLoS9~Nw2Pf^>oyC
z9SoI>&EyAouh!`@^`|4T(5_F$2H06D<i+h)(^2TA9wT~viWSG-0W%kXM1q>?$3EHh
zK^&4I)2={G-sMrSosH4+;s6#WZ93aTYxKqE<tSJlR77ORklMa7vAQA<C1iPzRRz)}
zr^m;Cbt{I}I4(#hNL3?09&Y3%UMQ{J>@j7NUAbU|oI|hMNM^x}w?kC3TI3GNRPYji
z2)QYpwUxk!;2L03E}%hh%~!7t4+|sFu@o~C6A8c2Dy~~-wG%6wZopL7c`1IM;NLRj
zJr&(2cQms5!_;eee`bMmY{(0MJs>ew0KEc2rcpka9RVoJWRg-CpD&V{!d~D56ku~z
ztfa2Jf5nk?FU~(ZasTL<&ky`HojHzm3RliI*d^PPy}QMh0DZ#OStX{vOY_wDU?f$r
z@yf^ukqLRi%pf+1O$ed5We&Zm1goGylk3WCx9}bTf#vv@Z0F~%LefcVf{NE*EOfN}
zu}+BQ>7Ki{mZZBi0QX<+bOD}9a@sQz%&OC`8*qg3ve#%jnga9@c40@$2G}*4NZqsO
zRsE4?P1k@4wWL72I-qdIID=YzB)-n74#H{+DLVUN<vcZVrr{Cz7Z%}Um!bMdT(%zN
zNCU}5I_P{`^`*^K%$rq=*-@PW2Koo~dB~m9K~j^?EnYv?v08%1sGe$)jc4Vuf4&FH
zqs$;+!F6=So$Dy9!d5$w)u5F(M2o7h0b%)&lzo>sI{fdtmoL*jyP*`@4$w7yW@Xd7
zdvV6X%vdz>=Xq$=^aE}5c!q`?ai`1N$9KZ_G61WiTNPmQV%KcX5>>a|JGrK&2Ej~{
zO-3Md@&zpjL;X+IZBY(1Z;09nbaIvTaw*TLfYW+sS826e!ALYgWXIl9+8CA_K)IFa
zQl;hxLRv}QbI-V}_FrKdtEX^+IbUBr|G6vob_Vi8@m#-uE3xP5vrUD>KM9Ga0*!&H
zNTadYl`|sjxAN#F)Y?pSH26P6PN#E24Ui1I>96#+6ZtBdB9EouV*^rA^jv{LHbpbE
zFYe|>X3})KQI&eTB>==us}Gw#z!E_F(__rqNU6&Sz=xcv>EPzzSSeR~silw4EUBH7
z&MwYZbaHIKok+_ft38-Pp*u??9VI_{`SRu5>!Qk?@Jh1*2U2q9e0dYM&c*{<0mE$>
z*3(hIGhl8cyTBFbJrM}$%utdLtQMIoKi<Tuy0CE`nW_yNHk_v*B~KfQwtKDro-b|u
z$93sgxAa~?)(?>f33C}31W%z;MMK>H+yM;;?j%x|o~g?e1-)Cl%FB~f5=E}^UM(Ql
zcp2r5r6vX`dKv<l#Lj6^*(%b11+C{YH1~Kqpo#%%XaQlYJ50csBpripy+>pLIX;<W
zS2=Iy0j}LW?k|O{Yx7t~%}yB%hM$JptTQ|j-Gm#uS?*wF){<<+Ey8Lu5HM!~1b?(0
zXC9~z@!_nD%(V;v1EV~^YNG1Wvs<t_8~fjw$M+0NMaxnLpK4W3h89e`!+u?U{PGpu
zckT+%x;Ckg5G1so71dabfY6v62rLB~T;fcZ2!vUy_-yzcOMxh424kE$Eosp6&|HLc
zS*}zg3ScBwVt+`d7ai7pH2T~faKD~qP)5QI8gUG1(9BG$%FLs#XleB9Jq}o<V<>&F
zi#K&52va`z)_vCCUB~uULc5NHo`eQ(0)${%u$G}~lIBrz*pZDXkZu%^r-eWfPV@x3
zpqC=hL%=dC$j1jLzA<v)If)OC$W%a;gnGf+YA{Q#V3`FAB&RL=Y9U09buwRQx&{Hy
z3osr8JZSUu1GTK=P6Or^DSE&}K}gzx-r#%}fNVCbO-WN2s)2~7w}#$5yIl*AWD$ic
zN3)_Tf$pOh-gA^-m1s!FWRZ<;qF0r9WDYfYQziR+Z*#gGjB_1|EQtRA5GiNb?v$`J
zbPJoajjHI)s3z|f2iOK42)swVq=7ApH-2*cCmNI$Nevu?YDU5v)9|*_aXokA<GC;!
zCIS#kqvsrUC^HFTh3&Tas#TX?+;agC-j9q5zd>m=ph6aKj*J<{#ZMnXX1GIOE%%Md
z|BN)t_H$yFQYypxmWD(L(5p-)0fzQ72@>DWU-~}51uod48L~<S8VkEnbXpdioeOZ!
zlBU3K9Xkt`KS$OLPURcvIdjv$!Cs#di>Yz$sMO)+PCggQy1GP_rED~s`!mqU8sIW;
z?a*lHfD|ww;y=K+dL%&>T27W-5H?wX)JUVnFfw9iXd{2|?r~^hl8M;D<@Eg2oH;Yr
zTj36xiXu&W@~x0_5{J%-58ijdvbc+c_6iskS_7wm-9%Df5SVk&jVO&x4GMURFMRs2
z9qGfuY)4qen)sDZDIEot>T+@(fJ6x(#W3xjg|HY1tm@V9%H)M1y}kua<v1yEFoa4n
z5ri$ZclMk_9GlxDvsjI!3e^9((5@^n!CS`;#wgHjT@_GAX_NytqfOQcqy&vXRShV5
zKR0(6zX2;WPWpy#?t>E4H-354yh~~VuqhKZt1uODb0ZOoaU1EaXPWeG9h89;Juz)?
z(Vb1wv-)fO73}_Z0LVuZxU0NB@zJBp8O%x^R*>m~55&1(cC+4FH$Dsnjuh~^2na}^
zXyW0<mirpR3g&fcab^tLuXr`>e6zFnLF@ow%W1I`ycAonNhGM7(wdq8{ywkkDk)5j
zf+r1@U2%!Ae|ZSY2H;31GzCF&!6NN;O~|fS3JSk?3s4w9mt&7+mj14d%>0iFnUBen
zdp`%;ytS(D31<Q@>2$PdlaZNcU}$IvLI;nGu{NDhsa6@JzB99A+<^xmL0>`Jn%b1K
zp3`ObuUk0F+h%|Vss%L(CzsdUCEXWGz`+E<S~#a<=L2-FK?W3f>yFIwO=kh=VOM>p
zmVT~Ai8C-p82pjQ%+YvJ4o^-4Hi%>{GZh(IIRt?}vA9^Q7iuJWnFNhNy-=Im&@sb~
zEi_4Nr=))Q<rf;ZO@Rn0Y~5wm0+GT5poXC7%lmWSzWELJ=21fuDB9~YGuCA;{%$4^
zsA;^U7$m%+C|)Q@!OR2<y_csWL~L}SUktYhn8rLM%N3b<Ali;LLb|X@X`D{O0Gg3q
z$%;)K&?6O;AVu)$Ln{U1Sum7AqR80??3+TWs--X^F?o&CTnhiP#sK)|&+OQ-G~2-6
z2T5xV;IjsA`^yU~vg5@RtFhu9WYMwVO;;(<RTDtE%0akG1i4wEThEn)hY$N9z{R!?
z?CZ41>_}ggx76I*zcDV2*{7`nE$X-Hm{~PEGfVt@y}2<ID!xodbgyVA_O!xX&w=;t
z!VDJ!osBL78`!DYAfQF}n>EJl0(a^ecU}KG%T{0)6!jh3Gz2YPJt*iKvk@OoO*9_u
z$fdpH&8|jF{A_s<+8eNExYwfCCPsx*0y;o^eAP1(XS)2N7#fv{&J(?DZr$j0bZG;y
zn>$f-l`nAZJ<#Il)SN(CioqH<%}ItqG=;e1ixC0DjODEY8=zn02i&Vk#u!p&pGMm4
zQW^_O5SgNF!LK@DJ%fSlnaK?FwdQ(fE16;E6f<QiCG%)&L2<k9Wt&QK4h)N<3D9be
zkZ`o>Bmy@r<kNECc-9pV{TV`{Mk_!W97qe<(TLf-=HBIk84pVIa+;t_52QK~vff_c
zn{v=*x&}NPwt1i^e$F5742%*s5^AAjG6uAr3(4=`!Gk^^3{hx`lcvBH1p-+_?q)t;
z2dHB^m&eBw_0ggNsACvu&klwT7U<g$<Ffp5@%U)f>dN%wP3RY0@xhK86QB0hx^RJD
zXiA=f#E<}Cs=3In?|pl=5xRQ2pffZ^52JDsR4b=2cmS!Fpp$Qg?NsditlKR1;5C;+
ziCedMsn!vG{vd#KWEY6UYKDfK7z43k;ls$20P-8*xL;tU9uU_gzN!bX$D*pW)@gCS
z@mWYp&oS^R+pj7-{FndF1jQ(|O^wGg2b)n=<q+s`%{OnyKKZ7zC~O49Tq+T&Ryym{
zo~g$S5JT<!`E5`RWAj5h*I0h@cBFNt&w}cfPdjI~FsF=%%Z{BpO~)4ehDPrJjyog+
z%?zpp%En(^2W(jboqpge7#PwAF#`dYo=k$)j4x<=kt4Wp;X*aU!yT|K&oBK6UwTlv
z2!IOeWx}b~S>fS@ym)(`U#bE>YNXd538QiW4Fx^evJYzcm<DcBGw>oro$~MB>lI*g
z?IV6;c>XK2M1(Ftb6ZhK$zkn=68!n0{pLq@foP}(EFYo<ptw<(l?YtBxbknuPrkML
znIXsAeoGGZFzhje!KpM_*m6&2vPC2RAwE83Hjv{;g3-n&3VB#HL<#4*(E7>6AV+CV
z*T`Vs|1(40ztC6Pqyd$sa;$`KyC6KGF4AqVwrRA<!Ma4i68Lwh)qQ0=7v!jou?`Vi
zuitvo_aBCfO#__QRczk$W!kvvRIo}8mQOYM%W>bG!QPF2EuCRoTN_p}`6-EER2E_J
zQ4;jss*V1&qhaI$#D-dEnnaDzRgB`H;YHK329S3LcZ00+QQbA8DCPYQcTh9=VCG;{
z`w7qz$ged-k;tV8;m?%ZO|yVp7WemF&|}-yEw)@f`a%9Kym9>RL<_@O<a@?TN3wk8
z27=R2Nfbo4tpQLm`@pgm*kovCzB~)R-%%enn`F2Z^`t^_enXDWAv;l^mw2usv)38g
zHkeKvVsI8=w=?Wd3kej_%FA8w`GG${?Sky4jUEUjf?D7s3}jAt^nobrEMM?W(6a&~
zPH%{R{&h_NFfAky9D;1;iYX}W?YW_x0NrFF%CXDn+2qyModXgK@t7I=p(AxR(5`HZ
zXaRDN@t^;7y04{K)3s5v=AX!Yv}V6egu3v}FC1Y77+6z*rIrVUajTycJenWoKIo2Y
z$EX8|@_Ai;9PChGBE%6aLIAZP*^{X_1UG}Kn8a&%`-KY4<(q-`56<=+vqmdqWCXI(
z2`drtbH9Ahv^nVSU0L|Yex~qh>wK;1(iT5*cksD4vJ%8A_nzSugf3t|8ib)xUaM(n
zgmz+`hWyArrLciNAV#~F0_cW*03pi+e~`RENxJQ)DyT#HfmwlmI*8`ypl=vXk;^HZ
zHVq~8RLS@dTEmh<K@?_qi2sG!Vffa}e~#!80|27x6|ys2mq>z#!UT;)(-*nBcWhlQ
zAjRFN<si?Y)D-#wgMTnyew~zR`RPiZ6s7y0NO}Q9&Qhrxg2>zGxPyVg2lS*IVf7&|
zE_aIEJxzAvgI~MOssVa;_dK}qxz6@VCRi~>MnS+`*cFeXT*$FGxmXbVX5UCrrwqaD
z%-)bQtH;4!j%Qi{48no|u`9+a%s$}o%d2V(3|0nUJdmtVq7QYr2>iQqu{T^=P7XDx
zaxTrdtEv+Rqo}X2C5<%#cYlbtb}0z|942q0yq<&*aySC?K?O7{9l+y-cljISU||;I
z-2Q;S!Q{%VF{Sn}l3qTu!o=oCEvfYkR$4ZF>llt3kRc?XRiPFR9tR|-DE)>-lCKLp
z;t<2{jv5hoKaBuEAo0Ny+4-N2pQLi`E`uI4u#aP8LCjv@5zj$?1?dK*JHV@YtE{_N
zG#?GAII?@)XbI%U@Yaz<<`j02I-gsr0`l@a1@((4)1vOJgG!PUiiKs{SQ8MsV7y{g
zlsg*<yhUJ~or%1h;edk1mE{{gi*fEeL1QuXS9dVnu@UwJJ))z|nKNgQz<ix$z=mQn
z?8Ae5*>mukW5qi18^iI<#p_euF&$$HhC+e_@H+v}HVwLAR0$@AO?RsP3u5IL(of<{
zK!PV;;bnL(yK?;^Nwo5=oBRtb!DQ!OsMyE+=JeR|gxstR-v&|7(4s>i&fC2xb0kz0
z)a%^buyyFZ;ShW}o`r^o*Fps0>93kibD#9z1F`S=f>N{8`f*ezLXwe_1E_}TO_Yy=
zQ?<1gISy&mnP;Ant7)vT#$6vJBCLQwg<^Y1i_m*U^FhAz-kFK=Ul@+hz&~M8h1zT|
z%k#6TFq<SVU$O~hgE<>F*mGP|$%oyYd$+|qf)#sWXs_)1{fbBP>iPkS_okWn`TJ8q
zR^nkL<?`_@LXAxaOw2Qls$|BF2m<<N2i`5a*?PwjZ4Q+_QOAriQw`sP!=4uGy%nja
zvez?=YKwrX?Eyb<+-DK#lsZpvqAZ}o9~4?TsO~`u&((F4L&Z%n4;;C$LZ$KZrlzJ#
z+zh+fy4R79K+H#kg4=WX=&nrNLO%d17&N(|Un+6!{nlD`2jwi2pI;rH`)X3Kk72DN
zyiw+FiYliwwF|8@<m9eFuNL`*ArVzM1a1;eqs4kuH17h?I^Wu+rVfe2kRltNIAh0n
zWP_}aW71B&hKhOGEwPmcw!&`GFyzoig4{?peRg|GFqjC^5eWcqbD_$IkqpQCp`l5N
z)J+=Tq3Eu{jZIBjN7r4KzV3d#Gy^`58MebZy8>?<GbnI}xP5~XtPGp(f3-4f^@vwg
zCb%+zlE>R0>uuo`_|u5ZWU4dkAhqU>;aGpkmar?rq7X^+X_4$WqH-}9p|b*q<AwAq
zz0j<A587rl2DE%cMNqJ!4qwe$A8Y_7D3VF=Up3XY@k<O39y)AMbq0$=FI`$%LJ?-C
zmAxVkjUD%-8pY(j_v6hv?db)zJxQDz6Y@+bXXMtjWqhuMjN`ju?ZStAeo?}WC+Jc4
za(q|=&{G^JN`&xp3U)pbbfG0dni=2&t%_zS--6`&8EG8vfg3D)54QYiyhrZ^jzFch
z?`X(JsxYl79vVb2Pz9|CP^8*8c4O^;Slc%F?_sviGEdUji_6uIbm}Pg|CTTpXM`_%
zoEMpBO;Lfe?0|@EHw%~NCp8={0^@?D!QD=?ssYC{fwG$(ve-OII2o>PQEkU?3Qkl2
zt)o(>v8bt{0>p!<hx~?~oa-v`oO*wrEX0)6S=<H*VIMss4Cv#NK_{XlNNohdM+h<M
zgPH=zPO#6HpG_C8z;I_13ZlufzH-LV8Br>HhGBzrWP<O3B?*CvHUr(p1pOgN$9hzR
zqcTyQQZduXI}Q~<fu|cV_B`_!&3_+jm03Az`mbge^WzSdsj%$e@v$iCZk&Vp{1QIV
zEaiV|3s+@p?T#IfR(+sXvJG`=XXS4gI9;2VxqDh}Z-`#g*$AD4FQY*4P1WGRK?w#`
z*+QFMv4x4A!<h5x@?!A$*Iw77peHe!_pn0pa<`1->&evA$1m+!zq|`s$}d4Yrw;9b
zu-JKary!UFU&=Q)_aGTCaM=~rSrIkuJM)&8`|I!h@3$YHNn8?3FfR*m_{>+*FFy^X
zbB^AvX<N8*^pq9}pu`Vf-4D2rfD;-J>Bu+`;K1?Umfy!hN-PZ3LIc{(1)6@i&l`BP
znF;dI&Z2|$VxpJK&V<n?QPv(sb^DHnzk2SS-fL_z;k1jopJ{CvZtYF70$74ATW88i
z(pZgBdrd+cTmJV;)B|n1QR2gjE;|R?e&viWGg<IuvM7FYgj{;&(}xajO80-ho1wh!
z22)O{j3*Z_8@4Tl_TNiQS2PpfwDybm|79>Clgjvvs3v4oS;?1t#4zN0;~Cv7@9^Lh
zd#+uX#r^o_Vi*j*f1Vskcplc~@|@kjI$4JhY>ji*cnJFCk9&d%kRvX6cA9s~xHkHA
zTriCM;o67c+9WW!J(QUizx0Ts&Za+HI|Hxnpc?+VxzsW-z+t;zxaki!t_U|SIPdB!
zmaYYRy4^hN^8b2U4FFM7@$zzTd-bGalFm6)A*{WgWWj)*6*;GugD5`x^6{XK^0&I-
zNfz`?tS%HeH6-YDnZY*IYyaR;qZ{zXH-WEEW_)Y*0UP(jKE=X5k&8`mMGM^2>vf4T
zb(UX#7BaJr-_Y?uww4Uffqh4t&c>ARm0kMoT}yn|x~D+x_xHN@VL=IJoSuDqJEs-a
zL|5>!%3?s5QFI0OOWuqhyeRe|{IuW0iet<T?V+000MJcEfB*J%NqJbZowuE=w>Pj-
z&10hf$lv?Jt?hyz&$Q6L!>llKC7fW}UvK)uk~QLzHN5H_J7()mO>E0GQ+NG-!&D9M
z>$_ebaA+>iJG?8qQ8(`U?Y;nxrfTixe1*}y@G!c@P~H!YR*H`n0<NJQzZurNk?+O#
zj+L$ix32MQ0k+d~d~#Fn#hP0`d|Lv)?RagX9yPwcHJZW*K-ONV{`g*7rTnE&Bi#^s
zs@LH5=XV|N!fipOc~PD|&zrB9kaze;BlV%bJ=3T!WK{T2s(01J@%v}a;lXESE4S-{
zWt;zC_VmZVb5Q|*y8V%w7kh8wSyGAb_mRgjC_((r&XexW&x@C3r!+~VyqDiU(nT0G
z{p&Hy>lDa9rlA_Ng_yw~b^{91bvlIORLy3hD+TP|>__|Wg0bq$@`i@oLF2Km8dE?1
zGDOom!szp>S6_5$mzt^n@JvVGnO@UF94Dh-Zbv%i=r5~v6Tj)sEF2ie5d{_`rv7Dw
z5<c(%eJamNd!in*5Wg?X7=Ld7mmwTnZhIxean#nRlqh7zrxX6a-@Yr|b;`>SV%Yyo
z5X!h3R&O4uv6rf-!AjQbd$Z}AJ0CxV_q^tk?dI7&Eq*_)r4s$^u2an(Ca$LC;&vK8
zc-2mMhutHa$8~oc*|_m&<JpK3z8~D;9z3(-FY~T~kLZEMU+D**I|7WA_?k=h85B!s
zW(m}NZ@3~U7MlmE=})b)pJ#u=k4|yrTr>M~U(T^8`S65fObGwSmx0>w!5~WaS15YI
z=mf&rhI~8(w{CvRHqm0G`DLJ>-TV(;@m3ukyOF&o!*lPpNb3F_;g`RU1^_&cHx^j-
zif!&LbA%N=_G5gFhahNgWf4EKt4JlPQ0nmEA4AP=@WZvYY!WRhoz&%(;laOOG9NGm
zdrZg81!`WTlK&o4{9(!H-`%ir(&S^M&9TO_{{yhdfnhs7$&tY@W4)5Dt)+YSdpm)5
z#YL~N$xr#EUoIE1{viZCfVI}|tI$1lJM1Q|X7~>&0iPG9j9EG*r_zVEoALj!PhegW
zm-OVPbn0H${*TOiu?fbS$^V5E${(d5ryQYkuLi55&o8{&r-qqP)n4v}kRjE+bBNuT
z!X!*EpcPe}*Wae0@#1?A?{EOqaU{LMC;h*YuRA~FnMN_=K4P<&>{M$Dz{2k%Kn@jl
zXgI(uaHH|X*#w9HJHI<tArB_@Q`K)xf4D<f94O&6R(Jg&xsFESotaup!0v`?T&Q^#
zLN)&`dp?OfgJ0h~+9Cm{`=!`y7uB3J90&pEKVKf3A%_Z-?C|}iaybv%C%wC<nzjdn
zyK0%&AoAL$p2Q*caH#aupRnjWC`;+C$gU3z*0g<Y&;9i>*QNa-!cs@u($9D-N5t)-
zzC4`XuhrxcF(~)-GYsxbuoCA=2O{E{A&iqsyepRp%F7lSPV2s5z|j-<NNKK@PAt8x
zUM7lrKMQFEQp-#bA^X~kJ`S9k-zfgrdJQOzIA~T+wT79|Vf~lSoPc}I2T@u7fs{{O
zLrbg6?z+Hsnf`Dc{7h@mSl6DQ@g9Ck_o(Rz<((+g<?pU@{24et*%DYNucbF$cTZoA
ztC}v^mFuML>U8qkO$%KGql|UF82*}e^JLlO7(KvXE1z0M`3`)8BNiR-U~cbjAD9k&
zdZS)N$2;y1Nv6!+9~Nj6CRc$`1F{f5C(({n0(fe!?bxxrnBTrUJIrXKxdhs6p4d4S
zCE6%98(bvgH!Eux`~5<13W4j`6<ldK$S?a)g;a9+X#Z+l6cbc+b!lI(#K6i4+hHi~
ze6{HPn}@EX5-q>*+PPf-=)eCpw2shI^R6^X(+n)509*zJcXX7$S<tJGqq*`^o7hCE
zp^jANI=)Q!vnoi?rH{Ca3dw;vc!`?$!#ZdWUugD#esWr{Xs?UO?Zt_6I!kR@Mu8`{
zNDFP2{`3kaD&J<E`qh5--P6@=DOcPzri^4od0vNWUjA<Hjz2pOBwDmP$TE2PL$-Z@
z$z|u_RqVOp9K>*hpI<C*7@UL>+xQ!K)A|YZ9p>zL&QdcowR#Ol^z>MrE2ShWz5Bhy
zPk$?3R9D;5b~ZrMKVTvNa^3>N44<r2nw=2;^CclCYF)m`i>R~Td(Nvr$R!El#~sID
zctro&h2Q`OwmM#lXj8*;+udIm`TDPaf{nHFvo%YuxnTRDjm8W8!rxy=brk~0wC+f9
zV846q><cli(15ub_166F_hAJl^M@<4*_)-^3arBj<4L>fjGrQ~`?e3NRd^Xg%8l~k
z;eq8My5}Gcs`<_Xr=t_ZU+s?ZxR}C1t^g$S!M~j(+q-nI&iCsbGYkuWS)71cnlPOP
z4!D|OE*lVN8v6Z~t9rvvZ6ZCfwdp5AIUS>ymn<|MCnovBWe(tFN+t&?n-9v~C5#7C
z%xm%rlYY44RfxKq6la|pOa|_7?xMa9*C1t5%r1-4k2Im*(Q)YuNS2lYkBWz8;JFM;
z{Q4|7)CO!Pmi`6S?ffw3x)n9rz)0vT+;pQz)=mdioz>ZDEuwKnc=jXP#{Psvce#n6
zBMx`I;phimszObF;Uo*x`OMhHM2-F7!$kgPwpq|-A@sUZYi28FD>^`>m4AQRaCU-O
zAYR&+EiQ`Krf;fo)OKn1Q?WJq-4A9YOMa6uzq-`4DhKo-v8;)HAPEeC>K8Uu4d%YN
zh8idsgUzT|1tk<+!wo_ONw3L}_-Z&jfei%pOxyol7M)BT7aVFUp#dP!YY2=c3hi$=
z?3?IT*mUCP*S)%4nthI`0oY||zc{7@yMpu-2wZ3mzPN0k5|nlX`!3L9mIpo)nR-P<
zMPuM4E$4x<rvPUe3=@9UIGpmVKy`K}(?G4pfeEfY*Y}wLpb8}yxGd?N<Pm7{7y0^o
z48;54BMgzF^;SI)5vSGSQ8+<^$p~o_TO5QY5;nR)5y^scm^jM@m9_~eMhPI3Jq6n2
z)}RWInDj%ptU=8_RrVa)EAh>5QVvqO-8Z&ZQzkrOdX9hlv1z$j-YA%ueC##w6o-3|
zq3H&tE&(TLVVs3qz;1*~9u~PD#-QA=1NG}>_6QMYm_o!eN{HMiEAF4(+dr(nXydRl
zWG)kMcJN0lmfbNR@nmS`zD6lDvUAiIi=gq0Ov-!cl&7;-0pI5W!<ndhDr9724S42W
z5ZLsok2lWM`-P^%MlG8FccVzQaLz8?^)J5N^>CJih7hWHvv|llXp|^Mf{Cp2T__o|
z*xYut?Zsj(q^nc%ZLcScuP*x65E?HTBpUcXW#9V^3a3U-+H+hV9%<wCY#W5WCHua=
z7Lv?wGZ9wq_8e=>Jd)|tSEU;Beb9G|PH>gZ0|Kpp04J*0-Z(D(sfV&CSNF8uuK(()
z^_1~Up5qVR=X?lo5D<`AJfo`6J%DJ;W77p=&-V9!9R?*fTdgUNn|Vw|sv_yEib~V>
z$-dM&m_MotNN4(#InrPw2PLn8%B314Mlq})!dnpp7MOkijRh)t9vv0KvYjaxbe8`f
z_>#}M>o=D_dbHQJV)`}qCQ+A9gW-M=5Z%CNb(b(`@R4LbPl2W?=xbKCg5ljxd$3~J
za!@vFPPxyfadqj1B^K|{2>tSUV0-wv<Mk`h>-B*%N{mqF;sP^TiNNb+Do2exodFRY
zdO{ijT9?>H0fVe?8O^Qj2Lbl!24N7#jzc?A3?}0o7<Ud=v~KB@9FG!{?A0qa%P4VX
zdhDmrr^{l*u07i~_~kdDa1s%IpD+(y)OeO-X3BH{G)zYL_+0pRsny=qK)YfNW{-n$
zl&!1h03^2;2jbc3ttyz7$Z8%DFenuQu?kVW7UY5{f!L$AetpHDdtVuxbyL+N7+>t+
zGFv?Nuk*3S<vFg2Q;Yi<Z(p+s9lXGhGXe^}CvkP2Yi*M6{`jS1B3ewJ=I@85fj{VK
z9%=sESFRs-dM_ZWQ?T{W>#IRM6L_g1KaW-&be%c+mdBl~%P(`nSmQFpioQ$-)L(!A
zLI0$0>LKv&M#q07MxKbc(t2N(XA;9TFVu)~xky&JUo^dD8}|1_2Gw={uXiPmle(=V
zqCN?(K<;D9h`vRj8l!9JlO`ML2BC!()Z{UAi~#>iLXCMtR9o==4>&wA2ma0q5(|Hr
z1v&tvXY9yn)ojl!Jq?eXql_ANJFIL^nsSdvgmHy)Y45Vx;IQ@E$z;20iH40uoUY5$
z?G&8X!}({>s<EL~7W(y|(N@khTuH;(X>9jmXAy4?b3t3=z4^?d-l)S*AtpggIEf;G
zIFRhoyLcetJv%0O>FGb%;5B)9|B$%y@6$#pW{mM4bPF5iHB960H?)VF3qn)y^XoXr
z-P5*}pPZ#Xf+DyU4rM_pC(1v4e8|It@l181T;948*^eLBpMA@!3a52p>$DuysA)FX
z%*T!_w9A|pmvC|zrVqxhC9k#cp>2StN_CC#jmAMdPN~>Odi(F+iNK4CD_<yr1G|c6
zUPRKG(v*lLO?wRVYT_Fu|A-QK*<^f_p+g*+Fc+H!WuPgl{KFq-ki_=Kvu1?7@VqbY
z)fJsGuM@Ac3fNHlh9XToeg#jqzylX}L%ekls_YMemWe;kW^KjMR*-(l)V{Dj1p{9c
zJmmz>kbu4jH#eS+LO*$6lW2EN7j*&_1KKWxqDK5WB>6U5^M_`_cD>zArt7_?lm5z0
z9+jm%{gkwe+P7kHR)1NJ&`nr*aPWC{yWP(}J>jf|&dX>?_j-CS?z=xeiCb%!c*WYD
zr3>E!?L2sJY7TW;IFk=YD5z)LSMbQ=CcsGzbgdkSxalX};NE};pFJg82WD~m(#2#U
zsC57H*RPLu%L>>oPCgMU&}$Z%-6d*cslWW*U9G8?;T!h97~WQrFb|JPZ{Cl>Y%*Ql
z3aOU^CgjfP{R_j+H|AlJC@?`{fWk&NP)Vg4avd|+RfFl0?(|Iok3G}V3Ut+@AWUXL
zY`yi+lCo56PJY*%@r>A~I3S?TRGl3kd$;Do7ZU?ngF3=wNeE;V94iG}tH}qa-lW-}
z%qQDpke8lJVM82Z#LB>&5^TDNxgPVAWFkzUDuDjOAz}mTEfGit9oZa==Djk9n6ls8
zC&bH^YqN9cGLcdf-7brMJ3#PwSQ|{+z~Fhrg2$A-E65ZnN=6IkW8fSY(7`GU{BfzL
z732f@3fF&4R8nhYnQHr&FvAgI+>q=Su&6(qJqu}&MCiO;kxxG=Z0$LAyF)6xsDy}P
z{?)^i6wnp=K;x?hCA`v=mn&gB3j3LzMC^npSU$Kk+?8h9?fLE<b`d@ZMh49NYt-v|
zCLh>~Z|HBg`>;ODm{(;)szeDLvMv3PTnrv%GCkZ@5)|ry6D-&%C<dJyo==6V{oy00
z4P}<f!RqQ;KZR6YVD75#ER&nzw|!<!me3MfEjS%}dM`|29L8CgaDDqT*iNL+l)^Ct
zD2b^(ly3*p3Kae(+rSuUE4M&)<MMA0Pl%;?rxd^2xZcLW%fM1z-(`YL&>Og*<Zr+5
zJV>JLJgBQzIjAuX=XNkLC1-ZWf{wJHKxhR<|42IKu{%L?r=@TIVjt>Qseg=RmB5S#
zs6To;(oFCG06bF$)yjoOpto2F^V;p#q%Iz#ZW?Ej6(dyHG=@Ls4ZJ&B&be<yF+T!?
z2cKULW^ij!a_IttUnaEv8v0>MIDS#Evk}Us*lg&YVbH{pB|O3;t8!r&^oCpPcU5%a
zq#d3j!H#n1x+6u^Fd!C7xH$uDTIj(Aqox)mYS_{4T%ynAzJFkoP5hl4Fu~p~$uE|-
zC_<(3_iZ)eSU&ZbH}knMb`aNI*E>;95ec2y!~7)!_lgJZDH(&blK@eQ1ZNs>A#9X;
z6y8V$fj(B8cyfZ^#V6P4`g+Lnb*4*Js1U&P#?6g#J`AsT+9D{}u{n!!Iw&|ATt-nM
z3fhmCX8Ii_KtUCce_Bz?%Mw~y?uCOWfXQ&wcU$%DA6L7rK)E3?y#V@@FZXD0p2~I?
zm2+?e)T%#(;u@IvV8eVX1WkXNq$yYkD9<6y#1{Aa@g~5zNjUE=TKggcG5{Yu&vqOs
z#j}EFFd223FR}=p*A@o+(QJ9ZepcT<`1g5}Qng*>wbRh)>9QF=25l>S+H*-YjnI0C
z?O$GS-lARSOYkO~M#eE`W7tas=p=Bi)(zD7utr{9UK3FJrG+-}aN|5~#(PUnIGUmO
zLvl2Zb^3tPZtUtR)EVo3{oMzK0mZTT4^t6m7fY{?S^zRzks8i<-|pH~RTNyYOz8ed
zE+9{B`*H)&DaIWuOMs+Bpw%$bPiDb$IZz*p_&Wlc>vD3sjEk+4^Dyj;=cxoaG)arX
z5hnsQmu_=}g^VJw#xp^1?iR2<O8Bt+n4hB7qLvYyOqY0N7aX${1ZVEyEMSJl>Gf7d
z8T$i`>*a6PGOvmqlF=;@q{uj{#BAr#V<PQ)=58wfZNrW?q=m~*HNY8k$M8U>8aztQ
z1@4uCLO<-Y1195Q{J<M400(Q}P#_KvB*3669j+jvfD=BBpUa~T4$8w=ZlYaZrFe-<
z&XfevYMV)wgji!KrA<4xl8+)&H8=aoqA%f(Ms~7r-G{Gh#9(#GFl!z85l&AdHo!f?
z(QO?5<_6f7yt5JNK0u1KkTbWg=eVZI4r6ZO=QfiMNJ|#@xbsU%L8HBINJeYL)2e*#
zUCZ}TBvpJ<y2VNw3S)5;T%dB!xIRjlcV*UlRTR}$V2Rwu87=d$A>owj<DL%BW#nnK
zQ|skV0H^N(g)e{EYJ-dQ%1gcj-zt_4&rNw2l_j{|aMlDf97^Da;(Hup09E(t(8iyf
z+CDBzR5zG!=@?ofO}?BPelKeL8j2ESRFJN{au!)Vnmrs)rvd74X;ko_LY*CkM}T}Y
z3}ksAQF+y(y?EiiS67Tt$wR4Az0%@gkKf$?KIxU22Oo~|PKQ!R(0<@K#+3TVV+XuI
zI1eWtss?)kK}nmwv}o(#^)5Y7a5(NouA7M~llnJ4Z?sez9|cP18epbkM_7(a&jSDD
zFTmbu8a>s@oE?}Q9J?+0CGYnE@?!faP2S;p)Sd8fAEP+o-9c}%pNY&yN}0>+uYBxM
z)A`W}V}VyxJ&76(uBxMdY##4)i(ZskUg@7(G3pHp9jVKU_?D|KQt5>%9K{(KGO~Z0
zZ-lV5=DDm*LVOJyH}=PE+!CBW?jk&N|8Y}s6V%Y`0!{i%s}AS;<|g0T-i;4`Zm;-d
zbs4UYWvI(fK|({_B7lK5xSV3#@dnHGgg;1Lj#oWs!6C+PI$Hu3A9t#9wl7UpxO|+R
zC-hJ^`|!9C>%PB|O<kSib!C_mdo9CY^-RVlx+cQ^SB1K#V^wkHoe9FUBuOLx37Jot
zaMp^T(sy=EM9PAP8#*AG@Wr~H+>87q6*!p!r<|8AEY3-C)e@@Q4%xcriiw13neF-J
zkxz&=-f2DTXZDBS7!C3%0qo*OWftEwodT?uW%AQ#!JdlFnq_7#M%lTMF5peZvb8I7
z%a7W64LzG1x&I5HA#Hs%ZCS#N`pw*S9~i#eECp%@h#?37kqN>X7DPsL+W~cT7UieW
z`~NNy0|~M$oZfDhPTew<Vv4-!#In`$LAL(>T=o8rN{)VS70KOhP8N5Q9(`SHhWO{O
zMh!h+$bueoAARO7YfP!g;<;8;Fs+Hl4g{XuEWXzZCdd$D+Q%?GScnpO89WjD>OkCT
z{5!(S6S3)z^>bUoYs_su#l2_~mbqC431M`d^4hN~X~iCnG6K+zL!8wy;tu`D>wuIo
zr2(oGJdKKihuVNdONP*CnNow*CkXTE)Qqpkwicd+<1u;v?*G?6*k^gBlcqR082Vza
zRVm-3I-69BykL6zJ)P74{rW(oAAq}EQim}SfBGOmeFWq71Lp$ppdp-lg(+;<Sv<6<
zZ*|2v2C6eW;1UnIGl4^cN&+^i+9*uha;bPaYwORJtV_@cxxq1Bb;ZVV>23BzwT`p0
zpR=rP?9G3^{@#<g$6zd?&SW)(8k{YK(b*uF0ih2#!+85%C#YQ^aiTyK^f1TZR68a-
zA7lvzmx3Y34vL9Cnb|o&t+}Z%!6vZsu|v%6qs-mYqjM3W)NH?2yJ!~Km0GfPWmdt=
z$!`$L{gH%`(CP)y_}~-J;FvHJZ_$qzU0GgC84Cfp1>+`JfHrX|0go`k6CZ$Mt5dVO
z&S4F%<VCCbi8fbs3m8&)$!^)m02hnN8!WU8n&3jHxSE=d#v%&&)?U}86)O%!*=50i
z1OX;3tAR*kgToSh>BrcB;Hkh96nXu4Kog2+=?jX3fskTw6FDSPtw&r2q7`b6P}vcY
zk6&FCjy~L9>g~BTWyRK5IZET+4-1-L2n#BD_3aas#nJ~Zan6W_hxEXpB}hW)c?G9{
zH~|~rfmUp&Undw}2ZvXxW?t_U$n5QMMc-bz3A7{~?*zH{b=ZtnrbcL)>w5!kW{8;W
z+4tqq7+$?<TTGZwfeZHs9BcIPUZQZ(u-HWfDvOYXDvB55cL+lw<LCMLf}a1=8*{Wn
z%l#kff_>jDI;OrBRH-aG7_>S)H#b%snVVLg_r2+F()k<L4P=kaFfoE>w}4o8G|Z4g
zxB9*R=HRkZoJ>8PB3<uRqF-k8i&D(0X;of_NXk)cY%dSz-T8(_EG$6Z_#heJJ`U6v
z12m?=ARwN;!;R&^I<giWD_zWH=}dt^VZynkcQ^YxivQkO$DGy4(k`>&)h%IrDuBI(
z=M+a9Pp61oOj7mN6*%sxHQ_&m!!K}X2MNdfQN8S=qDX~{(vM#fvgs6R6r%*N0A(m-
zO{DDn>!;y_{ehDdJs!7OSZ-e<DPd_&Zz_d@GvEHC>(b18BrPS}yhi-?*mmYEW4n4f
zT{-{r_4kO`u)i>T%!;Sz!Jz^oILOC%_i(74El?I1(!`)}K;-o%=)bDG5=FMGRgx30
z#8#h=6UHbLwFUClPA>Jylj$sr!{x=Rg{~NbDACW{YyL9OHQ#gO>kp}NwE^D<><ykJ
z7J#!_5apObKFCPxYpauO13ZTS+j!vl-CQ`qYv?fln!v)PU-Bg7-xpJq_9icu43Ya2
zA0yH)S8rEmuEGC-Dur_IowNybG%cGFzy275W2J=*%+s*INw7HG`~PtF)?ra@U;HT6
zK~cdLL_h@r6{L{{MM1hjKtf5u0f_+_(!`)d=~OyLYRCZ$O1c{aq-F?-VI=3Sm-9VG
z&+p#<?{n`wj1teh?|%1Qd)5A|&&q)^t{1=Ti#OVJ8VteDLt-Js8a$_EimWf9(hrFX
zd$B)YhB<|)L)6aAPEKN@dV>=?u*Ss9A93sUk)yAbMtz#HM&V9laDE|w)ytTh<M%-j
z;fa{OU>x9!k@-Us+u`v2_3P={TWOGl6oJs(44!Ix%OD&BuT!be*P<r;$E@O{dCP@9
zxDJTlQ<eDDw{gqRKqJdc`c^)BIr(S5!;%0?b(JjH--&jZBT^{)EyeBk^W`S03hhJc
zEw}SPpe$h)9ihVP=ByJ!z6xv*V;f7&fe7ZDjYGz$GY$Rr*Mil0RiWH+0JTXR3t2h(
zC;+o{i9K*G>qp!oT^(54XrLEOgo@iX$-V;4%<(^D(nvey)dUil0+2IHmj`@P&us!m
zC?KwW@5_vU_et{LDvu)TenX!8qGo7KWNUdbGrZuwXF-4wAZWbE8x4r17>)k2^76Is
zEaPCm5cWU!(1d$L)i*Z>A|5RA07%iYpiKwoxpRPl)VkDd3x_vZ(I?M@0fb;I`V0AG
z-d||{$rK)cX}sX2<OW0iEHkA`>7(V{6IHvg&99r8J4DOfl(8b9@C;OcJ4YRR&^T*j
zq{vd5;voTa9|c}n5Sc)EPJtJb&+R%;S}&<*j&m@7)KiWqJqM35^KkCT`A#w1MTUhR
z9XF7-gErR1q3)-#gK``|AK7d~h5lWdpiAgn>#cikd=bhMBKu^%INb`wIlx<^%o(+@
zIxY$&<5I5{zY!#dRRLZBz!2t5vBFnyJj;-Bx<eHFyH3Zu|6c4&Kfzg?7YYH^s4HXB
zU28%e+Vywc9o4C+g78DG{xhLs#TxOQ@V>(VZ@rcrCDym@@*McrH2M6MteRy#lx28j
z+}?+)KSSrY5cnF$u^^$$-$EdIo;b!x-sE_`eE+0gTptf007^pJ|Gf@XuR1Ia)NVDR
zcl$`{A}bv{f*<{y;QrOj-upE8IJ$s$D>tYq7jt*>n4>#7mifqN%r|bD(Y?S%v;N+1
z+tr7h2T^r!KidQRgm>1zIt{ZhyZTFT(GrJ}4QkH6J?y>SEblPWI5t)|W{E(f3{-z-
z6gmJ7K*j$J7HA(VP~dHhw!~uNS%=Xb?mEOG|M8H8C<lSNe|RDV(Z7?YserJfnrXQs
z*;gG0-s6}TuSC=79-$*InOO23HYP*HXZWRno%?y?IDFZk#$5;i6+Sk^!JHc#Ytc`}
zUd|uzKP%a<LkxF4nH|70xN6|BR8IL?Hcqq^J^SynA$ZRnJ@+=rc1(0$n=gJX)qc4<
zywaF~ZfKw92{>x!cERr9;*(qWoU%Xd69UQfF#?q6=+|8<udFUhvuAG(>_AYEOw3xw
z5q{EY;YVwRHtza)qU{i5!r2S|-tc1{azk$a{flsLN%jxR77;GTESGBpg@M7nbmd(Q
zMQX(>+JjgRNziTJV$dHm18@^6o9h~R)e9AI;%8zvx1bUcnbL3s6Qd`u_HzGR?CJIs
zS!v_H9W5C&9O$Yifd6{fo7wG_#4i#nq9MWg{5l#eLUKJiD(@Y4N~zl}{f`e-2Eg&H
z`lSFur_>fH=Dov`{;m&Z^`DvMOhWt~{_@|$H#)K4(Eqv$3^)J&3%`X1Fkm}Ug%HT+
z`bW>vDi&tOVaake!F%~*l$lJP48N$jRbDXqN((*x`+*+8^3RD8MIO#*2^@t_FW0MH
zOG^lsi+ofn2+%LGG4q%oDID8hsP-z%ic3r6U*w<<7a%6i*dnH`Fxu&+#MW$r*@Hk_
zr`W{oo{YOcB;StZ53BicBzv6>(V?0AcMa70VVJcgJB(hB@`RmcaE+GcEh#|hW|;wa
zP{m{YQ{iZlNnjwgaokCW18qfrJ3TYS$Opl^ALd}`5sh8{#J50Eoju7NbxfOB<W_&>
zYiD5jNm+Xg-<F7%Zf38xalOL7b7R^Jct06c<Se%83#M90&+mnXY>B*=@dtyN5Xj3#
z#l|wrQ%0W^EeyAtO-HKyxg}Ok!<OjShl;uqKx`J_jV;~?7qO!RbV}Kw?%fzzXv?I>
zw7*^$^Dbj_QtIly`#UJ?5kS^4WqGdW++6X`pWsc(jz0D3<cSW`(XEnwI|&gPGs`gM
zXX8Wr5rTp8?{MIt;Z<lYBg5`XvSxIJH_K&XMsJLpuEf?#jfO_Zz=(rR-3yMC%de0(
zVBczzz@9)!0@yY4v%giW-~nAh>UdQmlC^bgI?K29%53a&0_J+V+-j$k3t%ySEgE5K
z7z*)6C|<4_+PVp;zt3}!k*hhQ+PSjD@!n;N31bL^;w2o^r+dzH1nSl#(Umc93@vI(
zNVs{u)79>}tNHH&r&16()B}g@^sux;#l!LRvF#vk#cnXMucqm;K8y5PO)1|@^2xC#
z@VEL@#tXuKZ!-iXK}ATKc(!Mu^Zk1DuS;@Yu}xAUYtHvIX3fH~x&0~bHki|%WBbAG
zx0^voZJ_*H^O4k$iL0DLK)O~338zdX@|7f{J@0KecsP`adh39V%}YCH=kB08AaqCd
z^kAtcvN#x=Ju`lbLP7PHIe_Kv+x)H2<7q+0<F5k8_TR>CnMiNnV8q~k+T=8kS5c-x
zO(-1C?QajOhIcZ`9)QWqgcb>8yW_?3UjPVH9!uMpVAwy=T2vYJ_}?jNc>rpmB_3`@
z57=1xvia}7zK;oQyPx2#-LTMcOldBF+zE`iQ}<Hh<m7n-YMpkZM5pCnk+!ymX}11I
z+f%Buu`(b#h5(>I^)Y$7nO%LSyWu@E=o)>RbcaGA-^ce8UB%GT9aG6S1${48)oz{Q
z{eEp9&g`_`L6Jj?0#$?T`-ELy?0?BveTakavNQGlqr0!s^X_dud@X(mU1IEF-|yk&
zJu^4ZG^aD|LRKYXq>5^qtR@7ORu8zAL=3t&Pnxm(ba;zvod`8X*-eLZ@zIF=`>e<}
z-@nVYoThrv5~an`CLKdzHd-&2U9O+RO@%e1H1te9oIxEFqFF0@9;9lW-d(q!fNtBs
zG-mHVo`?|I|M5D_`FMiLN5M?dLfzyc<<8nb#D3r%$4xg`3E#8V%jZOKpc4GoF$E4L
zSg%scQnq`#sAARFIGUI^!3yK06z$sF%n!@|DzNPf`@BB{EI`U)Ay?zFMa&*ap}$FN
zT4-eq4fLkLmGbYIV1?(18m^Dz#G4E9SU*r3_ol)7f`UyZ4Iy!rB<ALYSB)Kxmrd6F
zHg@*m?+t`poKtFhxR<^!O*P0eq9@+_H;I|~8DQ@A<zwX;$)scx_x!XZz5f2sxhf-T
zd06pud6VHRcylYHA6!L`mTi4sJuHy^V+lj^cgv&SQ_;2;3LJu}a=C<b_jKMm-ch&z
z)b0KcFB5UN=P`#c@8z$~@2w&IGIU4Y=ab~_N|&dpa`4=I)*KkCli?Hi{m+I|P`Na7
z7$(8=>0>D@ZH#V`Y>G~CmzCVr=H1Qvg=jEpg70<aXWG+UR}_N3Z7_337^b|P=qAF`
z8%U(6j_;W75&Z4sBSQ@DKt}h#Cs+Qe_xzcJcx?Y*l8|8^>GOmxKCjS9xo`G$KgP~1
zBeuWiqQas(H<Ng&E=Lo#TVaV%keH?_G`_`m?{E5)e;$~(7+Ixw<ciXiBK_b*+)%jj
zKwI)j;evalP+fdMhOM%kWFUus*L_rXWY+B3OW|+1iC<Fmh>31!u{*{#9ZUB*EvScn
z1E7#Ae~*_3vGh5Yf!#Z`OJ1d$Iw1100)g<Fn8ZDcx#JY2Yj4*{6c-DjfAXW4udQUz
z$e^%PsbZsESD|fSEl4$759Xnh_!Z`XH|bia1=9n=f5yB0zt5BXSD|)TF|(urYvHTb
znfoP0G5B-NQ{&#Qk=@NEy<P9EKQq6@;R>D=WN~Lw8xMh0rS6@23M1x|99?}QkUqYI
zsZRVI8zNV&{CWc!vjh9FpVJ*Tw44baE(9l9pG)g@b+#c*2uoI^>F}UXdLp5x-{K71
z%`o?8jU{m|_9ZB6XoJe0X0>ROlqx5JHuKK_h5uu;FQ3GNRE*tjnN-<RG1*C6sy}hz
zO@4ukUYK#uKze6Xe?G=3s$}g5@R5pyb_g&UEH^wqp%C0`HZ7TF<J<;WMh?NwwCr=d
z?5Ka0)b>17^T0NsQs$My%GWfdb>=wPka`79e8c$<4Yg;T6fU%B40XvGYD@MUY~{Om
z=?){l1TTxVn{Q!Kvkzut;xEudWMjdJm=ChCbn03zm#uQ&@ANow3qQKu0PYR{F2SrM
z-;~+)Mj(SUtu<LspOBSx2Va!Lz1f-F$c;Da8Y*p+#1t0GlgmD;1Z$sMv|Shw<BrwJ
zIIn^hvvl!K|F&V)g;0b2e#QUV7UiHEE~TYpZR??TG)50UpH|Rxa*Z=q40PEV4HX9A
zQ^GMst@M3ykX&`ii}ffK9V~b5$-l@QYnV|5!#20eI-#8QuI5YXZ!ZYB1Fr@m)TT$m
z4Rn&}m?dRdBrwm}(n@)<^>FB^DsGekzE}n3L0#}ppn_rbP5b%ju739t60x)Yyh5i%
zc}MD`lWl>yT}(iXRz{?woaBELo6o{4WI5le>qMAXo$gB&phdl7yKz(jU!14JV|TCp
zHLRzlZK)XR0-r55gXf<eE=r`0Fqq!me6l&K{X<&FB0u53n!;~CGSfbwC<5%$+ZR;O
zC5sKua#0DcQSSL-OcLG>N>MJZEB^PQ=7q^qgA>e~c8#<OnmR2hW|JLaBTHiw9qAw;
z$*A8Z7MTgT=g3%V#};mh-N%H`Mj0oml@}@m=O;*Jo-EA2N1AZK=Vlqr&&-CRs<RvC
ztgkiSw{8Uqj7F;*JS!xmoth?<nEX6wBGF{B%l8<~1IGV~$FpnKK%My2&XfdMc0XGl
zsmeRAAldrVczEc2SYZJ%1jU50aQE%Qn3?rWbaj0M9ZEeQoWvha^P#(R*OU|EHJi)>
z-*PkgX8)^{l-ZHFsPsxZu;%>j!v!&m)?*66d~$|{U*K}3#zTGY^>Br@gL8K0YaDx)
zxVR-X3YM>v+PYTCP~%?Mqhj`o6-`-c{sB@MxZ6A8QqFk(XHtBg>_M~#FFRL#TiJ!<
zkynyFGms=sD^2+`#bDeFeL;(4hZUkzlQ;~1mR}!L$Qtg?BDt3?;oP|H$*H)BJ&fI#
zpa%l0eF)EAJNp~ujm&pYBHU`vQJP=Qi;w?`DT}|V2?~;F_AtII%Lsd)*m9{~e7P%I
zwd(CN29n1a+#45{>-k{(D(>4f_Dd!4r@9?{uWem^_PA7>bka3g0-cc6*IjDJmH&Nl
zo<}x)yfutIdM#+8!`FGsA&*VLb0M*7j?htWD8Bq{#RXqhAnRVTrr1>z8k=f>`CyP&
zAZc$XdqYM=b+6=cDYa)Um$bE1_x?VtfXi2Y+SXH?Y5Wy>HCH-`zmhRlgL6$UjQfYu
zP$+y~->EJfuA@6(<wdZ|f}C`7XCI@Mt-Uf}8f*J?PcD}3oLV|{F<PV5R`$`T>xqBI
zi+n>%wLRl=4XGEp{UR63NG}t!jF>$8x=wU;;fzsAzAXZGmSe3y?8d9yqpRsG&yvL+
zICKqrft-<cHgWBhk4^n%`oTTgMssRBDdjfbcBPFwR@i*oTy&GHlK$=8BX_#Vij;q{
zABgKtC_Z?$5w&+(P{jcyP@O7yy1T1usAKO`)*X~eg`MqwTQxnCml$re+$hFVelm3h
zgiZVH@#|P_x>WW{FXm`cQVP4DZ(Lk*-|zh2hhaa8EWr18Q~glFrJd>6z19l@C1ta)
z065TqGQ;ptp<Tu(`~8<<1<y~BzGFV@BV9k**L#?u(`yG-KEy&wXE#3mz?#nS<)nHk
zoxeQOFDl`6cY8Jor$ifPb1kKv`~J4u?D*6S@;v{({9L#9M56ixnGkeON6g$nX9$;w
z%Fkf^0J<1uH{00-KH@!<XDx*V&yOqhou69RdU+mIK&tNOx}=b>KW&m`Pnqh^vWq<|
zx>&BIdzLE2CseXJKouFZ+<LF>)BiiXf_r{9y><@JiOLw`z$8)9*~T=w>3rGbBHVdY
zf~1jWUt3!ScNRX47o&v5C5I)ve0lU5OV$IfH29jBoBD{h>Rh32j=@$dcIxbY)uQ$9
zLm=On`6DW+@=zJGQl3q{5Ny$u%Z|P+v&wrVnZ%lD6YTHJ>X>YNKlpwUrmVFo;03rg
z%?0J}&&5B}|FopM*Hca<f~)?bhW&qR;Q`<6t9mUy<Z8_1?r!LFjLB`mH{$#wOB4Fq
z`ZBn=@iub->8z*q<k^z5Z%W{!F|&SOkEunrMPGkZuu|PoQmm43rLee5Z+BT56~@fM
z`;eziQY1kZWb8cQKc0fm0c8ED_IzKs6=|%MJS}8_nScEJEb)}tv4HcP)4N;D2+n(2
zweBnjJn)v0Jgys<b|sXnjln-_=$0mSeyRf;A0y(Xu@dZ-d~2Je6T-SSE=I+c9ig_m
zs0hoBcNynuXD|IZW$Fjm(D1WjLoS!jZqM|+Yl3+=5AF5pBe+X1hL}`RQ+VFT0_*8G
zsBe7lczF>BT<@5<4C!GTVkq$a0|MVy2>fvZuQvQ5bC(@2K1K9mdwTt`pM2;36HYvc
zM{F{cvgwXEd4HX&-~p4@>8t5URVcLl*ska8T91~UWh5ijZPkEjhh(j#guT1Z{Xnzj
zLnF_9=-F)}N<Cnw-}dgC=|vyow>0t1ncPl~r;%k&;^z#kxoA`I=c4(%+_p}YY*zZA
z-*y>?P?tp-2rX^~<62j6AB6?U)?dnFJ8Yc-g@wtf;*p%UeSJ;+^1+w)9Mr($Jim*1
zVUxi3RHgScKu6ff?&y5Bk)E+P^)mi8BTcICkFeqTz0-eAW4Oom)0tL0lnk9vNoxV!
zFQ(KUO{%7!B?e6FX}KauPAwKU_{^O0^{t$EA8TJLGlRxvdc0b}aj(v2J2&Cs_ew4q
z#L=c&bv@f@tbZaQ`A=(TI(iOKJwALE`k~3XVfSp%5vuxvRRviBN4l#$H(BGXK2L22
zBrnAz{9vgDTc6?z{kg&~E9}75fZo}^M_X%7PbWf6$HGe|EX7#MA}+mtBfaV3I{l6R
z=;`xv;HOei-O$pS>kckP8}Ju46_oSDc7W39o|^3MXYK1e+*<CyOS5KO6^sM<UzdFB
z&iQ?oE-8%U+m_LOhosCYxkON?&d<=JSxqEV(#QQyPQ!)Sw+##xXEE3TUo~vkNPET-
zuIdPG#f5awO8$DOINYE$#fa(f?3^-wj_@KigGb}B@kv#YP;GEuhq7CDcWM=`c-32f
zN)AI$`JLjvpV@+_8NmWg5P9th@`>axf3Azj6ymK>RTiE2=_Z&Wl_*Mjf0ei_ceHPi
z0k*%??A!}$Ju7y<Ar|F8_L*}lCC;POZEF+8S@v`2Eb=YH2%Zp1emuFK@lpXV6Jj*N
zQVajQK0WsDb^n~PcMUdN2&lbN4@&vuW=Dzyc9sa7!dS)ixtrP0)wE$9Zj9EQBbSTZ
zXGNh7+S=OQQY#yr>Fc_6e1>IH;*yUPzC=0HA|$ou&&wnB_`IcGD<Gklp;4PP@R<^(
zrqp@qwT+BoXhnY<iUGte;509xuYmBg3)bgqn;cg6ydg6mp(9{ESpHCxY2@KW59R>T
zdZv-0qjK}LU={<?1dfF9-4Kh|+0)I`Sw5E<JKQm0V8XC`?~d6s+vTNa{vkFtd+X*&
z@BaI&oPWV!T84(QfdTX;!t1&G<E>hHQj$eWiC2XQoH_+$m(GdB05#~18At$D{($AM
z!T9U7+sbWyG-(qiyfXhh9dQQZeFiUiTF=$w+Ms7zclJ&1;w!>l^6K+1*_ZnQRPnfk
z_JX0w9Kq7;+bRc-!$8u~-d|g4!#WY_k1x9^b(sA*KYqs_Puy66<62{2vP-#})ANE*
zGv)wgQ?dZi@?9?UUN2k&7>}!tp5Do#F!&_>L2Qkt(G3~&@kbi-Q8)VtFWEZ=!_=*G
zsqmLBg%1s$)YD^oKbI<=I@B4au4i&P=c$oT-l-4GxYkf`G4r1<;q*fV^OA2bB|jA`
zESL6?vRAyRLOOnt?$EVA9r^IRdm~Tw0tqLtY3%@!WK-?O0GyMNqYFhiyTSA(RdVec
z3dw|$1lU(YHHn3%u!OHWEns}df|`jMD9-pkbs49XC3<cY)7se4jE=)^SLC=tRAN$Q
zWB5?kbFJX-9>FoROVL%)Bilw?{2WF%%dI~}hgysEeYdZmy`s(aWw(RJBl{0&q9ZD-
zH-Ddr!osxdjSN#qp|UOmJzNa%d5ZyaDL_8P?<=vtU4sM6Or)17^36=92{g=wMjQM9
z&h<>1LSS>%?|qsR2tL|OK@+;CxDCacBld8_<`)zQWgcvl>??v42?P~qH~ScsPT;Zb
zr62nGk9QIpQEvUGF7C8Z4D=c5IMlXG;0NQ4h^i4M$v|r^3|tcY@Sh9&_Tbf}BY=F1
zMW_!60Hy<4wN)Q<nK2ehA<}^ti~!udT|i+wa=#RzB6l2DgkpirTSkAu^X(odW45iI
z3cC4)nO|#cwiHa7LpkJxTAxoCgFmm-*4;G}jzrpShBfhME~RvE2c?dw#~hU*r|g`5
zuPu69l`K92<E)T)+Wx0qk$V>J_GS>s0u9PNig!M$d_i!>8*c(@eiyXn%LI~n5#oH=
zlIpe<@39ZH0Iwd*?>pkM>?xyuu`do*9ZLt-*JS_E*3p)J9ft!lSWkx;VC3@yYl8o_
zHQBAL0wc>8batqu9Z1i)n0g`PYNzkc-G7dxGEu&ztQN>Nqc!HoNEkF=S_wdN=h!6)
z2z|3&#H*;(IJyX)wiwf}_N2k^Ni`cKTP!aBX+c4Oo}qyj7DArBPKGxZ9QJzh;@&_Y
zWkdA8H&#CKWf~l_=QOUrBuJ;!&2(&v`S97v13dNT-u;;<qm|m-$nm0GO?ptr4u#SY
zIjzfi?4m{>+?KcZ-6~$p15-d7XB<<=9eLBn-H?sPryOnz21GFw!?~<+vrn#ZP(#R7
zK50<4o8-=|k~Tw^a7Fhop`?pJUI)dnr1w78u3~o@(|vOOLo9gD9ukZ@Lm&_qyU;E!
zNB-9AhnC$65LZVftD*mdS|4JdkiBXFZqoS2y9%YdC7Br)5?kk*K-<qSYIw%gXmWnn
z;ry<}Ii+)>GVHSZahf&lH$%FImAm&P5_jX9ypHRyhf<fN?|lfFoZGYXX2(=|rm=~4
zBgTueZnfzO#%c@RA!D1Nx%^7EQX(OFMIq!UjytY~6<ZVBY+E<KP)jWMLB6$!kyv!k
zS{7EbF-i`oQM6x5Tv6glS;_lBwq$B2YP3vhK#;Ek4tvPzv1PaY3*3M1(I;oDrLN8l
z6bslJ$;!&^!yrMXU%)U?LreBq<f)5nLM8ummz(xZ85+136r9gwV!Bg6uI}t&)MAU}
zQmNU0<_;s8biSiAjJ|#BFAwIBb$srucBRKTUo!R4pKjVl6IumFrRM40>y2k-Fq<;n
zLEON9UUI(itGgF~p0QWQ&c;Ub%ppz<d-$7A&IsvNq>f>>e!;ky#YiQ3ZB(Ja7m=H!
z&yyqJeeUh!50t_5Weh<0#^vyHPnPcyr9f3L-q+{v7c776Xgk_JZ)X0PkR60+!75ty
zh8eG9@aLUY4y*S_Ltl+6W41le{D-^Xv+xpz;Q%cSi@zb|9xu12;h0{;a0Y|Wc%+7w
z)Q7cd`Wr9?2t*DCi0@d|R+kK=Nl*DQy&bkE;zqt-IdKQA081J3-HkmzcHeueYtx(W
z(Q(&yVEx|QXYH-7r5}Snq|>=?P*ymyJ!5WpUAjHvswZ=2=(-N$Vn(tAsA8Mq%g59s
z;#vp(Py`9GEgn0O60CSYkoOhLLHW!j6)R+p9GY^up6!%^uf}Y~Ua5t|EJ%B<pydm&
z9Ef*WAcEzaCC)j8inA_!j-{%`gXfOVNnE*TK*;vH)iSBDr@4Mt`o7f|84TEg$OcDU
z5xnp*byTs*dmp{fs=agntPyN9hQop^lYM=C%f-!}O$Bq0_g`*-?vR3(D8VaNkRG22
zeO6Eyl<lad7FbeJQk|*RBaO%7WkVtV@#&?zPmadrdv+m7#X1&kez{kP;D+^mUg)Up
z!Sg}T(ivGW(00@bN3IU865nCExAkcj-biD1_LPwbm}hV&wNxV1gvP<0T-Tmks+}Zp
zeqXAc&(XvhD|y)ygo2g`uANJqLD`Hy4bsNUzv+{9N(Fi_@0_c=`G5s+4v?}Gs<$V}
zFsI6@1G|)sB_$=#qlWpb!-H3-p5&qop)@iZX-6VPw6U?-_I`k=+N`VTO2w)QqJXKL
z{Qx#^1PAv_3CzgKQ2XA}Bm^kEF}D%5F?=OfKi!?!S#e*f)tqoCG%d>bq>`e8fXgSt
zKtB)Wy7@OAp=)9778Nz-+$r7eY*;o!&9<93hF?Mj_YZ!wi^ojs87f{ZU^%03HBjT3
z3AnOB&8eTm46<vv*ZuR#2RPhT5C2a5JP7K+p`ri-XOOZ1%*?>~8*tHJ-OpMo0O?GS
z9aPK)h<<6t_8=rJ=ks~r!=%6LS0W$9E-|g<RVcjOmqMm)(He19axh%H6+Hay>1P$M
z7X#mD`~ur~vlV=s6L1}2@6Nqkwr$fiId{BIEvqnkHVv(-JJ>-vs(fFgUsLd6!Hx6x
zehfD|VikPblW0u5_e%a~KA6cE;<nC`UF$_M2(AkJokjOi2#8-5K?69D2rZ;zZ3m$o
zA{AhZMb{;fXHvH@z9e+l$!$oy_=(kri}i{zR<&<wc3leZ4R#NZjmtx6zF&Itv@-@9
z?Z{TQmv~fz?cRmpcLwqd>doT?%EnZZN3W4BBwOAvsj`UV+eOdDE|(i;RLrP!lgzWo
z2J9q4opH*JZnriQxYz$)yj@&o4WHiL6uF7uoY%f`NWGu2Z-h3<$)$58O1e9qYunn{
zb(MRr%M~;OuF)CVyNHZKoszLUlu*!<4<vN<_h$h7P<$Nt)D-~ja()JKXPke{?eO``
zTg@5yv6E3Hog-8m4n<<EHSw+2(+9#Zv;LN-`t<IjDydgOwM_;>EfkpRgKL5_mowz`
z>qEyo7(UKXk#Y+SLzBRdj(PJ`Z2bsLO>olIu<to!nTT(?&FaEd7cIHnxmL@VS0!}4
znXu=*F`L#a``08OAp&0)$?n>RmoNO7H#xcJt`j??`=XgQC~zH~nw<j*%BaH2ir}!!
z)Nz#AbkP910uDikKCyA=Qlf<r(zW(kU0-sTcq7l;nj%HIpjW%W%Ch<bsl|M}`N##D
z#Yc*v2|Jh)a&t91J3GxfSp7m?y`m}SfgN=XstsS9zYhs0g5OurjVa~l9MDhA2!01N
zX=}hHaH6|&SnjjlIO>vCI&xQEvvyvGb4u$*WrliX%B%SG0{$s2&hIMEI(HfC?DnA9
zqlLX1C+`&}4=E-uto);8$I7rZFm4?gHLP^9C8ht*JDM5rZ|6~QAZoebo*4lwSCMUv
z^0C5W)o~NZJwv;R+;^I{1>mSc<)nt0j=I0jCym*#eE*Vjn`f5;Zr+X=*%}Xca62b4
z>(*yo38E0;V$=pn!EoXFno!+Wo41enH0`;BzEP$o)EOl!x*b1HdD-ip86>_LwmL|!
zOLiA9iO?93Y}z}wrQAzo?vxaw@vy;nzqQHpKK3w>*cn&g4YzHX)Zt|crdyMMWXzte
zir<_1w8-O$v9I96$3tnany05{Q;C+U=nddKjo<l5W}iEFny`RekZM97-vRC{+i}Zu
zNi)BrBF(g!R@6Q233baiiD^FW?jp%e4+ug6*W4{dLr<Hqx+VM?RY-Wq4+$-OQ2F8A
zW*5R~W2ili_iXCdO9ejO(&qH~h-%7hW)(l&UhNsm-%6_OvkVDyt<UAsSM<MZU#D1+
z{7o@TOUbcYrCW^>mM&16^UihVQGcB+Yw6~UnTThuIU}nXTf(w!S1!uWuZ`#k<&csL
zqV;K>TI&~VV}_%)yovJ^%Cy0X+c?=^Pn5->XKz>a!6(BhcqWCO7ssmxFP%}C7&#o6
zv~OusxSI10<K$^i=H|pzw*pi`;<Pk55oO-5*+zf<e!o7ho#Gumo>_w9%jK$jyM-Um
z$z0Z(aJ}O#s#Wvtf_qy<H~+c;Gl_+D)4h?XAn7c~fJo@}i&k}aW6dL=C^bU5d3Lt9
zk7YuyoSdznzS5~5KJa??Y{yjb$GGYhea+AN+f|Yzw(UK~!&sVxn$E*@yW7mta&5I&
zI&e=LYYCh&iKt@%7M~Xu_(bk@DL-WF3jDMXbVvV`;FL+|QAL(tk9J_oVmDgSYu*+_
z(|*KAZq`SThMZ-Du-O)MZ_&3E6#H<&xPcKvvv)U3p5Qh|`nNp!Nim)?pRSce9CD>N
z@-q=|?WFr-w9XtVD^k<GJ93{4PopA!+9${IIX?bm8Q$`c-<D#&cJ|q3h_u$D5Ze7#
zjn>~Gd2V4TRofRTdGs-;4EhUq`W5>&KmD8;@5qQ*lNv|W_wh^{OZQlC3DnE3DAV>5
zH>3i0_F7=&cMP|zCt5!KXkMrJSy+(`Rh73kYY-;*%4#h}@D+zveZ<;=dAePjaGFKG
z>b!j$@&2S0?>#HZzzRh|`T7ofO!ezb2BD4OVMcp`6$fwaq6}$sdJOY?UoIWl{v4^c
zi5J~Qp%tiLiH;l0j5F+SqVzVc7dLHWM(yz0iWz1Vr8||xa*^P@_X7Sh@#?u0V>kpE
zGne%Ek@HR{;pKTOhkJ7H7PWdV#VDoAqO-BDJ8sqJ&DQ3d-sZ8K+jM3Y%-4S=#+5Ic
zOXq)A@MMt`Mdz_N*LLKw-AK^D=UpiXL?s&W%)OIt30`uS*eo3aOYCjJ&R?%jch|g}
zt}fG7{v2@!6PCm2pdvmwTWggl$*g1?7;O>g6yt<-wGUg$i(ZXr+=?B~*6M1$YU8;@
z7IpeKgK`Yb>pJ+;Da12#OEzLhze1-2B~xK~zCd2IdvS*PHvjPahAlpCP(XQec~f?d
zoaC*)NulHwrkEJ@$DsA=r`>5Qx)X9U*O{2(xBWfS#WU8%cY#H<8#uf>hlb`d#YbPT
zE<@k;wB+REr;^)wI#Z84E~WQAYVajsTQ6T0$0rA8Rp%DWZ3IcthdUD@(9@)*RN~8J
zTw>P4&zzwRgTsj?Z>P-|tPIaFeR##OF6*-3%**pea9Zr>6&dQ&nSO89MsaJS4Y9oX
zY;Erq@7?uk7RZyh35<Zgb!S5JLn0qOnXiBN=3N^_m%Sv@w^7*sqR|!Jv|FLt6HDn0
z^BTp&QJ7y%^OwU`wLUH9`dq=<MMw+1_RFv<nOR!#9dY_HpG|#AqL}nb;8LATUb6W&
zb}P}Prlx5f%ZW1<mZZ83N`7wWI<Z!0OpUTiYG(csdx2ALWo_$(V>g+6*44<)h%C#*
zZY!E6MgHWs;VaHY-o+U4#9aF4yG<&~6+hMp>Pxi3#nM_$&AOJ)LuaVv+UX1|DH{w4
zW!^kef$MjeO`29-3cd#GN9D8dDBf)!YOq8HItGPCMn%P-fdsW1s?M{ktE-Q!Y)1q>
zPr@wp(=RS%q&JulvVYY1or?IVb!VmMQaqvx-bNfY*BV)wIpofMCSu>m8Yj6Z^t^O!
z$=QJJu$$>uBGpAg!Q7wUSA#6p+ZTMAY_Zz#wI1HkBp}S6_vrxBO*Q;V^Vbh$mA};c
z)iK#rB^(@nb2*D&rn5<#dGfVI#(y1bzEm2$#2U0I^GRmC$Sa$M_h|DY`l&nq9l13^
zOV`=+=NgKF#M|Y%8cuKKxh7f>KJugUtcbNS_%HZ*nLLYI5xu;*<rPDjQWniUL95Jk
zf@X3nPc9Ybg^37+9^f_8uoPyk=*Fw9aItS?mOR6p`E0)V(VXJJ^t2iAluS>bvo<~l
zUBSA*N06HB$@!vx1zOrk0stFo{B-|kkEj%@#&VARyhT&Xx<_#^YJ}nKDSye+D(P3c
zSY%xB)kk*OL7okc5;_=FN=(es4v!!rZkh;@5SXN3nO~o{mZ*Hp!#RIYv7r*!D0KTj
z=dxAQv`b4?o}s&Jj&hWo`hK6W70VyT#o)$u;a8i}qPyf`%C4VYCKrFrIelYZZQrq~
zEeVc(hIibIXAF9}1#k7U_h_}|+|*WG!;7s5UxC}i+))2_`Li7*nI%n9*$lfhM?4^S
z4&)&2cs=7A;%L=T8)5Xt%%braZ>3B3F164fBupDxlTPwinSM>P*LSlZTyz9YG)j?N
zOj&QEi2a;786no2ci$Xm(HzUy!<YETDy23vtWA0>yO$@oi!Pr+Z<I|P*K79uV1%zF
zf1bFxbfvl5B1>qU*TV}-@drU54nwlZ+SaxmHh6)<n4Fc7nOOq#)^KQyldkyR2B)GI
zt<Qxx9deL5dVDT*56v`L(`t^Sy%{juXPC2WD{;^9n?6<_Jr)7Xh=PU27{hZBYqDde
z37=AMq`-|K>Aq5pwk8&#zkv$M#??1-%omtrujIZH)D#IlrbbIuHA0gz`Ls2}tFdA4
z8@HR4la1a%XG&EmYYk86(iqtJ1vrYBJ#MnpPZk{eiHY`=-ze7>{a$YnW>nK{(v*Gl
z=6sY9?Jv#Qom4?rAu&`^T1uj94O7~I3n~T;DWA0p%DObtf9d=T1FSiOV82ZD)6!Bl
zjn&dh`R}qzis`zTO2#Tb>q*#W*m_;F^;czm&et>OBQ7P;#?0}mY@zCU_FozHePh{M
zn=29QCt#p$nRvl$AVKwB{H*@EMPQk5OHOrO!%d^6cXNtawSJ1{754R8epTAO8I@nS
zt&9JY@S5O_p{+`{l~i(X!q}w$bNhq0yTA2aaA(_WaI|^1wWW@?sM|MbIIX|qHe}^u
zmpPXs7^ozsd-w6v?KHRC|1&XgoXaX8!tLf6TS|lD9BxL+EjJ(pAKe=&o^it_r-HX-
z=EN$II&L59y1QVH^4{J&c5~_Fpa_PuB$?}7Gqk!}e2y&7YbAaK>C>4yp0zeMhf?2I
zV~>R!-6fQH$~vW^t?dJ}g!Lob0`itJNHtaGy8rzIJ#@MlwzF}CMYAqw^gUogR(0#m
zC8tARZ9Qw1EY8gJ@$xuzkI@~{K*{uMuhtQ|W2e+m^#)yADrV97jeB=)J+YsEO}D8o
z!*xg8+b6%h-MOEgWTQ-39%Q%0$832=Mhv;Lq5b{bF>{#~CQU36xs<6;295eLhYCxz
z@!7f!KAuTB^qh1yDR4rlg=OTgwOvL-j$K=u-h1<-rz_VfE!Lv#0pi}r8rHvzR@T&I
zTPYLnJZWgyQ<tf~pI?A!Sz02vU`SZevhi_{-W5Ez)4<H(+C_>?*r}|B3+~kV^L?=u
zL%Ce%QHD8B>!QZbE>$W?*y`?0iOxUH@*r*t6G2)qCs+Z_s9=8SRufC~W`q~A_t#_f
z!_vl<3>MvUi7A9%5}{>HUYnCsXs<bmCKeed@9II~Uz9IXGHA)r@s09n%9T)({Wo)p
zdt+w(#Y-sxgOpOP^<k@tNmc=IIRa&&vOa=AoS*KKNd5G@#(?lEt8$j!dR8Vrr|@Ba
z*40MQp60uj+W3LE#sTT;lL13vqH-%0uN&a_;Z%I)FeTg8$6aw<8NXqBZOb81TZAoQ
z%R6R${>qz(Vm5sA+7-#0gUj=8l<@;4P0itJ*ob0jJ-NBKF9RhJ-P#rF^>v9^Ll16g
zUNlwL(LY<!Jh>rFGH|4(j&42dOMBMb-l?2tdN?W~j@TgdZS!I;PJ`ddvrX4{aK`rb
z+}hT>9wiX9LNeCx7mzN^_RMX&+)K=zXUe506p_(kNi%WS=<**DwZbh9j^2gl<>cZD
z*IX<KC;4#m2UY)Dee%Iwo+kXmQHe3V72c2`p)!S}?1z`5jV(Set8xWRsRrFhw7F8H
zUR{%>O_OfDab*1n^2+AOuZH!t26x>_r<xs7Px7Oi`PRb(JZn9K$CoUz#CiLbNt52V
zN3d{lPnrw3Nz4-Kn_d$F3k~Zc;~um@;xfu}le;6xb`hJU?&oX*YST&qg&B~Qd2S={
zF)Qp2HA#~8PziD#u#V1zw7bo8TM~8G*}r#$skFs4&uPsIrMV^$1tH##Oq%;ns>-P!
z()V3FG+2bcd<~88=QwKY%6zDHUt&s^0z`7<5BWDkj8)l=zdYX=_VKui{cCen;8!g=
zVlkl_MM`2BeJxU>^o{)&l519`ujZ7z`RL8pUHhuSj<0&oTHr})Eb5#x2k4(wscrW+
z`FkYFN1E<SB+b3hd{5R6k`$1AG5d5FRqD<D=#gB9Yx(U*ZiWhmZmeY-!4a(k{<hAt
zR`0%+Md;_sT|Ir*@`QcU%~uhqP|bt`$xRxn7X&`sp*VJ7tU_7HsY|=Enj5^ncW8KR
z4sd6i(dOH|7!%E-sonf|Z{-E0H;+n$6g~dP^4NowU<+`PMwxSlCD*U6%+U1KXTPBs
z3GB28h%it+HAc`%_H-5XHf(w=@H&{d;ke>pc`!FOlsfz+$ND6@dv$!n{iC{F4P2qN
z$#L3-I0Xu0-5NP)bDq`Sy=ewoFi|KOGv2Z;mdOFjl#2IOeYKH;Rl?aC;sOaywAzK<
zE)g^Or~TT!+9GB@aYhJfb(jj6nOi=ujW(IEI(N@X(o~?b;d+y87ljqPt(T7(GSm-@
zjVx_Du7A&8C7YRkD8R!WOWCKD?xYG{lbw5MP8bR6$Z!|E*Pn)tK-FzI4$QIi3b_wq
zA2F-n3|V4vsT~w8R24bhbqNkW61jSD&R^XFbkE#ME<zns>rw2%jn-*bj803PZyUdr
zTTuad(1&essG=o}w_l^L+B?K()<&nF$ejKY!(Z>n+%RQgWsybn{`Fb>`&KVeb9mR}
zr=$HY%-0_<f8<SDYVzv&>{e7qfBx;JfH&+!<_UVj)xhN^hhApl7Fi+{I{}g>SmYQ_
z59e+XBZq(2Ga?Dunz5W8Z_Zcf1y@hX@){X=q3z%CL(4E<;Z#uGiJqT(>S(j*glw<Q
z&7ADbGr!rJrx(7%GM9H(hZAm@hmov&td~ymhm_!(h~9Grg0(YrqI4=hE>HHwz34Pu
z7ZKn|q<EWK>eMzo50W&LwcL~u;92IW7aEgYk*Zsk-P4qLQGd9!0cyiDsSolGk2O5u
zil9EjPbX+@X<3&Y<;KajRC$vt0@L#K0!JG$*rn@agwS_W^Z8~TCDV7?hs%F25%RCh
z%VHK6s_Fo~Y9>{+o>vxoy3F?VA2$s=f1l$vPu;#LGLN{tD$DTZ_s=qP(&lt6k(&>m
zpz0ZVhlL^1rM^7l>@zH^pSPRdg4>F4DWlwhnQcnig|gtUCEtWgtdn@k`wQ2Ac{})g
zIxg)@lu+f&u~lLo(GcuuYSb!4x}{-nuK=&P^v7!_R=-z~PgKclV+DQ>&*#ZI3w@ej
zTzfoXHC^|d587AF(LS>N6eWIkqwHhJEHheK$0e{W?m=fBsM<%}bT~E3cNFCZL&9@D
z6-NoKjn{Y|;2UKBa_QPd-Meny7s!E1VR>n;>m96*zVAM|yNE+kn(sY=dH&B!WUqec
z`MB<HG_UzlpC8OPe;;5{ER2!yWRGWV-q&rYYqPP|kWrHxtue!$QE|UcG3@+>yvyG1
zgiGj5s2`D}giUJJ1UvVorKG3(?d7Ejj6+U2|9r^=*O*<HVla8MaarxQ(4#F!SqmBF
zK%ElWJ8h<y|2VGr`!hb<@Zmo{4<h*g*6&_pJD4iJ9`lzo8V5?!cc~n;UG41&z-lBq
z4xEl@z$5t-N%>Y){x=y9M+<Ha|I5->{s@U`Zfu-Caq6nmOc<~|m3}*U`@z+FV!tzG
ze?P49PH5BE4VC+^x?)qTxwm8IKc|6x_x7RapTDA_I$2L|rZ~7yS%Qg)35w=Jv|<IU
z0v>-d-RHhrq3`3JC(splXUYa}1Kb1ht0W@iusLb|n>dBpt$Y1XMwK0FEtn2t6dxC8
zme2yP_lE7TUVi8U^|Y+4&eh2{6M~&7J@g4{gN_qA1UvZbIi#WEA6NKKPS#OB*Ic2l
zZ{=MkayI{;S7p)l|3muW&)={8fADvsm(bKyelgY6a-L(1!w*7`!=de-+Uz`YU?Re!
zkw$=~i=)7T=2Q35o$B2y3qQW3xj#=Uw`az5<LA4QKWxh1)8(`93<1ym_f`4-=igQ7
z5ooFC85ytGbpWMlfmsc0)m=Ww&rkpSLf3sEB^pTmay4D)sC@QS(I)_CVG2%PTA-tQ
zM{DbQQ(iDhVocDnTMXj*nvJ!-!NDvz`qTwXK>!#lZDZr?<Ye|3=vx{o>NF9KD{z@L
zzyqa3AKtw%xGwS3sZ-0b<T81VIFDS9@*MZq>J3gisZ2i*h{Sd{SV=+fCbP4%G`n|3
zO1k4zf!B-s)<;w2{)=5!)KwB3R8%x5;1a?B6<0ot``MQ}&~eEYxCF!iQG*6X$cyv`
z=Eyn4khL-Ocqx~F0YcDxyFsyn!oo{OUc%4~ptipgQ~=VUNIw)^Im>orA0B#HVwM`O
zboTXK#CB&ulg%k$6zGN>DD;<An>{qZTB|qN`T*=iVv|mQ+95!@X)Wlm9bTZdJ$Q-l
zlADjt5=B1IQyuLC&H!tm47tm8@RyFh{=F^eZ9g>AUx@86+Cyb}8&FjOu7l{XVrVnp
z04$?_;c<%mUKPW)AAH&~k(LrG_TA+Qs?S_%;iX!*%o$!&!%&L|=(Q6G4ak|)#o-9g
zWMlN?1ZW$k<uaJc2EB?ko%Z&2fwN8fsC@300+-fewylba3i!ek6P8PWI~oF-!~+<V
z6i~`F0%fyv<>uvor!p-gqkh1#p4$?*HD$e|4(;9D0JM^pUO%6(_z`{qAs-cQIrP}7
z_5dP?E1ygs)Y?+u#|G$bif-@gD>0dMc6E(HxL1<P%e`8JX~k!Fo!~5-p&v&#6&;Hq
zT*z~tZaX_~CIwxzBB7xai@Nys8a~g3wOazJzVIl!k~%I!Tm!Te^RKg7Eq<sb&DXTg
z>~*{-?^P0s6cGp<6td;5jaxSN@3S@Rp{hKCu#{buZT;f8IXk?SH9a#kfOYX+?;nm-
z=M?<l!d1_IWzf0Z%kxnG(2!jykBQW%lfg&RM{cmZH6+m;RO<IDfI^MBEOxfpw;g)<
zonHBb8(8<Z78E16yjtzOnuAQ_we?}jrilRbhScyB=7s+bv&5}aA-ZNvo<Bkm?(lZu
zp+rLY3S}YC`?QWBwqk4TBVCS#x99g=EWBVS5~Mi#agD(A#5?K&{3;F5&h!dg-%XW@
z>Hv^d#PYGg2|^>V9QopD{cp5Hlb4T<Mf0w3nfV=tO@~8y{$OQk{X!=_V=zy`2)WMP
z?(FEWUYckm%*;5W=$>6O$hrG$i-6rq4<7`n8W10OdK9)8&v;g|@~j9DR8);QK(<n7
ze(fThwmSzStd|-p0&YOeMSynIZ&3Z&B!7SZNU&s(%q(!4R2?76HUYU@1FAadQ5PT^
zLO@J#Tv^&hy>kbwoZ2T-bztt*w70i6mX39w69tMW>w%b~V41smd(#pE#1zF{=NkIE
zIyw@O=6t_yHA^h{4MAgZ*mgx-06w<?8MCps?}m20A<FDV{nCqX0;hoAlyTwMVAaM<
z{H@8|R5I^9q1|>j(onvPoCQ?3phA+9l_NgfWcvX9G&7Qts(Pj1@n)ehanG0&%SQ_G
zp|iJvhz043x76i(?JW20B6VoFWP?I=0wu`e9No@GK0t<c))R#KG%f}RHi@u(od%_E
zYFbz%0asi+TpWhX$sAaUqjZ{VhQxX-D!{>@|F39h#(QeB#(TrpHpW69(MP8k84JKS
zDf4=WHy#AGtu;6Yk{JDGVX38mJ=$7ep*UPe%dhKuT^?#z^K-iP=pFw!ttn*y`U1PD
zE!=h+53vM|q8ek)e-9{Ib_l@g>fzymmfrDFQHcgfZ#|qe&^I)+1wpr2no0<a%+9{7
zhrfRA4zzi`rwTp1%WN0qrPo>y7ZyU-`LcT{0g@}XmH_BjLcjyt=mea=-ZVBOL%ZA;
z{UtypXta)zU|uS~dNu+EP89v2HMMwsu2co!nHh7VRj;9SGrcP^jX6dzjoEL?bFp_4
z81^J(WpUe^hkz<y0&P=l`KzbgvvhS}QYy?9zk#3@4;SiWHq&m4^szP|`-yOkS^G{c
zZH)jTBFoFm%ho_31Sy-t6qM9jYan6_4!C>F?Lv-|{dmYj?)F0k)3vODf<Q%@`m$y$
zyQHzPaRRUjO;HR5IXSH){dE~YBZ>msjnR5%cXyP@)?yg@iPxee14F|`z4FD7ver&4
zbW1)Q*N$-S?4Y<TG>Oj_5wdROd_L{ez?Y2y#GoZ?8djT>aCpE<b%Z>TPVAfusBSOs
z(74yzyG~tXcjKdd$sk&wgNnW0dx0)t&ffxa*8uGTZ<8vvNZ6A(H85yzB;OVRh`(Zx
zH5HN5%s8)eZ+H<Z#HFDF<ImHnm#%m(UCG{5l~}mm-rFl`YSfQg>N6;MaA$~maF;$D
zo-bhV*`D)21$32Iu{sfzjKg*5Pymv8N^g4svThf*-3qlx?AT?9u=$eLzfALZPAg@5
z0u|!?+8Lp4?!tl5bQ3Ktff<&M{uk!JB8njvaT>6XLa<w!NOkjMy45Yw?wi>b1JuZw
zH@(TwQY^VBTM4>_J)WMPW^jyyw&kCHc}R784X<?-J|CF0l3tI<@B(qrFLjxXCwY@H
zOo|EX9_3~6zhpkNS`P_rLT6~_Jyp4AXXkKeWdBKrVozDI_eK3Zs}K0EE&+EYXnIYj
zF9)xL!>;JQ-<w+nG|i3;k0;7f;^-%zem-kWtIP{*jtRt*(?AZ)Hx>2850aQgD{1NJ
z{M_8!Hwb*7T!(?L_U?z)snivqI&ow9Y>b&s`Z7lbp(v4?-@AL~1n~Kua*Ts+MF_H3
z&k$JVYCfWjoLg^Xdm=W5wp49yuN|1b?87$|Jwe$X(-Ju?46V_fvvnvx=_ntESyZoF
zxiaY=p9@USXURFJ{%D74hHTIBjd32je%%}?an1?ENZdr4U>ZdM?a{FR^g=P6$VbFU
z$wUnlwW1}1&2_QHk{>*s<d-)xD%LWt66F#!1gx4)>un;|`3nw=x5wQMZ}(qd9cKC_
zgJfy>pn|^E6Z+v1PbCc!Ns$oR94uDjYX)000^CiXfVQ#=2#v~6X2%65B{#o{a2yMj
zhdQ2q92zLWO1nIPattC@I1B`Sdo{pwm1xb)&2=HXOplijL&jt(+Nnw7%pv+tDPyc2
zGS9Yy(0fjJ#tU&a?)STV1NxYk0V1O>&XO-&y3~HM;uoZofrHDb*0Zm_p9H2@#G*cT
zxzXOssb|X*kkh(aT5q+6O3)cVLCW_-YFp4=&Dxr*M?&*GPK8Ku*X*rvlP!yiKvLB3
zh8#enR9AsBss)aI%3)%;l!kv*U%5>Le$mSeW`7N)gvT)*BSu4Tln8Y5x4`f^e>-XX
z@creopP%_qPu6{oZClqW=<{wg4$6=Se7dJw%UL$3<hKLE0|w-r8s>H|9x;osEtjX&
zyfHn(j@V&PBv>(Vao+(B@)8NzEdW$zbc_Q*(mZM6k)x%`{??6rr1)>sYX!W;V!%f<
zzHsdFU-j;5?-smNIk_HBRPJ5`RL?!Yqp7-mdc4P=#CiI~3Vvg;-e??<<kklONm2RZ
z-R6W`FzBA+w7$LrlfZe3&xm&SXKtuEX_5(`mXr<X8f*ic`A@pMfj2hfCR_AkTar?N
z>8Hnc`oUXpW1*pQ9ahf-L5dGY()pX9EgyUXMINZFuU|Y|;|?^fM&oacdBV62lYtmh
zd>lBKrywL`$@k)u7U%5R7EBO%-;C&W`SzSjPZ{a$NxN!j7?mMm)f9br9#rmaz2bxf
zvzC(L?s_$Vz<>EGBT?IWu0f#L`P;(7+ui^O{41*h+%M$uRExpUKMDKi<Lu4V?w<ov
zlNXV@@EZS&Egya;#c}M!RVUM(Ztyrp<Ky*Vfm14m$#%GuSd1co^vt@h07xhSeEu@D
ztbZl6M8|Ybu>-P6Z2X;;T!*fmk(u%k{wi(&g%JPBcLcoi9{vdm|A0XuqK;*9zK%H<
zd998svcC=1WQ_u+qIli23`uwZ*gG4|iWdf4Q(lu;eSux7t<%3-lM|lNxlz>xw*EAr
z*iN+z7tuW!gV2wcxuc__=o{>`uj><CG5i+a;?b{3qMml*FyM6Cps@GR7(yD&vumnu
zNCW}<N`651J>u>y_>h<F#z@iX4-r%F11KcXA@Zk2gA<`?tqpX{R24n(dKytZ39&Fa
zqO<=4y?%)&Ibp{U|H?yZ_(i`ssYA<GVo1GT&T04oP(oj#KOi;^t-Fh31)0-huEiq0
z(jqkbG_)^UfO=dUP8VXG^&L$1vwa1xYQ|LEz(I<HuG=rjJYABN*8iv?PNa?@nz}x*
zqxQC$|GJL{;+8AU7L5fsg#hRLmC^5Q5X2cFu(ErV7<ckqXRM@qv?DKl2Shn?12uOC
zcl`oo?nIxNbr%`K*pB(>CI1?yYa@`SVh;jMRQwH^YWzrDY0ipsmw<T`Ln!f$#=-RO
z+4<-y@g&iJIk3hk!7mV(1<~KL{U-xbx#!dX%@|cUgi$ISaD1(`)#O?(kWQyht=T}d
z$mtt}jKu?9gn!j&9Mn*kz88dH&JeQ^0F$5trsmrFARseM0huD=`t2Bhjx0TT0N&gz
zK>Ij>iOgkB3|N5|%>03J32UF|yuKT_LKhCYTUW?yNGt5K6tAF5WK8j$FMk26@8JTS
zOn0B{`9+?MToH4(@Y!dmMp8ZmM3MbClv7p|Xy?>`H-_G@@YDl5Ahvmc+N;-*TA#`D
z&5U!O<?bl(0KW8WI;;Bndf({sC7HhMouGaIhav(kvmV!pw`?(9Yttn__DlP#$jlQC
zXwVsJYio;y<4bu@?joM>vv-VwCJ<|2Fc_q1h;3c)`R&pUJdgk*H8E^QTA>+g!QCnv
z1am3&BS?06oX5LYh^9xTh>pIT3E9j^KV#-S<eAmH%_o2~zXWM&OKVV(kRZ#l-cxgX
zM+%+il<ZoIuauza<}SRku&{7C@XcSSxyy$dDl0yNI!<IZ6-0cUhn4PYBmE3?V0xh>
zRQV8?SpI4ei;7_^Fgykj=z;CkF(fJ~swUAM4l(2qwXnS0u2$M*<kD*)JNMRoHU_j$
zYF`6BbRLps*Jpps6-uKHy8KUp(F2pQizJYD1t-d!i5H(Mk_DQApAK}(7*TM~S3RUs
z?)T$BS1$cIJK~JtHDj${{ojyHS7ji^D2Fb%M4-CVb#*1jlZxnSYdj%rmk<Gl?PV6q
z0!!2S*7bAm>o1ooBby9bPUuPkKQ(NJtwhRd9HmvL`*s3|BLU!t4l5>5?cfE?Bj_+2
zFf)p6NN^=#DK|Gspk5R8d_T7%@lD)+#DDcUcBiSt)bzANi0u28R7#JLh6djcK1eRr
zf}=d`6t^C2JGkfM?RaEo)owas@x|Q}BR{$VYsOy5CD8o!#H_DLX8piFQ_>=AKa`2}
zB4wzq0)=`ot@OrEKGBs&#ys5hnr?2zm%a+O6cqn!1&@JixN{U5(`OmGjUQJ?v+V@0
zFauaW4MF^M?d`G1uQ#DEs1VrF$7k|DXpogDfl3nU`@a9v0@bfGvP%?wnOwpVU~iUD
zsjj9?_1gh`wOq)V$RJ)ocF`zJ_lv3GSD**kj%e^lNW|k_q3L)dao`%#JyeUcmxw(|
z6L=WZ-Z}C~i<NhptUAWVQrole^L~F9ht$HpZ6Jr9mR15_nLT?vVU1(mxml*d&03pe
zQ2xvQ*4sMt+B)EeGlqtY71~C8S+aS&SL@AOX6_nAakPOl>jR^A2+Xt*tF1yCl%a4b
z*jQV)K+oi5h_sFMjNOu&B+0jppiy-RsmrhcOo<V=e%YHyfVA1|z1i(E`+((vDGFRO
zYxv1K3v9avpz`o##`8gF2&XcjSAlUD_=`bjozk4iRO{_0$dJ)H;75i#oFE^hK8aZQ
z*6!}fi_|+GX|S-mnz!6!bCiMFcvJq8k>`xdHPTzH1#^fq4CLJcGv(A6NfzsO^N+G8
ztIF*EXMB<FANjBp3Tdq(bD)7YOT1{4Z*@<+bpCrvIiG&o5}77pND#IWU^}rqaEBOk
z3n;`55gTYktRQ9CkMHR|KevlEAL?IdW>cg$)1X08YQJ46gj+vvgSu)5`;!H1sMAPb
zkZYGVw__!R8_6is(<9oAdybLzGGeFBk<(&taS0R5t|zpf4TL)I=Pv&@y!6BC>nu|w
z39^Ay3t!ELm`8o5X3F3H9cj-D;SG@))Y@vWk`l1~c;EXtu}|X7+zz>RDENNH+k>7I
zh+Fl84BU^iWYR;4wK_Gg(NlHKph6)H-uV$0gtr95i1Xb;iR{4ti@mpus&b3khVgiW
zBciCFq7nuoC5_UE0V3Ukq=1w(n=VB`krHW1X|{k!H%F9|?pEndNlSloZTmd$JH8*^
zuWyWJk8>Ox5BrXFuY0X)Uh|sQbeYyI!lhwzoL0@m<y~A#%OGkp0dszc_+jmA17B49
zlSZ@$6o}FS*)0{S<yvS~@X1reUn|YYeaq-6QZW!#IjwJ*Ki%*|9w*ulQeM5X2_|yn
zaNsGFSstN=_m^ZAfI<rxR*$RM_2`FAcHTCpnrA<-<S5ikR<v8A>8Zo_B}!IkXRQef
zD`y~=%esvVAbG0Ai{2R1=o_m9@n8eveuZMX$Qm46eGGS?uhPV;>_)5flk|p-E*1B4
zp~rfhI;KMjy>C$qz@a4MnsnhH4(Wtu1}VM0YHM4r4Poq8X<#j00`Lr^Ahp$=NVDRZ
zivz&*?4~YP{J+h?XB$sBC=ZbG>>rnPbaL_%po0h74RH0Pj0;X7!(>iOXyMQR*vdEl
zLwEL>(@aC@Mbh*6WliHJzoZWAHZ_QK5nUI}kwpONbU(yej$i&IidrCb`+Bvbv(wLk
zpUAP4t7^K7DF4w<P9-Qbw0>&2s1w3rBd{euD^vvx)Ffl3YBpWwfz@4WC?XH)g^o>$
zD14E20NJ+V$TKk*wx!dn5F;!u#nWun<j#7YG^1#7XMD6C=^E=4v3o1BO!oUwA^^2k
zHOw3L1=4|J?||1-7s-QZF*Y_PN2Lp|XT}2bn#1oklplb`NCnNZ?2~J6Rr9|qts6JK
z*zy#14I(dR<DGsJ2VLFa$Gx=!=5D`c>}fq2OBbjn1Ll;4@dmol{$jh#Db07o^M=sB
z#bNmueOFf+4gAy%>ZY0<8{2Ljufn?BP@(f}e~E2JdzZ=@G(I>vZ_pO1VH*xz>|~Ss
zdwbJ}sO?W>4#Jv!&Io|~Rcg+&`E&o5-2;KA#|sNq4N?4Exn}U%#`B4qZ2@Js>AnFp
z<-gP5quvaUaXg}GGDtzX1!&<EAarrVquaZRWV;q%%GHc7@{R)e<g&4HBq9A9glKMQ
z`NS*?oCmnkbZ3I?YoT8jK(rFR%(C?~Y~w90))x^Ggps=ve(od*XZ?8IDrID3P<Lz>
zF8B5IE#lzKbmm7e(Yux+9*FkV+*veJAFr<33-Cb|tol?mK$#Lp7{YsIX0}_VXAb^j
z2yxHu&I_I6OL&I1YWH9p=}@CR1ftX^2B_WAUxQc8*LVkot_}bbo9C9hw7P)u5?Nka
z;+cX*L$6SO^R$$d?o8dIQ);uT8@U#r-fo#Oo$xgVY<1T7=?1*No+`f~L>`F6G)94Y
zP20-$kPjp$ezX7={|yKJ16cq>cc#A(cH72-w^-Hd)|uvzwNPX}deZVF1URty7ZHXX
zPfG`oOTGZnCEC-<cV@_oFMJDKcwv=RAB8#{vv(V_u;79DtYw{Rx$xXoDB25n1N+Pc
zt+IaZ!##(@DC3~rxFk0>_l8S<zX|GzG7+I{%+mxIF`)XSai8JrN!VV_b<uPgfaUN6
zmm+?g=+Y4N2?RcadC!~cpnasC-+o_2MA7ggTcHmqFk=A>zQuCgW`mmc@89o$`HpmA
zg9sg8-^<Y%pPq`Pg*HDWe9&5>3;Nq@%#;C}d)6%e)+)i1YgeG<47J?tpO0nyr2ltR
zcM26CVM|~`I<704Gl#gg!a86s@VSZQ{RHkF?*VXPI#mq{w{Ho!Z95A>5&(#FfmmG{
z*RP*4o>e(HIf-rFP0P<$f&E%5@{PW#!zy8qRX+N~v$F<f193_FFCa#Qt&`gIs}B0>
z`1PWhygv2nu#2W@no-c#r4R?i*(AjN7U?G^b9*S_5wFv^A7lC}&oBz?#Ix$Qo#Zz_
zC1camP2X*d6Jo8BV?tmk-UH~1Z6|bOy9e(ayhXGBjCpL$e1xx92JgeiZ_ubL64pww
z_C^3mSPP!%AX0c^si1)RtQi(9g$V9C=FdAy(+i+&9&%5~Xi1QEfO!WoUos31tLywe
zl{GNsrK!)@MPMP@4e2PLDprl4V7;+OHT&))tnAGw*^%H2^j?!vW2q?VSbq6dI>7nJ
zg}vt^V<8$mptb{e)z<gz0@#d0zXGYsJgDAv+a7l->fHZ5y4QY%3e5SYUS*3eV;~MJ
z_LEaq4pA|PCFNg*palv|0IT;f$Up-scsCbfm!JMjJB0IL(xgGp)Kvs4k@mF>y2E=B
z;?2N7%P6YXEUUY=@N2c&se{eX5e;5r=Y?81D%4=dA~>5!w>HG7v;??aG4hPw0?T;l
z`Y{@`<yzhdQ&?oGDV$Rb{Mc>lfT~$XtxE3kx85JxO^!pa4XeG(khwq%88J${+_x8?
zMqo1o17$voug&`*eXw^6Qc1RTu!R)C`b*q$9b%|gd3hJ%^-+g6(x#L9;zb--g?DYP
zEmpsZD&HtBe-7O)*Ps;67v|ul5g{l~LD)`XiluyW;q+>1W@Z<F>yJUc?CWEf%6aeY
zAgZW~ToKqiG<Kf-$q1)ZT(hk`7Yd~oZyA93ZmZlr-A54*9ZY(1XCVy8F&XJl|Cu~E
z?J{c}V;zjj)|FZeZMwHyA&xa^iWZbEguEZk*9s!SSM@HJiJ_p+eg3n{oKp;T(}QO7
z@{?<Ku5Dzf<<@a4j9;#<hu;&51qzCP6s11FPntT<*Bm_mGp1I7c9(-p3`wEP?D!M}
zzbF9o>^B%F!mLAJKNUn&6k4P7BPHlkXD@u7JZu+R9Hjr66qS`vh&Oh@gqjhVf`(a@
zA6{H)Qrf|4Z5bbOA_1vuR|lAR0CU{6{03;@5+LsP7|j8tWSfim{N#K@V`Jm4sop$O
zq1D2sLLBR@*A?&XWa;z)XPXY>(l=K5^c3V-if}`)?F^&GN}mgQxS|Xl7}B$YtI!C?
zYK9+Hfov#96qy==ykEvTtanMdxtQ+Xa$nG~VGQuAtwxN{a;i+Y9tZD)5b|ms%w2g3
z03iT?Uj>TOHow9X7%8F?=TLzJOhA{Lu8#wdDk!U^v4IlAbkA;(6gA1XE|95gLC!cE
zA{mjX9h^$JRmZAh>0JPlmC*`ZC%EBqFf&fT5S-}CBYHK|(_;jkZvIx;l5ze_R+)!S
zv3|TvS@SbH_y|lN!`#adlrEhw1=OitKK;Bl!2yua;-DmyV7BGt=Z9u20%d;{UYv&F
zyzMsOt>+H#E{bgdO1gN@2^`Kd_CC6jv4V0Z+3-rLltN<Yg}?L2SqGO5^mcr<Mgh%K
zZ`bgA7&7wDaILz-ZVmVM0&YcRK-Z7A3V_F<ZyDg&paUb7lbxN)oPe21wqSA9>7sFk
z{z%cQvLUpKa6G$r5Ss%}fEbuZKt2<Lnr-R?TAIV5BEJBKxOGUXxYcj$o&+pu46K^x
zJ&*4>$ND$=^$QtN$x*fus?a!VaQrib^J74qaKhtr*g!DwY986s_a7i*J=&XGRyMe~
z5L#}N3prY2<W4ZQpV}IlZ2EtVgx-A7CEEbjX*f~79y>@TBcS8QJ4T^WT32^78&<UM
zccje&`MVG9Zu#YCvG0n3|FeJn(smqm7>6gMO-3W3UV**R=PIdoe-;$N#<-OMg><OP
z5z^<t8~w;uPE1&sva+(`3nc(w<~}ZkY~^M)v&jcPydMHIZt8YKy)|s06Y7}MzK-th
z6livmR#>RYZ#!Mn=i0#CH33Z+X)N8|etHcpxg^tJfzE|QwIPsY5Q0PE|2eGB>V3(O
zBt$9%plc@{gIEA@r8hwu8U(&1V?ltKasW#mn%jY(Y-%^n*UN)T9iY955Oyo$<KxRQ
z?YnsHxp>#gVw12IRL^~UJa_vSm|O>LD;~Cwrw&ueGBFvjGGJl9{02j;P0b;Wg^sZp
z;7CdP2v>J)xIx;%Cl^t-2=)QXwV0=*tc<dsjI69GCMH;DzZbMxm`CgKX`fcJY5u^)
zd$*w%MH?jEAs>`iVvE8-K!vbVGNF`Yj7_UJ+QUFtA@q5%XdQ=Lejc;=E*EOJ8(>L?
zG;S|2QEF;x>V1OL{wRBe(*?5VaUTPI-wuLE(g=<Zwa6&ISZ~_dY&t#lCEE2)BpGqS
z!_#&IM5mxbjRZ>}?VM4}zhxjQSP&)OLqNcAwlvyr#DuG>4~W@nh#~yHc0;_l72BqM
zATc+$V-uFy<;Q&}ABMU!iVaBP(;4!|pH5zkfek}JOH26k)F^bZGlo@C6#n;54jXvS
z;`xoOvt`j#fX*GxK0M&{eWq9Enf4Jz&f65QmJgYu9;0$Dd0XJ45dn2fhw?4qb(!g!
zh^p0V-5eI)9)8>h?=ae<XHtILZHxL-!j1LzWFhXCdEd1JO=k(n<W*I}5jivk3FkJ;
zYN|_yr9jzhxGZ--RyJRhtB4U+%joj87v(Lmxui<4mMXfIyEQ{QRZCB#W7wK1-=1>y
zT~gB0%7iSSI%46EUQpxGD@8<yBRE@#avkow?2UnkFR6!TC=fTQqJas%64xBNjL`AM
zT)9>`yp7}}glMpSjz*WdwXdez76!2&SId&4U{1OM9fw2k`m`4%M;Z{^9}&Op3M;D^
z@?t@0DI<_MFW7tfH3R8Ak%rA^J!xJN2Wko<#TZi>r|B$oP0)VJMI3{{cy#h3vJvc3
z)OUbVN``v;Lo~vb!}AlUxEiIq5w+>jx$p14)**YFfvS5^Rv@MCnYSI9&d0uG@v;n#
zb5?8$5zs|~RBr+WIM9ys$kX?Vw{K%13b}%K_BvQc20*LF<^ySK!Nm`ph-mKsjnJDn
z91tt1px=S^rQAuTrSnxM9f2p!1<nB8K5j0q3BfrBe>~x^6CkAa9bdz%05De?rm%vT
zm=`4T;oFJNWrQ4Rh~BM;{YndHXlOXX^0ovUc3O6}0wnN}pA9H+UREC9zbDy?hR}UJ
zvAFoQN$0B;kOm+%Q(2i181G=?;zXc>l@IETr&Bp`_K<(|GPLKJXt*?_2rn#Hx<FUk
zxN{OA)2pZ0JbJQpy^sO|bO2HSYM|RoD?Fx`ZPNTwsK&YT+k1dQVj|C1!|(o2f;+2^
z{%ZdfClVZBL>71d`2f@Nzkm0?V?gk~^MK(0pJPxaZ%06eRxW%rHvS<zUYqUKTf7^{
z@2@(M|NoE$$Helk)B{D1tR#YpPX7Me@N-4N7trNQLO{!^<`n%h_kW08!w&vOnvV{k
z?wmMFK$iqsoPs)tp#xwKQ+zpiDn2<`3|I!+I+SOD1iD1`S;)kZfJ4J?GHdwy?`9~_
zWvUq;z*#RAKLSs_jdK(vMRh-v%jlq3tc26bv}GMQU_4BN|M-7B=&7@p%5NJ+zs$oM
z7oh9?@u67#Ki$m#m%pR4X)ejkM5YOev;X6Cl!`D;6&A?11`_9nuwCH=r~36rO^3>0
zqtW1~x)I1+FOR|GjzR7Lk@aD^el3V30GO);B+oe^7`s0|VYjOEn^Bgr2==ojNQ6%S
zHVQQDYs`}@5O~2Z3bH}S5;miK95Fe;LBz2%ZkzLnutV_J806*G`GM5?<MZ+Iab5EK
zL_>L;#LX9KZ}~EcirNR>(z2O77=vs-g4Rob7O%0Ee0h*9<kY?}-Svq=SI?&f3UYJq
zHk?2%HAu(1j120KL}b%-r#t4qbBV4LjKBwxE6GDJ*Bs}jl;9g}FH(#eymeP&cBNft
zZ?W*g?b2E9((Q)Ha{~O5N6(Q)cvb2QxNgj>e<F2D0VS$<YJk02tbh~kBIFAJdOf;w
z8ZP|FwRUhtfh2|@e-%aP*UwLjpmn?r@(JrT$?VAO(!@fG<F}E&l~LhuzN!?=Hh4m0
z&B1b&Yn(fT*sWMockiS4+IB1xPqZXaC7Jj6hA;c)k}a5dwL6ben4H@easKdL{PiF(
z9};qOoJfd_{!$eZ1f#GDsqUy;mRk6r892A~m?Y@UNbNsBKnJ;b3G?!;yg~cn{Yb^^
zxYA<)yZk6++Z*PYfnpV{Eu6~o)|{IbqRuf8uCTrRkCwdR@1qjkV5W(8#WmV%0uHQ#
z^7dx3{ZzE9dh{g+59i5o>0m$1UBIYV1Tcv=Zz3}lP(bc61zphcwMXotkeyEu0%~;%
zUUpQyFfwm1>fG<?Nr)g5MCzS@#OTGrkBGH=^9`P+1I`HH+Q@Z?6Y<F{h}0FK$KNUj
z_;a6`C;!17+8^E>&dba9f`~}v#chsEyxB2W&jzl%Blexkv|tu*rre_v7`uk}>=UIW
z&@UWPZK=>&xXBRcP2JuQ$m^oX59%-xh%a_tK}PSoFyvpBA&IRP6%`ejfDj{GzY3@g
z+w%ScY1sI(9;Ta`&K_Jt&cQ}A9gQoG>BK{vZ@`2Dk70O04_KYs8zGU}Qf~m@&+V(1
zTl2WkDlf26M-m61@%0oW@3P7uZpWc~8bZ&;NMbob^~bvck@pcP+T79$FToC1`mr+@
zT)w#Nn2YsbTeEO{Ns{677KYy$c*FHNm9m#8wR-=uQFIdIpxOb8XF{8HW#R34|7D<h
zifOn0bLg-Xa2i8qHs0?#!neWfAInLp0xc-qG&)xrZ>T#JuhVIS^@A<dp(SuQmMR6`
zV{lwPw8U2rRf)kOtF}zX+xpB)(qdH@7jMq-FLE_Rt3~dLW-kn;H7qk~qk|YS<mIHm
z{mYj@U>${qKB(vqn_Re_>%lp`j_Ze0E$sPd2S*=PlFfc8U}B5zJs<K~!UZgH4t-$^
zI^gN`SL)@0Deh81uz7n_wc#;*WS3olz{Danv5;Fui*{D})@V_HmS)q0|85^;aqyTq
zrx$H1u=1zXG=Vlf6wJ+*JuHe0um__|sNec%ucGA$CNyAW-JZ-g#w-z`s*q8*d|E1%
z_`peW8bhb>CMHrHGfDI?|Fb+w+KTt3oR1)Ow_82es%1HzYZ8D7mvsOO&QG`Av?t3Q
z4rf~{VL07jl5)cZnb(3Td%G97cZL)_t$2vKE&TD(4mvE(Pjy&^CvGkpE--#8Zazcg
zuaR&J(XioXT?R6`Z~o$OOD2!TWcC`_Qt4R5Hve}waTh;pbn^Vc=)K;_b*TsKW%vjN
zX$WZwG`0F!mQyLo1;Ax9PgpWqkZ&z=-D;1^!`l1uy!0MpK(9J}dUOm`X`@03h!nvC
z$vCwbLh9c?D<15kE!T|yo|il*VD1oLTDWTgx$U&^rtER1?g-jY0{1(qLgCy<SE+QF
z(u<0O?d_MtC@Gq%FwFvxP(jn%0a`x8Lh8{u2elJuhC;4RVcx!9b#C6?20Bwu0&~&@
zj5cwv!S82=#0Rn0>iZd+0{$Zlv%Yj4CnZwsxbDx}@anvfk7-SiJHC`!^kMw(cZ(jM
zg*qOBML^H}1rFgj%a7XZ86IOuux0~7&l{k6iUBTz%di<5vKp=}jH2*M&u8OKpd{Tg
z!Y05qwLy^+l4u+)nfvn+Bx&<Z$)6ZJ_*kn*(ZBaea$Tva&V01tl~0B{dH3Ir=v7L@
zr+rAqy;1y9T=XG1jiWrReqbtXv2#sHX-olJf`u}Z+mpd@f}%@g5d!O>y}mKvuX@!_
z)D@bQPxKhX1naB5VXi-Nu_<VEb^wcroy^jeh!V}{us;_cNoMftA<KK4rC7j!ScZ{O
z0{)?=lwzcmVLU96(3RL#9q9co5a+vWUau)_Dv`o!7P`K4is9oT&!;bvUy2#S)EfVO
zi>+__G#`oS0IxYh=LP#$@-c+&9u)=qBgDW*26l}%ui9CQ;TqhUi(oNahrGi$aafFP
zuMr<;#yVi80BJ9ShZTm1OYUiMO~PiD7159Xd97gr93o$CFi$?L*a#)_cqNm@I!Hr2
zXZ?0~cJv1G5oUv+FY~aG)Dk~75G9)<^)7xptnaE4Tf$K)G@*M+gJ8H4t|${!B3ZX2
z<&Cc`03)_vZ50?11v|TZ&WJV8R3F(wOlkHOlh&HIH18-m+TyAxwxH4BdF1Z=+vmCD
z86zw!)n9W@ldlYhzqSLf60H@he(8?mdQP-pR`ym|3wCFLxEmkn{t>zlb6RQ7M&%%M
z!(sH;iu-cY;DxXfE7nY@d5(FbG+I7}n7}98@+8*~@OjzlKqo*#2CnTs5p_V2<KcVI
zfN;I)QrV|oA@f0jtwrITY$R+EZw1ju2Gj+ZfO+kni9h$<*f8jbf^UJs9qDMg54joh
z_g3O&B@#S_#os@ttsuP!%@P<N^n9|2!A7dilKN~Y9bX#VQ*-<}CBcGkU+miKKrwW<
z;fw*W9J_q@$-`|J1u(c0Zw1)Kn0aaE*f#XyPGHI`k+0R04_pG43UBxMYmD6YR#3!`
z&8Co@LZ<j&>YbOjVto7dwF`nR@^**f4}NhZCw7d|wpUVRJg6B?c96#Yh1L;~%(T`_
zA<=_G?hlUVi!6em#-K1Q&A8bSC4hnIq)ogs2}I|L`!Wy@TreD<NtO#l>}J*!^RIKm
zPaoZv-aiN6L48V+x^rda_4;RUQsXh&<3f$MQFl~2bmLLkRd^t6`Uba~$Ag$WG{NTk
z^t|`0qFpqfEC|B>sWNTu58GzA`FZ|p>~gfrimE}+-0D>6oXcD+=H!VJk+BXcDqR#y
zzn3yYz;IC4=WSL307cB|`wnm}w$pmcFgJ74SGMc|72^=7TW~Bg_AvHfa0#spWbGVI
zDM`DmMVj`Ew)4d>Mw+IMN*W4~FV!`rd?H|DfG6PqFgu~Wp$c@3gz5P})`R@!gAAky
z_IUg&GDY`X+<=$A=FizVaZI8cH~TdC>ZkC`Z7cB9%K%?^!tM}_yeCG5UrR&kwzlri
zZtI&jzL6Q-)#Q%3GD+ZYy%`%YG~PQle=AYwIzfeqUO!=jK~wY$^Bg3OH&$j8z;XpE
z4I)18BTRa#sZWPNMZr754jK2TQDD8;r;(xUuTM~j>B&dK;>%0t+R1#e=X=W|_j;|0
zlHvU5TBq7Zm#kzQUF^8xZoOq$o+Q_(n34-0ieIoXEM}(qVjB{LiB|mDH>af9xpKeq
z^VV6P(2e8ADGAuJd|+Ne%?8X%#!FQbDDSxBzw^~UgKxFp+8-!#1;|3{dr-4d`<p;s
zQ<~ldy&3;)wGuU3RuJ9Sofev(ht0i|G-yNLzxVNK;$OppSr0$Z(unj=WpRN;zV+1A
z2n8B>g{wCdun&m$tXF;>z2E<_Kt&&1G9Okz4i(K$VC!)x^ns}5w60ItystUFbPhy*
zr^Qjsv=p4_{C@AtuC6W<AS^wo=Mbj`V#*-mL{_z&6pG|OSGf2)J}uCTCKngw;%MxW
z-FeSQ<!$uH-UVhQk(jZIU}!BgQgX3F#n8;<2B&d71vhjoV&^dP7z?z4{Kgd2DS${G
zH#rHV^Yu0$r>=+T3c6i+j_@FM(GdMfP;!PgUHUpH0D0_2B#{NIt?Hxj>!HC<?mC#|
z_sbuOFR10Z#z6B08PrH*ATcnD88wX<YY$ESYh?dqOYM|G+(K`F78s%zy&xc<T!M{x
z)0-)y8Iiwe>$4VR(SnkXY(eX6Hr*-`m-(k!lnb|j42{Wd0<3(hd`PRn?++AIanOfd
ztr#nT9qdU~np&MqG@ZFA{Xni>Fhmiy<g^c&IIHrzUBPhv@I!U*aFkRsVw-N`GX5f9
zoAjNEE!0;<vK{=+4^z`J{SsKm#zW#}5(Ih5rlvEo)ebO9xe)f94=ZzCTexEPCq7mO
z^Jl>=Gvjv)s4Z97tYK;~7b94@f&=h*dQGJ<>tKb<@XY%FSVLQCpe@oVxMlS~V-u5Q
zlvNpYn(F}RQ!;GBVxYSP#cey76=5K)mzI%XR-@Z*+h-amFSF}r>l#!!e{uce*2DVm
z^-VNrM4>T{21k0-gys&Znwh@nK~jX0Q(O0XCTlXa_Q_9eO{!$_(^D^D&rAe2O);3k
z9(h^ZcO%xJ3c{ci5EfaKuWW%<?Io%mkS<J0y8Ei=PweFfZnxW;vqI@=<uWqm#WEKJ
zv)K7p+S6jCBegP%0)&qclRCm)kjlYCKKrn`t={JpEG{cu5BEmUJcdPA-tLb%TOmRZ
z{?Ri)c5gKb3BvVj_O+bm6patr5C{q3>M)wr@l|u2P^4my_ipBaEJK7iNQZu(aqheI
z`$S);IWG6E8~|_mtC^;A%|!zP-sfi6O>`YGs*JZ>Z^c6|h3g&q+wA-of%-ATcMSM-
z4EJ5rlhbY3N!c}MuCcVfB8?Fv)9v*fGMRjsv~E-2oCeL;YEwobXRChd&uv`0b;{@6
za+RO7LBc|pt&JfxyE|ptkanRf+>6_IGA>oFp2_1~0diuhr25qk5xphi=isgfW@i;+
zot?Zi2U_B!HXQeLYF13Oh%Zi|mUL>4x8jdV>9<?KmavV27$}60=bC!WECH!AW+UVu
z^Xo$%ZF<twc(MiC#~<~qu4`siUciXBcXqnS>Vn`aISJCJv~soXSYpu&Fmhy);<K80
z_j=CMztNM_G4CX-Y;*s0B)Hap&q8~#;%pgcH_<yt4PAR)=jC6|fmUd~4T7-z5)XQD
zl#88j#CEk-CK;p0!<P6U5;n20=9}wb{Wr`LIPNd)E}ZA63gYVjV_VVQq0nuo_DkM9
zl6q>*@73is6eHWtTZw=hIrO%hEa2tlv$ah}eX~&CzVT)9bdc5&JW__xxxdg_jOxVP
z^t7Uwn3(1*u-u2KbQ7v2a5FPXfWM$LI$N+!8np31l~s@ZNx}G}-e69pnwdOQ3x?=}
z)RqrM;ofk*=e&e(-2aeHI@!&e3n%vVd!c}hzuTE2@bQAG8=TlXj#gw5Rb!@YgD{zC
z3s|L6G7c^9uSA)BltIE5!z?bxh@N@Y3^rV#>tx&-ciT?ZbXit1D8Fr1cpYhKDPq8a
z?AOQr6VL^=cB0Tm>t)o;v+YB*ZTA<T$(p>KTwuHGp&MRxCYb5S6I7hV%F9RY%iBSB
ztJsN~hq-8cQHHw>iplB4LAKrw%7EvmD?bax$bj<d70SeGIL+ijhRijw2}%$^{MCM5
zcuk!9(xoX7>oLjC{r=v$*TzB9k48Vq#{w^-sB~rw=SV~5W@ev5-GAck!Xa#YTYL}=
z^#7~=kkJCuNHs(W2f81Ep#Fjb5lBf=1lcEjAGzz-AEEr-1<ewB#p<sp`GqQnP}&a!
zM;OR!PCX=(Zid{fa;)YbD;80V1@oteHFh=$qO^8(Glz1QeEbj95)Cik0$=%HGzEt5
zcHzQ!xz*ZlF~oE!;Lq+OG0bPU{hrlTu8Kl6BoXc3)R-CpT~K8fK|LTkUwxh_)GtdR
zo~gPZ|JR_yynd(WBL}@7qM-Fi-NPDwj<e5`HTxWO6KBUBx2yck1-(Nc)^12;>VhVM
z2f~$8(NsXZ5)wkw`Fuy$(Qexg;(<eRT|-*~LsZT4pvJLs0qvJEsB|Gh$&`9=(Fmk~
zx-Ck-pIcj9qE{3+4CuFXgmMTqCR%x{FbI86P^dpZ`7E~SIq|dP%8~3godw>>%({;S
zzSm&1CFzG(G8oA?@}bM!c#{eSvkY#TNlBu}JP|6rqK3)-SPCotJw+*zAa+LPQI(zT
zLxoJ3@2fV;3`Q^H?X0f>n;@q#_II_y=>?mx5|)Ijv{>kagzqYHz;XDZVHs30<Ds&o
zU${%;^za`~YgnoEqVePt+(49uV{V81ZAD{$HOG|Y3*4*kQ1?wgixRktU}&`k`>Tm^
zx)av|8p>A}6q?5`V2;6^(3QgYHL+Ep{wBIsud?{0vo;%sHhn=CG*r+DG7KZo-6c-`
za<GQcs{+trTFMTd{%sk4H*mR}F9)FM*FgA*C}(q(n8S^RBdWWm1=G(>F5ci$-h)~}
zWlz;{7qcX&VkSp>ts2spROs1eb&);Y^TC2r-51;@A=xE%bDE_f0^kE><I09TqWo5-
zdxj-na9WRj*gbPcWNU6`+g!8e4@QK{i(ORX*)>L<=mWlU(q1(RQ|mdfKtk&fT!x>w
zzIYq9KJwJsE6FecjcC=|863iA17Y|iZP2jV&)*TZdhiL=yi-G7c}r+8v~1~|c<M>}
z`ZqaxwSl|m^zPf(+JX!x1D?k3lZNdf0L_(z%Fh|696BdP5Ro%L$(>n^nro;$cD9AT
zYcr`M|Kf0SKnlJ`-VNa?4cPzjf&6tDwAOWaDIq?&4K@I}=beQPS5e?|_Mqg6+VT@x
zH$-lMk8ta|ib27j0~0`89$sj8b0zxE{&pqqwUrd{K*AOCZ@_Iv+qgT+sKjKKosXc$
zsH{rpHzz~0fC$ywNve~FY7F$taCO|dX_A^vHH#>u<l%u3q>y$1<WhW~nbA`X@nJ6-
z_T$fSTsC%@x;@ki6%5;_caK+5ptct4AiQ}6WgQK?p8Bfmp>AuTXQJOv2tb&R3e?Yu
zQ~Qgd!brq%hZLy%c8vr?M12>H_f;?K9tpQnF;-kCD}aYHd#pY%NqNuW+hI<Vx+qx(
zOt`t?ZsdofZFK^e4=~J70rAx%By{5!nMbTE#D;m0+)hI2=sAZbr}pONSXuf63!YcN
zdY8f?;|Tr(?fv~=H9*UIsoHJT&Mg@ZlOL4KjZ`O1&SRX?F;G^00`=`VH+$r_ORRX;
z3>`YO>=XeJ=j3B$@U2sViJ#2@_?XRjykaYQL_+$*!9v=#3-Lv<ipRM4tOiLMV$_C-
zgJm)q6e`9U@NcWdK_G3c#n+*PlTlo{Tm7c5a+yIB^?=*X7QVctpY@WqnNQ1Ts8$9P
z7RCQlIw-8Q(c6^+wwKT!wa|K*b!UN9E&Hz26Q|#Cova!#h>7QM536C#NadwEHPlBX
zb-`{q^(-YTMuqh8kcp9_RQO%RP!mynpD~lW>lo;zmR7R2bYYXTjzqNVUQZlARTfhe
zlUOvLSQW8`irDO!ElGH_+o0kg8RYCGsZJbm3zAzOu<W8$6iD<4kG~As)U>`_apC$O
zWu2-;2jE~oKjdNTqEXN69sgdi2`GWcqBy$_+R1fM1)F*b5897qB|t+jXd01p9L>k)
zWVD$V+Jd}e1*lD$b!{-1+7r&4a45E<Dwk$~VeoNiK{Z1t15pJ05yJI7w%c5y;mw6W
z$L}-^MtRjLbsEu>%gi{b%tcA_>Fhnd@Af~ZJWl<6_+Y?w1KD?n!bNNdhwQFhe}CwZ
zUi-g=>eV9pmqoP#M6O*Xq)`1U%-#Eno&;Y&F2BT_#LR}nP6`e;dyjT<RME~&xJ#+a
zAv#%Fa9rhqV-apv3(1t$q0q$%Sy!Y^G}~T$(SZtCoP}7$QC4RO3YVKcr~MoX_~C%B
zQN)QGdL755QnBO|bKXfER8-V<!O;@qD<9k>xjj#J?CF1haLsJjLY(f^<_lf}cQL8(
zwTfLYIAH#OL(KZfdEr!qBA|Q{uOxv4-F^}h`lC;w4xWkfz|`Zje$5aHj;wB^v#hy0
z!&==Alo5+Ku~#K3E7Z2U{`>VfF_KFzpX0!JlMuZxzBy1`{4$ZiA%TLvX(ZXzUH!!D
zwLwMVXO|w8ydQ2Zsf+iF6=NYf4d|9Yl2w7xPzdd*PJ@forQ?EgSw%tYsNNq{!Gw<I
z57>eLErR(Kv=sdL`M}lVEK-`~QzQ{p^|boXK(&|+ZPO7+Y(3Sb125vL0u1s$ew{ew
z;NmVl0^bP>PrBFa%tud7j09l8wR*JT2%{i>il^A$vpH?|jUv2ht#N)w`2Fpwy+!61
z&W5mLuD<6~4&|S9W+{%iLe2fKf7EZJL1|Dfs%C1_tmmF*M<P1JkteZ*kC64t1XPoF
z(AJJxP5SZA2Ixp`ld=LgiJP4jO6j`SUtrlTaP%p|;U_)@syu(s+dG$A!<-bf1f*N>
z0cp7jILQEUxZ7mJ4%&+fX0@J;R+3>>P=9LBK~)cZ4CG`4;_tbx+tAQ_etkTRw4nW<
zO|akWLV?gndEMsp^;iklP?sHE63+Nr_l);U<BfdoLi2$!@Ar2?G-F~5uLJad33Nl2
zz)ovuSptyDYV!3n`9m3iR`n&FN`P*KG%7{1H=8EX>toUOdGsR`q`HUWO}OsTK`oW`
zdi}A4{Ij0httN(B?s4}&EKOIK)+kmmC>16!hNdzaOilz|VPQ}|u|XNwP;>Ldq1QK$
z(9}{JVZHss-I0|SI?7!?5I{?0$~5<%iTV2Od|kQR8;eg@@bu*SBfq`&?l+$?-7qIT
zq1I<IvFv*FNq_tP(32dab~lxz8AdpcFg~jFs!n>#!Y~mueG!XAYZ{y0%{>jE&0mJ>
z^F*+Urks;gF(DBV_@?Mb5Um<SlT<bXvCb)?REFwUC1ixRYzjkA6(&n){ToL(z>kmn
zveh1=O6@G`wJ8_Rz_q#qaN;92M~i*EA}gb+y{g}5jQkCuJb`<v(apabfXZ5a#Of@9
zlmTygro&b^q!c`L21xIKF+?jQ7__3njfbWS$Q>P3!9aXHFEo|m$Il8m2o#JQT#@n~
z64k@%#}6nAP}3}*XGb(LR7>q+f;sm5KC1D$%YrIh!@|L}%YE`(6w}1}hAqdmT(PF|
z0jwdPIY!*7o227wRN7*(B8^R|>jVXbYx^GcwE5zZ*Gje&u0@}ak}Dj#!-SiFhB!pC
z{IkcHjL=#F>H_xuPL|cU6_dwjU5Id8ZtmW_J1_wRsc55qlXprSGV@S)NYO?Od9*lL
z!$`)r_8>&uw1yc$h>Di~m?JoG{Y6K!tNGFPx}njkLy5QeE-`eaFYt97w86Sg9HOAU
z^ta%sW%pE&FTq+6l0@TYKBw5JgRhs?k5F;Bf#=ZjeNtx-m|u}+*EWl|%WEOjA@HSQ
zH){P>w}?stVI}H3@_VpHcb(&Tm-We_rS7TXoU~ktCm+7ru-Nh5tNb+Y@^|%=hgruS
zbu0eqVJifg2sp1^Cu#uqAxEf#wj#2wWLtt|O%_$9*}UC*J_U4bLNQIB8A<!0QPSGN
zR}i(eql%q<*x#ln0U5)yS|cTs&}jS8t5~(-pbvf*)MxE_Fe@)bqueB)ALSxcqOoBb
zv6P%FCqeOStd-Ke<`U4@!Jz9EK6Ox13Z`~hl0zCo<xtxiLPEm2VTkCa16-S-E~<|@
z^EQaoLDKz@UHNz7qlz(v#j~Eq%``OH2`iC$Pe*cNPyd1RTkWxn{!xjX$J*<M6POSJ
zbFY=l5?Mr!C@>%8v!1UZ6AT6_j^7W~CRV1M#vjp{@oxg(S%w+P8<VRqoR&9l57doR
z9c3YPq*N;`kUK`nDUnR0BvydHvHLkjK({7c84KOO5(0Dg`t=F;>mMIBg@iW_7*Y9N
z?l49geoyLWpR<@E<o~5CH&J$o;<K}iXW_98k$$1Yl<#4I&pepGpa@X=OEB7U3L}dN
z8@>w}hSNwDt)JcF;n)BPoix~d$^^{s-D?+I?dQZrg2{uw1e!NLK9UAWMvQ@V`@L$2
zBu+2fxJl(vYxTNB*wSKXh9>QGQ|jW;OOp?V?@ng=r3BaOya<{?>vqNcl<rsuQ&Uri
zX$!@B;Y6dbXb4PnO>Yl^9vHcIXrBA+X|r9>Tik;YYr{QGXg_isClt-%ceZk%hAg}!
z5!3lqgU?+2ISWsocrvvdQ*spk&fM!1v-Zw{*S)6veEt&<wVPRT|F~dudg3G_<5SO+
z400%r&iN#?A7>z}<0n!`T+VSEHVRMb=$O^es7PF;ZcEtb7oMnaAoJO`0dr@Fz2h;K
z9BJhZ^|tZgh=_&Mvw7KC5;9N2sQz{a?Z|{TYl0rbi3dimPDMdOfx-tJiPSmkvA4%*
z&{3ljpOa#r4cxt@fzL!x26^v~IO!ROB->yqzPfWW5Y-AyqSD^?O1}+-`xd*DLY9rR
zM$4TWCHxV}DzxbthBq~eedALjJ4XHXg;pl1Dp7NL3h*Tr-kcLm5<lO74e|XpCJe9O
z;=e6{AlGk2GA*c5LPeylD6m%5fEwDLA5K4`2%L=G)6?@eiYQL7`WDB>dyLfH8^l>v
zOVtd^Dg=v3DQR*)>Q9VMreuy$8kOrF57Ifvh~_t2siIADKN|^Z7+C~1bqM8YST}GZ
zc`phgz}&kRxA5~!u&gdvtvztOa~yMBVxZb??lQF$mOoJ5jye6_U`|oLr7nfMMn!s3
zKr#g_jb$X9O4n%GPQ@k%k0IzJukUc-Wx|0rz}uoEb8m@5HoR?flfcA0fyoqyx);GJ
zx?nFrRJsaV6Yt<C{=45UL6%*<XLF*$KEa;Zt2W*UBc_1!HPDle*({)~oq3&_+HP6G
zJ|d@3qk$8eRZ>vOVsZdrm;4>r5y)mS@)^2zD5?0ax5<0z)No0ig90ds1|5;Q-eE5F
zFqbft2I)!X$4vb89KGIgNnH;tY#FGIPnC~#=20A^5@(8NRMnU*IN9^4gyIaVFV^#~
z(f49CQxw79B2>x}?*kE}>{8jAyFbnf|3fYLj}v`LBkA*B1L=M<u;ExcnFN+eewt@c
zkjpReX0^>)SnZKlEOuA^9ykCfsu**I<`8}6LW<DG5Dyjv(;#2PC}b!^PD|sTF=#{h
z+X5wdw2kYC)Og*e`8bx5fvh{#^Uprbm)Te0tj<fq6u4Rgc(V8_y)P{A+<yxi|5MmO
zu|WSZ<Lf_o8sh`~ku8N@aCtJt{4T->IzpU87^u4Zz76+8zx`xAiT|+C@-@!)mmL_c
zMMozMRPVo<whWsn<?&Ojd#c5J;NT9Cw%B@n*hKjNhQJLfL9&Z}+kYy!F1BNw6}1II
z<jk4V2kn!-(qky+;OH?m^jcwMZc=?ZoHwJY2HqcZNFhHnbm6k*NsC&ed{qq=bo<(W
zDcSt4#c=nKqYv{=vcgG_dknkGzgo8=^$D$dZ{Z*szPU8bCluIaM04as=8zcC!s{-E
z7Y(nA)X`xs@A-qKPPvtOPt#@o&Z!dNXUC~S-wtm*xkI$@*}BJQ$kN$PCnmey#hHgo
zljK?GUn7Z@6;cA|7z7p;U&Zn30sg<>$+Q-h5FUQdX746|y?;$~D{)QMy~I5Ki~X7B
z`9;&><FAh=eza%j(iCpLUm`$5T}<_3pd=Ok&>s`MhuPM~yFZBU@#=Yw%3TZZEZm)A
z$xqQZn$dYyi4i+e#uxefllYJ}z;Hh*l}7mb+beRnfZ-&U8`S78dkJrfz=DmNn^WOB
zp#;5hk&qDlP>^^KxUu{q$!`~p{tT0sY-=BjF3{}nj-T18E%mG>I;=WMoYax1{LsBG
zuGd{6*EQ656XAl5>1(r=tuA=#hYcf{8tVYMQ&*_oo)q3$6z=HhS@Rojir|(&6$i*J
zD=jtE=rH52r|+J6{yEb`;sqDSq+ErjJX<3Z^S8J_=3(1z#bXwsmBYEa>?SAV(}&U{
z+$AvEpBK4ZkA1R4*I1Fmt^Z&Q6-A)(G=^t4YPiI9?w06~?9{$~)&zbz3MhhKLgqI^
z+bj7<Cnu>^ioW^nBU@V(ZzNb8CxPv>Gis=!e&5QAm54aVQY^*o8k)An(&77$e^=1>
ztkvU^iys4<^l6r<xbWZP>;MC5Em#i`o1k{!88j~t<{A>TFWcY+kpr^)iWT0PltFwA
zU8?bq(-9D)P(FH)dI>0OKv-Bc{$<>X2v&m6(DO7rFr|&B_vekZvany9<9o;Dr7995
z4o&@CjbCthxQ@_pl>>DQbby^Wqi3~LTaZx%@=*k<$qckdLb=7l#ufymC=H4Bd{n#u
zrOfevjUy{AFc8E*N>Xht<k!V$%fD;mP4DTBSDGqbn)YPDTyIl`7D_|<D&6DqxkITZ
z%AKy+m+)lH^DE6*r(Tp*C*4qg)Ax@reraGk4r}H%uw^)8q;uq`FpyBo$e&FODoy#f
zJJ+C8B9+zo(>|1qtvkI8MiY4F1=wXdj{n|7o=)ku`4u!4k|v?s1%)!<&N^>1jq;BZ
zJ4F$1M2=pz&&}+1a5hZu9cNBIR;?tI+<#!>Vb?e5>;Wl&q2&v~ne;JDp}R+sp9iYu
z?3p22(Wyiv1(XC&pmw-Tt}C3!J>#5(GLcgXqWZa?CF1V`KO7YaHDmjr;cdBct}Q~O
z7j55H>1$O6_`T3b<mE2AW9XbuuvYWMzIi6VS9%!#6-lNeC%I%2<=4TnbI7V@6H<}L
zFT{;%N$BDWDd-8Dk9=9c$x;HjVD3$nXK)aRo@YM(YxwD|YYD`U58+xlVfc&li;jqo
zR_PDLR>!R}r*!e|J83iaz_Ej{fA|e6{ySlg2sgqu42KdA)S&<qy)y2kEoA)=1&W?P
z@6#(#(P#MH`-pA^*{`!yc|c%puz-7#)NxV<%O4?UE|4B=2=Vb!%6#Ww0rA*Z_0<~3
z@<z?>*z98$aS4@aqd9$52D<nouq7hI4W3&98BjJ`Jh+^}K4S+pVQB#HAvAZIMzH+*
zhb&H(jXU52^b%B-;YyFTPokuyA@=oGm(brwrun?hY@+?Faqi{>VN~gB5wDK5^P3U)
zFKXjbyJOd6PXX4*&B^hpj|S=Js7rBiakak8k3&PeAr_|%05y#@S`G$nur&*QFlUZ0
zvsWvf_bG~>T(Dl7Hx?8k#O%s%)V!66?qn$*M{c1qt@0j!l-MhZ)5%2C{>cKMq!@l<
zz!A>uKVY_6F#T&^)5g_}?gO|a5jVe-(roYclAP9mzgOJ`S@0oB;e*u;sjUm~Cv6&E
z5>mCPpF?{FE&j-Cj}&2yU_b)(B9c%K>uYCD$28g1YygHZ1yzLq)-T8)NeVbSq%l=E
zT1o3n{I<MdiLjhAi5(Tq0Y6f@Y~@fvGoVTudD5f}SnoB%+1d{-lq^2|HVi9qfc&74
zI)oM`vLa(5D(gZlDx3*XnDKlOtl-K0f%_LK3ZsCtJ*)Nq$J;;*wqdH_9;+~c7cnOW
zs;cYzPsDtG<P#cV?KLy6Z+`=^1YvYA&0K-##zHY9#NUBN5$m6im8U>OuQ8Ok5|ws=
zL*)`mCE<Y*N|J#g%?tOmUw1hzMW@ny`DOR@5_4AnPZ`6BmmSW;OJ-<edO{X>Ow;xi
zhzY2}pGvp+Sj-MgKISVYseE0e_-!!v!-P{0%qYS!BFpUn9SXFZO(w@=5Yafx0w2o%
zQ4rh=u}~FPt_;0f6Yh8|fne770pl+%eUdOho~DtGV@!l^H$EFJR{<LZfTAB3k^YlS
z^bvKc8n)L{7WnE#=GPC!%@@fHe{Pw}n8<`+96l9XDlIXR{a}=ZN)~0nW1<FTCipTz
z;eE#u74<k&V%`Q<#Nwsflpjz(isbC@-fxA@mab}!F0NT!S_FwSa@smt@~e0vc42g1
zQt$fV-sVFsNwWnoWv!~`2XCeB<{hZ27m$8oyk*w$RRg#N#oK(Hd%P-#mlIDp_zs;5
zt-On={WbxJ5?*?Y0e?^qo{^aCwQ;5>>3TMj(M5*=;L(w`f5e6loG-3`OdQdW)c&Yk
zG(A1NPLCtddw|^Y$`~X8(3eM7H!ipud^;%o>7FaurU*cNKb4&6#N)JF8|PxCG)3Dn
z&l(SEF<xe&O$*Y@J?in%tA&i~Oy5!m?pKxPV;m#FZou*5eWIlne^*6dJUB?M%Flab
zO47w6f2op-IYRH)e5`5&fDlFAImmu<^0!>-YkB6Vw%buHhvF6<U$nW_5mTe4(fbfM
z7^Mx3v=v_1b7opFCB1Wuc4ss97bwoe4}**WJ{1Apq!ALWNOb|{kpdagP6~2JrKF=d
z&PKz*@mQ$6VH3PsS`j%1RkJe21NiUZzpGa_wy(F9j1_a<KZrlrd`<?=8P-FR+N(n1
z)%8_9ZH51-&b++4z_fL0l`tiJ=1MWS4gbn|)R}LG80>+4U!paV*Ln$AErG_TRl{x|
z5$cdNk&#7Et=<Yx*Qgaf#SJxx7|^{;fmz_2*gHQjLHkZvQhUrZ+jAtQu2~wli8x?N
zbR^A&y>VEKx|Bk2+W#uR9bRV5DLaN~yO1j*8J~2GD3^HjvJaj3hU>$a73d6w^dMP^
z3^8cM!zJ-09DwPVk1CuxC<~YlN)^u~w;5R`{yb~NeNHKbXL`~DSvih(;GN*WG59*&
zfk5f$V#W>m0lDHgkK`wyo;MLA5m<SbeD^1!uPG~qBRPGqSiVo<4ffFoV`mH1Sx_}4
zl+NcCk^elpHYvcG?Q5cfXRizow7?mTFNa)(e9~)y=d6?;8_9-x`q&aGjkh9HrW63i
zohVn}cFi~5s*I}gs)8H#6v@ZKIiThAZW8?}Jan%O`7rYI-!Jy;%!BwSBit&i<Pv@h
zaQ-)^Xv7Y9IujiQS+Prm7#juIeJ`#=3>^&U($6pYhSCUbJ^amK|8CB`ve+{FBytK?
z!fHFxz}0u0I3~f+ni1gW1J*QbFBlRV!@>56KCCm@WWh>&g9n!%fradk8+A827u<jq
zmYb#<q-jcHeJ1B7pnzl3#9gMYPf6yYmb{zJ;?U1~&(kku)kH*>UD4G>;l{of4<e{m
z>h5yBV)?#XDqIHCqUAKhjl9N2*C4z~2j*lkcGUsZkwT&DY9K8zYT!hQ%yEX5=zg4@
zfbPacEm_!@DEtjX|E}R2C2-I>b6X%JI;M`Qn)JFtP5SlhE2C^{Em9&>a&w`5IzI<O
zQU`d!9e7U@z)b#TAe03=-#Be8jtnnGY>RWj&=5A2n9Y&%>nr)?f_}k2H<6Fp;Yq3a
z#djPnb^UEgXgiKI9U!Wes5WFYz=VhFEueyR956bT<)gBx5v2urj!FG(;zCJo$HaXq
z(Q&f$dYHm*7aw*P!9^%Oy1{6BdZ({^tB-vKX|!-)aQ_AZ4RDD6t!qTZUz6&ZZvFkq
zO6eLXCYW&%2VAbGq&aS3FEF9(Qc>E)=HSwN!QXn0s`l)&$I-s`!ba-8?Lcg8upc`E
zK?`g=eG_ZpNFZPq@bZ{IXQDL%X0Z2-{!_e7-ptR<-`%d0N@}WSzlK_FnFMxdR#^ao
zvjofGoRbj9Dh=BOU;WIz9rv-o<QnuPp0(<J#qu4<@tgt}2zomW2g-l`25<kD>@)e`
z@W1D;UC`P<92G~4{0dC9{|ZY=#T1`GCuz#Do$Q#|%^`)w^09fIR@zWcyRfI)WcTdn
zZsB%Q=XYuq=>gTJE7^X&e-w6brXi>Srmg)3l^ldC*|_Xx`>otd!P({mw3qt+iKP#2
zn0hZI=y=y96am^U?K)gw-CLn#V`mop+FA&qIUo+Ns;4F$Ldlf2ML+a_a0VKj0E;+K
z)aolFgdU@oTqmeP1%Gbscheegq>(evwMY00-UM9d+7R0(N8$tP^*pacb!!PZZ6@qs
z!**#9zXXSV{I*^bSkMEc5aJ*o92yd4aXLUk5>(9+YNEdKGXM-@Kz)?wDB3yG-&?Gm
z<Ucd~CbJ<x+Hfy6u}bBDHz`p~)ccPGO^L6Y&V_m)F&p|o0CLe#Q$^*xIIy!2=+xcK
zw&IKOD$$~Q4rvP~2Tkw+7(4Ov(vym>(It17uNro&v!wSnC(ar=s;}&Kq~x0dNSggx
z#DGd8^R+*n$hU)EK8H{#QfX43e=d#;C&Bst{pq<?C?#Tgb|YvU?7dGa{Gw15!oQ(x
zzoZVJZq7$2+pUGk*%_6&s$2_K*HO=KxK^q)&U~-AXWvOot4@bi@1*qiMMW_USVNS=
zL<&DzJ)+ET0=Q=JYa)vBo6nyPZH`^4Cg<8gp#`2V&II@UD)qD9t`ZoF#3#wtxF?DA
zkgII|T9X@R$;dCXYr(~`n<UE0-WNeU>!aMbFMK->HNUSu6<^`j3LEaWA&W_*{|{UO
z58}z<p{)#5-9x#gIkeVjw*+=Wb!;T!tx$S(KDmuIWBKRwlKdO+mH24;NeL{h#R%)i
z>E6?GFQK59xw{zBd3(m%c&%kWpOc&iAX!Z%&9dj;A41YgxK)YP(DF@vgjnIRB^lws
z9H3-<V`CXYG9}yMEh?>;rg01;Fs8uWIf<k)z$oHP&plKa#j&^J+lmbLKCOY)L4o+$
zTWXNXboBI_&vATte7Q<2(_n$D&%yZ><PHF-i=qcBF|J&128-|acfEoFs3K<&KH*!b
z)9ie$2H5iGiip<EdKYHMWpxr&0D@996B2y0^F>gKhs>v-_QW*-oH6RTq>g*STZxzb
z0`ATJ_!C>WPa3C8*+X<vW}j93)k3y}XYM%e2DOCAsb{%GBkYzvrjA{QxYe>!<N(~u
zO87S60>3{+pKM^nBw9&bzwAar3AIv3Od-=6h$(E44s3lYGjwQ91q6EFL2xH447B*<
z{?+G>2)73-x(=usL?t2w^m<|D$DD9y(5B}Zgr|dIq!J3sxu?e}Kf5G4m^0iOJk~`2
z;!-+`(-mNC)Ld;m8dcj<+WgZLDm3P`IKMw83J>p{znec<Otgs604J)Eh>Wv6<u(<b
zUOl~Vi#h2Qb6!`MpTMg5_L#X1&=IwtQoiQ@Z1zNVnejze<NcAx&0j56=gZ{nOOj#1
zkdfT^_!Z@WaetY05DmbtakKifv$%CIMA-OP>Id=kT<>N=#?`n0ylDpFe#@YgG!0xE
zTP*^d%_R}*!8iT75?`wcf}^?fSUhuw?p^zI8&|G1TVkiip<LT*xn(x}{ZtA%clE1B
zEqgt85AVvyJvi%Msk2bze%qBV==)rv(7EYy=PSr|<<V5Ymkpwn9vH2sBO{d1`rsx@
z9!lib(#SJRQVNW8$GK4TGcOwM>U8`VaP61CqnJ+kq+wJP)2R;S&UzcWh+S(@oQ={F
zxW1-t+d+sAccxfvDKkk6Z1+7o<`22%a$l5_L?3OfBt-~%rfrK+CJ{McjpjmCHv9v&
z$J%U$I+$<22fulSoESwB#xsu%w`5aF&Ze1`?!`)+<g5%ZcFy?p#l9k4%`L0XK7A;$
z*ZnqTbX<L*FsZ+b|NHloVvXj-X?H>1{M#qV|KDqQC`=D2?>Mf6_{f~yV?es8;21T>
zX{gT796ynX-~MXN;-|hZyY6)y7k+iC(#F*slU%N9#rY(391>6E3~cqgpMb!85!1^k
z4+gA6zelx^LV7vHfz0`1G?-m>4g&2CC3#${h0`1)Z-;+e2g`3qREIrs`<gP92Lnb_
z0p|^g9dT^7&9wK?tD^udiNQ6>F=|ErmNPw%P`geEIVEXO=S|(1r<vaZNCJWn%nB<C
zEnu*bu2rrHW!5JsDHY>nktJd>G;;a@>tHO^APdt1S(s-*e1sDJ&O-)f^q>R#jX^3Y
zrsu4;G-m0o(@NF4$y;Blid?uyJUNjR8&OI9;Kwz8Igr$zoB|{WzS0;)rx0p63G6_a
zIKyq|3VM5NX{kmaD4t%SUNU5wOg~0lL5@XB)<jIvq>m^kFI_9sl&0NpJ0Y;Aq$6=A
zbV$GJt?FqwpYf+x0Y|y_<i;|j1XLSgjJ|)H?VMD2>>9wQ@=5U3lBawA%xHFPm1}=x
z_qd5}R+w4Y+Fe+niU%^DvJ^dT9RG378#wQzoIKOmC3?boCSFA|idLQ6?K`n03<;02
zAE>pHw?-Og-pg}w`>71$;Q_+2X?zhcp#j)d!_^@8*Qlq?G`GD_E7I4X?mOPs&eV-B
zt^*kmhxA)ysvr{o_BnDss@`Ov@dmr%pirzz-m*#c<yPFAmDIYa_P0r~M|~$0%VOD2
z#b7^|4}SlKJ45DY;U`<*po3p;S}jtXkxm%uOHdmca1e@dtQLgIKbE42GpGYfU;@N^
zkw4+Rwi?7Ykp5Ha>&(nG-R+o|u4O*^m>xaIUV7=`I8e>(Kb=3OxRI069_Re&RcyqS
z=&kMR3g6FO@h){Db`6TtZK|p4f`qSSV6koZf(qnw5VHs=eVn_H9&$&-Krfp*E-BQy
z2?A@YLUl6bAFmg|bB6HRsbwk;bP*~yKP_Y!_P%kv=ZOF^WS?|v(meyjd`0{t(mbnu
zPv@t(u=Lz}So?j|jctX5+a^5IK*i~z!^Dr4;LIk_Hoz+f&$}(~L`j=edhX`~nzcGn
z`uk-Q&^<Sb=<R&b+`Hmh`o{FKN#91UtBT^<173zMKw=<q!KNDXunitu2)a7zd)nW4
zi-mVOPdxQUf{1M!FciSDOyDU)i-U*khke2LCo};{&rpT>*49>F0_=c4-%fXi(-C#J
z+!+r?{oCc8d8CG>x69YbdiItk)$6onWn*)4&b`tdS2U8&m4O??d6*Sat?VuRF)Jz@
z5mwH=>$|X4LR)K=KQ#<%ZrJKQLqmF^hT@{4s*<1L{NZ$2w%jcKq+HW2wZ4n7=!s&P
z7_A??2+nV}&&^3xlNk5JeC{{C5OE~1UPR35`*?(<!u=pYE+R<zpcg&)0k7&31E|+_
zp!5~3{&jra4w|M3)<l+3cnOz6^LAZtD}<Y9((kggPP0MBjk=vqruH_CwCMcOH=YuB
zlGH?C;9x}=wUm--D3}hHPOjGlXvFdpd0WIn4pfc*rCOg*Y;K?TzY7-x=l7P+K3jH$
z<4r4<!FBIDvD>P9Qu#+j*_{uNNaot=Jou<C6hOVoFI23pRdj}-v=wfC20sQ}`J{~D
z4KfqlSE;?E!)~W1UeLJ^b+U8s1)WDO8GB2A1c}<~0Ph^Y37NK*OA1*90k*l=7iXtp
z1U-<z4!Ld-)P(F^KO|w71)|zOyI@O_kYjx+HEOrG>Fkp!R@2?R>;aL@CVeDEBD$~p
z(>LE0cJRGyWPQ;P){M{nqw!8EhigYp5u=u%;)ep5SWeg@16G}Y5~|aMB?z)g7|G@D
z^BaC$y(~7vP>r7`5Dwnnct6N?Kb^$lo!8E6;ZZq`Fg4+JScvNVUyvzRXk^hW|3G_5
zuf4aHP<sqyl8}p;QWkHuD_e}ZdLTmV%pv5j9IQjmUl0kFlwiy@tOSl)_>VR*0L*84
z8RVyAW?O=t$1lrAwd5@9X$xGZym(b2;_9gET5E`LZo7Sb4CcqF*q*`sVG&1p1hc}?
zqvEaLzjzY*><G723$0{%^q!4rLd9O~K!MVVd+JO8R-{^0`*DZCOb~b3nPW^KlVmz0
zMeZ@WTl_tz16G2`gxJC6bIF}p=gI!pe&HJD{M*caDh=F^rva~r9I;WxEERmB02RZV
zQ2<taOHh%kY`eklRxk)I!ANn4^rwZD^$+&!vjGcfNt?;<cT4beKV7z3M{nv-SxYJ;
z6_hz{^Tt_$l3@6*$`StHP`;3+v<0<a>HvM<16DV-kT`f5dT(jLELn5bywq>ufn17R
zz{#K!1dOytc1lupD3HbxoGFrhPhK+aNK-=HC=n7ny7MZvYpMe|oNm9O|9&Bo3ZRsT
zR_{tZKQ!GucWIeXg)N;$Jdi8DN`)ji{~_jci81VMwWF_plPYW|wt%qGacE;O8DZIG
zFyLTHrC=O_wsVl)$delf4&tO)fsTJ}oNQs+WIJL^pgEni^r!D9br;V{JfAUC))R;R
zh1DXYQ=Y~K%RAjM{aVbcYYnX!=RfQRnj$ZU<;RE}P<~I;aBh+;=IcW+;JV=gqpt)t
zGQ0vp95;lxXzoyW;=>Q{t!>{;DiGTyBka;@@?+)nVQu4r`lZLw>MA6mfSy1Tv#w#w
z?NwUo&nV*{z(1{|8+_Z$%SGP!$L!F4DFs4D+9>NrUbS*p{KKAvd~BTUY(Ik$6id;z
zaY2WQ^r?(u>6q=M<Riq7=(2xc+X@~?O{}+dx^AsB91zUSP~ayF94;y!xSPT-B4=Gd
zK^>!{G<{Qu;}-P-k0`hB&r|Z@6{`?gwn9s|SbR1P7|C2vkRxyed7Tp!6ah`+3J_cX
zA!30{Rgo};(vM;J=#PV(gCw}g;B1RIJkPc%exPan9GXBA1UVK%SRC4)^vA1$isbJB
zt8UX_*`ErXw#pz$oxXb)FK84gf*Iy7wy=W7JK>FEUamyBOZL?!(+TA>&uJE;8<eP{
ziW9`gS&Z5lygClj{}|_w1d_GCHhE*%<RRf>yL3KR_f+c4)?Ub7Vu#Feau?109CqSQ
z{DovhjkjA9bnklUQB?6L{-__S`0$H{VVmVBe85Mj&?^+sj8-8ym~|`#BMo1Y^&`vD
zvAq{Q$rqH!iM10|Va$KbmAfZ??SURg#r4G=Roju!iZv9!Gz!->mn_o)?W}@-N{55Q
ziCino=MiZFWKJSKzZ_t6jLQwIbe6!7Qw3b$<REha)mFLpdN^D@lf^VcJ+~gl;rp-4
z&c9iEfV5;MB#mIvDkc=OqaA9AQRIrsPPmxamH`5_ss3}md<Hu4Y0lCTxF2WHF2VwY
zL0MN2|GP0XLCt_PYGD~O?l$K%gnSxc%Ib%xjoR4QOd!Wx$QoyW;<j=fJa|}MG+g|Y
z`+XU_e69{a{kzstg0*JLE^T>!BJb+j2~k%zppY6>yHgfJxNtu4xKT_``j4oZz}+Yl
zRxy<GMoJ+d6QTWzZTaa%Xc?szv(Z0Zw+lpqqJriTN#K(ww8ujPiX??2nv1^AG9>0o
zyA<7$0ng(AU9Jheq)-^6KmS>VQQ{*(XNJ5$4;58hAUA%&sFlI1t41gJ{*Mie;CU*X
z7^|Gl=npk13tQ0}Xw6JAs)N*n*dieQ>t_yu$MwQGIAX;4!xGB}T|VfFoWBoS*g=h!
zAWajR_9_1Vhq(8S=X(FchgGLKl{jscQIVn&(lFa3dzUSdojoJfDNQPyjFP>xw+6Da
zH<gv09isbseVXU{_}%wk_v7*NI0wh!JznGadXDS5p3Camy?5U#SuO|Yoh++TLT2`j
z9`i;XequzFiqho!S_>rjOeE_VSD*el=J{PDO#}o*T}_Q#V9?ctnK^-Jh(OdK?<ccR
z{Q=GK>WO(|u<Lq+{1hcrAXvPwUv2W{)P60guz%0-h2wnhM?%m(CFe^cug{Cjm>X-S
zcn`vmSg?r;k%ut2Y!}P%euw-|-{IIza&wi-ISP#Y0g?>pJcpa-eVfbMx#8R1n9}ZQ
z!o5RH)k9;>xAKZGx+s#02#2UWx|Mo}vvyC~@A}L@PluoxXDaCx(~M4g08t_#Obijx
z7bne9fi0}l`H}qb`O8=J_*TAWvtn%W3(ZrDa@sby>np1@<u)1Lf<rB<*Ba7P7qX<g
zTr2+T@ZI^{zkrQ{Ckvc1^B{Lh95X*icMe+52~|3d74Ir-GvUbn^3nY5mFJ{wRCll)
zUqC&<FA(oxG5gg^a6;`tsh%WnMjAjwR19TV@C6W1npK-sLmyCwPu{TqSV!W|PrR%}
zSzEnDC?YSJ_7fRUi%ZDCz3i_RM!MQhp_}biQ8c#HWg3S%I?GZmK!|?2PE>T4;Gm-Q
zPq&$ZLY+`0v>R%GBP}^T4qZ;m_+AF9TA8T$cqOnVSt6lC3U+0<&?Fmant)JXxup}R
z2N2?qA^8&GLM(vysD@PP_2?g&PR@4X(q%eI$9tXVj!&iC{kY`3vU`h`oJC_aR(N?7
zyEO}7cy%!eBITjlZ#$Edg@3Lz%?EI{!fX1*Y_GPJX`qXjC<?R4ToFkIp-N+YeYWN0
zb8~Y=k<~ZGAxdNxzuf{J7}P8x6BA{cj1-afu6h6deS`&}>_n%eD3CUpi?8Kh^!$;z
zdA|*D_sffl1J<V}3>>)5k?9?x7nd+#K=f4j&Q$Y?{KM?pr`dpE93K7oR8(}4aEYDr
z@&wuiO$Doo+6g0JQW+Q+H1}q3ct?|JyLOD}AS42aa)z4ihnswz_YXjdw6qeH%UJas
zWkT>mx<yT$SV&7GochpP)^o$oAI9jRNAZSa82i-Re@XdNosUNFnR{*I73;=m82MV+
zp_-TkrE-P9u|$sdUgg5KDIg^BeDr=8G3ZjCWw-v;gvyqA#uIv1!BVvTxw=ObBS%O|
zQ(3+F`N!-A$7c%T8*f-PYPs|kZoOQKGVgZ49U3f<lBwVu*ei&3O_@{8hE^mv403?p
z^CZ%v29Ta{SK$6W7Mvj%KJX5V9ap4yGa^womHQn2tpew+v@rH~xgvNzub!|obgZp6
zv!H|(X}=5@r#1=}Um<3|=2p{}j1CF<B+bNNRRNr~1G=fEd<|EtM5ShaxT&g2hOwcx
z_P$2q@(*4rCRGps7O_(5V8T?RNsd~4_Sjvb7UJDNjQ>ybl1~RjvGK_MrEeY98H`AI
z-RQ<c?&P*BHL&0};?uSEbPZZpKe|tW@9EB%jDxvjm!AHb-gU8<UP6+^X?2j3b6X9M
z6|kS=#m%$sG1w!HLp#iPlyo+kAE76)`gDedib_j_YY(*d-uzpiw;#RsJ{-?0M6kC?
z<I8oSFE44m8gB7r{FvNEl9VSw$B2|c(J>q}kDFz-q)qNb&PPS<8B4M5nN?eEgi70S
zokO}{H^FQE%!2?oAB{Bj>kseXLs1mpFr@%h*O5LcL_$k4T#nSbCy7E=>&xEk@e1f@
zbP7#7pgEXG`KJBq0#7I1(h#v|*Jh(m4yZg3>(B1~c=Z9yX0EL;zwp0BE`|Kd?-eba
zs_*BXHjqrgNkFDD0i5ShZC$0}k+?>;Up`(wwL(>gIx-^~RXNk`(Pvn8h!S6qbeoas
z^$7#g5CMLsex3tS*Y6&MX1#)n3je}Vso?(pe!+TBEq)4AX)43xBN%Y&?M!R7F*OAU
zc<wu=*29m&_&v3f-u3*ROow;HWIDI>zxXv<?J}q<Zr!?d5|u7W6T<2~%M6E^e`<Jk
ziV_^0CCu%JMbNctiv%IP*WC~V8Sr(A<EJ1jTf}H6w{P!{3c(rKr<y2kQV!$QIE*<J
z?AI^p;-7T~c3Yk^Hf|!H=T@sh<dnY21F3xn3p!hsgOsBj4X87wzo`B~9%-^ie1f}+
z)}pects8~kL3FW^F`3CAXA8uBlZd+9h>aUby`E)RORAn|skuo)uY8>rdiaF&pvvN2
z+WjKp$2p=o0;Ixafe=^O!(*;z&hjsp?WYqr=+!*S5Gul|G1K<e#WrB7YWvuh_@4TW
zMHW$xV@HM*y?<}CVSr>gM8&-JR>3<71M85q-}MzIvfn6Z3kEk-qTw*X`<IRb?jk84
zxvmGgZha9mD3xf+?EF5^K9P6`4S@quOJ?F;B8H{5*%@T8-5e%GU-9jM9cR@0&#21{
zo>kvuTIKo67?mB7+o31KvVLVB8hju9)Q}@rG=6p(-w(hMXp2Z87?#$lk^X*Jv?M#8
zquW{dAfyLKLNpa@HF)(DZEEKNmY_V2CiCF2#EoXtzU^Ase^;lLK_YsOpN!JpkhrI2
zJh5@-K=VuHwzG)QQF$ixzB-0IP4{Li4m}wQPZf(-jO8mFYE`=*m1?!jttwA`RyEPJ
zaTQfQ|NA6z)nDuwpA3{4LK_Z?*F5p-pf3LhDGxr>KvK{zW~><^L{Shs*K3V4BUKt$
zq0KdnN3pMI(q9XFA0bw$mmVK^^*NbW>r$`QeSYo!(=)<b=n^+pO0>DQS-+M~5sR|+
zk22fjR%a4fxUVi<;-*@4u6ft@KgMTIlJEaEA@xl@kU$=lUtBDHu6FgT6;F`<%A400
zrY?W8mVZmc0Lg9l#;pAQhn_nwpUsK<knn|3Ys|=O-(|@*FhcEyPQDz?SzdBOviML+
z$Bv)3I99M-PyHon)HDElL6<T`v?>8S+o<s4q>k>Ah-BWLQncFGZL@*f*5*eKU)+og
zNJ!lp7NshS=QsCVNxQV9P4+>w?Zg<{F9#z3dFLL#m6N2o11X{j$5OP`U4$l{n&s6K
zSxNMi8w^%Y0;TkO?VmKgM=O&D7CJ}HjoPa2&zX>=aGBnYOskw*0}$?aE!25%Cya+h
z_0&Yf2doQ{<Cw$JGC!W}T?yKYG_o11k9%(ce*0!C_F&z5%MEVZ)9~M*GQHR=$B$i|
zf3fp_XShCI<8t5Rz7>8~Kb>T%KAbzcn)s-Ss!p^}$*BEVqEqzVAxcT_A}TehZ6?Ja
zk`)bjH~<Mquza+{9sXLp=;n#1W>J547K}`fS1zS;-h7)Z(l9TeEz<Zx?6r%6)jPN%
z8f;G0X6}(4eQNGfF0K7Gq$hzwK2Wl&mG7>KVUO2Uu;bY?C=<}J&;P+cyinVCPzk5g
zCxbK|(WDUZ(1+Jcy#r1ciN=YK1-1F22_hgW!eHgGLFW^^TVNt5_Yv7JB@XYT=eOGC
zf#GsP>c*{@r+ln=xzOiD#9QP3H28g<IYV{KLr!*I9;B<Cb&cTOu#p{w34@h(wRC{k
zZask^6)2#xV>_e1Y1^rvZZ{QY!1=X2-p^Gz!U+eCggjORUcj{QfhpO%yF@f&3a)D?
zIgd$)X-dgz!<vsy5LU24<FE%SbK+qWi5?HUi<_R`9oP4ot8bQQFs)&~+_pi1^|D$-
ztH}X*Ce!G|l8~^6Di2O-jhRPer*E7%vM(B|;>U;QOAH3d#b=G=svv=byb%<4>WK?V
zlwr}2`?k>MYqWaEqj%bu!y`RSdP+~CnZ^mYgYM7G8%%K^!PmA|jkif4{j>Yg_d}80
zlRcgwN(ou4%RFOm%nuK7c@)Y!as!Z#%aojVvKV=8%|6{MaZqWlPIYK2jT>f?4(aBo
zBLHjZgs;LB{`jDA-g$hSsB=<OH2{~gvd0-*dTj(yl>V(9Uv3fgCOwD=D5H}i>9r7m
zYB;*QKYCKJWZ*d<Rmmo8e57kE=^hhKnk}G5!$Tf=4yLhTn>-a{ji!^)_<7XPefbCM
zu^LjmJ-1g;T_w{plJV+SGmrpF`@aV;`3pbLECy=fBuAyxvYhp+9~|5e|LYJk@$6|x
z_iU_+5{~am6)W}LFSmQW-{)U33>D`*W#S1Fg-E%R_!+0UD7>z-GrS0+9OtZmLh)Ec
zt+Ui{des0a#$#B&7kCi69Ch3rz)jTk(4AEu%3hE#k`oGIN(o}wTxgmW1}(Kd(&)J2
z=(n$;_C*e8v%ZX+pNN3EQ(z_f&~YFZ$(&y^;^4mDhoF`Nl^dRh*IOAFUcT@uuKPuc
zCBRV7AE{0ZExI1&+)9(h0^01SrkC|AvOr<{@uAC6dcEf)NBaB4YNjuj+RyfdPZB*@
zqVq3av)AR7%ZZN}s*)sXM0|@Z>o7y<>em2dc26SJD^0{eq3QZ$h3(<xm#~@ukGP41
zWr-m+W;#(eLB3Gs+z*h9@LAn4<$3EgJZI!3<*eFGmG|cks#^PRtZ3dR-%jUIs<n06
z@J9rPryF@Q7CiG1&}{-{#u(KQUy2|V(7;xBX68}b+v!Ww&YAjtI2rDcu!2y(tH97N
z%KLIM!LWgY<k%3*I@lRwRT13*9)~HTReQcWoz5(%R{~TdCKWqS+n<)@S+cRv_%br3
zr<wUYnNCLhI<u)Zgk8W05us($-+7j>enGsQ2STb2oo^>0>P0Ft?CYn#-3$bt0_E~p
zGKHiYHH?fB*N3s~I68rc4~1N%ym~Ys)WkAif406V`92G}sj-4)B%vo2H}I~sWmbW+
zW9Hz<o1p~#su*cDX-0OJk^t)y-9P7yDnfO`CLSjJ;ns}I9zR1%@EOiL=Jvf0!RXrd
z_PCcXj{|fp`WP9#BlL=5V)vAlm9c|k_B!M}tBPpHM(KwP+TQq^ueAhcy4=Yg`)Pp6
z1c2acu%_;hw1Qt>E{Vu~tGA<@!QXn_ABAJjwMJ-PQd&b{hEbj+-$SnI_;?#Z0|p({
zpioCVPFiDI50D@}A_aK+B!b)}NmFSDxLM*rrFNbN(-j~2$=(&+lXDU|_GBBXcSKg4
zu`hFIrq*xDHb9m~bkC;4{Rw>Ej>!Kw+3_dmE`gXFeghy^6Sov>y1s`~M5zW2?4<{>
z+sH+cfeMr6tlJ<Fopbc*6oSRM$Ib0QyUX3-M9aUVpR)qUmf%sy#h=_<9kn{WD3^`$
zhvbv=_Ma-OnrBfKp1)(nb*eq+-PFQG{U^1!S}HAzXYmHsu4eE;kOB+#ytmb=P-b#0
zb8wwG!>_C48~kMKNSj^gd(u27eCw@<CF_XT?!aOx<$W%7v&rUYkHuk9{lc#9MazBB
zte>SZoN*hj_qdnw6nz-s{=QDdwU83qA6P|Fl+-LK7Tb9H8{565ukJ9^e?Vf8;g7;3
zB{sfwF)Hc5SBTH~UvHus3MMIa7?6T;4*wR{fW4^W`te1}gpx>oKQf2}ih`P8-5)GX
zFLW=ayp8jc)HIVW5|8ov)VflVqyRRlsP!ybVMovBpml@1gs(`$p7%W=KO$iKo#x*i
zXjDx;j>&SJVifI3w&09(B~Ef!EtgMiT(xblCYO9*iK61+vH2C541RK*a$+e;aMTSV
zN25ZU!!uHZ7-V|AUz;Rlo}yfbd<x$2Q@G4$5nS)9JEGM>PIN_@M_kd{=Uc9#)_>fL
z-^edX<#smEVtn=r-BtiDMVJ2Y<Zyv7`k$e&oGK-<*<5$?$|dCa>Gw+YhDq)k+ivau
z^t?i#xoI{%%l-!gi=j>M*U!2v7S<?K<|$7Td>sTKjA$(y=N|Bry~Gk*aptUAsP-kE
z(2AGI+%u7zc6hURw}qY8a`TJM_EQ^DKw^hiE566$dRJ1E021Vi-YY0qBA?2R{9G-2
zAC~k0cC42;ZhMI(G)|9AKxhY=NCKE@C2sJQ<DmETE@aenU0g_J&8`2dCCktBI29H9
zn&TFfOFEl*l=Re5;sHgQWID$E%Zh7I^*?)jEP0vx`DihQ1L1AEmlc+7H<w~NCPdFZ
zojLgD)4c!Zr?{K#@|o*MWhl2*t#NCFkh7nnls>&gFZhS5jnt9bKgobMniO@*E$Ow;
zK^H+_TcbFR9sXH?#UjZ3xrlU8(g62R*rjNC?Y0p><2Fe}YbvXaeo1VhKDewc^mDq+
zsfVp9G#-a3#REm}-}^m($B(hePkyxD2Vxzy>xhPfA~YNV^}zW6+{^cZ(pa?xAR&a7
zyVH$7yiHnLFJcuai0XcoD>wCm3NmQ9<M%)%F#WjXvD0EY<Na;>INR4rMH5{Jqc4r?
zO2xA(I<umuc)@>+*p6cTyVyb$>)}Y3yfnv<n>79X=tt55qfN*BV@2hg;{nv$3FctF
zxp^zoh8=EU7K+}7KP)zJ*>dvaOEb4E@FJH9c5kzT&kN=-V|ZJ??C+zl>>oL9Jrln$
z!f8MwFdPw5H*VZuhujAGuvsA=bzbee&1`hL^0HPXKqe;}h!LZKhM=^`=-B;QR_E8f
z8cKMu@}3+0;lEt69xZQ`8)N?Mu~5}CddzMdhmTkX-@#ZA{BX!wI0#(DX+LsMZz1-w
zqZnW-kDn7cO>nsa-=C#CTmC~m_Gpqn?KYkdjq6@=SK<Rl_o0(8nhJ1Ag^qk&V;a4k
z$6oNgHOLXsV#_ECkbH8!acJlD9w5?#X38#2po)1;HZf#ocxpP-SWeCzc@W@?349J|
zzN#6@rgC1P=@$lLJv*M7Yo2iB<Ry8B8JXOVV7k`1Ofha_+5)iy&>}9TtzrB<mz+5Y
zSBU!aCp2rULUI-hZTfGIt+x^z-p`yN!bFrFyeOqUwgg;*V2sE9t)sZH`Qndo_7u9l
zG>`b&ZM+1~$yko)y<KJz9G?^eI$|?}!D>u1Si%7b7y@l(nuyr`Nl`6-Uwo;_Pbf2a
zPiZcs5*E;7;2cq=C?&-W(kr1KNJ<5qyQINmPA4Oc%SocOK+=Y1C@ioatA_siu%7b%
z%L5I2j0-ep23nST7H?u5nuQfozRI;5%+|<U%b3;a)2icH-R6y~vA<6VDj!LLqbA$~
zxEuRi%G~(+cavliUyqNra6p=a_jpbQ8YL1K3uw$l1Ntnh`<Q*sI@#|8WH||n1(cE6
zOIToSiSm6ugsuf3WsChfg8?ydJ%)V;)%O`k)@Lhga7i;-M%RxhI!11_^8`<JDB$St
zIY4_R!oItV?&u^Jjg)UScqrjWIWttNqi|qu_oT9ybkrt-PMxy5$goj)M*Jd)+3=_d
zS$p^Tf?EloVy>p7fg>Doq}24AfWPopie1;M0N9Lg?H|jT%0UT=3>6iUBpm2A=|-=s
zf$c73J0n5Anw*@3RE!r3+812=UNO`>yJx*Ppn#KOsX{|^XEtO#a{JqJx{$j>!rb=C
zRR9hGj>IePD&MoL)a`$2@_16`9H<-2z}4MWv)5_!uqB@rir{W`m2h&+-(`n~Un-lU
zY!%KXoX(T4S_GAo5=Hi#^kZbTtwbGk=&G;Z3ie`9R2x1g6ko5jaPzl?q&lX9+@&2j
zvINRa94PesDs#AX;_29tjrShd9i<EvYqRDK)HXSmm1;CLM(-0eixz9%lUEuSY7uR9
zAyQ=qzz+qx07dVml;5U&I&z8txasNlx``}%Q?!GTX@R?aVA8cTkn(@;hsz&|S8AS3
z=^0|oNk|j{;94FmiYP&Xfe0v(dZHA2Q(}9QvE`oMlkD903>jefZr!7A-_4w(wi5Mz
z4rkM>iIV4YPTaj-Jho*+)vB%c)Jnf4r$|K_y@Ympr%fcsds)YKv4C1m65OQ;gW^EV
z9gFC?f5y(K{mR1NtXNF=g{V+axO33g5ocxgF130)moq=r0{@Y%Qbk$$C*0ZPS<e2a
z2SYVy?rn7dEnKq1>b1}%E>1>M?<HMU$CY?bscR?$^aRcdYv`|x3|tYNC`=?;Y0EQS
z;@%vqoN^XA(hjtCc>s~630P;S5-s#1`R~x9<xxiit40n_8vw#V#t54Nb#{;!8=Dji
zXdFtnkIk;p8hiRVz4W;4??8ddRFmMSpr?+W5JVg1@pI5!haTSOAp$m}a0Q~4Xf+{?
zxw#x2r4*&<m>^%l>8k)I0^$$I+M(nN*$;wtI&4i-!c#Qu2j&Q$qf>z7(J0AqdUfWY
z=nGfh{GJ*r@#7oj_e~sZPz@pAJEXsnNgSD&jK8V|bLUNKKsx)7&T=wWujY}3Mk4QZ
z()-9-(9DFB3pP+d)djumJ&zSHCEL<(-VG%z;^ZcK^{~5<n~nr(gx9#n$T_|feA@8)
z@S`d-0(c}3^$<$HNKy&<ewlzH5+OXo+LK20j6ZjQM5|~e&xdY$D%~bMq?X8${rS}$
z4vNDp{qol0t}K^=dqp3P*<HH$aY@(Q+KD5U&5u&}<|FI_gz_ZeE|(l_`5w4a7BFWO
zY#-T)1g4Dw3^EmjojB@tH74lW*Iv>f7Ks3E(E?{@U{+A&oB&ow52;iFSRo;pQw5I%
zT10+=^nB76HOls7y$;}h7<8)u+?R;n<t1aoqrivn_XuT*RqBk@|MhmdKqn26Nxyl+
z7-FDe6XtWjGC@>S3BoXeJNwi|?7bHtLZ(7`%gaW3u&^<@4J^!!G|W$TXb4sENMOZv
zY(3~LPvfhEp2eiVMF=ROv6NM3`U!6B-hdX7bJ@0ot~#}N5Pn^?>CuxZ&v~EM^lrM*
zy^BeanVdkPlyUg}>5SiEfM&=`!^rO^64MQ6A;{Y=b<>UBjoO<w);}x~t>ZquT+UPr
zxDacaDfzp6Mt~n?*KJ05zyIDPXg5Rv!+%-7eO;-?l5WAoWZ9<34U8Q@O<M64yRG&f
z9eS$_3bLa2Z)ch+U!8(u*?0cMDMERpbfG6&bP&A?#Q|<}a)7k7qvTde2FmDA%SEqM
z<kkm-322{f8$jr|sp5!9Cs>*5cS!9ajLflrxv3-TM`TaF3n@gxYr7j@QlST2qiR~W
zbVO+Q{Pr-Yf@Oh#&0uL7vQ~4N>CVJKK%hy4`b;=KLqm1>`Od(JqM$;QxD8ROGB7bo
z&Adn+!ABNElPY=1`8#EwArE$SN658PijH?aRCJzPH1_ghegBJH62MO1lIA+r{?Fc9
zZ?~QZ982fwA`U=!fQI!SPa$qhwAW-MBp#r8eSx%4RP?4S)eUUk`}MZ@DuA7^@J9L^
zp#vx`>E1T;ZoFXI%rWlhD_SF01r7M3N3(9-=mcGEk~#Bn!_roB-oGFIvBh+6L9&7D
zb!Xol{`>Vfed~_xh(>kxMsZmLKpE5TZFxMCGA+d*Tm&S46z#^W`?yHA+32}JNp&gb
zKTMOe;CH{?SyY-xVp!b0Pkr0%@B1Sefi;@XzFZ+Zr>}QE*fc2|UcZqxU15`Dq|kdA
zwRZ|M4;#EsbgO%}^_%~8SE+nne2*^k7s2uua2EvIviQoecq;HH_>CNLWwE#$=2E#;
zeB+I-r8WIFGIrD_qoy**ae$dM*3=Woockgj(c1g5=p31iA0MnsI^L02_nE@vjEYM_
z8E4c|vP#~JUTlbwuF~_y#{%uX40%QTob~wV5}+dPA)W4U<Ori_bp25tMkkIzs}fSj
z{~d@@Q8ku(tn}+0w<GPN>!C1=_PeCubCH{Ed|M48pnI@Pe!&#jz<rk!FXb`H%U^f)
z$sWmG|Bu@PpCTKTd*K#$z3M;qJ^ARHU3d9X$uDB+hN%s1C!dZg=ZPJ}+4hyA&SP=+
zL7`1Azf%~$zf{;Ywl(A=uFd;en)m73eBQV4;o)JfAss(fO1Kt$f|~V3bL+YqufX)w
zi8m=3{Bh-iW+;QcJAP#`idg2F*@YvrjlJ?#Yy#@k?zHi%1({6wFVaCW?~hgDEUGvP
zfhzP@vi~;M5sy2_1eC7eBaa|=5;Tm|_J48l<cwW$``S=Nl~Q#ay<gWYAqstq?eTJ2
zL2?S};jciZU%!Rco9`j$uLTFMatREZHOGFp(cj;NN_-9MJrNbNLUR|kjrX8GQR?!l
zhc$LJR)+Ik1Zd5?5BDxD*2Xf&`@kRS6VKfQO$&k7TBA|s=dQeI9N5CJdp|>tB!Au&
ziY@qYWmb8ZPxMDoCh}>{uXrQ^(DOC3>#>+6tansusv<>2jNHXH1gQ|PWK%6^mlof9
zIu@I=LdSe*bg93kkhj`#x0Bo5<hI9Kyh*pEcBP}WVX}#gvWzPiKKI8)ppp**AmDOy
zvM4&vaoBLh$Hmp(pcRTps^BZ_uf_v8Zw`^>oSbz%E|`-mhvcBVp-652<H848XBJIE
zpWx}k23~d+M)ueT)Q{Z+4u3Lx3;CJ(KU{N&^`0tNR<2?DVX0^VM-SE*_$w>6ljvM?
zElTh?>5(G1EaV>ek)~2ntKPjR?W9wh+M=|OFQ1Q`HKbW8R^)u?jqMw|lf8cl(AWh`
zjcG{zar{Al9q!!-SdDIwtePa0u#JjjHHcqE5E^JagycLwaqe~|&4}jY0?hou%85a}
zw_y#wDE2Cyd?&Z^QOgEX{%)I=ZW~?@ifR7vCa8SK)zeyrC{@@uYee4Z6H#G05x6PZ
zO5#$nBegXHd?~4p$HI6M8<Jb7%VS49Uw64Q&@L@qg1S>i@1GZoAhgIJejDiioDJO@
zLyiw0Mu4Lil2bIK;>o4>Q(_NuZEw)0&Jb%WUc&ccJ?qizig>RrK~Jn({{3A>#vf93
zM#Bds-O?y{Owx6jg5|ioZxjA!#F<C(ig^yvDpK(SwMknu5Bik3mgXK$J1OFEvTG58
zy2hZa)42eRMTe(<5v0g9<<tsTkLgwh#u9~)$nZc}0Y(ZS?%w{~MoLND`EHu_wor|{
z_UP~in?5%cd(U!z?e-e|!z6vC-~DXaK8?m;9Fx?q>0Ex*|H+l5?@%zrM0}HUYwb4Y
zB`p|gkt=6LZ($1T_t@z(y<<pf)U|JDxOM#G*5#{nsxIsGj(p?cm~(NIBnlaOHXZE0
zzjV^_cghu)_BqbZ$7T+JJ<^-HVVBV83gTN)aI-`W0`gdj-sFtBzqN+P^KBQPntBjO
zL#^6C;NjbdqmLA>K1&C28LDYA0`5$S_Z(FXcroZmW~N+_36XrV)$6qhvc7-Vdn(Qp
z&=MfXkUMKIb45svD1bqFAmw85T0+UF)<Dzy0YFzCY0-Tzv=7#4az{T<lQABU@OTZu
zq?lQezlvS1clus=`FM-*tz;wh6ve#;wRa01@VyYy6#bn$#bZb81?R;3Il&~_gY*yz
z3d@;+hWG}s?4t<G4Ui59UK0hdlprlSwBWA!O99rz*;jYB^n&6g@yj-a6V^)dm#u{B
z2!5nl9o(ycj9~R%+;A*pKw+!tPmv4UjC`<{fz6)>AlWP+-q9dBAUQ$665kp1%X662
z-eD|EcO!q6)3&*;MU5+`ZgX8>b)xK&Zf4X5l@rsC?*Y9M$|m5L{RTr)F1{h=BLI;A
zI7|}s9i==6Od{9P0+$p0_=WsjoB*M5T?NBkmu)sK=~k;Mq4jV}hR3(z9>I`?c=5P=
zxhY1u)Q?Z}3E&A5ot@c<@S}q4nnLT>+hnMsAcTZ}Qu<l^miTF8@vmJ}^v@)=SlVSQ
za^r5Mk176sn>Z)0$+=a}c~t;wX80dJlFH{9+-fI4={!&NAmxluItY+=D<ty`0lEkK
zk}|W-E4BQ>p7lMM1-HMjrvCwSzu?AsaWEVpex#h=bkTv54eaCbZFINfyRD)Md&%jQ
zQW<KHCkBr3D<N;tUFyEL6uF*IzB<#n%G|@m&3nJ0;_g%bRSb8D!(PV%V8ZOOVmd^L
z-Z*K78IhV=|FHz`$a#LPZ|kT0FT)>3tJCAxvs8AiQuNMNf-OaE6!#${XYUgqp>!7-
zQW^TNfi0{2$oqNbHA(dU5pJX^JV}hTUH1l2b{n2N;NVyI!kCJm#0X9;{0kO`LEDD9
zTNOl_f`E<XQ@L++&aE}&x0KgDwrb0Dz9_EXkS#a(qP7Pkbv(9T`cF$uC3hSbU%q^K
zX(t3I2A;c1zMzOa-gjBB4WrvVvinxC3jZ_v=mSxwST9@YFW>peT5rMR+St+uiw@rW
z`zJS%GVPl7L}ih}=isD<+KP%Q2_I>Mal9AC`H+WudO*2fkx@p8$CzB{T)(uFJezcG
zaH1`X^dFH5z$0X6iMoy%%i6?*wYbWl*i%h{yNabKf9N@i$OGF6bqu9kq-on1ZC&ZH
zUys_r>Evj7Fjqw3L;qvHi@Nx0-RC$@gCM>MMPy_Zu^-)cJi2}2>BBl5?3%J8#p3D+
zD88#rgk0~wY*n~k3|VBe_*NQ!LIZ`|31mG6Y`k5uFw~+}t?ylZoc7uuX%d{9hjEM$
zngVIhiIO?^n0-(b%~+`XHglAc`rzzFQS}UW`0$z!qaV4D#}dv7oeZo#5?HLF_JXBY
z|FHZ3#C6<g9=TCJ-sC3a9q{=FkC8ICRHnDT8|!xaXribef&44cwN62^C(@RaatmoQ
zih^5H+x5-@?P(K3WS?Ky)$~7})}*V3jE<`bHAObU?>c}3@+$oTjF$X=|0L9Zzl%Fj
zZYeJ>pDn-a6hf+}sOXYnH;QCI`rRd-6sygtCEJuuBD12T0&R!`*$O){_riX5zOGa_
z+c3HWdMN$iAZ|Q#GdY1R0tIX3cXS>dYE^0B-*58$bN+pqD$zicDa!)|RVV<sJ5pCp
zsHB<HIdsgald?V;OA;m{YAWp<rJraA^eMpkE~e}jBx){1KG_i&v&_BVA)zu~JC$l#
z7P0@)K1Pw4T}g6#cyf=zcSOla`N*DgD?MD2BiJHlgZ+oQ{(OXEO9>EN%Z1?(yE8mi
zh&$j=jo1Xx>ci=S)F(4kO$5%uA*eaWsw7Q92TwT`-?qvl;Yc~3c|phS#&WOnbj^VQ
zCvC|T8+n(p(@EF)V(Rx374Bc)Kg#WBWZ^uz_=KIOc|yw|QIq_F1&8lbfalltWq<U2
zvex&>g}&^y#DBk{AGqfzlhmdlH=3J;V%tZbzPDvpNthjyr$^BMvXow1G<c<R6WE?(
z|1A}ij4u7d$A-a?3_JBsbqB)!eDP|iM0ln;O%@!u8*Xv-;ut;`e75Cf3EW?)-FG2E
zAh39kJ2Smg0@DBei#ZTFGrOA9xzROH7{D7q&X_OwQ4{|$Vl{GjiK76hoL7SZ@%f8X
zVQr`lpg|Vc_Aay`l~!4;+C!I4_CcirjSKD4w*$iTB5_1a@sFfCE+wr+Y&dp^3gt4}
z;1dzfMpQkAVA@j^Y{R;p3Na6ho>HtaOmW3ny6%3%Iwv9t!3*{0(mz65yz$NNa*E<4
zM<r(?4sl*0Sa-F(IbIbR!x&K=I$6&&y1!`Wo**^9o?~g@>)fZ4PpRy<1Q@6IR(uzT
z`?(~1EJ@uZ|6hn2&%W+a7i?(`RoI$Y+wPs5`J9W3n(dSI?oVdyT~_Q}o44?<A6wsB
zU=@DdVY8B5z>5~@=#Kw97*#f<@S>>-q1}F{br2fKexpGJZvV7Q7Sz3UyeqpOb{Ko-
zS<?G0Z!1W!HPz5EyKF{p%6pM{_n*cP@KK7?V<8Kob>lf_#gvV&yJH!u%v!R*sDTV>
z$I!#dgX{lcqPeqN_}_wi<JES<)pi_NS4>>8CHVv1{OKZ5z28O&v6sgpcQ_9E7n!`P
z(JD8J2h#@!P;ZT6Hc0+0d{(5R@pEV>U-MrQfth^jOMYRf882IVel@EL(~>(e%O3lF
z6yVCP4|@A4wjzS)wB>Pi)k8qSvL5OR$P0coF_HT3l?=RCGAe}T24#pKGtSKHPP<MW
z4W#ZgqriphQ$7Bu_LKA%IB3_NLVi+?k(TAf-TSs2N7W-om%x}+dnFLTi0)NelWi5x
z+|m+n{zcyVexH#t0FFrS2O1CGu#8I6rnlpTr3-P8)82rr5}y89WX!Wje8$ehKOgMO
z_C+_o6UgaRHd*`n<ML>*e|=Z=r(Ds>sZf0vyd|&nmjuF(_n6Z{$6pXFQ6=y!>4ppI
zS?1x|l#K)bb9T0u61V%FAosE^)D+k6x%oCEG6uKV5%~o7uDfh}VlPP3Gp{K6a$+j4
zD8*J1cl`c1a?ERKxkOFtfbCT@@8VR%UaQbM3vHPcAdXR5hrv6=8ZXbrBg9QF#4R3J
zB3XIti|yzUV=~$me3{2fL3y(NU-ya1`Q$~>ciGZM5`x>W1umz`2j2GJ!?#fs7{c@B
zxpm#gW~LUNgJ$GeJpOHeXJ}|)bduQ1G0=rPi}bN>e?K6;%RiD36OzH=IAo~dPK4el
zj1>qT#Oz&8#d(_LfSobmz>uwS<trpwl~2a5GrEA5^Ge-2DqB)v=1J5T--apt!wb=x
zfq%W&8O}2Sv!m$!XVi_T?vmHCa%dj+m>(GesOm5hi3I3_O4@S>BJXi8ChXr;n>cFG
zfQZ$tNE_rN&h1E2vT8_kyDL4LIHNPZMO-%}r`9(+cE8N9Y&*dQjCs5_Z!CHF8E|(6
zbtOA`#x;!b<Y_YQ8mfn%P!AtG^s4he@5<?ZehZKH-47@Do}Q&W`Zd7>rKB9xJsli{
z{`s5Ieg%nA1r#47%&n+&xe?Qp`=9L{1Mab;YvtMAyH7t~JQRL0EEpK_i`GY20_nLr
z1O)=$cHJFd4ulj5NKDZk2B$~)hM<kQ(w2T@Cc{emCo9v%#vd6nx-1^9_A7eo?6CXD
zum`4L*7!e<MisWZ^hVDC!Ha>C;S>=sbeU@#u>&HPc=T4^=BEcp61ROka9UjUCKbCH
zs%rdR^%d8iThly8al+&E`vUD;0Vdw|`6rJIvx4r~^q4hwS@3pQHG?+QZPC<i<>kte
zl`ZM=)x?4ApW#+!dPe#+bhPOVWZmZdfExdJq<m^!yLVSu_pDzDeRZMdXv=-EHnYv7
z0f;9sn?r%F(K9to_^JwC3ZP?G1kq(`Hac%Mx|n0L^HOXPYZ?~f!Ky8RN^$m)4qhMm
zBF(c+0&V%Ur{8r-i={{bTyULHgln7n%Dc1=963bTb_>yOna-de%NQIx0?@&sma>I{
z-(VT?Sosqt-nE{9+~>Dck+)w7_K|d4JPjx3@m;M6JmM*W5JRbmMHJ9_rmydpPl8zy
zfsUfp1Isv_kP6@n>63vfG3ShHwK5BmnPqIH-3efhEA6WjN~xu0aP(6l=nyF=e%9^$
zk8@2W{pfE$mFGb16VVotSVz)d0|ZA-gL2)ZK7iQri1t5`g(tZJ7J{^!CGFQqqlOn}
zmzn+zM=N!D7wBHn4iM8~l+ssXJJk;Piem@HPU_NC$2=6V0PtVMAlrcL!Pe_0eE&Vr
zs7(LH?YmSuX1$(O>Des=PG^gdF6`5G3xiEbR=oi>2EeU#r;IpFXU&FRo!y1pqoiIe
z)l@l;1O(7gWW8^n?Cpw@Pu)&QI{=E5qjLNJ92Z*84pI^dzA+rXF`i_so%LOUGnNAS
zJdDDHj3EKeQ)_=yv#I3mr4KMG+zi2(6c)BE5BdSy4P6*z`eydZVClA<+`K8%J4J53
zb-CUJrex@grO{%^9$IIF!qwEhPa<okPQFpgt$saV{&pa_xYvArgIFbPK&KU7Nw58r
zlkpZFK{m}RNJFMSJ~*53?(YjWX~^5y*w{HCPeioG`GOnJxvj1nd3iyQwErr<#)Y0H
zR;Oeg{(XDT?YaF0x$r$15CYMD%N$?Bk@lL^`!#Etc9*TKvBr3tMp}FWHNey~E&L15
zeqc?1-qA;y+%B<*aqB@1=*li~;ZcfH<uzD9ogF0>wApl1am>GnGB4?$LMKiHNC11(
z<_y(i56{+T*VrwzGSn<iGfJ9ML{Q1ehdHp6Hh{T%)y-<gEmkIoa^{&{F5;q&<oYCI
z(FjO#+LwqejFmJFu%9a&|NG+3T+u$kGnDS{N=i~CDL-0aur)OM6E{1AB&eDf<)wm4
z;KE?-10hg$QLw_4(Tzn?Xr{|7<{!GF5H(YKluXc2SZvTV*}b%we;G7R!aT~Q9wbbk
zf`tlw_vO>)FEO${*^wp8gz)0~b11%l)m#>FJ2*q%kErUh4k;_z%&Fx$%}0>tthD`;
zkO$>}Pdvq}=US}m7q-m335n#!YXY&Z@T|AFJx5+7a(`fAX6;=Z_hgwHTye16j#YOM
z&};C`{?vb_+)s4FTAm<L^a#XG7LEm(kiM2ND=1Iv^)0T3rS!M`ua1Y+kKA2<vip&T
zY!hHO3Le2NPl4h>axgM<!biovjI8K)5B|)<;=<yqH>~41EK$?Hmi4rhRAt0;za?MY
zyLYD24rOSzTbmsnZ76;aipF5W&r#s=g)-w%9y-Iq9mdCLO?tnKC)(><xYb*|DYUAL
zd}DG(0`$2RBU2F^j3*h3gcdHGP>(z%9w{16N5v^8J|T#EMyR$TL&<sIe;URdP}-|&
ztedpsI!V|<YpDSv8Ox#bYG?t?E5?VIeKIejI-lpj7()=<X|3j^K2x@Udel}`afJzT
zE|9X9SzD!7s;FPPT81NyT#89#g`7aW>k-8)maKiOkrN#m$<HMtqvf`=!~+=5nOitV
zAU)y?|L4aMQBm;^ZHUJtvjm`}DIch3u^ig3h8R~)6X|=GhJm(`!2DB@<`Q2I8r_O~
zD7P|;bdCv5Y7}kdWNCSK@}zc>z~L!d$&kQ3BTsXNnC%$C3|b$@UszE3uT`PS=R3u|
z>pZ*XmV-7KG(s5z)>$Iu?!{d`28(Ir8+Ba!CQY)=(>&bd##}^55AV)X|F?B2&H%o@
z%6BylGn5_hEjvE)NHTmntM?^^CkLE6L#^Q<lhz2h6v+ZdyFKJ6+IV%>67LmthAglY
zg7~l_uj8o6oQV2Bt@hC6p+u820Ng;a1PC+5*SF2I--Q+itAN??)ns88Jj^?sRnr)@
zU~@CnO!Ld$kSRT|X_{mj#9Nn?sUlIBHZl8LXxI5||Ii)m$ay_;tug1u%rGVna;%^$
z+}C$Iwhb-Gc*v}Mb^4aRV8N@C`&24-S9ln^8|hEv<vMvh4e94}H7u0-<+dHL0M3y&
z=5QxU^Pc~(5$FtsZDaoSlX}ti?b@!jD}2M)UUSmey4eckb_ia(V-5tiMy7McB>wi#
zj~a%3w|AXaQ)`QmwmamnfHv00hrqsal^IX8X8tpQPX5Rl$D9;*d_y$QS+2@pqw+@x
zS|7a%Q5fyG|N3+I<y-&oq~#C4{A@K)&rplO(8%kU|MSk|d(FMXnxe5J-NCC!@6moq
zA^3#)Sd~(X@R~?q7u@7@{`YUB7ag~#;nDF6tJ4vW0x!`qqz7$(CKi3W^cn1CSHJzx
z47heov(@0g<`S22@!Zt$LPyN5wYK)6)=u|<<QhpLG(dWZi~1kFTu;zK-KE}norWW5
zvnew!#*uOHgu#DZcI+36^UYOP+OzGtU0Q}xUwDY3s|kURJy6c-_EKq2jGlpZW(d)*
z>3yjwO&pC~&rbdCOC0bCJwEO`2<F4H#n+D6EkIjm0J7yRy)&Q^yaPpWRK}2qE?`0q
zYyF|1{QWvUzVwQlIOX7o_62>L37Q0_q_R7OV8RJF9c@J;dU|w4Wg#*}<U$;0Yl0M2
zR8(eK@l(fPiy;4rOqb_1eKO;a&rp_MA&gvE$b#T`VzxE(aEBVwwu-9k|HV8t49~Q-
zjc2VZ=ai^<QoH9_jP&kLqy-xxRNPlay@s%<kpo62cG83FzC92>Q0NY<hM?cDXpD6-
z@x{<1DN?85f7h4pW5S~GRy7B`s`3yzUNagU@@KStzlappL+Zz8vWbLwieba>8t(Ym
z*g6m%O!WVouEm4rPYAzjy#Q%pLTPcDjMnJG0&DEGbIQ{oGQe0};cyTPtNZ_VfpCsP
zpW07x(={zZIRXbzWT-LmBy#T`OwZlhxR1PmiJbsrbm^Y0bad|sw>U5@Ir;67HJDM)
zKjs~!G=5t6{F>Hq;p+^gnGx&K|F4l$;tNo8fH0&J2m$Q$G*Zoz4DQxD=^vsSj?|rR
zV=k|?)Xa-_DtceD;k>$V1^9+H{oHsxmQI-KKl?NNX$Ew8!KB=mm0JGd2fN_=ilKaF
zU;6`@`^V4ncT+&9pT9L_jU?=jn|JFBQoDO*U83faH|-8QsS$U=^ww(M6}{hA?&D)#
zQ!0^hxDHKOFV~;aeiqi`FrxO?@r1>Tuh+KC%zxOg>aEroI>_;UZc;!%O1Hw7w_{xu
z?}71v?{MIsH_cymr|0MQzrJo|@%!J|pfY;+Tj%!YFD4v+_Men5`T76<7rYgpwuDOB
zZVQiIFzCWBb+=Mc)n2&|%YMX$3rU;zt#9Zs0~VV)arX*|`-|T1V|~p@eR=tRk}%}a
zBKXSM|C8k*KctdQ`M>sN|M$lJi#<QkmYw<6mJkp0@#DuHK|%4ERs)23)ZY*vw0QC2
zYz8dsy1Nj{vi%&KdbuO+`E!=g@Nh_JC`YNJ#o_N_Q5A`=j*kzQ45$XxJmqqSkdCfy
zl=V>aT~}8{7_i#sqV8vn4N1SAbh8jwdfpxP3ZB`+{>X((_Sb~FU2)tqvhk(6<6^4d
z%#!kQXa_|nqvd9RfSv5Z!h%74jPLA3U&zLN%JK#(f%ew#i-i_Zy*F`~9<GDg@bvI_
zUK1`AO>TGlwloqHwJC;5<QlELx^O6u)jjt14u?vk;q-7DpWWDVb93|cTed{qTD>(&
z#ElV3Md({6ce90U@7`L{V_Lh2CNwOpq^L*)hFGyOP_Pas@7Yhq-&l6-G6dcD?3pvW
z`=xLj9?g8JrOTHqDf0&o0_Ui(XnJD9$vEwU+EfwsC>a}1e5!i=u<P@#BS#u-p>=hC
z-7ZO6e#j3-B_}8Ibb?REE$qf<T#F<4jbSv)>&nW?babh`4=|Q|dRuz>5$(ao#9MiJ
zd9PE&S>3s8KWl1(p|`=H$RK5lhSN-F6_h{M&MCyJ3wEM~#eo+uUPPS^qN4ho3<;!{
z!<icy7}Vk{7>+u%rhi+Qdk`EPyf(gE&|!)PM%#O3(NN^2<&?)B_VD%9&qg{$SX^8@
zvU2&?`S}NnmMo(KAX?qGq7r2g=O`WUmkK2gw_U8osMZ=@tqGHO04?+OGZGT}l=w02
z+<}f$=a~frvfmtPPBk{X0`;I<bq2B0L8Mt0HKRa!tyvi;MMiImij*Q4q^4F-Iajmn
z+_^bPx12lh%9yD3+lSkQ&zw0EDp0!L)nT^3YL`66+VX1#?1v9GqysW|Hm6E(F8uh7
zWh_wNm%cvQS>)}rerT{4v|=u9Ztjx0;fcPQ2heEnZszh8FNC_y`MBkYyQBhnFv~sW
zL7*^cVKhU|PJ>q=H3yL!2KCC4aZ~%zj|GxPjrIt&zb!4@W5FMI#rl0DWpU-rPx-G|
zvqmQD(h_{(2nsTzf?YXQr<Hi_J|K$Koc`rHxl<U~5gh|VU>hZmUf`eW#fi&RT~kxq
zx+}WQKmcTDj<j{_*Iy)EKXgorC<~lrk75j`@PjQnJo?pd`m7?j*Oq65$1^&f=tk!e
zt-p_@Tu4zSsU$;mQ8+Z`c;(9a4I4uFtonBg38`=1y!lY~wQJXu6Ev^QP7Mtdn=Yb?
z7{f*i#Zqk^`gTdb==SZuH>_X3d;9j6!?Wz7IS$kHuWqg?O)l#y^F3hodiCMA1}V*h
zm)`|n+ri9i_4SL{&Rx5rznIPwLMZ5=1Lpj^yu4ckY!k4tE+utzbZB7XC;<(-e$%G#
z!9k;}BiQz#_*Wr=43j>ds2}6{*xh}o<XK3F)#vxey(?2L6s#mrx02%G#3rVV8^e5j
z=*V7np<#Fo1P@ytdW24b@|BT&Ds5K~gKZS@)9Ed^eY6OZW9@M6!EM`)N%--|0I28b
z?jFJ{;<|h1&d>;f9Xn27!&=!p6)x%?!Hyd?g)cFHs!>7<6CGWI*FF_ubIwXgD5$DF
zY5S6or-#(fJ))KxJ~Y^trL<%jb;9POXJnBE!-{zN<Ow~4fUWe>rArTWgS1`;iuL+`
z{&{a6#47F+j7;M@-Js%&vRI?F!N-^EWMyqG_|VxI;(OqdL2ZN#M_MN_L(-3@+a09b
z1jcTQS(VQ2%{wyO7cCO>p0&Nqe7Up`S|4V%DG2~udH_Roum9ZMjoS4w=Y{3vJtT6V
zzCY^8NkvsKfqc*><;@#6>fUEM+U^5K?Gg5*Tzo>pJ3u`XxlG?5%Q74Vg16J$Au%!0
zx>V9?sJZSOXn7Jj*wJ)K8jdMyDqw>ql^SCqRP>xnYBKr?u;roZ>gv1wUESTAHLlL5
z$fTL5`DeCe*&JTEYE`r<#a3X@TXe(Xlj5~>e3$Orx3Anp*YndSy-omB6VHF>eE0NZ
zAy}1$HaS<&7fQ{fRIZXnplKHsRcjv^_AE)FLR&bJ_syiMr>D72N>cJr?zaVpPCvsq
zE-bKsz@OWO+oDH2%TNw0;66CVo5_${xiK9zA?;AD_k<kMdzasHK8qPY7se@J?7E7U
z_Hgd}lnV3Fqj?eP1_n{10P;4M#|de@`pbKc$8pxuWrSmDsO6H}Wq=Qay*bp3`fvXG
zFWu<qC`ZcoiSZ-Nb>3`F1W~4R{@26^!KJAaKYRA>nzd^$jMMfT#^)$rn8%S8g6BFk
z32i1jT48Ez^agZ<AU`WaMN;dDkYls{{Hs?dI)T+m%-YNzD6jSy;!RaO={Wl;-@bio
zdaeOGE2R#ROxE%?*TMFjmd~T3iM-Y&9&9`7i5~kJ%&*)xB3UGhgD$@Y;`&BD;;bk6
z#$VRIxwEcx-V|(`pj6&NtgMAS#|_MgN{hricK#8^YsPeejz27u<lOYgVI0eM<D{IN
zoI=}=Zt5*bx&OmDRY%J0@*j|mSI@a06B8rrc0S$o>4$*<LyK12SGk`HDnPyx^EltP
z@Y@XUO<Zt0x}Yc5(a_MqA=COOJ6j;ja5mkn(`ao$g;!vp$wYr$Y1qQPwAPQw6q`+T
zX+e~ScEt*=WweY&#JUy2s64o_oOTNmN^!}jEWWRO$`F^WlM>Jxr|p9zhV;ks4@Wod
zJ*R0Qe&)>WM~@$We0m#pYZi=IL#$2DJ|7HhmP>+m;e9jvR%j*~>5hD!o<5AdtkBN|
zdn7ziA8Xv~Ct&xYCY2nS7UV1$obdU0ApNw6NK4tVk3+;5iH1uB9a2s;I&T?EX<&*0
z{W98VeIR1*xrE?a?HI*qvzJRZHPWNuM&%LX-%D8^htHvgu+0Z_<@;wgFPD#jIg(FZ
zvYe(-)s1B{?rqW6`0;=}R$JPeOglR};foiaOUy$>tXh97V)A|CSuPmI#vUKo%&mCb
zP{t%6-2Klt!+77vcJsEsziaL<D(3^(yM+}2{HyO3Tu@Rxf2XIlM#)X+Wlq^*Y2$OT
zijGk6J~T&Kbo+{j$+~3$vCU)pKL6PeoFx6i`<qSftf6x5*L!n&&Diwx{u(YJAtC#Z
zcXk;~&4bA<={AS{RToo~@1<S_dkQXCZml*}$pmzUn+E5=*FP)R(^c#a=~~6A)Z&ZP
z*uI~5a!IbLs;cUA%Ze2%aHt%QETR}=+vdD)^OqTpm>zpfP9K6+Z|(IvWb6kg<8JDH
z!Ucx_ODLl@x9<dcwl)q@ZcZnBr^t4!>00<KUY5Vy_kak=GdAa<)s`h-Lkb34?=Pv)
zghv*^J~iBCnA8??{s75caWFW5pxkIwR9NVBZo2a5x7w#CUGAnL_9tpZZTeda;yVsp
zs*o2J7LF`he>Hajr|F(;+c2ekmY;%&PMTiJg~Y}OJMWHPgi$jk1o{4VPj8gzPKUlU
zm%1vfFtV=o-YJXVE$0F7mwa+WCb{j|=~b&41sX%Oi2{=U5b>whGA}_4VL4U)fCc_v
zKOBls#&KM|*=SRquy+@7eoE5P(p6KllLJ;?K7Uv{KvZCE-n@Cb1v@|yk?50D?L+=Z
z!0~TA{CyOw@vD=l2yHOFiUyG@X=pg6XV6SkJ87tY(j1HK?wvbq%|iAQ5jYa$E;vdF
z@qd;lFA!erzN^J((^Y-t&2-DZ(h|Qz;h~q8ZW6=h&3$8uGBPrUTKf9>cGmB6%E<-;
zCV*8@^wQ&=WvjQyC#y+I2P0DTK6jLjjTGzHn`vlh>OWP51f()IX}rEyr22%+&oPCU
zg7e?xyRrPv@GOsd1Vh(kM7#A+S*U;N)&4-^;>SBnDk|cV8xdV?s*6==FwxLIV2U%e
zLD$+nICvXKh?SwKcN-2;zcw*4P5r0?E14)ZbY`ma7P3%Y=NgEwlP|Y&<w{EwI!Z1i
zp5Z%?)Yt8;p*}pWqobpbhF6E?RC5mbi?gm~;H%Zu&~MO{66F6(nLzKZu8aP4#V}K0
zum*}`-58MwPkyOYe{Et@MRBp&Oyfr@lD)d1CM6iYzg_a%!n`r79h(HcZlI$(mz-lW
zqCV7-*U_EYT8HB*YVmNNF|^uV&QsnNrLab$EWbB9MTwgM{e^p}<KyGVV8orhON|gx
z&fNS(TQ2-6&vSWSQnGDE+Q%j*tuk^J=I6ZabE`{AxJ^DaC501_(`3glpSAq=KM*r4
z3ZT5QIvTbZcjT%)UJu7d)SXq~(-qiS`QyirS6PdT-`{f3plfV2aLW>p?xt%Lvyg=*
zT1=;zyhJbQ_Cy)oUouEu)fP~kHltMBdC2@@HAccH72(cMLOWs+O9U3rhCo({gg3-d
z^eK|dIX*sKjp?jGDaFHNe;?Sct}wW+)s3wArW}W~a%&vCC6-r4XA3Hk-6_!p+u=|P
zHa@W=iMio%-Q++{y6>F=PtPC{`-dZ?;LBfB(lvw4DPlypqH2mr6^6OQxY~}{_eG}C
zAunKX9jl<l$Y!WH<x}2aq=1OJIpRizwoEHbLIm_63>y=)?9oq<-s#Hl-<{552;iNw
zAaqg2TE(`1e`Tm!+4$Jl1hz=iU=|Ki2CQgDLyT(V2<_cHPC54<JW$ZmdMajYXF&bF
zf_PgSOr<mytOeROQZ}^T+u~xG3m2Z1=i&tB4hXTGhr_FQgah%vj)vo;XZh6U53j{y
zHNG|v#2MPbDJ*~8gb>d=^U9^u&0}P*ef}%DE{cC7TW)4%=JE4uOP2RTcQWp?-JUsn
z--i!_TU%AL?0*FXk_GF^Rp2+q#Ox*VF#S~_E<#`4FCps5*pBdaWlo&9j)N{3lH;mS
zEvZM!QTxCuALjc@o}|4-B=vqZq$zHd^?VC06F{$!S-^9}VuAt#<KTtVv%c&@m*;m7
zmHUw3bUNO9^%frKW5<qFSvxu9e(daQtM&>nq|)j85ouPmM6PtaKKDqEMt&U|8yh!u
zh+a!&rPS)JhxXR+!c-e1Iez^zdiKt0Eaxf+h!GJ{e@CE=RbWH&rVc@wMBI2|o>$Nj
zvsa<kcbSv|Q_pVsVYgl(Tqo_yL?0viWR(is>s~DXVO(*-e)F0&+ch*go*84%YHQ{H
zGc-y|4)~X6zQ4X|P-w3WLS+%)_5E(qGR5Ke@;v8e<}6sa2%)5AzVo74B;)Q4&#hXu
z%BsaUEfR~|9%tV{h;PheE6gGm;enfnCzb`~Y#*N!7LgyHmBF3f_)n9vkNtS`W&3%*
z$$ZVQKOIz)Wk|g(qy5JgA5tj57QYgpv`BixMID_cqvgw%dCln9z-~wOB4ZpUDker$
zLJX5eqN3wuN7{451mnJ>q<u+g^8GluYUN4~WD2kY6b$%XUD@<of5d`tD(%s7>24}e
zjQYe%$X2LT4TK^#z|uE<+(@tHFE}%pFGu#j8r%%E>t6_C0FjdzpG7R89v!^$#T3*d
z@usFGO2=aO$P7YP(m(kuUA6HOGR!qN^#&6d3Kvb$VSh&5B`PLZR(0eu0sJ;+`I0`0
zjw(o1)Z^SDqHLM?1!LV)`zz!s`z*}OFD9O2uca&x{Z$xaX9s%+g+wXSqq;EdcRqfT
z4$OJ(5p4ij?|xp)frVnU0{j*QSy|btT=<uI)z3vG-C{pYW`9X(t>WDi!XRRtVb9zj
zN1Q4XsP(zMNJ`o%ig&HSf|k?oovKaA;CryBq<SM_u!oNxX&p!cpB9XX$MVQV5GCKd
zh1uxB2V28S_vfh+$!hhh^HxI$z~JH~@|xhdo5ePxPkntP_<}LH3G#VQAvF~b4MB1E
zHKFwn?%u5?;%i*mg&uwvIqWV|O9xVn&U?D)iSEny%ube{c*ZL&LBb9r2wHm2&=*`<
zR$g2jDJ@P#_2eEA^qEI+erOIjrvs;jh4}<ipEX#BBwgXp40JI6F|(57+{cF9)>mdk
z@4iclqhT_MB{1`lQaIPK^Uat|hO_p(eTeyRJnFyCzeZDvU}6$vy#Y8>sZHPDay9Ra
z6T82Jy_pQf6#Vxn7*AsH26dP1=iuU!=`3<n!U2PTUc*zlK}FknEV%$_N~)st@cma+
z?r$~NNb(%dz9<R80_g~esH>|}mTIU!kN#uuXc+t*iPTP9-h#ciE7t!XbL7cm^4(wX
zMuhwc#qT}Mxe(wwE53KneknEo=o-4<y<~K0cB<KscBhklHQ}||z&@bPyw2ZO9O<xV
z$Yb*vyO<qoB5$ikj%yT*`+*;*dBiNjBKqOO2MUfK%1R`F$0PDx`I6u?KdJO!%Rw;4
zMIU>3d4&=C2@lxzPVaO*U9f;L7r=P=T*G~(9UD7u`Q5}R<o7oXO=^?%gwBft1$q5!
zPLfEszF?CJ`-Huo+GGPO(1)Ci%?$m9oIw8B6Ae|9t?r%6q$ZKAvB^M^#WOIFLljZ>
zBUZ^L=lYCo`<qn*U<|R^scd%lWm+H)kY#xVJ{XVXCux62*R|!uj|YdfwVS*Pc9&{0
zJn_q<)i1=;9#`;>2*7nKhQ&m{Tu=Hnf{@-vqFzx`GJO+x9Daekv_h(G{d-)PFIO`h
zJgIbtYxj}>^3>?n-BLk9L#CT6sxMx;<ZX`^dfy134QZa+tm+UC9)u%GXQvPNq(A#*
zV4(8D;FO|LWlhh}+5aFSi{dAjJ>iv0x>U*0>;$yXhK(B)`-bEv?Dgq{GZ<Sl$A5U!
zS+#L$D%co@=1!bAQJdpn<6X(6_img25bS+poff^On;C|@1=+S|h)Z7RWufx!$8Qq{
zZ$rM{jEKw%&3<C`cf>5Ei>-@FAK=Xi;ga}nKBc`<;gYXR4^LCtF!$Fqa}|9AD^kCg
z-QOz0m&E$@?xQa*E<V(Pd&PLbA&e+S=o&)=$cgtmhYVX761mNS*;Zj+QVz4RMT!?~
z%JVvmkp6!Tmh{(6VybR*Tqi<A+^og;H{_@@@>`eb?!AB#XP%See~y#*OK?RVHx154
zCqY}c*@!=Ln42b{h#aEE?yM3onw-GeRC7AyNKOz5|3jx+A|oRa-FT_x+*RMdvkwP<
zXy7<gfp+{S3wxUfi&z>Ef*n)H7tG0tYTT91bf@RoL76OT<I|^4Hw}u3i{sS1+dw)W
z-0VaanpGvsIyMe|kH5|nIPu{?=a+VL<dyaASy<ex)-Z-t^xKApG(CQtbGsy;n2M<c
z_LGFv!@xlGi4G!1S$xra_Q1ur|M*+5u&^}N!QZ_n5m1t=E-WI#ue#`sJMt*RF>Q@R
z;t72ho<7y#%e9?{4Qj@8zR$){fJ7z8+E~*snrxuqpME8~$*BJJ8fK#@xUXe#>TnT?
z+?dkr4u2RKiN^ySi5ZCOo%ofC{=-3FF8G$%ik!HwYX_E2WG{TTZ%GhYCdr$Uk`ngj
z2K<=l3&2V3<_m{Ah|Gck9yz0>q_k8Hev_Cn7Y>7bUMcncf^{3U5s*kAHMHD+C_6iw
zo8hYUGY^)f%a)NxmtL?&1ml7iVP|H3t*e2gvTub<c5aBm>3bR-H1tP;zka>q-Ll&$
z2U={Q<$oOrP3eXM%l~6`9A{caEmchS6QlSXKJcLy)EUfY8i|E_|MJG^7Cf0XZ55tn
zGfDp^XuUSl-C@{~n}b@dR2`OKYwtd%eHsu`Eq@_f5u=*%qITR<QPIonHI5@=^|-?(
zt?6%cA}BemsAxnNC6$%&dULompLy5q(jo@~YH6>Znepln5nBHt9F?AZUp|TnHAE{_
z#?f?AIo!tpws(Dc{o(~He~=@C2MY=4!(~E*iG`BLLYdMsW3|+SMM$%yz;b#wkCCwE
zzgtY0Bk!xZ{}9f9NTYbW&Lhw?N_C;${0!>LXYavl^V_j~`|*=5G%0rH>DR3b$@{jT
z0*^4_T}Dfa;iUsLw$|3ErHAF<P>?=gB&2__b?cK@+oq1I--gVe6byK!8by%!;+T#<
z+J7(LEaaZg1`X^`@9o>S6QXOP6!!S@ZQs6~xNJsK8TQ79s86oixHq9msX3+TSDH@;
z=>~59Z_`nGk$%?K*XLl;5J-&rGFG#;)A=LuRkF*gT(n$hnK-b2DmB%svu><m7DjM2
z06LpB$rU4M?A?4G;%1UTLTbrvVG>(Y^lJ+L&vBX4{A?utJ>6nvW=2HY#EM9<;!q1_
zKc?&*hHwpPM`+oac}7%J^z<Cu+$gkPi_)(N^Q<oPR8rSZQv13^?J>@|J$v><>t!j$
zs~f<%xig6eG&!)U^zlwH<gt5`8Gw663aN3>;?VaaG{We{vengC(xnqM$ou(Q{Povg
zjdf-d2}#)vr2sta5faMP<46A4adv_&G51%}S`3Aey1c7b{T-1dHt%ahA`66y7Tpdk
zD293+`p-uxYo!M}6zi{L5YdB-Z$%=y@A?lV?b7B|86D_`e>5a(UWeEt*|M+tneox1
zM^`fmWxtty^ypE4dyYeuHP)|gna_u0hRBcFR5myioujTh%zle3alGz80sv;+r1OMF
zSe}bocX8i?7Nq=BuFQ-*8+!Qgp9hSB%?#TYQ%M_#hKB0HmF7b_xhfaQf=|fXDq??z
zrdSXen2X#Sj8WE-$g&yXnDjy9L$uxx%?(2mS=Ma@Eo}p2Jr$CT>28q}RrvqI-g`zx
znRVTwsIS@vq!k4vwn`ENBqLD;L~_naBo|q75lx^XL85@<oQhb4BB=;S&N-P#RRqaU
zq`MyM?(d#+zA^5PJI1}|j&bWpkAB}4sb@cXuf5isbIr93IzBCaIb{IgtY^ED)9HR7
zyoqu=GSC>(2-Xq9GMkuQ%3)ATffX!-Fu;#T+p2Zn|GRd-6Y)K;6Jmu@aLy5+*UDfz
zQQcBnAm>2j@!8m*@rcDOD2CN9Q5rbEZQlSVj!anL=wJh|Y;2c}5AN6ufj=nO9?Qoz
zA_nYa?LN^u)3$o+dm#Vdk~OCCKHvp6*57^agf-rCnVO!CLbolBSMkAHOtF<VdG-Bw
z8~|{jGYjwiH0}yXP9(5&mp^{|=%1F7i0BeN6Je{}hlp;A7l%}s?#*RbeNG;pUO_;P
zY%tNr#>S0M=#KYHK*nI-I{<Z|g{i5jV4gKrHweD)V1^Hb@HY_jfPxsS>;>M4zezEM
zJH3wxKgz#e2Va&Ixe2Omfs5iFEClpDIs|dNw+#+@^@<B<1G&5dFsK;nD^SY}ej6K$
z2Qc;@q=*tCkMrW#@HEjG5)Ueh!1T`uzhL5)?+xM(S;*{uvsO@6`3~uWQXDXGx9maR
z`de@Ib~dR2{V2MP0V&XZ_%5%SU^EUh|2hDk*6>YBJ`=_Fw^9|n>FMcBpi)xYf#S$Q
z60S=cVY@NGdK6YDFA;M8-yrc&kx6{nZ`}R-C66d0n$5NTR3!&`B#zHQ?u)Hju34(x
zvDq&w*S^^Ze8++v6J)rL-ivtjczy(0|2P82EshYup`&RFx>Q?t!=s|6OKXs?35*y2
z!Xf1WY=}Y))c90~l!pzEd7DF}JyM~x7nzp~Z^2jtTXGAimzUvXP{VU!vq#hAC{vS?
zAQd@$*VWnA$THQ`)PjJ48>SVv=%9(CtOIbQc!xzUjLY%6Wc27tU&%3qp(JtB0h-O2
zJH`g10&$p+-)88c8mFk}xI1j+1;fxX2xDK`+HPV=8;>BTT%$Ggs>D1-0Z67>2->}=
zFT%*!;12KQ4HQ{Jv^-AJZK|V?ata8!&RaUkySrB`gB9&aX?ScbN(3IlHM|Mof2PC~
zVz?VPY7tCesN6nLp$1|c6>RyUY)Tp$o6ijmm3_g#0cD^P9kwR+q4Bl2a$izXGGZ$j
z+_*B%J_8E1hq)r8`PTb339_V?BHetd+mF)_z`bM=h;2ZAa$qw7ssM?#oth!!L3>cu
z;3w<qPNp6wNy78Q$Va+j5SN-?Uq31mAiq*TN;f@<9N=JILv02OEb#88S_mHlyQq1}
z9m0D){6{N@i2OUyxONFHEhwdLaBwK<=;-iI0ai2!;(&g*0AP&|v6l>>x-9_*l|rB>
zL|xF@#0eIVpfq|lzS0j{5&%*aG#Yn-oZPHP+onZ2m?j%Cu__U}<)8454yyw+7|kDo
z+za+_`9cDi0T&K}Ttlq_zZBo!3qsBQN+*j<+gs;Ye35(D24FMDH}@{m)7xuN(9q-o
zJ=qWWH6)O)0|QGuG3+vkBQJPHv1i&YOw{PX8&%fTjWrO4#7kXK5m`44W#xxz6Hrkg
z{w4qkxK<iLraTDD@S~DWaF5{TRz>g|z=NR5wrPQ)D?=&q5ayZsy%A-5t38|;85q=O
zX~3N-7kVt>R0#lXi4y-Ku>2@Y*zYu8TU>|}T2+C!EHPWFAYuGX3S_@$wF)vZyAe}p
zVY8NtslbtNB<>)!pJPmGPg+I>6;evKF7Vn<j1869>cxyMBe}<~s1|KICr||5hh5h?
z*a~I!<;u!Rgp>X!5w(Jg(mydVv32k;;covEfA$B-A!Ku%i>v$F>&y<H-QC?cNHs6t
zSypZ{{76z-x@xQuxitAaGhvJ|oyX7f(W9r%p5=vSHJtwYIn|=9Rb<4GXOy#r8cLM-
zyv2gT4|M}LUO<NWK_r-FH;#+c=44@cfGi=#ph`d1;1WQf9f)L(R@l|D?H$+3>MC2&
z1Tu;i@v4El`{tm$5mcu{2o-!DE2c2;Bc~6I466Wz%3_baP4FQEuWo@X2P{%Lf~Y_o
zVH_Y#)KvNGZ(Hnvt2(F|G+~AQ-Au8!jGscak%4dueV!w*FKVFp1Vuv}s9X#(_%EM4
zypCw$X4VB}4KC&6kIzoF+Hi=z$WV+I!a;;91$SS>G}R1UHV!E_f&{}Omg0dMdQ=1d
zAb>y(xsB2b3vWet9*~s`=K%#G4R=Hjh;ZccW*}8S+=3CoA)xlHW5GWWrd?U%aBslc
zai2X&*dfVsePm*SbzRT}{+>--+z1W}3bJ70=2C&5ABlYcWM=OnIQ6~0#8&Ei;1e(8
zB75Y>5jeT3fROh?(=^C&px*&$#{FO9I7-=^J*_Q=P%;ic&2B`E<GE=Enjffvj35?h
zB#L><?%l}~5m}%Aa0v7paP#oMD_{l4-3TNBKy_$JN<K%g%_hTgYRUqD59dx7mM9&H
zNEQeQ2wvLWu3>4J1s1uP90s^72(gOc-3BU9bHaH-H3icGkslcn5<(?lmq#axj_|zj
zE(|myRsavn%FC<U*<p}c80TV5dimBJD4-$-4x=Jc<9RoP60~dnJ2{uivJ(6Mq6gzU
z{lC5T?f*T6*nhW*|BuN1{{KftREZ@2ducHLKl)N?LGRhG2B}08hXE8ASFU73YU06l
z?Qn(v>mNzeAOq_?WS>uxQl&wN4#6s=+-@-g;T;aNdDXQ4W1BG`9g15(YVtn*`5**q
zv7!Ax_;4V9uOI4(q|<->g5Uq<M`~n$(hm<wLXY_lV2&z)aa3O<-G3VynGQ+oC0jsd
z^`HhZd;>E{+>Zj>LegP?git{N1X}$%)&MY=pA)%>pOco2O&$imTFS!r?W)E5@Ldsc
z7XK)KXQ~iFYmmP+CMFvIHwxQw`$<EHlmC~0E6HxE`#z>BMs(!=Y^izp)stEP9{isl
z=i#&e?h!Av|1WWtAIR7L`G<%8`oHrRde!{n4D<Dd?}A7UXH*m6fqAeHUv_HreOLAR
zP`C>J%lK0L_CI>&q{ddxQoOj68$N*VmDJ9;^eIcHN4RN`E~E3w3~{j1e}61^JV76O
z`1ueYZ2lAP-<)e3))v^p?X>>2ubs72yXMa;D51dqU#|a-k0F_Y*YNviP<bxPR=C|~
zmqsGq89s`~E?dQxI@HZPy%%I&L`Ae)zf0=tTPY-R5McMd`u<5pQxD;rfB!ZTKkk25
znlIS+G7)W7-RM<&|Avl-BEh#%P_uP2@z)*Gtma1{i9OgvOwX#5D{Anq{M02~tPqEW
zfTCe)>Xpigb1)U-_a{mIpT)lu6c%pZUEf<in?C89-G!=feq;QAOldU5B!=Nw>ip@O
zn%)(bbs#*w7f0Kc;Jd4yFhR5~m^t=)Rl+*t5eFH+;?;Ek`VK#L%w!(457bQB_a&re
zws_f~Tx;px<8;I)`lV~d+B!)F=O1)*k&D~*Vy?%e5A1lTg)Pp~r=C-|CwlR>$-zun
zT6pGqZzDtdq>toDHKklz$(M9>Y55$My&p!CVH>GaS9AUT^>=~_-8&r(mOq}(l$mCy
z{!gTK8NZl9IK^VYe3$aEDGF`g<q%P-?QU84&pk0Zt48s4aaZ~dzotX#*wMgg8<~M2
z&H>cd&kbn%k3I{+QR%z)a?6-4W)qeKEC-EeHkaWK3qHH@_wH5B*U9%6)2GRGqnr;f
zp_zyyO3rPw30Z!p_gtTP?H=GFrcIN(`sfp1Azz8!$D*K9R_O1Uwn7fqs^pvUUn+F>
zcr)`>*h&aU9R`jMl})$K+A4K)y&(<YwP-btZ(+Y#WM}t8!+y6&dUB$B^x1jnU{yEz
zcbks1e7Vt;%2Q_faq&m`!0IL+VJcV1%DLcyOiy@pS+D=|@DVi2I0p0k1#Xj(s!Lk>
z34glJ=OR?6OnxwwYp+G`OjMDOF^sE|KBgxS8ZxY2`SI{zIt0tKxAe}pHxsQ)!g?<)
zc9c0!p0bsb)rvMZUOWS+OOpSse>Z=U$OfHg`dfO8L#*ipZ(f-X-CzMrv%XNc`~J6}
z@k>-+-Rb=M=DkD(T}n=~56A{x=^tzvx@fR6!R=kr;t}Ry=~tl3|HotPvdy6zd!@^v
z)YlICcao;rO0}_;om@dXKB*nPw#kMG<yG=B(+67)DtXi?@XE71{@u3N)zN`^96mKt
z=p7o@hd!p;KWI;WBFm{n$kpuaw<l-<4#P-S_4jUY$jh4GetdeZuXEJ7m(tpWg}upY
zPfqB*<;S`ca|_+$vU#f;MmrJlS}WWWhr8p$z!P#E^xmc@ev^1@e7XI^UeRjS>6=sf
zcJe(&Rl!!B-EFdO!09Z>{IQ4`K>f%^`S>N>JmhDHkEE)5ahs%e*-adFZ1g4BpM@yh
zY|?~Jex7Z;(6RaPyLLP!Ip#?|b%e0Fy3rX|+TM~no57+skxUoD$sIyEo3NGp%zf?{
zdDLa9BsLs>x4j<OXuqfrt5PCoNN>HwXvKA#!{3N{o>zUZebiN(M}Waf;#0G%_<?jF
z+3YjwDlMW1yjIi&pnMPi<Q;5iSY&3A5ymCfl<wzJs*)W(nVHj83l+94o={yHkGt=G
z`zFuXxo=i_&Nq3=MXsjC%EEqlrY1nZEb&)Fn2P0is_pH4`73C)N1Lhlz-K0*A}xQ{
zn@0R;eDzJmrtO-;Uh5D3#+P-eqjQ?f?fN}9IlIEe?K!o=JmN=uaOw7?KAsQ#g!oUn
zXj?3n)x)94O}Ps1w9oS2{oOM9-d$D8tMH!n_a52&!IZn9NDPj4v72A1-1BZH{%Tx2
zP3lm+W%PGUc|l<v^XUG%$z^|^akO^*gh7T8ZJVT_ergwqw~HQUji<LjKj}(2TgX|m
zKHI%GVUZ2zTB#+DA+7Gv!%YtWvgyy+)P^Fem4OQe;vPXhBk$7QAC-&gFy@vyrZ+fv
zQowa^#Db&8_r9y@jJXT%w@;eq2-N;-&0)tP&x--?5j3^6@H=XeC`-v3L}yV>&#Tvl
zOD@6Yn$0KRSQ$PPwAo7fpTxuez(Y_Gx@kn6m?3j!lVC(y&c0fyB1+TR)?ZTM24~uY
zqra}mEh2$#LNQs<XL6B#OtyEBUiH4>6NBNk^#EtRy31JGi>jD+zrTFP%4v4pLPCYP
zok*J5j^Y<o#FB{0w<kD^1&_gaNldJNe;Hd{g~WBN-Q~Q!rUeVm#G2bWWCne@a6G20
z;oeU2&mDL*U0G=n6-D~TAHN<m9BvgrL@JL{)ZDVj%&-?!M~Rle=+Ce+oV~If2oWEb
zyE!)9$2r}3Q>%Y|sc`pMLy-_ClRm@}7B}iFG_*HEEL&Tt9v6z#n=SQGcTX~`%P1jY
z^OkzH3I8F^eGJqk)H%5gN9vHyVJF=PaigBmw-;ksgbS3LxScLtqGK_lr}%+H1HV|-
z@((FDtjX7#dyWY?vKHvi{PBy&+C8%ohtWzSetr&h_I6C@uuab~%Ca7b-m;-cojiXr
z1N5DOH+X#vq(mer9BZOw0x;6ZP#6HBkiPzNbRtM?d4q>X&EIia?S6Q3T344c(0<uK
z>f?LT0}=T=2)l9(YtV*^hYJk~Dc0Mf(wlpQDx+cCviX!0vzcUe^M+Q<lS+moiqsbr
zQG{gYD(?DFx`Z;RReGwdm&#0HS*OmW#}D+DlvzvJyza#@g*!_hToy$;QXeNqFBIY1
zXQT;L{U;+s(IXg}<_U7$B5|o>x>ZpXhU9P!bbS`zpkNQAqxzb=X*Zj3W>m^um=}3F
zFl3j#&~gMP>0SNPFF2a#W3{LHyZf83`(APc#bdZ#%g)DB6UOT{=aQazuEtP@!Hyk3
zsRzs;eXk7~&62CoNCJ4G6ewx>fr2o@?%V79^bKq6G{5W*oeF^eVL;bPD4+BRcV;?L
zW6_yF#r5bp$W2orkMzj<JAYtF)F`@veuy_uH=BvpEiegJ_7*C$NawJfU$PzufHU(_
zrZsZzp)29<OTec(myCx)%S1Cbc}bJL@3S~`Zr3(g?P-!e<69o#z@OJB*I(G)iCzDh
zzQDWRq;Rg)D5g%&iTP}((v6aWe)ZgQX|u~QqY>{D)Ji#d%9Y)`tL}=A4NLFo##f!%
zLe@oD?dK(3UX#76-lbc9!RFIZ+grqsSZ9Th4A?DzMO;?JT=w#AqY|%(OxW^>GtjAR
z$Z2l<zP5Xbw#CH`*=>0()ue1JEM1$Ez6A*J1erJb+Y8E4u%g$wNfGz+F{qyKo#J37
z*^lZ6k{hv4&IVDA5Gc104M;Aa%G3sj_ZZ2Y8*mVQnw{j`XvHdqw-wBF%ZpDA7N~}|
z9h|>HmsO4-=swyW+2XCKbMQ?sKhr7ba7`{%TrZ!}cXW!kd2G<8=lK~utBGgFTH;Oj
zLt2ISR70|E1(;QZn4&hOf1Zx7!`+TI%A)ig&*n-jz{+qR1oVf}y)V;gYI*C2tOiLI
z)ykQCqOrnL|LRJ~gM^Y2xJAXx!~I=ecFmNp_24r(`+Dm~qlKGy>9t(!b25yvv{4E=
z(Mb_`EA7Q_*(-;8h<5LS<`q$5T>{N77?Uf2(uv)1V8s^!{tz`IlR|8k<Bvw5HNycY
zRG^h^g5D)Vgn3FyNm(1QF1R5qJWBrelK7Did%QqDY%#A9@(sz`2yuPAwf$~wGFNC0
z0wc?Z&L-Ps{js9Ai!^_#J=zWkG{M?To+`(Nv{+pBuhl(Y&^lp+Aw9($Kd>~!pzcIB
zTL7l{xw^HN4h#UBM!QPxTxgx#w=Q_B|IVO>Y3N;O#<wNTL4n+JQM#OrY)0f6cACiz
zx+vrnfR<nNVG#<ClO*-9ewDCe7I4m@BH`f#&bTmvcEZp)q4q)IWRG0EkmQ$wFwICv
zvaH9FD9*J$K9fg$EajD63RjkL_!c_%8*op6b;}?cK0tL5&oU-8m9Prq%cn_6`~8j_
z>jUc0WpPXglu~M-QMgD)xA^Nlpvs751}03U!a=h}Y{r2M1-vY<!-_k`d47mX6tI6Z
zC|JN>Udigy^HYFzu4+&Ijzqu2g~==|KSi{^mb>At>gRtIyVg8JhrhD0HLPsrxzgi0
z66&C;N0vSsdBuP2%Nl#^LDwCr=O^45=pu}Wb6%qSPH`0TV$-FVx&j?CGTZ#BlVw?@
zl*0$JS7<Tc;;F6nH&R{Aq`4oj_qVu@i>GO%m+3_Qq|jbloZR8{@OV$tcIkT;4)*Q(
zpqQwOj_~G*!|Wof@Mxe$2|wf9GpQn3A*(vHh74*u*2N{noN7Eb1}Ej;Y|359-bg@8
zp?XrZ;|0ycJ-6L*&*3M|=(61GnzUVjB~=$NltHXtF?8N*Dh-W|6pX%d78gDWgQOdn
zbgQ3V-Xi+SHuFKF>AqYoB|!4kpfdy+<t!5h8awc5PrL0jjm(1sMW>d^UPQ!2QD{jZ
zf;fB~#APTTt|LcPkoQkQ5&<0hULn)ngZOg2?HB2t+GAB76=K!dRaE}&9ND9jgL!23
z^O*DDQEWWrT^JEQVIwld@jUt^{Pjs$#RvDNbbN0qkSUDM_sm;~rV{W$*0?N<`iJEW
ztDhMc);2LFtc61sy1vCf{V?L3s_W0JEAzeWhod4e(iS%T`#*Hd#5Q`<PdXc};&55s
z&#AA$)5G(tCpY8_$AzzdOwne{h<Du<^ffAJ+;}xm@sV9*qmB7Pr}XAon`Eu&r82hG
zj`G<!jg#xYy7=i{K|Px!^s9xskgcG)7?SWXfI)eIk%NI2cM;SWCW#oqYUM%%VC0Hx
zhkpal`N;zWQAN5h5m{bWS67M<bRz&MWcuQ7>wG_J$d{cb$>P?QZVSgV#Y|4rRr*v{
zg-HK5F|6+7^{2UcniBNv!TqG+gMtd&1q(B&l$hD1E1MfG)?t+ld<FDrMKjXEH(OiS
z`gYEs2A0%(k~Nlgv0ABOh8h@#Ro`9fMon5)iQ?aB<lbp^&An<a8Hq`qoF?Cq>BnJ}
zuw%>Dtua$dXS-LmoPTZcByxGDs0(y;8V?V$k>S<to=E?ILz2}}&EF5x!cilo*`N+X
z?)o|CK-UbR6CInd10-b~$WH>W6tRMiDTsFT;@@}Ln7!I}8KeN;AMz;eLm%-muW|Dh
zsBw^z-u7^8{1qt7-xGiAk?hR_2gkgq1>tWWUw&;{R;$EK<+`9(J8O!y<$M<h>oi37
z*1sIb)%d>gYFpHc-LWAv9MZjdB$zVpeKChh(WM7pFtV=n&3zj5`F&|pMT!SBRD-9w
zMh9Iqv`=vod0@#8A{1KKN-M5IXDGy~E82PfKEv<x0w)bJq%G|FKdMubw|TcA*>ses
zm?Wp+u*&)5ghcf_VNX%pQCoS;*E8>k=gF)&NWxERL~BiS^%oc!0TF>gv^I#I5;Puk
zAy)+jwL?TV-XZ@d!Z!dLx9R@3iO$T0-2SdiIWo(&%-l%0aZ%%0zwWf-3hFTUi9U($
z$i-&4(QIqeclAiZ6H_bI#4VS|N1agQb#Mw<KF!Y=5xSfoI`g!oT?WY9HdIXAlLAtN
zQ|&_Q=q~rP7{@fAX4fY--Fac4mt_$il5gBf{iP8XhF+^ZYcy{!J(*FGHJZP-Dzz$7
z+g?xA-!R6auJjN=`M`5iQ^Ki?f$-fe8vkuh?_zY=LXV@57*mj_xU3u<ecr*F`Lz2k
z)!`3)%JhhQoXgjL0RnV`Igu<Lb_N3{V{B7H!(4Iz9U^2Fh%HByQizwq$#XQnTv?Fy
zCL!U|l+{eIJ20;?Jn#-V9p7dnlHz32?(N_w#CvL8*|`JBQo7$Qo@W1c-1OfnBsbuC
zy*O-Hg<X>nyKPjwYVnF({wUKm?K-hrcT9+q&891DH>VrMM+zz-D|gi{d$`G!|4MG%
zK{E3ChI<rd+Zj8t>r`<1X8M6a0lmH)ask<gEanJdJKIdf(hMXZ7MlSfUoLdF&Q4T6
z`#1U_Yk{RA+7gTnh!fnRytaYo-aD{=M3RS~n$*&*qL1k9oDP)gC+|Xm=O?!2%6!^s
zqteZ1tnotTd=R!nt0OGwXlUkx%;hvLnH(NFlB`pjv=6#}ZAM$q)sm+V<W5-+u%!yk
z)pH>={Y>(#{$QS6(gcTD*%V+`Xy0!>oyVVFYo5D}^I4%n64Ez4PH`?KOAp#Bef>rx
z-F+e(N{(-B&%Tlyjndx7?~WG>ZL_4Z7HN}u7l|ldXJQ{5keE{>7)_)X?fd*ZN=o?C
zSZm`;7r#)Ql>4hDvGrB{2D;9i2Nj1ehEhA<f7)I=1<3?XsR>xu1#Uiql*UewQ#FvD
zI&ToOm}l-HS{3##*KUbE^^PxnfLS8Da8mWX!>9cv`!5JrdoK_oPmPj*cadh3(p02w
zx!JS7+C7r$?C|6Z*ee+ZTzL}D7peZqL)v8SiLwq(Le0Om(~9N#W}3bJ)Os7$6Ewa)
z(r=(%^*s7Zf~!<C6TT-exW3p{<_ghPZ~a}R@KruN0eQpx=$QpgL*uy;;vnf-Z-5Do
zP|qc*F7Rdfb#$$mxdN`Jit2gaV{IRy$&_VryS};IPp6w8|AG-A$jdcpI!~Hds{g}x
zEUl(VvC{c{$Z#3~&c=7?lZ16^91C|fis{|Kb&)SdCY>R!+O9Wwl*Dc2SAHLUvVT~t
zEN5w+$9&U)SOjhCy^>L|Ff3oEa5>2}=$<IK{&7)9D`A(wvrgLJVRjv0Ky7o3#WSIk
z03EJ<FA#Rwbfb=VtEP+^?@%CEE<QhkE4C&llbWF_8BVkCFXt@s0_-bNPuaynsYI95
zyN;N|;cZ_zKz~!T+em0NxzQ-(Y|oUz`-9uOXHRvdQR7<mKe;jQ1e?uO8o7RP*dRL$
zIv>ooUud?EIXsB2snh@AGn6JOZ6J3`ATA9F2%mgOd*UEKs@LSi!@;gP(fDO@(2>~3
zEO##JgDUZ4KXPhm|9x2Xzzv#Svn9@6+*YY7;W%+;cAQX!RNt&!lY<i78t>sEp%0$J
zbdfZ1Wtz_rK4!;hd<lF|e*0ZmzO|T6y1gc?v+ic_J{NJWwiab5{$=k;Wc@Zf-by>*
zj{q~+?8Jc=VrYrS=wRfe?~7VwK7P+oAMl?oe0;Y7-}ZC@V~6VEgUP7%wb=$+xtodD
z8#++LKi>8_+{H}@3N_brj7re)E!}}tTr1DJIE9|9MFs3vqi1Uyvlxwyi7H-FiKEUx
zJl>vb4O5)9?1;h_ja-HjrRpE<%}@5Nk!63YliOUdJ^$>_xgLvmi(P{1P%m|5Ew+nX
zG?+qZ3MfMW(fp^%sGB(|6wo#tme(q-olM&dxa*lSYrT|E3cy{e%x*+aCZ#2&WH6v8
z#wb6&_wBnj6n371DPdqGd4)hW*W(s#g;cRb%2KPOI=ad`TAP+k;n~mUn&liPlcK14
z%I?0J>O8=yL3SFkU48hZa}MZW!-OiiJtL#ELEp(#lNjjiv*4DwUd7jqY>KKuH?_bM
zAMJwsY8J2bsrrZoFX@OI(y#?a_LqWqxiXh1e`)G@!=rBK<943yx;iyAl?^gxRBuKQ
z2>tL2p#MWYsOrysy|K4k!poR$D{WO{?MneZF+Ad<A-p{pV@!L^dC>2&chR*8W2C5X
zF<f;p=*sxXpi{xYJeh(NP;=DpJ0E&OE`~nvUqoHd96k0{L%;RJN2*?7*~tQ#LN?(@
zy(|Q-X<!pZU**<o;&EE<%UjSbovG->-Q5>MFRq-!eXIWRnDN~~y$%0deD}EX_DMD<
zRZX-K;4N*@3(S?DI(CujtJdNnLjG01c-6sQ%!nehax^%r@T^Q9Q#dc&F`~{Y*4W@b
z98I1l^aG@dfts<GCNK6<bXfkR8%_6=L9F{#FqR=%$_;xp4N7ZS85tF*EHuDwL6tyv
z`>`a9CHGx}REHI+E=)BP_{c0HUe|eTw->){pBxn9sul_p_C2Z;r!GNZ#QQ;uykH?m
zPMB{+*>rL9bNWK*)o*EKG_vJvfCRps)8joze)9Sc(qCQ<d|xI~GuJ*uD2(`u<o2PN
z!q+~8YNBUT23FRy4FhkB@s?uE?G-hnp4CZ1_R*EU7t7WtCl^ZXpKMf4HvnV=>-EgF
z{?iO|yA<Eht0S1#XUXy+HV7xS38``DS)7tq_J>D_ByU5}x=L+h8A^>CS<0%1OiQ1s
zzp>@^{zU~tikY6%8Vy0SW7=e-w)y;gEFX7m5Qy~(^|@x1iM@cpM|{<4CNXl_K@d`s
zm~y%8^`K8>9jVemvU~cEKa|0y;u0Ml25NVFMx0#__+`uoZ-cg@ugFANAwdLWNjltT
z>lcx^#5hpKx`169D|3@iBlY1%BZ*M5dm`1@Hi0>rVdDz8w<xPTU_IKYVQq}fNXgLT
ztYzu<W1B{`Wc6<{NR&pW{o!T*42)b~<RMmWC|(ieQ&AE^S5sk&mE(@?`0P9RHR@dJ
z>3n~t{i3G&^YM-%JX%P&56G}}1f=$>#lRUY=dOsVj)GE1lAQz3_Le~1uD##XkxE2O
zeU!C5#GX-4|1Q9aH=_wr1p|>cn#WAT{N3!*CIO8Kf$?%C;cbjJaE77xU9ItjHl5vS
zy0f3~hVWo~Dv|C#ioxUrR2GWJ7DBWgI{5=BlFLv+AxbY$M|LAwl5-nWRxVSKTCAd?
z2L8q%5RKdd<Q1gE_K5S7;rws=(L3$uJ$EB8M9cJyJOyW6x3Q$CSVwy!zFfV^SD6}8
zCAcSOPv`Ort%Hr3R!|Lg9#@xm4}jOR-m=*_ozX}wZFI{1hk^C=9f@Q8bsFAq)zG<}
zd#Un6DA=Ozz`IGsR`A_)kv0Nvc<H?7@bb*|nRM*4+M`o3jy?E#XM94u(Uzwg$+Ye}
zTj@XAwqA9!Hu)q|UW)kNlk~S|j~(@{y{Skkub=1JK9nOWtyJk{!w#iZ0)2Wz$hm<g
zfpztr#$>xUrTEL@73s3NEQETg9ka{lGK3*%%KBEFV43j#eI8=R4qXt8OmF1C!=f7*
zo(`sDttc!q4^tCV=_-|}!)*swzV3zF)WX;cL^xzfU_0DNcU%C}Ft-s*V-wS!TRU!6
z^Ovu;r?fp%P;YD0U&&~F1Xs;C-(MM@S3)S8P?KDPMzaZoGRAbm3Yq3?d47Og{)`F#
z9As7a(NWLbyARl5k;IPph@t1dmoY}war>4_8=^QTeec0_DqqkxnSYZx_tMtRyY)V^
z1zsn8ajfN^^8*#&A~R8u5vbco_E_7{k`FoEvaKC%)-)Kw2v^-Y8^r;r^mS<Ss}A!K
zyT`oOwF=rN^L+}~d)QXp2oLUKTATb&ZWF#G=VS5NXVzCV;`^(g?-a!5xJbU_U^C{c
z&6l~NTRXWv{1viq)ain{Z=Nt4J|Zma5zkIVDt&k}1N03-qp%7X!-a!>DG<L&7*v8&
zss=rrkDZ(lwJy@3V_&yd1|eV9xf<rP!TwX%)+V-|KU_+ZA*7&0vX)ngWj8WEsG`Pw
z*a>r+0Vhz(aVO|rckCoo0J$Lr<OW+Q6?!q6w#{qmIq;{CrK&O8rjZv^m7;aat+Z|1
zD>l7?=*L=Yc`QBa;X3Y5ejf9kpE<*N-*u)WFnTb#w3ljg@BaAiln|}p+l5k}nG6@+
z&5uW@1hUe-MAj_+)Nj?I4_Qbi5pN193o-qPlqc0uW3-+ka+^MNcaJf13;@Myxn_~;
zvd)|vLHIc-k32wDmZ&t(dchSv-mgJYL>ry=inq5Nwrtu7$MFcWHx*8a|CVg4&RIe9
zZ8I?Zak8AXXkJ1y4-kWyzY3U;a>feuz5J)9-NqaPKu(qky{Kju7RZ2c98A4th>s~U
zhsF&eIM)SsEO$weS%Jqal_<Itl#xunzP`}si*%fzf4-G2bs=c3gBm)^2kbSVE&a!G
z?eC&M-2!g)+1If3qS@0sZ`tz8gq*WZU0Rw?^-n%R_Zoxx1w7>M5`j?YW#AR-GZ8WW
zEh{G^e0UqTQ$<Q#8Z%%mj7VVcat!#R-P6GjeLujHMW~Z2PT_ChZp$QO<usds)olR$
zH!0?OyWFDiA@LpL!`|&oqTHoH=0QuD{O7MmQU$V&@SX1Wm3Cz<@*1usCpr{a812({
zw)F$dP6*qKn(K^jvT1=EY%q7#&65|5s$ii`)Q1jyPVCD!%I{5IVjs*?XlA`xA5bou
z@@Z{uC_#5?_92vyy4XGMvCFpWUD;N&21A(S`eK)py`}sd{q^Tz^SVcCk+)|m#4*W8
z@dP7wHAHCm0~m@Ex?B*CnZwrKy)`ujFcW~th{B?}Q>4PuK*u%<js`cdLC~=cfP@va
zjYl>+n`_4V2LDaV%>htco_h|}C!KpSQI&YMDd^rY-u1;*zHcc5baCSqIaZL#9n|Ff
z{Lhfq%#tQxcx3n0Me|<C_5YG-#Xj6zRwZo1!S0EqT_0{2y+kp)lsqd^;G0XN>KRx@
zk-W0XT(6J)ENcZs^Iqj@mVa%D`V#w4LG``dUK3c$ZqCikK?w=H*3Kr+kmv%!rR?sS
z*huaD#i!o7R|xxz?MhP=vkP?_sIE9UUKfLun~V(3@<zT7m`k>+5Zol`v36dMKRHtk
zc3gT>empsSN3p^yui1I}*kWSidVk*Xo{LrH{h4{E_hDn=X%RZ@qPxaCkjj>O@O67Z
zU%1r4I5?yr0!hY!y}`QubkGON5{(`qwj-~@n&c2wEONO>$&uTSRTMq_dzf;cS4`z`
zs)HX)HNOaoQ+l#WcWSZ>B-(u~lZd5RP^ac#*sAzd4m5)gpl+Ta1c`5;1ygczKRoIO
zUS+nib8z;-eY<RzxWO}h@D-h{>!vwG$@3PG%#pEXCK1XcZbeG<*<H|J%)xa!H?btZ
zI%gW(3zUM_`q;}2qU|RV7nE-1JzK3P1aoi=qUp@Cy9_K8KSA4ku1;$z5>mfGwjX@6
z?To~SDtV7j%O;wM6?El5wo=JRakwvow<2u3OPFL1#^+O)OuQo^7KnRAP@Di>U<I_a
z)e3t}U#>^H9%Ekd+8d1C_mcRj9ktTi%V`1E;b8uvgY&-x&WB;902@Q|MKtj#T*+2o
zMI_byJq$4;iyoyCbVy3$k-<aH!W48WAfK;eg`zhkqLp-$A**D^CM1T)bOu=#aM~@T
z|4ND#u6Nb}6Pi5>Q|9gHTcfLeF0V#DpOI<EG$o8EY0mv%JZUPZ)4!8rrvSn4i3Zch
zVe%2l=d;As6q+crpBHEA8+KY<CV-T#;rA_vLNF$3$<4NLDc6&?Kc(r!9OemowNRjk
zBivO)QyZn@gDWoD31N{1r_}g!yP5;I&5WVwbs-p&dRt^05^bY<T5id0Z(3iFeZPDt
zGonFgj~7)TbKAFtd1u=j5Zbn;gvF3~)j;VM8B{dSfC|jX<mk&y70NYx_E6xyzLUtU
zG?!Oq^jx_BAqfTu`W#)l3XMy!@t_kRb6wb_*uYg#NN8lL3>-0`Sr6o@9Q3~rrvnGq
zS&7Mf#V)p0SK;^u5mpgp`mrGcgX{Go<obL)#dixAY5VTc-+a9LeR+6Tcy099-jv>p
zFjvtA7|%Nvng(o6cJFq2=TU>vPHE1d`79{ig_+H{I2Yd<=kr;}71tjqO=4ub*uCS}
zxJA0souJyft#^*lzg^`5G8?%jXB1UVG-f_4p<BZ(s_Gr_Zmhd4Wzd0Rc>M}Qr?qJ#
z&-FF41-7vP5LeXf@6g#QaZIjGrG?+tt)8jycYT@Va1(juC?*&){Z%q5YF-pMpq94!
zvAr`qjIU0U^%)#9aklL=KfShzLMvmPeF7cUUt@AfPi=%Bzd7Ws4}m|+B;0A{nL9|^
z7FX~OR{FBlq&Q!Gs*kVBgJl;SS}(ZSiSZ<^Sw)8&!q_B&TBg$!v)PH|z$Nrpa7Cw`
zH=rV5bA{P>KXgKArL;+Al@1Td#?`*B)3<m%hUn_@6iA;RFTPYW|8Rbi5L08YT1M}v
zTMnWhF<f?)F5lVZJEj_Hpxqkxyen5z^)T^}ynE%l*bnTy8#*CAfCm98u=`2YJaYlZ
ziF3<xBY6MSl%FOT;;H?5VSxeLy;uU^<+M@0WxR?tDfN7L?@vusowLBNi*H!MJ#y*g
z${SF|FdRzH{DNTn=D#`A>XbS!onp+KQJ?iPi0khbr7Z1BkEhZI^u*<WNB7*5GkSAn
z1%tvj58RiJcsr|+kx2s>|JB1#ffu@3q+U($?{`FNZmhB$1hzUkHeN^G1qq4P0?41B
zD!eWKT1*cTPs56oRJ(cF=2Z&jqq|;fiEnA;ysmC{7<uwlJ6-3)sEyXY^=OK%P#5L)
z4g|G=P`>cSE{mu@$}{-WB7f@iyKWxmB0AbWt=IYDJBrqJTeHV3s<_b7#rX&s+3#Vs
z)CQPZP=fZYHerBo<pXV&T6kxs(>gZ{(3`<1iMf%-EaS%8h^PqoMuUVShGAif2?L|P
z3%#6rVP+M-4wS<1Cx}*`UgTrBmeZzfOAeAAK|$6K|3&poR1R`dI&`2$LrMzV(sS5j
z7}AqUIbD%){LKDk_F2F%DKEWqNElWRyK768<<u*5POB`o?#D||stIL^hr(Ylb7BB#
zZ-tLw02~4O0ft$S2nZXzu9VvQ3t3mJJq;d$dfi7`Y7_`#&RM+b1O^2D%-oMSG!*5X
zHwv}afudRirGR3%OZ~Im^+rB}7SUB_g^ROGxyUm!`%Wwt?!w99`!P|=FY_OY++-^P
z`T9Vjr^|}-6S;*o*!Cb1%qwg4*+CFq1ySBdJ&)6cXPwOLk{1(;Wq6v_HP|P6#2*UA
z#2186G~uiIPjPUn&_ji0a7=eL;VwIr_?dREjL(t7WZxaWQ|NE6gE``nS%Y`lL*J?;
zMHb!AE#9n%S`<z;iP@khj}8cF?us;^n%fT?3HvpF;-C%Xv$RRQ&~o-iTi*1N{$%^=
z6D0Nc^B-huCaZ1isOa9RYy0S;;FT2{1V{mNmaT#!B)kQ`-71Wln;>uLC?ARh-JH7I
z2HoJ-GSZqqV|I|}oop3{WpDPGF8F?;3{y~EK*&Lox0iY7$})?zT3ttF&ye_HHa*{8
zY5OBC^3l#V3^F6YqEh;dX9HZs5npBefL)ZQx(4b;)dh=cYbs5>CBsRD^60|XAIj3s
z)`+;3?|W)62TXx<#2d?Pn0WJcl3L{p4(yx>KO#^Q6)DAD>mZIV&|T5|p64cR`=G~!
zvRvcOg9NzC`dHvPCtA;NEr`3y(#8#RH_%?wPM@PO$5H_=P{LB=rS=GSGxC;oB|{8@
z)NyLO_F&E`H5CpY#($cWv3bnE9~+`P`tr|!&7V$XbOVsjutBLyi4beQ&L)`t4ovT#
zxiVPH?%#-u2u1W_`?cmZ&Sui%4u+;!rF-j9_g#*zeJSKedra5`Om%h+XV=kXYo%WJ
z6Y{e*QW^AedA!iVUmy2v_QlKrFx4CH7Qb)KQ)d#q^-ck8F6lW=>=z1{DmbW>j)(8B
z=ey7kc_v4EFQD<j8u3Cl$A>=_cGZ>||Kw`^8USnoQ-Cfh7fieG73=k;c*A~76kwW*
z2jzyPIi}UtVC?EWCH)b_e5w<pkuma2<6{E4O1q`)n0a->WPn-*hE`Jz>=0Z>V-Q#<
zB6{lXUZK$4%$0qRmm%fzHhT@KuZB)GIz}(4!Lx%QigPqNB_gjS2CE;2KfX<P%1;VP
z#A7Z&S?T!%P0f)?6(K-$cQ|d*PiEScv6TQuk6ZIItilEUUdK1Ft0#URQWC}gI;3oq
zkJzzxfyBE+2YE6V%L{N!QJ7I8-(Y847<T97-pyVl16}^Ey+`<7X81C1#q2#tS8oY&
zIixBQ%21c{HtlCKf)()Ye--0xp=6^P!qrACDHX8=Dm4!rC#THqY8YNdwk)uTv$yWz
znA}aZscq#2v-=&h&$T8dU=3*Fp69?-&pzg@Pku0#>fKbVFXr1hi$fEOUGe=Q)qQ9!
zSKE}|$@9^wVnlzU;GMGN2Z1MvN)D8&Kg}FtE4gI0Uu<j+wr*z5BI=pBZwobl(u!CO
zev|jEn@;i>_R_w6Jl)r3fwe>iPp6^vY#-bz)xw3O=q)VFab-_0v39=f2ts1e#l$dM
ztql;bDpV9=W1shL+%tujw5Vlv0VsyUN}A-WVdmpAYXBm~mM^^Q#T|aVf<^sQbD<3;
zBl;4<+MIBtHmzS#if)|Cfm$@>?z-Zxf%jh;2eCy`(%kPmPm3aVfN=FQ)DBNoVs1ih
zUXA8wj_7ZHC5}ZhteU%66DFhGy5JO{)>gIYOJtTNhZL?e<#)YO+u->lp`+Y#j4bXV
zh$tY7&=<3BJ#iNe(gCXJ?+0}x;`9c-hG}AYR2EugOWIKbjhTSit?S#-BU`OL_~%(<
zTL2Gp&7%Gnr{-rui=Dz&McIiK&`)pDO0VtV|6}_=Q0K;s&7Z2daB%PB5>KC5D~MQ4
z`R>)m1QoVwh_E&`=ffk*m14mfcQa{DNkqc#qw!rK+4wS*kh5h>$nx@yQ8^ZX2kN`^
zekZMdRCdyj{psEn%YYm>TDKG6Hp+t8ENV~bfmum)``If7S^^uveSIzg^DQgD9D8=0
z+jFo;L~S3`K+Z4hyVMJOrl9;&F+KOtIt26pA<Qp%2fhaZOv$)1X`c46cj_5GK(oN-
z<G?m9ob}@9Zb#7PegCURh+i8QsaChC0-D8(nhX*@RwHqcJ1+=B7?!UUo91rdDpM+D
z;tX`!+WZc3E*V5_nZ*P_17D%;jpJ2p-H3PC77Xm~;sGJpz}Ef>msXvp=#X&i+gy;g
z@tTcaO*b9(9nz(!H4jHi4)#OH=FL(#=3+^s%bs}ZJsWVYhXvcuHJYm8Y{CUTp9gY$
zXHWh5y{j}BFSIj8vK^_;Yf>*ivW+yzLG%0Yd|+_!0Cb|&kb^=MK3T5tg+f3{msnXv
z!!hMu2B{>`@}C3SA&RKjd>2JBO6R?*iX)4%krX^=exUEn+ejlX*)qD=G93`Uy&s^3
z!#Pol$))mN`Hrmy5HsHgw5OHF(+|A&o6>2i=Z4QOT)ILxJN+dmVO6iM*Ce_WDY&8t
zTcNtOR00+0c1x4`PxhxgBj`W{6*$7z_jJ5zp=E!}Lmo6IeHG_gb0Jo%Yk*9w4n(oc
zz(Es|*B-~4SgZQi8hI<1uZLuhef`RH;AQy>HrgJR@a`r@9*ag;qs!6P7g~3)IR#xL
zb+T_RF8-V`8Ss9lhX;~rC=!^g^{EwiQPJv}!&v4}!1~tGaQwmt(Qlx0E0Ozqz$Z(f
zTIF#xdcd`P-1W27IHJ3?)^gKy$Y))y$O4tq+F)-q)~TYen4w0*zTCgmTASUF7Z7ex
z*{yxI7j_YvR7Dy1(w6|@ln;vb>pUe}8V^#1j^I2Wp7AbZDeFUnKz$Bq1k^(UKvQ`r
zD!03Gwj%DTXEukJ(b1nfn-GbFH6&V>=+u|f@%D40W5y|!4JgZ&5lIjZj-D=u0PNR-
zl$Ma1tpkhnTKKKOx7C{M8AAimPQ^Z7erxO!7O9mr92d4Wj{naBXMN5Ci`7-GY&ZFe
z6IiPxNT70D>K{Hf%_*|><nK*;vE#BEv1hXZS5a5RU3QJ~3yas`cjR0ssJnf$Q+}Gy
z;QoDoP|t|j2mFrxIYp3dJ0FC^Ip11$&R*a3Zih7I9B?JCDdXuLLnRSgc~ypRE@L<{
zUNADV{9p`8s#flqV&uf*3oOj-2X_~w;|FZ}Z)q=0b}7)t^+9ew<L`MA+t+s?%8Cp#
zl^ei;%GcZBi>@meKGUtiZeAjT5aH*;H3ju2sw>`($Icc7j%wo1{sO_b7F;~9<%*P)
zV{sfRWcow0STAqZd_?3GVWer8oRKfrN7?$rg_oyXzKI`0p7iT4XbO36r4<!#-+iWT
zu@9thIeE(8?(c-e_du1&YpqsXymGqL%j;RFE-uaF`H6C?tXg)$jh7)r>o*V?$DQt;
zLEgQ=@SIm*t>Kjd-(@Z?I9|rpLa{+1A)Dr_8UsMUV8E|XMpaokw77(A|HYwf6IyjH
z`zPNZTv+e1uZl3TgiCW3{e(E1LB~Mi`40RyTT_J)epzGoVdcWoAj9lP!dv`U9%{cb
zPb@SCfa;X%S31Hu5Rlp;z)jiKH_Vbv&4JwrWS0R54cTt9qUB}}m9KRs<aLH`F!)oj
zUjfVRf@ToaCk@M=nGu1!S~ONIGb8d~vjdo_>Dq4eT)Ba(*$tg-V479n#FzGrLIvYT
zP%T+mTgZ8a)Mmg}wm945`hP@_PR#s>Bv9t4A>q~twTE^1UZBP2LTIHCkSFpIKdTC8
znCjeUd);!$ONBVF`ZKfRBKiWGK?97m)XU%ON%=FBBE$?I-?19rb~Q695X!3JuvB5!
z%z;zy5D5@lYHDiDuvcU$GAA1BlEN6E9Tqxv?;@60(W$@T$o{+VY7^k%ud@}MFI}Aa
zyS#i(X=K>VbWPIj+?#F-xgYZ_dH!qjoTGf6UcTG!ayxFW0>g|HkBC6HgW|?nvXw#V
zLFewmbLSIIEz$tcQnBwodz5|1TmCpjE*$PG030XI<+k9du4I6=AO#8trdAz#cW0Xm
zhszA={R%7yR``44d>66Xx4zO(VuT(bA_Ko@8d>3d5S~~O{Uuu1S@uj8zT>1{%+=my
z=PUJo-ge--GypOR2%kaq!@-Pm4MuJ`zPaeJK&`9Hic?mg?FA*~Y|qL5hW?G!?kqY5
z7r9AGto22_{~s#-{~_++JC1N6iX&GuNF&xE4uUu&@^@e(8<@$y2Y&X>Fh#9->p;MT
zoJqb5EGTBCvJNkTJ1&I;hYX~gS-z28@Map+x~q^VKt5#3tr5>K?bIzni5(crmp-H~
z%h+#7K=n8~UE3~7|L)lGjbXIqh`Fs?i-WG)Ljn=KgTu7tEF4i;KkCxtciBEb#a4e5
zWaj{?joC^^=soO)%gn_w;DGUXrJAK*;(ZXB(ho9?MB2*A4?-eQ-j;#nLrohr?S1gZ
z)(8Xj{Pfpwj~qr%AY$|N;8SkyM&5dL(?m15V#wV`B5B*(`t_jhm%A<;DeS8L9;SJ}
zb5KPPnkC`jh>8^zD<Cs^+Pza<6gth@486}GGAS`V^qrW>7qH)MhV(A%#j$lIMgT0L
z%yTOz``$i`eIr1lby!+f7E28{^Nkc5Kl^8=X~Y;`j>Rp88t&R=uAGre^k&ByCW%q?
zJ~M4(z1B6Ug@~`*d}p?{I}t&1m|{@ht<RpPMxhv<Ze*z0A}9v%a3!`+HsvvM*yaio
zo7@>3DOo}c+w1#bw`q;krrSFOyqZQL^)mo=w8*4EJnXQDhzk=rZYRC)433@?ERn%g
z%aI3B_o>pxMu=Hs3qo`Xg77JC>GMNVX*QOiR7Mg+(Hy&&M|T?HJ2?B^vj-r1VnOK9
z!BsyV^KfUCoj=BV1a$ZG24*HUX(%py%^W2*S@wL|b2VdDqD1bNalXPAhx;eXiy>C)
zJTf~SoJzDx_qwI&Z@{zy&m)Jy-;b4(tw6c6Z3F|HuB)z_<V+)aFSg@mr$NJ6!k@{>
z3)f3Q)>KstWgpsQ!mh?m!i`hXb$XLfIXt@FN-<Ms1jP(tj~bV4LN4}*M7F0)<z<Kc
zc?_SUm8+Rk8O;D(uUg}O`5tD3$|X=_FnTH>%^9F+EtYQ@SmCpVLULTR@I?y#IL=ES
z`Z1h64=#IGUYocp(t|Jnz_Y`}&!Hy5lIY!}XhkGS#8!R_t-q2Le+rH}>5LRgK9Pkl
zmq1)Pf+`!`;Tw!@{SmS7wSKoVs=;c0yZiL7)#by)Dl$-Vl@&%L+yQM|=FKRcXPt<>
z8RC2V@7@?rNR6R7sAp<OH$@WCraWJx?z<*ClDGAHs3$hRN79yZ^Gn@6zQM`lJN#IC
zaQ(IVf#$j>Rc}ZAmDpQmCB2iJGE|;E5g<9fi{10^I4$U8Jvj~7!RqP!*rIQtfGqZ$
zQGs>u)2Aj^#IH}fS?_%;W_*X}OrmPLars7XKsdyzHF`ibaNl=cFy}Bq1U7T7_0lp`
z(^jV~(w;b&-yB=BUFaHZvhi33PjMwfZ%4}7eWo#Is#TQJ23lhVhibctHdgNqhF__9
zAd6+UI68KSJE+4Tf?Pkayiph}w~uB3!2#k3VHo_ouTk9$bVfZ+bv9adPq2OH?)9S}
zilf8#Md)PY7Mr^qTmLGRzurjiNfULj6HQCp`}Ty+BOAPee6-`T4m!rucR%&`)_wYo
z)9`}aYV^lZ)N4>{^s(^Xk>+t)XsI;{PpUdm78(*0@)UvuEB?c)n~1t@e+=Zs>Q+?#
zeLV<vX|j}cy5J8A^;WHeD{vTnsy-TTd!E^_g1!0rtcCx4OBj^D;$`&x(AcI+p2%9y
zvU3}eCYud1L2Phxv1?qt7_o>s!oJqt@~p+_*V(DwbVnTY;CSg&!mm|#Vu<w|1`CQc
zaNN0QTesXujd*%dA^<#mG&DdFz`2i8>m8tf{QE#G#CDv{{~GA%-iVa_)@orJ^qs|Q
zfpX0QKhqs!9aOB#8$;nMW%^EFJVrwV^MwmtylcD-wD``FI#|o!QTRbH8GvxSwlTQ_
zUmnyy%LzZITAC0E0yu^6Xf3gi70eOHnjg7{whCtWgf7-3y|}+_shdr|ZP+Jv575pY
zNU;`LK&_76fR*3aSk-=#cNFOqD&BmKv^zq|9q+?y5ccKQ2-PUFZmOqLj$NoPqx7rT
z9AJ{ihd}m{CuFG=^*$kW9e_OhR&)X)(n`7jl+A9i@ZvAoDlmFPoHDgW#NwbA0vN7r
z@H3J@3@cnfx9)kH^>B5RFVV_34*=3>6&*g6u;<+dQkf0h_O7L&y4H0Be(};^MtZL5
zSIB_nHZ9M1oimdVvsgwaJ9tb@U`{!9FKiJ>UO{9uItO|zZXN=+GKauga8H`lj>f|x
zB`>0#!qG!?Y|4SP{7!gu!(8Cha?6dv3J4BB&-ysXA!+fbW|RkDak{mfQkg5K7g|8P
zd#FnJVy7_P<Emf=AQOn@O9Ys$_cyLXgC)ym<%x3JBt7DNYvnlqJdWp&E8UdO>{al8
z{3bfF@zWXUWWIx)>6cuEQMipiI7LY7v$$VPgTlWjme0bFj<~a9z4Ei!(Bp^~4Ah7R
znX!D+=f=hn8w)eD8$2azl;aL{^Yinnkj>v|M`jeEYL9!WuI*NPx3sYBnR%2HlGO0!
z#0&AB>jncmk&CpfayvvHT(OhH9&WF~K*umy1A=f$$bGj`4<GezM?avmjrWXes#pwP
zD4OqBQ&1!cNMqHS8n3preBV67QlJ~gVW(xZ>N+AGZcw)LysUv<!%4tf{b<tkR~_&L
zQGsrV#*FK+)z?&*o#x3a<sSR2-Wu;Nk0J7sfp4K|8TFDQN)GMdP-?in>Q}!K4VWNZ
z&@uH)ezvgN<et~`R$XZZcjK9z8F&iDW=@^@)t|+2o@zX`DCLv2T$rBkNml9~emRU^
z7~&~4q;tRbF0_;}q_OgpJehfRl%(&$n<E_tIyxJ<-_T*rx6Td@2IiQ8e;I5si-50-
zk_6ui?d%kTb&p&Fwzk}%o1$`-a|gs1NuV3(#!P^N8?Wh?(3AzR$%5G(g{meVkT)Wt
zAB|ZWcq6Ln>gtSAzngLxnjb~_DDYZ0pD*df8GOpe6{TxDF^tzFxfH4KS55J-bS8Un
z;@k0w#uU4C))_}b*C)JFemcU6LI-<z3cG$%_WYdoB}(NR-<4#R794gdQOehIF~LP>
z!E2E7;Tup7^i&mM<195%3544x7Vz49*IIJ29txb8@#_zrnEaOdOeff6Vf0s<DCK<m
zj7*%W!idACyq~?7TA8h`JesXOp}C%C|79{W?C5_O_F^9ne5&8*)S~L=Uvm~%nq~%{
zUOXrG$C8O{J?!29qe4Wq6oNHkG$h>%*SEXuU;S=#Q3X0jlvKhzU_2i($J_ljd^7kc
zN2bllOwMnr>}K~ErNk=uW8M^_984q(wOL3bZw@Kpu8ri(IR;O<cM4f*R^19b%DQz=
zWItl9U5q|`CVF4)hc!2EPNl})^{u_5tZJ(dvvop~(`<I?w5^ri@m`b7k@^leq@RQ*
zk35n6W1hshMpZ^D3e(vZ%iNKHWks>4!wd$KJt+d^+h0&aO*O;{of<0be%8J-A2sgY
ze$g|)8_xg!)(!q%28pS<tG4Q+$^{2X9ApI=ukIeTu%h_6H5p}cTeVioYX}}C2MeV;
zJgt|PmoqeH;r*%Yl)=KNT;g#q7@ewuVPU8<Q?m~oKd>_2e0->(CrQc~3b*TV+i7p%
zxVa~VqV<(1FE3(Y`YiF}<Mj@EOUnhv`fu`XSGJk<e7@wVw6_ZM#`lL((DGXurs!xg
zbK<E_T*l=GNhv&Q>A&cw`cA&*!+lK!(T9n)q1Ieh60c?Uu6G2<z)2#}k9_ZKKJZoa
zN{5yiQMyLg|GuP7e@(*Gl4$V++Y2lPL!Y~znl?R=7yp{dT&4SyfZjw!+{-Ox8&3;j
zmLGBKNxOIQ)HWvPhkcfXw?qwIub)BLOOUkOTK8T`<<;=u_faVxu7EG)6d{Yi9RuSX
zun#{T1s5v$>-XPuf$dk$xB7tArc8710k@xD&WmmgnMjSgjk`pjI7c(v@G=ctx(GM|
zhOL(inmrH$i`R&Yd_GLF(0Xjjk_%2*;8KpU1joOL)2F}N-a>S>CQ#qr2HAaL{)0Y?
zD)OnDHHG%_?+mMo^FKY?a<5@Rjc~~ccwu!kPuBg>8WOwy;iVy}_g0qt4?m>$Z)a32
zUshzUbniSZ^=ze_-i<8HNO!M4`W|?nxfy%#O$az+s0V~DrC1a!sgzQ&kX&pk0YWIO
z{z@g$!RqPsg5cts;WoXC^$tGahzb%^cD-^Gr(GwH25x{iZ-C-7R0W-UHwNlnEK*-y
z01^>Kyx9sxWP1n)6ADCcw6tOAsjFBkqC)(!xdregaxBzox7~MIj|x1ZQC^4UiizP$
z*~;S^<j4LaUDg}A7*zA;C6c5J<FgKnBjA*;HM)t9OMwZ{Qefz)3dW0RJf~)0=0~ik
zXBo`&$_F!8Yp|ojfn8TWxP#jJ>sl?=9r%DZl3W;=?WVmVXGRPBB?B%;>LAHsa}NIc
z_2CQu;$STem;?1W5zfubUyaa_<^Wf_I7UHT*R>YMo0|&Y%ypf!=|~55Jx@94A0t!=
zG4ee~aR!ZngR{{YGq{Q5WXJ+>wYpdAwamHFv<_bPD*Yf1M@I6V(<YD30TjP`S<NH8
zj2aj!XkYGe*^N71VUU$Zjh3D(Nq2NLD`Hl(c0(n1*sFA|KJj=0#Z<&QjLWhW>H<a5
z)deb6AZ$mCkMy7JgeWb^45G&F@7mtbfv`<6sOgde4L+d6o=t!juC&v^%=Z{;TwSz+
z_8#ZguW)KH&c&`i>dP@Mv^cg}7?CG}wDN!mT2)`xwX*Zaj7d*u87Ut$l#^NwDj)IT
zv%V&w#dR)=YDC~%|MX|)s=bpvE8D9_NGgtlrHQmdq1zH!b*$w+*nG_byfT1zgh5A(
zB8(G>a-L2uW`H~P9&-JHPd&^EIXUh+YKwy@VIYOLZtrhk2g5@8z+y1447^aiA(8dq
z{0j~%iJxBExT`jhDhipa>{2@v3%4=Q)ypx-${R?L6Aac3xPzv4p;#;N)Zt+yRoy2Y
z=Ph(OMkaN_o;cWG%j>&yKA?^=?m{o0N&Qt)drD0_a!%+uUia^u*}VOpcF)H4+SB!K
z@90qEAJH9tnQs=z^P%O`RB&r&8%n+z$&~Hs&*5mZ2|L>dTa&0W?D&&&nLETH6het+
zfjI@d+5)90nywp^78M&@alAOKG`cGzy8j1R-yP2NAHA(m*()=nC<>wMod{)TW=q+7
zZ=vjwB(jRg-g}2o_TF3e{ABOvy!-yH-}OA#<Dc)hu8PlVo%`JPea^uXN1vcJXvQ_s
z(fd}#iy&L4*GFX8Nr|;O{hR#tEuN?5rBs?k@hOPF`L7zSLO`^(cIv@wRhE`A-qwV)
zflnx<61RiJ9AB>oGzsax3jB*=P|dlMAP0vc%qk9neuZCQipeUBeaQ#glA$u#hrnwr
zgYYoOpfDKJYYHRy+S*w>uYAv1go-Nm<1J|PY67DubVL93won{}dYIaW)-k}^k1|48
z8rU>dtS@T2Wg?Tapph|>jqiQQ1(WL(XI0*!FyEWoJND8*X$gBj2_SCQyD*H9Phz`U
zc<N%5XLW9uJEb!t^!xN!H!{xt0_{V`C4vyB>3_08ozHW$)MY5}A##2;P-sbU%v6};
zb(ExMO&jt(=bjhs{plXBK^lu}lK~4Kx);E8*m)=JuvIrEs)K!#Fj<tY?oqzZWMv!E
z=ZpDD;IGM+U0U{_yakxd-%Ui7-MyjOa+(!-h=cDwSjYffw@1I4g4!OC=hRq|s8gWE
zZ7^0u>GtPu&sKUW!kf<A!xKI<kNUy)e8b$4%^62d4h@Ekzj&&3i=5<uUGE#<MoH*Z
zsR#{-pq%*kE5t#H^niml`7%(Rh4}NV>W+-?sQG%vX9b}q`@bgAEW_l#U@(tUtL0S9
z3ouV*>pT2A$HCIS+xp+$i-%4xr9TA_<yE)W(Gl`~0!jZYX3p|Q5#1A6qRzDTng@A6
z9sMnG`fDh8YTxxbk;)V#H$=AAd2zgQ89|WZzUvWMbr)}u!Y<$i(~?AgO-n&ReNJ{2
zZ*I=}OCNi|tzr)CN1RUXtBETQGp<4#gT2Qpn^Cq^kLcK@@~zeH+yzmva0;oI0ntW)
z+c6;r@>nzcCXN!D1KV0uEA578(!yWc+LX0b(Afq!1t!9JE2U~@G~J9`+=Wq5^AT&h
zQ2tF0?g21d2~odTxC?DTT(+>}v|=)v-6I6g4l8dXKDi<-5+E&AHf{tARJ@o{Ds}(?
z(#Bif=Lt|%W`i6f|GaxD;mjvaf58Aboc_ft3pyVmMm#E*w$QQ2IZJg>A8uk~^u6x%
zuMBh?)qq$!;C&8#!dqM2IW3UY{lRb2p6mb}W`dFGZ4}hh3Vd`LLsv>>9(Uyvhh7zp
zrXO6T|6^j4s%y}nE-ry|l?tw~49&Xmi&O7WC)ulAnm(01X3L$T2g_h5<ClAAJ<X>A
zB{+{h@Xx&dkI2Taqln~st&;+o8h(3Le9hyKIt|)%oGI;`?wHn?I4A=@AS<APM2s5>
z=n&IN)p;>@)mk+k8`I}<3&Pajyaxjvww~Fqp`iyNPA!$&P<un?2q?j5Zz58i00XPr
zT6gX0MGO7p+2ob`6UzIycMw?yDs;b3OiHnQsZvxRu+E1*JWn4=MGxmG1$NvyjoTFX
zuu=ib@Rcg#uk~H%<H_x|Ytor0s1BAgx1CO2Xr;N7^2&ZicFxtqqXwp$-+{h=&&aiH
ze2^u5ZS+tP-WC|=Hw;cPs<Ra{n%}=1Grho5(Q;9d*>XE9*#e}g-Mt{HJrwhu)KBLP
zVgVNlm+MND4GEqEWHEz`ze(4Z_46;Ktw@nZ+OE(_7hT!Pw`Q=Rxkh^L9QDQt*L~6C
z(&xGVW4yyC3i$Dj0#+#a21aCcHjp_62t3eO{cDS5*}KPcLlZ8?MCcTpRDhSdnw<}H
zN7A6o^gYCDvmN#XspQmj=IBhDr+;j|vX?Em)*ZkOBI*s*lb?pLrRtK7pS-c?K3x<o
z6nxq$*PG1B`psTq2CYo0j@nD1pr;UN3<PB^uLT02P7C<3-QRM;CFhmN2)nmu{z6aW
z&g+fKs)}#EL}-56PI$dEi%TGdJiV^ax9ZUgXxO?8jN9P12$3u}hs*KSb-iG>a-945
zIX>I_L$`XKc4jn-`YO~fK8JyI$S77yr${Jz&iMLcFJSotV&MG`^1*~kWNM1VYKfWZ
z-w;V;1Kzkpe}vt}gv7YAbH&dQlSv852oDpJzq;=KOHp;<kJj!4yWsB5_uJ`?)=*N1
zaGT)LqYcCzY^e3qT#o7d9W3*^j72ccurK0j$5@orb0>op685(Pe+O?2JN0gNdHg_A
zxih?L@xA<fEW7_`pDx9-diATMQ+5&yfrYGeKbYvum0!A5cB9UH&yN3=?J%=L$>j_w
z{}I_)-F%>tm9(AdlZxVZ@DD=Cvm-Ri7~Ic)aqt^Nv$b>ZOju#DR0;{Y2b_Bx`ZAAh
zjm*D@s}oTYwSK0i>9oRP7Jd6+O&JseJ{#V*_#W7k6*+uKMWU548DHIa#4$GLAtc*-
zs=36S-Qbeug_$qz$XEw1nAq$I?RydHpAvn!0R{B8j2v9`IFeJK<T8PELhY5FF+lA|
z4-^cuu-ryg@)19^e0Q&>sKvlgYM*fmiaTCKqw)9$l)VhNb~a^O&QY{20$CMyVGy-X
zE2wH#izZsETbWgyw=&ux9+_N~v9R{-LU-^flphWm9JO3u!XbAuRg<Ed8W9I3okdUx
zjZ`JQ?EhoS!EV|LdTe*YM+>=A`-nwrXHOm{koL*0&VL80*c>{LIELY7|GWm7A`@s$
z_XGO44aGk!S_$VUyS{rZFa`2re7AMpRW^$>(7|}0ta#2}DeuYGigKKJdlt|PJ`HZu
zUN2!+Q$cnKBX)3NAB{&qqY-hoZV9~vaEXE^C=~?vOzRN0hn(j8X7Ayp*lYjP<aFXp
z)AiASV$kaj1b!Q<tg@iRi|xI=L1@69iBBVt0p<J0F!P<_zpgbCMuR@r<zkCoWXc<v
ztwJ*gy6FLNg|nL4pP#D`RfmQu0I44keEfv;f%CY5@KHa{Yr)vGeG3F0%@+y}<SAtY
z(2G0vLv4u8Xlanwq}fGd-)E$^67WP&h~xTL0Bx({|KrwGoB+ZipAaeh$0Tc|8z&_v
z(v*qNolu+gIY%B+{I!~ToBFz!YW9#~g^{HH<#M<GU}PcvJjd^|r;e>^IiuU|Tv;$~
z{t;ap)IA~MaPM-r8rc7!5CwcubAwBmU@9~_K9=a5O33M$DDvdkj`)P<3oPYboyl)6
z!p?2bmdfzMXuNMG#3km>r~i$u1Y3oQ%%DK85CelaR6g`OPkW}rWFTqcr;Es8Lo4UN
z6-Syrt<`TSVMTo;udI?%t`*2CMy!J8VNhz8N-E&R+?_WCisa^rZPen#w4PXL*4}*q
z>R({V0K?l}XO^G*nl9AXLMNUO-y`8SB;7hwx*-SD&aR9<1WM0#ha7ddfrE`_Kvz_<
zyzwqWLAGM3WxN}gA6k+Ld%kcV%uT`KM|@r7$6rM>u4gN=G15X^r3qB_RGI6CUOw}D
zP2mp>q)d@6I8{)7iP#EPTXNtNsJs9*jckiQkMhQc5|=I80G8;Amxy)*ev?5n?wGb7
zK}PNWwQfKe6mkSaBi5FBN9&9KV(&qVuedU{-jyt`J6K5bpQRRroOOFLeCG=~w{8IA
z9dE*-H5*2R1pKO*<z&r%pDY9hr#O284~Yys`TMu4CgZ|5h_Z>#z6s*Kau^JdyL1V+
z@89B*MkT6eFnNuA2P*cXW;b}XkYX!vR)y#$fXSi<>HZYUJ|y7rn+obU6uQUqz^&BM
zUPko?aHd>r)e9}^+wF;+aI?Jiro?!^><OZO{B9uqhPFC2SNG5{y}t_DLm-vHNI~CK
zdUQoftBqmM^j(3%<K&_4gNN1aRu)oZIbBB63r>l<U^16!jg{-$TY9nrCgDMjKi=J2
za*R>5v}2m(-nCP+wp;8Y>G{@MI`?d0c3NBU#mGG{ucW?+|M2mH>hY@e9n`^twYhMj
zs!WBF<FXv}-$ZJ33OZxD-dh5BA6ZX+V?#~LT5Bm*Ri(9kd5eA_l_uDV;1TVk6AWig
z|GGOk`m8V6HUkW)nJm@Zs?fMAn);5S<GZUz*|v#j5WP!T5Zog^#=ZL(VOL0%Er~mk
zVQ)x6hBR=7jQ92^Qw;QN*7_zH)`yT1FrXojyaZYCH`pP9k!kMr(-~*-pWLh{p38g*
z-AGRAoppNcsX5R;4}K9i=(aBF`C0^PHL*669$pS<KXJDTHSvId70W-XlS<!_@unPy
z^#pV9)#Bom4v6`=Uw9Zz-R*vJwoQV`<eWPv?9`)!HWc^K6DTQ9mQ20`S=^V8+f8E_
zh}7jF#250~bf`IfCHknErt6XJ2b^*40Yn;)2MghX-#C|I`7e$DI<xyz?vLY16jm=r
zs<*Z?+Dh<d4mwgHa<IRDd;4Qg^)sUPDShkLS@N>8&4FYYoa_$AR7nkVNWJ@PiyJEw
zqtBgS#dFcayWl3DaUUn=)6Hqejnao(_Y|&uPT*hi`r;fI|C*eIk)A#!>G|Y{ucz(1
z5x0BglUPHRSWXX{e5KVpu<d={MLOk8q{uHyR=F4&5hhJivk07@`((!w|3SODlVn{M
zqxL+6=7H-7O*}i*`;BXc!jzkM_AP!*+7CuX6I}7A3VahgB)r2v;AC?v;voa94@@0<
zY6=PnVJgW_sDym~(czm+f2ZQnMoZX@fB;&zYcSiuFDNLes{J-S@|EJXs&Dt;ylCN}
zS)AthgjGhsojQ%{UQ+5^qFOvHI59<e^F7fodr{u&a*`P#{ExL+tCSy_Dq1}e)*Yjb
zD5hN4uo7&TCc7xViTkO=<Gp+$Q`EEVZhk)w<2}l>Xkykd2UPEw0mW7~{tF4u$UNEL
zCry*v(|*GrTkKe<Y}U=N7%|7pzZQwU`ZzQ<UH^UN;1$Z8>Va6ek&C0-P06E?lFG@>
zyhVcSRWmO>(zTJngup;)hU?gumL9ejZak;;l4F%H3`vn`E#&BHUgt3Gh;R@qh;k)R
zA3f;Awfz~sEJR(emc^wuJ`h~Ke|t9j4w0~3-kM|Q$PK+WG`+5Y?aIQDmBI#jWG*(b
zEJknn8Z`8>8SYA$5&bdo7gvAwQCERYzX!f&;4x!vb(^(?#ZQ>%S_nO(s6{*|u$#jO
zGI*<FrvI$1g+QZneHf!@<80i@`)_6GSNZMUy@?>uX)>1T8=KspjiiOoq+hM$m-Za%
zTu}5nr-*3KMDUd~lu_kn7Ty;9i{fvi&Djx3cQGu4zJz%@e+{Wrxm?3BNXOFZDxP(R
z#x&C!+qUqBdI-bzz;_d_cI&sDeyrCI9exuY@{OBS2+*&FU-hq??Ptg|iDco`IhwU7
zYw=xND3v?hwPEH->=w&@T*>nQ-{^`9ip{%&a&BUF+I@a(+01L*?A}YeJcb0F&Y-}+
z0mk~GOM)po1@{HicfBN~r0@LD5Y=K0bZAa}B)Gix%dMjAT%0~~W^&YzW8vF;iorSh
z(<gkf+M)NvbgCIGtW;-428q`Le>X*bB<?lu3g_VLY4Dt7bJ34wxlG+r<$#-V&iw1?
zsWE+a4J~)reGcv(Rr@D^jU^_(af|IhOL)Vx<GqnOPeC(tYgQ>QPHnH^5a?ac51hpt
z<ov=o)Hqgk%a#Z__Oa$f*cte8lX~sRnRK#JWU`boPPzOHwRQ5C!#&VGAi~lrHH4k~
z$xI!4_t#3*BLf4k*XX(<Q~}&F43v*I8SL8DP;>iy3Eep+&RRD}JUx!}DJ0#>Cp)7v
zzJ13Sogc{}8u6I9&ANwXj{c)dMX&!gkrMA{+^XT2zSOBl<s;MepQX>?=r*>id9t|k
zl>I`Tt+3{Z#0_mSv-47}{oekAK_?{8M&j?5i1Xp;ZDJwOW&#3FXEBG<DDlk0StTcK
zCVKi+Ip3SGXwN;I1xV8S+Ij|C2Wf{SW9e3cD`(kkIMi#gsV9FAwq5I1*l%*L{~j1#
zkFuvvsmV3yYuKFiy?OIiJ+`3X-$JH5G*ZV-ojl8mbW&RCMgQun{68MWkX#A;SyHMg
z{!9ta-%TU4kP5l;oBbv|LfqV>U<GJ}cOfz=3jGnkHe1wh4#!m$q1_(w3U~&S^Sc9H
zBO4<jF(CK>C#%q7KFvwcp;D7_Zml_zxw^mP_4{J}sRYWm>dtq9!|CMjHK;T7q5imh
zk*A9vkV+;jn4Jh?Tg1C>>HOfTl~CZ>dc&;(&Urt_)fbG%0^0^Vd?{YWrXK@LdZ^KO
z653nhIsM#guZR(D%RDAJ-)krRqwbXBo+-=R0IwZt;<o6Bli^&uhTaWa+u}zS@@ah|
zDD$+yuDbAR&i}2Vsa&qzdZqRoZYEBf+=?=pR*t?X1K20z=qeJl3##2u4|jJcVRF)A
z`w`t}mR8u9C3$*qkiDTxLZ-_7Qr$wtspZeD(p2+l1+;<(y-gD~<P)L;s<|YzLC4JG
zGUBmR%U{1Mxu#e>_agOML{??Vgv(@eYs*_I<CIa1kdTTh80PD}gwnu*;^KbAcmI}7
zf=h8dxU-2xkVGQpPTeG*sFugyM-;4~yHy@}@8EmfTN7|<UJMDpSSrt^m@^ByYrV$y
z<r`D<5SKwyrA0#f-GGpHw~~^UIlt1e8pRCHHgBLRdVJUxuT(v+aZpF2rCn)Y+k>CQ
zVAhI^;*W*@Pmcu<kd}o`9kWD31xFf+SUzB9ZSPP0!9UBZHnsaItXs!~CgL<%FZo{j
zwu@I0-KVT&vvX9k`N;Ojw$+yaxJ_0WW+n|0OZMAmE3zL4xExm~A~!#25UvE9-U_GL
zERoAvX-jq4s%wZXJ}xnlI-=`(Wza=v{I)`40_7%NOkID6)Ids>Rz5vithd=}*G=HL
zELKr#jSYDys?;Z3cB~Yg2=5Vb5C**QDJ(Y`Lu6@h!I|uH(nMixEwUQ9ySuY$R}c~i
z?-Ih?@toPLe>XhP<R(07+udjC1)H*J)#D<9y;r;C_Wg3=eGGyme7(CU-=_L9_u?C1
zUve?BXZ3P*Drphar;}#=2`g_?TZf5ebLmHZ(38XS$YhGyG#W7%M|fvXZFq3#s4K>j
z*%q6Ga<&_42wg@fvXceiPAc3c#`+}bI+M16dS=KSmOJ9fPZ#1}eQ@a7ZXUn=Wjf!W
zy*U9t;KLQc`1!5=%kCb&6BK$kBr3G%1x~8^Mt%=GlrUDm*_fD!25)K=1H7rziETd>
z$n6w|C(f59M2k5GTPLoOcZAsZ9H1q=pVDg7zQ3*bF<@Dk<sDj04Z|h(BwEZ&316HS
zq$5O$0eZyP)w{Jd_ZlaO?VEHsJPQ~)!W^oVgnNIG{rUNoA0q!AohA_%ImV@jpK*D3
zc=+u8T!Tpuec4M<(zgS{!#4-*3E>YB5k>B^WLo`H7Hye%`N{6hiqrRFfy3o<4q5!7
ztwQ@7D=YeYDK--)N2UWILTIenmW`b8hJ`DrS*jH()xjR$@`Kw3>C(e{{$%{0tOHxZ
zqZ!;&%}w>*tEvUWl(o)3Ry#@5!E=0lI6ir<LDkF#w`9Qaz+YfiuU=bh;eye9((3H9
z1uONUQ+AMybLlTq4R4+sbaYjWRW;QV#yH|NE-|8;c#f;l)6Q@}vKdca{w~j|ic2rb
zhEJ$OerHkLpz}BGdI<a5nyCBvj<1Pj@k?4yrO8A0N?)h1>FzhGh*C?lK2~!4)~8`-
zz^!HxDhco2-#lK2BkC?P|JWOwp(l+xd@qtR0x<aFg6h0-Gsy1SVLBX5J%!8a8K{QQ
zdFfHI;>~;auzrNixLU&%A;d^-wW=few{}X?q=`B{(Kigobcn~gr!e%`gWejm3z_T`
z!kEh?|C!Ow!j$<XP8I~;Y?oJ~yPZy0@+YeKlb1Iqe|>gzYf(-W@4m~cNz8ROyXzLR
zyMr2uZuhQV9!mV(c(p$5YvGgL&HW~!-0j&K!V0aO238^>#|WQ1+ykkqxiu2XXNKw_
z<~Ab5DN<_W{7$797b7vDyM0yomeyJMeoRb<Gm%QP96iyFC#RN_8-{+;&NAB1dk0US
zt6|a$j|^(lT*vkySq31(Cq`&PUy3`OAl<rB@>hO~Zbm=&C^9dWW)Wk}%Y*LGI6QQ7
z8!s9(>cnayr;=Ij=m_K$CgJv95qxR&7RTRJ19c#zM1bS;kleHOMcyOLr}r<bV)Vi(
z4=jm<1-zg>H6lb16MVUa+`Km8!ggb8T}CpbW+SMJ^D#B*e1ec-?O>{P$z43r=3e&Y
zwQi%3aBo*u8hB-5uaDSI4yc-1M9!a~-#a{aPzxyQ&NcZlFRI<B&@Q>_)+0+&)V8`k
zw_o8TIEBtz6<hD2fc7cWW~u1~T-paAQWf*@QIv#hokg=2PUXIflxd|d&KIQAPggYf
zo-#)3_uB9Z@%~n~oGrSc(|J~KIKGoIPe=Exxutb<u%`UFD)(IL&ge*tV&Fuu+uKUb
znLSFeYqo_w4yJIDu{v|H*TYzT#6?j`-^*GVs3XR*aV|iorJH|e!;2%*I{d!cmxhD;
z7i~Qgx$_zy@$ax854fy0>ILYyd&kqY)by6KhQ^n5K1=ML+uMn8{})8kO@vIQv?kk;
z1%$8joWdvJ!%j>u-5qq}$A701*Vrkb(caj|{1lLpFHaz$ZFOhvZ<a*smuWl7lP@&r
z>MY-Mvu}yy6L(uzPRv?;=hf-T{h9TJ&2TSe+l!<??JkCgHzN+6Q$fg)jRK3ukAHU(
zM>{u^<K7SX66FP-RC@T-jSUlDm0NSHEE<-i`rzH~aAKOA<Z5fBvhKL%U+-s{-Ze-f
zd8A>O5s<e0Ghf;Cbw#YIPx89ilp%)LkIGPN3%;0l(;tFwsh^^(|GXS>WYu-2g$E2T
zLRVu0LW-N3oFNX6-meDd^dd!-T({<j4<LSZMA$UX8wwr0P?xC@W}z_dYgZZ{7+dl5
zT$knGoxrsB=W{+DiwvxiRFJ|JbyvPks=^X>hg8MT>g|<a1Yxhzhh6F9<mBXbbwhqh
zPfzs-oB2k;OGB<X)wh+_MLa`I#yA1`spVmD%;>w0uMS@-@qPDp!-h-vY<@5-*Tqg5
zw=6%@w@$v>Dc&rSrQp~^Kt%9Eu-cPhMn5`f=0^8=-G=ObKerMdxnHc=O)`ngFVEXb
z&{%1^{;i_>CkfZUE1e72mqe4gTlBskJ1_GA;@{}4JlrnNzME~i*&|}5CW$06->;0C
z=lHEaCKEE01*r^PJS|=FV%G7OvF~c@!M~gQ?MExrw6pE1S6kF-3YoA(g&uy?u?z|<
zQ87?OuM<st*STb`*#BFxsI-t4qLTj8kAgX&dv2MJSFxq+#Ua+yO^wA*ByZdgb9lhg
zL3!{_(6FGG-`kqR^wIlop8@Pu7HpjKBY<Q~@|&2wv$L~YQqq41?n<CN<${ZuGH%=2
z7{2PeD$SYpC$lz194gAMy=@A)8l?^b=6W4tBw9)SW^YG${&sQRpP>~rb~C*XNn=r)
z{A&450gtawp6tTIadUa2#?H2DGATlCIU<f9Sm5jlkkFkwk;>(6I6t79ef^GL(Z8{?
z>Y%CvUerrJDClmuj8QNY%eoFm(y{-UTM4S`>5nXP4Gnog>f&xqOuQ2<j{h=r_mzuV
z3IQ$60v7sYQsSQ>Qij^x2OUk=<VDOMTL5RwKQ_YjcP+|xRZoo`&~F;Gfb?U_CSZVM
z_!&y;`GZZ`KRxY(5I~Hz@8z__kx-71sQ0V{{Ym0!aq$Rc0;FuxyyZn)e!C65nvMJ>
zTlsSbZFR<mpJ;*;<eR=&=S~~C48%%X*me)mQ=;`0*fRWYUQ0lGj6@87$$ys^S=idA
zgn%a)ake(=`!>agt_D=j=DPd2)fl=M%i<QaDwEWpntL63QDIAV90N_*#f=Hoj<oL{
z|2Qsw;j%US5&vBld4x^VbUJ2ARWhl)+80TK=)(`=)|*d#i_>l~UKw%D7IAF9o7)^R
zK2MZ5=|yhOAWW%S$%Ga%(hRTdfCO{2{_ktfHN^^*?yp~1l0agt;%cd=IQqu>BF<OX
z7L!I~8W3%bL%HaBCvKIty+U>G-~>6&IH13I61d4qxptWNI`>+Ri%0pQfKD_#HPn09
zax~hg7-1kmPcKJ|aVfpO4F&Nxp+aqJ<&0tCexXGUIpfta@s{oiO$Eki<lwm2bn?H7
z8J>0%9%!bvXVB(g_!*SA98@A*5LLOn2AA$v^%R;q3s1pKNJHv;#O(jOxvMc$T7O&|
z)#;sn{wb|YOp*<DHo~0uF|abr%CUsJN8DlGF`->?a|8=5e_a;NG^NLeT(!fgZ`+lc
z5@A}%n#LxFx>V@sx(J#9Vxzr@Ov0~oce%Uh?Wk5VpY|9n>HGniR*aOv`{F~t>y~|^
zY>P^|Lx=jh*XS+-CK~GuvuW`5st-Oks_Ywq6?i7L(?Z5yCz9mD9+940n07X8i$=I;
z=2O0jCmEalMB(GaJS1QbI{LB6A4ca#cBRJ5dr0#8yT1S`qwZVDcPuToMg9IG>S&Uy
zg3N&Z^r+>U^tS$PC9ehsW^ii$!CsGG|H;er@V)j(A*qbj2kAGNV*46ALpVg6+C-`O
zwnZF2t+QL^4ojbNmHi@Ge}zvcNFo>WM1uY@jp}s099zz!uq3S9DklXOB#`ix;+6ij
z=n@N$B!ftPR_6wF2k!RZhikdmDl5NI4|f}G#h$O+`#8jPFKn2%y%63#82EcVbWiZS
zP!~?EM<rVRMM7Yl<ih*IFP+zHe^wV?ZD?HXlGpGFH$q-bS{-L1yGS<FUZ<T<)h4}@
zC^xn|nLkaQ!epO}Iclzq@8=(tvg+K(B;0&ioS!KMR_ib2B_yZ}tTIAI#S>7_G%Kxe
zd3{Z~Bfs&cvHdmQH+sau+xdEVH|vX%#aqh&3b!9%=sU$0t6d~j4-R^SW#uhqj?{#T
z?TC_KTzbGC4LA{}{`U2hCsX^(qKT-^AY8bbxa?Z~<8buTGO~zZz30+H62EYz<})<c
zTq76a8H?JQ^h^Nw<cuk3QiV%z7Le(~Hjx{)so^v8J=NFjoEn}Wr7sQtz%!AfhKKH*
zxt*+;*XE%_Nkf`6k8h^<tihfpO<ec;TeZkjeq+(#9Xr{(rXdwQxoF3P#LP<~-Zt0B
z@`Tx`7Ufwoo2Q-c%*>xO-Ha@`(A8EN4uu##6Z1C3shpd4pjp(2ps_Ytp1GWI$vDK>
zn4l5K2U}&#;<lHoLPpByvA<ub+DKAP1Y&DCsqLq?*OmtPPUO^(i#J<!ED2HM;{7l?
za=bY7f3Jl}>eZ>Kc-AqLR%lZAb%jvW&!~A%0d7~hC`q?*eaQa1LOjBZIooX6zELxb
z3{s=@^su%jLEje9_^?#pF2^cND~IaD>-qtAlxX=rYKP)26-Aeseqz~POuC)p?Vu5m
z_cU|q9>!PIOUu8xbg+o_7~z@9EO}@l@Ffg5F^MgblIA{Xa(LwnxY}rV7{{Cm-Ueof
z;@EMK;KeaH&r%_*jI{D`%cLQ4H2dztUl%<2P`D{OlsgPB+3e0casAX*nP>EU_`Wyw
z&qghfr$%$!b-4=uy?i+W-=uIAxk1M%8+LOfBRD@Z4_-d(j=@3RO+a|%t_ckHnh(dr
zF?u(by?KVZ$>=)peEcfKAT8wUz~d*~JN&Crwd?}WAKASvT3T$K&rYYp<m$Y0>{{&e
z4w|mj<5WP-9IW_g@O6YW+=Cwy`x3qpv-l7~q}&%Eczo5~*!im93}kk*Mw17O&vWC}
z1FjAykkFhQ_Hdt;us-V~HH}&SD_|iM6P6qES8=eXZ{-Z4ukD2*FDuoilX;3xY&NFX
z)4SIbf4M#^#f7AJc2g{TGA~hp?N&ba7wQ^cWz&Zxci%k1xirh5sc-asr7bXnDSDX4
zK+A#FOX!PV0`|iDm+o+2w+~GeAWeLC%Q%&g$W#K}J})^HtMf?hp8aPDNaPulV95NM
zntB`K>Q(c(0cKM}xqrVqSIl7Q{I=uLjPx@sgsbhQrEWx{B{J1E457~7kpH>AT*Fh|
zAN3TqnIHz)eb)E3D&tWKcI~#T9{~DO=C^?H4gAOWer#Ed(?o5avy6C%)$FWn;5u5r
z%_1k*(#|+;&OHB>o6%Z#__!ApmHFDm)0uv%+Pa{{Tb;V@c_?{#>$j9d`2#c#fRYz=
z5jq_~fzl^JTz%qPCR0<ltK&~M<WMagH$(pxuB`M8Ft&|TG1TPPu!$dS&1y?xc72aw
zXmi(h<c?v%02<eK&$!t3RMRwoiF$%<VDPexRE42iAOo($7A`~Fjn%mY_sftNxb%8X
zkH&`<dX*&v=3QChY(q5{IzY%eu2;9xHe}L`nHjbGJ#zlX<X6QpwbB>HYnRzxsUdIm
zX`DtNnd{Q@K$G_U0*@T>?^gu3g?$30n|u?SjW3g_aL>8vaI%K&4QM0Dpwg1*pFBMc
z4GJ=L-B1{JiL<$B>8!hE2@|?qU`FXS7}-+N(sm|=5tMaY4d8gYH`zSJ@MI1LsC(-6
zR@dxGX8IJ->nc3@%`GNBmr5rwTN*mh^zH{eip_rKhP`&_c_zk1BAx%rmqdxFz5j<M
zHZa(26%UemL3-FeRDtg(gkxX&18`bHe@lhT-kGu2-QX-L+j}Qf%RHhw9q+QEiIU=G
z`d8);-hNl(pE|ko#I*4vh0_Cw9G1Lye$fIWd^gK#eZY$hR0xA$Iv@|P(A<AvnkuL!
zx8H<5ov~>~yCBR_6{kMLYq*xC4PZZP0(-%{d4G9D`T^%pESYZ;L}Ojte*s^dIy@ln
zr62adR1G^km8R0I68zfFt#|@i0<d+5b0$=5MnQV^=#MOPx%YoA7<LhobE`>72&ZKw
zW5U%dSn<H`duCSc5c=C^Uqhq|2U5aQSs97cJHFJNs^K~S-+qY*Va39dUcnE;=v=Vl
zAIjH@fG&o!Fz~!JguN~hMqrl?5t5S!5Qv@<LZ?S@bMtJ?ZsbM`sB7QPYS1gwwz7{u
zAV|IW>o=~UhvlI1b)sI&F5{2$nRJGVYU55ZtI4#Vkf66Ni#MS1qWb}FUvo?dJ;y;i
zr^zRlk1abaH?|vx_;OxQ1(ChGjO32Pt^K{ahRQUV{}_vAzAY=vE?kG#heoEiH?=&e
zUM$Qt>=SrbW2>_;Ox#!<SlDrR0!eeyD0K+SyFrjo##*mjBneWmefk$Q$7IwQn;U1X
z4(t8qqI@o7A!!9%mlL<{-kMrcXZ1FQJEbPamgk$c>AHszCLj-kRObski`46?hLbC-
zz%@!KXaC(ZaMxzVD|C4)NG4I$nB_-v9W;lj&!nNBza$dZc4^x`MBky;hG5sX;wGK&
z_}0w9A9@d4E*h*OONFf3l?!~`rrielp8k~+l4pTeE(5t4qyA2Asi=^*4lV3{qPN2E
zaO6uM{e(nXa73Iu2pH-6c=nBs3wVaaD}OPEJjKL~L!#^S*T}DxNlCPE@$tQ5W5IOZ
zr$jJh8qCZTKCjz98BEo<a`0?=ke7yMWM?;laib6>oF)i5e;W_luIIjuhkL9$@&@)g
zgI9HkcRpw@Msb+c+FtM-nou!6nfn)7JarcToj_?}I9-6*4qNM9m6Szg=ss+u$?v|l
z+HVljz_#eJGj+43ibcuc)aDu1)M!j7WIK?(n;y8-IWZ#47p|S!q*6L3q*L2x3{5uE
ztNj(a>~)$WdgUz*y%T8S!Z~h#5-f=@E@`nZ%;gIIHZd_hIfw;91hVl)pr%JrVQ|6U
zg7N|nE+{YMo_Jiaqt+d`zF%rH<CvB6Riiv@_irqG<wx`IxJwGS^K8|*TKm=v=xRBF
ziFc?9wgPRv$~34J!cpf=@1F`@IFqcu3Qtp_O<`#+`Gr|i$%M5=M>CXvKE<7^n)hSs
z@G>Y2;REtc++9G8e3Gfuc=)(D8%udB%i5~BOvZJDGCm^ogXY_cIE0xbS|8y2vXyZ#
z$Xz<O)Q&mgAadg==7vHj**riU5aS;3PE_j2F*4AvS_9?jo6uqNuvFHRwGzGa0rGT}
z_(=LLf*H)^{Aj7rdb&<aYztb?&Q4U?YgF3Z`7Zm}_eHUh0gSQO-rZdvs)Ruow!6zp
z>r=HiV9fSoWo42#qL&xZIP4c?J$8GgCItwPx6`|iS$*bnXW_3*!Y$bo<LJHn8fCYr
z1l7aeCMV^@WHY4r<o(|)$ja|j$U1MbQmjIdavN?mu&RiC;WHV7BhlJ?S=5^`5-lR^
z?@z$j0X|mUb$ae&6o#FB(yZF<w4L;c-0=(8LWSqVPQv`yNj$Zj4b&Bd6NlN=#0Bhi
z1(TNII`_9%&<$+LH*4MC<GRHGu=|eZ^s}2e4{IVPbbD;~5zyCL!#>lqx<eEzmSLPL
zZOLmmpGo=Rb*I>?(A5UZ_2ErKm}J-C8v?cUpMCHbrK(<{t{u=;UUBydrrU2O56rvk
z03hHhafnT~eyi0N<C|t?(S}kEB6R{uQ?n-zUWy1N1iQ|%)rj^JAk6>SC@b>Fwv96$
zCdsq3`b{S^<)uqD#Ajohl){^V>|wPbj^g;h5otNFpcAC)Z;+jAY|qlWNMZu=+LyZ7
zZ}BN6jaI2Yd?@LDE@G~O{r#3CVRxJD8L^uEG4l!+6g`I68wq+bLqi6r5D0)ak(baC
z5)+9iDT83Jcf<5F9UB{)jf2D6pdc(<JiJ@iE>qpOp`@(r2Xj&WEG*2zqzP$h=|{4%
zxG=%QI`LqU-^#xOHa!>&mS19pT#Fdbss5dyp?YsQqS}?gPw=AyYI8vZ5IQ?TrTlvt
z7C(Z+dIP)7PZ(?bK3}TSB=+G@$Jx97{R+^(7YUpcPca7*H!ZHA0eO4aeX1zos&(+#
zkypY^W43-r9M!R?(Ti$3CKEb6!(7+qCmv!v7WO({UdI-yK6?0<Wt_kumtrZ5{QI$(
z`jE5WBneUPk3FDeZMf1f2E)AH4utL@!~#1l&Bi6qL?0p!9qv&A<G$|Ta8Kl(PI;DR
zyJ|kgL+IJ?<x?kh6Cx5&h0~NaV@Ve$zWEs1i|YV6cT;zSj7i|UtL|0kPWijYBuQG)
zy{CJfxg#F8$j#+nea)yU9B*aGG;OTne4)WVRed#!!ScPXW=JU$XCVWlg2Qumn2k~f
z1*KSrrlZ%Ss|#LU+Dn4ubHA1RW&e={WL8cq5F7k8_$Kgn7_<${$9BG?1qEg}WAc9v
zs!P(<3}_*9jo`&VM3hxis@i^D7!)r1`5`8hel)|l><F;OPf%4;Ug>Uc7x(fKfnnH7
z@^lybc?-POj5xk;z`ivLGfi7y7*O>QDIyZ=i6ZjvzRLGDTP<Zz$QpyfH-cCm0Tu+R
zb5Lu6s29yi<QuiZ83n_bFv>|>m=)EWVl(oYDkJmZ-h-jxhM15h+pVQ^rRfROkKWU3
z<TVdCuR(%St1*ekDW3S=y_Ob47iNJZ9o>l6H&PIJL3p3=Ngm<$LLrxj!(CFlcRh<`
zW#5kv>oJol=FP_$mcT{Phrxs+zV7YRk=xZO4hRim+j__G^qzv2zpJLf#|Uou^dJZB
z?yMi5ckMz~#dSJKu`jjeMbDh{zer3v#hX}maPPh;#TsTO^|tzT{=4-p9Oo7Va(kNx
zYgK(32C59e{Q1{U()6$_Z7O6@X%@^{kktt$z6-7@6P-UDy-R1_LHjC1-K)-5P3-98
zbP(mhO1*i-zG<rdJbeo82HR`BR!Hb=mY+R%TPC`QR)MNJ=x^4BKQwfL`Mes*lOYYZ
zi0Bem6_+^?JPlI0EQ?*znlfuev1Z<mg#l!eMnUk`N{9=Zws>H(7|=rYLW#-riA>OE
zPUDx*vNbR$r~&HYd|*iW*QzQiw9N`4W`{p)ZI&k!Q?(NV1J}XZ(bN{r0)MZ!N$(Q!
z*Dq}N%Zc;XpDFTh8e98XwoAQS_-@j>@cYsXBFTLH_Tr0Mtb)Xt93cks;5hFyek)BR
zM13IIbn%oV+;up%jxnPXRIYKJ))zYHUvMff1ya@h0Iy|0KVXV|od=u=4CN`b@$7$s
z!=;S%T5-nbwnx#$tIi5)ASur-`#GmA`J{SWe!ILmU`O1bA7^p^c<zBtP_yBBYxKW1
znb?Usvk{O}Ro^kE7`<8*H_TP0Kb|H8x$3)|zsK@h*4E!6nwut3KR9?x9jc;1vpks+
zi$#q-4b(}>7yj%668Ux^=gOYuamwE@Mnpni>wfj^duHhwjTI+<559ujb^{@=jEcIt
z;l<G_Y8I9A104{uxS1Yq4_BYMXIVTaseJsS0pO1(J$j+eve(&EzB&hgkJS%3p*}<$
z!e=BmoUgQqK9C<bG|q}Z92P#wc|ee^&tgM1Ln34;tN)ediDAg!w%hPr{R2LcL;b;D
zkK|xr{o($&b)nmy+4^)n9SmS^+N!?@_w~J;W_jUZc(_k^+<D<-XV(Ze;0K<^4p3=?
zethilUKY@5>a(Yg44<qkQthyG;h_PziDl2A<59;!%_%DQ?zHVn+Q#+E+q)4DMs{{h
zDl{qgoJJ)Ey1BuDaJ(sD5z=LDFDU%^`b8{F=t%&6XqP4MjkcgQgV3&4+4=FNJF=x=
zZ?o<Y11#je2BAyEX?}uE@0z*{Lyd>RaJ3GtEL#h3jfyX(ot*h}K4Y*?lLSghxte(P
zgu<&Xz|_}-4e(hapP$W02ZSWM(EY)#pdQd$5KV8KZ6PI(Bzth;`m9w@fRx+JrWk%t
zbf$C4>-SNV2!TEMnKiADtnv+*atxXbn_M;=<5RLEJ?!bx;VgYkPMe>$4#J2P86krG
z6Z<-!ILVRD$6`|^;mcV0cY4P;ofFj~O;9Q;@^#bP<ddPhENz|e=iW3dCf-4=vbj#-
zr-ybA;;k~3VGTicXpSH+QqsO(NBqjY<!}r%AZNIG$|vpu@Yb<?R`Er<%BkIzt3oBi
zmZ9b?28%C|n5rta6TiE+XFKhAFk5%BiH`Q_)vKEV0#qmz>Q_gHgq0OrR#ujxp7*?D
zNMs}tD=X{F>MAw?0m0k1Z-LsptgEXVclr<sf5)MabbYc8RXe&V!F)+Xj}K#Ws09s;
z#ZcIz_V#7_dLci?Nd4ozV=~rh!Y~>ZsYXSGq3lu;r3+P!!8fEgQix*I2N#_HTpya*
zV)$3Q$jW@Uu{{?0=;x9u^VwPK^4d)2XR%oF*_2-W2(VWThBQ97$SwwmOEkHdJ-=d8
z{BU)*Z!_$^UClu|>8XvHW6wDw`wc^n;~7F+JD#lpe)t(Pwlug$)ffJhMd0f+-KE=D
zG0RiD#`jsx-!UEFoSA9*!Ep3b4#@NfBL4QcEHy2q9~$U++-ft6-nx_UjHQVk>q#u_
zT!v3uiHy9faGg+6IC!f}K7}Z3l7C}})`vtPhk6T-T?@6j%~<sVLkbiB&%J5)OzThc
zA>T}0)~06IhUIJXYs#=d3p51VgUSH+!LJA2a;%NkPoGrCbh2pSae+tX`Mx8<umg-5
z)A5lDjV`1Gq@_R0QLc5M0};_1+m4tZj)}Cyb1NP`l=Kag*FC4*H8nLe3k&a#c9zVm
zuK#07r3f)<%2*0J7hEnx?i0cx-g2=7C(F68Ge^=iF;kTSNXNRPlo1h|1pRSWkIUQd
z<+sAm@&4(ZSfbTyg34_7WK-pUG?@7<*9$7oiI{t8^&K^Ke7g6dO~(QcEnGbK&dY>p
zuSSL3ik9Yp1RJi-S8kSPh^|^45b&~VwV#=Zv-aGcK7kdNV`@52yjAvoUIQrYs{X^)
zyrPYm){{4I4bMbuPyFC>RNZaVaa7*{{**6T?fdR$wMk%O3ON}G*1BHZsx8UuKN$x@
z63WI|h;KsGJkPTe&k=QCdg-uT4RvyPYg3`c#e4jc`~6Rv+J1wreTYryuRMn0o@1(u
zeg?~@upR&6cLvqt3O3hmiliUuWdE&=ky|Q89a1P%XqdGwp&yGIy`ULN>i7Ysl3qr9
zOWTLMKfZkvR+{YbRftFpvf63s4hsBR=KtBpO5vkSc_!o_n<JOu{T@v2PJ4;OX3$(G
zgnwN+7O?0Oli(u65JYgxa~1T;$8Th_JiB}0GX`2w#X27#|FirF@snsdB(<SV8WT|?
zCJMcU{Nmd0->Zjh$r}fUZ`EnPX<#E`J{ZOTS}~@zALV^Df6}ezWO_RPWj_f6u=L5J
zbAi`@ScHRAQ_NdAX{8UO;cOUXLr*_th0L>f%H_HCW+JeY^h{*+v=K%`y}MQCX>ym$
z!;=gM?lfD72g>m;3oM(ZF*03dE>9emqac#k)be75-M75@Kz7e@8z?gb1a2HdG|RAt
z%cB50<TuPQ)+)&m_0=9+WlaF#Yd?m>wG4s6zWOp;O49Wun2~a8HWN92Qo<uQ12XA9
z50Zz|Z9L?J__T2`v6U0+(aN;?q^8Jk80RoG&E0hdCc9WU3Jcfa&Al${^)5C1?6O~&
zNFsUhnQkmoGvh!4;x33<hxaS5J!ZP0e^nsPU{ph)<C?86JO^Zzm#^l3oOxzY+E>;*
zD0evX2zx3~xFUc#J+w~~3Hcknt~zd_yxctvz}^6Jc}35?UpM1DBz`a6e>?Q-J~rYn
zYF|^y(-%jA5?;v`78l?OqRh_EyAMwdFG!>3!8~&~oX}t?66*b06AhfGHf-M>JDmQ9
zFYW;)?NB|nnPqCxfTK0P?QVyn$sgu!M-6<ZpgwNrqgX8rA&mSEDhI@^pFB+UXm3FL
z@$SHHITN28Q2D&yAN@3$rN3^bSI#e?6V=>kBedW2B(Re%!~D~HuOoTkNv0B5<nRYo
z2Fqunbx?<QlFtgd2zhZlj(-s5uB?I#A+bsXK?wLjKHFRsQ&z@wVkYHwjv)nMolZ<K
z1TBI{i4&k4EJ(vR82OCDvVeh>cEQJnw|(kfQGoPq;u=nKLL(2SXC>wbV^Ngw(&Exp
zpVMSr|4$EFKyf9=vuq92E&5lvy!v<*3|W_O7;4!Py-WPw6qCB$%~R4PGBcK?{S>kH
zvOv`j82*5~>+|s>locOyh3<V5hWDqyA}6NrEu{{`>pE+W#T=3Q$Nc8=mDC;h_L8Xq
zs?Ke6J%6w-Nj-m7aBlIb?YHa(mSgD;K#sALb3aeQB_z-_-uO|b5(l1l$unmd{j;l<
zQ2i%NqN{*YH)-tpH@hPv0P_%Xh_6&xilX&KKSdoig41X8-gOmj=CF3s%ZNk<ntfVt
zTAXr-d4Y#G*Tc4-F?YN3E#R8<ZO3Guo8^TDqRp5Atv*Q|lHF{{E@%npET2qDKk(C4
zE9ud<Xo5Au=7hS_iFXBbRVD+ahID?Gkt%Ui=5n5-<zNGXeEmnAr^MAG2*bs$h8Nhy
z{?#5~I8A#=z>z%hTSP-RQi}QUYMsuI>>H3C5sb4hWS*{%l9?L{1C;h}W|%Or9zul=
z#gQ$W_y?p2NvI2&t6p=lS0LsQ;n8NGS#XaB1pVkG5d<N`3zyetS@@v0mBdh}(B$n2
z&2YU=G7ILX2&X2&`tSF`?qSRzR98$0LzN@`Y7JyvU;#AT_#G-=&$KLkCzf*cEmMn2
zY2{z#jBHF=%7q|ng`j+%!@pe^kce>YI#Sqdh63uesQefrw`4bV#>y<ICl&m{Kwjfv
zBf6hXhkNp4NXpD{_g-xmaf@j_&HZOfGV&KN{mP7lqHu$Mn`MHQWp36?vc^8^<{7eF
z0T%$)G|(xU)X>n5;iJewHm8Y|>fJE)0dB+QmIvjGFo0=O`Kg4Miiw5ZgQCe9H)}Zd
zgx-^60mnwJ@#CJ3^a#5*Yt+eBA+bt-?J{_O8$|c-e0%!mY~Bg+^40RyD7XKLa(V$&
zStZCNixU3Jhv116D!q4QTI4zwIO^ELzt7H#4@(H1O>`l`>Li`Nl1QAMFcOitm?k=j
z=T9wcwQpO{#w!PrWWJ^tZqn(D*COTfNTV1}*#Tqs)9T^-FM+n*{&kn21J|+dm+@j!
zfmty7ehc~6lJ8GV*BvzZ_nEH0l$5@}>gFn)x<=^jDMa&PVJ~rPc(joa2yz3lRPzY0
zdgA~A{if!}uYxh+01-R}oer`iw{?%yvArsovJw*pvVfvm_QdPD``{}@eB7{8SWGxg
z_!~Ph_q_PtYuu&^L=-wW#f4@?dg-ea8Ei{xFV8-+uFKrLUfViK%ge&RN;w;T6v?Ke
zn+%NOasEvyPSjUdu_Z=UV&t{E@jtgI^yan0wKBfc>zSKWrw{}JZML+_XAqjvx=X*N
z;J&<r(1Us^@9EEPF4(TAYLOSKRbHk$_afD!BPZZGe_@|}8LUt(V*&K;l@IAJziB%*
zLacsQKLn3WI-#!d);wUKa1&TpnH;Y5a7MFZSw)$r93Gs33|v#fKnoz<=2mga<h&ei
zK~uoaV|~k%>HakM`IP8N%!=|6AFYUh3!b5SMz-rSCGIlaj;77_ql}C-?f2KwUOmU!
z#4c-P_$EZOVl}!{?TLgzpa-{4$x!Z>5ic2#kCU4dxu#&s27>7-*cBy$q@h0|*d&0r
zxKZ4c;J#oV7Ovu3FN<mdu=1o;^mK!nM~J7UwOhu!LQ}=h(fDB@3lAL-#5niH<5%V*
zm6BjPZgk)|3YO*<*5RM?8xzu=pQkJzu6|pIC7QcNC)h7f<NVdcBGS-W`x4|Pk{-IM
z=?5{=Z@_h-LA(%dTUYk}*~$J1*#GvD^qDx<gIE;ZgoFOs2KwwgPr_-DVB|Okt|b-!
z6SNud*n^ZcmS)%;W+V34ixjvUVw!2-Ly;|d_0c#ce3}vr@h<!68vkBxFXSyYd4|P^
zF|zmkwFCwl=tYHQ?XRz^ahLtGA^dM7qTJ)jRPhB!2vvOiKNnSkt%^Ar>nG=zf2SUv
zl%Rp8^{xAAF<g#}B`_%ebn(`ruFj(7sZY=fnje1lV5;8y%R$zvRsj<m9T3U4Zq@G?
zNT3x!(Sl#o1j1z`2#(}d<bw$>6s$hBw`5$4^lcqU5MEIIJEqy!qD_`u)5e2z&ca!3
z9y2N}!BXote)mAu@iX<KxwW={wz}I84zj1f2b}1pXAt0Q7;AAEYAdl76~KmrXmO6l
zMeuv}2Vc>COizJoyoU(VlW}hVfZ{`PxV`gwVOz6U+b72Pw*K1RyZ&FTqqLMF!hrK%
zc+SSGnsYothQpwPn5pMxH??UZAqTwu>$mLgUeOpMxP<NQD8P%>r};1cCBnN|&qO*~
ze>FdonhrQq^>pX0<gM&}^hj-u+#;Nf>XZDNJ-xV~^;J72l|d%+vf1C{dI^cX>cU3t
zQ;HQsJkP|s%2)i6Jm%3X+oO?OfRRYClQ%4BI&cfzZ21}|1<D&Qn~I|AP!6$%{Y)54
zkJL)8sd0C&u?$14_=yv)zna1U#}AQ`Ub$J(m;Y|Y$6#RDX_7L&Lu!+#>kq2!w$^i^
zI4!~9v_nR3cV(f6ZRoHN1{uSmqUO(#xGzZ2FDi^#`K$T7JpJJV(Y`bVN7XJ!26&nJ
z3-_Murq>?;_f_~Zffq#WOv{(4D$ETb&PVf+j&q5*$lY+tD_NB2qy=%<*D}TqAYl%h
z=XXk2kJpB^0()KqNyEk%vv9e4dv?>-*A7vS@yQ}=wj%BKRI%T^A<0Y|vEjKPmG|k9
z$x_|hSg|f6kO+LhG^zh}Tyz>hcZM?tfAG~#A8;~moKpTXf^7Wf1@1+&&8@fsj=&td
ziNQ8PJyzU+yd3v--nF(=joP~igK%doQv(P$Kw;~yMh^K7*5hsu$n(z<MmEuBKzqBJ
zX?k6Um={Vk%oU90GO<L2b?JH^ebO6!v*caHIySrAjZ{bYRo*J#8d4U2or=Z7czk$1
zYY=;~K&is*P~$#<F0O+pyA&&^Pgh_gf)I%k11<$AR($J%+T2L#b6J!0Y*#=hWlxq2
zq#S7fvIx@AW1>Mi9(2hGK~gC-S0pQVe>C6{MC|^64Tx^hB)vR!$d3iyxx77Z@~4=p
zhpR4EfF~^x6x;7+#2hhbcCp6YD_BjO8+$M{O$M7|`;XnTkiP@YrUqa=@KlDNndZP;
z0OyMlq9`O>rte2XUdJ;+)ZXG^P$1K-5LXI#!MxvZd)U$zL~t-q!Ft$m(7D_y127FD
zC9sqZucC*Y-MoQd=@+Liyxm3vgk;INFc2dpQKdm5;JyKu<LNqU<=3jo?z;$~5Z9Pq
zg!~u%5mKq5;f>|UO#6m%WpbHyT-iq+sXQs=yS!5`!a{Y(mT%q?jModUkgQgL3$D^O
z)c(|~Qx}o+&W+o|Gw<$s0d^vo+sWoQ3F%K6_w>4r2X?=v-Wu=wujNzk)iYj)X2o+#
zlU)yYz<OUJB1{O`&rZVz+Ki#Guk|Mh{om!}J*?L5zV9=A@IkBRfT|~$Sq_(XwH*){
zSUAvsv|i0OsF5b0^*7?jzfJmbtyw6c^X}H)%36!C!kc}h2Y9yriK9Po9P;AUL%>>g
zH`nwzm0*-^xF_meEM=DGumdFPAWqf^eF~o+{&}&HK_B&ugJl2<f_l@mSTW5w&`!EF
zEND1&B2e%4WjgQW^EWi9&%LHr5w8fM6mL2(%76dl=;jSBw@td$U|^ma2ir2pnNOi+
z!gxKrvo(`zLBJer36Oq*()ucY=&PLO0`_`nXDbAQ_sYFTZz_x5-6F$xZaNd!F)&P-
z#vvsp>U~7==M^I@7kkdoBW`3rXqx`{f0RUr|3{Is`li+Gy%a$ILu~JZrw-AonU8lD
z?OQN1goHEwQJ)>u2?8PFy;Z<doNTHBJ40MR5)dgWli5UEZ{p5h43pA#3|<Du_RID(
z?KI09ntu+L&RGsU3|z|17^s%1;ua2D&-a;p(B9-HVu@(+rM6B-NLS+>6t!I4d0dbc
zrpntm$-4a;UEEFhBf><TNdfXiJTubIg8DSHbxFf@?>GEJa|aJqdu22f6cU`wVJr3z
z?EjQj1vvvjtl;Io=6qvh(n=1`0R{3E0=s)r&Ie3}kKfS9?v~CIMT6iw6aCV|QHZ9H
zKN|4XBlX~`{#CPuy|r{VH$x4Q)F`&BLk#Jo?&pyW>nmtqG9s`Yf`cmqF5WV!rCGQ=
zI;*~xz=j4$y6FS+5PM@~|M9yv&<~Lizj=I!L8bMPY3DpiI}|<WU4{GmZ<?#U9{~%A
zLhVhu)bsHgrmQv-x*Nd8w}Q=b*};BK3gGZKCQt)jl28Aq<gO&fX`-0{!O6Ni4c6-8
z>t(y^5_A)s70$G5oe>UCkof+klZ<8>ZZaXcJraW3mq3;aH|A;mnp0NUxpA4Mb9-;@
zTE&a$qnU%W>NAx2%Vd+71vsV}d^HdrEvvNQS#v7QxYBJA-cUL>=2Wf&GE2S!fp4;I
zcaGg7&-V3OU5LTOn?SdE-VyQ^H(!FJ_&@CdC$KpKrcj~~?9YdWWD(+SPie|@+n$&`
zJsHl<!aXQygc8pQ&XG%!ZXSQ^`XSgJqCD`sjMeUjk*f2~_BUk$Zg_;JvLCRhhUrz8
z5Ro6A&H!|d@(JQ)NruFqs}Frhb8O)-U-=AjqaR`37vRBvtPn6f^73K(>UPs-Rzyy)
zBxn2rh~oz!J6q=hKgsj($=oo&xpe>8rf!Hb-D(@Zvgp5xRi7QDeG9fzL_|2jzoV$@
zOgnycO&>h{pCl@q#)nd4m~Ydk_^|y_s`>p)&6UP1UbEEbAtOBAy&Ep?5WDL@#0Hcy
zfy$v0t^kD>S--t%HJjErecwOV@WXl0Mi6sphVJA85!4faR+=b~2Vi+$g>mYH8}px{
z1BUK-J^L#mS2S|vc2SKg-k@whb(lsd;EQ6zukCLblmf7P-vH!5yhbKws+(V(1Ub=Z
zYD9TchZ$=Wr%}5SbR+SUGfmU^Dd4ee63#7v&zVvqcyQu_P)FYh$#G<{3z`ff3tV4%
zaTD)U4{`T^h7ZXQ2YVwX@UD~hi}gN8-#%<z7pqu`ic0x1S}zzK<VwW0UCgWMZs{nO
zd${tF4cM+n2C9bX`;Hu(Kfs}mn&IH3Fn7IX0xTiCmM`@@lKV8|A#&-qHO~=kQf0^r
z!p{x%Ck<yt9I1Q=*e7aH#K4(zK2F#Mo*D`XcAteMqQJ-n^#hMSNIMUFh-0krzKMAe
z$EkrHy@wB!Q{CZZ4>!KjeiA7#MIkW-mdpICn;Vd?r<nYln7tU<bL?!Q6@#?JVEC|s
zW33A9uqP5{Dv%MKmobPfR3J}EmFV^7rdw=Fxhzq0Ax~ywk08NUWfA>h30d-LD@1#@
z|3lSx2XeW-{~LueOCqxfA*1X~_Rd~eiEOenD>H>8BQmr1-kBjgnb{+<NA~_*kIwmg
z&+q+5{^%UNpXa%s`@XK%x;OxJ<ag{moYw&9tp2vq;;JLK41{Q~K*lrrsLTW4I;+}u
zJ|B`vQwuGdOOYZZG-P+>p|v>rICNY{Cj!vs=(sajg@wEfu|Dzd7b~5_ic>edO3Kq>
z>n8%zgnl2$E9FoG7z3r3w%dJ%G>lPWuM{T9qhXQ3j)c@BbWrpQ-2Z`Ysbs<!T*`Q%
z20V>i!uOK9dX@z<LCOjU8KMkS<1BHsFtF7%&H*M@+o;<!3wz%nZGU>`D|K(dS$cpf
z$QMlN^BL8L`h(&M542qPiu+Ja&i#AI(`#BBW4O+y7+)S54K08UcF*%x^iDHF;LcOz
zRWuFYHI9A?#V=TJpb?!3Z_1Qj{<?JSe&z)ieEa$zSJp`8%~Q+v5MmWrk?5u6M9C)u
zAD&<!E?H^0du9l>)Q=z0&++)@p&3W^!fh+lL?1Ag8d)U9)7o1r>}<}>*o~d-@N#rS
zSTbf?x3mZM4i^brMgW+pPB0X}DPN(JLw@;ei&vBNa_e`zu@8M=GMl?!8Y<3W9l)4E
zQRDwHNwI))pnI<1bxCvOTYG`JIs0lo)YPj}0HV5@CgVGj(9vDN`G}}(ly0m%4rzPo
z%;T79X|x6PVl>q>0!BcWu{^zWSoa(JRIr)LTEu*4={8Y2-`M?l_eKuD!9cGfSGf2H
zFC^vK8w{J#t8nbmx4Ik4CVJ*8^{?aVm(juRdX>O3&ln8XI~`E0t#z+k7d^sU9||}x
zo^U_rD0}6;4DxM@9>b>&NF@!0QBwZrIHMD!^Rg<TAN-^izk&+t?YFJ0J;A*rhoi|*
zhrd(vRl-Xxa}V8chTFc?sJ8*HIKpN<M%<G#DJqv>3m4Z!RlKhK+7}kRG#T69+YX39
zd!fDYTeCg8Darb+Ex+lF0_F8T6Jld`H`i7~UCpIx7F^-D1S~2Lf8H-+h1zjn_MV1y
z<=7f}J(i1Hj{Y^gBSpveq-!RytQ7YT>|g%SQcz`@5PtXStP_8AV>1xrJsemGlrl}f
z;lRSR;fWzb^u26j9Xf)|uqj^YeF43NLHetCkj)`|8=wbR&iB`w+Yg0R(e-Nh1POXh
z9JL*f6OQr?RT2f|x7~N2shcYhBD=;sHZKU_zb~G&9{zkh$7B0;W9cX^Bq4U?4J_j1
zE-a*S@@pD5OiPoz5CalLP(scgv1`MVC3!$kPx&q7>KOYs)X=woL@HuU5&)n+KC2pe
zb3&dHH8}m|^WuKTHDM@qIiZc141cq+nWWYmaY;Q)hPJ{skUjq60yeiL1|r2Qe)Z`q
z=tTZ<mXVLo!Ke%>&^p<!C_VtmeZ=Uor_Rlm!=G7&S;8Q{3EDrF1;r~3UoB{4Z?Vin
zi~g*28U7U|ymaLKS&cU~|G=*wOWB{AnuCxl0q@@^58c%y9i@y507t~F#_^)QR@$YW
zKY2sO14>cPGK2TxVi8>8$U$uQ(tBiI>$iYi7F-2L>_Eh*G%a^yGd8u}J%;ajO3wQO
z8sH{0+VUd@GXP#6w4Kx8VnZ8C+le*_!0JGe+M2e-yDylD#r&r`H8Az6p<5s!0I!WV
zf1k29)c1}NAezxthPk{!Fac=++sG=vGbCaHgpB4?)K_W`r@ItwesTDA+N6t*r|r1!
zQcmMZIW*~K6zq{KE>6MWWxVJ@EZ$lt5b1y8RtR!vIGDuIV4-;_v9z=Xs60z0oD|Ev
z5Y7JeVvqc`vU?vc>>cW$sS-D+1N!B9<*RH#z-z(=|KffbR6p%l$aGm4SOYxzJV7WA
zYzL=C$_h{w(hNDuH)K8CZSSLzcOklGl!xk*uhexPBXnGB>%dUh4DxMkeC47ni@DkB
zi^DxOC-NDRXKhzy8rRY#MF%mXI_Q>gK{P)zN3oe<^E3k0lY&7%;fgOPg$~94W)v)W
zBlhj#Ps?}mEo*0;@GWT|KcN|j0G?<U5B0lCJOBi`nBq2;C@ChZKe?0w42+S!FVita
z)pf_*9NQMeXY6_l^G2#u+=PLWRM5c{7R1D`Hh97wSOo|#aIaV5w9ZyD<?jand6Ao0
zi@~_82UH}~El3&hPE~nw@dO=;G{6W!8hHVIyq5pqNlpUuJkGP0ykU=Xia5&UtMO4#
z*gK2q6Be*8?#o^h67s-(eGrEo-E-NYg`Ln7(J{V(>i&w8{=3hv-)kz>UvB@<xxf<`
z3ftlp^B@h(uFuKI1s(_XW8P;cY#b}P6lmv5ZPm(+7o_w#_5$EmIjaKQvRFJcO*#7H
zAt01|@XRUvhimqO`u5;^!+V`(kMeC>{^0hm3gI{wa(smt2Dg;iVJN!*Ajw;3TJ$|;
zw5p7L1FvnBCuMSNPLg(r%Py_)*2T?%OE$kjebGaSvu<yOY-6DGhE2|><If#Lu%K<?
z1n_Xnm!xizC8NyEo;LKC311jMmGs(H@yVUww)3v{iZC--@ASIgO{~DS%&%!+Qlv5n
zOw*7^x4b_v)bJk4MS*s7=d=^|_b;IX1)&aYmC7T}V)E?J)akB|4Wt=4E^w9_f|3F8
zWSNw9t*+aVWnQRY8b(F>Yc{29udP!=?M7a$^J;mAS=JbA7sIYXWO8REjfr$n-+b1T
zLv|8CYn)t#U;k=hybNpHFO&iuT)ruB$14g+OyPlf_2G{mcAfWB<`lVjF$u!nl*BBL
z>a*0d3+$#Lc6fgl3m<>T5lKT6*P#dPh>}f^|DzOk5|rVXrP*cLFr7Znw(fou1D25b
z6N3bzFJT%QqrMWBJ-5I{1N@utUCQOBKrG|?K;Thme_A?Cvo5=jIt#;bgDpeF(|2N_
zaiJOcl`2}n&;?NErj(6aBfctV=u76mAnx`+3@$L!99wo9Dn#8g6y$Fs7B=W6j&t-9
zDn@gkxy;7YS=nXW4=R+sIW#N2sNfIDF)Rb%+k6G@4I6jkMN^C_Jw8c;Z6HX2<fJ9#
zm~dpl3=Q7(eUlmn%bS;WAb5Y*k&Y{_L)2Pzl*#twBQ%GUB=bSEG-F4{lRhMtgq}#X
z<^7WsWMd6T*2@!oDTgkuQ*c<d=10tY7RwCm$X(g+x_Sxmc|*5>ZQ!ox2x{$BxLUu^
zIEK7yjQN9Mj~)gek-UKohMM{F0H_tRva>gLh74w5;=Ls3(j%F5{r#|s8~Z<SUq&u_
zDOR`8N~~nT=K;!h5d9C#VCCwf2>_74-bw1K=}Z?GlI~(yxttD0;sXY+cRL$DYK>2<
zo@o{cddV?rkY?xDHb4{9TwQ!aG4Z9uKYF1(15pS5aWsT-23s$%yrK#S>;_w08GG*#
zVxHVsK}32kdj`<8@<#{x))8)HZ_N6UACt$1RssCd1BL2Tyl4oNYPkkZnQJ(3b_(!L
zV4fY%1^v(hJQC`InWlpzSina}*VFadaV4-@s<_5seD!2SVPivD38GAJ#J-#g%D92h
z00hvjV7^Z+1ZQPUE&nCUNJ+C_zs2BikY{d^3dp;~m0qNJq)4?A2<1>?C@L7xmc)<{
z9YinLo{xNY^O!+Qpav_P#NdB}WcQgi`h>3rXNQ`1wF*!|g->~slao0f(IQuA3Vk@Z
zyWdQ_ty+XY6oP4+%$6*^wFV;_>2LXskfFd=n(!Kc@9LY^TrTeE2kw+Eit;#@syQC4
zDnP(O{X=d<gHgIgXdeCpI)Wm0%7DBNCDp5J_)H$hA|#*ga*SRCc`<xSdO8WDtb`JL
zAaY06IF(ajfBzvPZ08qH%s>ER;DL&eJadYUT(xnr5myk{16)wU(F-o=+g?Us7p)bH
zc%8f?nOX>GLWr_n$k~%VC-$ADAGDpbQC}Zm&Ad6z<N(1G6ct~kZI@BmH$@=j1vzAv
zr0BnYPZlsjPGm4i_yXcQpb>r(i^n<lngO{?6JVEdBYSIxm;&5CiNP`UMU4Wg(e+Cz
zn@3EQ1&1M@t|7OI>-$pT^GA=!YY*nhtVT*MMMOlP*o<9(V#CSFNvqt30`%Vj_4Oim
zb<1$%BJaLTO?CFqK(5@aX~}qTIN@Q2{V{aKhzPhPh;JQ4FK|Xm%41d&9Snb*TBg0S
zQO3DAPzBEB0yueugg#_(&|%fby1y^*l(=TQt6b-Dvk)tPO;+T&o<r=Y3~jwb_%T$Q
z36usVh{ha9AI$U9?&54ba@r+*!qJCr4(#|qXOn{<^5OXK$f5&OUXVG^1ib~xsxdl)
zaKhN*M5ZPOU@tYJRI%#Mpkg0*L(TC}yA^g!_Qg2_l2=S+aGAGx1I!7S;R?Z7;(=+l
zX9;eAW0Rl+v#0J;Ehl-(6I^WED*D=q^w5pK3)e{8`r}>nWMy}*s?W;i*b{>b{Dr6&
zpcm9bdcmzM|LZ{8Hhv6#1YcbGjWnm8h%TgMyzLP>!hxCZ`;ZRF=DebXb!I7J=N`2M
z1FHnij<@Wbom*y_-#mk{1qzXfB_icI6lj|eW|2jXm~)3thc3#;E-ruDfZbvEW7(v(
z)rcYHikF32en6+K&b@9jmdF|It>of-V@<1Coh7rL()WB;icvG8*(&7B?-EnPJH!cJ
zXM&3ysDAKaJ67Lvpm#vG(x&u-m+k%&q+t>lxBgSg-E90>P@)w`?GA47Swx|U_mMyP
z#?(QZ7tcxg>wPzw+fa1gf5P%u-xpgTCs@1{oKz1U=tsmbEs;R4oPWs=8aea3I_|Zq
zw?G|t1H8en30W2Dz2^h)hTk11z#^_UB+A|fG4=E$=2x)=K}0#<9W5Y#vVTFtCTLOZ
zn}>ZO#jYSb>EzwuC*Qz9dnlkT>VP8MKGeN@5B5~jiV{9Euke>IQ6ukazcOwOxkXER
zk+o(6&C1$(9V-rmvn?%>RueTh2<a7l>*|C-^L&<AQli#0fYcm2H6`y!b4Kj7=Zfk<
zy9}T^T2+zIvr0_BGdwDJ$gA#eV%Do%+GrO<-G6Mv15ps=caW$D+mb7TXG5$E8Rk~X
zx`N(N`6KV`QC)CD<yD3mrj+40<|YG|RI?nqB`UCfOvi^RJ(iN!uO6L%cA!p(8!+#j
z{)ac<8^CwK?2-TD=krmtJd`F3W%k6n5DY1bC)Q~3u`Ae^TWeJ1!XU2L1YV!9Rd`>a
zdW`|Wn*037<xlHowJI4fHsi1wbx9Xzkf*ZsM;uY{Dz4yMM)?js>V&6{y5hA-&^Z6T
z-|oKs39@$(d*G!rLH|0dU@;vT>wvvD62kyNrO?Ka0RPADf?fz9bd0fSBDN;ere*lA
z#_rr*1mWx-J(T>jz~$xTwZGAX9~KoA03r2{m-;iyt70c7=8%{o@1xO4kk<uW#J)bY
zH|ag}<R`L@1k<u=FDm7PN?M1zK4pbP0924~L}7dk({enP3lqpT-u+mPl&pea8@szy
zzde`Tl;VK3F#eQ~yPyRQX(8SZS@S_QKt9g0f-(iH?eEUa8yk8p=PKT5CxumI*jmD0
z0iacA*YzCHEmY>#hFk;0h9sNVZ}0H^2=XPw`|A)1Ug?dOdJ~f^;`mA-do8bt=?%we
znh@m|pXZaJFI>_0hOdZ*%;<3_rA1C#x%K$JtxirWyoe-lMDK;p%*9=Tc!A}OTE%$`
zR|iu9#X5m2kZt2<LRBr5hKI;rDuN;|qD-yx6Mi42Uf+F=9Ob)DFj~CquEB;TZX+?e
zM{2_jj7N0>sUO%Kg9wP<*bD(;mtw7^7fMU?w`U%a@Lav=9vu>bH{TT-@O*3j7Ck+t
z<xpO8mDBROD3{N(6J0F7ySloD3ym?ISB6o!xw(;Y&GlE9)YPwEDbxAqR-7$(X?u>Z
zjm|ffLZ58y&?acT3-B0EG}%;fQ|6sq>qc>Jez@Jay&<X~;<xR_Y1E@i5a~!D97jQv
z^Z>>WRIS)g?6`s6aeTh3WimX7tuve=EpzrNR0hY99nz~JnuWl>BM0Cy#=iaR-}~&;
zhspoASP4%t-ASOIe9k0Tf@TcDHc;L8a3WnI{zttLBeXR0MShKt{%^i#z)d3ME?Kwf
z=PH0TUom>^(gAWo1~T-+PDbw-;LWMPDgQuG;>!;$No|T+o)~}Pkp-{GNZ4Vmcw+sa
z<4H=+t5iJ-*(`0?QavVEGyR$B0I&ECQG??AK69ql;?6&aP{;Pq#Dmqbv9XPA{`Dyc
zMr_QlI}L)c&nXxzC2F}GO!n}V9Sy;UxHy>O>56V!cfz$+`I}N;^DALAmo;gM%7hi{
zJIS@vKIjaHRB3J>S{pXOugbwhJ2k+EM^D+NFWz5+X6IF_r28~zJ7tY?FQeM7Dm1pV
z`e)Cfu7D&<;KbOf8h=qSHDbq&xnH=Xod`LBgm^y%Y54tl5`>(4?VA&>Ht(s#DE{78
zX5=YN@?-6JPzx0v(E;8HW>ds~2C`~pW51J8pt8lQbZ0<(nOYeANfx-|S8bxZ7THMg
zKKbce{h(YWV6~1GlL)Lr{yP}xyLYk!R3YoxpnKW>4d&86s~3AWSwmA1w<a1n-^BN1
zka|wWSKU)ilx((mn}wwK^bvZ3bgwt_1UZQr)hW8bX9q2BV}v+@wpnh!0X?-!=C!_b
zrB-k%D66UYIxP<xL1_KAZ{I424xx<5&CMMyx1|Cv3+B_OPjj>i(K;fTeS(97{p--+
z#!?SBx6aQ(jt*lhJ?6+XY-$!cW`@rJLWBIRq>8G)iWFE3jU^3Vi2p#6(qvqBW1Uwe
z+H-y*Llh9yipnU^fGN-|mu~6kzO3cu@gUFFISuf=SC?KnwH3WXJ@%6!0jVSc-TXu2
zE}h<Qrdp?xRwgBzF?|Wm!#$d>S1_vX<EjnSK@wbzqYDZs4mBK)h3gDfccz>{8zQ9=
z1iUh3h#s>jrj5&((c>2s8nB-{#P?EM&AL}xR>3-U0|>}-B2<4O>;<TW9}ITa`7~x>
z{HvTUOr0gXm~xJLfH+`0Q-mLOU2lSwoI@7_6(b@^H})pvv5zDfT<o&_JvH@WW5dqZ
z-=CUhHdJJARgdnXsEEEkrM~xxQw>DGxz2xlx^cLi-}L?aLqb~QV9WdfC1c%cpeFAc
zv_v3>Fdj<Nf0C2$qnVC5eV%``z9t8XQ(cn9onu;Ouo@#`n7G1r{F4!TzFZs!|B7u_
z!Tvp{j=*-l2^j#63#c2QWkbjihy@xAeRsYrQ*JS}Ja6cHhzZ922YCc+U%Sxo@f^e;
zMIudZc@I&Q*3>bF=&9=qvZiKWxX)rEeOqf!tcHpYGqD4xW2xp=bB1@H0;<Rk6s^Z6
zZZvgA)YR_h)@}rFe~I)2*X?a+xjuC@HhmV5*z01+Z_;e-D-`6*?~1r|Dlf$dnT6dT
zd%*<ti(VcW+A~-k*w|mBhOy)gmEQ@bC01iU2O@PoPo`eDeEIUmxXa{p^?I$CvojaU
zoZSIXwU;i4is<Q4Ou$eItMMu#_uUmym>H_G3|<y$l~mv_I=CLGR9Y2)VGKFBGEt+x
zFIyvDcJUF{Uz!Lk#~sFtu0~njq4Dg{{&sLbdhpoGDSc1yV|yYm15=j8S5WAKUoU6e
zdR2aB^R$LQexn@t`14!HK>*7P6rCZk_Bi$0Uu+L$o+d+y1IE%JMCIsc^1~(}y)2XH
zXcoI*?lH?_dq(~*b#TPt<2XSsPtBBtN&l+b0=`Czo2#)T0br+h`A(;8HSci_10L(m
zu~el2iejn00{Xl})wl&jPz++9c#`FdPqDd9VJCa3Kplp-Y#}xzqUAj{-<dq}Ze*0h
zyIlDoss8ljsL-^Fl$^&7#lG@bBk|bu9XX1CfWXFALZzuNDar;2Loa~;U}dBfTR}mg
zysYEfxB6dg;bHIIp;%g4DtMh^2%wBJoNF-y!A?+!9Butm?53XwQVMYQC#twf*oF8(
zzM)!@ANthLrUF~QZQ<w{*O$M}CFLb&3_cZ(AbD|~2pxzWT#I&<L63sGDniW{LoW2m
zRfVPFtLXteGvnGmhUPix{!7TWhrNgpgEto0$v}9(7;F+X1eWMA79})-3#h$6hYI0F
z0-2Cpt39!39DQS9DhX0QA+Q0`zN*zQOKmlx31PdL-RGQ>!F1}fhYt0MB~_3(uvJ3B
zF+t!G=XdgekXJ|+nkwv#FW{Wp1EI}AnjyUd$kA?{tI$#e4;{Y?Ffn2Ob9O(}a-GE(
z?z-94HC3U;*!=be`#I9(G^AJlfC~=~FPhB=t-YgT6SC*0A$$G-`2ApIK6`dg=x~9i
z=4h=t=J*d>SBc^SeIi6P`Derw*^q(QV(Sk#ugdX~1HF7D{gtSq{OqxLFxj(p_h<yG
zD<^)w0l{0XU~7{#?y|plTF821Qb?dg_cz>$$%O7kVed+6QnR}8CK^GuZxblq;}OXm
z8Y+Fh{qJ@5f5tjzQ!X9l4ROwvCJ;*apnIDFcjDbi+0WGR8QEu`VcdNVUUa<M*I!Tg
zAOWTj(^D@}KD&n^XBkrf>UU64Q3G8n$N3YKX0Pw*L%=AR{PxlIY^RXuKk=kHp9#&(
zl;a8Pcv3GE*YX7K*JLSD3^hs<hrZqZ3SZ06=Ia0n951JLzx9HUspP#5=#crHa3$gW
z>DN6Ri~Be@sDUg$%fXz6FdC_wSdA3p9WTim$l-uFuZ?D2(<8OfAJ@UbL08D6E;Q?<
zeoM}Kbzoos?gBXQOb|?cfeT#!1xx82W+#*A>02L(DnJzmSt6DdMW>dCE3Eciw!MdQ
zG+{i+Y-&r;LK@<)06|>41Ya-`s~Ps;F+>Qh|0pEW{k4At;?uWXV1pK<V3>$yCxMWi
zcLpk`3>WSsj#VB%vAE$gk3QC$O}jt3>k0sQY4s}P5_<5kUc;Nmu*g8XJIKR+8I^l!
z-mluQgLdxdAtDT)mcJeux@EIP0x>|aK{msw&huWyewp&|(}R(ZQ*eW$Ilo>y<ipX(
z42M7g{ph6t0ClcVtjm8J8&so0Qb-!7*FN15fm`QqNDr$u`+<nXGiHqH=<G`5_@y@S
zoDV-KwH!j;=^TSPN)r<kln>QWa<{LJL7cZ%tp~4ZXEbW2+Q{@QCI-gLVtPW0@Y#V9
ziio77WWEPdt#~Rw_9BAx*7WTcX5J5OMVfvvu81o2XI!r9Hu*5SDk#I)iV1OylMZ4w
z3MDi<_aS&?a_v&R+~}-C>*Pf2+pr0)xHXP2P%(k8ChXnht`EPI13F^Exy5oF1w|E|
zq9PXUBWL?)yeu=)q4KL^?%*Sw@DQseL=z}NY6xV@?wP*?d2if0)A=qdj{Y6G|H5T^
zp%`5vSz`lArB)vxZcG!$!uX1F@L-ibmU4t7bDlT%Adb{DknlQ=lSD=AMd2um?+A7b
zn`d8Khpl}X+t;9H`Vjisj+X<!m}1J@7zY6R81cLYkktQ;`N>TLoG&en&T6y_7g>@p
zJ`hEu#J$qT2c6P$TN$Y&?Oa^e{~R<noh^LRF))}6ZinT47z$<ZU0rXPZ468l0C2u0
zKF9B<)(@rUZq8Uaeqx*)Y8xrJVHE7&h3$gmeu8CC#r$q{4J8sqqe|$(=TTF<IOlrU
zO47@+2st?Beqb^JMU8js^65O<eajD2U*mgnM4*6i<D*FMmGLxibiR06#EJKFZq+;Q
z5(vM}n6dn#a$l1&B5Dr!SpRxr6bN|eCNKl3h2Y@L5e$1-C%=P<!okt8Dfi%3*`xMm
zzs?|*1a=6#MAS5}-@g61qr(LF%kfbyLFe)DwO?@yvg;<sZzE>E+;b0PcQmQNlz_#j
z%y*vwja>c;{rxtk%tRe@B4jgR=v%5r_QH(3+Go?-zt`qa*vx<2MruTu`}HbBaj)(g
zdoh{Zj{;SQgX+bFpg)1idl(R6!C1}<(Z<|QF*f&};v?RcqMhE1hgY};BV+(1u>(>t
ztMdLKJ4i#z*Ku1`2GVZ@3b-7tNHc<D#=lr%bdpJw01`{DPmFt~j$^taP&>#VC8&lE
zVwvo2Djn>AzXGzCjW5vq;$Xo)>*29L3a0Ro-FVDpX<De$S)l^-$D=~~yD8$jA`Rww
ziYTyZy6%jGVU!vOi~1!B8&p6esSW`7P1k~xBhc|>Xq>r=T$(<?uP6SXVn-T=RU0yp
z&8O43&I024PUg8cBxz(Y0Y3%$lD2<XKm?{!S0A8A;uIw?G&c4Lpz<O?3h7X#R(^5k
zmad0TQTB@L?M~c&^v$B0Nf`o_Az}93w~un=PH=p>n!5|U$5x-@aeF#=6O7uKg^U(*
zh<YB_!!Ba`h!#@aYx_eWiF&MD@hzUaJZyFytoR_M?b?Dgk<ENwpL?v05_4Q@bT4Y<
zMZzB)@a?Ld?`ib|$q1ryLvzFxzp|~h`vMg+)@)Ji-<Qzw4MKX5&H5?fr!xi*K*rT}
zH8$i{g2{HHya{v((IkegAw!<^S#7oZoP`hPWX*vo-q^lC1sm%uyE;@WHjwW|=y@V*
z#0eG+6~%dEo#OT_CGx(2aK_fRH++bgGz?)r<@dh6<p_shN<q?(AKT^7fOWzZC+jgW
z?mPH|XYI~b87;a+xiPot2q~p%ytNQ$1a+&bu|)qB)(Ijc_X^aY$sA?ENd#|xw6pza
zl{CK#l^qk8IL$4CO1b7$@-Ezso%dLfrdv`OCx3ZA=8|+xo=4N>6P+8UZc*N&23R}V
z5He`1WHKdOrzckY2r@%~efSE((<Twkw12CV4A$q5-_j%X?T*{r5qaF4ku@R=1NRD=
zJG&2MMo53Nh^Bvjl(^yM$kv_g6XUsNByI}SO^|DLgAK@rrCSY*ujZjfd7|0h)Yqcb
zb^aoa%Vhh^!^d?YDF7)mE?{8n9v*;Ea(ZoTV)-{{C4M*)-)Gtwj8DO{^=7Li=~3ET
zZVo!ELlT=H6_K$uz+Gn=QZ+4d0{Pn8weW#}Z6XtXbJmH(T+|70&xhFP0i=ZZgFXy#
zzFBkVV^d;6*m^R;6TE>LTb7%fRezQ*J&(x9vVB<@W)l!1<|(DDP7@D#m`~Z;N5==j
zJOD9p4brxAukrt`4sjGii^Mndtw&P{b&nQ*oI>`bJlRU%5se-sxCI&g61od2Qv^K=
z$iMcvRyza)a8_IZpY&tHMsh0*<!rg>dBCq%7Jro=vj7b0YiVJjqdW6})l(%M%(yvY
zB*7Yf-^Ou>V6zU>RmMkRIdgK(WWjde5X<^zLIlmmR}4fRzl$1mtftuzg$<JaB5RyU
zTZ){$K-gXU-%fGykF!?`7>Qm&Q{}vLv}SwDOWqIVlMHK<pbrJV$K4-Z2y6=(sCZ5v
zV~M9<ahqZ^biN43{d=}&Sj#1!@2uC<xt(1T9uhks%suExz(*5`kZh=V1=4VF*>ATU
z;gos%_6J{TA%rK(<4+e;z@q<gHy1aiwWZ}TOf)P%tpV0fTvF1a0RATVG#^M!lV#S-
zm7*M)hzy)Wsw$PY;B4D9eSV^%E`}YMTjv{-rqTNhU5}wwn-p~!&$)!I3&9J!a|}Dr
zmU0&M5tbYRQHA&%aiNtR6^M)nOzo2())tK(zg|Ebs$mj+Vv%;G?r<9iM%U3Bu;hS_
z?J^)o7B0HrnMHUzpqY~Pu>T$<$zBbxj-S#W%)mA>GS&^n9U?P3P0GWvV^dXiLKeV@
z610twfvKI+a9&tenOlE9-rt<v-=7V=6()T%yln%<Kbuv-K!jv3aKay3O9{NctTtHy
z&QwEqP&8Ft>!6Wvi3Dd+Wy0ey<us4kg)~6FBnk+lQdY4cR~-q!UI|jidq2cg`nX-H
zwRbq#Y>Omh9s@D$KG0*XQu7f<+mFTOF~}n45RT9pOf`P*EW?!9&?nqNotlw0ZjE3P
zUydFaTDHGt{JU!VdEuTE@N-c?%;lP^9yKa>mmiC!U;~CJW+0W81nzU>YzL$aL}lQL
zs0{vHhWwvP$)%89*h1E^dFd4jF){J*$cRP7`O{kk^kbOD9vvnMHH{;tgLcbU*!ki2
zgUcHSTx%0|iwzFoEk!7*xqXPN1k6Gvm`K^>3xv>mDKS3KD}H32^X1(E!cP_6sTob0
zLcvD;Epe(nr`AuCUPEa!C^^n2WDWtDh>i}Z_5QUwT0&0>1UrWCiko3~l=$GCK|U6U
zTN%_+h<_82gKOPbHmXUWn3t%khs?>oIpk68Ff}O7WH&(>n^cna8rK9aSo(7bm)Y8s
zow2#gYjHGwetuj{a3=QGQ7AAp9AT=ch2ZZLd<8}r6-D*c$|l&!WJkXdp&_c-Pq$dU
zXgu})vo+@d%2&O){S>gPu{9gO^(hswvjZRG33!CsnZWDkcOZtY3k&>{BdesxofjjF
z>HB00zm0qu{e5#ME6cWlsd@&g6y@qsJP=-{<Kw){CZfNF#5DlevIk;Vh)?$6&_i_Z
z%5}sGcPKe9U;Jvcc*}0$pa@s~<I@_ww%hCQKB{qkCPv1QVMO;*1=`Q+J*U^OMJ2cf
z+Qv|_i6umoez%y2C4SCq&yxT<#h%8;6ItldAj>V^er$A*gi4SABlAOC;88L->>Ec1
zE5t^Z;0T5EXmH^@429^odU0Z`0%GB_%>E)uUd&7-sLRqk-HOKFRf^ji6Zz(Ld?vXM
zTm8SEB{3_79i#MQ%jZSkWFal~L_$xM39>MdODPqz<c|p@<-82lct(D{l&L8lBO{}s
ziAm~W5wZ>=mRhbOF+1Q*k!S@h05sXZC^`xOf{@28Yu!sP{!k|Dm3s}uA0!LyZrQ(t
z@^i{AA%KQz_5;VUei2L{Y8yL6G`k{{m3sTaW*|GY@Yg7|wasrs(-&|~-RvJoZnX^j
zQz9<=EbhlLgazNy#CrGas?T6J#LPb9z)c`m^m~+4B`1h7{Bda(QEI{A`VSj@{$xNP
zciH*ASlfU~>QMOQEDo5LqP#8x#_Nd+w3T2Oilt2fLP&Z1XJ0{#6wP_-hl<W<@7VA#
z2{IA}1!BPsVM8R%gNx1Z4r98IbGUf_zHCHO2Dc|0>jgmRBlHulK4Go$1kuIQz7F@C
zmeVv4qR?}<BqnAlJ3a#Mzq=h)jg><GqZ>OV2%5(~EIPLjZ_<h<I56O8-E3Dqt^i$<
z^ta!*GS-@4|M#=(o(5^LCHE5{hI%|qjbIamsrvVmfsc)pEP|EqD7s!}tRpscVyQm6
zy;gb{%t5%nElqmA!{&)PxF)>7G|la$*l!mho)R>O(S<q%LV*AX=0cfTEuWOMu*mXf
zf<>?-e0p@GPA<cs%v%et7E?doHzBxN1+OZTTw7N`NamJOd%6Gyor-trN%qIKE|`Wj
zb#bIj$rIa<N1#Q@aP|oibb}CTvQR}tiEPFK&y!+P?nE`SeVk3&{Wwz1*IkJB_14F%
zNhTB#B|1d7QLvjN4R8Km1H%8-A#Q_WdsiLGO=zH;6lStj39pzJBk>SW?;?GCAr)nF
zK%Jh1ta5PsYPo*9m_^~zGwL7Ypf^D}$5GolKgcyQF`<PaA)#Ucc;;1iVQUw%g_c@_
zN=}nk2&fX)iMf3HM^bdnDJYvF+Yos8u%z#$%gqr;E068|ok5;JH17*Goqnw=J6yza
zG*+%wJdnF)l)>ba@Z}Pc;sCOV3xY(qHsX^ZaTIEl-a-%ne{r)}ih(l=ksZ#FB`a%B
z{+KVDC~Fzs>6Zq94&l|;;tGgR>EDmC?Irtb>t-xiX6(^2;wcJ@OEHC;kGR&(4(}$P
zdXgZvyo#}Qy!A<q5WheUDXG4TB~Ow05=BLg0ObTh64MyqJS2i{9F!g#qBvw+IPvlE
zZ0zhl@^pQUnQ$0*K2Q-Q(jTKtw;$_hAxRKmyKvupbh?>AbQe;>^0(bUkI3>{oqDXR
zXtZ>f_;2NQJ7N}=^9BeyFeHzXM7!sQYpi@Fy9L4A&Y?yb?mz)8G27tKVJI4UOX&=$
zz?SjdJZD4y)Bm&l97+8>!jM1v4Jx#6G>98umQs&*gkxu4neKj?4}zgAGTjY#m3zA_
zL{n-yH-3$q$yY!$0c8F9#Fi@KYe<Warw%Oo3J0`;R|Epypx3nnQjmp8lR7N1G@_%O
z0MeXP^+$b8mOsO3OF+R5GAM2k-`Zr1t`!1%`~dW5^BQx;C-G{Xi3evu;2}6+N!C7_
ziE(lO>?n;QQQ`9$PlI<$QN7f?zDTf4L5wtAhE~Lje$Wykx;7*MGyI$T;;KBZ;|4IE
z$Z7ch?I>YY(y3Q3@WsrWd-;F8V#HMg(oF}3U<9bz_;!TqGZS?MJNiN0&@sE{3L`TF
z?FFcpZm1{_f4?qDU~{sb78@sIOSRm72JbXR4ET$fxSJsqSJd$lHL8%;O&lR-Itspc
zuug(*IxvJno@L2C?nDNDf5)J~gXen13l(|tuULttkR!7zqF01q!4Lzi(!GV?9<#{v
zKg}tsy#b?zrogtc>fhM}){>55<NtrpZv~I8IFf3u;Xo@c-VP`Rk_r3UD@fvNHRk4a
zXd#eX6&=8`6Lsx7i~ml_$SUMdy{e4gw-hp8PX!)lYI3hFEqKuAz5MRs&u$eznA<l<
zKQvZJ^h^)o1r8K^>o7)%HCGHG!)vVhuj{Ob!t<2lp9$-42~H?qr|?ap)Ue12!ng`u
zw6YKv0I-Q1&Wxq<=tR8=up$9C)4EpT;3H0r8XrI9^PBGAfmGUngD+D4{PW3(Jr}Yv
zNN}1(Fzx5O2<*=Tx*u}sk+}Z+*M1o`R;z=X4<2<QX_cT%eg;egt4*lpNSbyY66X(L
zM-R)pCBy}#pt}LC|I0uFvd>nnwQlha+p*}*?Y||E0##CVUu)boGtAc)2x42Ept+ft
z#jyE)oIQJEnCa@q7jtm%f=NBCCCKb1=_(T?hZ1w91hgjPowyti2kHkvW4iU(9KkZL
zt#*2sa`&}DW~Wg~`F{JA|9f4al!1VZH?<S}St3A4f~;-;@<wsRVa^ZV5g)uQhJbvv
zTQqCht=}==Cj`}`FO>Eh1@~%q4#BH}sDl15k$u=b0EIg+K<G?FOs<4sZZH+>7>L~%
z7BEU=^i+RPf62XfJBu9Oo}&qjM^zk^9iX`b7aKviY9@y>TpCl0lTghB6*-huFq<NS
zoC;tcZqp0dh18@OE&Ew`3<;3?0um!m4fX;e$S_6Jytm|b+OKjefIbF(=kwWfrQ_cx
zWQMX?%KZ`)FdAVtoNebRNy_<*ITrOWpbIgvXgv2U51BJHab$Y5wtn+;B9$rp@c|Dj
zm;&Da&=Pw?j?7L$^0PH4#QWKy|3Z``<EjvDPDEz>otoG-&e9|&08p%Y>HOuW3#zZk
zLC$rLcSu>M?}D!C2@6#<t1N47bFpx9kn?j)6wss~9i<=zi?!639#93t4lT`^t4hir
zq0^pN83Qv!zD_#dPRvS5?ONS-OmTJ6xfv;0>0t7oS}7S7to|6FBuu$B0DKUUn$a%7
zQ(QF(@e^&;Y;rJ2Ak)df!d6zz=%5RRKnOy=0<1H^%;Slq-*KR5-dvpK_%Oi55PSmB
zfgK3hB~rdD0j7!bd1Dy~7GR1#@Hgwlwg<t5Y=N6H&!fA+RZj7+b-SLuu-~&0MZq&*
zXhClpt8zN7O{ppbEd+F5E&uDjE|G=5B&wzFBAvBF)*@5DO)MOay=0`L0;nDixsvK!
zped261Vr}#sz&~vOy-|&(zEv_KDRe2#mKq^6T9x!T^{|sRd8f4#JD_a6FG1ldNJss
zUwpi!2%^=CnI#TA@wkpAt^hhr9I5j<sPqj7;XW<i?7xt}G6VsyK8R&I*-ILzgc=&k
zO9$$V<<CLtAnB^@a`XsxIS{}~`b)ptG5k}1DF(tQG(bAPy*{0W|J(2JyW&dHY-%*v
z0?%WkhB_=_->IlI89K05g6SM&o{*SkeV+YHgyen>E?xca>$G=~H^zwmPlUb_WMiP;
zqF#!G@o%8_7Xlsr^58EbRZogBkP*PMVf!&kT2-``J6~(&%MXPDu^z=?wdzM##}D+t
zWRLS<UU9+?$ARB{@*Vb!Vb4mDxqS!#g_KqI&^TM!cY=@?pR|40R&u|ev$RpG?##d*
znJ)z}da}L8oU1LQAgCBih;zT)DDyvm6`Z;iQzP%0O*FxN*s{gz>SAy|Mu*7S#uDK|
zdT<*`8bU0xLjK+4;Ae?;LAYyQ767KecQv-bo=*gt8ahqLYSIa^j{0K<FK*YLvIK)H
zP^C5h5^;xrvJvv|Nfve=ZH&ShjNp&-0jd!#?aMKR2M_Q-Wc<_kH%)T)a$jaXkR8{E
ziDwUYmNPOlUuI@9++}7qvbDvhh!Z+Vg>~Wdvk+*7@^3{CxFtXku<DMvvtYHC2`CW&
zmmsff9``wP3AF!Tl?7t~z2lbhTY>^l4U_YIZrNO~h<4I5UjZl!Si;noS%>LJ00QLq
z!@!jCYHLVQLe4yvNXh^46zA7|_$rZ$^uj-sE5&=5HDuje2}6-Wlb2dpD8zuP1-==%
z(qAG+`qpgwASTa&WVk9M%O6zcNTa*$ST)2O7g&|olUmr<*LR*X%kvDau&~g~!a`+4
z(!hWU!rDl<ZOAFTkDs}^S**W>1NvP=grBeP1qi!LDk{2qcz9S)QsSGR&+3myaTy&Q
zT_Fml26>80nj=}h*kLWrC@2V>P|1_r%32R0(g2Z%7U|4QcEIKBEhmB{?cYjl5GpaA
z3-=kK3Te7vRu2;?N8LN^5G2R3n};d`Au|q`qm;D<c7sX5a%c*c!?<7AaW*V8|Ea6Q
zPu^69u|Udin9CPY^Shaeq~Sg*oDYev>|8`B3kU>3ayTF_qN8=Ql)VAWh@F%(IItU1
z<~-!YrV#{hLrA{T9d4exSvFrm87IKjslZ%H5LP5a$id3U*ch%P^jgq;SA@VO<+si0
zSpp{)*UZY#VjlP1y9lM-881kd;C;$VPEOvhT_ih{Z_v6nUj1yU9+jNe{sM42jO9Rr
zHG@{8&|-k`Eg83Bu=-V4tg3y(uOReMxo?<}FApl`AvtQdIiD9W=IxgVKcq)%ny)}!
zxz1dW*NqHh{uBuA&8)5%=(npG{9lWBW!$_Z9~Uqrg~gqg>lYsv<r}Aj24iF`QGo^6
z!|gV10+oz9Z`*G=Opx0-dXoUAIiO1ZZ_y1ucX*NMW1?HX8lhwp6CaT$xPZhcA>p~i
z1~*5p)X+f66B~QyCD^F1EIr11Eq)Tb4O4htulGDjs#NFo-UNgfbTW%km4wN)iAoo`
z@F@iXL2eDw;2DjIN7P|sZerDfc3wxZpKf9S6<F=O+PFE}TKIg+R6brn`%Cj#o2rta
zAJAdRFx*gVw7jImaBppV_=`JtB#T}UqT@6*&5CNf1vI)$62ZAHiYZgQvI}}sI750Z
zIU$u6CIV;<mP3%#ju&x`6^uvwl}7SQlX3DFs*NM_DNKC;NvyJiiFo@1y-qNt(ePcF
zgLltoUhjj;7s_m}s<(1K+s9|ltYGUBiY7yoGZy4jL%7d>Kc4E4)(6%W4FH!4bkGL~
z!nhdokj3I*RsG@3+oCEKs;4RCoDeZRlV^xYvSjW|WCa4pF_>Hk;*N+G2#zWQpc_IJ
zI6;$W?)m)~mP53lI~OUbjc>*6t8KX`l^{ohN#{T`pc@+-_YVvt<>cUK=Icwy$#qbS
zMny##@2>phaoxCyXs&m6J_&p4b3B1Tg;NU)STHHBq@?5~KR=nex=XkCqdG4^6%`e`
z{SBk&Xtol#5bynk@0{dS$o6}n#o9^vqndc^vxk*o3cc8uZ#X}F+L}*T%uc%}esM*`
zx-I6R$YT`%QZJqbGc%B+-^uqMY-NE8Y?@pf6jAmZW4+|(`dmsNOFf7BSvbI!U=#j|
z*groWDE)iK;HRhD`KXghB3u}w_p<VqrYaq2V2sWjPmIbd$ecJ&#>r;Rp0P3t1m7tF
z$OBd!&s-D>^lKyd!pw*cW;Cc2T005orBglTWm9Z)C^+&VAN%-VtM~hNJ0><f6nudV
z6om16OTmBL&8?jRH8wW(*`HrB9!IO?!$l??xgt`Yo`Ul|38A17X!s_*W&8g6_3K&{
zcDK@$5<>17x77FdZx2R4Vrgv)r{8QLvz-E6MY(>R-834d*S?X}WUU?|_X)c`0w$B5
zkQ7MqfVmY>Yj_Jge2{DoiKTB~=%=hPY;1t#zdWIPasV`TuXYJ{7i`18V*<LL;rZFq
z8qmep7u75DhdLEUDf(dy1u_TX-~lkHhd?S9`-u@u)f4@vkPpl-e)UOBShE8B8&~vC
zJ~wEB<s70_rJkwNKt@Z$M5jm}?G<y1zk%csGKkD^Z9j};p^$=h&mDu;(94%e(oNUW
zT5GiV2Uc+akFmRB{Ey**8B$mWZ!;V4NHgA=83L_yfJx=?0v46-Ee9a;5k?ha$;}*u
z1qB7=V>m8_(<wB9XN3(0$p4xfcim#Rd-p3aVS(I}9;y@uukJw7Ba)(w+)~I!mM9Nv
zdqN`YB@jX>7(}1oadpyTt1Gt<a$2VrdOw2z<5iU`$bOh3z_U?j^G20-4RCiQDiXAh
zF+#TiAA=Y9x1OyC46Hz-IPx0)^H}5J#8`NSy})oA4$QV#<W6#15^mWa4y<Lts$bl`
z58-Yq4icU{NMsX4p_|i|uG&=FRgmc?P^N6s&i|)k$gqXHz88LBd1i?mnThL|T}(?c
zFr!pHhd<me<}8<-DGUVx1@`>^JSVz>f(#@0XAUMvD>_;)J-gR>3@&!EnF|h|YYRym
zK|2V!8JMCe#zz1(fx<QZISw@^aywzLxeL=18*A%ekmxmab>*tIl5km(D8>uC{`O7!
zLsS%aDRT6xHH<~K$|t?ZRMX_ItbS#YqzgCNSsFlsZDDk#&6ipTfJaysjEbNtQv^;V
z)%le?3$adG#T*ZEsjfp3f+$^t2uso1-iBW@LR1{A5Frh=TC=b>`JyJ)=RLrEEm@tm
z@ewS@Oa_l7(n>~%Zk2&i0}-4d#&R2Vkn)1+N+_E66%0wuIuF12ANGU{euS17vT`0l
z3OYg{@4TH^lFZ=^dkL0}5An~i(U52mET?+`)uo00jJJQ}fR!3rhQ^R+0pMteadlik
zhD?cA*p8*6gew0OmFHec!?pi<>iZzIu%d<$Nz5cM(8X1g<gk<B)O*2*WQz9mVA{>1
zEuHL5t~-7^rUUUUBETa!-CNUr`0&O4{yt@PDLcHq6Vo7XhZ&EoU$%cx<exyitf2OF
zIMD8a>l&t=1Ru-J)!(r*ZrN*vc@VALYPn9@B#TDK+_vHMw)VL0qxTT%5oulFTQp+Y
z`wW>`K<wA_^MOa{RcZwo6-hG7G%)}wlApPjCRKf4_ZBD=BDFpK)Nk|61N`1zdYqBB
z{+Zcye#e#~ZxZ3wyvIJ$|1JP}P>>_ngQWQ}@cI$K{eUIxABy|OWYhe(3%MR3Ppv87
z#0y)eY&0lqnRe9qEjF)m3Fgq@88C;`S8CEByrAd(CtiKwgf;3RkZht596@j;v6-SP
z{QY~Bu?0#qGqXTUO4mf)R`-nt%*~bJp5&yY>A_902M?<B(yKuFG#?@ihsNjHT1vpF
zNweTh;8+~@Y~zVi2E-DwQ=-YAKtXy6!eP;pvaiP$u;S}8@i7V^39y5IHY_^-u%%0P
z55w4%${;P|u&!6A4060RLCe^V0hupNef|{GHZC9`gKV9*P=0cihs(Y|*r&xyLf^y8
zS0CyByN=-J5)DiaK~K(X<}|a20Ltb_Qww6q8o<qh*o_y0h98$1v^QnR)B^jkFt-%Q
z!SuQ0I*?+Jp=Ds=(-4A~0+9>}?yr>wCu4vL7=s`*B;~`M;JBWgdI;15dfhznJ`@O+
zKJ&D++!ME9=?Vx6R#Rkf-M{}TJ)Qo2c=$Sxmw}Oa<`_j;R#p%+Vacdw9nItrR}Y{D
zL`Jz<yb2{{s_H`$fs0wS9amr&I3?*~(!?vbdZ?W@w&u_xX0ZdRjW)4dI5oukIbrnL
ztuPTsjN{CH6^yR4bii4lq1Zg=Epe{C$nz0A(wP2+(e$=Y{?Z}mHJ5S^mOTedi&VdL
zv$W#6&!^_rQc);|5wSHKBXMi(V|O*DMxoO((J@@VCY2(pzQcSzp!?qQz^=@dKhKb2
z3>1qXoe`~wrM-PsriUm~0?-X+Qeak)q`D(1|IA7#mE_<~1P6ON;&O1TeC$rg!0_%I
zF}JW-3Ny}<^YW_wVL3Rs5+f_l@g0V<PL+<oW^6?#m*1Q|CJ6R=XIFav0c2{a1X+A&
zIeZLo$pA=AM1tBkVIGL9rD*{v6ZH}w)JQnaTv8tT5$7{r@4o)?&|6uD4win;$+b7p
zcw=LgOv9AxJ-9FZ!t~dP|65I=`<Lh512o}0%MZF4OGu}M;hdoR^EcL%t9JT?1Yi7e
zr9#3~9-4^9^igY0?GLDc*sCe&*Hy?6@J{1@z<ACALS_d{6a>EiNRx8`(q5R}ZWJ{F
z+?84gCe5&DgVcz1Kk<_=K>Dq%tx-_`JEEsgmkRk98|%JYXKiKW_A7eH{?D&A{?nDU
zH9SE<3>|<U--C=8H2L6C-Y>D0qdAY9rV;Eqb4wsyxk<_)9HkrrqWEaC-QZX>WUQvC
zLr(k0UEnwocMG^?cdkqJ<9_zE0_X+!k6uPi5h*UaGr@J3bOx^$6#OIe&WDgl1XLh)
zK;_OT9_oLprWD@4vK@(p#oNRhmWkBQAVLUmVIkr7{#zg0jlP#HEKB#^zM7J68EFjT
z5d_%V%G29#tAM=bo!Z0TxO)e~gi)Y#nrETvfOs<pnor0a#9dR2)%EW^0)h=-Q|W#a
zk}DL6*t1}QPj7w7!R?sKd3kx|r`1q${_aU=-I#8w_NdwV-SfF*JM@Nwjg8HCmeM$c
zKUedg3RsVD;HrZrAO<WLt<aR*$tE%g6NBL_JIPq!<;|?T{|s?zEl#VY4(S5nsK?xp
z!v=uR059a}xjsIjjd%%21F+&(FF_6`r>11TAk;?%hgwxXmN^x213R)G_*SQ4WSyTT
z5L<f#Cihso=t3@;N0}WK0t5bI>-pQo;Hp6b14b~kE=(lo9*MDoDCI!H%tB^Gcp^gG
zWQbAz#~Fo`M4Ua=9%Ki=s6%2HL+U+bx+E-{MOFw<4uoC4;hJB*V`(fnMmFyYQR)z2
zK=wU2A4YxBz<{M^l@U<m(Otiln{pj6nkCk^W-oe25)YuJF)}oKk(NgGvI@KnH>5%-
zSO0A4Kc9=xJ<H9{5AFRd<x(x_bO2)o>}I~c=GS)zLmH<l4#>yjKp|xE^wolbZ!BUx
zzhi63sWDIn$$qePAUv7*%HFTXx%e?m*ANKk#$u`yWBCD$>BFeDyFs_)PCdp96pWbI
z8<>8hX7Td{b__kS+U&=!d)B8llO<1l`Ss%{d2U7~|D!WI-;BF}^+Q;)0@BNVD4A*}
z(|wTZa71{y5XODxU3X!TUWQ^UVzvfEWLvpJpQ&yvzC5F>Nh`6I&|~+iiuOVD4btP{
zf|<3IM2oae(oDIK<hh!AW)e9dn)JAIdlw=Aqpz94yhj$2-?rG}s5O^&5czjJ>3T49
zx)CiGP!SIXctF+ABd*}y)gj~H=HU1WneT;9dni%rUlY=A3>$|Ub;WSLljT2^WW++#
z$kBQUF!XrLxWka1OGAC4?Oj@!d;fB-BWiMU1iJ8hR>K99{8BP9Z4~1Z{M5|Mt0BTe
zgM-OcI$P{?m{suMI+GXo>^t0gK1>vwZ+~3NhS_<!sK07&`k7GVFu7jF_F>_Ns~^<W
zCaRt%1`F{=`ZY1oGPY9Pn0|FhKTT)PMG;-|X+CPBUY8x;EG*Ghm7tApY)6#23c4WO
z?~1y-F-h|g8Ftg!+9`s$F2C0-?hV(~pHwNTAb~jXQPwZQ34kkKg8_U$X@x5Vn_Wfh
zo7O5w6?#TnejzW?-y%CzBCkH)_o{~r7jflKL~L+r^UxqxC?pW{i|B&Tk7ICXX8y`N
zPEli2t4W5fhRwU}olVCvvL|iz%;uetnEvF$08McX<qeaaZ+C1(Otl3sXE$pmuLm_K
zrd7BUP^{q<`GNNuB{3=Qx(E3|fDw!TMj__|9rG_ghS!D#1cVx|bYJ(R!5-18!62!y
zu(afK^Ll*~OYf6Z-_gv>419f5QG%OTXz(l>8yhzg&W<q&>Ez8^&fw8ZHxnC(!|0P2
zWJXs%%KJx0li*MaRFrqDbNM}Zd4t__YRJI*%T7#XCGXX%SHZhBV*LC0a&Gws)N3xw
zBne)J3}6&;%YX%1#gMA<DnC#9mWT&eReI)&be~_}TRL|KWG`tLTv<P?rWOeikz1{~
z@R~W?P%mrArupUd%XhIKilF171-f<iqFr)+%5=sbC3~TUTMf_0YoegKmT33`oy5$}
z?xesMg^PDLBbrnUaQ*&+eq1?G3Mbzmz4&pVct+~@7gq7w$?x1Q`zfo}7o;wyWLT|@
zp{uwMqN;u(!YaEq;!$?!P<Bn~^4El)cef*rE8?7mG}BH+Z=pnVi9GvqK>}Cgna?lp
zGxQ}Lclv7}-!aU{eUVglc{tm)RyyIj9$kYQ5pRg{?&Z51DjA>z-f3@ZYbfp!a$oLw
z{rWW)9-hYNL221%UKFsQ%=i)O*F&3y1=0>^@yN*DKz|C&1TNfn5ou{GD=RBQE33Cv
zReaD;(6F%JwT4ifJ1)j9XM&EnW}^|?llloB3Ty5D48<+5I)Hb@odfZmj%1ghYPl#l
zeH(kERR7s)<i>qW6e@O9|NE%8_H&1Xd2%K9&EhpNkE8PrS^Eb?>QxN<J2Tg}xQWjW
zRI#q6sLQZ@lRfeha+g&WEZEJ$Ne#9>yD4{bic<4p8c(_BR``EgF7Aa66kYU0@syXY
zUU4a!P?6-;w>`cTLL{v`+E$Po`Vmbv?&AJO?01faLz^L&Ki&C|sQz$b|6%PlG3z-G
z4Y6@LO_bE&3L<LOLxaSc6jU>f?Nebk!ORzI%VekyRK;gc*K3<TGxiCEoSFV`zln=>
z`@&T>CGpftXk6ksFD^Q2ib{-^E?4Evjh9xJsk>Zc8b-mcdUcWDLo)RjzpGeD7av?z
zkxAG(ZMYiODS!Ck-TLpUib#ieXJN~4&*wWm<D2$EaXHx<zbB*n7p;1<9?3<@{C4@!
z=rK+}@?F-|F}d=#;8rI)pZ%{um`Os@ccww*Z*+%-Mr{4dUVM{tI+ulTgrN<If0|-k
zR(gWh2<<cO0jB(#WMX~ShNUrRaPnos=_(nhQC=`bXvh{jyZu!75qaA-JSnzHlcC>-
zxLw~gQ<F?=xybr=0q0N(6X7pqs`wzrlyYY`-eZErbrUn4P{xaH$4i$obdN+B`dc50
z`mOdHb@%b~dN}0CtfzlepPzDBX0Z0aRZz%MiF!zU5d-C)kD*k+O)+ift4#RU`JdLH
zOZ@23HqGte;yunA3Dcjx9yipR?EB6?_{KaFZU|S&VaBSfb}F59x08*EprVrhEtB|n
zT=UVTI~6h?Y3aGg?P<dm=_2q$xccjIZ`Ji3=l<w^!X;z;nEB8(fA#EjL`z%1V>(Au
zO^14I9r3Jw_w_I7A3j_Y+@JDcWMIft<cac<yL0wMwQVlF9UTpA8`_zrvQZlc?M{VF
zFmNw{jj)8zr3sgthgrs}<?W5CrL1hyemZ7mg(!VD^PSD92A_fAr#+@2IJoMM9_4`3
zP>l*;NG(xOQJc3|BF0)cVlG}qdQx)s6ob!?XVyq3xm)~{ITfFG-pkc3Xqxx%@{{Qm
zRO#m~d+BoHQ%Z7sa;)%3)>x58^&Fi{kKTmt@7od;Tx^N)q{SFoDkZ80)?G!s-ig%G
zpCwZ{+~2M}xrcLd)Qa<^bW0@8sdYE7|1&WiBS(Gf#V}Nq?~Y^GC@6_ek8x2@M3$TR
z5}kf7yNHlQt{3wr=q=_ef9|%5y!S_rvBb_Y^178!7KId9WP{C*wsj2~@*gsSqZhgP
z{J)~olAa}$77e%2pQYZEchSFB&u9Rnl-Y1rg`6Kurc(B~Wd0n9(w&LqnHj}ccwgIF
zRiRWe5^zvW>)+Q~!1yNe%V~DV^~li=Cm&XGTj_8$T&EUKC|E6D3^H+Y4&=_32Z}q$
zRLzF)J3^_lTsoxR2hx$;oV%kb(W;8H39VnhE&w*x4_3}BJPN)F#(uhkrR;pilbs=5
z+V2Kl2Xk&)UEFbHhCv}A8jg;R;fKg>W_Cwc$So!@@#bkY3iEgDH!kJ}Hmx%Aeg5<d
zBS|tWZ!<PoNC*-GPODXk)XQ8hXgs}UVTRvRAtd~#StEDcR=Dla`!&J3>K1zX3O4FQ
z^QB@8l7Zqc<KkP}pRN*Vdt4)<oXXjFl$BWWV9%pQS<Z#$^!TRCsgS84Uu`*ELhoy`
z2$UDsgWXY4P)ZbY;7_wJT9}_EFzCc(oUe8j2eqr)?5aK~F!+2}s)ds~5&pT-5`SUT
zT1rj-XF=SHFP(IHe2G3b6hGBp_XaDm38%W4Ww5LfXR~rslrfBYrAWsXw{r?GWVOvs
zZ5xyLJCif?H+Fs0DWO$$FODl2ucPpqFm-x;S>v%|!PT?9y75uJ3`;eFNp8M2W*Wks
zeed$oPpp{*3*pSXRqj^Y+~RXWIjB0JQ&VXMma)mrw`1f~V1n58uW+R$2;Jxpq2S-{
zV68(om)1}!O=ab<S3W)iX$oY`t*y&B#oa0ZnU7Q0`#@%dyr!O01S1nuc26(`kawQj
zVKp_bE*K~!7zVa&{Y-K$U`md^mWj!-Fx?m!L-%!3N~}t9DL}8)s{OjHaMn?`%h@9#
z{bGY+?{+&ap*&mnE|Q-EepgWQ?%iehVFWn&&lp!pdSc00a&v9J7$JAPIPMQs;ujZ#
zm)}QRo4sMH{qGio@-Ahf&<L-ijEd!Zpbf5Et<VR}WTipM{$nRh#|_se7x&#AF3ZQV
zQsV;2*?gtAR(3}tV|+!~<FFRpJbzMN?NaVy!$~#0Zqa*v=yp`Vp?iWn-PuV(27!|q
z;Z0WdOLKV~2XA@49D2ss2U)vtdb_AsrG+je8`fQqxQbnZ#k^~v9!e?L|2g0&ye|gs
z-f^-VOQm>fYU;pmf1Df1J;G;?YHMq4-r}H4F=l?eSoQRjE?_Q>1`TjHpI@sc&PHkq
z++~_?Va4f5y(wX>){3JU-y=<Mx>0#JN~}u}xo1U{^*e8=wXcPTx!LzHTUWlonS!~9
zK{ssh4`El`sI$0B&uB_=QM=A^@ZybJVmjQ18E1<pq@4fWq3?gh{q`9czbds}?yzbv
zdpgM4Ta!~H`=*>~V9WsD%tWUQXEaKAh~fK?+)(_Dbn~X_JAD0h(-i$;J-bPK<x(ZJ
z<y(;>3A^)VO{cS@BttC>#!XvsvMdG_a|PF}d6OOKZ%4KI9%`>DPV9Kg%qcf`{lak#
zL@{7Jd8YL${>V99L0i;3b>fUyto)P1q3vxg6X~PNFxCz4LayW+1rFgjh;`70(<&(_
z$V=|e4IaD1>&!YSi8uFXCs9!z(U(^|>NCwBc>Ra5dQP-fKZcW2!ssRQ|0C|L!=l{2
zu;Ec`2^9e)k0KzgNJyiANH<7}N|!VYWgJ08K}iAW&Y?S0R0JG)=m8bUAtVN5sCPY{
zqvxmJ_x}BIol8B}r4#$vd#!uj_r2EUKk8zSIU!m5%3omZ%bbFcgB%^5f}575|0BnQ
z5>p!8)rGWDiY44f&gMfa7FB<D_Z7Z9Tk~ilx2xEq=!8JUD~@lxw6grXcoV|VDYf<G
zsWV&NZd$=FIesphH50mw3P<&zoWaLI+E~M`hDqU&;94IUTvYZfBr(gnr$2dnq}u$d
z)t0&Sy}>=A0jVEPvepc$3Pz{(MK5&bWpk>9?n-DpNAA$h^L?~!92;)zD6Hu0Ko+2U
zX2(QJltH_UT)Qo#FTlS)^$pCsYZ@_rUceCO7S#|M&2ZY4FZf*tiEN#83uD0GqRN$X
zZu(_Oa3mxB%zQ`3p=YAwmg1iCJzy|#2V?b&E9izj!1Cr6o@>H1z^#H(Tsb_|bN7j5
z^|7<*&18|sslU9*id9$1Y9M(Y>dPSW7Pc65kp2=Sx7_uZ3ol_6UmR?oxEX!MsYDTD
zCZ085wzkhJ_Os%1w2y8zc76DZ3w#NUL`q&eNka|8<eEvxo-myak-yXrt}gc;rrsdb
zj?5V!KlyX%WX4uPD9SeWO*;fFJG9Y6=uNdg4H62N+vOl;ZDeXDOU){HK0Ul`0pFH>
zFpe{R$oBSSqkP5oGESq~tt{BuIu*2}I?)-(I}YN$TvZa<8!l>s+0x;P^uty8?}>Jp
z*9RSNqNk@yRW3WKUf%L^Ykby5>RYoBwiuANh}|IUGLcRzA&|N5Zah~?)VRA{vtC#~
z2-j>cE3?Jqwl)m-t295F!_)m`NO25{M`GR0_fItSdYvAfJ9zM*VXaqDO3I9X83pRo
z%D1;~bhb)Gn#|WFZ`Rnrextk6v)M+Vxhc}=Yx3TC{CFhi>Hc=uP=Uz}zUA_9c2z}7
zQ{PR+a+UvXy|H_E{3D;k_?Sg;P03D6=`3xebwZSG+O%u0?LyI@r@+)PnzR^gt%ws$
zvnd~bt{sXVqo22hw_UTOBglED@Aj=-QAQU`Y@ua>%Pn(C-*ja;L&7EGd=07QN)I;@
z`5JrvpHN0>Uy?+dccJ?B?a366G_Jp`oKf!>_|>IEk^W_e-WaL8Z{2T@Q|xU+%;^W2
z-y1vj)K#4}VWZI%ws6`kRM4ji3CC0nsKF(%Yn_+3JiE=eJY6mG#n%JE(JE9=MQ#Z!
z?sK|G_h`Z~EG8x!_yz(3h9<?_rjkHM$N|sikNBpv>@S(*slVWQ&0llWqk3ytu5a(x
zGdF$SO;tI04!*27(bdId5c{I5*`lZx-agUk;@3jorIYb)c!Nd0;QhBVWz3u?9^0jw
z%?{@oRf!u_yzItNXbKH`4uwq3d&lYMSZ<(8M;W>=81k#ozp`8SxoR@Oow;>ea;}Zf
zwzqK}NS1wag?)nL6(b0Nl|93)>s#7c?-+byo|jt1Sz-tUuDo%tu!WhuSYFM0k1=dd
z=A*eF_4%G!Yh}ySfI#*0acLK*PrGS0tX3y^ipxK!bSx3CG#&cO%*565<%prp#_D|I
zdb(lZ;xpxCjo8NRm(-+%iAK^wM&qD!TA`@}B_ls>;{N)~DCcn1>tvjr=A#K+U0uT(
zk9=YK{-mvqwcMm688$XH0rDZH6LvQRVpe*MoJ2WWs>dfLY-jt621~5eiB(xyXL(I)
zufd*l3_gtdWJnMQA$;D8Bm`@^l*xkG07D7y7sr|M+K%4!NR-%i9MX*D{T9&guA=T2
zKT{|nTK|+ev16n3`wn9k2F|hvHMVcIRo6y7QlUYt?Z)|)r7k?$dwO1su<h*@wk;v1
zXh~Ih>Cn$5)i8@^y?1<ykWM{bF23epIw6xvnq{U=d1U3*WkDvM<?zf&Th?BWJzTLO
zWHx6=dic7wp2c}VyLYBrqkl^|J|xE`LykEtVr9Fkesa)yGJlHVl8ZM%*iViQyUr;)
zu3PTp{C#anvigRGLa3KlhOoktav8E|huyBl(ZhY@4RMs=)|8ocM;}eBudkDuq_>kr
zT_>g>=BduKo+GbdkENrfow;|IucO>yD5w2FP!Iv;0cf`;NaBxCqh1k1zDCbPt@RC<
zd{fejlVQQe#-{w$K<k`c5Z>&zr1;%{+dh#UuBlgH8`nMb&D~LeP(OPEzxWw#z$lT}
z%hzMJQ}6g}vsO|hoA!2~xuPa|yT(L#vXz^c<Zf0F$RS^KDyJF=ZSz%erDurN`NVr$
zwZ!&ETkie~9(<3<e=cLv`U@;O5i+>@$)>m-^}@u8_iLfJv<qU{0Zr#ZM|=ryq~;=z
zR4Gpnyc4!?4UkL{Z&N*;UHL#1Kn#_CTL&cQvze8(om($dy9`WVPmZpNC4ODf<7MwL
z)6a1032K<F;8bqs>rLT}p&ym*rFf3xgM?}3gzbrDpt8f(8w=eIwcy?!vD6hxCre6@
zkn)*>BC*RN+9U&L6@xkB`R3sGL1UL@HWGN$^V{&y8{E>?)*HsAOy8sL;!-?VYSaDl
zGbG}q(iA0;h6Xi25wlS~F{<df!AqHVLjC(yTPu;y+R`^EG&P~q5*+MzX`}R}G6w8)
zH4JSkhTDhoVzk3)n7%BOo<OPNDr8^0+3gzv!!fkWT|CPb*kdy-XlzI?%LaWuu1<Ve
zaDPPJ^)b6y=?SH?=B=Nrh{9q#?Z>FG`#DN+ZzI%TRFL>y652QCMOyf1%&0_4F!QE;
zu&RMUy1WxcsgI0q<+kiC>jMz}1$d1BV0Lu%q*97J$ZgI3y5zj2#vZM(FWfjJ-0@_m
zF0$lY;?l+#Ughq0TZ2Aw2{xB(9@Gzc=<ML#u5ou4&%HM?Gi8^RY@}dQps#j+bEGWz
zOu+HB$<Ev$hoSQ6&E<9kCZ~l%Q5lAPN=rKPerdebp-pn>1MAZnyMba0dQq2K-Me?A
zDkIEKkO-4Tc7Eg7(f7ojX9dp-4!6jMjSazB!nn*y#BbtPQ`M9OE3T2_g*?N%iXSuf
zdXpa4&-9%i<{TqSXlu3adRB0hgeDw~E2gO_A<~Jw)rosI6gpE-#h|NWx%2wD_La4V
zy}{x8&-RQq$wO86@6SaA%xW@44hz$lOztpo+Z#$8Y;yZVIk_gQ?KSq-Ro;pYq3w74
zw7RW+vfBNYe*OL>jlJnKBscse?7C=b+tXA=2#6&$+vTpJ&Es&P%4v=0l~CLy1LZ)(
z+(oUd1<zYHZtW*T<k%x4^lhd?U@2NXn6<sr(>wiQRJ%IfUVS>(dqv71`_QIYuTv66
z|APGCp0-qKtkuUC$>0c>$y6e5?C_q9&pb%Yo&{H4KYsX7<NBxRvLS9r!K<q|a8u{j
zrJ!+YZLt~Bsdy8*XNSFGRO#`ohRb~eZ95xG206timIOq0!qfP92h!QH6_}#bSvuvT
zDQ5d;$9D5J7p7eW<TrHTaD<7@#8DAP9sqv)`26VT`GS)9#48-rZJmk&;-5W##lAba
z6pt}JO@{m>ij%7kd($G+=m#W~MocZ~<R&EiM@(adYlF9o&fzlh3iGsF{aZR5Qjg(}
zoMaquPLn~l+dYaPx0KqP*K=5Qo2z{vcG#ku&b}e5PyKr`?%!;Ub~lJ^n>#vJY=T8X
zigVxjEzf0h-Vy&&%^gaAr`wa|^rH=Re6vHQ&tLm<oju!8Y*F4oy~}F3Uv3$|p7p!O
zG`N}<6*k>h5NcE0iwtF$7YSS9?VCBu*8T4v5Ei0W7a#VmUTp0*Jr9xJHMq5mZ>(}3
zN2x>aBCP`yXrq~;NP*VOjYBCmQl~OT<tw%J{=JLI_{7uvH(iHd2Wsb2J-w>+3=?ho
zLQrN>e466m&;5j&@8D&ZgL=-In7|lU%%^!NHlj;0nqnqt>PUcy+@u7+iSFFit_aVR
zHyN|>y3^HA2yFWGclN~tFeO<Mjv^_*ZgpHZC-egKpzWJGlxLz-HN$RPcDuCTL9&dS
zdvD9IIys#wDM}x9V!Cv38HfupUZYkI%jvj$1sx5;jkqd87v|=R_iMqpO8+?;2IdYG
z&E2YFMS$@2^26L*=v>2_(_rk=Sm{*(5%Na-DHLk>FV@U!6@HbbwELs)zR-}ab2cg7
ztW{s2dfgbF@^#^+p3Q?f?z?dG(+sGw+7}MHwb!arwu&q4fs42Ii(1gRC#63MKA>Yf
zBs148iA1joA8KrO@nb>1wm~to+&(ses~0laxzCHe{hy;(*%pv-hET*`ZnL<WdT4d6
zxA5&%Rk74?IQB2g<6`!%dlN_{;zNF84j!6S6Zw)!*;bw&e%CuX?`ipcZ}s=9v}_2W
zi4v;xgXh4<(xV9_gMP({`cc{LVPURL$CN5J7yac46+uj1v32)r->pi*3%yyXjHmmT
z7$<h)^tIdy`KDQNgEyMEP6=dr@7^2xyoo)QMRaLMdQ;e8aOyF4!R?!ZD3qY7ra^iJ
z)--qX*idF3TXWw~o=8U8S+SKBboFoolyVg6kGsCimDe2OSCh<X2q6sch+J|BZWVG^
zHH_5h51`P5&u6Kss(CF*J-lhR8eL=fY2s`C4Tm<2N^61t_ih|g^w&%{>S!9wsr!Y_
zAI+!Zpwh^a-pTkGrCv>Q_KYe3uKQHJ;4nNMcq(29z(oAnimmyzr!1>8Gq0VO76ocf
z75746uoae&D=KaAgGd>8I6L@u>bk0=xG5a@q$Bl1vKOX@&a1Fi%TG5Ue1qOXb0=x5
zN$6~WYU+lx*XG~;!;$%rt88=)J;FVCqthzO@BTP$)1#O0LyLubCE{R_e7xg_J8`!J
zvX!Ix@@?<#K|Nbi*Rh`LybN@U)~&<-LSgT2K=x-SBUnFg6jL|p)h82sP{VgtgkgyL
zy<>{^m9~R(ZCtiX-7Pe_wD{H6XeJ5MFNJN&;rqEBWv1-n4mpOsu&i7*d9CPJ>U|U1
zV0g921y4dcTX~Ik&q}5%wfp1?M}cLJ-9JxV$Dz`4#vI{G_0{OxfI?#&wLQ*a<d8HK
zO*JzW_`xvrOR<t){pV*Bn-R(J3WphnM<8Q(*WC~tpY@8zv!&p+$Qy+mSt6}PI*r3j
z&7v|yFw&_bel*d#Gm~|qhDG8=((_^&bpvG$el_)j4@iVB{&Pb00eK{M^0Cp~hUB5D
zf}z2|x!XQAHrWiUyHJ&bL)_iOw<$WhTMN^DLs#5!P$gA-IP(S~Yp<tASdMy?&9!?4
zR~=1Q&<bl0qXy2@U?@qBL(kbcVx^1D=}OOGov-<~_dC)Lac3yrL}jLy)LaqUtf+EX
zJ0Hy!O9v>T06|x{#RawLuW<)|#Gy6E$*{_wOM?2uEJC5)F`KxWozk)zoEN}9nyS3t
z&HDNJo&-ML+^Y*aFO${#m~_mUoc=235cvL8c6Z;)lbNgLyVM>Y`HubccR5m0_Rh9K
z?HLxE-t%!cKsDQn{C793eJt<#5BsSgD+17MZoA&*NjWS%AdjaDbh#JAD#kr&49~!l
zun63GsKOo&DXiXaL&v~?)5$56f)3T8BK4h3c4rOek?V@#9b$DVG4OmkH<6J=KN*)F
zgZ9oI!+QmB$j`h!>;t@G`;nuwnGs$a!5c_EUv&+q$FJ3zQBC1-8;ntAJr(he(jrBj
zTV$Cfg(wyx;Y0x(aM;)x7KFg$gKD9lKP^&|#%daRNwfzPl^z0u;%u&s`L#+(G}bJ%
zNv-=mdWOsP+qZaF(by=?XvR0eWm#XWFJ<e^w)H|bx7CduqZqLbXB@qv-5%2Sz;Cd%
zZ)o`A(cK}~6F6PFks_@8^R&LG^l6;kK3x{8tp%&CCVW#VfH;@Ot#P~|N2HzNM>FFQ
zeA`iQbZPa==q^LyQ0(x0NAi7O(^KCC)8$rHUJIt>?P7~I{xV;PLaD6~>Ww8y?}TeC
zkc>P(cNqb=d?7i`Ky-HCmxV=nLPHPqG!!uR+tP(k5<`mOtm~(**8{fCF?kVif5SN1
z*_evIf2Ocjq2`FBr$vCN<er`zURr6urX{XT<fOEmX5rXw8+7|iU8KH35BPzyUU6Ww
zOkWDhnD3HO3*P%{krKuyT&t}|s>uXq?FBYTOOmiF0~hWwe1<aIU$Gtc*kr&k#v`wd
zBAIb<?L47t7ZY->pqR9qID3~QnrRe4@;J~PCA%(cak%1^?Z-e_4o|*OcNOuF<HCgg
zxyDVQ)0|3^t514G@sMrCaCuu~^Isufg{^$N&~ClHG@g@}*F#1nkp$H>_t~=_@a90|
ze7bG=$k8t#o{KlcOwtt+(&NwrE8nTun*IsB@n<o`M$BIc=?2=`$C;-HUp`1QLBc0B
zz0s+$2-Xg@Z82D+m|3{$pY7-!5Z{u^<Iy^_hh*K`a~u-SO4t6|HRg|=|DaALgIl=D
z7G)CJriObt$NX#N9&T?g?u*px-x2X`No-T?iN9IBD0cqmYTePxP}V8ro9Q-%?>ZRa
zAn_~{;RmnwT@{h*ExyJRSkBzm_ZUB52t_>9t>T*rby8xGNvlh~xa+!bJlI>Td3?C_
zmcVqmripv&2En0)T(;VE{=Fqb>u6J6Nwu7{oL0mYw_u&Vtn17kuWedWOY--TpGX3g
z*982IeFY{pZj0~e>|j0!hyfm+)~Gv{I$EabK(27rWUcmfMCj<)kY?RUUsjVJldrmJ
zx>0BwSai;@<oDejJ{IJTgcAIX%f=@nCm)3#(5iTGy+&P@e`KWaCnJ_0LNhqJcK`yY
zT_2@ZX8GNxA#~Qf``(vnCSjuS`z_$iIIaTWaBiiy#Z9v4EN*_DI&;rDdODGM|I|IH
z+NC2umnx|Y0o4FN!&XSel4oMYZ-4uKFn>5cpT=guFg_}OjDst-C$afV{E(y}LI^%g
zJ1hL?o`NVwU!3jAes11e=bGid466-|r&F}qM-?@driZWikIWi|^u7iR%{C2+iO%)w
zNg1kf87%(aFV*?1^*U7GGq?*XFZ+>JK64&HS$~G$bU)Fdw^w#AF(+UuKs+(vcDKP2
zQCM8GI?%_ZlM=8WMB=^|j$#cWkHWgZr(KX*F3z!2%3%bRz#$@L!^R4}YKN6k>2%VZ
zU8tJ(*Dt2S8mO7(zKyTieWME>fR3)(crnjBlt1u3C$D&Fr+%<tK-!|mkpC0{i_UOo
zM@lD={alldLE`k03|C0dKLi1YV|L50B_OWNlU_-vnA<$?ABoG2gbe*|-hK|t=NRSR
zNaiOv{Y0&L{-c*L`hJr@oGL<m%_mf3H~Y!&+pzW0ih!cr_L^2&itJ|Gsmn>@i{BBk
zi8Pbz4nq}ffhJW_RmF|&Y<q5PEd)e|hK3H7J8%c=1a6TD$EX0~5*nVe7bW=iBg@)M
z@Y5iMo{2ucCgKM=BVy0EpXAKl)OFBp=5(hiTluH(YMw9Srrtc17=puwc8_sL@s^KF
zX7OO|{=Qx0zPboo!ms@Ev&JPw&UiuMENLNxQieY2gblEU-LE&%(tEU`AywlSJNMO$
zZH_<|iWOxu_<Xlsm6`hI3ZZyMpFB62V~<tl@D$o<kGMsr@YO$3m)1e-1qOhAnZVo&
z;n{ZW^z^8{gCZIYA?C3zdbJRGBO9p1FE%clbV=*+^9`hvRk2anP7*BilqQAg6ON}J
z#h=ENn4A?~C9^&C11fjH9L0R_O@Fv0(0oqK^gwHCtNYgv&-blODk?rgb;R<H#-wL9
z416vehI83<C>UQH3Ket0oUHrSvo^KI1?IxD(gh9v^*D3ywHHUWTyF%>CN*?Jk$>az
zmRo7k#|(SGz3%L>n0`MMbv`BcYf@+VjCVY3oC-F;QK4SEv+oX9aB|}6SHbmsa-e{S
zO`ZX`)sKUtx10xFQKF&zt#$lGrl4?8!05X8GUT^jbkts*TV+*h<<bJ_3kdW4CogYo
zRf-#Nd$M%TNQ!dt<lFad*Ie0a13QC{ss9pyGZDMdJ0;#eG42G>N9d_#UiV%}Vu?R6
zg7E|C`Mdp$<dk?nX)*6$zTouDa3-F3w%~WC8_(Smh>49j=fS1Og!*La6G}535-R+m
z6EnVQz8&kiJWxHp<v!@q?=SFW$FI!fQRi#=SZis{F5M8?h?xmdf{v+$G^dnAheAn4
z#@P?;WBfII7Q%4|EjhB|-Jm*9%w)iYDgDqlWM46d5K!Bn_lcqph_m;o?^N-{?9oj+
z3h0nYYEQ9?r$_iPYz2s1ETdbx3Wt>J^4BIi6{}rUj!`}SwQF8IZobt88^?Xj#}0|9
zq;@Kwl}+6({E-+}T^)m!?gU0Zi0y!E@?MJz^`!&z5XgQ7PET4O+ZDK5HpyUPzp(=_
zvcPF&VU5Zgcl!#?21pYcYvi-ZqzN+d5@KTxpWi$n!SrqW$m{-Gn!|m4`sU{5hsep@
zTq96-0%74hN(bziq%;YW4!sQ8VYqCEB&ZBJ*)Rn<P~)i9!Sg=JlGhhqL8a>}_pGF`
zJQpFB+6^-EE)V^!qPQdww=yVn|2W-iVoO3%-Qa+#AU{uT5C60%Y+ws*JsrJlo02}R
zhO{@1iS;9vb?4dSJCnDu%={hnFI3nml%I5E5~v0v@>N<L-5QXJah0k!x3adyoQZ`$
zJfXfevEb@_|CIBuCA*DA00Fx6aAyeX1IXRNj9n%-YvO5}QvtUlRI+^B1tM&<7IE_(
z@um0Qh{A*?Z4QDw=arzw!R7j$b+T@<d1o&?{gksA(PfRO#<?Xr^r1S=GHbf+y;wpi
z_)vi(b`%H>oZ<vHH^jsY!MtC=SM~K6jMP=NEZH(q+l5MKy>Z@cvWWpH2pYxOjXTx1
z!~&JWZZwjuypOo0ny_BNm1AFlY2(lCP+iE!SzQP``*yU+z<kK!L?E+ip)Lc}SJAxX
zZ<Ps-qt5v7cwBlu5R$+OZFIZ_dCcJ$7UV9^c6=7zK%R2Lcc!?$w)(Jj%0IktcS*22
zBqewI?!q7|1h$6y7OConn9BYIC7F?viel%02`fD#|F1POq=M?&NWlY_C_Q4Wt!Hyy
zkkC|zo(&r*pvpU`qhnCY#cLE?eru*?P9aXH3b5I;aPcS(c^lw04t*P^_<XT$-77}O
zTxP%OiH_I!WE5{K6kIScza5FgDjDJGp(}(wbtt@a<@P+{Ky`%G6?#QS^?LDQ7eK`g
z=3}z1d!-!Wl*_?4m%Aa2x39p?=bgr{FJ8gEjQ!G;NjYj#r=FNsqWV^198pR@rHg*o
zeQe#^$L7}BS-&#T?jK<Zy%{!wv)iQzEV!dz#a341wj+JiE42?Y7qaGd#Xyu5^1f|c
zM=H8TF1dxJ#BMq}<r6_b?hqFt2fh9F{V#idE{F_45ESgK<r<U0L?56$#?00TA+FRk
zM`tGkKhV$XxU9dya)h@s)!}QBQ!Z}`N^oky<`EJ+uhn^~mWC14ahn-{t^XEUF=wQ6
z6Du-$rkN3CScws4=>-~vF)Fc}S82_xqtS8T?FBKLrstRI>bUTZ>ma6(COyTO%AFiR
z*Cd8M$o_}?VTmRdDTM|4f$-9=D%k#!9|xI#21??v6>P6cU9DS1qd#Ozs69-UM_Df7
z<sW<(^~HaBjAoCG*3+2=I*~~1Wi9Jv=-@PVGEGeRK<ko9_4B);<|pSYdRLB`tbDtd
zFKm%cLt9saPMjCT)G(aRO<Yn6)|+eYO|ewrTLYB*@$ZMgeko)dshkr%jJm^)@K>9g
zn==ibQyrH$IXNAl>_c_v3{g%J>ep92&qSyF{o$`V0q^Vi;$hNmnp99g_W+9YXf-pd
zR{(Fr#jQw@4Og|Pa;m=EmK*)Np_Gf=0R_rYi6WSooY3d^)Nu#l?5?(*M9{W1M?=g(
zj*$Uz{A*$8_lGiDgqgcbs{-N|bdq#WCLBkyF)qPg*Wig^fEf!nPd7v@K)|N?H;wx<
z<B<YkTWYiZzen6?95bwmR4F$fSO{Az<d0-dvsNQ#kmC7(zUg{Hd}{@4crbQo7!IUu
ztFULQ+rJ3ha_U?9`d#=dCkhoC4|`leUS2T_s>K^OZ{is#P)VFFk6Q%&R>dN9bOv~-
zKuyidZa#DvN`r)oXakI?<W*`@qijFXa!f;5+J^K%9xM-_q>-M&IWE539{9wOo^Q6h
z-`1NVanRs=a;-~FJ<#iJ9(@ODsE+u;rrtg$c<G0H?y?~*{jYouP@|2K##%R!P2Q&W
z&``zIukl$O<&Eo<0qq$UG&6yo{<zm;Y-Otf%Mba`pYAaW^o12t{(AlwzbuTUGX$|s
zO=CR!BR~V0X*63UK=D+4s(AFJ65o!kYX+^86WYZaIH4<RPdWi4(Z`<1F2CffPKmnB
z2Q~8om~n}thYuYZK5-Bh0Z75vMIcC8N)m5d3n=od(kH#0dWI&_(N3j`VAMM;F!_4s
zF?0J1!L*RkZkoyO#F8^*skT}UOu3{}st0o!{_|P{2meCuU1x___u<~_Ak_lmhSJM+
zX2{O%uRiBDGr{5evW!%={QXfqqo3Lw#sL(0N50Fj2Z)LI0@iL3qC^R{b(bbAgr|4d
zS9;5=-iuiM$23?!D3H9MhT&H(+1Tod<L*ZA4_`oyqj$PIcXx=*=LuG9l(AHX{u92L
zA=62>;4coZQK)Ev8gZ0DAO%Clr%%^U1||b_`^=sa#oII5Fj!#L<;oN_>SOBpbeg6+
zNs2mEcBKVJX5mtJaQ^u{E!D&lwRB1k4X3;Szr+lu3MTh!+fB&_s|BiVmV^u#KAE2W
zZgssi**C}n{(Z(Wy2AC!RheA6PlWV{wpEsw1~!wgpQ-mU_4HKq_MlUF`rD11OcF1L
ziT-+wvGNZaLNgNZ1C9elZv^~`=aZDboXySaS`!bMnfGRh+zfpOl!6^`nsooEngFsl
z|K3%E09C@aupYlUbJns?1uKN9Pc9I<n2#S%%Ppu@3IhTpd!WRsM)WjFM%J&^vcYmE
z;&Y*|@#rexb!SXAJ&x6o%Cw`=eEmI{HzJ2voVb!o!c2oBYUN9q31w>#`<1EWV8*8V
zgYN?OXE_BG?klST5f<<4m=b<^_g{*QVW;O3%%JRN8|Pq|I8QFi?^~h0);5}^6g)Os
zg_PMlQ6)mfKBd|>e%1Cegq5J=`0<6C(iHPgS=O?;+8BJ}TEO%Jn;l`N7x897-*!M3
z3Mk)xb|~(dvH$c=SZkk@#FZc4^D5tdRAo#>4OtczsUQ5nyw-xn;$R?9PSMS#9GE&h
zPk9)nK}8U7Ay)90i}5UU^2xQE;MCa_hQj(9Ayk%*T}_r6z5T&)9k5A`)y5ILV6Ad$
z)3Y=ag~RS^!%rz&#|HNyG;{M%>&?Vb+oSJSA7{I-EsYXDaSc$L<u9pPl$J)#2uqw-
zYPZeAt}k%~11Eu`Ww#xUIO4najb*Fd;=(#kqqhSAdB@Xm{d&%Z*$=Nvt4gt|EfkDc
zX?y%tA&1ZtK)I_jb7W?zfW%b>6b_VN+!q>3oBaa6d)ujCPMGvm)Io{})1gFtyT}}G
z&CI}%3)UvBvG-5Ap|4uVw&!de^vltUd*s!~lA(G|Uucj642OBlwIM!KesBdbz#Lj}
z7_@-GVmiBZ!`8{8NSNtEiP2=Ud^U)Cjw_Va-W)9me@wIEG&vHx+pkX0&i3)(o^|1z
zfK5v{z;%WFiv^3BfA1_za(rk{jXwX%CfU<IYj6)~>ieTZam5&6s~Mt&PpPGuW4_-L
zV3hR>m_Ic9=L@@tErlet-G0(z^wq|~xR0CSPX9iqzx53@)5|tO?BoZ}BUQLV(BW9N
zUhsq>7Kv|;&zF|jt6)=CNr(;I+}Ylm0rCp2@5r3)&a@k?_pAPN@37#l4@XSgak@%s
zYI-_45t~~K^%rg!8^5GJy$=-u=@U>t2u+q1!$2Mo`q*=|hhABpY#(h>hBDZ0vq6t5
zx5Gmv_!Lt9@m4xyRbSaFlC{vn6+Hjl`o4gR+Qq!(H*a^%jPRwsc5c)Q^*A-tBP_Gu
zMVYa8@h39wlz`1(YXx=0ne$p8u@6ST@x2~DhmjUH4Slk}%ReIZ>+z#FW{xl?9V8Q*
z>L!+tn*S%*y)fK3M=qNk^8i>a6>bkJJ5_W+3l}f;nAt-RsS^gPUVe2!$sGRD`Dh|u
z+_M}V_7N!&$@5L?40H7{fUQCLhdZUPM^B?Zc>=*;CN-3T-M<XhS2th&c)U=GLDSQN
z#J-Q&oUz~Et8-N>pTZQDYITno<+eL9<P@S3(Q6wked47zJj)xG#GB4c-;<i7r@3Gs
zqr(1lZa@u$T+eHhpPeNEC3QW}+;w99ug+Rw_VASo=em_9^Mh)Sf5~;ILJz_RK9YUo
zrIQ|}_1i$*A*MBK!GywLr80PVy?LpONE=Z~5uDwVBRe2ZB9FT8OjzqK6mK{{^TZn`
zt`Oz7dv@qo^&sC@pk3vwk<)6v{uR&`P-to=ePl9kOS;ONA;K(vsczt1^;jwedNT0B
zOj*f%CV_GW8yP~K&`zL9m5z_=nes9pg``JGjm0R<XMw59!$Afm$dheO<+<m(*YtQZ
znVYE-^V^@w2kH*t%$1<2P1Cs*Y&om21rTP)!ytK(ck@qI85VIHAsGg&(w0v@1C#`r
z+RplP^Em2G6h<B#oM)oQ?qqJ5_nns{y5}?^a0Bgo*(ombeq1*gHEa>IxY%e~GmcQ%
znGI?<vnFCrSt1a~E79#07kL(Fsq?@y_^gWgAi??tSgVB2@fm~qW)RPEg03LIxVI@A
zcH1nWpIzhN^GizYd<7+rIu8>^s-@(iLJ^lf_iL*q1)fZ;{^5Xw`cB{6CcP!sel%j_
znq_zM(?tYpK$*GE?ND@RbkzW9Kk5};LacKWItDyF51{U#`=d?r#HEb+gYR42L&S&C
zJ_?uS@anb1QP~>E;{$CsJzA~uuj;RPBtsHR#V_UE(*o;Sehc?pKq2|zZ$Bc=>hhGb
zb?`W(@bz30A;CMY6gbT@)0-pb(G!pTC8jAVI}6`_5TnM{yxT*VV`$T8628m#zdNZH
zbF&gkVOuaosv4*{dzU;p%5pRd$|T@#(m|d$%kwJJfV3C&irU*PYFK=sc5EJ)KKHXw
z^{STiflIi{u`02`01*Pk-j1>@dVx=3edccUY?<z$>qmdBud;~_n$cN*fy?Qpdp$U0
z5J!c=;g;F*3vp?E1t-(KKTNU=WoVeDr^#!#BG&{J38-3bf&S?DkGn(|qK;>aN?Tq0
zVXf^eU`o>na7_8Njb7DRKAE)zD@M`ftvaN*Xxyo=^Uf@{uZ2tNBw?{3t_^_r?TN*w
zmFArea!;sF!GO5qX`&GZHoBDwqLuZVT<$scSJC<gZp$y-NlUBAwk))NDGHFwQU%oO
z!mbdVUBir_ZGdMhdGme&3U2q1Gs!I9j)SR>hW1nHW*7wKX0Qm2^3OGLqkes~Pe<G4
z>65qP-;_-UICVOn1*75pFGJQ|H-LAL&q3<sm;u1U9b(TZcB8CIqoZcK!oai9nhEL-
zY~t#3DWhlP_Dz@B?Qib1ssjWLj^KDD?wm7<2oPT<P>RhiJif$4Y)X!gKf;Ofs=qGn
zBzgN1Rjw<0xZBQNJVN3rLJP{jKX;nW#i#%vvAaL$_ViE}g{&AMpP*g3*tsmYFj3;(
zG<AmJiJdHfr6k>(3a%)bRGz`GZ2fw5*K1M|oL%gkojAyi)1M&{1(-Qh7}bw!)X(qS
zT5iUB(PfCuW(Gj8a5i9xQStTK(}6}zW7QRhJHfXt5TiDKD*@b?i1_jIhvBBr`>8xW
z2jcRGam~2HecvCC4$4EG^IAopckzWBfs=k$4nD?xa4Ap0re=)t3^~w~t4|eHv48gV
z24I`Av8#6{^2-{G)}!Mn35Z5+aeg*RA?Ul!M8vK24KL9PDk$rtAUdA~TUbrw&$mXQ
zY=MnJ=oaLQ^z|8qd}wM-+vWTj+3m3DXK+$FmL6RDQAujN!krQ#BKq0c*?@g?p|_WZ
zHn>fC$M@aax0T|8sEhgIw9$5hvt?Y_S^r5Av{PeWKFnT-Q6X)l`;`K-(QCn2VU$_h
za$~I)6K${)6PEWvBfu)~z<(bI6>A_@m}r_Hp~0{2;dusJ^~SthopAkzGNzuCBek`n
zJ=b5<nBlwl<sJAMfK_2)x_DbA>^R_)ILGSd>7w1Q7MMOrCWDp-^m#6*6g7=DrH{FS
z9XGDmQ8M#8Uz_~ph@F|3oJ-#^j?PmyHOc<C*@dpyXtMAVJR1^^*!lnGCtp2SQ7`~*
zk6QV_8k$vGYaD3a9}%Y#VrHSGqm$Oe>;om8qN%AVJ&dFfPb9m25XkcO1&?W*g$-^p
z<0TD7p=m9wB6{HIbD=is!`j5|5yqhLwI_2KMRgL(YjzM$5r!;s#~{D!(1!Snoe|iP
zU|Hv&ll#?4^W)RpnOywN)0C6LWr8$AKuf-^o}Q()snNZ4PVhRUUY3;wHYg`2X9e0>
z#8`Dug^P(StA=bb%3UuLFRXkNDR;8?=Jx;N(d;?kC-yfIGW!giuJ#?+ayM3|llxWr
z{`1yWOW);|ha78E57Y8IGe02^qXJQZY(M1>&8pAT2RlDMA+&ejK7?w=HG#_KV)+5}
zm!e@gt8%-)opiEq?_)Ql$_Qq)tp!Du2<!o6s}Im_uKKH(zDDVJ59fw!cunQYzM18_
z&~veumTc~dxBZd~etd`yV+pZf$MKfPSW(vuSabOAG%ITvjLe2R?9|jHi4l%<L_I~~
z5N@+enLqLjlw7*GdfBzL!!gNZ*5j*;++B%MgGqx454@*JcnuqNDhc;&(^%Kj5f+yV
zQ{2z%L_L*?h-qIZ=e~TPUnn?_N4Q$-x{Mh=;vfX|-SOdnz~je<YX1xnDO6(VA(+5E
zpE`Al1dH$X`2n494(39;L<wmDhBFOTxmuOHJ%AD#kJhms^s&>ure|PR2PhuQ>S{6T
zY<!RNK0`Qzpj#%UckLhSXxB%M2v2+=c{U#{!{J28Q15%ImlzD3$E-9ZAj4$DbNky*
z`=_Fzc4ldRV{W~?tdaap_#}GJn?R2@gT_6ikAq3t-(~F+@coF-3VVzj^-{~?2klrY
zQd4P445f)eRI}KC)`6w9MHd%03g|guCfNrIGkpX!<}`-3y?P_<aH_4;-TD!<_mYgH
zL_u_Ma7)B-CLSmLQ2*^emL__!|4B7mD+F6oDbW0by)Ypo&e7H-v9q%i7tr3)V(Sus
z(&%0uo$D!%Z=&z9gnAag(`<P%@N*jI<oS`)0P0Ttuk$M$IvKJ-3k&=B`vLvV6pGCe
z_x)aYruiSo#vv*yCznJZrf~uMJUsUBKHq%I9K{faz*wOKB=Oa9>5Gm8TW(J)0#qcw
zOjFtZgx~i)>SCRjeTp|UrxQnqz-h|k7Zn+)yMu3Pa(=^s671=fU9a8=2cuAp+&-IW
z$w?%@0)OjQqpe)d;(o=8BJ8<#-p~(RL(zNl^47|G_vWSLRTqcrzdbqBD+>wX&f$|a
zp;i_ax;v5r0>uWTJ$GhP1Q2_^5;rKfvYWL+uC|X)-``qEB!uClK8`v{g5eot+BvbA
zdJ(?kk-D}JfLoy7$7r5_aPL~jnQR08BW`}Y;ir{PAUZ>HbJ&^2a}m#<p9+5GzuU?W
z38FDEF}Nle(WW{>g*ua6+m&*10n?XUdkoss!dI4XWZ+ciQG9%8u^Vc;rM03RLPce*
zl1N>J9;ngrv?Z*x!9Q993R2}M2@>uGW)xG$=c|)`|C)D<kky33DtE{K%iDwbhGjX5
zTi8VD2GRn=PcdjRitS-RUF`xfMHLKUVmu8CoAUtYpWv#bnBE{#22<&?)yaeo2bKbT
zdEIX2LYu4~c9Wq{_`5&sCi9@o{qd$X$eUI;;v=3tgX!~U6wz=z>GArPF5NZtU6hB4
z1OAJ5et+`N!Gksh8oNrhST@6w*8)Zs3OB(61A37OQ^dO&V04+vP3kxl)Ol1KT%Hh#
zKgrZ3NR{YsZ<xWt{`(tOMg!-k+y!hG#AT?Fpm{$Vs%Fe<KOJ3N8yK$(2Kr~@jnB?t
zUz|^^lHJ?Uk_S$MR5N?mYa>w2x0?(!kjzi|%+q(P0+)M{C%}^^<yZRN`LvHK;x+t1
zZAwU;*4U{=C)L(TkivdH6{y(f3RV;&&<6=3c~tYQF!K}D(;0Cq!>;Ywa3oFVJZJlp
zUE3w~cR&)&^}Mjqn;%zDH9^mzK>5kd<03}xg4DH4rC{?4iPInSFJxw%ebtxS>yHE@
zU`O#c85_($Kj$yERYg07f62!#esNKatJC9XncFd?SR!^I3;@-NyWt-(>eVu|w0gtM
zY$LwQsxeqgSb8wWPru&JqI2U$3Nw1@lpkq2GfbyWdmS#yT!FT<DY%-b7x9Nm`H`$4
z_~U9)Ha0d0ja1Tk?m_0LELBDC$c%P_+}RYDn(-0%bPXQQ#QxsjliL6&u+G~LNCi?q
zFD6UCjj%y*`}Y0Om8RLN7LoFZ7w|`6?G1es?zwD7nSXzJFB59<Ofn{X_1|yQ!R)En
z^EU(2!WG9)o;-;LNjYx@vI`8g>X;bLPIF;lVX0eg6Q_sfCA;1GZw~@7Mde>^U_?a&
z_52iDox5f9!+pdd(85d`gblPuNblzPrbXY4#|rzzOVttW_mYH1F>@Sra~&XFQnoI@
z6Q5jj?6V*H{fXX*YE1PMl(rzPOp~Csorap4RT0-bmU*mMeM_{=bv>J@O+7yN(WB0;
zbY&suFLD*wT{5jA%T0Bp8)N1{<A^WQ-Riwi&H|J&Q=Q6aaUOLnPlx2PaPtk0qjl#e
zFp^0X`sw044gXMXH%gxTBGLX?8^Z+J=hl3RsS2TbFvUE<cS*4fGKpABt)<I4Cx}}T
z62@?2ccsVtaJG44U=^!9D)Z5}c*)9&h&W?Z=G%(SvFMco|05aa5maPnr>L_7cIGX@
zM(LHcfR@0mCSqTE1rTWe(O#~<F93=+4v0AGhlPdbkiOpPo$ZY>n{M{}Yekn4XxiMY
zX=<9Vn~aR+<jHIpCHa(INa_B5s;COj;MmhBs?xP<&k>r1e1F`d3aaJ428?nP#<M#S
z51fS)uz}OjD*(X};e3AXp8KW5)u%H%V30zR^tdi<&G_fLQb_yRCI8L&-?K!Lklpi`
zzb#7)5J2?mE*YNb(4B5$aFQdQ$~Rp~HRI5yiG3NCl69m3$RMy+_F<UP4r21VfN1w1
z3<It3WpH1%zP<H*5?_8+7uL0xGq>=gV^#FPkE*cp;j4X3(AxXIKWhA{qyBU#HEksB
zN{wVmHa!ONWwpg0l%6~wUk<Dyf%?!2vDnB|OEVo2O*23Ezp0cAG@rsGI^lqTg*Yuy
zkO>N~u%Pk(zBCGToBlMEi;Q08i+sHJ=i`N&h&c*KL$#ANkbdjVIp1kqlJ#b*Cf+B4
zeho*>Q*W*t$V~{sTve%#C%UTcYW{LA{&?v--87FTP}iIE<ERLLiz!>%r)kqS+7~|e
zoZy@4R`U#<n<(@J+Jkw^C62r?t1R({pE*S*Xq;^94KcKp1WclknBE^B6MYIkrft8+
zMYA>R=xm_`xJ@v|k{YI#lN^?#z!|?MIzSLl71|&I5lGp(Hu<sxOhEdP@B9VODp`pm
zm3#(Z=vED(`TccYN?nCXN2AT0T-_@J1RX6+iwcRi?M8@*&-1E@Z4nAzNl#sAqAklz
zVcX3ed;!j8KzK5^l?x#>$K$gKhuZpD^N`YFN@K?|nC_2PXRgJIdPVb@3=0&1UY{W8
zGb|vCYS-1Y=zK!eOVBWVgm(#7MB6N(poFOrEP$>kEYK~{n|A47wQ~R;SD>YDrK#D7
zu!{51yJ2UbR}j2%w%|<-+LH42HiP1IthDm?gCo;A-Yd84x3LNU3240N5IcbwV=?AC
zQJ)u}@&ld;f{rAn2J{+dbGe3T;0$+E6nlkA{PpA`=N3NJQqxr1tYS#^?=|*rcoWP<
zGZD(3X?5)%O(OMz?C+Hdiuc6;e%DnF>IM_IWo&lc3+s>F<^<ZmvVKnJ<}H1?E4L6C
zofxXTagk@#z4WT5f34=IJ+>!U*+Vo>MDYA&DwCeZlc)aNLw_523hqeiFW6F|GV@wL
z;702a^B-(U<Z*<0U9;#q>8yn1>8Z(G8{jnyL+e1p_DHit0mR)p^fSnjUqo4de;A*V
zB~Et^pCkA7_MV2sh=XM94)6eOaBKw&Ue-HsXgsE%(pd92HBXtYD6p&yAf1?#RlHNE
z3>{%`rt@UXLchCnDx$rN)ggx10Cj|SQD~prLqgkt)2{5`lKTy}Ki<>r)6tl4s2OR4
z<&~p&I-%L-I1^L89i~oCJ5x=!!GCL*iTTbbB$1tUb4<dPN03<A!5+jh;dQ-L#rS80
zsL6r|Q2`MGVu_jqnF*u8Ty5P)G%@yW?6*NKi?1)UJZtFKDXkZ&{&+a3zH>FM-=JgE
zX!50N2-GdPncIw#XdRg2k+P4HF>4puz9|^E(gRjR{7Ej9BJ@a+mYW!eA4SA(o#@&g
zrmo&<qyU>&rFiMS3*@o^>rf%EAQeLAVx5~_^Z~jnx&a_Eq5-bn+nQOc?TEh_=5{I3
zLoMWw0^ud!aICdIj9i%7SZL5kqpOi|z-;X7hLH?`$-GA8QvhCcGu7gMnEkdl*SYxl
zJ+<G;5ZnQr=+_k)0;IQh_lFfvFfMIW)f5^5s+71MC`;LjVIEfOi>Fz5^N^m8lU)vk
zoWH`eSJ9JeA)R1nbWzp#<B61hg5#kLV`x??8n#qnd~8=sB)5CFstBi`piWowEmuXv
zp9%iMd>E0EYf|G;P3$!)LlbR111U<mkj9hzphL4!Ak3RA_c7mgmU~(OvPyz+1hnF@
zK`UNETBwS<#ciKEosu`*cGe|<!wEguDa}FoY+!c%&m@=7$a)ML;Pm0yDWuR1VH8cF
zlHSyyGroQI<3zYQec&m8?$ObiF&C_CzkmBY1F&s-F*E>LSsSs>$ucVx-mmF_r7>tc
zV9PhRcR_j{7GY9m{vdw46Kz1gF%Bvjae)vvPMC9UU}mZwf5{hn?yuenU$3N4#KR9w
zY78mAA3PLqOp~P+X?@)WrkEL)T0f^`yqIK_;C%(`hUGa>BzARmtxTnc=D_?@dLi40
z5x+GP8XB4^PTNl_G_#ZWNJ&lm>1IW3ZA*&LL|!+vH_$2Q1#q-*{|uP_mMmd3G+!pa
ze29Gyve5wd2Ex8Ck)VAVj8!BrlELHP18%N=9Hu0;C2$pt!aK*$*DOdm3>3@5pK)-}
zO2Na22Xe*Wj7e{QPj#$ayaMBY)nF24=a+ZE_~YkqqrpuJ{!a27HOm4>w<_I}dD_q)
z8O)j+2S|A#91H$|4)gd-K0>ZPQ=<voK@61}LT#qPAa_jSj)0-^yp)3E;Dc&$p5M1q
z$K|qz@R=9{t)Ib|E*d5&-)k_uxr(oDMa$dAM_DZ9s_ltDwU-x@sieNkAg%B4=g$)l
z?{>FBv(gqZC4sV4gy}=KY@*Y1^AmheavYI3K<n7%_KygBx+?WBt4j#<Wt1IW_VhgQ
z`<o{{uycMik?G~*WB2CogCrPp;s!H%>y}!$L9&WE6Qjc6X|!4@r~k=q5vW6V3tq*6
z#C++r3cZY)b18#}<Ik!A527tFL6Ts?pu6%r)&UaP&^~2-VVy+c`s**5UAuP8Q|`5<
z0WN0S*@guv`U=~^gSE<Ttex-MJ1&FvC_*3?qoik7k?I}OI!lPcYQbhfzkg;FHszhA
z9(`c4Q~mlCeKWtZ!7{raq8lzp7grNQcAGDNg&zsF(701GPAiK9puuR9*)Jdbwf}L1
z<rE^^dqXI2?`cR!T`=qK+!5-VJuhFU@A3q;8cHx+vh4n+FeDrPR~wv;5CSKGpcoxB
z3+GVpO_2{ie&WOw@;vn2vKmHSgq=2X+GtRM`6+JQKymWq-*&$BZ~I4Nl<wEUHsG-I
zG5s2JSN?#@9Rgl8;&(_NT3WiGR7e74+z#F`45rxh3HpA&tr{<;OU<FCbM@-O+{QhI
z>AT|J5PK~A+}k%*JzAl)$gOW!6_MO6O9&$uzhM@-SD;0Zveij6GZ(y`?gkQnEF$kC
zs5fCAr`_v&d(ki+sMKrm1{7C{Vlmep1-7E{2Gf=4RAU6>ktB2Z^1YfjdkxE=Balm|
zM8>H=1&C~L{h1mUE3dum!?P{&jQG1qD1ZyQ%WtxV9WHGB-=8~CAI6Sh^$Uy<xt{>T
z%Qb-$P8$ttRiC@TeC${XOj)yq0+(Lc{)Kqs-fz-Wrv0+wp|rM$-C}%)B&7<+#@4uD
zRqZJ&Z>|PN5>tggD%n^G4O6~h6Q=)tFBoi-z6j@OWyKAfAZg?}@lg{^YG!(_&K8+?
zLpV;zWA+-dk}#zwdvj|Gf0PTg|3@C2xdeGoX|7e&yrQjcStQ%_RuZ}?P?RkYWC|!%
z)#NQAJzB~B5uV@d_IoMtKe8$E{;#Ir<Ug8#8%w)j1RN6U6&u*1<5U{v1cx)#S&+Ac
zX>sweQzxS??&?v$_9J|PTLn`dH){frd%C1=x^*as>vTWHpsAqvMD_9*1qf&!*Cro4
z+QRX&rx`T;v0pASRS9)&PQ*DuwO%|>Cm|r9)o1Mb6dCPwmu!DErx@z#^W)Sh(d-l*
zrJ{DaV~OQwrU5Qm`WBiljJjp`)_%ka&j-T{9FWT>TX)<XQvh4n54`{5C|rz3-e#?~
zwpI{?eB_HDG04}~_l)A+y?fP8@(T*~aBl5>_I~9tq^p|g^#zXs-W@g|3f4yF5H3H~
zqE_BWfH+eu7&M;hBS&aEkB_zZBL?2-eRUJD9R|aI^&FTIAm+yduECZTrOZ&WQp<{`
zN;<Zd$+V8mrAAVdljKN`v|Za1yRM7i+L;<zM?{WChq|Dd6=*{t8^+)F!=2u@kReqw
z!Ve||npcV*TN?u7U~is5DinJ;z|T7jmTCg=iAeS98yhv^5=kQvpAu?v61z%*%OtzE
z;|@ZdksEg7p8J)_@~8By3vyM6vxs_J_aCAStOy{9(c`3le_t89@uLu0Un*VnWLH<U
zG%LJ*cYAxS!ETF0^k}_*|Gql{W#Hm(-CMWPf>X)pIXe1`Kno;<>FaY=R1Rm<rnGeA
zFJQXqDyX!BzTMeJG=Z)JCKiVdz*HWss_fICP4ozHRcaRf@x)3k9e;FRxhlF;ad2~U
zcRv*w$%5pO)Qgsvmq!zE`a+)b#(qo99Q5aJ{2hFHPi)4`IGL@D!AsSvSTh})BEJsS
znk-|mXymSoP-))5=+|xdtq#RQ*VoWL(=FWEUtD?j2UpWE2>Zkrd=K-CBXc15u&c(V
z0Y-c?Qwu=`u+ni_+6*|9<CBy6(LgGzAiF4v#~=m9%?;sBkv(;>j6NXgq39%8dp#Gw
z1ORE}Rc0J<>FX{c`ukUK*$V<Q0fze7ZZ+5r_HMPmFE%Pa9%_hE%XZ1Nm#SgwycNSK
zFgw9+VWe)b(EPrOt1EH9Dv^_qub{dOqKzQX9l3klUUi#z>eUvAUT8D_p@m6HUsPk3
zwjlh&EC-F_Z(c@NM5C>tV}HC`>^(VGk>=*+PHJ@tbNBh|%!ICnoh_$#@89bsUWmh1
z&lb0qLJV++o)gmM>-+nOZw~WUav3KXNC?+g?DKRa6mAYUd0G#I@C1#E%3D->w~0mS
z8qP++L?OG0o?Cm@tB4kVl<+8CW*Dil1Q|M~u&|Ki_4eUW^ryS~8G9GNc7pI@xvmH#
zcX4rfNz2+X>|2LLZ&hD<OtD;H1SK{iWq^(wSNucWx?M*(9@+0;C#<+rZTajA>%CYZ
z%iq7@onDx~B~o~e2R#|@nb#({+7F{ARLaU3B+;dTR4k@`#@DW8c`r|hvR!#)05r28
z!1qnR*&Pxm_^(&MYY0vI?%R(ZWRtv|3L5=cDwBfRrfG3`MSoz$QoS>=HLt<W<PNhe
zX2$qTs;!}=fN1M8_Ef}>iT_$*(n(e_<5cyab4bTl(6~=YFmW<eB&+4d)i8vC`Y}Kx
zz<1}GDoba2RpTG%!@BIMpyz$K`oWMXlu5k24gWE~Y;7%y->d*Oq-8M0?a0Ngn@EWn
zG~VjT++8v{-|@q0267Vt>h&Be%#{8@fb8!}gUVun2T|xeb|rW0lx$#64Gitg6GKjv
zv8idn+)#yx_N_a1c*LwXe{`;J6B<%&XN(%MYP)V!)T$<h0z}9IG-mbSf0ayliw>^m
z*7N&-nbHG)G|^ULBbI2ly1(?eWr-rc8go*C<|RtP@d&*^$px@1l-cde)*h0vdhiE}
z++hnZas%dt3^oKb;HuQ(#e}j#V~L*g!vkL{QJx2hYd^dXi))S=H4Uac+Oo%3<B$JA
zXvh$*q*Mi4SXs56doptq*h+Z?1(B>!7_)Yb2^0q*W&1ldZjTQ2=Jt_?4d^tGprtfP
zntX1y=fxijWmppEaOw#=+@o16MoCT{(X7)xJGgW3(4m~N_P`dvIbezVF*Fa$9(eEG
zJ@$8ZGq~Z168pm`>?bqD71*X8EOguD_tp3nGBYw^;6^<<`F*H6lXlH^$a)FpLEy8F
zzXrc{po>7eKt|eipoN4KDzo9a7sc+p-rCw?7*>#xk;(TH?jc|r3Yu5~Y8CzR3`(vj
zgfQA@u~_@VW$n(M2PlqNUFCdknU5{sDSmLBMOMG8y<43-z|Hv<sLDLN4#;7Qr6Rzu
zh8(@{YpLnaH!o%0Sc6{N@2e%Ep9Nu(mtGT0@S>P+Y;SL$sZ2{tQ#`*b;mxfucK4zz
z?PuoNmcl1VH!GljuB02xAiw=!AIrJY<yPGXcES7p##xw`R;HfBwoDAmSj*Cy{4^Sd
z^}_}_UM<eD%VHq=Bj#JnBmA=+H6x?FyRmD~GARpQ2P?&3`+uKJ`0sL;quJiWfF7FT
z$G^URNWYMBp4Oq2N6XQ<YM&dq?od+T!ZsC3#tv>}1CB_h5%Ko^H#rc6y3_keNQMv@
z8#~jxsEDk&;%5%Em;Y$K#QD_9%F13T1}<o002xnphV|mbYsty>B6BwIxD8hVqK`Vf
z!81Wf;MGz#gKu=_{Y?OKGK9DE$SViC!}8=)!$`o?Y|%HW79NDDd<{sJx?0}y@(+rO
zi|Kosl4XPPLgHf2{GnW-Pz?&OhxhK;bC!><ZDx3TYlERj0ZWoZuUNbML0<xac?b@)
z#)b2~8>@2^wu!N^hIRqJJsJ3)j^jw6y0eQcF9fbYF<B@F1#Gaw$&6S8?O|w`&!+O<
z42_@v7GBjEIuDJQ_zjBX0?m7J=b)Lx%3T6*)ZYF3MU3o!{>7ia%cN8>ke?Kgd;a`6
zt_i%NIG7JYz^%fN!(y?7bLN*_1OFsvQ1Gel57_rohzwtMpI>ZZ!MN%#1Awwcram-*
z!W?_iIpxCTKjI>)<1vH@cDSnK4h-$f4GsAIc|S5Yl#;+A=5|#C?-q`$lHOTw*Aq|u
zW1pd9T)%mEqVf^Yg6J=jD;=`9$?c`+25S)N&@fAyhJk@w5!yOQORfCGN$DK}I3Ab+
z2Jl??%vUvyJ11&^55fIqRMbSq)o(rkNc5yRkf28eqXq7CQzIwakiQo;+kYbxVfDp|
z_F322PE7WG9Ca5CH$M@mtRN>8{4NE?fK26L>Wjq|#>dmYfA@Ou-~p$kqzRlLy>X~E
zkqKRJBh<_r>wG_W64fhR%6Q>FkUMm)@mwpEfibqX2fQq!pYCA*ZXPC$_rbj@`EB|j
zzWs|LRKvXG<o<?vb6<PUxc+=(KYz~@fdOG3p(sSg8%nKzrKUJ>?v;5R%$fwf4(<?g
z>ih7V)r6Mi=imSL_dC5!w@w6pp8xAxTxqG`7OWW}^8ukq!^~U&WUuX1NAl9zny051
zZfR+01#~PO7SEX;MSp*Pm?PTNoS3N0Usp=<f+9{yMa9ib1}YJ@-y`BbLq2~VRd9i1
zfbgKhlIYqd3d*<V!1ob8hJ(bg7ccrxZ!ZP~1tCPhJSCa?RTUK-_~v*f*mJkf{~q*y
zi@4R~^nxE8ZpPt-1^)PUs8_7dt^Yqqv+#1tf7bGU|HUgff`9&F6!)3fKerz$@7e#W
z7kmt25~{-&rqCell_3)uY*&{5`*yuk^Mbtb+%VIhM-TPN^#%Tqk`=!BtCIg`U+n+g
z(gWdvLSb*h94^BW%cqpg5?M=2E}+p}fjHgq?3C=+g@uLrZ;<3Q{~4K3YSBu|B;ln2
z$9rjcSvyYHAtE9o?Lynd)By|zj=o`;ZKTKCVEVm#yOGc@C1olbNUjN!sY^o29EOAu
z;}3*wkh8VmAcHQN3Z@(FkrC|L(bti#yMa02;^oBw7%jWmL<#m6WESU4e^F|i#IlO}
zbXU4TlMAknOur3=+gJ@%xn{ytlr$(JXWl-dK6K!KhKkA)P@yv**R-~`V><mxVCTOo
ziHMA}fy=3pj$$yw>uPDGbaZF{qO$8qx&%iyqWPBX(nT;wlVBo$sXIY${Oi{bt*y#{
zBaw+(Xqci(eqkL8d2Xd!U7C}XH5Dy|uXgV)G;ci4$T)02R_UUKLV4|$>K37>3gLaS
zD@lF>n&X~cy1(ya*zEQ}N=jkJhAodVn1wb^2|zD2DlX2h7Vf5b51;xeT>lf)r>7VQ
zIm~(b|CIOLVNsu3w-^&WiLpmTY9c5aD<ZvVR1gaYNN)-X!q7&lASG%7D8+`2rlN=p
z%!n{j2UHML1VkBzjxqu|BTXE@q22Y4<|OC2_q%_7_ul97N6rz!`IY_dvew#r-?}7G
zb!olV$9zN{WqMJcMj400>9bsM{;9b7k(-0?J)2ymFngpJXto{{5>ho-O#LVjK0gMB
zVzHuvm>Bv&-#%60FtHOyPk@^`)au{jp$7uLjN*g4$=?6)_=?KjjNqB{M6@s0?AyC{
zzt&*+LcQO9`z>o^=CP2F7%;(0u|LP;gwO?tB;3Uclklj$OpXLR9@BnI)A!weEzHZ(
zGc+_zt-$7|O_M@SsRYJr`KjsQS;c^;XhGSs=jtoO&X}`u`VED#S2i9xbv8M<{U+5`
z9T(~5Gw^x~7j>tSOyKKjPW9==dOLRP$Z4%&MAd#;>tuM?9)4%-K1?nlAKW0^GBOf|
z8{E<Tjo1S+KVEJ3DLTIv+)oVJ)AV96UDH->!-l)|Trhpy-)G7ku8(QtR>nzUW?I%r
zVxjMQNB6cuc52VJV&OE>5aQ2pi78NLxi{U@kaedOv?J4_45-nI172sSPZN9XP81d~
zmoHzQ(I$!IOLqdMGN)T2&DYX5Gm9U5FnzALb56lS&LpFBvT%!&M|*KWO+;hpR1e;|
zDq@6$mIB^UD>4m%i~?pujACG~gjVco^6_%3E$Exp6@Az!r=oJdhQHTG{_4<`l0(nG
zudYQD`bBK*smG3WutWO)x^TCN#|m$^r{|VyjD5(lTe?n6OlJM^<=goUSvl%qJa(+2
zqGCpR`d&S~KdS|1=Vr{6$wVfi@=p2u*Jyeu+cL8TZEe^WZAj9881+7%f>2bLJTOR^
zBWkEyFaqr+z!@jMb~Fp4nc!kdO3H9w8?Cd`F+t6oVV_lAzAY{R#zV*7Fxpwc{1?}^
zsvN?aCX$V@#;gmD+`@2ft@sNUER(Pa%mUbDtgJ2_F)uwM<5|WyLM#Vsz)bore2YG|
z|4!@1bID<icAhTmNe*_`$rFx^tyWPnM+$~P;Kt6*Y4>t+XjM**j=m^NHO5?|UyPEs
z^y90vJ+BhHN?d((Hf-1nj>l~C=5Q3>xdHw534-4l494O$me*#jFTPtt{i<-V%952|
zg$&rP(AO9^@^Ej<?Ng_IpnZfT+H{0Xv4BVNwQp<o<!>eORkk`1FbrA#_19l_B8AF}
zDuY)b4-(_}%=^DsveZ!=T3-N*Y?|lR{w_|ri}0eNf?AYjPV2NyHSiZ6LqF953vrav
z@J%l?B=!RsWGR(OH5X|3e|Td0NKCB$Ok$#KNY;;Y=c+nCzkFbgZT^duD_7plu&}an
z#q8$i;yTCjtC^#i{**ov=<4dK!bswEl$H)+SiB9pCEF>23X^V$77*c0aHfrC!Q+gq
zCU=#|;k?qNSf9xD!<&u{Xg^wc_*GhrSsS292{;BBRgt&Az(8}&kcml&hBJ11fzo*R
zn@7dPsp8S?7yKEAcP5UdZaLM0k67^F!up5%CDXk<=%xfj8pJF@Nfcfxg1OQf2cKV2
zs?6^^wK^K5FS|*XFGf<y+Kt#1mq0OP*t4R621tNzGWW~5cP|>{U~6}EIwWr9Bh9^c
z($hBxhkGe(I{Zlmq);wk&MY`pPoF9hFVp;i9$G0KP#<jQ(^JD7YkQ2zBAL9kR`<%^
zaTqCPtTSik$!Tk|?7z6=`fk{?Z@!$rx3_od-|uE_RQKjt=aZ5=PM$nzIq_Qv@6K(h
z2Hn7cYju!)(u71CI!kVb*n^?v3JQ|2e-#4laN(={8W@?Go3LffbLc?2vk6m<Fdt?`
z2!3T)P?l%Iam;RvW-}=k36gEYH`CH2ppi58&>bJx8X<tQb~_mnVZ#o?5<+b^1dRM<
ztRnpJ{L_Ow6l5ENhMQa@Lwtnm!#<xjYk?GoCV59$8-CebQ1<8%Z~U8<4YtAk56loE
zA6)V8Z}%7-8?$go0S4?gF>Y%bLO=A?L+wh@3CX1ILbI+O9%+^b&|EiAOE%14fQ=N7
zA<Q4KWP8C5vJs6V1iGWIr_+S(?d>X2ox_NeO}4MR3c424wq<vKQCGsk$V{G5k_NNp
zi?#EPK-?`aUrwSCwIgI?DNchbPLT`DG_z?z_2=Ck8@s>lfy(3XfxgnIP@2O{><#oD
zeKT-j`L2tWOiV{r#9_Ak38QI}`%yJ)glS6Dr`@~vvU|k`Wsa?>OA%58>)!S^d_Dz>
z7Fi7{@JM<lCg<3eN-i~-KAFG{t(gcXJCi)z`d%-VmGxpyk18Urb#;;&_0c=v`vNAf
zHf+)-)yoiANz;dG_YOWiXWe4CX0a0xJ97^dP1iO4VaS95Wiwsdi;q?}>+-|Xs=pTt
z@4k=aGc^8Lc_-|h5xCj-=+_dI^g!FadmB^XJIw_hLHsO@%KW}UzQlN28GsE>_KH=j
zp032|0%kRor<wk)BOgC*G%+=$cyOP=U+ukw^1=zCR6RI>Jyt%;zdHV!LyN-~mpNv?
zOA2Vu))>LA8)!b*&7Pm$)k)|a3lvZf9Jr32ww?>()5H!(Rm4atI5>37U=Du#_!17l
z%dd}2<<Duuf7hOVY!ae%^y5JR2#^Mq$;n9~I?t+MeeO<R-d7JD&(}fJ;5osoK?I*!
zGdejCp^Fr)M5u<b%1}1kVaAA@F>RvPE9@0&A9({fDl*Kq?p=w_<fs!)t(k0`xZ-b6
zl+l?nP{W|D5*RDk+lvRp=d|Si<<6Bzt!I7T-AXWMW?^>=&88VvmHVZ=7XZHrzukz;
zjZKBf*{@=$3u^L37PbdO_2}I`G8bD^904!03L7nx<w>P>3xXjgc!rFQxu<%JANIM*
z0cj~l;zK0j`tqg$yArIK3MJ~QD7WCf#0dl%u**4T=;i*;yTi`iuKm7d`wjIM$1~9l
zhfk&H^u4@wS}#`0igBL^@!5mNzngVc5v7y$?T)bgvo{kHrC>`p8dR{w;1-)&P05wR
z#!HjEd-n<k5%V+3BIaslVjbvjDCNv$<D}G05K!*;NaZxS`<Uj_{Z5FU;_3->O9df3
zWW+fH_bL;-p|DgG*7Hj^RmQ1$tA6?Ax^NUlsqk}8igZIj7cyHIpO_1g5#VFj?@5@>
z5APs=PvG&^R$CgEc+?^fbT6{05YCkqDl;yLp3&}1kd)O)s3+N#ct?ctFB8t3D^rhc
z)wDP;9Z(mRYgC?fXS`8%qCXY65z~Pyld8#D3S>_7=2`b}H#9ELzy|)-mOF)|dB;8<
zS+rz{2qDSU13{CmZqYJF3Yk`JbDR9il{=8u-{Dv)^CkW~kfSonx_SsXggvX`tUG76
zLx>>0tWcC+(oKwwuS<Fr8{-X~GwgE@f?41x+_<r9Vw4+a-op#_z&sxbC1)sxo9Wf`
z!hqN0th`=>JqP!KG@F3-)E5QxJ4>JN3s)v_#Ax2)#m;`Va7_L8mHF6WsFBIf82hAL
zxfJo0y<$C+{4W%Rv^W^howlL?PVgER@=%Q)xCBv>H5AljMYk)$j)g0z1}6VAbWDH`
z^7-mZ=Ffj~H?*4a8R%SBZ?9edAdn0oQ_OhfgKY@fYjkx>d0be7jkc?8i&_oIsOcZ;
zO^sZuF0MUyb6~)m(D-|JUO4qg%hGiWckI{;u?nxP-Ha0cXRD@nS0XC=i(t{ygbz2q
ze_LZ)9pe|imfUE7<c9s>Y^s}^8*AYuQ97m-IIzD+fE}tCa{96RWXB-yKmj9&tuv#D
zd3kvysH;g-ks9j3!htL87Zqy0uhAz)k3eKCJ%W;IOE0B?(FFM8@a09T%`K%XD=Vu|
zUK|Lqu&V+<oaw*R>zyTXd>&jVtHLql8!@#uxL`XYt-)=ALDXrlt|ep+=YI@(pOH=Q
z4a1qGlR_GsDw2nn94MPH@!lxR1b-r}3urCLK#Z0^$8mDl<k6zJDJ^9h*9N&_c(>r5
za(DWYZ4{iX-DtNUXC$+xEsz`I&u=I$fuN{CII(39j2p>LMrLLv$C7S6UCrmODl1gV
zBJR5$C>{_0vUBIo+%Uu$pCk=Gx4p8;{QlO0oTpT5Sh>nVg=Jp3#Lm|^OF*#?PV!sZ
zQ53*nS2u-+hg&;zG+oHHRVEqGL%3ctmFwuy0(n>HbFQj*Sf3yB=;I$k7QJ)VE)QP8
zzr7lJ_ojZhj#H;j`GOsB!{bL%GI^^yL3Q=|^+t$P&z?Ma0eP4&Vk1JZ!CdW6XV~lZ
z58s`W9xNq#tAWrWA5@vTxN!)*N$=2<?PIBnU$wA5dduwitI<KOa_D$qksu+66@BW|
zxzf@w0Tmba^3|)PjTdPRVzaNn#FE2nKl#ORzxO#l1Z*dFUQ<)k#vfCKjg^)9z<6i#
zQZeA&ywb|bs(OJW;M+O~BtCKDbgh$$-ux-)=3zS2jHI!0MvT>vbtpKWpcvB}JEw{x
zxt`u^Vvc{{Vz%o}jpxWeMFD`QOA6#8rF&TR73JkmT3U*PjgHwX6%;Ddl2`Xj=aXDU
z&9pl2cc{Z-#1$WPKRgnQ*YXL+;5~-Fm>_(W1!YcmC-gwzaTJ&DF|a9Xvef~z2P8f7
zx1fA)K%SgMHg8}ta12?W-bSsIh8faIFc6G4j4F<;FB|gq?o^un`sJ%oI5p@P-8t&O
zx-;@Vi8<p8PZ#e|Yg)>CkE5N5uaC9+SxkHVJ>F4fEY{FVjvoqu<IaLPhYO!#5CG=2
zxcl{?;fRe>P4Tc8YPK0_w!L&!fHSbq+<XPjw6k=jy`(;W#usnA%gBg>%G)+%{8H)o
zc7k7DEW!KPbY(Xnhr2r+a{@g4|LwZ7E!U`g-x(%+m6D4SVW7Y;TjFpmdtKqXWuVuL
zA3uIf$WSbXN`=?3$g{e4Fv>Ik%?zTU_pdx4y`KQ?4@_H;TkzUhMU$Uy>$<{soHHyL
zyxDDI6w1Jm9U`Ju#=b?GbRFdgJ4ATy+yXdPW!E(pS<@soLia(;<GvQv&|AlU(@g8g
ztEDT$rgq*D;$qX8Mq*-nRRP-a=|zT!51Bfc%AQ7yujc+9Rx+pfVhcY<y&_xHJVZBA
zgF@bH3zm{&f6^KQUR`PgNu8YeEoPkVi@7rnKe5E$Z-tPAU1NPNm(0T<piU@|Y*J$v
zc3rd)2>}tIgK&RLpB_UypK4|EUSuIkP=>Vhsbx;Ts)8%7@kg^B4{AL&4g^jT!<t|5
z`0;zF3E3nkq?{{wTcW(#se-Ia3J*D!<~44aDQUQ#U<BKM5wNov46hSo=><xM4Gav<
zI|4>hF6=KE`-HN@CW-KvKniYaw5SMzJiRV?1#^5X=#rwtpZFfY{!1KK`J~nuC%l%J
z!cdLQ42sV#>*<>gLLrg(u<p`%r194I-q%68dgf`NdW52-4h;@2ev@c-$ThINWI1z_
zANk>4Aog*E{=FGJDPir-E(2GG`#TtbE7IW!gOK-AviS7>>OQJ(2LW>iRfcmbGS+4i
zJ4ovbTw~bO#MMo%Cah*vL6H5BwI`#tTfDN}GPYqh{VG9m$eITfOL@XkbZi~DY}sH*
zFI369sl0h>1>(U{DZ2pA!uKnZj&@&R-d9ej7LJroc`(nvuSwDrz_RPr=kUY-jQI{#
z_`ZGn-aT8*5G{3j{(QHA39L^5Pjwh+um%!HT%sS;X`=TFh8@$^<PA__7Gg`fAAFwr
zhYmYRct!MMut#EJqq{psvn^E<?V--Lj>JqAnp3dR3eK%=Q*GrjfU>b60Imwrhk-fv
z-et3ik?`B}O&LAI$uF1z+5ugK4@H@Zm1j|}N>^8x!8sx`b4dxg-&E<%W)>{}YVb9$
z-xVq$MG0aO=M1{d$*;!#hB{Jt(LIsVBm{Iviz60K#=wK5;VgAYyp90<d2}=#GHbMg
z78A2vYkBv<uMgiNALF1~+$k?C0H2|>m|)BwxJn`1O2;;DB4R)Mi`(Q4Eo$e<HjZ~g
znuueY2Gb;&5506Uh(_rr7!IkNgJzx=rXTIiI_g2+RZS`_|CDAAp4xD8{m6;*90GkP
zJx|effZp(^U=aI=G6x0*oMms{;p{5gAmPVtxH2;5i>WS$dXE&8JwWO<dB1)TG(xRW
z@3E9-*cCOT&EbpL5IVH)KJqpe&_Q-3LIJ!dqaG!)b&DM~1_S|0gDnwQs(J5YtC9D;
zS*}-uQrfMMilA{MT;a76Ypqe@!o;|3KG*(;jE>ITd5{J$2|4Zwp&B?5HWh2Ua>`K}
zzXx)5h%8*g02!+gPXrMm0C{)M_J&*C%n|t2*=`O))-tFT7*x0Wl;?k2>cE(VZ5)(*
z(9!umQ$tLV(GIG2TMJzndO%!FYvcdA-8<caHHwPH&OQ^R8hrRdb~cN@?ytKXhK*WE
zqn$WOP@)<F6;HZNo+rCfU0t2%C*66<f}m9f5p!kf%vw{oiuU@kPc_!lcL-9x2G1|v
zaY6O(i<jY{5-g?%C$;06j$1=NMAYZG--x~T9mdOecW&1@#K%_v!au<#kS^stoM4ZC
z(`?202%&^17=mHmhN8mfndO(Wcm{yQ#0W5sXDH)7y8zoX4>@@7U}Jq#J~h=Wsk_9!
zqO!6jWP0S^!4v3vR_xZTTP5pDoF{-=w$vYx-a7*Ch;$ODV9C^EFwF<wX9NW2G-Xn`
z6;?@Ouwf5!pH~3!3ffKr?IJK|9-Y?rs^~8~nxh@7@ce5USU-NK0ERw;X|!ig7863L
zfdr&2e5hjf0Vog@02OjWrtrfLKe+8np%-2p!@+<sH%5k*nYkNqbQZ@l6VW3*VKw6t
zUE;Jd5;@-&G>i2#E@<Ex%&CwY7vz!jHRIA>jACA&_n)}n)w;SJJX;v-31PZbN4w2-
zihalMho)43q(O1ULqSa_)o8Pw-6X4!!@3YuA8ER<B96<;er-$K`;-5I?ycSfS+Vy^
zI4~k7q$NEG8vby{t8~H~J)|*xdELx$L=$FE);>G)ENio`8T(z&HVxW(4w%6H0p)$7
z`edyc__Z!B*EFQqt3&6IvvWfci=<fvf7Kx;X3V~)p@W!zqM387%Lnmok95Pd`veVc
zecFEF(Cj__lM{Y>&w<f!=jB4M+}q5|kq7`kAdo3Tk`Do0mQ_xgy;H$oFQw&Ux%$J;
z#3i0Ny&nA#fk1PSzS(<}j`eBMxTG#rQ_F91oQ?+!2=uz5Q-x)SRpOa5*D#V9IZB2>
z$jH08GPm1<P|yU0UH*05x&|ZNiG3mN8?U%G%qX?UaYwhkAU4Ojt=V3ZN6M)et=RZE
z3+rj}w}g>woTlUG=;+RqhjO0{NTx1wzFcMvd!*T2{7>k?9R29`DLJ%0X%VlXR+UfX
zB4Zskvx49s03pAv#M#lT8Uz0_r@J|VA@EiWp8F>>)A?IM>RTVS)&<PO*r6N^|Ar9Z
zRW2#Hk@W~9Tp$hBfaps=1r$~fkS8dvE0dIZ-;eJ0sy6NUwnq+bbqm?S_T<SE9v4z|
zH;h>pxQ$`kj+M00<qg->F}mn=?wS8$rBU_93Jj`t^s^n3=zY>!fB~BO_Jt&#J9ne8
z(K14?9vK+T&XNd^$jn{!*aPB0BW(d$HEiaWwD+(lX|6!<cy&z|vNH1!AZ#u5*#S8@
z*_^6FRTsf&vxI6ZR_H-TpH?RPH&?I*epZ6io&~i!W103IQ2PnsDr}M@fswTpW=;<+
z)4*Br1d-(iJd0h>UpiH?C1j0VVLfkjBq4j;zM9{KSYyg~n}LC2@37xBU1xZ3ux|Sj
zySH~k(M>^|J&%i$8RwNzL%o^Cn3hN?7p?D7-iL9Y`W*KmRlv`n%vD>xPp}J!z@}!d
zCsIZBsYR<Z;<h4Q;K93`{Fm6~)A0!jG$j9Sa5?NAP%1=oyj)&>Q;S;II9K-2MhxbI
z{NCwST3SPMT5j$>SXM5(02O?8g9<_?`_?6U>k!z9Oz?+n4Q?=!nk@S*i)3XPFw^?x
z3KFlcEl<!odJpoFjJUAz!AgzNCn`T~C2fXKG>pHQYp%&?c>4_elPGiX1&VAVtvk}{
zL+ZhtHt2!~%FpA1qDu;!{Or%aH%Bc>lodzIXgBjlaRDZ%8ClJf17&l~y?D8tj#Wsv
zN&u!?iViVp?DUHr1de89SIWrm`~j>ayE(0ULV!&=)<CJ5_YJ@V!a`_qy;;lMj*uFB
z;M-$qx=&yAe5;@&mb((}TnVa-*#_*e<RIRv2m$tn9`=QRD`OJr(uYTp)5HdjL3#`H
zD)E3Hivagn74`bUAQYDel^e)+-}dyhWKps$qd`zKWrU)8EE-Nvqq6C~N5TP>gsf(y
z$q~#3&?9*nQ&12G)sxaI4~AxCSJuhp^4o^NW8HO2ICEw{sDmd5CKQqbA7zAET3$YT
z{=8|=-(=MqWdC{)eP+>(;PY5OTDtC5qnL8{$qE7dN0E;36C<cw&nP}7lWuS>+Vxc$
zJ7@xXHX2#MdGjSDDeQGXF4HqJeH*(mGA+++0<@|)GCv>7sS|T`?q)#Qap(gI>l$_N
z1|sglgP_C!EgQ2y4tYUiWyvlD6v~sTBnZT{RacL!FI}PYDlc07;Zfr1u7U4)*?poP
z>9(7ossuF9D9ADoo!u1`6%trp8Q=hpIq(|D!e(2Wss5?STFc@<>WQhh-Q5l9?R{-U
zF%KSWrVR>5`n?DvlF<&&AA<nhy5c@0knCVjj2X{!qo^drlmfdnr(V2tNwU9qD7Fqz
z1g#KyUnP(@JmoJOoThSDx38gLY#rTBGhx`_*x(=RC8aaxNZmcUa@V;*BoGb3Yb@IS
z0%rhRi-UmWNhmu%7k7etHy7YdMu$xfrKmWZ0b)@NvL@Z{$A)PnUMC+yz*pg<5fT-M
zYIec;WH1d{n3k%CSGOnO^H2{*!w@BXT~UA+jT|XulsVcoDu}CSoc%`Uq06Wkz;IwZ
z`1L)<NNtu);jE(t;XEq?&T}zaucp@6h~H*mr5%hWy$Y>Qy`f%YAghgC7(BaFXr)8T
zbkjc{f{WUypceOZ1FtbcCcF~0>D3Jfj-#kvO&dZgc`7c>0v82vA^y@O7c-f8^GJxH
zK<~0H8dX`X3uY;2F<_FeIFWu0V?gZus+Bp7#fu3ehO~(=h>+HLJnPTr&LQQBAhriQ
zi3U7ru79sJZ3s?p=Z+m`z7mLVVYH9nyD>&!CjumG<V(iXmy4Vgt-l#UHOK`Z0HXzr
z$%c&^lT%&Ir<IkJ#UuC?7i;x)lsdrJ&AT8JdV#;S@Ao_X?<DFK0IF0`gZD8YZ1ver
z`hYJfaC2xv%lkDhG7?L9s_OH+Iy8VoiwMLQt)x{8`q7oz+HGlTP}m_B5)}bV1AHgN
z$HC?t>QWhPJXyb32<$TOQdg;}a-0*9Q)eLUd;k7DV7<G)%4!APC6NWlQcx`gF+@~g
z`PE>!1-Jqr5hj3=z<Xppt9H)mu1TU{;q$%ruOw9(ibKFghfQS3@kJ|En>BwxL<t*q
zR>%j4?b&N$;<!6mtEt;nRzD0Y(cS%jLLY(xdnCv@MR#^VJMkoX5LmLtO3*4JUDAC|
z1&MG)K~J(xKNYH2cP#8Ic=+Yh@^|?Zd-^~~p@-wm4`CH|U!_E&8*D}#_unr06OwCj
z9UUD(NwQymdz*8_i^98e_9M^_ND&rvtm0MSB-91s2o1g``aHZ^^Xnqx;us_1Sj^~J
z-<O0}RDpTiX8ly)urUQbJt~1xM6nUxyy>m=+jq67${iTlC=NGvpX_!4`SJ9BquF9`
z`Z55C_|Z#*ZXRugW~9FYtd~@SB!u(inS(N>8*7F@Tw;ED$@&J6kMWl;TT)oSDt6f1
zcfZ3ibRQOBRJjMqkKXs{pxbz{%zpp<_c{F8Qfhm#k?D>OSTJm@Q^s|+YkmR2(g+Eh
zpz76d)8M?@#BR9ZYZwP%h3}XHdp=RIU-|(F>*^YQyqiSq2lj~Y=FhvEO%FeM^vD<6
z?A-tY6MeXZuUApn)FkM>(?q40z9&It4j)*dD^Oh_df<MNk+JWe#2UkDQtf{NsY3w_
zIfsvqEnYK)!EVU1A@Kc-IJp8(0)hS@@~hWBU-_}DW-LT3R}CmBk*@f@d+`en@@h2o
zBax^FDc|)~yt7#vX?^aIuARN@EE|MF$k{AFA^^-Lo0~WBE%Bi0;9KI6T47w#_HEnJ
zjZ$<pAV;K3EMr|4(JIy%0Gh}+LDINiRdpT(2iYe{X67`^ESD%VL*YiG9f`NFEfFIJ
z8{o9#p$|aWkD*hC0hI#L3eJgsh{q;YYXa>$+WXv)ed8Z91&D)bNQ>srzaBavyaRQo
z--=WYWD0*^aMBX;+F{Uex+ZWA%J`@ByuRT+f#V@fKEx>?sACq0F<3$}y!FM47tWHR
zI7E=}64xhw?pBCF_}KsWvUC-uUi^>m;1A)i{?AFV{@+xeeVpoT3%(G4=(FzY;P7cn
zH%CnS{adNM`}R&>oGiC<dfleeyQAk#v-@@3(6*JU4oJNI#cIaEW|OLT=bw-N{L*{(
zGUNFhcK@+=&Ns_eUi)$8m9tk4bdTuk4tK^ZZJb{A(2K)tu%~qNmvr<LjPNIC>1h4%
zPdVb5pZ_UD+<g4nKQ)ctAOEMCQT|{5eZMGmWUX~B;FIWj3ToYmV0n>|?N|MJLEiHO
zy)CNqGaQ(1>^CwfdGR%D@Ip%Te?<QY|8|Sd-+vNY@NG5tpufI$%=@P)|Gecte(|J4
zMDUj%RGTNM;vR_=oaM+4AOcrIYoGY-<RG_1Xn>8;-7sN40@OVQI7LN<8O;$2B4cZ7
z3BWmZ>BgyQZ%!`k1Y{NmHJ$kzRRbfq801}R7O%IL!cU$?`KE^`zDz*@WW3uVKyv=|
z_*b5dXj|4Ey#zoY5VaK-o-7Vsn-v$=+AKp^1O#&dP`W5a7$%6YjDR{6gP6{CV`ntx
zQ=KkcyjYQ4DI=+n<oj}3t%I$t6gnd9*I4JS)It1hEC~t5C#(jIwFG!06=H_UD23$w
z4nA@&F)^uiUmvbi9k|c=a6hq;k*%$*mP|LZZQD?5Z9;NNzYGW!AJa#5KvnMuk3_9n
zo43u`*|~WPm$^<aT6{iYaT~<}>~ShGvpU>nJ}yxXw<%~!0UDo=aQTcyQldrlg0Yd7
zHW2-eo#j!3pc5X0be98p@JJbVwCIwZzf~(@c^wp1tk8%yX<kop6-bPqp!nXrJ5fy$
z1;gG04#-Toz?h=o_O_$cdubgE3`X(blQWmWXB`7s+BJO8#YG>gna<?kz9_vIiCfwu
zsAD9cXFLt4s$Wp5zik`-z}4*dY-k(Y%cP=kyu}iEy9aMB7z4cX&yNt<#OtuCzU$qQ
zk!X|Zr%rzE>CXwU5DUWmH>9HdE7z=P?XgEsrfamUu1>55FP$}v(S35EAJ3<hBd2>c
zwQQ;b!8H|W=$#<1Ekmq3D-X9r0!i}t)l73Ru|G<xteG)e;)40q^`M#}h)Es;HtR$c
z6G`gKzNU$G7C5meh%+m}M%jC+m1yBXpGM(Wj-sxxwjB-fL_p|-xape3+J!2N%jDS8
z>a|6eA8~r&mvb<>^J!JpY2b$CC<5e`EmKlbg3@3qSg>p)Xxhh{p-ojp%{&SbLlMW1
zQPfv>WNSAP%v0F9L(J9@xexhKVs`N%DZa<RRzzvY$;n++rYsilIdPyI8}EDAWZD*N
zMBp^hM!h+B+F^VdXsu3gK!<LPXj#^(FS~(b1>9*VfUXX14t`k=-NLDmeP#n_vn=xW
zK;os|TY$mooshi6VJLC`pcRA!Hw@0NFvfL^BB5TWkPdcuZRo%W+49SZPB(zGEuxnO
z4tC}PUU$SJh~mRfoPy-NT(MGWA^7)b3|c71IUt9+w@Ks~V&x6LIEB||S~?b1=j7zz
zX5^sHO|{B#Ce}pcST~@SxHAdU-42RYZvFc8ez*j;K_nm*uD6rS`(!XFFcKmcO*2lq
zv0PvAPz^Ly4k*C->M-F#cv;EC5Lu`?1Sm@|TKY)mwjm>(&+^10%%2Z3D-Ko5O&vIw
zo*MutyK0P)g=76MSB3+JAqnUr4hcbJK<QZ9VeErCc<9jbAdst05OyfK)u#u-9!)to
zw=y_^F*!9Fv{vl^^`GEOh`B%_v5*6Cp)6nmBU|6P?Z!AO<t~uQ#-S;8w8~*}0WQ9g
zAJvT=N>T7eDrTH48&y<`vd9<r<hnO)tQ&(Qu^jfg;P(;qiadVw$E;Kg8|<QofqbsG
zu*!6S&s^B=y5fOH>3C>2^6+%=(0@Yy9)nUtCss#zSm8?yaaj77vpF&*xvAq8*j7dO
zH8nKoRk2)pYLW(7{UPgNYZnXsfsa%IU3=CmtLDA`M_C;$Gu??#uc)Y^ZVK#(V_Ug)
zZI<^%?4qkcCUrgo9okC~J$6uf?VM|q?cNNnBDy0<+?D|wrQo4xaOaE>sZ%{t^A+*U
ze269&axg`)L0r*s+di7P2SZ73TQMpzXxVy>=t^q(yF*7LXO<G!8AX0U4U)2v!tQ}L
zWlpaj{C-C`)-e^j8;&`y1Xe}-Nb1d-^2?T4=XpxcTCy%0d`rJiUu5J+N0=_Ymt?-4
z9K{E3?)U3vi+Z8M<({B_RvHCa`i60-!4%WtDa{XBe!Ti0#sGO-4RQe3_YNwI=Ali8
zU+ib(?EB-{$>(y*psAKd4BE-YFGk{%_qoNK7)+E~7Al}QEayRZRDmG$@`o$>+y3}v
z2;n3jY@Uf9e3|txLy?g{%ajTO`jayhjFJ;a_k6f_r0dzfCt97lQ;|QnojJPgUspsy
z!C}?c_jK%5E3^e?6B3GQOOQ<K<M^8jPz`nhe_ffEhv8I$5*!8Bh~&6~MKh~+?cC{z
zW{&v6sr_0#sMjk26`A_sayt>9X!W*>uIPjW6@!JnBfQ_@xoh>ok}5DBru|jVo{?A(
z{OQA_W>n;p=g(W?3`EDXM506(W&-*fk8bLM?9^zb>j>%)6j)G%HPG#Bumq-3`chCS
z7R+3Rek-&#;O1cvEH^@7KCAtv9tPGZIX~Y~58yKP!-o$$tIf`?GEI{Qxl>YTGLi=&
zAKu`(jGpILa`bEaO3z0|LX`Q-sne%dGv|sv)Z&k?sgc7{i0=V=6y=UdLiLuO=iX%E
z+408``^)JJ&^du`&+0qEhs7rk?uQOP1_$9W0Qej5`FMJg@USFv06e>+*igOeLOClX
zWZ0^COe(WyyyH#pu5|_Sz1qWvMwtBiI|~sz>7OKU?u}Ru5a7wA^mKPu7$_uN$mpp~
zSOQX<S`5-KzI_(C!q+<#zNB&wEQqXpA9iW<PNKAu=b@H5GKQBeLvojlNxZVoSk1mj
zcp-hb5$^Q}nNyf%y0!$O;t9#(A7IO?NJawLW^BiJx9T8j*>VzgQQ<_P&JAgIxbkBB
zVNeZ5ZtkcDl<V9a{GqDv;Q++``f#+v|LW-HGog{Mmq2U7uK_7zeMReyYp*J=2EHf`
zON|B;s)!1IA$%46UccxNc;|o3E(hYN^~U<rFD4K$y6kJ8Kaa-rnSSaIfyi~9C#oy;
zm5lYh0~d{uUisY;+j-FOL?hnFzE~5lh_dD#j1w{ENGS)c0{YFYnSAwrnf96|Pa;v)
z$h^=gB7#Vhj`h-ulAg8axYUYk_#OtcBu{5pGk;CS34e#-4!MWxmXRC?Sn$^8SCwgx
zdPFwpEx+(fhzO4)$(<v%LQ$tCM)aYfuIg>V$30Kh)+7!S=<zYam_i?V|AYnyUTGm3
z*Y|f?1Nlk?A*2{M@H!CbmlJxF&R)8-1p1KOEaWBy_~FUM2`k+Cc4S>coS)*=v$XAk
zXGH)!RRicDdCB&O!5<2x$YnHus@{2rW0ecrb*@RY>QWLlB}?sAo-;!10Z`~oOyM|%
zSo#=H!Xk+7f^ajd;S;4{pQm9J+r02lwLm)}e~1o5M|~=o6Ehu>R*sMN=o;A;VF(}>
zBUNLOlY9`glkWRxJ?eW&%7LbV$Vi}ek+6tgzfXWlbU80EF;QI8|HCv!kt*OT!?#^s
zkAYb(z`={eQ?0~VlK>+3Muvr1xu;IAD_kja^zGSq9i>xSl0+taE#s>ZJP5l3&dHZo
zzX}HY6A&4%pO!4C1m>^^&zj_Hm1v7FWyu~J*zyU5K-%srCS5+{={GPOQZf)VB?uD#
zsxSaQZl+WFJEr6rd{+(U(uqhwI~N}6@9z(cQ4Yz-vSl~myukY%dB9KOQXIytA=Xep
zSIxrt^GTXc&M%tNej@Q27olGsR??62+=<wJ8iOQ9&O8@2GkX7=(LH7Gr4pHn00~wB
zVs%NWkj-!eQbdsS<%XTfNJ!UM-Mjg0Bm|KCLct!@whO~7KrVD1bxtEW*y%(P`>T~I
zQV-BXrhcH}GT{VvHqbM<v6|};br&I1ibDqS*vcg-UWhDf4RUU(CAvEBmA>X{)sub_
zwNpMOU*2%3O^)I-Si{x@6Gby$%1tg*G34~!MpQ^V@@$oBhb9__bA#u`LlwE4m!b9D
z<ndYScGT-X8mX<Wj=_UgpqqWt6j{I8%fDVuC~^{~8<PNepY_rZ+;6OdmN7VPYR;`C
zix%DT>i`NuEZeo;C*2nLca)GQiIK;BzLq&r)G>j((cX)^=@cB$ZQiL+03P_#m-u?=
zjdQ1@#pw>Ep#>p8ETV!i{?fI-4iM6|q(0Ziyw)STK$Xk&hRlfv?ozH=`nh@^3r>F&
z!a3{ZlmLFiS~#zGk%uH%I_+TZbmM*dege6@hrMV5;19lHH+>Cj<F!R#;5!jd^qVID
zUkyg+j{idKjaI5znKzRA%5Yab9=B0&iq|o&WRwt|vbiU!5o)Vg<Y%{7mDl}IU?FYS
z%Rnma+lp8doY-!EmH^JJd^XJ@4wcIadK<cj1|j7#b1MPNRe>hgd2j-4+#F)aAP2<p
z!LAIVTHcQLok4#WAbOeJH|KxNNW%a$+b4p_p+aY00ez1o$?(7qX$yatHOr|rpGss5
zB%w9sv@j`4T$23E3n-97d_GGW#ZsVI$%k|2qPZa!Xlg|V+8XNlzz~gE$B^J_5>pc(
z_r3v@m?D%p+cp%zuCPixCV$}3Ebio3Kd@i$86P^z)Slp|EYKZms$IZACAG*Uk*c@*
z@`hg!i%TlCNj3==^7x8N@=_w__3p)U*@o2D#1Bw+D+DSeryAuuOj-9egVG!%2_-U9
zA_uQSog9e!Lq!l6*FIZPrTp5K6E|@DsE-#}ki33D>4~<rC__C<K6i_6*uS|`NKnu>
z<DxgJf9~(H-2`V$9wg`j=xd|!Q1mg+hLqWE4Vg;;KE`6Qn0K7m5u`#|NF*20n~*5w
zL+7zj8;Xi(h~xX}5)i7fR(&TM&rk8#=s-TwK~oMI0VL2P69WUU{9Yk(9;qbabc8GM
zXfE(v@ft`M*kG1d2qjFWY|F@tt#cpasdgea*kMO#KCZs5(6~O+N(m2~6vTLFGC=96
zq8f*ZmfJe?Kp16e<4j&wrkE2C#q@_9i~K!u97(hp$6qZ)GcIQMJ`O}>i^@$?Q<H!K
zB(H##<2R#Fhod~lESMXx`S|89$$|G*TM9n{5=sdjNOo_c*z@NDM~#6gZ--QOxUd0l
zf@t-L3GA$`L#mXI49#ia^}{IB=R-q7+?1*D9+?SJhah*_2Kr$&i*wy1RWF8=9$a*2
z#X3w0IZ*?l2q1tu+)Ft%h~J<}3CTAfaE1dJpQ5uG6)e=V<zQBa3-w6CjBZEQ{sOR2
zA%DI>oDv3t^L%9wr3um+Z<>Gp7vM3x%mby)AUI2XR0yeXSz{l(K#Bhd{1x!?2{o`)
z!%Yqdj+c>L?oLoyOUD%3!{GJazI{tD3hSZS5}Q^61igW`xM)}k4_tBogYZBcVuI8d
zQQFGTnkWZMR|33K6e<cmfB#|oF-cWVDYPfqL1d(xB>>AMjt;bFpxbh>AbLZFRb5}o
z6W2^Kak!%G;O<A>y+`FoQYo|`79M72uyr_@D3-?2E)Y#1CCrans_dpVCAzgy-jkIr
z6QiOV78{k#!|}GfchG>qMdZCB**+aPb@DONDG4N5aR7s4?tg~NU;zo_I4av<Bq$Dk
zMkCTfAoQNFv7x)we%H{lMcPZ^$kT?mO@--Tbe<%6>#qzo*OC$ivwg={lL)6=ygLi`
z)_g@$^WtbzpDR|DLm?we@FtXT`+}+o5Ji0IC|-dRLaXuKz4L%}8RLK-hBZLCu`Vm@
z%K|ZP`33)hv0ZO|^Uqns;c}<{FF_hb-b3ua6Wjd%_7exD#75@u*})3lX5<+dY&YCS
J|84*A{{gDrdX)eG

diff --git a/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_25_0.png b/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_25_0.png
index b919a8e42aecbb238a2beb2b5414b1210ce62bc0..47c8a24dff1acb28422c5cdb3e76cec4a8cb1e49 100644
GIT binary patch
delta 142202
zcmc$`2Ut^C+b$emU-g?&VHl)H9YtjnfkCB)qOS!J0Tm(A6{HJ@KmvrYb(~S8Du^gu
z5m4#9gM}g@MG*p_L=!rM7)k<!kn?Qj73VwOIe$6-b^haZab2Rx-fOS*tmnC(`@Yxa
z^=zTgx7<DPi>A_V`+srq^}Xt&rL636$@l6NFW)P!_DWvYoRts#^JM7_rxK@iKm3Tq
z?eeH(a{u-C@ZPM#2SUF8+;GgIe(SNz-j~11Csd6+8+-Q4tjIPC-^K`O5nSk0EJ-=t
zmr5>nW%FjaX}hn+WEjN{Wf-KiH_1hf1Whj{&gu$u#<VjOMjQD&_kjE6<dX40Btdt-
zE@62{<MMK<@e2XWzyDm<x$#vU!T3H_ZHV=rbugrH+H*r*P;Qx1<4zq<^nLlZ{4#s!
zp^DpKdGr7H_MMl>|5}j5R30WB{aIbN`q!1wl&~imweydewieGdJi5oq{oyOxhw6kC
z{`u`I7fs}rw_>)J;@uss+1xnVczS{r$;2}+TZ5Zlz(RT*<QpE|;As_Teft82ef~8S
zbN7f~ZLMx@Id}Ni+-DwU{ObOxg@vl7rbT^|@Jky=S`~l${AKj>>&!~#Sv7U<qwL_p
zvzElzEq7)k6U9emG<R&AYZ)J!SiAn1z#nz%QezY;XGBvF{y>-t;?b<c6;K?LPSJ5X
zm*`Vp{&vzMVdJ;YfA%=%h{-u)Ya=FDQ?NrV*1YKscWYF^%_ToS1K;%q(e+8~IpJO_
ztodizIaF8b@47X#cN~VWvy_(F($Hn+XaD+kAxx$47q9C!7tWp+8s$&)UMPROe<!lD
zb~bL2^~hmQqZu<n#=_#cQf{P<nSq%{(BdGDegoG%UC7M#{?q$SZ_)C(b!(Tx?q1%S
zFN{8NFQ1sJvLG#VmU_3oUDoDjh0uMp$|8Ylrw+DOm)jd!FLA{jhDY8du?^FADm~KC
zRKi}egkd>8Tb)2;^@^1WFtDVBLA#MA$;nfM1&W=WmDr?Va;RO<Oow<P75gT(K;F&e
zB+WTWF;bRKZ++%|5>FvqIKnKS&b~=|w)g0P^U`Z$8%T0EvCi`A=gCPrj&E7psjHm*
zYh<TayriLetn;&d?DLYRUeIF7bcFR5`jFpkHr7Q^on4ma^`eB9*CsvH2-XOHaiIG}
zIc+-5s*4bC_IP42J%-g^*qUT5LVR&s>`DlSKg9C4|B#WyZfQ1dAnWGa_2Z{68Ha89
zHk8y>xJvvpuamSwtZg_glwwXTN`Io(Rxv!jFstB>EjWO%4|Vy?vlyFfqh>u3wa|C&
z@LaUJ)#c3SG}>~S{E2<B@6P=B?RuEGT{V@JnCm*NMp64iGanT5wlU)?Z!PqD*@c){
zWi&ZQCGGm$_eOExyi5vzU^9y{5pBcfeSPIXi#K&X7VOohw05N`lOMN!mQF_OYBh5c
zit-!CdieTs|KP<3{th;-VWe6PS>3*2B(!|w{`L<FeVg{r9plvI@2kc0-Ot*Z)^!F4
zRP?fv1f_q<Y?VlQ{BG^CW6kiyrG1tbl5s23>G0(Eg{4>YAL$91?_qB*l@F;a*V8wv
zmuPf*CEE(Qmm%g2{2*WRAx%a_)2~^%VR-|5%YF`R!iKGL_5JnoFH;sOmrmMl+_(PQ
z9WHu(K52P9<Ye@&+8}0DL1p>1+j(Mh(;|ABL~{tM<3~~w%NhM|Xz!0UP5Hh@Y72I>
zRW8~UJbwD(;Hf=tY;8(Zb+5jm=lmFquzL)=ImmJSAA{E}-tiblzmpc}jB8A>v+1Kv
zQgwLO#j(%q9I*0Og<AfX_4FBzao>1(UXIco!2u2RL2En8r4sz6bhDBINHFI=ee(0$
z%joX7Tdyo*^>Ze5?(%+x7d@`j9g7vLEE8E7J6%`mTZUuUhHsC9q&mz`O#A~=NqMq0
zoz<$>>du-k5|EaZP1JnRJAZzk2%qI;>hS($uy@0Nvc3nY@C_FyhHJd-l_;slt>5AO
z>HZFBb58Qp;umVk7L}SKN=l)KqEhJM<f)woI}3}>6)@lau5r2lrH23VOUS=3#cg`j
z&pkhni|gN)@a_dY3?bA?Mb%b#$C08QY3&O39eFQq-XXs}c5SEUtN*;Rk?n@=blEzR
z$_qD1Skb4C16UEJ?7nevSbpg@T+W@%d}hV3&nYYea(b5^>v>=D9e2H;i$OzoRT#Nd
zS!SO;HTblY*4I7Q*3jQ@9O5$PEVV;>*_Mjjt#{8dq?L5-W@RCYQ6V8;c*Le1^i7Sg
z=%s(Yat@cs*H4VLfz%S(D#B`{L_e&{JtUf~aL4(9XQZM?3AQE=XTC4jpQ`KqN+o8e
zLKg84%G%J!lK-f?Q9~&~NOFT0aFuV!*NTd0h_05x((v?B-aCScj1&j4y>>Z~$So>x
zGaMG+ep21#y=bG{^g>aDmBM^P@i4B740)ED6(xq-=B3w5G_5W!EN_TuBRG-_{FaB|
zN#k(j<HwFc`SJeza9yAAMr!cC7Va(&N?QaUw0h@mzfu|P@8IFO{{l|J3r4+R($H#o
z_w@(ilAn}47sj$RT#zf5Mz{U=EgCS)UzWE{m5fx^>IQ2PEW+}T@u%Y|4xg@@N2xnr
zIMPpSzQ=lGrz4h^O`+J=O^)slk4;dZ6EkIDmi%kvO6Iq+-o40S<uxSG><mL`6^>R<
zDsw$vjdj1sbq{fl^SA>)uGMOnuGV>6`pU2~y)A+qV|ymPurVe76sG>5L9*}WpjXmZ
zP3_al0l$<3KIel*k8<^4OW~u|A{LXg1I8HJ?U56zFdbacK<0Za<!f->{@@`42{~nf
z=S&qr_x{6!2q}y=9dOAkd%$wLI%)Z?ct_dfn>X`Y4XSH1c8z!n67{Nl5uf|yPXr5{
zme76n7H5z5b6-z{g#0=-pdcWD>k;3m=W^we<(QU1^ab3x5}nUBg^WYYlJo+92a8Xt
zQ-ePYD~>0`NK>`^PN0!CFfoT;o5@POFJ2e(Od)c_rv3Mpp8qucb^g+Z8hV}Hwa=cL
zh}>azXbM8Ri!89$=P~Clvns;EXyGZX5q<4g?Gs|`ft(6F?&#5<y*z)7s0a&PozC&y
z$IqTKi~Ar!c~V@dBx6^;7ya3}^W5_g(k-$2U^#Bn3);I2`MHNm&8HXm$<nTM_jq)q
z?U6{#?GeI~_1f4v?tfL!ColG<h0zgMC;WotJ`wK$i%kummj@Z~a##fs>cZz>6{m~T
zg*Lkp{g1D|bb93~hjj)oUG5K_zEr>;TgQkm+AX5$S8&-OV1NG^i7>VM4GEJiv_FTf
zo;<70crz+t-hquw)Ycf(3AlP%_JbbmnTXLiuREi-D|e1dzSFvryz35{>5=M!+~PNl
z{f~4tEm(I55m7DFGLyOK*ra=!Yx^j5FFKVo%QK2B`X=fX)_ho0f|S0|_K!P!($76#
zc&`#;?|X?_J{1`U(RS%JPwjzu(<41TZh0>8CVgT24W3w=6I)W?a@1l!MP|kpV27y3
zuVH3Z`v}DMh|Tu<agxmBLmGps0av9oVYCv_ad`Ws_XU4?k6dz)+xevY$sSwlv(<`Q
zYJ&`6|7q-VpTy>r4#b{1ep)I~j2r%I$hh26)7-rc-u3lI&h{vmWf`s|^oz>!5Ui@D
z=G~3ybwrg3Nqo>2&Jl;r^v&|dlV1a<2=el+xNfRR&<L)*VMNrpFZbE=s99wGO3>sd
z9-YpVpwcb?!pbO&m%df4e&3wn&oC018yC?(-7h+)d+pulhicis6c(PVs#V$fBX_uw
z)p=WWWB=rYBrT#0lEF$zW%=yy6W9a!cE%1biBut;LyFJ_6%YR-bFF@r>JSir;U5f9
z2A$%&y9|PcYjIXv#L$YypZ#EQFD9vL@2sr0=f!?P^F8lrMtkE&qhah+=iOsL^DAWg
zVW`kE)Xb*J=ocPersr38#(rx%(J-|K$GwkxWPU1_<6SGF=l^h%_O|&ZeIRC2Q~25S
zJF8+ZF>S_UlSH^kl*~_D_({>a_OD~dvimGWl(YS@TXelzyPxGyy)V=$UMn<Jf!s5N
zeR8qy&LG5it=y0HE=QQfG4IwQ<f6$Qm}ZgbKu<I6ukR?LcKkd+hOEP;`N>Zbje>1q
zL}lFrN(<CcTE(!H<rHVO=|MfiC+@hB%~2$?yGX8v=-2=`U+q?npp<Ek>x~8J?JB7z
zXbYe*e*K=1&{;Dp*}U9Aiugw4(&B?(-M-Atm4!=n$Y*#7`kTm}{i#!bF3RBO?19R)
z-U2hW<u&ziR2jrv5It(vGKd#`w6XsAriiwaitA^u(a9;AJ8S*PEi7a->5mTwMV6;y
zV?!+frwqhwY^stMyaRtvGcm@W81anI)$B8t(Yx|GHKYgsJ9p+usK=47B3AZ30Y=*Z
z<B!V?3Iqy{#dS8Atwr78(Vtc}1{MR}Sy*P;hQFgMN!RMSp0o{`y}x~X&U3i#a(i!)
z{nX1~7E+`y=6q#vu%^)dW5@DCt^Il#TUw$XL4@Z2us+SslhgYnl<U*t(PF~4h8k`Q
zZtv_GUdtngPxz(O%f9qnSfLJvEV>&)BJsqV_kV@BA!BC|5hurV+62c0?G+ly(}<!(
zbfhF!Ssk0PMLA^e(OHLyuVu)aL5Gd(u&M0xO-5@i6Xr4`s1666LkOvVWvEcpu5N<Y
z>A?T6{s68+AZRLt1x*=s6?BHiOQ#qQM&ICFv!BTS{h{_0<2vWlmqnE)NXp6dKdRRP
zBsEhAX5{JoZZp6!dBqD$Ebf*F6Is<Ah|dN_J?xr>B(Yko(sJFXgP*r!5&1TUDGk-B
z{yt+Jb8p|9!JD&0!jY2i4~|1Gm$-CmSMAj%!-9%xr`vgg&_D4>jqi0Pe5ZK&f+4qS
z{jHZ->8b^fi}&3UnY*+1P1%MTE~j~XVrK0+*L9HV@TBzR<$?wkg7*gjJ7`jJv9qb-
zg`1M&2ju0kZQ56^;NsSv12grw5C!0obFV`cS!qahFKtpx2CEsFthMW=$oG%=Zu#Ps
z+Vt!57ZLnrUKq4SQ(x!NMGa#nJ^1os?fw`o<QCjtZkNH&b`{K$3l$ZBW=A>GBI=2x
zGeeiT?t*I{9ZP=t%MF<?)1$bC6kFR)fToKbD=NWPZ8s|X<NJp$K-8C&L3#Xl)W6vj
zb;ndtBI#A=+Gj9Y$#BaYGjdlGq(nqzpW6gsVX1ycfOqTjg9ofd);@T+NqA*rWr^&v
z$C39|riB2X3Qf^AaAzGMZwu}_9_U~J%<`Q4qOMk-W3u0aHJ`OY3Y7ZI>F|FVnKnAI
z^X4{Uan$*1VR^FZ9xg#k&+Z`Gv)7t?OsdZh$oheE!A#rL)?)qA$RVP~$zbNfm+c4T
z6coBPe21kNUjdlmz9kM%zut~N`%ok3b!zdXp01D67R9lF<7R(b3iN|kl4d>X!JJ+~
zgP8F{QH$pxo1>DpH5p4%kS@o+eYD)x#s+hk=;)kI(=&M}QN-CU!Ht}v{%y-)e1Ak&
z_Mv8JewN|dRAI&rS->~{_NNLG;`d)Ww*CjQmK`t{-}OKk{Pkxw{|`VAe*f@cbpV0i
zfBi4tMLWQ;EnQt*u8iYhqJC$0-;a%L2o+I~=$jpFk6K<{R)Q(*k!Jg1#x$oUF&zhc
zI@hH-=Vz`z+<wu7%01MYuGX4^-}A*Q@M)|9g3Bl|!7_?-_kQ2~*JV70-^F@=eYMH?
za&C6``|t6EIb8MMJ;?K22GO}n|Io7Z_V(6Z8cT~{6gV`UU=G$)<v*z*KRF&oEABLQ
zzd}X$JQc@x_j46sXo`Ou8uFjUb{>aodKar8+1DgZu-+yr>U4S9cKf3Z2eR|?e>2vZ
z|CFnAQ~Kcn$5Zbgs-$JLU@(;CWC4uMZIU!5a3fI*Gq(l3^7g55;_aJ)y5)u=trO7}
z-<KET0{qAKiM{T5ctP^t^%wouR7uF%-~W4TBlLpbf2~~iU%vbOKmXUiiok8hy1F)C
zbox3TAEirD7fx78V#b1}#xWf}J>>5Tx9NKFt7PLn>Cw9kDl5Nl84L!ml$Evl`%o|!
z$^UQ0jr<>Y=D*MM>imyw4D|C`ceuUOpGET@dE{*0^}_6|sj2Y#O#<QLotaJd^kue_
zmgdmRo!SDqvgLtd7|xEQYMx^$($LWGp^Cs1*SLD|bdr{;0Cp;0fU00<Xh?6CPYGA2
z1|2+g>eLf2YOPA}@`A?9TRDeM&(2C~k*fn{X^W#NNK}bC`2>kX+9IQ^!G<u^poPdi
z!R18~w=@sMV=kJUH8T_O8EuPDAM}?iz0&)0MMZ^*)BA_$$Lv2oHIUZAWh74?tk{Eb
z$=a}4w7x~TPAbbhClvmS^_6*Tk=51CFiH?T=-d`QSVLs8d)R)MF>BboELxay<MJXh
z>i|!4*1}>tkIRX=^yXH>t4*>}rWvR2oZr_q9WXI5K^xCZi6q++72%RQu~_VhNU80&
zrLLVs_rQAdnbNK-TvOf3eBOoHpa6OxdNX}llJC&yD4|FDk+m?!cVl}$Lyl8++YqDm
zio_X+lM4F!%HY`4R|P#iy+tI7zYukq-WXwHvk!Cm{YYy<OrK|!qUP-TgE2df-YPCP
zIdLpHCMG5kPjPLQi_-o$9T2zQB06Z9E^Bhl$A_SS+ZTJvsXaLf-TaLB%*T&+RLpl<
z#kh86l$7Vby17Z-jyHjeDJ(4XEPy%6D{9xR>eW?+wHT0ae{*Xq(mblmso15goYzpp
zzI#-_V7iXKf+r_EA6+_~`vZpZ<muC0i*1D3nA3?G-kI%8nEvJu`m#yZ2yfm)f?6wp
zNt&z}O*YhJ7ewu@A#w)CZFK|Z#)c;C_<`rnpD(tWz5n3B9=pYNxacD{H<oBL>2jxH
z`tf}p21kz`t?Hgb-pWPoDspZg9B<#9vQRG{oo*Oc>YfbuzFyPv!YnJA$gEMXn0S#D
zO^?#k-TUjWV|O`i2{Z2pbh?jmAs0%&c=2N2Vo#H9Y3HUfLhx2;t@FQbzFzZscbxS{
z`!fMJyOiZ2`IsE5vgz(>Lch2E$2_|_^VXPC>Csuam2PE&NcKRXqv18$9r@BgqnJHL
zr)-Ig5}iOJOouH3b4)1LW3);32<k0=RYC%?$J+%VbUc#fW7_8*Sqfu{yHZx>GA}QD
z{o)abm(>mO&uS{m>m#ol)1IH7Z#Ki(#9nrEED2EN!*ozGDr^IWV{KNR^kC}-IklYK
zuys5TTT_^bYYif};=ttU>gu8CDyHY`YTyu~<dgk(E_U43y&o>2>K4a6rT<5rjt?P9
z^+Pb4_Az6aty{NxFO6q5$*z38T|C*_+pFT)UoQUJ#l!n;Di4)<4#cwq>w<aqZHbz!
zX+j+~X80e6Pe=!jJ!UP>wyRgIbT{on1Yn1_xw|_o&J4G*--U#{5!BO`fsd$>F4JuY
z10v2TB>bU6ho0KVYWcc^OJmj4uJrtr1sgOY>Ys5ocnfR7fL~5>W?|t0h(hUh?b*|m
z`TqX?hreC=*WB~~_Q8IOpNEHsTPGf337)4P9dL}Yf@vdn=U6#3hHozjMCK=)@*-sQ
zbRQ^Mbx#x|`_u0DGl^8m*w|QC^5)H(9ccA;3LIM=T&TF__=!x;tO-V+cYr`0n25@2
z8}cD#bpI3M;Wyw%ZV;<mXmDzeg+z7dY}$#2yE~3Pfl+nm+DIm-dAJ3UV3i6y2P$nM
zz3iww)pdv6t*^e?fOxkkmK~}Zgs^z19dK=q(Wegk+`c(_cPpC<&r!}_;nI0bgs(Je
zk$>@s6lv~*tDfMoW3a!Qo*KlUcYu+LmXwsZ=GAcrn32fJY|4jBlN9s3@!1r9&~hE0
z!~d8IeY*HTr_)=cwGNqQU-*z6IW;v^KABrZLypQWoyi(Sb6pP+-BheUm4hCek@}Vf
z15A@Es+!`*ezAF^y(#d4dtOaxuh)XL?}y{f6J1%7T$m1d&LGk20IYd&Fia;)@)c&z
z1<g(LTHJ*T53R2??`}@vFONQjO`KzAV`DS(^^Kr>la$ZtBGUXD*_EqVqli_p%h{r+
zpm<Fmr<rzS<zNm~(yRJjB207ZNabiU(Nc$eT+HS^-pkzFe5NFiq}W>>;Ny@2iE)VC
z=Vh`{K<Z&Pp*!2cz{X}krVLFno0ZCcVbtdkrVTtaYc9`@w6+=_+pZTJbdAsBWReOS
z0AR#yR<6$PUE!q3BD`l^K8_O=x-stmxL#U5{J<)nfraVj#-KUXz|737Xa%Cpc!kMd
zQC|FdTQ3n}5FbbHBQqv3zPVM?m07*EMbu#KZgk>fC=^O@t)$wOlerZm@lGi{n!aNl
z#hg;Y)6!Bk&Cl1@f1H*)<Z@;v%Z|72`Sa(lQ#j=O`TIe`3U;pe9~VdU_=~V?k=W^;
zFLff-iz5lHypi{hVx;^(-*Dyq_~VbO$;s{C$;ppwL#8!<SX^AJ;;pTf2B7TQJ;ko3
z=gyqD%NFCpHm?!o=2@2PdjuQ8lAnk0CbwscV-8HBxpvR#hsfRjYb}Zf$B#ECamig>
zyHj{m&IvjfRkv!D4W-HQTqva5()bI7ev{qOY!4R~7d7%wb4)_b_F6drn<j-tMV-?;
zh8NOj&qqtt-u?SePa=IDWsI{ho$SDcFSldP?~6Tlg`56NcKP$BD&9tRT<u(XF;U()
z@gdvE-4vlhcK^5}2uUN3?~=6Z5h=I*Q#>YBQB=wL;T#ly@4DvZ=HBsH57V69gsDj=
zpIYP<E;FUHT?(><frUk{F*9K7@$G2an!xk{&Hyu*kjYtI9@R6M)Vuom<>kTZ07W*x
z)hjrryAGFU+xof>dk#Y7N>uIGldzAk5nJYYYwJYxTCP03#b%S~Yk$peyVV!w@4x?k
znLX9pEP}JX*AMgET<F*meOorT{LO3XUwdP9zRPFcDRi#<OOM^-U==hL)RL&_^KlwV
z#Yr^}%KgWo>wYPcffamOSy^es`P9{=N=#!X`n2^9lk#fVh$ZE@BwExa^Xh6liTe~3
zVxF63X0-avwyHC)<sNL;KFCNm38D#CRcDSHxY3)UOt6|yv&jF%FwUMmd+&6zc7-={
zv7Nu%J`lW~2nDs(Soaa24P&OOi_6JK9d=JKv37Y_8Lo9&hwOX}3X5oyED}Vq^%+ef
z;KzXfoM-3!fWbSmXbrAh!(+S96q+^d5qNH-iprZ7<(lJg@(xXrMw7^7PTBOe$S9~w
z9>ID1MoF*JRn8Nio*Oti4jVHcse9Q{Tt}b=G%qjC5&}*3kpKB|(D}oY<IMoIqL-GI
zobvq6FZ3NGa4rZ-D=1(n&jBlVB6U1=u{(8nL|rcS6l1QIQ$4EV{a&T%P$>%G+OW*2
z`x5I?vyy^ln(rkf^kons<mm4d@=jN=Idyze9A^M(`G<~<jvgrNMGjN|IO95K3eL)_
z)nZT-%lZpDi70MEGnE52_Edv<-D0avH(@Yfv|)ioM<n}N;*_H_eMY9{l25}#P$u2&
zxR(2Et2K7+Y>bc;<ImgiOZpsmE1bG0KrxN%f+iVBW<Wa;A84{KHq<MKF6m14tYVa&
zjDFfcJg{h;@*Hxz)$$iXBm@nW0#f64=lbo=i60&Vo-ZrNTi;<Jy8pu6ZW+kOQh222
zQcfiY&4rdkm@7p#k4xptDByChTrqk0+YXq3YMZtXzIwHgtnDYebFej;<J?}mrH^q>
zOj2~QHO?@9VMT3PSp_zBcGM~7n;8cHi?VfN<V_-Z>^|kfLM`PN6OV`qIwT|C{m{A&
zjI<?5N~(Lp_2Uoibwb*c2VM}{_4v!hY#xV7y%D?aFo)B&DI+B0$&)9~o;~Xs*Fk|u
zC&h@<_aM=)+u~ka9Jwc$-Rtbo8m|gl3#yEipPyd@qqoQzElGGy6G+uX>XC%`gNM=T
z$Y4zswKvX_?WjE~<0gm%nz3{SS0Mx59pTiWz-o_iVo#54nWv5R{3laIcKf%&>J8f<
zvm{~VsH=W{UR`Ugz+vYe$p4nash)zIT0(!hw=082qlt3W2KcLUbt`RW=>{sK(DFvS
zhldA5KH^%y2OvlLQ_R-o$|#i0N6yI=Ede{@naxJ-p2$8^$W(2#M_do$O#B$ipH%S{
z+Yw{;6o3Lbf#N|~Oso#H4QFj_2QQVZJ4{fAtl~t*<F7UTzG3r6<K4|IEnxtR3(b5~
z840@#ibyL-@wIFKSY>9j#}4tgf4;Fv#-+duwgaXr3(7zn%=NRpyr>Wl`CGxCk@d1Y
z4A#4M?`RNLhE?5^8a0T~ALJA7;ys*+9^ZP496<S*q<7s%Hc4+g==6T913LS0XzqP)
zty{nGX^Db@f)&ZDv-aq%Ev^M#8G~Y8TJ2}`rDO3O&23VCpU!>&c+tp)@!EfR{bx>c
zRuL>q(yN1nN*%<{$X6Q;3){f)^kTgGn+rlhX(u8=e-PetsMOufjRxz+Onrw*<yzGQ
z`W3m~+$88(fOL`;p^`^I$}I`3O^eiW%cL`=N=lcvTz$La_A%)GCHvgFvkkZrw4~O~
zM=-$y^EFJ8vMp)83)G$c`@<zY%OBg&TNBg~WdMJYeZum_;X-mJikDVSGsZmva@zrq
zq?W9(%j#5DX3XnLg$uRfNi~{WEsQiH^`^in_x2RMx~?y2&dC9La!62SnEBMD`ALU?
zN(e+~w3?uc>YtD}Sdz&BtkNrC8LxmA__oiFsw#B6|NY1#?I|fp_n=oDkK&7Tt{63~
zzh2V|(U{yV18`wzWTdo5&y;%&!ieJ9?~#;6U=FGT%nX^g4m)qfuzmCDRsw@Yu~gS9
z02ioUostFE=w_a>sZVd`x*lO2NJp*=NX2Twe6-a|kF%9PyTBXkH=RH+@#{Z+@CMY6
zGzuaMZ=_dVvm<-R1VjeolB43#`1H@%7g<?W^I^RJ_q}=Z<V+hZe|eVBQ#Kx~3N%Vm
ztsV7zC(jEq##tPW{G#GONhV<grtF~q$RID^fR327;ew<~9K_Xy0{i2X5+G$tUBfeY
zV*4+2Ojlx3Gs_vJeIF_ENr>-9W3KvuKGxmNw(zn6MY5d4We<$&Un+IZMbjZ(AKHUN
zWMvWU$AyGY?1sWthD=!+r*60<P9H@`!H<IeUA5zH)yW&hZ*z2X^j=vUMOQ(JKdZ+V
z&a0j|wM|S+AO2&%U9C2t8eyEZ_wmz7h?Xi^o-&gfOXS$|Hth~}n^NV1U_PU=v&$TQ
zv*Rz3>A1QjvoKy*SQuSScJZX;$?==A%NK*b-jwaX#Ai<<M{f&6=#rLJ!_|KLV|vm7
zTn2ltH2VjP)=lSWoAx64>^qSl0~NPGbG*|ax)E<|Y`jHME!)T;Tn5q6%Bg|2Tj2Rt
zOl4Ia429L>5S4C&<@EbN5$h!RLZ9ssUo>7@?M-hqZ|$6`9e9+Lp0e&h$sQot)cda<
zMN^iL5L72gU>kg%o_mgu>Ko*F0o?xp$gvR*6Sz%8M0GN*8G2f@!aNzzw1kOhfF0k5
zOvM!to&t&opvG)4kWbo{o}RAAF3MAPhM72s;(Zo>h01piQp(H93iD~#Q5S$_u$)bC
zr`K|)!|DKiv7o!t(&aC<Hvu-Iqv@uaUoUJD4&|Gm7)c%EDetrg(gQ0!t7t@R@?>&p
zNhO*3#le6Dwj<g;>Sgk|cOuE|WE+enWbOyu)8st61pYm&va(mE!#`dQx&Vgu{_>|-
zh7Ayfoiy0_)Nzs#=R*c}D)$h>&dN#>q7)@z1d^QfImL5-rG1YQ-|>$N6Sz7`&&tQR
z+HIYgCd|79eN}wuMNn$Q@+S{{q6e}kW%3|QrAyAOTeqAZGYpk$4%r5=8bW$XIYFP-
z%Z9`lYwd+jO&FTERmz0Vqw*W#Rox|+u(ym8iPII{?K+&Pt1{d`9~2z6G>F+r!QL~m
zhJ>c#HCP?e!#0pL(qqt*_CRFfYy%w80CttXtseWF(||zgOslP}wefLuGx2J%hAH_r
zBcnrJsW$N|fAZR*eBE+tN!)4Q)0mz<Xh!Rs^j26GO5DuoC6IpWx2pH&w-EcP)YS*#
z2#1{X-xsA-SaPrSSwO}qU7B*%lZ3$w38?@qS(u5d>%ZjS?3TwcCI(uK`~$h(-Bao3
zPWb^RB&1yZ6*M@Mh?0S2U!cqK#l>uV&#q1blz4^Q8aCwuG=TsfHC;Iaz)Y~}_}n^d
zVpHv4Z3QMZXN5ssQA{SWd&`$+TI8@;VkW5xAWWQHZ~*!p?ABm?-+|;>>}rOxLQ_Qr
zvMkk61FSpaoS9i{cUx<#$E>)K$v}2?_7xn&$AFD@g9wXUx1!3RKzBAPj>S(YF+G@r
z)oGpigkp8)8RzS(F-_~X@`?Zyb*#SB^pI!m;>Z9+&?@?QO(_=g`J>Gk%d~Xl?)$d3
zNa%6eQGf@62vF4v2>JwQ$DM%Z0p9d1`YqrOQcaByKq%-$^un5B<eSh8=_K!>USi)6
zT0RtQxSBiBCZpI3#~80V7^fqBz2;Bn>4B7%_H_dZpl&D`A@t0QjEq(XV3e8YvL*Ci
zeY2GXQ>wwB#NuqXJ16;>XH9!Ck->gnyTK0+rB&J^N8HB$dKyc22BpM%aX3!BRB!fC
zd$G7cpUoqRdsS!h-~*UvRZEJ#F8q7FkiJwwC$Xrokd~3)c?!G3_tozkSYv5X)7Kyx
z`$qn8z57K@PDE7DkeoNGL9B^V;x4z-z9aPrYd#OrNe&o!)KiE!8h-lpsU8BF2Ef`O
zXzZwLD7b!vgc8)fB4^w6^m5B-p`uO97dj-uNMYvs{Iaj_Yq&m(nyExkXDGXY!^^2j
zqyB3!HlvQ!i9V^-GE~io7Nx5F3<@w~lF*lVWHq^<OGd39_%N8*{8#dF$!RA|`ygkn
zt)+yZ^oEC^4VtvBeg>N|U7T6wsDyEec)-2~-E^uM2gQoU8eje>cE|pR^pX42#W2O)
z=uU$)Y2v^|!9c+5NP{ZbS{UsYfx15dNj6+c^MLNcE5XJZ&Y&a*#CFKtxAm4U^#^kq
zSgE=!_S^vSs3lq8<>2dsoay%A(d*MjXxYj@F5%9fpZe;cNXV$J4q8n@D2xg*Ax)UN
za>sxXl_~4L1{AMc&IeyvkIMGxQ=Ka{f%B9vUZbUiWa)uU%I&^3lpt$FIV8>i!Onpz
zueWFb1H&(9GY0t4yLxlf2_TYI=%N6ydjc}jWoPG18--tg4Y4S4DhUjyM`D$7c>O-@
zs*?_OUTuIq^@&6G^wCK7xFRU2wjb^4#8vF;jhWct`=+!^J)SG2L?O5QiP`N?-)=4q
zyR<#Mh8afa{wr*tHhX%SSPM&=pw9^8>eCG$-4LL@plkDx02>$xI#7xHPI_QEY_ylZ
zzVU|6;TW(c^b%ncZIxh76;+jZlYon#P6LM7t&?ZO8Gd~9cBxx(28YpRt;pMQ7i~S<
zt7qvcCc2A58o=RV9h0F#PLlIXxV<|e)!o{%{bRP>x11Z6!9_an?==PA)I2|^v|X3`
zw1iL`MIk2?L7Vcyv;51gVzy(PP4@)ncSy4R57j&@9jcx^`*j)?)QLx_;vYYj4U?7W
zP@C?`V36#pi`a7^-Fbgi-Vt`a>IYOC#E-+|hD)j|F(@EYQ4XExT!CWXMX-VAM6`Kx
zo!;;EW<DkT@e$?u(X~RMS3q?Jf2`kvJA<-Z(6~o10L=81dpl&lk6nFCfD`J$eBewp
z&prR7R2QkYvQVt@K-q3EyB&(i{q9TvC@gvt^J|RqT30qz#jJH@tnj#H<0~1wvMaqG
zk?bfEi98ERi%Zll0~)YZ&Xf|t;IB7xYV}~gjA0&&-J^8a7m9)*YJhvZ%l2A_Y5=Yu
zpGvb^KXGZ_IQFV!XA|gb(ZwyxKwRrqmL@$ak(K2|th68|@C6i}-GlrUj=qJ31-%3&
zt-)?(ChjfL($bfsa<D{sbZDp^$~S?S3)I#HfMQuAGesLQ#I)1X$fJ04lO(%9uV?6v
z!{K1`gj-2FcLL%iqvPj{u7ffHJo1%+N=Vf8cZB=UNyaV#<l%PF8p6^b7k2p^8sP6S
zNlwJSeb+Yf?o;w03d|9J#6-?$^PKfx-JyWiSZXo5zrx3g#Dsk1&|Byj!AML@bn+@)
zf7qn>;;Y{^SoOl4#{cXiP)T$k%7|Nlx+!b&s8rTN5wGlcu>u-=#!Y>_?jDQVKZC|%
z!re4~PfovdLFf!RSyOvKKM~T@)Qn&_wjd80;5Jqy1chwce-RXxn`DEa9p){~dsNnq
zH|-FV(Jr@;{dax;7#SRpm9uV$FzqfR1T1&!Qn<Lv&J*F{_tcn>FR2rnfQ5m{Y5?k_
z6M!*v!(&^LD-!yI$;VJZo5ENxyKvZ&2%Pl2T?SdmG!KgTy>8u=1-hJ`UfmnF_1ASi
z%nY~C{}47l$=yV|BP`ztVxl;pOb>Do#H|VTUKJ4KU4kwsBmqJ#t03uAdOTy6`Dv~M
zMxNj0r^cu%iu(K>)sJw%jO*3I1$r{_(3aQL0VJVni<G^l8BgbdBSJzPqIA?4mhK$N
zE(4S(cO~OX$xi<!B)hd1hhpRuCK-s5Ri&@%mBH$YE{^(EeN~>z$bWDHO;Nx@i40zJ
zgI3%_B5mA&s#t?VW}4+QOR$q9R>ti6B&_<omKH13_81f0?II!~5e!gf8j4*zEp>3t
z07elMBl{93;>Y~a)>k@U=$^{uw|5)a(Yp<4upeE!YRU!!-7UGFOidVXKVmLM72z2a
z|90Y_Fq(v_)j(|V2l~x^ZtR2ffu_BgiP7DjmDJK9Xu(5P`I5)CWh9`ZbSvNky1kDN
zd<V_)&xQ~7_YP}=vkFm07F&wltCqI^g63fx&FOMF4W{0ulf2`$RJ7WO6kSsMeD4Rz
zCskE^9Y~4=fGQi=U%$MehsZ^`6nw{zsCKs6^D+jBOpmS_;3T|(4obI4&>^w~xD+zg
z2@rMK@W@Ex+k0}IxjY~)u5=>ef;w|&EV)UWqN`0ic}(Dxz03}~0U3rCa<x*QYu9K`
zV~k<+Jp~R9z%xfM;Hk=I+pq*plVe=Y99m_3Mcezm`eGo!z$;5pguKxP*|ON3Z0*yk
zB>Nw-KrZ?(vzjFPHf-474f-bj#2rk&K1yUHBET{ffb7}mj<zunKEI(<$fen!rOuQ{
zkN|sDfc%#Qu9Z5j1Fe=fql64F9PI@I+f$F+{7YSzH~1N4&<GY(kFtRWY?cYogi*rA
z8kaITm)(po`SO~uHRV8KE8szM!<doHi`jL@J-1>HgMtK%CdJ{OxSFoDa7xu*Ul+3x
zJ_phF^P5*<avPL`g@Ac-zrtZs^-$r6k)KB*NkT84G_1o6FtOw=P3A=byJ<eag*nS0
z{XP`gNNnN4M^Vkefe1Ooo69I;UW@b!o=tG&vH$~ofvoWoNJ?ZWm|a4)m4s&lG7GY$
zH%R}7N(pmCM@+wE*1oB6_a`@ZW27&FPH7Mv9Q?CTgdG*GZ#h-x*7f2tw9>A;x}~l>
zr+j<N+wAD*=%M^K0)~CuA2)A~5wi;_Fde~8W*2=N(VEJC6OC$d`Ch2#g|;-ve99r$
zbLUJ=i&_Fe{EP#=EPa66M_s{Z@<9IST%rO!PDB~$G&K5Li64P9gxrW~Yj`GP+8AJF
z%6j-%>Y}kFAG(2E$B!ScN?a4@%t2bd_Hx~(rwsL!rB9hC4=TzGn0THU&qx9Zdmb?!
zE%-~ZL*T9-*$+cbDwTi!J!yK>AbQtkF{RTdA|zt*)KAY%V_=3wnHA$12|$-iY5ax~
zu3<c6Hi7zdzP+u@7)ZtZ7F9D~YhjiQt2j@>luk)VNZ@-ASiEM~h4I~dz`qMht76aR
zB+4wR8X&6@&nk_*2ohc2)Qu%zD~$VmCnhIjxYJd6y+<dWolT2Yv?}$rVPQI&L1i1b
zlCs=gRvW9m@cE|43I*t0rBSQ$tCvu1vv|>|E%6bGUR-%7&ABS%xK+V^v8pL;4`vs+
za-19;ciX{!{5X;Q{5fLa303X#=a)BJNrKv6UxH9lYBqZr<*DycW;f!2<UU|uAL7K}
z1EG47+cX%ffK%f(WQLrC8ZBEKxW;N*`i#6c0IM7&+2{2S^1A@8SX<TsH8`ZNw{*4$
z*_H&@<=nY*g`L2ht%?Y&CRys0JVX<8>lkQU4iRMoJ2Q-$(PrcF%j@e}rz?PR#cP=u
z85tb_ajTfPajV4rW$0&5C1e5wT0}eBDNL-PQpM9Q_a#&V$*x7Aesh9X?W1lcO!z4Q
zc%b57;5$LiGqhCwixMBbBcBZV4v`Oq;dCEja!XKlkXCn(ITaTjU4M{3un?A(kKI+J
zks_TM3+qRN#2c|m#{U;aARdno2c>f37V)19<CG%c;fl#zHbYWSci|$+@@>x+#T-zG
zJ>}PP0R-c#?~`?ialByQQkpbJ6FaBm9^Dd<mVn?0+bXG650q$p&B`Luj_9H|&zx*}
z_N)+UyU_+n?@IRZ@j<0ew5vzK2w05LYjmM7_(O4VpcK32BrBl#I&u#5^LlhDdr;17
zzg1~=7Y7wWSiooG_3C5AL`556m!y@KYk=^G7HrqNl)#DSXlgE58oi60)+IYbj_MGl
z0yv^iWaW9{N%bK@sK?@0W#y=bFfn~t0m>v0kCDKqYY1q3zHZZYX;ngfnVW4@rWC0?
z^Y#K%$0G8~?Ck2G%e<(+?%Nl-+wcLbs^(5wnBB7dGE8^>ur+FtN{iCfQu7+@zmnV>
zgUGLPQXp3Y57}u8)XEz=l)XF9?w1DI#93c6==be3awS7#q2e<-gQ#9$l%SSglN=i>
zf@=D(=(4W!e`)4%K%CYCeCShc%+#HKz5x7lMW^mJAypiN)KtK+L%@i8vJ(y~l8~(c
zZhBplFdf1rc&{t1I4gD=6?jhrYAwl1hdmr;H%QYzF?|~L!HgH5?Uj@44C$^}A+P%5
zNWdz%`Bj?rAr>uS2V6Q*&6&t(qK`2&^Ih=Y-^2*C_cefiEQ^x~<UnR`b3&E9T^T_9
z0COb!LctpoT7MS~{{I?Om(uZ?p*Y*Xy{KZEH3}`CFn;p<dq`VDo9!0<l{GpJ<j^&U
zlj4BCbTUIL_Jaqt@TevwZc6R_0}e*TcIFu0RAA)lnb5P8LlU6_W$RX8XKQO~4U#Ym
z%0=14&^gRSPS6~ZCL4h&hiwp|Z=0K43#h2R5~V$Ja+w1)$TO*26&}kUylC{11g}Vd
zO;g3xIN<60=euxLemg0Ve^t1}GK5>^4@@Z?(pO|KZ>glm3#v8B4b`JnXKbK~kN}zQ
z--100D1Kek4uNPuWxy}J*Yu{<FEpD3|BNz~U?eIr0<{K}c$0xTZ9FQdvkep@XV_BM
z!a}@E*mm$ZSOZPPf`Dy=TF{ljiFR&F+@4pv<Q6n;Xcr1JZ)uMgsADV$zMU(qDCF3s
zOP8{m5JRBYmKU1zR)u=+_lQbUagVaPx;koLX#kB~6ew+{gwZ>1`=RC(`HxJ?xIf{Y
zGbp^-Ua)TLmoQPq(9D!zE%Q8EX;oF#vllLi!~A-!a{M6r=PC(e*!y<x-VGStr2h4v
zo70Mm4}oz?7~S>2)9*VGB{T5Yr)Fs)i}iZ5ay^`laR>HGccY+;6qi|tNO{)_VR=on
zrSJaoK7lO>^*$}mj_QNI58ATEu@7k|@ah>?zQl<9rYKs%9aRX^{THAJpe+cx4}tzf
z9fYXgrgO)tlg%gg!GlI1FvS4;-GSuw?8S@F6@<ra`uyr9{pEVWaL}u~;?}Xic%Bo1
zJ|t>|LH99u6~r~KT}FLNRivLz8KSl?bXXz^=EndxY}>Z29_k|<uty}ccK@C2$r&*J
zcX!;_Mhbs2?ECK+#8>Noi?8qR9yj!M|1&q?U;e{YE8_Q2{jb{z|DWD=^<L;*5y}4}
zw_Up{JpccXPrG*8wY#qQKmTV7v^G=!^}F9@?*GeIk^gNj(^MQz43qR23NOkgH?pCB
zNdpDT=7P|-abqw#M-XU}vaKr$3rPLmO~)Zcofr{a$<Pm+`=Cz;=kDahf$yIl`+}PD
zG(he+cmQX9!s&1B*i@VUY&yMx8mWD6LGH{bVZxVC)2^6V5dm7GFi3D21*m4H4^SQv
zGyf-MN&q$QN&zfG&AZ<n%S@7}qUE;CBs{QAuAU_ae?U6r=E2mJqXQb)LluV;tC!rn
z_YT-oR7-(5qK;AP{x7fCd8mx?sPKTA@VBQz*S`u#$8W!{1;)33_`g})i+P(7oSE5i
z$U}cWCV=VK12*mJZTY~8LWd_$112^Mcz8|JtOe?RI5a76gmvdm1c1dH^i`<UKzPsT
zkoAJvzX|Bfb!bdHOSAv_`ISxe?@5NyyU?$pwIv&zJfUG>=t8Q14*gKe&rJ&7a|tzk
z2_zLkvQGx1gg=PpX<)fp0&IwOg3xdufHtD*vCrRDtabk3S5EY9#E@22$MXQ7x*j#<
zp=ux9-D^WFb+1A6uS!ahkf+f~OE*E5OpjgJ{HN@-w>!K61EP>p7;LJ|JrZ~)aIkuV
zxvB{TE(^fOZAT4n1zvzC5@9&ry^e7W(AJT21LBlyBvvIo(CWkE<Kvz3kj!(>HPD_v
z-aYpYP$8u@e;cH){#JwfnZgj9x^u%5d0f31tS&C@m|$1V8!9;7SYVaV^{gDv@CQ$x
zWM18@FO#rRp<obwEP59Uc-scF*gy|%giaBwqMfAF2Gs~q#`wbV;@7C}w0szOV1*a2
z9lQs|tyQZXSc{@ef@fk_dZ1f=s6+T_?L;ujG(wv!2K0#{8`?4%AW0tT5b@T4A6TGk
z=XLRk-`)MbI90!`ti0XFSuevBXv{oEeFH}6@=(@pclu^#f{=y9%zfW#&+RL@&z^-y
zdLrdJ;BG1Px3)^|w72??3fHlPB>CXck&zRCmgXkO%F(;-0AK_tOm$6#bCCXEyr9Fg
zMF0Xkpj0#Wv3%~MA==veh<5)DMKkcWpOdq56EGZT1wISf912&V=gpX3q=I(lPmfTi
zL2nWY;){8E02#3%G(d;+p;-8n+)fnTg4HJi?GI3kA79-Vj#=}0Oke{1P@`p4!KrWi
z0uG^D1^C%$7hZH&rqBDa+SS#Lxta~Iaf5C7p1)?@iNlFBWqYr$$~YNV>?yL63*se+
z{yWgCHGvK*v7XA@@(YKFQ1v2pm&P=P?jEIGUoQ}X+P$IQL{$>3R1x%90OEwbs#zzh
zQOp7n+Jq8WfHxu>GR{4}P!~)Jn$NO}fL2W@w{~e_0gPYN-SRikwgZh&`IMk*Qa<lh
z&Yn5bv0w?kxHj;7tMZEsvdn6nY)8QDv??M(_9%0Ugm$9|Wkf`mlfjUPKC(9uH|n-l
zAiD)Ew%A32=vKy}fGI*2mBP{PmSM4HZBAawJbsp~HS*q07D*}7O}<dP*KI-qAJDF6
zYp1+ylsTsMg^9LuwF?c(jc_*NqwyjFGYqh&ZUJnz+cJ{_-B6TsFE+Mo!g~{dGPLVi
zFQD;Dn+82RBn13-A!4@vJ61VGa5{;h76b=AG$Y{OwHDIj_O{b1Cv(GKiYgq$D8Hk<
z2#Duh8qgD<7I^GbOj43GxSdx!DW!^`=yC*HhLUzwV6>!txh-qF*{f^_^*`N@1MN!?
zyz~!2WJU8+4i&bJFmS#E)Bx)0V*{~|()P#lYzId4l-Fk9`UwSfTs?I(Ck%OSCEKn@
zmD+01H_hi(`+0ZOnJ^P+Jz0JUzSAgAh<`^oas*jbGg*(?gZ2pME_@b9f(oIqngHiI
zj?z&crh0)EwhE$A0i*$pyP%<g@4*{5loMd&Q##>Zhf-O_+N}DwAYYpCQEMx!QIZaZ
zpAdMiT-Flm0&DroK`sA4OM(jsfNWmye3sKICQ=51{VMQBTju@N<-hGLO_BnAs;p^)
zn03dC!0#!u@?7*TcvXk%YPfR(CLLBN(fEim^aL*DjD+be5#KX^<{M2$y&YT+XB5)(
zqasm7t>D`5J9Ic$?S&e-BB$0+u(zt1=UQ*q17Xbm6Y}hA#Qf#ga8{vz;xotUf9!7i
z%5D-$gwCyi36lR@GTK^o;p~#|j0aYpu1CKgi^DuJgY(V@4b_{<Wc_s_dCc4Nr;;*J
za8CRR?kg4>TVG(hv7sbW_NSwpmwYdl<QtV~B8$YgcXzO2^MZCO)h^J~Ha*6kg0gHK
zyg2MVo*vf-=N`mSs;%;AWVhDHLx+zP;J2U|6#{nNos|qac$6yxc=tLppCmAcq2D<X
z5D;+em^M(31x~H;jj7se!EqRu^&%&#B22x3YG)<uGm;ne1euA)M?Ioqu^GeU#v3<d
zddhyg!%6u~ZVAZ7bzT4R8Q8O2bGbSCZF@U&;=s<GJE2ojs+uZXc<-#+*_A%^>E(^}
z2{wJ8poe{7qUSGQS4s520K7q|D|O2)TY{jvDOA8XKesA8zuF)+b>jO0N{lq(;C|dg
zduHU;LR0lT>+SDb;q4aGX_ecebCJRvNf~B>%1*XyJm_z1mrdt?tm^i7O(mZC%1vU|
z#}B%KWY?ODHt-EA$~AN6ELU|<+Zs!L%3ENfyTn%pKlrabJSHi<cE=sZ<HPV8gO70Z
z+TcBPW%LE27%P2X1y0|w2DiVV*Ki|3EF3n&SvOFCA<0P}&@Dbu2CxSorMKd5NnBoF
zp)N>!K6GT6B^<~bYOUmAq?7giE?TcgX5!(U0|xKy(02|f-cj$4ZA_RnOsmX&d~{Uv
z;@owAYWNqpn;e|jTt11UpOe<oEsp(45CkJ-S$c_y*7(yTwjJa<OVniN5ufME&!B(b
z1;xkxY)JbD&&d4sK7&(|Zw4h#Wnmp_;RvaeiuajnLY>DA=Gi|<SVx&2o7YsGPpHo2
zKUL85F*ga^8o=^`-s#E!ms7E8+Xo~~KeI&Lv%e>UyLON3HKx(K3TmHfjpQl7@E<fv
z_VKN&3-(V)L>nhnnHZZ(Vf|dH35_BC?Q@q@ep4<p9lgo^X=N}sJ9~dJWMtI;yagCi
zRLc@qyK(^+S7HTxj})ZNOO%pMW2_=-p-CFVf!PJ=Zfj|A@2ocBlDoT4K$(L>164-!
zLnR<5sBLN%Q?$I_tpuPI@EX4B)%0Gp4#W8@ng4n|>!Oz#yrL&e&8+_Q*5LvddZ`|@
z|ALH_!sG$-qkMQLPx<qkG@VZLbn({kTFu@*+h=*9(b32YwFt-6Q+fSP=b`{h!U;IE
z^!A~O{3?x62M0`)>Xv6vZ~7BpM_GZuAS7OUx7Yj4R`o;J4}m7#9UVUbvD@ST$EG64
zC_M<aq&HyGSdz5|`Gj(aK&@@nqojhVW8e!D%|EC+9UsAZf3O{?uk@RwsBV;|gDrA=
z;%Cftfr}<^*6a<OtkJot^Y4RoI!2_U^|xMxl!!dPV0tgKL`2Aa-?@Sct*+hk+eY?y
zzf9dFAa%cf==aLEAcqt<8EKbfIE)r#a)K1hv&RZ6h}H{SR2{blGl`H8>WV@+McA>G
zl}IJ3Cpt{`SKQk*F8jk7J3Cp_D1ho#=vfT(OhhJmPzZHFEP%}^9?t-L8L;ZX=a`$N
z^D|C)diLp-Uew>g1el~2u@*!aUuA=z&sqz+u4AI3w%dL-<+AQrQs}N*^AX}*%DwE^
zG6wfX1tRo3w>jzP?Q!ofOo+}*mA53QYEyHcrkUXFV-3Zaz#DkLkr6A3L9%LKLD2Sx
z4zwJ4g2FhjP6s7V7l7VL#52H_9FAIX%{b~h<5eKJ8qLCTGOwLp+nuT07tg{jSh3nV
ztIIajDoj@GLClh<3U|&17=%x7@I6F-YDm8hcIJnw9Q{A*va+)1AfHBkV%xQ#x3!w8
zf<|ZcW}-^~n$qac(jr6A>JOcdG^7g<G~cJ_=>@|{geEwE6n)QcDiX!yC<6b>(nhDa
z=XuPF110Us$dJFme1}9DFM4W?OryRUTKK^>mrfm&V}ebRv)ZFILB;8twsfhQnVBh$
z%GUsX6h)hx;m|SuHX0rRpaZR)yTnX21K_cDJ$rmV<$};=^#@JpUbyOTJX^sv_{^0(
zLzgq{XYK~8_+79)O`LRhvE72SwKJjv2b+^?Igj*^;JDU-wAWPdo4IS}Xn_$HWtQ!!
zpxR?k85&BO@K)a8YJa9Kb(cXKf&NurCg5_mU7aqVWV>aW81c#icg-gvAD05jVD)^>
z#xGWBu4ho$*2wP=g9-sCpt(rw$-kt6T<OWjN8C$Ek%jYjUIfKrU@4RNR+z(r|ATE{
z^Ee}S#{<uOQR?Kl#@F-dtdXTcf1jhS`;}jWEZw?HUhn(+z4c4=bMT4>(q;Zb-6y}W
zrF-@E2sDQ!PGu`S&&-(`*Rj)=ima{*JikB$Q6Pefphy)+HLEE=mYfsEpmk9JNp+!7
zz4jh^>5m_TX($Fl9cw~DEC?{5?A#rO-rh?(vp7wJ9-0no76QspqdOedvR?)XCv0k}
zl;m`z^ZGnc{1jh4WRp$`s$O^U*5dH+=(8-Z5Xa*pEM#QqBjX4Y9)sk%A6^^fXR~c)
zIxa!F^Oo3^&4G1tQ#L($lP0sbbj+fDMDlE^)BqkW>9~D*CdviVUjw*h2mSZ40csb$
zNu7ffvK@7-!O0-~WA)&|D26CT7eNF3F;C#69E<YZ;!(;|t|hUNUiq9F@O7yTP>cC+
z-lNa@dnc98osf*aP!K?TOSEyVu7KBu<SIw(mTXDOsC{hc_O9(z!_yrB1?}v+oYI#B
zzWBj>ugi|<l~4DK<-+u$i_PM~2@X*%bV-J1&CNxH`hKU$C;5I9;EDic2y<)Cq9F}J
zLIqcP3nLXulTbd`ALJ5hd1MIc0s70Kv1-srxK&~YhojU6^uW=P6uI_-FN|;aty{ay
zJ)ZZA&J5=3z?*aSi+b%3o2bnFAwd5a`i=_r_Ol)0bA!(Q{D<26l><3$!6%hF@H+dJ
zlXiI_Q-=|77l-lCuDy|oYRxvxKwKCJpnt#kiqhV_<Jb4~d4a18wF)-^2_TWjfX%>y
zqYpZe=kxpMGQJ&0Tp4gkP7Q1$;FIa*InT85Z3u^C))u=14>~)%AjChw(Yw05P{Y>N
zLMktpA~CEwZ)1*_9!aq+X#Yz1qZ(eKfxgD(da$OAx$x9hbPf^zickShxx#M}-r4~s
zFMV+L)q&Q~fzs(=q{&I@N*F8Y%S>)z(BcfL71%><k067DEsly(aNMle#sSVL=z#B}
zUmULtf=-3cXd_VkRV~DpmKM4l!e7Fz9?RII`}GFC>-*vp(>}hS_&auW`@0f5xaqMC
z@E(UsL$_vl(b~}C#fWvx@jjDz8O>A^!0$@We_%Vol<I(J)W{^mi97UwgJ=i<f^byY
zTlv!m*e!<ddqQ$<Qc4Q-Mpx+o>?1m;*N6#FDiD__uZ_0FAJX&*Z>2I5ubo1hzX*nv
zS51l87bcu1kc0Xn(RW)W&;?FIzBuZ(b}IgQ@{7{i9fKFFB$RUkv12vx_Jzw!)f9Lm
z;ltl{DYLiR{$zT4w5j+D`ren|aM?sL&0dQ~iW^<d!j5W3;#C!3OKBzTGFV<B*F`D^
zec9|v3I;9s1OROZ^l(IN0}DMMi5B-GEb!6b&ka|V0<VM?PDzvD$PjTj8HsAKDxhHq
zB#d^ZtoWiQ`64nCK~k%WLQlf5p+bl<p9A9#5`!9p0%e7INfnCazxmI@CU<ukWcOGL
zDSHS8)KwR1jO6USGf{afyz6dk=)>PM@>KeD{QQrcmbCpz`1a6oPvsq6J)QH<zH-XF
z=`@tPM~`De5=M{1c*7ZhL&?^>nHJ?HT<uJ3<JARlD%it8D#b_+)Su&^=K+-zYVzp7
zZegUwB?D-y6>#<Hfc{SQ&w-=<s9Z92=za}$^*}b**uaW69TE}(jM2iUOkEL>dhY<!
zuRfWw%fK4n>sWC0%j-sWI6WH*G*zdxrPx~R`>H3jxYLHs8dx7gY5oD+=0U4{ZU+w#
z_KB!?o~e7Nm5uzu-d@1i#eDweM|c5C^;k?y(mjdmkz}YsLPDtghqk--PNhEqY>8UT
z9_o?;hrp@@$F*cY%!G;y@uvZPj5J){1HL>eR7ut`VRuZGQG*{{_}4)+l+a$tmAq2N
zXY#|)at#N>D2KYi-}3W%WRpO@YYKlUj~}YX8#jrNe~Q|uya6twCuUMaA0R;H(GBgE
z&h<lqaOK#6CUP$(rZb~*?HN<6`p#I2L@D)%o9w4-Q_lT0QY1m)zyapN8>F&7t044r
z`%Gq%wGZhnjr<sJ)TERCnkr^}^~f@uX!LrIBw@=~e=wn+P_IosmA-SQJroyjVEmZA
z*^p_fSjnJ$6}SAO6_w<Q+)?t5;GPXatbb-^lKwF`QM=o-X1*I`7{FQ&Z^&_5f|@Oh
znpwav-vG20dYC^VuWlIZHbIGCX{<8)PPY2WBYyDCve&RSmtWn2pGJR@he2k$CcL2x
zUa|3;fP|I!rpUeF<8nXl%p*jF-Ta5*m^Ysa1Ery5-qcugH;NLACs#Q?m7s;M3viU`
zr$7JvQ!#S(qoFd&0FuQ;^<bx9tLAjx^*X0WLo$TumxTkq5oC2wq+c9zMgJEa)Fch<
zy58IKch+Y1uJ>fAc|)3)XNW$vUpz{=e_OhlUy>R*wYvYIrqf}mL->pDrLey^!3b{z
zR3xd%=8sMSjtmKT{rWZ9@zCIWXebI^27_i>pFuK0&jo}0ziR7-4&`d51A^+(;UO;C
zy-TRWYOa!VNR~IIk7{foc_fCrH`#`8+{1$*z?CLW6|i!=ND8nQ%D3RYzXBpD3(`-r
zUF~iei+mOMfmDQ}?45;|c!{8IcG?u77!I$Vap>-Ojh0CIj#7+}&Y@-AgM)1va&7;O
zsw;t~YHQ!8`8LqkNXA=fGNw!sDk7B3V+Yqs88T%!II-OZDpM&MWG3@G)j2pCP?3;h
z9+Pm)am>U2T{7Ii->>g>zu&d@+H0@9*84us^FA*Iz)}l&0`$udQ7O6K07zIIwe+pW
zb#MiI7eVz4+-cj#OX4lNFP|QfNB9T?V$rarqe|~ZkQ#(vcpdJQIdja;G30}!@UkH9
z!%Vurbc+;hASXnq!aeSl_3!Ji%bx|8MyH<BXTE)p{?ImnA!Lqrs9zlter`CNHU5jD
zbEZPP6=s-wiVI49Pm@gu{CaH}air}-ZBha~?nOOo)GLp~$#vt#nOFQ&aJV9)>$Kp9
zi)(4@tDi8j>h;mIgrW3|BTQd?8oqlKckJ3!3=Uv~kDfA|Vq#c2@xKJ2TRw<D@9i=C
zk(QR~quAQa3dU+q_4UX@3;AcgarY|jw~!<BM|i;R7D<6{e&xZXm*Jt2F42*=XLTRx
zKfW-tlOV}{Uhu6F{-jGdzApl2JIaTWjph%Q9A6gq-^ytICM_=Pmg}Wi_+?J#_e3Kb
zXAV)b3+1iX^*E%gFZk~3akTw=lzeHD1fCOkNeprlfGUX=t3EMB{wYEU4`r=oJ7&7H
z4W+J@!#DE-##>_l-K!+BbJKZnyk*|UVnTEZ8J>VT)&t<57RZCgPQbP3fvziglP4!%
zYWxFgVXol(Wc5oIOAGaEaZ|lYO5OPP0`C%gux)}6*ZE>3jx(m|F14+?@qgon`$xv_
z=04%+tu8vXG98an^T~pXNa!t#OQ&<~2sO2NLY;X-pXP;0;gQ0m#!6&K`3d{%Ocqb?
zrUNnx`$f*FPX&<T0P$e6x4i<Y1t5O=1od^0d>j=J@B<Y>NnP8^>!21w(7eC?%H443
zhKYUj&J1z!Vs2xjT@FUZ+(9`EN@2o=Qw<3cHjR}oL%uugo3kT@^RaN|<lPYtOVfZg
z)+|6p=%v|GY*vvlOai!hB;m3<FESGV5YTvx&KR%mqti{Hju<HdOORgC1yC-wp&78H
z*oD*<o6=Sp=K2?4N)=|Hu^WTOo@M0ZfMZ}@6CqY($Iwd&2~}xxt=XCRgHh?mHk)*|
zJUZ2a9iDC33;27$50pLLzYnnd8<2Q8v$YR`u&n&msgt0m+6RIR3lR8GA0Wwo21)`y
z9+BArb>{bx&R=ddY$|F6-p0kk3{Y!Wg++W;1cHSVr6dIQQq>oycxCL`FC4kGPWl`m
z4M?S9--QL~Dg^RCO(zT5;LBf%CS4-j>L+mfpyE+fK8!>-U}|!D5tD_SQ!2r7W!CE{
z0Ce`LBzF2a9U3ViS0n4gFvG%AIyR{{>fUI2ca<JHsaU+}y8pw?a=#_x4Lqgg;KrbO
zqy0HIC&%+JYRfOcJpm}(gP0nGJ3=+@|8((A+NVML%6Ua#<!)oQkl`leD<zL6W^)c@
zn7CW))~#{=G#R~hrsUWrzlWOF{2!u|0u)f6-rz_tx%SMXnzp6-tS3))p&hPCv=wdt
z2$TX<^kFOv#7GE@fW0o^pz?~Vl_yu+AG9vRq$M|Eg1t$uwakZC@>VK1x!V6;bFlO2
z9fjg|og25@T1UDEH}c?{1QoC;l1#zMboLWBDe`<SVQ&heya@YS|B)Enuh-I<8$Z?x
zre<5kpDJM5)^c%Q^(!g5XkGk3R|SB`->5-9RE(u4EFKal5Wlxfx}QCJ*4uZg!oKyC
z%*u@PGtuenrd1`GF4j&;`5C!a!Jw~CnV_lfr0{E~1=Q>HM3-B3zv9sbv>4gAt{Iny
zIC(A@w8n5ObDq$_6#Z7WFcKM(ncy3|0YDv>@+IY#pg;MdhEfcXA<oZ`ZwYI9e?+g8
zblvy<Y^(H%!M`f6Nz-jVK-4Vm0acCD=*##4YM;h|5hIt5dwNjO3-QtY`>djtS|pVf
ziz!d(bAa&W#~(1l^b16d@Y?y(J@}nwG^c%OX7UR%kUgL6KMC10dxNb13rUfUU0v7m
znLVZPC}KtdbChthJ55`+7-_~@xQ0-8B!8o+9k^6Pp9~1?T$!R1{NNpmzp@V{JZZ_T
z@Q2r5SJ&DvFtL1N!A#dRtW*Z<45#nkhHM?{oVYi)zF>_(G|W0;lR;{>?gyw-ef~(Z
z7mIiR5U*$b%hnIhi9i*>2T1TeQiv!T6k=EKuDVc5LMjg+rki0AiBY?l$T%@->#rC+
z$zaRW*a2>SvG?*PII$|(l5PF$`uZu~M%O$_g&Q9PNAA#}kHC=ZOwi(mm>Tqir%<ub
zBqaCjL+(Q)BHP-_fjsjN+m|jXp`cKSz4St?#)vT#tM9wAvW9W}1~3F?D{b)j<bg@v
zx%lupwtjxGw0%kcT=1}IJchL*^TdP?qsZ;B#1hJf#@-B1pkxeGs9S%4r^$JO%M4Wd
z_@Y8Q&?<5vccFfu^o3o9+j~F^rlqCz5eU7-c|wLU$e#tmF?o@V#VDdJMb}EbvUlZ;
zkTPI4VOQeUGiI|*#e{tB^{EivO;_t>7xF77^_|d=Q+4Kj%_MZ+U2*)fR22!VCVk7d
zpV__*Q<R@YC1Ll?w|OewXqp5nhy|4&0UIhUm7iwOBc&9GB5@=Xt2jejx#9oRwRAX<
ze=0(400}2_OlRt)7lKJ^?X_gg3vla;oWa~DOLv#7A#D8kFZ_We5k(Y=b6w{c%e8M_
z6=oKK*V8Ecf772?wwUAoJD8WhXUrpM!4NyFv=d@V<lcq2H>k5a5}mX1O{Ej-4b<6f
zZR+<9dpO`N2<?>k{KL^sF&aBGy?)tlbw&Q0n|zP?&MAz84fE|*v~BI1_b5Y=D)(g3
zrXT2VLiJLJ&^{0Be3pmphIgpstWs{fA4+#dG6LNBL2-2wzbe&`zG$Is4Zg5@x56Xe
zc7g<n)J_7+;%7A{!G;of^Kl$^JHsLI`Z#vYm(J~4KyrNbzlhSF5zG)($OCi6iokMU
z0ZW0~;a*ak9t9j6P!&|at@Pw@G0I<nX~F;JzAbwCKnZ7M9}Uyf+wH(&`w++nXMF)M
z0`*&`$YLW!Bb9sBV`H8@ZAaqD=xZX}-LPAeGof(2{{UgSAL9R$R8~Tf2=Dj=tNSeA
zSbKzprMUTg$Pa)4)axEy&G%PdH+^HfD@)0m=)BN=>ElA;oldw)*YU*v_~>OL``*#I
zppQ>{#EPGQh3|{lkm$@@8Nw)Xiu~@?U0U<ch79Z*eEMRi!F7}G{u=kt9;V9g{|V}f
z1e1>@-aoR}`r#3H7-X#dwM#Tqrn|`F6eRQSa?_BRtDXm>#qA=to(0eUb9<4D1Ym-}
zj{69U&DL9KjT#LW4#+un2D~>DK5R7&?`T@vs_Bife0((93?FZ(m=LZd;6E226=c%M
zF4rv7Prr>n8g?c77z*n<9Da6PE3H9T0;y;wC9PwQwAFQ8C9ky1EM7}%H<E#Ze19t<
z9Z^}SmW~db?$&Ha07`{Cu0~0F1+{o|42+nQo3Sf9kasX&QeMf*1Xt!>X-M7nd59A{
ze!=ILYv<5Q5s4CJI^R#GGnBFV;R$fiK^`2wAU#Co`%R)cfjnX>_QswzuM=}+5KIb2
z!@2I({659(Uray0{ta)lXL#y2Y2qRA65DP?Rj!?im#}?1;Z>A{t*MN3yE-`oG=?Hd
z5&^EOb$Or1w0rq0j=0VXW}4_i9bVvcn|HevcxL%c+`mE(ACcO#DS(|-@lwOyaTzd;
z|Ma<np1*lbeIEmMGQ|0geZL$4rb_qg_fD$@#t5%pafv-M@dV>hy1}31fBw?iRx}UO
zfpp)RN6P2RS!uHVFHiIMzdFIg9CG~lbl3PBRxTVVohz>o#VEjlR=HFYAq+<jEftb|
z$r3#gcT53MMpg@gsVfJNC#Qw^1yfq^7rpEZN5i5SPUAz`aLQeO-|k`m^XYzjZ0za&
zkwK+91QR2h5-Xf%-WY%?P}hZ&`ih+vZR<8}yN|X}dk|z1oh+881D7v{Xt*Y=5$89g
znPaUg;P>l<mwQGN<)IQGa~OtbeN*Z4f0@}gn0%`OFawpaL9n)?Np-dmD$_PBTN^_D
zK~{s0mfvRQuu*;_f3R&TK-n=#Wj?~|W=h6(%q`|U&|tx|ad;M%DIUKU*RS>m3(g&x
zf$adL{^WGF<&k+7$OZDKYZ;9~j010LWISS`F-04XH$vF|`}X|jC-|u&R=+E)jolrx
zC}iw3&O8U=CqW1^?_0-tTn;;Z>gLUx<TnRaE*KU|PL(k-dMj$AuieZ#jQpb?@>x>I
z**$<$gR?e#$5X;noM(sZ?@XUpb=J{+wh1>-3(t(aDrO6H)3|a(RxT!}4p!$vX}tb%
zds&J#ReZ1?Q(&2$m>a2L#Rf*ynqIFr<;Q^o@8h`{c^#cZ6{~fg6*bX2tC4lXeQWOh
znNV;dn7R`0N6aL^7Nx7Rr&JUC))NkebiVBhq44)hlhcQCJtr#19!&sPAegWTM>c>-
z<Mx>BK+1MhI}XId{$F3Q5EkU;LC;(sA#Pj`>4(7g&&BfdugSU}QNG><Sy>uj^-9eK
zbKQS72wRvN{bUTP?-0kC=W*qn1Rcy9FsS>102X<9gkjuAo_qLJE7zj1Xn+We^mCj5
z06PyYEWmKku&sA={PSn*;b+?o`E5JKxMU@SZj#KLre{Hl4vUZ|{}Cvpov0jh7Ow1z
zkRM^vc)!F@{wSHuzdT$|L3P8n|A9I4-tl=Ds|z%ch>!2hPoMBCJk`5qsH3Tn8#)ev
zLpIvq093v*i9C3ab_GduPLE(8ci96e{}Cyl(WnWK+u`vm;PsifM3`S!tK1Sz6|qv(
zKER<&jHnC_cEq&jr<~7R<kb1!leQh>T!G>22~Qu1gXWNnWgh$z=GyMxpZs}5b|3hV
zJb*%gk4UnEBmk(c?>_8>A^=V8b)Uw@EJo{*N1qgVbpsQ#f^QbDq>Ts>6|tbwKtHn#
zM@?7suV~kis~&Oq1>5jHfFz(`9d<L0lM0Lg2--Pici<|cU<fi0nSA%K;$O_NkzN8$
z>hK@vytWAtShaV(ifP!Nzr^1F_Yn^k_oA=>c>odtb?v6g1PAJ#agVz3^ojZRt+>iA
zn7SDOr9cs{q7aX}7Xq{A>l$0P1kA9mY&8)lwP>}a;7_fWnQ_Cg61Ez^K2PW<Qk(lF
zZoD`AXXq?4j(>cxp9x2Lih>Nj=9XaW(L`#=%Art?2RIITYMem3w^_&+b~&`Ol)g*}
zs@fwQ>+!b1zUS`QkyNFowQZaJBCRXpN-Mob1^<-sRKb-~yM8N8oG0T~iYHOsl~73n
zQ~%`VD?6N+@TC8lwY};`AjrTG63CiL;R<IkBEln4X%v~fk{(2OL$4RpueD$FjI>7l
z?Is`r`|>qNU9%04A~EIdr0oyaDX=Km2>oM=>#2ZwSpgIyq}kX4^^6U2q?_@NogYs8
z{<=q9Gp~DnK4{y1PWUUy{WgW(D$hm624qXOS2$#f<FK*gtJqPIQC#Hy2Tq5yGm_&^
z2eVjjbrEXg8Z*+-;?m9`=@cRB?8D4oeICgD24FRBQ~r@{pMB=tZ<5KUaevFL3Sd3C
z5>gBI*Y2k785RrxlFbiGtzUO}7+H+N7FE!>erUF2$a~Kis_(;Sht}fo7grylCl@iO
zfMDfa9`Yy+N9zqQR}Ig75<In^OYjICW_F)hKLlfdt{{cTAD-5=1`ehI-q`|x!CgQW
z1$5dT<XMOy697<<H7cqbYGtGni=o<^OkN?R+c~jB%RWN!FY7f!)<=j`&fi7_u_wlh
zZV|ynnRC~s?&7=q!K&s_{y-bJWxbn=G9EPA)3g>RsTQzs%oNOa?7v?Uhx)(~A5h}W
z3C;vS!9OZrdjl~D0jhZR#s6=4qZqe=AUXJ9BfQdi|3}30$@Tdu&}RZ8C#f_?LQ*aK
z4=J7mFK3`_!e{fHQ3Uq}fWjCbp}24txj>a7dqwc0y3eDuz$k>7o1Bv}Hq5$q=~ka%
z+e3tzj(}g?QFM1-+KL~pv^I2#vU<qZ#!R#o3<zqX8bk9V=um>k|6peNQsy^;-I);$
zYo4n;*4pVkw)`?pguW9#EP~|7Db(-XVBY&y+nf-ucOm1)AO`jkBp+RL*#Pa_NiEUk
zCv-Fp5@j@fXlYXI_u%U(Fm*Ve+^Df5hw=5K6zTT6rgg2mHg*@kc{kT8Q%tfm+SYTV
zoe9^EokC#`_yJlryY;2)$PlOmCI}8{s5pB7+>Z=Gf56r00gAifh(zoGvY16+4al}n
z2(0%2SGE#V4vTnVUBS)EqZvO6@m;xLmMBso&gZ;2iaJ0r#nSM29Urhm_lG&oy_fHe
zK^j^R3L|l3krtel2EUX^$ScG5w#%U07nyT(LMh3O{MJC^0%qS8q+5b&k61YnwZx6g
zIl&+2;4YQr+j>fapfLlU{dA$7=IOWQn&tQ_yAAoQDCA_3=#66!M(&Kldh%$R=g1%U
z244eq-e`!6C1@>f{G9aQk(UVaRtF<9*5C7J^G2n|;L4CkpGQW9_E6Zjh~1-FPlR8B
zo)h?{w)=>vIH|Hs%6dvGtLr<V`(74o+>JPqq+Bg$3u$ouu39%!=ze>mJs%*X`m=LU
zmsPR86X$Ta7Q}9YjJp`AB}&>Sz+sjXc>y(@Rt2#QO20BdgobK(4<7gdCEr$BAo&Jq
zjb!H4<p-}PXw}w~W|fJRqI?5d92NIjshlv93xAc&DtlC@glUkaa}&d7r0GseCwx~u
z?+YI{@$q$HK*IGO1Fm%jc!B#sk>9_MGI?+~iokceBOfHB-Ud&zGUP{wDAq>&k>z{#
zZw7+g#TwuRXhP*;+VOhv_)W0w8FSs#-6iHlDP^LwbmoTtl=e`gvKmxUMurt}xN5}P
zA(w3`SgK;?sOD#YZ3q$1V2Z>K>3ijkTg!Lou>k-A=>$GTT^jlM{*o|p<Tbe3L+cE{
zFrv52l)$E7+8mJw54F*DD~V<7bMG)T4jJEwP$r!V=&Xmj13;sp5<twK0uV3rz7_5L
z2{TQ7Z0SWc|9Me9d{U0H&CpF}^6puPkKdu*H>|Sp#=czj;x{w1Bpo3#aMPQl00H6G
z1|A`fpPmN=*)F6vA@`xxD@)43XTKGMZ(I3tZ_F4J9tz&$bkYBpK!VoC`RI)w)iu37
zR>7ahT^lil@K^oJq3dv-oPFvYL!kOP|8YDZPCu8QmzOunnCd~1HUc6R{A{xuCYNU=
z{Rm3xg_42v_xPtc*u}N07J65~q?9nx>sZHSZvJj(eg3Se!vTj&w}P1{0Dn;E5(L=-
zyx=!LX8y<@t!Nq;0o^<1pZ$b-z5IFAAlvc@YC;qd;Xy5KPE)0O=Ai2e{M|LKT)STA
z2&5!2!o$)w!-L#Kf8oxQ_QH_C2B`!oroplXP}M6=H0?#P0F*~Go~{M)M`vPEDzcP;
z?HB$tMAr7-O<cZL1ojru>;M?&r{_zA8D|_aGRQcjoG3Z31vJ+3i-0Cm_T@GEGul4Q
zgEX1`^eqlo?+(w!xx;?&ZFdO~jAvodW`MJR1X3l1kp>^gF`tT|5f-((P`E1rPMEtP
z_t+^5VUbjV>GDL5J7Y>a;jlfhHy^@#J#`MD)OvVt&lCb3D{d{ltSU`N@!da7{#$kA
z2*u&>I$XpD1W|zimJ?0{AyX*Y1%4Ktc_XRB0zOnoh+@<qs2t3w;H3~HHB}Iigs2pA
zA9z37MuFYaNDX?`#WY>axMf4q%4{4-09gz=|J_u~_^(h-h3UQ6!}sMt@Uz&c*@SK>
zr@@gsGsWtfx%Ud%q%G{Wm6r|FOhIjz3v8XBE8s>Yg83kD4*)F&ObBdb+*ELp?F9cM
zCqx>9zJ&m4TZFcQcZ515?6w$4+ra=x9tnT^-VGsXZz!>KAHr4gSMZ#1M@R`WY^=1=
zfdZRZ32HNx6MI0%Ho+I#=zx|~yGA^JRAMMuHQ^wn!zUG(?%ye8f|e)SeeaaAlzdtP
zYD{STb|MbKwSxx`fw*Y?yDNMWlSj5}urlvkM-BqN?gBeJTUvqB9DFN@!jpI?FkF0u
z=LG-HlPgJH{U{1GU;eN@J4m~9yCum7;-M#3>pC5?Bca98Bm1Uvt=_)etY{oEry5=e
zt;cz)(hhzAN*zW0`QVqjh@}Duk3B2#oRE@hvvs{LF`J@EMXKmU(J|<!E6iJ#7m8yj
zfFXJXu{RBiC~t0((kZ5-O3DdnG(>cI@HjN+A%f4))1nqG?e^<XXe3o$jKfuC0bT*A
z415iX!S}+q*3eQV9oobK)@Eo$WJqEKUkio%BZ|_G{(a)W-wJz0kD6cE*<AR1NLS65
zt=Z1+b6v!PclfBy2X1c^DY|^#OIp%+jk<~6wdXIwQsUw!QWH}dVQt*nPLxD);=~|5
z)kAtTzErv!z`D6+x^S^0wL2St@C|4uuLBp5cqEus=GjU3H9hBnOieL2C|^hcFK}ff
zW!u2Jl3Bu&7hSREhz`4OntjmUV&TlSy1}`1!UNo_RrB-lUZx4k&rT_*d`f%bKygd6
zt}W9MPbm48+6up>qOR>tb?}#@A9V*~;m16Oc5Vh;YXX&O++n8&f3vi)sRbXNpQBT}
zSB@>|KmN?~YrZh$B&*}WOpe!$=MG99e=b$>^unniL5C~3hd8tH&&DN}_4gFG;<IW6
zlgxBcD}@UN2Repsc~Fh5>QZSSso}$K`E#j1|9ki|YxKOi#-@!5j&{diuUiM#JRarV
z&^bT9gOj<`>|!h*7MkxBq04aAC|j#w)#ocnqA9s`Rq<)F=-qsbvcQWw7h+ebZXC|@
zq0dK6NePLlb?X{TCQL4F+H`{A%*}DXx2XNT>F0X|R>Tam{Zt+CI&*E!ORX6?Nzcq0
z4+}>@0StNcn_ipzFv=A=e^B=>ee-{w|9ITaV~XBhrj4@4HU2kT(e{pXG&s@mg=Vb*
z%cr~KkdqyQw68e^p-gURHxkv&Z~r}^W_mS@7l8INIsdO$2hMY!l9U2GZZ<_cs--}E
zp!b0}<~%V7Pfp*Ao;T?gTUnqa{f<q#RGt;lL$(JS)mjY4`GvJ1Zw2X23^01-18pD}
zKr2JK%L!bj*35lvoAu-QoXL+&-z1?l>{`-F3=hxadHzt4v!lu1!AvfS*_0!R(giyP
z=ziw5##*1pekc<+<>%^%nmG>`H9S9+YWbzVzJUBzp^W{`Z`Xch>=}v}cgK^5ClJyM
z-kFAhjv$u0#5XCRgsLaj{_7cr&AaLc$J&BJ#Wc7cNcupyw)(@*pO76rx9(tL3p;Hv
z?8+ypC~0``0plxchJG1@>xk{yNOl;|{wiv@dCE**C$<b_d9P#W@7q9M^(+P}w`su`
zx|nEmPU}<!kq))t&x93#$5=SOniIiq@VkA6yUZPIq_J0pg<Uw@(-OkuGI2iW=3bAY
zCM!d_`Cpc1n(2R67Vrx7b8gYX^P>+bK6Q9ve4q)0ie2L%(68Q?LIi$l@g6*2SF1>I
z9>A&r2e_7fc=dVUvpZg5el)l`GV(cjBSBK+sjCxHv7`CX!SU_eOLHnmTT-0Xq*!jT
zJWue`5!sNFDZHzvBBzPI?st8EBk=K_v2ls-+rUdJxe1D%zJu0HDw+XIsvZpE7zeXT
zAV}0LR&vh8^=HyrtZCtNeO)uY!Hzg#cLjgvg$L33bg&~Yo_?3@cI(AQ6yf-w20U3;
z<l61lq?eX8Rjg`7eF=^)VAw^swLqW->Q$9M{<3N?lDn24W~OgIaZX|99qR9YHS+R5
z&xSi?Q5rA4lX$1a+v~@Me+I8$pD03s@s0^kYIV3Py@DUr@+Z*}Uz<3W4zi`(*k*ZN
z(~rJZdyg*qT7GG3&(%5Fc-tshnwvil67B2jYZ)IN9!>_Q&DEd8kWN;H;x0*%-F1;2
z7oX3l85L1T60Zj%-!OZ3Doeet?o!n!xu5AXlrw142$9CTvQuMq`V7x)*)fP-*{_Rm
z6K4-K3YCiK)N-)0+BJEgzpY>c@Zb165mvZ!@N@p(#B0|Fugp}cUGesYPxPwW$UX9?
zfbC!8e+OqHCdk1CwCN<?n`o`_vuQJof25?O1fIaE8Y#rIz|S$aWZJUpR4|^M=--=c
zt*k=q{Z=0XXJUULhC|xHL@i*bw{BPHoSWOiBeIvNa?C5`XJS@5BG=ZovKz=5)SX}(
zHlp}?qW(SU|I?;nvD^?(+mNnal?Mf6gc10hsFDhRB~+_2uNg=)%(GEoEQcHLE1uZB
zB%#2{U8HG-xAdHw>1AqIqj<#R0cN+n+9_Jq{~V3W*l)w)j*o@~;3sN8N!AhdxcuiM
zI=(@B&%6xi!fEyO@!+McRE^lcefOvf9o+Npti8|eFZdml_UivVRm5+OQuFSPbgs7V
zLxo242U-|8St)@*$~-$*b$sIY>?0yU&Dv?rRDpA>3gQuumnHJfS~uky0s7Ym#&<zH
z(oiW%hI%F^@QP4%O~K~7v07l_%=CkKdcjiQ>9_6>+JB0sa7DBC4{Nwt4n@E%<4PyS
z`W{rQVe;qU!|SXGuudqzWFp4BB7()&KXmSAwsa+2OBQT*8r_CoL~wB87EGlYfV(tk
zNph)#bsu4@D!@Z)Zf<TG%6|?9T>oLL{ZuuM(eTYf2l4qZ24CS!YeR%4{fcx$*I4>F
z&d$8ecXvO1uK(TsSWeBy1~*hJQ?|a5a>~QR*VEL)TXHD-<Hx-`!Da+)Q%!ft=p8-P
zb?O*`Xk8WTDQCt&dZiL<_Sv4p9jei4Qu108L0D-3a_7hXr+z&bt^>y>2h^}&47}zw
zVYdl{vGBuh^g{eyU1l_%x#je#S_g-Fe&NJG&)YlLhiN$*-682Y$>XdcqcaA<H9LPN
z75x1v)Vgj{OFB^ne}?PaVia`kEbSikP_OZPm+=_XZS+93o-<SdzjcTMk(ng896D{e
zch1t<8ld*j_Z{hOZf?6D$nz+bT%8ZH9V7!qstfKR3p$@m|FimV`Xv}ECx>ru0BO_M
z)$7#xH^ehot8Bbp^@}W0?c`}mi%U<$(yVGXC$VMcXDDzot;wm~@N=}@!Pyv1>`|Sd
z4%@W4BsBMKP@I<nX62f}K~r^EZ%v3296Aylg09x{&D7a4Y7H`LcP-GK68q2X)7#JQ
z!j8>Zw3(c(hp+gS>d@Ov;rOq8&n!k84_h1MT`WzFfA$POUHDEmL|AHmg?1y;VBC?q
zcoKn=YBJa!DMfb-YWNDDC^3hMv}phh5~x!l)E8+t|8pa~rBxr+EcJ+qw7q94b^obr
zgM+;p0-P8%6T%wn+JwFkr!S4yQf?%^ps=GYqiCC!^`ulcxNlKgN5dw1pzP$W$6TLr
zA545LNZVtyc)Kb_+zP-8=ZO&`QMcDAjT8z6xf%^(3#O|ARGjDA8g~D_0N1laapO$p
z62UJg-|sU|9<dlDU$ZuPwi$Dy-ntZRIj7AX4mZWA9SZ)~W^|e>04jcN;(=%ytREgu
z_wx4EHa1QJ`O_AiWt0Q{<1-AOxavj^JH5-!<CK4fDhhjEwGj!17ecZqZnj2s$$lwL
znFGJ6q8#YTdovI1m>0ZPiDq;8C+iz47JcWA@IVjqbtCT&aH$L5`nfd@43zg4tTQqc
zGXDOwp@C$brK(?-Y_@;lRqJBv{_A@n(|>etIL}^{SWc($^DUke{)6ex*VeT?mUlC<
ztgmFNR<e5O_`C6NX!dKRQhwV<ZC2mP3ebCr*!kDe75>Cuo|&efvyCJ<_U#&OrR{n#
z8k#>Uy1qct0&Z>eJ?2TRSZ@sNlI69<kQ1j;9=HXY03#vrJ_47j@9pgo9HmvjS;zbP
z`zt^DPXvADJX3Mt3w-|Q+EyuMZ(0LOknpIXPid}W|HJ9>)K;sPPSqH!H1o}$b5o`S
z7LS;{&l9&}L?W>dI&pb{T~&B5@=$e{N5RqO;fb%}ZOPDONI4KHI1^B>0tX|$<=KRP
zI=o|zj?`1e-x22I;P}i%ptJc}w4KqCB7cDMFip>$kL`}6tF`9g{(@ZnBgr?kp#DQM
zV{xN0iA<yqPsq-Fm+gfhFni~NyB8l|UN`i+7S7vAaMWIxozd41J`_+I?ITq9ob&yE
z7WY<vVFmZcciDf*NbJ_EYro$##P)?F`hjDtN)QhXzF~W(XjP-8m?w22y)mL+4O&@~
z9T#<T=|{E9CZ(`Y)jkkvgUYn!5TX3}qdYVHLm%3wQpt2)@LCP8uB?1i-J9(J*k2z=
zw?oS^|8wV~kDPto-jCdPL=vfVKQ2_$BFd_HU_gsp6WB;!Cz)BG8q(P3IQH8Oy?#Yv
zO<7+CAAT`~<X6_-H$M+VRlF1iTSm2c<qQ;B)k48-1a#w~1NhZ`*Q%zrmJVJ%wNSi!
zKnZQE%S&c)zOQ|~a6p=^or_Jq&g6=>mjba*Mr5}p#7-91XTN^s{hA5Sn$!4{$<@{}
z>HCfbXM$f^HQ?m%(P$??tP7x86e{o<#BACiESLtbAtD}>-h;1@_b0)5olsY1nc#PH
zZ!igWA5(`UYHDu&RMM)x2bVs1%3s}Bq+{?)=+>JodEO``lRP975X+PjDZFOrU?Y9^
zm@DreE{lIN3h&3_WCfH?l)zzq76k|B+`ueSoWSPw&4A14ROm)#1PYoT$u<88sPt|{
zJ$Op?&XjVbz!~jPXL7OSgYcp6TS<9?BQ?{~gte_O%h@*iDfwKKKTlW`r~?7v{JaM;
ztQ$;iMeJ4_=WK8c2nv(lzSW2GGcX3`(?-dQO29InbCLRm(cwiO8hX|?R0og|8j(3z
z1_gng(8|xX8$6G7ChRVHXTBR8?07@Y>VSXr*Q;ImFjAajNIVvND2KKoJ4!gd%kON8
zd2-cDvmBdGBr}4Y2KCY+L34ZoVu?BM3p*OBsuBo$9NJg0a+j~;<Idu<EiEn5os4TA
zK-ZsR-6V$kZPevjH;sW;;Mor!J`7HQ>d|6iu;paow~i0qPEh;UgIK;j59F0Ape0Zi
za-ix3$#>|;+{%z~Cb`9dz2&MfW5m)yxevme75-iCTR&tJbJK%)(ySAr_hY;0My;$h
z551~9iN9F24BgMjI1i&O#PfUtgz=l0IUeyq?&HTUo(TPVO02}h#K;4)o{U>0Amkae
zG!Jbm06s0<uT;Rj&J*q0jIl9asUZ--*MW2HYa)obra`#HM{Ses&RP9<&J=d&X~4dK
zZAhyqj3;|{vT-Qskn{CBCmLV0klAlH;3-zkEffcWfYTb+8_olZL$cI{7YlYxc?D3h
zq_2OHD}Z7h{!N_EO0{%h23xp{(k4-%2{)*H9)n^Mqk{(zZs<hrY3cvmnVy-+Ar2W4
zpE)Uk+SDV)P9hm#pgWlH!m3`(M0M=^PPdWDbY~9czGAt52Fkf8UZz2U)+KCKw+veU
zr=>_xD`Q{lI#3l#39)jYJ-w*v3Vs7|+P7jsc!MoAl$;zVo!VE{fHQ3I{*aZagTeU;
z^?G~PQ|nR;<52%0GL#xN+w$}`<y|(O#NM-vA@97<2_TuTK$E>_8EBpIO?g%Ji3{c;
z_%bsUGfXGaogKXyrmZ%u?vLeT%R){-02wXQzVWRTn*xzuVOuvT9VEEezcl?XE$vB4
zO3EdTUm<INw0_(vk!ek8>>FsI=t6Ck;|y4^Kwx}Q+ZZ!WgDw<(V0H5yT%}W??(q_2
zh1F1Mb<J!Jn~uY(!O5$69+E`lbm!|j4j&N^PSe5A*DDE%Kw3E$n!9D<sPlbP9S4We
z<eC-b7cN3FmJ_9{gmbM@ln-qFalH7}n3@gw5YAs+*DMM<G3zcmW|Oe>6CulR@mMx-
zhGEYj%eW?gg=pl#1A#?>Wn*6pFc3CPog+Pa(p-vt=^+o`6wrerj(-<J&wt196z8vB
zq?x&8lCiluOaMH=pxc^F&l~`JcfsetxED&B0(qF2ndu9_jVB?(E=yzaFkd=<i>;5p
ztVwOX@65QHlXN5zFTG=+pV<l$g&)V_s=I=;ScVe|_D76QdrD^hGYJ1^WWNpPD*J6q
z+rQ##jkRTOE`}xiYqc5TRp2wG4ASIED5O55v@rW^;q<`p1e_x_0Cno?{4zEPhhBn-
zX=&jp8gcy?6goT~CBYUYt1iLeJOg49f7HmuJ}rY^C48#<`j%gFa8OjT6b^5gC|I3b
zwHAe)76XHR=DOeNb&Yw{dw*Za@d$uCg7(Xce6ln*>Jjm%nzpR+;<&UPC(f|<A&bry
zaKa0WL8YS8087Uc3$Q-Crollt1Ve{z#f=ZO2_4&I2R<T6*9&Uz{esAvx5pR|^(SN-
z@VG&r5oMjLuI@8rMbQhM30=u`tKd|`2>_8uGtrHqOit9AUG`0zHW7jZFKZlbE=WLU
zA>C1)6bi^xe1Xia-!`^?CHkuY{|sA=$>St4wZRBN_-W91Cw%bUUd9@Yf(!cKU@h!_
z|G1tG+nk00)IJ8gqA_rP_bCnxa2SZqD;ThLz`LEl_u(HX%9GnbfHm<hCN+m&1rccs
zXogkCIfLqaR`3Q0Y!7rw07Rvj&BxyFaSUT0Y;D<9)jGce@}l0Ihm`z8WjGrkFm=0b
ztms^0bzV*__p9yE#h^+nEpaObX(dQd+rFbtrKkL(9VJsxLntue32_AI*b+*219=F(
zfJ6X$nF@Q=Kz)WBRCH6pI?7`o;p)X-A$!5NZW>j1ZG4wR!$3b3ef31|xcH6w2<Rtr
zd3qX^Q2#xPY?<xF%2wa_aUV3jZU-ByIBOx!G%DvZrf=p$7)e-CicGs#e!z`y{lDC=
zk_rs^X0;bi{*iMi!)c$#Q_#U=TS=X3qW030(t@9Eb{hip2Q`J<g8hQ55(aatTyHRB
zy-fIJ=;4utT)(G|<=@V=Z8NHr1(xQuqx;D0560@jQcis=Zy>eZd-PoE2Q;9t)_9Gr
zS6OV83}{->2c_uQ2`PdOH`2bp&UF*5o#y=$_TaWEYkNTs2lE&1FY)8Af=<pMObwP3
zdr!r7V)yhZNG~(__YrEUsv^PK0T1lzcz&JHX6Vjz>nsKq&zj^ymw9mY-0f4md)vnA
zS5|ZnIL{F#g(r>nE-vM&tuvbxuI{~1LMiF4ny!5$mg|*Wm{ZH-!d=CskwUe^92c|D
zqU1z6^=vp1Yjtfbbf**oFG5ElFEfY4-wDTuL%*>`|N3<e@9<T08k}ijpgG4Be_j-s
zLfJYE)ze|s3{FnY-);rUu~79=YG2dTl%$=c^VMJ#K`GL>=3CWa*UaS7y{Q0zGd_G6
z&*$!p>}&cdXh@NwF^q=Jl3GmCNo4&NG5Q}wQB#-JVXI_$<3<g7`*SUAbD^NmKZXJs
zG)gxTN%Z*fh{i^PoZMVXC^c=&H1koFopXfDav|4u2ebFq=}0wm<vPRQ*q1Nan6__!
zhIb&tpq;=A=@>o>yp3*qKr{{Q!jfz5Bs`Em(20$&!4>@|x-N;s(e8Y6!r>Cvh4<0~
zx>yJLxT{iI5sqp}9G}z}y6;7R`0j{G^}`a+TA`$>Ml(@^XA=fhtWWC%_YA9=yGRJP
z#jY*7UAlOwf4_~zUe7&|`90&RMn$f59|~{5<K;@P#@?78vUlH^*nP1ohrRwa7GhHk
z{X>pNi$bvTIyE&_KVj3!EI?e8pTTOowkEt6htt|FQ?~{e&wT5(kZQ(L$D@ax0`w$B
zOnWQuJDPvgxO`DL<jc^bmiCjDTa^by6|B!j=dvY8o7e1oddpc^L#+IN!GZr+D8^+I
zn)j^1?dmwmiSwivU3X*j_4g;&V+GGZeA4MtY`Vc6LX<Aiwf~+ui(S2XRRM_U<sY9=
z;cAm}Hg71TkJXXS9gkXC9M_-n%oB*Nji^y4)Tr|bqzTjGg)O%$pY?dTh}{(3{$O0U
z-=bf&?ZL+ytjpju7dP?J^5t+528TEkjs$OZB?e$W-?zSSZce!MsmW?zPMxn8#o_Ew
zZp6<VKK!3h`#JynwnsdGo$eX)A+8Pg{SjK|Q}-$rVJ}d!H}y2NA%oxzmM|ysFmM6u
z0~v8>klS(5#}{JSb@h-x6Tl*SP>=D8oBsb?ev>Bnz8}G|-41SMoPR?1<2gCdEc}w+
z+`F?^`5C{`mMQ7RN`oXn0nx^KgAmu88cS_WH7?5U{f@XZ`(?f_y$@;VY>-wD4OCPb
zbFD+r35R-x(R+K<9UQX2b0itM`nZ$9!H5svntznpQt`m!V|ZKN7g@tZ5{c5-)P!Bn
z1{b4HF#5d~%T<2y=<Xj@Yj9`wv@gD;cim`N!dm(atODeCZU^ld+phb2dw0?|9|2e)
z=?I6eEl*@v#~Zf;n-6KI#;OP=9r{_fW5B7g#whM!R_UbyCkWe~n(g?#(X;sBH^-&r
zZHrc>I)Gco3)pns^m~@V_HinVv={s&c%>G=0O}<Im0-K}ear)qTrV)XRgfY?gY-iK
z6)RnBqRmk%9)6%f3La9;@Mh-J;0?MtlWU!X!avK^O<P&G#N3=G7D%jJ^a`&$(g&F9
zo%GG!B`41g6<#>!pMW{io?O?je)-FQvubbk4r?dLr~*~RY_oH%W#{IVLCJ}Jcq4!=
zK+GVD!9Hd3hXS43xsf_V0lpNxckSiciWk<lvPMI(wl);yqEqx#oRe-N$T;wo2m`NX
z6py0t)8x2BCwXNVraQ6{FcFT+W*?efaK0_w>NIhFr?S9n5#qkH;h~#}-9`g1(kHxX
zAKjJ##P!cJZM^^}92gpU<hg_8mai1*+?S4kzOt4nN^~^n^X1s~?XxeXLJ3C?=a7qc
z#p%)Vw}Mnt6sX;vhwjHxv3+1qEHmi#XBg=GKhr3TcrggNCUSr`8jP$uPDFWh`Kj+Z
z+^j9dQj{kXug4`*%>Vk5oAO`@TT(I7zDG57>pcYd#+x~iB*phP1ItB+DAt)cBLj_^
z#y~@?r^sP{7r^eq5kK1=C{J+sh8*CuIp<NogRar3r`W~Ahm=4(Q46h_QGXpJ`~nc?
zd&iIsP=nyARiuxnutLiR`vOOaUHui?-H77~Ns`S1N?c9V<lx5q(0n<`DCzGu$s^jJ
zg+o8-pR{3n15n1U2@cgGuk6k>yhZIbdCUr@eoo9dfrGF)oM?j>l-H0_oCli|LTt!N
zfsUXzwF2xMLIj7&^+B3|dO<;jBI<J0l7T$#-Js#xsg9fyR;addhj_>M*2sTiFwc7c
zz|5Q9betHB3e8^xL0&mQTxig@)-qu<DM#>;`_$GFKqm~oU*V(Hr8>U-6aPji!)Qt0
z!lVOv$}+QUS|lOj%5;ES#h5~IGX~FMn_;OoZ<(?E;<KCqr2x5K?nRL}hgRWn<Y>I1
zV`#Bu>=w+ng{%$v!_65u9wot><5!Hr;&h<@DG!H~k3xz<6jo}#YO9dhht@mT!OzOi
z#0g(>WOA;`HAAwO)0N{8Ihfm@fx>EoKe?HYmbSaMsf$a!wYZpAaA;jaLjv?Pc+)Wi
zTw(Ihd;l{vz(zvy)?_*lf~%4MNdiB(NTlA<<=Od|2gQbDXe7*dS(pF1Ulb+}!r%=A
zlq+BYP1nR%zE{s;6FZ&+L>~^qvFF@9h^V|?zxX4s;vHQzHTovZ^^fjqwb?vH+xgo{
zKh!uS(lhwptYr#!OA1N<lTf&KxW(eB;aMCD$@7RyvvojT-vpF(96q+|N-n=uv)w2V
zRL#Rg3|rvWCLhnq!}9u&tu+IJ>0(Y2w3Ubk9$X&MGk({vKxdetq1#Y1GSrd6uRC97
zWi~GY?6s~chMf+8q5X$e2n^`~g3X-y>x`OO{)NG$1*^So+4*J6VgF#c-Wlj3D0}b^
zhZMgNXcOjp=WIxJcIo|CK5#RY*0#-W)WIlGNUCmVf-+z(BKcv<^<S`k&^~?o^n3(-
z`4S_zeTp61+$Vyr5W2nuNkUNgrL}WtesE|Ewg5Hg(i0d1Sy(SX^EFfeyn6t;awe*@
z<=KW84&M{%LF&ABKxJSBkXnkxb}uui7_O+Cl~A+G|N6NW=@)7fsW;w`h?nK%GPGh<
zIHC(=BA8P4Tbnake9bh->AIH>OT&O#Iybdc7m>(6+frS9;MnNb6tEls>-W!noHO^4
zJdQ^Rm<MS3wU7Aq-+$NS;SCL?NX4oj&@fP=&iAgcXIDcZBxd}(nbOJ-oSUI8kQ%I>
z2^hXcWD8XPM4Dzi=dplqf(-U4w3?b2aD_BCF*y?&8mb)keM22;7)c%^_Z?n1v+`0{
zb$JKOVFdx_zpm=;bhHvMfl#7Yt4X@SD2WYw9!te$vE52BM~v*kMC+!rW!SOd*2<mL
zUb&BI^X_IBipG%7XwSv}ku;kaY0-#U&~<8k9(;68F!u;!$G>~EFcrQ~1=(c_=UUWf
z!@jP3z*E5DbHBMeh<|#Zfq5<XGojprcnii0-Ds|UwrU;E&FkGHJGn+`)vNHSiLW{)
z@>mBGiTwA5Cn@4+WhguGPV3{3>!qBVTnD~vpvrj!zyhmhV`C!adDUq3g}W>TMHdZV
z_eB-)qT=FKaFY^NcX08nfp@$6uk}1iy^aQmgo4fP-7kil6A0X&;?uISvP`;%07<H_
znA`f<%4n1gdPHJb<PI<P#WhrAp8Yc)tQ++aV8?hZUSJW%w?caYP*@7=LCqPFgm;fm
zr1ii35wUni%vy|+tW#_gXkM^z2v&eBM+(H`l5y<o|BST!34k-C0wd(th?2Cc`oUl}
zqZA7MVTiI3z{0=9p|aR%AUcLTMiGUlrKqUbzyMS*<Muq*wu;9wE?))UZPbnG@6X%<
zZum$9kXXt^x(LBfT~U$etOpe=xnsdkKn{#wU)H@`Sr6B~*!x1<e7k78=t5M4>{E83
zEw0-GD7z+p>>X}2sMAPdU;-~ZiKzv^0rRn2U2R&Q|7X~|DX@P8jzJ~1GBo<~f%Jn%
z3Ed6Sf|1*&N?03mQ;?RvGnJ7QK38sS+nB8+ARv%sJbXY+Q!^U6O<V@kR-@R}TCzvN
z3RUuwyQ=*UiHZbPtL27nwu#H^j4j2yI*;ws?2U`}HJ~c?8Jq)B+R)`jingNrqeV^^
z^5wON282hS1NfE(H4#xRr)7TWmcQsH{ss%#Z@qvW@lh&Z9-u3h(Zn=RZNv)8#e^Bn
zgPt-lt`cF@cKlgSoPPyvw%lHN6M)<^6O|Wug?d7<Q_q}(&D}^l>1&NrYz4LuuQypU
z{uP#${j(<*{eUpdR!MSUc9emTP;7#T#i4UhIaUFiYXvu@1k_Z642r5!d?&)CqN?JN
zPy`R=^bQ6mm)}H@X}g3%$47IMCMKJJpKF^5-<V1@Y|=>*Sg?jf*vm<UqsQ8FXjl4l
z<th*EV%I6S47o&_WEU$Zg{<9YXqm(R6RDNak6LWviCF<9=pmgySU6(?l$-?^?m*B}
z1(MPn=nbTVr@{f+0%3*?jQU%6lE%_E194o1z3+6?W+$eS8d28o?;@WwGhro286+o%
z2orUH?>k(Sy)NZlFVBqeint7?q9|<|hNbC{(q=7w?|12m(q9Ht%ULcZL`>w`w9r%Y
zx;`?Ciil{@?f*RM@ym%~v!UNBD<cpau32ak75Z*v0DFDpzrbAtKXN*(YsJe&m&##s
z0H()~<G$p^c(H}EjT$kD!aXOOD{~<!-+EKLcJgONL+DbXt*Oaat>pH!yy~mY^SHMC
z!s1?cP)YowyPcgp{*Jv9lbOGChf8c3zVar;PCR&veVsoi(KttjHdT<h+5XR)H3|D4
zGN%6%f8gRHTzGc@o1tX+_sepxw^@qB(;efR6a(4H!!DoduL<2X)?njFi7bipil}RO
zOxfR~Dd#6n8uQ!iR<~)h-O?59|D#1ZzOIDz{4NXNzu4H6%mO}FdE`oqRfhHk@O%&Q
zxVvG0dyY1D`Q6+;EUROn>TlxVd}+3yf~SqS$9UTrTGLB$pXVvXRS^!qg+BqdZjs$(
z8*>x`ZAcPgje>s>yK}q=$6}vNGB<mqg>JUqbn<NfH0Zhhy=X#m<agkI)$K6jk%sRr
z{qu5gd-Koln)d(zHBrdDS4Hy2ee57F{vnS=sXr$f%F>*`f>N8jQuG)6Dx0v|KW_Ku
zLa))r4i^K6pB~FMhVFv@%RhFv($xxzLl!~hG)3C0F1Mx%;Ay!td~8<mx0;6~5t3|o
z40QU4>stHCe^xH8JPmGM2*w6ZusP5{@cc|>8mkJRLsF4^fm5h(GHQN=dqEu~I8T*h
zc{NtTBHG|o>OXTo66V|rt@PwFqD+XV6ZEPR93S}Tq_jl?-+@FYrF;C|-6*0caQdyu
z@miZ{om3sWbjTmgG>6R$E;$C9tTD=C9FYtO`jB~6V6qFdu5TxxMFsof)(YV7F@i73
zC<=R*S)igD58|q)7s3Xs1*m;Bxh?KNg4fjz_Wq_s4mR7g*nE<jN!;!-weL@^giF65
z@N#$pdQ!wf;kdqk=O@-^#LI&=H4bzdap3kn;PySFH3m)^S7;Y91@its<Th}8R*NAp
z;RJP}D%Tnhz-N}5?9!XV1xFRQtGAF-H1p=D1&}WD;#G^M3UXgs-m**{Nk|ZLxRE@<
zG8SU#ZW%w)^>s99e@{>yL6^KNC3i5dx=RJ2@DLqQ>R>?02Idk38|*I<oGsAKyf6l>
z2!kBUAn4W}g<OjzA<lLJ7o9KNqn9k_<PSsw9;O36sBgI3diGmGpgl7&^h|_pvf>o9
z@EFTUbH4;VGs9ZEm(NU-2N$Fiwii~WEX?BLa%I|5qHK$W+}5O}Oh;U15;YQC@1D5b
z4WnuG=@R^OFE#v4BnW;AWwPx)*K#%-K<}luuNSfR(xhRasx(6jrS^*o59nfU0Ehow
zQUz-?v`g+qUg3;z2ulKafT2Z16*$8hWSHIq9k7<B=0Sol#8N=wI}Nv=9RS*6DTL}%
z;+@2DykXR}U`Bol@sFcpAD8D_>sgrH71_BDMst%Bp^bI28of8aB#yY&sC9l_|K^U7
zXw78JBbHw_@t@a&zHasu<E+pco2B1%Fjc=tby(EwK&R0Hh=^p|A(yFxu8n*IU-p`c
z4SAXgPf^nK3<GD8X2wI}crMbayzRtBSg=U&Q&xaaq?Kb;u|oUG8ZF!3TZhel<7$~1
zJA3H6L)O<kGbJaFH2Q6$duw0l;)|js^Q7I9%cQus?Dl%ocSmNbVEk3hGCqDX<LHOB
zQ;V>4V_l&ROaU1TO&u{PUs=pqzjY5aN{9WZd?T!Y&X&b^4TvEM@GMd@#mk?fT9+3=
zsb%V?$6WW2Bx5$R!y+XE^r#i5qhc@GH6^JPMUzcl<2y$$PG4XCm2_4C`Y^Eq#Hox2
zmv5a52kVp?NT5)$q1$8!=FOPw4CsSM_*th!r;%+_F4&QqTes%xr@7IR9HB+n&dKGo
z+~j30H%B!#Zxb+h>n8QUgR!S+X$+EM!{?+i-RsHU?fKY$MVwoD1$wU%56p(zf+*h&
z(DEcyz7iM%YZw$Oh=jUx=gvcg=)jmptkD2+T?<Hs4YIEJ&3a&m%2sZt?sMond7}5u
z^tU!A`iS)0cZ1Fzf44arUoAcJiEe^SPe|96rK8PcB#Ass6sESNL4^?L@Gk`kF4KCR
ziQgO8X#!2-J>#<dn#MfBU48CcDlDPt=Z0LmB(YF(s(!H9*Rw%O16Sirlv%A$yygvR
zzPp`twuctFm*r(KQ!1C+NV@NfX@jH?tFhdZwRC~FRCO!7$+*wb{XBW)SI>;2LC!VO
z9Ch#BEh;G`Bmz${n@uBi1lz-7T^A4bhMHme)xptC3~+=^(&eTJ27DE4TN=pmJ*yRU
z@i#NB8nnobT#O(c6%%WhX4Skbk-G0ExfJryzLdC>s-U<u8W7G74vVA~TtRQ`s$3xb
z!ajnCM4(ok+C@-3N{X<S<(^Et@`)lI7=HnCk4rOtokulIF(SUdxgkI7Va?M0akRU`
zD`J_gYBqU;v&V@*zk7Ram1`Y&s@bct705idPW1NE^x~qQ)#;KKl{WDE@w}^Jf3E0H
zs9{&~-AMK}l@xj2@Rq`7d*kny$&Lw{H_LxxZUvn|eU80KNm?xJrOR~tahA!!6<tXd
ze~@!FW+G1^MbrEJ%!#&Iq|8zvjdyOnkvG^oOu8XpyN&M4ROVRRYvl+UCjDUT-sN)Q
zh{PQ$?QcxjKx8w{P|S%FH((tE>dbiIz6RzG$JlqB&QbI8!0l$GU8I0A`1asrrUCUv
zNc!^gmmgUrke=?i6q9{KsJ<XhJa9nK$;3|n+Kyk8zB@+Z<pojuWt&5-#cSdtOUeY1
z79IVh<5tJO?^?oIP|sOg+f8Pe66={r7&n){E;(B%i$5VpFv8!<6H$0o$H|0=hb0#=
zSAv?;8V`{_*8$<@p0<wVWzDZt!YWO&yD%~TR%PO}yP6QEzZN+rlfJg~Vmzp2{ijg?
z(BMfdVsgq`6s;{$!*H$R-m7Z^3*JWXozJ#aL)Spp3U#k~SlgK+kW;tm_xt9zoaA*j
zpD5A)wk#?F(Lnay%41y`kQ$AnwC8id9J%`&e(`*s|M#(L0R~|R$|UlVLDvcjQH`XW
zGRvH(3<ZJx)EiAN>z0r|cpR1v+okvqcZLQKFIL|YM+zUTF1XBH6B#-As--M52B|*V
z43eA#_NUl>=5W|QWFXu2aOtSWl`|K1F5>Mlp!hAI!)_a*f0xE&;*F)h$A;ON*4B<U
z1|vx?h+$J~H(Ly38i@BJ_4k^z>$QVuDi`m6=G5wP_0c)d)sqn_O`d>fVr1dz0f(<=
zhnXqGr5L_`mSR&wuI(2DY}TM*_tQGUHx1g-RNyXdUkQziqIRmhW_<FICU^JVArCdk
zu|15vmK|oTly*AmJlc3zINBn|r`!3pw=BoMMg0aRI&2BPC^xlkE5t)(9~%e9(@qJ5
z_a#7^TiqpG5zx}j51vND(TGs+ejj?_fcIF_K+BfW>w72RVJjMI5`9$lQbz;&)Ku^$
z!7Bw7CWBh;lQRi%Vbu^@Xty}k;X$83sK9_@YdG|wZDBHCAs$3As)5<g0|xFO{0i+<
zUfx_SKoAZn_J@w=Wbc#j2Ah+bq)QxKBvJustR3u|sCT11P9gVv+#|VA&kS_mHEZkT
zS@|VL^P=v0a2Hy)8@NN*8~AkUpL4=xnO}^kx2A5o&vs(mAX^2UC~B3~uzz{~D0xg(
zArj9<Na;xI_2+wh^H=O^&$`;Tig~?^BdL{E{ua9KkBXK*PLq+uFT+F;HX1aphy&*6
zGwaF2Kdtq3J|A}}L<I44kD_cMkjg57HgCK3;=vizL1#HJ<j3S6yC@fQ+y!wqovD<z
zOZ94iJwtvceV>1lc%T~Wvj6Hg$C3_P-r_3wRqtFde|L}MM330Lwc1lDc1sUgv{Ngn
zZKM!VP{vH#LMxLyux|iNUPSr?uv3c`&+lN`mD<5@V}Pl#dK;+xd6XtvNkWV3WpVxa
zge_{uUlfhod3Z{thIJsOgR^2|KJW#8kgy24YCw8iFb)G4yt~JZp>CZxLgK}*RK91W
z0zZK2H5p~b^VLiq+|Zz98dSlrp$BTDiP4Bndn{_!HVuYt5Q`zA?eXIs0BMygwJd*s
z$2m(_?{yvnc49EK8K@QtJJFaYveoW&%P!S;p`Fi8&I>7A5n;P+6pA(9ESUe;B{}m+
zXG8w_!sXnOmI7l4UzqJaWaiPH0WTK`r6C3^&aRaNFuvJYGPD+I5uCtXyj(eSY3kG}
zunPxLU@;dwzNja)tC6e44^E|qNU1tiq%%J)bc;&`GMce3egbZDWjZ}U?1n!l8iNFv
z>zh4YH9-*sW}CgBHzE=l^U(f8cA!0vCnqm23FI%{+qr{zq#+Go`M%2^4n{uba?4Oe
zvi8VGJ?wC0C=(H7g@U$5qP3GXI^1d?&$ni*0X0`m>m#d8CkM!$Apgl6cz`7xTwd3-
zM8IbPWTxYNKhG9w0EPr@&rM6Lz@DDmF%%XSwl8gIi!!lL1{r-}obmQTJ3aKLX*;tv
zIp$Q?M7?Fw_IOcDwj9rLberpS<27jzbT7lAAWj4UPg0UfAOKcSJQ$;PRHRdsosF&8
zS%M>MWO)k>_J^EAynn9VpX>ZiAClsbXPu4}{!EIK2MCpGesC$4-h4M$!WKSbxKdqN
z)E}QXJOP@~ri$&>-{e96Z{GwFSXSoYKhNe#7YV%3N2Ith+DE+^m0P_T8kevB@Zn9)
zDCzck)1pQ^8d2*;H8{5>$1EU)vF6?^*V>llS<zAhR4^nYq{Z3ef42_bq&!g3P*Evc
zsgH<|WF=%rOZ5*9&duQ6p_1TX?N4l?(UNJ37TEHF1kid<1*c(41o2w=P!6BL3Q+kx
zA22+kqvM4yvqfeAwm|*Z@0q#pMnhoBrFHpoK$9`_74fxN%6rn^K+^AaL-F$A;kns=
zI9Ywa_Ri(!1~J)RzuhDdc~L6MaKfB&h<)-J5v2=%)_<Zdf-@n-$qT)};|Q&s`T%Kd
z$*!Y7h~AtFqCh^Qk%#+e%kOjq>e8+=Z|i->LT0qX49RzUR{OumVFZ|O?e+B&0Lro4
z>&@u<=15OA%lx}<mp>8D7~ZW|ntql7kUy_LTOp`4{RPu4JOT0`V4oOi;DS52T^o8d
z-PmyFoqp1uKht`q3!=B(Di(jgRD&<_UvrH)*MWjLb?M)x0d5B$=u@uai;}X$a(Z4x
zH#$~4wl>I0cCM{mj-he1KR_b%BGyGj6X;UF`y&Pkpb*{=myMNxzIy(*(1He%MFRWd
z_*Jy7{A|wBE4`-DD9m>Ma7{Rq-2~L<xFb+|Gn4#@f#z4u_F}{l!S2Z*CbS`;Kaytn
ztXo{9ilODi9hZ2Sc8`ksb}gn~pxn6XTZI}Sa7x&nF@~hKrXH)8=wKkg9^JPG@nyuh
zMe>dXGBF3d<ezbP9w=djt!c5S2C9<0OeKF7{%GJNpWHwA?(x;wGA97j9aY8p=MoP#
z!519wsI)YGTm;lYO*VCp_CoOn4Pnm3roy0jLB$LR=WxVEidn3XXy=5i2pmH|s^x(K
zb@Q@n2Xe&Tu#+X`(88I_s5Ru9)mnNfjD1QmTi-Y1?_>z?AA;hZofdJ`v8tbPL1!#R
zp^(DU6V<tz68|WT(XT{&fpHiHLLZ}TbD;IALj05-|6wKUq5J{zKyH&GAU|(MeTAn{
za#;E7=SC_!JNqI_2r`dR1KB^fO@Ce-Xu%}^#8t|%`+f6B{v+bWMW!V5aTn9Oi}f=-
zVxdm*No-btkgU7|t+$EWpo|2*m1H;^dmr+pR)Xf}U>RGB*%4|e#ywf#$4+pB(*8aW
zS-*g?$@nROrE^#hr(vNem+^EX0CD#ZLkhv+e*OvDr`%0ZN=#ji2Vb~m{CwY*K$4v0
zGK{m;d+p$FNlOZ_tW)Q7+R(QAIYOm}l{Wi)a17|7j6f;H?F6=>ebCB+4Cl}<FRZ|)
ztK>3p9PVS$+%!bbG~`N$FYkj6Hr*ZfJ?gBFy?PpTOl5n^Xp}<r%{$F+?I!GQ*$;h@
z#v3(hXr>sKohvNQs7-?+J4q`|AES(n^Eva0^TE7-CJmCI_Qd%kd$<A=b+6dpgH{?_
zfF$n)?6_)t0BfRwb_XaTT5WA&PL4Jb?g5LD4F+P$&z3^V^27_LV~u5;&g9wj)o9La
zJmjiynX4yJ5APt*o&(UZ&|ePXK;|Tiovh{fo$$6+c6!ScC<Ze_mLn9LUOFU|p|)rh
zf%wz$^Py*2ODG?PGpU$!ZHw^8hvMJKPLK0DL49$OkPBksP1*UVC<he>(Vz}<t24_+
zvej(Z1D53!0H-G(thJnM#yfw=i1B#b(lygNCkFsoHT{kez;aMhq&C0+fR2BgtB~!s
z#6v%yAyjH<8N>eP`EdVoy32pFgcYh$B5Yb}6O2zgovGT8U6%KaVD_Q;EQpk99y2)*
zuNuD{fdD*l8Dqjd|4UYVX@A?B5UHm@lzwY0+nTG}w7p_Lu#KxpgWzXdo|KSV^V}Ge
zbvaPfb*1l_wV{K-5~K|KZ;{5?<%iId_Z4=FayTQ~03(?1^)ywwZqSz;FPu2Ro?=*)
zTI<y){LkQPz^Q+iKtQCwcmFctgY#5QoE|1ZjKMuWyh$3IQ|bNvHGq*TC@5U@eNin5
zY#;<@^Bmes--WL&2tG0Cx7g)8o2I1h?M#=g{Gf1F@Kgpq$&^Afda|!6vr7+v^>WkL
z<z<9>&Ic=*i-V&EsWIpNkGZ!Fi*jrGzsKInM%@MtCLl;7APixl64FSQNH;^*1z1}t
zr5gk(k%pl`P(VVuQ>mdFhURx(=-&7JJje08f4>|aBRMnITI)L3xz6}}ftdRWN=^mi
z^Sn9#@h>>$;R0Nw%hBny-<ymbPMMrta_vkc({S8WjY8C44Y&LaVSAzC7r%FUxPHj<
z>8)>}jbtmRg9ttgTldchEs0-F4y2?nhBr4wcSG--t?>hU2UU~Y&JEj*6cd{&Ba_^8
z`cdB+gVcxrzQ=}QC{h#wW_%GaXwIKBaJvQvVrop&DbL>lp`hT0{8L_6+o@k0`2Kv3
z&^eT;5d{YlzTU9ka05Tysc+K6b})-}$$3^K{hxy#1{0(JNg%Z7=%;1(z*rweJPBLD
zEx_`ofbQ76yxiPmaD6I&)AeGhz)G#&KQl$*ceIH6C+OndafSY$pAN>4=lY<{QcYST
zK0g|N-z0dk6!~|Tci*q>j>6hc?YDMqnM0d4<aW4G(WD@@-yv)-Nv>JLi{b4*9^QfE
z_t7{s!TQ2uZYGN_^0>`|vy+agP1zqHkdk;^S}V1Srj0G;mBzm=D(0gGZ1WeOn?WpW
zpp3P?6=K1Nndi{2v6E024C>Urk>c_0!{(Laq~2F^c1)9t3obk|hJ@|{%WR!cAKZzn
z!SYJetPpXT(zo0Acb8QbhtmE5XtTwG_p=J+H-(<R4~E)!3N&lLbJHJEmp@+?a_>l5
zI&%p0HXbPeE9!F<Az1Lu6+Tfzqd>M}F!XaRW=q-i|5}dE{?OfO;a77-40b0VwFl~{
zoKOE9Q{4D%o#T%4l|b%+d=*Z|CGPfhoIz~JV4C5dQ)pyJO-ds^=n+p4+dpS!4CXxA
z9|4UfMYGB*$m5FNl7_+Q3cW27LVuw>pU?%>%6;r*uEoWV;Bb6_WPH6^b2x;yk8jv+
z*WE8&990w`B=b6&^4|P+;oZ6qgWCjhg3za<>uonbn5Mu?pOOEO?pHEJ`}S-4HZ*_G
zL_<;cy!6zclZEM*s{9cs+Dg6I=O1peqe@m+qB^y0eEdj19ve+-lD=AtcFgep`wC>4
zAI0xysOE|9M^Ph&w(q}tcn=yk{7@x7@sM9;QRe*1Ct^a*+TTc>M4YuBvfI51vXe}Z
zh#+_KOG{LZq3&%woWEn7LQ*i>qHTZ62jLO^)m*UuDo_>r5z1vzZa@ntk;W=8C-}L}
z=n4Q&<J^fC%)tIbKR(B`3emK|_+;zMH~2#7MSKvO$>a8QLF13BDOuG?@G{}X+hK0_
z0rlzn=q7K%_+eK(ix<bglZs(Zg2NKf-u=yyqDclQk(+NLVK0eAPl~8B&^|b*IoZ1Z
zS{O2#8cv9Q4<9YT)yzRsO(H_Z^>gY=b@x6}+*U^*`BdZKqMVT)el%vE@K4MagK3jP
z^QJ|>#ZypI+Yi4wfi#0Hy3M0(ok2*UP6G)}8+rB1@0U4GQ*$lyq0!A?$keJ>(lB#A
z?x%Z0f8ICgspMF6g^3zmo8Fy%$Da`0(%>$-V$b$%AXI36xjX3kKZkY*!MN|c8bL~$
zDQGerij%Uy%}sAYc9%EdWX2BXB*1bpQT!=oBm{Lvpkd9AeEc=0EcDeBRsOy&N5o#}
zGQmL<9!U-SemaaRGPg$!on&OD?vMVv3!e*><TV+l?GS5*R-1{Jg}7pySKD;E?}dMj
z7Cdr@fsuAYdk3JxT^aPxBU$SGYj>~Sc=s?lHPxhUdhzZrYxy=n9NUYCaKuGhR+5^(
z8tJ)Zr|DjNu_20l>ST?)IF{sQo)GEnm^59b8xzlid<5L|p80C^Btv!ceqR&SKd<<C
z^rmm1)WgYwz&hHIo~(O4oLUaIrk$gMXRqo-1a6ma1=zHkd{{M5+<Yzb0+Po&J{B%_
zQD>Z{<{c+;AA--<O3cWX$W-^AP+RJzZ;M9-R3N=JR<eMg=jGmx0RFjIKjtvaq9Jx@
zozy!tNGhPCh?Y9!Nzp|hg8xKKF$@(HwPHbkC>r=BR9kyY508XqIfR)6&^Mm{j+1M1
zilh=_Z@O^vVlCm)whupA|5!Yal&M{S3g;7dh6)$pprfR#8xKU&>+1CTZ$|kKZ_X(?
z-|2)ef6X?Yav-8yhmZH_AY^(zzvR-y(Ne#rqoqnb9Sdr|S1b5he|x`+TrqSPh-x4a
zWqk0Zb?$l?d7PB*%l9PX3|_wSW+D7qv%!_zahE@`ZM%L)c)AKIAn*mHp!@R$k|VEC
zbVm<fWAe<tWARxKI>z|?EuRg5+m{}lytZ=x+@lZIHHJO2pon_J8VgVgbUn1Ik|+Pi
z-)5oly}~d0ew?#n>dd97?Wm-h!|BWRMQl#z{m%DaM|Mj1Rg20?ZEhoCf9+QHqeo(h
zKw)S53)==9r}WsJ!%zvDj|mA0l|lRO!aa<m`dYL#yz=?iN$BIF5B<9jxzZjATClup
zt?*E07<IiF_j7&4O?2qVD6tz-YdP#%ukq5PfJxvHHZx6?*NFXCrudKJq%6shr)z8V
z87Xq8nW{aU%~-@HI~-mTl4IjG?oKHV<>CiEDk$#Un+Gh`+pG43c@%to_!qS_IreEU
z6z;sQfU{uBhy4TDSt{SLgZ*Kj?XE_5KIBq7{lo6rKo3WL&SBn^KQ@Fuk~k?s2MgPT
zJeUrd&)nvg#h2#QU7J&5n-KYbbJG|W8z}*5U$)*Cy#qc1oVClMvrImEce_(&PIjgg
zbZ1d1(gTwM7Y~sSNZ*({13(>3QC?U6wKsQsp@@g-g+uQo-LYqfrHX1i3@@|y3pxGC
z?m?R`gbzAya*XT!jHG38@`Z&Iwh2YoHTC4L+sNCY>wad6o4>0UFc@w<$-%i+`JQ)0
z>p^_c%shNujcQF%A><$JzVt1&EHh|K+8<Z$H_aG5{*W$al=NGc>zitMaW!Fnywd6;
z)+M$+nMr2Z1av#@>|{VUO{k^&WXZF?oE2Vj-mlGVfoXH#N_#g0%@a2e|A*Jd*K!DF
zjH=|ra93(`U*6{`wmJ3m?%=a4VsO$DXMK^DUvHSUY~m{M5Ct9#g$v=I=i(C{Un+kP
zENb{q3&Qw|farVfMfa{bl@!i3wvVXujk}E;&W(DSFhn0)<ggtUo0s37)n9mPTzxKd
zD0-paFu8apRAU{9Q2uiFZf}uoYTpnP^Ftl<_*PWq8Clq&c}zMBX5YVmf8z1JuXyXd
zLa!{#*tK9%jhX>`JY0b?b6cZN)l&6vkI56U2U!Zeq((IXQkt1q>T#(L=Oy^X*?-0Y
zI;mr`-#wpt$^5vHt4Z3_v$B7xiZPg+dMkwM(wL3)$^>3M{?fP&vvb4IsHpaClcl@O
zQrXw8Mp01*(s3Ka*)EM!CAH4e$cTO_!Q@)p_%U-d+BN>CA#to8yD9E1ov1PA5*Eu7
zv4A-OBM%W}gVgkNDZuSu?dQM?KfSk4p}=sMcymIZ-_#FnNjNw+SY1m@M=Ah~Sq1l0
z|7kmx$!pQ%6Pk6!`s`O%4`g%}SInu}lg~p1AY8-sf1s%DRdr(0mrEi=qOZ9&k}CW0
z8GJl2#dX{KPW$Kn96?dI*=2Ao%C~<M^@o;!U9GB^l$$0yR<ILataSF%QL-oqB|!@h
z+FD)&_`)CrsuF;k_z>C3N7R0&jl7^B84U*WoV)&q{ROHwlRu!V*e_{afBnuA6}hyg
z_+ED<D5^Ey(S+z%$GQ+C{EY^(Til78!Ed4WCVQRXcPuS4lTu!vZz@jXL)00pcnHaD
z^86%gv)kW_CImd&Z;d$|6xBfp!vh}<F&FpccCq&)+-VoBn3n4b8fYo{x??I0YX_s4
z4ELM=3bg%eqPQ=RmP=}K8RteTqHJN4Q~HIXz4d}ZH}D}=K#)c$q*$J4_!-+Ja^sw?
z{hXuB$Z~RFXA%_m+%ebgi+?+m+Ix$kBx)L(1y79mbRe6hVP)*xe4|9>iJarO-ws5T
zckRr*C=%A<+l07VdDNb4UC}c<ug?%`P5j75**fgxg4~WB80{X68~C`~x0_WX<`blP
z<t_Oqwn5tBn+o53J21g`IEG}X7s{b;dIb&UKznT$oUHdt{h;pBEkOc(F~pso{hjp<
z8VTcT!F4nb<WAHCyrHM1m-8J?$sJ~T@Y*JEzHDi8aodhrXxt_JLX!zK*Fa7q{zIZ_
zIqHw5ucpGqC3=n+V)>F%I+J4kXo2&Eeq_2+2J}?)i0OT4OKDNMom_bBun@y%b>=^!
z%Drs#H7`<i9^bO7<wcBC7}9=ZxH0vFudI$8Y8Ml8(LJYpjz#+CTnoK@+!}eQcqH*C
z*X>z3k7>@ICR#1&J)#vR8ORMwv%Vm=R~U>aY-0nEPlxuONeFGFK%|X`_USqeOlu}V
zphZ4J%dHUYguvr@A*{!gQ3^F;{K@+#E3#uX2<Hh#IV#_T8#nyhE!jTPN|1Sz>U|m5
z*y-PJc%$W9czV4Q56K5;WIwTKdc+l7ZYP|$YgEgVIGSmxs3+1-Iq3DxB>y^sXI>k%
z^WdFqk;xI0km4ORZH>QeFfy8l8%aoiq?dkyThq8qezTWpz}YjrdPSc8PJcisqT9N>
znQ;7}z-1)XUen@~f=Hi|FpEZWW8aHj+IkWlXRl{nqBxR+eR_@L$8vB4Gu}rBFp`fT
ztOwEuS_{+Bto8n!_kpgBwozvpWtgZ0%Q3knppg^zD*9;g>r~R$X{4FLztLP-41`YZ
z%EjEvi-DJ4Hz;4txZs;~9`S)%rlfw1{mtkn$7cD@E$H9rF66Lljxajr5^ajCxcD^1
z$BK{-u6Z9V+zE5!aU|S-HW0Sx-gV{F`(Ih6%<gl~LvnP6x7R}5EnB)nGp!d3<#a`V
zj(ToAI_()P_tr7;^hzjZ^+rwi0jkfYR`w}tdX){)!<Km&_kQ#ue7u%I3H*auI#d20
zGjaG?-7aJ3k^czQ1iqZrq#gv{mw~AHb?5TtwDu0P^QO`5j~{tBS+c88BR?sL<sQEl
z)aZ-tu}aDm>a`EF3oNYWw2sW``atub<(|`xf~X6_jrahgUAh}ez+A~Vg~ns*skv(O
zE+Mxauy0Ke^5^+E@6G28o3TIgNOmv%N=zw#c=N2v{Esds&fp7A*#1&zJcacu9HgXn
zV{`s~wQ;OVgYB(g2W=E%uTIf=*;${nnnz{KX4GSQlClmMBWo_N2U2QCRS2G~RVJh3
zP7m(3&{^md^sYfwQczj-+L^?o80`bey$kvGx~U=lfKmRlaxpNvM<iLqKnvJif!5yM
zl%!9wJ@K4ji;^PP2l1{owT-erd8ArY8K{?}A|#Uq_{hI4I-A<wIGP*xp5g2qq^gug
zdwp(0&SZ3T;nA<COdZ#KamJhjKb|stju?_ARLjSYBs3=Fz_GKxu=Z0xISH;My#blC
zD&a4*b1OG|HNJg|2NQee6BqWDvxN89><JCylAE`L7@i8%(x%sZ(mGeCe;07N9FG7c
z;MP?<s5rTw01N!<b<#?M$9u_U=OOpq5=4f5_1@R=zOODHSDkZ9IH^a?lUsh5!@V0Q
zbr!WdW??tHe{xsw=9EJO-nr60=u4U5BzLgu+ju{rzRoWjr}4zxEmQ!R_2jS>$cP~?
zicXfMpu&(*rgD51)IVmZCHte$kT9`Ln?O?8l;h$v8&Kj=$&Y`<0d9se7wXpnuvh69
z&)FXqeYEYf!7z<ddzzDHS0IpmAj8wm@1UldTLx=6QbT8EuoZ@{F*{abSK)%Z-iRj%
zEjgP(Dk?XQDS6X9M0@|(2<Id3m!WO1t$Ig}#MkGYbu5-=ynm>D(m~S@%akyz`>em}
z=bS=GCK+_|=@MQAOB@asDr@j<noDodOz=+l;`)p$_<`@3;?^6*rDG;1o;8bhdC#$E
z#333}(R$DNcjNMeP@4|Ji;^19;o;bW4r8b=xK<oNd<)}x3%d2K|8P<&g&dP>Gi)dp
z;}k0&p%N4PNZ4Jb4)y3RI;<%Zh;{hmPf*kTIg*>9*coTMq8C|N7Qhy%+p1er@B2t!
zO58ef33tLSP0F`Z7RijaEwa?C*ccv2%2}2yYMmFxC|tUJmI^QAKURyMEwMB2Z(J*V
z;aVRMrt3^!-)KUp781q1$@o5SWnPsiyf(36t-E0&ZD%UE(96f$F68rwF%BFm{BMV9
zo0K$XqYCceTS33#oZ8}Nj3%YGUahopyKc6XA-9D}VaY(0fMK%KusitNlJof!4)Bkw
zCe95qD^L|}+hbRVvHx5z#Q_(EVg6?I=FZm*kMWgmcPqD{4_=<%9CeTEY}>Z!HjP!2
zbXqZ#Ay#R6ciR&VwZB(T&p3Xn-v3Jd2gT($X@90at&5j)hzBv=dO^oo)HV)0oktGq
zZs`1+JRG*1u}an$v!eXk*@y2j*PlXR;nrrnHv6ymV!sYYW~|j$mL&|_`!wY>9)HVo
z5(zy1*XX28u1<Qd<>-F7Vl|63OcgPo)K;*@3n@<4s4AFBEcN0|vAC7);H{<JVAFd4
zt^nGc?2+gayD$@>-~?5K7b&4#U>?OU*KqAfhGNf-p4>E5*|tRIUpZ9#h+qB7z#0-d
z@>fXInF@m30m2z#-6>&d{Sd;i`|{H}%>gHjLkb9I#@{K#h`ddfxr7-H3CeHF>ccM|
zY=~~bJG1SDs}C`&e&N2oo`0=Do$ItZ^F1NW1&8dBs`t}k-sJ77NUq%?Zo!ocy-=?|
z_fz89(e!O$mbQXHBr1f>_*vyvrWu32Mbm4=KYA64w4=70?Z69u7oHYXI-{bRlneMo
z#&q-c<JYM>WJUt?#DZCRf}g5A9w*2?PP8~rgPW~%o1?m(5mQ|4Kbpnb(}T$~Q^__f
z)S2cSDcn7WRGVEtJG0hJcH0Erf%fI<r*})mn4%h<JGIr8xV7*TU6`@a%x%#Vy(M<F
zv=Q~sKh#8Fr5uW5W0-}Mwja}D-m=t=)W7`O)t}Khw=~|&kDYq-dtT0tdf&)*%@}9L
z`+Zed9zXuzaPGm9VRu{2iJKx%g%~n51RpbZM<J|3i0-~~zh4WD(ZBl68sf6k3zq6a
zKN~pPq(<{lE{6Wye&5!UBHuDjIoS?>tZp!|9WMY?nM+3KI~ItvVfY*=HA;K7M;mvU
z{a%#ZhNbOCOHBbdPBNv-nrqhb*NT^WVKNH}FBu`4HwF6E)*pOn4lp-(_T4<W7xWhr
zqkYPfEO$ID?big+X~10jCC(>vb(UiF{_qr~)P!Y$LMFEFWDU=0`dE&4o9;?A8OF_k
z+?;%_=;NfqJ7LxbvQgl3-J;;83XeAb+(W7sa-26rHRoci#<_l}5Q?U9rCoJywes0x
zm>=1KJqtE$ZzIi$b;h<4JGUPKeZ-^p`{wf8YUQ8wQ(p_`uIT3^60@%Ss(X_77Iz#e
zALq$nnt;Lz6i|J(F@kk$4&s^a8@k@Ig0L9u0MjY1@Ao=To@1`8RkVg{_PuhpcK%%1
zLv_<eAt5-bF`k<Rabo&=&2V6g4W|q^&QV-J2oyhtq?{GinH;@YF&Wov<F-(!%{N_(
z_nMRK=bhXiH3wxo1`0FhoMJxbrOkwD@zqkj>LvT|wR0~c%%nB@uucs_JiWBk7Exk{
z3(Yc~f=`(A%NG+@z43GY7@foCo%0LTq1ojpg^@v1%6T^!Ux^oJGluGZLn{Iep~G(&
zTE!D->E-Xgwqfv&a-`&ooLy`4^-qa&n^UxLWd^{g3^t76UFll@-U#T78GhUP+TqiC
z3%wQv65XqeQ9ZIfAG~@Ks<US~bqhDDbPFA&euf6r`6et?=ge|0w~CI8e6LRnBh?}_
z7G|s%$^b)~k5BQYNE9<t68z(ayYZ8;V?nr@xJ+Dy@~(QZp;U`9TcEmNa0J#|g!+Et
zwBJ>Kr=L}{u!U4-+csD+CMV+EY@(e-q<`+r#<``)*zKwlH%;G$$Ata5c!hSDGuQ*P
z?I3qeBb~w?NrE#tI8gfTIX0NYk+8XVu3WsN*TGbR2NCx36!tUT#(QeQW0+erLDr|~
zu}{+#HD9Nqlh=N{6EODPUE)ca{<=!Lbb19mmnrYsvZaF+=2-H>yklRgsyu#reqOvc
z;#=}gCyin~hh^lO|2z9F_j7*+r+f*EeVml31rCw6O^>DR;?8mgk6x#Bb-Ge^^{|OV
z8sW4)a#Y6k#GSgqC7to%*i1b?(yMBAvVRxc3oUH-ZEQPvoBUOD{U^S0$~X3rY*v19
z^YxegX{cJ~Jkref{vN?6C%%9030>vrhqf>a#Ev-=pE{Q~!k1n?BNPcfvb~(*{9(F@
zvrm1EuGDx(U!O6s`!0pO4@p((iMiv1N@O73V{Z4M-0K@Ub1$w<<Z%Bq$pBx#h3-g6
z;o=M@A3K(8!BJtP{zX0nUTGVAiyxHF;$5R_ceI@i4IirI&*8SH)l81N9r`Ru8IbQ~
zo^44!Fg#%IbEVe%rQ!g-n%|%<k~^X4{7O`dqi%I%SQG2X4PPR2XoOnQ*VRI8OQfPy
zyhEqXo4n})!SO_O;$NY>K|2<is$xlmQ#0w3v~XrO)nX`^Ui5kSrY1KBOGfC8R(I$L
zp}l_cp!5A_O=o=wuRbK@-cO;8^SaYUZy&lAOmSWDmA#FI-R~YC#cGNxSGdO~D3WFz
z<^&%2_St*Ob9Bk#we(B<U3HOjzR&ac^7wG4D0fnb(yA6|T~nJK=bimzN}=w&&x(O~
zH><W0mQn{08QwirednOsa!(u{uhc5s772gI1o0cHCc3jm^lehNF@2o(eXk_=P;y7}
z`yRzVXd?KG`Cxsyfb&^^o&Cm1MQVq=zm3IL)k8cR`eD<AW0B4x_ui?4=EVt(^G7oW
zPTRY=_f5-wHn{#HPjB(ABSu1zjYW;-Iolt1ljs@eT?F{x!OF5|-DR;?xBAWI<m+)i
z@*j<<e0weewT+b+_qwc^mgXGlmyM@n2$d~mTst#@e&rM4aXcp~JKL0zIGzDZZsgUz
z&DUmNIFW})F`dJ{{ybBl)4RPTsqdzl%x8~02>UK?JzcQVZ#yD561ochMq$?W@{#w$
z!zOhlejBUjhB9t`5DX&5sh=)t-;$l%4mDttwMY>;JJKOA7!&n+S3M}r@xAjyr!K4c
zR@)>ErAcv_7ciVy0xo7N5^aw{(HqSmvveI%Yd({WBS=AMz$0j8m%sc@20HhlTbm*Z
zB==4neTFiyIF5(yDL-cLjuaAq;W{$98J5+6JS2R#TG|TRi{$OW71Dl$Lq_~a0I58=
zAGR<mBZufq-`4YXt$V@lhHSfZ;&D0MY8DB|9&zi;ZC2j0S98@VPN`;$7|28pG`tX-
zKdr%LDH!GU`m;?Dc>J0_ijuYFdOfvmvs>q36Oh+_bmg1+jXBcMBgs2GAsNZD*SY6n
z`tuT|au|u!?&!^jX;b35sd8Cah50mg&^*_dQlwp#COOjdN1nP_!qheLGS>6{wi!+3
zdp9q&Y;T-B>LhX4hVew3Ks7>1R9|%F)+9|hSbv0&czTU?zLaT8FkqdVXONra<7w8+
zhl}Ox4MC!SF9Y$t>AB|)ohk-@?M5F%8%l)tHkzg(LVL}z4B}mA4dZH9lgoV|sx7P2
zVp!;a*$airKyAufw!G==ZHVms-Rc`aDr+zvZkjAW9C9y2+*cuVVZPM6u}o5foBr#0
zwcO7v)(s*2_TWr?K#wf8i|9Dxxy&OhEgRZ5j}PhC99uo@jUmhRub8m1d&Oo*I1=M!
zugAuZoaypCu0!x_tU55YI8JsXvF8#MM#g7GU51}MR6Vl~_d08vBd12hs;`kms`%36
zVsBNKPd+u02XVcNFo2m)&(AQZo?DOnuJN>{wCtXPb)3di*r~3(`y0=gU)-cOn}2SG
zL`pq^lJTnA<rx*`9}yQ0M;UevTL0{o``Ma(X^`o#qK~AGp(B-6Qpz@EXuEl}<IdtP
z%UJmZU;_Iz9oiqn;OxDu`V6Z`+@gazh@1u?P?H&KMF4=0D=1kso8=EA0+#D8V2~OG
z75~7H$V2O3qbIP*3X=Ff2HxGI>iA-!2Elcb%64Ht)|jq3nt?3SA+>hmsAsn>!5KF@
zqq=c>*1aouXoQl~&Y<g$Q6I-{SL$nlBoCyGYbLmMY_biL#^rP-pK5A5#%bFkvTvOl
z8e0y>AHXO!X6LX=Gu(^B7MWSdieU8}xr%v<XJ-WK8$m&KmY@|75wW*4dL+D56z8Iw
zu^32^Bh*s}*LbI+P#Zs6yB&@aJ6I@k&b*{G*!^JV8l@9z*IO}oW}09eP<^URO5eV>
zc4k*Vb3o7J8%ev=CZ*RPf{eYel9-Lfc8DC49~8T35w$p%5Z7#}*!Or6H4>T4taS@~
z74wCJo4zPUYzjp(Q4k#jQNA%1H8o^$*#K)*L_pbB06};N{D2ru0Q)rB*J3y__OmzS
zFG^5o(Bm$dPXNek8-|Lz;~RM_TXngIj8l;7bjzvq=Vb2sgzT~1wY7?@w6wa-;aId&
zd-Q`>WvAY=&W+nsi<NDnu9YpAOq;RFs=|wzWACz9=;e(l>x191A{Q@W$;t3Dl9}Dv
zBwuRH`>05%{K9m{(tf^sT1aFmVJoLAUI^zTq@7t766Fb2$!HHh(nMh5%xKTsAhNh1
z>?Gsfl=a1l+ALk;<q3clv7>($O@A;rpx4Oe1UBCHxS}t4Zq(1lPh)pJ9U-!xKNzJ=
z-fxYqlbUi3dwNl&HPn{jyh4=ZtwPDDs`=X~KX8j%+~X$P-!nQKDn{x5N<_25%<fU;
zoJwSRkiD3X*`183K3p!c;Bjg~K4udM1d=5a)7jJI)9J-%4O((ej=cXlpZlx9`6Z&#
zB0IS-%7jvLOsE-@mkYVdqFk_=pP6=c4{}E=mDz1y@z6*E^g&x4Zc&D0A%+rK3R1@|
zy=v8|gMd(ZUdYK`{q#=N>xpj6(!FAA((;!U(Hc&|au-XER4s>XA>tgUdc}ebSv)F}
z#-irWL)8$(LnX+1GlaaKHJV%BlnW=(pY+tQK>Y`=g;1&jhrOq*b(7{AzcqUhKi}YT
zAPK9V{BnWElpId{aJa<Lzw2Kl;wmPy_FVY{Z+t+h?>nq&cmOWyu<e~Yh~Y?0JewJ5
z`YrcjBvv-o_RZ}!WcajcVd2OTmF4*hKKJv4$_7YHPtH)uaGZ{`W^Fs(?&~<TG!$aw
zCL|8)q3q~Ln#iO<<d10{S&n>#?qCj97+p;4xn0e_3-7E`>#nS=O?hc;KdI<O*#%JE
zeSw`K$4t!vRBCKdI?YT@Q|CSw4Yry#v}<e{c}Z;^n&E$d<x({TV3BRLFD61c@M2&*
zG^$>XYNU~u(r1}1*tsVwBHUQ0j?FQL?O&DY)!yk;UMMd5HZObaha&VKsv_eQCma+<
zh}VAPjb*_JkiHK;niVwJ7wnvywphbm(@>wyMewQ-!qaD%)#~;w^@dHCI*z<>MT#fF
zTXCjpVe#~y{XtJ8Wn&B5uM6EqxNu9GbH2@@2WEyjY-)EMiY5J`Q>7VKUp`e27OM}w
zvXj+naRU>m?ReEsI=eJ%A$2xrX1L7WbZCTn&!yJwwX;J>{LZ%BVj&YzNK|M1%%!<S
zv`$vC^4=vGBTtP7xE-dW76w-$5q>q35U0f2=S~u(#{vfbvhy!qXcP^#E6E^Ae#w<j
z!3kjL;=M&47oh`<33U$U5_y&%)YJbZIBL?M+|OJMkFD|NNsK|v=3F3K;x^ddubh&H
zGYN{&lDQk^B%aP+Ye1RNx!9-{_KID_Ifa;{PMXkozDsj^rV!WWbOEs@7)+`pE!i?@
z4r#E@yExqZ^oq{uTbgZsp;~+Q=c7fT)X&Q~t3`H4M2rEzF&hIolFjZK;m3Bn9~Dzq
zpmaeRt;QJ0qP8fJ-6QO6Uw~qu2BS16{e)PBhsR&E6#Lo)Cr^Im+NprAC|aN!Dh}|h
z^<Qr|W?w>#>Z#27dVXZXp*&|~<A4#^56QmZgxDs#yM33+mGV4hIH`Zo&HBA8_9AQv
z@m5TTTSkh_tgD;bPi+6d$qs2;5psHz9>X@}=PW!QB;`{&^px;;a;_!#s&s-EhC?V=
z$St{>RDzu7r5Sw&YT4REcRZIxb!Oy<IKoz4KT<B4=2CV@IX1Cmdg?H?U5b7|g$pAP
zhOtQ3%5|Q+*Tfa3q0M-fhRxnw@^4$J;)USU)Thn1Z;FSd#vN;%V-j<m%~LXPDI(M(
zrGC$U(Lbu*Hx1vK4dr;Mxh@<2^auVcjTrr*+yu{{08)LaP4(9X*Z#nhbtz2s+f0Wc
z_mXeyxw8II;kzvCqmt3ENyXl+#&Ao`ydVE&w_&tHVM@qmX!`izm*Ht#-T@ND-iY9#
zrDE5N*`S*7q#1N_WItQc41iq15#5-b83V@cAA(S=vI5*h@lzt(nOBV*9F$Q3&DHYV
zuD$Ra)dMWTV9217gtsB;xa|dm2J|#MLQkI5_Z--#N=D-F^l2)OQvu(K7F$rO-G%g)
zSlw>5BXMs`)f~%he!hWHC}czF&Di+Z_^6#Hp0yUwYH!Y_Zf0<|Cu|^HLOno)(b<(P
z(`@YEfTNDNv>h$XsiW?}BiQ?dW4`q>-vo6W1Qcy%Z$iichlFJ=JM3`I#)U^Ibi9l^
zz+$~oG2y8Zv0PcB+q-dKY<TS+WcMyj94CaFxGimc%&0GYrTEW=u-fNbjDLzX+9Y+0
zy)+`=LVD%kZet;9^U5OUwyia6!a0wUtXCg4W?c3jtE`nLJ?lr@-XKP|H{!Yf><CWP
z<7$$%W>mYAhsM7#nWH|`1%U}R0&uhvjxp_HqkJA^s+^-9D_2L7FO~VsBxONA<@C6N
zPOsc_(H`H6*vWhEyX1-+#zjmDaS7474gHIDUaT!4io2Qtq`ZF4=AL;q&g0fUr`0cj
zLqG>}^aErN+JM_Suv_gzZ3`qrsi|HNJ*gu_pfndcO~ux!mG41(k8AeY7)3&>ufI`z
z;0R<EybT%+ssZ5|sW2D54d+2B=}AbQZsio)UVu9Z6dEGwSGeb6n>wpIB|l@ySA^q)
zKCH4{I^w!rUq;EPZ$nP-r2QbVx_qduYA0^G?nnyb3F%_2ydR&v=X9%BZDuMW<bLGa
zQs5u_gje2Py(2H<>ot^U3fOGR4Ec<tH7%Xu2Hty3GyP%A9;Lk^X&)7&z;%&dHk4Zg
zGnsx#-ZEzLxmA4!cZ&POIlI@aM3%FIIrlEwX5$!(CCnC`g~z8OqvS1z&ghOAUq7En
zJKrP$w@Pj1>^aY-4J`w^l&HIX$aM{L1~8HuDYs?<52X|in0X!YA9-$+U}g6*>gQa>
z$lRZ5al5Y%#>)3l+O>8OI>M15tF3^P_^+k)!b-A#<41EOhj*geW8THV`RAph#NkNB
z9EsWAc!aflXr=~whRVheTKM_#>`#kx1t8l?vI!~3-4A=V$Pl!?YBeK2JdOb`9*Vs5
z(}XbzAV&eYf3!;S!lQy&55qfXokh+FJRXfZ4&(^O+ZOR3M!vm`2*n4#)q0TIG5PnJ
z006wKzo6<AWP|2ig#(douUmfL1Z2;>k@9YnBGXWc(_n5_FS)W3%3-5<6AR!UPOJ(6
z`F_aV@yS*Gp7^cos}F*yu{JDz-ZhMj)c1MuO1`r}i|bEkT5#2C`kU}&&2?S9af9Bl
zc$N&BzAL)U+0%|=-FWXY%F<4}9!rL|nL#y+A5DE@x`p1jDjh$bRmE;C5<)fuO8P!C
zjy+s>)|%|A@M8nL@$nZzN!ed!o+6c>MviFv40ACA1Bv5ltL~TLv4dF&m0!$VmrMFg
zQ`}8!m(RA#n;#qTR9zl)zLSkkmiy$lp%-=~nB?j{$Hyb{PO6z(hOagBmgnWoYdV--
znI|caICykrHwC8U++3J?>9uT^6eSsI7pLJ&;F84l&^}M1O0<nJ<3bqp5sG~e&E5F^
zI`xs=^%@bf6{sO>5YsBQ9urT~D%S)yS1>B!1FV=Fw2??Z>rSkb-W_o{mN2$IEJOap
zJ;>3SdzeW3C{t}gJ%`2Uk-U$uG0D`o&RE{Ob=!Lqx27YujZcJ1a_~pJjnrWpU?gQs
z@OXa0F3|2!>+2E?B+=_oXH~JRpMAQ*E$7IAw6^1~e~^!>q%?ohRe9m6=b3x|@lWN!
zf&h)Ctfj8(H|ZB>n>5(3@9jEHho~}c<s7U@t6(m2Gu@FIRQ{P)=u9q@%7YKyb|!C+
z<{PTrl}S>cO_;K83UWAgqKjhdQ~Z3sVz+%co9@svx#b7*K}4kFF|Rhub!s*|7a_;&
zL9lm1%W`&h6R%U_jzGv}k)I=8eU_oG>Bv*=_kHSDJ0Dsv-Kh-A{G`vsG5zq3ftH>W
zHbHtn{zO(jd~q`OOS0%~@%?st=X5;&v8G?X3rAjoNYAa=bBE$aguXZ&=o?-=F&Mz~
z{I=ILdzn@WqAD)b<v^$&NXu_`=6{{><(^i&+|NF_c|EHMyGjuY(fNHB52L(JX1l(1
z<-*3_;SmvpF-@qaMo9`K{i*(V3-)cHa_DL44@^g4p@6qC7pfdmp77>8Q6mqH13Yg@
zAD{BR$}=$+7}dk-tbH}*p&)$?MM9LM3>N8XT*=Y(m4(0xmfcLUqJXnbJ%ms5JJv$Y
zdQ@JOE@4bwb#OZ*C|Ekhbm$#l-<pcv_;uD89)a@CfXB@v*R=Uh^cro<<{k`=cMme!
zN^78UyPlIM9k%5&$W8B8n+xPEKe2k=FwVZKde|`h*i}8Bs|iwinm9u_Y=iL)1%yfE
zMVIo6+Yj{!lo?U8AtxJ(E-9uKyp+>v8Ezh1*XfbW%(-@Ys#ERqJ=M!MBKCy6T~9Hb
z?2%%)F_Rueq`Nj>N<+>3U2wiTBTVsJ!FS}S$;`IDuBgXQT!ux(GTD*mUPBRbT0?S8
zYTKszlA#VWIX=<()i3=vbpA?YAu^gFo=ge7=sgu2DEG5H<)sWGwSLsKOo6Z{6)$^N
zR{Qy$lWpLW@b?V3)z8XcgE(d#4)pq0^eoyPjsDt&lk{=Kl1WcD)n2tP`;PdHSN<=%
z$6pLP5c)y(Qj~g^w|WAl29*q$Fl|c^p$E{`4k2Tp>iVR@oxt-36dtyhxzkA~lmwV4
zA8`3b+KB&jxO3=T=+QV%()%M+-qkm5oatJ4sQ&B<=ZgntsC=u=`_kW_@nSk7!F)#g
z8^wcQnx4mN*RDtysRX}EYY4@fQ>p|*99NL*#J6`W0a3cyT>}0=YK+t&xseU^0hBo`
zjLs}bf3a<@ujEjpDDU!|j!L}qm)=f8miD@|06rx-e6%{P<Mg?P3#CXpL(okvL46h@
z$$MROvrQi*&5r1_wJc18x?6?O+6CFCU14I-vws!jv_z0T9sK>&-H;Q<2(MmpOOJ)b
z-;yohwb%`HsyQw#o#|}p_Q!tlg}Zo!!cAU;{cu=$@yD8_KtVp)*c^v}*9Y_H?R}zd
z$Kur&a@%vH&plyP8gaPYn+?GsLt6G9*pvn9*BP?ATS;d;ep(Zw>)0yfkGGAd|1Pg~
zPcUwIS<bly`Xf)5@N{b%F2)uXF|AaokJFeZKv$tj-0T+9)@FRsdU{(>=ea{jPT5SV
zo9~HK<Hu|J1<auzYhPaE=@@vBM3H#Iw`HL@lRD1K+%&jrI?DQQ+(&F&@QDDa+<Nn#
zSd$j2Hdkb6sLy{|ny)*YC@jg`ueL%1q6_SnY7FPpw$%%_&WjL22ma2P{uQDI%l*Pg
z_Eq`vv%1hi%bV)W8EHsb)Y2)ZJMWtdOr##z+vlSV4GAW8Gu@ntzEFEc+2xty1(?`1
zgC|$1Vx0EX!#>B!F<)d(ana8D8abRf8oKb*E{gM~v=5?DxG-M86G)Qn%dIKMNGuy2
zidRV6Shk)Hbbe_yO+gT{F}e2Fov6mhlrnPpLc0<|_!<?`w%*Hn80xtD@MgHJq!_2o
z(D_Mi4{p&KG3)HTV?ceyZS2@XHX2!%JO)Vdg|M=fhWo;HMPz2{eSmoaXf(>AW7ZJw
zxVHO@;?JW=CHEga`VLo9^N4TY1GRa9&V3V82yOzQjywGbcgWtCu`uxu^lEZWosXdy
z6G^6X%xd)94aM`+I#ykh?HL(l6mbxU5_r>f!D2Rh`DvlY;P7HfIyQ)yKI&pJt`(5I
zQ`^YC$U~V=XL2Sl*s-iuaOB>+iraS*U#7Nd@4v*;ynD8b1)vz3<{wJxA&d;|x@3kM
zFh9;06XQt1<%34I<gSu&o*;xR-uUa2EYGr``Isdtk6W>^B3xWt@!{@^0CP_V+*BIF
zpQ=))KhWU5-?;NG;G}wRm`G5##9GoBV_E~Z+O@+?d_4ly&M}NG?1>9>(&N+W&2)^T
z*p~iDmq*p@c$qKz8}=_7fYGi7BTk)@>X0;l?=t5Y&+y_pQebj0$0w@X<AtuA$HSD1
zp*K@Dawv?W4Cn57)QgD_&Uwagr#My;pE6^+rA~*lD%B{A`|`2xxzZ7Zu5sEkFEcI^
ze1EX~eu4e}$e1gKEdMvK*7yGa)*}1FS}WGjqK8-I7Ot~M0nPZuo%bqgdpMDR+`qY<
zf@=e{wzG2xX7&x%HMqa3I0-EEuGeQuZpQ1W^sIiGad0!t=D1~~7Ixl@@2XKP-jwf8
z6^C5CdjF-(FlRmAY}1}#*4kjRzOZ67I&g;!dwN11;Kynk@*nsGk5LklUEOMlPQ#$6
zfMU&7!@gF-9mkP5o@|owz|_<qy)%WA>S`Zf$}KMs5nm5_aju^C3rVpkDZ-gyI?U2V
zfP(vZ`zP)fP#C5hP_>Y;M+wRjP0;In-43|pmgkS<#kVATbIPAs5xxA*sw>hL^d6u;
zxyjI{s7L@oTZ{_G8q}|y3-8FNa#l^36O00?Qif`|bl&aqI|pL!xRo0pbaR}Q?C!=3
z#(RvIY6UY=bFs~-Wt&;>mdw1<bM9Xb)~m;thGB5B*B(=mwv#=#mUwP&R?omff>CbQ
z-vX8{M&V@4G&~;r-)j=ddFL0!25%FH(sd|&`>I&&Q#nvFn3|XfU7-TR5c@>aja*X6
zQAESqEJ5PGCmKEvCAUi;`CKUr&^ZW2l6l+O*-07s3~$uX;2ONA_tPBz^%u9+sYTy>
z>1`6;nh8uFDs$Z`m`47wO#l2^C|%{yHOp2p#`7x|prTAn%Nd0R4VL!JZn+49k71Rg
zK>5$_{{BBR_pjai9%%E#ksa{urK>~5G`7$V!`O1YdNNpv-&KE=iv8#BKFi?&#{<8J
z9P{VoF^G0dR8><O*hO`XAZM#B*X6diHN-vveA$E?6{`RI-Tr5pA6P*`C0(Pqze~j+
zN^#Qqqpw*XN)y!JumfEVNecWPkb6>JU2yB3?ds~9X0>pvR}cK%wDa$Cv50xGZI>dz
ziX)!?<1o;q6w$a!BCLu0y&um`O<mX85bxQ53FvYLp%BlkQ?3(tk6`~sD{KDyBQy+B
z6V?4DW@a6z9tgR&>+so_<_;yTv_r>E7U=qpOn{s~dze9V3J@}jN1z(I{eke(yA5XY
z{b$)f|NeSD<ufHgY#5TZ8sRlglufmXg7fWXpwA`|L_FpVLGBzSlf!l2;NXz4)OhsB
z$8EDM2Gs>|BVjw^S3|OiwVO$nq5tF67wEpT*#cl8d$$T59DxDd4MtFCUHz8{KZ&_z
z`-|}W88O&wm5-EQZA@#Vhl#Jno1t0{o73gHCTO3mRZZJuE=t<;@5;^1o$$rZHN%ke
zXz2xmn8%>!E=Y_#4!iT;;{l(`bgaz{q{>V2>uJvEHPvfmUOV#a_5jF>7mfI0ZD45y
zMUkI)VYAwyG~I(3#B=~M7HhHkzZas!aV(;H2f$?e4P7d5Y0Ut+b)Fq6*Lr>L4ycL<
zz!=#=G6?!v9{)#D{`Y%{{isq+AwPN9y@wZw?I>e)7VZ271MhjBRReu8c<bY9^GM0E
zlv!_XiuI8L7<Z#zaPoh!*S~GauiG{x8WeIsdZ}m!WrG<T8;6HD=PdJml#`ba0NJfp
z@eq$QC@28hY|C2pV5g@X{vQnO|M_GuVRRBM6Z5WLvsU_Fqlft%_^TS_zrXJ;8giTL
z;8`-3ga33o{^zIKe}!0)!_T}h{}NFC{h{*V5YE|yXN51rt^e;ge}6)D;6G5Ee}0zv
z$N%}-2+P4iadJ#-QC?o&BzMe)<ZU##BK&`@ECYe@06lN$h5O%D2*V*<+`8{;{qw5(
z|1-b|^nZK<zWDFA2<!OQ$N$HB{Qt%HKzXG+cvEd)UjrXrZSU{@r0dInAGL9w71}7s
zJNAh;XK$%<K1$0l0W(ZRK_PST^nTt~aOq(bsFUREs_+X6etK}hRe`-^!4m=~fN?{?
zZu@*+09_iaYQizyn-1Bwq7`(I0p2Ss!pbTD^6WOCo%C%ws<ixN1A+ShCR66?lYMLA
z1VOL%p&@oabfFS5-rSAN5DJ6mSzaY>Zn!|jeBu;@V!Gs|g%W@%$3*4xF){HzijfAn
z*A)=EQ}?)~-D|R3coOru|4q$^(^4&Cp6iy~Dv^i+vq?!2Qt-69n35vn00CB;{fTmj
zF-Uw8boWY8!O_*VwYrf-Lr}@?1+gSwk==C_2Y*@Ax}pj#hQR%sTc)vXmlc38gQAiW
zp9wLn$>1PHn{B~<F6<I9|1%{f^bo2qq$kBccJ4~bC^R@UZqE+wrt!P1TcTo-i1y&h
z>T1}9?H|X+!Qy)vnVGR}@iWz~y9ncY^g_bWeWjY{3gp8Et(^`n%JLztB{)Wh0WY;n
zaQ(+K?jpaDo8!==T*ePqTqmp~A>p+FCeWzz=R%wVjF0{l^zKtE^XHil6d9#;3=EhF
z14=Pm=9<D<j@|M&@_igEeWcaEe+Kl4q2V`|MK7%z(A7>soS%0qABu$LjBx<m_Xc(n
zP{*YZ#i~}60AHLB#<#y|3F_Fx&_iNke!jgbk&=#%7lk&f;7wI}oM~KFTE+n)>|sS?
zaKC8<q|`%E+H3zFn_~?Y8PtKwcAogH{do@#hDi*I0Upe_?uD&T=zRA|0m+-7`6%*{
z`F^jFFb;08Bn=;xS@Z{?q|0xMd}3QkO^y0J3+A)GC79+AQfBEDlzwD%^w_Z^+aaJb
ze+Nl4sVQL?GqfqQ4LCy}uEY<AR>mDkYCgpL2Bhm&Jl*z(ioASXw6-Z}(&dozbZ6Sx
z-I1)}J-5GWM&!$yTy{&{%A${B+*3AzHfbavAdt9XPXz5ev5Y2y0Jur+g#l7Y-_dPQ
zw{I;_q<$W_flLF_=%_s3lVRDj1u9;+hldA~NC3<jbdOAYm_K_0dC>sw3Fp}F>-PnY
z_tz4@&fh?_mKJb&yp==SQO;I^XZ0Joswob9X0fDsU;!jb8_=FR@9iu|m!?tt*vH2Q
zKz$)_$s~<kAOKgkHP;4?K-~rlImX@5b}A)l3P?8>xTCjtUc<d1&JfU>W6%`ZBrYa^
z?5wn{q5uW)^6bU6K8r3uOkAq5I#J!%+ncNZoyykU-ehlghk5sU1y7fHJmhPvT8Wgv
ztwo9HQ2ue=duzuHjktN-x`oPjUVis2Q~-+lIbnM;st`I0tyw%bZzjY3q6xRn1stqG
zTd*R;4_&C<mQ4k6gxv7y(+}DuDJf#G-eh2I&~4a8K*MtPk%dzLoYH*7_BN+Bww-re
z(5I8P9r59tf{@a;&~l}t&u*tdw+K{DpeS9yp>(vhjYQ8+On^R2*YoM+Lg5|scH|n|
z8YE7E9O+=_BFLc7g4QhfAF>t}8JY-q37j!R(BeibGuVnI;*QTR1ArGzI<l?L9}xQp
zYQ$i2c(}OYP*`}FBv~W^jlL;c^W|C~NYdK0Qocvr>*NEi08c<v<vOhz!&Z}$lY^6_
z1$IRKkX!NKdNrTarttEHB$PCa?CV67uJww2kD4MjB3_I3b5)Cyq4YtL(T9@R_7+a<
zag1xNyZktGfdfX&p75c~Xc<(#`afwD<FyAtXKsVM0^|M>@3^c_#AT$U)T+C#c8mk9
zk{4tYjx5Wi1ol8*m}5t(CeJu-6=YSHKg{QYh6`u;(j+R9RR~CXl5teJZX5;}X$y6R
z1dvF?N~tg;x6i4Y^x-$!lZ5p^HnX1}SoHmnBuXqSEYzL@B^Fb;ly*<&{aXcccor4D
zY8x?P@;#*=GBPY0e6PVh!wth|P+@`Z26<pd@P<Xkm*-P~8u?+KM+?b_*|(0&fOVYr
z+2}8vi1R`k;5IRNlGPoGI<0(rfRfcg+l(Wik4>^bIA+=<{Saonb2fiScsyqu76Ug5
zTM=UjID#uKF8+>+z>M?R;q&s&yI2$lKIJk3vcOl*X=oW*+sm;hEXsBecWZ{Ydx&P0
zWWg-lpO_;K`X9~{H>;Xe6_v;{96&y%;dd0%dOK4~&GI_bST#zR*{X`zV?|Alh}OEZ
zoH%i?Qu0i2rhc}+YuNJE6%H<yhj*kH!WNu_=>+K_ab<+%9lD66C~n7?dCpx>M4|w+
zbD14IJ$*GhvQf{Q`gX{!&rG8%QAyJ=VPNR|On26ojDe-GdLPhC`<|hpj;Ke<Dl2y<
zxE*wSgmDk3kM|Sq{aM$Nm6gS~J>Y24k2_YwQy~aOFSK%Gk;2N#%55)G1nfR7o}HPQ
ztF^6dr*{ZO<NysP2IIkFJEd?%NN7O0!16sDuG);Y5)&Cl5+lrwanCvgCi>r@wMK4)
zD64A9`=X+v#b%|Nh($zMc6N5J3*}%m+|HDjE?sgXl!5dLEq)IaE`7d!y;r`5_UD%u
z!L&OW!&dkbU^pLwU&Wzu_vi)kZnT$vs1R&|n-(6wRy?%RnBjWo&Yh(4Twd?bgI&j_
z!ZDbMlOgqb_wL`{9FNG%%p4gU!gmH0zIKdqL})S1#pvc(?l`{ECe8Qp<40fTM(}*K
zpTB&$8AU|#8wCWI)3a`~0sj8pP?uoSy$gYm1(OR@RJ{x(1JSTjd2uVJkoCLenO#|R
z^)R;5A_Eyau!-+TZ5<y4NggTT2VaI@@Tz+ZV$Ax1_*QL_QK~-!eY=P<0B4PUTFezb
z8wuBjQzS;x3QX(9&NE??&QB*T1pHLXTndNy;g4U|j2v(N`vB&TPYDtsQ~-k+5J*Z%
zN%>Bm2PB~Bl@j*y?N*BppM?cWUAPfygp%<1PEa9rb49@-QENQ*bL!<dK=_7BH5rxq
z_a@86Z$CbXOir5|#Kba!YwrE&6U$KlasYtrpO=3YwwaKL2$BwF*K+H}m1t&gnsl<1
z;{cXED7AH>-PQGC2JA(1H=*Qddpul8%eA9@vte4O7$!*|ASPy>ulykwfQ!2laB3NW
zP?Kk1*NrbL8>HZT@>~l7W^P4o!s(#cW{x0TdoWM~^E9u%=)g`%M~s9aUQg4fJ9Fj<
zM8IYX2#1$o0vRvl_nCoyVwE8fHgFA}7Wjz$6;_WpLKHF0cQrGV;B@|Js0=lVWL{$h
z1Qfqf_x<~-*#%)2WfbL<;r$oJy^PnOk><s#SN7A9Z{MCqX@YLZ)-D(~8>c~(E|k$V
z-B$+ifg9%i1tZaCDJfHm=gaymhALl>_&H0dsDur=EI%5;S3POjAr1{quA_20K2$t)
z`K3_M#Hv-c4R3aTwWVl*@^)QQQ^-{x8h$xc2SvJ|v!1;iKn=`8YoAndY{0LiBhq+P
zA#r=V^LuN3M3A8P#^HUk>G-ItYiv3?whjQ<&3#gJVrUJ;E&-|yipN5`)HpH@VVvqA
zP_u&v=t?>u3*sdRZs2k(_Su2B0)7X;1@Ry>vW{<SYqN!mwC#N`y|E68E%KtGRaQ+#
zJFaV}DiCO7j6`!IZrcv1%0Sud`1m+)45=!~ul1^;qCzF&R`RKH(Cedh(?DFg*4YN=
zqrT;rrdg_5RaI3s+t45x1#X#!0C96VgV?8;{q*(gD~B)_*J+)<TonTg1X~>c9KS{R
z4t~gPdINt2H9=3FJaOrp?#k!_H($VXuMw1FlVJ<VN1Jc2b93jkxqW(U62b0^-TL!%
z^8Ui?%+SgJ)q;ln9mw1~LhX|JJji%2_03If+k<k%wJTS?<h%a(d>~CFC-KXgM@h!1
zcDUo3X+c5a1H7<SV7Q0lj!6SJNP{w}s#EzwPrm-m7K61OeONxbw1h03?88Uz!AurO
zeUX)uSeX5k)3}5xwoHLm&1|Nttn52p)9xRAzCJ!3{e?K}>;q#gV?G_u#ADwekr4Z}
zrklDB<A9dhtP3>^zC&v>JvqfT+zvKa9|;Vc&&BTDR>+;r65gD+0zP^KFQ7p@HRR;v
zB3x_K_z_(wDsoHp|N8Z-;dn!s##E}U+82)AhUFS#S2T#BAF}zb*X^isk&V$SvqNB`
zF_FpD)%Q^zq_MeqaC`O>6hG!rxeA60@K569t{PD$tYlu`R1227)<l>bvemlq4IRl;
z@N1@t!D(Uv3`UjDk0-8Gl+5|<+dt|vXU=TnO-xLRUCgKPyb)<%@E)6*CAK0xJw0wD
zQ8dzacKEpXc-KA%jfB*XO<8M|+QJ)IIXYLkv)X0ixdC9-i7e2&>$fOQ)nO5%;x!G0
zox!ya_9C=LTx@??06;nads$#$;K{!H=#ZpG5s4t^#)c3>qM}^;oocY1QB4^6Bq_yv
z_nuog!}-ol`rL(#n86aTC{pduP6;P}kKmK=KXdkMo_2*NT}L0<cTP?1+6RW8itFja
zkfu}Zk%LiNo3}K<OVd~#be`X!8}wIw{Edrx*+~uNACvPa{6?*XGn_ItPF{d2vqlT&
zUL@CO7LRpUhy@8fnB>3zZVqIUFA}W2^?ec&31;c<<IB*UasOibru@1NFYiw@JjZe$
zors$YJjp<rtq{CNeMd(}x4!uMK5`BY4zFep@vy@p+Dxk+7%01Z`Ep=TP)9=9`lkb!
ziFrUNeH_}EvoD(!*+zbz8rZyy7xIYQJJ>;v6TrlZsFu5x4y}ECebyBjDzJ(-FW<3y
z6nRrtOib*JukT|gry~8P=rr)tKMWkh_kAgG8{WZSniT;RN+h}7(bZ)?SuwM@>4e6@
z+_uuocQ@@|foIIvZCRCPSfs6P6YPosqS>BMc918DIF6wA3UvWNc+u_P0Qs+59L%sW
z^a8tW-S?(wNux>w3s_cKGXjomtOHo`Z9IFy56=<2g^=G`XNkIu%n#xba|MNVE3Fqj
z0c8i#r4jiD2+V`dswS)e{}28B>Zq`UP`b(5c0O22h;8N>k$;1!(l`r|ynz;h?WnM@
z6tIoxvJU6cdjrz+<%Yn+W@cwsGBZnq6IueOOish14*c@78TO#*d@3d9&@Sx!D@R(4
z_Dc~^p9S`@Z$&@&BAXyL(xS7qw6rh9?sh2wtuQ6mu#qjEbr#r5NK9nr;%dzw0IiA2
z%1T6Y^12Mp$sV=1gP(=$2|FvVey`}ifM4{5!=+D0M~CW$p-jH<hlGR;vkL+?-vYA^
zU{22rx)L?O`){=vLe;SPajH%6FbVUIW6}s?!^6XDt8JSlXD3tRn=t#PLh7sM5fb!B
zBL)kk07d4iumsZ~U9*$hd=3lRFTmZvmuhB+i;MT>lKi^~816j2p4#zIB}YHck)l`#
zT>E_@4rtR%Ed2aJi`Uh(@PZ*|rna`WUg;{|?XcGx1l1pKFCN4AwV~L=DlhVB7_v5z
zy$$IB@CNJZ>RMY9BwPs)K2Ss8&?NV26$VDEHB;w{uiA2Q=a4RO#d&csye%PJ3+|;-
z59c}j<_J%P%ljz41Ma?xii#U%pf+Q_71Gbi$yt+j9P_z0IX71U72zBzgJ`q@yfk*s
zF^$mDxom2nnr~rhs^IEc3b%peY*HysVDq<eFFrwnkbuzbPF=n$Aa4x8J56k#o7^_Y
zQa*S7{22K?6N_}xe}*C*ge%SN^q~hDUi1EIDSC5-zzRcEdJI7)@?&~>CmfSj;pl`0
zhsto^xTK?_U;*lW#XB4s`Bfz+F%uvSZtYd=O&O0Wb*kY_-y!WwiGH0HTQ?u;>nDOP
z5*xZw-LAjNCrh5M0+ZO$-`@`=fG5d537B6_hlu@Vt2MAdls8~6%-ub<)vY98tB9_C
zu(6iei%z9QphFAa80`yr`!@Cn37L(Pxin$CbJ^ON=leubX<3mIu1~G&Fy@v$Djk8q
z8xOwfLyLV3aNP`}yhA))N87-Tm4VOt=FOWE<>%9{8Uakv|3hHlIadH7IRS`p6&0^S
z#h%hjmc&u5EmUT6{Cfn!eh}0nQbfJT^7>9KJ2h>B*)K*#<P3K<SJ5!?xie?1`|(#E
z__o8L|Gne#I4#1r&bm4f>2}$bGwBdUimV?O<Dlho@(*)x`Bxr-#^I11n6rG7Tus{?
zfTcZv#=nc*1K`1Kg4Z^ikAhyB|NiTWfKYxC4(FVzlh4O@Y>E5UZx05lXY&B6E5@2?
zC<sm{<Bbb;S7mT!9N#AgCP^^v=@SzOxj(r4PM}*57!lDox7gjSjFN9rF~G@u)H8IA
z;3)iBGT4R=NIFQP?a4oW983qOVBYJ7`FLldIT|7tmzM4V2M*Vdcm037y$L*(Yuh$%
zHxJ6*X^=vtQZ$D`hEi!lW*O699vTc8Z%tH^2o+_{JVb_&p(Le}Sy{1?WLTMJTKbM_
zY245Iec${3{=@n`zpef3-0QyY>$=YCJdg7@j!Psec#r<8R@5?+2j2_SF9@D^ZV?b(
z$?}oQ!9WBpYoe2qq%9Gn6jsihzowMJF-ih?&S=Qq#LFyRcb8s~nc4s7y`KE$5tF2(
zYf=-(GrCyj=pX|?ewPIur?Bz36)32Ne6BqlP?24MmV|Cu*|t+?q8N|#wH8U%n5GfD
zV^DjkH8%GVoKzsu?rxvs=!{US2CO9OYtKuYmuoYO0c=n{5uZLCBgf)tWmw+D(uDCy
z2sxbs6=yX$>g~mm<UKz7_V2gBR!ailm-{aIuUiGm$zJi{*kj4T%Y%i@t{(sc%XmF~
z_H4u=V{q;%BQ5Ff3X-1)c)-p|i7+Xd1G<jwX$~!+?pZqFWULfFremzAAA^+$Us0C`
z1kJtW*O$unkr>)1HR1Q!c<r+{;Fsk6%|?cXZI60OdTBL!LxJ`=My%%|3#<N2<1G3>
zs!0Vpne&O0GC54o_tvfG`}a4@;t^Lx5cVc1^x8uyPuE9~V<B58QW0hR?;i<EEITqX
zvaUvpSJB3%pS8+1AsdSM{02pam!9pr<65XTt(v?OjU~O(gN!69vYd^Ljk6V-2U3Fv
zN?O}qTUhCmjG(*LEXkz%BQjS6!Z-U491zY44l1E2H-kH+H?wo>_4!x{XIxlV7?#P$
zjP>i*DclfH>^S9#qm5r$jiRWtsBp8-S^L?B&GjA0<|Kh#<+g7>KgFcA{2p?U=O^D}
zqjK#>qWap34u0gjd%JcyFdVxFq{92gBPwgQ{0gWS@~FT1F1<GFof#p5jI%UUwk|dq
z=e_5VtKQy3GDQ@;Le@C7l9eV~A-4ozbhFm!G3|DBud$Mn5)u%}^-+<LwlTrg&3E_c
z^EgkL#!7(a73GfEm)sExkg&5%>Z_<2?)>ohyi4%fc=b4QQSCt6@k|+=%u=3&W`XRw
zb#ZBoJgG&S4X@H|9eY>($eGq^-p{uhCHA*B^JeA_)S8Jn^cmA__wK!nk$f%M?m%`9
zWb>?@<7j8=;Kv-1sQtQmq+0(OElGmt9UZ+2)eyEzaOtzv!fi&Aj}{G3XLQ)uCV8L@
z+lI6E1{^Pzzq-9S?M(L(Ql@3=G4#a;s~_D#c2HV+kZy}y;EIQb<Gmzg4j57;J-v?6
zQJj;*?@?IZN=V*CNzc&ft4$roKESNKCHW2IS!~@F{YgE!QU_T<_bN_uvyzaK%GGYp
zu+&$l)T<+B^*S_Thd^2NWCOeNP=mY6C;{Tu1zV+QiJ)T4a|Re{UXd8pOAnQG|LU^f
zZ(iQSSFc{#(x$XIcC+$LGNdJ=tsFn{!-Q{T^;tG%86|Z1#hwI_Wy3)Ll6#2`7~5wP
zZHs-+u^e=LuTYmSi-iW94Y*nG<Oy;_n<vgl;o?F<LiRg%ADYN^V$+OIUl&B@-a^x!
zSvJ(&$V_K27}+*s$p!)>9kj%ZWIi4Ji!DS5u|ri!sUc%MKYzHk2sYhLi<=3}abLeW
zp;>R8=~dmUkH|@1CRD^A8IKi7lXK2ejvbtx0;SH6bx6Q}8?pDs*K~~}y}G8;gPrsw
z_*#yn<IZZG-l2gZlZwv8>mW&*)vo&|8=v6-onbGO4hjz~8TR(rVO)}|+g^cRe@(md
zOi@<v!Vfq&wRiO52&T1!(_~c^VlZ|Dwa1K^GkYyM(eN1^q&ho$hR-65p8ip$L>{>P
zF~hm*!(+?4^bwoqt5>hKsQtcuZ%}dMu(H|W>h$H&%Dt~&zqY7lU$|+ftQ&hbV-VlP
zIK_u61qKA*EYPfV{qtC5&z2K!>eQ)6D{pyqZE=OD=gCZF`pxzF>k2kxS$B-$P@~Qa
zasVL?D5*|GT35=4!(aAeI|`z&zqEPNrd0EHn~*XNL=U`8KFU=8gtMMIdS+?a*regK
zaZZ8-m~4K-7uTN*i$ELuI|(%2&UtsvlA=Yt7v{|-Xd%>%^diy?RdH=Qb0Sa_HF}b~
zj?Nv!*8xo#NpaNa1OqM>#FN|jI_+#$5(lZjOVv()e&t;?(x3Da?@CQetG20E+dUla
zL5Gg$S$UG2H+_INw>HB{Kf^RtuQfZov#t5n?Z%8cZMsKBhY=1kLSE6KEDZveGCJN?
zj(sg<+>#E~H%HqNNHK`F(vI|;^A&}&!eqQ&-w{(04Sb!Ud+=Zg@rU2vEQQ$SLL<I*
zw`I|Lqrc0e@^EaU&b)c^h6i9U*NTb^<#fPmXX4TCLQ80-UBmt<gfg_pA{YlaqX^V=
zZ%3stA+Jh>M9X<|=UUf#8E}!7+SdV_frU%Rf}~`OO2ip!5G?KMMm7UK%;Wa<_QqNn
zyx4NNgc4GZKWJbOY6UvQ;{YjV2h>$R1ND?y+!0^CnCCfL<qjL(EU_>(E&o{P!5ko{
zM0@&iy>HyT;%xcm4Gj(4;?~jhTp}#c7LgkIFfp(F=z`^9>Wtw|(n)VumJyVe*5{+X
zCR<6YSwl`h*zVpgca<yj#SMYc^tjmAKAb;qTkBQXE0&YNDioeb4%UqHii|IsTUDl2
zr~nulooA==w)DAmkyoRq7i1-Y9RzNX?Vd{~u$8X&_4hYL_y#I8*uGz0zAEMTOJ8qq
z%0Nq+oGo@NS<{II1TO{g!uI`X96^P&j$^nz8-C}&o=%-gLAzX;o^HUE1^irshmLfo
zad0F`DSUS;_F{<+F;<Z=as&MQeaOml+x>(zY)4+26>K0Kuc3AT9E`8(!WL_<5B9VE
zYF6XtK&t_Y=7NByrms(LwTG3w?|2#P<yC{OCf!o|bN_r6a&iiTHew--ni4=^TX`mV
zS@cEAX>oRy_=`!<2#luN;bbZt_LW{C)q$gy^M;2c=)>0#AFeqnFghRb;rK5UemsMB
zh@UsiHoK9jybGbye#X!FOlH90O%vG;9MsV<M=HbZ<3#Z~bv>R<TQG0l!q(PSmv#!p
z!sHWAv16zoKYkA|veD3M=gXHbKSS#8crOnJU6&^%0TC;Rj*d3YNT^|mSYmGE4cxwc
zdpEihQA~m$-q%VPhnR~jf^Si`T54Pxo?K)S+-?I6o9)-n5qPOK!|(OKeq7+vY<TP#
z7Y;jXzt04CEs-}cefsp>D8-|gKerSOoF=-695zdL+Dwf(g`-(fa2iKwR2X(VLWuW5
zDlU$h7&7<>DZb0;tIHdoEwb9y;bh6>)^Li-ylPP3Hhj1F#MZ0Gb7sG>M~=^|6<D!i
zA<C21`%E~jeyEXJzWnLiTxZ9q8dZILtE<>WJKw`H+TE>&VUgjyYuCk-$h1e(@=$t6
zv<qCvy2?*>==l@B|IA9J7qw|yW)@nNn^;<k&R?(hp8CAf=Iwoh-N5_kvv}ED^=yH}
zW!x5^dIRZPFa6gt27=4W%N0gXXX@;>wocWjI0Y1h)#EiAYAli4J_8-3+R&T;tT&U|
z0rdAsAo=Ca8|zPEK6_VMdUzWe)4P?Gee`Lh-(YNR{zX}u6&+ty-dKNs|Gs_H)upm-
zsbb#<q&IrJ6K4Ch+lH3E7{V#oZ<_!n{MZ#x&RJ@%nV%=tjM=^!(McuGwyCM<89`6e
zhwrttl!bd@2W$kYRYD3qT>#Sxftf1>1d23so$LcULnV2=x)F!DFn|^%_U&mk*Jz5e
zvefyF22UpCo%XqL!;|>DyD))JaS?0(+BG4kfWX=g2k0Tfi8f{<Ye$`CJCrsGq7%ds
zq<>;=9<OxpVCj$ciC0Cd`QTu^*S4NZbf-;cx<1UN<Xj%P`>>yP>^Pjc@>bCiuDkJX
zX0z#j!uhiBQ#Wp<%I~?1%I?@9Y+AW$#q#CIln>&uT#CZO!pPQh+{kF^iB!yYe>6Q3
zs!1-0@v*Dzz*)J~kL-JEANi~n5m6%*9a0lj)S;h;h#HyHo%Y*sLWVdwWe=x|kF;}e
zaA@q_?S>*;6;U2wOJ1XBpge^gq8u+T`@IIK+1NItSuQ7zm*+&Ftn!k(64_2;pibC~
z4L4`lr=31>Bn(VXRXx2#bR*+3Gv9R%k;7GiG1v{C-m|2WF_GXu)<wk7yv}*B&SHQA
zTQqPH4Q!b`y}ebXr626Jtyr-Ok-Ze>`cSnde6JV+F65U!=bxPzAa*(uTOMt(ho`3+
zZWr0Os<Lv}xpU`4>HPc4fo-2A2m0L|NVA}cHhua8+#yw78nZVxAwdm01t3A@HN`+t
zc^GDU<Cn9XbTjH5;t1s<&w0MbF8<wD51N|B0#RDwRn%zro<gmLU{r?pS>N=%3AP^?
z8~#F7ICpRJn;I5ZKy<tjw%nO(+tdwgaNXyI3|uWx;H>xvx<ciKX$ov?Q^FEUJavaF
z?q>Gl*aBhl<vOD%8C$yiyo*$nm5)GAN^dzOjY#$^Qa&)NP7xUq@UI8Ay4&DSG?c$b
zL7}7P>lUl}ZQ)2WXul$i-5Mm}(`L?O^n4XIe057y$ja6Yy(_>zj&Q85>j>N`G0HiA
zzTr0P8FeG;hI`*gZ`tk1UDYIog7Fcj*1QK8s39D!Q{ChOAlH*1@#OLdd6dA-j#q1W
zn75@VmzyIWgCw>lPM0*A$98MgrcKJEp{_?UZGfA`6xd={A*V^Cfl#w9L1zXk=vQ`m
zI1**J>1F_qa0HkpZoUOwA~J(;MxVQ)abW^#TeNcy5G<Y`khD6-&Kyu#PTy4DX{Jaf
z9OXLpX%T(MjbNgPOuNd;%6QTPx7nwVS~3UO+LcR}T5J(Be!%(}b+$OJxHbZ@zUSfK
z9iYD>XyC*X_DsVG>o|_jHmJ{JIv1kRM7H`IghaxQIiAKVJ^uy}8S*9M>_&2!h^6yQ
zYTL<=JITR;q>Jd|<0Ed4NZdz`ibsn_1MP~N48G%OTKy=1_VT$5g`PTnTEct*@_*uN
zJAw`++G_7D2l*#=kS^m|)-(xiUgK6);%Ls6zuYoI+pH$Bsy@dN6F&saOdKSw09gIX
z)vI3^4S%tnpp;|c<9#byeP1?}V;7x=o|8AfoS=k+wuy;J<Uks_>=tkDZ%1!h0}dDH
z8d&2fA{NZRY_hkTOop&!J-m<8(Dx^97ziFSu+Wj2MJfinym!B^urs8C!D<{>t6|Sn
zPtsd^^W{AtONGy#nRb1k9uvG3h}hA6Scky56R}nqsCCu0Z$FD(COQ^TLqlioMMSjV
z#N-N8AQH1K+3kYiGLCqRL&BnrPS1?#)60<LiySZZ<<y&Yj~~5VtsI9m;-(H_D7r-$
znpFY<a^2nCk^P)M)AG%q#zy|ya`W-r37pg<hq0k$Ke%c?eumWiI)m=V|L3~ozaZtE
z`!7g*)BX$6-;)1|s7Ee{W%%dk|M3<7yc`Po|KlzG_Wl3cd;FhW@z>w}?_6;F`~T1P
z_&>WM>u>+JFG&6G2Ikk7j{oVueZ~LT1%G@0<a_={bLan!Tm1R`|KCjS|LZN1@Bjb(
zp?}<!>yp2z+8V$wviA0wQe#6$=>ve2=l=6gnLGc#Xt4P*2le%nSl#V6C>vL-Tsb8I
zas{sI<kxfojX6vHI9c(>)!9$%_g_<nBxTO^-1xs*|ARtX$`9n@=llQuTT7;080bew
zXv2}G)AHI^?Dv0enK4*qtY8WBh5)tb9HyFF@$_sj_y-JV&c}BEkK{i_;+dbH&-(L^
zf4cOX%&yDoE8p)-OE?{u1CP2X5tlAa%d^O?OPtQRvX5NT&(Dt%Kk>oU%JKcflUKaF
zI{$AMy;hxBRex-jrs9uh{PEM3^S_`2|96%Q>-qmXfr9l-|D7x1FS_!Y_U!A}*D9^;
zG3v02D}TqR!;c&N@iQkTI)1mpO*zVbotG3{WnGeiaY>A<O>@SxRm+4UlHA>w9OrWD
z@rdgii_Fo=dM#*oZ`Uk651$#m{dY}nr)aHpmfXoMgYo-uJ=V{B*HaI1yzPvAe$9gZ
z6-`QKqq5`!FP7|RUlUV(qvnnRnt`8VVrvBLUY?(X@182ixJE5b9;x-M5;kRw?Yrya
zU%KV|Q`i4^m6OsU4U<IM6AsqT_PWz?#Nxef;>x75R{NJ3Jmou#&RPce&2v>+##JRE
zYQ`*4rWbc>4{F%9Zi}MH4lT+#_v={N@td6{p1t2RwdZ+peO!+uC9YFry~Ac~sNVLj
zcToHb{u>8)m!9*kF*Bx}-6y8YD^|ywAhbr!`9=oK=FU1VmoxJ@=H)y6`@v=MG?Zx3
ziapxu`X#6BQ?pZ_`DV%M$!6rFDs{dOin|cKLB(|0!^P3jDnW6Y3K?>7?|JQGxgB&)
z*HI<PxNV_LYo1>CXM#TekF%A2yw;Js&kU1t->M|~t@G;9DI7VXnE7aTXP=s?dYmKu
zeSBU?;2I#X5_d!B(TP^&wNK_WWo|o_we1v7?4~pq>ob=O4ev$AM{bwp3_Fl8>p!^i
z&LtZtb_|XZ?uyKV7weVqq%qFNYLBRXh_M`eD-k$o_Bm^=v(?BdmC>DavjOhfM;SXx
z_@%WM+1`vve)mXVdi;EoV^>2rPIr5@+(G2q6Z3n^R|Kf@ZJqyba(}77zPLrH#dRqo
zDHZKy^*vfDjHqY3R#7#UDjmjwDea=Ou6R^_bL>BzlWAa;-A?r;B?O2I?mg=MG*Eo@
zB!2_0T3xO1Z8yoQODI{dw96tq#`)^L%j$^*k&7K;6&B7-d?WRumuKe8<bc`*C)WI1
zE3jlq8+TxTYssdjOphjO`_$w5_hS0_3*xB&fA6qEvja~p7WG`XSiqz3W6fu3o!GA6
z&UIFHle<P%Cb{%InZjhGFW%+OnasZOUezqEyYx8&7-m&f!;bTlrYji~r$?<S3X0Wn
zm*@9^`LUqTYo^$gYwpzTAEP+_Qzog)VU|Um{_1A$s+^imGO21RDXEq09vX?^E3Qk}
zsQN4`*l~>}wQ`v%b$iR9Z~cQ+rp88Vh6}4kIy2H+8iZB`h%4M;yq!8-JyV-^`3Cmh
zHusB~!pDy(zig|&rg}HPKjiPYd%c`z_5Nc8-7>VYPq>qKM67OwR{KD7MAqZV5!sLY
zUX!R(7RBkbV;-I@ww}`q=JdshHCofPwQD8EYK?Z`zgV|^N)4L5U}ZOM{asDZlE;D>
zatvGl^hNcZ(~Ckho3@%4+q@N)<lnXNpQq+!-`HD%i{0F&SiDQwMZElhDgYV}ZEN$d
z5eCs_)zYQgQ8BBc%zNg=m(<nOB}MUqzxey-@x7EprQlCDjjQmMW^SvBtoEACQFis!
z+@`k{0V1`L@s*l~)=27~9S&F+o?6^qMx`${9~@f0z^eM`KbMQ|7xq0|f9ICuJ&|0s
zMvZ1iRrLyEQ)g}ShnQfvK(S4P>7E@CG5u%jH-tQ)+t}DpBQ4=xec-H^G!^^Rg4z-g
z5RheUX>1(5^1!VSbpKn?(5Mk>z7zg@&zRz$TU>rJcVJHK!|bnnR%K}F&sB?lXLd`S
ze#Bd0|NgBh6HdoQUOk|ECb}whM1SyT8DHLJCH;=q74<!}qD66w0{R;lY-xF|O&JL;
z6Mot^R;F$*e_p-$=FkLs_I|Si34H&UR;6D3<nPzm9>XoMTtVIJ4c~IM2_}fSbn2Tk
z6Qx(B&x-v{LXL}e(=JE!-;gRwH-X#=J{5`UbBA_-gQ`E5hbLkGy3Cy*38v-tCs_5f
zjxhf1WtS>0q})QkeiUrV)S8fqxUE`OYHrGhS?qgMQ4K3=^JW#_xtx}!%xlYcx|=+<
z6EN%G3{G8in4XfP%#l0l*O7N-hs?9nDKS>2)kYF8wVu>ldWm)>9F*vO!)chjCyIUl
z!SuRFEDRO76{|O0T_Sx>yVNZ&E-tPglut$y#f+l>O{{R{`JAq~CO2d-`LE7_2@P^Q
zi`ciY%L23{EjMi8>sRyh%M$*9OV@cmGz`+W{B}GQc^+4n0u{9+HA}DcUCtpXC6y!H
z-l3ShzK!=+7g<(B`*Nu;?20Ot_GUE(9_@1<%rzYxIvr<cYN&pN$`v!%<0>cG#pM>7
zG<z}GwSs8jHWydKn)L<L7Ij6eq8Ha28QxnZCc`m@?d%-uhE5R4nc!2G6>vz$mzOK&
z<qs;k8LSc(Rt=Z)sz$Ry#QZI*=?cmm8byd)1R+sV^8<4*iZqxZZS{HCM1)xSlpAFb
z9TFlVJ^JoVA2GPf0;1dx2u>5__+PLQ`{U!V-$M35b^98>3bi#+**j?i8Ub3;Yo3P1
zg!}k1_Nd`at5nRHdnC=;<%sM1Z9*z>z1mMY+B19m<pZw!honA@X7gGz#1k8DW`F6!
z^S1gzcZPfd75w+SNOQ-1h#&*1o(GoFFP~i-{e(`=K?3DekEnIr42CA>78Dc^ADT88
z?Ug}NckeQ%fZ&=n@!I`3WVR2eOACnp)erzh0PH_#W1}yfK+q$QLFGtW18DMg(giov
z<^JP|*#4p#`?+hg@_NV;V%Yh|guZw`?^e<5%PQCEBF)|9D>nPg<Ef|kJ=EiNQ2`Qr
z7p!@1w&jivHrwDwsY>hV!m^=DtEIRMx}r^tvHdBFdBt<*F+UinFx=fWZ5$kYtg42l
z7mU=LpCn_&$ouA#zR7vS+8LOw&nNA68Xzc&MjuPh{l@HMkUtzb;xJI_4k9mMN|PSC
zHGC?fv$C|ljSc1ziVFjT0X5(O>;<I|9k8eYARN>{gyHC9@#mxUrROxIoyxl6*W6=l
z<8m3h-+=__tx`M022`8G%>zsn%R(ORQK1-v>6H(sN!m6o*Jbu;@myX#GHk7Jn^!ki
z)-Fg|wCg;hC?Llz{Y#v&{q_8&GNWwp6wI;hG`c?%sxeT(m~Sq>F>fC45mV<Sw<gIC
zIiekyqPb=hyDTt9X%N$iYQnUoG&HB$=)i%i>+-&`$XO)oB}^zL1?j2E$&<<K1zi`*
z{><iDtY+7UwB7hRN33pb==%wZX)*mrpU4XHZ!XN3@WA<C{2KocdS}IGmK9zl>5L?y
z_wBsjM&*XZH&;Y1c3+#%&(Ob8Z2HOC-WSiWmMhmJ;u%@3xc{p1wVbBB-dFK+?>*gp
zON)=1X1JXb$9)2fZb1~T>aWpHdj33L>L1}_&;KUuj4T-K^^s@+6g>w`lD|v;wPBQc
zkEclWDH9&h!ru(~#=WDJ%&8Vw_0+WNX~Xp5lM#Z50iV1t&p>LRo|iY&YCk(+?V7TY
zyT>iAA^!DMXh_xMQ9f~%4{r!u*Rk*0eh&G~#g)=~{$8*!_V%IGxxop2zAYitb-+xx
zR~K?H8n{40f`YM%MgIUdf}%%`9FN-%ON>m#qCYFAt@aDuLh<sfCVv*0ydpN%YEzmQ
zyY^N5>*fc_bNQ%+hNjH1Rl-kWrS(M)k?C!>%S=%%XN(>f%;2k$zfmQ=VPdiTYdvQr
z)kHrDhvx0)4#w?L*}XC?p1bpYYijDGi?j2mPIs;U8~)LxapEjFNg9njrsaV@rlafG
zgos!@q@KvTkwUe{MQ=`XIbwZ_N@-8`&j)b*&x^9iYL58@bHe+&nmBD5!xU+fLCrla
z-_$=dsvorf#e1l81G}umepBCOK}-AVOY(NQ%e!1(A|$dqQ%-##$lEXM?WK^*_a`uC
ze0%ffJPHYpH8ZasxqC0SDkq~ONjS~r%q2AyxwQEFZ7;p4*y#lyr+zW5t`l+mdRX`7
z-9#gec5LY1a`R-d_(g0~PaU3W=#$bmC82n5$R&NizXm~1Y^BaS+rU#iYdPky@4vb!
zP3lC-vVWz{GSUsK$fAtI7Bxcx{ZORa$|U5BJacMNQZfo6TNkdrGJ}KKm^LPJYA~b7
z6qSQ>W`;#e?mM%Vftyk_5*f{_Hi|#u*j*#pFaNssew>ay$)9*F>a~{^Y)EoB6Rl<x
zAuy~y$Sdtj2H@Z5n5$H3#v^qFjpQC3R>o>#b3UejYT>R`MP*11uyD7Z^p32i_?|D@
zA=`JeQ=#nQ%pUB&?>`SAIj-v;St)t)wd$cYOraKYE;k+bSgfN}Z`9(`Cf|#-Fn63K
zxzW=2k=5}|lZ7v?us3d>5R&n4$Zm<NAcMPqughiBE$W$)f?cYCTGHa%cH*`AHSWRo
zzOpFfE#<P{!0eR^)47;qDJuOpZc1AX4vlJan3f4-3sZ&;GDTHE9}D`mng)#T#c5*V
z@WjV&NgMkxG_C2aiZjQYh-bU7h-I3=Gx=k>m3kmG8gWw0#KVi}7LJ5P?|7|OkUFC7
zVcpiXv#Hji_~ct8u0BsF*@Va{ck34s!U?Aw``V&*A&S&V(1ei;E9~7rt>q!o_O!(N
zs+hS#c5@kV6W!$#_~y+ITX-8||LWA+W6gN<H+NGTI-xCNDMG=6NHu!H3pR&YX~=f;
z-cv#E4kuL9rAr^V{Bf6TrIKJFt;&12&mMDFPvu~2?8r)$Q7*qZ35&DkGiL{uyh>fY
zRQy%th%sZ#L%_J8<5VVxTAEmO>6a>t^%j+RmM@jecXsaDoTDd8o2|-=5FKtKvA?SH
zlSo2t;j+8_{vnIq<uk9T@-Al^2@T<(G&D5G4pN0KgsP5CJcv2eyLQ6t&_?cHS&^HZ
ztd#e)X?_3Oqh2wYnTM2L?iAV!@%QzL)=IRU!#5s%)<U)$Y{2tMf10$6KhmUNS$9Ti
zt?tK}PJGevSll~QZ>6-aE>4aTtTCv#^vPI-5q`R?A#{ZHXm!?5l0sDm=fib<suUx4
zg?^i&hQg*a`owuQ#i!@JY_**3bW?T9EiLJ_8}~ifO+zkNg?PQ9@Is&#pKInh@vlte
z%xz<%of>Ltv?$}{tv>tLm2oO=3$3hF-K#7sdwyN+&=GVWUM(0xTIZ6qWNX$pBt7M%
zVU06j_Yz%f)k_Ulocx9VOj{{3OI}M`Ggb`?*3v6i{KVVJk%pz}@;rXl$0i#Fu0%3<
zsj7VVa*Ntgx2LUn>jOd@7g$*i4h1e6G>xo_d>J)cx<Sg6J0LYZL=}r3<?^Yt2jyoJ
zpYFO~x?BB<p_n<@3nW+6u9ur0Ld~r08bt4tiWLYt^JGD_|EJNS=9XNvto9SNeU=S_
zWX~j^I^cYF<^+0C5M@3qX2lv8;sfBZKKFW^WoP^TKVyBL%4<E@z(Mur_QxiFo|Ro(
zRr=nxzd48t`MVgx)nZZkmwVK$&y?=|IwiGJVNs~sFfWz!z=X;!qH;d%m*@6gH5&4E
zT~)8cEa%LmCkqC~%r$Ov2YQw$ehK?KZr6?7%gIg_UnVNQI4HXG?>LkGzb2_4EMfPU
zzqTA+4&V0na5!bmKDWp%lyv&Zeo6A>7LsP`6V8_@76J&>*NG7@_?x6%BuK4WH;@*W
zkWl4CeLV`Yvg2R5-w$~GzEWiLH?qrS@54^+h|N4zoE~1BMb^k-zEITNr+7qPr$!kc
z)Im*ECb4F|2k)aVsuqHRH9_SlbIcY6k}}8mg;&bFh4afTJ;yVmwDP7h(+s)e$Ox`c
z8w=)#FLrzx+r2pj+aY#CXEtYnmM3&V*TmNUb;o<~NXUt*!Vt_HS~H*YTF@R>!8Ah>
z27yphzXQ0!fI-3tA+RiLSAe9WZFWE0KsJ)57s$&<hRAQYxDdhH1n11(V*thG%C<Vr
zvNYC-#K>Y&)mMwYrE!)uEDi9!{c&bf?bO1NloQj|b$c6(ytIpI=_%5PdqK1H>p-<o
zZAE_<OV@LVT^<vgyVtB|KlVm-ln>`VnyM7t_<P=GuwF1?ti?C6Mf%*juT1m>k&Maa
zPPh6x)D228mEm{$c597gbIDzD)^{q}PV*KlU_f%|yL{Kqoh_iWl%n@TOW>P7KN3Af
z6%+>K%vYyTedm6)I`l|$tkr&hAU0Q-IBd5_6tQ7EZ(UdI@9ej!_VXD<%}Z5bmkT(3
zk7le!&i2WB?+lK#fEwzrk;;gFE{_yuJZrP-v@{zWlCJsUUvvHa)VNdwyF=MayE*60
zKT_uFy%%*ps*I}DCevpm_bmDA#HML*HNguOh&f)^31k)A(g^u3Z663w(vH2D<H;vY
zL<oXw*ZM)W^>w&cg58CWAtycJO+>;?&CUCLAX77<IxhZk_iPi286v4R)IE&GsVqq?
z4NZh(Y}<gOd>f6YI|kqO3mbaA0l|#QloF7Q>y^|>&UskZxIVGYLQ8*Od$Qb<dQtf|
zdz23BEZrsZ*R3TjW5IbR8+Y&Z!Cl-DJAEvJCSe}v?Hx)sgrvMND$9y)2fvbagEmmz
zx8f{m0yTjmbeZGNQl(S?sYh`ua82E?Dod|@u}J5G7jq_>W23MQOyLo|w_^8gEk0&o
zm1)_xlDiRB-|X+rR<91!l0dRTR{yn0x$Y~w9!y>`t$^-0@THsS464dqD*X-Aax<x5
zhqf5q4hC9%z092W`1qI2e<o2QyN~OJ1EBM+Xet8)Gdajqg&}uAOJptoy9LEOTbApo
z#}!se{jKA3^htkn+)A^06|+ww3Xc@Vvy|22@KmFF&x6S!2_Z8n4OP{lv7n%!ddg%J
zq?=(20RaKK%%r0<b#-;y;qh}_evFIEVPRqW{^6ig*vN}dJN6X@_3%zUq5VKJLd9*x
zsv-kYs-Xm(yKw#yy{TM(8y<^n892@3V4fML-R#LTCko}i*fmx8{fg?UTcx$xuC2%F
zhMXcrQ!D7$WzaBNQ=LI@q1$R|YTg`04|R~419j!UUx0(V0T*CRpNb}L;D-U{=2Qs_
z2a*Ck+FvG7QVq#8kc#@@y<1!B24=Ze&-FEy-wx%La^o^z$75AP*w4>@<e#{82iwH$
zr5E$Oa6)~31!%s%_8*`KV~m5x@80R~zdwjqJXHwgF(2Q;Sd1_$*%bBM*Xm@T9Fm4u
ztf=FhWoh5A8BnD6@?97Q$<4bk4Nl3?gbhzD&fLu=Ij8bp2xxx3N=0b|(zoK%X%})b
z__mpwGVlLQc|=~@r<};IhObbN#}4Nwv(91NQt<=p>$l2ENLIyK^_Z&eO9e1E;-JYk
z<?nytnyyk+Uqn*vtu6xF1N2*aKl^~^Yd2Pw8-+&}n;jT=e7TVLouS1Jidmjjjeqsc
zHvD;nIvHS!O(ws69T4DbO;Q>3?pIZ!8JjX6cmMM}b2#95WlG_$04+YKIRBmEmNEoQ
zxzn&EY-80G<G>XsZx|U2i;UH%Pt{H1u_?qjLnu{)#39GHY?;{IlE(Dat5$W<#-LHB
zDw;+T-u&4cvX+;<p%L6JSRrV)|AXUcrOZce_WiQ)^RYojk3ZB(&poP$XK4GdwYZ-X
z{c6GG7DSB=TL$-na%ateymMK>hHlPZXPA>mWtVX?FfMuwu<J^bwIj(A(mY>Nkpteh
zxx;nvt}sX698h;Ub}Oczh{bT(y1NGll9M`GEUyRF{Fyn*jLuf8e$hJcycIEapry3I
z0dV%y3qqslDqyj@v0?$6>tRmchR%*D4;@oZZHaUFBSn_+sC6?;SWB(JidG7G8QwmX
z1L^s)%2*`y#bR@Yc*&X;nt2A|7FA5x<~PCg8d>rfKR>QF&Y?|#z6k~%c^jkR?r~G&
z0f1JkO}fe8VdrUW-Ft<7=EgKBHnxd-ag|^&G;?`>Pg*el*)>OnOua7TNFvLRP9?~&
z$z`Qnznll{qAQ9>&d26@b`93-&o5^Ep;xtLi;0E7W%Z6Oyn*M=(Z!V`C;tfzKp{vL
zylvnn^Nj!^A_ri4_Tt|?m^(WGYUIAx4&6qv>LZJ{x{=QeQ6DT-_gLw*NA+3(0S!6b
z1q6kknrdP8C>Z{$<#O1SrlQ3kMdOc(J8si?rFORp!zl9EDs&UB@vCj#bYtEERw}_(
z`tPr1-{AheDzZe1$V<(0h?P$Wp34)tSgy@GKSLe`@PPyzkzEUZ?jKKlPUq+Yjle6#
ze$A$b>Kq$`PKh|qs%ZNTGo9jzAvm~;@uPTk59DQRGBy9|wzw`i%H=j=Th*y7Emo-!
zHePB_o3|=tUi0_Mi86{<CAjTr<p>P&N6)P!OC?Nnlps@fF?SEuGH^@3eI|sCSMzc2
z*Ty+^l43r0t!UVaFBVqQoj9&}36l|T$qboJ=>v63(fq2<S+2*Gonvb_eyZKiD=nc8
zk^;-cD8|aBGhtK+5sw8t9|J^1!16EGAL_l-!&Y<}*wUz`n}PKRieGk>5e%(v+xqKB
z!N61_WEt?2V;()y`RAX1Af)qwSa6}yozUldP6ua6I1SxbPtdvt3SV?eiZY}V+#;sF
zhVXR+0h792JMJv@{gzH_>R~lWV$kYQuuHD)T%#h3Hi^`;S)MiV{05yB6*tI6gDesD
z0q1aRbCpcSp~qGa%=&jiChouHP#AJpMM4<b#;fRV0Sdh^gapjKDCsIxO;zD8GiT1U
zp*s(@!tzWF%v36{5#%(ZyEgv3XFmM(IzS{&4yu~pJ_3PZDcs*1f#^G#*wC&jD6(Ji
zIEa{y3K4yUd#oCmP-9CkkQH`Fg*N#H!GxfXG01LU3<!&gn!fN^p#`Bf3u%NwgvZCm
z#_YPzxFjk!7Fn(W2!lN7NVExgu0(3ooJiz}-f*-^u=Tf_VUwYh57nU2xZmk)=MI9%
zg%`-8`*H}u3+)YV$*un6c=w5Wa3_cuR;Jm9Q$VP*Jx_<$i;%IWaPY=M#Z0L77%&%$
z`P5pi2(U=^@i}#!J#zGD_WNdN_acd1M2`bgf6<MAfY^WdcaxO07RDY{$E`tYh6T`e
zxF9&7eo*`%`1{*(JsTDCE)E!+&>IA`xL-{XQ*VVuK`q+v9E+bXzv!+dABVdfxrzBn
zh3^8h5fZv6vD2SdZP}s$zlGeRkQjghRRJ1f)T2k+=wrl63M!&1C|m3ANugyx<n_ao
z^8^&`L=)F3&?c{0(E+gAz<j6e^%i<xJInx!plpkKxh^|i;3jeN{`r}-qF_VngNWV0
zX4I*yZCW?<!wUN4BL_ALuFyi@LQQk`wu#CPYt}{}^KI9V#GZ(#C<ctw{K$=h;%rO1
zaN$A=Ii7+LLqS$;L&Dz2;|Q#k7zgBa*~+;v8TxTqBaE6CcYq}mp#g@RS&PG_b|dY~
zav3V%8|k;Hp>*w9p}$c#UL~#f^9Jz;uAyQx?o{!-rx{sxN{13+`Z>c3eD5AtL1e@h
zhz^ZB2~y{#w!U8ixfh2q2<}LoI+aJ^ufSw>QZ;U~7P0831M77j-mmByBxflX3ndMu
z1Ih14u@^4iHM)w}AP`ZO7Q8TY*4>Qo_bVV1m`E{ox!3=GKYt(K4tRuK*8u{==<-^u
z6TDeNK(I?{QBeZJPwF|53O+U62rUoW->K86sb?{q)}ue%9w3EEw4zgRd{B7f2qiOE
zxXv_kp3gJNbCKQq)64kzZEzF~gBTjLYd<`(^v~RheHsg;(fSUitzM;MBl}=H$@wk0
zJadU`-1+%3Lyl}OkyO*xtzNAJ4MteFgiHHN=uX=+oXtZ<&k*e<aSdb%Kq+Ej1VVKM
zG`Lw}roTUONT;9r7GpnJ_%@AXKxhMMmAAF1Jx&p*;|YL&`?!j!&V})>+=;IQr`q&E
z63C|oU4xj3O}5$2G%z>8tA&+-c&#l2DLz_3woZ_^Xfl6pEv)==duVRVAtXFE##4dy
zgKEz=yo%%F;RVbE1cxSUmMVWJCK)jo6iaM|Gq&S+smB~Hr2<T?HS>uY&bfT>G8U*+
zpfhAUaBv|itn_iIf8w`G$Bc|dpU~T@{zyPZW)^JtBt@y-^GozcKYG4zY<lv$)RFlf
z)>>Lm(0f%8A^04D=a5DTM4@Pq237Mf>f`GTS3}sT=BB1$3omYweJIa)(xJ8rqz|A_
z&92gC3?%%CO*zMq&C2d&aXk=J7J%rYb;S1+QVt_S>fWl=k&W?4<nk|*f>P<(#!l6S
zet~u}O@0X8p)1a2QIoR%i>seit$7>&#?}v$P<ucP;IQa2)y<n!$U7U)LXM#iFOxI_
zcuyI}Uee5ZT!tg>=v5$BGY2TrmMW5LMu2mnjjPDP|8${&J6R{r_xp5cnU1K@c-RlP
z5@3SV^!qz)JoPHtwAYrC#<NWXf!R<=v$_oU#gNV0;h@bfi@-bbY)ft^k%zo^@xoa$
z!X^AS$u{*vvyBd+3z;u<Z5cMB2+_bVg_bP~)3(u)fFVrA`A?P^G^^~`*nNI}pQSP{
zC&nZuN_C)snKm1mTQ>H^ghR^C&iXU{`FT~0X=%XSCM{jfNRf+8f{CUZ(#E=;?zwy0
zJ9_hGAK|r<O5NW2a*3u?T%4+zsTESyr*~X>OzvjRiU^rSX|I`YpQ8t36&+##in3W4
z`U>3>k0*n0+RA9?UkH~0;?N7eX|6MP{aW`AZ;xtDAKQKv^jdW~w)pnyFD64!d40>q
zzIv9oN)y~IbZ<(-xi=I6TpMwKziIxYSFM=;|GszKiWa{af@?=&pM<qH&2LXg1`;Xw
z^g93J(p!^ck8;j>w=8bC+TKx*zIQGv*%R2<558)hNTiLU@tv5Y)VEQ34aC^3GX?P!
z1O<llRO`{1WOLC#61~k5Gk2>*9%yYWn^F()6BR>#!DTe)%*W}TTpPnGPBYrtzLFrB
zU1jIEw6j(#$O694KR@}GH?y1Qd!{n?yp_l+58GYvC_h8ZZFk|`qnwYpZx|aNi=TVM
zGH5X#P8f^S1ufG)0CBn`<j==eJ5NY`j}Lpn_2mG0%pRYctVP{Fe3oz_h+-Z#WrJvY
zUC&)?D+A)W3=HIgzYdU8APWMoY-<;WU+yCAtic&~;C!S2HQ8^UyD`Q?;$L!do`e;6
zevVAG@OFE~d*~gF*J_nui?S*k6wAjqUW<%8Pbe9wsKSWWyEgf#I~Czyl|QTCdSQ7_
z=o|DyA@ZnzrYa6*0;zTLV$atB>Wn5sT{QNFXgAtm4cpDMYkAezTEWy4xevi8VgKp!
z%9@zOfF=EP037{+t!yqWrAzUQc8tXtwDv6nF!eG9$4-)as=@*25$JEKAXc8s$yq_M
zn1%x9#9%IX_B@#Nu4m7({(M%9X(lr3^@zFR#p=2Kj2lljWqJ@^1bT9<;d6vHZ{E9k
zlaKH=@5!_NV*6~MAU#Z6yEhxY>aw5+FMfl;4w;eJBSzJ*(g@Xutpzdfi@|NQz`w2n
z4-^Z<wLaS;*rFZk%=?yG4^jozyh+FV%&gA=W<(QTQ?R8Fb`2t!`gtD)A-IXWICFum
z*wEU&=<K`DKu@USmSYUStt2x3tE6%1l3Cos(Q+clqUQ1O(HoOPn!#cpqEBFR{mb<~
z9<Sk8Kn1!6+SGuWDLN~ty%GOaWFOS9Y9KI2RLAp6f(aO30*8c+ZSk8I!coETT1J)=
zSGN?<ivl(tb%Q7&6p|BTl3u17dlO5gXh;qgOiGH6uLPMq0gg{v&^-i5Q@L=U;y!-t
zSYLma;&YO=u(Zt-Jb-?Po1wlgPtZ!3SdC>}Cr)+N=eW>-#QZA`PSj93<ca;T2ShB}
zae@sK4NYWCP2$QT{F@J*^frjB#-7o_F-3J~G#cIX*LNi}pvS91%e)^#5)}eiV=X#c
zuoqB7Wk^zQqFOI5DX9ihXoEYO^txg`A@GA*5DzLlJ@@uzBU7wolH}Otpb;`;f&0rQ
zT4HI~&s%!^o`$kbENIBu?d+q!j1-73!zU;X*6*O-1`Y2xO@&Y;fLO8mol}8-w0KQ9
zhSKKzRE}yQa$&;dw1%)~nu8n_hz<j~=M`xe@>iHt#j0tgnfkf(+z5W&*bSLR%6j*@
z80a5b33UMaOQ+G5ib3({S!*DU3#dM@(IjS)c#|-#DN6VqjBq_0hdg=x6+vH$#gr_2
zCfQ^<z#Un|k)}o3Vn{JwbKi{}r*qs=$9A%Wi+!kTFnOhXKm<&g4iKReu|Z*iD^dzr
zIQ-c&e~sthuz;LmpHnZz+uChG&(($5qdLkWE7;jI_Q73K4zXH5$F4kLuM07Q(oj*M
zZtRfF9ypp}S{2(^{d<0ppKrkBX6s-1ycLl4%W%aR`>Y=(zNnN+Q9erEAjm<jw^v{B
z+#2%{3LG|zJHEkgJ7i^Tx(gSW0ojHq{}Pe@99r|Zd+rA)!>#M>n_CI+?F`|FR=W9Z
z@LwEN9lua-lc63{1~$Ju20V*6jU=F=58!Hne+hQ#xokx{lHBrS=K3#XXMT%zY^8a=
zYFh}cg0yzuH;nTM1&S^R(JWPPYe<oQ)bW3RQON4DgHlh=W%&47t<*hmck_-2neNgF
zXXfT0$}tgcA5Npt+Vwr2%C~Cm+Sk5Pq<<v|VpwiFF-+Uv&<WknE^N@b?O0N}Fa_Cs
z{EzN>%OZ4$N6@UnvOwz!45;e{w=|@;RmOsx@qNB^%QhZQn<D3rX%Mph&fJF1&z}vM
zqu_E?hwbu6wQg0$R<+;MzW-|B5$}_qI87iVflr(1uHCx_noiK5M2>y;z-Uyx$v*;J
zHX`l8dyMSwJ<s=5#!GVGR%hM|Vsi%DH0{P`o{X+LSTw|-gylr!Q1R^dJ<K&9oqQSx
zFCjZo7+af*q@^sH=9Mh_(U)<5b&WT<?{QKIP|>sIGVXa>EZ;}N-UNIsJ!Q~EBGS)x
zqI(IO4CYN3LL{LNk)fYB!DXn+z?q><FGscEGz!b8esl@>w)i?PBTj!L3_v_|WqC|!
z=ysy;f*m47Vr68G0oO>YA|(iuE80zX4iHz|$ZIoyAMu>V@7QccMY~cr!P_`cgpUJ7
z?`#976!zy`T!<iWuF!QRMaC9hveJaIGy0@_w?t=v81#6oEXHNH3H<R{eH*fyP<T<*
zIM$Xqz$9LtpSsVaIt}+&Rt1RL#6psEjrJLi5(&^$QZX<W_DOYNIshTqPu@2v()F_e
zX|x16c9fBP!)}at{=nqW(({iSKdVGw-;4JMEjMdhtxMN^RP9f82up50g#45;^i=RW
zdeA&i=lVZnmCw4=We4*YmRScTD=Ew20T_{_F)UTwaH1u^EU}E(u>!O2uR4+44%bN2
z2c58el?c^>u&-<8iHsu43<h+EM5O5Sc$jbjNp+0nCIaU*9&zhEi0&5W`iYz?@!?16
z3Mco^VuZZ!S2bTm`kLym@W(kWFe^wrB_ix7A7=$FUnH=8plbyztp6YGBU*r9*qqiz
z7to0&nIt!G{?)}Q@yVU-#5Ze&!1CqB*iFgKQ-UL!au-`3%!I|?F)lZFV>uv87#Ucw
zU;!2P@ZrO{({Ov8&4i04d*$E12lCRhbt^>B20E4HktUwElf@z83z~b6RDA=guV3v`
zQa<pIlwfS7xyb#pb=Hv;PDY+IIQLpiB)A8$w22w+ZDx=<d*{xb`m@E1ongOUg%qZr
zg7LprQqDjC_aa)}^jjik{%~-ox4?^TM%T38Z)M5eCJ|77^~1(F$nEPAHm14g?2InK
zO(5dv$i?oyxAWzZ<Xg2Q(2FmyZs;Vt)Z`p5v>&0}twb8=4CxxZB4qpd5#tM=&72d?
z`rf8)$G}f$yO;FX!#fI5sKLp}tk0kCA@+rktHFPU@u2(n`?zzpEij<}UE-#3Op35k
zHuJ9xCAjGU)oYS!fc#g^RwOwCLC4un>_FGixw1@P>Qq2KFdi})`#LP%U#T*BMs=cw
zlN0r59X!Qyd8gp-{`lnYeZdgvt2$eN*WP4((^%KwavOIJfO0<o<#Ms7zKFhlz7NEQ
z(%l~Tl_~Kd%nOEGZgBoQ_TXI~I%sWRMt-Rp1MrWN8y@Jj|GurTZ8fEKC9qf_V8skR
zr&OvAn=(J$I&mESSm-bw%`nkC;>#M+!?~zL_WRT@st`?<r(JmLcpkCQfGgMU+3zm|
z?t*Ic2odestWq7Vu|jZ$IXKcxwH#7TUAvG|#Nm0fYTrscAP906L)kW5fhSNmegB~T
zU^}-L$nLOT`M``gTUE_e$JYX;qV~)J5R;q@I!Po1;Alxkby_qE-@$4@`j6ixKV__d
z+kvAEsvp`4ec;=~E_&tJ8L?tFxTj>jfI_zKvgZZ@$+C^81BC|!IZEmu@*&@kkIYS9
z)haVJm%aNx(3;9}uZXKKigvkypjzymZx>sx5_UXU^8o8bGUaduIcQA()eWdsLm8}S
z=fC<*-JF5?l%(>?5rd`k<#}&MpttdEb|6T}WKvX;yC|h5>wZVKzkffpjF0qW^?9~}
zSW2V-)${ygb6=R5@*SO0gmy96Qwh=N@{%45=82S#H<pmQ{_!cDHC3<;y>EYX=Fm9{
zik<Q^I-|88yuBMlC%iwIu78oV7R%XC&Nr&(`>%R~u3nQj<Dg$me?fxVmu|Op+v*Z~
z6?oQxV~(c3$|$~yAUnT@s!0ikNulz)ngsh)g;t2}AauI<N7@+%OP}*FGZAXht4uv<
z2Lc6PdQ|Bq1)L%l$L=gEp6SOcbW!Wx>BxJlrKG5mR2N)(L)V{h{r2OECZ6hKMD0N`
zS30hHqw_7we20$O&7{rf&cAa*t(tN>08N|RZ*Tk-?p`S;gidH;9Y$Mp#ZLDa0Nq0B
za|3Dt(y>iR2heE+N<8Pv11$`Lp;j-Ld42!j{T~jAiMFT|7+%35-wYUQ?3H)7$b;sC
zre7aT>1>f#k6YmyzVrU-B$Lv%uRdVwWotU_GyBn;Y~S-iLXBS~j0Fg~-cSMbD}XGK
z)NF%vDv!vKqmI6VOE{dOrE*}Cgi|@bR3!iUems4tu&5(CygmGJ8x}BnC7XJ<b)}`>
zG?jfzcvX{NM02#dK23a<yq<G<+yQXFnldG8bt-d$<4P_-eqfJu73BJB3RN+n1~mLd
zGL2hnjGxc};*y#~?@YsFVsuKV%{H|rQIoCN^_{ve^12Kezzmx*Gd0~bSVd;#kI$WW
z_JV_<QCcj@*O}DFkezD%lA}3aS(L!?8({UO?8WQAs!Nnr%XOcI`4bx%`mR!H@%ba#
z>I1gl<p|q33lSz{uFnWiyaSBN)Y$kSDA%wQ*hO9fDV{8|T31rKBp*N%+Rug1(&0B^
z3R4QL2Zo*z{(%$s61lLS)NkR2tED)0QK}%Floi+s9A9D_=wC#vNB1x4FKN%*cV+sP
zG|`bHaIw(lu_cqI>~S%Y721&kKeR;0BSPqOCDwvq6X&Fa8#<BAwHQf{-u7Ro+cs05
zocn<swG57VVMzHHgj_}R2Y^<1`_(ixqk$o?>ODZpZ47P8N!JF1_7a0HUJ!bC`+p0z
z`7gqND=xD26M6%}I4%6OqmcEhqz1=A#Fx6^kp&KIqfr#LtUgPl7S6O;uR*?A7WEQM
zwQz7-MAKY$Mypk9^6^7P2>JkW!((YZ4=}o}xtzFZcoyd{2WTXfcY!&G33$iY=+H}n
zs6q=iVR#t9z1Y4gkc%YZ_>f&kpIt43bD{{)EGVD_1qJuH8AK;ii3%{&tsY~J!n!Y_
zot9w+PvzSC2Iq-sDr3-?PwqV3=*~$uG$iQ6Z-sH=8`;)=d?rmC2j!4;qXV9>dX<{R
zQ6R$yxaKD<c9%y#(%}=6r#<~pZu|A1Luo2C&3)!e#rin(l0w&x3Z|8-t)VcE01snA
z-yzsVMHC8+GbLDhg*B{h9Gi?H>3v0GEX;tikHG8k9<jY7LhH`AWv;d`swK|##O;9A
z@1<Ke+M<#Nyc$Y!l!6)HTvZeZ{kUKJJbYOEpppPyvRa3~+AYe1qbBe-Sb1EG)5Rks
z7+3X@F_`LDFZ-=NvO;c!$;V4ORf^8%ztz?gih^7#(LXpLB>-*DsPT=a#5*_++o%eI
zOaQ|f1WF~70)AS{h>o2arP2Ffe?mHNK40~!j#+o}D9ODMqip>;A3oelRzX&izqT-p
zfNmuHcDJq{Y>A|A=00upL0iX9QmV&InJry^zkYr|2}SgwaEs!7p8YQ;EoKlto@e&U
zH0Fl}(IUiL=Fq)aS`ONYbHbh+;NgFzLH6k36@=1*f^z#ww5}SL?GWLogbEi7m82G0
z51l3?fW9q2O{GkWeOEhxq~A}VGh*$F$rOuVrVpUrtRlUCYX8ym4RYV4Ahd8swnhL-
zYaFs5xNO;u-)>#T)O?@~!R!?8p|kd^er<O8Ds2tTHRUEI(+OZ(WKiKtv3O9nt?#3|
z{$|?LAJhF>qlRM0^0bqpNkWJH=^JCBRx`HKT_WF^8LKFb(^+{*yLu%jmAC_(1YYkJ
zZ4sA`T{|zjS+_UCoPn*y>YUV9XI0qguFL+iNf$<WJ`v~6O(0Io(5~Kq$wnIZshIB3
zC@Lr)!ZRfP1Y8?;0H`5nmXbj@ZfH2rbo{rcDYfEqTRIpj*u*p4F^Sayp&hnSM3!Mv
zZH$&xOaO>cPg}Y;6PvgeN6~dQZMVzKjj3zww3G^=66+Go46lkeNFBZgWfTTrKyuH)
zLp8@tnXjlyE*2X%!gRM`%Ex7gx_R)O6AN6YQyKm!#Naipd$Mqzv*g_0Zspvw&|+~g
zp;@|s^M3u1Xn9PlLYh--lRD{i1-2sH%cDIp1%!KC|Ndb{UE{Y0)^WhJDlZsJkL<r(
zqE;z7t}7@krU0eKMLeq<d9ks9D0a-iPBTzJdw`V!x{e*mcl%vrD$^X>7MPlNA8oP0
zYv<LF=p}0j+wTlR8}>D~Ma9#G%QI#{09cfa<a4aKT&2%mDRah^IND+><DRzk1zXzM
zX{Y8c+HM|}L3X^tpi@+Wwf!gSc?^%R8Q=T)S~j~Kd`^A&)z9zcT}B>$M5yD)kCz~G
zbFN<%xF4(Dw3m%MQLfrzFJiJYtRvg_5#W(g2S61e5R&E8re^E_vWNc>x#Wwd2NRcz
zO(_&aK83Z@di%u{4|WPnU85t*cJyc1t60CFt3v=o`+VkXlvbYBqeEVLVlOJyiWChI
zDN9w>B4%LA0nO0*VJ?uEXR|N&CU8lwl{}=>uylMsIrrDE##w1%F$)7LpTmCIQMJV+
z`_$(mi7Phk4;ZOmzumoe>EM$sSZ|a@_P$t1R0y@aYIudXj*;c6Eh6i7h=E6kvf6F=
z&H$}%JFhO{PWqN>{KzG9=%uaK`eCPFL&JB%gIE9gi}zi=l2%;^q*tB2AAhPk2@NB>
z$;j-W6aAYG**%>-f2nY<FBchq_n69^lv8e$&TC!G$<|nJ^|cSZK47LX_l%3-7T>P%
zNQ<g`f6x+A)<~n}LPg%OfOf*C77e_dm)b19C<eFeU{o<?>qQ?NxEUb4NeTaL@|k0g
zV-k}|L#>yo15Q6uVQ&L<aBs`j$bnGw>cc~q;qhp*;tBOLm%kJ7pJ|6c`iz!XCl2yb
zM-GCNgkgL!KI)&tPT4dpOk6h;^Y7%D9V`JR;U{$+jI-zIn*%oL)b$c-fgXYo14q8@
z&f3k=k>)TP7w@{*JO4)Yk8GRzGu`HND-;6!g8Ft`G53uuN7u$*td0D=5vf_*k%U3%
z)RB|%{*IaqT6e`8*wqauu`Q87Yhm=DU#BMz5%X0LG2iW&jcXyOmH$DnWy)OkVz1C|
zman$s)lYaveNMl8rY2Od?%Tk8W<yWK$YE>do3mHMzmuej+r6V=$F-t<j>nF7;_a_a
z{%3r$O%eaa31UCYLcok}0~N-vra@lI&Z+U2WSh+KyS6v0jaBzu9Y#9ly8H!8r>Dp&
zbIulvL;p0M84Zsil-<_KTYpz{o6?|@;~kd=)@Ip7`oq><sIbfK-KdzbEhU%iB59LX
z<W8Ob-aQu0fbShr!c4utT1doy>J&^iSKcK*wIKOyN4VTTB_{y8y(KAYq7%y&UC+fk
zn=>|rEq^Cv<M~=E?|~}*J85gv^i52emk!2(HJ1_p#9XoJ?b4GeT2n1o4Q73@c2+(Z
zc3hhy-SO*9k1dQ;Xp0Vb&gyzTfnd4vjzU`XlNx)V5!fVR|JqT!6qE;^plBUOOby6&
zR&T3h5t-14`9&;pU1CRtgejma3h~1y>zmJ2)U1AS@5)kUw0t$HHZhZEcp!3dMS$b8
zX)2>raDk5yw%CT0XQT{f-MwdOylHT0qDuKk=iU)099eWP7s@bM6EdDCladKhpsl8h
z>D86S^W{8L^02xq_F*jhi(t$EQwNHg7V3oVjhKG9f0ZvFvSQxl*Zh)o{Cx~!FjI@|
zF(lKn^csKtEFH#+;*+V93XpprPj<?g(bD(olWhSV34=TjN<M+}%z0`0?x*T_{fMG~
z(%ivnpKn$6yXzO``*s?}e`?^<mJ@4-I*z@@QG_BH4{H0<nSLYCfb>b<3@V=*zFBdX
ze7JgZMm$%i^#U_R6WV=8#iw#^f`n?9Ni>U>MOme_>$P73n2=yI?EM~+O~n#(X3!o+
zJ*vB3emO-mqN03`))NU3{TAMi=sIZT-X296MYOzAlc0T}YwN{fbL93uFf^6T^%`Nf
z$!|wFp1pwg<CW<gWn_Cxruj;UtKClL%5#6X1iFL>i8p-V?qf`~?4n@(2m2utZ8@jn
zKIP0YfyB@kama>Pnw|DC1dH24CzVJ&jxT=C&oSo?(P|4xYmoY1M*r7*m5~NZ47LlO
zw#}KL{Zo(Pz4#vL=;C_9tK{OTZ#o@ye7W5ja|?sjBr;tq0|hiDTBn3R^gToemB)?<
z)|h&)ytYTR!+VSE0R*M{*RSz?+~E;;YI7Q@DL#w!P))4YqZ9rv)km9`;N_yHliAqP
zT=<<-+wwR$_l*Z~|9PC-pOSj6g=6U0kF;Ax`k>Q8JwzaSTRj{c##?ekCU^L4wC^p>
zL_d(CH=b`yWhH{u^L<;82{>*-3-VljhY$C6`8~kAd1*utNGV=c2Sx%Z0RhsWS&8Xa
zoC2}uH^;)Ay7!kTf`v<4EUiP2py}0y)VOTzeQ33F`f|mpn&1U@*Y!%TuRX$#LW?E!
zH96q~6$069Zk_+N+c<|rhVC_+xj(H9l2?5m<3$l2m%l6MFl$9ABGNgCP<1!x_IsD1
z?E96es70sE$K8!A&s*Ue2=$_!i6{6sVNZN56uT3iywVnZ<vnLnDADWn25E&4%s=`K
z@+QiuQ;6C2r28!2ortVwhR~bUrkFl@89q*`TWAP1_uSXwJ@zMGec1W~(O^3)Mg5AQ
zlVl#$lzz^cHx4AF8fav42LN?z;F=qUS_WLR$m0>KM8gJMW^I7p-b3nLmEpIJa_;t|
z=&I5S(Rz$UsLZNMKUFn57B^YXGrRXP9N<X1o4cZ_=AM_bI=BGRMDZ1Cw=Y%Sk(pz#
z+wh8uLp^28G<kOod%E>}WPn#n7D+#>DH;zj$|d|9QxjR{7xuvm*3NJBHJH9OctN|U
zyu70FPUA}^tam+W%CD5e;YCn+0Kbsyd^2=r1Wgx#SjoL0l;BST{#D#li{y|?oIz+M
zb9Z~py1RGg-=_dXl+r*WL^j6)datCV-&aNBcl^P1K+1G~=rgavgX835x>rfEnf&(4
zkjbYyer-;24z+`9Gj(S#pCwI0j|NpAE&L}n-=FIqHml5V3qsU6^&KT<o5X8JFiY0w
zRzz635*7T((bw)++B4l1BI8yAeOHe)AwB_!?E3)h$@#$yWAJ=P+e=@k?(<uCY+%S-
zL+lW^flz>e+3Ub+N05E&XvXm8{-x8;I$oMNhT?#w>cu1uicN1|fTLPBR=zvOse=9D
zMNP-b8nh@AR#U5>=8gw{dUCsyf7|7^mky$|Duc#{xD~+7;9c`Ba<e%{^DpL+)0pbu
z{Y=;Z^X3%_t@wdU<U}REBjLum;qLkY-<M{$H0Z1V(3j9533%p-tmFKxn=1NV`3@A&
zC*O140A+*2XX90a#&2m9i@5twC`pgLdubrAyiv7pRYo4yT)r5&q&pFv(hn4NO9VGd
z(bOX4Znl@|(z$5~S)S0OCP#gJ9^1`p@m?j-kW+eto95bpt=k@Fn0^ITCOJErl%g&4
zDLO-rARH3+9&!MzY5gPntOmD5!Z0n%<)!Ac#sXzA8=6wXg=%Ys9J9A+sT>Szb2C&K
zeV$sliloa0kzhjYyEFql*V{-08HH)tv@h$sR0&~!O5uZZtJy-neJ}TlPp8FqFf(6-
z>g)~JutpDfxS^Obbfm;w1I*sj=u-EGKfsx>Gwia9zENXJxsgVZE%9YBJk_`Z2rI;N
z*IJ!FggYP%-J;tD9%5L?^27r`D&?Dt9*KvCbN^Z}CKZcXT5((V1;QPHQ{Ub#b=Efs
z#RMIVT;3n*ktLUa?2f9NT4zbt>MI!$th>wqeum^Ol3fezq5`$@wqVFxyD+nqb%jrA
zvdK+k%kN(3wiH>J{#!?j_1m6`2ro@Je%wkfHU%5NoJ9tv&V9MTLCvL@i-<GT9o^ps
zi<?IV6;CmFPYJ<bCl;D*-9plBGU{nAeA=?%xDg}=r@x4L?wK9*hP41*ZCO%4cQbfS
z9Z4P<%Y{JZ$AP!PIIXG<PW$4$uQrdK@%=tJu4B<8qwZKk3~Yc8r?Ygt;{cI!oXE9B
zbJoXI9WmuQyJ@SGa_j1{+C$0}Ki7&BSu0p594AmT&f`nmlIC&%)-=Z=VRk_sirJV;
zSL5G$(M>b$L-q}u$HBcY?Rfk7%wC)GmXv+Ci}kg-jI4}z!3zp@tRaptnV`CYez#%G
z_NCiL?9nu1a~;MM8wF9N^oF7otDOQfKA+_*5rC?$ZUX7#6GH=d)kXzcX6>!fIqiw|
z-iU4K5B%L#3fnGG94;8p`=L0x%Ez7b9_z4Hh4=`tCJ+5)q)|jF-`KiU?dQwD!?RWE
zhhp(y*du<cFDLkj<qkAfFZll#U2g&p_1gZAJ9Sj2#A#7d=oClU6DeyYB%zXh581L5
zLuh<d2d8Wiqq1eszLT9CWQlAuAtdXVEZL3icil5}p6C1h{{QoO>Unk2%x6BI`@XO1
zdSCDN``U}=7g0X~VfXj66#VPy-Ku}LgCgh+m-Lc#X+3FwsEum1q{ixz(`PeB#;``y
z4mfcXd|BR}MJ)z~=S*))y<w`UCUWqYpQ&M?03D|TQ8~a@_)5rt%@_S5`>s~c#nZ^;
zv*cTDlQWu&2o0-=rr4k0-<IKzgOUSR0?KoCj(_~?GFVu^YP*9H_i6Rp<~)=z^`-po
zX~~;B>X_!bSA`41`2ky-MVpTPov5n>c`yPvvP^Bb0x#rLWH06Wo7XD%AazrRL>)6R
zp!kqGUsksE1ax3$)(sHxct2M+|2n#Sw)^N=ynzmuq0<L(M5W0$t7n#hhLbxOnc{#I
zq->nE%?Qj5krGt69GegM=2B`8vqC<^$Qc9M1pnY)F-Tq~7&;l6B5FDvkfg5~Bsl_d
z3|csh%G*jn$IZbBuukeOH4gSF^jNk*NvqHK<(?bhwDC1K)g&U<{)2-{zmrTZ7V~C6
z&X$IgYNE-QNKdhyyTDwAcH&dA8%~{AOjs#Ht(hu=t!LKo>22{!6zFIXL5BAR%*d<|
zHVn8cxFzWC{ycJz40A)h5&VfbyhNBu_!<p>#!I`STLG{0t|0!mTV+9-T8}J20Zrht
zBN4RN-Ai!@G(UR-6L&L@dv`G%s2_g<WkKxNxhsI}_a4#LzjhJ29Usl;UB`RF0UWiH
zE$3wJ&i_G^oH0jy`|IA_OCayU)~C!NQ_AXPIiw@lo>BwJT)s7pW*r?FbC8w}Q4)4w
zT&(QusX!4h2sidDMh1~UW|}A-!=g0+zl8qsRtGDqk1%E;R-*sDJhHV#-v&|Bf$l&V
z`WL`-9!70=B%Kh%q%{Hh;R3OA<nh?-)2HpO{LKt#hDbzoaGXq{g54r~VcI@<Wca^J
zXSdy8T?D~G`WSY-Tj^r)bH&Iqmb<Y|z-wNBvW4us`T3)DZ*W7Kpu;1ezn=D^A6oA9
z%B&l*#P`9+7hpn{B5H4J0N0rUcDC)nd`L@5A|&H>$B*<rHY{}^qhY99S)^Y(0QOn(
zz3woYSo7XyWE7P)=taF@XqJaTNyyjQm0Pl;y`32ZII^Pi?{~hYfj`I2^>k9VHo)#I
z-_Y%yK|hd|UUEVC)F57jkKueaM2jhTO(o7Rd_*|9X>v4Q=;W(>0(s?pGwhhXjS3_@
zB{Y`0@~w?JU?H`($a~4#`bSSsF=+-JUZB64ma?uHP}0rN`tb6!64=kTJ>0r|{<Bl7
zH7qth&`FgyHi<BSKmZN9K*&0xs-QVEDBQq62`=NGX+Y!=$fF9^eW7dlQ-$vNrYr6w
z*uwy<=IWeE6PR_~(p!IcNAdO*-+J(p!MaxV`TXiA1_V%AS|3=JQ$rRaR$p-%_+;uA
za4oZDbk_qG<-b8uq9$7uMfrHOR2AygTE)L6=I702g;67Gd>=0!7*sG0pbchtiGi;x
zSk5mma_xSoB3Nl~f{(8qB*?ZhFuW2eSLad39T=kcQ|9n*q5gqG<0B{~3;ucsitQ4W
z;S^B`HUem}bdj$_^vk$UJ1Wk|?WuVlCnoU6ja9yh9@reH6X+h7Uov?4a*57i*YLCk
zTdVswLJ<k=lZLpxRph|*>D9D&IF>nod*DE;>feh%Ri8jp2-PIz$UhpY-1Vrb6T7Yq
zK_w;27v01!-1SADLXb}njK5Yy6RVM3af;oSRUK$-00;-9Bn*iA$d-Nr&4~fR^HxcF
z^cCR6W|c~b0ABF$<uAR*A$V(0colu2c{;~f1A#t5Z_^x_xIp_*@ROLbbAswknah6%
z#_&iRuaPI)tD97I{NXfoCAua^VW_1EEz$Wf2X6|>Vwr*8vJ}LcN%t9`jymVT)5*E~
z6D=xL)ldX~C4qksO%Pu5+Prqly_AIsAh@=n3E4;!1MRqqXG?94Mcj!XW`I^eClE`M
zF(eg7`v)BS!Hj;|RvSF3p;M<0R=rwRuGD80qI?IvmVdav(MkAp#a8^>zS0w&Hf;br
z{I@(fcc+!Zi|F9b_RAh1PXn}<?+?Co7DYye^%xM>-pvNPh0%8f$Ybdn=6<;cAfejB
zOVo|soKoDGJWNxUyYlDF6}-0#{f>Vf*W$_nwI&TcKz;=0V80=p_z1XCAp#1NmDS1z
zH~Nd6k`D+Oyg{KJEPKN{XT6`+-umucoS~H_R5o)FrOlE+dBvxBAzN(3hyj_>{CGST
zG|{HSr3sW9*gJ@yh^!5O^<!sj#7TR5xSJl}7Z^Gw1azskiM74(b%11kT3j*j2<@1F
z2b!~ow@-Tzf6M*$s$V>t&pf&1MwFDjqnFaJ4C|!W8XO4gPB{1~LAl_|Op4H21s972
zpjSW;y@m}!>aC(KyLVfW_2*~zbyXkinD`{&G7L6;5o>jU|JDP7{1KL)Zmsoyp;?es
zHH;@L*OkKPWWR&LMD&D_?GkX?GiC(~ey!?gZ+C{FNaN@sgRVg`Jv<~N4vj#e_AgZ=
z1CtVEu0E#YFf0>0_;2}Ul?e3^ltT}yv-=XE7*h9VM*c#u4bQ34Ojb(%l;P2P{PkGR
zZjnqu?zP=thP8S>ro&N#cPLq&s2cQ|@BKI&*3q|DgjC6+e`h&3EKi>^g#8FtcZPfU
zdR^eZ9tMa`@Z|Vj5SEK$8T8ig)@m<iZ2D7<Jeq|7WFl^u0hcn#Hvs!?fJ!WKp8Fs{
z0UBUvSI>el3rgg;Hka$Eebb)2%5X-Ghf#h!EH9IGD1qzxEbf))zv~CN-iWyH!KCcp
z{h<BO4o%hS0&uz8f9oq>(%Dn+-W}L!jrlC$#e#E%g|8D>6n|yF*VFXpBf-VlKMbjW
z2pp1}dO<~YAekqoXiZ<{wqvA&<_z6G#@eYRx*bpr;CA^HG}>((mIB?Q(iSpEh<*sw
zX*rq5N2a$kt}u$t$YWA`9b24|Df>`bvAIdX2oGer?9W+8{*xn^NG?|GIjfrB-@euC
zaK3-o>67Ur*}1#7nN^*sll-(SYVx4+an9W&!TMjeN|vm@X6(7F_B>y;^}~hPKU&U(
zv|8%6udm7%etjJI+(Ex^?SVnK5Lvc-I*4UfP$g>>k4@SdYaIRkSwg;Hg?6SvirmJK
z9I+JZUqE944d*vtw%ZGLX)$w$qMxOU&Q5`*8!GpYc6EEW+9vzN-PK<j%si=#`^C=Y
zjh3<}vAjHr_0>dLyHBODbbs>0Ha0m42HnX^(~hFt9R6?4?Y}-|(n+M+rtTb>dwWyk
zRhFS(RFCUh{hV{rhPauVmCVr-BoemOuF*--SstHQW*<(x$!Y_iw59s*9;vaxryKzd
zX4Er9DdL(6g@``_44z(#zS4BiB)~L=n1q*x6+C2^r9t!<ST#b5p2miktKl$g=TGw0
zY@p!NUWCVvn{Qs#OL<869+T{rxMhpa<5uted^>gDIM0Z8Ax5lU`_z6d%EeRKl$^3C
zRQ|R76(i>!hUS=uZ7&w{T>@Qe15Y@+R}ra+jOTiYFQl2N(n%Akei}`(j!m8GtB*|!
z{@Om4Uuh*BJ9;homHeVn#vdeWD-FZM2b`IoXpz~ABUfmxueVo<=Wjb*!qtvWss8WG
z<*R+ugx<+$EPP$<*AG7;_|e0=m4<qH`xDQL2w);{TX|RQGGbzeK8{jTzfbiG+3D!=
zZLiMeNp8~=$m2E+e%N-X$)yNz?Ckv7eS~Pap7^TeFrh_UbNSq&`8j5JQbxuz(qxE3
zc}kjXQ`nGmbBU+2XP|w>9dGoDo2=97IDwtz*rCvQTI_c3C!tO%D^EjKjah0+>=gw!
z-K88c+8sRfJZ#fK-Fank<$*i!*V3Q?z5wdHh;Te;L_o;@LFK3O+56D@@7h)BJv5@q
zV{E3i#G<otT-xb;DlVsSrOxXmS6GK7?J|PAE!liv+04)E;=L_^^>*BL;MHZ&*})Md
zKkNH$mg}jNw#p^^@l*N~iMLNjoFZo34SL$ICAL5M?3byjfBlU_e>Th(G@T3`dMn39
z?C>oWKTst6Mm9<}M8dOxIlRnXgTHClsGTwG+67#74{itv3&UjRzQm=nF&R`ajpDJD
zm6g=Aw1}m3TK)k)TJ=DxT|D9ZiMaD3aia%TrF@KT7>RH)oi6xb*C^0XR3Ll6H_2yy
zaQa~2qRk2R_*H%{g$!4#tEGC!W`np`q)L45WEks)re)Y`^vX26cD7T9%~;?)%i)u0
zWEh*Ktv!yHC>B4<-Vt4>HYpwYZ6~Lsj7TzdpY}Iw#_g(GpFs}ol<@Kn#Z6~NpS^pL
zkug&FC*V5PE+w7B3m>%o`GxG7sKsrcpUy>=<m5<p{gw|aq2?$XVG{0VM@tZofDHbv
zzHE@~Lx?<ihy8fxuPOztRcEhN|BY71Uje&w=OUTyQqw|7W;UhuiTqk&m<2btVPs6a
zw!Ka){Q5&#vj24YdB<bJoLWn6ku$gz9*bGGt1Hq!ukdPP>dX@IB7#ZXBI4_WqA~@b
zed4#l2hv}DN@9eMr#bp|{&j3_ucH%NySOdiFg(Q!S8|UyYa>qbTkkJ~U)5Eg@cG^A
z*!CN?l<xw#{j=)?ixeK|XVTwKvb9YS)pjcm6;7G0Ki#_`E#xMX1YhsK2rG-0=KhJ@
z66tM+?RDZWp_R5e%;G&Fx4%upw^ZyvQGRavd843{T-x7gmtt~V_rbJSb2T^<!u&#P
zGYgtyV5C$pyO?v8<)<JN6Rpb?U{vSbYX2eDyvpwVs8?^v#4-3tIF9V^q-(jEW@aqp
z%%45tbB3f%4i}Dkm2*d3-*TJcuO`KmBW#*SLzz6bU)&Ql&XH44p&KF`Q-7qg-8+TV
zLWROt%j_GQrWH;}?HVarVzt*LzC2y?_7?3RWAIbg0vexU2<FhKLrCmO({37x^Z3CD
z^cvy$ESEa^y`}%*HJxOtqTSD>CF`2IYy4H}F@4H$DX}i?<lUH@Yl8N1v%9ddPD4%7
zRQq1XK)t}ViEAhS@BZV7L-<0X@Ozw<6TaZVkm>Ypew92v?kH8`U9|hteIo7M00xI?
zXy^m^I2agT6aV8J@>-D)JwWO&6k@!%XHt4U@!|9JLSk#v-EkjWSkLI05vNk;+EmeV
zNB(-}=%Tt?(|Sy4=(kDAIFYF61S{`ogf1a*otMhG!+V59F~2B7B|hh`jMcZfg;$M=
zRAhrvRB>UE|DeKwfk71e6D|Tqp<I!fnwV((a+&sD0bujw-b*l&ZA(6X;ut29&f8nc
zT5#@*OE+yby7eX5>u4vwlip(gR^+f2Atn8&68?D3p(Z%5VJ(SR!aWPoF5cn8x&y~2
zYBC)rq!o7XJ>}HM!rT0e9K9}{l^@}R;jIp)%ev7I;zbYvh>3|M6c(NV{!&URAKn!q
zOX#J;7r4X(xAQ$ZnNGyDRKLks5I!mVjAYkHIaN^57Md37b$GOnERtVbaV2O<PR6V|
zy0C@Ge@E2~iTwfA>iXepzQ=a{^-j{+2BJu<n_Ax|9l@;LP=|7r-M4cvrpNq-w9oOX
z<%L@T8YFiJ)M;G6fWN}M7iqv3z<W}zqlSLN7`%3gim-P~5n67#P7`Kc!zr`<a~Wqi
zyWy{&O)8V96ZwPFmXhvb?0)=mzJaq0w-2i8xRdW^tyK@_I1UVQDhw?elHZwLCZ(r5
zYaI${#mpw?+wu;QWW!6uIG1ltP#L9HVAHi=3}#U@9rnO#`M~q~p@#Wv+K<5_(#pAi
zv6YLWd6ez?QMR^<K>OOg1iOVOh&$;*;o)9~4_wobr}8@m*6nqlx}0@}<TRqjpusW8
zdBu<%2gyO}#}^q36fD&*B&u9S2yRNo>^lQ}8hwd@LEx|SU6?UYXK30<7@?&iBO~Kp
z%Wo8mCe6dhOO+(wpOod3|3;2C#XNBv!RT#2e1+YUgf_75FTY3#q&Eql)Ufspq$*i%
z*iTzeLh^ck{kWO?F)$kP`@84Zq7gX`7Ey0qO+T#9|KuAC@6mOrwCfL0wSd`94%%D}
z!hQYcxAF<iEJREO6yi3TVFGG+K7Rh^9SuA`F?j+1itk<#a%==Swzk2RKlCZu`$uf_
z%|yRV;`TYpkB*$>DDgB*^z^_)q|tg2TG!oH_v_KTTYx4@^&H=XI39hjJ^pQgcFFs@
zY&|;KCu3+UM!Sp9L>Bq^tg_k64VOLKU-ybI!InFJ^!53pj%~QzHP-}gJB%Ub0D5)v
zM`zEk-LLw<#bj&jTmDOtL2@kYz!du=?et?`FrV7wlvqi-<<*oL!>4t78=LN$o7`kL
z@YF)rX8OntleK@6dG#bajCPv{eHQ1LEm2*OPI|?jsu>c4<HG1m@@g<{+Ea)4l0}`V
z=EXfZw?p*LW%x8{wRDZ|8^;dCPKjF%Rd)Y<n%@i!$2ojI)+B~?X(vX>i4s2Us&~&%
z5n8{;prYM+?dFGAHO7k#RZDGCrf}<6#~LVVM&x&6r*O({5LoHQ)Pd6UHFJ}=-Xadu
z=_h(WK2)Ty3Zk;U<OtWaRS)~2{2zz%RqxMQp{$?uxL!DD6A5W2T2o@J9@ORbWnhMX
zRX!B>J%A@aD_#oswPUW{-C|71*6kc~U$&52;;yXT&XMrYhUzdO+6PpL5^#glKvPVX
z#Qy+Wy`sMUrXtKq34?5;#pKc|vnxL#TX<0G83pn-n-y46a74{|@)Gh5lI0^N_~1;u
zEzsLROkC;<eRu);<;A|H-2IX=ucNB}-cp^tn2$@8sWuZjlP;hdy*7N;dai_zb@ZGa
zQv1UzJJiB(c{Qh=vuXZ!+J9hRFb^J*sY%Ipbvh9j9Sd2Lv9A7~S-pvqoEnEIe45Wx
zs&;o<sL10${@gKX__TIk<6TKnh!qEqiHf9-Uc)X)u_)}{;f+gmfLuMZ{lUuTqz4pL
zJEw@$lOtDtSaakxl>BU{W_L3A*X5TcYCODlX>j9uMkZfpU+;vbhT_c0eSdCBL$aaw
z?r|Y8>*R;<)}i)A*aNyD4z_rc9P8AqD^i#UJISwp)p_#OQ*Vyny`0z&VScs_`lRI?
zkvNDLkV8GAT+@JP#i=11q(adSbxfqkDzCXqKe^vJCg1-`JQ%pph@C)5GM=OFDNp)I
z$^%T@cVpoLiOd>KG`9+EQWA*%2oIBWhvNPhBV(ryuAE~Q7r$vM1*d_Ac2?%K3z_oA
z&7K^>P|$!B`oP0~6N;`QPsbY;?G?;U$K)Ms5=r3K4r>~N;PIm8rgqBOkxXa$@^d`(
zFFRt_7ptXbTdK{MnjwMgAI!<i9FW-C!G*0V<A+@(#`}5?@7UG*&2jAYTD7_5G?YOT
zTu-|7Hgi=PZ(yd=6kr@4h4Ye-Rd&SpOlC%BCZPqzG8|c^vg5_W>%W|(Wu_}7<`90S
zV)H7`6kjV~^-jrNA}VTV*1G)+W;&TWu%SprMa6s=_0DnD@qAxjA9B%{=t_gR^9eA6
z`6>&26`5RTXA7`)RY_i}#<JI9|JJK{e9+*ysdCK5z*YzrF|u>iG|d-EUdy)@m=p@q
z5}l+Ua1nzuVJk-PtTbdVxoh-(PO`bO>rMuX08VwG3-C+zLfpyd>|tleN4rD@+<oik
zFR9F4${)RUX);P4l8}bhHiD0hov}kB<&37o0jF7djaem)>9`C|gH4b|vRX=Mss8%<
zIylDB<|U=;#70IYexE$+^6drU*TU=(E0BDGPg-*4z(9;1>LDyHE}om_J;F>uST90t
zb~7h!I^KgR=+xzUxlHhUz_+Z@_o+_f|Hn4HlY*^(bgJ*#%lT2;)gY_q=|z2;+oe_l
zv9PL!`iM2|gF0DvsCO@4aS^po%?fcSD_!ylOZ@e-SDuy4n=4Y4m{<}lTW1Tql`TO#
z-h1w=e>ZwfbY^|6jZ;6!Tg^AtRx7-Tau>Qjosb2GQk*{({Wvrd3dGu5oI2avu{gj+
zDLnvRMZ1k23J45r(cy)en1?0oJEzxLrjferIv$VTrYKnof<Zh8d`Y0-b_R*1+rWTX
zkPOWeF!=ItItAH4pi$$f--1WAz7c2mH~Pml)#pT;uCRDN=Md9BHgSkOq6lB^T&AA2
ze(OAD>`76CjnUfcN#KKnNLuFEw{ILwwve{a3jQ2Z3v0e_<&fGi7CYbC-a;yXFA_d<
zO^om&=Z>o_@1R4~Hi8BDOM@rl#W`0218qa@tol&WWP3nB7oTu*0gajiCsm7JTFn5j
zdUPjvOs4sAx?d)Q-LVk4D~I*HfB!dXpvrvzXCJlMQZ`;{>aDynwLLul-bkbOk=b;b
zO*Q!U7(T-@Zuw=se|kOI$2ZxCS{N615#sK|)D#g0ckK^~ajThuIiMe_s=Pd5o?yoy
zJ)1VqsBU}VQsmOe<!@yvl1_A_rX?Dtr+$^j?N+oj?8nZrb?H}DD8=0WFyh`zD6!a-
zz4{gt>-r$}`%cQuf#xI@CsV?36D5;Cf^5mmMnU&s#=^sqgrlSsThdJ{IJ^8UJbFr;
zgR=hE8oz~5(f`IdoAfc3Q$EJsH0#~gyvo40Y^(~!Jwz*;h~7|m169r7p8?EqF!v0$
zwhprq#|0hS{7OnnZaj3T5xK$jw<_~P^^IrExdIT@VNQCoJItnBfKCU>e45@dZ)^z*
z4Ye{csUqmX9(Nx)JaCq0*%0;vh!{6wj8=O{Nfp{kL7ZL5vI{5iBmBn78A~XaqrN*>
z@tOrpK@0uqO}3*Q7=2f09^K#8B5#V4cpYiH(?h>t8tlMlxAb;c-(jMiHPL;%c!-+D
zC70DE)_0fZBq4kLdrW0Qc;FJbqBrZBXUi;ER^~{6t%FB69E3#+H0%OSb6O1ioKyzU
zNQ)q}>Lp{6EVvsT7#_VPp_g^;VQ24G{4yqr5@p+Ps8c!R?q3lMZs1z@AhR4b<~&Zt
zg$?zUs~YB{8mL7_&_kZz6)m62h=>Rrn6oErY4$NqxY&2@+&QEgxY}_O)T!IHZ)bgS
z$$93>1#l5n)ve6S({TNK*B6G@??Ki!FV{kDfHI-Ly5rA23|ieV7cbBL?cg@RGRQDW
zGdFPq)EK%Pa-P!ShQ`KOkXLD@X{qWuz;ItGWPmIghNW8AoCNm#)h5g442%1i)IdsO
z;}!in`fkhq7`Olh1Cy;KKnd3_$;g`(5(lz#Z}yUSrIZJ<Ghrub)bth^>!&GdKf(If
zt28A0r9^F`xTM`KRd;NqE*06`RJC?-Nl@b!K-+ZVn$AO$OHkJFv#WDGtppl4+znJp
z^~xljA!@cPyD?<95~zIZwy##oYs@4abB)8Z`1EH^{o-c`pq95;dCI<+Z?bz-Lxf5b
zuF9(CmM)FrH2Io`bYO=d{3H7XBo!Wxj@MuoaI%lDuf`wt%I+Nz{RuRP5ZWPO;o%Zy
zrB-5ml>=E0Fiep;$b!9?7(WdK6S5@rW##}^uTde3rB|dfMcSOeJD-mF)||4?)z^;@
zT;1VYL;ZTN@~lNasbaC<Q1x^Jl;A<+@XEDB7i{YDN#%*j$*wWkrE}{;avSl2Kg3Zl
z93ID?nzWw`m17H*W7AT(|B*<&tz1#)adm~rE3Y}Wud^bz(12zArS_7iWz1q>>$ec8
zL2^x>`{cohg|H$A1U%e1f*U<D$$8@_yv6>u%xjnW&iGE-^6wnsnEqD&!7erRJ~ry%
z%`jSzv*iz?h_!FiUdqQoHc-bgbtDv9<IYF1nxk+@=f1Mrg$bRvsM-@7Akw=m3@?lU
zT-q4JHY}s0-9%Z}GLen0=lpS)eln8Y3mgO*I0<f4=`0VvY^CX+=Etuqx5Mm&d6)^Q
z6|?*gFLvOo%H_12oV)!?-;tJk>(;F)WXLZmfNRQ9&HzZi4}L6kp<?BO!-Pf*b$|Fj
z^sVc61D<W-Q2Ffk<2`OC?s#nf0NDfJB*SwrX59>WvUBI-`gu>O%q~QEyqagJystda
z-FJ%O&bzAUJf<D}G1=HP%&5|O0W<K4-h+oyC=OCOlS`1*+3oSDS>jna2cVl^4DX?P
zwjzFR13TY8dncWoscRUU%<S5#Q<z+!GW}UA29lGPq|^JmG6};^tN^S~D)F()kh?1|
zUy=sJcIWt4w8YQ~qou8=TH;$a1V#pgGT20mk8JK>P(6888Uuz@E-58tp-ZR$xwWO(
zTKVsu5bM-mLSQd#wJk-o4LIymn1@je0kao0)}TjLk@oIhes{|((H@a{Y%1O=FF^*N
zqX1uypoYUl&6J}gYn48@IkLEV&5~Q1#T!#&!Y%AKg6j5CKL%mZ9RMTElPZfYoKsWk
zSvc?*RkfUC<!zrInaa54Yk&U>Kj@KM^HDslX=IKY<AlQb+SIWPPlhH{4S7w-s{0p(
zbO{PaS)1Nj31s4ZJs_Eeq;q~P$4GaPW9Nu7TZz4@H=KV)67?79mwV^A=HN6~Oc>P%
za$wI**+4fHv(juSbaq+B6o^t(2zD8+<rKq^grWK%3b<yIz^u-0?AgJ9hYx?5{9NqR
zSk`M_I$2gI&Lj^3!4Ec7R^sS7Y_Xq2QqkLvVo5S~QLr))d_u@B?@Ra)Qg-w0CrH0E
zv`lQ*$-u(OkQV2`+^1a^Pl}32Qrrjb*mqQG;Hm8q5~;ao9gOOoD`@!twTC7;ur5mV
zOT|Rk^Ke`Xw{yy-AdfwKI89UQnXN6JVG)vhV&!_FMcrCv^_xe@+w(mYthXsLHVZf9
zI*)gHtqs+`lgTh-=k)6>31amo7`d(RmCruav$KAK-8lL4wxox_Z9%M%8?&nU@7`T3
zC#yQV(kPdxT=_tqliPIK&BBsAVQIU(3LXUQpFX_|)P@zISA1{8&7l&ym?r`Fh`|$E
zV7MoGMQR>H`vc!|h^dK-i2?^ho=Zao8x(eSM%rOa9v)W>3v;hBWA*P`lFK~}!!}l1
z3(4gyvKeE6i!CKaPH!}_){DF<3WwYQb}#fgE*<Y=auaR?eUtFID?zbB2Yh9VQta$C
zY*U{?I&89W2hi0H(}c<8%H)FQGZMLV<ci>v_3t{?^J2<yr&9gq@{2PKSmSwd*2Rg9
z56D<j#AiPKrsf<j+Ep{`LLgAGdJG}-+a(ly;^K<&;~F51qf*_=VGy#$gk}8t>Jn@W
z_=B+3Dw(35MMo4(isJj^cm8^O*7sNs>(e(LR^L7!HuQq6FhV~O42%;W86*RCRR?_!
z>Y#^xr}5@IBeqVeFw4tyO|aF$mPZV!Yf+@}E^R~6=^^$*e(0{j{reY7Y=uq@!1#cU
zKClnZ)G3%5^k7&#(bC$cSuo<hc)8WzqO`YUe(^9#aQOv;6ceqN!Lu1_?mGS7t&Gkv
zAt8O;q=yeZ*B6U0-aCEtqJ-f<W%^Dm$Ul;N^rnuEmCA=lk4}ew<KQSL;;*@N55~}+
z>;PG0@$1RtEDUTW0{s22u58esb+f|e`xntx<g2*0I{{2RdIJIt&q-16jAfFvLIqov
z#CP6{qJ6Wcnj_SDUt_ITqs1W!u~r7yEY^po=4qEK94tyN5*L8}409CwGkPWO(JLt~
zO32QraQ^ebG(d-LhG9GBM7b)qn*=5Kqyo+cOFCCT#izP~LuYL-U;r`W8b|j-tRg<2
z?0Z`GP3C}6PO1Q43JVmzgSAhr=nwdP8GK7HDo}1<iIJ3)#O|cH4ZjcWH0uzvODSek
zowliPUzpg{<5~NZwXvb$y1;?w+&p`K!@0Pm78Vi-Io>scyL$fIzf<)kCmY)}<Ct=e
zWJgcPcUmQPGcXXg1(F%OuKvv<P=3G6xKyvSptM{`_y|Sg0payr?RzKop0`N`5@&AO
z*y6pV#cS^+Z1KgeFDxib_I69Zotv=9I9Yy!Yoq&@1?KwOhSPqPzmZpt8&c<Og*BBu
zTNJoRQrqL)<I0uP^KrJW`a$ouE_z}%S7!4QD=tb9wQZV8c4~$XClUezm~8G03mo36
zb0gCzxWY{Z0HxNVm>c+o&zTdyHHT}g$M^IRqz}lp5zSUHO(iYD2&Im?(@s}AHVMa?
z$|vh2;Lps$Cn=4?Z)k64I4b3MmHxZ2=eBLzL<^_k2C7fVay+?&<~#r~#?ZMOio>hF
zabyAQM{P*IIcan6frAH)KQ01g1Lk_KuUiV4S&@VnA>*(B=Qq4lYL4r)5d`ga0`}d+
z9;(X-@*nVc4x>+M-rf}Q?s$8(k^FWKCdO5!fPW?@H{6wj6IGf7?cQW}>e!rYJrwM7
zTyi7VdCfxKwZLTK!Q=Ubt!hzh(d!-y_p_ndIxE!YWNjecAsU{?L5jbdQXsdIclML*
z_@`&(a(>djJgzSjRrm$$P88*1aUy@#u!X#<Us&@7Tr2w@|9`mU)pTF2xQ=)Ped{8!
zo{1)JZLYR5upyszHd!lZXvjNaE3EjiBP6-GojN=GJv-?ryg{fMSBFz_Yx%nKS{5_I
zd6zFQ=)Xs=f3vxPKLwEP<bt~<P&?Y%AOQgv_Uo|sD$B>+F|O55^*4g%=H|dhe@wcr
z*2eu>>FY1=gl(mbe=q}{m2ay{kS|x{o9W{ykhrNC;_>G<Je*#$Wlj!xoi7;MjSW~y
zuJX(>q>&f6a*Z0FjS<FVm-~B5%I@9HZqH2^l5dz<f5<7Hf%mBsSoP%e5$FoL98S<L
z$v+lbg6%zNxj4Ax{Vm3G?Gv}imtT!4#mWMGU-VB?<@#-5J3WdP4?pd_FW~(ChGB}c
zwcEwRd;%GQ!I6s<^Yp!6CHf4)284?~mtH&f@mODOWi)vX9&<((l)?ATwroD<Zpxhp
z=7TmeZt?%FvR(o3ZONPS^EXb3VxkKr#cpl~H<YbBJvTzhpcL07(tBA=ngXW^BIesJ
zBu=im=iFmb*=^CF)*h~<qo>Jzj&R`(-@wUoC;Y`q$=hTs@T{ew!&&?BwCzWzj|+b5
zD{H(l@ad%*WxL1jg`n=$52Cw5Vpm}Sxit#1T6C2LGmOUNG`9y=zr--7i+CRa6;VR<
zfV}{7U%aK-1IbQXE0NpN!p5o>X2ly4M;e3%7?13te_atJ-K7IGKpxGefa3>>Z)86e
z4Yr;5DW{kYRiK5nOrq%rcwj0KEAXq}xFXOL0-Z0pwHi5rs`1m7?%6Wz0Y;7iPXZ}e
zURRk8(1Wt3KwM2bS;#Os{j#b9<YnQ8E-wX#s=W^#ITCsAH@^=N5@6PH_Awd_d+@QD
zB*T|mP@bCHzYK@|!X^En=&A9p%L1kpSt4n_tqR`TK0T|mPTYd8IZ9?Na?<D|S<_c?
zfyBbmX(ATw!lB}`QCf3y6H``^IpeA)EcpJqhGB6boJ}hDL@jM|htrG<PG-MKEqn?x
ziau2-Ve{Up??6V1Ky%fb>?2GHL+-PVLD5!}wtaSeHkACLU{yEMz`}Hu3Zdehii_c)
ztphpFHoJadc+Y&KgZ_JX41`U~I|GoiR{$$@K9&W4IcEJEW~nhaiP&B!kwTNTQKpFo
z=KWhqc6E2BAX*VxJYdcuaB`(OftIszQ9t@RcWG){Eb~Vg!!T}*q-57Ea$Fr!c=ar|
z92*;(n;IIYliA^Kpf`|bA^jra>(^VJz;`$Q1s&6Q=lbO0&I5vZUgQ{dBF=GhyjY@y
zK|FC~)*bq-NrlC#U7>qLqQ5Otnw)F(G^X9ZSQjX4vovfC{Nfr|^RccVEG4GCD~*5H
zfH{NsOLO<MfzgSY`?>^ir*6MwC7eRvQ*W;Qxu{C0HG?Xlrst6V_{%{1fZC3=uZ9Wq
zCcvy4W&rJ7s3)b(>M#D!)3(*q#ll=zk?38?Lp~j|;;$AhodQkYYjw*7e+V|Andvx~
zjM7>XCB7jvcWJygS6~w8JDX#hYcY(6T>n(v<bh?g{<zv%py^f75BON;vrqipNxNw%
z$WUVh`idBUgUvW#9PqKG&US_-^v_*zpL%s;{Tse_ery0!4C30^t5;erKxj2<9J_0+
zVpecvHg8r?kzp>qOhh|8L!hIHmx}Q8o<tx9oY@+GvG8n<4EN?Tsz)wCIb29w>I{YE
zUqRnc`zV!<)k%Q9yQ{V|_PEARVU?SnhS>T7bzDg<6*m7#t{r;TdQYzYTER;=c;LW9
z%;rkWLd8<KLv>xGa)*>HmyG-Tqe3~{MCh#?9h;s($M<5Y`gQbH)PZookl}Ln?@$<K
z@mVnUD2mJZ%Wh$}d`U^60W_uol@GwNHn!gF(ZY&P2sd|04BW*Y%dR#(rUupP3zeuT
zT+b{N|5J*Vw`d>eK6{0sNf(L?>Wr(t_BO@GiRqX*^fpR|x#adt-qEgj%LeH0Y)R{p
zUC@ur|B19Y{*ikI8hF^EC6<JCd}rl)ZzUOOuOzh?Ss6;nzK1j5ATbVZAjFup1}x@H
zVWCT`&#SwTd6PrQfq{XLd{C*-1?4;OC8UX#w3z!nFF&4gUK3gjwDzaA_JKdQYTAbv
zp3zFnJ=;p8HidPVj>l*9zi!eFBWHQ8MJm>{aJ08V=jx{R%ZVwxg<WwZvt-8BY-0fT
zE4n_*nr#3g$w6js$6oQNow?vnZzYCpLVkTsXI0XDzk2hXHK&}}Nant4#wW9++`59H
zh4=ii84A<zI`AZ&8B@7oXP2=-j@eM&oLApW*t(tBsx?|h670rHN=wB>L|%u4?A6rL
z0-s+seXyz6^T<f(3|IzOP)EXzD_&17O%Gs1%$@drCf0|Z*5yN03ne7uqdko3ET8l$
z#pIME&S*6i_4CB^-ZaFuh4L3w8)cQ>4r6b=RX$doj7zt?UpK3$o%0~tyecClR*z>z
zceC&hF9j1Pd{;U`@b_%g?T7AU3W?`~;GTt`e_G1h7~eW8Sf}Q>!j0HD_jUrK7sw0;
zk}Vcawt#8#4xZMpPx&*i+1qE<)zxt;Pfkus-u?QQQ1NicTu()Dv0LLj2m)-E=ssCh
z(ocC2NTj`Ca%savN1bbmsPnmk`4wQ#6ynYIV|N=Ur*I!8?m@q?u&ch<P**>5)2&k7
zczyDM%;Lp}r;%7l>F0JSV|+X@%f$kO>x5W<Mh#2iZf;j!pw;;rSiUuGo1%AZoa_Co
z>iNz|nbPfT;^s6Ol~K`4ce~s;|FoC%PN0dkj@F<<b0+xP_7*vi5VGA_ro`!X2}UZN
z9V>)T1zgK0(P{B4w}44A+CIb>7A1RR66j~(PqfTI#IIh;sw6)#_DUjSaUrqZn;&+I
zN11JbXF89#gN3dq@J&pLZ2^Pl+OVfIR#_2}f^*(R1Lx@I{_9I7Hx*XYptd9;3|jwU
zeLuaVhs+!r)x<m{_t5NVX|a*9v(T}}_*Q!UnQRAC|K8cR6F(jXf%DzKWjDakrFhoX
z0|4<VdpA(IKIs6aWrSBo;FS$SVn;3fX)EW;*M%T%DXVQ4_rSU-P}~M&jq9OlP!P%%
zoy@B5oM;5L##V-0b$H&K_rK2guW%U3OSVa+t2FL9Es|P`eaIEyINO~Cw4*5K{uSb^
z0a?#&DSFjAB9dTByy?#sZdhV(=Y8*Bo2CS<95<2v#_=yjW(aynG{om>9}QSk``JKe
zcnK8glMVpm&4WiDiT%h`&zVxEF|~CtLu=zVUTM@V$`Mdm>cd<ILfxhxj7g0+3Pejh
zqa_(Sne;X^KR^d?U6z6KC+y93efSwNenbodHA$;23_?{vPSXvaw4Q9|Aax0Su{w~|
zv3|TE4fpGDU;yo03W|1$_=`uTqb(j-3G-Q%zoncN?%wkm$QZbYd!*3!n8<;}w;WSk
z03luGo5ANg{6nPDV<14!`9QwXZ0r)G-Drkq_#N2sMC6%4O~z7NAB>((aRW!{pe+x*
zV7x$>ON2T`?SM{-rA-M8BH-GX@g2jb(|7lufJ3}>y1a4i(uTaLOj}P1VhOCyK)k^&
zYDQ4fZbr8DsxWx@jq!cZj!)AO9DJ!A|EYsme5Ymr*fo=Oln{C+9|D$B<~g<i3Te9<
z`lS-0MPTL5`ZRcIOz^}{`1_0}$8#X$blx<U>F?`JT;M+-`|$^tOCUde(^-f!h^AOT
zEUG{(kQq=6>-+b02sl&2OdugkRD2I`V;@<uwznt3gk=8}kS=`=B?kxZL6(%(w&{8W
zS3twDlD@i}R!QaW!#39?-DVTGQ!+ACku<1sgHkyGJ4|&#wF9QgA<!_iVm2)6YWIf2
z#;C(hO>vK8y6Da%D>@c?<5J)Zy^;5TkytG?FENkCm7Eps`lAzqt%V!(I(}h*R+k!V
zvq{)yqQ`@?^Eb_nQ%e!-rC|2Nt#7QMV$Ztyz;*OX8SNv;C){L&0AvAz&KM)fpLRY;
zbak%+nDDf2a$PbrUGO<Hh+KkF0&rm%-}~~JyY%COK}DD7Q|aO1v5=yUzrrHB{QBMC
zOr(u2q2R>30SEAv#4XPZI%T>}be=?NHS2I>jZB3)Mut)d><olTztIM22KympBm3{*
zHB<ryMVc^w8nBav`S~~T@$%pAKWHpOlY}>Bg*Ru{Dv#3ne~#4<sTXn1LB2Kb+&6Tx
zgl3K`<pCAK3cBxZG7W04A<=kVb78wGL)98!UF}PY&do0d*bjA*EB17ST8uqW0?N@1
zUI%gCV^5hbotBISyaV^eys~1{K_j|@Mz<hs1tw&;6M?i9K+^p?cxw7lNMOPOx+OEi
zEEtDk=Z71tx_A~{Ptf5U20}<W6D9{;G50=-QL^J%>BJg|aH^CXI$6=^VT8OJ8d^%G
z&Ckz|gDbOg=?DG(*#pQBXV`<r68HK52gdmP`P|31PsH_QM6o}uYb_sSd@(*xo_h;t
z-&5vS444;VL1L^s5jC;qU)gF2ez6*Dbrj2#v9ru1-UaL~W1(s1J?F`uD;(0@$r|{0
zr&QJe-uX+m$<bDmotBu}HClJ;1(OMMHtsdrKCg+AKZ9pfR&YZnFo9%ev+Ax9khxdZ
zgT9Fw{sb<;61Ynz(4G_<cDcgZhE0)=!M?kD8-G(dEYwb9WD2maI@0&}__SBLdBB4v
zJ8UjGEcjx}zPqI9IBKr;Sk;>?v6n?}vigsk5B#06V0&dwBTBjmWzM%Pl{6Wu+Oy-*
z&jViBbL{bH!lX6DAa`(@o}=ZTX43-Jk_LvLntg>`C|7w~DB8ukI?=^?PtxD*+}(6b
ztE()Pju+wAyQH?5LE3rzeoPH&412r8*?UI{r++-E_Xki_0aX+v9nU7VnCcP$o;`yY
z$tB=;i6n?H7Jw;e_G^Aok?{D27PPiu21pYXd@3=7Sy*pwtWS{1*Nu##ZES6!S<hY6
zTmG8f2RV8;z`u6krbp3QY&H<d+*dBeWGw7*in6hJ!#@3*9Fpt<PtmJ3F{OgfZW(ym
z+OG-@W&tCFjs)5;D|Cvp6%=?rPScTfowec6XHU}%k()E)1oMC0>wK-)(;MqKMMiBp
z7h9d3)6Rt+Avr;OeD9bQiR)5;O2n=22Q}++GxLw=sB;nEgueFnW0eCj6N`qrx+yVh
z!@P)`6-<s5wD$J)E?F6kzyV|5()|SOshcyx`3sEiHQ$gw(q&*F26U5t>l8^yvuVOc
zaJ*6v?cpwY26hxeW91VhQdi{e&%XCxiS5|>l*u3Hx2;ugTI@^!(awm1b_dilHovtp
zWw$Dze7RWFZs`SZgi|w4lzm|bco&-4X^@L?l}+AQU=AWbAs{AE;P7M=Icv>2xiQYb
z4i}L!)o+_hxc!`yu0LWhIrTnu#uer|Ck$_Y13ecAkUOg#XxE;7B65lg@&;wx*4;}%
zJ$jXeao1$kMZcA2wB5A08;uUg7wW))$P?=AB_<A(PJ}QX`S1;ORgX|0pVfhwgD63_
z%??lgp8)Dxu8mPpAuw%Je52B1<ce~8aAxdH?HR+^ze{}UM$3YrZ2?qANPt&x2A;|<
z!LQ&4t*h!DLd&1?swZ)3YiUh@MJ;e}>HSAS-k_C%M{nu=9X`M{a)x6lV?kxs39>1@
zUQz^5xVj>0MVT&YGI%9gYhntqz`CEfOqzi{ww}~!_6^x~ac)$Vo=68mySwigXJB-*
zp~kV8dgxxKrc~(FuIyTE`v5G|a_c!g^os|Wnv(s%)LdDR?e5PQcQc9_hO{JvH?CL}
zWhLHj2Dl&W;1?`Fw{($w?!e9N6YJx%O?C#O|IMyHgR|#npP1`3a+PIgcdj@}G8yKT
zK+%MnoSC{Bu?ERCXmyzDK^_(8%n@}8Q`aXw3XFF!M@eZT7Xqo@lsmmAQlTaSj;hAP
zX~?jk8FlbKBwXrqZ84j(?4;+DhC^8n$9k|h;R0-mXM}OKq&`dX>lq8eu%+WL{hX?r
z4Tk$$U`tBk4D$CkcjTu1XOnYhyQPk8-nX2FR5j}Hb$v=!notPItdZhEuB)>NJ2={d
z#%0weq*Ppy^K`$xyjs=ZiDOaJ)Qyx7UJa<-=RRc><ih#%#P3hXyN}C2?vC|NjGd!#
z0ii`CTciR(;(1Mu*elZh(Q~MW)k`YnR*u>lKXJrARfLq%73MAWqrt%-dK6j(5b?uI
z*#HNEIsp_<02!wW)0wO0AAt!6kCb%>@VgX&*a2Oi+((h$)m{q$yZ{fq8(k_^dq+in
z0g9DESF)yddcK7m5V6ahE<?{p>hYdQd-Baqt7byaJ|V;Fd@;P;tfjC5yxkzpw7h~-
zu1F=%0PKuj<~Ox?;%#khaZQ3n<H1UtZC}2;My*jL1j3z`5up80_9a#4ExUh(OY&X-
zX8Nr&E?ck<^n;g`xzru!9jTJA4~`5MeM8+hnj?(R7oTg4_Rt$UurbBypIXf0Hj7|`
zoan(~Cfv3>ZaFt;SKc5pf8$PQz0f{^fngtwzKZ&3^DssC<N-hOajuoW?JHK$Tun=u
zp@&{3zhw)kWCUMee1z=*-b%-B`PG`~lblFAKsz8~a1O2Db9x;dp)}+RJgLnzaxu>6
zT1FcXthuJtI692a68W1d=88h;`i$XIeP%W_jW-T$<8`DXbqp^s-6ir@G)w^<_U>R{
zm?7`WOlH~m!a~z;0&@M56Jw`ckj`Jnw#M5q^GH2B<9p1yG@#{wpKY}Az8lsRobsz9
z`|uMSoEmPX_C1pVP3^H7Tj5NEbh`&okind{8D^zxA)+h9h}mL8#4?5M$4`zJgV$Uz
zZqPxw1mY_)Ty({t&xqJ1$a8-j?1^KHHmM{E3<^pBt0DAi`UKfP`J~~_U*QuZ3pP>s
zaf<Re)1?V`Oam}7Yg6x?<0u26an%aS6r~1o6OwyMo#!n0Z_KGJ1%VDISnh@N6N>ZZ
zn+!AE>x3A|Xa}bVQI%O5r;(n29X!roGY~QlwTZ5(s;c`3TO-uuqCAgTOORb@Q0IfW
zP>|q((Ch7Mcfj-$6D^_@>BPWM6*|DR)8BQ;lj>L$_Z_I_D6xNM3e0rA#-^Sk12BZq
zrlk6T1S#gs`}O76?0b>SD=a*v9$aU;F$YQoL{n&X^JLW{(&W^uy{{T*-yEpl&bf=v
z-y_}?%+c)xSuPz)<yMHl4$XtvN0jf|glRvjqNFtT3<5MLmmr-^b-mwA<0_O**#MCe
z+VOh2o~FDnw=#ImLxK-PIGOp+-w!oGJ(!K5-vU-cGBX9kY3GQwnW6WrQ)D5Ux`SR`
zE6>6Pr-kuk6{=t5@-FBF=@MXm)#(2I=pBKy1K+DQ#qvZdFcly+BA$7>_#NBzcLvEo
z(;dcveC8^Y^P$KL^#v~?>({`fO+(!i1NT39*qRk!e13DI7IO5@Lga6lQHx9mfNZoG
z`f=&feJNpKb)YhUUuWaY3EIl8GPq=h=>CvDAy&mMC6CbT@1N~_|1c~lxkzh<K397U
z6z<naRGVI#u+gz`I2u=^p(gCqJa~7@4^F8Uk09k8ScflHZNE|N(3A^cX*{AP(b(?`
z6jIZmqq+OvwCnn^1#sps<cH96Hqn7RRcwC%^pSt&C&2O+(KHH0eP{+pPj<svSX!)y
z*vh}yAu4(Xjpj}=n|ReV(s+0F=j>mvZ|HdMD|N-v5)*m43Wh+@OGGMVP3>M14?QLc
z_(LTN3Dhu)Vkb~SZw`2GuWCukJ)y1NcN+?zSA3cK!9nbn37ZdaQr1bQ>sxHsx8>=`
z0?=2VC<u#B@XN1s)-WPr!&wdPmCqAyj!h$6<X5_@p$>KoA)?lln}|ifA0eVNr3ETH
zXiMj+AqxA<HAMJ!3?6G_{ys#+0><m1CJ@kQ)H}gv^*nvY<o}ZnEP5P1sl2>Ar5@-^
zB^y-A>9h1B#_`OPzY>A3)xfp03Gn7*7Q`p|gp~w=3J6dlB<lDn(SoydROxZrrA^$l
z7Je~&JL?peCQjK%bY{|p{8Tft>W4#v$qI;pike13Ld&>^VeGo7H2srA`r4-c05oJp
ze$V7ig-#i<+qUa)n$HHh{?bR($(RUvfKUoAZ>NQcotVshSB)f^`WiiJpmupzlG1gb
zyMHE;!mER6&!0hgg6mMn6j5k}2F}qzmXMfuAd=CF%_P9%4sDA%|Kr?+e`B5uJwlIi
zBRWm)yr7`q1cjoZtE-#jOS|@}+&v(U0*b~8Mq@wIqS0?ng;d1ZB;9;ZIRj1elH01@
z_oA44C+KMvx|FJ01Xwlz<M+9ZMS(1}A`z&o+d1#UsGel(71@+_1<$%9C4KXq%JN*D
zR4%O}AsZ>8cDCesRJjAvBU}JM(SNQKGf9EevxplHQ$;$_0D;U*A@D(q`3SSgQ=v=}
z`yu=|YCBI$X8#5uM@GCY<T+u$Dx5eoa+u{oR;EDiPxKdErGO@sBStW)gwuWekM)h|
z%?iLnanKne!!!mGyzwh?EbR)ixdSlBNuYCo|C3bu7~njn-P&@k<d4v?2EH>2AGb8o
z>Z|xECV+Q8Pk3K4<Eo_O3#G~eg2!o*ZE?|MeQJ6mv(UaT6;-AYXY4|nMs8fVP|Zj(
zDs`EZ_Gd$v=2cspD*$Z_NJ{eO5T^u_5~q<$fF0O~NY+2v?2-QlEcB1=gyIILVFqA>
zNQ?9K(@gRN3u{uQVGm?y3MAmb<h&^7az+nHrJB3}D9Kmrbyq+6<9Aas|7j=WyloVf
z5n(8tIP^62T<1xQrmQrRsVOA7hK)9jVP{749e|p7Q<dx;6Z#{WL08&j2%QgJ9YWa7
zhw7j82?3-86c2Mde?&w7?dMq{zi}CVea0=*aWZPTt%<r(TGTiwa$ERUlk0wUdwWQ2
zf^__BENG#z1&i2qkE6%HI0HhAQBaDCcBtApU2rp}$3J*yL<U_G+_Q*2r9}z2D-h&+
z&T%r}Q_J<+?te}1{boJgrXF)HiR7SHem?$J5p9CSNoV)-bl~^jQxR=uT3jwj$-Kc>
z{2x3J-nQyqINV1N)9`o5!yg$Al$*(5H7PCqrFxQG;&5WJvwV}ix_nq?SaQ1}JRvyv
zAaiu4VRGT2prC?#HQioVX9I2iUYbK7dKY+!Xu`d!T*z>+pxN42#dXMuCiP_)HdMKu
zh$GH?iL{k<O;7@jL|0hB$Ql3Ny*L!>zPiH~+qj15Y{>5^e-H;vgddOrYV@4y8s`J2
z|H7&NY&`~%Qjp@HKR=_RlW7Pu7D=}Znm<9vzBAD3>cU0bbX)H#Br!UALRJJvDb&C~
zUjczZxT4u_y%UFhoN;!=4<OA0HY!PrPq^MxA4BKrk5`p9-DlJMm;VbFekz+@8f)bw
z8E<Y_@bSNoTEhQwS+Y+8t^g_}^!xiN(1iiD5R}HH`2{(8VgDiK)85`dn&$LYwYQ7L
zms4A*hx}ub6Dut6q?fTEyl|&(U%)u6qjFkibPP5Z=C7BV>6d4$ZgZ@=hUi?7gB^$e
zN#0Kd3L-G{Km)1=<q@dAh?)l*M40rK{&TJ{WN)Xn?@gZkO<Ri@bc7w`t3UU65{<}%
zJ>b+pz*I*tlb4Ij()a+U?e)!1&F9Bm7oEp5GblU!ww!qa5T0YYHk1Ug*j6P=KyW+V
zFa^Z6+c<PL;T;euF0;c_|G#hMny%L1G@NFWFpC8^f57q7JQn;h&1iuyLnr50hdg?e
zx3e(;6~V!WAl8CfW3uy_m_0=nN{v@LJsdK#Zd^B_Vte2S?$a?NYuDf2RwfVBvg8aE
zefCs4j>FlJM-WT1EN>~3@HbWQRw2>&|Bro+o`8v|Ik=R-%n#s}S0o_FLelX|73Mzz
zvy?6akFTg=L6GCzy-OkaHeGyuFwkaBRtx^wYZ~oKl!1MRf^(cJpwpm{aqDZyE+W~b
zCZ&HVE9xsb7ONC^C^s}|b!n;w)I_^J%CG^P4X}eL=qI7c1&>(wLSSL>2axRjm$nRD
z6a!|isO4KJx7yu0F}*Q0y+1(FLie<AFut&<orQ^Me0p<Z+JemoMuQm5y&`@51F6~z
z5C30g7N|Vmq*O>AT3mcl;;cZ{HQpYhYEj{=keGO|)Y5*lDO_#BdGl|ZE3I-@nFV;`
zp;ux*O_1Av-3c-Q(9-aIs7yI1SZ``I)YI7Mv`&M-gr<srDv;qrb;@!@*EoXeaWKAe
zVoYwc7`nQ9>`yTrw_|&di>3}C!61w<O1>|hISq9LN>=?=j+}viN<iBQPzXqlL2Cjn
z4jXd;SSmHoG7U6^Fi;D$+S!=VtCX&#%<TDFOU)~0cU3l5hWD(K`DF?xcj+|lee!+G
zou>BzDd-)1rpgTfw0dQPKnhwYrj^Dm$G=(oBVmS5WVjj<7Q<8t*U^EDvXm_!^@9LB
z3W0KnyA2}t)fV0OH*YRyxnUn{=fOonJ7ivNb54#nWZLn%+EM^i=<m7w;rA1t<!tEb
zi<<%D(nuJ^*OFJTVGcx))Rv;obCP-SGfPXHZ82r<REtan^m*NVpSRALTmor0V8PH1
z(u_=oc94;40c=zo{+&OiT<AI(bg(tZxH)$GI^UJ$Dc&{NE2fGEr*>kR@%lukB<(^e
zH<l22y#fpa8gm82B_#OS1Nej;HXb2${(EM5WK)1UDZ;r$hITH{1*TCuA4t5Y9b2Fh
znVIE#3XjG5O5S$aCr3P;KEQc4Ltn@^=cw5)qJXJk{|&0K^ytaS)yIBV(1dh_*M){S
zMbiA6X%`DYQ4bFKO^EYqA?CUS7~D0#JQQRL4p9d+(^67SF5Gu^z6o<?IyJQoEt<~4
zY4l76Az>WTs*LBCIHs<HBS1z4Ja}D=KS0k&8fwR3w){vJqqTWayi12VVCGZzmFRCV
z(CzdBxRY8?+pWaQ(4kD4b;Li-W7Pm&K^AH;TVe=UmSH6dbEnnwe_SE@KLKMvnWm<u
z2m{mVIGMFxXn^}Md#bd95K0r!%Bd-P<#s6MX!0%We28;`?d6Bpa%t%rjjV61uL&ZS
zPH0dZK72Lo;7E@iCUoUqV(*z3?#qdQlL04za+&lDAQTG|bFcM4=iSzzEP+nh54L!*
zk^c1)0G4!nKwhDFN;~35djnltvk;``56_?fe%~Nu*jW;XL3gLQ_%>9%d~0~>Joy|6
z&c&+sP6<(v{7XLPz`$U_mAPB566C4+hgA{vl2ZfMRa;0b>OCxxapmwvp#ZtmLvp@N
z+jvd+#rZ!0^OPgWo31?G!%?_WY^3)rJ|m!O);*-JoTPa$QO$&2)uT&c_+pUDhppk)
zmhUQun6jY(843!uk+y{J@MO-VrKQ$l<bXr)`5)a=CnXP7bsZXBj@pv+V5&_b@Yj>G
zGd3QSCKcX<RBocG;bm6v=3L5gs-M0LFdlB3m{)nf+%UivF9+RukjhW89xo{fn=Mww
z?DQYCqM^1wUR7U1Aj8hg%<LRWP36<K0X{+!=h*0I&y9WX6MjJ;OATNObX^Rz0;7;^
zie`mV@zt6Fu$Vw?NB{(GShz2I2oPL5{fYS^Igh(_xxh%?s641Xv1n_1T152AH|UAz
zn_t^-dytuBeEk@f)ytEWtOo!tpV7PjeIYUDTb076I+E3sIU|y+RiOLx^&qq=_hm#u
z=#Sf`onw?!p~&nT42gfXE(y_tb9)`2tMI*l52#iTjL;=Njg-47g>j%>c<EA)19tc3
zH<F2%tXcPC^&@|-VF3#99w5XdT%H&Xq@|@2DzhUv?PST7tP}j%|DD#;psmS!#oRnn
z#T>E^be>$g$Xs7*thNe%Xh0FTWaADK3Y|EQGnm|E53DOFDp#RL?Col5GEc@`iKX)a
zrJ34+b6<n_8AKh-1u~%oY{JkXx03=y>0E);iLsHh&NYx1^dX2)s4_tyP5sD8(C$&r
z5GSNcrEUv&OgI)XM}F(@39~rFc8E|4aM&vI5;PC_0nr4dNuexh&glr`xjeA`8!!Zj
zDK5&A{wpu>Q-TUFt9bNP=TL0yAL5vVMAO(DFgVe`^ptwFWDR|wYIH2avqniUT-U5`
zNS`O&PUHn{y`IH;AuKAxeL!`J@d%GsF(9AY)fRb6*L{xl^b%8obGl<F2hD04+ix<=
z`yiEz6YSm`4^VdH1MEd@nW~|7a%c<A{GEM-1}b2`z)pd>5;d@AJ~ao<JKaT=7a0=&
z*26+sW0F5tCu9+Kp<G*nnQ>s2l?6qQa(m@kQ>B6Pa%onUy7BV6gE#tmk(MZoK<E*_
z$#8xf$WN{?o_F;=Ho?hW`g(G6==*{4j1;vIh2lA}iwrK<*)9p>Ir3jgq;}{?vZM!K
zWcR{Gd-brC?(E#FB$DPtN=@Hs!n*UoDg1m_cnY^%+Qwwnu{}_l&n?eMBehygdFukS
z5@+3;JDvIjbo>A3a(;$8>5t~sfuiC$HyRCCvbnK}!yqskUJHPzjsEUaj#&tIjfgW$
zQSX85r)q8DFcjW|mhabeF4+ZFpX;J<Jt51v6pkOSEUix3vn9KRo|w*ZUD<Y?C5k<}
z!1H>vhAPAl_mGDU>Z-~Uzh2-5>nFJR)4*Ys^B8^6UzjL_SXzf5iD_6A!T=|jWKzth
z;fj8<kw|p{n(M`dBz8mT|FLe=QRsoL?F~qYHZvX13P4r?hbJ^MJE&i_%biG``y4(Z
z>-fd1e=d6I#P2&2WXFvwJ<I!N2}R9IAAezR5<8$iUVK~zbhAJK*$;=<)R6uM%+7s`
zh)X>QTTRGo4w)r<*O>-<KYQ_KxH7#Aa^YvKG`EMl^uAwLt0L;wXdWC(aT1X<>qe!A
zegtuETrHytH==V0qss3@T)CYgd%$;t|4K{*9H2B85)vYkDmQTDQ@AWl<5=)DX-*QN
z<l@2clSs1^=*d?`0T#eQWc&$9wD-k7Xv__;mplykU(*#IMwlYDP(F<h%HGK!a1lUz
zanU!liip<fWefN~kyQePOS^+S)wee*lC;k70=hiUw#0(U{O?yZ87O2i6IkQLl{zU8
z2fK7Fh3nAG$}{u~)NnPbRNVZ2zscVM_Li-&QW2oN5bAVn)D{BXb6t)8)OO_Q2~m-`
zzaAA|mL-8DfKW8$KU>6$=OiF|A@kd@neEgfY*nJqW4b&fWUvaxYg2$p*c`e5<r#e8
zwnKA&VvqWZi;HPc3YbmWn{H!fIGa3G{RXZOEvlNpr9fa}#huUw#c`qdD=N^NlO}Ji
zu9tJThi6?tqKiHWuhUeeIuIONras5?&EcR$R!u!RkOM);gB^6v-L^vyZ(-?|E_Tph
zxn+tO*gdu&kb%cDgtabY1LYqUF4#kWUxXTC1wh4TbAF(d=v!qcSo}ZM-UBMCtlJtb
z^{Q=~T2YWBDj*poXV6AOBq=$g1j!(w$Z#+sS;-ldBsu4(NX|KflA#EabMdbOc7NXc
zk9XgAV?0JT+O0*MI%n^-*P3h2x%4*G{D$`yY(c2?$Q<k>vCHFiohNtwD~AX=2*6h6
zpaTQ*7h6&J7tCKhPQj1)?scdm2526DSx^$2_dostqr>@fpfU#t0NM>{$_mI$H%j}6
zE3g?TT$Yx-OshwO%6`^f-)kUE&o#eR!AA58r#er4?85;su6INCpjXYcrM>~su7+9~
z8QK~{vymAz#`k`K4CTBq@;9=x6P#E?C47vsQu`wOl&_h?;|lzLUjQynYG#3R3l>C8
zSYw2K9Dp96fMyrH_&v^DOV%}o4~_Os&;ssY&!HeRpUZARY~u69Iygr4^x}kmK0iMV
zSaCVzf`pa@7!373Vg9R=6X(Hu<sQls_(w#X2$CU8&O!62IR{r7XP8>HU@kSFf$S!<
zT<vhwYWuv#@Zhkhn<4QN4w)MrA@lfbP6f~*O0fH_HPgl7O!PXRvn%c_0=6@I(=#y+
zi;EmjAIsMQ&AW4$_t3;C1<l3sAptTcY`{ssDb0r}A>~@nKhnK})yjBNB^q(119zwc
zLVTCjULeS=dAIgl(2pD$8Hx7f!5tN-z|PL4=(I4@>w}k9W#9COt7zQ8vHce9X}4NG
z!4-a{Ym&iYh9*H^rTR>B02n}P&(*qd=`PE=UV1^)&I`dO*Sg4i2KW@Og|GQ~9X6&f
zAaMKu9`>tD7(4P1f?pEI&~amfX8jLiK#@+4BaX_PbA%MnOiN^T_Rm^PjhByrXkv5~
zNlVyq4)I+6I@l?$uKBx9+Tmt=%&Nvkr|(wz5w&qe<9xi^QA~p-_%nf73!HiI)};s(
z_SxzIKR#WW<m>8uNF7}R{{$q&-+g0Y@uX6CP&s+Ow+}F!#&@&nnnH2<d_~xy{`$gm
z#I+B=hGEKgJ+{bk5jXuv;K_);APdwExFp#lt-#f!RE~H8Xezh4W=zZ8Y^#?Z@ZJE7
z!J+mHk99+2fLA5hHOzz{w0%|ks8i9^9<;3l_6O^0b<%in>1e34`VDr>kYox67V$N!
z0}|ZB=SvwIkcR=K#WdOcN_$uC1l+q|ki*gn*BDC7lO>2y6s^gYE;{bCGw%<kpPMb-
zFv--@y3GY<3ITtjW^l8ls0RarnlMC`a`z~KPB3bUZ*JucY4<cxsy=6KHm)w(+SvYu
zZ$645=Q68wP!0a&1>O+M-qjcL7AocBZXA>e5{C@3^0dR-e(?Q1T1u|O{<cQON-4n?
zBb65Ye9gmZAf9^^pl^)q)oD6UJOE;5Jdy8OlX5rNtUS<cP0g%^*UvLP1(TZoq5i<x
z@e#t%K5B&72pBF$TA`c-6haMG>B1Rz+@b3-!<y%mOr!F8_^|}*6n$kdhk)d4auiG{
z!bBjsxiD`uBj1C1@jVrf%Hs-y?G7P3m5~L~%YX4lxaz(aaDyudfeQj4&#x_v${5+h
z%QSQRcF9mmC0zW&UU0}Fe)DAHT=Ul-w^n~BnSvwyV7See@c!}#mu}_K$74s>=-{y3
z(kVS?%f-itWxjHjS%0C93RL0xVXNDjG2JmAkPSw-b|W)lDdy%|-OP$Pj>UX^Jq;-b
z0aGN3b-kJs&WyR%0|jXplnPLKgRE8l+{xeb8cg$RxajYnz(gzP=i{V-Mbtb@bS$sS
z78XS^L(Y&+eg1G9VEVK@Xo2#&e}Od@%nU$%4$17oeP6_7ZJDlg)*}$^*sXoEl$+Xg
zCvW<A2q={w^igSw9nFEDawn~$dt2EK&2BmaXzd&xS7)J;ZDjp{@3JM^1~)l7+Te!*
z2W0Hv<N`iE$pzjtHdo$QE0`8SXBlF?x3PU1ihKR~LM=o9(ri|P+M(&=eP}8jR1Q6g
zmw+{e;%fh_lD$GO8>ZMAl33eDkQ?*MEqlu!%nD6jM_4n75~4RO?P0>vkK=XZZv1Yb
zOOy)(AF{Z4uyHZvn4oV0_d2$gTW3mh4O8*>a;>OLMz9ytDs_@Yo~<ECXS~i3z@9SJ
zz13BP*cu0)xHm7DsUxNEv1Fmj{`cJFaiXf_k43N%@tW(obtX3_M^|?Q^NtRd3)&IK
zZn!Mjr8Dk?xhC<nK;bNfntcF?Py_q^jLy=V1nw?{naQ)9+iTA|*n@yyJ+0qD)`8<j
zQkyGkT?^g+Cp99^X1A%u;xkwi_0^4xwSzOE=XT*a$K4<CUE7WOwht?H*wg=9iX3t~
zh3MY~9$1(f-{p`5|AP9{R=I$_0^2@%ATGD-ALGbhH(cV@?$GcsbLB1FzEB~7^Zylq
zK^RXSIM02Ebo0}=4eD=0T0Zt1+f1@I)@!pKx)|$HiJr7WaC7hV&*1bT4)j<o`#F8R
zbTUk#zClG*iy(;N057J+YS+NRoy{*CTSPsB@`Z>ope#LXzt@Z^DZWb{NY{viHKU6<
z4{7`vxe#28#B&^@R1^9a4`-X(YhMyl!vQG-xbx6S4CpbPt?#!?bN#J`st{0vCvoaD
zakb|32VM+gEY!G=*zLM~1rh+?6Dy)tI5$kS7E!|r=<(8fESsd3x1#EQl)Zk{VPVrt
zPpsH&?IjIAk~3C~JuQ6eJDG!9bxtxhu#n##{g?q8VL)|60bNIcgGfN~r5*>aX*KPu
zc}!SnA^WXH4v}x~^k`;QP}ZA4cm%@Z<v*xp=6Zny)u#Nl+t~!nD&fUo-wALMeZ5-N
zzU*%>NCp+tjLhsQy<<qlYf|s3wbdVRAk{4l161T6iAeuTSE4C)?GCR}v0Zz0ez-_;
zf930`s*CL97W><{cX)#iqp#0u3s88%dKHc#VI)V)&_$0$)x}3d3R;7OQb2(KG}N$L
zu!q$a4ip?XogK<lh5Kyfk+tIB<qgErM|#}{O=>H^_lK74uiRX`M;0KwY4YF03Hbln
zsqkxU3Uzo6&_<v{O?R4L$OW5~((SJFppKbF@I-su26SpS>9es%KR|5Z5FpGy0J^~a
z5gI;aYs?zO@qZUeyP2*L#bBRi4nM0TaW<E!@zO)yKY!%xs8qb*{F)y!HOr?eL8@PY
z&f$Ng74RRR?kyHFiwA4T-W=Hm87=sB(brDo5;e~Qz5%|gJ7U_~i&H*0Z|)S0%{xp_
z{&h@%Uh<>qRUmKvTT%ggt6oFytNU+9Du(ff1jJv{D{n5kzk=zZk5GbfQet8oYIlZ;
zc|Zq+jdweVA;uP&-$k|0OOJbJ@F?pkjS&T003l2Dj<5H$G)*nPw7bP1xLwyF#z`Up
zM-hc@?sRH6E@)!o_-O{z&F~@B<xL^fe|7J&-qx~xv-K{g#Pp^oa(d&WlGY~~{HPuv
zS53<6u&!Q}w_Ha(%i5xJd-+jkxrH31r~+jI8Vg_%FxaT)=gHdh_BmhtEl0;l^!c4e
z5a7OcqL%M96o`5d_U7s{pb9(xIIY!6RQB_@2DVF~2vVU{)U*<k_1f<SM;;?j&WP&t
zcXWdy{N#V@tnl>>4UEZx>LY9MXi-+lZCg`~g!BcI$jlUTs`&J6HT2x8t*Cw1^yU-m
ze573(xXV6kAD(h}k_Hr_q;YcL!k0ef86tPm<ZR7UQ`1lO!+8U4LQ(wfF+l5Q+^rzY
ztc-k_h-CbydvO3e2DFqq$&CKCRSv_hZOIt9Lk<7%M<1VaDCHY92E6>|YuN^nVx49w
z`GBGjonk`E7f=}_`YEk<=JOd?SXf?Cp2X~YBS0y7z~@IhW~fJjcgx&uXEewmO<^9;
zmVis5?GYzxoxzXWlB|^ql9ODCB(C3i`auKz++po6;FRdQ0S{#~QxgbNE9%^5X6h9t
zmc65UGc(iv-r|*pHaWp?Q5-WG9<L5M+x5AXJHGkU-_;&^#>wp$DJAO#864tc&&`9o
z6^T&#*>C+%#t3R6d{l<bys9&}_w>{V<1N4oK>~HHUW=jFyAPBGz<OY=Dloc`{S9QT
ze1E5$9&&fmk<ELhAkOINPE}(Ubix!oJ|tKdyK?2<MV)Pp_c#?$w=D0o)<*;>pf873
zj#_rBkds#GAQpQA=6@G0t}_5Q$f)PWe^Rbx?H?SsEUDx+_>jTD4H=h0RRo#amJ(V|
za6xnQDdP^h^URTbHib(}(IZt*?V+5CzYeT7A@vEfeiXyofCbHUp6nqf1n904)~!|O
z$Nu%@YJz0wR*v|a4izb&+<h1)A-V_l4_)XYMujr~6L;Hrd`d_VgNA_88Z-$Mz|y8a
zQ^#!>ZcH*D1C=o+T~`kcxAf_+bPx0D8t=95lA$J(5Hh|Orxo>rKC5b|%t7$y-C{nN
zrxxUFZtk|Q#AzUPnOqTJEspy5IZPxH%=QGVcbS^4jKC}oEMgpj4_Fn~A2OEYo2rC!
z?Fy1eOxMUzbP1NdIpbyY1QITw<aA!<2mdrfJ4Wc+b*Iz52fJZ7bF7>LJyx<uwFLk9
zobc-{DMjj|%PT8u@!X~2s4m5Nd0bp@6Z!tq;rsKGtL+jjaorF?KR&#V+M=v(z%yRn
zayr<rV#ul(Jsn-$_vs^ywmRUDDA-PUpqm8B!a99aM*7*$AU`YOynLvfL@jp@=%NiE
zTlZNgtxJXIGC|w`r8Ryr=8iM|Y!6f*eF<RSTjPkh1>&qf4>M|JX_$yXNisAncy--p
z`gk}$p6|>|82rpaAa10qsU?<>Md4~o)R@&iLIC6I;s)FYWB}7<=FW<dA<v4CyO1<O
z<`*XNK1XXqJ%oB|`M?APC|Ig0nvYR-(!XCBJbDV7j#+^WO0x(|a>x*Zal*zUWzMLU
zAr*E0^tjt>O)5!s5%mSLSr|+d-s=$V!f9^sp-~wI4I7^jJNLK{4kJ_GF#e5tv>qse
zysAxx#9pc}9%Kzhpp~ru6GF3ZY(7_N7x!)|YBQwM2ONT=4ZQSA0fq<cx3Z8pCK3q)
zfOrnObDBK;G43nZ={+n_<Kro5o9why+QzxZEnh*V#K(E<kUjy<A|%8Ty+ytb*~*#+
zGAH1)1rjZ**Npm)Sx+~G_ss4Kkwj#s044={3Fx@6xg7uXgUEnf1wiEdT$ej(hzJx<
z6QoF*xw1-_!V~*{?-5Go0!D;jADF%8!hE8=hVorcbZGcN^xOR5#>vHX)3~H-vbFUc
z+8dkQ8QCw}-rkm%mk;>y=veJNK@q5u`Q)y*9-!*!5r2FGHoKrF|6k!!K!|kMXS(V<
zPQ!vUDhFl6w(QTo!;j5}v)#&y1A@wH>GM>@BW`{)cR^`v*fUQ|(oxwG-3tH0?9u`c
zkdpeD3+$>#ME3>r`19jWytoj#ke@$hu3!P>m14t`xXp&9Mjeij@vM2#lhm@!@Q3{j
zYEJqaKd3L`|A(m)0xYcj#c$^hx}i}e+HM1#)rUsezx-GaZ%9zQ-UWQrJAeNe@U+uF
z1uWo}bKnSuiX@_3whdumR6~~RlV_NW%DYqA$SvYCjv!>2+hz9$7p*tjfa%w@;?T78
z$A{AfH%nZ`jGQNy6myY#C-fP2_BHtuf&McM>;1HFhA1=m!BA6y80+cA>Pj2QRe58(
zha<yXX+U7dbt{5%G<x_0mBX@mWknkr2`G>jxm%fMyW4c2nMsnTL85TVNs2*4)7=e<
z)BBDy<6e5@4qE{LAR36O+&OX2#^ul?3qf774(aYtxTrk}nH?=hjhRqY>2GV{@r)X<
z3F?-Ym%$t4^Vh$NMvrEoy`WR)4W>R?*)o?YL)R%DQ1b)XQIRY230`S8A-e&Gy_pSA
z#_VR73w&N2?8vEzQFTId?JTr80#1F)h`Z3h1}k_+gH16cZmWe_<g!(F#hmi#MjRaZ
zMrkevtw+yEyzbt}v!x@+P9ICBf>NDJ2<%U^#A>v3ee+EdB}nl;G>m(X7cZ3V6Uv;p
z7pMttB;Ys~Cq4SW_C<?&z~q!cgB=lcn&eAW7%RVj`>m~xL2ec6U4|Z>MEULn3o59_
zeRR9pUVA^QnD)qNj8z-&t)$zuet%|bnoN!MrO-CWVAqGif%%}Qn_F>I()`4rm<vwN
z?8lm*`y?@^m>>H5y<YR(`wpX9OS+E1_e{@;M@xX$?o}83EA(HgM3m|rHEso(YY?m=
zQyZ4kx{LSOFnoBYMKZ5`zGk2kcCJMDl-E8*%TIH{WG1P1+dGc@t7YLN;35yPQx?26
zp%ExcV<TV{D+Ufp!E-2KA5vV129WflW{2S)(=Y$_02!qkZtU*uuC(A#kG_qv4Z%o0
z>;qXg<PS5jCtx6+q-A96gpn#~sMoKksAwn|v#Y4tuO`)jJT2kub~8ZK2i*m4H@P8B
z>WJ8^yI{;Mw3{Tc=3_1x`U`;o<abTDW$T=Hivq0M)E{@y{Mr%>yt@^CTe^IH!qFcM
zBoRl|V&LkHng4`rd5o5+wynF`2=<CJ9$?@syzQ}feh9-ze~x0bgWbND9@uBCPUAba
zhZvNYYn7pFcANs^&jW*Xg9EC3w=+jb6)+vwhuf7?vtImbXpG(pc4fuoc<~K_73}o}
zj<qF|0kt4qgu0nkW~xxL$T3o9Yin3$$DuiIp|EVYZUmQ|#y-C^ZYt~Bk{8u%Rhm8T
zn3s+dD5L1WCG2f!G%i+d;#;<|g=;o!rOOGX3incUBDMt?d}^#azJKsqvKO5fZI92F
z7%u<oaem5KBwEN>bp#;nwTffsr@>PyI99&Zku4f&NkAJvXU*+if+Zf2LkK1a1Jx{Q
z+VmyE<^<(IQ;l_pkLdp*Pq{IFkmy=Wu|*wfLDC<VIR}achw%xyqIr+kr#P7dz;_Xt
z2|m$(U3gSG`OX^7;yey{a8gC<bGVPPJN<~Z8A7A=4fYu5UV)`0w2>^5L|fnGeYR`%
zs>dt{21z_yoaUcpMw}A?!WI?(RysEpYieIzM0zZAT`XJ?2m3l?{Q+iV$jVN$a%AY`
zBNXbocB#8r<O1NPK)lfdiIBJL`uc%D=wFRI5HH?bJXPmGa}0UwZntcq?Zh`<vKeYB
z+n}A@7%tC!AzYOvS3qdZWm|y3;^sBpv8PSTJgjsNc?Y($O=J3k6J}NPRk-YnIlf5G
zNZyk77h;d%3~?;$2rG(q-%B_h5`PtUv^$c+1#`NYF41J!>yh}&_l;*xsIWluzs*1%
zrTbW;d;Pr_a`lnGRd^P`i>J<>J?jA653~{YxvWe`u2RENRaG?yoNCAg9pvF<fq{Xj
z$016weDUIi<oWX%3I)gka5T1fP?jA6M{-~di0)jK2z_H`U7sgcwv$In<&jkinOF_{
zmKJ$?Q*i2iY_O&W>RE(bg`%=N+V|Zm7@WxsEvAFkW>EaPVT*?>V1(lBK&S+)8J)@i
zak{o*Fe(`+65At0GR=wAx&-8i^VMK@s)+$U<*rnTpJ-*$3!etM?5?&EH$W}~vb!X1
zPt(3014jEQpMx&vA=`-eBN5Xf%@px(!5*q!bWZJi|I;bE-EMcEN9ud;gV^&o60WKl
z{#~2hC}Zj$5~rx$KW6xRYGr8h1x{+j%<{3F1G#a~7Qx3+>lGR%32r>`%4jIw6oXMu
zaVJp3gzJfJ^8fY6G#Yb!kzJj&_4NmQ1e;OQ>l&)6N!%qr&LU_oU7~4z)2(=XcTQip
zgGP8u9Q^lK=9j@epmdCUv6V(>=~QTZVMWZbIaaX6mzy8@)zY!|8G4n#0PS&a@4DtH
z)UY_oGd?2V#hm3{5q+tph_{Q?_f_bj$*kc%PWjAWdr+*4c^I4zj#n3tEFMs!5hzc9
z$)5Ob6&0)Vx4g5KGJLLSJ7iS4P}GGsw`JCRPkyO{Z5WuB{L=~D9eLI6KIQR>N`y?)
z_~&ushe!bRjHBZ@oVm;XvS$+imIpa7^$u`|r;FjAP;M=7H%ZI;MV>?fhWRTjyB^n7
zsY4d3mp)%24II^}t67>3t#Qv}*LW|N|1D>xj@*w%s>MzqR~kQ^ktX1v?6Z_%{v{o+
zRBsWXl|PZQ(vG#2CSr>s8;`pB>ZZvVj4RXJoa70~EGTG9O-&E7`tL8#CxI;4Pxj8@
zsdH{<=9+9X*ZUDHUSBSB5*Ywn2=wR9`Dp$Yg)IelJLoWJwVy{eMfSkIw;QY{i40y<
zRE3kB86fsDkTFh3z20VpWxlt*(`t(AcmvKPT32~vN6L)?mn{hw7r%W+y}$V<&fk35
z32|Qy7fdv16ke=~)^KQh#Ok~_>6?#d85(k)=!FjFmfoR+`cJSAoww3=*{QsV276yN
zt)n%*2qnRRSC~XmGJgiJl{C^Aj2ocz4Q*+n5OkKOD4LhOJRYU>dE0sfz%syX^M{n?
zp#MW5e5VRUyuxJh>Hpm1m8U9W*Jqa=V@<4O3;6i3d|a%E_3T)u%^3Eka1<HR9#s&{
z^exgR+~8ih;Fcc=P0Ms+>uulw30&Lt-pU;RI-&)BuB#BW4S@6P*9^uawymYh^@jk|
zys3FdeINrnTXP)q`}@=2RS<LNo49DrmkYHtICmzerZROIYWRKqk)a7SBn~VXy1coE
z3tB}^G}8?tC^W-XOY%oQ#hLJF^*K;u5(N`QX-#SCCIFWh$Jvw84pSz;E1goKa{Gsz
z*y=hHmiUT_18TzUyB06~)-FCqO~+zK({+;Xtf{(M;ylj($s&XClrWXXI~>`0z2a3?
z@q`^QkWkbjz?7x^^ivdn78E*%o-D|LDa#Z~VX$1qCy-JMcgrXE;(JcB2d$m)p7}8)
zGrbnxu0{QXhomPeZ~PQJ*zduX7~sqMSK!iLHcTxeb?Vp9kMB;@7wZ;2CrT$~j*oo{
zx#Sx;6Gh@1sba3};NaF?7b#9t6sx+lIwVOmNfX}_nunwXhICJGu%@0i(w>|znUoR8
ztL86`#A#+Th690eGLuq?UgLV8=8eGA2S~J5R)(-GCX~Ca)ZxK92i4nrrWxC0N9N?G
zx7Cl#Wo9n|q5CoKrxotWIzI`9(ka13AThHSIfch3=9f$`p`ixNZ_ao#J36grM@L@R
zbwPmRmkgn4OcnV0vAVZG61-oCc2!Zex*s}{0*z7*OI1V)nwn+b$4=Zt*`JB)W?R7V
zIxo7K)ue%dKpUJA;`#9XZG~6*CSK;J_3W2C`+MnIBax-nF7_dkV*y?x&FRVWPT-Am
z;I#qHv_Wqj-c*|)sf&qp;)242BKM2pb8e>>o8_X>2e>gW6SgOW0|c(?g2!>n(0;P#
zO0~?~eW4MhppGS?j0R(XvXR~BJ+LxIQ9D65xgU$arvE%yJP?=UFM5!JSFMIx6ra2C
zfvSmZW8+{rfa~g!S@&~sbs0G^NV;0!%j?97>gO2ZBe=x33Ol-^i=-?a+OuY`a$n}e
zI}%N6;?snwI|U6Xf}HoW#~8PLqMYqr`s}XkUQA3`Zyak^&|mP>@Y7k+{CH70=i(B_
zlWC6UQ*^#9m*b`Lor_G{k0;YG#bkqk)Bj4{`J?)!!aaObkHq+DguLY46A_$D8ozU&
zdUrV2uu2VgLl39U-pm@g;cQ%Z@WPHo*+5iF%YuMQo>jj~KhK#OdsB7+zNE*uXzSil
z8}7vgCq0|Ld1U2D9Y_VN_#%tIJ{Yy%MsZ<9GqbD)Gcf2o(sD{qPj5xmHgjfft`oJ?
zl@8sQkwIXZ>a<t_(rCp)cA)SFd#wiCT2g7LhMzUq#2(bUVA50enuP5dojtXyNWux(
zNKY7-(@Zj1a)sANcS;-O&g#qpTX$u+@QQ?5sY(jBdb#Szh$A!@I`(3=*VAnF>KI5m
zOn8NXD%U^M{!9MQT?=zVzz?+Z)CEK{zwon2`<o{QmGx}kbQy1JYR+Sob^lgMUhM}+
zyAI}r=*(sX>RJ9&KRGG-NYh8N+#<KEG}*a23$^s9(mDxF2m2)vAF_(F9+p8{77}O$
z!l^@euc3DJT)N^YnJ<ApA3pY~TF2Gs)aKW7#tuY%hC4jzJKvBw4Fi^f?9}E`sUqwd
zeYPCsOvNN+$_c{$9rnBH*(tgY%`LC+vC{PlP*Vx+E%n-`_%oYC##gWYTw85j9d}k)
zvT{+O_REurx)7Fbek~}rt!$jh^4U6;j90d<e(Ba`&cctXr<Rd98r?LuLeZ&eJFD}}
zvNZEtu)l{boU4(nZ=HN^MCcRW*Z16?NUkwAj$KwwEO_&CN1JnroB#5&s7tO<?ST&4
zrmD9cy3chJAmJUalJtk3%APcqwmkCAl#I7-znH9fsm5nqN5`rEhXA|za*<p&X|(jv
z&6E5cLpP7p>laI%(;&HLDcq4Cn%TUnmpx}*oh7~!%l@OS4rza5&SL41cWcJvc`q?e
zS}W@2!|;Cnu!nPnN9&0Qo=JO)8%{>*&1IYMyE&#!d!8W%A(L8*xm?Fu&tU9VJwM*6
zuWIe!YH;HtO!Mp61|hiM;tn{<W&)Y*Ro^r!(CGrZ{IKCN=VGDd_r5R$3|x~?mt9<t
znwdD#w1b+0qvU5LEOxEL=kgyLzDc|RU>*w6k0Jz`#xSJ@430vusP`7zAS|?&=#jZL
znRRkGjwbg%fG6ZR+@7O2@w#v&3ylCPP#6xCZeXn$g5pnaRUNu$B<b2&y1T`mwv4Rv
zDZk3zA#x-r5>P2AQUo6q+ahdPo41FS$;rjgxsw15A=+UsS%Ow+&?k4y*f{3LI7TO|
zIYa+yjbFa|t<G=OvldS6%XJO$QZg!6Nf)L06W8x<@?fJw2%V%Q)r6e!t7{!5w%rw(
z&6&GS`O!=vf`*dTij7{QB#BvO0%wG_@|cNd8#GF2kXZe|yQ728r%(T>lHeHebbt<-
zdU{GfVSvUp^kSy&$t5f7g%(AhLES$}oTRe&w*#JS&W>m8woCf9y95R9_O+1M`9#(1
zT3?^7Tj@BSCzh~R8l@Tij5C8V?rXk(W#!nB5%-5n<E|D)_*GM~j&3VS4X(I#l_1`m
zU#kqe!^seVC|2ZC_bO(^q@jHtad_9yYgjz95|3!zH0R%Q<m3Gu$!<jv@yBsYg<p=Y
zoEO2Jvkjw7SborfI^)zv!q^s3i9idnS0Q*VOss5;Z|qQu!xV+>mi=9sy*hCCD)GL&
z39PAE&+^RtKyjF7g)(tEaa_6aNuVM_lwFwVSUNShyJw8?dBhDUlAIfvy4|3pVKjh;
zHelUB@olUm0bT&}^8v*$4`BAyThv$iR*NB^nhn1CqKVMf2VF)(chP^Q&iyO7JaH3)
zDZng+G^-~$3#71F7_KW@PiA%QjZYYMOQa&~rUTYL=KEd@{+CbY;MpkYVIQGT7yG(;
z-78M_ZkI3<Pd3zP^)5yE%InY8-6^vESio_lP^GeCs!fA$euQ|3PwrU7r=TnTMFal%
zeU}2FPrXbyI_h8aHdylpZ->*knTkGMv8CVMVR>Zhk<?yz^wXi4p@amttOQ13HE!Hz
zJ4EnQZ^e7|8LmQ+<yd>eMXQ@wMQNd{63Tzt+BC3{WsSFb1P0S`r$2VG5BqD;>*qr3
zUyA{0jm_c%GPA=1_INLhMUm@j&coH?#}ivvqi>>KMMQdaZ^~&H@8*|HT)H)qYaIOb
z$1|g%k00-(+f^|%8xKHG=f4c=9_$D8kyquc=`dNxD((H)tsDzeJ+{W8;W)HYp#CY+
zGt&;@_uw{um=gLi_Q&1Lx$<W~lrv6WT5z6x=aBRUww%Mf96djzVNQXsTd6<I-OhkE
z-qWso!!A0JOg|R?p-=fU*Z`O967{Iu%eKU4tF@hft&O{45tH%3CpfwdN~mX<3Z(My
zJf0!p2`wQX3V-_X{h3**bFY=UsgF6oc`qd3tQna^^R?5k=pk$r=2oe{&SMCp-w%(^
zcY4kQllqKbri)FyW1Pl=Q5O(1Th<lSPaAIxj<A=N9^M>t*Rvm6q3I6K5y-`VW3p`@
z<5=lP3l}ymMh3^5-t*HHn2hMxS2{a8Ea#U?NsU-{NYyhjWFO21&1&(J5J}M}4r<75
ziE*QFv8!JN38TaC$fBBgDYkwU9fI-Y%N<bn4{!-R&N4My`IHBq<^FdReNmy1yy`pS
zmD()H8}BEV+x+>DNtM4^9hVUI)%vslftPR;Vl8apKGQk&EoX}WM-$%_QZm%Oc{}An
z`?1_1knjmwJ^A}j!A&hoPM9cc^)>6#)hk_@3r_%B=!S|LvLrrnY<q<7959`Ld7G5V
zC=ZkWk~~&)Yu`yT*dNO<nGh4hR<%*;$5RZ@P4smzEp`RPA&hgaKYL$ey%}n;h>?Qm
z@}h#N=#S70+nUX@7{b4$qvQ*vlWZguyk%z|@DV<#yOmNv<{rW^$V4nMwV)|j$nfK}
zb(xQMgbEksprGN<w?=}eM=D%`IuQYT{w)5Emq%_ru@q~U=kDYfl1#8>juc>#I@zg`
zA3bPlE$hlMzw3VPbH|iazuC4E@ffmCAX(lsw*n_x(9X1Lr|pfc{FKF4oygy_{LE(u
z9Q8OOPBOd~`0<Cb5m|>wr@fp=i+`}xDJ_}v5>ryAjbuz5^Z3ScpAOJ0CSxkH-e+7S
zarZXh5DF{u{&w$@w;1O|ibpRd&nRmHZKRW}+b@jyba3KuQcuw`Hk9;IR<|E=8p+Sk
z*LV0sWxh{8I+CpvWS%R-Ho#<8%{SAS)dxY;8W>&U-jJGQSiGtrh6w<`nv4e>b0oGD
z`ib^}=p@guq)*35ejLw838e^<!9R1VKRKQ41RAHkK4xliQYnJoC?~p}-p~qemUz?5
zNA1;pK1D=-rP$n8Wkp;DXvLcgeNEpyUmAFCmHNkH-n!uNC;>`=1`nbybTg^#ulH@W
z#b>XUAF0rOug0jrO;!0FEEJ9M<%ei5E9f~`*XCI7??mPruNCrXMvHymYg%sa{j5n!
z`kuAz$@*5Kiy9`PJS6r}Wr%~-6%y7nqTY!cF>gLz`9l0yC6kO7VQWABLGJmib;HdU
z-MkM-!=#&R@84?<ZCy#ZZd}uuu@<l=t;$6iFkicxZ|<JqmtS%tG4_${SZCGJv|Yk%
z+0stbQjMV8lp2xEQ6X1Pj-`#RACma9*yd%s(Z%_IfgFGB=4~GRewUkgmuNlV)60CU
zK_%2D;$FpO9`BvF$cYHBrHI71$#w{u3eq#uHhzDLIgm&+dPH&-2$E2A8JM(ZZEj|X
z4|q}Y@LVf)X86feI-iw4Lm4jYH}mn@X{N%T+><@{y`2O}FeRHH%Gm>VMIq`B<;cFv
z*O3q<m_~VTlmAd!2188ap*wdyL8qVeO18^)AwgQiImk>e+xa%qe15UR>21rzJ_JzL
zxm#Sckxtj`!_zV>RcW0Bu4r~$n+R^iW!%%IoF$)E(8^)4CzTqOX(aVI8(m!ct;_DB
zMV*IKQIU*Ezk<W4e|PvU4|w;EuKM^8_{!lVXPZOP<i=iy2t~OsB}`UTr#oDdV8<(m
zrRyP1d+s}ZWee6Dp`-5txLxG%n`bLbnz5;@{Ar?u;e?&ij#gBQG=C4hDm<djBa!%X
z$l860H0WePthRX7MCjF18I9CbqBZ6nPgl5!38I=-(YimQ_X7T2{m-8nVDKH^sFwY-
z>3CO286L(<PYEzTR@ABFbJ`v%a+!Z?Nsa_9N4{B(l=XR1_wm&^a_Q-Z#euuehDgd~
zsQjM4l*DFpQBC-a=)OHa2>c({xTbdj<|_5sjF%g2qC-NiK605lUGFI%Y!&&5WI}=S
z^M|ZEg<V&P<7tS`rK<;*%w@*5ZGPUq=g`I79#{4;WBW~UCx3fl*?Y4!@AZgN*1l&y
zBjzR#oinemRlVs7zM>G&?6T7GknJJ+#H@hNlA75~7>NAq%asu(aM8WLe>VsluP+7e
zlwfen{P~`#>3qNpC}alMxE^yAr!rT0v?PqZzm6YA=}O9jKKBL1bghNscOSD|iZT~}
z^W$N(2+79*hJSu42FaLv|LSgiMMXux%%@K`h>148SXx*tsTgZ|!B1r%JEzsk_3@!W
zH4n7*CItx93%-2$k}cgL9$#dO66q=gvd)N5zWMlG^8CM7iXk%P;Co=k>e=_+w^aF6
z`FplKqRbB;zP~WOL`WEwJ6W~49DHCfW-iUSj=*Foa8YWuXACJWb=-J*AYlIY>M(??
zBI;)eBt|l0YE}oDN1i%d385h;Cnp}@{$U;q4)pVMpq8@z!U>J-1OQ~uw-`$Pda>)u
zSb$t%y@Jns|B|I&vm`@KO7qGs3XYQ$>MwXlbUG4Dl_1e2k(ROzH{;3QP<CkD#4xIs
ztat{@M3%+0O&{;MLQXDdEOqb`-@)<!s{ZDX2SL!B9qEuC6|XMw8%E-+@poQJJ4>8r
zRQv)<xC8DBN;5Z*NxWgKWEJud_pMoEs(fcXxfGgi>f|zulgxH<a(EDFh?BvWx%+Tc
ze7e<xEXYpv58v_%rFbX3{lTjKWYic%y!8HG*2`Q@w158kEDgut(*^_tNQOoYpW1bj
zTwE0_ovbPV!!uM4s);_K`=C)2Gjdp&Y&w{*d-$a8%nucFbC%k#px!$Jiu>OyjRWp~
zqpGB9_z`d>L6;|XsRl{3_gUH+^G)q^;Ps>A9SJKL{$}I!-N%0Q>ynG0ZHkN{+jYu>
zj@J5i0S#Hj4-)k&3Xej<`;Hu~pwh`?Mdz+?mRe3cn3()&`zK~pQC-KK)w^2kveaEp
z5(+6HA2H6bE0Ju%B);+S^Y4N)WPi*r-&NN;B@uT@LNDkeTV*1`Hu1T!Rw0y9CeFtx
zkngm7m|SdD#EppwLUG>XS(eNG8_h@QXtXRR>ntb5dAI5ur&K1TG!dsLvgShPNA4k)
zDEvns5iflX2wAo3ccK)!StS;?`EBg8ZWWc-!e*}@Hv<iCnS1(mUUTiau`!!T@#^KR
zee<$?Ft*2>1`}-rXt&S7OB?;Zy_}8g<GtpA=PVn4GS}_R8_<Awo!<Ou_3YWt6i$|V
zQc~Wq>n7!6Vq@<DreJmT^kiB=L2q|-l~(B%Vw!wh_v`z#>!mO=sEgXZ$N^ipY&Cf$
z=!mO*I|y@8CQafDz#QLj448uZL3)v=JZhSeS5;!8l==%XFG*$uliJECvp$K?H!&!x
zXuhuhlU`mEZDP@1z7!zAdtBD+N2ntW&E)s5eS76_#kFU^#u}r0@}KX0Ej~_jRX29M
zFLiK$W;tbvFh@s6htGLC2Mq5lhB<x6`MLBQ(mtAEYHdKckrSUp+ffOhA5(D@>ea1g
zrwRU*xDrX<|0onIWJhaPF&TESZ;gNqBsYn*wpM@WJ%cmv&3Mv0_8Up+V4;HJX@}%_
z<8qjc0ml#=NiC}2Wb)s>DypvUdefzquJ_Y*fGY*xB4R2N!SfO3$aPvnOk*zJwG6-a
zp9?sAEu%Rk=~WcFUnnF)T~^z~^i;a-O?N<|YgE4qcI&awC>Z!<0sWshW7J1q1e_$j
zu%|{vxw#79AKnWu1Bh0qeib^MW>c;3x6#l4@0Y8d_fPKlX8;HR(~DJa*m+~fws~O$
z=541>Z0yK&#KpxqRFV$gq7eB{useKl5H*~&KTxNu2M->|cK8b83xrleTN;b-nL0J7
zOiD6z1IEZA4^CSA`)&unf%mr(*(7BTiBCk0L!;Ohbtra17D_iX59VL70H_SZutL$E
z6MB<&VG`Di2soerefD?u9r~k`2`$+KF^tj_TeV{71VVHk&HbhwAoAEcuO(|KXlO*j
zG(xb@jgN|oq8qpj=GuTdAi`kKnVE4c>Z!H0b-gw1rTW(Ux4^m?bf2B8XjWkz1Up6I
zSdBH9Z=9?FrD)dS@BjI}I-&2tjCC->3c;DH3QY#k75T0<+m^`*Z+<y1yE3*^psJ?U
zVFq5Asi;Q1KhJdF@3U5lVm@S{Y|b?ZxvlQgYX`UDKi_JN;7{9tctWJB<#W96bWrSz
z7e}4<cSd^9CX$BZd~SA{lDc{d=$e#R%6GFgmpqghdqK1K-@aTYUNw*KB!JRu&0!Cx
zmHv=67RFSd8at1BF-f04x9SCJ#3L}dCh^0EYaNyU^O0tSfsbV1%pHskZf2$phFC|1
zX=Xq5hLeV!=hFnkr9Tgi`uCw}<v%drJm?A=N%RLg_hJ2e!S6F54u4;^>*eD!Tf09p
z9O-7L(&Y=vr;0PhXAu5+wiFLl?Si<s+Mj}+h8BuW`V|HRYUOh<<Vs33#LfHDJ@q90
z(`dAMHp)Qt&x<$0U!1NjJJWc}Y9Ie|ieMw9<re#SuM*83&Yp?FXshO(2>0X-s|9=E
zjg@Bp(Dg4TDafFk&+9&4H{x{T^5x4_FoR<vwe(C9t3m6BUgr&DD1V&|<9foc<H&b^
z5zUjXGADS+F+#-S)|gG*cOCiXL0mpSZ?Tg9K>oLzJ1e@x{h$B5&Hmw^;h^aM{6PG0
z_ctS#)-$yD-*&XYiAx7ABEMdaa2)eEv|>8^A7MwTMt=Pf`b(4RsQG{X)Ai}?|Mh(h
z3Ui4tPFYaGFu8!APi#Nt_6lXfu?iZul5?2oGnRZ9C$`!d%+I=O3H}oH;(k<Wj>xan
z;NVR<nGs`_|GXW>wTSM2x%B^EuKY0m{C~P-|1a-*yCJm{^WEQcVX%-D6z$4629E-Q
zg6<_qM{3u;yKYf&<m6pkcWVCnGUjozxF14A$f*2RdO6;KQ8kBFF-@H~RW;W`I+CyI
z`<ItG(a!6JtLlYMo5JiC3a3V4l$sZeN*4ojp-(W0;vCO+*IvVfND|KO#!&V#z(18r
zASVGE)vFps*6uRSvvQduE<2(PLEg4!>L#KvkJU1tRKBspZ%vJr@9&wXfDZ}s=jqci
zwu{5)U#xOHa*TviF|B#6aF77g*)!LdJtn-rC4*jBce)mZX@8FURY8Yu&HRgG;N<5G
zuG6MVgUfsvj#O__?&{?9G7?DjJ-}W6uDH1PTt?-&Hb^P2aG5BdA>p6}?h*-^K8z+^
z8uetU_9AB2?#t3^Lq3Um_FB}C>!g>id%)0=(hD}r!k$DdK17;!*9n=`L;70Kw*!x<
z$2sOBWo@J5mNJ;uOA&>n_l8Een2#77g&AQo;2R%w_inB*G_GaJRw5UvB!ZcT`X!FL
zMyQlI*|R-u3>kk#Abr0GI)XY;UT<R|;P6g{==d3SAn}|$3nlg|e*Q8-ymX9=DO`%0
znwo5u<F(_72~kE5BRo7j#@7eTWo(A+r${!s;v|B{!Jhs;jLH~bT>mf3S!}yFjY6Fq
z%q>^r=<%bL@I&>H;&8SaHReK~R9c1}Fqhp>Az#lFT=_$aWTkZJG?0JCLaLJ+HG+VQ
z)gRcdw+f+mGRs*G8$}9eYFg4LLQMl}A&E11W44Q7YsnXhU<7Tr=V*#b_RTGH0--f5
zvFa<ub6FDTCyWJ;-8+t9T&Ak%)vJyK!=S>E`;!1xf8ar%=RmNiCn5D%wYLOw>1zA<
zJCSqZeAd%QEA2)zgVm(jRmV?HAToFxLBOd)W47w~*6@96T({ICLd?Kxh-T^4e@_&-
zNb^nv=mI>Ub_tf+q7)n_;R+EYB_-p)U>*7to(shC`{(%QGEz1;9#^>P!h;@Ijlz8R
z2-L+NMk^gbgB4MHSffaatI50gCu7x!_nMD?gOzg{+{?8!I9@1c+G4~?n-^ftM6H91
zy@uw=4>JM5!89<Q!O$-{T5C}q55AUN4Bf`*rHWH0tGM=gN8eKV0hhGfQ3e8RT@{g5
z1UABEzMmSXfk^2Xx?oI%VLnXXIM~`g$m)>h?+R5SiP;<oEw-OS+}|`v_)(p5z4!3Z
zqYE`x9Zdv48lH$_Qq6e_vt#bTh&mno?pCHzS5hYq&MI$l|C?1{BNp8-arCzIJWO+4
z0z13p`ZeG3o0%|j^pT<>;b|h~6h8*a8|$#1Lj>$COUtq>=6W+NV5PuWAUf-iZ#MKv
zCR!*{x6ThyF0r$a7rN*Kr>ific?m0y2L`6F!V?S;a(0+?c=F`QDz1kGx}%q&w(x||
zs-RM{(jrXp^2FKxNpu0`!ll7MGNiLKTBV@Lz|8D({4}AiU{7|75Ge{5LfsOZMs)eI
zD3~nkz*Maz*iU!u?d@mR06FAB7RvYWDdlB+!BgAn^QSPgxN1^!FJRPX)EA<GJQHr0
zC^X(7<)(t?UAhV8i0tVb(`}T)z{2O73hv2wlEJ+51~Yed@R!M>?-|H5C52HGUR<XA
z-?H=?v|-Sm!m=}b4uQ?@FPC1kIDKs9$<}Z2K^Og+3v%toC+D4YR#+>vbad`;>36}V
zZgy>NV?;P!GW24qT7I*G3=9i&*#&a2ltx2CgXeImqZr)6QIsy1<wMA%`VDf5G4$bf
zTUZv-VDczn29TWkvoFV0)>0QsSL!1Q!)u}psMNBT$u@Nh7m9F6PG6@YteV9MSrE;L
z89DHs_9EqGo&js|BrTY+?0n;jt4t0g$qfD*u>bvF@w0^9dh&M`j3$#WmkQ^ihH=eO
z_q?t=qE2^#+^&gl7_eA{35Mu5=g7k(L)oai7SRA}IhR?Z=#DbZbjfk(`*}CDw8*%u
z#!yf<1hcfP_7zwlI+ifSl?o*TuI<72Uw@PS9K-Md0t`ndc$$YgjCN1*CJM>0pm8ja
z=F=r2A{sD;e>@w`1o0?xd@j2s&2JyQ20d~HC3u7j@Ce892F#NEY|DPWW}$+tTR97Y
zKK&jMi)O?ww8x~%pgVgLPNqaF*j@<iFGFu~{=5fieWv+Cey0d-QbB2zSSAs~D4quP
z-Si{9eee)EOXp_;0t2bhX}oe{e6|Y_gYf?1hK7dmBk+{{&~XC_QGdI@*`i;X01G9*
zzx;9u^kfoU{BKG<Scg3;Uw)39k7{MArG;LadE>t-)+b=8At_6BZ4kCcMjfXDd9Ap8
z1>5Y<Ff<Al?~)Lo-fau7!%oku#(NSnJpkW{;Mx5#ihUk8h7+!X{qQG#E_>V5FmqKc
z3x315W8R-b$BRqJP9n>oC*tcFH`dxwH5wG!ne&Ia0ZIL35Fr$_&yw+E)HnT{Z4Bc?
z7!2D!i5ksm*bXV^>=Uy;rGg!rh}Wt1>WfEr7bkYMlL}-c?wivhk5G<k{^p5wNL+(R
z1_0sfZ0fE@gSq@Z6nEWQVmPY0`9dKj$D^*>A?*6n?L}CeWEmYT?dLToH5INxan0bK
zBL=F`+nour_D{iNLb@AC(@?{O)cMoUFsbE9dhcwtAey)kVf>PBA|727Pn#oDboOB}
zEhggOZ=}eUXhgzct60|t3s^d-*Vu|dDNS-6wx;~PoHTD-^I@!y_Vq01hr8!R#>-Z?
zIqK;TWzfyjgpBv&r6c<dsz#azn{;G2u2zj8w_^&%{Ul`f-CSDlob0lj-tCPQ_m|Z{
zZ@m++%z0H=G2`8oz5;$Gg9VwII<TwdC+^Kpx5ePdxZy5!%5g&m;m<v67t_rOk*f~U
z7P(Bb(zR|royccjPbIRBY*us6lF4m}8^IZwNaJQQFL>m(NQzdu5HZiQ$H>hMC!l}~
zjFR@kic4G`_lE%6#TO4)81M9lm$Eugk}mV<Ow6iBH^A$h$t-KHC#qYGIB#&y?k+o2
z!ZX5#^4prxm*2h128%kSTG2&z5(2cA&IsHmbe)T@mBSnaoR`IN9Zk=U<~}&P?sSF#
z7)b-yi{$!*@>)HeHJpTqe;*Pp1zZShBG>sA*wKNF5xnf6k56A#)wzuDAFR%T<zpY7
zgB$E9Iwnk`7>G!e@E5vay0U|T*A><q9Z;l54Fao6D(fDmx8H|v_A^j>Q+h(sI7J<-
z?pL(_jX<W8*Q_yw6~y((d&6k!cJWM5EOqn6L%>C+;9kSdqgxo;2)UH@V1Y%b4df*1
z_7j2Fw8hm98Q;;-QAx)iUthBtw#Ta0j2l5l^bjrkv>_q5aG@>Vd}I^m>*iHip*C)r
za1=`Je0X`z1MD?(x>+IgFM+Wz71k84;5|Ym?yX~KpVra^hqMqFW_}ET<2l|LERk!L
zYA8lNvyQL%*5WW6fqmKf3H^}I`M^m^lbng_apdMvMq726jKI=e7@x);YE$mAHwssn
z&VFygNdTyB%DF}gsXFu{EoF0A4WHoS<@%suW)yBgV-CdfWTb@qGh?vh(Z@UN;6~RA
ze|AH-eCB~dWT9|Y>Y{sf+$O0(284g1AZCp)!}C{eUAqT`YgaFOGY@5#3y8o-E%Y$0
zyQVIWJ>DwknW(_JD(hxFFTDFKNrQK!dmP@#3vPg3KTGMVYzeqNbFN22B!?(V*Kw8C
zQWrUM<<^I0o~^Ad1v9dqwX4_A&HKh~CN6jsMq$_IAYfZ93dXhhc4hE{X}g-a_Q_!h
zw!!9?+<`;N7lARooa-$UzqWnBTDO|qd_6*IdS-chAH8v}CmMni`e7Z`IhcUPMQ$EV
zReJL+@%%8b9)~dccx-<DIS*N4b528j{V34WQZx4<D;^D0(yYsYBZ-10;@RJk7B`Y6
z$Lo#L7|{!iA8p(d9$1btQ1>W*ej*$`P?B@+6E{8VmED5H$#|*oYbv6^vS+0fzk5fX
z5XueXoZKU~t$GsW66kx394)IMwU?8j1{YC8gX~$w5tXW>L2hwBdV3eEKrQ~=pOx7j
z1drAPD`4Nt>WrX=N}fC%%MljU#AtPKfkKdV)e{Er5w_&R&9&Q}Ns#NwYRZMB#cI^~
zxih!l()3pqp=%6jNuqgQKdeL@fOE_{uPi%+m7KdRhXMgf8i=f{-&$|IGbGHa@J>TS
z>16sa<s|WBWam@(wGGe;NyqiMK34tah!h=8bd}QaSS6SyJAPV%;*ex+WFK#v6luz;
zc=ww$|NN!B?X`v+2?>cYp!gZY1|GS+l5e*(O5amtJtM2LUvF;@k;6NPN$n}QIsEwC
zW<+Vgc+LKNMvtbWXtH7|+dS-C?lt$({!tFJV3I(=j-_o}y4c;juv=mJJk!}Q^U20g
z$n$~>Hx@^P9oFYU*EZEBR<|Q?R*-zucB4?m)=$N6VluTxCd&>AP#}4mq7YQBep6T=
zcBu^<d*tRJW6SS%p+KB|-YEeiqEH)ncWr?6RMJ_McuVyff)v&t%B7&UKI{d%t-`=)
zl!4R1!{#(rOe%%>$6dxw6ZpWXW3KakkbL<Y5fKrIL6+scXB8L>3rSXHEX_OxO0=qq
z=f|==CQ46W28tj|Q=;dU<PNH|D>eJ3h|mOYu_$E%+>j@VZjmHG>*>};q<niec?o<0
zxgnt;fpf?Mp2IzKb3;+*NorQ`vH!e@l9=YmG7simD&%7c9)VnDgCSifv$@R%ub}DI
zY(KGn#3R#@`Yof@4|j5h)6-Kd@s7CEd~GNJRjTS0q|NIgpd)h6CWn`K3VlS!WOvVU
z+=s%k1fq4KF`A(CkOl6*M6=>AxTCHz9;`amvHc(l)J@%Em}-l!Z-9hYVyMCteqO59
z>%Mo&h5ZB=6L(11c*eZgKjq|b+_(Wb+69<y`1IwJ>D+GqvmamK4vPf36bkfALDYOm
z5r}w}pFDHrFXNsxZ#1MpAxQ}Hmd#PydQaG>_uzUM)eCOjlnT2<q~&CB=BfY>dLngp
z6eLlm_mGQ0`os!@D9hjO?(PSE*#*|F57_@^SL5MW_8RlyXI6-{2FSDqwzY=o{+r2i
zv!NnU*pTe=?d|O$!U*Ex{|)X%dduO$?jhG#>m~4(S>kTJMgd|kDsewUqDz<VZY&Jp
zIK|R6OJ(P&C4xBW`!OF19pUGW%_KxiYw*wKbQqD#@%q9f*J4dphN%fCQ=eW=ST104
zSLkqbMHBnHZ%p}v7x#f*NqOT_M#e4lh^}pDK#YHuoNYp&+IoxbihjtCGEMrj==c6T
z6X=m-1s4|=GV9hm3PN5Qk$!%D1?&CBW3`mNUYy3=oxL)e=?~yG(2?1FhaU0>p391P
zB*#!L!lvIabFplv8io`RL6PnYqGSQU;LEE(eL;a%2=FYa5OzGeq<|7qT)jmawtwRW
zyevU=YFBw;r}u0r%dkAJ;Ef$vD6me_jMN4RywmlDG%!Ya5O?H@p&(nsQ`(}{b|e8N
zPYkkZU#NTdvGXB1KJMri>$V77F_;ig3$EXTO`#3tZyPvu-}9Cm3JDuNHg1H#D$s(Q
z)R!Tx;6?{}N}(u?H=C`<iWVHYIfflK72Yapq&58t@Fof%u%qk8FW8oork8CS-E|6Z
zEZeMNsfL*k#OUb=XIvGEgn-An))*9kO<tiKhO+3*Sb{o0o>c7cZY4%IE)-@qFeAm^
zY|D3sLLdjZ>$ECG5m-L2V|DDrnYR+mg;dP~D~+B0l?)AC&P#CHX<1mzAEuz8<{KOV
z-y1c$@(O53qG^z0XJxncGr0F3B$(tHcEoq{9(n922jwhHw38(Y-dLsT)c9DyWQtDS
zypM&2mYQj1&DC$^G*V2?Bl%LfG?>rn+0A?m0M-*-xFGW8j5|yqDruW+R!B3I<}DS<
z#9SkV==4DF2mKB7RC0g&mRY0do1Bc`W%Qth!iIOLip7OhT43p00aRDoXuNh<o$f5K
z7^|+*!Y>CKjn(4LdLD-gzp%xb83rxJ6-<BfghP%H$pw;ozPY+}n<)Ac)zjsad79EA
ze#!tA4xH9sVDg=CzP%C;S-`uqq+GH6FkNRYDrQ-ZTjIe3lAN0ug9YZY%~3ljv}Akm
zu>0<T&S#cvpmk7vs3Rk}p{da6{JUf{aU6tcL}<SBc5|AJ1kx59;Y4NFI6czCl@OsB
ztqdh>82(xxTv7;}rjW>e>hD3%-h;;oHLr}uKAM`xg$VquX>Py>cMRm$`<`H<S$&p!
z@ZrR%{=ia*TNY52i9(#zhTJN5eMptpSwg#R24lh!QJ60fv~i><eAN8I9eQLmAtxti
z*ve!ODabr{TyU`De>)f$n2SDn^j4-+b8~PjH8*rM^<A+@^89BbXRSf9UnNSU5nzY_
z11>3($3$X+{<n{@TnkpdslDdqdq(8p(a|tspprsX=8s50M;n2)PC0D#`yL$8X-pQ3
zh9pGse|2;zF2N5Ky?b}6IdTk@o=d{0Oz2u&fR4)&kZ`M^8Y6?Dc}h2+6uNOEh(+tF
zeuTw6b*H5&&gSx5%k7|V0ATlO@>rSMGcFzr<E|RH{Sxpxp{}$Axms-Eu6uO7)zOOe
zQJ{Z&p!s*AaYCexs=DnEf>mj0L!TCn#cxk5uM7n3H9+i#Sf$?*wbY=6&uY^d|L>m?
zt&h7lj~qMk?en7;*`6c@d{7T5A$%|_V5QXnFT68cgt)?LHQ87IW2m$NOmzp0jFy)6
zExGL=tB=t3^c8NzOdZ7~5^u!8sdjy#V9dv8YUKavvF%f=Lj{_=r`ph;qL!a_@Zf$z
z9Q+1QVOV_(iXIJu{Xi&<uWpRx)zk=moNOV|5X6+cXKf}jCeM9ypwd(hVsRELq{df-
zU7X^b5f~FC2AM@njiCWv!cW)JY=P^aPy>7UrfQDC`=aiTB5s=~IIu<r6KJ0$?Exc5
zX@)Xgi8}pQYcHgGgrJCZj30vdV>kX@483K!K09FZd<jC!qHGVkMN+QPx65x-B|G}I
zx9*He1$N*FPD5&e{GMCPfTeuWwbYap#vn!&*>qk)fDDWP94>v-eFD=|)tM+a27b4`
z0D$PFk=yh>v4Dh5sqhN~S9@!yqSr{Ycv)-#{7>cytT7c&IS8;QIMh^*-})Tm<3S+J
z^HF@1gJa(r68FRnLdd$2HgK{3{j`}%xbyOasEf-wA~XPXw(R`u^5+uGK*$(%iYJ29
zWarW4qcU<@2+llh=)O1S1T(1-LR?R7Pye{Za$^)?ZYs9fS3%QJWYLpbLE5+0<q$DW
z?kC*1J0^!_87P1bXmX;lIa0o7Q#$#15P_(FR+9+BLBvwFsqF+ZoCnUY+mD<i`wKm~
zzJC4c(4w^E+bk6rjHU}vi8tmz1k#>qkCVE{fTrXoDe8r6D<cFqCEo%X%7>ie9e>wJ
zhC+euQwKS!jLuX!6mCZ4TQ~l}+H`s}5L>BSTS(m<ywe8}K<5;;YjN0`_W%>Tr4rY!
z51>ml7Nc;s?jC~)<zsLUA80s2ww=6cY=BqG(iNXIKLQaEMbC_OCTGj+09V0CeF-gI
z;2HS(D`8CDMf%N4mhP2Sw}f#fcW*&&ViwJASe$;G&a^@1`x|r7S&~cBF~0ZUAk8yq
zR-c#vecctFXWt2H`U@;{8pF9+l9h*zF&TpOdUzNM&P(oU+6HJ}kdZkG3XE~H*m%4(
z0N^iBXn8xR<_O#jXe~hQ8CcW}g_1+lqe28bVxz*V)fCCk4LZwgpGUm=fWq8bL*51U
zYfsQ8a6`6@X7=Xx5$ZLcEYQ^6<RE8`M(|rHHRy1;%*S6|NEUzi@S(oR2D3>O2Jwsx
z7GWaIi+Qj~fI)#WcLA-<w;Qgv---*?eBc}>rvZL;g{o7@^SRq4CpQ7r4%~jZ4TZb%
zn9}VDz=bnX5fnO&K^u^o8ISLv?GqK>7Bn9#6oRVM)dUV5#x6WmXS1AT%RSd^;ekJ(
zurH;zCwHxuA9Bl|RdX$|*Ytb~XLEXc0&KiCz&7Ov(PKlUrdB~3&4kcknyids@EphW
z7n1U-1lu+N<2t(zhbF?>6$SYL<au4aa9p!>2>~*XBBI`TT$o-$CdfVSd6NWgAG1K=
z+tF|CZ2-odXc7d1WhX&lPpVq*+7WQiz%G3~&7#{61(?F}?j(IVm&NF}lpd8rb5pG^
zp(t>oaZuXxSZJ5JfIaJ*PBO^&KDX<)A|LJ~3X$_!zkPX*j~2@`pdL;oE*Zu_hfU2>
z4^OsoMi*2cU>Gqsg!d7IIb0ZVVbm%Uz%2!O#?T|2R<BAlL(#pNyilnG$(|YPrQO)Y
zu!SxPZ0Dmy8#%O~n}UST7xE&#%Tt9@QJyf|M!z4DWUD#A3=owXqol+1PI^G`*Z%B9
z_Z`dyY|5Ex3MYd6zQJe?z}_e$m?NcS));rBf@=zpcTWH`H~>}&DC~x=3W~I^g2nZt
z!`++9Ff|Z12teck{sF&k_(a<DCsyOp3SxGK4t}bq{mLZLMNA{^x49C%A5o;(bC@d6
zT0(fLfkS0>EgR~>xJd)J!GS_uLw^mm?{Whm2zk3p{16NmJve0NqJQ--`evaLIzu5O
zfHeisM#Xywp1l`^un^XCy!L(9dSK5!S}tTl=f4`(U`P>{UVQ-K(8OxLJTC9Ja?;Nv
z%@SV>HA5EtyD)&X(8D^^=FXivWcK6lIoB6~4+i@*x-JLccv6VGT7U_VM0JFt@Q-{%
z;l94wFoCxOR1S|Ju3A7A9hZh${$Q{EK_e4I)*`SX89xLE3D1vL1Yz3lm<%apz?2bm
z3$fZR3^wLMWm5IcogfApQ{IGAs1NJ-#DrcyjF^=HpftIgm6XfmE#Oql*m*DZ=FeD3
zG+BT=yDrC{%AFy-B&&JNo0$FP+L{aieJBoa2u)=$@y^8he}cyux(;rx8dDVM@b{C`
zis-TPs~w1pj9^<%mU<M<3e7_gviGk4Q{0tCMRlfW`gAgxj$@1nf(S045>YgW$PUSb
zMIxJk?27>u0w_Ubk?p24Nkp)~Xd@y^CCVa*AYch7OQKN31qez3B1$X-gaQ$;ScIbH
zc?<M(pXr`HnVgx)kK_k|yM6b4pY8o_4L;lBgOVPWG;^OlCj%`#F1PyTd-`=t%@uba
z@H!$=0jIX={pFJ-R-9lA9d+PFJNmGr2J>6BcRFf!Hzeus<DWgc9+6v(nt`6KKX^Q(
zH^nJ;Y}Bdt6_1pMc~R>5HAdhK!wVuHXf>^twD7Qz#cFpJsuAPs*1e3i`49eY!BDj7
zEDvUznDq5ScAl4LNPEtC<rPvdtLsiODOTSpQpZwe$Th(3Q5*|?W+eYuzVpmbnN2B-
znt&Nc2Zwcj?AY)EG2GUZ_n3S4^m<5Uyai`~RgK#lFzkgbJO-JE2g(oFqXcnxV8uCD
zt8LlR6g7mpc2=T1`$AUHnyBqN8)zrO*QL+E8Z3nNJI`xRT!Pv96u0iMT6YHSiUsNd
z%V!P%*6~&*#X<sDD#dr8O=UJPAY~~&%F4RqcQpeW`{Bt0E?E!<Ypo!O6rHjl<SwcE
z4q~{{>oxtF3QNRjXp;_Nyn5?U?TSKeR%#!#d^Itgt`Oa%+rJJRk~iT6<*yXwH18EV
zX6#lNSK>d1F0PF5Ow)`Q2gfzXO7)K;`OHbzmW?`{pDi(BRvXvcUK<8@_$dHriE#N6
zDXSp7KYPZW@|?UfEi-gV9vg$_6>C1u&<#BaT_TsiSV?bt8f43YZs=O4U~65y-3ioM
zQ#cvk=Hi#b?d$JXC4JORuvuN*ia=gA44nL$=%EtE+zv|4o(vLN6zo4axOb}?Hk60f
znKHV>@TKiwa`i~nxOK`BBU6(xpwMjs=LIMC#3cyT>v3cAGc>W*pOR-QbgMkHZw$ex
zg4?T2BR~awg@Zf}_e6VdnAF*}wMP~0*0&Q&z6FWBpzG)ycm;EcaOVJ4WQqnz+9fS(
zSSqE^Df76ncXcbFs@jKunV)vYD%-Q_`$PDv`>OLWMS1Zyb+U@52nARQ(^33Nfj_ff
zae%thu<NMfycK5LY%~+ZVJ2&ye7n+aIvWn9)*P$q>XP;Ak7-7PK3K-ERJU3TMjGjV
zbecqMT;d^vv7c4u4N$-pdROkUjg65^NQ<fl^3y<PkvEEoe9vi-R3*l1+x<hWGWE2e
z%DVW~;wh?{C9m@6-INCFV>z`k$Q5;9RRvXK4K+Z6I4O*xFaZJCs@XB-PQCyLhV)c_
zxXJ`IV6CS|RYQ>(?FwzKhg+EuR!9nl!hNuDK78w2R}vEweSZ4tZI@&QV5lcvCF#N#
z51$M)r9b10XFw7XeN8q>L>ELgSTMQvjp$PgzRrKgc<SR6fguw;aU4~<0jEpq`EFO&
zvr*M}*){OeO<^Z055Ipq5_<<tFzkp}G}-gzAK#0g9`AQLatYTaIcf9}ynCDb8q=M6
zjSiXAeIw^7uif><Cz&NM6J#m3fPPu^X$F`giAi)Xu~)ENv4ol&=wz<=<1e($=wX@h
zY4Cf{6|w^qLRGqffRD#Pu|E_>L4l$26w(#auun-9&}8q-y<)Fg=ZY^r`;R^ybc&v4
zW-jzn>}#vD<b~qYTi7(p!dh=2TaEoz!9X6aYEM?%pQP9(5Bj)4af7GyN3IE&qHH`=
zm*&_OQ#syL%6A+Wi0EU)(gixuQg?Ym|BPy&m-qXU?OZ1(>nsD^fv(EXw&~!P&suIl
zFB`<^EO^H~G*S2NyYH4Z*-$DWMK!q1RAPDQfsBT8y6arpZP3pS;d92=&1sBWEhZa5
z7J>YEGgjxhJa2@FRps2j__!^Eas_n3lnyZb!{yj~n}hiS{jK&kjcTVQ^<#1BYAMzQ
zpb8=(4N&4&WAXX1M4&Ko6w0j{<*sxwtdx=J*hT~7-UuR)zyS7lp``occa&rZ00@ei
z+PK853SMPs{~3UzE__y-f?2LJ9?0^O?&~KsxiUx&%VqWHu6`pqLej*SNtjlciA6s?
zhG)EH4&g#`Xe57qGqBl9GT?(cae3F~41CZrIX$>Wm+q*%LF5<J-aiY*kbkwxLll58
zKn3uVH!9tCY_Dh+OF-EktbbO!7h_0)ji^VvR_Pt~uR?1Kd9W|l6nUb6A??T)zfMt$
zv@x3Ym64U*XDDt!K&}T!H;im(vZ#G(!Ct0bwPWmX|J`_^V2?wT50$G*`K!SNWMBij
zd!BR{H{2n`dWylK1jw8kAbyIV{<|F<WKd}W3DaO4rJ9raj?}{xB#;^?T!2P#3<4mf
zna+buX~ayuc!r;y=SGQy?LDLuq5N+}=J7mM-Y7n4u7(^<t*bcF(~Gch&?ZDR`3zx0
zm$Lo!Ap7SbOTuB7#m;?Rmsa2Clzvlc>5vq9phF^oNrnv2*D8&mj>{b&i3;n#rQA$l
z<5{gyP~!&VdAxgXr%>1l)Jo|bj$eY}SO?1D==bNy9e4rxh>0MBw4b4j0trziKndK-
zpWVDV_3;N<E(mQIh-b$~amEc;<L3}}(!jmedGX^PeU2)H`(KgRtOV!3-93pu{kMU;
z?Xx|Je-&ffuKSNOb&S+M3)TIGFyB8(rv1Gv-<$mH?+Fq9503l#_AvkV$9;Glu+0=z
zw$HEg3iG*Jw1wnkE;qiu({4W@RCikKu-E0iKec&EOMd!L=N~`A-}(rzbagJ0{LwFS
zFEhvhMfRonc_U;**&)DW7PhcdH^LS<W{5mojeLw4ld&hvUuO{1LeFYF^yzaDz9reZ
z5BGi)Gic9FfQYKiw=N~om)?;yO+PO1&q~muB+zhEAax(OFeJyDp-L$j9<&!_8J}Kf
zQN=*Y9|fG#hQF$SA4!ZJcrK78B3L%etNU(>kSezmcI1Bb_97BIpjIsfAxZ>1)Q%J(
zEJAg%VIk000v17y94^w@1axVX;v}@bgQK%_#XnA1ECfg!?MNkka)bG1>C)Zq?uy7J
z#K*^LcgJYr8>p-6!0b@YWcP5x2JWpV!$k*gBhhq0Bv2BIf>NH5Prh-T;rO>GQ@BJ2
zaX!9&_p~*krtwZTj1C?6s~45p0SOfPyJDDh%pqA36j&AW;-@o+$pKxS<1)Gwts;rV
zJ^aBwDk!d#mn+@HS;iCe?xU%WlTf$Wr8JuxM@ASd(QQr0lgMj$n>dCxpV3e$s(Cd?
z0<<?G`n-vaYHA6TC8U&(DQufE&&ipQ+SRn>5viXALMAyrzR2S`x8G>8a;lHfu3cfS
z7-G_<LHFm&VJFX%>u$Aq&WE8*vKO&blo8obP%%#`EE{Psy+xU}j6=6K?zXeD^D8``
zq^Cv!E+VLxL~!8bCB1HTYV<+yQ?WKdHLU=LTo1R=hywv?bI;Kni70r6;)}NR_%v8q
z1%qLMvO)I6?(v3`U}eQMdbEFvIY6kR&yLk@=>P}yZ_ByAPrdr0x^uDh(f5@2z}87P
zbU{<iGF9v2@H>r2>)~~+UI}1m0InlcknHju#5S15mLRw;o39Zps<Tkdl);hl#BFWg
zr+#?u9COYYGFA$IfPRj6gmih=p<S28<4D=Q`r>-@<ntCRVp>rYdxR`0jbeV_ipbXo
zv@M@<5ig%V($@<cl%a4Q+ELx?^tca@OPZ0z$vOb4n?w%Dmn(|6oBPVp+yp76dc;?H
zkP$(ygQ0*Op{>IM(r9Ff^@(z%JL9QA>FvS7VyR|Dp1U20<ofX}!)P<$FmhCc*`stU
z3UgNgtdlHS95P3*9gIFDqC+0RIA@CP>4gu;<Dw}+NN5d*-&c)EWZabGBV*e0_4na%
z_1LipyjWn|5CJ!xgXkI}<T*7U87l{M=I57e#EB@;rt#U0ukUhZXJ;daIqlwnKE*q@
z_)Mw{P9iw>5qen#(mp+ig(U&JtBH)EZBu#SBl>TS&)@8z7-s9K+HOEAiRZ+axHBT*
z=+jd+`#rJw*4%Q^g~F96>igv8QKw35n=tbX%7Y>^J4)fbwPXn6R$B@4FI<Sn<XZ#y
zhazbNeNiY^e8As$P~g1IGpp~^k4*azn(M+ibABMfv)O0n#tAA(I?7mxsZqm#8%zQ{
zc;b_kxd@0In}n9GKswL1VHC9nT6=lyxTu$~+If?dMy6e=cFFz_fdWHSQGUC>EoYq6
zCQ>kS+eBq-(Pj#uB6VXaRNu5I5na9xoNzgvB!!Z*=9{&zBtuX4A9r7UZVt}yO+F$o
z$*5p^^mD7K3?#lDx%rh70IJ3Z0VxA!ybj%39|C~m*NODgF$fxl7psW?pehHr-$CP8
z4&vvRYsk#aK5H-batviC(Wf;RS1^v%vfkQI*lIb0FmgPeKzW-6Q#pk-352+86eZRu
zZ1{S&=wMf559>Q*T?Gm%${V2FP_~FuOrMdV4C%g3GEcEAmH*{EMQ1?<5Mr!5&MIvp
zwdQ3`<g*`+7QA!=`#wC}sfD<f%)F)N<auO0PQ<?dnu_uq_6p^mSo=xo;3CQT3+f)M
zaho4@Yy1)S?tPNeOV;S}&*ynKZTTn;#Vue++?O7_f9^VXPsr_!dA`+I@Bmw{o#xuI
zZ;ZspBYU>q+GzTzWi+HOTI*4bEi$$J@$n?|@Q@lm+!Zt4=pq|uZo>Me8?PqtVcO^A
z2bhODF4FHOZmrQtGM<~VoS~IE*j?9v<T88XY|_+)%f=7!f7oI+9RK)QEi@4Jzfu4W
zdC2w$!O-pr-}!b@?}MM<_^=#t)$wlVU@mgX!FP6SoqLQ+ufi_7LWORe7Oh)kTu*!+
z!C)E1A>+df^w2uOKc>p3+-9x_9~Q!1btB0hVBKkA>yzua$*a9`n)}4o%E}-(SNG=7
z$w-9-&PC))Hl|;rEV1lnU_E7}iHq=VpprN}42F(zdlmuiiwG@FH7qX<b|cp4Fas^)
zQu|cy6k}*S{q$7+#Kyb~X%JYUtCJOWB5!e+k!<VC9l>>xqHH+>w=4gi-#DYXbSY_n
zC?mX0l8rP!+5s(@PUe>(Ss9tSUw-(c?0GkwActraAl?MtgqwE5<SdRtm{0?ccNx*z
zkpqG1%(i`>x-;maq}6?Z+%U{Bf7m#vK{;_~hN-O`#61N%R>J5aciUJvOZ%11Z%J={
zg!U!B&39*YKUIdlTEtrR^a@dLh}PtaDBh1<oTh2cK%gW+I^XYD7&7snKy*|QQM;e7
zF)$nKh8g(U0W(UdE+xpc?CYc;M&g!hAAqVYrSvi^!mv@4GcHj0)u1P=U$H%C0vw`x
z{8ynC8F?Rkibw*}Ve8`p&NYn+H~Iz_bdQ6i+EYGTUmzd$pvpNKEt1};oHaE?Y=p@-
zYBr4ZB7epTC1J4BKG%{8o;g(Z9vIrkfS&X*e$e4RBbl<hYV+m-K!hHBKWcH(A-WoP
z1VK&4;fQNE8YZMEjV<84gbV;)>Y?{YQxXPn<DJE>yJFozDMRjRG*E{sj|7O(-v6ka
z$&YJJmviXv?WL6UN|}BibfJswbEUiQ-@gNjqhu@^utCCbYgjA<k5PED8W32N(BKk*
zV(VS}GG%e<`J?lnzO`MpfKqJ}$rptvE8XrMYDKbmrxCi+aijwz?VOaE5v=<X1}en{
zkM_)n$1A{94-C3O@Z&?5lH?&MBO<ST(B1OPhif-jXPlXyMu%w27IaFdi;KX}S58l}
z#`_ar8So6;II=+`gpv<Yhb)9k&$4@RTioS=AlZBfwJc2kso-yPJPVXy8>33sgTwe1
zIn|#ZuNI-BgiQCQHY@@9qfr-fzBJH;BC+uoMbCNV0HJZ%-o6glYK4ul=<s3Kk}d7_
zuC59+88FzXjo8jq?^b37Mql#7h9Cs4j5S=$gW&C4;tgk;0;j0a3OQ0$I9Pr-iP!MF
znFBAXI1eCCc;EQ?Ph^7E(*Z~uK-bYDRVQZz&=w;GN%0hU4EgD8x$Ohj=zYVe*23LB
zk%NXzkDUUbH2Fjsc8FVdMu7_&5G|Gst`<7xj?h5OmHXC-fSXrZ+Ufde|L-N+hGDN8
z97IaBkh=w9QA_)M+f4n4CK*Q6LYh)Q=Vclxzz-+c2WJNOWM?N0#U$WG)dQNH>47Z5
zLK;I$S(!#A>AjX@?eQ4RaUv!$SGrN`KAh-g#8YUV>8Ci7lGW(c6BEoR)1^oS9Yt01
zhQgIh_4+YN^%T-ysZriEB(NM)JpnH(C_7|7Oj&qA31r})q2BEsl=dA?^@^+9-Ped@
zK5BsFb($2wX_%#5uOS^-2lb7>R<dqrUmXRpkEmOmtbjUGejB$D&;QH5*VAm<zk+qD
z10~EG+ETtiB3+8Kw@N2ZU69goOE(tkD|@GUgx`u9`pZW9(&y=$RLqX=<g6V3eBh|!
zp`8WQ>O-|qlaN3uYUP}r%bdS5>xG7~{T$x!yiU5G98!r=1E8N7F#`FNMfC^_mMKhP
zSg#I|mC~eBn}|Qy4^w?9In;Z5L)uJvt?0n{yAN$Kc9DQ?=G&<y(wE%Dj*Nu~Ie(Ls
z#7O<HL5BmiO_}<6ofLG}GXs^f9}Z$G#FF}niOMqkaUj=3ntr(NSslfM#Q=64{7Ypr
z29gNN%-2s6+;Icf!YlQGhMX2|CAOAa&W^l=QNMr}wAED>)cwMlVz{j|e9_9m%)-Ua
z8WgYSf>=CFKc#*smODAcP;vCwim~;wPBbe~mh@eq@$@Ylq&b+a_iJD}@PP>8Qt~p|
zeNj$%p227Xvrs>+JZ2tG*2vLIJjkgbcGjdPD5Q~<ly<t!6a=H05|?JQP|+3$I@^Z-
z%Z+c{i&u*#S}5Jir~O5%Ufftzg7T#%`jml*j@cN=_}Ptb7HPJFUnr1U!U;`Ab{(s7
z+c3OB%_P(hdCmQn1|WMB<GI_6KxwK%G-|7PNJvPo2s&}yizhe9BF*<6SZBc}VF{km
zX39Ip$9EuwRz8&4QU^CxRy-mW)wXg-jU)bBOf`-I9k<UQ7_<rBeCz7~*79k+uv@Ds
z7Kp2!wr2S}6~uw+Euk^e$azg3AnX(iqNs4fQWdj0U@AUHvpu@=o`LJ2_7x}sG+sx#
zZ9S3+nXe))SJHi?DK*lHnKReofX~}1;^^^ynap{k4IFp`#={@HSe!(x3^|ac6feWl
zqLe3v4J5_sg9=nL4UB&&^bbMDmdtz!SAemC>DGMOjTe$+gIo^~F2ac!h~%dQ2IH%C
ziXZ|l`SwhnJTwFhST#VKQ<6-j&;Zsf+S+kg!v4GB7F*GiP6Vn|B~Rrp2fmgT&5)Sk
zJb=H<NnAj4Edh2Q?Y%8j+^1c83{!yFeOV|)q(i#mo2F^es9p3n(elAWWLuy}&|b_W
zlYF`grbvAJ2cXWxvA~1yb1okHGLXGX+n#5ivQF?x(#`cOk=&%=Qwy)$g`@Dfh*Ys`
z!js}jd?bnkq~IuUr_M{6bn+{riY2*~z@o|xMHT#n*84S1$To?gi54&~$yYVNhhJL2
zbfUohV771#C%cxBLruxfowBW|I2)KvS0kSz7eU)TNTwChUANJSFGd~T*w>V~GPJ+x
zQdknekr|q3X;LMwRc}!%jMuwU;16tJ2A%zn6Y6h<T(9oA&*a%8Q$j8Tu$|%;gG0v9
zpQo@tq5=&`LD#Nrnj?Rn@ct^mq$%9o6~v`T=pe1#wHwycD$1Rp*R^%{L7t4Ka_<RV
zH`SVFl)|fWgR$mG?=y>(R^ljtnw*IdJZL5cOgjO1*iTDL^qZoPgumeCx%r9lG`P1^
z*^1x;i4=?mD%+jYjJH9F%rVKY0*mo<{h%E2X#fk?su_Ziui2f`Sv(0U^;O;aU^Zk8
zUAojq9H_xQZG|qB=B8y(=)#n3P&QKg^*Ns15TG-G<4ad-=rq9y<S`O*3<`v4>J|@g
z5~N77*zN3G!E58$f>YCc?HM@bn?0^=Cr=4U!erFcdaTn`6k|j;QO^>SsEy1}cgf*_
zdxQn91U=YBq?|Ai@;8t0O)i1ch%SzyK<YkOP!w>(;!LL0^TOb+1{AGAqxs!UY}5XB
zDtgVECn~wj-!it$Nd2yf>(`Q{<hcL+r1#$!X(jxRe*3pgdjIa@N=_L6V*a0w`}(kv
h^C$AZD8m0)yricl&@0DnRzig=Os!3d{(bLP{|RDwizNU6

delta 140561
zcmce;2UwG7w>BEbQN9`5U_+$MfQpKMi1enPg+T;VlxjdlM5Kv8LVtA}6{#bPpweuB
zbm^T~fJjGLkPxDY6bT`OmOv<HJ<N#n@AL0{&OT>ff3C~x`y7+^ead>)y4QWL^>i~=
z&vmW-<LJ-o3VZkbeA(0Us)wecqRV;DtIlqo&W;xp+^#t&s^k=;-?&t8Y1t1yV%<9&
z+XCo@)>f9sKKx$y^t$cI%_7mS*Brj+?irW3C+pdZ7dyl28j7!9Zz%rdUoVQk%k46o
zs`&HYJQbHH)y7IJ5X(R8+Lmt+)wBQ0__X)u_kp^J;~E12SCa<<U2S+hd=i!s9$Z?p
zmL}-5lwmSuO+tL4ZWBzsaDRO;9Bz2s@1$jqg<N)~-;I^T3zK}W8MUkKFA`Lq-p!ae
zR8zG{)+j{7PKaoC>+j#dPZSq_vR_4(d-G0oH8$sB*{)WgH$hXB+vLzJArh0gUp_f(
z=U~l`%f9}mi~cWc`GMOmJvJK>XF5E%{JKnbrEC#qYZiyK|IY9F!PIW{j~g@#iLXDv
z3FJPx;odzK*!FHTr%n3*c}6WiJs{AP;Qz5PiouvRy>RjTf@{{=uiw8u_i$7VckzJB
zAya>5gdt-J%WyRpJiVde{@&e`@rJpB{PBhc?d-SbSKNT_W1e~Tv2x4}B!AUt=RI^M
zs_-ozZcfqhuvOs?w@mv)JAQpUZr&hm^<#;&p{>l<=BoMzNAPi)ey5Lb(R`U=_*CTA
z$Zdgh?cMw_j!pQZ+sQhG^22n+tUy77`oOy%-s{O~9oqEW*C(~a!b%KRKWZH+=1*a=
z%!0=I!ouz}shBE#`o8U1K7x#Qb;l;JdJSjSIf*emh@>sNikI%$E(6AUg2kE>S%+49
zeG+c?-l*CuN!YDw@;oOs`Xll1VN;LwdVv>@WbDce=Ohq|@sAw)KB!O`Q6yj0PS?y2
zc4e@u!BNT8DaH?lH9lBkqG{h&u=f~W?A*FaE_plmn0vKr_S}~wM%U?ii=NphGIkaR
zZEnliVeLI}RS}YS$%Sc(`Li624|ddhuwu0FwGqseb<HQf+7%8rK`0bmary0>qFXR^
zA;&kuCBf2kGiUnKQwz&e_n)M7*}5oXhpoeCGl%_I&4#?Kv${V0LHssS#gS35uM@n!
zR(O78R~Um`;NF36E=p$)wGMeVuiIQ>nqEloI5V6rX;rGx%bg>Uf3x52=NuCiAFNpE
zMCkGTbdnGv^z|u^--5Lg-nYk9rHpv1)}@Jc2Q(KB-7fwp8$rgTY`VI_?&pwFot%TS
zdwJK+dQziKTAd-03r%kikaf((r9_$&*QcJ|{m&hYnP7eS4mYKy=kIN)UHFt)=1oyk
zC6h7k@eHb}oG#(b>1A|XS9^mq{*0LBDnnL1uY0OOKQzLr`O$_IKGFEEBg0K$@f)|k
z64S2mb@J!kpmdkd&)e;&X-hAvmaE?6QaD|cXmeM^I-S|AJaCxgZ2PiBgf`bu%4bw4
z9yt@xCQ0oZxJ35i=x%FD&{8;bf9bHb#QXpzmS2(li|%y&z|&NX59~>q9h<9Tn&UPU
ztTA%0Zce;QBAK;bvc>c;e<SA}8?l8+MGqOeYjeO+d%T@Lw>O57;6ULNu%9r6$YBG`
zVYP3^l~0H+4P59u>(=iMACBC{Eh)1NowQdetFU=MQLMz~d*0PH1;3fsaA)1puP)IK
zr`siBe)b-5#5TPP9zHI_Kabz8D0cNLXVs93<oMWJ&W7X~e2j!Ff6dp2ABb5$zcMID
zE8(md=Iw45OCilC9J=tP<%T>NEY^L_Z(W$2No)=|(ALUUk~9DMskfKuk(iEdvD59U
zRW)ptlX!YpTau@hhsSy~y;AAk@wc~f((=Iq?}&V^VM}Rf>Jn#O-=j9KUxmYg|3jYx
zlp#vn3fx3KI0HsK$+o9|ay&flCkJuW!j9?FyEiS&bl$uG{t20({vp#`s(=#HQRRod
z+!;9W+Q)r%;uGGCH~;);;V%gmB`TZc6~nMSiecNU`|fQM{$QD^?tT1xL#+C?@CW<1
z^&4$n&kSC9c=!FGFOgI%>3Ob#?i_1?dC$CPxPWcFcB>>^gZFZaWtZOaZyS2`XIPT8
zmtpU?*(7h(D`Jm`egQ*4v;5YFdAu(22M==fi-U8_)&;-abxuT5o7WN8M1H3%BbKAH
zzgNx8KZP~o*fAN#ZBKLIMr=yTx$NUuo*(G9A5$q>OHu<r-a0ep?HJ(GGau0*EUa|p
zgiS;2ei(1D%xYbS;g53{Vuq9Zw{<#ahMTKKlOq|y7FK5^O)`46Qq#nyylqJtr_WAK
zJ#??NPCja8#`#yl^1s#<hilOSQxwW~oBwj(JE{0W5#?H59)mP*@4C8yCeXW&xh1IX
z8L4Cx7mpUo+R+Lh(Fz@%jTv=Xi4k7ZG$y^5a3cG|oTl{Ctejm5c+)};2Tr87tBYl<
zRy?OMd90Mr45{XlMM7kxrL%3##5Ds)!{c8QM}E>R|2bQ(-p$_nOu(L93K8b2W>u|>
zU>JdRv}@Ys@k2P5hw7f^dia>42Xpj?BQ(>UUk8jUhq!R{mG8$sBW~M~bU2b9+8nCm
za&A&-^oe`7+<BYG)%tGOZX1iDibew6?_+^QS;3A<Zo;9d5Q%JJ%9B(L_$QtDobqk+
zIZyW|SQW?*ZPnTw>Y80z)9-z)qY(Xx_K3Q(rbl=s9h>%OdH3dB?;U&rODxuII=ar=
z-l0V>DN&(zpW;<IqP$K_j8Y*mI@zU{(iX6Ezz_IA3=w+%)iL_aW#%2NxkWY_xk{2*
zB_L<(5}f_VVw$U$8>$w37*T7^2nnM)G44n`5V<Kd;qP6Xs&O|)?N9R4c<ek!*4#BL
zZ6;YV-QkR*SH|wl@UlcpDPf3%bBxiuN950szLnjWbSo=r+g-+3cl<KWm_(V6TSicy
z_3mdmK6JuPxBBaG!~8l+4@SUv4KIyGT^i-!-0!3t%Z(u<Ed-K1axzQL7g6*#_g5Fu
ziG#XAenS0IA(#|))y99aGKN>2$A8+F<lYpqx!OfULIRG+aGQl7twiIlxte*^Q~M-j
zw_JX3>&|SKhRbUtPM)M}!LZD2^Yc~)hYvqLC9JH!sb*N|<^{Z^(H{A%Aht@`0zFTz
zQBry5JnOd-C6~hoiz2>-&N6hM)b%jmi$1e<N4~gbC!G?e>u^T5nEgRXRm#mf#Z6Ma
zMCSjpoKdQ$aQAswV~Q>9w5Y>E^P6J!n`aCo=1Ss~hQiYnwVGi0Gu-a}V2i?%eKzow
z=8WFWNoOYUx}ERdl&iaTzw{o@Kd<C8@fc2wut|3BW|Qm>o9k802_MuA)^o9A?nlZ`
zoqcZ~Fk-L}m%;y-6UC@2Y7Q+A{2(g2VaX;5+h^B^pGdH&ke?r)H>Z{dPTy@~@V~q*
zyO;sqNh+YeeT(`2pLM;+9w#YX<y3<8?jc5x)Pl_Xz;7~)&z3SgpK`X!njB{j`#n_A
zu|OB}zh^~Rg&o+iN1~tdem%8bZ7&OPEf))MH5ZHH5($Zgb~EQU1T%V`?Kph+u%%ak
zc3HBs{uckqLUj7t_wV<zb`k8pAN*k79=%K93Za(IgsL-?Zn-!7`Q+sOy+tX8OAg>8
zP8P<#w~(^GN*0@T-mk(56dM7PswO3BKC8#b0pwroF0_A~=f0;V24}hq7vk~sjUx2<
zi~fBXYSlvF@$Hvv4I&R4*w48h>W!5fDlMPFZ<^wWkdJ>5p3rrjdHFyocW-X)DL;~u
zbnwdln%cahh2Qsx#Xe%*rw~`tr}HW^1iuZHDPKHiZ1_s>@%_yU{ey2FD5dRP`MH}W
z_P77X;jYg`z_C97Ku#BH4sp31vTA$=fNebrXG784hzIK1MoRv6Q}zZOuGIaq!nVb{
zBslz<S@T%l)8cZ1ezBkFkv;TNqS-EKq4sb-GD@FVU+IazQS!mP{qqy|p8WINe#f^L
znz~-!qcy$c^-k1dO~GnmV&ZOF)zo$Udp-vR?d-J?YtEQ<*y7K<&3@mKFL)WK|M_Cd
zg_(D6)V)1Fg-cYBR|ntJ7pVz)Q=T7Rw{+IdVLM=%e;V<>g74wIsZq_w*fh7`9}Wo-
zoYq9$?jzG;q(ow7Ii}q9As3fI0r#04qwq-jQ>-$l#G3UXzC6%U$*uK|=$E`F%#c#~
zpGaEIgm@8c>qIxCNbTDpEe#QeQ9Vhue)DnBzFmeB=1Y}s3dkAY5G<dqvg;f?sN{7x
z@aVwOfN->fe6hjtXqT)|#W`2^SW5FlHQgpn|FcK0h(;!g=?MmvxL>re#0b6NUqTa7
zKN2B^G{jqMbW70O)2r>`jsE|fN3QZdg5N~*hn#W#$*4O=tHLFE%wr(H{04`}I(_Kb
z<#WajTj!b|^8noIoK;ymFrMo^xji;XcexyFGCTI+h2!OJ_GIeYab~?kQ|)CVcrKIF
zlqk00HnuucN+(y_j@dt1ADk-lBjfSC6-ogY4~nZk*%|&wd)vzXsf47T?;orF{ApUr
z$2+i`*vn5)Sb~<geQCUH^SG(klUU$(lt8cxdWJKKxwidB18w>jb~V%;t9IyBlH0Lb
z;wRg3Ueaab(k#ww&+r4?w7tm@JA-awN9UA>bpqeLde?N!i*{gX{0ELg*bL~rX{cK5
zvdX-`XYLWP0a;31$!`Sa9Qk-~&P|~z1UOGqIDy2m!quV=HVB3733ZfN5FORMeEMLh
zWV_y}UB9?3J#o+poUyK>sgKK9FUl$x8$ATK-(lP+W%CxvIBlnkvooz~<fH|jXNF?B
zqgO!t<8t-O7tbH3i;gB#!Wc7t&K64h=0AOdN7Y&ePzJ~$YKy>D03bPh-o{@L_jW3s
zmP6m~dR3eMWdoDg>=M+tsX!_BC3ak@KxR<q1!im#v$TL1ZdeG7Yk(Y~5OJj5kmx6W
zKbSvKa)&%Rca<_cukpcs<m3k5($!1zIHz~N1E88`l0&C+2xoS(Lp9sh_f7r-tYvhy
z+4oCdYf(XueO6xh%RJk>`KYN!E|qo1XlJlVqN>c>GfP&A;XKzMOZn^E8|Dz48eOwR
zK2L^-HS1|#%bm+x8YynV_|zW<60cI)SPWjX+vV{YFHWH8@vYH`uY;G~iYtwVWnsyK
z53rq~F;ufjydbW7=O^Yq3*n_N9iVKOU+G&=T9eHGj439D4b3FeY!dfr>t*RU8Q)G{
zN)&N}zHjIUvZpfj6vL{;>R@SPwKl0G=&AiUHe+ABd+B-Cv%tenSgotb)o`_ZncK*s
z%#tkK-|=6IX8ddEOOFMB7%ENyxIgPNFb>=AUIW|D#Z3Q~k?|lZ`X>EW9_UUhTX*;M
z@@rOjF`GX`)Z#oWF4`ygKHj(Vyz3*spqDOijGi$~7N(-reaBkz#}|A?&MQ6Ln|&wk
zAG44|vrsL(d|-CWj$A`Z+$WQJN;oEAuZr;lCG2TR{Xf2XZO*q8cfF~u=~y)>{@RJS
z{T_$KwJ)78iCGFKxE6V|vCNl09MQpl`{jY6+962EzJ0p1fIV3HT(>#EU&<<RxW^(t
z&97Yi$G`ml;0nkPm%fbiUG`u8b`Vm91H>2U>4sW1h0d1i&ivcn-af-JFS*aVHgfyP
z6DL}3Zd!&Dym_=17jVZ*0yneLNfS4*rqdHQD3<X>C;{)0TGnq?7B#9C(28{zIJJd0
z>k5Vwc6~G6>$)!N?gIBJVf~92GcrO$|1q%xDX@><hC1<G>NbPO1cmQPtt>4qwa4Dc
z$dfnhI3849UM`t2_^QD9L$11DIL|tqQ^D(fpc~-Zyy<(~@CGy>iB2!vAO<7CwWwLN
zEqm<tGFi%dXlUpHyjq?to8Wz6<EBma7l#8yMOW<003NDIla-O6aFd1})9_F^C8ZvR
z)2#hZW_epCT>YtjP2<qLlmA%R!Jq!a%3}ZfZCRY)e{Wa5?&yE-r)d8li=Z9F1yF;8
zWf+FGGpD+ha4iC~B%GI7%MZ}c83|ple2wOLQzicRvF!$t6Q18rGq7QnW+o!vwgmU8
z7ku}(zfXMkhW_nuxQgpQJ1u>%1ByS-E@i%uF0J|x+xuVq_J881f7w607BL#vD_y3?
z`nXjaOsYz~SWhb}b#Jd0U7vTUF_;;yr7*+e(A)Z!9f&Z9+-8<(wMns(e<8!VNTIH-
zPFhh>5q(ui!t;c<SNlnPrJ333(^YS7tzveV+lGyejcwU`?!fU!`*($FjTCRuA~?r{
z7kG0S-ZR*j6gy^3*!%YOjc;$S2~n%~V&vYSL_SOk#Pha9)@m$!HUG4*P<6As)yC}d
zv188ELiuYR9(J>QF8W$kd#Y)fXU1_Iio-QyT+2zr7=^HbL`=6c(^{mXqeJQ3tLs}-
zuAFXopd_<aYxw26`g-a>WhM*PQUP|(_~4KjhHW>Dt}1k)K21*Ev2xAUjWYV-p<ubv
z$tH=Csi)K0nEO>#YnTywl8O}*r~ZtNK1v`ELPeE#^f$(ec=R`X`R(Cs+sq`3DYd}Y
z&#<W|wbxa?)DSd1KA?Z_;K7)%D44+k;%Ea+;~FL?`Tnt3k4Ljkt+2lMy~9<m*pEN{
zh(%&^6wIj5`9o`4(su`HoN1*hC6k@jQSd!AQC)xUSk}eE+cY#ZYGJhT+Owl?<0XmJ
z!JBG@bVqM&Q&S@r*(IGy{ro)2_?V>6=X=IsZ9P44J=f~xYhje#ag>+m-$p9>e_CY#
zE-L%M2ApT-^gts0O8le!j;*?r1wPn#|7RGycVuEBz28d9t+)JhuOEl)Nqgv<)%-aQ
z_gJQ`zCMbTRi^N8pM9NVBHhEoBMPP`OBeL=G}}qw-byK^COY@a7v?x7u+dY+(Gg;5
z3eyn_{D|lI`JRb2sV`pqs=F}VEPLqAS{IMdhsq8+ih6yWT6=nWQZ+Cgm{P5QY<xbM
zvR!KSqfpd+Idj65xZ_PfTwh)fRv1~vsnjazf8-PyWumLGYtNqHNL~}M?_n9S;cgSK
zgQcfkTwKQHc_b`iU5neSsM3YsPi)2s{VtbP{Iv1$SZ|>2#FKT~sYPz|6n9QBGyLMm
zS2x&OW$z!`PC4t*sEbutmbp+1KewBjxbWhVjpHAcPqZ2eCSm4zfxI$0n-I_)qxMqD
zckoF?ptm!Hvd+uPOF~jMs(JF^BUsLM4z?h;*#6?AgfBq~Osz~%lx0m>HmGZ4WK^p{
zIU9A+-rmbwexWUKzWqs#jrXTp6@e#)!}Q!R0W~T&Hy2ymoLW+%Q8JX4-mPOC7#Mhs
zf+u=;|22Q|Z!1<s5=jDDZC8#RuQbc^2%oA<<UH6AA9E~n+Yi?jT6%iKIX=yPj3UyU
zOZW8f@bCk5_Y12fwbjkC&WggMP0DnyethNq@rDqi(JjzpNBxTHp_&jRm|s#7UA^Wf
zvoQm481?@B`>TdJ(ycRriPo12@YLpiyeTnLSEI!#S8mc<n7!=kYOJMqp(^OX-t+%<
z?k(5o%(j&((_QaBWSSVvfpua_roaI**8K9^n{ijRwP~QgzvgpolnnR<vkVL2GiT0Z
z4q4=1K6yM*H-Hq2%y5cLwQGvgz;XiTX3Qu%j2>-FH_N<2v|Y7%Pf(k&mi(0~S6Z7y
zHf^f@bC>A_YN1OGf!%%i%9Uef^9j6i*^yFK76A+J+L6doR*}UGzCeTEelfvHJ3Q<c
z=I88&yK<EL`J*pVQ}2d}sU0;iFsPqA`RwQscXzH0Y!OEQ8?2*H@)?Wd*O~;j4$Z#0
zvD~gZ-x0Zto$~Two8-*$tvF!pOgIi<#B!#DpM=Ag)+8ltem0-7Tv&L;iWN#;L+$7V
zXVT4%96EHUds2-w-N=QlkVmd^L?eK)Au(uSp0i-A6>}`<0{AQ?SVD`uOGfFMxp{fa
zCM`}VhPIat_T=&1$5-46PJrV49DShz@1ix+JDsKN!3)=Tf5dm6&)Nxj`fR_Nb-<f}
z+<FB=SbutJa%$?X62VAW)ZGoT$pvD1f#oLOFK<a{j8)6Fr^)T!eJrb(5&U@_#y@n*
z#wMXND<?<EXYk3L$fEK-`OMA^UL~)BjY+zF$hFU`9Q*R}?OMb6sUlKRfnVQ!^GII#
zfKF!33|LLuNRC3;aBjX|UT$urVjwr97Tl|KcY8<2_97a=df&x&f8-Z>3^W;S6%*r?
zfxmOWU#s2jsO4JWZZ!06WV>~dt4o1Q-E)JeNIy=68ar0LGYcPk*7r+NW+w^b_z!5Y
zCFM-vJjH=sRG9>hMVYahw0gPWpG~kC(^J;g)U-Kpu~7>G(t=?xxSbsUaJrlpI=xt$
zI2wGF<6O|$kf5VA{Z?xJ4>0BD1+;P>k8A7#O5sdRld#Ou=M_a2T5Hy<sr}`ScqD<|
z^5jqy)s3W+nw{;2IhtEpJ@TI)Pjo9-H7%>ly-R5R0zs;a&H2$LV6r2huugmQz%^L5
z<N~=pdyd1o`jF+mPeT^Bb`O1Sgh=htD1H3+Tq0eIe&1oFS4p-s91?)-$BrF~eDmf_
zKGsFzhPmZ`2&9#I)xv>j>s}yZv-&ILR$gy-q~>O`5AzxO{8?6S+Tau(UqP7)3k$0y
z>rUmEt+Oaw8BZNkTo_Yy<ViqCt%U<aq!yNxlyq3MB^x<SUgD4JvCQ38+tg%S#uFj&
zkHoTVVNIU{P|KXlEb1+_ZTtbJ$$D}}^4O`=)9Z7XhfdOGI*X>J*9<oYPUSiBj34gX
z&c+%$@pvN5%aJX#PO_Y0Yb9%N>F+ZYM6vOc|3W8W^}bP2QF-J6D!#zMC3n%i*|_<(
z#kO`nl!?&Zmzt5$!_--)%RkqeZA;1*RDxUKa1$URk{#K1R*OdNu_-TZoYI33(ALdk
zQL@i?`w=1jT4lbE!D9qVt1ea;V2DWctjxJP;IAvfQQW-x(MM+%Z(;Eu&a>5^_KZ3H
zevi*Ud~Mp9^vFAszAx(E&=X4h$Z><3kY2<T&kUuv%^%eJ^!tWx|3QD+@^#vO5KPXW
zKY!}fsmLe6%YM!qfk5&+D=X^|4?=pO@3X1jRw;C`umBX;yR*b#VQcFZN#$fe@H~+>
z*J|!`$%u6=b<DPl_Zum3<oS=@l<HyWl#M)Bb!>f}AAiW#7Z8Uipp4pl$2P0Z1WZtb
zQ3upss=4*{Tp5TGlJq_o!P~E<CMz3v_21n(iGrSiDNVEF1eP)w&tMRWW-gi27`R|s
z(wsF#OHCHn728)Ilai?@d`}xd7`v26iQk7#ebI-)tSp6x;CrnE)~+S?*>?UPmrD(f
z`pxO_U$1K&(HMMuGxE`+M>@TE0IKq74S9KaQ2=a)xe1GLv^m$lKC(JQL>7WlW|r5P
zzoQ6VP7a4@qVVHd#xBhFu$>=^k-2}2&8;k|V7qXmi^`Mwil07}4xD4z5)H#}Ep5Hi
zO~j+FuC8}R!0f`)vnw^(J?K{zy+l@K(NS=h^<2OgQbJPxqmmqCJO2I3i3VD{sKnWe
z5j=$6Mk)m((M%vM(eHRx82yL{eq+=QqbnLV6~12Dqpz0xmCKrXe0RNRh9N@QQXiuj
z5flUtu7*-MRS+=3GAGTPIc;WED4uxr%@5qdq6(J?-t$wb{MMMtKM5n9)^1_j47$sg
z;&T-WwBB%ht-_l%2IABQ;yg+{SF>i;Lh@5u-<pIOwuO@;q@?sEyiS*m1aSQ5v4nC+
znpCOteTvxK0w+qLxJiPxhC=D!(~9sMmaqRnJh2AOBG0=yi{pSz<t2vA&k4ZZKjb-(
z!9FS1dfMCDhYDV2EjkHpIIAJ>Px@wNyd!6HJUA~&&3Cha&GqbldL-mRJr*r5Pc}_4
z4vR9@OvUqT{JGVRWL~eIn~2#;Tm@ZB>VdTMF+;;=rKP?Ryx9>V$xy4;tHPxo-Xt<T
zJuOc*(Vf2N-1YKF;}ph!EA=!dC#R1OXfQa{G-bpqE+Zqu%uSrbRymN||2;0A`#H*_
z2KoJH=E8L1!rk9~!+r}G;VhyDcRvaV8D;YEhppGno;@obp)+L>%T}H~o0tktUBAb(
z&B&2lHvcqV&VsT~Te|iDv8&X}shj)z4;#7zI8}Eyw1wB#)pa|Uv1W+xkBG2U#5d*0
z@Ckqi3dPj`U?7K}O3j9hA;G8rVKno%-+oi)VLcu^P2<BX6(7nY;(hcYBDZ1AL@*vV
zXa(mwW$x2D>{l(_$#zrf<X*lnRn3?G>CUsXw6yvw%dhvkzWd{s%qF(uKxmR-Ofvfz
zpjaug1o3QHO?QDy<hIPtX|?XjZpW1P&MbFnE7HnhPNksRIToDDtI8{vF9(GvVwLJ}
zaHKDg!=G?`DP>b7BydIE6YurgnhJ=SMoFjK>vp9)kEga-#ikC-z-hvDJ3%yZr006U
zK*(_#i{|93i9*r&(T}ek$to%;>`Ee~@a>&-Zh0=ry&`n4I7lFLU7khhXw37Ob^t)-
z$tY6+|C))>?7S#|MYO#8jf+T4cM+B2*$v=7hS1Zy*X(c=AV0gY&$aU8$;nCXSiK_g
zq&+03nYN_Lj*|0lZx?(Bq<BpaK8pwk997^~4sQyKm}7sj(zjXO5UZvLh}<e%&AIE3
zsk;oFu(o(OSOJq^ZvHaJ(tvHocJB@rb1Tm=os+jISKqpIt5tZ)@rUc5=|{No<{nzL
zX^ovtgylNmH#gmkxlWIo0Rx*``pBz71!jzAkG@@JqGgkN(V*<DzKJ&Xa5wk8zElO3
z5Uc7E%wRC0nZQG+GlFEg@pj3b6KIH!4q4mTqhw7&+oPyZ5Boah$nA!$;SS9Sv?l_h
z3&{z?UHw@gpf4m4w87oJYixAM3rOzlyM6n1KCM_w)6B-k=6I}XCf?2`6H>&Hz8KqZ
zNK&%;_%p0*PK#JhSyb($qN$x_+p;bxo$1d}j^sNM1dDolV!+e~J_KA*^w-s*`><^*
z*NSh6k~Im3Bu2%x_|t7#;5k2H21}s37MD-qbD3>#jsfIn&zvLR@pyl55QbVrZnX%f
zke$WqnE2Ah;|I=V5%3Gq^46One6zR8nL7Cr!B9$XI*bHJ$+p?OF962MI2Dkhic{zF
z_*n4ccV@Z@=30{swJ^L0ZekP4hq}3d$t%fIWm(T?1q#^~InwMkJ<FD_9OkC))lxP?
z=U4C)e6>O)pYb9)8@wXIaqhL-V?0agSvCu1*)|dd1qGSi_G2Zw$bp7;sm$sihzbl+
zh+?IBIBWzzdtoLUk`R%n85w&Z&|SbLhdQ{gmanUV___(D@BSkmiH6iLCs+bMDKeaL
z^Wym#mW&)LQRxAsbPGX%oGKcpAf4obA5oNl?pY)1P$lXRmqVS3$1fZW-6A|E;JImJ
z!4Od@S8su=Xdi^UwFxCW-KP1yvZV<Ck#=q0zWv@d1FYWN*w{E!TqE~TKq%ymZ13^@
zM*Jx;C?COQnNmzJCnGH)40wc_fTz6YGReft4R}hQymeu|QyiW{jmhcuUGoPxj#rh%
z+!`@v4p}lJC<t=P;xEau()W*fmC~ECuxCfZf|2!!tEs8!J32b16CV0!HMhs%u*Wil
zIkR-|lj;B+tOB<95n)*vVs!q|xWJh&Cr_Sye@Tnw>x9C^+u7-HCJA7ubwa(Em>BcT
zD#g%e5xSa{(~WL5I=r3@7{ExTgPy?BA0dCow&dyn9*uHsAvGmoD`~b*kvdGuW7#gA
zX<pX=ix41HhUIZWh?GZ?@8^;01EQdNUR}1-hE$=p?6Ng1ayJ(e6Rlapk=fZ<ov9yi
zLQgZTi)xZ$Y#tFthVz=k;r$R8*rnkkrA;G^S!}VbTb}|}fMZ|k8AqeFbvj)?j&R_W
zzPkI|TZ;Zq`uKT?bIuKqRMHJ+MuulFl$pG`zRdApJ}tB0??riB@hCRd`c|RdbgfBE
zUbC*4uvTXRv4VwnpZStPhBE;Ucy|IiNoX~?UY<WxuY+LnS+Fc_EPeJ;5z~TVotL$|
z1+PGw9ebeRamjxyDlrrwh<;VL+f3IaRk!!@P<yKTB-UY0-X3!{ve1K{wQsW08xYEV
z6&0^58OJlfNQkalJVc#SWR3Te!{8X}vKG#fxN`B`PB3J@;SIR8`x_e@xi{AN{|q?^
z<(eyaV0UJi+Q?*YQ&wYHR(%;OPp;+z&4L|J%+5klEMVA9Dg?quP|yPvCo}&M%p;MN
zTd7>oIhCwZA!7%Q&Y51R<8miGD99nf2ZXQNYs5B3XpjAhVsA*jLfM|)$t+?u3cm=M
z*+EL^{(6PTa~hFHBa%_FH$o6+`3}^;`SWF2)3>7deAcJ${=NA43Uc8FS>sT-?DBmI
z#UFpuSxJ)d#axZ(F*(4&itp6+;0P>igeF{3+-vXbOqPHwnot}8@H`_m^~-4^TniQN
z)p3TKqBpmT-4g-l1Jdy~HXQbFcX=7@C9z1=VeGF!FX3J<UsMGc_$Og4!h3WQrW|#g
z4U;>&d-Ict7x&;UM-&GB@-RML0z;^QA@Al>53<L`ja3VfeO5!jj3&&pY!`gf_xk+q
zRgnklqCYpNuNRDLdW~97%M~FuKGQO92g)!XX!~62@nvQDIa5uYW-3GNjf6fP`1H_^
zP9D>IOa!Sz)1G27+fJbniz7-)OBby;5@L?h{J7o%rp)dEickVjl}*_Q2WG-5Eicn8
zyAVeU>-gJvyPbd`bcdw0P#l|hCgq|mUYNb_A3K&`5bh8EJ<Kj#<5C3glD&O3<h*b5
zCH@_wfZqyx_s)i#lSOofS!YB1@CN2eB*r$-$GN9iNg9*By;frD0#|B57{xDyP={D7
zJK!R@W%N0PUP$ruLFNt$LdULJ#8%uK?{y}2j@SOEQV~TL^%LIxy}X?AwSzR|<ah1d
z`KwkhE#(j1C7APYw=C={WX!fzDY}!X`5q!R-sk<tW9iOBsd#)Wk!R3@(J~t!`Y$@L
z1AYPE@gjf}3JThy<Ll`U<});=^dDw=7IJHLDTWTuU&sW{r<V4X<lr7xptHtD3HvpF
z05D`mbqKTDDLd?0xaRKQ;9zN4S=pLU(G3o9?nisTFS&yvkbDdK6UDq@b|IBO#Y{54
z2=+y*NL*ij3x%$F*7aT^&~X36FT94tsh3T$8ZkI?rbANj{q^Oq;SnNI^P_j#I^QF%
zfC~`+x&6SssV*-GnpZq67Egs-&#fr2i9M`dR+dt+u80Oq&E*KhDUFm2Bt$b2b%vch
zeY(Am=}DL~jJ%lG+rw@Glm!lJ@r1*fi6>;ee7U#3DV{o_YYbUYJ~Ji3H|>nr-!8fP
z?K(a2qMmDp_1t&&WSEd!guH*I&*4@oH)|`;4`B1M>>h}09a%<4N?8G1TE)>X!vY4a
zVDS*3h=f^Fr3MZoK~B@2Kl;h>Mn0HG7tjZHW-Lg70o~}aWzSV{gYuF<0iyI_5n}gz
zb?Md>uglaRWV1RmSuJzmab%8$UZ$v*^%g1G6-;jZ83p1gZze^LG#<|h1zcBTUGrsl
zrU(vFK4r6|C1#0eDl_)JuP}SKRM#j`S0^{Odx4b)Txqfd$5o;rtLPG5=y5vDn;GUv
zPMm2^>7KJ0y(MC^1=!}x8VW#IL3UeqrVK)zu&|h_bC+FWHnm`?sZy~Q@8o+}Fz`K&
z_F9?fBw)rZii#<#x9t6ShjH8kjNsb%NJV1Ejxq&2|Bxph*J7;=DAm3Hp+j=OtyXuk
zgb1(4!{jq^MI=1-r3cR%1J?bSEh$hQVci`2`e;{Cxy5<zl#_Fs79gYeHR+V!oCe^o
zwiydZ>(W)5b$Dm3VgaTA)0a<mt9<v_BLG1fOu~KiwNQ%0x~=<P9O7}h1A*$x?6#or
zbi=(Qt~Y@!yU0n!YcWHWy8|#YGqZA7$m3c(fV_oQC?6)>053R_Rsli`Ln%)oLfnhQ
zH6ZF<fx6)B-xFrfL`*?`-}6Ke1GEWw9``ug7hna(kl=Xhql7XpR*UF5%Z<Q7A=Cy8
zDVwsc0WtxQ>5@oJOHU6$jG)t=Y!#gPPxja5>f0?q&zsWEb7(f!%1Td%&39X^b>#4B
zarKvAoimjY%W>=+V3xZo1N^#6c9|x7zKFn6IU`gBB0f7>(N+@hJX_KZqfEL1?dTYr
zG3<z99u1KL2q^``9gjFd0XbIlZ;%b_X&HxB>~5D~Rgl)FE-RbT5zEH4h{)qh_Z#a>
zw&zQ{UaQOh$gj7ZUw>0*UGG335g(K2KUSNc?b#iBYn8}I4oL^kdgpXKB{5KwEiz<R
z6Y4mQ$vsASYL_htda~E9U9$=|2Wdx4&DHsTh5r&DMmmyVF^0g(10}{lSFkF``O7Tj
z*duSPCD!~*a>s`R0Y`hK<I=?7dnMnA!F=~b08xa9d#NfFz&<7bIZOz<kMfDHPzPHC
zgN`WoY7LhHqWag!=Xf3>rj4|8!G4}Se*74vdUNlApJx7PYHT1_!39c~F)!dm0JZ0t
z#<9%1l*E3A2gfubln<euPu81%e=Y7<l2zuoTRy8mz#)-nUL)NE6WiAhQR>$_3p8gK
z`p<{-FtwhmhpeOkAHt|-CcZej{J3fr^Y;zLziLr>3semv5qI7SDQ0G4yGOtdI7jTs
zCSs*>ZtnavD;EH64FC^nz(Uri7kJVmjW?y0awsu!Axc$t11<ub@0G$S>`li8wCE2B
zJ$OzCN3#hs|EDleJaf-ktavXNmvy*9!vkS-mU$M)OCk$sV`qCjWr30yPv~(cSC4*r
zYaMP5T>3h_*{fwE6@Agxt>Mj}$Y$+XgPYQV9ScRQO^z$AG};5=We9LEduzhPDBJDg
zAl-({J*&v21a!Qo5McWF?_?rEbL_~4*oXOvHWL%7PT!|HQsH3Z!(MUQ3>W}09J@;@
zDptM&jC{@wd0zFtKTS0!ltCfN*G2qxTH{FXPc+IT2N=d+ScpRLh^;ES!!`OJs8SvN
zPC=%&YFxW0j?Ho%2N3T=pi#TJwjg)XT>^ZEW06ZH##*?DJX;?K1`m|%TASwBZW!x?
z6rvNmxngSDe4gPcB&us!CVp8euw!Ik30tqlMMZ4@EB1|L%N~F56L8Ni8d;F#*e=Xa
z{3K+bjza<lF{CuaVFc?=1ow`Pk-VIooI?5Nn|u$*yV*8U{{oKcH6Mc<v-jFr3yUoS
z34X4`KmUaj+(Mxq`FG%++%(jHvM*kk7uePPxeKq_q^~=651gAknKaXpeF5Ztzw#8&
zCs7KJ9ACy~ga9uD{4x`;xvPe8<hDwH{hWeIYqts&HY*Xy94T?v>LfX}XkD>u^Vsx&
zKBzwRTmbT=6k8ivgY_^xm+q1fh0>ia`fd27Y_1q|skU$utdBk(PqX2aP4~w>D=seX
z^KN;3kP8+a0L#hJvH<Z(!Of3KZ4D>!DmX{%?CgNo`g~8nwesLK;#|cDtgm0FlwM_@
zcSqV8cZ}*%#P$kR#HQ;VSzJHBMq9MKfxG)KIXCPUI39b_+PaMF7rn=FJ+a4Kzn(i{
zBR0G{3WD0m$A^A*<^qg8bH0DP3JA092>M7r?HfR;Q!Ja3X&rMMc(cqok;>6qtIby9
z9-aXa0*S+pPPUL%jll{L>b)@p{JJd={gxXbn4%TBfYhW=G0~bo1+176U@@L%6Cjx=
z$k6irFo06SIRWr<D?#GyXIVi9P$%g+6D@}vomxR)03HfyZUhc^h{#D6rRc*v2t|fY
zdfuZSkr<iHy!h2J!A%`otMM88BY(0Vbc3s{z}ofeAkHhjU|czIx<L{^<D!y>vbwg;
z2SLArTokF3H30H=cEyy{KEiNUzF@89;N#~&x;6R*0+3EFklJ_g*IXcBRjCWJ19~P-
zi2z~t;7dKTJ>ZxdqKHf2*vH_bff(i#&P}l~YSdr6H_~(e;=NyqdG%L}<9+%^B)|bc
zIRL(Y5sC{9LHyEodG81#M*zxfU8)^9+z7<$PLJy42h_B_Jdd)<jIy1sGmk{h?X4at
zFEp0-;$T4q%;zEv{4){+oQgcZw0_p%_?Ten-H6^T@^h6Ebdg&}8tdwgRr0G9lRGSJ
zZ4-M4RF~R6emT_IjnIz)h+JhgwMZ3~^XgwS+}hfD5gdKy$O70@>{NX(VoPT}njYSU
zgobXNuVnY-Y#_sbHb^?<=+v|;B`#7M*gQuzqWn4j;1@FkcWFW_dR|`cA7N)f0eRjt
z8;Hn~4l894+dkhAlDs^r>j?_pwYO{3wn1of^=ghM_Lh*{!8)QeJo=y0lsyMqK2K5I
zOvF<1^Y>4`5vgpsviv@hfN35x)rx`d(}1hs1k?g!{it)~wUE^DwO};)gcU2W6}7>e
z<hoUi!L`4-;qdym6=ic3JV<f=C`<_5s*=`7zsLD<$iRT{u&k)zZdTN>$co|SwJpYw
zcDi?3I-mqUhM=pZrG<_*j4d2I;Zdl>>7E4YrfQBymm*7H{wvd0TyHp*bPC@|IkZk^
zqinoqs||H-$ea|6tx_m{++n^iY>SdKSjU!FHMejtR%b!!{9O}&Y1eXY4S`~c$4j7c
zoW*q;0FoW~qI)qhB64OKL9mSZEZ`a=Ai@K@S4PVYxC;LT`E#_4eco^~a;|iw5$Fp5
znrf)@`6)A7_Sw^?H<~6N$L>NnK$0Y6-AQ;oRz@Bp!EeNo$NJ;AF2&&r@N~iyTPy(4
z_>NwID69w0@?WH-1%ofv4+se8o&*xI8cphs_%%qhUM@<j=iGurLYSbohoHbvYIfqp
zPw=k0uq20pe95rLdD<a(CoG&eKY^t%Z;QEhu`CRu_uPfL0y5GT!<1dR^O3TO7IRTq
zMKp_KApa98BPkp3s@`N?wV301Kx57h2mye)gF4d9^}$NIC&5*~tF;)w=G&H$k-e(F
zM7{_ZGFqyx?(C;^WKb5{bPBl*G)$}iu*jF!A2nKpI@;T}LGegny2*D1lghxUo0WNe
zcfhv2w;)GHPr_0QIiGFISFXNHq?_CL*vHz6mdGY0CDmGkwG$$gk&p+7d~j5KQOLZX
z#UH9eF<4a7^Ku!51pEARYGx)I{`oKF>9GvjU2GcwIU1`C;FmSKyhQCUQPTl1dD+~K
zri!`GpFeLyM1$6Z%@|^Zp@t@vW(Of;mmpv6lBucb7zaoPwxk2F%C!>2p7)>r7AGYv
zt-EKYNAKwMWaa!NBl}n`Yq<rHTMAK8%&fAJU`U>@@<sya2<~V_5j@U{H(Yi-U>xa=
z+*U)y>J=hqe}d{0mqIO|#~8?_kf0*SXLh0}hd>yJWV@hRrjkrR5*Gt<WM@kF<+3P5
z$<2SxbPI4QpB<}rpYDqx1C>Q}RwIt|pe%I*5(H&1MaAs5OI|sgUnQ$6tXMw!?>J(w
zkDq^k+&=qb{tE(n1Z2*?AYZ#S>^!~*h@}9u>j)&w&QM6<048?j=1z%+h-!OZJsUXP
z$7O^l)<7s|d%}xc2VTz`7+WSHzJT(5-bg@1XOI7QBbS1a=2aj$?xroU;2<#c7RL3M
zFhgsgR^1%)@St^}!j^~_8{b_`K=G}O*l@1|!B;RHu$+OU$ct)pyX7rM5c?aUH}9Qc
zOVS0mgxWR{8drx@>KV2$uY;GwiJyQ{8awbXT*%6gz-NL(*Tc}EM=k=$&n!c-FB<1V
zhlItV1=!uyVyb(3Cz92wmB`|GWZMMdX#_z9A)r~Hatb5>g9DHTRuiC}2wB{tK~nGc
z0>yokf3x=YV`9jFHW>vJn-Dg&2o)uPSKE!KV8qJBgktCMLf1#moIELPqBD6KPNp<G
z4^>>DVklIR1xI`TrRIbR%5`r)R3`Gjsf9vM3+Vk<k~}2AAxA<sS3gp5LXSX5`ti-R
z!RS~&K6ta#8CA6`GI~w-1XR*uk=)<)XV5PAUHk%HdZ{iBhoc$)pC}E){-26H|G$+L
z!aTm!m@ZE0KPhJXd;b4ved>Sp+rJj;|KCrs{}YS+U#u~j|Npy{`k$EX|9!Gc_J{jV
zHt2u;wSScl16LBFFX@9l@AH21`wtM!DX3`p>!(Eo%4@2jHrP5GXvwObyLZ>U>v}_<
zFhEpv95C-X_`m9x7k?1Y1I4{!4)I=QM{DZp-hFFj!{I!kO#kacq0LdTB|Bx+^%0<r
zf6r&4Y8(t)ckiJN@vhx+a?HE3370i=X9l04tXoJ9I$Uq5K>6}o$Y__!mlunL*J;;B
z%ZGq6P}b*7>A6B&Kc8#@&dpFO;qTJmJ82p(!T0F9ySvwO0dI-lT)Acl>g2BnoJYm9
zZ%Y@8Y2n$}*V<@8V<sUbBQ)$=5j5_VF3@t{2LIpNAoY)L=pctd{z7fD|Mn1^=dYv$
zg~wW`&T{h}i=Qy$_Ousf#}PuZC?mN138RkOCM<jpbP-6bl-wCrh~O3n{Ob-`R1KFb
zB?W$w_32wLEDraarTmEl$#WGdk3u3d1GK#G{{H@Gr);50`c8vLwA~P37UK|)v=DH3
z_+Pi0L7&A?Nkt8cvTZ3Q&0XT(MmiYqC@QGqzObTeV=!fN)?GO|H#tG!u5Oz79TKQK
z3{(g)3-LV^qDOh(tQe6O$V~K$+GeoLVEy8x0A;&F4OGoZeThv-duPs$H%G+9#bqV_
zV~w#2;{ZDk=zw$V;SE3H*BgE~+51u^d*;G)4S%6mR~C547~5OngZDpipk!xJVS|i0
z5Y+%m6TUAZuJIwclQ|%_y=!icxUpR5Xymq&rluR9;P0fZExljeAhHTTs3;=7&q6sB
z7mDrn>4L0u6p~)v@zr1ZZ&Cl18T2M-mwS55+H;e=`S%;4!LN9{PER=_R*uD-_Oe$Z
zGX6-|6{(wPev5?Uw;D0GP*yfc7iz7nzN2T~DTfZlgEv=JgGy)DTkhlj?w31NP?diH
zl>$P3K%26(vf2vVXb_;c?MVLi1~m(_`}F44NgJEqRtKQ@jbc@+QNGFtu}2JYKnKwA
z_`k=$`gSm9isd(XabHCmaVgc+Iae(m+tpM!lL@5Casz&>>wBj+bkKop{b$+|!+{u0
zVvogdGeEUzNJt=CI>c|%7k57lHK7G%bOfAVV&~s7{y4Puu1AxGth6-9Zi~?$AbOEI
zP^OEJthMs_^KDtBLuvf?5rG>~(HS$I<L^K*-ne8e*Ch)|^LNkZ7OT&fdYfujT(TbS
zNhzkymlmsMO=EebDe7x4SL~P#K%_2v3~H*jh@8E85Q!#O7^>4ZBtz5>Mu})R$R&J8
zOtwNxg7YFo0zPqHp&MONSa|WwH)+bDI=|L`bW;fOwi#UD-X7z#7z}<r!zjno^hnB#
zb!F0S)co~DW>x8ne|J5h{7#Y~%<6R(yE7T|c{ZH^@G|TT_U27!s1|~Ruh#|sgz`p)
zzTV8qMa+j1`0w9;Tl+!$R_N-xd&ZiX9imo^E*5~k^Y{^Et@~Bnw%gTT?u3Ux*DC(R
z_U9~>Pn=J0?~n%oV-^GYlu=VF(!a8E=guG`z9IXoSr<5ZlV78TRlb+`c4q;u#a(66
zh&$+H%|NfXne7~$J9OKaXZc5rgz}GD87=1x#Lxa&rDgnZ19E7;U3pSqvtiyQ=faVV
z8;7N=>8R`#DLs79%DUT<mi7ntQV2Svm`}`A$+nwC$j`D}?snhqH|A;NVtW;h<8^lT
z>N<Efh~C?<N;2U2s@=CL&;;hT^XHBV?2NuWMd)ci^kBQDhi@0FXZEe><BrvgS>)zO
zcgA#h8Z4OJ86Z1)97TVsKQTk3<>WRA3-|T);am0c^|Sxkl5pVgVYnruyfjtX2J1ah
zDKlK5JczfXbR{vxRRgcasXBO;2Q<BSpqu;BP;ff{`h1p#@G3C<d9G^=t-KyumD4U?
zF)sDb2!#H2!q7`~m!=o$UQ;i&D0?4>iC7l@&z~KLaJpiA;f&*%Zr5H#x1AA<o4jb6
zBhWUijyd)Rp<jBl8cr&CpEE8B7lL0_n1b|;WQdcePOU$5d-Yw&RJ4zVi+^ad|7H#o
z8k-8%<m4Ai+w97^{Gm~BKWx*<ww6EdaqsjG)a(+92yMF*5k4)nCIXtsL`6k3)0=OR
zSkT@A<va3ZC~ye}cLn@sB_P$T^07Kp2fQANMCAvd&Xv)i+#HHDO2nXMs(kC_m^cz<
z)Cji;Xxi-R6&?wMesXwwkQepJKAU>fES&%08IuhgP;PMOsV%{*#_4*~kjE#^VzpIz
zR_ShOX((kyiTh>cSsIX`7|2;y_E_cbjNE2W)(Kq@sP+r}9R(+-9b-*(f19yydSqQ`
z{&k*Qbsu*qx$cFVbJJys)YDa;&rd7)H9UTxLpUL>oxFD~v^*bOHzWnQ@w0*h<}7$W
zN+IPBj({rW5m834g@VtVtDoKgk%>W^YyIwHwiKukyHJCV_x$vLj21O|{@b44$Si!a
z3yyM+@MtgJx1~(_*lyN78$)PUexTxeLcCmgPzSwTrF8Gu_{`bEo28#dEUq*Pg09o2
zYrySwT%tnyLB>FmOTJY(&<tf-kn{Zc=bu;=$lII1N}(16Wix>wfs!|wpS^d_XK4U%
zEdw~10Lw;&G9UA*x=3VqQ@sa8#7;io-KLcjzpqGH-5fEzr_9IW<Dls$4k|MMYU1#4
zSBU@$-dyw8=pFEpFAGFuUPQKkpPW1e<)35>30$aJ2}tk$Shvmp030*VbBAW&#sKN)
z&E{3DMq2a!1;x_4hqfx)7@F<A&>i$!KCOHmTI!Ac_T`(q=o6`<Jm8Y7=52sl0S4YF
zk4+=eC4{wf@9%T%DgLWMF#u}0xE<MnDu_%B3i8H4gSr4;R=U*i!1cwq@c&rQ(XLjV
z+jt*tV)3zR@$)!>k7zfA8#bWX`uHl}W^w!$XkztyF4;w&g!h7(%3L64(SZ<j$-@KZ
zJ3Gh0B2Hf+t1ci<f6<|6UY`NICt;|90TsMSGk`$^c=ZkcPWd6r_I2_?&GY7j5k>{L
zakvg3cw4Q%j%8{PG{;`(Y<kRVIC)FS>G=6jxvedc1K&?&ncO0!r=`KHEiAe&l>v*c
z08moqg8qqF5EkezWl#o<0Ms7aN^NOr=o{+rs{Yo_=82t(+?JDCp4DO8n+?HYrlwyq
z)zSN)4cPHjhgW_l8n)uMwCQi|Y7{FykoY4e>&D-C3>cwU_GX2%U1L0eGwM@ac2s9A
z;A^5si9S9)BBy^?vYZxo`;N&)7ePB)VkgUqKM$plE)F}&OkGJC&;$PSB`mC0cr*db
zxB*sF=Erg0=aT9_p`C<;Rr-SdzsiAOZzQbKnK%LFG$}x`NqKqU!aX2iuT1;3ZlHTC
zy$llk-K?$50;5fEIMMwWqS+&}g8txA$#lorKbFZuqqH4iR>P<3)nH6~hn>pn8!t?5
zVI-(NrH}ETNudTf1P~j99bHfb640JOP$erY49p)xQqWWAVqH&P_2ZAFFRhl|`0;=l
zwPj<yDoJuAt4DMyD+uDDj#=BsWg1Ezp6AihDMO<;+AurM^Q(sv+w4@9Ewrb+p@U))
z79^qND{FwQ*AskbX3(MTqpf=uDsF=S=0y`^6aBBn&Z$7Y4=p%WzcGNH{gV8x+5CEC
z8eDT>cK>vBE6nza`;k4g)56)%^nNDk%=@^B<3>Jnxfg0)K%4#0nuDD8x1d8md%M~X
z!`RGez8|CNt~(Uu)<8|#76UA@DyDKG=>gQj$3a1_urL=;>SElEzRE`w6V$R^$s-~D
z1!xsyR{o&UJ$&#jFK98mPf@MfAXiueZV{k|eL38-Aa`u{vnQzzr+UF~;pQ4>Pgh%6
zAyi%UWVwu%;X5(kSlvzY;XzQmg2^$UpCSZS`u_d<?qjvGWPpsQRKf;WB+xR!FF+Al
zHLCo8^0B$Zk3f03P5VLlAwqQ<Oqcco=X)1|vrVssWJG!Cc1g?9?Flw{@{fEkYjNJ+
zoGBXqsN>Ndu9p(ACkVok&Z%`RlQU=ef=J9z;V!DV2?O4wV0g6RVr^tJD;wMN8pJTh
zq6{Gb1*6PrL<72ZWPmFa{P?XS+4mOmP!k1_-WR__dw=ebKY7YB<~>(v-MhOFpZ&;u
zd~$`nmQS<znJqVk>NNgsOBr}WhnRp0;nl|*lo1u>hz$U02JZ?TOk7}wRS_39JOI8S
z1l3t=-xD~LnnECcS(@gnz!z|LhzP|k;!ccfxV{Z-eRH;a$Z_Y9_{3NVp|IVdqR)PW
zoySH7b)LeW)Bh!I0rS+7ehP$`WWdDx;_)V}MefnuHb4dJ97FKj9bQrcs>o&t^&jk{
zkR*Hf@V44;EFKA*uG!zPW|y9G>WdH(snE`Wqi;)F54X>hx@7dg<plwU527Qi-22m%
zBwU4&oBMnD4Qwdt6NeHH*xJ2GV_>+gLJzoKCJI7U;n42TbH;wzH~%MiSd-k3F1LUj
zME|1zE;}gm4;{`p4VPh%ls}@Q#QB$xiMZz88G8q>Y3~E{wv@N@_><uE+O{t(503W2
z+s{RNW+?n6k8Vp{-&YWZ@8qG2K%D=5vR<JVoyR{vP2Zy^metqb)@r}O=u5wSwd_ew
zsz$=u0(r?mFAwcpPs6`11{t`R!gyzJh*|YehVlHN%!U3JC~)ID^$z_0kKDe6jPBF2
z)hSU8oUVG*Ra{Y6KO+GPVf;u#+Bi;0Iy_ca`^T}WeK$t>Y`>+hkFjQuv(P;?f%9FE
z{xKMrosBhV4&WZuFV(fELzB3=WB<Dcs?zk4-o-l!mWCTpGxg{;zY)6O2O_e=`>fM6
zY4p$Mmos6A%qmeCMpx?F3kO^Y;ErFq`5N*4pkhf!GZ2z2P!4#Pzd}k_|5qdEKB(E@
zoDME^fTHhmXk3@qrY9>G9;>R2MvngMSl7&XvwN9l3S<$eKmp7+^7*)J*-J%#`ETHC
z*cM$=0nPbAi8_@#5^Svto9B*8S^gy3xJ6jDNnBXpNOJ3($MDFS;Y1jbz>B@+JVugS
z&pCcs%OFy6P29I9WoGV44NlZ6)Ex2m#XDy^4vx;1%;hrByq-VQBJd>jE}m06{X@$n
zni)7WiM7|<pl%SdD6194(0%a?ik8O4$5EXme4aw>Z{G}y3FPxO;8ojIzFq8s;ifF@
zOg*$;Ui3gKkaNzmR(Dd#vFnxhg3<<NoX>^}zXrOBnE$I~4!sE!TZt<R<B1E}^OLp<
z1>5;LD02c5KpZ5cQ^+IDObQ0tAv?C?GOD={t*VgH;SF-3)kqmM%`u?qWsvb<X%lf6
z8=~)`$?!WiI6W&aFMP{$NRsX)jJ`m3(Oj6H_;l}nM^4z~WmB?{LDGcjz*jJa(zC;Z
zf}nt(59RsAWE_zcILCknvXHlHwe~EgSIq#l_)v)<2~CI*DA&sI8Sm$}r|8~)GPDF}
zKfa2_0QOngY}NB~_L`mSGlEOnJc}dH9ej%VMnU5d30v$!R(c+~k9&xmSA_1&WyB%?
zR{T6LKt{D+5Xsmlq);W9-PG_0_cuSt9tQ3K6<jW|HNXXwrCwQ@?m0iNLp_slf!Z`P
z-L5)1Eno;^Hk#f+x6EuzX%yWA*NfrL{&JuhLH1_Qyx>Ksw*>Ox?oS_iEGR=0L+U(w
z{CjA<gbq@pJ!gwwhNQ3f`ikqzO<_CX4wy|LYR(aGYl9tJ<HGIf&VAu>dE~0?tvNxp
zU&IzEDRsgHu*|MCSjTt6QYb1yPcOI2F5<}H!%%hP|5--r%V|u+*54h~V~5Pl%y<vy
zkT1ZOP}5=7&P`w6hg-V^T?z)b2<_>8sbd2dqA9v{NoH>}Ka95{-|TgpNOr%;xJfn+
zR$eP6w%kK-dfWPQMKkRr&uk%*Y3k|Y7&L()lT4joxlU3XiFD9OQ8=OakDSkiYD4=e
zM9nn<Zj34#9<1;;JOkH$;9D^pa1zXyIoum|veoCALQp3;6dAb8_a?+v8>pQFxU#CO
z-$|^ut65C26tu1{w_HSg3Y*=!de@I(Hsw#WJ6+PhM01k?#Pg(-mC$u%8`m_jqm^(|
z#DLwibEOcUpnneEYHmLH?TA*Kf&1Oki_FN3HB@HOq}FeXnPqF*jH`s2mfWTkN$uUb
zSBIaY8<^hJ<<_fRJ;#FFz@1rBFdG1g6&KKO^`|?SRAuv=4n)I~XV3PG-Tw&yG~4;^
z((1mXQ%{`~K_PCSGrD@5RmYevus>--eUQV|UsD${9`R_)!nKL2Ejq<mP0fnGuy{|2
zhm5q<n{H$sptNBh2Ih4EW5L}u@l~+o;!sHyuekp2SAhSUk~!lQ#%0W~x&DSNetvNK
zN^KW^F87ps3jio(!ife^t(|K{)zsG}p4d2-o43zw`2n~00OCMgyb7A@FG#050vGY2
zDt_pLkVHLUR^NSMgYljPUC(`Cj#tz}L~Ixku|4>tr6oL2<m8N92~UdM4YOKU?MDJa
z;89dVsNT=AT9-JheK>qkaR})_a2M6XUAV_FX#IkMP%n6s_ILxtAb11n3R=7{050j9
zRA=Y6O>Ozk1R}~<45?~Bmsf()N$VN-F1H{%K>lW47YQnxYmg*$-`wV|3l%Fo0va3C
zqv)c_p$D2?SN?H1%qv&mGU1Y{Di5c6wD9O0c{{$Hg0s~0j(=!ne-&KIX4lkpP-{8;
zrd{3W?7OBb3zxEhj)n8PSg#J<5mk+9^q%#-fm0tGn2?b~Ko>Bg2$@E1z96q*s(Vla
z2(<AbssZ%^?y-3Jv#MLKxIr#c=6JSHwcgmhh^jKE`c8;5djymed4q|zFp~OEj~*BL
z#*vg)KWPxPqZ(QhHFcT*#xqdeEvVZmC(oQIpsGTNVhCb}QN@6lulAC4P0>2Bv>ER}
zTKNA(*O!1py@u^i$Ei*Woi>SRqs>yv5)vgeQT9DcN%k#_B7REIBBCrUmh8L8*v42A
z3fTu?sFZz1*(Uq{c_(!K-*<g;UFFnie(SqD&wby|{X{YR4avEcEy>e<e$oVog|K6G
z4%K#DP8Dnqz-4K~u}m8$nGX2H-aVcG+R2~E{<~x^;pXk6yu9Ne(&>JOq&n2DT<d<=
z$nrpi)D!>0u1c2To&#C_FVL%PMi-3A#5yY7p>C<?rBm(wpr9b>9Lya+P^4D!-{8?N
z=Q#FJToK+!ZtHS<?Xb(*?!skS&L9GsjKE4Su6%3eb-5d<pY*9T(**U606XRWU1+@E
zbhc=iLw4Wzhaax6$s@p-8BQZO4C!f8R5p-##y!b%L)bq2CV+*J*#@Z_o3p#<P`Yw^
z8d(r2<&izY_U+aXnW9nyMW_a7pNfu-4g(q(NwcAFm<#?+uyvk+Er=t^0%+_}yx`Ik
z^%BYg&&v>Yh}4ROhrviJoAH8Fk{7+g@z<T`<2G+UAGf)wR%qGz`F-8clLk32qgR;9
z!n&I<Mk~=9{slEoJgBA_DJ@`u?@Xo+o%(@lLr}^5-knotnPf2Rd&;4OD&XFLv~)_-
z?U0)xzS^>M1dRfK_vcW2ZE%~42+<4=n)OexSjdZ`A~=AF-!9B7%ohtK!LG3we}0y2
zXlvfVuatZl4}Nf2nJ0`sP4)TEh}%;xsV;arw%v*5zBKYv-m(8qDRNS(s90?>GVT0J
z>ayaIX@sLH9VUx_qL4{DEGkey(~3ZL@Ng*yy)Ht;1Ht2=iEl&=s$x-Tn7`y;;&BYv
zo}R=Qf2826_^>~+lWz|A{ce9&F0t$#DzG^q916*dti?h2!9<`@4FlTV1^bFegEQWt
z`b~)22ZG#|-U4p=Ip9>+%}l557L)px7cU5*V2p(js_OJEUloo`;D2cIY1bCaYfd9_
z16HgCmx>ZVw1ba3ALJ&Wsv@XEU`OsFNc&~YuaiC;Bn(Cwn-(DU2`111uCd!X>luTp
znSX{B&+?)C;@&+Zxb1W(V|Apuw7TH`pr>Rl*mnxDB_s67v`Es8ZJ~;IGmi?)`kB}s
z8MeqHyzuKd+Y8k#Zxl8>fP})mc%#=`)rzh2^pEl9U$oFnXOUb70=$r(%@>aE?wZWW
zrTvURb*$5kp^fcSD1bmq+{gVTOOL^er8eY2fo^soFS{WBqm|X?0Kz(7Va=fzmg}TA
z5`FyUX|)|kF;53b%%A*}WSTu?|3XSC0&o-Ca)*k5IAK;}X0hA=9_<9+96x)-i}S0k
zm-(BvNKJ}p#b*B83h=)LHUbYBLttj6M$$dJ-0F%NNW8&!5Sfo6JF|-QJdZOuaqk7b
z*zef`BljqJURbtMLO4O6L|-g*WUiLikXZ9S+}rd^A4rWnZS|DeW<H6UHMG5Sp2_o`
zW<1n0T~%I++~2}Z30ni=z@Y`6$(Oxwt2ZqSZr-@@;kLv6Ag2%M0r~U0{)QOP$pU>A
z$v*E10ck-NS_prSe=i=`N)U#yE;P8}tr;J(c}z^UAI$j)MvUOx=vl)lec<Y&gMQww
z^Ac{V3$UM1#e{HVIT$*jAt4ps8%}c6ofuP2H6)24t^#!FfxO%^v0AVa!8ZB&Me^_s
z5E$X5BBb33zVvS5T>9MaC={K4oPDU;>hc$BK8U`V(I{WA3-xF%80;ZTldUWN2?IS!
zaE1FeQB4o|iR+)6w)y3b9GWJed<kSBPjX@=ia@zshh4UUiXcm5!PE~z=^SGazr%*7
z0G1R|hRbQJtm~na^Z?`#yAp$8@g*xrS4$tNz+p8-qYuosiFe56;Hvm#<qbngph}G+
z*JBHyiGGA=B3Ske2*2n<Uud=Mu&}oOgn}$7=Y>SIm}n5?Hvp)-aU;mn)PU^*U~wpm
zb2;9Y+=~xtQ`g&8Ev8kG0=qsqPh&sd7SMh+`p`0uS+$t$+rY(9sj`!2EMY@{xC$4$
zJc|>FfWxt!^$?mRgR1d*oqN}23PtMVjo@=}d+<Ra{~jml=;=Kpyo9CY)eJ;8sICAq
zp7voUV_V<|k+01wci0g_2p9GAwj(w|eFL`!?2479xG2}x@!$#GjU*^b{z}{h5Iv}=
zQ~s^a)hr(k8q`%&=dRAttbEWo*k;t!CTPmW9k#hrS?Gaj!j^L3Cl4<Vf1Y0`Y}4iX
z{+&oYDpWs)47Sp%-qRnR+%{h{TE!2Q(*<S;BQQ~sz!xpkj;L@K$~sUXt~7S>`@DJU
z!ro!1Rp%1yXfgaUf?Q}2aY{%I0PJlKDA`D{Jd%Atj`t62!g&cX?V$cJ*g#-AUkp0)
zRQ${qorKG#=ikK-YTBgY%i{;{4aRHc`iH*=em8y!U&Hb8T}>(8Z+ja@lIHn$Esrvt
zt=~1;Vcgtpk$O!tMNk?#-qr@;E*VNgeu3b#J*$K9xzxJ{vBfo!GQ|l>9V3*Mcq~~!
zJh#VKI4CqUlyFf)qbce6(t?BAL%3524y#*QW25t~Dz=gG_H8YvY!s#L81*#NK6L-X
z%8kcXiC6lS;*}5G>kkbMe6=D9lLu5)1~vhhfPirRoB;`N#AjD2T0xU5VCcRxU~|@C
z8o7K5_tEJKU0f*|nVFK8HO?Or(@uaNy{EhEMXr8^5XOA{h0E<xywm39g@5DDHbIn%
zN(rXiVRc8_ytMB55p`A7$ArS63!iD3Ll?R+D7nr+Rb;5NM$#EM6?|oCr;!Yy=d<?)
zdXQUx7;J6X8LyASU+tBVDgHl{2|L~g@!n6<V*S}3lB{b4!zNd=5{)2jb?&XiN7CA@
zVW)054nK`Pe)XE#j$=s&e71g@)Wrm4M=qV0`2pNRY-I%<B3wAvy|&CD2Dmx^4w&G}
zKPXLLwJM(P&LRi#4&?k`prP~aePH+?s3dlw+=*|ZUIH|9^SL;$lO_Ml4sAL)O=|)n
z!s4@<71l2fT{du$3uqA)kP(juFc9-r*!r#%mQi3ZD`PLQ><zA(W$TgczI=XUrr^+C
z(qWVvLWQLuHqx4*az3qt#+h!EYi9_HShl5xh}ittW>=7#q9P)pq$N-V*R~L>)mjNt
zyoUn#Z2Qpek&jye8|;p8VLjZoaWn$lPuV#2{#{L*#hk#`J)mVJ)E2Ao#UiO3-26Hh
z#j@(v1<dNB4DhvM7UqD8w4oSVxX`+s>kb>k1J`-Lk3w_A`4;9J`$5i+PFEb_1kb=@
zaAT+TuDd)9SxhESL?SY|ibH>Sr6c?qc?_YLza<Sb*oGvpK-r*Ldbfg9qf0HRdX|-k
zi)XL-(v7pOeBhZ`PFUPBz!TcEA*)gMbt-JChX)#{g^F8>ct(Z%yp0x|uAExGadg#Z
zTs@ot0HSnojbiEvH*RF|%AgM(`I7AB3yNWsV}qFO%$au>#OzD_3xQe;;c|8l;0lE)
zO3Rz<(JNpgjT90B7qvIR2K6qvanP^zN5x}?A41kOPa%j&p5<=gisKHz#kih%2mIAW
zROK)WyZOV&xnXn1M*RMA2$Y(e%xH|o0+V}aWBQJDB8~9Q>3ZKuH~Zg7Tr9KLsi06{
zY0^cxBZvPdDLM4EPwsl)+kMHM7k{;aCnS;!rDwmP-i7sU>u#{fTEj{mel8A)c<;Lw
zjfH2Ll-|z!7NqnQN`@aGBQRmG>xmGuFD4)@z};fZKL2|N;chW9xYEtPW>)<Z_K~k)
zrv3vV%ln2`EwH!9{nZrhR6q3bNMO(`F6KHE1{=iXiuZ515y&s)IxckY(#jiDNg>=g
zA6&;AstyK3|J4SM15lFoFOn;dfG8J5!^ba3e18gIHO8DvXkf81s1duI?FlFtN0}oM
z8Q;4HBSi#NfR!Vat=I^FmFeWkcBrH@bXG&wTGnYxPf2!$55EcR8qwdS-gYhMs&T@s
zrykY5wBRX7a;<BFUHlYa(`QZ@0i<IG-pXuS;2JRxAmHN3vcI!gO&y&|geCm~_N=(h
zgE7IJ?LEd2arS_*fV~6*Bp4!#Au*LE-3<zYuTY|HE5U<=r%;?rHR4~opx5Rvs;y1G
zjG{-z96ROTiH7YMvblw9{%X<gZ_=ik*d;$s-fyfpY8+&D#-Y6U>UH-FjnDGp8|j`#
z8v;f)9996Pivt3hG-SjzW?2cMCY0(A=LA6gPQ~!2Hyt~A5FUpdjbN|ZyXP{LdOM<$
zp-19f9H`o}8uJB7c{wsr`33cDF9{1s7r_C#WZz!w&uk_^kf-~GD5-6e^kA#OA*L5O
z;k;g9PJUK(<#ULHdLc5X+$nOxKjMT4FugW^L*bE<$F=Vs(w)@6$BeG&_MOc92!z-p
zL@GqhUm-iD(uhc@{j(`@KKrA)FBIFNLXtu#V+m+)=Z?h24Io5%>C&PHg`h}~{$D*h
z)g4Lf0Vw!1zh*gzq2;6{pVWS;Suy%iE3+{6D7swmA~!v6=^`TLs#0rtvB3*$j2%G{
z^5b@)-S*Wui%1j;K#8kc!5yO!sO`FkcmPBRylcTgZeAN299)jXY#r{XE;|qG_ZSbk
z8<)-)m1|{T<xez7F`eQf{4~mpI|OGhhg98eXEE3hBg5<+G9K4!B2S*!$HskNGw5>z
z;e=2HDbv}+H@?Q+W5}Qf8B|zjkj3nZ=Oz=Ome!V(@6=bZtqA*Z7X?*MzASf2iDFoN
zDtGUrQ=he!ySswZYk7l)PY%R`0o%9e>Amg3y)H)ZWRVSk!P~j*5pm<ab9NWZ^^teT
zQU+O&YHPEDpOG-I&c}PhOfLgtwmof`6?J<EEbHQn_QSUFNfAbCzX^4bo&-NQe&wcG
zO=OX&>&V`HZ1<T&)jH6`yzL&Qc&%XhYHROqP)M`29~w|!8ssL$AY8CXO6}M<*rguK
zK5j%x+xPrv-a}V|_($(iOAzKS(*(AMU?%HUz>724`5z_9usn9&Ln9Ti_Fz7(G{R_l
zqV@2==$<`$f*)|mT#;4gHu|?oVavPWOmAd%e!1rJ3TZ=5T)q5%+*<cUgG2nI??k~@
zbj+tP1?5;Op{>!oGK`hE19zftH-mGt?UhsLk57ERD=xeoju6^_>sMH<`D=!VfiSv~
z$6Ww6oy|~s`Vjc90|EkmyXAb|K|Rq|Kx7{cG$k;cpDo?6tT?PeIa=bNL*QO90+*ec
z?5tSg4%!x2q#4flH)fh{8*!9pApj<_!EI#L>B=Rv;6w;*#PzT6I5}$w&N@sQ3es2q
z^Di(l8gUMBeL+A3qAXq^fG7Oc@?h{v*Ea0t;8!BtA?eW9-^ggYQAmz|l~8RR82N&1
z+DHGAvAS^G<@U3r>nu1@D-crRRA&vQ`V44XD4wOYbID9iR#+q18&W;|1m)O%=1@VV
z3z=@q{Fm2cioK0Lu_L+ZEj5L(s_<d8wR=G!JJ6>;<yIle3f#Tt{k?Jx3!yxq-n^_k
z^x`!<s`?i!rMDXIB6}8EbKGndLnP`av(R#+`9qiDle`Qhbs^#qx)5B}!n>pjczA3Z
zse(>0*jDRY3N)=5+M%kl{lqSFuu#9wd{s?^Rpk9_+!ovHaZySDDlw2QFAu67ft8N}
zlnZXI0(_egQN4rEb_oSQNP-B_+kt}z?;{gXR4;-Qo%;XGh8Wo0N4OtoCMgjwm#|md
zxZX$XBi=?J!}b%=-NjN12BJSO*qK+YU0{2Imnk@H7I*pU>s!Ju*Gp?182(-iG?+wW
zT7|lBvbks)qQGUM)i{#<^T}_I<-P)iN_gounB?x^I01;LXnN7~AhKK0zukk%XkKFT
zQ^Mx8FO`*-78eXR{StWN2gVz&hiaEYdmDHrTT{*@U!L$}|KWd+!{{-ij!JT+f0K4O
zb4r=x=q_L%C!BBKN>(JphEWWaS_)7mp!9x5Zs)Wr^8E}9rxs<LmjER8D^$zgN5(H`
z&4K+5d8{I^;wxZggSCsx8|1%HItby@kf+tEx=z$`!yKa2zILU;`D^Pw`XAgo3drj1
zFtDeNXR8Z9WI6z?94B^x{Vxm7A`avq*4EZN00=oL{cxY3ono%^Wpl7Om)qu2+0mvC
zfY=Dc0m!$)%Gw&$Lm28UI!VL%$MvnxrI?mx1^t37?>cB~wC&R#wEN8Qe-4L!KKQax
zgSS+Tt{=w`pA67PJr|N^fHNj4G1#*QyzQ7q37Qysz&l`b*_gRkw+O->X2GiA{-udK
zt^jF0>U$z^GmV#B=<t0IZMhX-vG?wsjoVYf#`CX+VyrJIf$i|UO!>JO&~B-O{)4OM
zL-98<M8<XsUp@o%6~f_zBWP6!+^bjtCa?w*8<tMsVTc5*nOP$2Li~^JR-gMC{Ph7(
zn)&rpbgsA5l~R9o!IS)2e+#-nkrb_2aPRK`PvVJ5$3nPv86@C_jeX#xr9Vy0GE)j2
zUtX<eVqmpKw6uV(_NIp+!3YbF&Xo9I!CE%zWIFm|Htd9e49eS|xcZRyvvP6VopU;>
zzYbRdlI*+1Gf=2I%7C>%Xy8qJZ=~`0Cm=NE^H>t#l+0sMuzW(FnoJ8lOLPX6^mb0l
z#ChsT`s>-wuyME7?z*1JR#Q$U>o@&ag|pCrY%ySm#L+!_AoNGRmtc(qCGQTAGb!om
zsP-*qaOcwVa7z)5Lxd(?;G)r{1zjUfkU)~mUcQoI>C%9P1mD7?Tu0ilQYo2L8~%l3
zw{3&uSK57Y7`+g6&~0;}K;fcugZxZqH&iwS=8Q2QMUXVyyL5sp>Uc9l#2&6Q?%4Rt
zC^zluh`5XFp{+;@cieOO2O>|DyS7iV!_oWQ;OHK0mwd~Sp%&mGh#hhID2g$)2M9sw
zY(+l4&#lw%16Ba|H2k}sdbtfWpX8;v8f7gaz8_kBR9$z2HW=vF)sHzaUn$>vlzGIC
zAcoyPQtSjYFVJ28v5qz>jKf)^!Asb?2h|<_f<h&*6g~uU<;!TAfKLv%2xBi==_iqu
z&UJ8>;sQ@3*gy9FTz9ze^@~GGOQ)BIhF&WNkWfnN+;y782vf())?MrQ1!AV2_ze$X
zeI`tCfD5%@<T?z4I=%ZZ?29DeEJ(Kkl{m~j3{{^Y3%f8G%yWFG)vX<zs!(e13j@zo
zPNam30gl?Nbp#D-^OIxGc*2&G$>GPm_|y*V&2_1tS~g7@0XY5qN&3zd*kN}ZGqczs
z{yX%!>URkAN|Xtf6}bA{5Lg>R%^y%g89m5f0g4&q0g8){kN@?l&XXNUj*!OY>c{8U
zR#dwPjteN<hl0}UVDo~Er}q#FRXwGDQ6Vw-1;7_K<k!+CjxD`;Hr|OxA2Y9)O@?rj
zBcUmZ81%}i)|L2_bo}zy$J``$m7VOq{)ZpD98R?Z4<RoPL>@DhRF{#3<WG<qA3;?8
zNtZC-pTU`?5+3;DN&Gur$USxfily{}1bP*~l_Sdyx<*6%>XwE8coIY4cl{<c9`JZd
z$5)W0L&KF$K`>$7_y!ttOf~sdI>ImLRq?K*^W=8Ezi|B;)5H8ZC>Q$)hEYt04mn#H
z{MAL8nSO?e`;-}XPt5ootG$PSCls^!wJbe**>3IDwk<=sly{929bZSvlXNwnJ<!^k
z)HWMXNpk7@P`0W9oeg0JFA`WEzM9IXO7h@vh8cA}FzV3$h?ZWa2RHae@*#(4qy>Nn
zT%^?j{6?4*=4$s6Tp1hzajrbIn!A~0>r%E|-ytfxU+tn1mvyf8$gfY@n}>3=E8A@;
z&?4cyO>*KR%u~&SklS1jE&rS<Sp9Eo_I(!!cfApQ_Lt6)%mi?_qX{a$#m3?w9%Fil
z?LjXVQWR4NwA26%L>9Ujq@!SBq6~G8;|_64?{|0vmKUIMLrfU$X_QPm^VjacK((oA
zZ)QFJp-XD;G~)h1V*3?vZl*S-{-<{gc7gGC^$J|S#OgD@W*#Aza1dso`i%&or6k8|
zPV<JXTx!SmUJ&o5f7(9Tg>4*7x3m~+m<BxLhtF%qW3<u1o)XaVH%^uz1w|xF*Yv|J
z&z+jXT}tPcp$K9jtzwf9AFTRzrSi+>2ba5IcT4@%4Y&UJR?!z8+-F(dWddC`vgyTJ
zDv1-cIKSI}2|R!fj1{G(Ytbfysf4b?%`Oa6Z@j#s<*~59AQK5L6Jq2V@O)!=i_2R7
z@UK1hyb=YhRO|Cr=9-Ld-Xib4=UrmM&Aq)dqIoyfqSqX<mi_Cd{FcY^yR){3tqAvs
z9Pmh+i|F2|<w8m>NG@<oqeE*mMQOT3Uza<*PkTdG5}omKRWI)Mox%S5`xEBc>y#&@
z6H}KcdLS)T+-+v4WoUeF=-}h{0xP&goR>s$@7r!Pa$R7okmg`K$=l!T;o5(q4vKS=
zi>Ff{6VMBZ15`uRcc<qs=iucn3{K#+$xnEY8*ibq?>#x;@Yhv^O|*@ywND&eF^zB)
zes+qbywX{I_Pg29aTlt$!4JMz1(nmq;A>6KG*7eFG2L5=nmIhxhKLI}J+~Kmka#UY
z$o*ukr&1%-OD4(giULKMR*2aEl&2KUPem@=;N)L=?dT45!**0AS|5m~dITz$NJUF~
z3}}%USN_7f_Wsl)R;Qk=H}6|X(h*o|c|+*{_b;O@>ZbKNqn)>I-O@`%br!@ZxO>oM
zXT*+MQL(8|Od>8HD)u+@1JfHUtbp|0`2SXy;iCRJSlM49>nzeteZuB!uJfa-X`9-K
z0ZmfPgX^|RN-u^+wMUF1)?ha>EY>6m4u_iQQG}2zXx*9fcZ<bs+hf5+k2D7^5Ekdg
z3}F*2m~6M^!%Go{>!IM^8T6AEEH9w@TKstFKfdNSIVqo=X00n#;7d%iKGQ++C6cXm
zNp6Zy&pxvX)eW@CbHAZ9xwWc8$Zma=>1I0YFC|!~Dqx<VJ}sa*98ZM`kZb40xM7)C
z>stn9kU_lQ#Gv$_Yzw||`^#|!_nE!zU82#p&rDn;`=wJ2r8kyqIAIfZ0k)QwPO8-}
z<W_#6O9~oqY5eNrRmekXH$wr()+C7AfPmq}hnQJ{l0_q;E=vGQyO`g|8ax7`i7Kb1
zums&x4WGYhHnuC7W>{ud;%?A-kKnw)TCwil*AMyJKlXLP3h8w>HvVn*r>zBiNtv0B
z`n@(577iWW-rh}MHu&dLVk2CUC=*m)G@m%R;S4e3@L+lund}Y^NpZ`IvK5$papeVH
zlEG>HmWv5ya6dWZSmCpIh^kqq0B^#{yPZrXHyt$xH-wJKnYsUQaOblU3RF2^@iua?
zOtQzE8aKy1D`V`;%(0$^cb%-Rty3pV|Dt}1Vmt_uB;IAovQ7E@D4b5ip*z~!dE>B!
z^1*aN($llN-^Ql)NgJQwX@x&_db+#MJpIG-!PO+5IWf>*Qeu%v(tSPi*+dy%sYz<i
z;{3}<yfd8D<*+^8w=h(+)l!XY93s!W{6sX6;9x^Jdjs$BxDN}H!J{N%{3cjjgefQ}
zbj}0v8Lx>ghih;SFJT4m%viQ>sV(8#Mr&<IK3^$9#LG0+72NIm85G;yX?7{CgDq}p
zR;(~f&YpL9Vw{JfU)iGPYq3b!ML^HlSW@D}I{@^>^wYQ9$S<-#XJ}|>0%9M}D$)PE
zU$6FHWr@KZ53on83T9&3W&U;Q`1F`@_JkHqeSfOpF!MBUwpS7>MkuxuYgbzxx{bbF
zqbE8cZr_nJ(UM!Fg~AA4Jcz;)p^{z!S>tVv0ME4P5ZLE7GFsPyNyLrOsb8Uq@WrK%
zfM!`{?Bw^m1!ElplmgRhm@<(+ys*Wsn5+M!$o_tEvS_T&u6(p~MYUTUnia$&lUA@t
z7Ta!m#jgQ*J5Eea4nj>-)(M`vyuJkWgNE4L#5CNWP<7T7zd+j#$gqk;)=`$WQ8<!U
z{;uyFa|0z&5gngW%w5L^r@}qjTSPC?vf^v7oY+YpFE-0$C!G-^Dtp!6g+mlg#S%3B
z!m6!+b@QBa^8nAnCUEVQ)(hdQbR3830p_hcL(P6asHIz>orG|2Ayl==g9)Qo(F}FO
z3WNtoE8b@bz=8}_{TGo2A;sZ8g`hYSv2n#sZkKeh$OvjfN*`f-@UEc;=L5bJ!<IY0
zq;uHCS2~|H4G$4n*_2Tc#b&wI%2C6ugNN<?P-coD>OACyPtO05%R4u+FjqVQwzNY)
z?7HfVbY+=6gq+vP5L9}}85ueT1~1-zc?}#eM1XN$zKj>witd{ug=Y1IgD25+D>jF<
zT6HZqphY*WP{Cn4u%pFEs_E27A7OH^Znf{z`|E4)6P^KZH7;j0!np>Gg4Q@6uKl}k
z8)e2!Pn)QO*7+meR!3)P6K*cu9*hA<h95(m!^7}CFV-dvj_|Kuy^8MZ<(b)r{oFPU
zn#BVdx%;~J!Pl1eKlVFcbkz^j>D7OeXU02gjV2!PCL4yNz)<{J;T+}ec<QxOhuuHZ
z$%g1#UJ_APH9kTNT>$p`695()BkLieQ{M&rp_#)M9|<3=umEFK<Y1kMcE;yv*Onqk
z_;!iNkqQe}`!jX)ehT*HnHG5Bb3N9$t3{qOlH%?n$Wd=Ozn9uPXw=D}sc0&@BEx+0
zAsS6b4pVG?Nev+>378NC^n;GO1*-F7p_1kzIK5~cedZkS(~Yd3YZu=2!$-UyOdVnS
z=-=}x$FO`khk@hB^6mNgblzO0IHLO*8|EfMicv9?%iLZ}kDea3qOK6MT<dR`PZCb-
zeVj48hglnUL``((d<btqWsH(g{uw}i?n{XsKb}}xdI~B865-iCp#10dyv`l>ZwRT~
zEnS?Va(H;KX`R%}_~5NmaaSGm>?1218>*i>zpY}o)ulwogot5Qc#X%D0`zsw^4tGl
z+vNI}X>v4k(wvTju-zCLq^u)Nk5?Mbar*I9$~dlVf4c$>kjk2(kB-GnPQ{<vd5YQZ
z$*Jr%>wmlNc;)YXes<tHdCtw#{HoT7p$9bmd)8p>W}~DiVCmqEb#ZAEEp8}<3sgUE
z7(z=-$rb1-0za2NZ>k*qa$GhH-F+Fwi1%PQCwWyNqhrVDz@1=0$xCK$F2H@5hKi@0
zlyzo)dp0xbWa;?$xCLko2L}e!Fsyh-Wdmd#2>=!8kVKXa3qP&2>m0C)@Q5hr5zVHb
zB<H25urz%0GiLlKzl0u8&#@G@3`=Ot$-K2O{g1xI8+cA6qMiw;Dqy^dKf~|SLW=QR
zvc<0%0J$1q@8HFQw=@O$RBg}rBNN4WO;vEj6b0Uy9GA}~i9`Iekv8ziLIvx-*FopT
z*IegjU6J8$7--bsUY5+xog1xp-lb@Ju@Ji4n)v5z*g})c+!H{KB+Gjow;!9-kqe$_
zErd#(0&oX5(+fGl69CxxU)ujH3q5f-f#VPYeKyf_ue?K2%-#F5Ikn|mrXY7_?5WB$
zixx-z=Pt8V*wr-4b=5g~6!g|*ZBv-)u|@zVqoF!o2Q0yM+gE2j@F{gNgnA6Lw8q}L
zwIWXa;M{nE3<aDWMW2}cXQe$dbos;6$q9Mt5eEHsFp0rX3_csYuzg0^pyguUDsq6q
zr_`8Gv839OB&+l2&Qw`3(e3l|;0K)qCjW|!7WFf~dIDOZ{2>Lfj6txp{Fdi*=k#tv
z;I0dR7U09f3XA)13#*dC%YF^AywBJO4Eb#FvG(v!^npi-P6y&0TU1_Xeeu%CFzgcH
zoHS}k^{+4?GFzQ_NxXts63Y7TfGir@nU~^`OC%1Ul2~WpzDSkGDtE^l&g;S;|IM@`
zPiSv99fvZuLp%Z1BaAv-8oKbskMfzL-{A?@uo6b-AMX@Uqg*xRGOrM;1wQ1&whCA_
zqi1{*Mm>>s>BjI+EFSIFxOHkD?-pmko5T-bWURBTEfD~4WvEMj7uzcdCU2bt0rn<v
zN=in4s``V=JSYxlasoC_uHLREZ}b^mMTuxAL4@}7m~RM;r!uI*#7%T0cwj3zK(8fs
zTK1znvnQ5GMRW81#I7zQX(;EUCwCb`k<otc*^pQ$14xOB!{i~n3W|lz>+XMw8E__5
ze!`shc!~DAqUYw+Xd?ku##c$$u8shUU6QUkG@**V%<Sy^3)NW}Aq}+WIj>=BNHN@l
z?ykmK6ks(loqu{Rju+1ppty7Dy<+?cb45j8uKAPT1NOA<KLe&26RQ*+<nbr1pxcCX
zuwFNx4m^|G9(O^P-2G%*zfsGMYctgbgUzt<<)m4KGBX;{6o@+A8T4S3?SN_{F+cxC
zm6MpLXg;m9wDe%ze<na!x58JvEp11OKRwVnPs*N1Ph?|L2u9Jk#KDJybMiVgtk#G0
zV(=#2zK6Ny)Sys`F+QG0gL<Iz)h{!3Yiwt37Fj;~Q!#)$!kA#IB<4GveX*gp7~Vm$
zVSDU#8zm)9Z@Vye6n;OK({JC=JQ^Y?Xq&F_-dIBzEljvX3DJop+kM&t@DQRp?5wR{
zgh;ITv;X0oyoD8Jrek8h(3RWVt4nvgY^Y2z?7DM@rK>^Jbze=mVL2Q;oLzj?_e?z^
zbB*qO0~ec{o2zsVJKmjX>bs*7!k;G*_7HTo=h{bh7#BDX{C`1gP0^LA2LFhS;@`-8
zo5#DZIh-xSQ1-<YYKG@#y;Ug7$U+xgb*B)XImdWa)<3T3UC9cjK;{6(-a|Qx3JPd0
z46@o^FN^IXfy+~8PGwKSkP)NcAmzVLUa#TIIRnWtmM-k%kyskF>|Crev6{asD|XUB
z)>mXDG4^1h-1Ej1XKZKVmgcV&Q6UgJ<26)0N4{;7VUCYxAGkTlW4}m(yByJDhJbI(
zEt*Kf`xdWWwaOVhrVADhO+#^-x}02@bMRX9gnxg+Zr`$aYgjJDAf~vUxyoi%=)uE(
z_<cCr_15nb9bA8n-uQ>WJVAwy#us}$HeIs%WE7WO-l?GRhsLv~W#)edOaMORaM~$W
z{h-HZmYwyBDbv6M{`9a({r_yVaobIsMYpJ|CQ12bPnDRsxjUR)z3MVWNX0WF?fWyW
z{3d?FYGSOTNGBQYR(^M}O-C{z30<TYry3;EFvayje@l0zeEM|I)Q=fcfZ%8vR#w4)
z%475?Xt?&^BwWuBNS_>Z=1xMVu8A&2k!!rtpC|3L@`EGfor)Fd->4F?MS*9C)f#g3
zDK6ZX(yTWm<UVU14S9UxZxZvW-z37i2ezjL;9NN2?^-D5-*W&9J~sgMEQNLj1N#gK
zpUl8C(>r#&_}^RsC2$}}b#rsezA<)n4s?$VpmFAcDEw&g>eTe~3o>&9D%4%a7auzO
zF!>4zzBzAtZD|-`((l{d!dz!IOWufN!u>&uf4<!%y}2|p>4TFw_IsYK5#2T)5}R9$
z&CyU8s$&RB`Jl#ABivGie&k@-<ygVt0ky(AkA=+D)5oTq!BD#&_J+vtw}0M%NyPU1
z`zaC}8hs|ptB-Xwcxr~r4G>yYFCK*C?4n)>;22N0{V;lADNdP61_vngF{51hWe@}{
zegZ6kR&WEcL3aj9^>5KT9b9uFVJj7=4iZ)X>0}Fl-mjWeWIS4||MR@9&OydmR?b4D
z1DN5>qQ+WxYb)vDPj?&ccI^xoZkWC<v`cB089McSNrc}u)XL{R`V{jGfUK$oNcnN<
zh^G#!;>mU$F~Vb0w?-~ja1gVz`yrTs`kaY_qGDpTSM!+{%lz$#wYq;pdc;QClgDFa
zcJ65G^_FYzl!%okZ=6_PDYBY<ApPw75l3(85W9H}y!MM*f?7>=NWWF?@mlA7i}?Ur
z#z%r5;zWL(#|3B`7|nytI)cR;o=U~2P}Zq&U<<T)dI6q%s^OpnkObe??-MPv9n5u$
zc?~AF?(u0L$N?@ud3E8jj5Nct2m8aVLUl5GAFKG9>HMHuytgUSZ{JcgL#tzcr8#Tm
zlWU;NM)oNFGV{NzLjN2N*JO7*q#0t=Ic!SSZRLo~dw27?d+K9m+wJja9epjupy-e6
z2VDkybbqib{}<Y^IyK^O5v$cJovB;Bn)v<mV;KG^hWh0KO8WT+VqIucD@KUimqb^2
zP=IEJkC(*5|88wH!T@X}9F7(Ymuqj7z)}UD1sCS-*rCpFC~W^8tl2k~7$Y3z@8i|Z
z^_LaQAa2%DXPGgpG)6T_y_oSBZj9}}AxL77#h$pdeCE1s(E2!fO+x#3e2|Tj)w+OH
zxgBLITtu#VQ%jZeq{ta3O9G;p!#50nb2MZeOb)Pquu*A-l7?5%H2_9|1$f&`w^g2u
zN4|vZ-~WvDuu4Gic>JwlXRxL3XbkZ#d-j;|7`C4)kU>8U$!)eYRMho#xkJp6PUSm)
z%9Ti$p8${bly-o?(T8I4YX+<*8)at8Wb)>KqR<1B@cIX=2RWJ!fq*4MSYh9oVKB^v
zA*g+>2lM(UC{|ZSJqWIJ%vJ%Jvh<1L)9@!uC@ARssMY&Gk_k#A+!$>S(I)yF-954@
zZRj6sxDxC3p{=8>odN#LJ4nQfGS<w4u}B~kQ?G%28$z3Sn;$c%{-*`#j_u;(wt7<w
z`c#e>B-eu0F!iV}+xE0SxkyRp7Nz)R=`A`i*CAcHZR=JDQ681N_grjpv7c*+wY9Cg
zuCE}QwV}J@XOFh8ueF7eL%XY^b(mLOV-D-hJh<NV<Uox{V^*anIK;pmhhS#;{Mccm
zLR}UXmIr6{(0@U#919sh`&MwEm2CR{Csqkt`Qd2{{Y%5=$u?QnW>!MoT`kf>%YWRC
zrCj%=Y87r6vZ&AMmHd<=I{!OjGwDKj8`vpAZOiw`IuH*9Ri1=82IvtM+DR!=ksRQK
zAR3dQfXh_yS>AC_1}FlAN+d17i1-6vLqv5c^U68tGDjEI3X13sjaCz7D*VCB8*nf5
zR}xz^M$TT0pML19a|LgzYArR0h5(Tkb85ujrzEV~?sw~ED+7R|7GQ75w~eezKyJ6+
zGJ$u(>iF;#98+_iR7jEZGcVtDr$ba92kkxjp@b!fmx?XS!E*ug)EA{vO9KFhdu-;{
zpem4*{wlA)w{Zm>{q(s!DAYz^Ef@Lsyf*0@e(Z(-ce@zpk6IscYqsxJw&vyl7v@+f
z+0xwXx(3W!cjL1JlrZQz7*yGF@WwEt(%<I!e*qIz&+6F+7V9U!`xKjIhJjO%Jels2
z@#0q1@@(+BYqsloy?^>f+Yz3YTelKPr(GEio{3T+&dG*@*;dJ8XfjxLIC*znO0!6M
z@@G9fQb_?m*}<XSR$Npx_=H^_RvX?9p%d3Ubai?7kjE7m$|N$`5L{#bS5U0_$_9W?
z#%qEzYeFs+0kEm`FV~6HFN8z*1nrZW3SK$@8f%yV^N!K?^!KyctOe+<u0U^2b=veC
zsIl<p7M>rk&H_h4a?-a<;N~%hvTg)$Y93sExT;*MV%FFi%dUWIUESb#RE~ozK!8MF
z7Axx?DbhPs6-`xrb0|agIc_UG;xHp-P>egG2Vpn#d~xlVo15bcaEEHe&PiY&v!S3p
zsB(JFGgtcP(WB*S`+UK&2{22Cve>$X&#PsyMASF!MbYg)r`P)A#h9dDvdt*<%C*I(
zeX)5gMYgs7CsA<Nm8FZuI-sKw9uWLHqL8IDYyKh;iCXGz@*`j{!v9RUYKYtYA#%9n
z1DJULXte`UOzf8J?&&5S;G`BmM-(|sn~&{i#ULD|4PWprbR`5>DAvugUU#=qg3#P5
zO=Wf7n-1qhDXe4DXtn9){@<8@lQ9fqREL_&{jhu-+8+%8%%apK46DURHbjXG<DN)>
zw#@oEi?QMkqil<Y@vZBYPKvA9Qz_go{x8K}`L(+{;~rr$76W3SK|1=CZdN;=KY#xl
z3Om~s?SaV@;>oC-RmD;*>=$x7-mVaBow~Ag1)}kge}L1YpT536P?!5n{fa;ZGMM_N
zR(;pMk9i?C_83qy4ti=tE*Y8_gn}wTiSF8PsWI(&BdDeEYmw2J%UGBwc7`{keRCFr
zCPe11IhU{S=_aLkzz4npNFL*UM#^0wJH{O-ggp-uURs47_vyB_zTp(!&iR*B-QRfz
z$5zm_#cRU5d}euE1NSqJ;dM6=F3%`KC)AMe3<Y(t*n$|x2%6yCMx9tc^o<RG5VsZL
z8k(nEe<3zj+XVK`O0^6ao!=)~GmK?oChD|6uf*Yc*LnG}YEog3>k7<s^WEIuL`mo)
z3Aj|orYFW3Fy__Vr+eBvb_NPl(wzm0TU(Olcc)pUwZ8fTR|Ae5R1tL|wVt`1<5UA7
zvR48qVVsZZh7F~0a&SE6MbQWfQO5j*E8geWf?<q{Ss%h!Iw!#aqq)6Za&G1FT5NI(
zFQMSE*KtCA(_r<Te0RmKgIphMl>91#?xqmjueHy;xv@^(jHPS9#MQH8=5a-o&0k%=
zZ$$$!SO5eKp=SVYv5A<jfLL#9(X$%$)z|_9%Rp`5zMTN&y&JSeppNNH{dqO{5QfR}
ze(@pB>|HaVxM`3J@3$)d*d=;bpiPui`YnsB_Ba*&OfSZ(v<ZEACV~t>v3B!C2sc6M
zxC3=ks|R7N1o9nLKPoZJnhn+3m@>#_$f&<(mJ@1j)%5=TJ>Cyl0toRqroT+M<B|bI
z`+g^<=@M=J?CCkU0Qp9Kdxrd`fhgD7w$CTn978vcx@TWgJDZ{}r%%<-2u|C*bk9y_
z&ZF=Xip&~8>zo1j4YWl#A9(f*!l|&TV19l{477{|(7lN|4?X&hX~Zo@Y9?y|2k=i|
z>127PgYmw)Wzop3#MlhT0M%3M9k-GsrRc6+ZerKSszM!Ni?!ATlHl;pv5wyhJ?@Bj
zGXSP3nVD(;a17t6<FYq%SRPTz8%+H!<IORVm)svWIGD3gSsH3|d7o|Wy|DAiHD#i`
z>4i*z0eE%VIX>A|YGiGqSx(ySG#l0=;r<lD{ojWPyYqYlX6p9tval_+K#NMHj!xaQ
zvbCkaoZ7nfZZb#}7e9$vkocO2B$Zn1%i}&D+Yxw2Dx@ot&2B>lRzX*Z{l{KKGrTgf
zbU$&`BYy*uvM+aIkRDko_PgNjl^zjRphH3*FH41ATs_kWtOAGnL!6I#AGwo_Y};O+
zf(cdxH56-!xw$$?rx(wOJ7Ne4kt=Vyx3{nhTg9~;H5b<MW4CDIRvq5YRGCyuR-Y(V
z+^VhgKA~5#__^Fk;+<KmBu0|NH<bY2S*!H1q&UxPd%itxUvmTUoj{{238{hh&)+d9
z?g-aEnCV3*?+}oTP~JBIrL{xg_tYX@^ph2#|I<(bED&JsrW1G^@CHdKsr}BipqCn(
z2Fws<Tv%<E>Z>nbzF=7%fNabBP+pCTBK)|x+UqWVh%;ohI;{>T$RCS*HUmeL;(k26
z_Enyn&*pQIsc)sX^C(%RjwK!J*U|{rd2}k{`QS~s*5v%!g!q@Xgu<lS1j$Ij>a0VB
z4+39?`NRYksznSy6r*zU0-QtahqCc?;3JX=wr{(`*aNY%XOLm~IHYz%Q1qs8d`Co1
zzCEm4R45_Tc#hoo8lb;pLed`Pa&XVmpn-U>MC9``Hk>T&b3PkxOsDE>L+lnjJ}_1!
zC`*JRbk(Lt+0H7PxLbGPZsl#kYjFzsbc?OM5q#r&PSc!QhGytMt2x6x)=M!f^~z8D
zX6#w3*A}&D(Qui3=0R<jem9yS&^z^r)^#s_Fx<Pwc!0q%Zh}xP#qdGwcva&fEtO*x
zdUUTqbt4dNZqWU~z6nY=5sRz-kX<mywquY^Z-I*1goWvhg+^!?Whx(|#wY|$y(Spy
zB+<b&6tgs5vE(}CrGLq9i&Nt(n$2~o=UJVeB7E{e+66S8C*1*3ck-)$b||<+IG-ie
z@>6a!?xs1Djx>h@vpr)(YF}cz%0I%@qO=vEsXIZ=G68ZQZPaQ{Yw#DO`KhQ*c^Zt?
zqOWAVt70AN%*Oy~MGYw>0bF=U2gE2TM94518dB1j<OwKZHK8szmINIbMq1B{Q)7$g
zI*P;7f|f$DFwukNB2nBQ?_|atOfjk9v^Bro(V%HpZ*X)^p`pKE&#GKGo+P2oEj+Bw
zFO=_*uIIhLEz)3Cfo6Wf6ig_3Q;uzQR%7?yL;)qJDhPxYz$*PvAN?$NXaQErMs!kO
zc~mMm0DRC3QBP`4y@&}tREU8NxLvWRoR_W(;3BZ{@u~+zzE2^5zP7e@P52uCE)1cR
z({2GuxX8#@@nGxi`&>LcwKH4ULARO(nQscFyAepQ^=O1PvqcQy*t5=5L1Y^TWK`Kt
z%>r%}%L5_J4gztdHH70Q+j$=FQF~U!42)gIC$#4tJ$z@DNA&L#YgZRq{o_U@9RI9l
zT0fN2G^5Y~z%$H@!J9EMTyJ$xG_AlTvM&9;EdpFF2V64T!#l8nI4sn;Sxxa{6|wJQ
zRX23S%D2n^&Mx7<*VGU3XH*zG`b4W>Kz$ekkRT+WMB0+vi^deV2pJ#{hG<&JhF2Hk
z2UZp}pt2-)-Q2o<)dXPYXTaY!mc%(->b-gblyu)$zTTe_6C=mX&8_i#?+!Ltm>sJy
zqRAjUhbesk<5{l9v*1*vuBWqv4ehA3I3A(w`#R=>@&Wg3=`A0}O{eB;+z*INKiJ$J
zails(e`wCi{-0#>?kGU?9u9g~{WIPC?|8EC8(a7fUGeq}s|!cnblu#=exjuXrv2~V
zGd#XH<XN>wh=V8J47QMgF##?FeL(-m?>?`gPkcIJOQ+jgi{in-a{%J1Mg$0j1HX+r
zr+*)tI`mwICjj*%a|2I-36T71gDdYnupilLDe@!S1^SH+z+dJ-$;%P4xI_Rf))&D4
z^c57<+PT^kEGvq<?tcGlf@!+WUdr|S`POJ|;B!=O<;an6<5?r<rgN)?pL>{Y@lR~-
zBZ$4d2QAho61!CHEheO4a=qtaUx&UdXQaV~zzv$zI77Yj05BI$jP0h`?hLy*Xf*9R
z-y<_G7xeAt^=vE3AwlD!i{R3K4;WccgkJGe6XB#_pFc-IH`@fz7+d$%f)rK##u&b8
z#^p9dVD;VLOf>`yb7Y)UgmLZ3nc>1841^k%FFUG?-S6R;v$M{%^KgTn%u`-LZ1+b#
zSg=hg$aSuKMeoe4h!P($-?g|S_4?k}I$w*r;w2*}6?Jl*z>w2CignEV9E<VKSnc*d
z!P|byk_SRlP|EmARab}^L?f=VP<rqJTpG`XgH-aEgE-L?1SB`+V4fY)S_&4z4!imE
z6pVc`oE~VBB9h%>+rMiD*O|hGtzjCj!g?x$^R^M7G7=riq}tHjjDt-uL{hQF-$5a+
zIq$}HxHvFVNJVYj+w+|g5cdLTfJ_stLf_~a&gBo@*5?T;K(CUgs15lblrlv^KX8e;
z^449Eb)l^)yqmO=m*|osp+MVoceuL@HA#(sUKrLn@m-S?dvM*f&0Ut0qN~Vj&2P62
z$WCGwM@X5qQk<WKHhp8eZma#927t-XQpkpxgE&Ur_}ANQ<IuVB6u6`|L!TBUM5HiB
z9)KoQFoT-fa%{=Hq8Sa)s&L##yzmsXRN=Wbf^RFvz@i0`yK})yAwh5SeBd3rrzd(}
z31T~UBoi=+pjfX~9#*T>WUGIE9qnkybhw%-y!q>eoidz*Y^%wmnP0_zf7?q>b@P{z
zi%^E&j2v1$z=tB37sx<bXTAoBKvM^zQ$eAz_~_9sYoVpI9^{tw;D-2(F_WT4I1iI&
z|L8RQU7|YQ;>CF7zvYPO!(1l@5>!T-Jr2{g1SBT3ifonaa8f&dB%l1OcD!Fh&P2q|
zaOc_8d1Vr}XoZAb9)8_Q7K?MBWEP>=r#*{}LG924QF^lBx$_`4X@cNASeyWJQzvOD
z0+S`ODr_$3yGv${4a@G5*j`-<XQR6cW|Y*LX8F<JnN<H9GrblOo!@8$4(DVHWUH_g
z5;Fi~o$pcHoiSD?Q|z{KY3E7)8YewhFfqVgz_?51yq~b|W>IC1Gkv<;t@~h#^I*I7
zC$Zg3+7|Bj{hrxJCy*K@5em2!VQMAz|GIKC$vQnBQ$WQaV-+%J*8wBoXZJTPoxrK)
z?1`iqqs|nA$gU(AIz6&^NA>d|=T9H_$LX7O03|&BaqHsPm$lu6P5@#I8ILdPjOzhA
zN=5*d<jn9Jmv<W#-w%CUh-vKZMvYixkr(I$NKwDh3`hhl4LIEbz0{=#rOKKyyt5uW
z!IKtl+6AmK{jes|-k|Y?e}DbV1--aqZ1$nPT1AUj7$xww`v6$CMKr?N$%kM2_h0%&
zH}k<=64sk$Dm4uf@@mJI!hM2zS!bYCLzKROfjQ~Q;_ELFQHtN;!iJS+=N<;-)JEX=
z3TQNdHL~qZ)W<-f)-Sz3tvPOgT76@0fo1yA^;vEEV(-V#{?HQ4h<kV#NCpJ${~k(8
zN?v`1bSdalq8Ym*CjC{|<fPeYDzJw`4=uW7uXgODl$M66`#%dA(hC{)DQll7($F5n
zhWAQ-n`mn~dieKuaRR(|iUa#V)kg_b<Vix>3Pgi|BJ@*F*_YQ)gLiZ8WB2T%+T%Tb
zddW+bB&z;B94@StquPVD>#j2tT}*Z*e-l|rs<vD=<MQD?zw;+NKQT*Gq^ET;h4j*g
zQ2qwW$}r?k7Ze*9xY4#VPYuknT8lhppfr`Y(y_U-Gc_cH9a=pbp>+QQk4kXLZx8~c
zZ+j!0olSOech&mjF(wp=K*P?>4YZb)CT>c4wt)~jHZ7Xw);**>*g2u}jgeKg81vwi
zSHmkixMl6YyUi-iK<nsYH1)+BH#x=Ow@;z0bIz2dYxSz=LCyy{q?#be3g*h!RJ(jS
z$PcjIMcTE+e;w8Pr!j(wPuz|e`l~Ru_3s+_ok3(*ECZ}<rt)j4XciV>*76{Vkc;{S
zeQGo#B1^q*r^mh9Cp`^#e;)|L)ofYb5pl|=+=z7Nd+rz}+2(PC>j%%hfzPs<t5=(z
zlZXda79U<0Blp}}l1bhTW@jHvjQi*!IZ!X3I=Z-u<KFzo6HlI3-CuHb6Ti#O_jd}M
z<lwHFdLXDGDurixwSvsXS7bF2%TBtW^!&Dd`^)XtYm<{|_g7~nGX+lQ@&qjYNFUc3
zF;tZ|*h@xr&*v*4g=Mi!JpD^p&8$(XLC`lwn||X{qfB5(_ce#FaXb9Mk9cR1=Y#Qh
zQ+Am@c)g1@%+nyp#Ta@Ox}krA6z2A=rYy>p+#SPrzH~e#6E5a2{YK3MzDK77E!Ph|
z?u~*#XOTYxO`5`=r!MeM1qZp%Rd$2zi6~#At_+>Y4cesGEcoa8onde(jvA`IB3E<H
z51255;-ffk7cKJFa9?463g|GLDF3r-<292!(&M8m=B#>F&;}lAl6Y^}DuE&dv_MDz
zT<FCJQYHZ{;nk`^Q(s=6)-SUVvghx=)0n13N=?*PuO1)LVN0~<6}E1*TQilNC!ARR
zXkZuPYkU-N$Bnd_6k^xmj-0D$`<Md&^jEU9UCUzWFov6NkK6U871G&1y?7R!5~A0b
z>-TNAZVOk^`!|UthF0@{3TWg?kJM$6e3-<#eY3WH4X`|CKxmtkke~<{<h31&JAaj|
z&UTv^v<h9~&%&Y%rT0!#Dp=esdOW4yq$&Di&!%6d7|2PQ&t2xu$tyJ^y90*($i+yH
zJ;h}S?@3PSE$c?K6_OR}ArUYnkQKu+bK^}9eTpGiupfjP)j(dfTLYRhw_az74n9!H
z5d-gK@KtEjt&va$n{ss_=s{wgS`WA6D+)GT;=Y8PUPOj~CQ7+l*8TJPMd&aajM`NJ
zR;wDNns5L4Fsz>ln__Z<jlL(xcQ4CqytW6Ol^EoCkm7VCzV?-c+Y1lYxLlI^-uBj=
z;iJUDoH*FrC~&Hl7XcSjVX`ziEIkTbP2uzP7<9Yz>qo>O<hFZFYr-Dqn(4`xea>gb
zH_$I96%uzoJhUfIL1ow5Q?@f-8c3HO2`kJ6z6<-;b<*XCd7=67lQG2RsS}c8qBKcH
zV!2zBHG9gzZ!a2#mIlj_%^=V7fYQJL00{_k-GI$Pi=Gx{{s)Z{`k_*OCeP_Q*@z4U
zBlU;`!8d}d%h{!gQ*zrJ>1>i}UxgL;UWUaf#ri;R(ORA13$ay)-w8iqr_4<B?v--x
zQd4qnFAxLRB$=`l7?wCE=Ko+or>+FZ<LX#FcS5}&g7%!DOD@*Ot&@keHWwXkfgES&
zdyW|B`>X)yl#9#kw94Y`68XM!O!!|p-RYfwi9T!M%22&Xm2)iDxIk1AvMi654A1qv
zUn^~OUo6!su##uNy>#(=xSDN<_o>n;+H;URT?0Mt$Dsw+Alm1fFM?%bPzQD)P96Gx
zKDhwx$1RhVR32B=1)izbIh^3`<?&ClKEmBg7uqyT-|k<X)b$rtgV5Zl5KdyAG1FhS
zQMKXgvWT}v5n%X}4xhcCq)3m!HG{B?eMoKN@6QJ4P|BM(8bRX_H$p?i-<OD~Zmr;8
zJrgv@c_E`D+~~g4=6;?F8Ew??PpuMLnoLCWXhG(F5Z8<j%A*&H=82&F8ZFY|@{Pk?
zl?Ol^+Kl42KQW4^0CX4ZsKQ5I+S=OM{UW8LSXYfL4gp!9yA0(VK_PwhZRfLHxm<Mr
zzDN5p^|9+W=xctohk&GyD1OCAf+qL;wxr|49>HXW4oP|GL0&b4(?BuXVMN+Myr<#e
zG5563#>U3E0{|tB2qx1COYPeh363TWgM#tqUOVahI3<`16d}u&>^nE*S*Qb^z`FpH
z-l0vbUaSqka3;0Dlet3l+EL4Dt5W{s$LkL0=gJhvFi$S9LoPEmqN_b^zqspAlAxiR
z*@6jC>A5!O<@C!uLDg-wZgq2mE^iz4UsY=lS<ehZ+OpU)tQ9oG)W*iIIZoc6H3ALK
zUkrjED8cN);%yQ6u?Xk!*;*s%{Cp)9yMdO6r0J*5pj3JOIOMTP+83|(Yp1CLIM?{@
zBL#>1$3RrD^gP)80K`VTVGziY0ejdIQ2CuPvtPO!77BAH%FCaFS*4t>uymqwM?Wz-
zmqS_C0y#|)X`IZy|7;7<KaIg1phWu0U(}dV`fwFmT;a)A_g%Vcw{))FXgC+rciTzv
z1PnoT^ayIAa+TdsS4)cR%$z5a9#aP(kNN49G4&%j2lRh{XLZdf)H65EfV}s<UPesU
zh@(zltzjIAc(P+Bwe@1(6I%o9jnUqv^Mo0};Du%icUv-y(MpaFD*E}zURiRMY~Ouy
zE^=WEyP`L+^WIBnDZ6%+ca!#_(}LGj4~PtlI~v1{sP0a=KuO2Xapv#|CfBXXd2@<B
zp4>@$M#+c%=pM2zo^{!+OQ*y2=D>Xc6M)hgq#lQng~;d$B(Mg2jdrJ(W5<qW<JofK
zL;zNU0Q^CH0=6fz|2{&=MOb^=JGOx~;vQkbzn?PaJxvX<BfFV6v}X_tL}N8ti38&<
zwvvuo5B1Ad+|UhQW|N<y%mvyLo?gI44=+T6=mgdsSdlJ(u8aac4Pf^m`pA7AWqXF;
zH40qu{I~RJ4{#9~fJC@MIh5`9C7Ad~_%}dQ3#Yfxsk_HD+C@1QO(k!n4F$XxKITLN
z>MkM8nqq0);XGKN4HNfc&E2Kjm;Hzo+*n;-6Bwu+BM+RnR~O-gQWj_d${0uJ4O$Wb
z!`OvqD#;NM>js$6vK$9YojN@#I0W^x&mtjy@=cw^BOkitr-tzcZX3K{zjwgP)%eWM
z*kk;}J1j<IyN`Rq3v$jU>}-iIlYC3R4a?n<{`v_#5qmt6AC3b~coD&q(G7{bme;TA
zf+61vq`+q!2dWF+Pu~Pze(6Yj{60h(BXyQJ(R1<C<r4mCb!RU^_}bpR^Wz=TUOK_G
z?jB?@$<4itHpxp`PFKxF#JEX?c%uNrV8EOf&o676`~lSLi1cs+UleDc$Opl(q1Hfc
ze{ML}uUdaDQv{O@K(5pXJ@8JF?k>g#>j0ZenqyJpb}$x(3}*1NiDbWr$*y>BZ#6!3
z2wBes2`EWgS;ky2XpL`b$r*^HJk;!CXp)wRNKCcNN({;j5U5~u{mV)!^dp!K#iq7N
zgM*-58%dQg3p+*&Dm;J&mLpGW-yB+8>W*mLe;NZ~C#Y(1Ir8Q*<5p=H+qk0B+b&^N
zEz5HH{o~nbMhWS*l!2l4@dD8_0QPnMtlF==nw<eZI>CblK(keKLl6akSh)e|Ku`!p
zA{hV$wf635DA#TR)kgB0T1h3KYtjfGen07wmu3JTeL?gBuyWJDMsed2tpU-Soeb9?
z9k#7^&(}NY8?<Eri2-t?)}fZ{!E6^>D6`R_Wi7rgT*+UU_85Q)fL1pmHVoChw#P00
zU#(E*SRnS<&a6^o>3n880=yF<0G)1ApZ&bvm_!(8ir)R1G75B`MN-p)PFnP1P@x?B
zW~|zMa&d9T;l^+><i*3Wd+8pHQ#2ZAmae1MWA+mk2M(62hhXF5O%x&93aEKN8kVW(
z$DrG2dJv+z?;CgH19=zV@;_vy{t2CNs=`a@itHnhVyW2ETF8foDJ+L+uv<R+q``H|
z9IBuoh^(dtlbT-9ZAdY@cXgyca$mlt$I#OC_ce)@b6_zL7WPN<G5U*S=#*QxbYW|G
z3X-OWT(|W*w=@S;ctO-mMf@fFZ02NpFPFExiU{mlN>98vUqhuAqv*8`qbN_&K*~48
z@B)5CMjGT4S*3LU3a{HIReqOAFi#xjWOWj`1daR3V6&${8d3;cLlO{k@_ROJH{B-^
zV5WzPB#z51VQZUR9gZZ=Eaa&$3Sa9SrVpm*a|52Hf7+9H=cZMa&5NO%un8VrG16e-
zY8tK+Qf@_EeCRmb6SX&AM&{=|=gMSez31n;7m&6=<9RiQ95b{-^WW*MiO4JCqMNdC
z1Bxnk^q^yzqHI-br=$@DCGky1iLCO`#Z%PxrWM;%^QO@h_$#OI!{ozFcJOkBG7~a>
z^JD0gPc@%FvlGDN-72NJUI9E<xV1=6ZSnt)sR=x5jL9GjZi5$A_JZv|i1R*7S_kBc
zF<d=v=ijJ#xAxa)sI-jwvx`!uwv6A1&s+vNamu;R*I|HfS4)l*j-dqoAos7@RVA>m
zqw_-Kak2NfLUDIXnlU`04YPG8S<gIq5>Y)uWQL^;j@8*;9!EvooE3KMzQjFYWwZW*
zBAQE!>tEO<h!cyxKH!HVe@h>QYzAr2p?u>klj%1wKz!mwKIWNlM<9|qJi=WM<E{a4
zv!?l5{jq&nf&(zU3i|WRBu5g<0S)KHgQTcQOX&A^;P<O~5JKhJe2NB8qtlQ^ii?wX
zrD86j$jsL&^mjbywerC8vq|>6uOhzWxhWpafZu&K+ZCH1k8lMvN;J-zhZL>1jhm!t
zFEjbCi^%KfC13$Wx=3yWz8?n<l1~T00GekMtNd6Tt4({JpEY~iabIuUH65$-24o>g
zyJzq#Njn##)K!TtB?-7AGY~J4u?5du2%4gigRfTVVxa1zRwX<b5X;ix0p#<eca$N!
zCK`a59ku?7n;{IZYijtn{%)lZMZQw7siB&aSr@dF0N_g2gMv7>vL|p+v;BkRH{9dJ
ziCy(;FcC<CBTb{@UV$L7p77Jb^)ySv9ls>P#FM6f#|LL)Li@k=gbX|tO&e{yxv;F~
zFxidn85nFTL4-5y^5W}B)I2c&6^E)AFLz7rs{ENf1B8XMownu-bK`^N5%GEZOGbz;
zlN-6=UByXaW1`$^r%zCX)-1m#Ep?4ABY>zuG3y78m?c$9T9U!v%zqLl?qiB4XJN8j
z3|1@Gk$cmMS+D4M(|hsa&C;7xv`Ebol{pZ4<P5y+2^kbrJPe$W4lQW;=tovJYaVs`
zlx4<*x*=N~)ZygeX+d&3=IXoHV5wQe)DQ5SY)9i_F*<jByoYytp1mxTl$u##m*{IO
z*SirDyD~!DiuF$qv_`u{K9`#xzkbzSS5a4RV%{or`4M?ZAh}A=_#^w9a;Q=-=tC^{
zvT-69AL_$02$<gQD-+!vcrBag)W+&&-x<p0&R7rP^Y+y0RryZd9fG?f^#{jhKD~Bz
z3Ri)Ro0j!wzywgBiw1Q2Z9+6%094AMSP68!bF>05d}?lJP@^^eK0|NtEsN6`!(7c3
z<hk~|!`$QZP4m0UF_go-(Dn>S>wSE9tm^MPxdByIr}qi$t~W(G&p%#XYjG4+6rdEq
z0svES9BjZeQ6VLG=pOKZdW~kV4;JDEKh-ROWyNF7_6iOFQ!IMA#t(IIG2+U)Kt}_6
z&1nH8tM&dq#23_mbL%ixyKP(>rlfBc|5?PwPBR4)(3hkM+GR(B5>pmB6gBD;jc)b$
z&TkwDAsZMyZCSr+zX<4s+rct%z1Hsse#GTqrNx2g&9Wk|K&IjSY*O}u`w{pfuW-Bn
zzU(*dXIL7bI>dE~obw!(>|~{k3XH1RS7+m4Rr~*Fdk>(fwys?lMNvV-Eh35nZUs?t
z5Rhy}1VPC;hy=+w!@__82?CNNsU(q{X`-Oel5^|^R6>)Q3@xcSW1)M0-+%7CRku#n
zp{y<nuzRh!W*BqK@jh>WaF5Ajyckw^kN0BV(~^H%_-+}*CtGOEJxcJ>cOPQ_6{2wf
z+XXlR1F9)~d~kQdP6K~d0MQ#;e8*0i!__CbqvmD&IdP0LimXD1?^v7{aovr_)PTlF
zv*TUuohzc{afci>x{o4Ew6?G%>sBSGw3#hc0LS9b*Si;xo&b07;dZD81lc2KD^0Xi
z)*l&(T>2gS-c^vCH0OWY_WEQW3$1l37E+hjj91wQ9pjjO9s3HUkj(D45_|ZpyVkEU
z6_8(_gA%#0W`F!Y?{8Ro;%xE}WrXt_`$)8PMw6!G5~sPu%bz301GS`HY@)d5M}r8t
z29XPU^>dR{w$(kpq^55jdQXM+H}otT<yx1wxr&q%Nnq8~m2~&K>KNxvt9|wYg*4<W
zLE@RgJosgxM=AqE%mYy61vzP|4|26(iiM4_&eFlj&djuk_8e8kN$1hOX<>0o|4+q8
z)S)01>HS2uhCN9f9$u34Z9$9=2~;ICb;=7CRq~;1!AW+rm#->&eH5>KH;!Ht9AP>=
z8Lm6-?Bq0oJ}E$sTxACNLY{TmpAuYOF;IX}QbKt$vmR6r!c`4^@kh=b(+Al<sPJV?
z)fZZ*Dteutj*%9<|Lk!nD9&Y=fAokAPg}a$8lG`e&!X}`lZkS-PwIHF*bxO;B02#A
zG0zh{vx^8P3)i5Brz=p7pq-hSsh0KoG(}ZM<a>owzJutlxu5F1)MNW9B1J47s<dtS
z(EJnsQ#h(~iut&St37n{_&^lg2-|zb>S{O5HO_lP>br@sDr`X|n_OtwA_;Kr3cuGC
zUnWyD2!(^<u3K<o?xqatq-P@+(0o4>*A=-Ye;Gto-g4aKwkbUuQs@b>CPB@(E%n(u
zjX>vU>$cQ7aU=oCN3?M)J%2sF_z^Ab`4ixR^sphS$-g}}wmZ=g7tN45nB!<9aq>k_
zAj|YK_=(^9shV76F17qNn(@Qx<hp~<5Iac({UWPqIF|j&ss202qlVl#jKyp15A^SP
zvXupL2p>I3>rjSs<CJ1yEo(&tx+~M8M{;@mD2Fz`yPV6>D~LPb0wLsnkFk}$?#h4i
z+Lx09qCZo2=H_lVKIy|4&3qu<|3Qp>=Q1P~jbT<7q~e4F1tC-qPb3mU*&6c-oF+Rs
zHFhrHGST5wVzs6+TCf(#)+KQSt)v=4*Y_inE{LhY@GV{MXm508kWUc)yGj-!I~b=-
z4mEwWyAo=mT`s*oWNo%Vd&R)CDc!CuT|7ZH!bELD{hx6va!K*!UFY)azNOy;>K_}^
zXe6=NB1SLD=Cz-uKRR(=Zpe4^z%7=9(FKB4K2dojx$Y+H6B0E<ZNqcR9k`L~Ajxq}
zzX%pBV$99JPtcp-&=9}+`SWE4uAR|jI15*AE|`2vGWph-WT*nXg}=uZGD~-p9=04G
zZ7<Oeu)Iu{p`V|9#nu&hz%|vvy`FeP`>OPBt@^ShWal7qq~5pqWpHH|9hc3Y&|C93
zHD$Nrk13?9-{s}POBMs}482cr+?pqFNuRcCb`>_$f7|n+!uUytOPZbYY%W>ha@jjQ
z2X6XN-cw7*S{QE`m{GA)Q57W4{Z)+g{V<aY3>5MJFAy~Q25Di2L(f4On*>$N=7xrs
z12?fdpBlCA6c%F6qj!>y-bp!n=fS|st_j4g<{mdk0qmZRI#c3XqH<^4#PL$%$CRM7
zZBQ(eeYEpaj|cNxTK8f<e2%NiHx1(ApN8)`gT1i-3Smz~+=I2d1uAx4oLY7^XR3%S
z37sz4As^>sN6jmq&52P8H7q{Z-NddQUsNIXV*+pI{6jOu1>?^35GPg*{tIUpa0-vK
z?m@`0zV4ytP|scpjI}pV(1oS|kR?$><<E9axXwOc{mcO<BI^<c#mil&g0~O+8oIaI
zTDJYc?cB!{u124s0DAv});zo~=~GXed0UL{pYV`>JMe&VWdAX#EX{fG_qj&Xv5t6m
z>L<No1SIHyE1`+NmcEs>izb-JtS44#^-au-+x5oeLeR>uj&!}UUDMPxeYNBHQZVaK
zGzmZxM<k&vyY*T=pZD?cEZ%F420c(WnAlrBF5Jz#vl8-S8EBkb52aii)U%P;f&@9&
zWRyi@fS_bia*B8Y%s~&GEpMgc@~$9r9+@_@u*=B)r>O5U-ZWq|$)TpD{%18IZ-ZLn
zHSfWz!Nu9PK9*|Em-08=dCe3>nV~Qt_By|&Kt^UiF-tv;R&Aeu#&tcm<5D&hWx5h(
z0cxLiHK{;IuMY)HVEkY(fz~g0czS}m#cUEQL(YY>KPafB+KK<8jG(gLjkChvMN(`a
zh@BbaeFvOc@3G*N;aF(hS9EL9^XQ_d8tXS3A!9L|olC-S`rXfo>rD&&mBYr3KVyo1
z_lS?@jQyV`EInynHB5N>ytqN|LjO7T)<fAx=NS&jFw$HnJbO<wfZ<YLOn=|;S=d*s
zrGZWrhg8NII=ws1M&vtEw95E2-w}t|MT}KTukc%9pmE*kOt)C__MnLR3+`nIN+KZX
zlLDYoJ=TprEL!edF~(!tj-EE7!f-g(zU%2(_PF}{Q-VImVeFvl&h)<o!omyK^C5!%
zsi)ZUvL9TL89N_;ac%3(y)#;Eh?3Z>CO3VD;+y?8Wg8A8!l|&q(7`O0VCyurpzpO3
z&)-#Ota50r`^aLN*c%&BvsKufbwRvv>GIv*;n}-$C(fSWVCHpE4wW+*P*jx27lxPc
zhp7$&3_)dEP0q2SmyE>-ff5n4a%R`MlWRI|)C`R5m-jH9{fq#H-~&YzKQ1QU!s)U*
z*R5bJ_tMa<z3EDfdKIO1g;SSbE-2tu59d(%?nUKm=^EKB8;^2Lv;^xW{+xUZPrCCo
zDht+UKSd`jewnYK7ih7{OhN3zr<5!}W(3t-A)3md%QcnEmnq|m&*zKiu#-#+aciK}
zfc$sR!5>p^BlY~slA$8hs&`Fr*KGTI(aG^Fs4TuFExe~Z&$_bu_d05!(0<s_@5iX}
zBZ2_d6#Ggyb+kM@XiUHAYm`ovkjT8`xhTCKTgOue48$lLqfeTzMjVpCRUUaXapcOx
zhbvRJakaY#DxIL(+gA*|7p=PEqR5{3K_+GNyz28~lYkIoHf+>#V~DbiM8yZ^hT|s^
zopALtjmykoLJ?x3W5->EXH-q`0{uny1i5kRxON{-L~`srQdQ>ce=0#=%#$tN;mctE
z=T%6GUH5O5DFm7B(PfXL^X|}pLD_VEBA;Q|9W8-elcZjkqV|ybge>8rjc=0ndB+PF
z&q_vDwi;_}sQ(V5RF4ZW)ihJN-Z_^sJ=;i2DUnz`?0@wOW+_NCf|KQ_h++tah7k3=
z<fL-^_0!^_o4S{7zBut&*MvZo=t$Xkqt4p4McP)eWFUq$k5{rKj*D=K^y~4OzhC;2
zg;6qW>{H}H7KgCcpa*cDb>Vmk^0+7#L4N?39l5$}X=}j7{3!ze&e_He_wn>MBJ4e9
z7oxpW3~8E@AVXvQ!xut_$<u|R%sEiGv)AG3z@)v-F58rZe#URONXK$pr=~BT2GFtT
z>&JOg)8&XVXUKusb3(-H9tk+b+i9cb5+^T#{@0yGL)eAHLG!peJYM7Q^%hG;s))Fs
zMkb<8x`&^&(B5KvR^k}Uhu=pLRU+{B82+-;59%)V-97FjVvo!vL{I4*F6S-W?Sk>h
zb3F2nio&oO(z>L5U;<<jiGKFG{UAAL7y7W>-<cgrT27@L_FNZ6B{CP^eYAR4hVaa2
zcwW0}ZB&JxzktTWc7GB{_*b_!>+9i0i~gOf7ds=mKkl|_4~tL)ttjDHn*552*rS=R
z=9k1plce;kG-0iO3w(iU-Xd+M+_wCxZ`2(()Zg*;A9|y2Kfqgf#5sE8fHEb8uX_@7
zI;hr=FO)~$_n0JH88RT85lLE|MqE;#z$WRCIe#79)LlKyquHX^!=jID&_BF<&z(yV
zE35HF(PX{<yMvL$T)0pKx1jn=-ve$!)DcJV`o}Kgmln9%C=8{AB6zq&+4MMXNu}tq
zUAK;;Q8CkqYI(?*rK?v`x>EAqhV^fF^iazLyE*i|k$kQ(6gbi$FP83Zi<fx!<5Jaq
zm-kBp5ne1h(!uOiy)6Zu-U(Rs(?a7bnv0%Ni=I|%73!uj?(UM&nc+gFsllWa0`*ar
z7>RhHV~5-hIH61ZXD7jP`GSt7$!1{<1uBVL;*3GjGt#N=67=qfb9~dCq;5BE;So~m
z@6JMK)GHNhJFO`$uV@uLa2HI!?I~!jq$nX2U*;TK&zw&a#3Q0^(PDqK+Zd+j57<3f
zodmjrvdh21s=|!jM>X~1R#Ni2`}#DAu4oyr<MH3O#UOuT^79L)IaWGzN0MSifh%MS
z4xJA@Jt4)~7oo~J&$&}!rB*SFgbj@seA$(K)hBz3GTuqzyIPxNW8?un!n0yo%E6wA
zN`?6WGhCjvy4%j=7A)3cZYO3d(Y|o|aVrK3ogpst$JAvf&1sTVn8_(EhohS-UqXAs
z5U(Mz`dEz+0g`t8CX>8hNnDbq5fjC^w9v!(CQ3_DvU$YzY0Sn<E^=jZW^J}alz?BD
z;yuMz)T(o?T<>Rzw*H-g6L&jQG>DvVSNy@h%N{HO1a9UgCGLcROa7@xn;*&^iS~iO
zu;#*q*{a#V*@ZXH675$GxplEd9;pi@|4OwO9*=#X6N)W*pc5hKvJRMFcPokf!clmX
zqqC%=Gki#23eJTrtz?>D0$2#OORo>uZwOtPjr9@#3>(ya<wY6V<iF9}jPGJV{T{DZ
z868&sPlK@%E(+#u)af$8t_qmDPMFKin1S1Si}SI~_PgYsj~#MSPdiLO6QCw;9?RHO
zh^6vb+Fd^qh!f+I(hz*E#>Q_Ix&JT5w9LL(W2CMDKUY#c{y0Y%K7>MH*Us}Z4BFXC
z!x6Ao6I%T~)Kp}4DP<_+Db<++fnQbp&#4Fs<<0jrJ$u$$&^<k2a!0$W;9lUB1S|PG
z9<Hq=wnm4I2wZCsJ6C6`R2`?i;<_8G>JV9)XO>sS9ni!@FF1K#bh9?88AAH)(a%*g
zR5+ItbL+W_%FA~Cg(#OZ-Wd|{ua);!kJM+(#AX^bNvGz$w#OKArDb;A?(}<PGorcy
zrg!<RwD|E1&R8O2*6mov{j1~UGe-zVvp(t`R(E;QAcia7!d#icZ@E;jVK#`lOIj`=
zj?|=n<7^eDVYh0t%6-4%pKjQ@z%@hx@vDnf?!j&xCR_7LlxG36e9sWwIFCHKJre+G
z3><qihaK<|-2=*6bYG--Jm3WF)l>({&f4|**o7Lh8zEf0sLw5Lb5k+TuqC!AEMrxI
znqnz|7$#X6o9Q|uvhnHToLs9~(Oh$t{tw-L4=W`^5y$0TzSl9(k=MusZHdc)zm3Zk
zt_Y5OG(&|4QKip*PMr!!vpg?iiUR>Cs2y7ZGQLT<_D48r%03Cw?@3CGOnx#X%hK_2
zor3iSya|6RCS6uZfIsf`tu(jwF*aG8`-Y%Ss$B-2?6zJy>!wJ1PL1eFM`00zn8?Tb
zjxMBL`-ai6J#EG<p>LHj-!~)j`jCw0=H4sTh>s?2gm0)Qf9dF+>;t!U_7VKHO)+0<
zV}_Pf$}1t!3V2MZS^Li9t`+#?yPnEHa{Ee!s)@Nqvn9CM6E7RNb1u7H(8qNcy*laC
zlvk(FS0*RR`%pD*SOrpJu>a<d9L*GG=A#ebE#|vdN~HRTjn3Az!_gXQ9R7KXbq|H1
zwiYH`B2sfw%ysXiy@nUB?%(3BK}|i@F#0QQtVUkK(kgFI*K{w9%jTdhNMXqGR&?0i
z4XP_lOvtqufja9*37gV4Wi0SJ(_n#{3lQxJE;T&FQm-2$tHJ$|4!9@nN#by@BlRVB
zZ}C;qb*Mg)J=O6pF*4=a5SH7r!!qLH`H8a=@X+su3QTr?xWk$6yogieWR?_(&Kwyn
z$lo~Usax&Caj#7&Xf@wxLSTcwHrVw`NBxoN$;WNN>57z^-?$~19f~TV59g=8o4dmr
z)=LUtiRn~A#Fbrb69UFUa;*xTxkFl6n-XA`-x9-N)>K5Zj_D-@wV3oz8s_Vy#|fnt
z-|`N}8&n^c+2Jt(QsZ0s4$S;r>5fXNOX#QuoIX~3fqjw6?WB&sr>{5MJ2MH9Z{n6}
zN&@#p8TOc%^usrHH*tPOwhDpPa?a97@YE&?RIB6?V|~8K3}|q^kJH)auHxaz5-MHZ
z6#Zm<&#}-JYIa5&sC@I^50@L}+LN@NY<+s=S}%Ar^lX`H%uzaqSdHpNsh-h*RR2@P
z{=Fu#3eB}sye@I73u2AkaiZ~3*?kMit7ARUo?DMe!V(dl<f!ajkIX29E$|e`{DW&X
z$ydLOjq@?a^D4`_NH?nF@CZnU7%$aTh*^6M46TPe+O<Uvk;Rk2$DQ54c%-2D_X(sg
z=Aph{RiMBP+*lCw%ggqF+^B#Y!$EAfM64a<pj5wxVwEeK_rV|2Q;TS=flp!2Mz`{u
z3RFp;3d=O5VBzP8*C&!Ec28OU``LHH5yQqxQ^f-Z4<4Kd`5^-}x323xnzR&Ar;Tr3
z`^0Hw_P7LNJu(_AI%JR8ioKle@TJ0UWNcNemT;23l;_}KjU$4*!m`x@H!MX*@G364
z>~(RQj<2YKRhU#7qeiHAH<?g&-l*yDdp|)fd`n!^)o`SXjNID&x!lyfBmD${>M7R&
zlD5kjHsVzAt36Lrk4W<Fzeu3^@T&RVMTI!2VV!kNlJ?L;TEU$`2v5HWp1y>liOlCu
z%fO-VwGgU1KY2V^FdOeuI(Kv#TO`jM<`nSkgPbUSFbpcHpI3}91?4Xq7W!?*J4H#%
zK-#?O7~&_0vs07+fj`~HzbC@2m$+W<GIVktN_ulUsaTOSpJ#?pNsX<W)^H2bxpv>F
zN+N<=*(@dQwuM5opJVcowQAD^x}jYZY7aI9uRMe#%lNX;mG4$Ksg#@qG3@HGSc3=B
zu0!@&;k}T5uD8MI*flqiXLLKuseeoRJ|euv0q`Z@@J9dpmv7$$f+VC<4EWDo7;K5=
z+K|D%i5K#0o~bFECC5%bATjoYdU|0j-;|~(giM=|%*GQIy$Lg}+0mGU8qf1;wm&v8
zbF#UXWrbt9>0MjFB(mx{IS>;mXiz0UB3$ynes<P<*yB4C=e&?#>BVlX%xuXbzI%;_
zo>t|P$#dKKXo<zpHko7z?6htk^IdRC@haYZ`~-2RLI(=tczAfkf4(^D1SQ9u#}BHb
zhzYi2nWkEmAOM;{;_t>!IT(1+z2*`XGHEZ$Y@HCnvGXQu949}uY~GL_WMOt~n&=U5
zpzOnpOj?h}o*DVbQ-naB2pvd^Aqc)qgnAo%ropQz%4oH+P9RuTr4F!Deq;{{V`a;_
zsHuD0B?~&j?I5XQO*`>9rrMhBe#`M$t_cFMwP_#4=kf<*&*P*XISr45Ns^ZvSA&WM
z?G3KrWS9(Hf_WVDF%&NSZO$Q;#E^3fI!|{d%B9xp$dfS4m5%#Y+U>u*yHWFTb}j5c
zsJcVunO@#wGfkO^R>&EW)|m-yk?%UAoA>A1-3-F+JBF`|E3nzc>L}%AU;DA4KI?5y
zxIn+jbIhqFo~G%9z{B$bszlDaiQ{^|Ho&yR6}htoMtV1Yc^*UE71yB{?E@j+Gi!nj
zz)v~^Tpv-XUPe(-kyh3rig8in0BFi&<$gN$XnPZxyJo@iNL0%#w}{PRoh+e2szo3)
zZ0oy)VAcDfs3y&#%-F3&!lh`MOE+5T<2c4{T`*pJ+^IM#LjR71R>cgf_Os6kCos9y
zZgp28q~jJhb59VPU^P+GL-NKarp|A9Nyv@&PEln%l1wu;H2dnTfao`7WMX4zBM{B-
zLH=31;|BZHPRy3WhCkH@M|F4J6VG^$1hMe>5}&=NPxM~cq@?H)w7l3AOUqVvWimF$
zf!$hESE@y~^RFo0qA8TM2HmUtbSfJy$1?`ud#(mZhC#i#L!HL6W~p=8dgml{kQwo@
z@wi*33upYZ#Wu|D7xpWY)MvI>`7Xt5=m+#07`3^UUv|LA&>7EKQqPG|7*74#R&9FY
z4)mA~BqUGM=s{iWeE}3shVsE0Swtc2w}WSc(<dhoVoZSoJt~=cmm&ED2QzVkr6;1r
z$ePFm{EU9$YpM?!i0%Oe(QNDbxx!AqU><azfolyIE1KSnFF))$+b{?d<)X(g;qM=f
zZhWbGnY(KQ|JlLDu2hxZ1Me6Xo_STzqMJ}Y@~XRux*hJdX7PdggViL;x}vxtQ>=CN
zgZ^ljb5CQLT@B*f+Z@`r{P>g<qcC3=Y)VQs@0Nb84BdrTT<wHrqG?yi64&89uQkh*
zY;pb=C(ntmvEMig#2m{=%Ezzr&{OjiBjxKH4%x8&^^@+$wpsRTKd9Ri{Hj8OUXQ0_
zRCi=E`T}0&0-eIT48(H$*azDz`xJ^nQgiN?)Di&jPzmE8Al=+Y0LcX!5>*wIFjO*!
zKxu%hftgt<T3IgzB0!I=X#*J?JLTnWpc_e@>Wu)}4D1mzfvl6`N_Y+256wU(VXUPN
z^}Y(5l7YFxu_et#lMUFc*<O(;8`f!KC2ZnzTs~Vi&MgEyD|W2G@3LLjRUfV^5H#IX
zOQgBhY*VFqy7crfNz>ZN`&cAUwDr;tLV0%@%~aeB-DzlwSYf8~MKeeExHzJ8b6S}j
zt%&%o_zp+Lcr&dVb$M7VW9-s+j(@DmPyM_f7RBmHWM^WHJ2i=MD3T+!u4dk4wIueg
zvrEwZ_coPiA#lU_(x20ASK0*AGZooS=~#4Yxsng;;o&$L&vVc{0x5U#<I1yOQjd>o
z*tq-fxXTBwUBE@__-@rd!sS&3n=aqApsL>cDNT*@qNYPKTb(k22Yis9^a*XKfSpIL
zR8?90w9i}xX$nq&x<Kg9E_e3qSr1a#a3&N{$$?vHzP>0$(=#Z3i34>-hgb#*o*Ag;
z9QsJx{C5I!%n#N4rn8Ttr?}d%EIV&ZdiNVMJ<%uMV_6haocz8RW#~BT--dZKb%g6?
zBn@Q9{7QA*N(~Q@qVRT3S7er+oCj#4rr555$L}&F)H&TO8J4j)18%oiF;fL*F|1rI
zDNu~VEGzF3LD^^w4;h%ZG_5>Ge0fib@E$>UU7obBjXe4CWqqr&^_yl<0C$|nz$6e@
z8mfjrkfJ>KHl+?z<k+bmSv|!kDPdaMZ?As8nZ`PzPv+9ak9zfbvvi>bGamQe-!j*(
z$sh5P#?rJNi$ThB%+l^!^|x6VUBY}NEBAiOOx3+x*ykxUWM4~Gc(cx=y1c^qS4Oip
zBJ*l*_D;U3J8TONW3guVcp{WP8Nj*tQe7ng?5Cj~&1)#i8n;tY0tF6QsR=%%;4;Z8
zH&Ee;3fXKYpPjUc0!^yn?a&LdJJIcyKYQnvW=%UB#9HfpaZh)jn>q2<D>q|aD7us^
zVut~5thMOYLG(p>6MN>(?IboFo(zgTnjIHUKzYf<%D4cz=Pw*Jp1Ek4@rtTf3#8$_
zZ|rV!e?N7`Xo5N@U8&A#wawQmEYw<gvFc}t!j%_q>9Q!s>2WbjiwZdg_Y<&@LgS4m
z8d<`a8>M9Ohoy?Yk1Yh!o=eyK*x&Fly=Ej2oWKw3o#94!)Q-O~75<57`nB)kkv{1G
zI=&DlrgV+NzI)%}8}b=4UEUIvoc!ADuNEd}sacU19;X{WE@iLD4bRBak-J;+fWPFr
zU6F2IiKaLGp%le0oaM=tBDIx0Bjo7l%Wg&ZgjRQkc<=Ioy*mqDN==cKR{yrvWTth$
zO_|*^O9CQf!?or3ICMkE*u6L3A*eo<qdqRXZ!Os(M@T01TYO|DmS{H4VjF+I_w7VQ
zK)+1OhQ_C7_%-=%Tdj!u?NK7u1EW12%lBlbg<ly6r6bRB><*Lbj-whU;Y*h$R$DKP
z9Ml7!2likCIGD4kTr)*6$Szov7`%mS4<p;DJv}`qG8sPC?369OukgM{B)&4YMXu{T
z5vX;j8L+XFh`zlKLqj>Z7i$_U7c#2Pzgu4i&dL6+63t)vnDvfz(Q}M6kjEom6n;n@
z7coYa@hT7(-BxH_Z>=FXo6F-al?Vqdw@DR6?7`NAy_iAc8GI!Vu47Z*8yAH<4v(SH
zc%{fN(@HDFctW0CRgzne?UIyfORSHPl<_!U(S4^hW5@A$tYl~nhH`1ch2m(Q17*|D
z<~s+Y`F<7d&6&<9{({u6Ykq{l=xqK?UAFUaj76(QhvycO=Pa89^dHw#SlnbcYP|g=
zD4a#2pW#kp9ABVbZWry|$OBJ;5Dm}VwO8HBGwc0Z$ypj!!>SsgJiV>nbmuk-&y&dE
zw1P^V3BSwIO||R!%|Z;Vj9%x@_uU@9z!23OBf+t(&D*BU$*;rvPP^WFv`0KeBrVVL
z!a)|L?-iT}N5?A}!z)w6_r!55O20XHB(l_}+EYsZ(j}75t3lb^%T6xHsU;jBqaW)$
zw%N`YGfT@2@zdZJ+K-$K9=U{`L@m~3Rgu^2S$WM8yo+|QwhP$K#@h!E9hyQB8V2CZ
z)~cCFVIhApq=QU$%JEb(xW3_EAp0}TA`<gmD*`|7xK4S?N9Lq|3(ZZ!o>U(B#9b5R
zr<eO)7n%ez7h5CpP$svopjpPtk;BYsZgy>OMa()_icW@m%r8;M=;!tO7Zqa5rW|Yx
zG3HUF3Nn0OW23IigsT`E>G=t#+|-B^*>t5f49fR5)_BlDjKoKte38YUq3eXa41Xt|
z#TMt-?BGnu<Rpez`D{=gOPSR4E7C0K9yEAR+phemq2@F45Oa4tsRkoP4Egp#Kd5S^
zM<&ns`Xj@4dEKg9T8~^pNm^4Cr);;c4^7`spHVMZw`5QMONsxM&Ln5ySj*0+GAzBr
z_D##Bwrg^K?6E!PUuAyRKF-E-EIw|+B0pLEIgg7?u9`g-Z}#$?Ek>-tPmUv>I{TzP
z&pk_{l0sd?x2R9JVXrwC=J{>%DK&>GP(2+*6>xC4p$yA1&LH135UmjNZ>fOVmqDnK
zMMdFIcz7%HH&7oX$r4`y^_;Dfh1SKDwl-aF98wq&>IGd70f_}5E|dp#4L8+&cv<fD
z^Yr`#xlY!*`9yr}h0c+F#75*PF+O2(%%_MMsyHYII45-W>frJgTk7?Z79YbFswBs2
z&1vT1hOr8_7Gmd}L|*6?$kcy#8iFMA)fV<AoeX<Q4#-kQHR8Y9zvM5#iZa>o4)E^h
zVbuA7(KN)UQk3EsW(&Rc5+cj?DHRU?_{3E@Wgc?(zRiAB&Ppar#75Kp%=;#PbvK^<
ze&Z|&v?_|{Uub`L=J2Q`R5(JrTG-Poq|Ls4#8Jbmd4|uWxhD6%vso1trZ@efoi_JD
ziTB1_n;wy#*{`<xKbiaC9vkm(9e2z6J>6ZlzHsoYI@4=r(LGxCD;LH&88>6UP36<m
zwndbZ?i18Y7p>BeaOGEeNI8Q6#}R#IA%l=L`4)ket>A;f6a6xhG30z>E%C7=m7|BA
zH~R2!Vul^&T+_ZU$8KpsphB^Dn0zySW3Eaj8-e3Q)y>Ea)E+o{8w!Rkpl%vutDWks
zzG<({DKot|_vY3Kb`?dB=}WiS0*?eG!eLZ=LqXg!BJff{##7;pr%l7Xn)+GRINM7R
z9Yd8bfN56%Nc-yzsvA9KP&1JZNIM|)T3X9i^c46NW+m!#F?Ki3#hvA{6u)qRftB_o
z2jpGwhE9s7#TZymM)11m8ohH@_*iNfK2a0?jwZO7_6}o9w5JrsM?sYt6ZcU!Z^0s^
zx)CHix5B|;pl-3jt=z$CZX${p5_W)1qKKnYX<sCBV_5WAW)Bx`3GeuD=@v~F(<&aV
z_+=kl7$zdFe=fv06*nsoA$v~9lv_f`RL;c(S;aT`XiZi&>~Z7Le6P-gF!Xoq8vHfk
zgZ*aTu+NKwxsB(@3!cI$E29_AP2@8qH8*6~ePygk!wMPt^kt+1+drapZ>RKypKg?j
zgX=)-F+GNho;KCgDPh#H<9qn!sd#C=M3_9zXK&3kbnf(joSFPMA$*TX>fu<Pm3W4Y
zfl(i+R~2+4-p&4cRiUJsj;|U;JmBOWZYFStYLjw+Fi2SK70FaHHa2G9g7U=Ah^{Os
zL80>7ubd1+PJ01^QgNob(|3@g%3udj7kJVc3jgmI`%&aUn(1XKTEQ`Accy~*WxDI7
ztazwgy!mtLL{PddTS$Lhx6l!di&tKoiT61i%~26A=udUd+?A;}+WY?1Ztd=w&IZ$j
zx0Ipbw{Iyb+@?*r)_`~)NaLXYpvdl6(`9nC%UC^)H~;=h36~CbiS*ED@1j8Q*fW#-
zZN4$h&=6tMgxoxDODBa;?qtd|%#v2(h9kj2aM-g|Vn}(&Yj#uJ##>!~SgI$Fx`o+u
z#(YbCe1NlLb>dV%>1(ckSG<^y!j?P9GVnyBl+Q95i|H*Vc_L0WW98Y22H9s;2<4*g
z1`wL?SYA}H(u#J3Gj>hE+qTSHTj?~XT&o<`CpY9<!?&I5x3w93;v5YoKV}#=^$DD)
z4=7rCQZ~s?{K_treI_7Db$n-9{LH3ybcXvcaURA{ZpN?Ng;l!e3(eaK6zmFAC-U(Y
zAHy@8w7I_{+T1U6xL@mjD*9m5*CZ9pxX9DZSMD<8sXsa`HoNj_Pdu02-^G1Ww`#wX
z(`zL&?u0RKM6$mOC#*G@b#-(Ia`mFR5eO8`#<aV^G&rT43`SBMPU!34ysL|J&9J6<
zNBWi97qH)U4#G_WPH`0TxN>_cc*N)<(ViE-#U1aQJ<$R{et!;v%4^)Wv$Fch{#6-A
zMjS>9Lnkvs6OC^NuVpMpDJAL{m`W!oFLH{BN1Py)Ed>N^WF&c(YlvYrFRW$0d8~e0
znu|SKuE97o`c_g>NRwr+RmH2dM>o>kE{lkMy`W`$ErmrsXy(JNe*6f>k=H!O_z_|C
zD|eVqp5i(7My=ZW7@b{2C1H}f;VquT%ST}u=p#j4lV|*;R&0a%YQbc-fsbL4mwDl>
z&wWgpsWZJibC!`R+fmtJ!XDeTk2}?Uw))rn_dcbmBG(29H&A&za;S43iauhEHd@r<
zg~t)Kj{|Rf$!4GukZ%^&pLbL+FF?Nk3cb>I=25Txlfv+fdF?(v4NJdA9e8n$(yOy>
zb4E68P9GkPIT#2%yeO%&Jk8&d@XCRUXi7K%${dgk>bTBi2(>D!7^@8ZA(noWS`tCk
zpsd@!uv%q3SAS7yu^nkg0cdsV8JYs`#i)Z3aBWZ3WxN{^78-zzR4UgeeR%ow4E)P2
zIrFmQ!@|@1FH0b!bU(M`Lp^-|^ykZ`@CQF%zPVoyuSxKFBrOTP`B|TI1pcL|n#SA}
z3=bXsb}1-8u-94Hf%|OZCBeFTA~p{RUwL`B23s;LIU5@aGTT2+M0pENnrDrbZ1prX
z7M;>sL2fpP^@N7xcJwv}zDvpGAu&y|3I<d}cm@fJCkU>L)yX<y9kBt)<>EJ6<qd}O
zV%tv>qb|??GWx!G+IBri`F3`sIFl-m=3dR(FP||#G(`i%$K6aScNYbCu~Swi*W}i!
zZ!=fu`y9WNJrrwSlZ!qIU+vJ7_nT;+L};@YAy$S$`2Yja5aK!3cgoSHMVR_pMzZ<^
zo{X|S`($-5o?|sUCkrx1E4c3&-}rm<wz_GvkCemi0^`b-v8&a7TlfF|VXq#?Ln{2Z
zEKM^kdo@3bFLWqrGh&$NX`L-ul<Lp0v$+Dli6-2<Xo`V`Mr6PVJ<fK*Qd(l7@l{8J
zTc?OS8=3!J{V<L8d<s;i<=e<en<e8OZaxa&Te^Bc{r6~&vJQX7Ebqr_lH|snHS;M6
zByNj+@p<p-Uq0n!XUI-hD9P{B#gOh<Ah~VUL!PSDpVInCCgsz2WIsEVYd2)pN!VC3
zlqVS1r5;b=!8BNhv|YUNAN@}X7HAs;s&D~k5EHs@0mtVDYXiXbBI6td5B(R<XI#J;
zlG6&9V+#2BdAokH>h+%&*o8}}cc}iOO!VcdKIilK4{JGPG+E@@?9adTqOgzigL`k*
zXw-dYlL0@HD)%q#8J0ZkxV~+9>8lFSaHNY^X}~^|Py<x}uX#3B&i}Q{ql3NlqvwP1
zNYlN7W9iEYFBdGb3dm__l{Tn!M^=G{G%7IMS-snYx`-j}{u^bBn?R;~BN{f0z(GO&
zb2D+UD|%$MUL(Z3)>r?w_y-X5ouJEGf?rZMZ?fr8@|A;BPw3+lv$C>gkk~MLsGZ)~
z!FHT#m0Su~og0=J)8==^$mqs%Vs90F4Q7N#m~x))sJNm8AGfv@a^-Mgm%Z_T-;XW_
z;u+yLQmveNb5R4C9UFD&iTI6B&tSiiqM4sdZ=$Po2a_%MNMyCe!V5Z<d!2(xW}oo*
zv7s*uQ&ElzFGW%U&mxdwZIb9*(_#h#2)b4v{#j7|d0ieTb|kcX((@cb@p0t<3Pg{*
zEF$j-eP}n=AD$yAxdmN5{kk{!`Wcl^2hL;T6SWS3093%{bKb4<Laqk&MQT1R0z~SX
z*M8UtYKI7M8$qL{g{io2_>mSFKhnDnkH%&qQY8O06gDSEiHvJP_a4%Y6cH>g|6BYD
zgC#%!=K3goKQ3?V$ijivzh2cgv_P6bolG?z?7#bk$>RtNQDEcT;Wu$s=kq$&94#-X
zInLsfgP5~t^XBTW2k|VD?~F>-+caisOU$lt*k#}nzEM}-n9arKhWA`CME?7;DDng6
zMRkAJUCK5nIpW4nt+CD<>nMnl-O$23dU3O*@#a$CgbtGbV7?q03Jrfgd;WaM$FCOs
z(fa!O!<g;4xlq+>{Su8yw%C?s>*S68NMapb{zk^8x4Mgg3*Lbw7iv8gWJVd3_+B27
zz~y&vRhv?VC0cU{$*z}J;qqqMPp6m!w?Ikn1G80J9*<65p0^TYKs*IEBxijjvJ+Sv
znry7OWbCF^GZl^t)BkFcYAK?gZD!Kj5)oZ~NEni@7LjmWtvEqCo^IIk*x0{2%99mi
zg!7-R&66LN%_Z>>#A|00`#dk2#voFaUki6N*$Qm|pb0uJ;dkf#@7ML3d{|XK>BT_X
z_+23aDg!i1NBaPz#VbL0;N;}=5oVIxj!(ZB0iI3~#-<)<gA06kq}%N=p+O<T)8Vbt
zA;Tl^8a_-2AD&kG9Miupx#;edN3?p4^tBY^^T`*Dg@@B$FT!8WlPwwx8H=9|hX(k2
z?73a%dqO+Em3~Rl>0de}dnpVnqh`_Z^^pZwT@+nZJKU~+Ukh!;7jwgI{5qKh)}(*R
z<U?*v090MO+|9n$5zM+dvSqs4MEK)_D~X@b0-b;BpV5h#f}d-~g3%WPBU2G_q)jJr
zU=saw{JbRj#o3@J2fdxY`LCD#NaAH9O-~?E(TgMCH)8VN)&@I&$1jHN<v<_NI4!f%
zrNWR<22xt<;mSechL<#uMSPIAoPUn9{(awLA~nO`mcVmeDtj9Mr;W!|+A(y1=GNav
z8KWp5Z{Xj*{r!!vpOyYII0<Cf7b+l-8t?IGU|;}G2K4X>Y(><aW>Ew1a`FvEy^C?l
zzn6o5-vFVY87QYg03O!t9p^Ieu$wn^yss#e4vr<<jZsW&iO~k?t>dw|fF1O?cJ0X!
zh`U;^zx?+Net+}%@x?hP&Qs5HFnDwsxYb=rz}C6>!>*4gv)X68UME_4Z}AtHi%y(n
zUAr~Zw^O{&@D;A*f409uGhB=DI9RMt&XH&57VE%sm~R5W`)xxNo`V8dd#6?@(gdKw
zx`CNBxR5L#IVA*fpjG&9`Ne-OihKVM`XDF4PP84QJjp14&jE{CGfhEh)+6;y?PL(J
z0tXZPuf4t93BYJZv8Wo3j!QGgbWb09H3l|UL%PYg`QbbPj8aRtK$ba{$qgs3<NN-F
z#r;1zAdQj{Zy|rNco_7I^SB~sEVJvx7glnP50LE3M$%MoqgX8uMN7-{+43btw7rph
zHB&jjH#JNf0IIJ0!f%bye{MwB8r&Sje>|l(9n)UT(A~SF%zwNK6%6l)Z)ZzJZnJ0@
z7-XYbW<Wu90WML@a^-(}#uPLMxBwM7=h0iXoaI^}A0ukp1N_%acpm2wmf&=tMWLiP
zMG4}?y-%QQ?LShe-`CM~vGKU^C%rxVgD}4>gZb``xc&U${i?Yi{4D>S6QxM_MDV#+
z%RUVQ>B0`~j#;wzztMNUzh{is<WA8mbHQu@s}c#cu(62`x6EG@wNQ3+%tuX9Eg~PC
zfzF$n7$gold5D)T{QQ^qc==yIBM>0r`%3;;yP5-Pm@K{U^nc}M|1{sZ|ANu{^Rp((
ze?*%*KlzR0Fl33mvb2kybLoG0%l!T?QTzVYi~jQ-6rUv{t558vKQ_MWA2I5`zoOu$
zi_BfvMUS}q{x^00_wWC=58wHrA<Mja8AJ@3fSooVF9JFUDmchL{y*<HsQ<wUV}-(3
z|M|o}-@P%@fcN?53)b_Uug|~!k1n6{|NrIxUue3|I>g-+T}3E`90h>R4wf+lW4l3;
zcsA(LRdj-TTn%PEloaovA`SJTBoh#_Y?<qvI=BSxe&h>eW+wo7Pl7$j+f}nMK<vc>
zAOl#52x^Z9>^+pm7Y8=s+Tf5<pg#>#^Pc=x>2&Xn)eGUb(+~h!1EaG*QEcp}dqpQn
z;DN~80T85|VG7)#ZE<Cne;69l2WUD<^h=F(@CWyLq0HX5nXN1N?n+Qa)`i;lfPK(C
zxTYQ;`l27{ws0TS1Bt_Y$gqFUIVsUrXDG)}2d|%hz&QXp0Paa(AQCt=G0}E_BlaB(
zCs03MFyM$*m_p&M0K!X}owae7WMOvr_BZO)0Rp&>4@<)Cfp2p=ICDx6`*OcjBz&Qh
z(M3;5YJSWY=$Y+Yee`Fu*|fh&qSzA@QM*vP%!hzH*>g5+g6q_wn5wmyDgn?1ck?np
zZ6?y$*=g(pe7R_pVU_|yqEScX(1kUxaX`k1fQoS9O?H{{wE7b0<Rk0mNQ@N`CR*cQ
zW^fTCd3|rPMlJ=dfqdEgkj&<MD5-m3zyc*?zXhK%cYCyTx|1XI#mB`8ExJF+H9OCL
zzp?~G(hfA-_v*3f%c(}tb8?j|&m!sG)^Vv_6#RdIl6D+**%AZVK%<WjX=7^~c(<a%
z<ke!1i!wV6*~3bn-?s$Zp4L6PALX?!0y{Dqhz>1IuC7W`;$TZT4oop&5V~`^2cklS
zaiC6w*q<`HO+t|{Zy~bJpBa`%s~=(zl#e_qjy_5N&68#a$HD4Nq*vE5P;5FQUjYnf
z3cTjZ4<}#<SSce57+z88?dDzOfCu2(*d<Qt9vV_SPeYM&S{FP`*q=0vRG>zsQDib%
zPLEWCvk`iK_v43Y%?J|^V!oC0&Mzu1?gied7{hQU0Nb17`H+f>sc45wLlqYHme(#u
z+ZxIM`f`Lkl3P<H^Ywo20RuA;ncmhOv~li{23-@h%H0;8N|ynUq~zzCd)AWfK8mJT
zRZjFHn2d3&PZP!f0QdcA)(U7J?D5F?hz(Rmtzcj<{B*9~{?rm+VvBxG!y|~1m@7vd
zYkH%bJ$F-ZpF&--+(F811XNh@Sw|D`nqjZ;cvjNWw0x)qrQoN9qh*QVHO2q8qh+|^
z@~!uGZ_&wY=IBER(TbXyG<^cb!9B2+nTcAM$(GYCcgsEU@+r6h2#jl8kVC1gK3mAN
zfzpJA$Pl0yO2Phg?2_V;8|WG!eenLtK01rmcqz`!TXtYnJq1n)8Nb9~&;T<i1u9jd
z%k6;^*qyGyb$GuyP-<Y$#-f5n0{|FF%^0{P!%yoQ8_vLzcnEJ}yqc+9cpIQ^*=|`J
z@orbqvWzn!w~!$>Scy<K6!4m~fb|!pa&DlOEmJKLa~Z#SHCo3B>;0u1pw2@tp$j$a
z7D(WFQvD8v=R)iWI)2a%E9vR!&60sOjtVtLT3Q{w7fIl1%r3p&9y~Q3W6L7MLhV=J
zLez%n14TYSaKOA&PTD}WO5sXSGqPaMIMh`ahO)=d@IJN+rw&;*zZC{Q26WOp^78r+
zxfe2D>)>mVXvnX*AhkstCTD`v_1tJ6aNkjfFIZH7TX{<5Ik+^TU|`qs5oQMHJ<z{$
z?7TK`!#yDHRBO+r-x_lNTiUTT*t6y$l4iPi>az7308Ft0+cgCGgkMkl^UZ=i6*$8M
zU5CAkwJ;7QL-vi4d<26%loS>N0KUW%^r6i#prCI8$ha*P71d&p{JF4kfcOc3SJcTm
zNsZx^ELSzJisl&6;I(iiK!Rt$NuW=dD<0<4`$N#WL6{yuN2)C#g(w4fG|m!Nv<8^?
z2Vg>kH@=@6g@N-Z1$lY-p<L8cM($jg<7_-6wmt>JT;<MXGbSB&F3&?wNvSo8;T}YI
zCO}JxFCVtbwe00xS_nqGnwI7gVCLhh?6LCBN<cff;TbrQ92aPfgDH4e^4Iq#=a>S6
z#1J3o8{;T^wHcKa=ijgOw<i!VEso3-{P_@a1qrmiU77BUfz<yn+mV;$#(*3)w<-DY
zOrR<^zoJ5F&J$P5cI1F#lK^%!uGqZ&%o2D;_I(|(CzZhD?MN)_4bt-4wB25{<*H1@
z*_35Yv{IA7nPZHZ1o3`MFH*T$=#B*e8ytEUsX4{<mvg|r+5tSTB6P*XPaRTP_ayki
zrcY+}K<D#$QQwE{-Q6bO7Q9FfhT@HFkNOeUxgl%yZ2dUbP3K-c(O2Lp&R+&78AJG)
zHF^ds1YQuc3-u2=yujM@<kGHm{t4X%lSsV?*C9EMIOGA0kyk9!ms+AMGa*oG68-Y3
z9~fB+Zj$Z<^g?;uH(@1u@HQr9DQFv{*cVf0(do)#G(<MQMC<15?Zl5LXgZ8cwaN$)
ze7PfRU)suTi;k@W(D|*amx@YfiA;frsmnie*k1?)$anaUJ0*xpCuAct1l-kd$r!ia
z^H>I$m59%YIRe5IT|@E+o8a~;qMt1~SB{EZO<LCgb&%ldB`^QVeQhjKo&`JwipQDf
zRy?pzi(kF0y>A@fp784{$6}2>1_Ib7z%E9)ZtjCg**;q+21cZ&Ti%bkXV0h{0X_2;
zVrwLSJV;`eALa#701JOB@T*$;uf|>g6D8ic?KU{s4hUrx&u4)}z1$I+UqM5oms{;1
z#pfB=Y^?Lqzp6dwq$n7DYAJ94C2WtxlK{;%mRb6Pw9yQQ3XSpfFRwQlJ!g(^&13~s
z_lW*9I77J(iOnHkN`;k5wPP<-OIIJuJf%!TyDBG`1~&nKe9){F-9E`7lpC<IHCC5)
z2aa!JJS@6mBLPB-Eo)^}8)f#{!a51lW=rV!cmePjt~o8o4Pvp<^rmEQstYW_0cHU3
zbZ=#pgL{roo50tE3!>Y+NJ)`nuaIb9SB)}n!_LJHZ@D>UKpuNq)r&xyYAntX;;QId
zY|5*#0IL?W6?P298bb|$>T2xoUW!7um%xuUMwL_8blB<NrfWP#p%C(4N6H>r5o)XD
z5OnFFvjd(CF`7-)*6NA0zbJeW3r`$w=v>vX%zH=hwUj1dTE_ynph)f%sho9dLe}9F
zmS_OP925r)tq8E|wN^yhb_S+YUQb0^+iaBdTnzOIJ4e^k8}rX)GN2_fTd27h;DdNQ
zTdW4*OkHIqna^8Jr^xCzI&gcQZkvA+<3mQ45-iQlzY!Dc>rS8q*T#5bt%ypnG$91S
zr^|#2q>PH?uVH+O&|d$t1b~Dq-_8b?Z2d83tuN4jLhxXMsN}PPJLLVA)?udXg>j|Y
zjzRZei|eof<8=q^!<sx;kTnvs^((X?dU=kmxcOu0SC9lUdlx~Fen;x61gIGEx>9YJ
zGb1+Lo85~RaTO=rPOjS=eAi|*L{DM(L#xas6L4L8xOmJ*&IC;7__~yl2N<aGn<z=G
zya9UPAQ^%RCwR(`2Y@&?zPAe2mXusU*vMAl=@ddyP;5v@2#hK)%l$UZC~Yf`xVL%^
zC4wBacpIGr2g+P+>qq9Qwtd*c<cdMCXJ!G5(1<>^h$t|8iDhn%BHtMe>uOO0jcJs;
zjoU^o-Tc1%kZW~?%Bb-bE^qRVu^5DJgJi(@uWW!x{OH1e8u1rc?!E5i+q^RA^c{mp
z`$HPa@Eo|fj;?5AXx>}EpxKyd(#lG>WYPQ@34%*Eyxp5Ohbk)oQ0BDp^7>Wq1--m8
z?`6X6wGf|wF{*&gJ_|{_qtDF2sM*hS8XXE3yvA9CBj*lm%Xqr7u&Q~_A)8WR-oxjt
zc3llUaqOP@UbX`kG<wL_%%04GW7@72+H;xOIBjG=Cwssny<^A*9J*#{if&jk+^FB9
zwdd%br_pTOM$Hd~Bcqo#&Kfy|QXCMAeo!;L<~Cmv>sU!x+G4S7aBUg&uEgrXu5-En
zq;2_MITeQ0k68u}vRH52fJUtZ`ud%dP_y@*mz_Piv2r9c7RiOYtFM~e&!0apy3XQR
zR=P2nM#uHW*Z8KqX1EO2ZU?W2l=@I<=H9Orz)>x|rJ%T^1wnf0_*=`-6%Y2p?BgEQ
zdl_S$DoRVmGxaOP4GokC+OKLqtSu4t%}5OCp3K--H`Ip#AXSKLlYNV=Ah&TRj6+GJ
zkZyeCa?><I;Y)t~=FQQZ!M?t}5l4yQ!hS2P)ya%GFjWkTi8-0mR=+ShKF%p3(o<Cd
zp34~BEEZC?K`+EM$oF`OHp7Z@QNge-Nuy@2ltrFQb)iZ35^p}lm)MhuK-5L^Ur8am
zC=Rbfu6x+TKwEo20OuMXY1aHUd47JL1tCSo#CYPN-m533l|sm@MO9Vx4YKk15#=tW
zqpaBp<nnVA6pB57r~C+P_uHYNVPS<Jx0mk56<Nms-){@2rKQCraQ}L6D_u|QP|QDf
z3k9OKYpg?|0~+51PcC_AG(ie6AMTyU&=VWPEOm$#5^~apo;^s0)?kr@*RPyI%;KZN
zLH_<70Vjk!B<l>DC&4j8FYAY<_g=KsC+P5wuENK&4~(DPq<z;4LF`{S-awyUnc8e2
zN&sv4yQ=!#yN7e)qNCHkefwryMAuV}9~fZDVRE(I@b~vuR#a3BUjLa7qQM*-1avHJ
z-*;`VexXBT@acT_%jlcd_%yu*QYk5@5@}D0?6Fdc6Yt(8LHslgyNJ%NG?nns&<`Lz
z3p^>RNOO$44SXj$h?KZNBYl}QAGo}<_C(ozPs3rE_FOh}f$$7mSaN;6VIy;=*jl1h
z!=-GiWVqM0$DQn0HVCA#>3as%Z#y@ZkU#g9;3ZQ%OEZ23Zcd5m<jF#aDtx_->jEa#
z0$YgCG5ZPju>Vk%1i}3-9C)CR?cpiPZdmH`^70<~wOjPux$%<hni+Y6>QeN9Jp*ov
zaIbD6^Z+?!AD>DTd@bE8>`BH!Yzfrr(gK=sZ||I}1JM75CQgxo9;L3Q7l&?Pnxh1+
zp`6zOlg49eYHDd<DT8i!f&AkPHNFi$>?Sy5*5p20=%V*-FM~c<AxS7^&C`|%uEs;z
z>pD2bJ2b7r|1F)#YutVHHfjg}h>jW?9Lbq1ucV~m<Wzv5NG5i6c4VtVBWHYhNEe34
z)+5hZdXmubtltk=3D%>7U*>IPuU|Ls%Q33>1XCwG^wDpBsnk(XT>j{})GS&E$Xj1;
z`3P>YS+O`mJ3Iu^r`DDw2k4Z+>yutX^~=r~trWl8qz<fV3SkEzr~KeS5*k|7-_Kn}
zFg6B+svlpTb4g0#tB6Z$s`~mv$$I>A(@&i5P*5!2Bou3FWvW9*$Zq`7H~7%XstX+p
z5W^Zk<`ip%Egxc75~p}4Gdeal2`HY-?aK1Z%sX&J?HOA6FbC5;Tw5ITfGBAhetQR{
z%Q`qX6x^?+-d;od5d!aoG_8BbZL>Y>`gjkU>8?tc%iUP7a6_6Wm0Po6)gynFa@BaU
z@Yb+$o?dj9uW@>MI!#|tU?6dY4>R)fXOIp(g|EuJ8#nyWu-MldwJbMne(Q{15^qh6
zBlOcx;wvG^(%jq(!$7bwVYWrA$IRDI4;s8eO&L-C0RAP|sx1hcHR(ANFDCQw_7qV-
zR#Wc!6;&4lf(%S%QBhI8r?p)jt^aM7H?4%}BLkIC_>D3W<%o{0dl_}#p{<fA8?5f_
zT_IvSlz|osh}(Vl-Hru=X^Z+$b`Myg%ZSh2E4ap;VgABQro<o@jcWD`y5Ot_U_MD&
z74>%V!C@Iq%*-I4FxvqUHDiv0n_2a`emV3*{p?~T@4c3ab9W5j(d?VWlraVL?pexR
zg!ylBlWvvKV6sa^KtKSSd4S?`8VYEI{c_N4dHJo4(9lp66G9)4>&0M}%JF)4gw`js
zvg45<{Aw>ix!QD4ZzxzF*yW%Vx|Dnt={qg<S!U5Di&cnv!|&-~<G71USFQtWMcL#Y
zK76?T{R!PN_{>}r9db)V{lHOrrN_kk=LIlL11a+S_i_Mh-ht3>x`=grrE+C2#bsO6
zuB-#La88h|KF`fPQ-WB-beJwNTXGH3CE_;oI!q;ETMyRngMUvfX9+=it@78Y!vm1_
zm<UTrN%1AZ+@{|<Q9fk~RW%89unK%ETWXO2S=<(v;V{$z$OfJe8h=Vyb+(zc5uTYH
zA2;-&+TVlT;7K${0Sku<Y2FNhsF?|eFe{5Zk~2PV_id+VCscb#*=HEsdgkXh87Mk*
z*9V*vup=WQE3oTTL(#gsSGQZYH<cp!F<;AvdMjbh%S3Vh4fXZy=wxWJm)Np8JD85T
zUBV$vCu-)3FQODqO;9jsnuH#@mxSjSW=x>GdJ<%x-W_W%6Cxra^pRe38yjgG7;1{_
zXf+Vr$h5CFIFd!8{85&;;s5ctjJ^NdjLJSbmM++1{}`@Z9k2{OaY#a|`mN=)hkv@p
z*WB?C%j^pEe6v>RS&VwRS{1lVQmXGC1grz(W}eWmekmU+L+=4e>*-NP&3H?pGde+j
zOitOt;uE+9>7y+A@bGYphY#ByQSRG$(!;;%L?7g8vdJ+pFrX0tDtchaCtq%!<mhB{
z6o)maT8dOpswMcgfz0!A!dEPIpOq`==;BK!pP#9Y=uI-fp-L<oeecX#?bi@Gsjd+w
z@3@#U&m+ZU7_q2?U5|Gro-UqZ{D)7UIORHC{B?F2rqqsQ>Bqvo&pKd{MKAoNar4<;
zJy*Tpme!V*p3Su-%2j7ZvZ?-@4mvivP(&D{GrFs!EPJ@o92;pEscI3Q=4R*M;5u{W
z>RH*Kt{qP{w2x+2kv%TonuPp8JIda+nWpQC^%2}z4iy>jjtO1{UWfKuqfwO{0m(VZ
z#8m2?Iko^9EY@OJY*HV?7RG$xXYH<~3CK2ng2Yls)#5EBr+8V3)_jf*jJKPcTd@*h
z6`gw~C!4JibncN_fSrrw8nQS>CZf&*_4V~Bq|V;8R{nsnb%$b^e<om61%-f`nws6K
z>}+i2>&uf;S@uu>xW@_s$)(-^f3|CF`5_8^9OMVGAz9r}B?V42nVPv*&)y8UH0zF<
z9D&-3%x5?p=Q`A%c;m+UwCBRCbmH=g=K>k}J%V<8bm7UO8WFDcLhtcqvnkE~q{5=2
zsc#`?D?a6-aU0R-%d==f5ekZ{wV%||onY&mo|)NPWn2AtuLo*m?^W%22G*aBjt>2}
zVRwB+C8fE0GI6aE(Un0fCr)dk;hK`y!b&R*BX1XjhHg9G*0M7h!X7*1vl&f#H)l+9
z&;M9-*6MAF8!d=y*{$akq7LT_4oWusUT8jB!6>2atK3?}d2Z^>17-W7&!*e9i@Q4p
zD87Zf#C7p)xen;CK{`txTa4$XNk6xQQ{|<q;6bfZlJI)8b(W9UcIK0)sN;dmh=hpY
zFdX=KswFyZyrkz?b=Re!c<K)+yvYx*c&Xj|I`A0BLa#N48#iyRmg*A>#7Vv7v2k&k
zP!qE4Vr-n)(9p1+oV~N0NZnIY8{6K1!29o4P)#laqeK64Ae?+TJzQGAvhFJo@FX`j
zHO;IxGgg;p)9uVk$jPvCcc}Xm6%=N=*Wz#*>gp~Jzm)s@^ofGgEN`RZ&xRnP@b!ie
z!YH`4SUXd@#Lnn*C1f34ANKaH&A}7eRYmh6AVAf{#iiK#$x4MA|DQFJ^m60GL0BDN
zgVtsix7|Ke>Ft@_0^aRddmy#*tD+PygKcEJS4~5MFf8i5?o<I@-Ttp$Nu<54ej9oA
z%$YY~VU8W%!lI(-B_$=Y-V0ik6n31{<C}~bl~G_b^`sr7X2)^{@pydtQq^&n&?E@q
zd99;*l`2^&C_Fyo<td|U#ztOVo(I~xvqLWZ?v<RJoXU__3*MNJ+#VYpMfPy#Ih0pE
z7EUD*&{>)lo%4Mj4>C5v(fZ|t-w`ux0i~o#kbyu&7>LA~Ganiwi4+tEZ=g0gkN48-
zR^JnI@nI-=YYySMaA7iO+9f_x)T;0B{Owfw3*6k2HUOHRZZ`9U;FW6gDnw8rSg*aR
zI}L?Y-(MW9_kZzX-gXX@hcK<qEUf)6_TD_K#`O&Y4ci<Ulc6$XC?r%A6_S(&%`{5V
zq@q!!K4T%(CMl7mc_yWK*rGIPo+qSv(wx<F?stXF{+;WbbDclWb=Du-u(j5=zVChC
z=XvhuzV8Q_shznNd%n?3hUdZO^SzLDjeh%#mHplF10vtQ+fKon$#jVS>iByb=?5vA
zrk?!RbwxO@X42{tF6p?9%|l60Q4s_HZzLp|UIzWS)v9MxtJIggOf_qkT<uuwZ{FxI
z)_t`b0Zq2AuP?}D5yRDg^nnk0i%<?3yKcXZi5UbayiG_bRW@#`%u|+^8?Dh%b~7zP
z+#UkhK#SkWe8SUa(piw*4vWwB;Kz@~ZX*jm(m#1?KSq;j0Ew50o&?ggX$GGo6MF*{
z=u&UOr0@Rs-_LCZT4r~3wyPVlI3i50e%X83-1ucr=M5W@2bR6PwNN+CVX`yk!GmQ=
zii(4y;~zhVe2hN-$gA>zjwTFCB9on>7q8o26u!nsg(h5KJ%qi^Sk)6vDme@zT~l6(
zo8bHQ?Gb>Dqw(2p?9qKq*&|d!^d!mH$=<v+r}M`VRE<ri`?`9(_Wk?!wo?-$b{0JV
z)>po??|Z-Mi2xW_b9{i#cOWXn(6bvrjDzT#aBbR@`B0d&b+2}R9O!<P0{#r92@jZ(
zF|BL(49j-cqH_2?BRn`|YI2N=n_F0$5^>ZfeZTot)-Qf0%3skTDMm!G8`5jZe>{Wl
zg};AtY<thbVt1b@(#^*mm>T}}Ig<}{0Sc|@LW1Gyvo-t|yv)qaXANU~f{~D5Jn~?m
zF-%p~0Eew=AK6Tp)2l$7B!*amGsei)rZmehqp33MO?txmoK=?3rY6T*MMk!*$1M&(
z8zm#1b?H(+<dsui-P!k6`?r?{kHvR(bf8tS%jYF~Xs@{~J(^5lmU7{u7NhHl<Y?O)
zULLmj#G}8u$S6J8%c7&WH(}48XQcb!F{6F|Ld0<%L&o!ya&msu?1juBAt4{qcL@oR
z#o>zGD%IIaFq0iX=f)5ti(FX`xZ24;#K(s8j$YFhTq=axtbDVl`j<w2Mp1l$#`)PJ
zXp_xNndIENS7D&FP^3qIUUtlJ506TcymSfWI8H6!Z=RL7;V@NXs>0X{`(4x-vC@y%
zZM4`U$Y$F|PvN_XAPcQ};m<atCq%D|aX^--q|?exYWGCPll3;}2}~<*MlEsX1O>&`
zqGvAql_lTi<n(l~(p}w=h!4~EG{JjitFkc@VigZNg`r=H^718@3fsr+heeqvp#bES
zP0wCqvRx{c%Vc+t@LCII`qme@SXgiJQfs-?d4{X=an9cx9lb32V5$%W(B=H$z`($)
zgiB2`YZ}o(JiKl<1H(T`3JN+Hoq2kOxt5t(%qwgthxT4b^%&dG%lo(oOEKBFbKv{E
zwI&H0p4(ely(uoCO3dCmKpc7gRAj|fb6?aVO>bmnWESgB!G?2j7;Z|&1yEfUc)9P{
zyVveY59vQ#HOEPC55v>hUr)fxbx9F?cyqSx+9jeb@++<=Xhv58G~PS2<q4WXYc-=s
zyjmkPlHcUj+2Coh$FsLsk{V=+vO#G!vxP+>3$=!I-S;{k#>{53)&9p^7VIzle31pS
z9kLI$;^eSDGzOSP2e3!>g(*xCnG?&jaN%BfN!z?N!n5Mfa%Ek6u?Ly6{^*r7lUfxO
zm9=P;oj){26tY{pwt684$XGShjC4Jrb+L=lbsMZyo&suWyl~1Q(Ju|@=gcjv(UnF!
zw9-=Yq&q_7{O{qOUt0_1_e2-EwB37T$==x404I3OsnC#+{n`|_XWnfWmql%+9c7_e
zNK9tdU_x1G0(M98y>8@U9er^c)Wpz?R@F%5CgQ7}v0qJ9^`6Mo_rhb6l9GpNIVpu8
z7T{Sz9rO#l`k2-~o8%I^&%V3O-HN1{Vj9)$#h9j~gQFZdqs_ah>ZY{omOo<y&Pjt)
zJ04vX(3>3po`pXCilV5nu-s7sWpbylU=?vlkyB7GuM_sFe2jQL(FtZ_=blQ`%Bz7g
zcJ&LzQrS(r*=)Wp-NeZmnkTR;eZ2qD7(&eMEwaznU6es8-shP<zWKRtf4^DNGrOMl
zn5(%k#jI;GBL4hI_j+L*yVt6_W@S+i_8?!7)Z=AE+`_^Q+qk)V<ggmHpbpG#x3C(s
z>jmwG<BafX;VYxNDRGAsrw5ET>;V2IZt==K%d+*9FDrxX2ULt?QVVPuXAV2vx<$6K
zgoFeaCnqu+rHnpgOsNP?xdjD_zUa?jn8)q>{;X;w;O=(PMb}P7mU0+nTt~lT(BPnj
z3+n-8ao2Tn_&T0Vr0_?>vZkpIuGeJbW-+;2FGZ+5d*lID5QOT|rB~oC%yg~2z`v52
z*>HTcPpD@*Ngb~~ZlI>@k-yqZcIW3y!66_yyOjXh<eW%OsSywmz+0RS3kxejCf7V<
zJIj?2oG_rw#!bZOuYojSdID-nP?{u+g-PyW-+lJxF*mkI8jZ3=EfX3TxcSp%RaI|t
zy6?rfubcKf(Z1uzM=ox3TFgpPKqZ_R&8MzhNx}KJV)=4^Jdc&DR#|^%JN>|6G?Tpp
zwS$1R)&Z-olbV{(kmDo`y1TpkXJsApc=#|RE{^ZhD1k$8?rq<)B`CYn64*^A*;Q1A
zFaU<#I$4P78i*(NvzzAcH;Gdn!gJ`r!|lBE<0<L_(X4r`C+lmt1Cu~N-p#*Md$Evp
zP32$ijAy>B%UDuYRi&?v$rc*V{t~S4jg5`v4!4}iz_Vh=!lWz5w;|t$R9;J$FE4GX
zv97z6y<2Ow&B5kosyPeV;y|{My(V@0bx^FQ_Dt`D)A^Szn=$v(2<PMQ<w><z{rkmb
zWo4ZX{~p$Oo`y${9v!^w;LW3b#M5J+>`faV8&-xQ3WacO_mlmygIhOle0v#~fbEb*
zKdPJy=gyxWPH+P_>{}M*g(GT|LQP%BHnM!}S|bvze|-A>xWV%#0u{bEx<*2F^(2Zm
zc^6qL&$xOAp?C;GDmyWQsTleT*J{9jGW!~@yoN@%!QbP=42F!H?-&#pC@V6dh@~ih
z^ys_YwLq_A3H<Fa6t3k{c2_MlG}IRP5g@P$N`fGh-45_|CQW=H)uoXKe#EU^04qrE
zn@l!;pOwY-x_+>|bO-}=I;!=25uX*K$L9Zx{S65KeB*!^5)dRiwKYFCx1`BUBnqse
z51%g%hB5s(ZddO;f3LxQL?abPl;B96xh@sQCc^Cg@L|9clOzc=<;%$duW9dj(6U33
z44M|1>~jP3YL;)c*h!Q`!v=>jW7M!C36pZ9t_Q?4ggk;%gk%UCH*Ffia3wWg2U;kY
z#CJ7EHqGuO0PC(gbEhmmzup~=#RYa37&16a;??kNOD89&F+z3v`t_W&H?KruV&Yv_
zSGKm#Soo5-N%u?JALcd78yiRBzVlgiooWUWj~uHk?Ttx&7;^J=<mCOfsmQ@fK^cld
z<DPxQvo&=k37=<P>H{o9Lns!Z6~_lBcKYhotG!VVU1EPLxcZ-@Xr>lE3OT<5CZMQ7
zdZ^Cri4eJxojn?Oq*2zr)mT>7Ik6RIF%+c(Gaka35t@*23U6j;XefX7><iSJyt=t7
z2)S`9<L!~7AUhH=A9?uA&CLrIE-XcX#0MDJU2|UZDO_e864CDZWEDu|C66EfJM%LO
z*{{o%FPHA5FMHBdNH+a2vB<=8!@8Nv_5T{2=o9-%bNl)0p>$yIKmYLa*A4$C6ug@Z
z|4Z3hG81v{=MVk(_0T*zuTA*>e~;-K^8eqDKNraV2k!^9Vf~fo$l{#<@!4Y}@MJfM
zfm~dM4C4QxC13S>4$|=0a5u_0FxMoJ97{IdOyA8xek|$hgA;RX|8<g|zHcmFG^q(o
zGLQRkd)}Idnt$F+{K@_ZMYo~*<9{4EG<t{$jW!$?^Q#;thAMUAYv=0o2EQS#O|A3>
zI+XZ6JDcg#ZHPH@&H$o5({=`>$Qgb5?|=PQWGJEDgs3<syFVtDPaYjzD=Lq0k@2S9
zr1EQyJN-Q=Z|^m%f)*drg$znKHgDOo`2X%r#artPshByDAcFn*%8>Yfa~l1m?EYUI
zN7Vnr<LD53+;<LZm%-j{-C05_7_~po68iHB(ZW$wtzI9T=Rea^oV_>vK_O4<w|n=K
z28@=kSjom;lqq_952L7<uBEMAo<n4nwnuW-3ua$2igU9VttH~Z>BvXd*fu(@|L<9P
z=6I;FVR_A$1uGMBzgpNRc7^tqv`FaPvGT0!lvVMSIKE1#CDcgSFL~ndOnkN|pERp@
z#3aARqR1MDm&bzjI2Prv-yHyW?9Z#+5LwV!`e>q@i>hkAU#byQU>9E2vRCGELoTcM
zV8H8Bm4$ip-)>ua*IPBU=4JBRbhGi{8+<~7h3&(TXs7=>-tx!09{JeNKPR&1RCV&p
z5B@blZ`{KM^5U&JA})n`3-iDKx^&};?^hhH)`i_K#iw-_(;h6~i~nw`k=@=;wPl%K
z4q+e--#@Q#PHIP%vi+&DG@9A9Jpa^{i-O9G2UaHyeJ`+WdUIv-`=eg+%l(>9TDmV#
z*ELHL;<B*&8fw&TUNDrry{xQ5$>ONj{7rueLyP?9+cKho?<-ub5?IddhM%NS=d?Vo
z9Ge<cQmqd0O?jh~uEDFp>>Nl5{_|X}u`bARb2HaP8{GJ>g7SKX6ICC03JN3?bVZ$y
zZF%pdy7qr=#GI$vEHxSp&u6MUUZBoqe@ZbbsfAx*?8BS)p;Zcv-LLk&VJoW}GN282
zNhyrI%NU?hjK=j^JN2>)D}quz42Qf&IIfj%g!ek{hMc0C=X{y9{tl0Qc&@Z#mKJu{
zf4<7?yENvNNwq3kR!KrNTIKqN6N5LmHP;&FvYRSI)dy+MFQ!F%3Rf6C8MdySY@Mo$
zdi<Evd;iAbvP<g|7&D6+?#@}6Kpn)Liwz1<FL#Ly8ovLSIsA%~w9T-EkMIupwIVyd
zu{+Ou?On6#+rI-=e_X_&lf3B57WYW5OvuCiiS0h*5<29+DZ<;sBgkg)lm2!5(k#!`
zJHKC6)RMkSScAPcvcqcjw-$b16B{e{NgJbQXE)tB<@kWYcZlRPWU^b8aF8pKcjWN(
z0Izw?HKh;rWL45KgLa3$n$+RrYnfa(D?2k?>dzbSTx;}-;~Vyf`>%B@dl%uk*R|}8
zbIb8x=FzLq^z~!Dl$^!Us!O%mVz)Z?IR|yFO;_`OWRfMXs2n{tGGa_CZ&r|IrMBh|
zy`MXOL47yVG8RXhp-pdhc<<&9w6`=BPx|=bxV%@n*_Y+l9HtFDfBSf^^OTbR)2KZH
z_06hH!yzeY@5^YLmiZr;naJN<V;u8dd?+dFtDHoV-e->bu@U2XHp)gP)U2CdV~>!3
zbmlFIE5f3+i(^HOQzzSo^(T7nnq-dIe`aBhKIkO<eesj?dV!qWcfEGbVRx7w!SU;p
zGbF+d49lIwnz_18)hAaem`*Rs92tdY^+7gG#<>!1p6^RWR(eH0a}PZdFFw^^l~CIx
zqLHSgkj=3@kGZln?V{PR67Q&ri<ZJyIbBLLJaovo?|yDv?L)nI&c^#uOC^efD(2E=
zP5bWt`e2449qb%_JjQv=kJ{Z!3~lTk4Tg3y8%Q3L&t8z<j=Lsy|Fd#w2i4#vmOkF1
zXrGjRXe@k9^15i#z7CORwE>kT&%#5#@sy5<t0*nnsj8sZ!*r#Wiqz+cb5KlBdL1KQ
ztKu3TZ1m;!U#BDOslOTKZTatE_{obg^Q7Eqp9SjO*XrWlH;0+_dDmonm9aEemwNQR
zZ13vg4SXl&tvYq;!P!l*gKDLg@7YDV_%qV5IN7cY?5(#h{`&l0t72(gOL<nkN4K_V
zJSDC+qLRncD=_o)j*@o&2M>*K6SPb7^H+$8FZn92|3Bx_%*!3#N^QfVvl^WYy`>_3
zSy-dglcmXu@8Z`DdjD88ec$=>L2a(lQbn1j_7&ZIdKFz2bx(Qib^C9xce~KGn^Dx4
zHO!;5S?*e`|IlWt+0bz4aZPsHLzb!B2EEn*PQK$z%NYJS@}J#)RY6_HS*6oj<wHA{
zd#Oeg>s%wLNcN@Yi*cpR2TDAQ;@j`t<G6KtXV7r*s(tx+72Yp~U)ovsT+wyjqQ}9x
zNnp!|O$+VC4ZUmFH@0ErM7*gkqe<*uO6A4{$3^!T%T`v!o>J(Xb)$fD)uf4`Aw}CJ
zC}?|C{7(r`M#i`AjK(6TN|)yDEOo3$-*m6_?3O#5mXWnnsa@I?q4@sT;)V||$_<O7
zqrHL#Jee)l%0!u5C}(=s7_lnK*Z&(&C&N7+3*OjR0}Y8=Pg$w(tlop_Murm~c)~xf
z^ZcU4=g=SW&(QiAMU8WM(*x`Q-);yl4TVPHIA*fmvFdGzgs3DmDCn4@_VEWmZ&B8e
zqK!~?6oRCR3Hh?FP93=AqHinzUA*LXXQC#QT+EVk6+iXsOUos4b*M>2ickl4OX9d|
zK5)U?NVfFl%_7wRKA{bgZW;p8jW&ipf-f9r2-hftimUXL_-OgCjQ_LftfsG@@!YUl
zc8-fSv6o~6Ra07ok6*0$a1`%>U1U*_p!oi%<6~9!d7DMIjBBW?M}R;lvt2o&2prYs
z0sBcNr{aS2w~pE&VPTR=l#)_Sl4bZL2$zzEOt%W33Mi2FA;uw{i?6+5^`D^V75FAp
ziJjRauib>Hnc1#GI+A_W>L7t?gUOX%-I-r>U)>yDke`U@k^+@cjZtRScH`E)tJ=FR
z;5dGR#dnc2^1|O_<>mRm-RKs!MpZhJ^1Z*#|DfF%)tV?^AnEM3Cr)@t?h(Q9`kI<B
zf^?CddYnnUAiAqjJrnPz>^3@Tf8b%nU?_1KWF8jiTM*c{gfy}V2EKIZ(iW}%B)41i
zhiHR2X<@@Fz3SQQQ=%m`wf3Dj<r}~gE~Y?<Z{;k!A9XNUx-@&s_Ko?5suTZw8UDUF
zayw@w7MO<XYDbGPXLeZVeNeG|8@l{>lmKo0;p{@?J2_0tIF33QnaFrg=^3f3Uict#
z9C-QPj1(LT_1jAViNMklJR*tif4V<TiB5gF+X!wET7L=`E=1rQL0c0YC91r{tgBL)
z#nBIpFn!Qh=yQ~7jpN%v2~$v4SD#Y<PvR|fj>Yk=&=Br;C9`LERANDyaLk2E_+#Nm
z35BzRMOsJs4(5ktM|($+)t~$#E;#D+)KEz_EpOy}jv=e}jAs65S>K1s218MkHh)v=
z>kNzUz2fW+&%zRXa6jPNhEXhj_9Gaxi>4Y8WTNNw2vY62X>N`wi!x6F5$Nyl=QVGU
z@&TRJd;g^u$6VF~0<|(RdgSTZ2~r;o$aO5_M8<Uqa<^{X3JVSGtBgIhLl6W!;Hhjv
zcyI(Ev;-&~LoI{T>u<01A-SF2%}Az@Yqkxy9kO%}9dg&%gkv#oZdy0Li-ZeboI?nU
z$2xy44SuD{ons-jO|!Y(CSN8U-{)(@)c=9z^fLoGJ$B<SvF0$o?}x8H9o*k+9nmGU
zb*prr+=i9fv*)<{{ax!LAXsV;r0A0-@dwU$m}c@=fb7@4wsF*vJ8kqV;ixGa75Yvd
zKXF0<xFsM<M&e8PfWx;inBX!P)0F&ZmsS*%A!D_62ib^%lZLl@4%Af<yNGw>$e+T^
zwxY6*a>x3WljW)L>=C=;9L{-BN_+R#s+@V&dc5+h+#OLXzRm{Ki!~SRcUeE3R8L5*
zZJHVz<}UasS)QNYvnrUii>p&j@zmG8sSDlFhP-hOhUx(b>dgmg*g0@dbPVl3DX!e}
zxfgQ|x1I6my?OI5GEElji%2wBwGFiZW)Wcx?5CtAMyRQHep9L;f(T{M`$_>Uv{?d}
z6=ZI&YHITBW9Y6gzn;y-j(+;tOX>?5>&N_EQHm#?6qX5ZaXqfDRb5LCUtapbLv2eO
z>s%hEZGB6)zLC$OcW8A|_jk9UEMpr<SC@5Hi-%;)pQt<;Rt(Kh+7e9F`bUn1moLrX
z6+4%pq~~&?FS^IR<~SP%15Nb9&Gf)wr8H<825+#BAA)%K#<FG0u9x%@h%mWvLM9y)
z?ja|U@xyK+lL?@n_A<Tr6ZN?mGpxdw+s>tc6Ne9LA;ffd+~i9nsRA`BXFkT&^6sEE
z)L@?3X~oN44g-;^lg=1i>rhM4aP5sYEYpa-P|P0vjMG{7;%uKqnSXa4N8o2e9TW>~
zu1lmwwBN2RE>eThT|(QpD-|z2i0vQ$_k0S-`UF7Fazc*z@Zp-xkuAc)ngD~H$jDu2
zt<y`<+S<RqY5K<gl1tx=@(x11p~#IY3VGA}XSNER*~c6Wtdzt0*R|(bSAAt+7HPYl
z<HwQ596Xgc$T;!omRqvW<nVB&>hNc_ozD(%hctD_oHl;Fpq?$#$|JO>yZE#Bd4*0U
z*?aQOgzBf(AGSU6Fm!0f(^I&w&d$z&07Z&2slPeUDy3V$#Z_t^C=&g1#WI?aa-~5f
zkUVsi$HJVEk1F-Nlp782e`N8AxI#}n&fk-kJC{%%OZ%Jq*&{z(`!0LMn(n^24OM<f
z_yvVFafUP@$uP7>yftxC?&T`gb23EaSiTgIgF@b$Z(en-_3+Fy8MmZH?Oy(sp03a1
z`ln=>=jD&YOHg@OQ%&7(x~P$IRzlK=dO=UG_Th@P+J^QPmW^WWaR#Oyb>baK>x}wn
z7M%l6h6>dNR<M^-#Ztb7MXsWJtW3);$2wTwQmwfXR8Ga83gBEVk!xP{u=hB*h$Rai
z|5xUFqx^o&mdHz<V?T4Q70dn9?Y#TD$YbH%!dFp7+yAQS!^Mfl7ps%n;$`@(mnU%d
zm31~$ij+r3KRs`l?iBV!@e}f<dbUySXskhpV`leST;e$kpHWdH?%i?bjT2@t|8JT6
z+;fd;O>9)akz3quxOKt@6sHpvBzP7P85c^C;4gTsD%CwJb)WXp>ywSQ+=two)vzdP
zS|+@ak;OdrmrZhWe8eBQJ?bR30IEXu`Uu{JRrBROd%L@nI*G#KTI0#Q-T9X9&h?7Z
zQz3qBd4^Aar3j0X1hx9r%z+>wCBr@|jfuov*zzZF=TR#lYhJll;+-iEts-JK*N*-+
zmz{I*`LFTKV|VAoFgf?i3E{2>&kdW<?sc)_D(~QvT5IZ6{=z`#frnnaK?8O9Nsz0I
z@?|fqABOABkN;eLS7lHqKI@+&P|qfy!4}Ec9}*Obc;~qF7;aq@p1gUt!J0=)j1BDv
znb6^xyGuw^O)@+-`o*ifzAWYF$)-eU+lfc&V&XR~)pVu{rbsKB*sN|jUDO>q`7!S)
z&ccQ?!||t2c{d*u_flog$Pft3Y}AZaVW-Al!IL1krR6*#vs6>Io650cS%;YZ%!rnp
z^BCHR?;TRqO|qJj+^B!x_2$A@`m~Fe+EyvNLzCJ0eVOo*b$5!F(hB0wx5YF@%RPzi
z<f=y!UcRfLii*-wH*@3&jY^a@a?Vxygrlm~|4WRmLbm&g<)5|qgbPhdy~;>t%yQ6C
zYBU*i2l)Mheu~bjph+zuXtat<B|ftsK3!gEJu!C>_Q*v<7f3q76MW!V{r1XWFv31;
zdWM?$pjlELshO#JQ34I_%;)3uJK8v!tBsLC^4edxyD>Ae#q#P5K4YmEhrzfzwx0@E
z>)EDiYf}4HKN!2{!`~g*#n0govyon{ilJicXKlw$m(W(m$t^Q`1M6f|baW*sn=goG
zE*L})i+6Vh1qK>`%SI+4hoDFE*VBRFTFSIDEIdly(lRdHUJuHh;xF%=hX{!oqz)A+
zDXDa=Kb;_+Q$TW1bIbYFZVDy^%t}L(fyXuCZ+6PqxDLM@sm5-)=gJc&Jjc_hKPEQl
zgTK;&yd<H?x}?;gAzgOn#-+n=aHe{Hd2vK==7LWg)UB?mevyem1$!v%DnV&Wx|txv
z=0+xmllGA<^2{OjsXq1x0YBJk;8i(uu#Q-+2Ezi~(qckMCKAdi?mx56BGc*6Ly9Fn
z6Sv$>Wc$8rR)wNi`)>8bjFn%P<8@VADD}=Tu8l&e5-pOZ>SW|C9qEIAgmlz~^;++J
ziSY|F<`sV%AqpEJZNj53c_mth<r&}TkQ^lA1NR?w4f~;T5J>O8KYL%BC0qS_?b%nO
zwS;>^GSDGJ&gYF$)2;|Vm%<s>U$KVz6>P+Bsqt;!Ue2ED!@E&Vx%1Vkjqi&*aV;d1
z6qAkch#ot2*6paHnq@`8iXZiE?0sBc8y81rBc>xUeeJtE?$_INE5Tma7hD+O`iq&E
z%UC~oas;hiOQf_0J#~qMz5_)Eqz9U*=;qlHhNY6KYKQ4q#w@0`M`|W=moFcmZ2hC`
zySgDfumEF$lyXQUul1K5RvO~GTKo9@Mjv4=iRsd}fR46!*|45D?&a<iRGZdSq_bTk
zRnQdWJSmLK1B~c}QUB}NpGJ>x7a+5aKweZpvFASuKq5Dm)R(o^66qiK`ubo?)2H5&
z%!t`p3?s@Ol^YIK5=|4y?RcM?fZV@D+B-nPeTxBdb{h1HQ8*=zKgi9?8zKxaVwZcv
z+VAHLr~i7Dn~|PGJf$Dz6-eIw-WySd?EVU8Yi1=5TM%SVQavDaXg6;?ZL<^pfsyeg
z2!aB(9_38eDIdk6$Xw=sWB(Bb(Ze5ZrE6h{N^~zzcz?E-%o;`)`M~o6=vpv?eEcx_
znCRpOUlI02-%?1T<RPcRAe5xE1p9!1fHuN6x;cM&Sw+PbO2QU`uK#q)G8p#DmvRIr
z3w3EA2frx&I&^5ce<o^t&Po#npkn#4`xQEy%9fmuYjbN9P}nDwEN65#ce8}tXXS#A
zG74$+LF0Wt;I};)n`Wb+;~r0Sn6RB{G&eJY4Z%d4m+@3*I%FEc)b{`|E1H>{8!tDx
z2+^F((Sc9;bbVng*r2C(Fr$Phe%SYCQs)}&7bx9%ce~;zz;@Hc`)wX)#KdhtkFdS;
z&T(+&ORisK@3~^FUu)D8|N3XPfO{VTTWq`i@){fS)eRh9-!_u|Ek_P@_Ej5bUP2~+
zW81Wm)F1q-4bWGlq_B;6`1(debDhV-=<(>$v+?JD2B++=L#u0&(p8ZcAulwpQ43X$
z);45cF3z#7&_v-`X3*)=p?6&v6Gzt_`LKU&c=oI|_6_^&(9<5B64uRqxcm){ol<Jn
zo4w#1)m4J!G)_fFMfLRA{JCnHYSn<}T0h3JsaW9=p7g4`jF!3F{|7*sILy1~#BF|L
z4-B(5;9Eg2GFhIcpr9aIm1h2Abaa$$Wa#~yMT-^*2{ZlizDq)q{FIFD0)M?oimllA
zw{=p=>gT*ZqKX7~i`XjA-ZvSsnt?%7467|wGnzW$dhgyr?XlapZ@c$6-vRyfx0j5}
zi6^paM-;<~kmEF{s&4kx)|^0rU7E$Qefn-7;;&qKo)3LyhB<zJFZ|J;{gwaDVbA1=
z?YxK-o51O+D(Zd+;Gy>Cg0ndraW^+<yne0V!D8R<d}AiVlDq#|unaP}_r14FX9@T(
zYp5}QoP#QAqXWvrA79t<atUOW(Lgy~^B+X-DjQ{Rq0dl5A`y2N53!pL1%+G;Kd&3e
z6}{=3kipRx5z5Y?_6!V5a{v38NzDVRNs|NRCqeS{tpUXdD~#<I+9XF1ooZu2bM?C~
zHocXKWMarTxvb=~sq?L^Nx$7QG`OUwx#4RMylr4xnIuH7PDo}*>Gon7hhDl59F*bA
zwuRN-;<Z@_JvF*;{=&|B1#O&3j@pAA>;8DJc`Jtsj8#?Sqso&J+=a~8L-)ox98pYY
z`5FL3k-)P%)YdJeW-?rSaD;z+XM4br>Qo^oiV08nx0mCxXryan;q*)-&tKYZJ5n~&
zYx(3+0>CZQCi_3s-+d+9dA>+2I!v)zL@m$#qrG$WS+z>>`d|d;aC^&Mv{4i~^?p4@
z^B4t>C9KHR85oMVIWnont*^iA`|Sc4AW1QNqB^84mxz=X&&1<0U?$&XYyA$2+lqgx
z4U)^*P3{Xr{ldg=))EpqhnW7{mUBFmqh*l=;&{-iw;M>~vaxU38MAuz`SQn2@<{Rt
ztY3O;ORiy@mohu-ss^R7ys-!8+WtE*oLmpjbJQHKf;DAS#&R2sob!|_>&|0$J8s?9
zSezC!^0V5-HW<{zJN$7GpA;1}D|aA2IbuE5(J(jciBzPivm1`9+S*x-*u2tM5)3p~
z6dJ$QKlw<;BhrzY##I`hap7bCtWoHkj>$^X`4w6xhw0fQ43xk?by|-NV>e<#2rPgb
zLCiw`^P#-F{MU>7Eie_8Q?RBkxCe|5rBkOuAo?emCGy7H{QNS+WAJphDU8kh<MU<2
zgnj+G=NbW^{Ls!ljjtlN$_}`H$GdXru<*ajnSr+IbMF#49B>dcX_%%CJtASo=HN4(
zH!int7lFoX32{1x(td1LNQVF~iAbt(%z^_Jn0E=8I(p3FL@d%m6+Ed7P9tCb=j*D1
zw1Mu8A3+ia-itJ&W5K%|Hy<U<0US$!7fY7EAe##H!Z+)A2{4fu1dr2dZCVd8;v!w?
z)Ja+=4W)KCiNlhh{adE*N*#Ov(SC2JpWkOMW0Vy{%wb+g38@er5Zje+hczeu$U4@E
zN-Km5@8BAln|9*hP*8;%vU=dl%>pH)a2fx0xgVrS#tk>qMKh8tJChbUfXin9%JCZE
zD+wgS(JN#F>3?qQY1rh$9c6DSAg{Y_ZoQX$K|qWlQx0-k7ha)^rEG1JK*RBeSVkYV
zB52ae+szvX=%!2ro&Y-C6C{7$8}jR!HiH4$U|$*+l6bC*9=}>4k<13UZRY^u@68I@
zwx`Ze6+4V4;imfN7<k5$R|kz=of?GE+M?r)S`VK-jSzEYHu(JZ7L{9AsB+}U^(n8)
zPNl<#uMttw$`va{N1~xjA_7Brd6f7IgE}}!n8fQxg}|922p+MU86N{RHPm<f>sDKM
zV2qDJj=H<KwY3vZx;szzkGr`chljD7+w|{Jr66=ZE0{gP-w@<-4^?^=x0AFys;l`o
zzdwc&40ly?!>UOK=+duGu347JGw0yA5QuZcCS4T_xwB`_CK)ehW)8xiRY2P<0ar7E
zXw4>dqSI($pck%O(jffEccM!#EP86(a*v*m<s*<tL*Ko7chze77O5iQ_XGjS?&s0T
zR8P_gqThL&DcY0$N2$>k7v_l8AlFEAkA`YBnwwv%<~6reJ#2l+`Ku;lxLUL{rd>UA
zbi91)KZ|e7t%X%eG|1lD_Uwrf@6LStc96hSPxy^XkT!m8By{6vJmaaxwfxgG=no&L
z;}hV!wl5nO&PDzK0ZSzqwqMN>IpU(%O`@_pO_yI~{c&YCA3WkGov?>!WgB{mtDOk{
zx>x3krmUFbY0H*`6;zd7e+XJJPFBY-Ed|G$W4ha%v_+Zo=3QqJ11#A!B%rOWO~;wv
zucTB)M<%d92n}3hA{GfFHHLz$1gS0P=+Mv->je$WdziF`O)AHqO*zK-=puwf;D{s{
zP~=W-6?^AOIwE)yhwMS|r1LH(l%&pi0d-=?C|VNp84&9VJUBx7OX>&<f9UN^2^NX2
zvFINdnDr(vFR#3)=y_F@PWo^&Q5j|oubNe#ZBBfF2pA5*Cx=0%<07K=1ms+vC-+BQ
zI4k-^8sw=Y7Mmp<6g*jf&TwMsonkt&^U}b?Rl0q?96fM|D&UUnWOl}PRgR+6L&2Cg
z!cKsr<SQ~W=hm&hvL_sY0Ra}(DjFL0`QJw1b=(XBV=egg-UqBr+-QO|!zlgkm$7}M
z;Nfy=;*ZAo2EW9Da6lc+K(Igcx0TK+-@`c2L7+StD_}<a1#3-V8=r#K5m=lI)K!He
zh#u+iHs}BVrvQC)!0-}cS45Cyl6;<jZBYC|EQ!_;PB?Zm*t#=n(kaT*9*kd*#EInT
zLZLU`iO-*frk3Uu6bw0~Cx`!0?lCf>Wnv#76Ry!6ZU<w*lRDv*u{369-p!jrbtubT
zivpG>FFsrhHy}?bloLrLajkX0z?*i$0FBYjBkpPv-RxN$p%D>ML_i6VGpUv6wGVtP
zBbRPE#`yK++@psNAI`8sCJv6ITeSnqs2R1vK31yHw9dLTbD=*@vGpF6d}t$4TGh!J
zN<o=?1wj>s$6e(d#H;MkQj!1S>Qj!8x_>oJf=AFiowCREl*7JEgZMCkLR*SzMuoU-
zG-mNI!hirqLzbRou@?A>Xpw^px``JMUCdZ#|Lu1{dv9Ih-ZOUjUb|B}qRq!XQe?*C
zuh-Qx1gHc+f<07w4FNb?{FV_O8y%}NQFydd=0^Z#NZW?0SBv+2J_{Y}m8ED3aMn>K
zcTUr`eKXjTIPg%w*raNvLPlqcaOg3u3poPCWE>X!T-(u!mI7*DBYFbivxaw`j`^C<
zx=z51<BZ*4TXAZ~UQ8W2lN=Edq0;)tIwyKHE7mB5YvEGJJS#jNhKH8xsoIB&c?)hi
z{oR$`O61>idZ{*g;rPL)$3Hj$QT_N-z<aNrE3va8A)DMlwAM^*s8mgANeWT}BHVpu
z+rdd1na-<rRg|k}MdHJ+v85S+X>F0YXdjd=AcFB4RYWvBgYsb2OV(AZD#INP#G4u$
zE18>{`>Lpln0-ASed8->rzFF26~qbQ#5>PIqoYN7AZwhr2pU}mMph!wVVwT`A}YVV
zWm~jSaZSCFs)BrI+-4=Na3g^9ZC~|$d^{pvzn14UQjb~YFXE=oS5X;ST-E?%joShf
zWu*e<Yo(YodG5{V6q>h~T{t3rqT5szu@<aZE{Pbln{2DawO&7X@+2Gt=g$7TT`FhK
ze!1!rAB}xal+?soslS&xAt_k`Ix8O<Bu&o_*z}(xW)|rakO-g98mp<at+Pg>?yQc^
ztYMh+A$=K2IA%>pH>jxz`><CQ?ed`DzmetpwTAE;o^Pu5@|Rr1e3jH<U+2D<Sj3E#
zbaS;C$7=7t&*`6y*`O91+KkP%<fS#$t4W0Elih?)3(F4B?nDindwUq!h1H4jd5E;9
z2o_1k_6bdm4@`OlxH)DlT)2>=b?mSi>c4#OuIu6gSz{FZdWXKLVM=ed$TObNon%zi
zj9T;@Q#+EASEj99Yx8F5?^n6CkSAakwW#Ey>q~1n7qLi3n`@@wjJjKnFL%G<{r3g^
z3n?qkn><62k-;k}JU$@?KipCsH=!VC9FCf%eGh`QWqKP?aU<yp7+!xF_vwmoBZJ%E
z*Buz5MM%@LhU+1CN$SttZfD`pnqhb1M6c$x(z?1UZ^4(Ie$n&aj5~_)SJ;LdP8-V^
zhbJ0bP>c1u!@AwBcKM?BPvto=uy{$XOpe=5cy&N^=1iJ&2Jdb!r9n8d%XxDwdnE)t
ztrpl}bGy`H&fOsbDz?hoZf;CfJt@mZ?^wj=TDN*{ZGxLn1q^c<!p-YU;<*#QLSGhf
zntIJ^P!b^QHanHLu0lgN7EfGDpv9cA4tHz+>i=D``R#BM>N9LgGmP9BTH>_X!_1S<
zOI2ipp!(~*?!5aBh?&R8^9nwmy=VqQi?)o&P*>F}oeY|A^V>!!HEloNS`~YtdJj1)
z9vl%KrBdP2uoGefNA1xO4R|2g_UCN1vo!X(QG;<KscXbrhw-(8BF2g27!Xs0EZ>)=
zCJ!&Ks@7M}dtoRuI)yf!C<BDy45D*k62Txki~jq}P~PA%K&7l9#fB;bJmBvMRdlY@
zQzcdT-mER#r?Y8_pd|;<_C~ho+N$VXDywvUhwGIXnLFR&D?7{a6nBAMF9rI*NyH?M
z=|hdu7u>W-l=$aK&EGLM4;jn<h?OBOsR&o~nN@);lPU;x^TdqW_V)HvL8N#8zC=~1
z>0Ez79b4ojJ4MVFGo09=_OzcBJ<Srntp}9m7cZp;Z5BpHi%~mR(~>RzjGDB8YF?sa
z&8X|0JH>FFT|{mnF<TWiT>0%MQVTI1)reZ>m$JA4px4Yd@PDigA2<;2yZ_SJXe#^P
zt3h4m=l7G~F#MstKcuQOIljpExV+R&vu=Z2>i|F*KhSubV8=wc$L%5J$GJn%qY+WS
zKNvsUyGyY3sX4I+C6fPHNrSYU8qD1!qtC*lqCTdZLSSwSX$q0H$Wwd=oZ{l4s|X*k
z?y9;}XZ!o*uq4%_2;0l6Mxt>;o*-DAm6d$)<}q(0nMu$_17;vAxRXx25!Le($ItF%
zyz|W(UmQAZKJwst)5fB3-~|+_d>1CRDo|F?kM{ih{A+7We7mW-L<LxcoYd6~q#BX*
z4FU^QgB)qAhILJ>!Hn9q{1;BDsXcj{X*aARf9llb|6HIT3aK9SXEby{QykR3lxX;*
zMyh+$Rw0=mk;um<Xf$NQQfm|g`wccs7__1JS%j2g5Q@D~l?-8O&8TovjkUb|1LDd~
zo+9Q_4If%uxq|q?Kp#pd6jNyt5j%(|N)nQHg>ERwxwdW<7ZVeE5CBuF$kVTnVPPEj
zoQNGmgW=vpEdY{L>K70DX8j&Hul^GllO_hBvf0AZV2Ild_`J6&CBnxokCKwCxN74y
z^d36a!O2x-pG`gU0%1DZj;R<=ptac^YV_&AjvZ3Orh@)(EsDiZ7OnZP;YkY=8uXc;
zB_;AxY4`3il3FkK!v~%~H+15UgFF;MjbRu~hurAc`&C+lbH8nFhr24QSo|re)$l3!
z_ciAjn&ZIHa@$dZ59@bM8d@H;kXpLhr2O<WdI22g2YLam8$!r(v(LTrmv8y&uT(db
zjtapt^d!li7(u?&&YYuvbHe|{iz9f%-=cGON?;2TQO-jT4_#Fel!YVI5bU47PmQI0
zd%KX3GWc*B>O3ngD+_R9v)y*45&k|pHD3Q-AI{=+j@tUIpH<P2Lj|g;B|i<+Nv*s8
z9Mw4<yAL0?((e1()7zFnzxEH`mrUMlgqSxD-ih>Q{Pa>5gIgF?RzX#C7-@}pL!8;t
z=!voveT}gMzb^MnwDw?NyirBYyVABv5Pu9bhT<UM294Xwz?8)i_7+x&>fBRlU0oOK
zr^XuhS^m-MYGVi2?5jn*Q>qV|FNRN~BSm&>kR-hVJm?zZ>82qbG^HScY5=sBQ>VNx
z@IN6iMk>IHi5g$v>L5&w#kuJuBIv}zo8hYc)R5NHX18pASj`NFRpdY3OH@O3{ONGR
z9W10K6@aro2p~kBh7=pyu2<O>2u!qEWT_{J#ApbHwX>g2!LElB>UxzXQXv1re-2&z
z$w@<dfZNTAEeb>zsiR)!^@6!@>ZJ6wn=v|%>>30}bv121AVH4rSA?ews{jO^V5b9u
zjBRs^2&R&k2FdeCfFZ0<xz=aYvnJO^!L&jWLaqKO>j{*g7EpI`LJ6mn?OIOV-z2UG
zzpm!=vB>y_usmQB0Wd!L?KJ?RrEtAg{i`I9KDtBYNer=f7DEJ(u))4r$4^=>;7^e9
zf#F*s;6UreIs+EZ6QhzkxK=y*5#M+h7njKiU0q$S?b|ztpAsV)LfyE*cl6)yk562e
z2^Kd2aqD%#&Ws9k1aRmkb4QNUl6FZgEdiXHxl0I+%h0BI+%bL3$iwt68quLa64eRe
z>0?cY@49hstf=KYrVR%tpku8RXnnf;>r!%~f4&Yq0Pl<tjtOiFT5LC+01BLx0N?ne
zE8<CeO3$8-K=41q0Q4j|AmH3{rzIA%R$CDd6yhpP_KlgKk6EI6SY)IUYGlZA!_kP-
zJ$H_=YV_v^kzZxR3XC<4>l8SH^GLT6Oc&kr?>NA?8hn+L`#KnKDpgumEU|#;wKv(#
z3^J0xu*l$mckhn+KuAAJi1D|#ysGe^P_ON^XdL>tUoAY1>?IPtRqer?c4rI5rIY$H
zM?&F>;s-Hj{FLKCVL$S|zg{<r{^>YI@BSWCRrq>8IEn&q7)Udr-}cghjgYS_Xg1?y
zAvPtyeh!Q7@N?3mj%StktM#7=f{HG|#2DiZ(N=4=yqPMy_VKpx^0S5{zPD-p=VxaW
zbqs}u9IH8PTpnfUJRCJ~QZutoc#E6lAmx-=xW+TA?KY430sQ2llmFpBT+iTPT<KAs
zM_8xb#o!%>neN_~=1xe1bn*#V6+4A<>8pa})PFwKL0+>m;_Zw6;f(k^|5@eIK^3yM
z7gyFjjdjhiPRa^=56Fe`sv*Vl95*m`Ff)O;=CQSD|HX1o=l4aEkzrss)Zt(6!F3TQ
zN<3=_C>%$<fQX~#WTFt283YOmd5F{R6^-|jg##Z+kSrSQN1*UEKa?E<K#;g96HReh
zO^x7l9ddhr?f;C7zVD3oRw2sZRuM2|SLjJ|I=zZf_T+RwhHx!bt=w~;++nXB=Y;3m
zrfQN95AWp&_!vnN`kD70UCEQ#-!QtQ4MKho`i6*$n;mD0pZWD-T>O)d(k(}!k1ng>
z96IC#fEDQev($;gm+$oHOJKbP2ssC!D8!s_;K-%G+ky#~)Vw9Xc&Jh<I*&V93Yf@5
zi7B8NQuPXb|LU#^|4Nc3O2}LAvPb;Bf*g}*EnuQ>s$9cn$lFwTUl0H{rMjqk`67*r
zK^g;vEIYGbUPjHVFosJ7UIZc=QmS^5sR5BVWXaU1ElyJM{zNRg^VG{jgsBFvj)OQ^
zhC-v^7(v{h;WLy(&^{11Bz&FHHn+cBxT_3~vo*mEzwQ7QTH|GV$2Sj^snB-{(Xpf3
zNb~imm*sP(TT845TM>OjGP;Xfh$LdoR^eAp5p;01V9k@~Az{m0oIT%0me{=B^U%xd
zjH(FUmBseRmT@0wk*V<}b`oI8SPOk%RS3yAn^B0VzB+pge%Bi$kQNjXk}#~RG=N1?
zMQz3Yd?=ftd4g&gtm2T+PziML1TTMkPPCNp`638E!4Sa$n3!PeZqolYMkueO>kE-D
z(+4u8=+{fea*S(DYz?S&bRHR>wDF`4sP~<ZH|yc}G-D9IeKIyUr6E0L#QQ~t?{;eM
zJ!u!EXE^tgrOnMRNZxfhDJ|`y9Lcpij;u3A;|<Fvg$=>lP<?sk8es_&I%ow1r{meK
zb7J8!1II~+2~=XemJ?v;+7Av1IBIX(w@-y|BAB_=tFpKMx-|SIRj$4&jEPQkA^Z7R
zKjo<NW&#=*6FcqKll`fuV^nt@Ubp#(w34VZx}EWJ)9Q6=REmMVzH7UPxD8T-g^RQ^
zAL(~8$VdYm67C?+m$*z3O6C-Nm<EyKh5^wiY4tPa<jJ=`C|>ye3s>X(`SXcYpaooS
z4d6JkMT@>Rf9}6Fnea+NF|dqys~$wC29>Y1rcOM-PMpxTyl*00*+AVm3SXK5145sH
z-shrSY+S6hh|1PwAi)p5;>?Y&eJCQVLCUFnbC=-i&XCR+J1{#&Aq<M4!3sDSCT^mH
zh+S)%9yx37Vhe+@_teRR6)cP*v=R7ew!;eaF78>#Lhgh^a&mGLcE4|*r(e*Ix`TdH
zym+{^P3)Tlb;|-{`>^lnroAN)I-%JZCgFcH1pNM}O_q~R^C~<PLqISvWOvo>>)vh$
zcCU{|P(sMxZePg7{k?XN?|x*4ltp`>X9oa#4-V8LMle+)wF<~Zv6yRv{zbT--$c3W
zt5>fe39Rtit-}i-Ui$GKF=FCrWi@<A^Y>gK_+~B?LCi$2L`d!+T!jv$3X8@IrJpvf
zdldaJ;kbg4&No5%+Oo75oXDUGI#Go3g5H=S&ba9lovv&mYj6L3ti7Hc2M(xJS63%t
zR_a{aZTRb-uc!UqwJ9~R!IHCy3Wg0brpR3>FO#*_g_xB1L|=KaHO=n4XZ$T4ef?h@
z-d`NLA01w_MX6^(8tB-6Fl^Q%l*3ti$OeDA>lqoipUI0WbJ|f4a+N{{$M?oox?86|
z<Y=?N1<BhBDmR(-cDyMdvNem(CK~RZx`vG`Ea;;d&o%t93kmJ*3(;7#uf9Wq{!e+w
zO>p$d`QNtikFIpa3Y^YgE$kHAOtfw#(sdFL_IRM}fXj`l-=cQq;nXPuU4PB0m(jHY
zY_!V7up@mDs`EgamFfBqlH1CJ8tM(#T2(Yoe?&@F5tSbeWmxwACgmdQ|LMb;AZ1Vl
z^|fFqaDVK$Pw0@&rcB<g2c(hU5$JQP5NK?HwQr_+-{#b$#7iDvs<aZQ=-?Y<FElN=
zt2W@VV!3t)i*_1lnUOoWJV{$Z<?-D-(zji*GD<+eU^2t{&CVV#5fyw|#u?Gyv?cib
z!Q<0gFNcr}TkMtkX^f>EYCB3E-s2$3lGc|7Syd~<9M$YBOwdbkNY+o{rWyOKQA-6)
zH(bBWO{@0Blp61i=e_HjK**3+rE^t1#h(l-hZVf@4WI5@kEyu$3JRY7tSvN_NFw{3
zwbM79e1GzKt~L#l&t}!`r7L^d*F^L<-}}l!;QF*p8s_FD57(`=DT!N`xqNWId28oK
zl{Xf4;{}mUgYlMjmP1eT-Fs%WRH+m{FOS+Yb>lCby8yIh+z1&4jgXde!CV`>S2kj4
z@$ml6vc%^*&?^;?Fyfuem@0Xn07~LgCrX^gVzzu%PiCVI$vV%1b&)tWhd}HE53hG+
zEuYSX801fTB4wYRveY4cN>9ir!P2$MUHd;ovut`-je%vy9~6d*3&M>|s8ZSkv#E{-
ztntUHBKgQncjpieplOP<g80_$K!yz2wO~g$i0UlL?L@eux^iRlWpF*>h2u|8CGg4i
z$-Ub`JAo$%R+d2=B73+L#07bsd!@qJe^jm0QaXCo%GbMr-2F2zzi>|<9OV8F9{&Xb
z8E+$Lk1SXtoec|R(I*){#Dqrg;7N3ecaDXlr$V?Y2Bv{EQjE=#4|GnY5c+$yvA2&;
z*v+<i3Z1^k)fE#aO^X>d#;IF|1_PD?nq8rlLV58W#oL1)Cv}X5=7Y_1xdRddazq(Z
za)2yqrMh*%6Pvo*_c|+(Ys;Q|dOCS%+i&v9B&E_a61Zw_Xq&i69@J3zIDSUu@uP8@
zROQJ^fx8Q~UYUiJ9T%LUIZX#41x(ErbJ0X}opL+p*|nnPyuZTOaKe4J!MPWp>ISz5
z=ZSO=Q=^^i>_v6!o<!|8lKNYm-$Jo09%Rx!O0({FMWuP{)P+SS){Xj-XnN-PZ%Oil
z0MH$LyTauUi(hVS!tNP9v$6>0o3yc(S$_p1H90z6@1*Ex?kY`NfGc`+UmKwy4672=
z0_DQaCbCVXNR!tn0s*&^&fj(D6%j6veU-CD<lqL#wxkIwp-!QFDv@SE{id>X9nKiJ
z`FZ}0{#NuE8CZ`0n|<#WL*chfHkHoz#$CRY*x@Z}va;yyMkdlhoO2Qj2>TOh7De)`
ziW07&PphNY7QR;HwX~22Wx~}$daZi|no>5bgYey-X7-*4kBfidm)94hJT2nvW4F0`
z=j+ZbKA?nx8@)X^ncflcR18Th&@*Ir7vIb^4RXR^X9ep&Q|!3Pt4$kzDSb0I171+{
zp5as0aJ$~<J$fsPEZ?u(!+67EHH>%AdHyOa-%9CZ*L*SKi@hw}z3G5Zlu#Flj50Fb
z%ltP%0Jnqidb?k=(^`5fL&|IUmi7wD2aip@l4jE4*JU`iZ**Oli99+r2-;{(9<<yU
zRKb3qC34)=BXyvQKk&UcMS=isUdO*%ljkO1yiMFC>p*~_L~Q_;F5_~Q8A2T=rQL^>
zdAIG|`EeXE{Lljkj+OmtOW8)PY%D^8a>{6LMvo&$niN@0C^GEF6^?D~%+~S^@Qto&
z=<Zu3QWN~zyu>1B@E)XCpUU~-4Kyw#Qd5U#j*v|JXM>U;uNR==Y`u0E*Y%v~_v?a6
zsc>=mkK`EV245WQddW!Amj&c3^$I~J6)IS=L2H_eU2pKAK3ouQsH|)_#+5&8A-(SY
z`f$rON3(R#12XK)o}E73PRT;lWNEV+pRUD&n9`;j2V*`N4$xwCDa)}ZXQnB2{Ju7_
zb5(bitoC>JFf|1)W;zx~hvQUWc>3F2Tk>VG(mqH^f{<0nSSKE|L_?AN8A~9LgMQZT
z@T=SowyAHdmkX<@$wzJfxJJhim;6-n3MDLhU~&1V%CKg7@{yg!EyC|CZI8X6G<^|M
zQItYHaUKbLdeQ|XW|oGOl8$H7&`!<z{<`x!(`|bG9_x)kGCgIkbn2^|vBHzkq3ob6
zE<%#34@wmH#b;p1X#Mv#E;$Q&Qye5DSQ9ePfA(y$#ErT$50r6`OKNUtBVUU-C9E~u
z!ph1p{&O!@WRUADc-#K-Pe)dvXyOkpu?QEZDuJ`04MFLmdIi!BQE9Ua0goJvlA=bN
zocgU^*9_qy*nV<hrS+bWVZU1ZIG9fPt5LaU4f1#N3!|#kUDPrVJ8NIXSf>FGDm_al
zCNX@f`uB2qKE&DD(~zMr5Imy&1PSt5Wx><a&10dIrsI{+j@@O9XL~&XLexa!MQhIq
zh;HMeUmVtvpFhRGaB&A=rKzbmu#NUOIVF7Am}y}pL)zfYZnBu<%}ssUz<nJ1)1p;n
zYnvZM1gq&$_5Ng`POjdB72#f4(o%{-4EVC*r(t~~0}rY3gb+gbgzUs2`^H~%x@T@7
z5nJ*saRl83>a&VP`|6&9<slEk(=4H1JUQC}I+&1Cy>x-v4;2YD#^Sz)2oQ4{rbB#n
zlewO5AlpDy(gXUkzH+TnPyd90$5U3nI%#Bb=g)Fg8r~0bl~8ubUV9+OMMC*#)hqTd
zN0K`SUdJIR@9o{uIXvp7S^c6^eDk83m62`^n(C>YZLRO8&k!9jho)NJ6K<&U8ndMo
zL4VhCEJ~DZAznckaZQ+0Mx^5pe?!vo2VoWeg#OA_Qm&0S5}%!(G~(yIMWJ)+wEj?p
z<<Vni>ykM6rj2~sm#+gl>^8fAz0LhZAZeHAY=-q;yLXzNyp}8_<0%gKTdBSZU6xA*
zuLw1Q(vz(uocX(Vc;Y5QGYA=dmv0!9gP{GIdp5-t7{l^F+V*=^Vz&JzNgkUH;i;51
zi^o$o#mwyG$l~cLke`e;hKzNjC!&Z7k~ASZU|j7FC+ncj=yXlu6LsXs+usBfe6i5r
zQWCco98eya>^fPw-cswP+D@W=7h-9730?Ptx6<nLT`=;w_rZcv0DtQh75BaA$b2z;
z(gbP?V#-h<d-u-CD4~M%1$#}adBm{~*wPJ?n1x;`*YC`llrV^M_+Fux)Ox-2y2yTs
zX?9(4nqf+wKX`69zga2m@`woDg3i&D{kU}?We~0>R#GE`oNFCFr*A<1PsS?}juF8v
zRn#xtib@?;^|2qWo^|jiF)JhVUFY6dODc}8$Oji#A&@y5+PHO@U#1iHQhW$B`Hj&t
z&g%slR6E%vbv3FLKfh5<xvY7-qSVwL1AU4hNTOZV^1yo#kxF1}SnaoOId|t7QMXYu
zkR%j#8eleWix<hyktyvS$nLD{?dljFb^o}m0Dm!F>NqlX3c`LIXdkNkE`FlhTs$7J
zDL#8&hTWrV(x%v?H2)Ik-33g033Zd<>L(U6>T@r6Jx3|<-3PI8z#^1<fOHQUQ(||D
zCIx+79~wgz@M`HqWvwhJnVYeA4ShZ0K5=x1>$jbn5i9j;Vg2xch>MS|*ne<0)OFc=
zw{IWv%C1<c()OTz_2IP(;Wdbj*e$5OHMo`{31g=uS6R^)zBne}KS=oxuzitKQWp_^
zck596Q@u(=U(m36hew&TCXbp<uGd<f(;`x5^*rpAy{>7zxKSOOM`b)3u$u&q5kNY-
zmUmxvP#J1X0%J1FOWR-_e>rQlUfaI(?=Hlgtes2;BqPK?{5e6%XBd9MV4zA0BL_Rs
z*smb&((r|sK@acap~ax+&|gcwgsoK@(!MV~n>C!Thtm|M0T<2zoDbxhRu4BhWsjwg
zEq+73eL!|-NJLuRE0}kXlxRaQTtWz!wZ4167W%wtzk}Dc;{&wY0n{fUd`)d-XSQTB
z?~hul>t!`6zgZ%qH?ebgt(oB9y76s<eMdODl19!5tsbEn-Ex=q`3BN93UjHO$CQ#p
z9p4%XU=S2Yd;icB3pzsn@Dj6x5k7fh6;T?=gYTBhwt>?S?z&E3WBWjP#uIPvLE`a7
zeQ)*kgk=u;^ugdsY!?!;zZ(7HJ2UW9;p|7jm&@Mpo%oc%p8_ny4zF`47zp;cC&l#I
zroEv2cM3yjzMH{+2E%hxS4T59$JBc94s5TLCfHqyB%yfy!&u>_*<5GsJH@txxdybJ
z(oS8<V!*}(kctrPBX|8!+?~|0+K!=icY7t)@GQGn8!eBNEe>ZG>3QJ;s(ch>?%2AM
z1n=TWSXBLOWR(lMh$b9MqX%J>-vLmv(p^?K6z!Q@o_klzGc>q8$=Jj8XLdcsn3fwr
z?Al5HZqDVwAK#I>`p;W>P>74}>YmP2)lvYb8#@ygJQ}RH#}8578rtJ~N|u{<4qH`g
zp`rlYe9?(>vlSRJK9%Ewk$7`mgpa|*s~kUd3Gahka)`|>0wsKV_lgxSMK8=L5eliK
zX7yx;<lOr1zQ$Cofa+=kK|L@#R=MwG6ouYXK%?9A#%VQmx@AlDV_oOYQ+y3Uh=dPW
zgf5HhB6Sl_Bj?gdVXlJ*)jc(=9&;ym?IAvE*uVjV9s-eqB<%{{H_YQm1zn5`LLk-z
z8nEo9HH@SM17iBkl;C<~N>p_^CGU@N{htm*li)0|H%_VObEEtd+ze6U1Q=^IyWJwU
zhZfyZ_^ydzX_r|XHvqQ<J4!?3;5)uOoJrV%;z?$>MrL+hsPNKjTMt06sJ_-p#ma6x
zU32`@6z#A>hNwxiWdRqdQR%qktSywLl&i~06B#?I>Hxi`Ui3YM(62MD1S^h?Ig^G8
zF^Nw?`+AU2ap<OeR|cd3jEY<`vPj7$)BXsn*pe6kfXS1DNe+UfT@s^eBfad$!(|l1
ziEkg9EDwkXO%IUuAp;;Hm0ZaooKA$?PZ4(iUwPl&>ARe8F7N4g<jw}AYB~Ih7!yRx
zrMWEPMKVaOs9bx&a3TTrJ2^4~bJNUAn#N6ikLd&RH*Aj&>KxBr2<}2n^JN=*XM$v!
z2o&}JOzA3gUaHMZ`eQh}LE=WiE`RE_gC<dmok>&RM`x5OH!KHTI_~1<BXFuZPWWSF
zv?;K-G1FYMCDD5)V-jJ0zYUZNG6e!NuxzU@yqS?k76f@6(wff8X_*b3C%C3fXi#i(
zQ7O{jVWcC#J-q_ikH3ChlEg%<Qen){2XQ}%pf%emZk5BxW1yGCc7+O%hRwYopLn%w
zymu?p(fhu{Zd&8^ppLP^D`nk%aqm~Es--lP32$YT)t^c<tKvPdUFp^R;44qbQJb;*
zV*Iz+#GM;5Bwp}@$+sAx`f@I0@}S2UMi{xa@Dn4#{|?fx1Cy7UbM^!MTOb^{UkrGY
zlP<4AMa&f3!&#FaV?7Obg$~Vi7Z3=w?en+S7*2LA%TcI27!Zx+_zm<3<7%y)q`Nk)
zRcP5O)t$L}M+5utGY^kxL_p^vt>oS3ByN!tvMU}Ao|w>{FE{BM4y+Mks<A+dR9_6D
zC`%>gE?!pzR6Y{J8C8i%Xc^zP2{C$SH6$%flRoqkJq|L%q=<O3mt7*?nY?U+Q29WK
z2wA@fU7|a^PKX2uEjhE2rkQDaY`KUPtOHeu$v#xphxM*=VDnz3ph-1OGI-MMFbH#k
zG^x2hZHW4O(@gfXwS(o0ir%QQtz%&{_3c|WN)hQqE731($$mMi&@!-*e7Ly}XtgjT
zAlNc}m)Lgh1nH3aFs7fN_5dC1U=qw4#~Mb6MWj&QyXCNKO~eSHiEemN@KMy{71Cb-
znRN2OPaZ41#h?9;sz80WcCRHQmIecliRPPRz0-JBkejsfQKH%PJEv%NFDoERUrS8K
zQ>XK8-h2pqXc1w4glKV~p-Qfz`)TPaarQ6M9mX7)ZL3%zE~RyoT@yGvm2&uyLB!<t
zg99(5>CE-<vhnGoKs{J})DC1F!ib`0B4AlOsG;Vcd39=B+XU|01STh@0BJb?X{KG`
z_gMUht2<07>9S4OgTQ)10y<4F59oj6tTldi{Vq~Dl9$YB0B;mQT0a?1HH8{MGIU-w
zq90|>Pd)j>N&W-drN_7=le@H3<mvJ4W)k6EcY}LPH_(X;RO(;7RX95MZ3MG4yL&{P
z>&c$j-N8Mg>ib{8JBn7Wt6<U8$nW#AyQ-{ec;7!&#@<gdTj^$(6HO22QSnq<9DQ~V
zW+4uOe2{~%rIZ#p%y9YGF-MYT5?U+WRJ{{Ks`gdsqqow)H!^~f65kDaPZ~Jl62I`&
z>jBd%aXJ=N`>CQvIOb#>!Qf4l6FTxbpg}y$M;zO}HrX*9i#41AVh_)v#Tg;sRU#55
zx|LZTL={b*x<1^EoODiIv8%##s;(}@%}1B#iWS;-58kJJzTQV`w6>bvh4j1VXG1lp
z??(o*w&Zg!6esN%%t*LC5QcJCYBUpp9pQSweteF`#eZhUsM<{)rA7$ph$w7rKV6&4
zAptM?3A3L`*VW-(YwL9pLr$rcy-~o73S()Xm7UL@qjy~=M=gBr--n6I`&gQ2_0z6c
z9X2)8@LE0xH{M1fa`Spj&H0s9DX%giW%<0uys=8#*-btzvxVu3+wr>)%*hDFJ5>8o
ziB?a*7{BYbpc3++=^%9G5F?~Tpu3UwDcLb-_CF}==4vEI8}Sn+4+T1!gYA=Gjeef8
z6BQL@Kel*!RWY=6U6fX^+m??=m#qFw(ENsWb^9gn%2$vKb)g1Wlo(xb<E(|`y<?lj
zIuHFHbiH+4Ra^HxjCxHFt_3OrHqwZMw2DCqNH-$g4U!uRE}|evihy)?H;RaqNF6$q
z<`7CZ?_B5L^W5k6{k-RM<qxliv(MgZtvTlyV~%Nu$)Q~Xo92cVAb+;cW9xGrEdsk;
zJnklZzJl7e1U25?y}W`^@P*L0>Uic{_*FLa@V^)wuy&AWT=qHedYknC3)In^*RJV(
z@I(y8;BlVe@@iTzVKz4IuDCK@T32w<b+uj#2a?T1P}L~^_~BZyfq}|E?5Fpiqm2Eg
zt4$aLK!?A|1+8gN2br}gXy3Roc2isN)TBje1vn6Ma$0j;=C=rulA`h-lC!pd04u)T
z13lzzi9<@^ZkHmQAmsWB&1YDK6B|4-KYz^nl-FcnSQB-6`@Q)RH9s?@5r&u)K=~~S
zjPB|ay5$JS4q&NVAVf!_Ep9^e(9P+})vZ0M0XE~=J=2>lv&|oPcW(EGu^o#7qRR)d
zVNJ^A5ycCrB>}|Q(IiE{N?{rpS@P^sN$T^c0Q;2(kYxKm!p`q|C{Mz^0(hl{Ctm*h
zRjPDgsO3zWL|P(>^;rf=2evJKeLiw<THEqZJHxNt&A`xXbUYZ}@m&MBtq%X#oe7#_
z!H)$sJHYF}UMPiGYAfm-U^1F{>F>L)$P`~!ce{6GY-A(}+Hkdu;jUxo-&V@x7L?xH
zJHr8*Dkyc7;L4H~b6STup|*hQhx>h;j_YE@Av#-71KE`fu*dq^D~;E`_1N!t8<H|;
zcSNF$ry8J~A@n|`ErG+vB5Z%b2Qo<64{aFJ2cGqCqJ-zN&|izyJod-I0U&JSix0Mv
zXo-r)Qs4-~47z?Qzk%`$V4FH>>w(Agmo6Fo{#jl8!-E>BWZh=j7#YPwLqj+F|D2)E
zlL;<~!@u`e0W=9k7%%rN*ioZ)sn+lOkRQ!LLt(Bmbf`{pR~Du}hZ%x$YD6Egl>yt|
zT~XcGhlQS&QRIxl-cfN;%|*ASu4|e??6CB!8HKVfH=TR`@rpn_LTy)}9?p3OeS7e7
zk9r7d85q@@I6mI*Dn_{L@Yn3p2wYwUaM_%~*@7X>zo}F`WXGsr{!79B4wQw3c%_8=
z99Cwg#Gra<6&B|GKGIqIb8gqkBP3*Jz_0?`CTVQ6cT8?9K6qXNjfCd*io51^;pV{P
z!Kz~$DNmceiOWgqDhJzuon2xj!N&4d!K%g7*X(M$zgo=r`04KhI!utkLAsZCO7_Pr
z8uf>&Fw7YM9=`d8Yx^<F+vvN%i+ussV01PgbZ|kpVLPE7UeA2I&G>=%{A0`eX*nQ?
zu&3VITDb9g@?Fo$Hnsb?>4$bY&Z6BTV2BViw+2FUU$NIH-K;tqjZ{wHvy+?#P#bi%
zri6Q%EXL;C_S04j4-AY3U=-puhQN8GHw#{_qUN{m9$Qc^AqEP<Bd(O;i7nULL{rE>
zCkVaqFi%VpY1#HWi4rdxAb`Mj?vzX&y;;QPB2PLZoja;^&{kLWRnlU!FtA^+YVp?=
znr9A$^J^==0sGm_1dtQqUt;zAiq%Wdp7O12?O^kdx<;eO{qtas5MRCO+b1NPxee0S
zmvxitZCc(P3AY@I4)u+er=2_B5DnHcbq>G25Kr9(_SjDY_wP?vsI!xapy6;%PHQB-
z2X?N|!{ZBO24qsG`3x8`aNYoE9ntcSA5&bG>VW;7eUyxpmv_PnoD(6v>>cUsegvTQ
zt*}4fpG9$JZow>*1@@q@J$gdtZ6J_ZTi{(nP)|EGEd74)V{I;$@W($EeBQPkyC;?s
zlcRt}S;VdU_&we6Bh$#<KKaoF&$f&>!dg8kBQnW~IfIhj{~}Nv+EpvN|McQYfy<6z
z&l6)<3v|=zNVZL&Gx9U^jiphXI!<#$-`Mppq)~*H#Imf#hY&@?8#KfyNuFw3I6gbn
ztH;<m(){kcFBt9t9T*PcFo+aBwy@O|ZrOeo>!=g;X$~}a-HySJ!yz@K>U!OU39@OL
z^G_$kyMA8a@5d2L(JuH$ucxsQ?xU2Q2;+tzw2f{LFflUjv%I)-cmH;R^$%KAq?gY8
zx^Y6}-sL+Ee^ntbI$eUF+dj{(u0GF-f*{3|sJPedTD!=M+YhIK{OrNa8W*gvAo^q5
z8bj6*Fsw0(h!MOJj?RtE7?e<*3QuK-d23hiXy48QY=u~n;;gaOiC)*(bm>Lk@~jIe
zK8Ewy6E5U$Ns&ov9H{WXMLszy>LzrZCMPD!7#mOCpdcJ+@UQm85l^cJPf`RA<R|p@
zx~pNtmiPILzuvv2rT2dEuv3PIpDC?p(@aPNWVk>e(GDNkRhdNuLXg$~6_T>h-|j`K
zd(T4!75Icwe@obTnPvdzI<UU&N-`rz&Y;qE{@~ZS0G6(3=3U}v`0ZOR=eM?X`MvGF
zPfc&u5_^iqX!5d|IreU`<o-MTnhQB9KGX?3S#LXcC+ew1WbdmyXG}WSf7X3|eb2?u
zyNrLoIs36={&(e`%GHQ@UM<<(6bDP2#^fhn>RmEAC`L7^KcQd}JPv<E{e2t`*U_CM
zE%I!0q@{P!a1-+?HJ8)mQJNy$r?cT4j2jxzI|6gc?+dSC&A!KdYZgJ1I<bdt<wkMB
zAJeb-TNh7v<d}ue?XSu?YH4$_rTB#2T(P8Mhy2956MA*_d{aHUDF1S>^Q$AxmfLsG
zfyV)8?Kg@+dPxjz!PkE?{EbT=wO_pVD1Sz$(f_<-pTb<>eXcgehI%GiW~Ta%uym~`
z9*v4y*zdwP*Ba+xQjcN&<G5B&e$Jygxw#jmX$u(GcCPrLW+*nx+3=lS==VW_sPpUB
ze^3X`W;5NUU$42tf3Ml*TQ>7vDf->!uRq?dG^-qS@0qOI?bsVHkj~gAqm|UMVXB7L
z`C)aP%q%qx{8>OPmk%RC1rJ}_o!ObUe7K%Y@_E_j$4n_(iZ0aw=DNTRGat=~Pi_AG
z0&4gsZqKUk3iT3p?Q^ny>*B%8_=Zqz^(;c-<i1Kj^l1P=GAKDmLnUREl!7$@XqtSo
zpY}Uo$M0v7cH&cID(ND5idAk-f$+(eqzFt~4IfOUYm!qR&=EMkyEr<!61Vo1>~LNn
zL)AbN-30EtsH9`pH_nDbm5;7+9&K$+3!mF7GPnOzw?k<SZq2ca9KK6{1Lstky^+=3
z7qQnSOb8!z_L!dfikg4_)>1(V=)bWg11uh9PL9LG+`yWlX0?Gef<0dz)E_W;#ILnB
ztbS)dQ|3qcg)HUTex)NbS&w={{(dSi&rK>wq$7hB3<nu9QpA;*nf$75ULiB+kWuop
zcDzX0(Vs76md-Qw_$~=YsGdqU7Ok}PP_+Dpva>25=b6_%#lB1?;=@)7wRrA4Z3SNX
zEW$^*=hhbf!j^|`fV)99vxHlPmRrSkH2$<llp!Zy>%^b?_3HCbkfu=c1&p0p>uhX-
zD*uTr-a-4WOPA(~?>nl;r)z0MZJgO{UdV0|9F%yGO+rvaZa`_EulME(b>*w@$y4I$
zBj^A27w$~Aj4V?sR#tpR@7ohlqY=ll7?{jl2j3q-;gEw>*A@OM&{L_c(FwjtT|y`P
zN7zJK{X6~U>K^UYr`pp8Jlgx|vhubr#W~IuO2W$G?g&d}ygcX;YQ;xbR7301(x-a*
zS|U77?NT1!cmBb&`NW%98D+Q~o>Gflsuk=tNz{`}BUYhv=}EHTDk0d$D}>rYf*=HU
zqrf)4rvMZvS*nD;gQP%d%_F9}y)9yDrtkP<LEhGidl+2%8sl!5nIvMSSNCf<$4uk2
z&zFF(JNFz(TD_}Truq9>w9Z;F_c|9!_=UJC6<hA?-IQUwuwwl?q1oZ&J*YDfUJXIm
zD^kp&uQ-r`VdoQ>uQV&Gvz@^Q3I7fM;o!;M8Cv3sv*aU7Q`2Om*u~Qj<x%`Hpx!ga
zN)U@xqc)NA-J@LUV^4lD!?yV(S&GTS&$zV|M$)6<Wqk_8QRT96t6llXuK`{mq^F>-
z8%o_4!9T|gc0D0~Y1iu0bF1Vp+B=Uc&dRjm*7Ed!7#di}@w6!=U-WgS9)-58fW+%O
z5@l6#+ZMCt=JXqfZ<L8#=ngGyxueXY?VMrzz&|*SB}UsKqo?iV%iU@O<e!8jR^-cn
zj2RvZO`bYjxVrn?>TYcsbGpntH<g?D`E3|Im~T++;)&G?YK_7x2IDFAi^zSkFR&fY
zXkn)(nK<#fdU2zfb@gtB?e8f+)@oDu$e^n4YYHPfhhg$NSza3Tt0xIxP?z#QQZ9DV
zM^+MSulDu2JbAN}-$iX%Z-2TwGg+Xkap9%jU%l+xoNWs6WEdvwS}5XYsLre6gZ8+3
z7PCa!^qx82h?U%&B*Bq(68%pK8W(-xbTCVfN|(J;<g!7u#^a!#{iA1fzmINzRsYrv
zWkHdpeZ4ql2LBF?OTInwB4#fRSwHrsnpYDsyHhtJa>1(XajFEhVjS)MOiR1Nx7$u)
zroqA?2VCf;&*bL0-O=_DNdFwj3h^R1V6=yzOh8EG(A%BONKz>3TJ>Ps*f`iCH^#`O
zZ~(Wa{~*28{MspNHCcZH6W53N8Y*(hPtPRmJD<g%Q}T`<h~!gekG?jsP&1hOoIvGc
zSD!t5i>j{HdVH<ji%H`#rXJ5)5qq0F-In+flv#JEwtVi!@YzkGVfXDlJt;qbJ_-yD
zHUXcdx;j<cfj@ON)mh4dg_bOHc%)_&&-0co6QZrwkDC9_iI_$9%$=Gt-84ajS~0Mm
z>HuMHMlJV+c!=!^IZ~f}Tmb7!n0}JE@#GCW@>qe7I+t;*EK=_`hSOWVw4bPJvg-bG
z6V9m}HyVqNzwC-v8)yMzxJ6*gjA7<z9UlyIQLS(*)BOEXX&i`#2Qba+v_4YA!0TZ-
zZ$DQEnKRG6s>#zQ;p7P9x-?gqoZAjcpk(V2y<?cAh<=y6QYgp9rlg5ybXH;Zd%{9^
zabWpy%GHy&B@yk_XXnBvL)TP#B;`+x)!{itG<1x;>*ejPDisqyBt+`2XN7cxKoY3S
zm3#%}{z+}d*Q>Ou8<-m!ilXU_(7?Xf)#Rp#Tqw)u0yB_3tqrdpdN<Uc%(r%)sqjg~
zL<i-1ra8OiAXSCnzg@&Ve{a*4-w?eY<)(&LqJj@kZ^1RYhp7aZz@siVN=y?iDiuE3
zDpBKX+xRlODEx1Co71PgjHD%M#<SQ{H|d>B(;|bXWkk&4PzvG@2rKiaILtytuQ3Ll
zssUdNCd>6N<rgHy#9TPF!cBHz^-J^0-zzITZ<*`;^;FiX+1#RF)~hV)g+=q6nx=<|
z3*}8S+F!KzpjjD0r0L;1@mktc^;cD&*_v;GW{Q%gYIwW~EA4+zGwdDa8~U};IuZ7f
z+QC9?zhWoHP;4%n1}+&bj9#e~>X7|Ett8fcmcLSwCBfa7OACK_JE2^kEl68biIoWw
zg1R9T!iubO3R{RrSY`FsufI@_@J!(l`C)Q$aq;IRHMja;R2EXIOSZ~uYe%iEub&|I
zw1Zy3*RNhlDp!=1!4x<vPh^*YA(ym?4S(W2$*iQAKJ!NS&3MEFg-5&Kb!*(Bvq$t=
z&U$1^jb@JGY?tai<xYLip7jyZi+s+rhQ>;S^D<aN)}PE-tDsojvFop1;81mWbKUfc
z>^bv@3%=s<9oN(Pdj~~p+&4acp1s8XRNIdH{iefPQUy#dD3Z+CBArL!L5PkCf%_(L
zuV#dFW^;417~oW{>(@ibtR!00`sXM&yCOGHe|7Tv_emD4cg`Py79>Ka84&$L!=6Ta
z_Uu7#u^az>4m?B}COSB@$POPi0v`b6*#O%)5G3;8X#k?)n|5|~6A`pK_(y3Q&zbi0
zczbj_Kh@^)Tw^P|+Y8uH#s1vc$G$@AUs3`iMch{&!I704ljO?jLf7Kwb5ji;cBjZI
zbNRHKntFV)_q=Hu1+y(NbCS-py+T<qY-{<Hl)a~T|GnHC2CMU`I_#;qrCn-xsu6u7
zIgPmd^s6J7RcJC7DX$~Z*6|^wE(k~lV6dDW3@p|CRRS_{;niAcn1U6bV;&4wk0wa*
z^x3`@0yDTt=sjqIY4DfCU{2`wF`;E%SD0}1-ka^mdzj`J4>Ony0ltQLV!jb>YetJp
zOP6buVD=dfhdVV(fBeuy`T9&Nh8#n-w%X{V%sVr0My`COM~%!)j|sRC2??nRv-w1v
zPT$<#Tivt=OlE&TG!A0PqjTIUe0R%r?MpshUM|Q&qEV8{>q}linoVwxlPKC0_qWgZ
z9}Ry+ZOk-GGd%;j;niI{gUqKV8D&{#X?%>gy=dW_=Bv538{-)0%*wn9A)vxCw;rbx
z)v9gSHz(`ZtI$2koo7zS7S@Rs7KwWR2kXXW_^b>4sf~2tbz#v%qlM#5E^hPQD?mXY
zqG5V^8gjV-Xv=Cjy=4xwsi?|-?S)}-YA}m5a&Z%(sc4D7c4#W@zt$vx#UND$3_NTF
zdcHA01DQU+i=#aYk}mQWM`<|2KEcTun&%36O$^xjbtny#YaX@>%*l^@QHRp~<x70d
z`)_skwM0D;ZNRyvYN)=41)Z8EqsblK`DEU%@g)cBhvQyf5?)Okqtv0ZGMkx3po<B!
zT=IE=r7)2WTZO@yY%Ag#CqAEJhKz;a<!)1-_-mPzt@H!5$w5C0fHO^QOF2|$HfVF8
z4bgS<S|rS|UX7IS_n{L`M^@#?*CAX^$O1Vjxc&ClYN##WaI4rF_uUeG_Uss&tD2%>
ze0R61oxMGp`TVl_TdDTSAoh$RIU@s_|0kp;ED<jV-(7LrQoiI}&j{)BJi)(ZZLTb~
zx3DTgZ01|L(CSmLd~adNVj4%Ug%$N@8MQ}abeG%s-%sgJQh3-V8*~yPvOu~xJV8j1
zSr}?_@`N%&hV}T-jhHr?2;s;ti4$3VjBjCQdiVp6`H1c>IZQLju`ofke5H+NbM^>T
zOzK%RwaNQtkfavhPfNU#xsxK3u&UpEQL#MTL(kCMbyiqd%fhi<O<#UC&B8ThYb|4&
z@6^-&0t>C~PXh*WFj=*{Tu99recMJ0xB2IT%g`pifNjrix0@C81#R5;4UI2r3s{11
z8X#i$+C?{*Ac~jJTJslLh@$A?AVvvyTgYh3Y*W>?9xC@xyvQv`le=)WK7Xx91=T&@
z6&(UgO(ZpnW0idcFm<}k3DVP2%`{5>(N+?XD<eW^$*h}K+$vJ>PFP=fcrjiujb?8D
z(LNjnp{UW!7oo;Xm`3bEp*Bs^P%Rzbv*aGm-H!6IS)&OV0rT1)gNys#*Wb99+}B7m
zxA&9N+#mzjiyEwrXMWJZhmbJ(+Ank4RcFicI+_s)*#@RlC$@xK*K$!8R67or{NbT~
z-~K~~4#}&jr37oaIo{f|g{inWkHgfUslhr>&qXo#RiP1aU`o^+v=+JEJ7x=$8HeX#
zxvXxwL6ev1CnqpO?dT|9;!0C5lHLAAvHeDUi*0+3%?W%_(qK@c+9)xlXYcT_gZlq~
zk@MNqFAtS9_FTxvX_e$Et94xD6Vs|FzUA0;tg5F#OD<+D`Fz<J_A$Y()Y-6`PuOYl
zpWSemv}TnvCVw_eiPHG0L5+E@{Gll1mC^<Fj7KGCURHnrKqD*lHmy=%KCBHb1)-6F
zJ?^gD?TX{VvdpuzXPTe9Eh~0WseRvcFl3e(p`{{LbeFvh{cQ;~+p)efp7eAXH}aZW
zXt84??yJl(p9Vxk41~z)o0<yZ^fmiKDZY7lPM4WZy?6{R9T{roMR!a((gUl0gs|gU
zS{~I4-HLis4h^Kz%1f}eY{Y*#msYHoRJ6i6qefOR{%w7IY0!^5zt(|mDyfYU_Z553
znkUp!y{uYiQ6(8KQ<1jVh()WL*@exei#<>NMxW@acRz`CmC1KzvtM)CD7^cUL&G&s
ze!HGv>cvF|^X~wGaDU@-g>3sP-GO>4GLw7ksZwE8%UpV^bFb`|`hRllMA6m~d%dFU
z@Vj+`lQTi~p9U%utEM;SUPnC7k4e(#d$vSN%Er(rK}*WhfkFNF?N1F#qOSxSEG0Sl
zq8Q*PF#A2|9nAI=6XL$#8uX4Tro7w@xBMIIGeuopU2DhCAVV-2x5XhhGkOS92l1|B
z`~%G}aM6g>i->uR#wqu<ln+7t_|)xoMb~+M<9lY$(-0NlI~S+q+8bfN@03YDq8yoY
zUgjLva?6t*=;7LQ)}VLxupR4gn7n3DC~nc4>2g_4+B+iZ$h4_(FschrDoS+r|8jj`
z9t69k4lnh*ce3GDf5GmSi^4TYcJ=iO3qQU=`X9!>FvF5(6msH?o5r3a(}v>j{~3i-
zDpuz^ZcV4=mT!tIE#?q7nR1<yxOJFyVe4pSJ-Rbzb^Q!(rmqD2pe&mg(a;T<D*-O$
z)+2SN8ZFA#!7+>X$12QXb=B<Y1K&_+!@X~RR@iY4d5Ols;NOo+yu{zA{%T0$8A3~F
z4<uPp7K>$QsHUcwB%?~$&F;lqb~u*b)(FK&tAlzRQ&8Y!q}yss?zu%#&UA|HVMla+
zRBXrO)mih&rt{Ff`ux*rMpWtiAmxFH)tJ7Q)zhQ{{4^do7Pi2S@Reodf@sOE`IYru
z%-o0VP5^x4tje#eLit3V`A}7J$P(?0zwk+aD5*(niuJl2t^~Adk9VKI^x*7qR6j$y
z6Zh`J0uZG2MH3bT1enFvKt`F4a$hQq+1ag`S$|(J+8O!%SYM!K6~zruu0>`}fo(2t
zhS}jNgfykXCJ3w5Lz<`^5wCmh1f7qugG5O_-65!fk{EO?c#Lwc(4S&mDwM<&f`_|Q
zvFrkKbp`<Qw&t9jPs~t9*q6<6=8q31(YlVL;T1GPuBF)UX%VnJHtg(0j6GGOk!F(G
zr@Po!HAjY4DLQBQ@M0iiZGAq3ignI5#Yy4q@0@y$_psYfT3uChVy_?0*{vU}r^08O
zEM4BL^J8X|{IJhw)4m$a-$f#$*d<ic?#^piONk*m=g_f0a^p)~;x7VqRy0B>2$R(P
zW5M_dj|6&<DBR~gGd9o+aeQ+`Yb!3|@lP+^U_UjBG6hG+ob_{>e8?l}!UY39O*nsz
zq1YKK+YDk+yZpQYW<T@tClJvHOGUsQF#sQP1^@PE`@02pdcw57>s9OQGu?&7=Z`Ge
zhZUO|d7beonwE0zn>@7Hq-EBuCA9D=LbzLS+UfO2`@W?}H&>e;JJph0U2=oRcbx|^
zH$Fl!iGC75#ER0hYGE4jrK~7JRg^#82?pa+U75-1Ql5U<L)$Z>_SmO^uO}0nJuaUi
zm0p&@=5W=C%ni+76yg}Eg_0yBSOUkydHTm?#l)O^HVHR|B!nL}Nl+|!HAtg}tg8~l
zT;GDSNYt8(L{oO)Nfu$Vj?9bcR=?iM1*_##9_Q+_hi&;5QN*xeUta*>*Y@)<h(_4z
zUNd3G&DtFstSS3kn;UT6mp@b{6bY~1JI9b_+8bpN>o;zk;uW?YBa=;UD>0*Tw+$+u
z@mcJ5v|IUdXD*&*;$&N-i=Ah=<rrVtQnG};tER|>tAaL#ay@&_0e}X(NylXKOoB*9
z8f$QkSsgyP<wQL;D&#^@N&BKmr8qfWzKLc6Fxiwik=au1MF=68RXahOM%;E;$TGVF
zw-fnAH79;%$6W%AYCGMQ<tN<Ey?MA*U-)Yo>=+loV_X@<QU4#)U?`%9rq`mTU<j}e
zP@eXmx(sXY?@u3##59q125h958JahH2-zM5wo-*m>RL7e8`HsOUFvc9qw1HW-EMPh
zo)mPr@84zaI!F*#l1++(;cfn~?zbA8VO5*?I3927VXI33EEObOx->uU`nuFK0o@gd
zobuai<Mrar0!sdze|t@o4PXN)6;6Q?n9clYwW^jNy@~HhLOX~?LYxKwHa(LP2A`H`
z9Rj&g)RbDQS|*ul2HcfnqpYOp!fL+&`#|;o3y^wsJ2RI)QV|2h1t6^^?_<rxrSrxO
z@VB5?l*jVHVTp{Au;H-J@To{41Nw{jzHiw_BLiN)C}*i}0Z)zvY^!NI^sQq;>qKh;
zy?|D^3oMrkVB(?Un&I?oasQvc3%h%I&`9?B-dj=M_nI9e+ea@ji^t1J`<BrpSh_*!
z+q4D(d}gvUt-DAj{(i0IH1RmYp3}it$s|})0EfkNa^4*C_8a2Nb-R;fp2*ZO+Ragv
zLOLS4kNm>qgjZ)lj*8`2d!XtAmpGxhU%Tum?j~r=)>lwWzPLcM)?gN#e1!)e?Tepw
zh2S*Z@-0Ye+IT`Xv8jJn+U|i5cYDx;N4K?^sz!WW^!wq?uQC;PJxu#(+&PP##N6se
zew1U{W#65|^8)wU2wWK4ct1&lOAq>-WoaN0Jgr2dqUxVQZCsygWEXl`F*qsuS3?#%
zM`y;@!Ox4UYBVJ74V71kKV5%M;PDbYFsfvOMT|n=mpf7;x<W5_$Kc@LbE$JW=VpI@
z**%~$w7R;w27^}|z)N#BEQdFt6&v1u4l{D7a?7+&A}dad+0gov-HUYVDO#J!aN3oh
zYVU*sQYu2gsNRMB8tBr*mXAYqa`<}u9uoa$dSX%MCLR_<J=5^mE$HF#XvrQci>+*l
ziwnX9sU)-I2Mm!(JkzGZRQR^C(@Nf5!^#xbaJv$u$fUN*oH)Km@nEoFXNoxEC&fbR
zCw%aJA#J^3D+Sj(&Bi<3%eE<b$tBW{N(2X~sHzzoiMh#pTs`~32mgW;0M>_3PQ4WO
zc%RL=I95kN+Y{3$VJgtNKjg`ekhV6R@yhYp%k)jwsbwb}r6gvKN|=ZrtErkSO(cua
z;&YX3iWFL2di%S85AwgzCpvs2=%>@DiHQliiQrBwhAk>#al)%_g_{Y&Y<}=k2$;bQ
zz25sNm{}zn?u#1SL1(iG)iN1La^tZPjTODu224AxT^vdS|JWw>i0&)rzp&(y{4%@g
zc^Ml^p8PwT88gb$*QD3NQXZ|jeUBRBKZCWVI~afePNijQ9LAqsU!Sm#iA91>=*@uv
zx^a%SULC{F*|P&xZ{ig0pqt9d;%%bf7r?b?yGu8-l-bBJ%J$-&`Iv{kKsFd1D~Q$E
z6!$0#b3R;cDnQAnYow56Yh<KuQqP>m?EijgYX@|9zjHRGYQSG!PA&#p2L)Jb#b!^%
z1k6q9E^{tGPIlkENr975QevVgNZC~Lj03sNx??5S$~gPvq`ae|ygC{iYm0tK--1+t
zUV!e*nfLnl3Hv)!`OeU>5;3}IrMhn!R7~}m@=E^W7hR1)<Mj6W>l}HWY#rWG((!q*
zM<>Y5^)v6Z)3d80*ueF{?|8*8cC8!|f)Fv&=%%>a_#e&k)Hg^xpMje;%$g_v=J)K~
z-Pg4iF9rE5NN<K7o4Ts2;-7WE&0fcC{EU#z&(<h&k<ACr+w%%zm)z)!aygqv_<eT*
zIf=gEgR<&e`)O6MJmc<b4GF&Nu;z37Je9i6<Hswh7#AB340lra!A|yR{=t3Eiidpo
z{hN5qdlSK4q9JH?@YBNbvUK6|YLGI5<z2`ZXw7r?{s<e-GK;xh_AKWI4VLiku9i51
z?qaUvh->0%f6*<k77rU25N)ryoCFGCgJLDI;zgN~tu4Q2lfIv2({*aUZ|Cxk@Ec)C
z^`lbwYDX1gWrgHMyAgP2`iS0AsqkNC=TAPd7ySJ;IDvWA=cKh(i5OGed@DN>^S(WP
zArXANap5c)@VQAr_RiE@0iy;+t&;$)@w5d^__uWNQ_jKS{_ome`<VkCXt~qQ4_;JU
zEsKthKG;O>uv?h!EynLEo$i<gvzHV!Gz4Q#gQ*qpaanpoA)%i>-P&RyA^A#zPEG@x
zVh2=+5o2>y{|_4r+g-`tHY0#t6r)l-nf3BRf&OVA5nl$zZ*6=uXgfL+X)nnCbU=k%
z1g{iCzb%-qsd;(qkzpSB!!Mdvj^$)SdMY@qEh|`x0{>D$V$@Cct7ukXTrgntRD|-;
zpofp^sKjr&+4Fk*hs~4~STjn*H4Bm;PmZ5f{yH#8LZZ9R2OJ&I&(NmyS5wu{Fe@gv
zeQar2di&!;#vr{$)g5VKgT*sMD%wNcR7-ULUs<cEh95KFZ<oQ8{KyoFQoMCf{`IQq
zi-Ja~=Rm<7Y_rN>aX9KtS{}aPmSbOh=?tS;a)$f^R*S2<3oU_ALU|c*g4gs+9c(=N
zT{i20I|==1IOgsWLm$2ROxupar&D!61BjEJHoyoVZApCdXFZPoJ$v`2f4nVD$Z27G
zFJ^{KwlUm^X&$!mtUA}qX%<Qp!G>r75X=@i?g!0PkQ)+!;Gz6>9<qtacYux|N*Su(
z_+I7&vqdUzV3u9SG&=A$6}*WVf?c-Fc6Eoc^Wub>yLtdGE-kJ6qOx1L0l{yc6>5Af
z?9|M<h`(y@J%<g9dl?#(%vDz>UTN^Fu^Ux>44G9LQA0-JP6`K$Kid1YpXT%P^9k7p
zSRTu2(gv#&2+Ff^@26<R_a&cxrk%HZ&or%~R6zJhqP2pNjcZ&TsSW&vGB1sX&{lUI
zROMbmYpr8koYpgM+<?RKP#CG5!TObO0d*4}TKP!Ufu5f%d7UN~M2A1!=I663Epbry
zSS%$lV4r|yA%8JK;Q;`QL{_1m8X=AA2&x|vSGOCUlMo*sR3@rz+#SykRSje*O9G^o
zi>A_mVm~P%;oh@n&$3fVosWHo*?r(m77?csKhT|LLXQbxt0Tc|hU=T_%aM7sgmX+;
znLwP*d|$eOmONAt`8c+3jV(}O0Bh>sp=Ye1_k`@3OuK23SszDx=!-RP&xq!%^p6Zf
ziwZC1ijVCo@F*5PF`~cG<3Td|<aOSORv+MD&u+Xv*xkL~zap5E`$aqdalz%gRD)Q{
zrRv&m{eT+RHT_7wu#;j(axBTcpP4atg|;Z12!&v*%oWZU)gV1Rmz8xeC6lqY&%b;3
z?nSY*v@|}epU=Te(tp0YJS)rU(>&N+eVJh(RG;oD$Gs7TqE-$G^$pRcGc#?X4dvwT
zq&%Zclmcq;>Hallm@%NOdb~9G?KMS+I@7``Q!F-qe293Qb!V?st%C7HUq;zYlOUxy
zXD1TMsF$D4Ahc`0^r94EEYgMhG6fFjd~#_PydE8Xf4Y{*H`k5~mOgWxUya^nj7LTG
zQ@bRww9I7J)m?EO`xfq0Mcw}azED&ZPhSicuLP4&($oqjk+-gO7c%$+)17b|wfgLZ
zl_6h%SYWtG|DSr=nZQr{9mIB05y15HCr8xq9icB`XgE`HlhSWhI|9{P^Ma|d38(c~
z(YR4m&bq6`znAmdfyzfnD*$61Tl_q_m?cqN(AUiXbtLv}>z_l`;ob5|h!<DLcK0c}
zkX0DL-*O318tiQBn2#-k`GBKgU8A+3j@g}`4#$)Nfuakl`Z#u%_^^~811enxZY6zA
z__Rs9g!;=)_E)+jE-rtckwez1$36`VfC{3CX=4*jMgcFQ+A;74ejp-JmD+HGh_kDg
z_D9{`e;*_U*xA%kO8|!IVgFX^A-2sbOjT3!Ay6YXMuG5Zd&W;8t<sOX`r%mLv*K7z
z;lnxa2A}`~*oz1=0Qu$V5%c>X-lZT*fm}qjAlh`HP(98>DTu+DR>Q6;o0;py+TE&t
zZ2+T{u#1>JUWm0M_O@mL18~=iDNF&u)46}});Hvkua`rYOM6F0@YI}{N{~5J!+mb+
z)2J|!haS?WyM-Q0j<8rbdT)XAjppX27lj+RNv0!<GN<+wO|EHf$nJq?2+N!T2bzL2
zKCAQlI0!e+TY;0ZV;;L#0yy(9#q&i1yKNd3I?b621Q#5+aPX!pD>Bm4aPA_ZdZ=So
zor@41+nPKso9kh#c-vW-pT;zVTK862)mZOkbFrp;yNRxR*3FvPXN(PEwtp=nXd_;|
z{f>=Wb(_p=rakA2w@+r@3X0_8GFZO_)|eTmU?t*+ym?tvyrT%<kOl+<y3>Z|SN;3S
z!4rHjCvsCV!)1i2RWNUFRB^E?n%0BpOzxs7pj-V0W;;M*E(=qjnq2Mz9wIHo#=_Em
zPiW3hh2WL|vIF1=fbxICb`p_={9!$?`Sa}PW+tO`6ER~Q;K*&RO-^5e?$s2=Zwjso
zQSWZaW!%jwiF0lCsPMe|sJpf0oYvWkT4z)D&^sHb=K$k5Oa2}Qe}XQ;YLZaTsxteg
zD&xd{v^4@pzG}9?_ocqxJ~1laI=2LIo}ueEkhdAU*gH5TTZ_JYxhv*H!3TIvM+LUE
z`09`w4>z~5rKM%v98{uipXQ^Y&IxX<3^AB6gS{6N>8rWxE93WsllRj;zCkY?5pXdt
zH`fp>FV}pxS18KBS5&&(2!h!I!g19Bo~;LawyT6Uo-s1U&$bbPu&H*gzngs!!=?@8
zCC&8vwf>Ki@|2_4MgHeb;JvK+4%mAV5(rUpo)uyIE$m?n|J1Nd3wOPpnkh@j1Brr*
z;<975fv9kgvpo9a58xp}1^!f~KJ!mazWfYYqM4PlUve9@OgMklxx$C2`!P!CX#>SX
zU48j4N8;rGu?3I0W_La2FR!VY4i2ubjo!cBL~<oO)S_eRvu#OoPSC6wZaoFH6v8)z
zr}IyoII;HI7fVG*kBEp@JqxXni!z#oRaaMs(Dn3mRtPr?l-~C{mzH`gItI3;2O_qq
zi106~G6>rG^S?D)DvKh0i(Z$KFnv81^*V)V*ovO-iDrK8ZSwtSvF9X2<=+zS?BmvR
zv?<gAa7JFkKa`#pX^A3A+fR})^-_u9T|gMs4!eFWKI0CrlqYv8qx6=LB_aY6R$Ylq
z>+Q~xEC#|MIhL1u(SkD|;`*ty?5|toBgEBEdCUI+_QN9KT65n=pbFaCt&A$X(cn}x
zUjeaX?afy4IpQECgFxC9&{H~XX3^?UK2K9VM)(Dt4Zwd%1OH{XuL^RB^e3WIz0{iS
zSyGWewL{MH{pK+e_5?uHr1%l%lc7~$N^Wq?dEbSr)8@Q3c|IZuYo=J57D#7I6oc@C
zwdfm0JV+B|P7iOK?(XVMZ`vIFKulzgRF=Ehtv(;PPhLRLxp3m*@!{)vN$Ik*!AWT*
zlI=<-es>Ys2NHHEc&<w6>Y*+IxV+O;V+t8g1Q$94)*^&~ll{$FV-M=X++OkBP87zV
z#0wodMOYPYin`Z5Ajvoum3%{)-g(M3GN$-CPgkuCQaf1b+sp3-7(qgOPTqtv`BJYi
zB!3d?PY+`?1o$In`pFwW-XhGW<u6heq@e_7EpVhZy;F&e?QN@eL!Flna3M|$)yL2u
z?#3-L&vR*N53tJ_OuwCL^O))b-;QLu3KO5qjrgxX{*5+<>7DfA;Z{>qJMsE17!LDl
zJu)%Dzn`%{GruO4Dw5e;evo!&>3G8IQ5fCCIx;+*2#hhffPy!q4Ie$>mvJ<Mgb(<W
zIx4bShbyVU(hHrc!oodCprUFfe}^epE48BMQ~{`IEbvaoGoo2b8+N58GQ?E>^~X%$
z{oue%VFZO>p=wS18l*N-n`ED%Sts~-eOatBHkzbB6l)rjT^?QB0GSetT7un2MJ39w
zL);@)3KJe{;(?<E2@bb*7T#`u-daO3E-tQOY;`mYS->V$RA|A$k%8v4#W|~zYND<M
zu-WSazqE-6qMGH@M?{>qGYv%DK49WtQ{oY`WVf4(Yyu=uj_O`gA3$Tn#8Ypi2It5W
zPDUw;PS8%bZf<Zw=*7B+9_2{9w&EBLcz!@ViKh_Pi}-z5r$IEL8`a~1PXf*|nW4U7
z@HDRqIaiwk5<JlrkH|tcZ)hM_vhnfuy*b=co6=UUoa4=W?ve&whuIS+_U4pJ6TjA3
zIX!vMJPNIMHBCb6kqqbQ_8Ot3tLQ5*f^2;`m+;Rj&pQxGsq0;bIotR#Q<Kei*)EQK
zpd$!!k{fx!i^OUVxgi&ZU6T+eg4woTf_OJk{WA#QDwct$H!dGD0nC?g`@-GbB}EVx
zK_=H>Hu+L7&YEwkO!c0TTfU~vh-}$tU7UaH1)4pz>4QWtwc#-xd^3%TVs%=jpZLNJ
z|D(K^=@!(9=tWdrbg0|BM*VD)g0qgk<TQM-9c43ighOv~a?)0sC{tiQ|9`8ge`y*B
zk_Nq#&yztv(3+j?l9u?j!_Vg<B4m16Pj3meTs@1DEB4qg4@<D({RXr?v`ru~%jhA9
z%<^Sl6xpmdH-e6?Om9dW{Q-ax&UHWGkQJv$<7xR}fea~YAoIMAHUH6Hrt|Hb>kS?t
zD3oY{?Vk4Z>F&+d$n7H3&#GZneZmQv5EIZzj||~g&jN}CFRYW5wqRk->$Y+oRbggk
z+v<+c&=>N8zIa^P6}kLYZM33V(xUQZ@A3Iasg#$*=}3(=U*kj>F9;(b?=m(+f<B<g
z^9vhv2iN^uTtKk<yh?jKc_gmb*fFVpz0T?I-A7Kzc{&TrVVO&j<6`N~Vnz`H=~#1$
zPoZS!og$<6I~_%tk0DP6<t#`Tkd7RY)5zn{by6;}B|&JBLC?SdDQX=Z9jWA-*vViM
zHMW=%QtZlGw%N|2<;~yvVbgZ6BjQy<<t$R0D#W4W_FbeIim{__J-QEVE`%oinB7B<
zIk$JwJ#f_imO90A+8e(bqX9e}JeyRM_)->_rbG_eju*F&AeKl&q4<#s@DVSXV=n{G
z20T`--TlCeNo5ww)0;;|4EXa(1tRr#QjHs@bxl`Fva%k~t$p{j9jx4iH~{g|2h?7-
zZRdt-Nz-enk*zc2zaK2F1HC;BLT|W}hu+WR<hhAy;tc7(qYRKpS^!CFDyK1$s{@pl
zE&harS(mS#3f%NJ!hg3c-B2!h*WJ#4CNFL&L;_AEyp(OVd19Kpn@=llZ9-NhAVr)H
z+_4BRqd>nOvnZKc3#(YAt{$?&RDv#5vFji2rzHiG8+f@_>!lt`6#mW%>(ij8_;_B{
zF~gm++_JO1zJ6H!Z1{~`B$+*Pb1p1Qjjg|DqR_%Npxm07@=eTQu$qH<9S6l=sHEMY
zoGUz50uGmaCM3jJrzT^F*Fa)fj&v;09y+;)4qK^w{(V{bF+UiJLx&~fl@BJAgnx#T
zMlR3w5sK#{6yK!izZWOpGP6^v9pcDKDhC<nspTcG<)x~I1<<B2N!3IfhIAF1PmK@X
z10d7VU@m0qXO?+a*VrYV`Y5PCy>yCu+PdSa)o&8lJxS&JuZ7Z7!Wig?E<YOpY7H`5
z!lOciG8^A<SzkXh5pkY)Dfd3PR>9HE_k??26+_AEZ?}<jwNMff*M%ua0ECtmhZxd$
z{#vf5cWQ%buw`Q^phqPr4ubqJO7K1MN{U3DUAU??X1@yBX^=kIDNGc%V~~~E{}DNo
zkbJub;oAX7)cE*#U5D2x8Re>IG!2v*p`*mvyzZ-ycJf>xb)HRw)*EE#|Mwgm|LYJ*
z<*H$##HL%ho*0C`@%Z|ni^1VDG_pEK$Zd7BW1?SMZ4Aqfy~yJzFz}NDBE)~MgY_<4
z2Z*Pb#A?3{AT`keH^S=3{EE(DJ{-%op~I{JxdY=-@19}`2fGIxXHdj>`u>$u{azAx
z-kjp>8|~Fa49|L{X%8{fBMo+R$ugvC`7`_wd+*zwfI9~&%sNa#Bfyk<XLXXy-?{60
zw5zW#+(jMGS<o}FzRn?5&l4>;p|e`-7ok3+xj#KgdnPNdTqS@BC~OE_P%N>qG=k8E
zgv2`##?M1BdoMy*NN?(6xaZ>Fw<_)#iQ?jTdFW~IO%cZ{1v2bJNqD-j|3VG}p_iVV
z>>u#h=5bfHo>2txln-Gdkmkn0Y>06u3f>5qnbJAX1J{^M0(4=!doRhuAdnJ6>d>R@
z=vZDFqdnUQR4iLVXyE3~dapFCg=_|f-TV7GJ2|ynCmPPPhn;UOT*C70Kr8}U27Gml
z#!I!Oc88NK3b4!ULc4!*OrVE&#yBx8H%-yXJjgsTBcLN3Qjp}j-BN=)2Y%)U2)aE(
zzhTr4k|h`$8_P;d`+|XJiy(lP@M0GU(qphJW^3-^|GQ--i2U$v)DvQ-_e21ShhPlK
ze#t;eU4q#7edJp5EjWu0Eg#nUmH%&;WCtb5VVBtXPNKfA4q;T3lmrbPf-dgFJP=$N
zeC7mJ@R2h{gooztZ3&Sc&SIA*nShUlPZs?m>Ff$1bX!Q;FKa${@X!Q<onvVQ0t&@~
zq)1`ssZw0VHPR8n#v&pb?<;o-)3Y|j>|!xVdcyA>?Sp&wJcU3SIN6@zmd6USf^DUV
zl8g6<v+QGm^YjxOseA;|5~9ADx<(WWs~q5`^=u4&Rob1!pfh{ui!E2w#ld1pH|%}K
zp{V&LTp+@kkOfIskr7uO$yq@JaKOwEM1!vl32*5jQPme}i#Fr{3^vd06A`Bgx8)=~
zZ2=NYx_o@&;`s@Vp@j2Ug=y=B5H;XtYbph%t7)VI7m47-%BjQ@#+D{*wl5W>`B1_`
zH-y+<<tWIUqPN`nY~@n;>^>YW{@uINn?PI@^v_2uC+6iTWH>gO<T}plBh%^bOzmf2
z925{7?C>!Ua(`aZYXofa;qU9+yk{{`+pwq7KpMOMF2IqgoYE1^tRK`M9wHery8rAB
zfK11G&X|iXbM8Fehnmn0u?&Ne^iCS^xq;}YJ{#E=Om}7t?R1D3*BmEYqcW$H=iS_t
zA`w8?AY*R)0aE3iTnJUC1xb84mY-nI;eTFl#NaCle#uT9_AJv`#)9<XY{UuwQ@7NR
zIh_j7ZwC~B3Bl)e&+>91=ro|?3H5vYPeStN7lt%(0AY1xiTIUSbMq(+*9{G3zr<%c
zc}|sCE2gPj@nvf6T1Pqfu-+e0J>lJyFJED#*Z_F%Ohgc`@Kl<!B{0dK=Jp;f$oB!M
z>P~Nv1Lv<YfRS-)e?FCjBvS@DeAZ#UoOy8q;csu;J>^xs*z1?xoarD6twXZCU%bsm
zuG18o+ZF9Rer3h;o>iF^MAn+{ChW~QxE;g;NB>JUbKY|Zc^-M2G7!{z+Ih|ZPGS;F
zS&xS@Ldj@o^3cfG^*;(p!uv!2fgzR<wR8CT_+x9d_|mqGtPQVRPGii@HM$t@Z!1ma
zo>si?yfIlmkO~O;SMK<bwwUtL2C44SujXOZddbDtoBrpEog^BhvVYtm|9$t}0Kpy&
zjg_c>gjulE#<1SzFTt=8%aapfSKLo|-5%=h!7%_p17|aOeA!beAUgm4rCXd3Anzl<
z)Z=5*zg+Y~4ieflD$i5-VrovSw|(>BBO(cvqcEPkM6G1O+rW3?HFZ!^GxM&706mpM
z5i@V$6b2&T&jB_Bjd!4+0wEDJb%5+z>QB!Jx+|5B3KBmJ(9qDpq_zq^K0YKzdn}NS
zU<*JV#g^O99pkl2HDT|<x}5LHl|Y3@r9Sjg!xM}E=GBQZ5;(M=o|Rf6>r&nJZxH|>
ztrQZzxN_ORJ4JsQYS4#~NCy1=G25CTjYsy;K<!5&E>Z3ao%2*yz>nEk805xhJ^ar_
zBM2?Upb^n`O@}vCnoiK1ZcZP?d~Of&pH#F-#J+Gv3b!Ek2VeJ2mRkQnCHW3Y3sG+%
zDii1yP;WqI2SM@04M${h2BW|B$^4l=!4rNfd)X^;44biuY!%*Y6=pc<^))+{VaQ<J
zy%@Ag+Z#>W%;2dBG8BN6BglWi8<a72=y?D^4GTIZh;Z-e$Tj}olCBmV+MAI)nr6-#
zBx5yDcwPs^?%7nR@}W1)@cQY3v~tkocLYFB0>J3Tn|0Qp@--!z2f#0eS0VeUQ(HdO
zj}VfwSg*Au8>Dytpd+=19|xIGKPph9U49<JCMPByFjKZtxc%C***LJ#XexyWph81{
zO)b~mq1JQpyoIok{;2WP+2!CSnvQ|(-+CZ*idb~rC3?N@8sLI(T)@SrS`K8UEt=HL
z-Q#yw`Vap?R1*^~=rLT78>)gl>D-iCLO(3$bzqt)NZ!b#l->pwE#)@2D_5?32R|bx
zG}9#b;wIdw%V>MLJI0xujfT`??zR20P&cok$!7LbQ-VZS(r$4Hzb$|h&<p?Q7vi?n
z@Sw~@A}wuwpXC=MTx9{tD@3ACe2<esK63|zHqev>wUxjOV_G^hb{!v>O}IMZkBPTd
zKP)N+GAvw%sB0;b@3*!gX%Xm7^J?wj`j*2x_IDB5NVYd;>mftt=4L$Vb%oy3#{k|h
z>I=fTGSo8)2UwNUZ^Pi91{EI~_tskRAxs$^LInD-ZbEdUzqh|DZrcTm7Aw0Thxu;x
z&v5tUIdW+~sJsj$B$cmSJ%K8O#-W6}kCw_h!-2&N3Pw-6!_2tvC5IkAa3=pc$QmM`
z@Z5%V_cr#k-YiUYLo;EGVPF820<;^PTZL8~<^2@2Z3;>ndi4SjI^1ob*XlkNh3Yi~
z#&_Jfh_HAK*&X@#O{q!YBhV|?Cu25IP!qqe^jX4sF2HDElZ<BJ)R`e?%x9qsDreaT
z4tkI1|DRg#&z-I81whHczyKz)Yh@1&3U3VwYuduBwjd0qJc4=`BD%Og`I+lC2z+Ty
zGL&>%56_c;rrR#s`73Tsp}J_U@Tj(hFCbxJ{f-tTj$-P*G}m+)?kvv|P7SILIl_eH
zu^*a4tN2>3!UFLxWrhV(JJZj^cnEqU{#*-sPLMpUZ@N`K^1`J4kxGzT?zL%pWumC0
zL|-+KARQTDB?$lcVJS3%M)39~5s@`O0J8GjKr;<onO9~(Pg|VC%eV9rnu&Aj6ECeL
z*zBbGws_X1CT(ZmBzrdrOOi8R2H9hT(R2WjEJBYI9nB-`(3|2om*=)P$H7a3Q7x<f
z`zdNcE-d7>QAiL^x3|N<Nz*S}FlOMbud_2?@90Z-hk=gJGFyp=k2$M$b8#Leo(9mN
zgLGvad2R|tpzX|WUuMo)kR59qxo}ltd$a9G*|vgt&(dCI-QO$YCW0qH^oPUb(rEI_
zahH<PlV#LUld>d%X*RzLOv=A**tr;?Q#0e^qC)A3iJ5VuzkXTHSpf;i!UYQ{v+(`Q
zDbRL8ctFb7M2D4LY7KWiXc2|@U=;>=G(i=-`J0s`M?Taf$@Q`Ey&rTlywS_$EqAa2
zZDv*a;{C@m=5HwTzx#A(`HpFt)`O53$Q5ao;$V}u2aNnr(!jnAd8jjYv2CP~E8l7-
z=Y2I}$Q{n+9KIb5O%Ug{4G$~~GB{_Pl?xea1IDzr(ji>sN>xZ$WnR^rd}c4aUw%^G
zXJ5O+lN9@<@S|9La6_%SyDgp?B%(GY5Oknl@!c&Du{9AGw-?Y5=tUE(C3}89J#uf~
zc~wRSJ@_%3oXjW&O<H^|E14h!J40S`r-L6tjnw<=elF#vUD>CaP;YA}JtfLY9XLpe
zmo0-KBQ;y3@yLbN5|BkA{cpO4O1$Y<^>zfm+~M}1t>q9{c3gT>&LtGbw(fp;3HpD_
zT#22BP2s=@;eoGE`9Csr0aiKC6DnygwH+hY8^kO0MoQ|AG__zC0e1Eez_>V;^?Ro;
zzm43uxY{{kYYf8-uf3x~^%qACP~EG~ya2ViuWfhuRi`SZcLx<Ga7LOZUQ<wgV`j1$
zdZjMNT*YLv*%i<RC@CkPl?tGdSZ#gxO&?n_LuW$vL8uIKm-95`g4i<no4cF=xCT(_
z51UW8vd~MQc83|7&?cA$W1_K3Vp<zNsn~!TZEcM~gK;@teZG_jLrk2&J*|gJ0p7&S
zE(l_QuC|Er73u>?d9IKKUUF3FR=0p=G>F5h3Mz&st|euTRoMYQEb9HsD@yLuVbk59
zi3lm0`Pkgy#E!VDD((uL_k4D4QMoLw0~EuPbx(%}!lC~B&+EMe+_OtY(2Rq8_b!T|
zRzljxgS*fWM=JRXl-}rn@!3y>pv{R!+2j*kcjr5RF#xy<vO74liOOh}o=F2<GL4dK
zNuVO&UP7Q|te?nC+cH&Cl1t5#@U|P&oejv^cEi^G*&1y(AJOM+WxqD~NzyG%*dbPe
zySy!Mn$vc=&e~BUKA*X=V+SKem-^5Rl?I5~nXK5tR~$d|0wl07Q-?NO$d!Pg4BJNV
zyI2F_wOOsT@nXXb%U<t>fh7z11!ZRW3D7?3&Iv6_jX$=~!H*#tcEGX?=h~)l0@1y8
zN=9<7aWAc@W-^ArI*cYvae|J{&8SWpw9MPHac_$^-@W5(AiK_ApA8C|U<*nuyh+gU
z+M#6s8jU26Zt)YWga7Z<N`HWHoiOj)Rl?$N6*br`obaw*03J(SYjYA)T7(H!3NU8x
zO~6X!06Uo=xWY`1kIS&1Yy+axd2?&I0(8^askc)KHa1)~2lopPa`cj9etqubnU_<f
zf!HY}g+!(FB%s8zNWOB?Y#xEdY+eByrl!Ob&JxWxFqjzvF9SfT(5R4)lP=&-Pir=C
zE7RKa>%;=GJnd;OZ@hMlfdKL)-g0i(M@QosLBk5n!DaqcHbM*0e-;S|i4GJDEMghX
zefR~`0EJu_FI{@tk3_Ks!s}fJ@qjv^hCsQX*L%xya&i`dcvk9wfrP!1XGq>~|1Wfn
z)%G78%21Yzvly@oN4>9&f4kFo08|UbE6RXBzJa*YJkE>de<kz`0}x73?{E0~zY?;F
zy}~15p{x}%J8c}sv9$0*3bMqQJPC;w_G`@c5Pgv7o(-D+PvX35ip^Zp#`Jk}v+{3g
zIz+$S1~BeDJK4Y1f5<UN&w^r?OUDF3THgVZL#c29M~>B-8!MK}o%2^=Km?5R4T`%Y
z5i}0%7G46hP4An)g>d~(%c!L7x@&QsoS(8w_hO{L@s^GP9=t10@u+1Gi9VvBuXlxw
zFHw`3NK2G>+>Hn3?Ny!x=KXfpcf69)72z6?Rj<y#dW$0i0Dak>2A42fXzXL~y!tDl
z%?)iY&}D((Y2Uqg``Ab?gd;e-MurT;1MsI)+P_lqQO{S0;rBp;NGwQe@Mx<n1ZKG0
zfj+!o&a1x!BlJ`l+1NU;{lY6ZT?hBj$%q})IJE2R+cVQ=MEBhQ1DDA&CyPEzS1jqi
z?QhN$kK5ao!zn^yf4%)0)LH*dc!L9mFY`NF0O^1lS+;KF*c*AJt0|->P&i1PTCeWc
zSz9q-lY3x(cf0Ee=RxFsv0yiYdev(BJJwn-e*wo{{kc4IN|u@b-8WxXj^8yhN)iII
zAw2ZFB%-08&~G|DP@x4)hQNl%!nk_Sal<)^?6u5#3m>ScoZ2?q<$fp1!0<L;()UEP
zwV|Fhkk4?qlAK{?fqH0h@uzlYR_)})G~$0@Hz>j<0r9@+BX>9tK*3{AEFA2bpEK3o
za2h+DA`4UIK&rAab+t^VH5rM_^bIe@g9il&CZIPDw{=Vqq)A_`jG>zp0+CurNOH!{
zygwc4uxCOr%KLR)Fn1G1|Fy$>@35I95)%DdbYS}0UaIPE!e}OBnIqPWDX8!zuUVXy
zN86KGp=FQNeV)o2q|(aE042PEWh{$GO;qs!o7tg<x{#s47djR4+na7@6j>)RlG?QO
z(}i*@m1%e`E_nn#H?;Z)>vF%;QXSOYVC6xOrIg|bm<e$+d-p^i#K5hQ17Z2(%W!tX
z`Z7_nDFMrgoZ_Y^zryl4&ktK8OH2lK!X%d=r#wp($N;oaybobHr2<<0aEONCnG_V?
zKy@e?95&Jx-jVjbBf|6@hy9>##!|kWaaHn5I50^YlOn)-!Rr`5KyfeD<0E9)k9l<z
zV+<K3FaPJk-#icXj<O8j`UPXtLx9B`A>;2C)csTLbjw;yl2!<YWP49jEay%%mD#BR
zjE67Z^}d;(KnT@JZLttApfNqD!SpzOl_h6GyODQu54GYXz9&pKu5j?vvVGo>t-Ioz
zRSz&|i9G3==4m@FADOo3ZIc6Spu{)1vo8Gi&~xAW8gPz>n%@5b8z7=WMw>uJshJ8b
z4ai;qo=Lj~>LU93L-hG4IK$S0waNta!u&$w*aYoGwOr=8mhS(hJPZY|=5@p*-X^iN
zz|?{tS*>eM5k#7oHSqvgrI?5Zj@Wi8(zPH8?$Q0gi0dccaV(9K(l7X8E}@Lx)(|Qi
z=uu-kEnZ4J@4`tO`1G9i>RMKH;DeYOEeSv~h>40v^#130pL#*~3aILdazmZg(2`+_
z?bJO;Q&kgqz@58Ikx{rN)Vi#IE48h_rIDe;d;4dR&RV?^&ZN=*k=ock=F9i^_6?I>
zEM7sT2B%(jt5%dJ%b+mNDi;Q*UO;_DdCoKqVe^2L%WbaV!R83lBq(QN-|mn`C&5{y
z1;i+e-5Z!{1{diFYL-Cn^$#X6WdFJEqsNZ2lep*N3Cs>N;+;7aI-M?q?)5Frje<f+
z1*);|4tqdfTz`#nt&KNnhH&Vsw2WakG6S9V;b}H|IaFr9|6|XcS5HR<i#z2~uZ77b
zXz0vN9d>t8j}HnQ!63IDvmfd<hBO<nslk9Ox+(Kk=Tf`c6sEH_b_@5~z|+KPAunQb
zyGjyNzVy;Rp+@+3Q!L?P=ewSgVjb=6ae5Vm32qE5>-*d#j}e-(<p7y1zoCrUvXSP5
zK>Z&=hDgQti&8L9zEV+fZ@gXPTI*iA%;g#HA&;%kF$nhHl0Y-oWLtORE-?jW>gsd0
z*jX(^JlT)TUDHkM;K!ewHhg4dc0-3;w+cMtU**}Md^8prAb5mob02>FdBN5s!R6y^
zs%>a?G8IfW;6I$ErIMazax{Dc%SdGieEUBe&`;I!$1Dz~hzP@PoF5$vJHMYLxdAB?
zS~mBp`awX&^|x1-BG?eZQoXn5gMw;BvSg8pl$x;tFU{Jo6t?ztw@jXql`+g&W}w30
zEzA0Dj*osmPjov=uAWqELJlv;v0!SdEGr*_e$=H>oOUEKgW6tp*!B8z{8sr^nTFZ(
zifcCn%qK>U2@;t}Aa6ygngPzXRHCXS?ls`m7#lP!vEqNhyX;JqQ}p9Dr@lns!6Q=l
zDd-DN1D;AF{``io1Nb^s#4~H5URdxv$dFgLi9vQ=<2)TGVW)TB3y7s_dZ|}C)1h%z
zO?@<Vnl7Y?h%R5~s;#&rkSDPvDNi+adCv!eX3YkW&~;dX6F8=zNhP-TAskEVdlGC@
zO=KX?G734Rj}*yp+JV|(8mt=bVc?9T4^1f@NU{wJ0Ew<JMBV-8#Ze7<7K!KCyB-St
zA6WAnT`Pb$h+BdO7nRu#Ei3lt-vGybp&-3oW1#D;^~L}!e2NYw>%ardhjx4~u-p>l
z5cKlPv~$*&9)FvRS}2)&x{B;)6O&y|HSr1<^eI|NtTjh)yRHODz$R7y=U;uEhTC2=
zJBBKty7jI{45sz<$N$bVELlmpH$a*AUMfLzF<A+f2u{-`T1*M8p+Lh_Mr*4CK{;7#
zUIBPAaoG@pSgT?I!|Xdo4J-$mCZ#;h7TMZ>fq@4KV!Nw)Q>zy=)~4$d5Z4Tc-NPm5
zE&IOIOo$zeWOvNcFan1uX1m-^Kwmec3ciD4<Ew^7Y~@5bgAQtD-kMY}AJL7QHQ<Xs
zO~A!jk?Lg6IYBH}?JBkO6gxq<f&s1y^$A{S?%y!P;3MYFUtml1vS!F><9E#>2w_uz
zpM^=#J$Yt5+#0Tnz`4+p=*IfM>U#DAbSOi8BB0S>sPDjWp^a@uSX{8S48T*@b&4x^
z^7VG!HMAvcWO%glZ%Y*io!_%S_Sz7;m#MM!@aM2k)$|_t1)z%T(1-&~71(JhD7*)o
zmDot_lTOZ6;+yl^r2=})ZTzVN<u0-rB~B`*^*ycvZ_L{pIrXyw2oTq*rHIa~=<`~%
z9UB7a|4IcjWmuqMLc%cuLk4!rdMXBd&Eb4@{~vL09ah!abq{a5<tQQzCP)ZU(x4&&
zQc8n_h;&Ie%LHkq8<B1)X*P&-NjFG0Nau!cF3|J%yzl$_`{TNfa1_{k-7#a#F~@wU
zhHN$mEXOc9h{&s0_|4@{GZdjDGw<u7%^XiK3Y}0Q1OzB(W)9c9V5GRpc0g9$6LgtE
zy7Mc1;f=x}WOm;m4ToL0$DWdcQX<2dFLLz7IY;ouvKuLcI|UtJ!QYoqGUUGi@JPFv
zP`<(JdPvCVBLNl7&prsY;34Es_0t_(GYizD{?nPe^PeEVRV5g*D>#e4Bk^XnSe*?F
zIKL>h32>P7f!B`Wi1L1Nau=bU^53fiX>!T0wSxW?hN5Yec`49MM^jhc<sSkkkG`ok
z0XFlH8{?Gm?yB)hx|f4kPOqsO`nU+nrmd-tY$3yhdVqU6xuUxjd7tnHz@{Yc#m>yC
z&^upNDJrBthf$FRnw&3951Pd)ulGD*6i2fKMqWo@Al8X-DxR9w|2Cwd=no9JYmzga
zU9G9AA>2D)pj`^}&qIta7eI%$PCC>f*KN8Wi`3j~k`{_ZK$K$c(J%8f<+gI&z^<Wx
zHQw;m=TU2&0{jo1vP4s~<$ysNxca6?{wic(v{1j}rI=?L!?)lfIe{Os{?>A)GjeMl
ztA^^9K;hwLZ3YbU_}ml^v%Yubcek*+Nie0nXrtb^9nEkM!~R0fzLsMzZK9(1uRweU
z1Shn6wX^p~)}E!5K}FjDGtfM8YU_SzEdx!H;k5tc$8j%7V(xCvcuL;3SVQ*Y@GX}E
z<3D(B0R*WIYD`c<+VAp4aZ9)jkW>Q{B5RVGvXXua4W$975ZUZcy^TYb4CU(<+?k(%
zsfVr*NL}%{e*^q~Do?<}-XrOnUYv>W{d>QKi5En0K+?;GI^>?RF{9(csl6i(qqi@(
zl>;ttEg>mMbYQ)Lec8j<UI4nPy1yER^ut_Uy|zSI-EdwEQkOEQBL>ubU&ap;b45@*
zeSpYxe}=pgaNcYS@z1Appnz9amCW#o(sqRew^@$wS`p_syfbWd7dPoN`gZVxB}!N~
zoPKDz<~XPmAhjJ(q?5PDU)$GeLd*2TYf6=MD7_y}&}80K73m7cuWRvqo57;Uh7G3x
zb%8SkR1!b+PCF6Ixd8wrB9h9Jn)v)@2>x$2&)#DTr@22=8W|g#LiyhPi~cziBh^08
z!|99;zt9^fcBr<1jwk>QAUhSoE%EPW7OJM9ha5K<v6JBH>e|d<hY2Ky%Epg`;^!E1
zNC}Q2pwNic{DE16(jf|BA9f_~bFOe3I@yZxs9WBn!qWG$CvRD0P4&y?quI6AUNbng
z+NE=tJ~-+-?;X-CMDl^rX47#k-_BS;cE8U2F{nRXSGMMt)%+uw-KA}lqK!>IGya4l
z$p}ZH3UH2&AZU>s%4_>67_2!ejR%2Iq8rD)w&MPVzqGjqu-WiUm3$err+N9Km;HUv
zhHpcQ6O_0Kp_9&K)fRw3eg*D3+9wVJX|eskoY-|xcZAZ2D}oM{)P?Qiv<NGaWxf?C
zrPS6gAt>Mj^yD#HX-IJa#Xr8YDQkqA35o(yy*PJ=Sr@7zhv7uQ4TsX+laRbJ5lI{^
zmPCtk=q44;v5+J6SGabDz7@?)i7qFo6(!G=ex%VD=xKYfzD0%#V35r|d|L#ew&Apx
z&8UkTo8|ccAW=N4+@qZOAzjoaf8}IfK4==O+ZC=%cj)6bS2l+TvGGEb4l-y<DTTxG
zV~~%k$~a%@^6J$Av_8}UJ<0+r%ggbfK3%HMJo<8p{T~--^cT7^Lw<bvX9DzCK_wi>
zpEdWbjII3SWG~WhGJFyhT{7<&Yul_?9i!3UMFbu@lyyXV_2%C`!y-UM`u={l_DnX#
z>FE*?JGJSJE(ycLqz~H5BWJmGzMX|YyDv{YuXxlL5>22tfdHWBw484}?)z9e2wYTu
z`A;!K@i9t#>&Uj29QrFbdm5>9VQ`)a#If>mt3BwXD<lcTuuy@6m6Ahv=&T{ow9lZY
zBR+ZzbV2rT<D7j=dM^jQoqcGf+>{8_B89RByfjUZ2;q<F{w-Hq+*7NNO{X}g#o}S(
zHkA<hq<juWc%s<%zx88&0=46eK)$JBLSYoOW%8fiE#wuG@jw=>CNTaex-G1moUsK*
zuXH!Hh99>-Ch&I|CF<2YZA$XU{l7qP%*8}Of*lG*ppHRbFLdupuE}727LrX8ec%tA
zIM@wxh8-pEQ&!|<MySjB*;|{AEZh4Mj!i()y4gGokvd2qAj=WZ%Us-(CRSGW<pa6V
zykv2^6>D~q-48%VGNA0cC!C7*_uZ&V&*uB^5Tz5;`tlQnGq3VDQg(nAvnDN_0?#ht
z)jd}YJvD#o;0<PvGQ*tO)93?G*|OATSvKIOP7M83*0@8ZfXD!3oUt4-u>D}~klBo!
z1NyRv_vts64!_)p+hZei`;>m!*UcJ7Pt!xV;s*yn;?WSA%X(*1+TL(K0a;8q5^s17
z^DCdk5fGRwlrQgB{izQQWd2yl0ohA&3qO~+e*Hul?Td>Ac+EqAbfNl(ctXRQlPp3O
zUO5cVN<+HO6|yP!1i>8=GM6rB=M;7QEgcHT-Mc)uDM_?A_2=WFhv3KK)`RZ|_XYp~
znDt5`jO#khIT#x+(5~ZF1ehu6;q!sZCG+TxFkVS7>3;`<5Dlj1IvC!8`eH}w4Ii|A
zblU<-Q+xGAwPaV*O!IjU1_y}mC5cI06gPJ%@Fq?fD+M?OX@<|eI!8WFA@j8*QYrIA
zsbxC3XT`FZ{<uyjcQT>j-1_vbXbZZU5epEgQY**+iO0hBkB(^cC;HITB2nAArW4N+
z$(b~gG<*;i_%~H)&{u6uEKm?ieqavwIEPuR|3#4i5WJ@I*BUhCuYjdf!{b~3q-s5Q
zMC9SoqRJ)PqCF?K9Lt`Nn`@mKcjD=}OnOlOMW8Vfg0MYZ-ZMgmeL_$d@<M=}s;LKv
z_rt{u!xXQ#IqZ=3JJfUa^RjBr0Iji2t@yLV+;uVy2ARF~35k6r)FSIFQH^l{UxMeL
zRgNmxYN>#bTz_@=yWbigdns?hlfBxU1!cBYBt$QOZNMR*tsl?!+@P+V{PBc6wH4W2
zvwm8m0DTtR!QJ5LW7V}*iQK<uLCZ<801yn-0X5av#}yhlLqkIi_ofnP8F55?Tl};C
z@cHNVbD4GS^DYzUWO9@eRY_qdmaZ@)HKn0aT-n;zHVKktaiC@Z>Ra9B;)EqvTG{@2
ztnr1H_#kgP!Ul!X#q>MLunxS~L+L@+7<&@%dS}`z9ao2Z({ryo4CXj5XImKwm(^Xo
zh$=?*1xz?niiQ5e^p8u7a*qDf`(DK_?xFvAqd<HL^i2tsHNMN6#1UxQSE>PJp`~cm
zay#WlUDFs`oj$k&wY7PACoVeWC}XA6kR69xqkcDwq3z}MNwk##C0aA?>BNB4uj}_2
z?)0)-(R2ZRRH>bV|0!(#RWik#l78?Oi*v+M*#EkVTaM$GwX({IW_dNAZZrVXG;CW!
z`lF$vuX=bP+AJS)@nd>JdGVbqM61x#8IyY)DHW_W;WBqH%;QPDKe2+ga=!R+gq6HM
z*D<en=Xqvu8BV%lXQ0@}c1Jh>=-44!F7;-E#e*j<Px|mm7sN$)jXlB!3*hn*p`?s3
zC#dD6ra*WORXxBJ%>2~|fkc8rY_lVj8AZd!pd(BwFMo}DV@av<UUn9%B$8Ha;DY{s
z2#EmpfDV*qZ|_3Zx|waVX%gRkdhtGaz7tK|`Mb)I|D&i@@-63S*#NZmq2q;6G3$hw
z7(s9EyYozEodwch=o^}~^_f(X*VKk!@1Dm5K_>&W;bq}|AbH$`YG^^qC+xQ_mCBv*
zH}r7=l=+$-{kw0pr9F{3_G2qWb|hoga!ZY9FGn`CF$*6qRs%-N7T<$s{%E`ku@v8K
zj{q&{^!s^Kp;J<5+q$=T$A`E2$0js|34UI+*!GKtcBlvADlyY?EenJ}!zk8|oJJ~#
z{2jB#-}Al5<UW)6Fa@MUKsVK!+9ZanRGZ4xEHg_t%MDwpnb=Mr`l9^GUkCqX^+jNS
zf$5G#w;s500?k1sYwI7r`^l>&z?M&v&s#hDGpGdn+Z<Hj+3&6KQEzvfRptr1emxJ`
zUY{r<(0jLsV{AA11x&bVig#Pt>wW5?6f<|}n_+IbmAOoOO2n&(FI;c-$CvuclV+17
zL`_kQp+Dfi57*jc3G|Y<7lQTTlVl#xZQ!_o0Dt2ghTNeDWa<pUw8a%27cC?z)t+Yn
zClz!S+q|y`<H~pqZ3jG7KvW3=NrYyf^<It79nRYfYE{LemK@|xsfv%Oz|H-pdc;lY
z|0ZGXJ^iT`NP2eB;@;`9z~<2&R`I>06>hlsY<5Sit|jK;8>k^e;tH%VAm;coPjKTO
z;gmoT0&Wrs3t7UZBj&GOaGZ7+X-7m?Qs?>n_OTOed|KT)(%5xYq*2B!UadaTu@4(1
zy9dqs=_f%r1Msf?!%_2VI}U$3l$$)UXn%4;6bGXy;NXB{a&4rg(pRCg8GP3jHvo#?
zT#nj`a}S##ZU%MeX*T(W`V*lLJ!d<B(2g(y$RH@kp!9hQN((TPQE(31K=+uj=PyoN
z$pwGET-{z=r>RO+AQfM@kzK8K@;aygBu|H^MVgs=#5^uKZQNiwe0rLEw}@|=%ZI{i
zfNEpiU>d%IyQesAE5BiQiz&~s2En>*?B2@XHQVWl7~bNJLU!@T^Ke;aq3gVK?t&H?
z2-dwx^GM2(W+$tM;n;IebB+-1P&7`O@9|AhMVRi6V!5s^mrH^m@N&0~31s#&r3g#5
zwIJ~gn*x#?+7GbD$#|wV{;cdnYAOvN8lXko3f)*W)t^sI+*@&FlLdjLd(cY|>yGe3
zx!0@6{$Y;tM@9LVi9MyNt%(!o)Q=dR75NX56V}!uL;FTv8jf1avd*jGCK#0;;r?e)
z$Wun{Lkj5>Z5!u5O`ze#c0RK~WrT+lgt^k_Ma>u0G^VD4o3kv~k2cRrveS2rJDP#T
z#S~+=ekD6$5$>(45bmMf2D8rJxZ3yJ0T8Q*XJ=+j<P&1)LE`w&m3w;5wemWh|M5!|
z8ZMO!P2)5H<qZZtWNs3f2FiWb=Fh@LtPO^6BbsvCVs}hy>VwBj?3F*6=C8!{JZSNv
zo>tT*CdI0_K8-V{B1$xgAeJPp;~hZ8gL5O&ad9UDsVJupwv)3X7_$Z%-Fx566{HXj
zUZ1-aPks!}19@-&H)pj8l~nfWE#}h#P4Pu47{XxCBcAQb6cX~HkiB;Pv5ie$d#Xwj
z3>l$gWMu4XIb=XEMGk%1p)>1td-)E)k|=Nk?I`;caR?}p=1KslmI0uW#C`>UiqO$c
zm|f-TwJsIg%dV9my;#WIg>$@puNlZ!R0RWRPaen4m|WIf^L_@AsdscskpLvEqafW6
z+dfzvR3uk$^uaQgF)Z}4$3g|y4z!j6Vj202Yv-VMbjEA4BF8>vXnl#2u7Ids=^HK)
zVLN5m)$xMWr2On&SrIL&NOIhDj5Q5CtCEw?v!G-piw-$3rT-bR@Mj~0NzR;zJ5Kgw
zq4n;_hz6bDahu@LYQv$HR#!<|9-3eF#XZU|imH1&@J>snYKUjr)60bFZRDoQEJb9{
z`_Jv30Ra&<%Wo&2W*#9$c5b`h|MV?`CGV9lh2y&VUYk#Uk)5<BApyap%Dim=O7gB@
zt>+9Uf9i!=6(b-tp?YEMwJbAKJc^)!XB01K&aet{difcbHq1^3jpw%!*tT`+;dV+U
zX*8f$e+uA|$uk`cl4W%a{iv|1P{F`FXvuTldWPuZ07R?)unbfkTF%A~<`yR5{V!%s
ziw3(#0dj{Q_B>iD0HEl{jHF)%v~*SN{+sEZ*jWDn!n(<wSMNst6pZmKDckwC7e)zX
zt+olcVRccj^x}IzyI==e+R?1xw#|0lCfnnF@2{g7q>_DzhsHS?F8cBZ=e;yOvUm+&
z)y}^{ZM-1TSZO6pv1VU0>|$Z+e9U6Bsm#>f$zjQH3oGS6yq$kDPT4=Pz%22@!uqAP
zkxQ)V8W@bh-C}~w=cP{A$~r-7)AXZt&d$yjYkgMJAgH^e*fBjl-6CJSNPq)LyLL%r
zQyRw3S$HB^$CV2gO22>zw<UW{56O96`SEAZi}40BsL-6Oc)jy7Zb7!z*M|++m0ZRX
z%1+8tf$&ARk8CO@+YvhLzCw(=-<R|G+1O>>vRlxX>;l!SrU>JdmVdpq`H=zRB@uFO
z-@Wd3lzmp7(R`$@jT{LSg_Av16MVkN++a$7iMo-j&Dl6l#-?kWFp&?~od{f?Im0yq
zZYVklNj_qN7&S3JFsc9dSuC;1Om6yTMdu3_7Y~CndC)o!*H?S%q2UqBD2oby3d&}R
zf(VB1VWuI%N?6`J@2N^g9m$saz`vze2<?)tD@MS+s^*zq?=v00!$Z*l&Rh$(7=R7^
z(hDS|(HcK!ERhuzb;~a(@X?5TB%J0jtB<15(@vyDfzvdH#xLyM@N>^sj?LgXd02p?
z-}x{%(WS;TGbZL1g4%~Gn-;apkSInlTSjX<;&<G9-=C)|OQPkkv@a}-RO4R(@Cp^C
zP`k6AE&_xN&5(X<nUvlQwU(Yv8A;1l-V$qPK@c1I=TT|6NVjd`pQ#Ke?>4F6A*nu{
zOklxJ$FPHAN6Kz3dpgN(t~aWMGPkqc`Yfcz5y@^O?BMsdR9LLw$$9Gd^+W2=L#jXB
zyO<dUVFU63(vRt2T9ANQC*5n*ez`>8MV%i-#U1kiAI_r`y=|<W9=)wrQTn(Kx3A~C
z7+3rCHrJRrEv~OTe0dGg7Afn|3~>u0DvySxjmNX?XVTfayJnTwg0&2SN1KBxJq;49
zU1aQCWHRVi9E_TJcM1*(W7Fr_@L#w39Bf8t-n5M$e@ZRTF@Ex?SE$1P?5kVT6+<5P
zuAHCE4?*B#GW^@;UwMrVMh|~&q4IevE|T{5YW;8e_#S`13tQESGAN+!T*Uk6(W8)b
z6^SqwEqgx{6&<(lFR}h_4)xc(X=4#pf3!=nQjMF7@M<PvKhdVYJWVKh<6^|7TW<hD
z_#+}!Ep<C|X<Cn)^pDc$@uc64<kJO7Zr*8L8~=ExGBmW1rmCc}D;FpOq6cWt#@Xu<
z$Gf0>Vz{<j^DPvrCFj%DVq(m<kIb0%u+=W7Vf)j7#q66tKg$=o7j{Cffk-$NMF7VW
z_Mgs@$jRJ7TzBzW>Pk|LZ-`%UMC0Bmk3Rdz%i@Ok+Zb}*Fxq*%nlH~x6&`D53)j?#
zXYG79Jl|O9A)Xp?TFc8Wyw~#i&?f%9zTs8%t~GY<hR@>@U6p2{56XT92w!K@rXXbG
zHd9l$&N((uK>NyujzSbU=Ol5?I{eqVvig)vPh2;Z>=)Z!qOj`+FhrHY_Wy9q;_})#
zy||h=SJkhHiYX)ZebpK)ipI_MabW|jY8}2S)E!pQ@d0{@iSv~kMs|F=mg5V?+)=aC
z^<^(YJ8r)zD?54kp!aD-?{;O|ghhFye{PXN&`g?{2C&mEXci+}>RsA=OT?&HTf~<-
zU}~H(wtj;OT6<OcoF1d(wUt5nc@_f>YxQlu2lnWW=8245dDd2DTUqK2ZXZX5!T3XW
zCa7hos@|Rsb4&ly7W~e(#iDklj8`bu9a`SnaokEPAhZ+LX==HA25eXY*f6vvTtA3F
zyGn>4C)`3F1~XOXXb^?yj=2tklJ}9~?76z?E3ZC)n>0nIZ#<@gqQ0i6=*Z9Pvvsbo
zL+Oi5aHbHTTGVtxg>Q3l{vfbot>h#gDXNAu&g*pAL8)X<0tgVFmx9kto#p8qJu4Rg
zvGn#z5%Fr28^5>8hUB@;L_36bIM{D8WD)ju$&L3@wQ)JIoKvEILgSb6A!BJ4$9{h-
zGx`1_V|yYlK88-7%hz}}7OHHLeP|8BK39zVS{eDVyzZcwZsw>c=>64G?Vr#T<4YH#
z2gASLw12T_L8d6x)ZBDyJ$i2Noz}B3UzPSsE53--CtD-44N}y|=<ODF@wr?+O?tj`
z&%qC_i80LtQj?2KJtq^g9?&LJZS$#l;OZofhE^W=L~R}uZXa^tL-^M>E1ivF7b2&0
zbBH@igME}S&q;*jhUOWZ&6d-zE_4z(%)JO#yzSU`V%mdGvcKXOw{%~{5!$!{4?Go;
z2haF@a)L4%M$&ax*%3{HUgk+LYx6?i!5%kzK9xZ8{5vzg7SvvG$6Kk^g05V!OMSUm
zemK;}Ov+VLJ7_Dbr{QNf-R2eX2e!7Y%`46z(rm#_i;ceK!E%r4>b3{X4sKvqrKqTA
zU!WHn(9?B0(h?Gya*9`O_SrAhS8J-FH8)Tu?nq2dMnc>24o3E?vmM0`v%^8Z$J*H*
zC2RvdG};}{G(ttMg1HGb1~;ZddN;fJHC&@P&JF5I-kvl(gZ2~i(rtO}@niSN0db#c
z@;|60gTyb0MGC*ACbSmjV(bZ;Sf9S#!b|Y9$;Pg%X0P({O~cusM-p24ZX*SI5EF2w
z6Ep4{HQt7!@=k}ogoE!62VY6xXO@|u%BJsz^rzWYc{Vt18`*DnMvvoHL`C)KR<==`
zqNJW5_Icg7ATsO|AgRh<ZmE5SY?^F%yZ~ojI%8t?IrEwA1v$~tB)I>>#!2{*#WNdr
z_iP`yR5p^oFaGqwBC!&o`5N}i%qaMUWun8uKw1iM^MCl|2^Yjht0dl*h{|8fkCHs@
z%qZzTdNIz<e>F4tI~JGqYP`yZUXtK51(rwlshxFmBdCX;kU!)m#ca>b$HPPNi~JOU
z7akFH0PH-IE^p=c^b^^Q%wt5S2;&rt?SEO<ET${@9I9*ai0R%%MlNNW^;-G_i0>4;
z4_Gd)ncF|&)}P|zICm2B@`%fYt5okRhd1Y}u-xih{JTs15iCSzE#mCe(<?ROOCsL3
zc6KZ1xB%OEy8&)}vm?d7dfvcIoHf*x3XZtxMRyLf@`o*QF+%j=l10avq-AlnTHo}!
z(sK@GcA3b|YP+am&j89n-jilQnF4E{io#4GY?BE8#{~DM=j!Q#2_uahOmu-sC7nv_
zwV)pC2VH52KrvU!DU?x^=q~~P2~$zpf<&{uoMbB)>M1<L$VL0TC?(TXO~XWbVow@N
z5{^*4a4%2YH6OBEPG}t#L`3n`f6*LRkafAGCO6mHux5XaTiOe+Ip$<}%<(F5(r`Yn
zxn>%qp|c~11T&Vs{=T&<I9HrHZCQ+nhxi}L4#C3p^VLB^SFLKfmNIR-8(M~u20Z<t
z#_bE!5_5dKoW8v3+Xj8fSBKU_N{)Iao8&Bzdsp5RwTWO$%`_ca5Jq~X;)|sH2R~eQ
z_NOnp-^FPYHB57_Q2JZJ@V!F(d}i)SL;h5|rS(zigWnSlHv8ObBD@gIS7{oY1+Gc?
z!nc9Ir!ZdP<hy&-<1kUec9c%dMzyEkVXd$FSI#xw?O$bYu}n#zK*W;zali54N?m^J
zL-{t}n!G!3v33yKc{-KN!SJh~W(;&i2@SWo?m{Kma>BJpjeY76^`QZn$iosgcUbH3
z96-A!_wHMq2+PED&WEulXI$S_QIov^!gv$`)dWziQ+Pj=?ovAOoWoY<*?U|25-u+D
zxi2ekw<0s$#ZMZw82+P>yD33sB(xSy#Cra2(E<|rYf;waPK0{&S;+|bxT*A87Oo7f
z2^4WB&GEk<dbyz;&uzlJA~j`F_A&R$J60y0dV*sw-NvgVLX*eq{6q7+S%hk8h#h!t
z`?KWR8x)xgSFcnWbq!O*DTJH$^@{XpQX6^Bn3QU6@kvw6Og2eF!Dw;A117-Ec%Mh7
zKp~h*OCOv{4tw!!PP`pQp|ZxV6}#>HVRTZ%)cm|Wj4v@<I#kg!#LTEyz?FIH+;fy6
zt@6%wDZ?;a(aEJ!TA9z*Ui(vZzy}J1X0l22?vsV4%Hg?62Bt83vWP5u1KfHv$#BjS
zBFgu&!nR*xrR{pS;?7pY{1o7qm+eC~aERv<e;7f-)y^3Y%wy;yq=#nDht0IQ6+)JA
zWj~s|dD?u(lb{$)@Q#!Dxp16c?g51?)2n6qLlYla8Y4OxMmt+>&S(3_Aoa#AKdatZ
zc!2tno}%K=xU9J1PS6*to{2M<v3qi!GQ~=X<{|_uQVUXC7u8E|B?po%An~l7G{lF-
z=Tv!%=?34K7ki!!BMvz~N0DP>KXoD#^ORq7iZ?u$yvuK{8bicVd{sAtRL|8Y7N0A?
zQ_cVLp{cAMuld_t3?%`k8w$r-t7|I^*=_RNo3JOS2J|;aTZ~xh9ik<tI6tl{wwv9X
z;=Q~=7I1X7D1)To4S{6ni^B*#t7rA)sZf{5@dXY1Zyi2m&73~3yES^kQyO$qCReMs
zXfa=OzNh3meHI|`JVR|P&~#bw*|{_2P8Z@h4_yp|N+6pD>-C6$w-06X+w?3Zr<TP}
z61)9kr?Li4JQdGCq0JJs3qkjAifWM(gtOk!!q+|rs5vTjs3AojEXd)8TW+Q>z~DT{
zGn=)qKp6lGO0~o%=ouQ*aUv|qh;>xp+bLT(LHS8?Y0x%tC9c5N+?+%3soi#ra+LPL
zfo*0(w-xVpNvqbPmrt_=n!THPY(-2P?2V+CJ^DRE^#w6*BB|Fp){jK$$`u}$5HSv5
z*iOS2l^REP#SNLcmyp45?kMG{xAOVXHMR+#uJmOJ<i~9G%qOcPHMT8m@*HfVsc?%b
z%fHEWO(VH%=F}waD~1DkEK=sgv@mvy^*NC%5&pdM8G*;ISxP=X<IH#-eew?Vp|*sI
zir4Lm4~c^%7_5XJv<LlI%DSvy`5i7J&XY9ce2r!FR!`0yM8V~W#48yY#stTq7xc{C
z3HjQ~#rn%89p6XW--W%z`+3C!XK%rQ&o2<x!b7oT!##L=GCDAmuOT=qB0XY2yQ?vD
zJDf<ocL*n?++0OAWBQ7OjHESkP@sBT<)%taW3A+{JLcyM)a6@U!`X6q$w=E@nD;6e
zm>cxozG`yzEK+4Ku!6|X=gyFCc2;=B*wI$q0h3j_gWIC!!EP_sGU{yAGGcj%cPNsC
zAZ`WKz~J*c4W>j&*bMuu0$_^<1XNE3E+4JPqRf>%`!FKrNtZe0kBFtRtqYVxW*huq
zCNHtLFN;#EiS<&|GDciDNqNRezEvboFq11Kasv5~?_AJ^KPl4dgukXydo=3JI0LgK
zTe!7Y#^>SGIs7nlzwpl7l?r}O$(dM=yr|*M;SsLke3sa$R_n`DjoJn~)tXIBNfd(8
zb^O6Y`ot>8jAMQpcbh{#$F))SeqV0eL$|`rRP^gS^*;(yAIX;PCv(|Kf5fhCgduDI
zhYb>i_RCJ?r8EhTq||luwGYypnuvUC>WMDj7OMyxZ{|gd<8@YVu^(AEIO0J9^F*y&
zvVzH7HmWq_*UwVo##4dqRiDmAIC`Z%;fywLj!}+_RF3o2R4~1fD&RcK)v9-$PdVqo
z{P4p4pO;knlFx{AMX?hP9kPO7`&1p$`%xu-Q+iRGmcGi$a6FnEoZ#qNwZMFdyV<6+
zQ@--5aWfBdPPc>|cMeFCP+nDNeqjJJ*7~N1rye!l5$1hEK1HD?-}orb)~TT3aK6xH
zD@$`)_{2-w&u^w&@aXPoW4p9(oQn^7VL?$jag&>z<=<NPougN_J)&Q5AoR6dEtyo@
zP(p2MBb14Xjn(?L1kJoX%<NCO3X;zXo@$I6{&?AOZ|YUxfTysE&~tOX$lg0wE`;lg
zI1N7X5+M29LkCZ@|M6ww<J<dEYH9;-$B!I2!p!jN&FHsp-!j_^iJ-4f?#6}uPlm5c
zfBM=iFE7_wGvvNNV9b-Y1K-ujss6nt@XRXa(`SgCoSJ#j)~)>y<*dP~TY2TDwS@Rq
zp8;>Wd1UuJDu1JVm7jk%nT@dmGzer-fiITCbnQk8XqqssFe|MCWRkj1`p+-f`+Uob
zct|f$VcnCQH<mwZ_i|qgQbMo0r}}&XxvHz9^Gq^5G&J;7>c@}}?!gTZ&vySkyaR*b
z;FsX^OEJ(SR&*hy+^0;uRQ&17ve7T_^`paBf7Qccc9Vv!g&rC@&fBZK>Kx5D&4VR{
z<=j}|;ND574=TU#7#tkTY~PoIn+zcIN_yUlq}(#9_RMLzDdH{QuU_SSS@di*Q4ys|
z*@x?jE_MP7Bg5YnHWiqk6MHr(y4&&C8WTH3vv@Vy8%EXosA;<b3XDG`ri5&-iJNB~
z7<(6F8Nk)C>ZC4)duiT#EDWFT7a8uB1<>e+a|Q(-|6U6Pvg62AkayPkwp5LTl{8k4
zNDTPstGc7(+pPL+#`jdzEh@>au0<F_R|Cuy?Q$P~eX3(%APr_p?f)fchJS72U!OQx
zKK!`)MU5%P^)edbv%1SBra>C!8xtCId8+;+!t^;6Y3yIgDRVV07Fid{H&RZ2^b@YC
znq<w&;t+zyjY)5$S=nzCl)+wOZZ!y--v@O;F+R0?^Ms7db<QIgCc4;<>+hX3%3Qx+
zMZFJ2<rNZ7JV^x#7eVjF*$?<RAg|wSEq2{RwA@7Wx_{*PvV`-R3E!*aT&@b0dnyxf
zFUf?;e)3K}IQpDWSkWdU!xVq5fro}p-gK<WbWB(=&jqm?SM>K#L(GH78iE`iFT`IU
z{rK!0ar*lUlxx<_rdMxYDR+wL>>PgoT=2Tk>O}8(b~-vHY<k+|bOVFAs3prW;fkS+
zE#t4da!?XAd-GdL9NNV|Q2dYRa>y@sZ>nS2$BrHaMwt7l^<SA-EP8GqSXg8upuDUt
z69wWWP|08kKc$uW<;#zD#LcUC1u;xI5?uy?Ie5mXn$KiF1C+^AWI0_6;N9fpWE17i
zFUbB#I|i!e<uNZl!bQfv;1zzt4X@m(p;u^=t<^yW0D$L$M`c2#<Gn+f{YLx}Sg}&q
zZ3t>=>HIPlqi42R)({Day3%)<TgDa`%m)fo>A7l8DIfilC4zjT(@k)lR3uin23n7z
zZt4g<cwoM{tb0>2W28_Yh6)>eTMBaedf-wEFw%dxy1q7-M0uZP(5N)ZW#%_s=V#i?
zi}ths{y61wwGY2!`(s2znjf9KghG2e(r$^`QY;Kk0bBa#`~G|$G!|p#OvZs1Qv0#G
zwKmVM|2Owy@4E<{A=bzp3)Y8NBVD7?#@KG?`-$wYU#TLrwY8_=Cv$YQ{`|V%pG$@w
ztdrsw&RL{|{6c_UO9>5b<Jq$)yY@t*G4%3)&STeYR952ekEcwKU7xsZ+)v-a>eCn!
z60-6gUoy1gD=O{^t`hh}v!Nt^{{VdbCH~w_#bKxc&)aqsP|<^I{8mF3ZVU8c!k`g*
z(Z<9CN$&^DuD)VlOyoa5{!-B87klh_XC+@OsCADlp)&|0a5LI*D!ZNO8mNu}75!Gp
zF}_=oZ#e$*Q+r>%47v<3fLKc_nizt9t`)Log*M4qpd+0!pp=7l|A(*}%YodzDY60L
zrRcN@>(to)x%~7e6(_?nV{u*%Ga9JMAOz=X*8^~X90Y)hnz0U082`Y-`T0}^0kEsP
zpaLtWsMysjVeJjZ?nL98(x<^%uVEAyAe7f5<_ECIJ#8CUm|HKzj$tmDy+D-V6U*NJ
z{QIYxJ=M4r^|+ii{-AO6$P12T=$gHc>PW0WAJ&aLo{Me?d3l_pw?Pqcb5asXy!qs7
zT^nnh#Yol16OHq^!=B4x`!@DJ3qw2$BhO$;CN6ZQmWkS~^fidZ#Gw}m+4~J3nyl5m
za`MzEi}|9psnGzncF?k@;SBkk-i!XSFM&TcNXM5e*oz;d0tE!8k7nDg8If~aihUHm
z4>Kw9XEJKDG;1${Zhnki@PA%@@2fixSRk`#<~>njq@to?3p8k-MtLMzA+om@B#Lzw
zfG4~!K2HgCpgiT&WpR+SR=%*OaDLWv`fs)K{jc2`fBpL6Nyh1IzvC)jcn@CREW?-!
zxq1~AW)k#ACD&L#ysjN>CXiSDIHwE93h_|jS&$L_nG1hv{Y1aklHC9FGq9K=Sq;l~
zbIT%HGwpx8agYJO_ghE(uYb9S`FGarzkmA$6Og`e0H5~A%RN<=fBzo+f*$i0`xn!2
z@86yt2)Mfh-+u}-X#8K_4F98m`8_}AuYV{x7*I-l5Fh!!U;BBC-9ZfV6Ioe5v}Pt@
z4SjGIDgNKf7bxnasVJ9E{XgFnhJ#xupZc#?-T#t<{om*P&qewF>w$L>nD$Pg>9@PP
z%a(BM+CTSBG)ql<df6&_=Wh4Oi}z{VZrr$1!;orEAajx66xijNe8>CWEmR&Krg?Zy
zH~rO1L*0)g2M_;qT;%1o=zc8!Hd8IHPf)K<&}m{DzErx7(Y*1{B|hrf?0HVBelt8&
zB@A;I<NCk$?-J(GZ|$DHmg~UFq5rj+$1#@!Y!6@@2T-L_xUUm%9cgdec;rFC*7{lS
zl#1PKcA`{FO^hlium`xS6LDGp`h;?6uL^nIpraE-z?LLFj&cgVZI}rBx(y!ra9%sP
z0mrSB)tRm+7VU-`SFYS!SXjtk?beq~Q{|1Q=*}~Xb6g)hhTctJAtfqi#-<i7KD3x@
z7QB49nA>UHOcH~Uxj5b!(E)=URv^W>!NMY~uCA{4<I}5r^U2TA(bpm>7CZ9#Bj8XW
zCDH{}^X(w~tVYghdLJh4y1sw^NLe`)`nqF*l|akGQ`l?f({TV3uYitNnT(6@_4fW;
zS0@IRAOS{bBudA5cIOzUQHcjvX=U1w$AQBK6Wk7SI(<KWjQsiYDXLI8^&|3)QjXE=
zo3L$3Tibke>_%1=>y~3M0xx6Kmrp}WTcd5Vod<`L_Mo=W{G&DbYGTZz1OD`i&$5gM
z`aj4_Ny^ChE(Bn=%Q<WoM`BcIF&IZPaM&9Cg*Fu*E(^HBd&#Tw@pgytK^i|xUOg~1
z&FC<}Bjb4LPQ>)>_^DHLZ2t9HEFk8rZV$Tns^v5^G)(<A$T)eFVx|_unrx<)xJHdH
zO+}vB_`zqegx30<U(_XRX!-bxmCeF9OunrzMeG{Lq$ss%rq06Sq?=DlrFG1vnHBM`
z9mc$*ZV2Vn5DU3oFDxczZs&-1>J$r1OVX+fW~+_bs0JG+lRS^z?au|t91jq=$jK{o
z#BEkcBa*zD&!0c{rEBQQ)b_x_koyM)2FbNVKs8_o0=~Adudj7>+{noDD4-%34FvRp
zV!#7Ey+rxF#VQ#ch2vH+RPN>h)V#WhuFSY|<m9tHcH^ELX2&YRf0Dz=UT@u48ux%P
zAT6^8Hisf$Hh{G65lk+<J1KjECK73?$PWod&d0DYmDJo*7wK<Zxq9_xd3u*Y1t`|b
zrG{h&1qD3-MGX3RSg~qw(Lyo(pmW#1x!fWtS}!@8_o_&}%!!$wXKa1vGC%*oFvkv0
z$Cf_2f>W7|d@MslLp)psmF4A%;Sq%rs>d)gp)hxPIK7gB<@M{=2v5T(j9pl~(GG{L
zX8TiTIX}>|Su!#*c>1M9L|m9mhVIvzB+8}e(JN(D!SC+H$NXGefN^X@IenHPkJD6(
zn3r4`U9p^d&&wO4PUM6sIR+|utU5m~q5jjHmxDtAZll!7RGVLLaN^92LCf^Vj~~$?
z!l`-47zx5ah{0f|I@6=jYZW(5SWLylz#sv60F)ZdYW%!g^USnC$G;&e%|JQ#MI?-u
zjP>?D-?9QLnFNoH&M9baX&JS?X1zY>%w{$wR%-%N=31kK@C>^$%FTH}A@{_I6DRTT
zI$j+*=Ii4#oaO03%AP3VL*-|LpzA~jNh-X69Ndf+eYotXjTp@<-WVfYneCB>gfMoR
ziHYf<n3#-~RxCQl1{Sbo`pwZZ&7TF`=n;o({>GJ^ZrGIYU{14G3Sb(?nQ8jb%F2Mj
zfKWyhegi5l0ro``;e|(oPIX2z$3@$XwZkwal0c)vy{R|PEG!jb;6#(*3e*BTc;E!0
zIX`{p%f5Zff|HI81JtbOf;l?qskY_6B=Y$2V_0P=Rn_pb6g=@TQl(`YmDp`hQ%j?j
zO;qS_Szj1l0kwiM^Lnk!gu8E!CaM&C1>N0|EKe{BH<fcAp&N*d{@zk1{dRisz+0tK
zYLfF|+(UKbM0T&elxu3tFjz*&+O0O<HUb0nX>?p%&(;M(LQ*$E#@i%pde5giKHWXU
zckVQ1U1b_>CGULs_wPh7_>%>|4E0b>vx?TV`1mTg%5R#A?5yGnZGjs9#1oYK`Qp;-
z^?H!6Dd~<lTWKt4!Iu-U>U@w+mgL*`Zkh~=@FlFbZ``P|hH%CM#SOMb#>Yt^&P{zB
z_27*304R8N-=L*UGzTkM@&wk9iPvO$D0HN{MN}e!Z+7{<@A|s628_syHU7#0Ixb9>
zGYWCrYg0*SN}i+tS`B2ZA#B>$3hlGkfUT;4s~WZ$7Z*ng#-i8+-Ye-D(%bZ~SzI>c
zm`CXs8I&h{^}*C>CM%?WyFpLSy21;`Z=sH*k;6el8$_AJ*S?BMD=6qFTfnqcll1iT
z3P^snFI>3Lxdn!X2r_|z(C~X81Y})nr}X71ayJ2?L+4;6phH7Ez%U1H3JM94CL|>I
z523E)Wo>O;5Q_>QCl@?x?^*-7$=F$U-cqX@J4b)w$4P`Se~yY0->uw%9<clwWTfic
z)ytPlTXz%U!WV~K4`6<!!Il%j@vndr<^k~_^A^y5h}Q-XY|DY#^dCr<A3C8o)&zRy
zm@_gnMQ5K#Nex{{f@3JbUMA|}?fvZVv11_>4H5hUn!Dihh~Or~?tnyplFZ)1gTBWt
zgWDmOajTK>0KVq+vVV!N9y-y|`ThI-mU=lMRE}l^tb}b4l6Cv^Zum4V8=MSJI?V2i
zdgw=stlHy~kW8)GPggxOx{`)EI)dpMx-{0&An;%$Fx110TMOtk4}^u^>-NIX;-8jv
zpf;yDr3QyU1OAy|3jt<q?9S!OmnF)3VyYV(t!kQ3!hb^N^fPG3cmPEdaU2|*Ip*R0
z^=y1MG(Rz+gBtXT?}!V&$GcO6Bp{&0dfTA$XXcP`L+`6}>h-hb0~q^f&Cw#Uy8>8f
zHBzSFVpIT=r7LD`o@D})b@)?=FqmDXV*5NuI_H}LKYzXwN19#cjK^r!3vXRavJadd
z3uK?8mSS*8o3eDJrIR_$#(atOt3F(gWwPqCkPp&8$ReUIQc@b*npW+b^{V=MHWGFt
zgGOGEXI86qIJEWM1z$pvPZ3_b2PGv|fCthhVF4%1tg$hzJl_aF;!hWdT7pgMD=Cqg
z7VvicvnMbwGiC;`1HNk4h@X5lNM~(d5D7x<Lv1mH5f93o&WPg%K;aT$)<FI9upJKO
zEAoqLKpdnuGCG<^o@q8N(b7@}(``I_eJ|u_ps_*8s5bOsRWyWgdw>Ou>#&#It`FkA
zwR(8q<avyBLUi<f^m<Nq!-NCP%&uO~>9(YRe5-jQ;|PeUD3NWGcgg24FGUSYULJsq
zXh}!x#S7#M1TN3>zuK-$F;{}qm4qi;UHzh7)|#Oynxl~@lkgcHs@L3bt~bx9^0}6l
z-ojA%>eqU$7I=|b<pJKc-YB@|cHB2_io+yb3+@WFy<-VRBpM<@MiIT%_>Um4v0>V{
z(j-JV3AbQ%6;?0~PGlarMkdm=41P2u6@6(Z^<1*yMzbR9DjH9~Sj||t`<*>7A+h~u
z=^F=<H-$T-i(HsbpJ)1udRmPjoa3^XdagS&n=@dfH{X-fs5#J<B#%ycShc)&?=@5@
z!kINV#)jd1(Q<S5%o0^@b)mx;;h}~s#lY*bnol5)><V&oJ12`?T}9?jU|wwK_U5L8
zeTn(>$(2rav<??iQ%t>n5e{SFSCioffIv3)OMuz;qTr?XAm_EsN+<&FK$&_P^RmQh
z;gyItGc&VqNXWEhU0j5to*wtu6M3hz<cTn$y1NG*s|!QjRaVu~pS^>oxtUoCUc~kW
z4NXEF$KAVkCD=V{ZSj7lq36j1+@W53vf}D0%$1A;1zgj{9H_KN_vr0RLy&VGAAfds
zxYBDHa0j!lfrxQi1Wp`A5-e3^tL3&p0^^5{UFX8HnE2xW&fd-jBcWqs5cR-AY&JQW
zKEQ!|3EenpEfZg4`N?3<N{5=UF-YE`v@nJ(N{EB`ch;kd$O#ByE(<vM5(}9&RD0@M
zj-YSt(PIv?T}jP;e0w)Y22`KGqa^gjNm7bBm!5K9^bZY94h<!XNC76~9eFSjar$%N
zq3MuHw@8aX6q5+^8QP`jD~p}fIYu|!IE#^XnCmTSa&3GK17oPIx;oIH{8J$uPd3|S
z9T2J#-JEragjvMWdE?wxbD_nXQ*<qCNqOzyQ?2IvWA*1uQ?LyyXjp`ii;0U1Db`F<
zDX`S*%g>h7_|Q>bHH~@c`l!g(LYkYAv2jtMvN&6>&9tjSZLOSq*c?Qr+m=7!Tjg8M
zCRSAm+d<=BUw463+Nc5nY9-N7JhLXBdYuB@|J9{N{*Z1txAbcXaCjdI3xCNub@F7E
zeg^|(3cCFP;o)=UDO&Zx3|rh|qi{*-u3h_XT9~R*kRR6ArypK;Yyuqgx3aR?S+IS<
ze2-@sBm~hz_j1v8p`0+=VarlPN=(dMt1*1G%^n_B1}daQVS$115OOF{%%_FURqrP)
zC5esvyS+PuHz#wbpKp-Z^bvz|{HYJ#k0o1LN{XJ45)Hgs;mVpN;2d!)^5p!Ez5QqL
z@Z`5vXK$u8sd4hcThql$eYSz@WlM(}dK+km!++XcKo0#<dic;ap)XOE&Z~lZ)hZMZ
zTp+9$VyIVHSxHAvU#E#6qX7NiYiY^l^&p_2NQE@(iEFvt8O$Bc99Ru;NCNsDCu0J5
z^3OdXLB{xz@+)zNFtkx{rh~hodmwvv4Q6;nJ5$&S8zMblWg$4dKunzQ_U+-7$rd3N
zt-2d4hk9Zzrv>mso-fCy^P@y2**7A>c=Amb*iJ~y{3p9%#JlM1bYNOAtM1UMW6{;h
zZ8W3-cu&!FG%Zy1BYKLiyclCZ$dJm{#*eqpl+dA3p!vjyNhL78yuoGZyD$d22vJ3Y
zUqbMwEZG1<&VR*fP^82c+Jnef%bDKy?~muGy_*!LN3Yk*MYyU>@WQf>9$gZd>Mts&
za3>yKDJ*V4bai#Pkexzle&mmT9aPv3^Yx838*k8Eo@jy=+M*rM+tP=oQJ5%$293?l
z&FJVtL_Cz^F|1e{G~9|y4%42oDVpIJvB11!EL?6DJ$Z6djkr!)k~aoWzDCVmxV&hu
zN$i!F5aVJ+7(EO7TU3u^>vzaOxH~fogAqVqT~3%D88w##D|$!pXPt^=Xw8@3N7xkn
zCb5&cv9psCnr&0mKSr>Q4$`QL<5L7Q_mvS?fK3R|#5x{$kc?WoYfu=1nH6FpUs|m8
zp}tveLM9)sklKG}9nEiiD`&bugUf0_#2YF>tlAA^T?V()##e6dLS9Y-C#;`=hUPIu
z(toeZdWsy_x_t2Hp?vNt!AM~tPtp32+tdIS+Yrt>ok6*Onjf8mLmvxTUzDq6Hjf{F
zgtrp|S4w|#c~U9kxeMR!wi#}mf42#O?6zDJjhkx4y?n)z5qz7!%J5Vg<_&jzqQRx*
zg)JSd-0JM_CrpbEWl$UdBSG+rB`32cmwH`QxJw07f@H%M1$9F{M6vs^0D8BL#t&nD
znxas+2RV1Uxgn&|Q@weaJtm$utxS#n<>?6A%EhH6IQVld95EgyD-<PrE>9Xl#$aYm
z*CHIC$MJ%07x{vEdbYJQyDpqNr%rpH?q<j4lJY!iLZ8Y)Vkh0SijIFnA{;kGnvL_u
zV?&Ijc6V@X5Y5A|mkID5&CqL~29~0`GFhcS4vcp*3Ki>-E#|ZAc|jf!CD-dO%!6UO
zmuGwPq??TgmIv3xdtFvB7`h6P`*&pwDG5QY6k;eJR;@as7N0Wmjp<@*IDD>dZbj%l
znG-?i_syKPpNS+$ccJ&hbJy8nL7q#I;M}=EO9>2yfgNH35~gQw0g!x#l>=K^AI@7)
zm)<|7RavOq9a{WeT8Tc%xyN-c1Iypt+4A%8xhkU9^!Xi?WcW;G9?nny5KWxjc>vew
zAjB1y%=M0jXymcCOr{nh?f_^6noIAs>B1^B|A-V|bJ#SW?Js%)(Hy(SlS1qC9cV@j
zAaO37Q-Sx(V^Ma7U1z#I#df(#h=7c2=BqKR&N7T$&|CjCXmko2BO1_Bi|-bC7L%)t
z+5r0gV~1}85@0>Ly|EN)39~*c%{cfhSimD7Ng%ZO&jgi3XuNEaTu^G!O8XN%$d-aM
zit6iQc+V7M_0BY|VfM32>+vuv^wNr8c?C59?-;FdPF<(#I4UKvb`54b$mMRndH&+X
z7s$LK!L|f!+5%+LkL)@?7TaNtdq=4Hf|e?vYP!DyGA8L=P)&%dKs-qG!Hh^q{;)Kk
z_;DHXVhKnVg4vD3dx`^S;{vEJa>6{)Jh;X?3Wa#b4NwE^I!x_&^5jXLaHx_F%!UG#
zECD_v1%&YG%?)9<Nx018V;25vd*tU2Y@;468O?*yes8-ih{(+p$q+f!oto@dzAd~i
zFx`M4b+HN<PH_w>`MKnz7cNADiD_AW`=hVGY8rqvdX}>`eVGb?;KIDrc<#Ae0kl69
zf^8}hR}YUi)J_57fWmE1A?C7Oj_1QI?@h|f=rVwgjyOquu!X@KCZB6J7-c0GCFxDM
z5V&lmK?U3C)|CHrqB)gceEbdRf-K3)0p$z${GMh9;pqeUkz%xkq9q$rPhBtp{^P%H
zvj?n-^fsGj@(=7=BZU>P2as?mdPjrlK%xFso72c>_nn%_@b9qTC*^IP*99{9S5RNS
zz8^Zo5FkECN0A#mxGef0d>bS9b_HM$P>3<yW{lWks#OHit&?ZYv;oo{iP~I9yW5*%
zM8yRkf}i?<P79ve{(tY&uKM`cnEz(B|J=yPNNyNDxYE+a(ONds5n*Wxa>%yFkcKdQ
zWbr-0zt%-qSU4e~Vdb`ju0}G25uF~`S%vf$gZH2?_q`u{c?SVL{_qk<{}_MStr`on
zoGZVf4>`g6_iu1;beiAf;!;8fk&kkwg4Cz<7FZFhDFIch2LL^-D*-A9bzpM40x<t*
z?u1OWr%YS+-puL!%-(NZW^Oq2-Nm1RR_gIuxu~7j(5%CP>;C$o-qcc#c<t~&)H>-)
zd9ak4w89V$w7TF^^8{bz<?U-t1t?Uq{V3+nClqctibs(QIoVb=pTp)v86LlRe(95o
zl{~hPB12#?HVWzX6W3vcBb6szx#-xhj&LZ#3=O3r6t|wfbUIBeb=jn_9>MNxCQTLz
zK6>;4UU0Ov@!v?7V=N957l0==BO2kh80iK03sp}yK)8od3*oi<nt%gG@T8cKN3Zn;
zn|^y;e?wK(55Nh-Hf=(7uT6p%HyST2F_}gFPv5W?@0~w)E)v{MP%1f>c~ulA*;bBD
z)3<30Fc&wVR+tRp+U3gx58>9O080D$%a>bT<7V4YW`d)mfH1#%`?j+OjW}!;pTGOQ
zxc78d=;&&92I%CHKh$f{EyZa!hR1Ru5C?>AZf`F&#_~ZR0pM4R<I=l-@hqLLjF<$K
z6eTt^7Hh1lGl!jI<UWgrduN~_1(2JTY&e!(i|ZiNaE9nsiTNzxGNx1GhCAGVi%c3H
z0N@*7Dq7Heopamz6YObLCY-i=Jr7|KQljs<8&?Ejn^#3#baZYQgnI)?Xp1L01_H3)
znU2)rO}!N5JSnh`H#<UiYHA}RBlUW7P4YBG$H%8Z5#ImVg6bj63(iIDoh5m=)0j`p
zuv>l!VQ5~y?&cPDjfR=IIRVe6Xxjuf`3l&FuCA_FeHRxO3Z#Cp6PsmRU|uH=;g>uJ
zFOCv<ZlnSH5$l|s>-PVM<@Q5e80D;EGXtez^It#j+Hte8woalmim*a}=*t0OD<*kZ
zAJ$JUO*IAPJp|aPRu&e*47?V3F2B(pW2^;tL_r6_V}l1Lg+FlTlVdr7eD3zi8+##n
z&1Z}0cDVT2Ie38XX;gYD{&xVrI@=S{jh;@SVRG!*Jc#-w9IAewKib){C*c<_sfBi2
zgFgAr1WCY8WOIyEq<f+MS857IX&EjC8lp?lgQh)1maZj1J&KyW`1W3CPE{}&8zMFf
zK^>IlMK!SNL#Lz*;IKf(xY*T=!Nj}6nQ1MsGH#)KkB^U^tyNFbma5W|l=mb{N8R>{
zm4t-n=piNe6TqzJ#ytkN<9HCJ$_~gTHEhB>9r_%O#G#1&9Yr+5hWbOh^)ib_#pxC@
z6B82&2MDO2P2rQ1r+}_OgL%DPD%)uRNbK2E%mVar7O89F=snd#5rmVQ0}-`S($W?X
zE7cB@yG4;<gB7o&abHP0gt1S_!c&9vxE1bA#PaNvD(>ZhLt?Y8`HXW!+JH{hR9B~8
z7>7+jWQ~kci~5okS<qW*n!Fc8pfQ@FX5XLXKubSx_UyLctUZ*F_{zE(X4TJ>ZCtb>
zr^Wz@=EHG7<<TA94^SYYg-k;Nlup(}+|gUwYFU?)O0V$c4!~8JIoy^r@n~oW=nR6i
zAT83~K0X!@<uMNvIYpwFPprP?Z+mp37Bb^B=-IPdZiy5))<PzmC}T1u-F>EMaSRLC
z#0@sKwti1HH|YQd73I~n@x1=MNH7s#iFjDESbgvoUs=CGMM%`2o=IgIlK%vU>gip9
zJyQ%}NNO%Jbh0HI-l`pIC3r2dSvN{yQ>M!8wYh?MCE5?@l!xN&MN#x{%YfLbRTmZj
z_k4)ZXJ^4rO%8kUGq8A$I7p<$|L@cslGEn+$5CP(;L3uS@!mqf)Pm-5#zI0uC<I23
zejik-Y2SfgPJ+l8eJz|4<dEorx=uy4N#6kD)9FI$wTuynvmT0wm~f9l{1nXRkiVCI
z^?AC0(}z8oH7vWu3cixLgWb5Fr=^3xys|P0l5?ZVy);3F3KA}90C#6+(~36}>-bek
zog%fvZ7R%X!|jkcCf=sjkN+AxB5up%1RMa6H~9InbtcAG+*eOZej{V~Bsp;?jL>B>
z+ThqBKe{702^*fk3#d7o^pR|VzYi|Hn~}A_Fwv0~LBgu@)R>aT`VjzPMnO!m5@C8Z
zmFPUrMESHiOZQ>i3@BVo!_y!}mC@1BI;F3VtXk^MpCQ}$V<NE$GXTI7V}2e2<`OF~
zHBO7HMi8pmH)-xd=@LbBV|S!u#TNEKUMN%&p$P71abO_PFa+<3AFKtO&$5+IwI=wW
zr6s)qD4A%?bf(Adh=_<hfD|am#B#1z7dd?7NYEa-Yq$$PD!mIPS1u8Sfb>yhcQhKn
zwOS3BxWTlC=%RjBb62+|HfU2mO|`G8s_F)d=by!s1Gt0~Ra>JOa3|wzzQMp?$vK81
zL^WYv{5!clkXNFK!;t;X#&|5*)oLOZEqA2H-iXL)4-E!TBh~~`Q8q9%%APh$_3Yv}
z{nmK(ncln>&0PqPN?Q3L!03cBLXaJ3(y*iD2a+vN*CB!gA!fC)q2V@!mpW7Y&%VFk
z2YWHfbs_)O;<(Y+HHc8vx^(gm4s@l0y(LhVO#1Wc^Kk-tMYb#Mb#-;3Io6PnC;+?`
zx;O&mGyP=6%!=U%IHLi?#`FC}51v1drcs47+5_%i;3gyqJ!sXUrJHYOU8B~n0J0Bp
zjW@4fyF;$joYepc@h~K)Y=A<lPxZu#lSTmvONtCNx)`;^v7?p<q^FO;HpeYM;~fhw
zeGSk<2}*nK=Y_zK_5T;(ecA@$${Dx|HJn9uYqyJcckHCGkc|(w$*WmxM(Jc~)w{zP
z$o06ZN)6_ZR<1FeH_TK#$EeR8>^N7SGoQtjFB4eVC@?(|8fY2gwgfo4CM4fNza_A<
zQ(+h`8%ue0Rn@Inv7n)4j32l_#r_sJqG9b;z}O?QTb*HA0SGay2_8cl3t{pw>U)0|
zDH<VCm!29Z(yj|sub~D@&anGnZcaeNsWgcD5dg&L#1DuYwU1%kAQ&*8PRY^RSR9R=
zE-gh$UA?^If0qW~fm)`|oja$LspSEX*i>cPLCjCJ+adMZW`><#?(*6$-^d&Q)u`yf
zGG}gw>(XE>+TtHyh2ro%Ax|<BTL<7F1w-&Px`JwnZBD0kK%K)lpW@=hSg1z4b9P3f
z1uj7GCRYGrodokQi@?g!SMJ3}zha=wyE4<&((mcz)lvH9*iElpfFfGc)cE7lEInOZ
z9zT2b849Clo&+a7L=_9US~i@X+IfL(o76Ql#`^UgCfY}%i1nl`c-`OGv<@WJz)84O
zqzXlD^hw~7$ObW0O^*&B010Sv!dQsZqE#ipPPmFL0_q9x77eGbUKKcYmh45o7yX`M
zy;fb|W36OB;~_K<2VB+CwFRID-~rIEvB^TYG`0gkyJUC>_*sSij$D)B<*^Xs;8Z|G
z*bF)!p(qUqbD~v9TizQtgrU~_gB-EyRp<0X{c&#LwQJXIz_hm{&Ig`k38*JB+Iq29
zY!YHnALM*F5D8d`23nMlIBdEi4UZBRXK@ho%kDWFyVAzOH7m46^#1+(FFb2eUVKxF
zbXWV(<7AXOc(m5!{Q2|l5Y^d`RiXfEiM;(nn;$p?7UY?R#?5goY$z!(Ce}FqH74PK
zu#x9TNc1@Q*^K*pC#yp=FYlL!#vwtEam3eF<cC18VtKNKH$MR4SAJ3mUi~$eA@fz1
zET?B+xJ@M%_>@xuE^PYK(t}>Vt3PhkdF5@5P_@5Vjutr)yVq;44`cXbVjdDWK>1iH
zOXnk8webu!a2RYxy&Q1HYMXsT4`A+WYy%~%8L~}Q`}G&^oSgD!poG&w!^x?b2K<k_
z@LU2?(j<r?bRaX=YmRp5arB{<Y==PYChxfv*1Zhgulfvo?<c^ACrgh}%D_9TMI6yw
z3C;W*po|4I>c5wOh~Kdg%JQ+10?zroW>i#EQxFeyz~8h&G0E%MhYsL1ymW2(qAq|2
z8~|yg1HB^h)1@^{CMZO7yuss(M<wH+)W0;LY6?aR#dy*77nJc*>xjaB2wZ{`#^UvR
zkZ1ajV=uwENGn<mMytrMkOeb<%EtnAK^mf#AHee9@w~cW3@M?^g}o}7b)yzhjJ?+x
z%z9O^!xlO<4IuEOPoM6<Z_|U&7sa)q7_B@tdI0l65sF{#Xi<H(uRtE+Nm+mlTM@Xw
z7PH;5Xtbo8jJ{jAJgpE@%P1=+1JpVNu2=@bo8Fy;Ug<rQ-A@nzBxqxehl>EjG#aSx
z!w<`&-{W{ZiD&yyj5r0wi0)jIsMWc?B;Xtf3JTId=^b7{9>7+I^KYO!ai^RMv0F_>
zttmOji#w3JqbMrWN@C%a6JdEEjgSpGG9-5aY7_8NC{+>aD;LPflIc=&@4@e4Tzm6K
znG+_1pl=8q?j+btS5HqlsCu@gJYk2hvHt%n?#iQ@ys|jjGPT8yol{pvp)QDkLXUzL
zS=<-Yh_WgQD2fOus3@Dj$FbT)E3_zBH$V_DtW}6C5<|6=5v&LV0tA7Ifb6n_C4ppq
z7YTTr{xPS^%y*6+4~gW<``)|v{+9dRTeg}^fSxxjP2_)4P#BjZ3Dy~P(92)eoC(_h
zt?MHNkQ8tIEKp%(=i8pr|JGE6&;M;#6_Vv!AG1;+@;|+L;}!ncJu-j9`SmB<<LmJs
z`KEtlAb;mLj(+~{8OX?Dur&blsj_+M>h{fOEq{G&a~?C#<*0oM3c~z`1PM(?6e547
zp(Ar^^z-S3KYMavOg?#GrX&B%z_Ul-p!Nrcw28&C1dWa5#l%=8*)(Cd$InlF{&|5>
zM`vB02&keNg6ndG3segv%>d9q`!KP#^NB$v?3eMeD_%&0g@AZx==i0VmpDy^5`;QN
z&$)Q*bptOeZ|{u4IxBw1z<BT!*dvRq-XKG6f&iX^-w3c1?^qC)#Wcq!<j;yj$U-0d
zb#l@mXvagzrfi8-%(WxFoeYEUK2So|*VjjswuTAdd0ky8;YPQrjH>RD#gbt-w!9~7
zM3D{)(}j7!BQ5wsAve+F@Di?Y0i4kmuvzFPA7FmDa>dZ+&d<TBDKO3zbfPl5g)c*=
z__#?F-+M0sQAqi-Bau?<$FN}O(lRMPzewkqN1;(n&}KN@^c!;}_sZd2qe6r>5q<RN
zQ9gJY{S6oZxBCMJDBP3$BQ)CmxQ5rLL?>^f23`e3SOn^f+1;CfWFYSANI}qzhX5JY
zP>nDb?%%sJ$%eBDkX4Ig3RQ6ZH`8wr{|}sXJG-5<-+{&m0m9ugX~%36h?>Qe+NjY3
zFe!EB$av(`7aW-W#ZmS&`P2;DudN$)6Sea#JT}K(F0<`o_Q9e#&%pZT7ZhkMDFaSV
z!w)RkX1RxXbS?}fUF_PN=>9^M4rC~5e<rN<v>t2=N_gIZDo$hW+=tPbaG=mB8mw@`
z3NwZo3fI9ViG{|JMA|!W%H+vAls!6kcmY@F?tSp?5PXCSIeWOeMMXto2IPz0o}9qN
zlH{GK>ml`9;qa@NOr4jNoCL~M!9S18-VDRe7w#kf&gpo7GiP-Vm48YCamea5YeJ!t
z0zQ#Vk?rl9dv*$Fj1Ur1J*y?{_fq%|ItY`)aY!ySJgpO02&CmZF_PQQF^I8nG?>Fs
zW&z21-TL*-5VmNI5H}ILce+?uBZXOij**COu9BB{t0r~c6l5fb4OFg_xRSZF{D&J5
z?AZ{%k%G0r^mS!<cz8g(`~>Zhl}#L)@GyFUw=e!~?uIR<$}5oh+_;R$ipm~kL+>97
z2E5v<*hDfz<&15CF%}y0Hk9U;NX5vYI7FHa^wL#1@+@n|L#`Pakz2a?k{2Bq0o0m~
z2M)x;Zsyxt;Y6)I3zjdhtY!e6u6(-p6q&+!Q$g&b7G$H0jbVN+&pMHSn*5Oam#0r3
z1pBKD1dgS3T`WTZFUB8Hg;Rv{uMf_UNW(If8bd(<a@8>Ltq-;C$(?3jZZ9chq|q4}
z8D;{2hZ_*Qm)i!PJ!=Noujl=04_W72gm;tRt7Q38+<=XqD;2khA=tDs0ytba&L#6P
zui5<Xr}fwM`TcnM{f~74>`ORs?=(G9kFbEf--6>}S>g))x&)8D2jv?HKyDF(M&Qzi
zun*AP^ysK4nqIUvVHMP+{VQb&jS)<>#c37rS)@3zuIOr<kdAxjZwH}s#-3Oq8)Ukh
zOX7kdUvBZoQA!+~1WT(<)xYOElp#M~kY~pot4A|@Wbp#`&OSvH9|HXHWE88Hmd9pf
z?0`q%0?CpR4s~Gw3@+rzpuF{<fGQWFcqxJ7VxP0lj2SaDH8hT9hC#|`;`TAssp;nC
z=AqwyyT`d*2~?D=G+<LV?tB+mhP3oAC(ejcRGOMGt1cLmjv8%A5@iWU^ZVtRzBzaV
zH^BlE423y7UYq<6gUC=`dgy!ZGawUsI|L<9gGQ~C>?qhRD}=N;IcUEIz78yx2pVP#
zwAFcRsFh;GMJRsT8?w}z;3M09Zt9|iqLIDvj`(Ov+%)8AWLzYcy$wXUf>ZmO(&KCm
zrhbxQ!^wA)A-a~!3^X-2k)O6+#82E=(q`lL<An$ACRtd8dJaELIC&EGq5LOLdJhJj
z?Kj$7yo4z<+~+b$)p$3~CCiGzJGGT~*r^2OqP8|TGB%&73DJcG_CpoJ@@i>oYT*{y
zeC>@a7H3+(X5N(EU5f_8*8VE5y$Z%kvq5dcS2^zh+Ulg*<D2jSnNOa?8o)%~&Gzr^
z>S~qEnml=b=0{BC;(HnW_dwABT8j?)ts4x7(IyH}ze0%k<Xd-snW4=ktpw|oG9BKa
zgS$+W!f-75{eQfuVcse`JpBuB|Ihi2&l&ZQ$;!$$L7^j6<jRWiGHpHikcGel<dW%r
z&$F#nW@}~^g&s*W2_QnDl&Nuga;?ZI!r!W0WH}X#cx*4c5ig4RF+2=hA+f!nFOsmZ
zgUjmk^Fy^*ZTH%<3Zgp?SEg+Tzy+nolUA||b7foS5gTZQWVrz(`|y?X!I-aqoDLX7
z@?KQ&06=3saj}P>q%r$k$cE-CDz!#$NqwiXmKnrpf*%`HeAa^Nu=4ZEc5rY&&Akxx
zhu9f{vvVukJZ@ng8*y;osuRH0i}Dr}<a$^q*;p>JNqglT1cz{?%s@>ow@C#0O7Czb
zzZx#}R4Ml7asm0Pk|!^<y%sQ|)%3gBs2-4UceUGNsgqqNg&#l(0Y<IXVYU<gF@(Zv
z;@-9dV($<si(nTmNI#;w<L;r$i1<;Uu$pidQVD`Bu>_7zP6=T59(RUMzGKbr@WHLd
zdcu!5e80JsR7x-(d~i(jE1ec6ETLX~7*}mwuHoBVscaFXSPPPEDY3u`yAsU(4bbj>
zcjeX8CG|H3Hw8{gyYtH?M)O;MiN%<w*TTbWw{43A9DVt<diXZ@ilEy?!zD<rE-_#v
z*XEo$<Z&huo@h-yJx-Me?6tevcWvi2o>PiHy!0vwUY6ICjvYH@SQ#-TIo!?|uop4J
z17d1y3mrHLA7%`IyVmU4t5E5X@XmoCbcr`<<oO>tGuEzAwGl=Duz<I~9kP)JV|wA#
zv_5u>xdW6Q37M<u#+NF^$c$RYZP6(eZs*szu7m^s3pudCa~8o$oaWu>khJ6WYMe26
zo7{>r_R|xgdAYm<EL26sfri3q;fAGEnqG=R`VX<Jt9&r*mrOUBZr)r%Fc(jUjfF+*
zQlS7HP;@c1eOhmpsQKE97n>1o$7D_h`)ftd0iq=-ZlFKRYryCYA+ifV#ohp&ya{=u
zbHEpAwWoYv@a1GWJkS@80%~VS-v<)9ty|=+1xXYx{*aqhRl7qkUhKcAU<g5q9|ZcE
zIi;wS9WJ;I#rq)229n3`LvT@3S8sukR*Z*pP27(CPf=8r;0N*Ve>~sfW!CF^R~R%N
ztMdcbAr_M<2!_jK5WqGdgv3%whX`ewt8{PxJt7g@4q$q4kqaQF6*snQ+`6@l!TNP^
z@53N>WOoO}+y<@GJK&oQD#KMrx^&_iIE=eYiu_Oju3o*`!op&n3Cc8cL^5u;;4Fb)
zqzOLQ6g3kn!^NT&4(y2jXj9Q#xbR|+FhCV%cw3KO^u!NUrL~thBc;QH-S-Eo!Tw|X
z=`Zayn6+8xwCsV8`XH!F3VT~j6YV%Ip=M@geky%nk(Y4t&i~WNrxh+7f9}i_TGf0h
zzM%i&6x2?L65Q+Pyn|mwyx+|fvCVYu`fr}XpHDCW59yKg%HHX)=QpVBgyV{?z#Jsi
zQT$`PC$&Nse9wof##P6Mu(mZgvfOpgi`^A-tZWDQ?j~e)slScskX|LQCs(VBq|3Pp
zIY*bH^-x+@SEt8F{Yvz#Ww5LaU$qWn__THnNFP0p>GkE7E<9xRE0yT<3ETJCROak7
zDo^`bQh4NS4Xf)fP?hy>FqdHee`_l%t22*>$iuJR?VErE3g1j>y6up`$S?+;8$%m`
zlk}%{mO*df2f%b5>xmwf`st{!^f*bzdcGh=!I-Dd%(JInA=H5tLW=tlGG@>z;<3?@
z+LgdfKMhC8YQJt)#N)Q}nCRDG->>!*b;x@jwbzeUAqv`62-{+SszrU!h54}hu>`QU
zZF8w9OWJj{5=rmQ%*IOB6g>{6lwHGx=(sJHqu_+K3HsF?872)CJJ*PjGJdp`%~n%=
zFGr-47`sV-|9cq<D5tDm0Z(d4^2>%6A?)$(JXrt=TLbP=j4~Ov0CLg{Ysj67WqDFq
z$^(%)d}T}@V<a+uaLD~Y^s;CTimKug(-lcr{u;}c3E1}FkO*pNGqzd8c?NKin@|Co
zvpxFoc1*|KUaaIe)Un7oJ}5Xsu>q;8Yyi!VtbJHBzQA-sAx?(0LXalK%KUcP-s!|Q
zA*n?p$eD!L+&#4OzWsW|mS^|Ur8P1M6YEMv1_miI_uMosb@e=F?2_m&=|PQ~0EM?1
z?<>gs65TC8VwFOyUka(1$vSYCW%D!Xs7>rQX${8O4Dq}a-ECa-24kTeZ>H8{fS;Wo
zD(gO-<yCcK(u%>SJsq_UAnl9nrCrmEy8bmcwgpWRM>7ND<1`G-<?}x<Ut3LW9YH2S
zegr)6)nTfE(dK*Nyx~WTi>gJSg;Bb&{u&lkk*(J=$L3Umov?<m-LV1zpAV9^uNm7k
z7lAEo6;Kl$n8A1icvRkIM~~hn{5jms@l$SYQ&KupT?{w2b-4YSGgl$(QF9HlM0r3P
z8PH&?M$`JI7;3GhORbPD>9}{?&BDT=KAy}LAvHErfKmbcA9-Dky--Zz<%{9r$(X&t
zWf51eCcp^3Ho~v#>v<aU=dZ_lutX?Bv_9VdYA*pW#<>b+b6qTeV26=a^(KZ7d=E=5
zG%T#4>(P@CzyN>hn?dRAWgvhsM8717^q~_IRSb6kvX`JL+hQttf(3ymP|GK^)CNb`
zfV40Ss@Iu4dp3a%6sAkKa975j5}#sDjRzf?Ugd#db3yS;je1ZE<1wivuHY<56@`zz
z!!wAm?wmOfjy+CHOkAwQ&--RZG$ZYf2!mg6_&VaFl*J$IAC0_IdB^dbUBG}N0NiGh
zuTz-|^I1JO4%-=%hk|W>tLm&-OcEwo3)pi3^q&Oa9Sw{$rhi!3GB`&l+hSpysEEU-
z`@Z;YLmCKVTc7&Sum<i$!rUj@^<yAk@^5WF`}NmHg)6|YVqesVyN4<qoTUg@q)cD~
zrdyEeEDp#}7P<WN)jz0q4^lCT&&c>Jjxol}F9jcR9Q+F#|HJXg3=la&w|gvmW{A`9
z_;U2OABC67=>+AgM?33Qj?7?%m27%CXop0dsA*(m3sO0>XV*9{Buh9F3?L%g-4W6K
z0sc^z7zhAZ{#cwtAPM>(s)E(^WFD2(C!v`EZisa-fC%6+4aKa1x>cHDZx34C3XyDo
zYDK=04D+}L1JwejEP{^(8d4zUCSzu^K_mexzw&;9!odacVEK{SK$LPxNcRBqH2KMl
zh_ee^n;yzzY3*0T#gTmdz5YrQ6jo6)>swdgov|=K`qH1o+P?Ame*ydVoBg_DSrK36
z_2>U3)Uq5qj}F;y^%OJzU;3NJy#2I)>n8ugdwk>hEBvqM?|zv=V|-g#?6RPC1qxUu
N>$a{<UcKkkzX2jgq+I|2

diff --git a/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_27_0.png b/dev/recipes/on-hoeffding-trees_files/on-hoeffding-trees_27_0.png
index de067f5558d5343721d857289777c17740350544..6e5336361918fde89fb4e85da4e62518a437f321 100644
GIT binary patch
delta 181874
zcmag_2Ut_v)&&e>LBs+_v7k~cNbjI@P*jRkDIy?EM0zKL0AXW4Dn$sr1*HiH2uP@*
zSZF~aHPTT^=n+UUguu7rx$n8&|Noxn+e%1d@3rQhbIdW<8uJ(Vzy$rkOP$jy(z24L
z-GYMr0##*Xyq$vlynKVa-0w*H2Dr*7?$*PU-#h3AsZykSim$E`EW~tz5AgcE+N~yc
zQTRyPqfKr97vAxX&QEJz);Jw7mXi+RIR4-r+b+4&s@m;ujtga$(n`=1f+P3txzeaX
z@cM#!;z#>nO*B1xcAef8L#?c*Wu0IHNAs`KU-{S~#o9@gLFCar{2JAl(dypiQF6l#
zO<G5x)8nPY>fpJ|l~;1^I9~UUSpweQ`f|26o0j8q5k{7(?mdc`HE;Aqq|)+Tq^j2F
zZ{^&%S*QK?kPY3Pwu_C;Iw{XZ!K2Dcr$ib_*s0%J+I_!kpc_5X`%222={z`I+I@L>
zVYGz9VO&bz<ITQ>PmhgM-B%T%%$j%lx_EC>m#<p)mUsMnR@M`pCzk@**e2cW&8ucF
zST0_XnAMLT-uPfsU1Gk7fz3T$&a`Q&w=I8k0)?sL$O+vnxv8;rvsg%Mt~zLXDOAeg
zYwpbclU3mns9BTZco+WxaI!xRQTCl*cCbO0ZsZeg&f#OT^0c%<lZ4z)AMTS-t0#XL
zG%gt#f0c}Z7ik?9L}6b0=UBmEivxCJ8y%G$4YO&c@^j&rQAv2;{_^!xmbNXrzm5Ix
z`FXjoy6#|$+-1hUZ!RIP57yZ`AD??m{Z%!Uy71VWpEU=(@8Z`jX#8hN6=7VkC+%Q~
zP30NHKp-xjE8RM|_6L29J=$j2OadKSoCWOr-=o>>kQ)~pTkF+kQeRKtDb`Iz{^<WK
z-|D8@7HR)&un;z)O8$W1@*bZ6COvTuTUBl4ySzMr&zca^FDl+US3{33lt3#j{GuDB
zK{t6$R$lxcyE_)K9EL4wKd$2ayIP(7>7L{`9+v4D!1NmYA>)Ng!ElKrS?9rfzRLLp
zN>%goM&79n*S-wcG-ith&a8iQC5X+@qOH^aJ7(}q_QdaDU(c__Xp_`DlE&fvlKQ=n
zqfn_;aPa)&{-bz@@t(X!KQF_P(Lv2@@8w;gKWE0`bDu|=v~_!aeWkGY-q8lO){_xl
zT6`eJedK>m?)UC2Ys?8omhEM58SlaR<ckLJKR9!YN|2!l-2!`X=45#A!t$VIYI*%!
z?T7M5EJGc~z@XW;o<&?9mjjK&*|6957Yw*Xi2sGWiOk#7^juQi`9Bxo;k`f0#^(6p
zCqt6|VzKelFGYRlMPyzIQ4U<A40o^G{YmeZ##U4s@H6B5-J1eW7j@s4#@1CzxZ1Uo
z`Wo_!?oQI*dq{bec1y~n+$>4kr+E|j-yc^<QUCjIa77*)q>E_*zq}pmC;o0!PfX1H
z>7#h_7NT!sU(T&N2V}xl&Kx#zGq7#Kyr+bfqU}$0?s;Wmu;-PGS831dS&yoI%l3+z
zld6B5nl17n2W#+?*>|jyj$I_UIR!>b-_o>-hZK)QFGi1zbxf{jW&%?sQ-in@-#d6C
z4^>gFk=J5MGhmjNn~q(y^1Klkee9y^uCK?9%X*eJuG7PJzxp4yOu4mJk%Ntm1KXMH
zAR?WX&vV=eH@!SB&@Z7@M_%imPQ*Km43zju-bmUys52Gn>035FJdzGoN+0>@-tvCo
z?j%B$4O^@8BF$5=pxkUnQ`rHHJ%7$XcHp=F2<HtQv`j-9CXPd0UixN=Szm3#LMdzA
z##n5biJI!3_c_j&>Qy<8M5k9!6b>7>nR}uyR#9i(?*AqE1$}e5=49vw0sxBkjt5^^
zUT^sCC8D@lc+i~i7nu>%)v$+Op{ylN(1dq+`9v?38nJB1j`sBxY?p*{UB&e~izUep
z4-b}=cr)XvYJ+r1cOUb_V}>X>A0878j)7Z8Es&O`iI*Y7p8xTn$?PlHDr{^iy_=c_
zfv1WUtIMz3q}4aNiWc!>ngbcmaANkIXKS|)YB=R)@;@9NlDid@i9-3DgZ%jp{yR?a
zB=XsBWQ^RoAn{N&)aPML*2v3B>0>5tCbX9dY*-DXB{_m};B)8Ksm&*^IhUik+<oz1
z|6ZU=7Glo647b>0fwBQH^hP+LtGHNi#%fQ6i#Y<8I5Y!7E$Q0V|M45y9$<Cgq<)b*
zBhUax2RGdZR0o`qgiBXPhEbP^Bg1C+W#;g>W;S(8uY(bmeOPDV#PjpJi1oVFCD{|5
zyNl`{5>6FAuu;eA7`XO(SR0hgUcBjl9`@&avJHQ`4zEb`z!`oA{@&H~Q-OmZs;Y93
zr+GpeOCTxA=y=)SxZk{*?b)818-z<1cDs6<a5wiLZRD{xargQ?+zr+cLs2$TY4#=0
zBb#<(+5bF$zBKFBvUO%R%D>4WUYwh&J*cv{=d7H!i38^t>>3$f_q6lt$Qti+Ve)L2
z0JgTEEsj)~%idR{&_XouzCVYO_d5ZlI2K*m!7o>DuiI6Mw^lk5?djSUIkiBv+jR(b
z@alj@N_Y_Xb%D3XRPn}>@z|zEahNJ@H(N%e08~6s?i1?$IO)rqjy<mmvRsucIxF7F
zQ+WRzqw=r6Sg7vzSK)yTSbLW$zkH?7w~8x0HKBWQ7AiQhA?~YuJLbpcDTSLs*3Q>u
zQ~bXU9=~bpa0?pk{oY>2MOU~?TraTnG`^B|PwA#hmZ!w8TL=EUz_&v;K)lN8Tkc^N
z9oV>#(-?4-a+ot`+|O{VP$CD;tuvr>#2f(nmY*T=iX7#!H@8)t7N0rE)bRVaPqnY1
zjY~qM1UP(=GB%dAxG26Wt<N-XC^|6>#dXP~;8c-f@hdan@z28k$5RuyS*XM2_meJp
z!Ge9j^l1N1OTnMPuUD@M&oyWr#tFXFOpvUs_cTqnVZ5()bvvMIo-zI^<suiwW2$ky
z!6nU8J8dlGz$Ft0h!YunMP3#><lP&z5}%_bz9alOz2;wMSQ&tA>g*h?uv|5_bpd!h
z<+1bd=y1Uo2IGAPwE>H~1js_cI9C}~>os-j@9}SAQ?=u+`ckl?X=A2&)|5n#BF-Z4
zxA_a?K)vXJ7Q?!7@i%Pu=ULbJ7pPORe>-dQBQx7WU;Wol!A3WAbRz%`QK>t0*+S({
zV;zv+Zy{7YQL?>_S?{QnM=wgvtxlMli}Pa7P%;kc(IQGq_O@5N_2=XKa{`xG(7?uZ
z_;Qm8%b|fYV@Yqk8s&5tp&w$3(NF=}HtYrWhV)ZlpU|gwhI?*=XJ${BU9prG#g@uv
z*L7wu6!+qG3|Q4ASI)CuiSJya@3jDs{O@te={HrI%)V!xbnk+N$IUBw#%g!#obB+Y
zih%>`AXyF%4wh+*Jq#@f)AL3;oiNWgj@4H5=?9($*-qT&=pk5s+liOR#LZ_@vY)xP
zMHo=dxa;J_sqn&&{d*PggzI;vWJ`Gu01A)-ecW-ynYS-K9Udkq<ihLrRDsizg19|Q
z2&VFXX;4O0{*t;<*5&}54|wB(z{&hvtx9sV($;T7xEqut<!2@D`|_se!|X|RX5%WV
zG4LeB^7XCv3k@!N&iOCT8}E5dTB%@v4d-?@5x<0$;gfJG!xd;9CKT7#Q5H&lbR(Kw
z-7g2i#DA(E{d?RjCr`n{(&2=px$mFMN7m-IdDsvZk2h_4&5F-URUvR>07C^h#5FQ-
zn7Tb@7nWj+0#Cm#h3-!WDkq6Lkqpj(R8Z06U`wSfmMhUz2EV*1*0bAz)xmjLX`{zX
z#c&m!{my?H=g7lL>tY{xbG%0|^Av&uq13Lve_$Z*`SX1jE?k&@d4;P-UnDcPv$Hb*
z&cn*s<_;$;m~XSW(k+#|hF-bd&MagjgX{mT%F%m&ZfIs@hpS&G&C!C>{K5UGhlAtq
zY{EWnG)XPTY%aXi0&I+I`StkkgkR+${_FXl`AGKAU!Uck!GePqUOg4)B}LHGm|L_a
zD_dLJ@ro8lQ({J7Olkd!Y9?Yf_59L#P)YRPI6?LdzLI!P$pSiRClV(MpkDQ1FC%9`
zAgTy1nxFORP3Y5jT!*fzR;{%Bj<VO9e?4R2MJN&u@2*R#+b5NAGm}%@MAPH_n_8Rb
zfmib6Ki$UB<LuY)_ziM_R*4#^!8}ec_sv?5n+3N9`K3VU+!^M812b`Jd1A|mLZLuY
zqXxjJ2il00{kQndoMCz|EERrie7~hGvKI>%Ey`*MdRgabr%WFm5WHzi%HDHlP&rDx
ziHUVuCLQz~qjqP&D2Y2jy2t@AU-bsi+%yz^e|qtSoKo#1%i+d9?PPdwcbbVAUW1n5
z`m%O{L|7WCyZ{Bx8eYq};XTo4H#XKSp}$3hwhG2W7o{||eoYmXlt@`71J{z}65knN
z*}riCi@9@^5*ix%K6u%NqMJ8(rqKQSq2aMpgxNt&z|#x(`FCAU@m~mP)I9i^JY4F7
z#YoVu&#{=7p@IF5F@dPYceOT^r^Ju`WLzJy1{41mjv^oO-JdnT<m{XyjU}6926n5c
zGRb|GR#wT@$79<lPqj8LJ8bkOlfjcSsO`;l{9*-8x(`Vkvx<=h`M~kP8_w%59`+VZ
z-BVc!;feWNRpNaqoknvH1iWdPWFSTJlszef(BjDP_Z}q_M>9eL2iciPv(N=gPr#D%
z2OjlX@K~6`^o$117!`8@<W1=+<0>o^&P{uXF3$ObAx4VDURu}p>Un)_<Hzx)HwH3T
z3rkDOESJrQrkka=3#&W{3u&IEJ^b*|IBWPeljgZRTxC<`VOdxQ(pcoftJj*#KHeLc
ze`gJg*}VDzax=l3&UO-6LuFAs5{a-i?q|JmGWwLa_ab2sv-P_3>87UDPsG<_2?D)}
zR@tg8zj6FOk?1JT^0hkpY<g8F#pAK|tJj?jW(1NHHqi%0mJ3@PbXg-ctQ-F(4yKA|
zeD7$h_!sZDy(gYxlm}tlkdA`Yr3qFA9XVc*$-nFQdBfDu42i=gZ>p5u4j$Uw%E7!T
z{4*S6{d~fcelx3A^HoPviR88SX)iZ5AN2kKJI$YTvgtywZWUrwO$_j^Xr=KtFQn^4
zQM9?7*cg{;FksooAbD2Zxs19g=@u~E_Jb4K2VKZr=4TFrLWzHQp18xCG)DluBVXXS
z%t<B8BzgRS3o=Q5$9%UV+0@uH)cnHbKPR89zE|<-$N2*Eie7bfwWp)wrGbF~mxV*%
zQo{9-69w#`l>2(Sq>tObR6+PeGq$9}VMJ}udo0#rWQ5~ANTVae5Mw~?h=fUj?ir_C
zOyl3f8)pHdm%BWVxkMh`XH-^figdYT`6|*wJHpaxw*^1Bxb8!lM{L8(2@zZxZ_C27
zM>mABOqG*Nm5*6T`b^WT_x+M&57%VhOajyxWI#;=p8Sdb+$z>D!)v?KKE5g}6cZ_{
zuTKZd0>J+h9=lklSvwS_QW8T6@%P$PoulgZypCZbdjmq+^SgrdSlpjnNN^T8ksu&2
za0{Gz;7pT;_72OZW;^CvB8w#A5tghsB3vg!+hv+u#Q0tmJotc$)f2y@{^Po9av<*8
z`fGoM*Nq&^Lbf1u;y!JfnA?6G{O-bfsMCdTe!WOLV!(2bS*#<q_Cw}zQzOySf>v>V
zWZcM0fy&F{ST&Kd+S;KUAuthB`y>}ubmXVKj6qMNR5ohy^~G5Hwb31O8Ft`m|B&Df
z;-#yZ%p0s17ybe-ELK0Y@66^FUVU_fWV)2yuo0%{b0oSAG8oQx(XTqXV_@U~eo_2k
z5sW>LQ2%gHVppm>c$a;%F`jzrzXpsf*U47l+xq!bOSyOdacK+DBjV=1;N!?$R_2Gk
z9;#e~eOlUhFmt%@(o*+*Jz-vKj@Ika`*R5gjnSP^Jt@0qt=O4GB?sP=p3NS%_=)iL
zfK<|tTX>7Rjn!(V6!Pr*>mA^(|96OjO6t2G!49s{>xxDOr-rh?G~o%}weXedn<op|
z#|rb`sL~(wjWEu6EFLT_k$KBRI#p40STv*U($yz5WHA@nlLSY_QYQBM4R4VnKUE$K
zFf|Q0ofGu*IGyhrt_{^f8THamLe93ydebr_nl)Ha0>tf2YthS-MGrj;3yQB4l~v>w
zYkL?n8HL3xwk0?G@c+gKrX6tZE8mGG`WIhvzqyIZnsohAg`9UzRh1=!MiRV{X_S6Z
zr5A}rF3iuL!1*}s-e55H=Y!&Kx;aBj-JtjVOGQX(I{9R=kmZcjDFa>@*HzU0IZ!gw
zM3R^B-pCF|N_R_-o&G75fl!iZm?YxxmBLSlbxN>KRWY)9<G%VJ^$b`6I-J;G7Iwwu
z(m~6X=>W@s+B2}DwCC9-XPQhdH|1q7t0aU{8GCIwT2HNOu(6r%3e^0+N0cD6&g}0e
z5Ud8NQqDD{0=Y3UTmU)!hpRlTgh>zfOMiXHt*M;996lrA(DOxULcPd)G=i~g^Ukq_
zm$DZaBIVW(&O#YsicVhE-+AM19x5J%O7XXkKeOG}mZzr;9{jYQ3-9gxJpBU|HzC3<
z>5|2dYrN|h*6bROk7<|=gdUp|ah)94ox2ceh93%QcUZac-;{YNLM?ddYZbvhUB79y
z^_1$uo1E)|npTQW4?laPNiC06dC+z0<jEk$a&>aK6&Or&|1?ei;H&8?7pUtG_M^{b
z$PP8H>xutTr!O72jz=dSbmSI^A31Q#>}&FY;e)w5Lzm~FQJ1MMnxv~dF2dk??uLNU
zimL>w+pb`9@o>_Q_`{rHrFSPIgw7mn&3#p)$Xl&=1hr?XBWU!mY^@vT?~28}d~LS&
zxoff6;bXRo1-~K3t5deJbyEC6dkP4<<J%Pe>nVo8yVI^Hjru)|V2n(tu0{O%DCn`Z
zNq2#Mqh(~=nzdP;7Ts@cpQ#qFj~6T(DcP7MXQmhjiXu(!&u_Z|<droNV^nZ=7e3{R
zwk&XzyeaSJkE=V^tG9FTN~6~#0@hq(-0ml3JA{&LAmk-S(^N+)@$r&eO8*_hdF^ca
z5je2cV*12p{y9?XDc^qR^Kl1t>1DgXFBj^5e%P;T;o6sPR5$rxzx+JPMAKTA&;JuI
z?ZD>+kQdkST4;TA_*zF1z)$B`VD1pdZjR^2qec4Fj`V@5ZrtxS^Lxt1S%1pYsfGP*
z{esVmWvWJx#}xF+QAUy&AN(IXKMUYyzxG^6^4eK5;@(~TZQ4+?tJq}q5wdlmi-WMt
zWaW{dygru)^AxJ;ymxIcb$O3DA_5&4_L1_@^VB?m{rGF2HXk*IbAx-`obm7DbX&OA
z1@CX~UcFuBanpP}7KqoVTd7;*LnPY;r}|(igZ`d@tx+e^<eXdIU~bsj7H@29$SoYw
zDb9!TpAdF|RqaR>LD9E}1DHe4&c|O~_|B?~06k{W(0NeE>U0#_Fy7wH_6b!%<atU|
zX52>G5Kmdt{Ul$lw7g-_d4Z_>q@0D5QAJ^Z)LYcKii!uy-sXeipPi4o!<q9c0Dkv~
zq(KqahNayhQ=|5>BGWaMbZW8i5y%ww*kPb0(nEZ+h>P8Vf2~+`+REr!sLG_C)$Z`!
z?_v-C|Hmu{SyMo+2dSKj=+^>>=|jlPDZ%9OYwY23R|I=A;o}{GE-Ye@eL#B$TKoRX
zG!N~WmFqXaGUoWJyI&DBN5!L&0v&tuxxq36#Crp-s;7Yxydin{(T2snsD43x`yObU
z*N5OL#*=M-lq_-NCs>6&zwKR%_QrvfdKA-;U3~jms2ZjrD04b-*xV|L#yCRGE8WVf
zeICA+I$`s#$N9+ua<tE;KDb1>;ivZJJLQJs3j@WJN;K0jqNyoL1F$+HHa0#<kWj&j
z&4rt>lCtBxIW2Ct<8Eokg=m8n8UTBWS?$w#eM9O~ajr710c&T=A8mY)14t=<pM?DW
z?I-`I%Mwnxpx(ps{Dh&>?w^M$O%iuBi9Z;ECC<YN0}`ogR7xQXtOJhL|93HmhedX~
zfSVMocdMCPxiXaQg%0Qx3;o%`adaO)pVb#IG-c5f=g$8yJ1cztbs;fGHAy71^Mx)J
z*Dh4;d3eZ0EcIe*l54;^AFLNKH1qXqcGCMT{rL0Fbm(X}EZM^DoOMLU@t5Hd9cx2L
zee}lu0P##~YS=X6_DE%76~QQ(T~_Q?Qr?2;rE^gxf;>GTXJIasyFU@cOTo3Cvah@E
zzp3MR4G0*h6?0Dh{mEd$+E5&%Zgr<4`uUxs=|W9-vv%JO*Uf#m;w1}zL=&egTYbB$
zBs<L3AY?NU6`ph+?UJ|V<t||<j7m1DNB7O3@EzXgqx2)l^1>9otm*hK2KhWH^IlmU
z>lxBKYYe`Y?Zg%@S*V4$b5Yz}gew<)We3_@@Ei4M*``&y38^XMY^@1!p&GgCLQ6Qa
zQQpr}!o;d~1Q==Ls)yPNLCnWcKGUz!#~)$@6xp90<FG{^#svR(=B4Iu8`iB#T^m~7
z2){{<VZT59>`W@^>gGI;PWZkdV^Pk$HF=z-K})2`Wrw;f{7pjqM1?#<Hc9U;I{Jnr
zHWn*TNO|^XaftWxj}FRe*OqJ{?f$fXMmV+b1G+K&;e@{{2P3eZBr%kE5-dZxorPZA
zQdfCG4p7W8m2^5{pwY%t%MkuPEJ$S@F%ddqBFgZJ`my@xhfeL{;b;A;$;h$_Au(n|
zE}8LCo^gKN{+?}j2W`fu%~g!}DEH;W6xYf5sn5xp5fvihL$QSqzsH<>c9r_AYctG%
zf1OG@%vel$$B#>;xw}l18L4Kr&*C5<lj{lr-TQThXh+X6!sDbo^<#KG?%e9<yc^-L
zaJ8bpQbKD|wOrCJ_-sS$f9jggtT;g|R}sjdaCQKMA+=!!m%OOdM346H@aX&o63OIC
zF1?GK9iQxQoUxma6x_aLC38s!7hL!dFwf>1Rv#0zFeHJuyLf0FDkZroR4P;;=8+0T
z>Mm`>aftfVIkUt!i_ST+cqhzz^zzibJG&E)7Mu0TQKRf!7WAH2hW`w0luyz+`hC_w
z^X^bDX0uBb?VXt@GGW<vNL|oxPj`V}DjPG>I}>?8fWG1wj$jw&jcp(og)gp6yN+}B
zlGNy1cE@d8@icKr$uMGL!l<PJG5%td9xvacF;CDS4q=Vi2Wzv=6XoJmFr#fojZ^L7
zrmczHh;8Yg5i4|iv_0C@ZUd`;l-HkiV$h~;5%JVMg^A=1`1d$ct>7~L<MO&w=dD7*
z=i`Q3r`9-*lIa)5j`s&avJBs1;nM>g=G)^HE;GeR;n2Z&ne2C~g)(PhkMmaiv@@Lw
z)%dL19Oe0+rS=c>qwrV!bKE3f`E?=kD0<-c!ouPexg0<BtU?qGEhcLWe7xmRoU`$>
ztDS>_QBmjE#suE`o;NxBj$@9o+(OsP>m6ca8b*ewX7>pw`ULEP_gyBW3~S%NYZ`h@
z_C$ye#BGe<oy!rvW}EbSKGHAt(rU=rDn6y>=i}jG&pz{{s`7c}Owp_{j%16Mb!Cb>
z=H_`W_lkJem^{~OYBT(dzm#;oZOowe@{*<mU0|6Kks0AzEol(pK;10?Wzn07m3)EM
z&I*{@+OFNLcoerX(PW>5Cn+Dw`^tzIy<qZb2?~6$cs2QvT&m`;lIdUJg_pt{inb#p
z(uviHMzbyi4tdxTMXHUHuiIHP{T(sgFl$wG-fqxt+-@-B{mf`gTPUgYp1pIEH~MaC
z#xS=%LQ^8_@I%7Z8nMHm`p{3;>bOEuPZlH2dqf0n5<~3l&x9+#`c2s)J6H=+cc7`(
zXd7&3m8zPW0s`=dM*>LljSGs33FWSAZ2BEd7eD2gsmC|;`iH6r#Ql&P2~e!ktjZ|l
zf$w=bbGz^x|7|bsnlFKmoy)xS3bh{8JU}SmDh5_>QQuA-<y|rk(+IOG9}>vT@a40=
zJV@WU`rHm(5TzkHq~ND+67kCMh9lG|kCCX|b;;c_SK!N3`D!FzQ#UXCD^lD$z*x3i
z{(_-!r%sHE_06p86SxzXoNbNUlN-_s-Y$LAd)6OtmZE3BqdyBHPdQAjOMQwQ(fsTp
zChg#%Lf7L(A1>T{l!05d7%uk;&vM~Ot+?sYWQ~ziVWe2Q1~9~IC)P_0%8a0<vqOh)
zH#^67sbDe+on5NW5H9wX#pzdN$~I;&Qw@nLG=OHAq>z$miOl(OctmJz4W*XONw3Jc
zFt1J&=+Y3VoT6xAo^ZPb2r$PBd}fkXx58H_r1Rfbiv0xwIY;8+{rFLxh?0HpkcnpM
zwJDk<{zB-a(YCv)G9z_ykV(9WnW)JLID%NWB-+q650gATTsGvLki-}`9U_g~Y3W-t
zs&V<uJll7$iMCSO+{!C<*hFZeCMVBpD3}o69pmpU7S|}Lp8uB2Z(H;r!t(JZAZGz8
zlU6RGRo$3I`8ixi@xBf35q%#8<XLmo<}v<kNOqL9Gs0S6fjB{GZKf`dzy_^d3#+Ow
zpsGVw?`TlJIeWOfi#g-j_}-B%VG35A(f9gR->mPeOKk`#%FtCkWUxoU6*CiO{K;;_
z*dbAQ#ZWED8$p#g;VQssHH`TZbTw*9*NK#!{=JWyNxYFiIbgW|_63E?S|?20IC0)_
z^^t_1I=?z=lPOY6h9|-O3HOsP?Z+=@W|eEue~j<QzIs?dq$YQ0*QgY}VU!X-UHLq0
z$}nY=M`cu`R=ICzhOB?A!~2&yZp1`*?c~FmI5cTDXIX`N>tMlBWn~!^6&@vdJ}S`;
zley`9cPJ^7zQQ9jDzZAQcE;rwk4b=uaC~vpv?j9!LIlKFA!q_sGOi{^S2bJC67Qaq
z$n4g*@p17}?)0saF8y2nBl?z9B00ct>n>snJ8mzHG_x8F<{OTE>}9f?(>YeXa<0&{
zHhd(gdf7hNSB;LDi7*PFl`sar*tsHdmj&}PwvBJMHLYrX|ALgyU`%kzrapG&1pi^&
zNm~%y7oCXhtXuC~(xf9F6X-}yy17+~NtR4p!$sr=Jz?1KCy$n`1M*QK<-$1OsJjUb
zJdqt<B6c@ByVjG=8w8B{>zK_-v(LjSGTcGvQS_7I;qosNu{oE;Q^k@;4KbNZMxV{#
z&2hzVi|y<9hdPHB^AJpZ;aWF|sa4`B$g`%RFoJ=!hpWA8<3GFvH!_`F*ro<h)fxQf
zbUbn|L`~N@n^L<kQ`HQoznDz8e@H^>ed|0q!Y^!AW*+*Ms^*qUd*#yMU!fBUS*J3W
zM`@`{w+>j_!zF&MCHUo^b)Hw6eQl|4k{cz!x;9(n+BvWSH&W>uJHDPpb0a{dcBu`u
zfy9N?HbKH@Em(U|aNPNKTkc%sk^S`#BDn9?{#~&$NeaI|veN8dwkE8(`RgCTDqQGg
z8Zuv&vRK(ma1dm_a}T(YPlL(NcN42E5j&r2glHgjUuK|gsW0YMOVpYyy_`-BRzF4C
z7-f_xn;a-VlI$Qy&%n1rXq7`}k!ug9hC=%N-<l+wl+qS_zoYmA)*Ctz-bzjZq<7z8
zZSjvJ${ei;7oRF@zBLay#c5s2%o>kHk#e6tVwUb;<MOy~;TL|IKig-&F&+^z6E@9m
z*0Q#vY5mdTOAEQEOX7Pq=^57<;i>9?2!16QXoxKC1`CCHnyJZ<tuS^h=w;IkEa2(e
z3?7LkdQyUF&qwPCR~M6=^QWG>i<VAz><lM@NjLMJ`5JzBac=G_pygGF^;tuE1A`X=
zVM}sS_6;?FpFe;3w?R)!e~uPK{q(gx6}Z$kM{%gxGQ#vZnK;2^^>Po}Y2CHhp$^?N
z8OkKw{kI*V)+ZD4YZB68SxSG`nEI7>te;2b7PQnTDfE&poYVzU!*Tp`D>vFY_cRbk
zk|e`ULn~jNSB$(PRIZWRhLZbdQQRnIW?M=>=e|Oo9lh{8*~07~l-?euh{+wjiEC$G
z)~P~kyCbhIT)n@PxWybQ-gGsVZO?Lh8RohNeW&#tgb#*4N!cu{m;XlkHrW_>i8>uk
z9#e{E1}0TbAkKv)bB(A@HAp;V?2gu6Y*^|1-i^<?Et|ES_9RRsrD!|e|J}3mGWb%x
zTD5?$9Zz=9(Ts-=cAy_bptZNA17#gm`nD`oCk^Y9QHz!CO)_Dm`%8BFBz0|4!G>za
znt;n~XfZ0@YQ<#Y!Uv=-Cvf?@5}ppe^0-1Z<6M$OoQ=rLxW<P7N!ytBcvqK&+=p9%
zD;J$yOm^!{c%Mb;J|^q$r=s|m;MRMU2lGfZPD7=eGOl(bV<*E)Gf9*f^!i4P!AwJh
zz^PToH6tzd{PQSRmvQf-)kFOkpEK@eG%_ASGv>3>e^J$<bJuHdJJ}q?S>dq&bUWy6
zI@*)DK}l_BZPj6twztY+lz;p!Pd&if%HxonwUvM5lsMeP^v=BCM;^=!S<s*%BDb1Q
zP8@-*ChKaHsj6^I#wyZFRh_q;FOOc|ekpnWEja|`?tEV6-H%$$Dn66E=)^G-Dn!YG
z=H5x_YK<b}XBzLswrM7YH60Tu3v-yUqkenDj1{$C78ylWWw`KAH<I{V_+e6It2Qbl
zu=j}q)x|y^>?fS-AJ^axBqvprxi@+rq<Kx~$hq{*FMSJ$O@8F;K-`|X*}dp$n;bi(
zt25PAxt3s%Z!|8S)bXX-i!$o13K8lT)4BxbMwjzw(MWXeHnxEF6saY&ZD*@8!{<^$
z4%4A>TX0H*@!i9@jNsl@W^6a|EqXC|WALJZW3mq7nYWE+Qb%3jD(DKR|9!F%$pIZz
z(jZT;TTfjQ)LwS!oso3rySzI8-UWwi2rk`)kC1@5ZtajvpQg$)O{vhWPWvmysZ2+)
zFv+~KbN9A$%9!}rdATq#b(5J68ChcOgl*sUU}Yfe;>4C_>Q8acp&0Kl>k)=-W8<+j
z+lFzTb!_s`Orz@&(Zvn9x}RYtTbMPhpv!td*pg*i<MtAovx3yo5kG|Ar_VX!`O>i7
zzKwj*D_S-7%80*57#^zJ4y(M40Qqf3Eq3Y$b@3gEj-WdE2VM)#u!rr#3aj7|Ts3FM
zZ)z+YSr<FIO_U>52q4&t!oGNRyzi*z9;NJCHlZ;Zncq73)Tl6w%9;9c<=OeAP*kX(
zarDTA<6+1ZF|vvXBLXFjt}lt>ooD!;v2U|0Pd~B3>|6~6^{?GB#k=jPZe|?Wx293R
zEwf>}abH6UNAK3&bkT1$jG7={98ywkT~b$Ns%l9U!$XxXRT)2!jINa44ohS(g^0}7
zn%E96jiEJ##pp2g73ST83K6le6{gjv7TKir`_o$)i43~LSTz~Wi3q>=4zhwZG;(|^
z*mc5SLk)_;dh0@76P69Jmt(WLx`<ysXoxG83Cu12SV7e{QRRnRNb+mnlB~8zN!9cX
zw68tYMr(W@R*S{6mQ4g;*saWATFJUHJVLjd=KGOpBkZ-<u%Y<6LF{DI9Yi>{USqY|
zN6-Bw@C01=3a%8s9Tq6)X}=!FR7;pfs!v1x=+|XV5jwr?GC!s?g>!$zzPOvU4oAgN
zYvv-v>1}~!G>^86-W{VA<IBVbIKu0r)C957ORJUmD`@*ze5ce)ai5s-?GYo4!fga0
z0$$~ur=Zb!k1^e;M9uhM5aXTtA&}x%OXsyMXmIegEuHDEUb59I5uB}37{bPHU4`O0
z<mQnXL;`H&@*=F_lOLqiiS|_qXsDhV<vuqQDknuOtx~^!YlweY8r1<WQJBYzubd+c
zHD%!6CJ}ljjYqVWj-K?bfH<vB#^=4#X1=g3)VdiOC08j$tA~kAk8oUHF5bBq*f~3r
ze>`?|CR~nh#k^Kxnf_URBBBeW1$ABAC-D6vbpm;SyYG1H&B|-KKi!7|wSS&`rd#W@
zaI9NRmO4+MN~h6Hf`?R^giN&G$y0jWC8~Os#`a0Jg<2<tSLF7qqGxXV=B*@5qRyjd
z4ED4#!q*Ctr5qOSvO6s7e%v3hpn^R6aDr%}!8r1hZZ0C1n<Qnh1FeOSe+ZQrF*ZGG
z87_-~-6<trSvjSinN|q+P{#YPbbCbU=90NjQ^VQ};y?(&e?6@jYyAADq}8NyaID0H
zra_bN=cPbe1dfobEdr}yEJ>&P8m>6wGKq-}KQH)UTR)QrFn6Y5>LHXiPrF9{VFHap
zb2kY$$zRG}F;Z<<@U!HAieId7%bKyadWo6vEc=k}?7HLIv~4Ej;py3zcEMBUeF=}T
z@?|3<Bj+KF(^|Nbm8S#Gi3y8&SU<NhGfcW8fb+M0ZrdT4GF8adSQFo(Jl{MXzKBB1
zkK7mD{(3xFVxn{F{#kzIl$Q!_IDykC2W5lz936T1XzBhHYW5ihDk6a!^1_5SMz#e_
zL>Et_%3~S}a3Sn^BioaE*;2k8PLfFZ*vZ=a1&8^qjopVi!ag0$=fgW||ENjHeohbj
zlJaQr;7FIA&53$8?S$67S3C{!N0)ag|B5CjJUQf>BE}s#Ldp|tqP3|as2wvaoWgQD
zzlaoyWQCj+AsK*O;vJCp?_Jm0fSEbf-ACpN+aokok!i1QivM-{I{PEr=PpThm!}G0
z^Q!YR%J*^q4COzMk^16%1_vDW^0H*LhN8Z0p_Jmf;Q;u5R!aWwTb$k_Sxi-AL~Ypp
zjxMVeF1AR!)27xd(*Jz^@2<A&cT1|56#TeshyS&8{Z3wU@C^<jz26d<KQ8<6ti_S3
zXs{i~bKP-R-d(pgY=@y?k|bRcJr&noc~^`M86>%gF}3t7Bqhuz%9o0q@zn)=BkzbK
zgc-0&uH-s&vM~$EN2La~dI**=hM=3Jj@X?el_aGyWYT!Bu?naSqZL4S=o5z=>|qV|
z)!`#u2_04!*AwammqvJ3F6e_L!S&IyQcQF}6MLS?YlE^%v;|R`B)wQlY_HDi!=+Mm
zVuJ2ri@n3bgst@}gwWwJavPFhIbLNjFBVN!h0`-&fI~_XHHF*mKx3D-Bkd#TGhN?e
z93mQuii#3UwK-8Q<%H>Prvrs$s>ZkUlJyDt?U<z-!j((q-tc;r`JJ#QYWLRop~syy
zI1$4S{!6(dRk2?p-sXW7vyP_mOkcy<i8r>5v@Gq#wMH4Dil#gXNp&6y_F_m9$}{X;
z)2oFLjWW}V=?o1BS3F_A*nmNtE`#J;u;b{4&d^5PgMA!fnHhx7Rb^wOa~~UH%<iep
z6T}C;y=!yYP+^j~ve25;r?w7Vn$jK)D#z9dr+m8G^~2tOw@)TrC26;rkHZj^(X^)T
zhZ>F_*-)Bw0|6^L&gf_y&m@Wx<sjh%BtED?ur5YQ9~xgYM^GtLno}9x$DX8SkN)1!
z88&k~bmB|FSB6jdKCD$#u8+Eam4kyrzK^$eiDXk^rAC{paIEtLGqv5$Rfvfr5PAkx
zUP8s^wv<#db`|~DIN+_O1F}D2g|b5L(JZKo^=7Ewks(o1wbnJ>G}FqvFlLlBEc!@z
zQwJ32l~+q3GQ7UiHDs{ktcnh1&*<{bZTsfemPH_n(ZZ#NG|ls9wQZh`+qWId6wEj_
zeC3f|`fE$ESo}Ob)!w-s9yJqYw+~B-`UTTR;&D+eIn0b&A$*;7Q(tc1N8~nd2T`}I
zVOJ=(8k;@IAQdLCZI89b---ArN`<iPtB4`$)?F>>Wu{;9`bh`lEUVhHi?il)6me?c
zH3(|552fcBhJIc<3U+fk7<6Tn^p6Rw6+eS`@q|hvPwt|!<_IFefLM#mOU90084Waf
zMT>6O)>)>khd<$4S<lf}9!PFr8eg2W8y_e$bZFR;LPX&kNn_}e{rMVDV_%+Do0{fB
z37SIvR8=rPj|zf4W`Eewqr>dmMm1bI`kZ2MdV{jh3{`8LP+bborYp?wg_I6eouov`
ztgRV&>%qHWPKNg~6UF1-qKt5H7b_CRpCqolaSrS3Fp)|pdoKB?VKYjS$G9=8op;;P
zucvoy+-=k+B?o0CNIH)|x~cMo>Zl=1iH2^vu&r>WF-@wE+m#<qCu5An@vADb18A~g
zaw#c;d%4ZAj(Y{4(M^&~wM#+R?Zb&VHDK;----)oN;-#?n3omiEd|!apFbx7lN3&_
zw8hU3VZ7tZd^acx*V~267_zXXQz*R|R|f->J@H%4&}yp1Lume7?bjwfTp)MI^4lfl
zHDAxWI2DQ=1Lvuta1*yBgRkWtor06n?=s01iS^!j4l*Ro0-E(bteC|`Muwenlh0=w
zUZb(!)@aN7Ry<86%o(41Pp;#F@f1^4mG*4;f|C7WCIUzi(fkMjjx?mhY%xg^T`yIJ
zSsLlCKNtpOB|>)PNLVaBP_R7=U0+M9w?dTBILo|`4JTEWcGz~y*CIZiSQ(pG4EZ_y
zgxI7aQJ*2TxCwWKj5Mm9RS5cK+DV5on`Du6a?_Y$qs!2s+BzRSX-3q%i;;mP4Z)If
z&{St>l4eB-zB==2<AwmE^@bL{JZoEp_PLt|`TEpMwKBd*$>W`;=xu4p<-qmCRFbUq
z0r>Qe93Wnf<UD_C?DmSZo|*991Yi2P79(JNJ+b_52Weex=l*5rQx#F|!BcN2q*9C6
zkLS5M&9!q~T;X=MSt#@kw;=`1#wJrEE5ecF+ZcA^(CCq^oSFn?k9sC?>$`@Xuq%Z1
zJeBH=*!({9s@ZiR9JV%7-?sEkOMr1^{bvYrN45o!Q1u_0YtKzKaVQ0Qf$~(ammO7=
zl_gMOSAP*@U*&O>we5y_$xz$!?~8Tr=3qOLq<N<0Q{L6v9g{(ffDg5jC_4=Lt0VqY
zsbTlV)nMi?Ze_%4{d-Y`u8fLjkioNC!K3uG!*ayN3SwT{4`oCPCPT@gmM-9OuICMP
zc_`<uo0GPJZN3EW&w`CR707o@!7611*r?TZgMxL6YwrB_&YGm<<<L?k4vYX>{+kTc
z?j<_AvGls?E0KEXrf?DCA;wgdm&u69^ML8F_e9v)OO0F6irbf_Poao)&`y!<ny@NZ
z$A=q~{k&X2J1d;<n6e)>-n)K;%zpGIyFC7!HP^<2eYbY7GYdC?6N*ojFL50c$v*yX
zR%}kVYh8#VGK8Raqu9%FPeMb<@r#evPKzWT#4mo@i*Ix5{A9NrvYRnd{T(JBnG>xp
zvR{ZJfj=1;H4ni)?jav9@0R#m%iOzWa_c+BDPu7jsd8hwV!EdIDKX&nuZsZ-?arCH
zwT7y8niCu3MNyKx3Zm5#AMrtNLz(Tla}(|g7o%zZ*{5kqLu2-*#M|>I-`n*&fw<XG
z+gdL0@y)K=H&%T99W7V~>gbNR!!CP5dFAUQWYb-1{;-XIzx?8Dcer2Ul=V2>?7pf!
zK48zcu&k6Dleye&W4upP#6Gno3iu%9T=tFWw<zE-(cz4HR~f(9sLXdGUuC_I*GJTp
z*O`pJ12Bl-Po^6SbyRwn%{=V-Ua3-kVCn^{t?18#3-nFZoMV^WV;QC9rZG{Wc55ux
z*s=3tdG|^G2ovA3tk8u*!;L5fyeZNgcR%`&=;7b0=08snnNT<NxM>vIe$-fWrK~Ie
z;Xu6aPAU&-9}T$!RhsRD^2yeKcFwG@$!7Q|FX{OXzb`b@xGw~hjP7kQ{Hf$82Txgm
zz2Q~?&@{CAdxsqCjsLq({?DHI@7?lR=X+yvRV>nD7@`JW3CfkJnS8JowENG&fajv9
zOf|U2=jTVyd*ZDsl74^sheQYTX|SVS;f_E_a;*vVJeSVc+1Z7zb%-d;)7Oc`uV258
z(nuS==sldfU%KWrDq`-{)AwFO6>Bh7xb2?}2RbpW*)TS?{5`Da|2%?s7=>bdT%N@4
z%v5r!YgS=vYioR!ps2M$^9~O$uYNia?Xco=I@Sf3H2)?iMjQ?#4ttxGSwZT!hH45w
zDxiAv?X4Ra4ARB(#Q)E9*ksQfLN#Zb05c*`j<4Bm`EJ|T*pO#rprJU>C4%B+X=6}=
z|1W#)8l5fjttkQU|Eg2|b81=tU!7xr=<xn|yZMtg>U;YtbUy&j*4*N<KKmtfi%taM
zMCog153%&QKR<F4#0oNA|JLVnvqf^^{y&twinD?4zQBhNvnQDICXktlNi5)X@BO}O
zXM&yUGzHi06iihFGZg#l$BSXG8v0|kCM_LGhmm`97wt%)$vf|tGlp+;%I#p2?Kybx
z;QWv)dcf8fo7dfK{8;<Umvki)QYfMR{oRguIqzbNCXR#v(9;rC6@5x+VZh2^0H})<
zWo5IAip0NUsF*($2z?WywxVolVZrwklS<pcCW`h`ew*wNVSVbNm#M33!5$6{O9uz2
z?1aKSH{d=t7_U@G+)0>P`7J}10(LxP>R?rMHI78{q%IAqet*Oh)PF-ax<#)dm^5Cp
z&1l;016v3RQ!3=IS75{L1%(()aM!TdObT$x3ysCGT>gJ^9NNWpEd=b?e|dgXB1#Ca
z3wRtoy@`IR658GhfFA`m&4F@qK90f&cC)WvNXz#XnBdi7ugkYMe!cD2!an;<B#ReT
zZ;SDg6J&gAGZa0t=7p<}tPGgBzCMjIi7Cpwde$idNGsB>aBC=%fHl*<9B=w{t&c!}
zn?`Yh_zLJapFG{=0EBSr+1^zcOqvO_(U+f}FHGVK|G+7d@>Bo{b?awJ50G}`@qx8!
z=fZ-5w$T8b)V<XopV^yePqohkg1M#oLX44f!<y^B_hu@6##GmPVk7!3EG&*>5J3=l
zHj?vXQmItxE{}rhYC`z;{SLA~q=Or2b$ZHw56E1kQF=l;7xF><_2KR9+KNfnq^3<=
zlcH_44?kq7_WR22^});X-0D9gYv=RRyNWD}4ejj4QZ|K2t#HaiXG2~!pja1mb8~AC
zC+7%C{rWt#<>u~wrg=XYbJqpJZIqH|Xib{~P)?_=tgP(vpaDBPE-WpTF5<^kbQvj)
z1zpnm5gZ(xHc)JJTqFOyK~jp+c!;xhEW|R?TCoGOGjLq>rC=2hsjDq<dadA(e)kLc
zw4AU;i!_S{-zleRZLe$Ho9#+Hx6!Q?INSBQ8sC6#;ARtz4N>>;shby`TBqx%<F~2R
zP5d`(%dphpcOq|x1cA<$VQ6JV_`5AZPpIpl(x_>W8i=mhM#jo1e=v0jSiO~Zo!n=V
z&K>dd;l1t*m5<#j&WGH(|CzTfz^%P(>OzM(ku2@ZM{cd9b{c~T^lkS~o;>*;ws;c;
zhpP=*Sy_30NxKkAtDmszT-nJc`zj;k$FtDY&&LDT`l=sis08M}?s<dpoyby1zTVp*
zf_~d32!&s*egieo#=~I1O+@4Ll-F!+Y!sobaNsE(w3V>{h`k2fJ#vXJvUn~S#7h<M
zJPjbNj@dL7&As;GSmEo}+Q2(+KoDfCm4&2QOMzD(Sms*o9UCjjiBY{?4<~h7R|5<H
zO#mfx-Wou%9SUqx+|qJcHv(w6p<^=kZ$X4FkJg&6hK!bPT@l=Pxl&JReC<1B*EzB4
z`UPQNpyBF9iWHMMv5oAe^>lV#|B|Yp3xb6(IhhTDGY5mgbZw+6xO$Mru^)ZVi0$UK
z-E7TncK)A_)&coU;97BUaaGW~8IWNYVzG8$kR5c%zxF)_p$l=(GfB|)yJfG@ni~a~
z!D&;K4}=B=2R%CC&!6Pu>&JMH9EsN9Uz}jBz!zyv+gNc4i5CKWuA1v7m;eLn&*3X1
zS<oaUB{f{`tZS?VRNgCCnmzL8LqW;9OtTo!`l7!9oT8+OQQss{w4?{2;8XP*Epoz9
z4+C^+p<OB8So*IFg~z3J3*wbwv=zft;86l0lRGLMTXyQ!f?W^)K{+<Z>uLal<k#;2
zqt(u)d3KdSOwYc)zA4e;FFtnxIph~0bIwvAv4nflbyy(moEdFhsF{jUXxe6y_-9y>
z{q<MeNYR242C1oO4#T>ft{sHNyKa3K+-eC53cB0*=%Z60Z490c5JGnd2t3knNnZTk
z3Tzot`{TI;pVh8Pyydyt(CfOoy4^>uk4fKlWA!VvWvU0SjJlULtaWA}Rvoq}4ljKE
z3($F>#X-T<K6INg;mgt%roDRgDq{`!#YOPNZOBD)^E-DsNXFdt$XW1@?Gk-7jD%HL
z{~D(&;2yzTbVzqoTo4@@9`^8Qq|av5r(@o~SB{XO9gf!dBG<+@^+w5iSaDr&dsR^Q
z@Uy#}0JGC&00gO6S#5W@YMPT%B355GglsXt1MEZPjDq#8uJSg4MNtqkt{5L^%*NtB
z2sE)VSiNR{s2+o%M*jCEv6>}Az`~dU3<ir)S}sE_dm?b8;VPV@&HMHnTVOTGj5F*2
zTl1#?7NCNlNCorlI{*$_&0{Z8o3<94y6FKkTt(ePnXMl(d=z8Vd7-|sv1RLvaeCA5
zA>fz28G;>HuW#>Kela6-$HG=>MSST1ojxjFyr|ka0g?lc2P@MhgXDY?R}qz<F1L7U
z;#cdA?KjL?JhJ-+9DsF<pyQZN?aVP<`jaP5oVMG&4c#Gb(E*T+cny4;n3%%1Z@ap5
zTO8j_((Y8>nU(&JsreuzxhF<t?h}uRB2e|PFRFvC4&lG{DlZ@@#ST)ph@hL|RZh6M
zIm)^z0y(~>1u{GT%&z;FjI(Q!{gg8lAP3lhWz3{pJ;2()AJ0ToJGVji%D}tj-`V+F
z-mWi;3kzMHi4tD`_Bpa5qtbiiVrB61u-tw{V*Lih3WVP_t^)#Ee&0dY41b+*nRIv0
z!MFbXg~i1R-rnBl7vw;<Fz#He`kfEofBwDj=}viupq~tu-0QT61%DPJ0X@<_vp$>d
zd+e=(va%bO%Jg5lwm>D^_RiZbsOMUYDnw6)RJ{0UiQ~X&=W0UrYgq^p!pjTs${Hj~
zf9cYP7}Zc%vzA9S1~Ypb;R3*veG#B;VCxB2<%RIAb+jO+#EdCm`kS95-7K-0NqG@L
z$2r8zQ&VZWN&uyFoB@f7snv}~03!9GXtA-ASPx*M!R6!kcd1%XW6owQv^nK8^6Oih
z=EWaPg5dxM1EGGE>6cf!CMO+RR2K<l@Y{J~BwG5xQx}iCHD_JQvD&>)xDv1?p4hg)
z!D!2Dm5BlF7&)iSU%T0)pIz04Pavz$kt7`G&n0o<w@4Z!M69tElWbU5U2WC0IV01S
z2XwH8L97-h?N^{t5V+V_$W(MpgRUSl=vycp8iOStes)zD2~xn^-{)dPZxvKjn4y_V
zuKWo2IKs6>H50T;Pu}N{2V3pr#IkUgZ(GP8Jm{|<9`9F}UmmGur5*Oggnv8~`t=n0
zI{PL4y#D@Ul3!UFQ}8Dy0vNBjtV{-_ITfWq&Z0FGID(}752b}eKt12Vi>fvj``2B$
zwKm@kmfEH3JA{0H%+ma}y+1h16k<Vx3P9<exGW{#ED%MxuNPQg7n}<KAfb9GCswlv
zlmhp9UmTO_713OM<}~7m9{|~{tggO(|DgZ2&K8Ik<HW>7cA&eRj;jMSRAC+pQO{bh
zZ7E4`-pSVNGvAx1y4HSfW<A<LRB~bfCA^U$8v(i?Urw>e=oXWqFb}AM+e~NTwE^tr
z8jgP+@JzP@VS9jL-gUb|NPl^G`6Mr|=q;jE(?;UC<*eBCT5uUl5I_1&U?!np!F?@h
zn^Fdq+>%VvFS}g}nHeNWp1|;<@yY8C*Zk0j{z-Tr_scY7vshOu8Q#RO0|`msCsiQg
zmy>SYH-!H{k)=&Th`%rvPO@jN#zI}TjR}Ac|Go!#YAZD#<AS&SVUn^WS2@xyw(BK;
zW5#7^K@mN$<C3=~_24krjMQS*DXrCKCeVRXUgyv6n(tO$?=n<98NC1`eMaMJfQ|Kn
z_MilP!r<Tnpr$7={9^FNVA@cL1u#@L->=fF3vq3aR#_`0d`(-FGX8lMv!a}rwHsUk
zwAB=Xwo@nvLCstGyO-tbfZ9c$(YYG=>qbV=#ws%juZIJYFSJgFLtO6y%fdh;*jlKW
zt++nY6+j624zbc^WcjgZ9T<HkSwbMz;JW=hc@(H&CznAXIbT?i6)=5Njl(u6ud-Ve
z7iZ<h^<N6>0+>roeGEUexysVxw*u!uim;rg5NN!Vdmlc0c#Qx{&eTMPj#uPuKqa87
z4V-VG#?;_UK~`u{5rCX|R!y;7Jt19JvU(B_w7gb0tl*idaFh&42Gb!kiB^2`cXrha
zxdXOl^WN=-F#ofwWPm;~H}Ih0U=tJwnJB{OXhwBI*tjNr8g+E<?&m%v(10OTgVkQt
zSvvtTxv}ZBYuALCP`4UMx%;+se|O05Ia903kVa)qTXjN(<>fc3Cn6YcFx4RZujTX@
zh60=i>FZ_>#oz)Vb{a6#-+!_OP)0iNY-&Qo1Hvumar|c%E6tmV)f$f*f9G_ir^OJj
zwcT(I398<(q>*Z0G<9VxUEj*Wf>riF*J$My>&Vl8wV$}sh`Fu;aO`?CX`xp5M~ZAC
z%k!N-J~<de2Dw<}^~TED$}%Y1H~(y}>9lRMSPxb_3A^nQp6d<DEjJKE*Q!3d9s*_M
z0_e!-m#Q9(*M~qN7wn0xw7;$*lT;)%8wx(1dwkp;^2?0{9^18slWBVES%$sY#t+X(
zOG~q`vnv6~)Sa?0P`8X3Q8y8%eQ1CtmqCx%F#s^cy%GDKe!P}>Nr3AqW!V=RiGu@A
z2}^?ELD4NG@k@zKi8fjTpc81iUk{59gllE7{I>sBX~g7mLY-8UAh?st2e`N(53ni`
z(*XEKtlO!TwNpwye6S{P6UA9&i|)o!C0_d0xCTCk(13Np9c$yR2ON^!m#LD5`6p1#
z&c=P6Np>jJ6FRHU>PLiqK57*eTJNkO;{!S-uUxsJGIO0CcBzm>qHYCnq{&!UTm^}?
z4uD71ozMH2&`1+A-9Bvj0-zV}etxFl{t&=D4*GadX^zz)OVrv%v<-Y{kR@?XaDn>E
zu>lDznT$_9m`CqTZp_{O6}#PT+gMy|AV_c5<-2Sm_^X+Ne4jhKxpLEu*qW4d%<|^V
zd%babS6S6oG8pwl>|MYn5>8GGJoR-w3q^7MUMAuKX;!}VZZBwuRk+v1+jH^%A?rH8
zqPn&&u@M#U8AJpr7K(xjB1i`XL3)uc)gaOwKxtAhHY^Az$WW9fUCJO;x`rsyJ0lE5
zr85FDFd+TkXEa9h-|u}9^TN!z_nx!Q-fOMB&PY=%Sp_!35Y+*A9H0GXpA3xA)}6UW
zx6Nkq%h7H~OoP)k7s@N3ntj%Dg(&^7h~lUGJ?DU#TekMKMqVvUA@SkV%Rwd+yx7EK
zh=&C#MK)YN#TTo>)6w4_hw0_~2wl7j=9Rv3@l6tg51ul=t$?>P;b^%W(<HZinL0l|
za<ryAKFEukPR9Y|>1$98$j-(>jx~<@JW)vlV1dC7xvvTSPz8-wL5WU{ZuV7%?Y$E+
zhp>z)=zoNLO}@TtwY4cYOw_jN=?xO5;ktfOu4ka9#<4Q;KKHl9PBmk`qOr&F`o|!;
zC^bM|ImRa@CU&l0D2LMd19-@s%7HE<bFblukaB>HPVvY4UVZI7<<%v-V@Hj-9DA?;
z2NOU`e`=6so`+ALQ26%pyGz|OE5E)Gy3`iG+&k*__2Zq;+M=_Vw9t8c3V;@8b)FD8
z%zjinY6l*@+xow%`5s3qEDd$FCe6_}pB(fkP_?nTy6O${9r9<YCE8O&+`F<&lIo9@
zX)Fw2RkqR%%Oz0&c?`QyJB`S>)lBWsqLfJ`6V=uF`~LR3!(0Zt*cj@rVZn^dKE6Cq
zYMSBi$j!l_FT=%cV44y$eYOqyl_BhXe-dV`9050%8{hi}J^vFH081}~2^Te<Kb9bs
zNi{F?u%SRPirSoZn0gkt7W?$i_ly`940am6u)JL3h?3U`ln5IFt;0hDmS9>h9w&KZ
zj9?#b+UIbqn!<l_duqz5hUF#CcuJ&27>}5^I2m4Q2f?2zXbw51)IzVTJJhq}E>q{u
zx50be;Xjj_kp1#%<5KhbigvR{)WTUJ4BC3UdF^xa++W*;#+Jy0$bo4gV~^K2HkYm~
zk<qX0W)O+26CdDKF>P3&qx)ix`HA9Y*e+1O2`HetrRCv4&TY4q%NZ9s73k^d&vx>o
zM$x@aFHcvkPC9dX$wRFohOMn!AcuHN&6m~{6MN0;ntxA0n}qep8u_&s@E#5x_F_RX
zZ7^V@)>lo8o?F9pRiy`nPDh5852ixnE2`XL{g=k$$c~Pf<F}UxJ0SVp`h-zc4D-ul
z$EQP!jzZbn29UtUqvoah#eE*8O%bpAJU+iqbj$(o7)Wu<J^KuDFPDrgZ?q3u<f5Yk
z%qjJ#+(8X4KuTO(;w5+8JEL44>>^?2@`R3VA9<eSOhkM8*?wBztx)C5^rfWOGOTa5
zh<LK*87JZ2#hWebGmjo#bFw@&LwlG}R9Yt`=H`yn5`P{XfOESkjVI!lI@0EHJS#G-
z%;THWpnzTu$$a7jCA2o|ce#_o)1g`D1{6fuJI~5dcAEQ9I}zl^W8YI?4^7&Y$)oX@
z1!R`@u`>tI1fBx~MvZ&r1HFw2o+l$INfCg$5~r6R1ItN5L|<$l?)bn)x>ugm)`B;-
zw?M}2O2<@KSQt`3jBcvF5-&N_4--<Q6qblvpF5G(<|xwVP0m(wwub3QWUI1p^*k(p
z@LPt|l_tMx58+ld>{4S4mg4L?xYxR0Ib-Cfndm#)z+z;ktG|lYNmhQ-)giCVF(;-&
zbI?uw1!yCxq2<HT-i{E>KHu3!$VbCF-oH<`5>D^Vw<T}n{u+R4cv-+0LK4zQPGy=X
zRUe(N7yEbe!Q8|CnS8Lku31^Vn%y0Ddv0NY6|Jy`|FlkdHso_JwcK%o2X!gO{~W`D
z3_0%3oxx+=zi*Oh-oS;Ews60{`lqx7Y;jhisK%20`r#&Ij+L{VVkb|Acu6P4vIZZg
zpSwAIZH-IkIYL+s0h%3JSmwmM;7OdX^z~{bNz68!;2ejB@ZrZ7{>dT$vQv;>n+ZKJ
z4lwB$WC5*NCi&aCpv0Y>>zno`KJtW(YL~n2<C&%GKJsk69p=bQSftGp9S2{dX0e@+
zBr1WL;m&w_n)D44fKca6P0tnt_mz1{x4}+>?Fp}h;h1EZml?8JTuc1(g)yHqZ2C*x
z&Hnh=>S>qW+0TIND(er(Iu{od?CK0D>%U$Q@lRB>d+&VH4?4=nP>veup)4&lMJjHd
ziLM@<wO^VW|7G)SwuICmyxC|azMDz<v+&s-$2gmQ?0dRIa<1xoR0*5@{WZTuw4s1g
zM2spljgzm_bG<t(-czC**6%eMdxNCmy5Lt(RTxV26=IS9Z@TnB$G{+E^Y+~m6j-Xq
zpW$BCbNhe&m^1~fRrs6l-CWIAOw;wWB$d!srGP<ERy?|<NWIobf=WOa60u~6JcY2Q
zUa`+a$+nefX=$lQJxa1|OuBMI4KfWSi(dFG&p#C*lvC~}H5fMs0l+&~-hTGq8Va>F
z95lDIlt)P$tTRcQsD(SucVPDvws>%S2_UcOcgwHn<#aGNKUlzErcm=}0TR^V$9pGx
z-}m%Pd|g&Jw5u(QS6!waKT$kS%QVjcZuMnS<JR#DzTlO#@6IygBII`*x~r&M?S&Ae
z*9jS1DxhSm6J&Dz&{4}uGt_#luczmFigcv2oReV-f;%=k2?BcpF9Xs<4Dv*}yq}Z8
zcLzh=lB-dfPr8}LlTlhKmBui;cD)$Zza3b3JTEIFJp#f0r=7TJ^L}zu!c@s+sJri<
zIiOVhF>Y_<G4qL+DjJEjL}&Cn7|(r8I>l-G;2j7Xk|FYI*Um0MId`;lDo)l1D*#|p
zLuwDh$O;{>Q2GT|29>idDtfNG$vYS#p%5y!lnz@P)3vfGuVqMAU3ya;B${PrHC|7$
zoEHyk{#~f@>$AboFZs63O+KcXw8wOB3P_)G!h0H$i*ck>h^4#CtMp+D@ZWI=u>*Sq
z1r4)Yk{2dP;_z}XQ$=xc@zEbEj)f5ZPMQ7l<@i~i!(5@GkE6vL@ernl?G~U9_DE^)
zxyA)VP{^<Q>f$000fG`aSIxh_zmuAh(KxaS!6aY#ZPc@gHKd_ESmDqq00m@-2_$k7
zdytWbZLB+fFtjd+Rj3QI4wX&C<MFz<WgvV!&Fsso02~yug$!c}rBE6@d09`JfAg4f
z50#xaZe4YYJnB~8O1saERQL@tU}<|>2a8Bfu9vL=9hbDhA2l60q-SK5cA`n5`jOFR
zj>`{PA*1E+x;^sY2Y_M-YnEftw_6^|#PyXLaB^}g5k2WmOPuwN9-l6wRjsvTo?L`L
zoFTbbzLpu7UPl<lL0OBc!ZMQAe79}qz<{N`G`x!kTefTw83zhP9~I-RxM1S<*DkHu
z@y}yHL>5FK+cv;{TbZ?GboKkV*T2?;%wa&*$>Ys8g8dA<L#~QZ+38k@HzHoHqfMPq
zL*;-_KSXseMBX@BIluTu$Um{NvQlFSAhQlgW+{02<<I$?MlN1}eu3f^e}Q!T?HIrO
z;=O~;FTbY-^b-vynmbHvW5u4;Y~_FY;k~krjg1x?fm?M4=4N3A?WaXfeKD%dz*`}T
zi-H##2116GRM<dQuoSgJkJAgtkyE<v)-R&Ce7~GN+YgYMY`w_$3jgq~ZT0|!hm;Sj
zuPq$xc?$!dQXvVY|2Z1BcN(aMXB%=(I`?NmqAC*3wB@E0jCd9w5114l6S9m`U4WW8
z640XDNtLAdX<EYec;i54J|E>f`~l$<W!bJnXs4_8-|4$DLvvi&AD)w$*{;NDF3M%T
z2_9ifYilCl0FJOAqsyW8KutA(RLn#b&em%CpTpbOv_az6TzH{6@P<18m2PA#yI6{H
ztZIdF!#!2=Nh*znJ^6MF0Ij;*)iQEU8Aox!`Ijz<O;Y5n8}784i)l-w+0}c{b|^9q
z1-rw0d5^u;ZnKTz(i8xFv?X%>o-q88Mz=*6S&XZ!*H^6s0dkm2^#}II9$12CH$T6M
z{)Nf~%nNVRul;8~J>BDKpZ-~{vrD9bwIbMsblpL^rdrc?vZ5$*zH*^L8}6x|wxWTY
z@Ogi-k`h6X7@63CeXVw|vpZy5v$}UNySV>3pxZaxPhNhXV7c;%HM)Q}x~2G^;OX6V
zhMW2Aqu=Oufcb(r<S)?1g3Um|f7E#qlIUotcO~mEK&y{j2>lI=3qS+X01in*QG4NP
zgaR4$!TZj<2`Ty+Lyi(8l(*^n17897Hv_3@iAoM`9(C=gfNW?R-ud!nx5rgLg79~s
zRS^Lgved})+uK=pn=hn3ak`3in3rFB(Ou!=*-t6%y&9C{tEhMrXk6~1*mFN}1z>6=
zN207bW?Lb}y#h$&Y}eu-_j=TcV*mO4_@nQkFon`IK?W6Gk|W%zBliV!4oMLAS~tM9
zii4;ynC|&*|D7nnyo#G(d+<BE2n++DnoH%_FffwwQpl4g|H|Oo3pKl#?lj)myq&|H
z0Q>~-hBEb@o}N!&$+qgQSpyCF9a-MxC|XrpHe<f}VGr|L<oHNgLdo;2($YjG!9(K^
za7i+d1W8(IjW$GEL#tms(F=_6E(3HYIXfo5HYguc!{u}NZ!9z<B;*3g2Wl5hclW}i
ziop=+CP+rQbe*70a0J*W9jyU`^X`@{LM4nx2NjHGxq&j_SbcEjzz9%PCqXD1k_Vrb
zbx%1-sgMTS>TF9|)Ge#1=xc}(6HHq}wB6UiFHN!1u3D907b|Ciop<#aX26bsecKWM
z?LLmTksL`zcvhBaag#1Atojvl)tX}y_c<_z(gCVRFBhOJ8bgwGzEHrHOKP&99X{Xj
zsz1pI2nr|?p4bCJn&CQKv9S9x{~!0FfPt&iwS}B}1l=*mWC0>afDe&m4L}^yeXe#)
zcA+=Cy(-jq=-|jVf&^lRQwYTRvn4$}=kp<;J^RLL=lU#-r^&82`IB(Pk|Qxdw~eem
z@l6a*dhikGED=EQ>!U8@oZewo2Zf;&u!L0D$t5VO{#5|Erw0aZL4h9B7a>%rAiY1o
zsBxRfNW){f#eh#A!P^9P7Xj#-86jxIWI6@qVF|cAwLK+i6c!ZNLH*`{!y$(MY8N(g
zBEA){ISV%o;=bERw5>y5DL5qoBl#wX70PZJBWY<FZ@oG<-cE(UJJzDO<7jC%jmA9(
ztSEnhL+5i1?~i}o?kU1yrmfU??}@_|(|*(SC2!n;T|<l{uo;u@UntTTNwAV4g2{;S
z+-UfZC4sMmbc_r>0hhJbAR`ua?w6r1ETFgtiAV^wv9S@E3WUI_xj?e&m;ZuNZg$}I
zI?+ayZCX5%m6@I0`RPp%1`i`;e%=GuoyR*`Y_GZmq@qus2MJeQ1W|HJtVfUi#Msz7
zds{%&5Fgo<ZOVf9NT{<e%Fg`F3~9W8w;9pQHMkOJK55F4`nHwD+0M4MXMoMacQn4}
zhuDqSa5>7O&|Q66hk^Jw)$>d#r~pz(T^u$D3;Cr#B|LuDk?L=hGrbMNReG2I?cv}M
zg+Qy?#HO^!1veyV#I_llbp$-7j*J`+E1|~uOdW^8D>*NtQqd=<L7|64YK&j)j%Vp>
zg3oV&T=3*44;rNSmSo(UIt%@7;-Cu<^tLFUYg2D*X=%Aj!Vdq~>c*i&6$gfDY!N{g
z^(78tE>R0exiS30x`wH_49~Ri`<9y{Q2%qbPPe1n*W{(kg;v~C=;Ne!Q)&j*bZu&S
zEoaOFFx*Bo&36{l7URQ{VP5<L=ti&U;=YFryf&TADI1#<3`MYU*dp6ubn(Sz08i(X
zjIdXLUzU>j6&8RcLaV+8RkiGOE>A>GUazf$ol79IQPh^1xwSTIxi%b-^E>E=MA24P
z3c&-(hi(kZ!7fJz&pV-s`#6j5yB0QYi=~PW`05#1`W3<uXYr<$zF5gr)w=&GSiu=6
zDZRN%`ss0;h+lkwkU<pkH&<mIvnV$nSZwl~>v1F?^E@`)y$#C^%g|mJ1sgdRwsK{a
z5?R#&dBN~33sW{kf)-S$5-l-vkUzBF^^cbWOkxgR7TGm^8DXpxyz~(EP!YDoRTU&?
z$pjtKv*LKL)t+l~_SZwau{*5!(W9(Dm4o6#-?@Ay@L1{Cvz^5UBb-p~2}uGXqb{4g
zz1CYpPe*xQU>^?iLK3W?OaXEX4i1+3boU@y=+E==@_PF~BY#p>R@NInl?rb=v$z;D
z0zw9<8MI`n`i;QfXpp>DsYEKU5BAM*viEBS9h{uHTF-K4dsdLmH?Mz?J4e%tF!urX
zk0b|Rs&X2l%C_4IG18tn{nQ*!7MY!&Pd%Ra2Oe*0Z%+buCPT!60uy8a<|;@k0NA(G
z=iF-SZ88K^*cm{%VnpuBZin<0nG_}_?(ZZLGfPTLP+$*{p}f0rvo&aEurd{Bh;e{W
zbbNfufa|~19GA@^Xvp#9wi%_n&@mDX2}KUL;1##;zr*pYT}v7n`B`^;TYDKZ=6-H>
z6=kl`Y*3+q{P}X(vwAq<=YN5I4p6-vZWZ?+|HC`pjo}~O1!KQIF5CFZ1<JxXkO5t4
zn;mU70y2Tk=fB#x2LzBj(Ks>NIImRpy?fQW<gSlIeTjq2#vBAj!&6)S^IbY8kqKdw
z{Q62*;i#YqavuEU3b?u9`bhY#2G*mtU=9>A2U&MAY!ML|;$viT`@aS};0yS}faja&
zZMV}8m7PaTM~L`_#^WD>Es$t~I<Q^$ET=(ge?nT?S%{I1$)OUTeut+Y`Qj}2_Q?p3
zZln`lL;#Sm813h8|Ac>|SQnhZ>0B9B^xxZ{i~16t8+QnkNZ;6aCooV{R8;>LDJJcn
zUM2r=$df*>9dg#Rc>nY0<!2HqpF=%p527Y;#CmjGIqaed9%|V_P+}ZCU!a2Kf|kt8
znOM+C!n`~-iyWqYxjuXl*}oAQ{koq4kB^-Vm7(0V@SRcO{o`Ikshdkl>~Uq7tbiuB
zkqK2f(C@hZ=rO}7d895wC<e^>&ODoXwUhtvh9rzNqfX7*i9U~CUm0Bj6Al8TcgMn=
zs~k#Jy(Hf5VY4m(T^8&tS`cCKQGV#R)Q-h>@kW|Gvl5n=zxm53;V%KI37@ZzY}~l9
zbakFYjh*&Ao1L8<7Iym6RKV5chz7;Phmc_d3v<-^0Vsc<*dnO#gcWmIKS2FLhyy@;
zy*oI9Oh7y`(9~@F%E0iS?~uUgz!ApB(in_gks<12wz;=|nzroD4Cw=_*J=d4zdCCP
zEp2Feb^VZOg4dDV?Xa!q060npxkde^$h3`h!N;Ey2t@m|*i_VzRKwx=fJuJEg{J;V
z)tv3U1ZxmL8BWm8n9`!gry!vT5M=6VYinP6g15o!2P*w&CXCL0WYZ<U{xh3>M^2c1
zME<!yUxNZ*@ww-&on`YwFco!}WUJd0K26@5S?H;<>>XH8C7yNb3US_swamM7Z%9UE
z*jJ+m@p+qK<bf-uZP>V;g4!$8tJV)7P^cj357K}VSTI4uccZh6`Gr{{BbN}M9c3NT
z3Mj%MRY;M%0gawR74&O*?v5wUmC=U>y1G&k)-3<^^$sMuQiqDS={?h~pGOKmhsPaP
z?z^dL+Hy_gp-e?NfuBvkzh|%E5%C)<0R{66JMBWQ7~oIMLe7shU!hU}Krx5!0>D--
z1tQf0dsDf1ZNT-Op^3cOF25QMeK<C&V`t^tn*HZbfSgP{3@uey%$6^V!KD5_bMe({
zL5?8svU|3gkr_HYz1<OWo^Oq@nA$G*hx+3JXL#4=PbTFg<NkCwjHxG-JeF~_p6V@G
z0-nnhQ6|qb1sktd`Yv9jSx|~s&M>ZipqFn!SS2zLWS;v)yo*%RpgYaEo_<ut_YjK;
zm&c~C;`zW%**!-+J!fH3=}#TA3KLXMSVYO*Z6V*&XmeFa)R8Pr=;^`En`5igQbeLa
zey44@+(WEu+wq^r`5?AGF}<je(azDQs;H3asJ)OdI57yOz)FMk>znT{P;b@uZPvJI
zvsH8;&n5^>%~rP%$Tp!i)j}|MT_0o#zg)X62>(GWn)k<HICVMr$h5YN4&*0hzrFu0
zJ_X<NP~qNwv0D$=Z?w(a5$aku70K>3ujVm=H+Juji%#M@m;V#Rg$-_7UzxPyR@o9J
zeWQ*Z%%jtL`}T^(^Md=m-6g-2OC1Idm;A1JUGlqqsl&+Ok^mt?D!H=K0vZLdlX{+=
z8Qb-tiRf*=wZI|u|F)IU{V$lmAm4hQs~BZ2qPF<+G*P<mrbue9EQe%$Sd62<)9Zuo
z5z)gJt-Nl>I0}M$<?z(8hphik6TV0M?ViH<z(Ox9zNm=z=DXr#yr&0~KpZ9^@vK(S
zKmPoL07od>_;AnAY*wI}CLvp=Wm?eF^SUmW6aMn#=;)HVxI=h1){?@m4}sCJqq=E)
z(bTjN#$z!xH7u6R_jv2B_>bFonuj_KpH~X(tN*lZC`yXYCcz65ew27XQJs;Ip$g*0
z@=Fa-7zptHBYfuQ0Q(&C*o_(cD3?zyb`|-9?Y)5K0tXn8?G`g~gwp~pbsBkZ33H`h
z*;lod(+>FMLZr#_24d%VPEM1lznz^5<Wv<?;F*d@`O4<ecEE+xpwcx5%!rdH_irQM
zw=O@9h6l$dIPUi3sKsqN<V+@p%>SnJ>E2b(;|D+F!$}W?yEO@|pT@;%M@QI^tGgtl
z0cG*!21%C$KDUHLr2go0sb&mIKD93a<ds8=qM>j8MD{f((_pjaAVYDEM0m1*>7cv{
z0jyW1rD>1V@W;b_b*7y)B%{z4!r5OAlbqn!y}xk&Rv|$=%THFZC*GUu?%(x&oec53
zjgD%Y!^LfH*0E|6TEPi+^UDL0!pG1^Ru=~1x3bFv@*f`Gu^R&&0i+4O6;N+emRAS+
zzuoJPFAO+0wxgyaX4@ad^be-VSGGJ{&`zMcS}e&J5wee>)L1L{7#$6v6JT{Up@Z*o
zf|mqGSCCNUp?bCcybn-tHJcWdm6;B#jEHmoHiGD0lpey%MVY!D2+JQ}$9r-w+(h~x
zWTtz#R^rDufBQih!jdu8L(G()BzMM5B{mq;E>QUhD*db>5oJM2_HY!2D)?<{z@x{<
z49(7+hvO;?Z5{^wzLB~17Ne+Tm4C&7M|YElSZa;3&qox01|Q+H{{H@k&W5Tlw}r;I
zG0+>HAk%(+tf$pl>9-8$SA_b{9Ryv$FGG2C3!ePA_Of^373cYxG`|Xm>Cnezj!N4%
z`=0MG%jRu&EKKJ0a;Lbtc<E`v%HF7xymhZ#B_vf1SD_dM8vaS~=Z!mL_Nl_TCn%6Y
zlK<Jb3bSiHKnvbEFF$QlZ9}*_AkB6F0uHvg{xSf%fWN?!4i3<eyiq&QL&`Ldy*WH}
zz7?#k;RysEOz;KY@extO+UfkdK3TdX(F+?J7e03bi77TK={hoW6y&AIQ~5ML!dI~r
z>bBv9(SE6q7!WRP21e#7*JO^VdV?Z{<`w_*7o$D3r%x=s`10!^489k<>369^e}c1d
zA<K1rVK2(_q1w!F{+o4~ip(b&SYGXk%3c3!d3pInJup&U^%aylLCCzqA2X;SfK|30
zoO|uTQ@N+yE1$=ngcaeeiQjvK|M^zo6gKM)IAJD!eqQQE-9SGvb4{hZw{KS8lC7m-
zNv$!9(D7gi?A0?n8kX)~EX`5a9+TO5&h$~q9^rrto4>Zi><yPy=VuQdIl2I_!A1Pd
z*9mI_>&uu|{Yi0g+YOCA-rjE$BENmxkLhle7xad?XMnY<e`^0EnXL`|F}c+izd&Vx
z!zdnZ;Zr%~WiAllt8A{sch~ol`9w*jNWK}{Tx3}$9bYcIY70wCp~Gx0m7df6<--7h
z2%;yCAZivC7O(oZgWcjTKKs6{=YMXLE-D7|zVj?2U?z8d{{6<<{Ce1~c==S`f1K-i
zh%f3ue)lJ$dUxwvaK_|*Or&Evz&sy&vA^6adKgWT^|e)SI=b?h4e$hjk3@<uBI<9u
zQ+|H_iF`9tQ|Z88Pn7`erRI^imZqlDIH$?>shrmrzg)uv?#fj3%Ad|rm@?#~kXxeN
zr7mBJ1?%>-YTM5778Cavizn+h%ISphu(}s1!}Ywp1bwFmVBENXk*A?f=dhV78RcSR
zV7OS94QST%JWzw63XRyR77t^Tp#EoLc-(f1C-vy^GTLL{m=hZ;uDFo#&>z~5%X~I(
z_Qm51I>vHMsI^v<=-LEINic-atgD_oUhO&w(-}l>2|#lgB3TuXU$B6(P&x@p(?Fha
z)H=di`Q^68zNG^D_JXU!b>$?V!C-Nq&v|%nwnZK_HN1b}$AhQ+5OBP$A7kwrSzYbv
zm|!?G&?ja*mBV6Vp=ZDswUTR6TN5^3xNWoV@Dw}UP*m>qkubMLG2+<s%$zVA6PVKN
z(A|LKAA)-rkUk1D7m>%6@iyb2^Dx@?5R*Ve%cT+kvOo)W@o?a;-%Y2ykG8C^bWJ$x
zp_@ggAI=o;T(~ci&6ivaEA39oje18xKDYOLbP^h4o>_u9FBMRkkpU&}N!MWtP<0o(
zObsdc{{h|6Wh=kjeS=gA6oH64N(VRJd_6<{PbhsSxSQtnT_LOJod)Mum@c2VK5UBh
zEQ#*Yd|Vdfb$f$c<8wks7A&yK>F~!kw9c{uFZRPB29TmWE_*^J?7%G5(wlcg7V>2&
zL>)uBgES@hI7Bo6hrQuhbiRKd<tdFY9RRo@w`y2ccvG)=a{hAf0f#fd?;h<}q*w+p
zF+8?I5>HzCOD*c^f+(>7&Z)91uT;oq7tpI851<SsfkFGdvzx#;N`XE`{Tzf+Xl^^)
z2hwtMyMVW>s)7^{x{^W+2^QIzIi;XX)ZAQr1<*~^um`G5YM@Y9d%3Xp*CEvM#0i7f
zwxc%YDPrtvsV#6#hUHA0#Z)NTJ~{BbjrQqnPv2hta*gjH{2y{jl?p7sVI^hT=$PN$
z+99f41{zTJkGEO5bo!8HVrj~=>WQkjsOSR&KtX@LqINwv?m!N*n_ZK`bq^3Ab{1s1
zp7Qa5I!ZPfOr+mn{GV*z;UH^4TUZ<Z{Lxt5Omr++L5i|^D)G6%ng2=BJ^1@Hn9Z_7
zI30$TkY6rl1s;4$NQghqVUmb1l7n@vy*Y9r!}gHj)D%lfrO3l=2eh|@r$vrxXW|EJ
zBg%Z}gb!=n`>CDqTl`CnY4RWKtZlCc*L1#>h}w48RW99l7ul5FnMWH_6251f$?o5V
z>4f9{yFq-|#}Wj|r>l<Xx$8sGnc>CM%q019=<5%BK!->@g%rU)!vMGWLI|DCvGR@o
zrd8-pFyZSE>|428A%(-)100%`qg9z*VcGii?+U3!FrSCBQydH-B^J|9#S4n%?_ZQD
zOFYMDa5_heiH`Yy|4m^v=)f4bD0M?^d+1TzYENmEwyffdtDepKhZgKFDJqHB$*{=8
zZROG4{HQsdpriE45}V4|K*ywGvY|2Zh{?H-OD6z$1zlo&5B)FC`Bew=JzluUQ>F`~
z4Yj1Dg2HS|NWq4uER+=ubkOl9Gjr^X_E~D1!;EOjytsZwMoi|zjjt}EiU6>P+Poud
zdT}Io;gVnS3XJZp`hg2j47~GDqFfeUgy8&!dH7ze>#RxJ4f{?wPYq7>5JP{!2_S_R
zy@TNZgS&+r0}kgLxjth5MoVElV6Ii;{M)F!Ex@e*d@(t_-2=zTI7~7+LODFY5ZKuh
zZss(}m{};kxS~Z&2z&;)t2mYf4(Yt`(Qe+sczE4yc=n_A8J&NPI2deN8J^;T^A-f4
zDg!1*nM1T6nt^h1a-LdUsTv?aJ=)pPkzQ40Sy52|_+B)1+ye<dL9B4{*OAa^u)|Oo
z-WCu%RkCGk17`eWyI;+$%6@DpeT~`qer3d8w^4=Tr3#XH*;FbEQchCSb=A!`8}ZZA
zJurqh1N8gJXS9|DsyH?)9=&fCd6pu)8=b=dn!t0{PkHPCIvs@fL@<-ih+i4vN4bm$
z6}3{Js@YnG{pV*x_v%cS*#BDbMgADBZ-4+H(LvMvx{MDmCHKIKd#;-)3UC+=oT!rY
zrroi?&iiD7FT7rZ+b^Xg>8w6K=56uiSp&J30W^0c@j%0%A);%yZwaE`@Eh_pmjZm&
z4IwHJoHFE~(;^dcVAQ#g(bZ)LaxZt@3F!I$c70oa3A_G!@zSGU2knS7sNV3ae(1C=
zn8FidkY)ND{DZO#<0#w>$^57BLO4T8IY{sIaHH6Mv<wd2g92v_R3GCY1$=y58T!(^
z5k2C&NZE-S1XMU3!5Ohv-m)QgZ9uaLC@q0(yhnnH{rX&lxgH$vAu@O2iz*?tt12p@
z+QX@B9v%#bF4Xl^e=@<NBfx^vh&&r>(};Ge6Lmwb?3qIgJ2hYw1T+ee`xw6617|B@
zOKBMUm4{wEkTbXgt4jvJ+6Y9p(8HGuDCVUHKG5^`)3G3Qd$#+*(uKl(cJ14D7f|iV
zYyQ-;pN6OU?`jYjQ*t!o%B8$S63Uggry696j0t%z(9ykfgoMwzJ5LFEGPrqFLHCN@
z{@KC`;91QO$i@PiZlF7LkzxRJFmIn_et-0|KQK5^;D15<b6x!w73KNr&XeP+7AfV~
zkmtkVKCe3=xG_c-o!Gz<qidAG=FZ}fk=gQKNe#}0$yRE2?9gF3;wk<coy0!olPxlw
z7oFg&I$)hZYO0GPBqp9l$_&sro*?o6r$AP#-jgKTr|4~i*+yFQw`QOd1>xKo_WOKw
zeT5wD4hr1~Go^0(yhKpKc^hKj^OT$$f?*r!_zF=~0hMgSRn%B{xxJEedH$D(LT8-`
zM>IH=m2zGm&aN1)IOhd6;F}x8Vj?OxlnBD-`K6q95ZtxxYh8~vJb%6$@PToc9w4>E
z+l!{wg}-jrrwnB-6l4@?6Fz^|*o^l?1yF==%5+e37&==mY+MY^Ni$SG?@*8o8<wd9
z?KB#jh)O+h+rEV|M({;EQNKJt*$(VB2yEon0*V(9CxE#79a~*Tum(09>o4QCQbUC;
zI)ksSX=t#}ybm1S)q3vOizUL042A?T^+v6uC<eLahPANy%>XRf!3+=rjZ&D%^^ZW2
zgo6)k>&E{6QMm<4)mp)1=q^BbS!MXo@10J#mQnfB`E^XolcF{Z=V#W19GjeCKcY%X
z4A2gW$t>h?wV|9p8W+cK$T^ECA{o#o4Fh<|E6`ih0_|!^u=W+-?#Tv)4nnFxSTG$t
z*2VjF4>Gt%tf&YTSi?P>SN`5P*!j!Zae!%qeRRn6yYn79G72$I6`!)|9@2?qzdps<
z(T*h7eNt0?TQ~bkd23aK#Iz?s^v|%OoQJ(7Y{1C3d=ChZq+uLpVJNhL1-n<?5%srw
z`mWS)qP}lxC&AwUC{Um6Tb}N<o`VBfCT8Q;%Pl~+*>DoJHq^=!>GBg2iQ<pWc{sIg
zzGuVR_Ie1<R;Yv(b*gKtg5c-2vhucJ@NPF1@?e{BSg~JDCT?tmzG^enl(__zG9Wn^
zNSNJ0$>Rt~AmXy!pc5Q1E>P(~6Ad`XKS52{sAUw`--zS2I~2wf=CpyU1T8S;S}%Fz
zmlv}q!cHcUIz4B%D&1GR@2ZiGSF=u5uX%%A`nF2|XNXRG^$Lb#&mzi9fmGN738`5a
zG~9B7!N+?uHH&Otm|3OkYs>C%B9Bj(D}5A*N??3sXd*v!n(*sP`#i^lDN)-(j%Aa&
z65$Y1bSfjG67%qsVMsu8+auQrP9s4}nF>DU8ZdNc;$oIV$?qzhNz#E*e^96**V$3U
zvr_3Y=5ni-9{|w+{iD!+SeXJGV?natFG)ne#uvptDnggZEu}b4?+d1BaGYckc8jgj
zBBRmqdEKVRLY6J*Nh6}^D6&Bw36=C0*yE8%rU@Q6;ubsmJO{Et*?IWUJK2LmoNiOI
zi*69<C&J@blI7rSJckb24)b4t-hd0de<(khUr5<dFHMC*RwpMoj(8Sd?J(Tr*1Hm@
zk6C$z!?`Z$_|p4VJ>mU62*1e(PZZ!$ci2ED>I4b;$ioMykMFcU6h3-Dt^x^Fc(L}6
zSeL)HqEi|?BY%0auPz*7Cz8v`3Y|>Tx^U!{_2&NAnpy+m;u;p;vtSXfB_*xCVbi9I
zt{j-z!#6lO+mB2WoE@j#a+>@eNIA_`o1YbGa_?ZHFFxHNDD_g>Y^L<L10U|zJv;Ts
zJ|58LPH7Peg+@CTS9J~2GM{Hkn|j7iOc7^i(rZj6Ix?JX@GX4=-=*SZKG6ull2SfG
zP0Mp*9Y4<f*{Yig`F1{^y3-N)yFKo#$`kKr7{!~xhzQNSyPLzdHIm_kELos@iLhvV
ziz9zcw)0~ec2mpzn3pugj;ya<Kg^O%c~*hR47JCcQE`+KXuZbEwZm{e^SGDx5tTE5
zezPCx{!i5J(X169k3TAilZfh=-Xd^eF}xZZ-|SPBJCKQU{B+(l=p8xrq0wKIgaf~c
zK&Pb3=dVJ)5>paPdL>~V9BLEyZr0Y#>zvbOOk;IB@8x_XVOHFoywYf1YUNtoG^YJ-
zntJ5@GPL+a1pQ|SBUYp$CXJ!FGkW^|RtJhdVAFnW&(n{k2M&)qPj!vN-DRjY{;~km
zeIGg-_1`Pt`3?K=jE@D(5%O2k|G=kFMs8FUH(QT2xO0{!ut&rfXQ)@a_;902h>q@*
zpt^u4_ujqj&DfeN(oOeNVO7v^@wk5YB5D43UyU%s>dL6RWr6`b-yir39i8zuJ`p8b
zDX*gfm^aVSeBaBmZ8P^b`+3i&9y8C)_yZrGpQ!gSz*Dy$YPk<8_%n8u*t&nkZ0vLg
z=)Ytm)KZvBBXOja2?&w@URJkrai#||Zp2AGZH;SvFdt$wH>Yx5nu*ulPM|8?(F6<s
zef*#2Hqe{W-o-OSZ4rSdH{UUzxClXt83lB;wDzW<s|r{C{apBo%9pP+$<`<lYO8#r
z$F#3`ceavc=z!x`N9UE;vKQ@J>xu4x_VKWe!&<u%7m)^0emP%W@8pvR{jCi+lE)2%
z@BMWDf@h2D9H#BMqBSg{&&74YX!-JZiCvv+Gvo(_TVr3GOl&9exhZ1`{Dk|5Kg_-?
z;saKNxf4Dcq;4KI`6i>5Gi-s0>+=I^Pu3_x>*tWr5*5WNJfA26zpT%O$Y&;9YxlBF
z1Y;}F@KiW*ZVlf_r8o&`Kk+_r;u)oob3b1MoK|9!hEtsV`0>K(5WoDD)F@9kw=syL
zEg)h2Jm2=q!{Fd=7jQfjO}u{3jQXh43Wteg-VFCLu^~;`2z;0wD-fGe)9<&uSEp^t
zY$P+}>4PkMgP!3Ov013(jf%_{cPLj2&t+))-GBEBVr1vao0*lEQ9iGIF*eFKm;Et!
z)I0k_-CX;<D&p);+0OAlO<#ehgBuU78>POi5QUE=7U1ms$6EH=%0nBZ0l_;WDD#(_
z->15{y)Ch}d<%ct;0(4D<$EaF=!yGKNX}hLQ}3jnw!Q7Dna`w><P#?yna{KAs1s%=
zj)b+)V%T$1wE3b)m_U02Qtd3RuGIbF`eKzhtd8Hp@A&bW*3Izl(uXo>AN4Ifo&*ks
z?b>>+Fh%@%V})*P$KLF~;fS-gJRjyMFjhSFpokFxl285O3AjjzL;qe!4oBZGVGXl&
zw-O=#FDx{5yOYq&-4zSPy=4cRE>aq0BOb0K8|Y?Vf}(`(Rk<DM2EeP$cpmT~+epv!
zviz6C-pXEOSd&wQUrfSeYi$~TxKRjy9&d5vHGPahabo&+a<V}?bFHIZ9ZcEbS?^wp
z1Mrb~T}6qDz;dQT!x8z_^^NRoY^`V(gCMD_;+H-9l~}4lm&)@5MLkJItr2%Sk+h0~
zdP~(OxHNY$1hXdh68wkfMMBS?eNUj8U&x-`=4fu4oHx16QQcV|gS*+bSm7iD@^Py-
z4y2Y|VFnP0Ot-DC#INgx%|TbJ8E8n2%q)Ik=W>NFle#VzCBLqH%A`5$?jn+QQ%{mJ
zYs)Y_zbm^I%f*Pv-2|uR@qY82Rb8G28!1gLVgjLdexAb;T$HkkGw_fbVDh=DdSmTR
z`jb(@R0=%c9BSG`rUTScVnx#=?QEML@1y33si<J<SAAY~c?pwywL3J2Gf#VIEuQP1
zwBZVmv$K-oB4`~Xa1zK2g6x;cGU!#h+L718PB17AEcY+W0bFSYo(%EO4YLJ_jJ%tD
z`jc|7oMW`v_2X;k8Xg{tT3TIGl`Y54VG_bA*JrYrw1!2akCs#vSQiO!Wj#^mx}rc~
zG`~Rg3}o3_;O-AZV*qnY*LunV%KpMoVlw{W^-XWk)S(rnhN>X<Zkxz)q<{khZkBDg
zLl*m?VY|;)2xI;Co9XBTAJ|7whg{Bweva=rxXeHI4u&BXhR2mv<Q+W0V6S<gLxjhY
z2vbPWpFpZd->cpuC5lZtS_)PRKeetFzDO#{>&}AumkDL0HGa+tzmOOlnXr_(y5$q^
zEND-Bysj#H5|c=|+<Mi=<fxZyV3)_OOJ|12Wq+L?U~7#DdALC?l=da3o;Z+O%HI~@
z_$3HCnjM=tv(KmZgV)qgSAt2Z)x?gQdJ*=+8^gUXt|teEc@;+v-`!L`G0ouU-jR0o
zl3Cwr)4tQ=O3ji+q^^bMLR^oV)m3OF{)6rFiC#x}<nakBDht2t`4c8KrrM#&)OTxJ
zWzWSLt_``U*+gDtj<0Ef;Vnu31s`sYvo&7j+z;=fu;^fxd+Y_?zvLhaUlhpzeKE;v
za&fQpW0hNdCJ2qi2Ny9lI#*B)izELTJ1zbP*uc$kKCL6aD77E1k{8a<XgyybHuv^M
zL_j%vsL>5dZkI=3LC%S+eq$Yr&=+=O6whJ`-R;(5h~;l5dH?w^^rzKU^<j1Is}d=k
zch2>QJ#K4CO!i<(d0h8o`&xhO&Z?&FmJHkM>5kN?yUdRkcoi^MeB0O7;1^%rvm^hS
zEJ(1!vhIJ8<-x>T=m?wsG$bCc&N%7z4@`@}K6Iz4uO-9vd8nrBPPQ$2wJ@e!(Va0r
zLqArkY?`bEvn_LOlvqWWNly)59}((dKS@3FG-2f_V=M$&EHNSU@GyM6vE1*!Fm5e)
z3a>gmd%@$2a0iS=d=U@<gGxg}axHC*Zc<#u-6lRW#!iH6+3n7VM*ru*UlNvp%sLxW
zdcLbYl8-xb5+8dor4=|HN|8-sGqLf}C4QRS1K-^$ob8K23<|t(WI_r)gRb~!cgqSi
z$F^asM9kjG>J&3Gvs%hI0Jo6R2XyUoPe*0WrairSpEfViuh6bju`?R2TkAX-Qwm%U
zrKAPw)$KZJ)Rmtoo#lGcO;UGpe(jcmKS9tDy|$iO$TLC9G>4E9%N6VstC)<8@84uF
zEXqWp5!A+32%REI#{0j2I_(dh!<Kn-9WLHd0^B)ghVSTYHtJ&DzU0;u^HR%nFR@5$
zYiuQ7PPXGGwOcDWStxU}^Hk>(UH^-<G6A)J7(v#76JLXa=PWh#*Lz>zq^E+1j7t1h
z>~H?Ld)mGVaAI8bv+QE?;aM-#i`i4+n#RY+x9j&!1<)6Vyf;u^T(1Q*2mQE=|IEtE
zhUnzM$Xl2%mEud(QS+%05g?uQU-g~c)25pFXGFTMrde9H>R3VHm25JDxF}>SbaW#{
zJPNbuR?#u9*wee^^W3Y2gCk$c7h~L{va1%KRJA($&+hdD`zhmO1MXO`7(#t;OUP}3
zp4(RkwQxeV_jDBeB9be77UI4vbIAD`qpm|L_^X*jYorc~p+=8>2)h#?BbWQQ@bj#!
z4>Bl%>@Te%$z+lp)3P^mKL?4p{jeHd&~YMov-?ud%OdI<_nw|xTBgjz!<p8wM7}*!
zW33)JQ4GaxQ>PN8bG%%|IMX5=vbrxF3R8E35*U<_MsNafw1#q0Qj#~+!R}BiBchJ+
z^&<4oKdY>i^8>EiWA@<q+VV7!Uk;nuy1GQH>Vh}Iy0VI>G?Z_$b8^PcmLjhr?>B-L
ziAbV?+7Q5hV7D*s#|wai*Zg*As4v&JvhQiO8!#?fdS^Fq&5-`BtH601qb`;?uYy9M
z99-w&;WUD#9VzCexAkeGF7zm}HsLs9<iR4Q6a$+s_CIGpuCBg5r@fvGsM*ThswP_F
zOFyVoCy-YXbH;#MyC%}#6oT0J&DY<Lu8xRTwL;omq2xtao+{H8^69RJ{GPqZ)5F67
zlz=f&Q7vGzVPn$1p7#}cLZ7#d7gWM~n-xs@Y52yM&S8sfZfE4;T2Gnl_(L8BJrh;w
zCq2$Wd?*l$Nbq&lJ|jn{>AQ_qj-ZC=oe{KELvJawv?buErNemBAu3lNLA(@fjoi={
z2Ph-asFK3M7Pue0Nzfrz18eietXIctj_wXR9IMq_Ujg%aE)w8a#z%{(n%I*$@VGl~
zWbPg@JL>)6)o*@72f1n8`lF?Az;xLXWWE{26_dExJZMV(apg)t{$W>Y9DPO(-&r}6
zwdJiyk2JpX)iuMx=w~GL$2{(5H})8cr})BCFPeYD!dT?qgm#v!J-B7dkwssN_38Ch
zdH%pk3~WWvtX+(n9-O)6M$Rrq!naoV7wW2QNQVFm5)S)aG@~33su@J2o7?NntYgp?
z-LaX~ECDFiu`u;x(UHsRNCC#gB{Rl>z)+NEz!{)>b4)=1A-7P{Y!6)!Na+_li#kx}
zAivQ90pn~Q_VbDsUf;s+TDm^2yGoooE_>xr2CVKpCGtBVGoI7&XVd226l4qOMxU!J
z`XhpHfAgXy8Sd!RUPn^P3X*fk#YghM&WFr#_}!|WIF+h4`%Ss>8XTBMGXxGu?A}V4
zy=Dg5G1}Yeu}`ZVw8vHowS4A3M@KG5_<qersCwbYUXZ!JVSD6NL9?NO&$C8!Usy8W
z3~-vnY85pU+!AK<Xu;(B8*%vrwd3kU^zpv+xpc1gvZ)a1iz9hdMHuY$JkOB2{w~C6
z{skj1;TIs>NkA2bu>+*@162TS<X1ss9SU(U>QR|dNhHcqR#v{$i@k`2Lnybeae+o;
zJ<&~Sk^?n8!I_s9QlpLx)h){a`v;PdY2Nrj?#18NZ~R<&MqUg`<?20=L+@eqiNeps
zYeSmK$q?V2zG>L6{q$mn-*Kf4xF^qO^`C??Op1lVeUSQs3i^V@R0(tpj8@*+!5tUr
zQ<RAU8)FS<l~#2trr>3_g0%T*?F1;3mk8@iLaVLZtF7e($e({)4f=U?kOW<{`E#+|
z+WXr2Ew!#s_~F3)*_yWsGWegvhK>7t&rZ+N2Ghf|&&)i;bcrY`Sj`mT?6|hwe!6=3
zq_ogN!|>H6f8jk(VGKzSnBf%^f9!>J>MQyLF$P6+;3cv_kQ#;V#MP=>BR{^Kj_%0A
ztKD58iOF2oyfqf4xjJw9yp1?|e*0RbKOvE^Zzpad#7<9JA2R=*UTkmc+o|*}&)X~%
zA68$qlhgGWkpf3DjR>?-2I9NGlSY9*?r|)vLS}g)rEG1jo>qpO%NU|fb!fSfoFJlC
zbVbo_4SH1?Mp0$;&t=>SD;3t|f>AZaa+0h4TI$Pv(=6MU3SLNm(%>DydYL;@r|Q;v
zLUPNwtWWo&bof;p5h@=x+o$_8L@r3jH`99V$rcNxgOvjyZS5Cs^#w$mzx=I7p><72
z;9yVuaH~UQ<)Z&T-wA(y7=t>jI^%{vwION>^VVOGmrZ?L+xy7!+5^~JsJ(jLb6Hf*
zp7$7MXKLJC?`OZUZ8;8g99@7l`fg}z&0bsc=ah0&DZ<fAHKDccYPMz7TGX_dghV>h
z(2FoA7U431erxvb%HM6(Ie7t62*w(4#gs@Tid$AW56FFs9n2O#li&5hI1+pPE_bT~
zX|L}+Wx_Nr3ND97tisPCN{;;lp60g3tnqD`qj7CXqj9~N1~VVZt57}>5RpkMm>}2B
zgt(LhUk_UxGGC&Jj$yy;*Bu!Ac!#?y9&`$hZf^SLY!sHj_oHX?`71tq*x2kP<hmrP
z2n21At)|+mh1Ct`L7xH!a_3Sn(}O=fI(4Oz%Z{SvEo-IM%H;&OpEew}6cG9<RhsnM
zYHO|R(qOm`wI*%O&f4>qJxUzucGLC+h}Bf+>C}Zm!O%bt?cL-Wmt<5iVc^|Iwnzg}
znz7tKL#S0uDztjZ`9UXG7p}OE537g6r_gNc0{0y!R=LJcs%h<vHLZ4YQ5Md70{{A$
zdoH6RYeB~1;K74ZzF)r_uNVP;J|_vSw01SV@Yy^rjxr6t6Qh9E8FPJ==B5i64y~I0
zjn0@2{Usy>G7Smyj@KFGOqwjB5)s7L88?_ZaY6WsG4lOQ<x<Xym;x(qszd3CwEM(F
zx{Q{wlh}$Gx8Pn3wDP$tGYhMAqL!;uGf@A?fI$j6P1gdFrsq0`-U%}R$wmMdyQLmt
zB_Hsys67EOvC2tx7exS`ZEuQ|HaN1I*T7%a;Hb!s+X_ptXL~h#q@HVEsnSo>8H<{J
zp_7QA*6%E2S;_Afin1#RD{xm&U&7BVsImx9dNr^JOB!48gTCrI^cTa@yrWRo-!xIY
zlh>A*m8b|m%!b*N$+Pu3zVG1(FY<U?SXkYa8g-a3n)_Hb>!!8}eY}g$ZDWBSPsk|n
z`t{$82CA|#RD{3YZjnG!c4t;C32JI-NmWs5i*PJKN~^GeJAW0^DUw|ky31JYa$puL
zCgGkf+u;PRa;0^Kpri54;vDoFu3J~072b1MqG|Xlf5Fci!|ouQdPZAAg6|=Imij6B
zVX?r=*+FWi(wA|Qx$ftB4Y3yT{Btym5}r+>p}@SzL2T|4_xd0<2BuDKe^QoSm8Z&`
zA!wec0xcWjG995)EvVF~SX5h>py?^U`?kV`CZC@Q#}+0^s@ox>d$r_h%7bpX(_R&w
z`%Tjft0{WSMbG?;jcCf??4$PpXvew;mIN>vQ3OaS8Bsc`pIEC}fYWXhdB(J919D5D
z-e4_1g`}!TJIQMJInMY<mrJkA#}zyHe&@>38E=GPfSt%llm#*%C9@E&-FEc_dCCO*
zEk2U^;k}JWbf9I)75G;I?7^ik0o}>{-?w*x{X1P>T|FmS-*W=r*?uSAd%`6(kF$16
zC(mVwRp?nlbJAK!0@UXPIi-`+zdM+pRUu0&PTb>McIKrIxm7g_Gk)#svcC;@XWq%2
zuDYl&8v{KI6VRC&rXSrQD=M0N@7~swcKbJf9tRnV{bWxeW!Y#-ei4U-{B<I_;dn)V
z(d6dsjYfc0Lf>(Gi|n5-qW-w|cX<{)|1tJpiaZqihIDJlczJPf+6Vr!1^$w&pS(Sl
zt4}`T4n1Fi^`R7V#lwKdz7rrh328nGFhW2Ak*#Yf=Wdt#?ZV2;(g8L@=OZl?@q&#(
z_x>3&=5G^4@L#xgw4|lnV&DH-fUPYA>wx0OHp3XjDFD(q+R1nB+_8yCb^SbZt%F38
z7IH*;<@YNg4oB)a5!0#&sDquA!j8!&E#cLNOFAFd+^BSrkmC%MUN*nruC#J%2>pWL
zEnd!;q(V@Gh;i&-V1SQ)f)NIw*9t8Cwi1AS`R5AIfV0r(`KVuBC3E$NBecXpM$fE6
zY)>F;ThFrnuEZ8WwdZ#QU{41{qcit%Lm$#mQ^3TJpjJc18fa_#`*?*vm}0s1|AdqT
zBwZjCZOOQ^!H*77YSlBRG5xpsovOp(OMR7FCppxPO%zZJW)fhFf`qBJJFbUSkLMJ?
z;c&Tr%uKbhgN+Yl!U7R$VW{+)Xh~Fr2FXd?iZv)_oF(J{avt%y_s?xW^sqT(R4})^
zF?`8f><D&o{g!fj(1~oB-E;PimKvlko&qep$eh{8Q#AL`G;Nsmnvmq9jy39iqw>Eb
zE1@F<&HoQk)8M+XFa`+^fmb!~0(3bnJ%VO?Da-oH(lgi0fNGef0%_ALxzHZJrjM|#
zotBQIBGeTq_OuMv?WkYyPs2)S1i&Tgok1EFp*@y(U3F68@#B(LgLSJGih!<<7l>WK
znKKhEFMom=guk$7`%*vp<=n6Wz+6z_4g`Dz4^mXrutcl3ZMXcYu=(a`nAPeqG#ExX
z>R@jeR3kNmw6^nqF#}-5e3m6<0My6GKF76}NK6YlR%&;;3;T)SF=qb;c>n^ZRu-t|
zef3aE4HKWcgn@C(!sPw0)X%}@Qb|=JvA(93W>0*QC_b{=mnzY?0#5E|#1IGiTw^~f
zEUMyLDssxEq>w{o9?lfOk9a>je<Vp+UtV96w-I4eZL3)5&n%n5O4WtDTZ1n7L0Yf`
z4IJ-XrrZfWu`*nhr~c)C0wP`&)Ioidy=hqSux**=k)+LB`o}`|<4nU_^2I$Sy7!jN
zebzMZ>1Dp4hLRY%m&w2za7ue1qi4xVkd|wY`6HU#seDFRwGBkp1>n&Dt+K<nu&M4Q
z#8FhOlBzmE8#46N2Yk_JH6>VA9JQQ%5$dOb%pYBQp})(se2{Z{<-c-xx>ujz{%(es
zIH<&V+@PfGNHY+v56TN{x@$S=6ctj}+~1O6oQpfL1r5shO3%Dt0Wz=d2I)OAd4o}5
z&@xsE!3OG6o^kNNa1|4+0`@qfe)!T;4C?p2A+w9+XciTgpM_chd77CF3__b#8qGN1
z^%{7O%=M1S8Lb23@(i``FB%O%)M!C=Nq*p%>3nS!88v~Mwe^x28pgLPU|s}&%#Nso
zp*QUwUe~V1+Mr~5^MZ=0Q4RI8W~`K_bz~E%BPHBBNkfp9f1N~dJTy!4)Y8g?kGH06
z_XHv{#!a6g+u`?ZW}bo0`SY+qr9Zh>S63rzI#am?KnRsB*C>&;emGChVyCKAj|#}z
z4WU@mj~?--Vx2~r`T2TC95XbO44v2)O$@6Y36)C@6;Q6rFYTc1Ro}+!q6q|*%Yn3w
z;gYa+>=#sQ)Brd+9Gn^yR2g^eozd1hagXutCsOY7fm*x@Yf6^18T8>RR23FaKwf_D
z%%2uhY@s!=tNH?BLJOBYVbx-@)N1ZE9n|>zwx%X_Hat8W=}1j0M-X#^YO5Kc8e06J
zjUhoZnZk<}?w^!<Sz7>r$4SSTtVEUfXJlG?dx!~O^KXyEmJ9HO-ien)VfxiMcxqmL
zyX1}^KQ6TJ^yyQ#`Hk0KmG`<HzSbIKk<1^5T~nX|N8pI6)P1>V*d5ukq4Pb*kp0+h
zuiM{l9sM)+Dx~xoX1qM{AvO7(>E(Ix4Nshe^ul(1@p7ws9)-Z7E-_kU25>Ut;9mt-
z(Qg?$L7h20IT@k9c*7fjAPAk5<<Rx*?SH;VUtM0nq-!z_v!y<Y#odK?dTe4D`C@_v
zWfwS~Zlxh{u%q(v^8@sQ8Utxa-0)+)-R_#`!@pg*e>|=)VoKXCB@x~J8u42B?(MaL
z7raVafF=vBd3bU`xiA}mAjeNVT}E5$#of;iA&!Su`D0c)5nTqJlG4j1W?E2q3n3hZ
zb72+PEsJVU`aw;-HQPA@Y#LG50P<cdt@uemVobSAG8A#1_;69{PU=<mrw!zmi#dNp
zXwyiUj~6X?>pgrj^3g`zRT)*3tqM3)6d)q*l;=@^iIOmN+HU$X9RddGR09VY$g9JW
zL;XHm^Y4mTC#+Meo~}Z_r2JBnJUphKvMhYDZ*7~7*dD^|!N${Gsdgn54M}aBj2ha9
zEa6R5zSNowI=oYAZQvNK6V131-owH*8Sn0Ti&O-%h9yI1=UEJ?#i*dzphito!GNy0
z6u=6bJs>EDV&cV$f29sBcF-PlKtz@uUhHXuqeL_jc#ObQ3VT-I5Iz5_lxRe|UFjHb
zT=y=h_{}_oa@M_W!kAZJI>?bsgWbM!JjMzzh{$AULPwUMWtT~K3`g>=k?v1~^r!-M
zy-1?ZkYy_XTykHo?-&E0j3|cchd2@i#tqJLWxr?s8<go@@|ubQj!F2K;czA{>()@j
z>eIl~ZS0z?y%r<KXT2G+RrM7p?uc-^Z|7(GUebcJ)9}^k6`yEyGXXXU*^09#nlK4P
z=WEq_=a8jt;DvOvV`SvOO$~PTRdj~Dat=J+^pR5V0TDU7gWM=hZn?V|VN`H1Hxyd1
z-yz5?KaP5YRgPA^f18sZ&d?T%y|Rp)Oaw9Uq6OjxHHRb6lA*r*S`oM;U47mts0Nch
z=&tK!?D)t2#qw`SQowCuS7HgAiv?aU|8&E<rG|m5$J7~&bgAIMZVnJDDs|rc3B){L
zr3QTAkMtL}+~gT`r?Gd=>+{Y|{y*`^ua3aZ3ZOY7|17(fVR44q8H=F0J7HeK>X=P&
zq<8NPxzH|mtviF{vgiA~Y;7<DnL~scmv2vst^l(`IvX%F9Rq$a8~HQT3kz845D!oQ
z7IBm6qZCq!Yi}%9ZlnBt(HFN4)-eBERwjo{h64k9c_N(e)}9U9%$P=rX~6edOl+Om
zbI$TWGPc$P<j%Lenn5}==kCCw$w}HTPhM8lCxSCHLV>A^&6ou`I|5F|*r)31KLaTw
zz#1S_ra~Qb7SR<I#`T;3`BJ(7me-KqXt4?;Uh|WPd#=GvP#U(5e)c^7Z+rpJ+>X)?
zt=IH)`q>OEF_}a0#KHM*DP9z-`T<db*WliPbRzhaAz6DSS|3XQP-cK=Q#sXMiul!6
z4xIzy4M8$3vV4fQ6t?%_nJ>3xetbLK({zv*SktG?ktzn48vD1dIt|=<dqSx=QcUQ;
zZ-g!$1+6ocMF-6o-=*EeC@NN}t9NOl!vN;Ip~`LZ-W8?bX@Yt!Bu39=Jcj>4YaIM9
zDD&UMJ7D-=w9t4Wh;b(2<1bCW1X3~MhKYxdNlljECt^%UZWqsuPMnIPw8DA4g}d~A
zu4{%k+5in>cv)kL6|BCur_%U0{;3vyt*Soh#DOVJ;!PiZU~vwUJbVoDfyQ_avL4GP
z>Dy~Em*K-E)Nk#g`1Dx_&^}AODeo83swUN}h&uPNz(3!!H`*BVHKX%+98ihC?9SHO
z)ESw+X|Par$glIRufY-8V?XyB?fZ@KKYxtX=0j2VH5V(jstm0o>ywpRHHX!LDnXH!
zRCy+s{n4Ec)*TrH_Hg3a;TQYln`q(bm8SZ<;OxxI6=Yd3I5-G8>~ke%T|t&Bf5LuJ
zLX$;&e?2Y{I8w|zJW#t^`Hx_8*sI6)FL}N0Zy!BgAjOsS_n`ahuM=PA{4Ecp1Jee~
z*n1i?M)zvP_*~-RyLWfxW}}<Oqz4wkII~xBa4~*uF72q|EV66`Go(l!a8DrvQL}o3
z@DpFZTdmY;qUE9%eHw+4q^RcwROfihMT?TKLMv>Vd)|kZ@L+ny$KLm5!!9*(Z_Cik
z{~prbcCOix=$4iEwek2lWZv}VPu(J1^ELk*Y$i@CjSBv_rvfj$BU6!3?IMMid(!SR
zpy;uXiB!j~hupxb>WJ-=mx$04a6UE>0~CmPZizY)J8gba=C*|O+*+|{eg-FDqw7dc
z(T>KW^=)txNfJs6@^C)NI!=3(>%WxuBi%G&JYahQ$))A`5)o}1?)xKuih4&iZGd~_
zNx2Cb($lO>Lxq#DPtaz5dbpvI`|DJv*s2nB*2_~}sd4X~R#$z~|Gc6A0lf&|&LzL~
z<$*|1&WL!}nh;2iT<j{qc~9<>EOar#!3=81QYI`Jd>g}m;%Z;rcv2TJ_w~bLe|d=%
zc?PZV9rUT9!g20CCXBvZincjq7e0J^Rff9kiv$v!)QqlUj$|5YkF`V7)aFskw+r+0
z6ZMvBc?e&UB9@~seU3@4Llvfg42dAxK;tL@l9p5Za;+Ty-G*}WcxHKJ4TEdfbr|ax
zf(woGkEy;$&~EPUNZEeU;4ca=paL9UO1}$6S}q~I02Czrs*t!tbi-&F2XFzL#0sz^
zIbd6*bI1;WOt1;dAF+w0jg|HfL;S_c<Qn?$rRopKYr;_-+)Nq{&U=-I!$8Y7aig)1
zrhNNH!NuVf^-b4c+zlIUJdja{P_aF~2-aY6gCPyP_Q2U8PWNx@?^`^`CgNxOI^<A<
z(D=;F+CbG|ALSEHLh4}`Ar2VpI7(N8RtAKF#;dUrlRZta&j%I?aLGI$_~Y9g&>=t;
zgUAlNjcQoA1;GL)N&FXCKs6?BjR1hi?<@cB4b|U)h1h`M=Z0MBw$`n~dT)NYC&E){
z<;16%HHcpxw>ki@_&=(y1DwkK{cBGZN<%1-GLn#ym4@u>JsL8Pkv$(RLXmMuR>@w+
zJoX9+WyEpf*n4EJ%>MUd^#1;?uHLJ+t8<?D{oeOy-LOTt)%Ue6Z~VK`83ObGKoxGZ
zz|jG+3I+y42f8vL7rs?4M&bW`{CGVc!(<ifuIPYndJ@#!VTon|4sW9m)-6RT>txwf
zJ{M*_XD5ct7>gF;1;Pc-PF-np+&sEbDA<LCg(rSPVpn~w(4kk)Gba~Uh6W=3JNR~h
z5ft_)UZ^f~*Sy^~U$w_-dgqJ^K(yi6skaPLz(+VHfCAhaBntlrr&3clu?@ZJ7@7|p
z6?4;meiQ~JIXQMZ6EL@D`$x|eo16hJ3$5%4Yd-A;!^Dq`)&us8JVJiXd?BjE{br(_
zlByNB%oe-RWE)ROt>R+Ewc8sAFqxjG5vjo$;`IGl6!*h8r?{tiY%ph`eUSvx!y~i;
z{bv^!7bB<6{I@TkrOl-Fjf?w?OgCDWwaQMO<cYYPU)27&<@EH8fEreL2agsTch8!Q
ze^Bbbg~^ZqHpo=(K>n_wo1b0#;tDJP1HYY$bcBZZ>-j&;|M&6lyq>7<>Qko;`=0&g
ztI5#U%sZ`kQj!oS?Sm4k0OFUKdF`zu2mLLGo}3}HqzzIDAkm#wVtEKzAL={uBcM83
zc&S=WpsPEt`}v{czEwje3ES5S)LAOrwE|cD2%YI~7T!F`w&VU>^axwkeOq>biSAvF
z|Nf=Kn+|bQSo7hFESoPYt2odqo8q7s^s65kM^G*JJc2epIENo5J|lGCT?7?W(^ijJ
zG$LdEVKMu^f?G-*)T^NX5MZGHVOS5<EkN_t8h3dd<hVZ)Jm0BQy%?2(O>V!D4MTLX
zeOiVLDz;_V{&o$(bvEiZ6g~lJD+mf-==2dBJPSatng<ZagnGy9CfXAqOX4l+?F%#C
ziGipwoFgHL+HvIHnTwJ*Yi)1cE4E`x0+Mf>rq=Lp#ZTYr0tQ#+Gv=?q^<nLu;Q*A=
zKcey>sm+?m&<Xf%@z3%bqkh(b=T;hcst%_M?mgEW7)SG@2vPu>!T!IoEI~~RcKvYZ
zN9R#E6_6A(q?w6?*C#<0_p1B>8r^R2KO|F7IMh38u^)l&x!Z&tf7TdH*V@;prXvu>
z%RN1|qJJg*efy((9gQDVDqn!YAd5xEvudT+Cg2+t78!a`eBo%)=2b3jG#Ud;c)ee4
z{Wg@ae`9&!Cit_+F9Bi#1UxT~mH`Rh3H4w-pQ?8aB(lN8A(e30E4(u;{#~E$-61OK
zz(OBUEuV%u*A&Xmrae##f`aVF)8OTij&X_*8uw-@@_1gAL*J0NTQh@J+J0_@bbBSX
zHbFdWf=nai=JqdSLFWjwxYn?c@fZed3?q(~c*_0!hRzF2E*s${Cv}~tqj3MZM&ebu
zYd?IW<_WrD%%({H=H%GJnHpO`&A<*i%pZ!=?#;9ZxNrAm>lF&qO}^dNjL;tVHZ*^B
zDR%StEq?g#hwn!7b9`uTY-nU6m%bZVPj%mvcx&k(M8boqASS19fPJ~3WaS3L@l<ME
z`FH9nub&$k2`V0_s^Y@h7vgpKQ_Y(8yw&%T9FWNgb{1Q{A=cJTLm2Fg$Iso)PGF8=
zFOo+v!#<b#7xRm@%Gd7!OjKYq6&LqFuk#oR^njg2ej}0#jtEBi#8y{Jz-T>4vPXpu
z8^RC3)oX?{73lxtV6W7kV1H}ieFi6c7I&u~t5?`?-1j8*3fs6vAr{27m<dl~EA#05
zx5lXch{h8YPr;AM8z%J!PJ(pkEk%+44xNMl&aF+Y_%VM#C37YdeC~&MI=RQ*P{w`m
z@vaH3G2?KOPnLKYX4DU`psZSP!i_`=xA&X<k6w(X|EK>7Bb8Wann7Aj<tz7ijA4cv
zsmk*oSn${GnLRb?xu=<ST52g;V5frlw-%<M$ONwIbH<8txBbXOST=E>(%*k3{yWPy
zg;c{4Pf&JU=ZmS@8#m`sb{UlmD&sqJKClYHttK~C7L4yy6HV&9nc_w8p~(q<P$Eco
ztNO758lr9GD<zAJDKq$4Oe=1lr(VMU$j;ryuI*6-rS`ms@%{BbdJ1xHqq8VK%uE=H
zCOy14|22uE-a8F{(DChAc)EH0(!|&b8dlL99>{H#0I!4#+8nB?1_O^HSkyyq-hjr4
zZU}ETzfYwQu`X-wd!gv~TkmIxQt^s~#=^DNHRQKOy>`RV$1|U~FPEIrMN6K@tRX~y
zD4tp4Kou-BONDef+hyPPhL%m8I4T^m^!W?p476cHL-_o%oXe=9BMi#A0t<&~2^Bw(
zAW*k$^w2t>XdOjmF@gwPvHHJaD(#g2l0O}N*pV9g8+;&E3t^(dIvrNNHLby@bMlSq
zF7(`s2p<c%zBF5Io&eiXSlEyeDczp{Ei;#2wQf;2_;EVqFGM6Yb-}URc$W8o+jSSz
z0xr<i-4~=IqE_)e+PvG(6;dh*CW(=0xWwq)gBw}&NIfT*@R6%m)sFFF>L~Yg+uU@j
zp=gJn=)e*L<E81+ecAf*WKsC?E|_aow+?d&ZVY9cJCR7B06ko8aq$CW(OwRbW(lt4
zAKFOQz^#wsP@vTAQ}91NA}yH~kv%aNTGZ!gQJ?EtWT17!!{1vVq42_QTtzS8QX#8G
zD9eO7--MS^)C_#HFh|MKmI{aO43rWg5O)A~*v1D{lGQnmqwW}p_WB<3&=nQQjOqT(
zM^`YcB$Er8Hi^pQhfqy_z<<>qLRMu6>fwEhZ}=Z9ad~ig&lEShsghQ1SP$kO3C|Y(
z#I<rGE032&q!ATx!cUqWRFnwdW#KMBx^mSETD6$`?GstQ<qv1R^O+l}ax#HgD|II5
z95QtLUOr?HGD)B#2LM%uSk#kKRGS|Kzq29QK=6YE-!;>~@!?YUW3#d@Ub;D>m3&EQ
z*Op}{a$=ZSe5=5cu?0h2O@R^<L|793AV2{{V*k9OLD2#{?w(vJs}N1_lwsBpS+qlj
zUJkwzyNSkLN6dx|v6XICGU)dPMSwV??voOTLpmk35aAD3t_(X}6f3R?oUy@vTm?f%
zTQlwXt!<mEO%$qz1f`nR$>HJm^ZCHqg8aMwzz^MNFV(EV2{4^P)devKQx7D50^xk%
zpN6y4OBqBcrWTS%RhWbRLWl0}U{%(M<LV`<j|pTIm4KfEN<|tZzr3#Ae(QQ<1%ntK
zEjLAo+_U@W>_rs@h`eXqKb<TZzLTj_0IC>fw3vS%hy7QTWR<{r22$nlv{k#6i-D}_
zhh}s-8bxgs9KLwg6749!#jx=sB?0HrXxQ8u3BB=rKB}gxTZd3yB$~&h<+~&1+%b!V
z%+}urQjcvN3)#mtKvh8xg=%TF%P$M@2mS<=ppcNiN+S!$Hz%`E&*3l7c34Oaq^I8y
zQ`*84*ev0>Tm+9vqBKiafgMh|DX^Z`Di6)x-zeGv$XO5D1fnnaeOqNm{;A6J{5BOM
zw&WX9)j0R2HlDDbishH}y$*3D6h1N0O+JHNM88v`i9(}cThf^J2cAdtMF?LL`}7eV
z_~lqFrSsF`2Rd+LyrX~^@_HInpcZvbN65+fS2{X6W^>fG&(UoU0%d_QC^zMNqUZbX
zgA&q0GNAP*czdee;TP@rnrb+pC!a9v{>HII1f)enci;-k49s+s0<<35xJie4Cu?-{
z<f+4w{$MhMQJFdtSWj9IdVpG}GI^2a)G5RFhOR#)nAwMy{}stT%76+W2|Xht{c<XC
zSl{S_-uS&G#D><f8*n!ZWwgCKR=pS2w~n>#7TiGiM4a=$1_xhz^{~+DRJkKiGbzO4
z1>lp{P=P}N+L+gI&!Q4Cu&Ra5yP|p9<<p?%N&h(ZZ>5b-$7r@j#WY$YH$RU=eI3OE
zI=0|zkZr;jvx_S4j;`jn$wr6JeCwV;I_=yT%^|bjO5Y~6=eCWUh8%F0r8Z6!ZOAte
zYoNP^VU&VhIS(FF+c!N7LdCAI#pOD?;=bRJ0GL4#VaVS-+Ypx9iSn!(*uW9)pv#8b
z%E_ipcizSx!nz4+Hyg&rH!9tH6!Lr~CIe;(Fjn3Y2FQX0fYKAhV?z_a_4IEv(~}TH
zg3bd$XRELrKUg<6bB2UxlGvCt0gK>w$4IDG&{VjmI;a}hqO9d#yk8LhX)DrV!Gc4T
zu=MrDj9A>EZ>%?$)bit}P2~yn%2_F(XUCwtMsY4a#nVVrR}7?d1M{cET{$;zi`$8m
z6p&4zT+=)(uPT$yLpSD81{RRQdNf^e8HN*l1VJX&s<RJ`D+LV~N?@T0a)<Jt{PRCG
zbV9=fs0mumcd4nYe9b^5t&?gh&_td1vG88lH4AFzwiaI6z{s|fL|}X1;V|Raf?i4~
z2@3!jqmlR<%dGUpY~U9Mp7Ntvz94)QpI?SKnj@%7(#jdkpd8H#+fN}P8v2b|hMA#@
zH>hz#$C=T_8_|fiQP(5yc1nu>(mhZ^tN`6(s$!C*gB^GGKi?A@7Yde2FjAr^?7mAj
z;vej{)&N*z>44%7VK<soySu9oWUP~Fv1bAdfXTIR@~mQ~oEmM03p!t!MF>HZ=!m2C
z_uU`lZdagWtfin3-#)(58I>yl6JWW4Eo4({MsJPu#v^-e&szg80Q;eDHHP+>#XP^L
zzmIatA&Z4t3H*Q&YWIa2-8Pt?GlAeqsqH&|Y3u3lru)y7k%s7@^o@aW))`OUz5VDj
zSm(;l$U!p0E&s8;vpSmKUFGzdfMJV|P!lam7qt<IH!Q7vyuob26M%X99}0Cg8=9co
zcuIG<$6jaZRq&7MEUWqHq*T9HS|tSaB~}2q&Oq*G&%}tM6KAc`ZggAK0ec6s1zNi~
z+%nw0e5D7K;hbx*EPj9FEBdHHq7?QtjqBGJpoQ~qR62MYjGI`Dof3@`!|48%j(@83
z+%MRMRSsZc=h2i~NXgHjF&8Osk`qbUW?V>8+ZR@^69k$IZ;&ThcHButk4_6&@qJ$F
zQ-7;}5Z+33Q9N*zM#Nay_Y+u_;PLoH+c-G7?mqDvC9R~k)<QRYpQPbBn(&B9hUQVK
zb<)1-hBf5m=BFPPg1G->7@d_0D{<1b9|OG4wgLLI)f1>}JJWH{0=WyC`8F<rwNtXx
z?K+)N2Ud!jC>+e2L+*oCH4)Buqt@IO2}pUd!gC%Hn}F)i(W1v19Smi@K4^}5oH=To
z{-P#fRMCY>ieMeWV@|uLUz*&8C`;3?rDULc+7o~aKELH$DnqK<Ga@AEjxIM-9g4pJ
z<t|%%XPYZGdb`))-3O(nvf#JH?L}LUE|2m92vO(rL6@O?C0L(?N9m9;P{J0YS(7j=
zk+7U8vciRSFtf`MXb5fbO2_0VT8aTK1MT&Aja{b@oo{N%`m%$I$Upz14=VZt|6pgM
z^rQR2L5=Iv$&xKI@zoc0;Hcg%-rj{3$B4A#LM47_cs9T2@z$oG=BIVw2Z|t$0%7&J
zb0#Qf7{<I<JfB3hDQNHoYKlg4#nCox4%JaX8c;C|*0f$)TmPJoq^lv#kcIIH<yi7T
zWncrkX%m><_S0HRXSNDit=dT2gal4=R}sdo?<C7itHImS%Z{a^_451XN6<a2b_ZU&
zQ#&3*n|b?N92DvBbmw2dVgMMayT?R5`BpjB*G{!>`d(?aE`GbE<J+6+!n=r|o-8z!
z4s8u!$5;Ws>{0%DC<Q=!G>nc-gS`#6fhM9C7_4|EATt5x3;0;rdO=B^pd{&Wg!bpN
zUHedSs5axw1am@<>XQiI>Oko&932cKy7!#t_H)lFzv)3<zY0CuzBmMJ+CcrHklO4j
z@9=F>UXD9HHC|WSujcl2>OH2jlU}m);7G3Fam7`Q>u>K~)NW&sZs{y!1dr%*IE8FP
zZ^NRWg-&Gv;U4or+@0Q7BeeEKJ!pf4y0aBe0W&z|ZnX|fQr^aWf)-!fO>Wl){{dH4
ztr|7}xg&9NYCM;a-+Br+jxs4<txZj>o#a6!Ptd8OS|@;Bh0>P|3$MKeyfQyucI%di
zu2=|h<MMuI)S}!qYTQ8~Yc!pI7?pHH;3Dq|2M>yN;4$bHfdWc%s8j-m1co7hE4NQd
zB@9Z*R49xUmdYg6ioq`AuTQ^Jd8qW!y|Mf+s4p}qLQ<(Xk2f*E@ew$`s;m098X>Y$
zPQ%cK!Y8oREPEh-_N|qRtiAv(#T;bRsG-wa=RsU53py!<Ji*20lfG4-fxjCmoULn`
zMIsgjQ9;JVE8De6*gLJppHv?HP>YMgp+Er`GEnJTLu3@Qp>WVI03RuN$AdjC(u~{x
z;JYvW(2p?;4DA9F&kNLED4~$9)?8ZZwlLX+1JOKKgC!IgDdi0e;?RK>5FK{u*#6u+
zQ}pVg*T0nV2^Ir|ZOgXhPw;92vxlO__t-+_GZs5%+8$ZSDTO*+@Y>zBthusv&E51L
zO1SU}9NRyP<|aZMe(!?9)?bm;R@}RWLoI%OYhcFHQ$S9IDdg*2sc~8nbO%;`?ECu5
zI*qPR<7)@Pkqm)UpEN|ilYaYiTiFu+Z7Ob5eykdR_y<CB<M&Jgcq3TjR*KwZL3k80
zM%mWc^(nK(2DP?`xlTqE{zKQRp8xxZG&XE4`nvK;vv%=>`77g3t`_YQ_LbEQZBQSh
z6hJadP=cfZDx96d_0LQGHO8~g{^uj!M9(-r2Alh2Wb@=v>C4(zPp-O@yW+rUKa8G>
zmMR#V)oBB|q_*1tGPV5wC_O2$;>SiHXzo!O;8f@k-n`H}d?A`sJhzcuIv8D|e7Hgc
z9FBnX&&2m2Loe&vR&2Xf-u|~OTFAf>c}+FZz0ZM}0e7_esA}I#f|ijyad5-U_6eAl
z(ckmo0#3K$WJllD-1_fWcKmlNvsW~^c|Nd=S_p`6oyC3aLoQSUX1ON>jM@W|{@1NN
zZ0g*vgoD$5$aJ`Q)3>%w8Nq{c;$P$Jb^%tO>cWIZ>zkiZ)U&e2HQ%A+W22J%2A(u@
z{OHn##}5pdMgOr*AhX3B+qMX9Z1V^}g*0N*fSi84R%t9POzY<x;|=yD!0WVtyNXMB
ztNMASreX$kP&4*m#NKLy$wWyE+kQVc{Te(5iXqg1Qw(Uha*k6~1_)7%*tGAr+B#up
z%|X*XWcs@~k6<(a_xk>~Nnpmd9yf%H?ef2qN_z><3NR+)v_FjX1e=ay4X{_<n{im)
z-0e)4;<FF9o@Stdzy}j%Gyss+f5zQXfAN2}2~(F<1K-Xg&4?R6`o;iOG5CZ|Yw=y=
zJC+h}+t3^}wh>g~r$5R<F$0A#k`uhG`z+14^Y25hz|{?jib;p7JM#QUoEONAC<Bka
z(YMxPV9qoxC~EGbo^gu3V99*us6eCrcphxm=mI_PuP^>*#4Igj4aUek_C`y^pew@`
z`5)7*<v-hy8l~mo#(hPu?HQ1gGG}n$?p8KOvAX+3>25%h&R0CwQ0@SPmVg4qbV`EX
z=C&6*vY<2n3P`?#Oa3v_->um?Px+qY);RPH+8Bd_8eY4;-MFwWc~*zKQdRlFOoZB_
z`<*n0Hh4g-?L{KZ4Sgy%*Z*WbaQS<h%2S2-0|5j_i^ar352AlVMM$^cqH0FF&cXrD
zy@hSfVAhCuovGbKk@&U$CU~q4{t-0e_y?zC9r$-(QXh=4JXlsE22JDBZ+s0#>r`_R
z3P%<)PTmFz>8+4FCbhC3oG25>TQCwR!8rqH^`CH6&+{J+*+300#{?~jfYzvsCM45@
z<@m7B_z84NoUP?K6Zr#*_t5a_{-&mC&{YvArVIYn@nmVzY}F0zcnsR7qQYn(6d;-_
z=4oCsYUm5)HMezg*gStSGNmQqPOu5s`bP6WLwHT|KxN!$)O3Prqi0nBE8D3^8E8lo
z;j8jLxBa4O9E_;BJyY{|<~74JrX`H_ze`%dmJoSQaC!Lias{*SCzQbkfecqf3Ai*g
z-3QT0S=m-YvJyW*<&1LYde2mmt#Ty!EZ|V^a4>uPL;(+2{x{JFqnJY#eg4f93rWn>
z_s!Yjbqr|2EK`}OmAJdH4^EkJoZkNKWD97AIH<iH$27YVQRA0Ci%72Vb>dMSbfhWp
zDIJ??A&}6(@V?Dz#I5ft62d-O-IE2N$6WvYngg{Pu>|<|Lb_YfzBr!wGTmm$bWRtZ
zM4!E&vuoG6w*7sjoJK1Rf}uY~QLX^Sx?CdR4R#N>Kgc><KL(}`+|L`G(?@8*7wT^f
zQIKddnpPUmHW;K0$U{P#^~|R%B;t&O5#S#e5}uQ!G>=VxltXK`s2w)qs|3tE-<kWW
z(fD~dcO`>g9KIe2ecNBJw`9E1unOc^j4O~qD9n0l!>Wnqbb<w^A#$}k^6WGn02XKv
zS|Tb-@{gv6K74zzaf^;(dfOj^+*rSw{H*_D#2Hx=I1FfkbAoMAJ(6Qolb`*qOB1{M
z*K4f&+ypL2pWWW@$qZWnbEdUx{{&Tjx#xE|@CCRG-RNorRaUEo_H5v+3Mv&q4YZ&g
zbDS#Mw*@>t?$|th(E<oX0o-Hn*GvTE5TV#!@Ew}_-V<$m)PYC-o$2YHlaba=30!{!
z-32b}u(`WB>h*^}5z&bYpG=MzgI_+oj~}t`(Am_6z=r}w3Wo&o!Ve^{^zLXX7SI>I
zkb>)^wU`g=WtdBCMixwe5d!g8M6k`)GG!)t5|Kuc2fac{e+c&A`8_1r!iE!^A@f*L
z2mzL?EcElV&uHGDq2QS*I>NjExZ>|6fE0o*A1$ovmTvxpZ|>RP^<m_sMz%5b2)!_y
zL|eAW$!+Q*=p}TgW!1)l!tx>ain~H&BM7(g{R@-`Exh}nM+~pR^Kgc@om6Gm3MbjT
zQha*>=;;E+7uI8cn6rrvN{E;ea&mGCZ*aD@o_jgDbyL2_fn?ujlL`U1UpBCl09}wX
z&4L>NmnzTD--PFxc8_-k**mJj*i=2jcOZI3X_$mS?2JUhJO6_cmqnCYsTByb5|<;D
zE(ygRIwE+X1dGLmDOykNiylK5Dpm5<E~mXg33_gB)ZTatb)!c$4j<^^^1H{kGGe{_
z?~u;oVA=Ae5Bu5oLPE&aLeiI9b_9^--`5Y-KvxBIn2DxtGGZp_pq12jb0~*md+$h#
zdVeP&(9f$?@{-PXCA{oeVV(=eBac*vM&zV?cC2-aUSFnCSB|n4vts)QcOfn|Hf(KK
zboKV*Yo`rF&K?29z}DM+0o?y{f68j2XR40Mj-;1G!}Dgwk#w`vWH^w3eZATYf>1>H
z^9+DY4uZQknS4+tGDT|zeb?gIZBFqrID@O6mXJ*_sfi^;rbS(~={JF-3sCd|r<18m
zA0GL-Eznv5Mb?71{WpNVneeGMKvxF7X9*!!3(1XO8yWxO3r^YK$bZ{Gk>ax)NYjCf
z_?RVNii~mS%!t|8+2(Gff}nT3RPq6qMX|`+C?U|~V6o<%Zkcqu1b)VPnaNQ89FdOR
zZ_FJ!JA(4y)K`|58I@i6asX@sv(lcO&%hw4C?^!t*eE|_4;%n<;oZ&0C|L_uSD?=C
zDgoS{k4F=!^6S!|+R8>gu||=15X4p0X!0+B@agd>>`4CxKPJ>&*izNJjkJ_D#_#D}
zN~pp2H1=u<Uq0b4GZf(8N_R^mC`+tn{DfWz>cWLy(r&@KG76n}UewRT!9THr!jpBb
zU~I7h9)L<?tIAe!ho=?tfA)Sw0BFMrM$k#1?JU!*_unK=)yfyLe8q5F=%vEB4}k1#
z#zDxt_~Pcdfa5pgd%jUutNO(kyfR13BiJT(_u72oduG3K>1yNILtqn}g%)G@4Od+Q
zFs6(W=+q)hk3qCaMJKNbG<~>I{O2K<njJJ*xAy;_RNy4;C3fHLM_-yZ`iU$2fJAv0
z$J#f<jYF00f)NXKimH1Fx(SL`yE^aaZc|8lJAT<RwOx5K_Izp-|K8|L#aQ2D(6oK*
z8BgN4=ET}w*)X#53w!z$(rz7Oiq;c5$fPv$(prx@!Jn6*2~sX0qDsYn?Ufmhx>gI|
zW&e$F?SSVb0l9+uBLD$FnnAvAFTHp#Mq$~z<j)})Y3X|?_o4}1>>f4t&1=%83>E%g
zdzF>ZSbriNAUiDY^@%MlwB&|Ph#1;lgsjmDSZRyV<TF4OOKjP~kH`$m5SAZd6WUJw
zaj}b<?mZzbe!1hFi*e~>g=Lb!`MqZSl|?V9bZe%~RlLt+r+oGMC9daM*Alu=_Ox(4
zOzOBSBlT?4gFRpnT$M3*3_#OHO}dI*V7WrU1e7;-fu>wpT)c`x_b=Lxnmg2LqT_}H
zn!a=7cJD-rpgq+cj@jH9_51>pGcIN_q4i?@afsC+9`l=|sWoQI{-ao}IU=>&1|VoW
z4$g;=>x>cqw$!<;^3#jJ*7~c5&uL5Z?&+;okD6AmL*Ewi!51P54!ap_RvPoI8e@kB
z^DnY2l{c*vyOAP~yjR80A|GI4Aj(ZPc<anDfMFGo543tfrvN5`ok9T(a$p2d0i5OZ
z>r)<%!=F6R*y#;b#zaR!_z>WiU~E8}{oK_peazKRe`SCo<2|Jl^4X1}t}x)jY3hTx
z)rEp<Kz>{h1CXA9*wO+pU7bi|!>IkYe^Ec8PU+)ctGi;fnp|zJ*_XTGEm`c-mkYVr
z3(X?=oJXej`W$npr*tLm>&gtDeZ}NkH7>WG?KIG!Qa@pOW;FQH|26;@n&d3ZAptf_
zbS94tj3m54gwsO#VDc*euy2Jh7aL6_$cEtn$Q|v?uC-JW_`|16QUR?B1AeRmidEtE
zf{M-dwvGscqEcc*MB^X7ux1*}dxhd6kxzB|6{WV`Mz%*mmrp2i#kuA<H_PlZf6t;L
zWFFkH&dn!Z{O<T0sj}NPq(b^&G0&EvUFl!-+DQvWtZ5I~JpY;$f+=dg8+maQ9sGli
zNbQ_TdO5Z@);H7UK*B@NgY8C50Xg0$?Rqx9OezdRk~|?T!?UL^AkRNGqiH3e)j#%3
z1Z#pJJC6RaJw*7}xTC@fFnNOuBNlq|;kj(f%3H4gwMr@KPdcI}TVtOQ7m^e!#*jtt
zkdbqR!jv5P9qW(z%)ZWXTOjs9Mm>aa(;j;lZo1bJ!tOY-+D23tVg%+E$9_NY4c!T<
zJe}`;aa7=eLuT6&5V49V_PPP=(76&ew;;7oH3O1t5#O<a--83chY&Cl)y@N}O7ol{
z>;j+uJf06zM$FcP-Va=&n=t}dnFbu`zi3m;E<mHv*yi)jS*IAL>}~w$MT8sf?@ZGz
zkn3>G>tA550L61+nO&A;n;kn@B+n7wItpLa$k|uX`R7Z>u7;u_5`W?t0eu!=rh^J-
za#2YWS^~i^HKV6b6=19yNZ8vDP<>s=>!$>!qF@WwbfbDg!e6^1rI=@Y7M1|wm+8v}
z$P?)ZmiHe9y2fyPu;`O0Y#+{$6J8fm2io0zpS!hlf0WH*3t1XzD4@2z@yyQKD%(YD
z3R`wEG4zji=u`*Gkw|t*T*T4aG`MMdxYlH8Sz^Ey10k3{nwVrwP*%;`@^!dT0p|cM
zEKJUto9naCa1!T&E<O9XyH{iyEXecFH~l_hf;#w(6{`y_02IdB{fK2tW0IB}Slh&D
z$En@XvJmPUdeNi)b%m8p{SYJ9&_Z0f*z_x!UDNlYC_Q;^_Yo`#3CPlYkK=HU_r51n
zD-JT<YJk&@pZJjhS$$_-%s^V=X&F{smV%-}JQcY;;RHDfQyVaToOrE^mg)@8YL@Pa
zY7?xmCaVXR-_LAai!7$vFs6yrP%_~Q2XR6QDUqzvtN~@|f=golB3is7PWZKZeJ?GL
zT*Rov?72PrnZ@Ofb!)PQg>9sSS~OqJ*skO8Oq}@7Hb88E8%jPQ5CP7`1XLNpejK-_
z7piiTM*C)5>i&P?fOu2QYlpDir0=S`rrPIjPYzx!{B_pbU{r^zYF4FjT+Zv&;I#6s
ziC=6@Wqk@CEA#XaXMw$T&!=Da_c&FMbzj`-U)}m(F#tfsy}G|3Nqz}L#H3oyWA5i|
zUapNEULjB%i0AsPY7kv%S%LGhD0R3(u~0h+>37as&*}(EaEw^S%r_!lQQ?!o2pmoM
z#Hw|7hjVIAt}sKrght4&Ybw`2pRw~uLM-jr=Z@6BDW-{<%6AhyW+FIrNTt~CFqs<l
z)}<)*GSJYEITY*9uUPFYQLUAbpCWQSu3mM~BfvrCv6LCd-`v}~beO)lGKsmu@p*YT
zkG_%i=n41xYEeiTybpk&{^_Y(TeqeAJ?z(e&3!6n6>V0ksaL9z5tD<LDPG{XQ^ck$
znwlp^^V(cLp7FY|B183MY_jhVZ1-e?S<zxvkmiCCj7LRQ(*Riwl)eZ(&0|v40zGhN
z!$F#6>Kk#AZ~=Jr55}SqnHGr+*Qn*P`GQdhXXX8?H8@6LCNZp04~Q@+&qq@E);pUm
zBU*y}0lp)>n^#WsLTy;YZk-xwx#cVRF%R(*d9iB`*Op?5)Cx^yxtP4Q1Dsug>mvuc
zWe@8JE&MovP(9y87Ua%E@j*^T1`Ppuy<Y!!NdyfFIM4y)%hv4-I*??F=6-@n;S+e3
zAx=y}h(Aoh!Sm=mGBUY;q@TbUuHce9=>@kHceh^{7R)`)FSm|K?L0hFvof4^sUv(&
zXNRu6O}BS2T3-<RUe*_|Gri`*sifWQ&kVhDZ?tD&1csz3&GYNV>{h6DlBIoww4%Qd
z2^S**6J`iK<sLK_gK1W!Hft7@Fh3<7UnS(ljQE1w=;FTEpnmSB5|D<90bxU+GGvrM
z@{xd%vUIv038~$J-Qst5tBhPKpD^`STQ%&P3X5hE*4`;Tcu4v4gvPPX!nd;Y`!t14
zhC2K{l)DCdvD(kJ<+vaSea_VZy#;HytW;vfK=!XfVS&xAsaCESc??QxMMrx*g`)tj
zUf2}?>rP%#@*{w^te>)9#Cg~7&fgA^+@uEGUOgcBtDDlAhi1O&SnBgGAI~5~QhUP^
zCm2@UDWh1j>qk@Rg(j*}8Xc}$6PE|o<%_`g8S=vZTmouV1%Yte*kBSq1#Qw%J1PvL
zM%d=(<}Ois<`znBJOUO`pmYa)99P0B<kO+HV09gVzYU;BilhjL%n*p(XjWFAY<hIa
z?m;PRe}i<&?#2%L;&~qOs(IqjarVd7>W*TaPvwiB%6E!;?e1oM#lg6Titzph!_uuL
ziX4t+`_vvjEzg2A8((Cwug%2yJOk)ARpOzgfax(Qs9m$}_$<t>-PnOqckwpxDckTE
zKP-J2oYc{DLRS;2O{>TCd0y{vk$mYa`Em`yza(F}5K>BdR)MLMRi{-nXX{>l<god0
zvr!Dp%vG3Wo^c@VX>V#O9CBPY)Ik*AR@4qYcRq}Z9)8|_`cO?@fEX?IzF=TH2}g3L
z%>1p2o8HsD4u(Iy(`btUHg7PF8a_GYj*dp93fZU|8@@sjmZ)b8;srIp?WE=VLd5h%
zwE5|3kMmrOAVf5Ne~x3OoJwS+yziW<=KM9U#*%F~puYAc-DdsohSwwcD+hXo)O-%a
zdKo3W7s_9Ic$Y<AW257u4f1NsniS2l?6t!<=87=v`QT%+BFos>cq(wEoU)vCD}%@x
zyvD7Qpy@~RM+$P!Y@D<TO2lJX`5XVJ<zJ?Yv@DK~8mPnd%{v?pZ;Wi<rx8E^@0NwQ
zR;f@Y2I*6qkEO+}Jgv8dTv*I`<($YMFG8PKzN2=&nTTRh@xrq4RWnU9ge8l${KC>_
ze-WjV_C#|7{-Hy>uX=2D<WU8P$KvL%zTZjKKv!d{9>f;ob@@&W#gZT<bmybfhNQpa
zGmb_6j3CqlG0gOf2b<?lEqYD&C)K_H$5l`{o|PVLP^t?N6!yDZ!v;|X=`rxg!4Xnp
z?Tnoe50ro2@^Ba;cy{%e8pcV5`3HlnRHaf&qn4@pktxE#p$^5=KD-`1udMYS@}2uZ
z>q5NvlHTU5aIKY&h+^3evO_g2nk+$pv)@6mHB2W`YFyK`l&V?y>v<ws)n3(@jN%f6
zbUQfIUY%aF==?lVd?)j%i#nl9P9(6{WgsUz#VND^5mjpe<xy9z<>$=E`XOztO>m~R
z{2t5X?g12bc=SSo{Eb0}P74Ki4TMZwJm8&z&_9S&-FAbxEj>a)nkekb@xr_7vwezb
zB5JR9KDv8JN64UHR&%7`uJe4Et+zyXaRb`}yWAPAbB_Jz+D|f&?ISyzsUmOZVz2F;
z75eD6LSFN=;RwqPwnn>8T88^e$GXam`?``9Sc2Pic5XE4kt^*(kmCwPI1s}%+%jO&
zE7%00kv4Y>2{5oGl*TPQgB5YTn&78Y22`#eD4-T9$6~Q|BmK&p4nnJ<n@}kmB`GNR
z;aI6t9ma~$m76p=cHreX2|8Wver?|If(J8pxp#Kcsnl_2SuN_QM|@-}$8klve2Sti
zN@0GzBIaM!w-C37EQ=z^l)owGkP1?EAjM=zLzalwILjLuIi6?SJ>FhocF;ZqIc6vA
z1vLW`+S@;MLP=~OIOkAt4aCc#PRvx8tez9RAu15n<V?wkfG}dJ8hY>mF(0|O;j|o&
zPxjfDB<}z`BG@J_oiQbut8Tm(Zk3NoZ_npYSs_DAnfE=je|%XD#~rQGFIXFCyyE;R
zKkbIn+w5C5H|^s&cjJ63VAwp)D1w^K_;78}dY{gd%OTCB{T+4FBcTx-Oq8?J%&^v<
z(=QMd!Z-zaw*R2Y4Fx|swFmF+-XB9XZ*i>ZOd$(D|Kw3VVNn|wF+3=xsrk2m0m4Ck
z3L%T_7n-<X+Ha9mu#RH_43FA+x=T+0v!Hz|!Ts_8Xn>WklB_arl@0+t{c&RSa|XSV
z9NR}Ag;Ayo@QG`G=JPhriw*Nco<%?Up$ZFZ-{>praMlRHTS;eC_}RZuI47_+CH`UI
zg2K6$Ugr+iHS4Km<bFHN;~8EuHYz+uPP~JCC*%=elYhQRXlF{Yu5jWhNmagW7UWrA
z*(<sF<Sbhn1bEi-51s9Bp8eriPSg1jL?#Nz3^Nji&JBbZA=`~)V=}U^t0X+!f)6!8
z+<xrF44Tc+nYurfY;M3G7}Xn^Jeo3UCS;lpt{_ENJ;4?RI^3QC;9Wl7w5i(ZwYTtg
z|K{23tqn{Tvk|bfd6=V|9Qv!H5MyH&l%kBdc$;{sd*<#DuRR&`JYDh+?Q6ogc+$#r
z{+gPiXeYzPMR7)R%{UISG=>oU%m<@*DxKia=fB%{#HpST>j*Mr96@8L7e>DxRL(-Y
z%s6%c`0<yxfEwKHF-D=ujHsjn=8D^*-m{H7r!g3$*1xX>Jt{Z+I}UXlKl}7EzQKvp
z_DrWDzXv6*fXL~Z>QkPxIO6GP697FR^)a4<!t_lQsHPYj7P>ZpFumztwu-N{<G*%f
zhFSGaifQjgbULoz?tP{1Gn=XVNYm`Ekskl_pCqc{TZEBLf(!E4u`P!~>1}c7qv=2G
zJ-Jn53vs*L^nbwWrMBlp%ENN}eKX6{k^Bq7NFY^_)PR4@>Rrm@`>wvaJIqCQ$N?v&
z3%}nqzrSO0&a!-#u;#{l^-;;XPPWw(Kx;*Qzf(nYmJUZoSe032VSZfC5xE|e$23jV
ziwf3YKDM_tWZ)Y^hRQNz{wE(r{*;5taCBlq<<fD1*cCt|ItuJK8jLr3O(}qYE{Cuh
zB~JK#59Y?Q8?hK%k1)@_qN*Vv(x4r%f8@K(f$+vn(mOBp7DUFba{}26{Eyz!rQjHa
zyDZT$u;;}luE2him@yfaG!dN9v?G^r*5+hf&Xb7CooctgTDOlMRb4E%Id7lKxgRH_
zO7EUic(J|L^})rLyGrF*NCWN-`PVB8TqW&X+=Nu4wMEtn2*;-`Xe)Y`$%H0!sPQ|~
zb-l4D;IFf-`$1vByRTauIq@ooZ)ow)$yEakz>b4;BY$snwdBcoiaxyAMWsfmOWTh6
zok*n4*zg;6=o)IP-=+!j0+{;F*#*lyS^$*JJjP;TW@ACUuHc}U0A0fB?`QT_Y&uti
zyI#IUUsKp@LkRKC@L>GsLHtPhB6JBsw|q&5>hBUU`QmGqoJ|5pBGYw?j;LL7&*5BM
zcy(si<fD%39-TkV=klIw<R??>fo(Nr#`=E8Sv`8AF3<GP-tlD%r<U35_=1sNy}Vv}
zuZ(ye6g)suhkRxPO5(0iN3x#NeHl+9dWYmOh<Zupz_g_d_8(A3U427^EF;IV?`^nO
z8gvfED!(n4i5X*<Rw|CRF0%&qi6=p9%~C&<J+<9@ba_L)aD#IR%x%`oZ7&~|;s5^J
zKvcW&PxAy4Zion_kV1L^&0|WM$a9zIS~5Ny5gD;x=TKF6AN6!@h}}xSqXaHN>^aiO
z<gaaIl5^vNh|hFkiE9ZzBM$6PX2?@AwficqPcJ?t#+viW4Ww3*<^u{snK8t=5Phq=
zs~u-;=r5n;t(6}AS~I`}^P$I%I6gmUzw4mm3-~4bQJZ8}=b#a0Qd~!6=z!DsHO?t$
z_E(<F;-==`ER>!31OxllQ_o+;z3P1?DhgQ(UdU~RD>zE@y}o*~iDj%oNe9)V@oqBd
zKKeY|Slr%z?8m~~!*QM*f$zFLHxXOpE>AYJ@Nn*N(b+0X`80l5<|xOL@B*szw)Hlt
zYA0XuR`vcUv$xT_lD8K3Y@mkb-73Z9V=00f<Mm6<w+~jtGP@GLOlcgOK&ClIXi7nr
zQA^%vv$lI2<`b}ma@Fzguy4v5Urr!p+ZQ+(+m$;t-|5O8df)sOn@nxPD_6hZwAK`m
zlcb0bWwzuM@BTr|`LJIHNnQA`ytwGJI^+0f=_uJE8rBn(Q&{N<Y5Ogsgrs?VPLT|n
z^a;U1EWRGdd3bi={Tu&i7?h1LBt1({rV5;-tu@qROIo8+$FjK=G&fE;9=35`8-4SK
zZ~~g{UR3}dQb%oFRMki;R3HfoAeJAMN8f)vc_2;Rr%De37QMjL<f}06%t^}C{eCZ|
z&8GFM3xFTY<&u36PBAOomcq+dHG97L<h#{IN#xiB?ru+=YhZO3S=)0AiTvh*)=fK%
zq}^Kdcx$Y6j$?;uQ<3JKzKV7?mK}ChhT139=rL*IyW6s7Q@%{{Plz{@lVzfXISQ?3
zIW3x<CLk`W$p%7FXjUbmsi|+K2EDs_`ZXum$=?vgW@?mXG}K(d^Kgyb;e{H~Ks@%w
zPWc7UZ1`zsQCf<U-kXtvyEpb93v0GaN|WHH*xZ6ZoLSP)6PGKRA-zJxvC+>{86ykK
zl?p@T&3i~?`P~KinEj<M-V*Ehj?k`*xqPbO-<!8XjZCHWLqvo&FS`@o>Pc&ugG`g<
z?A*lQ*qMuNIq!dW6q?W2@goiAhRIpFLDolp{Wq_v5LaZ<(X4R8f;-<k1V@}&_}csI
z+C<4jSHL8>`pJXNpV1&{2@N#f>^w?$0+9(iZtF577IPLXhY_5X%OiE>OQ@XJBBOgj
z;iPuym^{O8Rq<bOAT#=ULEgpsC0O3-j729FXUa7<>TlMU7^zo*fi%J2#h7}pdlN9@
zxvnLZEiB)sotyGBPU*liT%CS=>5IZul@L7bYt_Z1s%#hLmLt;QZN;T$hemhJbbq(n
ztwiSIk0D`?6<p32?rmYY93nB=*!!6!iLDmTJ^lS<;STe$1}C4=0^8yN0gc=J`USri
z=xG{|g+g~bfiDpzy$kZv17<dntzV{=-xb<Qx093csQ~#1zykjYI@2B|kx%v-qxbUT
znwmbSLlUeztr@1RKiO&-^QDf*9gO|C^mA_Md4hDAR!h^jTds1C&eDDGsW7PM^ZPm`
z=6bZt0xcEOu3_0P-?*ZGL@I>hG8YRHBc8b85i{2_YDMq}v2&_a^I7$iY##T$w?2CP
z6z*<sy74JG^EVaYvob4X@B4ivJMn82{BC<-j0hXam{z5pQ`sr?)QVxO<&Bjf{yvSS
z>oj5$%PB_nef{I&Lz60{3O}z(6c{-!_+7NB-(MJd(UK_MRo6d}Z$fj?kr=*+I2M}y
zO@~=0?QIK5!^%l2sIlcff|S%Ip|6!26@X9r2ZOHNQEY`1E>pnYKTw)kS+gWvmaL_C
z6?<8urX@0Ig2(^?+)r07u;;2Z#MyrvGVME$*l2d(ZqO%1$SL8#gO79bt=<4s&PhZ(
zmAE_lF|bpcyfPI0zkVZYzvC-jDm2gjNen1VqACin?j&H?cbgw6cR628AL07vi#T6u
z(UotE7J3KLYM@<dWU1Sa3%9aaseIaaAZ5t0m|B;p%SBonW40vfYG!$5WWR|sCA*Ro
z*{n@(vm#g=6p$ZUJ~4Q#(3NIxFO3fI*t>d)04~A3WNu3Y1PtBo<TPPxkFSmNZa@&p
zjCc&-ovfP46<tsQo2Jiv!%7c$VTXVm;2$a+(28W|-qS~z9eJJ*Ref|A*gDAFYK)CQ
zW0S$N#_ir`^UNOFYhpR?OY{=5apAQ?*0pOSDy8*JPB3OJ8~)-@S)6TJQAvMlN{wh1
znhoX|a^h-#1{_p7y_CY%Bolo0Go_Z(3v3#RCi(2jb3*A2wMFM7t8C)S*m|OF*RePI
zXmCcaYhF+W@IAZpfMnapJ^pm8n#hH3`EJz49%oJr4`r`Q6;Y*k;Vn^3(DK*k`1|0L
z8%faJpyfbY_A%0d0!``O2nt)G%w#H3RJ_g~Sw*y%JY7$anR}uDHBqfDaB*NOP2bGr
zvIR&4;`DDb^ekk+`H*F$n>KC=;RiT_E*rCHHvp6WvO8}yaBA>=p(}k={ncnN(8J}(
z%FCe)Bzg=q>XBSequrZcOeQA5^twAK2Bwsx2#@woA$fHtGG&O|;hGiIi8>OEcbsxg
z#9nu}aV%gbx?$0Yk1dQvde^ltzS7*?Z}TjdSg&NRNB0ZQUW<R?i%W?Wbqe~Hzo3<_
zWxAB_UQLo&G(7FA3Ej?jb~3wD*J3Xi<B-9M@z{~J?BN2}4+~nv=6zkV6&=T|a=fZN
zVH70d7TUJJyb6IfXapAO=LK~)Di~iTm8@xQc6WMyzwjy2jX&-4<I|<f4t%S+hKj5}
zo}xAX!P(QZ$GdEdGpm^O_w9a^z<@y8!Oqt$c6W}?%*vRf7p$rbpX6sJ%Q7(ZYtTS;
z2QG3LGpH%Or=BRJYKl0@bXZ%2;#h;peM=}ESIT94NAoloC&CW#2+^nR?IWl;-xa0P
z|H7-n4IS%)IjsP^<{rWi#e4%&q?M#y9%GZSP@WUsvvYxA{Q^B%iUYZMIU@$Wp}&7S
zt=9GbR%y!9ywf)Sz&`16Cpod%qu+)iuYRh!o8BHCPHsbl1cql}@Wzdyc61(L;Zz75
zUqk&Q5R-U)^ZdJaLofh3zz1d-!0e(hJ!fuh!}oV}xR-X6XztYUy}oceE1^IDTWBWW
zfEcKuLO3QAZ&uqM)Oy_L<cFQ(s}#NCt8D4^JVzo;Oa?pF=hXIhiZYEZ?D0j+oVbZZ
zu_<?Z!RAw*PNe0kkEMs_sd7vd(|=^NUOn%GcVX@{`;aB{$Z>&!VM6`U<nmBr8Fc6G
zcV)fRP#WhcWC_(@>JO-zx<nyyY4~W}{yduAiP+Kt<v-d;-O#jh9nJdbo<-9|>!_#Y
zRgcY3eM5XoN%_yNE*zvy{Y9D~bmJ+I%EM@~Gn!nk?YcCu>1-*LhxT!<0>&Sll@uU>
zA&f<1^{VlCpxJuQM?Ng8DQ`WxMC1EHxKd2GGKn-YwCeTB+3QcyjQ#s|^z_;IND)3+
zV~@rMt+*~)(~sm0iWW9J^tbbNAw6A@+da<Ge7RBuk!mC;WaZ?SB+kT-i#xJ3mAJqM
z(*z2x-qL3)UN&?}t^Sn~YgT_kou2bsYGfNmPOd#-TxjgkRFv}L-s=r}pWG&h(<tN|
z%uKSJH$88cw5IV*+bKLV1u353(J3F3?v}k6fM#li*FQxqVt)5>=fwdka?_7Wxi$Oj
z8L|U%LS@kb@$>Gyg*PErkszfrLn*YFS5H7wSU`I(2LWjc&~XhqnYZU!?odJfOUYUT
z87VG(>Oz)jlmSG#eR1#a*)c-Wj-Q)cx*ldsk#Sg=yIV1<GH>~wAl(e_ODJkQbjMMM
zDoX($`)I7Kob8@U0;Pr~5|bCnYcgcAGLH)x(@N)DPetf6&tav^{#NV7Tr0Hs^e2&e
z$I|HNlCnay>dc;Y9Zs#Mf{nJ-0%8r?C)Md?{s<9;!qN&x&XA2WcOQVcTdMf^blZ+R
zH{J%mJ)}OxQmXfst}VkWr=Cx>37Jgx%>Mb)$pVMb5DlmQBa|26T@^2Ua0>W(@Ts}c
z*>{761oz<7H5n`6Ak_7dd@tmA0y;dy^3uTWfdY4h8tUN1{sOmpMb9s5I<IzijuzuG
zCUIxQX&5XcqX<^yW%i|-<W(VO0Wo*eFU7M?TjwdgO_U;tr(fy4(h{CT!W~W56w*DE
zX%L3<{W6Ahe(rIt{L0rpn8WhG^Qs_LqI+JkEPPG0Y00hs5YPC&(Nyxw20_`g2|^i#
z=TA5v2w$rTW^J*9aA*FeuP3!zuhg=IvS?JcP^J4`JzpT)E!;A$CBN|IUg3zqMa-`8
z+INk|^obr#DPks(L|zkMbS-VmcTdFX6;S)<9R5m<Fu*H8rgwR3!pqPhpSBkK(i4<#
zNNX_F{BTLqPJ}<!MX_8~2@)U17JmAC8{%vR5)id!vwgI0z*&?gS@0Z;%$qb3q@QB>
zjT>J9rOyD<e!z0wLTd&_1AjX+eY`%4{gsc;KFC%7!x58y#y!WY+*_eF`4ArYp<Wjp
zyUR-N&Q(U__KXKBNwmXt-1(fN^-vg5)z{_Ih<*Ew5|Lt7rhqf=b4mPH7EQ4Xo(?WY
zbAIqxI@L-1nzyv^mLQS)=3B`!>IkW_oeW<gER5Cj?rSG&i>#l@4)fQhSE%QaaY`LC
z=Cr?+jNx|gohSalr&N-3e2L?T$Q3wMvf5uLzx@SO`*f>;2swjEsue1^Muk{C7R{`{
z%O7f}+4Q~NR?U+h<3FyI>yYcmgz4WqD%7)3B@YFz9iU>yd=tOLcY%pNe68ks{O$J#
zP(oJiT=s#^C$RMUVoa!`6;dgka|&aX1$Bh*UeIioYSST+{fDI1@9U2pQrR^gE78dw
z=dzC`*12BzK;d#&&P3<BA=AOdSJC?GHnGb1Fo~8Yt){<35=Tffq|Xsq9S{HBYbqLJ
zHVgi<k3*C+d%C#g)0`=VO%uAK^PMj+idxX(Tuhf9K4QOX#=&{!)aus_g#BHcAUE>E
zM5hn*cj(!xf*lId*&z6ZTfyb5>>y7ByKGgD4246g;bp}%F!eJ3nK<;f+zRIIR3|+g
z9ow(`d>S0-=^9scz)Z2zYu^DhA~L8bgU8CtHYtHLK&_ZtD?^o+GDswZa{#QWpJ+Fm
zoh{n_UCOp~T!g(;$UW!sw^ZMP7fr|xdyIk|`(csBaY4CdolEmg*#^4_E$x0bwPTtu
z308aBevq6wah}aD*+>fN`W>qQp~@<)c?K?}+Jk>{c1S%x|Cux2>_a^5)b1xQo5Vl(
zYlr%1mxRwQ4_fK`eKR}n$Jet#6|u=+)@bt}CX28+u4lFmkBK?-gb6t-M~$Z4+`Bee
z(T~OGfDZLyW~~8(D#(;!+}kLfWjK&dAANPlp?+xys?kpfOcC@78A-{E#7c(L;NORI
z+TjVaPd=S_9%@z5LwGbD%Kl8<VU;V+hS&5KdmWF=cm!wB4Zd2&DHRF_Oq<>P8MaQM
zB_5*+`cKIxgUIm3hxI2O<-C6>G-f0II&a9Ubli6Rufge@lVn+Or~Ry5>|LV2IR;JT
z3;&8>pGdvalRdUGqV&z$pi!qzd9COMF<2V4H9AO*1*Yz_D(Ghb-7@^L*JG^Ql9`3$
zdwJIMg8r$WL7nla?cy}y<LX@YffR|Ce|DUL`(LVM>g1?_R}YIwgud2Bu~<`f(CkB9
zhcTu7nnPy8aX;QyeNXDfMJP&{zFJ$1r+n?uL9~-Q7zQJVR_qU-nv2ey?P63T;SDMp
z;OQ)yl+L;Gjhl<*gd|IAjJqe1Ya!`@P14%nxk6<|*Jzhoonl>)UA5TrpU;Pxop^}w
z%9TaTe$bd3pRP7I>mX+5a>_M&g~Duu*SgBPLEvtv(@o11Vhvd|xv>f3H-=cs?D4;>
ziz{cdi5x=~&Q~DtDlR4AWxv53%qcP-^JYD(hX&!QMVS^*+7cg{b8)D@k_;Rdd`h@?
zgV#l?hIprUX>^)X$6yeFVx+Wp>Q7Qrx{067$1_j&eW0@$a!yH|l@o81Jw|6Z{bAY4
zrqRCK#CT|@f6lZxvwlJQwdZC>vNd%Sk#-9il~`<``Ujyb;fQh5`W<Tdh3+}}(j8AV
zMV=LyQQEQBnH@<9z3{s=)*<I0e#k4Y$or4xrg*OO3+kXiK)MT|G8<V=HT_fN31WS&
z^PE9fE-9udWkIlrgG5^cZOe!W3#S7{V`(`xO^_}Y^qp>f_whbYB^T4R6chKh6V5r`
zJr{w~;x)J(5&XDdEpFFY5ntVzSFv#qSDxEceaiop&p*5%H|eyRQtz~jFr*>W)ywaT
z>nW;%^+(cOi6s;zqtvli^YP}_BXhiD1+`0bor-OXDJ@Ux2W4K9QH*)0f6&(DcgHbH
z5?8$@p69|bTxE}bDDM2TfmnxL+uGOp!gUKO5r^z|lB3ks-~8n@H|7S5;-&d{sv=F9
z&INS!sahm$d7lS&ZRmpARPqp06R_3ZW5M|;-SkNE^?TguS!KfMP+W0z-B$%x*3CkK
zuN%q<WyWqrZoZ=Vgyf#GHObwk^Wvs!1KyEwHc15=O6d2soUces4vGO9T=VbVWL);e
zRR8@Ge|)Nr){b{FSSqESw-Dd#$o?`^t?sbz#;V)bauNw#?U9`7Sk)@O-hAq{(+jTp
zrRN!i!dO9J>|wcq9!{m?y-nFDVw-w2&(}=Swm5@FDuvlVGiIc^(}t?pr2b@WrTZ8e
z8wW6Dn5VY0+~c1^zPdD16=)XOrdGFS-fov}-z4w(1U97SYWJaX(yHEgnBz^kkXM6`
z6v1K(sZ={>8m;A3LX7-*c0`JUBzV*Dq9oX_3g^z(omA0V7Mnul3-HGShf=$K3AGKG
zYEeDEkH?>%JIK<$V`~*!!;Zw&KG8nj;jD#d8wtdlxu*8ai#vs@pR^=S68CldSRoAg
za<=<O=EtvB6sup^jkJYS@zw^8w?&)jHZ@OtHL0a5k;ciz665@<SeyE=Bh$(`Iw9`j
zu~iqE`jo4$CJwD#D}w)_8e8?*zXv<Q`S?sk4UfAx6PD)UL!w6uc|dkJM&}7Xd*<UB
zwhx)Ilc^nXYN>+t(HOm@_gwhYLpi?JTT;7nB!d=&GUe#Xkqfv&fxaxRz}FYgL`^L(
zhD=9%{ch77_4GLg-I-vX!&?nh0_M+1!h@9uK(ql#g1C(cU=QpXk|(A055<C1s}*)4
zmsDz>xI<3{@}go)%arhst7A_@DuV7CTpLM!kpC#{(Vx`!Nm4ChKZN2$-~I+^E$up2
zVEq%X!n0E0X?ihI83(af+mdB+uSh=mWY%lbly0T{X_|*Cl~w8U*z+@e<J9u@N%moC
zRD3K!j7?V`?~S^)MuFY=AY}cuG@=$H!q!CZKXRyMZjbWJtH;YbvF}Jz-)8LfYwlM+
zQkr;R|F~s>x%yhP|KX4f)**df;omcJEmx4*WZyiKGF$C$7;MVOw*swq>UGk(<|;ca
z#Ndh<MWhKm`eHL~pE@-5+$^$3v6SY$%gh4Xv|a6*`g2QMk9GrY5pv2Vgza;zoT~?=
z0`D<W|6bd;a%5@Fxs)GDhVO6~^IboD3xy+n?K_-9#?ilSlV>Nx39_A#M6j6R{2wQ3
zYp5aRG6+&yTaC{~aL7&)#ruAcmWjt+*3<i*pRXAGJU#LbQZv=aYi<F7%+hbLe3Gnt
zziGi8X>IQ`&6lJf#Ova}*msSOD*et-f193k)3)`4Pr8Aifvg89C1O{18V{{eaH3|@
zef^#I#FfO|znC5g6sJmmLsAhLo36eT>7MH>0Xb5Hs<}Cg98>>w;|Do$ln%tM0#{mp
zwsJ!X8_jFu8It-^L6~Zl*^}h>Xz6+RA|oUD^?N(m&KV@yu;U{hA$Y(7$MRA`#vMuw
z2Iqb+dy<6fzmv63PIEojGW5L{PklLvS|hkZ%JAdraok9=F0byOqX;}{j__heDMPwQ
z(&H99!Rm>6OzaTLkm(6_DW;~>+dTwPLEXd&s8y%kXUSKv?+e%Y*?eGfuwg^ez0$aQ
zrAdZq`Q(3&AGnT5suzDDd}kMy*sK$}e+e)lNZ86<^8*YxZM=qiG(&wcZ8TOn^sR5(
z<8kD4x^+@+KEcFfk%K3~DuR_~FOhsDW`tPx%iQ&7Cg;>%l~bC=A8fS`M46;#F_n8p
zn|!hz*T_6$t||LXn}0~UFJk&bO)=s4Lrt4{o_5lGo~hKzb~@($c4C&wx6E2ul>5dj
z<x_<<R-d()?_j&++hepp%78riZEGYh(p8%t(W19BUSLzq8S9%qJ#C9AaTF2}M`0`Y
zIhS`F^$N04!)RGO!KApmKOG$zQ((NF03TMhG0dk@ig2&cu$ARQkYrGbzjBazY^hoz
zD7#dc#kgWUabEif6brm}`w1AvZWO2gx#QzAeaR=q0`mg8F2_IMD`WfeU67=uynKwy
z;R(|ppKst(LWbN<BvU`ulsTV8bx-xhn8tk}S|eVM)+VaV@M_XL&isTeaVNkzq?R{M
z*Grx(M`WiJEb+AXd!NO~Tke{znw)0sCg*Fk#*|#I%y)f0Z_1eiV8__Btcbz)unIs6
z*qN<~QKq;ml10g&XB#^+;;h<ra`SY_f&_?SRwTUUM7<3VQl+Ngh+g@6&CwV==O*}1
z3}_J{GT`nVZ_n=9XAkZPH5zkAy!WY6Wqz)4A}KtnqW`>zNW;E{Cp7~j!)+`oFUFof
zKJj586lauQ44b6Yn~tl`NCL%UV_hVC`+C5xK`c8DQt>_Lfzm};nu$HF;s+~2U;G}N
zS0k*DmHIV59CqXF-rd9%t3zMDV90E~PjJUjE?yTY=D&pNc;VT$b=2L$s$#QGmxdqW
zJ{KLSGDA(@;5f4BE`9blvS`J}H&OBz-}?7xUa`qzV$5zIvtsU1r0IQ9Q`M#!Yg6e!
zv@4&VQ2jslzA`SVwtE*71+fuD;xSMpm2MDJq&sCm=|*ZOVOxL?5{lx`A>Gn3go-c-
zQW8TeAUQM)DR|cA$@l&L&&Tua@B#TL?%Dg^_r2D&u63=ouImu1uz8i*FW%nWlazRJ
zpH;%;r>zI~-c~W6-IT3=A<7#O@#3>$!yM^y+?TS|Xs_mj^$`(btASKhRGPVl;;ys(
zdVBUCQZ+R-edkX}Ar-Os+*V#V)SFE)7RF#ITW@~gNM`tKPPu4(sl!@L@kW}l$_y*h
z*)!Hufk@op*L0FO%qMbMkJ`NRr}?e>(1^>?-oiV@8kTsMo|7rqk~_sqxOR`I)Ft24
zJ$)&y?w`A<0z&$z9&f$c(~xb7o0fhqylfzH0Aoodj^Ce8J+MFRc}))su}FAdUel0`
z8^K^tkAU0ARo^-l%9>s8ndRpU8O{0qGh^KF*ug7EpKu{Yz1l^5oDr|EyC--~dHiGc
z5ZAbZ(S;8A`+t=ItNL8;YcmLq^_;pQ7g}rF1`08{BCGaG;Wg>BFe@?{fxfXD(4Tc6
zHY6wr9isr9?cJrJ3ggiH1O<f<XKrf>qkn$ki0Z=ZkG><PB$b=$PkAi9XFuqR5T5$8
zZgi}MKU2Nw@6UGW{EfDugaC1}cnbX8e5(dIfFzo8j45MYReD6~{b@hb?#idB3cEYZ
zrk~ldo5<R_kr_YOb3d}b)0Tc88{4it7~8P^VcTlA*=&HFXwgz(c1LsjY(uhJF}6xM
zVpRQ}byRzESIT^m^4xul)0eSjWZ>jSF4|VE0k;LN-o3|nt*J~(VJBOQCL7nIqsmzl
zCXXsSQ^1T%mE;oni$q<yKG6>!-^;yDmG|^$?;p5^f}(@!I!&Y`kZ{3*KVbC2?beFp
zqvPW?Ki3vEpi)4*tFfWsD=D`+hr9ZxdV8u05oY7=;ghD2Qo48A523Qfj?#9A>6mKR
zKT<056&R(B-Hz3<)Ukim<XH7X7kmBX3PV%dij1DTXyk<_Qe`R}r>=-_+)x`zt@^0!
z?Hjkvbmr7s%l0zWo4$G~MF%-u`!mwssiqcd`}*r1RF7YAd>TJ6{RWetSbX7t_msQb
zMEMt0tXleA?3wY#LsnjhmC;yEU+UDJUFJB&>lRU0_Szj*!qL_h3(sg`oMMKhT;yWf
zM?;H!Q(BiNSA0_lW^<)>gCF|(i(g>!cI|!9Y3w>6jK8SKIJ{hX(US3a##M!Ig@@%?
zgjsAC*Fk-MeHEijm-^lTetF7jg*a+hwl9AzTYMyybt_`Pb~tfC!LPxrq|X(aiP)?_
ztT1x!+&So)Q8YX<5*8Zzc7zZj+KiS`(qUX)j<v+)!emEE&w7f7RQWwLv?qJCR84h!
zUMj8M#L{+8H*j-o8#-02;RnXc%Y0Wf67EN7sP@}ag%ax+2{kg$Yr2OLw0(VbPo6T?
zx{;=Z980LZKJ%>g=*_3J%+C)|JBrg;YuyM>bv~Ec`=&};4kP_N?eX+V`v==Q2~mW7
zwY%)@Kxc-&yrp8iNpkR}OV*FlY)z@39kbm`GCw?OiMxN+t2(ardbnA+C0OHPaupiv
zvFTilp<MPx4{}<EJ!w?%cX88q?GF1#5j1~vR3TXdC3c~Qx-L;%|54$Em9y-H{n`A%
z+Nigb+Cdaq9nHfFB)l2^v)vJxyuAvoPsX0UW%~xx3g(XUR6~Uax!WWp-D7DiG@xdp
zz@nuKntdHfPEM8!b#ih-b@k}%#ek5IBWtC*;zih4^GWXZ3Blv1Sb0-b35YXwn31W@
zQ2Kl9%Ypl1T!m-Y@?%6S*d+`#rxqr2)kn|E_^zmj*kNgV6)0;}R`>8`-n7O@>N7@u
zO1Dcq-TC_=4Oqo{!S~%ArTW)(<sOP<X+G;`U@KwkupSbo+1;q^!j^=&&sI7!lvmm6
z#dzmYBmae^{7zo)lB`=Vvu+_T-v%QbdegOb@{fCh3Hws3abg0tAGWU~P!)V;DCVkc
zFCyG6xbR?x-nv-WHzuaB`H?;G?rH?4UHGIuyKDCO@wqHoij4T9zV&RG(kI{6&)@d-
zEA>lP@Q;P<Ejr_b@pL2QPi^RbJGN2$Is?hDwU!b3%dAnn&qW?H@yU)h)YrE{1&4`<
zfLX&yacF^(Vxpa?1;h2Upq7c>vh}y*loa{UukG#R;pg1eUz6k>(eqGf9`x`PU~J*k
zG*?dGai((N7<X|OTk3F>UDu2k<4u+7*|(Sd3FjwkwW$4VQ>{YEuw)tDBf74Z1`J#w
zhc4XTEqJJA3pw1ILTDB>6D2xs%pNcp7oG|Cp3<FkPIl|YMzaspHO=<zI^gK9AkXUk
zTftX+7XKky%9k@DjF!dty^&2rX85RgMM`57h0apF9)i~RFOO`Z8q!8CJ)N;9OtlYL
zEh+aji(29a*UMkT<m}Qk@j0j<TI86+Y<%$AoF%52!VU4*)6=8ZqhESI!6X<;1~p<t
zTpzD#{B=etq_m`4EBrR0Yh2>1%PJxbPbiOcLAA54`PL~;{Rya1Ykzj^0t3O&bCP8M
zCL~$6#$Qhkrlnw$@p;*FY`^3ijqeA7m>aLOE@%*V6=oz1)tlK1DZM7<&vSfw`^oRx
z6w)e`BcwQZQJXR9Q@YZV)4u-tPsA&xK41OvXyWS!b1yZ=(jFGw8HV><u7mdvL-`Me
zFd5!RR;Uttil-4YadgAr-j_!Cs7}JVWH$D4mJ7LH7!-@~2X$~y^JkKKdlY8uF~>WK
zaa7WDL{8DP89P}UH~c-kU#0k*rQL=h;uQRX{~>+zu>dTc{mMJM>ofiv_ui-vS#n>W
z33Z-*+|hi`)-A!_;oSZ9ZjN&g9{<(Jh=RhJ@$ndc_<%hTCe_^)S%H!^&#l^t`JozD
zn4)Vv`87E-304fo|5km4QfW0SlYJBpwpf|VPAa~&Dl<$z9GjYVe|8`XCc>#9IEKc1
z{tMRJVxKaKgN!>8<f27lsa}^jFr+@+-$q4!&^W%|gKj!>KEE+f{ewlw=+|AADrTuG
z#nOU!lW`p5X+d#ph7J3H!IpykY>Zi4j7i0f#v<CD<JMl2FY=>Iu&ED=oOSUo>n`7W
zHQsHw%=fqisfV=i-JQutHu;CdDDJZ!6!zDZQTC6DX-xgtlq}Q15)vcR%`zkJpN^S|
zE`OsC!-JjRVl2keo#rMnJlbj9vV*_k$9}Uke3Gr3Ck;K7v9J4z?J^T&{5LwV;QfN*
z%PuxW@;{FkR#gD8TGr3=a9R#KSd8~+nigc%+t-^5DkI+9TE%DBoY-(VI+pB-F#|7}
z%+p%!bzU~Ley)&aY!qfZEg5@<wdTNz${q{08v?%Rw};p?ZPPHWAEyIo_)pSV>nNsS
zt|bW6KJ7lqXsq)fJmJ(rg7q=F40Fc>>!W5`&m3>()5#Z~pS+Rak`=#}SNTrJ(vDk%
zJb)?FtPC|llHD2wqGi5emx76YO<UIzSEfDZ7~XIl6r)49EAK2%QY=qib)7UBvxz>e
zAC-GY<RZqYDv3LaLs|5W=)OD!vQa*D@qm?}RXe}gAWNUC@>o?<!aPr~aE?s<VG6dl
z%gSGq_jH-<%ZsULPp%=(?*7{RO<~T7@s#mlHGkiy!U(n(+gC@7FJ-x*{XFcqKV|)}
zv=6Nx#e2{QxXl`9c>Jh;De;TP3;sx1e0%fx>zJ7A@bJ^sK}V#%pX{9$ab#olIiD-U
zXQ<94fh~V<zNFqUD=9YSq1(PQ`3<Virt=$ejuLQ9<nUTU^U8JtjUFe~SEQ(<A*c0g
z2*N3lN(#@Vv(97hXY;)?LsddgRX|Tw(oL&9<(O8h8eW_8P9vm-^Cp$f%a8D#H{fK7
zv~RlVxmz!#gQR`CsZSc8Y_R6u&hMr&&7-RyW@r$&NE^H1ba^07a%(s@^xZp5j;Mm@
z<NB1={MwF``tv<97w@ELVf7fVn`)@)jUn-cRwdSgTj7MehuPz*Qfwafc<bV`Y=nIa
zF}r(`Wr+5>hxQIp#LHi)jB=ffdB^y{ToA8|lVCDG=xjGv$XmR`)iW#I5lrEFW<0t&
z3fo_N%0F8H+l6&w#Mb_8#V8(rzkqJjJ0;DUOjqwdY_Wsx7dg<-iWTAK*9z5CRgH&S
zSf)yxgb%|xRaa;+jX&2zBZIAZaqsr6^!zPN^&*$Dq@V;1yBWVk_~V-bhFDSHwvh9U
zWpvni)@>KJNKs7*9`l|$rq^t|k6~y&G=6QNUwZUp_v5V#*hVL|(~KGCeLttPY8c&P
zQ%_4?^9q-*qVtcEK@@N*-baeM8A56_4KDYq|M)7QDBv4IC66;mz4#g<^4{*SYaixg
ze{gGQRC%*q7SBoRD~I*s4xf)ZEGkO*2osE(t~a<P)$Q_AQ@8sC>2MoY$iT80?zM(B
z!7}P5PqcJ{-^kiztJ7zpK#dWh0YrBJ$Fias)4tU=@TKHOq#x2AE>D>>6FcUR;k;q-
zKIbci>XI&J7f;QX<^+q(+9`TttVRpVAxy^f<i_k&1h#X<RnVTf`B>FIYfG{B`1K`v
z4q@RQO=Wp`Hl59+q>Hkx6+b-pHmP;~pzCqr$(`uXNf_BYC`gDV#R!pws_SPf6M1Oo
zH0{C=%&Eza)nSv=>OKs%pm^h`C3di~7xSWLSjhZUr#i!t;DT<=aZT^WbjEDXApt#(
zT1WHDWFZmTmzYyFjz3FFbIyJC$%q|T&`oXD<G`LttuwwdsezTPW|`qQg*O>w*_!Nd
z&0_Ph<qRHHI5nc#)SfUFde?EXHE-aGra`_aQlus##rfQcQ@z+=PgC*}B1;HOnO_Wz
z;K==_X<Em9B=|iQo>?iTZt~z}rm1Ks(k^O0FA};jbMglEusCjSwn7{`qj9l*mi?Zl
zS!^Ztum;}Cz*BbmD?t&jgYUEJ^QUl=>#@$;ze)J3WE{@6RFIC@<1utb_7u{YN%x|P
z<J9w09J)teR6S-96x6Y@vPv<@)-Pfs@<)>#00(~@*<G<diEAkm7`;r}8=$GHnRMRk
zrET$f+8(?7p68|oeWrr8`F(czXNrgI2nvtePSqTv!@lOko(Yxz_|=g|Q`YaKRsA!b
ziwX<DKWbeAs?Vk_zE2gnhBK{4W?jC#xcGeNz;C!h_Q}d0x^k?&LgH+);&_c`<*MpF
z>6ios>@%)|Qn;sY6eM$cvr=K=-#qhk4ttqSu2J<zrnz{1oJs1`S91SM$_&Hs;XJ?a
zkKYF$LkJmDr=S1I?Q_wNpZN0|n?l%u$g_S4&_|9kE4O!E4H=OQW_Wq;bg+}IZaBT5
zy+Mvmp=H}gO$nEY7e;ma-SbVCIGPR*7#OgAzOqR+Vrxw2rl826d(M&a&{6M$snPDQ
zvC?fUcDB9(GktiKE{$JBcJ112YHemKqX&%c7`UaboE+vlsDhKw=DD2N$<S(k+2&vO
z^YA^Ae*C)gnvu5ln{)<7M$GI(Z$-sGf=%r2GX94r!I_U*7eLJ{&M{iZ@$E;G4-uc6
zn^$ymSU;LjsEWSc>lafkinZa=!n$RN%?xz9>QrC+GHpP}V_!*^Ds1$yd6tzQiES0Y
zlmzAp&lO|3*1yi|xgsihCo?$5>GkHNWmgZxXF?}9WY0BbE)g;_tZa3IPY~>sjQw3u
zOleKI^If8Av-dM|&p<#L&-JCRc!$K{=wHS+Z{2#cNw21+#+2)EY0!OyMk$gnU7Dzi
zHXmUSQXR^BFR7iaKU%3S{9|-XF3+d(B84VZW>$XvaQ94)ao%f>FKTa+g|4y~{7#k+
z4znM(e=wSDG1YFxF&iA#Df>B(iv1vq&N070dgf;<tGF5GT?_~(CuiE0&hPPMzyP4b
zSdx;G?jA|5t8Z-N4TFJyjov}eo*mE-78ERzHrf5)*vH_p_h<5=H*zo5lNf$-lER&D
zcaMg1a6K|66mckzs*?u{Q#*<bdi6NC2>n5y+Zm!|nr3s1w6AU`e(WFJO8UDK^3DrO
zQ${jwfL4bKng+nEXO9m|w}OT$o_Jipe!VRBWnA3cLZp=0fxP2<xz<^JreVrAZB!(S
zr(8;VoTl=GB{+l$zc<}8&YX1rOdH@PQe6M`=;vdmTpqTr2L<ue;-Zuh1HNIEG0t>8
zT%_pK?TZl~8)kQR$9`;U-sPa;`Uf8fRQVrY^EkE>eEvvnU0q~DvuO0@M-NKpao93w
z3q>7653{n~X&8fKQWxm>8VbfUxiICL?i@nAJv}`sA^j75CAr4N#y_!JGSr9`N#=9#
z>BULbeB~Kd1BPLmJY6$!a^+f{Rzg>MhzjBxO*QT()Bv%_n@|Gs=Ebn;{m=HbsZ4Yw
zSAym5a3)LFp2{5gIqAtVBQ4)4j3KgRx>$>yvXP5Rts7(Yueavhw$Jl><2VZu_|$Ug
zvXC@`a}nF8YY42v<dU}VuP@3)1xN4hf7qLqPlWw;^g-4t&>4rb*8lzve^aCZair?z
zWyI1@M%e$Ovy+qQ>04#H6XAK#y$6Rj3;H?fOwi}$<z;B3D*F<4|9bZO?+c_jLUv;6
zgtpQ`9~tlrvgXwU*>|lYBykFgqCGYee|_g?CV9_%51F>Vsr#f?luZeu5T%60j~}IB
z{CJuHu^YNrryI0A6#9Gp|9lM(r%;4MOG}#!Jp!VFA>s`q(KnMt0Wu9a{z>8f^~xyH
zHV#A*?>2v!2S1O?^mcN}Cy@Tf??j-#Bkr}r7p9Y?JLKF4^AXwk`8(APQ&VfGtMiA(
z-TIf;+qrZ+8;UD1AT3H+{JFjagZotMkd4X98Xn#6?gaz|k^6ApU3SqSyY}o)yI12E
zyqJT*gMB+6*INZsK2sJa+7h>f`ToAkKYxIMt53%K?yXfc3N#R`i_kH~Jd9C%R-weW
z)yMm&3tcAFWrex8ltD|bWp?P;fBknU+LIme9`f=5BQ`*SeqfLfA|ueJfK5Q4OU<yt
zJuen|qw<jE2Jkuvw2o3D%vP=%wl@P$_n%+Z#jSU-u`ga6{W5Fg3ldT5;ZMIuf)yO;
zt~nx^RanR;4t>6?2P?`hezq?mMLATpp&d)$?_~#IE*z<9bC!CoRH3n@MKH|m*mU=?
zq?b~m`T6;Zh-~PqS4y}8*j*Qp<}V@^*6dNWR}Wo?7eD`f8jHUb!jvuSJQg18|MV&A
zxpUVS78c~?<kZZ~Qzjp1YQBam(=88-S+vAvUo)xO>-uLt_3t~Ex(We@LZWQQVO^x_
zA^B;1o_=<ufL$aEr>1XrjA}~3;IZiRXRdQg`)TNr3H)3YwP!uU-~a!QAEdq?<I80^
zf8hcdM9(%G%ac<*1r`xxIJPd(4#awCtchO4#q#|hVv>LT-}tF_r~U5j^*VZ>0v$d(
z+7Q+Yn-?XCOYAYOlV5K_PgTtwlGq|NA|k!^(sMe|25|K1H*VZGDyxY{r>E_h4GZ8@
zp<OMdgA}N()p76AcAbRYr>aYNgGA-^^>uWHZ=#rK{n5_sJA!w&{P$;F*1Q6J@pA=!
zz3<4rqk}&G%2icAxJ0H6G3NslY}+)`om2h4pKyZkc=U<Fs_g5ydbeEz3j4jl1L1B3
z(NF$<IreJUE()opn#d#Yo!|-IXWpYf|M4CECgkp)5B;x&*!l5cMh*RR$-Miny}OXs
zyYOGg`Oy3g9#?*Ha*~aeRkhNS+)?IiL38@5)XSGIKQJ+Yb%0^V@=8jbu-{(C#^!wa
zqImPhjWokbPnh)l1YX3*YSHqK&#&~W9+VZ^^`)G+tjx;BCV%kYL3%*-k%A7h#GO9C
z-+VTe&~b20<{D4-({yAbcLV`(s3at8d-WWv7v4po+BrHpYE>IRi%va)E<q{t^Y@Ev
zd(>epmj=YJTfI8*gGUbhhz5^b0#U;h)R3!sdX|GBJPG#Zl2T+EkV8{#iT>x$pKo64
z&DsQFPQ}Nk3ObHu{`g@EZ(p&jtPHGSFlrv1k!E38ct5yW<MjKIH*en5FLO$U(Ya}X
zfrosbK26grFo*IGTsd}T%0D8y*lo^mYq62Lx6-S`L;^0=5r6&3$+KrkB|f`_q0(`8
z5OIoD=5QYUHH!C?ycW%T%}@|16WW*C-K{$^hy21juFVhKJwi+IkZlC)OI=601`?E+
zQ{G?+HYtLXcJTrS$CuN*=7D*ZZEvYo@is)R5tyMV__3yDV8tv?HxL4N`sZ}qIjWLa
z{DAwft<@k{p*O<0aAHoQ6CGZLg=uk)?WK@1D}zCa23@&NpFZU+?}Gmph@UNQ1lxbb
znPwIq=$cJe7FCF->KsD3*GZXzVy*r66PE*FkYn9#MaAW{a!E0<{?mMxFBafwaioo%
zRcwjmw+V#qI?d0XJ)19WE&(v;{&Rs46c`xDJ2ejPz7B@d8+YUyRezBXah)<%j1lR4
z*Ed5O*$$C0dh4DKnip}^KWlHx$)&4GK8l9!j4TLHcR^=(%}*bZSc|`A+kN!{7gsa)
z+-K2|B2RMoiJ$)Q!W1Eki;FuSJVHJG)hLLb|NQrT6c6v62CQKbkznaygm$-e1X_c4
zYPFHOn(u95qW2WRI1jz%_cN8hdrfy=GeINumUQT@TL&&Na~bwe8ppQ>OGr?kD@k#%
z!w_gT;@R%#IGrEk<2ZbrR2B2lG}w;c9bm_1w<XEFa;hA2_?q;43(;c0Vt|rjuYZ#{
zFVlnh>owLzZ@gl_XUsKr5;w@C%E5Qq8>Gk7TvvH`c^kzq;|J^&!>@iA_W>gnIN6?x
zUc4FfrZras(Jnt{V7v08=fkyWLAEuTIMr#QJkR|c5)c&cY48I%jw5we|2y*@)hsJ3
zYqR}xed=2^@|!<3g;dyAyqg2i&JJ7z1&LGna<4A2ad31%FC<Kty4)04AqZtub3)Rh
z|FpY3Ggzd30jx<nlvjuVu1BYgjpHbw8>A#m^yOafSFyLx@jJ{2gFLh&f}yKkCUnaW
zc=oInoF#A_Gd#hx?AmECbVSP@Hj(BV$OoWi4YjR}Wlx{(1#))dK{h<*Bs3t?fon~=
zL$z{PcsP1Uty*Hm^N}9glqF!n+m|QXy|+)#{j=`emCK>C_&Et2F%fJK>dxo|Y}5Mt
z4Q9So8$oE|7>&LTK-AyKus<y%;QX;ob=`63^qQ+2cM=m3(Fbp$0iKD6ho=xu5^j9=
z(9jU<;Wy|s!Q3rdm>~9KKSIMJuJa-Z4k)O~wZSrgjnle!FJbZlOk^ellh4k>^L1Q!
z02*<7Zjp&f1PBQAi*3_k(XMcEHkmO%LIibVb-|1z=Rfvz=rSA{QrxefsVc6Go3RzV
zhM77c!U@Os@{S&kxAt1;vB1opMEgGlLCHBHGSzJ>0IucCEhyztBEz*mV+a%v^|N6%
zV6bbR{XlsgfNHBRF``5v$Kj{xhH7dr^X?BxkMujYxazq<=E^B<vk!WRHWGBn6KyqX
z;24$2#kM`hfoBDqlXG*cw|mE{(COY9cQQMz#a6ma>e>=z8C@5bmV%B*AZzc_baG?U
zXL~H-?7)I*m7z9R4=p!nx4)ZA%vhX`by}WK!!ha|KYlzA>|un&XA^jHb#^5KWBFX$
zAj&6~<w{jDA4RSAca^!y)!4>zm$sX9XP`S`<x3oHTpfWCy>(CzZEOGv_FEv@t}u*q
zjdNwDI4Xv;rzkn=`G<yv29`OG)BF1Rrj$cJI36(MKNcrb;IQ*l8qF%h8)>Wba?`-W
z0Xxg=z5G&5RrPxs?&ZsSVvF?`xbT_Z-~~yvOv3SZ+1!NriNz69O>vUngkV^tB6)at
zIPmrBlW`jdQNO{u09!k(>;l%-cKQrbeR(c$=g_4w>}}dr>iVPp7;1u+Lsnj2(P+qu
zi;cC1IoEZXzZMA0N4`JXMUkfONuDx8|IQtrH847wA(#9-ARw?B#`|z_=S0hNnyoLz
z{j#+trm8THtSyY#lX_>cxGLy5Ilg$QO!V{T&$n*hewIFC5$COKj%)>;6{L41b@6w`
ziMU#aMS@WbGaLHo>sM=hEot~{0St=&6}=GEi?+8(w=*_=J4vH!(IU1KnC|o7K?Tvs
z>Flk$-;NyUKpkQ#c(Su_Sc1u{;7CSD#uimsn5<2(-VeV|c4bFd(M%2COTbBt<~0FY
z4Qy>yMKtiAUtRXHT>`5SHAT$SY3VMs(ug2SZY^tQY{4(!;!8yV*l>|nXG*@=Q-y@R
ziHNXTo*n4Z{N;Dne!f@bw{h?{RxH<-N=t=ICR!8fo*q0AXw{x_COtj9?xQdD)+xU6
z9b3*udEJ@99-UmCf@D!1VKNwd-gF#U8gCIS`u+KH-e3md<MGD;OBH>I;3d?zv~cT0
zy?uKgCwM=%az#IP2E4Lm@A?IBOx+b81q1;&tG8gMD0){yC4k{3+Nw&?4*H{1yW5Ra
zwBM1Ier&ZGdbi~u#lwaduU?se324r{GyulI;%CYVcq0R>VT2@tR1pNgm!1}Lf0y3$
z>Gu<rE3|2=dv?6}Pb=`sDMb^ICs-tr^;X#hP!nSeBSBM&WJB|;JK5rTMdlWJs*ztj
zq_OewvjCuYI-w?dwQ|J~Yz1y}mC~VN!7p72ZZi-JSBM?(nx?(U;ukM|x~;6d(tYWL
zGqRf^z7UYa=_mm^9$SH^Mqy-MlSj6p_}J416Iu$PyuO(Y2oV$su@4sI^f*q1%=jH)
z&VhUmQBA^QF{uhhEX$v{0Pt*}md+?^etzDm2ryUMo+DzA`GD4Ko_2z+Q<`AtW!EEZ
za@25K7_s8t0|Pr`j)<FztPFY~a~`(N;2)d9uhpm(>vDL){Hn^}IhZrA4J(*f0d=G3
zIWmcoYC>0X5fYl{P}I;Qi0nv{sc{k`Hr@-hbPWv+POsseW<f6@FI$s5-Q`awRKkOm
zVnnq3?!E50^kR;3uk{YjnC>Yk0`n!i-oT#Q5e8NElSB#K)#rJ$dng{?0~46y1hexx
z;poKm8iKV*Sp<aBkhKYqVhvU&*a_CjWvNNfYo^8{I~ELYFtg-rUeJ=)5A=XQU?5&B
zColiCQ^#nAwa-=nH%`;M^D^bZEyaM9F)h+IJo$z=RhzL9t?F?g64`S<&$yNfa@gH!
z_Rs(boZXa0-@*tXs-eKVX>p_;aKgHLwQgt<Ai~ifKiqBM!@q?$G?N_la;i40Yd?Jw
zYJlzzT`-8TAxjb>TnKxG8-SzI0<>DWVp61!8B|?a0rJ`zUNujaVWmFU?EJ)^P|T!~
zmxtt8Jg9^iCaR$-=Q*0?gA<<k;QRYsTLApbx{mzV-1P{x3ERsssSZB2+JH>iwbib>
zx{Klw7VNS{8MtPN5*UYBlpY-sp=XX6FIs@Nw?bbW8t}wL#_y2hYieq0G7LZq(~}ez
z$5j8?#wRkIJ)4Q*P8&%il;k-hW^S?bAs^n`klQmBcNS*P{cC?`I4Tctq~GcNBfl|I
zk_Qf=y%Q|OXn5*?C{#>a_nu+7bm<o8Z06Is$ID@2jo1;^ruBZbE>%M^l;r#r;>Oo{
zf-hhN-hZQ_rsntg>E2^2AXh6IgBU`D4oi@g#lfkU|H<VGk-ytG&+^l=V<{%FUElxq
zE8I~3!w>ma*aMwKHagWSAIcr-%8yVSC|p}}N0}|+cyY5U^jFq3ACRgf&i1#wf3!#G
zY>_+9{qIsYWo1PhVDw%mgk9dVnfWL}j$hCp$jWF&4u(M%jDik!9&ea84OUJaV#v-E
zrL(eS@$piSi&$yj*#dx!NKRSTpX@j5ax*MJ$s@3hahqp_?#B~I9wIui;|CyUf^hL^
zVuiyuiT0Sr*p2irI{;E6MX?Q{ri*f5<vYQc=qHfudJE;i)df2lBgSt5K1?}pN@bHr
zJtTkIStBE#wINDEkg58@VZ-gZ^0@%iRo>pAKVYtw_hDYJG!2@w0O<NW;m~!!gb#*M
zVEAI3n}sDpuBno|EiKa;85*Y8(aZhT>YhED!PflelY;+AaZ*=TFY6U0x>X?%+OhV1
zO;%7E1-$0#?~l`yR3%}--&)}o7FTGG9RpEc?|KJa%S{T3&Q5f$JT&|eYG@H(H7Fa5
zcD?+9WE{U?oJ~ogDGSFyAk1rdV#vJ$gn($$8I8jreoCE~umn-ktqZcj9n{<v-&azc
z+;mr4Du7l+ekK4sOqth!5KTq!#rXI*X%uH9z<5^Dr=qXC*+vhRT73KP`-r99QlNX`
zaIJUf#CL5rpwYvlIvX<Y+~rp9gvFzLly^g;qEMEjJrC&B9m1)^en1~3oK)|iv4|!U
zzx8XvMl;FQEV~azZtzs*oux%+M;8($*}v+<!=`vB35GI=S<-OdzI~xdaQ`|bgPu0N
zxuiK)M^LXdG&VY?)767};0I6e^Ruw!V6ojDGcz+Qh&@Ecqn($bUnV*~y5Y+gO#r)S
zdFuMoxXs6>2hkw;l5(_VFXtD=F7LUmcVO<=`rs3g%b8@C*JA)Kh_cMyn&w*&SYsS(
zX;WI;lW~R_3fz?)c3S~CK>&o*O-+*$4Im-*`t@u1Np^V49)E3QFG;O?%4r4wV8R)M
zoo@Ku6Kc43@18Xj#g*l>y}e1TvEG^><8ubo75rcbyi-{e-h>B3^Iat-q)#Zffb&i<
zhfZ8GX$7n|L{4P`5zAU#&QDR3Pl0%X_J<52Dfv(cL&;!;>W%X>*UD)zbijMPg;OBg
zV_(94;X+Na;ctHdk;~&M>-qZG^x#z+X+~y$jEz}0yuLyY>#rOure`kR<*kE)5~;I%
zh~*1IWXqLcvjM&yy(2VCiS=I5Yl;@uko-CSWP9${whB5E6!23j*b$-L5C&oOIAq%+
zf|xte%PI%7;i`Mn@#eW8o#o1v2K_mfZzv)`&$pIV^a(bdv{g0=>sMf4G=xP&%KNKN
zhX4iYt_1Z|Qxk1SV);yQpCSPOcp{i^PuuZ|W|7>QK_MbH2ZttXN}v&b_J-gE%*18?
z09iOdk|@Czyoj=GFxhZ(v=<hG^VIXVZ{I2=2*8-2zt@2dKQMdzX!UrkS0S7Txmufc
z7M#IbwZvQLu<09`o4b0EA{+AO%W;;VW(gJmEJQ>ldMIYMlg!%8LCyS!`vgX}G+=Mb
z(aV6ePp&r3e)#9X%gJcOg4PD^fc4;s=G|mNFW7dr0(X2js5(52Vj&@PfX^@!pAq0J
zJ!&lLOmV6C*J<4LGd3e&Iy@>aL2D1Bpl_ypdxI1f8#^m>ikUfA$H=?WrY|68pqW&R
zx7kQ!cJp4}_*jhE%!k!KXB-d>EVlS!y7PE5N;!VZVNByM6#?5dNez2N$meIN6(yG(
zsyJkkqoL<(9j<*&28THT@REo=J8J)0BsR0aZDc_E(=MdL9+rM%Z6su1rKa2{4{mur
zb*Howd@7pC0Gczz6>N9*=3KQA(a<ZK@S^Nw1$kR>!?==?;vo<&lh4Nw@T1uBl81*!
zij%v$ds*&K#T`p~dwbtH*a=F4_df3R23VG2;!wR+3{Buii|lR^?cMMX@OKdWp7lR`
z^Lyq&<d-*q9V7JxE~Yt{!E+}2mD#}#dV`p*jm2eP93)?p6~4gzG{uPOz`MxlTLNBv
z1LP$x+w0>|!78z0JuIbMd9yl&kO})anO!od4rXTgBQ<yF^r2Y}PEIAXH&<_=RbskE
zs%O^OEO-j!e|d~%x5Kn2Prlxy2i(n+3ujQ-3q6A%aEc5@;d42FK5uKIwxT|d0j!%6
zvOJQLn;&~&2ViM-fCSGK9#*sg<&YZ>MaHB{Wq;_j%H~!v0-i@kkMzap*9M$uVv7j`
zgh+<qw+HQocnA;f6uB=L=Yna%<)ZhW@a~-|3~BV8Egd<GhTNf=)jr<h+iN3e7k!kN
zd;EAe#2YF=q69(o%l^Ffyqt*Va#~#obMC<tmtK2p-OXOL_)3Q+6)LoIZySlprU@U2
z+;jE!pdkLRfq}{CprsfYq?rgs6txnY)4W^Ig!(EYeljI$dAguw<3SK?!r9q1*Agt{
zqOde-pQUaNz)V@NuCFih+x2Qf-2n1)P&U5n`&3~U4w_oQO=r1|0S7=%_XhOEZ~W;1
zG@QNe+_e=zM&Dn*BY|-Pa24Yy9Cd4mJR|uKt56^>6U$vmK$R#0MZIHofsnF<;S^wm
z!Cl1zjr4wo{G@0wPR<BT!_4bfp7&m0NC1J&;rE}`nMWW7EMXp+l!KE&uz><=tLA9o
z@`bvyN~Gyh$C2ehuQ_3?<bv;#2&v+**NA~6A794|<~{8U0dp2HGV0duLfNQi5Hneg
zeTfKOgyz<7;f6LU;{tM?``wFfJ6jdD?jyD1p{j4(ym<xU2Nw>=GC%}b+(JCfWBLrH
zfHu+NME*1xkI+O$B~VhqPC%{jTQoDka&?&C8@cd`pa=e?NVudJ{=GegHH?fH;_>6$
zwBEUu`CXgnF8{}H(xFERl)`+zeSq0qA+&SCm_Q_-f?jls2Lv(J0lw69Qvl>Ofs`2O
zu79~h%i}8+NI^>?QL(XxIV;U1Y~rn;2mk3*9tN;(UF$~sIOHG@x5-bYz9kKRctWYv
zBAlh22{g}yI``iZ!>gr@&MI#&z3i%Ak=bAdQ}XeyZ(P68k|Zd8dtvBJ*3wvq@g@E(
zbKKuh_tBk~m>B)?Wdgjy(vD2jK>&hSLLG!j?u6G&Ska(D6&e&ujTDt<W(XmdHMt&-
zQ7D4#?kIP)Y0oPxEOf%ul=(oA!!8CDxYhw->^f8Vja<Cm932mL?csACQ$%6Xre||?
zT27WL9=HZ78XDKv&d$!RK<}cS8AbZ%f&F6)L9g<>c9?8~hLSeZgOz>B&G2d!U4K?`
zUAlO&bPGue`J3`m!IZmP3nKw54C_V?VH6!x_jmo~1&#nBFXPjGKHKXS?Rh997+-<J
zBf5T*-JvWB22)xtc49!^O|DV{#bpg)OkepU&s_1p(O+d;#??O_)IjLhlL}CzIu)rv
z1nMphsXOaTFb1Tz&$-kg*FMq>L{K&~q*lIm?V5#4lT>yeiX@0R17}vl;^J~4OY<if
z6yp3B^plg5OL6k?@hR)Y9afQ3cX26HVC>rBg&7zR14OXX;Si<Si@XUB*UhmJBD$@9
zj&HW<0oSReil}~fW^T4I0Yj-6^~rq-g*9eCkD~8qBVzT$L=_sP?YZApV4<Sn0cin=
zl7VWZT3thfarE80t2n{$z{mul-sI&4*+?u0>1*<HpPvh|wFE-kf@=(M1_G5RiIj$@
z(MFtCMQp0`s7LTTbmWWhIruG0uYd=U>|#TgDL#YLTa@|n<41<kjt)(*ci+ZW9_f=B
zmwcp9g5zOzG+^tLMcAZ`orCL%31|5%zoTzwXB(>a58TimeCIsLuM!F|yh03&U(SHz
zoZ^FQ&IrbB9sp;oshI?k8i3awzT|Iof}p323+?l*;Q>@G&!L*sV&Gn0rHcU<4teYi
zJ5cm#%HrMTx<JmUSgon7sMrB7up><^kqUzcH=GhvS5Q#k?c?|c#Ail6>$PYY&(*R=
z-dRG>VNfiNFlj(+mkr*J()`is5Ng^CR(OnVc*1CUMWQ7F9I8SL#6?k4pK5EZ=D&YP
zF{#S=;CIgw?&iNjFm%IZb+uP(E6B;U0f*>qKgUq)v>k5!yX&_zJIAqqU~jR*w<<V5
z9+XS@gTK)6LQ10zp1A{MaljO_ii-B7`n=&b$^nZhTZSfT5IC<b-dd;yM4?KQR;O@+
z(=k5E@5b*R?y~NFmylQi+%2bmVSQ@xT=lQ2bTQ9mOXM&OsPHU+p{192Pzo6YMuL8!
zWx_t{Q)xG!pYoX;L62)#3~?kMo$0#6O+fgV4Z#ae4}q0SVKe}ZY!d8c4`haL^^o`4
zkbjEF;GZruI{loC9~yy<ZlJlJNqS(^xLo@y<vdeJ!$4?(@O_w7(CX_AK>F+jK7WHf
z93&Rdyl#pKulbkxbUUz#=FQPoA_6WGswJ*^t12dF+Cbgiy%b1jbCDGwmvJP7p86EW
z)i4EzH1)`(-Zhv|zjm^;x6C>96u03ttG}<W72K~`mqTYpShK{Yt*kIRyMmE7IgIF&
z6p*_bh6W;!9}%ZgwMK<^f}rmo|063+_ZTkU3gI2&DA050B;c@XHz0-R3yDZ;NQv|!
z@KGbWV50e8uRs*PksXIl5Iyz0R&5&q$Lr|m={zMv!&nrOx90&b;$^D=H~>;Mb!t$%
zry{YQL|g!xr*rwGHk$0aX8e>OutO$ven7i)fErTP<8vsBA<|=a+7DP=QxPmMC{b8r
z*HdUDh3te<lf`}ZykRJ)hw=<ByH()*ryzb0b^^__3(P7m&2B6=3PhveLpA<K1jAsP
zo9%>x*oEQR6qP;~x60#m2KOHUdp^kuV`rv72tD6Rc%>p?rJspX<X{fGY)5`FtrxUN
zWL8v&h1fx=n+%aABGND$D=0*b62s^Qq|2OgmS*9hcpn5GbQbWP-$erPznI`QS!S9)
zm|*2-p?i?HAQTD!d?M=Nv3AMJZh+OIHm2HBLm(yPSmHW^#XG{Bsfei(=rza6$~xcM
z0_NcmaPiigJr+ouN0t%fpf$E&$Y`!SO0r4mK`zQFTmJU#`LY>!5KHT*@Nmm%U~a!`
z{7wAAo*XLrCriaG5Y;p>Sc$h1M{x&DRFk%O6ij37G_&)Du^3_ity@sCB$(M}?L*=A
zZ5}ja2(hWWcN(dKvmXQ}BdQlX{0GR{vK2IeWWXxJ2+F5S03(@o{eoH<C9;gPbRZ;L
zJ!8;Ny$yV&MjzPG0m$&#lb9ua`X}H9)3>2j=p5V^n&2|h`XTON0nm>Lq7Q&d@rt0j
z#(JFdhIl6Y)XAtO^DoiDNc(856;LigU<~#-%tNxa2SQa$S6gbVXG6H4gJD^9Fq8OH
zQ8h|!x3#sUY<n(G+~Y7PZPj!D&ZZ9Vl|kJK(0%J-B`5=*QmmcX={}uDP#oc4WxWas
zz&(kOq)sWfh;e<RrmH)l@kf&8k?KpqB3>RIBoJOkd10KmSMemS#%pETv20yFbOEwA
zgz>7~<iD){7vMnO>2jp-TY+7ugY&CM2LB!evwf?%s&9ijAklI7)4>q$ce4d3k!b<$
zo{I#E`zL`w0N^W1ydkYH;!9_E6$)oCy^2u+O(v2+GTb5}IT$6oDPpd}gXK2a6d%Y{
z1p*JyVYVk6G)wK{!rqWB;u^sZ2e1e3BsDm7-@X4MsbOql613xu|775SHg(?gTA?#^
z|4`V)XZ1Dd%eT*7<-!eD-Lnlbz$V|jc8J@nbx&H+e`q<7r~el*M=Fp?`|lTm^MS~=
z)`5uDA)w@F(IB{i1T=ITHA#VpeuoH#S0=Bb0zLacIZ1+-LG}<678a%lU<xSOM1+Zn
zDd*Ft8z304B<5hi9`nRFfN4h^0RoZ4M*j}`BJ~@Ge&KE4K%HNoIDY(fWF)X+KvYaX
zD)s*QGZG5XuU%7%h=`C^P<RE&*BEAT&qA=UlS@ml{=rZ{Ohq$!PMpJyK&ny;79|Ya
zA~Yz?h>w5oQUwGa`XJ?QbF)Q90HDMnKqJvQ0^f>i9j;ut!Zi--iY6%2(pb$*P0{=1
znp0BviVosMm)6Dz_%nLQjHA2qs|vuD*T6afB|v-XRAlq&OrOs4Q=ICMb!=HGhcK25
zRBU80@b*R4o$|ope1&8znqq4A$n4Aj!=#R&Ay&)QP7?mLWqV2xkVcpUn|}9^ppE&O
zBjQ~{f8<WCAC;M<$an-<gc0EJ9m^8ave2x5=`;@&g_PY7QtxD=jz?D)gw|i41q3Xv
z_pW|JkO=0NlXACi`8f`Hj6*ILsPjO8dVeOMA2y7IgiIm^@9sWQ1B)I<tVLfcNCw&6
zBp*GH{0&91@8eK_jj9sH$(ew?x$u6Tf0+_s+DhW|r)JpUU)^DsHj=lO;T;76jTpCi
z`kL{lGcyxb1wDZXWUwSwPcSmtq0V@8_VNzgy5Z3klV4?Dfh^pL+Zg^M*WbuSG5;G6
z4@&NorKrT^#EN^p(c=bY84a5S90sS`gEK*O<Mb2azAkXK=*TsQ+OocQqO~tTPcK83
zIj$}EE<7X=FcM0cqseI|l2Wvg8gRq&Rs;dJ9r7;Y3Cb}5y^D0A)AA-JCU{6OaMsZ1
zkPK-Jw)5xT_HXT7+<N#235NEmD)QO##CVfCnT#?oUAi<CreoL!*~m^vB{`8l{Api-
zd9?cQcpn7lxqRUE^|E6V`9UaEJ`yZic&}!^_}cd;qn=2)YzR~DyL(TVeAbYA08n$k
zc!Hv?6(moYaXuR;PfT9lV1ZD`@WH~(EqEvqC=74RqJv`u`U_>4PTZlTgVCDxNqUbu
zjJx0eTtDN}_7Vh2iiUT<lx7>0sRQWIx_<r9R97}Cb+iO*5&&6@6|k~WS1v0@x8|EQ
zB4{(`7%>lZG{+7}g#CvZ(f}K+73HFnx?mF#fln|i=mDk69=I<hz&uN9)T@hBsKZQ~
za&mSqg7gW?<;(26yc%fm(e~CCR%LwQ);r!~e`nH0A+x5RXY32$Fb6aO>;eMX5PP?Q
zwh2j8juBZ;KGNk4Kn4|&=ogp=0ppwuJSYf((&1mBa_JGwW8h$6Vez0z+<XhX1iMa7
z2W)^ai1UDt;kW5h1opN0jKdH0H_OW|Xw3^KwSlhHFffP)T?5$R{pgi^NIRPkCBzM{
z{WzL=<nb78*kjjmx`1!$;IRJlD^2YH?$PG%6TANZAOG){0FizoPO(=TfTnhqZUXSp
z6Ww_xR7Z~Jpvk~oBT2O7Uw6$;>z#kSCDnaL`Htj+io&`xLkm@I!SPFm-7o?BD0m6r
zD2O?-?@L1cQx^z$mWTYA*&qV3ivd6|3FE)pL4(EK4@rofX1{-5_u*6{1lz}^uXhME
z;<$-WL2ZN{CX`0dB{E-Mf)Ha8O<qF4`5*(Sp%I(_PF)LVN8gQ|j{ARKh=M{@JTKAX
zD4O#FHQ1rsM({N45V(QMun{o?)CYhY2n&<m0>mN&0>_bl0yB~YOtn&+1m@IpmfuE}
zFT$|`#TWMVog(XG5MKQjeBfW#M}JH+_@BDN6MUd#20sRwew+;|c>`9r=!z+5M&m&S
zeG`YlMqIcN3o^dcI3InK6h#sT1JCfHnx9Ev2XRJq>c`+yyTY%TJQn|NCFgIJXkj_T
zf0S90Ht#(jKZnSIhEYWG#3jX6;HSZHXTT-99Eb?eqD7DllNAQP&90r*3hS*T8dd^o
zK8H&3n^=#rGa8$K^W+J^2;)+p`+q$ch31iIOQyYO^Z#baUk}hye(g+QQXGU|I0+cz
znFVZclgl)^_y8aqZI{4bl3)QlfD*YVM+jjiqPeEyQk4I7UDbkPd`BLGjnVRCHhQq&
z3({8x$x(>xZGgUE>eJ;oCBE?KM2aW)yUunMNxR-%;;q1Wnz4ZZP61%(k*}67!ordv
znip<BrBAlDwnuRp$-B-Y=nOA3Fv2!>XbV6MVh1F<l|~_G+U&3gL@2MQ=rWz<^Jj7w
zR9n^G2oss8GQSM2GX)8b7q^~(aB^ZN^np~pW~y?`&I{N4qv$}Q3*?`FmLB|7fZ%mI
zC!)8(oW|1O0P^gwEb27)jMemPs0Khu0|Ky$BWT+8KfiW_*HQoq)FIoK7!8;QpzuaN
z+~1>=MeyW5`TvLfxsw9wiCF)xC-NHKXeR!1D1l2tztHu~eT3$P*-GGG8qCCyfHk}W
z<UB3YUkxq(_)XBB?<mDxCR)#FltD^l)cRYJ+zDSlKamD39&_f*nVp=~U%$Nb=fe!{
z4li0wK|MXa6emSR#j@Tz9H$_i0kbh#Uzr&$DS{`x4JV#UCki%xcmw>N2<WDa>Ygk%
zq7@<_xaG&?ChuYp_>uc_HsZXc;U3{%$>0|`R{7c3Ew^DDuqBic)0WqP=*<P*RdpaG
zE&3dHkH7-%rNF&Q-gOpv@#f8`j_reyM5%vr4#$oiyT>*@Hl}T<`0qQGdN<maluXId
z``}B)v!gPIwe3XKh9Ae9yCHC};-}7`H!yeCP*qiR)&{hPUT9k8pD)!ITM_fN<&&Ag
zW4NRS&fe%NuE8ZFq@ftge~}56{w@>&?*}<Me^`v0(5mm!;jw0-m<9@%V9;-aQ&A5(
zT{`g02rCLrfnQu`bXWEHk9vp4Lg(R~ozYzaB5hrF4!$$){0!g4MMJgX$H!(mLhb~Y
zidT=8YW?atn$bsDB-2H+rO(RoiT*pxhW*Y6RRTa1F#NF~%?b0<)6-5xf`<~nJ{|*g
zlQz)B>@GndvFl9NAaIXduLaS>{5bBn^j&9=FZP1V>r;H<n_H)1zOXOq3Rc(W=$uNP
z+Wc`LQmh#7S(45iueO`wzPaw44J9(_)^ZYdV7+jC%nrK@$!|@yw#myxphE$#EH`%l
zvvRPx8j5GW%ZZDbJn`xC&WfJNcd5NoJ<;NQhm=4q$}S<26?zK+YpO~{_baMcWlM!h
zh0IFlEx@o)#iQ+R@bAk>nKiH%PLku?CT3VfwJdp?*F@rS<=ZQoD3`lg<jS*TodM@d
z21CpIc+Ynr)Sj;fhBK-GKqi;YCP<G%{&iH~x*3t9qQS_-M;vZ~>?_K4l84*zVy4up
zPoA=%A|`p@Opnj$a{$!H=nDqh0NkW9Vyj|pov||aZO(ZU_{^vVkOV9fTmOBN6x9v&
z4aDjy{Z!K6o%D`C5T}Xq|K7gbw@He~8#qaKW~ylW>x`_+wt~WPwXZ0mscO1PcmX>*
zV;>kSRlrO8k1*Ma0NNId>!tTgfbbRC7J5!5`<#XB@jqYRj{rs38!1k|Fl^kk=ABF2
zIF_I-e*fO(rZS5c@YZrk8!VO|yk%UhLdFdH#^`#;KSS=gE@UbciICGI{h*LabrhBz
zN#Mdy^JuI?rf)TU;J?VVCDMPs&VX)LAq8R;Uj0N$E-^1!I+xrs)2b@D<@|1zq1W)<
zxzr09K_lnM&|I_2UvO0|b3@HAw_r>4P0j-n9qfWiXa&FqjqNBNW;AbFRBIlC033mA
z+$$iHi(pVMqoj`koQ%*U;H-cz$^^IzIiesC4V{Dl_Qx?wB1&k1PFlq0mckp3wid}>
zSrA3!ei4AB0F#ko0;#luEbUp@f0hYe;5_ufAa@PNczO>(TCb<F%i-vW90Hfz*NNCB
zGbB&9Axp4O!(?kY<ecZs_>8B;XD-?vnh@gFgH9%BFsT}X2~BGBbgzPRixkw9JQ)Ln
z17zn*R3ZY3nH#A62BuNPPDVxsRmRXTiRq$L7${Cs>g(&}nH)R6mO7ejZOtJ>&>M^k
ztq`lX(YRP+X>3*!nYDn#ynepv?}-eM{Dr)o=7#RS9EDV;MakKXd+ocnDn?%zUyyda
znfqp{>%2sFjWlOIyh*0myz8Qe&DJ%4WejqX=Wk*(x$8=3*b_g>EWh`S;V-wKR*EjW
zUOgi*mbd*fYxlrrt%%YuPK-*(WXjY1*CfaYm@rk0W?B5YisV)jbnpMw4y-%_-tnv-
ziSco>+pae`8oIh^@G2%j=-&zvY6@scf|$hhiAG2zSH=9thM0#TD3Jh>F>VC;x!|83
zjl-p)tV`uBfCXW%Uu&8(qgir^jfo44LieRYUqj;(sQQ@zo}v>0k;T;7nUvI2RLs@|
zgqEH4ulfriX+B#UqmlpiR%sr^OhW1Y7FonOtB7C|=B>~So6X{`J#<*@2b#L``~B(X
z+?5kzx6bLs<@SzfY(cjGJ`Y>Bt#rF5yi>Uv?LtcU?CL7rO4$~lxU3T%v4RAH@~vk`
zGh+4o?3uyqC9!?lLkV5HQ}CJb8BqGTG(ByLrtnbJaS>E!$fZ6R0V3wuGcLwUZgdjd
zY9deVPy6K;6rhzbB;b;DKsg5e4VVU;8acrsWCu>J{U|5pv;*T%*(K^eAAc*DVREG(
zW)`QRQn@e1Xq1fLjC|6mxk^zt7McV@YiyP$+A{8x<QzY7A`A*)Ajk)ymPQ|-JrXF~
zSG}eGwYTHzK7&E*+}B5L4#lwLM-|Zfo%1f|QG!%Y8i8BsYqABubC1x`v;#F)muEJ%
zJg54aL_)*nihSIkX}KM;OBID7*BUo0Z~JZ(>vNVD8`%Wiw$4f&zetLbT=B??GcqME
zN!oX26?yS{7`TzGYQ}<LZ8oT_Tg9Lh7et|vu|OCzihFjP<l+XsyfhquJQG<ICxRwK
z2jqKDnvn_H1v(tS?m%@qpt9&KafkzT^jiROnQ8UN1HpuQrvZ84wt-Zb`u1zP1X82%
zpv{{L2o63xQLzV2iA0LJ<$ybtM?cF^3=lbp30?cf(CN64tn*m-7s8a{EVkV|CeCB?
z1Z(lbJ<IJPyV2@{D(&{J>9%&0@+%3~6(8Pu7HQkk#vm?YlXwbo5S`Z8s`{PG4|}D^
zVftIp<+FsFQ=O7=+3cQ09f<`3Y%^)&znF`e8@`4)Y`wMFDEulz=c+w0albkkKK8d4
zk5BRa0^rN<IDCUq%pFEvhD$d=9F8VNP!j;Dj5J^ysHoY$$#Q_IU;;fxAj&9$3^6bZ
zNJ}uLlI6;U3o_`QKzoHKMey8&x!kX+Q?y5;0Y~(L*cNE}Bq%SrEi616HxHSB9#0@E
z`R)5lDMJ2T3aUvfo4-i@(r?;ifG;tad1Ej6cF9Qpt(Tu$Yxa1tmU~)JC_H;st2wd5
z?24z=Hgr@%dMmp^La5p=B$iJE7;>5wX%D6ZJP{T6s`85=i|8|$Q$mNIou8Y2-s#2X
zuykD<D;Iie)wbC$1a^Wxm3(W3QMC`s7+e&_UMtbh-kHpVMr3F<Q&F*Y)}97p$T$h_
z4iJ<MAiuUy%?*$mxsbQG0g~|;1fnh&*$v<y)g)G~k3~TkY34gVmf6wK@sP|Shtd>h
z9V!w~rWa)}AZ>u23$!T7vP~M;5Dkb6_lW!(vspihete#@2YX{E;S||cONDHbF?N`+
z<1Ss4wk7l;gncbsf_|RI$Nq9eGR?CGGA-(`I^RBR_%N|p=ev6Gkmmzw<Oh+DsT(Gq
z0>nhz#q@i&H&{ZlbWL@iT{39ZOY|yk{dDGRq50~y5HfE*^>PR|(Q3nC(sLn$$=cvD
z&u=3jshG#b?`?t_0#49n)LiX&cTZ-=1%N0e1Hv~g{W!?)mJcHV>@KymI%ve<2?cb^
zu<=y@JVUKl@OLjII;f}uNf;UzePBYTB>egHz6IK)G`cagz07(*R?5nKLw@^2JW0RF
zTyXg_ce>|Ls$D|^cj!gvbLA=*VqTtyoF!Xo36}2RFdHA@P>RmG&aM%>G3wcO#0;6t
zC_&mfEALDuJQv9{q@^JyS_?(!q|y2GXM`XFoHa|ghCpjfP9!gf+tc^wsf`|K4hmKp
zUhcHpB9BIjM=oOWm4DEBWL4V|j$t+j<(n!kUYwVf+07>pem;O&9_o@oUzq`3+ZsfA
z0fC362RwloMyFcmP%^nAQfN}~l1gkA6aemZ`FE(h)}D*!;d30@KO+*&DVMLC^e=P&
zLRM$fw%5(zgoSM@^CUruKy8afQIPr1DjjkC&d#8%6MPw7yf^*!U49U0WZE9}96J@{
zE2?$3z$^RPRRMz#gZ`U+hs`Gk5Ei_q>1s69wo?;RlY&H=>@C-}QfGaT*sa-2q@DBW
zc9&A|DUhR7{^5~**1Bk->w}$ZiwEOLTBiQ@#hun|ao~cUaVaf->2HcGvX3KdfHSsQ
z>GdwpP|2hSg2Nd<{P;Lu9FJK5>KY(S+L!3%UtfP4)zQ#{#1b*|7!8XL{ugiYkk^p|
zFZHTgp{wxQS0w_C%!Z3uq))$#YKT!}l0u91t$INUC#rzu#h){th+crLp>#Jg{kfxh
z#dIp7>~ndF13QMueZ7gapGzTcv7_mrsLDX}_a?Ycuv=KQ2QCXi)QMgK#0$KFQzZig
zM66ge5);3fvXzjFs1U0OE*kh-d9+NT8P(nqWuC)PN|7b&@hapNdyI}oovY4xiYuFk
z>1aDEmo&GLb1~Okr+P?pZ`!~aVY9B7a<Q8_EB2qOYvHkb56HLvgly`eTP0=?++{hL
zpo0(o+P^1iuJYLo7;bqX%K5?Obg%X1ZfjS~u0P05$?i!^g`1Fm5>64EkJT$BzhH_g
zQkx_=HFk1cz)StQQ>x*>)tmlOPRYgOm%!Awoa@QHYP`-=e4}{I4S0!hQ_veI^Ro#)
z)4|xgeW6*2WZC<lTeYG&Am&W@#AtH4iKQqqaIFy2iglE|-!ttevCX2z{j*f7XP-bW
zdE~q_86cDkrh2blww7SLx4r+ZjRoc>WI*l904LcB%LXm;N8ihB<@3ty=@mTm@c`hq
z25wf+6a4dae++A3N@~+PTja8NEUu4Zz~{zzb>WTGLv!(WTI@3C5mg(O;CEPI7akQA
zZ@iv!*IaI^>$KnUNXnB9$M)+*@GNIHFI!FvbJCt2igWkBcRI4tK5kpwqJT%NKU(!X
zv_-0pBN~>_SY<v_T}`;3+4)v-)h_!OCUPR8EQ;S|Kq7?5@vev5@OWlpCB)m)Q&Dht
zNzs=SZvww#-TtkYh);j&2A!)-#$|3&#wFMD>>j4u$DnQAX7gOq(yPd+{9l}}hQ?%Q
z294f){08KQsvQV@|9QOlNLFZ?Hjs>8o_ajfz@3l7d;7e^wEQa7D(IT+<h949-CFOS
z@%saI&3OyXE%rLHDugr*CXM@aXMl%AI>?B;>Si6P!e1PWZmqW4BhFhK)hn==-EzzE
znCE(A0YF}DPnWL!$>!kCt4X(ubG#jr>tY6^(L+pQeBr?qWv7Pn^_PaR)tEVMg_ljU
zm%L|^JRT&)(hn$I2#wr(*JYbnX$Hfb?BA|`ro=RJc01j^BAXx9{XzwYQYKo=|MAK)
z9(}_ycY=K{B7gr(Wn{CNz=0vJaF>_6v5UK?S#&->xLR|-24_d9Vm%y4qhGKY4#$Jk
zuZtsF=2%tFv@J58kyw2dW|VY9CuypNHl4nsD#3mtE`A6T$@<B9A>BJ6du{Bg&e$f_
zS$Y7OxGK(*@jPduQ~lI)2k+McX)-cf^C2HRO@<Cvmbs@=*~QA#^1@m^=Je-t)U?3#
zWQJu)G*fpHK63gmPh<u4Mn#Z{ayrish+S3tMc<=#&8PRXq;B02d6%<%xTGU*QYX^e
z26l@9#qe<P|Iqc-VO4G2*I1y^AYBRy3Ifv7N(<86tu)d|Z2<uh0TBTK>F$zl5RmTX
zkdlXnL&vwa_kDlY_mA)Vak)Omv(MUV%{k^6W6qT`-3z?*|M$t93t5JU*Mx<w`d{Y^
zn7Gc|)Cj~jcC-obKd~~6`B<|~c?L)LC{P1^T1&QD{_!-$z|J~c-E0skywx`;QxlMx
zHXm1)Q1c^4!pW8~s3n>0b$Q=`F9WK*pc;j;UF~2gJf1o%rjq;tl0#`GMH=$*Yhupn
z4<%1ro?B<}&MygHLsor}*XlroDnPW*HH+*Q?D4M_$20H$>cZBFwjVM+U#RqR+eTu?
zTxeItlTGS^S!k-6kP3Ey+;8)N@7*!gUiI>*>e9@VN5;KMhmkuG0>uqS%9AH+1Gynz
z4inz+@>t7OZBmTC*y%TXPD%z=VGFpK{%;A;AcY$Moo;bc_Jz;b49tZe>mdXDG#uML
zJ6AbqxQqMytyFp4ih1hb>$O+BaL~Vg6~VKC`%eY6k?tI9bk5~R{<<ZE4*nxb9QHLM
zr5dTolhTp*n0!n^9hCGNefQ<$gE!M}kvI-?mAa$)KdNj9eBao6dhlv#t&*k;T1h*e
zbkq!8f|IoJ(?c=6o$gx+`THA7@4<XCw^uJ+u-E$fmf4Py!F)W=opNCqXT+T87Kbd}
zam%geLw%E|Emnb#e|0Z1@mC=pK&9S>pk%aCjfp=s43b+SdYeZ>MV$-<uV==h|4=mh
zz=cMKg#})gHhNkAduSD@+p|bKvA-C@d2aot`tB!?f*V}8XwB+CueB#=MuHp4LH}#Y
z*_j7GoEm`B8-hEXfD2CL-o2Wmf0q)_#UM=YAFa!tyzRV~uy$y!EFa$HtRA4m|J=ob
zJj1*kv}9Qxn02_c^UXcjKJLuN(%px4l|TfJ+a#R``E2`0vIKK~A7s*<Q@w;W7Yl-n
z_Jyh(brHc()I4poqv$2~NoNAV52M%qLRw+j;4Cki(zylr&{n5kM&_^9ry<uzoSZ;b
zxQ$)3l4Z+-{EW-P@E%k)o>Khh=QRfr2g}w)8jhx0YUI;tf$1Jmx#smyntQIk2@g!g
zpf+euQCn;X&Q#+RX)+SVwlc7jx3RW(D!!6VR~E&S56UvHNvQ7r@>wX3??z4GYCGP~
zX;_?+WO7DEdIxeb>Ht#3BA$_mvloC|*yRt~`oN%^j~_q6;suxeZz-qsvHmnKK=)F=
z{f%D>u2O=E$3F^7cRY1s+5}zuKdi>t;g&7nx)}f-5ZDKG-BIYTCH^WI)}0T>m3m=o
zWt*gK%X7Z4BsW(T5?w@fM6Oy;Cy=%OBmj^f0VV;30Fw8iu0aVh0u+heG^hJr0LAr9
z*YoSB^Y|OCAZMUMfEr?JH(Qxs0R9NUQR8bPSf1A;>WLew^_UpjYDVGOB#+8T5hYw#
z>gP)DW%UwR4zibWTgUaH+#d`Lezijk7Xg?Nk>&NWBEG&j;149|nwoXOIQ)^qTV=P#
zsB5prA;?t~yVmv|!{qx@4lo<C_F&Jsw$?T3Bjiz`oNTA_KE5V5hh=R<g*XUVugfJa
zX7(ZfQrQQDbj0G+beGO~#cCppP>v=ZJZ5Az77UqzR@0*Pf%Th_iK%nr7%q=Nd!$;5
zM2kx{F{rYD0c{W{(3ErbK`lmZyPEbMfN`ms^}*bS=T?9rG!X*8c<`_8)M4xCmJX9C
zu?KP6Sr=)7F~}mBs!Thb)X^$zW4ir;VL_*3US`xvF1B>{uN@8H#sB1?`2XY~V>%CV
zW!W$G_q9X2rV@$xIXtq3Gd)EG1=C|EI6q}6)H5T+k&T%A$muyMPM32p&wZKfkqjEB
zppQ!?C<tp96qO?hr$3zC9Yjn|l_$|pHm4m>J@dM5)_+(l!I6X#uX^woMu%cgGCdc@
z(1HmTi#*yur8{_9&-BHlNNV&@rtdu_f0mqy2ATwk$LQsw=eCchvaZG=XHZ$4%5d0w
z$CxL5y&G8T<!V9ubBIC3eMr)v=Jy!f69@!8cmM^k_`pM=Q9yW!Zdy#6jH>fjp?p~n
zIuuYq+JV*l2*C$`?MRO=Ak$r5{7|LIlZwElxRPU9`w;%p{>~OUn|0_;W|=e>Y#O$Y
zC$h?kD{+M)xg9q2U@nWgF0&+`(HI)tk4aDb1<K5={YTfCiMEE$#~$c%roYCl-|V6U
zK%~>XbZD_w%K{Pq(zAwE6#%5elh;e_Ew8x-2|;0IN43Snppp)z)FK3Hd|5X7bznGZ
z${*sIo8ZhTxiGDfUw$zlb8TXHQ}-a}%cooqK@J{Gjv5aokw7`wc}@ssHl#Dv+v#$&
z$Xg}M9DA2y4z#N}D**-=tP+e~8_%|x#eg@RZz4vw@WqjFEowyjOV^zJa$hAF$RQ^;
z0<=!2PUi9z;Jo4)-kPc`LB}FZ1mbuO(8+-c4s(S-EL+cv0zMU(*Ej((=v4ukf7b9H
z$wmhz8s)pDGd<h(YDTb&wDw1z2UfX=VTcDL92cDaNsM7sP9ROVnJl5_z2sdhx&Slu
z1UZd;>n;R!dp~XDgPN?TVk_ImX-yF>@FsC9FFBoGtD|hP0$A6Q-Pq<L`10fP*0Ps}
z1-W+SSx)Rx)aNq=)llh7Kt0C;ts&+*UPF!L-I?zS(O|#!Gc2600TyU=#(^6trS?49
zN&Yv{s{f(7c}K-&Rk2GBS_nZ8sN45v2Dpx^O{h8_?Q03s4rRMbI{u*=G~g_g>t;j!
zZH3#6<%M!o+6tglsAlm2L_VU=e7*-}82!_T+yHdj3W-moNnkV{pznc^0k+4287Q9(
zN!>tyw@LXVa6g!UYmEVk>IueOkn`H7P9p0;?1cMYfR_q91~}<I07X{{oT`0;f7MdY
zzBtah#>bz%3ildwY{)nf)M~Zdt=L?f5SX?#Hk)~>rVpKM$e)}X*1Emgd{yAxKb+OZ
zxfYRjGC5mTby|b-@iy+xq=wJNA7arkNJu|o>3@htY3gXr=CPXyuH0H1iYc|daNN}e
zeWg;P*Amev(OhB#dtvYeZ?UnTja=qS;vjLDH|PrquDu|;Gmb6$txm6+1G3*Q2&1xW
z!8uj!&TrQIgESC}$LJ9R%g$z*|EYwesWpcqhiXF5e^|~QT#jyWua7pT@a1TFsQeQT
zH5K&5!F&RB(B^D^y*vENmwnLjlL_o<>29D0oE*5cxUI$#fu9lyCK-g?hizcMBO@&>
zZSM}90~5FGw<piRxraSARBiPqYfOS?+szT2On)8osgiMQ&no-!i@jzw(?t@1DJbx>
zVNe6Zcc};OEr3Z42C>cjJR@JX!coKIhy0t74~r?>jQiz0tGz(UQUzi{widrhRFom<
ziSxOaAZp_k71Z8ws3i=l7=Hh<!MNs$N3@)-&rkdCa#+?!R7jjHvjNOr3)goBlko>5
zlnoq)cyml^=tDR1ZJ5toK&bo}iUX%0P$Lk#tyuO}chuLt2~grFMm0qU-XT+SuyM`o
z03+w!+;>=N!SCGuxUPrf`EYC0IO@JRIO`#hj)JvIXM<Sz^F7+?q)=!)4}*1`^1UM1
zr>?|9iN}DIyPI|7rw#f_0c*2Jz#f$fddT746tH|sz}o|CXfW~x6jf2z=eOoA2&X8}
zHf_2wRjK^t2WU~hl!Nam`R|7nVA8&34sJ6L&Jx!2`%0lekohd1IalV95awU%^ovF%
z3@U|oO=MD0i`0Gjv22!ab7D{mQeSj@#r+q-NHMh&(sOH=z2tUJA+{F$o4lmdZl_~P
zfXNvc#mqIyY5hPII@7d|@ZLi0XOv+xE&><g2vbtst7$F;7wDjx*zE`^i3WM)fTnkl
zA7oVYBK#|qh8rb}i<$I2Q?sZUKLYhbo3sE)nDE>N+Yn{1x7=zmcvmZnjF)9uNIN>+
z-C~SE9Kh<BM)Iy6VAf;GI>K2EVy)8v{4Q&W4dBFCYWJ1M{O6uJ1NYhK)F?2F-X{u}
z;IW=?^ru7rk7Nz6PojrCrd7<YXEDoL>uC9Vd+AQJs2+W{)s6an9I0P5KfJ6dR;t~5
zhR>H>R9rI;r5dMDr%uxl39>e=TE%RyuG3Cc!K?(i(ZbKQo7N)a*R_z+v_G|oK(*vS
z+4lGL2G+#<SBt5;oesBg$j9moOR=J(K0v`YhpTz^M*v|YFmh^r$QJBr?ALq6>}&7N
zcM-vTlp9?737vnM<o?(n1)YY+SI<8dS+dc{BL)Rm|3)P!PhyHj_80)$g?3$3!FLIE
zunJIvzRS@QxJ4~%3QWyNIOPLlFepRejuHp{|Lz6wvu6mJw+?W+=nO&~%<F=Y?V(Q0
z#H;C7tT`|1?(x-I4x$D30oqm-5-#xdI%zYAn@ma?J<&WCcXP8ThJinmGxe>8o4r#Y
zQ&p`4K5qA%c#+5rw>5NgTnBTLA{i80k)`Aq<vA(fNYR5jgn>o&OG|U8(1uw^Yi&_Y
z2Xu+ZkpmHQ-N~KntVR692t{3hY8K5sY5Rb4axv{NTwv{s_NnlsDp_5oMcltikjF_A
zoS_lm)_V8M;j-(&Gr0fXbqX|W7oZ3Ux?HkH3iM}|>!e2of#~16@z<AqSkTO{huY$e
zg@LF?cEV6@NctxTnNOX!rncG8ktEwp>gfTz^?PkX=k9<OGFf!bt%ub^PnR_K08rOf
z$TYYZAO1GcOS(86<c1Lx7LEP)JftLY6>Rtl<CG2>N`b1k2FY5(K_iHS0#$&Jt>Xya
zidW5@tH!YBSLd%I7#rwvHQ{>$=t{UP+PR^v>T?;T`kyCmudEIkYbPO90z&+3nB}jQ
zQP$}JwOZbTNnA^_jMjguM|W_a0k9Z!*sqlh0-3S^m6DPo3pc_foZiM2J`WQB6rWJ}
zUj=4^x^s0>0Z<=)Z+&s1?LFN}PiYd_?+v4J1X0J&AG(ebZATPqJSIn!`1w0q<t}?o
zCHJbOzqRH7Ao2XWhm;GhZNZK&0TDhq!Mm`?jeuZ`{og#Xj<aI%qg6?2K{ixVYKPxP
zpDWHaQt`-9gbj%e+H4i<(BzFY5^mbMf(RV*#IqJ?AuIZH{KeNid5I?0-E7T2>S-2s
zXCN*h_>la{WC1x{M)ClouF~!qMQ#0K@l9@42-3U_6C(;~*SXB}Vp!$*-WvVIL1$(=
zQ}Sg6a&TLyt|=z0;cg5`T~Z@t^VTHY3n)!MxeeO~ScEgusG}>NYvX+1*AOo6OTRoT
zR2>{mMFw_OSxDhIqeC;0fO=@}bn4q!+!K{cP{6@LjFY)q#urI{j{ZFHnpX%eg>sJy
ziT2aZi~FaeB%2Br5@mW7#R0U<6`BnsmV+Uyb7Y{?g@rBswS%DBi!T7isqFr;NC%|2
zlVN%KiB0pBC<mExnto8v4Hv!8`6p*)uOUDa%b!ce|LoCcTwegO5wusfnyemqa);fP
zlp<;L!lO*6B1^vXW^@#YoAGtE?w_g2hQ*8Urw_{}o$hP%$myold)7#}hb&z;c>kr_
zX2o~$5&6!{pXdYl!>xW@6l&wz&1iMI?)enXje`sFk@me{On_oXowo8+BqE_xM6cpH
z*wNGpwTcVVI&;du1d^Wr5F(pV2mC7cy}jF2rUpjQ%R(CQ<Q3lFNnM}!QT}_V-f{de
zTE-7{GtJV|E!6enZniwPJS`pZfw22Q38OwKkHWz$S_7mtYR^-Y^I)A=sPx!;?|HDR
zU6EdNz3y8-s7#t-5@rK8gBN4=H(r^NxNa>c+sJZ(0CXaA$z80Kvz#DlO$f5Ri|tAt
z;38df4<|Q`$xmBvZ#?}<lo+N;0}9E2;8U*omX?#FC<J@W*&q>M%6pPF_7{ZFkyAL{
zmySL$nV^hEnUQTBOnsUebmFYO+du~h;Wtny82<3@XVDRG--q(2qOi-LI>ZF9^+|TQ
z=`+*%Fq7cn2r3(mWAf3!AsO|<A@y<;;G^yIkjuc`FmPtgcbWDeW~*6lVKGcuA+>$x
z0kU{B3K_b;08)Nv=#V=I^Rc6cO5uEAUR0Qj`eq+g!zCaWP)5+V51x~oOFD3ZMbIkR
zlbS_WZ`tVV8qTA+OrN~ma-kBK^K3hG&>9{7?vB3d4ZJJtkM9F~O9f_8*^vGAQr`n&
z5&(z9fADRE<lk=yjRx>bWc3-AM?pbc=jD!Ybj_=}JPV0&@2y-zK<Qp+5~Qx4gh<OC
zgzx_Ev=<+PuuxjUAU<E5?2sdN=Pc4r@CFWF%|(imAI}rimY76YIrnOFXfqESJr9gI
zEVxG=Hk8`rNml{DcBaeafVLv_)jo#ZkB-f!Ih@wr@n|8rYenPO@S&#D)5$$<pSIoq
z5Oi}GQ2oG_0%2r24DwweHQaDeEPrL8#L%=+84A5neI6$G&kI^SC(?W80dO~H?TK#V
z1Acq<hy-xiO+vxe6!E_=?%l4G;FmnfSJr(n{W(n2m+uYJb%0L<azP^rxzVufH9{ap
zEuHk@YVQw|Z{0gfD5o4KhBhSk%YmjcT6pmBHtUQ{dw|@M`9OIr7l?j<0<u(;RBnw=
z+hkto9LCKq!+#<B=MY`?ud%zVu>4PK>##r-V!L7gu-meO9Y@^jsJhAe`?^!L(bV;v
zfrtwxO^`@=_H9#^p{X_d&DlT?&vL_hsP#L7_9h(&*iYfo7`wR-jLA~k<PN8oWmKnt
zrMqp9L-v2aCt4cpCh7SS=Z0FQ654p8EljTysy8KW+-q|$D9!nX7DcPXim>{A9~W>r
z?Tk~D+HKtmMFDjRMxU?p5jm2qo5`5fT&NR{l1XgOI*3oU*Q;KfVeey2$Rj=hF_^;T
z!8QZwTIi^j9ymSpN;b(ewS{z2exI9O#<>FzOWG?Fq#u*}!+~a&6)tgSuQ0usa#`i5
zj9;CvF2AG_JEiOot>lwW`yno_El$+sp<rg-rKIeoZhUgTVC-m>69A+t%&Ne67mwx&
ztun|jVh8X21k(fl`%$2_R0_42@4}vNe$tt7qP}mD6_`?$>GjeTXrLDS8BT)Tb(Wv&
zG~&|1Qf7C37YRij0yZ;59vZErB3}!XsfI7}C`Xl12>`!_RiABj=rL!=TpWevz)HS_
zCTrD{!!y*tV<Bmfsh|RPA^jY%6&OpVzoSk|Svl0Vru(!3t$+1tQ-fCVLT38e4+(b;
zhuV=+t<+cy`ZbWo80nLx#R4sYK~+ocA}8Zn$Jo6%b+W&30`MEkMp^jJ`+P{eukD20
z{7~JXCiNnE{bi5M(tp1o_^D6X%RKZmngW6?X-9Y1<*i6ldTll`Nb=Vek)lfvL~@B?
z84Yub0=P^{BU#;>nSpdeQdc9UTqmQZcCm`+*t>1*cG1R6j_OcK5Lp7or~Ou^<&|0r
zcS808abVT?172G%Zc!u8<0G?*Y;*fUMC%aGl;S8^KMRe$8lI`2_^k~6s&Nl;_1rsr
z?zaBrzR1#Z`_2Y#(|C7CQ+gI11jtzlg@k)xhIMe`Iv5o5e;-&sb7}UHJ#j9<N$3I<
z6u2{l-~R_(LH#E-d9Ag)x7;1utV)+>5)^U|AK7T%*Qozx1H5z9u=FNNISj-TS_o(e
ze;BQas1{n?(SlOyioy~Al#k8PGv!1?b7MHGVE(FK*?~JJSMb(g$dcI<MJH-R-_874
zwZAu&$%8@)|46DrGr1VmO71NeC$WHll{!G;gD-v?`__q2M5$H)y&X`F!y>Kt*X=jk
zWHCTh6QVV|><`#@JctqiDhLox|NH#{1SK5+lN_v7G0e5TESJEIg1tOWW&|)%L)K%V
zsm`%Unz&k@fvnsre@Iu|V0`Y&u<Nu%g7m2K%AmK45y-E0doYwUE2T&{1(cDzHF0D-
z>j+~;!%|sKU=1x|`+`xHY*berI<O&{#XspCxyFE+-A8FfZBBNx&{c7cr1m3*l5JET
zgksf^3qS0)?whiaqPhH%Ey*HxyD1aA2p`J7g|NG0AFZ4{3k6hJdnVJTL=L@&;Bv!a
z9w4i17beZTX&0PUbG00CK**$Feu-IYd8-YpB&HCnahtim_<Jp<tq(X=sHw8s#;;#}
zWv9&wtgFhaZ-stnVaQipz6Tt4(yhT!%gM&@lhI#tA8!{3CA|C$080*L88QxN*uR@E
zrN9>CTrJM8D%4iie0(;k8iBRhr?5fMX3>`PILbjkcOnJQGb&cs*}$L%JlC2gSdjg1
z{WYWoi^!%0tfL$=4^Ho}*8?{tbA@eY{_14R=eIY0N(Pt@tk}dj99FIkE_J!w6)p!l
z)qZbdm4lhP$kLk}?I73gD$B$w&YXp!X#kx$VKiX4aD`0b#+KL3cczn}D?Y$z87$6U
z>)!=x9Dm^5g=X+{p`lu`dwYyPZ(mvRww{XTkJe9JwLG}){0ux?#x~>MVF_unNV3{)
z%Wb|+(OLTT@>T&#dpL9eI1T%AAR+DGdD^Q_nkMdLy`}PW(uQgsokH!;btrzmM=s{L
zuPtbwFaqz-gAhEf=OsrEWvgrJdyrFO#!zi-bY<1~rPp9EP3}m3Gq{mM{RWxl@yT)&
z(((M1sg$3t&GNiQc3~eybqxeon`}Zd;&=G-FN683)7B)aFF8&O#Ix6l&X0g9`i|q-
z->F|Arwh<}box5EfU)n;wqgBg(Qhp}v6ca0_4moei)KINAclhA)RC32MUh;OBdziw
zDsH}D4WYDm$t7moW7F8vwXJC-mN|Va!-^OaZnQ)^n8ZQC3kjs7hZ%_v(Ngw$K>Kv*
zt5E;?3Js3YO#mGL4F@=kT-z<`R0pku;|%n>Z<3ot9j#|ZY<f3(e)R(-@sA#e_K0)r
z+JE0aSZ%bscsP{3gW)q)1Gup`M=JG_IaM+2Wz}C|Mzem0J^unEnEe&tv&{wph0Q(O
zYdpe`Z>CJ~6U@ed{t&!W{NyT3>>>-WPb>zXNXU1OKW=UgruX9(g#~*}%fq$)pm5;I
z7)(Tr74E%>r0*F^jDQ|VdS=h?PUn*pY~-cBr;iX|KbpiopIrH~sh3zXvpSVH*#m`@
z!Isqk<CWm&{O7|J1K@VJz7ncQe)4O?mZ!`7*arpJ01M)FBXV<MyB=mj#`Dg9+&%v6
zHR18B&HlC%U@t(%z!ZP)f8E_R^NI~M^eq$UY*o;N8&x;UwuZ~)jsOBRz6JOJZ+hJk
zRd9N4n0-|OSE#|?frofg#k+g6)XOR}smOYLZMaXN(@pUT>AkZ&bB|QGMtxN<xop1~
zrAs#PW2>TyI~2DG2(HhpiM}Qt1NMB~ejOg!m=#}NfdpC+{Q0x~3ME$4E+V+SQ;EU{
zCZGyx%9lm`Hq{;G6e_mIKo12dQ{p~X|4rvD@dvyG4us4#wwV|FwmBDDqX_#F?;q^|
za#d{uKZB`4GHhim!jGV+)U0F@C46nu*V8->;MizMmRmqN6Me2yfFD`~sE{YY1w?ST
zf>?FN>HZ+SQcVS)CD?e@*><ZE)2ljXnf34WPF(7E^l*R)gqzy5N5yYtK1*8=(`02x
zxC~19fFTw5G*s#EFNQs}R(Hl@_d$t3jU)lE6W~>_C^-N?tN`xiJl?Xs>rvm=t+JI)
zE_aXY87Tjs4a9=o{Ns&bm42Jg?8<m{K8W28r-wVSPN(~`esRMOS^mLjrHbGg(*T)-
zdQooZ3iWScg4rI*f9>ISYnD4@&9GZwgE!_MUHoZc@Fst~a@v*xR-y%9e)R@zw+hJW
zw}!uNm9I!aYISWV81lc7)2t(_d#dI!j+#797Ub%4Y^454wija=Q15syo#qW73j;G1
zK&e%pX4+u;tbm3(BI;||A)quzpmzg+2eauYP}loDVNBdz=Z#4L3WD-N#Z6#9y{4Hr
z%rT>GR$|#d(yWybpT&)B#98&)!Ovd<@)dlHmialcR{DTTV6)(JWqrY4{B@=l$nK-{
z%T^;i(E47M*XWWHr>Txh+;FkYA`aHGh}S^Qd|MDnK3*CKgFu?g;z?x2y|XH0rbKO(
z3XCH`#8-&>Rmic%&rBx?c*VrD$<QN}OL=P~Y)2o?hZNVzPyyLnXQ}iSiagTp9J;pO
zP8=6)U{RK~S>!5vWcXV@;ESe;DSH)af7=Qur$w*@4gmYB1Hu+%usnZo%6wwv6eFGN
z3DkKDfH~F>>0WOz)a8KfI2#6lWxkrgu$wP6i~(HX%^E^}z*P?)32;3aG&}0{&q;v)
z`);BWv?KgwE6bxH`r}1`rE9~e7ihqt(!BpOP1ngOLCwwMfId$eNFj9~5S*Jnf{iis
zA#1rsuggET%umYI_;FWHYsVz36I&_kB_S-&s{kkQ6nHJxhX<C06a>NQJ1s!(uFGE7
z1a($|cMKb3U~NO1rVW<@cO}r4du=vwu=)%tpL@5&Kr?`7o<Nq}+qgsB!S`{O154C_
zXMHpk$jsX>_A;cb_`jtwO`nKEo0HPGx?|!y{jZ$~<rBaEVj6<H#**dL<8eBp#tIKB
zE_N>%J-sZ2hl6o&d(faOi9Wy}Ru6^-oNN6jCcrD9z;7+=sstb4wIO40fJd^zU^@$7
zSJZcZp{cx%iyisHN7fx1C&Wj`=8DQamwFAmKMM*j`j&GB01&W|w$N)cKA#y3F$X@C
z3H#Yp`2{j}XG}piI}$Df^(n1|HK9BR1$<;3j|wZl1IYO?Kl3LA4j_Yv7fN`{r<Z(f
zdPzXFGxK(GA5eCjuvC`yieH|tVjy&J1wQ3_k)qT{&pM0HC2OHf*ivr_NQX)aE3D?H
zP8_ZDp_XF17lc(izQZT`1?P7t@V{2jqp931|N3tjhtRU)YD8l7&dhVbmL1CY{5GZH
zX06x8c#NAejmQ$3hf$FRI4YO`i+r#V<WWpg+q%~VR2s*2OBoc*g9()PEuIC|hzN39
zrm#Q`et<Xz0sX|pLmwajd6A{kmmVtL;8(s;$&4Yl212vf)*$dn&h6d_KH8dx*hQDp
ztcAa(G0mKa<MxgvF7W0<Gu{IL&DEAWMoH9F{IvX4^YcH}dBhW^2;k>Z-@WNs4l0#$
zuNny#kCW<Aim>~up@4#2Y##1#a#*;DrsDK>>_i*x_!uYY*v}+`TI=OkG2^Hisn6H9
z?7keugMc_bJc&*CY?y?EEFFN$fy2Z2oC|9T*F0OEKMtZA65`byE_ai13U(~f8QZaf
zFjkfkhWgFOxy6Khg&c7gY|9LqOEwLl7yZtNPOPdlH%RW#DobaPL4jWRQ+czgSf>5o
z<*^aEJx|u*opA<`8QAIUHRp>Gc@SJL2fsD0jE|4aood<*$cy@qYxt6RD#?e|{lTX`
z9`5QSI1TT%w8SK=Yirc~%>J5kGRa{1RbOn3@)@e>^}ly;!lO-$$8WeTRhs~p3?kzM
z4hcXrT#Hbs|00Q=$h`s#F$stXdHC=QI(&hg0Fl{tLI(bd!HFBF<YcjpnmlnH97tzo
zz$QN6+lR0yhBb_uNwK%4#x`c+ZfN5$qNK|Hi0U+zH@Ity4XUSgt7N!OqXT}1nk*tW
zdq*u`GcK^Y2?<{RlDd9ITgz!1o6aJyc;f=_gS$cNpl-{HDRnNO4+Z?QkoDEmGn>J>
zg_x(tz86x$Uag}Vpflj&VqB}%4#9gH(=gNkZTQ-y*1x+JEt?Y*U<-$i*pOWj*m2tm
zIUp#$aM%=z`pk<~59r*g-?>X#Z)WfiLaA|0A~le7)5MRLpiHy_b`9`gpcg^-p2O2A
zb_j?Fb_+v2$3#UYlZJ7ZyGFI8t{J1BD~D-Y#9e0w9YH!HKOQgwW{HfCR*jQy$s5bx
zbl!`*93ZGI?{fbo05ByAvd9||no2DEZ>tTEElij09GU+x?F2ddD<Gc7k)58mqsxH>
z3-ALS0>Z$`eU8i#W^{y%!AKke{=Y3z*||e><8CG+=*%-=bIRx58x$+iYzU^BW+pUO
zm>dsaS<7W|FCT(|BIsmdbgT`k=7CwXEW0=<;b1&2@k?jyp-S`bWt2}>?Ki>Rh3Rf0
z8X~oPOX~Ix@YcC4N2)x9hPOt*1*hqs1Z89w=uoijXCqBlFG*LqZYEh1c>pWTu=-xv
z8nCv}*De6px=Z;pTgvA^FtF$t+XUfF@^33i&GjPqT5BB3UA-AJF9t)~ad$e7`Dync
z!XG0Rd8N&pg1V~6wWkO{-HDNj!3n+waJfq+^lSRzU5fGIFNcQ#!g*?U3#6sqojT|)
z^Z5z1pI#T$`!Y4Nd^%eBay_WCxykOzx3O`2g1|LD;X~vbBOzj|kmn;P3Pwv)rCafv
zTi&Aqr+4df(Ul<qzq8#KbjxyaUEieqJ2ZYyJ}MJuo`m>Z404<dV4&r{ABZ+jS-aq8
zIYv0!Zf8oe$Wmmh+mVOGlZ!(~R{yTR%of-2Zw4F6f;-I%y0PC4CP^7EMbCCkT+Uo1
z)cw1%o)ZLKmq*#UX1`U<yNc{J{aDBR9Ix=ggWezV5>xp#rJ(MA!}z@DxW(%;e{6HB
z3XQ%j(q|@q?G}m5aOmODcXsH#m&?_l^48><Dmll5A-+cuS1$w{h?<4@X&Fsyav7Sx
z-15E&C9z(m>_$UFuVSy^=)mRIr+8;$L!~k;p=ipicX>+i>cWGV%r64hlg8%J!-B>f
zB+GoeesAFI=+3yVqQx|?ZzS`$bb?eB+7t_+uiY94Gz$8Wxxxv44<?PEdpEyZ$sHCH
zl0VEIaU>biGjmKnR+bk`3tN!BPjCj>XvulGL1X7%AFd4#3AQD1F}g<uA}Z%}i0^Ey
zKWV69ZtmgXX9bDTHm=;oz*;j4x}=tFl$RV-$Wh>aOO<uqwW=OtnzSEZ!i_w5OM&Uh
zy)pcRb-8Ddq4<ViR32}%_xSl#5}l>nw3*iP8{l7GaMUWwG{)H}Z5(*<{LQ9;W3Q<%
z--N*y(sz5<+a#AS5?!1yJmbdPtI%p=`yvygyAn4hg7}0=SsooPexslcb|4!S9}dMT
zZ2l7H{pN|SYomxHzp|BUKJJB3LSO{{f})!4%<l~<NdD2!U#y|?5rE{e`Ao9!NNsho
znb$(f<V>Hq@4wapo|nCbYk;KWqyn+WzheCQW2rpq=e}wN#l~g1vbsTLhVP-oX|^dX
zf2p9;?FR>@GKCDvw{bbHgjXtfYrT`WRMIVZB^xCC{I*X+BJW?Th>sse%vh@k1%$dV
z%=U7qix0bNP#C{8!#i=)%&Fv{r5(mXnYg-tWu<p<X#HYc^z^$%pF!MIJ0(-|@0<4Q
zwsSaHrR;qXh7zni9!__U<2#FGYz%C5s6~F`VC$8a=>^h+rq0KTKFZ9qb-QvMO(S;2
zc;`LZrwD$|Xe9ek_}n?07kro?hvpQ!M<uE4likcYtq%2)*;u5?NG08j2A2Tko%6Xn
zLyuH2^ftQoi%dlDBrvk{-rS9l-qw&;$2u%;E~c^@OE$U<S=SOIY{)c4W?E|CGfK+i
z%Jd&zLsQzdWodmH%VNXU`b1dU<LRT!cEn?!?_EVpS@GX0Pp}v*iLgh`pf~r4;3ekk
zTCO`$WdMhVjt?UzByGV$@d5b5I~`!g^hLB1pl9u=wybO3f4xeEb~j7KC$8v*YcA_$
z{9V+*#|L39HkS<%yx^*$zdjp5cW;h)@nXo7wImfLOPJQ?zw{wNe>>N5Z~94JMCr2t
zF|ql#8W6?8D1*BJ<RKJk6WTmH&xrOe%Nu8!bQ-_pq)L?DRLFm`_tSL#(Zw4J-@#po
zH-6fn?+o7m{*rXO`cpXfLQ#$_rzz(KHmOWitPlo=m0J?)#Z!kHU~`L!%MONT9JVgb
ziAmp^rd|wUIN5ed;^L3cdVh@HFU6FrqR9k>p>eoZ&&4U$J>kN;Ji3ZTq3r3wm4`|1
z;?(NX%~qbEOcDM?-#be<z9|Kv`-JBwF0q8{maDRILBk<<L5L)OsGfZ7b^H5gpKcYC
zL^!RlO<WNc9=}%N#TyhOLG12P$9fB_uK-7b^5RC8_A5Ig5HuUZQXyg)DjAlaMbK}N
zeqm(`J=0;QBNLaa;K_+DsSPgLFE~G~u6IC$N=$h-V`ki%h_27&_x<nw#TWd>Bp#8O
zr{08^AgyB0N}n`W0sp?x7=f>7@XL$y^7ze24SeG3w%W>EE)rt<v-b&ZNn9T@tf*%u
zu<x|1%_c@59z#hPDYd<AsR%1s>?cBF_^~V|2#4`wrcwL|O&<diOtiy#(GTT$x77UG
z`<u@j(}_A$2?YAvsOYY(7nx{3c5eC2t*Nh>)9}UGZp?#?f~o2EO~>Dy(<7;=*~CoF
zt;Xla?leKzJEa^g9qsmxP8-__ZB%3Z=<YM5A8c{x7up~?^GtQ_IF7SZ5Bh_)Wy8j&
z(95;;A%ll!R&L-W>q}U8uN4ZQb*(IZbdfx5oZ7x<+N~AL+g|9hadqA}mSCu7oy5l#
zt&OcFF%yXs3DghCSO_|QkqO2;;;}BhmhP6syT1Ohy=6YH=ohQ4E88Y2n=f~aEL1-I
zdifidp?JtlyiW)3`Y#i9EGoV`VA_~(+Jkb9yZ<IA@PFNS*OOEO=#xvNi_9#sSag`L
zplOh<&i8M!xYm?Y=sXVQoSdRsBa%L|r=`+gmuU^=^XqWqxJK25z0(FZ&E3U)Wm<~|
zVD-<ax;PQ&rTFqF7uzRhIB50XrJ%N`Q0La36`jY^#6dn1B7rUg;V*V}6>J4~ei5GV
z;eyq#t-MkP)leL?EzbEevsI{RAxu0~)E?1$xL=h&JZwNVdm5x>5Git7yaxx4+zDHz
zybT;UHPhO<kUIs~hm9rI1Y)iGQ@r!Bwm$7P|Ll?bMnWCDr=HKFsjs351@T;jG`0SE
z{5bZ0KtL%QxC|m2hh`(Bt*RtCIRv?P8N^m4JZpr7M!IepWSimA5(MeIOAh;d1`fxQ
zp@@6AIeXV=mY?f%JO=N^?C0{=tX%7Lu<T6LztM%pD1EQpI%c#SP3Ud<q-Rr3LV?f4
zllPQg=03s02<i2idKyV6l)42BAYro{c?_<KdPcv%{rayMA|{rq2cr5&e3ef(NlBPf
z?QQVxpS(_Fs1o@7yCqUZQ8~2tklwJn-Ek^f_fb}MbExFt_Fxew)?P_-xF@2&cTzgV
z@FepRx8!Hw%@VYR_Xd)WDM{cn4xb3I)Y_{MRd@g7AVJ39$E`}MwnfUXD#LS9Un;0X
z+}>&!iGQQi<EHOM(EDSIC6!01`a)4>u_BLVGg8dQ$l`p{!hN3n94#9^O#(yfb^MNF
zjo+yJUdX90=ZXwKZ|s&9y!SiAIbGvYhXy43K%#$gp2>OOSK3b}yASoK=dpR1AiBp0
z>d>a!_(5!rsjg0$JWE*n7LinsKsH_2%W8Bz-;%*2KdQyMKD&Xf2;$~FvCZwiHs!RM
zDxAyl@DHUK1)O8@%A04=+@nTBRh6VJ-R)R#%uy}PCRvDC>)iJGApBcyip*z7GUfRm
zL%b_|2N9SphW3Wzjh?cyF9fE4fCX=um>?g_kZ%JwpY($sZrL*F|9K16LoGR@Y&-(w
zw<;IQ>&uzwNve(P*(!55rYSZswK=X~J7-SRRR}y*V&hf8dw0I`c8n$ggO$R#YYiDv
znBViH-?lO|n8{+z)?ZW3l#`6Fx}{|RPlf^A@ChUezi+g687FT!NjVGDZvzOp<X`oT
zW<FH1ia*DxKRU1uM~IZ>-3whx_yLMWWAt<)ozBr;UAo~c@bS+2?p!J48s4n0RcU9<
zk#KIub{&st=z69;xFJx;=SNUaDc?qN8m^$5p`vMPo*Uq|aK@HZ5<Wq~C7nj_61fU}
zG`w)usH4M)W!1GTt^;|y_wYer-MLV_ng%ub_1;%{pV*2@2(yuQTy2#deWRFe%nmz|
z7$!2cC<s@-5<G}=Zu@PtGotZ3lB;&_nX=9K%W|EL){hFjZf~`?i~~009n~H!N$5zt
zEURd#(9QjN)cZ+q+x~}|!|S1Gb56552#ihk07L7>D(vXM4RJx>-i|93mEGuQckbMo
z1w-#!L7RE-yLV^;$G^kDFdX#tjSa5x{{@7lR~A9?aFnO@vNiIVNypS2qYC$p@^@Rv
z@}hpyH=O^%vodX1qlOFIdF-%-bj!LCabzI#SZF-%!z09Zh^-_vR*ODn+T7NPh1fb+
zT#nH2NZSP9d8*mKQUBux1^H^PCGd)3IJ;(WY}6zwlvJE%M&pLY@iAL(G)$x}I^SIV
zZRO3(ifWDtCp)sTC9oOd5=?SAU8JOZ!PszWe73EDt1gumGwHqf{Eq67K}*kdF`X_S
zB{VF@v~LKg_gVptwI?(vW*Rwr4kAd<l=mxXp4VNNI{|4$@iF|>vB6nFb>ByBgje$#
zH!gj{I6+i|`}KFXkSbSY*|N2!2<j-R{>eYsdU^$Sw$CnEW&xr|OmNxr|KK2PJL}6_
zG)5A|kUjlOJ=;F)xyADb)aR?5>CP^#Q*0M2P+wE1_eg$mI(0(e*p+!=8P6-q0$*>g
z-)(4pFKnq)A28E9cMr^gnFH4yHh@WFU`m;HnoLwOm=@_WeADM07(G-2Y@=`N>>9!A
z*#@n>Dd`fZ1ET*qK4LBe*RKGW{N!wpPQzUq#k~_zdfTIzAD^xsdiqhYsIna6wfPs3
zM+O^NSXh^5zdQrFw&2*t#lZ1oy@c-D@gCWFXs55p%c?n{-;rtyl{3A@JzHVms5G(=
zj~&=UHNve0!#9e-c%~s)`iR-~y3Io;DiRsdvjY`*58gbPApb7g{;(H2U1;*_?hbeT
zPqL=dQfsQH_vj#_?O&l8pIzHRI3`}=UAOw!)wJCkYB1=RJ89Uz#`mC3`1?mieQF{u
zJ3R`Op&?_i)nA@$zR&zzSsj6Y;gL>qu_yH=2s|%mLmRrg9?b`Je>YL}1A{9&g)DMU
zx0mh;3?Rfe2*+O^?lthIcu>^VfFt(Wi$8Ra>~Pay`z~}x`bvGgRNHoM$cNj}u`9^g
z=0NX?!Z+_%5>`+4G)WJR@xe0<d>nn5*LYDO@agF6rWbD#p9xF2^pl(@!f9N!yM@=F
zx*_}fK9Ls^pwXHfTrAiK2EZvQDP2YTEOj66`rJWyMON0Mbh)^X(HyVgvA*Q%kwcHT
zxrxCA!BBa9NMPV|1A|0#ev26}8PaA8^qYWNYrMgnM|6G|2RP00CVcpixv_8@4}N&g
z@?PqVO54b7Ptvg3_~?tCyw#rbwe#LC<*{z<`5Dyvs2L<ekPj6*H>x$r=L4rVzfB-+
zEJ;-1dhp*)dp9F*IxFlRuArz~P&_TB4`FPD9JGMs__?%rH4PlIRtK+BCGhTrm~awx
zT8>r0>x{la?dqu^nWtIv;O13liDjdmGL1W5bEw&~=^|#wz2mh6@dZ)}3HEp-7sRB)
zIwo#4hG;xxLE}zmra2-p*4Lp1kdV&8E-^&KXBp$|++xzSRyk7MUXK@)c@UUh>ZFLu
zkCXu6?)?=ZZi~%zJvg)2;-+!BRc6)UT)38z@bPL3Y_k~^L>TG0awZo`gUaq!aa;0^
zcOef?#d+=NdGrCQxT*NX^KF&A{1<tudgI-3S<WpVv^l5R$Ki_U4O`f@#|(}+pJ>ib
z;8O4^qM8SuAM$$%TADAYzYvj<x|uo<yD{C-8Wj;Sx10dA&NlkaEiU>2|1lo86Ewyu
z7HsZ+KR-T$G`zg9N_ue|7H~WIS_4R`?>#)Rg35j-re>E#^Qmk|<XxrNkZw4#4GFp(
zJsC?ji<%Q!F>`_DLb~QDH0n60Yt=T}rl9T4WO~gD?Wt1s_nr_zP<aa|+e2}Fv|aFE
z%moT<Vnq|;<7XKp7x+rLS8!@NSO$EqG(Ib3Ok@s|dQujC4X1|dX6T`_Uv^SIO8UwS
zja&<;W@q0Rd3AC=QUZ8%3n#;1C`wY?uj`6|<q#Lv>H3qAxZ2yM99y(|Pj)-9p`Ej>
zFo0y=D&E`6&nF(+SwpK4>`+Gn>US0tyY<WajcwtJPk5|6=2BlWHe&kL7A~W_$!~$L
ztWC#14?=vfo}Qj^C<CZB-cOxg#@1w)QNzHyshmN~uZyZ1yYM7%^XOv7#sxgWj_{zi
zTspDgepK7`@P*0H5EHaneOM-ad>LdH06D{qiH5wt_`)^39V6lEd@<#qN;@2w2wcK$
z@9b;_BM5|Zb*qViRUYjk+TIIUe<cx&KlsLFNes7h<&T(M<E$n3-E39u1L1GuTJJIV
zVNKI}_@7_#w3tL0*?<jH(jvQE!rd=w=vM)uRr-aoOjGvb54u;0NT%2E>Pv>-egZjg
zvIp5_=0-@p)AXF$8Tl3Rgsac*?#-uhYM*B0UV%b8zk_+4VqmbD05Ai_CW%yzu@wob
zrKlqdh+U0FK0xHBH)R+0_T10ApKcabU#5Tfu)-a`H<@7hfal3>P|D%s750&EQ1(G}
z-^QhhZN>E}L*CByOLZ}ks0xEJ2(#_Dd1UCBQxUmtjT;rH(ZfD7<!|}#zu9_6lw^N2
zX8tOQa`x2<Dzrp>r#=6A1mUiC>|p5(#X`WUpzb~E8=<Ud4|6jccd2$hRDAOsJ>;*}
zFfjc_d4UrH<=?q}WslaVaC3cF!ZH_<-7mVRoK9wVPb2-S4S?!jEQ;qR&l#`8uOq#A
zB%R*})`fJOw0fln@BCo<gDc#%0<&#%2<&Y)Upnnr1ZJM#Uvbu8zlRq>>|b8rw&!`8
zx{76;b30~c6-?dMD0zhqCPN4K`1n+_R1-br;p7xEG`xp}#d#WVLy%JT68h4Ia4iN=
z&Vt6Z2T*esr>=GI51NTn-g=M~K|RyN$}k}AmlO9GtH@F5F8a|JNK;Hl_7Fiwjw*%?
zTEl419hy(L&xCD}0qZ9=Hx{`x%II*kS#$|ix3&!YLqSF8Tx7PryZ(jZXknk|%!!4t
zCE`#x7%|<2#^DB4&KK$B7jpO4Ba52@d72+<j<)z|zCt;*Y14U%`)PuRYrb**eMXP!
z<*(*y3f7duWMZXTPZ;~CG23oT5lLHNTOcDeoE~HA(@06k)+(9S!t?<}UUZ>@xUAqs
z2?Kt8T*otq)J;cI0s-M)n5@`4HaqRJft7%y@**P<gZ%Z{u8#w1=Gj<uY0u;7<4;Cb
zpSqfY`EK~1j2r1NNaksMN>aJ)YALD`K4*>La4i?g#|0%Hd0snf0pNw~#_V_{hwQFV
zbr-U~43X<0KAdC^VIVPTTrxAcqZ*i<Fwjl5Cvs+Jp6fExX3=dk#Q29k6N>bA!)JZD
z(G6i0x{BPDz!;x#AnQHc2v6tuk;iT3CFAKS=)5@<2=0Ex=67CyrmIW!E;h#l)R17P
zsG-Tw5h0Vgu<Ygk_vP*JUK)r{6WfFk2{vx6UV!}JDoHwn@5Yjy+4%bO3)p<B{m+vX
zy`7<NGz4hsIRU4il(O!?#Hre*^t07qz6g{*AUoNbFyv8tcwCybRdU`NQ4ZUa?O6Tc
z(_5(w;YL;r7y#bm06x~ELJ(&%`KR9;4Sko0m>9NY@ABC}K8Qo=K{*EIsTh-L<Y;w^
z(uxmY>Z9nlh$2c?m${JkC64fEB=k0S3sB1#+D4z1wepJ9<mttQZJ8*gA1i2LqHU^y
zti))0ARdIBm9IP->)U<=&Qo8`!sXU8j_>?^nYy(if$Af=Kl8s$Nm%MI2Nzyc3KjCy
zpC0OhFS9wN>hXi=`H+#Os4m$w`8dA(ffJM&7Y(rd(Wb)fI^i!L`TU@@Hf0Pt-7Xr6
zckQ}yHn?|eIz#A*3>ue6b(qztTBD@iF}PLFZNT(UpLL=C^69~gL9z+8dQKflNenP>
z^4YUzXaJM%fcatNK@9)$2FV9<c-O^DBWNGgU#)Xa2aMsuX)|b|IV-+Qm?dGh@k#iH
zN`%1g)GxC$5qjp{j4+m9RewBA98j{_!f0zNZ$*vs;Le}yZ~oy>x6!FcX}6P&RRH}p
zhYAfY8v5yk!RpUMdIpM%SWw$5SMeI#t25|yJ#MsR;3(walT>+n9nFGB8h~wypDk7^
z?WvinnejpsI)=pHxSs@s#mfcV$b*KvyE{lN6V?Lr6h{jk-6?M~#Y}rHHVqSeIz4$B
zPxjv;K#;I;uOqw2)zT__ZiXM_SpM^4;a%6snEC&5arMdVNU-A#Th};lBIn;98=o7Z
z3gV1oIs?CobcN#ac*pSf{Scsvz5_GxYcst(RZ?>nc0|ytT%q9l1f+v|KvHUreHUhE
zD<Gf5sa|I$FbjPry}68oE8H;pOx>SH^2A8wwWMN!VZQqC94LmzNTUN6lk9;$AmE5Q
zky)6GE>k`;)1v~=#>&*M9u={eq3m9?w6wG%P(~Gk+poR@aENwv@9O`Nz>55M;gIgf
zxA^@P-%-8S)%5w51;%@+!uj~KHkwUoX$SPA)7}a9=(R9Z6Da7VxcMZ6#beoq?ziOD
zZGL_cd+~{_ub&d?T@8-wuhb~w&|CbvujNNDLnYS&_>>x+3EU*z?{`)0ux2kau29Rh
z0KQP{MqM5V_+}nxtxoo?v%N1KKm@m{s+^yxkK%k29-%?@n9PTAsDIYiqdVdL`MxWb
z13pk<)=_}g2gD8OR6jAzhFn8ciF#}<sFjcAq!`Qe>-_&qM7Sw3-Z*=Ez$ip~e3%B}
z;F90b0${1iFAtELHi1+HI1Smx=0|JK*e#H;7uAo>jJR<i`Xyt%55m-|OH)>w74sch
zk0<d1wlT+(*UqWBwiUloHe%J5Jr$&S1XECO&7Cpukv#VWbLP*kgkGcWb?T$@A)+%^
zl_^d;ZDr5L@pvEDo*#6_m+Y5uwdgyz85^4rtF3>Oe4#p6%^oMm)cTDz6fnSgu7-sq
z`%V+B?$BGGHx93}iTGPKUYdfcnxYwinhWZ_MI)U7r92k1e1(UFpsNvv9h2p@lCs_1
zJWsDbe<w!ky?yCE0%p9=ft%|Byq_bd3H<+shZl;P%Au?W59Xo>SC0-idjjA7(If!X
zZ=i*%-nG7f4#HM9Cm=Z>W=6JhN{43F1+e86QO)NY05d#$I^FhGgf`3o#5k<I@)jVY
zY$N9GsEl$^-RWR0Xw;@i-acJr(%=+A)79bU)N*Gmy}3u19Hyr6C_7=bR%d7Wq3|~f
zu9~zH-$E{~2Fqs8?Ir`(^}y~Ha5Tl_#jQSpnwB^eSL{O#blm(Hyx}3|Xo^)MfDpo~
z`3&{MIKS-1#NUkn56xlB&uh?Mfi@N+sPpRa>g9~_hdamRePePs@B@^kA5yE!OMZ;<
zcQwwPx`N5wHtQ;F7+$Wbq#EGDqt}`as6E_>=E;Lb1|dwJcOQbrHL3)0Mpdsv-myue
zJdmX0Pvvugifh5>WGfwoZfY)FXU8v73hd?`I{LO!d^0Z(DNL|GOT8llUu)A$_!}AL
z3^D);mwxbVqWMPinRSuJ>zWQf?9$s0>ZTGt<wfNizRjfctjQf3(Uy^FFKDRwV5Z1#
zc%>mRgj<7q^};!wcHq4lA!oho4lT@gKwz?7QsV4V8QvvM6a_gsEhpz4FgUHL(6ISP
z;>N#6mBRkXJzPwlw(Re;+Tfefgrph`7jb^N{R8lPf_=@k*Je2AX)S!=*naYi-HR`V
zp3%VYb$aLn!mDhI+l`{Vuy)7Sg#p6k!UEz}a+yf|S52S3El7?wH-`|=SQyp(KJueX
zmPJOd1-Qjt1@hH}{=<4B5WC(dB%)x6cN#-TY1Th{TUW{zjPPcT%1@GLx!%K%xLpFP
zf=m8um?e1l_==Mw2n4~TKH0nBwm}$WM?j(L72kbR0&lpwe4=@^??_ad(-rrhECkgF
z)7psqNU6LM34FCrApVHb&5_ol^VRq<`&DwFiHItiT8E=v4+P=w4ztxfIa>Pp`WoUi
z_UOB!M&NO5iXCm*T-MlSYhR8t+sJ5RL5aIj>)POlb{5M*i7v~#Af)Bq3{Q#GUI@~Q
zjg1Ht5=xjLQ?BAOzE~nn&Cz%n3#n-YwyDLn{?6}#0}xPOnTO2xGPBfYt3|Vb>XMy_
zh|iVX-)=m5_6hnBm-RvGiyB6^gT4JWXywKQce(O|G0#_Koioqme9^I+!0f~)rX6>_
zeG@pk%>ozC|9l37j!lO(ikWfvlN~mZZlDYXArnxjLC+vb4uSDess&%Fty5o35Od|)
z!ZFzZ{LjCmngzpuIQ`}+)^&3?_Vzd7!O0`K7E$P`ZIv<?%I~NDt9A36P7UWz3YZGd
z|3(S3sYXQi=w@FXoen2Wf8*%GbmL>@(PjVDPMPrX7R>n^0ja@D*hzdbnN{{Z1P~^u
z?FR<K=35%4Nf}&z`Y^LSjZx_{@q8;Q2mn+gI6xpZ(>3(J9I}(kATrdesGxe4N&rX}
zYFA4WNm67`=<JL81TWX--#@3a-tN!8e&#7u(tZL`!EH77VKm^Hcp>&&MW0Hlwd~;z
zVFL1w^qC_kz^e*WT^MK(ld+)36CpH0A1X4pVRk@$YMiRDzC2z1+L3i}nO;EGlGE$~
zGv1C|oN;vp<y_9S+-Ng2#3W7Aj`uJz2zH+U@i)}b!G<Wbjl*308wq~!&{aErWx<ib
z8(X0Vwj<z`4S_{l8Xg|LiHq9?CJn-uO+1X?Nzk#8Wvz$6pivAk8~i6=e&?2!(zBcc
z|1XS6$$pi-0>#&U1aUL5rs^WehUq|yY<k?4jLGGJft$R#!1N^c!4htHFd!t&B@D%}
zBey<C{%lDHeI+fu(|6!ZJf9uk=iuTZW96A8%q{V1E?n@|!(1~yPCdZcWw;aUK<9HR
zDnlRm>rsF}*j3;a$PJE7S;QD<)WGe<-E9Keh232tTuXV~#+|QC)fP5>nt+%bPd0P~
z8_R-5C-2kt<T~}3GcN{>O^~RexNJBoie2W(#pHr(dOe07#pX4-TK^RL2dbqSmC8;l
zr^G-Z^teMWSs2uEKJ7QjM+zb579PdnjSRm~kwT69Tn3aVn=p{m$)k@b-!Aek`AO2T
z73iqYggU!4PT>h_ke24(8rTR-E24%S3NO!h?y!qJF23>ZiqMD#_oJIOYBBETZ-M|G
zv;DO40R)V1H2A?Bt1Yr4Q9xKg1V<IIv12lc_|95ePKd-!Ck#M9j+!D0t?vnRpWeQ>
zjLUM%w0q4+>(n2Ucns#d!??w2ssf*gh(~>9>UPvjj1jn1G6Y=TXariYR|Yde=_eoj
z_mt7na!GeFw}FT&rdK(gTdu9683bsY*#NpMQaYCgSP&T=PF8&>1(t5>`tt_=JxvX$
zh>7}vsuLp;+c6C;5(E%4q8o%~sY<uf58gDARtnY$e)G|nOHGmM-{(Fs5?P}9<XB{2
zh1OKXXWm^YO&?)kk|-#FK0)`Oo^p<)IP9zX6@7O?fe8drzM4dgO&Hir6Hfxgd;lj!
zI@FI2RM`fa*ZJI<1q!#1cFf4jApM?3z#Eayzji-me~xz{{DV5jzUc0oy{%3jf0v)i
z-A9L}tny6j9@c1HPrv&EQ-dP!&Rc*gaOi&<dwsanh3FzxN9P6+fk!jZOn5OD9Cy%W
z&5b{SDt-SzMetr6XYWWC8YD1Dp$t78{=F%p*6Ie6V}$&cGmdVy+7RIxSH@vC+HsGo
zj`~i_CQuz)J)E%My(c4DQ~D~P>C|(&4@8H>KL!PceWxD_LU;()VDxTq6LJxFJ!f0c
zW%F<jkNgS9V4WuHpTITpa0Uan&?goY6tqYFb)bU(%}@SrX>0Nl8&KE&&ViKv{iVo-
zt-a`EhJ;b38&*M8{?>}4eSkVEIjy7<+ibw~_>d`^m8EX55&hWeL*(<Ti9w|V=c?D$
zf2djjw}vF_))*YzvLOS~Y9~E<sq4!4&@&PT(KEKZ45moy&}Q%VXBc&P@a6{LA~N`8
z&Jg-^S-Pvs4=5ph5>ze8&cG`}(kZ%l6S?*Y7RP^CyZN{PHKj|>0mpa|=g7bV4KB1R
z9-s#hVv14Oz6<4wl>d^mxFzFBMF!59uU#XK1|GhzvbnhI2#Na0d>%mPAVPtEQVQ(J
z<UF=tOqOm<{Aep3_g(D@dGsjfTMnOTkwLJZ=h%u>V7K|v@2HtmTfx$5j-?QDkBM4t
zz}~v1{y(<9J09yb{GSFglR{*qq9`JJmWaxTtjJzPC_C$uWMt;Z$SPF!Dtl&^Ei-#W
z_Q=fqT{qP^-|zSL{GszYua5IPpXa`>`?}uiE+q2!)uUV0Xb)q#$%hiTlQ0IJLo{a{
z^FiGw0kT9k-5K@6tmlDiJav6Ins41+T^Cdw!e%mUy~hI`t{=EkPs6<Y;>J5W_u9Fm
zCr<bx4h-%`dy<-Jw!iZ|w!*Xr1pjXw_#s?3*`rN%oa=y*krB-_DvE8cKb+uE<plh2
z{Ce%rG3C{Tr^bY9v|?M)6X&FB^o9s0Ath02&HenQ1K0lhtT~b9UfIt@jg~B{K*j*w
zVBXvm1^NA;+li8>G-P4rg|Ep(Qi&H@UN(+Tv<T)*r6sC*Mb8r8bTIR=tjQ8|_#bld
zq*%;^(M)k}r0Bb@neS-KmGLRV)aph!xl)IwoJ^V~;Y@K7rqiC47q^L;EIlr}Y;!C9
z=)*X<m=7Ia)yNpK2A|ynX~%>(FI4E;0t?6U@_T&6E5)60u<@aOTqxA@IXuo?yw}~n
z60*)$4}=~LRwl%mx4T#oyMUW&DY*5eP5iu<SN;zhQ+?(GLV9KVJCxW!npR9DU_%R4
zB?byjw3OF&Svy!`5nGSl_6B8jKRNEw(GHHFs3<zPP{;qyN24q3p{99ke@hm|WO7LI
z7=n!G$*Dd{wr_lkK(wo-rcm#)v%WJO^y>b{i+dlQe|Oka?(N8n&tD91ZmB7_rIVf>
zDXch0-T4KL^}F@F_ZSnCU!m3=A7ENDV*<ZCyf4Xjak=KUeO4jSn8#XKxwK<^UsT=M
zYQX2;epmHMdficR5St5VN!#G=>ZIH%N?q6hU`r2E3=*<H%T)ITl~!A<*$~>m>iacc
z9Ys|^E#EESe#I!yu<g|dgza2aw-wKV@$98}BlV=Q&#2IF=7e}F#p|kP^|B<^yQj0Q
zovTvPud1-6hp;LSZ|jK}Xr_x7D?U+Wiw?p1I_qc(wHThvk;M-~%f(7XW#L!gQStnj
zy@f9W{nSu5Q^1e9J7<ujs>{?ccCw;?Uw2y#Q+qWIDy7(vxMNqWt{i3z`l4fI<_EVB
z4Fhg&A`&gZbOkQ!Ru8xWv&HY1EX4s_TBeT~(BIyWkeLAO((v*S9kyVNk&V~B_3OC)
z?g+@n+@Obfp>oSLJ3dXP;+Kq4M^ZHZf!^K_zF|FHCU&E^<m7|V(?48c$djcH@{(?a
zV`S_m8Y$?jg0oqtc0k+(^sm~*Mg|z8?^i9&Y1~MjoNt*ukRs%$tW=DYB+qKIZEqB&
z$#}=FgYZYsSPJpgQ!r+iE?ySt71EJRI3a2q$$lN)_tG1H+A@r6$)>#;%K&UUdp7;?
zo1RCxT&Pu8e{5>*dHg2yPrN+Tzz*uVNDzOOlyln7eH3*98djXy-j3?eZ;#~NIc{3u
zXB3lus$!}_s6SGrL;#QFwoiBpM{-&Xw&_KUNH6b;0-MwAml_HY6F4JV9v(EYTI6Et
z6%kAhbuO-y%<g?`juac*)cQt=m7JEAXmE^HcakwsnvvkCO0fcn34XuY=-*K)=>SdA
z_?F5iiD9oGm$lc9m-vs|*MC|?u@9ly0A9EQj?EZ{CEQqA=D2&I%srV3%_}vI`9ll+
z<`)FN_<BkE`j+27g>B@V`>IVbsG5rw?2bYgrTgtPG)g?QN{0{N_HYakA3Ua#KzeZE
z%Uk=$34GDC-w!#{&Ai4jH;fGcfCQv79_=+>A2JtH1AR*{GyDvIE&<JdU{NO=m&Q&X
z@tyka1;o!<Q?$h9T>UXGzt{=sGa$xe(K<dE$0Jm~wEUqutrHIhf^%!z(yWt5hi83*
z+)swc5|$qp)8Mg_ZWD%eJzsOF`0yi6+B(|=t;^0sCWoq>olnS<&BOHTr1Q4ZNug`&
z(PJQ<iPM5rl2h6PZ=j}Diw=7)(nKrKAPY}+ht%Z~f!v?xDAC5--o_?j;YkFQaPP=|
zO2;r+tbcpIX8r6BSD*zR@t<QTjsE;8(_q21zUTvuQx3<W<bZg<GZ=v09di68@%B;n
z@(s``dZd$`j(e}QY-OS^_x7kR5Hbt3hb`(`Zr#I#rW}r;Hb16vpTcsn$c0QMCDJC9
zJXxnFamPimtdG0LBa=yMJ&6s#--pG~d~VMT0;-6VxoJJu0On*1!JieN%ou72ED3A=
zM!s0C|ByFsL2^U!(7ciuSrKRKDc|L6CK~C};jk;!RE>$3KUPT<9+~gVuV<dZrh@mm
z2>;_Tqae`mA%m$jg$5`nJ!9*%gmv~bO0~r5#7T*6@eyMR8(9v(<^Q~9ESXaY7MWrL
z)fvqTf7}<jJHJg0%_+~ofkz<Di*d1qTIBh1(=r8wow9-7@)Y6^+iZQMwE+<WH?QVr
zL?jfMX=r~6#JsvaKC@MUB*?9@=>7gndhH!-3*SmkKX_i?F&gx=xvn+oj=5^EgZmLv
zIitak#n)q_eJ=P~qaGPXX{$0aG@>WAwP)|oDgMgvS8ZXpx=)tEnXX8vo9dE7{+0Km
zLiGqNOcyB5Jjq3|EAG73rx}VbfW6W+yvOo6s)48Nq{3px#jh1oK|;EgNB|IU&C*3O
zX!#p3HjWJf&pyupZpJuWPdqzik8rsrt*RP?%kA-~_;Y?MRZA-KMOWHRaMN(h%gc9u
z-!0te;D(ic5QIH?qm3||l}wzI$hH4Qt#7Etvb2O{D4A+QE2PGI-QVuoFI}j4=$WNV
z@kwZ|?lpr)Ifz%PecX!kc-YCa!{8zA3iX5?kLisG<lmvui*!DC09WlbyQHp7E5p<H
z@2Z0>ujTYj?2PN_MgmBy8*;mT4CsBhx!|&9SZNUYJc$gpAc&H64P(=DqCtOy=FgVk
z>kGY%9M3+}%`eOz0HnzMz!CVw3;on${9&{v1(Ge$k<-r%A+^fdT+GoEa_669mJp+<
zL|;TR=2Q^;l~|+JioPR4T6ByKEa@;`!xZZUA8B}@e^`9*`W^!I8L-u5Zoy`HAC?2<
z{hQn6*tA6It7~p!Fja&L3;)Gi$P)wqmzXSC#yiy0VI==bbaUtZ{K1_37<PmXf7=p%
zhz2xR90n$VwNvfq4mde89`A`u)4j8+rOmyZE77KP=qZ}`)ru}q0zbq$WwS@d$Xn-P
z6<_qFaB${q6dCVy?k|mQ6pxCXc)1SLKS+oI;4zVRS0&zw_f+j@8}V_zzBVc$_>!so
z=f8Y)L&tvfuCvA1L$0maI;gg>nR_T}Se@<ZmSFIy#@B1Hm$egrtK}f5h){wNApyOz
z5b6`MtoJ^Y#J;EZ!E&ngTHSXi{LhI)hj!X+2sc-65xVgIlk{_$6Q>J!V-O@zAHTPA
zdDSg}r}bWQB>KkoUfU-L_6=LsK8#aw1?H!@^?osfz1F#;N}%!hL++kUD#?vM1IOLa
zef`+6Mj957EO0f`pV~hY_(H=2RsT)#9IW>K_51F@VI`BqNOHQFxt*ajvZT#q^7=!X
zD(L+(`NLjtB$X6u;HMA=Rn#;dH8k9jA?ggm<sh@HY15)d8~5Y!D%pOyM}`;r?=rai
z1>S){G~rHk92xU>-JvlxgV2QjE8;MyHC~o45^TfwaOf3yir#;?lqqQ#Mv<)wliHe@
zE!l(8NH?<Fn#8+LoCT7PQk01JvurpFHU!e?#_q@~tr(b`9FwYKEY46IeRSrCjtYYv
z5Y?+`J&Y#uYoGruO<(r~s;21=jZs&Q_7WdZj~E~7YjLrE8)5ZPE>Jla6j{ju??#gM
zBZa2m8xkDsPur+&E4DQaBw7kZKi#M&C|~Bc0+rg52qG;CUbwp*dK@OkixX7HJGc96
zKx~P2Y10bx(6uOW!{S$fb$je$-7k1ECG&Dq&uxdd($b##nBfH7jzt^K<3c#`FRmx+
z+duy7HYXUUt>t&7XE<ZT67x@32bpUMF+zBzO9CJp4!z6*pru>Wks_YH=O({;dJsVQ
zliZxXch4csW(~rb0O>~hTF?=||3-qYX`qZ4;T_X@Ey8wB5`y*qvreSl;4KUV^&MJU
zobF@rJ*v_+jF5nHTreGBqw_iKy>Bft*^&N9HWZcydy;6155-u(vS;w~l5io%hjhNn
z^VMGdNG^`xT>}$^pyf{Js#ZJ_c6RJcqbEw+GN3?LDQ92gYjb|noXm@)aMu)$37h%q
zrdE}bwS8bW4%PwY9TXgi?087$`<via!itqx%i8GKsV6O<ZyqLh;Vk^J-tBNkbRhIu
zL+A%M4gLG0Rbu_)lnB?R?gBgLE%n%@xYj{m<aj^bjAxBw;gt>vH4w(2S2v5*RZS3*
z)*v*ImOk}o@#n~|Cc>+Zr$Or#i>FULeq&*M-XB>Jtn>HpBX=8<!1kUu{0wLOBW~$p
z&UFw80^yxDT@?r(7KU{=sK-sT-{gSk5%p#k@m%4NOx8Ysdb5D+^$1huXkO;Ekyly-
zr7!#pg@zv%Crv;+!dH5ibq*exn4|MGuh>xG{2tu?sa%@sBdqLSWvO#Nut-%EKeF5^
zU1J&214CoB^;^ZM3w!c<w`fwa`Ue55CX)-_`Xx4Px%S^6Z%S;WLp1ndkF{>m<^@1T
z7k<S>app%{f+74=Ttd>9^{l5i0KVe}M#cB>$?b8Sl+Jr*NNeLEHL@8z73*;PxQ^!;
ziv2_IeQ2+`?$&>Ja<AFb=rU%4x7U^>1WUR?Q!H67a-Lj7sFLV)QdPFNF_WN#cK!b>
zsswjK&Gd5MO&?u6sjcK3CH|=s|DVhYlQbdngyPy3Gl%ZY0pAdNA@lQvWR*L~pcI1A
zZEu(mpn^B194LJ{theF#)y)T3rg$Z9Veb1K^Zb{7!Mah+trZ{K1<eYF(}H!)CAed`
zZkl81Z<9#1r{%$4@ZfyJ(Pt%E-HRJ7VH&!><ZW4EW5-?}YI=%u0*Tqbi>n=<LWj37
za06PAz60jSHnhv_CanwXN|}I)Hp?UPU83JURqE2;(S>cTUv{!PdvJi^x+JRn8y=s}
zpOF4svw0YMW@GbI;%C#+!la=4cn>a)&?V>duMR(Jxw1m$EVj4c61JLyuX7r|p#s+h
zQaEmmR22`bTL8v`01KE@`<K^4ytt2Y2l)9(g2s!PwC}9h^uK^icL?^ovsrY$o{(hb
zP%d}%D-j!~^YZa$Hjm0tHRftjy&5_UsyqFb4r(}<DU=5qQuiY{d`qJesGMFSC~#5w
zBC}nwz!qO>#)mKKZLk6W7^I(>eVTvLCF7!r6Rg2$&?9NnOP5dTPu$|>8Wu=>bEH)@
z>I$Z&%2H3(GCe1C8yJ_1pl}VHC8*<joA7~Bj83)!n5X8}z9X#;1&k82&8$@SUzq$W
z=9Fy57mCn_Y1!3(tfAWzfsX!5RnHpz{~QYc1BhWhSk_v7AYRc&LWFN0U;LU_OAm=f
z0QS>8(be3lk7Td2G%=18qSkGv=*wi`CG*MkruBYQ*EY*%H$CiVD?!pK-CSh#qtPDH
zkTUHT^z!oa@5j3Lq}|PbN+!C71Gg=*OiqUPPrlsya^UCq6-nb@J@ZP`_=qf-xog)~
zmsL8|gAEGAy}9ErqpC4xPQL24%cy;;Sc*=2aigb$`;DlUO4qM$94Czb7$~YFQ)4G)
z?EXxayV#wT;6G|o{}5*A?8=0^lE`4wyB+!!CR9xZSa?f#O`o6o!4kn2zA*UQ8jw~i
z;WWPF{fIY?8k}BCSw0l+m#y7P-Cl(YzQaPSOPdm_!2xrrmg>?*-qrWjH97w|KlD_E
zZzP3p6VDi!kYB?d69cpL@O0wkHcqD*nM}(M2&mwes@p4KZH>SK_>z8*!Wc}Qa`-@b
zQh)ijMVY-i_=<KpG4-_Tu`1H1u2{K?yFym$FkFR$eToYZ^@eM1@FDZsq=h~<Hg+B^
zg3^Oqs1wpvx2oy>v}6i$6C^1@R>!C=-5dlA2=LVLu?|;V1rtR>SrO5mh-W-3tr43#
zBa{HB@!MRi{I>Ky{&NTdon=Ea7sM%5p5c}CsB8t5Q&`r`Z!@<Cg;@ZM{|N(tCxhqG
zEem}bgEwpyw5J61oH9;<Ffe)3jx8yrY6{aX5ZUT%sLg|_;7nL+iZlW_*E9>~rqC}h
zmTl=e`pORwsri=q(_3-Z4~Ah3y1%MlDtC^6J!`%vk&VEXw<aq(4VW_m3THp3-m7HS
zr>!?MuHkj*eyO~SL3^8&o4LaheB)xaC&K#l0E&I4{n`9OiAv1TV_cn`fy3EDqV~2=
zSgZ}dj+un+EiM8aq{#!N8hJ<6aTtWiqcj~lM+>+PX;Dx6QQ<p2`oYgc&i%77E;^dt
z#MD$lTRT8(XOjZ0QHRx8KLM*T9k_SwA}^-WL^}o7l&Y1wh^VA%`GE+2KMouOEJApA
zW~*+be+DiGeju%~<GJQvi#$(AQO3dXKSQMlIw+8DAO`YcTG0N1m+u`xri<#`!&IeZ
zl~k_81Ahz}32ax_+#eureTwz{=V)1u>acYGA@?g<nEfNKTy=U+=sQv0EJk;X@A<cu
zUYwgO2X(_rH9xM#rWTpW&aX!HcRds(s<W$(Te{Y|V_x$Q5huvl>}|RlGTud#R)pl9
z1>@o$XWiCAH@!xqqo=-4=>q{qkxsmPf8obOxp#!_uM45GV&~IufPW<RLo9%b*a?K^
zsmrS#5Q?x`xXp%oSq`$(y33i?XC;K=yCaV4z_$cEtA*?AtRWx@P4TwXFX4DE6~LPS
z%cGM99GIEHFMS^DeJ|2m-y6reHWkG-ACD2bh&dmWJeEB^bKf1Tq4%85;wqTN2EKXx
z;K6x_bRu~$P_l*DHg7CVwDLQzb8FcT9rUH<+y@tsR=zlSb#Q2C;Z_aY>RtmETxUF9
zG(vt69v&W??L%SvKdmi{EI<suaNDCl#U{bbyI92=M0{SgtY-5+!=|~4icb=<*;VU1
zaHs+Zg6C*O%5!fHG?$=aLB=KTBr;oGtOk&ueRK^Lm<60$#0wv*YNHmq4!>&uzC3kw
zUx{egQ>2S|@Xv`Y=M;vd9U(e-_R7tM7Er;z`=G*#YXq|9h*N^)d317WA=2h@J9N-T
z0R~9^C==Qbp5+@(1uWzm?1uUPYW4M9&`7vF6a0_1fBsMlf9NAkrQ&of>fDjV+K80I
z+XT>_C_V70-K9giTHXt&oBgABPUortv&yv0t^9EuB$x+GlFzcvTxaT?X)YDdbEJIN
z{=u~X2HiCPPSwe%p4mR~$Pb*Y=sl<ybn5y+HAc&VVu^kAlOz7QQol^9+SW_p&|QYw
zYPVZkuV7Jd{?qL=0ZI%lp}wLq3%kdjiYb=z6bvjM1B8rL9(7u?Nu-8I<Xw8ySH3dS
z<9U#Xa<DhwiZ24;U*(z>7eR;-1DAah5E4F3Pv?k?iW(Xm#G#;|pr)Y_KYTz*RW+`t
z=mh+w&$YGUdU{N;adD4^$`2YC?_i79wo`jbY(gEE#$Dk`NqxBL>4Ia;L`h|JHPhL%
z<13wTO%wq!ahs0RjK-Zi$4TLK)r~2*7vK}z&+Ue)W1!%Cq`ZX00j2hik-DufYrpdH
z?#Ds0$r)-og8(qKwzOxvb$BG&m#C7tD<bk~FsSc&LV|+G^KYPa#b83#Gh(p&{MJuQ
z1<|`M65NVJm9l{(Fr_0AlUXcB!79cEx;YSY5~z}gl$>d9Ax4(w-fwqp9tk}XJL!xQ
z)?arFPB6E8-3=lF)y{MCeKAL!u+z#)_U+&~_Nl>Vk}wZwy<Y{phUPvKCRU9vq&LKm
zb%iHk#tOG?xCo57*9<<mmYJ|#R>G?$$VmXhEZiOy?(iU>z{BPT7nt#WUKSajkS0H3
z5O7~av!$*=(X>f&=K7Ui4HGLGah0;Us{qzZ=~^BrR1;pxY=6`!zo)&3lvUm0|5t>#
zdaLF%P#EtQY($Y#p=J@=)r+PA0+9QlCx?Uq+vJps7CwYy=U!&vyv4ZfOq*LW+nLVL
zkL@`7(h04cEO;LhJE47~fL2}wPQ5*mZ(BdzU-7DlS=KpA7AxfZ0T@>24V&rv*>L=(
z)dmjHcw%IBy>4*P+TaOYhz#*FxBM6^VLja8AVR=^seF^8mH43_h$v7@&P(%ANk<T5
z?nD3(VSSRe&aQIEA$an7J<=r##GW!*wes|rVO{5+k+g~0H$X2_Mkb}Gz@gXT4Ri~o
zAK9loIno*C6HZ*&hl!Z<UZ|X9KPws+%GST7%DCf#iOG;y&x4$$9vh!<&@N?=1$|*}
z=UZdWosI<o!?lgO%;g*gF)8ZH!@(YKevh=!vqQuDD~9rQvm1!4e*3v3w`gf5Yv-#8
z7=+uK;=V=SZntTq0+jvt+)2Z)ou_eJ7q-50afC%EYOzdCX#W5~dA76gK(2gvW3ve+
z*}NG-M8!S`ataPe)_(Tk0~d~+XMI&pce71>eLYfBz$KGJ<DlZbWGg%Q{{K9Ct<g|b
z{VzHEpfPDp$u%`h@dO1DXIv=lbVK{4E2}OGP6JN=%1)VCr5{)rJ%Is)leKx<)gKE5
zu>T-9*SNB!m7L}6Zk{J2c^VLvSlhi+exp9Xi_*DgC%RI`?`ADq4e%yFEVC!MK%Id>
zpzVFd1`^D`hz*$Z^Q5_Z#!rKFEv>CSKFDG&&NxEE3w{Leyqbqpszjims2CU;ir$fX
z*WYV%`GMMYW^|f8mc@Y8`&G)Or>EW4QI#R>h-x^>bEV*<pyIAk6;37M`5g0{Cn7wv
zy74`HR5q^{eOOI0ELHab6>&A@s7?Y~MeCNxJlG{uHSS4)j4I^s!roigDhC@ZAOgP3
zpH3*ldBb+PuaK%Ut*_8;ur%J0+nVnL9Y)6q*OI`>rW~pEb1M;LvB|m?zI`U9aKQke
zfHu+H+A`w@g}5(2dF`I+zFJ8c+V<8!@9?k<3wWQ^fY=#m+gaOWH;-rOMbAalw7lI{
zKXAgYUa!4%?l(@t^Wl^XNu~Qu+EWDRo$G(|w?C@dk(AOId>xzby_|mSDh4<;s6viu
zGaTh3>F$;w1L!IzIkBhVO5#xfie;R#ZuT0C1mfDRt!J{Eu_rt(4bXO`JEuiA=4Om<
zFD@@-Gp_~ioqTJOI@`*a4Dsa|<kRgN*q=_e>KYjBpRn<;Zn;(+IbTVbDt?OQFsqR>
zOOdTsV--J>-X2pLoU?j`I0+_lu~LsUp)g%AG%qdL&x<BJnhZ1Fxu`xF`5G{j$bU@i
zyQnLsPc~V>=C|Hi6a4LrDRKU4?Xj}m&uMmYeSMvPi0G=C8Z{2%g$pELn96hd#r3td
zHSpD|=U=}nDk&@D;`sXd>gej?Qc+=4d4+{>aML*~a9Uvz5uV3<e-HFyc?0JvDmEW1
z483Jk>O7aRVt@w=E#`yD$;kpRTlB=H>1wp`SX=Veq=?Q^Q%&PIN97!2G9x2oGuiD@
z?-tleU%LvXTO`eHWT$MfbOI#;_z|gxO??7v&kO?Nj>&d(0%YlU_FxaYb_G%*4C-5L
zd_a&mFm7*)2o-8vQsRmF8D=;fj$GQh${MT#?F2DdM%CAug)YkWr4eH={YP&upH~rS
z*@~fJx#zPH#os^BLj(MDSzou>5+{hZnPHgJ(WZ4O62hHo<lWkc0rkVgJsR7)jaZo{
zY{mKkxlztPRGQ{pH7!t^se0H0+Nu>>{!<pD{~$iWP!0nA@M6a7U1sP$2VJTOtA4!H
zoeYRS++B6Zs%EXMLq^E9=N?g;NS{{3w{V(I9WMj^h(ls?B-q@>cE%<q4goeU{PXjv
zaC3Kf(tG$rx7VcL!4iQmUkELT(MlAIGB!=xmCgx%-d@RSIYZ$#zj9U^3FAS)_(DPH
z3<Mp2ys^;XaTbYJCPXwj<8o=Ish3(Eo-*oe0fgT5>Ineld#VzBP?fX~>@WD>NfgTa
z)-97>H2UiyUCpSA985Hc*g;u*+H}wjk2M*CG4qrbZ9lmfgM0;?)82#$4tp@Y++}mT
zocDNZW0_Ee+Yb@tR&ooF?^vsIzTdK@F*RC!=68RpFMH7?eoTXAN*4}Ru#hi(qKLlW
zyMzEm1gsVjwELX<aMOHnVxUpq`i%&sEWr~vuH7&j2<}kJ5WuUsO(?nC(pg7{zPHxu
z?<I3rZ!cNDMZy{C{QX=$$r(dV%pI?r#Le$ykN1G9!_WEAV|(X3GVm&>)-5VI#yp=`
z*_#95=VS-#Q{<Z5do@fz0096*K7<}OPk$OI?wxPErya&~QK731y9`*=V}zz6w`4lY
zrH9;!m#sP6k7ci|>kyU*MS62u;v<C|NapGJ)viyXGy+g8^G9f%PjbmfCkc@we(Lis
zFkEOqI8JqcnC_o%4diyRlD2taxA@Mq>%1jRAwc(3p7ulTWm=IX1NERA*ZVic1H+R%
z<)a0-;7}lg-0*6=WA^8f#QdnHvy{h{N=5_C8zH*{NDPMt%gx!@kgn}ooD|XMT{^8G
zZDJsxFEbyI?kCV&4ue_h#dQl&hRzPQ%*n2Xo&<98kU5>VJ_q&pAMRK#tN5vxuSSDN
zGJ7+7sT0?0k;9xn40|y!u0JVvjAt210H&IZ^}ak~Tw3^`Xqs;3$4G#lv!PI>d@i<Y
zjdoi5?=!CcH_#)S7KsQ-PSFqTiU_)2ArV1euOK*xZkz#ek<Uq&9Hd6=zI`0#RrgwT
z_XPcRq_n~QN^L+!Py?D;;uT07fXx8DN1)_mRbmA1nwZ4~z@!N?-c=uV>gLYCp<rFG
zBv0FGw}wO@3lf_+ptxrJ*Xx(at^zK~Fya~Gl4HM~Yjrj~%_4(6E`czpwDdUdDp&-E
z*coKNUV$nB7M;(Ug!Q*}Q-esx<dERhgfu{%7QS=Vi>o7E-UqLMNffyOK#h%JJyOM8
z9}jUNhmuVZFu0spirD#xbLTliz!r8(ObA%Tt6Fz>kj4&bd7gTTgRZio)<Jp=8yXN-
zBED{Atk>{rF#ap^Qeh=hGE@Cs3;b{VwN04%Z!k|RuRI(ju-Cn#7V@-|<3DI=OL=#C
zYz%(GjcyL^+sRU}(4cVQ=OnNQ_*qrGcxP=!2`|knEZU`RG)1u}Uz8Bp&uqoWAoIe_
z)rv&mZ`{UF<sCp)i^RIg#;&c(m0Fwwr7QMdscWFup~4*_qc?8QcrT#-?_Fftz!3)r
zwc$Ad=ZjOFAA$eBpTS&Ogcq>+@JMOntgE?s2*h;RL)DS8CJlMuW4ct*dtH?o`Q-Ls
z<D}L6#;UIbP$UlbX<OT#W8Pi`Tg21Jp~3kK-s+#%F_6-j^$C-R9T2*-A2CE6#6Qau
zvzQRv>p1dLNy`F`m=nHD6-b6z_!>XnobPf(d=K<+doLaqyoNt`VuI{Vwge=2j+A4g
zR2KVGvwH?KOb*5Sg&!YBvUd9q=|ZF|qa&RZ$N59fyVP3iVv-j``+~e=set@NP<w;e
zXef<s@rT{;jH?DA2@VVz6iCABS!nwVJM+Je5+m=D+5co3*h-TeddOYz{>R4f!s{k(
zLDzzm0{TlTugg|173g}sHY2i2Q2&z<@?2J)xb$5-RtOB$!Lpv5(f3&p%Yd{1uApKC
zlw)mcDVX`{_Z#H6R+_I4{1(Ku(}$F5D<&QQu?&PO(6A^HLL|m{#h+~9MRJCz`;aaa
z%0Gf>w?)Ir8cluRAWhXP5c%`C6s&aq#jv~IhC5&ff6Mx=q>3+RDS#Rl0vet?kD`lk
z?oI$|;`hqF;c~Dey7m+JDwNt;B{anldg>zRw<~CSrpsdk{^|%0L;Z;r`=dT#2h!p}
zHhZ-gfZ<RTxvgv<m1{HPkg^TX0cmrlyOX_Thmm|NBYg#P5AC5)Wjpo2DTd5w%aRnj
zf07PpHWsG47uT9jm4&rcCAteePy?fpw!Fb0z{J3%uje=H4Jq7$JOlNYsTkyl3$`}M
zJ3lvR1kak&LAyc9P#8i~CzgO9?q8EQKvwyuudu`^Ry{tO{pLu(juYNVOULunv^ba)
zQ3d6s(#FgFgbJ2IiuoVkl857aJn>@Esjja7IX~JQIPd)xMwm5zThgod@lp}UTn>>c
zEYvagVmR;H#xS^&!voF4-bu~>8p%tz1@qSiP(`@xL_Zcl^67h;Tm!wA#T*wQTc(Z`
zv46Z*1SU9u&b+Im^BFE{6RVRQdld2$<(jYa%dX%M-TAl%<l*41hYSJtYayAu$kYJl
zj*)KzjUx}PRTOfzu^lYhUOjU+J*jHih5kbCP#+Kn7gt4}c2fv}T1;)`RrtWSs(l*T
zv%u_G;Eh(X@8jZWE(0+(7S?gqO7|Cj418sgu3Erx9YW=!?wu)X`o~LxRGvo&QF{-y
zWdFKgH&3=^6tqcy;!=Ln1TXJGe&5}xvLF7t%38LD<MIAJJfoK??Yg6B_TW6ctyCQE
z$Eo5`+fvthqamuVt{%=`gwcUE_7OiP1ER6jK1@DYgp?SB9?_n<lnKR>gEokAJjX0K
zRh5_elerB3sXmqCE1e(;eRr}S6ll1B)L(UyJKme>htyQyn>8&ESqJ&0W{PtGi`Mqy
zK1jCVQ_nTfIN{A^x-jPDe_`7`B_Xi}TRDvZh)8@^QpVmkle_Z6bxV+7#chwx2Kmc)
z8{^sQ`S#)hWUZ%tS1V{4%6R6(NHGZNQBSBCGd4DQlK$QX(f^tEZl2!-kxCWfk*FGJ
zjA4Gr^64Y{OCmR~%?(C5<k@}lZtmdh`_0#Rc}Zi$|EXW?AQ965=6+&@A4p1=PaukB
z_%KJ&#jcwpx9>p68<Jv%mC}I^ZK3B28;s>QM<x0I*Pvb;<xI_INLBtGtOvpkh2t4T
zhL)<v^$b&FH}#k0OI4}(*!01MVp>3Qqzuel4D@pztGtr>t?pRLwJB&kpMtpQd=&M8
zJ4CV_{j?E2k{bGfyM91edT{L6n}v}PFK2D!*M2EKoE$yw519#Sy`Q$MW_7%f`ROf*
zdy1o@y6#5u)}Dq>`$(<nWJHQQM@nT-X>`SB93hnZLUl8aax!bQjqRnH71w@5CHi54
z?G&>O#MY4P1R8G(&DNPe)!a)-_uo>bo}DbItd?;G56sCD?W;dCDnWo&WR`7l1d+|i
zx&bjfh-?Pf0CIJbZw&(!)#U&V4$s<_n&eTIkGU%E_$KGS%pO1ntpnsiVInDhnNErr
z<c(=0mkPuRjemZ-W_^Q2jb;j*Elby?Pz>EWWt+9l7P$XP_{+IsS4j=MOx3E&ISs)-
zZek;osq1tQ6%+S5mj^sA9(1AUSHQO9#3Cgu)V%+igV|i%G0-i_FRy;e)4@k>$kAW;
zI4h8dC9R07bz<7@2Zr~!{IY5x6}i{3C}r<_@=)?1P3EMM3o^NXDSZ=}9!I{voKCm<
ze>wcyNhy4MZB;;&0mXmm^pTlpUX%X=63cePA5$65RF3bd%rdiqH6|2im1(bW9@qpW
ztYZq4mM);Qq#5or5`WpYF8%JvUigQoXn$1my*~f(6R0?nT`DY-D1AJzt$Oz0rVH!-
zoo^A(2#-;u<9?FR{*$3_f!DA&{}p6NG?3GwZ5D3+uBt1XW^gev3URtl8D}UL1Y|u^
zO(%mv=6M8!Q00jKpdPd66p!L91-$JyZSzF2a^LA6q#j7p@7B|7?f8cVh{gWH!Kz33
zsHW=!tqy81$#zfMJ;IQ;Mb*l<A^DG01KfaQp0^c}?+M;2oe)Q|bzSh=z0Z1j+4*K&
zffnXYmQy*bBVLlQ{^#TnLUCPhK`e&47@hkmK7bbc;pqm%AJwZ)YF)FjZ8FlZK4O<j
zY;`IYfI+4t7nv6#fOtJC7GM$1RT1gEkU2BzvNlq$ylHH~RGS>3b@yR*9-;h>Ej=2h
zK^O^&$lP1;4JaA{qcnS7mi^TvI2+iCCgNSWUibI^d|;skx{H54y$Mfk^3?nU=mcHX
z2@jCBUh7No&3K@A7(C$#r2;&hNCZbt#|hDb;D&p|T%7l1eM;HJ-gv|iwUfI$Mphjl
zaOLz{dMKwThwl)^H+htG>hA`$bIl5t(|eyY)k2>JVup%jOjE+lxxz;tLMIkJld)v3
zOlUp)aM5W{Um4)u<gG7i&cbwr<foz`=m44hiOKF2-%g!i5ac?CJmk>NBe52YZHkYh
zAc~4VMA7R9zQeG4S^veh*4ZdVOYCOyE*(%ahUkFIPqnuMAnFmM<9|AqK->RxELqV2
zMpEpRCsmcW4_Z>JWs$DY_BK$Sth0r_Glzh{^^f!fgtlN+<++1_^75oMUB#kX@%*q(
zI`5)Yo;FO+i0jSl#A+ql1ITPE%O7oL@Qj`|`l=P@Hk}EI*ZMoO&%MJvSY+cVmGo*?
zSr#^0MTX2AcW(b8V;O@W9BKp2Qh-AK6BHmSW_!;RDH6WC)PZCpZ&+B2%!Ly_T0zc%
zhz#g47nCqEnVDJQ+xz<{Csj@W9*+G6#QnBO`@W!^<omN~q_~f9WFO$fdoL5IHGhsj
zkK=weBq+?6g{VyjGyxUf-o6ha-{wDBIJRDc=9GS7_9@C!%0#M|h-h{VD1=k{zjRT8
zT%YA&OnR;jkxkNL81^71h{;9h6)rjE7B&Rk7^1tp(o@$XPMP327pE+AM)rp-*WEd)
z+`+3BbX(m|%7x!u+jv#Nn2JatHv*8TcReE@b_h>czbthLlu>a(AM<+RJCc)4@gF=+
z6oZHjZ;<W80#`;94O71koM=w@9EWZ@=Am$$hnd_aG3K;XuWx%`u(|SHqD`dq1~6%Y
zr^NzPjDJ3?!bZivBj)`x@VEf}m<KV4QU|M2N5tgRU@@dP_O~AU$}4Kt1{BK9<I~iD
zY-BJp;en_N>K>Jf-a0ZwPd@u~5@`u6V&r&!C$hMKG$uG3L)&g?6?sH;-qz&aOtd*<
z$yEbBa~%Lg0yV$Re_wzt1oAL$m&k7<`<))p6km9!xJ&x9q}jhzPL7^`04ONR0)Y?K
z9C)2!P4w0jQ_zihdka1!Cq%o|W|sB9F`vh9BrYs2SZ_Ko(Z%hXL2JR<4x7Sx{?@Rm
zozLVLG6t(K+`L-sbxfLc3=d^WfL2Z8&29+J?oybkl$ZOB&Lv0!;Ru`ZAt-$}r)7bV
z_@|89L`lZ>9B{`YmMVz}>zS7L=V=&lG(N8Im}RAhf^$t2oNIDbcAkm~5K4N7!Q~_^
zK1T5qSyJMhy8A0vd%v2T@2BwrP$72GEvg}tNyh)OlohImqM+{SS`yb5UZ^{MdQ_QP
z?KUu9w@n*|e58c*@3Pc5vdcHJA?H(MT^{>n*wrln4Z+51*Su+M@|74g;;=^a0k(F^
z;GtAtdhf<|5b$PpAOW%1ppOD5yY_JYert`vxiUnWWeCu`sv_a|oEP+$IxClW%@FtR
zx~R7M{QE&j&m6I`!0Td4-=xr?1!+$@!_+?T3g8KyGQ0h+3HdPRgQIek9%~Qv7@>zs
zEETx$ZcmPm?b7YL2A)Sj%izSaw;M6soYC_L40+;A`}sNROOy$@muBe)><9C@puO=+
z4*IznygRh7UF2unARe$otP@{8Rlt)-^(GBs673E9B%v3tsP8w&c>-X@Ho%)=CqQ5$
zz8sYH$9mjbQWdWdgxM0>W?rr&a?XV82RLw_z-o(4vjYKO`j!o9#YX_w32Ui4MtRMl
zp<nyTV-l3p*^yuxJGuYFF0iUY<zqC5pv2u*0?*gUH;*BQX};I0X{bet!96A>=6%n}
zy;2gpp#;gMeE2~NBZ1s)J8V2f7#}+sG-8#TnYc8Q{#gxW&xFtSp75DI(%yRQ-H}1a
zx3SlkkfOHgK8y3IuKK`K^o$?ELvl{Z8Fq1SeIyS3U_d>4R_B>jYfy9c9caRl&gvhp
z`vo|V5kiVou4R#Pe2P7Kx^F#qL|TbBwjbewgNY`E{D2OK6z^i`DUx=%q`&FxSaS>_
z7(lfQZr*~1Z@k&KFzbT&M$h_%2vWq^7^i!y=i%TyHsnEpYX~t#Pz>M#K+@9}u)xk-
zUwoyO&xZSsPswwD#KzFxN<s?c>Wy@Ef1Z%n?3?!2<xv7wc*af9RTzX5_)oOPkGwLD
zzS5bd$<s&qdzC$ZD4c&7WjftZP>$mPOtc0P08sL5S)+8%_fX|s&c~~fT$#g;tETp%
z00SS`wExJuGA(yq%sh9}51;M|0(Sw^A>Upf@~>4X?t;$tXtV#kd;~VTCnjFG=ID57
zKj1lkc6-CcudQWCECnMdWWh3V;ZWo2xtm9vBj{#i5ReX#OG67&wk|9PSk8x`Z}S@f
zdnTMCUeOP=G~+AbpHPSaXICd4j8>O1iz)o@$k)Jn1AN6F(XvF!kV_1)!Z4~1>V3oT
za@PK)W|$bM({Y!+OGzPyi>6PxY%WMy&-Na-8mlh@iGZ$&Nzz2e@25Df?F~fG11Vvk
zQ<77Z2|sNNg`$+_NcwN2yAjTg#~9}1F7HYePXQ!HGvNb<fi-&t2wx_9EqcTkSb$wJ
z7Vu~J^Xv&BgH@&aoZl5iDSe5agf>*j2Pwz@tprK&0|46s$AIF1W%#$EyK$kZ)|Q#^
zCB5bVlf;Otg%kkxke0U#k+lw4u|G%S8A3&2LPQuQI4Hp(DZaE!#zG0{P`IETg8FMG
zGmT9jfND`YrHn}9AtjJJNw!JYm{Oo9g(sPop7?}Wf3c=)eD$3#-i!pf1B1{+Q*mfj
z!acKIxdSe=z@JoIY7%x6bzZ-hck}nTy?l%HV5A&J`InbW{OZ(t5U4TTK*HUp;sz5m
zsO%6IbR-aKWy6Ioff!A`uW+MHrv0;I^~_N+*r}hj5DZqaWd`BSha=AuxA)%Z`C3y)
zSg1w&gn=}EsMJ#tWlP_|&Sv7&{1c$3qHTu^_ONj-v?cPR7$C?Q(1@dX-R2aJgO3jB
zQ(aZ!#eU@XkAp=4@Kk7R?X<DhI*W-PX6~aw-yi1ks$8{e)1sdIa<Mp+ject4Y1|k0
zwO{svkjUay;-aS|(BE^^4@8H0ut|mcYe;h5lkCv_Wq7-m%9AwSuUb|)#%*QI3IiRu
z_j+rVC3uhi*fIClF6)P|%>{h8xfrLncUu*N2`ZS1UPCw!ib>ks+Tt{BJW(GhKmi(q
z<_E36|MP~^0&_-(SMOGfUAYT6+9xgXVL$t`r*$vV@Mpi-hdN!s)HT<%op1f8K_}zs
z^G@NC-L>xbkeIWZaEk(NlVB|R6GqS+-Fl7TMTtK#%4S`|bnubGCnynYlVJTsE<W(V
zA&XF0N5wI&L?dhk%nr*xE}>k4S_zp_zP8{YKXOs~B)}pEZWo>jl%Aiw$Y6c31L=+!
z6I^QDL6Au4zgY9OQO>FHozW@CDNBeW!L!M_0xDD&fk4-JqB{wR77h!o7bKzoKr{oW
z-95mojC8rXIp_ajbqg*a{1F<g!ot!00}Q{H7E|nsM6#G+VBP52ScJvHnQorVl^zRf
z{)ddub}+up#&bU&J$ht6;>S1m{rfYx_~(R_|A`iZ9(DbN;VLLnWSgbvmcdp#*FF9z
zK{E;BeLfaHC-}AArFqh7&<MvG!qOm`{YcC})F(J?5^i(Gs70Af9z*$F)lQG6U9XPe
zfWOhzT|>{jbT&R`!rt;E=<|;5h`dbVGGGtHkzaaLQNo9>YyD#X{(y#0AbrGyTZvxF
zlXyU^WbHa6odCOCmWNsRkG%ALSRnZBd#HZ5eEfooeOAJYUqIGPJL@w-7^~jrNP<sr
zkRtM_IhbclcwL&$_=LR`IDz;V1wz}ZL6Z-{Z+;#->VEof<T>xxW7DDSMWzB2NEZMM
z6(quxWi$mqz9EA6SHS%RDQ^&Nk80U2|2FHUBF|k~_30DNZzq1>%a_lTlU0TwPEyLn
zMf3^wK2lXxRll#mMlIKzuBf<pVfteea)4)KX12_W{4StJei#7FWZ{3sLHAv4w~y-0
zhYORE;9$@(V&IdRoA4%ODvxUz{Wn)x3s*-SBcno6jdKI9B@9!CqwAnP=|FU+FPZIw
zS(WP*sLDD?0jgo-leGGguL{Ic1tu+R+c}Vq!kChL31ra26{(@*<iYVj1pzy?f6#v4
z7t-?p5V(-Dco``(=x&s)f-)oiRxv>p@?@|a&`F6TqVU=_gLJJhjCOK3f8dnDa=KLp
z#1BJoZcKV;wnstS%4ew59AwKdUukubJCncxC?#VH!~N?UY~_jEk9^)h@S^Uo(*l6H
zF=T1}U6{7X?S4xWU74pux0Y@j7#iwstuIpxSbm=&^Pi2hjE;=-UF~-yV`pa%6SQGn
znZ^2&zvTRee8QE$FrV8Yr3)=PWNX^hLLxiBX)TVUxJ9KCTt}?x+S^8D0h6)ES`9(r
z`w~(xL;X0nqg!biWM>aRv;a6Fa$CB=S#V2TxifO+A3X5Az~1GYhAJUGhF747Qh|E3
zCYoO4hugrG{U;NnTQIsM(kojzb2>wam#RWk>?stuH?h~24Hi5ffp`T?j-B|oA<Hiw
zN}Fpgo-RB#SUfbG5V%SEdd`P_mX0y#h7O22_#n##jCvOh<Y?mJPf6aRGJfEgFNWS8
zq;)snl^^+^cPs23rD*-p-%1X<*MFFfj!xzk*TLKGWy;!<RWluyO(4w!@6H{*T<Id7
z(W0Cj$_QSw^7pb4gOiiq#?g=QJ{mW9#OZHD_^-U)*~Bj^D~p}#8EM-6wO$GD;G^$#
zYNVYETsoS!(<^@~)s_}>%BM@{!J$T!n1wz5DhTWi?Dq40^q%G3>cPtnT<1_z@dMvc
zTwBU^V*_?_&6v+Oko6VAwG_vA*6!H}t9aO$ghXGrQY2!%D8@ts@y(L$Wb>Io=vd~d
zXV+&vDEQKF)Lul|^|Sb)mG8%yN{c22X3!iAgeo{>!1&HkUck@OV}%jp+)YoZ`U#>x
z>Q`ry`fEB4AREW5`L_omhqDcF{f)ZjowOk?gX$sok2#K(3MgOWqxaplqE$cx_#AvH
z8QXV_+zJQ1INyN@z<sUa!$G@^-|a5weSFl-nm6Ur($d!EE69*&db&HCC|M=7yfI2h
zK^wEeKK#Sj>(shg8_c#g=th=d@a2f_@k@A3q8sPM#l;zea|+64$=+(@np4A->U&;9
z&~NR8YN<UxxSRfx@XcaF;s!a`8q4}>2_Q6oByg7fVhc8(i<?HNth;A+I;=bZA=e1y
z|G=65gX@(N2z8=RO2#eh>|B&m(d+|eC}(l;_-=8j3bMuf#7@XnV4%lRiGkE)vsg$?
z-goh2SWp6wBol5y4}txCMZ3+%CmF=(Wv|LQx9-5r_B{5ESC6`g1-p_}<HttyRLH&o
zArzcsnb4zovq_#eXbBwto5wOUQY;cXR{2EW{v`|n!!!(DeAlci*R5WG{Q(c=b+3Z7
zy<+g_hS=Ys>(c$(ugj(1DI&2>1fNAUTBLeag!T>&`|<=xMmk<DuCF_Qrj!l@q|XWq
zg;Y}1T+7SFN$fx!h+Z?2a8CjV#i|oH0=x-dfmC4VqZIZu6Y?>~f0)^5*VLX(DhHGC
zc=0ffXfLHFAik?MTYz@{PW6QPtpTy`LFb|}X2125K%_1r+aRHYw%Eyh&9JRatn0wd
zuaOYmM(}Hrx}@v%X;uT#R8OLnQUL;>K{yT44*D(`*jv5jK%t=!>f}`*r-VU}JBA%e
z-?1SVcsDHmWG4i2eQGdKH@;3ONFrT@RFvpW{Nd3UQItfQt!so=U~lw<#lPqh^+3b8
z_pI!Bldr+o=O=GjOgot2T>X#cAVF4!+rq23Sfs#q#(Qv3=k2?9ecRhn4C;hWpQNOm
zKYusxFq>)`R>_uL^wgb*s=T@R7R80VUQ<Enp=ca6vX{P8J(=Y4H;$PeN5}pN9iW{V
zV-k3%^NtjBx(P9h&{g<r%fHvpE2RTjo)m+8GIK>SRk{{ZjGg`-;v;xXjfw@40T6Qs
zm7Zj=ta1Fr-3ujro6(s1t>?)44pwx@KFa$l*Of1bb_YKxZ8Y>wTK%w>c=@=0p!|@@
z@kTn|`GE;d&SZH`V0tB|v$=vhzd23NtW=H-{ko!sl+GRw@b3~+Rfpr_5>aDgA}Jht
z2P7e7YS=T|q4EQ$Z;@h_^y<ZhHV$Rhb6_>CnU@6dmv>4p$yvzXL>vTz&}|aCzxn5F
zD_Qw(et}wlzK)}GFjU*6ESOCdr<az!v^DkI4^WP;t&Id^x@<W-=qosei;H`qn{RK$
zW}6|vAL4-aobP+<nTJ2HrKz77j*Dk-oq=xOH!NArk##@Jld<Niho69?1hT5}#T;*=
z9`kurl+K$C`LkKb_cVLGaxOl7P@5x;_PdecGw7Gc=d7^SQzXj<D7p8Q*lQ3y&yl`^
z%u0Ow9xm>;snF&_=Y+a&_hU&c3R7SYzwgBt%`gr2@b)VPUsIy#L%OhaOANr-N;v0~
zw`m6wZV$rk32K-q#azHb1xP6U@X8r%TS!2%C?BPHetubBGs9Z%@UT_gppH2X>|NR1
z-;OyD%Nb=qf0M76Sc$}TChe@#{l&C|?!&fkHdS7n!0GMl+ihA_roVXw1k@xcCqq`2
zYd97j-eYtB{(WjuCw@=`>l#)is_$*rrHAkVR_X*$Y!rNeiorijpQru6v2AlNXmimq
zA+TQreSyda{;vK=OYxcSAtT^QShKfddcSI2RWE9uXTK~6$>zt7-R%s#G2hE5U&PfW
zWti@Fd{E!58Dp1239U_r5MYKxxZqGe8Eom0nUk-s<bDv8{2<3saz-lyLe%Q2B^sg>
ztp2%v#y-oF4LnC{E6_Z{;un-`WxIvcMZq>ynJY?ofW}L_2>LZshm3y^H@3D|U%^pu
z5|&?GuQXM0{w2acRSgU5&IZ8b+BH1nKHxF!U}Ryz|I|P>f9A-SnZZ0I)~dWmBM3{;
zZA(zdjX(RXr$_(ui<4(rS<9)kom@Fe*7iZN6XXCqe*AcC*oPzI!8b11ev!R)$Yd0B
zO90kVpSW?9s&WP;eSlIWv#|6~e*xDyW?S3xN~}>X@8Pnbi%6iWW{W_AsyDS1>6^R{
zlml>kke%mk#-FU?W40evwKqIp^OoqOh>@Ndi+h_i8(jzf0=V2m27u=E=9-u>=<oEv
zGXPiN9BQ|@5xk6SouC|&DiGik><iC&o#@IA4o!WKbe<m|4}gdW)^scO5y5H<b3Ado
zFn<sdHu;0Jo}t$1R)3L?`Cb>X_fuL#U9-8Uq=X(Gnp(EWF;aSAf-8q-!?j3zi6ev{
z0uZp1vX4!oL{d^U*DRW9YYFQ@E>yrV+MH{l{W3WC3dmV{a#CgS8ca=0UW9~r#Ktm>
zPfnJ@U-ZZP{V#HHeYG~-TXbgE5YZ1oBR<Ywfat$<#{$$SVB>=b@6i|*J3E`1CuwPP
zFKCAY2IP&=wLy=HU2*KLLR2G!ea!dY6N5gx`@!w%RGB4|lGNXSxP5RG5!wl*7l618
zAGKc^WY;Q21_0^L7pRmOFp%yMRj*gnN@&B<y14+Nl*v1NkIv`gpg^l#1N>7Q2o$3L
zVUv=ac<JaBsy^5_e_slVCOlUb-@ZMaqMl7?+L<N^>Ap`K<3o{_gI<KDNo4gzwI3~h
zq7v)+%GTD_!p}Mm{a-&vah_ylacB3E$zl!@??TZtnVg&~EJ=@xb2INN(5(;Sv<G{I
zuD*U;4C!8XtX`T>f25uy;M!feR|)HXBgPwOE3^6dAliL%zV;TwgA#x`<P8`(<?r(6
zaYAm;ZbG3^1H)Q0==LBV3S`FP(RH;+Qwu{m{7yo}rr1Y@(8_b^X%#383^ElDOBlHS
zWtHYdJaSB)1dyQ2Gb}RS8?I)EL6E*WJnui05pP8-?Vsi!{pny27*aY&emzKNQ58BN
z=5hjuCQ{4X!Xh9(+JO*tR=vHw^lSE-h1-j+n2oY>`&H|-sNvyZnoSpo-EE8%aDy>y
za3nyiy2ql_Ptu3-3bX09+4IH4MTJ|8=5=ctii(P}>jkl~`wR_-_tCa(va+#7Y=*;P
z?Z%5<d_JoUj;s;^%Y&Gt76s4o-Y?}ii&BqcN<+^uw!KM7xn@@EzII2)*WzNM!OKA`
zV6pQdcRuy-7~#P8duaiW<BB+&dyZVbP+MyP*L3!h(}zVW_;%y%OYGtJq2sHKhi9*m
zG}_g6@yg+cjF|)sGqRE{?vt1uAQSQ{w$+g?><~q<uv{2o>c8lG@5LDpd)tcDnlzm)
z_Y+<x)LjBvaCz<R@>qZ-x}~u2&_@sT|KsLPe$XTR7CX1CfMQ%!S&6&2Fr@&+C!Jtz
zfGnhn8M4zBvgb<|OFc>Hydj~T9<^EijTOdYWGDEake|BNE9x&E<KSpzmIH0>`8yxJ
zyQT@*&+8yt0W&i*<WpVH#<qCwYHCI#p4$D!Vm+xevGZls*5_`RD9aB9i+?&Sg&P+Y
z+-$nNTpW9}7sr3>#-sO_pVCN<h}>k<$#8qZ$$n!z_g1>&$J1Sb-{#BCFCQ#oNV%7(
z)m=En^9g>BUYbu2XJenlr#LM_ewxI5&adhM%9kZPjy``kY<E2CE7iQ0=aFr?td$El
zey<s9_fNlkhh<hR=jHYu?V8F;#)}s-s{GU2DH?9xylLTC;u?W#Wol}=dj-cOV^SEu
z+bw&AuFj-JL7)df-4|`A)t08eQEYE-XJlnPQ(~1=P#|Z&oA+d<CwBzkxvMrd%QI7S
zqE6IszfeWU1&ubYZ+j^M2^X-liMYIm(vR}h?k0R~fHxjZ$bURJpYzxQV%IP9H+*ey
z<*1||a!Yx~h8Jq%7g7_2jlFwfMYJl|rtryK99MvE)>%{Q?y0gynRDWO!54azgyN1q
ziOWASoyRJfs<^yy3XcnSL^F8Yl;v_aZ@*j&{&$@NMT6-Q(>A!|s&CJxVCI{+F(m)9
z-X`3KErf6GQG%-jmnGP5|N6oC_@}}6#6;q#+SbOZl!HToy153$HUAUO0lAKYt1w8k
zX2-_~q@<*LY57QrD9?Y&y5IV6xbk^V=v-Ik{sHGzI<3c}!67EEb_;rf@-4zo3wICY
z7~hD%A_-i4N@PCqQ=M7wF;zX>c^RL2no;LX@xIK?Gt%dS-z3o!OftUt7;NOw9=I>S
z;oQ8kFY&%vvq5S91AVEup<{hde73b!&djSO5x)u^-d+&WI7dE~_AK9_KPg5<nhE#m
z`GaeB&OYQ;eJXNCQE6xDv7!d$e<~;jHkj7a<*BE0DJPp__8T>XyZ?xGB!#as+&sP^
zIW4WaIp*r4$+pCvfsfhQk`4}nzP{nc15=xuPH;m@Z>J5^f{XXRzU|S5g%BQqPm+|9
zQu*zpu?5!m(O90Eb}>j7=CvG;A5O8=XU3FuzN8(kyo*BS>pKbO0+ya)QHkL3H`NH4
zlPepbvZN`hZ)&8*8fW*>ncbMlwN!?CLhP0Zx9!+zZ;;O&6$qQq-@a!-yp*GaS-s`G
z@Ti0%DWPhoQM=;R(iHIJRcA3%-_V{b)dSk_f2FMGTtjouzd`&nx1F4X!T;#IF%=d@
zeE<(H2kTnMc8Gl}2f|kfh3~sosH>y%1iIbwfdPi)<z*amyo%M?K1bE&4c)Hp?(&X~
zl^FAi%1RW9*6@`~L|9*6f7=lbr0o5|KPYhjLoA3(9O*3U#Q|wtxff0$<#^i6E3qs7
z73ZoxvnA*M`0%Xg#xp6Zp;s7gG|l)l1J5$y-E&D#L;bENlQz&p)O&eD=4bZ&J%W4k
zhbxSS&oWC?N>e4sF4O&RymU!t=)jawu`DY1{QtLlg@xU-O*T&5l9(T^@>zpyZVODr
zdEMH2u3yuQE}N{szd!7v?!NMh3JVNN7l0Uo-B)VMP^A+>V^gMvpXQ-aIyJE5qVC~U
z*H_2KkuF(yr}Q%(#}3?a1DczPst=EC2MZV=TXM}no{5RwVB}iVK7haNV*_ZRGbP!t
zdB<mU@y*Y=B)BQ<gslXhu)y}fceydO<IGYog7U+KP!-&(Kf6W9>`nG-dj<i^1KKXX
z&~0G`T*t>6qmm~=y8U!3Jo=7t%ge~L>G18n*YMl7v`|_HazH37yZRfU-^yZ4w9MNf
z2NvWSP&`fh)fN8uKx;#YJ`&GI$IDFjU)>-6FWA!EGk|dvD|lLvzGsAdFGwV=e0brt
zUOHqwEKVyUjq+&wGV7EdOs$S@IY3Zh(H_+ES09D;^7!^Wv*x0L0vc#pA4Nq)p^jn5
zznhGTDzvbK{p{K1TP(ldFNEl$;8m4bU#VAKjA=2*tUm7b4MVq12IXA#oT-6}BKcpb
zKl2WE+@bS7eN3lk^n?t7t&$^ys!vh|1V{NZ^K8=yOs$g$e_Rpv*yzz~S-b2fqoI1{
z{}<q_PMws?x%*iE{P-%UPRpH}n~RSlefkKjWs|&!6-iZ0bu}%0YI2gh?a*#drBHYC
zUm`il#VefzQpn#1#|-Cu0t*IGN5rOPl4xxY<){(=%yi8UG<ORXOZgv?DDQ&C?KLM9
zh){R6h|~B5WJVg8Ow71|suW2oQTXffjVp%5QLjWpWH5rtgv%S05!AmyioK^L(Jg7}
zHs<fYBXf$4oxMYcWk%lId{$H2Dts@ZCma9YoEiwRpJd!+b;0zemcQRE&twI-e~5jr
zepmmGBRgA4LRGEi!rDE{cq6gdfA8^Hb?ysyID?7ygg!&+V<36(Y}$HFr$)ZGBW6t1
zJ4vbI#%Tn7BigH;xQ(L!uOtjT)7XwH@E!qObqfq6PfAL95gqM6Jgldtu5MD0YC8l5
zT|4KQ;o;br-;x3K>MxK4KXAegjAaVTmmKnkAsFT&#%FLi_Y9=8A%cFBuZA|VdJFtc
zC`}h~M;a`PeohJ^4{hn|l6_Z}!`+w-!7I|iVa&<=8QlQ!sii#Qj>FNogjhT<KAjrY
z%Smv6=@|*8K?<^~aR2>8(I2U<O06W^)m>fGklc@h19#AEV%r;YG_wiLXV0B0188%0
zy&saNx!Yjig;$`%F_dJvtPobR?wj!9v6K)6TrWumn!e*)7P{M{Mu0hdY^nPpDFNYP
zU{;1P?B83SY(C!wbr28<UdQj1!i{MaVq#ArwmLRP>(?#kBqjo+N~+Ajc6k9>2u6Y7
z;eq2jvHw6g=q+1Ebl^4M>I{{;H@CL#$APx$z?Uyy;2qC6Zp`GuB}bMUrXnIDFzj5j
z+I!RFiLM}00+WO8Yr1N<4UBba8E%#X%|K2m7fxSqUQft-#M40*9Enzl3I^~C={wPR
zGi|nfSL4oW!@qVkFKgZLt6l$n8%@fqJ_-%3NlzVPD}Yv#ln3K94*^X>4cEwsK1`Q|
zB=}GI$#NqQXrrJAUI7;f23;u;C4kJVr)e_D1I&N6-|nBWl<(VLGp=uJj6ehF0o~Je
z?D`=*GJ0QVD@+SgRfZr8K^%e$$JYVMD_ZW*So`(k_1yaQVv`sdKECIedBAQ1A*~M~
z9Sdts#`EAnx=Te~IRgx0aXMKgM7F|xmhBvIJ;M7nk3;klZSf|gL`r|*C{6EC0^uOg
zYv_AG^XC+VWM(|T6c6)?4p5b>yvKP08BPRPCIfH~&%>>Ouy&nDZbY}e1!}7}Yj5$s
zf3M{VB5wWq(3*#WscMGB+0W@q!aI(`<q3y6ua$40uj}utt>pd|%Xi2b{gh+Y^Z)qz
z3aBX8uI-^gq(nhl1Vts4E-3|336+u#rMvq9DJ2!8Q&2z}LAq-Ir9`?J38`V|=D#2H
z_@4J&-}+}A2aZP%^W3rH+Sk6ezKKatQWAYAE&mx<Bpb{$@_}1E!IUb1zcN}|tdyAo
zC%ZUVD++!qsIczf3}|}JE-b9iDkv>IzdV>P6Tv9H4whodDLyuq`Zhp{|B0pU<pWvP
zy?M!7vR-cI*&YY048pi(h@2k=bU1?kvkE*B)|;SG!jhN!#4i@8bAc2t@bUGRRrx^1
z{NVu_B7$-E9L)hVCzwq;-Js<M6Nm17h^*#{lD`6oA0z}*wEiC_lJ7D=0Hi;Ayh7-G
zJ0;vb;PH1ST3Cb*u1~-N_Sk2gGq-IU!Pq1NLlZV&VxzbJE(ZL~_uD^Bt9&!tpM9#X
zi-E^-VId=!kQfMsWZHASa!%gw-E!IDkB%rIw+&W=q@EsAcXv0BpkUC$t1=*(zIE$X
zn#$xMb1A?xomWTk*x7Sqtp7xlBcNLe*7Rgu0Ggf<prHa3KEn^59hX4>C*h=5eh3P^
zIRSI7;Mp_%9+d*_$DM%cS<FpTp9I|tihEcU1>_7B&$NJMgXqs{R}c1{-RA*fYcD`w
zEJf;*!Dn6o<AiB1fq>fq7@?q%UsZ7H^nQ*xpjp7G3)gcdzqDNh*vjHu$IxHa{;$V>
zp8(|2@n%w$JZuz(tF}*FbY*A1fB$}opI`O44w!lcc^_}918-C&ss4MpmY!&;21Pit
z=}(O0rZ0)-hXDe5_-);@v*x^j#soJ<0A!fobsw1Yi)%Ju_@bW&muTGUpnyc9H#+t*
zb9O+Q{2$iL^3;^pwTW?#UwqKxtqY!Yf<$xt3f!(r?VQNBOo|5g1tAkKJC1Dd>Ot5b
zz=i+L05MnHNoR|K*$b}TMX&?AvHV|t|0RNxECJ{A;Z2)x{r&tgbF%rKR5A#H=tZ5+
z5)lz8UrU(89tUBfQCnyuz)eGaaR|zkWB)uMP)#cXvd^%dXC9RO=xHH))HGmZ0$E_{
zvyL<^RwSSnVF2o<N%yW-EqvNoa_1Of3_4XryB~V|M@Ih6MBU$E%+NHW)3e6_GW9J^
zrY{0}0%OxP-Jn}!C9JDeG~<}3HjDk);|`rzJ<gn_-vaNKa)KsPVl*Je8$q#Q9XJyN
z|7EDm>j7urw(WGo80FWWC$K67i)<(Y0eV-NxwuHdJizvj4yn6$@!sCQ!ok4-QUn5A
zTwF=6SedIvalgdXUTW6Es>I{x{F+~ZU{ZVn2upGh0--_kwa|np!0iQ<DtHPgWWE%=
zt6hh(CvB?w0Vt51CqP*6H+7}|Nh?;vJ*B{MF!@AHZ`VV5lB{#^J3>!vlqLdbbHlPq
zZ`J#rc|CRdrBe?dPM{nBCIV(ZxmJ9jAp-Po{Fkac(CGT77k?Uh1Jbdh!@LLk?@jpg
zz6D8w2ld`tH&7_=y?T*`23BzZhNwDI>vs+7aiTjIq6sw5s1GCUG75g`BqP9dO#;xM
zfAe!S@%ygx*D_<~tgS-f{xG1cK%!e`>u{Ey<1B^STvhfM-I`4h85@2R@Y2F#HTmUP
z7o2NE5b(Ue*u$tts$*LyXv-c{K)hVA!B7Ggk?Qkcz-W3n<zOq(CK3k4>V-%D%Y&pl
z-_QLgT)$)Z&&<e$w_wl`td7=(-_Z6}YJ6}F^b7#@y`~@Yip7TkFfuoPLty3C3~hK!
zd9K$%Igp<f#;N7G4GmL(z`+PPmvnU*4+{u?Tv;lE@h8t;iXVv?s{ef~7XE3IOIOPU
z@`3Q6yeY-!lH@8wVAh`@kYxoti${>d_sfpYAH$57mBXlXMjV&Odp=-l0o+3yN3)od
z)hG*8Z)WTJs|_`w2yBkgb0MWoodk890L1Ab*=S*x@q%>|+PMDf%4LiHrJh^Eaug?f
zVn7h0Z=&lQ5Q|8#{`knl&;jlQvHzl?lotaaYM_{-OE-@rBaUx3mH_0%*6-K|9yu=s
zFD}DcA-_eIv!P<%0eGa>&@xUDd}aWnZAoO#OQ${&C=P<A{h>(}1%`9KqM<);k33rc
zA;rD}9`P>~sDj23jB7CPc7~hWfX4xi2&NTZ67BEX==TA-aM%|>7Y02*pt=flrJp#M
zyqAaj2ceW<v1nqzp8nu!r6C8m`BdF9BX%0%gJs15ybplJJzEd@omZ}wiL{H;U~0N+
zUJ(2H$E$l2e+kO)7NwVcP$lSEUS56^8ymDdR0!*hWM*ap;0kADWhE`6KL<_w>j55x
z2q)@>R?ft}(NuOD+J(TTGo=QBfB-09H-f=FV0aO(kylsE<m3PJ8Gy!s=xvyJ=~BuK
zl)U{yrN#w%fWSIPZd;P3Sm^RWDIpAIFn#{kEq@HQLu31n3q8nUL9WowQP-zG0f^`t
zd|Ze!V|>nW=6|myMDdv|pDQdmAt4ylGf|6u85LkMin+P@+Q+`3AvwSZylL8dNo?~g
z@A~?BuVmKmdpnvB#%sgV4M9m=bujaXI?QDR14Xfb`c)&P*Z8!x)i}1!<av+$^P5c>
z6w#_T{?#PlSFzgBF6jZ})ui_Vpq1*nzZKr$M;2E%1)pc9G90)+1@o#t!UKlD)ZVpQ
z)4w>{_@Id}KT!0)7k(#g0JqD*!2tuR4~8IC0iGRUeR^82s;-Xu{_%;B)6)6dCwoQ@
z6ZnY0G&4>xEKBPX7_J1r@>vNXY#=7p{B0Gug6!JD<rwuL6g;zM`<V#7OKD@&u^3Oo
z80}+zg|me@`LNM|3C!2tlGNkZL}gTBf!xf02Fuy}XHe->){_?ofOIE*voc%#%tgv)
z5!Cn|K=WIvKYxN61cQ?o`zYYc6<=xy$Wk(FY!07~3n(Z6{_~&Lu~sf&0(z{R45&aM
zPm5iqFjn7pPaE`HvN+v(TEa`pN&I=5&w7|3I7{oLchje&1^va(-KU;#jKzEi;I=x~
zH0e=DF6x1Fo)u?(y190{UP7@yhVx1>r?8v&n1|=>-O30>rjGF953!R&0|OU`V;@99
z=X0fFE($5-J-q7gLyiqI11t(_1EJHyDWT=O5?L3_?;RI@rj$h{C_d>kR9?#T-OV4N
zJ2D=wE>LcC|0AV(vJgdVccE-bm)JvgJn$a^6V(ONK8~e27jR(dY4gxbNtobk58lLN
zafOlJjaiS{G@fBw=<3{g92fvK8yey{{N9c~@14O((C%@5`D7CSPiA)M8nNXg4YBN_
zI{blr`#TVjDrQl7d3h}^E;h_}B~p+EMa{c7I~xLO7w9pkww-;&X1w|5jE7YfWuk0s
zR$6RyM_)*4Fbz7O7Z;nYlq?&#Q>I>gnO8?aIh1UM_v4p+dWPnSro-6;raPPtZzV{$
zc-~&q)eK_EAtu|q{a<$Q<;!bN6$!cy4-Rbi=VF`DyGU#l!nXB-`t%pQlUs`7+bS9y
zINV+gX9CvS=xnCJbOupIudP0fXou9EAP|etYn560HqTrfK*FQ7K>zbw&eWBkZ6xf=
z#UERv`i?$q=GbE1pfK$O9_vkkYKxbTtq=`#Dt4!1)wb}jrO;cRW{s>#0vTsNaeS!1
ziO`{T{oyBjv=tv_OYpsPbJZA6LdXQau0sFZ!{DTm%m4e~S%ew*PDgsY17l)hG}@t>
zoSZWe;yV`=g=YMjZA^+aZ0F-mX20_Gv^Wjv{QA5K3JM4xufv_01&LE(>)P$XK38~T
zH+Vfo5&2^_7X(o6TwG+HkyE8=%r<PaItjVCUq|TA<k|B%9j@1-*BV)vR-_`hlQ3TO
zbsrcCZOoAG&k@ww&a!HIKZN)<tmdT_Y|OUxM>^d-E2>2^HdAlr(z|dZge+b5ei`>G
z;rjaq%K45R<<Jp|sra><ygbpQ`*MCM^X5so=$cRsri~7y(XW!1ZJ@(3!u;QV_1d97
zxoepLQ}@Auh3k6q7PCVX$4o-N6U?|%R-5!Z^l0ENsWx=NQRC4ZU$O8_<SS!ry`II$
zz|ZDnCF=$vcHO3~T)Lus&dMD3OL;TYJyvZ2^<FF)uic)eT!8k*`@onVBw#%KUXXv@
z`CizUrSDfcE%%O(pOufiB!6zLa)A-V-SoYcz_!|gf!inxy+c}rK7^jEg*kQMLbESt
zv~T^g1&;>7KtEIrl5Tyrtt=Eowi^D`v4aM(cPC$OS{mb}ikIQ13L9hJ9v%ogoSJ;K
zFjy|#&wDMJw1N8DdT(_#2K=(yD1EG5tAB3A7*8<209l}@H#+v<-1<JsVjZ<<O!AZ_
z`OlL6*53gC5-8V=-!)XIN7j(tVp2Y42M#p-_x*yJjr*Rv)l&ge=!oQ0soxh>M8&kK
z;n)V<#GN{IR`1>eQHdVortO8ErfB4#WXy+9v|J=By<p`<qykslB%=hQw~gS*u{TTA
zfiz7js>_PO)j><HZPP^Uhe7K%NaSm(##@Zl-yQz_&EoVA%ify&uIxHC`Eys5QqBgU
zrnz3N*$VN1N`U2X5%bTZ-)n88Bjdnrt?@MCV16=mUeC2fXxzPz{_VmT>5?~+dK8o4
zU@`-B$Az=qo(LhB90>?C-jzz`eKP>2H<&0QhZLcB>=1dzvd#YrF#sm-E=<QqhKpTX
zH(|{b<1lK8A-~IWkv_)Z_n&+IKu@`~mfNyXVsey7=Y6W4swq+@#G=?%ZNI3X*;vhX
z|0bpF$dS2cO%~m$rv-J2m8ow+M>;;dEgX7}Ltb6!HqgEfZ}z|K|MMFre)G?tKSv;#
zaRE(%mt(=w(lT@#6oG=~l72t#$ofX6+IWq-c!I}~*}{!a{@kg+t%-Wz)5p9y<W-Jj
z1*;jlPZ`p;L^?g=;)2pcqoslb-(rM=lc~l%`z$$b=bVXT4I9?{$141@|E8wkfm3-5
zr1Pfw`g~!EBB0G4VWpyLcPC{$JUlGxGc)JY27i;9-;!9ZA*>-ooRplY*uEoRB#R8?
z8O(Ff$hto7xUpficOuMd*G1F6?s$BA{B)4ZbM(9D4=b)zy|CxhUuEC)-@2dsj}QL`
ze@<@%8-Da7N-40*Scc~!E<zhR-gcdfRwv!_6dbpe$+P*aX}8hA#<WHI`=iT-6c8UR
zAB$Hk#4D`h4wP;tQ{hR7xm+y}u{(EO*dw|z|0>>tdC;X$WA6DWi;k?OmRM;3Eu%qM
zEd1ntvPRu|QZ_}-j!{VDpOyG?0<S#<_h8jov|D(A9_Ak+)(@5oQS5sAXHE`=>ZN1m
zLVH;J8wBh36>1Kaa*Mwdq<XAX7L8c4e6s%KztU&QDqH>N)C!uB9`3gipq->M<K7+G
zq1>erD@{B7H3^r$=3a05L~Mep+|D1Kfck<m*r;Z{j?5J$ytU;1jW7M@GModeLXWVn
zKX!(zf8^4VguaL9dL~e4Z{_yJ5Cakw&+4k4*S6}>qMSq%*fQAK`1_<^t3ZW|${PHE
zKnU2#2iM19%VxO?(LOmLrJL-VRu!v}l|qGOko|czm+8(;%e^Kd%97f%|K*7<U#67?
zt}a4s<KEFUfkZ<Obh4?Td=#a0yYZ<>azH`?EzIJQqqoDo|KP!lqX>u?gSr9j``~Am
z>-K<pvB$18LRQt`mjU!%_hU*cDB$`M5&G-#OigX9R~y2h+~wlx{#fS%RxI^o9twB|
zH!SBLY5dh4{SzHAEZK{gk|K7;9mq5_HP?G($fP1!5wf<P0Jw6U_9eitO(vK9TyNT-
zP3W6jP<2M)_@|QG^NHWLh9JF}u4N-jz;J-Ai>518H9iQor@9J#goV)WPxGG@CuI|6
z;v?`OV01+w=^f4%)StNT9iM1S)4wF>q`cca*N1YhW{@}>>9OtN9||2!_<ekKg2=HW
zyoUWR^jO{jp3b?hivv|vVm!^xrWE+}jhsQiKc3;wS4SZlN+}1#UMh6D&%~iqbspMl
zW?GMcj&Q83to*ve0(;nR+*pq;^u(Jw7%W#-Rpr-w081&QAAhG-d1sD|N9s@(Yp=|w
z1Bk6;tny71s(7!TUq24BSIH9e-yOU1Bi`rwK90n=e6xi|HT4&@K~!zB4o#PYl`PGg
zrgjKXYdD4OuNKpP+aHF4wuj)f0W7@;@EY?pim_qBtp~a`XrkH$5b)`A3#qwciSORM
z8z*FU83YWtpjm-;>;ar1r!$vs;oI)=(qX<=c_};j0vlAc^5SWnM6uA=gE1R!Ytz<M
zr2;vlSSAEdaf`1OcZwgcvFbB7==ot5clNA0Y)hwrExm^Du}^HMLHV`!^lZ$MN=d0U
zdlgzEne;}&UuR||>n(knjtN<qon>jeqVw~AuI@|Iim;DqZs{OQGyvT65nX)v@Gu}0
zs9hJ6b@o~}wWdjOiZNhl0qWb~WMedPVEO>sG!A=pW+5NgGp!QF9==l6F!b8i%$Hs0
zCGpY?yX97gM{jJtd^^Lt`>joFd}Gg@Z+9=MZ4CQd-EJz0>k*_Fa4RskbRl8a+0k$$
z$KBEf{l)Yo8mZ8*z0k!-dCK*lgZ2B=uh5nJ_M)_j0xM3aKuC3aue8{tR~U!_lIafj
zzDVRw@OuzWpBfdN$geZ4hhA;#NtrmSB&|_bpS7&Deh@=?w4Ow>*YqmN{#Nz`F!MWX
zgQ%YeF;WYBE1R=#$abbTkxKNf(F_1#WF9}>x+->HU?jVl6<rP6=<r0bog2r$S(d+U
z*025V8~&40hn>;8*J`6X9$a5rNtwX)tsA`_XuHZ{ff)``sIai(yc#VcXAm7fQqdDe
zr#5J`N^Y=!XEH5#EM?dyI+f3;HB#ie+J(>+Y~J_F*ktwffJkWE|Jq<bJ5{`zW}z$p
z&Z{pXXL3ci2$|RSMr$iJJ4hCx72*%cHa9!=x|_`Q+RUEl^Iy*>lKvknlrHpJB*L_%
zwyKguFyC78NjX$+b2wi{nzXq*<Z#(`napgj>wy-gT+msv>k`M*R8^*~_Qs(grWG#=
zbL?Q;@VslX5S6GIT6Dw)%yOMVp+u-h%7lL^q~rXfopXi!b9D(B_#ZdWX^8@(J^S_?
z?53|jFZgp!{#YUS%Omr%3Gf2;y)@g!wL?bHtpoBR*^(HuDzZ>__yuxEz-T{GB#Gz4
zvpBW5N{}w0+6J~wS4K~ZJTL1YPF*e7Qp|>f#<=1`92V3_q5d<xl!SB!`}K9+`Ud-_
zhebqiY!@D{B8ClC%AcPB@-iau-kep#RTW=7k`AH~)a9Xz?J5jpmH$5$>U*0?uXaD5
z=iW@v9&n~bCOwQ56V`jj-h{;v7L9!5`fmNHm%kc3+vrXe*ZN`*6jpKg^KatNsz{vR
z5lCuhV}owK0J%|vpVRh<*f?X@jHciUSq`wGa8Qi0;3fXj!x&4(j4SZjH882XP|(Ll
zY>T2hLC1HDy6dj$J1?Oa(f5L7WvDh)lHvY2(d^Cf(kd*alb#Nq>l-}zE4^Eo6l)Hx
zr>Q%T2ebLlnj7P;GnQ{F@>pta{qNRqwcY8hDKDpXnQ)?JbYEmHEiY&KUg=N%dlSOa
z^79!vv{XS0yORkYH1qY=cyTt>2QGH+-3_&)D}MLKPWQ8kxUDQ(KT+fz<t+mv{r%+y
zB^&8N31Flr@}?mV6D4G<Qo>oR=~SE577)_~NaT^YRZ|O=&91I8>`gx9UH=euHSC^i
zHF$a*@bpV(n>R{sY_6k9uA-2q`{x;aeS20jsmK^zO$6)gH-60ME-^PU5TZ<atecwA
zt5R1}UnQ%6yy%LPkeTMscJA>1G6^95x+ViW4z!T$0+a8-<FTo7Ej<4L+cx_LAmtz{
zv}{h*9SbjtxvirWW73*W-P0K0gfgrjJKn2#sv%Z*M$njC1-%o1$vDkxhHjfTfgcs8
zwniI+#6paXifNR6!a}2$I!#Z0@_w;hNY{LL&AFwm&U;BkTUn44D$jo!0Unmb%|q{A
z^G22+>?1XbOG)Jvu8GCXA;DDzcV7eZRd#FdThN@9G0jjiKaQTT+pQxnUG3XXkzRDs
zZynHH2E<$<hV&rzYyLDS<4cYWEa`L^|8n0vQD(AD<Q31&(vN%HH2wO#S>7rCHrv1A
z&resf{N@2@hOAsjAub#uZXk@6Yo5Ox_00!9r+haiCkK7$)CKiT<n&}WL26FoZ9mCz
zez*%KyeqrCIYUq4Cdzyky}Hu0U+=~+C1Q1>u6BJIyV|!XP@=AiVYjx5Go#4!;}^m&
zdX6m>Z<+k_xa_A3)fxOi<^;{cpK_M;tXmz);kd>IvV8qCJoP10vLNMtnQxC$W?8vM
zvE_5|$+7sS8Ykaro@>+zF~;oT#Hn7hRVfbttb>!A=izrFM&dI?eXac@Rv||D+c*lz
znM=3+E%Tj(3OPSyNC6`WG#A|dkJF6<MfFQ<!ujgDdu?%VI63x0k^?Cfr>1{ZmB`z-
zZyRQ8-wPANW+&`zK^-~#V@{7Nu4nA?-q5b_Fk_fk;$XB-;drpOQp_*p;Ebmx_L+iR
z!i>@0TZ3|M^~+e%e19!4+zg5-x(OJgm+mGWY}VSU5ezhebA@6B_(Q3XATeI}yXNUg
z)|r~o`$=N8=j%Pac^O?jsJEDJtw<xtYOCpb3T$YUG2)c}hcBKFnV*hiL1xvYUWszF
zTzo_Rjet>W^QW%o&M=XX+31PHRL~jKei*x0&!sK{3F^1*o|1Um<NXrbxnlm;&vl^>
z|2_@y)v#9w0eHF1RJh=zD~nodO?Ecn=`k9z13{&UhZ%ezg2b6|)x5_;t5ZgOtfzV}
zAh8+@Bkfk>`!S!NWHuPmYA|^4z$RdOBm*R2OQAP1(Mwx1r^m<Q93tjo)>XEoof~;(
z=!|M^{|k_kO^XrJbE=qB-L<CR4^BeP-+B%9Pqfu2t!rJmV!CtNKB;bJ+pICzdsnl)
zL?10viV0*8xp3v&Rgga*)2J)Yp!}jXVC3PEX|PYU*Wp&8yjXNrB&+vKl7tizRsBRg
zxPA_UFXpiN@o?KoT;;pq_H3Z)a&PZSx9LfMTxc_tPu(TuR%hj}E(<@}zg~{(_5!9S
zgIYFNJkk%r_JH7<_jhq#7+eG<YfYVq@-io5zSH++S2XG>NTcp26L_W)XGYJ)TPQWc
zL6cWuX5sf^;Avf-KcOp-fCLJJoTWQE`=kl;bjLzPQ^#Kx)IrLl{2C8$)p@k>c^-fN
zNN^#iAeXCQ$wIM72EEb*P%>;AWd{2b3$0PRo!Y{g(UQ=#x~DX*Q{r{xYu@gZjK@Vc
zGx#5}yBGl9PcJB3Qgmb%jZNZ3YKdMa5h|;#C4)R12<p5JN<+Jyt}PPh?9FXz0T@d(
zC{K||6=coOpS1aZ?c&Rqrg1O?@ZrOiL2f`c{}8L2&^iD!k00F|z$^@vp<D_rW$rPE
zERrCA*8-a=5c4(n1uMx}cQkk6)5hKI6yFEov=NDF{L{${G6WM7SQo%Xq3W3g4fZ27
zzvg_6U>F<d3xX2W1g8NeK{K)Y;v-nc_oPu|mpM}Jy}9CvdEX&a=u8jtT=5h$QyP13
z<pE5IiJ;LoY#`s0BJbpUB>qnz^Oc9=G&!JuJ*+>o!Ti3?JF#tdU%H;eK<-m?)mtQV
z_)SgQ{)ejy^@qZ$+k>?g?=F;!P5RW<LcXOA7oIi&59E(d7RMod(0%grZ%4zQ)A06j
zcP7L6R>Hb_k^a}8WS)H>^y|)$ehsRwvTg2kx3t;>$6#ZBK*PDw^1Lu-b6w2G=Uhk2
z7D%HxWS7Q?<AM1Prq#8!yY*g_dg9N|)YaPFV01ST^adXa!sR9WLM{BY;%AevhWpmK
z|J-3NN6}2(sUQ>VH8~>+<s2fQ_I)kQ+KLq=^mMQh8ysM^^rVdW3zivE#YouQCyL?z
z*!uR?5LW(nTmm*nvU&gr2FZk|2$w5RuSoX3s3HOEg?yprf+)|IKYizu{V%`(a((`<
zGY`Jy%kf7vd0$$@$6mWPW)wOXhSSDCbZy?UFw#^5J`kCiR|m;(zo`?$2txYI&@wS9
z3wYZ4sVRV+c6?BT<2Jk}59Azmz@7QFKOkhJChUFi6%K<@uQye7rj-h<kW*9RzC~AW
zRYg32A`05VsJmq`gy6|Vfdy4e(tI&hQ*i;DTQ)wgMgH3xQY?)7`in?<)kPju8?iC!
z)BXSfLrne5ixF1?BX%(?r-tGM!P40|g?#X{TBRoB3i_OUgQmv9;HWocx&b+n+Mhm6
zu<*5IfcUD^n|%W?X8i99;2*Sp{ya_nQe^se*n^&u-A6ei!rb%->{=oz%%p!qMfFs1
z%dx-P0XdpUOS|EHl<)N{I;{~7pVk<&(9V&x{TFz|0AKVcAO37Xt)d#x@wVg(oGFUD
z2d?`^l~={_E0V=^{Qho1_H%Vk2YjA~TOW(!<<JpdE@$EGYD3PrT*&~J$z3YuRA}9P
zC_Nwb0H7lkRK%HKmKt>uL9PCDTybHon>j_6s^Ygt`W<=~wA~#qSl8$a+}9rT)4SyJ
zNsQgwaqS^Ujt<@;ZFEW)i8Euumv$TOjeeg6ur}|hW|DXocDddyY83G*OZ8thr(bIT
zrQ^H{^r;hQ7(3%ShENLWbJs!{^{K78+_kp2t<Nr0w5sp7)OTi<xsbw}%*{7Vu-S{7
zeB&aj?{$U1R&+m^W;?N^+u$&wL6wFqSxK0rok`-;t`c9Uy+Eaos%k(<w+JzJ%{n&V
z^Q>?6E1@DEaCPi_g53<MTbR!6@gv9;M|Wa8rSAxJ^@36BH_eCaJ#;hCB*lRpt*!^v
zS<{6!D-AY!qrqu8=w0Ej!TvTW0vCojl41tZwpQ%+_t1jWAV)(tiuYLGe=07PEPsU*
z9KfI`*-IR!`zW^A)7zGJWJ)|5irct=q{Nc5=jjSfvv++)2h%qI$du0(LI9CT$gC1W
zsQl2fJG}B4Kr#T4`Kul9>u$W2By1RpzRbb6%I4p3JVD?s(RlA(wO~oE4H=Jy@<aj|
zp_l%>8c1K;l%Q_Ma<dGv%y?6vA={RbBt=P#sA$f_!Rd|+I4Z&j(8nVVBQx)EZg_7*
zrA~k4g?jw^e}4V5B=R|#D74KQh=Q%9E+yU0PPXAS7{M#S#2a0ej46R$v4zOD@e^mw
zq_ecmWDbzH8d=D-m5Uqy4sd>67$N?`7Wtg$6z%^?$iy0N&E1}5(6dS~V=Vp|3owqn
zvRoX6?dR6CRYE@O){?xr?01}{4wVCh?#4qdhQ#`ExH<smU@2UBqm_}k+D=V(j@HNH
z+e_l*44nSUN@Pe9vJ5Yyv9N1>s2-9MB?c}8cV;M(&_;Awe%ZNyiw(DTh;)k&yVm2f
zRYqSM_75u`#_pZ_{i4HZ2k+i{MHScXvPnMnSu#R3LYqrQhyb+2lePArY5R}l7y=|t
zFcEtI>I>)8Dw|FZ;c9{h<ZNtgSaOGfW3%{r!_0`RK<{Zp*de~!N5$uTk5G2~0S?MB
zu^!x)9&*^tHgYosu??;^GbmX~1_=r+j(a8&32O7urFOk3@`UEx1det(-6|}-`K`Tc
zu&N<Z0y2r72>&^gP-zWsT?L8)p)!1c1@!nBF)JYREe27(+ziC>owW;&JPebU);#TT
z81)jLW<zSv8R4+fjsNcdgq7(yY0HxBltdxBXD9L9pG5qW(RLeo_CP1zy{Kyx@QwW@
z&D*oB8Axsrh__KKLgf;R#rJXpLN}joVyO#H=e#V9#vIoKx4Ys^U&H!AQAb;riJjtw
zS$FM8`620MLz#vz>rC=iM;eEAioa~g`-}!`lTeMDpPtHI;7jM|_z8}g=N!8pC~-9m
z)$Q=t?2KB(iMntVRL;|CgJz%X{CtKdGhkHm<#cUOK7ue`7Rsl6U7SDUDNaoJ8;SyW
zDyZN1ZP?K)=?hl*iXZJ5Aplx=@2p&f(Ow+JsaCmgWEXXptefr{q<IV?a@V!0Cz!+7
z@|m7O8##>-i8!%DqM=j?Vs%C>-W-E{rc_bNo_rfAbv@IDVmKX-52ON_kM;LKhcth3
zXs@1(u6XvR!s2$;>D&7MA8hA19HMq{w-kGF(Et8mzE8cV(@U{M{5uEiPnpR15w19G
z$4rE+3721|FcH1oC5!L~lrUwejp(+L>4`eu@YLA9ck^7+;;Y83#hYI)se6(VgciA;
znnOSO1}7|W!^~Eqo=FO{O7z;MN$cu=DQUyMIA!yuw^GG<ONu+n6q@q{$Q)dap$7^g
z6`<3u*bXH-?zWIzvDqzZAdvV-Tl;LH)oBwzMGX#jR#{3lVIQDupb&IfUij(L1Ldvp
z1FG?ZJ@?Xz{?kVV?dR&;JD+L0%tL2~Ia!Wgts1E43&OaCwn#lK<oTRVU~N<1@D+}p
zgwq+tRN=CMNF!eMy_rhhVprV@aCYGAvac+9n@La1?6F$Y#$83?0tp)`@1rTF>k(6!
z7JRrmsc5lw(ovk>6#|?K=A^qIbUqdQt_x>DtG^+Vu<$qV`cM6Vl(-6i>ocA2s>Vw(
zg9r4)WZv7Tn{ve1URG@}U;L?vw#yNmAE&6piYsOZ`c?zlhVrs*?lQ5^u+kLG-_1cU
z(5a%0RxcSH?{X?i6616+VTtPEIoDwGdAY7Lv97}=5o=KVg&2a^O4YO{F=VHvQgZzw
zV!s($$0aA>a(Ti8ml8QWLD?G#Xt=<k-g!^|{Mvcx2M@k@r$(suf{4rhfl(}3y{k8`
z?&vW7Q*5qmx*$=ifhI%4&(m*;3Wp-JnDinR1fT8v+{m~JW)y#LiW9e9IkB^MRG>I<
z-Yy=|Z7Tw4c~f$T1;E)64bL}oL5&ykSjVKKU?DZfv5s~=<X3>GBBQ>D??nzsT|dF8
zD+4p9*M*?Rv3TX$V${@3H^ZR^|J8rzZXhu|nMh7+gCo{=;|C{(yt2^fM^1SE;u$wz
zF7DpXEMx2)NtWU2&s|gzZWlTZ3(hp?;`$%>NTN@yn0po>G5ppu8alGQQwh{&l!rt{
zRO(AU6`WH-0`zFSM2?0Q$}p=L63GUV6>s9<lVFP5=wZ)Zmp4qNvfgPlPsUP%_E0vy
z@e4TWVxH?3Y_xZ}3A$kJ0Lo+PkkL^GpyH9N3@Q~Yw2uLIJAH-NLgeIRUh(Llm6OmP
z1&K*)KvFE@zC0)El^HZk^s)(D>%F4Y>mdu}pyJ)%?ebJ%597v*0iIOaAlmJ|RLqs}
z&lRN9$7>{u*S)(=4iiP~ejb^B&2FbVZY7zWU`W;|%V8891qFO)&W_Ud*sbKzG*85O
zC+zV5d6CLUBcxp3+q)1?qZPBEvpQHp_YuO#f(i~?^2M7us=EShV=dl#9%btCLA-ME
z^$o72`41UOs7aJ6L@p@spI8x0#N+j6Txg|~nTcOv7e2XNz&S<uqDO3M*aZ#FoUw}N
z6eb7rF3Rr`5eZM&$%WpZ>yaSIhnxR~bUmydH;B=mFW2C>cI}!^b0hR+5P*qccmGlV
zu54#%q!!pTZqjMmt&Mj+(j9qcc08#Iz_JLXM0ef<Xmb4ixO0cL3e`H<Ioi=A%zlM?
zBkR#-z79_K$EpSe06Z)XDYYL2$pZX0&()pf1l^n&I<Lg;G6RmJkn>~AKUeS9f<P}9
z$ysrymv1$pQcUsCruA1CIe?AJfz~(geWFNkioq%PbWe*^=*wl_k&V5$jPuHY%%oY%
zZSIFbNXoIkoWX~0+n?c_xrzv&3@u9&(RCBMDVah%W6_E$6dV>dyMe@!(=oG@Myw`=
zHtg@H`{>wU&wM?Tc_qHeb~sl4?KKD>KSMe$Ivmfq{3YGogzG@A4@YT@fKh(wi+x$_
zJ>d8cJ17ZJ3!wBagI<%ZA`Tm`GZt5j*F6(-B@24qVOIA#Sg#&L?2rRnsmhhCLmGAc
zW`+=RyPc$9P)K69Q3vgu75i+>G%D7Jg`Auk2^g?QEPkfzM5Sh|4fQQzK7(m(QFOf{
z)b~ZF3?ngrv$)RUrHlUxI~?^vW^%h>%19-B$A#{KW3#s;F_w9+DdnL#d2f>s)yfbV
zO+Hod?m)X$RCrL>SjW{g%d5%`*gP|?M(o+0JG}POj$PS<t0S@WR7|~P*zSANXeDT|
z?Rfh$j@8er4`j1HL)XM_hBA_5=Qf^a-Vm2vwic*M3Azji!U)R%pW5EVF=EP{!|jax
zjx)I8GAi%aXsR?n_XeI8$1%v>wXpDh-^H6Fhuk5c8RAa^R8FrYE8aSr+Wa;RJyD-6
zbbbV(g_PKxl<$KV^Ghs$+<4u;7zMGWd6pF?uXBRTxQmIVag(7Cp}=IkV`H*m#!{yu
zQZr|1zC7VF)e6RL8AX#t2+b1KY<+tGcorQS@U>c$6d&t-p*6&pHXzOun$S5pi2%^6
zz|oJIrz|y}Wct|?$80y3@cEp`9${UQj`88<iVH=j^5Sq=be{y@6TSiU7{mEj+vkWp
z1vk(ACv<jgd1uZK%k4&OR*N3m_AlYr6n|#P*k@C`-nen)bKK*3+L>q=5Bu}#^L}uF
zDq@@0G>B!(^U@Yikxf1v-5KTSON%+`I*2LXc5%I7N0ueY-CM<IYKG8U4)eB&qG-C{
zUZyTch_wfagynk~l~MD4%G-ZK?sa9F<0tibB?`uJYMOH4I5e-;iLci^X~Ik2V?}%z
z9ohW9PspZ;U%#YFj%EvIS}~#t&ZM1^nT)wdDE>H1Vs!T^?su8VW6|;wF()6fDjW8T
z@yI(yejV5k#rlS+CiRaJhmT_(t$HGJC6V)3f_=f6kmU6wo&$>y&G*BP1JCVZkW4s|
zdFlI8Cp&__6q?$yYsJ0CtBl1#ZK!Ih{CIg;)b?keYMfe%AU3-w-)A>$ILCIipQr;w
z5r)(VP}rP#xzzMV=OV*5E@68y#^W>!facTN<~7qb6{bvuj!_SUcWO2zgxA;9;naIU
zX6!G_52!^#=#2pePY+T%)fK;jf1P?hY=54dmvYThCu_dG>ncVgq#<PGllLyH2A*C#
zq@q^lm(bm=If&~cPCA3-h{o2WyZmkgKkWnK;)uA&u997(CGMNe8wmBL7<C%5=<3d}
zn;|S?xm`)DSZcAwVFBjQNpQy=?T_cS7uM4R-`#MRCY%5lY)I+sTWS?7<HMKSsie$h
zp8E6qZTN__+I1UODh9N8;H@6_;qSVp{A_+hKQrINlt^uYmO71kKxG0o+lN%@GT;FS
zr2FyUNX2{q$apJ&bmg%|4i`9z+j~Pp;U)+^lHar`=>O(TiJf78FB1>qPoqbi-KSm4
zgcdm5S9W1y4PbUCYw>^L4)!jsrUQjlBZpkJjQs}@1x*Un-BQK<x6+q?CAPocMBe-3
z(BQT_QKd)Ymnz3lB{zfUZovXP%xj<E$_~{Yh?tGmcfzlV30Q=_Y-L8`XhFoguaoNU
zaGu#+@o&q(+Ay^hGx+)lyFD~C{CXe4tK++6p+70XK8hm6f#$X~CzijYZW;}e(d*Wp
zqF`8Q5=laAbTk$ZHaiL#(X(L=uBa)*&$kN~C-W?p6eMUG@SLLe^wkAN#bIj*jr6_Z
zPhD$G&uDANN-TNyAeR8H7B^2!fNC*-PS!N|s0Z#EEk>=q-OP-xULSapIbEk-P9~H|
zJPlGnjs1MQ*K;$cy)Q+{;X<W9z&&to@+{nL02D6(3pLC4-w*QQ{MUIBRwBH#2og70
zgjg>fnID8akfDEaJfd}IAa#*>eGq@Txt>m@;J$v7-J6^RHbk_OkJ)S2cjP`MdaMt1
zZB(w=m?5gla4sJY=$CZP-g>cX6kv(Hfuy^TOU%ZqQd9tDYTO9Bbc>?THj>qKWq|x{
zR7veQk+T3f@@(=aDy|z_?Vi2=Yf<*3Z>)J4K*T4QK`vAGgHjXKcMLG=jMqe6A3%H+
zYki-uFHCHIm*q%O4fjlRi&GaX!)v1TJ_Xioe|z<M?!8@gP>!*p?r8-pnn$k0G7E@C
zf&3P`>6Iqa#9uAwJV1RdtYV`g!Jt}#85dxPjaC+e8Jw*Ist3FPjBy)P(Jp~(X)TS_
z{QpW&UQWNM_ykLj;71w5HHV?*yzR#HeSJ4b{1UbdL7RiP?Zb{5&W1LAaDc9gDg&-J
zrshBcWik4gsZebV-MQv|=nz9=uwN6*JbI?VlAx5QcL;0iGuGtJy)h78%Z>VD>PQhZ
zD$F9v#z7Q}+}Vl{VdPK?&YT-cBLK97lbQh*kOW|w%-B~>Z@U+Rk!7HK_+w$g#N#R0
zLq30XfzNQ}e;p_DxZ~XVT+~&Rhe?l5zxmPqPm9?Yz(W*wbwM^zi;s#+9WCv1!X%UC
zy0zk^RVxNKG^-ZzGF!O+mxp#UPYGh_G`KkfO8V1uWL|GK_5hI5RO!I}Xhm#~*p=T2
zj15CAV>U7Y4UUPon2<f@7Y$Bo>W{_^>;(E=_VG6i5xHtnHhq5M&GC$G#4{OztBs&y
zP06?^`he-lSJH-A-18yD@p(9hSJ~6zuv$g7;tX%*gdp3}kr1b(4R7z>?cP1E-b$;z
z!NLRI2^$)%vU|Z4q)fNObcrfD!xmVd1k<!LnOOd8IjP+`EPr$Uu@d12V$S`Yopj$B
zjD{8th;;<DK#D;61gxKnNOHvym{0%!d&-=g*+v4U7MHrsWJ~T4aPA(PIuO_%f}$~?
zNzil^=0Z0bCLSy+YpK5G;{q%}fQ@WIoA}?)5A-s5nox5|WYRhysm}^I3;kRoo+-!@
z&<BVvps+lB6LjIhG7n@3C@i7vw%ti*l5XaTJxA0asoTVREStn*8SEMs)cF~9>uZVY
z-3^F6nK@UFKw>PIBtfT6FN*c)D>XKZBLB(1t`R#<Wb>?A@k*m)0+FH`R$@<#3G~@<
zR|5P>@A}cR^I*?pJU<zb9UGz`OGdkq4IdJveW*9IP<u-3)2W%3PL&;p^FtlWEn3!R
z9M~#*=OH$B+1C@QDc;swQwDmXmpa;vimPP3!@wyr(f|b>tm$r1bV~w1WYIe?5TIpK
zd%+~tOTf50DSq`i*wjb<|FFA80J{sYS{VSlOUUGE!EL+C5AQEg(`Tyxm|@R)*p{j{
zc60p%7Y-DTb`yz7zZ7MYV}lYI&6iYA9MTh7^~{xTbLmM6FH9A|o0m$lNq+;9JL_s!
zuF26s-!u_uJz!l<!m`CAJiqPeRqLVG+Cir7c#$~+Z33`>^@}g<D&}T|ul1Cy?t1+R
zIlvc&onN8g5Z+#Om``T?*l?+>C+VFU1^%n)c!BtIGD{Ba!4&76W3a;?vwUCTz*}kc
ziDY?0%}G!6vK*13)=<(z_}W8rHb1KsA}re}eE%@8rKqCznu2YOo`uLBdv^E@lj}Uo
z*hV-Ae!`W*^TV55y?ZHr2O^)!>(V>kD7)#1Y3ph@EUIk9JV#kfb9C*%Cao=$em`II
zGX{k;0+ON?U}$6C#6%d7)|&MtU~CvMNljk;@awaemAb=a1J6=GwuSahy10<5A?A-%
zO_fA<t4$svDJ^ylZEW-#ij{V@ri&wJF(XD6J04v|Bi-S*$18FeK{)Z%^rWNzezB)K
z=hYY?IRN2*9C$0VzS^HPGf7?kUd+3HY1?&Hl#S%zUDV0JiyOcNQ;nbQD88?lZbYaC
zIFEmRPep8EtX%uzpYsO-Nn3K#7QfkJKUAGR!tE5Ld2NhcN$S>%wpTNoErXBF2tA05
zNbIkdi@rlbdNxF^`Nykw_IhlFGkeYPh720wo|2}Xua{;so<0>Dipy*8M}?B`+}7{=
z+)S7(SZAUlZZjoxv}@svm~)sl*yv;lHJ=--nW^W7``>B%0ky0SFeN~D)y(M$`gDD}
z5MroyUTp&K%CAwgK(q#Vw7>aCO${GlEV-0}-|2N{_~7!xIYNuQ<1b_D3q$2Bc^{y@
zyvtL;W8A-}9nziaQEQ61L?CY`m^zw@c*1!4lQ$7u)%)j#ks1LnaL6A!9qmJ(K_?0O
z82SB2G*$KIvhW1;_y|J6X;U$q3Ve_2>_*S$9lR$3W)mmeYJ36Cf7)Ud6OwksU_upV
zgy8$5b$KY5V<wkPsqM3gLQr#LKeYBAe1PF1vrpO>q0nK~!RI%es;1i8CLVLm_ga|r
z76!js8#>E2K?{AC+*$ptxo@TPv>+<WTZMLK+GepW@njL>YUD(I)U3G8lo8v+VQ8Dz
zsgI*r)1KuNGi(bReVroV^SW5*KDjk>a_rGv80_C+;oB*aq3MQsh6=`l9lqHQ#|^`8
z@8#vOuEV6n23X7nsF57{^svjHQ&D9IzXHg#5Q7?g5$V(ivd)I>u*o-2E5^NKN)e^p
z7ufWk8Un7s?%?aKnyN~#hGJ{IixM8zlxw=~E#9dv9Q*p})G+hc$_g-lq4_xTaodxX
z=F2`xMt)s+(>r%RR~#^*v&2qdfU&8{?plx1absW1<Zq%^flbHaNq8O?)BuXUaiVq8
zK0^=T5B=)}*Pvc7X4t#tG`Qwms;npOjdvTinYo>oMYcE+wHh6I5sQeq^;OZVPi^+O
z;F4$h`eJ2z!`za(h--(ZDU4y*9uz#smf-eso-O?@cOylvX(7eS#}KR2ST-kWs4X{I
zG`OVN8$^h-{SY{Nj92_$;&o@_lh>?(t?M)WxrJYR^f63hU*17>(?oS1(4<A^b)Gmj
z>|O_^tNs~Rciryx+vpmm{WOYLCf=E}H5T>lgQmToC3G0{E*)=z#D}l1=0cWjXTL`R
z{!-tGC_qrTvU6Mdw{e<h0bgql6uoT<;$ry<enNn!RZb`2;mljpxMdR~#CZm;<#^7t
z$SXfn+xXjXpH5G{Mcb(4!aE&an&Ks?hisUx0Fb)*^(=YWIVW|z=^MP7Chwi1=@Lum
z8HLt2s#CEe&muG`Y?(JUH%n@qX(@tfvPDLR758E=z87@(>vr-U!<_kM$nqg>7Z&~(
z;EO&<5&E8be)k9ToIB;*$011qC7~8QNxp?Cw`h#--d;Qe?FB96N~s{(7g~=Dl12y?
zS~n!1lRVpT9|VxQ8cy6E2gswNtDo6|)_IyK5idXkvAvOZwa)ZT<uXiE80cDE4=BY#
zGU`=Rr-~b6Q-N+32w@5C72JGLR?L>+kje`xRon?7c6c?cY=7;27&de0YgaxYM&d{`
zo|s42U@aNpj<Lswx63Q7@=}-|m)zWrU})qU(!+m@fjGY3KsHh%ECoO-m;HTmatW6W
zvZVuZkmc4kf}^DZroCyz3?#`ULY>~{=^<mevRH+nq9oD;K;s=4(ZvQQz`#Nd5ReMY
z8$?A#0OHfR1xVxCK?#vF$8+0>SqiN=N(q28D>yxTP-}gJ21&{G3;AaE)RErdxn>vu
z8jaZj@e*L*v&Vcl(5$oo<O0X_whO~BN5XEGH}NrJ{XPaiuWFw42*O0K?v2rjrY-%H
zc%~U|)4UooKRaF--CV2vH;oa|HoGxxC0`(U`_jESvfgjhA;RyU5+gjA$w~O+&mpeA
z3sZcH&-g%CkJht}_NBTvS(V%ZhT-hU*&nL31{e)d()QeY*^-PO?{(|*fTzpu4G4ct
zC4G;KIWK^Mc(55GV7Dw0V_kn5+ZT;Z4H2Ap&D1~A(iF8<y2P*puEz<?d>DHGLS&H@
zTvZat-HG62y;H!S5AfjtE(%-BSbAZ`NWP=!Rx4w?a-Cb$dIt*x*CS46E4zg13(n2C
zl^N8d`^X0!k5FC76}vF_*e@}K=gb?U!$Ta~lZx0U|M){OD2O{%j0O;Lvx&6EYJkB7
zB)AvVdVzv*CuGL**dX*c=k2=oup&?Rc;W2dvoV)m#I!g{<Y*mYIp$li3ZNFcOW*3%
zp!TAQ(45g7>4ryVheIwO-t|L|o#D+X5WoCA?UBMHh{~NV&TN_{ntz?*_taC05<f>(
z$TwPJcMLWCh;ECky~ZpAD{_4F|I8z1Kz1PQwH{at{vpi9@-HQSp-C>qd2u;t>%7s&
zc@jI0yr7cf*K%)j&~iM<iwK^0DeyOMq5*#ca94(7E)ynnUO*}WMrx1ryJnD|Y6_Lm
z12$$$$+8wc{U)v|%n}_x9LbP%Bw;attc=KDs~UEAE4QI|FB?<wGhO(a(^J^XH}~YV
zS2);l7VLegz8+z6?pCTe7n6hzXp>?)PrA7TVvygj#n<T{C3~#XEaarq#LnG7TVcOG
z$q6<Jw#25tg0ad@b9J23#f_8QIZie(`GmcX3gx7%!r$`<C3olWfhNJyT#eYT4(s*O
zotqwL&)+}$nv!CbjTzVRoeZ@I-@8^#g`vnDN-dp5a&jNZJnl=E{4rB&Eybk{cb@46
z7cY+D;_~VEk&4$%ws=1a=laGJ(dElHhL(tNCDl=KC}<zW7p;eJ>Bh`q{YM-{@s>q5
z27h!tW8g97y*tki9cxCBP>tMr<}0PD-bWqo-z+ov-kT5f<L?F>P7}G|cN4i$e3M~0
zC2_qd{M~Qst=DfRqPv&D`0-a+u?;DhZ5tz846|)hAKz+X52*$bq^s2f(IW$^hGNxJ
zJ`^wVVZ*B@ayH@w%>dYp?XhXZL9e`MpZ>g(U<eVw1nnm#a0Tq8Y%kH1`$dkr7u3AJ
z)##O%Bwp}Dul(HGkdHEW{DVUCKO}h)`Ju4jsoE}|>JCCn53R7*CHzc9^ECOK?`*n4
zc7v+Vl))La{mUAM>OS-&Ug$tW*n8HXHio;$(mz^>4&T)AM=&p{Jj1h%fiBWfJfb3E
zPFuRs<StZpWz}NlSY+~aSf6<<6lc=?hih>0uvLba*Ys*of|8NaseYBbKx@;i_dx3E
z+@mvjriv_KFu^&!IgT9L0yNzKnvoBw&4E&=Ca}r6LKk4WoCj1?)Gi9v^@dyYB!s8l
zF1bF3(PFe|crqo92T?t-4pL6lntB1(Q>a`nt!jgYEsHG0MwgyA((31qsUU>6Zw6KA
zrZzQK?D*qi4-g=XS7uXD1~dGt6q??woi;=mu`7U68|G78IdwAWM{JYG(1Eq}UeaCq
znz(>2ITP`AE6$wG8y>d>(o5rlo)HkM@O{Xq1|xamnjun_!N+(`>1^0o2$mL>3<~(F
zdxTi3*}aY=IR(;`njC2iz1W0i?_6{7$-lqoNThw3K&|43Gmrb-d!a9M)4x=i<Dw#;
z+MW7Bvz6(di?n9HjtN61ZMA2GofD4k)Hgsnpv*iE7u_CmI)trv;_t%wG$g^qZ=jXv
z(omdlrF(?RPu`n1ds0&LX=`Wuzz$A^n*s*`)R%j`1>}u9xwjx--1({ip>40Pk{A91
zK}qUMpJoR^Mh(jJByMg3f9-guq>w%?a-!?awCj_a8*YvgKWJw7_d>f8N0xsWy?Bps
zi++RHhlDQGpJ|UMbwkafjJ3+t@899$ILGb~R7x)Ger+%!uv`Xwkwp^FM@;GxOAQQQ
zIq)0!H>v6b+z1u8c`ZdK#g#)qaAqgrqHC+=<#ZV$zj?lIZGg9vn@db5XnjXro!Z^Q
z<K41IxA?BMii*m!Uz9#!&b*&{pqhhOriRKdkt;$Ua{$x%3m~3+Sx`3-UopGcQQ`22
zv*2hM*qzh))_k}wd{NJoX)c;X5x{&PRrbb=1DNky{mVBd{4>afu9k&}RKlmm6)~g=
zsS@6oK@~)nkFOd8;o}(oy^hKfWl$LUg<$_SS#8<MOue;qiz5(&jmJ()ZshKBHOIhn
zaVwbKJ)cYat{Kvmx%98y?moWUDK1TjTZ-^BVDii%t1BY&9w19y@{^!9Q-wNF#l`-@
zeF7jgHs_`2(=d+dYRim69ZR)63^r?^kFumKRTw{ri2v|)9{&K_6`j1BCh2?mx`ykh
z^xN|>&koHpWX7SmGYRFNKhFT7w)n~6)TtrR?86DK2ie?pFhBcfL85PXm=JMM!dtBF
zV4yI35ANi$?_=|*vV)mm<{f!AIwIXv0iA2)njLJk<3;VviKv+P8FPo$7;L{X4ca0p
z>DIHjzxFhB;%nBI|7s5D&jcZKs-D)XF)k=RB-m3;;GIn3<p+D)CJ{~1kQdo%%HH##
z%-Cw%T@Z=#eiRDH{jH#QIsH-AN7*ZLY1D*nELJ&QWaJxI#-hr~B<BY&S~cP}w6WuS
zV^$=l^Tni0NbVN+zO%>g*iC(pxEtjYX(rjBk_M~^6G`+~2twED&C5?;3lI7rx|O>w
zX>=2f)?2vly$mbiVcWQ2UM{_1b+&qsO|>PTdF<c0LsQaDrE!6s<Gy{Oxn;JIU)a+N
z>|a08P_^E2wbceg_z*@S)-$pxNz`NIH`LlAlUgtI&FFu{?E?Kvsi+t&YpHAJUy$QK
z6tqiB6+_FdR^=1;@{wuR&T!T3L56C#*?{>9W&?m`>R4#d<V4u{QyK=i8V=X4ulIkE
z^|m9>9|<VId}|YMeSyP$y18;dRr9HmgxDm;#NatT=%Fao13eV=pob#mgosJ+cR6a1
z{={24D8g*c4Mt5}J*{|m9QSQIw{9hgmY<`dJSF_4Gyx~1ypAO6yNEi)hA9|e;BoDZ
zA}8O?97a>Kst3Khm6_i_m_+^XrGGDrl@x2}S`Ok_-I}EEL&AnJb6jM#%ax$^Y8RZf
ztCH{Q?_dA$TJJe-$Aiq)^4X7)eRm{hR4=?T-NOBjaDO2#D<pRA9GYmSr3oeOo3e*#
zzs>%V5+Y$DI>##i0+;9$E3#~{j#XZn^?5@aN5|_oy-GriiGDx49SbgBzurTU9P237
zdVC`?a-%LC$*&`$OnlV}i{A%?c9J3GQk<q+0}VwGVJk=C8<P&d{uTc(D=)sxzUW`M
zlQME~8RAe~<`LD8+4rD%RM5GfR+(w`3pCq<P!TeOH!mr3$v^I#c1te=T#}0Y`NSFL
z>UU<eCm=JQ0j1^1dt8ZtIL`(sZZNF@lt(lt8cR17KKa`u3E-VNv^frct@QYa+z_Ml
zw(UMUzS9y2sA-_VhfP;KG8(zwIWI6?Q-!54aZ>8=oM`WxJZKUrF=;UC+Sh}=ybPT1
zHzcTYXxp;n_WKjjAS8!;CCNaU^abiwqJ&6{E5|vGp6L7WzPd&hR`<NL1#s|GF+A=Z
zFH^`B1vl^enSEqt!JYWbusF^L>Tj|i+&n1sx$)^+j}P8>$vyj;0J-9y2JocR58|g;
znApeFnDnfBE*h3zQ$s;hD&En>OpZ8ME=SE*H4k=K1EVuq2qWZjqmbBhPY@wSjziLS
zo-+GO-Xm+I-f7KoKUB&1<T9&q>IIxr0OJ_nJ1N39p9bE^3^`>eI3%{9q&Q!u=#w3p
zAMBF7LP)-O`#$)~)seR<cESQ&@rFiy%AYtol%HKGOEu3s9mg_);=jI1>S6s*hkkE#
zeNF4l`Sy*5!{EUD4BvGPy4Bq&^ibz(ETZcRuQ4T$2L?3u#Io@yRQ#?tFZI>s<l2Hd
zB##&~JtN5*t@97cn5ZbiijtK0bR4?)HEP}YeO$9WCu`h<0LNYV$rC?|g37C_!t|sB
z;My6A9j!Mbe6ms@?*ur05c%=D2YLwA8kP@A5mlMwF5KeSyzHCbl)ABCika(5#?`#3
zWky>|mn<^K-Ac4T)S@jrKdXzf0Z^|RqLz1_^VOH#U~=IW#&#OO`&Vcn!a;<E>wc-+
zM(QqFQ$tnoYYz3G;3S@kk{BN&3H0W$nrzj)!+n@=&lyeY8D7Hi?y@_HGCxzCeHF#q
z+exMdEL%wzStH77!<sj}E?@VlwSBGZZV>F_{@$hx$LI<xD{gNs<#09CaJj%YZ*$Ky
zmLd6D(>bA@7c)9dHxIXyKgd9w-}PA?C%HjI5sj>%>!mEPG<Ux0-+ma-K?apWS$Bd6
znQ@PDnATp?rMxBp?`vsvDMbV+uh|&SDiMa05r*a7-fNc4CE^?-d(|dXVi~AV+?cMR
z&wE$16+hRIUm%KqQt4vI#MPNHwbnBsmcDn3TVBk7O!a&trlnV?@7`<Hjy>HcU)!69
z+XKlT?aVp$9(4&$wdkE}#6rVt!JwPd?(}#Ub+p+N(N#I0U~%i9r4NWPV89Fs8A9Lk
z$^S>ySwK~lzFnM@Qo6gO5s>bZZlwfi1Qd|&IK-tprKC{=q)R#^6)EX1K|;F2@4e2y
zzPntPan_8p=Dzow^Tgi2?TwYEiB60qsRkBpVbs81x4dTrl--%5B+7rxm9vLnIaz{O
zIiltFs>S4ubJ0wnTF!rNBv$!z;Xb$q`MQY$sdm>F{LS!k*(p?NFvXDcvtpP><lplR
zlY=_XhqD90niwUiiv;~&m){m{K}Za4$w0<l3MJ@;z{r024U%^z{uL;AXKcFt91(@+
z4&8lfY8R_=D;B2D{Ij@O5OoxgpADnDe}|z>h0G#+OKfY!H-R4GVUH%_!H6~pZjlUB
zKL`w3MEp1X=fAHaMKUv^L=(tBeKYsTKip5L;4j(Tr1#O1@oW(>-ji$}+}eymCOqz3
z2pf29_k9dmG7MKf<vd^xz5N4L1A&&VP18MxX<OKrg22OEA5%}US)7OGI^wt2wtNEV
zmINHts8&5<w2t-(F>T;5kR~HsRI4$VW(Z4nKlMlQFs0}AC-4lAKC?uHm<9-&)lS?;
zFRO${*AjSY?9?%%3@xz;g*_^+)ZnxT;aj$T&W3rUjtdkE5YA)lv&a7}gMTOByW7oS
zU*A=yDK1E-kT)_~s4PnYH%UFde{cT=cYAiQc6wJpZI~+t*%{-XZDr8E@R~=txxVd1
z?2{>pHJmDg`Xt*ucf*bD+5W#(po^~X8x!~{utoLXbCDQ&Bi<nI6@=WYK*!aeQIyB0
z6dEq7YH0-6U#>?TOL|_SAidj=PqDFPU5-3sFmzqgMel!pCDn#2)sF?5J+|&UY6epB
zy&bcT-Eceij#WMbm%=(B2tQUdlq;a@9^(0;r)f(A&!UGQMdwkKCedX)ZNSgC=)Kb%
z#7SS%qmLawc%zrSMhoq@935pym$;}5(e8m`C0kmuQ0vF4{Xt;}o#p$l9J3z!mD*lR
z;$o#h_&CY?*L)r_8(=NnAH{?eLckz>^o9G<bIp22pH?X$=kpl^)~j3b4`EVLSRE8c
zT?A*~o$0*W*i$CklS23>hL?Guv%@qNV!~dS3|ZnsGVCN|_arLp8ivV+%st06O5|AK
zZwa@GOIFnn$U*y%T*C%-Rik+fz}mmCumI1bGViR+eADzR03=9h8X3jp=hM4-P)Jv*
zWeUJk)}be1(QUbrJ|@#aP)i+w_q~cWZK7GH&DGCvb$LJ68`$l|{BzIxJk*7@K!->$
z8Kna1#vZS_6fgk-j;E#emIsi(pJ<oC6?2Q&a3tJlcx=nAz;W_#MukOmIa%HlsG$ng
zJKC{|JiK8=>ZpyJ4{7mQl#4<KqL($c*jY^RF9+RvQNn@Ob7xX<V#T=7Nfk6*l{ksF
zRk@zS9UY#3)&dtVE*y2==yC+3jE=*`#X03Y#mJfVZ7JILV{{Vl>mNl2RQ3=#aH_8@
zscJAR&)8~@(5OiZR{m15Kn_@Cm9IPgXpNAhfmrsu>BhGot=0j>fIMIlI+d~wuG&z(
z@Z!5@4t$Rj{Yg@9qvOD9fUiL0N>k4i7inuY;YE=ciOk0L#|%%XA0&~e@kyNEK1ijp
z<9DVfcabqbZx(N^;%s?<h$kh5GE!ZKY9^dT2+kr_L3XW-MZ#S%UelCQDz9#2+Af5p
znmBL7YPb1Yxis?XXq!$IbaV@6bI)>tO&)YSB$)TA7t1Fyf4S7;c3V29rlm!Un9sp>
z4a*g<F>O4(p0U3%ZlxX)|EG``97u9nc+R_A`*XJf@w%x0Rj0=H%#a<u_r<2j04_7R
z_Jgd*Q9pF^u&wSBvS4}1UoZ!dI{V&Tklz3|Uw^|P6yo<!!<H|b_?`)^_tpsG7B)2H
zAU1+kc~l^+hv*PhRcFP$q-%;YUKf8Tc5i{9?Mw8lyo)Am1*S}`Ls4SvVHNBM>y{jA
z*wJ`MF1Y7qo(lkB&!RZSEmHx$uh8v|Ct8thSe4&_-kK}{L9C$6=CfghUMl{ZiK_z8
z#2A88@Mh3n95dBsquCp|<;G7?)7Zko7@_MqC+J<16fe@3bW(SvNpPTBaR|0~9cJUr
z?0+ADk&NDf^2z9va79x?{o}w`PF(w)z<RabFsrMaDa*)x6-5c_G4H~_1~ncM3|mx$
zv@G1qwSX2UsY3p<K4h<yfSK%S?@C-qBG+2wh(2+W@oI=fmU?(ypn$W9n(oO{$wPg+
zH|>Ygn+gt0h^UiD$+OB@c3h#)f9_l*i^6cXFwSxZ#$oar3>hs60@X`DZ7D7s8tFuB
zi@t3b9%)&!%j8X<N0aDdtzIVx@MvZ`4203M&ln59H)2-K0%r9d`^E?Xj7m`2<CmD2
zSXek$S*WO}Rc(E1!du7S3Z5<i=28FYaI~eF7;FdJK8;7cZq{mjwMQH}>B8uLc^J;f
z*#-?<1($9*sBmu{;9-nuP+Lg2d_w`|69(X&;9Z`E>!Ys61ORa+(1Z*X0WeV-cRm6c
zg%f<#4=18P06~+Z@2YvO@^=;S<Vk@ddcka61Jyx2xuJ>Z@(v}V*x~~eoN*~90aS0C
zC0Y-15rhW;Y)YDlHVL{Zj9X9nzOYN9<*JODR#b7(_gT0Q^1Axa!8mD}CZ=KhI3Q53
zY2xJx=Ogq|tfn5+g;kg)hf?78FWesjq{PP4Jt5WL5g*!{g#eIS5RRrq5d3M>PtE3#
z7%4+3n1waW_ywlgkDj;?-;tYzkznx=<kk`7H{rPy1|!ILM>1H~7Q->v7&4EuyKNMO
z$X}~}RyxFWvO9JNwVA%pH*YOQ^Bo^uffZIDbawNFda8raacm7X60Hp4g~H=W&`-%S
z%OR$Pr)h}V$-U>#){n2z>Q5EJMX&q_s3^Ggl<z|-7!9bjnYmvvVTDrtk72l=;Ps03
z-gVmUYINK6VRx4t7`D)j0KMj$Tv0PsB+UdIT1l0tZR8*JQd0uC>?$fk--rybv|e+`
zMYN(6ojpySUA>)7Yb6Ysl<6PihKdVmAUYJ#*DnNu_S5HHJ-iED{iJc)`O2MsDKXC1
zwr%um2YiVVdoejsxoNs44nlCjSau8BwSawu$RT*a5p98+-HK2j^PXR<U8Mb&QmL?0
zZi5!k4Ur=rF=;$|nmbQ%7Q1yzQ^Key&%X~-Ucye&TA6!O-JP#btKw|;3}Qr0WF+Hc
zWN|J`NiOsXw*UCXI#Eq2@DRxs5wi@Hdq9|C4t|4(9GF?sZ)#eFI7vuIaJ~u$cbb+t
z2+%Y^AU#^^b*905quEQpr=*tvX9e$(;0D}As}(-b7yZF-G$Of}y7qA#uE|`+C~QxD
z*PXu{_Ah@Dl;E<AdW}c>6(2r_jDpUYP6&In@6LL$;kKXmdb@wnFk!eC`*35~k9)4y
zU0~ay_PAW!R;0~+kT5MJ!O!d;E^2uAV_KM2%;X&L4E@TXO}rR6TU_j`JiCfC?5BSQ
z`!g7Of@{O?l@;k18)%3vaz%eql2;VHj{%jSzzD8-JquIlEl5io<|St1LOZ(?QPdLe
z-TU%TO7`Z=z!D5w3T&em)uY5xSEj7(I{l<@rLc+OC0Updm<R&!9?9gNB4rzQ)Z8u)
zKSZp|h&OaNd@WCFtm4FSwa22Q_-IOvPXHY62*GY=eYU;q_h*c#J@u=F2;LE#)X)=+
z`yV#bkAX@6=`}4ep$29IGpvswcnwS>V5cYhT9h2k0}`IJ;ysPs>j9va6P!URH4jk|
zi1B1GL`T$^CcuhT=w3rU3qCZx>=X;Ln#nr)yd{~+R`y1Vqv{xx5z?iJ4JUO{1Rwo#
zsg3O-A6%yTu0KCo<`Xp&oI^u_zF6cP60Y6KZWJ8SA%cU>mwo=I6*Iw-u_O-6g?g@v
zii%{vF@nDxAz8R0$YtNNv^b5(FNY~yhH>+I!`^fC-;Uqx=07ph^Q)a;2>>GNJgx>1
z;0AqB;-Q`knQQb4j_aSXTpc>h>RkZkMDD0MfeJUI{DJOfF|JQ##$w&M-n^Iy@Njnp
z-ooTxMSt-RsY1MMIR8AJ=tZOqNFDdp(Bqg<2Cl;fyW8w41U~#ikVzPEWx>x75MV%|
zibQ!YJ9_(NEqp%=KWI9FZp*Q7f}YNne?{NCXV0cXgt(EK(e#t@z={*+Q#xVdN7nIh
zL5h3=6JPE1+~{uy4bdrj!`yz%n-YjnxBd_4i2NtwbWL`obH0}7n<a39E3F_0SDvPf
z<{e3Mj9uWPlfp_*^M}ql8Yc0t8eym0A~#f92OhiU_6424;;Cf-58;C3ZSc`Iul@$G
znX=iEm~6nF*T{NH`VGG6%-{VwlM8u0e@=$5_N=(sV?VTfCw>FD0&{kHIvfDddd>hc
z7#gt!AO8ILRjNCjY2ec|)}G#2Iv)1t{QSHUXrSOmN|vuRv0ZSkgYCt_S3}@deHsRA
zmHiE?XDWY}Yhe@2C+*Pp`(irnV6?UzV6p#ut7{F&E)H8tpxRB0k0N&`v+g%{DfZ|e
zy=N~U0)K0p4K^_q|6Qeb8(g&mUh>K1$cx0eK7u1PKBhZwfDfg84hK~vAb_!xS>-^<
zxoj`#;RPdx#0=>%Gv)C^${iIU;%Jmk*#ue-mZxf9dJ-;gtcXMQ$4v4*N*yiOD3M;u
z><V+`La4Y0Xw|LJ(F2M1Kg>jkxlq3#CV62+a>RLA<)7U{s!_I{O-ksIZlt+B-yX>a
z--Eqm0>!NYwU=et{_*7ja4fGRKT{W8J{CWlwy0{$X8~k60_@j*pm&)QX_214T)!vQ
z=DzR0bOw~yhlB6o4@0{bPP-^THnd~|e3!OpZ(bP1Exh~HeFRFQ6<7q^c#`128`rh%
ztkO0Vo%tv$#+K%J=Fx?bhW!_^ny+ELZvYG}1db#z%oHrRzWK&8(YSIm!{S`s1oo-$
z*Qh@p(t!u^Li1C!0UvTi`{l5KJ~qS-&aYtW;mV3BvY}YYvh~#Rgau-{ASAe{ro;-n
z2A>}KMLP1|ug3iuMhd&iJiZFT%laFAZZTr~xY`K6w=<}3d$vSXv59rzS4DmrkY5g`
z@gOGwIYzlrGXg?8n9*Vj+5C2Ib^;`=UVl6Z@R<NasNf5L^U0^Ahjsk%oBjt#@ux$D
zu@RXKO8r#Q2T8sG6o<Mropj0Q(r>CC1jB*XJshl#0OJA}zjQf1b&tZ0U)8_~07nI0
z$9P&RYz9~WhnW8<(Au~U%p>-JjnvJ_Ow7gkbqv|UaPnhjU5=04V4sBTJ@l{Af<X=)
zB*qzSDfz*7X@D-Ju&xiSzF^!(4v#sP)U$iD`kB^Lr>wZ1uyc1YQ$!I2s-b&T-`uCl
z;>N{_?2}nz;uZOX?VA2+>v8%BtIAeT>?6F;$P>L1mHPH$@I7TA!jRXc%^(ZkQ!r2r
zzm0v8yaVoc6J8U&KTCRHEf~e$OXU-^VM8=_q0b*<KH>y0sY~dx;1BiR7L&>V7~ruH
z^o-ny-~cRyjtqX=gAQbz<?Ygn#R?ajj8Hh}X=q4ZQ6rJj_j+!L`m7EgPwULy_86{q
zZE3GzFSyiAip~`|!3zD4|5^AYcv!iJ0AAW3jSp~W(?5cPI4(c^MXOY&1qc8?Qj(av
z%laAG+Fk(q+;8B!*}T1aP<+)PXz^E+muV6+TY`kpdwlx^_-ldudh6hQMWU6KrSHzk
z$BVPBkChpoGm?LAllgYwQzc^SG9Wcum<C(PN3K8#@W>Sm3)C3xM%S8}XpZ@8q`CMY
z5<W<fFHcs75VEPp$S9-b7#Oi6%V3lxXsU|s7Y0kuNg^t&sC7Rby*x~Qf%n^Ne0dM<
zJ1Zc~%f=udI%@yv@DNCcbrd?1j?0><VukQ<j#WI>SwAWz6$#Ll>!emdJQKniHLEKa
zx0HX&QSgj-3*XH3wY29+p0UbEbj))nkuUv;_sJeJDq%dnO1zKpw2c4mt!_OM3p7&y
zhnyhJmTh?k?PCMpQw;`2h=kEhObmm5te5%?KI@9L{BENiQdUv?i;aL9O2-uB?~fI&
ze8!H`ee?ZI8u~3XURbZLQJ(0pIw`Vlo=Xz9iZe3$RXk~T|M-(>??Y1sZ2!|2i>k29
zRUb|ssrf(GEKZW{zTx|y?}lCe>~BD-KhJOkte<jvR=Qx<=M30mTi)GeyA(3|59MD6
zOAMoF#ylqxnU@xX-|Ep_bGw%U)u9hnG<#^#^`ay1CYEXlm8A3PaOLXr3k7C9GTv=r
zop(|>7YI&cdGtT<7_qO#uqY|F+T*dzl&jC}*M*rI@?d@nLI0IE?Xrg9wG8RqJ~uM?
z*viNH#P}V_ujUd^JE~oZgA2M!b~Yho0?q>*&Eo(r7Rj3x948Ppl-JryC4=sJT52L^
zXh;F~v&d?Vp*0KG&%zvh*C;$ePifI^3BOC^T+eUHd;bEu+Q|R!_bQ6`m*4AyD#YFc
zm&7vA+&Ahpm_WU|nZ<@%S>Qs^keb0T+h53b4(F@AI;Wl<3RY&9U~jxqE>~B~>J&Yl
z(VGu;4Hw(2&kjCJ)&H+z+vfM-8r;8Yui_)WDgMTp2YzR$b)ItM`locW`GzY)n~S=v
zcY6TZ(Sy>vWWy7EMVyDIUESWE%Wc%5B_CgHGRBTh=PKjZKj)aXs$z_pbEA6JC4b@T
z%@`oTNhtsMeT=W`&@Rf0jS-`lHg3db4HJRlOsGDL4}4rD&tTpoTMh~=XMXRI4oH8w
z5DPyfU6Dw#3sMs3<j4&Bt-_Q>RN#>MO<vLr`o@@U-k=4aPkYjg`u|yrEc@~uZ?EqS
z2^px`q)l*XTK)v$O@~CIViD6yazRpxVooZQQTGB1{6r91)jH1J0t1{>uJyrZR05#5
zmy+Yud~RFY^9&(fjw@U~gJXcDmMDKjH~dwa!1{b8wY*2JC~Nd0S$No3!P>h!bx){E
z)}&J|2!>=UJ-F<dP=_8;A84N~yq^m(VbOsPsOsRe=$5=gu^?+aH3in^6%6mhUY!I(
zI=51DxvEd3a`p5KQg1k8Q3tN$NamtexP?P~v1JWxMgsf?H(5w?2*zYzP1&Xn6+7!Z
zC{~uBb3iJ2q2ri#xkv+lXl;aMLD(6h@6>Ph9H8#Rhbp-{B7ADyr&A_zaF=r9(I7hy
zbmxoEOCtqf2k`6)uwDgu51}9?l#HwW#(CHI9fE1Y++hWD8^sj_I$fE?h0zGO*}tF_
zG^%>kf{0T&f)uFOYl%*httaRl;!Wi6vBadnetd)d_Su>WbPfgl7lbCdb*g~+9|=x<
zbobG})Hm6Gj3~YUnJtjNXI3=`axYKM_ENt%u82DXs}_{5Upf0o&Ho`+aU5U%MaBga
zeB!3Gr8B*0U-De%|75eVibxd2;F5n^C}8GWDGX?HYOkSZWtb+iddQT47ZfuQwn{HN
z{`4gX3C_l;tN5xa4R_+E{JHmyNF+A|krTaG8l4}ZI1uK~U5|kdbT<pnu~#@kxSyNv
zM2OSeX0LrYJ<UntOFi(UVnPG06iYQx4-y?}DiX_QVgv#t^b_E7MnBz^b{JMaX*|#E
zt08$~A_~pZ;rQy3_<9i?h^j0%Y;@yLN$}H&QJ*pIm-?gpkeV#D9G~8N`?9RTplb28
z8u{?Jt13=d^!6qO(FoQlAJopS+o6NlI7t6V>r~GpHLu6KhgzwRdLc|a&v^H2Pqv^L
zFD6?a7Q`uag_Wg+^mkU@!9kJts>aT4ln3PFDPd}|S0mNf#iA7u1WyE`6!QvGKhWCO
z(-g_ucKdVS`ln<v!eX?K)JU)P@v;7jz~`!GGh}lWuG;iV={Y?_RH2w9fdv(h1&ol-
z%2DgvQC#;0kk&^{y?Q7+jFa~!@U`xN#py<@bF%0d2{UGKU?HwZK;g1y+e1lqNJTLb
zLJ6Be44Vm5w%+hx&_zeg*_M@hKwB1uHztGrS{inOD`U{E7DqWQgTAw(FL~(s=d&3n
z<qj5|`O3Av>AV39-v=YIOs>ia^h{;@>CXwVl=q5Q>fZi7w?}?W`%;k#p|`o$LS_*q
zdFHG0zJuc5DvIzg>XF3A74Pl<3X@!(Te=fKSi?yL6vDM^pWjLu%&-n{4M>`DT!q1A
z?0O~tTxxs+GjCU7Fydq=fn$CCQhoSe@<9flu{^jwbyp=tqEU*1J6-nGZ`O+)3Q;%?
zlfD@Hws&Lvl+)8s1r08HylE$3k`eA-KJ-bbyg37$FFi^~6kH*aSRvRdR|8=@fQDiC
zC_BpGZeM{lPI7!CanuXGS{F3pLo0e@do=*BtN1vyjbd667yhVi-dT(a<`DF0FBC_X
zZNIitnI&QEy`tKIn(rE>TseuQ?mfnd+pL|cZVjm78cwuEkuM85MDS*e&lVz{vkR$Y
zyFPD#Lf_jSz{Iu=J#rg-n7w^G^7<BF*=@QfS97BjThbcSQPe{?4b<{+_fPsC-#ABV
z7T%|wf8ffGly0OSW%I?0kc@_1z}HUYE&xh@Z$ZwHX2tr&)tcx%(NM>aBZd{0cZlK@
z_?#s;&Eh^ITd%&nIe)WzxZ6p6er@ym&0ul<I;8(HQOz;q9hhwQS_v35&rr{KWMmyW
zB~b|^ak7$9KZxo(yAJ6suozXI=kS(tjkiQC&UUbS;;IjXRp73cH>i`cS9G63DG@Zd
zD}16gt;t!Iyfdb}Gmo!m|JLSO<zFx1%z)b+lRm&}KMdjWMdak<tRmqd2woqBZdv}~
zLt(hZ#USY?PCOLQN4<Vbfs^i5K@S8@PN%6PiqV<3cWN?#PT@hU-Yuw}iB$UPpag+9
zDcTnNUJhWEhT<yvb_{33#3}@fS10**s*cu};GmA#lAv3Ej{k)jT|WDo)#&pJPw352
zN2OR1<o<geGfSf8e#IN=tDSUZwW_}=fRDU`@}_^uH5PHWNI;BNYO{;g$4<$tE2=I`
z9jhm!Ugk)Q6{8@tIy&N6>nu2`4=*M#yej)@nAhf})y~!tnr1?fl8~)9qm4~Oy^)Nz
z9yE;>oaO1^baL{AyOL>OB<T6~efbKtKOuXiXIU%TBWi%Q{2O6D2dSAx_H!$xcxN^b
z851aISgKNTr*$byzo%OdM4^7!WAYJu^2WW;ZYWZC4(H$dYhX<=v-tTgly_K+0LXB@
z_?<6=E&aJWpKkWx3J82V7K1O73qV`IiAz=9dS<Y`d5jO(YZeQ?q3;s#HrK&!uixe_
z0*KLo=Kp>2=f`bv5f9bH4vH)e|5F2F`|Yg*i?Y+e{?Zp9QU^9qab$Jdt3S1vbbm2q
zaNOg86>h3tH13U{28{@Z2C`Bj46c^1+4rN$<4f5tP_2elyc0oP61Nc9C6-tMR}<vA
z6>dD)^mQrZyA*R1l97_3L#3d7cow2#{tC(M`NY`U8J)KsGF?xUW^e3gjAf$16CI57
zzfj}EDO^9SPb~CL_=FC=s<%r2>YG91TWtfRAc(oSxd>Axkhnj9iLZ@oNcK=Tv;@Xb
z2<F$+5P(E0M<GfUPR<?(L;_VguE<b&%sX+X_x_Lt%|GFP+>^jONd^n~uXl0+(P&UF
zkr7UMf)fxVp*IA8evNbQ@&y5c6iElb4n=&}IbW&&XABVhePR4pcTDLHFZS0Q?jq8!
zZ-1C=`@=R^XTIPw17?;s@X14;UTuE=x7aI@Mic06@~ePw77eAh87YDGGqmifNpTgN
z4EC@DNx1Q-xl&Syx#6Dd$lMrGp#k{m49MKlK}u73Pu}EAv&nSuP5ZA?G9jkLx6s%!
z92cBpAUIo^;LDpt+BsXs9NzY~^xHEcDkkuM#c(O?&$_TjfM{1<AXT~aa}(1sM?d_Y
zLNLi9H_3t+|H&(U&`O3aGp0WOwH+_(bjfw)&{C*<>)h!2tDp{CuG;lnP)UPghNbH5
z7q)ojvw~|dvj<X~y^Vhm+VJ}3K_*XnSL#c$<DWy_Endgft%N}QVg_&3s#8v<=eGxJ
z?x3-PQb#ynWw(~{#E)cp2u`z0r8bIr3Fqa%+~VKec>uSL!D=?Om%(_&+&53$odc-J
zfTsB{^KCs4lhLGdxu!X-q1HG77#uR@*Jssmh)=1+tKQ%0CWC8Ux&;2di>hD+6A}ON
z?{_ZQ4@f3j7h7$P`0}8cxva-Ri)33c=H!`uaDy-CdB0PMh1mQkdeSmj=W!?gS$Ekz
zGh+H`M62+1KRWzg{PTp-y=2AdSwalWzM!=#Qx9K`mxR;C_X)L`9WHgGI#F_SF&%Y~
zA~8m~q>6FSy{_y02Ck(Y+AVH3y#gEyEAGi3f93Oy{D45@5*P`I<W>_G7o%d@2K3v4
zg~5AdA;}dcAUhNF_As?Q*R|W3v9ep3t2Rz=nbwWYXd(ZxK9$Q<agX4b*<9uh<;VI2
z?Qr7Ti8}bl{m<*=*EF~kaWt=DispN9hUbs}=hRe~B3z01BTNRU<;=hy$qoV0YX2pd
zolTG7oX7zX$t*`<F_{^;ILJ$%7Q>?o)<SBuR;#i5jxYj%>*DuAx5lfSeTK4!knFp^
zdNR12%(xsLPAm`Zl<xx7`Mff~ljccM1BP05|4)0FOs-}#(fnIFxjUOET_t%L3W_-G
zr}FKGd1t)u*yW&e`c7S?siabYzDY78iyM9Om)54Hb(>%M9x|6LF?`)hc!{!6W%zj-
z8x^%0Roy6<VLi+UO95#$LfWZdKm4p<<q#$u+utUOK25Jv?~*w12G)aB5_t24+lx^#
z+XPjgGX0$j5AvCe1;wKba-4VPZV11vavKJw&2Oy=xI+CQBl9QvFA}6KUofZ|KSxU*
zMl8FAPz69mG&^SSBXyD~=KP+IMc(lzad~t<a2}1azgN5omCTMJE(t7dF}w&!nf_y<
zpjQrrOIrq6WzfZpmV)N8_2b+%xc7W(=vUyXen3|0w9=KgGqRHFozi$0iM8FkQV;R(
z9Ip1}1xvQYhr(w-aEAT+H;}y9Wds~G^&%_@h+p)x15n4652s3FI{}hEXy_9)B``~x
zNW6OdJ>yxzU)L8Rw?EF)5KOhH-0hMo4F1I_ovjf%SimFKEt#-@(YTlg&SC;+SIfr?
zVA+E_yCr<jF8rJ~o#q`x&{{EV5eE}2un68`me5l=zL)NhI@I5=&H%nwwWl;L=|=HI
zD{=ByI+7XULpEmuIOvIjUKzL}y*8(_l;#PXl`OqL2Ptn--%*xkmO8{ez+{o6dgyN%
zMfLV-??XnxJ+q_Jj`bbbz!YLq1M>Se?0#v%LMg67O_Qe`B2G}SB9&e2&>o4ux`@KM
zp-Cv-VodBnLe&?k+CwV4Z%J~_LL%NY&X!5^7LTtg1o25pSc6uBPk8h7V-PBOP=Ow4
zX*aqSI6$Kts{HS{e8Vnia)k^?w`P!C_fVQVkc>p>4J}QuUN#-o-Xy||pUcOYqYV2H
zJ{%iNa6(<Ej)oTd4~?G(Dv&Ma2r|)W7VoeG1R>2t$|ND9riu~HMrtjy$mZEnVjh)h
zX>wzP#6Bg|$I`Ungvlv2;-}&Ey40U1UDP{p;%-q**2S{u6=^B?R7S4Hu1<&HIVYMU
zB_nm{BCN-u^-?8xXO|-`>N2=4&XHo|F`s1yw4DVTsz57G7!-jkd<rQfDFE-zR4%X%
z;h6oWg@4b@G*DPm-C;9$F1B(SBeq&o+>}UectzUx$nzhyVBAlFLyG@-Q8B{#$-P24
z<A>7<YM!qbBoI0PMydPdF|$;H%f-;RG)U_Bm(%uJi6HgAowhw&t&ozw(RznTg~&a)
z$^h<P?h$ttDz0Kb&CHGfkL!Qw?N0C>d?j$a`sNHuCCa8pv~isf11N=$z{m?M&q&T5
z*7>bSTy#mCe3Z}RE5E|kL{<tssH6^Msx7R#%RAK0a~{OTz|XSU90_!F*^Y0bXPusF
z5M-62hW0!zM5}K{<YR!u$;`b2EXu$yv&i$kpX+|5=4xcv`;wEALfJxn^oFL1YVo1$
zd~5(`5{;H9+Q1A;@<0g8Uiq9;)nOSI_L;NXb7@<7k*0RZVQCjdHTC3NG>ec|^YO&=
z)wo+nji9jaVjTm%;t~d|<=Rcpoz8TMPaArju9t?5ycH8NoTAUtYRD6`H9FG7ro=Mh
zMmVTTm}bOMOPtrC6@+B{5c9Z1Q&TgzYxR{Qp?Z)dmq;US9V4O~0O<TSw@^i2^NI)=
zo+3A7sVQ}fK{uq+e9UQ<X$~Jn?3m%xrV=rPXG>D2IofRtuLK<WQI`fS67DOoLxpGy
zy<z-=o21f5ky`>Jl^b!tEyw{Gi^U~5@0(UA>Ke(HUbj4p3J<ROQ#;wr3ivwdU(bL?
zU+?su|M%{b{{tQ#M&3Qif4Ur+a;o1h=#Q}?*x<Gd(p%PkKUByqZ=#JV{hC$stMLzz
zb4_3wE@Kq|sEPzg_js^5+zUJ;{mT0KiHuo#h7n00-mU`ULNBtzeo(e_&yhAD(RM`w
z@bGi5v=+K-2GYmO^dfv1bj#SEM~Sv%KC}}U1wL{`ZCZZd6BF;rNJwoDgi>Gpbkiin
zFwi}Lyr$cDcW3VH-bOTel$|tK&i2%s6V63yL6+=~kyd^Nf^AK>m<aK^O>SY(JIEK(
z-&7JH9n?QW>u+{PFq(@Aa2k1cM`;lQCXp`(GT9gRld>*MyC0#bd1(@%sSB^YSSKNV
z=`+EglDd{s&YY9stBPKL@A~vYj4m4Z=659sRoXlJoNeocv|c;q^Rf2l2yz!Y3?aXO
zh~yn5C>RIO9Z570Uvu&Ix-NJ=N@UA{Fk#oB`~E}NYqCf^&j^q<650wpFMe-oTGjWo
zy9a9p_nyCgq~}Ew#4!M$a=RJ3)xU-3{rvNCRfK1$+1x+6H$jK|{lU;1j;rp>#_cak
z7oEbq@z!6*GRMe@9kzQOltPnvdBQcCeefoRlVn)hD;uC<Q?tLm?gAX>o-wZpxUK$l
zDBa|#7&s=l+GLnYuX}_SOeJx(jewF`l$d(|J}KhScPVS%r6nQ?JvUv{58ZF!gD>KI
zP%_{fGD^MS0t2c7!-!8PJ!j@Zs<A7j=-Hf2R<MV4v`}v7>Zb;NeA=ai7oWW)OLaCG
zZT)2&TL>LfaPB85AaB%n(a9EO%*8AkReV8P@4mrQNwgHpDR<8Oc@fxmD=vT0(tr`4
z{3B(tMyu10Z{V?}$k23b<2>m&f3AfsqidVMSUK_!y7GgEnSrYVhp*Qo0*_jpECg=E
z-lvt#7$g2qK0yrP^9LCk>5retrQk6t;nzDXWv5&-(TnKAs}tabH8t5EnGkS81Ww~e
zV&j!KaN^|V_fOwG;wpi(7WjkldTB9Ti~pcR22kdnMuqTxRq%JZ{#f3GP+B33fzwul
z)$aL*_i9Z0W%~i}eV+o~(=asbub&MV8jRpMSyHO$Mr0;Vxl<|=^4_-UFo0O<Z96-w
z5cM8d_=0}SPLeXeED<4D#Y$UQAX)zfOFePkv{tP7lxH%bs+3cwC7~$0rX81jU=f1y
zBh(Q7iDK+Mdw#YE;McjWq#2Rvos?uO4`HyljVsEaK>}bo`VBFt2T4ddX2jDxK<s7v
zw_<J888a@F-Q$2mP%S6U)3Zxma#)CND_f{O3yd9--)gaEpmoi4G#67f=0?=iM-BmX
z*S}sYQ!h^^Zj74U0U{1PLAuM4bmQqrVmQk)&Q`?vdMoj4qorz-23t3@!=XR7q}Jcr
zfGhlGKGf9DBg6h}J_<}k7WvG)5Hu|@OvJtZF8nD|=luu_04etuoRnLS-%v3Vf3&0h
zpDdi;AsZ3lHm(?St8{jGy+PAexb=$Y);nNz@i3&G--x#tmeG>KZ8A;}d%iSm>c4u+
zT<+AtlsHv{Nz^n_b4y4N!-@?V^}#A5U-MUotnGQ~$j#37$Kn?tIU2vTq@a1#*JEfs
za{v<wK|m5eT=pze+w!5mZc|4nxX#dc6CYin#K9=>#Ag2aYi*WITOHS8Emf(W7Z(4r
zF8m!e@vt-F`xKDfQ15Aq;*EBWxd#66^APmEo44`|-bT#XEP~YRJ$-zbfwowr&G*PM
zjmV2sFFjqi0WTGLPX}nn2@|MrS~`F<L>~->4mTo+-kpS8dcLw?hyNjVuMcU#Ww{w!
z(OGBx(;a-1wjA@e*}%3Hk>9k|0705R?FWucV4<+maaf~~B2E#G4R5$wfoiH{KM+~R
z)5)enmWqjl6mh2y$?#Wizo;b;Dkdh+ciB7}()cVLO3z+(9nnTiOE-2lr{QK-d8mDj
z1cnjl8vO)w^K^n@bU0vhUYPwm*^-W1eL^#ZUeWkd3Cj;R${*|uZR$X|PU;)Wi2VJ?
z^FR!G;fxC=L6waGZLxu!hlSi;rCLfijxV8V<ep~%s{`_xCj8uDzgYy>#Qs}Xfa)T~
zNjS;f0s-92a$~OHniKI!fwTKxFK^5qcu3Y@z`HCsRlDiEjsEw{*!NgsH;Zp>c4xkI
z9jQkJ!F}E!JPspLOo09Rop3Xf^*6RK!Q!shPfdAZ{3~<9w=^N(pfx$|rZ^)v?4O1^
zA-nv*IPCDy@g_}hUCyKtXR(q5m<|HS1K6-{f(>0Ss#yhuzF`ZgV#D8uzUwcUIm3$v
zP<tk-au3`J44}yAnx+mZfo|)h{T=-&&<!YK+T#%8zGY-()l^3gId$CK3>US74U7Bj
z9h^YF1N+8N8XAbGAvCd0<Fz#601EP!>icmV1sc>B%|_YpKa?p-Nb6{QEaA3Zabm&~
zS+?h?_XCK(jCm`kp5XN=`;yV0I@MP~|1QShKdA(q>1`gtb9#i<qIn%Bbo5q$);cV|
z@`wPBs_lmELA(DeETFkjw}HH`a8|W{&N=qcuQ~sK!}?oW1L-y$ULP)l43pl?*wW6p
z;mi=E>;4|&GeHgrkU-|!Y#sr#uY#Rgfm|Zv`b;%7D`=~Az>TZ)=v^b~4+I$l7F|}L
z+qXE*2Y4a<7Oo#8SP{(kzVhe2yj~e4UE>zkH0P3Td$@!issPEXjK+J~ar87U*oBQ-
zQZ{7n0twRLCNEWVF51>B6z{W+`5H7i@n)?bHE5u!J`o@%>f|j%+Ddxa_!2`}Nk093
zK5g*Q9BY{V@ipoIgW-U70h@65`oEuqe}8_)<9o}9(Nw<HLt^qJimNUB=*Za@Satez
z^dO@UhFBr`xvvc1f1$$uRi9ev&gk@phNxa*K&eI+4E_~*RC>s8TllJ|@jQg{qR&v2
zM|)BZeR<{lY#Br>HD2b=z};fq28Uw+x(h}Es~`|CAj3@m?o7@Fj@UCZL<XaXLM$rK
zRr{^9P5%TX3Qrx*Vmyh&L>Q#*z+8V-Pan;#ows`FkrT#bi!8Gqfp`>8Y)+abpNxV5
zRy^=R#hjhTz^8puH^|(F2h`w|nP+RkwlWB>anVl^S0res(L9?1;-^TlPEU8vAI9yF
zFLNNiAPOkO?=KBMVaV*^an`us^HDn2k9TAiq1GOu$%9P#C|f)LYWJD`-HVbdHKmn{
zi~#-K;4qYK!S@>Yu`czw0A}X=cgy^*w|u^8a=t%(l6Qe<G`_eMt!L!0@Gx9MLqoXs
zV0F=FU(U#gs=V#$weN4299a<KL#(Irgs&Q~df=LjvP)M7`Qy##uYJH+=(rB+Ds=iV
z0fRChWdsBIv*TKc_um*VFCgJ~aoullvsu6dV2s2BDp_b(xaPpm7z<G9rW$X#_U3K$
z?*Q)F^wfc#{1}D{9-0}z^1d*gD~pat5p@=V3-XGmVuY(M6VzzUvPnH(Oz50^^aBz%
zRe_w0J8Ku^>(KMh^pTIT0`b)#*@*Xf`UEU^QdJ1buxRss7Ca_G6{h5VD?(+fw4le!
zd&uj!xvmJ2Wbs4tnbi(RwT?21^MZ;t^H|aAXb;OS14`%7^O^?(TLuZQi2_<l0j;VX
zq6?^OU$QvI_N5M@mP!ol!jKxZWQe!rqP#n$<e)<smdJGzA9?Cf`)LOPLRN-rH_8Wz
zNC5IfTiZIlH5V07iE~g(8aLO9qd5+7^M6o`JZ$~V!Fwm3#t~p1QyX!6OZx0vz(9ug
zx7ER+eWU+(Mm&tA(#IgXE9zpEq&-~R=M02bQFqAVh{NpX>U(0b=rm1C;%M#;!Y&3h
z{DWMe@4{$$CVu=N5&|WvC$T4m0Pg_}K$o3#%LCiCd?WJf*-Q7DAo#4+MC28|>fkb6
zArL2&-JB_TUbHS;T-Ic+U|{?G7OZz)&hjzo`-{SP_ZXFU{0=Qkq(xr;?yxRBWW!pa
z>qy}g>np+NiW-SXmp7>yXLLMuWWy-LGqm+Vgm^M$?RFD(dcmV+qv^&^jIIaHw49Ek
zYs8h)OBF|!fE+Y>Y(EO;UU{#J$*5tzC5T}}eN3nn#5$HiCyf}bhG2ydiJPDnCOpR0
zbWd)|Y>Yn*R~Wm45d~;8X>-v#V1d^^`?I!ql5my$T-L=7k1^5$qTg5(BSG$Tj`}|c
zv72H(xy+3mTM{gD(CFidS9Fo0U7_||2be~7c=jjJD?$O11-o(yBVLT2)O#dq0!T*;
zMnodJXtM(HUF#1H-AUP#<4Ak(F{0i+>00Z#JyXFyi;AAi$hi|H>bmehss(U4uzGn)
z@oQ%#!7sW#=mL$&e5q#R5OOb+d;<5=|M$hf1WvFRgrCj(9uL92X@_TX4r_PU`+e~B
z#NwhcIvo%XYU}iXVSBK3w%0wNBfi+RmW<-^UBS|?G?lJ@tMSCOqrT4TFNLR)y(a!!
zSNcmhYL1Bzqz?f`Hax`c`#Zx3(QIfs;KO#<BLxa=x0{>KL;AN$yl||VQ&JRf6Ac{-
z+|~*pp9J2wzk)g7--CDJRABCN6mRB5Pi_dq1v@%V8EWtg%%${~2#z`k)Ok|5rz)=S
zCc8A{u5cD`V;D2dgC{IQbgFu<1mNx=we308`a<D<j6+J0Kqq4+cwSbuDG5j?5EfMI
zaEG;UqqcIRifJN&_jztqZ_~5$uJxUufhp9UDb!~sXsW<F6^*<M@#7#jVS0imclm(_
z#oS^X8a40CTHf*MfYfNACvB@~kd~3iGZ}=|m_u4G6-MpFy4w!fBL*2b&MmH@vbXr|
zH-`p<4Ol;G#GCynhYttPJ?Ah3s?&pq$KBR!H_14nsb95z8W^rJ-y<R9+9#onJ9)Pe
zFe7ji_y1$`oMqHeh6p(ID-p1WIXd?J&qd1&>M(I6E@+KCS5R#-?$Mzj<bL>o23>wN
zIe6QCgZk`)dXJ8t5!ghnKv*E_%sf?3<4JP5lzunJAq%~Rhw(6tedls}?lkw%#nEg*
zND&QJ`(uB@3QmqB`CI!p-9x1mBR}?NobM(DxFn7@Jq~6pn3k`Ot@HYYFLW5e$yWiG
zJ|K~%j2futJP#E;XW-CyQlNp3=+Pg84WWtLzT3L*+C}C1;Q<usBSQ^SXnM`2h4ege
z<#BWLka?|Fs+iD_IuMPnt}!vch&O$!TFFYR{}RitnoPInex_ZZ4o^2*x>?dtWxtgo
zuKY6pET;IPf~DjyPZ`N|)GLvIHunyaR|VIj{{5K~n=v-}K#FP~Ty@IKDvvIz!|$s1
z5O9M(%Xsa07eLA$lS)$lI$}?Z^^;mQ^pM~4IH&MGznAr}SNFN#g_s)Z)ok?kVJtpF
zhs0RQ_J5ZwpzC1S+;}k}Hu$=3HIlu&gGld1U}`F{*vW{j=Rqf0mH+M4G$kDhT&@7e
ze>E()Q>Y7X5WWBul*ZlauglJ#s~f%Pq5RI))>dS}Pp>;-V*%q5`VE{C04~Z>xv#+A
zFU^tlJeH)*gWUfb#66v!QfZ#%$^M4R^hpHjS3y{V1z=?=(VhQ2xrNwk<Aqcw{kwm>
z@z@;TGe;Y)xUyI|#D)~ch$*m0{qFU?Duf%0BOe;mi9LNNHVkEvU_~*9_8M7{WD#5M
zdS(k#GlCkZ7kY-&D&;^hGIzf*GumIFV6=&0?IxRv+)oA>Ji6y%^{QJlgp||)U5`<l
zDem4tX|H;`ZzBs+fwJiT7J_(MK|nIdTvAr{CC>kZ;om8a(eRK*5W9#j0*Q#2ySN(l
z?NqeKf^8Mm(&hKRIZ-d%CUk_ZcWY`Io$2!%f5W4JTJio|7K3P@@vla3`vE*ctZb*!
zkP!ZH>HBIE!G(ni&COw1OA{nrIMmmK;2+2E;QZM0<1xwN%why!Fj-TkwjVW;zkooz
zk`T58hlVT2r$<7+4$!2*Jt4L4r<lVDyB_&;HG=nBHf&qYx)F!!HH}D@ae*4?&baK1
zKVNcpmKAdr6LS`ELEWJ4MU!8pB5@ic0h5k~Oq=);dPF9^%xXezqfTsXq?=T@*lYs}
zaerb9g!L>K82Qj+8XOdX(-mpGRZXg&_pt`1Rx;Fss-K0CiN!6t>K8ShwrP*rVjs|e
zdQeL~(Z(uDh@~lj#gc<esHR-53a0riH?7J4?sxzGWO>Jf$EV?XGODn)RS?%(;eX05
zcKYKPJTda%$1@E&N^Dv5g8cH~&F*d-$!Iva+Oc^7tp1)G^W*JR00%cjQ1l23fK+eN
zD*QQN9e}*!H1&*PbmFbVI)D;f{01SXr_Vlgna>V$FZ!oFPY>&BbE2odt;F*H=kYs0
zWc>yxs8?@H+5-vRr!Jpz?vX9@oj8-g6`WPwl&Q>vUQO61b!0lJ!Nv?I6x&fJ@Ys*e
z^EyqMrfxr*ab(B_EPvApYgw6UGr>FNED@?b6Btb2=jnyi#{v^V0%XyJ+^E!zI*N82
z(F<rO2t+6oZVTIlaj^A-UwG*_{?q^lRr>WV40)ZUpw~c)($IiY(Slzg)pF1u0zwv-
z{)B8dX{o)?3#^>8sdMyIeHH_;|AsAln#Z?P*posz{N#y@6f3W47N%7Nq^ihM-Vv4J
zedB))#_Q%0CqoGqseH#A7}u#6_Lit4{A<^ASAKaf$Mg_6ll|MM(?961(mB<*nA-SC
zE#d8F?gscGv?pPh`AYTA5o6>wkosw;h(%K`oVyiDE-5CcCG=$+h{3L++13rXeRgJP
zE_EQ#vFGLsET*cyOap+*|9NfE#(d88N*=K6%9o3(v>By$b$4F{J8eN~&t*hkaN_Zd
z7&77d2U+30srL59^T*$qV0=`6(SRgV<}!!VHy!=uaH3rkwS)Z*Ald;`X{E0d6+Gk6
z^X0wgsg4^?{6dhAIx0MM3pBDIR7-K37c)IhS3lc^1wbI`Xn|AYpSkvhHVptmAQ{l=
zn$5harXWgFt1oPOu1=tibs3FoJXX&;_M4h7iy5Ku<qUe1g3OwI!ic92pVV*7kp$}?
zCL-+3XU>Y%TS&y{`mg{c(ofGjZ19!@`(iQDAOsef4$4<*NSda#Oz12T*BHk{q?=mi
z4ZN}G_<i)(j}hb$@T1!}Olv=$q0)9XYOo4WOA^nK&_5ndgEVQl&`}UI&?+5dx7VsM
z9!&{l6^iB#3iQha7u-rn;mbP;lApwccwM7~Ok9ZN(R@?>D49g%n2I>r;z>Kd%N;Zt
z0nr`mFO60#Rn#$_5Mg!Tl;5XOLbAK;)fKVd`V96cwt`5%vL_X}#j79Q8^0EbF>S1a
zgxZ$wa{CxQ@tfIIRy3C*XZ!U+b4_4OE6Q-&PrOT&zG=?_=%o@A#bq&JgI_lhu>Fe@
z{^w-j<F@FTTrc{l9wexF0lRr2d#?z0R2pAq83-NGU&*uVfeT6KdU48eFP#@4X_>mq
zqGLv!^~VpV1l^Yfx!%ZWs?k)kyBmPbaSb4S>*%sIzHKUNJg-q$0sCa9@on$j?Gvhl
z&%~;nfGX{lCZ=rWrJTDXKcUM>_;E`_Uox#T3pY13vF`+`#FMXG??Ha8tJi)jW>M1^
z9a(ohP;s?_;T3HP=GecnuFbC_z$lvsn$B^KWvWH?7a>v#RIBP@W35zqT-FqzHf1_4
zQyCIm8G>AiiP((W%Y(Pzs{hVv+Lb`8I(pJ3Kr9d6>p+TaEmn((F1})?UuF7*x238I
zwTFDy@+c(Lh=&~q`;yQ?^B|fRUsT4GsJa=iD8eG!j`Jrjb7u5%V-2BWviw!Tt$zD`
z$5hf!A%FbD8Ck7pp_Zx*%;)zoOQP+^1_t_I2saA@0Hr9^)UdI<-(GpIxHw9iB~F`_
zE89-H^S@hyzhf~i9;@J=U`u&E54pham6cXQ<S0mp2>r@ZN#UWx7Bu`TAWv4wBCs+R
zo}vlQy!?kzms~XQ@@JCx0(=l8P-mIi-wc9I0A-7!=ld<r26h8{Z7{Q=-a})u5G&c*
zBu;!W{3M|puILXgQm|&`kYqwfgxyL_qrtA|KjEOClR?+Wy}rnsp)DUbixCUF9Up0d
z<Rerww@!PfyoJ}Y%`=hMNf=a-(e4;q@qD#Abgo;7wWSg#zktt3M4o{BN}J~DiY6XG
z-?9Mnz>MBxTr-)Jc*=GC7c%m?S5r1OQ~ZaWO1TopILsM4-z8S74$GKxBHO~cCj!qb
z%V*Fp2ch@US3mpb3H@dWB{fRsY|Yk|Y)7zQVp1anzkFi+clB2(xg2q6Vsk0Yxu_Yr
zjmNmej4`J%pUY+SQY$?!2^WnF+`dGP>np!X$S3y(87et$#CkGmV)!gvIS-~W<@Z+p
zOKXG|23oHz9@(_LSr8GsSPs6JYKQJZCMR_f<hNdcsVyA>f_pPn6ftMlgr3QRu(bn>
zK)e{YcZ;;TVy#XiAl_80@uaN*#3lSqd8b>lqVAa}+I&!Q(c_YN*df}Cd+Eei_}`4I
zaSduYfq!~OI6w^uKWT7pG&bN@$Xdu}yi^)D83ZVhI=6vr%N}W74dd3TqlOEgEe544
zp;NGrG!GFNCTiI+MUp)ye#gT63b=3+TM=4%yKxi*2H7&p3$({IOUUda-pifwCj<Jl
zN!cr*Eog?A#B>PF?ozfERldk7$<0(~KADt|#`1*##e_%SW4dUZ7{+_U*Jz^z=YB@L
z!%)W7Fa>}7o<h7|;{8T82A#Chz#Fz))1ayxlb0`?5EJndQwm{iS#6Tb?#|o(C$i{I
z!g!;Qi_<LZZeN+@HjG}7{bWJJWRAVffy;^h=M`4WlMrBIYy0=f4<DH9OnyQR&@UDn
zW&My91h*FCTz<2L;(=LX_ABKxux<cjYKTTnO$|Lnh6O~@fpqXn(@vymyLyn@jr|Pp
z)`A4NUFEFk0>BIzC)*H^K!H{eh-Y2$z0aaRoWt?jgXyVEiFIpTM&BKfhk4fZsxm!A
zOMf9S9pr*Am`Ec22VA?<VR^9rlsT0PD(?w;^_eJ@Mwc!JZ#nv&FoEp5RzQIDlp5OM
z(Lfz3XR#5;9I<<BR-x1>k;KZXz4)nr#ASJjriWD~_X)y75zP|hA{m*F*^M->+&bt6
z;^XP3KPXC-(5MlAz{70#S%e-*a-WLvt;@N!k$M7Shn=86^4^kt2!iiBc~9Oe7pSCx
z)oW=^g#IhF&KW&z`z(4wU)MUuwbxoS4h^o>@2B6S4hjEvqz>*M`B`uxyCw^4qEv4U
zKHUHjTnK8sk3La-GN$H#IY<clV3Q7bgEf%EY;F75qIY|u)CmNjuCB#Ns(a0EIIl#R
z+|a*iJ=INyQmMN-wFZ?or((LFy?g|ZdIkB-IKmAJ1Yh|7T#<r_aUT`fQ#7%N>T$u@
z%5wxehpv7q^2c%Sv|&Ul+uW_@X%hDtK(+^eS4i6<hJ&)wfO4`4dN;S__aQNBO_*CN
zbek#$q;G$bKguTR>iwy{yK^Q(AQkl@a4@}Z1={8P9$MF8<4LUodv9xV-oQ|dnuqZ~
z2Hi#mm^lh2+_pwy{KO>|p~ZkX4DtG&vpR0vUDO-#fOa(ckp~w(h>#%eRv^$8=dI&@
z-pVPhR|!}kk_01m+YjXhnp0YZuUN)h3Af3QPPKUz;_l^g;xjJUKF*sdG_7Er70jD}
z3Z-6#i&?WIMnAR18?=82*1iSv{L~xYm6LD(oc$J+7-ZY~bK$|%Dvkd%06}R>4pCb3
zRZu`V6;^+KR3mY+(VT^K)v`h5uEFGyAy#=)KKfJbCq;5{gqlvT5>inVSup4@ex-k#
zZ<j=}_^mp~n64^w0j%T3>si8XmY;vNK`uk-%QkXoa_k~P23pcum^kQfCxFA{17nmN
zW<-gnt60I6z(&zn6*50_{$`QjKAN>}aES)BN9r1z!~w$pzA9{-B7TD8`U#LI{sCZ#
z*|@kg#`Xs`nALf3u&~Apm6G_}wzXqOdGdwd`iueb-9(JwB2*0e^xR2zFhlX)NYUd1
za(7@8(kzgVIU2(Z#B7}nd)0a3a<bCvmr-*gxH09_b-BIt__RCQ?sn-SyUENm$k(qW
z$@G^a>6Oizzj&+I=kNT=PxW&vtc-ssOl8eH*R|ywXm&T0>+&4M`*_fQh;p<R*Tq0|
zDmU}gtjIeI+EH4|CZ>IrTNs#PviL4XS=3s|pYExh<VRWP<L$4f*LP1<O$I7{Iw<tB
z4?XjiZW=otPtl6cJGohcBs4vxHe#Dh#+f#`1|+43n$N{dx-|`(W$|JO1~%K7^A&`4
zF&jj)By%o!J5Ilfy|$b>BpC4XbL-f@$mvV!*EoIR41s?mz^wS2dBP{umU{_-36U7F
zTCSS4=0O&0{@Ag)sw0Pz!4`ygxO6n?s0tRB_Y@N%h~l<M<$KWVV<8LK!xf|1cKcS2
zP@~Wuy+aHmB3w$g=YH(!jP+N#S2N(hBas3SBJL7UOY8Ba@~t0oVaGuv%<Dhr27$k-
zMdzd+731gE0b}IsMd*)T(fjwxiM$_gP)a9C0B+iL`^U!&ZnGdHWLtGpSbF!$2>;ZJ
zf+Cf-8QXrozCVgoNb2n8bU%Ll`2CB&(%|6WSO|%6aSFTMc9zTc!?Q%E$QlFArn557
zn0<=UKFPWAVL_U*I=`x+a^`kLXmVAJ%=>B(tczVdlgb@DtlFpzYUL=#UrV~(!r0ZZ
zrn+VQ2tO_r4%xCljHmgi$X1ahWt^yY9>5v%NmF@A^H8Y#+iC$ak9hLvBjaCYTFU3=
zdZB|U#>pQ~Zd^C!jxwD(Ib}ch<lC9hy!?8*zCs(;ofU4(D!lMUs|Xwh<k~;gFu|CJ
zCPIlRa358a64QwkQ-BiF9lYNU(f;HjL};G&=RWwY@SSgXH=X2wS4NA`(~mv*=23D*
zx>3p;%+!;Sl5tZhSEwI_3g0OQGw9$?%RfZSV>`srjmT+NF7s!rdRy|txMcOnJY`lz
z=c#c%zE1>_AJV&l)s!S?pdh$_h=Q`zJ#cS*b8RZR;*n-V4?U`auGnnOBg3#2<?j(5
z5)1wpq^Py8#-p)N{&R8&2mylpw)vNt4s9RL7kxCq<1OjL$N6*7Q}g*(AKtzH`0T@W
z(@yy_kQSEt@*Q*YX134whXXgjzBvJU;j&mNQRNavdB~*6_2ciRokx3f4H}66*at3C
zrdQz4dOcVFX=>`h7Z~bx3Tv?I#Iov;L5mLA=bpOyCI5pork1nTa!F?C^Q@(3W`Wsi
ziyWK{JEw)>-u6S(DKk`c^$&Cs%CQ&n$#@KjlA3g9jkZVY_ylF2*2n4mYHOR_-zmK>
zX;O5p3yD3x@pYT-QwS|mWK&Ol{bxATo2A}B$F6Q$>gz?8esGB44dKf}omw-UTG`0z
zx9qc4&yqiBYdw^UtW9B=HZI2vla&q~Eo?qbU`w2Spk(Is^hwYqUOI~$qp~z!R_*=&
z`MeO8lgG-^Uypy#c-v$Ce{H=7TvXTEJv?fnF;Tp+A}GXIkP;N7OHr{QO&E&wC@LMK
z3PU+WBUU7WfYK2iI!GDH&@>1LNM``)f`Bj-ktR*Pbx`kplkY#jgb-zheRg@C^{ln_
z-gZ*?G6Djp2`XK=7PfS!lC7qq^@DmhhK8(UaX|u|857~DN5-bODhtLS2(B8a8e->P
z{($}2ihgzV^^-WZmYQx_J=N&9Y>Nr4*sOTbr%oxsc_3+2<Kh)gu}rZw1M=*;iQYy9
z+EAM3Y>|2Rj8}O{Ml|h%e`Slm)>)H~Gn>qb#K|p&L+M8SGdA~aT_2t1(3>#vD%7~D
zpk={`HP{@WzbmBp*IWTrhMc<2m_xAxpQmt^ri`h4?TNsi>WZhvImN#!Y~mBs-=%wZ
zgFJU(lVTcIVML!*(tN0ToNWLt!E=;qk^0^%#rJfgwr^gh*>-m!Ig2d~s<lUIZ*$yE
z{zOP+clXCe#@?RoGMiTW;4Hc8hDutHqI2um$v0!nmO(+;{vl(}`RW5@cJro1JFYu@
zlD(g2ZCch;IAFZ@|Mo3KQZeJ<PIUwX(Mzv6Rl=mTwivlY&Cd(?)nKYM?iy#9*V7eC
ztj)tcrgVGz`wvwh#@DSN*2$bPnaj?W%%5!3=aR4qO&>w@sM&s{Ey{VgMQlKCS&Z7~
zU+(iyR22kx?i+3z8cnz%JQQ8A>g7OGvHnEiLJot&?)}+YHVdQGT%8iy>HF9*_ud{^
zt9RNcRi)Z}CZsQ^Q)hU5hd|?5_CDsz{*z&r?Lx+PR9Y7nCL<ybFA$s?lU0^26G*rJ
z@qnt0^?d7IVNuoX3Wo&5CADi`Y%1&FTmLHh>f?<+bM8nezQ!x}OmDZP*E{a69Fb-J
zczPR4kz0KnHxv@Ilj`EN5`x`@?-W1At&)2pVsdZ<T0U;^qccl++>|#f4J)RYl-^^#
zy1#R>{R+{k=!YY;Cvh<lJG0fsGL5lZ)j6atbDw6Z-_aRc_k_p7dyn2eL>&}8=5c6k
z<74w{lP~&YWaoN0Ha%$kT-dv*b)9AV$WCVeyb;s$>{a(R<yrAH3KdyAb<q(vRBg|=
zft(Fxy<6rIn{pZ#2fPZSCja<@DIc38zH+5N=MDb?BEQmtGcCS5!<l#CbGnwdyTxoy
z-Rcp)U3auTjs+AC>Wm#(m5_2_(!V%HBggyLVEkgTo+qysM^V&#ntShqcd@V4`(`TL
z4nHIwb|38tEqiAdWjDxKr@mc5kmuazbCvnW{?ScdL5!Qn#y6~{d7l#<Kdl$!M6etA
z1qCZ~M_L#OCwzH^wL4+cuYF`Dtf&+yw4-*Lk-GB-9m5Vejm^j7sjfNeE38gu1SGJd
zoCcYT+`9wcY`nx7%*CQ^m<x+4*4;kQIl(!+U%Em#!lG12D4ji4on<*jUnA?$J0SgA
zvUQTB<X`%UCj)Kj%ik4MG0V$(7l`>xGwL(ng0*WJ(vQ@ZPgwuF_#phj{2B$jOnWDR
zeL4oNInu2HpNBM!k}u~*|7za$m-{Z=dwe(acbn#GZCaOAeRrMN&g0X5k7<gvvVx!Q
z-VADa33^~oWsCQ@*zCNsQctDI`Qu#P1@;Y^m8tgz_DNSr?ZzKfs@o_g+bcN`7h}g#
z$Bz9vbL-t$`Rd;7M-RpNEXIxf#`}+Mqf9emhh8yNTc-Y`@;~x;lXj8*@zYpOyQdFM
zEU*>e+r2yP*|Re{cI@EM=RFga&!6y4^7FJ~N<_re><oY0>5%sy?VT9C0%ISV2K6r0
z@3giaG)o`!O1k6eL`>A|IeEp}e&P)MSLKDw3-1QfbQ%Y}-`<)n9FyI&{`b#(H6!~c
zKCd&UjU;Rr5K8A&6YhL6KHtc{*Coc2<72($kv~q4`C9+26FGS+_0UFBK`LKJ`=c`*
z11|Qi8STk?q!wj&&AJ?z89S+|#?Q>!*l6_eXky&G;`8dyjGtXyxJqo=!qu<Mn_HA@
za<MeDY#?WMP9a}>P;doi*wl{UFvWKP8_E@Tt@=lIH0Y%73BEE=S1ef88xwC#EU+|r
z#H}8-dK_{div9D?@0LO|fJg@Jw^_UOz{_ib-x)mRnT@Lw*DWY0|2WB-jXX^Ch>bpD
zSV4#jyvS6ow-&!1^nH9}M*39XRO2OdhzK*j;X_laC6(b`aYWX0#+<7u>sRynEQQjE
zE<XJX%f%x;Q!|PlEs0d!51tF#-o>0;G2i>_n0T3|{<Ysbe|P$k{X{h?Y~z;AG5Akt
z@4cw)o8E8uWqZY695#U**7zsxla#G-4<BJ$UncfQh8eW6P5QIj%vldany8!4e~6p9
z_UWGy=0ofy6;b*7FYnUthlW}SOe0|%Eg-pD&e}c*4OM<D6>M<2%7*{hh%<}-nI1!G
z|KX4oD+E|P_anx_x~&tO)E^n1yLcom!1>H2a|zk8&M>`Zx0_7jBxlQKHqjvXY0L3q
zQ%jG?hysS*?(4MkN`6oFFYb5ZU?W<lxF>1xXbsLMdR9ev2+1W^Ye|%h-8jB^>jBNN
zMel9i3f^nBdaJV|IEr?}b?ftPvzKJ=*1D@6mycmqzI^v!l#I+Tc_ZuAGKMwB-h0%J
zIvqdxv`C*&J1n0~=t`$i4-EAcY);sC|15u0UeLz&^j}kDetB)O=<|<3r#v4G7OUN+
z5|f>Mc5dpC+D{{GIS%<Hu`QmZ_qO{OZ8;-YbmsJ2xQ2~=yEXg8rBt)5!b0b{h*+t;
z1Cc@{S6ZljHH|`dJ~Gex%5OQd@XO@Jt__g~&l6QOZ?;!8;$Q92R~;WyXI(A~Y44QO
zIK544O!9@c&_$b|H0my1dH(nt8{epWwCsvm_s>=xzV??{$LZ}-_Q$->Nmh7zdfvWu
zE3S#t&d!c*f9VqaWaG+)SLbVuBBWJIrhX|pU%NxTdKaPpoA#t-lbY!jNkZS$Nm6Nr
z$x4Cer3V$+F&hhi;kUV5yyBm~M3j4drGDJitIMfUl9FdPD=n;dQ8;?kVte|I0|yTV
z1O#ZM$l*8>XWi0NlTxny&7#WZ&Yer@EumSzv@GBI>WrRe@YLoZR>yq)=?b;_f_V>t
z%q?z?$tAbf4HJ(Kns~o|{r;fSiH5B|j<0i&)6qK{uMr^LGf1JBh0^cMP$-czKaVb>
zNQ(Vu{M%Qvg`3kcd(_^Ih_AfWm!!!mJp0EVe@yc1+`BinQG~mj(PUIV^d_BKti*dQ
zx8wVos1qsQho_z^^4`OpTQRoC9e3aUPie>3l5`<AA~0>6?k1uo{Xy?5%_#MT3zo5o
zNmn1dZ&GTXXU2utN-$D0@83H;745u`(ZiRLxQyb}SNA{!;W1;)OE(Hdb?r;{Z(sG~
zyBFflbryJ3O3RbYQdGPZH#9nGJSwt$`Eupn?(RURU{TfIwC`xW4ZM6^zlfzEt4vgH
zlDh4wCU@n+z^R!d7hBf}w(fnPUU+R@<>;COrhZR@RAeD@#!2UaSbRt4KfokL=<<PI
zYx4r)X;*QkjBb7@O4=kR<;bT$WUdtK;7W1nbfJ1YQm0$FWgZy5;gl$(C6(z2XPD!2
zJpp~O)jglRql~7A-*?YWYm`Pt4R&$!@!s*x%KYc5r_`J$=ht;_@8EJRHOVU{SsPv_
z+<Wt%gO^^{yu7u6%`%-HX(x-bbQ#C@XC?>gV`?pr6m8tRIk>>y1U>)b>ec2Yn^?v<
z(FUu=H8OTy^bsFj)N(F5xp0W1;r$URv9W7QTU*5nO5|~~wPd$8+;dq+c^>!2!*5>?
zvrp#9i|yv)lVH#93eM8d^8fqJ?b{bPy@gnz-3I=D|NXaKsnyMg4>^lpz7(~%TT^p}
zM0&NBf0+LH=bxbu7G-}e+R57YS=sPS+fU0VUAyekN-$zwa^12NO3X^)yd3!-@@o?B
z*yo0>TW#$n&&1h7W(U;O)#*>{Ln`U7U%#gL+`oVSoPdP*k!}B68I<Q`e;gav7TCM@
zlp)hmgoUV>Vq~Ie-HG*ZBIT!l{qW(6laE~mwZ%l-5jZCYBSJ$71GdY*eu$#Sht-z)
z$BI?K<tT(4az|fWTNMx(*e5hMGZSaqi2`-&zuw{%co&Tr!o8*<0XoSB{6+iQ2IsJa
zIi%dA<gU7zS!`+J%748jvKMn_6E}gp_4x7Q(B20RHtycNJBKwroYg5FKyL0=6JGnT
z_Z<G|1e$+|p(keDB1&z?5@XA-+*$Ejvm?2qp}o(Z?N<pFjpGme;a~5I{GObrZXo(M
za&Zj`k;fj=`W~5DSVi0??nmLU$}-I>pyz^BPy92_QYbad8S&}mH5q84s6(G}WvEoW
z?sSd9I&-Wt9M=*k?L4$s(Oz=f>_okq6F$NMe_oZj{g88m>~vetq?3-eo}PukXh(kT
zBkkpi7^VFS+Z9z+#XTNknrCplbSEejEZe22@UNMmDQ22!p=wP{=kz}3;&j3#47@{c
zka2Rg{9>EtXirb{r%x_ld57}KN-<g9jT@hA64W;P^x>_MK~e(f2@Bi^SjqhVb-dJX
zx@pI7f<jJHUpkGj-XqElnghp6n0<U#MyKVrCu1nATQbUam*v4tJ==X}U$AW1zo(to
z^nEkW+dr&X({O{=GZt)@Mc4B`#4>+cy}A}R+mgV;hex)>lKrnhPJxQGrf7e#+s_{g
zu-)BqftZ~vfA#8&uI{71i;7q=YP@TC<qQx^EAwUi+u95~FjQ6c{Z(O`{_~%jyMoe;
z;f{R$4SSR?2L=i0J|1b$mFT=mp&a)ArEdkLX7>LXrs;Kc8pEB1ow{<<Z4R4wct#jA
z9{J)M^d8{d5u!L*=Fb~9*th}9cI0B@O0-F*&~qW!4KqCQ*3Ss|WT>Z5Lb+}t5q$ZY
z;rVioS{ixeTClZ^WqRemgCfQ3eo&OeiZZb=>yhs%k~zmto@Cda<P{58Mmfw@c)f}e
zIk~fA1?92@+2$`_$(Gk{6Y@UCSs64ow|oO<#X0i5|9<Ih4jS9BVvP9u!I=Ihxy7x{
z&}d9xy^TNr9MzHUX4(Ag+>dM4SdH{9L+IS<9hH$GYW*hd_`tvbW{K9}!(Je;8=_CH
zBXMC*s;IoYaVN2uk6qw#D9ZQ|M#jd*xUeWOv)|X|z`a6`AU^A^TF3LC-9`tOhB>wn
z<}m|_=~aFyDgDBS@^bhvgPDGQ>yz}eR0|iTLhSAB-;Iu@sD??CKc&%>Y$z)olr^%-
z#X*fiacMy@JW;r~poRts2ncALIrG<f6BAatsoGl3{fBW{(^{U8>~)mG<NV-9aZ@><
zU1DhHwoO~MB;Wv9V2j#F;>fjze77sQLV+i$sYXW0lk6r-#HsSCsz6^~Uunnpk%(6b
z#$B?H{zIY2>{UE=M6=M7r@3tOz<~o3pnlA-9IF;4hTc$jp16C43{HJxec=?!2k~SU
z_YIks{x_FVY|iGnq>7l;{Gy=2@I^@P!3iS?_X7f0ZA!RODY96SHW4dwWyHV)_kW!L
zYvg06>Gu}bNGd<v|Kil?(-sqX{QUeSPECfUpXTOt4uNkGL3L*hL!E?AaOvC2oVJAv
znYCa~xTaajhKEcV6Ln7<Ir2g=6}=f;jy>6i-8$~>?$=7nU%wW`snvy?C~m|`y6|M&
z1S&)G31+^N{PPgE<&@|C=+a9v;A~EuKYu={Y2owd&m}j1-g$S6v3sgf&xUEtVLADQ
z34gm`MiUo$m{BqVzM<#-Ngk23^mJ}fHBZliv8=*{%A0rZCfeOI%BO90=&usn#&I%w
z>cN8t7w+=NG8?Cbh}rsxyA2HuCy2_PO4((U%SSQsQsGM=C6N6kcek4SdHuTUyKr|c
zgM^4}Zdd#NGUJHUMocdO9MWfBcj!Io=qS3{h(h7?7C~Cz@_)>xDThh3UR6|B*5r-%
zS4&*IcC9_Q^n5DkI++iLui13>y?e)rYhJIosUZ-FW!kj2Jf8lJLL8>fAlxSS=&#L*
z%fD0^EG_?Bd(z{$xw(0M|5)Dw#ddf#g`x)YQ1#vB=4P7Vcxg!qQ&!F{0u#%O$2qs1
zlzi7Du=GYH3*Bcv(kdCQ<9*Wd&$MUkUVOiWGbbl!vctdV?S3Q`xvc)xt5=I`7B)CL
z>bf-7*Ax-^8<Pz!4eW|HodEJ?-<1kKCEBmI@UD4-OvQOl?;Vs_Z-nXq9izDa2Sr?7
zQE^a~ciXnaZ~SndJ;q19N@iSn0Ty}TNxp*32gJp-#Y0rwrbnD^J@pd>u1V5k#2Xo_
z=Rl5A2`6N9Ic^kb@^0UrR26ne@Zdq})~#FPX{#^ebb|qqN0;5gySvXL{OTb(>}So*
z%?a*+Np;z>(~64f=gvh{NH-?w#~}J;Wn&m?G{Mp|q!`Ozyl^2~W(zNG3L-{Usa~#g
zQkDFoI~HEZU{O&~z{7{JSceFt#sqqBVxD|Hgnj0MSVu=5Z&wmNs>^&|-iNCrIsH6A
z?Aqtg4?7Jt1H-5b2n#o2-a5T|^>hDtYHBK0=$SCgOYQ<pb}n{bh%GqT{9X;RS8Upu
zWDI)q<_#Ka=en;8kz}`0;QPOS+L!1Mj=XC2@+R>!efmc~Wb$|%K^Ko|2m_z?#){rw
zE!Dp*$tYhB>BDAp0H)O@!IZr7@#$xUK;Zj7j*e#7cFF}gefspt?8VjZnQ-m3g_n{`
z=&+xW``Gbz92#N6NE(%$iO<%Smzw%>Pfe)q2t_Yb%AqgYUE@7kDTWXdN>~r!bSzB#
zXr&-wx^tMcQ#ZAg;ze!2>I}0Jk&$V8C}QHBYW3zoPjjjX-MP27w**+>?){x7nJ?QR
zyp|8oIFEJ=e-zVRv2x9R?Gz(xi)maQxK+o<^=XAcg!{)^tq$!pb`rfnJAvi$>Fo`l
z=##$Etq%eNxkmHG?~z-^&RMvDu;s)@|7L@*DjEP5{EZ{m+1|5S5%tu2JhFrKzbd)A
z=cQgKTVF5XI)wc9P|ky?WH1l={M#vm4km9Gxrn<IZSyAsl?<4;f5oS_B8VI7Re1Q|
zfdhA;1$Sl-#wXmx9BY#Lx_IPZFuhFNQov!6Vu8QuUuw1zr?$R+t!y$i^hUVThHLwF
zYo@W$_K?a;p`Nn?bWecRlHAb(%Lf4gQmq$FO}PmiV`rcWtI1Srdq+L)oJ?3)D8g@0
z8P-lVFrOIh>}(Jg5_)|~Ma9XI$97~j#cKmRtk=2JUUIBmYyDwqXb5s_=tmw)d0C+B
zeEGvqKOJ0%UO+@>YL=6gZ9jGD)P4r>?*7TV?DI%Xfn><U8ZN;cySQ=FCf1vRyu7yT
z-avf=;pF%|T21eKiTUWbPHaZHo0peo(JH--BPfUI#O|~A&yy}IEAvKDi5tc74Hi~~
zPHgR+GK5T9gC~mb-z=7TUd*zQbMu|zzXaqSP9o%pf1*#p?S5e}<l#dz<}!U2&T}!h
z=tLrwCd#ZiJMzD-6-DII1It&e2pfY*Ev1p&9p84#@TEt$9Lmj5=N5B1rz0RL7j!|!
zlrF;};4PbNIXDeZAB$+|hYc(%4hK&DnF>=qS1SR(DSfJ-pg{OmHw%f==~gH1Fi7Bc
zO-uuZ>Un*zT9gqX?PL))zLbPDogsIjmpNr=8RUs>TQREp>+0(6;!#|LYC|n2{Y})&
zxON4vh;3Y$?zEjPr8UH=iy2vkKeO*WO(h}#w0l6`8tI8pD8E(oM-Vocn~e!-uC6&1
zYuos#^oBRK$jEem`T2zfACx3bEnqF0-`7UpFi)1~aEY$U*`pGCVQ@S~ej%<(PD_n;
z$JbXP%&4vni!qWPqqMc1>EuxI<j`nd`U?rh!Gi}4RgH9W>@OyHY~vtqh*&i2rA9nD
zXxb?CKV(sdRi~W#9uoW1`%f<yN{|G+N}AJ6efg(O9T2x_?$Yjzk8uxosFD)P#Q8g7
z(pN^TEEdTqAytY*tuxxmn>W>BNtctQP3*sY=Z@uYx^=5~J^!$roZJb3nk0{<_%F~(
z<hx~|-^Gh;vT|km<BmBbv()a%Mh%?(o}wlB532jl(?-v)RCieK{JgsdE-+9@J3#^_
z>;gKAL_Im!10)YOY^UZlJTI&_wwPF`C#rzO{Q-v~^ya=>xpEM9EQ_*8g@7?tRz{T7
z!VwsH5d=F3^~9>l_RB?UZ~OXQ2}CKJfV|S!QmRjOWE^?tNDF#kR~#Z0t@{=qmrUbH
zUXLdrQcPVjq+Exbt&%*1Hs9g#m{6y-gHO81dBVpd&sF|8`2o-U1BDIaF)1V~Ht?x7
zpxl;~US(HEEVtvO)NFGeYCb1$Ww1dJ?3Z1_DC`awmkOPdOkDIBFg4maD#W*I*L!bn
zn^=c?@u@U~TqM5)h8oN5-8$KczcBYLhwHO!jmg_ns?yqRuDEud36Ma7KYS6RPemnl
zks)p<Ak;LtNpCj1#_^Qss;=|+8v<z8J`l?%zBc+~E^E4k+g3CH<v{lfK<t`Xt%9?D
zPoQFL!dh?X$H6yZ0SP5N!3Ov(AOq|IC2ZPaSFGY_X!Zn+42}Lu;7MPeB&3o?DPNX_
zQ%;mYliPPBgz%z2v+FsL|Ec><)yIUCg7})6nw7p;jst`S^-X)WBvhW=d-h0I+~C+=
zhvVwbnz-3^tozVN0?8+s<3*auvlUuuzl<#21MK*ai4Y!K9(L%8bFOWWu%U8qTU#4f
ztG17iMB8PBurh6p<G;4&^oO&BTCK!A#)`LyWvZ#EHK7dd7P{f>Z2_3+RJAjwgdK00
zj1%n1(UrQK^f@;$k56>_`pRY5XEUuH#mRWiyJQJ3SIbBe;^$Yqe*HR+J~wy;Lj(#X
zL+h9;L(Qp0jt5SkI&~AgR9~ZkRx6kH+mCXQY4O!z#OE2C%AGj{CqDs(SX!}{vV%Eb
z2)SO7@SfcESKXKM&TpkJp>yU;B=BpaX1~pv%(FtIY8989$vKoXx2&UW1BDXq2d-ka
zkz^3!jRC#Gu4UVvXj?^b(QnPPRsj!nK5I_fntRhGEfam)cFPp$>JM*=R8}z>E}?e)
zk%PlI5G{=Y<jaxOp^{k6wnwi$@#)BFl{?2AD8hv2!lVv{;H9wQU~_8p`t|EWd(+Ym
zSa%k*D+q$fnXGvA>T(>67=}K>2_?f(3OCA|nwo}Zyw~v>v0WIlIuX-9TwAqH8?{in
zd)M=2^?X5=Au;@zcwRBG$RjOj7w_!imyt0bNr>nYv^m5|JS0672xw}!HMgDU)K4vt
zn_jUL4rF>O_7ysM{-qo*`J$r!=3Mg4IkcBc@^WNcXlUq=^MQJFwQ?^Sq1`%y#{~HK
zD}CF4{)6&yWlsJH+hR9kW8)y%2GJmL=6hw?C5^=I{`RDP{{AYx1s-#a2?T3Os}!}g
z*xX1iF{<2)+J9zluBo|0->&si`Xkk?DWe8Jc!P~eA^HwuM&D5`r?nJ#<c7O{R5YD=
zWEvjVRQYDm<)ARlQuw?qrRzclW;WIJx!1C)2np+!Cx=F|6@W#~V4?tn2mJL{By11t
znf{2BDTQ9vju8oW9+Xm1*f&Cd7ek<+&XPN9q^B2;5~Ei03hw;SZq2k1Vx#^{DqIm8
zZ(-Gj7>U*19*pCm3zoVTPuB6lHBWX+FvqPviZTK$iN>ggsO{NeCs<REF3cvF6pEzQ
zYXA<-g7=T_SXdj2Rf%i0sVXXpv^TH)@yGbd1bOa*L{Qcgx{437B6;74fS{oNE}v~u
z8WNrE4$-BPjs|VHS5i)&K5fwAI_~N9<!7~2L`R{p{rKaLj%ck#P2`c;rozupD?=m%
zB_wo;i;G+E9zc*d^sgEcK+Wq%2UTLOYu(5;o|bv(K{-ryLpHv^_#A$EoOrd<_kH`7
z&ZJZi8=GWuzah1*{%Md=ywbk>jFu@MAD`OVTGZf<ffeQD4}ybRb~(5Tb~Kri<u7;B
zpi_1Idf=DikEJO7J5SzwwSSH2o9b#c5fPC|uARcdRhm_=Ur)Sh0sA~$@TvPJkGbJ2
zIunc{t0l%FbQn@Sk>8idm6e{B))pLNQRw|ztNu&8qNvekdV?mB08jfN?@l5vzB->O
zN^2`ADQ<2)J2N{gT>*s_*jjk_e<42Jvm%IP!HQLnHjvwI6o$n~t^p>x|3O5AvB{zC
z0zqJs=dCC1az+857Q3nt#wfbcAal^G!3Rz6ZQf^;h^3-|lVAxav^GIM%VriqBy0iV
zT*^B6`DZ0#+}zx|NrHiK&-EzFt;Z+7sY7$j$e#GxC&_x<;WZiVY@6j>nxF)J7g)7w
zRny#-9u&Qddk6pq+hu8*@GVHNR37dpjwsyVNMabXB=yBOnx0y?fg;6}{QkS|TpYs(
zXB#GXUMyuVss@#c<RxS}6Lo_}14X~=0cVq*$IxUzI`GFOF*%1S5wTjxh}C^r(qR3|
z(d-@$Fkgxq$E}#}{FEGPk8gpCL{&gYr~y>O1h&AjpZ~D}N5%>2Bsaj0GEv4Cp?L_W
z;%-{Xh&H*y;**0cB^;%819)#wTz9KVyl0W(MT9aIfK<v7NdJ|l)xm5sGB6OzZSRQ9
z$=fu_KDdQK=_p|^69TjOM1NBi#f4InyglW{>b=Q0#Z9jirk6_?In;MyFkTR0PC$Ju
z#2o|E&XSUmc)MJth^&a3lS7W%xmHawaZ_z_dN*&{y!o^rZF(dLK#68$Wn0|k&)?W&
zDvXu9W8c1N$K^r07QlP?J%Q0+08Oif<0Bp@Su4oE{#J5MA6Mn0gD0^pYaZ}Zl^3u<
z^9gJ=DJ`zV%{iYCFw1Jvs7A;@(hkGh$FZ?EhBY#qBTQ5l<q!B7dmbrOZW-l1uw@Lf
z1I)_7Eu%l{twXMQ2vXr;ISGDvq6f{Ru=srRFa}9v{S|{6BxLY2W>Nw;2&XA)tD)qP
z2cv`Mvm`oqx>%Ca<!=JDQW9X!>3GRvEancgGogvMvd(`f+sIz($?!Av`$XWw$Up^#
zxRBP;ub`kXNe&(+r36bJ`(uP}vDe!braF)X(BkOgg1i&EQ3b?mUPW1#7U=|nym02|
z(W`yfq-fdNcYAv6qdP9^D3qo_pGUYd;)kSFWDJY<w2w_pOzbcr(XaF#3I(U(?<-6s
zFJYuJf$5B!3><v)7r41|k%k6KCSJ<0D!(mOuM%mO50NAJ(?+H<^bnhPk=n$lyiM2C
z=x2>1asVOz4DNodBGLXNy-GTv5p|$eO5tbbfMY#MVB$S#d2-<C_FU-0SZ_dbB7;!7
zbE`G^nY;K89MHhts++*Pkto?Aj}2hfE-}=_n{*HoPWhogjEB!aeY*XMu}_bLPM+;6
z15!=O*0z0ZkayBd7Fg@|^;}#Q0y@)*{Y=e;cV}2Id>p<GqGW(b&K)_x0vo5|(D&NT
z?JlO^pxl_g;m}mySKw4KTVee9F-i|b4N_-7RYY^=_`mZniZ1B@DlC^(h&25LKQNCu
zbbIzJJrKE$JU*83Reglz2o$$?Ww@+9P^P+zOZNQyJkb!Pu*#4&@Ki6u?0%q-?)lR+
z+UJ@6$*M!6nuk0cis!svAJ1Dw@v@vOd0p)GXX7=1hCNz~BEkX!@l%2_mejLn&$1ta
zH70|U)52s^NcRxQW}sxMaGkw%D8A%Pyj`}iR_omCto9s#S4kDs5V!1Eg)G&3C0B{>
zf68zfJ8wFJn6H5*mG+xk8HlclJ|f{B^6_iJiTUpP@5@R{OEtG_-b_DOC0R5qLS6_<
zoxvKjH&<x8a-76NR1FQk4$nDjQ>i_BPJm|3X#pIzEUXaDRZc0GjgK2Cm>Y3mpLsw>
zyl9^bD(SGajdw97mL>?uN<+p=*(me-X4q#eAW;6G?Na)i2ag`<rig&0Q0|2y<(s&c
z;?;P*GFU8F((W<xahMb6v?eh2@9lfCgtdCCXf#^>td37BGL}sxm0_&V7e63-mCTuk
zO*B7q4UuJv{~i?!KUXg9E$evYtZ3Ee2!<Z8b#Gl&2rv)>9F9z!#rz;7jzsNgqOvhT
zXCF0(DXH}J8SAkmtOPnyVcB#Vhl^VW)24<7giK9MEm6e?4|5?gkkfN1F)$>ijNGWj
zZe<H3HDPU#kZW<v?P&e4wcETD5wn?&1Lxygvj|&bQuZ#mGTH&z8-b&Ec9QM%=3>yJ
zg3T77zx6=fBLA^vjabH-5?XDfwZ`4UBa8%|7cX8ED>GP%`9AkULfG@JC<4;X3OUJ<
z1SXdWvV^ep629bCD=h|7WKlR!^uOm+i;$z+6CvW(gRDY}IV`Rq@s|tC{v9JLD8aYS
zaq8vM7;z(YO8cC03z1WC+Jja694bu17BO-0OP4MMdlr&}7HSRc)<*s5mVA;t&-1aI
zWcHh{{`#PI;un8`#+Gli;P3BmxqTgoC01W$h|}BaTjC=lpX**JA-0;n$GDsU(Q&P0
zLc48he4LtP)6ONMV{RU&p`p<hd}sPs3T3`3#I9D~;b-<T3STrpWN<lbA(Ml~Qm&`{
z0P+l5{xi)jhnVIQ;O`%eYrm+5ppTb2mT9{(iK_yXPBdV3R(~uPcdirrlJqWmCYh~5
z2>Y*Ddu>E%t3%WPc;4s57($g^MA4k~Y0zM}V9_CPVSe7g<~cMDftFO08znI6NT(*E
z2WN4=K?CyRN?(038{4+*+6gf&ry0a&%k3$d{e$B?Z^0;tFRU*>nium-`ce`qHG!g>
znVFHQFel8-$vD@#kiUt|cKAwyQ&+C#x*)(S=bmk}kl-oii1N??PiXiv=<g1V_8YGT
zLxv2b`V{?8XPVa+c(?(5c8HE5<_}G6>$p7)CavY!xoi3ku1hinaM~A4LKdCf|I{^!
ze+!$>%ScIq3}XX9<+pC#(n~#m9c&}F7+Ai16+R!t0H%kX_Sv(syq=$4=%Qp29$g`j
zFxz%}lVF>XDJzrV(Hpx|_$vHn$kY_nPK<5p0r-~i`I(!WT}KBIOc#fvflJKdL0nZk
z!D+?mP10rY3BRp$V|#inRSq!*?qkJidc-0_&%z>}WB{EP<IA+Q3h8>kj_7;|H%`3E
z4_)p$j6sHEuQI*3ui(>pYEIk;Lnq7>g*9-Fqh<JL;i3`F&Zx4l4wof$nXG<`_^-<`
zgwcS2Ly!@t_}RH4@HD+bF;AZC<>xO;Dj*e&ICmgWH+yqPWsV#?GO@5@2!N8)#T8C5
zS_#_xfVVpx44mIwq}#Jx4E$%t`xD4HEjRx9Ad9qav>F3*(Ki_Q`T0HY^V8^;@jmw+
zr(gTVB_~h+u(WE|I*kxH{T6N6c9XO<JTlFqCw~7uQYmE6#!p;SI9WIS1Zl&zx3WsC
zl4d7zwYq()0AgOgd`U{9bms9pn{!xMd3gqRw(JvUfhK3RWWHSJT$(7PaK*7*`r#l*
zS;G_&?IgX60y=VMo~Fg8j)h(6uoHZ1Q5{am&VsWJy(lj)Z;0lKWsbJxq*qDytTD}r
zj6BRs%-e;HpYrjYRv}|!@QS9vLd!BNZkYMJeuB~p<osyxQgygt|Mf07B_~ixNL65T
zgGJ3Xs6szey!`(LuOWs~e)vs#sqPlg{E&6`iip&jjL*+h6M+QGyMgkv?{~tJ0AlUQ
zlVixkA}q2NVocq-d$-}w)f+-D7VV$Fxt6S)f*gnb;DR~<IXR=A5}yQyFB#mQm;DZc
zX^9RYsaR>^jWwq?+GHJ{`_`LVnJ-nl_rK6s)MM6>Is<Nk3Z6Y|p~IaRSJ^J@_)6EN
zp6(-@-F4NXb^Wy9f3VIf&w*f|e?)AG#Q`^2vz|!tblcWMo4gJ9x#doS4Q0BjN=o%$
z^{D;Zd}XH|@@pGopEs=5)=3`m9XlgXO`veI81{$ihsklMf|YL@TOL9}+SY9Cl$yHf
zKlfZ9D2K(;($Yw$g@WJs*guKi2#E4qbk<M~SJ)3Vi<rFnYX`lVlZVH~swE>%u`d^Q
zy(&A9(kDcxgv=y}Jan}P8;eecrA{=UT$L;0(xtOW+SE=U9af7R$oz)SNm(5h>SP=A
zA=8=MCbS@t+Dv;uL{j$HuwjGG-Me?WTIEU8^b~-(7(_L8Xz72GMzKv6H?lsHpU03`
znDlP9NlC2+U-W*xKCk<S7kWq@q(oQQgFGo(%&S;}i)sxl6D8IZH8U)OYEw;4QVV%x
zX(ZJzHsuPmqCyPggs;0~3DErc+B&)YjOnp45wLOU{o#naKEj5~hqE1!51>NDW_lPI
zh1;QY4Q?^M((dF`wItnjC6~1Mr`-NOJZ~*kO}5v5hexY*)}!UO+ifIJ)D79&A#k7E
z$;THnvl)`?L2mHHO4~dLu$VrWk_~fq6Yp!K`&-e@pSNwZ3bqBGKq?)|!z4Fo;MyA?
zY&rL-$G5$s75BNGAGBoX`2c$~XH+4t^k6-SK#Nwko|_)Eg^-Z-RfY8~X=|vesuBkl
z8m%J+>Ymb#IYyyh^cLy(-8GHyXe5Sn^1f-GR5A{p71~TyCd2O-*uF<Cy?uRLi=Rs6
z4Lt9I{8S@F7)I5RHa;>*<606EgYFtq9z_MgY-^RXe|K{XIFK9t=`@i3`-`i3{V?Pn
z1f(z>+^}`4c^()6x+})iM>qXa&Nj+H6QX2I*0iW?hfL6<Ar5mW`66E+RD6*yL`54;
zLA&}YUz9^D)E^#w19ay~O}ypflQeeaBf~?od_OcMUGM<sMD9vgMXtJB4%U}B|FL2c
zfj@-YSF#aO9A={c_N73Pr1xLDdNt#G@sDeH3AwgA3Exv9FQ%Rq;^DRgy5o|iIff0(
zAtb0gI#AqeNk{f!VOz?@OP9#-+dG4b$q*%aS}U7hK%fq5IU&MxwoG#r;qK+jW2l_>
z?%sVIQUkp|`tjoll&T4))!{bPy-TXzT17=gu7j8Erf>Tq4iZ^&1j>A7*VUg43c961
zlp*KAT)+azxRBMVQf=%x(@TP|)={<p@eJXveOqO*=TmqR^_5PHYM3VSA&v(^1@(Hu
z9qBp)dvL>`8aZLCY(IfUHM<J{U=Bcc8fuWMu&Ageeh(NNLc`fP6FA3UL`A6%Hk)X`
zg@URakY(id1TX~+7sH_=#z7fGGFVau!O_M)d9n*nn3JEf_28xVNZ}GJU7^iRBW=3C
zz9jo|Z=2M4T89P0;`96r6Bwoai#FH=7uR>g5KGlf46uG@s|jp-u;l*2`L7l=@jK+@
zf7s40RmAn<g&?(!tO>KG<gg0<Xr#c~Z9ZWmGJ_lR`zu3CQ4r+FPUTN`==bPcXxww?
zP^%=$=eBUZ9Xnn$=eatN(lz<e7U7?QtB@<VLOZi5EryuF2~BPijroSQi2>Mv#8C*f
z-80UoP1;L|We9%}j$DV}{%W!PEs0Hf5kO(Z7){5Z(K<Wj<T{3>N_@DRA~(nm<FYOk
zez~%0moGoV{VMac>aT*nN4KhqFHYj<NXl7KK)<U_`;lVtGbd*(Se!FIZ4qt&%(En{
zwya<O7`<qQ;?5>4`$uHC65hp6C#~C=TG&o<AH0#z@ChHTr|5EwkHcE&pM_A90@u`_
z=!}B$+|**z_xce@wCeL^YAvZ1T^BBQxb~alBbmdQtsA#(Gcd_T;iN#yp>m(PR$!7g
zxbct_JMo2MC+9RS#c0G{EGnB}asK>cBig`j>yA8K@OQCI+rD;*BSx6qlu*h`BqFa?
zSR~0Z=veIJZ^^h=u~`;ZZcQ@fWq*H8A$>BWGJ&XMVbuhWOg;`jnw@2*kglPrPkhJ=
z3*!-sL<6>SirK$=#nlQ9mfd2R!h)bKW?uJ%R=v~J7T{QP)zb8MkuKz|UOD}Va(FF}
zU=n;Pf;)FApal7G-8!8JIS*0yPnW-6xl(-kBZ|@8@DkLwFf19CO~Tk7BDjD585sI%
zK^a4W?}rn|>dj*;tmCWJ5&yH|d~xE$Pi`5R>?H~NnmYWjC7|4CRaI?}lPH7m2Qf<|
z!n&(sfBx5oQx3G1fnC_cEes`BSJxlcuGIo&0)~D9`-kfV4p4rSN<Nr_NaG0B+Jlgg
z1bBSlY&<bcv@ocPpBGqJww==cwe9E9-(+|$(8$3DwFn+a9ji^$tR`|L?>i0ui3vy2
z7=&S^!j1~FB<VfE9>}BM*~Xe>mh=KpU$maTZn%8>rS{)8Gs^FEpL{i!|HBDI8l&_!
zWx)zHyevp|=Zd{Dxczwj`U|ONLw%n-dD3UJ{@afyZ3a#A^_8)|p009mH!J4pQ?Y5k
zZ+{xYNm{Bd5Z$e9|Mf~fZ-uECTT?pu5qcqjz``(ybyX!dXEpQblc-A@!qNczbLx6}
z!LZ)!xlt6=)%82vpRph_q=z%=Nt5A=ux!|T5DN@jpdWB-lZ+(oRlodl7|WWhmAw&>
zyu2vB)vcN-<Eshpb7r_CU>4p-NaasajeWmrRRUD@!TK0qRgA&PYO1z29oSMGAPoAK
zU(wq$j*b~LB;faxkc`Nw3))o+kr8+M!YmgFjkdV7l#JsL1@y0#uKN1OYX9rxn!#PW
zc9ErvAMpf&!qTn*(g=pl;%6Wj1{A%$Jb(u{1lJ=qtJ5==3_0Pp(D7&}MoA}CLrYa@
z>B)t;=_WwV2@vX9c)F^7@L!8dx?iR-N=hFS?GEFMDBZ}E0J`QeqC)SxcMXy!AixOc
zPiV+a)DQ|>qGo53++tt1qBk&E>VZd|7T?(dcT0MSGYk(TAx$Dt5PEe<sVH~jv7ikq
zg7)bgIdX(y4fCK=yHnG^+Z(*v%F1_J*>ccX!d<#ogW*qUT;%aXt@0Fmkkm+W&IvA`
zY}s!e;&QuqKd~k{XUjL^@nJ&&J~5>_02xvAuXJnpDKDe!%TaXIu=wuoZs~p|6hQ{A
z%)ruZY)Q_@&?Iof1_O-Hw1hob(uvx%Nt)(l9vg3IcwokZ-#aukMDszmo_M)I%?xqR
zhB4j9cE5U#lLjOOHc~B)KoU%n3z%!9qb|A9>5+v??->^UJqa5?ACa*aLW^CPwNB#M
zs9PKOAX-)@rK)kF*d@_L3fAh<t7~;A?i2j-GMj`ygO2^@6*gH)ei*q2kgq;`xC}_Z
z+-0#(KT{b7k3u6dQ<vrs5cH6El>@%FZmAm>gu*Ni>OTdx4umRKEBO)BcOn75ZN4Mz
zH2CD^%^xr|1rZsLTVdd{&lFjsK)O0KAqwD*%qJZV;w-k1BGwJLv+(chKN*IPV@oPz
z9@6LrPFesl5P-%eB{w$q6lr-Wm>nbpgZhE@QlPI@STIP;;&_z?TbeKwI1d<LKwq8r
z>9K9iR0%So#R282Op<=4Q>V_Lbhwz+8W0o|lkYZNeknaC-}ze)2>h=%B>6=rj*a8-
zk@7M3VnOPYeeX2M(h-Ca;tk=6dCmmVV}LU@4Fh)r%7v9;TxikXoNX74#X2YiHgFJp
zT>ZpA{U%=C(YntF&7|*m;*Dv9ITi&;u&VF{^UAo8jq+d0d}Q(nt6rEcTts-aVz7Vs
z@y9y6&2SKmG%Lr!R3%hWo!ll!tX#txi6BDdMle~+E&d+VVjYzFcZtCEwU_{rI~2S%
z5B0C^a0ye(Mmr}-SMK0Sj%~33)MvLCT>j(e(WA=Mq&v1E@V9!b;7DBc(nxze1EiAz
z-o9EF1{xNrT5;QC9|4e}XW}rZMRH0$T;D+y{r&d@3XoV(4Xk?v^k+7zxcy0UxA?+q
zu+ge_bkx?u5`i8DnBR;G40t^z?zd!^7yUQ927mwGZ@v7FDr~4!;}kXft6=`4((#ig
zby`bpve+O|Y7sRD5_8$fxf3vSX|{d_>C@y;E!$5BRfiqYffRmC->nQjPE;;-zad`N
z!#u<milNg5fRZrfVXcfGn~bX>ci59SMK@g3+^?@X!q}46Hm}NW0v3vab;PG@aU=H*
zjnv>+hF{?FV{`2`hew<=J20v%Bjk;7d#Q6EC@nfj*3(Fq2~Nao><(5XS46ryh(vQ#
z9m2!##gS11rK}>U9!;Wu_F{uam6K8(xDGnR&Ro{!uaiwCZX`G5>-Ut<cj%jF@C>Yn
z^Fmso-G*XsTxUM^ie+}@IK+(iqi)6aP}cBd2J%HC!{w-lAMtv(IXr4iY(x(2#T)te
z?SqS7v=|Es9#Qdt80ANgG4sokb~z}D?RmuQ9R=sdYh4a#<$`4fexy+mQ>j+>GpKfD
zf=`w7PeFg##QW=Ii86!rir=ze@Y??kllCYKZCWsyh7HpQs6?L^<_=W=hifba)H`?X
zgvskCX=nL|>!+39>LdBFY&(<uS&3qH-npWc3m=NsRoS0C`_Kp_5G^TT4{7@FoEw%f
zodFN;C|EZCC+a0B0-G_xvpAbmXGU-BLJ|=B{pr_k>hj=QG_?KlKXAbhUak~t%5Hat
zjl3#iJI4)h*zwj)s6YFwNn}J8eqED{jpWSz2r&)>EWc4dM3ENFaA~J_#D6v;IT#e!
z7W&7C&l54UNz{ymWDO}q0PG4|3oOdse?s~+#6yyd#=o}Vu)~*Zh~E#de>~b}jvCe`
z3z1MHVEp69cxwspFw_|m<H>&>TZhuJ(alGt44EB59}|;?4ED&gf~bPv69SV=Kbc~b
zuk7KwO{#lJMrBf3N2P<*vfIL=g#$`CR<BmaRAS4XKs*eS=i}qcmElEIMWDi$tYA0c
z%9_s~Rsk?2cR-t3dU;8JZu{~r#f<gq&Yit*3^~vJ_1Ax>Ioa9SEzYp!Xu>NK)(ck;
zsl?s(@nMn5JzR@sfOi7?{N~Mn%l?ijP2!G{d)pJp9H<9N8KP|rpoY}8dxZ2{6Q6lO
zoKZh2vWMk3)RW%o(D2m$ybJuIiP{NDA3Y3x5m2N{VSF}YjLYDbU;0N91_Q#v1v5zQ
zswX!BAPCc}jK4OwYc(unU%%I5Jb(WD#H7TU=oT7?Gmoh^rou5^K>-2g#kV)ni1$%9
zMD5?5x_;w^<?DwcGz_B?$hs3rPvtIEg~qMkuxGEB7~=&gv@zQ=(aiF)*3Ea01DW{*
z_x}l0NgJ--E*4fUhr?GEARdx_YZB=VkVZK=77uE;-7q5s>f6+!)t2tqaIy4yt`CBj
zLRlJrO5`6*n2Jb{ttL0~1i3!mnhKKx@CqqS4%R)rGXjAa;9O#W+2Jv5NDAe2W@O|q
zq<Mzi4btJ#v-UT=j8kL?1`r7K#SLVID$Hc>*m*r<%?gXj*D2zI4tlw{7D^l>Q|0;T
z?JAN(B*en!p=TJ81qb3(;_UQj3K$j__itMOyM#OYjJ~#+Lg7nCt{_nvxj(;P@v|H0
zdPSA4SLm4!V}*(}j0Y2NtSez_D5GFuN+00V#a$NZoJNvXPUHk2Rk?Tw(~PP1R1@Mx
zX;b}KkdWnNW%qYM-N6iZ+4V+7QnFd|9vDR!HMwPNtih=Y{RZfO-2e?ZtT72aL)sHa
ztFAfXskY(e7vImqU>b$6ovYaNZ3hDj)aSRhuY0DK#3g0_<ix#gaY%5QzzKl2gg==o
zfYsE<ZG4$%(G9F|I{4|od)Ns2b#IGqg!PVB%Nvs{v%ZHf=<TsdQSA#Co*b-g+rNhN
zf$m&A`}XbIkCT&Hup#autAFsFmJ`_{uqj+iF8oX*={LV8uzrziS1;|tExCmW>KiL@
z#Z7ZWK9;g$l~n(G^vYB@%oj_quJ5Yd3wM2M`u?%;@p`HL(C9bmrj}U0G@u6+KD&L$
zhp>tAL`(ybiUydze)!h869uL#P0FVc#m<uE_wiLW<|}x^DtmARC<nUkr?L}J5|AN3
z<1+E~C|QY-PC7YLPH)okdPx`37Wwj`a{o7IQ$$7J`XftkDD3oE)QZ}z1@=don)jiT
zm&ZI((m?QO8_A_jT4c4-D&Wynxu_PtTpnIEYR>K<d62*;GY+y?6EGWnc?GY>r+=CM
z5ZB9qPE>kfOVMl<-nHwcCNxufw|nu51DHEDlZV_tJ%bP6-IPqn#~bqVFEl+D7p4P3
zLRxp36ymN{o~>D_%qyeGFq@d>&<nwSFTH9WEUmGn@1HBh?^$#5*fD(kfdh4#s>oAr
z_ojx4){yd%aBp%lC1hkY47=uCghhu-<ouuM?x1lOdiN1yubQ#%=L{^4VZErN*}yf^
zubO?-Q5K;ar7FZo&2Z`EMLz~_&DYo3%h>fW2*KPg*F#nH9(d8;sO?aw60BPxC{bPC
z{(ar1`XFJ|yr1e?g)@P|Z_2_lg0pm=4@>0@W8Y2y$+m;QiOC&dR`gCx1><bp_!b(X
zHW_FnSnaO;VKflkY(_!Yp(}%kFzP2xcvm=}pX)(KCP5_3Ds{tD(K0X?5^DNa$A4`l
zA^JS+Vg<?#Gq7VizH+m%WMsfP%*hahd;aS8KRGt)nwleZRLw41vC<5*HtQZ}4zndi
zY_wo5IXUm+=}KTIR76eX0fJ$^ka0R1K)zHX#pk8x5sRMW&MI&)`*&Xget=YGKL2Tq
z2AO@gq6aZMVcvYF_9Ymin8mrm#e=N;i8>`N85eT6pZxvz(I9evZ6gd$PjKinl@t=}
zhEQBs&Q1(erBz<42fzCml)@mEXo|BH3^}A3hz#T4Nls0UwCmdz%+O&}P$J#pWaNTt
zz7A+D3D~w~Vw#9dBRh}WWJ7Krpe~Rkc+c$D0ga6NgiUzgMpW_<bPqPpi)ez=Nf_e*
zT|p6b=SNf-pgc%oaQ$D`u3ZzmGSbFXdKWu}fCu=fA*d!T$GVG!t&u;!?PeSR6-pk=
zM1(pWx-!y0rcD$$PQxu)&bfwU84AWI%o-b%CP#@yeH_?f@v5Tc9ZI-p6uoTxe&<Hg
zFN7=H14ya`nf%qOQ)G=JD{DyBIKB&H6qb^bqNnPl7$xDnunJfarJNC=>>V5e?%$7s
zkU>i-Bv1-15nVIyHYk#w0<suFxdQKoIj9!W@H!v?f)VbgD5L)(13oCPUwg&tM)R;U
z$x@bVNZ1XTO%;}Ite|Ta+u2Z|8-VQ?=ZkOCQcSvE-Xh1G^hMS4_n~F8cxXeS1)Zg9
z>^^q=sDc7pHYn>8iXGgytN{&h687<je$z-PBwk(VJKWzN18v6gL)Q!1P->|rrnP%A
z%2v0jq1td+H;e5%ajXn5q)qjskPv;-1M?Wm#3m!R%HeNEhCKIAtjzy5oD|g)+~R~)
zO?tIl0x1{n+V!Mr3fu6Skh-r7H>WD}hdQW+4U+{5iS7Zy8zL6xP1kOdJVOrp__%|m
z$3fGo26FxU{@jbJ6~3$!nD@RUVOFnts4~ee#J~7_E%|YnIF^9spx;s$9^kudm;-yP
zo1eW%BF3;>JaO#PAZ`)Rf{SU;3Xsap5RwULG9l?31hpV>SkRj4o+>%jt4PF=+jH)1
z1=Av!oP887`Y<y4&&=9i!mcy&<<)miM1c3=f9LIBrA?Ngl$0(Bu@NEA^4(gy?vxf6
zn|D1Y{y43yY?V1lHgEwuB45AO#1sJ=7$fH~OXj4&!a^cFDIpa`T+-G9l(tt;Pzf?E
z$=so4hg=bZf}{d|KbLv*9vkUdOItgT+>FO&CqPGt2ctu>NnpwbWiR2caNr-g)01@8
z9p8=*;^5GR{gd>IA;M%&zWA2C%`Ady=a+BX{C~&n%M*gNtO$B8wIb@@U%cj*%WUyq
aTz5J1`Y*OJ$NkA4S3ItIEcxi~SN}iL?gY*N

delta 181131
zcmZU)2V7Ih7dDJySx{jWP(+GKZ_-3MxT16c=}1*Xn)FV9i=wh1MLGy1AiWDnZ(^ZC
z=%FSmC4>MGLK2e@_-@?)yRZ9x_ov|!l9{<@&UwyrW}ZQjPygII{aWj?;x%cB%dP<d
zKK{y5Ql1Y2e4cm*JaKz?&D+mKO1`eKvhML2S74*_-tEjkf5@4C?oT|zcrDFQxVQKD
z_t`5bhMKy&=bmi-xp2kz6X!(ysimG5mnA;jJpH%*^Y}0oOh75t8H*<MHD%Aub*e2b
zZKtC@**{iyUc-}zYlsc)B<Mv3@cJ-&d5oJOO0*x>5HQ?vf=90QFPLIPY`o08U%nK_
zWh798ZswUn*7V$S8{%|x(r>=e6oAz@^o|j5i!@C)ysvt<@`3`U=*#5$aINZabq}+7
zMtZaVUh&*d*BBUXW;nZ%hwn?mtSg6%tVML^m&ReO^JIS;R(B5@R9RzlyVK2f72<kN
zCP^oStK!-H&N5wL$?THpSHK(ku*^@;3TJr#ZIpiF81l#UlMD>dH3f5zNYh637WXcJ
z@@j>uGYcy3A5j15d{?OHy^S%Ec%W5WUGubz7ic$6>RuAlWu{2-$d}rSU%276`cbRe
zcC6_5WW^(ETLSlgn%8@J)1QICI1Ruwa!@9n9%GG?vL$sll0NSZ));oatJb^}#66|2
zqC9mWHc8KByk)$yy?t+E`x=efFwn8~##nYe{6VV4HP<QMQ(WdYJ=(vF{cjJPr*69(
zVTjT+;bC5%a-Iy_sK=xfS*aE@lL+fCwoW!afW$yik;W&(J_3a^_7I2L*c(mmOT8=R
za7lgThZc5gacJ-X+~?x&lf(btk!!`#M=mXPG!mAWjmURelV4vhl}wh=Qeh=xUu^Nr
zzoX^b&qf;Cu<Rw=hxF=r<FMlk)Udrtbg*v9hS*W%$iJL62GI%15B^FmnFgL~Rp0%0
z-1M6)v0sLF`dBX^>0UGbc8x3I=Vx+C5fS<B#-`3mXz+T1nq8dWa9QIVTOtNC;u#j^
zZzJUG>)&W`?a81mdB0yNkeL!uI3x6*W(~BCy<udCN^z^H9)l!7t4X|SHYwNt1Y)u2
zc6nh}0>9uhgM%s*xGzoLXl^EXKp@?`;h{+a<yXH>`r<DI{H#)z%(ci1zb>2^nBH^G
z^U{AB7ZN)<a)jaLoY{;0Fhu?1P7mt_0a2**{Nj8i*y{RfUS3$JWen17EB#{UE2ucP
zJ~#_JVGkhMhJdwcpz*0350fM7=J&5xYn?KAy54{lUnN-=BF_G&56-?{c3FOja>4vP
z7pVL&DDRp|Xn{aEM7jbI6et)C4-VV_pGA50!o^gg3Z-yk9XD0kuIxW}D-s5Ju^=P7
z)ZF8qf8gVYZ|~iw&3@D|gY5(V_VHomuiNYl44jA!@n~h*I}H7Qihxsm5bvgu{f;U}
z_`wX)>&v;qkU4+W<d9o73zYYb)$oS!Fi(+7&($R_KJO}P)q0gvDPLG~E91-b*pYu*
z25$_we_5aD%fFLq_udaG;3f@lAud7MQhLzs%6q$`hK&JdGTu9QcAjk{Xo0_v=GIr_
zJG^>R+1LWM{ifl_`l}Lzt6@ZOxcamIb`UT1PY1QK>xc~yEy8x*8`$e3qU`79$}@un
zYr_xbx39f$iml)CzRZQ7`y;Unnp<xSd?<Y}S&{k_jbYeZ<C65M8DH$xJHFo~yT|$a
z_&=Cw=PQA}nYMP_%x$#JJPcLmFQ7Xfa?87UZ++E{C(H$?1r3E5ji!OvQ|r2zRt-zG
zx|L%CX>4s&D3$oy@ZRx$<FflYW_7>B{l(E^1^<Jw|Jk4O(y!r*URft$X31xigCe8(
zH9!YKsFm?-Ma-u&tE0w|Fl*d;Qbu_n2s-bptBn@sDFQq`X=DjEd(-suhLtn#-!RKt
z9G|x0G1D~P9k=4qlZ<w+Vy@^CD;v!3R%QIpgh+q;)0KsRp+w?(H+-U_S8T+iDkWnz
zBbZgIaq@hK7qE%pG?usrtt=3Ze~<kd6~x0W5|)#i;l&TgaG&|zGEqA(>39%zl<qc<
z?V~|Vcx`(AmGIPr{QBve-bM}_okmO1b+UVk-94_isBfdeXHu-%3ePaJIjf^C##o_S
zjW9o9#-_J6ZC<o1&N$ar-qAL=8FuG`@X%FPp5MFT#DNh28cgx_k#3_?CFUMI@7?p(
z?jh&+#)-s7V`O5}RDGwB$~<)%c|kN3U^V}!1NQvrYdE5x$gyXWeKRolN0^hju|DWS
zbM$O+uQD{j+*bELorti}Lz$sh$0<-p*eoXC<Fnq}`t6>wpeB;bQFwDRP@J(9;l9O`
z+0`I;F5yxjQG0rhkEh<td91W}(xWl_adpOs&b3T<)%17Fbj2S(^~Y>}e+I{mt6)B4
z$UptNxn}rc-Lxrs>owwUaa&vxn%+ZB(r?CWB$vV}ix-k{4Mm`f%Jra!o;+PY1DN;7
z63+kLWR~qOVBmDA&^%I|Wm?POzu`Z4qR@9sc^20~33UN(=h5y3o)O@q|6sX&o%3#5
z0m(_~LDAM0Z61lN?L&D5rroWr9^1{9pS!#_<!7i_EHNZzn)JKFM8(GZnr0@T2#l}?
z4I2ToRc%+WELzJOynk0r@<t)~e2*0c7Y~_|F>SqRj?zx~V6tpNzB_nF$mj1?UV$IW
zlghHFZatI!X3{F%ddA{M-G3tN9sk=CVAf5kI88CXxaaXWV)b2<zCe7~N14Flns}uA
zpXNA61GR5i0Og|$fu(oqQj?l3rpw1bEn4P<(=s}4gO=Q4kD4q-kOD$8K##iKT%hOA
zB`#lDASV201o%i|(Ff;9T)!Jmgzwi>fBlNLb}wVDC=e@qQK7y<yS*H1Fc#_{kfC7}
zA#LqqgpRt=Omuf3eHBS8QB(t>Wjwhy$4h8xp;&`TCO1P(u|GS<TXQFk%jeFdgNa9Q
zQ~vi(q=SCV&WEf&C=u>eEai2|*Q>U|KFN5xZB1t|cFsSs*>=TQN<8Q-Yt~emy)fR>
z^*3^HVawXfdLn+FdBm-1Vw1uwU)+l{<1wf{Ic)}{8Yq|vP97`yod-E~{PHS>Wj)V3
zH~XYqr*=+p@sL4y0)v6)$ofFko;u;B(<-JaLB_<x$f{cHLFfII8-W~0<Y$g`Mo*aP
z2S<Wm%y3S?Sp7bBhF|uPzH*GFPN!C@avlUEP}o53<=n!tsMJJ4?VE_thY;Vc?mc(f
zVCn;b;^z3mF<J&!1qkSY=|`r`PnbKT+q0H+O>{!<f|FuydG<A2J74+tPMG=W0l~ob
z#@YJK(k~;@GfyHIbFOO@XgY#+RvSs$G0rc@mx2BS#EY7iYG3A3aL1mhtR{^`uVclY
zJVmFE5(37;#y-EtM-@|EUvHS`H~1WV{I`ChG_~jm&LC7x8O#Tn!o=W<yGqU=MpN5Q
zHa7d;l(dTmd&tii?Rj)L%zbX0Pg1kC&z_>UvBAatwnU?WIHGW1G~_Y^&{>hBz9bmo
zbZ&B-)>XG9{aZf_dWrNvVKC4wi25H$ZFB^lxa!*&AjmjQ3Yb4S-hN^_h1nQ)rcZAc
zyNh}CnE>*-&bIcUWWFEP_h)VMm{pCAzlE2L*Jn3Y)Sk{LZ~nb`fK&9>tVF3++=`UU
zyH{B(=Z>Gj@WxjZPlnzBv)bRvRQ`?iR~3aCZ$}SSWV!Xz-jRyW>T`Okjg?lPr0mVw
zP<fo?uwo{bH*3fJ7ShMi*Ubj&t7HlP3CAy&$HL>JdtIr-tuPt^(Fh@V1qKCGynPGY
zx_kGo^5(lw0Ta5y<@NRTi%ZAA#OMR!Sp|$|>TCn{Wt9qNLM1;(6bQ`sv;pNm)!LEM
zxYLF>uD`_|=t;kp8JlOb0KM?Axp67kfoIpHCLG$uOaYAv@;!^Y#~tqd{=B`6Zxw~=
z5))a}_Qo_RTiyUriK1->yL)&H*(h+H-sr%OP7)9fwGE@nmCrlrLs1rsjvR3-o5+1x
z@wQo4AinKn;b{I|S6SPMw;LDZ>2wL&vl6zOJZhA2r}vxr6qVn%v#V6U7Ka%W&ADe;
za0dk2r|1Fgg!2lPm37KyFOf%o8xUY{_t+aF6BuQxGyt6!EOsuI-CTn!VWS|1cS4g@
zt*HCwtM0Mx18jFiS65^_gb7TI1wX?xxGFWl=y!{Cf7uzKcplDl3wJTT0&<zBXj|-*
zaAsb_gGR#XGL6RxJ!QKpV{9`|p&;2^?-#ACJ?*-)#|2W)>MtYm_h)l$fotwHGsA7n
z))sD5I)VE?mvr`7S9M)A(xN}~ueHc-2miI<@ha;E_5`g)r#_azK&4s%GVG8H3!`o|
z6MilyRW>y>nc0Dm`+>9UkDuoEu!z0+xCfqyrKM^D7EviB*2toF_mzdKk&&&<eMsZ#
zO*+4^{1^}1E5#Gsq)QM%!I;LSR?Azp%#=o-`@78RM8~~5Bhzm{AdEs;&X4|XGy@;d
zW=9ed65956hXRqIXiU7^sIi@$U5Ny55|-3`*x9weDn-AE5IQ8IVF(UO+XCu#doA~`
z5h~teOUpP`+>iv@Y5|t-DxOCWn<71x#wsPNJLkjm6&jJHPhC4!XxxAIHo5^xH@0vC
zqNga5Bi<D=n-smfl}zR3LS-czuisQ?zOi64aSD{qcHl8Fy{jXCMZ;1Xtn&W7OnINK
zrOtl;@`_L_6``By5ZPK$QK5?cNsFwuZqULaY7syn!md<hyM^C0=#WP8piv0Q)XfI{
zX=ktvgFiWrbSGxnmGVDEDDAQ_6=rPr45!1Jx!0%HX6(TMmoln-tKXC7Qkb8aDfRS>
zxHHX4j;Q&H&zBTYD{`y38Tf4Sx1{^w(d~S)Kp=iZ!DDN_k@>}~JJTd8K%*c@U8R}!
zVY-)YJn3ISg`)H5+>s&QRUK9g@|iaXt717;*f$X@*WZt8qPkXZI*2iJTDG$g#WSRx
zH+*Y!R10RH@9xK(<>UiN(FZ(OfxfZ7G!fs%43dC;iAn6F`>kE-A9}yrW%&|lmnpC-
z1z^*2KeF4a4I^adHz1hZq0JW&aJ?Nw+9=T~FoT+(%6aepAeCR^ypgLnIzNbebCO7m
z+<IapMQvPmgq<jil_j)<jJMPsFD0>8VH?tzbb?P5#`ry($f;oZPnWBI<tuTIDlVVa
zZtLu{qtR#;H8s~vljww5dhYnXy5y6ZnU8DkdA?Z<!&#3QNW>=fEydYdI$a(|f<Nfo
zrWD#^6{@Uul!+g#C#GgDfrV%slV0=jw+IW@FuNBU;*1r{$IaMwE8DK-Z{#~nb3~d$
zR$lC@ThILb!@@`){_4*2k9FX`mWRIl-Efl+E}~xnyy(~F6_PcW>36NU6lh=s{h!NG
z49vnTAUrCCg0B6~q)12%O;l0zMk;EjMD=rcoO?Mz#t5e5g$3&ShE50Y$MnplYo@i@
zMzSJms&DIlMr2fIMovFJTES9$bRznQxupOe1p4Xc`=cKSuN@+X029UYM#o<W^Cg=_
zg?vsQtwGP!q3<j|wu)e-m>yYI7w34sZ8~;79?E1AJUx^l`0Lx|Crpy^H$bvo{YRO4
zqkX0|NVu8%`^;B<^EPmu#t=f>92oHM@*<XjxW$l`>1^#@K@XlHw)5B5(nv3y=Cc9j
zTc|*vn<sxInKCfRBzBdt5Vfa%VZGVT=O0J&g;PR4mbpmZxKew-n{esQ@=i*Vo3#l@
zFakM78TOQu^7q;Xqc+5zDIM8tOg{o<&=lft<#g0In5elU52+}XiC5<(O+?tmOufO;
zL}^-@o*jz=lnD1_O1m68Z;SoeUZ|16q@^)?;hqZ+FnFTW$hlYKQWi=+q@P-Tnqp@2
z;FP-2@qA&%*;H&JM~{Jhi7H2jea`$f_REgHaeiaoV{blvt7+f%D}sagDN6v82!qHO
z7~DiN(D=;sUy>Gg?=7sHGW5p1DytR-=%kiq@8$Fi+dUg^E6@(IpNow|qsO10C>%h1
z)Omii3nUO?KNfKfY0_G;&%u{ofy`0DV*T^v_-cvdeg_n;Vak}p%qh?~!TgH6*~#fF
zL(3R52WNfD7gJdjk}1fMoUIE3j{P<_tDs3_BWYL%Y&lR0^;@y?{lZZ=ujS!U$-=ED
zUFCVh8@Z`NA`&aw+acH6Up!dHV#TvS&rD_XKNyqHoxC;i{<VX9Rf<`9vzNq!sd2u$
zC*G2@rjVJo%~A_tsXZG!jCCmqN0vd-YYdSHJ*Qdz`J5KW5*vx9rF4+qx&)BRSstG@
z<}i)`oNt~<KO%2-JQZs`KzKR$$okG$>1$MJKFVaQ>2z^MB;Q}y4^~cHCm9^w9Y_Ct
z-F51My$ORrhUUARPa~$I1Act{iwD@>neT)I3*p|rx;7Ebs;*eE6;7yhm>y9;slJU^
zjHGVeZ}IsiLihum5Kf2hOyvqf1mJe@_TR-&S9dk{a^Vz-SDz(cESm@v49lim7P*l6
z%1A2`#{MqNT6rd7;43i-E|8hnu~Pq0*!*OI`%#m?o8`_ocy==(X<&>0yI!M4^Hd1g
z@Nl8j$mIlufW2|;%&LeQR~Er@Oc8s9e1XTZ6|P@jCoXtPIn~k^OrpvMaH(9k24dVd
z(Z?^SYb!o5IfC|8dN%fnNj%#{96zdm{9Wm8d{Mq+zCx=P`;X#@@^Ez9y$^cRgtANt
zx$MAhK|zy<q)ViU!6N^mv~Gz9qSr)|Uw_v-v8{b>;-&#tygs9^ezc6+WvD^lnIn{C
z^II9`m_)?{VZ$9PG(hR}eXZ%9fIr*nju$RW{N>^kcK0$l?Ek`0)Bs38*LyE{PkBe;
zFKHuLfuEX^R`2o+)aV&m+E!h^Vo#XQ(Ei8<+qYhenEAf%&2n0CLK9$_u<AHGSS1`i
zX^P2eFnpYo#V%+S@iC4|-|lR=_|-GQj%q7ICjhABNdw{vGa{B_u2|GK!(IXD+*Axt
zk+AprKNe+R(0hIpFS-qy4AI<0kkm%rRah$0m+A~Km~Y>1!v^epxxng?Gm@i#1t)&6
z$Vi?1sM@OTVH)J+Ljr+-0SN&7*PGK*Q)zYDU{xp~jb{Avs+SGqZ$P=mIZGqO8nE>1
zOm3lk^aTr>jaQ$ibMAwL8XP#~3AXcF%g4IgtufE8-Z__X^=!FQ){%|ED_<Mff7mcn
zs;1hcQZM|lW^S9nDE9=kM&#^u*}739qsvvh;jP^Tly8km0rvgg16LnL+B*LK-53D<
z_=eY_)eMp8I_%?iE1K61pb-)~#A{k^H6z2YS&d5mYX&2^O8&R0W!2T@5fL=df%(l%
zO+Vp}FnnPDkju(GqsmPE`}4Vh%LID;T4%-(W~LTB5a6IFRJdYZ9bqABtk4PVm}pG5
zHp(a+4F6Qgmnb*|R={8>ZwD}rPlcJ~G2RO#XisJ9feSXyLLa~E2xnV;6v>e0k_gt;
z`cPl=E14YuvqlB8VCLL=2l;}i3i164mJ?>C-z)<~@;6>POke4C7n{Hy(Qdo?l|L%t
zc^2*nLl##SILQCK?q}gjU(V0Z*EbpEli3OV{7LIhd{iJSUQ+C9G~a&yb_d?@K;#sC
z)}8-k{>je#e9DYXEZ8#coDnDCv{3YD`-!*Z40}2^q&qb#MxXxOHu2X+_Ws`wnXXQi
z$~qNw>FmZGoYeB|R@cvFAXMZ#fc4T}-BM)lq0aZ!Cu!>Dv%Ak~!Z<Lr+1-f+f!Zl(
z+rthJW&o)`0%_QMB~?Gt<6ngQa2sSw?C&FKVRqp=U*f(z%<gFPKx~w1(8~8Cfs2q|
z(UB#xudbgkeMR1aRZ1GkS@_h|7A*h3rOfe`dm!#zhxV^I51xzrY#=1XWwhpky{K(J
z)fF#1CC52*tRt9sXPPbX%+g=X23Ab`gy&AnyP>Jjr-Hyl>0p!tN6!;$=QauR+6jA<
zJyh(}&xloZiGRa^6aB|^*h45}TglF+=QtPlF!B*~%G78YM7%O%*N3+9te}hElGW1t
zTzuO=@3!esfO3X#-ry_kPHevb$>ZEyplJ4u-V7H%JoiEoz8+i9b41dURw$s~yDJX-
z-0&)I)2iQ3k-ALYPg!lKF2zTb6U>wwwn_b@RwBB_11`+Tk_GY*6L8)i^p+;YxvG9^
zsRvgq&3MWy-vt*<U_R_6cA4cpEu--7Oaz}#)l+@IZp3*frCgRIG=OVh-?iDj*nKoZ
zK))hnS&3X#m*X#~U!NAv_k%0|1Dpq3tjkqx=JlorR-7ldo!cV)vKK8sX02#TIhiD_
z=w6%ac%+=1I=YsvIuwxi8ioGjp!W3RX!5CRr_#(z47qirG>^cxIA;@MYWFSsjrh7l
zu7(<XXDV!w(G%Fjnk;gDSr$UKzVpVaH^ojxF2YS0vmPmb3rBt@5~hM;0|34!5qh)p
zvBLFRpFOvfn(GkoZ3McZK3L)p0i)9cxdPvfVA9`B`-5)jilk>+gS_oTI5TBpf&66Q
z{;rM18fWjh*v$#fFB5&U*kmyy0b6}1_E*9~%h}5hOmK_?2wy*p=?jerg_GUOqbx+{
zew_GR9@jahLf5MWR;o%b_kghLP-BS)?D~?+v5QWrK0K^c-#FoOal&bKT!kU31ugfy
ztgj}A(VS7061sZbe4;e<qG8{Gj?B3k|J{gI>W=lG(iPDaQcJWJh45X!JF;F_Y|g=y
z?OP&O>}_IWHkp(WHnM=y--J7TB;FtGFcPOmdQM<&-*6BLHF=Zl57<i&cGYsup<g|*
z)A!5eJpZHA@G_oiBAim3QL`U~{ahcgwDUt9T<WD??5{vvUTH+wPg0R2bpnz2c-NxF
zw%p;6f~Y7j7j=TuX_oTkIROE-;A&yjUAEEu2M~MZmf5@+X>2iL?$(y_M0v3#wB76K
zT4!hB$Z!wPzgC<y^7n2U4~+VAjxSNx2BnH;YeIh^F1AGXj{N*XOx&Wl$Z6|h`_NSK
z=^=irB}>KpCGnqj7Jlu+pY+sB+_YOgmi%oHL5W|<H=B&I1KLo;-6#)kUh=kMc<6B<
z&ZLfjdtLRa<ZM#(9@Vf5K)e{-B-;~S_8T3xi&_B+a7eO>gNaa^cE%R@z0{JknDN?u
zy-~#Nw&CQU%y#?eP;ASD5)O4730Fg5Cs#uUCvbTfz#%)5l2=Q9kUrP@KuUbimfe3b
z#<m1{!9kkr-Opz>hDHQ97*CqIk@rpR8dsdsIoRuN44}CWSW1zf#c}GZoz>5KP+0Ts
z9)Qlj=OR#$c%-n#=hnBW^yyD^E7NnIVpn8S4ErWAE9o<zWD0#G3VMd8=k6W=B_B9r
zh2u}0(@InlJSS=cNUJ6?1VF~R(p6(W@aE&1Pok-5dM=E6a!0;nq6{HpEdys3@1C@C
zazAxE<u43J&!excYKm%XuX%qAcw5JnFn$Eoj(%B=oeEnN$<RS!1kM{hYWmiALpBoT
zd`ipccsW&%EefiBRv=>HCAH}9J;<=x{^>{7ev6#~JfF84QgGw;I0S|iG*rJrayELm
zi_T^ZS09qG6!<RF*eGZuq|b>tVDui`CG!ZsWe-CowXelyMHu?zt*1x}O`hyd=Y|1N
z9IiNh0?OMNzu(k0(D5YnazjD{WXShu3|>C%?kxVU5IkgRuLfI#tMO2s_uK9(Tx_q*
zM$dYy2xZHZCPrIA;e*i6UjsY#Lw6@UkL}MzJZd7j!fE|YTd8p2zIxd}G>>7~FYk5S
zr7ud&zIRKVsj;g}OiVOD#sM7Sd+UWs6$J$a@oJ0=I(i3<S9sIEC$FR@PGWcdW>1=%
zDJ<We3VWQ>BBv;U*88&k^}DL28m~K_$wt!U8b{=C+mnTmDu~xI2j`|CM6$DH_6e)W
zr~*svWo-D<kS`hNawk?j;%RPps)L9Xdse-{hS+x9j^7n422&j!6MtYsX5*;ezOs|7
zu+;3ctu69Ji^r!@W9yTh>@w1YU3>jbjLpul?F!beiHcLDO|CXVb<|QU85&Eghl7}?
zP1!LKDD^MSqSqYU)yO)Wuyf`6&vUUmX7hDk;m|;~>^6&y7V9P%C2E$aTq`ZD8b=<S
zD8{%5s63o=pMzZPg9363R&x>kjY)g{k4!v;<E79cLX)k1YTxd+;d$VVzQuBC#8IQ#
z3zqg}iS1h}vnv(efam-|K;j4%6-VWOjSj8aNgcZQ*8)<bPFVka8(pc@GI8xLh-&Cu
zm!2rp_AYQcj${6sOZc4cW0(xJB_TW|JmpsCMhjMDsJ`$Az*~y&XdUw%*)Dv)2tU!|
zk6|kd`?$Kz#H@Srj~J&|zu@CHdUcXRu9ACsB%Ozv+A{*UxhFHuVTL|B4eFulipAC~
z&HG{ZNrDK*Nb{E_&P}_6`BK8QV(ZOY!maSVSOW}0aB$SY?UtN>7QES*p01gGNaRro
z7*#@-H3E(Dsm+fD&Z!Y+o!-beX@R-^>+9m$ikwyxuj@I9+If~^BP(A_391vZR<c=2
z4N^$~SGVVbDHWf6T|grbH8>0?;&oaknj%X0A98CjLryN8&THc9D5y!nq`RPc^DIBB
zPxPtsNI-p#CzeL{fs{jf)%lsMou!ckr&zKS;2tjF1lj%4$#^qXK-JJkx8Y=q4K%cM
zHD#qC4zg^JwaBKlDBP+zIY-!wWgm$6sft}N658d8UQNcS%NH0gU3bEo24OZUX##xw
z`b<vF2v6ZdPV1&XmoDn(QzTtg&AiuFL-V4(2Fe-)*>1mI4)`P0W?S`{{VL`hN%!_B
zaNf`GM*Re4kxjnC&_FM7dNB7nM{}N;vqK5OX;&C~3Fimt+|#x5UBHeV-0vq&saGsI
zVIu`OD$vMMf4<gGqHL|_ZhE_s&j88$6ZO$W9Az7-a+ud*f7nZXu0R^_*#?9^JUX8!
z$WG^=#uQX%gauIyJ1X8lT_JH?1Mt-F-O?Iqn5x6oP*2M9WP^=sr<oQ^{7O=V>x8gE
z@sH9v*rEnDh3zZ{hw&ML`hJVPS|)4Pj$>r4N8dZoH4$_(Qi5e)r*{_qC6-;Dxp=oJ
zZ}aXKli&}@l{Fsi0V?&=joz7S1mWzJit5BZn0HmdMhRt$6uPu^lQ&rB9e|SmE4%V=
z0Xv7^436il`R4UN0KpSoAIwsZdT`Y{l48i#w14}f1!8tvIynqF;naxwi$CJDq<&(m
z{6yc-Ah$QV4$iHuU!%hN2<PM2ed-y)?$Xi7g|@yROuH<dV2WrQ9T9HL$T;6gUBb<5
zFSW~s;@3tdmy<MVz#AaF&gFyRpGmsok)r2=j?;Jt!_EdS^`OW{o0pz0+Y`=Y`u(#D
zVQP6pq+`!=m|%^;)+RB?$bCq=pnwz(nZ^Bd)Hw2mw*%V6uQRGE0bM8}iMJYUzFx@=
zl1OM*B^`*5vS~_uS+z7!M^F5e6TVes6zL<8(03zoH-PpCXte}R!8Tv#pv~bP-At*F
zm@~C183MMU$FXfhrL+Q<ypL3!em@@4o^Niru$sVT@EHc_g5xGVJ5K|m+9Va#(BIN=
z$rp5v51rf}WjlYgJk{`M`Q@K0vgt&!Y-l8`I3JaHU#OXhQf=@$Tvn>tFsvJM85qK@
zuvjYzTg>7CNzuT!G5!!(x*AhauyjdJON#KRA9?gxEd+}|-OaB((f8Yfs+*q7r3~cA
zIKAYBY|2Pl*B60k9ryZg{qd($g;I^1nOA9Zgkh)4=_p)<#^pPdU0jT~4kN>BesbUD
z3jauE*pK$>zj8p~C+~}7j!bs=rQ5b5Ewd{CIYoVO(s8l<qF0!hG%u@mwlnK2d`T-?
zIpqn#kLWVTNY10y<M?eJuQs`~QXRVo11Kz~Q{Y8V{lX~@3Qdobnogz-6z_;W*w5~0
zkq|Vv%uxhYJ#sl&`QBwnF#r(74zt-#*9&v4AhUZ)vv&F$mh!G-Y{2k|XSP*NJ_3x8
zI;aT#ISm&zYMw%UFy>wQMYJ6l@~-S+_%`tv6^(BpLcN2=xMQX}4zfaTt#)@e<I>vs
z*$i)-?mt|_ah(3izDH%>70&F5S~Z}WVxr`>?2R&9CL<k)NXEH#cB{nqva99$xfI?&
zTW75SaZ)Lc>swdhb6VnAFf=lOoZ+yIL^h0ydC_3eV!O6gV&G6Rkz6%=1CxK4+kV$-
zj_t@qaLzl_y`ISWXfW0GzwX4}qKF;(<+6Y4u!)w_h&ahu>x5HBLgA^MTBDKI1ya2!
zqSBJtsxLxG=8ImYRFUmeU&=OXhEcYYYxks%a#A~3+LXwGQBe+s0=zT~?4TSOjvq0w
zcXBE`AOVM#;ucjRPVlHJsyk;e7;oLW9zW7C!Hk0|s#nlS-v?yTtZUnzn`9#2#FEB)
z^ny^|mHlKPl)1~M#(rP*9_xU~N4+G4>>C!lN5!G5<%AG1&#Fz|`b-GT_oG!tA>X$8
zfS*HM-*VPqgzYIH2@z4XTLanrc%aQ*OJv*bx<A^+L?vc$2l6be?ceL23;NjTxt1jR
zA~A5IRku6w6c6m-t{R?sKk6HyBUEyWDq7;DoTv7<we|vfXZL5}{Vp`zA|q*8TWfi^
zVRuTu<StS<V_>BD$wKQwKt_A>U7B_Ow1Oz)33sieQc?^+QxrI~vr!^&2UhPvw1})`
zOyblxLo-87nW7E3hh5yxO_8U+xTLjuWf{$B7(CuJ*lufwN(Byw*KElGn`w2^mTPSu
z>ZxZ!ni0B)hIrp&UrHx4B&G0+t@a_rg>n@<>w_f$l8g5w(h{z*IiGlkBJZrhyIba<
zQvg^_i~;aQ1jK`a3Ja<kU0Xnjv)KnqE_f{1BZvpdwYV2+=W9t{!g0<)t+<QFH`*fO
zuk9mKyQ{--+H)O?ybw<(jYuN=*c@r)(a5IDi=yXSN;)NWaHlXnVz=Pz!N{CLyZQnI
zN<-zW4yFpgBvIrxsqGdPG8Bg91b4?C^<jX<y9H<gmu&QL%i(UsqUTG>g<P{?bJm7H
zwO!^X+w!%7lR`KdA8*U`kTw))tG0R*^0s>ThOi=1j)$s~nKMQ8abK)yoyGba6~L9|
zNI3zt#fUT7U7m5-(Muin!C$r?mys%Sfx=#_5+O3-(I9hp&?PU_OH=zr^|%ls3$V(>
z>uIr#HSr4?q1_K!vrY`_^;jaz8_r@|)<WdWA`Pt%NMa<a?x@E=NcYTcC!*BBdmHz3
zZU=qhdn*>5)|%vSR;p_}msNm-u3CgUV7iLC_4}6_o+5&$H6vJ6rs|5iKg})#?e!7u
zzEI`Y&!%OQJFHu@UmSGuZQ#EH+o|o{MCG|aobv8$rsZKSR!A7$0PPEpEOSz4e{oPf
zhv-D2Wdl`{GD+R=T!ap#r44!ET+>!(v;RT6FKJ=v@Cv@fRXT2UN9{5hxqZ4;(rmlT
zB^P!{4n_!Ahd0Xx`oVCU-E-ySeU8ThXAZBbZzG<Y?PITIXVN4$jDaxQ0FCth<yHiw
z(FxX6grKHbQk8HJUaHR!sa0wrsV+334LGH{dR~X)P-&?P9S+N@C)o8ZO(&+0?v>?i
zrHZ3w{h4c(h|bLLY0cF&rzd&4kzJmx87ioH+!b^6&;Bm;+Jdy$tEo;CeTwi}Y}18m
z#K1XC+L8#+wb~EtAd~Qlr(JMg-sjr5hfck#KRItFoQe~Z!@!lcEo&M8C788jt!1=|
z=ixg`*N%Y$!AEY!87EE=q`Zm`tS~aY!(EHGnc~+tPLaSW<}~!wNVAa?@A44&q2g4j
zpXEIMm2=l}mN4*l1bg@k);WOW5%PCe$on_d%JD{%P&VKhLPvGuOr7Vq4oxRmq$loe
z_TeK$9$tS};Fx%Wow8?f(3?)1r>i2F&I7{wdg){dm)7()(`QB*;TcjFIdS*_n0<5j
z?!rViVM;c;X@x{K)rTEb2R4qqoN99x{fZw#>o=u(O{yQ>-;SK4<jOr3&q2@5n6H_m
z>=cBzkU*&=R9vIIe=F0nOVxo9xzG|iGHow>CuBzahjLJoI7&ueL--4~Kgo9pk$Fov
zu!Xu=ljCi$jmS`ei+6wZ{~DI~3R79__;VrT$3~K+b(in_5QG{wea?GeYH6|2THAuB
ze8VwtoOa|TSi9?PJUex5?37f;*M2H+h&wE=)~~*covFSw(`Bf-b6Z|sKH)JbYG1-*
z>e+BYQC3lzv#7hSjCEXSRiCq{mmm<3SbFp)xX`>@#0bHs3*#r68SfqxNL{n+N~iTG
zr#?>;0lb<byQ2C6PzjYNBHucj-^>p4>!!n=vkcN*f)bX}G3gEeZ0wf2+hY=7A^v^I
zKnYsQ6pQ$xXg-Yc&=R!u?tlDpa&Z0lpKq_fym;S6cGZjT!)Jv>Q?8etP9>@gUfrpY
z`%pgYijr_<yKvTLaA~Xv8uC3hw?hgb55)lQYr*YjkC8X>{MbK@i%PGa+*{vsi@RTb
z(?^u!PcP97>)_)FrGpa-MTq5N*M8T)fYOd~9|@TNLC0gD;zqirgAF?_g>@fV`j1=<
zRCYCPNuCVoedb3xnwnj^2yO(m-^{OHwEIOb{;wX=aj|j}X|a9HF=aV!Ej3N11pEYx
z?(1LLj(<M->-FXBGLc>`P=nl?{^9-V#3<M*CrrY6*IK=gKhI^rWcM%wTzR7aDUYlz
z9z*)tJT5`H)bfxqYAE}RNh3s)5_V>6VXp6rs9diBG+m7s*j8WTPTC7<TK=x6wH>y-
z{LLKU;ZWn;Rkx<*#Y-k9ns<42LKSKwR47@sUXPO@5}VY1NQYhD_yKnt3GKBYLb5TY
z2($%c(~6XXBZBQnL5blu%AHVA1-~M>)taWQt?U)86hZQefBd)$c_kHoZ>=Fkfm1K@
z7Z@f2kTxhSB+<eeaOk#=B(Dz78d`-u12@6bjBhcezLpVcHS^0J&`)3B!zg1s&(&8$
zrUt4Yn+r=`hHW2iXm&o{?L1(f_C*+FJzs82^+F)wdsv}P1CjQ-ZHe)+5u^$juJf~3
znX+bQ-wGt6XfhE|zty<aU|YKyknM>~0r_0l>)NDkl0@wRBt3KsZwx(rxDnQ=5jsr>
zPvUyZO7!%DUO?0Zbtj!BtLlLd39B@7N<`ElJjM^+1fW&ysmBLjDfQmr5$|(9*n0U!
z&ToR(vt?*EZ{2R4kk%~W-L!L{kcWoVp}R}3YIv-uo#>@1&J8VvCWi6iOA%B6hVal&
z2@a*GO<H&XeAo8KBasHX5sCf?(xB8DttD&O3uh$5+-Z5*Wg$eh28l!tH#Igk*6&-4
z9(34W*d}(Ud#_TPGn>6N;|sEAr_r7JuNQP9km4_L;bTpRvQ9%~xZO6CZ_B@e+UN<>
z-XwIY<4e)6!%;EZhgv2Jz$!#T^k<|Kq&l<{*EWQYX!@~VlX<P#FR8kH7@O64CRknM
zr?T9rdMVnK6n<?^-LyC<6pE`P;^0KWHxVML9+I;MK7k9c8DR*F?r~{$A*{O4#be#1
z2O3gM0Es*g&(_~f<DldT<?(J%`+U?j!d7_(@qh?3P3t=Wz)fSx5^D<a4^gkUVVRyd
zBO;=>lW#lBetBpBUtzh@L}m}`ilf<%BM_hnAyPEezR@q;-Q9gRQhl`ORW?MesSfZ;
z#Zo=PF7>&nB!yvAgFE-y5RiT!C@#D<+n`ew#)qdt&0Sdn5Cb+Et(x%gV3aIPbn1dl
z7j3p3(6G5+00}SE2NHwBPgrid1oioEh233Q;Rvyt#h$0;=xlru5xpXKZa~uDYO3M(
ztN`BNYHQ7utT}YCx4%ARda~PA2>a%VRQy1ib-CDpXT6)!gjU7`_1y$d;HHrhxjZm6
zGI7bX6w$EYoD}WZAv?EIG9hFN9ag|Br;MfoDS=P=L`;R#U1X_EjcC`&N7$lCOqVm|
z+hVVsMyGe`pno`v0YO-8vbZ^TG=_+9Y-8Pq=gz72T^UxDda*hsZ4YSxs&v$Wj5;+$
zIeN+@C>z<IlAInUDTHW%z}M!8o+)r|3RzCCUtpqJO~`@TD_}yNLr)Y9acf^!0n}67
zrD=NC_M|3uRgc=A6jw>yr*a{~Mu|1ctBuf$mClEmXw?(jJMBq#{iu6w>FwUdFe#jR
zX_9rxB6~%S-DAWg+-8U7pCm=FNCo&ZR%k=Wb-8kD1HXjH5RIf!r-zWy1j|-AsW~WV
zA{o7NtATGQd_e_^-X5xyq;>)L?N3mbRVneN+U=cCUdB*9e+7~+sRBn(|JDe<MfLOY
z*qv!G@Io5D3?q@k)IzpC;ZeEicJP_NgZ2iEQYf`9*EuEab@6ja4cdSZ&08VW+q%im
z1<H@A=MIbug*A}WY#u97+<=i)HLa%IaH{Ug0&;m)u?dd95APlZBI@z9oJ$*l%d~k(
zS-2CL-1jD8+aI5vjgz)MO&Da<qRWk!_1?9$F50~=GWfF=wTpJ3_~G%>b&m#cc$K54
zwt&x#Xq9I%o<K;0lz$VP?e+@GQGA7*S-uG+WEM-21lE!=iBa|85qq}H#|^_077hwq
z(hg6CT90aI0jp{4#N@fmK0YW9b9l*U^WCm+QWj5N-?HK(US7BqLz-T2a5KvZmd6v~
zrL|@e5fK+5pk6@B27Gs>)&Zsqiy+PolRz!dt!>=TJ|hjN5+8%H73KLcw#;+Bg3*&(
zSSECvLC{j<t!OpxGG(RJ*eB#!R>e7st56=7tB-+8E@FAsMn!`$6*=$H`x=~lb2}Vu
z;5oI6;Yn@Du4Yu5`)Q{o4P0m9G0Z3S%e%6k@NL5FM)7X7PO7^Tm~a|c2bU?=1VY-O
zt7~I}M;zk@m2^Kq9lox}*0P1wJCj#cd<T(ywSkcNNRu>vRH0fe+mT&@cMCb@Dk@8L
z2w=|LU*4W>Dt@IkCFLjridZ-;&reHD93Q%7Ja)nC!-al{m<Hax;~ejfGE+dUZ=kq&
zpVJbLgyBu8f3sqg=s>+mCY+>u;MfX@zIrAo7%PZiTK&uL+NC#$*t5{2<f+*9`JF$A
z&A3n4M-1ARLJiOBu!yK!qjIVNL#h*ZrX|^@aCI<Eb<xWa!xPxps|dk9J2h1V+n46#
zh6L3?)eYwf423*62`}uxuB=={1QFS^CTbb;6^ZStE7IAA@lrxA&VFKwsxAdaT{2tD
zH}jREe4VGO%<s#4n76k7yKe{%SxAK%%kkmSYai5$tJ@tzz%8MzQKObYTeo1JE+<i3
zc=?AL_WDYUn`%bGB0du_%U-87bJ@Rt)nhfcH^{psY#stebTV%CG?w$iWXw^xW*v3M
z9q3nG-R{E+q(0Y%wnGtESyZe*C~>P%y@y*VhHXf?>6aw<UweVOe{b>R&pe`0_Z-a@
z|H6`ImU=dX0*{*VCdzuul9$xf!ZylFWifJaWmCdkITp|Oo5}xg3k(cJQ#?}ptJ`<L
zeF$FwR5?^-gfG8?Wtp|$PNgR2zZHxsc>d{ub{M#J<-EZpP~O~ZsS><BT%_Q&SO#wL
z$`V15Mf~y9Z?H?c13Yrr#Bv^wV{e=m7aBb>`6I^fTu3kF|82nZ+nd>&^NsqI7S&?b
zkgk+Qx@zV>SJt1YJ45V%RWGOMwjsldfS{m_0b$s1(FF;s{J6L{S)!}^LPu!BwYK%Q
z1{At{a(8uT2yq2mU={kc3k~i+|GO9c&%QK$N4msEvC6JJ$a%sgFEE4a(k1<w$oxl=
zM`FtA>h4{z4|&kq{!}$X%Auw(Nxf}xiGe|d!6_aL!Ki^GGA}6IcuK$j=M{V>a>J_8
z-F$AIm|jiS@BQcc&~iIf`mJ#)svv|zGauxM(sjdGGMWzLK}&8oaFFCwu@NqrLT(AC
zc+p_t|9qAH%W}*wZu$SbA1?*ASCERzHwO$PY?_{o<KY^$OYOnp=VHss%XPJRLyHX6
z2xjIM78&gja6)W)IXzKbcD3CHZ9zC>_2{UfxtSULlQBF&S%%7(qI)oXNLbk3upE3n
z3D_@eYi~C<H!r^P>O6+N!@St)uiaR!fC@L2>iYTi{{8#xNqy6ci&ajEN`O{ru3y~I
zr{mQ5dxJ7Z7^IPBSXegZTwv1+-rd!p-uA_<D<d!s2l}TnuGuM9c&2ZVqD&H*7^23T
z+$M!ODI1lbc&C{{rP6z}Zg`CO@rf0&)5li}POc_W0k6)$u*${~L)aaX@>uRo*jQg5
z`|u%Z1B;ah1rpW3{YJf^Oes)e#_uJ3{uQPb4V}E?-BCa!29arzBiVA=!qAYniRzT~
zdz`v6P&LRNa7Y5?>zuS7tH$s}wis&A0;XqX67Q=NbF(op6!!)blCQA%9+hD*etwcK
ze5~qz<DkxN_|(kIn{G;Fx9mw4ZjoTiBMh7hEWE0&+id{$n*V@cW)^0$6rHCnnC&R-
z3VPyI{AV_!_?D$=o8~u3k$Z(g?98D}&ej>=<0XXd4hU;c8D346);QQn8u=E(2Mx9z
z85vpAcc7$Twzl&AbUMo5SY;bD=d6TByuI^^#m%9*x_ZqNMoB9#FCS1j2?Oh#HPF+!
ze7p4l0_3J>-!X;{t{dCiH<}myURl>a((S>~3XJ=6BPp0Q6%={&9TDEa%5anXWcFQ8
z_zFLt3<n3taWolRch06)OC_D1wAb?(3`WLb|NDiJ_imHBO}?McYG;8en<0U7v1b!7
zvBFSkkC`gbpV_PM!+rQjljnlglp)wLz;JUOT+AL}WL0%kr_RCOG{DstrlzM82EBK^
z!YyH(P9N8=jB(%mD>^!Qjh~OK(<3!X?2ErB_s5T;pk~;ADG;%GCXRPw=K`>Ep`@}B
z`+A;6h67#|m6fmD`|2g|bMQ#w>&VYr>+8}Uh#JW-1O3cv^4=Xgy)C@pd^rKDnce%v
z=#D@U4fk4y?#O<&*+rQ6$8SI0Er_n-judl&A1d4e4$#0bC}Vy}$OC4otnc7pp|DUe
zq4Ms8EQS*sVDjoP?^*mlsC3E!71Z+{wP!*<zM<`a3Rwh*H!Ki?*xMf$LzWZnG@F{5
zI&HV(x*jx}(%sz=h?g<apuH!5IH~Tq1}eZ-a^=7>?tc$nA1|ZD@byZv&N^M_!W;SX
zfp!-Phxqv7)b!X`zUM+yd1d9$J$pO5>Okxa#ts)|DcXLD;RC3uAh>t_{QgRbj9Iuf
z`bJ)V+|^7aXtO_CUl6mBYBU3G8mL#<m?0s!PM~eGeke^xfSWA$czEpD@l#Xt^A&0k
zVn2=JD+ODNXzr$My<ND-$vO5$-eYec4_DdMEO>oh%DyTtj@fgm4HOndDA7WQb1seH
z<Xv9{A+WO5v$V_vBgS=Wu0go;$@tql^IZ{;acUR^Q%Zo6I<y%X858$=d$qvz_uBvu
z56?UZ?%#J>3ip4>c|ha5^<K6cL^0~ggRN;TfJ5-4ZJ`c4M@i#@gL)%*DwdO_MhGy7
zK_meKD?=0}?~y^=T2NRy$R@Ey7=VB^Wo~I#tb*&16Yg$nyFZj6X=-ksKEJTg2pTFE
zezd??hgEIwH8?*2>CD_{$=#d)aBwQ>;yNatp4A}ER6KopgpPRQu2c68YJxlUyTM%-
zGiz(>y)1Pa#t4f#*g|oKg2#Oi_Q~LW-^-xw#q3GeZd5I3QcJ#{A5O-3m8OAlcr&F7
zpNCubK3MaPt58`)tt|blQJ3j`E@M0@63no?Pz^LA-}sk|$s=r?h?{e%<!niPzhdG1
zHQV(v1>hijRQUvhfg1=y4<Fk<dGh2s!Y(+EJY5^<3}P$~;bC2tb$4W^(sI0<-`Wyu
z#Jc94=4LQ1a~G`Wvc|RGZfag35`<Du^`AYgYwrU=vG|3h8L+$M*%>rGyBnakcgvk*
z<sF`4iutq#dgcvKIGorfZdr4^&g#ix0*JG9e$(cbN;M}Q!zy3;6VjqjvaZ=CJA>0o
z#}L280VhO_-P%TF?P&G|P}qOiy56MsFQ~NPM^*gSib<Wgh&6?7a>7M!?&0!o7dS88
z;gI04rpX6TUR%F&?0HRfLAoGMGNi#fNyr>IR-VAAdux{6l=N7w!o)T@e68hpkG8gB
zz{uzB&V+z(ugy9o1wK*moWG9eJKW(DIv4wBqp`U;M=EmXm6E09;a;hR-CkDY0Uic9
zpnaofr9xy|Aa>Zdj|W|5X=&*KVvvOy@DFagp$>@6c>A;MO-;ntKx*)EJLNMcTki>Q
zMMV{?tFznMl>2gsi0%VI2O$`T^aKN7v+QtH3TS<<c7nk%TxsVs+X$%FAIS?wx`DYU
zri$;?mtlv4Rfo0kR=<<xXuJzRbV=L-bEUxK^z^E=LbA@v`^G18Kz%OXK<AUimT^$r
z-`515n=nFBKzMrT_T)*xR5!M!y>~hbl%0x$qJM=9=Zm7D4`)C_j%Xg-qvBzQ9Zm~^
z`WcFmQGxBN88+O6OI%zp_xYP;ifL;3X7oI8y%U^)lKOgCIlkl3J<m|Dmg2J8vq;&b
zBMhtl!!mt<;#&T6rDYDdg79oX_2B2S(4T)OHc{shT^2q$d<Q>b5-FfVtbtP4VVgr?
z4RUN@<mEs6nb=9^SYrIwa2_?Sy=PS)Bj)DZENbny$`0u;tl9Jrwq>$hudS?<194wQ
z0U1)*N<u<Lz%U)55U|>kW4X8uHYWq%*OAj70h4zXl#o?$Rs3*=!;Q(AY8&)yNlne@
z`^LufvOX}NrIJ*4{{~(3)4b66SF8I`5b|m!YwHr2s=u}DYKySTdUp~mdOS~@I5GSn
zhG)$co2atQSzoPxt@U%=2>rkbYij~k&l^;7``LCDEz(URu}3210M0`h&TB>VlQQZn
ze2q3XHvTmo^<GPD&lUvEG5N1$yC^r2KVImPeq>WjQOl>twBz+Wu((QWUBA%%^Gn$_
zEEIS|e^&t9Tl}5{5y(cvfUYvnB};NRq8$k;ofWxnoH{SzGj+I=1gJi`#7A4#j-ipF
zaS=F3q9?$jvMDVmEp6<R1M;Iu-}Invls8@2>61ezISlQfJoVRKJ}>X&){iq^NB`rQ
ziAD@xQ*;CZNK3&gyIqv&TJY@DvxQ%K!xvq0lXXdZd%wn)wRe^wY6sLvj+%<FbIsfU
zkK`yU1*G!h#<lr){My@r?Kb_`+gDS&z%=mw;g`5)3uYD;8~!kAE<bKm1t-yRS6e%V
zH{|P6gZX-i2v8NxQxEVR{_JaX9e-;O)|be0zuC*K$MY9d_YR_HODH!GZBvR~oVu_<
z4@=fi2MsW-Pc1Cu=)ywRvcpL2C_A4^L-l*<wc%fqg8>lF1~^lLb4(6+jVtH`@C`lB
zRXcR6r*?rUED#jwCb?f4rs@t;8KV5?svCYa5Ek>mb6<BnsRgVN{mICVP=91wk~*a<
zXlp)QSIWM_iax}cC!f?TQ6M01Y;1_NBv2K0;lKgDqN+-wW#pbVPcf(jzMcvs_AM*H
zE-mUQfw^WgL(-<CFi}}g9X0#n<F1{WTSpR;6aB=g5;THn&7<gZIn}-`pd6$Crc+Ip
z_Myf6v@uQAgomJDEo*<%Aw!`2pEDxIo5>(HV07Bi%i-5L_e?+q@uLZ;jvvp6+e<mk
z2epv|@h=bS48lg<0P^?S0x++)euj7D@}<R4;{P$idHaxW$0N`xsDUDS=Bc2=5{U#d
zTa}3~qpw_G5ltBq)+xHUg=Qn*=O@118L5BC3I_CipQ0temgmec)tYWK6P1u3`TU)Z
zJ<r}Y+qL)<@Gs5HWjEJAy!L<g0UGL$-leCVE>gi(HOOLIuUF7jc%fJvveQNi9J!F4
zjZGyOMUVP}iWbT9OD@eO^qk=LwLNSmgqN+C1w=@{{SC`*zalUV*i2=92(W7hv(+t-
z7ZlDYfQ4;)Nl8g=$1O`7eeRYAMZnzwWx|x{EB7Hg1ZOIMSNbq&M+9j>X&Kl9{0}hB
zb&zep$aJA3c%W@iyw*&WH#4ncf-cAg`l;Bht!Yyuwbg#{AeJTxeRQsWB7f}|w`~PT
z80eFzH3YJB?qD93Z_nB2D*9jEWBAJs9<HuFMJG?3h(pfU1ylAgq1X&yqd6#8r8F<-
z&GIeD7r8<6Zs7Tz(IPOd6a^zgOV^uv-9bK)#)X>B>&0gMDw$s|r(4?l7QH&NnNrm1
zk9Obr{v|s=;M};4cc;7_DE@NXU1)GIGPL*i_eWB;L#ZI+vbg-GNL%Nmp-T1F1LpKI
zXt|FdAua<86ayd(v~;3e=BY-x3_6Ouj{uKXAh5OF*L2;VJek^RMybE0b&}U|;<O60
z_fiD)uY-qU-Zo23+iK+g`@r4+bzwO6k;yKzePKeV#s#qE$xky>{B}$`46JzWoRm{L
z+{Y*Gfo1QewfErxZNAytLCEgjy#n(}z=C6`&t!!3^yX!k)8u)vRWR9z$ahtuxssed
z&VmPtZYCyNQc#}I1~c-zreH)c4pa%Rkf^9bWU~Ex*W_CT#z;!>o94ptvSqR5Po9-;
z-`)ZNK)gay{S`R*6`&SfAFL|$g=h)h;Z9Z>qhX?}9aa@hS=b{CY~!b-fR3*^d>~WF
zCV;2htYPF~mw^49zDlsPoSvS(-w{$r*Vrw1wU6M7{G^7dF-TBCTKE8DU5Q^yeH3k{
zm{z{|Sb-%<Fn1_D9$oB6woM=reFiZ4_`?%&6ud1Y&Wl%6gHPy0cMiP-+(r!2uzca;
zELc@Uefj`=hyuAArr!o6A@7LaYP#JfOU{x`fyMdve~;okBP`)Jtwt7Xn!N(|-`Rk5
z=8qW|zZ<yXPfrHm`3$?z)%3XAS5u1QMin(J?E<I&CaKKhzk}+q>xzotQTVj(lWBA}
ziWdsbCr=dXPcJM~ir03SfQuHu1U>a^fLd5n7nf2neZ2>dNrGjcAw6T#b3??pQw~11
zlEbI)2V+~(I7Wuvd4;Z>Gvx3&<2Ur;i;{HsGpU21qM}0h#tO`y-P=1LbvxjIL#i4!
zDZnciB&TBH;&K=rfwy46kf3W*{00{z_dio;HUo0J{~uXb0asP_b#WZUW^6<Ol~7s)
zL>iNB5CjQDT0*)n7W@rbQYq<1x<MJGJDySxr9nU*<pUmk`#cNhd%qdM8Myb{bI;ka
z_S);bfB5<e^dx)b=bt;Ge|B8&Oi~z+(H=8;fM{&uz#&S+TLO+~53x@s>{|r^LPp_d
z@DqijABl6LRH<@HrEUY2_g;11wRAXbKD_e7f`waDknBk?BtJ$-;rk%!%4RqFcZGJm
z^t@RY`<B1%$qM>QCY51jB8fUI7}aTVlp{A?;!1QCjQ~d>aO#;0N;@FtvWsw;E}9r0
zuhsJXvA8=>+D+=IAZd*Vxv?K=M}3-s3ZmCxwWZRze!W0iEU4&2iTY%F)LJgG>Hujl
z=8z!MSb+b+JF_Ms$S>UnxCFLynx~P}^Gy;nqfaI3u)6=)?Ka-k+&AxR@yz&3j}?+?
zySUWUR4<a(Y<PJ1{T0BNiXeSYO3f_q_IChO3T0KCg%K{1cFk3n?&5r}#_sT{N=Zth
z?zAt>uU$G4LtZMmcY5;WbeG#g&W<BbBuJ=XkiZP^3=xMp>XjisVMuB;+r#<GkTXGu
z`1k*?5uT{ldaep~76JYUY>g-gAtE0*K-8f!9HQHWk#Lv^*pM)D2$~X6`^OR<t1uF3
zSSbocL7vrA9&X})3_dMJqH{-(Aqfa@;J=Ii^smKuTYT|2mhi%s9NzzL`dSLU8;m1h
zGZG!wl1ElP<!+%6PoC8l!8sn=|Nf9VFXh01J5N6TRf+~|fh|iAoxI%l_@U@>tFP(O
zbYEDhr{~r_XqLKUA=FmXBVy??Wy9M&Ynu9jD_@_S$<?EHI3+VvA;5OXUt(gX?zgu|
z*i?zC&DYD}lV0thCJ`G03GLl2oKnu?@ga;-fND4c+OYt?sEoT|<J}~-?y3#{t9YSH
zD-XaQZKw?TOhI(Ai8P9>C8CFBFoV6@I)4fyoO1s3=&)_oZvL8?rNe0@R-Z2RSyk;u
zoxeM`hRF=Sn4ya27;%9<PRPgt^1nSh;{;M9l{c-dTKoZYs_OSzF^z#tm0<!9fWSU)
z{{AHV>IlD2DGG`;QOeTeV>`WATvveW)vjya_8lqsR4uml8eYUH7N>*0(4IT!H&W{=
z%}1#aSLA-)9HJW<0%>pi%+e&vZBSP!)=cQ`)`WLR3hAGVTslvvSj@{D{yRek9>tve
zD3BD3gGFsOFX#)lF|aC#5OSC(p3G>6Yy*0L9S){xWf0dCyKg~{kjKmfgUQq8ZGn{F
zm5=or?YVN&|K<$Cu|0=-0U4|*CN1_Xq+7bx`#V6mJ>Dk6#bJuA_&4=UJN1}F#kTsW
zYY{~mzFR6)8JVXRg`YfSmjk0#Z}*GmTvAIBa6H+#P-TKf<!2d596BYItRgQEKaejP
zrGQ^wTcTCx?*ktn=}M?Q(Q~t(UI!#co_Rt@0li;U#f#l$oo#I=rNYmdhm+f^!JVs1
zU=m&@{r?AHQL645A!Hd~kmL2)j8c%1c7T^nhWMU)X04tNPcN;7${Am^-5AYhkK6<b
z_A!r3kgGFH*(J;=!lM?U^%(H~8llWuSE$E(r|jNmZg@=$3&{#@(h(660dd&J9HYq>
z@Me<=l>YO8MH4BhPrBV<n+QUXam-2Q-pJ4OMc!0vD>L8w!_clOhJWpl6anDBZ}b#V
z+dKG3cag7^o1MpEypCN^DCo0)F<ri5(M$?4D;Im7k6`725^Pzr`!mnmA?Pxupj>)!
z7sDz>IfQqPL+50n0tLl!5ik<VAF4%1LXE^AgP(wyyx|5Zj(XauON5H*ownFN+Z-de
z2)BWqVOe}SrGbJo|3b*L76u+r8MdtxxYl32fB#;|(kO23759jr`&ZifpU8>~*c=SH
zs2MUdGUOrSXLqJYXf{LH%_`5Af<FvM{5(LBW3a66GK&CUipCQz;$r`1inDOwFE@zh
z?S}dgvZZ~%AkE!uX?gW*8@{{Rw47cZ-ad+(zFX40Xm3Ht<ui~E3!<y5>*kbxbEJq5
zHs1gkE7`*wLVfM;4(q0Aum{&_0a??1xjkxR^{dc<CE*g6rRkgIGm!;G4WUY^Leo=w
z;?rHD_I^i*-nt+C0$Hv?$PMe-&|IE2r@Ij2_%t7bWeGr0bF%s`EGf`sO|#s!$hG|Q
zdV@V6l;_(pojLY7cg_(v3UL->ynN<C#X)y|re6KV-A8-GR=>)kwr?uouj2A${!QKi
zxsd(eUy-$0a*M0T^X+uyIw<zTmlnMr{2m_;C;|`-`Hvt0A`5{ux57z!ry+la>BC@0
z-q|#ZB<|!ath}q01VOAC6(;&E`eIB=*3#(Or3HvF0G(8W1RF)Ew%}E{e?%FKf<i((
zlNFHX-M-%C=Ziqn*S@qu_Cp;IdZ-_>yXvBI1ER6f2vKRGi+J*T1n6qMzV(PfTUS>s
zZK`iUNKnvv;>-+H-yR4K_0p*I=S%RUCjTW2qJ@gFDL2t5SXsgn{V=E?70WmqeQ4?(
zoY@IL8H{KlO$&U3%+CPoQo+xl!Q<F8{}4D`GmRe0-on=dYmf(}=(tSr0p2sCVrgl~
zY&l#Pw7igsrGw+V)NPbJG6nGk<lMB@S`fHt6<`(|-$O+|wK}KQs^;9d%@oY9AP1}S
z--ELKemY?$6bcI1g)UK*0ZZm;N^I;U=xg^W1>j?s8U6IKj>A^FW&@~VC^7D1Dc-%*
zTX^T=+3G8|cI2t05Z&1U+(urE)Y8LZr$sFUrNo-MZ?~U1-_|U;a;FvR$<NJ2@*-ij
zXQ&&9n^1AaxIIA*k%b4J7yyN|1xh$6o}QkU`ZHbn-NC0ED@dlP<7pOFvBDnJdCndA
z1Z&oIb+3EVO93ZKGqvwk3|hDO)*V|Pgv|rs0cKBmelZ?=+}uyF{d%5h5JMTZzJ|f7
zF6Xm_TtS|8gmi<PrpyXHzEk+7X;DBS;Pb4bd<2*%GXMvD>~*25@Kw+0PFDbz5Egs5
z^BThLH#ljOjlF~@!Dk+zuG`O8OncTz_;tHD^sMAE1s>ke|Lnhfh_PuBsGgM=#$Xo5
zURTq#1F&{AcZR`Nxb1HIY<YS4tBRK>#8_XOSwCx1pKUEl`Jt#DSktdxt&C@AjRnV&
zQ#7v$s6TY+`-+@Ps|E)(@}U|~a_b3p-SS?gM6fDGkxmH=nI8l`P{#uDDnQuL_~sXk
zlKT=eEQ~<R#;*0`o4<g7N@IR;T90kXJcVh*ji0<Fog)6uhaGows>*H4oNL8;6#R<M
zyauUj+F}%nL~6Rax=K?38u3ibfH9^LTeYHf|Muxh0pB%<&7(WMWlZzxRFmUdk7J3E
z&M65BQIS%X09z{nPfe+3?Hg7tW*-Nc`P<enAj-5~9^76(=(?DZ0~!EQ$DxvhD|MQ?
z8t;v^t-pP9HF0%yR7@Q$?lQpH0gmRkB*C`E-Q5y`5?{y?XtQbcpRK{f_xba87(FCs
z%$MxCt{aWNv}rohH3$~f_Dd{)a)~||&Nt3WkShadPB01eaYl{d@!(XMEJ?or5lzFU
zm&uSjHu^7BZl*erXAS``^lXmRXhAi|NPC3DPM#`MC=27M_6L(pvd~y@gfxoQWMzxm
zmtLE}J_o$EYGo$`!t)TR8n%Il8jW<Z=wIH69b#{(3xfQhzh8&+`EUhwHm%9f9Et2l
z{!H?}xr(BGf1lD9mgP=0T=#v&)(Vk4f>E2Don7%zfXw?>knzcGL#`X*>^#`yvi-2C
z<^iWQsSRL??P@wDI=lUPaFEpc7bCIu_ufZ$TJT`!&nH3R-0ANCcI;5EVRx<x6k?yA
zHPL^DP^~>5D^L;Xu1qSbCBp2n6v~mU+hwbd1|i7_3D7MMMQP7f{Q~Z*0HEbC%nZ`6
z7Y<6NS;VjYgg~|37CL?A#YpX9EXVc$Xb2TGhN+$cuE31UObq9OU(snU{s}NS!t_l^
zVt~9TLE>oIb1KTU?QVfwCMmuQK<v&{;vCO7{O%+m<#gHJ0OX|l6fIw(U-qM+q1j<E
zxaPgurDfSUFrfFw98kQZsP)y+uw2~e)&g?LQhBh%xy_M&|9ihp6jV?OJOhnGdC-S4
z3l>tuk^~jl7w?`P7Mj4uWpr6@g7O))F2MYaQl}z@LXX0y5fTAm<|z~d`{ZxLl0#7{
zqJ%-L0yy}tk4^B7Ok72y^m`z4O4K7ku|<Le<q(@2C_Yd|B?J8o&0J$8uz&7Qzp}ei
z0?kV~M$M6>3zkL0ha^Z4%eVLUi{2LcHI2Fd4&Jr-^151ZB-YpMi#{`*?`Yo$>gsRm
zT`p>N%g}@98<{V{H&92K48<73QZtHE25wkB<DMy<^9d7j$j~pT+9TBP@2%IRJ+82F
z96BsbG1Ie_<wV2&Wieu{!m$OCd&NhnANuJ-{$%*+?IR!a%%TyRKuJd7Ne4T-FhHtd
zazG7%Hamhmp|`w;_?`I6Qx%>;wg&P0@ytxODKi1xxXiLe&FY8ypqc_(C3J4scDlDH
zAvQMFHG+~Nx&Z*ZKptPTL$NX^cqUKMdB?8<ep6(yyto*YTVUH;0+D7WsQ~hfxi?hq
zXA&&|?&W$y*MqVc!TbfH>&VR7^vp5Rv&$!c7fz~a9bQ6l;z}MF0$1F@KvXIzE9+~B
z6cI97fr9ae+3HWg8yJ4+%2ew*_X3Zx7pE(e=oHCbLlZIwBlddh1}TJUeayP~AXW!Z
z0U{(fqc6qc02uw0P*PH&cl5_h3xMR0%}7kUf>>XgRsrW|!4%~1Ha+GAVc@S`nomP5
z<^I&=lY4Cd1{2B;gTR{zXaSXk(A3lvDi<p^ZNc;a<|uwuZ3~(sL-R9L^lMo}*g<1S
zOCZklV$esZNhvcwzw1t4abA-BcaSvFec7bBA_s5ZWA0OA2vg~_&a68&bXp%0DJ1|C
zZXV(Jmv3y7?DfZ&F9H=JhR$hdFS%|Bwdjr?b<3Qbb|oDMld3T~7g<2xG`G7z;qu$9
z>s?7xNX$qx$FR|F0WHmOsM|LeHFkg{$%Py)FFzk8n*zg8Yx9YAAR=|RFKTVgJp!z2
zH_R7<*nu6$*g)>x0+R_q%jD@OQMckqr;#JyJ%btonH6_{{eRdby7C2+b^ckP>=6Ta
z!+FL9|H9_!<d5<s<OtnGHi_iw<lCKG9k7@*=0^j6+2-)_S#QfRfXi>luYLKL^CK}w
z5IEpLK7^4GLqM{1s=;sinV*@VUq+>z$8IZ&WLg9ipscYvkPyp*%?Bwp0*}FaG!o4<
z1J)KT*RMg!(Bo%r-dm`Y80B6J4l=a8Dchi)_skho{AHo>7OX>}6(Uf40%|O)^9@uS
zEMF(h2oXT<2a378`tNs5eRvItlDUs{RC6LSN6TNm#BSLBD0@A{4s&$@PIWcc7j5cg
zXq#EOZ}+oC`Tn>A#r{etv=%{iz`E&l-IB3|MN?7TX$lGzrPnq_+`gY0(gBa~O-6Ue
z^Y>a^>esq`sI9->Vr@uaEpE&n>ST8y9?bhu?mp9Xb?{sS@*J=jHCyLC4`U&^7F#JP
zz53*TgFpp%_mb^fA6#kHl{j|ya+BI6ueYU$fN<Iua$o2EJliCRGt|5gc1~uwN4bOF
zXn>XV#9|QKgFqac^ZUWObNtvGKNQ=wwarlhkgDXnwaER{AVj32U-?k|#Plh-{6vju
zi*6F%6ciM+Jg$JHfYz77sh3t4`<(%$E(VQ|d9daO?cS*Y(dOLRm*zEDRGQ<gSha>?
zTIoWQ@SH4~{N+S!Iq_2QNkF*Uk-I|>d<)Qsc6O~m=_nefi<Kp@<vXz;j4N~-nwrZ{
zt_sco8-=N<tB<A^SPx70Ve6#jS_Su401Kn(`kAG*gM5TZ8D!7J^KCLa-PN_TZ$AbL
zv~~qdTsR<vSvT|E{Ft6CI}eSacm+zJJCRVBsP3nhaIoas@CJ~4-U5&z>@_MB5a3Gw
zJ1`|M9jNa)P14$bxCuaxdmM@v)%5lCTUQk3h_1_B1?BNb=uQwS%!IC;k1j6PNQk}y
zaPkt$wz7KsrBTVh?%wy=)DGsRTbkYEAq+}DX%Jx`B`1i=m7pW*TAD@-1Tk_j)x*=E
zah)|jJ>Au=fQh9;NGBm8A_9e(4q!Ieb(<ubKt(6Ey9-e83?wkd!NKvQdeGFuqKmuw
zSS%TGpzrlceU9MQ(xZS8LOPTRD0NCsjtV3!s4bEu@ul+D%UPF{epLlzfEkI3ILO39
zK?v3P4I$RT)KagF13X3@bPmB~r=ubx>C8W5U4VV4h5%VgN{BHtVIR{v^xhSmNQ(8`
zb(QNXFHI*(<j^#LDJ86s!g-8*uBB_*)f~32H3IwOj0h1nqw+vzf_j2HnI?spLO7i7
zpG+Z+yPpBd_zyjcV&Duxg>@r6ko*IIJ94;d$NmaD`pV!3eM?o(Bj*1T+K^7s#t$`0
zwMy)#>f)LOC)xSVoeNHL`>T<AS_F9aDA=m|Z*Kxc^c_0qu@l;kpB@EzL>>NX|6hUJ
zwfi}}#EXO|b{V|6zr|-BX$%h6hlC{ak3l^K=wm=R1>7ekJ)J|}7u5VT^a8n}X#~R2
zLU_tqd^*s+y2FPL-4&Q)=i7jCg@X#Dc0zx2Nr@JuU-fb7ut1iVmwz(=kI=eE7IEk4
zH>$UFZG0T^_k(mWBv$M&;B|^X4H}-_`rjXUei_(u)LaOs1?#B^Ds?RoB&9&`20S2b
zU^@kKUxjvvU|@pffn(u-sv&aTP{vM2>lCm@1G@kB3ZD6}&>EN9O<wDR_wPJ}`Af`y
ze#*t_FL}GJ?ai<~$f|)lB`ib4lmpJtnuPLM3KE3-NV6^xg0~y+;9MW6|2tN_T)86g
zIRHkYNMDSWAtld*&BGaJSz%h_CU8Li8R*o5b31qELo@_-*H1;*28aTLHJ**tbE=%7
zK1RCm-wRe!Lo@Z}_%=K~6QXOiorgI)5Jrx!I-^$K?h8pccsN9*0i+6T&--tpV|{Ro
zs749!{tk{Cg^H*U2n$yI$%%~`if|{r0P6q!i=-IcvGt}+BHt!NNroW`OLrE>T`Y<7
zt?Q2Qy?s>&&WvXo(>;A>3qE@CDE=Fg@VWiW-{5-K@w@uZVAAb?aWjdg2C~mo$UBv7
zMw>dpfC?{t?C;Mgb6J{z;uqNl9M*MJ0%pK72!(l8a@l28mS%7v<?~E#D_9S{YgBYe
zzzrcqJ4XLA3o18wBU{#{FMyi2spY80qWzwfc(da<<ppNqph1kgmOm-uuwZ<AA9Vif
zwz=z@hIQN<K{Nxh?}gG))8Cj(t@u||5Jr?9Tgd%QJ5$sM#3vPnE;cqf;V-Zkb`U*I
zz!C^%ggQ_eSZL>OpS%$EHj!S?U)N!2X4a0NWBSzr%4H}@q=WUbw58mrm<faSxci1W
zcClRYoN=km@gtWbcd%NHA5%4Jj^1Z@dn(s<xAFM1?$&YMft$*d;kuyR<C7YK>+FQc
z2;;&5CqpV-GtX4Dhd^}IEOX973mE(6g|)zPOG9J>vS$O)LaDZq#us+*5UhuudYktA
zXR@eWHv1Q9x@_%4s;Ca<bIL1R6giUF{0nS%v+k35=A^4xymdQob!a$Dk}@I959P+0
z*!ps&-9o2&YDPu|<5b3tB4s=%y399Px~m|xa-IwD$8IQwGLHdr^T%h{=$Vq``H^jO
zVtza594MbJ?ed9wx_f3E*pY|N)XGhW&jBNH@~Q6XE94~~U5v?|kIB!!1|h}+(YdhZ
zG055?%h#}l%#e8cKQ0WRiJay61QFWJwc@kWR%fk*5AwQ43#EYs6Nq#6YSZR}wyw3q
zroi5~J-qm0yHr!N>S~Tn7i0V~D%PCc2!T=T4(k88BD449w@L9Xidt0jrZQ%k`NziE
zk!G@WG^V~%QZ+o?pJbe|Jw<abpBgpaqqt59uJ$mAhSLrO-ydXl-)#$iI#e{;_yMBp
z)xqxY9VhXoJDKs$(Uuo={Dsr=PevDc2Zi@&#0%_J3-9sMv`p(jvgj$9s<)#R03dC?
zW3$LIhj5SgI{K_wD7=l0SB39G&p+c9Y&qH$e9h=M5e>6l;JAKWU)87WL^n`24%@|O
znN;_;U$c2yTd(Jkvzvic4e9b$>6Yu2kN(?LM)6z~c974asQKcy)1VX^RN`_ac@L2<
zC*vZ|BpZCeF2W%Je2K56=V;gh)tN2Rlx^fBMWfWFGyd{=`2Vi<Y`gxmMcX~b?d5dl
z-T1&8)03@To86xLyMK5(GI6<1oY}FLPI9^_HU0Rm!+}RLzwSk@1^HRXY9JmPf4E=1
z1<>O9K8~M{j8r(`;}HOo)lypm9L(C+bj-VP>0;9}YL<?!H)32h<xcbK_4;RYG%W9c
z<28n-S<i6(W6TLafEmEgVe=5Qt7&UT6s!#lL_@t}YabYDRBCsHXc+(>aTJTfw&723
z`;UV;?X@N;i_LtQvD1ln#eDb7KdkTLYfMXR$v%>&DHwaT-mt?Isk=w$#9m(r+OfqI
zqMkD`aO-ivXEH2GXRk^rAO8m>DRW!Ht8a0qYnNODlrHTVw@)^7ba~M^OQOG`WQzl^
z&4!g9+DYG^7~;`GuVx?e=NBv(+^@y=<6_~FTS4~U`jKL=YKx`GT^l(IapE(YPW7$z
zIBvaw6D>E|NK5#r?KBkw_jd-ibR5DeunNx7uNM)6ny^yj_8%ip9R)(g-R<1bA`zRr
z+gqb<*zYl(yeEp}n<a&n^2_<IQRcZ`HD~oJy(*J<YIEITUNx41(TSl-jTeM_BM@*P
zsyBOm03(VboF?t<?8de5%fSqltMkxsXp$I#CBSP1V9M>gD?Z9^(SgteZHGQPrX!R;
zAM^WH5ABmTK{vH)x@nWv87J!^klNkv6O`vGDx-NmUC>62V8GL+&YIbo8?JbLWvi^6
zifGF!P}Wo#_xV3BJxK(lFrr}EZK*FYGBUEEvjHXY6W9!3n>@pavTO$sk}1>-&b}X$
z5L@2+-z}vld->M(sY#lS=_Ypx=#I|l$hOH!Q)hH|0UEcUu{-|tx}D(Jyc_3ot{&Wk
z6_sqQta?s`Y>Sy;8I*${9<A3}x<^Up$9P1a*Ro&OZJ15>?sE+Bl-9petw#e+A{{Uf
ztl{MN_$%O#k35L{v!m3bwuw3x#B%#h$GdtsM7nb`wMqm7?hUQDY&DQAatQ@_=p+W^
zH$xH)8MwVUW_BOa1kro%<D;lLGrYM`OH-4PDbrBYVY;^zF-8$;SD%fsOSM4(nX^J{
zX;(Nfd@iF3D<ry%<4|}5a>So+V3)}m8XQs&zBAtSKA5hoEK}>KR&gijlRMyq)wa3`
zx?YR$2~`8h;Y7Ne3EtR@llv{V>wOM~wE#?@u!Z8{;?e~jr{RO3_9ZR7H9pD(n=Xo6
zfJi*oarTz^$LuQsxsVsPPO|@b-_WlR``~H2-m4xbqDTMGmY#0z_T<!iJX0M>R0hsa
zxcygcd;OO7l;NelihE?Q!K?56FnwgN_lVfwXqZuCZ5ri+3nXkEzaCOx0WdprODU+6
z9&q0N!*=|<9rLSswK{zEmgf~^maE$Fpqt8DQm#A~p2AxJ<}ExW*Y>J6JYXj1oU0z0
z4=H&{A$_NgG8~7)$w+T~2}CR|Na-fwFFK8`3lp6HK-6gQ)A<^8P2KzQ^B&*7>RH#_
zh%b%DgzVx8bY&tw8Ol0G=1p|;gGTt&%+MEpH4_W{_z}KqW^kg0VW-;Zfu*357ZmUP
zS?G;Lv9;9(XF#bzaW(n~kKy@=UAuM}_Z8b^rwvWfHVICq-Cy}KHdd2{gQkRAmVXAY
z;sUl*S3BDTlFGC<n_>`Rz{w<whQXUn$7xj02cOnjr%8QKD^BR>tm#AQ%VFmwDNetK
zrL0?I9UnIoROijT44cN1)QDg8j%@;w+Ss+VHQ6#qzl3JiUbQAcZ%Bj_Pb7~~6ZL=2
z8G<_SJxSsH;?vt0XJ}MqSpQebj~!!k;D>V&lqH%n+CRaXfmV!>p1*3%SQA_&PkShx
z8^)^v3pxofD0vi4KjU7mQ!jtDUqM(2RTUV=JJoeN5d~BB_%eizxsc73uDF17>Ve0X
z8JQlmg>dQWJh}Pj)KgHwnE_L<kspxY`K7l8%uLBr27&bwT8SN=PPlY>pnY%7MD8v5
z2%neQnQ(%I-Ov|7zRrhru$5G5U?xK1fRQVYb_%ppTUurUgmi}%s(VoCah^;s0PchV
zigAkxb!-Q>4T;_H6<O3UECWsy@TXK+JU5O8)?ejw!)L9LO`FQvyHgz&m&SZoTo&Zb
zGSAJ-DCsilOivF7l}i(!2rLL%%a{3tt0aQ<`VP=^oTl+$0dZ9+U>XIj&55n6pz6*@
z-7uKYhUZ$rOj0Nt6<sQ7KG=%`qZjhg5EdkiP#<@9!vN6wPY9J2C$8y~wTJIGjmO6}
zrk4#0Mwh}_L!h6ZePKAKNLs*S)?3_$>A_p5VeUn`k%+}l3r8=2DFYsl$m-WB;Hfo~
zL;?Hi3Q_|9J0TPB&9ss~-Bo4Wb4i$a>8;P#?_vrg(!+xySo95PaGmd7I>Tupi3~<K
zGx!!xz8PU1reHqsSECAaRUy97IJ7BrBHNAHU1%WN2i8uW{gkG*O(P#P{IVAM0zFJl
znQ(_is`%^Moz<oI4G#(5%HAgaRfjj3Qd>X2>vRFTiJ|r*bb5{j23rii!jT*!=a@sl
zkR-hhPkp{Q^xHOGhoT?MQ~I`iL~K~-n{nq4v@kZNPPh1TN1ZwGNdNBC%nma+o&)lW
zq<AxN7eUI-wjkM=X=-ZqO~Z|RCmN-;K(v+A@$xDJWFW%5rRc1H2a5+LKBuG#JoIVK
zKgX+h_VGg{EnN*4A-IkJa=)-WJNi`>!SP(69orsK^5O^_YSCz(Wc_zQv$<@-=s8sr
zd-A~|)(-nA-ftnF%dp{^9w0xS1kroorlE*r3PMLv&652dQ(X)feRB44QAwCSOu?QP
zYKc`wT@zW?^91i)iwJH`Q*$%O>eb%tvHih+<G6&-dy+TOMIL}zZsLSfD=LIu;JRtA
zDV)DHopA`xJ5jDz*);6coYGD}7c)$s*mj!IWAFFlHGU7v1CzQ1F6Cb2ov)6}R;4bA
z=_2Fv(Z!V24irU4Hw{xLIzy%Q_m9JOXErhJyXsQ1HFrTgD!*XKL|6TsV`&~l_971^
zzW4^0ykIyBH}`5xX}_85L8vn9G!;PCITR?^;tGWk19N;fJ2;y7G&k~Pf7^dqcJ0kd
z5ZI)`xfJL`Bdwn7$xj-udi#$0zn_Rki$v%9TCo7*eD0blocEKVxQAk}0c8;7ZY2=q
za3`=&X$7kV+yA*d2DKQ2TOcayc8=yhBI|zqh+;Wh-@MG<+F=Isd-#`Vqs$f`qx&^S
zSykQ))vivo#O=}g+e6Agx>W!0(XSDEB<ZC*3b_-~-c0>56CSgWFK!mOX)dK3wki(z
zJQL0VPDK#rzWcd>T_#*jJz2oJwsID!@CWWWX(_qYUm<n--Fw$4LN7DhMH2yK^Aa(^
zplp|%lf!dLqjzQr5;%DXu^}y2QBd%Os&j&HpS+4nAp8djqd)Eq$tqjq8Pl2FX1Yt!
zi*~zBF$Im4lC62EvAS>)RPK~`l=Iuy)($q3aI8yXT4qZ_F;bmxLE`gi3K`95sU2{#
z70PF@mKiqG3CF~nyt%Q!5-*xYt^v)fV+-nG$LE6B)F(jaHx%j%u_yz-C%$O)?iM%V
z+K@S6D0*`S?DuR>pLIP4qIF3)eb?stIb09r@gvVnF*M3Yf%VfDrW8aL?ZDPk&3jfa
zsMTVr_uU&Uk~$iSZ+~?Zv~o3c6O3R_O%Cmdc?jyG|9<f!piZ^o@q`zQQqTf|P#N7b
z8p8i<8-vOSxxuj6b%ckE*ebY9s6ZsshWZRr^YYXH@O69a3`E%tpVL=Cp%soBSt;Jy
z8NTB<9*2n}bxq&a5ezNm+Q;RfzBBx~dJG(}#FrYQoO4Y5WV23KY5qwdL)G=EQB?kY
zCAugM3fR(!y#I_FR8m{P5yrqI0a25lSR-NEp(HU5?U$;$x=|>PN!KaU1lVhtdI8GX
z8$JU}$DW$u-ng5KU@dU;Lv3|ezNoq7Wc$7TtcGu8+lnsP)qqx#S?+CT@8G7S&g>(6
zVYb>}3Hmlu$Xfp$F0<S_Dtb!)8I%{qR;$Ez3DpRj1H*&Negdl9X=qi;i6W&zxg|_#
z`d6ApzW;GJN32J{Z#$y`WuT?mWG5Esvx$jW@=Q^407N{A_iF<;4a=xZLPA+|QUDOh
z1lMRHWJe2LVyhPd4(!^x@L0k1*5#EFaIFiExLhyzOI}9Q_gnzuIJApxd5ix9RaBmE
zUxfqQ%w@E_g=r<xwu3_s4nAh8>wEW#PmPjC9b?uNjAcG3tCI?|4re`upibAJPzYGZ
zkK5E|=R!>e!L`QN?%lgJ0dqfR-u?We*ne^ZKfE|&2DnX>$Tth`nNL0iQO(kmee+uI
zI?+i?;o7>%j~Z)JpL4xXij8kK>qOerhoP_=j6pP44i(o1XKn4iq%ZB|^ebzBSP#9F
zr~mGQSYIM@db-1`L}&qyjzf`x<J;#YW=?##hrBuE$}nBLB{6M#6-9H(Q8M1CN*mB@
z6jYK5)VpxFLnK5P&|fKGpc|V<zhNDKf_!KV=o`b>!#oIZii)Yej4#<AFiXMNS19()
znFzc$nA0}75EQPa8*@Ebobbe>qE~whMf4DQH$aLwJBgq;U^2hj4{UR2PykAy30Opk
zMZ-)0&Z9MfC*APT>*aZGEa8GpNDpf}V{Q&1{U3Vb*Cb2tz!?^8_f0xA&z4<9r=pF+
zQ8RZkT3@OvI=LAf-#-b`g)h-9fvCX5GI#S6T4HoSu({Na9~E17lB%VWgH}9%<``xe
zH{1@UfZ;tgpnrxL%8@$uRPXEBqOLC-Ox7c4a>#ALg191eV&UB%-0`ih4fv7F#psRP
zdbfiXST4>GNZx+d3M?91M4$z@sXwbpmFN8!qFw^uCHUhs6nZ+J9AtZ+GWo|n757b*
zeNsK+bT)dmvrik2hYHE10)NdB;lG<v_twtSndzZ~7%Q0nXM%?6QmEq=G3l-cGwDAA
zzUUrA)nA78tAwS}QIRd1o4mQ5p?XVR@tNhzriS#v2IHu)M&|sV>z%q>l0Amhht9Q2
z3eLCy>8d10;COJxrl}cDIYp-{m808^>z*xR-mf8=&di(k)C>Cyn1Tov03K#0N<qb-
z{&V);XReCXY>4e|fcB9LDm(fJj8OCO^QS@|C7gZ&)k<=IG=kKjE${4mN#&6T4T#u*
z$jQUU{x}lOgT><bK7}oQRJZ@C=BWo`r)3$(?v}dko9%qBGe*l_p#+D7wY;9A$+LC#
zhO-oFVP8;f<5eI+z5TdUn4S%bQyu|TOMz;M1N361C_<>RmpT@4=b;=UvEBI(Q6;^U
zC-3)n_jiTt7q(Dv*nPRaPfiLBOBsigI0Y@-V|`xWq6bI#C%#_KLs2v~4VJOyf%x13
zXhwp%w<7AQK^P#Em`8dInP^obGoo~8^kEes77LIXw4g=7N$O7^MvpK__iO@A)<oxx
zp1U*C!d4>4_m6i-iIxDnjHYO8mwh~8D>@Dj&OU^2ih`m_Ji4gfAVfO>gQAHpVm+W9
zF0(4n-O`Nv1QyyZi~?;jRXwu{&8ZtSTPs2FHo0Wi#x<t54?sP??W}lo`s>VO@RVlQ
z;QLo2rnEwt7pj{rJJ3m?^QBumul*Cgm7?-;^_^*iovQ1)<z>JULbNWRtYIBcf9`;D
zDS`O|{Ygl;0H}VQs4)e&de#nq9OYX1_aOypNcx5LtCqvDv*>(GctS&*`a~fd6X||o
zaJ{)dqPp4+x3q>ueRP-#VA8Tb)blu^OzL>t3C4#MoA!+boNK9X^Axu<#<tJ|i)zSV
zn%yljF=fxw<u>04{%g04&2|ngk5GytzjUcD|GpmV%TBXfB(1K6@<<q6WzfoJqLrF+
zUgY-8X=-rjrF_j%w(8%c)e|#^vrFRHXXSDo$`X7338$BU(i|cV!xCwmir+YC6dFv~
z@3QGW^2e_i7%5kDV?r@rOL8pQr%TG%uWZ)YZSTqVVQ*XbWyuG$CwALD<n1`pCGcST
z!l{cETyO#^``(K-e_ri&Y;CD~o`dqbW%EP#G6!XaW6~+gd?B{Gwf9zRO>=aW^Ew#l
z(BVTVbdg5#;Q=aQh^-uqk$4gv=Zii+;!lq)x5E=6kH}5F5!t`I5Gh46orznNyM1_y
zwCmD@_m(M^A+uwfh~fXrD$)^L4x7V**IPL8^BUi;<e|bF-eA_lqu;&Q5b6&9`Y`T%
zx~Nn3I-2uiOT3KyWE)eWomsu!jSKX9;p{mLjfV&`{vH2PSlx%2aVLIxm_K!_JwHO<
zc|y{(wcku1YuHg=Vpr?QX+HM2=FGDzE@JR|k6TLPXJ_w{fJV@Ps(_@GB{OGd&7vFf
z2_}&(lhmFuh#~(pSNi+le*7sDugtfAr%cQ<A64qBu#u&j!d|E*Ti+6Cb^W`PE}|l%
z%BPO2za>7ySnmxUW@x~1TO;k_3_jBV8mO0{cQ{!&*;YE!Ae?#K^q-Aa)_Ak%ZDhC3
zBLP<qrTf5_G2WZZcO~8S>gw+Hx@PU37(aZ;0JP0eSiRcT$brfewqV*>BjmB3!20d)
zHu@)zMeZPuiF?|E#n>o$9?^SP6_KBOJob5aU-5h{r?$0bNYj{i?O==wz@_z6F-38R
z@>c<^DCu|d7b!1Qoq@MW`|^*(MZ;~!@DsXjp?LGI4np-Fd9xFNCo-#Bup%=d+i_1#
zRrgs>i-mU&b}k`=`RV__A_J2$;m$cTg){uSE>$eKR3ze|Dvx_~SSJanE>}+O4N(WK
z`|r=d?Su7dwBdCo_{-N6=%q|xB`kyV^{{l%7Ply#<yX#Y?{&0aCo6<Rw5B`OR77V&
zWSXUlb&K+fSr7fM9C&RXRT_XGj5t7JPuMc7Tugpn9KIW=<&~4W{iK`Uzq9;t+M-rZ
zx4XPoqD>0tKrW$p+E~1z=(Tz?{zEFhw<9$#;?pAxIwmL%=eilML9dN!!or-zuBsNq
zAV0Zr3t<#`N9saY1<zdHS-r&fa463_zc8!4R%`pRuyfORU-2#d%#05msT^ke2#(LL
zV7dVF$9FBp5I<FUO_88fiYp!A<IF&F_UmJ;+nJIaJARSlIDNC}`fYtlcgE{h2HK;$
zm~7uA=!jtYQJezlGAAl<L3U{Uv&ZLCHS&Z#i-Q(&#OHOdzz-kXxF9G}15LUv5;g}4
zO!{};t9`ksaNEx6Y~p4W0hsWDkmS6Go7%5mt5*~yt5-M~`a0jbP!IxTo~$95+L`h3
z*Gq2Y`Rl{KhKIh94>t*?X+ALJ*oz$r#1%X;_$Pq>DWzNSmj*f!Oa-3!Lu$KA?Acva
zQwA`S!j!4{2~>tqo}wxOzMz%UABTEY5)8r<nA7}9Z=Nh-U~Ucx)|Ko}<D%<0Ql=!*
z{G^^s1IH4D8(hqW`vwQWvqsIm97R~6pw)oKRZK!+0a5#bHFFo4&)=WQfYfVp-tjxT
z#lqU(B){AvKVoaen)=|HhPxGO;+@x@7ti{#eOWRs6iQp@+q>IABDddp7yOyQmL?fJ
zmBRurYS45=jQfaZj6>O^_Hw%zNkytnLX@!A4Y)WCPC%wXpC9(8@-3Ts3XRl=S!*E!
zvzXqyA+H1i>$TwZ%hpwXTlb&XU9hbd=OEE>bZ5<(T?HYq?z3Lyxfydxp;oVQVx#2*
zAol*{2+&VS&1j=v`$S({_6LSM@bGy`TmR#i2|fpCE?JuaFHt?-w79GRb9zka#<SN+
zf#W3obc!5{ZqLDMEu(fWk6~=7G%-b{Dvi)*Z#jXQ{J~QuXjv3pLCmFoFd?8WQa?5P
z$5J^frxN%vV2|9eAzi!Elt_4o4@pPvP~7cWKX)QqL5LvN%?kC&;<q%@KE~SWADlZD
zG4xn_7kPpJX|KHUa3rAo>mx470G*{`+;~gOrEU<l5e39+k|&DzOlSVp;qd?&P9<m$
zfo_5A6^oPbbB}cXfR3)kxF)~yA1~b_*O}l;A6}=$?L<1C=$b58ofD$#M@w+|VpU4#
zPbK7261(Z5HO#+<!80&6NBh5edWz-Jc%EqI4Qe#BLq-4{5;Klt{=?0P=b4%ctK^>Y
z=l6nlWKAP;xKC$f`}S|bd5?$eg+=%KQ^E6Gk_6l@>($<Hq0j`A^-uXks-gswrWL!1
z7(q;N<6bou!}Q1RBX2CRs4(8TSp`{_PvfOehKmyXxfEcs%+jq~&5bn7(x<E(I8M)a
zZyla(sfKrxcJ1Lvu(PE5$qU3;ot<bU{rseAm8-Vy_exmEIv6aLjY(57+gNa!>FhfZ
z)n#|(i-&6jZ4j=>?#k*eP1vNym_E!oP`o$`LVjKVef2QN(BxbA;`l!D+FH#Ti@DfK
z)uLiyuYHT}@56(3@e)RAhi3ioQ%v9h^o}s_hRiSHD*C6GHeN$7EUf0h!q^&%$ek<0
zzF%Ibp4GK?xgNk+fosu|(6|(FV&RG#D<=CaiOo1g=ych9-RM}Fo!bs#nFSXzoov)G
z;HH0nx^-f1v<7+wEXEOFvP@<iyYd$1FK?##vdbiiAuXn<`)@)_lcq*_K2KV|h9aHs
z2`Opjz~Y(mON%xNGmrlX18+3&eD5D<T6DT9mXZ)jTR%4Ypxyc3aE)@3qVhXwJE?xL
zfjNy&j+1__Sqy1b9*U?8H2o`PA&;ptg)f55WaU$w{V{pdl~2#}eHj-zB&8n_K1&Q=
zqCKHqSgtafIoV+%vX_mKD)khN-%aYLYtR`&QJH*v!-|6aU-?8mIlNmaG8uM#U^ln&
z&Uz~OVUP-p-IUp+i%Z^#bC`JhNnV?`H+k){u~h$2xQ52Aha=Umfmwyf>MiANE4%^%
z$*QdJP%;II17dE4XxoPY^?GcPESparG5pbe^{j523PWDBD-1kR_+?u=#=rs|lF144
zJz-AwzsZwqtQA&;GDlbR3C^L)u!=o8AGDM{^7r2h;7(MV*!rB<WQ{*Vbr}YXBb2rE
zLpLB2h?U8@`12<4)uS_0lP}Nmq}I+l+MC3CweexfI{bdyx-SqPRYeG5+Fq|NWTWRu
zv%pp#7t|Vwtz0J|(Uk0uX|nBi3Js}w)+aigroaF!w|I%hA@QeWC`zV)^)aaJ+PK`a
zXk<rCRqCQ>lCKO4?Ky>BLD=UuS(eP5D`kR~+FqPA`3XUpGaY&oj#4-@LUziBBYV)u
z*A{fwJAJU4$(v53LIGjR@>3D=lc<e&VSNq1-VS5>$fUW^(NxwcrKsku7+)2-8qm!h
z(57-t)!kUIV#sWkx-HC&Tl;)(^3yI{LXaih|7u4U*om5GB8EL%dI_MgqmKIM<1~dW
z!V_fMJ5WLmgptYnJ26g0D4;;q=xo?C=ukt)r%a&kNeKGz|GdOQ9Ug)GzyX>bk=R(9
zw(>R!7YtzM{d>5F>QvsOv1WYTmz&sAj=cnvdcBKUTlaUHn)C1e_W;2q#1$b>mD1u+
zbceL21t@&2|2#!4B+DfgE0_?XMJ^JHT!Hf24l-Br=<p8vICo%yjU~LWXcHKHnR9@C
zqmi%V`*`cRLT6&2Z`_wcS5_@eLLRlJt+QqBp`gsiJ$H$E5@UtC5&O>L2-p^Q_9ci~
zKVV^weUmzgK)+YfnOR+XHvz1cTV-R|6|Od@QKz9+;Iq3f1xa5)B%^tRYd$Tqg^T?B
zcrdB3tJ~?F)p{W>isoLi-0~!w&A8WdHad9Z@Q3-ky6zKlDEAAR9nY}xU_tRlWwPl?
zi*Qu)BwDspHLDQRwE$15XumtHZ*?pXzGX@ru$eAMskx+<ryVL%T}3k$Pv`o{Y&6jt
z2ZB<P{cr0~GH0VmU=2b)1w%W%lZm9j>)woW8GjFZo`~q$-}%vJ`>@v5LCu~chSJDx
zWl2K#gNKeD0<n~__RKDn#0tdYN}!Nu3#u(LDObKgt4llf6ug>>4gfE|K9$MfMfJyD
z!F}?ulc^AQ(S|mdRvZifZ<efjH-^z6Nr6SoOwLW(>4wqne-G}?cN;-VgFQKaegC&L
z(#jO6yj)ZSDqkXSKj18P0ROGEwe>oRcz=DeJ|yJCcOSs4CB#Md2aP^wQ|r}v#aKJa
zZEL72Yn1+Y^2?jTCY}Abk7oO5zHH-;M<!5XD?C#s<tNXZ#*U7T7j$eK9Y47D#Y*^c
z)|hQvoV@iRtj@;xIQMe18x<c!GH4lC;t2)?A$;}WmyNF}y2BkdMO9D5!>Yn4LNE1r
zjeyQtf3soEPC|Imji!F8tq<V4Uqgj&-dU^p(uTG*J9tk*X0p6cE=7(#4W9m8+AaJK
zE*Qp2^FJ8!Lm0BU5#zvO8~W{+(Gc#ftgJ9eC7753(WTBX2MFn>NX{ScAePCOG#urR
z_=|k6^GfilPwkPL+{DJd-Wj>w;OW7F5GLR6ulK>-2tL28Q$bV?SB7S(Ex6EJR{mwY
z(&7q|-vNwOXP!5YE`TDH8e*L}wq-8C_jxy85n=z>)R}FhJx#@cYwhrVj?%O!Y=#EM
z6-BGgs%vk+L3^{J%kB|h@4;3p2aPHeRbDY2EoK0hn30BzUS%8HZxyMIx3u`}p%-Ou
z=UqUuTsR$<bO4DR-e%gtNuCgG&IH_~hDCJg(j`Fv(hlG3BtPHxKlJ#{$f=b07|zf2
z4K5S03~th`rH9OC-!VDOWZ|`T)|8y2na<cUG<i(P>Ew`ZfF1fRjYxR$A#wbrpLQeS
zJbTp{-gD;?P@6q4R|+^mqQ<G>eHE2h8lD(|Z~gEgG_L#MJo@RUxwkglI!k#fss>U^
z{bgdtU6Cxa8?njQ+gGIum6NUA8CLq@7FUL=v~Su;Y(Be7Uy1DKD(fV`Y(e3%U=94k
zbzo$JkpQA>JHn1o?z3Dawt5M5WD$sl>&ij(2G9~97q!wmI*-nb4w8NsY1j|T9~8j)
zI9)du0kV#TCP0>i-QOcSk_$$?qE{p%YK9JXueSGoh5E^%b_1jNF(;$Nv6nG>b~||S
z{X<ndc!Ck37FO2V8kwL711&bX?dSmNi!sNm&Zs7<&cEYwS3*ph;iVNjpgyO9tS4yB
zg~?tW92^9(M6W0Bx_wj6%C-A6fO=dxw79d-U~md|?S?6;yu~f2vOnw<8Wna{t4F>g
zpE7OmVo~ww8QR^BcH@u|t~StVyBlWa?_wTeVnqYW@_?M4%kj<5d1#>0My)?k#4=bn
zKHbi%#(ro45L&*~Lio#+Th*B_HKjnzMT1;`{ynS~6Wf@jYn+FTBB*Y<t!DCqlL%w;
zAvT%p;OOR9gq{o=Ely<fINPoj?;(~&GC5hIQBOT?>HUOq1L*DGCN-}U#q4Ivmb$r%
zc+O<nc#bWYj3C;-DBydhaB8+pvBic*wX#mIVZvzd?5J5%Xm~g@e(__F9kI-llV-Zj
z0Op;*<6`^CD)FLwCL8%|$(G1%jW3|VM;^6WAVL@-sgVm$plWgX-L1P_W%tCz4{O`{
z>wLX`cnp-%x<JwHh6eYBTN=OPg_Q28;CZ#3)l}q!4#Q>16&(oRpQCPbniRAnhKV@$
z9}y`Y%H_QB*KIOgv$qN_Ig;fxXNc500^)AHPJ(DiPF^0s6ShN7L3>aJnAbpl=gd6t
z`%WH<su`+$0S(Q2YG_v;S$9quG?`55U2M)_4%T&cF4%X1hHMe)DX|!$@ZW!JLj5to
zYjkjdyz$p3*ZX2ooAnOjrr+tB)8oHOHSx5qHu5uFb4_EdHqB#74!i^8<d#BDo(yUI
z{QXKhzybR7kBC4L;QJ-2gJC5xmt<x7?%~jdD2bm*7^bc<SqwY=$%oP6lkP`6KYb4v
z_4>CpZt(e)0WWF}m|Y;2vxt<!#ipZ_$+5i64o4Bqo`d-eg!f6pY2_Mze#lEyrd%|J
z$nn?bklOda-PsZUobR=(^a^vf1y}05W_{zNoI{v5?v#Hf97re8K~GipV~4ef!VW`m
zh;wJEjsZO$HktFAojO);Gv}ZyO9Pviz!;^#5TKTfJfPq{(q#U1Hvps}O;9+U_kiHF
zq+qfV6Za%2C3SMgvg!U>oF+H1yD7`=7}%Svt)DLtOEEfi9Jy1VemlTesDuejU0+5M
zFzTlchRL)c1x28VqoSkd%`udPBBc+`0qG61qoI?K!z5y@2ebU5jbY`~0%S~bhB7kc
zK8`g;5<@V*K7OV7Q;)aarj{jJ5D&mVL&#XVGG@Mmxc-fPeek)?q+O;<1d_j~;gxfP
zrZ~n8X#zzWjmpPY5V3doRI!M-TWP`zZJU1G5K!u1$)1#_eKXx=>(BxzBWvG@^?)?;
zmhQd+ezm8-5?536KetsN4H2H}%z)fjJi3Ks01}%3tRK6E>Z2@W8z)dE8BoeE8do8*
zoT~Qmf4)ay^f0MR#M8Db{2`|%K6$5?klmYNd4NMt4=r&nyO3{Zh>$L5tL<KStsx6k
zO^9`RvJIXB(XMSgmk~7(pW665>{{&~10ZL@@&>Jxd#Dm~-58S-uPU=XPn9980rm{h
zw24@g7A^+B6dQ6pW~JS;-iS`HLe7>~SlG2L(}>y=x)Uuazko*&8pdsKvIDAWDG7@{
zMyA|F`qIQV$-M#TC-!}vD9xiSwsK1Z6VvgiN?WsiuO^nggiI(!gD%Y0LhWDxsDK_C
z;v1YTEQ^wkhwnDL=g`dC<#DuLDkw**i$dAc_vA_I!A;2*%lch$1WxVTauxWN;3OiG
zPabt;t=URT?~z9e^70TQ$;-)oT)8K%cR>ebxafo8507H$&JGAMS5Bc2GbjH#ZD@_j
z`t568iE}dL36RVl-s3QF(oB;G779V=u4KF9@u+n%#tD)KMgS(K@D;l8%2Mv@wd+?3
zG}sZ1bMdaOE}kV5Q2U>s79B`EZs*^o99l{Da9zKA4WI!`@JyIm@~z_h@}~dkYIQN@
z+MSMh+C|Pd)2DXTpV@`$B2TM^hB<vWlCE2H=L~6Aa3}098@s|KIF4IE%~}A{(p!GW
z`t-buJel8bs1cF<&YI(R6a%xu;W`U0CAv$3v0Mop54M{hd1gkA*qM3#ux<N-(E6!l
z#R00x6msJgs2wu}N=dchR~zv!m5tX5u$6*EcPMDUvW47%;pfu;@GvYjEF;JpgnN^m
z_8p$fe4uhoL)5?1F=1?R-%;C#q`XkqGdC%RUu?Za#udngvVr>$6^hXL>MUl5jALxN
z#3zj7DCH7eeC<1KZt2pqy`l?2&6DLT(>d#%i!wTdeHDUn2^6ptq%L1jC6d;*XmkA@
zMx`6r+1IjfcGTFwU~-lqdk1_p^>#n$@ld+suJQT)q$WC9V>35pqTWTbeJ2v0lQ+yA
zm{6HE%%y*h4k-!-nS?{cHse?=gmQT**MZFhXWw<M9JZh*NVIeUiKpJC_LOaZUOm_f
z;9EJ9hRE+R=*1xLJ2L<6T_afwTKe#&RE-Qk_XedOy6(O}gcu&8WcBlv&#lp~RKDID
zF4GO9|Ni*|jDH-7L+z?k%+u^dYNV5BLM%W=5>b!^p|#dX#1aG!i;<WL(u*l^m<fiV
z?z#l#5cycBo6fvV+^|cIJv9jrZwg9t6vqGa9F9F~RlSffz9>C<3R2TTp;w<W{^waz
zg?w1?^#9ILVgK@Vy!kP#+7rA5tAk=|v2X;<cGL*Cs7e49-FSYF1=9UV0Js~fbP##P
zx0`r-d$R=AN~XB&y%<0$MTOh-d4=_%k!pWNx33TAJ5YOa<UHuBF5@PlF}3$C$A*5e
zU~%wBHMaeGYN@d6?KM<#&0maAtS?~s-81Is6VNCbCbq5g>6}DUrg!ipZvpiFlN+9@
zj`l}ex=m^=R*Ypx42|1rj@uRw+e9ouAM{!VI*6O-y4v<TAF8~_gYvms^^T?IyoEDg
z+rF7Hi2TR*#BTeCe7zWL{zXDqsB0nHD}-86sX+A?J~B^fx{=~BY8fJTdZ5>z3-ll<
z!4coRaJ>YRiQIPicGR{{YNaQ&DszCa%FoZWgYCS0K^)^>ZFLxGUxN9zb7a-+k#BxD
z{0>5qc=F8|FG8x?)s1Cjb=jYjKSe*WtA1vouuPVG4Vr(#3%)|R>AI!s;L!!pth+=m
z8G&prl7wIlsb3U<`Ya-=gZip#8N}M1uxzBAgzHzh*48I65*H##&Xr@v#>RWq$;+L5
z-RKC_)CA`2E8eoWx3m5xY>WliNy0Mm?T)84Oe|w_A2t<{#!|KnnF&>aZ9Yl%l@w{`
zACB03buKHOeSg#ridNmD%E`~+A0TPNKTlJI1fVnObw=?7YeC5GWvCaFOh)hR&x@Xo
zX#aG({E)B0u-*BvcK`AFPlAFD`^zY+|7$5Ri0!^ml!vF?eQfTO0ePHR`}T%Mc?8jf
z{vLJZMX3=P=R51Ublv>PP%WJpXE33a8d1v#x8RzJ`zGwUb>9gvmS$Nzyll)ck!&<W
zT4Xka$6^)U^Y@r2IMh-TA_c;`F1egq_)3nYlrbTR_Ot4WDmqbDMWNQJC}C`f-%-2r
z8NzQ_2C^}YKC%%96%XJIW0~+LsX#Op2jhvCSW;k7gX(RXz!ulYL$i)hxLp|ZJGrTx
zgB*iZ)(%*9RC9B4$Do$KTemK@x3{NUOLeZ5(Rl^x&IMQq*-d?4Ij7QZB%4-CN>gdw
zBv!2?%zx2%KV|T-l<v~z+2}6Yj<>UX0K4SdFtg8)W0~*c)8mH4az;=vMZxv+{$l7I
z&eN~<xsI!V2JC`ejWYl(Bj*&r%gY-Fl6~G!%dh=hrqHG1i?yoiP|du^rVDibQkiQV
z|AftK)@j#n#)~u)yG-OHG$0g6mwITb#l}A4y=7?5`Nnm6@~<D~jT%P617w}Qcej)P
zUoUn~!*)l>CoTYQLecYP&E&$w@?@s2ePHv>NB5CnKh$eM&3<8i9#*BKzvRCNfcTaF
zYaomvHn|jb3sh(mrXCN2cZ67EQ>KJjnx1P?)!qOH$WqE$qlM|1{l71jmwUstSc9IR
zHWs&)9z58^ocq>QXEZ%W<&g>pg1%V7WK)!r$3VL+yMHmifMX<Ep+>8paA*pYMTxHE
zpRm&A(RL*Fgc#MRngh+BH#ATDnj()PNK<c@r!mx)UQI}s$X+~uYl>cKgfEzrv)0-9
z)g}~h98RBU8@X4wHQldNi01qV#dqJ7b@rcdxKnV!80t8Q3rkvk^^~&>p>8PIuym@|
z@>!wr$0RSK46Pituv7q&N-#JWE(RU39L*v11|&jAMF+?$%AnMzcq+eV{vMU9xK5_S
z8ytG!0Q^L@*dUgh=b)`%`t7qte)Vs+*W5l^Eewk)b#uk&W{Rxk*%k0pQ}^(5>OR(;
z9U8?+;1FVyyB8(v?!M%lz$8<mU6Uv3mR;#C8STDox2AYwG*f2meoSGPy{94NaOZvH
z<oj%9e|y-!pCJNZ?7r%bkA#Rab;dHZ`e=LKR#cQiG>5g5k`N0azV|qglX7Ne9fcPc
zkN~7JNJoAk<RQ^9B+(TRr+7LJj#mfm#Vc!nr(?gpMTbo$7!hRQpS-(OZzyL6Snzrh
zbTe?ZJo)`T;se>Gg%Wm8$s7%5l~lQMsr?FzT7WD^6w3guJ8;v}B^o-4lMZNn6Zl@>
z8=wa1qeM6zZe{(;U^P8<1K^;hfHJZDjZ9^*Lqags6(t*xh#*s705gc{Fhj^)-V&_Y
zNaqO_P||kVncWn4liCx)pV4uYYe%92CWtGbmLUiRzI`i1eag_WzJe`4FS`!WwcouT
zq`vk0Ku{X>Tb7*;{GH8t*ehm8m#BE!%XZRH=3w=wm^0^&BzWq#d4?vpr)QP&j4h^+
zAJ?M?K%c0WFFODrwNzlk03dd0^DkmVx||?A75?(&b4HL=sLE?u1>obg&RjUo7`l8o
zGO2d&Hh~?M>G+Oq9zoxTt%*b|KH@5n3_M8b#jSy}woMM+^?U!N87HvNHl`j5Ryf)%
zV4kF*t#j8&p{2v~L&Lv>yuG!fcU`_us9uo?U~T}Nrl_(8FlORXkx@xp4F^`1y|JjE
zpmSZOLpEp=5S;KO7JY)G0ZOqJ&{(`k0=T1~dA(DY6l4KXE1(Iu<$8O10oI=a>Uy&e
zBSAf>c}y+SImqosRKp&J+h>o6d>eSMIBOF*ckD{6(qVN7gAe_8Nrzl5R_uD<k}#js
z7wA0jk%jhU9sRnL3?t1~)5-AACf%brqS<~y>~uz?MkOj`AEN%bC)`}%IDnGW84M3$
zZ_FhxMk$D=p7As(8X1kUR2YKXG|E5Zi3`Efrw_CpF8{g(bq5VwUsvXMz7CU$`^P!z
z3Gg5mmX@O0%>KQ_&9v|Fmg`hdCZZW$2NRjQ$-S8pNHT*<z~}6SadSO0?d;g^6|g+s
z6uVs8F0s(kFBOzJf(2x}ITIIYtY{{9Uo&u<$??5ydJ^U(2@I-2xV_54V>wtc4@I=w
zR~I-yB$d7bU*M&|J~FXV;ASk!&3$vm!NGy6#_Z6JEiU+(CHRgr2owk=JdawmAbHl6
z-iw|izqU@Lv@Ne%3+b9hIdD|o^Sry_k{R8M#quo}_wJ~USTPg4IN?h<yyScc(R<Jb
z0%WfD^Run3N-h-z1rC5Wv!Hf|-7!R@eEiw;`%l529`Wkz>I<o5`<KVt_2FNMg`rPF
z_siFgxG8BD$7nr>;AyYMX{gi+y>(CU`|j6qP(JU+;Z9*v5OssBo?4z}P_+$Vod9ZN
zBth42S#H9-ytZ{_H%zOls&-dP_|n;#{H|_Rey%&nCK%9Q#u$c!#0?U)Wx$IOqWSTb
z!JGb5bo1l<{EHKHjc&!(HknX&J@h5-dmL~Gq1}gXSlug&YBXHPiNZ*-sPv%#kTxjG
z_N?e+szd4SBTIVoZug3cPlc+iMY;QaJ?OyDb5=FrB+!I4O-g1*x2pE{i`}|149-EB
zNO)CRaZ_?Mb1LgCK@`$wmWrhzN}ca_WzU0@haW@;KqN;S)F}KJNnkEWQeGoi?QLZw
z1@eVn-vQJ{S?PSaI+Q~iYHB2_?z&9~{l4=#2r0ujKE`}AKAQ<Kluuk5={5&5z0Bd;
z;W8!p<&KK#78C6|n<m;19(C!Y-OWDrBLF_62oD?pht8GBW5f`QMG8svQXvimZW5w%
zY`?R&bo?t^*|eP?=yRh=NgwnO?JVz#&jKjdbMVurn<3YJ1=Ak(N>nuxDo);-D%BDy
zTf8-U!UiFpOX;Z(V%$<nr|s+<lF_UBLeqUQyinx%4)XBz;-Nlm3wl7~=<pXGIuTZY
z&ENBvD}fM_GiXNRIWuuCy`wXr5QB~`$usXcx3W_LI^Vm@z^1*^S-$@wJiHBhH2bWk
z!_6}8n(+Oa6^-!}H6T2^0;-w)Wzq1u=3`p3-&#8e+iQG>pY<nY^!6sQ@h>6R^uYG7
z9N!zNvjk_=sH6ZFCaY3n_~mrGa-ODIymBkF#9E-%>yA`ilBqVR+y$7b901Z#CPp%o
zla(EVK0eSUnP-4*cCi4+!P2A34#SZ?;1Zm!$g>rKf93_;m!9&kO(=Jp-jMOsUyC=L
zXi=4Ssm>8RACxyZ+ns;Td(}84+8XP$bxA(OafrNNJ3bTS+<===SjI?xL=UD5#uiTR
z*mPn**#&EZUVhf(LqRirCF+QGTIB{w2te~oX*LT<&^-YW%yiq8!`ivLJEN-5(I6Ah
z%$dhP9ApWpcZNd%&`BgYpH>lV*UZYwiaQu(WZ&a2kAu;4fip*%jhi*Dy1dWnIu}dY
zUv!ZB5lB>?h=S-EQYBkJoV1II6d>4nj-R~ylbj{v0|2y~rE?%sp(z7My%XRrT3?|{
zgyp*tPz2d^2@q{PJ#mx>+Pyod&Q9!pL6*r~`^>d=F+>-0FLIw*a#tV=>p}w=(bapM
z3*=9_AQVdVg=2o4L8O-^18FfkL&Ipr!_;5#vd7W7pcJ6G08QemOEph7PjLF9;lU$N
z;z&`wUbMfK2@b3^KEAgD$dshVe*eeRmB2%}y?^ad>Poa(E`@B#QrRhW?U60nqq2pN
zeR;J|C`x1}vhUki$5PTnMY2qceH)CiHrD9>yhHu|KCX1{otbmq_q^vk&-eL$zt8R=
zj@*g8Hvcj>Nk0tQxxBau^vWZwro7fpU(exD_r=xrblFrijF!AV{vzdLc@H2vpih|q
zx7uu~?@r)lbKXcVBQWChL;Zf0way+zYsu}ctsHO?LVOF&W)wRnr+68m-9#%CJ&YmR
z6`+Lu%|QzG@}-qHaHbd5dyrXMCcA_Yem;~Xc3E$9vMIPxCIBc#=RXYDPOh<Hh99^q
z#WNuFW?Psv3wx5sK4?^fHZRCU35h5r8*Qax3qwE7&W>+y4+j@pPcJX3sRi^x>4d{G
z?#8S4k#8?0y@5=gb*Uz@dg?aBXH`&>tlgQt{qOl-m#K23Niv4dN-P@RdOxW&k#GFl
zWdjW|#VW_lD*0bsWR6N73gDnzegwxvheN~Adf=+#r;tW3i)|}dwz9}gRzLxt;BU$9
zD{P^PXe9t`B2^aY^d5yg(TJ(?_r@BeCkJ%-A}spAKJtDq;)0*h0578ZAfh?b`aids
zvyp(wx!>VFE;s4^ENz~iSlnRYBV@Jm_y>h{q}d%SWD+ag$9^zV&Ls9~ALG7H8uFWv
z_W<9e1zmLEcYv8WzcN%DQJoG3dcCm9dg}vL&|#wsc3P-T1}b(geTWCKkwmG){yoh6
zI&uxpq0|y-?RNLdH*;#O372GJ1jO#*+IH!xmtB`efYBNtsem!-iIJ6kvG?3vFLOv1
z0|=_SsLyPjG^Aq>z~>wlGlM%(AVKx--PhMuYXcTe=VPXj(A40?4fixFWX~v6n?8QO
zqJAehD<{PhtBgozw0R~==3YpqmuU@H*MH$4v$_4|aC>9^2k#X?UZRtfqbzku$cD3x
z^Iwf4=Mpk^jifd;(c=s!Lie~BIOwEPF6Y>h3zK+~PYZ8ydE^NL{1<s9-tA#;$H^`V
z+Yyjbbtvkgmjg|LE>usbS8lA5dQHF^Y4b~-0BFa=r5oB^Vca5D)NcjHd})6;mw7jH
z&|So^{c>7L=G|+fOwx|tHM<X8l=|20ztIz(obZ4#b8fyKDhbLcctKTEEzZ^c`do`f
zsm^_9BNC%B1pJ5rQ?u@@`6ezRl8iPk0;DV7WPS2F3BtyQ(rhis`Bc0WeY-dDw}Nkz
zWn)_1(<BZFbzk&YcNeKzO?%#=C@`*VDxFcIuW5WU)aljR$^B82^vXh2^ICSs{fcUv
zYr}7Nh+ioT<(=q4{0bqx<O~*B2ssx08x2xX*%aYH#m%Gic=fh+fa{qWhT#a-PBdtD
z`HMy5+yTB=f^;|<$$1WaD?uv)#TLh+$@q}Z7D_UO2GV_rR(%2OM-FDmvFSi*!0O(Z
zp&y*_OT8D&*&U`s51~9L_%btD<_k+<Uq{9j7dO-)LjU50Hvjo7rhY8oriK{%(W9Co
z(K=@$i(3$g+&gSd*9=gf@!)E2>dWDWAJbRBofAJz1S+(74ryv>u;uxWDu(SUi!2@I
z+?LN8h%d!hVOwPL?7Wg;fqtAb-C`{3{|kW~+F1i`{30?3#hnYpYGD5-vLU}5y@FO!
zBTceUPk%`W$>Bl$35usnsBN^*Iw;}(A1TaFAyccP`J}ZJHv%6|ILH;j7v5{q{c!J!
z%w*v14#s^MJjXTN#4`Hy<z))Heb>D;1Tqq=<bn7~=GwZTf*1s<7GT9dSc8@@;0rVz
zx3mF<`mrh9I?v_*eFHvl2!3|9V7;fTnrsJto{?xNQ-#{j>U891@Xrt$;`wtypi2JT
zzX<4HlYt7Dj9-oQ$}%!xdWBjpUe*iDvz|7N<xD|K|LvG`eLRG*6<vENPe9h_xA2GT
z%LF9V7A^`}+$gzOzuTkQS>yRK1p1a5{I!DtC#F92OtiomF!c?qd3qd7qdK4hUb>h6
z?*pmN2H^h7TNi<x1ceA7cZIhn%so?UBX@kA_1y1rW&32%at9NAhSZ@9ySKh1Z$Zv{
zQ_{0lgz4bw)Xc%HErOgvR#?#CyFKG}|Na*b&@iom)3eFizQBB_#2NIVE|92hV!ZWj
zSPJ+q|MQnk=cjv8`!s3dBro}>@<is#@KW}xlbI)Z)o-h+RvrjVGGH+YeSdNzKnctC
z!tK91A&eZ!TOw@4tOl}7p~G~G8-;1na$po5X2U1}K@aMSFEPW)##XAZ(Ae0x%iwQi
zwC;PRjyjxP?dJ0@!xN4xqlgvM6jfzYv2twfok@?IkEx(ldplM&D<8MZYzJG#H*Zma
zK715(C0N|}`SWMo(mGhT7a~h7;6D#Gad(=%c0BMyqjC*L|G&31*p^cpN^Y1J-Z&*K
zjpr#pfwwWAIvJKf3aCD7gad1i|ESO0Kv@3Pg0gahwGCZ|l(N+M`k|26G+N|c(^V?~
z!1`}4v}6ea@ljz@XB(TQzR=5@`^pggKcD5Nt$F(T&o5R6QU~yt=E-_SzGAxK8?TP&
z13O~uoN&D8kDE_MEe~=Z-dDO_mcRKbzuQUVS}iy5d+%QJzjTr6HuOERrerVy^*BT;
znfu=^4j%zrBg>*@3?ifI{@&i|d%>GW1-wTT^2EF%O9rAA>9Ec=MJ>-eC8fPGudk)r
zoLTXN*sJsacg`EE0?Hkyl6EW`@msODnJ-*SIq)=X@90PcJS93U6X34uO7Kx(+TZEB
z(*goo*mh-=F`1~A2kl2j^sWXO(>XOUIk3@Vc3DT~KwbZDe4u(EBlWtqxivgNHmp;$
z(-i{2+eaQcDML=$#n{wxOvQ?1_5=zCi}H}%Qos1oObXr^b)Tg`5lRaZXw-(;;nzop
zae4>$N8ss>ax5m$)`sg1Es@v}F5uEdEl?nIdJr%SFvgk5Rpig%ST}k7^mUtyD=$l2
z*_5r*&n0=cxOYa-j)onkcgJ|B8(HQnk58R*B)!yB{4mqxxituZ97wFVCZLH`A_}1a
z+NTm$>d>Eu@=fTMqSAPQXto4UD9$?<I{Fd%`pX^PdU|>?`@Y+H1K|Jb@`dwv`-r`b
ze+$&8Q@QDi+TA2`&1vD3HqW=NO1_;j<)t~wK$q5s>Oj-xaQ!-hfU^vil++1Ax~-X{
z8AW+}LcMs=pMdtiLtPSy-tll>P;+`rA_Cnl1iNGYPPQpbNLTFj87pbTxZOSNO`*;;
zi|FpT2aP!}dE1%CUKcA)Q7-S0^ePZO-7of!_NN&aJN2y*_drfi0Y;2avszb~@g_=9
z)@KmYQKzmNv=b>6PJ{n4X;JeLwQRgyd_x(?Na&${#O3SOThP?WK*=oANZDcnd9Nm)
z0OQauY5vL*njQ>MAP#BeIyCE1;_+`<@$N(2B1iEzI!oqj+`mx?W9VDxbAv3VS%S!d
z_J+D965Wnk9~zxWPfFqdY}41Ir5}9a&~jcE4yaLNb3vv*A1n|CC<x>|)v)4YKiP<#
zLqz?(rDYgjYTTl8a$H`<0-Ar7^xQ@pQH$L@1VytpsGUjX@^GRGg88j?5dhA#AoN8;
zYaBeku*2%oe|O=}3Q$=>H4^H#Fb>k$)x$U7WAq6*bfZ}WRcQ{{&?L|`bc_AdmbOuT
zp8i9L2l8(oL|b&rd$;Z^O`Q1uBvu7{Tnk)Moc&TS9e0l2V2x>RG#YFBWS9vB%{@ci
zKTLxgf#Ui1l>-0&m7MTAw(2A?hlN^}I3%-0Si*4LHr{O{b0vt?;$t+YqpsFu%B8g-
zo^R*9X#ae;fb9SK@Vii}0fr6=H>e%~z}ZUL_!~rwi6_cR0VkhNg&E26!i*B&d~JQ>
zX}*o}@gZC)n=7NjcH<nUx30;+ng92e8S|+@+#(b!ti;iL#SzQ`V>FV?r`ty6N({gJ
zDZNks+ksDB!=2}uH8+py>bW<bZ!dDamqmFgf07uptJ#ubs6VHV4+x(-At|0%)H<b%
zTH~Q%{x~c?7}=kdK9Dl1$^^0PafnY}X3BgNg%FZ1=2N)X%gkM)!_mpeN3C6{bihsY
z{x136H<v_5*yfRV@>ISCC)67{m?-??3zgMITQ^?+Ec(&Fbk9S}f1c2zD&Gw|%nEMZ
zulPwnz#&(5{$=Pm8a6hKMyxgbp|93WTEwJ@Gz4*fzw+FYF2o9(p^U3x4NDJhI?RvQ
z3wd3H^tPl%3gT@B;8ZkExhZxq%vDuOZ++^$hO7f$xiXE;kf1q1JGD^~O5@l7bXp%>
z-eK4!*kXLB$N{*~oNN;VeVE+tKMaF{Thyw5iX~12Qq)!m3P8YfF2PalWDB#d0CK(8
zWHK4$@!(lI1E$knV0n^Vj2ZlYsc~-IK)*XM=~}e02l(n{8ss(sJB4J}|Glh+B8W20
zk=Z@S8#LYTM05W)Gb_*wZUU`aqU}wnA&rgcr^r*V5u%pq7yA!xP0O!Q?OF>szgx`b
zutvy9y}riR&n9Or$~NDn{RzKxxg<$Si?DbZ!_5$eumAUtSEM)tdI!K#(y|&<DV<Q+
z&_kPxxXxBXhDBWuAe6s}x`?4VPbv`%1S73r`98ALGJ`W?4!(l=eNuZdpx()q$V|=J
zeIQLLi#mGrqD9t8U>KF7j7I<U7k@@tdKMIMU4_qpP-4r7GTQIwjV#VHt4oh)Lx`PV
z^``Xsu6D+KSuNcMTspZU-2VIZOXu33H6srrj!Z$bF_iC%!V2htfVM-KvQ4u_ChH#T
zSOpe;26{qL0d4vd=TUjmaTXRG0cLG+7APw<5f@ZawNO|RH9Uet6d2WF*o2e-p<8G-
z1S&}$l4|=v**|PIv(%X8*FRBYBG-Ck@$vlJv$Ue@liW)X3L3V$I|!I5j>t4y*b)#{
zdLZ#sR^Qqhu9a#wP<*|*KAWVWsro9^(%w*g6j3lCI?Y;fkfMY&I)j>rf;MMK*fih6
z9C8+rYRUeFYLOB8V7LIBA@Dy=K+dgH72T@PJ3u~zB;l$}73C<Zn(3w$-JdCnOkH;q
zdy`-#cwi=IcMk0c#m}3Yb7t?_=-gQkluU+xy{;;y{Ys*$f3665zC;TuLOv6!R3juP
zsr1yy_OoYqv1G)iWrFvyV!p>$+n;-2SsK*(uD=)hlSvi~sadMh3$MHak%h;pT=Ue)
z#qS35h!;J?#+ZB3%xJ1G{j|39dB^to>G3b6qK95Y-up8Iz4+37Ur9hV%CB7xuKi~E
ztHIO?!sFHa->X7Y%VJ<&=AtCAj>*E>Iw(HfNBbOrBW5SHKt94H#Bt`&^K@P>HwJV*
zc2Aqi*smfJ13Jy*ROK*8nLuJW31Q`KsY@YN(pFD~ja&T=ar9%K6m4Dx8XCR)Qu?p)
z=qOMb7Aj&ml?h}Y7)UqR*rlbXgK~*W87v_HVK~ml)(M%*_6L5kpZ`Es<);h<Y;j69
zcT`rAQXq2X)y_f6EAy1<vgu{6#rK_PwU%k^etRLS&GY1B@Gja<e>TyD|E&5hflVJY
zE5JAo|5q0I2amni1!yqtpgO5h0Yi%_5l}2FZKB!py>I^cpO^1@<}<xqx;&*k>f>lm
zrJle{>fZlP|57J{#C##u9=wWY+hap$)w$V-XPEao&J(tD`M!aJAomOp_n)IxVB6nw
zMBeZ?pl-|Wtk;(wq-?AWgu_22A*UkG^DTMox0?z@nU;o64oP~cUjiWuT2z?(=hdGS
zx<}zJIZ>Yyl7`TE{u(G@D^3fS16JM1#$RE1N&G`VD1W^ELsCDuX@_h}Sl*x4`mOwL
z=sA3pj9;~+&>gBLB2Iw(L8FBE)j_V3+h_BD;RGdX;QI0Y8G8=Mb++!I9}}->mX+9z
zZ+V3!Y;tEAk=N`ACY2e|$7d&}-T@B;h7RO={zC(wXa2k*;(7l4KO=Zezsc;ds(jOW
z3(Bb2kTkmE>P$IyeSI3EpH(YMlR{pU9_TQi7woj4=01NSMyTE#{L3RSYmI<1Lf72#
zlojF)HJK=kX^u{>NgkA3U8cJqQx)Z%E|Wb;kEvqLKiNWE*#9Kgap5RFwi!82JpA|_
zqlqmwEvcV>JMj=;nzBf#bQno92#-OUKHh_2m2|M%b8Qm?0vLt-SBHhr-2FPYtR`*}
zjElzs5|x!k;FlGCM9J{Bd(v!tJ-Ep$@K10;6XpNckFWcae&rk70kw(f){>C4Nsg5-
zm*?lyu8q+J<A_Dv4BtKU)O`E8yCG~8|NE&l3m5<1`fQy%{nlX+$swPBhk;TzrxU+J
z+(!6paCk@H8@K|Q3!Sdxzg#Yl{(1P~`2H?H{at*z+ER+zp11nL*<{Y5;ypC?)y?Af
zlJ>a%A%DXi5Z^be<`*e92pfR@&b?V3x=6u23VQO(Ug%FO>ZglNXDk2LJK84Z0=iOQ
za~NJ*31^cN&qXDs-^Ute&gEEPg_-m1ESn*|qFa`>7y85W@hiRjcS4_g?w6)09#orX
zT^DU@!hHuArlam`%CwR^z=fW}+b9hWJADN?XF{J$y}^nOMcxDn!!elJ=I68Q_`5a)
zPOfNOfhRdP!`lG8DAR?{_-!+B!<31k>Ouo0dv=^H5oY6}xP9IuTJ{gmx6bB&*EU$d
zG;17U?85sOf%RlZ5|@oR^Me?+G9Q)$O1%`(Rdd(VeWWP)K1+2j9)#`BL-NMw@1m$v
z{>1$GxT*x_9K1#5&=+4BJ`JXDJ^^)U*+}<K>HA=9J{&v%HLg_nHA9ZU$>mRSLSyD}
zQcP|)6nLOZEQ(9VL`8l3`LiUeBa1321<sVSm&evEW!5a!M>;R3Wdy2SgHVv0b&YW%
znY#!x3?ql)5>@`u6V`7Fspln>g1MdK=&7N@7CN}8E<xX=j_#6G-+Qz-5>=yydK=%D
zlp(Ex&2+1X*l*M&9&i$+Fm%s8=sWu;7^}7Q;kgi9hplKK3gWV@KdqiM?rVz7vKLxQ
ziEBN9o(%gxGX3=EHTh#CVwMz@rqjf$9}`v^_9{Ot!YyB;+bMTsUwO4;jyVY0fB^%<
zMZ!<8?>Er0OZE`0kMf#qL7UZsuE`L{FlYBy57IlB&2K$|_kt!{mv5D(G$MDe0kN1{
zl(HVio#V_H-Ms>j5mdDJI=W2z4JSM;E~2_NVEo>HcQ3u!l#m(nw4W-<rcPr_D_5l<
zJX}M?KLw+HW34U3hvb>$J5ypImw6eJ6DeuBWUcJrV>HR)I`Jrp75FG1@XLc%CTMA0
zMBQ0W(;hkc-{;w$iF5fL&->;=+{UTU5_-COws`dwTiI?MU3xJ9Q)I@89u|_^J`YOI
zF=rUH-c3<oqnCcrIe=^xr6=3YAUSsSk3&cuZ&BKl?5LMP*0!;k4MU4HF1asUdm`=k
z)|1!GZRR=+^grNtU$(MGydaOwMZ4}TUa$t(Dcgj?R}DI9^z1a53%bs-bPhVm_>#(e
zRZ&-Rz><M;PP@gX_@)QtKcES9OT>I{Q~ZwpG5z)!Bup_)ufxv(7@17kZW8Kg0h!Sw
zaIvsm6SkEzkz!MNeBVF=Xiw<QwE6sfv2H#s-8&?1;XgQ$W`?~YZ?D(8ace<asj_-|
zjuWcTtst_Y%oNvKHIQWYf)OCv0rSLkY)i@;Xb@n6jtx}sXxGnCCkg-h!7Dc|Y%a8c
z*O2jUP!oI71i&kq4vK9}k$Rtw3>?w?Ml3`W=436!`~C^v({W@U9|L$lY=u1XULs!&
zJ{c#j8YeFMd{G|EK1FA`-{X?A0OWJZs2?y&J4V|ppoWC!#KqmU@?D^S+`R@8K9qHi
z!Y~4#B)9O2R6UTm%DwCWhzYJzr|4U28J|<B#){VwH_d6v(aE5l?W~k+spFa&5QJID
z8@I~W@0^#l-R>cvB)zrAm_>-H+wzq&Jtd~+u1`cee<cLiwrO#6^xa&(WRWbvd;{Ph
z#SoTOv|73ri&_@1DJzbJq2f1?!?DVE>V*9SBgH?Pj9FK<j=GE#^12nq<f>M)pkz=L
zlBRn)|Ci9}lV1q|`dAb_ZhMP!1Wo1VVGfq9eX8K0ZnhS$6z<mS&tHqA2kx@gSB99%
z<d_7~_8&{lA$;B>GPn3HibkjLDuXTr^-jCGa?Z=ki~P;|?%k<y)2$3he=o?*kWFc!
z=jyk|zNP(dTjTDZvK(hUxS`e%G_iPy2-|=AG1?6Np|Y~xRzJy;dID^;8B(1`Uny?4
z`<<$}wU>O>#SHCzFWTu_%It|>Ql%_c)yReVVZ$BQUfn#?=XY35>0S9@mG(siDC(J+
zkHz^^D?|rfuk{{LjzF*s5!E+ke#b=ewYE+y^1K5ccE9nlW5-^e0_OrCbMPI1o-l9A
zeTddeF8*rbJTxq(el6ULNrTt}l|%w+gjzzFiGU;$Lr%(!Q=u2Gz^zQBn&h40JwDrM
zK9^x?{L+s##I2*OHO;xzFL_)3IwRfY8v*{M9DPS{=!`yN&A!ZT-#*8w{qIO?IJ(Bq
z9&lC1>StBnMIC;s{2+sJk4-U09IJb}?{L_f0?)6-h+A5j>0u!h_pzE|)S7~Z2H^P3
z^1pO$iHQEDI}PCcUs)m)fkh09IGy;p-KN;1zGP|wJ6erUnOA_Er{Dl-${U=1>N(k3
z&Kj{s-FLY+XX)o^wr7Y}@znIw`h~B=IF5>8Phqa;&qPu|RK4Td+3_#FXFJ_lo~k@p
zo?7$17|3zjN)+&4=+V@*);#`8r#(GIRZByo<so+K{kk7n#{l8J4MY`->h0-ahOdxD
zo+5w&?1da(4^9`rU@+GIrm3$xf*5<^OHEYryVBqDC$|Q}c#XFufu^tEYiW5x?68FT
z((597MDmOOX%_v`?hPp!=+hcz(_iNWG&0bAG40)Xb(I3m7oi!a@EYhfm%}%eE}+ha
z`CAcRqSYg$9VGRh^QZ=cC!#45(lUVn4$vKz063(+!_iA?C3^kZ6<BPy|JxkJ8y{W%
z=dWsmC%W3|$ckEgDTawcw^LPG$r_IsN9UB^JwB|WPY8*&AZ{2f?PLs-Q#*V>K-=y&
z>l?^`KlBMP2UGj2K;wc`&|#XcBHtsY)}hk!`ApT+AN7D=$4x-pUp=wm@Q@MccM;2V
z0+NC?{eA_5{$9@0Gr_zpvz33*k@PI1oQ46)M1RA5yGb`6q;utGp~9TjN3zCa7!M}u
zXrDBfH1{q}!Ao-?2B)u{I#A*dknNKvPcUGaXV;I^7>C_QJ6_zk@?D<2xjyrIz4o^L
z?^f(k5dUF1f9AWno72|cfUVx^qv+!Oc>|+_?@A&wi{Fo*x+Kem<#O4hPI;1g!~}bV
z=rDfr5AOB)kqw5~JJJdBqADt$w(dk}m&N7bW2B1ckG36Qgv0|2x7}@9%j6KP;F=xR
z8rgHnYbUkN%DSDJ4lNOPU|rj8kBHFlUq-nz_dg4kje{;B7LF$kWL0||Y6%z=>Wcej
zo1(?Lj<U|Vd3d-`1{p8~NSrxaQN!merTU$))L6siJj79TO%pRLG<7&J>y1HM`UI25
zpNzN|-=ueIxvTqS0+l<=)UNjULJx-{*S8MRoU;-0YCG)}d%Gmw&!#ObR!B_q5_>l8
zi!%hIGyCje#JrFi-(US!bS<|T+-nLydih*T^WSlg3B*)r%MGftH{b*S8liMYJTTpU
zLN{!Epb_AFHW29ei%{7OsGiR&hFbaqxE{)kTiBUE7`S;rL;``?N0D96(}QCQO82BW
zb^D5E0-hzJ?$&t(km9^-W^nX4xkcI!S|;b~V!{OGpYv_ryOM2f;Y}WyFG{&RFKDdV
zAE<A8zHPaX`|0%{djye;<Fovi?OEB6P}QW6kHcn0bm!yuU8a4%Ek^~;U9^TUACW%}
zR*Oj><OU=S1+~#`<lmq}YJRX#jeZz-9l@OeLIkVK&u<EzW6_q-nr&SPTFn&Y0}4Am
zZBMm2U(w#&@#->lGHqaeyH{p7@*hRmTMnN~aGfGt^tc8JNHUi#xP6@PJicRo_3=EE
zli3dTmv7FZj!&CcwV_>Y#BR@!-YJuNM%jr%DJMe7#2ZAlC1g9t)t7_u(evbk7p`tw
zQp~j*5hvDnmdx#ZAWrjzF{xq{ZB7Ru9ktf`3e}{8d;+jiWIYS*Fxb5WBL&LnFg-2|
zB%;t<6a@Ic^MEeqqm17?ufAOc@<5(WE5ys0ca<Sk<1Pl$fX&P0sB?im$HFy;Dgb3(
z-Il0mnIA}%_Z;Y8gI__OUsWx(IR&#)r$TGGa*J-a%#QF`Jv>p|6yYsjpD6fefBs>W
zFKT-aelSqm<#zQzXvcIk|DmPbd*5%X+Xdk*N>N5*8<f!MITXlq+_$8_x&K%szGVyZ
zL1oD29r~~(`!<hh{dX|fQvJe9b73GiP`_>aZuwSPMq|!`QKRH_og^a{uL+*i3(VhM
zBkvxU9x%8D%AEA2|3())drY=O!g`f9SNKMA6iXcqn=$2`lj8gl3&kxFPNEmxJJu6-
zKfN+g%<aF26EjXoIMBy;$blh9D%6ML{XH7}U7%A%n{c7bA*v#m`Vj14_x^U^b8JUT
zA~YlP#7Xrf$t^#Udx7BUFYKf)4EZ%a-7FB!h(!omOJ0vXFAIP2UH`6AYK0qlrKco&
z`^rcI)A8r*!MGP!XP(=TG@q32|41%?ny1qrGL*hO9~W<8I9*%k`7U!QV*<05I$jI2
zTowL`?@E~*?dyj^J>nFpe=ux?(X;~(qJ|B$#gIOhSNMDnEV|v)i6~h0J;akbfDe!o
zYhS6%$jI0Y4hCpJ3%EtlA&d7=U<QbqQA0%3Lk~PbE4GkBzg-bRx>IIg2hpyRmojlz
zS)1aHT@g3@em^heev%B&F)){lw^QfsH{Uginid?GeU7G|5dZq=W*T79ye(7BKMK!D
zwYlfLaNRlBCVJD|S;?fiVm+|ng>+5y-X3=AXYbdY<Favm8wDrnLeKY+4jNt*uh-m0
zqx$IEFY(H%Di^R`9fw-jy+0z_g*!26MGZk=D`8<3MNrA$g6{<`+kVN&{P@IDHf=rW
z1R*51hITmpJxREoi8kW`T6lOfZKZjl@?^K9PVi?iCmNZXwJ_U!ashnFQKuEUonqd}
z;BR@N1@r6!u^i`-aMpWNR62xqfHGcnWtkke+&gb{Ymu}JTeu`}hIekI)^#Efpr;ja
zK`}yQ%+h==S{_a75&sd<rHJe_0{hJ_5x_&Iu2V}Nf@~nM$h9-;TnQ;iq^fFxvXP{#
zqW2-*d1r6M@P|QXV~1{`JEw|=7I{8gqWh3jN9f)?Ec(vvUp=p#7SC}HGF;A_Ut6g$
zBkzN1_WzW@XZWGmD3#DL{nnZ+8PhtAygdegcEVq|7N81J#@Sv!muFR%Al=7G-^u+#
zamQrHu8tG*83a096{qhC_Lut&>a*-;k);<RJm@9sxa^pBUDY1BBp?>rFU*Sd5Lq@N
z#^#qjtzTzotj8}vwa(@b`lVVYrv5yQPEXz>xX~@NWAUKc9i(A)?iicG<g=aaOmo@%
zhcvH?y#df7{#tG2$p_KS*J>qCVr&BoOij2$s2G@^t9Gu+DE3*;<M$219@)D*vafg?
ze9TpQ^Ge?h+~Uz!?+kmjVN0Pcd?CGIP(VJYy79uIzkAgnF4o1=`E1|e5U+K6L}gPm
zkKR?tznC=v3o5n{WmrKk3R|}S=)|Qpb)JLnGLz4iJC4m|pOj_{z;lPLk)LAu4T77%
zIEw3%ugBAm)KyWZqL;YSqzPYo+tx5~yGfJOqO+C+-rdNivUYGK1b>=%O4(6a$hGFT
zw!OENfH<N<$CYAc9%6_40^}Yz|Jx4-DdzY|tL=m|=ju^l*g^h;cDVkD0vLeof{9Vn
zsFTQ7#uI{fjs}3btoe!o7xfnC+oW0ZnP%}tL)n>s0_ecK7S`nB@qvvXK<qIYOMbc&
zJGp<YgMl_fbjt0+@PlsO@(J;w>z3kgUi6==|Mxg`r$lHzU&cs^R#!!prnyTg%ou6+
z^iIZ|JS;@KgBu==)8VaLR~}3oYwUOaIK(R0jI&*P_jrU0CHGDbX`DO&YNFNb+ExDb
z#NP|Z{h#Bl+Ma@KVq5vKegIhh!J`KcV45?=OgdV~kN}UO6WQB$%z*EDQA-3L53+c$
zHS*Pq{<oJi&fkz`SmbAzmslhLGWh~~a_V{=(EeAblYJ8p0LU4!U4vcGKExIC;00H;
z1Z%8bQ{{SN0gJmghDQ3^;m2$BqOabOh*Mq$@%eH%$L1rirM(5|K{av!%(AFmA9MRx
z1tAqIx#=XqScbkaLZg2SMhRta67ZN%TOapIE8I|9NE8;$5!zpkQI3L$Qsr^3zVyg-
z&MD_9ko3nd?eZ}xliNhW^}q7n&WsA9n=t4xR%%&r5ApgEL`dA5Cw%8g@7IfN3+=P=
zDXuL!;?mM<5;cAV_J3&nwDpAfvto2T_HR5u7@7b0CklBqs)k=p=js@m_q;p(ta)>J
z&7X}IoItrmj#dUxJ(+A&P?iym(W|H|`pq^&If@D?!RRi&^+?AtAL01c6CEI{gi0oC
zw;R_sD~RF}9V&J)ZoEPf&RUIgr-?R1)f<^{ZgDLw(&;ORfv_m!<0D!aC2XAAnzni4
zQN0qMxMi<S%4|<tU3}XIpNoO{kG9dgmH;z}tk*gjhtcnx!zW6cakR$#7mTwzODnXl
zPIw#KF3u@x%JR+5a_E~?x0~u7UliJ<CyasxplSle&iZo$faRdlrJwIT%J0(WLCj1~
zS42REHL6R!lHYtGyaMyH1aY0<MiOcp6-Q8&;sPU4)|ZK0?8WcrqdMIb56=p_Q2%$o
zQqA1X_jfJ77$2-L^zWx9jIAvczjc)2E}PDO-LvsQ-=<Gqdzm#_hReITaX@g|C17CV
zEQdHvolBZ^mcC#!9VR&9qchSlIrXVZV%gp$SC^&FyE(i{;RRjC!|a*Rj(p%kq4HC(
z-7QOxf~0N&+8uX-A;9Ps{%6HM3WY742q<hJiHiA>_r>AiYIOh<KFCjhJ*&xRo(i6P
z))!To{L9LOx`<yhwcTa7f8@@S{!#BXpWNnA79wKsVVGj)e>%hNm<j)n1FALO)XK@B
zO0a9m39P(-j@w9E%O$1<>#h%756u{&&zhp0T)AGWADG8hCdUi1R3Jx=+Ej4V$?ARU
zWkD{;wRq&|@O~wE78=}Ni+tzOKv%n4-w1M2wAu74owd+}n@3l_SjsBgqM|BwoXBV9
z&k#-DZWw9voY&NnqDt^V*7z8}W_pH|V?LMy(54C`Mox@Srw|toVbL9%P_4xu+uSSe
z+28|fIFv<LlH5{ww;w!rxtCse89U?nVaVk7!~B{UH9p4U(GrDwXSUJk*JRz%U2*C=
zP)J%^Q8Do^!nnjaxM(2RYs8r8YNIp{1-@CLzCaNqXNv#b*?7?lw4#1+lW}z1q}%7H
z0cAv|Ca3W(3Qxj0>LnE+X!G6C+e$JWO$8zw@oYY93UE{ezR&<89n1_ot;MCjYo6-{
zPlgSE!LqiQM-QB7%h}b_@JvUoqtw3yafvHs>-5cr%tpt;=s?M>_Ou5Vq|G}Y&wlz+
zv*7*t$%4eS#uG2ViSpnc8vP`-HtA`xnK7lR4$5m{R<0?JX*0ew0;|#yvC?+aagy#f
z0AzVEh3dZU3y<jQza6uT<9JlN8UA(~<>?nbnwz-W$Pn1FbGR+!p0|i0(hdRAqzEnU
zk!&cc5(nZ5EclbmiGlS}EW#c?W=1y=7r%~FgTgd7qAlS{^iF#jfGR5HeN~RVemYo~
zJRNm7$6*+wSYH81$puy%OQE~W{)3E<chKC)jOlnSTq@dUf8d>l*s|;x#-^2Qy1j&#
zJI10A6fiZu$hxPOTw+UA`Jv4fdNoZGMd!#Y>bAs};jSTR6kwz&hIueG@e@zf$1~La
zk6Z^FyT=fTHW5KKOr4>;l&E^9R+0JCdltf&b4Axu_?Sl`>tjQlTFHk%5ot_gUyY_6
z>0rl0d(-k#%99iME0>Cq`nigbbq0u=t#(A%-0o{DkL7W?fE&Q=t2C@OsW5O|i=?q?
zoSEYvr{^1>KRr(0-0jA8p`UR+;CP;Hu&zw;@UfNs&~tM_qIQlYzHCgoOpa5p0`349
zcsm_K-5gQwHTVLpc-#H{Ox-y2xwKZk=e+R-0OY_`1dhsXT0=Zjr-TBn9qUwv-4m+c
z)pIHgdRDF@OQ%W6zvP_1lrDk!J0`HFp+x6PGwYM<bPG=#7PKn#2{zHc#)Wc>WckNS
zPtGtGmMz4v20S^wV=lnVJH*yRPFS2K=TNGRvcJ>20|g5aSXLK&3lrZisrl0U>+|$2
zNHKeebAw7D2TQ&wzg`eu?BhQD1n&TiZ4PG5-@NiZRq6iY+p{LKZI_QIaeIOvmTl&t
z&+A(vnQN~7mh7fKypj$jeRx<c`%Q&#qyvib9bhNM=>tNA>)fweQLe!W`*M(*Q;(8+
zm*5~q54vcSWd}Bci4tMok)(-;rM4<iUqY$0dooI=1`HrV3i`LWR<BhsoDoHs*{WMI
zLD2^L?Vk)yq*(x7JX8+gqV`R$h2`TX?M=Ti*h#gVyE0hwZEEd~U43pMage8UTb+vb
zxop97r%1lkkD0yd?@E30JZe3kk<{P0$sEm(Vk$$9(ZnLS*)NZOBz#n=s+n0@B{%kN
z(=gNf(#vw6oy110_6;@N<{&StL9i^U;~hjUhN0f@>&rMfy$Ykwsy@mYR1XG?>g<K6
z?0>%-V8lXZiH}yRaTZIBSNwilIS>2BC=38ca7&t^9VPB}v9dtaIDWrZccZR%0px63
zoJ<2D<h`gCucd!4phQ}WNAavJLit~6iROXsJ3=(2+~a<%9mU5pxMwT%cqB$*x6{0q
zY7;y0erim<Y6Z*s;JV@E0m{0?&S45sVn#?;)&92#jbI0l-4iUwuIh{a-W*b&@@C*K
zUdPLvYdvDzoZHFIKE=Mh$LU};iVPzSVQ2$7Dt#xm?<cp+`p-5`RC9w88z*OG+%~g2
zKV5L-1cdHf1V2ET-*1&>Slhx5XuX+mz0G_G?%B3ty7#;q7lsy&QX^LsH@>b|UP=zy
z-F|G&7V5$1{1s42tlVpA_BGakh^4A1XB|#)DrC06FP$jem8o_AF6+heYSSpzV5zbQ
z3#0tQy&DmADuZ*Xn?UUNm-9!<LP$jOPM?rr`TJuP8&6NJ9#CQ1cpyn5Z~@NNyRSo~
zJl6DIRT*9x_(}}%$ZblzR)AfR*|?!yM$YW(#N61n<k*Y}swcI*i%0O%yXAOy)uG-G
zJ>Cmn!Jn;i^tItn5Q$z!+rLu<%x=C!xBE~6X*#={MlEZY*+b~7ons}U4cjO0;g;88
z6Hh<J3j;4FC2L}FGE|UlJ~Nx`nIR!hN$_aCgA^-Kiqp5@k?h79hz4oi$mnEaczYUh
zbU=HjHk~J@(z#;3?(KQU(@m!P3^9};P^yf7n4R5X*X-^w;a0DNSMTHBxjPF??<-$d
z-uA|>BxX$JdWH{{w!gSpH*{T+(%7e4Jh>>e<DS~KmZ?C3vwu%6x7(iTcip#UJ*3Mz
z&Y3&V@G~S;)T6pCRNdQwVn?9ICoK{X2RxWNo~iAJ_7?Wg;FHl)1_<ErBwsNQL|3R;
z5&$zsnaLAInTwxZ-&sVn*ylLWP{?PmMDTsa9(CXtSwh!z?Ama*_&;sOs)Z>Zw@>Em
z8M#^OZBjE!i@t|{ww|q{+xoRH%{vEEd(=+%6__6U5X$&667XY0GDnB4Uv0`bv7Owr
zN)+#(^p*N$@0ch<+cOuVwsVi)E+DTpbvP-E^!{UUA>JXRcwfXrl?bhRof*bToFkil
z^ZsLWD>ko6A0@2dx*#h(P-AKAAUU(NOk2He;M6}cQokVdXe0D*xS~DSp%f`*`#`=>
zzx~svPqejc$SVRm%{m#7c<kYEUTmMWcrql4*E1K8We1=VymY#Mu;bGnL~Y^pU^eX`
z{c9a9qWkSrD?XoD6`rk5Izwa)eK?FA_T-&;+CX}_JhzzJI9O-o&0)K}>|CH%bDh*_
zk4L#JWzQN6dr!nfb5D;FC^Yx7U|NX94;jS_JH<Zd%l#DFuWgl~M?&!@9kjC-NKN|j
zlXEVgP88Koy%EZIaY4+v??_z5Sb6&BP6rKAW*_}p%^UCCa<oh|dYl_$HY*=^WRp;t
z0N5*lrZnJY+;@k&6eX&5q(EG2B@8UxauhrJ*y6I{W1C)s+akaD#YE)%BCx&HcL>`Z
ze`*VdzXIz{D&xQ)LyzU<l6M)d$~8{2$3yNK^VRUdm)qwI4zu0=F!<nya0s!ywnk%?
zo@PPoTsHm1QqlRmM1EaWK3x@hTyAND&9zjO%)#=Nm4)b^#^<u_e;3j4e6d`<lXll@
zy?EG8V<3vL+E9Jr0J8cJGeDsGXxK1FS&N>erx9oP_58+n6wO@kN?gC8U}kc#0f_OE
zl9JF-&d7wCdX_|j5z-FE=BHTUQ;E$OOC^&d2Ul~z5jfxt80LPsj0!!>xH2^;2~3vk
z{6h&Rf7;LWC~>^y=fRC+z8*Oj93(xA>1(OX@9sm8viF>G{-=->W~a52VRF*N>`(QH
zlWVQacoH$khv@w4W?=2ocKy+;J1(woj6;iZ<*OvVBs8m$3WqHCVsGo}w0>%4@YvD!
zn!`PNfY6u!q)%{YrsxL54ZFo}%Z57m3NIPqL+7w#%LEUZar!0*B?jm}5jnbc(B!cE
z%0sxQ_%mxX0Er-{u3jfQ;OQ^)yn$w?pN9uc9nq7s?ow=X#GNZ6Q0bMlBJ(VeSJe0A
z<1{UQ<b!7tRJJ5f`nvqL9dhk+KaO?Y;XlK!w3EuH^-DF6(c<N2D%Rq)uq)OQ*7j<L
z9hTxHnwx0Z|Ek0MDrHd_nRH4xEzoUvQ(|uUl!NB{t{l4pW~8i#ZP?&5QF3VqCilX?
z)25S$`b~EgTw-y!R3<v_%9c3wczTRj7PC=pc5!JqedAD_c@+Oxp2pGv_wDrB<*_?v
zR~KShFmd~*y+fEDddZlSA+QhVUjxSeZ;<LIK~~e%A14RakQ4;%`2l_B&*W19f4x$u
z7AuM5ZxWZSX596>z?AAjLPy`+m#l-0^a@~!lWQey_GFlKu)CChvTTWL%TPOXk$sy4
zT&Q~j9V&wDXO5&4Sv+*hZ1^yv)hygAg*TsctC^_ynjak-_~8NLZTD39ob%5K37xFj
zWCEgHrJ#PX{N(<n-FM8V1Du=foYm}&Prlh+pavVC6DH?Usu`~1D`OdH&0i&n<GsDC
zeZq&{Ntc+nGXXSlw7pge6zjfX^Sdomr>kyf^xp5AnPXq@bXJ;Pn58%XV=e|lL3VIQ
zP%h$$`Te=}*S?qNKxw{cgGu&-#=C+DC&(ZBFAJh1>y8J3aj2G9Rr)qZts?79r2b^<
zeWQaPrE<)$aFR-oMB?j@e1R45Ent$votGevsT%c76COJ=%F+^VQLA--jnN!4=WqFb
zM*i~rlic-;lZW!zrP#oN1}D=ePd4S);TH_BbeBNOfg;1p-Obhn=Xk_+Z#8l62lDA2
zPkWhv@^Pc`QX;p0aFjY9cLP`A@Ae)w6F-i5h<-f*{;scKF6rs%<aLZKeiCS{Td7gO
zFCaE{+p3=`+cb8MtZ<hb^Z^1}-|*uhS(nQOxRHzOrtSdu)X`bQ;1b)7&xo@%doi0C
z3RbKm{C*uDjudgbJju1oV1BqKS?{0eC}#V_P<`Ub5YkOWrRnnNeX4Asgv3L2bi`*B
z@r)&;Yp&S~sW!uyRf-<L2_wB`(B@2D%IY89`(8cQPiSCU@TZx%mZ0JteSW{#YA@Aj
zD%ADb99k+gpSiJ?(Ajb`432R9OjwIEB+@GcEi(geKXTJ0C^%a)?{tI^f4mUC@o9cQ
zw&I2G%}h-yqpB*SpFz4@oIIbC=KNXe(46Oflr;@*`<QqJplut5cKafnN&=A9`lVXN
zsBd$19_ckVbm|*^T1#FCA*Ff;ujQ<(yd!Ibc6>bMvGy=T@sneo?mq}yj87lCy{G;7
z4<c<5jTo9RU3AUn?I}*4VP>q3UusVrepX`0S|+LSh5pOzG1--BU#VGm&ICgZ?MU0)
zeJ;6uE+7O|gnERY6j(a?>#Yb3;8h2hIsk#BI#G?=*a$s>X};vb#0)d^g_qS1o5<zG
z^LH_Le3vYpLCU6i^|I`da7F-{P|urqrkD9Hm%%jfJxE^~iK8cW!K(`(VEI;Q^n$P9
zqn(7@no;V%5y7G@(Jsx7xniOEJ-+Pb4g&u=n7R7(VkwY3VeD##TJx&+=$!M;a^EgV
zid$d`aXH_60cX2<DGaYMMPG8PP0W_Dgcwzd^m_L=b(b#msbr3xnl{J$nC%%OUclPR
z%;$ycod0b9`_Q(}Dl$cj$7FR?bv@|wz2`GU`!WmU1QKx<M02Zn$MX_xYtq@g@GUDc
zAxE(dGR?XD!|<F8K+(vrBN-qR)Fl=y0YUl-6)`Ok6>`3$HmZS#^K6?KqI>#!#F_92
zUij7t=j|Mty_=M*_O4q9S~Y}-HhlDoT~dwckOBh`VdzCuT0a+%IReJGA09nM@rch6
z)+!w;7C(%6&wuQqzv))vLenQQb*6bIAvf<Fuzy{$K3M9TZzz!&o+@o*9DXXfNWv$J
zF6}fgZuu&)OZt?@BP5JC^82ce8aG94Lw0TXS?+cIFH7%f@aJ$nxcDMtx$Cw0yv?qC
zh6hUw{f9<vJWK|~{r)qlC;X!&wc{?)u})O<d6mScQXaQ5ci#oEnTmuq@2{52xAcGi
zD-f+VS+jP>x1|;&Q4^*x+Ivl~=s|x*b+SUq^g!uHP_+Q)k2gMSW@-Aj0Os(nNWNQ`
zYy{t#rHO*CBg&R+@kN~!qe5xY2K?K*)??m+|K?|C8CTz2ZXAEMj7gG8rpR2gJ<%F4
zjrx{BtDO<bSys3O1CMo^v?O>U=Jo3AWK>qVR|05zzAoLk=9(%d^(%qt!4br8O6ros
zoTaINXrFxWJt_BRQBpnToaL0+C$XIKp5kn=v>!A$w(ltrpJXT#O?KUlcV><J+SP_3
z<y$0{CEp|ZRv5r0HR!{V<i0NHS0TY;Pq^tS^I>t7>E~|4t3>-6yvLbwdbWki1xj3!
zIAbNceKx~&h)f|k)EfUr=GcEH1qRak4D4Y429oLQ;%3Ruo0q~a-6)gKG!GzdeR#N&
zq>CLKSc!D0DxZlRElezf?lPPJAloB7x(#PjAY}z?up}v+`Gv!HAmlhYBrncC_HA`T
zqU!uVn<dz~qU|JR#GB8Nr9w_ToU_<D!i^wl4JsDm$>e($<#vg3&A#;uCyN#4wBt7D
z(HsMEnG-w6O;gH0aL+180OtCHc#&=OmVJ8Uu?-U3PCIy-E!@YL>@OI(_23I;o>o5U
zGy6<xc5bG@)|e)KN0W^@V<;mB1O)s<eGfcWKAYw>HYywYP&!t@kS5<2$*=C*%(b)s
z!`MvL-MR|BYbHQC34%P7QwB_D@>%Sc!?U*q2Nv2dIpHqP)60My5#(SO*|mM(Aq&om
zuuz*g^F?jIKI$Vms>zraBYyoZK;vb5aj_2_)4VLMoMmal5{qT}WZrcV*BN;KvD@F|
zlhj?Swd?F>q>%fb6wh6$d<mXAQi+6nNH*Ros3=tG9bt~;RX@>Bdkj*19!tXqn3mz<
zrPwlO8QjbqIK`FunzZRm2Ux*nNPObG6P@VYU+NdXqmof*OH1`eMzdMekN~Cgxq#<6
z>Ej89c2wpKd@MB8@g-A82wv!Y5UdHmQIH;kpSq{bDKv4Xzb2vmAu=79>A?5Y|CX-*
zDIWwv%}VklkN~2W0JZ>ydH1K6XlUeLsVMW^VF|r&XV}!asJdcMV9a}iFs9hZ;GFp0
z1*Xq6e;(KM)zQuBj#inoQ<;xLTm8(l_lCW;@k^_;NKI=gUq=%*-Qoy;L-$*QlDDMQ
z3at*hYW`G-nZo=F#>F#*dHDV#b?@ho)krAV62qtU7_speT@~7Uz55uP@uBhC+!sh|
z(y|`=edkfOqF=-#(B?sF_s&_1$o=&%tYbU}9_;y9x8q>kPtx&#B&TS0l#uV&VJl$#
z4f-ztaUO0t${$84aXJZddxZS`THZcpdWuu<wIyi_jA&zh&x{nGoPX?MR4c^xlm9rh
z${39`gt~@G+Kt$G<HRBIehf*wD!40J92;)8zrj)_Zd=80IRqrHw)5al6kKw^bsour
zs)eDE^J4<+&>kD^+V5C#eSRazV9Sucan>D-W4CKtJNZUid8k|UMBi}3V6I8Fk>CZE
z>&s7+>mJS@?}H~BzvRQ#pQGs|tBoZs5g4mt&1c6nu@Zbd<J5_3D_TvPp^cyLWoeC}
z2wJa$sw??uVu8YZdTxO?r-IIXPC%Mk%>;jj?5+;ld1vu0vMn+BB8g#Ea(aRH(9h1r
z9T+d$S2!QKRj5dxLL<ruEy1QUqq%lh`G_`z07X4MscGKP9NIpAX2)gPtGkbw_J0r=
zqBnTy68yBv_iG3?wv--jh-(Nw#wB;6Lha{AI&;Wh?^L+R+jYL5gm8ub!<p-!V>T0r
ztSE#DGKg`}xAVKPi?_&QX9=>%b_RDJ!P~D8_X7d}dfG+Am>T1Qc_%L2wal@TvCLbp
zS}9+VwQMJgde3_<uO5a;(&XHEJf^<)Gc4l*1Mr;%7rP?=A`pCu_!l{)cAqEQGitrX
z_m;KA^ZqHG=eOw9fB!mSFN7%X@?pDzY&Y>=H}T(r`B(ZbtKxQwGiHGU@u_u{Vg3o%
zgO~ZIL94sWze|S#cF+u@sbn%cNA45laFgg?K0Rs)Oq{WrG7c68x({ScX&&7`@_-1W
z5KoS$Td%QdW2z~$-N&rUqUL+On@J8q<-`@1_x1v!%@c&LuL?t5?(XUR+FFJTZv+fi
zG@siwi1TXMwa~wBNvqCg@L6&EtBP12n+toonvZ(r+0wtNiZ!up_UjQ&B@pM%{km_D
zPMhZSA)xu-Yd0wNLNIrls8`l8TP_{~#_eG9#@Ln2`ivNaA%Aha5^a8?>(Z4jExTCW
z*FY7}n2nF@4}DFKtjK^fE8Plq(f8Yo?kCImL{eq{@Y4@ILHgS&Sf8!=EIR&}iNM!i
zn4zH&=jsSLcY^?~5eX&6_teH*r;iZbL!4vWlB{dn#aF60_BsT8_!>m0<+Jh%7=a>6
zfyId!pXMV)r578I*za$MC;GgLUOTbfi6je`NuXA)&4PlUG-Ls?bl!{<kEzr!Nn%Fk
z<6`*(_w1uRjz)t^yUkjUirm@N-8}3WWsEFipW`TAKL1(7$=Fu9W-nPu;#u4;m@XYR
zxwLsf#?U4kV1wi2g$8|A3Kiy8fJn}&PIM8|ZEr=%><>fI_UY-E5ZKh2bWSH)okD7`
z5WU(i$QyyAQ?ghn%l(raV<7&KV`3Qx+rd?7@3w<bFT5S2<*TB?7UnQ=%<gEF#yck!
zw&Iil4dl$qv#x99tj<SblV^rlh;(5Ul^atJbw6q+ek41@yf;pqwzG?0qvXonZRj10
zUJua3UJ*B`?LTrW`NGvG>zJ%4CWMK<;l^Oeg3$s6g1_?q`+000Ellngb`Ihyp<2E}
z|9HTDsnSxO+fv&n{dmsuPueGZlfjRW$9Vg`=AR>0tJ`w)p$|Wenaid&hWRPT)grTE
zJ~7+AD`$G%!W3#5l#Mub%X4OY-QgI2`^so6{sX2^E=~VMZ4lI73kFyiD$)+BG_DS7
z+DX}wBSpeeo~G=JZ(+Z&%4HF;;GZi0At+~u=v?XgtQ4nx?XXe7T;+vtKYy*tY<zUC
zF|!cwn>X^~?YH|VrXKONsHvkfH0(yqf<adtZRUxDVR8IFMpE4O@%_ae)q;tnxSi8c
z<;FROZ#O}CjKRu~g2+n^(Ems(tu_tK%RO4vA=7*mTZ3tlO2D(Ymc`VOu`Q&z%5_yL
z_kgB*rwdv%@7Ai&(oNI9R;sU;rb)OH8$z~&pe)I^I$5nS=kB|1X8xwbcM=Tk+Gc()
zUEb08cs}ZUeqr+bM!6)XZyES))=g@Lej*jR&--CykW=bXi@8r(#5vd!4?n#T`t<Nq
zgSFP~dUk8+goT=n;e?FKJIk^<cGz1UHcq_TD}B4F<1#sbgp2BwNhC_vPwN$m`;A)X
zHFg8kq4%a&TCj?v+4U;HKBl{u??-~ImfM2u3VJqeIBphrtQ@Fh41Ff(btuF8YaC%=
z{DAv@MT$k}b+)~b#}%k`OWLX%#pdv$rpc&lu!F!jTJO|94y6!YgV68Ni@`ki5M{n2
zu->%p+`Yr+!An|9$6cD=ZS$&T{cGsck$oN9j`(!PvbZT@xeIuTMA{>GMigy{1$T9_
zBBIADtt(x!oxi>+AjUkCK@#ycE61D52|+a^;x++O*|*I4Yx@(i0oOaUblK`pCVEdk
zbKx65*nFV0Iyc>tu+~?hK)%ts$bu*3k1i~OBQl|O)3cUF`~X#;BVY(}!{Lc!rVyfG
zzz9V#P2bV0g8y}k#p&ZefsV|_GRp_QjK36VRBl-Rj+lLDS50(#a7NNXetquW{=K_t
z1oD8nsUMlEh-O(@68-o4B=PrhYi6F7XOE`6scJv!c3oAy)otY{UNx*SQ9@5Jw3gX(
z?V;W{YYc78iRL{g9^FGygb&N<Nl#-Vt^0$MZS=^?m29eI-OZN2-CHs>zR6De05Cp>
zb;1ZB+IFiJVScxpZQU)oZ<R%P_u4#kt*>_OF_m+@O0sA2E%WTZfgg+d5U<AV((kj>
zg+qm{hXNm;sC|Y$c5q{EQmy(6GJ;04;HH8FiHLs?M$nMR*L2th4+N+*<q;4)2f>MR
zBWRb;WZ*9F5I;P(c9_<aiGCS5OzUoYkpFmSd&@ai#YdQRtGe`ib=iIni=hwfH@JU}
zTHfb<xmR!$x06OdOlrXOLzn9(W2^nSBu5PE4g3-bKjvlHMzQIlYgo`bZDM7lP@!3M
zgj_${&TUW7E+wsXhT9V_JD7eG#z_4TdnYAcJmY+M&{t8QZyq}=Iau};5mT3f#*YO&
z)-6Of{+v9Gcd%$(7o1fh!c^`kk2X!M#pmOtJF0BvUj_R`ynk^M*Gkd!)GL78zCR!E
zX8$fh1@agP=Ml2tQ~gU8J%%8*I=?B5QpQB3=m<yf>xK*Ih70P2^TT(C`K($}j+!I)
z{=_w-E71t3iwZA|3$ugsv(KhZ>!S?;h?mu!u4CeFl>FpA?x-ue6%&$CY3=D+uS0es
zDR()`mW@QU_ewVq7@l3&L)h6Pen2~5e&g!sx-p;E!^z2sS3*bQSzWw#<hHEj&|ujf
zwn!eiw{t`=p^e)SN(`<6t*%POf|zG{#2_Tgvf;-<b^3&irT;((Z7&7CqhS81$l=-D
zhi7*lx3JEyb!{%3iSr|i8fXm2CV7oTjrio;i@OJS5c;LRq34n`|Ncl#!QhXx`_XQs
z($hZsZs#q*h9%BGp#c)hY16Fc?(S>unaNlTCRA0la5>J9<k6)C<pa57AA|#1Zm>RI
z`grHz92n*J%mMKrL;o93{n;K95gLJN8wd<tFjGI=)IR3Dj$KrB?lH9YuJ%wLat-Jg
zOmKKIg!2?6v6)qzmDalZJp5>wWT#3X2DAR*i*M7j@tQ>Y;zTECbjANtsl0r=Gp-H8
z-x0)m8E-M6_p0Q?duyB{)c;R#l9?GRV-rR3;be;|!!&wq`Gc7BI}lP!l>qJ_m9coN
z7-CQM=HZGww2G8(t1%89IuvK<kGaj^&ESR~k9;|rYQdM;yWGidzfd=KR#H@&gk||K
z)**?71Xd%^y(=>Y7?#js0R0KZfzD|@dHP6R)_8fkW?q(N)@f7pCuE!@J<K7FHEiK+
zh)_H<Ke76T<{|K0D1FO@UzF<7t>EXk?%m!&yKM<dZH}BHsP_{P_2d}0>nDUTxe*wP
za(TN_<IXWu$(|!P8a0*4%8@(-P4d~E#r_)EH}JUQ<a|iiT)AYYG7`uU-MTG8M)JS2
z#@}^3^AEf}m}S0h>MfQh&u6x$m#g?nxQt?5uQyr$+EQWiY=z0ex~A)iMdY}gUXkRy
zL^rijSJ^U-ywXzoi{&_;TH!S#g|mG_5?}GlRe!Y0IrE@1q<I6@&d?dP<yjhZ3;PeG
zF6TtzuNN92x=C8PjY0LQA?wLL&9~k!(J?K0bsWi8KbWt<kgvg{@z@9BCp`eoG?Zjc
zFL_ZJva)_1bz-)Ii$)BtGFbb)n1$T>yTnp0o45hv+%IZf3FqdjJo<$*4S#LyW(noZ
z<jlN#luwoKSU^*N&g+{qk+(A7@5ee_)?^j9l#$z^*KdbvaOLNhQrvYH4GcpSx!%@L
z^z*g*B~9^fi!Qt=T0hPf!4;uyTjWN0s?xZFPvh%>I*lJK%=6d(sneQ0ZsPSG(=R2;
zRpr6yx*(B=!~f%iXQwFl<|a6QamGoOrnxZMZyw*U28t&YoZG|<r`bv1XIe0yGLLYP
z!GPUB$7J8<r4UP<tl70+HUm&Ub-6qY;jTyC1vVYA0WNbIJ^r>h)|)LsnAi~hhO}?p
zg>w9#N{7f}*8R<a{enxX#dw7;&(G^c*a(*}se`1!vs{MJE;84d3+KCD(Y&rzzs?pR
zBg<d2*u3)xn*%v!FmqRr-d>aQh}eR6G=`$o=}W$*r+d3ir!mK1E#k_mX`yxX8cXD|
z#-4(axVBvKk8TDNsTO>?NsA=@e$V8_7(VfwGJ&oEq%!*|d;&B~Lh)o%m=pD@j;}OJ
zVZp}5SI*a~W+|5&fVc0D4$5Zi*mEF5UjW*6s-j)K!6etoAGyR)7P*H;UKP9?m5K)T
zHnzFyqy1vNJjA!LDFp5I3A(}9U~w^Vh~syh_9yTqcL`XByc_pmmt~BezOntYY%*_G
zNMIK|KCaa@)5TpT(U$TrHau49u#m)C#|^L0j<4ZN)`V*mwG^^6@mYzgb3m8#mF{AN
znJ||;Alo4KB6BpW?z-npbh82DTMVn$XkaVYledSE_J8EPg<DnY7Cs6l78YR;HV6`;
zbSeT05=ys7H_|MGX#ra)r8}i#(H$xR(%mH>u~;m6ks^0Y-0rjY?>@ih{sDKL=OBjz
zYp(gt?;G*H?-+wA^XH6WhQ#SH^CxwUhf`=@+I>17Li^0wW$An_!!u}=x|2S|bJ^7J
zx!}nCGN%#K1z&~V<{vVV256aq7g~uu{=g5k`DpjDNc(={Io5AtBb(iYwp)Zke5?nZ
ziIX5g<%vU^1qm&@JPy)khqubG7}D7qKzMiw1`d)pK(!pjP+d@eWbOMnG*^v|`>b|`
z?${>9p>>KQ7=n?28nQ2}#fdOg`%1{|C*4z@o#^r{AIluIpFF>$+aIXcA5cx0Ws!P+
z{czETSm<-wvt7Ja@=m$BJhKO;m!=|&-{h=Fa~=g<NV||0kfYAnI&`IF-nSrD){pDD
zw7*ZwR>hJ@^K!&;gw6Yg2krAVuVxz94?BG0BE&=@Pp4-6_@D3-uD<HOS}(I^UAIsW
z|Fw8eVa`zHf_)u6wAty12lbmxSqd^niMDti4b8H6yO-)AB{BC-4B(gJ3Wn>ek<Vt@
z{8rREwgE?-oZpnoMz9SNN=?#Sxbb^GD{)+*)$wHH6d`avAzTg7G}$_(Ltr=6`96?W
zRZvJ<$}(ASwdG{*Zuk%TE@m=?^3F17!~2O+KA&G7?5U4&($b3B?T)@!O|9SE(Ad}s
zpkD^I@|T)?l1x{YPSq>q_FWPYJN+~6OSCc*R6K#|-{oW~M0xkS`3}ZO$W(M>Xyi1%
zweQ<co<P^!omtgq&C_S!u1)1hL2DszTO|Exl&kA))^)zP1*gSDzLQ*HViAlI5+O*C
znX-nDPgaUdo6W1yWQyvS<`S-tLi$<c%!}xk3w_j8N{f7$DdtuIf4Lo{JT_`S_-KVD
zne=c~?;NSoyw3C(iS4r&0|P#NiKdP;HUna%dSnBHu<BU+=~4V?I~t1~8wx=O3h%ay
zbx+Fo$bA(gBXA0sA6h2U4W`QBa?6pL%hY^2WnS5!)S_ADSv62L_h*qv6vZ;g!lc|T
z7WHpRolELKQlRqb;Vr}F+q8v!W+p==`D5eb`7qfNRH~hJb1(rDnglN2c9_#RM#ID2
zD|&z=Vqa-hih%QCRczU-(=E$VyitY@RDraa1snRE%SYc+2i3iyen*ze)NY{Y7@|1J
z<$0;<@ZSCpI$UDzs+5!#zs{bW+b-&f9#s-^bJ{tJoE0adakb);eN#fa-|Z!CY-+Xa
z)YM^mo!VQr^&dEM&f2Sb?{V8HRzI8@k*vOy#GI}fWg}MmlBxc^Uhogb!y@6XRQtor
zw{7=-EYD+R+{7JL3QnH48X%MuMP1j^y!1r#0^4W&DSik3GD04~-b;jw#8QJ4|M+hI
z+^AW2*CYZ(NFOzodP}{8$_SZ+jGdh3#dEvb6x5Q%JT;MSE#umGikk@5UL^}1u^%u;
z`)PKSIoUjL(^(0<WjF&(`yNS3zPoarpI`m-wTG?l$IesG)6=g!+bd!hXz5^LDehxW
zU)NpXr7_V@%Vc$i$%;qui9_nAQH6@C_e03DF{hVRstT-wv}K=&>@os{)PfFCj#2Yg
z^;(_ow4WLb^6zJy>wHvNlPzgR5nq(dF7`&1QYV+`Qe&!>`snfkd)g&4Dz;k5PG)ky
z0kgx$a~TH>6hpcmr)gWpP{<fQis^T8Fk>s#ckZkUoYk`Lw-0!(r6yPQ>G|*n<)T~%
zgtzlUXXTHV0V2OX$CNOUYda6P^$VI2D#r6RLex(Tuw-d+2oM-ger07;wLfCPFX@$_
z*Qd;0mKQDaOZuSIsT(<tvkIEsE$z*LiX$$Dgt099I(vP>cTJk_<b`g`47Vg2f>`~f
zxm5ML{<<ZxaQ5#WjIg#E=+YJ-lCrWrP*`lp$7S4pMR4ic<wRYjG{x~6Z+U2h&8he8
z#8n0cDZj_^@<eEAYCQJ!?dNkGBnnPYYbIw9Wl!JSJy*pm$s3U|<dGX_&7+<oVVyT`
zM9%-*Lk^Co`T5#p7k`y}Q;ij)Q}JBKbSWq;ju02*{B$VXqdn<;_S7LbMkZ>CZCeW3
zLgy59+hNtQvUt7nSM3AA-O*K2g7TWeTh<g5?w=#~$NrKRzk2HmZ^9{lnP@UD=Yf>|
zQ5HGPOD8m!_~wMA`8Ave?v#2(<_+!b-LLhki*DS`RJq8RYt)n^u+|{k=}k)c{sb4o
zdjiV~O_S+<mIzIyp)u27WYBnka$h0ks(G#wwkN#yu+R1SQxi-!;{ZPX{gS+7q*YZ8
z4b}Kue-@<)nDw#?{#>X^=(cQ&a})ZxAnHxS+io`tk{+ORh7w@=Db1*m#xU_lsmBa?
z9C5fhqDgUO#N|p7<HM+o!99k_9x8ZFA5VqSnwy#IPRQ1^Y?GcVN=cpGeqCx(dpXFR
z-)d%iI@GZapX47g@0l$-RjOMWEY-~hN6RkwsIqdF*}_hISPYL5p~a_j|J7HG!+W|j
zqpMpyGe<k$X61EIVuB*H?BUR?l-c7h&W+LLHtCmE-^$WlQnTOVM3PFDRz1V{f^b+e
z*e)~RJ!1dN5q78rwzh2ctESqEfy-s)$AX(oK%Q`nNPqdy8j!p=MHbfcL?%fdw3csi
zy-*lNuyIH|d0E_Ox;0j<%rW~4Zhdil6D9(o&Q-Zzh4jK10Kcn@lRxP&j*KdZ+q<?|
z{17YkqpwpkX>0x=k<;f^#FV9qut*xoNf0zj{cDS&Y9%~^hqD}$%p_b#o7;U}eVJ;0
zby`13lY{2!mlkpxk`j&DcfS1ZGI93ld0F|R36@xW!?ROIWZrx^Cb95qT*S|b+tfo{
zq;K2s*@b@N4-zWHdhh#}i=2{-eOvQzn-&+1)=IQC7rU_%3ngFkha76I86n4FN|=Ym
zN_oy`T6>saKO3uud|?^Ys_NlNYodr@Jv+nNCtGJ-j1M~1ooT^u{C6M;%aXi_E(r7#
z?tq#`jR_b-VY0q3>Y85;V`?<-*gOckse9m#4M{zV6(e&Ct4GKCAyt}w`>=X9;m92R
zqw}?%4L*;3_>Uodv@(=Dhq0P+D*~`gFNZQTerA;h(z-7ZWLj3qla5~QMBZ_FNRx}D
zikxJUdo%kki-IDMkg5A2Xis(j?7L$&Of{2oUQMxlV!lRcqHRHY#MBAza(XFMSN_W3
zqoBXo<;|{8I~kDA!(M?E>6ib0RX~e|v4yR_t&~yD$6pDdiDD%erk+<K+&Ik3FSRdO
ziY}MhEt!^yPwSG3rb?2Lgo*ON=~08E;1l^9VUr?uIz<CEYbMI{b2{dsue-Fc*mlve
z>d}QUd`a`koC9(VDtgyBY0NQ2(aiB?x&4h3adP=hCd`Alk|K>u&ou{YC}#@-Tr<ej
z@1@+a-qZhg{{wWJ$??6K<m{d8%axvZ6feFp*i@!B<VSTwB_=)nM)lXP`^=t`yn=aT
z`cwS2Hwr~+Z3uip@-e!_`}u-&dlm=x&@wwU5(hKKulh_3M8-ymQ$G22E$PdT_lh!o
zgLK_vHdHM(T)Y%YaXuNzG9ffMb)%`u&k5|jh<<K}ud_zd*Nw4pH{qrpGG-Hc3p=JS
zV+tYF;LQ|@s#6xm_^4V;DTUMBMRmkqnl<lHjoipdr9tSAQYP9G0*NCDD;7h0JF5Bb
z@eAx}9QNgvI&_O)QX`k=g~rcZJ@(S|q(a@RTMImsR3TgkKMIPrRl5WnMM_(8^~eTs
zNSni3;mcn-NaS?p_%#M@aLJ$Phu~{WxBB9Y-_+3SZvFk`4TGcu)=v-4`n@#kXzsNz
z@8kSC`$w0}gbiIbXiY0Uap(1cjiCaVhiKA~=<Vn4fAzt}#s=D)PKKY5N8)oL*%eNt
z&V8Ax_qo$adaUPV>RR6-Il0N+L&o1m+4UUwq-F=rS%dRM9q-i9exh~be-Z9*(1D40
z<k6XPE!os=RZ{NtM;tC4Z;Ouj^6pE^s5Rrm)zd93<Zje~Qc=g*__Wro7~|H96Bd%v
zG;D9&y^0^8)X5HS5bJqnIe*wwz3feO`TjZ<h8;tdva}rL6=oa8-UZ~;0sE1gB|l_Z
z6E$N!`-|z>$d6Hb1T0_G4*~=GEZz&x$KsZ4Bibfrr)cXPcXQ@mYd<c->}Jcv*Tqx%
zZ2X4juhyEK^WA8vf4|YvdV&2IiK2VqcU$Anrs35F!`c0Wq{5QS_XX;GCfS%b$x`m=
zn)-#G9E>koVy7evvftDpCfS=V{%q(<{{2#fAokpi>(}cP=3rHe`yB}VU6i-kD(LRm
zC~&&#FfxzCHfoe|EFZ4wbZa@{fD@X-XgU6LjGD?8C>_6de-JO5q{nmhY|a_?bnE&{
zqDp74y7K80d6ZOom_(I4M-Q`<+UK%jEDm$4m(lo(BHeMCd9s~00Yy>q$<be?o>6&>
zO#~z)`&2((5h$B>di1eqp?E;H;q5dQUz}hiJ3TfP6n9hY$NR3w0&Ayx^3*g=KIw7t
ztX5q~QaE#hOY+S-&%Iy8-@bqL%=hZ+Du$Uz9_0(ym4o79Y!~w-wR)^Ck!g1GddmDv
z2|jwWL7k)@q4~<{l;JmOxb{B(b=Fyu7>eO9-3h-|lxn4nO7y;${NVN7v>-L^m>aMt
zRVkg10cV@A|C?0zWyik}7j)G`h>!8FCf7!g%9$Q4eL%$xD$mKLaO!}H!HFL~7_VNf
zwoO+|Q<^Aociv`EFHmsQc)?bAW9zP||5P&aN$Y|~6>Dh+m`K*rs*6eJH`kMNf;({U
z<z>#wcw8ql&pzXKxT>}KgB~TV|4}#9qg}zVn^dCWA1{iX^GmQgxHP8bcd_c}b-u@)
zIgDmEBFU)vJ#!v6$DPR2NllAO-gsM*IVD!>>7Y>%Om?l*;PItovjFP~o?e<w+He*m
z)ve(W$%Pm1MC+71yC@}DH_CX9YFa0mbl9v#i|H7qRhwYu=mu9y((KOi8*Js47MQM?
zvBjEOy5ubXE=>8oZIV+>6ggVikD^^=ja=L~*!_Znu2r#XOe`!|aT>RO)aWsSa3SUS
z!uQWFypAz8j5UoIyPM1FAfMG%$jGGH_L96vt-a*g^WFN{iDJF`eS$pZho4ew#|O0L
zZE+4d6tOyGUcic|@ANZ;AFgdJH+874`N}kSYOYWF?NH`WOCZ%4c_Ecnt;wO@ry%?I
z=k<alRq@Vp|CL*}pmrN_uHx78FbhmyYgvy*z){YFoEBkH4u?4#PF~}^*}9Z;M`UD_
zl$1gz-sS9LRV61$zI!95$Uc~rT-KQ-crc@6x`^3K^Jy^N&s^Fr17b=}f&pPzwk|6l
zS8_E|$G@r5*VJ&9qvrdgLY^n)*-jbS9QU;-tGZQHRZ}bRU%5$%maQY>#8~YJF=1La
z;iG4BsF7{>xNns7D{FySQQT_^5Jvv)S5A7o3xVRVSqrhwk{G8t%?eYFwMvyoj~-1_
zh}+uQhV}@Yg~Z6`noNXBtZ6z_dT*`GV;59ToMBc<Cz~c(+gK;`zR)^FvZtQORL7`f
z9ig%*=8@9`z{j_EW>a25Nfnwa3a%l175$+zPQ_Z!wsK|yX>y$fkxf=1rHkTiqSpI^
zL}R%;;`f$MqEE+zNJsLb|0O5=pRZxQ#0vZS4;~yc-pOY&vdC6JIsRmjZpYF|CNwlP
zaRy$#bgACO8)m7<^D;0b-PPUmQzz{ef61P%m(DRodX7bTB-w~Iamc55M*RjIK}xGK
z&vnQ**f3q+d!(mKz$f@r94jHj8HoOWy^?Vzime)cX!qMlyKdE3I`IUXk<S9c!j*$P
z-x?a2m%~K#v(yfSDT^tIik%$z60MYS@16>WggZ;(g`BBHzYNc++V0cGJrkzOGFI4g
zk!q=;z6@>4(eF!dA{q|M2lv%9tPj3!b={v-PGBcYI4=I_DEaqi*pt$|zsc0Iw^v2J
z&&9(ADVl#W=i1~C1Hv4p+ojl8cDIx9Y;Sg^H6##S2~xp_efKjkFf%u-1fJjCT2p@b
z@ZqOS<jJpj6FaM1W~6iW-34m559VT{+G*8H;nhrO)tA)UY{I+X`)lF(M$}I|6{$R%
zRtA+;Ivy*uJk6#Qx6fd>+gz<<qzp_P%@3Bt8)}mKf?33Lj6AzE3X@yLxZEbbWUHj>
z<fxhDs2r))Vfkfz$iLczS-|l6Nk-P;M}344=7?FXp2zD`a>&oYeCFJ(MZem2nd5}#
zi@YQvgvWj8HY5}5b_Vx)u>1KxUxm82`me+5<M!#>*w{hQ(Y`(?+K$)IX-iK}=fFB~
zgxxldyCxl`FnumNJ3Cdq(88_u)u+Ee-ZYK)s@FVhX9=Uof_knOpU76iVBe?GbqCAa
zkWwNi`i9<Rx#u}3go%5f3%xtBfB$}%hV!I2GaHJR<cs4-pZ#kG?0(y|7rYb`AD<c+
z94=a^2M@0Ia1P=f<_3$ujPF)C{p%0ph$nAjKM%eh4d*gvQr`Ob@goOzxA^zZ@1RYn
zVhkY7c}<+@YEaNBFbf?qUxPNAKYr*b^9l%PfMEDo>$g9@{^wr>{e4ZQSx4B)S{0)B
z9WswrEu;~k?CL2KIw$s>xA(E$-ao(k=U)X)^iLpANr4zIbIFI6Kkv!sJxFE0E6fr}
zTnf3VyNCDhW)Xk>1k8d;#-XtD?xSlK!<=qiCSA~o7Sh#cWA~yFav0^%F;?xVIZ;%^
zBOSsX%i;d7IiI_45py^dJbEO*LkZpMgU88<R<CBDO}LVoS$bNT)#%;w^)XK*p&M=1
zZc&1}){%|&>CU?amHYpBy$EBu(Ycq*{mq*<a>p;~o#Qn5SWD;p&{uFZ1mBDHe+vkp
z5M2Cv+GMz__(~nlb_U+217-$gz|WP3z#HPBjP&?skyL#{!>zzV^8u>34<ddXw{A%z
z5`KIA{2m6EJCI&RS{h3XO_;;0*^gdM7Q8eaA(K?&bv7Ql(}(&Dm~maba)nVyNQ>WL
zPC;DU9rxVUHgB>q^z@SEcf;n0lx*EvciX?)w*2$iMHrhxZi?#GcpXwhx?_lM-e;?2
zgmLJ<L&4vUXC76t(4NkyDU6fu;xc5eLNgxpu4GQ%{!j;h-bQ2rcF@(hxVV)rWo_e`
z&Xk$1H02cH)2C0--Z@4ciPGnUo;$V%M>>*HOTEFIAgchMV!W|5fwrJuT3%MfY6;rm
z+Qr}jC@Cl?BzqY8efSXTc7(d5K0_`4v7VkDnS??q{Ealsj<gFjYz&EoF0URI!8?wm
z7d4%noS@fiVcPSWvxdo9<(NBo;TuMRv69A69-qH)FcE%q?v}yBWRI7RVMMAH^v*p(
zWQVBas&3OkKqYm{;j8@<^!wjm;U`$Z%@y3~oQjN#!7fguL*#DI&^<L6`kUfYlA<_*
zjWDDa4$5Vnao2f+3HHU1Qj*RTCSXYSks?`g$06LwaoMuZ0H2eRl2YH&qP)FH>?*b~
zJa+E(eQ;2*bab$1gLKYF%E)wY67hjyVX0nTN5#a&Q(d<=XIf)lqw)9)Y(lLseNTIW
z6d(r(4!rL>A0MT=cV8VicJ3qWmxFm&=)PqF2eQA=GMa|m;~bY+@*FZ$YWnE~>ER<s
z1_ykeG#>)L#LCUBjHaRRH+Db1ofNxwFIm4K=p6f#7tp)od~3xkqLT>w)IJiC7<7Uh
z@@pdrz?{Iy$c*V}BTn;v1=s+caMP(!kK<p#h4Lb?{=vaXoTlB3*RMZ-j%tr&WPE12
zGt$7YB_p!YLeOuv{05bj+gA1fL9OC>F_&3y%*u*wpBY!Ab^Z03fkG^dB%*o|8j%IY
zYkPIT5}li2DuiC={Y6oV^XJcJxxV6MAR&3>y*5KBN}hf0CdsGc9Z9m>@Jw{?FKzzi
z?ac|@XMVNt*iOqOdmJVa348{<na0O`g%Pf64?~{ZHt9_6v{UdqNk?blMa$o<lJ(?{
z+tyIFuE#UzbAR^a$&*@JIVv}57DlVQVUsB((<gNpKL-mV4wH<fB9*J$k9jjhIez)p
z*qBabvxg)mM+0V?(BYNu-u?1G6Av@SYesf{Ib6PWEwPic$a16t4&f~(fw?Cx2;GTB
zUQ&`1_u-9h0rJRu`R2{+#<gv@WDVS$7x%DZyhe}V&_R+5wn`al`Wj9Xf#R^eL-Ef&
zsaU+A!4ADR2DABqX!CRZ**FYE=RSAaNX~>llFyDq=-0Y#70y=4NahID4R-hA^jedq
zL21~Ee&{T2=(c%iG6Li>j6q>xCJ)28njF_hJ-lzV2z31A>3QUv;nJKVoUzF_pDr4a
z$2=@9F0Khw)>553Kjr5OyZZFP9+FS&Ui@=K@ttyc*jmmW4P6>R%*w~jZ5Wa2&1W~W
z-sKB}mFZm9NBuWXtbO}%xhDBh{Ld(P=vnOz4XG7yaMyCG70A{cKNjCz_dHn;`Sm=S
zJFB=d6OB3?gg^fjxS#@Sd7-sp=a+N5aYy0=re!40SiTb5Qqod#oP_L{N0p8MAZPAf
zCM;`wJMc8&?(Fd08)Z5fKgOYB&P@&uj(R}`cvb??AJTOV+Rx}djO5E7+}>v+LqY<l
z@dl!r^Ysk3MMN6_z|p2M7hX-4DP!qu?mkgHTE)JPM2ja<#+zPP>;HnM+g8X#@`Zt`
zIzUcyrg3x+iCn@8zDG7q8N14(^)vsybU2sdojWh>3H=k<L6VPA02F!(S-Xf$1YDQ5
z1qEvZg0l_t;kFo50khQ(90yulTwJ-o2u8=Oz)Efn!JKy=cyyVR%t|3X5^xsbJ>PkG
zb%Li02n?LK=>umo1sZrOTEHDC1i=VZG)zFvWNq=AH~Ro=-EzseDit(6o7<uU?}c+L
z#}ETTLXs~__!zCh`M2hwNn9SU_3i0Qk>CDxZvKfY-HCK`OBw;KQZiS$<LbK>4^{|u
zM5vdFh@gQ=Ls?^E<IH9FEM330w_LQM?RI{kCP8cnN-j5{(}<u?Pl~(*p+8?9d`f#q
z2RbCnw`UJ*YrwJGq3^sv^tKslK1AJ*-Yi#o{}JLp7?h{P%gYN^P6-wb_Y)eK6QLI+
z6BASYJnxXxR3rwFK<QdY2Gt78QeYP{Lnf$8!xawGs~@%%JsbZy#}EQLM#OK}as1d2
zj9zl>VwwMECrh5F58!{1N^qmrS}2mAh9Tg$R%emE@zilE9xvElXVJ)~%d{`&ssrs_
z5|eyT9+z}iV~8_nVK|+~P3KiJG^I(z*q0842qP6Y!It>itwBrf)zV5d<9Y7g1!m9W
zCp@-93{3mus_~_tZ|i}+k$4Afbly-(Mwba^)bphW<6uWJ`M?X+Fd)&-pLSxkM-&Pj
z=7-&fEU?zQ<^!!<O&t0pnW;F@CpZDWPRE~f<;s;aw+tHDUHoh3hDyDm8(NpH=5r83
zY|k;PL?7JU()-%M-%e+<qElXLe1(4*{g|A@bfU4yA{d~aB8W}A!HjH_3E|;CU{jF9
zhoNKH<aU5bga|qpJd%@plIjfY>vaK`ryH^8FM6iiF*PwvK|yf_jyHS0X=}{OMVKmk
z7LcG!3=W5@fnzt9UAhTZ#H~eC{Mt0dRwC>0*d7umn<GtEX8Sr6*jQPc(P;+Szt0_o
z^ER<ZYUm_C7uPECQP|(1)|ohSlR<&})g(!oq?FXHv9Ym-J4Ui$w^KSd!6S-8nE8;1
zj@R1v>?>??7If$fz)-r+!DNZs+ZVKK!ZRs?Dqn$Njbe{fspZ1T?WWE-=*@czrZdju
zXLjILrqdwiq@(w9_74cq>owz&AAra*rxV#c&t=9A(T8E`4p>j+lwGql*%EE3a)U~g
zsq7{mrbqGNj_1?6tUQt*`1<u;$HA;Q)KJTBudh2sSN`Dg`u^v{+=r9L2|<kp{t>iC
z44wBJ+Q_xfp39xvOL8{}W`ZViCc`ulaRUQ`P;>CEhHu>5kkEcXD4WwSaGEb)dkC|E
zv6g0oMcJL3(1zbF#Z^TmL_%&bysS*Xm$-%QShrfq`fuOZAqp@|MW8MI`t0tOkNG$j
z<D{Vxar61Q)i?}8yA6-^=@kZs^UG5$$5mP*)@HhNWs+(3HIX_JVVpwfuWULlGnEue
zF2u7blE0_(8a$!<Qc_3S{~{$xRx@Y{)BiCs5t!yZWU;nRvi6QSbD_@yoLpk0@TE(a
zyx`ae_CbfhT7c?br(gl688Orbs*F9q7)n!I3Z9MNST0`toag)?88r&}-<q4bz-LdX
z`NPoUIsadBTi_At__2+V{77LVmrPQ}87{MslqOwY!O2|g6PG_e(J;y>(U=A00QO*9
zy0_moL3r}67slKg6F%rciv9&#Fjx8<CKzOOmcVHePgBZxNEF_jz8vkMVb`TN@oW1t
zjEK}V0Yv@aWk{GCxnRQlZ9=<6Zl4#Lv<&RCLBMJT<iUhaU0|r(t40@isFP!O=eyEs
zBjlXY_mFV^o1tLwtbS}z`Zq_BxwJT5OKjpEQK&xyi#Eer^nDM+N&#u!O$HsJ#|8EH
zR+n3Zx6IaHTQ%TpOX`EQU5iU>agPnclam>L6JzwQ$huCbxepC*92lVDdYC35E4-c8
zvZ#q{6D9B8w+aNXFrnCHDyZ)o>I-9X1Yp<%I_URG3OG6pH0G4uRP$$$RWdM0++~uy
z&)ke_<U-#^kQjjLg=q#is@NsXPdB#geZ>hO(jH?7@Wa4-r9Fq{x-;#F6x%T)`t&U~
zl{He5*ev}9D*0mYD13yxj-MvZz_N$egU4phpKB4`Ngpb8D6Kc|&ufDSb3XU@T0W*e
z;5r{>#VFK#Q1~(e@em~?rJ_N2re^6(P6Mk=AT7B^E&hPR=Lnb`autdUW#9}3cBwf%
zQW;L8PMxv2JApuW@u@0Dzrl98^`dey*ii`li?q#QgtEB26NE{mG(g^RYS(c~`2|Ep
zWzNpdatu6&;ckgJTNsTnl<NlkDRZelkU1YL=wp0Kl%P6T14TM`8;Do&x%6pWGm9^C
zusvj-!{B2C>P*xJV#d9>H6{>fYk@#oDTu>2MeuegtU724z=*iOkdSGnc^ENY32{dX
z@%4t2NY*55;|t|5>=7=@ro8v1-Oj#l7ZQ;yn8DbqV9}KtIARWG-<-)%PcJ~N$Hlx1
z#uK@?XMFr}VF)_5i~=J`F%6cPG2hk1B=y(&_;F=kE-ocFGRQ)o?~h`wQr|w!-HR|T
zXl=hq6<|=H17j%A7$rEMK*wnkpl!Lw6CZ@8w)*<|`_0TqNIGr7Sgi3Vb<feN7nt27
zc%3?ZIv(=ijjmv4%_?>Qt%CtST<5^)ucp!0EpvWJSIw2oIKLZf8UY%BmW)kU@mxVD
z=WH>fZq!jLM93ZUjoL1C+(dewcvi=KxGWLo6PSPCAb$D9Zek$P2W|wXUJD*E!D6Un
zf8OzKms`85P+eOaUJstzwa^Yb0Ab!^`~iHf{hI56<8)^I$q6^mEDZuujR^p2jqu$W
zu4{^_Xk^#xh(#=v6sO+Qi?!0c@PG{xR{!e&?CrlPhqQ?3Xe=0;{YH9l!_Q&P@FGt)
zh&&$SOR)ne^aJK`WcwJMKv5$;&@NK|dvbG?kOuIWvJ*_cOXLr3m`UQ;%~YhrZa)Cq
z8hi!POj*lvqLN!?Aq<|Nq(CT&>fgS7Yto$_Izo7z-yJ;l=WaJ}NzoeIeh=LE9rvHZ
zS1_P=%DxL{$Vf^q@i*o@tv)PYjO_f<!qf6ti2z#Y5P13W<*p=I`pVif=|rI$HyU-x
z6Nr?%Yi?Giyrw|;=T1L<ydqx=z}|!jGOsnV4Y^{BttT}bSK#SWrzlRId}OlyBx3-c
zQle%}!+^4$-bd7WDr=Wfk&-AtCr9&kAlRpvD#4piY+7}PKgsm`JmZ;9M<Pd?p;6qf
zAVlnx*KFxYSB)>7E0KaJBB=nQV<9L<gGY$r0$^qwC6+tTxvB&~mO#sRf()L_ZMW#9
zrKO)GF9q1xWa0d|<9fZhho|#1x74~J7UDn-es6X$AB6smv-YLhF8fW(v43t&c*FJ8
z5H-_r;3u!45!{0ZH)&0~QWc9cN3{^hB`N8s0fwyxYh=b04H=_;w}ZAJv+2-qHlpi+
zdp$HgJ<U<MnR^M|6zuUnzYbg{TsryZ&*u-?L9t@D+{CTCUh-0l8a0Q5gBre=f3k!3
zgRdvSYAAvMM1?^_7}kvrRLX-<?fQCpj0_ACs90^V*oOH1_3>0u4|2?GcJ0GbH>awS
z`BM6B1fHrsYUlLxqyB1#Y-*<!q-;ti+mK*uTGw4oZ0&#!@|<Afuteq(E-r}FAr{WG
zc6F_QPROd)(Pdz3fkcXoYdCFdE{Un}P@rcFFNuF%-fb8%#RPqU(;oO=n<x>7bnW2;
zoP+~<yQf$9=fZ0$7BxhV&wj|RC$O)^B@)tU{o+kq?ZV@<d_R<h=Lj;1*mkYU`*zo)
z9pb%&5oAZx4>m|FxLnXIC4A}0m@essoXE}1t+*as6$E5-#8>}VtYV}g81OAPG6EK7
z*1%&+_+OK5-<XQVLckxa=4&^CoJPaIUiYJC;~-ESAVv*a6yjl?E-Ka_emTNs1S#|;
zoP?Fioo!hNDwAL*CINg@-0ypiQ&QW7sF>1&o$hCR0R0WvV7T?5@OkXCAt5k-Sn5{`
zxa%k5<t0g}JPZn%>?4XKo!3G`LldpR7SwNhE{24Ja3EMHY!N_5*`i(DaJ>*=cU2-g
z90SGJ+&O#}+Z^%8;llv{9{`&0Vs$)2+8BHCw<FV^YK}x(w`xpC(7PxA97p~wpSgrp
z?FN;YghcpV^WpCA|D-aE5Mo@u;0k&QV}Pdo4)-Ji$S5}zjZ`3LDglHr+ISxN2_xV{
zw3Nfa<Icb$bfB?&K7F@~Z2A_-3%Me|9=cNG8Q^(oLSP*K@rxz*vmJMTKkzeY9(0)d
z+#}C(jU{XyK5wQA!djDi#NXZBAF@!ayYKvbm!M)bZ>)88JTJ-VHN;EGRV=U)iHnbK
zK)ZRf+weugRK>J_u&^0EDtdY>tVe_4z`M+$IebB(>47+UOP7tUnt~kE6~zo%H?Y*#
z*ZW<ugG~sSp~|OB+H}q$z6|07?g+^X5gyD+vhhLI2b&O`UwHuecIOwjBSe4?aS%jy
zKn9eIdQP^o1>{wMSzjEKRDbETfRK9g=W<K=YE_ALb_Hzdk5D@+#jKp!x%arhh43aN
zgCQ6(jDLBMt`){nJTmyEad-%WaJXa-?<M)<OnkReZTcqcHM;rXh|2R`x)e9qV>QBZ
z`!`iV5|ik>@cAGSK2ewtbdqv50k_$L6i{lGmJ^l;<^=Ld(>G7h5Km-%I^Gvms6B8i
zbu0s?%aAV<bWO$7%1#a6&;gd@rCdMTO>?ergO;ca5KCYL>Cc}}+xfLt$+tC|D_si~
z#>w}4$=4F?w%xmcUcSnZiFPn!%f_;A6_o;lIt26rN@@!s;EXx&?k45Jp%p67RqlH^
zMr?D8^KR1}@JMgBB1Iw30fI<NODnWz9|;LV8L?X(L&)!WSW5>@AT;g!^j-5|{}U=K
zoJ$QZfD}ae&jw{e$0(tdni><(`ZlFArmyaB_c@Ko?#-o@{SZwA6f_Uob|BIKtcir0
zXDFq?1pA#3d+wneeNQS@jp<za?(HQa`kRn^O`}7{ps!CRq&^S)0OE;3ksr0}r*3HM
z2zHV%RH_Ws9FdR}Gk8{CdVr4hLhT_s7$ZSTSOLFXw^EG(ieSErxt$t&(gaPm9zNAY
zbfxtz9r(7!T&Lb|agvgV#6nv17)C+(;(B45CsAuTex5f~uqCY8*J2Cv`9uCbeoQm-
z$WAHXD_0)CiRzGIE2mfxdX;}bIVrzFzy;6)vV@FV=y}j4K>V1t8%#JI3Co>RPG^HC
zbQDm$-OLzaTALryhOoTz>)X?I{l-Zt0KOhRe!S;;Qbb%^yo4!iN)$1;zOqu!jGgwE
z6<Wk>%Vn%FbWr03Nk{;!xvyO&p3F-YwwL|cPAv~V{{UXuG3Y8Yb6TS*@D|41>8d3#
zTv8f83vwbN0QDvI!iyeTeE16TtuZZO1iUm}^AjbZR{cCO(yP=Brabwt>+*@H;mcft
z33DkB@9Zy&ewXP99e6qqRs{aBt}*u{BpM8ILw%~Nt5YEMbk&9_q|NMs${A`Ck38%~
zb}efNDmd{K+We+y(f;u*BvuLV-@v}PI8WAgFsYVEM>g8X87uvW2|^}ItTxl8s<VE^
z+U_y_$9S@$4+|W(-r+(@*bI<8O_bC`2|7QV01S2Cu0x@KOs@xWRi@IRIX*6vPDZE*
zSEi%?xj9zRoS2vxlvch~G6dOt<>oU>2M(9O)pHloi~@<dB<Xr=bw9O6%%RiQ6eNWy
zHK~u&@I1jZ>Ex?*B|p0SA6MIh1r)u(gt*gVh60R%<r%r@TTy80@Oh<M-7>Tu-8duB
zY}yV(b~C-%@o5ndvO{L$)R~6*2uOihP#;mN)z;F_Y+4@tGa7-A{Bw=7ovh+BnPd&f
zPeWi;(;&A=)CFE_m*H=Y<X3jofB=x9A(*WPW&-MMswG3DYYb25-Cut(t`P^3N1Cd5
zd)R1{R8*5pQ$Kz{Vk#QguDlykive6(6ouvmRG!sS<BhjkZfokoxH&qz?o3U@H1+hf
z-A%|=@!T%^mLd4twLmlN37#n$g%(5B`3_hFP;g(q^sS059l&7~sH)gk0N)7n@um~(
zpI-%WBejOrOI&Z9n5V#uqOG}r<3bE>?Fc|!CIbZ-Jte3&Kw&8~+?^|pPGx6MK%b{T
z(a!5KW27U|I$u^&l4%)&z)mFyjynM8N@{F;*poe6?u>_|LGIzh#s`|WRPgqWjz!>p
z9ok(cCMJvlFm@$Yo1TO&zhsjoXVNQFfC%h*1UDu<QC0?}U~Ffj{60d^=h&$;1OXcC
zYZUWC2jV2^Ldc7*t&1fxa&)F5$b6(i6AYc}+Gqt=foV^3-h+p#|F-~-4$gx<6PtQL
z<9q?kLz1_)&Pk)8qnleK6@VBIos0-*I%9{AzGjJS8=5t)udn-8WNwv|TTe)K^eAlP
z*y7qbu*%)VOJ(9JNI51S7rc-ZVJ(@WZMqvP%XPuymmVD*&9sL1h7C96u=NBS+lIh}
z-DQ+RQGF(Q9;+A)!cgV85}hh{Iy;p$!EAt8UR4iB>rEWv57t<|Qa5Vl*a_!96s8s#
z%s4>k?z(m|od^MrY4tlw*WZCTa^k;{`Tqy`nCt^sZUSn}TvH+dMCG-aOnx?ALs?=E
z(TFs^x1qlN#`fe5W@f3%?bYx!vuu^$iI_!m1h1=aN4bTnHt#t!YPQNT0EUg%2AK@N
zFM?$Y$!vo#&X*6MRqn30o}L{LvpuZ+sfqUdgy2=WwqEV^>wkzB4Hy)gqbB=P%^WU^
zn)(sKP?gf0wmeJrT&XT>(tZAg;h$FLhSW1nflt@!pV~q9Xo`kvbbjetu2~-kq>&q4
zE$s=26fidgCWqPHN5H47lw#}ABpE%Cxv6}AZ`7keLwTR$v<`&dirs&NYQ1X}2uhB?
zTCcOGzr_NKPnZQj__DZ{93;1R6$o^1TMi4Lx#R^G4cj)c->Et((ue3T(;C{Sbe1_S
zKgOe(0Fp?nftpCT`1))u`%xDNHWPI#{*HPh(r-2Mh{CSD(Gw4>NuudJF)^pk#np?r
zx=c^h;DK$>oJf=oHvtYSq5Jsc6zOh~|FZq#gVT@~_S%|(|8ULE2d%x3O9`$Qkks;S
z{UPC(cJw4C9>Nht_$C76A3{wHvBz3dceu<c(Yo%KqNKI<dEt#8rz`g;qoao)cG5(3
zH<9pxL8c3F3ubMOz+`A(Far)^vCmgwkyx=CnEf|>SIJNU1+QJA?r8>@l?`z#czaaY
znnpuSorGH3niHf5Nir2Vy-+%^*T!&~GIfEY>yF^H0fhccma9e4=mC(GC_63vsFMUE
zkt=Osk*Vc4y1yg)yk%K-BNs4)tq?M6>Vr*!sq<SjBWRAbDVYY)Kgfu48o74Yqw6!s
z-811Wm7Ut+#DtJO^E2)sbTQ(y2b`%V-2SN@^*;a+4h%VGptkFVK3JIzvo$~yjUXAw
zv<4c~4pbtJ84V@cf1&Gl7#=x$;0R#teFXc;epZd5))n*c;Y{UO;9ed<+NTL>Ej<^I
z*9&bq!gG~2C8Etbz#ec66d~fjAiZEjxVZrVeDL7GCcx&J`j*)9gZoi#1`U^wC=z*N
zPn{+CUJXH>7Z3!Ze)yXpeR_;fN=!TlIMmH91QLK(JWqjZ4FaO~Y*M6sn6Y`fGBzq3
z+^i&S9#E)TL^P~8E8$B=RO`Xno<sAD6jzjng0LwX*#@|^ElVvUu>ig0P$GU83PuTi
z6be3Z19lmAhT@?^hg=I?0B)8x2NKC!(0MgLZKrrD>Q<MpuP-<78!oH=gV?G^t#-lk
z3<;Q~n=xv|BWb?8HDBqRp5ZI7bU$=E>o+1i4wWq&NQSVV7i^;9#{6OSqq<=K#X*4}
zP2jVge$q==eLMh7U3PAEVYMqOvcb?i#v_ocCT?<tH}yiC*uo3XXqUIhFDDcX98K!L
z<^O`H$s2g#&>3yOK!vt;X}$$zF`@t9?2<%?^qtE-AQh0*NFRAuHOihS8$Gu;nFrWQ
z4H&q)+>Rt9cg@xpM(Oa=Knv<NhTcjV^sVwZzBOx!eoP1e9&SfsW>RW?ZY)a^A&q$I
zMK3(8Yc}$H*?YvM6yW%WXjr@W!(TS6CNw(Q@;U4TaiF%P?EuL!hNyPEOBUH>lGzo0
zh!TMEMTH4;1kfHBZ%!-#$suS_;ip#yN_p2c%5*?5QIdC$K4?<hfm_UY0EXc<N{Xfi
zfB2wN2GBEI<$dT08UGB_LL6I9hEUq{O*1;sIh*|0S&pwEz|_?m4ghsk1G&J{ueG(#
zNM0z1zGUe3kEyBiu)%NT8~;skgl;~NmF13d96RKD><QW>wsL9(4iUy{9zyY(0PnHM
zm%~z>g{^SA%M%D@``O-6W#QjYCkwEO`1w9<fLS`!I`WJ=9wQ9e-A#|&KLgXj0ZfOd
z&U)`_I+2VC#FmfKpz#dE&<(bO2D#TSUc9roxY!S53B=Y9r{d$|ISrd>z?AX)6~CV(
z#w1(i25D(GtT-ni{n5m0vQg=1@-}Xoc#hj59Zk<5IFyu@Ch{Otkq$E%g~LnE0-!45
zgV;3Bq>HIz@vpzgu3o#w#KG~H-+opO0o(-lJYL*850GiQ+1|GKirw`3mCol^`}qJ3
z)tva7MI3Sk)4?|tVIxp6Rsun~<im%kXyozZ$3Lui9!`NXh{ts#ZEbH}2>~+dH)a9j
zY?3ADoW71b<{C70=IBd-+d<77XdIyPXKz;QB+?R%r4z)g&%XY{AkG5v`uf1Jb};RN
z&DAwEOgC>nYHMpl%SwRr6c7pz`Ur<f8(X&YuBF1y&+j&rl%Ozdd^EV`>yUP%Y8I3~
zd=6GC9}rW31GWUQKvj$GY(RU~UH&lNBmpudK;RHflyHhHRLejOkX_x?;PoNd-94Gj
zW!`TD(G+D#4cbNu#pN6Mev`vj$#lNaig3R7_t)L!`5h<%SB>CqYFXnUh!g*n8Tm_~
z9$JM1!3)yDypbMPNCPFe-!<lZOiDU$7-<(YvJIP}My1dK(JcObLX*v2u7@?FI|^+2
zg0m(%FUo6haL{_~e})AIkCWp|Rn@juCB$_<vQ3lO(zj4(q7_twi<=n<6?70b)u7l$
zW=j%$c2nRC*N19>eU~+nkOjJY5G6&|K(|Rd4k0Y7gD|qYVXNzN=K@xXAWzz!@!aK#
z$Zn*O#2f{v3r-dGw%v>ZgimRm7{J;~#(ntV!(Xek*MLrPf#a6QRyeBIK-;px)f2jb
zP}|LVfbZD&Xul6-41sfqM<FYSZrE~%f#yjB@Omp>H=G;!+druqMkW9_iG#dQ87zxr
zMo+1Or3>)An?SHqoIR^L=lVZb3*~~<3N0dG#8(c-NZPe)*Jgl+=>(NXCwK~L;^*J`
z0F>!82A_)(NVnm*sAlL(m_jw29lWh6*dYwqbEJW|v4W~q-~-{?B<L04=TaFkR_dS&
zfEAl?;uSy!QHj-VG|8WVeb516Oe$O?NR92%rMt>nP6_C}gEJ2(rN8coM)?$f>n1?2
z9X!8C`r@$}sF2Xi1@g53z_THIPLT>`JtyFp1ymt&R}jKxxi6r#%G0YmWj<7*2r3?B
z1%&{(!3+=tq=|V_krL34i()w-M-t+tA?Gyfl>;)S=LPBB`k!vjPP;Bs!49BTfU`p3
zPy~P)3JyBNV0ZT(ILf#zp5=e?9|Jl85d->r1r|f82nYt4fQo>%SBw^N0S!RXi<Qry
zt`M1~5q8x8My(xH8o=8Drqh+7u8e9oAeZXOH&u?-u|>d&8=<nSES=Zj1@&|LZ0oNl
zC4;Dkq5OlCF$jb5A=pg#_>mDrMbDl+L&Zuc)(DU4KzcOL3S7GoA*TAhXu1&O@PG7S
zfBt>Q<SMn>A@<J?(0^mZF9v5{-AnQR?=Pp3Lq~`tCp18sgFZ|cR+SF^kP&tzVELV}
zGdiJ!NbWd5KL2@%P>Qq)d|jRGiw8vs`sh#)2@XmEAi|~13?K;P<t_fQO~Yq<AEMSy
zh*44j9>v<B%nC#F^T(k4OR+-ro&Tz|f&Y=p=BaZ=Jo(_Gfg7473fPLx)F3wPOc>M_
zq{@Bb&N5^TsF+t#O)X5Dh=qZz=;?ywJz1{uKc5bLCkFf5$6p3AE1N)`cL!%$=XaG4
z81g_cwtQM}7QuPVfI;goN1O{m#fBdQ7&jC0W$8#h(Dhzr9KRt2ZV5%um<mn0BtLxk
zaFGU*<o~?nih=3Kb6&Gh-T=r(4iqu3x`pp7GhKs|067Y2aW_Ojwtf1+p?4=C^uqxE
zVAm!_U3~Wk$PrN1l{oA?j}*_qsgd4Q$$&WXg;&BQxN0)0n#7#>&*eZm_^t8Zs>yxF
z;Vhs;Cpv>nI+R1KuC8tq^qhO`x4~+v0#0HV2yrHQcD&Ets>zdgf@qou(?Vv@m@^-N
zk#M#@kZ&Jec>hOs3l|k3e)5^T$PRo(q|0jG%wTbDx{^&d0%;;CZX7@nphkY_^Q}Tq
zpA5bxp}+}9TO#n(rr_NDK7uir0h!k#S<L@=5|jJL!aPKPN?|JZt@CGK2K~Q?A{|(I
zaj!Fv49){z2sBf@!aUfh?%Wn3(|%IHUbTD^eWn(;Z!r7E+l=0ahNi%tE2>ACJ2Nvg
zvfxKaq?Zuj-n~I`r)b~Osl)}PN1YI8N>4y~(}uN21pvTnYVxQ3&BvCaLaT+Fo6H;>
z?Fts48$^|Xac$qhqM;M((*_fysKH8czqt(9U*N8`9#r-JZ=F4zpJ8G_j_T|F^T?}9
zw{FQp&dNc{59sl>iD|aVZR;QRJF+1b-DO(;`zTX|;4`*D@SrFs_nnF1+O<^pJ=a2d
zFy|>{5P^ss`&S*y?w^WqinNv;fb@+c5Y|sEb9Ih5^gp@u?ooo@i!r!tq_r0)plXy)
zRxIu>?HK`?qqU6<vjFRVKLQEKu2>s|0TZoZV>EQZ4BteMaM}b$$|Qjq-pj%DfBuD{
zcE~(avIVLSo&es(I=@im2Ixb(7n#OurOGGkKph@KPRpl|$px9wt-xEiZb2}Z{le?u
zL~vlBq5*L5okxRJok-7f$;T{}GI+)~9sBVs7^GRLgFVUVF&m(>h<|&UL23FNluNXN
zCTuSUox<QdO6I$Q0GprKxBIt<D6GQ$Ee5#JiOI>ZddR;4TL*eGCBoldKKACHZVS5~
zJ~n9TJ=J`nQOC)&OXlt}mQRxn_li<UB}+i%yBzVvZS2cI2lkeh7Pd7`IL!5bCZhhn
zak)q{fv0VqkxI19%rb%MdDlf}Xy!YS1oKFfdjjwVpWeU?fO6E7sdV0I?)VyQ;}F&<
z2MzNzF}Q>YliWYMa&Dxu5=0xP-Y>@d%*n}ls9oGvi6Bru_{QoUJT#6hDCLk_Mi~$}
z{@w54YcN61ph7DwS5w$hW*OtU{3uOJjXrU}JrC*T+^YuV+{c#4NJ$pFq1pr1<FN4#
zNZ2o|$vHxyOmY8t_eaKemQiUe(1w&H;vg#rt?YTY{urpE*MXuCVfh4Ff03qIfb&HN
zyWhiC?Qgi-D--W1%GWy#Xj}4wRk>T{I>V3n5p^GjMbY<&Zuj3NL)sZhNqBhE*YZXG
zd5CmBWTd58l((Rj$2SY%#j$>q{8RuVNYDPOBoG$7?-!qyhOdk|wyV@9AXhVy)oG1;
zI|DHlw6-v;?m2}0coGhrf(zV93`inF>rwF5WL`@c@)-ysclqCcc?`Kw>mxLntsZlX
zWt%-EkC%T%voiDJ+lC#kf=)Whnzkl3syBj2e4xWv3?|DRLqb*t*+6*x|E=6Wfm5n5
z0XN7^g(GYXFi~)@1qFp-OBt|Tob=e%%$0N!FQ`(F!CXXO<Nd}K87c}GczIPJ6<IFb
z2FbMa1Y|V(O^p71Y(zv73@33F*q)jC<H5|lf&+%zGScI>i3Kk)78(xKVs0Dhm5!!i
z&8{vVehDJsON3^Tqi{}Q)GokffD<t8&C*HKwV7;OOv}(YgQ`S9l?~|YUatR~&I>1?
zp-6yYDd$qYTUddCZ_DNf9VJ0q3x=i=)4w+JjboS(UNIWpHZ)pEX>uuO%H=06wW!;E
zR`<;3*s!|b4*p$k0>t#OtR>Tz<Tn7ATS^;({19#Vpj-@g0p*uOYIoa3{INA83SWIh
zLrcb9PhhNO_-N}`7gP}=5An^3VVx3PM`yPInR|(0U&V0R2tGp=D=eLH?%q~ebNB1e
zl9i#6mFYaY`ftrxv<DDLDq3UP`EPJ(6SP4MX1agyC4iS2OeRp07hZp^f0wOgt~K1I
za0VV3zK|9tgy;5p+j>5j*gnO*qI|b1<T3u(eCNH&K^ynEGs7}EN}erv*Zepu24bk`
z+BbGDYjj+COv!~i8lq4NcW-A6*)im^?~vv;2H8hKLFjE`KcLosFxA%9;=$9U=iP+O
z`Oo!V%v8ZqsoHeGJgJYyY?gZWXH6L;d%RIIsBW$*`JQnV<1(jR@4(~I9o>pem>fdf
zYgR2TdR99)3h;eN^k^oe_c<I>9C@=Tgiq3+#Lv~4+BMZO=P?U5V0qyp3Jf~2bRy>Y
zkWT~r<w&yw-c@=6qUWYague&R8OCjy2?D90{#p=;eQXihdI-{y;46f|VtF*}MQMIO
za7wjA0OXnhrDYeSY=$N~>*Ky@KzdAs0sr~WE7_@FRyb24DWik_`q@m~=<T3%=FP%x
z(J6bS=eCa12RzQr8`;0SwjJ`Zj@e;L5vepEKO-od=^<k|u<&sc-+4nu-hwt6{y)4M
zy}#{^9xa3IYV{7=RlKKd&tK#vPDbitZ?e|_bV)u7?*AKv1}UK6+DLc!{)rS2;d*?r
zDYjfG8fNXLn4bqy62*7{A@BjOb@c(NyMX!|G=D+?hs&2m5wHrV+_(UO$Tr}Vzwf;H
z=T;ze@%C5$-m1P_FtaVjujg<s(Z>CvOSXWRu%$o@;x(7rCMdgT+GF%d8(Kw_3a`yX
z=W<<Vxiw<AO%|@`5YZ=7c-%7S8@syOW`P<@$x{(i@7ex)EzOec4#*r!u4~j#N-%;9
z$DK7@o!(d!v{gIL<p7?ki@va>%L=589Z)DPXn(Ec+qYKW3-aLf_`cr&nmZQmDUAEi
z&P6|N=ToVIL|g<|o5=kGf_EpcCg@M%GO^v$VZO>n${3{+5`Tr6t&N&#Zq-Q+2|~@{
z#bwgB$5yI_L=n4|Z$6adp~0P3SS2mDGYW+&V=L9TlgD#^{W)CU3h@CS2+o6W9YxiA
z^02AN$rAe+aa=PW{sthP4?vMnI@5W5d%q=Ybs{jrGk~$k0mF4d;@@>jsqgX{1%|eW
zYMR70Yl39UYU$8ehB3moMHJgy`)OWZ$j|)jO>*5~8kzD^t5Wgu3OUbTd_QtspW7B`
zl@uji8?i3w=YE@!_v{$HSEX{0{~;or8~G)@g4;5p%(_s#tVHfs_v{Cq0M~dhEFy|j
zR5PUO)=*Lbz}G!AX9Q?J1+ZVjaJf40R5s;gxzHv`9VyI2vswUFk_}s;K$8>=3bAx3
z6LNPvFbqodgZY(!<<EeWxDCn_QbC6Z8|DGbj)zP^4y6^Id?Lw*3^-Z$_j%skJTY9~
znhQ<aw2j{#%}rHK!=%!mJ~?yI2|55Jrb~7$3tzi_x%B(T%nybRGsr}Ap;1kkAlUEY
zL|L=%r6=X|2y;gTmk503!^j|ZQ`ecZ=QOpwVtns+v*a_B79FprY`a6WSXO%Th-&%x
z`=P{++$Fn~9#c*`yHu@ktJJVg@5A*xV2$=9NFHcH`4N;p2e7Lh;F1a3sG^3h0a{lm
z@`l}i7JB2TRl1g=<qQwpNLN5pHS_8SE2^cq^Iq*S07Vc>$AY@_4Tf>|&UU}NAhF>&
z#xfL?2oDrKb4(CXYNy*&LCB+{6+xuBnJa%LGzyenxeo1P+62S#{dW${^yO$79aQ<U
zDjJG0>&LK+IPqNQ8Njg1pGJnaq=Sj32@=hC4gD%&@;RR=s%co0>B`D^KjCns&_Yh4
z#T%a)OM6cF*Y~pN-rF)GgM4z@*#3+M!dGxAndOa}QUwXPcgKTGS2aZ`&Kev+BMK6x
z%=&#zpwqeSw4}EUcy-&-M1y9n)SQ~uQW6^s=z%I`wS37mB_yN$Z<Zg2st0!ag?V@!
zbp72)^!Gi#`8LnrPV8MF_CI5k^3%({ue@TJfg0U)BUcGJv-4|oUAi6J_?FK2#PJRL
zq>Am!iX*>1q-tf%;Naf*Dr>J_JQ6+GN4webihwcvWk10m0q*6Uck4K{Ez+iakc0R+
zjy8wCL1sTvZc;dTg|>Bk%Asvth~;OqOU&xJah0neap(XoPta7ZMMzg%xJQ1MrlFmj
z?LDKt14?OP2!2HEa_Q|Zn_O<=n*MVA?h$)-`CC1+U2i(n_8X(-jFNgO#tB5}!x%hE
z`Nm{88d-n*^1f*6yeHzv9||QLjMuJ-+s$--&^c3ISNBLsDF_PMqzFKMfq3sRXp|yR
zX$dMa0tr$n#p%=Xyr=(_j$I)0(VM_;(EHuiw@bwp>D<@8A=}n+{`fR~e5r|Ti#*xJ
zfjjmUnwE`2slGw80aX%Y&alTbw)sR`5U_?5+v>P_(nBU%Y<%}G2L@3OMP!vvPwvzH
z)m<4#$6M$*hNl=VVoy}I5fpNHtu)UEg6AsLjS{3jdUMrRVcW*cA>WineKb3Mb0GXf
zd{V_gGH*Efx?-g)9)odxe%#JNsnAbCNyvNr60v5Y-0s;(5awyk0Wd+V`Ey9El*3HB
zE6YY9<i`r<FCGaSAlR3qiW(p_0)v7k4+m2ptUp#I2S_~HDTqiIP==}lImlko22Gqs
zt)K9mFd-HdX_*g~se+=j2hJJlYJop&w3CK36yn(Fw+vCO0B>dj^n4FAYYhAmzt``#
z6;GwCV!y3Rpxd@l@*_74thlMJW)L;EY2uxj-k)JuYp;A)luIw|#&RJ|r~fT3M&f;$
z;{F7~3H-=%Azt`M0e&{K$+jtG-!Zd+toAR-XZtq7R;mnKU^8hG2e{vMEstZ8kvLv#
zTd}I9bg^Abg;u`0zw7<>Yz=25v`4i-P;;t0TLpDi10IF)3XVn&AH|J$BHV@4gVL3e
zzPR+~9xa>CKImYk%bH7a5L49*MeZ2EJW<{4=Pm5p&cC=&i+z|TiaX3~7}2OSm+gsJ
z^}fq~7rK-#9eL3D92LBPW(taFxH{%0+;z|ba^MCcKue9p3%P85Gs)j{83Lo53dH$L
zTf7Hr`O4!5sN|9!`56lhuu$9uSX2KrS3qk}fTDbBt`q}-oZaXB)cfq?{%4y=6kh$v
zyCHZ}_jQn&)N*MPxER#Kl$hQ-I3$plap}v?9T$Om`vu3$s0~+tgA8OqITp`z4434+
zMeL~Ao@u?J)N|A%{#m&?BNqqzA~`%48-muhP4~5g;Ab{>v%#_NI4C&}VZ_zN+?|fQ
zsb^53IfwUbyFyFT@W->JYe7y;&pqTVn+H<ocLvko;W1dLH<<O>DELZiW9?ETwo?&T
zUjvMZEjMSzc}1k|@)56OtMEDDQvc?;{)g1t&To&hX`3vx`9=OL2PmaW{Qj#+YE9b@
z#%oJOMLe3%swqowiQ%0|ve7SA^+6DW)fNVzSat_kxqO4BT@(ui$Zc1QMm@VhhH4Bj
zPtHI?MK0C<R_TQtxLe}qF<J~_I;=F&7dc@O4bJF~FKTWe?_z{{!qy*wrNl436%Na+
z*o=d>h(ElJVHRtfN<LajX$rF6A)KoPjkbLP^>9R%V;aKEaMWx-4mH*VuB&}KtdA`<
zhSh^t%o7+4(v__-_Mt?FkNUfY`uRK+=I1L`lQZXWh_Y~URL(L1<I=3X>1dooBg2L>
z<z|@?!-w7+x*aFgalwmq0h8y|^H;MpCpYdzZLw4MaT7zepi}1;31~7lbEJ<@2U`j-
zX-JcUQ3S1gxtX8zc&Kv+;PvSDXko1u26q(--3B@7{<jJwsi|E^DY@xNWf1CeA{4%r
zeT3WO94#MNS?ILm8>?;ZURddQIUcq!Y>6@3#8lp$G_<pb<7UMtd?uU^N{a?JC!`sa
zeiF;t*+tLN6Cr)@=iwPDW-A_I^DA?7-?_Rq;=2y@Gz71726G=@;Mdb?e^2hc(ji!7
z+oMXLw8%x)(}m5O=pu<TcU{Uk#G0)ek95`kY&ys}lP|XUWKkv>amb*;)m(gwW<p(D
zdRnvaeL~ch9<KR<4+M+ff4J#MEJH;-4r)5kF?*SRL11$94%FtOLO)xu-8eLO`S`&9
zE(iI)?!Dgnc9IC&Hj<~Roq7L*uyb<0v5JH!|LtzbdiB;$&iur5Su{Q1i>Ymyk6Uz6
z`}TB^_Gl*IKRNRAorA_S)G0|44jmhUVRcWljm+k$`Z3>&er;{Bx|H%Ms&0^1ZfTWq
zN_!S2rihgrp_>ME_M0~ordWU7=Z&jPL^66Nw`;dcTx0C1eGeDnDsuU8aS+|CuMpKL
zmaRgGzLONwqn16UGcyW;+?gJHT;6GNJIUwB=^DDe&T|{DZo0U3{XbM)cRZEt|JPJm
zNg*>z$}A(B$Vg>n@9a(Dpkv%hRzfN}d+#KBg^=udaBz&wlNHA}9Orl4J-_GsJkR|{
zyky+>eO=e*^ZvZw@9T4|_p_V4Z`)PhrQM8{{5&mM?{oqomR0<2hj~j>#7aAVjdfUg
z6VZI{mH>DG#K3E|4PfhEYg_UmN(?&nSYG}WICqg{(tS`Rjpf|GUJ)Na^})CU-1aUb
zV#zDH7Y`Z@I!k0E9r;;%D+Hq5=a-9cd)pKS^#FyfQi5=NbQ7|N?^B|<c0B1D-$;4u
zXD2&&;&9~H927bL;6Q0xMB2LyfP-7ymwSVvQi4!+EsJE%`|OgjjNTXyhl4bp#`aRq
zt4jQbRr)2}`w!`o#*I*vju|YxYOmZP=V_-tZnQolI;-Bd)dRR_HI0p#VPEPr^u^^u
zK}Y4s;=BS)+#!{+_&Z~DYeIB~lKW9&7y^6Q39+cp4ELp5@;c9?nv=$_o4M4QatB+*
z3e^%&p`VtIz-u3WarRu9e!#8f=}&q*HD*s4r9=@)?bRQT2RE2RryB;9lQn$@Z|9Ki
zUFvh~v+=Q7m)gMu{QJsHdK_tqWfc@zL3NW7@-h5r4aux7Q)g@3%4!mxVehJ#R2%Xi
z#${SaA5Upqy^nhC!ZWhkf`*oNW>(Ma$2=9~Htp@1%Cv)iZ5H_DWV^zd8>f<MYF%HI
z`_x$QQ+Tnolx$~34z|pyBO%fKuJoOB&yGq781-fst7@;0;M`Zg-T@D=-L;$O-WQoj
z(_9!R!g+|%U*%qHtZX&!&mtJu$;W0VY~OpAU8u@i)-Y+egn>W?J&@U5PO+%PzKICu
zwld0PGTdQBSq!2M>YB1fzw@aGxjynN&!sa4kvcpKDU1q}andocZfLLWjaeO_2%vG$
zssce}NRsXBf5aj+k6_IdzK0_P01kt92ok7<8FIUhujcTJ&SwK&mc=5Suwr_s&mg1b
z&i}BR2|s3LC=t|~a5Y(7Iw_p%`^2-V*ctFKmC&z#0P*%G!VTZCw-o*>qftcyhdG5a
z=6(QL<nc<BBV3`r*Uj7;wkZ|9dD-Co=aF^1FU^X>J9PKi=sej+uS^=YwTve!vsh9Z
zps7X5wVT0Zh}Cv(ce7s2?XI{r`|~u^A6#dD{-hP%E-|fKsSlK}+?`Vmz3v*7;H4pw
zgszY0q3wlg?O%Vtl~^n;B@2MvimS7lVuat6*Il}8Nsg0O0lL|M_g?&S<@+P@E+gH?
zkxmodT_t$B9Z3Zu{UoHVGMnx*;soM|2;X|qLwv;mjo=8NwracZ5`&-JTZ21=i#^Gt
z{AB5bUx#lyJQD8c0r_3pw?4*6ZMWd)?N0diuhBL8!5i6fgo;8_C8`v(zGvi8>KJxs
zxeW8JoDXLkSgilpNGpy-tfcJ7!@!|Lgj1_x5PfHe%9CmsIw130GTo}YiCN<*J1RsR
zeBs08N;j+tmAf=o4(lqwEZ*jF1JO9@T9`r&e*2`<Q&rC4@N-nnOLFV^gEZSchpRt4
zy^f5DUdyV$LP?VP;HvGv;_TfsRtsae6@J6*vCo$KrPQAchQdb5=4!BAswzJwQE#EY
zvH|!tPWayDRj#KWCTF&Vy|=Lo0bsK9B=q$j6fX;_G)(60(mL;QpX9x?HbnRtXe?Br
z8|J^(#RLk%R$s%F*#_Qn56>WJ)0jei?9OwF0zCF*a?Vm7S#7?uF$O$vHW;ckrheHB
zHK$@uYsbEKPIgu!YGNOuqRn-l;ac6@;Z%=u?^r_qH7P;0-fq=_FT&o3`Iu_`kIAf<
zd%gy!lAlr9yy50og#iv%(N6;7jVr!tP%Ck--e8#Q=qniK!)fBK(7*U{fB%HCWq-x`
z3iT8o9J4^BK5q>|&#=4NWmGw^D+~^xn6V^idv3Bt=t}8}Uc8dqx$W;?a5cXO7&z&2
zPP!mI3G9F3^KvLPN!p49oW)s!f&G-Pjjhpo%rT1iTI_IsvPo@WY>X5#1`eXCq5P)c
z^pAR5-)EC{_}xpsax4P;c`6z7gx$6y<koK!`-_Yk){_?19>!b!=T!8|CGKZH_w4@R
zIzhJ1Xf8tTP4wW8df3YOK(@s<!P(ZU<F9O+>SNy(u|OGo4*=L<U<Qf?yFg%G92mXq
zfm)Rt{D;&0YtH+39A#9)Tpr$T&aZJLWn93dH*!7AxqI%!ouC~RbK(7%L16@h$WJD+
zDJy*@`%%~^upiO`xs^I!9?|hk9+vH=uJ8}%7>cl_TM7ihy5Q1HzhM`&sA6v<HVw?*
z!k<y`U`-5RdL2s)K}|T38Gt{mwB$u6o7np%4yQAouI#VR5YJbYAAS=8who6c56iVk
z+}Yg6d|eaTo*qJsM<*9S2a1ToOA^0yg(WDhR!Wz#xPyomzhTz#w5Vi8^t~d+kvuT)
zPR5BSFhMBlkBB&s5x1B2lMoMl+|MY2=T)!Z+UIL_uH@$BRRN}$*OB}OD!&1dunk<z
zOE41zs>$|!V5nJwcWG$r8f)vfz~}w_bwMOw`6)p{idi3cDC4AIojhqnh&z)A4`(sU
zsA9!~Gs7Ub8IPAlE4tg|gOcCw$I6fZ7M&C{rT@toQ-UslB9@D1E{*EfbKN4p2fmPF
z(}2kY%tQ+As-w=NbY0S#Xju|_iyYk-sxp0q^RBgcrMDmf0izT&pD%Qvl>d2)8ALI|
zHtfEu)KI;`w6kjNBLEkK?R=<=2Py+u_C4w=Sh{%1*U%tNh)F#xF+%32H#_uDP-*KY
zPp0kCuJwWc*)K;!8KY<ZUoF^rFuB?&UTyX?9hF)Q63rIIV|m6+BKV0Gcy6l#0og4e
zVb}lu0dd1tkS}19>t)3m&zG=^0yv)r2)mdPP#DFHAO7y-uL1ucwwE+?eLZ0_Hf;TY
z_XWZB(e#Y!gMZ2C=Y5n9B9{B@uy8Oi#r3X@Ytgj3ll|bUq%ddS{+*Sx@~(mR50l~d
z-7TZ?od+w_(~n3g96BYX)~^XzruQ`((b8>q(Y~UFYCRLFrwl3gwHqPx6EH&yvu{76
zqSe~7UGa;fYsifWRfA9SZ#}ZC^3>#flcp-nvL-&NkKwNi^=&s&jU<^KrR`fCR>qe*
zr|d9y4<G36YPXDjzY_6jN56FI6H8))3Hrwwh#2d>Z&$Sa{dtmhiK;wDWFb<lz>Nk5
zqaDj*EQ<|B$?3KBqaq5RmP}}3SUGlo0Mv;?pj(I(aDoA?3RH<7%;*Kk?*R*4g5>kh
zr^)G4;ca8X*1$+mvy>hNj3R6;w=7Hb9{AD+r2%*J5-@p^&+z;ess5_J%QZkl?)QF=
z1_Fr&v%25w^e1oBdm>EhA(h6|S6vSo`YO`=s=RXnj?|zwYq)x<47EC_4?ur#m}l<S
z8SmJ+{M4^AL*8VS4ntTn!nt_R<Q3Z*=571CJ4d(r?Q>;BMl+&6uN$!#Ldh`Epixd9
zPZt(WK1hn%h6c+|(EKQ@M53s}y=~$#$810J_WwE#^M5K{>mrRuZ_vUKpku?wVmiJx
z<?j<@1;goBQck3lCxGvRBOeIiFjJLw{&(s=F!Q*N=-b!<D@@kuHV)u5Ujj`5`a$QY
zd3SLHd_skc3<l948n<@z_UE}oe!Wk3T^gW$>+cKy0=>D$v`2{hHjSm~>%0?e!Gi}P
z2)Z)8Ozi3M{*MPi%O9P)Ukb^oflk|#V(9^X)i!ZK9=3p_wN5tf?NJ@rci|iPylN*)
zKht3dQUhImz1DlbiNcBFn5Mnk^$89@Gg|sQXwYd89)KR!i0jS|g)C~YdHu##H|HXy
z=bu)zHI{w(L7Xg51wpy1+*@e2{8R#9cYE>FcM8^+$24Rnq_j?q-yv^4Q&0l1f)+^Y
zC^uIAv70%O2tdGI8I@xZY6RQ>U~a6(eJ9W@=B3|3qbjgla0K{>U;;vy03-`L*u1Q)
ztl{ho6-Thj6a$Cz3h4Tz4G3MMRbX!ids#VaCs|hh)eD!z{?onoEq9czgFApqUk;u|
z^x}<G^9n9|l8+41AvBG&yv{2w873;;#vca#f40GTd(MJ_646T9iQDBT_BCWQEHXW6
zcO%>0_GA=ZOUimM4taLj5Jo6gE_T;fGPEk5HhXoW8y7%>YZ@ki_V3)~J*7sq#rw8_
z*@eHEUn`Em&8W-4dYyca;`$>rfIQF_xl2Agc~#<uK;bq~@y11(%h{c34)eH)oqPHQ
zUxs%pdB6-gsd+zO0k|vsfKTqxu`NdDDp{b4t6M|X*+4^vZJ3i_|K7WEp4Uyk*hWe3
zO`kvsgU$%fFnbR6gsTHh{}R~b3H;h{k1McMuH@)&OAWV<!D!1!Ng0D?G~{@re<gq6
zltDY&->)x*dm&yl9JrPA;`?XrNO}0#_!6N2gna=rTITCB(7y;`7%T`}m=rP6CAVGG
zO@vWfp)TzTkTE{kcxwGV<9h(;&v2wu6sWU#TH39ffb!<dI;ARu5q#7leuuJ$WM=St
zv5N3~#S43b%I&^J5Nn(Au-iX+4M#8GuU!=V2(zvXIJ_J$zSDEG+9c^|y}PoSFJf!i
z9wmC`40N?nteAO7G0RuBS?Qt2X;08pi$waBkgYTzj+LLSih{@N#kNq%Xn{QfTEL_T
zteXYEONIpd{c*rP)NzYkEJ+paF!U}HEI`<g$6Xc)dSN(#DF*xx@3WR_KfFf&i!42V
z15L?-nqMatD`gj<pi(0Y=8IIFrUl{I`mITU!lY(LHlo~b0ia-`0B^U1Mw8~bm5HX{
z+|Dr}mveZ<*TY*4mXvJE1%62+WF*R%W}%NN4Gtpb@r9ULgp@C|cwWlK*+xO>lc1aS
zV7)?}^`Y;lnvahwyO1uunC#l?B^u7MOD@lH`9?CC`j;Sxw;wKOoL>yu5H{-#ZDD*R
z*Qis>LwSh$?8gj)eUuxh-<L~Hv<FDCgD^Z0xSg~DY!pVlXt41X>;|eDssV2!1^7R&
zEz92`fbE=H*i#Nb8FvS}_<!7}5q=JwnwOYhCGzhp5GE2<V|m4)_3l=KD4$Uqf?{bD
z9)&?--(yuZ3r-_m-fI53oTzS!!HCI6reG9Gqs*kS>-Rec%7K8+n^H2c)-onbpZ4=9
z1Kx63HQWc0w%{l}UFK(G8FwU>o!C(C^tSiBpd9wYo~=r?Q)<sItsUpBvXRxnXjVR5
zq_xRgMI9;TUS6nA#@e}WkH|B)Kxk;`K1+dJf<!3$9Aa^gTSf;wV}o5p$W?i86Pyp<
z2Z+Fp<yqwzgfPHbnAu!1&LHFG6QAY^C|=-$uWp|GWBhGAU=VlD1@t=7-hM?n;$OBa
ze;QD;48k$eu+drbMazah(FGzHI>4_SDCq^Os$XaB6gabt=rm3Ng%{^Mqnl`Yhq>=N
zigu;D+K?NOvmEXnX@4m3P<Zn|<;RcR$=>IaiFRussII(lvMkr>C9;&OI9kR@S7oOZ
zGsmf0*8^*W?Sw?<m(?fM;DO-;m~!XWAp8pfAnDlHRD*PZqR^N!_Gdxe6BjzybH$;m
z;Vcee-|1Ddm`#Yv2UNQ(3vjFXd$Lg^uuA*Ps;+^F4rW`ux2L8UTiP!X3XXE%9L602
zBY|J#zh?T^$*4jfsax}E#+yygR_}NI)^EJ`UJo9P>*4EZd*U;)^k&e!!()*LC8i?}
zVOb|Bv30)?W+HLb(DE-7on0}jM0l(c&<=WuZ!^QDO!8$keQk~>v@0#QIN+CMbaC2)
zH5$j5$+%46Wb#)o(H<c%fQnZ5C@IUJLW<WbpkQ{#`sdO@uP(NNNJu-;?z6r(m4lG#
zZg9wDfh2Rv(wm*Yovc17)pKt-zh0ZLIF^LBS9tJN3Fg;~LEHrPSI^0`$yC`BDY!}O
zx3)I?q;5ye*jUrP_`iHmce~Ajzz5zKbqVj#&~QVeiUd%=NPE_DfjB>G=j1~)sS{sG
z8NFM!#*_|7Gi{K(dLcYqd=D-bxVPkRNB>f~kBQv}1m}=L*!EF6^E1)Tsf026C}*a3
zxnjFXX;H?BI#n*OM*hwa#)>3(y<%gO&lfpOomI-&@A)i1gadKR{-*<(ut>*lI19`=
zcsP2hfb}50?|Sv@kM+8>oLsxf`z4FgL|%Pp-^1pw2kqFEsCxi>^G32;z^0pmYiUtC
zDqdT`M_c^8Xu%xFXA|Rn7}={=l7${1<jdq5--AiH3yiH=zrV}zcUcc30%#C3dEK}H
zwM1Mu7l!T7RD^ZtU6Ealm7XDi(6#eA;s(aKgEVr{es(ue$a@l!Zd~zn(a?S0hSQ3S
z*_>Y?)kgn+Jg?a-i_Ti>bMj<>Eg?Xz8S%{!|6yNbeEA)Pcf<;ea{VILCyzLmU$THw
zx8Hd9)#MSkwtSp6irg_H7MhxMUHe)pN%-WK0^AbRFQY(u`gneT8Fp&?edR(cpb0%J
zVI*y}u>*b=hkb<|eyBD~Cqj{XiEZm0$jK{N4^@Z1NSw~|4y%#IE=qhvf^QG^aZ2+u
zYE%o-*opZ0?x#Bg-h7`AB)c?FrixSFk#d!#>#nl6#kR$e?;YmVy?5{gFp|F--ToJY
zUtGO^m8_@`lB`WmDpv+b>08O$=V8cx{9cV8emC0pOgw4FC4wQhK$o`<b}mbTfENLa
zFjXMXUFkop%4hA4U>MQkv3Ijgar|q8k0VX{6e!GnpaM^oksO3?_u;{L6_faM@Iunx
z{CX*XUGMnbziN7z1|-$xJg9$is6n|9g@l+zVb=y~#aQW(p&hX8fc6q8FEq`WaJ!3p
z_Q2;$_QV}^Pv3IIMrUa~`+C4K*nv|=;7G&Jrg+-*Ra3bw+Drv+6}@BmbFa{${62`A
zL!kWRy8iBl#;xJV&^{n&;)Y*ejC8|3j<g5l!3ru{_6+o=gbex5pgeU0*cMhx9!?Ru
z!CNuUp!a>gMN%(5Gi1Ojz;9h<;6CqvvbzzgDyHu<pbcBU1Lr4T+_4*=QvT6ZRtq#_
z(e2;8i3q@c0yHiX8a5=MyVD<x+PP|J67~(W6(tlLtN^~#Y6LpC%5iUQN<Z6)lu-L3
zQ(P>8-}iT{HgUIG5zGcDgKyYcB4auy->(PXCFPyzC$fj!Sb4`T*S!EU$g7E4{_lwd
zKE4Lgbqu>}2PU@r|L9_w47mQR|0~4cG-a;K!PPs!XIW6!L~eO4B>1Dy1<Y`le#&mW
zTBXP&i~@xPJMP;X+yihn!dPr8yRlemIg5B@*@|KysqN`p)n-Q>6{bOBCx+Igc>P36
z`YCJ{Q_skrXUxiE!GXPctzr|iCC5{SpqO8kwmPr1#V9eJ2yke|KNN4Nn$-=2EklkO
zGowR4WEw2lL#T6Y1jtgS;Pk{T5WnRzfOgJ%G*R=fMyq!@#z0L-jH{O2s`LsX)QJ_T
z_`*e@xKMb(&!}N?DZ`P$FKAM`71lghB6`sKgTL(Pg~jt4@81TEC73+Bzj!2?72T(q
zxo3-6Kj!M-uuH`qki&<h^$ml?|BqsT#qR?=?WyOlUX)o8U8RKP)d<z?jfuvJ9(MUQ
zZSI;|$o^<afjulxK}-S`RPVuz2OtMg=ih)LCiV5nZh+o_6}xk?&6*oCISsl_DcV)$
zgVNyd%bo)qEu<E69a}C2-OTw4d0#2dtbv)?L%y0qQ*Qkt$9x?YpsLg0%Ll}a6PL@y
z47V{D&kp@2(>bLeeZ=nfKPIHWGC{!y^ke0D4M0zIK6K2YWZF8SIOUO(Mw(MRXi6G^
zZO}-%$gJVp5PzleYg*-k)9^u<$M>~%VpPIN_|Uho8n~7kJr8|;eR(7;3e?ua6#?-q
zi`Pp=i)xiTv>Yv?L!N^!$_Cj#>RJTITT0qHzHE;CTfzfuD#O-#7IH248kngtuUEaB
zlGccGQTbkXd=~r4VK#0p0kNo_G_vs1^9oGp_*Hwx0rOh&Td}&8DRvXVBWXQjNHx#U
zx}J)5eFz2xITQy43T>7hBSLUEtS@^~+xxZ}OhLh0vTkb<z{4dI`1biFP)oZLrtg^a
zGWV7_tmIENF#}+9H;QYMuhYEr<ov(FtN%5?egd!(&Tz1vHD0wF*Nf-HDq#n)YJ-hO
zBZh$bg#gYQPjlF+!7q%wKoUM{4vX>)tb2epu-#@=zr9aEi0-?UeF6BFrvFBAD-)Sr
zNN<5>?r5S8c3e%zzMZW%DXZY+_J8d~`i%fK`+6T-krK?Vg%^+6D^dM9?@FzD@hKvs
z?`IzUp4WZ^_yq5-%4eSU)z#I$)7}2pz2{E)ja8Xum>A}Q+0v=&+-qIFG$J^@66+mo
z=auvMUcRUpm|b(o%ApeQ-V=ka{aCQb*@G#{HGt=U66PpQ+SvL}tdYj@;jwRfo?Aqj
z9NLuu$y1g-$t4IfTS^2*%UNKwY=DiH8o+1?;5!WCady7HF%16Gp%I^!Umq3KC!+yh
z2h`8g&*7-^Snq}e2GAfkYX9Z8A7Vd<Tv*9lzq7O$PI{ORfjTQes5rRhPKja1)qrcj
zAc_qotF><igv(G@INnLA23qz(x7|ki8BxImPKaX23LnVg-)>J1XKTDkjxSfXTGiE?
zzO|il3N38W%>we8ZW1O#|GTr}sNW6ENEO49c?qfm?MAmc%@X5&HDj6to!{Tj+rk-m
zmu~g#WT&w@D9`gWjQ3r@0)5GyDZBORB*ruJ?-dF2FY45Q0VEO{9`Y0G6yl#ZcydMn
zas>FC2DEcH_a=-PU!Y3uJ~q@n_g~;(9^y~j!QS_H(X%okKX>p&-7=1MsO28TQ^K@7
zhfrY#X?Ee@OyQvbc1<Fopb^>+K3(aO8w%?4gX^xc3|XM4T!IY~N&D#N^{4nkR*2Es
z)`&3&e@9^Gv!DBU#gMxWw5(ZV$mDe3>qLG$0I%rInp8euZU3l;>V|<r|DpG-#=q>w
zu_QH^Kx;i41xI3&2`kHBZGZ5ROGpFjMCh}FX1L|9iPpO^agpJ}gJA41h$oF8o^+lb
zxDLbx2n!odgV;i0J*QPs$N8uAeuTT!9pCy2)*rtMj`mEthxmE4Z_h-F^PFq~xUc?n
zH^=h(tPbS-faafiOg&Nm9%wuA&vl$v0#gh#rQ7I-hv(u`F0f|pmaG-*7OyFNXUgRO
zp#j#`afGCMLn3KV1L#zllLp4BFGQElc41sffSh%C`~BOfq>^9|$*k6iYk&|lo}>5m
z?OaCU4=G=DIfMXfQ#5{ev$otD2%{!{@T0vTtbY7``7>BG#RR|#IGowUd>l0$%kQJ8
zoHJk)TUIMfHgM*c7RO{lJLIt9bOLm5%gNVp%<RRJM$9h*_bRTE$`2JoXg?uv`i}vF
z1%_L|wF3Okz<{wE@hg{gjV1q>6XR)1@?3F%j+by=4J~<K4sn0aIL3tmwP8Z3b!3dw
z!}o(I-A7txrzmqkd6){DSzL5;eylwWLf!|*(L^6WQqq6lGx&J2BW><E(t6~&FLBWX
z08^=~Vw0+>U}Qz1ZuN;1>hoaZJFJ^2A{_Eji-%dg5PE(~F&g8^G_vo)JAw!pUs{V|
z))HCyS#eQKxp!nvaKvGAL<o@oQxz|g8BzCO$AlJ?i|+x%Eburus3}4b?bR^(4W!VU
zTobe(N|}0j`x^Zwvu_r7a7$p)sErern%u;>OEn&;<CVHSV9_2(!|!#obN3#y12z1Q
zxyLl|bQ-gfSkV+h4>rU%Qd-tT_}1{~rx-yArXshG-PLZ5`1k18cLTdIz+&NE5aI<u
ztV>T5S!5c+8V(wpUUeOd0}euzW=5?YFw_mn^m~5qpAp&yeiU(RbYQ}cQa*VLG0BUJ
zNtIyJ<dH;ZlY?RN$}4QvL*z<9q8g{ANlV~ZVU36SOO%^k{JtMXU0XxOX|yB?ltmy1
zE`i1S`<E(!<&7X1LQzM;E(O0=e~r9(4;<RoMd$+f`*gWgUv+T+&LO_$=U@JA@dV@Z
zHM00+J?9t6aiu1)bq9$zE%}tOyG+BwtVQaI9)2N;q$HP={&>Lue@NAxoTQ!_#nuxO
zS&}MD+MlgTV^hZRO5r>RSJ`HF{H!3#ueCkhD=Sz-Ra9c^?&J5O7>WLD;GS)8IU0Jk
z>o=HP-|jO)ygDDENOTB=Blj~hpI}5g&N=Nln93mB1K-1*FsYVHY|@AUg1v6t;0U~`
z`I@}6I>37OcLS!viD+1KqW!mI&muYriRdu}hVE8_c%Q0)wigM|P2BwofFGid);op8
z-6yuzbxTWhevO2nXyL^ORvE$aa!?v8YJKp#F5L>$nAq~0gJV@9+d)P@uS*hBq6v;+
zJ4a@<C^GT%-hF(z9uFRLs*)QJ`Da$A&H}Opw%8&)XTZ(|pWsM_c7+vCFCk<jHK8+h
z$2Y=&Iu0zQ?mCOtk-!h+`&mfe!)A;)Rrp9cos3wn2S;;nC4?5R;w~SSP8_d49Hb?<
z>=!@27s>$#U0`xT^-+d%3tZB=x7j~a^Tp+%?+%(T(k*e973Cr^Ae?KQ;lHyCsG4<`
z4&?qK=o99-SJ;SqlFbMC{%6z1?%3(_*vXlpoX&SZG=%}$E`~qvDJc=&Ix#ey2bZ!O
zLT~bZYT#`^`EhjHEnwj~^LD{eO06JVLF2}7giFRhD6WyHYlBFA1c=|$1d2K{deFw_
ze{Up}!LWi?C9-!kHOUUr_Zx0}cGm&yZm@~`%1N?!m5<8^R8ZP5{sYv|5V-x$!gZlc
zwO9_=D6lfKC60Y3&1+S9AD+`WjhH1wu&pP|N=juEtw|OHq&J`C7|}sds17YQdN<yj
znd9g<Mv=e?h79KWz<vIh+yPP^tc@l!0R5Y8=VuU~7cl(ivcy-YGVbghwUN5ex~L3}
zmGBQ3Ulm_kNOQVGO`G77<tj?yTfw7Wax{zSrspUceXdt|=ou|tSCPWrvggTs7e_si
z`n>d=sZ$f#zczgNiv*Yb#!dXX<_n}QQ=9|{8JU|>AoR;}M3UcyochxK%B|KVm>e4$
z4m)D$ga9@NxLMKwjS6Cf@SD&L??p7=gm8ydEGt7ieCJyfg=0o<C|(s8*~EL=Of8&v
zpxXI3QSlObMvN1aCbH->Od96-ea52w=yN7mN<AebRUXOC!>Ou)ZGj1F+`Ywc@QjRI
z(>iqoA(h{OXi$H+Mcb`crUWabgX1T6ag%NJN;qI~#tY(>lua!_6eVC{9kzR-eJ?tb
zspzqg{3v*E=wMZ<XLQW1<rWw2pV1}BHG$m{1)9RM!>53vd$qX`x$#jIhQe|S;HVt)
z(xv9)ZsGMKL3i}!Rp_GgR(;xR!Uu8?#s20>su5S%+^QySiS}b%Gi(<RU+Zn7*x*P9
z`y2rx9%gS<z)uaj&zJ@at>6NYE;Alu%suDAsfN+Ox`hCH6}D$CVA#L`FX;uUE=41L
z6RDrz$qwc^EbLfWE|S`Q$?60i@d1cRxn^lK3*4vSP?!#=|Fe=)z|2@XSm-@BISQIs
zX<2y)fm2h(v4v%DclH9=6H`n7;@C2i?DUa7cqCAL*K`TMl?GfRQe1T{+$0}3t_wVh
z{D{(z-EqjvX+-COZ4_MY^<>D8={bs?u+n#ofz2Lz{bH3NA0TDeZ9X?4Qs9A|(qO*C
z%4|~X0EfXb{X}3)g>&KmlQd;}H>4!I-&^BtV-2T=GFe3&faf?ts390xp08WHnTC|`
zlGLSX#AE_0e_xw8q=OT(GonH`ndnxZq9kBZMc{2wj`^M}<t&*f5=(?AmpT9<eN?hg
z9PWI=4uA&Oo14Ybv8j&&fE$!~H8z%{%#txVXV(eY0qt9LBZQ;g`qz||D}kF*R^_Mt
zCcsGr+U)SzL_a~}3ecjD*Wk;2xV2Dm{f+_L9_RCz<DcUy{$BrAz5~jqzR|XJ3Xe#H
zey=uo$~&E>N#o@Im0ntJp!J~d3`w0GN>nEW(`PE<GEo9sl#|IZX5Zey-VK>{k3s#U
zM4f>%8j}DHOy0VtL8W56=*kC@wTsT$NO#LP9>EJx3$Q}N%KqiCQ_@(aV6;1Nj2SVR
z3OkfIlwC}w3;}o@q7j5f9Cl|u6C2?8;0l}=<tNZfz>LMkm+$k!ON$?Pwf@-(vsUK!
z=#>gp5?Wt3Dfx<_#;7>gL^f<EyIpUv1k+(iRFptTRC08QcY{d*Yy|_(m}9m58-1IS
zu!GfiHwO~2*tVrEQJ(Y81+<j%<3vc&3_qKF3^rXLL^}_Pv6#XK+Tc`EHPfMRRPl|<
zFGit_!e?k5Zb&pPBsc))_f?>!(g%880bO3;Zf6V<9)<AsA+RGz7dqTjx)B^ov{`S+
z$l$8H<1ZuleoA>=ip|M3?cS2`muoMt<c6H%+WDO)M9ua@p|v<QL>H2MAP3rv$FK}s
zsed<R)3ZxTSA1W<&HuyM=Y9^82o>vIF>g5m&iLn3gbQGA=08_?>Xw}Xec3F%G$!!|
zm!<U0K5Eh_hLFDE2=bNl8e#A6U!%YDB$(hr@r^q`D($K!YkSlK|9DyF(cKY}pf2U2
z&7@T`yUamo+NnS-JIy2wbb@+k8o}u&7Varxa_N!)>qP&#gDD38#ej+RS6wcQOHP6H
zF67_BPDrhte8qV6OeK=3;qb=Jx1YIE{N*aj&#mGPw{CWYzqtT@bjBut;6n9nyP=rE
zkLo#^-lB!f_Y=<}N*XC#nR_JXr6=EXl{ULyIHdb|LXK~)oY^~=pddH&lh^t_aaSqm
zlm+3uSnYb!+kGdKJ8gPfj%v{aqTh<R^jOkUP6_2q)g-x4-}nnYKKen3n~(k0)x&Iu
zQ}==CJnH@X0MJa>;`>`RPiaWPD>)@466|hH1$~CzcFu#&y!>z9x0s3ogXljO6vCpw
z5siqWp$-g{Np3C&P1<m4F+q9~Wg^mvLzkJ!87Zf!smm|=GGCs$JVxz3#pEx`T*OfH
zULwvM%T2iL@`jb%eTei%==K+@xRK7RCbU3U!6%VBmHh`JBi!&q(&KQqKdRn*?9KP^
zs<DqmIk%s0HYt+?y^ujp$<-F<Aj9_)Vfbii)-rpiN-S&GYbKH`u6#ifoG<P2TgI+<
zMf_CE`onKaC6O~mpthPLd18?s#+pL@tyj%6I4Q5;he4j!OHQQt+*fqKt+cTEaSIgq
zyR8lKCrd<w%JNnkQAFOGB~0%j>G0h<3Tc@S2<C6nq({IsxGRamtU}4H^p&SX<IHho
zQf9eV9kxYfSd`mDNZ+s3VivBQIbLDf-TZ1@H5IGmy*ZRGNVP4Tm5rFet2{cwU8umk
zO;de)rBwcP((V1CN!s!gMGQiuBqvUskR)smjy6Iv0|Nt}yf)3fyuC5nSI-<bx3Jg*
zJLa1^I?jS^?QNiSZa9>Xkf5`Y@Xvt2pC!D$a%R+ngX~@`Ytwj{$k2#G=QS^<Tsn^i
zmRHg&w7h+6)KOl}F+qE)@hp3wV0wCoXsV9VQzK)2gKJrcJ(v{SkGY>@SD(a~6hp*r
z4xN^Pyo2BJ?}Vb+eugIZh;EGBeY0xGj|@I<3Q5p7BJ=pA6Q-%I8so?~ciI-rN9mL{
z(4ks4K79O!j!AmJC%=Je|1|qmc<81U(eS~wuY<lupNK^-!20qwT`UzCX+>$)n0J>+
zts_;P9z8Y@V<9eIoI-nDYK<<p$2zQB+Ovn)UpCUxsaJyU-5snko8Ml0dP|czhSc8~
z>S6x063@~%Y%iH#?PJQW!}3tWyD;XhiB_nMpG2_kQOzsv<<=^F{a_2!xs5%woQ<RF
zuBMvy23f<1xJI#aG>~Ly=HZW8=L2`>98o2A%x>uOeNIl!u0#<k(6y=s^#3VE;_M4e
z8(F~q*YhWjT?jpSF7P;LXV40I8JanQ^#|IMF@e_f7hk@7X;AA64ios-74BO9>3BKr
zL6rf6Ty4~ogzNYD^S{01?HB!c#hJlWP1lWwg3Lds+*Qz1QGR>++J&=02V+m-N`z<K
zempJvOgm}Zm6pM7dq4Aac_~X9qM|)J?DFn}#}Qt|NSR-8k*G=w@#p2YaMsRkqAugY
zDoMT$j-Tml{<$<`e{{FLBlCjfWc#J7hmNJThU6X!i@Aj*x!O<qEA$*2K7?kOJ=;UO
z%_tZ?HqsBvl9>B+bXboGqgb}Jcw-gW(cfL&axcl#ADsH!AVz{=stZbq$V@fqmJ4j`
z&9Q)t_!c5@3is)Pq0@9~A60mpKZX?C?<qMy-15bw$IJ5r)gxT_dcp408tvY4-rX)E
zw}yW8qj$`%eJDGh-IohGIqI{hxxBq+5PLqB!6feUnvwWUYyMHf<3Z|^$7<8vHfBcX
ze?|*Se%QCRVvbpR(~?6YQTfz!DB1RxO9^Pf0Urbs0*-AdgbXVwo0^*5K5!}LK2w?O
z`0(MwQeZ{^{r%3cu&}^gci$$AZ=93**O-7$Z#y@#j_D{^eYo=LfQEi=%#57)`v&sW
zdi&=jS1>|a&?2o?iTdnfMhE@oyoLsw8;umN57YJb8vXjq0-Wi0?y*OGMfIHYmEwUe
z3*-dvI6^2zmjj$U*Gbv05d~xP6^9|YZ>u97y{-)MwrF=Cu!>q9sa0fy?mQiL3w>%N
zeyQMwMoc?yH0`;lJz4G%)fyR*+1}54e)gq5Z>->L$9-z>H;s93PB9NtqfN2IKDI41
z2#|Y?QPQLP$n!O|xiZ#f3cOQ?2hh1>BgE;vQ$(`&v05W$s^7FFj|OjopXPVC>gZES
z)qHP$msz0ENnObS#7;)bfwq>hEfJ-w=`UKZb+0}{-fn}uk9T^siZBQ?7L}IV6fWwT
z<&q>GJnuT%GS2{ib|j_uYxMSPjNO}Yj?lg!6ONAV?pEk%ixqU=KdqqwX^=P*<opiU
zCQR4{!B&z-DbmcKA@|GBP~`&LLltm5)ahf#j(JK)L_`ENHbUGULk+-NGYi^wcuKQK
zdfkG0K)hm+ty`d>rk28c)pN45+p~Ig)0L>EeM`_pmF_>jF5s+_URis}n&PPv>iW6X
zFRy+lAbBu}A;X-S{e6(XQs_zg6^j5Hd`)^vjzWRzmAZE(Z^WoILb~}bY0IRANi9Tf
z21J)S!%J-F&SnC}@6rDLMb(^(lUvVu^G*^Zd0nq*Oi;4bPjr<EJ))%I&d7GA_Qzu$
z9(9)nNYe(NeDYKYYrP!IqKeeuSa6DH(}=>D5~O&kSr|y@$e<Q$ta9GPPe(rDV4B{u
zE5`?(6E}EVCooFGM|)bYPKxr>e11!9<@(n6d9^Nv>3++wzzU<DZ;X(sqD7uc-pM@=
zdy$i!s|i1S@;_*!X11ix>bc>O8HC*Q435@<0IAy!sS@txmMWMh5@1T2<2r<RFtH&&
z3<AfI7RHySAgY~K8mW(L+NB{c#w$j8`YQ5L_hpP5d@h3%o7zBY)u<F`_tdW)GIl&3
zf8)jt|Kw!+rDvJd1_!~f*4kuLLqkHwNlmuE2NvJDDKH4-RCNnsZp&>dU7;azKB{9<
zl~N<!)a&6ZXB=p{{d!$Z9r<XTLlt$D%X5)b-1neI>)6+yoFi@x4BIcP9p%(zAL+!+
zYbE6N#lNqXj(PR{b{{%_Kk+KNeT!GOi_A{j&nF|SbS!&E+4%7|x?f?+lgx&e{bR##
z48uCjKY!!pV?=jQvcB00C@-}pu4Sp3*a-tbFm2{^lBS+C(ZA^I%EbqVNXbj4Yie2&
z%M2t?7X{+elR|#E8qqJK>1~If8D6c2v@Ue&s~{(&*J#gBQTTfc^0dJSsjl*7em}ZA
z^TOew`<0-*QK9{67c_HH099q%l^m@J3{}3D0VA8_HIn)X-h|Y$9d53SlWy!K?Bbde
zTUNSc9MUNxq%1oz-4mC~|Gel4?$auT5a@fg2ewOXfDZH)U{l{7a9)CTIg~@g!^f)1
zwUxBBFMW7`qZ9p4{IQ3A<YPC+l20C{nZLw<m_Ev!6doJwH9O|#+&4H<sO&4oF}(NG
zf!w40=LCxIZi{=PDTW@zjbk(?m^ZapW33Zyk?7<&CY}k1ov(}i{exSA-<eCOa4#Do
ztbMz|U>Xdnn=)4E(N81I=6x|DneZyW1!QS-uAU$0=qEQcSqmQK9NcJ~lGhr}OYW;B
z*x!k`wlGKNmv(w4=VJDp0!(J5pIPKLPTf^Ae0`ZPu`&L(VppO&dfU8?l{!!~N}}%(
zx%;<?Cy;3z<BE`*jb|Ci&AOU3v!!>Hx-d+HyE2SKOY5f&x7)sbT`+ueyE<5q%i)sV
z7v72{G&cIU$ziX;;m#wf?JdXg^R6Dsm!wB81o)O#r?23?WZBbQzBV6i@w+!?)5}w-
z>+)(CCGVkB--!0m&|KKT=&@!a-LLW@9ETETJ_$gxsDuR5M2ZAVc2-s(IKHN(x0kA-
zq5||mV>Lj8iTvli4>-QOGI37IC0y3oE_@vS+TfJ*WRgF^W)Q$oe#O19FL>{F=d%1|
zdX3t1YH8F>G>Ko^Z<ge>ue`U@^A(_wHHnFvYQGfVTgc|ntgZ|0n${T#Z8C{6i{VqX
z)}y^1T*Az`3CavY;v=pmL0@A=irhPy<(_By-zjhUyp>QqH98x>ds~z~CFgTPVGYX`
zdj_@ZZJLMo=9g?8CghT&MP<*Qhri(QZPwaqGO1S=b#t?2#`0HHLU=3NF(>ob7_>y+
zp&wUKe`_4^r@fhfRvPqd+iCXhHsRdw&&|mqr>AT0DerCG7g)K-o<NNKR7@Es(nd$i
z`-$E{QAR{-gbPf%SEpS{igai9F}-t<i^%lag8h3bYH1fgEod=FW8V9}26lXDlCSr5
z$<`xQLPB3e?9Dv=oLjPt-?}`?8XLTC;X^%MKA$~aX?0-Xw)q13o|I?#_-yhoGb(6C
z+#NkqbIE>g+`X?ZGxH=c8@GVQ15feWb|lm9L2UWgCCpW@H*&I(;5sYUOLWi3y}*Cy
zdJM$kjV3*!4w+P|Rjj$H{6)STI+119NMr7=r4FZ_3ZU8t>y5#j;EZFimD$+5_}~}>
zeSFZe=uM*#IO7tmpN)HFH;^hpP2Hbi{n(QeP7(sbDBXCim!JyUXj69ykIP;iZ6Ybi
z_H6ITW%8-wJ-u(0Pj_fs<ws+o!_?AsY>h#RTCynh_|_ez8TrR%8Nwp3xo#6sW%QUa
z7P2;c(~qk8>>rp&Um``lb=aADdu52j%QcnM<nnCZ?b@RyyUvZBkO=sj-L7Ul<y+0p
zJrGM|ydo(?B5T51dwzPpDLFS^dm4RDnPSRBJLl^gOwUYje0^U>|F0LlH1D-vR#R(K
zz0+XN_jC8>rH$AMF3MvenLwew<fhZQ6g9pKrxW;e?Hb8icb&sy!)QaKgAJ5#+&}A)
zz4FucwUya3(aXwDm-=wlYY|J%PJFh->Nh?)p!ncS4SN16sdN{aik_79muG*mfw9t}
zq@?r$?Mu^ga!?7Y|0#~6POINIRVW=F#J_Vr_3^ipGTN%wF0p&H-j*EeyzD}`8x$|w
z>8NX>UGNnJkrtcHmtGtS8`Yc~k*Cr`iksPJ-MQe_sgS~1pL=dbVs4|g;p8h11dRIE
zy=%N2b#5-mE6#weKZlJiJo57LL!wuWc^|FKyozquWGq_#Wf^3Z_H1v=VT6(mU~LQW
zF}CmM3n^<6?I||SxgQgZOD=AJHR0Hp5ZFp{{1%j(f#?)2Py{utX++jEmVi>%EV+nC
zDboG?RFsX%?WOPS>ROsihR;=em0uXhD!)DPd{fouAYvoPLx#;P9bBk)e6zV*C_Va=
zjrpNxTSjJnag#WhF#1JxZ)aAir*aFmr%^N%M9J9Up}A$#Uh~Pqsg2V5#+1b$<4B)!
zBE+D>tQIKu4MVj9;GL};Nzne-gj?ch$+8pYd$QXrSPL(!Pn;(7OO(dE-Fk2O#y=28
z;~z|#vyt2yikeA^V%~IT)%~7%;X_PJ#obtWPX|HSEs#>oK=)L=S`S{ZjsDmKBOeNd
zQnIzZ0ovI{z}+Bg#QwLEAow#j@iLI9($7cVbbgtV6-9aOhlBRWjSt8DWDv}T#g;Qg
zGgEQngO?2i>@>fiQ!_&M<0{3@Eh}i-J8)chf@IaLlWJycLymj-=F$ONG_(8OZj>?c
z1odSalDmkc3l6>67OP|K+wOwx+tS7KGcG5bE=87ou3#};Zw!K>RnA+pfpan1q|g`&
zY)=-Z%poK#f}AMw`D!xW<%Wi4{F0r8atKqJG4pK^_H$>et+=p*Vfh<fE{nH{%cN-E
z-~!8Cv3#nv?sL}{<mK6<l@J(;n?=R#A46`)8+CmhP#3FnG~h2R(mJ=d^^3x%hTBAI
zX;=@0r|_Mop=79sf##jd8!fJ;pDaD}D-Ho0%&sAIAD*LDjX!n)&cqKx#+8^L&))R1
zv>`WN^U3NMBgM!?-I1A&Xq(cbz^Qc3SFEplTMoPXzw_u2cLTJJOAewaT*5bkqMYZV
z9wST#p@Je^^Zf>S$A*ugS!vI<Ju1%+A7pSzFOKwR#X3V5!}3|zYyuSK2xczXn7VN4
zytIDVtzW+!;V#EbVE_3G;CUvgqTwRuyf{H>4k)iUQCIdiZ>omjlOzARj$IPQ?L9MP
z_mhg^;xbx&k{#K~c60_rb%265hmYhxXctgUz&8#u$llgAh0m=XDiANB#M}eJ-<|fK
zER}cwuDqbV?qKsWvgPN}FH4U_H?V+wC3*VVTaL%KYJ;Cb6iUQKq@No$!$abU2CvHU
z6g~;}|0&thV+DD_-PiNQK0f8{dgofU-27*4-trdoE%Zt!o0{~xbZWl*?F0X)qYAxS
zBPA+<7fu+9KS4tUgw4*<i-n@8egb<HMAeg!ix&1(Qm4bp=03nTeq1W^*F0|bWKGI%
zyteogDp`J=m0FES+WYucGf0eD*Hy!E2?3&=G}Ag2<NJ#86X97=d<`EbP7NZ(Ean2q
zwT5rr`qs{1Au;CAWlkO{|8nCki5BW61BDCS2h@{s=!c*lQI1tKo>2%{SohhUXPh>S
zS+`6f35}UL30J~DZLim0(58d!HDcyR3pX+^>Esuxb(DPlkfLVsIQ8nn>4WqDv(8^<
zvTm|i+T>HUA6RJlJCV67WhTqp-l(2@onKG@2I&Pj>USC(-l@*zLioM0zB~_3Pw`sG
z%h`a2G}W3~TeE}>v#n`qXafBG{UunHl$2U9n8r&I_88EL*A<-HAi+w-B1wP!dO~?O
z9BHK_{|mWS_Y<I7(sX2Z+C!43ca0#Cy4zihLBumsbTO#uFyBx;@13BfEB*H&#xAm*
zfHH--8LHbK9vs8==0Fx>d^q&L3IB!)auAuD%Lo(lDmo>_nokaf9S{8kRJJpBp@Asn
z+u84#vv(1J0rAq(<1ArLy59n4Sg&=-$;;=LFFN>n>*^auGAcyh66t$^U?d|UqH)dV
z0FDa>tcm=vr`w&Dfm=X@H(c=c1qA>AYvI4%*_W4#3qUvV&kd&4<r;D=Y7OYGNNP>}
z45MXyY@G8|gtTNP=jyCn$;6B7`62EPUWiqWU$1-YzO;=^kYG)hP8bf>LQ~NXjyd#B
zer>Ov9p;NU<xC)K?3{4{OAwdlVVz&BIeh@|%nXcYRvJ?P)W5|)N@q67bS27Gv9D2#
zS)~gCo89}XG&05CP)&@-uPjgTdD&Ph(PQmI^jfgK2SekR4Z`&q{J?P+a25oIvdBMw
zF6QsuG7k<A@jBdWzwL{6-v%U)Fq-3kP%&NS`kCX!mLIMF-jCVo4fXVs0lukJ@ZZC~
z9}p#)_#aH1l&KE5857t+Jq<lU_Bzoj*61;CwFBw{09;xd&9EUB7qv%xl;CKN{VO7M
zlA~M2(=~c9$ID0^US_w_OG*+TZeX_giX{3y1yM!KFbLo*ufoI=d|7mJaP++#&CA7f
zoTU652RpwN`|ros-7OyvacQZso=V_@$4s7Rk(x1VE{k1&7R=2;gnM_sjd1IOYLgaJ
zo1UiCUK#Wjx=ig`vTJZm-$o?ZuzNY%2}@5fWC$D{s7V=jmkRNaB#*R-rs%u^wQ^OA
zZt-tvyTItUir?hTHR4aE=uPzw9TwxVpRwnWY_SikrJi=?ZTmQdy5thM^qtw(ASC9|
z>yjLI1qjEw$Y`AT6d<@ko#ZwQ|8{j*i@Le<<po9gnL}J+dXFGjy{sx5!4|>6hD>A=
z_}bYbYUbMwVfDF<X0N&IH+O=#N8N9qcqn&#+SzKmPs%p^<!=U_DL+U;lb7s$MyHHN
zHv7QEBIKzmk5vZH2_yjSQ~^%LJq?ed02EISTK0bhLwMWo&}(VsPOriTm;(Ll8jo1&
zx&TP^=HWL)l_#r!+Ook{vuB3gE32=5Brl)vFz@2V;uzzeyRPI9`ysUr!9I|a!QIuD
za9cheCW}5?RRQ2&3XDepV9174l}og5Y!U$asC)ZjS8@N!mjdWSrhU(`Lr;e38Oy_`
zqRLv+=z;rmWbONAj-0imCXO6Y_xrl5Pt=HceyzLRpyz#^8A`5>sQb*&-+rD(z~#p+
z_)rz88o>NrcX$1I#l^v=PJ+ducS>E~6abyMsK0;?+cEly{+bS?2G^-W#Lj7H>ivVP
z(qvi?ydd*uo;ebY2MAB$_6XF=4G@cce5V=gvD(C`i>^0mNNIlG5*ej?f1EkdQPx*O
zfj8z?h{CtlM;f40<Mln!M-e^b^%bJbyf^*41kMWr##HlWAjQbFMcwH<ZJSc0BGsYm
zinyORmvcf@c_|+V(vkhgDpaHkn+C_}vDUaYsEzEhUJW;8YjnP+m3X{Sh=wV1OJ{)8
z=Ij;EC3`7`lKHdEMM-%vYZog8qmwHxx=?Pll@TUWyIZR=(}1Ak@xC&fGy1{$xoDy$
zD4#dMi4PW_Q_H6c=NeOz+*P~PQlh(vbE7Q4iP9ShYVz{iOLMs7@%+~bNS|tc>P9H}
zzJf)qMB-&ziw9ITlEdw%D6R3l=quDwL3aG8!&e^#x<ad7hl#va7^d1PZ&IQ;G$WFr
zLV+saC!O$eor*K9M%DK#14)f&yxEMXj5}hj&$c`7r(9Rq8@UIvfw>l{tMhMm74ct(
zQfGLFSrwh?FL;|j+c@YD>er-Zw6KB-!>HQ#uj);0o6)kf<5PB?b?59X`WR3zC@Qku
z*2T$hpxfL(w=|dfeY(@{ox62?A;t_YY(fr=Z69$;@GvcsI?T28ChDla*iX!GmCU<4
zblbZ+{;H%Fr5ctrWsm2~aS#H`H+S?Vv}g`bIR~0x7}mmV4ys*A6LfSmG@-~Gog|HY
zk%$<rm-sf5D+i>ZCxMP~c|GFyi%^q6B4NWNa9;Qf<}xSJETA@;EhgwkKYB7fUc$jd
z`T2Yt<7EzeP973^piXhH?N$n2D%oAtbg;1%HdhNn^WPX99euJglou!MEB^D@b8b&?
z80_Cy!p|o@e6)^GgocDdMb&3YSoYv=e|&2IbGmmMagPc`NnQzN_RD<iMLpMP2Jp7o
zxt^E-?FAsE+XKX=d%ehUfx0_GWH6=CvF4>=m}aWVueh|!Nj=hiY(4z<i*A{hY9ieB
zrS3LNcZ|NDNB99slLtfuP_drC@dri3T$~d}Ll#Ch-tsmvPrCDj9IHPDG6>U%67#bD
zR2JK|D{J-<t0kw+<~J|;)DsiNF-jgr-EIA^z#<zb9Yl(UjcYHYfIHkfQorCQ%I+-p
zr9O`Zru=eC!p9ACAKp9MG*W|qZ7bSBLr<gN^U))F>X(huL9wTwPiM)p^bJA{jiJ4E
zr!K*Wn)4k)@@pX6T|j0nSmYK2Vyw5neTmMYPSPKL5+2w1LVTRw!Y+>K1z$Kn(#al9
z8b@AqkA5wLJWtGs%D2Al^uX^__Ms00cq6yLnq3GI=N+;e%+1pUS+L_z89tc|K-xx=
zDD=iVBq=Y%;GH6Lvip>*|D)>xSy>#EERrGMG{!sFeGpFIqe}`Gul$qLvZHhj!WGcw
zrB}HQ8rE{u&%seGNkeR-QUvBIZWoskd>d~+1W|E$ul#rynkf()7!6kl({NJtwrsyn
zCWTI1p2OZW-qdoT1I2T8|3*6`qhQqaZEcAOWI38%oM%h%k|WO)OE9}I+tq2Ey@PVL
z<T&B4C&UeSMT`Z$HeF0n{q;sH!*_Fc-yKAjnsEiwBI-Z&gHkLQ&@$5J=7ke1Tb1&2
zw&o2#@Sg$GJIWNU0?J+?ElL@0*FitEoQvkpud0}>GcQcXH#kFH-mgT+5v@ua;F;w>
z=1Q%+Bt3O05~vgfg>baSy>Nh~uo-~dH`Qe}s{yavtEBm9)*t^wnnaI&Wjess4;1Xl
zX}S~^`6b~Xl{N!t7@E8SB^JkAETO<P7am*dbIw}c_Lbr-#p13UKGici;|-80Q%Gm)
zN9c*^qjT<#G?+-6$LDT~2%PKK2jw_2J%?tBbso(lp-C-F7kcH)N^H3hO`g`dQ2FDs
z=hu*rZHr@$p7RCD_46#IW~0U}h|A6segU9)xbL)ZN=V33e>+yH{M3Ui|HhuFuRz|>
z&@skbYZnq30wqt6*XK$yM-^6QMkn@ksA5{9ZUI2yD?Un0?EInVpvm?TdKmbwO!%bP
zWIg^00`#7{n&i13!@+IGU|wflc(&LDNEhc>xPlY2W?H_CT?0%C>Fl%8g&_o!8dnvz
z0&Nx(FT2Le5X8xzuC$e)HgsP0{HIm>JJJAMgX~<66UaN+yX)D4ZGq~Zqz*p4Qdjkv
zw$me@$``ZnPyHcNB(mgpFCU)H0JbqR#P9h4<+Ce1L{82*K6g%KZS9p<lk_b?TSJ;m
zbYk6rJuF>hhU!_80lFkc@4^KVy08)Afzj%rED7-ED9g)g%~WL8Jp-}1Iev&;0|zL3
zh#EXaPPvAl=gyM{T-Z;Jx=bTUG`n>(`<SFWq~y;Yt-7g7wW6f7;gHY;qRI0CTAlRs
z|Esz*wv@l$Y?=6e!>G!yOgYj}OC!vMieskzY6m@UWeZpp{oP}4N*>Zsmmhn1<w?sv
zInU&!;MpJI<v8T{V;H0NLO4%{owI(A^G-(YK5e$G=53IQdu6JYnXa0Ktcr<y`<(QI
zQy1%8@F!<V2DhSj${e764x>J*xc`Iu9!SI27Gg3oim0!9dIpsR$~siu2jMmH7N~N7
zy#O%h=l<qB3UzOsI&>F0g)XtSP`*!c`alm;y$Yq_=pl`;Q-Ct{a*Gp5c524(dsiDi
zS;WfjZ9YBMAo1av{p?oLo+Kz5>am{R23Ev<DkP2(#oU<9t?OzGFPq!tGSyEGtKH8T
zv2jWdma*Aso0Ca?TwVJ%FpUya<}I)I5=4@wfTjqi`ahXokP_ICRXnekpUG&HeoaRT
zAz@OBq3qraLE2@>R(bQs6ScZqeatlm37hmGYq|xb-G;8I;{SW&)OK+o_v$cJ1*Jr0
zr<&v);Vc+wjOzr`uU>feV*^Bm{`b9rAFeR&Xs+Pa2h|%OpN*(_D(4ub<+tzW<>@}@
zjwg2vc_({3wIJ&PVHi58E2RDGsA$<UNAsIXBjYqkWHiSOB)1(4bHc@%8sY{9FF&kk
zpS|ldb%NEw{7=ExTo+{*odBd#dXOdUR__3;n9D3Mj8gQ<Wdg;;ZGM4sn_~H#UN*G4
zW^F5O^j9leNaqgK4NLw~Zr}2+_<;F&hAK*=a~+DUF%YPe?jp00K%4nl3aMsY%rE56
z&uv9B7C#wmC4UwCBMKC{frc4ThWUQYwH1$Ejs-~G_Wnd_dcw|vscU-^)nyX5J$LhG
z^yGmH;Sb+Ex%b6e<x153TY0rKLHKc4AOwIj?Q?@F+jL@pG91nx%Kcngw;Gd6g6{Ct
z=UxDc-rW84s>SgCmpp2ttyQf!FBE78EP(F*MbDA{O9Si!SK3Q1J(*g#VMJF-@k#!;
z<S_cw`kP;8Xr!D-z9zQR14jBpW74C0ufOx=9GkXRJPSm|bIqRLEDZQ<9${C>9W3fe
z{g^<OnAUp6wx9qhvSp?IA6xGok9FVvkBcJNRLBZRMj2%kk&u*;6(O@>WRyMMQTD2&
zWM}V@y*J6;duQ*m=l6J1UH9kl`<;JubzR+e&hve~#&JB?fmxO!X{kTKt^UY{gidze
zIn0<S_p#M|__o}`7EfY)x|4fx(`RX*fNXbwogf3~dj61iZg8c(x2Nl7;=3>U)(tx%
zO=8aV5?ZzQIp6wB*t2ecB;N@0Oj~D;ERb1S@)n5v_VOAR-P*llN*1TGYx&?L#JPNp
zz7MH{42Z^g7isdVBqfXw7pkWG>s)|;J$ahp>@3zTC__+O9vF6ototct)fL3(N)ARe
zYXZDUF<z&I;sVNQsL(#sNMNzN72R}lZu0fk`__ZK!t9sO4q3nIq?DT0@!Qhf)q0xT
ze>-$Y9%$iiGGMNUdE}a@o?#=0EEcqH=lC9lO^ak1v<#qVfGkw1e_!V!(u=SG)2II>
z9~*B=r>4tJ;2#xi=2j?V0!2dHcAkzZ2RZe9*0Nc$1pW)JxG6DCf9xKa!|Gn%8@&KJ
z3qO?F%=GWw6Fkij`Oqpe@+<(A+M<m9qw=Sc5LDB^2@vPx@tv}UTV`>Cq4(K<jlTp8
zidSNITP5z2UsrPNwfHC=Xw=9208E<L7Yh(D(E~|{egw`l!<L+7^qm?x<w(JPw$<_;
zkm<-8{dVqU;a6#(c#1|og%V$e#mLn{B5I!u51BCdN@Cb1`^6{5$#E`7KJ2l9L0owj
z(KMsb!T=J6XB(n5jrTg(PFQD+>i5DulQHK5mnJ{uFx|a}g?ZNX6YSK0xPfkmD@LN;
zURbm7QVqP{swV-Si9~a=uWfYgwY59GX<J85b|i|J{kL)rN`YN>Mca+5gw+PI=Eq(`
zKY^Iw|E@8EhT^h6uPQVWoN#Dj6{oxdEf!B)s&3_$xnf6RIREj1zE6D6M?)<g8JtNO
zHZ$c4eGF@}q*zBE{H>GqUr?%6{0s<A4urye_g>})3U`syKc>EB@IzJdDUHw&jbHY2
z6Zgc8EZ&N?2l+KJ`rV%-S6(0pAG$s!c1SbU=LvQNh&n2>v7p8teOfv50e!4QKk5cB
zlAf=Y`NqFMv8ml}K{z*jlU$AgF+V-kYb($I{Mm?Ct-yH3EgZPm?j#0zH2Y8<k)Y#E
zMt&4u6fw#1D!YuMB&QAPmTNdoyq8}1p}hik{)49uh`z;5Xf?DCj~eW<+OFtF+llcB
zwd8hW8QV{0Zz82URn-+3qx7#<H^!P2SEsdGLeO1y^ioxS<#-+K)TIpG$8R4SdZ@xp
zkI{4%d&7d(ijC2pe~8Y+Zy7!KH}{^LYdggzK726dqoZE*5|{U4f%Uh~cwxx=LR+MN
zgS&gxUAFi#z+(8r6iqcZ*plO`VV9MocLphgTkz94>@)D3*OPK@i=3F46&1O`%>tx}
z2RU|usPg^a%&*lbW*7t&ef5jg(|@U)YKlHv4(}Q;<7E{-37O9qU=<i(qI7^jFXQ+6
zQ{hXJWk{2{+j$1$!YK?@d0YV4eU;s;^&1GU9<I2x^Y+LkTw!WSAO;5ByyR@J{qPP0
ze?7x^lF%c_6_)9~eF-_*Sk)1(;l*>PmwIHd`HtrUSR>SdhkZku++~n1*o!Dfj4>%%
z)hGp;t4CM^D}4_dZHCSp7~lu}BeM_^IWP2_8zLmffZ80WXu2A!93)=95CHYS)@FkC
zoxCwq9a=|$_pZ9#RXE&c_YHv&5la>f^UN<7vMwt0zaxFX6uF*|qk%FQTGdX5Q1Ksx
z^aYYhx=?I&$`#${A<I-{$qId$Ns7lSF^*zw+e3?!fH=9?Q6U2thnfNDWVSlG$lPi5
zfmv6fCVkyvObBq3%Vr37z#3rT%PYFSo(22va)OS;+E7fwdr8MHpQM#PxN9@gA>p`n
zPB_&N^&)}j<2zUhM(^)lfRoUjUZ`u+T3N6w^uF>T9MS&%^dT2XM(e084$QpJz*z)s
zAtGlBZv<;U3VbGHLd?UYA^mrG!M!RTBFRz(1=fWrul_}wdqm7d!hJZ;u%-D#)L8?x
z-2DiEhzFbvA4HVe?JiP)_KFRbMkf+38F^O3dVN7ZWSm7m9zSVo)M&*T0~C*UZl}(c
zd8>e^%07dG1Oa~IFK7QVZx(Pyl&RI6mp;9w{^D7Lj}0786w}{N2&0=Z`XS%c;nng-
zK_F%^IEZ)6$2@y^G2R!t<`dim!`d}}kPTP788~TZnUpN725CpMoS%LTJ#Tf*C<a|X
zUz`Yq9Fxgt@ywB`0ryMl^=!(I!e3^dy819&G=1PKz(18WVoN1WaX?zk0cr7#jrJ+e
zn!z0UqlVx|^<jtcxd&ms0m9?&t0UQy%`oG|bHRO~G}}|HMzjxIRnB(d-W+TQ&gPIc
z)MdwgKYnm$8;VeKl$rAmR*GD-<IP46>o90Xb=J=l8T;H9yBrHo4=o!tpPSqw?&vNI
zoYPG<x_<naN;s>yNcJpWx&N;pTw7D^u=E%wz1PKYDr42=f19*^VX_t;V~xCeU%b-|
z=(=65l61~Cqnt4G%Y<Zx%l>07PP8XK^^f`wuJn-kW8q$ae;GrHTZPiEcA6}l75Bf#
z6gW^xDe7RfJoxnjwaj<7uc`R{g#x1>8Ep9=sa7Z+d9l5+lUhInkF?mo8wglxY4)-M
zpLus?J1;yZ=0C`YdMQg{oZNoSrfHGz3+W|}2APge+4oumK}oc~zn}7Q_Kv>Jf43_l
zX7mNbj<!sAUb(fA5k(J;ia~az>}n0~b`ey8`6D?ACA?uouWG#=jroZ`dx7l16i&{C
zz1h>4Xc@Yo{4M>$H4Hd4)FRXv4AS6~`bQ1(7|dlqB`xA_!IHJgEw9s+fLY@f@*_bx
zRE`3nPB&a;KW-!c%Zqn@m5g&!29-j+gU2SQ!9Frn{A!xS1`*1wcb}E~*KVO-_JW^(
z{;`tKK7ut*HSNwE>K{4R+NvvF?yK;pW(&|8XrSsL=%vSR^y=<^(xaAcfW4=o;OC;I
zn?9q14R;I4NXzou1E`{*z<-O-`CUlb+o8aCA-%fXdK~dy&UJ{W6@f?gqw^aOi7>4I
zJ5{rE=v-WBJ;eN?a5u~<j>%R7&hF?HX(>Z|9B$tc0Idu<rKVfWyYbkTSJR%aY!}o=
z-kvNTY|Pm4IS)`xlDO#P!37YTR1BYmn#*U<<oGD!5hl~pDSyCidr#y<?EWhd7nC|j
zo?7z@aG!R3I*?ptRvKPgk32qohI?tT?U;ZjUPV9SjiEKcSVk-j%4yHXX*gfwlC~c+
zqEK+xHX8~+%l}d`QM%GqzNzlL`Rmd{T4g%!&yq~m`tNYoi@09XAC?3L+`wiA6MjL+
zvL)cc+H|->WdB=CS%orbcw!c+R*<z(qbnjZc0aXW<|4V)YIp5&WBG+_dNYsX*0E`9
z>!ml}(VH3Msy>a(zx}lgbPCHbbxQ|QcQ#88c7)9V_7b<tdo^)NXlMTFi=OmjYinz>
zONZa!;g;?2z5=(#2_!g*B>3?ujPz@5<%Lo9oGdLFl5yX8b8oniviZe~&0%@Orz+5W
z;=9^XQv^^!M|$myuA%c&?a<}LyL9MArt`g!L~vkI5a{$~B_GrpM1B^AeKVy1cXs|e
z%fysIZyObR0N{Ik2k>6zyGk!e7WIw1Ei!Eplp}k)<Si<x6#8R4OzKKiJSd$5%85$m
z#55l9oHg>Q20>!vtbo85?MLy{j_VgU9IhOF@wTgXFOYiAz3_v3fAfW`;J%<4ma}I_
zo;(In+SN_*FVOoRr~;CbtAtb>Puy>WL%9s3M{9LmvOX<_991W)h#5jdL(|dK6<Jb3
z3v+kL(IKL%i-S}!E}#N(@wN5zdf2B@;^WI8(I^9kWrqkkoGVMg6~TxA)dBkydaob7
z<YWFFTT70*(Gf*wmyC-%0r){t3h3|Wcv#vdKzvj>!wTA1u}(J&3lvaKM>yq$UE;IN
zIXlXsdEr9kekQ&(bw6(htnsnUEjPKc9qd`*y)F!xJSfaoT06z$$!e&bm^NsjupE33
zt}rNSMvb4Iyic%i6Y<G{CyD~)(fyTJ6CD9pWT%+JGt_;#WFr|vDmYY$D{7yTVGc0M
zm8XfxprJuoVdQ^kVGCM-TMEo(RC3q|pLWi=2~*B~UtYXKC)nG6g8PiN&Cw?{2nTI!
z#sx87l#!GX``Fjbka<6GrGkz;BJT1T9tu$qT=BWujOXtgw@fbLh^3HS^Yid1hk?2t
z3;nsnxVA9U@EoLf*WBkQemja(y6IND#ial#B}~?&omGc^kKcTn7-r{r!%(uQ*vT$S
z=L+GW3`#y@FdY1<ce_FKuNiOM13|Mu)T`yu9EzWxJFlt!?d$*CC)4_d8WB0k1%z;)
zRxQP~Daw!(H2$q7zrEO>401>Q)>2oV>D2kE3?wVw+}lw6Q7L;6<I3_86V?>XFwMQ&
z#`**e_*q<0X;d229qT1c`7xcFdTQ*Jcb<z98ynRQ47ZcYuA0|yYEn(>tu!{Q#}~!Y
z!VfK5Vs4K(y%I9pBSPmTipiN5wT2bi3<L|*JLS4gzJcTjrAt=Dy?`~AtuukYe(M7k
z2ck4hSPuGG>#D#)i5;;}wv^zh5C`!8c_O0e=X6w{^o6H_dVHaCe{(^MwPe6bdO=1@
z=l0}LZ(rG45s}b$!Qi(lliuR!s8fnsuFi0YL!dAs1cRG!M2Mk5fAIJ*;salbE(~45
zz`()5F<%;@0lDjRPX;434UP8m=QxavjMH;-qQu7)m6fBClks70?f38BZ{5C)36nBS
zSH{GjJli+fu2?V|ApEj1-HHX{Mk#n-s2JK){wt#5Lx1Ew@*v8o8qu;3i{HNC^7;&j
zZu$e*-?|--K+@tSk7sRE{nQ%$MDg?E!=#kX5BHWUZdn!GsJhRH^?MffxZv4o_i~{w
z6;_?#!Txq67v!hk(q*P+o(+NKtUxF89v3tBDdje=CHqtW@<CZy^@;5Zz;CZhXoG(L
zZVl|$-jcOqjwqT$$ihw><F`z~s7*anR`CGG@5Bw@pNN>PO}QNlGdOt3>0Wr;vGmxU
zQbZ+JJB6cb+8UfKHk-M-(0l4^P+=|aeZ8=2dV$j7R42;@0Xetnc_E_T&JHNzPg-{n
z>j#22F*+QK7gFXKdiEHgmlIkYKZ39$*G1JXRwo}jtUxMLxO(WA7uULwOawMQfHRyW
znR}0sVNyA^6OATRLiYTwM0CCoUO(zDD^w1{{gwtS1@Ob(zjtbAkb2SkZKA~QpgCb8
z7uh@^atnglmSIq#F67)3ZJqr0*xjp=lrKRKbq?$!Voy_MaRmq@FUOa41Ux71>OA(l
zX0xK=ZZ0KKaGE(T8iYl6bY-Pd<F^63KmmtbACUBS?E+(sq6*f@{V6l0EGN#e&iR9u
z^M5<`h?SYrj`T#;6D2p^xd+tBf7N?ZWrp$qaFqfU#mSLpfe0yj)L*HF9$9@~LZyV>
z<xKYGmfk6pzY0r5CIR8XEq_(Wn}7FBM?FhIjT;mS0v9C|9iB@l0Mbx7Jd`E5)6)KF
z^oVwaZc%Lfh3fks+mW8CMa841cpo>EL35O+MmehZ>TCSeosq872XS_VBOp%FPL3N?
zBm!I-ef3e+5@Mrv5vcyWh;lutJYV*YBZDRjJ=NXw?&rD^MSl0!_)xh)mf0wTMs9*r
zB^`$9g0cy->~WIlaW1>m{c)j#5DnuOFT7!}-dN0MjoVsUT4chQe=Y&|M`tAWJx=Cd
zXdOzotwQpd`z0r+xFmLT<gIGYhJI`BxiDD+?EYx6oDeE$l1FBucnISC+)kk07CJ)a
zakt94c%}Fu$Bm`mGay*>0yh-rSG+hz4~q+2bbyo57suaIbO7j;+Pk?jq*W;J^mH`W
zu$5BwDU>$_`-q4GqxM!!h%{S8Zx(}n9uGEjp`0;sSM3+1f)W%ABsX?K4B{Cp(Yh6|
z8{~GE_i#Tj=_-6le7qXp%i12lyp)=(@Cf0~Cypw);R=2m{ttm1v#63FW1j0eFu{7B
zpFpAuulUd-o0-?X#-~jk6VPjY!B*lZ&r>^x_QI%Br~85&RWu1aSQrZFs+9C_6y`+3
z^dW(ChaJoL!9wy!$fHLa9s4^cHWAkrlY`vi;+rsW$B9d~Mhj*gwsdwj%rO1yFnFJQ
zR@7ejLY4PX10PT~6i0yNitqK|9;!XkF=2QZE~T_pQ)2`as_AruUuHsL-1oB}IU1B!
z&R9WfPwcYxf-Pp3+NqCYd(yMaT*JJ`9b-3_@m(#W$nGMaY=)agl+Z=`yae=`kNe(2
zsT%51*xdOQI#YV(tSiu2{r;K{-vIQNvG${Qi6mY@{gE5ks?gRCK)|x30OlDw>4w^(
zos;!^u~`MQrQrp>!?KShU@c3{%y`4!s~w#rXR`6`u1;M4FmFXOg6(y2v$9FC)P`z@
z1Dss7cKq7$X*?BHHS}XTu|Kv|UE~$Nt1I94>~^!!e)div>uFo8!RN!L?|u?PD>}&q
z-aScICkY5Pi31+J>R#yM#??6Y(*sc2JTvO|C(mvDh)PYpQc+PseD*9h227<ccflh!
zU7J;kj*VrwiQJ@NVq%I<PCkx7K|xX0(10%^BV%e|As#0aN=!j<(#mS1%bw!zSFq}i
z3M;P%xHfNGh=_a$lMCT6HI#WQ@%jyJ*vy=3M(F%z<h4Ov<Va(aOuD9){R3P)-NOWc
zb2)>?hlKoU21vb(6TdJ(_jfW1IV2-(wbr$opKS>xgYE|agw3t^Jjn(SB%(OL*Aiao
zA+Ok909*}c+5#eWR4xY0BDo;$;T)@vvNoDdNssY8Tlp!MB>;0D@UN!=;}29*94j(e
zpSODw7mZPX`Jy&Sv`BT=rXXVXp^9Vu#12t?k{MGJ;P2d#>DG4afO^z^3~QR6*ZWZ|
zYjjH~1yhseZq*Onga_`{GTERqi^Schm@(<c92~5#fD4>%$=L(2b`!H}@_4MYn<|Hn
ziXY;<M~|vw_or`c&+aSXU5m*Z`}__xE)&_)3&u`R7-eriz>EZ7=FQ~6<l-ViXk}$3
z6?84(zm6u-^5Vs$<7SMJrc6=M<^54t@2@}^sFHJ)P{Hmxz}VzHK|%%X!IB{ZZ3gK6
z2D=eRoGh<ZgTx60A4~R&pkW8Bs0PeHAPW_p*qcLJMX3r@VnfOxfpFBj%^rmNYO<9T
zH6n6(8Y&4n4_S{rTQ#dxt1Q;;A!KI1e<3&afN&-I&ZaNFn1=k#z1&z0kLL9YTJ;lB
z>;wToVcXzYB<6qk^@Ed{;nY-{v1j5Eh*RDUa0l>MoH_;-Q%w$AFM0(-Z7$uKcn_FB
z0`fMX!i)OEaiDm<Tzwzw=6eNI7bN9#<|4ZRY1FreP8L&b8DONcf8oOw<3@J7@J2<)
ztsQMzOdfwmkbH8yDkv7ilv*11w7O&2;hwPehtIS}_$h^{Wp0PwuHvORcxoK~=7g>T
zNn5p(7yZ#TiLVBIDkv+?$gaIKT6pN*m)wOY!T7Sn7vg>wOR1}CJZY~xeKs#oKMK8_
zCbfV~5Al{emIDabnZXbNPN~i&(4(T&VECfUEgPS0$p+UO+`GfyeXSGy*q*f6l`5lm
zWiQ;7>@6f9jDHuBX6yMvRhtR5H=!pmDD(kequb0l{ppD7?06ev@;evdE3ef?ff*&u
zUH44rq(A!?j$|17M&91`$e#$@9A}X_6mV>nDVFY?c$@T>>;J&cT4eC_k9fn_tDQw0
zW$;-H9EFTeD~+K--@o<$_~fJ0A3rL)9)e{LFC;oXkYHmEBgSAh!@?*lLbx1z9GUP8
zn`6fB*{(Q*3JAsF(;0hJw>@4tNtj-Z?udzJO@{BWzjGAw3_8x8dD*_;upqVkGLSkn
zv>_-rV<$T9GP-WKN-|L(cl1T#_R??Q69TE`@ytX+&rZMl^Q7H_NdE^fI(T&nN+&)E
zgfUZPFRK+vQa;t=$U_DEpy;BO=M3=qIsP;N^)<Gz=zqwYmj^db#nboYEhLV-D%Z;C
zEMvC~4DAY!xWbvcr%G;qeA{Om^JDw_{V(88a06%(F2>{fSoG^PC!Jp(u`6f!*g9o(
zx(-aHP45UR>(xa^Hn$_Rs~g{{Q0GDan<M8zC%fv_Tm!+pPaU8djuicMse^Cd$U+AW
z?3ny1MVxArU{s&7&-iW_bg6}l<5Rj&%K}_fiT2Maalak*?BhW|n3pEm{OL{s!M+;R
zjDhw&+2`m50H0*n;R+3$g6R3Q^1}!L91vPCm&VVTBgXpfbvHwnRU*bkFAIIeiBdeJ
zf(W6iZjWugj_yyRjiVX>a|YiLj<Z#z8eMu+oUw8n8*YlPc}{aK0Li1;nC{FO%ih1L
ziO5gY5&xvtt;j@xk}jAh0RkPoQ0w)By<C7Q718Q#;zySlAlF)keS&1>32Ta-tji!f
zP_bbbH+px7%%tl%3*Xu9AL_OND;Mg-t+D)uYL5B8sK<XR`3raAeL?L?pM5g=%%aaK
zYH2+A*rz+Zz9iXT&Vk;mV4of@?9oNlL+Ygo-%28iu+vhJ;<sdyC*Y)rXccL9vjB1k
zB=_knw_W3HO{h@03}Z|6ZdkwZfj!#k18rc+Ai1_-`pDwu*%!Wds;cqfII8S!`sqYl
zX%zPX54g{J<|_Mu*jXcB;v`U%P20%6TJAQ!x3aHC2lCCDzRa1(i+2wc0Pgv;;8}}2
zw<F~oXvPNNKjZiIZ*`dK9eHk{#%VLCqRqmpf|$;bz5k3KEGxMArVZ<tQE@AGl|SCX
z-(}s$kBvH(4qx$u3alrRZ(3o|EqO|Oao3LmFf5zQ>>wdM^6W62z(j8TT&l)yC?OV{
z`Ka*A!(HT(Kv19`2x#7EL_2Q(IpA_7tSePyE%Fr40E@A^WzQj|@uLddr<W4rl^!9y
zDJRR>d`Df5(fYXV8_WejG0pvuT2Ywb+#!N*8iEuC73`%KLA^HWBBWw<g@$fPUbxqK
zFyH*NQio18*<-r=h}iQdBTJEEw-B_naJ5BC3-c^RoTWi_f|c+oTWw&u*n3I5oL_a;
zmYRbav-G;})OO*h584prm*z?vKkU#!d0k@4PaEHWQsTUBn>R%DNm(4kppZdua$(l}
z<L)bTtpoHD@t~@EDaSVE_2bs-J!e9(GzG}m&n@|d8E=69!lPZWeWCvp9+=~W60V|-
z(cbD#(@gHSb)xE1e;@K)Gfq93zg5VB>K}b(Q4mXIF2PzxjRgHMfT^?^>#m{7Mo(3j
z_?<4c6E6xsxELva19!@4U@>%FJQn~(pDKOZdIT&z0M~TjLn4PlLTGs;$eA-QUN046
z5CO&pe^JlJ&#HPHYH-#$xINM1#Yj05^{$9$JhU<{G%Y>}K+C&8wJWm&(D+yEMA>)B
z=)~Fq(1LCzZO1N@kLyp3-{0@03ibjkx0hzv;{F3KvTKqCAj|U@&t2Qds`~H_!2oNu
zr;r=`+`Gy+__KO0vX-^F3y(U5>A=Ya8UwA`&KLG@;sGq(5e9uYiq};4SG!DDWJOzB
z99lEJ^0KG-0*(Rb+SmdK&CByw$kFq>C)@ty?C%Dvs;8gFR>EcIvy!j?FL%nuwd&Vn
zZR8m&xtz}1g}lk;D*aPWf`^jnKWa#>P!Uwnxj6pI?X6Gb+V7<JSY+rG2}4sm^o6$o
zoFeFkhA(#n1iyFLl21GACs8t4+f|-{{o<BzGn6X@R6Z1|?be_?+1oN_AEfmAkoZS=
zavhW>>sD`a=0}l=7=jtCcsO7!Z>#X(fY$Z)tCCr*@wJM3g4wg|w+nj4*R+RJECf%0
z?v$Q2VpjX9JD`vJ$MSJisOCn4nZdDVOsJC1q&gwCi{63&q9lj4l0B>`xZLx!y<j+D
z3v_6p_(#c5TI-5TpgumiSXAgj?Zzj+r-OK+e&LNB8}DDa)fFp_C>M}^1$9t?B*9wV
zDPCyXRz(XWD+4Lsi#uG0u2#K#nmhFis70S(sy$=h9{bh3Byl=Mpl8>sC&yrm1P*m)
zKMITM^ZFEI{158y_&{+D+`)&hNXscP`D2sfcRY|&uJ<pL`4_7%BT+XhqMbY{{)czF
zt0Uwn@WmAjFOhaEm(r>|;fmTf<%S==ETySzZpLdjqQ%4$j!KF^BixHhivH-bQ9ps%
z^IIHNc=Pr*PYTmXmh-uF;MnkKHdQ=(-`->KM81J`-U(nb#B|${jH;vcoZT>mdm52?
zC1@Zc9`J^2`qi;kN7KoUfyXZU`-XyoK={K8tkVwt(cq6H0M<n`^x}2IO4;lM!gG?%
zFAgo+VGHj4&JehWQ=2+5{?8he8|G&Shtxz*if)Jd$$Bf!mYQ8I$CGpGBic3@A>e;q
z?sE6Fb_nDH1E)6lbfB*_T(z4G|6w=_odR`NxNL3Y%CYHj?bhUCA%o*0w|>S*vG>wD
z7VH^{3UV2iRm$f6eXI-y#{av1<v<R_B%lL|5CugC2`W^#z}OP=rVmZb06d9eUe_lY
zDSCzWGXT^+kV_)ANQFZRj*Rq$duJgEckD|^Dn)ypZ@C7+1K102dZTv64%QC0`<H#p
zGfYG1;2tz=J%NR4Xl``ifu3`?JL{>EZNV2^kS2w7Neg&<*_Y6iaNUsD0kV)MY-f)e
ztg}?f_t}!GbcF{_1tV5jJjSC}=%gf-XoPxc{7Rb!h?SP05?Fp<lX*N8HhJL9??9#u
zZg25NJ`;H$o`UI>GgzR3R^bo7mJPV;UHJLQ<Hj($o}t8S2)@M5@d@0U#B@~nD;eMu
z<w2YJ;i<NlQFr$$5~~JY39i)ZqjxDVi|ITQ$Q7zt!SCif%MY%Dwk0_K>Yo+L5E)zk
zU+r-77tl&57Y@<|fTcUS<_siPwmCT(@L##*%<sqfpan@3?lIK6@Io-$X;?O9w7`94
zCiijUrE;~oG*klAFg@i8Hbx456Q-k!`V;_4k;rWL^o;k0#8z3y$k5Ej<?7BhVnnc;
zOzp~ZZ4eEg_6$tlF}Y}~iuyqPF+<M>yee?K573`)>GQSn)!`<F(&X6kAkd>rZLeH#
z3~t48x-xlr;46CQNGS3GePFbjvFi`2rGJnx-;!A<Fjx<pr|Any<B<kZsm(?H;~37{
zqa&u7*Tk&vVr<*0@=8|{mDh+7aS*LnL8oHM8be$fE>$_yqn^Fd5BepX@mIj6bHHb+
zN$4kN`8l)x_i_@G_V`cqVH+Lu^RuqTp{xqjA-}%CiI0(|BDk9+2YMwzaGW6^Hm)A6
z65O?9AgE3C2<LwMK*I~|v_KGne#g0X?Xp`ju7jXQXgg}>6pKG+lYDZmYpqneT2Ts`
z0($5!;-k)>t0((;yZEq`{UP#?!=^j<Z2sN3vF9ev(j4p6{z*;OZa5VHC;>)?a-w!j
zuAbTg7eqjj0|ZzAeAawD-bq$zGB;!38>4+}Im<YoW#LG%pbIG)sI^vDMpK!RLD}<U
zAezU5+D2*Td*`*cc9;V0wKTVrqEQ53x4;8GI!l`RgafO=8K4qq!R4ddx{0y6VX!w+
z6q{#S94{^PZ_6SK2B-epv#pG%i~uOXaj=-ipW!m<0F)MdtJ=58cP@tWvVuOQsuo++
z{>8hG%Ak*FgF+D-Y}t{E;U6@eScJoCs?gKvnM%raNTO)`d^N+`j@na93bVWQ!`P9M
zSClbsKZtT#>yZlcTMM(Q0R&J<1(GWk^AUbb;8N(2`(07L;F0NajJKo%ryaTJmN(yq
zZJ0RsF))8pP$;~E5}vkdUZpFLTY$PBVyjA_+f%#>4n}Y+>USn=U9{~3A7xo+xL?nm
zlA&31tC`o}Y_;yqQdij*S^J$;)zs~rzWs!?WC$TRV}PWJ)@_|Zby~;kjkpkRy7|Uc
zwRnX_1$iYmS*zOdv`oG#*<2uCHHlnT|CN~wI{&F)Xs=JTc|se0Z=tHIAB30{AzE~S
zl-H2g{h)iKlRjA{z$l;ts0o;<Qd;Y(4|3p{5mrs;3{eORb~Pej>#$Z99;=F+KzXN?
z3ojL>c5GF^2SvzN?-uSjJYbs_1PLG_n-eOhP`Wt?VJ~?HWM*n9)eyzx2`rkDC&{?U
zK5Oa54^0|&_XL|_&+w>#gLS!P`14ChfVsMzpKa=9RfuL<Y>;$>wN%souGMSm*agRV
z&-OiGBw5IY?GlG2$ve~!;=Nw|kv4_=6G9AyGo(#8;XbBL2mL%a1Lf_)L8-xiI@}K>
z$HA5~ZJ^)Pv5saGS>Bl#sWjsH&XFXimUh{HB*uw0FvY2T;qT*eUnC#q`PUOk+&R)i
zxuKf{=AVL$6o^GMGa*0I4$Ph?afK4Bgv>v+A+3VCFji>9KJy#l(u+LsFSnt3UAF)^
zJo6nrFjn$Xlo?CmU@h=cuA^%Vq@>U=5(?8vi>O<&s9z>nfp%fDKRCzIZj}Sowft6>
zE&A0hKJQm=m}LyT*l9$mV+`!Am-qKnfFuW44K)H>5|^^J1h@DH;_KM9YCMS9hUEQ=
z)!>FgRlO)a&cUnvbT6-I9j$A+YHTleXt!r_wN^q7(HOKoDtS!Uhd$M$+0#IbVxl(y
zw6Y628*jXW12(np;}h2K?O)xLy{RsMXzA<q2mV*3T)_UXN$;41T?A$C3i?JrCWZ4R
zGWzSRYY`P)`ySO9_YHtO{I%?I2x<VW9++;sGiG#L`#cKb6g)Y%lXTDh=I=|y>-OU0
zV^B3J`;~*zg<#7Hq~bkT-I*K%+ZSAMhDfdm18O@T_jj^3Q8*1?zFyWMh(s^Jfj5SG
z>|J)tlZ9fg>nebA^)huD7(SK9eTJPszV=n4rMq>0iWTRo!|pG0t9g&QayJ4&p<2Ri
z6f-RMd+@ygGFNg+yXcUR0$+{|rpV-2ji(L<A}7MO7N{XBL91Lv7^);B>p|S*IE}AW
zkdc5w$ceKZvUz;1?XZh9r51;BV*CF|r4lOtyAZSWXT$>_f9aeA>2LAQjaOGCKzke3
z6~u=-0P-84`atN$fzsLl)Ca7A53ZySAyDIZPJf7tUl#sSoWcshM{$J+Bp>xs5sOLb
zSpIkUAZB6*5lsGLnI559#_>KNtKwDN=>9@=DIvM?S6yhZ#i-<AcmrfN@Q{_7%#DHP
z9MX{bzFbq2ZPbn~y^KmFr=nPTUh?3<5speRCNcOZrUh+HQuOMht#eALYAkaqQPU!#
zfnaEvyto3zm+t!cL+P_^Ur@38t>k!b(gu$r&*=tvxi!BEbGv_qL5-kon(JS`y<Fjg
zxCc6&D~k5n!X%v*_p;YunIB!&H1zNF*0bcGv13Py@Ge@KRTVY1-n|_E;8D2z11CtD
z+q)~(j~E-nRsCF%5VTltcDdKum;NYL;O4b|a{FV)`rQ~(I9Xltzg~eGZdh*+goqy%
zJ4b8dFtwxmvp5de0GH!O5$TJO>Wx)~7?9*rQ5batM5&+827eps@CMCNm#Ik+&p=_e
znm!k77u2>Q*xF7Bm!1fso-><nAze(0(uR;^qQc)q1g(~8e0!`iY)A+YK>M2qo+__@
z0*4DGKh9~A|0(KMP5!&Q<butv7gH>m6nKJWO>XT!&@VVYGq$t4owQJcsA2<Z$Z@oW
zRK%I(1Lg_=l?Z&i>MCC1coTg}KDHG|N`VrXOlf#91`E^*qfd1Q8bV1DnBU)BZaYsD
zNM!&Biei*V&$#4?!1a3lUEZkY{+DlfG_qEQfDb^D<E)bKjj}@tSJy|fu>M5a)XARp
zd&0Xmlh_ay`1+ecrxzZI5#|p!>&E*bJVF#xI3UnGJDa6H?*;7m<<{@42@loS{O>K>
zzoXKin}uoX|E_C>ZDt?G6Ui_D${J=Hal(u!V25)p1^!k)9Y4@y7;sJ}Uw5E!1_0y=
zga`o)e4Zm|(j*m5*Lw83K$#nx_j3IHs?o@|eMrdKHyKw#J9nr;1yg)f;r>%P$_ld2
z;3Gi=(`#r6NH*_PHyC=}D9S%@rj-X0S)|r1mir3s$ETQBuhfAc8m$4l1iSGFpAqf%
z**-P8iQci$oZP0@smcUrlu!`yYi&m(t3;kXKk$E|C?f*CVxd7AzqP5W9Ya&M>}h?O
z&WuzaTLiTZ^Qy%)J7)Kg4@W4w?kf~!=dS$r;QH>obkKj5GDG8eqXle5n5>5n^X9Ji
z^!946&-dm*?ww)o-{&Hh^2ExV(3L~jb67Tk=!>Bw6O{E>c0(}#HWoxlKlP2?f*58T
zHhfF@Ej6P(u!kH+9PsPm+)@?G+m=I>9b_c}PD}!0VlEA}kfgSsi-Vr^_+R{M&YjWn
z>bz=Ob~ktB5jRv5ft!_*7bZOVw?>E2ORVa%#7cYXX;2Ow$t2fHc;3<fmUss~b)itC
zhms_7hoMZwp{xK(uQNFQoRHpyO4q;57Mz{_wGXg&7i5&APe{NI2K`?;U;qpnv-R)(
z0gKse=V-2#retNE7T#MWvt6BbDl5CS3r5IGY<B77wSRBnc%wSj)QSe4sg*J0%K5k~
zRU-#Tu|QrRDZ)cE6IeHm8pEP(OaH)bPPsVRIuTKQz<-gl5vo_B@rO_3MexkyucLh-
z#KGN$rxBPepS|Wwvx@pW57kptbJ@4t#E9b3+?cu=X9@5`L+==VyPwE4Q>%~{qQSnM
zXY;fZ^>GXS?!W$IAX#2*2{AEoZsgcN4HAmj!wCy>lLN;fT?j1(IXjh0M_4lRPN6!i
zrEhNl(K!pJ{^gb!+n2pD*7I)u=v4}Dr?bm%HMe{}7hA_xQ~7%y#@|m2jI$3>5S}xJ
zhuEvBQjW(3Z+kPJR}Kx)Hs9Q!mI=Ml`Gbb^F#iGsEpldnFQT%}O2|Acfi`2Ura%?l
zIX)cgE`AG9H;p<59^iz_&U%+VTX!yu`P#SL;e1N!u?;rls(4rY3OYNTZm|IScX<L0
zFT55{S%nWG26F5?-KAhG=npLFuifns!=#qV7_AmE<!m>}nuLuPi|oHT2km;2;3s*O
zk+iFPobz6cV9>a~kiVAwG*GyP9F1C%@qM<@e<NECTvM{^mgQfquu)CqJNNI8Ayq^d
z4uAaJd18(2<`W_J*_<Pc4L>}40NE;Vy>KlCk4K)>?D;Y~Iu(bx(ylP5ZTRB^Y7#np
zbqMJr<}JHF0@$n}+E?4az;;Sp;?QFDKu*pHqCBIJgZ+~yPnN;7HB(44e7G3+2M|05
z;CLP+ZlFppk7!Jg&87^J>ZgNS`mG~^NoV9IE}nARXpbVFhOp=+F}!J~EE;L5g*k1H
z#~}Ixh^FOz0+PTnrh{B`y&~+qHr1}i^s{^5)xE@(F*<TivqqaIpW~tY?XAP_*O>7G
zBMxWF=!Itn!2koL&%~50=WrQh#R6ybTsgdQJ&6G4d)mY&ZAg6L1PfhE=Pa`z(Mm>5
zNiO!t)_E&E59`IR5`Ot#0ePy+u(1?2x8x?Xpo_r(JQ^tnkVU(~iaCAa)x}xW>5X#W
zx+qpUNp`I-P_7v~JTG~n418P}OOQ7?YSPcJ_fPz!&k858%9YRKpsW&k`0%VF2*vOz
zI7d>;uA3VEuz3MCM*H8={QUeQb<E*a?-?ajGmZY-;oG0VS1h$XbNV)9bOM^F%Vjh{
z34wVKX4rw_p5Ty>oFcL5s{)Pj@ziG@<5R3af1WrqxH6b!oX)Es-hRgdSODEXKFeZ~
zx1i)UnDYXu+G0(T^}4X~dxc(1f*cGN`LZAdebmST8DQ}#d~hj8uI#&8Cvfb}(=E(w
zbO`kX&1&!AAr38#y~(XXpbAp%)~KM1OB>!&w+O`q@zgCy+~EHl>-f$qAmd6&a32B0
z9=Yk_youEd?aOPDdFE~pxt)amQE!}brgu=FVQ%h9)B!Wj3aED>XoUAZvh|e5+Gv4E
z>R^q%%`35^Yp3^%*6(>oRGI5-o`1Mckt5t*y69ePT%226nC&Sv3X_zKY^9!~bOcfw
zZ)s@IzCMt?o|c-5#?fV1E~r}D+UiU;d_i5s$;rv8TO@xBdD*g^7N_jZmd`of`0%QE
z^?PwfKB|hC^jR^CS-jhhx<-IAft(h)<No(vg9jQI9b|~}2{As#i1yRB*;nlt$8;`&
z!M(ci$BlqB^0xaguZy~BuVcCj<P6eakA1t(1#Ii*b}#Wx^uPg91LR<6WJ@;ZRV<p_
zsV$Aby(w{EqD<275WhYF77L(L1|j8SXi^Ud8TT9e|H3V+-8>Q;-htp50e@K(=8Fu7
z9_<P%22F6@<EyVg;t%0&=qI5C6+NV<jpz?<nmcA3MRw@+rdoegOemqnJPhq1*r@S#
zObu!K2Xw9y87+2lo0yo`ulGI&u^6hI<kG2f7ie!hxIVE9578PDk4Jv~d|OM0Ak+{=
zYSftX=gjO29S2VTS5nmG;)7o|A-)kdxVU-jc0%W>UOih%O+BJf<=r_2SJ4DvEcA~^
z1KnKhyRFhcDlse*q6z>gv)4|EIN$stZ~sc%b-Ygea*K9*LZXsZZz{j{-eevmTZSgk
zIy$lv3*YQSLg~$HpHNQgjed?R)V)n^7hnOGuFmX_s~{a7;=R+eDo+O<I7rP5XuvmL
zZhebEjhLL&pq#hSBx_27>59J_(CFgh&AXOo;T#?A9bI>1nh4PT0pV|sVjtt*ZH@Ro
z?Ae*bXrLZx`tQzcEzRn+8q3PcLO5TJiAja<lkgc2l)*uWhP6I<@&sdZ_~Y-sKGhd}
zS-3Dr(m6BpY8bL_0}?N+_4Pzey+4R&B#wfL#v3RKh;3LDj8fz=hWzq88#8yfdfEw5
zDZ~vstnhhXiANU2eYRMTU`*Re0!+yAjyQptD2+XD!qo}{?aCll=4TS2|D^JL&YdL+
z{E`reN!uIIUR!Q|65Rg2${vDLTvi#@@W?J4#VuX^#^{~L>C|`3=*wwMp`jBr_~w9U
z^kG483@TLl!zOKp+#l-=oIVVR`lTg{a#(51^6x(?U)kPwBm4D|pPr7!2=N9JlCsqF
z7{*PbFMT^o-7Mf5J4~YJ5VJr7llg~;bbne}VN?D)_*w^PJ(S%-Lqk_uCFtlcUnc0!
zF{<2L8K?K(TN0N5VIzc}XqNgC32kDerltmRYGdY@3=_tDTQJ6h%nbD9Cj>&*x}TW#
zCCtb%BsAYC<il*2ae~|tQ-S{&O`_h-nQmxvJ5?dq{}KF;lER@Cuy<iiRDy;U4r3Gm
z5uM!liYm8?Y2Wg>*@#M1buT=)Ep*4x<}_3co<W%XZNKUbmizkd2ky^3f=E+g$QlkC
z1YzIC^Y0;jX8?tE8Ao=ZAYQ^LK!j!S<{fk~08*=acl{<O<W0w}zp5$W!x`ClZm07)
zx6@%zm_8RDd?mL_{fKn7hK@tN?7rPHd<?Wwgd&OT40m{6JtfW%tvl#&V8#CJ*yKmw
z+ggdU2I#=@p}6`C|JAEk>1ZafdOj%&!op&uaq0ANR36?_;vj%NDiDGeL0WbokKtMN
z%a_&uoo{>1*#q1t(~zGPY$X)O6mVX}*bVHht)2OzY?fBJKX(tU%`+b(aS9H=z{S-z
zd{$->TY7*}KM0lkU$-U@rjZmWmUkd-s1YOgs@c(vMoKTg(IUF5eBTunIH^AuKtp@L
z0tvKR04BQm0qsZQy%cQuMK2l+rWMQE7}D1R6ng1Q2B(Oonk$-63vB)ssR+ya4DUy>
zziLbaodnJ&(FX?~L4iO6B51F7R|xH!VlFa*4e-ccXL>mozclQwXcbb8-_hYkbzD(J
z&I^iPNGv&xg5$t-mV+$`!}Na$X<eH~RA3UK9(>$Lx4H7qgq_XoH($sOk&V+`DO5MC
z7VW#Im`Lj$fBo(4jnA%<j$v$U94KIQB{8u>_O=0=(C%`jfZvA5{sxSP&srBnj&Lc1
z&uEGRTo%sC)P-zM2nSBRHZK0)(y}DWu)0Hr+CwZnyZTd-Vl|x7HZ-9e_PuHPof;!?
z_B2pm=bQsiQ)fRyeX$)7tUmbl$&pK-zz=-WJj79$^Ay%EFc%-*+5Ekw0nP7ou<XUB
zpeEm<?}#28iTpANxN&@O(M8-4EqiUq&m9eM?`S<`H%$Hd3)TQECCp_sG64(<di9FJ
zelnm^cFM(O$Vy@iH%sWX?xoO$=Mp!GUoF5`q-~VUwp{{{2Vgnq6>dE?)efu`Db!gE
ztWQ!Q6&C(S8ROaKT|v{~esur3FIF=0v{GZhDCXt4w9>I|O{5y!9>+#f$*T|cT(cqZ
z@BGPlkl{0j+dIP_=t4B(^6%e<ttEf%4UCZ|uTVaXF+1Y6(LxMWH57-WsHH@aj$CSC
z#+#6#B1(GVg#pQ=n7a6@l5LG+THxh^>{rCoLpVfCJY_7bpNRRVE`(0O*FW5mBG1Og
zo+g6IpVWVxR$9_E^oG1?^Y(~@JwFIRaE8QKNVGXeXH@L@oh{#BiR|-ZPc%Cpd_Mqw
z<W^hK0vYx{;ZNjWRnE<fRjxQvFiuq*k3=9PmKJ~#>ruoHFMhY1r>C)vc=YSB>A7+m
z;RJ{^nH>qB*Fb|Oz&D&eYkg>I8a`0GM=ulOu6De$xg8T6k-!`a=@lLwY4Gbp7bD3v
zW!8Y63|$<@tqJn^p<=32E@LtgyO!^$G1MPF<}#a<LT`|lFJEp>eAXnUr1S`a{B;2f
z78Vu|ZBu}vTT5HJHHgUTsLJkp4-#95qpghfnq}tRSCaw9T)#i6-bT4((2I#msEt3H
z{`Kj@j~giI3;HebPCJz&TmFE_Ug%!aci+Rv)IP}V;3}&>6^q>`-Nlhj3uz3ax3ST{
zVdUl0&_3InMl1j{_y9c=(J0x`nD_?l`#vkK!3<<1`rOe0Nkq?<eCx`~-wFy24-;hK
z*nx6+OG8!gi#lp{zDtrZjg5hxmUdD0T8Yh5gpoDJZ60ZldpTs78eY3HbjdZ~{T1}j
z(IkxaPuZ4s9Wpa|k|cedB=pE*YE#f5Ci%wcXsTZc(($K)BLe~tyjoHT)nEU6wyUop
z6I2lH8aCzBDs<F9Xehx*7gZps$4?nka02RpTJ;bSdG+(V0LR6?zCNAhk@8FI>{XvN
z9X&WocS$=tJHf1wpP#?^tDYm{N!^Dodu!>%-$=8t1mp&ZclY<3Ia43radKv4$B+Hh
zNqU|fOO#hgt3quVpyU9Gg%eB&uA3^NtVTgvKL20q*k>HOv1$>pj%{ncUYHR|?XhPe
zQ|7+6m@g}0LjX2lH(<|$qMsYwtlOFhB&S4NfHaSseN)6vLd)QmDPAvCAFHPB?Zk(9
zI`6^x@-TIWp8*N`y{VA6x>H%#V@;0*nFAs!%5E3!e2={jK9dd6JZ?VZRhJ7^KnC)S
z^LjyH5Z@yrF+Xl$JF*wn85|#fsId&i?rt&JQUDYQFnde=ASO7ppkO%H6@DKvF(s$w
zx`=&4$2gs%LCrLm?(eM?DIph(7X-<XjNII$rMtiIARgu<gpUMi5DwUm_m`I+YZg8b
zeAX0_z|Co-t?k5g|C1IZFHQI7azJPuJ&Z0e>R<nUq7lXsaOt%~bQ%AVf$y_rAF}&s
zKB_+*vLhx>59y#4ofR1EI|U%qj&lnVvX4q9{S)GH=v6&Q6i`|DV>+xKqGD0?K%R-8
z=+`1E^$AcDp;DJHV|?Enah|nPyVm?CP`^mZ?S!16w!4Si70_`qR7OmsxGjVC%IfI{
zSGubO(JTy}wZwx3<kt@a)aA1|4)L`VQ>dVssPoPFlaK-F0d?I2MS2~2FEqzxr3%ag
zEv;j#c9A>PudnNKljDg(`jQ`?o1N}nzB$StLPlcb;@mUfj$1HR*V121UilA+aN{Mr
zCMzXX1xwKxvH&9@A~4E|+v}MSQrtT}=Um7&tdHr|`no8x?n__P)YL5c^@Hss5fKhX
zbaeE!`ad29hD2BekSVY7k`p)EpdthV!09(YBXf3k*O)LXEU^WKJyh|4t-b=e%z{IM
zxWH`|r+KtmoMHl9dUZDrsodkZ66LFqC<2Hl)ae0VwGmh*z#`ZBNqnFxa)9`E&?`)*
zN=`>_HN~Dq<KQ96%l%Idz`E0yV{twPm}n(?u9XB0Ecy0$1wd}K1qX<`8_A%Pfo!y!
zLsSb`0t$OOLMAAyHCP)N>=*_?+V3kn+m=h3Ap*+C%_Hp4QVESUlEKGIUP>!ly*0;2
zmE8GBa=Gmm5ojZDZyN3I{|8hkUb~^Ep`h@FfQoknc62!eH6M#8pP7Jb-x|6c^kmJ;
z%M)DQ0-d|%R4E%98(rid>wlzcs4QCrY9XHd1c?R|)9(53$oPuRRMAX6e}im>$R`WE
zcLjl&K^)=mL%^INePt}yKY?P>@C{mA)ehc%O0nDK6O7I%L6PODlYdhNqS+GnfeC>i
zBp)jAjeUZ!5KSl4L*S!56zMCTk`(=o5Tfz5LHH%NhWc^Ou}W}Qc|BY7rhTi6gJsW`
zO%;o5uvA1Eak<yAD%pjD2r-@cA~^@)Y8iS|cFWQIvU?}a_q%ozu6nGvVaSnTROM!I
zp0GrHv=F}Ucr(m1RIWGvV5x~8^;ufq#wdE>t(LFw-zN6EgVShaWF)6*JLi*($a3Jk
zh6LELYI4o0x42{xG0567QFT?-ag5D7XO1d-o_vrA*%z^)DRLl%__v&krg-Vp$n1Aj
zn2Q8{;J_cE0K@moDI^x^A{ZdN(Scw`R5~0L&IVC*d4|7qA6d|#_Z86AwaCG7WzcgT
z<p>nyDl4s8JOQy{nF5m3#B05?;r0R5n+m2-o@-NtE?_Qz9YUZWDo8bZ4R!j{&U=vK
z<eRBFP_?u-Qmbjs_yH5R0zGo}K@Gsu_W8mjz;dJU<EUP=wY!(rvXua`=w;Cm*D;S&
z6+wE8m)B!JT=d54gjL>OE|S6E3If<E2MbI7T;nAXVl91rkLO`F*x){Nj*EMq8M0v+
zA9{2bPiq4gx1gK}&?h?@g;b8xB-dK7;4&OKvv2GU;1c>S!R4QEm6fVA^ll}%4O@$=
zr#_ArOUrFOvBHd9H?L7}P&b8WeD;BJjI4R7OaUV<h|{9Ah5P3|4JxvqPhH^BL&ImA
zji7u3pN%er1;4wuWmOQHFQ?+Mr4|VhCYp*4CRS$95|3&>?Pv&<Q-Dk$)HYrIO2FQZ
z6BmkV@zC3Ar#^zLs;FR{6vd9g9DLH0v)D}SpT<RSH<Ahx5lW>9kgV2VfvA7QG>?D}
zVGvf{)ndU*mrin&Y%<W=&2vppUu*suxxcr=$itKU$XEL4R$h>Wc?Lp|<SB|f=A3sF
z2{}^2Y;9N{9oSNRjI4lvWXvZRCFw22pGIl0=ulsO4fTw@-6h;%IvMBhh38v<U_n4L
z+qym)=hF;#z9_B@cwY*qax(SBQRyv+LWNgt;`KXNK7ttbj3(V(F^f=nqv9`4ybxWe
z1M#cm>$%#fU<;@TmYbnyxX8}<tInY*^*(9uoppX%BJ&EM-^Zj3=s}7i?)a$vO2I$l
zGVGDaNd(e%%@h<A(76*FMa!7>4i3S4_5}q6rY~RWum8@94GKC3XK+M%Ix!R#>FMdO
z!6Ji?kFQ$DbGXMMCGHg8_869l)&?^tqz`m-1yAd1!(B?dw7A;hUs!nE>K23se8*j{
zri*J|1`;u^_txt|dhiL>*s5BafJzn?hVeI+amaH6yq5@Q?zWKyG@->kjR%N*80@e*
za%C{j17bGan_0+|==#7tR(t3k`yvqaX$&@KKzkvoHnfNE(C%M9l)CUR(Ih0OoF53_
z?&Z*?=AQj#-7C0z8xjWpd)b=8(r-qZnq0$ZhsBIp;b!8NmzM`mqC@voc2*X+PuyFh
zBy@pGN&oiE0CEcscS2tY%)>#C!k&2`CdUQ(b%DrLq2x0n?(R|~I^b)=Nh4gTn7sc5
z{pLwuRlB^Qy874S)3%Bv34>P;iYxHYFNgKtOG4&Qb{j^EsQHI~u3||_tpq{va_!6<
zvDg!r$L#n=l23b|QMog)Kfvuh+I*ES9ni9SiHaP0BxKxwxng5yalU`$BK&4``5LdD
z<4hY9gtXFG35DITU@fp(8uFyccF4%cAaE=J$MN%~5TyLe)#uBA(2j&!4V728;L-c@
z?B!J1*w}n;CeMAkYa}TtY2~1<q2b#m|I&%T!1m0UGpxcFVd+R<rEQ6Aq(#Qk4pZm*
zT9AJelg0+1wzB9w?evm1;YprLms9j}hMXN5I16;2he)#3gimFfuBwt$HJ&M{9#SMX
zld#o!jl^Alt@`m3przqvu7#r+SqvS##CHcliXY|BM*LHMh<z&76^<S(T$fF26YgI>
zIu8ngy|RwYWqh_~<{Xyejg%VZ#pVsUVF&7_+nsmhx1^clcqaO@<(HlJugr?V$^jTN
z_U6|wH?`BE@K~(Ihd1FL3+S6iffz(%sXy-5tIooDUOtcW?q5VZf0b;{vK6g$slB^<
z;a%O6?+pz<qZf8oVIG1`Yos`l`LM_vY;AyiIJvpo0>g~eGIUOagoXykGal^+H*%#5
z<u!^do`#*WBLTO|wCs*WQ3tf>Z?6?c{!~6DVHVBr&mrOyxe~@i%=ujgDbr(SGFOZ5
zE~X@%ziYDjWU>HrXen7QL!+-Sn5PW>qDYf_;+=)(a0BPRzAhHI%6I1+TOp~tlt05)
zv4IVI43+6qfj?v4n&9}@dwe)Ls}FyTR~pQFiIrBo-WTc@6%{qQv^1<U)!pMU35@`T
zv2@cbbMq(PcaI^G7f2!e6-T$mTckPc=IGoUo|&21-5B}PXU~p-3lYU+0NLLV&@{u~
zx8LN9mHOg*SuN*U{gb2eB2q%+B|$oiv}cm|*zJHTcQ7v&vW;l<H25TF#}Ell##T`<
zkC>Mnm&lB{l$<YMy7=UhmZux0@8y9xV!LDHc*l_^%7av|ITGJ%Juf)@HTB0Qqr_0T
zQ)R8xxRwL&A|EC=uDlJCD&v37wB2{|T_Q)EL}2K#<qv73Tzn_QG(Ja06s(w>lvXiE
zzuJGVMEw^n16^Hb%Tgk;va>DNtlD{fD7Y{c;$&QMbFJRw_V@RrEEv?A)w{^nZ?305
zPKWTn9BWz-u$+f^r-+}jrbBj&DlBq%8IXO%G!>L^UYL;&AS*X%-!mc34R<~9axLq%
zh$SX=4=b^mg7aHaT-G_K*Myh_Dp@jVs`6Uj+%PpI>d%i1Vf#M0-!e#QYS2fJH(koj
z`r4w7J6hR&r*=u_t5{$XDH405aeyM5k`$L%>q=RzlvC7oQb6D$w>6vngT76>{?9k+
zuIPW#IpY0J+aeejqwe@P11jO>h67xIT49WYhJ^)>D?X(&e9FPCknC(8c?AV+t(I#$
z8dDP!^uodiC5!$aKfZnU?wtS~>VSv+FWp8oh?IJIo(j2Xx876>zr6iQKTHXq+qMI;
zVmtEQ4!mOT3wS%73~~Zd0o9Q`654dGE~CjN+RLMb{>vNH^VlZ8Gq|cWuNK_C?=Qb&
zr2aGobC|B!_aLPHt$Y=mxaGxyK)xSjSb~kgrPmF3FTX72U{9+?nx2nJ%}NK(8Up^7
z##B>hbbh><12{;p(f_+S(Y=|M#}9!BJ59B%t*y%&8zvdQ4d=yvJML|lmz7}z1O(ib
zmKK+jBWY;dntW>3*4~az%ov%Q^V;6F#mIFY1$4idZF-M0IVFY8_IC06_astMQbEyl
zw$`q$uDcuO5%`92tQ(mz6AjL6&fY@jpSj!?BkgWdBto@orozEzrx#nVniNsH`Fp-o
zQ#ETgTFF1-?#@~Ta;TJ+-h++~Hq+vCQrd@ijBZ@BI|?B`pP6^hC<UVM`<n^SaSvZp
zYf6&Eh~lrOZd|-r=DhMr+ExmdmM#A-6%zh`LMt71w4QVnsNKA;wl>d7aqgVX?&b;x
zMvT0N@cw30fPev=;LV#$_Jbz<g;pkP7DuZ-ni6H%H$O`(L%vWpa26s14kiS!x#<I8
zbU^Fo>pLa~pe$<hVpU6zI$%jnu7+m7{81JWV~L<AV(hgs>Q#&ja8cGhVItPFtaBU(
zHYCldLL975u2^wacPVe{?yCqNf@S|L#=c`d1VLD3&L{99qN1=cRI>C)QXdy~{h-Mv
z5_Kaio*RGr_U*&jw?`+D#l+{%k<RST=*)t5FBKe!yCGXXSaZ>hsAdZ(cNZCO<7F*7
z%XkiVP8q`L5wmN@MfS|MVi}-?2CF?V|7h6|zipCU1Wq5+#sdE-U6?nwdfVm!82b)F
zN_@Z8`+?pz=a6N9;6>4&^CeL789DvAwEw*EyJULiEiev*Jn3Ny>>iPqFS!tGf^%;n
z=polq%}<b-Ij&HsskzxCM&{`Mq3R~jf{r|(*sj(FH%Cm41|YsL!X;y3!V$6+yLJh3
z7{Ncwcdk$PL!h6LawkqqaD`9BJ*k5QY7I>*N{IX?=mMg@;<sVbI82Pm?#r^UfS^Xs
znK%Vhz4f#cfm9>6tv;;-H7r!Cr&o7v`{{qF9)|~z&N*QXBS027`S@xxn}xh!Hi9?x
zby9fKwPdn_PGL9gDjFLJE?&F{w^hpEETgEJ+D@w9-0ZAs0vY-jFhqW`0=W6_M3rqc
zl?wb+=m<!&=g5M8XoAqb33y@$&b5y$&>4A^-{N3{0Wo~mJC%tFmok2-u$BnDbDzBt
z6Tbtx!O9H76X-~eX9VVMsSoE$pFvMu<_TeYzgtgtfWOTtm#eiJs8;Z*;(3U*Vbc6n
zY8`f0?_*o1BeqH_#{r9RK0mU#f{gP!?rvhyDKor}DACyKNL^=TW}Zs*O95i@-`iJ!
zA7SP80gdzSVClelnc{7+GRlt}ucxdsG6r&81VDOq8IF>v9O#%(B+!p;uH)xaF%a~J
zBQfIUOP16C#3F~7ImiGy3mf~^QrAxZ_GUKnC#iQfE*X3f3dN6?0W;)mH4Ix8`bYSn
zzsV+mYLWcyL2(AMBfZmqaM0WzufHcywe<66$Gy;_{vq4jA2d<9*Uu%KICD4}1R@`I
zhwst_&R%uzCq0C2>QF@jfy}rDU}>P_ZRtk_BPE-ZA3k;i&Gm?1d-qB@hcL`o(AiFV
z&V|aTU}nycRH>xx>8Stz5JYIE|J#-DyFN^`fjJ1)89H75tG_m~<_tNxZhNe>m4h*i
zQAp{4t`J^wzgZLG#Q|M@ffNMNCzeDtrkpp9z<ZN-xe><lWn$-z1K36|Bm`mt5REyf
zeo8LyM~62;bay(8)dGSRW@1V*L4>9U$OR0rX21h%3i$wN+%HT}h=^ix7!m4(QG`@i
zo}k)$pu-bVy4ydEt$iJ`7Wz)m_ib|S2;}{Pzr!0p3vZl=g7bC1Uv9e9kPQxui?kBz
zYHclt@GP~oqpl_L8IWrXf1*-OlT>wfg0A|jMj++Sq2y!Q@(b|$9+Chb2wvV8_ym&d
zB_gKr#?c`n^ZDd4R{L}&Z0L@}563Z}0UmmW-bn~$wMzQ+bAV7P4#k0<0$f*UA`t3$
zf=Da6cx9yzzw6ONQ&5$Pb>i4OVh|J~e2(|#;*!h%eeS&Q_UVM|^Rm_SGKKaxb@Azh
zsGXdgB4_93=2nL6mb5z)m8=~00eE;D8v58Q>j>NFKv==P4QJkIzyQI-yrk~|${*A0
zYcD37?$Us35j>S3j`)IYasU(tOt5?tmz^=K%lZ)$vIZ)F<9hHY>4BO_^8aJ&E5M>m
zzqbbv0g;gI5e$@;?o_dmP`U*K>27#w5GheW0ck-2>692kK<Ngl0fv$qy5s*0?z+3b
z@8fcZ+3Ql6_lXntxzBxa0(b8o8kj4Z-uqZi)#75$FZXTPTf=4byd)UV70ovAiW>Av
z-`bAj!^~E~WKScv6&(@bpFczDlP!%mLA9IAoDz^HWA9>ml}JEZ<M3MQ?5O<xB5*I=
ze{O5i89fmWm}V(9$l9~<4~`2;dwYA*50_S<jp%5y<*`cgWbcEUT>7=1Lspmxl9T=<
zS_Kxr?a<s4paH8WAHCj@dbNKrhh#dQ9$ExMSp5J;I|EX6|E<lE+n@;xjIF7jF9VDW
zkn-($s3nUr+o}RR3rwrG^80Mo*GR$N2^HPNptcOMM@zGQ7`ZcJ7jZCaQOTnkGrEXJ
zk_0&3<}FFC?r4eOk|VyGfk3N0aOUWO@-`^tO#!5Yhp*c$W%F<T)9=SrKCDaGzp^^>
z<BhP}##NZKx;ix&yvQRU5cuf2oS`8zm?xX^bb`L1ptQ6J_&ZFsqL=eGL06y>%tOZH
zg%)nJX^X75XMnOO4R1)!OE5sOF?COTEVtUleh<_xF+e(Kr5AEP4ULC>%kU@N3sg~p
zBmtY4gLu6KG|!Zlf_!+-@tG-RveSNo5ow%mc2O-*^}+NF0u4J30s?8!{j^PVtZrw*
z8M_kJf}96*@;}v&{qq0Xug@(hZek!L^q-kAE_DJEKlfJ3kp@;)&^Wg4?`IcC24$4L
zrYHQ}0wi+a$`g|F!&)I>w-L0_@Vg!s>DmEcaC~xR9ms8gn{yt3zUEz5Vqjf64vj!T
z%J7p1#GQKX>pP=S_G3&Qt5GX-$wXkAZ3*Sj%Cp|`1gHsyfNrF^-h1UeS~8?i!S(|x
zDx1hVGHzc+^uU-7jCLD0v3+n}>~bv=-ruM1?0c#H#dxW-xsC)pfDki?IbU2~Ust+8
z!L}407RKYgWrhKbX?P5z37zhr<pTZUXOfveF+)XgXa5qT<Nr<$RMMpt6J*B9&gM^G
z^jltLG<aCE=j64dtrYHAo0p3vW5^z9R)xTta4zoI<2}K20@u}V`Fiw6-v<yMJ!U9D
z(T8<_R(;-)1YL**Hjju;Y|g-pphs+#OFQ&n7zpATojm#Go$c<?aRwlu?y5m6(QWhx
zd^>Z^&`yp5UI(xduM-kpB?#H10ga>P=4N24aV#w@Q$7x4!bjx(667I<0iBH7fLH~N
zVbq5Y;Ts$FH)0;Vd;cCFn#7<S>94~T{HU1+_xECX0j?mXI2%m<4cE@&YrN454HzJ|
zf)*?D2f+J+`d!%3;tj}r5!lLFl(}HT*STl5@)l?FF+eL1;ORbT!m0!FuwCvtVn%82
z22{h}f-$<7*+OqTguxu8o_p<_KwM8tHxh7t3a30psTA?0O<DQ=4OflVMD8Ln(p5mg
z90-{+zvl1w8_hqn2@p%#&c}|Ag>T-x!RVVYNVrqPap}q`bkw~0<>=(p^F>?eB7-m~
zrqlxTxwKNBbQl^K_<#&39YpqD#xI;bt8(#YAT<Qal(l2rTp<=fhW_vVdve^+&nA^x
zbkjgdY0uF`*1w#4n1M))&kG2aKj~i&+J{Ca(+N%rg!*h+1<@FV490xisd6AtY<?>6
zbTE6uYZ-{)$<|8w0<n!tF`9m7064)P0DP3f>JL93^HG$=2A=!PiHL~2lkgBMvmU=7
zLkgShJU!J0*%@G527>GiBdH{F?;d{b@s9TJ2sBb=-1a+j?U&8Vphg`7DPNPg(Cp;1
zPL;j-)_35bT7&lJGQCc7o`4UkD&e_cbWlsdpD=55xerlla@MW_^j$DLi(p8DnkF|s
zM(j5&V&$2k&l;)7frqBxot!^8GUiQx=Y4Ic3hUO+$|zo?P6SBnr9VHNAb9uiYP81$
z>|}l(%(_$geB0Rg>Wsa}4jPG^uKE42%9|LV3N(U{ZX6Q;VhO8**U^ckqNl`1cDcDo
ze89>90{X5|o)thId%Oh~jByW9#&Ygp;yq;*d*X$`{ZCx;SjRYt;ERw3Mtv6af=bw2
zH2D56X<gu5<HMR2`K9cp&&kTlDygdCPO3%eke<W$gK=_l0;f~g+e-$LI8c_CmXX0C
zBO_B%S9ekO0%jWg{Q?`L<k?&z`0x1hbu$}Be!XvnIM7({885{F(Qk?K!|)M^{EdI0
z_Oqxb+1}4eLEmEgWfzQu?SW_!)k1`piuydU>(_EZFbGa}%L}0UV8U&LbthwdA>X!N
zb#4JrX3iCl4YeWGlXLp8QM(FX=*9YL9Oa|`2ENK3AHi5NkZS|26#`XNRTpd&j0XS@
zkoe(pF)&&%M@PriN_{IUPM}V7rc{JRoYwNa&Mhh;1M`4opFLx_c#&U-0}OJ(>|C!o
z80-C8qdq{Z=}St^ZS0HNg_t=MJzaficd{gMqIFdQ!GnQO@aj*!s9C|dzBfRpAOk?A
zgIgfP1sYU2&QrcgP#Fc7d)mAyW8EG5qWP{3rjrz-o+&npN?$1)W}WsJ1#`{OkvsL6
zDY-G`&0W{euH4@*5?Y`#b@D4KnFWOHkH3$BvM9Zvr8Ih?Ms-3_ULFrvrY10~c)gk4
z#-KOjKA_{lV8*7VD3HiB0<=*Znr8kTJin+0)70LP+ff1nxr>(`sSnVQ2KrNkiWXPS
zM&#xZ^YmMNkh(?*hF1QbS*fh|B)@FpuR)tYKH{<w>F#S&AX^`Kea{FC$))>3uu*i|
zHH4v)D!y`W4CqKB#K#%&P!6Cfe-ZF8>2m4&en7;8EeG`@xAOzV{*!j4?c4_O4g%;g
zeu%0J48(n$D2!!lI)5m77ocQz%fq)YQgwlWfgmUbC3)k&+TliOrdzczwIeXdFXxLw
zD|?GyM-Q+&;@5FSr+!ItVFEi}E}NG>*y_hX<KK@Pm!N-jOUR7v7zZFY8@`HMo_;o`
zn_LbwG5rcL(>3&ru5ae>g4RP2lVVzcG2~o<?Q&;YThKSy1jaamR?3+FeE?}A|2g%1
zy*tmJUk9vq@JDig=FK7Td*s~ma+pb5tRdi#!XTgATn?QYcghc<PUfwD>1qK<Z_<DK
z@`~4ZIheKzDl1K(%6jYj=!Gsoy@<-XmeIj~s~R)Ma0L;h+VUh->4N#vio!OAq<C?&
z_(iPuy-hE{LO#n!TLrA5ctCnS9mjGV(?deLP~vQ{i;-#&f`Y*)5Z_hdH!1<dL81#Y
z1|ybI7H4Pt&&z3Mq0A;`2w*uj>A}IlJ6@AkIY#tL)}%U%{F<Qb>};_X)4$h0fj;oG
zYeKiB+zxq)1C4*6X@5Ix_im-^Md;1UcHgp5(R(fZRx6@x4t@15O9hOM+R)Tco_6_F
zh4=3(i5)J!q`j)?xx0UCC#zldcDQI}W_xUPeSd6TQq=3JSRQB1mLzwcha;ojc<-&W
zO?<5Cl1dzItJS&^UNJQWF<NS_T;IND3?E~#xvi{Qwk-xzD_i26^Ej~d_4p~(lWq|b
zJ4~E>erP29`r6TQuE^d`O$U3Z=B=_JAFW3He@&k~iw)8XP+0L`_o4$nKgnu%UL3yQ
zDVRV697t0T5q)&$Z@Q}^5B_JmRW~bE@7)HfJ-LxjNgnd1Qr7O4I49_zoE2t<M3@iO
z*UEV4RqI?1FBdPviiExjrSEUDQcXt8ZVN*hnPv#|oB!l8AtBMTR^4Oh-IfEj9H*z9
zFA6oD9ZO&4Vf#yOwABSJurs-+N;KTR*lBjMfjc04+WpdXuja0Jc`8x-_hoN!U!GMj
z2WrfJj}uiR@;T!D-aL<yi~Sg8o&N%x1ThqlELD1m3ld&|Jad%mBRk&(a&rScXBN}4
zduL(6KQFEKCeC-XwY6cvWbye01tVHT65`{Z9RsG8vIy&$6;iIX-<R7%m<pnN%U$G*
zNvV=`%o|r8F4-MDI!1qNa$xHoLp7axR9ynUH$>0o2I|n;=^-B2L*JVp1tHI0!#6AT
zLg*+o!sI%Uy>rb1Idp3$t4U#<P;26au_9wh^H6!`ZT5dY(b>BPoHqV*z%Ykyi;0P?
zcZ#7W7t$jq(3>@zoni|M3n42*gM$%fUcV3V{sVm9tfA8B%$C9Qj+hhk$LBR0${)uE
zp_6$ba;o%b$K1@}pq$PmfA+@-FXg4I>uYRQo11I$!iI`++FohHkv{rTRkYMhdj1co
z6`I2?m@ZdHWlTqMTQ}<KjYmYqOA_g?@}n|0=TV6VZ1+^2$MpUoUi{uzGbGs1qdin)
zaMLrH%PGMud0!XVs<(O{9ztHVIr+7sgF*=%!Y6wu`Rco8kC*0IoHZ0o&!ihXFH~mA
z4^7QH%hgef{HfY$IIkIYLDIvRQ_|tv)#~ybyxrPL@~&JP|7;T_Hbx(zPt#x*Fgt<T
zqcy2;?4i&IzmTWh=cB7TQBodvb&^niXgbTZgSL$KY^ce<^RMuTYNB}UFKUT)&9s=|
zt+2@fb^WHH+8u$T6&uK(qm4FM9^cmnIvmBcYZwizc8YI4(%^{A%}qFa%p25C6(%Qy
zT&9M)tb>F;Wqv@H(AW<Abg=#Lx-@FGo!$RobU=D$?Bu7<*M|F+8vW7B%?N11O4bG9
zK{-^{ej(4dS!#DZS$@E(#+BdF@S}+q`5*ip{F1xZH@`U(k9lr(M48Y=UzNf4^+4B9
z%QLwdH^+47d+(btIQ1)6%qMv~n+JcPe9Zn~tom==7=Gu$(k;VZ$rE*7B5l6kF!o+$
zCSPlafmo*q<rOEgqV}c9U*+!oc<-<_r<ygXPR;1|b<yuwQl0I&e^_#&2tAmY?D{_-
zY)DA6#)KL_o{t=f1clT=PvlyT{xKUB#VI(7wj(dHQp?JyUQu&5GY2#H6&3LguQ+e*
z?xyGD5GH$W5a@vq3mAG3y^r32PV`k^X5hyQ@}KhayVkG#E@jbGo=DTUNS5QCx*D<a
zzxMWadQDl*`K57Mgfjgr@hj!}CizDVcL*)k42?)m_M5rHYw{?k9#IJdpiJLR?U*>d
z(=mrP#ry~U0|9d8hNu}a46)b5-90iAiU%>d!lE<ON62Ljc2~zM(6xs_)gMURQ<&*^
zsAUbBLMiw5yCBKyOTUXTt(nw<Wm7@&L+KxffMsQQxVLEcWUaB!wElVPM6z%6Rl}(n
zGLA$YKfyRSNo@^1NzG`;V@0sLZ-*5sPO2+4+%=q*ZKVHomjV9I!b0ca1_#S{xLQRb
z&tP{Bb2;`rYcmRD_?41w&~ZgFm1}+3o8^XpjA)f*``~XD_gkVBE{vs>NU}8RFgsxu
z2-K*|WpWw_)YQ`~YQ-kZ6E_m%5;HlUWI6oG134V17=IX4(_tF%seZt;>rUY~|J@uA
zah(1Bzduh}YS^n35t!%cA+lv-wrxF2>X_hkuSKeRVnT9e%xte31cUmABSUK+(4Kgp
zj%QT*^z5IeDQ5wzT00t|FIh^kDlNR?S#=*{RKtO}@MW3L<E{9aV(`<Cjze3?Ka$SF
z7MW1BcDqq8pnoqI3d)KZEwtB^h<L9SU+nb1yH#XI4i;u{@r<mE;T~oxB&-2zhuN%!
zU%z}?P`-^*(f~)lu9y^N6d`iH*tDy2JNmczxohc=L$98HKmixrdCq@YQW;ZW^275H
zN6~W^!$UI}t6Nn4R1hlOVSf7kw$<?X&&WnrYE*wEB@g{u9q!2gaY<xhIVo;w0D3a$
zOcbfqJOA7C5AMmad7n0@9c>RV;#;t+9JONeS=Xizn|9_0T%FAebrYq0OjzsKKBL3^
z7ya(fj}5wI4O-2iGYgGZG*sq1IWn-+J-nhIrXKA>+4874vMZ4&t4a8L&hP8+_a?EV
z;V}N)qwbc`M_y+YX;T*;xogZ1J&qxF;mB|C8=2<F|1T^X5<-0a?@rxWEar1eZU|cT
z<dG0P$mYh1=PTuA%B+rtp@`%Q=`4};7yie3FBmlZ?);wBfi8y?uqm1G&AH#al4g!g
z#OW0U>sP0ijs2*@{Q}|S=I2^2=hp{&#7<hN*NJ805sWvKYhEWNR>c<&P3PK=m1Pi@
zek}Xfr~4a!C00YRndN1(O+cq!o?AWgW|o!#ZL@w|^ZulVRGzP|5<uWk7MDE0tU~Kk
zi)6Tqc<CiY&#%;m3I@PA=s0vTfO{9c^l_zlUZ`Yhu4O3w^C%t+*nr0H`L4#B4r~t9
zMP7Yye3Vd^RlO8)m_XqM%;&-@sLn4P=f;ii>(j*2z6GIG`V$dVdLt1j*&jdX&+$wh
zY|c&j$+5~tH>2_XQFQ*jhc`~cU!}O|>%*<9Rs_Y@>TrPHrRk{*ApO12v+uGA#McJ7
zj*!LLlLP@qDERVfeV_{>@L@!DlPd5))Rdj0I*|8RiL*qK)=)fuZB^T={E0)!&d>P`
z;4{mc_+FD|ZyzJ1AGK{vU+U>ydPrxp`*3dx5}NEmC*zwisK4kEvOzC22;|Tgq^C=z
z?<b4dljVte5Qkc3{%eW<I&o$U=Q@z?OPn0qWfvJ48SlrmY1Twy_~&MxBLyviIqE`_
zZ!#yC=Z}`gF>#bYKD$s(e4QL{QryWC7hPmEyi-`|wbmD7A0O?7Y3}tFKH1>?G(NY1
znrj(E{RkRlp3fa!s&7OKOaq6))EL9~&b>w^*VF#W$)>l%-A&V{!UdRX9xr;rcKi=!
z@!S9Q?8OYpYyx6C3Umbow4kt3g4@y!H@|hCHQCNe+3<uRB@mRm)wSf=u_rE-j}=Md
zq>gT3PBF$KyyZQl$>rsv-~DR4oqgU@{E>#_YgW=`g-ENq>_{!luU*V0EUv1%_#(c}
zA1yc?r)Whd$N}GO%MyZ6x%;X&oZht<IN>pYdvWYaV(sOL^Yy_0#e4z<EfN^eTBlzA
zcSNl;xqp-#|60)-T>1WU$m3O)sdd+9lpKMi!l*F50^=WK{X;|PpdDB+fwKi;V_DK1
z^(Mw5mIia>93pL!Z4M909w)Aks;}2HRYD|V2YzfIdR80T93c+i!*3(mdf@=)+Cwev
zZKQp!*a*|Kedn;dw3n$S!Evf=WYb*!Ycp`|RhH0H95rKnO(0{6v&%N>tH9FQ$Y5vK
z9GvgVIrg{#Bdy9SZS4PJI9Set#?f+{7Ld45<vi2V(Fuc2kI|4YNX#5+_|NW^Qq^cA
zi1<QUB!BXyZJukS^WGez5<jpbwANRPG|G&L=)J(VyBJHXQ<aIOFn-7stC&GB3Ab7P
z_GD>iI+Jh?)xNKqUeeIMCkJ!}Z1Pw2HBO|^LMT4qt1mryP*m_0NAcdkTKSOkq)V60
zoy$-zxM;?F(<2UZ(`kVMg(PGD;{q<t%7OqK>53wPxl=mak6w#I90i`ukVvzW?K{Wh
zolhVInP1U4u^ri0OZOwIBH*lP46Z15*+Mx$Ui8$Qx%C3`?RQ>|^J_h1Qpp07ia<Ck
zFV)GxA>|^A6#$WfB!bBqI5?@=?K(VRE9-04dc5#~JA*wMs|9+aBfb{u`Eo|(F1P0^
ztvf`;PmWQBN;Aq@KYf2SHZ$oIXEU$t%Jzd4q+(~R5}c#rYF^AT{_87kYeUd%Vq}DF
ziJF=k)j-JIK4p$MKsTOfkB(J9&z*24MhD*X95ofU*|kNBz7=WTYkZ}>K6H`I3e2;A
zUR_W~G-O=3QJhI2z9B=dCRs_~RjYLNtnegBa(xMFwsSsEZ6P^`lqhSXM3+~Rki$Vj
z<u22~z@)23Bt*xoIfKU@WkUK0&<4%UKNVT;>&u+ZIz2x3;iMkwTFF!sTJ^9Hs&zi<
z@6lVts-Gp9ic_>{@JI8@8Y}U8+kYl1=l;^N&ev9i5dPP5Vcw=)1aZBNG6l@%>iu_u
zR&?C-n6&Wi{1G!H<o<&G<fzp-pnvPvh<z5VP?8kM{GejUao%oU@tnagKXaV~8KoMi
zg}y=Y<nZRnp$s49gwaw)D7!Jh3cieoTc~|$tg8A7Jjcee{n*zG;x`X03kz+Mgc;}i
z73&l%hEV(AD}YEt|J3gA4SnH3I#sTYUS#8S%5dZTTeA4#8{T|slBJhwz-V8$e|b24
z1Rk~GfbH(xYqMiw$<V0ZCh>cpR_wM<x!cm01L~eEY1#FSY@4~@^>zw<C&OHR&(pW>
z3uvB5b^Xq!{&}s8E|&paiX$3G^zOXvnNauJ&V*4>yOnq+AcK(HbHvS{`zkp}054Dt
zm10!P_F->VD6*p>eh+v=yB)M%lMD1XJ_5~^bStec52RFiapkDhxN;MUSZ;>}!+r3{
zqBi_r3p5EHSv6g9eeOkXtyRVJC2tDKA1@dRG;yi%<z|wgl@hTKyujg~oczcNy*3%I
zO5|#4`rL4z+%yKY^KrePnxrm~#kcVRzvq}lMeA2!7*`Z6zoEG`3y>D#P*lm*fo)^F
zldECh8k{XeOSqB-6GP-aFpX8e`_H4Ey(@vhQLZ)XVYwH5j`^JP%1EHC@I`-Sn>a9a
z_O_$a;ws7X4y)ia1Q!G;o7zVl%nxe#ajmiQjR`@rL=jb1=a+A-tb)%5<T)EVNj_sE
zAUlYD(7Ol^7;(%2<MqC%Y-5FPC<PAfN@Ky%HTi+*?BcnUZb45PkRv3jst2M>V=?Jf
zvzN`PpWve)UHnN*s`qRAi$7bfDGRBZ-EP~c-O$6)OHWHnHrp7k>p&CYI<^bn-tHD#
zh6WMUkok>F8d*sF^zUL4A!z9shIZ!<r7x)oj2+H#9nDp8uALt!+5A9-gK?u=B1Ke1
z?Ky&fV~u}(S3aTBiRV!S10hf)eUEW~u-7gM5E$Fn&oq^9fL#03yF2U&bZmc@*`Zjb
zS+O%QyafTtjzqbo@dnc(_LmJ^7nbCW%->6_96`FedY<cO^yEgclx(nARFBuz7T)?B
zdCB@~kcU`$)PDN~Jf{7f!U`idFYp6cf`9tZcO^&4bBi%;4+OrLcd5YHn~;M~wVisB
zgzHX(t)f0K>I%=h<WH4%*>r$3?}zf6wWAnT!ykqNazQ^|wb%W86C^oA6qzT-lXy}~
z4BaN`@X~M}qOVwa8BQD6-X&p-tyi&e)pKg$$Hn*m`03x_?nK)t$PbRDsS!RspMqVy
zcZ!o!5mLpAXQT4oN{ERat_ONd)JMYi&q+)GBh}J%qmfTNF{F*E%jZV{gz)ByR}RYJ
zlER8h7A3!a?N|g1opB|3e7xP}y71wyncQ0vkaTU!cL;9_<xjpQ=?$J$I8Q4jX+)K@
zS5F{p|6tB=-*?gCLg8Ey%C>KRbCM?u@vMtoTh#X8L;fVVJj_79u6l_6TNkh>l7~a(
zg*8`>(d~R}40~PW5lxJ3Br>l%-a4P$SH<pYv%DE;g<gPKB=XVsJR)%JgV0u+Y1HQ$
z<j%OHrSoE5omz9fiyQt@%In1TDTPL}LE!(~hTB)-K+w`iEOyal+=&`y&=$)njIQ1U
z$x;$Z6|-K9iGGGekgFkk=g~@ztAmI7$o@5F^3|i|RM$xhdBX?i2fp_~g*22$Yg?MG
zi=pJU%O$-fo4O}Fd79%suTD3p1WT_6`?@&opdLaHwbaor>%uBYro>R%uaB;Cq2%i|
zPoA&3fKUgdp&NEj*1;}MEq~1}*~Bp<LU_9KbsisExI*eKmOA*p0`Stwetm==gh0OC
z5x?#q#;%wg$m(q?4n@SryJ_gX_O<AWzcsUtL=;eVNW53og`<;@-9dfO=7p2>=EoP;
zX@h9)#IA*tXHHkLD}DnhxKZ)H)7Ud-pd4Kr@GQb>#X{e|<9VY{?K%;rB8r)~!d11*
zs{{JOKIP>2UtN#h#H{4coryea9jG`POM6s;wgcg6NR%gH$`!iuE5`(#^J|t^d@DS?
zaIukU>}007L}s1Dd3UlJiHOKg<{?7riZhZ_m(n93Lw4+`{(*XO%F(cj)iE~>Z&xQZ
zZ*(qO$>b_G*SCLSXL{$8SV0o5gH7$dvDa_b0@GPhab1=T7Lc|W-`9Mjte9yGVxYD<
zoL|bMtQ|5-m|eJ-9xKyw#7u+%Eg@aIZ{8m~#LXP`G2X$|T-W06CducU-&#$!Kjm<>
zZ?ygu6aMwh>=6N0fTwgF?w;=JGIhAaW7u_u3pbi|@}te3&Uke*z_==B$QZn~dLZG`
zqs>A))ZH=nr7snL^=rsar>vra{mKN%CB=Q84D=%>eH_5VM2x}*)yzjnzZCX{s={eI
zlGUEKth6oKEpKg6w#dsyU)8B7IG@%2xvndD<ZW5Jiw61LCj036?vTsnjLe&IjCNB~
z0%tMG4@@NgWL|B0_}9n1acEQ9u4_pwHS=c7@tTJM08zIn+p$u<KmS%b-`)h!mAeeF
zh=asRS8F5sv6HnFqb_T+##ad=wylY+m&WC5d-#3vkUzuqC2MF`>n2+yqQ)~O<xSh0
zKfcP8e!ZWn@t=T!E;yjOu@6hgpa^FUTUyP&Cc6hB-@rMk4M=g-mo*p@nADi|ZV<P6
zw;QQIY|y%1SMp~2izjhiS}Xr!kZbMJ+f@6q=U0<q6alW@CzU0cIOi`@QubU;ndeeY
z3dUFwDdPLKNRGe4+~miKmsdRBu%N_P6<b^$ClSB0TRy4aC`(v-^@<qGfUvObSUhhD
zJ$zy(@g{1&pI6LUUn}?yU+Z0q_q2uYFIIqu*7%x2H<W8#Cm&vOMRI<lF~@F2#6fwH
zC(_dw*81W3(M<c#im9IMn~}&0X9+3=Zu&m7ujNSpV#7cV@2OUzWImaYJ`Wt=YCm;p
zFb3hzSI-GPp6`isUEg`gbp*>LXTp7C+NeiRd@^)w4!cgJ0vf&Nzu0qOS|*aoo5bD6
zhqcj*gw$~f)e}`AdKA+q&#{12CjP|+Hu)(uxHId+If_T^tH<QwY1Jh0r|Xf>Hxg)1
z&Mf{s^SP_{>6Dek=~OVi@M-<=Uu!Br5L;dJ@k;u>yu!`;)>+8uo$?eozCoIe{bb_%
z{^Vz&`7XERoy)l^n<8PM0XE6}jB%VvulrKA#t3ltkmb}<sV?;EAOrcB%aqjlR)roD
zx1owiV^14;-mE9@CKTi~2`p$lDJOBMsb*A9lC<bYyKwA-Ea!Au?Pvu6C0U=jNMJFz
z&&NDvAjZz@96JAjKTC7=<{BwB2wL%9$sZCJ+Us|nAQdl@^{%!pFOK?Oq<ktw4U?s3
zAcND;P%ob3OALvBsmmf+qEAs5jy&JR3dhs9U|%VkW;pBAeT1pffL7lm3ZBUGO$yh^
zFAx$M7XP%b#^4BPICA#}ptZ;$m+8*=@;dkfct2MOon42BgYA05{ixY7{aek(QFcG4
zX@${I1ZN9ZR?3GOWxDk{L~3sx3!EkYQ53+<#7Aq|p8pL1G2ccMcZ?my{Bwh65iSpQ
z8a6^XrUtJ$X?X7@+}oz@ltNT9woyIBxPJECWUGAZUr?PBsM8dVCOGF^#bS?m9|lhr
zp@00!?xLFfzugW0`G9W!np(X&B;F_AnWzFo$GlJCa}8L>g|wWNdiUo7U<YD86W3Gn
zZ}kfPvf_}QAT~?Y$>*<meSU!CDr3Ud$_FW&XzYFlT9Tw82CN&Nr|&o9_3%3KE1>C6
zjqkugfXc9r+iXn5q#q%V#URJ`qQs?ZXI6()(t5FJAcwBN4n4^>@R>uzk|j^fg{k$!
z{w{XIjEdOm>7ugha$ilwY$4IY*14cWZG&5riy)gTmY<OamgYhhE&^eU#iga;zXaC}
z9@mu%PNu%wuu<H6nFEnh2GJyo6;YkKm)mt0resewmoC{M)gu^sVnUj={*|otOJ%jb
z6pI})lCiS;pSZLH&QFB@^@`#8M5;EVzj({)9XmHY$7l8@B&F~1meCg7VhsH`+VcLb
zSmm|*c*`Vi9KG2x$DZJxDPsw<L07C)J&~HUwE5>OKl&uj<{0J}*3}3xF`=y<5x?CP
zz%hiJhomd7Jx%##<>fIu@ACuek%%OdICpZoy3Cc<*Pvp6INlirZ3-S7iT2-_gC{{R
zsq~8%FCxyOXU9p~L%F@}Vr^Tbc9n`+7L%FEOALtJa=*B>7*TO43c5-_%^-S-wS%n%
zz$^j56IKjcd!15_EO}BkTx^N^r!TfKar}Gs$KT-kV?l`OuZ+wBCkBasB2?!`jT=%D
z4uVOV$k5f3$rK}SsLqm@$HxJ;w7X8oWG6xXZ8-ZTp?m(}=}qWgnNWHINiFp6bi0Qp
zM2*I!94o0T;K87kPd=+ti1;M-?E`jAbX);^ZEyP!2@e&ybGha+*$p@ilS`y`x2QW@
zcQh!O^`=ja4oRvQdBeiX`mKdqeWdE1#QWO?dv)eBJ%a;jc;PRO-WW>@ev|4hK=?5k
zwDldFUqITt?uII#dhdnA-7m=psl^Fsa>16)P1~2)9c#ph#q?ltWtb~ny~)k~5FUbg
zLVCDvq?-DHR4b)sgV5u5L2Zg32y$+=B@Z**2~@j+F+_yagCG(tGeqeLU5^`C>mbjX
zn)yjtsW%~siM00Ge`1XKZcQa&RaMQW1P#cC8!mK>)o}k=6@mH(0X(T=WEQaJ8^h-l
z`EwYYLBaHguhj}j^uJ=ESC>Jx5p0~@Xs_rawn7s(lYd`PN2TtkDF`_d%NE%g0_|xH
zvxOXZq?kEu!Dgu3^LqciPY6mgh2vVYupgK5Bdj@l5zlKB_D><2`>a2Fe$sxf)lX5c
zi7b^1BN`L>i6wBZ#1ppcN;|wx)_rvHUFzQC3tUMrn!PPUVJwcar|FF%aK_Gk=}CMT
zZu;6eQX)>9CvYz>uR?FY^NA3i4q?c{RE~;1e;$2x>fy|%H#|(NiQV}**+)CJzuwrO
zbkcHobiNT}0Z@wrJ@298zTult+m;RY_76PXr@|uDWi-1w?#6j>x7eOP6}DaK{i?%_
z1GDXEpc6mRyCP}1%;=aqFx!IG9}dmwdM~y^`0xryy+GQ9Nxv>V?NhhA;qcD3aeve9
zgqi%Ay3T*20G01}6+2f0nn$r*N}vmF7UH*8SK9Rk?C7**NV-?BzPik~Uwi*1d_8+P
z;z6C}`)c&3bGc2@-~jHfj8Zv9b{HwO`}|DBJMQSbmoThI!zZGG_l*<xps>f4d)NT`
zIy}ebRsQ)SE`weAx}CVKHMjY>OUOIE2wdC~sz+#7s_8r=PI1?H$=%8CLysZ;0C0ul
z;^J^%SMOs+UOH6Mflu2S6qP-N&PRW<Nk(ErH%?kM0kDZdMii5!5>q>sGt9Af5v7Eu
zyY0`nfK2kU!54y)psLtBADMJ9P<8Gmu4b(5Xs>(XZtzgb%leknlY_^Y_zWNbMu-aL
z>Y8*yOk5EWaRP<S>D%{T)o~isp%@9%)cZ%~2b_MHd-#_pL!Yi%<9bRE#H6xgY2Um&
z|C2H<5O!4KUb<MD>6$Q%Mt|RC`(^N)ospC#vP|`d(cp9sfqGV2&^9#s{(PT_iR}&X
zH(uAekS(PY3o>gzO_vVyB<dp6x6J3o^aiCMM_%k`C9K9_eXI|}TyFII$^0)=a?$pc
z_7!Aj>wu}(08rJ^T;cfUAO-+l4iZchL70cBI~!;rvPdxaK5K{9FIF7ks=NzAx;*e0
z+#~;hAR(J)SX{dNxTl^*Ln%gv3X>R!5^cluW|Jg*%iUjNf9CGd{P7%k{;p59XH)?D
z2lxML+8tqJAPiWVvU4f%bCF%L4rj2yX{b%@y7I%_Y)}tNxb=z^t^s%Cx##TDMrFLx
zw1L8jnZK48XqXf&Te7wIlv(_7ZzP_hQ0N_Ph<BY`{|0lD49KU%F<|cz3T1zX&*1#B
zmH@(XwCu&1AJxZpt|1QKWq%iId@!x7e_c5Z91A#fv~DZRu;wa`X+zWDqP}q9f_)7K
zSQk3xa;X4)iDFuk>Kb~MJGe8qh4OVH$)Cr9Lg1*r=0=hbO*1$;p$J4Rxp{VUAI>9=
zlF8U>PQq4tvoyI8#1tSbBE*z0pq16S6eD0-n&Tt23oK<6h}F*HGp{YQc-StX=(`8t
zkHnD1Q2;6CTl?}Rv)=N1@#3FpWWf5AKmjmWy}F?s^|YPs1xiObNogYNDG&+lG-62;
zVVN69OMNOg=b0dpw@z>r&p<vFgv}QFju`pemtWF0bjnMHULR+DmWEg-C&YxsB?|^*
zuOOsSe+|?hiuvp~zPTpRFUs>aiRNzP`Tj;<3g(TwHBO}q`dv7$D`5o`*s&~iS{b`v
z?hqW}9P|ttoX1`@WK-EC*8ifFftcQr<Qv%v9h~#Kx742_-?9@-GX(6TgvuQR(OSt8
z+<mR&m(ejJ1nWhVCsEEdxQ2*-{xYEL;kLsT)i?a+YRa>`TE<PrV+5QNzEHo($c@#3
zHr=r?UA(WU&&D{mAy{fsK5Dn#M2$*5Jz{N`;#ozUdV8WhkZs#V!1S4&OVS$(r60<Z
zOR%t|5r~oS@;;hsvchm)imHon`W_~X+Kn$dnL)o8GqI%uesg4`M2)DH=c<n>UvIqa
zdj>L~&Zb!sC~;#C1DJsllCl}&sA4C2b+H8mZBwORKG1eZIcwbnZYTJ~Y{Y*{E<2jp
z1+KZtGGcV8^T(-yJ~i;AQ88$+<pOJy+<d7#4&|nN7TK;Dv{B_#wk<s>no=r*-W6EF
z(oLOpcakIZDSwC*6`XUw_0xnaBw!&2=JCrX#Wi%TqErLXaj&+E1NU&3nSj~H09u5(
z*Ts;kxcu_n$?$PCtWFk|>}}y?5Xgl{+_bJRZ!O^6VDVCvm6lleS?(~(u7dYH{V=v+
zS^PR3{Ni;3Xru6;Y|s1{`E6C`%p{x_6(afiQcIlZK~F}ZK0D*njb)*?#^n?4?1Oo_
zuU1#B$6Qh2!pB>Ew>);1b+|EI6gxdhdT?u<DyA+gv(fp{6A<E8tgV^1f3kgUXo8pd
z|L}h10S@CubZYkT*rgsyC0|GH#;?uo<jJ&%O2{0|{p_f4yFdOe_gaSe-cpX(+97or
z3+dxiS<}Lf5R0x4!Gt9FLEkRG{^fdqUJ1~<BTjd+vwe{`Ad9ZeNyD>b`%bo5{$YNt
zjXi9D5T)8>5kXT`_nA~0ZJpwzEBnclvAfv9ay5Axx~fUSBF2s--!PXO8r;fAdGPK>
z?9#yV$I-B2Ib^e(0MwKM5hc><;EGS){}h@;D!aSAIIvBQGaESI(-Xfv+lTc{1=cR9
zjfB;e;?)u<rf+_bS6bC0d00VWzOp{)^!4T_U>rySmAhQsDk{Lt;Bt3&F971WV=hQ9
z;bUm4?|0>;)hC-^Y!`r#2R}O&NLG;_3mW3<0EY{eYQxY0Goy>Yz6SF1z5OTjQyam3
zo#pQ-bleU&Jl%RIXeit+s@=omy&r80>IaF(RqB^i@bn+7Vt~=<E7w!=M60+%y$h<&
zO0jc-E_XNM;CkUpogae9s%}C#OaF4tqA3AKwCBD(QDs15^&3P`eP{u(+C2Ngd03QB
z>;4bbu2<KWe8(@1M^c+1@k<20`OjJ{ZD;qsmAEc1YbH*2Ir4l9c{7Nk+rKYVkP&sq
zJ?`3muwbCXb1Bu0daAKZMH*lDm{WLT4OQ>*dtD}=;o%Ta-and`I;|H%>?<sfloFbL
zaH68()CzdTT}wkq&2`(&O@xE0O)|NvtW1!Q9WXJ{J~?rERe;Kgq}6Jh*@?)us;iAU
ziRhTGOv}VbPhe^Ca(#4TbA48VgYm-zdW?rUtn%QEhZX^$sv1M6YUyDezlrVujt5#|
zebhM4Z3B0FM2<<t3<q4OWMbt{<*sl+_y9#CXY$Ot^vt%?D+-Xc#%gn!S~_mfZ<qcN
zY`Ao3R)X2bhkkAM7nUugFMg3}iNt|pxov!e<N^yBwmx3<sf*WlW!y)iY%1GFG%49k
zJQpu<Q()Vo!vlVh%g#c&qp;T}<^>`q_6zlg>#~9{T=MPEfje)=*ffR~j5^sQLLS}b
zkH1P_Z9#N<y<aCkfEe42kw2N&<blep%Kd0ivA|507D9SDAiCJ^lQm3jsY-(Sy&2NY
z-*9YyjOqv#uB%kK^`dK$h22_{7mmT;<0G|fn-EfuMbKpsfaBK_F97ygdiC^r2V7;n
zg}Qu-JKtJS^Nu@6-e@r%-E>H`P*&WeX;<;RZz&ut^AJ5r{Z0zxg$Sfg!!`NviD1b<
zQh<rEW6BdNHzhyuMk&8uTX~a-Dr(|0xK+e*?w|BN+`*h?%~{qO>1(f!6J`!cJ<6ue
zbQ!{Fkn%JTN8+@+ZST8asgq*^Gbq3w2wtKDH@6>=X|?1tsJLxVnJ-qp#Q0q=FL?Gt
zOk+umPtS!<%qa-+1sgnZ#@A(x3+Rv%0@U#b81E<95=5Awgt(r@8`7)!;dred$Tb21
z$C20!tX?FU>^DE99yMc?ObAodZSH@VFk&|z?{pI<6Xyrn+5jpORL{En#&b^}6wMTz
zQ46bUjMiQ;F|NYAH<zi#9P5vi^><yj=gJ;qQiH0m)i35BkO5fHVjWbscybjrP55Xr
z0r1)vJjr`*N7SI8_I@)*=^1<<w>sXV8Uq`-+CA~O3vEWe(+P`dTe_9gD>8lfn`U%&
zBU|YdaI1BFvN)M_=zq1ymc8y6#g(qZGEkltiMKD_vTHKKdE_j8>sLa(c!doQPUt12
z_QuGY*K5AIT5`CLo!csfDWI_xY?dgS<R1>>Eqlu$?eC~;+3pogt+UlBr%JKDR|%7@
zht7FDWZglV)FHw%qTFR~=zjW9UQR_q9N99WRi~DQ`1t@-cB1I0WgL9!?ZM6S99+dz
z`~@dp2G!7Z<mYpJV{BVeR@Z&U&vl>7T302w7}}+4xS5@bJRRiH@w`nmr^vzJxNOin
z4*zDQXjzv{qRksGRn_|vJaVC}y&VKXz6>!Vy=#x(KY7gyn^+>6qE@tO;N}uFMs&gd
zKXn;N4=zcUnXBJ<_3{59Os6q)^Vc+#Wa*NGlh8AvjZ4=b(OUU@#4n&C>Y*aB%T*!?
zh>Qu0TeN~q#T_U1hzsVgNGm{1gNeKlHMXjgv<ah<2+564=Mz=kea6%-e7CnAVr6%a
z8drYpZM}swgzzr^I54^;R)hfT#56k&Q1*}0bAOQ+Ck?z%V>iDqNZY@0Crz9ymr8W5
z?f44;ZFw9dbN|?{vHbySt#iZEquCfnf3zZO=!<6Xsh3BO3@sI<>?8!i%O*k5ETx71
zff&*<YTf^d>2w0?V}HAl`GgzvASY3n?`okR6}^N{;>~=7L<>#v$qxd`OSG4yi<|>I
zPY;CUr4_)qO)!&V4xG|d$?~SxKlhDs>nuVXCE9b}^2yAXNA@du65p;?5TIh`67Hvg
zRFdV?wo(deE*8Z6N>%fqHQ>a0EB><jQ|dkGYD(L3tkoK@B;#~8<6qnhoD8m+^j-^U
z`qK2m4~3)E_&q;GG;`UcF419&2-bjQfM%6OjY{{4xi77>j~|3)-Ibl)Ubl5qUh#d>
zqaC}Q?_0w$#11+s>4Y|B%2)eO0JRR9b0+|c$REIIWvx?_jRi<DKg>>EH~_utXwj$C
znth$e^*8y}hg&=fvbv-eh5;1oI^%!cbLabrm)@ppS#dFZD9^esVYCV?!BH45Fww+s
z9<G2yO4+>hqoC*R3~j~cKIhFy{qpA$OotP8a^4HZDS%jr@OCBy{9uSxMQ6<*3S%6#
z)q<Of`9A6WZgIfG7l7TokCIvUt+%t0_3@mv6L_~2lXdx*4C>pO+cOJxoaK){TEc%J
z(;tu#UnG7d-#GI!)!v5s+0>r@`)5o#5}rzCo*|3VMw*)9NSgM#jPxy{ZYhJF8aa{=
zLN)2?66j&!18CQx1dDQW+HfOVZp(c3`TPQs8f;#;1u#<pv`srgk$gVm1H`F2Urv-6
zDK4rDOW=MN$v3Yq)4KL%)wvi;C{c<8c>=*R#ZNY~1gFB>RBx0;7GJ<UWTqKhi&HN{
zk#Jkd@!T<4@O`jyRu2Uj-m~T|A>}g9r3~nW#<4P=vy&Sh<7mZDAl}48cs_%Wz=|8D
z`~&#6aT|@SI5&#|^5IgHamj+@g5;-Te-wPuaH2@1>ptQo){Ntfe2-=0XWE%{pH069
zpsJz4cI#&)@(uGnm))-hSaX8b$ZxR9ELEx9`sNuJN1!`wOofKPpBlP;u8};F2tOQc
z5tXb0!hXlEwTwG%UhP9T{0Mly`^3>n?Xd^fr-*<bOFr4|HCk>zo*su{VRq*Qj~iyI
zXg!RyS@U$qqj>p6qpo7<i$3S|_MEc>9_`RmAMo%ReiXN@Z_Gu=w77%%$;Nwy3Zs4I
zL(hrznHt^k3dmg*r>qnN2dD3^nA>jubRY?ZNrsb9rTZns^-uLe)d?GW`N`vc4oj>l
zX?4*Xu7G+b^hGx^YpDFWBNRMH@oW~LCdnEVNQ-}LH{BCi7*@HSh`6eOxN6kc5RuuG
zPQK?6!8T6Kv9yg*o_e$ZHkn^2BXiSv$ij}2!sFLuovS6nOxmZKRB7i}LV^xw@R`Bf
zFA#T`<C|H9JQa@&=o<IHYC89}E8;0@{wsC%@|YRtWAOC+dAH66L%wiw!>%}f8LqLd
z{P6B3AbAwpyGan|b55WS-(Dralp(T^`{u0=CNSjpSCQ}d(+<C@drgo+-X~jk0p%+*
zY)+BA*a$of1K$^ZKph%!!C;b@(5lGLR%47DAGO0OH0_eC9mSt#4#=Lx^q3l>xbK!t
z?6ATGZ~Hk*43k~SSZ(8v?rxem>DofQqB`MZXvu1gG&DasSwYphZGJQVzWc0`{I$s&
zL)wr>hSSr9gc@w?;kD_}WLPv%l&N;Tg-Nulom($7;Xj4v*|d1dpVoy^4I*}ORWNMk
z^e`EW`U_n&Dfa1c3Lr9$g)rG1P>XQhr3Hm$LVYPiwy;p~=|e!oZ*fsfia<(A?L`O6
z!iMrSgG-TOCq4wR7ns+EvpnqDAS3$jUj!K+Y*dUr=VyNHtxo^qq2EipG@HNFl@J^!
z<Wfif6pztk3nj6)eCbVr*<D(g0ZNZLPjA2{GQpisbeDFM&2BS#|DDqJhxUK4)E*j8
zoO&tmk1D*p&s0|<Z=>tsE3KcW{$RgTE1H5rkvLlYTjbAH*flSxTE0T3FiM2c`0C4n
zg_j}3mu2w-N_k|p@C`5B)p%tik;GJzv!awLOHMcF^rg{ou+e~T#J<K-)F*s2#m&V<
zNYHvL1Y~7f+uJgI{E2RJ??I1d`*tsgYKZa0M8z>t^29{Xhn^z<PE755-2TMLIIgdU
z@alEUJ^6%N1yxo1M|?q|8csbtgaBS+@)XTS{Xcag&U#Sg1ZAS{pvrmIS~e9J0#S0n
zzq~oSj)C+C;uwQ5oJkM8QQv~=y-CH{b>Q-}w}5!(u16)<uHRpd1uBg`Spt>V`ze?A
z^`sT_Ip%zxb3x~F?@04^U%d~@2oMxirt{>O^?b3gZwOUyc+?39M%r98zA~BH+c;h5
zj6IqA=#w<l=X;$7JOCVf*Bua)LU&t<kR>CCv{!ar(2ia>)*71B$bb{Rly8`N>AU`I
zJp=X9h~-!nVWfw9;<SoTQdf(sVT~eo2V~QUiHQiQ6KFkh4|#eLtdMX%_$e4)Qu$V9
z6FS*UHZwQJhGBHc^-m9mifh$?DPCtIRa>8rbD2~G_Ec%0x04%SoX2jPw)Lsx%=gpv
zHOHPkwe7>*T#tcj<2LqA0Xk$lq@LRFf_D3miI7>fQA!W_QtS**Jl@nb6FB3%L4foY
zMH2O3)zNluasA0Uk|e&cbewNtcqhV}<16ZpEB3EbgV?K+y+&D+EvB78jpBJHiT@a2
zPtpynaWUUKtJhXy-8H)KB^$ik2SbY_G}nXmR7bVW4L^Wc*cl^S7rY)S-yJ9|W?|^g
z`Qahvd-7Pqlga8y_&)Si9=-IU;BLdTs{2wR&*evYA0stik^5mv^lu;BBdTY#PnQ}d
zQT&br{y@W#V8x}0E~lJ-cAOIXjANkMFe0!-DGzS17Fg<qpGm$FZ1<*pZy?`0klwli
ztKNWA_F|r`uuee-YJD;@!5UO;l_+rMlOaj=WKgdCxV8#)T4Ms}QI&HtPxe7&9@8(O
zWXxycr2EQLeG+gpUERHwQjAtXCxCb*0braI<mNHc|EWpYJf{?nWfHtJ^g)QkgxCQD
zll%u^p<&u_gHwS0CpNqONOsPFm11(fZOLvSBTZS4@lRyLsMOEXT?NrEOuQz&5gE{W
z7>|{K$eylXp@(U|@TBLu!8Be@g%h+~i{M+l_&mkHuhvBN36_37lWZhzH(0^EnOpji
zOkZ8ph;+lpbn0LtmwF`0WTnaJ_)}#l&2v=+1YSrL4g{RX!ovS{fXxz1{fObAnEdUz
zk2J6H6r`mGo|<(}FDXFF@^fY^$LqrkW`)PTMla{POZewQbFDsY5;9-DPsS07d%fx0
zt9tC>XOAZ?&dC^2$r;!3rU+;lL@PAT@ic1D9H-tB7GJo(D362c@rFLZS5rLJ5>xPG
zINu%H)cJq+Wy;7JYdcJ-%+9U=n&#`EHKW;E_<Ho!n<i`GJwvt7j(`qyylq>lqohhn
zBC$p4z1IkT?(h?AKk7lUP=Hv{NJuc!qSfqV%U(>4E2pKW??G{OQ3ZWxa<F=tV;!{$
z9;5cLG9jiLhe6D1d619H8YIq`;L5{-y7!0<2lk1xaH{O1ldzqd+YYt-+dD0f*qZ=h
z5_qaM$#JOD{3)h^VmhZqAyDRZaf#?3ID#%V^A{!JPz$DB%rv}H@{8Hj0n5O7@yoST
z8|UW3bX{C+d{C(qxjj*;i{4$vZnl`bs}e1TDx1b)-iQu3zK2{k#uug3#9ID%aQF6G
zxxeh{jk0SiIjLTvl54|C%&TD#&V<&CYiQxHRl1ke+DdSOBC4y-;H!+_w}$E30`8%<
zbPA20#3D{K2>ErLX$NyPaV|5l=@%Gabqb>{_74tD7qxLc^dL^?2-@tDf&<b0%P`Eq
zk+Y6W`~!FihHHkQoIX}OPz;NwparFLUz=ocqUZj+=h?#&APwWD*{qIl7}mzd9w^8(
zl1m9=U6=C?R!aW6-W4acqT|moMbG-Uk>v4vf}9tY$u?=0m48?n?I*($Zq6c@?Cbuu
zf0OGMF3+R;8c|-H()#3+=ClO}Q{ySCbWRbUB#L{A_#a!E2~m(y{fTQzwHVoT^axhK
zfjoNM^vxFv+sEfFTwfe0v@%(ba(!ktriHC>ftHSyh3*bm#XB_FnJl@1sTTyOx_5HC
z&kYz<6CGYs!ug>s9L3K~N_;UMMM5GRnVrn~>AX?BZQ44^ftLab4{BB_!<6K=hxA)8
zMpc7y6Ep=uW1xXa81x_;IRKJSks<dGX{qBc6>*^3K`J{LE;J#{Llf|Wzmp5+4hVKa
zF`mg8Fc`jK7!2Sb)dLF5t(vjdvoS;~-~=^6QfAF%&Rr=+fK#;BLNB%sK!|w*lV2+<
zo0zs8R3(uWZHK;+b^xZ9{8&{c76KNd>|b1t#!hyJcXp`#O<Ryp@g8^qd91&_f-Y?=
z)!V5*yed4wkpF;boaAdU7~o#(#_RFHQzwd|PLq-#N)dJ;+ZO=diKK8LE&Cc3mNezp
z8t=tEoxju%8fp2f8NoL}QOO0@tsQexLr!tCJcw;e$yI~!3dfe<ts6<gNv<4%LQ^~0
zG@ebCfS^F&Klj!OWCDn(AbQvWB7=g0VlqZuuRc-4VP{E49`5Cs3&%fK;I{c3P|hP;
zMFH^B{03C6l7^h8UAQfMrQV*XsNNC0f+1FM0Vrg%AnV6v6d)v2%-GmuiJw%>L*>(;
zzv0=l*G^&Tg8ZuF6{lZxF};lGQtwXgT+I6E1K6wd`oiwK#_y-JM0vabEpV^vP(fp8
z`hnZgCg`OzHSLs1?GSGMhip!l<##OIWu4;UQoUjorhX0GmAi298jl7lbzMeg<E49`
zy*%#a&Crb7Z=nEEXf4OvRZh>fQ4hTCHAsRtd$O+ummj)`3&!%WjvNeZsasuVq3a4O
zQbtl3RZD$)Cv)p!yipX9T-nRfx?AaQf9gO8IJ$b9nBs=gRoqvKT4B9uy*g4>jy`~_
z=%%PhqUW(vs*$qtbt!KY9VG(m@9$5ksGyOMkbwCpBtU{^@iX3sKXtB-T(%!YfX}`9
z7oWSRol&fY;vq>whyWZ->)1}MV%KIwzc$a!*<RmzUi+UC7$^p0)m{KRXHWslwUosZ
z1cbTKE~cP|Cp*NVW9w~s9l5^?UC$oeCszN9-T~b<dQ7ww^A?Bc^1{oK&pYGN-Gnq>
zqz#Fbp5~MXzJU?uU%ZDWERccM?BRkVQFR`SV!4WRiN>Z-#Cl)~``G6j{o=rz|EV?S
zdDqfAEUmZnhl6XS;qFHg3j|v!L&#T2(u$V3LMiAfY_6@~gEz7$Y?h%mR+iRx_XX>7
z-x;bv(W6g|8X4)$+rj_64d=O;nd*-!VxGsP>9ZrCPAuXpvr%i0x+W6Lp5`wDi1_-@
zvD2q)z3nH;6rRUR`tWrTb~d(WGMhU)JC~9{&y-m;hVU@Jm#|(pTE)!kDq)D>kAof!
z0=_mtcsRE0sB~CwXm+l^@#j9>+&X~NKoc_~@bxnWCZd=&4@m?i1bMMO<vb>rEbR8f
zc=G<r7^4Ewv?HGMhL@^H%>dA$Dq~bm&Dzd^i7^d$*CGvmJB>6uIpCqWCs(jN$9?kU
zN&NA~2(auFLyxg!Hr8%DZ7!i$^xbT@|C-c2gEK6khcU*d9U5Z15lMjMI<htC+Lbp$
z>egjlH*hgzOa10YiVNA>m)(Aa?WyrH-blK5qdJ@AQaB@sfhMH$nTR6;zIou(6t21E
zBZR!gi@GjM-%gkJI|mS189$C6g`8gjd5_{gE&uh0yvA3z$Rk?jE?2@o<lKAE>TrAh
z8l$2eA~YkTIv-lFQ+)!A=luk!WqBo{hWzGHAG+>C!_!X?e$x&5DSi<{p^+&b(McW=
z1Mr4_(FiB<qq)^x`Y>LI`%0@isMsQl<?~h8?vsJmxF9gX;8JpNK>^R8OBG+v3;^%=
zlL(mC+7Hym07Y#?$G2pYs3^eZ13wW`%H%pn^AS|jZxq<)2mf%3P)MSTd3HTvFt4Rh
z{q5Y$EzHJXGNw5u{gKL|bDpympDQ-DGz$_ocZPV=a^F9F*0}rl{re@8zrk=&rI<wH
zR*$>eira18X(5Ze<2Spna<dPaWn;CZ0*?!qHgpmXV<Jpr4d@H6?lsVwN@0ThRwsxZ
zts={l1Z#S$OKA#XVP(-+$KPu?d7pC$3;UnSE_CUG>X?VA{k!UNJ4Z>!TWS==CpOtB
zT1b)#7=r!8kX+!{gDkyOEjzd+ec*?Y@5T$CAA)5T%xQ_FvX?*MP7%2osn*mO))cu+
z57!+R;LDL~Sw0E8z<j$^&Hp^4(6=SnVMP{+*AH{YsaXr`LqPY=!9p{xs_F=S56TE_
zE42GTO8+V-+T?x^3wz=5QzM*tV!Aft$1|SiZ*F_BGCs6JhGs~|;vv=fEg6XdT1L#E
z$rB&7po{nJi3D#Y#~5-l5GI44jy)yaTHsy+A8aU~wQ}bXXJ%j`dYu4KJjsJoT{6*}
zxE}wts><j%Iyzce#Pl-hsnMX>468v%$U<G&?^ftY&<btBf4VlpGFEs*+MZ{hnVg7M
z>)LJ%`a<{y(RM_lWuSvh3Q@$Uo+xP1KG?UqgNg-6?YiRbbACkLSC|Wj*tu<iz+bF?
zXx%I95Hk$oo{8Z6w0uZ2S3uZYfHyZZalT>I6_j&WcDEUZ2ck=(nATQ)3T!cWxXIqx
z(n@h>=2<b8xZ%a4Ea$!;a_b(G)nWYIc`!!+X3;~Mx|K$OwS5n1hTHrQh|HvzvKGu6
z8K7HU?~otkFz!E&#*$Z^X`~r&*XBvF;if-<EG`k>mYv7JUcefX46A~1PPJy<^^gtm
zlzw+)aOm>D(T7yFj{5)jdh4jDx;N~9=q~9RI;2s$J48}akQQkWP+*8dgCLEFfV6^y
zq=Ym`i-1Z<hX_b_!@EbH=lwpv_mAH!9B{doy3RSX_r34?x<1!YJGoaoy;moACwB)&
zl1_TBp3nuH_Fehh-3rQ<gxO++!9&Ho$6q*Ut#o+=Zn7KWCmL3^m8KmITE-odc3an}
z*O!?HcCuzz>fNoh^wHCiQg)XT6w*q82$ERW=A5EQw4TR1W0NeKgEg*NH=qenrpNfX
zxoKZ`f<yf>E!Y^32Oj_-SH=RtTS%}A$UPoC8fssOFAnF4Po`W&p(3NM<16djYn;97
zdjXW_Ha|if@A7?k^o0KZZ*bQl2O(R?$QKrsa6zOF!7sD&pKX*ISZAQ@y*)ghIqZgF
z#*3i+9f{T^_^fU!IsY&Y>yrMgcBL~+9;Zl#E-jEtU`apc3|p>ms?PX!VZ`CE&+*e7
zCsmJ}rbnU)n>krJOQXAuM+;Bcv2<<;Gg)R5OMH1aEcUSI!lt=DPzI`^;0Wt+S{%K?
zD?V)TM9q0#;hxc6>UKoZ#LpjQzC5%2wR3x|V^b-GwD@fY>E0^SuDzwyJPjQ@{wTEX
zvLf~BQkW(izY2x@2YS9W9a+6dA7ZW`%^GF>lQ`xNSg3Q9!I7b@GD9-H&d=Vog7}+N
zsG+t|Gwg`Q)r*PD)m^AJO;?0Hyi2nRCpxp$=n^#1`d>5!kQX#H(FI+tb<H$-D}EE|
zWvTbSfA1dY(N?Sx)oZ}YjT#61x)e8HX<q;w2z&d!jjnajqMPsDU&+n0^f$k8v@>aK
z281=p)|Pw^(BX@|e-IA^p7xeLz;yc0P!LLoCa1wvuz(O)zDi3Y|0(u3*)&H(5lphg
zM<}_U25H5A(F<h#G8VJa>Sfqg?R>``3CFH6S~bYqd>F(s8Sgw2pg|%KavY?hgw42$
z;@^yTVFeC~__0SHh%IZBMYt`i*2@IHraR;Gxa3pH5n{o{vwSR@%5TnkLmyV#_jYy2
zPlFhPQi%>WM=el)p<4ca)^>33VBU8Z7?IuW6;|i?Uz=iK_4_?to10tG97d}VS)Gy_
zTSQsdci0W6wmBFh#(M#g5v6QjvO)^WNu^l5F&t>h$t?1Fp9D^oE+75yVN_4dZt&N9
z&1o7&j4ZiA;Pr~GJ|S7q&%j<Z?_qj3mY!*OZyud39A8F8k4qg+#VXcLyNY(liVnM)
zt~0}Ya*<G%VZjW+Duy*v{8T2z#b?1DhRfh~Nx1(6Y=>b*qS(V`bN*Ri)XQ}5zyZ7u
zKqX)uGa|$2q4WGKP>n22U;f9?UxRPn^Nz0yT1;izfBbv8e}kWIAQ1734(Z~To^5xF
zZfa^k16+qrlP3oGi0rF`XxWx}0?7A?q=MraM^lGG_aNox9`J`Ic>hqLNJhl+-yt|h
z#`E2-%%#ofj(VmNWPN4nhS6rvCHN@~m_y+K>pxxuzITQ>KTO>5_4S~VrhwGqBgO_W
zjWptAURLJ#Y|poyv@Kn=oSfJjx*Fb9L?8JU_6>|ae57dDx81ZM3oHqB%)46BjLsgP
zOp_m6i+-CzuRmTTKj_hG!*E8J&B1bP)6F;i&u+GTCxv;&@yRkApwiO@k3Er_Lgcu-
zZduki@#Q%|1pPWbaA8y(oriXO*YKMq&fJYGiBUl-!t9L{gTnHWhqMx$AemBoO!k17
zAT_>*Mw}#8QeHS}W@~1GViL1;r<4D}er7T{f$k3OGY-t2{M^TeWDK&bYMerpdA()@
zZmG;<88N!HBggbAJqakg_H(6ar-5vs`LUS{giS-~!gxrM%lijVBMjDhI_P#JX}{&s
z;J)(OyE{-<Vy~BF!RyzmG90wMbb+l?#H`g$`aUASVWs~+)6vYjR?LlLAe@wybRv9z
z|3zL1L69ljG#&_6+wEvx_ndcw>1gBsrlVcJbiwgxP$f8=f8(Dmh#pG#?(ShZJ^p%F
zd!h-g`!WE=E&IDhVBTT0t``57Dej@=N1E^1Jwe>-8lTf>o|ms%+E%QRx7sYYKk`tM
zS_&wH_iIyz=yf6{n#xUxvW|t>4>At#?BpJlFU6`I69)KgyPZX{v(uLWoqRUxT`}i_
zl0(&!l)Cq5lQai9zUz=gv)-w3<d$k;l=FQB3$-B3n=RP_@awgkU1!fc^UkCE@o+38
zl{~SO_Dy$fV2pjlUvP-Kl9`x;U99+E5_NJl<ye!VYYgesKIWlJE&NQUnd<9_m~$ds
zW~`;;V2G_qb{CdO8l+aTt&N^(03;(-hzer!9B++1=A<!JOz1OZCw%Cqhz;*YIFpi|
z?5?r%R?sffimDqsO875@6q9}(Yu;epU+ZTnvSGxl1P6IPW}dTqlcq~2id4;QxiDO~
z`8;qPyYq@$fIMeH?>TcdoQhwFviq3D#X6^{O&_KVY!=R-JeKJF`J}@c04?fIe`TP+
zoW|dG1su;rLK^(dUfExAi@6L-je}EDDUepqlP&L7$=^<Fpyw(74gKmekaP=j5|xlf
zp_BfA4vGuX+g#mqSP7F40;ew~Fr`K+0MG}x(SS$okI}lRs`B_13;P0cSef5FE8lZ;
zaJma*JPXCuE6-PDKAwjHvl)T$p%s`^Kwwny_`C`M%==P#=WPGf@q(;j=9`n>kY#yU
zE85Oe0uuN(3M!UEj@ZkK04%ozNdkf(Tt*&sY&0Df7vrQH^%3H7bb=su<<RD{mMFUT
zLEY!WrOL#qsP=XNq;?!a8Yn{5irX1<#CBLh$!O_)gt=)+sIQ+{X`w*DK5uXDrs3uu
zDx4r-78hc~L`m$JrupZ^I!nCh`k}bG_fg6U@QaJ$8SoIfXNVjOMDFQ%&glbU6w2>G
zw9Ko)G<#iHv;;T83S=Wk65NHpXL=+rGA&O0n!>Miy<M^8dU;y$QemQ>(V4Ash2y9m
zvoI(b&Jk8@#6@(Zh%ODuRanf`xPRphCy{$-;xDT#16f*{#)Q@9&xwvy%6q=~v{RSj
zZVIbHNiUUC!wut6#75DyPwXk`f)}zMnD(^PSsy1~Nc~EgAUN2-MBM0R1;z>(u18H#
zAY&83>MW+$iaici1G6Vdf9XTc52<;d??>FyXkwtPPR;E#LpOnFmWf{AGlm6;ZYamM
zVl@ZPw!7qDHDCFx6ZnCXhpr`;TyTS&8`c5dV7R(X_*(@kqIt&?1#BM$_`!+a@aA@0
zT`;^Jm5+*?1y?g%QAU-bE9sDHn29&cP@6kl3K7)wV<wY~^Egb(r1=`{1-g3quh%+j
zQ+C7L{-y+mX=D(ot$LgbD4>&Ao5RtS<7$$UgxrdFM>p}Sm~IM~+w&>qHV|Wqm)n>k
zizL{dl>OBb!!;GqPKioLNC5TAc<a@Zn!38Be(v(Hyn^enStKE(%q;+~y!8sPL3w3T
zZMU$WeeGZS5=@SK?eCS~8I?P#;j+kHt(i+zw?O1`G7b`arc!7WHdhd;8E}*%>PT9B
z5Tw&r2QRCtUy)s`KL1gFFwrN#`6D3rwvnw#hl|F#`xS$EWNKbozjXO#TX>|Ajooq!
zlp_bMp8*pgQ`r+ia3og)m_Pq;HMG-kmr&09|5@{5xfNDN?WH#pd2hhuA<Vw~+`ZK_
zn$L&P4q=#vDu!$~;RVV<mb_gGqxTngQLLN|zA8>KsbOo#apsoCtKPqPD`Mg7Y%~AT
zo=OU|)7c!{<;+m^Ke?5w94RJ>e5_sF{N8Ir9dLO@F@c8;`>Bg^N+Jjopn};pU_YD0
zsy~MvA0=jMpjwJBw$3gZK!<Wo>5DoCQ@TkBX74lX{gF$I=x~fn^Hjv^LN(kwJZ$PH
z2YMP(QCU=4-ZZZhSOKk4mV!(hY&&5+q@hdF!SJw}gKgdCDWjJ35(!z>)1Yc7NL$Qs
z4gm!>BW)GDpF*LH-lGR~X=LXl=Tc6}SSD#@n3MLN3?*8R9uz*R8GZ99+<REs)?jb!
z%CLxel<0eCjB;fTSiB1Ra2iJlOGYF(8R2-bg;WewTnsTpILHGheed3yL6rQ)Q^~D{
z6*v-Vk^WZRz`*Rus6qYu`$qRZwwIO*9pSJ8Sw<kDz+&(Z0#h>QsagVLst&N+Dqkk=
zC30awh9!U6-cT?rSRzmUgJQT>kf2!H#$Y}j0Z<5`Q>kN1C$Apf{F1~VQup!kVX^kE
zRdoN%yG)TfR8jtSicb=S8}SCv0M^X9tMZ=vc19y6J)RTlxa>#5>$7gb^8>%OI6|WL
z51I+xHdjG69kfG$KwRQ31kMQl49I62ylS$)6VFd36T`K!lZfzNL^d^;yO@82p|j+!
zS+WY1xgoG6sBrFL<)1(6A(<m$N3SGR9L`lMQBfFM2(tmcl6;hP<9S3>&=cc3gz+Vb
zxP6uG;&WJF4;L)JC8uv8^g5CF`bD^powEwnVnP&?d&;{7svv5qAaa?lCCMHD9iSvl
z0WOv1Y+@CLVush0jj_%l>@@`yKGtk|6{#(Z-|TT5n7qy;M%LD+SEf`#zsd7P&B|@B
z^79di$7+$K9ai`Vc`>YKU~O}J(ZK(OeBO1pK+l&B29P~c7i-#3hj;7w0~n&nAMkqs
zf+;W&5(19BlJ_4ShmD2q+6mF@4nbF>coaNT8R4WPy5>HBl=j}VnlN@|ZSL;$=yDh3
z^%Y?%9dh6!NwloBPgN>#8<d7f+bR?w41899Bx_*W{b<yHUZ0`jBj9KF<XQ3m)dtRv
z#Hz~9hQ7fo{+7i7#1Sh-wa`*LQXPS$P&kSraWB9qO-fX}@|Wlp#aSW*)XJ#Rjlr`s
zp{7Q7j9;&30vU&4Cu}(8CLu;iHZH9^?cC}+{by<X3u&SY#!yG494j{bIeJ5AqT?jE
zrzrvQyhYcMmyE<ZXxK$4jOXDK`&kx;o!k2>2x{tNh>iMbKD|~xDPb}iVLlqh+YsY9
zhp#=P+zlL<UGyqgWa8K5%zqM}UiF<L*-ZNxbDscMboUK^08Uw1StZQKIzj3q1DpB{
zR`n2KRFFvF^D|M^9i;BWpH4oD;zVxS(`P4`QfIqa0WF*P4BPEUu^Sk(?KZG*$U~`~
z=PFa{rjmeJfVr`ChanX((lq|khhkBYKD3t>xI8Lm-dkp7S35nx8gukXCG%FD^~B0A
zq<-Qb`NYSeAv)bqr!TK!m}yJ6|7i$qk}|<8h=!}VpCmNZ;pkvNY5q+&U}ja%`IJ@a
z@^UodYNA6RS7nuQ>mVb+HjaTx_j;qG`S754ad%`L(4U}O)rN}KpZSopg6vbsLS=$a
zf#!zfb3(DZh&j88%8EbZ_O3t3s!YW1cN6uGS+-ivT`!Oh$Si4uyMUlsmKt{O;-a<L
z<1X_0i@gC|9UXzx0Z`Y_k^Ezef{?zbsp}g!d=M4Vl5s8L+zn<50)>tn*OFhCapr4*
zK=M$P7Z`5uEr03<#KNXpu>tM@H>7KesC6o=?x`1n#Mm0nyy%c?yC&sl2Cvc~+K!>@
z*(j)aDrMyIE_a>Haurfl;q~cXiAX;M6FNG&P%hh{FqA7VCx?wfkZAMFGqH(?QAB3P
ztg_+RN=T52V1Bnge!i3TZ${9RWL*8Sdt2xh6C(v#e0Vp3iq|WbRgv@&7l9m&)L{aG
z8Rjs7uNLq4ezbf+s)qDgS95>Z`-s#sI506CN#4+Hsph2kF6MMFs2i&OMZM_pSdKAQ
z_pf7-PKa|2<0S>Z`R7u6@M$H9ov6o2N0S2#TO@?+;-FBr3mG{(Zth1wE9$BQc;pB_
zKj5I$F*J+-SV-&L#V)bmMfsKj&r2e~iJS#+J3^fdMdxQ;&rlDF(%PbTbhcWav~R&P
zkI%<<KLGpq-n~a1cKuh7Q5Z20a@j#hLJ*>Pt#c1><^X;$9N4?bRg}M+o$bA5Rj+@t
z=AU^uHBLN+*-GtqZQAG#EJ)uCC{c&AxBqAJJ>zm<SMK(v{A=^A4t{z07ktx?!Gzwy
z{5*k1LG7EU!M$7@0xOaTcA=tlTmx3;k)``S48Pt%I%x2s7T2N{c#(plv2IR3W>Hm0
z0;}SSheb*F2KHwX%qu$KG^R#gJgiB_VBBg&5*Hi(K|nv3kkCUFb-FVaZP)#KTbnl@
zby!=94)*O(rq9?B0xVz42_qAS%B}5TlVljNwF*L$F~bhEc>|V8bv*eR6sr_WUhh!q
zKP;G2Y3Llrnmm?h>rKL^zQwBk^*jk5N3V4B`lU-9Dks$Ybe@JZ9%)IW)UzNhm}OUM
zaaB`*d~PTe#7a-I>U9h~pLqNOHZi~`&IR+sm@k5DEGIl4D_FUGs)W|!n%I+7|K(GU
z_|M8uO9^^k7<_mTerjrG=Fb8*^#(O3wVznv%$Yvxu_p3vJJ!%k6Wcv^jM^?+XmKMQ
zlHECllR@{j1l@`(J=sS?B0tXM9^Gyjy!t#h?rEp7IL`esdu^PVG<|p;w|;nSU8%5@
z^mU7t*XhTDOZ&O<I_ayd%&V+7YPiv#l{st4I6jQD%-E}f0rmSFyntYt`Vc-)H8yKf
z;W;g2N=KO8?qer2jn=ikDWBMYEze8k=Q=Gf>JM32XpI}$@oL~Ej*GDbs1kr8_GW9(
z13t>9YLXrm_mRtlL_+KUSuM|iF__6x@E9kozab{kh3`b_PhajmMgBq`J$S+1Pscv(
ze&wGjkdKcn?%;uLC%9w4mBe4Z(MFoR)m5hPyTV7S%N_vDHtCUEACzrds#HkU%8nZN
z!s93POvlEA;6O3Hh!l)xTl|n&O=AxNNT<%?Z5{=Uaw4Rx6AZ*{y=L<ZQ|FKV2xh1@
z+5Xmiv9t=<CkC?qRt!x{{Qg!J{k#aCB8+4ZV$3V#V}Zw18u9ImI$i!Q6Oa0Y-7Tw(
zz7!$XQK7qd(AvWOt3=q@b4zUU;`Gba^btZat{n0H5j*~F*J^@TOpMs1XqCapZcF}j
ze|qfhmZ1*VG~z5%SqleIKg39DnLr@W-@S02grcInP0N-)3*2&Q5fd(CCeJoGW>srs
zcj`e;+f(Vha!R?IOkKW>w#{$5r4Z{WlMTok<8+58@IjsD`pjm8(Y;3dJhz;B;>z0A
z4`=6?P`b|jhK@KxQ7KyS?}J)f8pi5X4Bgtkr02{UB+)#|X~EQ;UkDtSz#$>2WA(w6
z52v)m?DBeuq|gPq*`GGWTl_dmWQ&4&S|auf$@N`OB~l)Bb*#J(!V73`DUd@r^Q=N4
zlJi<LL@^h*?=r}5gIW|kN$V&cp6WbmGVV>E?xSY3i)?sO>4z#}9^8{r4L-@tp}5;<
zp6{&mAs?Y*u!mghp6Ykn{q+08*-K&QeuyOXa{eycd{F=N3EESyqenv5N%`#HX!<K}
z_^rjQtrbdm$a3U3fbkIkI{Fs%QSG3#jQ{fNvAb3PxvLZ;!-dDgVh-su0*m)H;%0S)
z|GSwmq#6Q`@O7`Kcf<}5@LF#6@J@VNVer~mdTVZWG#u&U;3;{LdobjkI|@83uVP~2
zix&3(?CLBOWdd|gOgYz3Tm5j!k}@|XQL!=IE7DDl4>(JT(BQ6*ls%znPw`IKx;a*c
z?3K#X#rrqLrFR~)eu+KG|8|4n-d9h7yUVTep5d59OmYOU1(?zVQP?HUrJOS<aZnk0
zdNXQ=Ii?i|QX=(}!IbKGwoWU?;lQ)7JBcqsmX4?Ed@~M10UX_Q_IP3J==0lX(|#ly
zE=m^LS_dC#ppo@iw3l^g?{jF)tI^CKvH2hAo$z4n$cKt%62pm3!ICI*(s`9>)c1CG
zh-1LmO~j`8N2bu0zVK&!jKkk6IOM}IH9}A;w_Teq4)E)hnI9Tezsw)CRmm^N)!ok%
zoQRy6ZMOFi&h48Cv&<n^gMx400n!%G!KZpCE#v~F9P&!o>D^UFaa1<6Oo`pK^s=r%
zM8Tf0_vADE4#2&`q2COr%7Pn@N<8K{qsm{8HnfBDn_*Hu<&v~HH>l+zo51s^&kNPD
zB5*&!4fv*SIn>j3L>ZJdg9wtM;VI9=GLG?VzR%nrq$`rwVHk{SdbX$p&Nny12pMmR
znOj0!y?t{xl33gAYQR@+zxfC4I&y_J+M^DJBbI`2V*OB1(xHD~4EY_T8AEq36x$)!
zSa$cMt$|6HY#ooudg0vIa@y><p3}|f!LG{3CG8T$+Y4!Aq{y>)b?WB6%cT|6pCAFd
z>vi`N>k4a%>A?QB+aCgP9vKzT|3Z0>-xbP6z0h|3#KIs1ph_y?f1pZ<_wHcQOFBB@
zAh|dvQ%?W5q~KSv1OGDm0Gr}uQ7}2Tm&I@7H(&xGg@Co*wiKz?|5I7L$@V9%2B?dL
zi_?>%>vP8C-NLaCc3xR;A~iofSs7G(1$AZy-uhD)L|0J6HKR!HXs!g?jEH8F_H?A1
zlZ%&z+n9cBQ4<uIpJ<yRXlgEgR|&h7;Oiq*-kh|K@f^}hzDM*tEoKqrK#fuQF;A^Y
zw58$ErW=95y#>OLBkSu*7DHMGhnouJCDhHAF01=ba)#z&W)rZQi;4Jn!_29n)fW#>
zev;*?QOgx!t-7M#YOJ8p9cv(`X5gamw^zRkf*OjgFz=>Wadq7F6SgB8>Ntp2wBpXd
z7&HkVtDtP0iM96-wW@Oxr2#*AHOKr){q>ydPMi^#On`G`M*CYgmF5pLWt9Pq?QjNB
zV=F=9q>zP?3Lj@k&R$A_q73>pNVGs2n@|c9#ygCSBE+Q##Tboq)R!#w1l(*E^&1;1
zk{c?1?_Vap<iW(_!PHP;P-6U3yLuxbkR$cXVF1l(inMZ$%w%6_DkRHyA5#pq0r)az
zwbg1d43KyL8>!7V@^0H<{QI5~Q;7)aazN;cbsD!BjU0sP1OBiJk54OsXSRN+PxIvX
zTocry=B_<JlPQL{za}#`-v0aZ=?gKS05YGIx){IxV)6#9tLgnn(1v|v^~a*V6i<wb
zB1~Ljzd_K&O~+v<sU;4NHd4?kb*I3KRaICV8TP615FPPZBK(NFFRQMfPecqug~fv)
zSFfC5%TQf{+@Yypb4c_Is0bp?M+z|(Zg44md|nvV%iZdK`jU{2KKeOf*ly}1PQ3)m
zI7i!-s*Y8eoLi2!Sw!EaisT*MH=LCzpNc&tG`|?jO*>X}EB;h5?H?<m^1KxIXW{||
zov?0i8lWJUo|qYpNA9dnrjR6{zirg$AyxeVB2GQmgCtIz_b2|3IDrps{sd@S_jU%0
z9zeT`24EwMM)6DNemJr5c^@2k^zlO6!XDs8U|&sf5HX|{wu62Ea(1xh-4bn?e&<jZ
z(6B|dG9wr=Z;=|3X1gJ43U?)br<u)lNabZK-ptijK^gz>OA)0nFtL%<n5%Qg&7qBA
z4NE4gJl4&YBQt&`l~h|~yWKGsjG!;|G#vC=C-m@R+LNvdkjMJJFY!-Ei0X2)rE%vR
zP|6~Ytikvl9bG`3Bkc|6lWIb=@+|`g7oV6HvsSu69B>!oKtwIyumwMpZwaGZTpqp#
zQfz+LkCrlDGA)FEPIvS1zkl#8;j!^mngbj1L3z8aXq=U#$@gKY5g>1X{W$&URtR!h
z+ywV5#_hKJ^DMAX>M4ezF3nu2B}|L@NaYY-%im$n3w<V!-HWQjm;=*MYI=b}h0drb
z_v~{bghIvGp5vgyT&L|!&SXjDDC<EO3d2{MjsK8%I&zK<6R+aOY35fJ5wGe#3A8o;
zDUt*$=uKY^6W6oVx)6aSxbp>1h}*8lB#?IBSn0=cN?~?Nf%V#P6zu&-=wHceuE(6h
z$E|fVci#VCV><U#s4;^{;4>btZ_y{c5Jy#E=uX+2O5C5%E>{CV8?cIX1@S*Uu~I&J
zq5QtXx3|kb1KHF&K!}K^#~kRPxXDcXWe5I0R!4_V$4Jp_?VCdYi6VG!G8&{=)>&@l
zmd!ASm`D2$tf$fjXmvqf@6)z9Gy%;20mdx=hXf#!U0rxiN8CKxeJ%g0ryHAY#!C)j
zUL2JEI05h*rUCyJFz4btMs<YwN%;+LxT?eyvORQf8kocI7O0*}eiqdot^CcB9Mw>B
zwG4<lZG&$93>Bmc(v9lHq+&`s4p*bnm{wJ(yT@$hq{u2q58=i2gv{(#rs!Ha;piAA
zH<9b3g(nAfIJW6xaAE?3cY7e?D)sOY$2OJ}S{y;<3Z_D0;Dl_d#oLj4w+M<diS)<N
z{@R=L@s!L5IMD@2+kW7P?nX3CjMSl-`qf&SZi|4SSn7WNPqx$>-zA)MO?6`KKjLMl
zyxUz6q7D^38S8NH_~qsD?^CR#)R6Qo=M;ob0d?vp_lMTiLBZ0%rZT>Q_lFv(xJ_uG
zDdl(v35_i2c?@t$0lbmp6ib!c4DtV^R9>uz)GWlhyhbWY0O<7QNzEJn{2avh|0%Cj
zZBEglHXy0)sX~~Y4SAF3CAa2&d0)~24%w;?QTUCJ^^*+%vhzWd=`(|1q0jyC?YW(|
z?Y>a{OvY3N{YXm!GgPBo!pf2x8>`B7)28jj{ruIJ7kPe(;}@)&8t*6ROdom0?%F(i
z@mg@+*q;ZHb#gXS_1xS<MGoHjBBo472ja}I9M_)+kue_CVP(vsA{#%iwU?!VYp2=7
zY)pj(vFSaO<fkr07&ld5a;o7ctA_l<7!B%6{n0?iV#)ipZX_>aDR=)$t8EnZKzQA>
zbT03>Yo}bpHoT7nCuL~7^QXa~T$fZ^)3%XiHML6-cZuQylLudYEOPiMB<3bdBB9~B
zBj<4LRhlx@L^AelGXe3j?4OyR1pZ)gk^3@<F0MF1x@zph9luh=P2|%Lj@4DU<Uq><
zyoz#@rz*-Fb=v81NC00~k*&)E73FSG?aVmO-yHLAKdkRwbT5R;&gMi<Wlcn#&rXH|
zt&fV;X&<n<*47Yme1>=!-Wc4=15~V?I&#%{0A6hWB_bLJ9E;^}QPA2h`Rg<Q(nPHA
ze`KMDnqfe2iVspPiHE~5HSLLq&nqjLfc#0077<ubj{8K1R=OvLU^kF`Ki@O&nA~q8
zamUWfx#;rK3YWty@qTO-3C3GgG0iUpF7s(n=V}m7Y@pCr%A?$_nGg!?fgDEP>PG6o
zCz*!jgDgOEkeuhH@}ubZA?fpI5|tm=q0YmieZ<?UwG)pW+5(Hk7hw3a=2T4&1bif|
zcDy=<=WxX5Lw;_Dl5J??8gsZ3zY86#Y(VQ6QeTPkUmqCmY#b33zm-ZAHP#w>=v$=*
z5vr<gE5<qN+1~ALJ5)hkVpi7ij|x;FtA@`Di^K9Yw`n&M$1u}2c16a9EaOJr8oh+8
z5uh~3${lOv_+br`Iy8F2q#I$vX>0x8_SA2j*-|%0!=<(jQq;IryU7oFZ=EssoZDi!
zKJ<cSslwzE-{unI;y%0`jjYWxIjf+QoMFQ_<{or1itjlU?zqv;5C0B$(UX(h*#wvl
zR-A`acU`u7f?D15$HAt-)tq#sL*D1YR!PM2ldTKJ86Nb8m@^Igt{Cf~lqQCh3t|<>
z4sIxDZ-}B@(zBRqjMz%6!SP;<qbT1C;(;pihRveFU*J_%cvv~##SVtFEDFJY_XkCJ
z&}<_C&_gf#f<qWW*kgq?q7STe|GZQ<`9pre(B_qt&bdn+k!>BR-8FNy8%PZ;=-8<1
z{FDmZ_re~OHlN;EMKsmlto|LVdY7*owA0cI#Gks|7$ckd^MC;kd%eRZqUK`+5ti_i
z!a(TT?L_%hB>7_Dyzuj9N8_xZWMB&bK%ZeD6kob4S|;YajeEl6vlW!H38L>di4Hqy
z_wr;*>(rO5_%Ey=Jw~a5@ElxW2WLxrDTAU+55*Al#t|8R#+fvUg7<2;M+e*LxCyG~
zuUo#D$<_$k=<)z<Rs~Hd@{4*gpac^11%vZ@5uI3Z1o&|rUj+Nuscbt}$@!6dvaZhF
z2UAOzTl7J#-d<aeq=3TJ$~Bo~R#Y<SIC6t%LUa-(^ycMd9D5gbiq?2tun@WXraED^
z_xAJ1uZoxFUPgvYmIem;?_L?D5u&Wdgb%$i=4@6S30)l-Mi?jQ6ghAvL4%w^5<0eg
zD!FB3;YVC#;w8Gq#xZ$G)D`5K!M40`lc@ViL5qE7BG5Y-_OL4{aC~N1Hx;NZdGav)
z4L0ZN#0t%CQ%Md%r;Iqn1JQ6Le()qn+&aumU~u17FAABZ+g9Z+z%o0F6z2N1CcoFe
zD#B1f_I%TZCW2L+v+hMcG;J8COiJ0+Eu3o=|A?se&{eUqjvjnIzf*kA8#BR+beJnC
z{b#I&T#OujNrWgB<aEh0J^F7NgJkkAGUv{mxS>y#l-B$wr(;Ttge@<#PT0aMrY4^u
zy@ryh^dV>I9dz&cJgp2mLF`BeFx#P8a9(EYYsH`#LD|xj0BWwM)zOi}x={L&i%zy0
zAD9bnZ~1vr)?8jReFrHS$k>DG62c_WPy6VKAa50moeAAcp7P`6%<A5g+^M-(S4dQf
zDIR_!KCC0(MMxm4ehBv)76%H-A5%+c^QE6>bEcq8k|uII3hIs<8aUCsT?oc$F)@e-
zX!KNj^7frEX+d>w$kh=q!35Lx^9X7JpYG-dMATSjYeRywy_jw0Sxw@Lmw|n8$08dF
zZSzXTl?!3_z9`A-K7McX`au;cz|hpa<6vhXRVYWN$IISUdmpv6-nSt`_ARI_z{5%C
zhpt^!KF`hGZ92N|qLL`8Z{V3j7?Ao`v}v|)K_BrB1vS5A#}xFhWN&!NLB=W>S4_c4
zYdFDsYIBCS5XYX3x9=}nH0Fk(ogF*M82K_p@bn?Oyxk_r$bQ(rFZ$s<W_w`N7Sikl
zZiRSY#DSBOlb>`@wgi!L8_p*vM${{D0`AafQfwkxck^**u_H;gIgY$X3mNBtQOKt1
zcIQz}ZUd1~`4!pv{abiXGQE4zL1RryUyK*zP9O+@9Tf<m2PnVKr;j3aSr<u*;6@?^
zA~KLZuObqm+==aE1=>Q^3%h)+aK`OD@2=y3nabUY?&5i`og_AD<zLJx#&>iapP~0c
zWo(s<zFyoPQu6*bdl2O9p33(QD+*{}9qm5r4l(Gx-mFGtgb0u3#l+i`tmr)hFx9Sk
z0bd~<*PApVO2r?|R;Ld2{N-eN@nqGKuKXpqf+lS+8IBKE=Trj*5mB-RZ;s9M-5E5h
z@KXkd>lFFMdW6VVr)h9rLv#+Z5JpF**@A`$D%-uK(Ov-k_=2+}av}Ae>T`oR%D)#C
zbj{JgCVUO%(ruT=bMZ#zz8DIhl9OCqT<Sp@&tsse$!lqW1IPGHnd{S`eBgSBEMa5r
zCjz(>B!a7Rwq>Ej#Ss;$F!XVpF`xEt_XmU}u5txdXsu%p&4AM6@l=%rPu7FP%dXA*
zvvh^hh7GZ4;IF@!yPYw@K-a=`-f&3j4onL=x(VkMQs9FqVh_66cr2yiBwrOO1C7IS
z9UYyg%g;tO*R^otRL@6teB5$G$B8023t3_0NhAhI7;o6I?tJdztTf2^BuMfmJXuFi
zjGPTRx|3q8p<~Y#mZU9jC0uG034Ay&K1sWr1@>YU$p_F<-7r#2tPJka8ze$yMsjnq
ztWljGBJ8CE<+7@P;gRd@bOi<4XqVjK0>BgoCX(k^$F<NueJ1lZBKXT7*o*>L7=?1|
zJ*4iMi{a?(<zfB}RQ!8s30R=Q@?X&DfJHZezCQ%%ON%Y%Tb*b`Qb8_LwOw&|OvA&&
zK@1?F<mAUtV8rb&Vra;VoXTT@n!E;kj}L}#3jed~ZhhOFG(+(}zYd7Hx1vA)i)~-`
zp7Q{bW<MPvGJUctv7t_yiUk9r{llfg#l3HKU`zqciRJ>*9P(UQ{kzzGf0E#4tJbKp
z;q*r@8Ld~qjRWS;8)3w|#I@KxArqCvwQAUpoUDozG;v)@(u?Dtx$gyb_#0@5$+*jg
zo<2LyuJ4x5P%}3I_XCG_Tw8FY-uj!+UEFY8(m3Z_A4x$vl}RHx=Qjup%SQ&b8B`7&
z&?i&B)73BuB=Bo@-ltS^wUOm~mJdelUwjb6vSyKNtmdCKT;F`RTqjGF0hgVIJQ}4a
zv)t`})w+_9>$0+SIY{Cew}7PI15HE|(ErpQ&-(55vc%_<mlG*qgX|r5vFC+Z2wZLH
zLo$HK+y$4D!s6`fOQ^zrB+raL8_YpLw9$2QGi@uv4w!y-0<%}bH8%m%nDV`t3wsOV
zLZ6D<m(1wZs{n!l2EBVxNPErIrw*QS=YzYPHH$+Q_oI->IYvXy-2gWa=!G(n7cZZz
zaBJrtSg>O%C{*MK+8B=3%YLge(y9GEH-Nro7MylM(|ko3->_<^KfM)13h8@;(0db@
zG{BrBI=1HwOiFeG7vHMkvZ~*4&8>U)t8ki8G4aL3rBZn<3)5G^3GFA&MZ5avsG#`)
z0~*IEuqz;^3RV`iSz9T#Oae@$c@oMAP3&|kiEpix1Z~`2wtOg~MZ~e99Yy=>G+8P@
z9>g;y7-@8Wyvsg%X-%5ttbrA`jDhPyu1~+2w+K!LuY0jdp7p<42$V$K4NV%2y!UIc
zV<>n#7_N4(O3j;Tj^`j*;4c*~D#m3x;4*G*Za!JJxQ=eUIxJ6<36MalulNJjG}5Ge
zJ>rbCm4rTuY&}_kfVlOf{++k}mleEs^-J$Gfmt6984k7Zp)22G)(hN}cJHT4FSQ?0
zq}FASA%V2WV2+&LrRu7=cb?pjL4=qOs#O{R?%jw=q9algdNm;b3=h}|lRBgLF6!MI
zd!-`_bf5lGWU{|-5W@_=BRPHs%WgyhGlKEPH5OF2I{)x*QAF*GERAS4D_aIFLq{#c
zbp<;ZsVx(*oRd(ZOWQbUle;m$Q2R(K)40?ANFbYpb20L%>*?iWV&XaEP7IM4sS#y2
zo=eVeIfYHl;J+*+aJAr=4wku0`yGkLB~^tF#$p!mi^kXYM)eF?v&jhax#%JUv7q?d
z&xT>zB`uqmV&f%!ZtSX3YVfJI>t)r^qdot<=Bqi0-tzmL)jysO1#EeqfW*AWu`xqr
zC`<kI)p=#$<=F%s6MC)7R0LoMlZeiEk!p!8pqnAq-qf$=#mu{ldDJ7A^AdM43@(ns
zdFxj`yavpim+4SBR|UYfoh=u+0kVzX*+u7<x$nQbL5j<>AqIn(KV<*0jV|*eVIHWv
z=?)Su#7=g<y7p})v-*w9)J#=?gzwf@!|N*c=WxBMV2~H4Dj|dGpjG;gz<h;xW*Ii(
z!#|ORE*I-~UhdduR#H17Ymhem9IQ^3+#P#02$-O(ryXlLT!-U<T#D*v)8+oFTjcI&
z&6;v<EYF)`Ik#qF7iU#+>PS|dzH)~=;>6FfNL@%`LuaL4cBBfYzL?eW{bVM<s0ROW
zr*Pe%kDC4ey7T|EDd#|@QE@TX#ZL~|cJ07DQvZ{M$Ulq2PA5@*bv1NbMD?3c)7kG5
zpTkj*SF#;I1Ssd7(d69jXKRHk?#rO!-+IAIU+=1kmi`gQL+{*hU#q;gvvm6ru&P4^
z45In?|0Z}v41B~!kvh?00UXZ=V6Kk=0~4`ga{d5$@>V}9ZV)1f)dBJ;+(7yUpf=Ea
z5GE!Wfh@szk-&F^;di`CR)Q)6-f<`C0cgMH4SWkPn=Qj;3ah|g`cao1&Q%hs@bekU
zUJi{dRc6(lkT7cvLFBmTL(1>6Cvr|Vm7je;2MrEmg7ckrVWuE=81@lbE)a4M)i(y`
zfWP;MX!V#Q-igbSAA{~iP(H4^p~_j}pe-~oORD&I0XAGoZEuQI;|4=K1!eNAO56Bt
zM{gS#vPS<73z9wFOMN3?{;bIh;gK_K*#Dp7IoQZ_X_lDnvsS?c?E!exxVej8qCShy
zINav~ju!x)SdNn{gcy-9vVOSOj>Yf^tOxw{W_%OQ{)*XAdyqw><e$J2fV2WwlqMVu
zq{fo)wLX{+iqg8z`0iJD$qNJcMG!LGOIjNca|al%sE|+ur043V-uabGSej9t*ZgDv
z{=oI>bCc35O{JwQVpJ<$e1oFlu(xi*jA2o+%I6B$_QN?2XPFZhz3d4B+W0c?v5r+D
z=$Hee`}U6}tL-c4`sVy|GZJO$aJhHIdHM#rdAKRKIZCiM3Mx3ZDY$5NSkdl0WQk^F
z!AGIqIY9Fu){#Kz!tPaR*8Cv~bL3=V-J)j}+Cr&1mB;Z!nJ*evT#dtRGkIa%dIMN~
zs-(`2`O%5@2z)lO+J_X|2-I%r2;p#MLB&`eWy<-jS&26QF$-gI46Td+F;xp+olcr5
zUor-+OT#>@+%h)A+<pA8rl3XxX!?D#oY>{|q%U9{Z9nmAxY};FL?!bC!jbLHG;4JH
zU*B!mRRaUJ|7b`4t>9u=miX-94+W@@;XOPiRp<{MJU9Wu!f*%~$;RNngM!r7FjGhY
z<J=?Q=h!~ZaPLX}2gF80{s+XiTz^x5Q!z3DBn?&mZtPh=uFFQ+ZV{3K7Y)eM&h0IK
za#^t_>@ANwgwpWO;qJc=K*Go0AR5sdsj|*;##vE>uIa;oWq|#T3H^g*Q*0dc4k0^Q
znedi@L5!oIEB>8=vN2u{>H_lS-6OfdRFs}B^%k6O$$=r4r$fE&-iKaaC9#`kS}B^i
zwwy`miUq<7A*ibvu;(x`Cgu!Gn*swN^60niIqw7*J?Mhuv8m8=<m5<#P(qJm6U9et
zFqx9hx_f;WS4ah5)H7c=(V<d>`JAGgbna`W<FyMbec+fQSkL^iT8{w>6TRy^ST4HF
zXZSU<T5Gm!erZ?YurI3*d=sRrpzZy5k4wLYvPsKgwfAKADH(Ulqgd<cur3Xjh212S
z^EWu>T?i*Wwd+jWTu8RT6b1MpVAAhm4?gHTWypUca25P);@DZa2^u_#<ovhG@aH?H
zQ@6{oRAQfP$f#rZ=trWL%HW=$O-=q$BF#>{Fj2GH1!*x=$h4SSje4QX8pe+|$Ur%b
zi^TYA3*lSDi4c$Nlspo#8C2y(cFp6`?<b91+NZaH3EMxIWCq(x8e}TNE9eB|`$ZBA
zkdB-%hsl8w1$dDD*pRx;wsL5<bfPQ68i0I`NjR)R?K+n>&$)_|B8|W<joh=;4fv3_
ze-*TJS*oYo7=9K&b`H>hPe$bj)jF*$8kzdYc{--eFq&c;TId`>*Bn;p97bp>rHTau
z{;Vri(~M5jjKBfp-H{W1&?W}EGTLX%2}e1G2MkcQzyqfC7v^m+ous_XZ;^9m-5@HU
z)cRVY@#qeAPWeqZt6OYXe`DP?q$b17nq!%FbrS7(_<Q>(`X`X*Nx4=%;)vo`N3>1p
zVW$ki5m|57EF_P0tPa%H9W(;t$oBzevMUJYTi{j>dk&?JK>q(JCP76_39dijg+I#j
z#XO$zW&lt~yw*7)SVGkU_1GwTGLVP21(JWZ0v{rM8YTQ7&ZvGZ#8<2?up{s?vxKK&
zUfV*9j?(4dd|J_si?U6)|1W;Gg=!EHW$<W79z+xVoOam+TnI;qes*T`^~im#tOs$X
z$4orhN;cZqC`QBKS{581{U5CEc>_P#8-nP@P}i`k2f{BngL(Sgsp!w_zMB4(ju!S&
zGl)(s1fiGM1}O`+^JG{i#tLw&7~7J5jwZ3|!;WzdQx{PP8AdA~F|^;6VLn&yAw2^W
z;yIiHZ`iCFL^}g)PFa~UZc6!F8)%cFz;=_A7g=%7K+#(tfH#u3Q|ijbOT1P9W>&vP
z@u%{KK2v!DORBTur6%Tqpl1EK;@^>3g%N=D-;8J~(<iFfKO{$Py#K!PLTBso(jl;!
zBI}f0;G)SbD<cSkM<lW0D6CpRnkA#rY#OQaUp4r{2fG<rNS0qBqn$pEu2<w&BNN|b
zKT;qM*SKZpIs_irNM=R@Lsr;aYdtyS_XMkBa);>{WPXILfZ`iOY+0W!jH)wvLuy?Q
z_}zK{_HaOq2l{JvKc`IP$V=Q04*-_d!t&b=F|ntIG7#EQ3a?!kezJA65q7*j4iErW
z0yU`pw3SzzYgJF2Wb7_u!>Hxxb;ja0+F5#V3q{*#>9J(S!5BI9%CXSM1S!{WwZ`1j
zrj+&3PwE~D2~8row|s@v%%uZEe&#C+t{MP|;(uQ^fSS+O&rcHWzau{lT|<{A2j8?&
z7%8~4y*h9>{^W6fek6ld_)UClfR&lXP#YqylWiaY8=iRn>1}R0<n|bsrgy7&y&|0q
z8FCB~H-(XLDj+=sU$V7PHi|)d59d$V6u^CfkddYj&3FJH)(fsfpYDhH-=*T5HL+4W
z+FrvXsMx+xu%#@q`?||8=@e1;*$C-T-tF)4Un;O3<OX_yIVknss!NA)E5+x-{9GwJ
z(Mc=O2`kaHG82)`sikoBTkVY00&nbaaZ51}YSRhCH{#K|20aQ4K7&$aq(xm&KJ{mS
zsj(QW{{F$RPftYHjt{Hc>^MI9%nx6^sJy#`ZMNFb1YOVw#dgNVnUpr|yaSe0Sx}>9
z`H^NrZd%ZuI2aw|_}uY$xEJCmx3K-@|MNLjS_7^uDJp#H!F1tzkZQU!?KuSG(Xid$
z%evp*YIeSCIbbU_u0#bcputg(XZ9(VV7`cib_1AAPr#uQ6S^VvJp@9|1-7mZn6$y*
z8n7pVk?C9PoiY)-N9`M29PrbzO5{Kq45nk<PdhumKu)TZU#>Qt#Ox3^LBMh1NWkY(
zcS|HxO1$67H?OJCK@Ms(e<(gbqFB*>r8bvLOvZq)AUMkB_#XdsFrH9ZrBPnK(ONDN
zyyxX8>O#Y=I>Rgj#7h?|teXrb*b`wv558HBqtm5hW`MbZU~W6b5AENyuPem4=G_fD
z%>{7A^7;~D*w+jK@SI?(Y-U-3O$T~SsJn}bl=(S1mUEQF4hZ4Ly!c^eh?0m!8%N{o
zy2U%#aU7U^=ToY?CizzCRq!m4`@lAeLRM}512Pd@Z#N4yA==@3t2Rd^Zg=PQP)dbc
z^qh(zOmnoAA#aY-k^NF22lLy|IP*T)Jt<b#8Y042qTf@C0iomx$4lO~m=1`reJ5E^
z4h);OU1K9h^3P|#j9eq^?&!g)Sx$XH9V*?3BAn3pWHTnjF~>tj$Ar>Nqwx0KKdHTc
z(?O9pR!G5@QV4)r#vfl_9xt_CU-E+}a@eQN<q@u5T7{(9r=K5HSa;#l0b+H786U~R
z_S&aJfxF+?(G3W$&nqxMf?H?74b%58KUh2kc|w!I1U@#VMk@R98M@I^FUM@|ypkT^
z{syuvkl~bT0BLnjicD04Qmt0740u5dm2vp;vj~<ydmt#kWxo@-_2Rir;DPtn-|+^<
zwKSy&47*UacWx=bFc)H;^JqNC<xv5>L?#EU-qwUDMpa>HGjZ5QRY=bD-td}`+>2<H
z{hQ*9uY^!Y(eEm>F&>O@=F~-P)7tc)<AY+w@_7v=3g<9niiV!@=`!pT_h6s1a%!yy
z|Cvo<0Cf;E3<qIBF+2s%_6a$2g=Dnx3^AK{B(DmG7)E;VE>PNLa5iQV&gz|mIt5XB
z97Mt%;@ReQj6D`*#F4J2B{=KuXhI9^SU8c&jkSFvl+cblF@REi8;&hHc>okfYEol$
z1gk{$nmABE7y3>pcfM*8Oe47E#UNGSdm7X;$pg8Mjl4JR#yf+ogDXBc5v7m?w9Z{L
z)AM;)L!Y2eR(s~z`g?~UKceC0d)M2h$8lj-e2l{{Ulh6+V{+>VQWh1*MD}z4LJ<ip
zO8oEY7sW{n`m-&bD_UUfyY!h4hOjEYZ(vd3{rR>g-D`!}aiC3Wx%Tx<g)vWqF&D|h
zSH!4b^^(&WRdhh+J^)EW2#7@ZE24eqAC{_E9MRfbi`MEF2y&7RP#QrzB2dm#f((+C
zf)Kw~ozEp@{iuNU0CT*|q3u22c3`&C$N^Gr0l(M7$)gS72x*p27GtuIKZyK|bt{b&
zy`%l7Aa6k=iRKp{_rVRSitX>B$%^I1jqyO+I4xfX#|KUJW+pdhSwVp;v0r$ndS7(I
zKh9|vGT*G=v`b^dFjS8)A+50I;&CKS)q$lZFxW9!k}Jrv=Vf44&tS*cfq;S$3W5X<
zeP*H!?T66x<9<eTTp@xlVS~BK-QRyc_G86|6NIfV?5L^H^_`EjiLq3g<b^Xert74N
z{rWO$P3T@FPZT#b05dfyVE7S#IDq2!ZZ`d7ZX7Mi8p*UVZAo}|iHj934E&i%0Q>-`
znDf5JacU?2kD3l^;x4XY9E1DM)WY}FUhE~kgAnBY|8CWPztfN+TfHC-yc5~#1=w9u
zIVrO&s;bhG66CRX2Y4Dt3=9m!O*lwmU#rU4cY~zA<Zh56^+$DjS&zZ}a(roLa*?i#
z@Am&qu}?Q)-Ub}7`d^t1oR!;toZdC`V9ud!@fc#>LikEvUF;nh&&!e`r6M4DC6ys7
zqvE2T5uBG$nD~h|;%m$qzn5<l(d-ju(H<0Jkshs&hVDPJM!idjU5&<<j7D#VC-L0Q
ziw1vH-_}b_uCO3a5ijrFem={FGIH-Kw$abclig6hZbQ2POD;7+kx?Tn;)*8iTr7q#
z3q{1_w!YVd38ECfZNKrZLz>+-|0<x6>JYbiq{TX}=54QC)Mw$}Ocp-`l;)^jW#sYG
zF0QCQ@e?k%_9O5K%Av)sX9<X(ji=YhTtZRVS-|dR8B1b9U3!DovGg$LO_aeWhl@nk
z=IHt|^db|n6Q2r;)(%zc6RF7~5%%j+=(hNcj-QEgzFlT*dSZ4&|Jt+v`OxSMI1FEy
z&Ye+!m?Qn>rqWV;z=B_?1nVG=aRsAl2}@q(*fNggax!hCRLi^3SiJ2(QVTIqF-e1p
ziKko8>;WQ6jtUv#Fnv76hksI;`t?r^lwqXuw}G$Pa774kAdyLFpj8p&WF(4@dsXQ1
zH%IFNdmvsM$xav^2cvzo_rJmd@#be~abBpPF7(c4*ktOUcyZs7Aj5clr^(U#QAf)x
zC3Zg?2ESa0WTstCQU0d$wwUwYVXeLa%Q;tn$Nf@^yke@|R_A0i0ZLeEh}EP*vtD&O
zBqc`Ys)ip;B*;g5_`7lG74cc5%8k$!w9wUO=d-Stk2DWfG$*pxRTKP@@WqOOFr*pR
zQ(jm{Onr{OXso;*rcTs%M^C{*YmUK7Qv8YR854S5=&L?BQ5;c0L{JO+<)gGRGR}0V
zD=6@PQ#|CUm_BEZ9VM3&!UfiV=g85}M)gp{ND-FW13n|et@4~4M(!1Qeh^L;2b_46
zgM)+En4z3}eBA&{1X{+-#yqJYL7Pwy<Qhcu$oebWF$G;8KmF7B$@r2T@A<oAFAs6F
zPdn*;nZI69#~;u;RHSzDMg80E3=$a4WmEC-@^*elFcjJ9i+^=&h35)cqam6?liTkN
zY}5xh&{GWdmRDR9;UW%UkA!+(QT^VC3F}u3bu^Fnvx3n_hfw6B@M5AJQITy3Qza=Z
zOGo4L5?x*DW-_DS!O$s2c}Szf&YI%z>^#vB%^9?jZ-xX!qZgcw*jBCQx>w&;)@gD-
zVQk23g=E+FqFGT?N?LOL=Ulnb=QqE0W{hOt3mEJ>&#Eou@w;mh6C6SV%-VR#92=4x
zs|+%Xc^7Y}kW`-kCp^=a*Dd5%`r8E%57dUo-;{884odumogJ$&w9#~7C-?QTy2VZ+
z*}J~e3-0p+7FV}in|4*8lo#}E?YjLMkH)pzNS6tbs62*^E$XMPpf5B4KO;s4zVT}O
z%6~f{qEd1f$!320H;ox20gJz(LM8#*z47#-U<AtEZ=K<@uF^ZD;V7D;CCZKq?HM#*
z<DIPJ@CYO=^c3sW?mfQBWE83OvT}>ZDu&%(zN0?~$>0`ddq{jH@5<jdX4cnKjx0w5
zy>x>jVg)&JE}`7DvbXY~Z?~OxcZ2saXSD1qd27ofg5((H*D?H?iLc;o<5<8|3-O1w
z5@EAP_I9<IeK0^RjXQaaPiQ_ROrV?|VQrvXS)8kOPdDdHy7*Il*k&RiW`=^BhvA1m
z(gLBlU}9KB^zKJ6_i^5)%<s^P_AtfZq9fI{DpzPIqgx*HNSW%_wc}L;u7Rpg#T8g=
z>ZltJ&qN+aaifN#1vw23%OAA-nhtdDw$!AD$8Q>~Gx+=wX#Qta&l60fl;5`(wq{a_
zGxC_nMMpiE9B+vLmO*!D!WSNo1zjvL>~2HXw8*UF$Dnr?H-h`jtGXo9=cj4Uq6J92
zUeR_5k<<*|rxg9Lgp`&eMNR#W?^)_}vs=D%Kt(cF0Z1?HX)J8MhRPizjP3qlVV6&h
z#Mv%pk`~z7L_d>cLD{>Wn+=FmHF<N*aQ(SJRE>x=7-Buh3t}uH!$5h!Z7fIepzA}m
z)A^(cPdx=sV_xHLH{N<zd6I&n342{K41Kfv1#Boc?kK1!sJ;Fy)hdf>lXbVB_0?Sk
zpVp@<%qySYu}~gMX>W;pUg-MQ7q_r#bG<j*c9(XT{)Wm*kR()eHqf}l;?Q%qk6$Tz
zo&O1(@d*vY_(bAC^d`sY=;bDM_;Pr^MCzN;Jf4RP1BQkp<+9uSv*mYGX-@<aT3N4Q
z2Ai|{{sfBjn}o_@TCCaE=~3{73hwDGV7pix3Q`GEZ;Z=U+gdNV4D7j<%2Em}=14lH
zi9p3yB&UdRc9|EB!hf#s>|SaeVogR%XL{)A{@fZ0jk~6%D~SriK6U33OCJ!Gi@y5~
z@x5E1SjOJ-f|m9U(dPG-vUOX*jaxh|ckXPx4Vu^K`)&G!xpK5_atQo82k(ydcdPbK
zmo{Gy8Le;AGGas+v3*8^{qL2dX;1p>7l(0Eg0Tk|d$lAT;RDY>;rk!G-o2acfqGOQ
zG(I)xiKBm!n3&tqp|IlF(9rM^j0|ePCiB|!i}<YhEv?6fCCcMjFA8=q7Y}~+Oda&S
zMLVqJ|M27RpwQud6A0ll_x*_Z6vu7kaI^pBSC$wNCq=u<!uQ$kT@DKfue5mP!<d@2
z3`d-wktIp}B^(Ja$wj0rO>MYStDrF>>9~@LGe_vI_CwVt28PBpRTpR}yctz@JW>o&
z-Y}c$a`V-@6xA4~tgPotHDpWC{d7+w9VuZeoZ>Bh%G?l7cy?Bsp|mF#sn{NoYZ<A?
zL<oLRY)8K5CZs<hj8wqi&j!C`>d7X0EcTN_Tsyf=gR9P1-?6f2+7Z%ywfw2`{^82*
zM~_y<7ZFPDq{Q?d2Gi?P5EtLNwtR7VTJrgK;Vr+0{>^2sG9H75X0wMc9&%N`ygAG<
z{EEk1`jr&Bn9Ae!$I-aYld|J2xLZET2~)(7(U}~sdVO?$t;)$O+jr_!NMb`aoU6#q
zNBqD00)x$dFNU7|0@wfWj>qY{EwFI9tKUL~tsMf0C6|nhxt_tTv#)P7o(xGJ+i%Y{
zClw?m-O$XEGBK@n)&;k$xy6-HghvO;mM}IV=o|UmWt#Fp=f2yy3s#)tqSM^(2+yP^
za~REJ6stde4({D)G}oRf8#mlG44=_;o6>o)`g?c~N{2TpS5nWZHTFw?OdYdR>))^B
z8W=Y^_YZinxl+P2SFBo_T~2s<(py2M@y0>#^7_Xx_8Y^JJ7tEOZl>9!PBrc^*?GJz
z(SoJXkJUzw?sQE!N7ugK{#dG%SbGa&ikp1VowHZqzE_=$p23YqrkgzS(P|>H%=ZeS
z$Zm3mBtxkv|GgradiRCz@!q~&EnFze_H%i84V(x>Mx_Bg5dr2lnokT%42;Ykher_5
zzD;CNmAIa=efV&#9J(yA`#+6+30zI-|Nk`xWB(dkii}Z`7NT7XB9cU_Hc{GD+V_sN
zkVI)wDoffIwA}WPYAS8&w%s<`Z+p3={r^5;zQ38*e><<4CZp~-=UG0V&wF{Er!2TL
zfpal`;n(}L(fNbwdy|@CsI`~=8jPL}S-whUs=M)&TW;smu=MOacN<MEpZJS1UF#%g
zJ`=kJ%J~wL6{1fH)=v5Hk7z`Vxrvlg-yAVm#ychTR=4-vvprNz6>hIRe7<FR4|P<d
zG{d9grZN~A>!sB-W5bfejdzxgba#GA`lR>pS*%|$pD%CWop5j6J`N3UT}7KTHKIJa
zmMWX0nm5i(J(tp2ciccN?)Z7Fl(kZuwjWzU9OK@#TUOw(fU5K|_1Uf4Ht48s-`YO6
zyk(7N$1Az<6~z_@=b}Z=tLwU@c0ZZr@tN1yA$EWMvS`Gs^1NNX4*h>lPElu8F8QBr
zNa4EUH+@1yuxGM6T>b3Xhsmy3KhAjO$sU{RcI3$Q7Uw=bZ#I!~jR{b0y+mTxppuG;
zidW*sD^orE#YKuIw`wwP_eWkJbS16N>MF1VJw3k3vUYxaW6i-2?_L;W&Pq$0y5@<N
zWoGAn$-fbt^60%%NZ9nu<}xiRwYFPECH8e~xNS_ugm^`!T<)fjw8`nd@Wa=pq;_kw
zE&fI2V&{fSPnyKPIh;9SeEo||JF#)|)~KMi*p89)%R8zLSKa;X-UHf~cWbwwOYjvs
zG`f1YDd->XMxj15Zn-bEbxjV8Cy|+4VU9;O$<N9q=@;FnMW20<Vpzs1r-YZ5`qs9%
z*{Wz8Uz*z6VhiQ2v|D%g@&wF#P-7X_|BSeK;N^#Bj6l(&UPR>TDneu<W3qzUch1Qm
zC!ep?{)CyswpzE%Uu#$A`*pS(exhDvnYvthSaPmPt#gxqNZ7%L+U|EM#6yi{!^fxD
zG&i$EFS{XbDs$oKU02awIz8TFM_lY_vk9aRErY)h1>-{7_~qW5<J_*R5$~?L-S*Yg
zNi}H=*I56)6A?t><WOv9-Pz84Tld87R&v+K-LrLLSZnsOo0Fd0I|Sc7J{9(#;HTdn
zI2L%SNxq-S*L1n_&-U26vnqR=R+fE&wT(?^r$uw7$=9AL3uaDlWXO{{UA`&Mdwwtb
zym8|)_u8AMAHLbGbjRQSWXvn>0<{>6Wp%LxJvZr*(naN<i#=04=YwnJSMik(#Lr(U
zFwLquW<OaYZfJE&N74O_f|rP4UjvU~oyqcTeLDGb*OesGY~$1-wprJzD)J8vQ8n0Z
zxP7&|@HA}g-Lv=i&OZ|N55Ktkom$L?l9QY&YO!7kj0${vi@r@&uHB<!XR)<}SkoS{
zJ|ZF3Io-|B_0TfzNa}`Wt-HOdOrxW|H9HQk=&w|wKT=_SzV)d@sWY&fCoi6H_RRUG
zI}MJjoH^gd+a1`+lP96O#9+yu-FqMGc+p@Jeu6{#p~QK~XW2H!Ms~xN4LSca4N@ql
zr7j&$(}%o!z`w|6duy9X7mZG+1qKJlVgdE7n>QUVyB#tgk4Spd{;joI(%rqWy*sLg
z)m(2653Nk?=_484Sgl(MK6WYGErv9AE%n*{o*3`@?2SD=F<XxXO_`VT*K`u|vLg3S
zRLy%2y?j6!lfGu}`HUa}V>aj3Cw^HmJ<>7iE#7LGaQ(VNPthOJIh!^_6Sni|n_QPP
zhM&ulS)R6nv$4PY=IDp^;!XkkEX|IYi3dUL#T`Z4t8yZOq?FEa6_*jqB+K+(@2=l_
z?w$L)lfNlkiSuc`SXXuTMR~{9>u;{>uBVlj8F+p^6ZPP9Imaktc_)YOKU;y->*$z$
zKa6q0{+?^M6NC1x-@Z9=a&hq-IH2sSa`x;qIFC$vDxa}y?`Yf8-nMpiS}~KUnwXSS
zyJSf)LuOk6L+$n9;<VJvGDBs@V^8n<J5+^@N;P)$JS&qG@n@TJo(@^%mNt1c?8%kj
zLT21a*7?r1vxu~Y15FNxUbwlRX<cKcx-MH$dg)~E2!$ZTcpvh;X&wKgUgX@Ji+4`0
zQcyW^?)vXC>q9%gRXQ%2`t`)|<d=&>B5siX#JTe~(PxSea%ArEEBYF6onA!j-5%%j
z=Px@qtYg_hBmN`(&&k1l?77pyfPetQJrjL(NoQxLnSI@-1f>Po?{pcP^iF0)yd^y7
zw5}jptirWLjT>0bB#ghBxhHh;O~R*wjrO-xe=SyP(GcrwX4Vk)#uvk%G1gr)rdQ0X
z47G)C2z?Y~cg=mO(bRtf;|`ykFD7RVXS|myU+!pXtz%8}Sn`w@G5-2nx|BMdrD*#m
zjZ1%>q0O%rTjhym(5qgwNN@9y@x4T_cx<&6?c-GSKAVtJpm!GK;elro5`ScWwv_J)
zI>+p>8A-C^JRTvV<*sl@`jwn`w#@$iuG~#=8y+h0J<FzUsE*t7TH=q7`t#oZeD$|U
zCj>2{PRh%tIwVb|wDId?3Yy*v9-vybw)ERAV(wq{(t*LbF@g|kN?Uh%lb8MEv{~T%
zo#4g3YyQ%UuNqw8nbx7HQcrW9$++6M%p`&mP6=J-;!!^6nnwORb#(lp53OjYK9%j=
zL#2`oRiE{jUn-RgbMMs|9r1haa`Is_XWlONsee8bMR56@u7R4*`JM(W9?AXP-BI;8
z9ZS~x;e!Vk_H=i2I9@V86w144V8o>D(&e@Lc{gqQI(BN5O4C!FkDe3H8fy@b8`EIj
zaPY(;sW3TB>6hP@$Ss$NW&h_*p3)u|x18Q~(Eg*!nl)<z3O=R(+VbGR1Lva4cb`1j
z+S%1599x0K9on^42$Y6I(h>z_ZEbDKjEwValfiF%40w`vE4$nus0w>7S;AXV)ABrL
zPimmEZDQBS6$7yc^gS6LgAUlqe%-QSY^{xy`g!d{Yk%q&28CiA7I1%(LV3zuH?)W%
zD)gVRpI?pVZ%;(+QtTKMUWT_<Da$)|?b<bO_MN+TN4E$NAzxDK&R554BnJx>PBHuJ
zhTGm5CM^jg4LzIKvie5my+7?1JF-pObgOEjq<Y5vrda8Lj1s$!==$9zuZ2|V&K^EF
z(w8vk-b`zzW{ICWc;Yz6^tvqf`nenGwG_%BGn3+=wG>L@(l3$}O4L$4sh?j5wvWu!
zymBChTGT?r5fNf>sDjG)#DwKnmPLyeMOvGf(8U#4qc+7A#9on4f1mJ~5fc{KeR3<h
z6@F#A^wF!M{hp7EHk2AS9iH2@!$VavemUd!eTSN7op(2~HuqRA{b#pOM7JPwpQcH?
z%f=S2Rr-$t1M3d%swC8Y)w<z-NkPfi(l;jeee{_yNky@78F9^r&s?<v)@+$<wB!p+
z2)JoQ<e#y0d8IVt!XsrIBtE_Cuc9yQ3FiO0N6HqCV5;)7XU`(7%hs1SpB?*kELdN-
z0K16@nS6?nWj3Um1@1ciq=$DcoA4>O>2dRMLbFn^pr9Z!sfk7Sa?p_@M>MUDd8_wE
z*IavVYp$r9pfK$o&^%mq_1j@qNype<X0GG>MhYcm`M{2@#gwa4|J%NfOi4-k(Wr(>
zj&gE$R^>l<8KC^6PYDBUC`e%)8(Um`eZ8UgI#yO9cFi_%K^MN^dl;jBqj|?KIYvxz
zg~sq`x$K}&o^YJ5CA+Wbc=j?%Tg{=5KfgYm-Psb(D)BIjzO_~@bQ|9;aq-4SDi+BN
zkNo_qqXph1Co6Pz?YjNX$WNh&iji|;$&w{8*c~l0Gsk6u5GJF-G+GEz;a{z3JdF@i
zWV*}4lAe}ucDDM{C&o5B%s;L}(?=0vg^er!P<9$<OtqMhywY}c|A7Oc2GY{f%)Yt`
z8J>SXL}dV3!s)=77CCyC*vNB<#Y4+2Z)Q|p+391Dz>3wYne~nTxCj1_qJxgr&-Yxb
zZfl#8{3I+av8G0aI+Y8o6ghfx`Fe>A!GHgXISXc65Y3d9Vp4s|F4?fmbXaA~pGVyk
zdSSWsxpP6g6vLW+C**(q*DqEs#p9Jreyx<jqu2%HO)+?IShI~?T19zzZJ9{x<=8BE
zW)!$y{QJF(TV1tW78z?)RaFHG=zAD`4Eh}Jf}Lto9EQ%9JrPqL85!~A@XUYaBQ<pb
zoFa96u(|It!M1&S5<ah@uP-q%JG)7xCDlxwC*fCL+x~ii>eh!TDT39WTQHHQ$<8Oc
z9j6qV{(-^w{cGjix*<gcAsa)pu9`GKuZt`m>P9!~P#-G<?K>RqI(i2fzv#G}oE)S6
z^^5)c_p6*abHQ0^_wEy=fqUz2uiHsvr)#Rq2lCT!FtC>0>VLJxAv4W4m+tI371WkG
z8Gk-xc|xISs_DDyOM>?s+>L3mpz=sc>R{ItTe<x{ckh0R3U_a0$aVeeGbq_UN5_<p
zUbyhI#brp6Pdok0jisw&lXXs<xE-k&P9&1$1|so!t?mE%#j9!gdMqAQjb2wLFtX(P
zY7><0?bG(`*|UM2Ju%m9+VsQibt_h_x;)VN7`w*z#3kig2V~jyKEHbPs+x9HjCoz6
zPOzxe^W<!lz|5Yi7tBrQYRz8-wo|T-`#7$pyp6dQw3hh(AGGiBY-u6?z(BLsT(|O3
zr|!_G;o&PuWkY?bk1HebeC-jF%XUdfXs_RO%6P!w=^9DruV?1xX0=?<IZtn`L$oL3
zJf<w`ZDfJ(UsH$=)5=9{?`~+BTbRX_t@_ssQjGmh1fG>x>=4K$vH0wT5%Ym16w!<i
zA3pSq6m1vrUxbH}`9F4g6)UgDMV5q|-~Rofhv*l{5Bm4jxG8YA#Nww5w<3Prs=p;i
z3w4pYnpyzatn1c2lX9Cf{x<yid$TU0R2pvac=qw*Df5<W4P1M6?>=+r&{Z&tHOWSb
ztG9{Qw7*V?jy}=qKAm9SU;pgIaUcB135+&YDcAHb?d_7{b+rjv%yu7X#@lO)-_Qr{
zVwjjj5uuUE<>K<WxnWuT!(Cy>oqZ)GB`Nj;nzN&&drK-R0`v0neqX=du2#b+MvH2}
z9$Kn3u{b+}i$akwpB^8W!g)4P@$tO=j~?yD{j9947__*+rE9iEqwjb5C^TWFFp*%)
z+6%o=0V%t?=i)!KXEMtQ3JXn#+lY6Jsu!NTo8*(bW8&j$GVS_O3`#e3c6JtHu7n<R
zsC}?QrY4Ud*7kR(H#9U%4Q36c7*()$yE`)(FTp$P5frS^A9J)(q);5$>KJ7*>|f+Q
zP$(k2=d<fsBphGuh^akpY#cpOBt2L4S6P|)z{_9Ilv~BDKLj0gNEC1)MlbCY7tbsg
z-BUa~Hg*6R$v7f1QrM;C>sQ-$?Bai5W_H#rv{dgk3tCh-7v@yMvUV8Qsjcqx9$K7w
z=rr1KzhU_kldT8s`y=YtQz(1}C6BoIM|iloW90NZJ33ehoN_|u6mk7wyv;pWYy@4J
zTQaw|l}RolC~b1beb|M<+^0--a&o?G>2oItLcim$horp0DkNovIZ`_YvYrL|A|<T*
z&wtKXK2bbzVjsIpzrfDDdz0kGHf-Fug_x=izxUT)e~EYZG-pu-uz^<o=PQC{iZL-U
zg_v)BZWW_ht+VC$3FBIcmL@zU7wDQTUYiKM{p`*?JM<CmWf6#v4;I&bNS!Kv&E>e}
zP8Wrts-|YBjUx@1=S9^COos<;ODI<_vQ$SYCLQ3;d{%_>H7qx5+9Yh!GC9`G_U_#~
zi!%M!=k9s3J4efjSdER1d5^RgW#vQx3~vEbW>W3)3+3rc*RNH_QwtL!n`+JzInH)B
zwqJ;w%zd7non1BNIOcwpI{qu=YLpvx6_Z7levvrY{^d&(dw9Ac22;wKtY!-dWCRbI
zsk!Zwc}PX9LUq`Nk#**+CbI~<oiQSiptIr{&wOaw%bLQtI5<9>OTTjQMfV6fzW1%l
z|JD^^d5<SC{rznpAHs59m*byRPt2Hp`ijH6WYnfS-PHB`LlT7|_D=5TVU;zWGFW8u
zGJs#lG<ITS<Sb$t?X~`KgG-m9U%jgQ>U!nMEBNW3fu1c2DP2N&svtJ@@c=o`GHb%W
zY5tX-F4}Nec%UJ<h%?~ir&kyAQ>;6&PWf2p^(89>4N8a&ND#_yr%v6=M7%^*Y+z&*
z*Twl3dF}<)mlO{;VY&!8e>G0f&1+E^8X8LK=(x1x*I#!d+@o0H?>h&+!5KlZ3ypS2
zo|~6f1)<PCG<0B8TS>_m>A#u14~Mo^E!nL7^wTFLWj9C1bR{LFN8#Z~sJe;?3u;JM
z-e?I=>Xpw&sCk9-5X|mWa&`~A3_Sk{yS8>)OR}=CL<5-~JAOPaB}HY~nyr<<4WSi9
z1qHo`sH^WQHd#)Qp(9btBIB544l_XB4Xu*tFq8=G4s)4XQnI--=PDuzmdiZ%;?InJ
z=L9(~5%ZS_Zs9qhgc*vNABpEPFf=SNOnw|D-TlNS#gw*Z$<n2nIZ+3PvXzPX=~e~1
zO<u?D+_`i2&Yh~392cG1&+Y9mmaN>=bC)OaiVM5t;BpE@F8F}a{lT1x^VCM+@i12f
z?YQu*E=N`xm+?AK%<26N$vRTpw$lStHiVOMVZuJE`CN+e!z$!@<*W}Mj!N0BqKLj+
zy=DK0Et~W6^Syhkqu5BE;y$~isj0e#h6&bJG_nYpK9X<BVZ_EYg;`=i@ayUeU1>X{
zPm5KJv`78qW!34gp81gp4|b+x-2k=Kf?<}b>5Vf~YDBF&ZYL`^Ic1Dsn?ja##JY8n
zX+}@1JkDxpG<@&FjLrb9Q&HDceN?^0Bs>3lxRamj4F@OZ7Wn{P0XM%Qq}zLRi>xwE
z{<+S*gp~`wW%JsF$UPi^gR@IH`E_iojuje(yBR+6_s>W{sU$oHDTz&8$Iid%rF`Jh
zBCf&p6N@MbMyMY639D$`Jomyk2BoV)$_!0I{QUGk1`CyT(iQae^iEBbn(1SiL$#uG
zbIsoU`#(9th3S3w?h2!<>}-j4MPkaYw3p$~;uJ-936>_@6Y_}q*ZQ6@%`hk2TeNs-
zP~)vzx3=K2C~HToqg;894S-d;@6-bpDCBbKuCHSdTt#7(`%efiI}?YJlI`Nfi;+Z1
zxZ8MxINb;Vs<^l~a@<F9JZQ7}D#{@`7UleBr<hoSBLvKQIJ{sf9SM^auN_xfpjsBe
z$ebCc<_R&`#BHOocJ-W@mS_Iw{p#5MQOCl!ZWkY4+3?7S05ep?@_gt3^>TyWO;5rz
zq_HCOAXQ;tIpu1=8)v3zK4QHRZCP>{F*muZwZ<J~<0&^~C8bj$7tEaDom3b=ps#P+
zo3T1S>*riC=`IhIMOk%#eR6V==AxtRcks2>_jnanefnV8h8yJ)W2f5DILvX44C|Ml
z`})in6CV#$)DZM;tSR%1aXWhS=4-pYT5<aPMBcnm>I9Q%C$}Mq94PWLWNocbtWVpx
zVFL@iu#qvaX6d%JeGlZc&=Z1dSFSuD>%(qNu0bh8V7Z4vK_5qlv&##1YtDAmuh(3P
zXl}}EyRkNS?zI$Z=)UZFiHq2vnTV7^n-^j}=cMQXoCYXLO@7$Dcdw;NcuC30K(=&v
z_RL?k2JmT};v&9HPqWd}%;TtvCMGdes5`2)Eg>A=uz?WLP}5Crw|T?OH0*1@qeqv-
zb0ShF$dI&~n_B>(VDO$CDs2ZpOH;55=})rX=oQ>`o)#sxu4#u`P>A_hKp!9E)aQOx
z1iyBb%X`a#!VO*|ckDyPmliU<+{Xs}A$mR>`m<9^3ub~-Nw-+{6J;a%=&1Dz6wZ<3
zmU*-GH*B;Wn(e&jX5X15<rp%m@t7}w0$Zd|y$FSV8f<UZk9hf#MlT8>a}kjV&1Vbw
zSN--`r8Z$Fe>T{CRmzzg`JXEhdh}ympu=8zeU338V&8wxn^mYpgMZJSPY`V@|1|rR
z@-!i<!&kmzXs9(>3YYWnyJZS?y4dyg#%x!4C=bRUuj1qV#}IB4sEQaR?C#$#DmQCc
z=BQeR6EiceSiSX^M%p=EXWWHVyA|h9MMp$%7&%phNou~<JC@_N5Rf%XUie7Gq6OrA
zGwlarv4SqPSU(3wUUB>Wpao&Uw7>o}bNn?v2&q%S&ZfKE|00Xs_(<0*#S+7tqDTsj
zYDwn@col;M<@|OjjP`6LKFsd<;0AJ{KG!WP=1EXc^fMXHOxi?jT;hIzhk-^_BRcl5
zJ%UQlCcw|n&#v8DL|F8A*}0qx?|#|1X;Upds!MVdo9`b5k-{ABtxF0|%;;h@YOtVY
zWm`rUFSK#yU~^U+qRbmjE&h!GX3Rb2Zi>cY9KnmPytXF>G{FOjmo{hGRijX8X1G)<
zh&(PqtQ%=7L6^_R>hj7doj)IT^5jXzz*yDs?T<@@+O2fRyMQMaEoBqFTsb#8-FG?f
za>IU<E8?Xn)YPaJ&8$y~Oj9$P&RFzFUMDMxL-O^nF5c++__&=HKr#-9(tOlWo(SX@
z@^AL`(l5Ms_T<UC$*#j~huz?Mv`d@#yV;!a(x*Cu^R$`2ewj<UJx!rl9z+z6X?iKY
znL2Gh&`49^2ZNMsURLi&wolMyPF2oNp@@s+Z^{g-fyFe9rcoo$!d_f%ja)~<-8Knd
zv+TqSqAU1&?=G%m<e8Sb94XyVwe_l(L21C_lJ+7WhrU0DkJ1@R%F3$R+WoInfdg8O
z9X*;}LvwI0!Y(wxzBg~)Byoa8nQC&vMHc>{#j$adP003bwGzI4E;lisBg)ejO1?0|
z<OW+bZ?OecK32T<D?}%AV$>1pWi{4on#bCREY;%OL<&+W>bk4OYcXudgsNDK6ynxb
z|Mc(;vJ&?xKinj;b8s;wI{``$$xg-4-OSRw@83^MPM{8^);%!-8jQT5$vB|*;kPBr
zmK`jmQ!VStLk~8G#l0j;JE~QJMXlPz&46Ri#R$q~>G_%Dn0^brK#qc<A|6C{jl}5a
zN-Up~NZF5wl-J*XIWRLiI$E4wcaNU$vDz;wDTzMj%_8u;WrX39C@{3nW7QTwO^T5d
zN}otY{yGsw`Gp5}8E-I3#d&3NN+^2HJ9YXSdQ<KC)KHHZiE(ls3l+DsCbIIVdT#oI
z%f3ShEO{peXa0DsVs||-YTbm+IwZ;#L6TAtL-rWB6bT+Bqy!HUm)eYpVvEsYB=*N2
zhi)ueYwzOSa(5y(H<#Zf+Khjp<t*DZn^m=%)@yD;^wmbxd0c|XWu`J8JYaTz8HK_~
zGbGAbmBqNZ<>uz*Fyz#l(i$#DDi^XHR0p^tqh_sk_U1*DC-G>GND|a*xI;l?wX;bR
zZn@{x66|$uE!aHs`d9B%*hQ~A9qUH#F`y%U0>;}25ZTst0eXVj%(oHek%G-;vuB*l
zZ%-?P<KBuVPMs3ulqP_)vzUuU5kkuzA1G3B!1$mwrWx|~RlPW#leaiJ#_)0zsJcBo
zJk7zSS|hofWOPOkwtRKUmfWmKqJ&cm)b`MMWt~8N9abUPvT<~{<#;AYF4d2r2kTUF
zaTsKux$c4TG?k{)ii#r5^{ZE`h@FU&4mm&sE>!zmsDhg@He;isA8;VRo}@Ne%O@8x
zZMB<{jeLMewHuv&A_FekDZ!!Igpm8&4&s#0OgXfDZR^~)h<Rg*9Qd?7e0;PpNf**H
zA&GRzXh@C%|8r5CFJAb=tVj0_j9kq|n+i#;IA)znFj2(ratfApi_n3UD^}QI;bDkx
z7!C*AuVdd}keX&`arxezJ7*EJleMA8+`D&g(_^v~1du!4($m!}9mCRIai#G7UbZM`
ztCZ_GkBg$E?(Xh)y}iwASe7qe9%+60^!?+tODTt3CZL})`jYa5X$m322`y4W?U(iR
z^oSQ-@mr`iHa5+{QHFVuA64sIe*GS-ol!!5h3`||7Z8+L$Y%MM-fKaZrmn&QK#Iw!
zDe+Pu6DKDp{()>K>9<<s1NoV{mj;4n%jgd48@)Et_$VNN(O?u#E7+_X52ja~{&PeN
zTX&q8MN#N*o~T9zW!jix!V1pSup%s>z-x0Kwri-vxwPAx!M!kAa<Zf*JJuV`1IhK}
zOti$=%ybpX%H8$xNpkqO-n50yaxiB+;O>O)^p7@ipl!n3?xbH?Pl@tHh<IEAT<ASl
z;CSN1i5&f3C=~VM_qSG=Q=1KALqrLy=Wr0HS>^>>r5VD0ST-&kq5#OS;<w+1dn8hs
zF@4{o_tmINmo6D2dY6yx^pSWDa>Vgce^=o#uY%{m7|Kdo<^uJ9LzjZjSJF9dx7yX|
zG^Sg<h={GX?R^=@_~Nx!O;7LnT`#Xxt9IGh>DKw$xmjX5)QyC1*xIWXAUocU{Yx&-
z7b(h9p(aSy41npu%szhe=1f&38a1S5#>0<6A{C_mGZ>;?y+^+WXWJ#^02bM2<!IPA
z@wA8-@>3|;-ez{{=2nWv8c&SZyp8CRE~Mo>FVQ)C_%QQ6Lh8q@YdkSa#SIJy1F=3N
z@MscZzRNB1ZWd~{H`jO83v2AMo_L?O5HF*eocj{?@bd?7Fng<yA29qlTwmXE_Z^pV
zyyZu2@E%7lUVPU3E596y({K*CvuDpDy(Ix+Z|@zaTh;-_#hN8%$R-<A)FNXD*)dd<
z^Nx9K>czR3>IuZye<ngiHd82JwsVu+W{7N=C@DJI+m$WpkY*Tj(?fMNG0J@k`vW!i
zwul(;lj4PToQM&B%g&VT$l?3Ad3X-^&(6-WNy?U0ACZ@r4@upHu${Gx-SH3#`NuJX
zWPuBG`{_b!SK(me>B&SsdzY~v-AZs}qqs$xb1NcNSpo<8c<(AG&UEBi8h+|^u7J%V
zO51X~+25-{1_6Xs!&YGGCM75cj60EM_ioANAV#=T*OtH!c6ZWSn=&Yr;fVQ(`MGcs
zdT!Qrt?Ilq91LzZ4j>MRMFSO40@d8CU^?>SF|hbo&6r)p#{!p|<2R1lP<9DPi$Qa(
z>Jca@E$!O|ie!d?<1eeoW>Rjxu~sS?tyq{a_m4h`{V_Zb*=!TNatO=r^O9Z1%0%iO
z8`rA*ql<K`y1+VsL<?25)QzLXBH=dMX7-K~19Ze3T_Mr6R(VznuZ!_dT1*Dh+t-kv
z>APXemKZWfk?bJYGq8gx#Jsy3`Zx2-T^>}W9CGA*QV?m5K$AZ$AJM`X{NMD`^3|(_
z{LQ8#Y-CK`t)si(<#<|>?L>^(!qF7O0D}_QkZhDCDQlzeU>^Q0;lF)!$Y%EQTH?&%
z!`C>l3-@Vfj1x&IPpaN<@rFLO1LV*-qyB^htxb&91TB&8^@G-PF{?bMg%@&YvhtLg
z8J00}LIKv8X-67_ULtw!0fp;MxVtl&GLp%Ni!%5GiE%k4bC55bv6G4Ew!G9YZpvMM
zf@y!7>TJ{XMe@k>;pbu$zu<Y6z(<d2rZ$E#x%v4M9Zpdwf4Genb7kV}(o<IBIG>{i
z^>wGj?1ycLvt^=94}u5W+}zA&H(aslj!E#z+MLEmvK+xUlJ@!%&1}cl$=I=dFOP&o
zQ~Q{p2tTVwRRmiu-_O9ReEs_M4giPPl`B_PZ!voEd7K507Zx*vkddCUV=(JavL50V
z5>f>MNlV@eRS9+1Q4|=l)Yi^G>`kKUMpK!1yLfH7-R_g}&1Ojhv+_1WsZ<pr`}-UQ
zcbVAuC9Ludpzwv?w{0^&2?gnLNpZ1B4GV~hNbAd&<G<&T*TAR>sNb>qetG7Pm~4|V
zLVpl!CCtMUcXrmSa3Cm%h1wO<ki>06W3s;^bHh`ySYq`!XQ1};Vm~hGUlL{BUY={V
z?av{)Uuh=Xy?@`{q`Pc-QA;C+eVEDtcaZclYH`zfhJA>DG1^*BIDfojFQ$Zm{}4{W
zpbqU-3=vkyab{WvjE#<_z=<7bU*_%Yi{UxvTqN<Vt1-kSDd**voQ^qww_)K#noeqg
z<THbZh=?5EiUGWYXv6?+a8|$Oa%+@gxM9*t2m*S*0-5Ly1>KcGuHUZkozFgp>d2ct
zscUbn)*cIozFGxTS><Hm38RhHC4MqrUhM+?d}S^hAV7mRVCi<$^8((l!4wAyFVvUb
zQSEx5y%Ji#e*XJCYp9mGgmQ+A=NfN@Zn7IDmpt1S_k2XeMHIe*eX9~QQ%h7F8Vmw6
znp;g%8NTxT0yH;3Z~lQ`HZ~h;>r;8U!xIzgz#69JeF-iD3q^KIVXAZfSjw#*FM6zu
zctAC$*GA8on3&)=J!l-JSQ1CNI!Rf{h-k)edZs8ntFmLq4h7a~Eh~COg(6U%<)}8&
z;7LVA2Q&T_FSH+zW#KW)vSabfC{OM|oi^GN7#JuLOE5dQ<yNd&V{ak@5>A3b5hQ~I
zwpCHE<L2h}W_@8e-I%*2#gLbt<3dI3XbO8_co||tR_}wLsjg8qjK5+qGi~OG>2B<n
z6}JLpFuyiK7uxR+Vyn--b@8$_yAK?w))*ffv&mRQYBk0{gRIi-*}Wg$fQ({z?ae&{
z3=tw>QI?)_F@I@wor+Kg-7+u1A$uy!?4!%g?ZiygSNU&10Ckh8Y74Dou7gs&v%9;S
z;59NdBozsZi3v62v!9q_3%L6jS9o7!nMsqhN+dOYRIZ5&;45Z8=90I9eIol92cS$=
zN^eb!pST_4Qt(94cEZ+Wjb*_!xC@dnO~_bKD*l^|NziG9Vj#GLM=k!rMHUN!N$MO5
zodWuWyGbd*Y%!(n5~(ndVeG4X=c7j(_;ho%1q1}jShEzvr6T<by}Z1db47Xh`D;OF
zbn^l-1x_rY(OfMiM>{Q&k`3uoxz;rcz&8e@acpO4pa^3G#Yb@AZ7(kylj;}8*D8nL
z)YMRy!Hh^k+(G}<MHVsv#w6yl^e12vlJtC-HO`dz%M_!{6#SVu;3j?6Y?l^oCfM{&
zy6$?Y0&NPZZogedGoq8bmUr1T&*y=KIMo@DS$EzhEY++oHa)#Q!3o@v7Q`9RQcP5(
zr1LpV{{&&g0t%yS{(tDXr^QLu7yK2XPG`LQi4&D2{yhE<9<)6a<IkJ1R?aY#NGzS1
z9J3?G^dyKa6<1f+&;d{fdezJMp+aCFTV7$$-}e<qu2iekRerB>uj2iOvd081n$l$@
zf#UR7ckbI)`BK4LL@tDNvwG6`gnRc^8Hw@ppEe4$Qwtr)`8y!%>FWytUMe>~KjziM
z(9+O|LE#$0012jJru7r0k79O$nNzTfQ%f+dVc}6v6!eeV9<8KPR{ss`Yr6lHRKjJ#
z!VMX?{7raxI6r`jINhQ5(=n2fO?Ji0xPi=BUA|LDx`kRpQc$b4<K|=ehL-brqGaHY
zSBpd9L>((+rgmfVb=;r^%PXFAqIW@wWpD=kMtLju85Ax#UF?Ti2I9TIT#t5^`uqBR
zdF|vrKTWhkTM{iD%A3#q1W|uLSxt@1!yycd%-%@n>7%l;re8kXDSva3<p(>|lwqTw
zYl0q)uxc*^i$w<awoCgAN;WLeA>@(CH}d+0SIIzT-%o2sK^5tD&TRB=G9z;!jee?g
z^LfX^2rsM02^W+raJn2eBI97@@cWf!=JHKRM@Xr~<F$TKaEVRV$Fl|oFX0P-{9a8p
zuN7`tj$eNUQ82U<JR|A;TwOl$VZCCI9#d6a=Ou92cUlPV5f>jH-y((32YNU+S(`rX
z5&UyTey4$m!%HbOe~O6I{f6>{Lu`8m_V5cGNPhF?9&`@n>Bk3*oUICAI^ZKkGLJRe
zOz(Ps{`|SH(3?%`QZ8AL3D&{P?fw4!dri7HxWqKN#eq~39U@{>wMme~9y%#zDBEd_
zVZNQ6-NJOfR?9LqhKzZ9aq3Bga6v4{HtMt#G^NN~#7tRxd;8FVaOiQ$(|8qDK&;i}
z?SCf?Ju2Os<ij`kSfqbmnr}g^Yv`UH%5%7ruvU8JN)D38ooOg`BXInNeDUFE574rR
z5vgVKxY*y6Q4PUKgxLo~3btRvC>%u~(Oe_-=b!KEGwsYm2cU4w2RBZC`^G{7=qrlo
zAz1`r1viq{grHZ`m<!2C6#A0FKvLFV=F;zy))jS|zh%3WRe+*3y^oE;tTG<tw%<Q^
zYEgj&mCbAOUi%DDZ`^)#Y)s2?1TFdo06*&kQH3K{KAp>Tb$YQKWu(cKT!^l$0Bn(y
z5OIx3v7^QU@xBmwzxPerTB&bu2XdN$M<R38;%JVjLKXVrqoen=ICXEsK`8HQ?B8Bp
z<WXj5WA<H*u{17M7=T#t0+l2GEcgM_sm@_!4Q(k`&SJ;8Nus+s?7xb#0T^kI0zH8O
za&T{vo*BLo1I%=71Us@7bZ(A82-^=GMssNE2R9O<G4FvS=3L{wRqPY?f4o(iGeBwx
z2s+QJpwI<M;b^C5hcM_BFHn+1N<Dn|urO;V*DRQ8@7{_aF`LNJMf(UynGP<BV_ju7
z8m%bA&B3AO*I0Cx_!nMXwD{D(5g8e8NWs*u1@SO?5K>wUbE2QT2Y2m<f&$v)!O1D9
ztp+TRnRvn8y<(AFY*e{e)ZqWq_d&q;0pAAz(<ZNiFRx#No=rf<h|~`P`jf~@C)aS#
zu3fSymQ+C{ym|9RJ1h?~j)@XCJtqu{5J;YJ3PRH<CaR>9a&l+E9DS;;CL?k67z{8|
z0D%yy_foq@Ysosy19lyfT-P{#@}!a>VHioD&6}Gt({cQKi(l7j8w?%Dc~Bb2RSvKT
zM0RtX3VMp*nl(H3;ctZ93yPr{#c#h-Ym71%=dN8yHs`V&hSZK7yVW}x9lbl@e2ymg
zD(4$Ez?ZpXrQHsxkn|$J`Jx@Ra`kFe(5=_66W3u5h(Zeox>WnxVw<8~kQJA&B*JWi
zZdJmNw0a!nGBF5NM^#qV<Far_@8rdN53<w-n6?-)oBG=8j4ZjJ4C-Xso`sS+-rw*7
z10f`gs+mB>oy6QJQ|kArBDw=G&}F>eq?Sdb>B<d^zY3z3?y;c$8ZSx+>R8jpaYj?q
z^wi`11)b}U*-3&n(LTLp38hV?!=m4|yDF$&+_FUihQYrEUL$DFiRb;9EQJtJi1|J=
zE&;u&kQAdOeMlAKko?$(I@?6;j8C`k-LpN&e>zm0wws5??5jje_V%^}G6oTHN!g=!
zKcf<|pCQ?X(wJwfcYN39^w9yS?q()3YvJG0$4hx<O3#9tVS}l|qCx;Ub604O3Br-f
zP)^-|)AbuSydlDiI4?}RtLs_UJ20G^uH>(_C@c7`=m$X3@Ok_85J(&s&GakJmab-1
zA&jyfJot}7xRefr9h<%nsuW7)e)nm5t10kyF!0OF%nakQ14zd`X!5<;!$<s1|LS$`
zUJa5MNlHZ{E;G|YV6qanZ0WDPW1c!fjxy0LPavN)rkRJ3I2I&uUBlqL1Lc*T+ufr{
z><)Uzc%c=D@68}biipDIjS&dxku7F448a^g0Ps?N&Uo`^vD?$6O3~Nz>7|!iqIL}8
z&>`^r0W?j*Q~|}OUC}lNV@zV_o@bt{c7$`=+iQ*4PRZ;}9qT}s*EA#=RfIVs(=h?Y
zfIFK8Miha2ruuY=`B>FBHItg?vvz4?6BBiCfX4A@r5TTQ6nlpqA6<~0kn<rSUpLZN
z^&((sWYJae{{OoNY7ynCDZ*5&W$vWxL5D$*3J-r-yqE@s8Km_Qu!8KNW_VVTuCXQB
z-Y#kwus?R}SZ_-8>1vW67~0#!R=Q9>Ch&mEY)m!t4+x07e*Krp>1m!FI}VejP|o&W
z%~t#^{60ccn8KLVm|+uz(tjf>E2g99YIccpuoaH~g=h)4l_DO){oChdFQcO3o<2Qf
zU|_(ge0D<<16=UJTMBsXq<5EW9qKO&CumVmldGB_x}b=w$*?iPC#4rJ%c$Yx;gN@1
zkp}4<N=qcF9A<xm^3LP;Re&WWgcrvA0yiwtu_H_(@QlRb4Vwk$cl!U^0Cm;=R^T&<
z#ayHV>e+w49AA&z_2Xx`ZXy3`@NVkfDI}Dd8u-&0cqj{+!OxE~|48}?<(Q8jKTgQu
z`0;<=a(n#!*^<P!v~duQ->=~`*een=l4Vg)ziGG((5sf`K5Gk&ptPtcs<@q9!tsps
z>{mZ{ChWfUoJ9b~xaAQRRwK13lc><h%E~iq2R}%Hc15q&z)h73B4(SUvkF|gckkU(
z@nM$~yj;Hz?hHrQmH_8a#eKvonGj*~PkZxbo;NjVLoeGSDtZpAfD)W-cIbZ*Co(&c
zdQ-{Ak17@xiQol63d7P>VVKlvm~4V!()h-}3-r8-wst%;4hZU~QRK*C9<p|bgw&6h
zbNmHF{P*jsJ=gaj_mG3&<mBWg7ZarD7p;+;8O|paK-@z<4<WiNNI-_1c9`LCF@Oev
z4K(m;vqLM5UJkx(@zPb5a3V6{oH~olS>8Q2SwS!`<>OOzA;yL3tF`LdVce4B32Ey<
zS^=P|6O%>$A?X}Roa;=mN;qE0afvRcCygyf6sH}ika&Sy!o$TCRc?=^-<e=~?C9oH
zaspa-=fL_UIoaVi1y>Ky@oP=`O;680xUi~8>9fB69JS63rK%*eMmZQ7e$Kok2e-Jm
zCN!H|l+vwz?^o^x{QJ=i!yCLV5-hT2dw&jqs~#$frU4Pig=X-}tWJ}BNXdceMF$(5
zAiJ1fNtEZ$IJ$B(zsiLR7jgzb0y62Xb82MeMC>dgK_Dz_HUWL|f=P-xce;>S@grt6
z=io@ePNt7YkExoAWllL4i^nt6R7w>RWA_LWaWj;2EZv}=*bU`OFv$2KB$g`-(cKw;
zwgt))thWbp3>Gx;#oTRXoxZtlr)&@E3=@@~Hq7B)PYXh_S-4VMNFwwRxAG+_ol_24
z1DWz6Z&_PgYyX}a$fvcyO(UtC%xP~SKt`tSdR$akxCem)!xYb>gfarWiDcS#2N*;i
zm;7+l+`fGVo*kIk9%1n*$_gVkfVC*;S(0aR?tfjxDrqF2Q|@~yxj_&NGQ6XeonmmR
z*#Rt})CN1P21n?M6)T#~JHTe0i2J*Z6cC&FE9@9Gav1WyG*iTkfZku3&!7KEI%4tm
zVY?K9Rl2{SNT(SD>O?|=Qg*}W@%8f~9aad7m@%eM!7*DclD7S50L>L#!9V@Jr&lOQ
zR!M+I#7n?=aq`ru8b3v;82C>$(ZJ>71UXSj%>sI=SRa4@nPQ+8*NGQFbs#6JQA1sw
z`dW$Ms}@-U!k8@?Dblt*4{GBzUie7QokIax3DeFc@v0e66eM@a(F==NWuUHmuj`=n
z{G`v9FJDY`j-NU6K=vNXXQl0kyjb30{3$|?WOxJ#n1T>QLK;Q4t^pr$9)aLZ3o-9z
zl;h*$lRG_l5XQ}vS~11p!ampc<a3JIbOkYxmm_dj6%u8^27ju8^xTrY4hxhnw7zF8
z>&q+wK4!6}t95{kjLgEw!pu}bNfk4I{3=aOk;t191VS{aI>TdmJfU3|oWd0Tq|oQi
zh=~$(pK(M+32AJ+ENtHJ<`~J<lh0<?Q+aLu&p4d2;(vzZ$X&tV`y7WaxPbzO1uG`i
zr`cjbU#O6tZ_;o8k=#S74XLvvTaKrHM`(n>Wl3%>KppoD4CM06KijQIQq5pa>NH?S
zJY#MBMoT~ZL2$5Ucb{?yfmb3a+$Wu2Db40)CYj?Sg?+qoFjL%e$6=&F3{s8BfpYyu
zGl7|S)|adWfKqFxnm|4X0R$UmAk0jGZK*-(b0&&hu?NCe*j~g-(PtUpV;G+jU%fhm
z*^5;cj}`<KGUkKdW!0u~kX#U>I?6}O{6`*o>&Kcqz2e8ndn=a+jI5brd^WVWLW#!R
zBIFk;jrFkN^dW$TNgF8p<etaWy7e13st##NJQE<~x_oU>osu!kCSWt4W(0t$tasJ#
zNy!}WzL%<mp~5=vK50Pp{m4mse93~vBh2y3aw9QO(fR}q8#!k<7zIr}aTra)PbfSa
zD!OCm&iu!h_*LC*zir0fb3Fbr6O4txZ2fr5#vG7uIw%rXrxAU4nWd{(+IWYWGL%s#
z#ig2qHXSpHqcQA1*=VUrR%O3=qXZ9yvC4daV`yEHZW6@Ma*Z|MxiO0xqwQ#&;P)yq
zaKG@PI&Ze%d>p>drt4I9g~vr>9LULU9y>4jkV9HJZ&&<=9}kc{)REV>;9n>3e-14s
zy&6>d8dkx-CX@3&p8>v37=R-=RC+sdC&)o7zq&fLh=_=w5*XMoFFeS?8idKIz9e-=
zN5>bns)1lgYgFc%hurI;=a8AsM4b+O(kWzCo1PLgH*wUL^a(XwPLnPB*$4^Oz|T;~
z`{N0+y0E_K+TvxI%~Ha`wJQ9)yz!~c-$vRMI$;q~BlrA<7r(McD2-ebfO4=GQ?okV
zs{IjI7Ff?GzkP%D8pMtfCCrp`{q~AJmQvHFeZXlN<ILXWzK6>8gB%=3-KM^pd}RR!
z!PXW50bQf6N=k$;s@GoLBve#+lVz5$RX563{4|8O9R0hLd8&hw`VnFReLw~iz7tfC
zCY>btVHdCo@PW_`ia{6V>ddz5D+OJ~FT0_ZMM^Pid@o7v&_sHu3(>oIbIc@7_{&Jm
z3mXWCo!)wX>9oi$liZi*yTK`kl#uQ*x{Y9jv!0`s)oTPq`B66+pVe%_qD3$!o=6yz
z0Fe_pikesu?AptSh#pcx1i*-rbQyOH+_7)*MxrtG?c2A8Op#SDYMp^hNS`i$)KzsA
z6=S3^XV~QgO>1`F^7J%=r<n9&VpUB|e5zm}z`CH-m!mMw3Oe0Gtf$h`(29~ybgZF-
z1zTh6$r)|!rs&y>7m3OZ=-*hx*NQjicq6C4GR<Pc`%1uJ|0l$gc$9`D1)-U0sv4T~
zGch`@{JR382%ZHgk4Ps?E?QSZ3{5acvi8UFQoM>fury7_q4_0B-%UDoU<BgemaJ9T
zykSGcO8)di2aL*VuyOM<Xrv|$ZwxeZT{Ff;!slnAj@8jN&~%A7m?|_9!X|05`lrnt
z)ZL0dlfQw87KNFTUhdaA0sM*0nv}_f&>#^~Tqmu4lGZH@MXVp{q&Keo@jZyUO<T9d
zZ#iJt0{~wOPk9_hABk<r6&9GNG3LP2bMQ3wmhR1~0bP_(Ur(I#Ns4;@{K#6#uMc7d
zVHV7RQ_%^97>(`#qJ|wS-N_<RT3XjvMM>#TxX&J58p9(ii|ya9G}c{VIiAi$E=*2=
zR6+`jy6%%N;V)vsQiwGq%`XSw^|M9PWb7p-z2Kv5*`x!FZ{e!(z@+*<r(h6%iv=hs
z+gRa+Wyd6jt7aDa4o}UDRR{qhBWIO1*yDkMh0TMdX1^-?B%zd71yHaK*qh$=OAcgT
z3#KH9?V`GBZL+{TYLmau_S^79AC4YRPtQ{1Wt1Mi5ikXVi%)E5&wK&>HuE##!DgGk
zTI7W9_)u#SiQS5QIj68m11b{^)qx3e>cSQHBDq3}&I2}o;kvqBVr<s;`}Y*e+YgXf
z;4i*U3Xxcy*h31ip&w>pmEeZUr+v&F1$pEs7GgCIX2CC{xHqZ+^Vnq1Jl>C;*iwf9
z%gtS^!4GtXVnTAiy*`{v3w7%ITPstsi5j^cWAX_&TU6AV20A}(eA@iKLAvri?<OxS
zx=Dnj8qCPUHu;)#b;T2bvA*8Ts<VPNawHi*cT-Q$?6lXi+=4ca=`U7pE4sIZ(BDJ7
zHOs}xY0{GI6q%VxifaqkA^AV6w*L5@vUiJs-pv?S7{fH_AcliVP}^8koISvu^Zfby
z1b1}9Cz3f2E7DrXt0g2TS49x~Q=i;-X5z2(G8%LWILyUL?Pft#EL>Lu5VC&uKjpmA
zfs3Lz|GCy4y%%W>c7|?a%cv<<v7Zp^Cmq|MFD)wC`AB~bDDcoupj5_M;@gemg<)6>
z1k?KGg~*tcH<x+5(BeLj5WrN8Mq=L(ED{S>3deuT598}(u-0`WnEvzutSn%H$9>Fz
zXrN?9M=ykwpmeu&34@b0qy+fMV)$r3*kf{ultsm7b5wKbXz^qT49#Y^fB2)Qbp~Mh
zQfooC_CS<Kjv2(-l3LW=4bjYj`qwg~s~C&-bizVJEW^Uw#x;O$vLsvLh@{+Dj7g)6
z#5~Yi3s<EtRNEilKV6Rb6N!3@bUczqOePRjOhm+ompYixI5YSux!wj8lvVB8t)gd8
zP$by(<Ht)YDk?PG;jgxX@5gkoDT4$-q=~7!9qC+%DG8kf`KW`J7@|`Zcgo(Y1OuaR
zrvYggQ;pOY$R#9lB(e+{*GqW?0=0L3ZfO3L9a(ge4mrfL7)U}d$tAa6QRwuM{$pf0
z##R?BSRr+RCk7#9k)$t`j9es!>8pnufn=oxjmo!@wk+g0q6%%q9a7fyNtla^bfcVs
zsfMK3NLnc}ZyAkjJaQK!JJbu08J2Jch}ZpGtH@VCP5l{7C~u2-`S=t_gJPKLs2sBF
zZUF%jYLc=ISYMEsI=?@-apQ)N!(fwm(LHZ(+K~IiK|(nM2-0k!_VDxdef0QoFYol^
z<U(EEQS29L2YYrUx!U3O?cZRiR<O^~A(1E68ay#awf@zIn33rYbFol9@3Bb)sdNC%
zx!ra6TCxT3T_!C3F!Clr5;!I+OOl%3*IHe1>Cc-Tu*7;O5yCqM>k^QBPLeA0YaSjR
z%$uh$DXOZd2%64fo=qW4kk<c`zVGw%Y2ZhZ4C4^75e$`q@#XWll_QOz>UanLOau}O
z)ynq~B=Nz5Y6j3_7j}+M4rGz$s|(kbu4UIAItL}DD%XveMnV~tP0|O*ApPh<sK%<f
zy8wTtuvSf{k(95XUc`X8W|Iu0ew&(NfJ%mzmh%-x>&LzmJaCX8lJ4>uKve|B&<v+s
zkm8bxrY6HR5#=Cw+*(+6`?g_alqSitR%jqHT-ZOuM{RmuN1S&l8xZ)rK8{bw`7!db
ztAi|9q&iTPNvO&9aBxJt8^_wbO5{!>^VVD)gxu3N2J+v({|b(`hcuaCpDUNutb!-V
zPKUf~=7!-^iM0rVSK8i+(`|by4`V`vgISCxUGxc7T7Ct3L>2jYFJHbi0Xw*2?b;-&
zo3{27i`+@W1;uv+eR&u)Mh~1z>JU%ZE|Uy3(!CuD_lIQ805ujUOl`IgA=O!oWeq%|
z@s*DtS9GijHfkmtdc%gadUDFz@W&28S8Fc{#BMkE!6P94v6f-IC5A3Z?g8}zG;^qO
zdSRgvwzWSDJZ_T8B%cqw5eyupc!o-YG#i9sT>}QUWvZP}AdnMO{8Y$~@IOO>I*K(B
zCr_Lp7c$bK6t}~~u*)aUtd4&c`Lv|0OckoWtD75e@xkMEuw}zct_sNiqUd8;84c#p
zM_7joA`Zs!qY!U!62zHsDK|9ZA*xjR&;H@_CYiDIoA0FlcMksiJBqOu$=5mlmyP@T
eJA=)m^NR%N9dco2j}~@-kvn$!XyTCz*Z&uQA?T(6

diff --git a/dev/search/search_index.json b/dev/search/search_index.json
index 70a8d6b1cb..448c004d4f 100644
--- a/dev/search/search_index.json
+++ b/dev/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"api/overview/","title":"Overview","text":""},{"location":"api/overview/#active","title":"active","text":"<p>Online active learning.</p> <ul> <li>EntropySampler</li> </ul>"},{"location":"api/overview/#base","title":"base","text":"<ul> <li>ActiveLearningClassifier</li> </ul>"},{"location":"api/overview/#anomaly","title":"anomaly","text":"<p>Anomaly detection.</p> <p>Estimators in the <code>anomaly</code> module have a bespoke API. Each anomaly detector has a <code>score_one</code> method instead of a <code>predict_one</code> method. This method returns an anomaly score. Normal observations should have a low score, whereas anomalous observations should have a high score. The range of the scores is relative to each estimator.</p> <p>Anomaly detectors are usually unsupervised, in that they analyze the distribution of the features they are shown. But River also has a notion of supervised anomaly detectors. These analyze the distribution of a target variable, and optionally include the distribution of the features as well. They are useful for detecting labelling anomalies, which can be detrimental if they learned by a model.</p> <ul> <li>GaussianScorer</li> <li>HalfSpaceTrees</li> <li>LocalOutlierFactor</li> <li>OneClassSVM</li> <li>PredictiveAnomalyDetection</li> <li>QuantileFilter</li> <li>StandardAbsoluteDeviation</li> <li>ThresholdFilter</li> </ul>"},{"location":"api/overview/#base_1","title":"base","text":"<ul> <li>AnomalyDetector</li> <li>AnomalyFilter</li> <li>SupervisedAnomalyDetector</li> </ul>"},{"location":"api/overview/#bandit","title":"bandit","text":"<p>Multi-armed bandit (MAB) policies.</p> <p>The bandit policies in River have a generic API. This allows them to be used in a variety of situations. For instance, they can be used for model selection (see <code>model_selection.BanditRegressor</code>).</p> <p>Classes</p> <ul> <li>BayesUCB</li> <li>EpsilonGreedy</li> <li>Exp3</li> <li>LinUCBDisjoint</li> <li>RandomPolicy</li> <li>ThompsonSampling</li> <li>UCB</li> </ul> <p>Functions</p> <ul> <li>evaluate</li> <li>evaluate_offline</li> </ul>"},{"location":"api/overview/#base_2","title":"base","text":"<ul> <li>ContextualPolicy</li> <li>Policy</li> </ul>"},{"location":"api/overview/#datasets","title":"datasets","text":"<ul> <li>BanditDataset</li> <li>NewsArticles</li> </ul>"},{"location":"api/overview/#envs","title":"envs","text":"<ul> <li>CandyCaneContest</li> <li>KArmedTestbed</li> </ul>"},{"location":"api/overview/#base_3","title":"base","text":"<p>Base interfaces.</p> <p>Every estimator in River is a class, and as such inherits from at least one base interface. These are used to categorize, organize, and standardize the many estimators that River contains.</p> <p>This module contains mixin classes, which are all suffixed by <code>Mixin</code>. Their purpose is to provide additional functionality to an estimator, and thus need to be used in conjunction with a non-mixin base class.</p> <p>This module also contains utilities for type hinting and tagging estimators.</p> <ul> <li>Base</li> <li>BinaryDriftAndWarningDetector</li> <li>BinaryDriftDetector</li> <li>Classifier</li> <li>Clusterer</li> <li>DriftAndWarningDetector</li> <li>DriftDetector</li> <li>Ensemble</li> <li>Estimator</li> <li>MiniBatchClassifier</li> <li>MiniBatchRegressor</li> <li>MiniBatchSupervisedTransformer</li> <li>MiniBatchTransformer</li> <li>MultiLabelClassifier</li> <li>MultiTargetRegressor</li> <li>Regressor</li> <li>SupervisedTransformer</li> <li>Transformer</li> <li>Wrapper</li> <li>WrapperEnsemble</li> </ul>"},{"location":"api/overview/#cluster","title":"cluster","text":"<p>Unsupervised clustering.</p> <ul> <li>CluStream</li> <li>DBSTREAM</li> <li>DenStream</li> <li>KMeans</li> <li>ODAC</li> <li>STREAMKMeans</li> <li>TextClust</li> </ul>"},{"location":"api/overview/#compat","title":"compat","text":"<p>Compatibility tools.</p> <p>This module contains adapters for making River estimators compatible with other libraries, and vice-versa whenever possible. The relevant adapters will only be usable if you have installed the necessary library. For instance, you have to install scikit-learn in order to use the <code>compat.convert_sklearn_to_river</code> function.</p> <p>Classes</p> <ul> <li>River2SKLClassifier</li> <li>River2SKLClusterer</li> <li>River2SKLRegressor</li> <li>River2SKLTransformer</li> <li>SKL2RiverClassifier</li> <li>SKL2RiverRegressor</li> </ul> <p>Functions</p> <ul> <li>convert_river_to_sklearn</li> <li>convert_sklearn_to_river</li> </ul>"},{"location":"api/overview/#compose","title":"compose","text":"<p>Model composition.</p> <p>This module contains utilities for merging multiple modeling steps into a single pipeline. Although pipelines are not the only way to process a stream of data, we highly encourage you to use them.</p> <p>Classes</p> <ul> <li>Discard</li> <li>FuncTransformer</li> <li>Grouper</li> <li>Pipeline</li> <li>Prefixer</li> <li>Renamer</li> <li>Select</li> <li>SelectType</li> <li>Suffixer</li> <li>TargetTransformRegressor</li> <li>TransformerProduct</li> <li>TransformerUnion</li> </ul> <p>Functions</p> <ul> <li>learn_during_predict</li> </ul>"},{"location":"api/overview/#conf","title":"conf","text":"<p>Conformal predictions. This modules contains wrappers to enable conformal predictions on any regressor or classifier.</p> <ul> <li>Interval</li> <li>RegressionJackknife</li> </ul>"},{"location":"api/overview/#covariance","title":"covariance","text":"<p>Online estimation of covariance and precision matrices.</p> <ul> <li>EmpiricalCovariance</li> <li>EmpiricalPrecision</li> </ul>"},{"location":"api/overview/#datasets_1","title":"datasets","text":"<p>Datasets.</p> <p>This module contains a collection of datasets for multiple tasks: classification, regression, etc. The data corresponds to popular datasets and are conveniently wrapped to easily iterate over the data in a stream fashion. All datasets have fixed size. Please refer to <code>river.synth</code> if you are interested in infinite synthetic data generators.</p> <p>Regression</p> Name Samples Features AirlinePassengers 144 1 Bikes 182,470 8 ChickWeights 578 3 MovieLens100K 100,000 10 Restaurants 252,108 7 Taxis 1,458,644 8 TrumpApproval 1,001 6 WaterFlow 1,268 1 <p>Binary classification</p> Name Samples Features Sparse Bananas 5,300 2 CreditCard 284,807 30 Elec2 45,312 8 Higgs 11,000,000 28 HTTP 567,498 3 MaliciousURL 2,396,130 3,231,961 \u2714\ufe0f Phishing 1,250 9 SMSSpam 5,574 1 SMTP 95,156 3 TREC07 75,419 5 <p>Multi-class classification</p> Name Samples Features Classes ImageSegments 2,310 18 7 Insects 52,848 33 6 Keystroke 20,400 31 51 <p>Multi-output binary classification</p> Name Samples Features Outputs Music 593 72 6 <p>Multi-output regression</p> Name Samples Features Outputs SolarFlare 1,066 10 3 WebTraffic 44,160 3 2"},{"location":"api/overview/#base_4","title":"base","text":"<ul> <li>Dataset</li> <li>FileDataset</li> <li>RemoteDataset</li> <li>SyntheticDataset</li> </ul>"},{"location":"api/overview/#synth","title":"synth","text":"<p>Synthetic datasets.</p> <p>Each synthetic dataset is a stream generator. The benefit of using a generator is that they do not store the data and each data sample is generated on the fly. Except for a couple of methods, the majority of these methods are infinite data generators.</p> <p>Binary classification</p> Name Features Agrawal 9 AnomalySine 2 ConceptDriftStream 9 Hyperplane 10 Mixed 4 SEA 3 Sine 2 STAGGER 3 <p>Regression</p> Name Features Friedman 10 FriedmanDrift 10 Mv 10 Planes2D 10 <p>Multi-class classification</p> Name Features Classes LED 7 10 LEDDrift 7 10 RandomRBF 10 2 RandomRBFDrift 10 2 RandomTree 10 2 Waveform 21 3 <p>Multi-output binary classification</p> Name Features Outputs Logical 2 3"},{"location":"api/overview/#drift","title":"drift","text":"<p>Concept Drift Detection.</p> <p>This module contains concept drift detection methods. The purpose of a drift detector is to raise an alarm if the data distribution changes. A good drift detector method is the one that maximizes the true positives while keeping the number of false positives to a minimum.</p> <ul> <li>ADWIN</li> <li>DriftRetrainingClassifier</li> <li>DummyDriftDetector</li> <li>KSWIN</li> <li>NoDrift</li> <li>PageHinkley</li> </ul>"},{"location":"api/overview/#binary","title":"binary","text":"<p>Drift detection for binary data.</p> <ul> <li>DDM</li> <li>EDDM</li> <li>FHDDM</li> <li>HDDM_A</li> <li>HDDM_W</li> </ul>"},{"location":"api/overview/#datasets_2","title":"datasets","text":"<ul> <li>AirlinePassengers</li> <li>Apple</li> <li>Bitcoin</li> <li>BrentSpotPrice</li> <li>Occupancy</li> <li>RunLog</li> <li>UKCoalEmploy</li> </ul>"},{"location":"api/overview/#dummy","title":"dummy","text":"<p>Dummy estimators.</p> <p>This module is here for testing purposes, as well as providing baseline performances.</p> <ul> <li>NoChangeClassifier</li> <li>PriorClassifier</li> <li>StatisticRegressor</li> </ul>"},{"location":"api/overview/#ensemble","title":"ensemble","text":"<p>Ensemble learning.</p> <p>Broadly speaking, there are two kinds of ensemble approaches. There are those that copy a single model several times and aggregate the predictions of said copies. This includes bagging as well as boosting. Then there are those that are composed of an arbitrary list of models, and can therefore aggregate predictions from different kinds of models.</p> <ul> <li>ADWINBaggingClassifier</li> <li>ADWINBoostingClassifier</li> <li>AdaBoostClassifier</li> <li>BOLEClassifier</li> <li>BaggingClassifier</li> <li>BaggingRegressor</li> <li>EWARegressor</li> <li>LeveragingBaggingClassifier</li> <li>SRPClassifier</li> <li>SRPRegressor</li> <li>StackingClassifier</li> <li>VotingClassifier</li> </ul>"},{"location":"api/overview/#evaluate","title":"evaluate","text":"<p>Model evaluation.</p> <p>This module provides utilities to evaluate an online model. The goal is to reproduce a real-world scenario with high fidelity. The core function of this module is <code>progressive_val_score</code>, which allows to evaluate a model via progressive validation.</p> <p>This module also exposes \"tracks\". A track is a predefined combination of a dataset and one or more metrics. This allows a principled manner to compare models with each other. For instance, the <code>RegressionTrack</code> contains several datasets and metrics to evaluate regression models. There is also a bare <code>Track</code> class to implement a custom track. The <code>benchmarks</code> directory at the root of the River repository uses these tracks.</p> <p>Classes</p> <ul> <li>BinaryClassificationTrack</li> <li>MultiClassClassificationTrack</li> <li>RegressionTrack</li> <li>Track</li> </ul> <p>Functions</p> <ul> <li>iter_progressive_val_score</li> <li>progressive_val_score</li> </ul>"},{"location":"api/overview/#facto","title":"facto","text":"<p>Factorization machines.</p> <ul> <li>FFMClassifier</li> <li>FFMRegressor</li> <li>FMClassifier</li> <li>FMRegressor</li> <li>FwFMClassifier</li> <li>FwFMRegressor</li> <li>HOFMClassifier</li> <li>HOFMRegressor</li> </ul>"},{"location":"api/overview/#feature_extraction","title":"feature_extraction","text":"<p>Feature extraction.</p> <p>This module can be used to extract information from raw features. This includes encoding categorical data as well as looking at interactions between existing features. This differs from the <code>preprocessing</code> module, in that the latter's purpose is rather to clean the data so that it may be processed by a particular machine learning algorithm.</p> <ul> <li>Agg</li> <li>BagOfWords</li> <li>PolynomialExtender</li> <li>RBFSampler</li> <li>TFIDF</li> <li>TargetAgg</li> </ul>"},{"location":"api/overview/#feature_selection","title":"feature_selection","text":"<p>Feature selection.</p> <ul> <li>PoissonInclusion</li> <li>SelectKBest</li> <li>VarianceThreshold</li> </ul>"},{"location":"api/overview/#forest","title":"forest","text":"<p>This module implements forest-based classifiers and regressors.</p> <ul> <li>AMFClassifier</li> <li>AMFRegressor</li> <li>ARFClassifier</li> <li>ARFRegressor</li> <li>OXTRegressor</li> </ul>"},{"location":"api/overview/#imblearn","title":"imblearn","text":"<p>Sampling methods.</p> <ul> <li>ChebyshevOverSampler</li> <li>ChebyshevUnderSampler</li> <li>HardSamplingClassifier</li> <li>HardSamplingRegressor</li> <li>RandomOverSampler</li> <li>RandomSampler</li> <li>RandomUnderSampler</li> </ul>"},{"location":"api/overview/#linear_model","title":"linear_model","text":"<p>Linear models.</p> <ul> <li>ALMAClassifier</li> <li>BayesianLinearRegression</li> <li>LinearRegression</li> <li>LogisticRegression</li> <li>PAClassifier</li> <li>PARegressor</li> <li>Perceptron</li> <li>SoftmaxRegression</li> </ul>"},{"location":"api/overview/#base_5","title":"base","text":"<ul> <li>GLM</li> </ul>"},{"location":"api/overview/#metrics","title":"metrics","text":"<p>Evaluation metrics.</p> <p>All the metrics are updated one sample at a time. This way we can track performance of predictive methods over time.</p> <p>Note that all metrics have a <code>revert</code> method, enabling them to be wrapped in <code>utils.Rolling</code>. This allows computirng rolling metrics:</p> <pre><code>from river import metrics, utils\n\ny_true = [True, False, True, True]\ny_pred = [False, False, True, True]\n\nmetric = utils.Rolling(metrics.Accuracy(), window_size=3)\n\nfor yt, yp in zip(y_true, y_pred):\n    print(metric.update(yt, yp))\n</code></pre> <pre><code>Accuracy: 0.00%\nAccuracy: 50.00%\nAccuracy: 66.67%\nAccuracy: 100.00%\n</code></pre> <ul> <li>Accuracy</li> <li>AdjustedMutualInfo</li> <li>AdjustedRand</li> <li>BalancedAccuracy</li> <li>ClassificationReport</li> <li>CohenKappa</li> <li>Completeness</li> <li>ConfusionMatrix</li> <li>CrossEntropy</li> <li>F1</li> <li>FBeta</li> <li>FowlkesMallows</li> <li>GeometricMean</li> <li>Homogeneity</li> <li>Jaccard</li> <li>LogLoss</li> <li>MAE</li> <li>MAPE</li> <li>MCC</li> <li>MSE</li> <li>MacroF1</li> <li>MacroFBeta</li> <li>MacroJaccard</li> <li>MacroPrecision</li> <li>MacroRecall</li> <li>MicroF1</li> <li>MicroFBeta</li> <li>MicroJaccard</li> <li>MicroPrecision</li> <li>MicroRecall</li> <li>MultiFBeta</li> <li>MutualInfo</li> <li>NormalizedMutualInfo</li> <li>Precision</li> <li>R2</li> <li>RMSE</li> <li>RMSLE</li> <li>ROCAUC</li> <li>Rand</li> <li>Recall</li> <li>RollingROCAUC</li> <li>SMAPE</li> <li>Silhouette</li> <li>VBeta</li> <li>WeightedF1</li> <li>WeightedFBeta</li> <li>WeightedJaccard</li> <li>WeightedPrecision</li> <li>WeightedRecall</li> </ul>"},{"location":"api/overview/#base_6","title":"base","text":"<ul> <li>BinaryMetric</li> <li>ClassificationMetric</li> <li>Metric</li> <li>Metrics</li> <li>MultiClassMetric</li> <li>RegressionMetric</li> <li>WrapperMetric</li> </ul>"},{"location":"api/overview/#multioutput","title":"multioutput","text":"<p>Metrics for multi-output learning.</p> <ul> <li>ExactMatch</li> <li>MacroAverage</li> <li>MicroAverage</li> <li>MultiLabelConfusionMatrix</li> <li>PerOutput</li> <li>SampleAverage</li> </ul>"},{"location":"api/overview/#base_7","title":"base","text":"<ul> <li>MultiOutputClassificationMetric</li> <li>MultiOutputRegressionMetric</li> </ul>"},{"location":"api/overview/#misc","title":"misc","text":"<p>Miscellaneous.</p> <p>This module essentially regroups some implementations that have nowhere else to go.</p> <ul> <li>SDFT</li> <li>Skyline</li> </ul>"},{"location":"api/overview/#model_selection","title":"model_selection","text":"<p>Model selection.</p> <p>This module regroups a variety of methods that may be used for performing model selection. An model selector is provided with a list of models. These are called \"experts\" in the expert learning literature. The model selector's goal is to perform at least as well as the best model. Indeed, initially, the best model is not known. The performance of each model becomes more apparent as time goes by. Different strategies are possible, each one offering a different tradeoff in terms of accuracy and computational performance.</p> <p>Model selection can be used for tuning the hyperparameters of a model. This may be done by creating a copy of the model for each set of hyperparameters, and treating each copy as a separate model. The <code>utils.expand_param_grid</code> function can be used for this purpose.</p> <ul> <li>BanditClassifier</li> <li>BanditRegressor</li> <li>GreedyRegressor</li> <li>SuccessiveHalvingClassifier</li> <li>SuccessiveHalvingRegressor</li> </ul>"},{"location":"api/overview/#base_8","title":"base","text":"<ul> <li>ModelSelectionClassifier</li> <li>ModelSelectionRegressor</li> </ul>"},{"location":"api/overview/#multiclass","title":"multiclass","text":"<p>Multi-class classification.</p> <ul> <li>OneVsOneClassifier</li> <li>OneVsRestClassifier</li> <li>OutputCodeClassifier</li> </ul>"},{"location":"api/overview/#multioutput_1","title":"multioutput","text":"<p>Multi-output models.</p> <ul> <li>ClassifierChain</li> <li>MonteCarloClassifierChain</li> <li>MultiClassEncoder</li> <li>ProbabilisticClassifierChain</li> <li>RegressorChain</li> </ul>"},{"location":"api/overview/#naive_bayes","title":"naive_bayes","text":"<p>Naive Bayes algorithms.</p> <ul> <li>BernoulliNB</li> <li>ComplementNB</li> <li>GaussianNB</li> <li>MultinomialNB</li> </ul>"},{"location":"api/overview/#neighbors","title":"neighbors","text":"<p>Neighbors-based learning.</p> <p>Also known as lazy methods. In these methods, generalisation of the training data is delayed until a query is received.</p> <ul> <li>KNNClassifier</li> <li>KNNRegressor</li> <li>LazySearch</li> <li>SWINN</li> </ul>"},{"location":"api/overview/#neural_net","title":"neural_net","text":"<p>Neural networks.</p> <ul> <li>MLPRegressor</li> </ul>"},{"location":"api/overview/#activations","title":"activations","text":"<ul> <li>Identity</li> <li>ReLU</li> <li>Sigmoid</li> </ul>"},{"location":"api/overview/#optim","title":"optim","text":"<p>Stochastic optimization.</p> <ul> <li>AMSGrad</li> <li>AdaBound</li> <li>AdaDelta</li> <li>AdaGrad</li> <li>AdaMax</li> <li>Adam</li> <li>Averager</li> <li>FTRLProximal</li> <li>Momentum</li> <li>Nadam</li> <li>NesterovMomentum</li> <li>RMSProp</li> <li>SGD</li> </ul>"},{"location":"api/overview/#base_9","title":"base","text":"<ul> <li>Initializer</li> <li>Loss</li> <li>Optimizer</li> <li>Scheduler</li> </ul>"},{"location":"api/overview/#initializers","title":"initializers","text":"<p>Weight initializers.</p> <ul> <li>Constant</li> <li>Normal</li> <li>Zeros</li> </ul>"},{"location":"api/overview/#losses","title":"losses","text":"<p>Loss functions.</p> <p>Each loss function is intended to work with both single values as well as numpy vectors.</p> <ul> <li>Absolute</li> <li>BinaryFocalLoss</li> <li>BinaryLoss</li> <li>Cauchy</li> <li>CrossEntropy</li> <li>EpsilonInsensitiveHinge</li> <li>Hinge</li> <li>Huber</li> <li>Log</li> <li>MultiClassLoss</li> <li>Poisson</li> <li>Quantile</li> <li>RegressionLoss</li> <li>Squared</li> </ul>"},{"location":"api/overview/#schedulers","title":"schedulers","text":"<p>Learning rate schedulers.</p> <ul> <li>Constant</li> <li>InverseScaling</li> <li>Optimal</li> </ul>"},{"location":"api/overview/#preprocessing","title":"preprocessing","text":"<p>Feature preprocessing.</p> <p>The purpose of this module is to modify an existing set of features so that they can be processed by a machine learning algorithm. This may be done by scaling numeric parts of the data or by one-hot encoding categorical features. The difference with the <code>feature_extraction</code> module is that the latter extracts new information from the data</p> <ul> <li>AdaptiveStandardScaler</li> <li>Binarizer</li> <li>FeatureHasher</li> <li>GaussianRandomProjector</li> <li>LDA</li> <li>MaxAbsScaler</li> <li>MinMaxScaler</li> <li>Normalizer</li> <li>OneHotEncoder</li> <li>OrdinalEncoder</li> <li>PredClipper</li> <li>PreviousImputer</li> <li>RobustScaler</li> <li>SparseRandomProjector</li> <li>StandardScaler</li> <li>StatImputer</li> <li>TargetMinMaxScaler</li> <li>TargetStandardScaler</li> </ul>"},{"location":"api/overview/#proba","title":"proba","text":"<p>Probability distributions.</p> <ul> <li>Beta</li> <li>Gaussian</li> <li>Multinomial</li> <li>MultivariateGaussian</li> </ul>"},{"location":"api/overview/#base_10","title":"base","text":"<ul> <li>BinaryDistribution</li> <li>ContinuousDistribution</li> <li>DiscreteDistribution</li> <li>Distribution</li> </ul>"},{"location":"api/overview/#reco","title":"reco","text":"<p>Recommender systems module.</p> <p>Recommender systems (recsys for short) is a large topic. This module is far from comprehensive. It simply provides models which can contribute towards building a recommender system.</p> <p>A typical recommender system is made up of a retrieval phase, followed by a ranking phase. The output of the retrieval phase is a shortlist of the catalogue of items. The items in the shortlist are then usually ranked according to the expected preference the user will have for each item. This module focuses on the ranking phase.</p> <p>Models which inherit from the <code>Ranker</code> class have a <code>rank</code> method. This allows sorting a set of items for a given user. Each model also has a <code>learn_one(user, item, y, context)</code> which allows learning user preferences. The <code>y</code> parameter is a reward value, the nature of which depends is specific to each and every recommendation task. Typically the reward is a number or a boolean value. It is up to the user to determine how to translate a user session into training data.</p> <ul> <li>Baseline</li> <li>BiasedMF</li> <li>FunkMF</li> <li>RandomNormal</li> </ul>"},{"location":"api/overview/#base_11","title":"base","text":"<ul> <li>Ranker</li> </ul>"},{"location":"api/overview/#rules","title":"rules","text":"<p>Decision rules-based algorithms.</p> <ul> <li>AMRules</li> </ul>"},{"location":"api/overview/#sketch","title":"sketch","text":"<p>Data containers and collections for sequential data.</p> <p>This module has summary and sketch structures that operate with constrained amounts of memory and processing time.</p> <ul> <li>Counter</li> <li>HeavyHitters</li> <li>Histogram</li> <li>Set</li> </ul>"},{"location":"api/overview/#stats","title":"stats","text":"<p>Running statistics</p> <ul> <li>AbsMax</li> <li>AutoCorr</li> <li>BayesianMean</li> <li>Count</li> <li>Cov</li> <li>EWMean</li> <li>EWVar</li> <li>Entropy</li> <li>IQR</li> <li>KolmogorovSmirnov</li> <li>Kurtosis</li> <li>Link</li> <li>MAD</li> <li>Max</li> <li>Mean</li> <li>Min</li> <li>Mode</li> <li>NUnique</li> <li>PeakToPeak</li> <li>PearsonCorr</li> <li>Quantile</li> <li>RollingAbsMax</li> <li>RollingIQR</li> <li>RollingMax</li> <li>RollingMin</li> <li>RollingMode</li> <li>RollingPeakToPeak</li> <li>RollingQuantile</li> <li>SEM</li> <li>Shift</li> <li>Skew</li> <li>Sum</li> <li>Var</li> </ul>"},{"location":"api/overview/#base_12","title":"base","text":"<ul> <li>Bivariate</li> <li>Univariate</li> </ul>"},{"location":"api/overview/#stream","title":"stream","text":"<p>Streaming utilities.</p> <p>The module includes tools to iterate over data streams.</p> <p>Classes</p> <ul> <li>Cache</li> <li>TwitchChatStream</li> <li>TwitterLiveStream</li> </ul> <p>Functions</p> <ul> <li>iter_arff</li> <li>iter_array</li> <li>iter_csv</li> <li>iter_libsvm</li> <li>iter_pandas</li> <li>iter_polars</li> <li>iter_sklearn_dataset</li> <li>iter_sql</li> <li>shuffle</li> <li>simulate_qa</li> </ul>"},{"location":"api/overview/#time_series","title":"time_series","text":"<p>Time series forecasting.</p> <p>Classes</p> <ul> <li>ForecastingMetric</li> <li>HoltWinters</li> <li>HorizonAggMetric</li> <li>HorizonMetric</li> <li>SNARIMAX</li> </ul> <p>Functions</p> <ul> <li>evaluate</li> <li>iter_evaluate</li> </ul>"},{"location":"api/overview/#base_13","title":"base","text":"<ul> <li>Forecaster</li> </ul>"},{"location":"api/overview/#tree","title":"tree","text":"<p>This module implements incremental Decision Tree (iDT) algorithms for handling classification and regression tasks.</p> <p>Each family of iDT will be presented in a dedicated section.</p> <p>At any moment, iDT might face situations where an input feature previously used to make a split decision is missing in an incoming sample. In this case, the most traversed path is selected to pass down the instance. Moreover, in the case of nominal features, if a new category arises and the feature is used in a decision node, a new branch is created to accommodate the new value.</p> <p>1. Hoeffding Trees</p> <p>This family of iDT algorithms use the Hoeffding Bound to determine whether or not the incrementally computed best split candidates would be equivalent to the ones obtained in a batch-processing fashion.</p> <p>All the available Hoeffding Tree (HT) implementation share some common functionalities:</p> <ul> <li> <p>Set the maximum tree depth allowed (<code>max_depth</code>).</p> </li> <li> <p>Handle Active and Inactive nodes: Active learning nodes update their own internal state to improve predictions and monitor input features to perform split attempts. Inactive learning nodes do not update their internal state and only keep the predictors; they are used to save memory in the tree (<code>max_size</code>).</p> </li> <li> <p>Enable/disable memory management.</p> </li> <li> <p>Define strategies to sort leaves according to how likely they are going to be split. This enables deactivating non-promising leaves to save memory.</p> </li> <li> <p>Disabling \u2018poor\u2019 attributes to save memory and speed up tree construction. A poor attribute is an input feature whose split merit is much smaller than the current best candidate. Once a feature is disabled, the tree stops saving statistics necessary to split such a feature.</p> </li> <li> <p>Define properties to access leaf prediction strategies, split criteria, and other relevant characteristics.</p> </li> </ul> <p>2. Stochastic Gradient Trees</p> <p>Stochastic Gradient Trees (SGT) directly optimize a loss function, rather than relying on split heuristics to guide the tree growth. F-tests are performed do decide whether a leaf should be expanded or its prediction value should be updated.</p> <p>SGTs can deal with binary classification and single-target regression. They also support dynamic and static feature quantizers to deal with numerical inputs.</p> <ul> <li>ExtremelyFastDecisionTreeClassifier</li> <li>HoeffdingAdaptiveTreeClassifier</li> <li>HoeffdingAdaptiveTreeRegressor</li> <li>HoeffdingTreeClassifier</li> <li>HoeffdingTreeRegressor</li> <li>SGTClassifier</li> <li>SGTRegressor</li> <li>iSOUPTreeRegressor</li> </ul>"},{"location":"api/overview/#base_14","title":"base","text":"<p>This module defines generic branch and leaf implementations. These should be used in River by each tree-based model. Using these classes makes the code more DRY. The only exception for not doing so would be for performance, whereby a tree-based model uses a bespoke implementation.</p> <p>This module defines a bunch of methods to ease the manipulation and diagnostic of trees. Its intention is to provide utilities for walking over a tree and visualizing it.</p> <ul> <li>Branch</li> <li>Leaf</li> </ul>"},{"location":"api/overview/#splitter","title":"splitter","text":"<p>This module implements the Attribute Observers (AO) (or tree splitters) that are used by the Hoeffding Trees (HT). It also implements the feature quantizers (FQ) used by Stochastic Gradient Trees (SGT). AOs are a core aspect of the HTs construction, and might represent one of the major bottlenecks when building the trees. The same holds for SGTs and FQs. The correct choice and setup of a splitter might result in significant differences in the running time and memory usage of the incremental decision trees.</p> <p>AOs for classification and regression trees can be differentiated by using the property <code>is_target_class</code> (<code>True</code> for splitters designed to classification tasks). An error will be raised if one tries to use a classification splitter in a regression tree and vice-versa. Lastly, AOs cannot be used in SGT and FQs cannot be used in Hoeffding Trees. So, care must be taken when choosing the correct feature splitter.</p> <ul> <li>DynamicQuantizer</li> <li>EBSTSplitter</li> <li>ExhaustiveSplitter</li> <li>GaussianSplitter</li> <li>HistogramSplitter</li> <li>QOSplitter</li> <li>Quantizer</li> <li>Splitter</li> <li>StaticQuantizer</li> <li>TEBSTSplitter</li> </ul>"},{"location":"api/overview/#utils","title":"utils","text":"<p>Shared utility classes and functions</p> <p>Classes</p> <ul> <li>Rolling</li> <li>SortedWindow</li> <li>TimeRolling</li> <li>VectorDict</li> </ul> <p>Functions</p> <ul> <li>expand_param_grid</li> <li>log_method_calls</li> </ul>"},{"location":"api/overview/#math","title":"math","text":"<p>Mathematical utility functions (intended for internal purposes).</p> <p>A lot of this is experimental and has a high probability of changing in the future.</p> <ul> <li>argmax</li> <li>chain_dot</li> <li>clamp</li> <li>dot</li> <li>dotvecmat</li> <li>log_sum_2_exp</li> <li>matmul2d</li> <li>minkowski_distance</li> <li>norm</li> <li>outer</li> <li>prod</li> <li>sherman_morrison</li> <li>sigmoid</li> <li>sign</li> <li>softmax</li> <li>woodbury_matrix</li> </ul>"},{"location":"api/overview/#norm","title":"norm","text":"<ul> <li>normalize_values_in_dict</li> <li>scale_values_in_dict</li> </ul>"},{"location":"api/overview/#pretty","title":"pretty","text":"<p>Helper functions for making things readable by humans.</p> <ul> <li>humanize_bytes</li> <li>print_table</li> </ul>"},{"location":"api/overview/#random","title":"random","text":"<ul> <li>exponential</li> <li>poisson</li> </ul>"},{"location":"api/active/EntropySampler/","title":"EntropySampler","text":"<p>Active learning classifier based on entropy measures.</p> <p>The entropy sampler selects samples for labeling based on the entropy of the prediction. The higher the entropy, the more likely the sample will be selected for labeling. The entropy measure is normalized to [0, 1] and then raised to the power of the discount factor.</p>"},{"location":"api/active/EntropySampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to wrap.</p> </li> <li> <p>discount_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>3</code></p> <p>The discount factor to apply to the entropy measure. A value of 1 won't affect the entropy. The higher the discount factor, the more the entropy will be discounted, and the less likely samples will be selected for labeling. A value of 0 will select all samples for labeling. The discount factor is thus a way to control how many samples are selected for labeling.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/active/EntropySampler/#examples","title":"Examples","text":"<p><pre><code>from river import active\nfrom river import datasets\nfrom river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\n\ndataset = datasets.SMSSpam()\nmetric = metrics.Accuracy()\nmodel = (\n    feature_extraction.TFIDF(on='body') |\n    linear_model.LogisticRegression()\n)\nmodel = active.EntropySampler(model, seed=42)\n\nn_samples_used = 0\nfor x, y in dataset:\n    y_pred, ask = model.predict_one(x)\n    metric.update(y, y_pred)\n    if ask:\n        n_samples_used += 1\n        model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>Accuracy: 86.60%\n</code></pre></p> <p><pre><code>dataset.n_samples, n_samples_used\n</code></pre> <pre><code>(5574, 1921)\n</code></pre></p> <p><pre><code>print(f\"{n_samples_used / dataset.n_samples:.2%}\")\n</code></pre> <pre><code>34.46%\n</code></pre></p>"},{"location":"api/active/EntropySampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/active/base/ActiveLearningClassifier/","title":"ActiveLearningClassifier","text":"<p>Base class for active learning classifiers.</p>"},{"location":"api/active/base/ActiveLearningClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to wrap.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/active/base/ActiveLearningClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/anomaly/GaussianScorer/","title":"GaussianScorer","text":"<p>Univariate Gaussian anomaly detector.</p> <p>This is a supervised anomaly detector. It fits a Gaussian distribution to the target values. The anomaly score is then computed as so: </p> \\[score = 2 \\mid CDF(y) - 0.5 \\mid\\] <p>This makes it so that the anomaly score is between 0 and 1.</p>"},{"location":"api/anomaly/GaussianScorer/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Default \u2192 <code>None</code></p> <p>Set this to fit the Gaussian distribution over a window of recent values.</p> </li> <li> <p>grace_period</p> <p>Default \u2192 <code>100</code></p> <p>Number of samples before which a 0 is always returned. This is handy because the Gaussian distribution needs time to stabilize, and will likely produce overly high anomaly score for the first samples.</p> </li> </ul>"},{"location":"api/anomaly/GaussianScorer/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import anomaly\n\nrng = random.Random(42)\ndetector = anomaly.GaussianScorer()\n\nfor y in (rng.gauss(0, 1) for _ in range(100)):\n    detector.learn_one(None, y)\n\ndetector.score_one(None, -3)\n</code></pre> <pre><code>0.999477...\n</code></pre></p> <p><pre><code>detector.score_one(None, 3)\n</code></pre> <pre><code>0.999153...\n</code></pre></p> <p><pre><code>detector.score_one(None, 0)\n</code></pre> <pre><code>0.052665...\n</code></pre></p> <p><pre><code>detector.score_one(None, 0.5)\n</code></pre> <pre><code>0.383717...\n</code></pre></p>"},{"location":"api/anomaly/GaussianScorer/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/HalfSpaceTrees/","title":"HalfSpaceTrees","text":"<p>Half-Space Trees (HST).</p> <p>Half-space trees are an online variant of isolation forests. They work well when anomalies are spread out. However, they do not work well if anomalies are packed together in windows. </p> <p>By default, this implementation assumes that each feature has values that are comprised between 0 and 1. If this isn't the case, then you can manually specify the limits via the <code>limits</code> argument. If you do not know the limits in advance, then you can use a <code>preprocessing.MinMaxScaler</code> as an initial preprocessing step. </p> <p>The current implementation builds the trees the first time the <code>learn_one</code> method is called. Therefore, the first <code>learn_one</code> call might be slow, whereas subsequent calls will be very fast in comparison. In general, the computation time of both <code>learn_one</code> and <code>score_one</code> scales linearly with the number of trees, and exponentially with the height of each tree. </p> <p>Note that high scores indicate anomalies, whereas low scores indicate normal observations.</p>"},{"location":"api/anomaly/HalfSpaceTrees/#parameters","title":"Parameters","text":"<ul> <li> <p>n_trees</p> <p>Default \u2192 <code>10</code></p> <p>Number of trees to use.</p> </li> <li> <p>height</p> <p>Default \u2192 <code>8</code></p> <p>Height of each tree. Note that a tree of height <code>h</code> is made up of <code>h + 1</code> levels and therefore contains <code>2 ** (h + 1) - 1</code> nodes.</p> </li> <li> <p>window_size</p> <p>Default \u2192 <code>250</code></p> <p>Number of observations to use for calculating the mass at each node in each tree.</p> </li> <li> <p>limits</p> <p>Type \u2192 dict[base.typing.FeatureName, tuple[float, float]] | None</p> <p>Default \u2192 <code>None</code></p> <p>Specifies the range of each feature. By default each feature is assumed to be in range <code>[0, 1]</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number seed.</p> </li> </ul>"},{"location":"api/anomaly/HalfSpaceTrees/#attributes","title":"Attributes","text":"<ul> <li> <p>size_limit</p> <p>This is the threshold under which the node search stops during the scoring phase.  The value .1 is a magic constant indicated in the original paper.</p> </li> </ul>"},{"location":"api/anomaly/HalfSpaceTrees/#examples","title":"Examples","text":"<p><pre><code>from river import anomaly\n\nX = [0.5, 0.45, 0.43, 0.44, 0.445, 0.45, 0.0]\nhst = anomaly.HalfSpaceTrees(\n    n_trees=5,\n    height=3,\n    window_size=3,\n    seed=42\n)\n\nfor x in X[:3]:\n    hst.learn_one({'x': x})  # Warming up\n\nfor x in X:\n    features = {'x': x}\n    hst.learn_one(features)\n    print(f'Anomaly score for x={x:.3f}: {hst.score_one(features):.3f}')\n</code></pre> <pre><code>Anomaly score for x=0.500: 0.107\nAnomaly score for x=0.450: 0.071\nAnomaly score for x=0.430: 0.107\nAnomaly score for x=0.440: 0.107\nAnomaly score for x=0.445: 0.107\nAnomaly score for x=0.450: 0.071\nAnomaly score for x=0.000: 0.853\n</code></pre></p> <p>The feature values are all comprised between 0 and 1. This is what is assumed by the model by default. In the following example, we construct a pipeline that scales the data online and ensures that the values of each feature are comprised between 0 and 1.</p> <p><pre><code>from river import compose\nfrom river import datasets\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.MinMaxScaler(),\n    anomaly.HalfSpaceTrees(seed=42)\n)\n\nauc = metrics.ROCAUC()\n\nfor x, y in datasets.CreditCard().take(2500):\n    score = model.score_one(x)\n    model.learn_one(x)\n    auc.update(y, score)\n\nauc\n</code></pre> <pre><code>ROCAUC: 91.15%\n</code></pre></p> <p>You can also use the <code>evaluate.progressive_val_score</code> function to evaluate the model on a data stream.</p> <p><pre><code>from river import evaluate\n\nmodel = model.clone()\n\nevaluate.progressive_val_score(\n    dataset=datasets.CreditCard().take(2500),\n    model=model,\n    metric=metrics.ROCAUC(),\n    print_every=1000\n)\n</code></pre> <pre><code>[1,000] ROCAUC: 88.43%\n[2,000] ROCAUC: 89.28%\n[2,500] ROCAUC: 91.15%\nROCAUC: 91.15%\n</code></pre></p>"},{"location":"api/anomaly/HalfSpaceTrees/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p> <ol> <li> <p>Tan, S.C., Ting, K.M. and Liu, T.F., 2011, June. Fast anomaly detection for streaming data. In Twenty-Second International Joint Conference on Artificial Intelligence. \u21a9</p> </li> </ol>"},{"location":"api/anomaly/LocalOutlierFactor/","title":"LocalOutlierFactor","text":"<p>Incremental Local Outlier Factor (Incremental LOF).</p> <p>The Incremental Local Outlier Factor (ILOF) is an online version of the Local Outlier Factor (LOF), proposed by Pokrajac et al. (2017),  and is used to identify outliers based on density of local neighbors. </p> <p>The algorithm take into account the following elements:     - <code>NewPoints</code>: new points;</p> <pre><code>- `kNN(p)`: the k-nearest neighboors of `p` (the k-closest points to `p`);\n\n- `RkNN(p)`: the reverse-k-nearest neighboors of `p` (points that have `p` as one of their neighboors);\n\n- `set_upd_lrd`: Set of points that need to have the local reachability distance updated;\n\n- `set_upd_lof`: Set of points that need to have the local outlier factor updated.\n</code></pre> <p>This current implementation within <code>River</code>, based on the original one in the paper, follows the following steps:     1) Insert new data points (<code>NewPoints</code>) and calculate its distance to existing points;     2) Update the nreaest neighboors and reverse nearest neighboors of all the points;     3) Define sets of affected points that required updates;     4) Calculate the reachability-distance from new point to neighboors (<code>NewPoints</code> -&gt; <code>kNN(NewPoints)</code>)        and from rev-neighboors to new point (<code>RkNN(NewPoints)</code> -&gt; <code>NewPoints</code>);     5) Update the reachability-distance for affected points: <code>RkNN(RkNN(NewPoints))</code> -&gt; <code>RkNN(NewPoints)</code>     6) Update local reachability distance of affected points: <code>lrd(set_upd_lrd)</code>;     7) Update local outlier factor: <code>lof(set_upd_lof)</code>. </p> <p>The incremental LOF algorithm is expected to provide equivalent detection performance as the iterated static LOF algroithm (applied after insertion of each data record), while requiring significantly less computational time. Moreover, the insertion of a new data point as well as deletion of an old data point influence only a limited number of their closest neighbors, which means that the number of updates per such insertion/deletion does not depend on the total number of instances learned/in the data set.</p>"},{"location":"api/anomaly/LocalOutlierFactor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_neighbors</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of nearest neighbors to use for density estimation.</p> </li> <li> <p>distance_func</p> <p>Type \u2192 DistanceFunc | None</p> <p>Default \u2192 <code>None</code></p> <p>Distance function to be used. By default, the Euclidean distance is used.</p> </li> </ul>"},{"location":"api/anomaly/LocalOutlierFactor/#attributes","title":"Attributes","text":"<ul> <li> <p>x_list</p> <p>A list of stored observations.</p> </li> <li> <p>x_batch</p> <p>A buffer to hold incoming observations until it's time to update the model.</p> </li> <li> <p>x_scores</p> <p>A buffer to hold incoming observations until it's time to score them.</p> </li> <li> <p>dist_dict</p> <p>A dictionary to hold distances between observations.</p> </li> <li> <p>neighborhoods</p> <p>A dictionary to hold neighborhoods for each observation.</p> </li> <li> <p>rev_neighborhoods</p> <p>A dictionary to hold reverse neighborhoods for each observation.</p> </li> <li> <p>k_dist</p> <p>A dictionary to hold k-distances for each observation.</p> </li> <li> <p>reach_dist</p> <p>A dictionary to hold reachability distances for each observation.</p> </li> <li> <p>lof</p> <p>A dictionary to hold Local Outlier Factors for each observation.</p> </li> <li> <p>local_reach</p> <p>A dictionary to hold local reachability distances for each observation.</p> </li> </ul>"},{"location":"api/anomaly/LocalOutlierFactor/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import anomaly\nfrom river import datasets\n\ncc_df = pd.DataFrame(datasets.CreditCard())\n\nlof = anomaly.LocalOutlierFactor(n_neighbors=20)\n\nfor x, _ in datasets.CreditCard().take(200):\n    lof.learn_one(x)\n\nlof.learn_many(cc_df[201:401])\n\nscores = []\nfor x in cc_df[0][401:406]:\n    scores.append(lof.score_one(x))\n\n[round(score, 3) for score in scores]\n</code></pre> <pre><code>[1.802, 1.936, 1.566, 1.181, 1.272]\n</code></pre></p> <p><pre><code>X = [0.5, 0.45, 0.43, 0.44, 0.445, 0.45, 0.0]\nlof = anomaly.LocalOutlierFactor()\n\nfor x in X[:3]:\n    lof.learn_one({'x': x})  # Warming up\n\nfor x in X:\n    features = {'x': x}\n    print(\n        f'Anomaly score for x={x:.3f}: {lof.score_one(features):.3f}')\n    lof.learn_one(features)\n</code></pre> <pre><code>Anomaly score for x=0.500: 0.000\nAnomaly score for x=0.450: 0.000\nAnomaly score for x=0.430: 0.000\nAnomaly score for x=0.440: 1.020\nAnomaly score for x=0.445: 1.032\nAnomaly score for x=0.450: 0.000\nAnomaly score for x=0.000: 0.980\n</code></pre></p>"},{"location":"api/anomaly/LocalOutlierFactor/#methods","title":"Methods","text":"learn learn_many learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p> David Pokrajac, Aleksandar Lazarevic, and Longin Jan Latecki (2007). Incremental Local Outlier Detection for Data Streams. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). 504-515. DOI: 10.1109/CIDM.2007.368917.</p>"},{"location":"api/anomaly/OneClassSVM/","title":"OneClassSVM","text":"<p>One-class SVM for anomaly detection.</p> <p>This is a stochastic implementation of the one-class SVM algorithm, and will not exactly match its batch formulation. </p> <p>It is encouraged to scale the data upstream with <code>preprocessing.StandardScaler</code>, as well as use <code>feature_extraction.RBFSampler</code> to capture non-linearities.</p>"},{"location":"api/anomaly/OneClassSVM/#parameters","title":"Parameters","text":"<ul> <li> <p>nu</p> <p>Default \u2192 <code>0.1</code></p> <p>An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. You can think of it as the expected fraction of anomalies.</p> </li> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.base.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/anomaly/OneClassSVM/#attributes","title":"Attributes","text":"<ul> <li>weights</li> </ul>"},{"location":"api/anomaly/OneClassSVM/#examples","title":"Examples","text":"<p><pre><code>from river import anomaly\nfrom river import compose\nfrom river import datasets\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = anomaly.QuantileFilter(\n    anomaly.OneClassSVM(nu=0.2),\n    q=0.995\n)\n\nauc = metrics.ROCAUC()\n\nfor x, y in datasets.CreditCard().take(2500):\n    score = model.score_one(x)\n    is_anomaly = model.classify(score)\n    model.learn_one(x)\n    auc.update(y, is_anomaly)\n\nauc\n</code></pre> <pre><code>ROCAUC: 74.68%\n</code></pre></p> <p>You can also use the <code>evaluate.progressive_val_score</code> function to evaluate the model on a data stream.</p> <p><pre><code>from river import evaluate\n\nmodel = model.clone()\n\nevaluate.progressive_val_score(\n    dataset=datasets.CreditCard().take(2500),\n    model=model,\n    metric=metrics.ROCAUC(),\n    print_every=1000\n)\n</code></pre> <pre><code>[1,000] ROCAUC: 74.40%\n[2,000] ROCAUC: 74.60%\n[2,500] ROCAUC: 74.68%\nROCAUC: 74.68%\n</code></pre></p>"},{"location":"api/anomaly/OneClassSVM/#methods","title":"Methods","text":"learn_many learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/PredictiveAnomalyDetection/","title":"PredictiveAnomalyDetection","text":"<p>Predictive Anomaly Detection.</p> <p>This semi-supervised technique to anomaly detection employs a predictive model to learn the normal behavior of a dataset. It forecasts future data points and compares these predictions with actual values to determine anomalies. An anomaly score is calculated based on the deviation of the prediction from the actual value, with higher scores indicating a higher probability of an anomaly. </p> <p>The actual anomaly score is calculated by comparing the squared-error to a dynamic threshold. If the error is larger than this threshold, the score will be 1.0; else, the score will be linearly distributed within the range (0.0, 1.0), with a higher score indicating a higher squared error compared to the threshold.</p>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#parameters","title":"Parameters","text":"<ul> <li> <p>predictive_model</p> <p>Type \u2192 base.Estimator | None</p> <p>Default \u2192 <code>None</code></p> <p>The underlying model that learns the normal behavior of the data and makes predictions on future behavior. This can be an estimator of any type, depending on the type of problem (e.g. some Forecaster for Time-Series Data).</p> </li> <li> <p>horizon</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>When a Forecaster is used as a predictive model, this is the horizon of its forecasts.</p> </li> <li> <p>n_std</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>3.0</code></p> <p>Number of Standard Deviations to calculate the threshold. A larger number of standard deviation will result in a higher threshold, resulting in the model being less sensitive.</p> </li> <li> <p>warmup_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Duration for the model to warm up. Since the model starts with zero knowledge, the first instances will have very high anomaly scores, resulting in bad predictions (or high error). As such, a warm-up period is necessary to discard the first seen instances. While the model is within the warm-up period, no score will be calculated and the score_one method will return 0.0.</p> </li> </ul>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#attributes","title":"Attributes","text":"<ul> <li> <p>dynamic_mae (stats.Mean)</p> <p>The running mean of the (squared) errors from the predictions of the model to update the dynamic threshold.</p> </li> <li> <p>dynamic_se_variance (stats.Var)</p> <p>The running variance of the (squared) errors from the predictions of the model to update the dynamic threshold.</p> </li> <li> <p>iter (int)</p> <p>The number of iterations (data points) passed.</p> </li> </ul>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import time_series\nfrom river import anomaly\nfrom river import preprocessing\nfrom river import linear_model\nfrom river import optim\n\nperiod = 12\npredictive_model = time_series.SNARIMAX(\n    p=period,\n    d=1,\n    q=period,\n    m=period,\n    sd=1,\n    regressor=(\n        preprocessing.StandardScaler()\n        | linear_model.LinearRegression(\n            optimizer=optim.SGD(0.005),\n        )\n    ),\n)\n\nPAD = anomaly.PredictiveAnomalyDetection(\n    predictive_model,\n    horizon=1,\n    n_std=3.5,\n    warmup_period=15\n)\n\nscores = []\n\nfor t, (x, y) in enumerate(datasets.AirlinePassengers()):\n    score = PAD.score_one(None, y)\n    PAD = PAD.learn_one(None, y)\n    scores.append(score)\n\nprint(scores[-1])\n</code></pre> <pre><code>0.05329236123455621\n</code></pre></p>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict | None' </li> <li>y     \u2014 'base.typing.Target | float' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p> <ol> <li> <p>Laptev N, Amizadeh S, Flint I. Generic and scalable framework for Automated Time-series Anomaly Detection. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015. doi:10.1145/2783258.2788611.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/anomaly/QuantileFilter/","title":"QuantileFilter","text":"<p>Threshold anomaly filter.</p>"},{"location":"api/anomaly/QuantileFilter/#parameters","title":"Parameters","text":"<ul> <li> <p>anomaly_detector</p> <p>An anomaly detector.</p> </li> <li> <p>q</p> <p>Type \u2192 float</p> <p>The quantile level above which to classify an anomaly score as anomalous.</p> </li> <li> <p>protect_anomaly_detector</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the anomaly detector should be updated when the anomaly score is anomalous. If the data contains sporadic anomalies, then the anomaly detector should likely not be updated. Indeed, if it learns the anomaly score, then it will slowly start to consider anomalous anomaly scores as normal. This might be desirable, for instance in the case of drift.</p> </li> </ul>"},{"location":"api/anomaly/QuantileFilter/#attributes","title":"Attributes","text":"<ul> <li>q</li> </ul>"},{"location":"api/anomaly/QuantileFilter/#examples","title":"Examples","text":"<p><pre><code>from river import anomaly\nfrom river import compose\nfrom river import datasets\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.MinMaxScaler(),\n    anomaly.QuantileFilter(\n        anomaly.HalfSpaceTrees(seed=42),\n        q=0.95\n    )\n)\n\nreport = metrics.ClassificationReport()\n\nfor x, y in datasets.CreditCard().take(2000):\n    score = model.score_one(x)\n    is_anomaly = model['QuantileFilter'].classify(score)\n    model.learn_one(x)\n    report.update(y, is_anomaly)\n\nreport\n</code></pre> <pre><code>               Precision   Recall   F1       Support\n&lt;BLANKLINE&gt;\n       0      99.95%   94.49%   97.14%      1998\n       1       0.90%   50.00%    1.77%         2\n&lt;BLANKLINE&gt;\n   Macro      50.42%   72.25%   49.46%\n   Micro      94.45%   94.45%   94.45%\nWeighted      99.85%   94.45%   97.05%\n&lt;BLANKLINE&gt;\n                 94.45% accuracy\n</code></pre></p>"},{"location":"api/anomaly/QuantileFilter/#methods","title":"Methods","text":"classify <p>Classify an anomaly score as anomalous or not.</p> <p>Parameters</p> <ul> <li>score     \u2014 'float' </li> </ul> <p>Returns</p> <p>bool:     A boolean value indicating whether the anomaly score is anomalous or not.</p> <p></p> learn_one <p>Update the anomaly filter and the underlying anomaly detector.</p> <p>Parameters</p> <ul> <li>args </li> <li>learn_kwargs </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/StandardAbsoluteDeviation/","title":"StandardAbsoluteDeviation","text":"<p>Standard Absolute Deviation (SAD).</p> <p>SAD is the model that calculates the anomaly score by using the deviation from the mean/median, divided by the standard deviation of all the points seen within the data stream. The idea of this model is based on the \\(3 \\times \\sigma\\) rule described in <sup>1</sup>. </p> <p>This implementation is adapted from the implementation within PySAD (Python Streaming Anomaly Detection) <sup>2</sup>. </p> <p>As a univariate anomaly detection algorithm, this implementation is adapted to <code>River</code> in a similar way as that of the <code>GaussianScorer</code> algorithm, with the variable taken into the account at the learning phase and scoring phase under variable <code>y</code>, ignoring <code>x</code>.</p>"},{"location":"api/anomaly/StandardAbsoluteDeviation/#parameters","title":"Parameters","text":"<ul> <li> <p>sub_stat</p> <p>Type \u2192 stats.base.Univariate | None</p> <p>Default \u2192 <code>None</code></p> <p>The statistic to be subtracted, then divided by the standard deviation for scoring. Defaults to <code>stats.Mean</code>()`.</p> </li> </ul>"},{"location":"api/anomaly/StandardAbsoluteDeviation/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import anomaly\nfrom river import stats\nfrom river import stream\n\nrng = random.Random(42)\n\nmodel = anomaly.StandardAbsoluteDeviation(sub_stat=stats.Mean())\n\nfor _ in range(150):\n    y = rng.gauss(0, 1)\n    model.learn_one(None, y)\n\nmodel.score_one(None, 2)\n</code></pre> <pre><code>2.057...\n</code></pre></p> <p><pre><code>model.score_one(None, 0)\n</code></pre> <pre><code>0.084...\n</code></pre></p> <p><pre><code>model.score_one(None, 1)\n</code></pre> <pre><code>0.986...\n</code></pre></p>"},{"location":"api/anomaly/StandardAbsoluteDeviation/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p> <ol> <li> <p>Hochenbaum, J., Vallis, O.S., Kejariwal, A., 2017. Automatic Anomaly Detection in the Cloud Via Statistical Learning. https://doi.org/10.48550/ARXIV.1704.07706.\u00a0\u21a9</p> </li> <li> <p>Yilmaz, S.F., Kozat, S.S., 2020. PySAD: A Streaming Anomaly Detection Framework in Python. https://doi.org/10.48550/ARXIV.2009.02572.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/anomaly/ThresholdFilter/","title":"ThresholdFilter","text":"<p>Threshold anomaly filter.</p>"},{"location":"api/anomaly/ThresholdFilter/#parameters","title":"Parameters","text":"<ul> <li> <p>anomaly_detector</p> <p>An anomaly detector.</p> </li> <li> <p>threshold</p> <p>Type \u2192 float</p> <p>A threshold above which to classify an anomaly score as anomalous.</p> </li> <li> <p>protect_anomaly_detector</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the anomaly detector should be updated when the anomaly score is anomalous. If the data contains sporadic anomalies, then the anomaly detector should likely not be updated. Indeed, if it learns the anomaly score, then it will slowly start to consider anomalous anomaly scores as normal. This might be desirable, for instance in the case of drift.</p> </li> </ul>"},{"location":"api/anomaly/ThresholdFilter/#examples","title":"Examples","text":"<p>Anomaly filters can be used as part of a pipeline. For instance, we might want to filter out anomalous observations so as not to corrupt a supervised model. As an example, let's take the <code>datasets.WaterFlow</code> dataset. Some of the samples have anomalous target variables because of human interventions. We don't want our model to learn these values.</p> <p><pre><code>from river import datasets\nfrom river import metrics\nfrom river import time_series\n\ndataset = datasets.WaterFlow()\nmetric = metrics.SMAPE()\n\nperiod = 24  # 24 samples per day\n\nmodel = (\n    anomaly.ThresholdFilter(\n        anomaly.GaussianScorer(\n            window_size=period * 7,  # 7 days\n            grace_period=30\n        ),\n        threshold=0.995\n    ) |\n    time_series.HoltWinters(\n        alpha=0.3,\n        beta=0.1,\n        multiplicative=False\n    )\n)\n\ntime_series.evaluate(\n    dataset,\n    model,\n    metric,\n    horizon=period\n)\n</code></pre> <pre><code>+1  SMAPE: 4.220171\n+2  SMAPE: 4.322648\n+3  SMAPE: 4.418546\n+4  SMAPE: 4.504986\n+5  SMAPE: 4.57924\n+6  SMAPE: 4.64123\n+7  SMAPE: 4.694042\n+8  SMAPE: 4.740753\n+9  SMAPE: 4.777291\n+10 SMAPE: 4.804558\n+11 SMAPE: 4.828114\n+12 SMAPE: 4.849823\n+13 SMAPE: 4.865871\n+14 SMAPE: 4.871972\n+15 SMAPE: 4.866274\n+16 SMAPE: 4.842614\n+17 SMAPE: 4.806214\n+18 SMAPE: 4.763355\n+19 SMAPE: 4.713455\n+20 SMAPE: 4.672062\n+21 SMAPE: 4.659102\n+22 SMAPE: 4.693496\n+23 SMAPE: 4.773707\n+24 SMAPE: 4.880654\n</code></pre></p>"},{"location":"api/anomaly/ThresholdFilter/#methods","title":"Methods","text":"classify <p>Classify an anomaly score as anomalous or not.</p> <p>Parameters</p> <ul> <li>score     \u2014 'float' </li> </ul> <p>Returns</p> <p>bool:     A boolean value indicating whether the anomaly score is anomalous or not.</p> <p></p> learn_one <p>Update the anomaly filter and the underlying anomaly detector.</p> <p>Parameters</p> <ul> <li>args </li> <li>learn_kwargs </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/base/AnomalyDetector/","title":"AnomalyDetector","text":"<p>An anomaly detector.</p>"},{"location":"api/anomaly/base/AnomalyDetector/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/base/AnomalyFilter/","title":"AnomalyFilter","text":"<p>Anomaly filter base class.</p> <p>An anomaly filter has the ability to classify an anomaly score as anomalous or not. It can then be used to filter anomalies, in particular as part of a pipeline.</p>"},{"location":"api/anomaly/base/AnomalyFilter/#parameters","title":"Parameters","text":"<ul> <li> <p>anomaly_detector</p> <p>Type \u2192 AnomalyDetector</p> <p>An anomaly detector wrapped by the anomaly filter.</p> </li> <li> <p>protect_anomaly_detector</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the anomaly detector should be updated when the anomaly score is anomalous. If the data contains sporadic anomalies, then the anomaly detector should likely not be updated. Indeed, if it learns the anomaly score, then it will slowly start to consider anomalous anomaly scores as normal. This might be desirable, for instance in the case of drift.</p> </li> </ul>"},{"location":"api/anomaly/base/AnomalyFilter/#methods","title":"Methods","text":"classify <p>Classify an anomaly score as anomalous or not.</p> <p>Parameters</p> <ul> <li>score     \u2014 'float' </li> </ul> <p>Returns</p> <p>bool:     A boolean value indicating whether the anomaly score is anomalous or not.</p> <p></p> learn_one <p>Update the anomaly filter and the underlying anomaly detector.</p> <p>Parameters</p> <ul> <li>args </li> <li>learn_kwargs </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/base/SupervisedAnomalyDetector/","title":"SupervisedAnomalyDetector","text":"<p>A supervised anomaly detector.</p>"},{"location":"api/anomaly/base/SupervisedAnomalyDetector/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/bandit/BayesUCB/","title":"BayesUCB","text":"<p>Bayes-UCB bandit policy.</p> <p>Bayes-UCB is a Bayesian algorithm for the multi-armed bandit problem. It uses the posterior distribution of the reward of each arm to compute an upper confidence bound (UCB) on the expected reward of each arm. The arm with the highest UCB is then pulled. The posterior distribution is updated after each pull. The algorithm is described in [^1].</p>"},{"location":"api/bandit/BayesUCB/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object that is used to update the posterior distribution.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>Number of initial observations per arm before using the posterior distribution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/BayesUCB/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/BayesUCB/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.BayesUCB(seed=123)\n\nmetric = stats.Sum()\nwhile True:\n    action = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(action)\n    policy.update(action, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 841.\n</code></pre></p>"},{"location":"api/bandit/BayesUCB/#methods","title":"Methods","text":"compute_index <p>the p-th quantile of the beta distribution for the arm</p> <p>Parameters</p> <ul> <li>arm_id </li> </ul> <p></p> pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Rewrite update function</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/EpsilonGreedy/","title":"EpsilonGreedy","text":"<p>\\(\\varepsilon\\)-greedy bandit policy.</p> <p>Performs arm selection by using an \\(\\varepsilon\\)-greedy bandit strategy. An arm is selected at each step. The best arm is selected (1 - \\(\\varepsilon\\))% of the time. </p> <p>Selection bias is a common problem when using bandits. This bias can be mitigated by using burn-in phase. Each model is given the chance to learn during the first <code>burn_in</code> steps.</p>"},{"location":"api/bandit/EpsilonGreedy/#parameters","title":"Parameters","text":"<ul> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>The probability of exploring.</p> </li> <li> <p>decay</p> <p>Default \u2192 <code>0.0</code></p> <p>The decay rate of epsilon.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/EpsilonGreedy/#attributes","title":"Attributes","text":"<ul> <li> <p>current_epsilon</p> <p>The value of epsilon after factoring in the decay rate.</p> </li> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/EpsilonGreedy/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.EpsilonGreedy(epsilon=0.9, seed=101)\n\nmetric = stats.Sum()\nwhile True:\n    arm = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(arm)\n    policy.update(arm, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 775.\n</code></pre></p>"},{"location":"api/bandit/EpsilonGreedy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>\u03b5-Greedy Algorithm - The Multi-Armed Bandit Problem and Its Solutions - Lilian Weng \u21a9</p> </li> </ol>"},{"location":"api/bandit/Exp3/","title":"Exp3","text":"<p>Exp3 bandit policy.</p> <p>This policy works by maintaining a weight for each arm. These weights are used to randomly decide which arm to pull. The weights are increased or decreased, depending on the reward. An egalitarianism factor \\(\\gamma \\in [0, 1]\\) is included, to tune the desire to pick an arm uniformly at random. That is, if \\(\\gamma = 1\\), the arms are picked uniformly at random.</p>"},{"location":"api/bandit/Exp3/#parameters","title":"Parameters","text":"<ul> <li> <p>gamma</p> <p>Type \u2192 float</p> <p>The egalitarianism factor. Setting this to 0 leads to what is called the EXP3 policy.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/Exp3/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/Exp3/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.Exp3(gamma=0.5, seed=42)\n\nmetric = stats.Sum()\nwhile True:\n    action = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(action)\n    policy.update(action, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 799.\n</code></pre></p>"},{"location":"api/bandit/Exp3/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>Auer, P., Cesa-Bianchi, N., Freund, Y. and Schapire, R.E., 2002. The nonstochastic multiarmed bandit problem. SIAM journal on computing, 32(1), pp.48-77. \u21a9</p> </li> <li> <p>Adversarial Bandits and the Exp3 Algorithm \u2014 Jeremy Kun \u21a9</p> </li> </ol>"},{"location":"api/bandit/LinUCBDisjoint/","title":"LinUCBDisjoint","text":"<p>LinUCB, disjoint variant.</p> <p>Although it works, as of yet it is too slow to realistically be used in practice. </p> <p>The way this works is that each arm is assigned a <code>linear_model.BayesianLinearRegression</code> instance. This instance is updated every time the arm is pulled. The context is used as features for the regression. The reward is used as the target. The posterior distribution is used to compute the upper confidence bound. The arm with the highest upper confidence bound is pulled.</p>"},{"location":"api/bandit/LinUCBDisjoint/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Parameter used in each Bayesian linear regression.</p> </li> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Parameter used in each Bayesian linear regression.</p> </li> <li> <p>smoothing</p> <p>Type \u2192 float | None</p> <p>Default \u2192 <code>None</code></p> <p>Parameter used in each Bayesian linear regression.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of time steps during which each arm is pulled once.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/LinUCBDisjoint/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/LinUCBDisjoint/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> <li>context     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Rewrite update function</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>context </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>A Contextual-Bandit Approach to Personalized News Article Recommendation [^2:] Contextual Bandits Analysis of LinUCB Disjoint Algorithm with Dataset \u21a9</p> </li> </ol>"},{"location":"api/bandit/RandomPolicy/","title":"RandomPolicy","text":"<p>Random bandit policy.</p> <p>This policy simply pulls a random arm at each time step. It is useful as a baseline.</p>"},{"location":"api/bandit/RandomPolicy/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object that is used to update the posterior distribution.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>Number of initial observations per arm before using the posterior distribution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/RandomPolicy/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/RandomPolicy/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.RandomPolicy(seed=123)\n\nmetric = stats.Sum()\nwhile True:\n    action = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(action)\n    policy.update(action, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 755.\n</code></pre></p>"},{"location":"api/bandit/RandomPolicy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/ThompsonSampling/","title":"ThompsonSampling","text":"<p>Thompson sampling.</p> <p>Thompson sampling is often used with a Beta distribution. However, any probability distribution can be used, as long it makes sense with the reward shape. For instance, a Beta distribution is meant to be used with binary rewards, while a Gaussian distribution is meant to be used with continuous rewards. </p> <p>The randomness of a distribution is controlled by its seed. The seed should not set within the distribution, but should rather be defined in the policy parametrization. In other words, you should do this: </p> <pre><code>policy = ThompsonSampling(dist=proba.Beta(1, 1), seed=42) \n</code></pre> <p>and not this: </p> <pre><code>policy = ThompsonSampling(dist=proba.Beta(1, 1, seed=42)) \n</code></pre>"},{"location":"api/bandit/ThompsonSampling/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Type \u2192 proba.base.Distribution | None</p> <p>Default \u2192 <code>None</code></p> <p>A distribution to sample from.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/ThompsonSampling/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/ThompsonSampling/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.ThompsonSampling(reward_obj=proba.Beta(), seed=101)\n\nmetric = stats.Sum()\nwhile True:\n    arm = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(arm)\n    policy.update(arm, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 820.\n</code></pre></p>"},{"location":"api/bandit/ThompsonSampling/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>An Empirical Evaluation of Thompson Sampling \u21a9</p> </li> </ol>"},{"location":"api/bandit/UCB/","title":"UCB","text":"<p>Upper Confidence Bound (UCB) bandit policy.</p> <p>Due to the nature of this algorithm, it's recommended to scale the target so that it exhibits sub-gaussian properties. This can be done by passing a <code>preprocessing.TargetStandardScaler</code> instance to the <code>reward_scaler</code> argument.</p>"},{"location":"api/bandit/UCB/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Type \u2192 float</p> <p>The confidence level. Setting this to 1 leads to what is called the UCB1 policy.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/UCB/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/UCB/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import preprocessing\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.UCB(\n    delta=100,\n    reward_scaler=preprocessing.TargetStandardScaler(None),\n    seed=42\n)\n\nmetric = stats.Sum()\nwhile True:\n    arm = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(arm)\n    policy.update(arm, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 744.\n</code></pre></p>"},{"location":"api/bandit/UCB/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>Lai, T. L., &amp; Robbins, H. (1985). Asymptotically efficient adaptive allocation rules. Advances in applied mathematics, 6(1), 4-22. \u21a9</p> </li> <li> <p>Upper Confidence Bounds - The Multi-Armed Bandit Problem and Its Solutions - Lilian Weng \u21a9</p> </li> <li> <p>The Upper Confidence Bound Algorithm - Bandit Algorithms \u21a9</p> </li> </ol>"},{"location":"api/bandit/evaluate-offline/","title":"evaluate_offline","text":"<p>Evaluate a policy on historical logs using replay.</p> <p>This is a high-level utility function for evaluating a policy using the replay methodology. This methodology is an off-policy evaluation method. It does not require an environment, and is instead data-driven. </p> <p>At each step, an arm is pulled from the provided policy. If the arm is the same as the arm that was pulled in the historical data, the reward is used to update the policy. If the arm is different, the reward is ignored. This is the off-policy aspect of the evaluation.</p>"},{"location":"api/bandit/evaluate-offline/#parameters","title":"Parameters","text":"<ul> <li> <p>policy</p> <p>Type \u2192 bandit.base.Policy</p> <p>The policy to evaluate.</p> </li> <li> <p>history</p> <p>Type \u2192 History | bandit.datasets.BanditDataset</p> <p>The history of the bandit problem. This is a generator that yields tuples of the form <code>(arms, context, arm, reward)</code>.</p> </li> <li> <p>reward_stat</p> <p>Type \u2192 stats.base.Univariate | None</p> <p>Default \u2192 <code>None</code></p> <p>The reward statistic to use. Defaults to <code>stats.Sum</code>.</p> </li> </ul>"},{"location":"api/bandit/evaluate-offline/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import bandit\n\nrng = random.Random(42)\narms = ['A', 'B', 'C']\nclicks = [\n    (\n        arms,\n        # no context\n        None,\n        # random arm\n        rng.choice(arms),\n        # reward\n        rng.random() &gt; 0.5\n    )\n    for _ in range(1000)\n]\n\ntotal_reward, n_samples_used = bandit.evaluate_offline(\n    policy=bandit.EpsilonGreedy(0.1, seed=42),\n    history=clicks,\n)\n\ntotal_reward\n</code></pre> <pre><code>Sum: 172.\n</code></pre></p> <p><pre><code>n_samples_used\n</code></pre> <pre><code>321\n</code></pre></p> <p>This also works out of the box with datasets that inherit from <code>river.bandit.BanditDataset</code>.</p> <p><pre><code>news = bandit.datasets.NewsArticles()\ntotal_reward, n_samples_used = bandit.evaluate_offline(\n    policy=bandit.RandomPolicy(seed=42),\n    history=news,\n)\n\ntotal_reward, n_samples_used\n</code></pre> <pre><code>(Sum: 105., 1027)\n</code></pre></p> <p>As expected, the policy succeeds in roughly 10% of cases. Indeed, there are 10 arms and 10000 samples, so the expected number of successes is 1000.</p> <ol> <li> <p>Offline Evaluation of Multi-Armed Bandit Algorithms in Python using Replay \u21a9</p> </li> <li> <p>Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms \u21a9</p> </li> <li> <p>Understanding Inverse Propensity Score for Contextual Bandits \u21a9</p> </li> </ol>"},{"location":"api/bandit/evaluate/","title":"evaluate","text":"<p>Benchmark a list of policies on a given Gym environment.</p> <p>This is a high-level utility function for benchmarking a list of policies on a given Gym environment. For example, it can be used to populate a <code>pandas.DataFrame</code> with the contents of each step of each episode.</p>"},{"location":"api/bandit/evaluate/#parameters","title":"Parameters","text":"<ul> <li> <p>policies</p> <p>Type \u2192 list[bandit.base.Policy]</p> <p>A list of policies to evaluate. The policy will be reset before each episode.</p> </li> <li> <p>env</p> <p>Type \u2192 gym.Env</p> <p>The Gym environment to use. One copy will be made for each policy at the beginning of each episode.</p> </li> <li> <p>reward_stat</p> <p>Type \u2192 stats.base.Univariate | None</p> <p>Default \u2192 <code>None</code></p> <p>A univariate statistic to keep track of the rewards. This statistic will be reset before each episode. Note that this is not the same as the reward object used by the policies. It's just a statistic to keep track of each policy's performance. If <code>None</code>, <code>stats.Sum</code> is used.</p> </li> <li> <p>n_episodes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>20</code></p> <p>The number of episodes to run.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility. A random number generator will be used to seed differently the environment before each episode.</p> </li> </ul>"},{"location":"api/bandit/evaluate/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\n\ntrace = bandit.evaluate(\n    policies=[\n        bandit.UCB(delta=1, seed=42),\n        bandit.EpsilonGreedy(epsilon=0.1, seed=42),\n    ],\n    env=gym.make(\n        'river_bandits/CandyCaneContest-v0',\n        max_episode_steps=100\n    ),\n    n_episodes=5,\n    seed=42\n)\n\nfor step in trace:\n    print(step)\n    break\n</code></pre> <pre><code>{'episode': 0, 'step': 0, 'policy_idx': 0, 'arm': 81, 'reward': 0.0, 'reward_stat': 0.0}\n</code></pre></p> <p>The return type of this function is a generator. Each step of the generator is a dictionary. You can pass the generator to a <code>pandas.DataFrame</code> to get a nice representation of the results.</p> <p><pre><code>import pandas as pd\n\ntrace = bandit.evaluate(\n    policies=[\n        bandit.UCB(delta=1, seed=42),\n        bandit.EpsilonGreedy(epsilon=0.1, seed=42),\n    ],\n    env=gym.make(\n        'river_bandits/CandyCaneContest-v0',\n        max_episode_steps=100\n    ),\n    n_episodes=5,\n    seed=42\n)\n\ntrace_df = pd.DataFrame(trace)\ntrace_df.sample(5, random_state=42)\n</code></pre> <pre><code>     episode  step  policy_idx  arm  reward  reward_stat\n521        2    60           1   25     0.0         36.0\n737        3    68           1   40     1.0         20.0\n740        3    70           0   58     0.0         36.0\n660        3    30           0   31     1.0         16.0\n411        2     5           1   35     1.0          5.0\n</code></pre></p> <p>The length of the dataframe is the number of policies times the number of episodes times the maximum number of steps per episode.</p> <p><pre><code>len(trace_df)\n</code></pre> <pre><code>1000\n</code></pre></p> <p><pre><code>(\n    trace_df.policy_idx.nunique() *\n    trace_df.episode.nunique() *\n    trace_df.step.nunique()\n)\n</code></pre> <pre><code>1000\n</code></pre></p>"},{"location":"api/bandit/base/ContextualPolicy/","title":"ContextualPolicy","text":"<p>Contextual bandit policy base class.</p>"},{"location":"api/bandit/base/ContextualPolicy/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Type \u2192 RewardObj | None</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Type \u2192 compose.TargetTransformRegressor | None</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> </ul>"},{"location":"api/bandit/base/ContextualPolicy/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/base/ContextualPolicy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> <li>context     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>context </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/base/Policy/","title":"Policy","text":"<p>Bandit policy base class.</p>"},{"location":"api/bandit/base/Policy/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Type \u2192 RewardObj | None</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Type \u2192 compose.TargetTransformRegressor | None</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> </ul>"},{"location":"api/bandit/base/Policy/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/base/Policy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/datasets/BanditDataset/","title":"BanditDataset","text":"<p>Base class for bandit datasets.</p>"},{"location":"api/bandit/datasets/BanditDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>n_features</p> <p>Number of features in the dataset.</p> </li> <li> <p>n_samples</p> <p>Default \u2192 <code>None</code></p> <p>Number of samples in the dataset.</p> </li> <li> <p>n_classes</p> <p>Default \u2192 <code>None</code></p> <p>Number of classes in the dataset, only applies to classification datasets.</p> </li> <li> <p>n_outputs</p> <p>Default \u2192 <code>None</code></p> <p>Number of outputs the target is made of, only applies to multi-output datasets.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the dataset is sparse or not.</p> </li> </ul>"},{"location":"api/bandit/datasets/BanditDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>arms</p> <p>The list of arms that can be pulled.</p> </li> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/bandit/datasets/BanditDataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/bandit/datasets/NewsArticles/","title":"NewsArticles","text":"<p>News articles bandit dataset.</p> <p>This is a personalization dataset. It contains 10000 observations. There are 10 arms, and the reward is binary. There are 100 features, which turns this into a contextual bandit problem.</p>"},{"location":"api/bandit/datasets/NewsArticles/#attributes","title":"Attributes","text":"<ul> <li> <p>arms</p> <p>The list of arms that can be pulled.</p> </li> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/bandit/datasets/NewsArticles/#examples","title":"Examples","text":"<p><pre><code>from river import bandit\n\ndataset = bandit.datasets.NewsArticles()\ncontext, arm, reward = next(iter(dataset))\n\nlen(context)\n</code></pre> <pre><code>100\n</code></pre></p> <p><pre><code>arm, reward\n</code></pre> <pre><code>(2, False)\n</code></pre></p>"},{"location":"api/bandit/datasets/NewsArticles/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Machine Learning for Personalization homework \u21a9</p> </li> <li> <p>Contextual Bandits Analysis of LinUCB Disjoint Algorithm with Dataset \u21a9</p> </li> </ol>"},{"location":"api/bandit/envs/CandyCaneContest/","title":"CandyCaneContest","text":"<p>Candy cane contest Kaggle competition.</p>"},{"location":"api/bandit/envs/CandyCaneContest/#parameters","title":"Parameters","text":"<ul> <li> <p>n_machines</p> <p>Default \u2192 <code>100</code></p> <p>Number of vending machines.</p> </li> <li> <p>reward_decay</p> <p>Default \u2192 <code>0.03</code></p> <p>The multiplicate rate at which the expected reward of each vending machine decays.</p> </li> </ul>"},{"location":"api/bandit/envs/CandyCaneContest/#attributes","title":"Attributes","text":"<ul> <li> <p>np_random</p> <p>Returns the environment's internal :attr:<code>_np_random</code> that if not set will initialise with a random seed.  Returns:     Instances of <code>np.random.Generator</code></p> </li> <li> <p>render_mode</p> </li> <li> <p>spec</p> </li> <li> <p>unwrapped</p> <p>Returns the base non-wrapped environment.  Returns:     Env: The base non-wrapped :class:<code>gymnasium.Env</code> instance</p> </li> </ul>"},{"location":"api/bandit/envs/CandyCaneContest/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import stats\n\nenv = gym.make('river_bandits/CandyCaneContest-v0')\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\nmetric = stats.Sum()\nwhile True:\n    arm = env.action_space.sample()\n    observation, reward, terminated, truncated, info = env.step(arm)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 734.\n</code></pre></p>"},{"location":"api/bandit/envs/CandyCaneContest/#methods","title":"Methods","text":"close <p>After the user has finished using the environment, close contains the code necessary to \"clean up\" the environment.</p> <p>This is critical for closing rendering windows, database or HTTP connections. Calling <code>close</code> on an already closed environment has no effect and won't raise an error.</p> <p></p> get_wrapper_attr <p>Gets the attribute <code>name</code> from the environment.</p> <p>Parameters</p> <ul> <li>name     \u2014 'str' </li> </ul> <p></p> render <p>Compute the render frames as specified by :attr:<code>render_mode</code> during the initialization of the environment.</p> <p>The environment's :attr:<code>metadata</code> render modes (<code>env.metadata[\"render_modes\"]</code>) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through <code>gymnasium.make</code> which automatically applies a wrapper to collect rendered frames.  Note:     As the :attr:<code>render_mode</code> is known during <code>__init__</code>, the objects used to render the environment state     should be initialised in <code>__init__</code>.  By convention, if the :attr:<code>render_mode</code> is:  - None (default): no render is computed. - \"human\": The environment is continuously rendered in the current display or terminal, usually for human consumption.   This rendering should occur during :meth:<code>step</code> and :meth:<code>render</code> doesn't need to be called. Returns <code>None</code>. - \"rgb_array\": Return a single frame representing the current state of the environment.   A frame is a <code>np.ndarray</code> with shape <code>(x, y, 3)</code> representing RGB values for an x-by-y pixel image. - \"ansi\": Return a strings (<code>str</code>) or <code>StringIO.StringIO</code> containing a terminal-style text representation   for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors). - \"rgb_array_list\" and \"ansi_list\": List based version of render modes are possible (except Human) through the   wrapper, :py:class:<code>gymnasium.wrappers.RenderCollection</code> that is automatically applied during <code>gymnasium.make(..., render_mode=\"rgb_array_list\")</code>.   The frames collected are popped after :meth:<code>render</code> is called or :meth:<code>reset</code>.  Note:     Make sure that your class's :attr:<code>metadata</code> <code>\"render_modes\"</code> key includes the list of supported modes.  .. versionchanged:: 0.25.0      The render function was changed to no longer accept parameters, rather these parameters should be specified     in the environment initialised, i.e., <code>gymnasium.make(\"CartPole-v1\", render_mode=\"human\")</code></p> <p></p> reset <p>Resets the environment to an initial internal state, returning an initial observation and info.</p> <p>This method generates a new starting state often with some randomness to ensure that the agent explores the state space and learns a generalised policy about the environment. This randomness can be controlled with the <code>seed</code> parameter otherwise if the environment already has a random number generator and :meth:<code>reset</code> is called with <code>seed=None</code>, the RNG is not reset.  Therefore, :meth:<code>reset</code> should (in the typical use case) be called with a seed right after initialization and then never again.  For Custom environments, the first line of :meth:<code>reset</code> should be <code>super().reset(seed=seed)</code> which implements the seeding correctly.  .. versionchanged:: v0.25      The <code>return_info</code> parameter was removed and now info is expected to be returned.  Args:     seed (optional int): The seed that is used to initialize the environment's PRNG (<code>np_random</code>).         If the environment does not already have a PRNG and <code>seed=None</code> (the default option) is passed,         a seed will be chosen from some source of entropy (e.g. timestamp or /dev/urandom).         However, if the environment already has a PRNG and <code>seed=None</code> is passed, the PRNG will not be reset.         If you pass an integer, the PRNG will be reset even if it already exists.         Usually, you want to pass an integer right after the environment has been initialized and then never again.         Please refer to the minimal example above to see this paradigm in action.     options (optional dict): Additional information to specify how the environment is reset (optional,         depending on the specific environment)  Returns:     observation (ObsType): Observation of the initial state. This will be an element of :attr:<code>observation_space</code>         (typically a numpy array) and is analogous to the observation returned by :meth:<code>step</code>.     info (dictionary):  This dictionary contains auxiliary information complementing <code>observation</code>. It should be analogous to         the <code>info</code> returned by :meth:<code>step</code>.</p> <p>Parameters</p> <ul> <li>seed     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> <li>options     \u2014 'dict[str, Any] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> step <p>Run one timestep of the environment's dynamics using the agent actions.</p> <p>When the end of an episode is reached (<code>terminated or truncated</code>), it is necessary to call :meth:<code>reset</code> to reset this environment's state for the next episode.  .. versionchanged:: 0.26      The Step API was changed removing <code>done</code> in favor of <code>terminated</code> and <code>truncated</code> to make it clearer     to users when the environment had terminated or truncated which is critical for reinforcement learning     bootstrapping algorithms.  Args:     action (ActType): an action provided by the agent to update the environment state.  Returns:     observation (ObsType): An element of the environment's :attr:<code>observation_space</code> as the next observation due to the agent actions.         An example is a numpy array containing the positions and velocities of the pole in CartPole.     reward (SupportsFloat): The reward as a result of taking the action.     terminated (bool): Whether the agent reaches the terminal state (as defined under the MDP of the task)         which can be positive or negative. An example is reaching the goal state or moving into the lava from         the Sutton and Barton, Gridworld. If true, the user needs to call :meth:<code>reset</code>.     truncated (bool): Whether the truncation condition outside the scope of the MDP is satisfied.         Typically, this is a timelimit, but could also be used to indicate an agent physically going out of bounds.         Can be used to end the episode prematurely before a terminal state is reached.         If true, the user needs to call :meth:<code>reset</code>.     info (dict): Contains auxiliary diagnostic information (helpful for debugging, learning, and logging).         This might, for instance, contain: metrics that describe the agent's performance state, variables that are         hidden from observations, or individual reward terms that are combined to produce the total reward.         In OpenAI Gym &lt;v26, it contains \"TimeLimit.truncated\" to distinguish truncation and termination,         however this is deprecated in favour of returning terminated and truncated variables.     done (bool): (Deprecated) A boolean value for if the episode has ended, in which case further :meth:<code>step</code> calls will         return undefined results. This was removed in OpenAI Gym v26 in favor of terminated and truncated attributes.         A done signal may be emitted for different reasons: Maybe the task underlying the environment was solved successfully,         a certain timelimit was exceeded, or the physics simulation has entered an invalid state.</p> <p>Parameters</p> <ul> <li>machine_index </li> </ul> <p></p> <ol> <li> <p>Santa 2020 - The Candy Cane Contest \u21a9</p> </li> </ol>"},{"location":"api/bandit/envs/KArmedTestbed/","title":"KArmedTestbed","text":"<p>k-armed testbed.</p> <p>This is a simple environment that can be used to test bandit algorithms. It is based on the 10 armed testbed described in the book \"Reinforcement Learning: An Introduction\" by Sutton and Barto.</p>"},{"location":"api/bandit/envs/KArmedTestbed/#parameters","title":"Parameters","text":"<ul> <li> <p>k</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of arms.</p> </li> </ul>"},{"location":"api/bandit/envs/KArmedTestbed/#attributes","title":"Attributes","text":"<ul> <li> <p>np_random</p> <p>Returns the environment's internal :attr:<code>_np_random</code> that if not set will initialise with a random seed.  Returns:     Instances of <code>np.random.Generator</code></p> </li> <li> <p>render_mode</p> </li> <li> <p>spec</p> </li> <li> <p>unwrapped</p> <p>Returns the base non-wrapped environment.  Returns:     Env: The base non-wrapped :class:<code>gymnasium.Env</code> instance</p> </li> </ul>"},{"location":"api/bandit/envs/KArmedTestbed/#methods","title":"Methods","text":"close <p>After the user has finished using the environment, close contains the code necessary to \"clean up\" the environment.</p> <p>This is critical for closing rendering windows, database or HTTP connections. Calling <code>close</code> on an already closed environment has no effect and won't raise an error.</p> <p></p> get_wrapper_attr <p>Gets the attribute <code>name</code> from the environment.</p> <p>Parameters</p> <ul> <li>name     \u2014 'str' </li> </ul> <p></p> render <p>Compute the render frames as specified by :attr:<code>render_mode</code> during the initialization of the environment.</p> <p>The environment's :attr:<code>metadata</code> render modes (<code>env.metadata[\"render_modes\"]</code>) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through <code>gymnasium.make</code> which automatically applies a wrapper to collect rendered frames.  Note:     As the :attr:<code>render_mode</code> is known during <code>__init__</code>, the objects used to render the environment state     should be initialised in <code>__init__</code>.  By convention, if the :attr:<code>render_mode</code> is:  - None (default): no render is computed. - \"human\": The environment is continuously rendered in the current display or terminal, usually for human consumption.   This rendering should occur during :meth:<code>step</code> and :meth:<code>render</code> doesn't need to be called. Returns <code>None</code>. - \"rgb_array\": Return a single frame representing the current state of the environment.   A frame is a <code>np.ndarray</code> with shape <code>(x, y, 3)</code> representing RGB values for an x-by-y pixel image. - \"ansi\": Return a strings (<code>str</code>) or <code>StringIO.StringIO</code> containing a terminal-style text representation   for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors). - \"rgb_array_list\" and \"ansi_list\": List based version of render modes are possible (except Human) through the   wrapper, :py:class:<code>gymnasium.wrappers.RenderCollection</code> that is automatically applied during <code>gymnasium.make(..., render_mode=\"rgb_array_list\")</code>.   The frames collected are popped after :meth:<code>render</code> is called or :meth:<code>reset</code>.  Note:     Make sure that your class's :attr:<code>metadata</code> <code>\"render_modes\"</code> key includes the list of supported modes.  .. versionchanged:: 0.25.0      The render function was changed to no longer accept parameters, rather these parameters should be specified     in the environment initialised, i.e., <code>gymnasium.make(\"CartPole-v1\", render_mode=\"human\")</code></p> <p></p> reset <p>Resets the environment to an initial internal state, returning an initial observation and info.</p> <p>This method generates a new starting state often with some randomness to ensure that the agent explores the state space and learns a generalised policy about the environment. This randomness can be controlled with the <code>seed</code> parameter otherwise if the environment already has a random number generator and :meth:<code>reset</code> is called with <code>seed=None</code>, the RNG is not reset.  Therefore, :meth:<code>reset</code> should (in the typical use case) be called with a seed right after initialization and then never again.  For Custom environments, the first line of :meth:<code>reset</code> should be <code>super().reset(seed=seed)</code> which implements the seeding correctly.  .. versionchanged:: v0.25      The <code>return_info</code> parameter was removed and now info is expected to be returned.  Args:     seed (optional int): The seed that is used to initialize the environment's PRNG (<code>np_random</code>).         If the environment does not already have a PRNG and <code>seed=None</code> (the default option) is passed,         a seed will be chosen from some source of entropy (e.g. timestamp or /dev/urandom).         However, if the environment already has a PRNG and <code>seed=None</code> is passed, the PRNG will not be reset.         If you pass an integer, the PRNG will be reset even if it already exists.         Usually, you want to pass an integer right after the environment has been initialized and then never again.         Please refer to the minimal example above to see this paradigm in action.     options (optional dict): Additional information to specify how the environment is reset (optional,         depending on the specific environment)  Returns:     observation (ObsType): Observation of the initial state. This will be an element of :attr:<code>observation_space</code>         (typically a numpy array) and is analogous to the observation returned by :meth:<code>step</code>.     info (dictionary):  This dictionary contains auxiliary information complementing <code>observation</code>. It should be analogous to         the <code>info</code> returned by :meth:<code>step</code>.</p> <p>Parameters</p> <ul> <li>seed     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> <li>options     \u2014 'dict[str, Any] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> step <p>Run one timestep of the environment's dynamics using the agent actions.</p> <p>When the end of an episode is reached (<code>terminated or truncated</code>), it is necessary to call :meth:<code>reset</code> to reset this environment's state for the next episode.  .. versionchanged:: 0.26      The Step API was changed removing <code>done</code> in favor of <code>terminated</code> and <code>truncated</code> to make it clearer     to users when the environment had terminated or truncated which is critical for reinforcement learning     bootstrapping algorithms.  Args:     action (ActType): an action provided by the agent to update the environment state.  Returns:     observation (ObsType): An element of the environment's :attr:<code>observation_space</code> as the next observation due to the agent actions.         An example is a numpy array containing the positions and velocities of the pole in CartPole.     reward (SupportsFloat): The reward as a result of taking the action.     terminated (bool): Whether the agent reaches the terminal state (as defined under the MDP of the task)         which can be positive or negative. An example is reaching the goal state or moving into the lava from         the Sutton and Barton, Gridworld. If true, the user needs to call :meth:<code>reset</code>.     truncated (bool): Whether the truncation condition outside the scope of the MDP is satisfied.         Typically, this is a timelimit, but could also be used to indicate an agent physically going out of bounds.         Can be used to end the episode prematurely before a terminal state is reached.         If true, the user needs to call :meth:<code>reset</code>.     info (dict): Contains auxiliary diagnostic information (helpful for debugging, learning, and logging).         This might, for instance, contain: metrics that describe the agent's performance state, variables that are         hidden from observations, or individual reward terms that are combined to produce the total reward.         In OpenAI Gym &lt;v26, it contains \"TimeLimit.truncated\" to distinguish truncation and termination,         however this is deprecated in favour of returning terminated and truncated variables.     done (bool): (Deprecated) A boolean value for if the episode has ended, in which case further :meth:<code>step</code> calls will         return undefined results. This was removed in OpenAI Gym v26 in favor of terminated and truncated attributes.         A done signal may be emitted for different reasons: Maybe the task underlying the environment was solved successfully,         a certain timelimit was exceeded, or the physics simulation has entered an invalid state.</p> <p>Parameters</p> <ul> <li>arm </li> </ul> <p></p>"},{"location":"api/base/Base/","title":"Base","text":"<p>Base class that is inherited by the majority of classes in River.</p> <p>This base class allows us to handle the following tasks in a uniform manner: </p> <ul> <li> <p>Getting and setting parameters</p> </li> <li> <p>Displaying information</p> </li> <li> <p>Mutating/cloning</p> </li> </ul>"},{"location":"api/base/Base/#methods","title":"Methods","text":"clone <p>Return a fresh estimator with the same parameters.</p> <p>The clone has the same parameters but has not been updated with any data.  This works by looking at the parameters from the class signature. Each parameter is either  - recursively cloned if its a class. - deep-copied via <code>copy.deepcopy</code> if not.  If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.</p> <p>Parameters</p> <ul> <li>new_params     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> <li>include_attributes     \u2014 defaults to <code>False</code> </li> </ul> <p></p> mutate <p>Modify attributes.</p> <p>This changes parameters inplace. Although you can change attributes yourself, this is the recommended way to proceed. By default, all attributes are immutable, meaning they shouldn't be mutated. Calling <code>mutate</code> on an immutable attribute raises a <code>ValueError</code>. Mutable attributes are specified via the <code>_mutable_attributes</code> property, and are thus specified on a per-estimator basis.</p> <p>Parameters</p> <ul> <li>new_attrs     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/base/BinaryDriftAndWarningDetector/","title":"BinaryDriftAndWarningDetector","text":"<p>A binary drift detector that is also capable of issuing warnings.</p>"},{"location":"api/base/BinaryDriftAndWarningDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/BinaryDriftAndWarningDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p>"},{"location":"api/base/BinaryDriftDetector/","title":"BinaryDriftDetector","text":"<p>A drift detector for binary data.</p>"},{"location":"api/base/BinaryDriftDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/BinaryDriftDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p>"},{"location":"api/base/Classifier/","title":"Classifier","text":"<p>A classifier.</p>"},{"location":"api/base/Classifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/base/Clusterer/","title":"Clusterer","text":"<p>A clustering model.</p>"},{"location":"api/base/Clusterer/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p>"},{"location":"api/base/DriftAndWarningDetector/","title":"DriftAndWarningDetector","text":"<p>A drift detector that is also capable of issuing warnings.</p>"},{"location":"api/base/DriftAndWarningDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/DriftAndWarningDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/base/DriftDetector/","title":"DriftDetector","text":"<p>A drift detector.</p>"},{"location":"api/base/DriftDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/DriftDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/base/Ensemble/","title":"Ensemble","text":"<p>An ensemble is a model which is composed of a list of models.</p>"},{"location":"api/base/Ensemble/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 Iterator[Estimator]</p> </li> </ul>"},{"location":"api/base/Ensemble/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/base/Ensemble/#methods","title":"Methods","text":"append <p>S.append(value) -- append value to the end of the sequence</p> <p>Parameters</p> <ul> <li>item </li> </ul> <p></p> clear <p>S.clear() -&gt; None -- remove all items from S</p> <p></p> copy count <p>S.count(value) -&gt; integer -- return number of occurrences of value</p> <p>Parameters</p> <ul> <li>item </li> </ul> <p></p> extend <p>S.extend(iterable) -- extend sequence by appending elements from the iterable</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> index <p>S.index(value, [start, [stop]]) -&gt; integer -- return first index of value. Raises ValueError if the value is not present.</p> <p>Supporting start and stop arguments is optional, but recommended.</p> <p>Parameters</p> <ul> <li>item </li> <li>args </li> </ul> <p></p> insert <p>S.insert(index, value) -- insert value before index</p> <p>Parameters</p> <ul> <li>i </li> <li>item </li> </ul> <p></p> pop <p>S.pop([index]) -&gt; item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.</p> <p>Parameters</p> <ul> <li>i     \u2014 defaults to <code>-1</code> </li> </ul> <p></p> remove <p>S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.</p> <p>Parameters</p> <ul> <li>item </li> </ul> <p></p> reverse <p>S.reverse() -- reverse IN PLACE</p> <p></p> sort"},{"location":"api/base/Estimator/","title":"Estimator","text":"<p>An estimator.</p>"},{"location":"api/base/Estimator/#methods","title":"Methods","text":""},{"location":"api/base/MiniBatchClassifier/","title":"MiniBatchClassifier","text":"<p>A classifier that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchClassifier/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and boolean targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Predict the outcome probabilities for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A dataframe with probabilities of <code>True</code> and <code>False</code> for each sample.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/base/MiniBatchRegressor/","title":"MiniBatchRegressor","text":"<p>A regressor that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchRegressor/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and real-valued targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted outcomes.</p> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/base/MiniBatchSupervisedTransformer/","title":"MiniBatchSupervisedTransformer","text":"<p>A supervised transformer that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchSupervisedTransformer/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/MiniBatchTransformer/","title":"MiniBatchTransformer","text":"<p>A transform that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchTransformer/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/MultiLabelClassifier/","title":"MultiLabelClassifier","text":"<p>Multi-label classifier.</p>"},{"location":"api/base/MultiLabelClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'dict[FeatureName, bool]' </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, dict[bool, float]]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/base/MultiTargetRegressor/","title":"MultiTargetRegressor","text":"<p>Multi-target regressor.</p>"},{"location":"api/base/MultiTargetRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'dict[FeatureName, RegTarget]' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the outputs of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[FeatureName, RegTarget]:     The predictions.</p> <p></p>"},{"location":"api/base/Regressor/","title":"Regressor","text":"<p>A regressor.</p>"},{"location":"api/base/Regressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/base/SupervisedTransformer/","title":"SupervisedTransformer","text":"<p>A supervised transformer.</p>"},{"location":"api/base/SupervisedTransformer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code> and a target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/Transformer/","title":"Transformer","text":"<p>A transformer.</p>"},{"location":"api/base/Transformer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/Wrapper/","title":"Wrapper","text":"<p>A wrapper model.</p>"},{"location":"api/base/WrapperEnsemble/","title":"WrapperEnsemble","text":"<p>A wrapper ensemble is an ensemble composed of multiple copies of the same model.</p>"},{"location":"api/base/WrapperEnsemble/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>The model to copy.</p> </li> <li> <p>n_models</p> <p>The number of copies to make.</p> </li> <li> <p>seed</p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/base/WrapperEnsemble/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/base/WrapperEnsemble/#methods","title":"Methods","text":""},{"location":"api/cluster/CluStream/","title":"CluStream","text":"<p>CluStream</p> <p>The CluStream algorithm <sup>1</sup> maintains statistical information about the data using micro-clusters. These micro-clusters are temporal extensions of cluster feature vectors. The micro-clusters are stored at snapshots in time following a pyramidal pattern. This pattern allows to recall summary statistics from different time horizons. </p> <p>Training with a new point <code>p</code> is performed in two main tasks: </p> <ul> <li> <p>Determinate the closest micro-cluster to <code>p</code>. </p> </li> <li> <p>Check whether <code>p</code> fits (memory) into the closest micro-cluster: </p> <ul> <li> <p>if <code>p</code> fits, put into micro-cluster</p> </li> <li> <p>if <code>p</code> does not fit, free some space to insert a new micro-cluster.</p> </li> </ul> <p>This is done in two ways, delete an old micro-cluster or merge the       two micro-clusters closest to each other. </p> </li> </ul> <p>This implementation is an improved version from the original algorithm. Instead of calculating the traditional cluster feature vector of the number of observations, linear sum and sum of squares of data points and time stamps, this implementation adopts the use of Welford's algorithm <sup>2</sup> to calculate the incremental variance, facilitated through <code>stats.Var</code> available within River. </p> <p>Since River does not support an actual \"off-line\" phase of the clustering algorithm (as data points are assumed to arrive continuously, one at a time), a <code>time_gap</code> parameter is introduced. After each <code>time_gap</code>, an incremental K-Means clustering algorithm will be initialized and applied on currently available micro-clusters to form the final solution, i.e. macro-clusters.</p>"},{"location":"api/cluster/CluStream/#parameters","title":"Parameters","text":"<ul> <li> <p>n_macro_clusters</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of clusters (k) for the k-means algorithm.</p> </li> <li> <p>max_micro_clusters</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>The maximum number of micro-clusters to use.</p> </li> <li> <p>micro_cluster_r_factor</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>Multiplier for the micro-cluster radius. When deciding to add a new data point to a micro-cluster, the maximum boundary is defined as a factor of the <code>micro_cluster_r_factor</code> of the RMS deviation of the data points in the micro-cluster from the centroid.</p> </li> <li> <p>time_window</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>If the current time is <code>T</code> and the time window is <code>h</code>, we only consider the data that arrived within the period <code>(T-h,T)</code>.</p> </li> <li> <p>time_gap</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>An incremental k-means is applied on the current set of micro-clusters after each <code>time_gap</code> to form the final macro-cluster solution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for generating initial centroid positions.</p> </li> <li> <p>kwargs</p> <p>Other parameters passed to the incremental kmeans at <code>cluster.KMeans</code>.</p> </li> </ul>"},{"location":"api/cluster/CluStream/#attributes","title":"Attributes","text":"<ul> <li> <p>centers (dict)</p> <p>Central positions of each cluster.</p> </li> </ul>"},{"location":"api/cluster/CluStream/#examples","title":"Examples","text":"<p>In the following example, <code>max_micro_clusters</code> is set relatively low due to the limited number of training points. Moreover, all points are learnt before any predictions are made. The <code>halflife</code> is set at 0.4, to show that you can pass <code>cluster.KMeans</code> parameters via keyword arguments.</p> <p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 2],\n    [1, 4],\n    [1, 0],\n    [-4, 2],\n    [-4, 4],\n    [-4, 0],\n    [5, 0],\n    [5, 2],\n    [5, 4]\n]\n\nclustream = cluster.CluStream(\n    n_macro_clusters=3,\n    max_micro_clusters=5,\n    time_gap=3,\n    seed=0,\n    halflife=0.4\n)\n\nfor x, _ in stream.iter_array(X):\n    clustream.learn_one(x)\n\nclustream.predict_one({0: 1, 1: 1})\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>clustream.predict_one({0: -4, 1: 3})\n</code></pre> <pre><code>2\n</code></pre></p> <p><pre><code>clustream.predict_one({0: 4, 1: 3.5})\n</code></pre> <pre><code>0\n</code></pre></p>"},{"location":"api/cluster/CluStream/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Aggarwal, C.C., Philip, S.Y., Han, J. and Wang, J., 2003, A framework for clustering evolving data streams. In Proceedings 2003 VLDB conference (pp. 81-92). Morgan Kaufmann.\u00a0\u21a9</p> </li> <li> <p>Chan, T.F., Golub, G.H. and LeVeque, R.J., 1982. Updating formulae and a pairwise algorithm for computing sample variances. In COMPSTAT 1982 5th Symposium held at Toulouse 1982 (pp. 30-41). Physica, Heidelberg. https://doi.org/10.1007/978-3-642-51461-6_3.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/DBSTREAM/","title":"DBSTREAM","text":"<p>DBSTREAM</p> <p>DBSTREAM <sup>1</sup> is a clustering algorithm for evolving data streams. It is the first micro-cluster-based online clustering component that explicitely captures the density between micro-clusters via a shared density graph. The density information in the graph is then exploited for reclustering based on actual density between adjacent micro clusters. </p> <p>The algorithm is divided into two parts: </p> <p>Online micro-cluster maintenance (learning) </p> <p>For a new point <code>p</code>: </p> <ul> <li> <p>Find all micro clusters for which <code>p</code> falls within the fixed radius (clustering threshold). If no neighbor is found, a new micro cluster with a weight of 1 is created for <code>p</code>. </p> </li> <li> <p>If no neighbor is found, a new micro cluster with a weight of 1 is created for <code>p</code>. If one or more neighbors of <code>p</code> are found, we update the micro clusters by applying the appropriate fading, increasing their weight and then we try to move them closer to <code>p</code> using the Gaussian neighborhood function. </p> </li> <li> <p>Next, the shared density graph is updated. To prevent collapsing micro clusters, we will restrict the movement for micro clusters in case they come closer than \\(r\\) (clustering threshold) to each other. Finishing this process, the time stamp is also increased by 1. </p> </li> <li> <p>Finally, the cleanup will be processed. It is executed every <code>t_gap</code> time steps, removing weak micro clusters and weak entries in the shared density graph to recover memory and improve the clustering algorithm's processing speed. </p> </li> </ul> <p>Offline generation of macro clusters (clustering) </p> <p>The offline generation of macro clusters is generated through the two following steps: </p> <ul> <li> <p>The connectivity graph <code>C</code> is constructed using shared density entries between strong micro clusters. The edges in this connectivity graph with a connectivity value greater than the intersection threshold (\\(\\alpha\\)) are used to find connected components representing the final cluster. </p> </li> <li> <p>After the connectivity graph is generated, a variant of the DBSCAN algorithm proposed by Ester et al. is applied to form all macro clusters from \\(\\alpha\\)-connected micro clusters.</p> </li> </ul>"},{"location":"api/cluster/DBSTREAM/#parameters","title":"Parameters","text":"<ul> <li> <p>clustering_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>DBStream represents each micro cluster by a leader (a data point defining the micro cluster's center) and the density in an area of a user-specified radius \\(r\\) (<code>clustering_threshold</code>) around the center.</p> </li> <li> <p>fading_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>Parameter that controls the importance of historical data to current cluster. Note that <code>fading_factor</code> has to be different from <code>0</code>.</p> </li> <li> <p>cleanup_interval</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>2</code></p> <p>The time interval between two consecutive time points when the cleanup process is  conducted.</p> </li> <li> <p>intersection_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.3</code></p> <p>The intersection factor related to the area of the overlap of the micro clusters relative to the area cover by micro clusters. This parameter is used to determine whether a micro cluster or a shared density is weak.</p> </li> <li> <p>minimum_weight</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>The minimum weight for a cluster to be not \"noisy\".</p> </li> </ul>"},{"location":"api/cluster/DBSTREAM/#attributes","title":"Attributes","text":"<ul> <li> <p>n_clusters</p> <p>Number of clusters generated by the algorithm.</p> </li> <li> <p>clusters</p> <p>A set of final clusters of type <code>DBStreamMicroCluster</code>. However, these are either micro clusters, or macro clusters that are generated by merging all \\(\\alpha\\)-connected micro clusters. This set is generated through the offline phase of the algorithm.</p> </li> <li> <p>centers</p> <p>Final clusters' centers.</p> </li> <li> <p>micro_clusters</p> <p>Micro clusters generated by the algorithm. Instead of updating directly the new instance points into a nearest micro cluster, through each iteration, the weight and center will be modified so that the clusters are closer to the new points, using the Gaussian neighborhood function.</p> </li> </ul>"},{"location":"api/cluster/DBSTREAM/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 0.5], [1, 0.625], [1, 0.75], [1, 1.125], [1, 1.5], [1, 1.75],\n    [4, 1.5], [4, 2.25], [4, 2.5], [4, 3], [4, 3.25], [4, 3.5]\n]\n\ndbstream = cluster.DBSTREAM(\n    clustering_threshold=1.5,\n    fading_factor=0.05,\n    cleanup_interval=4,\n    intersection_factor=0.5,\n    minimum_weight=1\n)\n\nfor x, _ in stream.iter_array(X):\n    dbstream.learn_one(x)\n\ndbstream.predict_one({0: 1, 1: 2})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>dbstream.predict_one({0: 5, 1: 2})\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>dbstream._n_clusters\n</code></pre> <pre><code>2\n</code></pre></p>"},{"location":"api/cluster/DBSTREAM/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Michael Hahsler and Matthew Bolanos (2016, pp 1449-1461). Clustering Data Streams Based on   Shared Density between Micro-Clusters, IEEE Transactions on Knowledge and Data Engineering 28(6) .   In Proceedings of the Sixth SIAM International Conference on Data Mining,   April 20\u201322, 2006, Bethesda, MD, USA.\u00a0\u21a9</p> </li> <li> <p>Ester et al (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases   with Noise. In KDD-96 Proceedings, AAAI.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/DenStream/","title":"DenStream","text":"<p>DenStream</p> <p>DenStream <sup>1</sup> is a clustering algorithm for evolving data streams. DenStream can discover clusters with arbitrary shape and is robust against noise (outliers). </p> <p>\"Dense\" micro-clusters (named core-micro-clusters) summarise the clusters of arbitrary shape. A pruning strategy based on the concepts of potential and outlier micro-clusters guarantees the precision of the weights of the micro-clusters with limited memory. </p> <p>The algorithm is divided into two parts: </p> <p>Online micro-cluster maintenance (learning) </p> <p>For a new point <code>p</code>: </p> <ul> <li> <p>Try to merge <code>p</code> into either the nearest <code>p-micro-cluster</code> (potential), <code>o-micro-cluster</code> (outlier), or create a new <code>o-micro-cluster</code> and insert it into the outlier buffer. </p> </li> <li> <p>For each <code>T_p</code> iterations, consider the weights of all potential and outlier micro-clusters. If their weights are smaller than a certain threshold (different for each type of micro-clusters), the micro-cluster is deleted. </p> </li> </ul> <p>Offline generation of clusters on-demand (clustering) </p> <p>A variant of the DBSCAN algorithm <sup>2</sup> is used, such that all density-connected p-micro-clusters determine the final clusters. Moreover, in order for the algorithm to always be able to generate clusters, a certain number of points must be passed through the algorithm with a suitable streaming speed (number of points passed through within a unit time), indicated by <code>n_samples_init</code> and <code>stream_speed</code>.</p>"},{"location":"api/cluster/DenStream/#parameters","title":"Parameters","text":"<ul> <li> <p>decaying_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.25</code></p> <p>Parameter that controls the importance of historical data to current cluster. Note that <code>decaying_factor</code> has to be different from <code>0</code>.</p> </li> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.75</code></p> <p>Parameter to determine the threshold of outlier relative to core micro-clusters. The value of <code>beta</code> must be within the range <code>(0,1]</code>.</p> </li> <li> <p>mu</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>2</code></p> <p>Parameter to determine the threshold of outliers relative to core micro-cluster. As <code>beta * mu</code> must be greater than 1, <code>mu</code> must be within the range <code>(1/beta, inf)</code>.</p> </li> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.02</code></p> <p>Defines the epsilon neighborhood</p> </li> <li> <p>n_samples_init</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>Number of points to to initiqalize the online process</p> </li> <li> <p>stream_speed</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>Number of points arrived in unit time</p> </li> </ul>"},{"location":"api/cluster/DenStream/#attributes","title":"Attributes","text":"<ul> <li> <p>n_clusters</p> <p>Number of clusters generated by the algorithm.</p> </li> <li> <p>clusters</p> <p>A set of final clusters of type <code>MicroCluster</code>, which means that these cluster include all the required information, including number of points, creation time, weight, (weighted) linear sum, (weighted) square sum, center and radius.</p> </li> <li> <p>p_micro_clusters</p> <p>The potential core-icro-clusters that are generated by the algorithm. When a generate cluster request arrives, these p-micro-clusters will go through a variant of the DBSCAN algorithm to determine the final clusters.</p> </li> <li> <p>o_micro_clusters</p> <p>The outlier micro-clusters.</p> </li> </ul>"},{"location":"api/cluster/DenStream/#examples","title":"Examples","text":"<p>The following example uses the default parameters of the algorithm to test its functionality. The set of evolving points <code>X</code> are designed so that clusters are easily identifiable.</p> <p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [-1, -0.5], [-1, -0.625], [-1, -0.75], [-1, -1], [-1, -1.125],\n    [-1, -1.25], [-1.5, -0.5], [-1.5, -0.625], [-1.5, -0.75], [-1.5, -1],\n    [-1.5, -1.125], [-1.5, -1.25], [1, 1.5], [1, 1.75], [1, 2],\n    [4, 1.25], [4, 1.5], [4, 2.25], [4, 2.5], [4, 3],\n    [4, 3.25], [4, 3.5], [4, 3.75], [4, 4],\n]\n\ndenstream = cluster.DenStream(decaying_factor=0.01,\n                              beta=0.5,\n                              mu=2.5,\n                              epsilon=0.5,\n                              n_samples_init=10)\n\nfor x, _ in stream.iter_array(X):\n    denstream.learn_one(x)\n\ndenstream.predict_one({0: -1, 1: -2})\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>denstream.predict_one({0: 5, 1: 4})\n</code></pre> <pre><code>2\n</code></pre></p> <p><pre><code>denstream.predict_one({0: 1, 1: 1})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>denstream.n_clusters\n</code></pre> <pre><code>3\n</code></pre></p>"},{"location":"api/cluster/DenStream/#methods","title":"Methods","text":"BufferItem learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Feng et al (2006, pp 328-339). Density-Based Clustering over an Evolving Data Stream with   Noise. In Proceedings of the Sixth SIAM International Conference on Data Mining,   April 20\u201322, 2006, Bethesda, MD, USA.\u00a0\u21a9</p> </li> <li> <p>Ester et al (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial   Databases with Noise. In KDD-96 Proceedings, AAAI.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/KMeans/","title":"KMeans","text":"<p>Incremental k-means.</p> <p>The most common way to implement batch k-means is to use Lloyd's algorithm, which consists in assigning all the data points to a set of cluster centers and then moving the centers accordingly. This requires multiple passes over the data and thus isn't applicable in a streaming setting. </p> <p>In this implementation we start by finding the cluster that is closest to the current observation. We then move the cluster's central position towards the new observation. The <code>halflife</code> parameter determines by how much to move the cluster toward the new observation. You will get better results if you scale your data appropriately.</p>"},{"location":"api/cluster/KMeans/#parameters","title":"Parameters","text":"<ul> <li> <p>n_clusters</p> <p>Default \u2192 <code>5</code></p> <p>Maximum number of clusters to assign.</p> </li> <li> <p>halflife</p> <p>Default \u2192 <code>0.5</code></p> <p>Amount by which to move the cluster centers, a reasonable value if between 0 and 1.</p> </li> <li> <p>mu</p> <p>Default \u2192 <code>0</code></p> <p>Mean of the normal distribution used to instantiate cluster positions.</p> </li> <li> <p>sigma</p> <p>Default \u2192 <code>1</code></p> <p>Standard deviation of the normal distribution used to instantiate cluster positions.</p> </li> <li> <p>p</p> <p>Default \u2192 <code>2</code></p> <p>Power parameter for the Minkowski metric. When <code>p=1</code>, this corresponds to the Manhattan distance, while <code>p=2</code> corresponds to the Euclidean distance.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for generating initial centroid positions.</p> </li> </ul>"},{"location":"api/cluster/KMeans/#attributes","title":"Attributes","text":"<ul> <li> <p>centers (dict)</p> <p>Central positions of each cluster.</p> </li> </ul>"},{"location":"api/cluster/KMeans/#examples","title":"Examples","text":"<p>In the following example the cluster assignments are exactly the same as when using <code>sklearn</code>'s batch implementation. However changing the <code>halflife</code> parameter will produce different outputs.</p> <p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 2],\n    [1, 4],\n    [1, 0],\n    [-4, 2],\n    [-4, 4],\n    [-4, 0]\n]\n\nk_means = cluster.KMeans(n_clusters=2, halflife=0.1, sigma=3, seed=42)\n\nfor i, (x, _) in enumerate(stream.iter_array(X)):\n    k_means.learn_one(x)\n    print(f'{X[i]} is assigned to cluster {k_means.predict_one(x)}')\n</code></pre> <pre><code>[1, 2] is assigned to cluster 1\n[1, 4] is assigned to cluster 1\n[1, 0] is assigned to cluster 0\n[-4, 2] is assigned to cluster 1\n[-4, 4] is assigned to cluster 1\n[-4, 0] is assigned to cluster 0\n</code></pre></p> <p><pre><code>k_means.predict_one({0: 0, 1: 0})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>k_means.predict_one({0: 4, 1: 4})\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/cluster/KMeans/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> learn_predict_one <p>Equivalent to <code>k_means.learn_one(x).predict_one(x)</code>, but faster.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Sequential k-Means Clustering \u21a9</p> </li> <li> <p>Sculley, D., 2010, April. Web-scale k-means clustering. In Proceedings of the 19th international conference on World wide web (pp. 1177-1178 \u21a9</p> </li> </ol>"},{"location":"api/cluster/ODAC/","title":"ODAC","text":"<p>The Online Divisive-Agglomerative Clustering (ODAC)<sup>1</sup> aims at continuously maintaining a hierarchical cluster structure from evolving time series data streams.</p> <p>ODAC continuosly monitors the evolution of clusters' diameters and split or merge them by gathering more data or reacting to concept drift. Such changes are supported by a confidence level that comes from the Hoeffding bound. ODAC relies on keeping the linear correlation between series to evaluate whether or not the time series hierarchy has changed. </p> <p>The distance between time-series a and b is given by <code>rnomc(a, b) = sqrt((1 - corr(a, b)) / 2)</code>, where <code>corr(a, b)</code> is the Pearson Correlation coefficient. </p> <p>In the following topics, \u03b5 stands for the Hoeffding bound and considers clusters cj with descendants ck and cs. </p> <p>The Merge Operator </p> <p>The Splitting Criteria guarantees that cluster's diameters monotonically decrease. </p> <ul> <li> <p>If diameter (ck) - diameter (cj) &gt; \u03b5 OR diameter (cs) - diameter (cj ) &gt; \u03b5:</p> <ul> <li>There is a change in the correlation structure, so merge clusters ck and cs into cj. </li> </ul> </li> </ul> <p>Splitting Criteria </p> <p>Consider: </p> <ul> <li> <p>d0: the minimum distance;</p> </li> <li> <p>d1: the farthest distance;</p> </li> <li> <p>d_avg: the average distance;</p> </li> <li> <p>d2: the second farthest distance.</p> </li> </ul> <p>Then: </p> <ul> <li> <p>if d1 - d2 &gt; \u03b5k or t &gt; \u03b5k then</p> <ul> <li> <p>if (d1 - d0)|(d1 - d_avg) - (d_avg - d0) &gt; \u03b5k then</p> <ul> <li>Split the cluster</li> </ul> </li> </ul> </li> </ul>"},{"location":"api/cluster/ODAC/#parameters","title":"Parameters","text":"<ul> <li> <p>confidence_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.9</code></p> <p>The confidence level that user wants to work.</p> </li> <li> <p>n_min</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>Number of minimum observations to gather before checking whether or not clusters must be split or merged.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> </ul>"},{"location":"api/cluster/ODAC/#attributes","title":"Attributes","text":"<ul> <li> <p>structure_changed (bool)</p> <p>This variable is true when the structure changed, produced by splitting or aggregation.</p> </li> </ul>"},{"location":"api/cluster/ODAC/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river.datasets import synth\n\nmodel = cluster.ODAC()\n\ndataset = synth.FriedmanDrift(drift_type='gra', position=(150, 200), seed=42)\n\nfor i, (x, _) in enumerate(dataset.take(500)):\n    model.learn_one(x)\n    if model.structure_changed:\n        print(f\"Structure changed at observation {i + 1}\")\n</code></pre> <pre><code>Structure changed at observation 1\nStructure changed at observation 100\nStructure changed at observation 200\nStructure changed at observation 300\n</code></pre></p> <p><pre><code>print(model.draw(n_decimal_places=2))\n</code></pre> <pre><code>ROOT d1=0.79 d2=0.76 [NOT ACTIVE]\n\u251c\u2500\u2500 CH1_LVL_1 d1=0.74 d2=0.72 [NOT ACTIVE]\n\u2502   \u251c\u2500\u2500 CH1_LVL_2 d1=&lt;Not calculated&gt; [3]\n\u2502   \u2514\u2500\u2500 CH2_LVL_2 d1=0.73 [2, 4]\n\u2514\u2500\u2500 CH2_LVL_1 d1=0.81 d2=0.78 [NOT ACTIVE]\n    \u251c\u2500\u2500 CH1_LVL_2 d1=0.73 d2=0.67 [NOT ACTIVE]\n    \u2502   \u251c\u2500\u2500 CH1_LVL_3 d1=0.72 [0, 9]\n    \u2502   \u2514\u2500\u2500 CH2_LVL_3 d1=&lt;Not calculated&gt; [1]\n    \u2514\u2500\u2500 CH2_LVL_2 d1=0.74 d2=0.73 [NOT ACTIVE]\n        \u251c\u2500\u2500 CH1_LVL_3 d1=0.71 [5, 6]\n        \u2514\u2500\u2500 CH2_LVL_3 d1=0.71 [7, 8]\n</code></pre></p> <p>You can acess some properties of the clustering model directly:</p> <p><pre><code>model.n_clusters\n</code></pre> <pre><code>11\n</code></pre></p> <p><pre><code>model.n_active_clusters\n</code></pre> <pre><code>6\n</code></pre></p> <p><pre><code>model.height\n</code></pre> <pre><code>3\n</code></pre></p> <p>These properties are also available in a summarized form:</p> <p><pre><code>model.summary\n</code></pre> <pre><code>{'n_clusters': 11, 'n_active_clusters': 6, 'height': 3}\n</code></pre></p>"},{"location":"api/cluster/ODAC/#methods","title":"Methods","text":"draw <p>Method to draw the hierarchical cluster's structure.</p> <p>Parameters</p> <ul> <li>n_decimal_places     \u2014 'int'     \u2014 defaults to <code>2</code> </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> predict_one <p>This algorithm does not predict anything. It builds a hierarchical cluster's structure.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>Hierarchical clustering of time-series data streams. \u21a9</p> </li> </ol>"},{"location":"api/cluster/STREAMKMeans/","title":"STREAMKMeans","text":"<p>STREAMKMeans</p> <p>STREAMKMeans is an alternative version of the original algorithm STREAMLSEARCH proposed by O'Callaghan et al. <sup>1</sup>, by replacing the k-medians using <code>LSEARCH</code> by the k-means algorithm. </p> <p>However, instead of using the traditional k-means, which requires a total reclustering each time the temporary chunk of data points is full, the implementation of this algorithm uses an increamental k-means. </p> <p>At first, the cluster centers are initialized with a <code>KMeans</code> instance. For a new point <code>p</code>: </p> <ul> <li> <p>If the size of chunk is less than the maximum size allowed, add the new point to the temporary chunk. </p> </li> <li> <p>When the size of chunk reaches the maximum value size allowed </p> <ul> <li>A new incremental <code>KMeans</code> instance is created. The latter will process all points in the</li> </ul> <p>temporary chunk. The centers of this new instance then become the new centers. </p> <ul> <li>All points are deleted from the temporary chunk so that new points can be added.</li> </ul> </li> <li> <p>When a prediction request arrives, the centers of the algorithm will be exactly the same as the centers of the original <code>KMeans</code> at the time of retrieval.</p> </li> </ul>"},{"location":"api/cluster/STREAMKMeans/#parameters","title":"Parameters","text":"<ul> <li> <p>chunk_size</p> <p>Default \u2192 <code>10</code></p> <p>Maximum size allowed for the temporary data chunk.</p> </li> <li> <p>n_clusters</p> <p>Default \u2192 <code>2</code></p> <p>Number of clusters generated by the algorithm.</p> </li> <li> <p>kwargs</p> <p>Other parameters passed to the incremental kmeans at <code>cluster.KMeans</code>.</p> </li> </ul>"},{"location":"api/cluster/STREAMKMeans/#attributes","title":"Attributes","text":"<ul> <li> <p>centers</p> <p>Cluster centers generated from running the incremental <code>KMeans</code> algorithm through centers of each chunk.</p> </li> </ul>"},{"location":"api/cluster/STREAMKMeans/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 0.5], [1, 0.625], [1, 0.75], [1, 1.125], [1, 1.5], [1, 1.75],\n    [4, 1.5], [4, 2.25], [4, 2.5], [4, 3], [4, 3.25], [4, 3.5]\n]\n\nstreamkmeans = cluster.STREAMKMeans(chunk_size=3, n_clusters=2, halflife=0.5, sigma=1.5, seed=0)\n\nfor x, _ in stream.iter_array(X):\n    streamkmeans.learn_one(x)\n\nstreamkmeans.predict_one({0: 1, 1: 0})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>streamkmeans.predict_one({0: 5, 1: 2})\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/cluster/STREAMKMeans/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>O'Callaghan et al. (2002). Streaming-data algorithms for high-quality clustering.   In Proceedings 18th International Conference on Data Engineering, Feb 26 - March 1,   San Jose, CA, USA. DOI: 10.1109/ICDE.2002.994785.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/TextClust/","title":"TextClust","text":"<p>textClust, a clustering algorithm for text data.</p> <p>textClust <sup>1</sup><sup>2</sup> is a stream clustering algorithm for textual data that can identify and track topics over time in a stream of texts. The algorithm uses a widely popular two-phase clustering approach where the stream is first summarised in real-time. </p> <p>The result is many small preliminary clusters in the stream called <code>micro-clusters</code>. Micro-clusters maintain enough information to update and efficiently calculate the cosine similarity between them over time, based on the TF-IDF vector of their texts. Upon request, the miro-clusters can be reclustered to generate the final result using any distance-based clustering algorithm, such as hierarchical clustering. To keep the micro-clusters up-to-date, our algorithm applies a fading strategy where micro-clusters that are not updated regularly lose relevance and are eventually removed.</p>"},{"location":"api/cluster/TextClust/#parameters","title":"Parameters","text":"<ul> <li> <p>radius</p> <p>Default \u2192 <code>0.3</code></p> <p>Distance threshold to merge two micro-clusters. Must be within the range <code>(0, 1]</code></p> </li> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.0005</code></p> <p>Fading factor of micro-clusters</p> </li> <li> <p>tgap</p> <p>Default \u2192 <code>100</code></p> <p>Time between outlier removal</p> </li> <li> <p>term_fading</p> <p>Default \u2192 <code>True</code></p> <p>Determines whether individual terms should also be faded</p> </li> <li> <p>real_time_fading</p> <p>Default \u2192 <code>True</code></p> <p>Parameter that specifies whether natural time or the number of observations should be used for fading</p> </li> <li> <p>micro_distance</p> <p>Default \u2192 <code>tfidf_cosine_distance</code></p> <p>Distance metric used for clustering macro-clusters</p> </li> <li> <p>macro_distance</p> <p>Default \u2192 <code>tfidf_cosine_distance</code></p> <p>Distance metric used for clustering macro-clusters</p> </li> <li> <p>num_macro</p> <p>Default \u2192 <code>3</code></p> <p>Number of macro clusters that should be identified during the reclustering phase</p> </li> <li> <p>min_weight</p> <p>Default \u2192 <code>0</code></p> <p>Minimum weight of micro clusters to be used for reclustering</p> </li> <li> <p>auto_r</p> <p>Default \u2192 <code>False</code></p> <p>Parameter that specifies if  <code>radius</code> should be automatically updated</p> </li> <li> <p>auto_merge</p> <p>Default \u2192 <code>True</code></p> <p>Determines, if close observations shall be merged together</p> </li> <li> <p>sigma</p> <p>Default \u2192 <code>1</code></p> <p>Parameter that influences the automated trheshold adaption technique</p> </li> </ul>"},{"location":"api/cluster/TextClust/#attributes","title":"Attributes","text":"<ul> <li> <p>micro_clusters</p> <p>Micro-clusters generated by the algorithm. Micro-clusters are of type <code>textclust.microcluster</code></p> </li> </ul>"},{"location":"api/cluster/TextClust/#examples","title":"Examples","text":"<p><pre><code>from river import compose\nfrom river import feature_extraction\nfrom river import metrics\nfrom river import cluster\n\ncorpus = [\n   {\"text\":'This is the first document.',\"idd\":1, \"cluster\": 1, \"cluster\":1},\n   {\"text\":'This document is the second document.',\"idd\":2,\"cluster\": 1},\n   {\"text\":'And this is super unrelated.',\"idd\":3,\"cluster\": 2},\n   {\"text\":'Is this the first document?',\"idd\":4,\"cluster\": 1},\n   {\"text\":'This is super unrelated as well',\"idd\":5,\"cluster\": 2},\n   {\"text\":'Test text',\"idd\":6,\"cluster\": 5}\n]\n\nstopwords = [ 'stop', 'the', 'to', 'and', 'a', 'in', 'it', 'is', 'I']\n\nmetric = metrics.AdjustedRand()\n\nmodel = compose.Pipeline(\n    feature_extraction.BagOfWords(lowercase=True, ngram_range=(1, 2), stop_words=stopwords),\n    cluster.TextClust(real_time_fading=False, fading_factor=0.001, tgap=100, auto_r=True,\n    radius=0.9)\n)\n\nfor x in corpus:\n    y_pred = model.predict_one(x[\"text\"])\n    y = x[\"cluster\"]\n    metric.update(y,y_pred)\n    model.learn_one(x[\"text\"])\n\nprint(metric)\n</code></pre> <pre><code>AdjustedRand: -0.17647058823529413\n</code></pre></p>"},{"location":"api/cluster/TextClust/#methods","title":"Methods","text":"distances get_assignment get_macroclusters learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>t     \u2014 defaults to <code>None</code> </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> microcluster predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> <li>type     \u2014 defaults to <code>micro</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> showclusters tfcontainer updateMacroClusters <ol> <li> <p>Assenmacher, D. und Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In: Asian Conference on Intelligent Information and Database Systems (Accepted)\u00a0\u21a9</p> </li> <li> <p>Carnein, M., Assenmacher, D., Trautmann, H. (2017). Stream Clustering of Chat Messages with Applications to Twitch Streams. In: Advances in Conceptual Modeling. ER 2017.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/compat/River2SKLClassifier/","title":"River2SKLClassifier","text":"<p>Compatibility layer from River to scikit-learn for classification.</p>"},{"location":"api/compat/River2SKLClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Classifier</p> </li> </ul>"},{"location":"api/compat/River2SKLClassifier/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> <li>classes     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> predict <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Predicted target values for each row of <code>X</code>.</p> <p></p> predict_proba <p>Predicts the target probability of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Predicted target values for each row of <code>X</code>.</p> <p></p> score <p>Return the mean accuracy on the given test data and labels.</p> <p>In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> <li>sample_weight     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>float</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> set_partial_fit_request <p>Request metadata passed to the <code>partial_fit</code> method.</p> <p>Note that this method is only relevant if <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>). Please see :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.  The options for each parameter are:  - <code>True</code>: metadata is requested, and passed to <code>partial_fit</code> if provided. The request is ignored if metadata is not provided.  - <code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>partial_fit</code>.  - <code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.  - <code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.  The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the existing request. This allows you to change the request for some parameters and not others.  .. versionadded:: 1.3  .. note::     This method is only relevant if this estimator is used as a     sub-estimator of a meta-estimator, e.g. used inside a     :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p> <p>Parameters</p> <ul> <li>classes     \u2014 Union[bool, NoneType, str]     \u2014 defaults to <code>$UNCHANGED$</code> </li> </ul> <p>Returns</p> <p>River2SKLClassifier:     object</p> <p></p> set_score_request <p>Request metadata passed to the <code>score</code> method.</p> <p>Note that this method is only relevant if <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>). Please see :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.  The options for each parameter are:  - <code>True</code>: metadata is requested, and passed to <code>score</code> if provided. The request is ignored if metadata is not provided.  - <code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>score</code>.  - <code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.  - <code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.  The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the existing request. This allows you to change the request for some parameters and not others.  .. versionadded:: 1.3  .. note::     This method is only relevant if this estimator is used as a     sub-estimator of a meta-estimator, e.g. used inside a     :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p> <p>Parameters</p> <ul> <li>sample_weight     \u2014 Union[bool, NoneType, str]     \u2014 defaults to <code>$UNCHANGED$</code> </li> </ul> <p>Returns</p> <p>River2SKLClassifier:     object</p> <p></p>"},{"location":"api/compat/River2SKLClusterer/","title":"River2SKLClusterer","text":"<p>Compatibility layer from River to scikit-learn for clustering.</p>"},{"location":"api/compat/River2SKLClusterer/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Clusterer</p> </li> </ul>"},{"location":"api/compat/River2SKLClusterer/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> fit_predict <p>Perform clustering on <code>X</code> and returns cluster labels.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>ndarray of shape (n_samples,), dtype=np.int64</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> predict <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Transformed output.</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p>"},{"location":"api/compat/River2SKLRegressor/","title":"River2SKLRegressor","text":"<p>Compatibility layer from River to scikit-learn for regression.</p>"},{"location":"api/compat/River2SKLRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Regressor</p> </li> </ul>"},{"location":"api/compat/River2SKLRegressor/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> predict <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>np.ndarray:     Predicted target values for each row of <code>X</code>.</p> <p></p> score <p>Return the coefficient of determination of the prediction.</p> <p>The coefficient of determination :math:<code>R^2</code> is defined as :math:<code>(1 - \\frac{u}{v})</code>, where :math:<code>u</code> is the residual sum of squares <code>((y_true - y_pred)** 2).sum()</code> and :math:<code>v</code> is the total sum of squares <code>((y_true - y_true.mean()) ** 2).sum()</code>. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of <code>y</code>, disregarding the input features, would get a :math:<code>R^2</code> score of 0.0.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> <li>sample_weight     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>float</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> set_score_request <p>Request metadata passed to the <code>score</code> method.</p> <p>Note that this method is only relevant if <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>). Please see :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.  The options for each parameter are:  - <code>True</code>: metadata is requested, and passed to <code>score</code> if provided. The request is ignored if metadata is not provided.  - <code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>score</code>.  - <code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.  - <code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.  The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the existing request. This allows you to change the request for some parameters and not others.  .. versionadded:: 1.3  .. note::     This method is only relevant if this estimator is used as a     sub-estimator of a meta-estimator, e.g. used inside a     :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p> <p>Parameters</p> <ul> <li>sample_weight     \u2014 Union[bool, NoneType, str]     \u2014 defaults to <code>$UNCHANGED$</code> </li> </ul> <p>Returns</p> <p>River2SKLRegressor:     object</p> <p></p>"},{"location":"api/compat/River2SKLTransformer/","title":"River2SKLTransformer","text":"<p>Compatibility layer from River to scikit-learn for transformation.</p>"},{"location":"api/compat/River2SKLTransformer/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Transformer</p> </li> </ul>"},{"location":"api/compat/River2SKLTransformer/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> fit_transform <p>Fit to data, then transform it.</p> <p>Fits transformer to <code>X</code> and <code>y</code> with optional parameters <code>fit_params</code> and returns a transformed version of <code>X</code>.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> <li>fit_params </li> </ul> <p>Returns</p> <p>ndarray array of shape (n_samples, n_features_new)</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> set_output <p>Set output container.</p> <p>See :ref:<code>sphx_glr_auto_examples_miscellaneous_plot_set_output.py</code> for an example on how to use the API.</p> <p>Parameters</p> <ul> <li>transform     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> transform <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Transformed output.</p> <p></p>"},{"location":"api/compat/SKL2RiverClassifier/","title":"SKL2RiverClassifier","text":"<p>Compatibility layer from scikit-learn to River for classification.</p>"},{"location":"api/compat/SKL2RiverClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 sklearn_base.ClassifierMixin</p> <p>A scikit-learn regressor which has a <code>partial_fit</code> method.</p> </li> <li> <p>classes</p> <p>Type \u2192 list</p> </li> </ul>"},{"location":"api/compat/SKL2RiverClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import compat\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stream\nfrom sklearn import linear_model\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.load_breast_cancer(),\n    shuffle=True,\n    seed=42\n)\n\nmodel = preprocessing.StandardScaler()\nmodel |= compat.convert_sklearn_to_river(\n    estimator=linear_model.SGDClassifier(\n        loss='log_loss',\n        eta0=0.01,\n        learning_rate='constant'\n    ),\n    classes=[False, True]\n)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.198029\n</code></pre></p>"},{"location":"api/compat/SKL2RiverClassifier/#methods","title":"Methods","text":"learn_many learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_many predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_many predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/compat/SKL2RiverRegressor/","title":"SKL2RiverRegressor","text":"<p>Compatibility layer from scikit-learn to River for regression.</p>"},{"location":"api/compat/SKL2RiverRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 sklearn_base.BaseEstimator</p> <p>A scikit-learn transformer which has a <code>partial_fit</code> method.</p> </li> </ul>"},{"location":"api/compat/SKL2RiverRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import compat\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stream\nfrom sklearn import linear_model\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.load_diabetes(),\n    shuffle=True,\n    seed=42\n)\n\nscaler = preprocessing.StandardScaler()\nsgd_reg = compat.convert_sklearn_to_river(linear_model.SGDRegressor())\nmodel = scaler | sgd_reg\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 84.501421\n</code></pre></p>"},{"location":"api/compat/SKL2RiverRegressor/#methods","title":"Methods","text":"learn_many learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_many predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/compat/convert-river-to-sklearn/","title":"convert_river_to_sklearn","text":"<p>Wraps a river estimator to make it compatible with scikit-learn.</p>"},{"location":"api/compat/convert-river-to-sklearn/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 base.Estimator</p> </li> </ul>"},{"location":"api/compat/convert-sklearn-to-river/","title":"convert_sklearn_to_river","text":"<p>Wraps a scikit-learn estimator to make it compatible with river.</p>"},{"location":"api/compat/convert-sklearn-to-river/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 sklearn_base.BaseEstimator</p> </li> <li> <p>classes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>Class names necessary for classifiers.</p> </li> </ul>"},{"location":"api/compose/Discard/","title":"Discard","text":"<p>Removes features.</p> <p>This can be used in a pipeline when you want to remove certain features. The <code>transform_one</code> method is pure, and therefore returns a fresh new dictionary instead of removing the specified keys from the input.</p>"},{"location":"api/compose/Discard/#parameters","title":"Parameters","text":"<ul> <li> <p>keys</p> <p>Type \u2192 tuple[base.typing.FeatureName]</p> <p>Key(s) to discard.</p> </li> </ul>"},{"location":"api/compose/Discard/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12, 'c': 13}\ncompose.Discard('a', 'b').transform_one(x)\n</code></pre> <pre><code>{'c': 13}\n</code></pre></p> <p>You can chain a discarder with any estimator in order to apply said estimator to the desired features.</p> <p><pre><code>from river import feature_extraction as fx\n\nx = {'sales': 10, 'shop': 'Ikea', 'country': 'Sweden'}\n\npipeline = (\n    compose.Discard('shop', 'country') |\n    fx.PolynomialExtender()\n)\npipeline.transform_one(x)\n</code></pre> <pre><code>{'sales': 10, 'sales*sales': 100}\n</code></pre></p>"},{"location":"api/compose/Discard/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/FuncTransformer/","title":"FuncTransformer","text":"<p>Wraps a function to make it usable in a pipeline.</p> <p>There is often a need to apply an arbitrary transformation to a set of features. For instance, this could involve parsing a date and then extracting the hour from said date. If you're processing a stream of data, then you can do this yourself by calling the necessary code at your leisure. On the other hand, if you want to do this as part of a pipeline, then you need to follow a simple convention. </p> <p>To use a function as part of a pipeline, take as input a <code>dict</code> of features and output a <code>dict</code>. Once you have initialized this class with your function, then you can use it like you would use any other (unsupervised) transformer. </p> <p>It is up to you if you want your function to be pure or not. By pure we refer to a function that doesn't modify its input. However, we recommend writing pure functions because this reduces the chances of inserting bugs into your pipeline.</p>"},{"location":"api/compose/FuncTransformer/#parameters","title":"Parameters","text":"<ul> <li> <p>func</p> <p>Type \u2192 typing.Callable[[dict], dict]</p> <p>A function that takes as input a <code>dict</code> and outputs a <code>dict</code>.</p> </li> </ul>"},{"location":"api/compose/FuncTransformer/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nimport datetime as dt\nfrom river import compose\n\nx = {'date': '2019-02-14'}\n\ndef parse_date(x):\n    date = dt.datetime.strptime(x['date'], '%Y-%m-%d')\n    x['is_weekend'] = date.day in (5, 6)\n    x['hour'] = date.hour\n    return x\n\nt = compose.FuncTransformer(parse_date)\npprint(t.transform_one(x))\n</code></pre> <pre><code>{'date': '2019-02-14', 'hour': 0, 'is_weekend': False}\n</code></pre></p> <p>The above example is not pure because it modifies the input. The following example is pure and produces the same output:</p> <p><pre><code>def parse_date(x):\n    date = dt.datetime.strptime(x['date'], '%Y-%m-%d')\n    return {'is_weekend': date.day in (5, 6), 'hour': date.hour}\n\nt = compose.FuncTransformer(parse_date)\npprint(t.transform_one(x))\n</code></pre> <pre><code>{'hour': 0, 'is_weekend': False}\n</code></pre></p> <p>The previous example doesn't include the <code>date</code> feature because it returns a new <code>dict</code>. However, a common usecase is to add a feature to an existing set of features. You can do this in a pure way by unpacking the input <code>dict</code> into the output <code>dict</code>:</p> <p><pre><code>def parse_date(x):\n    date = dt.datetime.strptime(x['date'], '%Y-%m-%d')\n    return {'is_weekend': date.day in (5, 6), 'hour': date.hour, **x}\n\nt = compose.FuncTransformer(parse_date)\npprint(t.transform_one(x))\n</code></pre> <pre><code>{'date': '2019-02-14', 'hour': 0, 'is_weekend': False}\n</code></pre></p> <p>You can add <code>FuncTransformer</code> to a pipeline just like you would with any other transformer.</p> <p><pre><code>from river import naive_bayes\n\npipeline = compose.FuncTransformer(parse_date) | naive_bayes.MultinomialNB()\npipeline\n</code></pre> <pre><code>Pipeline (\n  FuncTransformer (\n    func=\"parse_date\"\n  ),\n  MultinomialNB (\n    alpha=1.\n  )\n)\n</code></pre></p> <p>If you provide a function without wrapping it, then the pipeline will do it for you:</p> <pre><code>pipeline = parse_date | naive_bayes.MultinomialNB()\n</code></pre>"},{"location":"api/compose/FuncTransformer/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Grouper/","title":"Grouper","text":"<p>Applies a transformer within different groups.</p> <p>This transformer allows you to split your data into groups and apply a transformer within each group. This happens in a streaming manner, which means that the groups are discovered online. A separate copy of the provided transformer is made whenever a new group appears. The groups are defined according to one or more keys.</p>"},{"location":"api/compose/Grouper/#parameters","title":"Parameters","text":"<ul> <li> <p>transformer</p> <p>Type \u2192 base.Transformer</p> </li> <li> <p>by</p> <p>Type \u2192 base.typing.FeatureName | list[base.typing.FeatureName]</p> <p>The field on which to group the data. This can either by a single value, or a list of values.</p> </li> </ul>"},{"location":"api/compose/Grouper/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Pipeline/","title":"Pipeline","text":"<p>A pipeline of estimators.</p> <p>Pipelines allow you to chain different steps into a sequence. Typically, when doing supervised learning, a pipeline contains one or more transformation steps, whilst it's a regressor or a classifier. It is highly recommended to use pipelines with River. Indeed, in an online learning setting, it is very practical to have a model defined as a single object. Take a look at the user guide for further information and practical examples. </p> <p>One special thing to take notice to is the way transformers are handled. It is usual to predict something for a sample and wait for the ground truth to arrive. In such a scenario, the features are seen before the ground truth arrives. Therefore, the unsupervised parts of the pipeline are updated when <code>predict_one</code> and <code>predict_proba_one</code> are called. Usually the unsupervised parts of the pipeline are all the steps that precede the final step, which is a supervised model. However, some transformers are supervised and are therefore also updated during calls to <code>learn_one</code>.</p>"},{"location":"api/compose/Pipeline/#parameters","title":"Parameters","text":"<ul> <li> <p>steps</p> <p>Ideally, a list of (name, estimator) tuples. A name is automatically inferred if none is provided.</p> </li> </ul>"},{"location":"api/compose/Pipeline/#examples","title":"Examples","text":"<p>The recommended way to declare a pipeline is to use the <code>|</code> operator. The latter allows you to chain estimators in a very terse manner:</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nscaler = preprocessing.StandardScaler()\nlog_reg = linear_model.LinearRegression()\nmodel = scaler | log_reg\n</code></pre> <p>This results in a pipeline that stores each step inside a dictionary.</p> <p><pre><code>model\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre></p> <p>You can access parts of a pipeline in the same manner as a dictionary:</p> <p><pre><code>model['LinearRegression']\n</code></pre> <pre><code>LinearRegression (\n  optimizer=SGD (\n    lr=Constant (\n      learning_rate=0.01\n    )\n  )\n  loss=Squared ()\n  l2=0.\n  l1=0.\n  intercept_init=0.\n  intercept_lr=Constant (\n    learning_rate=0.01\n  )\n  clip_gradient=1e+12\n  initializer=Zeros ()\n)\n</code></pre></p> <p>Note that you can also declare a pipeline by using the <code>compose.Pipeline</code> constructor method, which is slightly more verbose:</p> <pre><code>from river import compose\n\nmodel = compose.Pipeline(scaler, log_reg)\n</code></pre> <p>By using a <code>compose.TransformerUnion</code>, you can define complex pipelines that apply different steps to different parts of the data. For instance, we can extract word counts from text data, and extract polynomial features from numeric data.</p> <pre><code>from river import feature_extraction as fx\n\ntfidf = fx.TFIDF('text')\ncounts = fx.BagOfWords('text')\ntext_part = compose.Select('text') | (tfidf + counts)\n\nnum_part = compose.Select('a', 'b') | fx.PolynomialExtender()\n\nmodel = text_part + num_part\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n</code></pre> <p>The following shows an example of using <code>debug_one</code> to visualize how the information flows and changes throughout the pipeline.</p> <p><pre><code>from river import compose\nfrom river import naive_bayes\n\ndataset = [\n    ('A positive comment', True),\n    ('A negative comment', False),\n    ('A happy comment', True),\n    ('A lovely comment', True),\n    ('A harsh comment', False)\n]\n\ntfidf = fx.TFIDF() | compose.Prefixer('tfidf_')\ncounts = fx.BagOfWords() | compose.Prefixer('count_')\nmnb = naive_bayes.MultinomialNB()\nmodel = (tfidf + counts) | mnb\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nx = dataset[0][0]\nreport = model.debug_one(dataset[0][0])\nprint(report)\n</code></pre> <pre><code>0. Input\n--------\nA positive comment\n1. Transformer union\n--------------------\n    1.0 TFIDF | Prefixer\n    --------------------\n    tfidf_comment: 0.43017 (float)\n    tfidf_positive: 0.90275 (float)\n    1.1 BagOfWords | Prefixer\n    -------------------------\n    count_comment: 1 (int)\n    count_positive: 1 (int)\ncount_comment: 1 (int)\ncount_positive: 1 (int)\ntfidf_comment: 0.43017 (float)\ntfidf_positive: 0.90275 (float)\n2. MultinomialNB\n----------------\nFalse: 0.19221\nTrue: 0.80779\n</code></pre></p>"},{"location":"api/compose/Pipeline/#methods","title":"Methods","text":"debug_one <p>Displays the state of a set of features as it goes through the pipeline.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>show_types     \u2014 defaults to <code>True</code> </li> <li>n_decimals     \u2014 defaults to <code>5</code> </li> </ul> <p></p> forecast <p>Return a forecast.</p> <p>Only works if each estimator has a <code>transform_one</code> method and the final estimator has a <code>forecast</code> method. This is the case of time series models from the <code>time_series</code> module.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_many <p>Fit to a mini-batch.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series | None'     \u2014 defaults to <code>None</code> </li> <li>params </li> </ul> <p></p> learn_one <p>Fit to a single instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> <li>params </li> </ul> <p></p> predict_many <p>Call transform_many, and then predict_many on the final step.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_one <p>Call <code>transform_one</code> on the first steps and <code>predict_one</code> on the last step.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p> predict_proba_many <p>Call transform_many, and then predict_proba_many on the final step.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Call <code>transform_one</code> on the first steps and <code>predict_proba_one</code> on the last step.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p> score_one <p>Call <code>transform_one</code> on the first steps and <code>score_one</code> on the last step.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p> transform_many <p>Apply each transformer in the pipeline to some features.</p> <p>The final step in the pipeline will be applied if it is a transformer. If not, then it will be ignored and the output from the penultimate step will be returned. Note that the steps that precede the final step are assumed to all be transformers.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Apply each transformer in the pipeline to some features.</p> <p>The final step in the pipeline will be applied if it is a transformer. If not, then it will be ignored and the output from the penultimate step will be returned. Note that the steps that precede the final step are assumed to all be transformers.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p>"},{"location":"api/compose/Prefixer/","title":"Prefixer","text":"<p>Prepends a prefix on features names.</p>"},{"location":"api/compose/Prefixer/#parameters","title":"Parameters","text":"<ul> <li> <p>prefix</p> <p>Type \u2192 str</p> </li> </ul>"},{"location":"api/compose/Prefixer/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12}\ncompose.Prefixer('prefix_').transform_one(x)\n</code></pre> <pre><code>{'prefix_a': 42, 'prefix_b': 12}\n</code></pre></p>"},{"location":"api/compose/Prefixer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Renamer/","title":"Renamer","text":"<p>Renames features following substitution rules.</p>"},{"location":"api/compose/Renamer/#parameters","title":"Parameters","text":"<ul> <li> <p>mapping</p> <p>Type \u2192 dict[str, str]</p> <p>Dictionnary describing substitution rules. Keys in <code>mapping</code> that are not a feature's name are silently ignored.</p> </li> </ul>"},{"location":"api/compose/Renamer/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nmapping = {'a': 'v', 'c': 'o'}\nx = {'a': 42, 'b': 12}\ncompose.Renamer(mapping).transform_one(x)\n</code></pre> <pre><code>{'b': 12, 'v': 42}\n</code></pre></p>"},{"location":"api/compose/Renamer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Select/","title":"Select","text":"<p>Selects features.</p> <p>This can be used in a pipeline when you want to select certain features. The <code>transform_one</code> method is pure, and therefore returns a fresh new dictionary instead of filtering the specified keys from the input.</p>"},{"location":"api/compose/Select/#parameters","title":"Parameters","text":"<ul> <li> <p>keys</p> <p>Type \u2192 tuple[base.typing.FeatureName]</p> <p>Key(s) to keep.</p> </li> </ul>"},{"location":"api/compose/Select/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12, 'c': 13}\ncompose.Select('c').transform_one(x)\n</code></pre> <pre><code>{'c': 13}\n</code></pre></p> <p>You can chain a selector with any estimator in order to apply said estimator to the desired features.</p> <p><pre><code>from river import feature_extraction as fx\n\nx = {'sales': 10, 'shop': 'Ikea', 'country': 'Sweden'}\n\npipeline = (\n    compose.Select('sales') |\n    fx.PolynomialExtender()\n)\npipeline.transform_one(x)\n</code></pre> <pre><code>{'sales': 10, 'sales*sales': 100}\n</code></pre></p> <p>This transformer also supports mini-batch processing:</p> <p><pre><code>import random\nfrom river import compose\n\nrandom.seed(42)\nX = [{\"x_1\": random.uniform(8, 12), \"x_2\": random.uniform(8, 12)} for _ in range(6)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x_1': 10.557707193831535, 'x_2': 8.100043020890668}\n{'x_1': 9.100117273476478, 'x_2': 8.892842952595291}\n{'x_1': 10.94588485665605, 'x_2': 10.706797949691644}\n{'x_1': 11.568718270819382, 'x_2': 8.347755330517664}\n{'x_1': 9.687687278741082, 'x_2': 8.119188877752281}\n{'x_1': 8.874551899214413, 'x_2': 10.021421152413449}\n</code></pre></p> <pre><code>import pandas as pd\nX = pd.DataFrame.from_dict(X)\n</code></pre> <p>You can then call <code>transform_many</code> to transform a mini-batch of features:</p> <p><pre><code>compose.Select('x_2').transform_many(X)\n</code></pre> <pre><code>    x_2\n0   8.100043\n1   8.892843\n2  10.706798\n3   8.347755\n4   8.119189\n5  10.021421\n</code></pre></p>"},{"location":"api/compose/Select/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/SelectType/","title":"SelectType","text":"<p>Selects features based on their type.</p> <p>This is practical when you want to apply different preprocessing steps to different kinds of features. For instance, a common usecase is to apply a <code>preprocessing.StandardScaler</code> to numeric features and a <code>preprocessing.OneHotEncoder</code> to categorical features.</p>"},{"location":"api/compose/SelectType/#parameters","title":"Parameters","text":"<ul> <li> <p>types</p> <p>Type \u2192 tuple[type]</p> <p>Python types which you want to select. Under the hood, the <code>isinstance</code> method will be used to check if a value is of a given type.</p> </li> </ul>"},{"location":"api/compose/SelectType/#examples","title":"Examples","text":"<pre><code>import numbers\nfrom river import compose\nfrom river import linear_model\nfrom river import preprocessing\n\nnum = compose.SelectType(numbers.Number) | preprocessing.StandardScaler()\ncat = compose.SelectType(str) | preprocessing.OneHotEncoder()\nmodel = (num + cat) | linear_model.LogisticRegression()\n</code></pre>"},{"location":"api/compose/SelectType/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Suffixer/","title":"Suffixer","text":"<p>Appends a suffix on features names.</p>"},{"location":"api/compose/Suffixer/#parameters","title":"Parameters","text":"<ul> <li> <p>suffix</p> <p>Type \u2192 str</p> </li> </ul>"},{"location":"api/compose/Suffixer/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12}\ncompose.Suffixer('_suffix').transform_one(x)\n</code></pre> <pre><code>{'a_suffix': 42, 'b_suffix': 12}\n</code></pre></p>"},{"location":"api/compose/Suffixer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/TargetTransformRegressor/","title":"TargetTransformRegressor","text":"<p>Modifies the target before training.</p> <p>The user is expected to check that <code>func</code> and <code>inverse_func</code> are coherent with each other.</p>"},{"location":"api/compose/TargetTransformRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regression model to wrap.</p> </li> <li> <p>func</p> <p>Type \u2192 typing.Callable</p> <p>A function modifying the target before training.</p> </li> <li> <p>inverse_func</p> <p>Type \u2192 typing.Callable</p> <p>A function to return to the target's original space.</p> </li> </ul>"},{"location":"api/compose/TargetTransformRegressor/#examples","title":"Examples","text":"<p><pre><code>import math\nfrom river import compose\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    compose.TargetTransformRegressor(\n        regressor=linear_model.LinearRegression(intercept_lr=0.15),\n        func=math.log,\n        inverse_func=math.exp\n    )\n)\nmetric = metrics.MSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MSE: 10.999752\n</code></pre></p>"},{"location":"api/compose/TargetTransformRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/compose/TransformerProduct/","title":"TransformerProduct","text":"<p>Computes interactions between the outputs of a set transformers.</p> <p>This is for when you want to add interaction terms between groups of features. It may also be used an alternative to <code>feature_extraction.PolynomialExtender</code> when the latter is overkill.</p>"},{"location":"api/compose/TransformerProduct/#parameters","title":"Parameters","text":"<ul> <li> <p>transformers</p> <p>Ideally, a list of (name, estimator) tuples. A name is automatically inferred if none is provided.</p> </li> </ul>"},{"location":"api/compose/TransformerProduct/#examples","title":"Examples","text":"<p>Let's say we have a certain set of features with two groups. In practice these may be different namespaces, such one for items and the other for users.</p> <pre><code>x = dict(\n    a=0, b=1,  # group 1\n    x=2, y=3   # group 2\n)\n</code></pre> <p>We might want to add interaction terms between groups <code>('a', 'b')</code> and <code>('x', 'y')</code>, as so:</p> <p><pre><code>from pprint import pprint\nfrom river.compose import Select, TransformerProduct\n\nproduct = TransformerProduct(\n    Select('a', 'b'),\n    Select('x', 'y')\n)\npprint(product.transform_one(x))\n</code></pre> <pre><code>{'a*x': 0, 'a*y': 0, 'b*x': 2, 'b*y': 3}\n</code></pre></p> <p>This can also be done with the following shorthand:</p> <p><pre><code>product = Select('a', 'b') * Select('x', 'y')\npprint(product.transform_one(x))\n</code></pre> <pre><code>{'a*x': 0, 'a*y': 0, 'b*x': 2, 'b*y': 3}\n</code></pre></p> <p>If you want to include the original terms, you can do something like this:</p> <p><pre><code>group_1 = Select('a', 'b')\ngroup_2 = Select('x', 'y')\nproduct = group_1 + group_2 + group_1 * group_2\npprint(product.transform_one(x))\n</code></pre> <pre><code>{'a': 0, 'a*x': 0, 'a*y': 0, 'b': 1, 'b*x': 2, 'b*y': 3, 'x': 2, 'y': 3}\n</code></pre></p>"},{"location":"api/compose/TransformerProduct/#methods","title":"Methods","text":"learn_many <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_many <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/compose/TransformerUnion/","title":"TransformerUnion","text":"<p>Packs multiple transformers into a single one.</p> <p>Pipelines allow you to apply steps sequentially. Therefore, the output of a step becomes the input of the next one. In many cases, you may want to pass the output of a step to multiple steps. This simple transformer allows you to do so. In other words, it enables you to apply particular steps to different parts of an input. A typical example is when you want to scale numeric features and one-hot encode categorical features. </p> <p>This transformer is essentially a list of transformers. Whenever it is updated, it loops through each transformer and updates them. Meanwhile, calling <code>transform_one</code> collects the output of each transformer and merges them into a single dictionary.</p>"},{"location":"api/compose/TransformerUnion/#parameters","title":"Parameters","text":"<ul> <li> <p>transformers</p> <p>Ideally, a list of (name, estimator) tuples. A name is automatically inferred if none is provided.</p> </li> </ul>"},{"location":"api/compose/TransformerUnion/#examples","title":"Examples","text":"<p>Take the following dataset:</p> <pre><code>X = [\n    {'place': 'Taco Bell', 'revenue': 42},\n    {'place': 'Burger King', 'revenue': 16},\n    {'place': 'Burger King', 'revenue': 24},\n    {'place': 'Taco Bell', 'revenue': 58},\n    {'place': 'Burger King', 'revenue': 20},\n    {'place': 'Taco Bell', 'revenue': 50}\n]\n</code></pre> <p>As an example, let's assume we want to compute two aggregates of a dataset. We therefore define two <code>feature_extraction.Agg</code>s and initialize a <code>TransformerUnion</code> with them:</p> <pre><code>from river import compose\nfrom river import feature_extraction\nfrom river import stats\n\nmean = feature_extraction.Agg(\n    on='revenue', by='place',\n    how=stats.Mean()\n)\ncount = feature_extraction.Agg(\n    on='revenue', by='place',\n    how=stats.Count()\n)\nagg = compose.TransformerUnion(mean, count)\n</code></pre> <p>We can now update each transformer and obtain their output with a single function call:</p> <p><pre><code>from pprint import pprint\nfor x in X:\n    agg.learn_one(x)\n    pprint(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_count_by_place': 1, 'revenue_mean_by_place': 42.0}\n{'revenue_count_by_place': 1, 'revenue_mean_by_place': 16.0}\n{'revenue_count_by_place': 2, 'revenue_mean_by_place': 20.0}\n{'revenue_count_by_place': 2, 'revenue_mean_by_place': 50.0}\n{'revenue_count_by_place': 3, 'revenue_mean_by_place': 20.0}\n{'revenue_count_by_place': 3, 'revenue_mean_by_place': 50.0}\n</code></pre></p> <p>Note that you can use the <code>+</code> operator as a shorthand notation:</p> <p>agg = mean + count</p> <p>This allows you to build complex pipelines in a very terse manner. For instance, we can create a pipeline that scales each feature and fits a logistic regression as so:</p> <pre><code>from river import linear_model as lm\nfrom river import preprocessing as pp\n\nmodel = (\n    (mean + count) |\n    pp.StandardScaler() |\n    lm.LogisticRegression()\n)\n</code></pre> <p>Whice is equivalent to the following code:</p> <pre><code>model = compose.Pipeline(\n    compose.TransformerUnion(mean, count),\n    pp.StandardScaler(),\n    lm.LogisticRegression()\n)\n</code></pre> <p>Note that you access any part of a <code>TransformerUnion</code> by name:</p> <p><pre><code>model['TransformerUnion']['Agg']\n</code></pre> <pre><code>Agg (\n    on=\"revenue\"\n    by=['place']\n    how=Mean ()\n)\n</code></pre></p> <p><pre><code>model['TransformerUnion']['Agg1']\n</code></pre> <pre><code>Agg (\n    on=\"revenue\"\n    by=['place']\n    how=Count ()\n)\n</code></pre></p> <p>You can also manually provide a name for each step:</p> <pre><code>agg = compose.TransformerUnion(\n    ('Mean revenue by place', mean),\n    ('# by place', count)\n)\n</code></pre> <p>Mini-batch example:</p> <pre><code>X = pd.DataFrame([\n    {\"place\": 2, \"revenue\": 42},\n    {\"place\": 3, \"revenue\": 16},\n    {\"place\": 3, \"revenue\": 24},\n    {\"place\": 2, \"revenue\": 58},\n    {\"place\": 3, \"revenue\": 20},\n    {\"place\": 2, \"revenue\": 50},\n])\n</code></pre> <p>Since we need a transformer with mini-batch support to demonstrate, we shall use a <code>StandardScaler</code>.</p> <p><pre><code>from river import compose\nfrom river import preprocessing\n\nagg = (\n    compose.Select(\"place\") +\n    (compose.Select(\"revenue\") | preprocessing.StandardScaler())\n)\n\nagg.learn_many(X)\nagg.transform_many(X)\n</code></pre> <pre><code>   place   revenue\n0      2  0.441250\n1      3 -1.197680\n2      3 -0.693394\n3      2  1.449823\n4      3 -0.945537\n5      2  0.945537\n</code></pre></p>"},{"location":"api/compose/TransformerUnion/#methods","title":"Methods","text":"learn_many <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_many <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/compose/learn-during-predict/","title":"learn_during_predict","text":"<p>A context manager for fitting unsupervised steps during prediction.</p> <p>Usually, unsupervised parts of a pipeline are updated during <code>learn_one</code>. However, in the case of online learning, it is possible to update them before, during the prediction step. This context manager allows you to do so. </p> <p>This usually brings a slight performance improvement. But it is not done by default because it is not intuitive and is more difficult to test. It also means that you have to call <code>predict_one</code> before <code>learn_one</code> in order for the whole pipeline to be updated.</p>"},{"location":"api/compose/learn-during-predict/#examples","title":"Examples","text":"<p>Let's first see what methods are called if we just call <code>predict_one</code>.</p> <p><pre><code>import io\nimport logging\nfrom river import compose\nfrom river import datasets\nfrom river import linear_model\nfrom river import preprocessing\nfrom river import utils\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    linear_model.LinearRegression()\n)\n\nclass_condition = lambda x: x.__class__.__name__ in ('StandardScaler', 'LinearRegression')\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\nlogs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition):\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_one(x)\n\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.transform_one\nLinearRegression.predict_one\n</code></pre></p> <p>Now let's use the context manager and see what methods get called.</p> <p><pre><code>logs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition), compose.learn_during_predict():\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_one(x)\n\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.learn_one\nStandardScaler.transform_one\nLinearRegression.predict_one\n</code></pre></p> <p>We can see that the scaler did not get updated before transforming the data.</p> <p>This also works when working with mini-batches.</p> <p><pre><code>logs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition):\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_many(pd.DataFrame([x]))\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.transform_many\nLinearRegression.predict_many\n</code></pre></p> <p><pre><code>logs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition), compose.learn_during_predict():\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_many(pd.DataFrame([x]))\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.learn_many\nStandardScaler.transform_many\nLinearRegression.predict_many\n</code></pre></p>"},{"location":"api/conf/Interval/","title":"Interval","text":"<p>An object to represent a (prediction) interval.</p> <p>Users are not expected to use this class as-is. Instead, they should use the <code>with_interval</code> parameter of the <code>predict_one</code> method of any regressor or classifier wrapped with a conformal prediction method.</p>"},{"location":"api/conf/Interval/#parameters","title":"Parameters","text":"<ul> <li> <p>lower</p> <p>Type \u2192 float</p> <p>The lower bound of the interval.</p> </li> <li> <p>upper</p> <p>Type \u2192 float</p> <p>The upper bound of the interval.</p> </li> </ul>"},{"location":"api/conf/Interval/#attributes","title":"Attributes","text":"<ul> <li> <p>center</p> <p>The center of the interval.</p> </li> <li> <p>width</p> <p>The width of the interval.</p> </li> </ul>"},{"location":"api/conf/RegressionJackknife/","title":"RegressionJackknife","text":"<p>Jackknife method for regression.</p> <p>This is a conformal prediction method for regression. It is based on the jackknife method. The idea is to compute the quantiles of the residuals of the regressor. The prediction interval is then computed as the prediction of the regressor plus the quantiles of the residuals. </p> <p>This works naturally online, as the quantiles of the residuals are updated at each iteration. Each residual is produced before the regressor is updated, which ensures the predicted intervals are not optimistic. </p> <p>Note that the produced intervals are marginal and not conditional. This means that the intervals are not adjusted for the features <code>x</code>. This is a limitation of the jackknife method. However, the jackknife method is very simple and efficient. It is also very robust to outliers.</p>"},{"location":"api/conf/RegressionJackknife/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>The regressor to be wrapped.</p> </li> <li> <p>confidence_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The confidence level of the prediction intervals.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The size of the window used to compute the quantiles of the residuals. If <code>None</code>, the quantiles are computed over the whole history. It is advised to set this if you expect the model's performance to change over time.</p> </li> </ul>"},{"location":"api/conf/RegressionJackknife/#examples","title":"Examples","text":"<pre><code>from river import conf\nfrom river import datasets\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stats\n\ndataset = datasets.TrumpApproval()\n\nmodel = conf.RegressionJackknife(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression(intercept_lr=.1)\n    ),\n    confidence_level=0.9\n)\n\nvalidity = stats.Mean()\nefficiency = stats.Mean()\n\nfor x, y in dataset:\n    interval = model.predict_one(x, with_interval=True)\n    validity.update(y in interval)\n    efficiency.update(interval.width)\n    model.learn_one(x, y)\n</code></pre> <p>The interval's validity is the proportion of times the true value is within the interval. We specified a confidence level of 90%, so we expect the validity to be around 90%.</p> <p><pre><code>validity\n</code></pre> <pre><code>Mean: 0.939061\n</code></pre></p> <p>The interval's efficiency is the average width of the intervals.</p> <p><pre><code>efficiency\n</code></pre> <pre><code>Mean: 4.078361\n</code></pre></p> <p>Lowering the confidence lowering will mechanically improve the efficiency.</p>"},{"location":"api/conf/RegressionJackknife/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>with_interval     \u2014 defaults to <code>False</code> </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Barber, Rina Foygel, Emmanuel J. Candes, Aaditya Ramdas, and Ryan J. Tibshirani. \"Predictive inference with the jackknife+.\" The Annals of Statistics 49, no. 1 (2021): 486-507. \u21a9</p> </li> </ol>"},{"location":"api/covariance/EmpiricalCovariance/","title":"EmpiricalCovariance","text":"<p>Empirical covariance matrix.</p>"},{"location":"api/covariance/EmpiricalCovariance/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom.</p> </li> </ul>"},{"location":"api/covariance/EmpiricalCovariance/#attributes","title":"Attributes","text":"<ul> <li>matrix</li> </ul>"},{"location":"api/covariance/EmpiricalCovariance/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nimport pandas as pd\nfrom river import covariance\n\nnp.random.seed(42)\nX = pd.DataFrame(np.random.random((8, 3)), columns=[\"red\", \"green\", \"blue\"])\nX\n</code></pre> <pre><code>        red     green      blue\n0  0.374540  0.950714  0.731994\n1  0.598658  0.156019  0.155995\n2  0.058084  0.866176  0.601115\n3  0.708073  0.020584  0.969910\n4  0.832443  0.212339  0.181825\n5  0.183405  0.304242  0.524756\n6  0.431945  0.291229  0.611853\n7  0.139494  0.292145  0.366362\n</code></pre></p> <p><pre><code>cov = covariance.EmpiricalCovariance()\nfor x in X.to_dict(orient=\"records\"):\n    cov.update(x)\ncov\n</code></pre> <pre><code>        blue     green    red\n blue    0.076    0.020   -0.010\ngreen    0.020    0.113   -0.053\n  red   -0.010   -0.053    0.079\n</code></pre></p> <p>There is also an <code>update_many</code> method to process mini-batches. The results are identical.</p> <p><pre><code>cov = covariance.EmpiricalCovariance()\ncov.update_many(X)\ncov\n</code></pre> <pre><code>        blue     green    red\n blue    0.076    0.020   -0.010\ngreen    0.020    0.113   -0.053\n  red   -0.010   -0.053    0.079\n</code></pre></p> <p>The covariances are stored in a dictionary, meaning any one of them can be accessed as such:</p> <p><pre><code>cov[\"blue\", \"green\"]\n</code></pre> <pre><code>Cov: 0.020292\n</code></pre></p> <p>Diagonal entries are variances:</p> <p><pre><code>cov[\"blue\", \"blue\"]\n</code></pre> <pre><code>Var: 0.076119\n</code></pre></p>"},{"location":"api/covariance/EmpiricalCovariance/#methods","title":"Methods","text":"revert <p>Downdate with a single sample.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> update <p>Update with a single sample.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> update_many <p>Update with a dataframe of samples.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p>"},{"location":"api/covariance/EmpiricalPrecision/","title":"EmpiricalPrecision","text":"<p>Empirical precision matrix.</p> <p>The precision matrix is the inverse of the covariance matrix. </p> <p>This implementation leverages the Sherman-Morrison formula. The resulting inverse covariance matrix is not guaranteed to be identical to a batch computation. However, the difference shrinks with the number of observations.</p>"},{"location":"api/covariance/EmpiricalPrecision/#attributes","title":"Attributes","text":"<ul> <li>matrix</li> </ul>"},{"location":"api/covariance/EmpiricalPrecision/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nimport pandas as pd\nfrom river import covariance\n\nnp.random.seed(42)\nX = pd.DataFrame(np.random.random((1000, 3)))\nX.head()\n</code></pre> <pre><code>          0         1         2\n0  0.374540  0.950714  0.731994\n1  0.598658  0.156019  0.155995\n2  0.058084  0.866176  0.601115\n3  0.708073  0.020584  0.969910\n4  0.832443  0.212339  0.181825\n</code></pre></p> <p><pre><code>prec = covariance.EmpiricalPrecision()\nfor x in X.to_dict(orient=\"records\"):\n    prec.update(x)\n\nprec\n</code></pre> <pre><code>    0        1        2\n0   12.026   -0.122   -0.214\n1   -0.122   11.276   -0.026\n2   -0.214   -0.026   11.632\n</code></pre></p> <p><pre><code>pd.DataFrame(np.linalg.inv(np.cov(X.T, ddof=1)))\n</code></pre> <pre><code>           0          1          2\n0  12.159791  -0.124966  -0.218671\n1  -0.124966  11.393394  -0.026662\n2  -0.218671  -0.026662  11.756907\n</code></pre></p>"},{"location":"api/covariance/EmpiricalPrecision/#methods","title":"Methods","text":"update <p>Update with a single sample.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> update_many <p>Update with a dataframe of samples.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> <ol> <li> <p>Online Estimation of the Inverse Covariance Matrix - Markus Thill \u21a9</p> </li> <li> <p>Fast rank-one updates to matrix inverse? - Tim Vieira \u21a9</p> </li> <li> <p>Woodbury matrix identity \u21a9</p> </li> </ol>"},{"location":"api/datasets/AirlinePassengers/","title":"AirlinePassengers","text":"<p>Monthly number of international airline passengers.</p> <p>The stream contains 144 items and only one single feature, which is the month. The goal is to predict the number of passengers each month by capturing the trend and the seasonality of the data.</p>"},{"location":"api/datasets/AirlinePassengers/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/AirlinePassengers/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>International airline passengers: monthly totals in thousands. Jan 49 \u2013 Dec 60 \u21a9</p> </li> </ol>"},{"location":"api/datasets/Bananas/","title":"Bananas","text":"<p>Bananas dataset.</p> <p>An artificial dataset where instances belongs to several clusters with a banana shape. There are two attributes that correspond to the x and y axis, respectively.</p>"},{"location":"api/datasets/Bananas/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Bananas/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>OpenML page \u21a9</p> </li> </ol>"},{"location":"api/datasets/Bikes/","title":"Bikes","text":"<p>Bike sharing station information from the city of Toulouse.</p> <p>The goal is to predict the number of bikes in 5 different bike stations from the city of Toulouse.</p>"},{"location":"api/datasets/Bikes/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Bikes/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>A short introduction and conclusion to the OpenBikes 2016 Challenge \u21a9</p> </li> </ol>"},{"location":"api/datasets/ChickWeights/","title":"ChickWeights","text":"<p>Chick weights along time.</p> <p>The stream contains 578 items and 3 features. The goal is to predict the weight of each chick along time, according to the diet the chick is on. The data is ordered by time and then by chick.</p>"},{"location":"api/datasets/ChickWeights/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/ChickWeights/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Chick weight dataset overview \u21a9</p> </li> </ol>"},{"location":"api/datasets/CreditCard/","title":"CreditCard","text":"<p>Credit card frauds.</p> <p>The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. </p> <p>It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.</p>"},{"location":"api/datasets/CreditCard/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/CreditCard/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\u00a0\u21a9</p> </li> <li> <p>Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\u00a0\u21a9</p> </li> <li> <p>Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\u00a0\u21a9</p> </li> <li> <p>Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\u00a0\u21a9</p> </li> <li> <p>Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Ael; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\u00a0\u21a9</p> </li> <li> <p>Carcillo, Fabrizio; Le Borgne, Yann-Ael; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing\u00a0\u21a9</p> </li> <li> <p>Bertrand Lebichot, Yann-Ael Le Borgne, Liyun He, Frederic Oble, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019\u00a0\u21a9</p> </li> <li> <p>Fabrizio Carcillo, Yann-Ael Le Borgne, Olivier Caelen, Frederic Oble, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/Elec2/","title":"Elec2","text":"<p>Electricity prices in New South Wales.</p> <p>This is a binary classification task, where the goal is to predict if the price of electricity will go up or down. </p> <p>This data was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.</p>"},{"location":"api/datasets/Elec2/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Elec2/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>SPLICE-2 Comparative Evaluation: Electricity Pricing \u21a9</p> </li> <li> <p>DataHub description \u21a9</p> </li> </ol>"},{"location":"api/datasets/HTTP/","title":"HTTP","text":"<p>HTTP dataset of the KDD 1999 cup.</p> <p>The goal is to predict whether or not an HTTP connection is anomalous or not. The dataset only contains 2,211 (0.4%) positive labels.</p>"},{"location":"api/datasets/HTTP/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/HTTP/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>HTTP (KDDCUP99) dataset \u21a9</p> </li> </ol>"},{"location":"api/datasets/Higgs/","title":"Higgs","text":"<p>Higgs dataset.</p> <p>The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes.</p>"},{"location":"api/datasets/Higgs/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Higgs/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/ImageSegments/","title":"ImageSegments","text":"<p>Image segments classification.</p> <p>This dataset contains features that describe image segments into 7 classes: brickface, sky, foliage, cement, window, path, and grass.</p>"},{"location":"api/datasets/ImageSegments/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/ImageSegments/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/Insects/","title":"Insects","text":"<p>Insects dataset.</p> <p>This dataset has different variants, which are: </p> <ul> <li> <p>abrupt_balanced</p> </li> <li> <p>abrupt_imbalanced</p> </li> <li> <p>gradual_balanced</p> </li> <li> <p>gradual_imbalanced</p> </li> <li> <p>incremental-abrupt_balanced</p> </li> <li> <p>incremental-abrupt_imbalanced</p> </li> <li> <p>incremental-reoccurring_balanced</p> </li> <li> <p>incremental-reoccurring_imbalanced</p> </li> <li> <p>incremental_balanced</p> </li> <li> <p>incremental_imbalanced</p> </li> <li> <p>out-of-control</p> </li> </ul> <p>The number of samples and the difficulty change from one variant to another. The number of classes is always the same (6), except for the last variant (24).</p>"},{"location":"api/datasets/Insects/#parameters","title":"Parameters","text":"<ul> <li> <p>variant</p> <p>Default \u2192 <code>abrupt_balanced</code></p> <p>Indicates which variant of the dataset to load.</p> </li> </ul>"},{"location":"api/datasets/Insects/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Insects/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>USP DS repository \u21a9</p> </li> <li> <p>Souza, V., Reis, D.M.D., Maletzke, A.G. and Batista, G.E., 2020. Challenges in Benchmarking Stream Learning Algorithms with Real-world Data. arXiv preprint arXiv:2005.00113. \u21a9</p> </li> </ol>"},{"location":"api/datasets/Keystroke/","title":"Keystroke","text":"<p>CMU keystroke dataset.</p> <p>Users are tasked to type in a password. The task is to determine which user is typing in the password. </p> <p>The only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes have been dropped.</p>"},{"location":"api/datasets/Keystroke/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Keystroke/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Keystroke Dynamics - Benchmark Data Set \u21a9</p> </li> </ol>"},{"location":"api/datasets/MaliciousURL/","title":"MaliciousURL","text":"<p>Malicious URLs dataset.</p> <p>This dataset contains features about URLs that are classified as malicious or not.</p>"},{"location":"api/datasets/MaliciousURL/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/MaliciousURL/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Detecting Malicious URLs \u21a9</p> </li> <li> <p>Identifying Suspicious URLs: An Application of Large-Scale Online Learning \u21a9</p> </li> </ol>"},{"location":"api/datasets/MovieLens100K/","title":"MovieLens100K","text":"<p>MovieLens 100K dataset.</p> <p>MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota. This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. User and movie information are provided. The data was collected through the MovieLens web site (movielens.umn.edu) during the seven-month period from September 19th, 1997 through April 22nd, 1998.</p>"},{"location":"api/datasets/MovieLens100K/#parameters","title":"Parameters","text":"<ul> <li> <p>unpack_user_and_item</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not the user and item should be extracted from the context and included as extra keyword arguments.</p> </li> </ul>"},{"location":"api/datasets/MovieLens100K/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/MovieLens100K/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>The MovieLens Datasets: History and Context \u21a9</p> </li> </ol>"},{"location":"api/datasets/Music/","title":"Music","text":"<p>Multi-label music mood prediction.</p> <p>The goal is to predict to which kinds of moods a song pertains to.</p>"},{"location":"api/datasets/Music/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Music/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Read, J., Reutemann, P., Pfahringer, B. and Holmes, G., 2016. MEKA: a multi-label/multi-target extension to WEKA. The Journal of Machine Learning Research, 17(1), pp.667-671. \u21a9</p> </li> </ol>"},{"location":"api/datasets/Phishing/","title":"Phishing","text":"<p>Phishing websites.</p> <p>This dataset contains features from web pages that are classified as phishing or not.</p>"},{"location":"api/datasets/Phishing/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Phishing/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/Restaurants/","title":"Restaurants","text":"<p>Data from the Kaggle Recruit Restaurants challenge.</p> <p>The goal is to predict the number of visitors in each of 829 Japanese restaurants over a priod of roughly 16 weeks. The data is ordered by date and then by restaurant ID.</p>"},{"location":"api/datasets/Restaurants/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Restaurants/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Recruit Restaurant Visitor Forecasting \u21a9</p> </li> </ol>"},{"location":"api/datasets/SMSSpam/","title":"SMSSpam","text":"<p>SMS Spam Collection dataset.</p> <p>The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent 13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.</p>"},{"location":"api/datasets/SMSSpam/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/SMSSpam/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Almeida, T.A., Hidalgo, J.M.G. and Yamakami, A., 2011, September. Contributions to the study of SMS spam filtering: new collection and results. In Proceedings of the 11th ACM symposium on Document engineering (pp. 259-262). \u21a9</p> </li> </ol>"},{"location":"api/datasets/SMTP/","title":"SMTP","text":"<p>SMTP dataset from the KDD 1999 cup.</p> <p>The goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only contains 2,211 (0.4%) positive labels.</p>"},{"location":"api/datasets/SMTP/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/SMTP/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>SMTP (KDDCUP99) dataset \u21a9</p> </li> </ol>"},{"location":"api/datasets/SolarFlare/","title":"SolarFlare","text":"<p>Solar flare multi-output regression.</p>"},{"location":"api/datasets/SolarFlare/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/SolarFlare/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/TREC07/","title":"TREC07","text":"<p>TREC's 2007 Spam Track dataset.</p> <p>The data contains 75,419 chronologically ordered items, i.e. 3 months of emails delivered to a particular server in 2007. Spam messages represent 66.6% of the dataset. The goal is to predict whether an email is a spam or not. </p> <p>The available raw features are: sender, recipients, date, subject, body.</p>"},{"location":"api/datasets/TREC07/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/TREC07/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>TREC 2007 Spam Track Overview \u21a9</p> </li> <li> <p>Code ran to parse the dataset \u21a9</p> </li> </ol>"},{"location":"api/datasets/Taxis/","title":"Taxis","text":"<p>Taxi ride durations in New York City.</p> <p>The goal is to predict the duration of taxi rides in New York City.</p>"},{"location":"api/datasets/Taxis/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Taxis/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>New York City Taxi Trip Duration competition on Kaggle \u21a9</p> </li> </ol>"},{"location":"api/datasets/TrumpApproval/","title":"TrumpApproval","text":"<p>Donald Trump approval ratings.</p> <p>This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald Trump's approval ratings. It contains 5 features, which are approval ratings collected by 5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of this task is to see if we can reproduce FiveThirtyEight's model.</p>"},{"location":"api/datasets/TrumpApproval/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/TrumpApproval/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Trump Approval Ratings \u21a9</p> </li> </ol>"},{"location":"api/datasets/WaterFlow/","title":"WaterFlow","text":"<p>Water flow through a pipeline branch.</p> <p>The series includes hourly values for about 2 months, March 2022 to May 2022. The values are expressed in liters per second. There are four anomalous segments in the series: </p> <ul> <li>3 \"low value moments\": this is due to water losses or human intervention for maintenance * A small peak in the water inflow after the first 2 segments: this is due to a pumping     operation into the main pipeline, when more water pressure is needed </li> </ul> <p>This dataset is well suited for time series forecasting models, as well as anomaly detection methods. Ideally, the goal is to build a time series forecasting model that is robust to the anomalous segments. </p> <p>This data has been kindly donated by the Tecnojest s.r.l. company (www.invidea.it) from Italy.</p>"},{"location":"api/datasets/WaterFlow/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/WaterFlow/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/WebTraffic/","title":"WebTraffic","text":"<p>Web sessions information from an events company based in South Africa.</p> <p>The goal is to predict the number of web sessions in 4 different regions in South Africa. </p> <p>The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and '2023-09-15 23:45:00' for each region. Two types of sessions are captured <code>sessionsA</code> and <code>sessionsB</code>. The <code>isMissing</code> flag is equal to 1 if any of the servers failed to capture sessions, otherwise if all servers functioned properly this flag is equal to 0. </p> <p>Things to consider: </p> <ul> <li>region <code>R5</code> captures sessions in backup mode. Strictly speaking, <code>R5</code> is not necessary to predict. * Can <code>sessionsA</code> and <code>sessionsB</code> events be predicted accurately for each region over the next day (next 96 intervals)? * What is the best way to deal with the missing values? * How can model selection be used (a multi-model approach)? * Can dependence (correlation) between regions be utilised for more accurate predictions? * Can both <code>sessionA</code> and <code>sessionB</code> be predicted simultaneously with one model? </li> </ul> <p>This dataset is well suited for time series forecasting models, as well as anomaly detection methods. Ideally, the goal is to build a time series forecasting model that is robust to the anomalous events and generalise well on normal operating conditions.</p>"},{"location":"api/datasets/WebTraffic/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/WebTraffic/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/Dataset/","title":"Dataset","text":"<p>Base class for all datasets.</p> <p>All datasets inherit from this class, be they stored in a file or generated on the fly.</p>"},{"location":"api/datasets/base/Dataset/#parameters","title":"Parameters","text":"<ul> <li> <p>task</p> <p>Type of task the dataset is meant for. Should be one of the following:      - \"Regression\"     - \"Binary classification\"     - \"Multi-class classification\"     - \"Multi-output binary classification\"     - \"Multi-output regression\"</p> </li> <li> <p>n_features</p> <p>Number of features in the dataset.</p> </li> <li> <p>n_samples</p> <p>Default \u2192 <code>None</code></p> <p>Number of samples in the dataset.</p> </li> <li> <p>n_classes</p> <p>Default \u2192 <code>None</code></p> <p>Number of classes in the dataset, only applies to classification datasets.</p> </li> <li> <p>n_outputs</p> <p>Default \u2192 <code>None</code></p> <p>Number of outputs the target is made of, only applies to multi-output datasets.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the dataset is sparse or not.</p> </li> </ul>"},{"location":"api/datasets/base/Dataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/base/Dataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/FileDataset/","title":"FileDataset","text":"<p>Base class for datasets that are stored in a local file.</p> <p>Small datasets that are part of the river package inherit from this class.</p>"},{"location":"api/datasets/base/FileDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>filename</p> <p>The file's name.</p> </li> <li> <p>directory</p> <p>Default \u2192 <code>None</code></p> <p>The directory where the file is contained. Defaults to the location of the <code>datasets</code> module.</p> </li> <li> <p>desc</p> <p>Extra dataset parameters to pass as keyword arguments.</p> </li> </ul>"},{"location":"api/datasets/base/FileDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/base/FileDataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/RemoteDataset/","title":"RemoteDataset","text":"<p>Base class for datasets that are stored in a remote file.</p> <p>Medium and large datasets that are not part of the river package inherit from this class. </p> <p>The filename doesn't have to be provided if unpack is False. Indeed in the latter case the filename will be inferred from the URL.</p>"},{"location":"api/datasets/base/RemoteDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>url</p> <p>The URL the dataset is located at.</p> </li> <li> <p>size</p> <p>The expected download size.</p> </li> <li> <p>unpack</p> <p>Default \u2192 <code>True</code></p> <p>Whether to unpack the download or not.</p> </li> <li> <p>filename</p> <p>Default \u2192 <code>None</code></p> <p>An optional name to given to the file if the file is unpacked.</p> </li> <li> <p>desc</p> <p>Extra dataset parameters to pass as keyword arguments.</p> </li> </ul>"},{"location":"api/datasets/base/RemoteDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/base/RemoteDataset/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/SyntheticDataset/","title":"SyntheticDataset","text":"<p>A synthetic dataset.</p>"},{"location":"api/datasets/base/SyntheticDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>task</p> <p>Type of task the dataset is meant for. Should be one of: - \"Regression\" - \"Binary classification\" - \"Multi-class classification\" - \"Multi-output binary classification\" - \"Multi-output regression\"</p> </li> <li> <p>n_features</p> <p>Number of features in the dataset.</p> </li> <li> <p>n_samples</p> <p>Default \u2192 <code>None</code></p> <p>Number of samples in the dataset.</p> </li> <li> <p>n_classes</p> <p>Default \u2192 <code>None</code></p> <p>Number of classes in the dataset, only applies to classification datasets.</p> </li> <li> <p>n_outputs</p> <p>Default \u2192 <code>None</code></p> <p>Number of outputs the target is made of, only applies to multi-output datasets.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the dataset is sparse or not.</p> </li> </ul>"},{"location":"api/datasets/base/SyntheticDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/base/SyntheticDataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Agrawal/","title":"Agrawal","text":"<p>Agrawal stream generator.</p> <p>The generator was introduced by Agrawal et al. <sup>1</sup>, and was a common source of data for early work on scaling up decision tree learners. The generator produces a stream containing nine features, six numeric and three categorical. There are 10 functions defined for generating binary class labels from the features. Presumably these determine whether the loan should be approved. Classification functions are listed in the original paper <sup>1</sup>. </p> <p>Feature | Description | Values </p> <ul> <li> <p><code>salary</code> | salary | uniformly distributed from 20k to 150k </p> </li> <li> <p><code>commission</code> | commission | 0 if <code>salary</code> &lt; 75k else uniformly distributed from 10k to 75k </p> </li> <li> <p><code>age</code> | age | uniformly distributed from 20 to 80 </p> </li> <li> <p><code>elevel</code> | education level | uniformly chosen from 0 to 4 </p> </li> <li> <p><code>car</code> | car maker | uniformly chosen from 1 to 20 </p> </li> <li> <p><code>zipcode</code> | zip code of the town | uniformly chosen from 0 to 8 </p> </li> <li> <p><code>hvalue</code> | house value | uniformly distributed from 50k x zipcode to 100k x zipcode </p> </li> <li> <p><code>hyears</code> | years house owned | uniformly distributed from 1 to 30 </p> </li> <li> <p><code>loan</code> | total loan amount | uniformly distributed from 0 to 500k</p> </li> </ul>"},{"location":"api/datasets/synth/Agrawal/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>The classification function to use for the generation. Valid values are from 0 to 9.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, the class distribution will converge to a uniform distribution.</p> </li> <li> <p>perturbation</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability that noise will happen in the generation. Each new sample will be perturbed by the magnitude of <code>perturbation</code>. Valid values are in the range [0.0 to 1.0].</p> </li> </ul>"},{"location":"api/datasets/synth/Agrawal/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Agrawal/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Agrawal(\n    classification_function=0,\n    seed=42\n)\n\ndataset\n</code></pre> <pre><code>Synthetic data generator\n&lt;BLANKLINE&gt;\n    Name  Agrawal\n    Task  Binary classification\n Samples  \u221e\nFeatures  9\n Outputs  1\n Classes  2\n  Sparse  False\n&lt;BLANKLINE&gt;\nConfiguration\n-------------\nclassification_function  0\n                   seed  42\n        balance_classes  False\n           perturbation  0.0\n</code></pre></p> <p><pre><code>for x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[103125.4837, 0, 21, 2, 8, 3, 319768.9642, 4, 338349.7437] 1\n[135983.3438, 0, 25, 4, 14, 0, 423837.7755, 7, 116330.4466] 1\n[98262.4347, 0, 55, 1, 18, 6, 144088.1244, 19, 139095.3541] 0\n[133009.0417, 0, 68, 1, 14, 5, 233361.4025, 7, 478606.5361] 1\n[63757.2908, 16955.9382, 26, 2, 12, 4, 522851.3093, 24, 229712.4398] 1\n</code></pre></p>"},{"location":"api/datasets/synth/Agrawal/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function randomly.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Agrawal/#notes","title":"Notes","text":"<p>The sample generation works as follows: The 9 features are generated with the random generator, initialized with the seed passed by the user. Then, the classification function decides, as a function of all the attributes, whether to classify the instance as class 0 or class 1. The next step is to verify if the classes should be balanced, and if so, balance the classes. Finally, add noise if <code>perturbation</code> &gt; 0.0.</p> <ol> <li> <p>Rakesh Agrawal, Tomasz Imielinksi, and Arun Swami. \"Database Mining:   A Performance Perspective\", IEEE Transactions on Knowledge and   Data Engineering, 5(6), December 1993.\u00a0\u21a9\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/AnomalySine/","title":"AnomalySine","text":"<p>Simulate a stream with anomalies in sine waves.</p> <p>The amount of data generated by this generator is finite. </p> <p>The data generated corresponds to sine and cosine functions. Anomalies are induced by replacing the cosine values with values from a different a sine function. The <code>contextual</code> flag can be used to introduce contextual anomalies which are values in the normal global range, but abnormal compared to the seasonal pattern. Contextual attributes are introduced by replacing cosine entries with sine values. </p> <p>The target indicates whether or not the instances are anomalous.</p>"},{"location":"api/datasets/synth/AnomalySine/#parameters","title":"Parameters","text":"<ul> <li> <p>n_samples</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10000</code></p> <p>The number of samples to generate. This generator creates a batch of data affected by contextual anomalies and noise.</p> </li> <li> <p>n_anomalies</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2500</code></p> <p>Number of anomalies. Can't be larger than <code>n_samples</code>.</p> </li> <li> <p>contextual</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, will add contextual anomalies.</p> </li> <li> <p>n_contextual</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2500</code></p> <p>Number of contextual anomalies. Can't be larger than <code>n_samples</code>.</p> </li> <li> <p>shift</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>4</code></p> <p>Shift in number of samples applied when retrieving contextual anomalies.</p> </li> <li> <p>noise</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>Amount of noise.</p> </li> <li> <p>replace</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, anomalies are randomly sampled with replacement.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/AnomalySine/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/AnomalySine/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.AnomalySine(\n    seed=12345,\n    n_samples=100,\n    n_anomalies=25,\n    contextual=True,\n    n_contextual=10\n)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'sine': -0.7119, 'cosine': 0.8777} False\n{'sine': 0.8792, 'cosine': -0.0290} False\n{'sine': 0.0440, 'cosine': 3.0852} True\n{'sine': 0.5520, 'cosine': 3.4515} True\n{'sine': 0.8037, 'cosine': 0.4027} False\n</code></pre></p>"},{"location":"api/datasets/synth/AnomalySine/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/ConceptDriftStream/","title":"ConceptDriftStream","text":"<p>Generates a stream with concept drift.</p> <p>A stream generator that adds concept drift or change by joining two streams. This is done by building a weighted combination of two pure distributions that characterizes the target concepts before and after the change. </p> <p>The sigmoid function is an elegant and practical solution to define the probability that each new instance of the stream belongs to the new concept after the drift. The sigmoid function introduces a gradual, smooth transition whose duration is controlled with two parameters: </p> <ul> <li> <p>\\(p\\), the position of the change.</p> </li> <li> <p>\\(w\\), the width of the transition.</p> </li> </ul> <p>The sigmoid function at sample \\(t\\) is </p> \\[f(t) = 1/(1+e^{-4(t-p)/w})\\]"},{"location":"api/datasets/synth/ConceptDriftStream/#parameters","title":"Parameters","text":"<ul> <li> <p>stream</p> <p>Type \u2192 datasets.base.SyntheticDataset | None</p> <p>Default \u2192 <code>None</code></p> <p>Original stream</p> </li> <li> <p>drift_stream</p> <p>Type \u2192 datasets.base.SyntheticDataset | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift stream</p> </li> <li> <p>position</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5000</code></p> <p>Central position of the concept drift change.</p> </li> <li> <p>width</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>Width of concept drift change.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>alpha</p> <p>Type \u2192 float | None</p> <p>Default \u2192 <code>None</code></p> <p>Angle of change used to estimate the width of concept drift change. If set, it will override the width parameter. Valid values are in the range (0.0, 90.0].</p> </li> </ul>"},{"location":"api/datasets/synth/ConceptDriftStream/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/ConceptDriftStream/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.ConceptDriftStream(\n    stream=synth.SEA(seed=42, variant=0),\n    drift_stream=synth.SEA(seed=42, variant=1),\n    seed=1, position=5, width=2\n)\n\nfor x, y in dataset.take(10):\n    print(x, y)\n</code></pre> <pre><code>{0: 6.3942, 1: 0.2501, 2: 2.7502} False\n{0: 2.2321, 1: 7.3647, 2: 6.7669} True\n{0: 8.9217, 1: 0.8693, 2: 4.2192} True\n{0: 0.2979, 1: 2.1863, 2: 5.0535} False\n{0: 6.3942, 1: 0.2501, 2: 2.7502} False\n{0: 2.2321, 1: 7.3647, 2: 6.7669} True\n{0: 8.9217, 1: 0.8693, 2: 4.2192} True\n{0: 0.2979, 1: 2.1863, 2: 5.0535} False\n{0: 0.2653, 1: 1.9883, 2: 6.4988} False\n{0: 5.4494, 1: 2.2044, 2: 5.8926} False\n</code></pre></p>"},{"location":"api/datasets/synth/ConceptDriftStream/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/ConceptDriftStream/#notes","title":"Notes","text":"<p>An optional way to estimate the width of the transition \\(w\\) is based on the angle \\(\\alpha\\), \\(w = 1/ tan(\\alpha)\\). Since width corresponds to the number of samples for the transition, the width is rounded to the nearest smaller integer. Notice that larger values of \\(\\alpha\\) result in smaller widths. For \\(\\alpha &gt; 45.0\\), the width is smaller than 1 so values are rounded to 1 to avoid division by zero errors.</p>"},{"location":"api/datasets/synth/Friedman/","title":"Friedman","text":"<p>Friedman synthetic dataset.</p> <p>Each observation is composed of 10 features. Each feature value is sampled uniformly in [0, 1]. The target is defined by the following function: </p> \\[y = 10 sin(\\pi x_0 x_1) + 20 (x_2 - 0.5)^2 + 10 x_3 + 5 x_4 + \\epsilon\\] <p>In the last expression, \\(\\epsilon \\sim \\mathcal{N}(0, 1)\\), is the noise. Therefore, only the first 5 features are relevant.</p>"},{"location":"api/datasets/synth/Friedman/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Friedman/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Friedman/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Friedman(seed=42)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 7.04\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 18.16\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] 8.90\n</code></pre></p>"},{"location":"api/datasets/synth/Friedman/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Friedman, J.H., 1991. Multivariate adaptive regression splines. The annals of statistics, pp.1-67. \u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/FriedmanDrift/","title":"FriedmanDrift","text":"<p>Friedman synthetic dataset with concept drifts.</p> <p>Each observation is composed of 10 features. Each feature value is sampled uniformly in [0, 1]. Only the first 5 features are relevant. The target is defined by different functions depending on the type of the drift. </p> <p>The three available modes of operation of the data generator are described in <sup>1</sup>.</p>"},{"location":"api/datasets/synth/FriedmanDrift/#parameters","title":"Parameters","text":"<ul> <li> <p>drift_type</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>lea</code></p> <p>The variant of concept drift. - <code>'lea'</code>: Local Expanding Abrupt drift. The concept drift appears in two distinct regions of the instance space, while the remaining regions are left unaltered. There are three points of abrupt change in the training dataset. At every consecutive change the regions of drift are expanded. - <code>'gra'</code>: Global Recurring Abrupt drift. The concept drift appears over the whole instance space. There are two points of concept drift. At the second point of drift the old concept reoccurs. - <code>'gsg'</code>: Global and Slow Gradual drift. The concept drift affects all the instance space. However, the change is gradual and not abrupt. After each one of the two change points covered by this variant, and during a window of length <code>transition_window</code>, examples from both old and the new concepts are generated with equal probability. After the transition period, only the examples from the new concept are generated.</p> </li> <li> <p>position</p> <p>Type \u2192 tuple[int, ...]</p> <p>Default \u2192 <code>(50000, 100000, 150000)</code></p> <p>The amount of monitored instances after which each concept drift occurs. A tuple with at least two element must be passed, where each number is greater than the preceding one. If <code>drift_type='lea'</code>, then the tuple must have three elements.</p> </li> <li> <p>transition_window</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10000</code></p> <p>The length of the transition window between two concepts. Only applicable when  <code>drift_type='gsg'</code>. If set to zero, the drifts will be abrupt. Anytime  <code>transition_window &gt; 0</code>, it defines a window in which instances of the new  concept are gradually introduced among the examples from the old concept.  During this transition phase, both old and new concepts appear with equal probability.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/FriedmanDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/FriedmanDrift/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.FriedmanDrift(\n    drift_type='lea',\n    position=(1, 2, 3),\n    seed=42\n)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 7.04\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 18.16\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] -2.65\n</code></pre></p> <p><pre><code>dataset = synth.FriedmanDrift(\n    drift_type='gra',\n    position=(2, 3),\n    seed=42\n)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 8.96\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 18.16\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] 8.90\n</code></pre></p> <p><pre><code>dataset = synth.FriedmanDrift(\n    drift_type='gsg',\n    position=(1, 4),\n    transition_window=2,\n    seed=42\n)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 8.92\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 17.32\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] 6.05\n</code></pre></p>"},{"location":"api/datasets/synth/FriedmanDrift/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Ikonomovska, E., Gama, J. and D\u017eeroski, S., 2011. Learning model trees from evolving data streams. Data mining and knowledge discovery, 23(1), pp.128-168.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Hyperplane/","title":"Hyperplane","text":"<p>Hyperplane stream generator.</p> <p>Generates a problem of prediction class of a rotation hyperplane. It was used as testbed for CVFDT and VFDT in <sup>1</sup>. </p> <p>A hyperplane in d-dimensional space is the set of points \\(x\\) that satisfy </p> \\[\\sum^{d}_{i=1} w_i x_i = w_0 = \\sum^{d}_{i=1} w_i\\] <p>where \\(x_i\\) is the i-th coordinate of \\(x\\). </p> <ul> <li> <p>Examples for which \\(\\sum^{d}_{i=1} w_i x_i &gt; w_0\\), are labeled positive.</p> </li> <li> <p>Examples for which \\(\\sum^{d}_{i=1} w_i x_i \\leq w_0\\), are labeled negative.</p> </li> </ul> <p>Hyperplanes are useful for simulating time-changing concepts because we can change the orientation and position of the hyperplane in a smooth manner by changing the relative size of the weights. We introduce change to this dataset by adding drift to each weighted feature \\(w_i = w_i + d \\sigma\\), where \\(\\sigma\\) is the probability that the direction of change is reversed and \\(d\\) is the change applied to each example.</p>"},{"location":"api/datasets/synth/Hyperplane/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>n_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of attributes to generate. Higher than 2.</p> </li> <li> <p>n_drift_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of attributes with drift. Higher than 2.</p> </li> <li> <p>mag_change</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>Magnitude of the change for every example. From 0.0 to 1.0.</p> </li> <li> <p>noise_percentage</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Percentage of noise to add to the data. From 0.0 to 1.0.</p> </li> <li> <p>sigma</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Probability that the direction of change is reversed. From 0.0 to 1.0.</p> </li> </ul>"},{"location":"api/datasets/synth/Hyperplane/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Hyperplane/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Hyperplane(seed=42, n_features=2)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 0.2750, 1: 0.2232} 0\n{0: 0.0869, 1: 0.4219} 1\n{0: 0.0265, 1: 0.1988} 0\n{0: 0.5892, 1: 0.8094} 0\n{0: 0.3402, 1: 0.1554} 0\n</code></pre></p>"},{"location":"api/datasets/synth/Hyperplane/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Hyperplane/#notes","title":"Notes","text":"<p>The sample generation works as follows: The features are generated with the random number generator, initialized with the seed passed by the user. Then the classification function decides, as a function of the sum of the weighted features and the sum of the weights, whether the instance belongs to class 0 or class 1. The last step is to add noise and generate drift.</p> <ol> <li> <p>G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams.   In KDD'01, pages 97-106, San Francisco, CA, 2001. ACM Press.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/LED/","title":"LED","text":"<p>LED stream generator.</p> <p>This data source originates from the CART book <sup>1</sup>. An implementation in C was donated to the UCI <sup>2</sup> machine learning repository by David Aha. The goal is to predict the digit displayed on a seven-segment LED display, where each attribute has a 10% chance of being inverted. It has an optimal Bayes classification rate of 74%. The particular configuration of the generator used for experiments (LED) produces 24 binary attributes, 17 of which are irrelevant.</p>"},{"location":"api/datasets/synth/LED/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>noise_percentage</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability that noise will happen in the generation. At each new sample generated, a random number is generated, and if it is equal or less than the noise_percentage, the led value  will be switched</p> </li> <li> <p>irrelevant_features</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 17 non-relevant attributes to the stream.</p> </li> </ul>"},{"location":"api/datasets/synth/LED/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/LED/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.LED(seed = 112, noise_percentage = 0.28, irrelevant_features= False)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 1, 1: 0, 2: 1, 3: 0, 4: 0, 5: 1, 6: 0} 7\n{0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 0} 8\n{0: 1, 1: 1, 2: 1, 3: 1, 4: 0, 5: 1, 6: 0} 9\n{0: 0, 1: 0, 2: 1, 3: 0, 4: 0, 5: 1, 6: 0} 1\n{0: 0, 1: 1, 2: 1, 3: 0, 4: 0, 5: 0, 6: 0} 1\n</code></pre></p>"},{"location":"api/datasets/synth/LED/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/LED/#notes","title":"Notes","text":"<p>An instance is generated based on the parameters passed. If <code>has_noise</code> is set then the total number of attributes will be 24, otherwise there will be 7 attributes.</p> <ol> <li> <p>Leo Breiman, Jerome Friedman, R. Olshen, and Charles J. Stone.   Classification and Regression Trees. Wadsworth and Brooks,   Monterey, CA,1984.\u00a0\u21a9</p> </li> <li> <p>A. Asuncion and D. J. Newman. UCI Machine Learning Repository   [http://www.ics.uci.edu/~mlearn/mlrepository.html].   University of California, Irvine, School of Information and   Computer Sciences,2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/LEDDrift/","title":"LEDDrift","text":"<p>LED stream generator with concept drift.</p> <p>This class is an extension of the <code>LED</code> generator whose purpose is to add concept drift to the stream.</p>"},{"location":"api/datasets/synth/LEDDrift/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>noise_percentage</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability that noise will happen in the generation. At each new sample generated, a random number is generated, and if it is equal or less than the noise_percentage, the led value  will be switched</p> </li> <li> <p>irrelevant_features</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 17 non-relevant attributes to the stream.</p> </li> <li> <p>n_drift_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>The number of attributes that have drift.</p> </li> </ul>"},{"location":"api/datasets/synth/LEDDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/LEDDrift/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.LEDDrift(seed = 112, noise_percentage = 0.28,\n                         irrelevant_features= True, n_drift_features=4)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1] 7\n[1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0] 6\n[0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1] 1\n[1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1] 6\n[1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0] 7\n</code></pre></p>"},{"location":"api/datasets/synth/LEDDrift/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/LEDDrift/#notes","title":"Notes","text":"<p>An instance is generated based on the parameters passed. If <code>has_noise</code> is set then the total number of attributes will be 24, otherwise there will be 7 attributes.</p>"},{"location":"api/datasets/synth/Logical/","title":"Logical","text":"<p>Logical functions stream generator.</p> <p>Make a toy dataset with three labels that represent the logical functions: <code>OR</code>, <code>XOR</code>, <code>AND</code> (functions of the 2D input). </p> <p>Data is generated in 'tiles' which contain the complete set of logical operations results. The tiles are repeated <code>n_tiles</code> times. Optionally, the generated data can be shuffled.</p>"},{"location":"api/datasets/synth/Logical/#parameters","title":"Parameters","text":"<ul> <li> <p>n_tiles</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Number of tiles to generate.</p> </li> <li> <p>shuffle</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If set, generated data will be shuffled.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Logical/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Logical/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Logical(n_tiles=2, shuffle=True, seed=42)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'A': 1, 'B': 1} {'OR': 1, 'XOR': 0, 'AND': 1}\n{'A': 0, 'B': 0} {'OR': 0, 'XOR': 0, 'AND': 0}\n{'A': 1, 'B': 0} {'OR': 1, 'XOR': 1, 'AND': 0}\n{'A': 1, 'B': 1} {'OR': 1, 'XOR': 0, 'AND': 1}\n{'A': 1, 'B': 0} {'OR': 1, 'XOR': 1, 'AND': 0}\n</code></pre></p>"},{"location":"api/datasets/synth/Logical/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Mixed/","title":"Mixed","text":"<p>Mixed data stream generator.</p> <p>This generator is an implementation of a data stream with abrupt concept drift and boolean noise-free examples as described in <sup>1</sup>. </p> <p>It has four relevant attributes, two boolean attributes \\(v, w\\) and two numeric attributes \\(x, y\\) uniformly distributed from 0 to 1. The examples are labeled depending on the classification function chosen from below. </p> <ul> <li> <p><code>function 0</code>:   if \\(v\\) and \\(w\\) are true or \\(v\\) and \\(z\\) are true or \\(w\\) and \\(z\\) are true   then 0 else 1, where \\(z\\) is \\(y &lt; 0.5 + 0.3 sin(3 \\pi  x)\\) </p> </li> <li> <p><code>function 1</code>:    The opposite of <code>function 0</code>. </p> </li> </ul> <p>Concept drift can be introduced by changing the classification function. This can be done manually or using <code>ConceptDriftStream</code>.</p>"},{"location":"api/datasets/synth/Mixed/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Which of the two classification functions to use for the generation. Valid options are 0 or 1.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to balance classes or not. If balanced, the class distribution will converge to a uniform distribution.</p> </li> </ul>"},{"location":"api/datasets/synth/Mixed/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Mixed/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\ndataset = synth.Mixed(seed = 42, classification_function=1, balance_classes = True)\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: True, 1: False, 2: 0.2750, 3: 0.2232} 1\n{0: False, 1: False, 2: 0.2186, 3: 0.5053} 0\n{0: False, 1: True, 2: 0.8094, 3: 0.0064} 1\n{0: False, 1: False, 2: 0.1010, 3: 0.2779} 0\n{0: True, 1: False, 2: 0.37018, 3: 0.2095} 1\n</code></pre></p>"},{"location":"api/datasets/synth/Mixed/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Mixed/#notes","title":"Notes","text":"<p>The sample generation works as follows: The two numeric attributes are generated with the random  generator initialized with the seed passed by the user (optional). The boolean attributes are either 0 or 1 based on the comparison of the random number generator and 0.5, the classification function decides whether to classify the instance as class 0 or class 1. The next step is to verify if the classes should be balanced, and if so, balance the classes.</p> <p>The generated sample will have 4 relevant features and 1 label (it is a binary-classification task).</p> <ol> <li> <p>Gama, Joao, et al. \"Learning with drift detection.\" Advances in   artificial intelligence-SBIA 2004. Springer Berlin Heidelberg,   2004. 286-295\"\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Mv/","title":"Mv","text":"<p>Mv artificial dataset.</p> <p>Artificial dataset composed of both nominal and numeric features, whose features present co-dependencies. Originally described in <sup>1</sup>. </p> <p>The features are generated using the following expressions: </p> <ul> <li> <p>\\(x_1\\): uniformly distributed over <code>[-5, 5]</code>.</p> </li> <li> <p>\\(x_2\\): uniformly distributed over <code>[-15, -10]</code>.</p> </li> <li> <p>\\(x_3\\):</p> <ul> <li> <p>if \\(x_1 &gt; 0\\), \\(x_3 \\leftarrow\\) <code>'green'</code> </p> </li> <li> <p>else \\(x_3 \\leftarrow\\) <code>'red'</code> with probability \\(0.4\\) and \\(x_3 \\leftarrow\\) <code>'brown'</code>     with probability \\(0.6\\). </p> </li> </ul> </li> <li> <p>\\(x_4\\):</p> <ul> <li> <p>if \\(x_3 =\\) <code>'green'</code>, \\(x_4 \\leftarrow x_1 + 2 x_2\\) </p> </li> <li> <p>else \\(x_4 = \\frac{x_1}{2}\\) with probability \\(0.3\\) and \\(x_4 = \\frac{x_2}{2}\\)     with probability \\(0.7\\). </p> </li> </ul> </li> <li> <p>\\(x_5\\): uniformly distributed over <code>[-1, 1]</code>.</p> </li> <li> <p>\\(x_6 \\leftarrow x_4 \\times \\epsilon\\), where \\(\\epsilon\\) is uniformly distributed</p> </li> </ul> <p>over <code>[0, 5]</code>. </p> <ul> <li> <p>\\(x_7\\): <code>'yes'</code> with probability \\(0.3\\), and <code>'no'</code> with probability \\(0.7\\).</p> </li> <li> <p>\\(x_8\\): <code>'normal'</code> if \\(x_5 &lt; 0.5\\) else <code>'large'</code>.</p> </li> <li> <p>\\(x_9\\): uniformly distributed over <code>[100, 500]</code>.</p> </li> <li> <p>\\(x_{10}\\): uniformly distributed integer over the interval <code>[1000, 1200]</code>.</p> </li> </ul> <p>The target value is generated using the following rules: </p> <ul> <li> <p>if \\(x_2 &gt; 2\\), \\(y \\leftarrow 35 - 0.5 x_4\\)</p> </li> <li> <p>else if \\(-2 \\le x_4 \\le 2\\), \\(y \\leftarrow 10 - 2 x_1\\)</p> </li> <li> <p>else if \\(x_7 =\\) <code>'yes'</code>, \\(y \\leftarrow 3 - \\frac{x_1}{x_4}\\)</p> </li> <li> <p>else if \\(x_8 =\\) <code>'normal'</code>, \\(y \\leftarrow x_6 + x_1\\)</p> </li> <li> <p>else \\(y \\leftarrow \\frac{x_1}{2}\\).</p> </li> </ul>"},{"location":"api/datasets/synth/Mv/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Mv/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Mv/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Mv(seed=42)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[1.39, -14.87, 'green', -28.35, -0.44, -31.64, 'no', 'normal', 370.67, 1178.43] -30.25\n[-4.13, -12.89, 'red', -2.06, 0.01, -0.27, 'yes', 'normal', 359.95, 1108.98] 1.00\n[-2.79, -12.05, 'brown', -1.39, 0.61, -4.87, 'no', 'large', 162.19, 1191.44] 15.59\n[-1.63, -14.53, 'red', -7.26, 0.20, -29.33, 'no', 'normal', 314.49, 1194.62] -30.96\n[-1.21, -12.23, 'brown', -6.11, 0.72, -17.66, 'no', 'large', 118.32, 1045.57] -0.60\n</code></pre></p>"},{"location":"api/datasets/synth/Mv/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Mv in Lu\u00eds Torgo regression datasets \u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Planes2D/","title":"Planes2D","text":"<p>2D Planes synthetic dataset.</p> <p>This dataset is described in <sup>1</sup> and was adapted from <sup>2</sup>. The features are generated using the following probabilities: </p> \\[P(x_1 = -1) = P(x_1 = 1) = \\frac{1}{2}\\] \\[P(x_m = -1) = P(x_m = 0) = P(x_m = 1) = \\frac{1}{3}, m=2,\\ldots, 10\\] <p>The target value is defined by the following rule: </p> \\[\\text{if}~x_1 = 1, y \\leftarrow 3 + 3x_2 + 2x_3 + x_4 + \\epsilon\\] \\[\\text{if}~x_1 = -1, y \\leftarrow -3 + 3x_5 + 2x_6 + x_7 + \\epsilon\\] <p>In the expressions, \\(\\epsilon \\sim \\mathcal{N}(0, 1)\\), is the noise.</p>"},{"location":"api/datasets/synth/Planes2D/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Planes2D/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Planes2D/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Planes2D(seed=42)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[-1, -1, 1, 0, -1, -1, -1, 1, -1, 1] -9.07\n[1, -1, -1, -1, -1, -1, 1, 1, -1, 1] -4.25\n[-1, 1, 1, 1, 1, 0, -1, 0, 1, 0] -0.95\n[-1, 1, 0, 0, 0, -1, -1, 0, -1, -1] -6.10\n[1, -1, 0, 0, 1, 0, -1, 1, 0, 1] 1.60\n</code></pre></p>"},{"location":"api/datasets/synth/Planes2D/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>2DPlanes in Lu\u00eds Torgo regression datasets \u21a9</p> </li> <li> <p>Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A., 1984. Classification and regression trees. CRC press.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/RandomRBF/","title":"RandomRBF","text":"<p>Random Radial Basis Function generator.</p> <p>Produces a radial basis function stream. A number of centroids, having a random central position, a standard deviation, a class label and weight are generated. A new sample is created by choosing one of the centroids at random, taking into account their weights, and offsetting the attributes in a random direction from the centroid's center. The offset length is drawn from a Gaussian distribution. </p> <p>This process will create a normally distributed hypersphere of samples on the surrounds of each centroid.</p>"},{"location":"api/datasets/synth/RandomRBF/#parameters","title":"Parameters","text":"<ul> <li> <p>seed_model</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Model's random seed to generate centroids.</p> </li> <li> <p>seed_sample</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Sample's random seed.</p> </li> <li> <p>n_classes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of class labels to generate.</p> </li> <li> <p>n_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of numerical features to generate.</p> </li> <li> <p>n_centroids</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>The number of centroids to generate.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBF/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBF/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\ndataset = synth.RandomRBF(seed_model=42, seed_sample=42,\n                          n_classes=4, n_features=4, n_centroids=20)\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 1.0989, 1: 0.3840, 2: 0.7759, 3: 0.6592} 2\n{0: 0.2366, 1: 1.3233, 2: 0.5691, 3: 0.2083} 0\n{0: 1.3540, 1: -0.3306, 2: 0.1683, 3: 0.8865} 0\n{0: 0.2585, 1: -0.2217, 2: 0.4739, 3: 0.6522} 0\n{0: 0.1295, 1: 0.5953, 2: 0.1774, 3: 0.6673} 1\n</code></pre></p>"},{"location":"api/datasets/synth/RandomRBF/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/RandomRBFDrift/","title":"RandomRBFDrift","text":"<p>Random Radial Basis Function generator with concept drift.</p> <p>This class is an extension from the <code>RandomRBF</code> generator. Concept drift can be introduced in instances of this class. </p> <p>The drift is created by adding a \"speed\" to certain centroids. As the samples are generated each of the moving centroids' centers is changed by an amount determined by its speed.</p>"},{"location":"api/datasets/synth/RandomRBFDrift/#parameters","title":"Parameters","text":"<ul> <li> <p>seed_model</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Model's random seed to generate centroids.</p> </li> <li> <p>seed_sample</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Sample's random seed.</p> </li> <li> <p>n_classes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of class labels to generate.</p> </li> <li> <p>n_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of numerical features to generate.</p> </li> <li> <p>n_centroids</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>The number of centroids to generate.</p> </li> <li> <p>change_speed</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The concept drift speed.</p> </li> <li> <p>n_drift_centroids</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>The number of centroids that will drift.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBFDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBFDrift/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\ndataset = synth.RandomRBFDrift(seed_model=42, seed_sample=42,\n                               n_classes=4, n_features=4, n_centroids=20,\n                               change_speed=0.87, n_drift_centroids=10)\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 1.0989, 1: 0.3840, 2: 0.7759, 3: 0.6592} 2\n{0: 1.1496, 1: 1.9014, 2: 1.5393, 3: 0.3210} 0\n{0: 0.7146, 1: -0.2414, 2: 0.8933, 3: 1.6633} 0\n{0: 0.3797, 1: -0.1027, 2: 0.8717, 3: 1.1635} 0\n{0: 0.1295, 1: 0.5953, 2: 0.1774, 3: 0.6673} 1\n</code></pre></p>"},{"location":"api/datasets/synth/RandomRBFDrift/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/RandomTree/","title":"RandomTree","text":"<p>Random Tree generator.</p> <p>This generator is based on <sup>1</sup>. The generator creates a random tree by splitting features at random and setting labels at its leaves. </p> <p>The tree structure is composed of node objects, which can be either inner nodes or leaf nodes. The choice comes as a function of the parameters passed to its initializer. </p> <p>Since the concepts are generated and classified according to a tree structure, in theory, it should favor decision tree learners.</p>"},{"location":"api/datasets/synth/RandomTree/#parameters","title":"Parameters","text":"<ul> <li> <p>seed_tree</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Seed for random generation of tree.</p> </li> <li> <p>seed_sample</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Seed for random generation of instances.</p> </li> <li> <p>n_classes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of classes to generate.</p> </li> <li> <p>n_num_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of numerical features to generate.</p> </li> <li> <p>n_cat_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of categorical features to generate.</p> </li> <li> <p>n_categories_per_feature</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of values to generate per categorical feature.</p> </li> <li> <p>max_tree_depth</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The maximum depth of the tree concept.</p> </li> <li> <p>first_leaf_level</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>3</code></p> <p>The first level of the tree above <code>max_tree_depth</code> that can have leaves.</p> </li> <li> <p>fraction_leaves_per_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.15</code></p> <p>The fraction of leaves per level from <code>first_leaf_level</code> onwards.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomTree/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomTree/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.RandomTree(seed_tree=42, seed_sample=42, n_classes=2,\n                           n_num_features=2, n_cat_features=2,\n                           n_categories_per_feature=2, max_tree_depth=6,\n                           first_leaf_level=3, fraction_leaves_per_level=0.15)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'x_num_0': 0.6394, 'x_num_1': 0.0250, 'x_cat_0': 1, 'x_cat_1': 0} 0\n{'x_num_0': 0.2232, 'x_num_1': 0.7364, 'x_cat_0': 0, 'x_cat_1': 1} 1\n{'x_num_0': 0.0317, 'x_num_1': 0.0936, 'x_cat_0': 0, 'x_cat_1': 0} 0\n{'x_num_0': 0.5612, 'x_num_1': 0.7160, 'x_cat_0': 1, 'x_cat_1': 0} 0\n{'x_num_0': 0.4492, 'x_num_1': 0.2781, 'x_cat_0': 0, 'x_cat_1': 0} 0\n</code></pre></p>"},{"location":"api/datasets/synth/RandomTree/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Domingos, Pedro, and Geoff Hulten. \"Mining high-speed data streams.\"   In Proceedings of the sixth ACM SIGKDD international conference on   Knowledge discovery and data mining, pp. 71-80. 2000.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/SEA/","title":"SEA","text":"<p>SEA synthetic dataset.</p> <p>Implementation of the data stream with abrupt drift described in <sup>1</sup>. Each observation is composed of 3 features. Only the first two features are relevant. The target is binary, and is positive if the sum of the features exceeds a certain threshold. There are 4 thresholds to choose from. Concept drift can be introduced by switching the threshold anytime during the stream. </p> <ul> <li> <p>Variant 0: <code>True</code> if \\(att1 + att2 &gt; 8\\) </p> </li> <li> <p>Variant 1: <code>True</code> if \\(att1 + att2 &gt; 9\\) </p> </li> <li> <p>Variant 2: <code>True</code> if \\(att1 + att2 &gt; 7\\) </p> </li> <li> <p>Variant 3: <code>True</code> if \\(att1 + att2 &gt; 9.5\\)</p> </li> </ul>"},{"location":"api/datasets/synth/SEA/#parameters","title":"Parameters","text":"<ul> <li> <p>variant</p> <p>Default \u2192 <code>0</code></p> <p>Determines the classification function to use. Possible choices are 0, 1, 2, 3.</p> </li> <li> <p>noise</p> <p>Default \u2192 <code>0.0</code></p> <p>Determines the amount of observations for which the target sign will be flipped.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/SEA/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/SEA/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.SEA(variant=0, seed=42)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 6.39426, 1: 0.25010, 2: 2.75029} False\n{0: 2.23210, 1: 7.36471, 2: 6.76699} True\n{0: 8.92179, 1: 0.86938, 2: 4.21921} True\n{0: 0.29797, 1: 2.18637, 2: 5.05355} False\n{0: 0.26535, 1: 1.98837, 2: 6.49884} False\n</code></pre></p>"},{"location":"api/datasets/synth/SEA/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>A Streaming Ensemble Algorithm (SEA) for Large-Scale Classification \u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/STAGGER/","title":"STAGGER","text":"<p>STAGGER concepts stream generator.</p> <p>This generator is an implementation of the dara stream with abrupt concept drift, as described in <sup>1</sup>. </p> <p>The STAGGER concepts are boolean functions <code>f</code> with three features describing objects: size (small, medium and large), shape (circle, square and triangle) and colour (red, blue and green). </p> <p><code>f</code> options: </p> <ol> <li> <p><code>True</code> if the size is small and the color is red. </p> </li> <li> <p><code>True</code> if the color is green or the shape is a circle. </p> </li> <li> <p><code>True</code> if the size is medium or large </p> </li> </ol> <p>Concept drift can be introduced by changing the classification function. This can be done manually or using <code>datasets.synth.ConceptDriftStream</code>. </p> <p>One important feature is the possibility to balance classes, which means the class distribution will tend to a uniform one.</p>"},{"location":"api/datasets/synth/STAGGER/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Classification functions to use. From 0 to 2.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to balance classes or not. If balanced, the class distribution will converge to an uniform distribution.</p> </li> </ul>"},{"location":"api/datasets/synth/STAGGER/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/STAGGER/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.STAGGER(classification_function = 2, seed = 112,\n                     balance_classes = False)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'size': 1, 'color': 2, 'shape': 2} 1\n{'size': 2, 'color': 1, 'shape': 2} 1\n{'size': 1, 'color': 1, 'shape': 2} 1\n{'size': 0, 'color': 1, 'shape': 0} 0\n{'size': 2, 'color': 1, 'shape': 0} 1\n</code></pre></p>"},{"location":"api/datasets/synth/STAGGER/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function at random.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/STAGGER/#notes","title":"Notes","text":"<p>The sample generation works as follows: The 3 attributes are generated with the random number generator. The classification function defines whether to classify the instance as class 0 or class 1. Finally, data is balanced, if this option is set by the user.</p> <ol> <li> <p>Schlimmer, J. C., &amp; Granger, R. H. (1986). Incremental learning   from noisy data. Machine learning, 1(3), 317-354.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Sine/","title":"Sine","text":"<p>Sine generator.</p> <p>This generator is an implementation of the dara stream with abrupt concept drift, as described in Gama, Joao, et al. <sup>1</sup>. </p> <p>It generates up to 4 relevant numerical features, that vary from 0 to 1, where only 2 of them are relevant to the classification task and the other 2 are optionally added by as noise. A classification function is chosen among four options: </p> <ol> <li> <p><code>SINE1</code>. Abrupt concept drift, noise-free examples. It has two relevant    attributes. Each attributes has values uniformly distributed in [0, 1].    In the first context all points below the curve \\(y = sin(x)\\) are    classified as positive. </p> </li> <li> <p><code>Reversed SINE1</code>. The reversed classification of <code>SINE1</code>. </p> </li> <li> <p><code>SINE2</code>. The same two relevant attributes. The classification function    is \\(y &lt; 0.5 + 0.3 sin(3 \\pi  x)\\). </p> </li> <li> <p><code>Reversed SINE2</code>. The reversed classification of <code>SINE2</code>. </p> </li> </ol> <p>Concept drift can be introduced by changing the classification function. This can be done manually or using <code>ConceptDriftStream</code>. </p> <p>Two important features are the possibility to balance classes, which means the class distribution will tend to a uniform one, and the possibility to add noise, which will, add two non relevant attributes.</p>"},{"location":"api/datasets/synth/Sine/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Classification functions to use. From 0 to 3.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to balance classes or not. If balanced, the class distribution will converge to an uniform distribution.</p> </li> <li> <p>has_noise</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 2 non relevant features to the stream.</p> </li> </ul>"},{"location":"api/datasets/synth/Sine/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Sine/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Sine(classification_function = 2, seed = 112,\n                     balance_classes = False, has_noise = True)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 0.4812, 1: 0.6660, 2: 0.6198, 3: 0.6994} 1\n{0: 0.9022, 1: 0.7518, 2: 0.1625, 3: 0.2209} 0\n{0: 0.4547, 1: 0.3901, 2: 0.9629, 3: 0.7287} 0\n{0: 0.4683, 1: 0.3515, 2: 0.2273, 3: 0.6027} 0\n{0: 0.9238, 1: 0.1673, 2: 0.4522, 3: 0.3447} 0\n</code></pre></p>"},{"location":"api/datasets/synth/Sine/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function at random.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Sine/#notes","title":"Notes","text":"<p>The sample generation works as follows: The two attributes are generated with the random number generator. The classification function defines whether to classify the instance as class 0 or class 1. Finally, data is balanced and noise is added, if these options are set by the user.</p> <p>The generated sample will have 2 relevant features, and an additional two noise features if <code>has_noise</code> is set.</p> <ol> <li> <p>Gama, Joao, et al.'s 'Learning with drift detection.'   Advances in artificial intelligence-SBIA 2004.   Springer Berlin Heidelberg, 2004. 286-295.\"\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Waveform/","title":"Waveform","text":"<p>Waveform stream generator.</p> <p>Generates samples with 21 numeric features and 3 classes, based on a random differentiation of some base waveforms. Supports noise addition, in this case the samples will have 40 features.</p>"},{"location":"api/datasets/synth/Waveform/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>has_noise</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 19 unrelated features to the stream.</p> </li> </ul>"},{"location":"api/datasets/synth/Waveform/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Waveform/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Waveform(seed=42, has_noise=True)\n\nfor x, y in dataset:\n    break\n\nx\n</code></pre> <pre><code>{0: -0.0397, 1: -0.7484, 2: 0.2974, 3: 0.3574, 4: -0.0735, 5: -0.3647, 6: 1.5631,     7: 2.5291, 8: 4.1599, 9: 4.9587, 10: 4.52587, 11: 4.0097, 12: 3.6705, 13: 1.7033,     14: 1.4898, 15: 1.9743, 16: 0.0898, 17: 2.319, 18: 0.2552, 19: -0.4775, 20: -0.71339,     21: 0.3770, 22: 0.3671, 23: 1.6579, 24: 0.7828, 25: 0.5855, 26: -0.5807, 27: 0.7112,     28: -0.0271, 29: 0.2968, 30: -0.4997, 31: 0.1302, 32: 0.3578, 33: -0.1900, 34: -0.3771,     35: 1.3560, 36: 0.7124, 37: -0.6245, 38: 0.1346, 39: 0.3550}\n</code></pre></p> <p><pre><code>y\n</code></pre> <pre><code>2\n</code></pre></p>"},{"location":"api/datasets/synth/Waveform/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Waveform/#notes","title":"Notes","text":"<p>An instance is generated based on the parameters passed. The generator will randomly choose one of the hard coded waveforms, as well as random multipliers. For each feature, the actual value generated will be a a combination of the hard coded functions, with the multipliers and a random value.</p> <p>If noise is added then the features 21 to 40 will be replaced with a random normal value.</p>"},{"location":"api/drift/ADWIN/","title":"ADWIN","text":"<p>Adaptive Windowing method for concept drift detection<sup>1</sup>.</p> <p>ADWIN (ADaptive WINdowing) is a popular drift detection method with mathematical guarantees. ADWIN efficiently keeps a variable-length window of recent items; such that it holds that there has no been change in the data distribution. This window is further divided into two sub-windows \\((W_0, W_1)\\) used to determine if a change has happened. ADWIN compares the average of \\(W_0\\) and \\(W_1\\) to confirm that they correspond to the same distribution. Concept drift is detected if the distribution equality no longer holds. Upon detecting a drift, \\(W_0\\) is replaced by \\(W_1\\) and a new \\(W_1\\) is initialized. ADWIN uses a significance value \\(\\delta=\\in(0,1)\\) to determine if the two sub-windows correspond to the same distribution.</p>"},{"location":"api/drift/ADWIN/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Default \u2192 <code>0.002</code></p> <p>Significance value.</p> </li> <li> <p>clock</p> <p>Default \u2192 <code>32</code></p> <p>How often ADWIN should check for changes. 1 means every new data point, default is 32. Higher values speed up processing, but may also lead to increased delay in change detection.</p> </li> <li> <p>max_buckets</p> <p>Default \u2192 <code>5</code></p> <p>The maximum number of buckets of each size that ADWIN should keep before merging buckets. The idea of data buckets comes from the compression algorithm introduced in the ADWIN2, the second iteration of the ADWIN algorithm presented in the original research paper. This is the ADWIN version available in River.</p> </li> <li> <p>min_window_length</p> <p>Default \u2192 <code>5</code></p> <p>The minimum length allowed for a subwindow when checking for concept drift. Subwindows whose size is smaller than this value will be ignored during concept drift evaluation. Lower values may decrease delay in change detection but may also lead to more false positives.</p> </li> <li> <p>grace_period</p> <p>Default \u2192 <code>10</code></p> <p>ADWIN does not perform any change detection until at least this many data points have arrived.</p> </li> </ul>"},{"location":"api/drift/ADWIN/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>estimation</p> <p>Estimate of mean value in the window.</p> </li> <li> <p>n_detections</p> <p>The total number of detected changes.</p> </li> <li> <p>total</p> <p>The sum of the stored elements.</p> </li> <li> <p>variance</p> <p>The sample variance within the stored (adaptive) window.</p> </li> <li> <p>width</p> <p>Window size</p> </li> </ul>"},{"location":"api/drift/ADWIN/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(12345)\nadwin = drift.ADWIN()\n\ndata_stream = rng.choices([0, 1], k=1000) + rng.choices(range(4, 8), k=1000)\n\nfor i, val in enumerate(data_stream):\n    adwin.update(val)\n    if adwin.drift_detected:\n        print(f\"Change detected at index {i}, input value: {val}\")\n</code></pre> <pre><code>Change detected at index 1023, input value: 4\n</code></pre></p>"},{"location":"api/drift/ADWIN/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Apart from adding the element value to the window, by inserting it in the correct bucket, it will also update the relevant statistics, in this case the total sum of all values, the window width and the total variance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p>Returns</p> <p>None:     self</p> <p></p> <ol> <li> <p>Albert Bifet and Ricard Gavalda. \"Learning from time-changing data with adaptive windowing.\" In Proceedings of the 2007 SIAM international conference on data mining, pp. 443-448. Society for Industrial and Applied Mathematics, 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/DriftRetrainingClassifier/","title":"DriftRetrainingClassifier","text":"<p>Drift retraining classifier.</p> <p>This classifier is a wrapper for any classifier. It monitors the incoming data for concept drifts and warnings in the model's accurary. In case a warning is detected, a background model starts to train. If a drift is detected, the model will be replaced by the background model, and the background model will be reset.</p>"},{"location":"api/drift/DriftRetrainingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier and background classifier class.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftAndWarningDetector | base.BinaryDriftAndWarningDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Algorithm to track warnings and concept drifts. Attention! If the parameter train_in_background is True, the drift_detector must have a warning tracker.</p> </li> <li> <p>train_in_background</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Parameter to determine if a background model will be used.</p> </li> </ul>"},{"location":"api/drift/DriftRetrainingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import drift\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Elec2().take(3000)\n\nmodel = drift.DriftRetrainingClassifier(\n    model=tree.HoeffdingTreeClassifier(),\n    drift_detector=drift.binary.DDM()\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 86.46%\n</code></pre></p>"},{"location":"api/drift/DriftRetrainingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/drift/DummyDriftDetector/","title":"DummyDriftDetector","text":"<p>Baseline drift detector that generates pseudo drift detection signals.</p> <p>There are two approaches<sup>1</sup>: </p> <ul> <li> <p><code>fixed</code> where the drift signal is generated every <code>t_0</code> samples.</p> </li> <li> <p><code>random</code> corresponds to a pseudo-random drift detection strategy.</p> </li> </ul>"},{"location":"api/drift/DummyDriftDetector/#parameters","title":"Parameters","text":"<ul> <li> <p>trigger_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>fixed</code></p> <p>The trigger method to use. * <code>fixed</code> * <code>random</code></p> </li> <li> <p>t_0</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>300</code></p> <p>Reference point to define triggers.</p> </li> <li> <p>w</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Auxiliary parameter whose purpose is twofold: - if <code>trigger_method=\"fixed\"</code>, the periodic drift signals will only start after an initial warm-up period randomly defined between <code>[0, w]</code>. Useful to avoid that all ensemble members are reset at the same time when periodic triggers are used as the adaptation strategy. - if <code>trigger_method=\"random\"</code>, <code>w</code> defines the probability bounds of triggering a drift. The chance of triggering a drift is \\(0.5\\) after observing <code>t_0</code> instances and becomes \\(1\\) after monitoring <code>t_0 + w / 2</code> instances. A sigmoid function is used to produce values between <code>[0, 1]</code> that are used as the reset probabilities.</p> </li> <li> <p>dynamic_cloning</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to change the <code>seed</code> and <code>w</code> values each time <code>clone()</code> is called.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/drift/DummyDriftDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/DummyDriftDetector/#examples","title":"Examples","text":"<pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\n</code></pre> <p>The observed values will not affect the periodic triggers.</p> <pre><code>data = [rng.gauss(0, 1) for _ in range(1000)]\n</code></pre> <p>Let's start with the fixed drift signals:</p> <p><pre><code>ptrigger = DummyDriftDetector(t_0=500, seed=42)\nfor i, v in enumerate(data):\n    ptrigger.update(v)\n    if ptrigger.drift_detected:\n        print(f\"Drift detected at instance {i}.\")\n</code></pre> <pre><code>Drift detected at instance 499.\nDrift detected at instance 999.\n</code></pre></p> <p>Now, the random drift signals:</p> <p><pre><code>rtrigger = DummyDriftDetector(\n    trigger_method=\"random\",\n    t_0=500,\n    w=100,\n    dynamic_cloning=True,\n    seed=42\n)\nfor i, v in enumerate(data):\n    rtrigger.update(v)\n    if rtrigger.drift_detected:\n        print(f\"Drift detected at instance {i}.\")\n</code></pre> <pre><code>Drift detected at instance 368.\nDrift detected at instance 817.\n</code></pre></p> <p>Remember to set a w &gt; 0 value if random triggers are used:</p> <p><pre><code>try:\n    DummyDriftDetector(trigger_method=\"random\")\nexcept ValueError as ve:\n    print(ve)\n</code></pre> <pre><code>The 'w' value must be greater than zero when 'trigger_method' is 'random'.\n</code></pre></p> <p>Since we set <code>dynamic_cloning</code> to <code>True</code>, a clone of the periodic trigger will have its internal paramenters changed:</p> <p><pre><code>rtrigger = rtrigger.clone()\nfor i, v in enumerate(data):\n    rtrigger.update(v)\n    if rtrigger.drift_detected:\n        print(f\"Drift detected at instance {i}.\")\n</code></pre> <pre><code>Drift detected at instance 429.\nDrift detected at instance 728.\n</code></pre></p>"},{"location":"api/drift/DummyDriftDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/drift/DummyDriftDetector/#notes","title":"Notes","text":"<p>When used in ensembles, a naive implementation of periodic drift signals would make all ensemble members reset at the same time. To avoid that, the <code>dynamic_cloning</code> parameter can be set to <code>True</code>. In this case, every time the <code>clone</code> method of this detector is called in an ensemble a new <code>seed</code> is defined. If <code>dynamic_cloning=True</code> and <code>trigger_method=\"fixed\"</code>, a new <code>w</code> between <code>[0, t_0]</code> will also be created for the new cloned instance.</p> <ol> <li> <p>Heitor Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer, and Albert Bifet. On Ensemble Techniques for Data Stream Regression. IJCNN'20. International Joint Conference on Neural Networks. 2020.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/KSWIN/","title":"KSWIN","text":"<p>Kolmogorov-Smirnov Windowing method for concept drift detection.</p>"},{"location":"api/drift/KSWIN/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.005</code></p> <p>Probability for the test statistic of the Kolmogorov-Smirnov-Test. The alpha parameter is very sensitive, therefore should be set below 0.01.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>Size of the sliding window.</p> </li> <li> <p>stat_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>Size of the statistic window.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>window</p> <p>Type \u2192 typing.Iterable | None</p> <p>Default \u2192 <code>None</code></p> <p>Already collected data to avoid cold start.</p> </li> </ul>"},{"location":"api/drift/KSWIN/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/KSWIN/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(12345)\nkswin = drift.KSWIN(alpha=0.0001, seed=42)\n\ndata_stream = rng.choices([0, 1], k=1000) + rng.choices(range(4, 8), k=1000)\n\nfor i, val in enumerate(data_stream):\n    kswin.update(val)\n    if kswin.drift_detected:\n        print(f\"Change detected at index {i}, input value: {val}\")\n</code></pre> <pre><code>Change detected at index 1016, input value: 6\n</code></pre></p>"},{"location":"api/drift/KSWIN/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Adds an element on top of the sliding window and removes the oldest one from the window. Afterwards, the KS-test is performed.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p>Returns</p> <p>None:     self</p> <p></p>"},{"location":"api/drift/KSWIN/#notes","title":"Notes","text":"<p>KSWIN (Kolmogorov-Smirnov Windowing) is a concept change detection method based on the Kolmogorov-Smirnov (KS) statistical test. KS-test is a statistical test with no assumption of underlying data distribution. KSWIN can monitor data or performance distributions. Note that the detector accepts one dimensional input as array.</p> <p>KSWIN maintains a sliding window \\(\\Psi\\) of fixed size \\(n\\) (window_size). The last \\(r\\) (stat_size) samples of \\(\\Psi\\) are assumed to represent the last concept considered as \\(R\\). From the first \\(n-r\\) samples of \\(\\Psi\\), \\(r\\) samples are uniformly drawn, representing an approximated last concept \\(W\\).</p> <p>The KS-test is performed on the windows \\(R\\) and \\(W\\) of the same size. KS -test compares the distance of the empirical cumulative data distribution \\(dist(R,W)\\).</p> <p>A concept drift is detected by KSWIN if:</p> \\[ dist(R,W) &gt; \\sqrt{-\\frac{ln\\alpha}{r}} \\] <p>The difference in empirical data distributions between the windows \\(R\\) and \\(W\\) is too large since \\(R\\) and \\(W\\) come from the same distribution.</p> <ol> <li> <p>Christoph Raab, Moritz Heusinger, Frank-Michael Schleif, Reactive Soft Prototype Computing for Concept Drift Streams, Neurocomputing, 2020,\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/NoDrift/","title":"NoDrift","text":"<p>Dummy class used to turn off concept drift detection capabilities of adaptive models. It always signals that no concept drift was detected. Examples --------</p> <p>from river import drift &gt;&gt;&gt; from river import evaluate &gt;&gt;&gt; from river import forest &gt;&gt;&gt; from river import metrics &gt;&gt;&gt; from river.datasets import synth </p> <p>dataset = datasets.synth.ConceptDriftStream( ...     seed=8, ...     position=500, ...     width=40, ... ).take(700) </p> <p>We can turn off the warning detection capabilities of Adaptive Random Forest (ARF) or other similar models. Thus, the base models will reset immediately after identifying a drift, bypassing the background model building phase: </p> <p>adaptive_model = forest.ARFClassifier( ...     leaf_prediction=\"mc\", ...     warning_detector=drift.NoDrift(), ...     seed=8 ... ) </p> <p>We can also turn off the concept drift handling capabilities completely: </p> <p>stationary_model = forest.ARFClassifier( ...     leaf_prediction=\"mc\", ...     warning_detector=drift.NoDrift(), ...     drift_detector=drift.NoDrift(), ...     seed=8 ... ) </p> <p>Let's put that to test: </p> <p>for x, y in dataset: ...     adaptive_model.learn_one(x, y) ...     stationary_model.learn_one(x, y) </p> <p>The adaptive model: </p> <p>adaptive_model.n_drifts_detected() 2 </p> <p>adaptive_model.n_warnings_detected() 0 </p> <p>The stationary one: </p> <p>stationary_model.n_drifts_detected() 0 </p> <p>stationary_model.n_warnings_detected() 0</p>"},{"location":"api/drift/NoDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/NoDrift/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/drift/PageHinkley/","title":"PageHinkley","text":"<p>Page-Hinkley method for concept drift detection.</p> <p>This change detection method works by computing the observed values and their mean up to the current moment. Page-Hinkley does not signal warning zones, only change detections. </p> <p>This detector implements the CUSUM control chart for detecting changes. This implementation also supports the two-sided Page-Hinkley test to detect increasing and decreasing changes in the mean of the input values.</p>"},{"location":"api/drift/PageHinkley/#parameters","title":"Parameters","text":"<ul> <li> <p>min_instances</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum number of instances before detecting change.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.005</code></p> <p>The delta factor for the Page-Hinkley test.</p> </li> <li> <p>threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>50.0</code></p> <p>The change detection threshold (lambda).</p> </li> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.9999</code></p> <p>The forgetting factor, used to weight the observed value and the mean.</p> </li> <li> <p>mode</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>both</code></p> <p>Whether to consider increases (\"up\"), decreases (\"down\") or both (\"both\") when monitoring the fading mean.</p> </li> </ul>"},{"location":"api/drift/PageHinkley/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/PageHinkley/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(12345)\nph = drift.PageHinkley()\n\ndata_stream = rng.choices([0, 1], k=1000) + rng.choices(range(4, 8), k=1000)\n\nfor i, val in enumerate(data_stream):\n    ph.update(val)\n    if ph.drift_detected:\n        print(f\"Change detected at index {i}, input value: {val}\")\n</code></pre> <pre><code>Change detected at index 1006, input value: 5\n</code></pre></p>"},{"location":"api/drift/PageHinkley/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p> <ol> <li> <p>E. S. Page. 1954. Continuous Inspection Schemes. Biometrika 41, 1/2 (1954), 100-115.\u00a0\u21a9</p> </li> <li> <p>Sebasti\u00e3o, R., &amp; Fernandes, J. M. (2017, June). Supporting the Page-Hinkley test with empirical mode decomposition for change detection. In International Symposium on Methodologies for Intelligent Systems (pp. 492-498). Springer, Cham.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/DDM/","title":"DDM","text":"<p>Drift Detection Method.</p> <p>DDM (Drift Detection Method) is a concept change detection method based on the PAC learning model premise, that the learner's error rate will decrease as the number of analysed samples increase, as long as the data distribution is stationary. </p> <p>If the algorithm detects an increase in the error rate, that surpasses a calculated threshold, either change is detected or the algorithm will warn the user that change may occur in the near future, which is called the warning zone. </p> <p>The detection threshold is calculated in function of two statistics, obtained when \\((p_i + s_i)\\) is minimum: </p> <ul> <li> <p>\\(p_{min}\\): The minimum recorded error rate. </p> </li> <li> <p>\\(s_{min}\\): The minimum recorded standard deviation. </p> </li> </ul> <p>At instant \\(i\\), the detection algorithm uses: </p> <ul> <li> <p>\\(p_i\\): The error rate at instant \\(i\\). </p> </li> <li> <p>\\(s_i\\): The standard deviation at instant \\(i\\). </p> </li> </ul> <p>The conditions for entering the warning zone and detecting change are as follows [see implementation note below]: </p> <ul> <li> <p>if \\(p_i + s_i \\geq p_{min} + w_l * s_{min}\\) -&gt; Warning zone </p> </li> <li> <p>if \\(p_i + s_i \\geq p_{min} + d_l * s_{min}\\) -&gt; Change detected </p> </li> </ul> <p>In the above expressions, \\(w_l\\) and \\(d_l\\) represent, respectively, the warning and drift thresholds. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul>"},{"location":"api/drift/binary/DDM/#parameters","title":"Parameters","text":"<ul> <li> <p>warm_start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum required number of analyzed samples so change can be detected. Warm start parameter for the drift detector.</p> </li> <li> <p>warning_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>2.0</code></p> <p>Threshold to decide if the detector is in a warning zone. The default value gives 95\\% of confidence level to the warning assessment.</p> </li> <li> <p>drift_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>3.0</code></p> <p>Threshold to decide if a drift was detected. The default value gives a 99\\% of confidence level to the drift assessment.</p> </li> </ul>"},{"location":"api/drift/binary/DDM/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/DDM/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nddm = drift.binary.DDM()\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    ddm.update(x)\n    if ddm.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if ddm.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 1084\nChange detected at index 1334\nWarning detected at index 1492\n</code></pre></p>"},{"location":"api/drift/binary/DDM/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p> <ol> <li> <p>Jo\u00e3o Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/EDDM/","title":"EDDM","text":"<p>Early Drift Detection Method.</p> <p>EDDM (Early Drift Detection Method) aims to improve the detection rate of gradual concept drift in DDM, while keeping a good performance against abrupt concept drift. </p> <p>This method works by keeping track of the average distance between two errors instead of only the error rate. For this, it is necessary to keep track of the running average distance and the running standard deviation, as well as the maximum distance and the maximum standard deviation. </p> <p>The algorithm works similarly to the DDM algorithm, by keeping track of statistics only. It works with the running average distance (\\(p_i'\\)) and the running standard deviation (\\(s_i'\\)), as well as \\(p'_{max}\\) and \\(s'_{max}\\), which are the values of \\(p_i'\\) and \\(s_i'\\) when \\((p_i' + 2 * s_i')\\) reaches its maximum. </p> <p>Like DDM, there are two threshold values that define the borderline between no change, warning zone, and drift detected. These are as follows: </p> <ul> <li> <p>if \\((p_i' + 2 * s_i') / (p'_{max} + 2 * s'_{max}) &lt; \\alpha\\) -&gt; Warning zone </p> </li> <li> <p>if \\((p_i' + 2 * s_i') / (p'_{max} + 2 * s'_{max}) &lt; \\beta\\) -&gt; Change detected </p> </li> </ul> <p>\\(\\alpha\\) and \\(\\beta\\) are set to 0.95 and 0.9, respectively. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\\\neq y'\\)</p> </li> </ul>"},{"location":"api/drift/binary/EDDM/#parameters","title":"Parameters","text":"<ul> <li> <p>warm_start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum required number of monitored errors/failures so change can be detected. Warm start parameter for the drift detector.</p> </li> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>Threshold for triggering a warning. Must be between 0 and 1. The smaller the value, the more conservative the detector becomes.</p> </li> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.9</code></p> <p>Threshold for triggering a drift. Must be between 0 and 1. The smaller the value, the more conservative the detector becomes.</p> </li> </ul>"},{"location":"api/drift/binary/EDDM/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/EDDM/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\neddm = drift.binary.EDDM(alpha=0.8, beta=0.75)\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    eddm.update(x)\n    if eddm.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if eddm.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 1059\nChange detected at index 1278\n</code></pre></p>"},{"location":"api/drift/binary/EDDM/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BinaryDriftDetector:     self</p> <p></p> <ol> <li> <p>Early Drift Detection Method. Manuel Baena-Garcia, Jose Del Campo-Avila, Ra\u00fal Fidalgo, Albert Bifet, Ricard Gavalda, Rafael Morales-Bueno. In Fourth International Workshop on Knowledge Discovery from Data Streams, 2006.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/FHDDM/","title":"FHDDM","text":"<p>Fast Hoeffding Drift Detection Method.</p> <p>FHDDM is a drift detection method based on the Hoeffding's inequality which uses the input average as estimator. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul> <p>Implementation based on MOA.</p>"},{"location":"api/drift/binary/FHDDM/#parameters","title":"Parameters","text":"<ul> <li> <p>sliding_window_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>The minimum required number of analyzed samples so change can be detected.</p> </li> <li> <p>confidence_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-06</code></p> <p>Confidence level used to determine the epsilon coefficient in Hoeffding\u2019s inequality. The default value gives a 99\\% of confidence level to the drift assessment.</p> </li> <li> <p>short_window_size</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The size of the short window size that it is used in a Stacking version of FHDDM <sup>2</sup>.</p> </li> </ul>"},{"location":"api/drift/binary/FHDDM/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/FHDDM/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nfhddm = drift.binary.FHDDM()\nfhddm_s = drift.binary.FHDDM(short_window_size = 20)\ndata_stream = rng.choices([0, 1], k=250)\ndata_stream = data_stream + rng.choices([0, 1], k=250, weights=[0.9, 0.1])\nfor i, x in enumerate(data_stream):\n    fhddm.update(x)\n    fhddm_s.update(x)\n    if fhddm.drift_detected or fhddm_s.drift_detected:\n        print(f\"Change detected at index {i}\")\n</code></pre> <pre><code>Change detected at index 279\nChange detected at index 315\n</code></pre></p>"},{"location":"api/drift/binary/FHDDM/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p> <ol> <li> <p>A. Pesaranghader, H.L. Viktor, Fast Hoeffding Drift Detection Method for Evolving Data Streams. In the Proceedings of ECML-PKDD 2016.\u00a0\u21a9</p> </li> <li> <p>Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/HDDM-A/","title":"HDDM_A","text":"<p>Drift Detection Method based on Hoeffding's bounds with moving average-test.</p> <p>HDDM_A is a drift detection method based on the Hoeffding's inequality which uses the input average as estimator. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul> <p>Implementation based on MOA.</p>"},{"location":"api/drift/binary/HDDM-A/#parameters","title":"Parameters","text":"<ul> <li> <p>drift_confidence</p> <p>Default \u2192 <code>0.001</code></p> <p>Confidence to the drift</p> </li> <li> <p>warning_confidence</p> <p>Default \u2192 <code>0.005</code></p> <p>Confidence to the warning</p> </li> <li> <p>two_sided_test</p> <p>Default \u2192 <code>False</code></p> <p>If <code>True</code>, will monitor error increments and decrements (two-sided). By default will only monitor increments (one-sided).</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-A/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-A/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nhddm_a = drift.binary.HDDM_A()\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    hddm_a.update(x)\n    if hddm_a.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if hddm_a.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 451\nChange detected at index 1206\n</code></pre></p>"},{"location":"api/drift/binary/HDDM-A/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BinaryDriftDetector:     self</p> <p></p> <ol> <li> <p>Fr\u00edas-Blanco I, del Campo-\u00c1vila J, Ramos-Jimenez G, et al. Online and non-parametric drift detection methods based on Hoeffding's bounds. IEEE Transactions on Knowledge and Data Engineering, 2014, 27(3): 810-823.\u00a0\u21a9</p> </li> <li> <p>Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer. MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604, 2010.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/HDDM-W/","title":"HDDM_W","text":"<p>Drift Detection Method based on Hoeffding's bounds with moving weighted average-test.</p> <p>HDDM_W is an online drift detection method based on McDiarmid's bounds. HDDM_W uses the Exponentially Weighted Moving Average (EWMA) statistic as estimator. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul> <p>Implementation based on MOA.</p>"},{"location":"api/drift/binary/HDDM-W/#parameters","title":"Parameters","text":"<ul> <li> <p>drift_confidence</p> <p>Default \u2192 <code>0.001</code></p> <p>Confidence to the drift</p> </li> <li> <p>warning_confidence</p> <p>Default \u2192 <code>0.005</code></p> <p>Confidence to the warning</p> </li> <li> <p>lambda_val</p> <p>Default \u2192 <code>0.05</code></p> <p>The weight given to recent data. Smaller values mean less weight given to recent data.</p> </li> <li> <p>two_sided_test</p> <p>Default \u2192 <code>False</code></p> <p>If True, will monitor error increments and decrements (two-sided). By default will only monitor increments (one-sided).</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-W/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-W/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nhddm_w = drift.binary.HDDM_W()\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    hddm_w.update(x)\n    if hddm_w.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if hddm_w.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 451\nChange detected at index 1077\n</code></pre></p>"},{"location":"api/drift/binary/HDDM-W/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BinaryDriftDetector:     self</p> <p></p> <ol> <li> <p>Fr\u00edas-Blanco I, del Campo-\u00c1vila J, Ramos-Jimenez G, et al. Online and non-parametric drift  detection methods based on Hoeffding\u2019s bounds. IEEE Transactions on Knowledge and Data  Engineering, 2014, 27(3): 810-823.\u00a0\u21a9</p> </li> <li> <p>Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer. MOA: Massive Online Analysis;  Journal of Machine Learning Research 11: 1601-1604, 2010.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/AirlinePassengers/","title":"AirlinePassengers","text":"<p>JFK Airline Passengers</p> <p>This dataset gives the number of passengers arriving and departing at JFK. The data is obtained from New York State's official Kaggle page for this dataset.</p>"},{"location":"api/drift/datasets/AirlinePassengers/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/AirlinePassengers/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://www.kaggle.com/new-york-state/nys-air-passenger-traffic,-port-authority-of-ny-nj#air-passenger-traffic-per-month-port-authority-of-ny-nj-beginning-1977.csv\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/Apple/","title":"Apple","text":"<p>Apple Stock</p> <p>This dataset concerns the daily close price and volume of Apple stock around the year 2000. The dataset is sampled every 3 observations to reduce the length of the time series. This dataset is retrieved from Yahoo Finance.</p>"},{"location":"api/drift/datasets/Apple/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/Apple/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://finance.yahoo.com/quote/AAPL/history?period1=850348800&amp;period2=1084579200&amp;interval=1d&amp;filter=history&amp;frequency=1d\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/Bitcoin/","title":"Bitcoin","text":"<p>Bitcoin Market Price</p> <p>This is a regression task, where the goal is to predict the average USD market price across major bitcoin exchanges. This data was collected from the official Blockchain website. There is only one feature given, the day of exchange, which is in increments of three. The first 500 lines have been removed because they are not interesting.</p>"},{"location":"api/drift/datasets/Bitcoin/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/Bitcoin/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://www.blockchain.com/fr/explorer/charts/market-price?timespan=all\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/BrentSpotPrice/","title":"BrentSpotPrice","text":"<p>Brent Spot Price</p> <p>This is the USD price for Brent Crude oil, measured daily. We include the time series     from 2000 onwards. The data is sampled at every 10 original observations to reduce the length of the series. </p> <p>The data is obtained from the U.S. Energy Information Administration. Since the data is in the public domain,     we distribute it as part of this repository. </p> <p>Since the original data has observations only on trading days, there are arguably gaps in this time     series (on non-trading days). However we consider these to be consecutive, and thus also consider     the sampled time series to have consecutive observations.</p>"},{"location":"api/drift/datasets/BrentSpotPrice/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/BrentSpotPrice/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>U.S. Energy Information Administration (Sep. 2019)\u00a0\u21a9</p> </li> <li> <p>https://www.eia.gov/opendata/v1/qb.php?sdid=PET.RBRTE.D\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/Occupancy/","title":"Occupancy","text":"<p>Room occupancy data.</p> <p>Dataset on detecting room occupancy based on several variables. The dataset contains temperature, humidity, light, and CO2 variables. </p> <p>The data is sampled at every 16 observations to reduce the length of the series.</p>"},{"location":"api/drift/datasets/Occupancy/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/Occupancy/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p> Candanedo, Luis M., and V\u00e9ronique Feldheim. \"Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models.\" Energy and Buildings 112 (2016): 28-39.</p>"},{"location":"api/drift/datasets/RunLog/","title":"RunLog","text":"<p>Interval Training Running Pace.</p> <p>This dataset shows the pace of a runner during an interval training session, where a mobile application provides instructions on when to run and when to walk.</p>"},{"location":"api/drift/datasets/RunLog/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/RunLog/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/drift/datasets/UKCoalEmploy/","title":"UKCoalEmploy","text":"<p>Historic Employment in UK Coal Mines</p> <p>This is historic data obtained from the UK government.     We use the employment column for the number of workers employed in the British coal mines     Missing values in the data are replaced with the value of the preceding year.</p>"},{"location":"api/drift/datasets/UKCoalEmploy/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/UKCoalEmploy/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://www.gov.uk/government/statistical-data-sets/historical-coal-data-coal-production-availability-and-consumption\u00a0\u21a9</p> </li> </ol>"},{"location":"api/dummy/NoChangeClassifier/","title":"NoChangeClassifier","text":"<p>Dummy classifier which returns the last class seen.</p> <p>The predict_one method will output the last class seen whilst predict_proba_one will return 1 for the last class seen and 0 for the others.</p>"},{"location":"api/dummy/NoChangeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>last_class</p> <p>The last class seen.</p> </li> <li> <p>classes</p> <p>The set of classes seen.</p> </li> </ul>"},{"location":"api/dummy/NoChangeClassifier/#examples","title":"Examples","text":"<p>Taken from example 2.1 from this page.</p> <p><pre><code>import pprint\nfrom river import dummy\n\nsentences = [\n    ('glad happy glad', '+'),\n    ('glad glad joyful', '+'),\n    ('glad pleasant', '+'),\n    ('miserable sad glad', '\u2212')\n]\n\nmodel = dummy.NoChangeClassifier()\n\nfor sentence, label in sentences:\n    model.learn_one(sentence, label)\n\nnew_sentence = 'glad sad miserable pleasant glad'\nmodel.predict_one(new_sentence)\n</code></pre> <pre><code>'\u2212'\n</code></pre></p> <p><pre><code>pprint.pprint(model.predict_proba_one(new_sentence))\n</code></pre> <pre><code>{'+': 0, '\u2212': 1}\n</code></pre></p>"},{"location":"api/dummy/NoChangeClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/dummy/PriorClassifier/","title":"PriorClassifier","text":"<p>Dummy classifier which uses the prior distribution.</p> <p>The <code>predict_one</code> method will output the most common class whilst <code>predict_proba_one</code> will return the normalized class counts.</p>"},{"location":"api/dummy/PriorClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>counts (collections.Counter)</p> <p>Class counts.</p> </li> <li> <p>n (int)</p> <p>Total number of seen instances.</p> </li> </ul>"},{"location":"api/dummy/PriorClassifier/#examples","title":"Examples","text":"<p>Taken from example 2.1 from this page</p> <p><pre><code>from river import dummy\n\nsentences = [\n    ('glad happy glad', '+'),\n    ('glad glad joyful', '+'),\n    ('glad pleasant', '+'),\n    ('miserable sad glad', '\u2212')\n]\n\nmodel = dummy.PriorClassifier()\n\nfor sentence, label in sentences:\n    model.learn_one(sentence, label)\n\nnew_sentence = 'glad sad miserable pleasant glad'\nmodel.predict_one(new_sentence)\n</code></pre> <pre><code>'+'\n</code></pre> <pre><code>model.predict_proba_one(new_sentence)\n</code></pre> <pre><code>{'+': 0.75, '\u2212': 0.25}\n</code></pre></p>"},{"location":"api/dummy/PriorClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Krichevsky\u2013Trofimov estimator \u21a9</p> </li> </ol>"},{"location":"api/dummy/StatisticRegressor/","title":"StatisticRegressor","text":"<p>Dummy regressor that uses a univariate statistic to make predictions.</p>"},{"location":"api/dummy/StatisticRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>statistic</p> <p>Type \u2192 stats.base.Univariate</p> </li> </ul>"},{"location":"api/dummy/StatisticRegressor/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nfrom river import dummy\nfrom river import stats\n\nsentences = [\n    ('glad happy glad', 3),\n    ('glad glad joyful', 3),\n    ('glad pleasant', 2),\n    ('miserable sad glad', -3)\n]\n\nmodel = dummy.StatisticRegressor(stats.Mean())\n\nfor sentence, score in sentences:\n    model.learn_one(sentence, score)\n\nnew_sentence = 'glad sad miserable pleasant glad'\nmodel.predict_one(new_sentence)\n</code></pre> <pre><code>1.25\n</code></pre></p>"},{"location":"api/dummy/StatisticRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/ensemble/ADWINBaggingClassifier/","title":"ADWINBaggingClassifier","text":"<p>ADWIN Bagging classifier.</p> <p>ADWIN Bagging <sup>1</sup> is the online bagging method of Oza and Russell <sup>2</sup> with the addition of the <code>ADWIN</code> algorithm as a change detector. If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.</p>"},{"location":"api/ensemble/ADWINBaggingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to bag.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/ADWINBaggingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/ADWINBaggingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = ensemble.ADWINBaggingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.65%\n</code></pre></p>"},{"location":"api/ensemble/ADWINBaggingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Averages the predictions of each classifier.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p> <ol> <li> <p>Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavald\u00e0. \"New ensemble methods for evolving data streams.\" In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.\u00a0\u21a9</p> </li> <li> <p>Oza, N., Russell, S. \"Online bagging and boosting.\" In: Artificial Intelligence and Statistics 2001, pp. 105\u2013112. Morgan Kaufmann, 2001.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/ADWINBoostingClassifier/","title":"ADWINBoostingClassifier","text":"<p>ADWIN Boosting classifier.</p> <p>ADWIN Boosting <sup>1</sup> is the online boosting method of Oza and Russell <sup>2</sup> with the addition of the <code>ADWIN</code> algorithm as a change detector. If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.</p>"},{"location":"api/ensemble/ADWINBoostingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to boost.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/ADWINBoostingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/ADWINBoostingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\nmodel = ensemble.ADWINBoostingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.61%\n</code></pre></p>"},{"location":"api/ensemble/ADWINBoostingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavald\u00e0. \"New ensemble methods for evolving data streams.\" In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.\u00a0\u21a9</p> </li> <li> <p>Oza, N., Russell, S. \"Online bagging and boosting.\" In: Artificial Intelligence and Statistics 2001, pp. 105\u2013112. Morgan Kaufmann, 2001.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/AdaBoostClassifier/","title":"AdaBoostClassifier","text":"<p>Boosting for classification.</p> <p>For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter lambda. The lambda parameter is updated when the weaks learners fit successively the same observation.</p>"},{"location":"api/ensemble/AdaBoostClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to boost.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/AdaBoostClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/AdaBoostClassifier/#examples","title":"Examples","text":"<p>In the following example three tree classifiers are boosted together. The performance is slightly better than when using a single tree.</p> <p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Phishing()\n\nmetric = metrics.LogLoss()\n\nmodel = ensemble.AdaBoostClassifier(\n    model=(\n        tree.HoeffdingTreeClassifier(\n            split_criterion='gini',\n            delta=1e-5,\n            grace_period=2000\n        )\n    ),\n    n_models=5,\n    seed=42\n)\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.370805\n</code></pre></p> <p><pre><code>print(model)\n</code></pre> <pre><code>AdaBoostClassifier(HoeffdingTreeClassifier)\n</code></pre></p>"},{"location":"api/ensemble/AdaBoostClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/BOLEClassifier/","title":"BOLEClassifier","text":"<p>Boosting Online Learning Ensemble (BOLE).</p> <p>A modified version of Oza Online Boosting Algorithm <sup>1</sup>. For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter lambda. The first model to be trained will be the one with worst <code>correct_weight / (correct_weight + wrong_weight)</code>. The worst model not yet trained will receive lambda values for training from the models that incorrectly classified an instance, and the best model's not yet trained will receive lambda values for training from the models that correctly classified an instance. For more details, see <sup>2</sup>.</p>"},{"location":"api/ensemble/BOLEClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to boost.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> <li> <p>error_bound</p> <p>Default \u2192 <code>0.5</code></p> <p>Error bound percentage for allowing models to vote.</p> </li> </ul>"},{"location":"api/ensemble/BOLEClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/BOLEClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import drift\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Elec2().take(3000)\n\nmodel = ensemble.BOLEClassifier(\n    model=drift.DriftRetrainingClassifier(\n        model=tree.HoeffdingTreeClassifier(),\n        drift_detector=drift.binary.DDM()\n    ),\n    n_models=10,\n    seed=42\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 93.63%\n</code></pre></p>"},{"location":"api/ensemble/BOLEClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> <li> <p>R. S. M. d. Barros, S. Garrido T. de Carvalho Santos and P. M. Gon\u00e7alves J\u00fanior, \"A Boosting-like Online Learning Ensemble,\" 2016 International Joint Conference on Neural Networks (IJCNN), 2016, pp. 1871-1878, doi: 10.1109/IJCNN.2016.7727427.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/BaggingClassifier/","title":"BaggingClassifier","text":"<p>Online bootstrap aggregation for classification.</p> <p>For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter 1. <code>k</code> thus has a 36% chance of being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6% chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do <code>scipy.stats.utils.random.poisson(1).pmf(k)</code> to obtain more detailed values.</p>"},{"location":"api/ensemble/BaggingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to bag.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/BaggingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/BaggingClassifier/#examples","title":"Examples","text":"<p>In the following example three logistic regressions are bagged together. The performance is slightly better than when using a single logistic regression.</p> <p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = ensemble.BaggingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.65%\n</code></pre></p> <p><pre><code>print(model)\n</code></pre> <pre><code>BaggingClassifier(StandardScaler | LogisticRegression)\n</code></pre></p>"},{"location":"api/ensemble/BaggingClassifier/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Averages the predictions of each classifier.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/BaggingRegressor/","title":"BaggingRegressor","text":"<p>Online bootstrap aggregation for regression.</p> <p>For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter 1. <code>k</code> thus has a 36% chance of being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6% chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do <code>scipy.stats.utils.random.poisson(1).pmf(k)</code> for more detailed values.</p>"},{"location":"api/ensemble/BaggingRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Regressor</p> <p>The regressor to bag.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/BaggingRegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/BaggingRegressor/#examples","title":"Examples","text":"<p>In the following example three logistic regressions are bagged together. The performance is slightly better than when using a single logistic regression.</p> <p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = preprocessing.StandardScaler()\nmodel |= ensemble.BaggingRegressor(\n    model=linear_model.LinearRegression(intercept_lr=0.1),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.677586\n</code></pre></p>"},{"location":"api/ensemble/BaggingRegressor/#methods","title":"Methods","text":"learn_one predict_one <p>Averages the predictions of each regressor.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/EWARegressor/","title":"EWARegressor","text":"<p>Exponentially Weighted Average regressor.</p>"},{"location":"api/ensemble/EWARegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Regressor]</p> <p>The regressors to hedge.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function that has to be minimized. Defaults to <code>optim.losses.Squared</code>.</p> </li> <li> <p>learning_rate</p> <p>Default \u2192 <code>0.5</code></p> <p>The learning rate by which the model weights are multiplied at each iteration.</p> </li> </ul>"},{"location":"api/ensemble/EWARegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/EWARegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\nfrom river import stream\n\noptimizers = [\n    optim.SGD(0.01),\n    optim.RMSProp(),\n    optim.AdaGrad()\n]\n\nfor optimizer in optimizers:\n\n    dataset = datasets.TrumpApproval()\n    metric = metrics.MAE()\n    model = (\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression(\n            optimizer=optimizer,\n            intercept_lr=.1\n        )\n    )\n\n    print(optimizer, evaluate.progressive_val_score(dataset, model, metric))\n</code></pre> <pre><code>SGD MAE: 0.558735\nRMSProp MAE: 0.522449\nAdaGrad MAE: 0.477289\n</code></pre></p> <p><pre><code>dataset = datasets.TrumpApproval()\nmetric = metrics.MAE()\nhedge = (\n    preprocessing.StandardScaler() |\n    ensemble.EWARegressor(\n        [\n            linear_model.LinearRegression(optimizer=o, intercept_lr=.1)\n            for o in optimizers\n        ],\n        learning_rate=0.005\n    )\n)\n\nevaluate.progressive_val_score(dataset, hedge, metric)\n</code></pre> <pre><code>MAE: 0.496298\n</code></pre></p>"},{"location":"api/ensemble/EWARegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> learn_predict_one predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Online Learning from Experts: Weighed Majority and Hedge \u21a9</p> </li> <li> <p>Wikipedia page on the multiplicative weight update method \u21a9</p> </li> <li> <p>Kivinen, J. and Warmuth, M.K., 1997. Exponentiated gradient versus gradient descent for linear predictors. information and computation, 132(1), pp.1-63. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/LeveragingBaggingClassifier/","title":"LeveragingBaggingClassifier","text":"<p>Leveraging Bagging ensemble classifier.</p> <p>Leveraging Bagging [^1] is an improvement over the Oza Bagging algorithm. The bagging performance is leveraged by increasing the re-sampling. It uses a poisson distribution to simulate the re-sampling process. To increase re-sampling it uses a higher <code>w</code> value of the Poisson distribution (agerage number of events), 6 by default, increasing the input space diversity, by attributing a different range of weights to the data samples. </p> <p>To deal with concept drift, Leveraging Bagging uses the ADWIN algorithm to monitor the performance of each member of the enemble If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.</p>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to bag.</p> </li> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>w</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>6</code></p> <p>Indicates the average number of events. This is the lambda parameter of the Poisson distribution used to compute the re-sampling weight.</p> </li> <li> <p>adwin_delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.002</code></p> <p>The delta parameter for the ADWIN change detector.</p> </li> <li> <p>bagging_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>bag</code></p> <p>The bagging method to use. Can be one of the following: * 'bag' - Leveraging Bagging using ADWIN. * 'me' - Assigns \\(weight=1\\) if sample is misclassified,   otherwise \\(weight=error/(1-error)\\). * 'half' - Use resampling without replacement for half of the instances. * 'wt' - Resample without taking out all instances. * 'subag' - Resampling without replacement.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>bagging_methods</p> <p>Valid bagging_method options.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = ensemble.LeveragingBaggingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.55%\n</code></pre></p>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Averages the predictions of each classifier.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p>"},{"location":"api/ensemble/SRPClassifier/","title":"SRPClassifier","text":"<p>Streaming Random Patches ensemble classifier.</p> <p>The Streaming Random Patches (SRP) <sup>1</sup> is an ensemble method that simulates bagging or random subspaces. The default algorithm uses both bagging and random subspaces, namely Random Patches. The default base estimator is a Hoeffding Tree, but other base estimators can be used (differently from random forest variations).</p>"},{"location":"api/ensemble/SRPClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Estimator | None</p> <p>Default \u2192 <code>None</code></p> <p>The base estimator.</p> </li> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of members in the ensemble.</p> </li> <li> <p>subspace_size</p> <p>Type \u2192 int | float | str</p> <p>Default \u2192 <code>0.6</code></p> <p>Number of features per subset for each classifier where <code>M</code> is the total number of features. A negative value means <code>M - subspace_size</code>. Only applies when using random subspaces or random patches. * If <code>int</code> indicates the number of features to use. Valid range [2, M].  * If <code>float</code> indicates the percentage of features to use, Valid range (0., 1.].  * 'sqrt' - <code>sqrt(M)+1</code> * 'rmsqrt' - Residual from <code>M-(sqrt(M)+1)</code></p> </li> <li> <p>training_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>patches</code></p> <p>The training method to use. * 'subspaces' - Random subspaces. * 'resampling' - Resampling. * 'patches' - Random patches.</p> </li> <li> <p>lam</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>Lambda value for resampling.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift detector.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning detector.</p> </li> <li> <p>disable_detector</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>off</code></p> <p>Option to disable drift detectors: * If <code>'off'</code>, detectors are enabled. * If <code>'drift'</code>, disables concept drift detection and the background learner. * If <code>'warning'</code>, disables the background learner and ensemble members are  reset if drift is detected.</p> </li> <li> <p>disable_weighted_vote</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disables weighted voting.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> <li> <p>metric</p> <p>Type \u2192 ClassificationMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The metric to track members performance within the ensemble. This implementation assumes that larger values are better when using weighted votes.</p> </li> </ul>"},{"location":"api/ensemble/SRPClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/SRPClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import ensemble\nfrom river import evaluate\nfrom river import metrics\nfrom river.datasets import synth\nfrom river import tree\n\ndataset = synth.ConceptDriftStream(\n    seed=42,\n    position=500,\n    width=50\n).take(1000)\n\nbase_model = tree.HoeffdingTreeClassifier(\n    grace_period=50, delta=0.01,\n    nominal_attributes=['age', 'car', 'zipcode']\n)\nmodel = ensemble.SRPClassifier(\n    model=base_model, n_models=3, seed=42,\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 71.97%\n</code></pre></p>"},{"location":"api/ensemble/SRPClassifier/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> reset"},{"location":"api/ensemble/SRPClassifier/#notes","title":"Notes","text":"<p>This implementation uses <code>n_models=10</code> as default given the impact on processing time. The optimal number of models depends on the data and resources available.</p> <ol> <li> <p>Heitor Murilo Gomes, Jesse Read, Albert Bifet.   Streaming Random Patches for Evolving Data Stream Classification.   IEEE International Conference on Data Mining (ICDM), 2019.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/SRPRegressor/","title":"SRPRegressor","text":"<p>Streaming Random Patches ensemble regressor.</p> <p>The Streaming Random Patches <sup>1</sup> ensemble method for regression trains each base learner on a subset of features and instances from the original data, namely a random patch. This strategy to enforce diverse base models is similar to the one in the random forest, yet it is not restricted to using decision trees as base learner. </p> <p>This method is an adaptation of <sup>2</sup> for regression.</p>"},{"location":"api/ensemble/SRPRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The base estimator.</p> </li> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of members in the ensemble.</p> </li> <li> <p>subspace_size</p> <p>Type \u2192 int | float | str</p> <p>Default \u2192 <code>0.6</code></p> <p>Number of features per subset for each classifier where <code>M</code> is the total number of features. A negative value means <code>M - subspace_size</code>. Only applies when using random subspaces or random patches. * If <code>int</code> indicates the number of features to use. Valid range [2, M].  * If <code>float</code> indicates the percentage of features to use, Valid range (0., 1.].  * 'sqrt' - <code>sqrt(M)+1</code> * 'rmsqrt' - Residual from <code>M-(sqrt(M)+1)</code></p> </li> <li> <p>training_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>patches</code></p> <p>The training method to use. * 'subspaces' - Random subspaces. * 'resampling' - Resampling. * 'patches' - Random patches.</p> </li> <li> <p>lam</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>Lambda value for bagging.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift detector.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning detector.</p> </li> <li> <p>disable_detector</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>off</code></p> <p>Option to disable drift detectors: * If <code>'off'</code>, detectors are enabled. * If <code>'drift'</code>, disables concept drift detection and the background learner. * If <code>'warning'</code>, disables the background learner and ensemble members are  reset if drift is detected.</p> </li> <li> <p>disable_weighted_vote</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, disables weighted voting.</p> </li> <li> <p>drift_detection_criteria</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>error</code></p> <p>The criteria used to track drifts. * 'error' - absolute error. * 'prediction' - predicted target values.</p> </li> <li> <p>aggregation_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>mean</code></p> <p>The method to use to aggregate predictions in the ensemble. * 'mean' * 'median'</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> <li> <p>metric</p> <p>Type \u2192 RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The metric to track members performance within the ensemble.</p> </li> </ul>"},{"location":"api/ensemble/SRPRegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/SRPRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import ensemble\nfrom river import evaluate\nfrom river import metrics\nfrom river.datasets import synth\nfrom river import tree\n\ndataset = synth.FriedmanDrift(\n    drift_type='gsg',\n    position=(350, 750),\n    transition_window=200,\n    seed=42\n).take(1000)\n\nbase_model = tree.HoeffdingTreeRegressor(grace_period=50)\nmodel = ensemble.SRPRegressor(\n    model=base_model,\n    training_method=\"patches\",\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.R2()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>R2: 0.571117\n</code></pre></p>"},{"location":"api/ensemble/SRPRegressor/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> reset"},{"location":"api/ensemble/SRPRegressor/#notes","title":"Notes","text":"<p>This implementation uses <code>n_models=10</code> as default given the impact on processing time. The optimal number of models depends on the data and resources available.</p> <ol> <li> <p>Heitor Gomes, Jacob Montiel, Saulo Martiello Mastelini,   Bernhard Pfahringer, and Albert Bifet.   On Ensemble Techniques for Data Stream Regression.   IJCNN'20. International Joint Conference on Neural Networks. 2020.\u00a0\u21a9</p> </li> <li> <p>Heitor Murilo Gomes, Jesse Read, Albert Bifet.   Streaming Random Patches for Evolving Data Stream Classification.   IEEE International Conference on Data Mining (ICDM), 2019.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/StackingClassifier/","title":"StackingClassifier","text":"<p>Stacking for binary classification.</p>"},{"location":"api/ensemble/StackingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Classifier]</p> </li> <li> <p>meta_classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>include_features</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the original features should be provided to the meta-model along with the predictions from each model.</p> </li> </ul>"},{"location":"api/ensemble/StackingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/StackingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import compose\nfrom river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model as lm\nfrom river import metrics\nfrom river import preprocessing as pp\n\ndataset = datasets.Phishing()\n\nmodel = compose.Pipeline(\n    ('scale', pp.StandardScaler()),\n    ('stack', ensemble.StackingClassifier(\n        [\n            lm.LogisticRegression(),\n            lm.PAClassifier(mode=1, C=0.01),\n            lm.PAClassifier(mode=2, C=0.01),\n        ],\n        meta_classifier=lm.LogisticRegression()\n    ))\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.14%\n</code></pre></p>"},{"location":"api/ensemble/StackingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>A Kaggler's Guide to Model Stacking in Practice \u21a9</p> </li> </ol>"},{"location":"api/ensemble/VotingClassifier/","title":"VotingClassifier","text":"<p>Voting classifier.</p> <p>A classification is made by aggregating the predictions of each model in the ensemble. The probabilities for each class are summed up if <code>use_probabilities</code> is set to <code>True</code>. If not, the probabilities are ignored and each prediction is weighted the same. In this case, it's important that you use an odd number of classifiers. A random class will be picked if the number of classifiers is even.</p>"},{"location":"api/ensemble/VotingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Classifier]</p> <p>The classifiers.</p> </li> <li> <p>use_probabilities</p> <p>Default \u2192 <code>True</code></p> <p>Whether or to weight each prediction with its associated probability.</p> </li> </ul>"},{"location":"api/ensemble/VotingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/VotingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import naive_bayes\nfrom river import preprocessing\nfrom river import tree\n\ndataset = datasets.Phishing()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    ensemble.VotingClassifier([\n        linear_model.LogisticRegression(),\n        tree.HoeffdingTreeClassifier(),\n        naive_bayes.GaussianNB()\n    ])\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.94%\n</code></pre></p>"},{"location":"api/ensemble/VotingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/evaluate/BinaryClassificationTrack/","title":"BinaryClassificationTrack","text":"<p>This track evaluates a model's performance on binary classification tasks. These do not include synthetic datasets.</p>"},{"location":"api/evaluate/BinaryClassificationTrack/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/MultiClassClassificationTrack/","title":"MultiClassClassificationTrack","text":"<p>This track evaluates a model's performance on multi-class classification tasks. These do not include synthetic datasets.</p>"},{"location":"api/evaluate/MultiClassClassificationTrack/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/RegressionTrack/","title":"RegressionTrack","text":"<p>This track evaluates a model's performance on regression tasks. These do not include synthetic datasets.</p>"},{"location":"api/evaluate/RegressionTrack/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/Track/","title":"Track","text":"<p>A track evaluate a model's performance.</p> <p>The following metrics are recorded: </p> <ul> <li> <p>Time, which should be interpreted with wisdom. Indeed time can depend on the architecture</p> <p>and local resource situations. Comparison via FLOPS should be preferred. - The model's memory footprint.</p> </li> <li> <p>The model's predictive performance on the track's dataset.</p> </li> </ul>"},{"location":"api/evaluate/Track/#parameters","title":"Parameters","text":"<ul> <li> <p>name</p> <p>Type \u2192 str</p> <p>The name of the track.</p> </li> <li> <p>datasets</p> <p>The datasets that compose the track.</p> </li> <li> <p>metric</p> <p>The metric(s) used to track performance.</p> </li> </ul>"},{"location":"api/evaluate/Track/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/iter-progressive-val-score/","title":"iter_progressive_val_score","text":"<p>Evaluates the performance of a model on a streaming dataset and yields results.</p> <p>This does exactly the same as <code>evaluate.progressive_val_score</code>. The only difference is that this function returns an iterator, yielding results at every step. This can be useful if you want to have control over what you do with the results. For instance, you might want to plot the results.</p>"},{"location":"api/evaluate/iter-progressive-val-score/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>The stream of observations against which the model will be evaluated.</p> </li> <li> <p>model</p> <p>The model to evaluate.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>The metric used to evaluate the model's predictions.</p> </li> <li> <p>moment</p> <p>Type \u2192 str | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The attribute used for measuring time. If a callable is passed, then it is expected to take as input a <code>dict</code> of features. If <code>None</code>, then the observations are implicitly timestamped in the order in which they arrive.</p> </li> <li> <p>delay</p> <p>Type \u2192 str | int | dt.timedelta | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The amount to wait before revealing the target associated with each observation to the model. This value is expected to be able to sum with the <code>moment</code> value. For instance, if <code>moment</code> is a <code>datetime.date</code>, then <code>delay</code> is expected to be a <code>datetime.timedelta</code>. If a callable is passed, then it is expected to take as input a <code>dict</code> of features and the target. If a <code>str</code> is passed, then it will be used to access the relevant field from the features. If <code>None</code> is passed, then no delay will be used, which leads to doing standard online validation.</p> </li> <li> <p>step</p> <p>Default \u2192 <code>1</code></p> <p>Iteration number at which to yield results. This only takes into account the predictions, and not the training steps.</p> </li> <li> <p>measure_time</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to measure the elapsed time.</p> </li> <li> <p>measure_memory</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to measure the memory usage of the model.</p> </li> <li> <p>yield_predictions</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to include predictions. If step is 1, then this is equivalent to yielding the predictions at every iterations. Otherwise, not all predictions will be yielded.</p> </li> </ul>"},{"location":"api/evaluate/iter-progressive-val-score/#examples","title":"Examples","text":"<p>Take the following model:</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>We can evaluate it on the <code>Phishing</code> dataset as so:</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nsteps = evaluate.iter_progressive_val_score(\n    model=model,\n    dataset=datasets.Phishing(),\n    metric=metrics.ROCAUC(),\n    step=200\n)\n\nfor step in steps:\n    print(step)\n</code></pre> <pre><code>{'ROCAUC': ROCAUC: 90.20%, 'Step': 200}\n{'ROCAUC': ROCAUC: 92.25%, 'Step': 400}\n{'ROCAUC': ROCAUC: 93.23%, 'Step': 600}\n{'ROCAUC': ROCAUC: 94.05%, 'Step': 800}\n{'ROCAUC': ROCAUC: 94.79%, 'Step': 1000}\n{'ROCAUC': ROCAUC: 95.07%, 'Step': 1200}\n{'ROCAUC': ROCAUC: 95.07%, 'Step': 1250}\n</code></pre></p> <p>The <code>yield_predictions</code> parameter can be used to include the predictions in the results:</p> <p><pre><code>import itertools\n\nsteps = evaluate.iter_progressive_val_score(\n    model=model,\n    dataset=datasets.Phishing(),\n    metric=metrics.ROCAUC(),\n    step=1,\n    yield_predictions=True\n)\n\nfor step in itertools.islice(steps, 100, 105):\n   print(step)\n</code></pre> <pre><code>{'ROCAUC': ROCAUC: 94.68%, 'Step': 101, 'Prediction': {False: 0.966..., True: 0.033...}}\n{'ROCAUC': ROCAUC: 94.75%, 'Step': 102, 'Prediction': {False: 0.035..., True: 0.964...}}\n{'ROCAUC': ROCAUC: 94.82%, 'Step': 103, 'Prediction': {False: 0.043..., True: 0.956...}}\n{'ROCAUC': ROCAUC: 94.89%, 'Step': 104, 'Prediction': {False: 0.816..., True: 0.183...}}\n{'ROCAUC': ROCAUC: 94.96%, 'Step': 105, 'Prediction': {False: 0.041..., True: 0.958...}}\n</code></pre></p> <ol> <li> <p>Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation \u21a9</p> </li> <li> <p>Grzenda, M., Gomes, H.M. and Bifet, A., 2019. Delayed labelling evaluation for data streams. Data Mining and Knowledge Discovery, pp.1-30 \u21a9</p> </li> </ol>"},{"location":"api/evaluate/progressive-val-score/","title":"progressive_val_score","text":"<p>Evaluates the performance of a model on a streaming dataset.</p> <p>This method is the canonical way to evaluate a model's performance. When used correctly, it allows you to exactly assess how a model would have performed in a production scenario. </p> <p><code>dataset</code> is converted into a stream of questions and answers. At each step the model is either asked to predict an observation, or is either updated. The target is only revealed to the model after a certain amount of time, which is determined by the <code>delay</code> parameter. Note that under the hood this uses the <code>stream.simulate_qa</code> function to go through the data in arrival order. </p> <p>By default, there is no delay, which means that the samples are processed one after the other. When there is no delay, this function essentially performs progressive validation. When there is a delay, then we refer to it as delayed progressive validation. </p> <p>It is recommended to use this method when you want to determine a model's performance on a dataset. In particular, it is advised to use the <code>delay</code> parameter in order to get a reliable assessment. Indeed, in a production scenario, it is often the case that ground truths are made available after a certain amount of time. By using this method, you can reproduce this scenario and therefore truthfully assess what would have been the performance of a model on a given dataset.</p>"},{"location":"api/evaluate/progressive-val-score/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>The stream of observations against which the model will be evaluated.</p> </li> <li> <p>model</p> <p>The model to evaluate.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>The metric used to evaluate the model's predictions.</p> </li> <li> <p>moment</p> <p>Type \u2192 str | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The attribute used for measuring time. If a callable is passed, then it is expected to take as input a <code>dict</code> of features. If <code>None</code>, then the observations are implicitly timestamped in the order in which they arrive.</p> </li> <li> <p>delay</p> <p>Type \u2192 str | int | dt.timedelta | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The amount to wait before revealing the target associated with each observation to the model. This value is expected to be able to sum with the <code>moment</code> value. For instance, if <code>moment</code> is a <code>datetime.date</code>, then <code>delay</code> is expected to be a <code>datetime.timedelta</code>. If a callable is passed, then it is expected to take as input a <code>dict</code> of features and the target. If a <code>str</code> is passed, then it will be used to access the relevant field from the features. If <code>None</code> is passed, then no delay will be used, which leads to doing standard online validation.</p> </li> <li> <p>print_every</p> <p>Default \u2192 <code>0</code></p> <p>Iteration number at which to print the current metric. This only takes into account the predictions, and not the training steps.</p> </li> <li> <p>show_time</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to display the elapsed time.</p> </li> <li> <p>show_memory</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to display the memory usage of the model.</p> </li> <li> <p>print_kwargs</p> <p>Extra keyword arguments are passed to the <code>print</code> function. For instance, this allows providing a <code>file</code> argument, which indicates where to output progress.</p> </li> </ul>"},{"location":"api/evaluate/progressive-val-score/#examples","title":"Examples","text":"<p>Take the following model:</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>We can evaluate it on the <code>Phishing</code> dataset as so:</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nevaluate.progressive_val_score(\n    model=model,\n    dataset=datasets.Phishing(),\n    metric=metrics.ROCAUC(),\n    print_every=200\n)\n</code></pre> <pre><code>[200] ROCAUC: 90.20%\n[400] ROCAUC: 92.25%\n[600] ROCAUC: 93.23%\n[800] ROCAUC: 94.05%\n[1,000] ROCAUC: 94.79%\n[1,200] ROCAUC: 95.07%\n[1,250] ROCAUC: 95.07%\nROCAUC: 95.07%\n</code></pre></p> <p>We haven't specified a delay, therefore this is strictly equivalent to the following piece of code:</p> <p><pre><code>model = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n\nmetric = metrics.ROCAUC()\n\nfor x, y in datasets.Phishing():\n    y_pred = model.predict_proba_one(x)\n    metric.update(y, y_pred)\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 95.07%\n</code></pre></p> <p>When <code>print_every</code> is specified, the current state is printed at regular intervals. Under the hood, Python's <code>print</code> method is being used. You can pass extra keyword arguments to modify its behavior. For instance, you may use the <code>file</code> argument if you want to log the progress to a file of your choice.</p> <p><pre><code>with open('progress.log', 'w') as f:\n    metric = evaluate.progressive_val_score(\n        model=model,\n        dataset=datasets.Phishing(),\n        metric=metrics.ROCAUC(),\n        print_every=200,\n        file=f\n    )\n\nwith open('progress.log') as f:\n    for line in f.read().splitlines():\n        print(line)\n</code></pre> <pre><code>[200] ROCAUC: 94.00%\n[400] ROCAUC: 94.70%\n[600] ROCAUC: 95.17%\n[800] ROCAUC: 95.42%\n[1,000] ROCAUC: 95.82%\n[1,200] ROCAUC: 96.00%\n[1,250] ROCAUC: 96.04%\n</code></pre></p> <p>Note that the performance is slightly better than above because we haven't used a fresh copy of the model. Instead, we've reused the existing model which has already done a full pass on the data.</p> <pre><code>import os; os.remove('progress.log')\n</code></pre> <ol> <li> <p>Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation \u21a9</p> </li> <li> <p>Grzenda, M., Gomes, H.M. and Bifet, A., 2019. Delayed labelling evaluation for data streams. Data Mining and Knowledge Discovery, pp.1-30 \u21a9</p> </li> </ol>"},{"location":"api/facto/FFMClassifier/","title":"FFMClassifier","text":"<p>Field-aware Factorization Machine for binary classification.</p> <p>The model equation is defined by: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_{j, f_{j'}}, \\mathbf{v}_{j', f_j} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_{j, f_{j'}}\\) is the latent vector corresponding to \\(j\\) feature for \\(f_{j'}\\) field, and \\(\\mathbf{v}_{j', f_j}\\) is the latent vector corresponding to \\(j'\\) feature for \\(f_j\\) field. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FFMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FFMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FFMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, True),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, True),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, True),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, True),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, False)\n)\n\nmodel = facto.FFMClassifier(\n    n_factors=10,\n    intercept=.5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/FFMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Juan, Y., Zhuang, Y., Chin, W.S. and Lin, C.J., 2016, September. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 43-50). \u21a9</p> </li> </ol>"},{"location":"api/facto/FFMRegressor/","title":"FFMRegressor","text":"<p>Field-aware Factorization Machine for regression.</p> <p>The model equation is defined by: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_{j, f_{j'}}, \\mathbf{v}_{j', f_j} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_{j, f_{j'}}\\) is the latent vector corresponding to \\(j\\) feature for \\(f_{j'}\\) field, and \\(\\mathbf{v}_{j', f_j}\\) is the latent vector corresponding to \\(j'\\) feature for \\(f_j\\) field. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FFMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FFMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FFMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, 8),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, 5),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, 8),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, 2)\n)\n\nmodel = facto.FFMRegressor(\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>5.319945\n</code></pre></p> <p><pre><code>report = model.debug_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n\nprint(report)\n</code></pre> <pre><code>Name                                       Value      Weight     Contribution\n                               Intercept    1.00000    5.23501        5.23501\n                                user_Bob    1.00000    0.11438        0.11438\n                                    time    0.14000    0.03186        0.00446\n    item_Harry Potter(time) - time(item)    0.14000    0.03153        0.00441\n             user_Bob(time) - time(user)    0.14000    0.02864        0.00401\n                       item_Harry Potter    1.00000    0.00000        0.00000\nuser_Bob(item) - item_Harry Potter(user)    1.00000   -0.04232       -0.04232\n</code></pre></p>"},{"location":"api/facto/FFMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Juan, Y., Zhuang, Y., Chin, W.S. and Lin, C.J., 2016, September. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 43-50). \u21a9</p> </li> </ol>"},{"location":"api/facto/FMClassifier/","title":"FMClassifier","text":"<p>Factorization Machine for binary classification.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/FMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, True),\n    ({'user': 'Alice', 'item': 'Terminator'}, True),\n    ({'user': 'Alice', 'item': 'Star Wars'}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, True),\n    ({'user': 'Bob', 'item': 'Superman'}, True),\n    ({'user': 'Bob', 'item': 'Terminator'}, True),\n    ({'user': 'Bob', 'item': 'Star Wars'}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, False)\n)\n\nmodel = facto.FMClassifier(\n    n_factors=10,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/FMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> <li> <p>Rendle, S., 2012, May. Factorization Machines with libFM. In ACM Transactions on Intelligent Systems and Technology 3, 3, Article 57, 22 pages. \u21a9</p> </li> </ol>"},{"location":"api/facto/FMRegressor/","title":"FMRegressor","text":"<p>Factorization Machine for regression.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/FMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = facto.FMRegressor(\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>5.236504\n</code></pre></p> <p><pre><code>report = model.debug_one({'Bob': 1, 'Harry Potter': 1})\n\nprint(report)\n</code></pre> <pre><code>Name                 Value      Weight     Contribution\n         Intercept    1.00000    5.23426        5.23426\nBob - Harry Potter    1.00000    0.00224        0.00224\n      Harry Potter    1.00000    0.00000        0.00000\n               Bob    1.00000    0.00000        0.00000\n</code></pre></p>"},{"location":"api/facto/FMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> <li> <p>Rendle, S., 2012, May. Factorization Machines with libFM. In ACM Transactions on Intelligent Systems and Technology 3, 3, Article 57, 22 pages. \u21a9</p> </li> </ol>"},{"location":"api/facto/FwFMClassifier/","title":"FwFMClassifier","text":"<p>Field-weighted Factorization Machine for binary classification.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} r_{f_j, f_{j'}} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(f_j\\) and \\(f_{j'}\\) are \\(j\\) and \\(j'\\) fields, respectively, and \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FwFMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>int_weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the field pairs interaction weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FwFMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> <li> <p>interaction_weights</p> <p>The current interaction strengths of field pairs.</p> </li> </ul>"},{"location":"api/facto/FwFMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, True),\n    ({'user': 'Alice', 'item': 'Terminator'}, True),\n    ({'user': 'Alice', 'item': 'Star Wars'}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, True),\n    ({'user': 'Bob', 'item': 'Superman'}, True),\n    ({'user': 'Bob', 'item': 'Terminator'}, True),\n    ({'user': 'Bob', 'item': 'Star Wars'}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, False)\n)\n\nmodel = facto.FwFMClassifier(\n    n_factors=10,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/FwFMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, and Quan Lu, 2018, April. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, (pp. 1349\u20131357). \u21a9</p> </li> </ol>"},{"location":"api/facto/FwFMRegressor/","title":"FwFMRegressor","text":"<p>Field-weighted Factorization Machine for regression.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} r_{f_j, f_{j'}} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(f_j\\) and \\(f_{j'}\\) are \\(j\\) and \\(j'\\) fields, respectively, and \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FwFMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>int_weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the field pairs interaction weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FwFMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> <li> <p>interaction_weights</p> <p>The current interaction strengths of field pairs.</p> </li> </ul>"},{"location":"api/facto/FwFMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = facto.FwFMRegressor(\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>5.236501\n</code></pre></p> <p><pre><code>report = model.debug_one({'Bob': 1, 'Harry Potter': 1})\n\nprint(report)\n</code></pre> <pre><code>Name                                    Value      Weight     Contribution\n                            Intercept    1.00000    5.23426        5.23426\nBob(Harry Potter) - Harry Potter(Bob)    1.00000    0.00224        0.00224\n                         Harry Potter    1.00000    0.00000        0.00000\n                                  Bob    1.00000    0.00000        0.00000\n</code></pre></p>"},{"location":"api/facto/FwFMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, and Quan Lu, 2018, April. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, (pp. 1349\u20131357). \u21a9</p> </li> </ol>"},{"location":"api/facto/HOFMClassifier/","title":"HOFMClassifier","text":"<p>Higher-Order Factorization Machine for binary classification.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{l=2}^{d} \\sum_{j_1=1}^{p} \\cdots \\sum_{j_l=j_{l-1}+1}^{p} \\left(\\prod_{j'=1}^{l} x_{j_{j'}} \\right) \\left(\\sum_{f=1}^{k_l} \\prod_{j'=1}^{l} v_{j_{j'}, f}^{(l)} \\right)\\] <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/HOFMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>degree</p> <p>Default \u2192 <code>3</code></p> <p>Polynomial degree or model order.</p> </li> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/HOFMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/HOFMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, True),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, True),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, True),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, True),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, False)\n)\n\nmodel = facto.HOFMClassifier(\n    degree=3,\n    n_factors=10,\n    intercept=.5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/HOFMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> </ol>"},{"location":"api/facto/HOFMRegressor/","title":"HOFMRegressor","text":"<p>Higher-Order Factorization Machine for regression.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{l=2}^{d} \\sum_{j_1=1}^{p} \\cdots \\sum_{j_l=j_{l-1}+1}^{p} \\left(\\prod_{j'=1}^{l} x_{j_{j'}} \\right) \\left(\\sum_{f=1}^{k_l} \\prod_{j'=1}^{l} v_{j_{j'}, f}^{(l)} \\right)\\] <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/HOFMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>degree</p> <p>Default \u2192 <code>3</code></p> <p>Polynomial degree or model order.</p> </li> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note thatthe intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/HOFMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/HOFMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, 8),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, 5),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, 8),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, 2)\n)\n\nmodel = facto.HOFMRegressor(\n    degree=3,\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>5.311745\n</code></pre></p> <p><pre><code>report = model.debug_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n\nprint(report)\n</code></pre> <pre><code>Name                                  Value      Weight     Contribution\n                          Intercept    1.00000    5.23495        5.23495\n                           user_Bob    1.00000    0.11436        0.11436\n                               time    0.14000    0.03185        0.00446\n                    user_Bob - time    0.14000    0.00884        0.00124\nuser_Bob - item_Harry Potter - time    0.14000    0.00117        0.00016\n                  item_Harry Potter    1.00000    0.00000        0.00000\n           item_Harry Potter - time    0.14000   -0.00695       -0.00097\n       user_Bob - item_Harry Potter    1.00000   -0.04246       -0.04246\n</code></pre></p>"},{"location":"api/facto/HOFMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> </ol>"},{"location":"api/feature-extraction/Agg/","title":"Agg","text":"<p>Computes a streaming aggregate.</p> <p>This transformer allows to compute an aggregate statistic, very much like the groupby method from <code>pandas</code>, but on a streaming dataset. This makes use of the streaming statistics from the <code>stats</code> module. </p> <p>When <code>learn_one</code> is called, the running statistic <code>how</code> of group <code>by</code> is updated with the value of <code>on</code>. Meanwhile, the output of <code>transform_one</code> is a single-element dictionary, where the key is the name of the aggregate and the value is the current value of the statistic for the relevant group. The key is automatically inferred from the parameters. </p> <p>Note that you can use a <code>compose.TransformerUnion</code> to extract many aggregate statistics in a concise manner.</p>"},{"location":"api/feature-extraction/Agg/#parameters","title":"Parameters","text":"<ul> <li> <p>on</p> <p>Type \u2192 str</p> <p>The feature on which to compute the aggregate statistic.</p> </li> <li> <p>by</p> <p>Type \u2192 str | list[str] | None</p> <p>The feature by which to group the data. All the data is included in the aggregate if this is <code>None</code>.</p> </li> <li> <p>how</p> <p>Type \u2192 stats.base.Univariate | utils.Rolling | utils.TimeRolling</p> <p>The statistic to compute.</p> </li> </ul>"},{"location":"api/feature-extraction/Agg/#attributes","title":"Attributes","text":"<ul> <li> <p>state</p> <p>Return the current values for each group as a series.</p> </li> </ul>"},{"location":"api/feature-extraction/Agg/#examples","title":"Examples","text":"<p>Consider the following dataset:</p> <pre><code>X = [\n    {'country': 'France', 'place': 'Taco Bell', 'revenue': 42},\n    {'country': 'Sweden', 'place': 'Burger King', 'revenue': 16},\n    {'country': 'France', 'place': 'Burger King', 'revenue': 24},\n    {'country': 'Sweden', 'place': 'Taco Bell', 'revenue': 58},\n    {'country': 'Sweden', 'place': 'Burger King', 'revenue': 20},\n    {'country': 'France', 'place': 'Taco Bell', 'revenue': 50},\n    {'country': 'France', 'place': 'Burger King', 'revenue': 10},\n    {'country': 'Sweden', 'place': 'Taco Bell', 'revenue': 80}\n]\n</code></pre> <p>As an example, we can calculate the average (how) revenue (on) for each place (by):</p> <p><pre><code>from river import feature_extraction as fx\nfrom river import stats\n\nagg = fx.Agg(\n    on='revenue',\n    by='place',\n    how=stats.Mean()\n)\n\nfor x in X:\n    agg.learn_one(x)\n    print(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_mean_by_place': 42.0}\n{'revenue_mean_by_place': 16.0}\n{'revenue_mean_by_place': 20.0}\n{'revenue_mean_by_place': 50.0}\n{'revenue_mean_by_place': 20.0}\n{'revenue_mean_by_place': 50.0}\n{'revenue_mean_by_place': 17.5}\n{'revenue_mean_by_place': 57.5}\n</code></pre></p> <p>You can compute an aggregate over multiple keys by passing a tuple to the <code>by</code> argument. For instance, we can compute the maximum (how) revenue (on) per place as well as per day (by):</p> <p><pre><code>agg = fx.Agg(\n    on='revenue',\n    by=['place', 'country'],\n    how=stats.Max()\n)\n\nfor x in X:\n    agg.learn_one(x)\n    print(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_max_by_place_and_country': 42}\n{'revenue_max_by_place_and_country': 16}\n{'revenue_max_by_place_and_country': 24}\n{'revenue_max_by_place_and_country': 58}\n{'revenue_max_by_place_and_country': 20}\n{'revenue_max_by_place_and_country': 50}\n{'revenue_max_by_place_and_country': 24}\n{'revenue_max_by_place_and_country': 80}\n</code></pre></p> <p>You can use a <code>compose.TransformerUnion</code> in order to calculate multiple aggregates in one go. The latter can be constructed by using the <code>+</code> operator:</p> <p><pre><code>agg = (\n    fx.Agg(on='revenue', by='place', how=stats.Mean()) +\n    fx.Agg(on='revenue', by=['place', 'country'], how=stats.Max())\n)\n\nimport pprint\nfor x in X:\n    agg.learn_one(x)\n    pprint.pprint(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_max_by_place_and_country': 42, 'revenue_mean_by_place': 42.0}\n{'revenue_max_by_place_and_country': 16, 'revenue_mean_by_place': 16.0}\n{'revenue_max_by_place_and_country': 24, 'revenue_mean_by_place': 20.0}\n{'revenue_max_by_place_and_country': 58, 'revenue_mean_by_place': 50.0}\n{'revenue_max_by_place_and_country': 20, 'revenue_mean_by_place': 20.0}\n{'revenue_max_by_place_and_country': 50, 'revenue_mean_by_place': 50.0}\n{'revenue_max_by_place_and_country': 24, 'revenue_mean_by_place': 17.5}\n{'revenue_max_by_place_and_country': 80, 'revenue_mean_by_place': 57.5}\n</code></pre></p> <p>The <code>state</code> property returns a <code>pandas.Series</code>, which can be useful for visualizing the current state.</p> <p><pre><code>agg[0].state\n</code></pre> <pre><code>Taco Bell      57.5\nBurger King    17.5\nName: revenue_mean_by_place, dtype: float64\n</code></pre></p> <p><pre><code>agg[1].state\n</code></pre> <pre><code>place        country\nTaco Bell    France     50\nBurger King  Sweden     20\n             France     24\nTaco Bell    Sweden     80\nName: revenue_max_by_place_and_country, dtype: int64\n</code></pre></p> <p>This transformer can also be used in conjunction with <code>utils.TimeRolling</code>. The latter requires a <code>t</code> argument, which is a timestamp that indicates when the current row was observed. For instance, we can calculate the average (how) revenue (on) for each place (by) over the last 7 days (t):</p> <p><pre><code>import datetime as dt\nimport random\nimport string\nfrom river import utils\n\nagg = fx.Agg(\n    on=\"value\",\n    by=\"group\",\n    how=utils.TimeRolling(stats.Mean(), dt.timedelta(days=7))\n)\n\nfor day in range(366):\n    g = random.choice(string.ascii_lowercase)\n    x = {\n        \"group\": g,\n        \"value\": string.ascii_lowercase.index(g) + random.random(),\n    }\n    t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day)\n    agg.learn_one(x, t=t)\n\nlen(agg.state)\n</code></pre> <pre><code>26\n</code></pre></p>"},{"location":"api/feature-extraction/Agg/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>t     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Streaming groupbys in pandas for big datasets \u21a9</p> </li> </ol>"},{"location":"api/feature-extraction/BagOfWords/","title":"BagOfWords","text":"<p>Counts tokens in sentences.</p> <p>This transformer can be used to counts tokens in a given piece of text. It takes care of normalizing the text before tokenizing it. In mini-batch settings, this transformers allows to convert a series of pandas of text into sparse dataframe. </p> <p>Note that the parameters are identical to those of <code>feature_extraction.TFIDF</code>.</p>"},{"location":"api/feature-extraction/BagOfWords/#parameters","title":"Parameters","text":"<ul> <li> <p>on</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>None</code></p> <p>The name of the feature that contains the text to vectorize. If <code>None</code>, then each <code>learn_one</code> and <code>transform_one</code> will assume that each <code>x</code> that is provided is a <code>str</code>, andnot a <code>dict</code>.</p> </li> <li> <p>strip_accents</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to strip accent characters.</p> </li> <li> <p>lowercase</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to convert all characters to lowercase.</p> </li> <li> <p>preprocessor</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional preprocessing function which overrides the <code>strip_accents</code> and <code>lowercase</code> steps, while preserving the tokenizing and n-grams generation steps.</p> </li> <li> <p>stop_words</p> <p>Type \u2192 set[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional set of tokens to remove.</p> </li> <li> <p>tokenizer_pattern</p> <p>Default \u2192 <code>(?u)\\b\\w[\\w\\-]+\\b</code></p> <p>The tokenization pattern which is used when no <code>tokenizer</code> function is passed. A single capture group may optionally be specified.</p> </li> <li> <p>tokenizer</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>A function used to convert preprocessed text into a <code>dict</code> of tokens. By default, a regex formula that works well in most cases is used.</p> </li> <li> <p>ngram_range</p> <p>Default \u2192 <code>(1, 1)</code></p> <p>The lower and upper boundary of the range n-grams to be extracted. All values of n such that <code>min_n &lt;= n &lt;= max_n</code> will be used. For example an <code>ngram_range</code> of <code>(1, 1)</code> means only unigrams, <code>(1, 2)</code> means unigrams and bigrams, and <code>(2, 2)</code> means only bigrams.</p> </li> </ul>"},{"location":"api/feature-extraction/BagOfWords/#examples","title":"Examples","text":"<p>By default, <code>BagOfWords</code> will take as input a sentence, preprocess it, tokenize the preprocessed text, and then return a <code>collections.Counter</code> containing the number of occurrences of each token.</p> <p><pre><code>from river import feature_extraction as fx\n\ncorpus = [\n    'This is the first document.',\n    'This document is the second document.',\n    'And this is the third one.',\n    'Is this the first document?',\n]\n\nbow = fx.BagOfWords()\n\nfor sentence in corpus:\n    print(bow.transform_one(sentence))\n</code></pre> <pre><code>{'this': 1, 'is': 1, 'the': 1, 'first': 1, 'document': 1}\n{'this': 1, 'document': 2, 'is': 1, 'the': 1, 'second': 1}\n{'and': 1, 'this': 1, 'is': 1, 'the': 1, 'third': 1, 'one': 1}\n{'is': 1, 'this': 1, 'the': 1, 'first': 1, 'document': 1}\n</code></pre></p> <p>Note that <code>learn_one</code> does not have to be called because <code>BagOfWords</code> is stateless. You can call it but it won't do anything.</p> <p>In the above example, a string is passed to <code>transform_one</code>. You can also indicate which field to access if the string is stored in a dictionary:</p> <p><pre><code>bow = fx.BagOfWords(on='sentence')\n\nfor sentence in corpus:\n    x = {'sentence': sentence}\n    print(bow.transform_one(x))\n</code></pre> <pre><code>{'this': 1, 'is': 1, 'the': 1, 'first': 1, 'document': 1}\n{'this': 1, 'document': 2, 'is': 1, 'the': 1, 'second': 1}\n{'and': 1, 'this': 1, 'is': 1, 'the': 1, 'third': 1, 'one': 1}\n{'is': 1, 'this': 1, 'the': 1, 'first': 1, 'document': 1}\n</code></pre></p> <p>The <code>ngram_range</code> parameter can be used to extract n-grams (including unigrams):</p> <p><pre><code>ngrammer = fx.BagOfWords(ngram_range=(1, 2))\n\nngrams = ngrammer.transform_one('I love the smell of napalm in the morning')\nfor ngram, count in ngrams.items():\n    print(ngram, count)\n</code></pre> <pre><code>love 1\nthe 2\nsmell 1\nof 1\nnapalm 1\nin 1\nmorning 1\n('love', 'the') 1\n('the', 'smell') 1\n('smell', 'of') 1\n('of', 'napalm') 1\n('napalm', 'in') 1\n('in', 'the') 1\n('the', 'morning') 1\n</code></pre></p> <p><code>BagOfWord</code> allows to build a term-frequency pandas sparse dataframe with the <code>transform_many</code> method.</p> <p><pre><code>import pandas as pd\nX = pd.Series(['Hello world', 'Hello River'], index = ['river', 'rocks'])\nbow = fx.BagOfWords()\nbow.transform_many(X=X)\n</code></pre> <pre><code>       hello  world  river\nriver      1      1      0\nrocks      1      0      1\n</code></pre></p>"},{"location":"api/feature-extraction/BagOfWords/#methods","title":"Methods","text":"learn_many learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> process_text transform_many <p>Transform pandas series of string into term-frequency pandas sparse dataframe.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.Series' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-extraction/PolynomialExtender/","title":"PolynomialExtender","text":"<p>Polynomial feature extender.</p> <p>Generate features consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. </p> <p>Be aware that the number of outputted features scales polynomially in the number of input features and exponentially in the degree. High degrees can cause overfitting.</p>"},{"location":"api/feature-extraction/PolynomialExtender/#parameters","title":"Parameters","text":"<ul> <li> <p>degree</p> <p>Default \u2192 <code>2</code></p> <p>The maximum degree of the polynomial features.</p> </li> <li> <p>interaction_only</p> <p>Default \u2192 <code>False</code></p> <p>If <code>True</code> then only combinations that include an element at most once will be computed.</p> </li> <li> <p>include_bias</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to include a dummy feature which is always equal to 1.</p> </li> <li> <p>bias_name</p> <p>Default \u2192 <code>bias</code></p> <p>Name to give to the bias feature.</p> </li> </ul>"},{"location":"api/feature-extraction/PolynomialExtender/#examples","title":"Examples","text":"<p><pre><code>from river import feature_extraction as fx\n\nX = [\n    {'x': 0, 'y': 1},\n    {'x': 2, 'y': 3},\n    {'x': 4, 'y': 5}\n]\n\npoly = fx.PolynomialExtender(degree=2, include_bias=True)\nfor x in X:\n    print(poly.transform_one(x))\n</code></pre> <pre><code>{'x': 0, 'y': 1, 'x*x': 0, 'x*y': 0, 'y*y': 1, 'bias': 1}\n{'x': 2, 'y': 3, 'x*x': 4, 'x*y': 6, 'y*y': 9, 'bias': 1}\n{'x': 4, 'y': 5, 'x*x': 16, 'x*y': 20, 'y*y': 25, 'bias': 1}\n</code></pre></p> <p><pre><code>X = [\n    {'x': 0, 'y': 1, 'z': 2},\n    {'x': 2, 'y': 3, 'z': 2},\n    {'x': 4, 'y': 5, 'z': 2}\n]\n\npoly = fx.PolynomialExtender(degree=3, interaction_only=True)\nfor x in X:\n    print(poly.transform_one(x))\n</code></pre> <pre><code>{'x': 0, 'y': 1, 'z': 2, 'x*y': 0, 'x*z': 0, 'y*z': 2, 'x*y*z': 0}\n{'x': 2, 'y': 3, 'z': 2, 'x*y': 6, 'x*z': 4, 'y*z': 6, 'x*y*z': 12}\n{'x': 4, 'y': 5, 'z': 2, 'x*y': 20, 'x*z': 8, 'y*z': 10, 'x*y*z': 40}\n</code></pre></p> <p>Polynomial features are typically used for a linear model to capture interactions between features. This may done by setting up a pipeline, as so:</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model as lm\nfrom river import metrics\nfrom river import preprocessing as pp\n\ndataset = datasets.Phishing()\n\nmodel = (\n    fx.PolynomialExtender() |\n    pp.StandardScaler() |\n    lm.LogisticRegression()\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 88.88%\n</code></pre></p>"},{"location":"api/feature-extraction/PolynomialExtender/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-extraction/RBFSampler/","title":"RBFSampler","text":"<p>Extracts random features which approximate an RBF kernel.</p> <p>This is a powerful way to give non-linear capacity to linear classifiers. This method is also called \"random Fourier features\" in the literature.</p>"},{"location":"api/feature-extraction/RBFSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>gamma</p> <p>Default \u2192 <code>1.0</code></p> <p>RBF kernel parameter in <code>(-gamma * x^2)</code>.</p> </li> <li> <p>n_components</p> <p>Default \u2192 <code>100</code></p> <p>Number of samples per original feature. Equals the dimensionality of the computed feature space.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number seed.</p> </li> </ul>"},{"location":"api/feature-extraction/RBFSampler/#examples","title":"Examples","text":"<p><pre><code>from river import feature_extraction as fx\nfrom river import linear_model as lm\nfrom river import optim\nfrom river import stream\n\nX = [[0, 0], [1, 1], [1, 0], [0, 1]]\nY = [0, 0, 1, 1]\n\nmodel = lm.LogisticRegression(optimizer=optim.SGD(.1))\n\nfor x, y in stream.iter_array(X, Y):\n    model.learn_one(x, y)\n    y_pred = model.predict_one(x)\n    print(y, int(y_pred))\n</code></pre> <pre><code>0 0\n0 0\n1 0\n1 1\n</code></pre></p> <p><pre><code>model = (\n    fx.RBFSampler(seed=3) |\n    lm.LogisticRegression(optimizer=optim.SGD(.1))\n)\n\nfor x, y in stream.iter_array(X, Y):\n    model.learn_one(x, y)\n    y_pred = model.predict_one(x)\n    print(y, int(y_pred))\n</code></pre> <pre><code>0 0\n0 0\n1 1\n1 1\n</code></pre></p>"},{"location":"api/feature-extraction/RBFSampler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Rahimi, A. and Recht, B., 2008. Random features for large-scale kernel machines. In Advances in neural information processing systems (pp. 1177-1184 \u21a9</p> </li> </ol>"},{"location":"api/feature-extraction/TFIDF/","title":"TFIDF","text":"<p>Computes TF-IDF values from sentences.</p> <p>The TF-IDF formula is the same one as scikit-learn. The only difference is the fact that the document frequencies are determined online, whereas in a batch setting they can be determined by performing an initial pass through the data. </p> <p>Note that the parameters are identical to those of <code>feature_extraction.BagOfWords</code>.</p>"},{"location":"api/feature-extraction/TFIDF/#parameters","title":"Parameters","text":"<ul> <li> <p>normalize</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not the TF-IDF values by their L2 norm.</p> </li> <li> <p>on</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>None</code></p> <p>The name of the feature that contains the text to vectorize. If <code>None</code>, then the input is treated as a document instead of a set of features.</p> </li> <li> <p>strip_accents</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to strip accent characters.</p> </li> <li> <p>lowercase</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to convert all characters to lowercase.</p> </li> <li> <p>preprocessor</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional preprocessing function which overrides the <code>strip_accents</code> and <code>lowercase</code> steps, while preserving the tokenizing and n-grams generation steps.</p> </li> <li> <p>tokenizer</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>A function used to convert preprocessed text into a <code>dict</code> of tokens. By default, a regex formula that works well in most cases is used.</p> </li> <li> <p>ngram_range</p> <p>Default \u2192 <code>(1, 1)</code></p> <p>The lower and upper boundary of the range n-grams to be extracted. All values of n such that <code>min_n &lt;= n &lt;= max_n</code> will be used. For example an <code>ngram_range</code> of <code>(1, 1)</code> means only unigrams, <code>(1, 2)</code> means unigrams and bigrams, and <code>(2, 2)</code> means only bigrams. Only works if <code>tokenizer</code> is not set to <code>False</code>.</p> </li> </ul>"},{"location":"api/feature-extraction/TFIDF/#attributes","title":"Attributes","text":"<ul> <li> <p>dfs (collections.defaultdict))</p> <p>Document counts.</p> </li> <li> <p>n (int)</p> <p>Number of scanned documents.</p> </li> </ul>"},{"location":"api/feature-extraction/TFIDF/#examples","title":"Examples","text":"<p><pre><code>from river import feature_extraction\n\ntfidf = feature_extraction.TFIDF()\n\ncorpus = [\n    'This is the first document.',\n    'This document is the second document.',\n    'And this is the third one.',\n    'Is this the first document?',\n]\n\nfor sentence in corpus:\n    tfidf.learn_one(sentence)\n    print(tfidf.transform_one(sentence))\n</code></pre> <pre><code>{'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447}\n{'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469}\n{'and': 0.497, 'this': 0.293, 'is': 0.293, 'the': 0.293, 'third': 0.497, 'one': 0.497}\n{'is': 0.384, 'this': 0.384, 'the': 0.384, 'first': 0.580, 'document': 0.469}\n</code></pre></p> <p>In the above example, a string is passed to <code>transform_one</code>. You can also indicate which field to access if the string is stored in a dictionary:</p> <p><pre><code>tfidf = feature_extraction.TFIDF(on='sentence')\n\nfor sentence in corpus:\n    x = {'sentence': sentence}\n    tfidf.learn_one(x)\n    print(tfidf.transform_one(x))\n</code></pre> <pre><code>{'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447}\n{'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469}\n{'and': 0.497, 'this': 0.293, 'is': 0.293, 'the': 0.293, 'third': 0.497, 'one': 0.497}\n{'is': 0.384, 'this': 0.384, 'the': 0.384, 'first': 0.580, 'document': 0.469}\n</code></pre></p>"},{"location":"api/feature-extraction/TFIDF/#methods","title":"Methods","text":"learn_many learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> process_text transform_many <p>Transform pandas series of string into term-frequency pandas sparse dataframe.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.Series' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-extraction/TargetAgg/","title":"TargetAgg","text":"<p>Computes a streaming aggregate of the target values.</p> <p>This transformer is identical to <code>feature_extraction.Agg</code>, the only difference is that it operates on the target rather than on a feature. At each step, the running statistic <code>how</code> of target values in group <code>by</code> is updated with the target. It is therefore a supervised transformer.</p>"},{"location":"api/feature-extraction/TargetAgg/#parameters","title":"Parameters","text":"<ul> <li> <p>by</p> <p>Type \u2192 str | list[str] | None</p> <p>The feature by which to group the target values. All the data is included in the aggregate if this is <code>None</code>.</p> </li> <li> <p>how</p> <p>Type \u2192 stats.base.Univariate | utils.Rolling | utils.TimeRolling</p> <p>The statistic to compute.</p> </li> <li> <p>target_name</p> <p>Default \u2192 <code>y</code></p> <p>The target name which is used in the result.</p> </li> </ul>"},{"location":"api/feature-extraction/TargetAgg/#attributes","title":"Attributes","text":"<ul> <li> <p>state</p> <p>Return the current values for each group as a series.</p> </li> <li> <p>target_name</p> </li> </ul>"},{"location":"api/feature-extraction/TargetAgg/#examples","title":"Examples","text":"<p>Consider the following dataset, where the second value of each value is the target:</p> <pre><code>dataset = [\n    ({'country': 'France', 'place': 'Taco Bell'}, 42),\n    ({'country': 'Sweden', 'place': 'Burger King'}, 16),\n    ({'country': 'France', 'place': 'Burger King'}, 24),\n    ({'country': 'Sweden', 'place': 'Taco Bell'}, 58),\n    ({'country': 'Sweden', 'place': 'Burger King'}, 20),\n    ({'country': 'France', 'place': 'Taco Bell'}, 50),\n    ({'country': 'France', 'place': 'Burger King'}, 10),\n    ({'country': 'Sweden', 'place': 'Taco Bell'}, 80)\n]\n</code></pre> <p>As an example, let's perform a target encoding of the <code>place</code> feature. Instead of simply updating a running average, we use a <code>stats.BayesianMean</code> which allows us to incorporate some prior knowledge. This makes subsequent models less prone to overfitting. Indeed, it dampens the fact that too few samples might have been seen within a group.</p> <p><pre><code>from river import feature_extraction\nfrom river import stats\n\nagg = feature_extraction.TargetAgg(\n    by='place',\n    how=stats.BayesianMean(\n        prior=3,\n        prior_weight=1\n    )\n)\n\nfor x, y in dataset:\n    print(agg.transform_one(x))\n    agg.learn_one(x, y)\n</code></pre> <pre><code>{'y_bayes_mean_by_place': 3.0}\n{'y_bayes_mean_by_place': 3.0}\n{'y_bayes_mean_by_place': 9.5}\n{'y_bayes_mean_by_place': 22.5}\n{'y_bayes_mean_by_place': 14.333}\n{'y_bayes_mean_by_place': 34.333}\n{'y_bayes_mean_by_place': 15.75}\n{'y_bayes_mean_by_place': 38.25}\n</code></pre></p> <p>Just like with <code>feature_extraction.Agg</code>, we can specify multiple features on which to group the data:</p> <p><pre><code>agg = feature_extraction.TargetAgg(\n    by=['place', 'country'],\n    how=stats.BayesianMean(\n        prior=3,\n        prior_weight=1\n    )\n)\n\nfor x, y in dataset:\n    print(agg.transform_one(x))\n    agg.learn_one(x, y)\n</code></pre> <pre><code>{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 9.5}\n{'y_bayes_mean_by_place_and_country': 22.5}\n{'y_bayes_mean_by_place_and_country': 13.5}\n{'y_bayes_mean_by_place_and_country': 30.5}\n</code></pre></p> <p><pre><code>agg.state\n</code></pre> <pre><code>place        country\nTaco Bell    France     31.666667\nBurger King  Sweden     13.000000\n             France     12.333333\nTaco Bell    Sweden     47.000000\nName: y_bayes_mean_by_place_and_country, dtype: float64\n</code></pre></p> <p>This transformer can also be used in conjunction with <code>utils.TimeRolling</code>. The latter requires a <code>t</code> argument, which is a timestamp that indicates when the current row was observed. For instance, we can calculate the average (how) revenue (on) for each place (by) over the last 7 days (t):</p> <pre><code>import datetime as dt\nimport random\nimport string\nfrom river import utils\n\nagg = feature_extraction.TargetAgg(\n    by=\"group\",\n    how=utils.TimeRolling(stats.Mean(), dt.timedelta(days=7))\n)\n\nfor day in range(366):\n    g = random.choice(string.ascii_lowercase)\n    x = {\"group\": g}\n    y = string.ascii_lowercase.index(g) + random.random()\n    t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day)\n    agg.learn_one(x, y, t=t)\n</code></pre>"},{"location":"api/feature-extraction/TargetAgg/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code> and a target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> <li>t     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p> 1. Streaming groupbys in pandas for big datasets</p>"},{"location":"api/feature-selection/PoissonInclusion/","title":"PoissonInclusion","text":"<p>Randomly selects features with an inclusion trial.</p> <p>When a new feature is encountered, it is selected with probability <code>p</code>. The number of times a feature needs to beseen before it is added to the model follows a geometric distribution with expected value <code>1 / p</code>. This feature selection method is meant to be used when you have a very large amount of sparse features.</p>"},{"location":"api/feature-selection/PoissonInclusion/#parameters","title":"Parameters","text":"<ul> <li> <p>p</p> <p>Type \u2192 float</p> <p>Probability of including a feature the first time it is encountered.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed value used for reproducibility.</p> </li> </ul>"},{"location":"api/feature-selection/PoissonInclusion/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import feature_selection\nfrom river import stream\n\nselector = feature_selection.PoissonInclusion(p=0.1, seed=42)\n\ndataset = iter(datasets.TrumpApproval())\n\nfeature_names = next(dataset)[0].keys()\nn = 0\n\nwhile True:\n    x, y = next(dataset)\n    xt = selector.transform_one(x)\n    if xt.keys() == feature_names:\n        break\n    n += 1\n\nn\n</code></pre> <pre><code>12\n</code></pre></p>"},{"location":"api/feature-selection/PoissonInclusion/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D. and Chikkerur, S., 2013, August. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222-1230) \u21a9</p> </li> </ol>"},{"location":"api/feature-selection/SelectKBest/","title":"SelectKBest","text":"<p>Removes all but the \\(k\\) highest scoring features.</p>"},{"location":"api/feature-selection/SelectKBest/#parameters","title":"Parameters","text":"<ul> <li> <p>similarity</p> <p>Type \u2192 stats.base.Bivariate</p> </li> <li> <p>k</p> <p>Default \u2192 <code>10</code></p> <p>The number of features to keep.</p> </li> </ul>"},{"location":"api/feature-selection/SelectKBest/#attributes","title":"Attributes","text":"<ul> <li> <p>similarities (dict)</p> <p>The similarity instances used for each feature.</p> </li> <li> <p>leaderboard (dict)</p> <p>The actual similarity measures.</p> </li> </ul>"},{"location":"api/feature-selection/SelectKBest/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nfrom river import feature_selection\nfrom river import stats\nfrom river import stream\nfrom sklearn import datasets\n\nX, y = datasets.make_regression(\n    n_samples=100,\n    n_features=10,\n    n_informative=2,\n    random_state=42\n)\n\nselector = feature_selection.SelectKBest(\n    similarity=stats.PearsonCorr(),\n    k=2\n)\n\nfor xi, yi, in stream.iter_array(X, y):\n    selector.learn_one(xi, yi)\n\npprint(selector.leaderboard)\n</code></pre> <pre><code>Counter({9: 0.7898,\n        7: 0.5444,\n        8: 0.1062,\n        2: 0.0638,\n        4: 0.0538,\n        5: 0.0271,\n        1: -0.0312,\n        6: -0.0657,\n        3: -0.1501,\n        0: -0.1895})\n</code></pre></p> <p><pre><code>selector.transform_one(xi)\n</code></pre> <pre><code>{7: -1.2795, 9: -1.8408}\n</code></pre></p>"},{"location":"api/feature-selection/SelectKBest/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code> and a target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-selection/VarianceThreshold/","title":"VarianceThreshold","text":"<p>Removes low-variance features.</p>"},{"location":"api/feature-selection/VarianceThreshold/#parameters","title":"Parameters","text":"<ul> <li> <p>threshold</p> <p>Default \u2192 <code>0</code></p> <p>Only features with a variance above the threshold will be kept.</p> </li> <li> <p>min_samples</p> <p>Default \u2192 <code>2</code></p> <p>The minimum number of samples required to perform selection.</p> </li> </ul>"},{"location":"api/feature-selection/VarianceThreshold/#attributes","title":"Attributes","text":"<ul> <li> <p>variances (dict)</p> <p>The variance of each feature.</p> </li> </ul>"},{"location":"api/feature-selection/VarianceThreshold/#examples","title":"Examples","text":"<p><pre><code>from river import feature_selection\nfrom river import stream\n\nX = [\n    [0, 2, 0, 3],\n    [0, 1, 4, 3],\n    [0, 1, 1, 3]\n]\n\nselector = feature_selection.VarianceThreshold()\n\nfor x, _ in stream.iter_array(X):\n    selector.learn_one(x)\n    print(selector.transform_one(x))\n</code></pre> <pre><code>{0: 0, 1: 2, 2: 0, 3: 3}\n{1: 1, 2: 4}\n{1: 1, 2: 1}\n</code></pre></p>"},{"location":"api/feature-selection/VarianceThreshold/#methods","title":"Methods","text":"check_feature learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/forest/AMFClassifier/","title":"AMFClassifier","text":"<p>Aggregated Mondrian Forest classifier for online learning.</p> <p>This implementation is truly online<sup>1</sup>, in the sense that a single pass is performed, and that predictions can be produced anytime. </p> <p>Each node in a tree predicts according to the distribution of the labels it contains. This distribution is regularized using a \"Jeffreys\" prior with parameter <code>dirichlet</code>. For each class with <code>count</code> labels in the node and <code>n_samples</code> samples in it, the prediction of a node is given by </p> <p>\\(\\frac{count + dirichlet}{n_{samples} + dirichlet \\times n_{classes}}\\). </p> <p>The prediction for a sample is computed as the aggregated predictions of all the subtrees along the path leading to the leaf node containing the sample. The aggregation weights are exponential weights with learning rate <code>step</code> and log-loss when <code>use_aggregation</code> is <code>True</code>. </p> <p>This computation is performed exactly thanks to a context tree weighting algorithm. More details can be found in the paper cited in the references below. </p> <p>The final predictions are the average class probabilities predicted by each of the <code>n_estimators</code> trees in the forest.</p>"},{"location":"api/forest/AMFClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_estimators</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of trees in the forest.</p> </li> <li> <p>step</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Step-size for the aggregation weights. Default is 1 for classification with the log-loss, which is usually the best choice.</p> </li> <li> <p>use_aggregation</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Controls if aggregation is used in the trees. It is highly recommended to leave it as <code>True</code>.</p> </li> <li> <p>dirichlet</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>Regularization level of the class frequencies used for predictions in each node. A rule of thumb is to set this to <code>1 / n_classes</code>, where <code>n_classes</code> is the expected number of classes which might appear. Default is <code>dirichlet = 0.5</code>, which works well for binary classification problems.</p> </li> <li> <p>split_pure</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Controls if nodes that contains only sample of the same class should be split (\"pure\" nodes). Default is <code>False</code>, namely pure nodes are not split, but <code>True</code> can be sometimes better.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/AMFClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/forest/AMFClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import forest\nfrom river import metrics\n\ndataset = datasets.Bananas().take(500)\n\nmodel = forest.AMFClassifier(\n    n_estimators=10,\n    use_aggregation=True,\n    dirichlet=0.5,\n    seed=1\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 85.37%\n</code></pre></p>"},{"location":"api/forest/AMFClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/forest/AMFClassifier/#notes","title":"Notes","text":"<p>Only log_loss used for the computation of the aggregation weights is supported for now, namely the log-loss for multi-class classification.</p> <ol> <li> <p>Mourtada, J., Ga\u00efffas, S., &amp; Scornet, E. (2021). AMF: Aggregated Mondrian forests for online learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/AMFRegressor/","title":"AMFRegressor","text":"<p>Aggregated Mondrian Forest regressor for online learning.</p> <p>This algorithm is truly online, in the sense that a single pass is performed, and that predictions can be produced anytime. </p> <p>Each node in a tree predicts according to the average of the labels it contains. The prediction for a sample is computed as the aggregated predictions of all the subtrees along the path leading to the leaf node containing the sample. The aggregation weights are exponential weights with learning rate <code>step</code> using a squared loss when <code>use_aggregation</code> is <code>True</code>. </p> <p>This computation is performed exactly thanks to a context tree weighting algorithm. More details can be found in the original paper<sup>1</sup>. </p> <p>The final predictions are the average of the predictions of each of the <code>n_estimators</code> trees in the forest.</p>"},{"location":"api/forest/AMFRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_estimators</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of trees in the forest.</p> </li> <li> <p>step</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Step-size for the aggregation weights.</p> </li> <li> <p>use_aggregation</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Controls if aggregation is used in the trees. It is highly recommended to leave it as <code>True</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/AMFRegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/forest/AMFRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import forest\nfrom river import metrics\n\ndataset = datasets.TrumpApproval()\nmodel = forest.AMFRegressor(seed=42)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.268533\n</code></pre></p>"},{"location":"api/forest/AMFRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Mourtada, J., Ga\u00efffas, S., &amp; Scornet, E. (2021). AMF: Aggregated Mondrian forests for online learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/ARFClassifier/","title":"ARFClassifier","text":"<p>Adaptive Random Forest classifier.</p> <p>The 3 most important aspects of Adaptive Random Forest <sup>1</sup> are: </p> <ol> <li> <p>inducing diversity through re-sampling </p> </li> <li> <p>inducing diversity through randomly selecting subsets of features for    node splits </p> </li> <li> <p>drift detectors per base tree, which cause selective resets in response    to drifts </p> </li> </ol> <p>It also allows training background trees, which start training if a warning is detected and replace the active tree if the warning escalates to a drift.</p>"},{"location":"api/forest/ARFClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of trees in the ensemble.</p> </li> <li> <p>max_features</p> <p>Type \u2192 bool | str | int</p> <p>Default \u2192 <code>sqrt</code></p> <p>Max number of attributes for each node split. - If <code>int</code>, then consider <code>max_features</code> at each split. - If <code>float</code>, then <code>max_features</code> is a percentage and   <code>int(max_features * n_features)</code> features are considered per split. - If \"sqrt\", then <code>max_features=sqrt(n_features)</code>. - If \"log2\", then <code>max_features=log2(n_features)</code>. - If None, then <code>max_features=n_features</code>.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.MultiClassMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>Metric used to track trees performance within the ensemble. Defaults to <code>metrics.Accuracy</code>()`.</p> </li> <li> <p>disable_weighted_vote</p> <p>Default \u2192 <code>False</code></p> <p>If <code>True</code>, disables the weighted vote prediction.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift Detection method. Set to None to disable Drift detection. Defaults to <code>drift.ADWIN</code>(delta=0.001)`.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning Detection method. Set to None to disable warning detection. Defaults to <code>drift.ADWIN</code>(delta=0.01)`.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>[Tree parameter] Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>[Tree parameter] Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Hellinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>[Tree parameter] Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>[Tree parameter] Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>[Tree parameter] Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>[Tree parameter] Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>[Tree parameter] Maximum memory (MiB) consumed by the tree.</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2000000</code></p> <p>[Tree parameter] Number of instances between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>[Tree parameter] If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/ARFClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/forest/ARFClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import evaluate\nfrom river import forest\nfrom river import metrics\nfrom river.datasets import synth\n\ndataset = synth.ConceptDriftStream(\n    seed=42,\n    position=500,\n    width=40\n).take(1000)\n\nmodel = forest.ARFClassifier(seed=8, leaf_prediction=\"mc\")\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 71.17%\n</code></pre></p> <p>The total number of warnings and drifts detected, respectively <pre><code>model.n_warnings_detected(), model.n_drifts_detected()\n</code></pre> <pre><code>(2, 1)\n</code></pre></p> <p>The number of warnings detected by tree number 2 <pre><code>model.n_warnings_detected(2)\n</code></pre> <pre><code>1\n</code></pre></p> <p>And the corresponding number of actual concept drift detected <pre><code>model.n_drifts_detected(2)\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/forest/ARFClassifier/#methods","title":"Methods","text":"learn_one n_drifts_detected <p>Get the total number of concept drifts detected, or such number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drifts detected.</p> <p></p> n_warnings_detected <p>Get the total number of concept drift warnings detected, or the number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drift warnings detected.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal,  Fabricio Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem.  Adaptive random forests for evolving data stream classification.  In Machine Learning, DOI: 10.1007/s10994-017-5642-8, Springer, 2017.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/ARFRegressor/","title":"ARFRegressor","text":"<p>Adaptive Random Forest regressor.</p> <p>The 3 most important aspects of Adaptive Random Forest <sup>1</sup> are: </p> <ol> <li> <p>inducing diversity through re-sampling </p> </li> <li> <p>inducing diversity through randomly selecting subsets of features for    node splits </p> </li> <li> <p>drift detectors per base tree, which cause selective resets in response    to drifts </p> </li> </ol> <p>Notice that this implementation is slightly different from the original algorithm proposed in <sup>2</sup>. The <code>HoeffdingTreeRegressor</code> is used as base learner, instead of <code>FIMT-DD</code>. It also adds a new strategy to monitor the predictions and check for concept drifts. The deviations of the predictions to the target are monitored and normalized in the [0, 1] range to fulfill ADWIN's requirements. We assume that the data subjected to the normalization follows a normal distribution, and thus, lies within the interval of the mean \\(\\pm3\\sigma\\).</p>"},{"location":"api/forest/ARFRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of trees in the ensemble.</p> </li> <li> <p>max_features</p> <p>Default \u2192 <code>sqrt</code></p> <p>Max number of attributes for each node split. - If <code>int</code>, then consider <code>max_features</code> at each split. - If <code>float</code>, then <code>max_features</code> is a percentage and   <code>int(max_features * n_features)</code> features are considered per split. - If \"sqrt\", then <code>max_features=sqrt(n_features)</code>. - If \"log2\", then <code>max_features=log2(n_features)</code>. - If None, then <code>max_features=n_features</code>.</p> </li> <li> <p>aggregation_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>median</code></p> <p>The method to use to aggregate predictions in the ensemble. - 'mean' - 'median' - If selected will disable the weighted vote.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>Metric used to track trees performance within the ensemble. Depending, on the configuration, this metric is also used to weight predictions from the members of the ensemble. Defaults to <code>metrics.MSE</code>()`.</p> </li> <li> <p>disable_weighted_vote</p> <p>Default \u2192 <code>True</code></p> <p>If <code>True</code>, disables the weighted vote prediction, i.e. does not assign weights to individual tree's predictions and uses the arithmetic mean instead. Otherwise will use the <code>metric</code> value to weight predictions.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift Detection method. Set to None to disable Drift detection. Defaults to <code>drift.ADWIN</code>(0.001)`.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning Detection method. Set to None to disable warning detection. Defaults to <code>drift.ADWIN</code>(0.01)`.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>[Tree parameter] Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>[Tree parameter] Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>[Tree parameter] Prediction mechanism used at leaves. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided, an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters  is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>[Tree parameter] The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters.By default, <code>tree.splitter.EBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>[Tree parameter] The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>[Tree parameter] Maximum memory (MiB) consumed by the tree.</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2000000</code></p> <p>[Tree parameter] Number of instances between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>[Tree parameter] If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/ARFRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>models</p> </li> <li> <p>valid_aggregation_method</p> <p>Valid aggregation_method values.</p> </li> </ul>"},{"location":"api/forest/ARFRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import forest\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    forest.ARFRegressor(seed=42)\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.788619\n</code></pre></p>"},{"location":"api/forest/ARFRegressor/#methods","title":"Methods","text":"learn_one n_drifts_detected <p>Get the total number of concept drifts detected, or such number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drifts detected.</p> <p></p> n_warnings_detected <p>Get the total number of concept drift warnings detected, or the number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drift warnings detected.</p> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p> <ol> <li> <p>Gomes, H.M., Bifet, A., Read, J., Barddal, J.P., Enembreck, F.,   Pfharinger, B., Holmes, G. and Abdessalem, T., 2017. Adaptive random   forests for evolving data stream classification. Machine Learning,   106(9-10), pp.1469-1495.\u00a0\u21a9</p> </li> <li> <p>Gomes, H.M., Barddal, J.P., Boiko, L.E., Bifet, A., 2018.   Adaptive random forests for data stream regression. ESANN 2018.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/OXTRegressor/","title":"OXTRegressor","text":"<p>Online Extra Trees regressor.</p> <p>The online Extra Trees<sup>1</sup> ensemble takes some steps further into randomization when compared to Adaptive Random Forests (ARF). A subspace of the feature space is considered at each split attempt, as ARF does, and online bagging or subbagging can also be (optionally) used. Nonetheless, Extra Trees randomizes the split candidates evaluated by each leaf node (just a single split is tested by numerical feature, which brings significant speedups to the ensemble), and might also randomize the maximum depth of the forest members, as well as the size of the feature subspace processed by each of its trees' leaves. </p> <p>On the other hand, OXT suffers from a cold-start problem. As the splits are random, the predictive performance in small samples is usually worse than using a deterministic split approach, such as the one used by ARF.</p>"},{"location":"api/forest/OXTRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of trees in the ensemble.</p> </li> <li> <p>max_features</p> <p>Type \u2192 bool | str | int</p> <p>Default \u2192 <code>sqrt</code></p> <p>Max number of attributes for each node split. - If int, then consider <code>max_features</code> at each split. - If float, then <code>max_features</code> is a percentage and <code>int(max_features * n_features)</code> features are considered per split. - If \"sqrt\", then <code>max_features=sqrt(n_features)</code>. - If \"log2\", then <code>max_features=log2(n_features)</code>. - If \"random\", then <code>max_features</code> will assume a different random number in the interval <code>[2, n_features]</code> for each tree leaf. - If None, then <code>max_features=n_features</code>.</p> </li> <li> <p>resampling_strategy</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>subbagging</code></p> <p>The chosen instance resampling strategy: - If <code>None</code>, no resampling will be done and the trees will process all instances. - If <code>'baggging'</code>, online bagging will be performed (sampling with replacement). - If <code>'subbagging'</code>, online subbagging will be performed (sampling without replacement).</p> </li> <li> <p>resampling_rate</p> <p>Type \u2192 int | float</p> <p>Default \u2192 <code>0.5</code></p> <p>Only valid if <code>resampling_strategy</code> is not None. Controls the parameters of the resampling strategy.. - If <code>resampling_strategy='bagging'</code>, must be an integer greater than or equal to 1 that parameterizes the poisson distribution used to simulate bagging in online learning settings. It acts as the lambda parameter of Oza Bagging and Leveraging Bagging. - If <code>resampling_strategy='subbagging'</code>, must be a float in the interval \\((0, 1]\\) that controls the chance of each instance being used by a tree for learning.</p> </li> <li> <p>detection_mode</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>all</code></p> <p>The concept drift detection mode in which the forest operates. Valid values are: - \"all\": creates both warning and concept drift detectors. If a warning is detected, an alternate tree starts being trained in the background. If the warning trigger escalates to a concept drift, the affected tree is replaced by the alternate tree. - \"drop\": only the concept drift detectors are created. If a drift is detected, the affected tree is dropped and replaced by a new tree. - \"off\": disables the concept drift adaptation capabilities. The forest will act as if the processed stream is stationary.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The detector that will be used to trigger concept drift warnings. Defaults to <code>drift.ADWIN</code>(0.01)`.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The detector used to detect concept drifts. Defaults to <code>drift.ADWIN</code>(0.001)`.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth the ensemble members might reach. If <code>None</code>, the trees will grow indefinitely.</p> </li> <li> <p>randomize_tree_depth</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not randomize the maximum depth of each tree in the ensemble. If <code>max_depth</code> is provided, it is going to act as an upper bound to generate the maximum depth for each tree.</p> </li> <li> <p>track_metric</p> <p>Type \u2192 metrics.base.RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The performance metric used to weight predictions. Defaults to <code>metrics.MAE</code>()`.</p> </li> <li> <p>disable_weighted_vote</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Defines whether or not to use predictions weighted by each trees' prediction performance.</p> </li> <li> <p>split_buffer_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>Defines the size of the buffer used by the tree splitters when determining the feature range and a random split point in this interval.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed to support reproducibility.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>[Tree parameter] Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>[Tree parameter] Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>[Tree parameter] Prediction mechanism used at leaves. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided, an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters  is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>[Tree parameter] The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>[Tree parameter] The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>500</code></p> <p>[Tree parameter] Maximum memory (MiB) consumed by the tree.</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2000000</code></p> <p>[Tree parameter] Number of instances between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>[Tree parameter] If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/forest/OXTRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>instances_per_tree</p> <p>The number of instances processed by each one of the current forest members.  Each time a concept drift is detected, the count corresponding to the affected tree is reset.</p> </li> <li> <p>models</p> </li> <li> <p>n_drifts</p> <p>The number of concept drifts detected per ensemble member.</p> </li> <li> <p>n_tree_swaps</p> <p>The number of performed alternate tree swaps.  Not applicable if the warning detectors are disabled.</p> </li> <li> <p>n_warnings</p> <p>The number of warnings detected per ensemble member.</p> </li> <li> <p>total_instances</p> <p>The total number of instances processed by the ensemble.</p> </li> </ul>"},{"location":"api/forest/OXTRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import forest\n\ndataset = datasets.synth.Friedman(seed=42).take(5000)\n\nmodel = forest.OXTRegressor(n_models=3, seed=42)\n\nmetric = metrics.RMSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>RMSE: 3.127311\n</code></pre></p>"},{"location":"api/forest/OXTRegressor/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/forest/OXTRegressor/#notes","title":"Notes","text":"<p>As the Online Extra Trees change the way in which Hoeffding Trees perform split attempts and monitor numerical input features, some of the parameters of the vanilla Hoeffding Tree algorithms are not available.</p> <ol> <li> <p>Mastelini, S. M., Nakano, F. K., Vens, C., &amp; de Leon Ferreira, A. C. P. (2022). Online Extra Trees Regressor. IEEE Transactions on Neural Networks and Learning Systems.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/imblearn/ChebyshevOverSampler/","title":"ChebyshevOverSampler","text":"<p>Over-sampling for imbalanced regression using Chebyshev's inequality.</p> <p>Chebyshev's inequality can be used to define the probability of target observations being frequent values (w.r.t. the distribution mean). </p> <p>Let \\(Y\\) be a random variable with finite expected value \\(\\overline{y}\\) and non-zero variance \\(\\sigma^2\\). For any real number \\(t &gt; 0\\), the Chebyshev's inequality states that, for a wide class of unimodal probability distributions: \\(Pr(|y-\\overline{y}| \\ge t\\sigma) \\le \\dfrac{1}{t^2}\\). </p> <p>Taking \\(t=\\dfrac{|y-\\overline{y}|}{\\sigma}\\), and assuming \\(t &gt; 1\\), the Chebyshev\u2019s inequality for an observation \\(y\\) becomes: \\(P(|y - \\overline{y}|=t) = \\dfrac{\\sigma^2}{|y-\\overline{y}|}\\). </p> <p>Alternatively, one can use \\(t\\) directly to estimate a frequency weight \\(\\kappa = \\lceil t\\rceil\\) and define an over-sampling strategy for extreme and rare target values<sup>1</sup>. Each incoming instance is used \\(\\kappa\\) times to update the underlying regressor. Frequent target values contribute only once to the underlying regressor, whereas rares cases are used multiple times for training.</p>"},{"location":"api/imblearn/ChebyshevOverSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>The regression model that will receive the biased sample.</p> </li> </ul>"},{"location":"api/imblearn/ChebyshevOverSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import metrics\nfrom river import preprocessing\nfrom river import rules\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.ChebyshevOverSampler(\n        regressor=rules.AMRules(\n            n_min=50, delta=0.01\n        )\n    )\n)\n\nevaluate.progressive_val_score(\n    datasets.TrumpApproval(),\n    model,\n    metrics.MAE(),\n    print_every=500\n)\n</code></pre> <pre><code>[500] MAE: 1.673902\n[1,000] MAE: 1.743046\n[1,001] MAE: 1.741335\nMAE: 1.741335\n</code></pre></p>"},{"location":"api/imblearn/ChebyshevOverSampler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Aminian, Ehsan, Rita P. Ribeiro, and Jo\u00e3o Gama. \"Chebyshev approaches for imbalanced data streams regression models.\" Data Mining and Knowledge Discovery 35.6 (2021): 2389-2466.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/imblearn/ChebyshevUnderSampler/","title":"ChebyshevUnderSampler","text":"<p>Under-sampling for imbalanced regression using Chebyshev's inequality.</p> <p>Chebyshev's inequality can be used to define the probability of target observations being frequent values (w.r.t. the distribution mean). </p> <p>Let \\(Y\\) be a random variable with finite expected value \\(\\overline{y}\\) and non-zero variance \\(\\sigma^2\\). For any real number \\(t &gt; 0\\), the Chebyshev's inequality states that, for a wide class of unimodal probability distributions: \\(Pr(|y-\\overline{y}| \\ge t\\sigma) \\le \\dfrac{1}{t^2}\\). </p> <p>Taking \\(t=\\dfrac{|y-\\overline{y}|}{\\sigma}\\), and assuming \\(t &gt; 1\\), the Chebyshev\u2019s inequality for an observation \\(y\\) becomes: \\(P(|y - \\overline{y}|=t) = \\dfrac{\\sigma^2}{|y-\\overline{y}|}\\). The reciprocal of this probability is used for under-sampling<sup>1</sup> the most frequent cases. Extreme valued or rare cases have higher probabilities of selection, whereas the most frequent cases are likely to be discarded. Still, frequent cases have a small chance of being selected (controlled via the <code>sp</code> parameter) in case few rare instances were observed.</p>"},{"location":"api/imblearn/ChebyshevUnderSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>The regression model that will receive the biased sample.</p> </li> <li> <p>sp</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.15</code></p> <p>Second chance probability. Even if an example is not initially selected for training, it still has a small chance of being selected in case the number of rare case observed so far is small.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed to support reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/ChebyshevUnderSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import metrics\nfrom river import preprocessing\nfrom river import rules\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.ChebyshevUnderSampler(\n        regressor=rules.AMRules(\n            n_min=50, delta=0.01,\n        ),\n        seed=42\n    )\n)\n\nevaluate.progressive_val_score(\n    datasets.TrumpApproval(),\n    model,\n    metrics.MAE(),\n    print_every=500\n)\n</code></pre> <pre><code>[500] MAE: 1.787162\n[1,000] MAE: 1.515711\n[1,001] MAE: 1.515236\nMAE: 1.515236\n</code></pre></p>"},{"location":"api/imblearn/ChebyshevUnderSampler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Aminian, Ehsan, Rita P. Ribeiro, and Jo\u00e3o Gama. \"Chebyshev approaches for imbalanced data streams regression models.\" Data Mining and Knowledge Discovery 35.6 (2021): 2389-2466.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/imblearn/HardSamplingClassifier/","title":"HardSamplingClassifier","text":"<p>Hard sampling classifier.</p> <p>This wrapper enables a model to retrain on past samples who's output was hard to predict. This works by storing the hardest samples in a buffer of a fixed size. When a new sample arrives, the wrapped model is either trained on one of the buffered samples with a probability p or on the new sample with a probability (1 - p). </p> <p>The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the lowest loss in the buffer, then the sample takes its place.</p>"},{"location":"api/imblearn/HardSamplingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>size</p> <p>Type \u2192 int</p> <p>Size of the buffer.</p> </li> <li> <p>p</p> <p>Type \u2192 float</p> <p>Probability of updating the model with a sample from the buffer instead of a new incoming sample.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | optim.losses.MultiClassLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>Criterion used to evaluate the hardness of a sample.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed.</p> </li> </ul>"},{"location":"api/imblearn/HardSamplingClassifier/#attributes","title":"Attributes","text":"<ul> <li>classifier</li> </ul>"},{"location":"api/imblearn/HardSamplingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.HardSamplingClassifier(\n        classifier=linear_model.LogisticRegression(),\n        p=0.1,\n        size=40,\n        seed=42,\n    )\n)\n\nevaluate.progressive_val_score(\n    dataset=datasets.Phishing(),\n    model=model,\n    metric=metrics.ROCAUC(),\n    print_every=500,\n)\n</code></pre> <pre><code>[500] ROCAUC: 92.78%\n[1,000] ROCAUC: 94.76%\n[1,250] ROCAUC: 95.06%\nROCAUC: 95.06%\n</code></pre></p>"},{"location":"api/imblearn/HardSamplingClassifier/#methods","title":"Methods","text":"learn_one predict_one predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/imblearn/HardSamplingRegressor/","title":"HardSamplingRegressor","text":"<p>Hard sampling regressor.</p> <p>This wrapper enables a model to retrain on past samples who's output was hard to predict. This works by storing the hardest samples in a buffer of a fixed size. When a new sample arrives, the wrapped model is either trained on one of the buffered samples with a probability p or on the new sample with a probability (1 - p). </p> <p>The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the lowest loss in the buffer, then the sample takes its place.</p>"},{"location":"api/imblearn/HardSamplingRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> </li> <li> <p>size</p> <p>Type \u2192 int</p> <p>Size of the buffer.</p> </li> <li> <p>p</p> <p>Type \u2192 float</p> <p>Probability of updating the model with a sample from the buffer instead of a new incoming sample.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>Criterion used to evaluate the hardness of a sample.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed.</p> </li> </ul>"},{"location":"api/imblearn/HardSamplingRegressor/#attributes","title":"Attributes","text":"<ul> <li>regressor</li> </ul>"},{"location":"api/imblearn/HardSamplingRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.HardSamplingRegressor(\n        regressor=linear_model.LinearRegression(),\n        p=.2,\n        size=30,\n        seed=42,\n    )\n)\n\nevaluate.progressive_val_score(\n    datasets.TrumpApproval(),\n    model,\n    metrics.MAE(),\n    print_every=500\n)\n</code></pre> <pre><code>[500] MAE: 2.274021\n[1,000] MAE: 1.392399\n[1,001] MAE: 1.391246\nMAE: 1.391246\n</code></pre></p>"},{"location":"api/imblearn/HardSamplingRegressor/#methods","title":"Methods","text":"learn_one predict_one"},{"location":"api/imblearn/RandomOverSampler/","title":"RandomOverSampler","text":"<p>Random over-sampling.</p> <p>This is a wrapper for classifiers. It will train the provided classifier by over-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution. The implementation is a discrete version of reverse rejection sampling. </p> <p>See Working with imbalanced data for example usage.</p>"},{"location":"api/imblearn/RandomOverSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>desired_dist</p> <p>Type \u2192 dict</p> <p>The desired class distribution. The keys are the classes whilst the values are the desired class percentages. The values must sum up to 1.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/RandomOverSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = imblearn.RandomOverSampler(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    desired_dist={False: 0.4, True: 0.6},\n    seed=42\n)\n\ndataset = datasets.CreditCard().take(3000)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.0457...\n</code></pre></p>"},{"location":"api/imblearn/RandomOverSampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/imblearn/RandomSampler/","title":"RandomSampler","text":"<p>Random sampling by mixing under-sampling and over-sampling.</p> <p>This is a wrapper for classifiers. It will train the provided classifier by both under-sampling and over-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution. </p> <p>See Working with imbalanced data for example usage.</p>"},{"location":"api/imblearn/RandomSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>desired_dist</p> <p>Type \u2192 dict</p> <p>The desired class distribution. The keys are the classes whilst the values are the desired class percentages. The values must sum up to 1. If set to <code>None</code>, then the observations will be sampled uniformly at random, which is stricly equivalent to using <code>ensemble.BaggingClassifier</code>.</p> </li> <li> <p>sampling_rate</p> <p>Default \u2192 <code>1.0</code></p> <p>The desired ratio of data to sample.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/RandomSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = imblearn.RandomSampler(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    desired_dist={False: 0.4, True: 0.6},\n    sampling_rate=0.8,\n    seed=42\n)\n\ndataset = datasets.CreditCard().take(3000)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.09...\n</code></pre></p>"},{"location":"api/imblearn/RandomSampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/imblearn/RandomUnderSampler/","title":"RandomUnderSampler","text":"<p>Random under-sampling.</p> <p>This is a wrapper for classifiers. It will train the provided classifier by under-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution. The implementation is a discrete version of rejection sampling. </p> <p>See Working with imbalanced data for example usage.</p>"},{"location":"api/imblearn/RandomUnderSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>desired_dist</p> <p>Type \u2192 dict</p> <p>The desired class distribution. The keys are the classes whilst the values are the desired class percentages. The values must sum up to 1.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/RandomUnderSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = imblearn.RandomUnderSampler(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    desired_dist={False: 0.4, True: 0.6},\n    seed=42\n)\n\ndataset = datasets.CreditCard().take(3000)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.0336...\n</code></pre></p>"},{"location":"api/imblearn/RandomUnderSampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Under-sampling a dataset with desired ratios \u21a9</p> </li> <li> <p>Wikipedia article on rejection sampling \u21a9</p> </li> </ol>"},{"location":"api/linear-model/ALMAClassifier/","title":"ALMAClassifier","text":"<p>Approximate Large Margin Algorithm (ALMA).</p>"},{"location":"api/linear-model/ALMAClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>p</p> <p>Default \u2192 <code>2</code></p> </li> <li> <p>alpha</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>B</p> <p>Default \u2192 <code>1.1111111111111112</code></p> </li> <li> <p>C</p> <p>Default \u2192 <code>1.4142135623730951</code></p> </li> </ul>"},{"location":"api/linear-model/ALMAClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>w (collections.defaultdict)</p> <p>The current weights.</p> </li> <li> <p>k (int)</p> <p>The number of instances seen during training.</p> </li> </ul>"},{"location":"api/linear-model/ALMAClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.ALMAClassifier()\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 82.56%\n</code></pre></p>"},{"location":"api/linear-model/ALMAClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Gentile, Claudio. \"A new approximate maximal margin classification algorithm.\" Journal of Machine Learning Research 2.Dec (2001): 213-242 \u21a9</p> </li> </ol>"},{"location":"api/linear-model/BayesianLinearRegression/","title":"BayesianLinearRegression","text":"<p>Bayesian linear regression.</p> <p>An advantage of Bayesian linear regression over standard linear regression is that features do not have to scaled beforehand. Another attractive property is that this flavor of linear regression is somewhat insensitive to its hyperparameters. Finally, this model can output instead a predictive distribution rather than just a point estimate. </p> <p>The downside is that the learning step runs in <code>O(n^2)</code> time, whereas the learning step of standard linear regression takes <code>O(n)</code> time.</p>"},{"location":"api/linear-model/BayesianLinearRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1</code></p> <p>Prior parameter.</p> </li> <li> <p>beta</p> <p>Default \u2192 <code>1</code></p> <p>Noise parameter.</p> </li> <li> <p>smoothing</p> <p>Type \u2192 float | None</p> <p>Default \u2192 <code>None</code></p> <p>Smoothing allows the model to gradually \"forget\" the past, and focus on the more recent data. It thus enables the model to deal with concept drift. Due to the current implementation, activating smoothing may slow down the model.</p> </li> </ul>"},{"location":"api/linear-model/BayesianLinearRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\n\ndataset = datasets.TrumpApproval()\nmodel = linear_model.BayesianLinearRegression()\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.586...\n</code></pre></p> <p><pre><code>x, _ = next(iter(dataset))\nmodel.predict_one(x)\n</code></pre> <pre><code>43.852...\n</code></pre></p> <p><pre><code>model.predict_one(x, with_dist=True)\n</code></pre> <pre><code>\ud835\udca9(\u03bc=43.85..., \u03c3=1.00...)\n</code></pre></p> <p>The <code>smoothing</code> parameter can be set to make the model robust to drift. The parameter is expected to be between 0 and 1. To exemplify, let's generate some simulation data with an abrupt concept drift right in the middle.</p> <pre><code>import itertools\nimport random\n\ndef random_data(coefs, n, seed=42):\n    rng = random.Random(seed)\n    for _ in range(n):\n        x = {i: rng.random() for i, c in enumerate(coefs)}\n        y = sum(c * xi for c, xi in zip(coefs, x.values()))\n        yield x, y\n</code></pre> <p>Here's how the model performs without any smoothing:</p> <p><pre><code>model = linear_model.BayesianLinearRegression()\ndataset = itertools.chain(\n    random_data([0.1, 3], 100),\n    random_data([10, -2], 100)\n)\nmetric = metrics.MAE()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.284...\n</code></pre></p> <p>And here's how it performs with some smoothing:</p> <p><pre><code>model = linear_model.BayesianLinearRegression(smoothing=0.8)\ndataset = itertools.chain(\n    random_data([0.1, 3], 100),\n    random_data([10, -2], 100)\n)\nmetric = metrics.MAE()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.159...\n</code></pre></p> <p>Smoothing allows the model to gradually \"forget\" the past, and focus on the more recent data.</p> <p>Note how this works better than standard linear regression, even when using an aggressive learning rate.</p> <p><pre><code>from river import optim\nmodel = linear_model.LinearRegression(optimizer=optim.SGD(0.5))\ndataset = itertools.chain(\n    random_data([0.1, 3], 100),\n    random_data([10, -2], 100)\n)\nmetric = metrics.MAE()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.242...\n</code></pre></p>"},{"location":"api/linear-model/BayesianLinearRegression/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_many predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>with_dist     \u2014 defaults to <code>False</code> </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p> <ol> <li> <p>Pattern Recognition and Machine Learning, page 52 \u2014 Christopher M. Bishop \u21a9</p> </li> <li> <p>Bayesian/Streaming Algorithms \u2014 Vincent Warmerdam \u21a9</p> </li> <li> <p>Bayesian linear regression for practitioners \u2014 Max Halford \u21a9</p> </li> </ol>"},{"location":"api/linear-model/LinearRegression/","title":"LinearRegression","text":"<p>Linear regression.</p> <p>This estimator supports learning with mini-batches. On top of the single instance methods, it provides the following methods: <code>learn_many</code>, <code>predict_many</code>, <code>predict_proba_many</code>. Each method takes as input a <code>pandas.DataFrame</code> where each column represents a feature. </p> <p>It is generally a good idea to scale the data beforehand in order for the optimizer to converge. You can do this online with a <code>preprocessing.StandardScaler</code>.</p>"},{"location":"api/linear-model/LinearRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights. Note that the intercept updates are handled separately.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>l1</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>intercept_init</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.base.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/LinearRegression/#attributes","title":"Attributes","text":"<ul> <li> <p>weights (dict)</p> <p>The current weights.</p> </li> </ul>"},{"location":"api/linear-model/LinearRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression(intercept_lr=.1)\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.558735\n</code></pre></p> <p><pre><code>model['LinearRegression'].intercept\n</code></pre> <pre><code>35.617670\n</code></pre></p> <p>You can call the <code>debug_one</code> method to break down a prediction. This works even if the linear regression is part of a pipeline.</p> <p><pre><code>x, y = next(iter(dataset))\nreport = model.debug_one(x)\nprint(report)\n</code></pre> <pre><code>0. Input\n--------\ngallup: 43.84321 (float)\nipsos: 46.19925 (float)\nmorning_consult: 48.31875 (float)\nordinal_date: 736389 (int)\nrasmussen: 44.10469 (float)\nyou_gov: 43.63691 (float)\n&lt;BLANKLINE&gt;\n1. StandardScaler\n-----------------\ngallup: 1.18810 (float)\nipsos: 2.10348 (float)\nmorning_consult: 2.73545 (float)\nordinal_date: -1.73032 (float)\nrasmussen: 1.26872 (float)\nyou_gov: 1.48391 (float)\n&lt;BLANKLINE&gt;\n2. LinearRegression\n-------------------\nName              Value      Weight      Contribution\n      Intercept    1.00000    35.61767       35.61767\n          ipsos    2.10348     0.62689        1.31866\nmorning_consult    2.73545     0.24180        0.66144\n         gallup    1.18810     0.43568        0.51764\n      rasmussen    1.26872     0.28118        0.35674\n        you_gov    1.48391     0.03123        0.04634\n   ordinal_date   -1.73032     3.45162       -5.97242\n&lt;BLANKLINE&gt;\nPrediction: 32.54607\n</code></pre></p>"},{"location":"api/linear-model/LinearRegression/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the linear regression.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_many <p>Update the model with a mini-batch of features <code>X</code> and real-valued targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> <li>w     \u2014 'float | pd.Series'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>The predicted outcomes.</p> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/linear-model/LogisticRegression/","title":"LogisticRegression","text":"<p>Logistic regression.</p> <p>This estimator supports learning with mini-batches. On top of the single instance methods, it provides the following methods: <code>learn_many</code>, <code>predict_many</code>, <code>predict_proba_many</code>. Each method takes as input a <code>pandas.DataFrame</code> where each column represents a feature. </p> <p>It is generally a good idea to scale the data beforehand in order for the optimizer to converge. You can do this online with a <code>preprocessing.StandardScaler</code>.</p>"},{"location":"api/linear-model/LogisticRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights. Note that the intercept is handled separately.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for. Defaults to <code>optim.losses.Log</code>.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>l1</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>intercept_init</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 float | optim.base.Scheduler</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.base.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/LogisticRegression/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights.</p> </li> </ul>"},{"location":"api/linear-model/LogisticRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer=optim.SGD(.1))\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 88.96%\n</code></pre></p>"},{"location":"api/linear-model/LogisticRegression/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and boolean targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> <li>w     \u2014 'float | pd.Series'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Predict the outcome probabilities for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A dataframe with probabilities of <code>True</code> and <code>False</code> for each sample.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/linear-model/PAClassifier/","title":"PAClassifier","text":"<p>Passive-aggressive learning for classification.</p>"},{"location":"api/linear-model/PAClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>C</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>mode</p> <p>Default \u2192 <code>1</code></p> </li> <li> <p>learn_intercept</p> <p>Default \u2192 <code>True</code></p> </li> </ul>"},{"location":"api/linear-model/PAClassifier/#examples","title":"Examples","text":"<p>The following example is taken from this blog post.</p> <p><pre><code>from river import linear_model\nfrom river import metrics\nfrom river import stream\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn import model_selection\n\nnp.random.seed(1000)\nX, y = datasets.make_classification(\n    n_samples=5000,\n    n_features=4,\n    n_informative=2,\n    n_redundant=0,\n    n_repeated=0,\n    n_classes=2,\n    n_clusters_per_class=2\n)\n\nX_train, X_test, y_train, y_test = model_selection.train_test_split(\n    X,\n    y,\n    test_size=0.35,\n    random_state=1000\n)\n\nmodel = linear_model.PAClassifier(\n    C=0.01,\n    mode=1\n)\n\nfor xi, yi in stream.iter_array(X_train, y_train):\n    model.learn_one(xi, yi)\n\nmetric = metrics.Accuracy() + metrics.LogLoss()\n\nfor xi, yi in stream.iter_array(X_test, y_test):\n    metric.update(yi, model.predict_proba_one(xi))\n\nprint(metric)\n</code></pre> <pre><code>Accuracy: 88.46%\nLogLoss: 0.325727...\n</code></pre></p>"},{"location":"api/linear-model/PAClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S. and Singer, Y., 2006. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7(Mar), pp.551-585 \u21a9</p> </li> </ol>"},{"location":"api/linear-model/PARegressor/","title":"PARegressor","text":"<p>Passive-aggressive learning for regression.</p>"},{"location":"api/linear-model/PARegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>C</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>mode</p> <p>Default \u2192 <code>1</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>learn_intercept</p> <p>Default \u2192 <code>True</code></p> </li> </ul>"},{"location":"api/linear-model/PARegressor/#examples","title":"Examples","text":"<p>The following example is taken from this blog post.</p> <p><pre><code>from river import linear_model\nfrom river import metrics\nfrom river import stream\nimport numpy as np\nfrom sklearn import datasets\n\nnp.random.seed(1000)\nX, y = datasets.make_regression(n_samples=500, n_features=4)\n\nmodel = linear_model.PARegressor(\n    C=0.01,\n    mode=2,\n    eps=0.1,\n    learn_intercept=False\n)\nmetric = metrics.MAE() + metrics.MSE()\n\nfor xi, yi in stream.iter_array(X, y):\n    y_pred = model.predict_one(xi)\n    model.learn_one(xi, yi)\n    metric.update(yi, y_pred)\n\nprint(metric)\n</code></pre> <pre><code>MAE: 9.809402\nMSE: 472.393532\n</code></pre></p>"},{"location":"api/linear-model/PARegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S. and Singer, Y., 2006. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7(Mar), pp.551-585. \u21a9</p> </li> </ol>"},{"location":"api/linear-model/Perceptron/","title":"Perceptron","text":"<p>Perceptron classifier.</p> <p>In this implementation, the Perceptron is viewed as a special case of the logistic regression. The loss function that is used is the Hinge loss with a threshold set to 0, whilst the learning rate of the stochastic gradient descent procedure is set to 1 for both the weights and the intercept.</p>"},{"location":"api/linear-model/Perceptron/#parameters","title":"Parameters","text":"<ul> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/Perceptron/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights.</p> </li> </ul>"},{"location":"api/linear-model/Perceptron/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model as lm\nfrom river import metrics\nfrom river import preprocessing as pp\n\ndataset = datasets.Phishing()\n\nmodel = pp.StandardScaler() | lm.Perceptron()\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 85.84%\n</code></pre></p>"},{"location":"api/linear-model/Perceptron/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and boolean targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> <li>w     \u2014 'float | pd.Series'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Predict the outcome probabilities for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A dataframe with probabilities of <code>True</code> and <code>False</code> for each sample.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/linear-model/SoftmaxRegression/","title":"SoftmaxRegression","text":"<p>Softmax regression is a generalization of logistic regression to multiple classes.</p> <p>Softmax regression is also known as \"multinomial logistic regression\". There are a set weights for each class, hence the <code>weights</code> attribute is a nested <code>collections.defaultdict</code>. The main advantage of using this instead of a one-vs-all logistic regression is that the probabilities will be calibrated. Moreover softmax regression is more robust to outliers.</p>"},{"location":"api/linear-model/SoftmaxRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used to tune the weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.MultiClassLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> </ul>"},{"location":"api/linear-model/SoftmaxRegression/#attributes","title":"Attributes","text":"<ul> <li>weights (collections.defaultdict)</li> </ul>"},{"location":"api/linear-model/SoftmaxRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nmodel = preprocessing.StandardScaler()\nmodel |= linear_model.SoftmaxRegression()\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 81.88%\n</code></pre></p>"},{"location":"api/linear-model/SoftmaxRegression/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Course on classification stochastic gradient descent \u21a9</p> </li> <li> <p>Binary vs. Multi-Class Logistic Regression \u21a9</p> </li> </ol>"},{"location":"api/linear-model/base/GLM/","title":"GLM","text":"<p>Generalized Linear Model.</p> <p>This serves as a base class for linear and logistic regression.</p>"},{"location":"api/linear-model/base/GLM/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>The sequential optimizer used for updating the weights. Note that the intercept updates are handled separately.</p> </li> <li> <p>loss</p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>l1</p> <p>Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>intercept_init</p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/base/GLM/#attributes","title":"Attributes","text":"<ul> <li>weights</li> </ul>"},{"location":"api/linear-model/base/GLM/#methods","title":"Methods","text":"learn_many learn_one"},{"location":"api/metrics/Accuracy/","title":"Accuracy","text":"<p>Accuracy score, which is the percentage of exact matches.</p>"},{"location":"api/metrics/Accuracy/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Accuracy/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Accuracy/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, True, True, True]\ny_pred = [True, True, False, True, True]\n\nmetric = metrics.Accuracy()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>Accuracy: 60.00%\n</code></pre></p>"},{"location":"api/metrics/Accuracy/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/AdjustedMutualInfo/","title":"AdjustedMutualInfo","text":"<p>Adjusted Mutual Information between two clusterings.</p> <p>Adjusted Mutual Information (AMI) is an adjustment of the Mutual Information score that accounts for chance. It corrects the effect of agreement solely due to chance between clusterings, similar to the way the Adjusted Rand Index corrects the Rand Index. It is closely related to variation of information. The adjusted measure, however, is no longer metrical. </p> <p>For two clusterings \\(U\\) and \\(V\\), the Adjusted Mutual Information is calculated as: </p> \\[ AMI(U, V) = \\frac{MI(U, V) - E(MI(U, V))}{avg(H(U), H(V)) - E(MI(U, V))} \\] <p>This metric is independent of the permutation of the class or cluster label values; furthermore, it is also symmetric. This can be useful to measure the agreement of two label assignments strategies on the same dataset, regardless of the ground truth. </p> <p>However, due to the complexity of the Expected Mutual Info Score, the computation of this metric is an order of magnitude slower than most other metrics, in general.</p>"},{"location":"api/metrics/AdjustedMutualInfo/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>average_method</p> <p>Default \u2192 <code>arithmetic</code></p> <p>This parameter defines how to compute the normalizer in the denominator. Possible options include <code>min</code>, <code>max</code>, <code>arithmetic</code> and <code>geometric</code>.</p> </li> </ul>"},{"location":"api/metrics/AdjustedMutualInfo/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/AdjustedMutualInfo/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.AdjustedMutualInfo()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.0\n0.105891\n0.298792\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>AdjustedMutualInfo: 0.298792\n</code></pre></p>"},{"location":"api/metrics/AdjustedMutualInfo/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, March 17). Mutual information.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Mutual_information&amp;oldid=1012714929\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/AdjustedRand/","title":"AdjustedRand","text":"<p>Adjusted Rand Index.</p> <p>The Adjusted Rand Index is the corrected-for-chance version of the Rand Index <sup>1</sup> <sup>2</sup>. Such a correction for chance establishes a baseline by using the expected similarity of all pair-wise comparisions between clusterings specified by a random model. </p> <p>Traditionally, the Rand Index was corrected using the Permutation Model for Clustering. However, the premises of the permutation model are frequently violated; in many clustering scenarios, either the number of clusters or the size distribution of those clusters vary drastically. Variations of the adjusted Rand Index account for different models of random clusterings. </p> <p>Though the Rand Index may only yield a value between 0 and 1, the Adjusted Rand index can yield negative values if the index is less than the expected index.</p>"},{"location":"api/metrics/AdjustedRand/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/AdjustedRand/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/AdjustedRand/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0, 0, 1, 1, 1]\ny_pred = [0, 0, 1, 1, 2, 2]\n\nmetric = metrics.AdjustedRand()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.0\n0.09090909090909091\n0.24242424242424243\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>AdjustedRand: 0.242424\n</code></pre></p>"},{"location":"api/metrics/AdjustedRand/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, January 13). Rand index.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Rand_index&amp;oldid=1000098911\u00a0\u21a9</p> </li> <li> <p>W. M. Rand (1971). \"Objective criteria for the evaluation of clustering methods\".   Journal of the American Statistical Association. American Statistical Association.   66 (336): 846\u2013850. arXiv:1704.01036. doi:10.2307/2284239. JSTOR 2284239.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/BalancedAccuracy/","title":"BalancedAccuracy","text":"<p>Balanced accuracy.</p> <p>Balanced accuracy is the average of recall obtained on each class. It is used to deal with imbalanced datasets in binary and multi-class classification problems.</p>"},{"location":"api/metrics/BalancedAccuracy/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/BalancedAccuracy/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/BalancedAccuracy/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\ny_true = [True, False, True, True, False, True]\ny_pred = [True, False, True, True, True, False]\n\nmetric = metrics.BalancedAccuracy()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>BalancedAccuracy: 62.50%\n</code></pre></p> <p><pre><code>y_true = [0, 1, 0, 0, 1, 0]\ny_pred = [0, 1, 0, 0, 0, 1]\nmetric = metrics.BalancedAccuracy()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>BalancedAccuracy: 62.50%\n</code></pre></p>"},{"location":"api/metrics/BalancedAccuracy/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/ClassificationReport/","title":"ClassificationReport","text":"<p>A report for monitoring a classifier.</p> <p>This class maintains a set of metrics and updates each of them every time <code>update</code> is called. You can print this class at any time during a model's lifetime to get a tabular visualization of various metrics. </p> <p>You can wrap a <code>metrics.ClassificationReport</code> with <code>utils.Rolling</code> in order to obtain a classification report over a window of observations. You can also wrap it with <code>utils.TimeRolling</code> to obtain a report over a period of time.</p>"},{"location":"api/metrics/ClassificationReport/#parameters","title":"Parameters","text":"<ul> <li> <p>decimals</p> <p>Default \u2192 <code>2</code></p> <p>The number of decimals to display in each cell.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/ClassificationReport/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/ClassificationReport/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['pear', 'apple', 'banana', 'banana', 'banana']\ny_pred = ['apple', 'pear', 'banana', 'banana', 'apple']\n\nreport = metrics.ClassificationReport()\n\nfor yt, yp in zip(y_true, y_pred):\n    report.update(yt, yp)\n\nprint(report)\n</code></pre> <pre><code>               Precision   Recall   F1       Support\n&lt;BLANKLINE&gt;\n   apple       0.00%    0.00%    0.00%         1\n  banana     100.00%   66.67%   80.00%         3\n    pear       0.00%    0.00%    0.00%         1\n&lt;BLANKLINE&gt;\n   Macro      33.33%   22.22%   26.67%\n   Micro      40.00%   40.00%   40.00%\nWeighted      60.00%   40.00%   48.00%\n&lt;BLANKLINE&gt;\n                 40.00% accuracy\n</code></pre></p>"},{"location":"api/metrics/ClassificationReport/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/CohenKappa/","title":"CohenKappa","text":"<p>Cohen's Kappa score.</p> <p>Cohen's Kappa expresses the level of agreement between two annotators on a classification problem. It is defined as </p> \\[ \\kappa = (p_o - p_e) / (1 - p_e) \\] <p>where \\(p_o\\) is the empirical probability of agreement on the label assigned to any sample (prequential accuracy), and \\(p_e\\) is the expected agreement when both annotators assign labels randomly.</p>"},{"location":"api/metrics/CohenKappa/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/CohenKappa/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/CohenKappa/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']\ny_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']\n\nmetric = metrics.CohenKappa()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>CohenKappa: 42.86%\n</code></pre></p>"},{"location":"api/metrics/CohenKappa/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>J. Cohen (1960). \"A coefficient of agreement for nominal scales\". Educational and Psychological Measurement 20(1):37-46. doi:10.1177/001316446002000104.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Completeness/","title":"Completeness","text":"<p>Completeness Score.</p> <p>Completeness <sup>1</sup> is symmetrical to homogeneity. In order to satisfy the completeness criteria, a clustering must assign all of those datapoints that are members of a single class to a single cluster. To evaluate completeness, we examine the distribution cluster assignments within each class. In a perfectly complete clustering solution, each of these distributions will be completely skewed to a single cluster. </p> <p>We can evaluate this degree of skew by calculating the conditional entropy of the proposed cluster distribution given the class of the component data points. However, in the worst case scenario, each class is represented by every cluster with a distribution equal to the distribution of cluster sizes. Therefore, symmetric to the claculation above, we define completeness as: </p> \\[ c = \\begin{cases} 1 if H(K) = 0, \\\\ 1 - \\frac{H(K|C)}{H(K)} otherwise. \\end{cases}. \\]"},{"location":"api/metrics/Completeness/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Completeness/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Completeness/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.Completeness()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n1.0\n0.3836885465963443\n0.5880325916843805\n0.6666666666666667\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>Completeness: 66.67%\n</code></pre></p>"},{"location":"api/metrics/Completeness/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Andrew Rosenberg and Julia Hirschberg (2007).   V-Measure: A conditional entropy-based external cluster evaluation measure.   Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language   Processing and Computational Natural Language Learning, pp. 410 - 420,   Prague, June 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/ConfusionMatrix/","title":"ConfusionMatrix","text":"<p>Confusion Matrix for binary and multi-class classification.</p>"},{"location":"api/metrics/ConfusionMatrix/#parameters","title":"Parameters","text":"<ul> <li> <p>classes</p> <p>Default \u2192 <code>None</code></p> <p>The initial set of classes. This is optional and serves only for displaying purposes.</p> </li> </ul>"},{"location":"api/metrics/ConfusionMatrix/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>classes</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>total_false_negatives</p> </li> <li> <p>total_false_positives</p> </li> <li> <p>total_true_negatives</p> </li> <li> <p>total_true_positives</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/ConfusionMatrix/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']\ny_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']\n\ncm = metrics.ConfusionMatrix()\n\nfor yt, yp in zip(y_true, y_pred):\n    cm.update(yt, yp)\n\ncm\n</code></pre> <pre><code>       ant  bird   cat\n ant     2     0     0\nbird     0     0     1\n cat     1     0     2\n</code></pre></p> <p><pre><code>cm['bird']['cat']\n</code></pre> <pre><code>1.0\n</code></pre></p>"},{"location":"api/metrics/ConfusionMatrix/#methods","title":"Methods","text":"false_negatives false_positives get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> support true_negatives true_positives update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/ConfusionMatrix/#notes","title":"Notes","text":"<p>This confusion matrix is a 2D matrix of shape <code>(n_classes, n_classes)</code>, corresponding to a single-target (binary and multi-class) classification task.</p> <p>Each row represents <code>true</code> (actual) class-labels, while each column corresponds to the <code>predicted</code> class-labels. For example, an entry in position <code>[1, 2]</code> means that the true class-label is 1, and the predicted class-label is 2 (incorrect prediction).</p> <p>This structure is used to keep updated statistics about a single-output classifier's performance and to compute multiple evaluation metrics.</p>"},{"location":"api/metrics/CrossEntropy/","title":"CrossEntropy","text":"<p>Multiclass generalization of the logarithmic loss.</p>"},{"location":"api/metrics/CrossEntropy/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/CrossEntropy/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2]\ny_pred = [\n    {0: 0.29450637, 1: 0.34216758, 2: 0.36332605},\n    {0: 0.21290077, 1: 0.32728332, 2: 0.45981591},\n    {0: 0.42860913, 1: 0.33380113, 2: 0.23758974},\n    {0: 0.44941979, 1: 0.32962558, 2: 0.22095463}\n]\n\nmetric = metrics.CrossEntropy()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.222454\n1.169691\n1.258864\n1.321597\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>CrossEntropy: 1.321598\n</code></pre></p>"},{"location":"api/metrics/CrossEntropy/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/F1/","title":"F1","text":"<p>Binary F1 score.</p>"},{"location":"api/metrics/F1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/F1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/F1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [False, False, False, True, True, True]\ny_pred = [False, False, True, True, False, False]\n\nmetric = metrics.F1()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>F1: 40.00%\n</code></pre></p>"},{"location":"api/metrics/F1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/FBeta/","title":"FBeta","text":"<p>Binary F-Beta score.</p> <p>The FBeta score is a weighted harmonic mean between precision and recall. The higher the <code>beta</code> value, the higher the recall will be taken into account. When <code>beta</code> equals 1, precision and recall and equivalently weighted, which results in the F1 score (see <code>metrics.F1</code>).</p>"},{"location":"api/metrics/FBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Weight of precision in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/FBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>precision (metrics.Precision)</p> </li> <li> <p>recall (metrics.Recall)</p> </li> </ul>"},{"location":"api/metrics/FBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [False, False, False, True, True, True]\ny_pred = [False, False, True, True, False, False]\n\nmetric = metrics.FBeta(beta=2)\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>FBeta: 35.71%\n</code></pre></p>"},{"location":"api/metrics/FBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/FowlkesMallows/","title":"FowlkesMallows","text":"<p>Fowlkes-Mallows Index.</p> <p>The Fowlkes-Mallows Index <sup>1</sup> <sup>2</sup> is an external evaluation method that is used to determine the similarity between two clusterings, and also a metric to measure confusion matrices. The measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher value for the Fowlkes-Mallows index indicates a greater similarity between the clusters and the benchmark classifications. </p> <p>The Fowlkes-Mallows Index, for two cluster algorithms, is defined as: </p> \\[ FM = \\sqrt{PPV \\times TPR} = \\sqrt{\\frac{TP}{TP+FP} \\times \\frac{TP}{TP+FN}} \\] <p>where </p> <ul> <li> <p>TP, FP, FN are respectively the number of true positives, false positives and false negatives; </p> </li> <li> <p>TPR is the True Positive Rate (or Sensitivity/Recall), PPV is the Positive Predictive Rate (or Precision).</p> </li> </ul>"},{"location":"api/metrics/FowlkesMallows/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/FowlkesMallows/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/FowlkesMallows/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0, 0, 1, 1, 1]\ny_pred = [0, 0, 1, 1, 2, 2]\n\nmetric = metrics.FowlkesMallows()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>FowlkesMallows: 0.00%\nFowlkesMallows: 100.00%\nFowlkesMallows: 57.74%\nFowlkesMallows: 40.82%\nFowlkesMallows: 35.36%\nFowlkesMallows: 47.14%\n</code></pre></p>"},{"location":"api/metrics/FowlkesMallows/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2020, December 22).   Fowlkes\u2013Mallows index. In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Fowlkes%E2%80%93Mallows_index&amp;oldid=995714222\u00a0\u21a9</p> </li> <li> <p>E. B. Fowkles and C. L. Mallows (1983).   \u201cA method for comparing two hierarchical clusterings\u201d.   Journal of the American Statistical Association\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/GeometricMean/","title":"GeometricMean","text":"<p>Geometric mean score.</p> <p>The geometric mean is a good indicator of a classifier's performance in the presence of class imbalance because it is independent of the distribution of examples between classes. This implementation computes the geometric mean of class-wise sensitivity (recall). </p> \\[ gm = \\sqrt[n]{s_1\\cdot s_2\\cdot s_3\\cdot \\ldots\\cdot s_n} \\] <p>where \\(s_i\\) is the sensitivity (recall) of class \\(i\\) and \\(n\\) is the number of classes.</p>"},{"location":"api/metrics/GeometricMean/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/GeometricMean/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/GeometricMean/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird', 'bird']\ny_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat', 'bird']\n\nmetric = metrics.GeometricMean()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>GeometricMean: 69.34%\n</code></pre></p>"},{"location":"api/metrics/GeometricMean/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Barandela, R. et al. \u201cStrategies for learning in class imbalance problems\u201d, Pattern Recognition, 36(3), (2003), pp 849-851.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Homogeneity/","title":"Homogeneity","text":"<p>Homogeneity Score.</p> <p>Homogeneity metric <sup>1</sup> of a cluster labeling given a ground truth. </p> <p>In order to satisfy the homogeneity criteria, a clustering must assign only those data points that are members of a single class to a single cluster. That is, the class distribution within each cluster should be skewed to a single class, that is, zero entropy. We determine how close a given clustering is to this ideal by examining the conditional entropy of the class distribution given the proposed clustering. </p> <p>However, in an imperfect situation, the size of this value is dependent on the size of the dataset and the distribution of class sizes. Therefore, instead of taking the raw conditional entropy, we normalize by the maximum reduction in entropy the clustering information could provide. </p> <p>As such, we define homogeneity as: </p> \\[ h = \\begin{cases} 1 if H(C) = 0, \\\\ 1 - \\frac{H(C|K)}{H(C)} otherwise. \\end{cases}. \\]"},{"location":"api/metrics/Homogeneity/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Homogeneity/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Homogeneity/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.Homogeneity()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.311278\n0.37515\n0.42062\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>Homogeneity: 42.06%\n</code></pre></p>"},{"location":"api/metrics/Homogeneity/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Andrew Rosenberg and Julia Hirschberg (2007).   V-Measure: A conditional entropy-based external cluster evaluation measure.   Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language   Processing and Computational Natural Language Learning, pp. 410 - 420,   Prague, June 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Jaccard/","title":"Jaccard","text":"<p>Jaccard score.</p>"},{"location":"api/metrics/Jaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/Jaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Jaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [False, True, True]\ny_pred = [True, True, True]\n\nmetric = metrics.Jaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>Jaccard: 0.00%\nJaccard: 50.00%\nJaccard: 66.67%\n</code></pre></p>"},{"location":"api/metrics/Jaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Jaccard index \u21a9</p> </li> </ol>"},{"location":"api/metrics/LogLoss/","title":"LogLoss","text":"<p>Binary logarithmic loss.</p>"},{"location":"api/metrics/LogLoss/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/LogLoss/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, False, True]\ny_pred = [0.9,  0.1,   0.2,   0.65]\n\nmetric = metrics.LogLoss()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.105360\n0.105360\n0.144621\n0.216161\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>LogLoss: 0.216162\n</code></pre></p>"},{"location":"api/metrics/LogLoss/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MAE/","title":"MAE","text":"<p>Mean absolute error.</p>"},{"location":"api/metrics/MAE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MAE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.MAE()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.5\n0.5\n0.333\n0.5\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>MAE: 0.5\n</code></pre></p>"},{"location":"api/metrics/MAE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MAPE/","title":"MAPE","text":"<p>Mean absolute percentage error.</p>"},{"location":"api/metrics/MAPE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MAPE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.MAPE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>MAPE: 32.738095\n</code></pre></p>"},{"location":"api/metrics/MAPE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MCC/","title":"MCC","text":"<p>Matthews correlation coefficient.</p>"},{"location":"api/metrics/MCC/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/MCC/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MCC/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, True, True, False]\ny_pred = [True, False, True, True]\n\nmcc = metrics.MCC()\n\nfor yt, yp in zip(y_true, y_pred):\n    mcc.update(yt, yp)\n\nmcc\n</code></pre> <pre><code>MCC: -0.333333\n</code></pre></p>"},{"location":"api/metrics/MCC/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia article \u21a9</p> </li> </ol>"},{"location":"api/metrics/MSE/","title":"MSE","text":"<p>Mean squared error.</p>"},{"location":"api/metrics/MSE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MSE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.MSE()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.25\n0.25\n0.1666\n0.375\n</code></pre></p>"},{"location":"api/metrics/MSE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MacroF1/","title":"MacroF1","text":"<p>Macro-average F1 score.</p> <p>This works by computing the F1 score per class, and then performs a global average.</p>"},{"location":"api/metrics/MacroF1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroF1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroF1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroF1()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroF1: 100.00%\nMacroF1: 33.33%\nMacroF1: 55.56%\nMacroF1: 55.56%\nMacroF1: 48.89%\n</code></pre></p>"},{"location":"api/metrics/MacroF1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroFBeta/","title":"MacroFBeta","text":"<p>Macro-average F-Beta score.</p> <p>This works by computing the F-Beta score per class, and then performs a global average.</p>"},{"location":"api/metrics/MacroFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Weight of precision in harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroFBeta(beta=.8)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroFBeta: 100.00%\nMacroFBeta: 31.06%\nMacroFBeta: 54.04%\nMacroFBeta: 54.04%\nMacroFBeta: 48.60%\n</code></pre></p>"},{"location":"api/metrics/MacroFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroJaccard/","title":"MacroJaccard","text":"<p>Macro-average Jaccard score.</p>"},{"location":"api/metrics/MacroJaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroJaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroJaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroJaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroJaccard: 100.00%\nMacroJaccard: 25.00%\nMacroJaccard: 50.00%\nMacroJaccard: 50.00%\nMacroJaccard: 38.89%\n</code></pre></p>"},{"location":"api/metrics/MacroJaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroPrecision/","title":"MacroPrecision","text":"<p>Macro-average precision score.</p>"},{"location":"api/metrics/MacroPrecision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroPrecision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroPrecision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroPrecision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroPrecision: 100.00%\nMacroPrecision: 25.00%\nMacroPrecision: 50.00%\nMacroPrecision: 50.00%\nMacroPrecision: 50.00%\n</code></pre></p>"},{"location":"api/metrics/MacroPrecision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroRecall/","title":"MacroRecall","text":"<p>Macro-average recall score.</p>"},{"location":"api/metrics/MacroRecall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroRecall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroRecall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroRecall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroRecall: 100.00%\nMacroRecall: 50.00%\nMacroRecall: 66.67%\nMacroRecall: 66.67%\nMacroRecall: 55.56%\n</code></pre></p>"},{"location":"api/metrics/MacroRecall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MicroF1/","title":"MicroF1","text":"<p>Micro-average F1 score.</p> <p>This computes the F1 score by merging all the predictions and true labels, and then computes a global F1 score.</p>"},{"location":"api/metrics/MicroF1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroF1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroF1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 0]\ny_pred = [0, 1, 1, 2, 1]\n\nmetric = metrics.MicroF1()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>MicroF1: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroF1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem? \u21a9</p> </li> </ol>"},{"location":"api/metrics/MicroFBeta/","title":"MicroFBeta","text":"<p>Micro-average F-Beta score.</p> <p>This computes the F-Beta score by merging all the predictions and true labels, and then computes a global F-Beta score.</p>"},{"location":"api/metrics/MicroFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Weight of precision in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 0]\ny_pred = [0, 1, 1, 2, 1]\n\nmetric = metrics.MicroFBeta(beta=2)\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>MicroFBeta: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p> 1. Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem?</p>"},{"location":"api/metrics/MicroJaccard/","title":"MicroJaccard","text":"<p>Micro-average Jaccard score.</p>"},{"location":"api/metrics/MicroJaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroJaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroJaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MicroJaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MicroJaccard: 100.00%\nMicroJaccard: 33.33%\nMicroJaccard: 50.00%\nMicroJaccard: 60.00%\nMicroJaccard: 42.86%\n</code></pre></p>"},{"location":"api/metrics/MicroJaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MicroPrecision/","title":"MicroPrecision","text":"<p>Micro-average precision score.</p> <p>The micro-average precision score is exactly equivalent to the micro-average recall as well as the micro-average F1 score.</p>"},{"location":"api/metrics/MicroPrecision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroPrecision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroPrecision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MicroPrecision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MicroPrecision: 100.00%\nMicroPrecision: 50.00%\nMicroPrecision: 66.67%\nMicroPrecision: 75.00%\nMicroPrecision: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroPrecision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem? \u21a9</p> </li> </ol>"},{"location":"api/metrics/MicroRecall/","title":"MicroRecall","text":"<p>Micro-average recall score.</p> <p>The micro-average recall is exactly equivalent to the micro-average precision as well as the micro-average F1 score.</p>"},{"location":"api/metrics/MicroRecall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroRecall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroRecall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MicroRecall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MicroRecall: 100.00%\nMicroRecall: 50.00%\nMicroRecall: 66.67%\nMicroRecall: 75.00%\nMicroRecall: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroRecall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem? \u21a9</p> </li> </ol>"},{"location":"api/metrics/MultiFBeta/","title":"MultiFBeta","text":"<p>Multi-class F-Beta score with different betas per class.</p> <p>The multiclass F-Beta score is the arithmetic average of the binary F-Beta scores of each class. The mean can be weighted by providing class weights.</p>"},{"location":"api/metrics/MultiFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>betas</p> <p>Weight of precision in the harmonic mean of each class.</p> </li> <li> <p>weights</p> <p>Class weights. If not provided then uniform weights will be used.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MultiFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MultiFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MultiFBeta(\n    betas={0: 0.25, 1: 1, 2: 4},\n    weights={0: 1, 1: 1, 2: 2}\n)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MultiFBeta: 100.00%\nMultiFBeta: 25.76%\nMultiFBeta: 62.88%\nMultiFBeta: 62.88%\nMultiFBeta: 46.88%\n</code></pre></p>"},{"location":"api/metrics/MultiFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MutualInfo/","title":"MutualInfo","text":"<p>Mutual Information between two clusterings.</p> <p>The Mutual Information <sup>1</sup> is a measure of the similarity between two labels of the same data. Where \\(|U_i|\\) is the number of samples in cluster \\(U_i\\) and \\(|V_j|\\) is the number of the samples in cluster \\(V_j\\), the Mutual Information between clusterings \\(U\\) and \\(V\\) can be calculated as: </p> \\[ MI(U,V) = \\sum_{i=1}^{|U|} \\sum_{v=1}^{|V|} \\frac{|U_i \\cup V_j|}{N} \\log \\frac{N |U_i \\cup V_j|}{|U_i| |V_j|} \\] <p>This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won't change the score. </p> <p>This metric is furthermore symmetric: switching <code>y_true</code> and <code>y_pred</code> will return the same score value. This can be useful to measure the agreement of two independent label assignments strategies on the same dataset when the real ground truth is not known. </p> <p>The Mutual Information can be equivalently expressed as: </p> \\[ MI(U,V) = H(U) - H(U | V) = H(V) - H(V | U) \\] <p>where \\(H(U)\\) and \\(H(V)\\) are the marginal entropies, \\(H(U | V)\\) and \\(H(V | U)\\) are the conditional entropies.</p>"},{"location":"api/metrics/MutualInfo/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MutualInfo/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MutualInfo/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.MutualInfo()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.0\n0.0\n0.0\n0.215761\n0.395752\n0.462098\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>MutualInfo: 0.462098\n</code></pre></p>"},{"location":"api/metrics/MutualInfo/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, March 17). Mutual information.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Mutual_information&amp;oldid=1012714929\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/NormalizedMutualInfo/","title":"NormalizedMutualInfo","text":"<p>Normalized Mutual Information between two clusterings.</p> <p>Normalized Mutual Information (NMI) is a normalized version of the Mutual Information (MI) score to scale the results between the range of 0 (no mutual information) and 1 (perfectly mutual information). In the formula, the mutual information will be normalized by a generalized mean of the entropy of true and predicted labels, defined by the <code>average_method</code>. </p> <p>We note that this measure is not adjusted for chance (i.e corrected the effect of result agreement solely due to chance); as a result, the Adjusted Mutual Info Score will mostly be preferred. However, this metric is still symmetric, which means that switching true and predicted labels will not alter the score value. This fact can be useful when the metric is used to measure the agreement between two indepedent label solutions on the same dataset, when the ground truth remains unknown. </p> <p>Another advantage of the metric is that as it is based on the calculation of entropy-related measures, it is independent of the permutation of class/cluster labels.</p>"},{"location":"api/metrics/NormalizedMutualInfo/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>average_method</p> <p>Default \u2192 <code>arithmetic</code></p> <p>This parameter defines how to compute the normalizer in the denominator. Possible options include <code>min</code>, <code>max</code>, <code>arithmetic</code> and <code>geometric</code>.</p> </li> </ul>"},{"location":"api/metrics/NormalizedMutualInfo/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/NormalizedMutualInfo/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.NormalizedMutualInfo()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.343711\n0.458065\n0.515803\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>NormalizedMutualInfo: 0.515804\n</code></pre></p>"},{"location":"api/metrics/NormalizedMutualInfo/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, March 17). Mutual information.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Mutual_information&amp;oldid=1012714929\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Precision/","title":"Precision","text":"<p>Binary precision score.</p>"},{"location":"api/metrics/Precision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/Precision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Precision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, True, True, True]\ny_pred = [True, True, False, True, True]\n\nmetric = metrics.Precision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>Precision: 100.00%\nPrecision: 50.00%\nPrecision: 50.00%\nPrecision: 66.67%\nPrecision: 75.00%\n</code></pre></p>"},{"location":"api/metrics/Precision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/R2/","title":"R2","text":"<p>Coefficient of determination (\\(R^2\\)) score</p> <p>The coefficient of determination, denoted \\(R^2\\) or \\(r^2\\), is the proportion of the variance in the dependent variable that is predictable from the independent variable(s). <sup>1</sup> </p> <p>Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of \\(y\\), disregarding the input features, would get a \\(R^2\\) score of 0.0. </p> <p>\\(R^2\\) is not defined when less than 2 samples have been observed. This implementation returns 0.0 in this case.</p>"},{"location":"api/metrics/R2/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/R2/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.R2()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.0\n0.9183\n0.9230\n0.9486\n</code></pre></p>"},{"location":"api/metrics/R2/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Coefficient of determination (Wikipedia) \u21a9</p> </li> </ol>"},{"location":"api/metrics/RMSE/","title":"RMSE","text":"<p>Root mean squared error.</p>"},{"location":"api/metrics/RMSE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/RMSE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.RMSE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.5\n0.5\n0.408248\n0.612372\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>RMSE: 0.612372\n</code></pre></p>"},{"location":"api/metrics/RMSE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/RMSLE/","title":"RMSLE","text":"<p>Root mean squared logarithmic error.</p>"},{"location":"api/metrics/RMSLE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/RMSLE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.RMSLE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>RMSLE: 0.357826\n</code></pre></p>"},{"location":"api/metrics/RMSLE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/ROCAUC/","title":"ROCAUC","text":"<p>Receiving Operating Characteristic Area Under the Curve.</p> <p>This metric is an approximation of the true ROC AUC. Computing the true ROC AUC would require storing all the predictions and ground truths, which isn't desirable. The approximation error is not significant as long as the predicted probabilities are well calibrated. In any case, this metric can still be used to reliably compare models between each other.</p>"},{"location":"api/metrics/ROCAUC/#parameters","title":"Parameters","text":"<ul> <li> <p>n_thresholds</p> <p>Default \u2192 <code>10</code></p> <p>The number of thresholds used for discretizing the ROC curve. A higher value will lead to more accurate results, but will also cost more time and memory.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/ROCAUC/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/ROCAUC/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [ 0,  0,   1,  1]\ny_pred = [.1, .4, .35, .8]\n\nmetric = metrics.ROCAUC()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 87.50%\n</code></pre></p> <p>The true ROC AUC is in fact 0.75. We can improve the accuracy by increasing the amount of thresholds. This comes at the cost more computation time and more memory usage.</p> <p><pre><code>metric = metrics.ROCAUC(n_thresholds=20)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 75.00%\n</code></pre></p>"},{"location":"api/metrics/ROCAUC/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/Rand/","title":"Rand","text":"<p>Rand Index.</p> <p>The Rand Index <sup>1</sup> <sup>2</sup> is a measure of the similarity between two data clusterings. Given a set of elements <code>S</code> and two partitions of <code>S</code> to compare, <code>X</code> and <code>Y</code>, define the following: </p> <ul> <li> <p>a, the number of pairs of elements in <code>S</code> that are in the same subset in <code>X</code> and in the same subset in <code>Y</code> </p> </li> <li> <p>b, the number of pairs of elements in <code>S</code> that are in the different subset in <code>X</code> and in different subsets in <code>Y</code> </p> </li> <li> <p>c, the number of pairs of elements in <code>S</code> that are in the same subset in <code>X</code> and in different subsets in <code>Y</code> </p> </li> <li> <p>d, the number of pairs of elements in <code>S</code> that are in the different subset in <code>X</code> and in the same subset in <code>Y</code> </p> </li> </ul> <p>The Rand index, R, is </p> \\[ R = \frac{a+b}{a+b+c+d} = \frac{a+b}{\frac{n(n-1)}{2}}. \\]"},{"location":"api/metrics/Rand/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Rand/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Rand/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0, 0, 1, 1, 1]\ny_pred = [0, 0, 1, 1, 2, 2]\n\nmetric = metrics.Rand()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>Rand: 0.666667\n</code></pre></p>"},{"location":"api/metrics/Rand/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, January 13). Rand index.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Rand_index&amp;oldid=1000098911\u00a0\u21a9</p> </li> <li> <p>W. M. Rand (1971). \"Objective criteria for the evaluation of clustering methods\".   Journal of the American Statistical Association. American Statistical Association.   66 (336): 846\u2013850. arXiv:1704.01036. doi:10.2307/2284239. JSTOR 2284239.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Recall/","title":"Recall","text":"<p>Binary recall score.</p>"},{"location":"api/metrics/Recall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/Recall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Recall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, True, True, True]\ny_pred = [True, True, False, True, True]\n\nmetric = metrics.Recall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>Recall: 100.00%\nRecall: 100.00%\nRecall: 50.00%\nRecall: 66.67%\nRecall: 75.00%\n</code></pre></p>"},{"location":"api/metrics/Recall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/RollingROCAUC/","title":"RollingROCAUC","text":"<p>Rolling version of the Receiving Operating Characteristic Area Under the Curve.</p> <p>The RollingROCAUC calculates the metric using the instances in its window of size S. It keeps a queue of the instances, when an instance is added and the queue length is equal to S, the last instance is removed. The metric has a tree with ordered instances, in order to calculate the AUC efficiently. It was implemented based on the algorithm presented in Brzezinski and Stefanowski, 2017. </p> <p>The difference between this metric and the standard ROCAUC is that the latter calculates an approximation of the real metric considering all data from the beginning of the stream, while the RollingROCAUC calculates the exact value considering only the last S instances. This approach may be beneficial if it's necessary to evaluate the model's performance over time, since calculating the metric using the entire stream may hide the current performance of the classifier.</p>"},{"location":"api/metrics/RollingROCAUC/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Default \u2192 <code>1000</code></p> <p>The max length of the window.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/RollingROCAUC/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/RollingROCAUC/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [ 0,  1,  0,  1,  0,  1,  0,  0,   1,  1]\ny_pred = [.3, .5, .5, .7, .1, .3, .1, .4, .35, .8]\n\nmetric = metrics.RollingROCAUC(window_size=4)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>RollingROCAUC: 75.00%\n</code></pre></p>"},{"location":"api/metrics/RollingROCAUC/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/SMAPE/","title":"SMAPE","text":"<p>Symmetric mean absolute percentage error.</p>"},{"location":"api/metrics/SMAPE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/SMAPE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0.07533, 0.07533, 0.07533, 0.07533, 0.07533, 0.07533, 0.0672, 0.0672]\ny_pred = [0, 0.102, 0.107, 0.047, 0.1, 0.032, 0.047, 0.108, 0.089]\n\nmetric = metrics.SMAPE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>SMAPE: 37.869392\n</code></pre></p>"},{"location":"api/metrics/SMAPE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/Silhouette/","title":"Silhouette","text":"<p>Silhouette coefficient <sup>1</sup>, roughly speaking, is the ratio between cohesion and the average distances from the points to their second-closest centroid. It rewards the clustering algorithm where points are very close to their assigned centroids and far from any other centroids, that is, clustering results with good cohesion and good separation.</p> <p>It rewards clusterings where points are very close to their assigned centroids and far from any other centroids, that is clusterings with good cohesion and good separation. <sup>2</sup> </p> <p>The definition of Silhouette coefficient for online clustering evaluation is different from that of batch learning. It does not store information and calculate pairwise distances between all points at the same time, since the practice is too expensive for an incremental metric.</p>"},{"location":"api/metrics/Silhouette/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicates if a high value is better than a low one or not.</p> </li> </ul>"},{"location":"api/metrics/Silhouette/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river import stream\nfrom river import metrics\n\nX = [\n    [1, 2],\n    [1, 4],\n    [1, 0],\n    [4, 2],\n    [4, 4],\n    [4, 0],\n    [-2, 2],\n    [-2, 4],\n    [-2, 0]\n]\n\nk_means = cluster.KMeans(n_clusters=3, halflife=0.4, sigma=3, seed=0)\nmetric = metrics.Silhouette()\n\nfor x, _ in stream.iter_array(X):\n    k_means.learn_one(x)\n    y_pred = k_means.predict_one(x)\n    metric.update(x, y_pred, k_means.centers)\n\nmetric\n</code></pre> <pre><code>Silhouette: 0.32145\n</code></pre></p>"},{"location":"api/metrics/Silhouette/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>x </li> <li>y_pred </li> <li>centers </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>x </li> <li>y_pred </li> <li>centers </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Rousseeuw, P. (1987). Silhouettes: a graphical aid to the intepretation and validation   of cluster analysis 20, 53 - 65. DOI: 10.1016/0377-0427(87)90125-7\u00a0\u21a9</p> </li> <li> <p>Bifet, A. et al. (2018). \"Machine Learning for Data Streams\".   DOI: 10.7551/mitpress/10654.001.0001.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/VBeta/","title":"VBeta","text":"<p>VBeta.</p> <p>VBeta (or V-Measure) <sup>1</sup> is an external entropy-based cluster evaluation measure. It provides an elegant solution to many problems that affect previously defined cluster evaluation measures including </p> <ul> <li> <p>Dependance of clustering algorithm or dataset, </p> </li> <li> <p>The \"problem of matching\", where the clustering of only a portion of data points are evaluated, and </p> </li> <li> <p>Accurate evaluation and combination of two desirable aspects of clustering, homogeneity and completeness. </p> </li> </ul> <p>Based upon the calculations of homogeneity and completeness, a clustering solution's V-measure is calculated by computing the weighted harmonic mean of homogeneity and completeness, </p> \\[ V_{\\beta} = \\frac{(1 + \\beta) \\times h \\times c}{\\beta \\times h + c}. \\]"},{"location":"api/metrics/VBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Weight of Homogeneity in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/VBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/VBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.VBeta(beta=1.0)\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.3437110184854507\n0.4580652856440158\n0.5158037429793888\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>VBeta: 51.58%\n</code></pre></p>"},{"location":"api/metrics/VBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Andrew Rosenberg and Julia Hirschberg (2007).   V-Measure: A conditional entropy-based external cluster evaluation measure.   Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language   Processing and Computational Natural Language Learning, pp. 410 - 420,   Prague, June 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/WeightedF1/","title":"WeightedF1","text":"<p>Weighted-average F1 score.</p> <p>This works by computing the F1 score per class, and then performs a global weighted average by using the support of each class.</p>"},{"location":"api/metrics/WeightedF1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedF1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedF1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedF1()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedF1: 100.00%\nWeightedF1: 33.33%\nWeightedF1: 55.56%\nWeightedF1: 66.67%\nWeightedF1: 61.33%\n</code></pre></p>"},{"location":"api/metrics/WeightedF1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedFBeta/","title":"WeightedFBeta","text":"<p>Weighted-average F-Beta score.</p> <p>This works by computing the F-Beta score per class, and then performs a global weighted average according to the support of each class.</p>"},{"location":"api/metrics/WeightedFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Weight of precision in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedFBeta(beta=0.8)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedFBeta: 100.00%\nWeightedFBeta: 31.06%\nWeightedFBeta: 54.04%\nWeightedFBeta: 65.53%\nWeightedFBeta: 62.63%\n</code></pre></p>"},{"location":"api/metrics/WeightedFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedJaccard/","title":"WeightedJaccard","text":"<p>Weighted average Jaccard score.</p>"},{"location":"api/metrics/WeightedJaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedJaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedJaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedJaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedJaccard: 100.00%\nWeightedJaccard: 25.00%\nWeightedJaccard: 50.00%\nWeightedJaccard: 62.50%\nWeightedJaccard: 50.00%\n</code></pre></p>"},{"location":"api/metrics/WeightedJaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedPrecision/","title":"WeightedPrecision","text":"<p>Weighted-average precision score.</p> <p>This uses the support of each label to compute an average score, whereas <code>metrics.MacroPrecision</code> ignores the support.</p>"},{"location":"api/metrics/WeightedPrecision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedPrecision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedPrecision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedPrecision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedPrecision: 100.00%\nWeightedPrecision: 25.00%\nWeightedPrecision: 50.00%\nWeightedPrecision: 62.50%\nWeightedPrecision: 70.00%\n</code></pre></p>"},{"location":"api/metrics/WeightedPrecision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedRecall/","title":"WeightedRecall","text":"<p>Weighted-average recall score.</p> <p>This uses the support of each label to compute an average score, whereas <code>MacroRecall</code> ignores the support.</p>"},{"location":"api/metrics/WeightedRecall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedRecall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedRecall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedRecall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedRecall: 100.00%\nWeightedRecall: 50.00%\nWeightedRecall: 66.67%\nWeightedRecall: 75.00%\nWeightedRecall: 60.00%\n</code></pre></p>"},{"location":"api/metrics/WeightedRecall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/BinaryMetric/","title":"BinaryMetric","text":"<p>Mother class for all binary classification metrics.</p>"},{"location":"api/metrics/base/BinaryMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/base/BinaryMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/BinaryMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/ClassificationMetric/","title":"ClassificationMetric","text":"<p>Mother class for all classification metrics.</p>"},{"location":"api/metrics/base/ClassificationMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/base/ClassificationMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/ClassificationMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/Metric/","title":"Metric","text":"<p>Mother class for all metrics.</p>"},{"location":"api/metrics/base/Metric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/Metric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/Metrics/","title":"Metrics","text":"<p>A container class for handling multiple metrics at once.</p>"},{"location":"api/metrics/base/Metrics/#parameters","title":"Parameters","text":"<ul> <li> <p>metrics</p> </li> <li> <p>str_sep</p> <p>Default \u2192 </p> </li> </ul>"},{"location":"api/metrics/base/Metrics/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/Metrics/#methods","title":"Methods","text":"is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/MultiClassMetric/","title":"MultiClassMetric","text":"<p>Mother class for all multi-class classification metrics.</p>"},{"location":"api/metrics/base/MultiClassMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/base/MultiClassMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/MultiClassMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/RegressionMetric/","title":"RegressionMetric","text":"<p>Mother class for all regression metrics.</p>"},{"location":"api/metrics/base/RegressionMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/RegressionMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/WrapperMetric/","title":"WrapperMetric","text":""},{"location":"api/metrics/base/WrapperMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/WrapperMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/ExactMatch/","title":"ExactMatch","text":"<p>Exact match score.</p> <p>This is the most strict multi-label metric, defined as the number of samples that have all their labels correctly classified, divided by the total number of samples.</p>"},{"location":"api/metrics/multioutput/ExactMatch/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/ExactMatch/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [\n    {0: False, 1: True, 2: True},\n    {0: True, 1: True, 2: False},\n    {0: True, 1: True, 2: False},\n]\n\ny_pred = [\n    {0: True, 1: True, 2: True},\n    {0: True, 1: False, 2: False},\n    {0: True, 1: True, 2: False},\n]\n\nmetric = metrics.multioutput.ExactMatch()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>ExactMatch: 33.33%\n</code></pre></p>"},{"location":"api/metrics/multioutput/ExactMatch/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/MacroAverage/","title":"MacroAverage","text":"<p>Macro-average wrapper.</p> <p>A copy of the provided metric is made for each output. The arithmetic average of all the metrics is returned.</p>"},{"location":"api/metrics/multioutput/MacroAverage/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/MacroAverage/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/MacroAverage/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/MicroAverage/","title":"MicroAverage","text":"<p>Micro-average wrapper.</p> <p>The provided metric is updated with the value of each output.</p>"},{"location":"api/metrics/multioutput/MicroAverage/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/MicroAverage/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/MicroAverage/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/MultiLabelConfusionMatrix/","title":"MultiLabelConfusionMatrix","text":"<p>Multi-label confusion matrix.</p> <p>Under the hood, this stores one <code>metrics.ConfusionMatrix</code> for each output.</p>"},{"location":"api/metrics/multioutput/MultiLabelConfusionMatrix/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ncm = metrics.multioutput.MultiLabelConfusionMatrix()\n\ny_true = [\n    {0: False, 1: True, 2: True},\n    {0: True, 1: True, 2: False}\n]\n\ny_pred = [\n    {0: True, 1: True, 2: True},\n    {0: True, 1: False, 2: False}\n]\n\nfor yt, yp in zip(y_true, y_pred):\n    cm.update(yt, yp)\n\ncm\n</code></pre> <pre><code>0\n            False   True\n    False       0      1\n     True       0      1\n&lt;BLANKLINE&gt;\n1\n            False   True\n    False       0      0\n     True       1      1\n&lt;BLANKLINE&gt;\n2\n            False   True\n    False       1      0\n     True       0      1\n</code></pre></p>"},{"location":"api/metrics/multioutput/MultiLabelConfusionMatrix/#methods","title":"Methods","text":"revert update"},{"location":"api/metrics/multioutput/PerOutput/","title":"PerOutput","text":"<p>Per-output wrapper.</p> <p>A copy of the metric is maintained for each output.</p>"},{"location":"api/metrics/multioutput/PerOutput/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/PerOutput/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/PerOutput/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/SampleAverage/","title":"SampleAverage","text":"<p>Sample-average wrapper.</p> <p>The provided metric is evaluate on each sample. The arithmetic average over all the samples is returned. This is equivalent to using <code>average='samples'</code> in scikit-learn.</p>"},{"location":"api/metrics/multioutput/SampleAverage/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/SampleAverage/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/SampleAverage/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [\n    {0: False, 1: True, 2: True},\n    {0: True, 1: True, 2: False}\n]\ny_pred = [\n    {0: True, 1: True, 2: True},\n    {0: True, 1: False, 2: False}\n]\n\nsample_jaccard = metrics.multioutput.SampleAverage(metrics.Jaccard())\n\nfor yt, yp in zip(y_true, y_pred):\n    sample_jaccard.update(yt, yp)\n\nsample_jaccard\n</code></pre> <pre><code>SampleAverage(Jaccard): 58.33%\n</code></pre></p>"},{"location":"api/metrics/multioutput/SampleAverage/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/","title":"MultiOutputClassificationMetric","text":"<p>Mother class for all multi-output classification metrics.</p>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Type \u2192 MultiLabelConfusionMatrix | None</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/base/MultiOutputRegressionMetric/","title":"MultiOutputRegressionMetric","text":"<p>Mother class for all multi-output regression metrics.</p>"},{"location":"api/metrics/multioutput/base/MultiOutputRegressionMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/base/MultiOutputRegressionMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, float | int]' </li> <li>y_pred     \u2014 'dict[str | int, float | int]' </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, float | int]' </li> <li>y_pred     \u2014 'dict[str | int, float | int]' </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/misc/SDFT/","title":"SDFT","text":"<p>Sliding Discrete Fourier Transform (SDFT).</p> <p>Initially, the coefficients are all equal to 0, up until enough values have been seen. A call to <code>numpy.fft.fft</code> is triggered once <code>window_size</code> values have been seen. Subsequent values will update the coefficients online. This is much faster than recomputing an FFT from scratch for every new value.</p>"},{"location":"api/misc/SDFT/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>The size of the window.</p> </li> </ul>"},{"location":"api/misc/SDFT/#attributes","title":"Attributes","text":"<ul> <li>window_size</li> </ul>"},{"location":"api/misc/SDFT/#examples","title":"Examples","text":"<pre><code>import numpy as np\nfrom river import misc\n\nX = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\nwindow_size = 5\nsdft = misc.SDFT(window_size)\n\nfor i, x in enumerate(X):\n    sdft.update(x)\n    if i + 1 &gt;= window_size:\n        assert np.allclose(sdft.coefficients, np.fft.fft(X[i+1 - window_size:i+1]))\n</code></pre>"},{"location":"api/misc/SDFT/#methods","title":"Methods","text":"update <ol> <li> <p>Jacobsen, E. asample_average.pynd Lyons, R., 2003. The sliding DFT. IEEE Signal Processing Magazine, 20(2), pp.74-80. \u21a9</p> </li> <li> <p>Understanding and Implementing the Sliding DFT \u21a9</p> </li> </ol>"},{"location":"api/misc/Skyline/","title":"Skyline","text":"<p>A skyline is set of points which is not dominated by any other point.</p> <p>This implementation uses a block nested loop. Identical observations are all part of the skyline if applicable.</p>"},{"location":"api/misc/Skyline/#parameters","title":"Parameters","text":"<ul> <li> <p>minimize</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list of features for which the values need to be minimized. Can be omitted as long as <code>maximize</code> is specified.</p> </li> <li> <p>maximize</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list of features for which the values need to be maximized. Can be omitted as long as <code>minimize</code> is specified.</p> </li> </ul>"},{"location":"api/misc/Skyline/#examples","title":"Examples","text":"<p>Here is an example taken from this blog post.</p> <p><pre><code>import random\nfrom river import misc\n\ncity_prices = {\n    'Bordeaux': 4045,\n    'Lyon': 4547,\n    'Toulouse': 3278\n}\n\ndef random_house():\n    city = random.choice(['Bordeaux', 'Lyon', 'Toulouse'])\n    size = round(random.gauss(200, 50))\n    price = round(random.uniform(0.8, 1.2) * city_prices[city] * size)\n    return {'city': city, 'size': size, 'price': price}\n\nskyline = misc.Skyline(minimize=['price'], maximize=['size'])\n\nrandom.seed(42)\n\nfor _ in range(100):\n    house = random_house()\n    skyline.update(house)\n\nprint(len(skyline))\n</code></pre> <pre><code>13\n</code></pre></p> <p><pre><code>print(skyline[0])\n</code></pre> <pre><code>{'city': 'Toulouse', 'size': 280, 'price': 763202}\n</code></pre></p> <p>Here is another example using the kart data from Mario Kart: Double Dash!!.</p> <p><pre><code>import collections\nfrom river import misc\n\nKart = collections.namedtuple(\n     'Kart',\n     'name speed off_road acceleration weight turbo'\n)\n\nkarts = [\n    Kart('Red Fire', 5, 4, 4, 5, 2),\n    Kart('Green Fire', 7, 3, 3, 4, 2),\n    Kart('Heart Coach', 4, 6, 6, 5, 2),\n    Kart('Bloom Coach', 6, 4, 5, 3, 2),\n    Kart('Turbo Yoshi', 4, 5, 6, 6, 2),\n    Kart('Turbo Birdo', 6, 4, 4, 7, 2),\n    Kart('Goo-Goo Buggy', 1, 9, 9, 2, 3),\n    Kart('Rattle Buggy', 2, 9, 8, 2, 3),\n    Kart('Toad Kart', 3, 9, 7, 2, 3),\n    Kart('Toadette Kart', 1, 9, 9, 2, 3),\n    Kart('Koopa Dasher', 2, 8, 8, 3, 3),\n    Kart('Para-Wing', 1, 8, 9, 3, 3),\n    Kart('DK Jumbo', 8, 2, 2, 8, 1),\n    Kart('Barrel Train', 8, 7, 3, 5, 3),\n    Kart('Koopa King', 9, 1, 1, 9, 1),\n    Kart('Bullet Blaster', 8, 1, 4, 1, 3),\n    Kart('Wario Car', 7, 3, 3, 7, 1),\n    Kart('Waluigi Racer', 5, 9, 5, 6, 2),\n    Kart('Piranha Pipes', 8, 7, 2, 9, 1),\n    Kart('Boo Pipes', 2, 9, 8, 9, 1),\n    Kart('Parade Kart', 7, 3, 4, 7, 3)\n]\n\nskyline = misc.Skyline(\n    maximize=['speed', 'off_road', 'acceleration', 'turbo'],\n    minimize=['weight']\n)\n\nfor kart in karts:\n    skyline.update(kart._asdict())\n\nbest_cart_names = [kart['name'] for kart in skyline]\nfor name in best_cart_names:\n    print(f'- {name}')\n</code></pre> <pre><code>- Green Fire\n- Heart Coach\n- Bloom Coach\n- Goo-Goo Buggy\n- Rattle Buggy\n- Toad Kart\n- Toadette Kart\n- Barrel Train\n- Koopa King\n- Bullet Blaster\n- Waluigi Racer\n- Parade Kart\n</code></pre></p> <p><pre><code>for name in sorted(set(kart.name for kart in karts) - set(best_cart_names)):\n    print(f'- {name}')\n</code></pre> <pre><code>- Boo Pipes\n- DK Jumbo\n- Koopa Dasher\n- Para-Wing\n- Piranha Pipes\n- Red Fire\n- Turbo Birdo\n- Turbo Yoshi\n- Wario Car\n</code></pre></p>"},{"location":"api/misc/Skyline/#methods","title":"Methods","text":"<ol> <li> <p>Skyline queries in Python \u21a9</p> </li> <li> <p>Borzsony, S., Kossmann, D. and Stocker, K., 2001, April. The skyline operator. In Proceedings 17th international conference on data engineering (pp. 421-430). IEEE. \u21a9</p> </li> <li> <p>Tao, Y. and Papadias, D., 2006. Maintaining sliding window skylines on data streams. IEEE Transactions on Knowledge and Data Engineering, 18(3), pp.377-391. \u21a9</p> </li> </ol>"},{"location":"api/model-selection/BanditClassifier/","title":"BanditClassifier","text":"<p>Bandit-based model selection for classification.</p> <p>Each model is associated with an arm. At each <code>learn_one</code> call, the policy decides which arm/model to pull. The reward is the performance of the model on the provided sample. The <code>predict_one</code> and <code>predict_proba_one</code> methods use the current best model.</p>"},{"location":"api/model-selection/BanditClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to select from.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.ClassificationMetric</p> <p>The metric that is used to measure the performance of each model.</p> </li> <li> <p>policy</p> <p>Type \u2192 bandit.base.Policy</p> <p>The bandit policy to use.</p> </li> </ul>"},{"location":"api/model-selection/BanditClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/BanditClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import bandit\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import model_selection\nfrom river import optim\nfrom river import preprocessing\n\nmodels = [\n    linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [0.0001, 0.001, 1e-05, 0.01]\n]\n\ndataset = datasets.Phishing()\nmodel = (\n    preprocessing.StandardScaler() |\n    model_selection.BanditClassifier(\n        models,\n        metric=metrics.Accuracy(),\n        policy=bandit.EpsilonGreedy(\n            epsilon=0.1,\n            decay=0.001,\n            burn_in=20,\n            seed=42\n        )\n    )\n)\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 88.96%\n</code></pre></p>"},{"location":"api/model-selection/BanditClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/model-selection/BanditRegressor/","title":"BanditRegressor","text":"<p>Bandit-based model selection for regression.</p> <p>Each model is associated with an arm. At each <code>learn_one</code> call, the policy decides which arm/model to pull. The reward is the performance of the model on the provided sample. The <code>predict_one</code> method uses the current best model.</p>"},{"location":"api/model-selection/BanditRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to select from.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>The metric that is used to measure the performance of each model.</p> </li> <li> <p>policy</p> <p>Type \u2192 bandit.base.Policy</p> <p>The bandit policy to use.</p> </li> </ul>"},{"location":"api/model-selection/BanditRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/BanditRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import bandit\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import model_selection\nfrom river import optim\nfrom river import preprocessing\n\nmodels = [\n    linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [0.0001, 0.001, 1e-05, 0.01]\n]\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    model_selection.BanditRegressor(\n        models,\n        metric=metrics.MAE(),\n        policy=bandit.EpsilonGreedy(\n            epsilon=0.1,\n            decay=0.001,\n            burn_in=100,\n            seed=42\n        )\n    )\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 3.134089\n</code></pre></p> <p>Here's another example using the UCB policy. The latter is more sensitive to the target scale, and usually works better when the target is rescaled.</p> <p><pre><code>models = [\n    linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [0.0001, 0.001, 1e-05, 0.01]\n]\n\nmodel = (\n    preprocessing.StandardScaler() |\n    preprocessing.TargetStandardScaler(\n        model_selection.BanditRegressor(\n            models,\n            metric=metrics.MAE(),\n            policy=bandit.UCB(\n                delta=1,\n                burn_in=100\n            )\n        )\n    )\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.875333\n</code></pre></p>"},{"location":"api/model-selection/BanditRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/model-selection/GreedyRegressor/","title":"GreedyRegressor","text":"<p>Greedy selection regressor.</p> <p>This selection method simply updates each model at each time step. The current best model is used to make predictions. It's greedy in the sense that updating each model can be costly. On the other hand, bandit-like algorithms are more temperate in that only update a subset of the models at each step.</p>"},{"location":"api/model-selection/GreedyRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Regressor]</p> <p>The models to select from.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The metric that is used to measure the performance of each model.</p> </li> </ul>"},{"location":"api/model-selection/GreedyRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/GreedyRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import model_selection\nfrom river import optim\nfrom river import preprocessing\n\nmodels = [\n    linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [1e-5, 1e-4, 1e-3, 1e-2]\n]\n\ndataset = datasets.TrumpApproval()\nmetric = metrics.MAE()\nmodel = (\n    preprocessing.StandardScaler() |\n    model_selection.GreedyRegressor(models, metric)\n)\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.319678\n</code></pre></p>"},{"location":"api/model-selection/GreedyRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/","title":"SuccessiveHalvingClassifier","text":"<p>Successive halving algorithm for classification.</p> <p>Successive halving is a method for performing model selection without having to train each model on all the dataset. At certain points in time (called \"rungs\"), the worst performing will be discarded and the best ones will keep competing between each other. The rung values are designed so that at most <code>budget</code> model updates will be performed in total. </p> <p>If you have <code>k</code> combinations of hyperparameters and that your dataset contains <code>n</code> observations, then the maximal budget you can allocate is: </p> \\[\\frac{2kn}{eta}\\] <p>It is recommended that you check this beforehand. This bound can't be checked by the function because the size of the dataset is not known. In fact it is potentially infinite, in which case the algorithm will terminate once all the budget has been spent. </p> <p>If you have a budget of <code>B</code>, and that your dataset contains <code>n</code> observations, then the number of hyperparameter combinations that will spend all the budget and go through all the data is: </p> \\[\\left\\lceil\\left\\lfloor\\frac{B}{2n}\\right\\rfloor \\times eta \\right\\rceil\\]"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to compare.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>Metric used for comparing models with.</p> </li> <li> <p>budget</p> <p>Type \u2192 int</p> <p>Total number of model updates you wish to allocate.</p> </li> <li> <p>eta</p> <p>Default \u2192 <code>2</code></p> <p>Rate of elimination. At every rung, <code>math.ceil(k / eta)</code> models are kept, where <code>k</code> is the number of models that have reached the rung. A higher <code>eta</code> value will focus on less models but will allocate more iterations to the best models.</p> </li> <li> <p>verbose</p> <p>Default \u2192 <code>False</code></p> <p>Whether to display progress or not.</p> </li> <li> <p>print_kwargs</p> <p>Extra keyword arguments are passed to the <code>print</code> function. For instance, this allows providing a <code>file</code> argument, which indicates where to output progress.</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#examples","title":"Examples","text":"<p>As an example, let's use successive halving to tune the optimizer of a logistic regression. We'll first define the model.</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>Let's now define a grid of parameters which we would like to compare. We'll try different optimizers with various learning rates.</p> <pre><code>from river import utils\nfrom river import optim\n\nmodels = utils.expand_param_grid(model, {\n    'LogisticRegression': {\n        'optimizer': [\n            (optim.SGD, {'lr': [.1, .01, .005]}),\n            (optim.Adam, {'beta_1': [.01, .001], 'lr': [.1, .01, .001]}),\n            (optim.Adam, {'beta_1': [.1], 'lr': [.001]}),\n        ]\n    }\n})\n</code></pre> <p>We can check how many models we've created.</p> <p><pre><code>len(models)\n</code></pre> <pre><code>10\n</code></pre></p> <p>We can now pass these models to a <code>SuccessiveHalvingClassifier</code>. We also need to pick a metric to compare the models, and a budget which indicates how many iterations to run before picking the best model and discarding the rest.</p> <pre><code>from river import model_selection\n\nsh = model_selection.SuccessiveHalvingClassifier(\n    models,\n    metric=metrics.Accuracy(),\n    budget=2000,\n    eta=2,\n    verbose=True\n)\n</code></pre> <p>A <code>SuccessiveHalvingClassifier</code> is also a classifier with a <code>learn_one</code> and a <code>predict_proba_one</code> method. We can therefore evaluate it like any other classifier with <code>evaluate.progressive_val_score</code>.</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nevaluate.progressive_val_score(\n    dataset=datasets.Phishing(),\n    model=sh,\n    metric=metrics.ROCAUC()\n)\n</code></pre> <pre><code>[1] 5 removed       5 left  50 iterations   budget used: 500        budget left: 1500       best Accuracy: 80.00%\n[2] 2 removed       3 left  100 iterations  budget used: 1000       budget left: 1000       best Accuracy: 84.00%\n[3] 1 removed       2 left  166 iterations  budget used: 1498       budget left: 502        best Accuracy: 86.14%\n[4] 1 removed       1 left  250 iterations  budget used: 1998       budget left: 2  best Accuracy: 84.80%\nROCAUC: 95.22%\n</code></pre></p> <p>We can now view the best model.</p> <p><pre><code>sh.best_model\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LogisticRegression (\n    optimizer=Adam (\n      lr=Constant (\n        learning_rate=0.01\n      )\n      beta_1=0.01\n      beta_2=0.999\n      eps=1e-08\n    )\n    loss=Log (\n      weight_pos=1.\n      weight_neg=1.\n    )\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre></p>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Jamieson, K. and Talwalkar, A., 2016, May. Non-stochastic best arm identification and hyperparameter optimization. In Artificial Intelligence and Statistics (pp. 240-248). \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Hardt, M., Recht, B. and Talwalkar, A., 2018. Massively parallel hyperparameter tuning. arXiv preprint arXiv:1810.05934. \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2017. Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1), pp.6765-6816. \u21a9</p> </li> </ol>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/","title":"SuccessiveHalvingRegressor","text":"<p>Successive halving algorithm for regression.</p> <p>Successive halving is a method for performing model selection without having to train each model on all the dataset. At certain points in time (called \"rungs\"), the worst performing will be discarded and the best ones will keep competing between each other. The rung values are designed so that at most <code>budget</code> model updates will be performed in total. </p> <p>If you have <code>k</code> combinations of hyperparameters and that your dataset contains <code>n</code> observations, then the maximal budget you can allocate is: </p> \\[\\frac{2kn}{eta}\\] <p>It is recommended that you check this beforehand. This bound can't be checked by the function because the size of the dataset is not known. In fact it is potentially infinite, in which case the algorithm will terminate once all the budget has been spent. </p> <p>If you have a budget of <code>B</code>, and that your dataset contains <code>n</code> observations, then the number of hyperparameter combinations that will spend all the budget and go through all the data is: </p> \\[\\left\\lceil\\left\\lfloor\\frac{B}{2n}\\right\\rfloor \\times eta \\right\\rceil\\]"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to compare.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>Metric used for comparing models with.</p> </li> <li> <p>budget</p> <p>Type \u2192 int</p> <p>Total number of model updates you wish to allocate.</p> </li> <li> <p>eta</p> <p>Default \u2192 <code>2</code></p> <p>Rate of elimination. At every rung, <code>math.ceil(k / eta)</code> models are kept, where <code>k</code> is the number of models that have reached the rung. A higher <code>eta</code> value will focus on less models but will allocate more iterations to the best models.</p> </li> <li> <p>verbose</p> <p>Default \u2192 <code>False</code></p> <p>Whether to display progress or not.</p> </li> <li> <p>print_kwargs</p> <p>Extra keyword arguments are passed to the <code>print</code> function. For instance, this allows providing a <code>file</code> argument, which indicates where to output progress.</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#examples","title":"Examples","text":"<p>As an example, let's use successive halving to tune the optimizer of a linear regression. We'll first define the model.</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression(intercept_lr=.1)\n)\n</code></pre> <p>Let's now define a grid of parameters which we would like to compare. We'll try different optimizers with various learning rates.</p> <pre><code>from river import optim\nfrom river import utils\n\nmodels = utils.expand_param_grid(model, {\n    'LinearRegression': {\n        'optimizer': [\n            (optim.SGD, {'lr': [.1, .01, .005]}),\n            (optim.Adam, {'beta_1': [.01, .001], 'lr': [.1, .01, .001]}),\n            (optim.Adam, {'beta_1': [.1], 'lr': [.001]}),\n        ]\n    }\n})\n</code></pre> <p>We can check how many models we've created.</p> <p><pre><code>len(models)\n</code></pre> <pre><code>10\n</code></pre></p> <p>We can now pass these models to a <code>SuccessiveHalvingRegressor</code>. We also need to pick a metric to compare the models, and a budget which indicates how many iterations to run before picking the best model and discarding the rest.</p> <pre><code>from river import model_selection\n\nsh = model_selection.SuccessiveHalvingRegressor(\n    models,\n    metric=metrics.MAE(),\n    budget=2000,\n    eta=2,\n    verbose=True\n)\n</code></pre> <p>A <code>SuccessiveHalvingRegressor</code> is also a regressor with a <code>learn_one</code> and a <code>predict_one</code> method. We can therefore evaluate it like any other classifier with <code>evaluate.progressive_val_score</code>.</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nevaluate.progressive_val_score(\n    dataset=datasets.TrumpApproval(),\n    model=sh,\n    metric=metrics.MAE()\n)\n</code></pre> <pre><code>[1] 5 removed       5 left  50 iterations   budget used: 500        budget left: 1500       best MAE: 4.419643\n[2] 2 removed       3 left  100 iterations  budget used: 1000       budget left: 1000       best MAE: 2.392266\n[3] 1 removed       2 left  166 iterations  budget used: 1498       budget left: 502        best MAE: 1.541383\n[4] 1 removed       1 left  250 iterations  budget used: 1998       budget left: 2  best MAE: 1.112122\nMAE: 0.490688\n</code></pre></p> <p>We can now view the best model.</p> <p><pre><code>sh.best_model\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=Adam (\n      lr=Constant (\n        learning_rate=0.1\n      )\n      beta_1=0.01\n      beta_2=0.999\n      eps=1e-08\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.1\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre></p>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Jamieson, K. and Talwalkar, A., 2016, May. Non-stochastic best arm identification and hyperparameter optimization. In Artificial Intelligence and Statistics (pp. 240-248). \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Hardt, M., Recht, B. and Talwalkar, A., 2018. Massively parallel hyperparameter tuning. arXiv preprint arXiv:1810.05934. \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2017. Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1), pp.6765-6816. \u21a9</p> </li> </ol>"},{"location":"api/model-selection/base/ModelSelectionClassifier/","title":"ModelSelectionClassifier","text":"<p>A model selector for classification.</p>"},{"location":"api/model-selection/base/ModelSelectionClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 Iterator[base.Estimator]</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/model-selection/base/ModelSelectionRegressor/","title":"ModelSelectionRegressor","text":"<p>A model selector for regression.</p>"},{"location":"api/model-selection/base/ModelSelectionRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 Iterator[base.Estimator]</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/multiclass/OneVsOneClassifier/","title":"OneVsOneClassifier","text":"<p>One-vs-One (OvO) multiclass strategy.</p> <p>This strategy consists in fitting one binary classifier for each pair of classes. Because we are in a streaming context, the number of classes isn't known from the start, hence new classifiers are instantiated on the fly. </p> <p>The number of classifiers is <code>k * (k - 1) / 2</code>, where <code>k</code> is the number of classes. However, each call to <code>learn_one</code> only requires training <code>k - 1</code> models. Indeed, only the models that pertain to the given label have to be trained. Meanwhile, making a prediction requires going through each and every model.</p>"},{"location":"api/multiclass/OneVsOneClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>A binary classifier, although a multi-class classifier will work too.</p> </li> </ul>"},{"location":"api/multiclass/OneVsOneClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>classifiers (dict)</p> <p>A mapping between pairs of classes and classifiers. The keys are tuples which contain a pair of classes. Each pair is sorted in lexicographical order.</p> </li> </ul>"},{"location":"api/multiclass/OneVsOneClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multiclass\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nscaler = preprocessing.StandardScaler()\novo = multiclass.OneVsOneClassifier(linear_model.LogisticRegression())\nmodel = scaler | ovo\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 80.76%\n</code></pre></p>"},{"location":"api/multiclass/OneVsOneClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/multiclass/OneVsRestClassifier/","title":"OneVsRestClassifier","text":"<p>One-vs-the-rest (OvR) multiclass strategy.</p> <p>This strategy consists in fitting one binary classifier per class. Because we are in a streaming context, the number of classes isn't known from the start. Hence, new classifiers are instantiated on the fly. Likewise, the predicted probabilities will only include the classes seen up to a given point in time. </p> <p>Note that this classifier supports mini-batches as well as single instances. </p> <p>The computational complexity for both learning and predicting grows linearly with the number of classes. If you have a very large number of classes, then you might want to consider using an <code>multiclass.OutputCodeClassifier</code> instead.</p>"},{"location":"api/multiclass/OneVsRestClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>A binary classifier, although a multi-class classifier will work too.</p> </li> </ul>"},{"location":"api/multiclass/OneVsRestClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>classifiers (dict)</p> <p>A mapping between classes and classifiers.</p> </li> </ul>"},{"location":"api/multiclass/OneVsRestClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multiclass\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nscaler = preprocessing.StandardScaler()\novr = multiclass.OneVsRestClassifier(linear_model.LogisticRegression())\nmodel = scaler | ovr\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 77.46%\n</code></pre></p> <p>This estimator also also supports mini-batching.</p> <pre><code>for X in pd.read_csv(dataset.path, chunksize=64):\n    y = X.pop('category')\n    y_pred = model.predict_many(X)\n    model.learn_many(X, y)\n</code></pre>"},{"location":"api/multiclass/OneVsRestClassifier/#methods","title":"Methods","text":"learn_many learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_many predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/multiclass/OutputCodeClassifier/","title":"OutputCodeClassifier","text":"<p>Output-code multiclass strategy.</p> <p>This also referred to as \"error-correcting output codes\". </p> <p>This class allows to learn a multi-class classification problem with a binary classifier. Each class is converted to a code of 0s and 1s. The length of the code is called  the code size. A copy of the classifier made for code. The codes associated with the classes are stored in a code book. </p> <p>When a new sample arrives, the label's code is retrieved from the code book. Then, each classifier is trained on the relevant part of code, which is either a 0 or a 1. </p> <p>For predicting, each classifier outputs a probability. These are then compared to each code in the code book, and the label which is the \"closest\" is chosen as the most likely class. Closeness is determined in terms of Manhattan distance. </p> <p>One specificity of online learning is that we don't how many classes there are initially. Therefore, a random procedure generates random codes on the fly whenever a previously unseed label appears.</p>"},{"location":"api/multiclass/OutputCodeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>A binary classifier, although a multi-class classifier will work too.</p> </li> <li> <p>code_size</p> <p>Type \u2192 int</p> <p>The code size, which dictates how many copies of the provided classifiers to train. Must be strictly positive.</p> </li> <li> <p>coding_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>random</code></p> <p>The method used to generate the codes. Can be either 'exact' or 'random'. The 'exact' method generates all possible codes of a given size in memory, and streams them in a random order. The 'random' method generates random codes of a given size on the fly. The 'exact' method necessarily generates different codes for each class, but requires more memory. The 'random' method can generate duplicate codes for different classes, but requires less memory.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>A random seed number that can be set for reproducibility.</p> </li> </ul>"},{"location":"api/multiclass/OutputCodeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multiclass\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nscaler = preprocessing.StandardScaler()\nooc = multiclass.OutputCodeClassifier(\n    classifier=linear_model.LogisticRegression(),\n    code_size=10,\n    coding_method='random',\n    seed=1\n)\nmodel = scaler | ooc\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 79.58%\n</code></pre></p>"},{"location":"api/multiclass/OutputCodeClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Dietterich, T.G. and Bakiri, G., 1994. Solving multiclass learning problems via error-correcting output codes. Journal of artificial intelligence research, 2, pp.263-286. \u21a9</p> </li> <li> <p>James, G. and Hastie, T., 1998. The error coding method and PICTs. Journal of Computational and Graphical statistics, 7(3), pp.377-387. \u21a9</p> </li> </ol>"},{"location":"api/multioutput/ClassifierChain/","title":"ClassifierChain","text":"<p>A multi-output model that arranges classifiers into a chain.</p> <p>This will create one model per output. The prediction of the first output will be used as a feature in the second model. The prediction for the second output will be used as a feature for the third model, etc. This \"chain model\" is therefore capable of capturing dependencies between outputs.</p>"},{"location":"api/multioutput/ClassifierChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>A classifier model used for each label.</p> </li> <li> <p>order</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list with the targets order in which to construct the chain. If <code>None</code> then the order will be inferred from the order of the keys in the target.</p> </li> </ul>"},{"location":"api/multioutput/ClassifierChain/#examples","title":"Examples","text":"<p><pre><code>from river import feature_selection\nfrom river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river import preprocessing\nfrom river import stream\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.fetch_openml('yeast', version=4, parser='auto', as_frame=False),\n    shuffle=True,\n    seed=42\n)\n\nmodel = feature_selection.VarianceThreshold(threshold=0.01)\nmodel |= preprocessing.StandardScaler()\nmodel |= multioutput.ClassifierChain(\n    model=linear_model.LogisticRegression(),\n    order=list(range(14))\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n    # Convert y values to booleans\n    y = {i: yi == 'TRUE' for i, yi in y.items()}\n    y_pred = model.predict_one(x)\n    metric.update(y, y_pred)\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 41.81%\n</code></pre></p>"},{"location":"api/multioutput/ClassifierChain/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Multi-Output Chain Models and their Application in Data Streams \u21a9</p> </li> </ol>"},{"location":"api/multioutput/MonteCarloClassifierChain/","title":"MonteCarloClassifierChain","text":"<p>Monte Carlo Sampling Classifier Chains.</p> <p>Probabilistic Classifier Chains using Monte Carlo sampling, as described in <sup>1</sup>. </p> <p>m samples are taken from the posterior distribution. Therefore we need a probabilistic interpretation of the output, and thus, this is a particular variety of ProbabilisticClassifierChain.</p>"},{"location":"api/multioutput/MonteCarloClassifierChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>m</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of samples to take from the posterior distribution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/multioutput/MonteCarloClassifierChain/#examples","title":"Examples","text":"<p><pre><code>from river import feature_selection\nfrom river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river import preprocessing\nfrom river.datasets import synth\n\ndataset = synth.Logical(seed=42, n_tiles=100)\n\nmodel = multioutput.MonteCarloClassifierChain(\n    model=linear_model.LogisticRegression(),\n    m=10,\n    seed=42\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n   y_pred = model.predict_one(x)\n   y_pred = {k: y_pred.get(k, 0) for k in y}\n   metric.update(y, y_pred)\n   model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 51.79%\n</code></pre></p>"},{"location":"api/multioutput/MonteCarloClassifierChain/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Read, J., Martino, L., &amp; Luengo, D. (2014). Efficient monte carlo   methods for multi-dimensional learning with classifier chains.   Pattern Recognition, 47(3), 1535-1546.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/multioutput/MultiClassEncoder/","title":"MultiClassEncoder","text":"<p>Convert a multi-label task into multiclass.</p> <p>Assigns a class to each unique combination of labels, and proceeds with training the supplied multi-class classifier. </p> <p>The transformation is done by converting the label set, which could be seen as a binary number, into an integer representing a class. At prediction time, the predicted integer is converted back to a binary number which is the predicted label set.</p>"},{"location":"api/multioutput/MultiClassEncoder/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier used for learning.</p> </li> </ul>"},{"location":"api/multioutput/MultiClassEncoder/#examples","title":"Examples","text":"<p><pre><code>from river import forest\nfrom river import metrics\nfrom river import multioutput\nfrom river.datasets import synth\n\ndataset = synth.Logical(seed=42, n_tiles=100)\n\nmodel = multioutput.MultiClassEncoder(\n    model=forest.ARFClassifier(seed=7)\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n   y_pred = model.predict_one(x)\n   y_pred = {k: y_pred.get(k, 0) for k in y}\n   metric.update(y, y_pred)\n   model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 95.10%\n</code></pre></p>"},{"location":"api/multioutput/MultiClassEncoder/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'dict[FeatureName, bool]' </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, dict[bool, float]]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/multioutput/ProbabilisticClassifierChain/","title":"ProbabilisticClassifierChain","text":"<p>Probabilistic Classifier Chains.</p> <p>The Probabilistic Classifier Chains (PCC) <sup>1</sup> is a Bayes-optimal method based on the Classifier Chains (CC). </p> <p>Consider the concept of chaining classifiers as searching a path in a binary tree whose leaf nodes are associated with a label \\(y \\in Y\\). While CC searches only a single path in the aforementioned binary tree, PCC looks at each of the \\(2^l\\) paths, where \\(l\\) is the number of labels. This limits the applicability of the method to data sets with a small to moderate number of labels. The authors recommend no more than about 15 labels for real-world applications.</p>"},{"location":"api/multioutput/ProbabilisticClassifierChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> </li> </ul>"},{"location":"api/multioutput/ProbabilisticClassifierChain/#examples","title":"Examples","text":"<p><pre><code>from river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river.datasets import synth\n\ndataset = synth.Logical(seed=42, n_tiles=100)\n\nmodel = multioutput.ProbabilisticClassifierChain(\n    model=linear_model.LogisticRegression()\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n   y_pred = model.predict_one(x)\n   y_pred = {k: y_pred.get(k, 0) for k in y}\n   metric.update(y, y_pred)\n   model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 51.84%\n</code></pre></p>"},{"location":"api/multioutput/ProbabilisticClassifierChain/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Cheng, W., H\u00fcllermeier, E., &amp; Dembczynski, K. J. (2010).   Bayes optimal multilabel classification via probabilistic classifier   chains. In Proceedings of the 27th international conference on   machine learning (ICML-10) (pp. 279-286).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/multioutput/RegressorChain/","title":"RegressorChain","text":"<p>A multi-output model that arranges regressors into a chain.</p> <p>This will create one model per output. The prediction of the first output will be used as a feature in the second output. The prediction for the second output will be used as a feature for the third, etc. This \"chain model\" is therefore capable of capturing dependencies between outputs.</p>"},{"location":"api/multioutput/RegressorChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Regressor</p> <p>The regression model used to make predictions for each target.</p> </li> <li> <p>order</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list with the targets order in which to construct the chain. If <code>None</code> then the order will be inferred from the order of the keys in the target.</p> </li> </ul>"},{"location":"api/multioutput/RegressorChain/#examples","title":"Examples","text":"<p><pre><code>from river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river import preprocessing\nfrom river import stream\n\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.load_linnerud(),\n    shuffle=True,\n    seed=42\n)\n\nmodel = multioutput.RegressorChain(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression(intercept_lr=0.3)\n    ),\n    order=[0, 1, 2]\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.MAE())\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MicroAverage(MAE): 12.733525\n</code></pre></p>"},{"location":"api/multioutput/RegressorChain/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the outputs of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predictions.</p> <p></p>"},{"location":"api/naive-bayes/BernoulliNB/","title":"BernoulliNB","text":"<p>Bernoulli Naive Bayes.</p> <p>Bernoulli Naive Bayes model learns from occurrences between features such as word counts and discrete classes. The input vector must contain positive values, such as counts or TF-IDF values.</p>"},{"location":"api/naive-bayes/BernoulliNB/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1.0</code></p> <p>Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).</p> </li> <li> <p>true_threshold</p> <p>Default \u2192 <code>0.0</code></p> <p>Threshold for binarizing (mapping to booleans) features.</p> </li> </ul>"},{"location":"api/naive-bayes/BernoulliNB/#attributes","title":"Attributes","text":"<ul> <li> <p>class_counts (collections.Counter)</p> <p>Number of times each class has been seen.</p> </li> <li> <p>feature_counts (collections.defaultdict)</p> <p>Total frequencies per feature and class.</p> </li> </ul>"},{"location":"api/naive-bayes/BernoulliNB/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import compose\nfrom river import feature_extraction\nfrom river import naive_bayes\n\ndocs = [\n    (\"Chinese Beijing Chinese\", \"yes\"),\n    (\"Chinese Chinese Shanghai\", \"yes\"),\n    (\"Chinese Macao\", \"yes\"),\n    (\"Tokyo Japan Chinese\", \"no\")\n]\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.BernoulliNB(alpha=1))\n)\n\nfor sentence, label in docs:\n    model.learn_one(sentence, label)\n\nmodel[\"nb\"].p_class(\"yes\")\n</code></pre> <pre><code>0.75\n</code></pre> <pre><code>model[\"nb\"].p_class(\"no\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model.predict_proba_one(\"test\")\n</code></pre> <pre><code>{'yes': 0.883..., 'no': 0.116...}\n</code></pre></p> <p><pre><code>model.predict_one(\"test\")\n</code></pre> <pre><code>'yes'\n</code></pre></p> <p>You can train the model and make predictions in mini-batch mode using the class methods <code>learn_many</code> and <code>predict_many</code>.</p> <p><pre><code>df_docs = pd.DataFrame(docs, columns = [\"docs\", \"y\"])\n\nX = pd.Series([\n   \"Chinese Beijing Chinese\",\n   \"Chinese Chinese Shanghai\",\n   \"Chinese Macao\",\n   \"Tokyo Japan Chinese\"\n])\n\ny = pd.Series([\"yes\", \"yes\", \"yes\", \"no\"])\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.BernoulliNB(alpha=1))\n)\n\nmodel.learn_many(X, y)\n\nunseen = pd.Series([\"Taiwanese Taipei\", \"Chinese Shanghai\"])\n\nmodel.predict_proba_many(unseen)\n</code></pre> <pre><code>         no       yes\n0  0.116846  0.883154\n1  0.047269  0.952731\n</code></pre></p> <p><pre><code>model.predict_many(unseen)\n</code></pre> <pre><code>0    yes\n1    yes\ndtype: object\n</code></pre></p>"},{"location":"api/naive-bayes/BernoulliNB/#methods","title":"Methods","text":"joint_log_likelihood <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     Mapping between classes and joint log likelihood.</p> <p></p> joint_log_likelihood_many <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     Input samples joint log likelihood.</p> <p></p> learn_many <p>Learn from a batch of count vectors.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Updates the model with a single observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class p_class_many p_feature_given_class predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Return probabilities using the log-likelihoods in mini-batchs setting.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>The Bernoulli model \u21a9</p> </li> </ol>"},{"location":"api/naive-bayes/ComplementNB/","title":"ComplementNB","text":"<p>Naive Bayes classifier for multinomial models.</p> <p>Complement Naive Bayes model learns from occurrences between features such as word counts and discrete classes. ComplementNB is suitable for imbalance dataset. The input vector must contain positive values, such as counts or TF-IDF values.</p>"},{"location":"api/naive-bayes/ComplementNB/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1.0</code></p> <p>Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).</p> </li> </ul>"},{"location":"api/naive-bayes/ComplementNB/#attributes","title":"Attributes","text":"<ul> <li> <p>class_dist (proba.Multinomial)</p> <p>Class prior probability distribution.</p> </li> <li> <p>feature_counts (collections.defaultdict)</p> <p>Total frequencies per feature and class.</p> </li> <li> <p>class_totals (collections.Counter)</p> <p>Total frequencies per class.</p> </li> </ul>"},{"location":"api/naive-bayes/ComplementNB/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import compose\nfrom river import feature_extraction\nfrom river import naive_bayes\n\ndocs = [\n    (\"Chinese Beijing Chinese\", \"yes\"),\n    (\"Chinese Chinese Shanghai\", \"yes\"),\n    (\"Chinese Macao\", \"maybe\"),\n    (\"Tokyo Japan Chinese\", \"no\")\n]\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.ComplementNB(alpha=1))\n)\n\nfor sentence, label in docs:\n    model.learn_one(sentence, label)\n\nmodel[\"nb\"].p_class(\"yes\")\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"no\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"maybe\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model.predict_proba_one(\"test\")\n</code></pre> <pre><code>{'yes': 0.275, 'maybe': 0.375, 'no': 0.35}\n</code></pre></p> <p><pre><code>model.predict_one(\"test\")\n</code></pre> <pre><code>'maybe'\n</code></pre></p> <p>You can train the model and make predictions in mini-batch mode using the class methods <code>learn_many</code> and <code>predict_many</code>.</p> <p><pre><code>df_docs = pd.DataFrame(docs, columns = [\"docs\", \"y\"])\n\nX = pd.Series([\n   \"Chinese Beijing Chinese\",\n   \"Chinese Chinese Shanghai\",\n   \"Chinese Macao\",\n   \"Tokyo Japan Chinese\"\n])\n\ny = pd.Series([\"yes\", \"yes\", \"maybe\", \"no\"])\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.ComplementNB(alpha=1))\n)\n\nmodel.learn_many(X, y)\n\nunseen = pd.Series([\"Taiwanese Taipei\", \"Chinese Shanghai\"])\n\nmodel.predict_proba_many(unseen)\n</code></pre> <pre><code>      maybe        no       yes\n0  0.415129  0.361624  0.223247\n1  0.248619  0.216575  0.534807\n</code></pre></p> <p><pre><code>model.predict_many(unseen)\n</code></pre> <pre><code>0    maybe\n1      yes\ndtype: object\n</code></pre></p>"},{"location":"api/naive-bayes/ComplementNB/#methods","title":"Methods","text":"joint_log_likelihood <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     Mapping between classes and joint log likelihood.</p> <p></p> joint_log_likelihood_many <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     Input samples joint log likelihood.</p> <p></p> learn_many <p>Learn from a batch of count vectors.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Updates the model with a single observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class p_class_many predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Return probabilities using the log-likelihoods in mini-batchs setting.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>Rennie, J.D., Shih, L., Teevan, J. and Karger, D.R., 2003. Tackling the poor assumptions of naive bayes text classifiers. In Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 616-623) \u21a9</p> </li> <li> <p>StackExchange discussion \u21a9</p> </li> </ol>"},{"location":"api/naive-bayes/GaussianNB/","title":"GaussianNB","text":"<p>Gaussian Naive Bayes.</p> <p>A Gaussian distribution \\(G_{cf}\\) is maintained for each class \\(c\\) and each feature \\(f\\). Each Gaussian is updated using the amount associated with each feature; the details can be be found in <code>proba.Gaussian</code>. The joint log-likelihood is then obtained by summing the log probabilities of each feature associated with each class.</p>"},{"location":"api/naive-bayes/GaussianNB/#examples","title":"Examples","text":"<p><pre><code>from river import naive_bayes\nfrom river import stream\nimport numpy as np\n\nX = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\nY = np.array([1, 1, 1, 2, 2, 2])\n\nmodel = naive_bayes.GaussianNB()\n\nfor x, y in stream.iter_array(X, Y):\n    model.learn_one(x, y)\n\nmodel.predict_one({0: -0.8, 1: -1})\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/naive-bayes/GaussianNB/#methods","title":"Methods","text":"joint_log_likelihood joint_log_likelihood_many learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/naive-bayes/MultinomialNB/","title":"MultinomialNB","text":"<p>Naive Bayes classifier for multinomial models.</p> <p>Multinomial Naive Bayes model learns from occurrences between features such as word counts and discrete classes. The input vector must contain positive values, such as counts or TF-IDF values.</p>"},{"location":"api/naive-bayes/MultinomialNB/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1.0</code></p> <p>Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).</p> </li> </ul>"},{"location":"api/naive-bayes/MultinomialNB/#attributes","title":"Attributes","text":"<ul> <li> <p>class_dist (proba.Multinomial)</p> <p>Class prior probability distribution.</p> </li> <li> <p>feature_counts (collections.defaultdict)</p> <p>Total frequencies per feature and class.</p> </li> <li> <p>class_totals (collections.Counter)</p> <p>Total frequencies per class.</p> </li> </ul>"},{"location":"api/naive-bayes/MultinomialNB/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import compose\nfrom river import feature_extraction\nfrom river import naive_bayes\n\ndocs = [\n    (\"Chinese Beijing Chinese\", \"yes\"),\n    (\"Chinese Chinese Shanghai\", \"yes\"),\n    (\"Chinese Macao\", \"maybe\"),\n    (\"Tokyo Japan Chinese\", \"no\")\n]\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.MultinomialNB(alpha=1))\n)\n\nfor sentence, label in docs:\n    model.learn_one(sentence, label)\n\nmodel[\"nb\"].p_class(\"yes\")\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"no\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"maybe\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model.predict_proba_one(\"test\")\n</code></pre> <pre><code>{'yes': 0.413, 'maybe': 0.310, 'no': 0.275}\n</code></pre></p> <p><pre><code>model.predict_one(\"test\")\n</code></pre> <pre><code>'yes'\n</code></pre></p> <p>You can train the model and make predictions in mini-batch mode using the class methods <code>learn_many</code> and <code>predict_many</code>.</p> <p><pre><code>df_docs = pd.DataFrame(docs, columns = [\"docs\", \"y\"])\n\nX = pd.Series([\n   \"Chinese Beijing Chinese\",\n   \"Chinese Chinese Shanghai\",\n   \"Chinese Macao\",\n   \"Tokyo Japan Chinese\"\n])\n\ny = pd.Series([\"yes\", \"yes\", \"maybe\", \"no\"])\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.MultinomialNB(alpha=1))\n)\n\nmodel.learn_many(X, y)\n\nunseen = pd.Series([\"Taiwanese Taipei\", \"Chinese Shanghai\"])\n\nmodel.predict_proba_many(unseen)\n</code></pre> <pre><code>      maybe        no       yes\n0  0.373272  0.294931  0.331797\n1  0.160396  0.126733  0.712871\n</code></pre></p> <p><pre><code>model.predict_many(unseen)\n</code></pre> <pre><code>0    maybe\n1      yes\ndtype: object\n</code></pre></p>"},{"location":"api/naive-bayes/MultinomialNB/#methods","title":"Methods","text":"joint_log_likelihood <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     Mapping between classes and joint log likelihood.</p> <p></p> joint_log_likelihood_many <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     Input samples joint log likelihood.</p> <p></p> learn_many <p>Learn from a batch of count vectors.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Updates the model with a single observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class p_class_many p_feature_given_class predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Return probabilities using the log-likelihoods in mini-batchs setting.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>Naive Bayes text classification \u21a9</p> </li> </ol>"},{"location":"api/neighbors/KNNClassifier/","title":"KNNClassifier","text":"<p>K-Nearest Neighbors (KNN) for classification.</p> <p>Samples are stored using a first-in, first-out strategy. The strategy to perform search queries in the data buffer is defined by the <code>engine</code> parameter.</p>"},{"location":"api/neighbors/KNNClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_neighbors</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of nearest neighbors to search for.</p> </li> <li> <p>engine</p> <p>Type \u2192 BaseNN | None</p> <p>Default \u2192 <code>None</code></p> <p>The search engine used to store the instances and perform search queries. Depending on the choose engine, search will be exact or approximate. Please, consult the documentation of each available search engine for more details on its usage. By default, use the <code>SWINN</code> search engine for approximate search queries.</p> </li> <li> <p>weighted</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Weight the contribution of each neighbor by its inverse distance.</p> </li> <li> <p>cleanup_every</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>This determines at which rate old classes are cleaned up. Classes that have been seen in the past but that are not present in the current window are dropped. Classes are never dropped when this is set to 0.</p> </li> <li> <p>softmax</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to use softmax normalization to normalize the neighbors contributions. Votes are divided by the total number of votes if this is <code>False</code>.</p> </li> </ul>"},{"location":"api/neighbors/KNNClassifier/#examples","title":"Examples","text":"<pre><code>import functools\nfrom river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import neighbors\nfrom river import preprocessing\nfrom river import utils\n\ndataset = datasets.Phishing()\n</code></pre> <p>To select a custom distance metric which takes one or several parameter, you can wrap your chosen distance using <code>functools.partial</code>:</p> <p><pre><code>l1_dist = functools.partial(utils.math.minkowski_distance, p=1)\n\nmodel = (\n    preprocessing.StandardScaler() |\n    neighbors.KNNClassifier(\n        engine=neighbors.SWINN(\n            dist_func=l1_dist,\n            seed=42\n        )\n    )\n)\n\nevaluate.progressive_val_score(dataset, model, metrics.Accuracy())\n</code></pre> <pre><code>Accuracy: 89.59%\n</code></pre></p>"},{"location":"api/neighbors/KNNClassifier/#methods","title":"Methods","text":"clean_up_classes <p>Clean up classes added to the window.</p> <p>Classes that are added (and removed) from the window may no longer be valid. This method cleans up the window and and ensures only known classes are added, and we do not consider \"None\" a class. It is called every <code>cleanup_every</code> step, or can be called manually.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/neighbors/KNNClassifier/#notes","title":"Notes","text":"<p>Note that since the window is moving and we keep track of all classes that are added at some point, a class might be returned in a result (with a value of 0) if it is no longer in the window. You can call model.clean_up_classes(), or set <code>cleanup_every</code> to a non-zero value.</p>"},{"location":"api/neighbors/KNNRegressor/","title":"KNNRegressor","text":"<p>K-Nearest Neighbors regressor.</p> <p>Samples are stored using a first-in, first-out strategy. The strategy to perform search queries in the data buffer is defined by the <code>engine</code> parameter. Predictions are obtained by aggregating the values of the closest n_neighbors stored samples with respect to a query sample.</p>"},{"location":"api/neighbors/KNNRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_neighbors</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of nearest neighbors to search for.</p> </li> <li> <p>engine</p> <p>Type \u2192 BaseNN | None</p> <p>Default \u2192 <code>None</code></p> <p>The search engine used to store the instances and perform search queries. Depending on the choose engine, search will be exact or approximate. Please, consult the documentation of each available search engine for more details on its usage. By default, use the <code>SWINN</code> search engine for approximate search queries.</p> </li> <li> <p>aggregation_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>mean</code></p> <p>The method to aggregate the target values of neighbors.     | 'mean'     | 'median'     | 'weighted_mean'</p> </li> </ul>"},{"location":"api/neighbors/KNNRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import neighbors\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = neighbors.KNNRegressor()\nevaluate.progressive_val_score(dataset, model, metrics.RMSE())\n</code></pre> <pre><code>RMSE: 1.427743\n</code></pre></p>"},{"location":"api/neighbors/KNNRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/neighbors/LazySearch/","title":"LazySearch","text":"<p>Exact nearest neighbors using a lazy search estrategy.</p>"},{"location":"api/neighbors/LazySearch/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>Size of the sliding window use to search neighbors with.</p> </li> <li> <p>min_distance_keep</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The minimum distance (similarity) to consider adding a point to the window. E.g., a value of 0.0 will add even exact duplicates.</p> </li> <li> <p>dist_func</p> <p>Type \u2192 DistanceFunc | FunctionWrapper | None</p> <p>Default \u2192 <code>None</code></p> <p>A distance function which accepts two input items to compare. If not set, use the Minkowski distance with <code>p=2</code>.</p> </li> </ul>"},{"location":"api/neighbors/LazySearch/#methods","title":"Methods","text":"append <p>Add a point to the window, optionally with extra metadata.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>extra     \u2014 'typing.Any | None'     \u2014 defaults to <code>None</code> </li> <li>kwargs </li> </ul> <p></p> search <p>Find the <code>n_neighbors</code> closest points to <code>item</code>, along with their distances.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>n_neighbors     \u2014 'int' </li> <li>kwargs </li> </ul> <p></p> update <p>Update the window with a new point, only added if &gt; min distance.</p> <p>If min distance is 0, we do not need to do the calculation. The item (and extra metadata) will not be added to the window if it is too close to an existing point.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>n_neighbors     \u2014 'int'     \u2014 defaults to <code>1</code> </li> <li>extra     \u2014 'typing.Any | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>A boolean (true/false) to indicate if the point was added.</p> <p></p>"},{"location":"api/neighbors/LazySearch/#notes","title":"Notes","text":"<p>Updates are by default stored by the FIFO (first in first out) method, which means that when the size limit is reached, old samples are dumped to give room for new samples. This is circular, meaning that older points are dumped first. This also gives the implementation a temporal aspect, because older samples are replaced with newer ones.</p> <p>The parameter <code>min_dinstance_keep</code> controls the addition of new items to the window - items that are far enough away (&gt; min_distance_keep) are added to the window. Thus a value of 0 indicates that we add all points, and increasing from 0 makes it less likely we will keep a new item.</p>"},{"location":"api/neighbors/SWINN/","title":"SWINN","text":"<p>Sliding WIndow-based Nearest Neighbor (SWINN) search using Graphs.</p> <p>Extends the NNDescent algorithm<sup>1</sup> to handle vertex addition and removal in a FIFO data ingestion policy. SWINN builds and keeps a directed graph where edges connect the nearest neighbors. Any distance metric can be used to build the graph. By using a directed graph, the user must set the desired number of neighbors. More neighbors imply more accurate search queries at the cost of increased running time and memory usage. Note that although the number of directed neighbors is limited by the user, there is no direct control on the number of reverse neighbors, i.e., the number of vertices that have an edge to a given vertex. </p> <p>The basic idea of SWINN and NNDescent is that \"the neighbor of my neighbors might as well be my neighbor\". Hence, the connections are constantly revisited to improve the graph structure. The algorithm for creating and maintaining the search graph can be described in general lines as follows: </p> <ul> <li> <p>Start with a random neighborhood graph; </p> </li> <li> <p>For each node in the search graph: refine the current neighborhood by checking if there are better neighborhood options among the neighbors of the current neighbors; </p> </li> <li> <p>If the total number of neighborhood changes is smaller than a given stopping criterion, then stop. </p> </li> </ul> <p>SWINN adds strategies to remove vertices from the search graph and pruning redundant edges. SWINN is more efficient when the selected <code>maxlen</code> is greater than 500. For small sized data windows, using the lazy/exhaustive search, i.e., <code>neighbors.LazySearch</code> might be a better idea.</p>"},{"location":"api/neighbors/SWINN/#parameters","title":"Parameters","text":"<ul> <li> <p>graph_k</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>20</code></p> <p>The maximum number of direct nearest neighbors each node has.</p> </li> <li> <p>dist_func</p> <p>Type \u2192 DistanceFunc | FunctionWrapper | None</p> <p>Default \u2192 <code>None</code></p> <p>The distance function used to compare two items. If not set, use the Minkowski distance with <code>p=2</code>.</p> </li> <li> <p>maxlen</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>The maximum size of the data buffer.</p> </li> <li> <p>warm_up</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>500</code></p> <p>How many data instances to observe before starting the search graph.</p> </li> <li> <p>max_candidates</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum number of vertices to consider when performing local neighborhood joins. If not set SWINN will use <code>min(50, max(50, self.graph_k))</code>.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0001</code></p> <p>Early stop parameter for the neighborhood refinement procedure. NNDescent will stop running if the maximum number of iterations is reached or the number of edge changes after an iteration is smaller than or equal to <code>delta * graph_k * n_nodes</code>. In the last expression, <code>n_nodes</code> refers to the number of graph nodes involved in the (local) neighborhood refinement.</p> </li> <li> <p>prune_prob</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability of removing redundant edges. Must be between <code>0</code> and <code>1</code>. If set to zero, no edge will be pruned. When set to one, every potentially redundant edge will be dropped.</p> </li> <li> <p>n_iters</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The maximum number of NNDescent iterations to perform to refine the search index.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/neighbors/SWINN/#methods","title":"Methods","text":"append <p>Add a new item to the search index.</p> <p>Data is stored using the FIFO strategy. Both the data buffer and the search graph are updated. The addition of a new item will trigger the removal of the oldest item, if the maximum size was reached. All edges of the removed node are also dropped and safety procedures are applied to ensure its neighbors keep accessible. The addition of a new item also trigger local neighborhood refinement procedures, to ensure the search index is effective and the node degree constraints are met.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>kwargs </li> </ul> <p></p> connectivity <p>Get a list with the size of each connected component in the search graph.</p> <p>This metric provides an overview of reachability in the search index by using Kruskal's algorithm to build a forest of connected components.  We want our search index to have a single connected component, i.e., the case where we get a list containing a single number which is equal to <code>maxlen</code>. If that is not the case, not every node in the search graph can be reached from any given starting point. You may want to try increasing <code>graph_k</code> to improve connectivity. However, keep in mind the following aspects: 1) computing this metric is a costly operation (\\(O(E\\log V)\\)), where \\(E\\) and \\(V\\) are, respectively, the number of edges and vertices in the search graph; 2) often, connectivity comes at the price of increased computational costs. Tweaking the <code>sample_rate</code> might help in such situations. The best possible scenario is to decrease the value of <code>graph_k</code> while keeping a single connected component.</p> <p>Returns</p> <p>list[int]:     A list of the number of elements in each connected component of the graph.</p> <p></p> search <p>Search the underlying nearest neighbor graph given a query item.</p> <p>In case not enough samples were observed, i.e., the number of stored samples is smaller than <code>warm_up</code>, then the search switches to a brute force strategy.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>n_neighbors     \u2014 'int' </li> <li>epsilon     \u2014 'float'     \u2014 defaults to <code>0.1</code> </li> <li>kwargs </li> </ul> <p>Returns</p> <p>tuple[list, list]:     neighbors, dists</p> <p></p>"},{"location":"api/neighbors/SWINN/#notes","title":"Notes","text":"<p>There is an accuracy/speed trade-off between <code>graph_k</code> and <code>sample_rate</code>. To ensure a single connected component, and thus an effective search index, one can increase <code>graph_k</code>. The <code>connectivity</code> method is a helper to determine whether the search index has a single connected component. However, search accuracy might come at the cost of increased memory usage and slow processing. To alleviate that, one can rely on decreasing the <code>sample_rate</code> to avoid exploring all the undirected edges of a node during search queries and local graph refinements. Moreover, the edge pruning procedures also help decreasing the computational costs. Note that, anything that limits the number of explored neighbors or prunes edges might have a negative impact on search accuracy.</p> <ol> <li> <p>Dong, W., Moses, C., &amp; Li, K. (2011, March). Efficient k-nearest neighbor graph construction for generic similarity measures. In Proceedings of the 20th international conference on World wide web (pp. 577-586).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/neural-net/MLPRegressor/","title":"MLPRegressor","text":"<p>Multi-layer Perceptron for regression.</p> <p>This model is still work in progress. Here are some features that still need implementing: </p> <ul> <li> <p><code>learn_one</code> and <code>predict_one</code> just cast the input <code>dict</code> to a single row dataframe and then</p> <p>call <code>learn_many</code> and <code>predict_many</code> respectively. This is very inefficient. - Not all of the optimizers in the <code>optim</code> module can be used as they are not all vectorised.</p> </li> <li> <p>Emerging and disappearing features are not supported. Each instance/batch has to have the</p> <p>same features. - The gradient haven't been numerically checked.</p> </li> </ul>"},{"location":"api/neural-net/MLPRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>hidden_dims</p> <p>The dimensions of the hidden layers. For example, specifying <code>(10, 20)</code> means that there are two hidden layers with 10 and 20 neurons, respectively. Note that the number of layers the network contains is equal to the number of hidden layers plus two (to account for the input and output layers).</p> </li> <li> <p>activations</p> <p>The activation functions to use at each layer, including the input and output layers. Therefore you need to specify three activation if you specify one hidden layer.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>Loss function. Defaults to <code>optim.losses.Squared</code>.</p> </li> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>Optimizer. Defaults to <code>optim.SGD</code> with the learning rate set to <code>0.01</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/neural-net/MLPRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>n_layers</p> <p>Return the number of layers in the network.  The number of layers is equal to the number of hidden layers plus 2. The 2 accounts for the input layer and the output layer.</p> </li> </ul>"},{"location":"api/neural-net/MLPRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import neural_net as nn\nfrom river import optim\nfrom river import preprocessing as pp\nfrom river import metrics\n\nmodel = (\n    pp.StandardScaler() |\n    nn.MLPRegressor(\n        hidden_dims=(5,),\n        activations=(\n            nn.activations.ReLU,\n            nn.activations.ReLU,\n            nn.activations.Identity\n        ),\n        optimizer=optim.SGD(1e-3),\n        seed=42\n    )\n)\n\ndataset = datasets.TrumpApproval()\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.580578\n</code></pre></p> <p>You can also use this to process mini-batches of data.</p> <p><pre><code>model = (\n    pp.StandardScaler() |\n    nn.MLPRegressor(\n        hidden_dims=(10,),\n        activations=(\n            nn.activations.ReLU,\n            nn.activations.ReLU,\n            nn.activations.ReLU\n        ),\n        optimizer=optim.SGD(1e-4),\n        seed=42\n    )\n)\n\ndataset = datasets.TrumpApproval()\nbatch_size = 32\n\nfor epoch in range(10):\n    for xb in pd.read_csv(dataset.path, chunksize=batch_size):\n        yb = xb.pop('five_thirty_eight')\n        y_pred = model.predict_many(xb)\n        model.learn_many(xb, yb)\n\nmodel.predict_many(xb)\n</code></pre> <pre><code>      five_thirty_eight\n992           39.405231\n993           46.447481\n994           42.121865\n995           40.251148\n996           40.836378\n997           40.893153\n998           40.949927\n999           48.416504\n1000          42.077830\n</code></pre></p>"},{"location":"api/neural-net/MLPRegressor/#methods","title":"Methods","text":"call <p>Make predictions.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_many <p>Train the network.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_many predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/neural-net/activations/Identity/","title":"Identity","text":"<p>Identity activation function.</p>"},{"location":"api/neural-net/activations/Identity/#methods","title":"Methods","text":"apply <p>Apply the activation function to a layer output z.</p> <ul> <li>z </li> </ul> <p></p> gradient <p>Return the gradient with respect to a layer output z.</p> <ul> <li>z </li> </ul> <p></p>"},{"location":"api/neural-net/activations/ReLU/","title":"ReLU","text":"<p>Rectified Linear Unit (ReLU) activation function.</p>"},{"location":"api/neural-net/activations/ReLU/#methods","title":"Methods","text":"apply <p>Apply the activation function to a layer output z.</p> <ul> <li>z </li> </ul> <p></p> gradient <p>Return the gradient with respect to a layer output z.</p> <ul> <li>z </li> </ul> <p></p>"},{"location":"api/neural-net/activations/Sigmoid/","title":"Sigmoid","text":"<p>Sigmoid activation function.</p>"},{"location":"api/neural-net/activations/Sigmoid/#methods","title":"Methods","text":"apply <p>Apply the activation function to a layer output z.</p> <ul> <li>z </li> </ul> <p></p> gradient <p>Return the gradient with respect to a layer output z.</p> <ul> <li>z </li> </ul> <p></p>"},{"location":"api/optim/AMSGrad/","title":"AMSGrad","text":"<p>AMSGrad optimizer.</p>"},{"location":"api/optim/AMSGrad/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Type \u2192 int | float | optim.base.Scheduler</p> <p>Default \u2192 <code>0.1</code></p> <p>The learning rate.</p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> <li> <p>correct_bias</p> <p>Default \u2192 <code>True</code></p> </li> </ul>"},{"location":"api/optim/AMSGrad/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>v (collections.defaultdict)</p> </li> <li> <p>v_hat (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AMSGrad/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AMSGrad()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.60%\n</code></pre></p>"},{"location":"api/optim/AMSGrad/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Reddi, S.J., Kale, S. and Kumar, S., 2019. On the convergence of adam and beyond. arXiv preprint arXiv:1904.09237 \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaBound/","title":"AdaBound","text":"<p>AdaBound optimizer.</p>"},{"location":"api/optim/AdaBound/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.001</code></p> <p>The learning rate.</p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> <li> <p>gamma</p> <p>Default \u2192 <code>0.001</code></p> </li> <li> <p>final_lr</p> <p>Default \u2192 <code>0.1</code></p> </li> </ul>"},{"location":"api/optim/AdaBound/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>s (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AdaBound/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaBound()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.06%\n</code></pre></p>"},{"location":"api/optim/AdaBound/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Luo, L., Xiong, Y., Liu, Y. and Sun, X., 2019. Adaptive gradient methods with dynamic bound of learning rate. arXiv preprint arXiv:1902.09843 \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaDelta/","title":"AdaDelta","text":"<p>AdaDelta optimizer.</p>"},{"location":"api/optim/AdaDelta/#parameters","title":"Parameters","text":"<ul> <li> <p>rho</p> <p>Default \u2192 <code>0.95</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/AdaDelta/#attributes","title":"Attributes","text":"<ul> <li> <p>g2 (collections.defaultdict)</p> </li> <li> <p>s2 (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AdaDelta/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaDelta()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 80.56%\n</code></pre></p>"},{"location":"api/optim/AdaDelta/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Zeiler, M.D., 2012. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701. \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaGrad/","title":"AdaGrad","text":"<p>AdaGrad optimizer.</p>"},{"location":"api/optim/AdaGrad/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/AdaGrad/#attributes","title":"Attributes","text":"<ul> <li>g2 (collections.defaultdict)</li> </ul>"},{"location":"api/optim/AdaGrad/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaGrad()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.01%\n</code></pre></p>"},{"location":"api/optim/AdaGrad/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Duchi, J., Hazan, E. and Singer, Y., 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(Jul), pp.2121-2159. \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaMax/","title":"AdaMax","text":"<p>AdaMax optimizer.</p>"},{"location":"api/optim/AdaMax/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/AdaMax/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>v (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AdaMax/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaMax()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.61%\n</code></pre></p>"},{"location":"api/optim/AdaMax/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. \u21a9</p> </li> <li> <p>Ruder, S., 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. \u21a9</p> </li> </ol>"},{"location":"api/optim/Adam/","title":"Adam","text":"<p>Adam optimizer.</p>"},{"location":"api/optim/Adam/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/Adam/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>v (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/Adam/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Adam()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.52%\n</code></pre></p>"},{"location":"api/optim/Adam/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. \u21a9</p> </li> </ol>"},{"location":"api/optim/Averager/","title":"Averager","text":"<p>Averaged stochastic gradient descent.</p> <p>This is a wrapper that can be applied to any stochastic gradient descent optimiser. Note that this implementation differs than what may be found elsewhere. Essentially, the average of the weights is usually only used at the end of the optimisation, once all the data has been seen. However, in this implementation the optimiser returns the current averaged weights.</p>"},{"location":"api/optim/Averager/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer</p> <p>An optimizer for which the produced weights will be averaged.</p> </li> <li> <p>start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Indicates the number of iterations to wait before starting the average. Essentially, nothing happens differently before the number of iterations reaches this value.</p> </li> </ul>"},{"location":"api/optim/Averager/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/Averager/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Averager(optim.SGD(0.01), 100)\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.97%\n</code></pre></p>"},{"location":"api/optim/Averager/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Bottou, L., 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010 (pp. 177-186). Physica-Verlag HD. \u21a9</p> </li> <li> <p>Stochastic Algorithms for One-Pass Learning slides by L\u00e9on Bottou \u21a9</p> </li> <li> <p>Xu, W., 2011. Towards optimal one pass large scale learning with averaged stochastic gradient descent. arXiv preprint arXiv:1107.2490. \u21a9</p> </li> </ol>"},{"location":"api/optim/FTRLProximal/","title":"FTRLProximal","text":"<p>FTRL-Proximal optimizer.</p>"},{"location":"api/optim/FTRLProximal/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>0.05</code></p> </li> <li> <p>beta</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>l1</p> <p>Default \u2192 <code>0.0</code></p> </li> <li> <p>l2</p> <p>Default \u2192 <code>1.0</code></p> </li> </ul>"},{"location":"api/optim/FTRLProximal/#attributes","title":"Attributes","text":"<ul> <li> <p>z (collections.defaultdict)</p> </li> <li> <p>n (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/FTRLProximal/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.FTRLProximal()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.56%\n</code></pre></p>"},{"location":"api/optim/FTRLProximal/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D. and Chikkerur, S., 2013, August. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222-1230) \u21a9</p> </li> <li> <p>Tensorflow's <code>FtrlOptimizer</code> \u21a9</p> </li> </ol>"},{"location":"api/optim/Momentum/","title":"Momentum","text":"<p>Momentum optimizer.</p>"},{"location":"api/optim/Momentum/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>rho</p> <p>Default \u2192 <code>0.9</code></p> </li> </ul>"},{"location":"api/optim/Momentum/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/Momentum/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Momentum()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 84.09%\n</code></pre></p>"},{"location":"api/optim/Momentum/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p>"},{"location":"api/optim/Nadam/","title":"Nadam","text":"<p>Nadam optimizer.</p>"},{"location":"api/optim/Nadam/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/Nadam/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/Nadam/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Nadam()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.60%\n</code></pre></p>"},{"location":"api/optim/Nadam/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Nadam: A combination of adam and nesterov \u21a9</p> </li> </ol>"},{"location":"api/optim/NesterovMomentum/","title":"NesterovMomentum","text":"<p>Nesterov Momentum optimizer.</p>"},{"location":"api/optim/NesterovMomentum/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>rho</p> <p>Default \u2192 <code>0.9</code></p> </li> </ul>"},{"location":"api/optim/NesterovMomentum/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/NesterovMomentum/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.NesterovMomentum()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 84.22%\n</code></pre></p>"},{"location":"api/optim/NesterovMomentum/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p>"},{"location":"api/optim/RMSProp/","title":"RMSProp","text":"<p>RMSProp optimizer.</p>"},{"location":"api/optim/RMSProp/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>rho</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/RMSProp/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/RMSProp/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.RMSProp()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.24%\n</code></pre></p>"},{"location":"api/optim/RMSProp/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Divide the gradient by a running average of itsrecent magnitude \u21a9</p> </li> </ol>"},{"location":"api/optim/SGD/","title":"SGD","text":"<p>Plain stochastic gradient descent.</p>"},{"location":"api/optim/SGD/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.01</code></p> </li> </ul>"},{"location":"api/optim/SGD/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/SGD/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.SGD(0.1)\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.85%\n</code></pre></p>"},{"location":"api/optim/SGD/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Robbins, H. and Monro, S., 1951. A stochastic approximation method. The annals of mathematical statistics, pp.400-407 \u21a9</p> </li> </ol>"},{"location":"api/optim/base/Initializer/","title":"Initializer","text":"<p>An initializer is used to set initial weights in a model.</p>"},{"location":"api/optim/base/Initializer/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/base/Loss/","title":"Loss","text":"<p>Base class for all loss functions.</p>"},{"location":"api/optim/base/Loss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/base/Optimizer/","title":"Optimizer","text":"<p>Optimizer interface.</p> <p>Every optimizer inherits from this base interface.</p>"},{"location":"api/optim/base/Optimizer/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Type \u2192 int | float | Scheduler</p> </li> </ul>"},{"location":"api/optim/base/Optimizer/#attributes","title":"Attributes","text":"<ul> <li> <p>learning_rate (float)</p> <p>Returns the current learning rate value.</p> </li> </ul>"},{"location":"api/optim/base/Optimizer/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p>"},{"location":"api/optim/base/Scheduler/","title":"Scheduler","text":"<p>Can be used to program the learning rate schedule of an <code>optim.base.Optimizer</code>.</p>"},{"location":"api/optim/base/Scheduler/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/optim/initializers/Constant/","title":"Constant","text":"<p>Constant initializer which always returns the same value.</p>"},{"location":"api/optim/initializers/Constant/#parameters","title":"Parameters","text":"<ul> <li> <p>value</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/optim/initializers/Constant/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ninit = optim.initializers.Constant(value=3.14)\n\ninit(shape=1)\n</code></pre> <pre><code>3.14\n</code></pre></p> <p><pre><code>init(shape=2)\n</code></pre> <pre><code>array([3.14, 3.14])\n</code></pre></p>"},{"location":"api/optim/initializers/Constant/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/initializers/Normal/","title":"Normal","text":"<p>Random normal initializer which simulate a normal distribution with specified parameters.</p>"},{"location":"api/optim/initializers/Normal/#parameters","title":"Parameters","text":"<ul> <li> <p>mu</p> <p>Default \u2192 <code>0.0</code></p> <p>The mean of the normal distribution</p> </li> <li> <p>sigma</p> <p>Default \u2192 <code>1.0</code></p> <p>The standard deviation of the normal distribution</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed that can be set for reproducibility.</p> </li> </ul>"},{"location":"api/optim/initializers/Normal/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ninit = optim.initializers.Normal(mu=0, sigma=1, seed=42)\n\ninit(shape=1)\n</code></pre> <pre><code>0.496714\n</code></pre></p> <p><pre><code>init(shape=2)\n</code></pre> <pre><code>array([-0.1382643 ,  0.64768854])\n</code></pre></p>"},{"location":"api/optim/initializers/Normal/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/initializers/Zeros/","title":"Zeros","text":"<p>Constant initializer which always returns zeros.</p>"},{"location":"api/optim/initializers/Zeros/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ninit = optim.initializers.Zeros()\n\ninit(shape=1)\n</code></pre> <pre><code>0.0\n</code></pre></p> <p><pre><code>init(shape=2)\n</code></pre> <pre><code>array([0., 0.])\n</code></pre></p>"},{"location":"api/optim/initializers/Zeros/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/losses/Absolute/","title":"Absolute","text":"<p>Absolute loss, also known as the mean absolute error or L1 loss.</p> <p>Mathematically, it is defined as </p> \\[L = |p_i - y_i|\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[\\frac{\\partial L}{\\partial p_i} = sgn(p_i - y_i)\\]"},{"location":"api/optim/losses/Absolute/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Absolute()\nloss(-42, 42)\n</code></pre> <pre><code>84\n</code></pre> <pre><code>loss.gradient(1, 2)\n</code></pre> <pre><code>1\n</code></pre> <pre><code>loss.gradient(2, 1)\n</code></pre> <pre><code>-1\n</code></pre></p>"},{"location":"api/optim/losses/Absolute/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/BinaryFocalLoss/","title":"BinaryFocalLoss","text":"<p>Binary focal loss.</p> <p>This implements the \"star\" algorithm from the appendix of the focal loss paper.</p>"},{"location":"api/optim/losses/BinaryFocalLoss/#parameters","title":"Parameters","text":"<ul> <li> <p>gamma</p> <p>Default \u2192 <code>2</code></p> </li> <li> <p>beta</p> <p>Default \u2192 <code>1</code></p> </li> </ul>"},{"location":"api/optim/losses/BinaryFocalLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p> 1. Lin, T.Y., Goyal, P., Girshick, R., He, K. and Doll\u00e1r, P., 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988)</p>"},{"location":"api/optim/losses/BinaryLoss/","title":"BinaryLoss","text":"<p>A loss appropriate for binary classification tasks.</p>"},{"location":"api/optim/losses/BinaryLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Cauchy/","title":"Cauchy","text":"<p>Cauchy loss function.</p>"},{"location":"api/optim/losses/Cauchy/#parameters","title":"Parameters","text":"<ul> <li> <p>C</p> <p>Default \u2192 <code>80</code></p> </li> </ul>"},{"location":"api/optim/losses/Cauchy/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>\"Effect of MAE\" Kaggle discussion \u21a9</p> </li> <li> <p>Paris Madness Kaggle kernel \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/CrossEntropy/","title":"CrossEntropy","text":"<p>Cross entropy loss.</p> <p>This is a generalization of logistic loss to multiple classes.</p>"},{"location":"api/optim/losses/CrossEntropy/#parameters","title":"Parameters","text":"<ul> <li> <p>class_weight</p> <p>Type \u2192 dict[base.typing.ClfTarget, float] | None</p> <p>Default \u2192 <code>None</code></p> <p>A dictionary that indicates what weight to associate with each class.</p> </li> </ul>"},{"location":"api/optim/losses/CrossEntropy/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ny_true = [0, 1, 2, 2]\ny_pred = [\n    {0: 0.29450637, 1: 0.34216758, 2: 0.36332605},\n    {0: 0.21290077, 1: 0.32728332, 2: 0.45981591},\n    {0: 0.42860913, 1: 0.33380113, 2: 0.23758974},\n    {0: 0.44941979, 1: 0.32962558, 2: 0.22095463}\n]\n\nloss = optim.losses.CrossEntropy()\n\nfor yt, yp in zip(y_true, y_pred):\n    print(loss(yt, yp))\n</code></pre> <pre><code>1.222454\n1.116929\n1.437209\n1.509797\n</code></pre></p> <p><pre><code>for yt, yp in zip(y_true, y_pred):\n    print(loss.gradient(yt, yp))\n</code></pre> <pre><code>{0: -0.70549363, 1: 0.34216758, 2: 0.36332605}\n{0: 0.21290077, 1: -0.67271668, 2: 0.45981591}\n{0: 0.42860913, 1: 0.33380113, 2: -0.76241026}\n{0: 0.44941979, 1: 0.32962558, 2: -0.77904537}\n</code></pre></p>"},{"location":"api/optim/losses/CrossEntropy/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>What is Softmax regression and how is it related to Logistic regression? \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/EpsilonInsensitiveHinge/","title":"EpsilonInsensitiveHinge","text":"<p>Epsilon-insensitive hinge loss.</p>"},{"location":"api/optim/losses/EpsilonInsensitiveHinge/#parameters","title":"Parameters","text":"<ul> <li> <p>eps</p> <p>Default \u2192 <code>0.1</code></p> </li> </ul>"},{"location":"api/optim/losses/EpsilonInsensitiveHinge/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Hinge/","title":"Hinge","text":"<p>Computes the hinge loss.</p> <p>Mathematically, it is defined as </p> \\[L = max(0, 1 - p_i * y_i)\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[ \\\\frac{\\\\partial L}{\\\\partial y_i} = \\\\left\\{ \\\\begin{array}{ll}     \\\\ 0  &amp;   p_iy_i \\geqslant 1  \\\\\\\\     \\\\ - y_i &amp; p_iy_i &lt; 1 \\\\end{array} \\\\right. \\]"},{"location":"api/optim/losses/Hinge/#parameters","title":"Parameters","text":"<ul> <li> <p>threshold</p> <p>Default \u2192 <code>1.0</code></p> <p>Margin threshold. 1 yield the loss used in SVMs, whilst 0 is equivalent to the loss used in the Perceptron algorithm.</p> </li> </ul>"},{"location":"api/optim/losses/Hinge/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Hinge(threshold=1)\nloss(1, .2)\n</code></pre> <pre><code>0.8\n</code></pre></p> <p><pre><code>loss.gradient(1, .2)\n</code></pre> <pre><code>-1\n</code></pre></p>"},{"location":"api/optim/losses/Hinge/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Huber/","title":"Huber","text":"<p>Huber loss.</p> <p>Variant of the squared loss that is robust to outliers.</p>"},{"location":"api/optim/losses/Huber/#parameters","title":"Parameters","text":"<ul> <li> <p>epsilon</p> <p>Default \u2192 <code>0.1</code></p> </li> </ul>"},{"location":"api/optim/losses/Huber/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p> 1. Huber loss function - Wikipedia</p>"},{"location":"api/optim/losses/Log/","title":"Log","text":"<p>Logarithmic loss.</p> <p>This loss function expects each provided <code>y_pred</code> to be a logit. In other words if must be the raw output of a linear model or a neural network.</p>"},{"location":"api/optim/losses/Log/#parameters","title":"Parameters","text":"<ul> <li> <p>weight_pos</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>weight_neg</p> <p>Default \u2192 <code>1.0</code></p> </li> </ul>"},{"location":"api/optim/losses/Log/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>Logit Wikipedia page \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/MultiClassLoss/","title":"MultiClassLoss","text":"<p>A loss appropriate for multi-class classification tasks.</p>"},{"location":"api/optim/losses/MultiClassLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Poisson/","title":"Poisson","text":"<p>Poisson loss.</p> <p>The Poisson loss is usually more suited for regression with count data than the squared loss. </p> <p>Mathematically, it is defined as </p> \\[L = exp(p_i) - y_i \\times p_i\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[\\frac{\\partial L}{\\partial p_i} = exp(p_i) - y_i\\]"},{"location":"api/optim/losses/Poisson/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Quantile/","title":"Quantile","text":"<p>Quantile loss.</p>"},{"location":"api/optim/losses/Quantile/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>0.5</code></p> <p>Desired quantile to attain.</p> </li> </ul>"},{"location":"api/optim/losses/Quantile/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Quantile(0.5)\nloss(1, 3)\n</code></pre> <pre><code>1.0\n</code></pre></p> <p><pre><code>loss.gradient(1, 3)\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>loss.gradient(3, 1)\n</code></pre> <pre><code>-0.5\n</code></pre></p>"},{"location":"api/optim/losses/Quantile/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>Wikipedia article on quantile regression \u21a9</p> </li> <li> <p>Derivative from WolframAlpha \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/RegressionLoss/","title":"RegressionLoss","text":"<p>A loss appropriate for regression tasks.</p>"},{"location":"api/optim/losses/RegressionLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Squared/","title":"Squared","text":"<p>Squared loss, also known as the L2 loss.</p> <p>Mathematically, it is defined as </p> \\[L = (p_i - y_i) ^ 2\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[\\frac{\\partial L}{\\partial p_i} = 2 (p_i - y_i)\\] <p>One thing to note is that this convention is consistent with Vowpal Wabbit and PyTorch, but not with scikit-learn. Indeed, scikit-learn divides the loss by 2, making the 2 disappear in the gradient.</p>"},{"location":"api/optim/losses/Squared/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Squared()\nloss(-4, 5)\n</code></pre> <pre><code>81\n</code></pre> <pre><code>loss.gradient(-4, 5)\n</code></pre> <pre><code>18\n</code></pre> <pre><code>loss.gradient(5, -4)\n</code></pre> <pre><code>-18\n</code></pre></p>"},{"location":"api/optim/losses/Squared/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/schedulers/Constant/","title":"Constant","text":"<p>Always uses the same learning rate.</p>"},{"location":"api/optim/schedulers/Constant/#parameters","title":"Parameters","text":"<ul> <li> <p>learning_rate</p> <p>Type \u2192 int | float</p> </li> </ul>"},{"location":"api/optim/schedulers/Constant/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/optim/schedulers/InverseScaling/","title":"InverseScaling","text":"<p>Reduces the learning rate using a power schedule.</p> <p>Assuming an initial learning rate \\(\\eta\\), the learning rate at step \\(t\\) is: </p> \\[\\\\frac{eta}{(t + 1) ^ p}\\] <p>where \\(p\\) is a user-defined parameter.</p>"},{"location":"api/optim/schedulers/InverseScaling/#parameters","title":"Parameters","text":"<ul> <li> <p>learning_rate</p> <p>Type \u2192 float</p> </li> <li> <p>power</p> <p>Default \u2192 <code>0.5</code></p> </li> </ul>"},{"location":"api/optim/schedulers/InverseScaling/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/optim/schedulers/Optimal/","title":"Optimal","text":"<p>Optimal learning schedule as proposed by L\u00e9on Bottou.</p>"},{"location":"api/optim/schedulers/Optimal/#parameters","title":"Parameters","text":"<ul> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss</p> </li> <li> <p>alpha</p> <p>Default \u2192 <code>0.0001</code></p> </li> </ul>"},{"location":"api/optim/schedulers/Optimal/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Bottou, L., 2012. Stochastic gradient descent tricks. In Neural networks: Tricks of the trade (pp. 421-436). Springer, Berlin, Heidelberg. \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/AdaptiveStandardScaler/","title":"AdaptiveStandardScaler","text":"<p>Scales data using exponentially weighted moving average and variance.</p> <p>Under the hood, a exponentially weighted running mean and variance are maintained for each feature. This can potentially provide better results for drifting data in comparison to <code>preprocessing.StandardScaler</code>. Indeed, the latter computes a global mean and variance for each feature, whereas this scaler weights data in proportion to their recency.</p>"},{"location":"api/preprocessing/AdaptiveStandardScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.3</code></p> <p>This parameter is passed to <code>stats.EWVar</code>. It is expected to be in [0, 1]. More weight is assigned to recent samples the closer <code>fading_factor</code> is to 1.</p> </li> </ul>"},{"location":"api/preprocessing/AdaptiveStandardScaler/#examples","title":"Examples","text":"<p>Consider the following series which contains a positive trend.</p> <p><pre><code>import random\n\nrandom.seed(42)\nX = [\n    {'x': random.uniform(4 + i, 6 + i)}\n    for i in range(8)\n]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 5.278}\n{'x': 5.050}\n{'x': 6.550}\n{'x': 7.446}\n{'x': 9.472}\n{'x': 10.353}\n{'x': 11.784}\n{'x': 11.173}\n</code></pre></p> <p>This scaler works well with this kind of data because it uses statistics that assign higher weight to more recent data.</p> <p><pre><code>from river import preprocessing\n\nscaler = preprocessing.AdaptiveStandardScaler(fading_factor=.6)\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 0.0}\n{'x': -0.816}\n{'x': 0.812}\n{'x': 0.695}\n{'x': 0.754}\n{'x': 0.598}\n{'x': 0.651}\n{'x': 0.124}\n</code></pre></p>"},{"location":"api/preprocessing/AdaptiveStandardScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/Binarizer/","title":"Binarizer","text":"<p>Binarizes the data to 0 or 1 according to a threshold.</p>"},{"location":"api/preprocessing/Binarizer/#parameters","title":"Parameters","text":"<ul> <li> <p>threshold</p> <p>Default \u2192 <code>0.0</code></p> <p>Values above this are replaced by 1 and the others by 0.</p> </li> <li> <p>dtype</p> <p>Default \u2192 <code>&lt;class 'bool'&gt;</code></p> <p>The desired data type to apply.</p> </li> </ul>"},{"location":"api/preprocessing/Binarizer/#examples","title":"Examples","text":"<p><pre><code>import river\nimport numpy as np\n\nrng = np.random.RandomState(42)\nX = [{'x1': v, 'x2': int(v)} for v in rng.uniform(low=-4, high=4, size=6)]\n\nbinarizer = river.preprocessing.Binarizer()\nfor x in X:\n    binarizer.learn_one(x)\n    print(binarizer.transform_one(x))\n</code></pre> <pre><code>{'x1': False, 'x2': False}\n{'x1': True, 'x2': True}\n{'x1': True, 'x2': True}\n{'x1': True, 'x2': False}\n{'x1': False, 'x2': False}\n{'x1': False, 'x2': False}\n</code></pre></p>"},{"location":"api/preprocessing/Binarizer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/FeatureHasher/","title":"FeatureHasher","text":"<p>Implements the hashing trick.</p> <p>Each pair of (name, value) features is hashed into a random integer. A module operator is then used to make sure the hash is in a certain range. We use the Murmurhash implementation from scikit-learn.</p>"},{"location":"api/preprocessing/FeatureHasher/#parameters","title":"Parameters","text":"<ul> <li> <p>n_features</p> <p>Default \u2192 <code>1048576</code></p> <p>The number by which each hash will be moduloed by.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Set the seed to produce identical results.</p> </li> </ul>"},{"location":"api/preprocessing/FeatureHasher/#examples","title":"Examples","text":"<p><pre><code>import river\n\nhasher = river.preprocessing.FeatureHasher(n_features=10, seed=42)\n\nX = [\n    {'dog': 1, 'cat': 2, 'elephant': 4},\n    {'dog': 2, 'run': 5}\n]\nfor x in X:\n    print(hasher.transform_one(x))\n</code></pre> <pre><code>Counter({1: 4, 9: 2, 8: 1})\nCounter({4: 5, 8: 2})\n</code></pre></p>"},{"location":"api/preprocessing/FeatureHasher/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Wikipedia article on feature vectorization using the hashing trick \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/GaussianRandomProjector/","title":"GaussianRandomProjector","text":"<p>Gaussian random projector.</p> <p>This transformer reduces the dimensionality of inputs through Gaussian random projection. </p> <p>The components of the random projections matrix are drawn from <code>N(0, 1 / n_components)</code>.</p>"},{"location":"api/preprocessing/GaussianRandomProjector/#parameters","title":"Parameters","text":"<ul> <li> <p>n_components</p> <p>Default \u2192 <code>10</code></p> <p>Number of components to project the data onto.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/preprocessing/GaussianRandomProjector/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = preprocessing.GaussianRandomProjector(\n    n_components=3,\n    seed=42\n)\n\nfor x, y in dataset:\n    x = model.transform_one(x)\n    print(x)\n    break\n</code></pre> <pre><code>{0: -61289.371..., 1: 141312.510..., 2: 279165.993...}\n</code></pre></p> <p><pre><code>model = (\n    preprocessing.GaussianRandomProjector(\n        n_components=5,\n        seed=42\n    ) |\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression()\n)\nevaluate.progressive_val_score(dataset, model, metrics.MAE())\n</code></pre> <pre><code>MAE: 0.933...\n</code></pre></p>"},{"location":"api/preprocessing/GaussianRandomProjector/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Gaussian random projection \u21a9</p> </li> <li> <p>scikit-learn random projections module \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/LDA/","title":"LDA","text":"<p>Online Latent Dirichlet Allocation with Infinite Vocabulary.</p> <p>Latent Dirichlet allocation (LDA) is a probabilistic approach for exploring topics in document collections. The key advantage of this variant is that it assumes an infinite vocabulary, meaning that the set of tokens does not have to known in advance, as opposed to the implementation from sklearn The results produced by this implementation are identical to those from the original implementation proposed by the method's authors. </p> <p>This class takes as input token counts. Therefore, it requires you to tokenize beforehand. You can do so by using a <code>feature_extraction.BagOfWords</code> instance, as shown in the example below.</p>"},{"location":"api/preprocessing/LDA/#parameters","title":"Parameters","text":"<ul> <li> <p>n_components</p> <p>Default \u2192 <code>10</code></p> <p>Number of topics of the latent Drichlet allocation.</p> </li> <li> <p>number_of_documents</p> <p>Default \u2192 <code>1000000.0</code></p> <p>Estimated number of documents.</p> </li> <li> <p>alpha_theta</p> <p>Default \u2192 <code>0.5</code></p> <p>Hyper-parameter of the Dirichlet distribution of topics.</p> </li> <li> <p>alpha_beta</p> <p>Default \u2192 <code>100.0</code></p> <p>Hyper-parameter of the Dirichlet process of distribution over words.</p> </li> <li> <p>tau</p> <p>Default \u2192 <code>64.0</code></p> <p>Learning inertia to prevent premature convergence.</p> </li> <li> <p>kappa</p> <p>Default \u2192 <code>0.75</code></p> <p>The learning rate kappa controls how quickly new parameters estimates replace the old ones. kappa \u2208 (0.5, 1] is required for convergence.</p> </li> <li> <p>vocab_prune_interval</p> <p>Default \u2192 <code>10</code></p> <p>Interval at which to refresh the words topics distribution.</p> </li> <li> <p>number_of_samples</p> <p>Default \u2192 <code>10</code></p> <p>Number of iteration to computes documents topics distribution.</p> </li> <li> <p>ranking_smooth_factor</p> <p>Default \u2192 <code>1e-12</code></p> </li> <li> <p>burn_in_sweeps</p> <p>Default \u2192 <code>5</code></p> <p>Number of iteration necessaries while analyzing a document before updating document topics distribution.</p> </li> <li> <p>maximum_size_vocabulary</p> <p>Default \u2192 <code>4000</code></p> <p>Maximum size of the stored vocabulary.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number seed used for reproducibility.</p> </li> </ul>"},{"location":"api/preprocessing/LDA/#attributes","title":"Attributes","text":"<ul> <li> <p>counter (int)</p> <p>The current number of observed documents.</p> </li> <li> <p>truncation_size_prime (int)</p> <p>Number of distincts words stored in the vocabulary. Updated before processing a document.</p> </li> <li> <p>truncation_size (int)</p> <p>Number of distincts words stored in the vocabulary. Updated after processing a document.</p> </li> <li> <p>word_to_index (dict)</p> <p>Words as keys and indexes as values.</p> </li> <li> <p>index_to_word (dict)</p> <p>Indexes as keys and words as values.</p> </li> <li> <p>nu_1 (dict)</p> <p>Weights of the words. Component of the variational inference.</p> </li> <li> <p>nu_2 (dict)</p> <p>Weights of the words. Component of the variational inference.</p> </li> </ul>"},{"location":"api/preprocessing/LDA/#examples","title":"Examples","text":"<p><pre><code>from river import compose\nfrom river import feature_extraction\nfrom river import preprocessing\n\nX = [\n   'weather cold',\n   'weather hot dry',\n   'weather cold rainy',\n   'weather hot',\n   'weather cold humid',\n]\n\nlda = compose.Pipeline(\n    feature_extraction.BagOfWords(),\n    preprocessing.LDA(\n        n_components=2,\n        number_of_documents=60,\n        seed=42\n    )\n)\n\nfor x in X:\n    lda.learn_one(x)\n    topics = lda.transform_one(x)\n    print(topics)\n</code></pre> <pre><code>{0: 0.5, 1: 2.5}\n{0: 2.499..., 1: 1.5}\n{0: 0.5, 1: 3.5}\n{0: 0.5, 1: 2.5}\n{0: 1.5, 1: 2.5}\n</code></pre></p>"},{"location":"api/preprocessing/LDA/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> learn_transform_one <p>Equivalent to <code>lda.learn_one(x).transform_one(x)</code>s, but faster.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     Component attributions for the input document.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Zhai, K. and Boyd-Graber, J., 2013, February. Online latent Dirichlet allocation with infinite vocabulary. In International Conference on Machine Learning (pp. 561-569). \u21a9</p> </li> <li> <p>PyInfVoc on GitHub \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/MaxAbsScaler/","title":"MaxAbsScaler","text":"<p>Scales the data to a [-1, 1] range based on absolute maximum.</p> <p>Under the hood a running absolute max is maintained. This scaler is meant for data that is already centered at zero or sparse data. It does not shift/center the data, and thus does not destroy any sparsity.</p>"},{"location":"api/preprocessing/MaxAbsScaler/#attributes","title":"Attributes","text":"<ul> <li> <p>abs_max (dict)</p> <p>Mapping between features and instances of <code>stats.AbsMax</code>.</p> </li> </ul>"},{"location":"api/preprocessing/MaxAbsScaler/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12)} for _ in range(5)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 10.557707}\n{'x': 8.100043}\n{'x': 9.100117}\n{'x': 8.892842}\n{'x': 10.945884}\n</code></pre></p> <p><pre><code>scaler = preprocessing.MaxAbsScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 1.0}\n{'x': 0.767216}\n{'x': 0.861940}\n{'x': 0.842308}\n{'x': 1.0}\n</code></pre></p>"},{"location":"api/preprocessing/MaxAbsScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/MinMaxScaler/","title":"MinMaxScaler","text":"<p>Scales the data to a fixed range from 0 to 1.</p> <p>Under the hood a running min and a running peak to peak (max - min) are maintained.</p>"},{"location":"api/preprocessing/MinMaxScaler/#attributes","title":"Attributes","text":"<ul> <li> <p>min (dict)</p> <p>Mapping between features and instances of <code>stats.Min</code>.</p> </li> <li> <p>max (dict)</p> <p>Mapping between features and instances of <code>stats.Max</code>.</p> </li> </ul>"},{"location":"api/preprocessing/MinMaxScaler/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12)} for _ in range(5)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 10.557707}\n{'x': 8.100043}\n{'x': 9.100117}\n{'x': 8.892842}\n{'x': 10.945884}\n</code></pre></p> <p><pre><code>scaler = preprocessing.MinMaxScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 0.0}\n{'x': 0.0}\n{'x': 0.406920}\n{'x': 0.322582}\n{'x': 1.0}\n</code></pre></p>"},{"location":"api/preprocessing/MinMaxScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/Normalizer/","title":"Normalizer","text":"<p>Scales a set of features so that it has unit norm.</p> <p>This is particularly useful when used after a <code>feature_extraction.TFIDF</code>.</p>"},{"location":"api/preprocessing/Normalizer/#parameters","title":"Parameters","text":"<ul> <li> <p>order</p> <p>Default \u2192 <code>2</code></p> <p>Order of the norm (e.g. 2 corresponds to the \\(L^2\\) norm).</p> </li> </ul>"},{"location":"api/preprocessing/Normalizer/#examples","title":"Examples","text":"<p><pre><code>from river import preprocessing\nfrom river import stream\n\nscaler = preprocessing.Normalizer(order=2)\n\nX = [[4, 1, 2, 2],\n     [1, 3, 9, 3],\n     [5, 7, 5, 1]]\n\nfor x, _ in stream.iter_array(X):\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{0: 0.8, 1: 0.2, 2: 0.4, 3: 0.4}\n{0: 0.1, 1: 0.3, 2: 0.9, 3: 0.3}\n{0: 0.5, 1: 0.7, 2: 0.5, 3: 0.1}\n</code></pre></p>"},{"location":"api/preprocessing/Normalizer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/OneHotEncoder/","title":"OneHotEncoder","text":"<p>One-hot encoding.</p> <p>This transformer will encode every feature it is provided with. If a list or set is provided, this transformer will encode every entry in the list/set. You can apply it to a subset of features by composing it  with <code>compose.Select</code> or <code>compose.SelectType</code>.</p>"},{"location":"api/preprocessing/OneHotEncoder/#parameters","title":"Parameters","text":"<ul> <li> <p>drop_zeros</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not 0s should be made explicit or not.</p> </li> <li> <p>drop_first</p> <p>Default \u2192 <code>False</code></p> <p>Whether to get <code>k - 1</code> dummies out of <code>k</code> categorical levels by removing the first key. This is useful in some statistical models where perfectly collinear features cause problems.</p> </li> </ul>"},{"location":"api/preprocessing/OneHotEncoder/#examples","title":"Examples","text":"<p>Let us first create an example dataset.</p> <p><pre><code>from pprint import pprint\nimport random\nimport string\n\nrandom.seed(42)\nalphabet = list(string.ascii_lowercase)\nX = [\n    {\n        'c1': random.choice(alphabet),\n        'c2': random.choice(alphabet),\n    }\n    for _ in range(4)\n]\npprint(X)\n</code></pre> <pre><code>[{'c1': 'u', 'c2': 'd'},\n    {'c1': 'a', 'c2': 'x'},\n    {'c1': 'i', 'c2': 'h'},\n    {'c1': 'h', 'c2': 'e'}]\n</code></pre></p> <p>e can now apply one-hot encoding. All the provided are one-hot encoded, there is therefore no need to specify which features to encode.</p> <p><pre><code>from river import preprocessing\n\noh = preprocessing.OneHotEncoder()\nfor x in X[:2]:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1, 'c2_d': 1}\n{'c1_a': 1, 'c1_u': 0, 'c2_d': 0, 'c2_x': 1}\n</code></pre></p> <p>The <code>drop_zeros</code> parameter can be set to <code>True</code> if you don't want the past features to be included in the output. Otherwise, all the past features will be included in the output.</p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=True)\nfor x in X:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1, 'c2_d': 1}\n{'c1_a': 1, 'c2_x': 1}\n{'c1_i': 1, 'c2_h': 1}\n{'c1_h': 1, 'c2_e': 1}\n</code></pre></p> <p>You can encode only <code>k - 1</code> features out of <code>k</code> by setting <code>drop_first</code> to <code>True</code>.</p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_first=True, drop_zeros=True)\nfor x in X:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c2_d': 1}\n{'c2_x': 1}\n{'c2_h': 1}\n{'c2_e': 1}\n</code></pre></p> <p>A subset of the features can be one-hot encoded by piping a <code>compose.Select</code> into the <code>OneHotEncoder</code>.</p> <p><pre><code>from river import compose\n\npp = compose.Select('c1') | preprocessing.OneHotEncoder()\n\nfor x in X:\n    pp.learn_one(x)\n    pprint(pp.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1}\n{'c1_a': 1, 'c1_u': 0}\n{'c1_a': 0, 'c1_i': 1, 'c1_u': 0}\n{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0}\n</code></pre></p> <p>You can preserve the <code>c2</code> feature by using a union:</p> <p><pre><code>pp = compose.Select('c1') | preprocessing.OneHotEncoder()\npp += compose.Select('c2')\n\nfor x in X:\n    pp.learn_one(x)\n    pprint(pp.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1, 'c2': 'd'}\n{'c1_a': 1, 'c1_u': 0, 'c2': 'x'}\n{'c1_a': 0, 'c1_i': 1, 'c1_u': 0, 'c2': 'h'}\n{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0, 'c2': 'e'}\n</code></pre></p> <p>Similar to the above examples, we can also pass values as a list. This will one-hot encode all of the entries individually.</p> <p><pre><code>X = [{'c1': ['u', 'a'], 'c2': ['d']},\n    {'c1': ['a', 'b'], 'c2': ['x']},\n    {'c1': ['i'], 'c2': ['h', 'z']},\n    {'c1': ['h', 'b'], 'c2': ['e']}]\n\noh = preprocessing.OneHotEncoder(drop_zeros=True)\nfor x in X:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c1_a': 1, 'c1_u': 1, 'c2_d': 1}\n{'c1_a': 1, 'c1_b': 1, 'c2_x': 1}\n{'c1_i': 1, 'c2_h': 1, 'c2_z': 1}\n{'c1_b': 1, 'c1_h': 1, 'c2_e': 1}\n</code></pre></p> <p>Processing mini-batches is also possible.</p> <p><pre><code>from pprint import pprint\nimport random\nimport string\n\nrandom.seed(42)\nalphabet = list(string.ascii_lowercase)\nX = pd.DataFrame(\n    {\n        'c1': random.choice(alphabet),\n        'c2': random.choice(alphabet),\n    }\n    for _ in range(3)\n)\nX\n</code></pre> <pre><code>  c1 c2\n0  u  d\n1  a  x\n2  i  h\n</code></pre></p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=True)\ndf = oh.transform_many(X)\ndf.sort_index(axis=\"columns\")\n</code></pre> <pre><code>   c1_a  c1_i  c1_u  c2_d  c2_h  c2_x\n0     0     0     1     1     0     0\n1     1     0     0     0     0     1\n2     0     1     0     0     1     0\n</code></pre></p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=True, drop_first=True)\ndf = oh.transform_many(X)\ndf.sort_index(axis=\"columns\")\n</code></pre> <pre><code>   c1_i  c1_u  c2_d  c2_h  c2_x\n0     0     1     1     0     0\n1     0     0     0     0     1\n2     1     0     0     1     0\n</code></pre></p> <p>Here's an example where the zeros are kept:</p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=False)\nX_init = pd.DataFrame([{\"c1\": \"Oranges\", \"c2\": \"Apples\"}])\noh.learn_many(X_init)\noh.learn_many(X)\n\ndf = oh.transform_many(X)\ndf.sort_index(axis=\"columns\")\n</code></pre> <pre><code>   c1_Oranges  c1_a  c1_i  c1_u  c2_Apples  c2_d  c2_h  c2_x\n0           0     0     0     1          0     1     0     0\n1           0     1     0     0          0     0     0     1\n2           0     0     1     0          0     0     1     0\n</code></pre></p> <p><pre><code>df.dtypes.sort_index()\n</code></pre> <pre><code>c1_Oranges    Sparse[uint8, 0]\nc1_a          Sparse[uint8, 0]\nc1_i          Sparse[uint8, 0]\nc1_u          Sparse[uint8, 0]\nc2_Apples     Sparse[uint8, 0]\nc2_d          Sparse[uint8, 0]\nc2_h          Sparse[uint8, 0]\nc2_x          Sparse[uint8, 0]\ndtype: object\n</code></pre></p>"},{"location":"api/preprocessing/OneHotEncoder/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/OrdinalEncoder/","title":"OrdinalEncoder","text":"<p>Ordinal encoder.</p> <p>This transformer maps each feature to integers. It can useful when a feature has string values (i.e. categorical variables).</p>"},{"location":"api/preprocessing/OrdinalEncoder/#parameters","title":"Parameters","text":"<ul> <li> <p>unknown_value</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>0</code></p> <p>The value to use for unknown categories seen during <code>transform_one</code>. Unknown categories will be mapped to an integer once they are seen during <code>learn_one</code>. This value can be set to <code>None</code> in order to categories to <code>None</code> if they've never been seen before.</p> </li> <li> <p>none_value</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>-1</code></p> <p>The value to encode <code>None</code> with.</p> </li> </ul>"},{"location":"api/preprocessing/OrdinalEncoder/#attributes","title":"Attributes","text":"<ul> <li> <p>categories</p> <p>A dict of dicts. The outer dict maps each feature to its inner dict. The inner dict maps each category to its code.</p> </li> </ul>"},{"location":"api/preprocessing/OrdinalEncoder/#examples","title":"Examples","text":"<p><pre><code>from river import preprocessing\n\nX = [\n    {\"country\": \"France\", \"place\": \"Taco Bell\"},\n    {\"country\": None, \"place\": None},\n    {\"country\": \"Sweden\", \"place\": \"Burger King\"},\n    {\"country\": \"France\", \"place\": \"Burger King\"},\n    {\"country\": \"Russia\", \"place\": \"Starbucks\"},\n    {\"country\": \"Russia\", \"place\": \"Starbucks\"},\n    {\"country\": \"Sweden\", \"place\": \"Taco Bell\"},\n    {\"country\": None, \"place\": None},\n]\n\nencoder = preprocessing.OrdinalEncoder()\nfor x in X:\n    print(encoder.transform_one(x))\n    encoder.learn_one(x)\n</code></pre> <pre><code>{'country': 0, 'place': 0}\n{'country': -1, 'place': -1}\n{'country': 0, 'place': 0}\n{'country': 1, 'place': 2}\n{'country': 0, 'place': 0}\n{'country': 3, 'place': 3}\n{'country': 2, 'place': 1}\n{'country': -1, 'place': -1}\n</code></pre></p> <p><pre><code>xb1 = pd.DataFrame(X[0:4], index=[0, 1, 2, 3])\nxb2 = pd.DataFrame(X[4:8], index=[4, 5, 6, 7])\n\nencoder = preprocessing.OrdinalEncoder()\nencoder.transform_many(xb1)\n</code></pre> <pre><code>   country  place\n0        0      0\n1       -1     -1\n2        0      0\n3        0      0\n</code></pre></p> <p><pre><code>encoder.learn_many(xb1)\nencoder.transform_many(xb2)\n</code></pre> <pre><code>   country  place\n4        0      0\n5        0      0\n6        2      1\n7       -1     -1\n</code></pre></p>"},{"location":"api/preprocessing/OrdinalEncoder/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/PredClipper/","title":"PredClipper","text":"<p>Clips the target after predicting.</p>"},{"location":"api/preprocessing/PredClipper/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regressor model for which to clip the predictions.</p> </li> <li> <p>y_min</p> <p>Type \u2192 float</p> <p>minimum value.</p> </li> <li> <p>y_max</p> <p>Type \u2192 float</p> <p>maximum value.</p> </li> </ul>"},{"location":"api/preprocessing/PredClipper/#examples","title":"Examples","text":"<p><pre><code>from river import linear_model\nfrom river import preprocessing\n\ndataset = (\n    ({'a': 2, 'b': 4}, 80),\n    ({'a': 3, 'b': 5}, 100),\n    ({'a': 4, 'b': 6}, 120)\n)\n\nmodel = preprocessing.PredClipper(\n    regressor=linear_model.LinearRegression(),\n    y_min=0,\n    y_max=200\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'a': -100, 'b': -200})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>model.predict_one({'a': 50, 'b': 60})\n</code></pre> <pre><code>200\n</code></pre></p>"},{"location":"api/preprocessing/PredClipper/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/preprocessing/PreviousImputer/","title":"PreviousImputer","text":"<p>Imputes missing values by using the most recent value.</p>"},{"location":"api/preprocessing/PreviousImputer/#examples","title":"Examples","text":"<p><pre><code>from river import preprocessing\n\nimputer = preprocessing.PreviousImputer()\n\nimputer.learn_one({'x': 1, 'y': 2})\nimputer.transform_one({'y': None})\n</code></pre> <pre><code>{'y': 2}\n</code></pre></p> <p><pre><code>imputer.transform_one({'x': None})\n</code></pre> <pre><code>{'x': 1}\n</code></pre></p>"},{"location":"api/preprocessing/PreviousImputer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/RobustScaler/","title":"RobustScaler","text":"<p>Scale features using statistics that are robust to outliers.</p> <p>This Scaler removes the median and scales the data according to the interquantile range.</p>"},{"location":"api/preprocessing/RobustScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>with_centering</p> <p>Default \u2192 <code>True</code></p> <p>Whether to centre the data before scaling.</p> </li> <li> <p>with_scaling</p> <p>Default \u2192 <code>True</code></p> <p>Whether to scale data to IQR.</p> </li> <li> <p>q_inf</p> <p>Default \u2192 <code>0.25</code></p> <p>Desired inferior quantile, must be between 0 and 1.</p> </li> <li> <p>q_sup</p> <p>Default \u2192 <code>0.75</code></p> <p>Desired superior quantile, must be between 0 and 1.</p> </li> </ul>"},{"location":"api/preprocessing/RobustScaler/#attributes","title":"Attributes","text":"<ul> <li> <p>median (dict)</p> <p>Mapping between features and instances of <code>stats.Quantile</code>(0.5)`.</p> </li> <li> <p>iqr (dict)</p> <p>Mapping between features and instances of <code>stats.IQR</code>.</p> </li> </ul>"},{"location":"api/preprocessing/RobustScaler/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nimport random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12)} for _ in range(5)]\npprint(X)\n</code></pre> <pre><code>[{'x': 10.557707},\n    {'x': 8.100043},\n    {'x': 9.100117},\n    {'x': 8.892842},\n    {'x': 10.945884}]\n</code></pre></p> <p><pre><code>scaler = preprocessing.RobustScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>    {'x': 0.0}\n    {'x': -1.0}\n    {'x': 0.0}\n    {'x': -0.12449923287875722}\n    {'x': 1.1086595155704708}\n</code></pre></p>"},{"location":"api/preprocessing/RobustScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/SparseRandomProjector/","title":"SparseRandomProjector","text":"<p>Sparse random projector.</p> <p>This transformer reduces the dimensionality of inputs by projecting them onto a sparse random projection matrix. </p> <p>Ping Li et al. recommend using a minimum density of <code>1 / sqrt(n_features)</code>. The transformer is not aware of how many features will be seen, so the user must specify the density manually.</p>"},{"location":"api/preprocessing/SparseRandomProjector/#parameters","title":"Parameters","text":"<ul> <li> <p>n_components</p> <p>Default \u2192 <code>10</code></p> <p>Number of components to project the data onto.</p> </li> <li> <p>density</p> <p>Default \u2192 <code>0.1</code></p> <p>Density of the random projection matrix. The density is defined as the ratio of non-zero components in the matrix. It is equal to <code>1 - sparsity</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/preprocessing/SparseRandomProjector/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = preprocessing.SparseRandomProjector(\n    n_components=3,\n    seed=42\n)\n\nfor x, y in dataset:\n    x = model.transform_one(x)\n    print(x)\n    break\n</code></pre> <pre><code>{0: 92.89572746525327, 1: 1344540.5692342375, 2: 0}\n</code></pre></p> <p><pre><code>model = (\n    preprocessing.SparseRandomProjector(\n        n_components=5,\n        seed=42\n    ) |\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression()\n)\nevaluate.progressive_val_score(dataset, model, metrics.MAE())\n</code></pre> <pre><code>MAE: 1.292572\n</code></pre></p>"},{"location":"api/preprocessing/SparseRandomProjector/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>D. Achlioptas. 2003. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences 66 (2003) 671-687\u00a0\u21a9</p> </li> <li> <p>Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006. Very sparse random projections. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'06). ACM, New York, NY, USA, 287-296.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/preprocessing/StandardScaler/","title":"StandardScaler","text":"<p>Scales the data so that it has zero mean and unit variance.</p> <p>Under the hood, a running mean and a running variance are maintained. The scaling is slightly different than when scaling the data in batch because the exact means and variances are not known in advance. However, this doesn't have a detrimental impact on performance in the long run. </p> <p>This transformer supports mini-batches as well as single instances. In the mini-batch case, the number of columns and the ordering of the columns are allowed to change between subsequent calls. In other words, this transformer will keep working even if you add and/or remove features every time you call <code>learn_many</code> and <code>transform_many</code>.</p>"},{"location":"api/preprocessing/StandardScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>with_std</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not each feature should be divided by its standard deviation.</p> </li> </ul>"},{"location":"api/preprocessing/StandardScaler/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12), 'y': random.uniform(8, 12)} for _ in range(6)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 10.557, 'y': 8.100}\n{'x': 9.100, 'y': 8.892}\n{'x': 10.945, 'y': 10.706}\n{'x': 11.568, 'y': 8.347}\n{'x': 9.687, 'y': 8.119}\n{'x': 8.874, 'y': 10.021}\n</code></pre></p> <p><pre><code>scaler = preprocessing.StandardScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 0.0, 'y': 0.0}\n{'x': -0.999, 'y': 0.999}\n{'x': 0.937, 'y': 1.350}\n{'x': 1.129, 'y': -0.651}\n{'x': -0.776, 'y': -0.729}\n{'x': -1.274, 'y': 0.992}\n</code></pre></p> <p>This transformer also supports mini-batch updates. You can call <code>learn_many</code> and provide a <code>pandas.DataFrame</code>:</p> <pre><code>import pandas as pd\nX = pd.DataFrame.from_dict(X)\n\nscaler = preprocessing.StandardScaler()\nscaler.learn_many(X[:3])\nscaler.learn_many(X[3:])\n</code></pre> <p>You can then call <code>transform_many</code> to scale a mini-batch of features:</p> <p><pre><code>scaler.transform_many(X)\n</code></pre> <pre><code>    x         y\n0  0.444600 -0.933384\n1 -1.044259 -0.138809\n2  0.841106  1.679208\n3  1.477301 -0.685117\n4 -0.444084 -0.914195\n5 -1.274664  0.992296\n</code></pre></p>"},{"location":"api/preprocessing/StandardScaler/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>Note that the update formulas for mean and variance are slightly different than in the single instance case, but they produce exactly the same result.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Scale a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Welford's Method (and Friends) \u21a9</p> </li> <li> <p>Batch updates for simple statistics \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/StatImputer/","title":"StatImputer","text":"<p>Replaces missing values with a statistic.</p> <p>This transformer allows you to replace missing values with the value of a running statistic. During a call to <code>learn_one</code>, for each feature, a statistic is updated whenever a numeric feature is observed. When <code>transform_one</code> is called, each feature with a <code>None</code> value is replaced with the current value of the corresponding statistic.</p>"},{"location":"api/preprocessing/StatImputer/#parameters","title":"Parameters","text":"<ul> <li> <p>imputers</p> <p>A list of tuples where each tuple has two elements. The first elements is a feature name and the second value is an instance of <code>stats.base.Univariate</code>. The second value can also be an arbitrary value, such as -1, in which case the missing values will be replaced with it.</p> </li> </ul>"},{"location":"api/preprocessing/StatImputer/#examples","title":"Examples","text":"<pre><code>from river import preprocessing\nfrom river import stats\n</code></pre> <p>For numeric data, we can use a <code>stats.Mean</code>()` to replace missing values by the running average of the previously seen values:</p> <p><pre><code>X = [\n    {'temperature': 1},\n    {'temperature': 8},\n    {'temperature': 3},\n    {'temperature': None},\n    {'temperature': 4}\n]\n\nimp = preprocessing.StatImputer(('temperature', stats.Mean()))\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'temperature': 1}\n{'temperature': 8}\n{'temperature': 3}\n{'temperature': 4.0}\n{'temperature': 4}\n</code></pre></p> <p>For discrete/categorical data, a common practice is to <code>stats.Mode</code> to replace missing values by the most commonly seen value:</p> <p><pre><code>X = [\n    {'weather': 'sunny'},\n    {'weather': 'rainy'},\n    {'weather': 'sunny'},\n    {'weather': None},\n    {'weather': 'rainy'},\n    {'weather': 'rainy'},\n    {'weather': None}\n]\n\nimp = preprocessing.StatImputer(('weather', stats.Mode()))\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny'}\n{'weather': 'rainy'}\n{'weather': 'sunny'}\n{'weather': 'sunny'}\n{'weather': 'rainy'}\n{'weather': 'rainy'}\n{'weather': 'rainy'}\n</code></pre></p> <p>You can also choose to replace missing values with a constant value, as so:</p> <p><pre><code>imp = preprocessing.StatImputer(('weather', 'missing'))\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny'}\n{'weather': 'rainy'}\n{'weather': 'sunny'}\n{'weather': 'missing'}\n{'weather': 'rainy'}\n{'weather': 'rainy'}\n{'weather': 'missing'}\n</code></pre></p> <p>Multiple imputers can be defined by providing a tuple for each feature which you want to impute:</p> <p><pre><code>X = [\n    {'weather': 'sunny', 'temperature': 8},\n    {'weather': 'rainy', 'temperature': 3},\n    {'weather': 'sunny', 'temperature': None},\n    {'weather': None, 'temperature': 4},\n    {'weather': 'snowy', 'temperature': -4},\n    {'weather': 'snowy', 'temperature': -3},\n    {'weather': 'snowy', 'temperature': -3},\n    {'weather': None, 'temperature': None}\n]\n\nimp = preprocessing.StatImputer(\n    ('temperature', stats.Mean()),\n    ('weather', stats.Mode())\n)\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny', 'temperature': 8}\n{'weather': 'rainy', 'temperature': 3}\n{'weather': 'sunny', 'temperature': 5.5}\n{'weather': 'sunny', 'temperature': 4}\n{'weather': 'snowy', 'temperature': -4}\n{'weather': 'snowy', 'temperature': -3}\n{'weather': 'snowy', 'temperature': -3}\n{'weather': 'snowy', 'temperature': 0.8333}\n</code></pre></p> <p>A sophisticated way to go about imputation is condition the statistics on a given feature. For instance, we might want to replace a missing temperature with the average temperature of a particular weather condition. As an example, consider the following dataset where the temperature is missing, but not the weather condition:</p> <pre><code>X = [\n    {'weather': 'sunny', 'temperature': 8},\n    {'weather': 'rainy', 'temperature': 3},\n    {'weather': 'sunny', 'temperature': None},\n    {'weather': 'rainy', 'temperature': 4},\n    {'weather': 'sunny', 'temperature': 10},\n    {'weather': 'sunny', 'temperature': None},\n    {'weather': 'sunny', 'temperature': 12},\n    {'weather': 'rainy', 'temperature': None}\n]\n</code></pre> <p>Each missing temperature can be replaced with the average temperature of the corresponding weather condition as so:</p> <p><pre><code>from river import compose\n\nimp = compose.Grouper(\n    preprocessing.StatImputer(('temperature', stats.Mean())),\n    by='weather'\n)\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny', 'temperature': 8}\n{'weather': 'rainy', 'temperature': 3}\n{'weather': 'sunny', 'temperature': 8.0}\n{'weather': 'rainy', 'temperature': 4}\n{'weather': 'sunny', 'temperature': 10}\n{'weather': 'sunny', 'temperature': 9.0}\n{'weather': 'sunny', 'temperature': 12}\n{'weather': 'rainy', 'temperature': 3.5}\n</code></pre></p> <p>Note that you can also create a <code>Grouper</code> with the <code>*</code> operator:</p> <pre><code>imp = preprocessing.StatImputer(('temperature', stats.Mean())) * 'weather'\n</code></pre>"},{"location":"api/preprocessing/StatImputer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/TargetMinMaxScaler/","title":"TargetMinMaxScaler","text":"<p>Applies min-max scaling to the target.</p>"},{"location":"api/preprocessing/TargetMinMaxScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regression model to wrap.</p> </li> </ul>"},{"location":"api/preprocessing/TargetMinMaxScaler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    preprocessing.TargetMinMaxScaler(\n        regressor=linear_model.LinearRegression(intercept_lr=0.15)\n    )\n)\nmetric = metrics.MSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MSE: 2.018905\n</code></pre></p>"},{"location":"api/preprocessing/TargetMinMaxScaler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/preprocessing/TargetStandardScaler/","title":"TargetStandardScaler","text":"<p>Applies standard scaling to the target.</p>"},{"location":"api/preprocessing/TargetStandardScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regression model to wrap.</p> </li> </ul>"},{"location":"api/preprocessing/TargetStandardScaler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    preprocessing.TargetStandardScaler(\n        regressor=linear_model.LinearRegression(intercept_lr=0.15)\n    )\n)\nmetric = metrics.MSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MSE: 2.005999\n</code></pre></p>"},{"location":"api/preprocessing/TargetStandardScaler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/proba/Beta/","title":"Beta","text":"<p>Beta distribution for binary data.</p> <p>A Beta distribution is very similar to a Bernoulli distribution in that it counts occurrences of boolean events. The differences lies in what is being measured. A Binomial distribution models the probability of an event occurring, whereas a Beta distribution models the probability distribution itself. In other words, it's a probability distribution over probability distributions.</p>"},{"location":"api/proba/Beta/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Initial alpha parameter.</p> </li> <li> <p>beta</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Initial beta parameter.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/Beta/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/Beta/#examples","title":"Examples","text":"<p><pre><code>from river import proba\n\nsuccesses = 81\nfailures = 219\nbeta = proba.Beta(successes, failures)\n\nbeta(.21), beta(.35)\n</code></pre> <pre><code>(0.867..., 0.165...)\n</code></pre></p> <p><pre><code>for success in range(100):\n    beta.update(True)\nfor failure in range(200):\n    beta.update(False)\n\nbeta(.21), beta(.35)\n</code></pre> <pre><code>(2.525...e-05, 0.841...)\n</code></pre></p> <p><pre><code>beta.cdf(.35)\n</code></pre> <pre><code>0.994168...\n</code></pre></p>"},{"location":"api/proba/Beta/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>p     \u2014 'float' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>What is the intuition behind beta distribution? \u21a9</p> </li> </ol>"},{"location":"api/proba/Gaussian/","title":"Gaussian","text":"<p>Normal distribution with parameters mu and sigma.</p>"},{"location":"api/proba/Gaussian/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/Gaussian/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>mu</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> <li> <p>sigma</p> </li> </ul>"},{"location":"api/proba/Gaussian/#examples","title":"Examples","text":"<p><pre><code>from river import proba\n\np = proba.Gaussian()\np.update(6)\np.update(7)\n\np\n</code></pre> <pre><code>\ud835\udca9(\u03bc=6.500, \u03c3=0.707)\n</code></pre></p> <p><pre><code>p(6.5)\n</code></pre> <pre><code>0.564189\n</code></pre></p> <p><pre><code>p.revert(7)\np\n</code></pre> <pre><code>\ud835\udca9(\u03bc=6.000, \u03c3=0.000)\n</code></pre></p>"},{"location":"api/proba/Gaussian/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p>"},{"location":"api/proba/Multinomial/","title":"Multinomial","text":"<p>Multinomial distribution for categorical data.</p>"},{"location":"api/proba/Multinomial/#parameters","title":"Parameters","text":"<ul> <li> <p>events</p> <p>Type \u2192 dict | list | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional list of events that already occurred.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/Multinomial/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/Multinomial/#examples","title":"Examples","text":"<p><pre><code>from river import proba\n\np = proba.Multinomial(['green'] * 3)\np.update('red')\np('red')\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>p.update('red')\np.update('red')\np('green')\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>p.revert('red')\np.revert('red')\np('red')\n</code></pre> <pre><code>0.25\n</code></pre></p> <p>You can wrap this with a <code>utils.Rolling</code> to measure a distribution over a window:</p> <p><pre><code>from river import utils\n\nX = ['red', 'green', 'green', 'blue', 'blue']\n\ndist = utils.Rolling(\n    proba.Multinomial(),\n    window_size=3\n)\n\nfor x in X:\n    dist.update(x)\n    print(dist)\n    print()\n</code></pre> <pre><code>P(red) = 1.000\n&lt;BLANKLINE&gt;\nP(red) = 0.500\nP(green) = 0.500\n&lt;BLANKLINE&gt;\nP(green) = 0.667\nP(red) = 0.333\n&lt;BLANKLINE&gt;\nP(green) = 0.667\nP(blue) = 0.333\nP(red) = 0.000\n&lt;BLANKLINE&gt;\nP(blue) = 0.667\nP(green) = 0.333\nP(red) = 0.000\n&lt;BLANKLINE&gt;\n</code></pre></p> <p>You can wrap this with a <code>utils.Rolling</code> to measure a distribution over a window of time:</p> <p><pre><code>import datetime as dt\n\nX = ['red', 'green', 'green', 'blue']\ndays = [1, 2, 3, 4]\n\ndist = utils.TimeRolling(\n    proba.Multinomial(),\n    period=dt.timedelta(days=2)\n)\n\nfor x, day in zip(X, days):\n    dist.update(x, t=dt.datetime(2019, 1, day))\n    print(dist)\n    print()\n</code></pre> <pre><code>P(red) = 1.000\n&lt;BLANKLINE&gt;\nP(red) = 0.500\nP(green) = 0.500\n&lt;BLANKLINE&gt;\nP(green) = 1.000\nP(red) = 0.000\n&lt;BLANKLINE&gt;\nP(green) = 0.500\nP(blue) = 0.500\nP(red) = 0.000\n&lt;BLANKLINE&gt;\n</code></pre></p>"},{"location":"api/proba/Multinomial/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p>"},{"location":"api/proba/MultivariateGaussian/","title":"MultivariateGaussian","text":"<p>Multivariate normal distribution with parameters mu and var.</p>"},{"location":"api/proba/MultivariateGaussian/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/MultivariateGaussian/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>mu</p> <p>The mean value of the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> <li> <p>sigma</p> <p>The standard deviation of the distribution.</p> </li> <li> <p>var</p> <p>The variance of the distribution.</p> </li> </ul>"},{"location":"api/proba/MultivariateGaussian/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nimport pandas as pd\nfrom river import proba\n\nnp.random.seed(42)\nX = pd.DataFrame(\n    np.random.random((8, 3)),\n    columns=[\"red\", \"green\", \"blue\"]\n)\nX\n</code></pre> <pre><code>        red     green      blue\n0  0.374540  0.950714  0.731994\n1  0.598658  0.156019  0.155995\n2  0.058084  0.866176  0.601115\n3  0.708073  0.020584  0.969910\n4  0.832443  0.212339  0.181825\n5  0.183405  0.304242  0.524756\n6  0.431945  0.291229  0.611853\n7  0.139494  0.292145  0.366362\n</code></pre></p> <p><pre><code>p = proba.MultivariateGaussian(seed=42)\np.n_samples\n</code></pre> <pre><code>0.0\n</code></pre></p> <p><pre><code>for x in X.to_dict(orient=\"records\"):\n    p.update(x)\np.var\n</code></pre> <pre><code>           blue     green       red\nblue   0.076119  0.020292 -0.010128\ngreen  0.020292  0.112931 -0.053268\nred   -0.010128 -0.053268  0.078961\n</code></pre></p> <p>Retrieving current state in nice format is simple <pre><code>p\n</code></pre> <pre><code>\ud835\udca9(\n    \u03bc=(0.518, 0.387, 0.416),\n    \u03c3^2=(\n        [ 0.076  0.020 -0.010]\n        [ 0.020  0.113 -0.053]\n        [-0.010 -0.053  0.079]\n    )\n)\n</code></pre></p> <p>To retrieve number of samples and mode:</p> <p><pre><code>p.n_samples\n</code></pre> <pre><code>8.0\n</code></pre> <pre><code>p.mode\n</code></pre> <pre><code>{'blue': 0.5179..., 'green': 0.3866..., 'red': 0.4158...}\n</code></pre></p> <p>To retrieve the PDF and CDF:</p> <p><pre><code>p(x)\n</code></pre> <pre><code>0.97967...\n</code></pre> <pre><code>p.cdf(x)\n</code></pre> <pre><code>0.00787...\n</code></pre></p> <p>To sample data from distribution:</p> <p><pre><code>p.sample()\n</code></pre> <pre><code>{'blue': -0.179..., 'green': -0.051..., 'red': 0.376...}\n</code></pre></p> <p>MultivariateGaussian works with <code>utils.Rolling</code>:</p> <p><pre><code>from river import utils\n\np = utils.Rolling(MultivariateGaussian(), window_size=5)\nfor x in X.to_dict(orient=\"records\"):\n    p.update(x)\np.var\n</code></pre> <pre><code>           blue     green       red\nblue   0.087062 -0.022873  0.007765\ngreen -0.022873  0.014279 -0.025181\nred    0.007765 -0.025181  0.095066\n</code></pre></p> <p>MultivariateGaussian works with <code>utils.TimeRolling</code>:</p> <p><pre><code>from datetime import datetime as dt, timedelta as td\nX.index = [dt(2023, 3, 28, 0, 0, 0) + td(seconds=x) for x in range(8)]\np = utils.TimeRolling(MultivariateGaussian(), period=td(seconds=5))\nfor t, x in X.iterrows():\n    p.update(x.to_dict(), t=t)\np.var\n</code></pre> <pre><code>           blue     green       red\nblue   0.087062 -0.022873  0.007765\ngreen -0.022873  0.014279 -0.025181\nred    0.007765 -0.025181  0.095066\n</code></pre></p> <p>Variance on diagonal is consistent with <code>proba.Gaussian</code>.</p> <p><pre><code>multi = proba.MultivariateGaussian()\nsingle = proba.Gaussian()\nfor x in X.to_dict(orient='records'):\n    multi.update(x)\n    single.update(x['blue'])\nmulti.mu['blue'] == single.mu\n</code></pre> <pre><code>True\n</code></pre> <pre><code>multi.sigma['blue']['blue'] == single.sigma\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/proba/MultivariateGaussian/#methods","title":"Methods","text":"call <p>PDF(x) method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p>"},{"location":"api/proba/base/BinaryDistribution/","title":"BinaryDistribution","text":"<p>A probability distribution for discrete values.</p>"},{"location":"api/proba/base/BinaryDistribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/BinaryDistribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/BinaryDistribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p>"},{"location":"api/proba/base/ContinuousDistribution/","title":"ContinuousDistribution","text":"<p>A probability distribution for continuous values.</p>"},{"location":"api/proba/base/ContinuousDistribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/ContinuousDistribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/ContinuousDistribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/proba/base/DiscreteDistribution/","title":"DiscreteDistribution","text":"<p>A probability distribution for discrete values.</p>"},{"location":"api/proba/base/DiscreteDistribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/DiscreteDistribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/DiscreteDistribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p>"},{"location":"api/proba/base/Distribution/","title":"Distribution","text":"<p>General distribution.</p>"},{"location":"api/proba/base/Distribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/Distribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/Distribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p>"},{"location":"api/reco/Baseline/","title":"Baseline","text":"<p>Baseline for recommender systems.</p> <p>A first-order approximation of the bias involved in target. The model equation is defined as: </p> \\[\\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i}\\] <p>Where \\(bu_{u}\\) and \\(bi_{i}\\) are respectively the user and item biases. </p> <p>This model expects a dict input with a <code>user</code> and an <code>item</code> entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.</p>"},{"location":"api/reco/Baseline/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>regularization amount used to push weights towards 0.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/Baseline/#attributes","title":"Attributes","text":"<ul> <li> <p>global_mean (stats.Mean)</p> <p>The target arithmetic mean.</p> </li> <li> <p>u_biases (collections.defaultdict)</p> <p>The user bias weights.</p> </li> <li> <p>i_biases (collections.defaultdict)</p> <p>The item bias weights.</p> </li> <li> <p>u_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user bias weights.</p> </li> <li> <p>i_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item bias weights.</p> </li> </ul>"},{"location":"api/reco/Baseline/#examples","title":"Examples","text":"<p><pre><code>from river import optim\nfrom river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.Baseline(optimizer=optim.SGD(0.005))\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>6.538120\n</code></pre></p>"},{"location":"api/reco/Baseline/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Matrix factorization techniques for recommender systems \u21a9</p> </li> </ol>"},{"location":"api/reco/BiasedMF/","title":"BiasedMF","text":"<p>Biased Matrix Factorization for recommender systems.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i} + \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle\\] <p>Where \\(bu_{u}\\) and \\(bi_{i}\\) are respectively the user and item biases. The last term being simply the dot product between the latent vectors of the given user-item pair: </p> \\[\\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{u, f} \\cdot \\mathbf{v}_{i, f}\\] <p>where \\(k\\) is the number of latent factors. </p> <p>This model expects a dict input with a <code>user</code> and an <code>item</code> entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.</p>"},{"location":"api/reco/BiasedMF/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>bias_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the bias weights.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2_bias</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push bias weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/BiasedMF/#attributes","title":"Attributes","text":"<ul> <li> <p>global_mean (stats.Mean)</p> <p>The target arithmetic mean.</p> </li> <li> <p>u_biases (collections.defaultdict)</p> <p>The user bias weights.</p> </li> <li> <p>i_biases (collections.defaultdict)</p> <p>The item bias weights.</p> </li> <li> <p>u_latents (collections.defaultdict)</p> <p>The user latent vectors randomly initialized.</p> </li> <li> <p>i_latents (collections.defaultdict)</p> <p>The item latent vectors randomly initialized.</p> </li> <li> <p>u_bias_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user bias weights.</p> </li> <li> <p>i_bias_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item bias weights.</p> </li> <li> <p>u_latent_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user latent weights.</p> </li> <li> <p>i_latent_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item latent weights.</p> </li> </ul>"},{"location":"api/reco/BiasedMF/#examples","title":"Examples","text":"<p><pre><code>from river import optim\nfrom river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.BiasedMF(\n    n_factors=10,\n    bias_optimizer=optim.SGD(0.025),\n    latent_optimizer=optim.SGD(0.025),\n    latent_initializer=optim.initializers.Normal(mu=0., sigma=0.1, seed=71)\n)\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>6.489025\n</code></pre></p>"},{"location":"api/reco/BiasedMF/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Paterek, A., 2007, August. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD cup and workshop (Vol. 2007, pp. 5-8) \u21a9</p> </li> <li> <p>Matrix factorization techniques for recommender systems \u21a9</p> </li> </ol>"},{"location":"api/reco/FunkMF/","title":"FunkMF","text":"<p>Funk Matrix Factorization for recommender systems.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{u, f} \\cdot \\mathbf{v}_{i, f}\\] <p>where \\(k\\) is the number of latent factors. </p> <p>This model expects a dict input with a <code>user</code> and an <code>item</code> entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.</p>"},{"location":"api/reco/FunkMF/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/FunkMF/#attributes","title":"Attributes","text":"<ul> <li> <p>u_latents (collections.defaultdict)</p> <p>The user latent vectors randomly initialized.</p> </li> <li> <p>i_latents (collections.defaultdict)</p> <p>The item latent vectors randomly initialized.</p> </li> <li> <p>u_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user latent weights.</p> </li> <li> <p>i_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item latent weights.</p> </li> </ul>"},{"location":"api/reco/FunkMF/#examples","title":"Examples","text":"<p><pre><code>from river import optim\nfrom river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.FunkMF(\n    n_factors=10,\n    optimizer=optim.SGD(0.1),\n    initializer=optim.initializers.Normal(mu=0., sigma=0.1, seed=11),\n)\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>1.866272\n</code></pre></p>"},{"location":"api/reco/FunkMF/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Netflix update: Try this at home \u21a9</p> </li> <li> <p>Matrix factorization techniques for recommender systems \u21a9</p> </li> </ol>"},{"location":"api/reco/RandomNormal/","title":"RandomNormal","text":"<p>Predicts random values sampled from a normal distribution.</p> <p>The parameters of the normal distribution are fitted with running statistics. They parameters are independent of the user, the item, or the context, and are instead fitted globally. This recommender therefore acts as a dummy model that any serious model should easily outperform.</p>"},{"location":"api/reco/RandomNormal/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/RandomNormal/#attributes","title":"Attributes","text":"<ul> <li> <p>mean</p> <p>stats.Mean</p> </li> <li> <p>variance</p> <p>stats.Var</p> </li> </ul>"},{"location":"api/reco/RandomNormal/#examples","title":"Examples","text":"<p><pre><code>from river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.RandomNormal(seed=42)\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>6.147299621751425\n</code></pre></p>"},{"location":"api/reco/RandomNormal/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/reco/base/Ranker/","title":"Ranker","text":"<p>Base class for ranking models.</p>"},{"location":"api/reco/base/Ranker/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/base/Ranker/#attributes","title":"Attributes","text":"<ul> <li>is_contextual</li> </ul>"},{"location":"api/reco/base/Ranker/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/rules/AMRules/","title":"AMRules","text":"<p>Adaptive Model Rules.</p> <p>AMRules<sup>1</sup> is a rule-based algorithm for incremental regression tasks. AMRules relies on the Hoeffding bound to build its rule set, similarly to Hoeffding Trees. The Variance-Ratio heuristic is used to evaluate rules' splits. Moreover, this rule-based regressor has additional capacities not usually found in decision trees. </p> <p>Firstly, each created decision rule has a built-in drift detection mechanism. Every time a drift is detected, the affected decision rule is removed. In addition, AMRules' rules also have anomaly detection capabilities. After a warm-up period, each rule tests whether or not the incoming instances are anomalies. Anomalous instances are not used for training. </p> <p>Every time no rule is covering an incoming example, a default rule is used to learn from it. A rule covers an instance when all of the rule's literals (tests joined by the logical operation <code>and</code>) match the input case. The default rule is also applied for predicting examples not covered by any rules from the rule set.</p>"},{"location":"api/rules/AMRules/#parameters","title":"Parameters","text":"<ul> <li> <p>n_min</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>The total weight that must be observed by a rule between expansion attempts.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>The split test significance. The split confidence is given by <code>1 - delta</code>.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>The tie-breaking threshold.</p> </li> <li> <p>pred_type</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>The prediction strategy used by the decision rules. Can be either: - <code>\"mean\"</code>: outputs the target mean within the partitions defined by the decision rules. - <code>\"model\"</code>: always use instances of the model passed <code>pred_model</code> to make predictions. - <code>\"adaptive\"</code>: dynamically selects between \"mean\" and \"model\" for each incoming example. The most accurate option at the moment will be used.</p> </li> <li> <p>pred_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model that will be replicated for every rule when <code>pred_type</code> is either <code>\"model\"</code> or <code>\"adaptive\"</code>.</p> </li> <li> <p>splitter</p> <p>Type \u2192 spl.Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The drift detection model that is used by each rule. Care must be taken to avoid the triggering of too many false alarms or delaying too much the concept drift detection. By default, <code>drift.ADWIN</code> is used if <code>drift_detector</code> is <code>None</code>.</p> </li> <li> <p>fading_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>The exponential decaying factor applied to the learning models' absolute errors, that are monitored if <code>pred_type='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>anomaly_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>-0.75</code></p> <p>The threshold below which instances will be considered anomalies by the rules.</p> </li> <li> <p>m_min</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum total weight a rule must observe before it starts to skip anomalous instances during training.</p> </li> <li> <p>ordered_rule_set</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If <code>True</code>, only the first rule that covers an instance will be used for training or prediction. If <code>False</code>, all the rules covering an instance will be updated during training, and the predictions for an instance will be the average prediction of all rules covering that example.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples each partition of a binary split candidate must have to be considered valid.</p> </li> </ul>"},{"location":"api/rules/AMRules/#attributes","title":"Attributes","text":"<ul> <li> <p>n_drifts_detected</p> <p>The number of detected concept drifts.</p> </li> </ul>"},{"location":"api/rules/AMRules/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import drift\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing\nfrom river import rules\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    rules.AMRules(\n        delta=0.01,\n        n_min=50,\n        drift_detector=drift.ADWIN()\n    )\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.119553\n</code></pre></p>"},{"location":"api/rules/AMRules/#methods","title":"Methods","text":"anomaly_score <p>Aggregated anomaly score computed using all the rules that cover the input instance.</p> <p>Returns the mean anomaly score, the standard deviation of the score, and the proportion of rules that cover the instance (support). If the support is zero, it means that the default rule was used (not other rule covered <code>x</code>).</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>tuple[float, float, float]:     mean_anomaly_score, std_anomaly_score, support</p> <p></p> debug_one <p>Return an explanation of how <code>x</code> is predicted</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>str:     A representation of the rules that cover the input and their prediction.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 'int'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/rules/AMRules/#notes","title":"Notes","text":"<p>AMRules treats all the non-numerical inputs as nominal features. All instances of <code>numbers.Number</code> will be treated as continuous, even if they represent integer categories. When using nominal features, <code>pred_type</code> should be set to \"mean\", otherwise errors will be thrown while trying to update the underlying rules' prediction models. Prediction strategies other than \"mean\" can be used, as long as the prediction model passed to <code>pred_model</code> supports nominal features.</p> <ol> <li> <p>Duarte, J., Gama, J. and Bifet, A., 2016. Adaptive model rules from high-speed data streams. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(3), pp.1-22.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/sketch/Counter/","title":"Counter","text":"<p>Counting using the Count-Min Sketch (CMS) algorithm.</p> <p>Contrary to an exhaustive approach, e.g., using a <code>collections.Counter</code>, CMS uses a limited and fixed amount of memory. The CMS algorithm uses a sketch structure consisting of a matrix \\(w \\times d\\). </p> <p>These dimensions are obtained via: </p> <ul> <li> <p>\\(w = \\lceil \\frac{e}{\\epsilon} \\rceil\\), where \\(e\\) is the Euler number.</p> </li> <li> <p>\\(d = \\lceil \\ln\\left(\\frac{1}{\\delta} \\right) \\rceil\\).</p> </li> </ul> <p>Decreasing the values of \\(\\epsilon\\) (<code>epsilon</code>) and \\(\\delta\\) (<code>delta</code>) increase the accuracy of the algorithm, at the cost of increased memory usage. The values of <code>w</code> and <code>d</code> control the hash tables' capability and the amount of hash collisions, respectively. </p> <p>CMS works by keeping <code>d</code> hash tables with <code>w</code> slots each. Elements are mapped to a slot in each hash table. These tables store the counting estimates. This implementation assumes the turnstile case described in the paper, i.e., count values and updates can be negative. </p> <p>The count values obtained by CMS are always overestimates. Suppose \\(c_i\\) and \\(\\hat{c}_i\\) are the ground truth and estimated count values, respectively, for a given element \\(i\\). CMS guarantees that \\(c_i \\le \\hat{c}_i\\) and, with probability \\(1 - \\delta\\), \\(\\hat{c}_i \\le c_i + \\epsilon||\\mathbf{c}||_1\\). In the expression, \\(||\\mathbf{c}||_1 = \\sum_i |c_i|\\).</p>"},{"location":"api/sketch/Counter/#parameters","title":"Parameters","text":"<ul> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>The approximation error parameter. The error in answering a query is within a factor of <code>epsilon</code> with probability <code>delta</code>.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>A query estimates have a probability of <code>1 - delta</code> of having errors which are a factor of <code>epsilon</code>. See the CMS description above for more details.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/sketch/Counter/#attributes","title":"Attributes","text":"<ul> <li> <p>n_slots</p> <p>The number of slots in each hash table.</p> </li> <li> <p>n_tables</p> <p>The number of stored hash tables.</p> </li> </ul>"},{"location":"api/sketch/Counter/#examples","title":"Examples","text":"<pre><code>import collections\nfrom river import sketch\n\ncms = sketch.Counter(epsilon=0.005, seed=0)\n\nrng = random.Random(7)\n\ncounter = collections.Counter()\n</code></pre> <p>We can check the number of slots per hash table: <pre><code>cms.n_slots\n</code></pre> <pre><code>544\n</code></pre></p> <p>And the number of hash tables: <pre><code>cms.n_tables\n</code></pre> <pre><code>3\n</code></pre></p> <p>Let's compare the sketch against a brute force approach:</p> <pre><code>vals = []\nfor _ in range(10000):\n    v = rng.randint(-1000, 1000)\n    cms.update(v)\n    counter[v] += 1\n    vals.append(v)\n</code></pre> <p>Now, we can compare the estimates of CMS against the exhaustive counting strategy:</p> <p><pre><code>counter[7]\n</code></pre> <pre><code>5\n</code></pre> <pre><code>cms[7]\n</code></pre> <pre><code>12\n</code></pre> <pre><code>counter[532]\n</code></pre> <pre><code>4\n</code></pre> <pre><code>cms[532]\n</code></pre> <pre><code>15\n</code></pre></p> <p>Keep in mind that CMS is an approximate sketch algorithm. Couting estimates for unseen values might not be always reliable:</p> <p><pre><code>cms[1001]\n</code></pre> <pre><code>9\n</code></pre></p> <p>We can check the number of elements stored by each approach:</p> <p><pre><code>len(counter), len(cms)\n</code></pre> <pre><code>(1982, 1632)\n</code></pre></p> <p>And also retrieve the total sum of counts:</p> <p><pre><code>cms.total()\n</code></pre> <pre><code>10000\n</code></pre></p> <p>We can decrease the error by allocating more memory in the CMS:</p> <p><pre><code>cms_a = sketch.Counter(epsilon=0.001, delta=0.01, seed=0)\nfor v in vals:\n    cms_a.update(v)\n\ncms_a[7]\n</code></pre> <pre><code>5\n</code></pre> <pre><code>cms_a[532]\n</code></pre> <pre><code>4\n</code></pre></p> <p>We can also obtain estimates of the dot product between two instances of <code>river.collections.Counter</code>. This could be useful, for instance, to estimate the cosine distance between the data monitored in two different counter sketch instances. Suppose we create another CMS instance (the number of slots and hash tables must match) that monitors another sample of the same data generating process:</p> <pre><code>cms_b = sketch.Counter(epsilon=0.001, delta=0.01, seed=7)\n\nfor _ in range(10000):\n    v = rng.randint(-1000, 1000)\n    cms_b.update(v)\n</code></pre> <p>Now, we can define a cosine distance function:</p> <pre><code>def cosine_dist(cms_a, cms_b):\n    num = cms_a @ cms_b\n    den = math.sqrt(cms_a @ cms_a) * math.sqrt(cms_b @ cms_b)\n    return num / den\n</code></pre> <p>And use it to calculate the cosine distance between the elements monitored in <code>cms_a</code> and <code>cms_b</code>:</p> <p><pre><code>cosine_dist(cms_a, cms_b)\n</code></pre> <pre><code>0.175363...\n</code></pre></p>"},{"location":"api/sketch/Counter/#methods","title":"Methods","text":"total <p>Return the total count.</p> <p></p> update <ol> <li> <p>Cormode, G., &amp; Muthukrishnan, S. (2005). An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1), 58-75. \u21a9</p> </li> <li> <p>Count-Min Sketch \u21a9</p> </li> <li> <p>Hash functions family generator in Python \u21a9</p> </li> </ol>"},{"location":"api/sketch/HeavyHitters/","title":"HeavyHitters","text":"<p>Find the Heavy Hitters using the Lossy Count with Forgetting factor algorithm<sup>1</sup>.</p> <p>Keep track of the most frequent item(set)s in a data stream and apply a forgetting factor to discard previous frequent items that do not often appear anymore. This is an approximation algorithm designed to work with a limited amount of memory rather than accounting for every possible solution (thus using an unbounded memory footprint). Any hashable type can be passed as input, hence tuples or frozensets can also be monitored. </p> <p>Considering a data stream where <code>n</code> elements were observed so far, the Lossy Count algorithm has the following properties: </p> <ul> <li>All item(set)s whose true frequency exceeds <code>support * n</code> are output. There are no</li> </ul> <p>false negatives; </p> <ul> <li> <p>No item(set) whose true frequency is less than <code>(support - epsilon) * n</code> is outputted;</p> </li> <li> <p>Estimated frequencies are less than the true frequencies by at most <code>epsilon * n</code>.</p> </li> </ul>"},{"location":"api/sketch/HeavyHitters/#parameters","title":"Parameters","text":"<ul> <li> <p>support</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.001</code></p> <p>The support threshold used to determine if an item is frequent. The value of <code>support</code> must be in \\([0, 1]\\). Elements whose frequency is higher than <code>support</code> times the number of observations seen so far are outputted.</p> </li> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.005</code></p> <p>Error parameter to control the accuracy-memory tradeoff. The value of <code>epsilon</code> must be in \\((0, 1]\\) and typically <code>epsilon</code> \\(\\ll\\) <code>support</code>. The smaller the <code>epsilon</code>, the more accurate the estimates will be, but the count sketch will have an increased memory footprint.</p> </li> <li> <p>fading_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.999</code></p> <p>Forgetting factor applied to the frequency estimates to reduce the impact of old items. The value of <code>fading_factor</code> must be in \\((0, 1]\\).</p> </li> </ul>"},{"location":"api/sketch/HeavyHitters/#examples","title":"Examples","text":"<pre><code>import random\nimport string\nfrom river import sketch\n\nrng = random.Random(42)\nhh = sketch.HeavyHitters()\n</code></pre> <p>We will feed the counter with printable ASCII characters:</p> <pre><code>for _ in range(10_000):\n    hh.update(rng.choice(string.printable))\n</code></pre> <p>We can retrieve estimates of the <code>n</code> top elements and their frequencies. Let's try <code>n=3</code> <pre><code>hh.most_common(3)\n</code></pre> <pre><code>[(',', 122.099142...), ('[', 116.049510...), ('W', 115.013402...)]\n</code></pre></p> <p>We can also access estimates of individual elements:</p> <p><pre><code>hh['A']\n</code></pre> <pre><code>99.483575...\n</code></pre></p> <p>Unobserved elements are handled just fine: <pre><code>hh[(1, 2, 3)]\n</code></pre> <pre><code>0.0\n</code></pre></p>"},{"location":"api/sketch/HeavyHitters/#methods","title":"Methods","text":"most_common update <ol> <li> <p>Veloso, B., Tabassum, S., Martins, C., Espanha, R., Azevedo, R., &amp; Gama, J. (2020). Interconnect bypass fraud detection: a case study. Annals of Telecommunications, 75(9), 583-596.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/sketch/Histogram/","title":"Histogram","text":"<p>Streaming histogram.</p>"},{"location":"api/sketch/Histogram/#parameters","title":"Parameters","text":"<ul> <li> <p>max_bins</p> <p>Default \u2192 <code>256</code></p> <p>Maximal number of bins.</p> </li> </ul>"},{"location":"api/sketch/Histogram/#attributes","title":"Attributes","text":"<ul> <li> <p>n</p> <p>Total number of seen values.</p> </li> </ul>"},{"location":"api/sketch/Histogram/#examples","title":"Examples","text":"<p><pre><code>from river import sketch\nimport numpy as np\n\nnp.random.seed(42)\n\nvalues = np.hstack((\n    np.random.normal(-3, 1, 1000),\n    np.random.normal(3, 1, 1000),\n))\n\nhist = sketch.Histogram(max_bins=15)\n\nfor x in values:\n    hist.update(x)\n\nfor bin in hist:\n    print(bin)\n</code></pre> <pre><code>[-6.24127, -6.24127]: 1\n[-5.69689, -5.19881]: 8\n[-5.12390, -4.43014]: 57\n[-4.42475, -3.72574]: 158\n[-3.71984, -3.01642]: 262\n[-3.01350, -2.50668]: 206\n[-2.50329, -0.81020]: 294\n[-0.80954, 0.29677]: 19\n[0.40896, 0.82733]: 7\n[0.84661, 1.25147]: 24\n[1.26029, 2.30758]: 178\n[2.31081, 3.05701]: 284\n[3.05963, 3.69695]: 242\n[3.69822, 5.64434]: 258\n[6.13775, 6.19311]: 2\n</code></pre></p>"},{"location":"api/sketch/Histogram/#methods","title":"Methods","text":"cdf <p>Cumulative distribution function.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> iter_cdf <p>Yields CDF values for a sorted iterable of values.</p> <p>This is faster than calling <code>cdf</code> with many values.</p> <p>Parameters</p> <ul> <li>X </li> <li>verbose     \u2014 defaults to <code>False</code> </li> </ul> <p></p> <ol> <li> <p>Ben-Haim, Y. and Tom-Tov, E., 2010. A streaming parallel decision tree algorithm. Journal of Machine Learning Research, 11(Feb), pp.849-872. \u21a9</p> </li> <li> <p>Go implementation \u21a9</p> </li> </ol>"},{"location":"api/sketch/Set/","title":"Set","text":"<p>Approximate tracking of observed items using Bloom filters.</p> <p>Bloom filters enable using a limited amount of memory to check whether a given item was already observed in a stream. They can be used similarly to Python's built-in sets with the difference that items are not explicitly stored. For that reason, element removal and set difference are not currently supported. </p> <p>Bloom filters store a bit array and map incoming items to <code>k</code> index positions in the such array. The selected positions are set to <code>True</code>. Therefore, a binary code representation is created for each item. Membership works by projecting the query item and checking if every position of its binary code is <code>True</code>. If that is not the case, the item was not observed yet. A nice property of Bloom filters is that they do not yield false negatives: unobserved items might be signalized as observed, but observed items are never signalized as unobserved. </p> <p>If more than one item has the same binary code, i.e., hash collisions happen, the accuracy of the Bloom filter decreases, and false positives are produced. For instance, a previously unobserved item is signalized as observed. Increasing the size of the binary array and the value of <code>k</code> increase the filter's accuracy as hash collisions are avoided. Nonetheless, even using an increased number of hash functions, hash collisions will frequently happen if the array capacity is too small. The length of the bit array and the number of hash functions are inferred automatically from the supplied <code>capacity</code> and <code>fp_rate</code>.</p>"},{"location":"api/sketch/Set/#parameters","title":"Parameters","text":"<ul> <li> <p>capacity</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2048</code></p> <p>The maximum capacity of the Bloom filter, i.e., the maximum number of distinct items to store given the selected <code>fp_rate</code>.</p> </li> <li> <p>fp_rate</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The allowed rate of false positives. The probability of obtaining a true positive is <code>1 - fp_rate</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/sketch/Set/#attributes","title":"Attributes","text":"<ul> <li> <p>n_bits</p> <p>Return the size of the binary array used by the Bloom filter.</p> </li> <li> <p>n_hash</p> <p>Return the number of used hash functions.</p> </li> </ul>"},{"location":"api/sketch/Set/#examples","title":"Examples","text":"<pre><code>import random\nfrom river import sketch\n\nrng = random.Random(42)\ns_set = sketch.Set(capacity=100, seed=0)\n</code></pre> <p>We can retrieve the number of selected hash functions:</p> <p><pre><code>s_set.n_hash\n</code></pre> <pre><code>7\n</code></pre></p> <p>And the size of the binary array used by the Bloom filter: <pre><code>s_set.n_bits\n</code></pre> <pre><code>959\n</code></pre></p> <p>We can add new items and check for membership using the same calls used by Python's standard sets: <pre><code>for _ in range(1000):\n    s_set.add(rng.randint(0, 200))\n\n1 in s_set\n</code></pre> <pre><code>True\n</code></pre></p> <p>False positives might happen if the capacity is not large enough: <pre><code>-10 in s_set\n</code></pre> <pre><code>True\n</code></pre></p> <p>Iterables can also be supplied to perform multiple updates with a single call to <code>update</code>: <pre><code>s_set = s_set.update([1, 2, 3, 4, 5, 6, 7])\n</code></pre></p> <p>We can also combine instances of <code>sketch.Set</code> using the intersection and union operations, as long as they share the same hash functions and capability. In other words, all they hyperparameters match. Let's create two instances that will monitor different portions of a stream of random numbers:</p> <p><pre><code>s1 = sketch.Set(seed=8)\ns2 = sketch.Set(seed=8)\n\nfor _ in range(1000):\n    s1.add(rng.randint(0, 5000))\n\nfor _ in range(1000):\n    s2.add(rng.randint(0, 5000))\n\n43 in s1\n</code></pre> <pre><code>True\n</code></pre> <pre><code>43 in s2\n</code></pre> <pre><code>False\n</code></pre></p> <p>We can get the intersection between the two instances by using:</p> <p><pre><code>s_intersection = s1 &amp; s2\n43 in s_intersection\n</code></pre> <pre><code>False\n</code></pre></p> <p>We can also obtain the set union:</p> <p><pre><code>s_union = s1 | s2\n\n43 in s_union\n</code></pre> <pre><code>True\n</code></pre></p> <p>The same effect of the non-inplace dunder methods can be achieved via explicit method calls:</p> <p><pre><code>43 in s1.intersection(s2)\n</code></pre> <pre><code>False\n</code></pre></p> <p><pre><code>43 in s1.union(s2)\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/sketch/Set/#methods","title":"Methods","text":"add intersection <p>Set intersection.</p> <p>Return a new instance that results from the set intersection between the current <code>Set</code> object and <code>other</code>. Dunder operators can be used to replace the method call, i.e., <code>a &amp;= b</code> and <code>a &amp; b</code> for inplace and non-inplace intersections, respectively.</p> <p>Parameters</p> <ul> <li>other     \u2014 'Set' </li> </ul> <p></p> union <p>Set union.</p> <p>Return a new instance that results from the set union between the current <code>Set</code> object and <code>other</code>. Dunder operators can be used to replace the method call, i.e., <code>a |= b</code> and <code>a | b</code> for inplace and non-inplace unions, respectively.</p> <p>Parameters</p> <ul> <li>other     \u2014 'Set' </li> </ul> <p></p> update"},{"location":"api/sketch/Set/#notes","title":"Notes","text":"<p>This implementation uses an integer to represent the binary array. Bitwise operations are performed in the integer to reflect the Bloom filter updates.</p> <ol> <li> <p>Florian Hartmann's blog article on Bloom Filters.\u00a0\u21a9</p> </li> <li> <p>Wikipedia entry on Bloom filters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/AbsMax/","title":"AbsMax","text":"<p>Running absolute max.</p>"},{"location":"api/stats/AbsMax/#attributes","title":"Attributes","text":"<ul> <li> <p>abs_max (float)</p> <p>The current absolute max.</p> </li> </ul>"},{"location":"api/stats/AbsMax/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 5, -6]\nabs_max = stats.AbsMax()\nfor x in X:\n    abs_max.update(x)\n    print(abs_max.get())\n</code></pre> <pre><code>1\n4\n4\n4\n5\n6\n</code></pre></p>"},{"location":"api/stats/AbsMax/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/AutoCorr/","title":"AutoCorr","text":"<p>Measures the serial correlation.</p> <p>This method computes the Pearson correlation between the current value and the value seen <code>n</code> steps before.</p>"},{"location":"api/stats/AutoCorr/#parameters","title":"Parameters","text":"<ul> <li> <p>lag</p> <p>Type \u2192 int</p> </li> </ul>"},{"location":"api/stats/AutoCorr/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/AutoCorr/#examples","title":"Examples","text":"<p>The following examples are taken from the pandas documentation.</p> <p><pre><code>from river import stats\n\nauto_corr = stats.AutoCorr(lag=1)\nfor x in [0.25, 0.5, 0.2, -0.05]:\n    auto_corr.update(x)\n    print(auto_corr.get())\n</code></pre> <pre><code>0\n0\n-1.0\n0.103552\n</code></pre></p> <p><pre><code>auto_corr = stats.AutoCorr(lag=2)\nfor x in [0.25, 0.5, 0.2, -0.05]:\n    auto_corr.update(x)\n    print(auto_corr.get())\n</code></pre> <pre><code>0\n0\n0\n-1.0\n</code></pre></p> <p><pre><code>auto_corr = stats.AutoCorr(lag=1)\nfor x in [1, 0, 0, 0]:\n    auto_corr.update(x)\n    print(auto_corr.get())\n</code></pre> <pre><code>0\n0\n0\n0\n</code></pre></p>"},{"location":"api/stats/AutoCorr/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/BayesianMean/","title":"BayesianMean","text":"<p>Estimates a mean using outside information.</p>"},{"location":"api/stats/BayesianMean/#parameters","title":"Parameters","text":"<ul> <li> <p>prior</p> <p>Type \u2192 float</p> </li> <li> <p>prior_weight</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/stats/BayesianMean/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/BayesianMean/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Additive smoothing \u21a9</p> </li> <li> <p>Bayesian average \u21a9</p> </li> <li> <p>Practical example of Bayes estimators \u21a9</p> </li> </ol>"},{"location":"api/stats/Count/","title":"Count","text":"<p>A simple counter.</p>"},{"location":"api/stats/Count/#attributes","title":"Attributes","text":"<ul> <li> <p>n (int)</p> <p>The current number of observations.</p> </li> </ul>"},{"location":"api/stats/Count/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/stats/Cov/","title":"Cov","text":"<p>Covariance.</p>"},{"location":"api/stats/Cov/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom.</p> </li> </ul>"},{"location":"api/stats/Cov/#attributes","title":"Attributes","text":"<ul> <li>n</li> </ul>"},{"location":"api/stats/Cov/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nx = [-2.1,  -1,  4.3]\ny = [   3, 1.1, 0.12]\n\ncov = stats.Cov()\n\nfor xi, yi in zip(x, y):\n    cov.update(xi, yi)\n    print(cov.get())\n</code></pre> <pre><code>0.0\n-1.044999\n-4.286\n</code></pre></p> <p>This class has a <code>revert</code> method, and can thus be wrapped by <code>utils.Rolling</code>:</p> <p><pre><code>from river import utils\n\nx = [-2.1,  -1, 4.3, 1, -2.1,  -1, 4.3]\ny = [   3, 1.1, .12, 1,    3, 1.1, .12]\n\nrcov = utils.Rolling(stats.Cov(), window_size=3)\n\nfor xi, yi in zip(x, y):\n    rcov.update(xi, yi)\n    print(rcov.get())\n</code></pre> <pre><code>0.0\n-1.045\n-4.286\n-1.382\n-4.589\n-1.415\n-4.286\n</code></pre></p>"},{"location":"api/stats/Cov/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many"},{"location":"api/stats/Cov/#notes","title":"Notes","text":"<p>The outcomes of the incremental and parallel updates are consistent with numpy's batch processing when \\(\\text{ddof} \\le 1\\).</p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> <li> <p>Schubert, E. and Gertz, M., 2018, July. Numerically stable parallel computation of (co-) variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (pp. 1-12).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/EWMean/","title":"EWMean","text":"<p>Exponentially weighted mean.</p>"},{"location":"api/stats/EWMean/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.5</code></p> <p>The closer <code>fading_factor</code> is to 1 the more the statistic will adapt to recent values.</p> </li> </ul>"},{"location":"api/stats/EWMean/#attributes","title":"Attributes","text":"<ul> <li> <p>mean (float)</p> <p>The running exponentially weighted mean.</p> </li> </ul>"},{"location":"api/stats/EWMean/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, 3, 5, 4, 6, 8, 7, 9, 11]\newm = stats.EWMean(fading_factor=0.5)\nfor x in X:\n    ewm.update(x)\n    print(ewm.get())\n</code></pre> <pre><code>1.0\n2.0\n3.5\n3.75\n4.875\n6.4375\n6.71875\n7.859375\n9.4296875\n</code></pre></p>"},{"location":"api/stats/EWMean/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. \u21a9</p> </li> <li> <p>Exponential Moving Average on Streaming Data \u21a9</p> </li> </ol>"},{"location":"api/stats/EWVar/","title":"EWVar","text":"<p>Exponentially weighted variance.</p> <p>To calculate the variance we use the fact that Var(X) = Mean(x^2) - Mean(x)^2 and internally we use the exponentially weighted mean of x/x^2 to calculate this.</p>"},{"location":"api/stats/EWVar/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.5</code></p> <p>The closer <code>fading_factor</code> is to 1 the more the statistic will adapt to recent values.</p> </li> </ul>"},{"location":"api/stats/EWVar/#attributes","title":"Attributes","text":"<ul> <li> <p>variance (float)</p> <p>The running exponentially weighted variance.</p> </li> </ul>"},{"location":"api/stats/EWVar/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, 3, 5, 4, 6, 8, 7, 9, 11]\newv = stats.EWVar(fading_factor=0.5)\nfor x in X:\n    ewv.update(x)\n    print(ewv.get())\n</code></pre> <pre><code>0.0\n1.0\n2.75\n1.4375\n1.984375\n3.43359375\n1.7958984375\n2.198974609375\n3.56536865234375\n</code></pre></p>"},{"location":"api/stats/EWVar/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. \u21a9</p> </li> <li> <p>Exponential Moving Average on Streaming Data \u21a9</p> </li> </ol>"},{"location":"api/stats/Entropy/","title":"Entropy","text":"<p>Running entropy.</p>"},{"location":"api/stats/Entropy/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>1</code></p> <p>Fading factor.</p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> <p>Small value that will be added to the denominator to avoid division by zero.</p> </li> </ul>"},{"location":"api/stats/Entropy/#attributes","title":"Attributes","text":"<ul> <li> <p>entropy (float)</p> <p>The running entropy.</p> </li> <li> <p>n (int)</p> <p>The current number of observations.</p> </li> <li> <p>counter (collections.Counter)</p> <p>Count the number of times the values have occurred</p> </li> </ul>"},{"location":"api/stats/Entropy/#examples","title":"Examples","text":"<p><pre><code>import math\nimport random\nimport numpy as np\nfrom scipy.stats import entropy\nfrom river import stats\n\ndef entropy_list(labels, base=None):\n    value,counts = np.unique(labels, return_counts=True)\n    return entropy(counts, base=base)\n\nSEED = 42 * 1337\nrandom.seed(SEED)\n\nentro = stats.Entropy(fading_factor=1)\n\nlist_animal = []\nfor animal, num_val in zip(['cat', 'dog', 'bird'],[301, 401, 601]):\n    list_animal += [animal for i in range(num_val)]\nrandom.shuffle(list_animal)\n\nfor animal in list_animal:\n    entro.update(animal)\n\nprint(f'{entro.get():.6f}')\n</code></pre> <pre><code>1.058093\n</code></pre> <pre><code>print(f'{entropy_list(list_animal):.6f}')\n</code></pre> <pre><code>1.058093\n</code></pre></p>"},{"location":"api/stats/Entropy/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Sovdat, B., 2014. Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams. arXiv preprint arXiv:1403.6348. \u21a9</p> </li> </ol>"},{"location":"api/stats/IQR/","title":"IQR","text":"<p>Computes the interquartile range.</p>"},{"location":"api/stats/IQR/#parameters","title":"Parameters","text":"<ul> <li> <p>q_inf</p> <p>Default \u2192 <code>0.25</code></p> <p>Desired inferior quantile, must be between 0 and 1. Defaults to <code>0.25</code>.</p> </li> <li> <p>q_sup</p> <p>Default \u2192 <code>0.75</code></p> <p>Desired superior quantile, must be between 0 and 1. Defaults to <code>0.75</code>.</p> </li> </ul>"},{"location":"api/stats/IQR/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/IQR/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\niqr = stats.IQR(q_inf=0.25, q_sup=0.75)\n\nfor i in range(0, 1001):\n    iqr.update(i)\n    if i % 100 == 0:\n        print(iqr.get())\n</code></pre> <pre><code>0.0\n50.0\n100.0\n150.0\n200.0\n250.0\n300.0\n350.0\n400.0\n450.0\n500.0\n</code></pre></p>"},{"location":"api/stats/IQR/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/KolmogorovSmirnov/","title":"KolmogorovSmirnov","text":"<p>Incremental Kolmogorov-Smirnov statistics.</p> <p>The two-sample Kolmogorov-Smirnov test quantifies the distance between the empirical functions of two samples, with the null distribution of this statistic is calculated under the null hypothesis that the samples are drawn from the same distribution. The formula can be described as </p> \\[ D_{n, m} = \\sup_x \\| F_{1, n}(x) - F_{2, m}(x) \\|. \\] <p>This implementation is the incremental version of the previously mentioned statistics, with the change being in the ability to insert and remove an observation thorugh time. This can be done using a randomized tree called Treap (or Cartesian Tree) <sup>2</sup> with bulk operation and lazy propagation. </p> <p>The implemented algorithm is able to perform the insertion and removal operations in O(logN) with high probability and calculate the Kolmogorov-Smirnov test in O(1), where N is the number of sample observations. This is a significant improvement compared to the O(N logN) cost of non-incremental implementation. </p> <p>This implementation also supports the calculation of the Kuiper statistics. Different from the orginial Kolmogorov-Smirnov statistics, Kuiper's test <sup>3</sup> calculates the sum of the absolute sizes of the most positive and most negative differences between the two cumulative distribution functions taken into account. As such, Kuiper's test is very sensitive in the tails as at the median. </p> <p>Last but not least, this implementation is also based on the original implementation within the supplementary material of the authors of paper <sup>1</sup>, at the following Github repository.</p>"},{"location":"api/stats/KolmogorovSmirnov/#parameters","title":"Parameters","text":"<ul> <li> <p>statistic</p> <p>Default \u2192 <code>ks</code></p> <p>The method used to calculate the statistic, can be either \"ks\" or \"kuiper\". The default value is set as \"ks\".</p> </li> </ul>"},{"location":"api/stats/KolmogorovSmirnov/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nfrom river import stats\n\nstream_a = [1, 1, 2, 2, 3, 3, 4, 4]\nstream_b = [1, 1, 1, 1, 2, 2, 2, 2]\n\nincremental_ks = stats.KolmogorovSmirnov(statistic=\"ks\")\nfor a, b in zip(stream_a, stream_b):\n    incremental_ks.update(a, b)\n\nincremental_ks\n</code></pre> <pre><code>KolmogorovSmirnov: 0.5\n</code></pre></p> <p><pre><code>incremental_ks.n_samples\n</code></pre> <pre><code>8\n</code></pre></p>"},{"location":"api/stats/KolmogorovSmirnov/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> <ol> <li> <p>dos Reis, D.M. et al. (2016) \u2018Fast unsupervised online drift detection using incremental Kolmogorov-Smirnov test\u2019, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/2939672.2939836.\u00a0\u21a9</p> </li> <li> <p>C. R. Aragon and R. G. Seidel. Randomized search trees. In FOCS, pages 540\u2013545. IEEE, 1989.\u00a0\u21a9</p> </li> <li> <p>Kuiper, N. H. (1960). \"Tests concerning random points on a circle\". Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series A. 63: 38\u201347.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/Kurtosis/","title":"Kurtosis","text":"<p>Running kurtosis using Welford's algorithm.</p>"},{"location":"api/stats/Kurtosis/#parameters","title":"Parameters","text":"<ul> <li> <p>bias</p> <p>Default \u2192 <code>False</code></p> <p>If <code>False</code>, then the calculations are corrected for statistical bias.</p> </li> </ul>"},{"location":"api/stats/Kurtosis/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Kurtosis/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nimport scipy.stats\nimport numpy as np\n\nnp.random.seed(42)\nX = np.random.normal(loc=0, scale=1, size=10)\n\nkurtosis = stats.Kurtosis(bias=False)\nfor x in X:\n    kurtosis.update(x)\n    print(kurtosis.get())\n</code></pre> <pre><code>-3.0\n-2.0\n-1.5\n1.4130027920707047\n0.15367976585756438\n0.46142633246812653\n-1.620647789230658\n-1.3540178492487054\n-1.2310268787102745\n-0.9490372374384453\n</code></pre></p> <p><pre><code>for i in range(2, len(X)+1):\n    print(scipy.stats.kurtosis(X[:i], bias=False))\n</code></pre> <pre><code>-2.0\n-1.4999999999999998\n1.4130027920707082\n0.15367976585756082\n0.46142633246812403\n-1.620647789230658\n-1.3540178492487063\n-1.2310268787102738\n-0.9490372374384459\n</code></pre></p> <p><pre><code>kurtosis = stats.Kurtosis(bias=True)\nfor x in X:\n    kurtosis.update(x)\n    print(kurtosis.get())\n</code></pre> <pre><code>-3.0\n-2.0\n-1.5\n-1.011599627723906\n-0.9615800585356089\n-0.6989395431537853\n-1.4252699121794408\n-1.311437071070812\n-1.246289111322894\n-1.082283689864171\n</code></pre></p> <p><pre><code>for i in range(2, len(X)+1):\n    print(scipy.stats.kurtosis(X[:i], bias=True))\n</code></pre> <pre><code>-2.0\n-1.4999999999999998\n-1.0115996277239057\n-0.9615800585356098\n-0.6989395431537861\n-1.425269912179441\n-1.3114370710708125\n-1.2462891113228936\n-1.0822836898641714\n</code></pre></p>"},{"location":"api/stats/Kurtosis/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> </ol>"},{"location":"api/stats/Link/","title":"Link","text":"<p>A link joins two univariate statistics as a sequence.</p> <p>This can be used to pipe the output of one statistic to the input of another. This can be used, for instance, to calculate the mean of the variance of a variable. It can also be used to compute shifted statistics by piping statistics with an instance of <code>stats.Shift</code>. </p> <p>Note that a link is not meant to be instantiated via this class definition. Instead, users can link statistics together via the <code>|</code> operator.</p>"},{"location":"api/stats/Link/#parameters","title":"Parameters","text":"<ul> <li> <p>left</p> <p>Type \u2192 stats.base.Univariate</p> </li> <li> <p>right</p> <p>Type \u2192 stats.base.Univariate</p> <p>The output from <code>left</code>'s <code>get</code> method is passed to <code>right</code>'s <code>update</code> method if <code>left</code>'s <code>get</code> method doesn't produce <code>None.</code></p> </li> </ul>"},{"location":"api/stats/Link/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Link/#examples","title":"Examples","text":"<pre><code>from river import stats\nstat = stats.Shift(1) | stats.Mean()\n</code></pre> <p>No values have been seen, therefore <code>get</code> defaults to the initial value of <code>stats.Mean</code>, which is 0.</p> <p><pre><code>stat.get()\n</code></pre> <pre><code>0.\n</code></pre></p> <p>Let us now call <code>update</code>.</p> <pre><code>stat.update(1)\n</code></pre> <p>The output from <code>get</code> will still be 0. The reason is that <code>stats.Shift</code> has not enough values, and therefore outputs its default value, which is <code>None</code>. The <code>stats.Mean</code> instance is therefore not updated.</p> <p><pre><code>stat.get()\n</code></pre> <pre><code>0.0\n</code></pre></p> <p>On the next call to <code>update</code>, the <code>stats.Shift</code> instance has seen enough values, and therefore the mean can be updated. The mean is therefore equal to 1, because that's the only value from the past.</p> <p><pre><code>stat.update(3)\nstat.get()\n</code></pre> <pre><code>1.0\n</code></pre></p> <p>On the subsequent call to update, the mean will be updated with the value 3.</p> <p><pre><code>stat.update(4)\nstat.get()\n</code></pre> <pre><code>2.0\n</code></pre></p> <p>Note that composing statistics returns a new statistic with its own name.</p> <p><pre><code>stat.name\n</code></pre> <pre><code>'mean_of_shift_1'\n</code></pre></p>"},{"location":"api/stats/Link/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/MAD/","title":"MAD","text":"<p>Median Absolute Deviation (MAD).</p> <p>The median absolute deviation is the median of the absolute differences between each data point and the data's overall median. In an online setting, the median of the data is unknown beforehand. Therefore, both the median of the data and the median of the differences of the data with respect to the latter are updated online. To be precise, the median of the data is updated before the median of the differences. As a consequence, this online version of the MAD does not coincide exactly with its batch counterpart.</p>"},{"location":"api/stats/MAD/#attributes","title":"Attributes","text":"<ul> <li> <p>median (stats.Median)</p> <p>The median of the data.</p> </li> </ul>"},{"location":"api/stats/MAD/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [4, 2, 5, 3, 0, 4]\n\nmad = stats.MAD()\nfor x in X:\n    mad.update(x)\n    print(mad.get())\n</code></pre> <pre><code>0.0\n2.0\n1.0\n1.0\n1.0\n1.0\n</code></pre></p>"},{"location":"api/stats/MAD/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Median absolute deviation article on Wikipedia \u21a9</p> </li> </ol>"},{"location":"api/stats/Max/","title":"Max","text":"<p>Running max.</p>"},{"location":"api/stats/Max/#attributes","title":"Attributes","text":"<ul> <li> <p>max (float)</p> <p>The current max.</p> </li> </ul>"},{"location":"api/stats/Max/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 5, -6]\nmaximum = stats.Max()\nfor x in X:\n    maximum.update(x)\n    print(maximum.get())\n</code></pre> <pre><code>1\n1\n3\n3\n5\n5\n</code></pre></p>"},{"location":"api/stats/Max/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Mean/","title":"Mean","text":"<p>Running mean.</p>"},{"location":"api/stats/Mean/#attributes","title":"Attributes","text":"<ul> <li> <p>n (float)</p> <p>The current sum of weights. If each passed weight was 1, then this is equal to the number of seen observations.</p> </li> </ul>"},{"location":"api/stats/Mean/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [-5, -3, -1, 1, 3, 5]\nmean = stats.Mean()\nfor x in X:\n    mean.update(x)\n    print(mean.get())\n</code></pre> <pre><code>-5.0\n-4.0\n-3.0\n-2.0\n-1.0\n0.0\n</code></pre></p> <p>You can calculate a rolling average by wrapping a <code>utils.Rolling</code> around:</p> <p><pre><code>from river import utils\n\nX = [1, 2, 3, 4, 5, 6]\nrmean = utils.Rolling(stats.Mean(), window_size=2)\n\nfor x in X:\n    rmean.update(x)\n    print(rmean.get())\n</code></pre> <pre><code>1.0\n1.5\n2.5\n3.5\n4.5\n5.5\n</code></pre></p>"},{"location":"api/stats/Mean/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many <ol> <li> <p>West, D. H. D. (1979). Updating mean and variance estimates: An improved method. Communications of the ACM, 22(9), 532-535. \u21a9</p> </li> <li> <p>Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. \u21a9</p> </li> <li> <p>Chan, T.F., Golub, G.H. and LeVeque, R.J., 1983. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician, 37(3), pp.242-247. \u21a9</p> </li> </ol>"},{"location":"api/stats/Min/","title":"Min","text":"<p>Running min.</p>"},{"location":"api/stats/Min/#attributes","title":"Attributes","text":"<ul> <li> <p>min (float)</p> <p>The current min.</p> </li> </ul>"},{"location":"api/stats/Min/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Mode/","title":"Mode","text":"<p>Running mode.</p> <p>The mode is simply the most common value. An approximate mode can be computed by setting the number of first unique values to count.</p>"},{"location":"api/stats/Mode/#parameters","title":"Parameters","text":"<ul> <li> <p>k</p> <p>Default \u2192 <code>25</code></p> <p>Only the first <code>k</code> unique values will be included. If <code>k</code> equals -1, the exact mode is computed.</p> </li> </ul>"},{"location":"api/stats/Mode/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Mode/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = ['sunny', 'cloudy', 'cloudy', 'rainy', 'rainy', 'rainy']\nmode = stats.Mode(k=2)\nfor x in X:\n    mode.update(x)\n    print(mode.get())\n</code></pre> <pre><code>sunny\nsunny\ncloudy\ncloudy\ncloudy\ncloudy\n</code></pre></p> <p><pre><code>mode = stats.Mode(k=-1)\nfor x in X:\n    mode.update(x)\n    print(mode.get())\n</code></pre> <pre><code>sunny\nsunny\ncloudy\ncloudy\ncloudy\nrainy\n</code></pre></p>"},{"location":"api/stats/Mode/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/NUnique/","title":"NUnique","text":"<p>Approximate number of unique values counter.</p> <p>This is basically an implementation of the HyperLogLog algorithm. Adapted from <code>hypy</code>. The code is a bit too terse but it will do for now.</p>"},{"location":"api/stats/NUnique/#parameters","title":"Parameters","text":"<ul> <li> <p>error_rate</p> <p>Default \u2192 <code>0.01</code></p> <p>Desired error rate. Memory usage is inversely proportional to this value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Set the seed to produce identical results.</p> </li> </ul>"},{"location":"api/stats/NUnique/#attributes","title":"Attributes","text":"<ul> <li> <p>n_bits (int)</p> </li> <li> <p>n_buckets (int)</p> </li> <li> <p>buckets (list)</p> </li> </ul>"},{"location":"api/stats/NUnique/#examples","title":"Examples","text":"<p><pre><code>import string\nfrom river import stats\n\nalphabet = string.ascii_lowercase\nn_unique = stats.NUnique(error_rate=0.2, seed=42)\n\nn_unique.update('a')\nn_unique.get()\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>n_unique.update('b')\nn_unique.get()\n</code></pre> <pre><code>2\n</code></pre></p> <p><pre><code>for letter in alphabet:\n    n_unique.update(letter)\nn_unique.get()\n</code></pre> <pre><code>31\n</code></pre></p> <p>Lowering the <code>error_rate</code> parameter will increase the precision.</p> <p><pre><code>n_unique = stats.NUnique(error_rate=0.01, seed=42)\nfor letter in alphabet:\n    n_unique.update(letter)\nn_unique.get()\n</code></pre> <pre><code>26\n</code></pre></p>"},{"location":"api/stats/NUnique/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>My favorite algorithm (and data structure): HyperLogLog \u21a9</p> </li> <li> <p>Flajolet, P., Fusy, \u00c9., Gandouet, O. and Meunier, F., 2007, June. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. \u21a9</p> </li> </ol>"},{"location":"api/stats/PeakToPeak/","title":"PeakToPeak","text":"<p>Running peak to peak (max - min).</p>"},{"location":"api/stats/PeakToPeak/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/PeakToPeak/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 4]\nptp = stats.PeakToPeak()\nfor x in X:\n    ptp.update(x)\n    print(ptp.get())\n</code></pre> <pre><code>0.\n5.\n7.\n7.\n7.\n8.\n</code></pre></p>"},{"location":"api/stats/PeakToPeak/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/PearsonCorr/","title":"PearsonCorr","text":"<p>Online Pearson correlation.</p>"},{"location":"api/stats/PearsonCorr/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom.</p> </li> </ul>"},{"location":"api/stats/PearsonCorr/#attributes","title":"Attributes","text":"<ul> <li> <p>var_x (stats.Var)</p> <p>Running variance of <code>x</code>.</p> </li> <li> <p>var_y (stats.Var)</p> <p>Running variance of <code>y</code>.</p> </li> <li> <p>cov_xy (stats.Cov)</p> <p>Running covariance of <code>x</code> and <code>y</code>.</p> </li> </ul>"},{"location":"api/stats/PearsonCorr/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nx = [0, 0, 0, 1, 1, 1, 1]\ny = [0, 1, 2, 3, 4, 5, 6]\n\npearson = stats.PearsonCorr()\n\nfor xi, yi in zip(x, y):\n    pearson.update(xi, yi)\n    print(pearson.get())\n</code></pre> <pre><code>0\n0\n0\n0.774596\n0.866025\n0.878310\n0.866025\n</code></pre></p> <p>You can also do this in a rolling fashion:</p> <p><pre><code>from river import utils\n\nx = [0, 0, 0, 1, 1, 1, 1]\ny = [0, 1, 2, 3, 4, 5, 6]\n\npearson = utils.Rolling(stats.PearsonCorr(), window_size=4)\n\nfor xi, yi in zip(x, y):\n    pearson.update(xi, yi)\n    print(pearson.get())\n</code></pre> <pre><code>0\n0\n0\n0.7745966692414834\n0.8944271909999159\n0.7745966692414832\n-4.712160915387242e-09\n</code></pre></p>"},{"location":"api/stats/PearsonCorr/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p>"},{"location":"api/stats/Quantile/","title":"Quantile","text":"<p>Running quantile.</p> <p>Uses the P\u00b2 algorithm, which is also known as the \"Piecewise-Parabolic quantile estimator\". The code is inspired by LiveStat's implementation <sup>2</sup>.</p>"},{"location":"api/stats/Quantile/#parameters","title":"Parameters","text":"<ul> <li> <p>q</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>Determines which quantile to compute, must be comprised between 0 and 1.</p> </li> </ul>"},{"location":"api/stats/Quantile/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Quantile/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nimport numpy as np\n\nnp.random.seed(42 * 1337)\nmu, sigma = 0, 1\ns = np.random.normal(mu, sigma, 500)\n\nmedian = stats.Quantile(0.5)\nfor x in s:\n   _ = median.update(x)\nprint(f'The estimated value of the 50th (median) quantile is {median.get():.4f}')\n</code></pre> <pre><code>The estimated value of the 50th (median) quantile is -0.0275\n</code></pre></p> <p><pre><code>print(f'The real value of the 50th (median) quantile is {np.median(s):.4f}')\n</code></pre> <pre><code>The real value of the 50th (median) quantile is -0.0135\n</code></pre></p> <p><pre><code>percentile_17 = stats.Quantile(0.17)\nfor x in s:\n   _ = percentile_17.update(x)\nprint(f'The estimated value of the 17th quantile is {percentile_17.get():.4f}')\n</code></pre> <pre><code>The estimated value of the 17th quantile is -0.8652\n</code></pre></p> <p><pre><code>print(f'The real value of the 17th quantile is {np.percentile(s,17):.4f}')\n</code></pre> <pre><code>The real value of the 17th quantile is -0.9072\n</code></pre></p>"},{"location":"api/stats/Quantile/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>The P\u00b2 Algorithm for Dynamic Univariateal Computing Calculation of Quantiles and Editor Histograms Without Storing Observations \u21a9</p> </li> <li> <p>LiveStats \u21a9</p> </li> <li> <p>P\u00b2 quantile estimator: estimating the median without storing values \u21a9</p> </li> </ol>"},{"location":"api/stats/RollingAbsMax/","title":"RollingAbsMax","text":"<p>Running absolute max over a window.</p>"},{"location":"api/stats/RollingAbsMax/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingAbsMax/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingAbsMax/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_absmax = stats.RollingAbsMax(window_size=2)\nfor x in X:\n    rolling_absmax.update(x)\n    print(rolling_absmax.get())\n</code></pre> <pre><code>1\n4\n4\n3\n2\n2\n</code></pre></p>"},{"location":"api/stats/RollingAbsMax/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingIQR/","title":"RollingIQR","text":"<p>Computes the rolling interquartile range.</p>"},{"location":"api/stats/RollingIQR/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the window.</p> </li> <li> <p>q_inf</p> <p>Default \u2192 <code>0.25</code></p> <p>Desired inferior quantile, must be between 0 and 1. Defaults to <code>0.25</code>.</p> </li> <li> <p>q_sup</p> <p>Default \u2192 <code>0.75</code></p> <p>Desired superior quantile, must be between 0 and 1. Defaults to <code>0.75</code>.</p> </li> </ul>"},{"location":"api/stats/RollingIQR/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingIQR/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nrolling_iqr = stats.RollingIQR(\n    q_inf=0.25,\n    q_sup=0.75,\n    window_size=101\n)\n\nfor i in range(0, 1001):\n    rolling_iqr.update(i)\n    if i % 100 == 0:\n        print(rolling_iqr.get())\n</code></pre> <pre><code>0.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n</code></pre></p>"},{"location":"api/stats/RollingIQR/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingMax/","title":"RollingMax","text":"<p>Running max over a window.</p>"},{"location":"api/stats/RollingMax/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingMax/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingMax/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_max = stats.RollingMax(window_size=2)\nfor x in X:\n    rolling_max.update(x)\n    print(rolling_max.get())\n</code></pre> <pre><code>1\n1\n3\n3\n2\n2\n</code></pre></p>"},{"location":"api/stats/RollingMax/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingMin/","title":"RollingMin","text":"<p>Running min over a window.</p>"},{"location":"api/stats/RollingMin/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingMin/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingMin/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_min = stats.RollingMin(2)\nfor x in X:\n    rolling_min.update(x)\n    print(rolling_min.get())\n</code></pre> <pre><code>1\n-4\n-4\n-2\n-2\n1\n</code></pre></p>"},{"location":"api/stats/RollingMin/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingMode/","title":"RollingMode","text":"<p>Running mode over a window.</p> <p>The mode is the most common value.</p>"},{"location":"api/stats/RollingMode/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingMode/#attributes","title":"Attributes","text":"<ul> <li> <p>counts (collections.defaultdict)</p> <p>Value counts.</p> </li> </ul>"},{"location":"api/stats/RollingMode/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = ['sunny', 'sunny', 'sunny', 'rainy', 'rainy', 'rainy', 'rainy']\nrolling_mode = stats.RollingMode(window_size=2)\nfor x in X:\n    rolling_mode.update(x)\n    print(rolling_mode.get())\n</code></pre> <pre><code>sunny\nsunny\nsunny\nsunny\nrainy\nrainy\nrainy\n</code></pre></p> <p><pre><code>rolling_mode = stats.RollingMode(window_size=5)\nfor x in X:\n    rolling_mode.update(x)\n    print(rolling_mode.get())\n</code></pre> <pre><code>sunny\nsunny\nsunny\nsunny\nsunny\nrainy\nrainy\n</code></pre></p>"},{"location":"api/stats/RollingMode/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingPeakToPeak/","title":"RollingPeakToPeak","text":"<p>Running peak to peak (max - min) over a window.</p>"},{"location":"api/stats/RollingPeakToPeak/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingPeakToPeak/#attributes","title":"Attributes","text":"<ul> <li> <p>max (stats.RollingMax)</p> <p>The running rolling max.</p> </li> <li> <p>min (stats.RollingMin)</p> <p>The running rolling min.</p> </li> </ul>"},{"location":"api/stats/RollingPeakToPeak/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nptp = stats.RollingPeakToPeak(window_size=2)\nfor x in X:\n    ptp.update(x)\n    print(ptp.get())\n</code></pre> <pre><code>0\n5\n7\n5\n4\n1\n</code></pre></p>"},{"location":"api/stats/RollingPeakToPeak/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingQuantile/","title":"RollingQuantile","text":"<p>Running quantile over a window.</p>"},{"location":"api/stats/RollingQuantile/#parameters","title":"Parameters","text":"<ul> <li> <p>q</p> <p>Type \u2192 float</p> <p>Determines which quantile to compute, must be comprised between 0 and 1.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the window.</p> </li> </ul>"},{"location":"api/stats/RollingQuantile/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingQuantile/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nrolling_quantile = stats.RollingQuantile(\n    q=.5,\n    window_size=101,\n)\n\nfor i in range(1001):\n    rolling_quantile.update(i)\n    if i % 100 == 0:\n        print(rolling_quantile.get())\n</code></pre> <pre><code>0.0\n50.0\n150.0\n250.0\n350.0\n450.0\n550.0\n650.0\n750.0\n850.0\n950.0\n</code></pre></p>"},{"location":"api/stats/RollingQuantile/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Left sorted \u21a9</p> </li> </ol>"},{"location":"api/stats/SEM/","title":"SEM","text":"<p>Running standard error of the mean using Welford's algorithm.</p>"},{"location":"api/stats/SEM/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom. The divisor used in calculations is <code>n - ddof</code>, where <code>n</code> is the number of seen elements.</p> </li> </ul>"},{"location":"api/stats/SEM/#attributes","title":"Attributes","text":"<ul> <li> <p>n (int)</p> <p>Number of observations.</p> </li> </ul>"},{"location":"api/stats/SEM/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [3, 5, 4, 7, 10, 12]\n\nsem = stats.SEM()\nfor x in X:\n    sem.update(x)\n    print(sem.get())\n</code></pre> <pre><code>0.0\n1.0\n0.577350\n0.853912\n1.240967\n1.447219\n</code></pre></p> <p><pre><code>from river import utils\n\nX = [1, 4, 2, -4, -8, 0]\n\nrolling_sem = utils.Rolling(stats.SEM(ddof=1), window_size=3)\nfor x in X:\n    rolling_sem.update(x)\n    print(rolling_sem.get())\n</code></pre> <pre><code>0.0\n1.5\n0.881917\n2.403700\n2.905932\n2.309401\n</code></pre></p>"},{"location":"api/stats/SEM/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> </ol>"},{"location":"api/stats/Shift/","title":"Shift","text":"<p>Shifts a data stream by returning past values.</p> <p>This can be used to compute statistics over past data. For instance, if you're computing daily averages, then shifting by 7 will be equivalent to computing averages from a week ago. </p> <p>Shifting values is useful when you're calculating an average over a target value. Indeed, in this case it's important to shift the values in order not to introduce leakage. The recommended way to do this is to <code>feature_extraction.TargetAgg</code>, which already takes care of shifting the target values once.</p>"},{"location":"api/stats/Shift/#parameters","title":"Parameters","text":"<ul> <li> <p>amount</p> <p>Default \u2192 <code>1</code></p> <p>Shift amount. The <code>get</code> method will return the <code>t - amount</code> value, where <code>t</code> is the current moment.</p> </li> <li> <p>fill_value</p> <p>Default \u2192 <code>None</code></p> <p>This value will be returned by the <code>get</code> method if not enough values have been observed.</p> </li> </ul>"},{"location":"api/stats/Shift/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Shift/#examples","title":"Examples","text":"<p>It is rare to have to use <code>Shift</code> by itself. A more common usage is to compose it with other statistics. This can be done via the <code>|</code> operator.</p> <p><pre><code>from river import stats\n\nstat = stats.Shift(1) | stats.Mean()\n\nfor i in range(5):\n    stat.update(i)\n    print(stat.get())\n</code></pre> <pre><code>0.0\n0.0\n0.5\n1.0\n1.5\n</code></pre></p> <p>A common usecase for using <code>Shift</code> is when computing statistics on shifted data. For instance, say you have a dataset which records the amount of sales for a set of shops. You might then have a <code>shop</code> field and a <code>sales</code> field. Let's say you want to look at the average amount of sales per shop. You can do this by using a <code>feature_extraction.Agg</code>. When you call <code>transform_one</code>, you're expecting it to return the average amount of sales, without including today's sales. You can do this by prepending an instance of <code>stats.Mean</code> with an instance of <code>stats.Shift</code>.</p> <pre><code>from river import feature_extraction\n\nagg = feature_extraction.Agg(\n    on='sales',\n    how=stats.Shift(1) | stats.Mean(),\n    by='shop'\n)\n</code></pre> <p>Let's define a little example dataset.</p> <pre><code>X = iter([\n    {'shop': 'Ikea', 'sales': 10},\n    {'shop': 'Ikea', 'sales': 15},\n    {'shop': 'Ikea', 'sales': 20}\n])\n</code></pre> <p>Now let's call the <code>learn_one</code> method to update our feature extractor.</p> <pre><code>x = next(X)\nagg.learn_one(x)\n</code></pre> <p>At this point, the average defaults to the initial value of <code>stats.Mean</code>, which is 0.</p> <p><pre><code>agg.transform_one(x)\n</code></pre> <pre><code>{'sales_mean_of_shift_1_by_shop': 0.0}\n</code></pre></p> <p>We can now update our feature extractor with the next data point and check the output.</p> <p><pre><code>agg.learn_one(next(X))\nagg.transform_one(x)\n</code></pre> <pre><code>{'sales_mean_of_shift_1_by_shop': 10.0}\n</code></pre></p> <p><pre><code>agg.learn_one(next(X))\nagg.transform_one(x)\n</code></pre> <pre><code>{'sales_mean_of_shift_1_by_shop': 12.5}\n</code></pre></p>"},{"location":"api/stats/Shift/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Skew/","title":"Skew","text":"<p>Running skew using Welford's algorithm.</p>"},{"location":"api/stats/Skew/#parameters","title":"Parameters","text":"<ul> <li> <p>bias</p> <p>Default \u2192 <code>False</code></p> <p>If <code>False</code>, then the calculations are corrected for statistical bias.</p> </li> </ul>"},{"location":"api/stats/Skew/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Skew/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nimport numpy as np\n\nnp.random.seed(42)\nX = np.random.normal(loc=0, scale=1, size=10)\n\nskew = stats.Skew(bias=False)\nfor x in X:\n    skew.update(x)\n    print(skew.get())\n</code></pre> <pre><code>0.0\n0.0\n-1.4802398132849872\n0.5127437186677888\n0.7803466510704751\n1.056115628922055\n0.5057840774320389\n0.3478402420400934\n0.4536710660918704\n0.4123070197493227\n</code></pre></p> <p><pre><code>skew = stats.Skew(bias=True)\nfor x in X:\n    skew.update(x)\n    print(skew.get())\n</code></pre> <pre><code>0.0\n0.0\n-0.6043053732501439\n0.2960327239981376\n0.5234724473423674\n0.7712778043924866\n0.39022088752624845\n0.278892645224261\n0.37425953513864063\n0.3476878073823696\n</code></pre></p>"},{"location":"api/stats/Skew/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> </ol>"},{"location":"api/stats/Sum/","title":"Sum","text":"<p>Running sum.</p>"},{"location":"api/stats/Sum/#attributes","title":"Attributes","text":"<ul> <li> <p>sum (float)</p> <p>The running sum.</p> </li> </ul>"},{"location":"api/stats/Sum/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [-5, -3, -1, 1, 3, 5]\nmean = stats.Sum()\nfor x in X:\n    mean.update(x)\n    print(mean.get())\n</code></pre> <pre><code>-5.0\n-8.0\n-9.0\n-8.0\n-5.0\n0.0\n</code></pre></p> <p><pre><code>from river import utils\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_sum = utils.Rolling(stats.Sum(), window_size=2)\nfor x in X:\n    rolling_sum.update(x)\n    print(rolling_sum.get())\n</code></pre> <pre><code>1.0\n-3.0\n-1.0\n1.0\n0.0\n3.0\n</code></pre></p>"},{"location":"api/stats/Sum/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Var/","title":"Var","text":"<p>Running variance using Welford's algorithm.</p>"},{"location":"api/stats/Var/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom. The divisor used in calculations is <code>n - ddof</code>, where <code>n</code> represents the number of seen elements.</p> </li> </ul>"},{"location":"api/stats/Var/#attributes","title":"Attributes","text":"<ul> <li> <p>mean</p> <p>It is necessary to calculate the mean of the data in order to calculate its variance.</p> </li> </ul>"},{"location":"api/stats/Var/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [3, 5, 4, 7, 10, 12]\n\nvar = stats.Var()\nfor x in X:\n    var.update(x)\n    print(var.get())\n</code></pre> <pre><code>0.0\n2.0\n1.0\n2.916666\n7.7\n12.56666\n</code></pre></p> <p>You can measure a rolling variance by using a <code>utils.Rolling</code> wrapper:</p> <p><pre><code>from river import utils\n\nX = [1, 4, 2, -4, -8, 0]\nrvar = utils.Rolling(stats.Var(ddof=1), window_size=3)\nfor x in X:\n    rvar.update(x)\n    print(rvar.get())\n</code></pre> <pre><code>0.0\n4.5\n2.333333\n17.333333\n25.333333\n16.0\n</code></pre></p>"},{"location":"api/stats/Var/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many"},{"location":"api/stats/Var/#notes","title":"Notes","text":"<p>The outcomes of the incremental and parallel updates are consistent with numpy's batch processing when \\(\\text{ddof} \\le 1\\).</p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> <li> <p>Chan, T.F., Golub, G.H. and LeVeque, R.J., 1983. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician, 37(3), pp.242-247. \u21a9</p> </li> <li> <p>Schubert, E. and Gertz, M., 2018, July. Numerically stable parallel computation of (co-)variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (pp. 1-12).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/base/Bivariate/","title":"Bivariate","text":"<p>A bivariate statistic measures a relationship between two variables.</p>"},{"location":"api/stats/base/Bivariate/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p>"},{"location":"api/stats/base/Univariate/","title":"Univariate","text":"<p>A univariate statistic measures a property of a variable.</p>"},{"location":"api/stats/base/Univariate/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/base/Univariate/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stream/Cache/","title":"Cache","text":"<p>Utility for caching iterables.</p> <p>This can be used to save a stream of data to the disk in order to iterate over it faster the following time. This can save time depending on the nature of stream. The more processing happens in a stream, the more time will be saved. Even in the case where no processing is done apart from reading the data, the cache will save some time because it is using the pickle binary protocol. It can thus improve the speed in common cases such as reading from a CSV file.</p>"},{"location":"api/stream/Cache/#parameters","title":"Parameters","text":"<ul> <li> <p>directory</p> <p>Default \u2192 <code>None</code></p> <p>The path where to store the pickled data streams. If not provided, then it will be automatically inferred whenever possible, if not an exception will be raised.</p> </li> </ul>"},{"location":"api/stream/Cache/#attributes","title":"Attributes","text":"<ul> <li> <p>keys (set)</p> <p>The set of keys that are being cached.</p> </li> </ul>"},{"location":"api/stream/Cache/#examples","title":"Examples","text":"<pre><code>import time\nfrom river import datasets\nfrom river import stream\n\ndataset = datasets.Phishing()\ncache = stream.Cache()\n</code></pre> <p>The cache can be used by wrapping it around an iterable. Because this is the first time are iterating over the data, nothing is cached.</p> <p><pre><code>tic = time.time()\nfor x, y in cache(dataset, key='phishing'):\n    pass\ntoc = time.time()\nprint(toc - tic)  # doctest: +SKIP\n</code></pre> <pre><code>0.012813\n</code></pre></p> <p>If we do the same thing again, we can see the loop is now faster.</p> <p><pre><code>tic = time.time()\nfor x, y in cache(dataset, key='phishing'):\n    pass\ntoc = time.time()\nprint(toc - tic)  # doctest: +SKIP\n</code></pre> <pre><code>0.001927\n</code></pre></p> <p>We can see an overview of the cache. The first line indicates the location of the cache.</p> <p><pre><code>cache  # doctest: +SKIP\n</code></pre> <pre><code>/tmp\nphishing - 125.2KiB\n</code></pre></p> <p>Finally, we can clear the stream from the cache.</p> <p><pre><code>cache.clear('phishing')\ncache  # doctest: +SKIP\n</code></pre> <pre><code>/tmp\n</code></pre></p> <p>There is also a <code>clear_all</code> method to remove all the items in the cache.</p> <pre><code>cache.clear_all()\n</code></pre>"},{"location":"api/stream/Cache/#methods","title":"Methods","text":"call <p>Call self as a function.</p> <p>Parameters</p> <ul> <li>stream </li> <li>key     \u2014 defaults to <code>None</code> </li> </ul> <p></p> clear <p>Delete the cached stream associated with the given key.</p> <p>Parameters</p> <ul> <li>key     \u2014 'str' </li> </ul> <p></p> clear_all <p>Delete all the cached streams.</p> <p></p>"},{"location":"api/stream/TwitchChatStream/","title":"TwitchChatStream","text":"<p>Twitch chat stream client.</p> <p>This client gives access to a live stream of chat messages in Twitch channels using IRC protocol. You need to have a Twitch account and receive an OAuth token from https://twitchapps.com/tmi/.</p>"},{"location":"api/stream/TwitchChatStream/#parameters","title":"Parameters","text":"<ul> <li> <p>nickname</p> <p>Type \u2192 str</p> <p>The nickname of your account.</p> </li> <li> <p>token</p> <p>Type \u2192 str</p> <p>OAuth token which has been generated.</p> </li> <li> <p>channels</p> <p>Type \u2192 list[str]</p> <p>A list of channel names like <code>[\"asmongold\", \"shroud\"]</code> you want to collect messages from.</p> </li> <li> <p>buffer_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2048</code></p> <p>Size of buffer in bytes used for receiving responses from Twitch with IRC (default 2 KiB).</p> </li> <li> <p>timeout</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>60</code></p> <p>A timeout value in seconds for waiting response from Twitch (default 60s). It can be useful if all requested channels are offline or chat is not active enough.</p> </li> </ul>"},{"location":"api/stream/TwitchChatStream/#examples","title":"Examples","text":"<p>The live stream is instantiated by passing your Twitch account nickname, OAuth token and list of channels. Other parameters are optional.</p> <pre><code>from river import stream\n\ntwitch_chat = stream.TwitchChatStream(\n    nickname=\"twitch_user1\",\n    token=\"oauth:okrip6j6fjio8n5xpy2oum1lph4fbve\",\n    channels=[\"asmongold\", \"shroud\"]\n)\n</code></pre> <p>The stream can be iterated over like this:</p> <pre><code>for item in twitch_chat:\n    print(item)\n</code></pre> <p>Here's a single stream item example: <pre><code>{\n    'dt': datetime.datetime(2022, 9, 14, 10, 33, 37, 989560),\n    'channel': 'asmongold',\n    'username': 'moojiejaa',\n    'msg': 'damn this chat mod are wild'\n}\n</code></pre></p> <ol> <li> <p>Twitch IRC doc \u21a9</p> </li> </ol>"},{"location":"api/stream/TwitterLiveStream/","title":"TwitterLiveStream","text":"<p>Twitter API v2 live stream client.</p> <p>This client gives access to a live stream of Tweets. That is, Tweets that have just been     published. This is different to <code>stream.TwitterRecentStream</code>, which also covers Tweets that     have been published over recent days, and not necessarily in real-time. </p> <p>A list of filtering rules has to be provided. For instance, this allows focusing on a subset of     topics and/or users. </p> <p>Note</p> <p>Using this requires having the <code>requests</code>         package installed.</p>"},{"location":"api/stream/TwitterLiveStream/#parameters","title":"Parameters","text":"<ul> <li> <p>rules</p> <p>See the documentation[^2] for a comprehensive overview of filtering rules.</p> </li> <li> <p>bearer_token</p> <p>A bearer token that is available in each account's developer portal.</p> </li> </ul>"},{"location":"api/stream/TwitterLiveStream/#examples","title":"Examples","text":"<p>The live stream is instantiated by passing a list of filtering rules, as well as a bearer     token. For instance, we can listen to all the breaking news Tweets from the BBC and CNN.</p> <pre><code>from river import stream\n\ntweets = stream.TwitterLiveStream(\n    rules=[\"from:BBCBreaking\", \"from:cnnbrk\"],\n    bearer_token=\"&lt;insert_bearer_token&gt;\"\n)\n</code></pre> <pre><code>The stream can then be iterated over, possibly in an infinite loop. This will listen to the\nlive feed of Tweets and produce a Tweet right after it's been published.\n\n```py\nimport logging\n\nwhile True:\n    try:\n        for tweet in tweets:\n            print(tweet)\n    except requests.exceptions.RequestException as e:\n        logging.warning(str(e))\n        time.sleep(10)\n```\n\nHere's a Tweet example:\n\n```py\n{\n    'data': {\n        'author_id': '428333',\n        'created_at': '2022-08-26T12:59:48.000Z',\n        'id': '1563149212774445058',\n        'text': \"Ukraine's Zaporizhzhia nuclear power plant, which is currently held by\n</code></pre> <p>Russian forces, has been reconnected to Ukraine's electricity grid, according to the country's nuclear operator https://t.co/xfylkBs4JR\"         },         'includes': {             'users': [                 {                     'created_at': '2007-01-02T01:48:14.000Z',                     'id': '428333',                     'name': 'CNN Breaking News',                     'username': 'cnnbrk'                 }             ]         },         'matching_rules': [{'id': '1563148866333151233', 'tag': 'from:cnnbrk'}]     }     ```     [^1]: Filtered stream introduction     [^2]: Building rules for filtered stream     [^3]: Stream Tweets in real-time</p>"},{"location":"api/stream/iter-arff/","title":"iter_arff","text":"<p>Iterates over rows from an ARFF file.</p>"},{"location":"api/stream/iter-arff/#parameters","title":"Parameters","text":"<ul> <li> <p>filepath_or_buffer</p> <p>Either a string indicating the location of a file, or a buffer object that has a <code>read</code> method.</p> </li> <li> <p>target</p> <p>Type \u2192 str | list[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>Name(s) of the target field. If <code>None</code>, then the target field is ignored. If a list of names is passed, then a dictionary is returned instead of a single value.</p> </li> <li> <p>compression</p> <p>Default \u2192 <code>infer</code></p> <p>For on-the-fly decompression of on-disk data. If this is set to 'infer' and <code>filepath_or_buffer</code> is a path, then the decompression method is inferred for the following extensions: '.gz', '.zip'.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the data is sparse or not.</p> </li> </ul>"},{"location":"api/stream/iter-arff/#examples","title":"Examples","text":"<p><pre><code>cars = '''\n@relation CarData\n@attribute make {Toyota, Honda, Ford, Chevrolet}\n@attribute model string\n@attribute year numeric\n@attribute price numeric\n@attribute mpg numeric\n@data\nToyota, Corolla, 2018, 15000, 30.5\nHonda, Civic, 2019, 16000, 32.2\nFord, Mustang, 2020, 25000, 25.0\nChevrolet, Malibu, 2017, 18000, 28.9\nToyota, Camry, 2019, 22000, 29.8\n'''\nwith open('cars.arff', mode='w') as f:\n    _ = f.write(cars)\n\nfrom river import stream\n\nfor x, y in stream.iter_arff('cars.arff', target='price'):\n    print(x, y)\n</code></pre> <pre><code>{'make': 'Toyota', 'model': ' Corolla', 'year': 2018.0, 'mpg': 30.5} 15000.0\n{'make': 'Honda', 'model': ' Civic', 'year': 2019.0, 'mpg': 32.2} 16000.0\n{'make': 'Ford', 'model': ' Mustang', 'year': 2020.0, 'mpg': 25.0} 25000.0\n{'make': 'Chevrolet', 'model': ' Malibu', 'year': 2017.0, 'mpg': 28.9} 18000.0\n{'make': 'Toyota', 'model': ' Camry', 'year': 2019.0, 'mpg': 29.8} 22000.0\n</code></pre></p> <p>Finally, let's delete the example file.</p> <pre><code>import os; os.remove('cars.arff')\n</code></pre> <p>ARFF files support sparse data. Let's create a sparse ARFF file.</p> <pre><code>sparse = '''\n% traindata\n@RELATION \"traindata: -C 6\"\n@ATTRIBUTE y0 {0, 1}\n@ATTRIBUTE y1 {0, 1}\n@ATTRIBUTE y2 {0, 1}\n@ATTRIBUTE y3 {0, 1}\n@ATTRIBUTE y4 {0, 1}\n@ATTRIBUTE y5 {0, 1}\n@ATTRIBUTE X0 NUMERIC\n@ATTRIBUTE X1 NUMERIC\n@ATTRIBUTE X2 NUMERIC\n@DATA\n{ 3 1,6 0.863382,8 0.820094 }\n{ 2 1,6 0.659761 }\n{ 0 1,3 1,6 0.437881,8 0.818882 }\n{ 2 1,6 0.676477,7 0.724635,8 0.755123 }\n'''\n\nwith open('sparse.arff', mode='w') as f:\n    _ = f.write(sparse)\n</code></pre> <p>In addition, we'll specify that there are several target fields.</p> <p><pre><code>arff_stream = stream.iter_arff(\n    'sparse.arff',\n    target=['y0', 'y1', 'y2', 'y3', 'y4', 'y5'],\n    sparse=True\n)\n\nfor x, y in arff_stream:\n    print(x)\n    print(y)\n</code></pre> <pre><code>{'X0': '0.863382', 'X2': '0.820094'}\n{'y0': 0, 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0}\n{'X0': '0.659761'}\n{'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0}\n{'X0': '0.437881', 'X2': '0.818882'}\n{'y0': '1', 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0}\n{'X0': '0.676477', 'X1': '0.724635', 'X2': '0.755123'}\n{'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0}\n</code></pre></p> <p>This function can also deal with missing features in non-sparse data. These are indicated with a question mark.</p> <p><pre><code>data = '''\n@relation giveMeLoan-weka.filters.unsupervised.attribute.Remove-R1\n@attribute RevolvingUtilizationOfUnsecuredLines numeric\n@attribute age numeric\n@attribute NumberOfTime30-59DaysPastDueNotWorse numeric\n@attribute DebtRatio numeric\n@attribute MonthlyIncome numeric\n@attribute NumberOfOpenCreditLinesAndLoans numeric\n@attribute NumberOfTimes90DaysLate numeric\n@attribute NumberRealEstateLoansOrLines numeric\n@attribute NumberOfTime60-89DaysPastDueNotWorse numeric\n@attribute NumberOfDependents numeric\n@attribute isFraud {0,1}\n@data\n0.213179,74,0,0.375607,3500,3,0,1,0,1,0\n0.305682,57,0,5710,?,8,0,3,0,0,0\n0.754464,39,0,0.20994,3500,8,0,0,0,0,0\n0.116951,27,0,46,?,2,0,0,0,0,0\n0.189169,57,0,0.606291,23684,9,0,4,0,2,0\n'''\n\nwith open('data.arff', mode='w') as f:\n    _ = f.write(data)\n\nfor x, y in stream.iter_arff('data.arff', target='isFraud'):\n    print(len(x))\n</code></pre> <pre><code>10\n9\n10\n9\n10\n</code></pre></p> <ol> <li> <p>ARFF format description from Weka \u21a9</p> </li> </ol>"},{"location":"api/stream/iter-array/","title":"iter_array","text":"<p>Iterates over the rows from an array of features and an array of targets.</p> <p>This method is intended to work with <code>numpy</code> arrays, but should also work with Python lists.</p>"},{"location":"api/stream/iter-array/#parameters","title":"Parameters","text":"<ul> <li> <p>X</p> <p>Type \u2192 np.ndarray</p> <p>A 2D array of features. This can also be a 1D array of strings, which can be the case if you're working with text.</p> </li> <li> <p>y</p> <p>Type \u2192 np.ndarray | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional array of targets.</p> </li> <li> <p>feature_names</p> <p>Type \u2192 list[base.typing.FeatureName] | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional list of feature names. The features will be labeled with integers if no names are provided.</p> </li> <li> <p>target_names</p> <p>Type \u2192 list[base.typing.FeatureName] | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional list of output names. The outputs will be labeled with integers if no names are provided. Only applies if there are multiple outputs, i.e. if <code>y</code> is a 2D array.</p> </li> <li> <p>shuffle</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Indicates whether or not to shuffle the input arrays before iterating over them.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for shuffling the data.</p> </li> </ul>"},{"location":"api/stream/iter-array/#examples","title":"Examples","text":"<p><pre><code>from river import stream\nimport numpy as np\n\nX = np.array([[1, 2, 3], [11, 12, 13]])\nY = np.array([True, False])\n\ndataset = stream.iter_array(\n    X, Y,\n    feature_names=['x1', 'x2', 'x3']\n)\nfor x, y in dataset:\n    print(x, y)\n</code></pre> <pre><code>{'x1': 1, 'x2': 2, 'x3': 3} True\n{'x1': 11, 'x2': 12, 'x3': 13} False\n</code></pre></p> <p>This also works with a array of texts:</p> <p><pre><code>X = [\"foo\", \"bar\"]\ndataset = stream.iter_array(\n    X, Y,\n    feature_names=['x1', 'x2', 'x3']\n)\nfor x, y in dataset:\n    print(x, y)\n</code></pre> <pre><code>foo True\nbar False\n</code></pre></p>"},{"location":"api/stream/iter-csv/","title":"iter_csv","text":"<p>Iterates over rows from a CSV file.</p> <p>Reading CSV files can be quite slow. If, for whatever reason, you're going to loop through the same file multiple times, then we recommend that you to use the <code>stream.Cache</code> utility.</p>"},{"location":"api/stream/iter-csv/#parameters","title":"Parameters","text":"<ul> <li> <p>filepath_or_buffer</p> <p>Either a string indicating the location of a file, or a buffer object that has a <code>read</code> method.</p> </li> <li> <p>target</p> <p>Type \u2192 str | list[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>A single target column is assumed if a string is passed. A multiple output scenario is assumed if a list of strings is passed. A <code>None</code> value will be assigned to each <code>y</code> if this parameter is omitted.</p> </li> <li> <p>converters</p> <p>Type \u2192 dict | None</p> <p>Default \u2192 <code>None</code></p> <p>All values in the CSV are interpreted as strings by default. You can use this parameter to cast values to the desired type. This should be a <code>dict</code> mapping feature names to callables used to parse their associated values. Note that a callable may be a type, such as <code>float</code> and <code>int</code>.</p> </li> <li> <p>parse_dates</p> <p>Type \u2192 dict | None</p> <p>Default \u2192 <code>None</code></p> <p>A <code>dict</code> mapping feature names to a format passed to the <code>datetime.datetime.strptime</code> method.</p> </li> <li> <p>drop</p> <p>Type \u2192 list[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>Fields to ignore.</p> </li> <li> <p>drop_nones</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to drop fields where the value is a <code>None</code>.</p> </li> <li> <p>fraction</p> <p>Default \u2192 <code>1.0</code></p> <p>Sampling fraction.</p> </li> <li> <p>compression</p> <p>Default \u2192 <code>infer</code></p> <p>For on-the-fly decompression of on-disk data. If this is set to 'infer' and <code>filepath_or_buffer</code> is a path, then the decompression method is inferred for the following extensions: '.gz', '.zip'.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>If specified, the sampling will be deterministic.</p> </li> <li> <p>field_size_limit</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>If not <code>None</code>, this will be passed to the <code>csv.field_size_limit</code> function.</p> </li> <li> <p>kwargs</p> <p>All other keyword arguments are passed to the underlying <code>csv.DictReader</code>.</p> </li> </ul>"},{"location":"api/stream/iter-csv/#examples","title":"Examples","text":"<p>Although this function is designed to handle different kinds of inputs, the most common use case is to read a file on the disk. We'll first create a little CSV file to illustrate.</p> <pre><code>tv_shows = '''name,year,rating\nPlanet Earth II,2016,9.5\nPlanet Earth,2006,9.4\nBand of Brothers,2001,9.4\nBreaking Bad,2008,9.4\nChernobyl,2019,9.4\n'''\nwith open('tv_shows.csv', mode='w') as f:\n    _ = f.write(tv_shows)\n</code></pre> <p>We can now go through the rows one by one. We can use the <code>converters</code> parameter to cast the <code>rating</code> field value as a <code>float</code>. We can also convert the <code>year</code> to a <code>datetime</code> via the <code>parse_dates</code> parameter.</p> <p><pre><code>from river import stream\n\nparams = {\n    'converters': {'rating': float},\n    'parse_dates': {'year': '%Y'}\n}\nfor x, y in stream.iter_csv('tv_shows.csv', **params):\n    print(x, y)\n</code></pre> <pre><code>{'name': 'Planet Earth II', 'year': datetime.datetime(2016, 1, 1, 0, 0), 'rating': 9.5} None\n{'name': 'Planet Earth', 'year': datetime.datetime(2006, 1, 1, 0, 0), 'rating': 9.4} None\n{'name': 'Band of Brothers', 'year': datetime.datetime(2001, 1, 1, 0, 0), 'rating': 9.4} None\n{'name': 'Breaking Bad', 'year': datetime.datetime(2008, 1, 1, 0, 0), 'rating': 9.4} None\n{'name': 'Chernobyl', 'year': datetime.datetime(2019, 1, 1, 0, 0), 'rating': 9.4} None\n</code></pre></p> <p>The value of <code>y</code> is always <code>None</code> because we haven't provided a value for the <code>target</code> parameter. Here is an example where a <code>target</code> is provided:</p> <p><pre><code>dataset = stream.iter_csv('tv_shows.csv', target='rating', **params)\nfor x, y in dataset:\n    print(x, y)\n</code></pre> <pre><code>{'name': 'Planet Earth II', 'year': datetime.datetime(2016, 1, 1, 0, 0)} 9.5\n{'name': 'Planet Earth', 'year': datetime.datetime(2006, 1, 1, 0, 0)} 9.4\n{'name': 'Band of Brothers', 'year': datetime.datetime(2001, 1, 1, 0, 0)} 9.4\n{'name': 'Breaking Bad', 'year': datetime.datetime(2008, 1, 1, 0, 0)} 9.4\n{'name': 'Chernobyl', 'year': datetime.datetime(2019, 1, 1, 0, 0)} 9.4\n</code></pre></p> <p>Finally, let's delete the example file.</p> <pre><code>import os; os.remove('tv_shows.csv')\n</code></pre>"},{"location":"api/stream/iter-libsvm/","title":"iter_libsvm","text":"<p>Iterates over a dataset in LIBSVM format.</p> <p>The LIBSVM format is a popular way in the machine learning community to store sparse datasets. Only numerical feature values are supported. The feature names will be considered as strings.</p>"},{"location":"api/stream/iter-libsvm/#parameters","title":"Parameters","text":"<ul> <li> <p>filepath_or_buffer</p> <p>Type \u2192 str</p> <p>Either a string indicating the location of a file, or a buffer object that has a <code>read</code> method.</p> </li> <li> <p>target_type</p> <p>Default \u2192 <code>&lt;class 'float'&gt;</code></p> <p>The type of the target value.</p> </li> <li> <p>compression</p> <p>Default \u2192 <code>infer</code></p> <p>For on-the-fly decompression of on-disk data. If this is set to 'infer' and <code>filepath_or_buffer</code> is a path, then the decompression method is inferred for the following extensions: '.gz', '.zip'.</p> </li> </ul>"},{"location":"api/stream/iter-libsvm/#examples","title":"Examples","text":"<p><pre><code>import io\nfrom river import stream\n\ndata = io.StringIO('''+1 x:-134.26 y:0.2563\n1 x:-12 z:0.3\n-1 y:.25\n''')\n\nfor x, y in stream.iter_libsvm(data, target_type=int):\n    print(y, x)\n</code></pre> <pre><code>1 {'x': -134.26, 'y': 0.2563}\n1 {'x': -12.0, 'z': 0.3}\n-1 {'y': 0.25}\n</code></pre></p> <ol> <li> <p>LIBSVM documentation \u21a9</p> </li> </ol>"},{"location":"api/stream/iter-pandas/","title":"iter_pandas","text":"<p>Iterates over the rows of a <code>pandas.DataFrame</code>.</p>"},{"location":"api/stream/iter-pandas/#parameters","title":"Parameters","text":"<ul> <li> <p>X</p> <p>Type \u2192 pd.DataFrame</p> <p>A dataframe of features.</p> </li> <li> <p>y</p> <p>Type \u2192 pd.Series | pd.DataFrame | None</p> <p>Default \u2192 <code>None</code></p> <p>A series or a dataframe with one column per target.</p> </li> <li> <p>kwargs</p> <p>Extra keyword arguments are passed to the underlying call to <code>stream.iter_array</code>.</p> </li> </ul>"},{"location":"api/stream/iter-pandas/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import stream\n\nX = pd.DataFrame({\n    'x1': [1, 2, 3, 4],\n    'x2': ['blue', 'yellow', 'yellow', 'blue'],\n    'y': [True, False, False, True]\n})\ny = X.pop('y')\n\nfor xi, yi in stream.iter_pandas(X, y):\n    print(xi, yi)\n</code></pre> <pre><code>{'x1': 1, 'x2': 'blue'} True\n{'x1': 2, 'x2': 'yellow'} False\n{'x1': 3, 'x2': 'yellow'} False\n{'x1': 4, 'x2': 'blue'} True\n</code></pre></p>"},{"location":"api/stream/iter-polars/","title":"iter_polars","text":"<p>Iterates over the rows of a <code>polars.DataFrame</code>.</p>"},{"location":"api/stream/iter-polars/#parameters","title":"Parameters","text":"<ul> <li> <p>X</p> <p>Type \u2192 pl.DataFrame</p> <p>A dataframe of features.</p> </li> <li> <p>y</p> <p>Type \u2192 pl.Series | pl.DataFrame | None</p> <p>Default \u2192 <code>None</code></p> <p>A series or a dataframe with one column per target.</p> </li> <li> <p>kwargs</p> <p>Extra keyword arguments are passed to the underlying call to <code>stream.iter_array</code>.</p> </li> </ul>"},{"location":"api/stream/iter-polars/#examples","title":"Examples","text":"<p><pre><code>import polars as pl\nfrom river import stream\n\nX = pl.DataFrame({\n    'x1': [1, 2, 3, 4],\n    'x2': ['blue', 'yellow', 'yellow', 'blue'],\n    'y': [True, False, False, True]\n})\ny = X.get_column('y')\nX=X.drop(\"y\")\n\nfor xi, yi in stream.iter_polars(X, y):\n    print(xi, yi)\n</code></pre> <pre><code>{'x1': 1, 'x2': 'blue'} True\n{'x1': 2, 'x2': 'yellow'} False\n{'x1': 3, 'x2': 'yellow'} False\n{'x1': 4, 'x2': 'blue'} True\n</code></pre></p>"},{"location":"api/stream/iter-sklearn-dataset/","title":"iter_sklearn_dataset","text":"<p>Iterates rows from one of the datasets provided by scikit-learn.</p> <p>This allows you to use any dataset from scikit-learn's <code>datasets</code> module. For instance, you can use the <code>fetch_openml</code> function to get access to all of the datasets from the OpenML website.</p>"},{"location":"api/stream/iter-sklearn-dataset/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 sklearn.utils.Bunch</p> <p>A scikit-learn dataset.</p> </li> <li> <p>kwargs</p> <p>Extra keyword arguments are passed to the underlying call to <code>stream.iter_array</code>.</p> </li> </ul>"},{"location":"api/stream/iter-sklearn-dataset/#examples","title":"Examples","text":"<p><pre><code>import pprint\nfrom sklearn import datasets\nfrom river import stream\n\ndataset = datasets.load_diabetes()\n\nfor xi, yi in stream.iter_sklearn_dataset(dataset):\n    pprint.pprint(xi)\n    print(yi)\n    break\n</code></pre> <pre><code>{'age': 0.038075906433423026,\n 'bmi': 0.061696206518683294,\n 'bp': 0.0218723855140367,\n 's1': -0.04422349842444599,\n 's2': -0.03482076283769895,\n 's3': -0.04340084565202491,\n 's4': -0.002592261998183278,\n 's5': 0.019907486170462722,\n 's6': -0.01764612515980379,\n 'sex': 0.05068011873981862}\n151.0\n</code></pre></p>"},{"location":"api/stream/iter-sql/","title":"iter_sql","text":"<p>Iterates over the results from an SQL query.</p> <p>By default, SQLAlchemy prefetches results. Therefore, even though you can iterate over the resulting rows one by one, the results are in fact loaded in batch. You can modify this behavior by configuring the connection you pass to <code>iter_sql</code>. For instance, you can set the <code>stream_results</code> parameter to <code>True</code>, as explained in SQLAlchemy's documentation. Note, however, that this isn't available for all database engines.</p>"},{"location":"api/stream/iter-sql/#parameters","title":"Parameters","text":"<ul> <li> <p>query</p> <p>Type \u2192 str | sqlalchemy.TextClause | sqlalchemy.Select</p> <p>SQL query to be executed.</p> </li> <li> <p>conn</p> <p>Type \u2192 sqlalchemy.Connection</p> <p>An SQLAlchemy construct which has an <code>execute</code> method. In other words you can pass an engine, a connection, or a session.</p> </li> <li> <p>target_name</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>None</code></p> <p>The name of the target field. If this is <code>None</code>, then <code>y</code> will also be <code>None</code>.</p> </li> </ul>"},{"location":"api/stream/iter-sql/#examples","title":"Examples","text":"<p>As an example we'll create an in-memory database with SQLAlchemy.</p> <pre><code>import datetime as dt\nimport sqlalchemy\n\nengine = sqlalchemy.create_engine('sqlite://')\n\nmetadata = sqlalchemy.MetaData()\n\nt_sales = sqlalchemy.Table('sales', metadata,\n    sqlalchemy.Column('shop', sqlalchemy.String, primary_key=True),\n    sqlalchemy.Column('date', sqlalchemy.Date, primary_key=True),\n    sqlalchemy.Column('amount', sqlalchemy.Integer)\n)\n\nmetadata.create_all(engine)\n\nsales = [\n    {'shop': 'Hema', 'date': dt.date(2016, 8, 2), 'amount': 20},\n    {'shop': 'Ikea', 'date': dt.date(2016, 8, 2), 'amount': 18},\n    {'shop': 'Hema', 'date': dt.date(2016, 8, 3), 'amount': 22},\n    {'shop': 'Ikea', 'date': dt.date(2016, 8, 3), 'amount': 14},\n    {'shop': 'Hema', 'date': dt.date(2016, 8, 4), 'amount': 12},\n    {'shop': 'Ikea', 'date': dt.date(2016, 8, 4), 'amount': 16}\n]\n\nwith engine.connect() as conn:\n    _ = conn.execute(t_sales.insert(), sales)\n    conn.commit()\n</code></pre> <p>We can now query the database. We will set <code>amount</code> to be the target field.</p> <p><pre><code>from river import stream\n\nwith engine.connect() as conn:\n    query = sqlalchemy.sql.select(t_sales)\n    dataset = stream.iter_sql(query, conn, target_name='amount')\n    for x, y in dataset:\n        print(x, y)\n</code></pre> <pre><code>{'shop': 'Hema', 'date': datetime.date(2016, 8, 2)} 20\n{'shop': 'Ikea', 'date': datetime.date(2016, 8, 2)} 18\n{'shop': 'Hema', 'date': datetime.date(2016, 8, 3)} 22\n{'shop': 'Ikea', 'date': datetime.date(2016, 8, 3)} 14\n{'shop': 'Hema', 'date': datetime.date(2016, 8, 4)} 12\n{'shop': 'Ikea', 'date': datetime.date(2016, 8, 4)} 16\n</code></pre></p> <p>This also with raw SQL queries.</p> <p><pre><code>with engine.connect() as conn:\n    query = \"SELECT * FROM sales WHERE shop = 'Hema'\"\n    dataset = stream.iter_sql(query, conn, target_name='amount')\n    for x, y in dataset:\n        print(x, y)\n</code></pre> <pre><code>{'shop': 'Hema', 'date': '2016-08-02'} 20\n{'shop': 'Hema', 'date': '2016-08-03'} 22\n{'shop': 'Hema', 'date': '2016-08-04'} 12\n</code></pre></p>"},{"location":"api/stream/shuffle/","title":"shuffle","text":"<p>Shuffles a stream of data.</p> <p>This works by maintaining a buffer of elements. The first <code>buffer_size</code> elements are stored in memory. Once the buffer is full, a random element inside the buffer is yielded. Every time an element is yielded, the next element in the stream replaces it and the buffer is sampled again. Increasing <code>buffer_size</code> will improve the quality of the shuffling. </p> <p>If you really want to stream over your dataset in a \"good\" random order, the best way is to split your dataset into smaller datasets and loop over them in a round-robin fashion. You may do this by using the <code>roundrobin</code> recipe from the <code>itertools</code> module.</p>"},{"location":"api/stream/shuffle/#parameters","title":"Parameters","text":"<ul> <li> <p>stream</p> <p>Type \u2192 typing.Iterator</p> <p>The stream to shuffle.</p> </li> <li> <p>buffer_size</p> <p>Type \u2192 int</p> <p>The size of the buffer which contains the elements help in memory. Increasing this will increase randomness but will incur more memory usage.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for sampling.</p> </li> </ul>"},{"location":"api/stream/shuffle/#examples","title":"Examples","text":"<p><pre><code>from river import stream\n\nfor i in stream.shuffle(range(15), buffer_size=5, seed=42):\n    print(i)\n</code></pre> <pre><code>0\n5\n2\n1\n8\n9\n6\n4\n11\n12\n10\n7\n14\n13\n3\n</code></pre></p> <ol> <li> <p>Visualizing TensorFlow's streaming shufflers \u21a9</p> </li> </ol>"},{"location":"api/stream/simulate-qa/","title":"simulate_qa","text":"<p>Simulate a time-ordered question and answer session.</p> <p>This method allows looping through a dataset in the order in which it arrived. Indeed, it usually is the case that labels arrive after features. Being able to go through a dataset in arrival order enables assessing a model's performance in a reliable manner. For instance, the <code>evaluate.progressive_val_score</code> is a high-level method that can be used to score a model on a dataset. Under the hood it uses this method to determine the correct arrival order.</p>"},{"location":"api/stream/simulate-qa/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>A stream of (features, target) tuples.</p> </li> <li> <p>moment</p> <p>Type \u2192 str | typing.Callable[[dict], dt.datetime] | None</p> <p>The attribute used for measuring time. If a callable is passed, then it is expected to take as input a <code>dict</code> of features. If <code>None</code>, then the observations are implicitly timestamped in the order in which they arrive. If a <code>str</code> is passed, then it will be used to obtain the time from the input features.</p> </li> <li> <p>delay</p> <p>Type \u2192 str | int | dt.timedelta | typing.Callable | None</p> <p>The amount of time to wait before revealing the target associated with each observation to the model. This value is expected to be able to sum with the <code>moment</code> value. For instance, if <code>moment</code> is a <code>datetime.date</code>, then <code>delay</code> is expected to be a <code>datetime.timedelta</code>. If a callable is passed, then it is expected to take as input a <code>dict</code> of features and the target. If a <code>str</code> is passed, then it will be used to access the relevant field from the features. If <code>None</code> is passed, then no delay will be used, which leads to doing standard online validation. If a scalar is passed, such an <code>int</code> or a <code>datetime.timedelta</code>, then the delay is constant.</p> </li> <li> <p>copy</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If <code>True</code>, then a separate copy of the features are yielded the second time around. This ensures that inadvertent modifications in downstream code don't have any effect.</p> </li> </ul>"},{"location":"api/stream/simulate-qa/#examples","title":"Examples","text":"<p>The arrival delay isn't usually indicated in a dataset, but it might be able to be inferred from the features. As an example, we'll simulate the departure and arrival time of taxi trips. Let's first create a time table which records the departure time and the duration of seconds of several taxi trips.</p> <pre><code>import datetime as dt\ntime_table = [\n    (dt.datetime(2020, 1, 1, 20,  0, 0),  900),\n    (dt.datetime(2020, 1, 1, 20, 10, 0), 1800),\n    (dt.datetime(2020, 1, 1, 20, 20, 0),  300),\n    (dt.datetime(2020, 1, 1, 20, 45, 0),  400),\n    (dt.datetime(2020, 1, 1, 20, 50, 0),  240),\n    (dt.datetime(2020, 1, 1, 20, 55, 0),  450)\n]\n</code></pre> <p>We can now create a streaming dataset where the features are the departure dates and the targets are the durations.</p> <pre><code>dataset = (\n    ({'date': date}, duration)\n    for date, duration in time_table\n)\n</code></pre> <p>Now, we can use <code>simulate_qa</code> to iterate over the events in the order in which they are meant to occur.</p> <p><pre><code>delay = lambda _, y: dt.timedelta(seconds=y)\n\nfor i, x, y in simulate_qa(dataset, moment='date', delay=delay):\n    if y is None:\n        print(f'{x[\"date\"]} - trip #{i} departs')\n    else:\n        arrival_date = x['date'] + dt.timedelta(seconds=y)\n        print(f'{arrival_date} - trip #{i} arrives after {y} seconds')\n</code></pre> <pre><code>2020-01-01 20:00:00 - trip #0 departs\n2020-01-01 20:10:00 - trip #1 departs\n2020-01-01 20:15:00 - trip #0 arrives after 900 seconds\n2020-01-01 20:20:00 - trip #2 departs\n2020-01-01 20:25:00 - trip #2 arrives after 300 seconds\n2020-01-01 20:40:00 - trip #1 arrives after 1800 seconds\n2020-01-01 20:45:00 - trip #3 departs\n2020-01-01 20:50:00 - trip #4 departs\n2020-01-01 20:51:40 - trip #3 arrives after 400 seconds\n2020-01-01 20:54:00 - trip #4 arrives after 240 seconds\n2020-01-01 20:55:00 - trip #5 departs\n2020-01-01 21:02:30 - trip #5 arrives after 450 seconds\n</code></pre></p> <p>This function is extremely practical because it provides a reliable way to evaluate the performance of a model in a real scenario. Indeed, it allows to make predictions and perform model updates in exactly the same manner that would happen live. For instance, it is used in <code>evaluate.progressive_val_score</code>, which is a higher level function for evaluating models in an online manner.</p>"},{"location":"api/time-series/ForecastingMetric/","title":"ForecastingMetric","text":""},{"location":"api/time-series/ForecastingMetric/#methods","title":"Methods","text":"get <p>Return the current performance along the horizon.</p> <p>Returns</p> <p>list[float]:     The current performance.</p> <p></p> update <p>Update the metric at each step along the horizon.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'list[Number]' </li> <li>y_pred     \u2014 'list[Number]' </li> </ul> <p></p>"},{"location":"api/time-series/HoltWinters/","title":"HoltWinters","text":"<p>Holt-Winters forecaster.</p> <p>This is a standard implementation of the Holt-Winters forecasting method. Certain parametrisations result in special cases, such as simple exponential smoothing. </p> <p>Optimal parameters and initialisation values can be determined in a batch setting. However, in an online setting, it is necessary to wait and observe enough values. The first <code>k = max(2, seasonality)</code> values are indeed used to initialize the components. </p> <p>Level initialization </p> \\[l = \\frac{1}{k} \\sum_{i=1}{k} y_i\\] <p>Trend initialization </p> \\[t = \\frac{1}{k - 1} \\sum_{i=2}{k} y_i - y_{i-1}\\] <p>Trend initialization </p> \\[s_i = \\frac{y_i}{k}\\]"},{"location":"api/time-series/HoltWinters/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Smoothing parameter for the level.</p> </li> <li> <p>beta</p> <p>Default \u2192 <code>None</code></p> <p>Smoothing parameter for the trend.</p> </li> <li> <p>gamma</p> <p>Default \u2192 <code>None</code></p> <p>Smoothing parameter for the seasonality.</p> </li> <li> <p>seasonality</p> <p>Default \u2192 <code>0</code></p> <p>The number of periods in a season. For instance, this should be 4 for quarterly data, and 12 for yearly data.</p> </li> <li> <p>multiplicative</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to use a multiplicative formulation.</p> </li> </ul>"},{"location":"api/time-series/HoltWinters/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import metrics\nfrom river import time_series\n\ndataset = datasets.AirlinePassengers()\n\nmodel = time_series.HoltWinters(\n    alpha=0.3,\n    beta=0.1,\n    gamma=0.6,\n    seasonality=12,\n    multiplicative=True\n)\n\nmetric = metrics.MAE()\n\ntime_series.evaluate(\n    dataset,\n    model,\n    metric,\n    horizon=12\n)\n</code></pre> <pre><code>+1  MAE: 25.899087\n+2  MAE: 26.26131\n+3  MAE: 25.735903\n+4  MAE: 25.625678\n+5  MAE: 26.093842\n+6  MAE: 26.90249\n+7  MAE: 28.634398\n+8  MAE: 29.284769\n+9  MAE: 31.018351\n+10 MAE: 32.252349\n+11 MAE: 33.518946\n+12 MAE: 33.975057\n</code></pre></p>"},{"location":"api/time-series/HoltWinters/#methods","title":"Methods","text":"forecast <p>Makes forecast at each step of the given horizon.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Updates the model.</p> <p>Parameters</p> <ul> <li>y     \u2014 'float' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Exponential smoothing \u2014 Wikipedia \u21a9</p> </li> <li> <p>Exponential smoothing \u2014 Forecasting: Principles and Practice \u21a9</p> </li> <li> <p>What is Exponential Smoothing? \u2014 Engineering statistics handbook \u21a9</p> </li> </ol>"},{"location":"api/time-series/HorizonAggMetric/","title":"HorizonAggMetric","text":"<p>Same as <code>HorizonMetric</code>, but aggregates the result based on an provided function.</p> <p>This allows, for instance, to measure the average performance of a forecasting model along the horizon.</p>"},{"location":"api/time-series/HorizonAggMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> <li> <p>agg_func</p> <p>Type \u2192 typing.Callable[[list[float]], float]</p> <p>A function that takes as input a list of floats and outputs a single float. You may want to <code>min</code>, <code>max</code>, as well as <code>statistics.mean</code> and <code>statistics.median</code>.</p> </li> </ul>"},{"location":"api/time-series/HorizonAggMetric/#examples","title":"Examples","text":"<p>This is used internally by the <code>time_series.evaluate</code> function when you pass an <code>agg_func</code>.</p> <p><pre><code>import statistics\nfrom river import datasets\nfrom river import metrics\nfrom river import time_series\n\nmetric = time_series.evaluate(\n    dataset=datasets.AirlinePassengers(),\n    model=time_series.HoltWinters(alpha=0.1),\n    metric=metrics.MAE(),\n    agg_func=statistics.mean,\n    horizon=4\n)\n\nmetric\n</code></pre> <pre><code>mean(MAE): 42.901748\n</code></pre></p>"},{"location":"api/time-series/HorizonAggMetric/#methods","title":"Methods","text":"get <p>Return the current performance along the horizon.</p> <p>Returns</p> <p>list[float]:     The current performance.</p> <p></p> update <p>Update the metric at each step along the horizon.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'list[Number]' </li> <li>y_pred     \u2014 'list[Number]' </li> </ul> <p></p>"},{"location":"api/time-series/HorizonMetric/","title":"HorizonMetric","text":"<p>Measures performance at each time step ahead.</p> <p>This allows to measure the performance of a model at each time step along the horizon. A copy of the provided regression metric is made for each time step. At each time step ahead, the metric is thus evaluated on each prediction for said time step, and not for the time steps before or after that.</p>"},{"location":"api/time-series/HorizonMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> </ul>"},{"location":"api/time-series/HorizonMetric/#examples","title":"Examples","text":"<p>This is used internally by the <code>time_series.evaluate</code> function.</p> <p><pre><code>from river import datasets\nfrom river import metrics\nfrom river import time_series\n\nmetric = time_series.evaluate(\n    dataset=datasets.AirlinePassengers(),\n    model=time_series.HoltWinters(alpha=0.1),\n    metric=metrics.MAE(),\n    horizon=4\n)\n\nmetric\n</code></pre> <pre><code>+1 MAE: 40.931286\n+2 MAE: 42.667998\n+3 MAE: 44.158092\n+4 MAE: 43.849617\n</code></pre></p>"},{"location":"api/time-series/HorizonMetric/#methods","title":"Methods","text":"get <p>Return the current performance along the horizon.</p> <p>Returns</p> <p>list[float]:     The current performance.</p> <p></p> update <p>Update the metric at each step along the horizon.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'list[Number]' </li> <li>y_pred     \u2014 'list[Number]' </li> </ul> <p></p>"},{"location":"api/time-series/SNARIMAX/","title":"SNARIMAX","text":"<p>SNARIMAX model.</p> <p>SNARIMAX stands for (S)easonal (N)on-linear (A)uto(R)egressive (I)ntegrated (M)oving-(A)verage with e(X)ogenous inputs model. </p> <p>This model generalizes many established time series models in a single interface that can be trained online. It assumes that the provided training data is ordered in time and is uniformly spaced. It is made up of the following components: </p> <ul> <li> <p>S (Seasonal)</p> </li> <li> <p>N (Non-linear): Any online regression model can be used, not necessarily a linear regression</p> <p>as is done in textbooks. - AR (Autoregressive): Lags of the target variable are used as features.</p> </li> <li> <p>I (Integrated): The model can be fitted on a differenced version of a time series. In this</p> <p>context, integration is the reverse of differencing. - MA (Moving average): Lags of the errors are used as features.</p> </li> <li> <p>X (Exogenous): Users can provide additional features. Care has to be taken to include</p> <p>features that will be available both at training and prediction time. </p> </li> </ul> <p>Each of these components can be switched on and off by specifying the appropriate parameters. Classical time series models such as AR, MA, ARMA, and ARIMA can thus be seen as special parametrizations of the SNARIMAX model. </p> <p>This model is tailored for time series that are homoskedastic. In other words, it might not work well if the variance of the time series varies widely along time.</p>"},{"location":"api/time-series/SNARIMAX/#parameters","title":"Parameters","text":"<ul> <li> <p>p</p> <p>Type \u2192 int</p> <p>Order of the autoregressive part. This is the number of past target values that will be included as features.</p> </li> <li> <p>d</p> <p>Type \u2192 int</p> <p>Differencing order.</p> </li> <li> <p>q</p> <p>Type \u2192 int</p> <p>Order of the moving average part. This is the number of past error terms that will be included as features.</p> </li> <li> <p>m</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Season length used for extracting seasonal features. If you believe your data has a seasonal pattern, then set this accordingly. For instance, if the data seems to exhibit a yearly seasonality, and that your data is spaced by month, then you should set this to 12. Note that for this parameter to have any impact you should also set at least one of the <code>p</code>, <code>d</code>, and <code>q</code> parameters.</p> </li> <li> <p>sp</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Seasonal order of the autoregressive part. This is the number of past target values that will be included as features.</p> </li> <li> <p>sd</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Seasonal differencing order.</p> </li> <li> <p>sq</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Seasonal order of the moving average part. This is the number of past error terms that will be included as features.</p> </li> <li> <p>regressor</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The online regression model to use. By default, a <code>preprocessing.StandardScaler</code> piped with a <code>linear_model.LinearRegression</code> will be used.</p> </li> </ul>"},{"location":"api/time-series/SNARIMAX/#attributes","title":"Attributes","text":"<ul> <li> <p>differencer (Differencer)</p> </li> <li> <p>y_trues (collections.deque)</p> <p>The <code>p</code> past target values.</p> </li> <li> <p>errors (collections.deque)</p> <p>The <code>q</code> past error values.</p> </li> </ul>"},{"location":"api/time-series/SNARIMAX/#examples","title":"Examples","text":"<p><pre><code>import datetime as dt\nfrom river import datasets\nfrom river import time_series\nfrom river import utils\n\nperiod = 12\nmodel = time_series.SNARIMAX(\n    p=period,\n    d=1,\n    q=period,\n    m=period,\n    sd=1\n)\n\nfor t, (x, y) in enumerate(datasets.AirlinePassengers()):\n    model.learn_one(y)\n\nhorizon = 12\nfuture = [\n    {'month': dt.date(year=1961, month=m, day=1)}\n    for m in range(1, horizon + 1)\n]\nforecast = model.forecast(horizon=horizon)\nfor x, y_pred in zip(future, forecast):\n    print(x['month'], f'{y_pred:.3f}')\n</code></pre> <pre><code>1961-01-01 494.542\n1961-02-01 450.825\n1961-03-01 484.972\n1961-04-01 576.401\n1961-05-01 559.489\n1961-06-01 612.251\n1961-07-01 722.410\n1961-08-01 674.604\n1961-09-01 575.716\n1961-10-01 562.808\n1961-11-01 477.049\n1961-12-01 515.191\n</code></pre></p> <p>Classic ARIMA models learn solely on the time series values. You can also include features built at each step.</p> <p><pre><code>import calendar\nimport math\nfrom river import compose\nfrom river import linear_model\nfrom river import optim\nfrom river import preprocessing\n\ndef get_month_distances(x):\n    return {\n        calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)\n        for month in range(1, 13)\n    }\n\ndef get_ordinal_date(x):\n    return {'ordinal_date': x['month'].toordinal()}\n\nextract_features = compose.TransformerUnion(\n    get_ordinal_date,\n    get_month_distances\n)\n\nmodel = (\n    extract_features |\n    time_series.SNARIMAX(\n        p=1,\n        d=0,\n        q=0,\n        m=12,\n        sp=3,\n        sq=6,\n        regressor=(\n            preprocessing.StandardScaler() |\n            linear_model.LinearRegression(\n                intercept_init=110,\n                optimizer=optim.SGD(0.01),\n                intercept_lr=0.3\n            )\n        )\n    )\n)\n\nfor x, y in datasets.AirlinePassengers():\n    model.learn_one(x, y)\n\nforecast = model.forecast(horizon=horizon)\nfor x, y_pred in zip(future, forecast):\n    print(x['month'], f'{y_pred:.3f}')\n</code></pre> <pre><code>1961-01-01 444.821\n1961-02-01 432.612\n1961-03-01 457.739\n1961-04-01 465.544\n1961-05-01 476.575\n1961-06-01 516.255\n1961-07-01 565.405\n1961-08-01 572.470\n1961-09-01 512.645\n1961-10-01 475.919\n1961-11-01 438.033\n1961-12-01 456.892\n</code></pre></p>"},{"location":"api/time-series/SNARIMAX/#methods","title":"Methods","text":"forecast <p>Makes forecast at each step of the given horizon.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Updates the model.</p> <p>Parameters</p> <ul> <li>y     \u2014 'float' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>ARMA - Wikipedia \u21a9</p> </li> <li> <p>NARX - Wikipedia \u21a9</p> </li> <li> <p>ARIMA - Forecasting: Principles and Practice \u21a9</p> </li> <li> <p>Anava, O., Hazan, E., Mannor, S. and Shamir, O., 2013, June. Online learning for time series prediction. In Conference on learning theory (pp. 172-184) \u21a9</p> </li> </ol>"},{"location":"api/time-series/evaluate/","title":"evaluate","text":"<p>Evaluates the performance of a forecaster on a time series dataset.</p> <p>To understand why this method is useful, it's important to understand the difference between nowcasting and forecasting. Nowcasting is about predicting a value at the next time step. This can be seen as a special case of regression, where the value to predict is the value at the next time step. In this case, the <code>evaluate.progressive_val_score</code> function may be used to evaluate a model via progressive validation. </p> <p>Forecasting models can also be evaluated via progressive validation. This is the purpose of this function. At each time step <code>t</code>, the forecaster is asked to predict the values at <code>t + 1</code>, <code>t + 2</code>, ..., <code>t + horizon</code>. The performance at each time step is measured and returned.</p>"},{"location":"api/time-series/evaluate/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>A sequential time series.</p> </li> <li> <p>model</p> <p>Type \u2192 time_series.base.Forecaster</p> <p>A forecaster.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> <li> <p>horizon</p> <p>Type \u2192 int</p> </li> <li> <p>agg_func</p> <p>Type \u2192 typing.Callable[[list[float]], float] | None</p> <p>Default \u2192 <code>None</code></p> </li> <li> <p>grace_period</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Initial period during which the metric is not updated. This is to fairly evaluate models which need a warming up period to start producing meaningful forecasts. The value of this parameter is equal to the horizon by default.</p> </li> </ul>"},{"location":"api/time-series/iter-evaluate/","title":"iter_evaluate","text":"<p>Evaluates the performance of a forecaster on a time series dataset and yields results.</p> <p>This does exactly the same as <code>evaluate.progressive_val_score</code>. The only difference is that this function returns an iterator, yielding results at every step. This can be useful if you want to have control over what you do with the results. For instance, you might want to plot the results.</p>"},{"location":"api/time-series/iter-evaluate/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>A sequential time series.</p> </li> <li> <p>model</p> <p>Type \u2192 time_series.base.Forecaster</p> <p>A forecaster.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> <li> <p>horizon</p> <p>Type \u2192 int</p> </li> <li> <p>agg_func</p> <p>Type \u2192 typing.Callable[[list[float]], float] | None</p> <p>Default \u2192 <code>None</code></p> </li> <li> <p>grace_period</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Initial period during which the metric is not updated. This is to fairly evaluate models which need a warming up period to start producing meaningful forecasts. The value of this parameter is equal to the horizon by default.</p> </li> </ul>"},{"location":"api/time-series/base/Forecaster/","title":"Forecaster","text":""},{"location":"api/time-series/base/Forecaster/#methods","title":"Methods","text":"forecast <p>Makes forecast at each step of the given horizon.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Updates the model.</p> <p>Parameters</p> <ul> <li>y     \u2014 'float' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/","title":"ExtremelyFastDecisionTreeClassifier","text":"<p>Extremely Fast Decision Tree (EFDT) classifier.</p> <p>Also referred to as the Hoeffding AnyTime Tree (HATT) classifier. In practice, despite the name, EFDTs are typically slower than a vanilla Hoeffding Tree to process data. The speed differences come from the mechanism of split re-evaluation present in EFDT. Nonetheless, EFDT has theoretical properties that ensure it converges faster than the vanilla Hoeffding Tree to the structure that would be created by a batch decision tree model (such as Classification and Regression Trees - CART). Keep in mind that such propositions hold when processing a stationary data stream. When dealing with non-stationary data, EFDT is somewhat robust to concept drifts as it continually revisits and updates its internal decision tree structure. Still, in such cases, the Hoeffind Adaptive Tree might be a better option, as it was specifically designed to handle non-stationarity.</p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>min_samples_reevaluate</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>20</code></p> <p>Number of instances a node should observe before reevaluating the best split.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Helinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ngen = synth.Agrawal(classification_function=0, seed=42)\ndataset = iter(gen.take(1000))\n\nmodel = tree.ExtremelyFastDecisionTreeClassifier(\n    grace_period=100,\n    delta=1e-5,\n    nominal_attributes=['elevel', 'car', 'zipcode'],\n    min_samples_reevaluate=100\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 87.29%\n</code></pre></p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Incrementally train the model</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#notes","title":"Notes","text":"<p>The Extremely Fast Decision Tree (EFDT) <sup>1</sup> constructs a tree incrementally. The EFDT seeks to select and deploy a split as soon as it is confident the split is useful, and then revisits that decision, replacing the split if it subsequently becomes evident that a better split is available. The EFDT learns rapidly from a stationary distribution and eventually it learns the asymptotic batch tree if the distribution from which the data are drawn is stationary.</p> <ol> <li> <p>C. Manapragada, G. Webb, and M. Salehi. Extremely Fast Decision Tree. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining (KDD '18). ACM, New York, NY, USA, 1953-1962. DOI: https://doi.org/10.1145/3219819.3220005\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/","title":"HoeffdingAdaptiveTreeClassifier","text":"<p>Hoeffding Adaptive Tree classifier.</p>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Helinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>bootstrap_sampling</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform bootstrap sampling in the leaf nodes.</p> </li> <li> <p>drift_window_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>300</code></p> <p>Minimum number of examples an alternate tree must observe before being considered as a potential replacement to the current one.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The drift detector used to build the tree. If <code>None</code> then <code>drift.ADWIN</code> is used.</p> </li> <li> <p>switch_significance</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>The significance level to assess whether alternate subtrees are significantly better than their main subtree counterparts.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_alternate_trees</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_pruned_alternate_trees</p> </li> <li> <p>n_switch_alternate_trees</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ngen = synth.ConceptDriftStream(stream=synth.SEA(seed=42, variant=0),\n                               drift_stream=synth.SEA(seed=42, variant=1),\n                               seed=1, position=500, width=50)\ndataset = iter(gen.take(1000))\n\nmodel = tree.HoeffdingAdaptiveTreeClassifier(\n    grace_period=100,\n    delta=1e-5,\n    leaf_prediction='nb',\n    nb_threshold=10,\n    seed=0\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 91.49%\n</code></pre></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the model on instance x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#notes","title":"Notes","text":"<p>The Hoeffding Adaptive Tree <sup>1</sup> uses a drift detector to monitor performance of branches in the tree and to replace them with new branches when their accuracy decreases.</p> <p>The bootstrap sampling strategy is an improvement over the original Hoeffding Adaptive Tree algorithm. It is enabled by default since, in general, it results in better performance.</p> <ol> <li> <p>Bifet, Albert, and Ricard Gavald\u00e0. \"Adaptive learning from evolving data streams.\"    In International Symposium on Intelligent Data Analysis, pp. 249-260. Springer, Berlin,    Heidelberg, 2009.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/","title":"HoeffdingAdaptiveTreeRegressor","text":"<p>Hoeffding Adaptive Tree regressor (HATR).</p> <p>This class implements a regression version of the Hoeffding Adaptive Tree Classifier. Hence, it also uses an ADWIN concept-drift detector instance at each decision node to monitor possible changes in the data distribution. If a drift is detected in a node, an alternate tree begins to be induced in the background. When enough information is gathered, HATR swaps the node where the change was detected by its alternate tree.</p>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>Prediction mechanism used at leafs. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>bootstrap_sampling</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform bootstrap sampling in the leaf nodes.</p> </li> <li> <p>drift_window_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>300</code></p> <p>Minimum number of examples an alternate tree must observe before being considered as a potential replacement to the current one.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The drift detector used to build the tree. If <code>None</code> then <code>drift.ADWIN</code> is used. Only detectors that support arbitrarily valued continuous data can be used for regression.</p> </li> <li> <p>switch_significance</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>The significance level to assess whether alternate subtrees are significantly better than their main subtree counterparts.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_alternate_trees</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_pruned_alternate_trees</p> </li> <li> <p>n_switch_alternate_trees</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    tree.HoeffdingAdaptiveTreeRegressor(\n        grace_period=50,\n        model_selector_decay=0.3,\n        seed=0\n    )\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.823026\n</code></pre></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the tree model on sample x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the target value using one of the leaf prediction strategies.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>Predicted target value.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#notes","title":"Notes","text":"<p>The Hoeffding Adaptive Tree <sup>1</sup> uses drift detectors to monitor performance of branches in the tree and to replace them with new branches when their accuracy decreases.</p> <p>The bootstrap sampling strategy is an improvement over the original Hoeffding Adaptive Tree algorithm. It is enabled by default since, in general, it results in better performance.</p> <p>To cope with ADWIN's requirements of bounded input data, HATR uses a novel error normalization strategy based on the empiral rule of Gaussian distributions. We assume the deviations of the predictions from the expected values follow a normal distribution. Hence, we subject these errors to a min-max normalization assuming that most of the data lies in the \\(\\left[-3\\sigma, 3\\sigma\\right]\\) range. These normalized errors are passed to the ADWIN instances. This is the same strategy used by Adaptive Random Forest Regressor.</p> <ol> <li> <p>Bifet, Albert, and Ricard Gavald\u00e0. \"Adaptive learning from evolving data streams.\" In International Symposium on Intelligent Data Analysis, pp. 249-260. Springer, Berlin, Heidelberg, 2009.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingTreeClassifier/","title":"HoeffdingTreeClassifier","text":"<p>Hoeffding Tree or Very Fast Decision Tree classifier.</p>"},{"location":"api/tree/HoeffdingTreeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Helinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ngen = synth.Agrawal(classification_function=0, seed=42)\ndataset = iter(gen.take(1000))\n\nmodel = tree.HoeffdingTreeClassifier(\n    grace_period=100,\n    delta=1e-5,\n    nominal_attributes=['elevel', 'car', 'zipcode']\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 84.58%\n</code></pre></p>"},{"location":"api/tree/HoeffdingTreeClassifier/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the model on instance x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingTreeClassifier/#notes","title":"Notes","text":"<p>A Hoeffding Tree <sup>1</sup> is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).</p> <p>A theoretically appealing feature of Hoeffding Trees not shared by other incremental decision tree learners is that it has sound guarantees of performance. Using the Hoeffding bound one can show that its output is asymptotically nearly identical to that of a non-incremental learner using infinitely many examples. Implementation based on MOA <sup>2</sup>.</p> <ol> <li> <p>G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams.    In KDD\u201901, pages 97\u2013106, San Francisco, CA, 2001. ACM Press.\u00a0\u21a9</p> </li> <li> <p>Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer.    MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604, 2010.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingTreeRegressor/","title":"HoeffdingTreeRegressor","text":"<p>Hoeffding Tree regressor.</p>"},{"location":"api/tree/HoeffdingTreeRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>Prediction mechanism used at leafs. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    tree.HoeffdingTreeRegressor(\n        grace_period=100,\n        model_selector_decay=0.9\n    )\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.793345\n</code></pre></p>"},{"location":"api/tree/HoeffdingTreeRegressor/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the tree model on sample x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the target value using one of the leaf prediction strategies.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>Predicted target value.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingTreeRegressor/#notes","title":"Notes","text":"<p>The Hoeffding Tree Regressor (HTR) is an adaptation of the incremental tree algorithm of the same name for classification. Similarly to its classification counterpart, HTR uses the Hoeffding bound to control its split decisions. Differently from the classification algorithm, HTR relies on calculating the reduction of variance in the target space to decide among the split candidates. The smallest the variance at its leaf nodes, the more homogeneous the partitions are. At its leaf nodes, HTR fits either linear models or uses the target average as the predictor.</p>"},{"location":"api/tree/SGTClassifier/","title":"SGTClassifier","text":"<p>Stochastic Gradient Tree<sup>1</sup> for binary classification.</p> <p>Binary decision tree classifier that minimizes the binary cross-entropy to guide its growth. </p> <p>Stochastic Gradient Trees (SGT) directly minimize a loss function to guide tree growth and update their predictions. Thus, they differ from other incrementally tree learners that do not directly optimize the loss, but data impurity-related heuristics.</p>"},{"location":"api/tree/SGTClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Define the significance level of the F-tests performed to decide upon creating splits or updating predictions.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Interval between split attempts or prediction updates.</p> </li> <li> <p>init_pred</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial value predicted by the tree.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth the tree might reach. If set to <code>None</code>, the trees will grow indefinitely.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Positive float value used to impose a penalty over the tree's predictions and force them to become smaller. The greater the lambda value, the more constrained are the predictions.</p> </li> <li> <p>gamma</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Positive float value used to impose a penalty over the tree's splits and force them to be avoided when possible. The greater the gamma value, the smaller the chance of a split occurring.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List with identifiers of the nominal attributes. If None, all features containing numbers are assumed to be numeric.</p> </li> <li> <p>feature_quantizer</p> <p>Type \u2192 tree.splitter.Quantizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The algorithm used to quantize numeric features. Either a static quantizer (as in the original implementation) or a dynamic quantizer can be used. The correct choice and setup of the feature quantizer is a crucial step to determine the performance of SGTs. Feature quantizers are akin to the attribute observers used in Hoeffding Trees. By default, an instance of <code>tree.splitter.StaticQuantizer</code> (with default parameters) is used if this parameter is not set.</p> </li> </ul>"},{"location":"api/tree/SGTClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_node_updates</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_observations</p> </li> <li> <p>n_splits</p> </li> </ul>"},{"location":"api/tree/SGTClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Phishing()\nmodel = tree.SGTClassifier(\n    feature_quantizer=tree.splitter.StaticQuantizer(\n        n_bins=32, warm_start=10\n    )\n)\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 82.24%\n</code></pre></p>"},{"location":"api/tree/SGTClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/SGTRegressor/","title":"SGTRegressor","text":"<p>Stochastic Gradient Tree for regression.</p> <p>Incremental decision tree regressor that minimizes the mean square error to guide its growth. </p> <p>Stochastic Gradient Trees (SGT) directly minimize a loss function to guide tree growth and update their predictions. Thus, they differ from other incrementally tree learners that do not directly optimize the loss, but a data impurity-related heuristic.</p>"},{"location":"api/tree/SGTRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Define the significance level of the F-tests performed to decide upon creating splits or updating predictions.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Interval between split attempts or prediction updates.</p> </li> <li> <p>init_pred</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial value predicted by the tree.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth the tree might reach. If set to <code>None</code>, the trees will grow indefinitely.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Positive float value used to impose a penalty over the tree's predictions and force them to become smaller. The greater the lambda value, the more constrained are the predictions.</p> </li> <li> <p>gamma</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Positive float value used to impose a penalty over the tree's splits and force them to be avoided when possible. The greater the gamma value, the smaller the chance of a split occurring.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List with identifiers of the nominal attributes. If None, all features containing numbers are assumed to be numeric.</p> </li> <li> <p>feature_quantizer</p> <p>Type \u2192 tree.splitter.Quantizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The algorithm used to quantize numeric features. Either a static quantizer (as in the original implementation) or a dynamic quantizer can be used. The correct choice and setup of the feature quantizer is a crucial step to determine the performance of SGTs. Feature quantizers are akin to the attribute observers used in Hoeffding Trees. By default, an instance of <code>tree.splitter.StaticQuantizer</code> (with default parameters) is used if this parameter is not set.</p> </li> </ul>"},{"location":"api/tree/SGTRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_node_updates</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_observations</p> </li> <li> <p>n_splits</p> </li> </ul>"},{"location":"api/tree/SGTRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.TrumpApproval()\nmodel = tree.SGTRegressor(\n    delta=0.01,\n    lambda_value=0.01,\n    grace_period=20,\n    feature_quantizer=tree.splitter.DynamicQuantizer(std_prop=0.1)\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.721818\n</code></pre></p>"},{"location":"api/tree/SGTRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/tree/SGTRegressor/#notes","title":"Notes","text":"<p>This implementation enhances the original proposal <sup>1</sup> by using an incremental strategy to discretize numerical features dynamically, rather than relying on a calibration set and parameterized number of bins. The strategy used is an adaptation of the Quantization Observer (QO) <sup>2</sup>. Different bin size setting policies are available for selection. They directly related to number of split candidates the tree is going to explore, and thus, how accurate its split decisions are going to be. Besides, the number of stored bins per feature is directly related to the tree's memory usage and runtime.</p> <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> <li> <p>Mastelini, S.M. and de Leon Ferreira, A.C.P., 2021. Using dynamical quantization to perform split attempts in online tree regressors. Pattern Recognition Letters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/iSOUPTreeRegressor/","title":"iSOUPTreeRegressor","text":"<p>Incremental Structured Output Prediction Tree (iSOUP-Tree) for multi-target regression.</p> <p>This is an implementation of the iSOUP-Tree proposed by A. Osojnik, P. Panov, and S. D\u017eeroski <sup>1</sup>.</p>"},{"location":"api/tree/iSOUPTreeRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>Prediction mechanism used at leafs. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | dict | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model(s) used to provide responses if <code>leaf_prediction='model'</code>. It can be either a regressor (in which case it is going to be replicated to all the targets) or a dictionary whose keys are target identifiers, and the values are instances of <code>base.Regressor</code>.<code>If not provided, instances of [</code>linear_model.LinearRegression`](../../linear-model/LinearRegression) with the default hyperparameters are used for all the targets. If a dictionary is passed and not all target models are specified, copies from the first model match in the dictionary will be used to the remaining targets.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/iSOUPTreeRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/iSOUPTreeRegressor/#examples","title":"Examples","text":"<p><pre><code>import numbers\nfrom river import compose\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import tree\n\ndataset = datasets.SolarFlare()\n\nnum = compose.SelectType(numbers.Number) | preprocessing.MinMaxScaler()\ncat = compose.SelectType(str) | preprocessing.OneHotEncoder()\n\nmodel = tree.iSOUPTreeRegressor(\n    grace_period=100,\n    leaf_prediction='model',\n    leaf_model={\n        'c-class-flares': linear_model.LinearRegression(l2=0.02),\n        'm-class-flares': linear_model.PARegressor(),\n        'x-class-flares': linear_model.LinearRegression(l2=0.1)\n    }\n)\n\npipeline = (num + cat) | model\nmetric = metrics.multioutput.MicroAverage(metrics.MAE())\n\nevaluate.progressive_val_score(dataset, pipeline, metric)\n</code></pre> <pre><code>MicroAverage(MAE): 0.426177\n</code></pre></p>"},{"location":"api/tree/iSOUPTreeRegressor/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Incrementally train the model with one sample.</p> <p>Training tasks:  * If the tree is empty, create a leaf node as the root. * If the tree is already initialized, find the corresponding leaf for   the instance and update the leaf node statistics. * If growth is allowed and the number of instances that the leaf has   observed between split attempts exceed the grace period then attempt   to split.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 'float'     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the target value using one of the leaf prediction strategies.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>Predicted target value.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p> <ol> <li> <p>Alja\u017e Osojnik, Pan\u010de Panov, and Sa\u0161o D\u017eeroski. \"Tree-based methods for online multi-target regression.\" Journal of Intelligent Information Systems 50.2 (2018): 315-339.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/base/Branch/","title":"Branch","text":"<p>A generic tree branch.</p>"},{"location":"api/tree/base/Branch/#parameters","title":"Parameters","text":"<ul> <li> <p>children</p> <p>Child branches and/or leaves.</p> </li> </ul>"},{"location":"api/tree/base/Branch/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> <p>Distance to the deepest descendant.</p> </li> <li> <p>n_branches</p> <p>Number of branches, including thyself.</p> </li> <li> <p>n_leaves</p> <p>Number of leaves.</p> </li> <li> <p>n_nodes</p> <p>Number of descendants, including thyself.</p> </li> <li> <p>repr_split</p> <p>String representation of the split.</p> </li> </ul>"},{"location":"api/tree/base/Branch/#methods","title":"Methods","text":"iter_bfs <p>Iterate over nodes in breadth-first order.</p> <p></p> iter_branches <p>Iterate over branches in depth-first order.</p> <p></p> iter_dfs <p>Iterate over nodes in depth-first order.</p> <p></p> iter_edges <p>Iterate over edges in depth-first order.</p> <p></p> iter_leaves <p>Iterate over leaves from the left-most one to the right-most one.</p> <p></p> most_common_path <p>Return a tuple with the branch index and the child node related to the most traversed path.</p> <p>Used in case the split feature is missing from an instance.</p> <p></p> next <p>Move to the next node down the tree.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> to_dataframe <p>Build a DataFrame containing one record for each node.</p> <p></p> traverse <p>Return the leaf corresponding to the given input.</p> <p>Parameters</p> <ul> <li>x </li> <li>until_leaf     \u2014 defaults to <code>True</code> </li> </ul> <p></p> walk <p>Iterate over the nodes of the path induced by x.</p> <p>Parameters</p> <ul> <li>x </li> <li>until_leaf     \u2014 defaults to <code>True</code> </li> </ul> <p></p>"},{"location":"api/tree/base/Leaf/","title":"Leaf","text":"<p>A generic tree node.</p>"},{"location":"api/tree/base/Leaf/#parameters","title":"Parameters","text":"<ul> <li> <p>kwargs</p> <p>Each provided keyword argument is stored in the leaf as an attribute.</p> </li> </ul>"},{"location":"api/tree/base/Leaf/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> </ul>"},{"location":"api/tree/base/Leaf/#methods","title":"Methods","text":"iter_branches iter_dfs iter_edges iter_leaves walk"},{"location":"api/tree/splitter/DynamicQuantizer/","title":"DynamicQuantizer","text":"<p>Adapted version of the Quantizer Observer (QO)<sup>1</sup> that is applied to Stochastic Gradient Trees (SGT).</p> <p>This feature quantizer starts by partitioning the inputs using the passed <code>radius</code> value. As more splits are created in the SGTs, new feature quantizers will use <code>std * std_prop</code> as the quantization radius. In the expression, <code>std</code> represents the standard deviation of the input data, which is calculated incrementally.</p>"},{"location":"api/tree/splitter/DynamicQuantizer/#parameters","title":"Parameters","text":"<ul> <li> <p>radius</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>The initial quantization radius.</p> </li> <li> <p>std_prop</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.25</code></p> <p>The proportion of the standard deviation that is going to be used to define the radius value for new quantizer instances following the initial one.</p> </li> </ul>"},{"location":"api/tree/splitter/DynamicQuantizer/#methods","title":"Methods","text":"update <ol> <li> <p>Mastelini, S.M. and de Leon Ferreira, A.C.P., 2021. Using dynamical quantization to perform split attempts in online tree regressors. Pattern Recognition Letters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/EBSTSplitter/","title":"EBSTSplitter","text":"<p>iSOUP-Tree's Extended Binary Search Tree (E-BST).</p> <p>This class implements the Extended Binary Search Tree<sup>1</sup> (E-BST) structure, using the variant employed by Osojnik et al.<sup>2</sup> in the iSOUP-Tree algorithm. This structure is employed to observe the target space distribution. </p> <p>Proposed along with Fast Incremental Model Tree with Drift Detection<sup>1</sup> (FIMT-DD), E-BST was the first attribute observer (AO) proposed for incremental Hoeffding Tree regressors. This AO works by storing all observations between splits in an extended binary search tree structure. E-BST stores the input feature realizations and statistics of the target(s) that enable calculating the split heuristic at any time. To alleviate time and memory costs, E-BST implements a memory management routine, where the worst split candidates are pruned from the binary tree. </p> <p>In this variant, only the left branch statistics are stored and the complete split-enabling statistics are calculated with an in-order traversal of the binary search tree.</p>"},{"location":"api/tree/splitter/EBSTSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/EBSTSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool'     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Not implemented in regression splitters.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> remove_bad_splits <p>Remove bad splits.</p> <p>Based on FIMT-DD's <sup>1</sup> procedure to remove bad split candidates from the E-BST. This mechanism is triggered every time a split attempt fails. The rationale is to remove points whose split merit is much worse than the best candidate overall (for which the growth decision already failed).  Let \\(m_1\\) be the merit of the best split point and \\(m_2\\) be the merit of the second best split candidate. The ratio \\(r = m_2/m_1\\) along with the Hoeffding bound (\\(\\epsilon\\)) are used to decide upon creating a split. A split occurs when \\(r &lt; 1 - \\epsilon\\). A split candidate, with merit \\(m_i\\), is considered badr if \\(m_i / m_1 &lt; r - 2\\epsilon\\). The rationale is the following: if the merit ratio for this point is smaller than the lower bound of \\(r\\), then the true merit of that split relative to the best one is small. Hence, this candidate can be safely removed.  To avoid excessive and costly manipulations of the E-BST to update the stored statistics, only the nodes whose children are all bad split points are pruned, as defined in <sup>1</sup>.</p> <p>Parameters</p> <ul> <li>criterion </li> <li>last_check_ratio     \u2014 'float' </li> <li>last_check_vr     \u2014 'float' </li> <li>last_check_e     \u2014 'float' </li> <li>pre_split_dist     \u2014 'list | dict' </li> </ul> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>Ikonomovska, E., Gama, J., &amp; D\u017eeroski, S. (2011). Learning model trees from evolving data streams. Data mining and knowledge discovery, 23(1), 128-168.\u00a0\u21a9\u21a9\u21a9\u21a9</p> </li> <li> <p>Osojnik, Alja\u017e. 2017. Structured output prediction on Data Streams (Doctoral Dissertation) \u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/ExhaustiveSplitter/","title":"ExhaustiveSplitter","text":"<p>Numeric attribute observer for classification tasks that is based on a Binary Search Tree.</p> <p>This algorithm<sup>1</sup> is also referred to as exhaustive attribute observer, since it ends up storing all the observations between split attempts<sup>2</sup>. </p> <p>This splitter cannot perform probability density estimations, so it does not work well when coupled with tree leaves using naive bayes models.</p>"},{"location":"api/tree/splitter/ExhaustiveSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/ExhaustiveSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>The underlying data structure used to monitor the input does not allow probability density estimations. Hence, it always returns zero for any given input.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>Domingos, P. and Hulten, G., 2000, August. Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 71-80).\u00a0\u21a9</p> </li> <li> <p>Pfahringer, B., Holmes, G. and Kirkby, R., 2008, May. Handling numeric attributes in hoeffding trees. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 296-307). Springer, Berlin, Heidelberg.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/GaussianSplitter/","title":"GaussianSplitter","text":"<p>Numeric attribute observer for classification tasks that is based on Gaussian estimators.</p> <p>The distribution of each class is approximated using a Gaussian distribution. Hence, the probability density function can be easily calculated.</p>"},{"location":"api/tree/splitter/GaussianSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>n_splits</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of partitions to consider when querying for split candidates.</p> </li> </ul>"},{"location":"api/tree/splitter/GaussianSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/GaussianSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/tree/splitter/HistogramSplitter/","title":"HistogramSplitter","text":"<p>Numeric attribute observer for classification tasks that discretizes features using histograms.</p>"},{"location":"api/tree/splitter/HistogramSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>n_bins</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>256</code></p> <p>The maximum number of bins in the histogram.</p> </li> <li> <p>n_splits</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>32</code></p> <p>The number of split points to evaluate when querying for the best split candidate.</p> </li> </ul>"},{"location":"api/tree/splitter/HistogramSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/HistogramSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/tree/splitter/QOSplitter/","title":"QOSplitter","text":"<p>Quantization observer (QO).</p> <p>This splitter utilizes a hash-based quantization algorithm to keep track of the target statistics and evaluate split candidates. QO, relies on the radius parameter to define discretization intervals for each incoming feature. Split candidates are defined as the midpoints between two consecutive hash slots. Both binary splits and multi-way splits can be created by this attribute observer. This class implements the algorithm described in <sup>1</sup>. </p> <p>The smaller the quantization radius, the more hash slots will be created to accommodate the discretized data. Hence, both the running time and memory consumption increase, but the resulting splits ought to be closer to the ones obtained by a batch exhaustive approach. On the other hand, if the radius is too large, fewer slots will be created, less memory and running time will be required, but at the cost of coarse split suggestions. </p> <p>QO assumes that all features have the same range. It is always advised to scale the features to apply this splitter. That can be done using the <code>preprocessing</code> module. A good \"rule of thumb\" is to scale data using <code>preprocessing.StandardScaler</code> and define the radius as a proportion of the features' standard deviation. For instance, the default radius value would correspond to one quarter of the normalized features' standard deviation (since the scaled data has zero mean and unit variance). If the features come from normal distributions, by following the empirical rule, roughly <code>32</code> hash slots will be created.</p>"},{"location":"api/tree/splitter/QOSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>radius</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.25</code></p> <p>The quantization radius. QO discretizes the incoming feature in intervals of equal length that are defined by this parameter.</p> </li> <li> <p>allow_multiway_splits</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not allow that multiway splits are evaluated. Numeric multi-way splits use the same quantization strategy of QO to create multiple tree branches. The same quantization radius is used, and each stored slot represents the split enabling statistics of one branch.</p> </li> </ul>"},{"location":"api/tree/splitter/QOSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/QOSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool'     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>Mastelini, S.M. and de Leon Ferreira, A.C.P., 2021. Using dynamical quantization to perform split attempts in online tree regressors. Pattern Recognition Letters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/Quantizer/","title":"Quantizer","text":"<p>Base class for the feature quantizers used in Stochastic Gradient Trees<sup>1</sup>.</p>"},{"location":"api/tree/splitter/Quantizer/#methods","title":"Methods","text":"update <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/Splitter/","title":"Splitter","text":"<p>Base class for the tree splitters.</p> <p>Each Attribute Observer (AO) or Splitter monitors one input feature and finds the best split point for this attribute. AOs can also perform other tasks related to the monitored feature, such as estimating its probability density function (classification case). </p> <p>This class should not be instantiated, as none of its methods are implemented.</p>"},{"location":"api/tree/splitter/Splitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/Splitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/tree/splitter/StaticQuantizer/","title":"StaticQuantizer","text":"<p>Quantization strategy originally used in Stochastic Gradient Trees (SGT)<sup>1</sup>.</p> <p>Firstly, a buffer of size <code>warm_start</code> is stored. The data stored in the buffer is then used to quantize the input feature into <code>n_bins</code> intervals. These intervals will be replicated to every new quantizer. Feature values lying outside of the limits defined by the initial buffer will be mapped to the head or tail of the list of intervals.</p>"},{"location":"api/tree/splitter/StaticQuantizer/#parameters","title":"Parameters","text":"<ul> <li> <p>n_bins</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>64</code></p> <p>The number of bins (intervals) to divide the input feature.</p> </li> <li> <p>warm_start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>The number of observations used to initialize the quantization intervals.</p> </li> <li> <p>buckets</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>This parameter is only used internally by the quantizer, so it must not be set. Once the intervals are defined, new instances of this quantizer will receive the quantization information via this parameter.</p> </li> </ul>"},{"location":"api/tree/splitter/StaticQuantizer/#methods","title":"Methods","text":"update <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/TEBSTSplitter/","title":"TEBSTSplitter","text":"<p>Truncated E-BST.</p> <p>Variation of E-BST that rounds the incoming feature values before passing them to the binary search tree (BST). By doing so, the attribute observer might reduce its processing time and memory usage since small variations in the input values will end up being mapped to the same BST node.</p>"},{"location":"api/tree/splitter/TEBSTSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>digits</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>The number of decimal places used to round the input feature values.</p> </li> </ul>"},{"location":"api/tree/splitter/TEBSTSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/TEBSTSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool'     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Not implemented in regression splitters.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> remove_bad_splits <p>Remove bad splits.</p> <p>Based on FIMT-DD's [^1] procedure to remove bad split candidates from the E-BST. This mechanism is triggered every time a split attempt fails. The rationale is to remove points whose split merit is much worse than the best candidate overall (for which the growth decision already failed).  Let \\(m_1\\) be the merit of the best split point and \\(m_2\\) be the merit of the second best split candidate. The ratio \\(r = m_2/m_1\\) along with the Hoeffding bound (\\(\\epsilon\\)) are used to decide upon creating a split. A split occurs when \\(r &lt; 1 - \\epsilon\\). A split candidate, with merit \\(m_i\\), is considered badr if \\(m_i / m_1 &lt; r - 2\\epsilon\\). The rationale is the following: if the merit ratio for this point is smaller than the lower bound of \\(r\\), then the true merit of that split relative to the best one is small. Hence, this candidate can be safely removed.  To avoid excessive and costly manipulations of the E-BST to update the stored statistics, only the nodes whose children are all bad split points are pruned, as defined in [^1].</p> <p>Parameters</p> <ul> <li>criterion </li> <li>last_check_ratio     \u2014 'float' </li> <li>last_check_vr     \u2014 'float' </li> <li>last_check_e     \u2014 'float' </li> <li>pre_split_dist     \u2014 'list | dict' </li> </ul> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/utils/Rolling/","title":"Rolling","text":"<p>A generic wrapper for performing rolling computations.</p> <p>This can be wrapped around any object which implements both an <code>update</code> and a <code>revert</code> method. Inputs to <code>update</code> are stored in a queue. Elements of the queue are popped when the window is full.</p>"},{"location":"api/utils/Rolling/#parameters","title":"Parameters","text":"<ul> <li> <p>obj</p> <p>Type \u2192 Rollable</p> <p>An object that implements both an <code>update</code> method and a <code>rolling</code>method.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the window.</p> </li> </ul>"},{"location":"api/utils/Rolling/#attributes","title":"Attributes","text":"<ul> <li>window_size</li> </ul>"},{"location":"api/utils/Rolling/#examples","title":"Examples","text":"<p>For instance, here is how you can compute a rolling average over a window of size 3:</p> <p><pre><code>from river import stats, utils\n\nX = [1, 3, 5, 7]\nrmean = utils.Rolling(stats.Mean(), window_size=3)\n\nfor x in X:\n    rmean.update(x)\n    print(rmean.get())\n</code></pre> <pre><code>1.0\n2.0\n3.0\n5.0\n</code></pre></p>"},{"location":"api/utils/Rolling/#methods","title":"Methods","text":"update"},{"location":"api/utils/SortedWindow/","title":"SortedWindow","text":"<p>Sorted running window data structure.</p>"},{"location":"api/utils/SortedWindow/#parameters","title":"Parameters","text":"<ul> <li> <p>size</p> <p>Type \u2192 int</p> <p>Size of the window to compute the rolling quantile.</p> </li> </ul>"},{"location":"api/utils/SortedWindow/#attributes","title":"Attributes","text":"<ul> <li>size</li> </ul>"},{"location":"api/utils/SortedWindow/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nwindow = utils.SortedWindow(size=3)\n\nfor i in reversed(range(9)):\n    print(window.append(i))\n</code></pre> <pre><code>[8]\n[7, 8]\n[6, 7, 8]\n[5, 6, 7]\n[4, 5, 6]\n[3, 4, 5]\n[2, 3, 4]\n[1, 2, 3]\n[0, 1, 2]\n</code></pre></p>"},{"location":"api/utils/SortedWindow/#methods","title":"Methods","text":"<ol> <li> <p>Left sorted inserts in Python \u21a9</p> </li> </ol>"},{"location":"api/utils/TimeRolling/","title":"TimeRolling","text":"<p>A generic wrapper for performing time rolling computations.</p> <p>This can be wrapped around any object which implements both an <code>update</code> and a <code>revert</code> method. Inputs to <code>update</code> are stored in a queue. Elements of the queue are popped when they are too old.</p>"},{"location":"api/utils/TimeRolling/#parameters","title":"Parameters","text":"<ul> <li> <p>obj</p> <p>Type \u2192 Rollable</p> <p>An object that implements both an <code>update</code> method and a <code>rolling</code>method.</p> </li> <li> <p>period</p> <p>Type \u2192 dt.timedelta</p> <p>A duration of time, expressed as a <code>datetime.timedelta</code>.</p> </li> </ul>"},{"location":"api/utils/TimeRolling/#examples","title":"Examples","text":"<p>For instance, here is how you can compute a rolling average over a period of 3 days:</p> <p><pre><code>from river import stats, utils\n\nX = {\n    dt.datetime(2019, 1, 1): 1,\n    dt.datetime(2019, 1, 2): 5,\n    dt.datetime(2019, 1, 3): 9,\n    dt.datetime(2019, 1, 4): 13\n}\n\nrmean = utils.TimeRolling(stats.Mean(), period=dt.timedelta(days=3))\nfor t, x in X.items():\n    rmean.update(x, t=t)\n    print(rmean.get())\n</code></pre> <pre><code>1.0\n3.0\n5.0\n9.0\n</code></pre></p>"},{"location":"api/utils/TimeRolling/#methods","title":"Methods","text":"update"},{"location":"api/utils/VectorDict/","title":"VectorDict","text":""},{"location":"api/utils/VectorDict/#methods","title":"Methods","text":"abs clear get <p>Parameters</p> <ul> <li>key </li> <li>args </li> <li>kwargs </li> </ul> <p></p> items <p></p> keys <p></p> max <p></p> maximum <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> min <p></p> minimum <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> pop <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p></p> popitem <p></p> setdefault <p>Parameters</p> <ul> <li>key </li> <li>args </li> <li>kwargs </li> </ul> <p></p> to_dict <p></p> to_numpy <p>Parameters</p> <ul> <li>fields </li> </ul> <p></p> update <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p></p> values <p></p> with_mask <p>Parameters</p> <ul> <li>mask </li> <li>copy     \u2014 defaults to <code>False</code> </li> </ul> <p></p>"},{"location":"api/utils/expand-param-grid/","title":"expand_param_grid","text":"<p>Expands a grid of parameters.</p> <p>This method can be used to generate a list of model parametrizations from a dictionary where each parameter is associated with a list of possible parameters. In other words, it expands a grid of parameters. </p> <p>Typically, this method can be used to create copies of a given model with different parameter choices. The models can then be used as part of a model selection process, such as a <code>selection.SuccessiveHalvingClassifier</code> or a <code>selection.EWARegressor</code>. </p> <p>The syntax for the parameter grid is quite flexible. It allows nesting parameters and can therefore be used to generate parameters for a pipeline.</p>"},{"location":"api/utils/expand-param-grid/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Estimator</p> </li> <li> <p>grid</p> <p>Type \u2192 dict</p> <p>The grid of parameters to expand. The provided dictionary can be nested. The only requirement is that the values at the leaves need to be lists.</p> </li> </ul>"},{"location":"api/utils/expand-param-grid/#examples","title":"Examples","text":"<p>As an initial example, we can expand a grid of parameters for a single model.</p> <p><pre><code>from river import linear_model\nfrom river import optim\nfrom river import utils\n\nmodel = linear_model.LinearRegression()\n\ngrid = {'optimizer': [optim.SGD(.1), optim.SGD(.01), optim.SGD(.001)]}\nmodels = utils.expand_param_grid(model, grid)\nlen(models)\n</code></pre> <pre><code>3\n</code></pre></p> <p><pre><code>models[0]\n</code></pre> <pre><code>LinearRegression (\n  optimizer=SGD (\n    lr=Constant (\n      learning_rate=0.1\n    )\n  )\n  loss=Squared ()\n  l2=0.\n  l1=0.\n  intercept_init=0.\n  intercept_lr=Constant (\n    learning_rate=0.01\n  )\n  clip_gradient=1e+12\n  initializer=Zeros ()\n)\n</code></pre></p> <p>You can expand parameters for multiple choices like so:</p> <p><pre><code>grid = {\n    'optimizer': [\n        (optim.SGD, {'lr': [.1, .01, .001]}),\n        (optim.Adam, {'lr': [.1, .01, .01]})\n    ]\n}\nmodels = utils.expand_param_grid(model, grid)\nlen(models)\n</code></pre> <pre><code>6\n</code></pre></p> <p>You may specify a grid of parameters for a pipeline via nesting:</p> <p><pre><code>from river import feature_extraction\n\nmodel = (\n    feature_extraction.BagOfWords() |\n    linear_model.LinearRegression()\n)\n\ngrid = {\n    'BagOfWords': {\n        'strip_accents': [False, True]\n    },\n    'LinearRegression': {\n        'optimizer': [\n            (optim.SGD, {'lr': [.1, .01]}),\n            (optim.Adam, {'lr': [.1, .01]})\n        ]\n    }\n}\n\nmodels = utils.expand_param_grid(model, grid)\nlen(models)\n</code></pre> <pre><code>8\n</code></pre></p>"},{"location":"api/utils/log-method-calls/","title":"log_method_calls","text":"<p>A context manager to log method calls.</p> <p>All method calls will be logged by default. This behavior can be overriden by passing filtering functions.</p>"},{"location":"api/utils/log-method-calls/#parameters","title":"Parameters","text":"<ul> <li> <p>class_condition</p> <p>Type \u2192 typing.Callable[[typing.Any], bool] | None</p> <p>Default \u2192 <code>None</code></p> <p>A function which determines if a class should be logged or not.</p> </li> <li> <p>method_condition</p> <p>Type \u2192 typing.Callable[[typing.Any], bool] | None</p> <p>Default \u2192 <code>None</code></p> <p>A function which determines if a method should be logged or not.</p> </li> </ul>"},{"location":"api/utils/log-method-calls/#examples","title":"Examples","text":"<p><pre><code>import io\nimport logging\nfrom river import anomaly\nfrom river import compose\nfrom river import datasets\nfrom river import preprocessing\nfrom river import utils\n\nmodel = compose.Pipeline(\n    preprocessing.MinMaxScaler(),\n    anomaly.HalfSpaceTrees(seed=42)\n)\n\nclass_condition = lambda x: x.__class__.__name__ in ('MinMaxScaler', 'HalfSpaceTrees')\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\nlogs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition):\n    for x, y in datasets.CreditCard().take(1):\n        score = model.score_one(x)\n        model.learn_one(x)\n\nprint(logs.getvalue())\n</code></pre> <pre><code>MinMaxScaler.transform_one\nHalfSpaceTrees.score_one\nMinMaxScaler.learn_one\nMinMaxScaler.transform_one\nHalfSpaceTrees.learn_one\n</code></pre></p> <pre><code>logs.close()\n</code></pre>"},{"location":"api/utils/math/argmax/","title":"argmax","text":"<p>Argmax function.</p>"},{"location":"api/utils/math/argmax/#parameters","title":"Parameters","text":"<ul> <li> <p>lst</p> <p>Type \u2192 list</p> </li> </ul>"},{"location":"api/utils/math/chain-dot/","title":"chain_dot","text":"<p>Returns the dot product of multiple vectors represented as dicts.</p>"},{"location":"api/utils/math/chain-dot/#parameters","title":"Parameters","text":"<ul> <li>xs</li> </ul>"},{"location":"api/utils/math/chain-dot/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nx = {'x0': 1, 'x1': 2, 'x2': 1}\ny = {'x1': 21, 'x2': 3}\nz = {'x1': 2, 'x2': 1 / 3}\n\nutils.math.chain_dot(x, y, z)\n</code></pre> <pre><code>85.0\n</code></pre></p>"},{"location":"api/utils/math/clamp/","title":"clamp","text":"<p>Clamp a number.</p> <p>This is a synonym of clipping.</p>"},{"location":"api/utils/math/clamp/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 float</p> </li> <li> <p>minimum</p> <p>Default \u2192 <code>0.0</code></p> </li> <li> <p>maximum</p> <p>Default \u2192 <code>1.0</code></p> </li> </ul>"},{"location":"api/utils/math/dot/","title":"dot","text":"<p>Returns the dot product of two vectors represented as dicts.</p>"},{"location":"api/utils/math/dot/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 dict</p> </li> <li> <p>y</p> <p>Type \u2192 dict</p> </li> </ul>"},{"location":"api/utils/math/dot/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nx = {'x0': 1, 'x1': 2}\ny = {'x1': 21, 'x2': 3}\n\nutils.math.dot(x, y)\n</code></pre> <pre><code>42\n</code></pre></p>"},{"location":"api/utils/math/dotvecmat/","title":"dotvecmat","text":"<p>Vector times matrix from left side, i.e. transpose(x)A.</p>"},{"location":"api/utils/math/dotvecmat/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> </li> <li> <p>A</p> </li> </ul>"},{"location":"api/utils/math/dotvecmat/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nx = {0: 4, 1: 5}\n\nA = {\n    (0, 0): 0, (0, 1): 1,\n    (1, 0): 2, (1, 1): 3\n}\n\nC = utils.math.dotvecmat(x, A)\nprint(C)\n</code></pre> <pre><code>{0: 10.0, 1: 19.0}\n</code></pre></p>"},{"location":"api/utils/math/log-sum-2-exp/","title":"log_sum_2_exp","text":"<p>Computation of log( (e^a + e^b) / 2) in an overflow-proof way</p>"},{"location":"api/utils/math/log-sum-2-exp/#parameters","title":"Parameters","text":"<ul> <li> <p>a</p> <p>Type \u2192 float</p> <p>First number</p> </li> <li> <p>b</p> <p>Type \u2192 float</p> <p>Second number</p> </li> </ul>"},{"location":"api/utils/math/matmul2d/","title":"matmul2d","text":"<p>Multiplication for 2D matrices.</p>"},{"location":"api/utils/math/matmul2d/#parameters","title":"Parameters","text":"<ul> <li> <p>A</p> </li> <li> <p>B</p> </li> </ul>"},{"location":"api/utils/math/matmul2d/#examples","title":"Examples","text":"<p><pre><code>import pprint\nfrom river import utils\n\nA = {\n    (0, 0): 2, (0, 1): 0, (0, 2): 4,\n    (1, 0): 5, (1, 1): 6, (1, 2): 0\n}\n\nB = {\n    (0, 0): 1, (0, 1): 1, (0, 2): 0, (0, 3): 0,\n    (1, 0): 2, (1, 1): 0, (1, 2): 1, (1, 3): 3,\n    (2, 0): 4, (2, 1): 0, (2, 2): 0, (2, 3): 0\n}\n\nC = utils.math.matmul2d(A, B)\npprint.pprint(C)\n</code></pre> <pre><code>{(0, 0): 18.0,\n    (0, 1): 2.0,\n    (0, 2): 0.0,\n    (0, 3): 0.0,\n    (1, 0): 17.0,\n    (1, 1): 5.0,\n    (1, 2): 6.0,\n    (1, 3): 18.0}\n</code></pre></p>"},{"location":"api/utils/math/minkowski-distance/","title":"minkowski_distance","text":"<p>Minkowski distance.</p>"},{"location":"api/utils/math/minkowski-distance/#parameters","title":"Parameters","text":"<ul> <li> <p>a</p> <p>Type \u2192 dict</p> </li> <li> <p>b</p> <p>Type \u2192 dict</p> </li> <li> <p>p</p> <p>Type \u2192 int</p> <p>Parameter for the Minkowski distance. When <code>p=1</code>, this is equivalent to using the Manhattan distance. When <code>p=2</code>, this is equivalent to using the Euclidean distance.</p> </li> </ul>"},{"location":"api/utils/math/norm/","title":"norm","text":"<p>Compute the norm of a dictionaries values.</p>"},{"location":"api/utils/math/norm/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 dict</p> </li> <li> <p>order</p> <p>Default \u2192 <code>None</code></p> </li> </ul>"},{"location":"api/utils/math/outer/","title":"outer","text":"<p>Outer-product between two vectors.</p>"},{"location":"api/utils/math/outer/#parameters","title":"Parameters","text":"<ul> <li> <p>u</p> <p>Type \u2192 dict</p> </li> <li> <p>v</p> <p>Type \u2192 dict</p> </li> </ul>"},{"location":"api/utils/math/outer/#examples","title":"Examples","text":"<p><pre><code>import pprint\nfrom river import utils\n\nu = dict(enumerate((1, 2, 3)))\nv = dict(enumerate((2, 4, 8)))\n\nuTv = utils.math.outer(u, v)\npprint.pprint(uTv)\n</code></pre> <pre><code>{(0, 0): 2,\n    (0, 1): 4,\n    (0, 2): 8,\n    (1, 0): 4,\n    (1, 1): 8,\n    (1, 2): 16,\n    (2, 0): 6,\n    (2, 1): 12,\n    (2, 2): 24}\n</code></pre></p>"},{"location":"api/utils/math/prod/","title":"prod","text":"<p>Product function.</p>"},{"location":"api/utils/math/prod/#parameters","title":"Parameters","text":"<ul> <li>iterable</li> </ul>"},{"location":"api/utils/math/sherman-morrison/","title":"sherman_morrison","text":"<p>Sherman-Morrison formula.</p> <p>This is an inplace function.</p>"},{"location":"api/utils/math/sherman-morrison/#parameters","title":"Parameters","text":"<ul> <li> <p>A</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>u</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>v</p> <p>Type \u2192 np.ndarray</p> </li> </ul> <ol> <li> <p>Fast rank-one updates to matrix inverse? \u2014 Tim Vieira \u21a9</p> </li> </ol>"},{"location":"api/utils/math/sigmoid/","title":"sigmoid","text":"<p>Sigmoid function.</p>"},{"location":"api/utils/math/sigmoid/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/utils/math/sign/","title":"sign","text":"<p>Sign function.</p>"},{"location":"api/utils/math/sign/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/utils/math/softmax/","title":"softmax","text":"<p>Normalizes a dictionary of predicted probabilities, in-place.</p>"},{"location":"api/utils/math/softmax/#parameters","title":"Parameters","text":"<ul> <li> <p>y_pred</p> <p>Type \u2192 dict</p> </li> </ul>"},{"location":"api/utils/math/woodbury-matrix/","title":"woodbury_matrix","text":"<p>Woodbury matrix identity.</p> <p>This is an inplace function.</p>"},{"location":"api/utils/math/woodbury-matrix/#parameters","title":"Parameters","text":"<ul> <li> <p>A</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>U</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>V</p> <p>Type \u2192 np.ndarray</p> </li> </ul> <ol> <li> <p>Matrix inverse mini-batch updates \u2014 Max Halford \u21a9</p> </li> </ol>"},{"location":"api/utils/norm/normalize-values-in-dict/","title":"normalize_values_in_dict","text":"<p>Normalize the values in a dictionary using the given factor.</p> <p>For each element in the dictionary, applies <code>value/factor</code>.</p>"},{"location":"api/utils/norm/normalize-values-in-dict/#parameters","title":"Parameters","text":"<ul> <li> <p>dictionary</p> <p>Dictionary to normalize.</p> </li> <li> <p>factor</p> <p>Default \u2192 <code>None</code></p> <p>Normalization factor value. If not set, use the sum of values.</p> </li> <li> <p>inplace</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform operation in-place</p> </li> <li> <p>raise_error</p> <p>Default \u2192 <code>False</code></p> <p>In case the normalization factor is either <code>0</code> or <code>None</code>: - <code>True</code>: raise an error. - <code>False</code>: return gracefully (if <code>inplace=False</code>, a copy of) <code>dictionary</code>.</p> </li> </ul>"},{"location":"api/utils/norm/scale-values-in-dict/","title":"scale_values_in_dict","text":"<p>Scale the values in a dictionary.</p> <p>For each element in the dictionary, applies <code>value * multiplier</code>.</p>"},{"location":"api/utils/norm/scale-values-in-dict/#parameters","title":"Parameters","text":"<ul> <li> <p>dictionary</p> <p>Dictionary to scale.</p> </li> <li> <p>multiplier</p> <p>Scaling value.</p> </li> <li> <p>inplace</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform operation in-place</p> </li> </ul>"},{"location":"api/utils/pretty/humanize-bytes/","title":"humanize_bytes","text":"<p>Returns a human-friendly byte size.</p>"},{"location":"api/utils/pretty/humanize-bytes/#parameters","title":"Parameters","text":"<ul> <li> <p>n_bytes</p> <p>Type \u2192 int</p> </li> </ul>"},{"location":"api/utils/pretty/print-table/","title":"print_table","text":"<p>Pretty-prints a table.</p>"},{"location":"api/utils/pretty/print-table/#parameters","title":"Parameters","text":"<ul> <li> <p>headers</p> <p>Type \u2192 list[str]</p> <p>The column names.</p> </li> <li> <p>columns</p> <p>Type \u2192 list[list[str]]</p> <p>The column values.</p> </li> <li> <p>order</p> <p>Type \u2192 list[int] | None</p> <p>Default \u2192 <code>None</code></p> <p>Order in which to print the column the values. Defaults to the order in which the values are given.</p> </li> </ul>"},{"location":"api/utils/random/exponential/","title":"exponential","text":"<p>Sample a random value from a Poisson distribution.</p>"},{"location":"api/utils/random/exponential/#parameters","title":"Parameters","text":"<ul> <li> <p>rate</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>rng</p> <p>Default \u2192 <code>&lt;module 'random' from '/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/random.py'&gt;</code></p> </li> </ul> <ol> <li> <p>Wikipedia article \u21a9</p> </li> </ol>"},{"location":"api/utils/random/poisson/","title":"poisson","text":"<p>Sample a random value from a Poisson distribution.</p>"},{"location":"api/utils/random/poisson/#parameters","title":"Parameters","text":"<ul> <li> <p>rate</p> <p>Type \u2192 float</p> </li> <li> <p>rng</p> <p>Default \u2192 <code>&lt;module 'random' from '/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/random.py'&gt;</code></p> </li> </ul> <p>[^1] Wikipedia article</p>"},{"location":"benchmarks/Binary%20classification/","title":"Binary classification","text":"TableChart Model Dataset Accuracy F1 Memory in Mb Time in s ADWIN Bagging Bananas 0.625967 0.448218 0.400658 942.73 ADWIN Bagging Elec2 0.823773 0.776587 0.598438 8970.15 ADWIN Bagging Phishing 0.893515 0.879201 1.31008 568.218 ADWIN Bagging SMTP 0.999685 0 0.164217 8006.78 ALMA Bananas 0.506415 0.482595 0.0029211 68.9731 ALMA Elec2 0.906427 0.889767 0.00435829 836.498 ALMA Phishing 0.8256 0.810764 0.0045805 29.7613 ALMA SMTP 0.764986 0.00178548 0.00309372 1361.61 AdaBoost Bananas 0.677864 0.645041 0.453154 876.714 AdaBoost Elec2 0.880581 0.858687 13.5424 10153.7 AdaBoost Phishing 0.878303 0.863555 0.873312 552.609 AdaBoost SMTP 0.999443 0.404494 1.33633 6617.5 Adaptive Random Forest Bananas 0.88696 0.871707 15.3551 2603.02 Adaptive Random Forest Elec2 0.876608 0.852391 22.3949 12397.6 Adaptive Random Forest Phishing 0.907926 0.896116 4.10291 743.377 Adaptive Random Forest SMTP 0.999685 0 0.327095 11543.4 Aggregated Mondrian Forest Bananas 0.889413 0.874249 17.2377 2954.75 Aggregated Mondrian Forest Elec2 0.849904 0.819731 287.315 18206.6 Aggregated Mondrian Forest Phishing 0.904724 0.892112 3.39106 807.573 Aggregated Mondrian Forest SMTP 0.999863 0.734694 0.211749 5848.87 Bagging Bananas 0.634082 0.459437 0.703124 1170.85 Bagging Elec2 0.840436 0.80208 2.28896 13164.5 Bagging Phishing 0.893515 0.879201 1.38826 633.136 Bagging SMTP 0.999685 0 0.207971 8814.84 Hoeffding Adaptive Tree Bananas 0.616531 0.42825 0.0618467 163.516 Hoeffding Adaptive Tree Elec2 0.821258 0.787344 0.435328 2980.69 Hoeffding Adaptive Tree Phishing 0.874299 0.856095 0.142962 77.865 Hoeffding Adaptive Tree SMTP 0.999685 0 0.0241137 2094.95 Hoeffding Tree Bananas 0.642197 0.503405 0.0594654 93.5302 Hoeffding Tree Elec2 0.795635 0.750834 0.938466 1485.98 Hoeffding Tree Phishing 0.879904 0.860595 0.132719 54.2758 Hoeffding Tree SMTP 0.999685 0 0.0170441 1543.56 Leveraging Bagging Bananas 0.828269 0.802689 3.23571 2747.95 Leveraging Bagging Elec2 0.892653 0.871966 7.56535 18763.3 Leveraging Bagging Phishing 0.894315 0.877323 4.0114 1619.65 Leveraging Bagging SMTP 0.999674 0 0.164603 17549.6 Logistic regression Bananas 0.543208 0.197015 0.00424099 82.0689 Logistic regression Elec2 0.822144 0.777086 0.005373 953.54 Logistic regression Phishing 0.8872 0.871233 0.00556469 29.2066 Logistic regression SMTP 0.999769 0.421053 0.00438309 1531.37 Naive Bayes Bananas 0.61521 0.413912 0.0140247 97.154 Naive Bayes Elec2 0.728741 0.603785 0.0510378 1230.66 Naive Bayes Phishing 0.884708 0.871429 0.05723 38.528 Naive Bayes SMTP 0.993484 0.0490798 0.0201406 1826.47 Stacking Bananas 0.876203 0.859649 19.1946 5236.84 Stacking Elec2 0.885458 0.864157 40.7547 22944.4 Stacking Phishing 0.895116 0.882722 8.72124 2411.41 Stacking SMTP 0.999685 0 4.88868 24733.2 Streaming Random Patches Bananas 0.871674 0.854265 10.5381 3551.41 Streaming Random Patches Elec2 0.868884 0.843009 107.322 22969 Streaming Random Patches Phishing 0.913531 0.901996 6.59559 1436.69 Streaming Random Patches SMTP 0.999685 0 0.17817 18142.3 Voting Bananas 0.872617 0.849162 4.58403 2790.97 Voting Elec2 0.84368 0.797958 5.7575 13925.5 Voting Phishing 0.896717 0.884512 4.8203 1436.72 Voting SMTP 0.999779 0.487805 4.60205 18069.8 Vowpal Wabbit logistic regression Bananas 0.551321 0 0.000646591 88.7248 Vowpal Wabbit logistic regression Elec2 0.697475 0.459592 0.000646591 937.011 Vowpal Wabbit logistic regression Phishing 0.7736 0.669778 0.000646591 27.8334 Vowpal Wabbit logistic regression SMTP 0.999695 0.121212 0.000646591 1631.37 [baseline] Last Class Bananas 0.50953 0.452957 0.000510216 30.809 [baseline] Last Class Elec2 0.853303 0.827229 0.000510216 341.39 [baseline] Last Class Phishing 0.515612 0.447489 0.000510216 11.9196 [baseline] Last Class SMTP 0.999601 0.366667 0.000510216 532.359 k-Nearest Neighbors Bananas 0.885073 0.870838 4.50996 2974.33 k-Nearest Neighbors Elec2 0.853148 0.823642 4.76604 13503.4 k-Nearest Neighbors Phishing 0.881505 0.867145 4.59643 1552.65 k-Nearest Neighbors SMTP 0.999821 0.666667 4.51822 17961.1 sklearn SGDClassifier Bananas 0.546415 0.205026 0.00557804 621.426 sklearn SGDClassifier Elec2 0.819099 0.772892 0.00680161 4291.77 sklearn SGDClassifier Phishing 0.8896 0.876122 0.00701618 167.984 sklearn SGDClassifier SMTP 0.999706 0.363636 0.00574303 7118.18 <p>Try reloading the page if something is buggy</p> <p>{   \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",   \"data\": {     \"values\": [       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.490566037735849,         \"F1\": 0.3414634146341463,         \"Memory in Mb\": 0.0041875839233398,         \"Time in s\": 0.00989       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5141509433962265,         \"F1\": 0.3832335329341317,         \"Memory in Mb\": 0.0041875839233398,         \"Time in s\": 0.123413       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5220125786163522,         \"F1\": 0.4242424242424242,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 0.315017       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165094339622641,         \"F1\": 0.4023323615160349,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 0.5849610000000001       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5320754716981132,         \"F1\": 0.3641025641025641,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 0.937213       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.3287671232876712,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 1.342505       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5525606469002695,         \"F1\": 0.3054393305439331,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 1.895068       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5530660377358491,         \"F1\": 0.2808349146110057,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 2.518365       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5555555555555556,         \"F1\": 0.2587412587412587,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 3.1930270000000003       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5622641509433962,         \"F1\": 0.2418300653594771,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 3.938137       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5608919382504288,         \"F1\": 0.2242424242424242,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 4.7351090000000005       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5613207547169812,         \"F1\": 0.2206703910614525,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 5.600857       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5645863570391872,         \"F1\": 0.20844327176781,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 6.476079       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5646900269541779,         \"F1\": 0.1965174129353233,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 7.428853       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5647798742138365,         \"F1\": 0.1858823529411764,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 8.473991       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5660377358490566,         \"F1\": 0.1785714285714285,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 9.59319       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.562708102108768,         \"F1\": 0.1705263157894736,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 10.745503       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5587002096436059,         \"F1\": 0.1679841897233201,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 11.962335       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5516385302879842,         \"F1\": 0.1662049861495844,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 13.252336       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5495283018867925,         \"F1\": 0.1688424717145344,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 14.603624       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5485175202156334,         \"F1\": 0.1809290953545232,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 15.981958       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5484562607204116,         \"F1\": 0.1967963386727688,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 17.395643       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5471698113207547,         \"F1\": 0.1999999999999999,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 18.850781       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5479559748427673,         \"F1\": 0.2166212534059945,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 20.422045       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5452830188679245,         \"F1\": 0.2260757867694284,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 22.049363       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5395500725689405,         \"F1\": 0.2285714285714285,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 23.763248       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5391334730957372,         \"F1\": 0.230005837711617,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 25.51638       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5411051212938005,         \"F1\": 0.2261363636363636,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 27.316788000000003       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5403383214053351,         \"F1\": 0.2214876033057851,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 29.124189       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5437106918238994,         \"F1\": 0.2203116603976356,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 31.016333000000003       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5450395617772368,         \"F1\": 0.21604614577871,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 32.984057       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5439268867924528,         \"F1\": 0.2127226463104325,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 35.003757       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5457404230989137,         \"F1\": 0.2082710513203786,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 37.068178       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5480022197558269,         \"F1\": 0.2042012701514411,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 39.232173       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.546900269541779,         \"F1\": 0.1991424487851357,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 41.450117000000006       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5463836477987422,         \"F1\": 0.1945090739879013,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 43.72876300000001       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5474247832738399,         \"F1\": 0.1906064751481988,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 46.072390000000006       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.547914597815293,         \"F1\": 0.1866904868244752,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 48.42327300000001       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.548137397194001,         \"F1\": 0.1828521434820647,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 50.870554000000006       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5474056603773585,         \"F1\": 0.1788617886178861,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 53.39424700000001       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5476300046019328,         \"F1\": 0.176716917922948,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 55.939767       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5498652291105122,         \"F1\": 0.1820408163265306,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 58.584779000000005       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5467310223782361,         \"F1\": 0.1814580031695721,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 61.26661800000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5465265866209262,         \"F1\": 0.1880998080614203,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 64.04445700000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5467505241090147,         \"F1\": 0.1908682634730538,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 66.91140200000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5469647251845775,         \"F1\": 0.1911387770047601,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 69.84398600000002       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5469690887193898,         \"F1\": 0.1976537504443654,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 72.84582100000002       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5448113207547169,         \"F1\": 0.1958333333333333,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 75.85667200000002       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5429341547939931,         \"F1\": 0.1941615750169721,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 78.94956300000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5432075471698113,         \"F1\": 0.1970149253731343,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 82.06889500000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7980132450331126,         \"F1\": 0.7834319526627219,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 0.687155       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8134657836644592,         \"F1\": 0.7488855869242199,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 2.092465       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8024282560706402,         \"F1\": 0.7300150829562596,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 4.064074       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8192604856512141,         \"F1\": 0.7598093142647598,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 6.824807       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8289183222958058,         \"F1\": 0.7613181398213735,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 10.234028       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8226637233259749,         \"F1\": 0.7528205128205128,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 14.344314       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8229265216020183,         \"F1\": 0.7589611504614724,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 19.167838       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8261589403973509,         \"F1\": 0.7617246596066566,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 24.744494       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8318616629874908,         \"F1\": 0.7833096254148886,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 31.081721       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8375275938189846,         \"F1\": 0.7975797579757975,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 38.163875       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8377483443708609,         \"F1\": 0.802008081302804,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 45.915004       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400478292862399,         \"F1\": 0.8089220964729151,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 54.352834       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8432671081677704,         \"F1\": 0.8127789046653143,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 63.489549       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8419268369599495,         \"F1\": 0.8117547648108159,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 73.399178       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8437821927888153,         \"F1\": 0.8167141500474834,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 84.03825400000001       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8447157836644592,         \"F1\": 0.8189204408334004,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 95.414959       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8464485131801065,         \"F1\": 0.8201110519510155,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 107.55183300000002       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8411822418444935,         \"F1\": 0.812780106982796,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 120.38661500000002       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397234808876496,         \"F1\": 0.8069954529555788,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 133.99787400000002       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8419426048565122,         \"F1\": 0.80987785448752,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 148.356557       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8451066961000736,         \"F1\": 0.8115849370244869,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 163.518734       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8428155729480232,         \"F1\": 0.8097637986520129,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 179.395561       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8393799788847298,         \"F1\": 0.805689404934688,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 196.009478       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8402777777777778,         \"F1\": 0.8036632935722765,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 213.342445       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8394701986754967,         \"F1\": 0.8009198423127463,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 231.348647       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8357106469689252,         \"F1\": 0.7954545454545454,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 250.064916       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8330471752105306,         \"F1\": 0.791441119395363,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 269.489469       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8298249763481551,         \"F1\": 0.7872875092387287,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 289.628629       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8304407398949532,         \"F1\": 0.787745962170661,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 310.508458       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8308682855040471,         \"F1\": 0.7889638709085066,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 332.12320800000003       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8277077547532579,         \"F1\": 0.7843678980437593,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 354.49968       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8270212472406181,         \"F1\": 0.7820039121930016,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 377.655941       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8260418757107498,         \"F1\": 0.780872129766168,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 401.520333       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8258992338657317,         \"F1\": 0.7797807251673304,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 426.09085       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.821286660359508,         \"F1\": 0.7731294287201249,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 451.387139       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8188619082658818,         \"F1\": 0.7700093428838368,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 477.46355       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8168963665652408,         \"F1\": 0.7682024169184289,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 504.189667       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8143952596723597,         \"F1\": 0.7647795037915042,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 531.635688       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8142016188373804,         \"F1\": 0.7627822944896115,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 559.807066       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8154801324503311,         \"F1\": 0.7629984051036682,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 588.709839       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.815161794002046,         \"F1\": 0.7614481273017858,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 618.344034       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8151476926311363,         \"F1\": 0.7609596955073744,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 648.6306599999999       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8162379998973254,         \"F1\": 0.7631274195149389,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 679.6980239999999       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8169275536825206,         \"F1\": 0.7661946562439931,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 711.4719719999999       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8186656855531028,         \"F1\": 0.7707241432780277,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 743.966698       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8201602840963624,         \"F1\": 0.7745390006918749,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 777.181242       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8211920529801324,         \"F1\": 0.7763613934089174,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 811.063029       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8216979396615158,         \"F1\": 0.7772863051470587,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 845.685827       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8211695274136145,         \"F1\": 0.7754109027129481,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 881.122534       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8221412803532009,         \"F1\": 0.7771292633675417,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 917.330239       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8221442443502824,         \"F1\": 0.7770862722319033,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 953.539999       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6,         \"F1\": 0.6428571428571429,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.005087       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.7499999999999999,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.014273       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8,         \"F1\": 0.8,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.080154       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.81,         \"F1\": 0.8041237113402061,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.160529       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8,         \"F1\": 0.7933884297520661,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.244823       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8066666666666666,         \"F1\": 0.8079470198675497,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.373717       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8171428571428572,         \"F1\": 0.8072289156626506,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.564558       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.815,         \"F1\": 0.8042328042328043,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.765703       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8133333333333334,         \"F1\": 0.7980769230769231,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.969796       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.82,         \"F1\": 0.8068669527896996,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 1.176844       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8218181818181818,         \"F1\": 0.8078431372549019,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 1.38745       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8161764705882353,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 1.62648       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.84,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 1.940615       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8514285714285714,         \"F1\": 0.8278145695364238,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 2.281543       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.848,         \"F1\": 0.8213166144200628,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 2.625835       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.85,         \"F1\": 0.8214285714285715,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 2.973623       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8564705882352941,         \"F1\": 0.825214899713467,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 3.357502       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.86,         \"F1\": 0.8273972602739726,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 3.744344       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8568421052631578,         \"F1\": 0.8247422680412371,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 4.182096       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.858,         \"F1\": 0.8297362110311751,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 4.631479       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8251748251748252,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 5.084116       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8581818181818182,         \"F1\": 0.827433628318584,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 5.539997       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8608695652173913,         \"F1\": 0.8305084745762712,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 6.065522       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.865,         \"F1\": 0.8329896907216495,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 6.594884       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672,         \"F1\": 0.8323232323232322,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 7.192367999999999       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8707692307692307,         \"F1\": 0.8390804597701149,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 7.814115999999999       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8711111111111111,         \"F1\": 0.8426763110307414,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 8.439065999999999       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8757142857142857,         \"F1\": 0.8465608465608465,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 9.067184       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8772413793103448,         \"F1\": 0.8514190317195326,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 9.744984       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8786666666666667,         \"F1\": 0.8539325842696629,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 10.426391       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8549141965678626,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 11.153806       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8567164179104476,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 11.884597       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8579626972740315,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 12.619003       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8811764705882353,         \"F1\": 0.8587412587412586,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 13.411056       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8845714285714286,         \"F1\": 0.8622100954979536,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 14.234524       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844444444444445,         \"F1\": 0.8617021276595744,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 15.105192999999998       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8864864864864865,         \"F1\": 0.8655569782330347,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 15.990264999999996       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8852631578947369,         \"F1\": 0.8655980271270037,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 16.878196999999997       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8861538461538462,         \"F1\": 0.8664259927797834,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 17.769031       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.887,         \"F1\": 0.8675263774912075,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 18.72316       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8868292682926829,         \"F1\": 0.8678815489749431,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 19.680949       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8885714285714286,         \"F1\": 0.8704318936877077,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 20.642059       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8874418604651163,         \"F1\": 0.8703108252947481,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 21.642509       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.889090909090909,         \"F1\": 0.8723849372384936,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 22.64645       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8897777777777778,         \"F1\": 0.8742393509127788,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 23.715816       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8895652173913043,         \"F1\": 0.8738828202581926,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 24.78868       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8885106382978724,         \"F1\": 0.872444011684518,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 25.864657       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8891666666666667,         \"F1\": 0.8729703915950333,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 26.968066       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.889795918367347,         \"F1\": 0.8737137511693172,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 28.075126       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8872,         \"F1\": 0.8712328767123287,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 29.206647       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1.174944       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 3.465965       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 6.937403       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 11.610183       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 17.462392       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 24.519273       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 32.784706       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996715712033633,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 42.234241       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997080632918784,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 52.882453       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 64.622668       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999761142693355,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 77.568109       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997810474689088,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 91.771967       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997978899713004,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 107.109486       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999774791682306,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 123.681834       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997898055701524,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 141.369945       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999802942722018,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 160.23044       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999814534326605,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 180.239632       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999824837975127,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 201.318948       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998340570290676,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 223.519273       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998423541776142,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 246.976714       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498611215374,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 271.56812399999995       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999856685616013,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 297.295844       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998629166761864,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 324.2115329999999       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686284813452,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 352.27523699999995       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998738833420916,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 381.597104       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998787339827804,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 412.116627       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998443004223352,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 443.867429       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498611215374,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 476.838798       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999855038324243,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 510.9819989999999       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997022245577158,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 546.274013       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997118302171444,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 582.6678519999999       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997208355228586,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 620.2082039999999       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999697447411583,         \"F1\": 0.4571428571428571,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 658.8625569999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997063460171248,         \"F1\": 0.4571428571428571,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 698.5852799999999       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997147361309212,         \"F1\": 0.4571428571428571,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 739.3620329999999       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934664564724,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 781.2563779999999       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999701751146838,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 824.198222       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997095998008684,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 868.202086       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997170459598204,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 913.268811       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999724119810825,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 959.416173       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997308485959268,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1006.608919       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1054.85163       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997433672658838,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1104.06085       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997491998280228,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1154.258062       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997547731651778,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1205.371532       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999760104183326,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1257.4462130000002       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997540277948592,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1310.5048250000002       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997591522157996,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1364.5437910000005       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997640674767015,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1419.4942320000002       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997687861271676,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1475.4318390000003       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997688007062088,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1531.3705140000004       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7047619047619048,         \"F1\": 0.6990291262135924,         \"Memory in Mb\": 0.8133068084716797,         \"Time in s\": 0.833499       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7867298578199052,         \"F1\": 0.7668393782383419,         \"Memory in Mb\": 1.3378009796142578,         \"Time in s\": 2.8663       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8233438485804416,         \"F1\": 0.806896551724138,         \"Memory in Mb\": 1.855398178100586,         \"Time in s\": 6.250927       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8392434988179669,         \"F1\": 0.8229166666666667,         \"Memory in Mb\": 2.3226680755615234,         \"Time in s\": 11.143336       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8412098298676749,         \"F1\": 0.8181818181818182,         \"Memory in Mb\": 2.776212692260742,         \"Time in s\": 17.797124       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8488188976377953,         \"F1\": 0.8267148014440434,         \"Memory in Mb\": 3.173288345336914,         \"Time in s\": 26.396562       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8596491228070176,         \"F1\": 0.8359621451104102,         \"Memory in Mb\": 3.5500621795654297,         \"Time in s\": 36.969223       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8677685950413223,         \"F1\": 0.8461538461538461,         \"Memory in Mb\": 3.917997360229492,         \"Time in s\": 49.692848       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8730325288562435,         \"F1\": 0.8515337423312884,         \"Memory in Mb\": 4.238534927368164,         \"Time in s\": 64.631677       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8772426817752597,         \"F1\": 0.8549107142857144,         \"Memory in Mb\": 4.491437911987305,         \"Time in s\": 81.765253       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8772532188841202,         \"F1\": 0.8557013118062564,         \"Memory in Mb\": 4.809717178344727,         \"Time in s\": 101.295253       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8772619984264359,         \"F1\": 0.8566176470588236,         \"Memory in Mb\": 5.171953201293945,         \"Time in s\": 123.161687       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8779956427015251,         \"F1\": 0.8561643835616438,         \"Memory in Mb\": 5.501619338989258,         \"Time in s\": 147.513883       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8813216453135536,         \"F1\": 0.860759493670886,         \"Memory in Mb\": 5.80189323425293,         \"Time in s\": 174.53874199999998       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8785399622404028,         \"F1\": 0.8579838116261957,         \"Memory in Mb\": 6.17225456237793,         \"Time in s\": 204.250002       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8790560471976401,         \"F1\": 0.8585231193926847,         \"Memory in Mb\": 6.45002555847168,         \"Time in s\": 237.091398       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8806218767351471,         \"F1\": 0.8613797549967763,         \"Memory in Mb\": 6.703157424926758,         \"Time in s\": 272.83416       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8783429470372313,         \"F1\": 0.8602409638554217,         \"Memory in Mb\": 7.075212478637695,         \"Time in s\": 311.419605       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8777943368107303,         \"F1\": 0.8607021517553795,         \"Memory in Mb\": 7.409914016723633,         \"Time in s\": 352.79492       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8791882963662104,         \"F1\": 0.8636847710330138,         \"Memory in Mb\": 7.730207443237305,         \"Time in s\": 397.065386       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8782022471910113,         \"F1\": 0.8626457171819564,         \"Memory in Mb\": 8.068941116333008,         \"Time in s\": 444.302777       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8777348777348777,         \"F1\": 0.8621190130624092,         \"Memory in Mb\": 8.392999649047852,         \"Time in s\": 494.454577       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8781288469429627,         \"F1\": 0.8624363131079205,         \"Memory in Mb\": 8.738908767700195,         \"Time in s\": 547.433225       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8784899724734565,         \"F1\": 0.8635761589403974,         \"Memory in Mb\": 9.069158554077148,         \"Time in s\": 603.367304       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8799546998867497,         \"F1\": 0.8654822335025381,         \"Memory in Mb\": 9.38022804260254,         \"Time in s\": 661.971994       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8820326678765881,         \"F1\": 0.8676171079429736,         \"Memory in Mb\": 9.675683975219728,         \"Time in s\": 723.088894       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8836071303739951,         \"F1\": 0.86905230043256,         \"Memory in Mb\": 10.005556106567385,         \"Time in s\": 786.780009       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8840579710144928,         \"F1\": 0.8691019786910198,         \"Memory in Mb\": 10.283010482788086,         \"Time in s\": 853.0146269999999       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8831760494630654,         \"F1\": 0.8683535020168683,         \"Memory in Mb\": 10.632661819458008,         \"Time in s\": 921.667133       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8858131487889274,         \"F1\": 0.8707725169099323,         \"Memory in Mb\": 10.90281867980957,         \"Time in s\": 992.810764       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8852359208523592,         \"F1\": 0.8696854476322157,         \"Memory in Mb\": 11.200468063354492,         \"Time in s\": 1066.389204       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8849896785608965,         \"F1\": 0.87017310252996,         \"Memory in Mb\": 11.512235641479492,         \"Time in s\": 1142.442462       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8864741206748642,         \"F1\": 0.8712293220888745,         \"Memory in Mb\": 11.797895431518556,         \"Time in s\": 1221.036812       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8878712184290869,         \"F1\": 0.8721518987341771,         \"Memory in Mb\": 12.102933883666992,         \"Time in s\": 1302.125963       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8878403882448099,         \"F1\": 0.8725490196078431,         \"Memory in Mb\": 12.41331672668457,         \"Time in s\": 1385.838182       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.889646133682831,         \"F1\": 0.8746650788925276,         \"Memory in Mb\": 12.665735244750977,         \"Time in s\": 1472.135343       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8885488395817394,         \"F1\": 0.8730758059831543,         \"Memory in Mb\": 13.002767562866213,         \"Time in s\": 1561.047711       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8872609883287808,         \"F1\": 0.8714609286523215,         \"Memory in Mb\": 13.407987594604492,         \"Time in s\": 1652.580672       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8874909266876361,         \"F1\": 0.8717241379310345,         \"Memory in Mb\": 13.751871109008787,         \"Time in s\": 1746.8148660000002       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8886529841943854,         \"F1\": 0.8731864588930682,         \"Memory in Mb\": 13.96497917175293,         \"Time in s\": 1843.750561       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8895281933256617,         \"F1\": 0.8742138364779874,         \"Memory in Mb\": 14.240518569946287,         \"Time in s\": 1943.403214       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8890137047854415,         \"F1\": 0.8735926305015352,         \"Memory in Mb\": 14.605810165405272,         \"Time in s\": 2045.776976       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8894009216589862,         \"F1\": 0.874439461883408,         \"Memory in Mb\": 14.917993545532228,         \"Time in s\": 2150.6554650000003       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8893416255629423,         \"F1\": 0.8748180494905387,         \"Memory in Mb\": 15.239774703979492,         \"Time in s\": 2258.064088       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8880268400083875,         \"F1\": 0.8729176582579724,         \"Memory in Mb\": 15.67698097229004,         \"Time in s\": 2367.913167       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8888205128205128,         \"F1\": 0.8733644859813083,         \"Memory in Mb\": 15.96486473083496,         \"Time in s\": 2480.267593       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.889580405541056,         \"F1\": 0.8746010031919745,         \"Memory in Mb\": 16.210702896118164,         \"Time in s\": 2595.134509       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8891291527422842,         \"F1\": 0.8740509155873157,         \"Memory in Mb\": 16.543100357055664,         \"Time in s\": 2712.434229       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8894665896398999,         \"F1\": 0.8743982494529539,         \"Memory in Mb\": 16.87101936340332,         \"Time in s\": 2832.294496       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.889413096810719,         \"F1\": 0.8742489270386266,         \"Memory in Mb\": 17.23769187927246,         \"Time in s\": 2954.746773       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8662983425414365,         \"F1\": 0.8638920134983127,         \"Memory in Mb\": 5.093213081359863,         \"Time in s\": 9.961559       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895637769188294,         \"F1\": 0.863013698630137,         \"Memory in Mb\": 9.274415016174316,         \"Time in s\": 34.997891       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8737578211262422,         \"F1\": 0.8433074463225217,         \"Memory in Mb\": 14.81954288482666,         \"Time in s\": 77.180768       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8746894838531604,         \"F1\": 0.8451568894952252,         \"Memory in Mb\": 20.35789203643799,         \"Time in s\": 135.799753       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.869728416869066,         \"F1\": 0.8295782784517621,         \"Memory in Mb\": 25.320820808410645,         \"Time in s\": 209.048681       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8658693652253909,         \"F1\": 0.8254728273880776,         \"Memory in Mb\": 30.94210529327393,         \"Time in s\": 297.509476       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8613783314934553,         \"F1\": 0.8220287507592631,         \"Memory in Mb\": 36.922226905822754,         \"Time in s\": 401.254404       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8563543535255967,         \"F1\": 0.8144715736945286,         \"Memory in Mb\": 42.8322229385376,         \"Time in s\": 518.853069       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8547773825585674,         \"F1\": 0.8211480362537765,         \"Memory in Mb\": 49.13461780548096,         \"Time in s\": 650.61595       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8564963020200905,         \"F1\": 0.8276776246023331,         \"Memory in Mb\": 54.27480792999268,         \"Time in s\": 797.031608       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8559959859508279,         \"F1\": 0.830478440637921,         \"Memory in Mb\": 59.58850955963135,         \"Time in s\": 957.298151       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.858522675006899,         \"F1\": 0.8360690684289065,         \"Memory in Mb\": 64.43849277496338,         \"Time in s\": 1132.655012       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8588774730406725,         \"F1\": 0.8365138697619515,         \"Memory in Mb\": 69.77676105499268,         \"Time in s\": 1321.3849659999998       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8572892848695104,         \"F1\": 0.8352148579752368,         \"Memory in Mb\": 75.08023929595947,         \"Time in s\": 1522.77491       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8577525940098609,         \"F1\": 0.8380665158750105,         \"Memory in Mb\": 79.94311618804932,         \"Time in s\": 1737.870186       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8584339427388755,         \"F1\": 0.8393863494051347,         \"Memory in Mb\": 84.43613529205322,         \"Time in s\": 1968.10555       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8584507499513019,         \"F1\": 0.8387335404645658,         \"Memory in Mb\": 89.24470615386963,         \"Time in s\": 2211.432474       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8561354019746121,         \"F1\": 0.8352296670880741,         \"Memory in Mb\": 95.6551637649536,         \"Time in s\": 2468.910492       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8563295183872655,         \"F1\": 0.8333445649976414,         \"Memory in Mb\": 100.85075855255128,         \"Time in s\": 2740.76049       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8570009382416248,         \"F1\": 0.834176,         \"Memory in Mb\": 106.8406229019165,         \"Time in s\": 3026.823297       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.858712220762155,         \"F1\": 0.8348082595870207,         \"Memory in Mb\": 111.7458429336548,         \"Time in s\": 3325.548438       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8587125583262255,         \"F1\": 0.8363361618040218,         \"Memory in Mb\": 117.0202569961548,         \"Time in s\": 3636.553219       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8564572635216202,         \"F1\": 0.8339348176114596,         \"Memory in Mb\": 123.3725290298462,         \"Time in s\": 3960.554229       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8540219840868325,         \"F1\": 0.8286917098445596,         \"Memory in Mb\": 130.42929553985596,         \"Time in s\": 4298.210438       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8531944015188309,         \"F1\": 0.8264160793526494,         \"Memory in Mb\": 136.64212131500244,         \"Time in s\": 4650.500753       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8528550201655699,         \"F1\": 0.8255134917438581,         \"Memory in Mb\": 142.6701021194458,         \"Time in s\": 5016.675492       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8532766444544376,         \"F1\": 0.8247130647130647,         \"Memory in Mb\": 148.4442949295044,         \"Time in s\": 5397.142957       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8514605589939686,         \"F1\": 0.8225487425826504,         \"Memory in Mb\": 154.72937488555908,         \"Time in s\": 5792.939295       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8521676245575306,         \"F1\": 0.8231490756761678,         \"Memory in Mb\": 160.280930519104,         \"Time in s\": 6204.791143       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530851024688179,         \"F1\": 0.8247069669432372,         \"Memory in Mb\": 165.12001132965088,         \"Time in s\": 6630.671498000001       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8528752002848495,         \"F1\": 0.8239904583404327,         \"Memory in Mb\": 171.1938066482544,         \"Time in s\": 7068.974646000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8532303128557138,         \"F1\": 0.8236415633937083,         \"Memory in Mb\": 176.66365909576416,         \"Time in s\": 7519.88705       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8538649362812323,         \"F1\": 0.8241355713883187,         \"Memory in Mb\": 181.78493976593015,         \"Time in s\": 7981.874679       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8542349771126189,         \"F1\": 0.8238110186783865,         \"Memory in Mb\": 187.08849048614505,         \"Time in s\": 8454.454599       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8525655176763695,         \"F1\": 0.8216125462662648,         \"Memory in Mb\": 193.5201120376587,         \"Time in s\": 8938.242097       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.852245899126169,         \"F1\": 0.821432541594101,         \"Memory in Mb\": 199.6366205215454,         \"Time in s\": 9433.534304       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.852003221860923,         \"F1\": 0.8214247147330909,         \"Memory in Mb\": 205.8111581802368,         \"Time in s\": 9940.639789       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.851715223516426,         \"F1\": 0.8209965286300361,         \"Memory in Mb\": 212.10033893585205,         \"Time in s\": 10459.964952       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8513287861206238,         \"F1\": 0.8197137660019906,         \"Memory in Mb\": 218.64550113677976,         \"Time in s\": 10993.026606       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8508788873865173,         \"F1\": 0.8179735920237133,         \"Memory in Mb\": 225.19258975982663,         \"Time in s\": 11538.003929       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8496432898102032,         \"F1\": 0.8159741671883752,         \"Memory in Mb\": 232.33557987213132,         \"Time in s\": 12096.169427       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8497279966360937,         \"F1\": 0.8155126798735239,         \"Memory in Mb\": 238.56606006622317,         \"Time in s\": 12664.877691       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8494493929203994,         \"F1\": 0.8154906093686098,         \"Memory in Mb\": 244.89648151397705,         \"Time in s\": 13243.508414       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8492336251661942,         \"F1\": 0.8164773421277635,         \"Memory in Mb\": 251.12543201446533,         \"Time in s\": 13830.859859       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8486104638328141,         \"F1\": 0.8170174918470204,         \"Memory in Mb\": 257.83575916290283,         \"Time in s\": 14427.278119       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8490941811637672,         \"F1\": 0.8186928820595613,         \"Memory in Mb\": 264.1331262588501,         \"Time in s\": 15032.883602       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8493929217256523,         \"F1\": 0.8194385787087872,         \"Memory in Mb\": 270.1314744949341,         \"Time in s\": 15648.679676       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8493802745648125,         \"F1\": 0.8194995590828924,         \"Memory in Mb\": 276.0468301773072,         \"Time in s\": 16273.986894       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8493681436262474,         \"F1\": 0.8189620164063134,         \"Memory in Mb\": 282.1419038772583,         \"Time in s\": 16909.074578       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8499083864985982,         \"F1\": 0.8197651300267741,         \"Memory in Mb\": 287.208477973938,         \"Time in s\": 17554.066457       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8499039968219637,         \"F1\": 0.8197312269727252,         \"Memory in Mb\": 287.3145227432251,         \"Time in s\": 18206.640571       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6666666666666666,         \"F1\": 0.6923076923076924,         \"Memory in Mb\": 0.2663440704345703,         \"Time in s\": 0.180038       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7755102040816326,         \"F1\": 0.7555555555555555,         \"Memory in Mb\": 0.4029140472412109,         \"Time in s\": 0.591649       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7972972972972973,         \"F1\": 0.7945205479452055,         \"Memory in Mb\": 0.5196552276611328,         \"Time in s\": 1.289716       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8181818181818182,         \"F1\": 0.8125,         \"Memory in Mb\": 0.6383838653564453,         \"Time in s\": 2.331468       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8225806451612904,         \"F1\": 0.819672131147541,         \"Memory in Mb\": 0.7669887542724609,         \"Time in s\": 3.724154       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8456375838926175,         \"F1\": 0.847682119205298,         \"Memory in Mb\": 0.9175167083740234,         \"Time in s\": 5.520175       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867816091954023,         \"F1\": 0.8606060606060606,         \"Memory in Mb\": 1.0086803436279297,         \"Time in s\": 7.749843999999999       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.864321608040201,         \"F1\": 0.8571428571428572,         \"Memory in Mb\": 1.1245098114013672,         \"Time in s\": 10.53336       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8660714285714286,         \"F1\": 0.8557692307692308,         \"Memory in Mb\": 1.2114391326904297,         \"Time in s\": 13.795268       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554216867469879,         \"F1\": 0.8448275862068965,         \"Memory in Mb\": 1.322244644165039,         \"Time in s\": 17.57486       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8540145985401459,         \"F1\": 0.84251968503937,         \"Memory in Mb\": 1.3987751007080078,         \"Time in s\": 21.876977       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561872909698997,         \"F1\": 0.8413284132841329,         \"Memory in Mb\": 1.489828109741211,         \"Time in s\": 26.743447       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672839506172839,         \"F1\": 0.8501742160278746,         \"Memory in Mb\": 1.5769939422607422,         \"Time in s\": 32.2729       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8681948424068768,         \"F1\": 0.8486842105263156,         \"Memory in Mb\": 1.638784408569336,         \"Time in s\": 38.477964       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8689839572192514,         \"F1\": 0.8482972136222912,         \"Memory in Mb\": 1.7178211212158203,         \"Time in s\": 45.357054       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671679197994987,         \"F1\": 0.8436578171091446,         \"Memory in Mb\": 1.7941875457763672,         \"Time in s\": 52.888585       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8702830188679245,         \"F1\": 0.8433048433048433,         \"Memory in Mb\": 1.8353633880615237,         \"Time in s\": 61.095765       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8730512249443207,         \"F1\": 0.8455284552845528,         \"Memory in Mb\": 1.909624099731445,         \"Time in s\": 70.024579       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8755274261603375,         \"F1\": 0.8506329113924052,         \"Memory in Mb\": 1.988790512084961,         \"Time in s\": 79.720297       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.875751503006012,         \"F1\": 0.8530805687203792,         \"Memory in Mb\": 2.063833236694336,         \"Time in s\": 90.078634       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8778625954198473,         \"F1\": 0.8525345622119817,         \"Memory in Mb\": 2.144712448120117,         \"Time in s\": 101.258101       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8779599271402551,         \"F1\": 0.8533916849015317,         \"Memory in Mb\": 2.199640274047852,         \"Time in s\": 113.251819       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8780487804878049,         \"F1\": 0.8535564853556484,         \"Memory in Mb\": 2.2528209686279297,         \"Time in s\": 125.935841       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8797996661101837,         \"F1\": 0.8536585365853657,         \"Memory in Mb\": 2.283121109008789,         \"Time in s\": 139.44840100000002       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8814102564102564,         \"F1\": 0.852589641434263,         \"Memory in Mb\": 2.343900680541992,         \"Time in s\": 153.77905700000002       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.884437596302003,         \"F1\": 0.8587570621468926,         \"Memory in Mb\": 2.418844223022461,         \"Time in s\": 168.92061400000003       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.884272997032641,         \"F1\": 0.8617021276595745,         \"Memory in Mb\": 2.468423843383789,         \"Time in s\": 184.940001       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8884120171673819,         \"F1\": 0.8650519031141869,         \"Memory in Mb\": 2.478273391723633,         \"Time in s\": 201.76583       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8895027624309392,         \"F1\": 0.8684210526315789,         \"Memory in Mb\": 2.52436637878418,         \"Time in s\": 219.457713       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8918558077436582,         \"F1\": 0.8716323296354993,         \"Memory in Mb\": 2.5813236236572266,         \"Time in s\": 238.014124       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8914728682170543,         \"F1\": 0.8707692307692307,         \"Memory in Mb\": 2.6200389862060547,         \"Time in s\": 257.461391       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8898623279098874,         \"F1\": 0.8702064896755163,         \"Memory in Mb\": 2.657014846801758,         \"Time in s\": 277.779634       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8907766990291263,         \"F1\": 0.872159090909091,         \"Memory in Mb\": 2.706361770629883,         \"Time in s\": 298.980548       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8928150765606596,         \"F1\": 0.8741355463347164,         \"Memory in Mb\": 2.730466842651367,         \"Time in s\": 321.097396       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8958810068649885,         \"F1\": 0.8771929824561403,         \"Memory in Mb\": 2.753351211547852,         \"Time in s\": 344.186724       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8976640711902113,         \"F1\": 0.8786279683377309,         \"Memory in Mb\": 2.807779312133789,         \"Time in s\": 368.101507       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9004329004329005,         \"F1\": 0.8829516539440204,         \"Memory in Mb\": 2.8523120880126958,         \"Time in s\": 392.980624       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9009483667017912,         \"F1\": 0.8850855745721271,         \"Memory in Mb\": 2.913583755493164,         \"Time in s\": 418.83123200000006       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9024640657084188,         \"F1\": 0.8867699642431467,         \"Memory in Mb\": 2.943540573120117,         \"Time in s\": 445.632777       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9009009009009008,         \"F1\": 0.8850174216027874,         \"Memory in Mb\": 2.9903697967529297,         \"Time in s\": 473.399028       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8994140625,         \"F1\": 0.8836158192090395,         \"Memory in Mb\": 3.035707473754883,         \"Time in s\": 502.224676       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9008579599618683,         \"F1\": 0.8857142857142858,         \"Memory in Mb\": 3.069150924682617,         \"Time in s\": 532.049603       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9013035381750466,         \"F1\": 0.8869936034115138,         \"Memory in Mb\": 3.114839553833008,         \"Time in s\": 562.838704       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9035486806187444,         \"F1\": 0.8898128898128899,         \"Memory in Mb\": 3.132375717163086,         \"Time in s\": 594.67778       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.905693950177936,         \"F1\": 0.8933601609657947,         \"Memory in Mb\": 3.1889095306396484,         \"Time in s\": 627.518257       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9060052219321147,         \"F1\": 0.893491124260355,         \"Memory in Mb\": 3.220029830932617,         \"Time in s\": 661.4048929999999       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9045996592844976,         \"F1\": 0.8916827852998066,         \"Memory in Mb\": 3.270620346069336,         \"Time in s\": 696.4079739999999       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9040867389491244,         \"F1\": 0.8909952606635072,         \"Memory in Mb\": 3.311410903930664,         \"Time in s\": 732.4743999999998       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9044117647058824,         \"F1\": 0.8911627906976743,         \"Memory in Mb\": 3.344022750854492,         \"Time in s\": 769.4892029999999       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9047237790232184,         \"F1\": 0.8921124206708976,         \"Memory in Mb\": 3.391061782836914,         \"Time in s\": 807.5726659999999       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 2.745403       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 8.183125       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 16.539666       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 27.755785000000003       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 41.777067       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 58.637769000000006       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 78.268206       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686198515404,         \"F1\": 0.9090909090909092,         \"Memory in Mb\": 0.0923185348510742,         \"Time in s\": 101.443914       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998832184981898,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972318649291992,         \"Time in s\": 131.805417       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998948972620736,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972814559936523,         \"Time in s\": 169.246217       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999904452512899,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972814559936523,         \"Time in s\": 213.148727       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124151521788,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972814559936523,         \"Time in s\": 263.357684       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999191527205108,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0973081588745117,         \"Time in s\": 319.49775       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998873916144289,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1091413497924804,         \"Time in s\": 381.401703       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999894899103139,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1091642379760742,         \"Time in s\": 448.60874       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999014681249384,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1091642379760742,         \"Time in s\": 520.91477       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999072642967544,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1096677780151367,         \"Time in s\": 598.09858       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124164306776,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1113195419311523,         \"Time in s\": 680.064697       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999170262197146,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 766.82968       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999211750177356,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 858.2478070000001       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999924928682248,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 954.233503       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999283410963812,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 1054.7914       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999931456772071,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 1159.67034       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999343128024348,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 1268.4432900000002       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999369403455668,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1298818588256836,         \"Time in s\": 1381.268586       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999393657659116,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1299276351928711,         \"Time in s\": 1498.4984390000002       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999941611521993,         \"F1\": 0.9032258064516128,         \"Memory in Mb\": 0.1434888839721679,         \"Time in s\": 1620.0599740000002       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999436968639152,         \"F1\": 0.9032258064516128,         \"Memory in Mb\": 0.1434888839721679,         \"Time in s\": 1745.8256190000002       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999456383865472,         \"F1\": 0.9032258064516128,         \"Memory in Mb\": 0.1440382003784179,         \"Time in s\": 1875.6542580000005       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248349068998,         \"F1\": 0.7619047619047621,         \"Memory in Mb\": 0.1476888656616211,         \"Time in s\": 2010.272303       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999830485489558,         \"F1\": 0.7619047619047621,         \"Memory in Mb\": 0.1510839462280273,         \"Time in s\": 2149.2802330000004       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998357829050004,         \"F1\": 0.7619047619047621,         \"Memory in Mb\": 0.1510610580444336,         \"Time in s\": 2292.3763450000006       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089111118188,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.1511411666870117,         \"Time in s\": 2439.4913830000005       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998145314601012,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.1534147262573242,         \"Time in s\": 2590.5360980000005       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998198306408024,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.1576833724975586,         \"Time in s\": 2745.6113380000006       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248354182784,         \"F1\": 0.75,         \"Memory in Mb\": 0.1762075424194336,         \"Time in s\": 2905.2725530000007       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998295696634,         \"F1\": 0.75,         \"Memory in Mb\": 0.1762075424194336,         \"Time in s\": 3069.514522000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99983405473428,         \"F1\": 0.75,         \"Memory in Mb\": 0.1762075424194336,         \"Time in s\": 3238.428366000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998383097984264,         \"F1\": 0.75,         \"Memory in Mb\": 0.1761388778686523,         \"Time in s\": 3411.935267000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99984235210657,         \"F1\": 0.75,         \"Memory in Mb\": 0.1760702133178711,         \"Time in s\": 3590.1136440000014       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998461972264232,         \"F1\": 0.75,         \"Memory in Mb\": 0.1782979965209961,         \"Time in s\": 3773.084966000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498592430404,         \"F1\": 0.75,         \"Memory in Mb\": 0.1782979965209961,         \"Time in s\": 3960.4867140000015       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998533509312216,         \"F1\": 0.75,         \"Memory in Mb\": 0.1782979965209961,         \"Time in s\": 4152.338698000001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998566839044082,         \"F1\": 0.75,         \"Memory in Mb\": 0.1783208847045898,         \"Time in s\": 4348.642178000001       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998598687437232,         \"F1\": 0.75,         \"Memory in Mb\": 0.1783208847045898,         \"Time in s\": 4549.410423000001       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999862915110182,         \"F1\": 0.75,         \"Memory in Mb\": 0.1783208847045898,         \"Time in s\": 4754.622131000001       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998546511627908,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.1783475875854492,         \"Time in s\": 4964.315109000001       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998576792967168,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.1972723007202148,         \"Time in s\": 5178.776489000001       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998605838320144,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.2118177413940429,         \"Time in s\": 5398.511461000001       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998633721846788,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.2117490768432617,         \"Time in s\": 5623.669278000001       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998633807997478,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.2117490768432617,         \"Time in s\": 5848.865968000001       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.5242718446601942,         \"Memory in Mb\": 0.0028944015502929,         \"Time in s\": 0.039715       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5330188679245284,         \"F1\": 0.5217391304347825,         \"Memory in Mb\": 0.0028944015502929,         \"Time in s\": 0.180531       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5188679245283019,         \"F1\": 0.5173501577287066,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 0.3338649999999999       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5330188679245284,         \"F1\": 0.5330188679245282,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 0.4937739999999999       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5207547169811321,         \"F1\": 0.5115384615384615,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 0.7446539999999999       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.5303514376996804,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 1.03169       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.522911051212938,         \"F1\": 0.512396694214876,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 1.379859       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5235849056603774,         \"F1\": 0.5061124694376529,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 1.737155       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5157232704402516,         \"F1\": 0.5,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 2.173505       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5160377358490567,         \"F1\": 0.4975514201762978,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 2.70321       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5154373927958834,         \"F1\": 0.495985727029438,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 3.270309       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165094339622641,         \"F1\": 0.4979591836734694,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 3.844268       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5195936139332366,         \"F1\": 0.4977238239757208,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 4.501151       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5195417789757413,         \"F1\": 0.4968242766407903,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 5.229491       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5226415094339623,         \"F1\": 0.4983476536682089,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 6.030342       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5194575471698113,         \"F1\": 0.4947303161810291,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 6.8837410000000006       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5205327413984462,         \"F1\": 0.4965034965034965,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 7.813207       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5193920335429769,         \"F1\": 0.4964305326743548,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 8.751116       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.519364448857994,         \"F1\": 0.4989648033126293,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 9.762632       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5174528301886793,         \"F1\": 0.4997555012224939,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 10.806008       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5197663971248877,         \"F1\": 0.5002337540906966,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 11.968014       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5175814751286449,         \"F1\": 0.4975435462259938,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 13.16512       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5176374077112387,         \"F1\": 0.4957118353344769,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 14.408045       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5196540880503144,         \"F1\": 0.5008169934640523,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 15.661105       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.520377358490566,         \"F1\": 0.5037094884810621,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 17.014893999999998       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.521044992743106,         \"F1\": 0.5041322314049587,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 18.454389       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5213137665967854,         \"F1\": 0.5032632342277013,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 19.942263       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5175202156334232,         \"F1\": 0.4985994397759103,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 21.473074       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5152895250487963,         \"F1\": 0.4969615124915597,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 23.106855       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5132075471698113,         \"F1\": 0.4931237721021611,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 24.747764       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5130858186244674,         \"F1\": 0.4927076727964489,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 26.464385       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5103183962264151,         \"F1\": 0.490959239963224,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 28.215584       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5091480846197828,         \"F1\": 0.4891401368640284,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 30.068049       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5097114317425083,         \"F1\": 0.4876775877065816,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 31.96012       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5118598382749326,         \"F1\": 0.4908630868709586,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 33.864206       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.510482180293501,         \"F1\": 0.4893384363039912,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 35.803291       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.50790413054564,         \"F1\": 0.485881726158764,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 37.844614       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.506454816285998,         \"F1\": 0.4844398340248962,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 39.968017       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5050798258345428,         \"F1\": 0.4828109201213346,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 42.128298       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5068396226415094,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 44.30306       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5080533824206167,         \"F1\": 0.4858104858104858,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 46.485881       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5080862533692723,         \"F1\": 0.4847058823529412,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 48.746465       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5063624396665204,         \"F1\": 0.4837081229921982,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 51.058035       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5051457975986278,         \"F1\": 0.4829749103942652,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 53.475871000000005       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5048218029350104,         \"F1\": 0.482017543859649,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 55.900409       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5036915504511895,         \"F1\": 0.4802405498281787,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 58.409701000000005       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5038137294259334,         \"F1\": 0.4811083123425693,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 60.970617       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5029481132075472,         \"F1\": 0.4799506477483035,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 63.61249900000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5040431266846361,         \"F1\": 0.4810636583400483,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 66.28843800000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5064150943396226,         \"F1\": 0.4825949367088608,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 68.97313600000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9072847682119204,         \"F1\": 0.90561797752809,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 0.679052       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9166666666666666,         \"F1\": 0.8967874231032126,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 1.978643       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9175864606328182,         \"F1\": 0.898458748866727,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 3.929769       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9268763796909492,         \"F1\": 0.9098945936756204,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 6.478699       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9271523178807948,         \"F1\": 0.9076664801343034,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 9.702945       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9269683590875644,         \"F1\": 0.907437631149452,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 13.508006       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9274676758120468,         \"F1\": 0.9089108910891088,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 17.915655       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.925496688741722,         \"F1\": 0.9066390041493776,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 22.910275       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9251900907530046,         \"F1\": 0.9100294985250738,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 28.53571       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9266004415011038,         \"F1\": 0.9135128105085184,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 34.833569000000004       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9293598233995584,         \"F1\": 0.9182535996284256,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 41.779447000000005       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.931383370125092,         \"F1\": 0.9217208814270724,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 49.40882500000001       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9313975208014944,         \"F1\": 0.9218568665377176,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 57.61408300000001       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9290444654683064,         \"F1\": 0.9191665169750316,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 66.44160400000001       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9298013245033112,         \"F1\": 0.9209872453205236,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 75.85703300000002       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9305325607064018,         \"F1\": 0.9222213640225536,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 85.90938600000001       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9308531359563692,         \"F1\": 0.922279792746114,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 96.62160000000002       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9293598233995584,         \"F1\": 0.9203319502074688,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 107.965593       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9279656093877076,         \"F1\": 0.9176298658163944,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 119.984123       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9266004415011038,         \"F1\": 0.9160141449861076,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 132.63841200000002       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9265741616734994,         \"F1\": 0.915143048047136,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 146.00166900000002       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9262994180212724,         \"F1\": 0.915589266218468,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 159.938555       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.923217199347346,         \"F1\": 0.9122710823555212,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 174.54734100000002       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9225073583517291,         \"F1\": 0.9101955977189148,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 189.73533200000003       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9217218543046356,         \"F1\": 0.9087540528022232,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 205.619545       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9186619120393956,         \"F1\": 0.905063918343078,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 222.161849       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9173003025100156,         \"F1\": 0.902885123133791,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 239.399015       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9144985808893094,         \"F1\": 0.8997643144322751,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 257.26822000000004       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.91424982872802,         \"F1\": 0.8992622401073105,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 275.765482       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9138337012509198,         \"F1\": 0.89909521757863,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 294.848784       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9110232856227302,         \"F1\": 0.8955049132343716,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 314.617946       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9101476269315674,         \"F1\": 0.8940927755417328,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 335.076303       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9094922737306844,         \"F1\": 0.8931701539676272,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 356.063392       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9083235943383976,         \"F1\": 0.8913093680240166,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 377.566828       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9062125512456638,         \"F1\": 0.8888722815933038,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 399.67833300000007       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9052918812852588,         \"F1\": 0.8879294706671989,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 422.4669390000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9050474315374978,         \"F1\": 0.8877050626212737,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 445.7904010000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9050772626931568,         \"F1\": 0.8877901387172092,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 469.7481780000001       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904539536989868,         \"F1\": 0.8866104144955793,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 494.2109750000001       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9048013245033112,         \"F1\": 0.8860483551327785,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 519.3392310000002       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9045119259139612,         \"F1\": 0.8854217139903738,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 545.0643520000001       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9042625880374224,         \"F1\": 0.8846092933388237,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 571.4397520000001       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904409877303763,         \"F1\": 0.88502624266749,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 598.3711440000001       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904926750953241,         \"F1\": 0.8863636363636365,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 626.0397580000001       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9055187637969097,         \"F1\": 0.8878667908709827,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 654.3634580000002       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9061090315769268,         \"F1\": 0.8892285916489738,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 683.2521370000002       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9063923723639096,         \"F1\": 0.889810361032786,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 712.7682970000002       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9067098969830758,         \"F1\": 0.8902534693104661,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 742.9022130000002       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9062711177186106,         \"F1\": 0.8894732648019762,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 773.7300850000001       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064238410596026,         \"F1\": 0.8897844569823977,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 805.1132940000001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064265536723164,         \"F1\": 0.8897670549084858,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 836.4982200000001       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.56,         \"F1\": 0.5217391304347826,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.003459       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7,         \"F1\": 0.6341463414634146,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.050212       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7066666666666667,         \"F1\": 0.676470588235294,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.100001       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.72,         \"F1\": 0.702127659574468,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.153128       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.72,         \"F1\": 0.7058823529411765,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.228063       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7133333333333334,         \"F1\": 0.7189542483660132,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.334445       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7314285714285714,         \"F1\": 0.718562874251497,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.511774       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.735,         \"F1\": 0.7225130890052356,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.692607       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7244444444444444,         \"F1\": 0.701923076923077,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.876779       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.724,         \"F1\": 0.7038626609442059,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 1.156005       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7345454545454545,         \"F1\": 0.7137254901960783,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 1.438242       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7366666666666667,         \"F1\": 0.7127272727272725,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 1.723391       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7476923076923077,         \"F1\": 0.7172413793103447,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 2.078902       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7542857142857143,         \"F1\": 0.7225806451612904,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 2.437997       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7573333333333333,         \"F1\": 0.723404255319149,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 2.800319       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.7257142857142856,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 3.165567       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.7197802197802199,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 3.585508999999999       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7622222222222222,         \"F1\": 0.7206266318537858,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 4.009149999999999       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7663157894736842,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 4.435539999999999       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.768,         \"F1\": 0.7327188940092165,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 4.959426999999999       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7714285714285715,         \"F1\": 0.7321428571428573,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 5.485955999999999       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7709090909090909,         \"F1\": 0.7341772151898734,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 6.028689999999999       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7739130434782608,         \"F1\": 0.7379032258064516,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 6.595545999999999       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.78,         \"F1\": 0.7401574803149605,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 7.165257999999999       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7744,         \"F1\": 0.7314285714285715,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 7.741693999999999       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7815384615384615,         \"F1\": 0.7427536231884059,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 8.363988999999998       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837037037037037,         \"F1\": 0.75,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 9.010548999999996       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.79,         \"F1\": 0.7545909849749582,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 9.660288999999995       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7917241379310345,         \"F1\": 0.7606973058637084,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 10.349524999999996       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.792,         \"F1\": 0.7621951219512195,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 11.041959999999996       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.792258064516129,         \"F1\": 0.7614814814814814,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 11.737615999999996       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.795,         \"F1\": 0.7670454545454546,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 12.526707999999996       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.793939393939394,         \"F1\": 0.7671232876712327,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 13.319008999999996       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7976470588235294,         \"F1\": 0.7706666666666667,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 14.117527999999997       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8022857142857143,         \"F1\": 0.7744458930899608,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 14.959668999999996       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8011111111111111,         \"F1\": 0.7737041719342603,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 15.804425999999996       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8054054054054054,         \"F1\": 0.7804878048780488,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 16.651646999999997       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8073684210526316,         \"F1\": 0.7849588719153936,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 17.502014999999997       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8102564102564103,         \"F1\": 0.7880870561282932,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 18.418237999999995       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.811,         \"F1\": 0.7892976588628764,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 19.337672999999995       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8146341463414634,         \"F1\": 0.7943722943722944,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 20.260238999999995       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8161904761904762,         \"F1\": 0.7970557308096741,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 21.242111999999995       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.815813953488372,         \"F1\": 0.7983706720977597,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 22.243638999999995       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8190909090909091,         \"F1\": 0.8023833167825224,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 23.247954999999997       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8213333333333334,         \"F1\": 0.8061716489874637,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 24.273946999999996       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8226086956521739,         \"F1\": 0.8071833648393195,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 25.351544999999994       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8212765957446808,         \"F1\": 0.8059149722735675,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 26.431981999999994       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8233333333333334,         \"F1\": 0.8076225045372051,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 27.515220999999997       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8244897959183674,         \"F1\": 0.8088888888888888,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 28.636604999999992       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8256,         \"F1\": 0.810763888888889,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 29.761263999999997       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.720966894377299,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1.027868       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7769311613242249,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 3.106358       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7509196006305833,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 6.233245       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7900683131897005,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 10.302873       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7826589595375723,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 15.393504       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7699246803293046,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 21.578682       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7722393213722694,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 28.779608       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7791644771413557,         \"F1\": 0.0041469194312796,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 37.113003       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.783207800548841,         \"F1\": 0.004824443848834,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 46.3898       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7891224382553862,         \"F1\": 0.0044653932026792,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 56.715322       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7832131084889887,         \"F1\": 0.0039508340649692,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 68.217717       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7821422315641969,         \"F1\": 0.0036050470658922,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 80.77427399999999       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7877440478596548,         \"F1\": 0.0034162080091098,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 94.432579       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.78188574431349,         \"F1\": 0.0034299434059338,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 109.06303       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7857418111753371,         \"F1\": 0.0032594524119947,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 124.719605       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7871452968996322,         \"F1\": 0.0030764497769573,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 141.369597       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7866835646502427,         \"F1\": 0.0028897558156335,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 159.093537       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7860979739592456,         \"F1\": 0.002722199537226,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 177.829964       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7771939043615345,         \"F1\": 0.0024764735017335,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 197.576045       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7831581713084603,         \"F1\": 0.0024175027196905,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 218.31617       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.779496033831294,         \"F1\": 0.0022644927536231,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 240.067789       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7831175655663307,         \"F1\": 0.0021978021978021,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 262.83473100000003       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7791130708949257,         \"F1\": 0.0020644095788604,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 286.65613700000006       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7808066211245402,         \"F1\": 0.0019938191606021,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 311.45044200000007       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7799684708355229,         \"F1\": 0.001906941266209,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 337.28748800000005       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7778810784591131,         \"F1\": 0.0018165304268846,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 364.149506       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7807944570950351,         \"F1\": 0.0021263400372109,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 392.065506       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7777193904361535,         \"F1\": 0.0020222446916076,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 421.04388200000005       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7785891604906953,         \"F1\": 0.0019603038470963,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 451.0228320000001       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7758801891749869,         \"F1\": 0.0026502455374542,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 482.06238400000007       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.774159646059702,         \"F1\": 0.0025454817698585,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 514.138052       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7746157383079348,         \"F1\": 0.0024711098190275,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 547.227498       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7704899759550311,         \"F1\": 0.0023534297778085,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 581.3499069999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.771274458285679,         \"F1\": 0.002292186341266,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 616.580127       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7721942797087306,         \"F1\": 0.0022358124547905,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 652.800029       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7705085537455479,         \"F1\": 0.0024111675126903,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 690.043858       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7685872945988553,         \"F1\": 0.0023267205486162,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 728.276014       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7687999557485411,         \"F1\": 0.0022677090171271,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 767.526171       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7657140547313958,         \"F1\": 0.0021806496040399,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 807.759105       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7665002627430373,         \"F1\": 0.0021333932180552,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 848.897716       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7657101111210797,         \"F1\": 0.0020744622775412,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 890.953712       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7636313590070816,         \"F1\": 0.0020073956682514,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 933.942273       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7647777682728617,         \"F1\": 0.0019703411801306,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 977.888636       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7652868676252806,         \"F1\": 0.0019298156518206,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1022.739679       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7642552694575816,         \"F1\": 0.0018787699001285,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1068.558668       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7644680024674998,         \"F1\": 0.001839659178931,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1115.337297       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7635312664214399,         \"F1\": 0.0018876828692779,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1162.988807       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7650091960063058,         \"F1\": 0.0018600325505696,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1211.563326       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7647859984771628,         \"F1\": 0.001820415965048,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1260.984755       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7649710982658959,         \"F1\": 0.0017854751595768,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1311.295723       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7649859178611963,         \"F1\": 0.0017854751595768,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1361.607838       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5283018867924528,         \"F1\": 0.4680851063829788,         \"Memory in Mb\": 0.0055513381958007,         \"Time in s\": 0.50714       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.4673913043478261,         \"Memory in Mb\": 0.0055513381958007,         \"Time in s\": 1.544995       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5345911949685535,         \"F1\": 0.4861111111111111,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 3.028568       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5188679245283019,         \"F1\": 0.4659685863874345,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 4.996646       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5264150943396226,         \"F1\": 0.4256292906178489,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 7.439068000000001       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5235849056603774,         \"F1\": 0.3878787878787879,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 10.329429       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5363881401617251,         \"F1\": 0.3629629629629629,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 13.751562000000002       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5400943396226415,         \"F1\": 0.3389830508474576,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 17.710995       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5440251572327044,         \"F1\": 0.3149606299212598,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 22.189814       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5518867924528302,         \"F1\": 0.2962962962962963,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 27.154882       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5523156089193825,         \"F1\": 0.2790055248618784,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 32.629664       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5542452830188679,         \"F1\": 0.2758620689655172,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 38.64774       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5566037735849056,         \"F1\": 0.2611850060459492,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 45.182197       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.557277628032345,         \"F1\": 0.2474226804123711,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 52.237503       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5578616352201258,         \"F1\": 0.2350380848748639,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 59.74126700000001       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5595518867924528,         \"F1\": 0.2259067357512953,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 67.72058500000001       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5566037735849056,         \"F1\": 0.2158979391560353,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 76.17010700000002       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5545073375262054,         \"F1\": 0.2115027829313543,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 85.08376800000002       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5496524329692155,         \"F1\": 0.2008810572687224,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 94.42289500000004       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5466981132075471,         \"F1\": 0.1944677284157585,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 104.22299500000004       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.550314465408805,         \"F1\": 0.2036595067621321,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 114.52672400000004       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5493138936535163,         \"F1\": 0.2103681442524417,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 125.29025300000004       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5479901558654635,         \"F1\": 0.21173104434907,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 136.49837800000003       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5483490566037735,         \"F1\": 0.2262626262626262,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 148.22051100000004       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460377358490566,         \"F1\": 0.2322910019144863,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 160.35879600000004       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5395500725689405,         \"F1\": 0.2304426925409338,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 173.00042100000005       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5394828791055206,         \"F1\": 0.2310385064177363,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 186.18470300000004       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5411051212938005,         \"F1\": 0.2305084745762711,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 199.85251500000004       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5396877033181522,         \"F1\": 0.227198252321136,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 214.04388100000003       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5430817610062894,         \"F1\": 0.2283590015932023,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 228.72034700000003       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5444309190505173,         \"F1\": 0.2247540134645261,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 243.83191300000004       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5445165094339622,         \"F1\": 0.224786753637732,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 259.480064       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5463121783876501,         \"F1\": 0.2201474201474201,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 275.54602900000003       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.548834628190899,         \"F1\": 0.2167630057803468,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 292.10262700000004       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.547978436657682,         \"F1\": 0.2123062470643494,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 309.14156900000006       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5474318658280922,         \"F1\": 0.2074346030289123,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 326.6693460000001       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5484446710861806,         \"F1\": 0.2033288349077822,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 344.5959390000001       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5489076464746773,         \"F1\": 0.1992066989863376,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 363.0622570000001       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5491049830672472,         \"F1\": 0.1951640759930915,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 382.0101650000001       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5483490566037735,         \"F1\": 0.1909590198563582,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 401.4423870000001       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.548550391164289,         \"F1\": 0.1885856079404466,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 421.3360060000001       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.550763701707098,         \"F1\": 0.1935483870967742,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 441.6814400000001       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5482667836770513,         \"F1\": 0.1934978456717587,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 462.3969810000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5490994854202401,         \"F1\": 0.1976344906524227,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 483.6474930000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.550104821802935,         \"F1\": 0.1998508575689783,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 505.4107300000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5504511894995898,         \"F1\": 0.2,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 527.6413840000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5503813729425934,         \"F1\": 0.2062367115520907,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 550.3366250000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5479559748427673,         \"F1\": 0.2041522491349481,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 573.5279610000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5462071621101271,         \"F1\": 0.2023688663282571,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 597.2511250000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5464150943396227,         \"F1\": 0.205026455026455,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 621.4261850000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8002207505518764,         \"F1\": 0.7868080094228505,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4.395754       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8140176600441501,         \"F1\": 0.7501853224610822,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 13.314942       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8005886681383371,         \"F1\": 0.7262626262626262,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 26.594138       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8189845474613686,         \"F1\": 0.7586460632818247,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 44.068779       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8278145695364238,         \"F1\": 0.7588126159554731,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 65.924464       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8211920529801324,         \"F1\": 0.7498713329902212,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 92.07692       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8222958057395143,         \"F1\": 0.7575822757582275,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 122.546282       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8253311258278145,         \"F1\": 0.7598634294385433,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 157.279906       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8303899926416483,         \"F1\": 0.780789348549691,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 196.124663       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8364238410596027,         \"F1\": 0.7958677685950413,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 238.938569       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8371462974111981,         \"F1\": 0.8011273128293102,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 285.711115       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8393119941133186,         \"F1\": 0.8079586676926458,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 336.134661       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8422482594668025,         \"F1\": 0.8114088509947219,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 390.124895       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8409019236833807,         \"F1\": 0.810445237647943,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 447.475874       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8427520235467255,         \"F1\": 0.8154098643862832,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 507.886031       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8438189845474614,         \"F1\": 0.8177720540888602,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 571.200551       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.845214907154915,         \"F1\": 0.8184587267742918,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 637.3575030000001       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397105714986509,         \"F1\": 0.8108264582428716,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 706.2352450000001       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8384454513767864,         \"F1\": 0.8053202660133008,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 777.6642180000001       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.840728476821192,         \"F1\": 0.8082646824342281,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 851.7523750000001       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.843950383685483,         \"F1\": 0.8100326316462987,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 928.4036970000002       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8412101143889223,         \"F1\": 0.8075636894266431,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1007.5500400000002       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8373644303675977,         \"F1\": 0.8028848950154133,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1089.2694250000002       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8382542310522443,         \"F1\": 0.8008155405788072,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1173.5296240000002       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8376600441501104,         \"F1\": 0.7982441700960219,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1260.4177460000003       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8337578536254033,         \"F1\": 0.7924748277689453,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1349.7468710000005       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8313302264737144,         \"F1\": 0.7887569117345894,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1441.5955620000002       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8278539892778304,         \"F1\": 0.7842711060613546,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1535.8662290000002       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8282712948161681,         \"F1\": 0.784486052732136,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1632.5910050000002       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8285504047093452,         \"F1\": 0.785431439359057,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1731.7068200000003       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.825357829523606,         \"F1\": 0.7809192013935414,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1833.2277320000003       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8246412803532008,         \"F1\": 0.7785135488368041,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1936.9708820000003       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8228644056458626,         \"F1\": 0.7766343315056937,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2042.9068730000004       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8227827554863005,         \"F1\": 0.775599128540305,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2150.889688       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8180384736676127,         \"F1\": 0.7686632988533396,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2260.9522580000003       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8156119695854795,         \"F1\": 0.765426320305796,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2373.012802       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8136746017540719,         \"F1\": 0.7636955205811137,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2487.1515040000004       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8108516323922389,         \"F1\": 0.7597934341571375,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2603.2748090000005       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.811031867323258,         \"F1\": 0.7582811425261557,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2721.3856460000006       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8123344370860928,         \"F1\": 0.7585643792821898,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2841.498866000001       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8119312981209282,         \"F1\": 0.7567887480852249,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2963.634415000001       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8118364343529907,         \"F1\": 0.7562636165577343,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3087.755988000001       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8128497356127111,         \"F1\": 0.7583601232890332,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3213.7594090000007       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8136162954043749,         \"F1\": 0.7616297722168751,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3341.6530200000007       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8154034829531518,         \"F1\": 0.7662732919254659,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3471.3422760000008       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8169929935694404,         \"F1\": 0.7702641645832705,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3602.9648230000007       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8180216993095675,         \"F1\": 0.7720681236579698,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3737.0799900000006       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8185936350257542,         \"F1\": 0.7730894238789657,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3873.415774000001       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8179708969680587,         \"F1\": 0.7710051290770494,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4011.612716000001       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8190949227373069,         \"F1\": 0.7729350807680585,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4151.679177000001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8190986935028248,         \"F1\": 0.7728922505749037,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4291.771713000001       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.68,         \"F1\": 0.6923076923076923,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 0.149754       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 0.457736       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8266666666666667,         \"F1\": 0.8219178082191781,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 0.892069       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.83,         \"F1\": 0.8210526315789473,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 1.419094       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.816,         \"F1\": 0.8067226890756303,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 2.091236       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.82,         \"F1\": 0.8187919463087249,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 2.916232       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8285714285714286,         \"F1\": 0.8170731707317075,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 3.840025       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.825,         \"F1\": 0.8128342245989306,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 4.910046       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8222222222222222,         \"F1\": 0.8058252427184465,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 6.121922       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.824,         \"F1\": 0.8103448275862069,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 7.479490999999999       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8254545454545454,         \"F1\": 0.8110236220472441,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 8.920382       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8366666666666667,         \"F1\": 0.8191881918819188,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 10.509974       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8461538461538461,         \"F1\": 0.8251748251748252,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 12.191812       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8514285714285714,         \"F1\": 0.8289473684210525,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 13.999137       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8506666666666667,         \"F1\": 0.8271604938271606,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 15.959285       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8525,         \"F1\": 0.8269794721407624,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 18.058664       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8564705882352941,         \"F1\": 0.828169014084507,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 20.312993       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.86,         \"F1\": 0.8301886792452831,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 22.675489       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8589473684210527,         \"F1\": 0.830379746835443,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 25.19503       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.858,         \"F1\": 0.8329411764705883,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 27.784241       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8283752860411898,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 30.514065       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8618181818181818,         \"F1\": 0.8354978354978354,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 33.400870999999995       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8626086956521739,         \"F1\": 0.8364389233954452,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 36.397646       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8666666666666667,         \"F1\": 0.8387096774193549,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 39.57675499999999       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672,         \"F1\": 0.8362919132149901,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 42.828696       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8707692307692307,         \"F1\": 0.8432835820895522,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 46.253182       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8725925925925926,         \"F1\": 0.8485915492957746,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 49.816151       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8771428571428571,         \"F1\": 0.8522336769759451,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 53.516545       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8786206896551724,         \"F1\": 0.8566775244299674,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 57.35818       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8589341692789968,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 61.281034000000005       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8812903225806452,         \"F1\": 0.8597560975609757,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 65.347537       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88125,         \"F1\": 0.8613138686131386,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 69.566336       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8812121212121212,         \"F1\": 0.8623595505617978,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 73.91498000000001       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8823529411764706,         \"F1\": 0.8630136986301369,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 78.39968800000001       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8857142857142857,         \"F1\": 0.8663101604278075,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 83.02084100000002       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844444444444445,         \"F1\": 0.8645833333333334,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 87.71921500000002       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8864864864864865,         \"F1\": 0.8682559598494354,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 92.55798800000002       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8863157894736842,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 97.51738800000004       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8871794871794871,         \"F1\": 0.8702830188679245,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 102.59954100000004       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.888,         \"F1\": 0.871264367816092,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 107.87282600000005       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878048780487805,         \"F1\": 0.8715083798882682,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 113.28564700000004       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8895238095238095,         \"F1\": 0.8739130434782609,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 118.79277100000004       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8883720930232558,         \"F1\": 0.8736842105263158,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 124.46348200000004       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89,         \"F1\": 0.8756423432682425,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 130.26843700000003       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8915555555555555,         \"F1\": 0.8784860557768924,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 136.21796400000002       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8913043478260869,         \"F1\": 0.878048780487805,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 142.31432400000003       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8902127659574468,         \"F1\": 0.876555023923445,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 148.52290000000002       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8908333333333334,         \"F1\": 0.8769953051643193,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 154.887447       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8914285714285715,         \"F1\": 0.8776448942042319,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 161.410896       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896,         \"F1\": 0.8761220825852785,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 167.984219       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.996847083552286,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 9.012274       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9984235417761428,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 26.992092       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.998949027850762,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 53.749217       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992117708880714,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 89.545782       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993694167104572,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 133.365466       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474513925381,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 185.067425       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995495833646124,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 243.739666       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774566473988,         \"F1\": 0.5217391304347826,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 308.57406100000003       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993577392421324,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 378.971453       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999421965317919,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 454.798992       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474513925381,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 535.937031       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995183044315992,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 622.51799       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995553579368608,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 714.221122       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995495833646124,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 811.098386       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796111403048,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 912.878884       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058854440356,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1019.269091       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99962906865321,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1129.962426       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999649675950254,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1244.872652       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996681140581354,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1363.91764       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847083552286,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1487.072194       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997222430748,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1614.171257       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999713371232026,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1745.093316       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997258333523726,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1880.714485       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2020.289668       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997477666841827,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2163.67936       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997574679655604,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2310.817497       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997275257390864,         \"F1\": 0.5333333333333333,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2461.472155       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.5333333333333333,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2615.677493       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997463170674252,         \"F1\": 0.5333333333333333,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2773.449537       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999597127342792,         \"F1\": 0.4102564102564102,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2934.793173       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99961012323496,         \"F1\": 0.4102564102564102,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3099.649087       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996223068838676,         \"F1\": 0.4102564102564102,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3268.0999800000004       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996019044889248,         \"F1\": 0.3902439024390244,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3440.0722140000003       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996136131804272,         \"F1\": 0.3902439024390244,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3615.503218       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996246528038436,         \"F1\": 0.3902439024390244,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3794.437889       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058854440356,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3977.094501000001       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996165371887916,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4163.174128000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996266283154024,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4352.606393000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996362019483408,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4545.416162000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999645296899632,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4741.562007000001       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999653948194763,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4941.079189000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996621875234591,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5143.951781000001       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996700436275648,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5350.231536       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996775426360291,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5559.929647       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847083552286,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5773.003953       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996915625214192,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5989.467769000001       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996869444661844,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6209.264779000001       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934664564724,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6432.452666000001       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997222430748,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6658.918178000001       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057277982132,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6888.546542000001       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057463533566,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 7118.179378000001       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.16057       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5283018867924528,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.37729       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5314465408805031,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.7064710000000001       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5400943396226415,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.0774430000000002       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5547169811320755,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.492379       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5550314465408805,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.9966470000000005       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5660377358490566,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.539797       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5636792452830188,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.1757850000000003       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5649895178197065,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.855114       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5707547169811321,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.635951       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5686106346483705,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 5.458947       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5644654088050315,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 6.34328       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5682148040638607,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.308669       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5680592991913747,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 8.359952       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5679245283018868,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 9.451883       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5683962264150944,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 10.590847       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5643729189789123,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 11.83715       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.560272536687631,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 13.126962       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5551142005958292,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 14.497203999999998       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5509433962264151,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.938437999999998       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5512129380053908,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 17.424999       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5506003430531733,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 19.022886       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.551681706316653,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 20.666828       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5487421383647799,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 22.355416       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5467924528301886,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 24.051772       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5471698113207547,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 25.858309       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5489168413696716,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 27.751459       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5505390835579514,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 29.665552       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5487963565387117,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 31.686176       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5509433962264151,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 33.740652       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5517346317711503,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 35.89104       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5498231132075472,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 38.079414       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5514579759862779,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 40.353903       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5535516093229744,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 42.668922       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5522911051212938,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 45.086801       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5516247379454927,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 47.540759       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5525242223355431,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 50.094246       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5528798411122146,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 52.697211       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5529753265602322,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 55.369587       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5523584905660377,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 58.109436       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5526921306948919,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 60.894093       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5530098831985625,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 63.717346       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5508995173321632,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 66.643891       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5497427101200686,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 69.6601       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5505241090146751,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 72.725555       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5518867924528302,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 75.798736       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5509835407466881,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 78.970205       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5511006289308176,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 82.150165       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5514054678475163,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 85.416127       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5513207547169812,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 88.72481       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6799116997792495,         \"F1\": 0.5482866043613708,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.820242       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7190949227373068,         \"F1\": 0.4904904904904904,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.329863       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6986754966887417,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.585071       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7047461368653422,         \"F1\": 0.4478844169246646,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.424633       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7024282560706402,         \"F1\": 0.4118673647469459,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 10.992865       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7041942604856513,         \"F1\": 0.4165457184325108,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.263433       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6986754966887417,         \"F1\": 0.4048582995951417,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 20.287068       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.695364238410596,         \"F1\": 0.3953997809419496,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 26.004014       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6873926907039489,         \"F1\": 0.4084474355999072,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 32.433812       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6864238410596026,         \"F1\": 0.4240827082911007,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 39.59982599999999       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.687537627934979,         \"F1\": 0.4433321415802646,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 47.447315       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6938925680647535,         \"F1\": 0.4717460317460317,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 55.964905       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6932416369502462,         \"F1\": 0.4715518502267076,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 65.217817       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6944970040996531,         \"F1\": 0.4755717959128434,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 75.258293       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6942604856512141,         \"F1\": 0.4842993670100534,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 85.993354       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6935016556291391,         \"F1\": 0.4860613071139387,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 97.389046       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6929619529931178,         \"F1\": 0.480957084842498,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 109.49806       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6904586705911209,         \"F1\": 0.4713028906577293,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 122.273049       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6921691646334379,         \"F1\": 0.4645852278468223,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 135.723897       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.694205298013245,         \"F1\": 0.4685911575716888,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 150.008391       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6967307894460212,         \"F1\": 0.467515688445921,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 164.94785199999998       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6958157736303432,         \"F1\": 0.4737435986459509,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 180.578389       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6933966791438718,         \"F1\": 0.4696604963891426,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 196.972492       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6968359087564385,         \"F1\": 0.4670978172999191,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 214.037594       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6977041942604857,         \"F1\": 0.4643667370726746,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 231.758696       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6952368823229751,         \"F1\": 0.4573285962657797,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 250.247632       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6978170223203336,         \"F1\": 0.4597281099254495,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 269.425119       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6976505834121728,         \"F1\": 0.4612250632200056,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 289.286378       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6983329527289336,         \"F1\": 0.4614757439869547,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 309.833587       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6959896983075791,         \"F1\": 0.4576304561864129,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 331.075152       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.695649077832372,         \"F1\": 0.4559572301425662,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 353.085242       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6952262693156733,         \"F1\": 0.4515207945375543,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 375.713328       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6939260151180681,         \"F1\": 0.4465678863017841,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 399.011341       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6941630957018569,         \"F1\": 0.4433020150091591,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 423.007885       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6917376222011984,         \"F1\": 0.4368266405484819,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 447.820686       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6893549178317391,         \"F1\": 0.4316805025802109,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 473.259187       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.688353916830738,         \"F1\": 0.4290944860374884,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 499.4094079999999       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6863599395840595,         \"F1\": 0.4245363461948412,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 526.1781749999999       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6869304352748061,         \"F1\": 0.4212013394725827,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 553.6489789999999       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6911147902869758,         \"F1\": 0.4267718148299877,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 581.8684239999999       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6919722177354224,         \"F1\": 0.4269831730769231,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 610.7708879999999       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6944181646168401,         \"F1\": 0.431171118285882,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 640.2659799999999       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6937727809435803,         \"F1\": 0.4308206106870229,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 670.4759659999999       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6930814770218744,         \"F1\": 0.4344288818009522,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 701.3646249999998       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6924208977189109,         \"F1\": 0.4391771019677997,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 733.0001779999998       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6933966791438718,         \"F1\": 0.4472227028897733,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 765.3741459999998       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6956225635244939,         \"F1\": 0.4550767290309018,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 798.4329459999998       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6962150478292862,         \"F1\": 0.4576097220511558,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 832.1587539999998       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6963103122043519,         \"F1\": 0.4557564992733731,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 866.6068249999998       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.697439293598234,         \"F1\": 0.4596278189560006,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 901.80814       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6974752824858758,         \"F1\": 0.4595915792793503,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 937.011341       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.52,         \"F1\": 0.3333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.00395       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.56,         \"F1\": 0.2142857142857142,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.0784219999999999       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5866666666666667,         \"F1\": 0.3404255319148936,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.1562489999999999       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6,         \"F1\": 0.375,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.2373179999999999       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.64,         \"F1\": 0.4705882352941176,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.4181319999999999       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.62,         \"F1\": 0.4466019417475728,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.6021909999999999       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6342857142857142,         \"F1\": 0.4181818181818181,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.7890869999999999       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.63,         \"F1\": 0.4126984126984127,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.012096       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6488888888888888,         \"F1\": 0.4316546762589928,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.2378889999999998       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.648,         \"F1\": 0.4358974358974359,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.4692909999999997       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6618181818181819,         \"F1\": 0.4561403508771929,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.7531039999999996       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6733333333333333,         \"F1\": 0.4615384615384615,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.040874       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.683076923076923,         \"F1\": 0.4663212435233161,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.33165       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6942857142857143,         \"F1\": 0.4780487804878048,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.715045       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7013333333333334,         \"F1\": 0.4909090909090909,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.101519       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.705,         \"F1\": 0.4913793103448276,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.491013       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7105882352941176,         \"F1\": 0.4896265560165975,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.92315       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7222222222222222,         \"F1\": 0.5098039215686275,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.358171       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7157894736842105,         \"F1\": 0.5054945054945055,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.796033       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.718,         \"F1\": 0.5252525252525252,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 5.248043999999999       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7257142857142858,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 5.702586999999999       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7218181818181818,         \"F1\": 0.5233644859813085,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 6.159983       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7217391304347827,         \"F1\": 0.5209580838323353,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 6.620335       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7283333333333334,         \"F1\": 0.5275362318840581,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.113508       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7376,         \"F1\": 0.5340909090909091,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.613357       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7369230769230769,         \"F1\": 0.5415549597855228,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 8.116107       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7333333333333333,         \"F1\": 0.5477386934673367,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 8.713363       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.74,         \"F1\": 0.5560975609756097,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 9.313647       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.743448275862069,         \"F1\": 0.5753424657534246,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 9.917033       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7453333333333333,         \"F1\": 0.5820568927789934,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 10.613639       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7470967741935484,         \"F1\": 0.5847457627118644,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 11.313363       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.74625,         \"F1\": 0.5915492957746479,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 12.015877       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7490909090909091,         \"F1\": 0.602687140115163,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 12.766805       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7541176470588236,         \"F1\": 0.6122448979591837,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 13.520246       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7554285714285714,         \"F1\": 0.6123188405797102,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 14.2887       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7566666666666667,         \"F1\": 0.6123893805309735,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.059985       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.6237288135593221,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.877357000000002       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7589473684210526,         \"F1\": 0.6288492706645057,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 16.697524       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7610256410256411,         \"F1\": 0.631911532385466,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 17.562951       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.761,         \"F1\": 0.6328725038402457,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 18.43148       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7609756097560976,         \"F1\": 0.635958395245171,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 19.325245       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7638095238095238,         \"F1\": 0.6436781609195402,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 20.222005       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7665116279069767,         \"F1\": 0.651872399445215,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 21.121639       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.77,         \"F1\": 0.6594885598923284,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 22.071389       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.768,         \"F1\": 0.6597131681877444,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 23.024294       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7695652173913043,         \"F1\": 0.6615581098339719,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 23.980116       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7702127659574468,         \"F1\": 0.6633416458852868,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 24.939110000000003       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7741666666666667,         \"F1\": 0.6691086691086692,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 25.901192       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7771428571428571,         \"F1\": 0.6746126340882003,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 26.865956       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7736,         \"F1\": 0.6697782963827306,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 27.833412       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.287853       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.780599       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.576109       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 12.534125       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 18.771881       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 26.322128       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 35.214625       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774566473988,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 45.441498       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999299351900508,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 56.927386       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993694167104572,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 69.74153799999999       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999426742464052,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 83.765543       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474513925381,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 99.065492       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999514935931121,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 115.581943       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995120486449968,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 133.361343       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995445787353302,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 152.36548100000002       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999573042564372,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 172.57866       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995981577076444,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 193.969825       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996204822794418,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 216.600052       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996404568963132,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 240.511883       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996584340514976,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 265.709607       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996746990966644,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 292.142637       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996894855013616,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 319.878347       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999702986131737,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 348.79444399999994       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997153617095814,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 378.9697039999999       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999726747241198,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 410.4053809999999       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 443.057614       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997080632918784,         \"F1\": 0.1176470588235294,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 477.025323       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997184896028828,         \"F1\": 0.1176470588235294,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 512.247669       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997281968579556,         \"F1\": 0.1176470588235294,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 548.612896       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796111403048,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 586.233337       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995931720712626,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 625.057298       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058854440356,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 665.0820319999999       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999585980668482,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 706.1803269999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995981577076444,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 748.3509649999999       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996096389159972,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 791.6670189999999       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912886086296,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 836.0877649999999       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996023348624504,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 881.7116259999999       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127997344912,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 928.538605       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996227279464274,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 976.409207       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321597477666,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1025.3623619999998       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996411314612358,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1075.4142359999998       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999649675950254,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1126.58116       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996578230211784,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1178.778759       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999665599770697,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1231.992839       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996730308869036,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1286.303482       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801389111014,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1341.617815       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996757639114052,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1397.839199       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996825188299177,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1455.075933       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996889980374704,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1513.261041       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999695218076721,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1572.312523       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999695237294548,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1631.370354       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5333333333333333,         \"F1\": 0.4615384615384615,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.089081       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5592417061611374,         \"F1\": 0.5026737967914437,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.244558       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.555205047318612,         \"F1\": 0.5154639175257733,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.453937       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5626477541371159,         \"F1\": 0.5066666666666667,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.76271       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5689981096408318,         \"F1\": 0.4818181818181818,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 1.109688       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5716535433070866,         \"F1\": 0.4645669291338582,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 1.619873       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5870445344129555,         \"F1\": 0.4555160142348755,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 2.197835       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5962219598583235,         \"F1\": 0.4554140127388535,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 2.834188       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6002098635886673,         \"F1\": 0.4454148471615721,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 3.5547570000000004       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6090651558073654,         \"F1\": 0.4405405405405405,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 4.339157       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6068669527896996,         \"F1\": 0.4260651629072681,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 5.220598       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6136900078678206,         \"F1\": 0.433679354094579,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 6.139897       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6143790849673203,         \"F1\": 0.419672131147541,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 7.157157999999999       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6142953472690492,         \"F1\": 0.4127310061601643,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 8.301379999999998       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6135934550031467,         \"F1\": 0.4061895551257253,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 9.487101       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6141592920353982,         \"F1\": 0.4010989010989011,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 10.730798       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.614658523042754,         \"F1\": 0.4037800687285223,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 12.063669       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6151022548505506,         \"F1\": 0.4080645161290322,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 13.448557       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6100347739692003,         \"F1\": 0.4048521607278241,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 14.939018       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608305804624823,         \"F1\": 0.4071428571428571,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 16.439687       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6089887640449438,         \"F1\": 0.4089673913043478,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 18.077419       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6096096096096096,         \"F1\": 0.4098573281452659,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 19.752885       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6101764464505539,         \"F1\": 0.4084682440846824,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 21.52049       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6114825009830909,         \"F1\": 0.4153846153846153,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 23.348549       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6100415251038127,         \"F1\": 0.41273450824332,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 25.279207       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6076225045372051,         \"F1\": 0.4070213933077345,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 27.31295       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085284865431667,         \"F1\": 0.4092827004219409,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 29.37924       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6083586113919784,         \"F1\": 0.4065372829417773,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 31.545651       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.60624796615685,         \"F1\": 0.4062806673209028,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 33.733231       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6071091538219566,         \"F1\": 0.4077761972498815,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 36.007059       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6063926940639269,         \"F1\": 0.4049700874367234,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 38.312687       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6048363314656443,         \"F1\": 0.4060283687943262,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 40.720334       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6065198741778668,         \"F1\": 0.4053586862575626,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 43.213056       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6086594504579517,         \"F1\": 0.4090528080469404,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 45.745916       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085198166621731,         \"F1\": 0.4078303425774878,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 48.41046399999999       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6070773263433814,         \"F1\": 0.4049225883287018,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 51.18183799999999       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6067329762815609,         \"F1\": 0.4027885360185902,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 54.01538599999999       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6088899925502855,         \"F1\": 0.405436013590034,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 56.94241999999999       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6106944108395839,         \"F1\": 0.4078027235921972,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 59.88324999999999       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.611936777541873,         \"F1\": 0.4118698605648909,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 62.92792699999999       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6131185270425776,         \"F1\": 0.4128536500174642,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 66.00451999999999       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6137946528869916,         \"F1\": 0.413510747185261,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 69.16846599999998       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6122448979591837,         \"F1\": 0.4115884115884116,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 72.34300699999999       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6126956894702981,         \"F1\": 0.4124918672739102,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 75.66876099999999       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6143845669951772,         \"F1\": 0.4130226619853175,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 79.08375899999999       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6153846153846154,         \"F1\": 0.4131455399061033,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 82.54849799999998       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6163420999799237,         \"F1\": 0.4168446750076289,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 86.09394899999998       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6150973068606251,         \"F1\": 0.4141232794733692,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 89.70897599999998       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6146735990756788,         \"F1\": 0.4133685136323659,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 93.40231399999998       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6152104170598226,         \"F1\": 0.4139120436907157,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 97.15397799999998       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8187845303867404,         \"F1\": 0.8284518828451883,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 0.90253       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8023191606847045,         \"F1\": 0.7475317348377998,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 2.668728       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.784688995215311,         \"F1\": 0.706177800100452,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 5.38565       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8032017664918576,         \"F1\": 0.7356321839080461,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 8.965856       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7979686465003312,         \"F1\": 0.7073872721458268,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 13.460125       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7937442502299908,         \"F1\": 0.6972724817715366,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 18.947959       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7982967986122063,         \"F1\": 0.7065840789171829,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 25.368016       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.790396025941769,         \"F1\": 0.6875128574367414,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 32.74734       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7841285416411137,         \"F1\": 0.6888260254596887,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 41.092102       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7897118887294403,         \"F1\": 0.7086710506193606,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 50.330249       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.793176116407426,         \"F1\": 0.7240594457089301,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 60.487048       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7960629196946003,         \"F1\": 0.7361656551231703,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 71.57583       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.792137216608644,         \"F1\": 0.7295027624309391,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 83.57396899999999       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7820704880548766,         \"F1\": 0.7260111022997621,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 96.505859       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7858562072264331,         \"F1\": 0.7383564107174968,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 110.378561       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7866850638151086,         \"F1\": 0.7435727317963178,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 125.162828       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.785728199467567,         \"F1\": 0.738593155893536,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 140.864825       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7806463481940271,         \"F1\": 0.7274666666666666,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 157.49451299999998       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7788880497298554,         \"F1\": 0.7181158346911569,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 175.04662       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7728903361112645,         \"F1\": 0.7138983522213725,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 193.534389       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7701445466491459,         \"F1\": 0.7094931242941608,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 212.921569       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7628317696051378,         \"F1\": 0.702236220472441,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 233.287368       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7537553390603254,         \"F1\": 0.6903626817934946,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 254.638983       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7508163546888654,         \"F1\": 0.6836389115964032,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 276.932282       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7509823833281822,         \"F1\": 0.6798001589644601,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 300.189673       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7457015495648482,         \"F1\": 0.668217569513681,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 324.33764       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7466170638976329,         \"F1\": 0.665839982747466,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 349.45202499999994       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7447865336854969,         \"F1\": 0.6611180904522613,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 375.51598899999993       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7448711605069843,         \"F1\": 0.6581322996888865,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 402.576751       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.741123661650539,         \"F1\": 0.650402464473815,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 430.58128099999993       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7390065871461634,         \"F1\": 0.6440019426906265,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 459.5402439999999       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7358145631402849,         \"F1\": 0.6343280019097637,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 489.4018749999999       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7320466936481921,         \"F1\": 0.6243023964732918,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 520.249024       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7297990455475116,         \"F1\": 0.6158319870759289,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 552.052505       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7256930209088902,         \"F1\": 0.6059617649723658,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 584.838155       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7215391690939752,         \"F1\": 0.596427301813011,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 618.5052350000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7176695205990274,         \"F1\": 0.5867248908296943,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 653.1230730000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7142359194818021,         \"F1\": 0.5779493779493778,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 688.6949970000001       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7138369229898395,         \"F1\": 0.5724554949469323,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 725.19325       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7174866856149452,         \"F1\": 0.5752924583091347,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 762.649856       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7169740207295733,         \"F1\": 0.5716148486206756,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 801.028112       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7183516858952459,         \"F1\": 0.573859795618116,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 840.263393       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7206407064198989,         \"F1\": 0.5799529121154812,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 880.2889349999999       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7217720693374808,         \"F1\": 0.5866964784795975,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 921.221106       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7228776766660944,         \"F1\": 0.5923065819861432,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 962.947955       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.724127174565087,         \"F1\": 0.5973170817134251,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1005.542302       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7260280406754186,         \"F1\": 0.6013259517462921,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1049.006993       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7277117299422816,         \"F1\": 0.6045222270465248,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1093.33419       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7273894532921857,         \"F1\": 0.6015933631814591,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1138.520645       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7287136581381487,         \"F1\": 0.6038234630387828,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1184.586595       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7287413652314008,         \"F1\": 0.6037845330582509,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1230.65543       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.005899       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7636363636363637,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.034194       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.8048780487804877,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.070237       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.819047619047619,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.1191769999999999       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8145161290322581,         \"F1\": 0.8217054263565893,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.172458       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.830188679245283,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.294112       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8323699421965318,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.432822       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8341708542713567,         \"F1\": 0.83248730964467,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.620751       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8240740740740741,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.8126760000000001       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.825,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 1.097418       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8244274809160306,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 1.386748       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8394648829431438,         \"F1\": 0.8285714285714285,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 1.708109       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845679012345679,         \"F1\": 0.8299319727891157,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 2.034368       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8510028653295129,         \"F1\": 0.8322580645161292,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 2.472974       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8529411764705882,         \"F1\": 0.8318042813455658,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 2.916035       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8546365914786967,         \"F1\": 0.8313953488372093,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 3.458949       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561320754716981,         \"F1\": 0.8291316526610645,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 4.00711       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8596881959910914,         \"F1\": 0.8310991957104559,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 4.560221       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8565400843881856,         \"F1\": 0.8291457286432161,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 5.117465       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8577154308617234,         \"F1\": 0.8337236533957845,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 5.6788810000000005       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8587786259541985,         \"F1\": 0.8310502283105022,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 6.3168120000000005       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8579234972677595,         \"F1\": 0.8311688311688311,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 6.9590250000000005       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8606271777003485,         \"F1\": 0.8340248962655602,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 7.670201       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8647746243739566,         \"F1\": 0.8363636363636363,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 8.386169       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669871794871795,         \"F1\": 0.8356435643564357,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 9.138945       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8705701078582434,         \"F1\": 0.8426966292134833,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 9.901064000000002       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.870919881305638,         \"F1\": 0.8465608465608465,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 10.713223       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8755364806866953,         \"F1\": 0.8502581755593803,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 11.569231       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8784530386740331,         \"F1\": 0.8562091503267973,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 12.458796       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798397863818425,         \"F1\": 0.8584905660377359,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 13.352328       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798449612403101,         \"F1\": 0.8580152671755725,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 14.337352       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798498122653317,         \"F1\": 0.8596491228070174,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 15.326948       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798543689320388,         \"F1\": 0.860759493670886,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 16.325159       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798586572438163,         \"F1\": 0.8602739726027396,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 17.375421       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8832951945080092,         \"F1\": 0.8636363636363635,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 18.429913       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8809788654060067,         \"F1\": 0.8608582574772432,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 19.528877       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8820346320346321,         \"F1\": 0.8635794743429286,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 20.632714       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8819810326659642,         \"F1\": 0.8650602409638554,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 21.817705       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8829568788501027,         \"F1\": 0.8661971830985915,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 23.016963       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8808808808808809,         \"F1\": 0.8643101482326111,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 24.246233       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880859375,         \"F1\": 0.8647450110864746,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 25.480751       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.882745471877979,         \"F1\": 0.8673139158576052,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 26.819711       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8817504655493482,         \"F1\": 0.8672936259143157,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 28.162914       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8835304822565969,         \"F1\": 0.8693877551020409,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 29.538844       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8861209964412812,         \"F1\": 0.8735177865612648,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 30.932979       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8859878154917319,         \"F1\": 0.8731848983543079,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 32.425237       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8850085178875639,         \"F1\": 0.8717948717948718,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 33.921729       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8865721434528774,         \"F1\": 0.8731343283582089,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 35.452877       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.886437908496732,         \"F1\": 0.8728270814272644,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 36.988201       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8847077662129704,         \"F1\": 0.8714285714285714,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 38.528021       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 1.286863       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 3.863138       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 7.731956       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 13.024672       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 19.659339000000003       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 27.654251       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 36.976608       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372397030808,         \"F1\": 0.7777777777777778,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 47.719054       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997664369963798,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 59.951688       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997897945241474,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 73.68853899999999       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089050257978,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 88.86509       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248303043572,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 105.535247       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999838305441022,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 123.661746       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498554859052,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 143.18039199999998       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999859865470852,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 164.12799199999998       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686241665844,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 186.627363       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998763523956724,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 210.517492       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998832219075702,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 235.832236       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998893682929528,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 262.657063       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998949000236474,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 290.942762       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998999049096642,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 320.716469       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999904454795175,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 352.027934       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999908609029428,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 384.764181       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124170699132,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 419.010574       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999159204607558,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 454.738206       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999191543545486,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 491.833824       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999026858699884,         \"F1\": 0.8275862068965517,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 530.367488       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999061614398588,         \"F1\": 0.8275862068965517,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 570.267553       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998912767730946,         \"F1\": 0.7999999999999999,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 611.572363       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993869221741492,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 654.167087       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9988473013289938,         \"F1\": 0.2916666666666666,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 698.17064       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9986369981115034,         \"F1\": 0.2522522522522523,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 743.490298       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9979139463040224,         \"F1\": 0.1761006289308176,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 790.115891       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9979443903494536,         \"F1\": 0.1739130434782608,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 838.025404       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9977478830100296,         \"F1\": 0.1573033707865168,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 887.160342       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9967302611411972,         \"F1\": 0.125,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 937.511304       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9964777730436016,         \"F1\": 0.1142857142857143,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 989.136144       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9964045192427364,         \"F1\": 0.1095890410958904,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1041.952402       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9958230031260106,         \"F1\": 0.0935672514619883,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1095.894331       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9956515456062218,         \"F1\": 0.0881542699724517,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1151.054816       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9951936633257288,         \"F1\": 0.0786240786240786,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1207.299045       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9946700031279324,         \"F1\": 0.0698689956331877,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1264.68116       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9945862052109302,         \"F1\": 0.0673684210526315,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1323.182614       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9945539883675102,         \"F1\": 0.0655737704918032,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1382.690018       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9939860335847912,         \"F1\": 0.0585009140767824,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1443.251394       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9938540274398254,         \"F1\": 0.0561403508771929,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1504.7859910000002       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9938618067978532,         \"F1\": 0.0550774526678141,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1567.3680330000002       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9939677917300724,         \"F1\": 0.0548885077186964,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1630.9013820000002       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.993543958990198,         \"F1\": 0.0504731861198738,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1695.428307       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.993483904192372,         \"F1\": 0.0490797546012269,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1760.949483       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.993484315064894,         \"F1\": 0.0490797546012269,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1826.472109       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.4952380952380952,         \"F1\": 0.208955223880597,         \"Memory in Mb\": 0.0192251205444335,         \"Time in s\": 0.143993       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5213270142180095,         \"F1\": 0.3129251700680272,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 0.331364       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5299684542586751,         \"F1\": 0.4063745019920318,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 0.6339969999999999       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5437352245862884,         \"F1\": 0.4238805970149253,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 1.026482       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.553875236294896,         \"F1\": 0.4099999999999999,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 1.502748       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5590551181102362,         \"F1\": 0.4017094017094017,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 2.038539       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5762483130904184,         \"F1\": 0.3984674329501916,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 2.585217       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5867768595041323,         \"F1\": 0.4047619047619047,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 3.2443470000000003       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5918153200419727,         \"F1\": 0.3987635239567234,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 4.029044000000001       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6015108593012276,         \"F1\": 0.3971428571428571,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 4.857172       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6,         \"F1\": 0.3852242744063324,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 5.757943       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6073957513768686,         \"F1\": 0.3966142684401451,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 6.7312840000000005       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085693536673928,         \"F1\": 0.384,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 7.793338       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6089008766014835,         \"F1\": 0.3790149892933619,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 8.86628       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085588420390182,         \"F1\": 0.3742454728370221,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 10.05345       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6094395280235988,         \"F1\": 0.370722433460076,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 11.283728       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6102165463631316,         \"F1\": 0.3754448398576512,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 12.570083       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.610907184058731,         \"F1\": 0.3816666666666667,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 13.875162       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6060606060606061,         \"F1\": 0.3799843627834245,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 15.24589       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6045304388862671,         \"F1\": 0.3838235294117647,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 16.723857       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6053932584269663,         \"F1\": 0.3868715083798882,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 18.213202       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6061776061776062,         \"F1\": 0.388814913448735,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 19.838043       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.606893721789085,         \"F1\": 0.388250319284802,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 21.528239       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608336610302792,         \"F1\": 0.3963636363636363,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 23.295102       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6070215175537939,         \"F1\": 0.3944153577661431,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 25.108421       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6047186932849364,         \"F1\": 0.3892316320807628,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 26.99552       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6057322614470465,         \"F1\": 0.3922413793103448,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 28.904989       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6056622851365016,         \"F1\": 0.3899895724713243,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 30.87684       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6036446469248291,         \"F1\": 0.3903903903903904,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 32.946938       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6045926391947153,         \"F1\": 0.3924601256645723,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 35.112766       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6039573820395738,         \"F1\": 0.3900609470229723,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 37.308874       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6024771453848422,         \"F1\": 0.391696750902527,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 39.588614       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030883614526737,         \"F1\": 0.3933566433566433,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 41.900558       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6069941715237303,         \"F1\": 0.4035383319292334,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 44.30438       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6079805877595039,         \"F1\": 0.4079804560260586,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 46.776579       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6107470511140236,         \"F1\": 0.4146629877808436,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 49.299973       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6123437898495282,         \"F1\": 0.4180704441041348,         \"Memory in Mb\": 0.0440921783447265,         \"Time in s\": 51.9923       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6143531164638689,         \"F1\": 0.4246017043349389,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 54.748055       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.617227195741592,         \"F1\": 0.4321608040201005,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 57.554127       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6218447747110167,         \"F1\": 0.4439819632327437,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 60.441345       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6239355581127733,         \"F1\": 0.4513096037609133,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 63.387968       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6259267580319029,         \"F1\": 0.4567699836867862,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 66.368103       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6276058810621022,         \"F1\": 0.4638230647709321,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 69.45485500000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6283508470941453,         \"F1\": 0.4695439240893787,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 72.676145       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6288530090165653,         \"F1\": 0.471641791044776,         \"Memory in Mb\": 0.0594196319580078,         \"Time in s\": 75.94439200000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6311794871794871,         \"F1\": 0.475801749271137,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 79.31100500000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6336077092953222,         \"F1\": 0.484026010743568,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 82.69585900000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6361313151169649,         \"F1\": 0.4905037159372419,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 86.19871000000002       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6383593298671288,         \"F1\": 0.495703544575725,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 89.82165300000003       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6421966408756369,         \"F1\": 0.5034049240440022,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 93.53024900000004       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530386740331491,         \"F1\": 0.8513966480446927,         \"Memory in Mb\": 0.1757516860961914,         \"Time in s\": 1.081486       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8663721700717836,         \"F1\": 0.8393094289508632,         \"Memory in Mb\": 0.2084512710571289,         \"Time in s\": 3.208731       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8365844681634156,         \"F1\": 0.809278350515464,         \"Memory in Mb\": 0.2330217361450195,         \"Time in s\": 6.394793       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8459839911675407,         \"F1\": 0.8210391276459269,         \"Memory in Mb\": 0.2330217361450195,         \"Time in s\": 10.694791       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8511812762199161,         \"F1\": 0.8157463094587206,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 16.02834       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404783808647655,         \"F1\": 0.8020095912308747,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 22.571918       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8334647531935025,         \"F1\": 0.7966884867154409,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 30.238204       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8330343590451221,         \"F1\": 0.7912353347135956,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 38.961308       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8344167790997179,         \"F1\": 0.8013537374926426,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 48.732242       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8403797328623468,         \"F1\": 0.8129849974133472,         \"Memory in Mb\": 0.2980508804321289,         \"Time in s\": 59.636444       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8398394380331159,         \"F1\": 0.8171402383134739,         \"Memory in Mb\": 0.2981653213500976,         \"Time in s\": 71.55266999999999       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.840493054916751,         \"F1\": 0.8200124558854057,         \"Memory in Mb\": 0.2981653213500976,         \"Time in s\": 84.613385       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404517279442982,         \"F1\": 0.8184014690248381,         \"Memory in Mb\": 0.3811311721801758,         \"Time in s\": 98.771271       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397066939998423,         \"F1\": 0.8184983483617534,         \"Memory in Mb\": 0.3811311721801758,         \"Time in s\": 114.088656       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8422253293104717,         \"F1\": 0.8228684732319893,         \"Memory in Mb\": 0.3811311721801758,         \"Time in s\": 130.484857       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8440841669541221,         \"F1\": 0.8259128023417038,         \"Memory in Mb\": 0.3823747634887695,         \"Time in s\": 148.034702       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8445555483410169,         \"F1\": 0.8246153846153847,         \"Memory in Mb\": 0.3824014663696289,         \"Time in s\": 166.630313       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8382903047770895,         \"F1\": 0.8146221441124781,         \"Memory in Mb\": 0.4081621170043945,         \"Time in s\": 186.33286       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8345436588624876,         \"F1\": 0.8052516411378555,         \"Memory in Mb\": 0.4081621170043945,         \"Time in s\": 207.149801       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8332689442022186,         \"F1\": 0.8030253635000325,         \"Memory in Mb\": 0.408848762512207,         \"Time in s\": 229.10312700000003       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8340604467805519,         \"F1\": 0.8008327550312283,         \"Memory in Mb\": 0.4101419448852539,         \"Time in s\": 252.195562       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8288595655009784,         \"F1\": 0.7951228302000121,         \"Memory in Mb\": 0.4740419387817383,         \"Time in s\": 276.349221       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8238230071507414,         \"F1\": 0.787570163763671,         \"Memory in Mb\": 0.4986543655395508,         \"Time in s\": 301.796373       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8251391252357081,         \"F1\": 0.7858028169014086,         \"Memory in Mb\": 0.498814582824707,         \"Time in s\": 328.42960500000004       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8245838668373879,         \"F1\": 0.7828843106180666,         \"Memory in Mb\": 0.4754457473754883,         \"Time in s\": 356.18046400000003       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.81761834005519,         \"F1\": 0.7712703652433182,         \"Memory in Mb\": 0.5000581741333008,         \"Time in s\": 385.033694       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8151342954090184,         \"F1\": 0.7656509121061359,         \"Memory in Mb\": 0.5002222061157227,         \"Time in s\": 415.059878       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8133401663578665,         \"F1\": 0.7649540828989824,         \"Memory in Mb\": 0.5574884414672852,         \"Time in s\": 446.384667       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8142199215925094,         \"F1\": 0.7659329592864336,         \"Memory in Mb\": 0.5574884414672852,         \"Time in s\": 478.875266       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8130909893667906,         \"F1\": 0.7650758416574177,         \"Memory in Mb\": 0.5574884414672852,         \"Time in s\": 512.642228       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.810646252447926,         \"F1\": 0.7611605137878379,         \"Memory in Mb\": 0.5575571060180664,         \"Time in s\": 547.6069200000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8084233037839329,         \"F1\": 0.755846667838931,         \"Memory in Mb\": 0.5575571060180664,         \"Time in s\": 583.7938770000001       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8039602635715958,         \"F1\": 0.7488322262695523,         \"Memory in Mb\": 0.5575571060180664,         \"Time in s\": 621.109455       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8052787066194851,         \"F1\": 0.7498540328634582,         \"Memory in Mb\": 0.6720895767211914,         \"Time in s\": 659.6567610000001       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.802863540319783,         \"F1\": 0.7460285215130216,         \"Memory in Mb\": 0.6720895767211914,         \"Time in s\": 699.5069490000001       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8010731258623333,         \"F1\": 0.7451889089623752,         \"Memory in Mb\": 0.6838197708129883,         \"Time in s\": 740.492735       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8010500880045345,         \"F1\": 0.7469934367768125,         \"Memory in Mb\": 0.7644319534301758,         \"Time in s\": 782.6537000000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.799663055160194,         \"F1\": 0.7444893120438633,         \"Memory in Mb\": 0.7656755447387695,         \"Time in s\": 825.979857       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7997056576005434,         \"F1\": 0.7438746335637508,         \"Memory in Mb\": 0.796971321105957,         \"Time in s\": 870.4262610000001       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.798283617097602,         \"F1\": 0.7418420680887131,         \"Memory in Mb\": 0.8215837478637695,         \"Time in s\": 916.052417       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7980347287656482,         \"F1\": 0.741577678263865,         \"Memory in Mb\": 0.8528566360473633,         \"Time in s\": 962.729846       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7942761031247536,         \"F1\": 0.7384913476314559,         \"Memory in Mb\": 0.8296480178833008,         \"Time in s\": 1010.431184       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.791975768154632,         \"F1\": 0.7385131646876614,         \"Memory in Mb\": 0.8296480178833008,         \"Time in s\": 1059.228611       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7917617841105787,         \"F1\": 0.7414904549842734,         \"Memory in Mb\": 0.8308916091918945,         \"Time in s\": 1109.092208       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7937158134857367,         \"F1\": 0.7465187775031646,         \"Memory in Mb\": 0.8308916091918945,         \"Time in s\": 1159.935967       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7945770845830834,         \"F1\": 0.749744219357479,         \"Memory in Mb\": 0.8553438186645508,         \"Time in s\": 1211.819823       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7952373124163359,         \"F1\": 0.7509355271802782,         \"Memory in Mb\": 0.8799333572387695,         \"Time in s\": 1264.739744       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7953871271874353,         \"F1\": 0.7515912897822445,         \"Memory in Mb\": 0.881199836730957,         \"Time in s\": 1318.691004       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7949676750839096,         \"F1\": 0.7499038303016982,         \"Memory in Mb\": 0.881199836730957,         \"Time in s\": 1373.745004       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7956246274752202,         \"F1\": 0.7508745492707605,         \"Memory in Mb\": 0.9384660720825196,         \"Time in s\": 1429.8385990000002       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7956346141113637,         \"F1\": 0.7508341405661393,         \"Memory in Mb\": 0.9384660720825196,         \"Time in s\": 1485.976427       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.6428571428571429,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.007366       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7346938775510203,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.021904       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.7894736842105262,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.108104       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.8080808080808081,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.262464       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8145161290322581,         \"F1\": 0.8130081300813008,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.426996       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.8235294117647058,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.670297       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8263473053892215,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.944361       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8341708542713567,         \"F1\": 0.8272251308900525,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 1.225091       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8190476190476189,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 1.620606       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.8205128205128206,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 2.072395       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8203125000000001,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 2.536963       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8394648829431438,         \"F1\": 0.8248175182481753,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 3.035956       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845679012345679,         \"F1\": 0.8263888888888888,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 3.5418380000000003       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8510028653295129,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 4.130076000000001       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8529411764705882,         \"F1\": 0.8286604361370716,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 4.770647       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8546365914786967,         \"F1\": 0.8284023668639053,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 5.418701       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561320754716981,         \"F1\": 0.8262108262108262,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 6.103302       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8596881959910914,         \"F1\": 0.8283378746594006,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 6.878701       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8565400843881856,         \"F1\": 0.826530612244898,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 7.659851000000001       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8577154308617234,         \"F1\": 0.8313539192399049,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 8.471725000000001       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8587786259541985,         \"F1\": 0.8287037037037036,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 9.290161       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8579234972677595,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 10.200081       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8606271777003485,         \"F1\": 0.8319327731092437,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 11.144835       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8647746243739566,         \"F1\": 0.834355828220859,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 12.149797       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669871794871795,         \"F1\": 0.8336673346693387,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 13.191129       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8705701078582434,         \"F1\": 0.8409090909090909,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 14.297317       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.870919881305638,         \"F1\": 0.8449197860962566,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 15.408972       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8755364806866953,         \"F1\": 0.8486956521739131,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 16.598807       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8784530386740331,         \"F1\": 0.8547854785478548,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 17.82593       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798397863818425,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 19.123649       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798449612403101,         \"F1\": 0.8567026194144837,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 20.439518       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798498122653317,         \"F1\": 0.8584070796460177,         \"Memory in Mb\": 0.0060701370239257,         \"Time in s\": 21.870793       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8786407766990292,         \"F1\": 0.8575498575498576,         \"Memory in Mb\": 0.1326732635498047,         \"Time in s\": 23.308925       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798586572438163,         \"F1\": 0.8579387186629527,         \"Memory in Mb\": 0.1326961517333984,         \"Time in s\": 24.793982       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8810068649885584,         \"F1\": 0.8583106267029972,         \"Memory in Mb\": 0.1326961517333984,         \"Time in s\": 26.327422       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.882091212458287,         \"F1\": 0.8590425531914893,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 27.867454       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8831168831168831,         \"F1\": 0.8611825192802056,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 29.49699       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880927291886196,         \"F1\": 0.8599752168525404,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 31.132602       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8819301848049281,         \"F1\": 0.8609431680773881,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 32.858381       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8828828828828829,         \"F1\": 0.8621908127208481,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 34.600804000000004       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8818359375,         \"F1\": 0.8613974799541809,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 36.37479200000001       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8836987607244995,         \"F1\": 0.8641425389755011,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 38.236126000000006       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8845437616387337,         \"F1\": 0.8658008658008659,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 40.114172       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844404003639672,         \"F1\": 0.8656084656084656,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 41.998405000000005       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8816725978647687,         \"F1\": 0.8630278063851698,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 43.96255500000001       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807658833768495,         \"F1\": 0.8614762386248735,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 45.93298000000001       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.879045996592845,         \"F1\": 0.8594059405940594,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 47.92952100000001       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807339449541285,         \"F1\": 0.8610301263362489,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 50.02063300000001       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880718954248366,         \"F1\": 0.8609523809523809,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 52.11693600000001       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8799039231385108,         \"F1\": 0.8605947955390334,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 54.27575100000001       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 1.086226       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 3.167363       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 6.335451       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 10.587829       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 15.968691       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 22.515101       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 30.177580000000003       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.0262222290039062,         \"Time in s\": 38.907943       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 48.927066       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 60.172540000000005       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 72.66801500000001       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 86.48422800000002       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 101.518721       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 117.73184500000002       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 135.114956       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 153.67141       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 173.49434000000002       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 194.59387200000003       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 216.90021000000004       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 240.42103000000003       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 265.208452       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 291.209442       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 318.43843100000004       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 346.89099000000004       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 376.5294080000001       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 407.49804200000005       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 439.6062170000001       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 472.95335100000005       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 507.474024       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 543.2100710000001       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 580.098547       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 618.1800870000001       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 657.2504170000001       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 697.447209       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 738.7766780000001       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 781.207926       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 824.7260210000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 869.3947840000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 915.176721       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 961.985028       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1009.890756       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1058.848202       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1108.7919539999998       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1159.7893379999998       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1211.840415       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1264.8087919999998       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1318.8158339999998       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1373.8298589999995       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1429.7685149999998       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1486.6476859999998       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1543.5553739999998       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5714285714285714,         \"F1\": 0.628099173553719,         \"Memory in Mb\": 0.0256843566894531,         \"Time in s\": 0.216494       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5592417061611374,         \"F1\": 0.5903083700440529,         \"Memory in Mb\": 0.0257682800292968,         \"Time in s\": 0.463954       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5615141955835962,         \"F1\": 0.5947521865889213,         \"Memory in Mb\": 0.0258293151855468,         \"Time in s\": 0.862573       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5555555555555556,         \"F1\": 0.5822222222222222,         \"Memory in Mb\": 0.0258293151855468,         \"Time in s\": 1.389833       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.555765595463138,         \"F1\": 0.5506692160611854,         \"Memory in Mb\": 0.0258293151855468,         \"Time in s\": 1.964112       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5543307086614173,         \"F1\": 0.5291181364392679,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 2.693957       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5708502024291497,         \"F1\": 0.5167173252279634,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 3.480128       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5761511216056671,         \"F1\": 0.510231923601637,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 4.453125999999999       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5844700944386149,         \"F1\": 0.505,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 5.580188       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5920679886685553,         \"F1\": 0.4953271028037382,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 6.755147       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.590557939914163,         \"F1\": 0.478688524590164,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 8.08575       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971675845790716,         \"F1\": 0.4807302231237322,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 9.558451000000002       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599128540305011,         \"F1\": 0.4661508704061895,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 11.087295       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5994605529332434,         \"F1\": 0.458029197080292,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 12.740385000000002       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5997482693517936,         \"F1\": 0.4517241379310345,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 14.490633000000004       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6011799410029498,         \"F1\": 0.4459016393442623,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 16.383274000000004       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6018878400888396,         \"F1\": 0.445475638051044,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 18.381747000000004       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030414263240692,         \"F1\": 0.4470416362308254,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 20.420329       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986090412319921,         \"F1\": 0.443526170798898,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 22.615668000000003       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5960358659745163,         \"F1\": 0.4427083333333333,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 24.891681       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5968539325842697,         \"F1\": 0.4425108763206961,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 27.295309000000003       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5975975975975976,         \"F1\": 0.4423305588585017,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 29.792211       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5982765695527288,         \"F1\": 0.4396107613050944,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 32.414577       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5973259929217459,         \"F1\": 0.4398249452954048,         \"Memory in Mb\": 0.0302915573120117,         \"Time in s\": 35.17394       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5956964892412231,         \"F1\": 0.4436363636363636,         \"Memory in Mb\": 0.0567083358764648,         \"Time in s\": 38.067898       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5985480943738657,         \"F1\": 0.4497512437810945,         \"Memory in Mb\": 0.0569524765014648,         \"Time in s\": 41.158639       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.600139811254806,         \"F1\": 0.4536771728748806,         \"Memory in Mb\": 0.0571966171264648,         \"Time in s\": 44.452629       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5979103471520054,         \"F1\": 0.4525011473152822,         \"Memory in Mb\": 0.0573034286499023,         \"Time in s\": 47.888765       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971363488447771,         \"F1\": 0.4497777777777778,         \"Memory in Mb\": 0.0574254989624023,         \"Time in s\": 51.476908       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008178672538534,         \"F1\": 0.4499349804941482,         \"Memory in Mb\": 0.0574865341186523,         \"Time in s\": 55.178717       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6024353120243531,         \"F1\": 0.4470787468247248,         \"Memory in Mb\": 0.0576086044311523,         \"Time in s\": 59.035765       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6012975523444412,         \"F1\": 0.444991789819376,         \"Memory in Mb\": 0.0576696395874023,         \"Time in s\": 63.084021       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.603946239633972,         \"F1\": 0.4431041415359871,         \"Memory in Mb\": 0.0577306747436523,         \"Time in s\": 67.283017       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.607826810990841,         \"F1\": 0.4452296819787986,         \"Memory in Mb\": 0.0577306747436523,         \"Time in s\": 71.628079       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6071717444055001,         \"F1\": 0.441976254308694,         \"Memory in Mb\": 0.0577306747436523,         \"Time in s\": 76.17092       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6062909567496724,         \"F1\": 0.4378742514970059,         \"Memory in Mb\": 0.0577917098999023,         \"Time in s\": 80.84267399999999       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.606988013261923,         \"F1\": 0.4353242946134115,         \"Memory in Mb\": 0.0578527450561523,         \"Time in s\": 85.696272       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6088899925502855,         \"F1\": 0.4360902255639098,         \"Memory in Mb\": 0.0578527450561523,         \"Time in s\": 90.658573       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6082748608758771,         \"F1\": 0.4341139461726669,         \"Memory in Mb\": 0.0579137802124023,         \"Time in s\": 95.895015       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6105213493748526,         \"F1\": 0.4370951244459598,         \"Memory in Mb\": 0.0579137802124023,         \"Time in s\": 101.21739399999998       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6119677790563867,         \"F1\": 0.4372496662216288,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 106.75825799999998       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.614243990114581,         \"F1\": 0.4387054593004249,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 112.41943399999998       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6126837831906956,         \"F1\": 0.4355612408058842,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 118.261679       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.613339052112374,         \"F1\": 0.4360337816703159,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 124.266912       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6148039421262319,         \"F1\": 0.4352905010759299,         \"Memory in Mb\": 0.0641164779663086,         \"Time in s\": 130.389994       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6157948717948718,         \"F1\": 0.4332829046898639,         \"Memory in Mb\": 0.0641164779663086,         \"Time in s\": 136.677955       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6167436257779563,         \"F1\": 0.434705359786793,         \"Memory in Mb\": 0.0641164779663086,         \"Time in s\": 143.134937       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6158836249262827,         \"F1\": 0.4313154831199068,         \"Memory in Mb\": 0.0641775131225586,         \"Time in s\": 149.736273       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6160215674947044,         \"F1\": 0.4296338672768878,         \"Memory in Mb\": 0.0642385482788086,         \"Time in s\": 156.525598       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6165314210228345,         \"F1\": 0.4282498593134496,         \"Memory in Mb\": 0.0618467330932617,         \"Time in s\": 163.516222       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8386740331491712,         \"F1\": 0.8370535714285713,         \"Memory in Mb\": 0.1553249359130859,         \"Time in s\": 2.212895       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8823854224185533,         \"F1\": 0.857334226389819,         \"Memory in Mb\": 0.2904033660888672,         \"Time in s\": 6.521798       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8715495031284505,         \"F1\": 0.8438478747203579,         \"Memory in Mb\": 0.1283740997314453,         \"Time in s\": 13.845606       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.875241512558653,         \"F1\": 0.8472972972972973,         \"Memory in Mb\": 0.2500133514404297,         \"Time in s\": 22.913433       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8737028041510267,         \"F1\": 0.8396860986547084,         \"Memory in Mb\": 0.3712940216064453,         \"Time in s\": 34.075607999999995       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8656853725850966,         \"F1\": 0.8300744878957169,         \"Memory in Mb\": 0.4407672882080078,         \"Time in s\": 47.709684       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8646901119697209,         \"F1\": 0.8296943231441047,         \"Memory in Mb\": 0.2620487213134765,         \"Time in s\": 63.50523799999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.864771629639851,         \"F1\": 0.8289703315881326,         \"Memory in Mb\": 0.2866535186767578,         \"Time in s\": 81.25362299999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8572304673126456,         \"F1\": 0.8312064965197217,         \"Memory in Mb\": 0.2866878509521484,         \"Time in s\": 101.144105       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8580417264598742,         \"F1\": 0.8370501773948302,         \"Memory in Mb\": 0.2865924835205078,         \"Time in s\": 123.033084       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8544907175112895,         \"F1\": 0.8369320737741789,         \"Memory in Mb\": 0.3109416961669922,         \"Time in s\": 147.021637       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8583386992916935,         \"F1\": 0.8434322895485971,         \"Memory in Mb\": 0.371591567993164,         \"Time in s\": 172.950216       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8529336842999066,         \"F1\": 0.8357982555934774,         \"Memory in Mb\": 0.4628047943115234,         \"Time in s\": 201.490822       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533469999211543,         \"F1\": 0.8362099330750264,         \"Memory in Mb\": 0.1895275115966797,         \"Time in s\": 232.642586       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8551769813820002,         \"F1\": 0.8395303326810176,         \"Memory in Mb\": 0.1939334869384765,         \"Time in s\": 265.763258       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.855122456019317,         \"F1\": 0.8397435897435896,         \"Memory in Mb\": 0.1697406768798828,         \"Time in s\": 300.834382       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8537757288487761,         \"F1\": 0.8365984617617181,         \"Memory in Mb\": 0.1694965362548828,         \"Time in s\": 338.213883       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8506776231066413,         \"F1\": 0.8316626339440029,         \"Memory in Mb\": 0.1640834808349609,         \"Time in s\": 377.804328       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8495904258409341,         \"F1\": 0.8278704873346187,         \"Memory in Mb\": 0.1691112518310547,         \"Time in s\": 419.462025       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8498261493459904,         \"F1\": 0.827752104830031,         \"Memory in Mb\": 0.198678970336914,         \"Time in s\": 463.148728       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8522996057818659,         \"F1\": 0.8287211995611362,         \"Memory in Mb\": 0.2572231292724609,         \"Time in s\": 508.959572       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8470222266820531,         \"F1\": 0.8238895627563102,         \"Memory in Mb\": 0.315378189086914,         \"Time in s\": 557.675148       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8434035609732687,         \"F1\": 0.8200121352529097,         \"Memory in Mb\": 0.3239650726318359,         \"Time in s\": 609.8923100000001       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8450535804626776,         \"F1\": 0.8196563353139554,         \"Memory in Mb\": 0.3222179412841797,         \"Time in s\": 664.4541280000001       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8452911828336792,         \"F1\": 0.8184455958549223,         \"Memory in Mb\": 0.4409503936767578,         \"Time in s\": 721.910691       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8424962852897474,         \"F1\": 0.8143700590413289,         \"Memory in Mb\": 0.4440975189208984,         \"Time in s\": 782.4177400000001       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8403990024937655,         \"F1\": 0.8111089607122121,         \"Memory in Mb\": 0.5018138885498047,         \"Time in s\": 845.9758730000001       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8367169945204399,         \"F1\": 0.8072053621299572,         \"Memory in Mb\": 0.5608501434326172,         \"Time in s\": 913.002536       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8375137974346287,         \"F1\": 0.8080226649278229,         \"Memory in Mb\": 0.3154354095458984,         \"Time in s\": 983.555265       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8378527539644579,         \"F1\": 0.8096081565645656,         \"Memory in Mb\": 0.315774917602539,         \"Time in s\": 1056.640488       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8349652839594089,         \"F1\": 0.8051456678017405,         \"Memory in Mb\": 0.187021255493164,         \"Time in s\": 1131.521461       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8328791693973991,         \"F1\": 0.8007566722868775,         \"Memory in Mb\": 0.2230243682861328,         \"Time in s\": 1208.700577       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8303843194969395,         \"F1\": 0.7968267959453503,         \"Memory in Mb\": 0.104043960571289,         \"Time in s\": 1288.239999       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8300490211992338,         \"F1\": 0.7958984755740965,         \"Memory in Mb\": 0.2261257171630859,         \"Time in s\": 1369.046902       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8277460657857391,         \"F1\": 0.7934815486993346,         \"Memory in Mb\": 0.378305435180664,         \"Time in s\": 1451.951614       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8227502682814656,         \"F1\": 0.7867025790502896,         \"Memory in Mb\": 0.1292285919189453,         \"Time in s\": 1536.962592       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8198442767220548,         \"F1\": 0.7850354180756772,         \"Memory in Mb\": 0.1289234161376953,         \"Time in s\": 1623.2849270000002       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8166264850262875,         \"F1\": 0.7809127190699289,         \"Memory in Mb\": 0.1940555572509765,         \"Time in s\": 1710.9311020000002       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8170265757224124,         \"F1\": 0.7804530172852923,         \"Memory in Mb\": 0.3470821380615234,         \"Time in s\": 1800.2335220000002       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8175446342338365,         \"F1\": 0.7795559111822364,         \"Memory in Mb\": 0.4078502655029297,         \"Time in s\": 1890.9592170000003       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.816610580158837,         \"F1\": 0.7771817349208426,         \"Memory in Mb\": 0.4166545867919922,         \"Time in s\": 1983.256251       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8155107618722242,         \"F1\": 0.7753456221198157,         \"Memory in Mb\": 0.1291065216064453,         \"Time in s\": 2077.01254       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8146674538593834,         \"F1\": 0.7751479289940829,         \"Memory in Mb\": 0.2005825042724609,         \"Time in s\": 2172.116472       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8157188370167825,         \"F1\": 0.7778516995282448,         \"Memory in Mb\": 0.169626235961914,         \"Time in s\": 2268.811937       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8171650028207706,         \"F1\": 0.7812151452891106,         \"Memory in Mb\": 0.2508678436279297,         \"Time in s\": 2366.696648       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8187402519496101,         \"F1\": 0.7846022241231821,         \"Memory in Mb\": 0.2554492950439453,         \"Time in s\": 2465.8461540000003       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8196848359597003,         \"F1\": 0.7859015113490603,         \"Memory in Mb\": 0.3113727569580078,         \"Time in s\": 2566.2285070000003       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8203141168625107,         \"F1\": 0.7864093592827466,         \"Memory in Mb\": 0.2909717559814453,         \"Time in s\": 2667.8448940000003       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8199265649989863,         \"F1\": 0.7853959731543624,         \"Memory in Mb\": 0.4365749359130859,         \"Time in s\": 2771.03508       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8212543323252169,         \"F1\": 0.7873854475750336,         \"Memory in Mb\": 0.4353275299072265,         \"Time in s\": 2875.842657       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8212575312837942,         \"F1\": 0.7873440987265327,         \"Memory in Mb\": 0.4353275299072265,         \"Time in s\": 2980.68937       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.6428571428571429,         \"Memory in Mb\": 0.0747642517089843,         \"Time in s\": 0.008848       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7346938775510203,         \"Memory in Mb\": 0.0748252868652343,         \"Time in s\": 0.123836       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.7894736842105262,         \"Memory in Mb\": 0.0748252868652343,         \"Time in s\": 0.332733       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.8080808080808081,         \"Memory in Mb\": 0.0748863220214843,         \"Time in s\": 0.575353       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8225806451612904,         \"F1\": 0.819672131147541,         \"Memory in Mb\": 0.0748863220214843,         \"Time in s\": 0.909775       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.825503355704698,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.0749092102050781,         \"Time in s\": 1.284417       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8242424242424242,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 1.765942       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8291457286432161,         \"F1\": 0.8191489361702128,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 2.25515       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8155339805825242,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 2.805996       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.817391304347826,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 3.45663       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8174603174603176,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 4.129749       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8361204013377926,         \"F1\": 0.8178438661710038,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 4.871246       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8425925925925926,         \"F1\": 0.8197879858657244,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 5.6743250000000005       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8481375358166189,         \"F1\": 0.822742474916388,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 6.528728000000001       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8502673796791443,         \"F1\": 0.8227848101265823,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 7.451788       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8521303258145363,         \"F1\": 0.8228228228228228,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 8.382915       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8537735849056604,         \"F1\": 0.8208092485549133,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 9.397859       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8574610244988864,         \"F1\": 0.8232044198895027,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 10.451359       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8565400843881856,         \"F1\": 0.8238341968911918,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 11.619284       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8557114228456913,         \"F1\": 0.8260869565217391,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 12.854081       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8568702290076335,         \"F1\": 0.823529411764706,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 14.155744       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561020036429873,         \"F1\": 0.8240534521158129,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 15.508673       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554006968641115,         \"F1\": 0.8230277185501066,         \"Memory in Mb\": 0.1140928268432617,         \"Time in s\": 16.956902       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8547579298831386,         \"F1\": 0.8176100628930818,         \"Memory in Mb\": 0.1419858932495117,         \"Time in s\": 18.498415       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8573717948717948,         \"F1\": 0.8172484599589321,         \"Memory in Mb\": 0.1422224044799804,         \"Time in s\": 20.046891       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8597842835130971,         \"F1\": 0.8233009708737864,         \"Memory in Mb\": 0.1423902511596679,         \"Time in s\": 21.659211       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8590504451038575,         \"F1\": 0.8263254113345521,         \"Memory in Mb\": 0.1424512863159179,         \"Time in s\": 23.289464       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8640915593705293,         \"F1\": 0.8306595365418894,         \"Memory in Mb\": 0.1425123214721679,         \"Time in s\": 25.025232       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8646408839779005,         \"F1\": 0.8344594594594595,         \"Memory in Mb\": 0.1425733566284179,         \"Time in s\": 26.77059       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8664886515353805,         \"F1\": 0.8371335504885993,         \"Memory in Mb\": 0.1426343917846679,         \"Time in s\": 28.602532       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8643410852713178,         \"F1\": 0.8330683624801273,         \"Memory in Mb\": 0.1426572799682617,         \"Time in s\": 30.506622       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8635794743429287,         \"F1\": 0.8340943683409437,         \"Memory in Mb\": 0.1426572799682617,         \"Time in s\": 32.425334       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8628640776699029,         \"F1\": 0.8345534407027819,         \"Memory in Mb\": 0.1426572799682617,         \"Time in s\": 34.352118       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8645465253239105,         \"F1\": 0.8364153627311521,         \"Memory in Mb\": 0.1427183151245117,         \"Time in s\": 36.371837       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672768878718535,         \"F1\": 0.838888888888889,         \"Memory in Mb\": 0.1427183151245117,         \"Time in s\": 38.48177       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8665183537263627,         \"F1\": 0.8378378378378378,         \"Memory in Mb\": 0.1427793502807617,         \"Time in s\": 40.637242       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8668831168831169,         \"F1\": 0.8400520156046815,         \"Memory in Mb\": 0.1427793502807617,         \"Time in s\": 42.878637       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8661749209694415,         \"F1\": 0.8410513141426783,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 45.126806       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.86652977412731,         \"F1\": 0.8414634146341464,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 47.480371       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8638638638638638,         \"F1\": 0.8392434988179669,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 49.860964       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8623046875,         \"F1\": 0.8377445339470656,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 52.359728       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8636796949475691,         \"F1\": 0.8402234636871508,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 54.917309       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8649906890130353,         \"F1\": 0.8429035752979415,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 57.530035       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671519563239308,         \"F1\": 0.8456659619450316,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 60.237019       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8701067615658363,         \"F1\": 0.8507157464212679,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 62.977514       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8720626631853786,         \"F1\": 0.852852852852853,         \"Memory in Mb\": 0.1429014205932617,         \"Time in s\": 65.816825       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8713798977853492,         \"F1\": 0.8521057786483839,         \"Memory in Mb\": 0.1429014205932617,         \"Time in s\": 68.70086099999999       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.872393661384487,         \"F1\": 0.8530259365994236,         \"Memory in Mb\": 0.1429014205932617,         \"Time in s\": 71.69850399999999       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8733660130718954,         \"F1\": 0.8541862652869238,         \"Memory in Mb\": 0.1429624557495117,         \"Time in s\": 74.74610599999998       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8742994395516414,         \"F1\": 0.8560953253895509,         \"Memory in Mb\": 0.1429624557495117,         \"Time in s\": 77.86495099999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0237245559692382,         \"Time in s\": 1.538486       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0237855911254882,         \"Time in s\": 4.672632       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0238466262817382,         \"Time in s\": 9.280899       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0238466262817382,         \"Time in s\": 15.518128       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0238466262817382,         \"Time in s\": 23.359252       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0239076614379882,         \"Time in s\": 32.724381       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0239076614379882,         \"Time in s\": 43.566998       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.0331544876098632,         \"Time in s\": 56.059492       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.023930549621582,         \"Time in s\": 70.315874       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.023930549621582,         \"Time in s\": 86.215201       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 103.810178       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 123.100461       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 144.232391       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 167.059054       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 191.625058       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 217.971038       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 246.1385       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 276.028821       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 307.727155       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 341.140461       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 376.24707       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 412.982235       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 451.376836       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 491.342412       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 532.9409959999999       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 576.05932       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 620.875381       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 667.2134759999999       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 715.0678459999999       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 764.41064       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 815.214443       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 867.5634849999999       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 921.307598       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 976.50125       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1033.066349       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1090.838953       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1149.858677       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1210.07507       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1271.441242       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1333.994434       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1397.762471       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1462.67416       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1528.680001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1595.853878       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1664.0432529999998       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1733.3243249999998       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1803.716354       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1875.203937       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1947.740223       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 2021.343945       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 2094.94949       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.638095238095238,         \"F1\": 0.5777777777777778,         \"Memory in Mb\": 0.6023197174072266,         \"Time in s\": 1.25138       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7535545023696683,         \"F1\": 0.711111111111111,         \"Memory in Mb\": 1.087297439575195,         \"Time in s\": 3.920593       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7760252365930599,         \"F1\": 0.7380073800738007,         \"Memory in Mb\": 1.471883773803711,         \"Time in s\": 8.005582       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8085106382978723,         \"F1\": 0.7768595041322315,         \"Memory in Mb\": 1.8271961212158203,         \"Time in s\": 13.704046000000002       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8204158790170132,         \"F1\": 0.7845804988662132,         \"Memory in Mb\": 2.2761096954345703,         \"Time in s\": 21.017212       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8362204724409449,         \"F1\": 0.8052434456928838,         \"Memory in Mb\": 2.6539440155029297,         \"Time in s\": 30.07026       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8434547908232118,         \"F1\": 0.8110749185667754,         \"Memory in Mb\": 3.06672477722168,         \"Time in s\": 40.88011       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8512396694214877,         \"F1\": 0.8220338983050847,         \"Memory in Mb\": 3.4897289276123047,         \"Time in s\": 53.580909000000005       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8583420776495279,         \"F1\": 0.8301886792452831,         \"Memory in Mb\": 3.93476676940918,         \"Time in s\": 68.252368       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8659112370160529,         \"F1\": 0.8378995433789953,         \"Memory in Mb\": 4.283300399780273,         \"Time in s\": 84.83204500000001       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8695278969957082,         \"F1\": 0.8429752066115702,         \"Memory in Mb\": 4.800313949584961,         \"Time in s\": 103.574646       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8693941778127459,         \"F1\": 0.8442776735459662,         \"Memory in Mb\": 5.391313552856445,         \"Time in s\": 124.497156       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8714596949891068,         \"F1\": 0.8454148471615721,         \"Memory in Mb\": 5.846994400024414,         \"Time in s\": 147.830368       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8759271746459879,         \"F1\": 0.8518518518518519,         \"Memory in Mb\": 6.193078994750977,         \"Time in s\": 173.44391299999998       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8753933291378225,         \"F1\": 0.8520179372197308,         \"Memory in Mb\": 6.296388626098633,         \"Time in s\": 201.557429       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8755162241887906,         \"F1\": 0.8523442967109867,         \"Memory in Mb\": 6.211141586303711,         \"Time in s\": 232.060182       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8767351471404775,         \"F1\": 0.8550913838120104,         \"Memory in Mb\": 6.65928840637207,         \"Time in s\": 264.912691       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8730991085474568,         \"F1\": 0.8522588522588523,         \"Memory in Mb\": 6.686662673950195,         \"Time in s\": 300.275827       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8708395429706905,         \"F1\": 0.8507462686567164,         \"Memory in Mb\": 7.210599899291992,         \"Time in s\": 338.470819       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8725814063237376,         \"F1\": 0.8540540540540541,         \"Memory in Mb\": 7.48176383972168,         \"Time in s\": 378.991608       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8723595505617977,         \"F1\": 0.8539094650205761,         \"Memory in Mb\": 7.915548324584961,         \"Time in s\": 421.86917       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8751608751608752,         \"F1\": 0.8574228319451249,         \"Memory in Mb\": 8.423246383666992,         \"Time in s\": 467.302026       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8740254411161263,         \"F1\": 0.8560712611345522,         \"Memory in Mb\": 8.870996475219727,         \"Time in s\": 515.200386       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.874557609123083,         \"F1\": 0.857779759251003,         \"Memory in Mb\": 9.376256942749023,         \"Time in s\": 565.505493       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8761796904492262,         \"F1\": 0.8600682593856656,         \"Memory in Mb\": 9.769472122192385,         \"Time in s\": 618.233402       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8780399274047187,         \"F1\": 0.8621821164889254,         \"Memory in Mb\": 10.359186172485352,         \"Time in s\": 673.368122       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8797623208668298,         \"F1\": 0.8638163103721298,         \"Memory in Mb\": 10.741575241088867,         \"Time in s\": 730.824809       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8800134816312774,         \"F1\": 0.8637059724349159,         \"Memory in Mb\": 11.09235954284668,         \"Time in s\": 790.5343869999999       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8805727302310445,         \"F1\": 0.8648250460405157,         \"Memory in Mb\": 11.562868118286133,         \"Time in s\": 852.458144       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8826675055048757,         \"F1\": 0.8667381207574134,         \"Memory in Mb\": 10.152639389038086,         \"Time in s\": 916.443171       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.882496194824962,         \"F1\": 0.8663434903047091,         \"Memory in Mb\": 10.670488357543944,         \"Time in s\": 982.391476       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8826304924800944,         \"F1\": 0.867244829886591,         \"Memory in Mb\": 11.057397842407228,         \"Time in s\": 1050.1851049999998       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8839004861309694,         \"F1\": 0.8680961663417803,         \"Memory in Mb\": 10.33408546447754,         \"Time in s\": 1119.7824649999998       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8850957535387177,         \"F1\": 0.8689043698543382,         \"Memory in Mb\": 10.692270278930664,         \"Time in s\": 1191.1920959999998       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8846050148287948,         \"F1\": 0.8687116564417178,         \"Memory in Mb\": 11.112970352172852,         \"Time in s\": 1264.4750769999998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8859764089121888,         \"F1\": 0.870420017873101,         \"Memory in Mb\": 11.59941291809082,         \"Time in s\": 1339.6069449999998       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.884723284876307,         \"F1\": 0.8687572590011614,         \"Memory in Mb\": 12.03856086730957,         \"Time in s\": 1416.6409169999995       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8840327787434815,         \"F1\": 0.867892503536068,         \"Memory in Mb\": 12.43459129333496,         \"Time in s\": 1495.7064749999995       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.884587466731188,         \"F1\": 0.868558831634059,         \"Memory in Mb\": 12.796384811401367,         \"Time in s\": 1576.7285749999996       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8858221278603444,         \"F1\": 0.8701019860440149,         \"Memory in Mb\": 13.12009620666504,         \"Time in s\": 1659.7498909999997       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8872266973532796,         \"F1\": 0.8716605552645365,         \"Memory in Mb\": 13.362188339233398,         \"Time in s\": 1744.8641249999996       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869916872612896,         \"F1\": 0.8713225888974162,         \"Memory in Mb\": 13.906320571899414,         \"Time in s\": 1831.981592       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8872064955014264,         \"F1\": 0.8719481813652217,         \"Memory in Mb\": 14.321008682250977,         \"Time in s\": 1921.07186       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8876259918507399,         \"F1\": 0.8728155339805825,         \"Memory in Mb\": 14.677774429321287,         \"Time in s\": 2012.226564       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8867687146152233,         \"F1\": 0.8716119828815977,         \"Memory in Mb\": 15.041936874389648,         \"Time in s\": 2105.5104569999994       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886974358974359,         \"F1\": 0.8715318256003731,         \"Memory in Mb\": 15.36302375793457,         \"Time in s\": 2200.9404059999997       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8877735394499097,         \"F1\": 0.8726941471191073,         \"Memory in Mb\": 14.241693496704102,         \"Time in s\": 2298.464636999999       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886966778061726,         \"F1\": 0.8716804284757868,         \"Memory in Mb\": 14.559698104858398,         \"Time in s\": 2397.961828999999       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869632197188523,         \"F1\": 0.8716939890710382,         \"Memory in Mb\": 15.019205093383787,         \"Time in s\": 2499.520506999999       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886959803736554,         \"F1\": 0.8717070036410367,         \"Memory in Mb\": 15.355104446411133,         \"Time in s\": 2603.016254999998       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8674033149171271,         \"F1\": 0.8669623059866962,         \"Memory in Mb\": 3.022599220275879,         \"Time in s\": 14.706798       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8956377691882937,         \"F1\": 0.8737474949899798,         \"Memory in Mb\": 3.453568458557129,         \"Time in s\": 43.63985       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.889216047110784,         \"F1\": 0.8638625056535504,         \"Memory in Mb\": 5.134407997131348,         \"Time in s\": 89.85880599999999       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8901462876069556,         \"F1\": 0.8665325285043594,         \"Memory in Mb\": 5.045891761779785,         \"Time in s\": 149.36791399999998       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8924707440936189,         \"F1\": 0.8628555336524922,         \"Memory in Mb\": 6.377499580383301,         \"Time in s\": 220.195834       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8870285188592456,         \"F1\": 0.8556652562294312,         \"Memory in Mb\": 8.556572914123535,         \"Time in s\": 302.539101       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.884245387162908,         \"F1\": 0.8540175019888624,         \"Memory in Mb\": 10.355942726135254,         \"Time in s\": 396.160169       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835380157306472,         \"F1\": 0.8516174402250353,         \"Memory in Mb\": 10.061070442199709,         \"Time in s\": 501.0892999999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8847050165583221,         \"F1\": 0.8605341246290802,         \"Memory in Mb\": 12.516213417053224,         \"Time in s\": 615.7123809999999       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8869632409758251,         \"F1\": 0.8668400520156047,         \"Memory in Mb\": 14.3945894241333,         \"Time in s\": 740.068354       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8839939789262419,         \"F1\": 0.8666974169741698,         \"Memory in Mb\": 15.028592109680176,         \"Time in s\": 874.678214       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.886119032287738,         \"F1\": 0.8712830110210023,         \"Memory in Mb\": 18.58602237701416,         \"Time in s\": 1018.727325       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8851150547677676,         \"F1\": 0.869464544138929,         \"Memory in Mb\": 18.284192085266117,         \"Time in s\": 1172.329585       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8825987542379563,         \"F1\": 0.8672550592850139,         \"Memory in Mb\": 18.56262683868408,         \"Time in s\": 1335.5183499999998       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835087202884686,         \"F1\": 0.8699794661190965,         \"Memory in Mb\": 22.36763858795166,         \"Time in s\": 1507.9316749999998       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883270093135564,         \"F1\": 0.870085995085995,         \"Memory in Mb\": 23.97218418121338,         \"Time in s\": 1690.157865       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8826050256476852,         \"F1\": 0.8679713743245216,         \"Memory in Mb\": 24.89116382598877,         \"Time in s\": 1881.87418       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8806034218433801,         \"F1\": 0.8649885583524027,         \"Memory in Mb\": 9.630642890930176,         \"Time in s\": 2082.713846       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880787776680416,         \"F1\": 0.862742474916388,         \"Memory in Mb\": 9.825531959533691,         \"Time in s\": 2290.8714669999995       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.881505601854407,         \"F1\": 0.8635178946030132,         \"Memory in Mb\": 13.432568550109863,         \"Time in s\": 2506.1422499999994       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835742444152431,         \"F1\": 0.864335150364427,         \"Memory in Mb\": 11.236374855041504,         \"Time in s\": 2728.16983       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8847022226682053,         \"F1\": 0.8666744024135531,         \"Memory in Mb\": 10.915810585021973,         \"Time in s\": 2956.9763619999994       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8845803138647598,         \"F1\": 0.866618601297765,         \"Memory in Mb\": 6.771607398986816,         \"Time in s\": 3192.2184919999995       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8842845973416732,         \"F1\": 0.8643665768194071,         \"Memory in Mb\": 9.905537605285645,         \"Time in s\": 3433.744552       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8832178021104684,         \"F1\": 0.8619303648796786,         \"Memory in Mb\": 11.60939121246338,         \"Time in s\": 3682.104731       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8819783485459562,         \"F1\": 0.8599637316139432,         \"Memory in Mb\": 7.878331184387207,         \"Time in s\": 3937.9632889999993       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805854216916724,         \"F1\": 0.8573800107416631,         \"Memory in Mb\": 10.65384006500244,         \"Time in s\": 4201.066475999999       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8791343083533725,         \"F1\": 0.8556497175141242,         \"Memory in Mb\": 11.591797828674316,         \"Time in s\": 4470.752341999999       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8801431127012522,         \"F1\": 0.8566616596112704,         \"Memory in Mb\": 13.86082935333252,         \"Time in s\": 4746.937765999999       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.881195040288458,         \"F1\": 0.8584082438061829,         \"Memory in Mb\": 14.463074684143066,         \"Time in s\": 5029.888654999999       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8798646964571836,         \"F1\": 0.8561807331628303,         \"Memory in Mb\": 15.367924690246582,         \"Time in s\": 5319.910836999999       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8796523058880342,         \"F1\": 0.8551019560612982,         \"Memory in Mb\": 16.377129554748535,         \"Time in s\": 5616.458601999999       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.879285547044854,         \"F1\": 0.8544934080554772,         \"Memory in Mb\": 16.05477237701416,         \"Time in s\": 5919.470834999999       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8791351491737818,         \"F1\": 0.8535347574648885,         \"Memory in Mb\": 17.57622241973877,         \"Time in s\": 6228.255318999999       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8775111167176511,         \"F1\": 0.851267519338286,         \"Memory in Mb\": 17.546963691711426,         \"Time in s\": 6543.393541999999       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8771117583933773,         \"F1\": 0.8510701545778836,         \"Memory in Mb\": 17.15481662750244,         \"Time in s\": 6864.899302999999       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8770621401509502,         \"F1\": 0.8512864927285193,         \"Memory in Mb\": 13.577618598937988,         \"Time in s\": 7192.851994       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8757080198681267,         \"F1\": 0.849643346568748,         \"Memory in Mb\": 12.363858222961426,         \"Time in s\": 7526.949055999999       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8754139189992358,         \"F1\": 0.848624484181568,         \"Memory in Mb\": 12.55275058746338,         \"Time in s\": 7866.609101999999       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.875134523579569,         \"F1\": 0.8474427699672971,         \"Memory in Mb\": 12.88097858428955,         \"Time in s\": 8211.574848999999       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8743034055727554,         \"F1\": 0.8459838363846282,         \"Memory in Mb\": 15.634392738342283,         \"Time in s\": 8562.247513999999       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8741163175737826,         \"F1\": 0.8451642099818981,         \"Memory in Mb\": 17.75814151763916,         \"Time in s\": 8918.936239999999       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8743743101368175,         \"F1\": 0.8458873913591133,         \"Memory in Mb\": 18.55082416534424,         \"Time in s\": 9281.578229       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8744951458746206,         \"F1\": 0.847381104908331,         \"Memory in Mb\": 20.39273166656494,         \"Time in s\": 9650.394165999998       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8750521229365449,         \"F1\": 0.8493434283686265,         \"Memory in Mb\": 20.04684543609619,         \"Time in s\": 10025.208226999996       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8757768446310737,         \"F1\": 0.8512911843276937,         \"Memory in Mb\": 22.40410327911377,         \"Time in s\": 10405.883388999997       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8760010333247223,         \"F1\": 0.8517603458925264,         \"Memory in Mb\": 17.905674934387207,         \"Time in s\": 10792.424187999995       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8758249591832041,         \"F1\": 0.8516320474777449,         \"Memory in Mb\": 17.979458808898926,         \"Time in s\": 11185.171687999997       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8758362804946725,         \"F1\": 0.8511557571829769,         \"Memory in Mb\": 19.483532905578613,         \"Time in s\": 11584.749531999996       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8765977173889048,         \"F1\": 0.8524053440354862,         \"Memory in Mb\": 22.38176822662353,         \"Time in s\": 11990.855977999996       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8766083291033082,         \"F1\": 0.8523906328378699,         \"Memory in Mb\": 22.39494037628174,         \"Time in s\": 12397.578789999996       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.625,         \"F1\": 0.7096774193548387,         \"Memory in Mb\": 0.4178829193115234,         \"Time in s\": 0.504078       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7450980392156864,         \"Memory in Mb\": 0.6195468902587891,         \"Time in s\": 1.506996       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.7999999999999999,         \"Memory in Mb\": 0.8261966705322266,         \"Time in s\": 2.945496       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.797979797979798,         \"F1\": 0.8039215686274509,         \"Memory in Mb\": 0.9074077606201172,         \"Time in s\": 4.810448       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7903225806451613,         \"F1\": 0.7968749999999999,         \"Memory in Mb\": 1.0524044036865234,         \"Time in s\": 7.208508       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8120805369127517,         \"F1\": 0.8227848101265823,         \"Memory in Mb\": 1.155344009399414,         \"Time in s\": 10.051324       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8372093023255814,         \"Memory in Mb\": 1.2272701263427734,         \"Time in s\": 13.451327       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8442211055276382,         \"F1\": 0.8426395939086295,         \"Memory in Mb\": 1.3437442779541016,         \"Time in s\": 17.363237       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8465116279069769,         \"Memory in Mb\": 1.4417095184326172,         \"Time in s\": 21.777532       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554216867469879,         \"F1\": 0.85,         \"Memory in Mb\": 1.652822494506836,         \"Time in s\": 26.610844       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8540145985401459,         \"F1\": 0.8473282442748092,         \"Memory in Mb\": 1.7137775421142578,         \"Time in s\": 32.103705       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8595317725752508,         \"F1\": 0.85,         \"Memory in Mb\": 1.6836071014404297,         \"Time in s\": 38.177642       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672839506172839,         \"F1\": 0.8542372881355932,         \"Memory in Mb\": 1.8154468536376955,         \"Time in s\": 44.724647       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8681948424068768,         \"F1\": 0.8525641025641026,         \"Memory in Mb\": 1.9355945587158203,         \"Time in s\": 51.783982       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663101604278075,         \"F1\": 0.8484848484848485,         \"Memory in Mb\": 2.1126270294189453,         \"Time in s\": 59.453597       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8696741854636592,         \"F1\": 0.8505747126436781,         \"Memory in Mb\": 2.2513599395751958,         \"Time in s\": 67.701448       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8702830188679245,         \"F1\": 0.8467966573816157,         \"Memory in Mb\": 2.4080867767333984,         \"Time in s\": 76.525873       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8775055679287305,         \"F1\": 0.8533333333333333,         \"Memory in Mb\": 2.413846969604492,         \"Time in s\": 85.91210000000001       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.879746835443038,         \"F1\": 0.85785536159601,         \"Memory in Mb\": 2.540945053100586,         \"Time in s\": 95.911251       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8817635270541082,         \"F1\": 0.8624708624708626,         \"Memory in Mb\": 2.727457046508789,         \"Time in s\": 106.551347       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8835877862595419,         \"F1\": 0.8623024830699774,         \"Memory in Mb\": 2.780088424682617,         \"Time in s\": 117.8137       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8816029143897997,         \"F1\": 0.8602150537634409,         \"Memory in Mb\": 2.84419059753418,         \"Time in s\": 129.668358       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8832752613240418,         \"F1\": 0.8618556701030927,         \"Memory in Mb\": 2.9667911529541016,         \"Time in s\": 142.149346       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8864774624373957,         \"F1\": 0.8634538152610441,         \"Memory in Mb\": 2.9313793182373047,         \"Time in s\": 155.146824       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878205128205128,         \"F1\": 0.8622047244094488,         \"Memory in Mb\": 3.1180286407470703,         \"Time in s\": 168.863765       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8906009244992296,         \"F1\": 0.8672897196261682,         \"Memory in Mb\": 3.1772937774658203,         \"Time in s\": 183.176428       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8931750741839762,         \"F1\": 0.8732394366197184,         \"Memory in Mb\": 3.270914077758789,         \"Time in s\": 198.116157       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8969957081545065,         \"F1\": 0.8762886597938143,         \"Memory in Mb\": 3.2819652557373047,         \"Time in s\": 213.702702       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8950276243093923,         \"F1\": 0.8762214983713356,         \"Memory in Mb\": 3.465627670288086,         \"Time in s\": 229.894704       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.897196261682243,         \"F1\": 0.8791208791208791,         \"Memory in Mb\": 3.637697219848633,         \"Time in s\": 246.719197       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8979328165374677,         \"F1\": 0.8793893129770992,         \"Memory in Mb\": 3.6838626861572266,         \"Time in s\": 264.274398       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8961201501877347,         \"F1\": 0.8784773060029282,         \"Memory in Mb\": 3.7807750701904297,         \"Time in s\": 282.500107       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8968446601941747,         \"F1\": 0.8801128349788435,         \"Memory in Mb\": 3.913633346557617,         \"Time in s\": 301.464554       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8987043580683156,         \"F1\": 0.8818681318681318,         \"Memory in Mb\": 4.011789321899414,         \"Time in s\": 321.06457099999994       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9016018306636157,         \"F1\": 0.8847184986595175,         \"Memory in Mb\": 4.15968132019043,         \"Time in s\": 341.449663       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9010011123470524,         \"F1\": 0.8836601307189543,         \"Memory in Mb\": 3.946676254272461,         \"Time in s\": 362.64137199999993       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9036796536796536,         \"F1\": 0.8877679697351829,         \"Memory in Mb\": 4.049928665161133,         \"Time in s\": 384.6765929999999       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9030558482613276,         \"F1\": 0.8883495145631068,         \"Memory in Mb\": 3.684160232543945,         \"Time in s\": 407.5276099999999       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.904517453798768,         \"F1\": 0.8899408284023669,         \"Memory in Mb\": 3.787748336791992,         \"Time in s\": 431.1052179999999       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9049049049049048,         \"F1\": 0.8904267589388698,         \"Memory in Mb\": 4.052656173706055,         \"Time in s\": 455.43358799999993       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9033203125,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 4.062379837036133,         \"Time in s\": 480.5827999999999       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9046711153479504,         \"F1\": 0.8908296943231442,         \"Memory in Mb\": 4.190084457397461,         \"Time in s\": 506.4991779999999       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9059590316573556,         \"F1\": 0.8928950159066809,         \"Memory in Mb\": 4.285711288452148,         \"Time in s\": 533.2628339999999       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9062784349408554,         \"F1\": 0.8934850051706308,         \"Memory in Mb\": 4.370790481567383,         \"Time in s\": 560.8485399999998       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9065836298932384,         \"F1\": 0.8948948948948948,         \"Memory in Mb\": 3.93486213684082,         \"Time in s\": 589.2233909999999       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9077458659704092,         \"F1\": 0.896078431372549,         \"Memory in Mb\": 4.19316291809082,         \"Time in s\": 618.4066789999998       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9063032367972744,         \"F1\": 0.8942307692307692,         \"Memory in Mb\": 4.349401473999023,         \"Time in s\": 648.4320089999999       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9065888240200168,         \"F1\": 0.8943396226415095,         \"Memory in Mb\": 4.34752082824707,         \"Time in s\": 679.2709969999999       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9068627450980392,         \"F1\": 0.8944444444444444,         \"Memory in Mb\": 4.031515121459961,         \"Time in s\": 710.9105619999998       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9079263410728584,         \"F1\": 0.896115627822945,         \"Memory in Mb\": 4.102910995483398,         \"Time in s\": 743.3769359999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1703529357910156,         \"Time in s\": 12.381202       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1715736389160156,         \"Time in s\": 37.073822       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1727943420410156,         \"Time in s\": 74.106824       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1727943420410156,         \"Time in s\": 122.30955       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1727943420410156,         \"Time in s\": 180.539768       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1740150451660156,         \"Time in s\": 247.174942       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1740150451660156,         \"Time in s\": 321.611977       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.23138427734375,         \"Time in s\": 404.612477       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.1771812438964843,         \"Time in s\": 498.19936       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691703796386718,         \"Time in s\": 601.949625       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704368591308593,         \"Time in s\": 714.510104       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782646179199218,         \"Time in s\": 835.601872       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.1626014709472656,         \"Time in s\": 964.845183       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.170440673828125,         \"Time in s\": 1103.12758       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782646179199218,         \"Time in s\": 1249.157139       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781692504882812,         \"Time in s\": 1402.52966       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.1626205444335937,         \"Time in s\": 1563.348194       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704368591308593,         \"Time in s\": 1731.617157       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704330444335937,         \"Time in s\": 1907.150595       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781158447265625,         \"Time in s\": 2090.214351       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704559326171875,         \"Time in s\": 2280.326256       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.163818359375,         \"Time in s\": 2477.410916       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.1715812683105468,         \"Time in s\": 2681.481315       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.171630859375,         \"Time in s\": 2892.603814       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.1794776916503906,         \"Time in s\": 3110.724932       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.3225936889648437,         \"Time in s\": 3336.815763       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.3238716125488281,         \"Time in s\": 3571.848368       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.2926750183105469,         \"Time in s\": 3815.616702       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.3244895935058594,         \"Time in s\": 4068.203899       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.2933502197265625,         \"Time in s\": 4329.847041999999       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.2782058715820312,         \"Time in s\": 4599.769264       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.3094024658203125,         \"Time in s\": 4878.106527       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.3095321655273437,         \"Time in s\": 5164.770925       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.2940139770507812,         \"Time in s\": 5459.39874       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.293975830078125,         \"Time in s\": 5761.313045999999       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.3251571655273437,         \"Time in s\": 6070.487373999999       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101234436035156,         \"Time in s\": 6386.879354       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101387023925781,         \"Time in s\": 6710.520715       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101615905761719,         \"Time in s\": 7041.446151       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101768493652344,         \"Time in s\": 7379.414547       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.3100128173828125,         \"Time in s\": 7724.224697000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101425170898437,         \"Time in s\": 8075.760258       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.3100852966308594,         \"Time in s\": 8434.038373       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.3107223510742187,         \"Time in s\": 8799.103777999999       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.310821533203125,         \"Time in s\": 9170.869249       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.2950706481933594,         \"Time in s\": 9549.506616       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.2951812744140625,         \"Time in s\": 9935.081315999998       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.2957954406738281,         \"Time in s\": 10327.647623999996       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.3115119934082031,         \"Time in s\": 10728.119291999998       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.3115577697753906,         \"Time in s\": 11135.737891999996       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.3270950317382812,         \"Time in s\": 11543.384409999995       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5428571428571428,         \"F1\": 0.4,         \"Memory in Mb\": 0.2255392074584961,         \"Time in s\": 2.569769       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5592417061611374,         \"F1\": 0.4685714285714286,         \"Memory in Mb\": 0.6304416656494141,         \"Time in s\": 8.011061999999999       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.637223974763407,         \"F1\": 0.5724907063197027,         \"Memory in Mb\": 0.9710559844970704,         \"Time in s\": 16.523663       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6926713947990544,         \"F1\": 0.6448087431693988,         \"Memory in Mb\": 1.262800216674805,         \"Time in s\": 28.175313       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7145557655954632,         \"F1\": 0.6621923937360179,         \"Memory in Mb\": 1.5703105926513672,         \"Time in s\": 43.218913       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7448818897637796,         \"F1\": 0.7000000000000001,         \"Memory in Mb\": 1.467294692993164,         \"Time in s\": 61.573557       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7624831309041835,         \"F1\": 0.7170418006430868,         \"Memory in Mb\": 1.877767562866211,         \"Time in s\": 83.22394800000001       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7827626918536009,         \"F1\": 0.7430167597765364,         \"Memory in Mb\": 2.3253536224365234,         \"Time in s\": 108.413447       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7964323189926548,         \"F1\": 0.7599009900990098,         \"Memory in Mb\": 1.7426891326904297,         \"Time in s\": 137.183902       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8054768649669499,         \"F1\": 0.7674943566591422,         \"Memory in Mb\": 1.7942829132080078,         \"Time in s\": 169.50541299999998       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8103004291845494,         \"F1\": 0.7747196738022425,         \"Memory in Mb\": 1.8575687408447263,         \"Time in s\": 205.462561       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8151062155782848,         \"F1\": 0.7822057460611677,         \"Memory in Mb\": 1.917165756225586,         \"Time in s\": 244.587795       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8191721132897604,         \"F1\": 0.7851596203623814,         \"Memory in Mb\": 2.1873340606689453,         \"Time in s\": 286.663235       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8240053944706676,         \"F1\": 0.7916999201915402,         \"Memory in Mb\": 2.2810306549072266,         \"Time in s\": 331.700902       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8231592196349906,         \"F1\": 0.7916975537435137,         \"Memory in Mb\": 2.585817337036133,         \"Time in s\": 379.560946       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8271386430678466,         \"F1\": 0.7961029923451634,         \"Memory in Mb\": 2.885595321655273,         \"Time in s\": 430.279509       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8334258745141588,         \"F1\": 0.8046875,         \"Memory in Mb\": 2.8240184783935547,         \"Time in s\": 483.724395       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8332459360251704,         \"F1\": 0.8060975609756097,         \"Memory in Mb\": 3.138376235961914,         \"Time in s\": 539.732848       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8340784898161947,         \"F1\": 0.8084862385321101,         \"Memory in Mb\": 3.5751514434814453,         \"Time in s\": 598.378334       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8367154318074563,         \"F1\": 0.813778256189451,         \"Memory in Mb\": 3.890401840209961,         \"Time in s\": 659.469374       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8382022471910112,         \"F1\": 0.8157625383828044,         \"Memory in Mb\": 4.414094924926758,         \"Time in s\": 723.240852       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8404118404118404,         \"F1\": 0.8185365853658537,         \"Memory in Mb\": 4.828973770141602,         \"Time in s\": 789.4489060000001       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8432498974148543,         \"F1\": 0.8216619981325864,         \"Memory in Mb\": 4.724649429321289,         \"Time in s\": 858.0992190000001       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8450648839952811,         \"F1\": 0.8247330960854093,         \"Memory in Mb\": 4.20762825012207,         \"Time in s\": 929.163006       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.846734616836542,         \"F1\": 0.8270868824531515,         \"Memory in Mb\": 4.517709732055664,         \"Time in s\": 1002.552295       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8500907441016334,         \"F1\": 0.8306683066830667,         \"Memory in Mb\": 4.757001876831055,         \"Time in s\": 1078.240465       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8521495980426425,         \"F1\": 0.8324752475247525,         \"Memory in Mb\": 4.690572738647461,         \"Time in s\": 1156.1945970000002       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.854061341422312,         \"F1\": 0.8339087073264287,         \"Memory in Mb\": 4.873067855834961,         \"Time in s\": 1236.185934       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8538887081028311,         \"F1\": 0.8340110905730129,         \"Memory in Mb\": 5.244169235229492,         \"Time in s\": 1318.3478       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8565586662472475,         \"F1\": 0.836441893830703,         \"Memory in Mb\": 5.473237991333008,         \"Time in s\": 1402.707418       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8575342465753425,         \"F1\": 0.8371607515657619,         \"Memory in Mb\": 5.716192245483398,         \"Time in s\": 1489.296647       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8593335299321734,         \"F1\": 0.8400938652363393,         \"Memory in Mb\": 6.05610466003418,         \"Time in s\": 1578.3688909999998       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8615956534172148,         \"F1\": 0.8419333768778575,         \"Memory in Mb\": 6.433168411254883,         \"Time in s\": 1669.799251       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8631695809048016,         \"F1\": 0.8430436166825852,         \"Memory in Mb\": 6.670698165893555,         \"Time in s\": 1763.5519829999998       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8638447020760313,         \"F1\": 0.8444718201416692,         \"Memory in Mb\": 7.050989151000977,         \"Time in s\": 1859.626818       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8657929226736566,         \"F1\": 0.84688995215311,         \"Memory in Mb\": 7.316404342651367,         \"Time in s\": 1958.012503       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8653404743687835,         \"F1\": 0.846064139941691,         \"Memory in Mb\": 7.61528205871582,         \"Time in s\": 2058.675528       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8644151974174323,         \"F1\": 0.8449744463373083,         \"Memory in Mb\": 7.967977523803711,         \"Time in s\": 2161.579262       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8654730220179047,         \"F1\": 0.8462389380530975,         \"Memory in Mb\": 7.394952774047852,         \"Time in s\": 2266.595873       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8674215616890776,         \"F1\": 0.8486806677436726,         \"Memory in Mb\": 7.571531295776367,         \"Time in s\": 2373.458397       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8688147295742232,         \"F1\": 0.8503937007874016,         \"Memory in Mb\": 7.877435684204102,         \"Time in s\": 2482.305033       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8683441923163334,         \"F1\": 0.8496664956387892,         \"Memory in Mb\": 8.180627822875977,         \"Time in s\": 2593.165336       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8689927583936801,         \"F1\": 0.8508618536097925,         \"Memory in Mb\": 8.39448356628418,         \"Time in s\": 2705.945127       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8691829294445635,         \"F1\": 0.8516536964980544,         \"Memory in Mb\": 8.710580825805664,         \"Time in s\": 2820.674173       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8689452715453974,         \"F1\": 0.8510131108462455,         \"Memory in Mb\": 9.014997482299805,         \"Time in s\": 2937.453009       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8703589743589744,         \"F1\": 0.8523364485981308,         \"Memory in Mb\": 9.167715072631836,         \"Time in s\": 3056.177406       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8713109817305762,         \"F1\": 0.8538864827900615,         \"Memory in Mb\": 9.482858657836914,         \"Time in s\": 3176.936292       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8714369962649892,         \"F1\": 0.8539526574363555,         \"Memory in Mb\": 9.87147331237793,         \"Time in s\": 3299.754349       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8717504332755632,         \"F1\": 0.8543944031482291,         \"Memory in Mb\": 10.20412254333496,         \"Time in s\": 3424.594329       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8716739007359879,         \"F1\": 0.8542648949849978,         \"Memory in Mb\": 10.538087844848633,         \"Time in s\": 3551.414099       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8828729281767956,         \"F1\": 0.8811659192825113,         \"Memory in Mb\": 5.258722305297852,         \"Time in s\": 37.408806       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9039204859193816,         \"F1\": 0.8804945054945055,         \"Memory in Mb\": 8.443174362182617,         \"Time in s\": 104.985856       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8873757821126242,         \"F1\": 0.8602739726027397,         \"Memory in Mb\": 12.445928573608398,         \"Time in s\": 198.514402       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.884902014904775,         \"F1\": 0.8576305906452714,         \"Memory in Mb\": 16.533422470092773,         \"Time in s\": 314.268209       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8812099801280636,         \"F1\": 0.8452243958573072,         \"Memory in Mb\": 19.26629447937012,         \"Time in s\": 451.77035       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8756209751609936,         \"F1\": 0.8372652864708715,         \"Memory in Mb\": 24.12981605529785,         \"Time in s\": 609.66041       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8719444882510645,         \"F1\": 0.8340825500612996,         \"Memory in Mb\": 28.348302841186523,         \"Time in s\": 788.9253299999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8691872498965089,         \"F1\": 0.8308351177730193,         \"Memory in Mb\": 31.66439247131348,         \"Time in s\": 988.709629       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8690052741322213,         \"F1\": 0.8387681159420289,         \"Memory in Mb\": 35.27585411071777,         \"Time in s\": 1209.125777       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.869742797218236,         \"F1\": 0.844162704701532,         \"Memory in Mb\": 38.39363670349121,         \"Time in s\": 1448.169528       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8681384846964375,         \"F1\": 0.8455934195064629,         \"Memory in Mb\": 42.49019813537598,         \"Time in s\": 1706.33653       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8687333272008095,         \"F1\": 0.849265870920038,         \"Memory in Mb\": 46.91076469421387,         \"Time in s\": 1983.228319       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8694064702386006,         \"F1\": 0.8495402073958129,         \"Memory in Mb\": 41.51856422424317,         \"Time in s\": 2278.772125       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8672238429393676,         \"F1\": 0.8479320931912588,         \"Memory in Mb\": 46.98099327087402,         \"Time in s\": 2591.81768       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8682758113179778,         \"F1\": 0.8513289036544851,         \"Memory in Mb\": 50.757638931274414,         \"Time in s\": 2922.748705       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8687823387374957,         \"F1\": 0.8527863777089782,         \"Memory in Mb\": 43.74130058288574,         \"Time in s\": 3269.777293       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8686448931887539,         \"F1\": 0.8518708354689903,         \"Memory in Mb\": 49.06788444519043,         \"Time in s\": 3632.343799       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8649659655362728,         \"F1\": 0.8467427616926504,         \"Memory in Mb\": 54.357858657836914,         \"Time in s\": 4011.129855       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.865392435949573,         \"F1\": 0.8447987139125192,         \"Memory in Mb\": 52.38222694396973,         \"Time in s\": 4407.893822       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8652795408135107,         \"F1\": 0.8448286822198208,         \"Memory in Mb\": 59.36540412902832,         \"Time in s\": 4822.718697       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.867700394218134,         \"F1\": 0.8459136822773186,         \"Memory in Mb\": 57.10729789733887,         \"Time in s\": 5251.994155       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8692489087351363,         \"F1\": 0.84882236918436,         \"Memory in Mb\": 51.34463310241699,         \"Time in s\": 5694.600867       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8691750251955656,         \"F1\": 0.8490085299656586,         \"Memory in Mb\": 53.35045051574707,         \"Time in s\": 6149.875029       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8700271351699398,         \"F1\": 0.8478682170542635,         \"Memory in Mb\": 59.89077186584473,         \"Time in s\": 6616.750619       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8694865115457636,         \"F1\": 0.8459614382490881,         \"Memory in Mb\": 65.61615180969238,         \"Time in s\": 7094.935599       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.868860114625345,         \"F1\": 0.8444690599667689,         \"Memory in Mb\": 72.22853660583496,         \"Time in s\": 7585.744803       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8682392379706472,         \"F1\": 0.8428648042513772,         \"Memory in Mb\": 80.47726249694824,         \"Time in s\": 8090.441156999999       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8672290771474751,         \"F1\": 0.8417888012025554,         \"Memory in Mb\": 73.03231239318848,         \"Time in s\": 8608.413273       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8684581128915617,         \"F1\": 0.8430802760624773,         \"Memory in Mb\": 79.16955757141113,         \"Time in s\": 9138.877569       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8698259685786821,         \"F1\": 0.8451234459814394,         \"Memory in Mb\": 84.2670955657959,         \"Time in s\": 9681.232951       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689335944454335,         \"F1\": 0.8434616202423985,         \"Memory in Mb\": 93.53372383117676,         \"Time in s\": 10236.036265       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8688558518160808,         \"F1\": 0.8423322551215061,         \"Memory in Mb\": 92.7535800933838,         \"Time in s\": 10801.143387       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689166137070609,         \"F1\": 0.8421603769785332,         \"Memory in Mb\": 90.79977607727052,         \"Time in s\": 11375.205452       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8684868356978216,         \"F1\": 0.8408063818917749,         \"Memory in Mb\": 97.95510292053224,         \"Time in s\": 11957.401901       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8668832192752847,         \"F1\": 0.8384553561177235,         \"Memory in Mb\": 105.25788688659668,         \"Time in s\": 12547.475367       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8664724819868159,         \"F1\": 0.8381943154374885,         \"Memory in Mb\": 94.53887367248537,         \"Time in s\": 13145.300695000002       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8661436114674383,         \"F1\": 0.8380553650701988,         \"Memory in Mb\": 102.01883506774902,         \"Time in s\": 13750.733881000002       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8648444534812793,         \"F1\": 0.8364441632394811,         \"Memory in Mb\": 100.427827835083,         \"Time in s\": 14363.910035000004       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8647723091727281,         \"F1\": 0.8357849876271651,         \"Memory in Mb\": 107.87262153625488,         \"Time in s\": 14984.863075000005       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8642346643119292,         \"F1\": 0.83420946219167,         \"Memory in Mb\": 112.83228874206544,         \"Time in s\": 15613.594311000004       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8632655808318751,         \"F1\": 0.8326689289361843,         \"Memory in Mb\": 120.66686058044434,         \"Time in s\": 16250.422859000002       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8627631336889964,         \"F1\": 0.8316135689410551,         \"Memory in Mb\": 126.15458106994627,         \"Time in s\": 16895.166705000003       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8634391765279668,         \"F1\": 0.8328620797989319,         \"Memory in Mb\": 107.22049903869627,         \"Time in s\": 17548.303432000004       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8644356922459423,         \"F1\": 0.8353041570157259,         \"Memory in Mb\": 103.5422191619873,         \"Time in s\": 18208.490072000004       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.865436974171552,         \"F1\": 0.8378745788758201,         \"Memory in Mb\": 97.78764533996582,         \"Time in s\": 18874.504490000003       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8666586682663467,         \"F1\": 0.8403940603728063,         \"Memory in Mb\": 102.76390266418456,         \"Time in s\": 19545.753018000003       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8673821657546793,         \"F1\": 0.8415055151702265,         \"Memory in Mb\": 107.51249122619627,         \"Time in s\": 20221.607860000004       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8677075907742544,         \"F1\": 0.841885392332005,         \"Memory in Mb\": 102.9560489654541,         \"Time in s\": 20902.251232       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8679071024711104,         \"F1\": 0.8415905775568644,         \"Memory in Mb\": 103.86480903625488,         \"Time in s\": 21587.715975000003       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8688933530541513,         \"F1\": 0.843053830501308,         \"Memory in Mb\": 107.29028511047365,         \"Time in s\": 22278.011723000003       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8688839354682086,         \"F1\": 0.8430092751631743,         \"Memory in Mb\": 107.32242012023926,         \"Time in s\": 22968.976341       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8333333333333334,         \"Memory in Mb\": 0.7029104232788086,         \"Time in s\": 1.141902       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8372093023255814,         \"Memory in Mb\": 0.9397382736206056,         \"Time in s\": 3.355867       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8783783783783784,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 0.9708013534545898,         \"Time in s\": 6.532426       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8888888888888888,         \"F1\": 0.8817204301075269,         \"Memory in Mb\": 1.056624412536621,         \"Time in s\": 10.815831       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8790322580645161,         \"F1\": 0.8739495798319329,         \"Memory in Mb\": 1.3782567977905271,         \"Time in s\": 16.293882       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8791946308724832,         \"F1\": 0.8783783783783784,         \"Memory in Mb\": 1.379134178161621,         \"Time in s\": 22.890072       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.896551724137931,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 1.4786596298217771,         \"Time in s\": 30.52314       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8944723618090452,         \"F1\": 0.8864864864864866,         \"Memory in Mb\": 1.6607275009155271,         \"Time in s\": 39.247513       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8973214285714286,         \"F1\": 0.8866995073891626,         \"Memory in Mb\": 1.686568260192871,         \"Time in s\": 49.014513       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.891566265060241,         \"F1\": 0.88,         \"Memory in Mb\": 1.9668035507202148,         \"Time in s\": 59.910523       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8905109489051095,         \"F1\": 0.8780487804878049,         \"Memory in Mb\": 2.071291923522949,         \"Time in s\": 71.88595       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896321070234113,         \"F1\": 0.8754716981132077,         \"Memory in Mb\": 2.2423620223999023,         \"Time in s\": 85.00905599999999       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8888888888888888,         \"F1\": 0.8723404255319148,         \"Memory in Mb\": 2.4750547409057617,         \"Time in s\": 99.146325       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8853868194842407,         \"F1\": 0.8666666666666667,         \"Memory in Mb\": 2.532855033874512,         \"Time in s\": 114.354375       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8850267379679144,         \"F1\": 0.8652037617554859,         \"Memory in Mb\": 2.8150205612182617,         \"Time in s\": 130.79065599999998       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8822055137844611,         \"F1\": 0.8613569321533923,         \"Memory in Mb\": 2.795191764831543,         \"Time in s\": 148.41625799999997       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844339622641509,         \"F1\": 0.8611898016997167,         \"Memory in Mb\": 2.962000846862793,         \"Time in s\": 167.06847699999997       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.888641425389755,         \"F1\": 0.8648648648648649,         \"Memory in Mb\": 3.03415584564209,         \"Time in s\": 186.853211       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.890295358649789,         \"F1\": 0.8686868686868687,         \"Memory in Mb\": 3.071761131286621,         \"Time in s\": 207.829       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8917835671342685,         \"F1\": 0.8726415094339622,         \"Memory in Mb\": 3.1551198959350586,         \"Time in s\": 229.951047       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8950381679389313,         \"F1\": 0.8741418764302059,         \"Memory in Mb\": 3.1928510665893555,         \"Time in s\": 253.214946       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8943533697632058,         \"F1\": 0.8739130434782608,         \"Memory in Mb\": 3.2878904342651367,         \"Time in s\": 277.566695       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8937282229965157,         \"F1\": 0.8726513569937369,         \"Memory in Mb\": 3.441771507263184,         \"Time in s\": 303.140017       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8964941569282137,         \"F1\": 0.8739837398373984,         \"Memory in Mb\": 3.515273094177246,         \"Time in s\": 329.715755       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8958333333333334,         \"F1\": 0.8707753479125249,         \"Memory in Mb\": 3.5807180404663086,         \"Time in s\": 357.461609       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8983050847457628,         \"F1\": 0.8754716981132076,         \"Memory in Mb\": 3.695376396179199,         \"Time in s\": 386.398038       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8961424332344213,         \"F1\": 0.8754448398576512,         \"Memory in Mb\": 3.755015373229981,         \"Time in s\": 416.468493       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.899856938483548,         \"F1\": 0.8784722222222222,         \"Memory in Mb\": 3.790995597839356,         \"Time in s\": 447.643877       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.899171270718232,         \"F1\": 0.8797364085667215,         \"Memory in Mb\": 3.939352989196777,         \"Time in s\": 479.929216       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9012016021361816,         \"F1\": 0.8825396825396825,         \"Memory in Mb\": 3.942519187927246,         \"Time in s\": 513.493128       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9018087855297158,         \"F1\": 0.8827160493827161,         \"Memory in Mb\": 4.2751874923706055,         \"Time in s\": 548.2965389999999       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.899874843554443,         \"F1\": 0.8816568047337278,         \"Memory in Mb\": 4.513812065124512,         \"Time in s\": 584.3583229999999       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8992718446601942,         \"F1\": 0.8819345661450925,         \"Memory in Mb\": 4.773520469665527,         \"Time in s\": 621.611368       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.901060070671378,         \"F1\": 0.8836565096952909,         \"Memory in Mb\": 4.815379142761231,         \"Time in s\": 660.1796979999999       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.902745995423341,         \"F1\": 0.884979702300406,         \"Memory in Mb\": 4.980830192565918,         \"Time in s\": 699.8371419999999       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9043381535038932,         \"F1\": 0.8862433862433862,         \"Memory in Mb\": 5.134486198425293,         \"Time in s\": 740.7850809999999       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9069264069264068,         \"F1\": 0.8903061224489796,         \"Memory in Mb\": 5.209948539733887,         \"Time in s\": 782.8443949999998       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9083245521601686,         \"F1\": 0.8932515337423312,         \"Memory in Mb\": 5.338950157165527,         \"Time in s\": 826.1813729999999       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9106776180698152,         \"F1\": 0.895808383233533,         \"Memory in Mb\": 5.382990837097168,         \"Time in s\": 870.7373769999999       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9109109109109108,         \"F1\": 0.896149358226371,         \"Memory in Mb\": 5.44773006439209,         \"Time in s\": 916.477949       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9111328125,         \"F1\": 0.896942242355606,         \"Memory in Mb\": 5.591532707214356,         \"Time in s\": 963.445117       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.911344137273594,         \"F1\": 0.8976897689768977,         \"Memory in Mb\": 5.678961753845215,         \"Time in s\": 1011.481541       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9115456238361266,         \"F1\": 0.8986125933831376,         \"Memory in Mb\": 5.788058280944824,         \"Time in s\": 1060.652982       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9117379435850772,         \"F1\": 0.8990634755463061,         \"Memory in Mb\": 5.880267143249512,         \"Time in s\": 1110.965738       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9119217081850534,         \"F1\": 0.9003021148036253,         \"Memory in Mb\": 6.120665550231934,         \"Time in s\": 1162.442095       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9129677980852916,         \"F1\": 0.90138067061144,         \"Memory in Mb\": 6.185591697692871,         \"Time in s\": 1215.005575       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9114139693356048,         \"F1\": 0.8996138996138997,         \"Memory in Mb\": 6.431841850280762,         \"Time in s\": 1268.8167280000002       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9124270225187656,         \"F1\": 0.9004739336492891,         \"Memory in Mb\": 6.484606742858887,         \"Time in s\": 1323.7082160000002       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9133986928104576,         \"F1\": 0.9014869888475836,         \"Memory in Mb\": 6.481654167175293,         \"Time in s\": 1379.6419000000003       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9135308246597278,         \"F1\": 0.9019963702359348,         \"Memory in Mb\": 6.595587730407715,         \"Time in s\": 1436.6903440000003       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1670236587524414,         \"Time in s\": 31.246172       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1682443618774414,         \"Time in s\": 90.057064       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1694650650024414,         \"Time in s\": 168.92668600000002       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1694650650024414,         \"Time in s\": 266.339332       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1694650650024414,         \"Time in s\": 379.700681       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1706857681274414,         \"Time in s\": 507.500932       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1706857681274414,         \"Time in s\": 650.046105       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.2171335220336914,         \"Time in s\": 806.74928       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.1745767593383789,         \"Time in s\": 979.905317       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.1744394302368164,         \"Time in s\": 1169.37771       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757745742797851,         \"Time in s\": 1374.513378       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757287979125976,         \"Time in s\": 1595.365052       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757516860961914,         \"Time in s\": 1830.528112       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757287979125976,         \"Time in s\": 2079.072293       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757287979125976,         \"Time in s\": 2341.33087       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.1756372451782226,         \"Time in s\": 2616.3107910000003       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757745742797851,         \"Time in s\": 2903.8369350000003       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.1756601333618164,         \"Time in s\": 3203.0985050000004       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.1756830215454101,         \"Time in s\": 3513.3936680000006       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757059097290039,         \"Time in s\": 3834.7595300000007       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757745742797851,         \"Time in s\": 4167.168481000001       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 4510.027218000001       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 4863.234695000001       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 5226.731618000001       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770639419555664,         \"Time in s\": 5600.511358000001       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769037246704101,         \"Time in s\": 5984.651066       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691675186157226,         \"Time in s\": 6379.477192       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770639419555664,         \"Time in s\": 6785.903036000001       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770410537719726,         \"Time in s\": 7201.6518080000005       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 7626.427031       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 8059.133738       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 8499.283325       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 8946.957028       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769952774047851,         \"Time in s\": 9401.959764       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 9864.111715       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691446304321289,         \"Time in s\": 10332.853073       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769037246704101,         \"Time in s\": 10808.168746       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691217422485351,         \"Time in s\": 11290.14581       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 11778.656001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 12273.787996       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769952774047851,         \"Time in s\": 12775.472063       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 13283.764208       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770181655883789,         \"Time in s\": 13798.661938       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782617568969726,         \"Time in s\": 14320.191281       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.1703653335571289,         \"Time in s\": 14848.294436       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.1702966690063476,         \"Time in s\": 15383.005183       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781930923461914,         \"Time in s\": 15923.647685       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781473159790039,         \"Time in s\": 16470.415157       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782159805297851,         \"Time in s\": 17023.687732       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782159805297851,         \"Time in s\": 17582.958979       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781702041625976,         \"Time in s\": 18142.251025       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7238095238095238,         \"F1\": 0.6881720430107527,         \"Memory in Mb\": 0.1032800674438476,         \"Time in s\": 0.213787       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8056872037914692,         \"F1\": 0.7807486631016043,         \"Memory in Mb\": 0.1952676773071289,         \"Time in s\": 0.888466       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.807570977917981,         \"F1\": 0.7859649122807018,         \"Memory in Mb\": 0.286778450012207,         \"Time in s\": 2.29757       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8297872340425532,         \"F1\": 0.8115183246073298,         \"Memory in Mb\": 0.3787660598754883,         \"Time in s\": 4.640547       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.831758034026465,         \"F1\": 0.8061002178649236,         \"Memory in Mb\": 2.6361207962036133,         \"Time in s\": 29.527472000000003       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8472440944881889,         \"F1\": 0.8245931283905967,         \"Memory in Mb\": 3.060887336730957,         \"Time in s\": 56.29478       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8529014844804319,         \"F1\": 0.8278041074249604,         \"Memory in Mb\": 3.5180253982543945,         \"Time in s\": 85.033958       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8559622195985832,         \"F1\": 0.8328767123287671,         \"Memory in Mb\": 3.9749040603637695,         \"Time in s\": 116.009539       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8604407135362014,         \"F1\": 0.8372093023255813,         \"Memory in Mb\": 4.4283952713012695,         \"Time in s\": 149.38786199999998       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8706326723323891,         \"F1\": 0.8476084538375974,         \"Memory in Mb\": 4.592366218566895,         \"Time in s\": 185.280189       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.871244635193133,         \"F1\": 0.8484848484848485,         \"Memory in Mb\": 4.394963264465332,         \"Time in s\": 223.461408       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8693941778127459,         \"F1\": 0.8477064220183486,         \"Memory in Mb\": 4.242337226867676,         \"Time in s\": 263.595386       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8714596949891068,         \"F1\": 0.8488471391972673,         \"Memory in Mb\": 4.1376237869262695,         \"Time in s\": 305.593042       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8759271746459879,         \"F1\": 0.8548895899053628,         \"Memory in Mb\": 4.233838081359863,         \"Time in s\": 349.622856       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8735053492762744,         \"F1\": 0.8527472527472527,         \"Memory in Mb\": 4.485638618469238,         \"Time in s\": 396.123591       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8755162241887906,         \"F1\": 0.854982817869416,         \"Memory in Mb\": 4.566784858703613,         \"Time in s\": 444.689218       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8778456413103831,         \"F1\": 0.858611825192802,         \"Memory in Mb\": 4.580937385559082,         \"Time in s\": 495.109217       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8778185631882538,         \"F1\": 0.8598917618761276,         \"Memory in Mb\": 4.553791999816895,         \"Time in s\": 547.400313       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.877297565822156,         \"F1\": 0.8605307735742519,         \"Memory in Mb\": 4.4779558181762695,         \"Time in s\": 601.303554       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8787163756488909,         \"F1\": 0.8635156664896441,         \"Memory in Mb\": 4.453892707824707,         \"Time in s\": 656.840699       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8782022471910113,         \"F1\": 0.8630621526023244,         \"Memory in Mb\": 4.4562273025512695,         \"Time in s\": 714.0315049999999       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8777348777348777,         \"F1\": 0.862782859894078,         \"Memory in Mb\": 4.439526557922363,         \"Time in s\": 772.745195       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8785391875256463,         \"F1\": 0.8635944700460828,         \"Memory in Mb\": 4.450131416320801,         \"Time in s\": 833.024947       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8788832088084939,         \"F1\": 0.864793678665496,         \"Memory in Mb\": 4.448788642883301,         \"Time in s\": 894.808032       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8784446961117403,         \"F1\": 0.8647058823529411,         \"Memory in Mb\": 4.491581916809082,         \"Time in s\": 958.170481       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.879491833030853,         \"F1\": 0.8659127625201939,         \"Memory in Mb\": 4.482541084289551,         \"Time in s\": 1022.970177       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8808109052778749,         \"F1\": 0.867056530214425,         \"Memory in Mb\": 4.4542436599731445,         \"Time in s\": 1089.131713       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8813616447590158,         \"F1\": 0.8673700075357951,         \"Memory in Mb\": 4.489590644836426,         \"Time in s\": 1156.687087       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8805727302310445,         \"F1\": 0.8665939658306071,         \"Memory in Mb\": 4.4426774978637695,         \"Time in s\": 1225.526022       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8820383768480654,         \"F1\": 0.8677248677248678,         \"Memory in Mb\": 4.4409685134887695,         \"Time in s\": 1295.666774       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.882496194824962,         \"F1\": 0.8678082191780822,         \"Memory in Mb\": 4.441540718078613,         \"Time in s\": 1367.284144       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8832202890002949,         \"F1\": 0.8693931398416888,         \"Memory in Mb\": 4.4570817947387695,         \"Time in s\": 1440.244405       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8850443237060337,         \"F1\": 0.8709055876685934,         \"Memory in Mb\": 4.465977668762207,         \"Time in s\": 1514.504066       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8856508465167916,         \"F1\": 0.8710888610763454,         \"Memory in Mb\": 4.4596757888793945,         \"Time in s\": 1590.040367       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8864923159881369,         \"F1\": 0.8724628900333233,         \"Memory in Mb\": 4.477154731750488,         \"Time in s\": 1666.899992       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8875491480996068,         \"F1\": 0.8737120989108037,         \"Memory in Mb\": 4.4705095291137695,         \"Time in s\": 1745.0344980000002       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8867635807192042,         \"F1\": 0.8724870763928776,         \"Memory in Mb\": 4.45665454864502,         \"Time in s\": 1824.4605280000003       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8852743978147505,         \"F1\": 0.8706606942889138,         \"Memory in Mb\": 4.454602241516113,         \"Time in s\": 1905.1708120000003       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8857972417130414,         \"F1\": 0.8712493180578287,         \"Memory in Mb\": 4.461682319641113,         \"Time in s\": 1987.1975480000003       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886765746638358,         \"F1\": 0.8724760892667376,         \"Memory in Mb\": 4.4584245681762695,         \"Time in s\": 2070.530861       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8876869965477561,         \"F1\": 0.8735751295336789,         \"Memory in Mb\": 4.494175910949707,         \"Time in s\": 2155.1880220000003       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869916872612896,         \"F1\": 0.8725614390676463,         \"Memory in Mb\": 4.517621040344238,         \"Time in s\": 2241.198533       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869870528856704,         \"F1\": 0.8729336294103133,         \"Memory in Mb\": 4.495129585266113,         \"Time in s\": 2328.452784       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886982629208664,         \"F1\": 0.873286847799952,         \"Memory in Mb\": 4.453595161437988,         \"Time in s\": 2416.918556       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8857202767875865,         \"F1\": 0.8715531463587085,         \"Memory in Mb\": 4.469174385070801,         \"Time in s\": 2506.628209       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8861538461538462,         \"F1\": 0.871616932685635,         \"Memory in Mb\": 4.478787422180176,         \"Time in s\": 2597.661861       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869704878538446,         \"F1\": 0.8728832693610296,         \"Memory in Mb\": 4.4154863357543945,         \"Time in s\": 2689.897866       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.885983880479654,         \"F1\": 0.8716814159292035,         \"Memory in Mb\": 4.439602851867676,         \"Time in s\": 2783.355406       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.885422684382823,         \"F1\": 0.8711842390127733,         \"Memory in Mb\": 4.50291919708252,         \"Time in s\": 2878.218251       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8850726552179656,         \"F1\": 0.8708377518557794,         \"Memory in Mb\": 4.509961128234863,         \"Time in s\": 2974.330637       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8784530386740331,         \"F1\": 0.8711943793911008,         \"Memory in Mb\": 4.434150695800781,         \"Time in s\": 37.114054       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8801766979569299,         \"F1\": 0.8453314326443336,         \"Memory in Mb\": 4.643096923828125,         \"Time in s\": 93.709907       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8568273831431726,         \"F1\": 0.8160756501182034,         \"Memory in Mb\": 4.667282104492188,         \"Time in s\": 164.56349699999998       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8746894838531604,         \"F1\": 0.8411476557032889,         \"Memory in Mb\": 4.594398498535156,         \"Time in s\": 248.378172       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8783395893133142,         \"F1\": 0.8399651466744118,         \"Memory in Mb\": 4.710762023925781,         \"Time in s\": 344.82085099999995       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8745170193192272,         \"F1\": 0.8360576923076923,         \"Memory in Mb\": 4.698677062988281,         \"Time in s\": 452.381486       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8747831572307208,         \"F1\": 0.8384865744507731,         \"Memory in Mb\": 4.6694183349609375,         \"Time in s\": 569.8523869999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8723609769559818,         \"F1\": 0.8348509194786646,         \"Memory in Mb\": 4.666007995605469,         \"Time in s\": 697.1091419999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8718263215994113,         \"F1\": 0.8430695299594534,         \"Memory in Mb\": 4.7265625,         \"Time in s\": 834.9178869999998       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8738271332376643,         \"F1\": 0.8493475682087781,         \"Memory in Mb\": 4.708610534667969,         \"Time in s\": 981.8103579999998       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8720521826392373,         \"F1\": 0.8501234277653698,         \"Memory in Mb\": 4.625167846679688,         \"Time in s\": 1137.002296       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8740686229417717,         \"F1\": 0.8545628386274301,         \"Memory in Mb\": 4.637184143066406,         \"Time in s\": 1300.798705       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8742464124989386,         \"F1\": 0.8546756942400157,         \"Memory in Mb\": 4.6933135986328125,         \"Time in s\": 1473.412993       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.872664196168099,         \"F1\": 0.8527937289217027,         \"Memory in Mb\": 4.810676574707031,         \"Time in s\": 1655.581963       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8748252262859666,         \"F1\": 0.8573824096587573,         \"Memory in Mb\": 4.703468322753906,         \"Time in s\": 1846.50108       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8750603656433252,         \"F1\": 0.85826093762229,         \"Memory in Mb\": 4.719985961914063,         \"Time in s\": 2046.373626       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8755924939938965,         \"F1\": 0.8581371242410781,         \"Memory in Mb\": 4.7149505615234375,         \"Time in s\": 2254.774116       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.872079475072055,         \"F1\": 0.8535112359550563,         \"Memory in Mb\": 4.6830902099609375,         \"Time in s\": 2471.471716       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8723058153721025,         \"F1\": 0.8517669274345832,         \"Memory in Mb\": 4.657257080078125,         \"Time in s\": 2696.3467009999995       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.87234394834152,         \"F1\": 0.8515118443859535,         \"Memory in Mb\": 4.7351837158203125,         \"Time in s\": 2929.434657       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8734822601839685,         \"F1\": 0.8509505232522138,         \"Memory in Mb\": 4.845870971679688,         \"Time in s\": 3171.2751989999992       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8722091214690683,         \"F1\": 0.8505193966782089,         \"Memory in Mb\": 4.855270385742188,         \"Time in s\": 3421.524358999999       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8678312616979411,         \"F1\": 0.8451765234989881,         \"Memory in Mb\": 4.8942718505859375,         \"Time in s\": 3679.924087999999       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8677735363105368,         \"F1\": 0.8427672955974842,         \"Memory in Mb\": 4.7196807861328125,         \"Time in s\": 3945.793201999999       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8669256920835356,         \"F1\": 0.840444679724722,         \"Memory in Mb\": 4.809005737304688,         \"Time in s\": 4218.904371999999       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8647845468053492,         \"F1\": 0.8373257061136934,         \"Memory in Mb\": 4.794342041015625,         \"Time in s\": 4499.001777999999       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8644372674870201,         \"F1\": 0.8359715077166601,         \"Memory in Mb\": 4.758956909179688,         \"Time in s\": 4786.067247999999       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8619860448614342,         \"F1\": 0.8330710914032329,         \"Memory in Mb\": 4.846771240234375,         \"Time in s\": 5079.937268999999       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8623301488219846,         \"F1\": 0.8333410127632125,         \"Memory in Mb\": 4.699310302734375,         \"Time in s\": 5380.101847999999       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8632767945840538,         \"F1\": 0.8350350705851016,         \"Memory in Mb\": 4.794769287109375,         \"Time in s\": 5686.612909       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.862061598718177,         \"F1\": 0.8333620096352374,         \"Memory in Mb\": 4.6817474365234375,         \"Time in s\": 5999.306036       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8618191852643924,         \"F1\": 0.8323989624299222,         \"Memory in Mb\": 4.8116455078125,         \"Time in s\": 6318.119808       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8607218115529987,         \"F1\": 0.8308417289567761,         \"Memory in Mb\": 4.769432067871094,         \"Time in s\": 6643.155825       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8599162419244879,         \"F1\": 0.8291562735083342,         \"Memory in Mb\": 4.83782958984375,         \"Time in s\": 6975.21929       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8578006244283958,         \"F1\": 0.8263832736513803,         \"Memory in Mb\": 4.765548706054688,         \"Time in s\": 7313.109579       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8558332055802544,         \"F1\": 0.8246307623452186,         \"Memory in Mb\": 4.726959228515625,         \"Time in s\": 7656.831982       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8543897855075923,         \"F1\": 0.8232354325861008,         \"Memory in Mb\": 4.798057556152344,         \"Time in s\": 8006.181245       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533128068086095,         \"F1\": 0.8218066337332393,         \"Memory in Mb\": 4.773887634277344,         \"Time in s\": 8361.39601       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8518099227351201,         \"F1\": 0.8192737815822173,         \"Memory in Mb\": 4.808341979980469,         \"Time in s\": 8722.632877       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8522310218273131,         \"F1\": 0.8186651315566692,         \"Memory in Mb\": 4.722572326660156,         \"Time in s\": 9089.688296       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8505586216179836,         \"F1\": 0.8161859664227292,         \"Memory in Mb\": 4.720252990722656,         \"Time in s\": 9462.293988       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8507792173661665,         \"F1\": 0.81590039556449,         \"Memory in Mb\": 4.766929626464844,         \"Time in s\": 9840.72504       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8507841979618553,         \"F1\": 0.8163523204751524,         \"Memory in Mb\": 4.769111633300781,         \"Time in s\": 10225.058019       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.850889295838246,         \"F1\": 0.8178809976101478,         \"Memory in Mb\": 4.736076354980469,         \"Time in s\": 10615.291715       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8509161372611543,         \"F1\": 0.8193329766363474,         \"Memory in Mb\": 4.725471496582031,         \"Time in s\": 11011.540552       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8518536292741452,         \"F1\": 0.8217770336585647,         \"Memory in Mb\": 4.700096130371094,         \"Time in s\": 11414.574328       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8529156196425636,         \"F1\": 0.8235028885444553,         \"Memory in Mb\": 4.746559143066406,         \"Time in s\": 11823.240959       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8525536367190195,         \"F1\": 0.8231074817920989,         \"Memory in Mb\": 4.826316833496094,         \"Time in s\": 12236.954316       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8525217939765278,         \"F1\": 0.8226754421602882,         \"Memory in Mb\": 4.775764465332031,         \"Time in s\": 12655.735001       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.853131415704541,         \"F1\": 0.8236541468974475,         \"Memory in Mb\": 4.767349243164063,         \"Time in s\": 13079.48614       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8531482421487057,         \"F1\": 0.8236416644579911,         \"Memory in Mb\": 4.766036987304688,         \"Time in s\": 13503.439196       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.0411081314086914,         \"Time in s\": 0.04635       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7551020408163265,         \"F1\": 0.7777777777777778,         \"Memory in Mb\": 0.0695962905883789,         \"Time in s\": 0.16308       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7972972972972973,         \"F1\": 0.8235294117647058,         \"Memory in Mb\": 0.0986146926879882,         \"Time in s\": 0.336872       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.797979797979798,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.1271295547485351,         \"Time in s\": 0.6777850000000001       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8064516129032258,         \"F1\": 0.8208955223880596,         \"Memory in Mb\": 0.155644416809082,         \"Time in s\": 1.226658       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.834355828220859,         \"Memory in Mb\": 0.1846628189086914,         \"Time in s\": 1.947513       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8426966292134832,         \"Memory in Mb\": 0.2131776809692382,         \"Time in s\": 2.9471350000000003       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8391959798994975,         \"F1\": 0.8415841584158417,         \"Memory in Mb\": 0.2421960830688476,         \"Time in s\": 4.26255       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8392857142857143,         \"F1\": 0.8363636363636364,         \"Memory in Mb\": 0.2707109451293945,         \"Time in s\": 5.830504       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8232931726907631,         \"F1\": 0.8225806451612903,         \"Memory in Mb\": 0.2992258071899414,         \"Time in s\": 7.819846       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8248175182481752,         \"F1\": 0.8208955223880596,         \"Memory in Mb\": 0.3284578323364258,         \"Time in s\": 10.199689       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8260869565217391,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.3569726943969726,         \"Time in s\": 12.972732       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8364197530864198,         \"F1\": 0.8250825082508251,         \"Memory in Mb\": 0.385991096496582,         \"Time in s\": 16.225203999999998       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8452722063037249,         \"F1\": 0.83125,         \"Memory in Mb\": 0.4145059585571289,         \"Time in s\": 19.962148       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.839572192513369,         \"F1\": 0.8235294117647058,         \"Memory in Mb\": 0.4430208206176758,         \"Time in s\": 24.257676       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8421052631578947,         \"F1\": 0.8225352112676055,         \"Memory in Mb\": 0.4720392227172851,         \"Time in s\": 29.175886       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8443396226415094,         \"F1\": 0.819672131147541,         \"Memory in Mb\": 0.500554084777832,         \"Time in s\": 34.714831       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8463251670378619,         \"F1\": 0.8198433420365536,         \"Memory in Mb\": 0.5295724868774414,         \"Time in s\": 40.875928       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8438818565400844,         \"F1\": 0.8177339901477833,         \"Memory in Mb\": 0.5580873489379883,         \"Time in s\": 47.66810099999999       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845691382765531,         \"F1\": 0.8229885057471266,         \"Memory in Mb\": 2.675789833068848,         \"Time in s\": 79.03492       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8454198473282443,         \"F1\": 0.8187919463087249,         \"Memory in Mb\": 2.7769289016723637,         \"Time in s\": 111.488727       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.848816029143898,         \"F1\": 0.8237791932059448,         \"Memory in Mb\": 2.8829355239868164,         \"Time in s\": 145.039549       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8519163763066202,         \"F1\": 0.8268839103869654,         \"Memory in Mb\": 2.989964485168457,         \"Time in s\": 179.782132       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8514190317195326,         \"F1\": 0.8230616302186878,         \"Memory in Mb\": 3.098984718322754,         \"Time in s\": 215.642079       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8525641025641025,         \"F1\": 0.8210116731517509,         \"Memory in Mb\": 3.2059221267700195,         \"Time in s\": 252.521109       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8582434514637904,         \"F1\": 0.8302583025830258,         \"Memory in Mb\": 3.3169260025024414,         \"Time in s\": 290.529559       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8620178041543026,         \"F1\": 0.8382608695652174,         \"Memory in Mb\": 3.429127693176269,         \"Time in s\": 329.62297       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669527896995708,         \"F1\": 0.8421052631578948,         \"Memory in Mb\": 3.5458459854125977,         \"Time in s\": 369.665809       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8674033149171271,         \"F1\": 0.8456591639871384,         \"Memory in Mb\": 3.659161567687988,         \"Time in s\": 410.977113       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8678237650200267,         \"F1\": 0.8465116279069768,         \"Memory in Mb\": 3.769242286682129,         \"Time in s\": 453.550102       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669250645994832,         \"F1\": 0.8446455505279035,         \"Memory in Mb\": 3.881718635559082,         \"Time in s\": 497.265773       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8648310387984981,         \"F1\": 0.8434782608695652,         \"Memory in Mb\": 3.994263648986816,         \"Time in s\": 542.189116       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8628640776699029,         \"F1\": 0.8423988842398884,         \"Memory in Mb\": 4.110844612121582,         \"Time in s\": 588.325742       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8657243816254417,         \"F1\": 0.8451086956521738,         \"Memory in Mb\": 4.225159645080566,         \"Time in s\": 635.696811       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.868421052631579,         \"F1\": 0.847277556440903,         \"Memory in Mb\": 4.342709541320801,         \"Time in s\": 684.216091       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8698553948832035,         \"F1\": 0.8482490272373541,         \"Memory in Mb\": 4.455658912658691,         \"Time in s\": 733.952375       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8712121212121212,         \"F1\": 0.8514357053682896,         \"Memory in Mb\": 4.573666572570801,         \"Time in s\": 785.013527       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8735511064278187,         \"F1\": 0.8561151079136691,         \"Memory in Mb\": 4.697152137756348,         \"Time in s\": 837.27657       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8757700205338809,         \"F1\": 0.8581477139507622,         \"Memory in Mb\": 4.820996284484863,         \"Time in s\": 890.836474       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8758758758758759,         \"F1\": 0.858447488584475,         \"Memory in Mb\": 4.93715763092041,         \"Time in s\": 945.663339       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8759765625,         \"F1\": 0.8590455049944505,         \"Memory in Mb\": 4.905686378479004,         \"Time in s\": 1001.665911       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8779790276453765,         \"F1\": 0.8617710583153347,         \"Memory in Mb\": 4.881028175354004,         \"Time in s\": 1058.849613       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8780260707635009,         \"F1\": 0.86282722513089,         \"Memory in Mb\": 4.857575416564941,         \"Time in s\": 1117.1299479999998       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8789808917197452,         \"F1\": 0.8641470888661901,         \"Memory in Mb\": 4.821175575256348,         \"Time in s\": 1176.4409139999998       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798932384341637,         \"F1\": 0.8662041625371655,         \"Memory in Mb\": 4.749619483947754,         \"Time in s\": 1236.7626339999997       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807658833768495,         \"F1\": 0.8668610301263362,         \"Memory in Mb\": 4.722535133361816,         \"Time in s\": 1298.0588499999997       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.879045996592845,         \"F1\": 0.8645038167938931,         \"Memory in Mb\": 4.706612586975098,         \"Time in s\": 1360.3228199999996       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807339449541285,         \"F1\": 0.865979381443299,         \"Memory in Mb\": 4.686341285705566,         \"Time in s\": 1423.5043029999997       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8815359477124183,         \"F1\": 0.8666053357865686,         \"Memory in Mb\": 4.653275489807129,         \"Time in s\": 1487.6393369999996       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8815052041633307,         \"F1\": 0.867145421903052,         \"Memory in Mb\": 4.596428871154785,         \"Time in s\": 1552.6498929999996       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.559709548950195,         \"Time in s\": 49.463009       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.594751358032227,         \"Time in s\": 126.299444       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.435243606567383,         \"Time in s\": 223.803561       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.493677139282227,         \"Time in s\": 340.763146       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.534708023071289,         \"Time in s\": 475.19475       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.455095291137695,         \"Time in s\": 625.35715       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.479013442993164,         \"Time in s\": 790.6914730000001       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686198515404,         \"F1\": 0.9,         \"Memory in Mb\": 4.445444107055664,         \"Time in s\": 971.128522       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998832184981898,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.544534683227539,         \"Time in s\": 1166.447296       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998948972620736,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.52708625793457,         \"Time in s\": 1376.022994       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999904452512899,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.493997573852539,         \"Time in s\": 1599.513782       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124151521788,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.490983963012695,         \"Time in s\": 1835.532511       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999191527205108,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.531465530395508,         \"Time in s\": 2083.498651       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998873916144289,         \"F1\": 0.88,         \"Memory in Mb\": 4.54191780090332,         \"Time in s\": 2343.113276       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999894899103139,         \"F1\": 0.88,         \"Memory in Mb\": 4.488824844360352,         \"Time in s\": 2613.833594       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999014681249384,         \"F1\": 0.88,         \"Memory in Mb\": 4.459695816040039,         \"Time in s\": 2894.8346570000003       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999072642967544,         \"F1\": 0.88,         \"Memory in Mb\": 4.475152969360352,         \"Time in s\": 3186.5040240000003       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124164306776,         \"F1\": 0.88,         \"Memory in Mb\": 4.543954849243164,         \"Time in s\": 3487.9588300000005       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999170262197146,         \"F1\": 0.88,         \"Memory in Mb\": 4.482622146606445,         \"Time in s\": 3798.7831540000006       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999211750177356,         \"F1\": 0.88,         \"Memory in Mb\": 4.496248245239258,         \"Time in s\": 4119.269013000001       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999924928682248,         \"F1\": 0.88,         \"Memory in Mb\": 4.471353530883789,         \"Time in s\": 4448.958874000001       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999283410963812,         \"F1\": 0.88,         \"Memory in Mb\": 4.53770637512207,         \"Time in s\": 4788.142489000001       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999931456772071,         \"F1\": 0.88,         \"Memory in Mb\": 4.51286506652832,         \"Time in s\": 5135.940338       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999343128024348,         \"F1\": 0.88,         \"Memory in Mb\": 4.49894905090332,         \"Time in s\": 5492.9415070000005       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999369403455668,         \"F1\": 0.88,         \"Memory in Mb\": 4.555765151977539,         \"Time in s\": 5859.118968000001       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999393657659116,         \"F1\": 0.88,         \"Memory in Mb\": 4.430139541625977,         \"Time in s\": 6234.600581000001       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999221486959906,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 4.466188430786133,         \"Time in s\": 6619.789592000001       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999249291518872,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 4.526651382446289,         \"Time in s\": 7013.956607000001       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999275178487298,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 4.485139846801758,         \"Time in s\": 7418.775951000001       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997898018882796,         \"F1\": 0.7391304347826089,         \"Memory in Mb\": 4.452577590942383,         \"Time in s\": 7831.9045620000015       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997965825874696,         \"F1\": 0.7391304347826089,         \"Memory in Mb\": 4.485406875610352,         \"Time in s\": 8252.946705000002       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998029394860004,         \"F1\": 0.7391304347826089,         \"Memory in Mb\": 4.502649307250977,         \"Time in s\": 8681.511861000003       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997770629637888,         \"F1\": 0.7083333333333334,         \"Memory in Mb\": 4.495584487915039,         \"Time in s\": 9116.499033000002       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997836200367846,         \"F1\": 0.7083333333333334,         \"Memory in Mb\": 4.500345230102539,         \"Time in s\": 9557.922914000002       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997898024142694,         \"F1\": 0.7083333333333334,         \"Memory in Mb\": 4.572656631469727,         \"Time in s\": 10005.892137000004       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997664472243712,         \"F1\": 0.68,         \"Memory in Mb\": 4.537866592407227,         \"Time in s\": 10460.064213000003       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997727595512002,         \"F1\": 0.68,         \"Memory in Mb\": 4.469621658325195,         \"Time in s\": 10920.643886000003       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997787396457068,         \"F1\": 0.68,         \"Memory in Mb\": 4.537904739379883,         \"Time in s\": 11387.046035000005       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997844130645684,         \"F1\": 0.68,         \"Memory in Mb\": 4.493074417114258,         \"Time in s\": 11859.048710000005       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99978980280876,         \"F1\": 0.68,         \"Memory in Mb\": 4.520692825317383,         \"Time in s\": 12336.641580000003       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997949296352312,         \"F1\": 0.68,         \"Memory in Mb\": 4.566102981567383,         \"Time in s\": 12820.170836000005       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997998123240538,         \"F1\": 0.68,         \"Memory in Mb\": 4.500688552856445,         \"Time in s\": 13309.287446000002       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998044679082956,         \"F1\": 0.68,         \"Memory in Mb\": 4.506959915161133,         \"Time in s\": 13804.219559000005       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089118725442,         \"F1\": 0.68,         \"Memory in Mb\": 4.503435134887695,         \"Time in s\": 14304.793113000003       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998131583249644,         \"F1\": 0.68,         \"Memory in Mb\": 4.498682022094727,         \"Time in s\": 14810.923265000005       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998172201469092,         \"F1\": 0.68,         \"Memory in Mb\": 4.491861343383789,         \"Time in s\": 15322.783751000004       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998099284436494,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.496858596801758,         \"Time in s\": 15840.351229000004       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998138883110912,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.480546951293945,         \"Time in s\": 16363.581709000004       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999817686549557,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.533571243286133,         \"Time in s\": 16892.331870000005       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998213328568876,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.517786026000977,         \"Time in s\": 17426.665113000006       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998213441227471,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.518220901489258,         \"Time in s\": 17961.110841000005       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.4857142857142857,         \"F1\": 0.4599999999999999,         \"Memory in Mb\": 0.1797952651977539,         \"Time in s\": 0.693272       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165876777251185,         \"F1\": 0.4574468085106383,         \"Memory in Mb\": 0.1805887222290039,         \"Time in s\": 2.027128       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5205047318611987,         \"F1\": 0.4722222222222222,         \"Memory in Mb\": 0.1812677383422851,         \"Time in s\": 4.089008       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460992907801419,         \"F1\": 0.4838709677419355,         \"Memory in Mb\": 0.1813135147094726,         \"Time in s\": 6.917919       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.55765595463138,         \"F1\": 0.455813953488372,         \"Memory in Mb\": 0.1813364028930664,         \"Time in s\": 10.429995       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5543307086614173,         \"F1\": 0.4259634888438134,         \"Memory in Mb\": 0.1819925308227539,         \"Time in s\": 14.687229       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5748987854251012,         \"F1\": 0.4220183486238532,         \"Memory in Mb\": 0.1820383071899414,         \"Time in s\": 19.647457       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5785123966942148,         \"F1\": 0.4232633279483037,         \"Memory in Mb\": 0.1819696426391601,         \"Time in s\": 25.366911       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5844700944386149,         \"F1\": 0.4193548387096774,         \"Memory in Mb\": 0.1819467544555664,         \"Time in s\": 31.800242       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5920679886685553,         \"F1\": 0.4146341463414634,         \"Memory in Mb\": 0.1819467544555664,         \"Time in s\": 39.029576       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.590557939914163,         \"F1\": 0.4015056461731493,         \"Memory in Mb\": 0.1819238662719726,         \"Time in s\": 46.984262       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971675845790716,         \"F1\": 0.4101382488479262,         \"Memory in Mb\": 0.1819238662719726,         \"Time in s\": 55.672123       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599128540305011,         \"F1\": 0.3973799126637554,         \"Memory in Mb\": 0.1825342178344726,         \"Time in s\": 65.02100899999999       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5994605529332434,         \"F1\": 0.3926380368098159,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 75.177128       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5997482693517936,         \"F1\": 0.3896353166986563,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 86.053176       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6011799410029498,         \"F1\": 0.3876811594202898,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 97.727606       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6013325930038868,         \"F1\": 0.3904923599320882,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 110.067211       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030414263240692,         \"F1\": 0.396812749003984,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 123.213825       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986090412319921,         \"F1\": 0.3961136023916292,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 137.051202       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5969797074091553,         \"F1\": 0.3994374120956399,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 151.568436       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597752808988764,         \"F1\": 0.4013377926421405,         \"Memory in Mb\": 0.1824426651000976,         \"Time in s\": 166.814875       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5988845988845989,         \"F1\": 0.4033184428844926,         \"Memory in Mb\": 0.1824426651000976,         \"Time in s\": 182.823981       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5995075913007797,         \"F1\": 0.4019607843137255,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 199.616425       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008651199370821,         \"F1\": 0.4088526499708794,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 217.084375       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6002265005662514,         \"F1\": 0.4073866815892558,         \"Memory in Mb\": 0.1830987930297851,         \"Time in s\": 235.279245       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5985480943738657,         \"F1\": 0.4028077753779697,         \"Memory in Mb\": 0.1830987930297851,         \"Time in s\": 254.250965       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599790283117791,         \"F1\": 0.4051948051948052,         \"Memory in Mb\": 0.1830987930297851,         \"Time in s\": 273.857236       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599932591843613,         \"F1\": 0.4026170105686965,         \"Memory in Mb\": 0.1831216812133789,         \"Time in s\": 294.204326       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5977871786527823,         \"F1\": 0.4023210831721469,         \"Memory in Mb\": 0.1831216812133789,         \"Time in s\": 315.311898       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986159169550173,         \"F1\": 0.4042950513538749,         \"Memory in Mb\": 0.1831216812133789,         \"Time in s\": 337.189575       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5981735159817352,         \"F1\": 0.4021739130434782,         \"Memory in Mb\": 0.1785964965820312,         \"Time in s\": 359.75124       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5959893836626364,         \"F1\": 0.4022687609075043,         \"Memory in Mb\": 0.2364349365234375,         \"Time in s\": 383.144231       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597369173577352,         \"F1\": 0.4023769100169779,         \"Memory in Mb\": 0.2806434631347656,         \"Time in s\": 407.324287       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008881487649181,         \"F1\": 0.4087171052631579,         \"Memory in Mb\": 0.3000526428222656,         \"Time in s\": 432.314941       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6012402264761392,         \"F1\": 0.4086365453818472,         \"Memory in Mb\": 0.3464546203613281,         \"Time in s\": 458.107983       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6023591087811271,         \"F1\": 0.4104158569762923,         \"Memory in Mb\": 0.3760719299316406,         \"Time in s\": 484.645149       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6052027543993879,         \"F1\": 0.4145234493192133,         \"Memory in Mb\": 0.4113121032714844,         \"Time in s\": 512.014837       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608393344921778,         \"F1\": 0.4195804195804196,         \"Memory in Mb\": 0.4392280578613281,         \"Time in s\": 540.239956       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6121461408178079,         \"F1\": 0.4260651629072682,         \"Memory in Mb\": 0.4532661437988281,         \"Time in s\": 569.3761920000001       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6157112526539278,         \"F1\": 0.4329968673860076,         \"Memory in Mb\": 0.4546051025390625,         \"Time in s\": 599.333749       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6186421173762946,         \"F1\": 0.4384954252795662,         \"Memory in Mb\": 0.4373931884765625,         \"Time in s\": 630.119       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6212087171422153,         \"F1\": 0.4420913302448709,         \"Memory in Mb\": 0.4377059936523437,         \"Time in s\": 661.732786       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6214614878209348,         \"F1\": 0.4437278297323443,         \"Memory in Mb\": 0.4275894165039062,         \"Time in s\": 694.0925080000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6219172206733863,         \"F1\": 0.4454230890217049,         \"Memory in Mb\": 0.3975372314453125,         \"Time in s\": 727.2725200000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6227720696162717,         \"F1\": 0.4449244060475162,         \"Memory in Mb\": 0.42584228515625,         \"Time in s\": 761.2761580000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6235897435897436,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 0.393829345703125,         \"Time in s\": 796.1318860000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6251756675366392,         \"F1\": 0.4491000295072292,         \"Memory in Mb\": 0.39398193359375,         \"Time in s\": 831.6856570000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.624139964615687,         \"F1\": 0.4467592592592592,         \"Memory in Mb\": 0.39410400390625,         \"Time in s\": 867.9313910000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6248796456768727,         \"F1\": 0.4469051675184554,         \"Memory in Mb\": 0.394500732421875,         \"Time in s\": 904.957506       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6259671636157765,         \"F1\": 0.4482182628062361,         \"Memory in Mb\": 0.4006576538085937,         \"Time in s\": 942.730038       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8651933701657458,         \"F1\": 0.8685344827586208,         \"Memory in Mb\": 1.5650663375854492,         \"Time in s\": 11.845084       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895637769188294,         \"F1\": 0.8684210526315789,         \"Memory in Mb\": 1.8734617233276367,         \"Time in s\": 34.886970000000005       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8778064041221936,         \"F1\": 0.8547681539807523,         \"Memory in Mb\": 1.7035398483276367,         \"Time in s\": 70.08374500000001       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835219431410434,         \"F1\": 0.8607260726072606,         \"Memory in Mb\": 1.6641263961791992,         \"Time in s\": 115.953507       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8878339589313314,         \"F1\": 0.8599007170435742,         \"Memory in Mb\": 2.069842338562012,         \"Time in s\": 171.720075       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.886292548298068,         \"F1\": 0.8580615525953147,         \"Memory in Mb\": 2.326838493347168,         \"Time in s\": 235.88433300000003       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8845607948273143,         \"F1\": 0.8556782334384859,         \"Memory in Mb\": 1.8882322311401367,         \"Time in s\": 307.801578       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835380157306472,         \"F1\": 0.8526021655606008,         \"Memory in Mb\": 1.7675046920776367,         \"Time in s\": 386.842642       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8854409419845456,         \"F1\": 0.8617115783239561,         \"Memory in Mb\": 1.8750486373901367,         \"Time in s\": 473.251889       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8863009162159179,         \"F1\": 0.8664765361680064,         \"Memory in Mb\": 1.8668012619018557,         \"Time in s\": 566.6143930000001       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883492222779729,         \"F1\": 0.8657337805019083,         \"Memory in Mb\": 1.624751091003418,         \"Time in s\": 666.8526760000001       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8851071658541072,         \"F1\": 0.8689539397754694,         \"Memory in Mb\": 1.836443901062012,         \"Time in s\": 773.0944300000001       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8819733378619343,         \"F1\": 0.8645224171539961,         \"Memory in Mb\": 1.461909294128418,         \"Time in s\": 884.893217       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8788930063865016,         \"F1\": 0.8610709117221419,         \"Memory in Mb\": 1.412806510925293,         \"Time in s\": 1002.145707       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880197218338362,         \"F1\": 0.863970588235294,         \"Memory in Mb\": 1.521845817565918,         \"Time in s\": 1125.032122       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8799586064160055,         \"F1\": 0.8644437519476471,         \"Memory in Mb\": 1.922499656677246,         \"Time in s\": 1253.0070850000002       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805272384910071,         \"F1\": 0.8643667993513195,         \"Memory in Mb\": 1.924330711364746,         \"Time in s\": 1386.9590420000002       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8794382780401054,         \"F1\": 0.8622670589883704,         \"Memory in Mb\": 1.6739492416381836,         \"Time in s\": 1526.6043270000002       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8781153779120432,         \"F1\": 0.8583389601620527,         \"Memory in Mb\": 2.050276756286621,         \"Time in s\": 1671.5850450000005       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8772559192008389,         \"F1\": 0.8570510348373829,         \"Memory in Mb\": 2.0607213973999023,         \"Time in s\": 1821.619573       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8782128777923784,         \"F1\": 0.8564702967230379,         \"Memory in Mb\": 1.4914274215698242,         \"Time in s\": 1976.682617       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8703025437760273,         \"F1\": 0.8482357776081723,         \"Memory in Mb\": 0.7451009750366211,         \"Time in s\": 2137.4066430000003       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8626001823679033,         \"F1\": 0.8387314820030418,         \"Memory in Mb\": 0.7786626815795898,         \"Time in s\": 2303.79277       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8638642321666743,         \"F1\": 0.8378259916721456,         \"Memory in Mb\": 0.8927946090698242,         \"Time in s\": 2475.479733       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8620689655172413,         \"F1\": 0.8337590464027246,         \"Memory in Mb\": 1.0149259567260742,         \"Time in s\": 2652.48421       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8552748885586924,         \"F1\": 0.8239789332369494,         \"Memory in Mb\": 0.7698392868041992,         \"Time in s\": 2835.224884       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8513143371080496,         \"F1\": 0.8171902488062327,         \"Memory in Mb\": 0.8274068832397461,         \"Time in s\": 3023.118045       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8471242165017543,         \"F1\": 0.8121670057153928,         \"Memory in Mb\": 0.9264287948608398,         \"Time in s\": 3216.225907       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8469912077037263,         \"F1\": 0.8116213683223992,         \"Memory in Mb\": 1.0318632125854492,         \"Time in s\": 3414.151818       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8476397218440708,         \"F1\": 0.8134600657687283,         \"Memory in Mb\": 0.828364372253418,         \"Time in s\": 3617.194246       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8442585009791703,         \"F1\": 0.8083260297984224,         \"Memory in Mb\": 0.9404935836791992,         \"Time in s\": 3825.52125       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8416405091235211,         \"F1\": 0.8035935828877006,         \"Memory in Mb\": 0.932948112487793,         \"Time in s\": 4039.267659       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8389804997156906,         \"F1\": 0.7997670742866649,         \"Memory in Mb\": 0.8770322799682617,         \"Time in s\": 4258.308337       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8374508976398403,         \"F1\": 0.7964054812344976,         \"Memory in Mb\": 1.024897575378418,         \"Time in s\": 4482.681616999999       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8326973414488,         \"F1\": 0.7888725275599952,         \"Memory in Mb\": 0.9494352340698242,         \"Time in s\": 4711.951208999999       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.827318718381113,         \"F1\": 0.7808219178082191,         \"Memory in Mb\": 0.861109733581543,         \"Time in s\": 4945.765051999999       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8271531278899794,         \"F1\": 0.7806964420893262,         \"Memory in Mb\": 1.093031883239746,         \"Time in s\": 5184.141968999998       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8255439044935661,         \"F1\": 0.7779174678302028,         \"Memory in Mb\": 1.133570671081543,         \"Time in s\": 5427.007607999998       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8253191067840263,         \"F1\": 0.7766196163590301,         \"Memory in Mb\": 1.1872129440307615,         \"Time in s\": 5674.462564999998       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8264576837109192,         \"F1\": 0.7764070110569915,         \"Memory in Mb\": 1.431788444519043,         \"Time in s\": 5926.318084999998       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8245255081437609,         \"F1\": 0.7721616331096196,         \"Memory in Mb\": 1.357222557067871,         \"Time in s\": 6182.753490999998       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8241833328953247,         \"F1\": 0.7704974271012006,         \"Memory in Mb\": 1.1449995040893557,         \"Time in s\": 6443.434381999998       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8233950252842878,         \"F1\": 0.7698534823041413,         \"Memory in Mb\": 1.147334098815918,         \"Time in s\": 6708.424011999998       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8222160901086221,         \"F1\": 0.7699250073044834,         \"Memory in Mb\": 0.7468709945678711,         \"Time in s\": 6977.570913999998       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8227329588658049,         \"F1\": 0.7725140860587366,         \"Memory in Mb\": 0.9336042404174804,         \"Time in s\": 7251.011506999998       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8235872825434913,         \"F1\": 0.7755388654820785,         \"Memory in Mb\": 1.097620964050293,         \"Time in s\": 7528.4976689999985       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8245696437378174,         \"F1\": 0.7776785714285713,         \"Memory in Mb\": 1.5453977584838867,         \"Time in s\": 7809.843107999998       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8251431462276082,         \"F1\": 0.7787605469886529,         \"Memory in Mb\": 0.8941831588745117,         \"Time in s\": 8094.859004999998       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8243867276372401,         \"F1\": 0.7766701042740918,         \"Memory in Mb\": 0.744959831237793,         \"Time in s\": 8383.886548999999       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.823770944170953,         \"F1\": 0.7766305716444221,         \"Memory in Mb\": 0.5983161926269531,         \"Time in s\": 8676.996437       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8237734766392267,         \"F1\": 0.7765871128395959,         \"Memory in Mb\": 0.5984382629394531,         \"Time in s\": 8970.151376       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7083333333333334,         \"F1\": 0.7407407407407408,         \"Memory in Mb\": 0.6633157730102539,         \"Time in s\": 0.427424       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8163265306122449,         \"F1\": 0.8085106382978724,         \"Memory in Mb\": 0.6639947891235352,         \"Time in s\": 1.324595       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8513513513513513,         \"F1\": 0.8493150684931507,         \"Memory in Mb\": 0.6639490127563477,         \"Time in s\": 2.554164       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8585858585858586,         \"F1\": 0.8541666666666666,         \"Memory in Mb\": 0.6645593643188477,         \"Time in s\": 4.28613       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8548387096774194,         \"F1\": 0.85,         \"Memory in Mb\": 0.6645593643188477,         \"Time in s\": 6.454494       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8523489932885906,         \"F1\": 0.8533333333333335,         \"Memory in Mb\": 0.6645593643188477,         \"Time in s\": 8.992416       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8620689655172413,         \"F1\": 0.8536585365853658,         \"Memory in Mb\": 0.6651926040649414,         \"Time in s\": 11.94422       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8592964824120602,         \"F1\": 0.8510638297872339,         \"Memory in Mb\": 0.6653299331665039,         \"Time in s\": 15.477702       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8405797101449276,         \"Memory in Mb\": 0.7024993896484375,         \"Time in s\": 19.458772       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8473895582329317,         \"F1\": 0.8347826086956521,         \"Memory in Mb\": 0.730194091796875,         \"Time in s\": 23.970287       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467153284671532,         \"F1\": 0.8333333333333335,         \"Memory in Mb\": 0.7302398681640625,         \"Time in s\": 28.914006       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8528428093645485,         \"F1\": 0.837037037037037,         \"Memory in Mb\": 0.7302398681640625,         \"Time in s\": 34.304573       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8611111111111112,         \"F1\": 0.8421052631578947,         \"Memory in Mb\": 0.7308502197265625,         \"Time in s\": 40.225779       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8653295128939829,         \"F1\": 0.8438538205980067,         \"Memory in Mb\": 0.7308731079101562,         \"Time in s\": 46.580822       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663101604278075,         \"F1\": 0.8427672955974843,         \"Memory in Mb\": 0.7674179077148438,         \"Time in s\": 53.398646       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671679197994987,         \"F1\": 0.8417910447761194,         \"Memory in Mb\": 0.804534912109375,         \"Time in s\": 60.683253       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8679245283018868,         \"F1\": 0.839080459770115,         \"Memory in Mb\": 0.8596954345703125,         \"Time in s\": 68.459501       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8708240534521158,         \"F1\": 0.8406593406593408,         \"Memory in Mb\": 0.8597640991210938,         \"Time in s\": 76.689807       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.869198312236287,         \"F1\": 0.8402061855670103,         \"Memory in Mb\": 0.859832763671875,         \"Time in s\": 85.340536       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8677354709418837,         \"F1\": 0.8413461538461539,         \"Memory in Mb\": 0.8598556518554688,         \"Time in s\": 94.431883       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8683206106870229,         \"F1\": 0.8384074941451991,         \"Memory in Mb\": 0.8598556518554688,         \"Time in s\": 103.968058       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8670309653916212,         \"F1\": 0.8381374722838136,         \"Memory in Mb\": 0.8599014282226562,         \"Time in s\": 113.947374       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867595818815331,         \"F1\": 0.8382978723404255,         \"Memory in Mb\": 0.8599014282226562,         \"Time in s\": 124.337957       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8697829716193656,         \"F1\": 0.8381742738589212,         \"Memory in Mb\": 0.8599014282226562,         \"Time in s\": 135.24069899999998       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8717948717948718,         \"F1\": 0.8373983739837398,         \"Memory in Mb\": 0.8966064453125,         \"Time in s\": 146.56138699999997       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8767334360554699,         \"F1\": 0.846153846153846,         \"Memory in Mb\": 0.897308349609375,         \"Time in s\": 158.32837399999997       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8753709198813057,         \"F1\": 0.8478260869565216,         \"Memory in Mb\": 0.92486572265625,         \"Time in s\": 170.52005599999995       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798283261802575,         \"F1\": 0.8515901060070671,         \"Memory in Mb\": 0.8633918762207031,         \"Time in s\": 183.100014       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8825966850828729,         \"F1\": 0.8576214405360134,         \"Memory in Mb\": 0.9612770080566406,         \"Time in s\": 196.144193       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8865153538050734,         \"F1\": 0.8631239935587761,         \"Memory in Mb\": 0.9975471496582032,         \"Time in s\": 209.598306       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8875968992248062,         \"F1\": 0.863849765258216,         \"Memory in Mb\": 1.052570343017578,         \"Time in s\": 223.56015       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8873591989987485,         \"F1\": 0.8652694610778443,         \"Memory in Mb\": 1.1531257629394531,         \"Time in s\": 237.949979       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8871359223300971,         \"F1\": 0.8661870503597122,         \"Memory in Mb\": 1.1537437438964844,         \"Time in s\": 252.78040299999995       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8881036513545347,         \"F1\": 0.8671328671328671,         \"Memory in Mb\": 1.1632118225097656,         \"Time in s\": 267.983484       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901601830663616,         \"F1\": 0.8688524590163934,         \"Memory in Mb\": 1.1906776428222656,         \"Time in s\": 283.604953       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8887652947719689,         \"F1\": 0.8670212765957446,         \"Memory in Mb\": 1.2457008361816406,         \"Time in s\": 299.652639       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896103896103896,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 1.2457923889160156,         \"Time in s\": 316.05876399999994       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8893572181243414,         \"F1\": 0.8708487084870848,         \"Memory in Mb\": 1.2458381652832031,         \"Time in s\": 332.972637       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901437371663244,         \"F1\": 0.8718562874251498,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 350.27117       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878878878878879,         \"F1\": 0.8697674418604652,         \"Memory in Mb\": 1.2458610534667969,         \"Time in s\": 368.008026       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876953125,         \"F1\": 0.8700564971751412,         \"Memory in Mb\": 1.2459068298339844,         \"Time in s\": 386.172169       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8894184938036225,         \"F1\": 0.8725274725274725,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 404.74234       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901303538175046,         \"F1\": 0.8742004264392325,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 423.719952       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89171974522293,         \"F1\": 0.8761706555671176,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 443.13918       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932384341637011,         \"F1\": 0.8790322580645162,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 462.955181       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8938207136640557,         \"F1\": 0.8794466403162056,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 483.090208       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8926746166950597,         \"F1\": 0.877906976744186,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 503.748339       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932443703085905,         \"F1\": 0.8783269961977186,         \"Memory in Mb\": 1.2550315856933594,         \"Time in s\": 524.8299360000001       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929738562091504,         \"F1\": 0.8779123951537745,         \"Memory in Mb\": 1.3099861145019531,         \"Time in s\": 546.3067460000001       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8935148118494796,         \"F1\": 0.8792007266121706,         \"Memory in Mb\": 1.310077667236328,         \"Time in s\": 568.2182720000001       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1599369049072265,         \"Time in s\": 9.565839       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1605472564697265,         \"Time in s\": 28.660555       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1610889434814453,         \"Time in s\": 57.169533       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.161111831665039,         \"Time in s\": 95.025921       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.161111831665039,         \"Time in s\": 141.315828       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.161722183227539,         \"Time in s\": 195.517174       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1617450714111328,         \"Time in s\": 256.578558       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.2173633575439453,         \"Time in s\": 324.084886       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.162454605102539,         \"Time in s\": 398.342181       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.162271499633789,         \"Time in s\": 479.30544       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629047393798828,         \"Time in s\": 566.848605       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629962921142578,         \"Time in s\": 660.867353       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631336212158203,         \"Time in s\": 761.190417       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.1628131866455078,         \"Time in s\": 867.1613560000001       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.1630420684814453,         \"Time in s\": 978.380996       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629276275634765,         \"Time in s\": 1094.7710920000002       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.163064956665039,         \"Time in s\": 1216.3368180000002       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.162973403930664,         \"Time in s\": 1342.8439390000003       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.162973403930664,         \"Time in s\": 1474.4175280000004       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629962921142578,         \"Time in s\": 1611.5765880000004       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.1628589630126953,         \"Time in s\": 1753.4950250000004       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 1900.0594340000005       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 2051.0940990000004       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636295318603515,         \"Time in s\": 2206.5822620000004       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 2366.582792       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.1516590118408203,         \"Time in s\": 2531.0740060000003       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.163583755493164,         \"Time in s\": 2700.0024150000004       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.163583755493164,         \"Time in s\": 2873.4051700000005       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635379791259765,         \"Time in s\": 3051.2280980000005       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 3233.6982610000005       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1634693145751953,         \"Time in s\": 3420.4241430000006       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.163400650024414,         \"Time in s\": 3611.3532100000007       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 3806.5756970000007       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 4005.990633000001       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 4209.692892000001       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636066436767578,         \"Time in s\": 4417.671552000001       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.1634464263916015,         \"Time in s\": 4629.924287000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636066436767578,         \"Time in s\": 4846.389066000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636066436767578,         \"Time in s\": 5067.145737000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 5292.119512000001       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 5520.9977020000015       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.1633319854736328,         \"Time in s\": 5753.467598000001       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 5989.673013000001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.1642627716064453,         \"Time in s\": 6229.466851000001       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.164285659790039,         \"Time in s\": 6472.919364000001       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.164102554321289,         \"Time in s\": 6719.889077000002       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.1641483306884765,         \"Time in s\": 6970.567347000002       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.1640567779541015,         \"Time in s\": 7224.925889000002       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.1523609161376953,         \"Time in s\": 7483.091298000002       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.164194107055664,         \"Time in s\": 7744.930013000002       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1642169952392578,         \"Time in s\": 8006.777295000002       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5523809523809524,         \"F1\": 0.5252525252525252,         \"Memory in Mb\": 0.1663923263549804,         \"Time in s\": 0.661448       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5829383886255924,         \"F1\": 0.5555555555555555,         \"Memory in Mb\": 0.1665983200073242,         \"Time in s\": 2.064295       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6025236593059937,         \"F1\": 0.5827814569536425,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 4.087538       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6099290780141844,         \"F1\": 0.5758354755784061,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 6.767182       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5841209829867675,         \"F1\": 0.5089285714285714,         \"Memory in Mb\": 0.1665983200073242,         \"Time in s\": 10.090322       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5748031496062992,         \"F1\": 0.4981412639405205,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 14.036758       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.582995951417004,         \"F1\": 0.4892561983471074,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 18.750104       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5749704840613932,         \"F1\": 0.4812680115273775,         \"Memory in Mb\": 0.1665296554565429,         \"Time in s\": 24.116907       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5760755508919203,         \"F1\": 0.482051282051282,         \"Memory in Mb\": 0.1665296554565429,         \"Time in s\": 30.255333       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5873465533522191,         \"F1\": 0.4828402366863905,         \"Memory in Mb\": 0.1665296554565429,         \"Time in s\": 37.077733       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5931330472103005,         \"F1\": 0.4925053533190577,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 44.554975       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5979543666404405,         \"F1\": 0.5034013605442177,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 52.671883       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6005809731299927,         \"F1\": 0.4990892531876139,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 61.596702       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6089008766014835,         \"F1\": 0.5117845117845117,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 71.17423       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6091881686595343,         \"F1\": 0.5121759622937941,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 81.390284       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6135693215339233,         \"F1\": 0.5194424064563462,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 92.365155       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6185452526374237,         \"F1\": 0.5354969574036511,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 104.03539999999998       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6208704771893025,         \"F1\": 0.5467084639498432,         \"Memory in Mb\": 0.1665983200073242,         \"Time in s\": 116.33010199999998       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.620963735717834,         \"F1\": 0.5561372891215823,         \"Memory in Mb\": 0.1666212081909179,         \"Time in s\": 129.40978099999998       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6252949504483247,         \"F1\": 0.56941431670282,         \"Memory in Mb\": 0.1666212081909179,         \"Time in s\": 143.16628799999998       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6242696629213483,         \"F1\": 0.5721596724667348,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 157.57341499999998       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6229086229086229,         \"F1\": 0.5763855421686748,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 172.619666       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.62330734509643,         \"F1\": 0.5796703296703297,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 188.342899       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6244593000393236,         \"F1\": 0.5860424794104898,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 204.771397       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6266515666289165,         \"F1\": 0.591828312009905,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 221.901573       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6250453720508167,         \"F1\": 0.5921831819976313,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 239.674138       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6249563089828731,         \"F1\": 0.5927893738140417,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 258.142834       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6248736097067745,         \"F1\": 0.5924569754668619,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 277.299077       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6260982753010088,         \"F1\": 0.5958494548012664,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 297.096451       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.62378106322743,         \"F1\": 0.5934738273283481,         \"Memory in Mb\": 0.1541042327880859,         \"Time in s\": 317.58276       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6246575342465753,         \"F1\": 0.5937397034596376,         \"Memory in Mb\": 0.1971263885498047,         \"Time in s\": 338.83455200000003       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6234149218519611,         \"F1\": 0.5931825422108953,         \"Memory in Mb\": 0.2317180633544922,         \"Time in s\": 360.725819       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6211038032599371,         \"F1\": 0.5894019212891229,         \"Memory in Mb\": 0.2662029266357422,         \"Time in s\": 383.343908       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6194837635303914,         \"F1\": 0.5866747060596926,         \"Memory in Mb\": 0.3123226165771484,         \"Time in s\": 406.679113       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6238878403882449,         \"F1\": 0.5915080527086384,         \"Memory in Mb\": 0.3201503753662109,         \"Time in s\": 430.743987       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6277850589777195,         \"F1\": 0.5970488081725313,         \"Memory in Mb\": 0.3261775970458984,         \"Time in s\": 455.494794       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6322366743177761,         \"F1\": 0.6009961261759823,         \"Memory in Mb\": 0.3539028167724609,         \"Time in s\": 480.941019       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6354606406754407,         \"F1\": 0.6034575904916262,         \"Memory in Mb\": 0.3679637908935547,         \"Time in s\": 507.154231       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6399709654004355,         \"F1\": 0.6073878627968339,         \"Memory in Mb\": 0.3758831024169922,         \"Time in s\": 534.089651       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.644963434772352,         \"F1\": 0.6130110568269478,         \"Memory in Mb\": 0.3759059906005859,         \"Time in s\": 561.67777       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6508630609896433,         \"F1\": 0.6185567010309279,         \"Memory in Mb\": 0.3759288787841797,         \"Time in s\": 589.949243       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6535609975286453,         \"F1\": 0.620384047267356,         \"Memory in Mb\": 0.3758831024169922,         \"Time in s\": 618.987429       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6570111915734036,         \"F1\": 0.6243691420331651,         \"Memory in Mb\": 0.3760662078857422,         \"Time in s\": 648.691271       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6607334334119666,         \"F1\": 0.6288127639605818,         \"Memory in Mb\": 0.3761119842529297,         \"Time in s\": 679.16585       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6630320821975257,         \"F1\": 0.6303197607545433,         \"Memory in Mb\": 0.4315700531005859,         \"Time in s\": 710.3587739999999       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6670769230769231,         \"F1\": 0.6330544879041374,         \"Memory in Mb\": 0.4394893646240234,         \"Time in s\": 742.192598       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6707488456133307,         \"F1\": 0.6378091872791519,         \"Memory in Mb\": 0.4457454681396484,         \"Time in s\": 774.746403       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6734814232356988,         \"F1\": 0.6407094959982694,         \"Memory in Mb\": 0.4518642425537109,         \"Time in s\": 807.9427459999999       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.674369343346813,         \"F1\": 0.6412051771695311,         \"Memory in Mb\": 0.4518413543701172,         \"Time in s\": 841.965614       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6778637478769579,         \"F1\": 0.64504054897068,         \"Memory in Mb\": 0.4531536102294922,         \"Time in s\": 876.7139659999999       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9337016574585636,         \"F1\": 0.933184855233853,         \"Memory in Mb\": 1.423478126525879,         \"Time in s\": 13.145088       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9491993373826616,         \"F1\": 0.937837837837838,         \"Memory in Mb\": 2.051041603088379,         \"Time in s\": 36.742593       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9385351490614648,         \"F1\": 0.9243316719528772,         \"Memory in Mb\": 2.3655481338500977,         \"Time in s\": 75.07794200000001       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9359646701628483,         \"F1\": 0.920980926430518,         \"Memory in Mb\": 2.6522607803344727,         \"Time in s\": 124.641449       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9361890041951866,         \"F1\": 0.9185226952354102,         \"Memory in Mb\": 3.339066505432129,         \"Time in s\": 184.399421       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9332106715731372,         \"F1\": 0.914487632508834,         \"Memory in Mb\": 3.582810401916504,         \"Time in s\": 253.628197       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9309257214950324,         \"F1\": 0.9124350259896042,         \"Memory in Mb\": 3.74349308013916,         \"Time in s\": 332.298993       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9232785980405686,         \"F1\": 0.9024903542616626,         \"Memory in Mb\": 3.99596118927002,         \"Time in s\": 420.603134       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9207653624432725,         \"F1\": 0.9042962962962964,         \"Memory in Mb\": 4.062603950500488,         \"Time in s\": 517.241068       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9214041284910034,         \"F1\": 0.9072191816523326,         \"Memory in Mb\": 4.2443437576293945,         \"Time in s\": 621.271616       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9173105870546914,         \"F1\": 0.9037158214536104,         \"Memory in Mb\": 4.387467384338379,         \"Time in s\": 732.039898       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.916842976727072,         \"F1\": 0.9044195390145908,         \"Memory in Mb\": 4.416756629943848,         \"Time in s\": 849.200167       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9150887322747728,         \"F1\": 0.9024580569644948,         \"Memory in Mb\": 4.712822914123535,         \"Time in s\": 973.870605       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9128755026413308,         \"F1\": 0.9002077124537162,         \"Memory in Mb\": 5.243111610412598,         \"Time in s\": 1105.403187       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9123555817205092,         \"F1\": 0.900890405259216,         \"Memory in Mb\": 5.419106483459473,         \"Time in s\": 1243.898383       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9112107623318386,         \"F1\": 0.9002402914502752,         \"Memory in Mb\": 5.619416236877441,         \"Time in s\": 1388.762519       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9125381468735796,         \"F1\": 0.9014414282578476,         \"Memory in Mb\": 5.888123512268066,         \"Time in s\": 1539.2398159999998       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9096093702091128,         \"F1\": 0.8977808599167822,         \"Memory in Mb\": 6.072480201721191,         \"Time in s\": 1695.9498239999998       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9093708243769244,         \"F1\": 0.8958611481975968,         \"Memory in Mb\": 6.119706153869629,         \"Time in s\": 1858.647702       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9071140791434406,         \"F1\": 0.892972972972973,         \"Memory in Mb\": 6.420571327209473,         \"Time in s\": 2027.885605       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.907910643889619,         \"F1\": 0.8927784577723377,         \"Memory in Mb\": 6.732544898986816,         \"Time in s\": 2202.439499       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9079323666649942,         \"F1\": 0.8936540133294696,         \"Memory in Mb\": 6.836274147033691,         \"Time in s\": 2383.16175       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9073283102174018,         \"F1\": 0.8931673582295988,         \"Memory in Mb\": 7.145352363586426,         \"Time in s\": 2570.030805       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9069585613760752,         \"F1\": 0.8912424063222407,         \"Memory in Mb\": 7.368103981018066,         \"Time in s\": 2762.468029       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9053379840169544,         \"F1\": 0.8884611382790553,         \"Memory in Mb\": 7.513260841369629,         \"Time in s\": 2961.014701       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9031203566121844,         \"F1\": 0.885441767068273,         \"Memory in Mb\": 7.7879228591918945,         \"Time in s\": 3165.900418       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9015984628592452,         \"F1\": 0.8830361047669955,         \"Memory in Mb\": 7.954785346984863,         \"Time in s\": 3377.578704       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8990026412267907,         \"F1\": 0.8799775133514476,         \"Memory in Mb\": 8.00295352935791,         \"Time in s\": 3596.265534       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8993263045712329,         \"F1\": 0.8800942925789926,         \"Memory in Mb\": 8.124005317687988,         \"Time in s\": 3821.215918       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8986717686449097,         \"F1\": 0.8798324461122262,         \"Memory in Mb\": 8.133870124816895,         \"Time in s\": 4052.049016       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8958874844222895,         \"F1\": 0.8761436801084379,         \"Memory in Mb\": 8.60555362701416,         \"Time in s\": 4289.013778       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8951398709944466,         \"F1\": 0.8747011787981206,         \"Memory in Mb\": 8.944867134094238,         \"Time in s\": 4531.544758       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8927986085560424,         \"F1\": 0.8719485396939551,         \"Memory in Mb\": 9.235833168029783,         \"Time in s\": 4780.469894       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8921533616855502,         \"F1\": 0.8705882352941176,         \"Memory in Mb\": 9.317421913146973,         \"Time in s\": 5034.96976       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8903465892964143,         \"F1\": 0.8684499262229957,         \"Memory in Mb\": 9.565300941467283,         \"Time in s\": 5295.565511       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8890387858347386,         \"F1\": 0.867226767435888,         \"Memory in Mb\": 9.898663520812988,         \"Time in s\": 5561.886477999999       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8882789892902956,         \"F1\": 0.8666547979348406,         \"Memory in Mb\": 10.141366004943848,         \"Time in s\": 5833.648399       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8878496528887211,         \"F1\": 0.8660444783679699,         \"Memory in Mb\": 10.462204933166504,         \"Time in s\": 6110.893153       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8864800611326522,         \"F1\": 0.8639185750636134,         \"Memory in Mb\": 10.841256141662598,         \"Time in s\": 6393.894783       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8857584370429648,         \"F1\": 0.8622387861040862,         \"Memory in Mb\": 11.13858127593994,         \"Time in s\": 6682.129445       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8846412706959214,         \"F1\": 0.8604643589827087,         \"Memory in Mb\": 11.587563514709473,         \"Time in s\": 6975.521303       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883682426217445,         \"F1\": 0.8588377878420617,         \"Memory in Mb\": 12.028901100158691,         \"Time in s\": 7273.546291       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8819210924865878,         \"F1\": 0.8569117830036083,         \"Memory in Mb\": 12.1774263381958,         \"Time in s\": 7576.403824       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880741539773725,         \"F1\": 0.8567122792211707,         \"Memory in Mb\": 12.330445289611816,         \"Time in s\": 7883.760394       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880423851455763,         \"F1\": 0.8574603081781235,         \"Memory in Mb\": 12.583298683166504,         \"Time in s\": 8195.49812       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8811517696460708,         \"F1\": 0.8591977712710009,         \"Memory in Mb\": 12.884881019592283,         \"Time in s\": 8511.394385       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8815199267278834,         \"F1\": 0.8597403319525146,         \"Memory in Mb\": 13.200516700744627,         \"Time in s\": 8831.520838       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8809069377055212,         \"F1\": 0.8591399896646449,         \"Memory in Mb\": 13.322876930236816,         \"Time in s\": 9156.403803       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880476651724371,         \"F1\": 0.8583404527979496,         \"Memory in Mb\": 13.499638557434082,         \"Time in s\": 9485.877502       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805713150400671,         \"F1\": 0.8587024655244463,         \"Memory in Mb\": 13.542492866516112,         \"Time in s\": 9819.626928       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805808744013595,         \"F1\": 0.8586874200203704,         \"Memory in Mb\": 13.542401313781738,         \"Time in s\": 10153.705154       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6666666666666666,         \"F1\": 0.7142857142857143,         \"Memory in Mb\": 0.6517477035522461,         \"Time in s\": 0.344782       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7551020408163265,         \"F1\": 0.7391304347826088,         \"Memory in Mb\": 0.6519079208374023,         \"Time in s\": 1.052047       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7972972972972973,         \"F1\": 0.7945205479452055,         \"Memory in Mb\": 0.6519308090209961,         \"Time in s\": 2.04852       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.7999999999999999,         \"Memory in Mb\": 0.6519536972045898,         \"Time in s\": 3.481699       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8064516129032258,         \"F1\": 0.8000000000000002,         \"Memory in Mb\": 0.6519804000854492,         \"Time in s\": 5.345952       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.8211920529801323,         \"Memory in Mb\": 0.6519804000854492,         \"Time in s\": 7.607799       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8313253012048192,         \"Memory in Mb\": 0.6519804000854492,         \"Time in s\": 10.226605       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8341708542713567,         \"F1\": 0.8253968253968254,         \"Memory in Mb\": 0.6889629364013672,         \"Time in s\": 13.384675       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8173076923076923,         \"Memory in Mb\": 0.6891918182373047,         \"Time in s\": 16.995274       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8273092369477911,         \"F1\": 0.8154506437768241,         \"Memory in Mb\": 0.6892147064208984,         \"Time in s\": 21.029962       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8188976377952757,         \"Memory in Mb\": 0.6892833709716797,         \"Time in s\": 25.500591       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8394648829431438,         \"F1\": 0.823529411764706,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 30.459705       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845679012345679,         \"F1\": 0.8263888888888888,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 35.865097       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8510028653295129,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 41.618205       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8502673796791443,         \"F1\": 0.8260869565217391,         \"Memory in Mb\": 0.6892795562744141,         \"Time in s\": 47.877466       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.849624060150376,         \"F1\": 0.8235294117647061,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 54.483198       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561320754716981,         \"F1\": 0.8271954674220963,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 61.544972       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8530066815144766,         \"F1\": 0.8225806451612903,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 69.002264       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8523206751054853,         \"F1\": 0.8241206030150755,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 77.051638       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8557114228456913,         \"F1\": 0.8317757009345793,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 85.50066       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8530534351145038,         \"F1\": 0.8253968253968255,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 94.362743       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8579234972677595,         \"F1\": 0.832618025751073,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 103.688841       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8588850174216028,         \"F1\": 0.8336755646817249,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 113.608321       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8631051752921536,         \"F1\": 0.8360000000000001,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 123.905415       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8621794871794872,         \"F1\": 0.83203125,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 134.725616       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8659476117103235,         \"F1\": 0.8391866913123845,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 146.008333       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8679525222551929,         \"F1\": 0.8446771378708552,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 157.728693       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8726752503576538,         \"F1\": 0.848381601362862,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 169.816185       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8756906077348067,         \"F1\": 0.8543689320388349,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 182.229315       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.87716955941255,         \"F1\": 0.8566978193146417,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 195.113196       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8785529715762274,         \"F1\": 0.8575757575757577,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 208.501766       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8785982478097623,         \"F1\": 0.8592162554426704,         \"Memory in Mb\": 0.7275295257568359,         \"Time in s\": 222.496546       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798543689320388,         \"F1\": 0.8619246861924686,         \"Memory in Mb\": 0.7627391815185547,         \"Time in s\": 236.948041       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798586572438163,         \"F1\": 0.8614130434782608,         \"Memory in Mb\": 0.7627849578857422,         \"Time in s\": 251.813013       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8787185354691075,         \"F1\": 0.8594164456233422,         \"Memory in Mb\": 0.7628536224365234,         \"Time in s\": 267.105354       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8787541713014461,         \"F1\": 0.8589909443725743,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 282.965748       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8809523809523809,         \"F1\": 0.8628428927680798,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 299.167544       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798735511064278,         \"F1\": 0.8629807692307693,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 315.934336       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8819301848049281,         \"F1\": 0.8651817116060961,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 333.021735       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8828828828828829,         \"F1\": 0.8662857142857143,         \"Memory in Mb\": 0.7645549774169922,         \"Time in s\": 350.749086       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8828125,         \"F1\": 0.8666666666666666,         \"Memory in Mb\": 0.8362636566162109,         \"Time in s\": 368.847305       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8846520495710201,         \"F1\": 0.8691891891891892,         \"Memory in Mb\": 0.8363094329833984,         \"Time in s\": 387.397742       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8836126629422719,         \"F1\": 0.8691099476439791,         \"Memory in Mb\": 0.8378963470458984,         \"Time in s\": 406.476047       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844404003639672,         \"F1\": 0.8702757916241062,         \"Memory in Mb\": 0.8380107879638672,         \"Time in s\": 425.926155       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8861209964412812,         \"F1\": 0.8732673267326733,         \"Memory in Mb\": 0.8731288909912109,         \"Time in s\": 445.763528       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8842471714534378,         \"F1\": 0.8707482993197277,         \"Memory in Mb\": 0.8731517791748047,         \"Time in s\": 466.112685       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8816013628620102,         \"F1\": 0.8677450047573739,         \"Memory in Mb\": 0.8732662200927734,         \"Time in s\": 487.01292       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798999165971643,         \"F1\": 0.8654205607476635,         \"Memory in Mb\": 0.8732662200927734,         \"Time in s\": 508.389566       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880718954248366,         \"F1\": 0.8660550458715598,         \"Memory in Mb\": 0.8732891082763672,         \"Time in s\": 530.180451       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8783026421136909,         \"F1\": 0.8635547576301617,         \"Memory in Mb\": 0.8733119964599609,         \"Time in s\": 552.608585       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.14459228515625,         \"Time in s\": 4.671696       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446609497070312,         \"Time in s\": 14.150102       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446151733398437,         \"Time in s\": 28.360088       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446380615234375,         \"Time in s\": 47.155736000000005       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446380615234375,         \"Time in s\": 70.60316700000001       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446151733398437,         \"Time in s\": 98.660415       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.144683837890625,         \"Time in s\": 131.464682       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996715496288512,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.3174581527709961,         \"Time in s\": 185.699411       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997080462454748,         \"F1\": 0.8,         \"Memory in Mb\": 0.3083944320678711,         \"Time in s\": 248.358611       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372431551842,         \"F1\": 0.8,         \"Memory in Mb\": 0.3005514144897461,         \"Time in s\": 315.58373       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997611312822472,         \"F1\": 0.8,         \"Memory in Mb\": 0.2926855087280273,         \"Time in s\": 387.343573       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997810378804468,         \"F1\": 0.8,         \"Memory in Mb\": 0.2926855087280273,         \"Time in s\": 463.526447       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997978818012774,         \"F1\": 0.8,         \"Memory in Mb\": 0.2926855087280273,         \"Time in s\": 544.0157879999999       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998123193573816,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579168319702148,         \"Time in s\": 629.1407149999999       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248318385652,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579168319702148,         \"Time in s\": 718.5008859999999       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998357802082308,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579168319702148,         \"Time in s\": 812.0251739999999       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998454404945905,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579626083374023,         \"Time in s\": 909.687376       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998540273844628,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579854965209961,         \"Time in s\": 1011.417335       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999861710366191,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1116.7962549999995       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686250295594,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1225.6397989999998       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998748811370802,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1337.9609139999998       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998805684939688,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1453.6581889999998       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998857612867847,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1572.7472669999995       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998905213373912,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1695.2891979999995       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998738806911338,         \"F1\": 0.7857142857142857,         \"Memory in Mb\": 0.3903570175170898,         \"Time in s\": 1822.117106       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998787315318228,         \"F1\": 0.7857142857142857,         \"Memory in Mb\": 0.3928442001342773,         \"Time in s\": 1952.439332       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998637602179836,         \"F1\": 0.787878787878788,         \"Memory in Mb\": 0.4802007675170898,         \"Time in s\": 2088.556257       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686260158024,         \"F1\": 0.787878787878788,         \"Memory in Mb\": 0.4802465438842773,         \"Time in s\": 2228.050331       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998550356974596,         \"F1\": 0.7647058823529411,         \"Memory in Mb\": 0.5258626937866211,         \"Time in s\": 2371.062704       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999281823118289,         \"F1\": 0.4383561643835616,         \"Memory in Mb\": 0.8453359603881836,         \"Time in s\": 2524.551865       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993049905071876,         \"F1\": 0.4383561643835616,         \"Memory in Mb\": 0.8887395858764648,         \"Time in s\": 2681.973264       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993267099105017,         \"F1\": 0.4383561643835616,         \"Memory in Mb\": 0.8967199325561523,         \"Time in s\": 2843.114587       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993152648173508,         \"F1\": 0.4266666666666667,         \"Memory in Mb\": 1.0689306259155271,         \"Time in s\": 3009.18915       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993354043986956,         \"F1\": 0.4266666666666667,         \"Memory in Mb\": 1.0783147811889648,         \"Time in s\": 3178.956821       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993393790162752,         \"F1\": 0.4210526315789473,         \"Memory in Mb\": 1.0915288925170898,         \"Time in s\": 3352.423608       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993577298670208,         \"F1\": 0.45,         \"Memory in Mb\": 1.0735387802124023,         \"Time in s\": 3530.715962       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993750887658004,         \"F1\": 0.45,         \"Memory in Mb\": 1.0788640975952148,         \"Time in s\": 3712.739099       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993915340256938,         \"F1\": 0.45,         \"Memory in Mb\": 1.0906057357788086,         \"Time in s\": 3898.148799       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994071359275628,         \"F1\": 0.45,         \"Memory in Mb\": 1.0906057357788086,         \"Time in s\": 4086.957815       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99942195772409,         \"F1\": 0.45,         \"Memory in Mb\": 1.090651512145996,         \"Time in s\": 4279.139143       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994232395990874,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1481237411499023,         \"Time in s\": 4474.636732       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994369721614011,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1493444442749023,         \"Time in s\": 4673.509203       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999450065992081,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1612462997436523,         \"Time in s\": 4875.758715       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994625646415306,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.161269187927246,         \"Time in s\": 5081.435613       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745077889624,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1612234115600586,         \"Time in s\": 5290.523743       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994859316631824,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1584348678588867,         \"Time in s\": 5502.994331999999       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999463327370304,         \"F1\": 0.4285714285714285,         \"Memory in Mb\": 1.2966947555541992,         \"Time in s\": 5719.643149       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745081724928,         \"F1\": 0.4285714285714285,         \"Memory in Mb\": 1.3124494552612305,         \"Time in s\": 5939.715090999999       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994316110074428,         \"F1\": 0.4044943820224719,         \"Memory in Mb\": 1.3362340927124023,         \"Time in s\": 6163.415711999999       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994429789067673,         \"F1\": 0.4044943820224719,         \"Memory in Mb\": 1.3363256454467771,         \"Time in s\": 6390.433574999999       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994430140297408,         \"F1\": 0.4044943820224719,         \"Memory in Mb\": 1.3363256454467771,         \"Time in s\": 6617.502892999999       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.4857142857142857,         \"F1\": 0.4599999999999999,         \"Memory in Mb\": 0.2237319946289062,         \"Time in s\": 0.813651       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165876777251185,         \"F1\": 0.4574468085106383,         \"Memory in Mb\": 0.2245254516601562,         \"Time in s\": 2.392298       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5205047318611987,         \"F1\": 0.4722222222222222,         \"Memory in Mb\": 0.2251434326171875,         \"Time in s\": 4.879886       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460992907801419,         \"F1\": 0.4838709677419355,         \"Memory in Mb\": 0.225250244140625,         \"Time in s\": 8.257922       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.55765595463138,         \"F1\": 0.455813953488372,         \"Memory in Mb\": 0.2252731323242187,         \"Time in s\": 12.416081000000002       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5543307086614173,         \"F1\": 0.4259634888438134,         \"Memory in Mb\": 0.2257461547851562,         \"Time in s\": 17.551695000000002       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5748987854251012,         \"F1\": 0.4220183486238532,         \"Memory in Mb\": 0.2259750366210937,         \"Time in s\": 23.418389       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5785123966942148,         \"F1\": 0.4232633279483037,         \"Memory in Mb\": 0.2259063720703125,         \"Time in s\": 30.181971       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5844700944386149,         \"F1\": 0.4193548387096774,         \"Memory in Mb\": 0.2258834838867187,         \"Time in s\": 37.806045       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5920679886685553,         \"F1\": 0.4146341463414634,         \"Memory in Mb\": 0.2256393432617187,         \"Time in s\": 46.336236       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.590557939914163,         \"F1\": 0.4015056461731493,         \"Memory in Mb\": 0.225738525390625,         \"Time in s\": 55.794626       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971675845790716,         \"F1\": 0.4101382488479262,         \"Memory in Mb\": 0.226043701171875,         \"Time in s\": 66.093431       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599128540305011,         \"F1\": 0.3973799126637554,         \"Memory in Mb\": 0.226348876953125,         \"Time in s\": 77.304266       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5994605529332434,         \"F1\": 0.3926380368098159,         \"Memory in Mb\": 0.2263031005859375,         \"Time in s\": 89.41731899999999       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5997482693517936,         \"F1\": 0.3896353166986563,         \"Memory in Mb\": 0.2262802124023437,         \"Time in s\": 102.388462       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6011799410029498,         \"F1\": 0.3876811594202898,         \"Memory in Mb\": 0.2263412475585937,         \"Time in s\": 116.266249       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6013325930038868,         \"F1\": 0.3904923599320882,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 130.90050499999998       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030414263240692,         \"F1\": 0.396812749003984,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 146.406164       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986090412319921,         \"F1\": 0.3961136023916292,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 162.81699799999998       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5969797074091553,         \"F1\": 0.3994374120956399,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 180.02605599999998       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597752808988764,         \"F1\": 0.4013377926421405,         \"Memory in Mb\": 0.226318359375,         \"Time in s\": 198.114263       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5988845988845989,         \"F1\": 0.4033184428844926,         \"Memory in Mb\": 0.22637939453125,         \"Time in s\": 217.043548       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5995075913007797,         \"F1\": 0.4019607843137255,         \"Memory in Mb\": 0.2264022827148437,         \"Time in s\": 236.778245       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008651199370821,         \"F1\": 0.4088526499708794,         \"Memory in Mb\": 0.2267684936523437,         \"Time in s\": 257.426783       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6002265005662514,         \"F1\": 0.4073866815892558,         \"Memory in Mb\": 0.2269744873046875,         \"Time in s\": 278.871455       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5985480943738657,         \"F1\": 0.4028077753779697,         \"Memory in Mb\": 0.2269744873046875,         \"Time in s\": 301.18545300000005       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599790283117791,         \"F1\": 0.4051948051948052,         \"Memory in Mb\": 0.2269744873046875,         \"Time in s\": 324.33878000000004       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599932591843613,         \"F1\": 0.4026170105686965,         \"Memory in Mb\": 0.2269973754882812,         \"Time in s\": 348.42370100000005       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5977871786527823,         \"F1\": 0.4023210831721469,         \"Memory in Mb\": 0.2269973754882812,         \"Time in s\": 373.346007       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986159169550173,         \"F1\": 0.4042950513538749,         \"Memory in Mb\": 0.2269973754882812,         \"Time in s\": 399.176002       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5981735159817352,         \"F1\": 0.4021739130434782,         \"Memory in Mb\": 0.2248907089233398,         \"Time in s\": 425.805579       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5959893836626364,         \"F1\": 0.4022687609075043,         \"Memory in Mb\": 0.2988729476928711,         \"Time in s\": 453.430877       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597369173577352,         \"F1\": 0.4023769100169779,         \"Memory in Mb\": 0.3531064987182617,         \"Time in s\": 482.040674       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008881487649181,         \"F1\": 0.4087171052631579,         \"Memory in Mb\": 0.3826017379760742,         \"Time in s\": 511.8008850000001       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6012402264761392,         \"F1\": 0.4086365453818472,         \"Memory in Mb\": 0.4367246627807617,         \"Time in s\": 542.835536       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6023591087811271,         \"F1\": 0.4104158569762923,         \"Memory in Mb\": 0.4704160690307617,         \"Time in s\": 575.071901       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6052027543993879,         \"F1\": 0.4145234493192133,         \"Memory in Mb\": 0.5176496505737305,         \"Time in s\": 608.725741       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608393344921778,         \"F1\": 0.4195804195804196,         \"Memory in Mb\": 0.5480222702026367,         \"Time in s\": 643.745138       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6121461408178079,         \"F1\": 0.4260651629072682,         \"Memory in Mb\": 0.5632429122924805,         \"Time in s\": 680.30634       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6157112526539278,         \"F1\": 0.4329968673860076,         \"Memory in Mb\": 0.5676107406616211,         \"Time in s\": 718.216367       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6193325661680092,         \"F1\": 0.438560760353021,         \"Memory in Mb\": 0.5822668075561523,         \"Time in s\": 757.397991       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6218827229835991,         \"F1\": 0.4421610871726881,         \"Memory in Mb\": 0.5884695053100586,         \"Time in s\": 797.943115       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6219003730524468,         \"F1\": 0.4429356611703847,         \"Memory in Mb\": 0.6275625228881836,         \"Time in s\": 839.803567       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.623203945957538,         \"F1\": 0.4455664247396655,         \"Memory in Mb\": 0.6328649520874023,         \"Time in s\": 883.024854       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6250786328370728,         \"F1\": 0.446096654275093,         \"Memory in Mb\": 0.6821584701538086,         \"Time in s\": 927.682473       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6266666666666667,         \"F1\": 0.4468085106382978,         \"Memory in Mb\": 0.6950826644897461,         \"Time in s\": 973.720669       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.629592451314997,         \"F1\": 0.4530091906314853,         \"Memory in Mb\": 0.7119512557983398,         \"Time in s\": 1021.080455       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6298407705917043,         \"F1\": 0.4527753560011624,         \"Memory in Mb\": 0.6960439682006836,         \"Time in s\": 1069.7402539999998       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6321971885230118,         \"F1\": 0.456459874786568,         \"Memory in Mb\": 0.6964941024780273,         \"Time in s\": 1119.6924109999998       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6340819022457067,         \"F1\": 0.4594368553108447,         \"Memory in Mb\": 0.7031240463256836,         \"Time in s\": 1170.8531239999998       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8629834254143647,         \"F1\": 0.8663793103448276,         \"Memory in Mb\": 1.7490100860595703,         \"Time in s\": 16.056337       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8890115958034235,         \"F1\": 0.8680236375574525,         \"Memory in Mb\": 2.496591567993164,         \"Time in s\": 50.652304       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.87523003312477,         \"F1\": 0.8521587440034889,         \"Memory in Mb\": 1.8562908172607424,         \"Time in s\": 106.697102       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8868341153739995,         \"F1\": 0.8653972422849641,         \"Memory in Mb\": 2.5584278106689453,         \"Time in s\": 176.150463       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8880547582247736,         \"F1\": 0.8593619972260749,         \"Memory in Mb\": 3.1707210540771484,         \"Time in s\": 258.677392       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8829806807727691,         \"F1\": 0.8518863530507685,         \"Memory in Mb\": 2.113290786743164,         \"Time in s\": 353.604927       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8814067181832519,         \"F1\": 0.8497802636835796,         \"Memory in Mb\": 2.472631454467773,         \"Time in s\": 459.993548       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883262039464606,         \"F1\": 0.8516310066643283,         \"Memory in Mb\": 2.354246139526367,         \"Time in s\": 576.649537       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8828652029927634,         \"F1\": 0.8585394756332394,         \"Memory in Mb\": 2.1453304290771484,         \"Time in s\": 702.348431       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8839827795562424,         \"F1\": 0.8639129871811472,         \"Memory in Mb\": 2.1982364654541016,         \"Time in s\": 836.637311       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880983442047165,         \"F1\": 0.8635840809753854,         \"Memory in Mb\": 2.4484920501708984,         \"Time in s\": 979.832141       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.881151687977187,         \"F1\": 0.8654446990210373,         \"Memory in Mb\": 2.578580856323242,         \"Time in s\": 1131.438926       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8799354674365288,         \"F1\": 0.8634344214796214,         \"Memory in Mb\": 2.730459213256836,         \"Time in s\": 1291.261447       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8768430182133564,         \"F1\": 0.8601361031518624,         \"Memory in Mb\": 2.090116500854492,         \"Time in s\": 1459.3451100000002       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8789462064905438,         \"F1\": 0.8639483913654784,         \"Memory in Mb\": 1.877275466918945,         \"Time in s\": 1635.065473       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.878854777509486,         \"F1\": 0.86444341516134,         \"Memory in Mb\": 2.105062484741211,         \"Time in s\": 1818.351758       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8775404194532822,         \"F1\": 0.86187197890728,         \"Memory in Mb\": 2.440736770629883,         \"Time in s\": 2009.388651       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8765560802109523,         \"F1\": 0.8599262403451395,         \"Memory in Mb\": 2.627225875854492,         \"Time in s\": 2209.910977       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8758496485214663,         \"F1\": 0.8567214213878646,         \"Memory in Mb\": 2.5119991302490234,         \"Time in s\": 2419.733571       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8760969148407749,         \"F1\": 0.8567600331780769,         \"Memory in Mb\": 2.716485977172852,         \"Time in s\": 2638.279319       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8772141918528252,         \"F1\": 0.8562284588872477,         \"Memory in Mb\": 3.019651412963867,         \"Time in s\": 2865.7383750000004       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8739651798705534,         \"F1\": 0.8535106134826219,         \"Memory in Mb\": 2.721925735473633,         \"Time in s\": 3103.6766270000003       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8716225944233815,         \"F1\": 0.8503663925714606,         \"Memory in Mb\": 2.4018421173095703,         \"Time in s\": 3351.3122810000004       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.872556684910086,         \"F1\": 0.8492300995701616,         \"Memory in Mb\": 2.248655319213867,         \"Time in s\": 3607.2754260000006       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.870722769217184,         \"F1\": 0.845275840202917,         \"Memory in Mb\": 2.611169815063477,         \"Time in s\": 3871.072358000001       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8645722776480578,         \"F1\": 0.8365611230658879,         \"Memory in Mb\": 1.8957767486572263,         \"Time in s\": 4144.250301000001       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8614120436613385,         \"F1\": 0.8315276811450154,         \"Memory in Mb\": 1.5607776641845703,         \"Time in s\": 4424.237452000001       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8560334292584855,         \"F1\": 0.8249113050148624,         \"Memory in Mb\": 1.3715801239013672,         \"Time in s\": 4711.1951020000015       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8558596277547292,         \"F1\": 0.824277295717136,         \"Memory in Mb\": 1.611249923706055,         \"Time in s\": 5004.571280000002       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8564332756907906,         \"F1\": 0.8258035714285713,         \"Memory in Mb\": 2.025979995727539,         \"Time in s\": 5304.465246000002       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8535517179989318,         \"F1\": 0.8215385950449082,         \"Memory in Mb\": 1.848848342895508,         \"Time in s\": 5611.580754000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8515746266082578,         \"F1\": 0.8178624338624338,         \"Memory in Mb\": 2.0671520233154297,         \"Time in s\": 5927.3319900000015       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.849048399504967,         \"F1\": 0.8140885684860969,         \"Memory in Mb\": 1.3224430084228516,         \"Time in s\": 6250.652578000001       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8473849949680226,         \"F1\": 0.8106344410876132,         \"Memory in Mb\": 1.549489974975586,         \"Time in s\": 6580.126329000001       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8429783342268756,         \"F1\": 0.8039377830281552,         \"Memory in Mb\": 1.5209712982177734,         \"Time in s\": 6916.182134000001       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8411773723746743,         \"F1\": 0.8020785572367416,         \"Memory in Mb\": 1.995222091674805,         \"Time in s\": 7258.669481000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8415023418155783,         \"F1\": 0.8033751526590429,         \"Memory in Mb\": 1.8286800384521484,         \"Time in s\": 7608.320925000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.839689778371627,         \"F1\": 0.8006357692446627,         \"Memory in Mb\": 2.242650985717773,         \"Time in s\": 7966.087012000001       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8395550901423598,         \"F1\": 0.7993487417265422,         \"Memory in Mb\": 2.1107349395751958,         \"Time in s\": 8331.986249000001       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400618118601507,         \"F1\": 0.7984280447937677,         \"Memory in Mb\": 1.8943347930908203,         \"Time in s\": 8703.460619000001       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.839278503163279,         \"F1\": 0.796356938190749,         \"Memory in Mb\": 1.3389415740966797,         \"Time in s\": 9080.992051       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8389267036345956,         \"F1\": 0.7946940006029546,         \"Memory in Mb\": 1.607133865356445,         \"Time in s\": 9463.837927       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8382832353620658,         \"F1\": 0.7942655607079877,         \"Memory in Mb\": 1.8687000274658203,         \"Time in s\": 9853.704286       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8387477109098663,         \"F1\": 0.7967495098969203,         \"Memory in Mb\": 1.466756820678711,         \"Time in s\": 10249.501094       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400009811376291,         \"F1\": 0.8001225677953119,         \"Memory in Mb\": 2.0175647735595703,         \"Time in s\": 10651.197686       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8407918416316736,         \"F1\": 0.8026413635146792,         \"Memory in Mb\": 2.1117191314697266,         \"Time in s\": 11058.632461       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8411732932528593,         \"F1\": 0.8035781708344224,         \"Memory in Mb\": 2.033967971801758,         \"Time in s\": 11470.822587       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8416538275806563,         \"F1\": 0.804308286915994,         \"Memory in Mb\": 1.7070560455322266,         \"Time in s\": 11887.700533       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8406280269411844,         \"F1\": 0.8019483246087955,         \"Memory in Mb\": 2.28169059753418,         \"Time in s\": 12309.209906       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404379787633282,         \"F1\": 0.802124397722295,         \"Memory in Mb\": 2.2888126373291016,         \"Time in s\": 12736.77001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404360971949416,         \"F1\": 0.8020804817957842,         \"Memory in Mb\": 2.2889575958251958,         \"Time in s\": 13164.474026       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7083333333333334,         \"F1\": 0.7407407407407408,         \"Memory in Mb\": 0.7072525024414062,         \"Time in s\": 0.45657       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8163265306122449,         \"F1\": 0.8085106382978724,         \"Memory in Mb\": 0.7079315185546875,         \"Time in s\": 1.426682       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8513513513513513,         \"F1\": 0.8493150684931507,         \"Memory in Mb\": 0.708251953125,         \"Time in s\": 2.873238       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8585858585858586,         \"F1\": 0.8541666666666666,         \"Memory in Mb\": 0.70849609375,         \"Time in s\": 4.790442       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8548387096774194,         \"F1\": 0.85,         \"Memory in Mb\": 0.70849609375,         \"Time in s\": 7.239611999999999       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8523489932885906,         \"F1\": 0.8533333333333335,         \"Memory in Mb\": 0.708740234375,         \"Time in s\": 10.202642999999998       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8620689655172413,         \"F1\": 0.8536585365853658,         \"Memory in Mb\": 0.7091293334960938,         \"Time in s\": 13.59528       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8592964824120602,         \"F1\": 0.8510638297872339,         \"Memory in Mb\": 0.7092666625976562,         \"Time in s\": 17.527801       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8405797101449276,         \"Memory in Mb\": 0.7491827011108398,         \"Time in s\": 22.029145       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8473895582329317,         \"F1\": 0.8347826086956521,         \"Memory in Mb\": 0.7771825790405273,         \"Time in s\": 27.026807       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467153284671532,         \"F1\": 0.8333333333333335,         \"Memory in Mb\": 0.7774114608764648,         \"Time in s\": 32.501577       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8528428093645485,         \"F1\": 0.837037037037037,         \"Memory in Mb\": 0.7775945663452148,         \"Time in s\": 38.42215899999999       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8611111111111112,         \"F1\": 0.8421052631578947,         \"Memory in Mb\": 0.7779607772827148,         \"Time in s\": 44.92146699999999       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8653295128939829,         \"F1\": 0.8438538205980067,         \"Memory in Mb\": 0.7781057357788086,         \"Time in s\": 51.897795       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663101604278075,         \"F1\": 0.8427672955974843,         \"Memory in Mb\": 0.8172750473022461,         \"Time in s\": 59.36314       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671679197994987,         \"F1\": 0.8417910447761194,         \"Memory in Mb\": 0.8571996688842773,         \"Time in s\": 67.416022       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8679245283018868,         \"F1\": 0.839080459770115,         \"Memory in Mb\": 0.9128484725952148,         \"Time in s\": 76.017673       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8708240534521158,         \"F1\": 0.8406593406593408,         \"Memory in Mb\": 0.913100242614746,         \"Time in s\": 85.092057       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.869198312236287,         \"F1\": 0.8402061855670103,         \"Memory in Mb\": 0.9133520126342772,         \"Time in s\": 94.603797       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8677354709418837,         \"F1\": 0.8413461538461539,         \"Memory in Mb\": 0.9135580062866212,         \"Time in s\": 104.609638       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8683206106870229,         \"F1\": 0.8384074941451991,         \"Memory in Mb\": 0.9136190414428712,         \"Time in s\": 115.080656       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8670309653916212,         \"F1\": 0.8381374722838136,         \"Memory in Mb\": 0.9137258529663086,         \"Time in s\": 126.050962       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867595818815331,         \"F1\": 0.8382978723404255,         \"Memory in Mb\": 0.9137868881225586,         \"Time in s\": 137.397676       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8697829716193656,         \"F1\": 0.8381742738589212,         \"Memory in Mb\": 0.9139089584350586,         \"Time in s\": 149.31562       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8717948717948718,         \"F1\": 0.8373983739837398,         \"Memory in Mb\": 0.9536046981811525,         \"Time in s\": 161.695664       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8767334360554699,         \"F1\": 0.846153846153846,         \"Memory in Mb\": 0.9540624618530272,         \"Time in s\": 174.565593       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8753709198813057,         \"F1\": 0.8478260869565216,         \"Memory in Mb\": 0.9818639755249025,         \"Time in s\": 187.898512       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798283261802575,         \"F1\": 0.8515901060070671,         \"Memory in Mb\": 0.9230222702026368,         \"Time in s\": 201.735875       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8825966850828729,         \"F1\": 0.8576214405360134,         \"Memory in Mb\": 1.021204948425293,         \"Time in s\": 216.085388       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8865153538050734,         \"F1\": 0.8631239935587761,         \"Memory in Mb\": 1.0604047775268557,         \"Time in s\": 230.90612       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8875968992248062,         \"F1\": 0.863849765258216,         \"Memory in Mb\": 1.1157331466674805,         \"Time in s\": 246.307883       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8873591989987485,         \"F1\": 0.8652694610778443,         \"Memory in Mb\": 1.2215375900268557,         \"Time in s\": 262.241262       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8871359223300971,         \"F1\": 0.8661870503597122,         \"Memory in Mb\": 1.2229490280151367,         \"Time in s\": 278.553882       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8881036513545347,         \"F1\": 0.8671328671328671,         \"Memory in Mb\": 1.235407829284668,         \"Time in s\": 295.406724       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901601830663616,         \"F1\": 0.8688524590163934,         \"Memory in Mb\": 1.263422966003418,         \"Time in s\": 312.749904       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8887652947719689,         \"F1\": 0.8670212765957446,         \"Memory in Mb\": 1.318751335144043,         \"Time in s\": 330.632511       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896103896103896,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 1.318964958190918,         \"Time in s\": 348.945693       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8893572181243414,         \"F1\": 0.8708487084870848,         \"Memory in Mb\": 1.3194990158081057,         \"Time in s\": 367.701988       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901437371663244,         \"F1\": 0.8718562874251498,         \"Memory in Mb\": 1.319605827331543,         \"Time in s\": 386.96933700000005       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878878878878879,         \"F1\": 0.8697674418604652,         \"Memory in Mb\": 1.3197660446166992,         \"Time in s\": 406.7503420000001       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876953125,         \"F1\": 0.8700564971751412,         \"Memory in Mb\": 1.3200559616088867,         \"Time in s\": 426.99481600000007       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8894184938036225,         \"F1\": 0.8725274725274725,         \"Memory in Mb\": 1.320155143737793,         \"Time in s\": 447.8295280000001       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901303538175046,         \"F1\": 0.8742004264392325,         \"Memory in Mb\": 1.320277214050293,         \"Time in s\": 469.1965740000001       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89171974522293,         \"F1\": 0.8761706555671176,         \"Memory in Mb\": 1.320643424987793,         \"Time in s\": 491.1150510000001       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932384341637011,         \"F1\": 0.8790322580645162,         \"Memory in Mb\": 1.320704460144043,         \"Time in s\": 513.5552500000001       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8938207136640557,         \"F1\": 0.8794466403162056,         \"Memory in Mb\": 1.320704460144043,         \"Time in s\": 536.4428780000001       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8926746166950597,         \"F1\": 0.877906976744186,         \"Memory in Mb\": 1.320765495300293,         \"Time in s\": 559.8515450000001       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932443703085905,         \"F1\": 0.8783269961977186,         \"Memory in Mb\": 1.3328428268432615,         \"Time in s\": 583.8125340000001       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929738562091504,         \"F1\": 0.8779123951537745,         \"Memory in Mb\": 1.3880414962768557,         \"Time in s\": 608.2234330000001       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8935148118494796,         \"F1\": 0.8792007266121706,         \"Memory in Mb\": 1.3882551193237305,         \"Time in s\": 633.1359570000001       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2038736343383789,         \"Time in s\": 10.878823       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2044839859008789,         \"Time in s\": 32.501535000000004       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2050256729125976,         \"Time in s\": 64.818606       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2050485610961914,         \"Time in s\": 107.076722       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2050485610961914,         \"Time in s\": 158.807432       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2056589126586914,         \"Time in s\": 218.4459       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2056818008422851,         \"Time in s\": 285.193275       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.2613000869750976,         \"Time in s\": 359.039643       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.2063913345336914,         \"Time in s\": 440.617697       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.2062082290649414,         \"Time in s\": 529.79805       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.2068414688110351,         \"Time in s\": 626.4074929999999       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069330215454101,         \"Time in s\": 729.8228539999999       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070703506469726,         \"Time in s\": 839.3276679999999       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.2067499160766601,         \"Time in s\": 954.865641       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069787979125976,         \"Time in s\": 1076.4115539999998       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.2068643569946289,         \"Time in s\": 1203.4939569999997       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070016860961914,         \"Time in s\": 1337.1471509999997       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069101333618164,         \"Time in s\": 1476.3596139999995       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069101333618164,         \"Time in s\": 1621.0863639999998       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069330215454101,         \"Time in s\": 1771.0710339999998       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.2067956924438476,         \"Time in s\": 1926.306088       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.2074975967407226,         \"Time in s\": 2086.761849       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069787979125976,         \"Time in s\": 2252.424258       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.2072610855102539,         \"Time in s\": 2423.176177       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.2072534561157226,         \"Time in s\": 2599.130792       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.1954126358032226,         \"Time in s\": 2780.435567       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073373794555664,         \"Time in s\": 2966.651736       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073373794555664,         \"Time in s\": 3157.874809       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073526382446289,         \"Time in s\": 3353.943675       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070703506469726,         \"Time in s\": 3555.07983       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070398330688476,         \"Time in s\": 3761.219357       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070322036743164,         \"Time in s\": 3972.252634       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.2071924209594726,         \"Time in s\": 4188.261246       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073450088500976,         \"Time in s\": 4409.191759       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073144912719726,         \"Time in s\": 4634.878548000001       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073602676391601,         \"Time in s\": 4864.934707       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.2072000503540039,         \"Time in s\": 5099.2875650000005       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073602676391601,         \"Time in s\": 5337.886074000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073602676391601,         \"Time in s\": 5580.709613000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.2074670791625976,         \"Time in s\": 5827.7179830000005       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.2074670791625976,         \"Time in s\": 6079.015463000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.2071466445922851,         \"Time in s\": 6334.716663000001       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.2075586318969726,         \"Time in s\": 6594.692589000001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079553604125976,         \"Time in s\": 6858.666542000001       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.2080392837524414,         \"Time in s\": 7126.604303000001       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.2078561782836914,         \"Time in s\": 7398.595214000001       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079019546508789,         \"Time in s\": 7674.616155000001       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.2078104019165039,         \"Time in s\": 7954.656032000001       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.1961145401000976,         \"Time in s\": 8238.690655       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079477310180664,         \"Time in s\": 8526.751857000001       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079706192016601,         \"Time in s\": 8814.843001000001       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5142857142857142,         \"F1\": 0.4516129032258064,         \"Memory in Mb\": 0.1802501678466797,         \"Time in s\": 1.958268       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5402843601895735,         \"F1\": 0.4756756756756757,         \"Memory in Mb\": 0.1808605194091797,         \"Time in s\": 6.1304110000000005       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5394321766561514,         \"F1\": 0.4930555555555555,         \"Memory in Mb\": 0.1814937591552734,         \"Time in s\": 12.559627       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5531914893617021,         \"F1\": 0.4932975871313673,         \"Memory in Mb\": 0.1814708709716797,         \"Time in s\": 21.158430000000003       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5614366729678639,         \"F1\": 0.4703196347031963,         \"Memory in Mb\": 0.1814708709716797,         \"Time in s\": 31.954501       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5763779527559055,         \"F1\": 0.4836852207293666,         \"Memory in Mb\": 0.4110956192016601,         \"Time in s\": 45.100937       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5991902834008097,         \"F1\": 0.4940374787052811,         \"Memory in Mb\": 0.5197267532348633,         \"Time in s\": 60.647662       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6210153482880756,         \"F1\": 0.5201793721973094,         \"Memory in Mb\": 0.6145830154418945,         \"Time in s\": 78.646382       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6411332633788038,         \"F1\": 0.5464190981432361,         \"Memory in Mb\": 0.681065559387207,         \"Time in s\": 99.167462       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6515580736543909,         \"F1\": 0.555956678700361,         \"Memory in Mb\": 0.7228097915649414,         \"Time in s\": 122.156435       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6626609442060086,         \"F1\": 0.5732899022801302,         \"Memory in Mb\": 0.8111352920532227,         \"Time in s\": 147.677601       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6766325727773407,         \"F1\": 0.5958702064896755,         \"Memory in Mb\": 0.8519144058227539,         \"Time in s\": 175.66982000000002       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6877269426289034,         \"F1\": 0.6062271062271062,         \"Memory in Mb\": 0.9361848831176758,         \"Time in s\": 206.112203       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6999325691166555,         \"F1\": 0.6238377007607777,         \"Memory in Mb\": 0.978398323059082,         \"Time in s\": 239.08437400000005       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7073631214600378,         \"F1\": 0.6375681995323461,         \"Memory in Mb\": 1.0816278457641602,         \"Time in s\": 274.51056400000004       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7162241887905605,         \"F1\": 0.6496722505462491,         \"Memory in Mb\": 1.146012306213379,         \"Time in s\": 312.20930000000004       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7262631871182677,         \"F1\": 0.6662153012863914,         \"Memory in Mb\": 1.231095314025879,         \"Time in s\": 352.32891700000005       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7320398531725223,         \"F1\": 0.677602523659306,         \"Memory in Mb\": 1.3021745681762695,         \"Time in s\": 394.808348       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7391952309985097,         \"F1\": 0.6902654867256638,         \"Memory in Mb\": 1.3571443557739258,         \"Time in s\": 439.684188       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7456347333647947,         \"F1\": 0.7020453289110005,         \"Memory in Mb\": 1.439896583557129,         \"Time in s\": 486.825513       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.750561797752809,         \"F1\": 0.7080483955812729,         \"Memory in Mb\": 1.4615755081176758,         \"Time in s\": 536.150148       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7554697554697555,         \"F1\": 0.715,         \"Memory in Mb\": 1.4801912307739258,         \"Time in s\": 587.578093       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7599507591300779,         \"F1\": 0.7202295552367289,         \"Memory in Mb\": 1.5264062881469729,         \"Time in s\": 641.095023       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7624852536374361,         \"F1\": 0.7257039055404179,         \"Memory in Mb\": 1.5866899490356443,         \"Time in s\": 696.665992       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7678369195922989,         \"F1\": 0.7331887201735358,         \"Memory in Mb\": 1.6338167190551758,         \"Time in s\": 754.18395       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7731397459165155,         \"F1\": 0.7396917950853811,         \"Memory in Mb\": 1.718327522277832,         \"Time in s\": 813.521765       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.777350576721426,         \"F1\": 0.7440739252711932,         \"Memory in Mb\": 1.7761125564575195,         \"Time in s\": 874.768076       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7812605325244355,         \"F1\": 0.7479611650485437,         \"Memory in Mb\": 1.876938819885254,         \"Time in s\": 937.838653       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7845753335502766,         \"F1\": 0.7526158445440957,         \"Memory in Mb\": 1.974156379699707,         \"Time in s\": 1002.618853       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7892418999685435,         \"F1\": 0.7572463768115942,         \"Memory in Mb\": 2.007943153381348,         \"Time in s\": 1069.033932       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7923896499238965,         \"F1\": 0.7605337078651686,         \"Memory in Mb\": 2.0704050064086914,         \"Time in s\": 1137.092928       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7938661161899144,         \"F1\": 0.7636117686844774,         \"Memory in Mb\": 2.141594886779785,         \"Time in s\": 1206.880705       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7966828710323134,         \"F1\": 0.7657331136738056,         \"Memory in Mb\": 2.2472352981567383,         \"Time in s\": 1278.374701       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7998889814043852,         \"F1\": 0.7685393258426965,         \"Memory in Mb\": 2.2915468215942383,         \"Time in s\": 1351.505796       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8021029927204099,         \"F1\": 0.7717661691542288,         \"Memory in Mb\": 2.3504953384399414,         \"Time in s\": 1426.3168569999998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8055045871559633,         \"F1\": 0.7761013880506941,         \"Memory in Mb\": 2.397225379943848,         \"Time in s\": 1502.8248519999995       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8071920428462127,         \"F1\": 0.7776470588235294,         \"Memory in Mb\": 2.4447336196899414,         \"Time in s\": 1580.9903339999996       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8085423392103303,         \"F1\": 0.7788930312589618,         \"Memory in Mb\": 2.513848304748535,         \"Time in s\": 1660.8236829999996       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8107911928381321,         \"F1\": 0.7816862088218872,         \"Memory in Mb\": 2.6076173782348637,         \"Time in s\": 1742.3313809999995       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8136352913422977,         \"F1\": 0.7852093529091897,         \"Memory in Mb\": 2.653599739074707,         \"Time in s\": 1825.5059619999995       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8161104718066743,         \"F1\": 0.7881198621055423,         \"Memory in Mb\": 2.711110115051269,         \"Time in s\": 1910.383718       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8173444169849472,         \"F1\": 0.7894327894327894,         \"Memory in Mb\": 2.7411813735961914,         \"Time in s\": 1996.892937       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8183015141540487,         \"F1\": 0.7910146390711761,         \"Memory in Mb\": 2.7629594802856445,         \"Time in s\": 2085.0686209999994       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8205018228608192,         \"F1\": 0.7941971969510695,         \"Memory in Mb\": 2.818455696105957,         \"Time in s\": 2174.887267       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8209268190396309,         \"F1\": 0.7942168674698795,         \"Memory in Mb\": 2.852097511291504,         \"Time in s\": 2266.3018959999995       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.822974358974359,         \"F1\": 0.795932844644124,         \"Memory in Mb\": 2.940415382385254,         \"Time in s\": 2359.381272       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.825135514956836,         \"F1\": 0.7990772779700116,         \"Memory in Mb\": 2.986912727355957,         \"Time in s\": 2454.120045       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.825437389424022,         \"F1\": 0.7995485327313769,         \"Memory in Mb\": 3.072648048400879,         \"Time in s\": 2550.44047       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8266897746967071,         \"F1\": 0.8008849557522125,         \"Memory in Mb\": 3.1882104873657227,         \"Time in s\": 2648.361542       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8282694848084544,         \"F1\": 0.8026886383347789,         \"Memory in Mb\": 3.2357072830200195,         \"Time in s\": 2747.950666       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895027624309392,         \"F1\": 0.8873873873873873,         \"Memory in Mb\": 2.537948608398437,         \"Time in s\": 35.61841       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9127553837658752,         \"F1\": 0.8941018766756033,         \"Memory in Mb\": 3.267250061035156,         \"Time in s\": 98.669065       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9013617960986382,         \"F1\": 0.8815207780725023,         \"Memory in Mb\": 2.908538818359375,         \"Time in s\": 185.051304       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.905051062655258,         \"F1\": 0.8859416445623343,         \"Memory in Mb\": 4.239933013916016,         \"Time in s\": 291.140366       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9059395009935968,         \"F1\": 0.8829026937877955,         \"Memory in Mb\": 4.5028228759765625,         \"Time in s\": 414.050546       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904691812327507,         \"F1\": 0.8806451612903227,         \"Memory in Mb\": 5.411556243896484,         \"Time in s\": 555.0527619999999       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904746885349314,         \"F1\": 0.8810086682427108,         \"Memory in Mb\": 3.64324951171875,         \"Time in s\": 712.2276919999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9038222712846696,         \"F1\": 0.8793908980792524,         \"Memory in Mb\": 4.176555633544922,         \"Time in s\": 885.4512659999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9062921623942108,         \"F1\": 0.8879107981220656,         \"Memory in Mb\": 4.873016357421875,         \"Time in s\": 1073.942042       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.907384921072966,         \"F1\": 0.8915600361897376,         \"Memory in Mb\": 6.068294525146484,         \"Time in s\": 1277.3056459999998       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9066733567486202,         \"F1\": 0.8924855491329481,         \"Memory in Mb\": 5.883171081542969,         \"Time in s\": 1496.0059789999998       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9090240088308345,         \"F1\": 0.8966238110170377,         \"Memory in Mb\": 7.123630523681641,         \"Time in s\": 1728.5474849999998       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.908805298463106,         \"F1\": 0.8963720571208027,         \"Memory in Mb\": 4.904956817626953,         \"Time in s\": 1974.076492       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9071197666167312,         \"F1\": 0.8948214285714287,         \"Memory in Mb\": 4.745685577392578,         \"Time in s\": 2233.08693       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.908234601515932,         \"F1\": 0.8972732515034187,         \"Memory in Mb\": 5.919612884521484,         \"Time in s\": 2504.250556       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9082442221455674,         \"F1\": 0.8976293103448276,         \"Memory in Mb\": 4.272552490234375,         \"Time in s\": 2787.365554       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9089669501980392,         \"F1\": 0.8979027090008739,         \"Memory in Mb\": 4.651363372802734,         \"Time in s\": 3081.637323       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9085668731219722,         \"F1\": 0.8971653217463273,         \"Memory in Mb\": 5.967304229736328,         \"Time in s\": 3386.795808       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9075698599895428,         \"F1\": 0.8943488943488943,         \"Memory in Mb\": 5.553913116455078,         \"Time in s\": 3703.059282       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9077211766653788,         \"F1\": 0.8943911066195048,         \"Memory in Mb\": 7.001399993896484,         \"Time in s\": 4030.541025       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9078580814717476,         \"F1\": 0.8932854446947099,         \"Memory in Mb\": 7.953182220458984,         \"Time in s\": 4368.505934999999       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9081832321509208,         \"F1\": 0.8944636678200691,         \"Memory in Mb\": 8.54180908203125,         \"Time in s\": 4717.761576999999       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064644622546432,         \"F1\": 0.8926111631494847,         \"Memory in Mb\": 7.284095764160156,         \"Time in s\": 5078.966551999999       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064066596145886,         \"F1\": 0.8909840895698291,         \"Memory in Mb\": 8.78485107421875,         \"Time in s\": 5451.144687999999       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9054704401960352,         \"F1\": 0.8890386110391294,         \"Memory in Mb\": 9.895774841308594,         \"Time in s\": 5834.807108999999       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9032052642751008,         \"F1\": 0.8860113988601139,         \"Memory in Mb\": 9.921958923339844,         \"Time in s\": 6231.782156999999       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9009443604104492,         \"F1\": 0.8825098191339766,         \"Memory in Mb\": 6.414276123046875,         \"Time in s\": 6640.471452999998       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8975046320022076,         \"F1\": 0.87847059923343,         \"Memory in Mb\": 7.025360107421875,         \"Time in s\": 7059.615553999998       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8978418909146272,         \"F1\": 0.878705712219812,         \"Memory in Mb\": 8.249675750732422,         \"Time in s\": 7487.941528999998       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8983038375216159,         \"F1\": 0.879888753693725,         \"Memory in Mb\": 7.590415954589844,         \"Time in s\": 7924.652190999998       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8965640021363718,         \"F1\": 0.8772448763997466,         \"Memory in Mb\": 7.862815856933594,         \"Time in s\": 8369.790676999999       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8963126487530613,         \"F1\": 0.8762962962962964,         \"Memory in Mb\": 9.08489990234375,         \"Time in s\": 8823.391086       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8952403251162324,         \"F1\": 0.8748901493968203,         \"Memory in Mb\": 2.6490402221679688,         \"Time in s\": 9284.744377       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8947505113138331,         \"F1\": 0.8736357966947302,         \"Memory in Mb\": 3.22769546508789,         \"Time in s\": 9752.904185       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8935948784256835,         \"F1\": 0.8721291594027135,         \"Memory in Mb\": 3.8703384399414062,         \"Time in s\": 10228.075712       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8929940211559099,         \"F1\": 0.8717194736455194,         \"Memory in Mb\": 4.073085784912109,         \"Time in s\": 10710.354293       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8932311088571343,         \"F1\": 0.872338148742643,         \"Memory in Mb\": 4.776435852050781,         \"Time in s\": 11199.826533       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8924390739826299,         \"F1\": 0.8711865585974188,         \"Memory in Mb\": 4.868198394775391,         \"Time in s\": 11696.46579       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8922537005066086,         \"F1\": 0.8703735231025913,         \"Memory in Mb\": 5.445720672607422,         \"Time in s\": 12200.310087       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8919948122188802,         \"F1\": 0.8690619563762879,         \"Memory in Mb\": 4.9837493896484375,         \"Time in s\": 12711.187581       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8920985327769552,         \"F1\": 0.8688996467355751,         \"Memory in Mb\": 5.313899993896484,         \"Time in s\": 13229.1012       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8916979842842501,         \"F1\": 0.8676919125437441,         \"Memory in Mb\": 5.129566192626953,         \"Time in s\": 13754.199791       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8912647277767796,         \"F1\": 0.8674924924924926,         \"Memory in Mb\": 5.3661651611328125,         \"Time in s\": 14286.524658       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8915535709806086,         \"F1\": 0.8687813021702837,         \"Memory in Mb\": 5.776020050048828,         \"Time in s\": 14825.184636       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8920012754789178,         \"F1\": 0.8703512852978417,         \"Memory in Mb\": 6.964508056640625,         \"Time in s\": 15370.438083       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.89250149970006,         \"F1\": 0.8717728547713092,         \"Memory in Mb\": 8.029548645019531,         \"Time in s\": 15922.831901       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8927925600619995,         \"F1\": 0.8723184068469779,         \"Memory in Mb\": 8.723072052001953,         \"Time in s\": 16481.133162000002       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8927495573389749,         \"F1\": 0.8722821622213702,         \"Memory in Mb\": 8.793426513671875,         \"Time in s\": 17045.039295000002       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8920775797986169,         \"F1\": 0.8710467526175545,         \"Memory in Mb\": 6.634971618652344,         \"Time in s\": 17614.470389000002       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8926687123336056,         \"F1\": 0.8720122143834896,         \"Memory in Mb\": 7.5638427734375,         \"Time in s\": 18188.843118       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8926529981682152,         \"F1\": 0.8719663069228745,         \"Memory in Mb\": 7.565349578857422,         \"Time in s\": 18763.342135       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.75,         \"F1\": 0.75,         \"Memory in Mb\": 0.6626491546630859,         \"Time in s\": 1.23946       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8163265306122449,         \"F1\": 0.8,         \"Memory in Mb\": 0.6635112762451172,         \"Time in s\": 3.937992       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8378378378378378,         \"F1\": 0.8333333333333334,         \"Memory in Mb\": 0.6635112762451172,         \"Time in s\": 8.007437       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8484848484848485,         \"F1\": 0.8421052631578947,         \"Memory in Mb\": 0.6476030349731445,         \"Time in s\": 13.398751       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467741935483871,         \"F1\": 0.8403361344537815,         \"Memory in Mb\": 0.9203081130981444,         \"Time in s\": 19.997869       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8456375838926175,         \"F1\": 0.8456375838926175,         \"Memory in Mb\": 0.9203310012817384,         \"Time in s\": 27.874049       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867816091954023,         \"F1\": 0.8588957055214724,         \"Memory in Mb\": 1.0861825942993164,         \"Time in s\": 37.283539       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8693467336683417,         \"F1\": 0.8617021276595744,         \"Memory in Mb\": 1.2813997268676758,         \"Time in s\": 47.998757       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8660714285714286,         \"F1\": 0.8557692307692308,         \"Memory in Mb\": 1.3089113235473633,         \"Time in s\": 59.952353       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554216867469879,         \"F1\": 0.8434782608695653,         \"Memory in Mb\": 1.3089799880981443,         \"Time in s\": 73.11976999999999       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8576642335766423,         \"F1\": 0.844621513944223,         \"Memory in Mb\": 1.2476167678833008,         \"Time in s\": 87.72028699999998       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.862876254180602,         \"F1\": 0.8464419475655431,         \"Memory in Mb\": 1.4594087600708008,         \"Time in s\": 103.60422699999998       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8703703703703703,         \"F1\": 0.851063829787234,         \"Memory in Mb\": 1.4950456619262695,         \"Time in s\": 120.70292199999996       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8710601719197708,         \"F1\": 0.8494983277591974,         \"Memory in Mb\": 1.5330171585083008,         \"Time in s\": 138.98074599999998       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8716577540106952,         \"F1\": 0.8481012658227849,         \"Memory in Mb\": 1.809849739074707,         \"Time in s\": 158.64485       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8696741854636592,         \"F1\": 0.8433734939759037,         \"Memory in Mb\": 2.068051338195801,         \"Time in s\": 179.646811       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8702830188679245,         \"F1\": 0.8405797101449276,         \"Memory in Mb\": 2.104710578918457,         \"Time in s\": 201.850953       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8752783964365256,         \"F1\": 0.845303867403315,         \"Memory in Mb\": 2.104527473449707,         \"Time in s\": 225.239967       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8776371308016878,         \"F1\": 0.8505154639175259,         \"Memory in Mb\": 2.132199287414551,         \"Time in s\": 249.917589       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.875751503006012,         \"F1\": 0.8502415458937198,         \"Memory in Mb\": 2.1503801345825195,         \"Time in s\": 275.75512899999995       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8778625954198473,         \"F1\": 0.8497652582159624,         \"Memory in Mb\": 2.187130928039551,         \"Time in s\": 302.922289       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8743169398907104,         \"F1\": 0.8463251670378619,         \"Memory in Mb\": 2.2971315383911133,         \"Time in s\": 331.41425       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8763066202090593,         \"F1\": 0.8479657387580299,         \"Memory in Mb\": 2.406788825988769,         \"Time in s\": 361.219435       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8764607679465777,         \"F1\": 0.8451882845188285,         \"Memory in Mb\": 2.406834602355957,         \"Time in s\": 392.26267       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8782051282051282,         \"F1\": 0.8442622950819672,         \"Memory in Mb\": 2.352017402648926,         \"Time in s\": 424.555734       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8813559322033898,         \"F1\": 0.850485436893204,         \"Memory in Mb\": 2.279099464416504,         \"Time in s\": 458.242359       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798219584569733,         \"F1\": 0.8513761467889909,         \"Memory in Mb\": 2.54854679107666,         \"Time in s\": 493.209676       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8841201716738197,         \"F1\": 0.8550983899821109,         \"Memory in Mb\": 2.565995216369629,         \"Time in s\": 529.352808       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8812154696132597,         \"F1\": 0.8537414965986394,         \"Memory in Mb\": 2.870518684387207,         \"Time in s\": 566.738792       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8825100133511349,         \"F1\": 0.8557377049180328,         \"Memory in Mb\": 2.941006660461426,         \"Time in s\": 605.3090109999999       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8837209302325582,         \"F1\": 0.856687898089172,         \"Memory in Mb\": 3.0508241653442383,         \"Time in s\": 645.053635       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8836045056320401,         \"F1\": 0.8584474885844748,         \"Memory in Mb\": 3.1606874465942383,         \"Time in s\": 686.031259       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8810679611650486,         \"F1\": 0.8567251461988304,         \"Memory in Mb\": 3.270321846008301,         \"Time in s\": 728.1933819999999       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8833922261484098,         \"F1\": 0.8591749644381224,         \"Memory in Mb\": 3.2882280349731445,         \"Time in s\": 771.57935       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844393592677345,         \"F1\": 0.8595271210013908,         \"Memory in Mb\": 3.2632036209106445,         \"Time in s\": 816.1995999999999       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8832035595105673,         \"F1\": 0.8575305291723202,         \"Memory in Mb\": 3.380833625793457,         \"Time in s\": 861.997768       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8841991341991342,         \"F1\": 0.8597640891218873,         \"Memory in Mb\": 3.4813432693481445,         \"Time in s\": 908.973065       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8851422550052687,         \"F1\": 0.8628930817610063,         \"Memory in Mb\": 3.5117311477661133,         \"Time in s\": 957.120781       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8870636550308009,         \"F1\": 0.8651960784313726,         \"Memory in Mb\": 3.5666399002075195,         \"Time in s\": 1006.3541349999998       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878878878878879,         \"F1\": 0.8663484486873507,         \"Memory in Mb\": 3.645543098449707,         \"Time in s\": 1056.728559       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876953125,         \"F1\": 0.8667439165701043,         \"Memory in Mb\": 3.735753059387207,         \"Time in s\": 1108.282035       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8894184938036225,         \"F1\": 0.8693693693693694,         \"Memory in Mb\": 3.808384895324707,         \"Time in s\": 1160.949301       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901303538175046,         \"F1\": 0.87117903930131,         \"Memory in Mb\": 3.94530200958252,         \"Time in s\": 1214.7027389999998       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89171974522293,         \"F1\": 0.873269435569755,         \"Memory in Mb\": 3.945347785949707,         \"Time in s\": 1269.5439849999998       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8905693950177936,         \"F1\": 0.8730650154798762,         \"Memory in Mb\": 3.972836494445801,         \"Time in s\": 1325.4243339999998       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8920800696257616,         \"F1\": 0.8747474747474747,         \"Memory in Mb\": 3.945645332336426,         \"Time in s\": 1382.2374609999997       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8909710391822828,         \"F1\": 0.8732673267326733,         \"Memory in Mb\": 3.973248481750488,         \"Time in s\": 1440.0925429999998       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8924103419516264,         \"F1\": 0.8746355685131195,         \"Memory in Mb\": 3.947278022766113,         \"Time in s\": 1498.9929549999997       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8937908496732027,         \"F1\": 0.8761904761904762,         \"Memory in Mb\": 3.982327461242676,         \"Time in s\": 1558.8210809999996       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8943154523618895,         \"F1\": 0.8773234200743495,         \"Memory in Mb\": 4.0114030838012695,         \"Time in s\": 1619.6535709999996       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1602087020874023,         \"Time in s\": 31.58816       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1608190536499023,         \"Time in s\": 89.620857       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1614294052124023,         \"Time in s\": 167.42750999999998       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1614294052124023,         \"Time in s\": 263.688726       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1614294052124023,         \"Time in s\": 375.655092       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1620397567749023,         \"Time in s\": 502.190419       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1620397567749023,         \"Time in s\": 643.105063       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992117191092426,         \"F1\": 0.0,         \"Memory in Mb\": 0.2446889877319336,         \"Time in s\": 797.8947989999999       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9991825294873292,         \"F1\": 0.0,         \"Memory in Mb\": 0.1627492904663086,         \"Time in s\": 968.143197       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992642808345158,         \"F1\": 0.0,         \"Memory in Mb\": 0.1625890731811523,         \"Time in s\": 1153.4864309999998       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993311675902924,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631536483764648,         \"Time in s\": 1353.5382149999998       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993869060652508,         \"F1\": 0.0,         \"Memory in Mb\": 0.1632680892944336,         \"Time in s\": 1568.364428       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994340690435768,         \"F1\": 0.0,         \"Memory in Mb\": 0.1632909774780273,         \"Time in s\": 1796.946914       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994369580721444,         \"F1\": 0.0,         \"Memory in Mb\": 0.1630849838256836,         \"Time in s\": 2038.09448       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744955156952,         \"F1\": 0.0,         \"Memory in Mb\": 0.1630849838256836,         \"Time in s\": 2291.954796       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999507340624692,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631536483764648,         \"Time in s\": 2557.874515       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995363214837713,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631765365600586,         \"Time in s\": 2835.65121       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999562082153388,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631536483764648,         \"Time in s\": 3124.961925       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995851310985728,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631078720092773,         \"Time in s\": 3424.787068       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058750886782,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631307601928711,         \"Time in s\": 3734.997034       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996246434112408,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631994247436523,         \"Time in s\": 4055.587677       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999641705481906,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638784408569336,         \"Time in s\": 4386.458848       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996572838603546,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636495590209961,         \"Time in s\": 4726.906731       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671564012174,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638555526733398,         \"Time in s\": 5077.03597       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847017278344,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639471054077148,         \"Time in s\": 5436.872312       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996968288295572,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 5806.300992       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996691319579604,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639471054077148,         \"Time in s\": 6185.544045999999       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996809488955202,         \"F1\": 0.0,         \"Memory in Mb\": 0.1641073226928711,         \"Time in s\": 6574.848143999999       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996919508571014,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637639999389648,         \"Time in s\": 6975.121680999999       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995445707579392,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638784408569336,         \"Time in s\": 7384.584095999999       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995592622728504,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639013290405273,         \"Time in s\": 7802.5953629999985       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999573035553001,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 8228.594131999998       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995541259275772,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639013290405273,         \"Time in s\": 8661.618187999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672400735692,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639928817749023,         \"Time in s\": 9101.660233       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796048285388,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638326644897461,         \"Time in s\": 9548.69733       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999562088545696,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638097763061523,         \"Time in s\": 10002.655236       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995739241585002,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 10463.105481       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995851368357004,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637639999389648,         \"Time in s\": 10929.695224       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995957744960656,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638097763061523,         \"Time in s\": 11402.447204       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058802664248,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638784408569336,         \"Time in s\": 11881.476782       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996154930660582,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637411117553711,         \"Time in s\": 12366.666901       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996246481076008,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 12858.057531       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999633377328054,         \"F1\": 0.0,         \"Memory in Mb\": 0.1521596908569336,         \"Time in s\": 13355.582794       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996417097610204,         \"F1\": 0.0,         \"Memory in Mb\": 0.1528844833374023,         \"Time in s\": 13859.323941       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496718593082,         \"F1\": 0.0,         \"Memory in Mb\": 0.1643285751342773,         \"Time in s\": 14369.189455       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996572877754548,         \"F1\": 0.0,         \"Memory in Mb\": 0.1646032333374023,         \"Time in s\": 14885.224126       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996533989266548,         \"F1\": 0.0,         \"Memory in Mb\": 0.1645116806030273,         \"Time in s\": 15407.418989999998       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996606198614016,         \"F1\": 0.0,         \"Memory in Mb\": 0.1643285751342773,         \"Time in s\": 15935.791256       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996675460609572,         \"F1\": 0.0,         \"Memory in Mb\": 0.1645116806030273,         \"Time in s\": 16470.041814999997       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996741952096186,         \"F1\": 0.0,         \"Memory in Mb\": 0.1645345687866211,         \"Time in s\": 17009.813748999997       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996742157532448,         \"F1\": 0.0,         \"Memory in Mb\": 0.1646032333374023,         \"Time in s\": 17549.605714999998       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6095238095238096,         \"F1\": 0.577319587628866,         \"Memory in Mb\": 0.7777948379516602,         \"Time in s\": 2.119535       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7109004739336493,         \"F1\": 0.6702702702702703,         \"Memory in Mb\": 1.3802881240844729,         \"Time in s\": 6.931057       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7602523659305994,         \"F1\": 0.7361111111111112,         \"Memory in Mb\": 1.8119163513183596,         \"Time in s\": 15.160032       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7943262411347518,         \"F1\": 0.772845953002611,         \"Memory in Mb\": 2.401026725769043,         \"Time in s\": 27.407145       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8052930056710775,         \"F1\": 0.7775377969762419,         \"Memory in Mb\": 5.0262651443481445,         \"Time in s\": 65.121823       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8236220472440945,         \"F1\": 0.7992831541218638,         \"Memory in Mb\": 5.88111686706543,         \"Time in s\": 107.288216       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8299595141700404,         \"F1\": 0.8025078369905957,         \"Memory in Mb\": 6.734616279602051,         \"Time in s\": 153.798119       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8347107438016529,         \"F1\": 0.8087431693989071,         \"Memory in Mb\": 7.555168151855469,         \"Time in s\": 204.76644700000003       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8426023084994754,         \"F1\": 0.8166259168704157,         \"Memory in Mb\": 8.384669303894043,         \"Time in s\": 260.019764       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8536355051935789,         \"F1\": 0.8275862068965517,         \"Memory in Mb\": 8.926264762878418,         \"Time in s\": 319.31365700000003       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8532188841201717,         \"F1\": 0.8274470232088799,         \"Memory in Mb\": 9.188977241516112,         \"Time in s\": 382.58132300000005       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8536585365853658,         \"F1\": 0.8290441176470588,         \"Memory in Mb\": 9.45701789855957,         \"Time in s\": 449.3987790000001       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8576615831517792,         \"F1\": 0.8321917808219177,         \"Memory in Mb\": 9.84501838684082,         \"Time in s\": 519.6596460000001       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8590694538098449,         \"F1\": 0.8345209817893903,         \"Memory in Mb\": 10.364198684692385,         \"Time in s\": 593.3590610000001       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8565135305223411,         \"F1\": 0.8321060382916053,         \"Memory in Mb\": 10.46892547607422,         \"Time in s\": 670.9077340000001       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8595870206489675,         \"F1\": 0.8354080221300139,         \"Memory in Mb\": 10.96640968322754,         \"Time in s\": 752.0270200000001       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8634092171016102,         \"F1\": 0.8410852713178295,         \"Memory in Mb\": 10.118447303771973,         \"Time in s\": 836.636461       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8626114315679078,         \"F1\": 0.8417874396135265,         \"Memory in Mb\": 10.3862943649292,         \"Time in s\": 924.5557       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8614008941877794,         \"F1\": 0.8415672913117546,         \"Memory in Mb\": 10.646858215332031,         \"Time in s\": 1015.887245       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8640868334119868,         \"F1\": 0.8459893048128343,         \"Memory in Mb\": 10.9229736328125,         \"Time in s\": 1110.420791       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8642696629213483,         \"F1\": 0.8462321792260691,         \"Memory in Mb\": 11.325839042663574,         \"Time in s\": 1208.24484       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.864006864006864,         \"F1\": 0.8461911693352742,         \"Memory in Mb\": 11.659860610961914,         \"Time in s\": 1308.9666100000002       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8641772671317193,         \"F1\": 0.8465461288827074,         \"Memory in Mb\": 11.19693660736084,         \"Time in s\": 1412.476276       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8651199370821864,         \"F1\": 0.8484312859036677,         \"Memory in Mb\": 11.452000617980955,         \"Time in s\": 1518.7765900000002       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.864477161192903,         \"F1\": 0.8480744815911976,         \"Memory in Mb\": 11.787381172180176,         \"Time in s\": 1627.8671150000002       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8653357531760436,         \"F1\": 0.849125660837739,         \"Memory in Mb\": 12.11353874206543,         \"Time in s\": 1739.675061       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8678783642083188,         \"F1\": 0.8515318146111548,         \"Memory in Mb\": 12.35980987548828,         \"Time in s\": 1854.131846       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8695652173913043,         \"F1\": 0.8529076396807297,         \"Memory in Mb\": 12.722334861755373,         \"Time in s\": 1971.385777       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8682069638789457,         \"F1\": 0.8515939904727006,         \"Memory in Mb\": 13.0479736328125,         \"Time in s\": 2091.3191060000004       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8700849323686694,         \"F1\": 0.8530771967271433,         \"Memory in Mb\": 13.308364868164062,         \"Time in s\": 2213.9114950000003       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8700152207001522,         \"F1\": 0.8523002421307506,         \"Memory in Mb\": 13.608009338378906,         \"Time in s\": 2339.1031470000003       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.871129460336184,         \"F1\": 0.8544788544788545,         \"Memory in Mb\": 13.70766258239746,         \"Time in s\": 2466.937686       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.872748069774092,         \"F1\": 0.8557536466774717,         \"Memory in Mb\": 14.051713943481444,         \"Time in s\": 2598.213051       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8742714404662781,         \"F1\": 0.856872037914692,         \"Memory in Mb\": 14.26829433441162,         \"Time in s\": 2732.2090540000004       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8751685090320841,         \"F1\": 0.8583664729275007,         \"Memory in Mb\": 14.518733978271484,         \"Time in s\": 2868.9613940000004       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8762778505897771,         \"F1\": 0.8596908442330559,         \"Memory in Mb\": 14.742842674255373,         \"Time in s\": 3008.2640120000005       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8747768426421831,         \"F1\": 0.8578048074138431,         \"Memory in Mb\": 15.053866386413574,         \"Time in s\": 3150.2484420000005       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8733548547305686,         \"F1\": 0.8560135516657256,         \"Memory in Mb\": 15.453622817993164,         \"Time in s\": 3294.8873180000005       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.874183401887249,         \"F1\": 0.8569069895432032,         \"Memory in Mb\": 15.755488395690918,         \"Time in s\": 3442.0965550000005       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8749705119131871,         \"F1\": 0.8579849946409432,         \"Memory in Mb\": 15.976973533630373,         \"Time in s\": 3591.8798750000005       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8759493670886076,         \"F1\": 0.8590849673202615,         \"Memory in Mb\": 16.313834190368652,         \"Time in s\": 3744.3343260000006       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8755335879577623,         \"F1\": 0.8585291113381002,         \"Memory in Mb\": 16.729196548461914,         \"Time in s\": 3899.4355060000007       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8757954794821154,         \"F1\": 0.8592039800995025,         \"Memory in Mb\": 17.032727241516113,         \"Time in s\": 4057.1530940000007       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8756165558653227,         \"F1\": 0.8594961240310078,         \"Memory in Mb\": 17.45319175720215,         \"Time in s\": 4217.5274930000005       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8754455860767456,         \"F1\": 0.8590412909349787,         \"Memory in Mb\": 17.6948184967041,         \"Time in s\": 4380.594611       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8756923076923077,         \"F1\": 0.8588070829450141,         \"Memory in Mb\": 17.917430877685547,         \"Time in s\": 4546.383704000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8761292913069665,         \"F1\": 0.8596770525358198,         \"Memory in Mb\": 18.09793186187744,         \"Time in s\": 4714.847995000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8757617456261058,         \"F1\": 0.8591800356506238,         \"Memory in Mb\": 18.51348114013672,         \"Time in s\": 4886.014522000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.876372039283651,         \"F1\": 0.8598865124399825,         \"Memory in Mb\": 18.89274883270264,         \"Time in s\": 5059.990176000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8762030571806001,         \"F1\": 0.8596491228070176,         \"Memory in Mb\": 19.194592475891117,         \"Time in s\": 5236.837813000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9116022099447514,         \"F1\": 0.908256880733945,         \"Memory in Mb\": 7.041282653808594,         \"Time in s\": 59.400144       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.906129210381005,         \"F1\": 0.8855989232839839,         \"Memory in Mb\": 9.07800579071045,         \"Time in s\": 148.928403       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9002576370997424,         \"F1\": 0.8772088808337108,         \"Memory in Mb\": 9.477606773376465,         \"Time in s\": 264.671315       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064311344189898,         \"F1\": 0.8849677638276212,         \"Memory in Mb\": 10.383838653564451,         \"Time in s\": 404.188666       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.90527710311327,         \"F1\": 0.8788477831121152,         \"Memory in Mb\": 11.437847137451172,         \"Time in s\": 565.129871       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9000919963201472,         \"F1\": 0.8723854289071681,         \"Memory in Mb\": 14.209432601928713,         \"Time in s\": 747.817107       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.897019397571361,         \"F1\": 0.8704622098789923,         \"Memory in Mb\": 15.688876152038574,         \"Time in s\": 951.56122       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8965089002345799,         \"F1\": 0.8694744169857292,         \"Memory in Mb\": 19.837779998779297,         \"Time in s\": 1176.633371       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8980743284680486,         \"F1\": 0.8778839088905216,         \"Memory in Mb\": 22.41482448577881,         \"Time in s\": 1420.714573       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9000993487139861,         \"F1\": 0.8828478964401294,         \"Memory in Mb\": 25.97023296356201,         \"Time in s\": 1683.800751       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8982438534872053,         \"F1\": 0.8827203331020125,         \"Memory in Mb\": 28.783666610717773,         \"Time in s\": 1965.440637       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9006531137889798,         \"F1\": 0.8870765370138016,         \"Memory in Mb\": 30.882869720458984,         \"Time in s\": 2263.780185       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9021822195805383,         \"F1\": 0.8888030888030888,         \"Memory in Mb\": 33.277831077575684,         \"Time in s\": 2578.868179       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.901285184893164,         \"F1\": 0.8879541793449078,         \"Memory in Mb\": 35.50911808013916,         \"Time in s\": 2911.085292       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.901905953344617,         \"F1\": 0.8899529431189631,         \"Memory in Mb\": 38.6168327331543,         \"Time in s\": 3259.512573       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9030010348395998,         \"F1\": 0.8917128773875539,         \"Memory in Mb\": 41.26064682006836,         \"Time in s\": 3624.142031       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.903902344003636,         \"F1\": 0.8922382408620941,         \"Memory in Mb\": 43.65532207489014,         \"Time in s\": 4004.812957       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9002882197829153,         \"F1\": 0.8879239040529363,         \"Memory in Mb\": 45.1539192199707,         \"Time in s\": 4403.507418       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8997850461860222,         \"F1\": 0.8856176646111001,         \"Memory in Mb\": 37.54582214355469,         \"Time in s\": 4816.829749       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9001048622992439,         \"F1\": 0.8858908082209053,         \"Memory in Mb\": 41.9152717590332,         \"Time in s\": 5243.603799       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9014454664914586,         \"F1\": 0.886177381169186,         \"Memory in Mb\": 44.45838737487793,         \"Time in s\": 5683.033974000001       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9011088254477948,         \"F1\": 0.8867826986041704,         \"Memory in Mb\": 48.213175773620605,         \"Time in s\": 6135.158415000001       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8992657292316553,         \"F1\": 0.8848411696933121,         \"Memory in Mb\": 47.78572177886963,         \"Time in s\": 6598.876461000001       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8986800349537782,         \"F1\": 0.8824376967821121,         \"Memory in Mb\": 49.35674381256104,         \"Time in s\": 7073.276106000001       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.898361958585368,         \"F1\": 0.8812912541254125,         \"Memory in Mb\": 47.91073036193848,         \"Time in s\": 7558.681153000001       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8961579282530249,         \"F1\": 0.8784656663022956,         \"Memory in Mb\": 53.37149906158447,         \"Time in s\": 8055.088432000001       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8945259801316381,         \"F1\": 0.8760211436809228,         \"Memory in Mb\": 52.06687641143799,         \"Time in s\": 8562.621137000002       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8922221784207829,         \"F1\": 0.8734610756271406,         \"Memory in Mb\": 20.63737678527832,         \"Time in s\": 9080.309208000002       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8928177216153466,         \"F1\": 0.873801201039706,         \"Memory in Mb\": 17.83812713623047,         \"Time in s\": 9607.290172000005       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8932263880201626,         \"F1\": 0.874632797649905,         \"Memory in Mb\": 19.30546188354492,         \"Time in s\": 10142.799192000002       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8915435285739719,         \"F1\": 0.8721564677243349,         \"Memory in Mb\": 18.23539447784424,         \"Time in s\": 10686.882271000002       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.891345590010693,         \"F1\": 0.8712498978173793,         \"Memory in Mb\": 21.229859352111816,         \"Time in s\": 11240.330868000005       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8905575810281968,         \"F1\": 0.8700246285850481,         \"Memory in Mb\": 25.24380111694336,         \"Time in s\": 11801.274420000003       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.890335356945752,         \"F1\": 0.8690697674418605,         \"Memory in Mb\": 28.836254119873047,         \"Time in s\": 12369.623812000003       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895266328171813,         \"F1\": 0.8679458664756663,         \"Memory in Mb\": 27.00519371032715,         \"Time in s\": 12944.308752000004       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8879963207113292,         \"F1\": 0.86634224872855,         \"Memory in Mb\": 30.17805576324463,         \"Time in s\": 13524.938061000004       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8870260433757943,         \"F1\": 0.8654563541407612,         \"Memory in Mb\": 32.107930183410645,         \"Time in s\": 14111.446119000002       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8857582711244082,         \"F1\": 0.8638770636486346,         \"Memory in Mb\": 32.29031944274902,         \"Time in s\": 14703.865141000002       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8850083491353692,         \"F1\": 0.8622665175090681,         \"Memory in Mb\": 36.271653175354,         \"Time in s\": 15302.291934000004       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8850133833715058,         \"F1\": 0.8612988050461006,         \"Memory in Mb\": 35.55355262756348,         \"Time in s\": 15906.592759000005       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8836182527931081,         \"F1\": 0.859061715515274,         \"Memory in Mb\": 38.75662517547608,         \"Time in s\": 16517.021041000004       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8830516937794013,         \"F1\": 0.8577547628180539,         \"Memory in Mb\": 41.063425064086914,         \"Time in s\": 17133.624997000003       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8831788895448828,         \"F1\": 0.8582198822393221,         \"Memory in Mb\": 42.255154609680176,         \"Time in s\": 17756.348401000003       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8836765923287259,         \"F1\": 0.8598797328740216,         \"Memory in Mb\": 43.78993701934815,         \"Time in s\": 18385.013164000004       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8840295322426354,         \"F1\": 0.8614708467623791,         \"Memory in Mb\": 40.85312080383301,         \"Time in s\": 19019.639174000004       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8847030593881223,         \"F1\": 0.8632106356933413,         \"Memory in Mb\": 39.29591369628906,         \"Time in s\": 19660.085711000003       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8851130786031328,         \"F1\": 0.8639675212724542,         \"Memory in Mb\": 42.33915042877197,         \"Time in s\": 20306.976876000004       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8848391473313864,         \"F1\": 0.8636091290375292,         \"Memory in Mb\": 42.20731544494629,         \"Time in s\": 20960.03932700001       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8848467100669024,         \"F1\": 0.8632350580555408,         \"Memory in Mb\": 44.13865566253662,         \"Time in s\": 21618.107174000004       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8854720854765006,         \"F1\": 0.8642027012878233,         \"Memory in Mb\": 40.63082981109619,         \"Time in s\": 22281.08467600001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8854582772395224,         \"F1\": 0.8641574621787154,         \"Memory in Mb\": 40.75471591949463,         \"Time in s\": 22944.429270000004       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6666666666666666,         \"F1\": 0.7142857142857143,         \"Memory in Mb\": 0.6122617721557617,         \"Time in s\": 0.640752       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7755102040816326,         \"F1\": 0.7659574468085107,         \"Memory in Mb\": 0.7524843215942383,         \"Time in s\": 1.992597       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8243243243243243,         \"F1\": 0.8266666666666667,         \"Memory in Mb\": 0.9228668212890624,         \"Time in s\": 4.151733       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8282828282828283,         \"F1\": 0.8282828282828283,         \"Memory in Mb\": 1.193608283996582,         \"Time in s\": 7.194986       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8306451612903226,         \"F1\": 0.8292682926829269,         \"Memory in Mb\": 1.3295679092407229,         \"Time in s\": 11.208747       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8389261744966443,         \"F1\": 0.8441558441558442,         \"Memory in Mb\": 1.3798675537109375,         \"Time in s\": 16.195196       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8563218390804598,         \"F1\": 0.8520710059171597,         \"Memory in Mb\": 1.4546594619750977,         \"Time in s\": 22.422243       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8542713567839196,         \"F1\": 0.8497409326424871,         \"Memory in Mb\": 1.6083984375,         \"Time in s\": 29.888728       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8436018957345972,         \"Memory in Mb\": 1.7997064590454102,         \"Time in s\": 38.482186       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8433734939759037,         \"F1\": 0.8354430379746836,         \"Memory in Mb\": 1.9343080520629885,         \"Time in s\": 48.311454       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467153284671532,         \"F1\": 0.8372093023255813,         \"Memory in Mb\": 2.053934097290039,         \"Time in s\": 59.483297       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8461538461538461,         \"F1\": 0.8333333333333334,         \"Memory in Mb\": 2.12460994720459,         \"Time in s\": 72.133375       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8518518518518519,         \"F1\": 0.8356164383561644,         \"Memory in Mb\": 2.201033592224121,         \"Time in s\": 86.30699200000001       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8595988538681948,         \"F1\": 0.8414239482200646,         \"Memory in Mb\": 2.2356014251708984,         \"Time in s\": 102.050203       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8556149732620321,         \"F1\": 0.8353658536585366,         \"Memory in Mb\": 2.328523635864258,         \"Time in s\": 119.413833       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8347826086956521,         \"Memory in Mb\": 2.316814422607422,         \"Time in s\": 138.615081       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8608490566037735,         \"F1\": 0.8356545961002786,         \"Memory in Mb\": 2.353947639465332,         \"Time in s\": 159.65345200000002       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8619153674832962,         \"F1\": 0.8351063829787234,         \"Memory in Mb\": 2.429127693176269,         \"Time in s\": 182.47163200000003       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8649789029535865,         \"F1\": 0.8407960199004976,         \"Memory in Mb\": 2.579917907714844,         \"Time in s\": 207.23725       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8637274549098196,         \"F1\": 0.841860465116279,         \"Memory in Mb\": 4.818408012390137,         \"Time in s\": 255.437855       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8645038167938931,         \"F1\": 0.8397291196388261,         \"Memory in Mb\": 5.000759124755859,         \"Time in s\": 305.327991       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8652094717668488,         \"F1\": 0.8418803418803419,         \"Memory in Mb\": 5.177936553955078,         \"Time in s\": 356.788916       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8658536585365854,         \"F1\": 0.8425357873210634,         \"Memory in Mb\": 5.324765205383301,         \"Time in s\": 409.723896       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8697829716193656,         \"F1\": 0.8446215139442231,         \"Memory in Mb\": 5.557343482971191,         \"Time in s\": 464.11455       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8685897435897436,         \"F1\": 0.8404669260700389,         \"Memory in Mb\": 5.701066970825195,         \"Time in s\": 520.0238599999999       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8721109399075501,         \"F1\": 0.847145488029466,         \"Memory in Mb\": 5.875107765197754,         \"Time in s\": 577.5076929999999       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8753709198813057,         \"F1\": 0.8541666666666667,         \"Memory in Mb\": 5.993474006652832,         \"Time in s\": 636.4901669999999       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798283261802575,         \"F1\": 0.8576271186440678,         \"Memory in Mb\": 6.097118377685547,         \"Time in s\": 696.8595059999999       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798342541436464,         \"F1\": 0.8603531300160514,         \"Memory in Mb\": 6.2616376876831055,         \"Time in s\": 758.6517739999999       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8811748998664887,         \"F1\": 0.8624420401854715,         \"Memory in Mb\": 6.510566711425781,         \"Time in s\": 822.0041369999999       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8824289405684754,         \"F1\": 0.8631578947368422,         \"Memory in Mb\": 6.659415245056152,         \"Time in s\": 886.863173       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8823529411764706,         \"F1\": 0.8645533141210374,         \"Memory in Mb\": 6.793304443359375,         \"Time in s\": 953.229371       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8822815533980582,         \"F1\": 0.8654646324549237,         \"Memory in Mb\": 7.087222099304199,         \"Time in s\": 1021.107018       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8833922261484098,         \"F1\": 0.8660351826792964,         \"Memory in Mb\": 7.324291229248047,         \"Time in s\": 1090.559446       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88558352402746,         \"F1\": 0.8677248677248677,         \"Memory in Mb\": 7.470724105834961,         \"Time in s\": 1161.497406       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8854282536151279,         \"F1\": 0.8670967741935484,         \"Memory in Mb\": 7.810632705688477,         \"Time in s\": 1233.983859       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8874458874458875,         \"F1\": 0.8706467661691542,         \"Memory in Mb\": 7.971014976501465,         \"Time in s\": 1307.96073       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8872497365648051,         \"F1\": 0.8718562874251498,         \"Memory in Mb\": 8.080266952514648,         \"Time in s\": 1383.548543       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8891170431211499,         \"F1\": 0.8738317757009346,         \"Memory in Mb\": 8.205357551574707,         \"Time in s\": 1460.7186840000002       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8888888888888888,         \"F1\": 0.8737201365187712,         \"Memory in Mb\": 8.39128303527832,         \"Time in s\": 1539.50166       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.888671875,         \"F1\": 0.8738938053097345,         \"Memory in Mb\": 8.406519889831543,         \"Time in s\": 1619.9124450000002       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8903717826501429,         \"F1\": 0.8762109795479011,         \"Memory in Mb\": 8.400672912597656,         \"Time in s\": 1701.87919       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8910614525139665,         \"F1\": 0.877742946708464,         \"Memory in Mb\": 8.453279495239258,         \"Time in s\": 1785.406003       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8926296633303002,         \"F1\": 0.8795918367346939,         \"Memory in Mb\": 8.421560287475586,         \"Time in s\": 1870.444315       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932384341637011,         \"F1\": 0.8811881188118813,         \"Memory in Mb\": 8.383057594299316,         \"Time in s\": 1956.980231       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929503916449086,         \"F1\": 0.8806983511154219,         \"Memory in Mb\": 8.433841705322266,         \"Time in s\": 2045.031177       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8909710391822828,         \"F1\": 0.8783269961977185,         \"Memory in Mb\": 8.58321475982666,         \"Time in s\": 2134.506504       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932443703085905,         \"F1\": 0.8803738317757008,         \"Memory in Mb\": 8.605175971984863,         \"Time in s\": 2225.421876       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8946078431372549,         \"F1\": 0.8817598533455545,         \"Memory in Mb\": 8.70528507232666,         \"Time in s\": 2317.752916       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8951160928742994,         \"F1\": 0.88272157564906,         \"Memory in Mb\": 8.721240997314453,         \"Time in s\": 2411.411116       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.776657104492188,         \"Time in s\": 62.937451       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.703582763671875,         \"Time in s\": 162.906939       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.683967590332031,         \"Time in s\": 291.999561       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.6548919677734375,         \"Time in s\": 449.905783       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.674896240234375,         \"Time in s\": 634.264648       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.68939208984375,         \"Time in s\": 844.6132379999999       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.6727142333984375,         \"Time in s\": 1077.972428       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 4.755153656005859,         \"Time in s\": 1334.293209       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 4.668712615966797,         \"Time in s\": 1613.416001       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 4.707424163818359,         \"Time in s\": 1913.210948       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 4.680248260498047,         \"Time in s\": 2233.020054       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 4.695384979248047,         \"Time in s\": 2572.124593       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 4.721019744873047,         \"Time in s\": 2930.571035       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 4.747562408447266,         \"Time in s\": 3309.032834       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 4.741054534912109,         \"Time in s\": 3705.221814       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 4.678241729736328,         \"Time in s\": 4115.910057       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 4.619670867919922,         \"Time in s\": 4539.977119       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 4.749675750732422,         \"Time in s\": 4977.188868       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 4.678524017333984,         \"Time in s\": 5426.058059       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 4.705173492431641,         \"Time in s\": 5886.859448       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 4.729236602783203,         \"Time in s\": 6359.258672       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 4.729305267333984,         \"Time in s\": 6843.735511       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 4.741458892822266,         \"Time in s\": 7339.76837       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 4.677211761474609,         \"Time in s\": 7847.750223999999       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 4.833148956298828,         \"Time in s\": 8367.044639       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 4.807292938232422,         \"Time in s\": 8898.416265       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 4.893611907958984,         \"Time in s\": 9441.1614       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 4.877178192138672,         \"Time in s\": 9993.506038       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 4.888896942138672,         \"Time in s\": 10554.524537       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 4.783634185791016,         \"Time in s\": 11123.611551       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 4.831531524658203,         \"Time in s\": 11701.029088       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 4.854015350341797,         \"Time in s\": 12286.755546       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 4.858226776123047,         \"Time in s\": 12880.441843       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 4.846561431884766,         \"Time in s\": 13482.274686       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 4.872089385986328,         \"Time in s\": 14092.292627       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 4.843868255615234,         \"Time in s\": 14710.448755       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 4.835132598876953,         \"Time in s\": 15336.827122       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 4.892154693603516,         \"Time in s\": 15971.964918999998       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 4.812671661376953,         \"Time in s\": 16613.982513       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 4.880641937255859,         \"Time in s\": 17262.391145999998       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 4.831180572509766,         \"Time in s\": 17916.095854       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 4.851375579833984,         \"Time in s\": 18574.918078       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 4.851016998291016,         \"Time in s\": 19239.025055       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 4.869503021240234,         \"Time in s\": 19908.351772       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 4.886287689208984,         \"Time in s\": 20582.843626       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 4.888690948486328,         \"Time in s\": 21262.535161       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 4.888484954833984,         \"Time in s\": 21947.343422       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 4.876293182373047,         \"Time in s\": 22637.362347       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 4.905620574951172,         \"Time in s\": 23332.514825       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 4.880191802978516,         \"Time in s\": 24032.848442       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 4.888683319091797,         \"Time in s\": 24733.238041       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6761904761904762,         \"F1\": 0.6136363636363638,         \"Memory in Mb\": 0.1434221267700195,         \"Time in s\": 0.374142       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7772511848341233,         \"F1\": 0.7374301675977653,         \"Memory in Mb\": 0.2354059219360351,         \"Time in s\": 1.3677169999999998       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7886435331230284,         \"F1\": 0.7527675276752769,         \"Memory in Mb\": 0.3270235061645508,         \"Time in s\": 3.238746       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7990543735224587,         \"F1\": 0.7658402203856748,         \"Memory in Mb\": 0.419011116027832,         \"Time in s\": 6.2520690000000005       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8015122873345936,         \"F1\": 0.7575057736720554,         \"Memory in Mb\": 2.719620704650879,         \"Time in s\": 30.609494       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8173228346456692,         \"F1\": 0.7777777777777779,         \"Memory in Mb\": 3.159085273742676,         \"Time in s\": 56.745796       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8259109311740891,         \"F1\": 0.7839195979899498,         \"Memory in Mb\": 3.6036806106567374,         \"Time in s\": 84.9251       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8299881936245572,         \"F1\": 0.7913043478260869,         \"Memory in Mb\": 4.0666093826293945,         \"Time in s\": 115.117686       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8352570828961176,         \"F1\": 0.7963683527885861,         \"Memory in Mb\": 4.521588325500488,         \"Time in s\": 147.5017       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8470254957507082,         \"F1\": 0.8094117647058824,         \"Memory in Mb\": 4.660099983215332,         \"Time in s\": 182.008963       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8497854077253219,         \"F1\": 0.8132337246531482,         \"Memory in Mb\": 4.474972724914551,         \"Time in s\": 218.357961       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8489378442171518,         \"F1\": 0.8135922330097087,         \"Memory in Mb\": 4.3258256912231445,         \"Time in s\": 256.411001       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8482207697893972,         \"F1\": 0.8112014453477868,         \"Memory in Mb\": 4.1847429275512695,         \"Time in s\": 296.011707       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8530006743088334,         \"F1\": 0.8180300500834724,         \"Memory in Mb\": 4.276310920715332,         \"Time in s\": 337.212047       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8539962240402769,         \"F1\": 0.8198757763975156,         \"Memory in Mb\": 4.522702217102051,         \"Time in s\": 380.364724       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8584070796460177,         \"F1\": 0.8250728862973761,         \"Memory in Mb\": 4.59923267364502,         \"Time in s\": 425.089112       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8622987229317046,         \"F1\": 0.8315217391304348,         \"Memory in Mb\": 4.609700202941895,         \"Time in s\": 471.420384       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8610382800209754,         \"F1\": 0.8319594166138238,         \"Memory in Mb\": 4.583279609680176,         \"Time in s\": 519.2335119999999       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8584202682563339,         \"F1\": 0.8302561048243002,         \"Memory in Mb\": 4.509037971496582,         \"Time in s\": 568.4848139999999       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8612553091080698,         \"F1\": 0.8355704697986577,         \"Memory in Mb\": 4.487088203430176,         \"Time in s\": 619.150273       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8624719101123596,         \"F1\": 0.8370607028753994,         \"Memory in Mb\": 4.479489326477051,         \"Time in s\": 671.27983       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8614328614328615,         \"F1\": 0.8357905439755974,         \"Memory in Mb\": 4.476758003234863,         \"Time in s\": 724.8282939999999       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8621255642183012,         \"F1\": 0.8364167478091528,         \"Memory in Mb\": 4.495999336242676,         \"Time in s\": 779.843051       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8623672827369249,         \"F1\": 0.8375116063138347,         \"Memory in Mb\": 4.492741584777832,         \"Time in s\": 836.3752579999999       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8618346545866364,         \"F1\": 0.8374777975133214,         \"Memory in Mb\": 4.535428047180176,         \"Time in s\": 894.373804       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8627949183303085,         \"F1\": 0.8384615384615384,         \"Memory in Mb\": 4.529454231262207,         \"Time in s\": 953.727688       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8661307235232436,         \"F1\": 0.842061855670103,         \"Memory in Mb\": 4.489285469055176,         \"Time in s\": 1014.523259       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8678800134816312,         \"F1\": 0.8437001594896333,         \"Memory in Mb\": 4.522076606750488,         \"Time in s\": 1076.832167       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8662544744549301,         \"F1\": 0.8419838523644751,         \"Memory in Mb\": 4.4861345291137695,         \"Time in s\": 1140.498025       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8678829820698333,         \"F1\": 0.8432835820895522,         \"Memory in Mb\": 4.490513801574707,         \"Time in s\": 1205.597432       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8684931506849315,         \"F1\": 0.8433647570703406,         \"Memory in Mb\": 4.50365161895752,         \"Time in s\": 1272.097546       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8690651725154822,         \"F1\": 0.8449720670391062,         \"Memory in Mb\": 4.519848823547363,         \"Time in s\": 1340.030829       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8687446382613668,         \"F1\": 0.8439306358381503,         \"Memory in Mb\": 4.534294128417969,         \"Time in s\": 1409.423343       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8701082431307244,         \"F1\": 0.8451356717405691,         \"Memory in Mb\": 4.515525817871094,         \"Time in s\": 1480.1764939999998       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8705850633593961,         \"F1\": 0.8462524023062139,         \"Memory in Mb\": 4.521697998046875,         \"Time in s\": 1552.3097799999998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8718217562254259,         \"F1\": 0.847900466562986,         \"Memory in Mb\": 4.52362060546875,         \"Time in s\": 1625.8426229999998       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8704412139760265,         \"F1\": 0.845873786407767,         \"Memory in Mb\": 4.511474609375,         \"Time in s\": 1700.7302089999998       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8698783213310156,         \"F1\": 0.8450620934358367,         \"Memory in Mb\": 4.530387878417969,         \"Time in s\": 1777.041184       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8707960319380595,         \"F1\": 0.8461981566820277,         \"Memory in Mb\": 4.540306091308594,         \"Time in s\": 1854.755673       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8723755602736495,         \"F1\": 0.8485018202184262,         \"Memory in Mb\": 4.5452880859375,         \"Time in s\": 1933.828162       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8734177215189873,         \"F1\": 0.8498088476242489,         \"Memory in Mb\": 4.5819854736328125,         \"Time in s\": 2014.179242       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8732869018198157,         \"F1\": 0.8494394020288306,         \"Memory in Mb\": 4.578292846679688,         \"Time in s\": 2095.838517       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8720649550142637,         \"F1\": 0.8482166102577455,         \"Memory in Mb\": 4.539161682128906,         \"Time in s\": 2178.6750019999995       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8719708342268926,         \"F1\": 0.8485156051763512,         \"Memory in Mb\": 4.509727478027344,         \"Time in s\": 2262.6244259999994       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8712518347661984,         \"F1\": 0.8472636815920398,         \"Memory in Mb\": 4.5496673583984375,         \"Time in s\": 2347.803564       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8717948717948718,         \"F1\": 0.8474493531852575,         \"Memory in Mb\": 4.560760498046875,         \"Time in s\": 2434.119985       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8725155591246737,         \"F1\": 0.8487735175041676,         \"Memory in Mb\": 4.513671875,         \"Time in s\": 2521.5491019999995       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8718301552978179,         \"F1\": 0.8480186480186479,         \"Memory in Mb\": 4.541267395019531,         \"Time in s\": 2610.1395069999994       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8725207009435779,         \"F1\": 0.848927430397079,         \"Memory in Mb\": 4.582290649414063,         \"Time in s\": 2699.957936       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8726174749952821,         \"F1\": 0.8491620111731844,         \"Memory in Mb\": 4.584030151367188,         \"Time in s\": 2790.9651129999997       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8795580110497238,         \"F1\": 0.880351262349067,         \"Memory in Mb\": 4.715929985046387,         \"Time in s\": 35.551681       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8807288790723358,         \"F1\": 0.8536585365853658,         \"Memory in Mb\": 4.9170331954956055,         \"Time in s\": 87.613259       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689731321310269,         \"F1\": 0.8344186046511628,         \"Memory in Mb\": 4.988085746765137,         \"Time in s\": 154.923184       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8793817278498481,         \"F1\": 0.8493622888659084,         \"Memory in Mb\": 4.879870414733887,         \"Time in s\": 235.92708       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8792227864870833,         \"F1\": 0.8405712620227338,         \"Memory in Mb\": 5.017077445983887,         \"Time in s\": 328.79801       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689972401103956,         \"F1\": 0.8260869565217391,         \"Memory in Mb\": 4.985064506530762,         \"Time in s\": 432.967134       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8680018924459865,         \"F1\": 0.8269588587967748,         \"Memory in Mb\": 4.949084281921387,         \"Time in s\": 548.642546       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8643576652407893,         \"F1\": 0.8194010655888295,         \"Memory in Mb\": 4.962946891784668,         \"Time in s\": 674.769885       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8671654605666625,         \"F1\": 0.8317016317016317,         \"Memory in Mb\": 5.020190238952637,         \"Time in s\": 811.067026       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8711778341980351,         \"F1\": 0.8417627118644068,         \"Memory in Mb\": 5.0752363204956055,         \"Time in s\": 957.595858       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8706472654290015,         \"F1\": 0.845165165165165,         \"Memory in Mb\": 4.979113578796387,         \"Time in s\": 1113.746382       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8737006715113605,         \"F1\": 0.8516156922079325,         \"Memory in Mb\": 4.9885969161987305,         \"Time in s\": 1279.290866       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8733972998216863,         \"F1\": 0.8507059176930009,         \"Memory in Mb\": 5.106616020202637,         \"Time in s\": 1454.661274       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.873294961759836,         \"F1\": 0.8513551012857274,         \"Memory in Mb\": 5.2120466232299805,         \"Time in s\": 1640.606202       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8755611156082125,         \"F1\": 0.8560973534167304,         \"Memory in Mb\": 5.144991874694824,         \"Time in s\": 1836.835898       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8765781303897896,         \"F1\": 0.8583643416989946,         \"Memory in Mb\": 5.178118705749512,         \"Time in s\": 2042.985996       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8766963184208818,         \"F1\": 0.8575500712624708,         \"Memory in Mb\": 5.108157157897949,         \"Time in s\": 2257.366998       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8711596247010487,         \"F1\": 0.8493366798135532,         \"Memory in Mb\": 5.1558027267456055,         \"Time in s\": 2480.044725       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8687038865973392,         \"F1\": 0.8429683157309616,         \"Memory in Mb\": 5.140070915222168,         \"Time in s\": 2710.925816       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689773166289531,         \"F1\": 0.8433623647400369,         \"Memory in Mb\": 5.172907829284668,         \"Time in s\": 2950.660411       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8696977660972405,         \"F1\": 0.8421320766732471,         \"Memory in Mb\": 5.328249931335449,         \"Time in s\": 3199.829749       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8659876574180925,         \"F1\": 0.8380132209351688,         \"Memory in Mb\": 5.355593681335449,         \"Time in s\": 3457.907085       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8617843259586313,         \"F1\": 0.8322851153039832,         \"Memory in Mb\": 5.442904472351074,         \"Time in s\": 3724.572015       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.862668445016787,         \"F1\": 0.8307064293003741,         \"Memory in Mb\": 5.347712516784668,         \"Time in s\": 3998.841393       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8610093160845953,         \"F1\": 0.8268045774647886,         \"Memory in Mb\": 5.363558769226074,         \"Time in s\": 4280.436632       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.85434090426661,         \"F1\": 0.8163571160948456,         \"Memory in Mb\": 5.3763532638549805,         \"Time in s\": 4569.013267       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8534401700666366,         \"F1\": 0.8138145936120489,         \"Memory in Mb\": 5.330439567565918,         \"Time in s\": 4864.708005       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8518941932431899,         \"F1\": 0.8121030257564391,         \"Memory in Mb\": 5.456484794616699,         \"Time in s\": 5167.517117       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530811098846725,         \"F1\": 0.8130931628897928,         \"Memory in Mb\": 5.321070671081543,         \"Time in s\": 5477.642376000001       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8524964126715479,         \"F1\": 0.8125672074430782,         \"Memory in Mb\": 5.426630973815918,         \"Time in s\": 5794.594214000001       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8496350364963504,         \"F1\": 0.8078795323233702,         \"Memory in Mb\": 5.366648674011231,         \"Time in s\": 6118.251751000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8475043979165948,         \"F1\": 0.8032575319300432,         \"Memory in Mb\": 5.4148359298706055,         \"Time in s\": 6448.591708000001       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8460046158477439,         \"F1\": 0.8002429711905589,         \"Memory in Mb\": 5.3892927169799805,         \"Time in s\": 6785.693940000001       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8462162776352953,         \"F1\": 0.7992881657556884,         \"Memory in Mb\": 5.532000541687012,         \"Time in s\": 7130.317230000001       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8429783342268756,         \"F1\": 0.7938387644403958,         \"Memory in Mb\": 5.507189750671387,         \"Time in s\": 7481.445887000001       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8419745515866932,         \"F1\": 0.7926122646064703,         \"Memory in Mb\": 5.485476493835449,         \"Time in s\": 7839.281994000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8421884788639957,         \"F1\": 0.7931492922499414,         \"Memory in Mb\": 5.629275321960449,         \"Time in s\": 8203.623919000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400092950300636,         \"F1\": 0.7894656371837016,         \"Memory in Mb\": 5.587969779968262,         \"Time in s\": 8574.868737       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8398947159878867,         \"F1\": 0.7879287722586691,         \"Memory in Mb\": 5.669405937194824,         \"Time in s\": 8954.118864       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8408896492728828,         \"F1\": 0.7879523389232127,         \"Memory in Mb\": 5.650286674499512,         \"Time in s\": 9340.118817       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397092475434109,         \"F1\": 0.7854569040069184,         \"Memory in Mb\": 5.652537345886231,         \"Time in s\": 9731.872155       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8398202412551575,         \"F1\": 0.7854402083993381,         \"Memory in Mb\": 5.638819694519043,         \"Time in s\": 10129.488521       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8406704828400544,         \"F1\": 0.787685992816829,         \"Memory in Mb\": 5.6699628829956055,         \"Time in s\": 10532.813227       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.841381732433585,         \"F1\": 0.7910235647949235,         \"Memory in Mb\": 5.623560905456543,         \"Time in s\": 10941.028498       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8422085408030612,         \"F1\": 0.7943480067772769,         \"Memory in Mb\": 5.627467155456543,         \"Time in s\": 11353.937417       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8431673665266947,         \"F1\": 0.7973835947671896,         \"Memory in Mb\": 5.641619682312012,         \"Time in s\": 11771.547134       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8438505436697118,         \"F1\": 0.7987529888919157,         \"Memory in Mb\": 5.640711784362793,         \"Time in s\": 12193.871152999998       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.843999356129418,         \"F1\": 0.7991592160577892,         \"Memory in Mb\": 5.725451469421387,         \"Time in s\": 12620.815871999996       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8432635775910616,         \"F1\": 0.7972256221950225,         \"Memory in Mb\": 5.7456769943237305,         \"Time in s\": 13052.460535999997       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8436830835117773,         \"F1\": 0.7980031379261162,         \"Memory in Mb\": 5.754740715026856,         \"Time in s\": 13488.904282999996       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8436803425216834,         \"F1\": 0.7979576118892089,         \"Memory in Mb\": 5.757502555847168,         \"Time in s\": 13925.545040999996       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.1740083694458007,         \"Time in s\": 0.162813       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7636363636363637,         \"Memory in Mb\": 0.2024965286254882,         \"Time in s\": 0.520257       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.8048780487804877,         \"Memory in Mb\": 0.2315149307250976,         \"Time in s\": 1.0500919999999998       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.819047619047619,         \"Memory in Mb\": 0.2600297927856445,         \"Time in s\": 1.7634529999999995       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8145161290322581,         \"F1\": 0.8217054263565893,         \"Memory in Mb\": 0.2885446548461914,         \"Time in s\": 2.765045       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.830188679245283,         \"Memory in Mb\": 0.3175630569458008,         \"Time in s\": 4.083864999999999       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8390804597701148,         \"Memory in Mb\": 0.3460779190063476,         \"Time in s\": 5.803779       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8391959798994975,         \"F1\": 0.8383838383838383,         \"Memory in Mb\": 0.3750925064086914,         \"Time in s\": 7.866028       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8348214285714286,         \"F1\": 0.8294930875576038,         \"Memory in Mb\": 0.4036073684692383,         \"Time in s\": 10.317012       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.8264462809917356,         \"Memory in Mb\": 0.4321222305297851,         \"Time in s\": 13.231482       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8357664233576643,         \"F1\": 0.8288973384030419,         \"Memory in Mb\": 0.4616479873657226,         \"Time in s\": 16.680039999999998       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.842809364548495,         \"F1\": 0.8327402135231317,         \"Memory in Mb\": 0.4901628494262695,         \"Time in s\": 20.639261       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8549382716049383,         \"F1\": 0.8417508417508418,         \"Memory in Mb\": 0.5191812515258789,         \"Time in s\": 25.174847       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8624641833810889,         \"F1\": 0.8471337579617835,         \"Memory in Mb\": 0.5476961135864258,         \"Time in s\": 30.349829       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8609625668449198,         \"F1\": 0.8433734939759037,         \"Memory in Mb\": 0.5762109756469727,         \"Time in s\": 36.11991       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8621553884711779,         \"F1\": 0.8424068767908309,         \"Memory in Mb\": 0.605229377746582,         \"Time in s\": 42.598398       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8632075471698113,         \"F1\": 0.839779005524862,         \"Memory in Mb\": 0.6337442398071289,         \"Time in s\": 49.849208       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663697104677061,         \"F1\": 0.8412698412698413,         \"Memory in Mb\": 0.6627893447875977,         \"Time in s\": 57.823258       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8649789029535865,         \"F1\": 0.8407960199004976,         \"Memory in Mb\": 0.6913042068481445,         \"Time in s\": 66.560459       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8657314629258517,         \"F1\": 0.8445475638051043,         \"Memory in Mb\": 2.87209415435791,         \"Time in s\": 96.818717       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8683206106870229,         \"F1\": 0.8442437923250564,         \"Memory in Mb\": 2.980504035949707,         \"Time in s\": 127.963431       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8688524590163934,         \"F1\": 0.8461538461538463,         \"Memory in Mb\": 3.08364200592041,         \"Time in s\": 159.977649       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8710801393728222,         \"F1\": 0.848360655737705,         \"Memory in Mb\": 3.1883134841918945,         \"Time in s\": 192.908804       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8747913188647746,         \"F1\": 0.8502994011976048,         \"Memory in Mb\": 3.300492286682129,         \"Time in s\": 226.71997       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8733974358974359,         \"F1\": 0.8460038986354775,         \"Memory in Mb\": 3.412938117980957,         \"Time in s\": 261.370264       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8767334360554699,         \"F1\": 0.8523985239852399,         \"Memory in Mb\": 3.522160530090332,         \"Time in s\": 296.927054       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8783382789317508,         \"F1\": 0.8571428571428572,         \"Memory in Mb\": 3.6335840225219727,         \"Time in s\": 333.391974       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.882689556509299,         \"F1\": 0.8605442176870748,         \"Memory in Mb\": 3.7482118606567374,         \"Time in s\": 370.807982       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8839779005524862,         \"F1\": 0.864516129032258,         \"Memory in Mb\": 3.861550331115722,         \"Time in s\": 409.129822       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8851802403204272,         \"F1\": 0.8664596273291927,         \"Memory in Mb\": 3.975522041320801,         \"Time in s\": 448.343177       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8863049095607235,         \"F1\": 0.8670694864048338,         \"Memory in Mb\": 4.095002174377441,         \"Time in s\": 488.481904       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.886107634543179,         \"F1\": 0.8683068017366136,         \"Memory in Mb\": 4.146827697753906,         \"Time in s\": 529.573688       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8859223300970874,         \"F1\": 0.8690807799442897,         \"Memory in Mb\": 4.390903472900391,         \"Time in s\": 571.6173799999999       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8869257950530035,         \"F1\": 0.8695652173913044,         \"Memory in Mb\": 4.504707336425781,         \"Time in s\": 614.6492939999999       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8890160183066361,         \"F1\": 0.8711819389110226,         \"Memory in Mb\": 4.624469757080078,         \"Time in s\": 658.6172529999999       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876529477196885,         \"F1\": 0.869340232858991,         \"Memory in Mb\": 4.741554260253906,         \"Time in s\": 703.5317429999999       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896103896103896,         \"F1\": 0.8728179551122195,         \"Memory in Mb\": 4.862430572509766,         \"Time in s\": 749.5245539999999       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8904109589041096,         \"F1\": 0.8752997601918464,         \"Memory in Mb\": 4.984291076660156,         \"Time in s\": 796.5006999999998       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8921971252566735,         \"F1\": 0.8771929824561404,         \"Memory in Mb\": 5.102375030517578,         \"Time in s\": 844.5149469999998       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8928928928928929,         \"F1\": 0.8779931584948689,         \"Memory in Mb\": 5.219093322753906,         \"Time in s\": 893.5084249999998       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.892578125,         \"F1\": 0.8780487804878048,         \"Memory in Mb\": 5.178688049316406,         \"Time in s\": 943.5715689999996       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.894184938036225,         \"F1\": 0.8802588996763754,         \"Memory in Mb\": 5.151969909667969,         \"Time in s\": 994.6247309999998       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929236499068901,         \"F1\": 0.8798328108672936,         \"Memory in Mb\": 5.117225646972656,         \"Time in s\": 1046.6512989999997       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8944494995450409,         \"F1\": 0.8816326530612245,         \"Memory in Mb\": 5.075950622558594,         \"Time in s\": 1099.6701919999996       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8959074733096085,         \"F1\": 0.884272997032641,         \"Memory in Mb\": 5.007194519042969,         \"Time in s\": 1153.6019869999996       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.896431679721497,         \"F1\": 0.8845780795344327,         \"Memory in Mb\": 4.982025146484375,         \"Time in s\": 1208.4750359999996       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8952299829642248,         \"F1\": 0.8829686013320648,         \"Memory in Mb\": 4.96966552734375,         \"Time in s\": 1264.1689839999997       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.896580483736447,         \"F1\": 0.8841121495327102,         \"Memory in Mb\": 4.9371490478515625,         \"Time in s\": 1320.7795169999995       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8970588235294118,         \"F1\": 0.8844036697247706,         \"Memory in Mb\": 4.8813018798828125,         \"Time in s\": 1378.3046599999998       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8967173738991193,         \"F1\": 0.8845120859444942,         \"Memory in Mb\": 4.820304870605469,         \"Time in s\": 1436.7224909999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.661611557006836,         \"Time in s\": 43.527964       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.557134628295898,         \"Time in s\": 113.03315       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.496244430541992,         \"Time in s\": 201.747221       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.508665084838867,         \"Time in s\": 310.754596       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.565656661987305,         \"Time in s\": 436.825284       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.554738998413086,         \"Time in s\": 579.964386       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.492513656616211,         \"Time in s\": 739.485002       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372397030808,         \"F1\": 0.7777777777777778,         \"Memory in Mb\": 4.532373428344727,         \"Time in s\": 915.25293       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997664369963798,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.528841018676758,         \"Time in s\": 1107.42481       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997897945241474,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.520586013793945,         \"Time in s\": 1314.036215       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089050257978,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.519166946411133,         \"Time in s\": 1534.186276       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248303043572,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.512857437133789,         \"Time in s\": 1768.1243410000002       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999838305441022,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.568696975708008,         \"Time in s\": 2014.7470960000005       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998123193573816,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.55253791809082,         \"Time in s\": 2273.6909760000003       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248318385652,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.554193496704102,         \"Time in s\": 2543.9053730000005       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998357802082308,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.487833023071289,         \"Time in s\": 2824.6447140000005       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998454404945905,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.52525520324707,         \"Time in s\": 3116.2234200000003       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998540273844628,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.608850479125977,         \"Time in s\": 3418.6303260000004       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999861710366191,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.462549209594727,         \"Time in s\": 3731.216163000001       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686250295594,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.517663955688477,         \"Time in s\": 4053.885436000001       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998748811370802,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.596040725708008,         \"Time in s\": 4386.480402       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998805684939688,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.581964492797852,         \"Time in s\": 4729.507439       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998857612867847,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.56077766418457,         \"Time in s\": 5082.623411       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998905213373912,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.554697036743164,         \"Time in s\": 5446.283888999999       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998949005759448,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.589784622192383,         \"Time in s\": 5821.612630999999       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998989429431856,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.514947891235352,         \"Time in s\": 6206.523862999999       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998637602179836,         \"F1\": 0.72,         \"Memory in Mb\": 4.571069717407227,         \"Time in s\": 6600.877267999999       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686260158024,         \"F1\": 0.72,         \"Memory in Mb\": 4.544900894165039,         \"Time in s\": 7002.824473       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998731562352772,         \"F1\": 0.72,         \"Memory in Mb\": 4.490015029907227,         \"Time in s\": 7412.266105       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997197358510396,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 4.520219802856445,         \"Time in s\": 7829.032212999999       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997287767832926,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 4.567926406860352,         \"Time in s\": 8253.169596       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372526480006,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 4.602060317993164,         \"Time in s\": 8684.574786       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997133666677284,         \"F1\": 0.5,         \"Memory in Mb\": 4.518564224243164,         \"Time in s\": 9122.220249       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997217971901516,         \"F1\": 0.5,         \"Memory in Mb\": 4.562410354614258,         \"Time in s\": 9566.245368       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997297459612036,         \"F1\": 0.5,         \"Memory in Mb\": 4.587350845336914,         \"Time in s\": 10016.770375       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997226560789408,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.58268928527832,         \"Time in s\": 10473.685786       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997301519670502,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.552003860473633,         \"Time in s\": 10937.213874       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372533292768,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.568490982055664,         \"Time in s\": 11407.187031       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997439905141748,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.501398086547852,         \"Time in s\": 11883.434676       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997503908354024,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.530572891235352,         \"Time in s\": 12366.124718       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999756478941837,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.565195083618164,         \"Time in s\": 12855.350972       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999762277134814,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.555276870727539,         \"Time in s\": 13350.750206       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997678056411008,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.523683547973633,         \"Time in s\": 13852.638924       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997730828486464,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.51640510559082,         \"Time in s\": 14360.452684       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997781255108952,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.554742813110352,         \"Time in s\": 14874.156807       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997829489244549,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.55351448059082,         \"Time in s\": 15393.589182       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997763864042932,         \"F1\": 0.5,         \"Memory in Mb\": 4.576028823852539,         \"Time in s\": 15919.905447       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997700973254656,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.509759902954102,         \"Time in s\": 16451.368555       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997747892671,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.633722305297852,         \"Time in s\": 16987.639193000003       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997792935290964,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.606340408325195,         \"Time in s\": 17528.67073       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997793074457464,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.602045059204102,         \"Time in s\": 18069.786262       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5333333333333333,         \"F1\": 0.5242718446601942,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.004468       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5876777251184834,         \"F1\": 0.5538461538461539,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.067972       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5457413249211357,         \"F1\": 0.5102040816326531,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.134988       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460992907801419,         \"F1\": 0.5025906735751295,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.20522       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5671077504725898,         \"F1\": 0.5096359743040686,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.337716       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5464566929133858,         \"F1\": 0.4875444839857651,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.474055       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5573549257759784,         \"F1\": 0.4875,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.646583       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5501770956316411,         \"F1\": 0.4816326530612245,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.822555       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5487932843651626,         \"F1\": 0.4794188861985472,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.00209       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5448536355051936,         \"F1\": 0.4679911699779249,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.292978       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.534763948497854,         \"F1\": 0.4590818363273453,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.5875979999999998       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5287175452399685,         \"F1\": 0.456935630099728,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.885535       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5286855482933914,         \"F1\": 0.4523206751054852,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.211477       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5252865812542145,         \"F1\": 0.4491392801251955,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.547239       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5204531151667715,         \"F1\": 0.4437956204379563,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.88734       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5227138643067847,         \"F1\": 0.4455106237148732,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.258534       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.524153248195447,         \"F1\": 0.4523961661341854,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.633124       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5233350812794966,         \"F1\": 0.456664674237896,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.01125       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5171385991058122,         \"F1\": 0.4563758389261745,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.505139000000001       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5143935818782445,         \"F1\": 0.4581358609794628,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.002779       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5114606741573033,         \"F1\": 0.4545910687405921,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.503925000000001       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.510939510939511,         \"F1\": 0.4550669216061185,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.074663000000001       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5104636848584325,         \"F1\": 0.4530032095369097,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.648598000000001       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5084545812033032,         \"F1\": 0.4546247818499127,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.226634000000001       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5096262740656852,         \"F1\": 0.458072590738423,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.863299       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5092558983666061,         \"F1\": 0.4574638844301765,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.503527       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5103110800419434,         \"F1\": 0.4563445867287544,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.147193       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5133131108864173,         \"F1\": 0.457957957957958,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.82546       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099251545720794,         \"F1\": 0.4563176895306859,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.507099       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5102233406731677,         \"F1\": 0.4538758330410382,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.191893       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5095890410958904,         \"F1\": 0.4522271336280176,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.975438       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5107637864936597,         \"F1\": 0.4558871761233191,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 12.764918       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5124392336288247,         \"F1\": 0.4557931694861155,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 13.557573       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5134610047182903,         \"F1\": 0.4544039838157485,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 14.3795       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5122674575357239,         \"F1\": 0.4546276756104914,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 15.204998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.510615989515072,         \"F1\": 0.4536142815335089,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 16.116361       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5090538128028564,         \"F1\": 0.4507845934379457,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 17.035489000000002       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5108020859200397,         \"F1\": 0.452473596442468,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 17.958008000000003       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5102830873457537,         \"F1\": 0.4517876489707476,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 18.927027       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5102618542108988,         \"F1\": 0.4525316455696203,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 19.900154000000004       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5074798619102416,         \"F1\": 0.4490216271884655,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 20.87662300000001       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099977533138621,         \"F1\": 0.4513207547169811,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 21.913356000000007       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099846390168971,         \"F1\": 0.4539007092198581,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 22.953869000000008       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099721209521767,         \"F1\": 0.4553039332538737,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 23.99911400000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5110085971901867,         \"F1\": 0.4556489262371615,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 25.08372100000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5109743589743589,         \"F1\": 0.4539624370132845,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 26.17171900000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099377635013049,         \"F1\": 0.453792794808682,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 27.26320500000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099272655789266,         \"F1\": 0.4536489151873767,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 28.44143600000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5097246293086848,         \"F1\": 0.4531786941580756,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 29.62357400000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5095301000188714,         \"F1\": 0.4529572721532309,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 30.80903600000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530386740331491,         \"F1\": 0.8500563697857948,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.224121       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8619547211485368,         \"F1\": 0.8287671232876712,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.785464       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8450496871549503,         \"F1\": 0.80958842152872,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.64751       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8418437758763456,         \"F1\": 0.8056968463886063,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.805953       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8388165157871494,         \"F1\": 0.7960893854748604,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.158177       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8413983440662374,         \"F1\": 0.7995348837209302,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.857693       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8370919413341744,         \"F1\": 0.7958094485076103,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.811494       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8359321098385539,         \"F1\": 0.7948231233822259,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.005109       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8352753587636453,         \"F1\": 0.8021799970540581,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 12.510532       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8358538470029805,         \"F1\": 0.8069081937410726,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 15.278308       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8372303060712494,         \"F1\": 0.8118765947575969,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 18.289259       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8368135406126391,         \"F1\": 0.8140461215932915,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 21.565546       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8374798335739153,         \"F1\": 0.8150724637681159,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 25.041396       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8384451628163684,         \"F1\": 0.8161177420802298,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 28.814916       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.842004562513798,         \"F1\": 0.8223417459660736,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 32.85712       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8448430493273542,         \"F1\": 0.8264794383149447,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 37.134508       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8460489578598792,         \"F1\": 0.8270983738058776,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 41.682175       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.844851904090268,         \"F1\": 0.8251313243019076,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 46.494991       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8443618195549875,         \"F1\": 0.8222177981286084,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 51.515798       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8450797505381091,         \"F1\": 0.8227792158595871,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 56.800748       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8462023653088042,         \"F1\": 0.8224083515416363,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 62.372633       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.847523957653906,         \"F1\": 0.8255753888538139,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 68.180409       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.84661899505687,         \"F1\": 0.8249917862227577,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 74.270057       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8452835395299637,         \"F1\": 0.8209495422610177,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 80.612623       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8444081416398075,         \"F1\": 0.8188733552631579,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 87.17507       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8451284228401613,         \"F1\": 0.8194595664654062,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 93.968638       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8464903315481788,         \"F1\": 0.8198781599270878,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 100.983267       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8462963692986951,         \"F1\": 0.8199492034172247,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 108.278888       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8477524454763445,         \"F1\": 0.8213168944876262,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 115.769594       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8495529636851982,         \"F1\": 0.8240457851026293,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 123.465792       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8509880719245149,         \"F1\": 0.825107610012955,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 131.36678899999998       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8521265220240765,         \"F1\": 0.8258237516759436,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 139.55273799999998       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8531959728400843,         \"F1\": 0.8268160833366216,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 147.964309       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8537480115573158,         \"F1\": 0.8267107743201139,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 156.664426       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530385694913116,         \"F1\": 0.8259895444361464,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 165.606267       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8536869538555879,         \"F1\": 0.8269760696156635,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 174.782391       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8541511291429253,         \"F1\": 0.8276032300151628,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 184.217189       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8549684840386905,         \"F1\": 0.8286724084685859,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 193.875362       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8555175048821215,         \"F1\": 0.8284321962695346,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 203.791365       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8545213720025387,         \"F1\": 0.8259146744155329,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 213.957306       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.854354556467896,         \"F1\": 0.8252696854208386,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 224.377202       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8545636119944285,         \"F1\": 0.8247736052181622,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 234.998191       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8548142824139435,         \"F1\": 0.8254213223038459,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 245.897409       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8546521837292728,         \"F1\": 0.8262981172802495,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 257.034489       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8540067207927592,         \"F1\": 0.8267652366261132,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 268.379106       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8537012597480504,         \"F1\": 0.8274320002264302,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 279.987419       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8536201592259458,         \"F1\": 0.8277177368086459,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 291.808183       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.853473451836181,         \"F1\": 0.8276626818845675,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 303.899029       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533777847858897,         \"F1\": 0.8271686890948196,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 316.239245       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533521711296055,         \"F1\": 0.8273155007928462,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 328.813976       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533027300214076,         \"F1\": 0.8272294856132872,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 341.389555       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.625,         \"F1\": 0.64,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.001863       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6530612244897959,         \"F1\": 0.6222222222222223,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.005016       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5675675675675675,         \"F1\": 0.5555555555555556,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.009415       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5555555555555556,         \"F1\": 0.5416666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.115037       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5241935483870968,         \"F1\": 0.5123966942148761,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.222127       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5234899328859061,         \"F1\": 0.5298013245033113,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.330326       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5229885057471264,         \"F1\": 0.496969696969697,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.439628       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.507537688442211,         \"F1\": 0.4787234042553192,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.550035       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5,         \"F1\": 0.4509803921568627,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.6616070000000001       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5180722891566265,         \"F1\": 0.4782608695652174,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.774476       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5218978102189781,         \"F1\": 0.4738955823293172,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.8884620000000001       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5217391304347826,         \"F1\": 0.460377358490566,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.0035580000000002       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5216049382716049,         \"F1\": 0.4483985765124554,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.151113       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5329512893982808,         \"F1\": 0.4511784511784511,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.299965       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5267379679144385,         \"F1\": 0.4380952380952381,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.45006       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5263157894736842,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.6013830000000002       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5424528301886793,         \"F1\": 0.436046511627907,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.7539290000000003       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5367483296213809,         \"F1\": 0.4222222222222222,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.9077010000000003       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5358649789029536,         \"F1\": 0.4329896907216494,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.0627030000000004       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5370741482965932,         \"F1\": 0.4460431654676259,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.2669650000000003       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5400763358778626,         \"F1\": 0.4382284382284382,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.491531       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5391621129326047,         \"F1\": 0.4415011037527593,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.717762       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5418118466898955,         \"F1\": 0.4416135881104034,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.945135       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5509181969949917,         \"F1\": 0.443064182194617,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.175983       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5560897435897436,         \"F1\": 0.4358452138492871,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.407996       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.551617873651772,         \"F1\": 0.4393063583815029,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.641123       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5459940652818991,         \"F1\": 0.4436363636363636,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.875357       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5464949928469242,         \"F1\": 0.4389380530973452,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.110698999999999       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5441988950276243,         \"F1\": 0.4463087248322148,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.380075       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5367156208277704,         \"F1\": 0.4412238325281803,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.650483       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5310077519379846,         \"F1\": 0.4336973478939157,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.940408       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5294117647058824,         \"F1\": 0.4388059701492537,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.231503       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5266990291262136,         \"F1\": 0.4396551724137931,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.523716       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5241460541813898,         \"F1\": 0.4341736694677871,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.817038       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.522883295194508,         \"F1\": 0.4311050477489768,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.111637       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5272525027808677,         \"F1\": 0.4340878828229028,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.407366       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5227272727272727,         \"F1\": 0.4338896020539153,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.766113       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5205479452054794,         \"F1\": 0.438964241676942,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.126579       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5174537987679672,         \"F1\": 0.4337349397590361,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.488418999999999       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5185185185185185,         \"F1\": 0.4361078546307151,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.851253       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.517578125,         \"F1\": 0.4386363636363636,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.215067       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5138226882745471,         \"F1\": 0.4370860927152318,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.579858       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5111731843575419,         \"F1\": 0.4372990353697749,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.945611       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5122838944494995,         \"F1\": 0.4393305439330544,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.312328       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5124555160142349,         \"F1\": 0.4453441295546558,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.680001999999998       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5143603133159269,         \"F1\": 0.4464285714285714,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.125450999999998       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5187393526405452,         \"F1\": 0.4509232264334305,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.572148       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5187656380316931,         \"F1\": 0.448901623686724,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.020091999999998       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5171568627450981,         \"F1\": 0.4471468662301216,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.469231       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5156124899919936,         \"F1\": 0.4474885844748858,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.919638999999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 0.335236       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 1.143886       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 2.36402       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 4.028138       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 6.117771       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 8.657701       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 11.631       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9985548183669448,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 15.05037       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9984818404764684,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 18.876176       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9986336644069578,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 23.061029       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9987578826676858,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 27.619004       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9988613969783228,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 32.587888       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9989489853666425,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 37.966612       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9989489884013364,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 43.747016       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9990190582959642,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 49.923315       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999080369166092,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 56.476617       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9991344667697064,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 63.442318       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999182553352991,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 70.796392       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992255780506694,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 78.554987       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992643001655324,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 86.688101       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992993343676492,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 95.30254       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993311835662247,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 104.31052400000002       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993602632059952,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 113.72316700000002       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993869194893916,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 123.549629       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994114432252912,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 133.72455100000002       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99943408048184,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 144.361296       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99941611521993,         \"F1\": 0.0625,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 155.342577       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994369686391532,         \"F1\": 0.0625,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 166.71976       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994563838654732,         \"F1\": 0.0625,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 178.568204       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994394717020793,         \"F1\": 0.36,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 190.760423       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994575535665852,         \"F1\": 0.36,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 203.318295       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745052960012,         \"F1\": 0.36,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 216.324675       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994585814834868,         \"F1\": 0.3703703703703703,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 229.751949       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745058036196,         \"F1\": 0.3703703703703703,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 243.519605       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99948952014894,         \"F1\": 0.3703703703703703,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 257.632512       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745062548352,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 272.164854       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994887089902004,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 287.040678       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995021642028404,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 302.255295       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995149293952786,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 317.85437       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99952705631971,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 333.81575100000003       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99953859167927,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 350.111597       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999549577729121,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 366.757049       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995600527936648,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 383.769929       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700517132244,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 401.126049       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796062311698,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 418.855408       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999588745330546,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 436.927356       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995751341681576,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 455.364423       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995839856365568,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 474.187698       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999592475816657,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 493.377496       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999600626385984,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 512.867881       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996006515684936,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 532.358985       }     ]   },   \"params\": [     {       \"name\": \"models\",       \"select\": {         \"type\": \"point\",         \"fields\": [           \"model\"         ]       },       \"bind\": \"legend\"     },     {       \"name\": \"Dataset\",       \"value\": \"Bananas\",       \"bind\": {         \"input\": \"select\",         \"options\": [           \"Bananas\",           \"Elec2\",           \"Phishing\",           \"SMTP\"         ]       }     },     {       \"name\": \"grid\",       \"select\": \"interval\",       \"bind\": \"scales\"     }   ],   \"transform\": [     {       \"filter\": {         \"field\": \"dataset\",         \"equal\": {           \"expr\": \"Dataset\"         }       }     }   ],   \"repeat\": {     \"row\": [       \"Accuracy\",       \"F1\",       \"Memory in Mb\",       \"Time in s\"     ]   },   \"spec\": {     \"width\": \"container\",     \"mark\": \"line\",     \"encoding\": {       \"x\": {         \"field\": \"step\",         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"title\": \"Instance\"         }       },       \"y\": {         \"field\": {           \"repeat\": \"row\"         },         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18         }       },       \"color\": {         \"field\": \"model\",         \"type\": \"ordinal\",         \"scale\": {           \"scheme\": \"category20b\"         },         \"title\": \"Models\",         \"legend\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"labelLimit\": 500         }       },       \"opacity\": {         \"condition\": {           \"param\": \"models\",           \"value\": 1         },         \"value\": 0.2       }     }   } }</p>"},{"location":"benchmarks/Binary%20classification/#datasets","title":"Datasets","text":"Bananas <p>Bananas dataset.</p> <p>An artificial dataset where instances belongs to several clusters with a banana shape. There are two attributes that correspond to the x and y axis, respectively.</p> <pre><code>Name  Bananas                                                                                                \nTask  Binary classification\n</code></pre> <p>Samples  5,300                                                                                                 Features  2                                                                                                       Sparse  False                                                                                                     Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/banana.zip</p> <p></p> Elec2 <p>Electricity prices in New South Wales.</p> <p>This is a binary classification task, where the goal is to predict if the price of electricity will go up or down.</p> <p>This data was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.</p> <pre><code>  Name  Elec2                                                      \n  Task  Binary classification\n</code></pre> <p>Samples  45,312                                                      Features  8                                                             Sparse  False                                                           Path  /Users/mastelini/river_data/Elec2/electricity.csv                URL  https://maxhalford.github.io/files/datasets/electricity.zip       Size  2.95 MB                                                   Downloaded  True                                                       </p> <p></p> Phishing <p>Phishing websites.</p> <p>This dataset contains features from web pages that are classified as phishing or not.</p> <pre><code>Name  Phishing                                                                                                    \nTask  Binary classification\n</code></pre> <p>Samples  1,250                                                                                                      Features  9                                                                                                            Sparse  False                                                                                                          Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/phishing.csv.gz</p> <p></p> SMTP <p>SMTP dataset from the KDD 1999 cup.</p> <p>The goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only contains 2,211 (0.4%) positive labels.</p> <pre><code>  Name  SMTP                                                \n  Task  Binary classification\n</code></pre> <p>Samples  95,156                                               Features  3                                                      Sparse  False                                                    Path  /Users/mastelini/river_data/SMTP/smtp.csv                 URL  https://maxhalford.github.io/files/datasets/smtp.zip       Size  5.23 MB                                            Downloaded  True                                                </p> <p></p>"},{"location":"benchmarks/Binary%20classification/#models","title":"Models","text":"Logistic regression <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LogisticRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.005\n      )\n    )\n    loss=Log (\n      weight_pos=1.\n      weight_neg=1.\n    )\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Aggregated Mondrian Forest <p><pre>[]</pre></p> <p></p> ALMA <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  ALMAClassifier (\n    p=2\n    alpha=0.9\n    B=1.111111\n    C=1.414214\n  )\n)</pre></p> <p></p> sklearn SGDClassifier <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SKL2RiverClassifier (\n    estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log_loss',\n                penalty=None)\n    classes=[False, True]\n  )\n)</pre></p> <p></p> Vowpal Wabbit logistic regression <p><pre>VW2RiverClassifier ()</pre></p> <p></p> Naive Bayes <p><pre>GaussianNB ()</pre></p> <p></p> Hoeffding Tree <p><pre>HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)</pre></p> <p></p> Hoeffding Adaptive Tree <p><pre>HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=True\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=42\n)</pre></p> <p></p> Adaptive Random Forest <p><pre>[]</pre></p> <p></p> Streaming Random Patches <p><pre>SRPClassifier (\n  model=HoeffdingTreeClassifier (\n    grace_period=50\n    max_depth=inf\n    split_criterion=\"info_gain\"\n    delta=0.01\n    tau=0.05\n    leaf_prediction=\"nba\"\n    nb_threshold=0\n    nominal_attributes=None\n    splitter=GaussianSplitter (\n      n_splits=10\n    )\n    binary_split=False\n    min_branch_fraction=0.01\n    max_share_to_split=0.99\n    max_size=100.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n  )\n  n_models=10\n  subspace_size=0.6\n  training_method=\"patches\"\n  lam=6\n  drift_detector=ADWIN (\n    delta=1e-05\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  warning_detector=ADWIN (\n    delta=0.0001\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  disable_detector=\"off\"\n  disable_weighted_vote=False\n  seed=None\n  metric=Accuracy (\n    cm=ConfusionMatrix (\n      classes=[]\n    )\n  )\n)</pre></p> <p></p> k-Nearest Neighbors <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)\n\n<p></p>\n\nADWIN Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nAdaBoost\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nBagging\n<p><pre>[HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n)]</pre></p>\n\n<p></p>\n\nLeveraging Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nStacking\n<p><pre>[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n\n<p></p>\n\nVoting\n<p><pre>VotingClassifier (\n  models=[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n  use_probabilities=True\n)\n\n<p></p>\n\n[baseline] Last Class\n<p><pre>NoChangeClassifier ()</pre></p>\n\n<p></p>"},{"location":"benchmarks/Binary%20classification/#environment","title":"Environment","text":"<pre>Python implementation: CPython\nPython version       : 3.12.4\nIPython version      : 8.18.1\n\nriver       : 0.21.2\nnumpy       : 1.26.4\nscikit-learn: 1.3.1\npandas      : 2.2.2\nscipy       : 1.13.0\n\nCompiler    : GCC 11.4.0\nOS          : Linux\nRelease     : 6.5.0-1024-azure\nMachine     : x86_64\nProcessor   : x86_64\nCPU cores   : 4\nArchitecture: 64bit\n</pre>"},{"location":"benchmarks/Multiclass%20classification/","title":"Multiclass classification","text":"TableChart Model Dataset Accuracy MicroF1 MacroF1 Memory in Mb Time in s ADWIN Bagging ImageSegments 0.777826 0.777826 0.765011 4.11628 3543.55 ADWIN Bagging Insects 0.579465 0.579465 0.570198 15.3074 60279.4 ADWIN Bagging Keystroke 0.81656 0.81656 0.815908 37.8558 41308 AdaBoost ImageSegments 0.804677 0.804677 0.79777 4.09839 3350.88 AdaBoost Insects 0.563532 0.563532 0.554622 27.943 60335.7 AdaBoost Keystroke 0.834796 0.834796 0.836062 194.794 51861.3 Adaptive Random Forest ImageSegments 0.818536 0.818536 0.814535 3.06348 1574.18 Adaptive Random Forest Insects 0.745378 0.745378 0.743302 0.361794 25383.5 Adaptive Random Forest Keystroke 0.969116 0.969116 0.969111 1.63546 7363.05 Aggregated Mondrian Forest ImageSegments 0.901689 0.901689 0.900381 17.0502 2997.7 Aggregated Mondrian Forest Insects 0.646981 0.646981 0.644352 1365.41 76295.7 Aggregated Mondrian Forest Keystroke 0.881073 0.881073 0.879928 338.139 35528.4 Bagging ImageSegments 0.77696 0.77696 0.764564 4.15507 3634.88 Bagging Insects 0.606392 0.606392 0.598542 3.69162 65237 Bagging Keystroke 0.669739 0.669739 0.669981 50.3449 55411.4 Hoeffding Adaptive Tree ImageSegments 0.774361 0.774361 0.763362 0.423797 457.311 Hoeffding Adaptive Tree Insects 0.613337 0.613337 0.604219 0.143826 11292.9 Hoeffding Adaptive Tree Keystroke 0.723124 0.723124 0.721825 0.724475 8998.46 Hoeffding Tree ImageSegments 0.776094 0.776094 0.763137 0.417154 328.067 Hoeffding Tree Insects 0.537306 0.537306 0.527364 2.51923 7445.36 Hoeffding Tree Keystroke 0.648218 0.648218 0.647249 5.09445 7138.73 Leveraging Bagging ImageSegments 0.778259 0.778259 0.766016 4.1005 8561.3 Leveraging Bagging Insects 0.695858 0.695858 0.690508 13.831 99120.2 Leveraging Bagging Keystroke 0.956616 0.956616 0.95665 7.40999 37049.1 Naive Bayes ImageSegments 0.731919 0.731919 0.730419 0.390004 248.959 Naive Bayes Insects 0.506897 0.506897 0.493019 0.611693 4263.77 Naive Bayes Keystroke 0.652532 0.652532 0.651577 4.86901 3544.69 Stacking ImageSegments 0.867908 0.867908 0.865603 9.18162 5416.88 Stacking Insects 0.754745 0.754745 0.752818 10.5864 72115 Stacking Keystroke 0.975489 0.975489 0.975486 18.7111 42471.8 Streaming Random Patches ImageSegments 0.766999 0.766999 0.764707 8.92653 6441.81 Streaming Random Patches Insects 0.736163 0.736163 0.734622 9.632 90031.6 Streaming Random Patches Keystroke 0.955929 0.955929 0.95592 39.636 31009.8 Voting ImageSegments 0.80641 0.80641 0.798999 6.07392 3157.94 Voting Insects 0.648533 0.648533 0.638 9.40652 48163.7 Voting Keystroke 0.779107 0.779107 0.784136 16.3925 29779.2 [baseline] Last Class ImageSegments 0.148116 0.148116 0.148116 0.00136948 31.4159 [baseline] Last Class Insects 0.289761 0.289761 0.289763 0.00138664 679.004 [baseline] Last Class Keystroke 0.997549 0.997549 0.997549 0.00504208 274.675 k-Nearest Neighbors ImageSegments 0.873538 0.873538 0.872136 5.26871 2666.29 k-Nearest Neighbors Insects 0.713115 0.713115 0.711381 6.27269 40639.9 k-Nearest Neighbors Keystroke 0.910486 0.910486 0.910328 6.32511 21326.5 <p>Try reloading the page if something is buggy</p> <p>{   \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",   \"data\": {     \"values\": [       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4666666666666667,         \"MicroF1\": 0.4666666666666667,         \"MacroF1\": 0.4009102009102009,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 0.450679       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5604395604395604,         \"MicroF1\": 0.5604395604395604,         \"MacroF1\": 0.5279334700387331,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 1.152847       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5474452554744526,         \"MicroF1\": 0.5474452554744526,         \"MacroF1\": 0.5191892873237387,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 2.278305       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5573770491803278,         \"MicroF1\": 0.5573770491803278,         \"MacroF1\": 0.5225713529323662,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 3.449742       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5545851528384279,         \"MicroF1\": 0.5545851528384279,         \"MacroF1\": 0.5217226223148511,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 4.939578       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.56,         \"MicroF1\": 0.56,         \"MacroF1\": 0.5450388711329709,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 6.667081       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5825545171339563,         \"MicroF1\": 0.5825545171339563,         \"MacroF1\": 0.5566705826058684,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 8.548779999999999       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5940054495912807,         \"MicroF1\": 0.5940054495912807,         \"MacroF1\": 0.5613773296963412,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 10.607026       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5980629539951574,         \"MicroF1\": 0.5980629539951574,         \"MacroF1\": 0.5624927052752284,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 12.811145       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.599128540305011,         \"MicroF1\": 0.599128540305011,         \"MacroF1\": 0.5669821167583783,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 15.144022       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6099009900990099,         \"MicroF1\": 0.6099009900990099,         \"MacroF1\": 0.592228619098681,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 17.683543       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6116152450090744,         \"MicroF1\": 0.6116152450090744,         \"MacroF1\": 0.5983340184133136,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 20.357047       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6180904522613065,         \"MicroF1\": 0.6180904522613065,         \"MacroF1\": 0.611527101723203,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 23.213992       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6158631415241057,         \"MicroF1\": 0.6158631415241057,         \"MacroF1\": 0.6113311881078581,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 26.205369       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6182873730043541,         \"MicroF1\": 0.6182873730043541,         \"MacroF1\": 0.6150189987146761,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 29.350024       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.617687074829932,         \"MicroF1\": 0.617687074829932,         \"MacroF1\": 0.6157912419016742,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 32.567265       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6274007682458387,         \"MicroF1\": 0.6274007682458387,         \"MacroF1\": 0.6216325704223051,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 36.027093       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6324062877871826,         \"MicroF1\": 0.6324062877871826,         \"MacroF1\": 0.6280704917469789,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 39.646129       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6426116838487973,         \"MicroF1\": 0.6426116838487973,         \"MacroF1\": 0.6349558095046656,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 43.417442       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6485310119695321,         \"MicroF1\": 0.6485310119695321,         \"MacroF1\": 0.6384515982514894,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 47.360213       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6507772020725389,         \"MicroF1\": 0.6507772020725389,         \"MacroF1\": 0.6399118827528387,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 51.459671       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6508407517309595,         \"MicroF1\": 0.6508407517309595,         \"MacroF1\": 0.6387857120889422,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 55.677121       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6537369914853358,         \"MicroF1\": 0.6537369914853358,         \"MacroF1\": 0.6398811322847953,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 60.129657       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.658204895738894,         \"MicroF1\": 0.658204895738894,         \"MacroF1\": 0.6463297068165035,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 64.716333       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6640557006092254,         \"MicroF1\": 0.6640557006092254,         \"MacroF1\": 0.6508930463144657,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 69.425449       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6702928870292887,         \"MicroF1\": 0.6702928870292887,         \"MacroF1\": 0.6599370641329333,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 74.368592       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6736502820306205,         \"MicroF1\": 0.6736502820306205,         \"MacroF1\": 0.669511465798708,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 79.42749       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6822066822066822,         \"MicroF1\": 0.6822066822066822,         \"MacroF1\": 0.6790074545382362,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 84.676077       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6841710427606902,         \"MicroF1\": 0.6841710427606902,         \"MacroF1\": 0.6834974476087327,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 90.04929600000001       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6874546773023931,         \"MicroF1\": 0.6874546773023931,         \"MacroF1\": 0.687676692272135,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 95.54439700000002       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6919298245614035,         \"MicroF1\": 0.6919298245614035,         \"MacroF1\": 0.6930786661709784,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 101.25523300000002       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.698844323589395,         \"MicroF1\": 0.698844323589395,         \"MacroF1\": 0.6985606658027719,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 107.09626300000002       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7027027027027027,         \"MicroF1\": 0.7027027027027027,         \"MacroF1\": 0.7017787722939461,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 113.17857300000004       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7056941778630839,         \"MicroF1\": 0.7056941778630839,         \"MacroF1\": 0.7062915374924865,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 119.38367200000005       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7078931013051585,         \"MicroF1\": 0.7078931013051585,         \"MacroF1\": 0.7081385387673028,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 125.72760100000004       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7093655589123867,         \"MicroF1\": 0.7093655589123867,         \"MacroF1\": 0.7109488618373111,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 132.27559300000004       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7101704879482658,         \"MicroF1\": 0.7101704879482658,         \"MacroF1\": 0.7132092257742534,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 138.94755600000005       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7143674871207785,         \"MicroF1\": 0.7143674871207784,         \"MacroF1\": 0.7178399485500211,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 145.68584300000003       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7172336865588399,         \"MicroF1\": 0.7172336865588399,         \"MacroF1\": 0.7191260584555579,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 152.67811600000005       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7199564980967917,         \"MicroF1\": 0.7199564980967917,         \"MacroF1\": 0.7217017555070446,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 159.82058900000004       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7204244031830239,         \"MicroF1\": 0.7204244031830238,         \"MacroF1\": 0.7234495525792994,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 167.13449700000004       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7219057483169342,         \"MicroF1\": 0.7219057483169342,         \"MacroF1\": 0.7238483512148008,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 174.57489300000003       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.723823975720789,         \"MicroF1\": 0.723823975720789,         \"MacroF1\": 0.7251399238639739,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 182.21825900000005       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.726643598615917,         \"MicroF1\": 0.726643598615917,         \"MacroF1\": 0.7268553573885639,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 189.97396200000009       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7269212179797003,         \"MicroF1\": 0.7269212179797003,         \"MacroF1\": 0.7276782991451582,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 197.92708900000005       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7286052009456265,         \"MicroF1\": 0.7286052009456266,         \"MacroF1\": 0.7283656039279267,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 206.04766600000005       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7306802406293382,         \"MicroF1\": 0.7306802406293383,         \"MacroF1\": 0.7303992643507475,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 214.36632800000004       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.733574988672406,         \"MicroF1\": 0.733574988672406,         \"MacroF1\": 0.7322842940126589,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 222.77231300000005       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7314691522414558,         \"MicroF1\": 0.7314691522414558,         \"MacroF1\": 0.7300322879925133,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 231.40748800000003       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7316224445411048,         \"MicroF1\": 0.7316224445411048,         \"MacroF1\": 0.7300416811383057,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 240.14309800000004       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7319185794716327,         \"MicroF1\": 0.7319185794716329,         \"MacroF1\": 0.7304188192194185,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 248.95897400000004       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.623696682464455,         \"MicroF1\": 0.623696682464455,         \"MacroF1\": 0.5870724729616662,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 4.116407       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6148744670772146,         \"MicroF1\": 0.6148744670772146,         \"MacroF1\": 0.5800776869595596,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 12.008893       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6065677297126618,         \"MicroF1\": 0.6065677297126618,         \"MacroF1\": 0.5714781230184183,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 23.636521       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043097324177126,         \"MicroF1\": 0.6043097324177126,         \"MacroF1\": 0.5697541737710122,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 38.735534       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088274294373934,         \"MicroF1\": 0.6088274294373934,         \"MacroF1\": 0.5727560614138387,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 57.253764       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6023677979479084,         \"MicroF1\": 0.6023677979479084,         \"MacroF1\": 0.5679597008529512,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 79.038555       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5995129211202814,         \"MicroF1\": 0.5995129211202814,         \"MacroF1\": 0.5652603100832261,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 104.109779       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6019888717888008,         \"MicroF1\": 0.6019888717888008,         \"MacroF1\": 0.5673514925692325,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 132.296427       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5993896664211301,         \"MicroF1\": 0.5993896664211301,         \"MacroF1\": 0.5644951651039589,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 163.68164199999998       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5994885879344635,         \"MicroF1\": 0.5994885879344635,         \"MacroF1\": 0.564565538599863,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 198.252114       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5972449418854929,         \"MicroF1\": 0.5972449418854929,         \"MacroF1\": 0.5631227877868952,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 235.999104       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001894088864336,         \"MicroF1\": 0.6001894088864336,         \"MacroF1\": 0.5684733590606373,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 276.973484       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6120783856632913,         \"MicroF1\": 0.6120783856632913,         \"MacroF1\": 0.5935173038317552,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 321.087465       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6024487587093282,         \"MicroF1\": 0.6024487587093282,         \"MacroF1\": 0.5841270876002982,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 368.414891       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5676494728202538,         \"MicroF1\": 0.5676494728202538,         \"MacroF1\": 0.5507155080701159,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 418.926748       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5418762947617638,         \"MicroF1\": 0.5418762947617638,         \"MacroF1\": 0.5256197352354142,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 472.672831       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5232020500250683,         \"MicroF1\": 0.5232020500250683,         \"MacroF1\": 0.5066898143269706,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 529.5973250000001       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5118640500868101,         \"MicroF1\": 0.5118640500868101,         \"MacroF1\": 0.4926543583964285,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 589.87103       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5103922643672432,         \"MicroF1\": 0.5103922643672432,         \"MacroF1\": 0.4900586962359796,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 653.257514       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5115772527108291,         \"MicroF1\": 0.5115772527108291,         \"MacroF1\": 0.4910837640903744,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 719.720849       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5140022547914318,         \"MicroF1\": 0.5140022547914318,         \"MacroF1\": 0.4932541888231957,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 789.2473650000001       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5154319659076234,         \"MicroF1\": 0.5154319659076234,         \"MacroF1\": 0.4943013417599926,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 861.9200270000001       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5184254951208466,         \"MicroF1\": 0.5184254951208466,         \"MacroF1\": 0.4965832238311332,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 937.628382       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5225111470623052,         \"MicroF1\": 0.5225111470623052,         \"MacroF1\": 0.499893079239698,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1016.433905       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5257396113489148,         \"MicroF1\": 0.5257396113489148,         \"MacroF1\": 0.5022487669255871,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1098.325454       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5301402294663996,         \"MicroF1\": 0.5301402294663996,         \"MacroF1\": 0.5051550433324518,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1183.302333       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5277261407877661,         \"MicroF1\": 0.5277261407877661,         \"MacroF1\": 0.5036945145235058,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1271.323869       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5204450908107011,         \"MicroF1\": 0.5204450908107011,         \"MacroF1\": 0.4989008712312768,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1362.446785       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5147111648107632,         \"MicroF1\": 0.5147111648107632,         \"MacroF1\": 0.495826840073632,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1456.7074690000002       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5105590454244137,         \"MicroF1\": 0.5105590454244137,         \"MacroF1\": 0.4941101813344875,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1553.9918670000002       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5075607148312204,         \"MicroF1\": 0.5075607148312204,         \"MacroF1\": 0.4931947798921405,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1654.355087       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5044538486579266,         \"MicroF1\": 0.5044538486579266,         \"MacroF1\": 0.4905626123916189,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1757.6376       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5020231296811777,         \"MicroF1\": 0.5020231296811777,         \"MacroF1\": 0.487879842488124,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1863.925375       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4998746622844887,         \"MicroF1\": 0.4998746622844887,         \"MacroF1\": 0.4853435061152475,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1973.177917       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4967937444194918,         \"MicroF1\": 0.4967937444194918,         \"MacroF1\": 0.4819418474093529,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2085.445724       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4955938445350519,         \"MicroF1\": 0.4955938445350519,         \"MacroF1\": 0.4801892436835747,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2200.821931       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4940237004427836,         \"MicroF1\": 0.4940237004427836,         \"MacroF1\": 0.478380783820526,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2319.178703       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.493508111745209,         \"MicroF1\": 0.493508111745209,         \"MacroF1\": 0.4785213801670671,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2440.443075       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4936988563242114,         \"MicroF1\": 0.4936988563242114,         \"MacroF1\": 0.4794201499427274,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2564.583087       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4938800634484718,         \"MicroF1\": 0.4938800634484718,         \"MacroF1\": 0.4802377497532935,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2691.651665       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4943757939715902,         \"MicroF1\": 0.4943757939715902,         \"MacroF1\": 0.4812132921167227,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2821.601336       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.494036211133909,         \"MicroF1\": 0.494036211133909,         \"MacroF1\": 0.4812388919618418,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2954.377766       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4944832294580131,         \"MicroF1\": 0.4944832294580131,         \"MacroF1\": 0.4818441874360225,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3089.8310679999995       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4945225232981082,         \"MicroF1\": 0.4945225232981082,         \"MacroF1\": 0.4820791268335544,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3227.9665449999998       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4956333256171216,         \"MicroF1\": 0.4956333256171216,         \"MacroF1\": 0.4833168636021498,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3368.688097999999       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4970869788986104,         \"MicroF1\": 0.4970869788986104,         \"MacroF1\": 0.4846703771634363,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3511.887438999999       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4987608551107171,         \"MicroF1\": 0.4987608551107171,         \"MacroF1\": 0.4862426724473749,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3657.6494079999993       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5009568528419516,         \"MicroF1\": 0.5009568528419516,         \"MacroF1\": 0.4881725476999718,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3806.011259       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5034497419940862,         \"MicroF1\": 0.5034497419940862,         \"MacroF1\": 0.4903712806540024,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3956.893516       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5068467205818292,         \"MicroF1\": 0.5068467205818292,         \"MacroF1\": 0.4930025316136313,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 4110.278735       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5068972694760346,         \"MicroF1\": 0.5068972694760346,         \"MacroF1\": 0.4930190627831494,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 4263.766907       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9852579852579852,         \"MicroF1\": 0.9852579852579852,         \"MacroF1\": 0.6962686567164179,         \"Memory in Mb\": 0.1935644149780273,         \"Time in s\": 0.780775       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.947239263803681,         \"MicroF1\": 0.947239263803681,         \"MacroF1\": 0.7418606503288051,         \"Memory in Mb\": 0.2889022827148437,         \"Time in s\": 2.463269       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.884709730171709,         \"MicroF1\": 0.884709730171709,         \"MacroF1\": 0.8705899666065842,         \"Memory in Mb\": 0.3842401504516601,         \"Time in s\": 5.15507       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8933169834457388,         \"MicroF1\": 0.8933169834457388,         \"MacroF1\": 0.8791291775937072,         \"Memory in Mb\": 0.4795780181884765,         \"Time in s\": 8.960951       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8921039725355566,         \"MicroF1\": 0.8921039725355566,         \"MacroF1\": 0.8831785360852743,         \"Memory in Mb\": 0.575160026550293,         \"Time in s\": 14.051639       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.851655087862689,         \"MicroF1\": 0.851655087862689,         \"MacroF1\": 0.8581984289516641,         \"Memory in Mb\": 0.6704978942871094,         \"Time in s\": 20.582882       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8598949211908932,         \"MicroF1\": 0.8598949211908932,         \"MacroF1\": 0.8469962214365346,         \"Memory in Mb\": 0.7658357620239258,         \"Time in s\": 28.649143       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8513637756665645,         \"MicroF1\": 0.8513637756665645,         \"MacroF1\": 0.8281280134770846,         \"Memory in Mb\": 0.8611736297607422,         \"Time in s\": 38.532046       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8422773086352493,         \"MicroF1\": 0.8422773086352493,         \"MacroF1\": 0.8409307955747314,         \"Memory in Mb\": 0.9565114974975586,         \"Time in s\": 50.273206       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8367246874233881,         \"MicroF1\": 0.8367246874233881,         \"MacroF1\": 0.8249418657104467,         \"Memory in Mb\": 1.0523834228515625,         \"Time in s\": 63.882498       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8203699576554491,         \"MicroF1\": 0.8203699576554491,         \"MacroF1\": 0.8300896799820437,         \"Memory in Mb\": 1.147721290588379,         \"Time in s\": 79.531469       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8192032686414709,         \"MicroF1\": 0.8192032686414709,         \"MacroF1\": 0.8269731591910484,         \"Memory in Mb\": 1.243059158325195,         \"Time in s\": 97.310117       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8172732415613804,         \"MicroF1\": 0.8172732415613804,         \"MacroF1\": 0.8027823390848743,         \"Memory in Mb\": 1.3383970260620115,         \"Time in s\": 117.35519       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7961828051129399,         \"MicroF1\": 0.7961828051129399,         \"MacroF1\": 0.8002006091139847,         \"Memory in Mb\": 1.433734893798828,         \"Time in s\": 139.817583       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.793920575257395,         \"MicroF1\": 0.793920575257395,         \"MacroF1\": 0.7746960355921345,         \"Memory in Mb\": 1.5290727615356443,         \"Time in s\": 164.727582       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7688064960931515,         \"MicroF1\": 0.7688064960931515,         \"MacroF1\": 0.7622487598340326,         \"Memory in Mb\": 1.624410629272461,         \"Time in s\": 192.151707       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7568853640951694,         \"MicroF1\": 0.7568853640951694,         \"MacroF1\": 0.757813781660983,         \"Memory in Mb\": 1.7197484970092771,         \"Time in s\": 222.243586       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7669889690862045,         \"MicroF1\": 0.7669889690862046,         \"MacroF1\": 0.7643943615019536,         \"Memory in Mb\": 1.8150863647460935,         \"Time in s\": 255.230678       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7676428847890595,         \"MicroF1\": 0.7676428847890595,         \"MacroF1\": 0.7655695901071293,         \"Memory in Mb\": 1.9104242324829104,         \"Time in s\": 291.218411       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7714180659394534,         \"MicroF1\": 0.7714180659394533,         \"MacroF1\": 0.7672011803374248,         \"Memory in Mb\": 2.0057621002197266,         \"Time in s\": 330.398823       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7702813120112058,         \"MicroF1\": 0.7702813120112058,         \"MacroF1\": 0.7699263138193525,         \"Memory in Mb\": 2.1021223068237305,         \"Time in s\": 372.827664       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7680222841225627,         \"MicroF1\": 0.7680222841225627,         \"MacroF1\": 0.7682287234686136,         \"Memory in Mb\": 2.197460174560547,         \"Time in s\": 418.63015       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7659597143770649,         \"MicroF1\": 0.7659597143770649,         \"MacroF1\": 0.7643546547243014,         \"Memory in Mb\": 2.2927980422973637,         \"Time in s\": 468.011111       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7586559084873864,         \"MicroF1\": 0.7586559084873864,         \"MacroF1\": 0.7552148692020618,         \"Memory in Mb\": 2.38813591003418,         \"Time in s\": 521.0847249999999       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7505637807628199,         \"MicroF1\": 0.7505637807628199,         \"MacroF1\": 0.7430512224080145,         \"Memory in Mb\": 2.483473777770996,         \"Time in s\": 577.917349       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7290468558499105,         \"MicroF1\": 0.7290468558499106,         \"MacroF1\": 0.715756093271779,         \"Memory in Mb\": 2.5788116455078125,         \"Time in s\": 638.790947       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7217430776214253,         \"MicroF1\": 0.7217430776214253,         \"MacroF1\": 0.7173640789896896,         \"Memory in Mb\": 2.674149513244629,         \"Time in s\": 703.666983       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7151361288628206,         \"MicroF1\": 0.7151361288628206,         \"MacroF1\": 0.7011862635194489,         \"Memory in Mb\": 2.7694873809814453,         \"Time in s\": 772.6431349999999       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.705603921900093,         \"MicroF1\": 0.705603921900093,         \"MacroF1\": 0.6976881379682607,         \"Memory in Mb\": 2.8648252487182617,         \"Time in s\": 845.8350979999999       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7094533867146009,         \"MicroF1\": 0.7094533867146009,         \"MacroF1\": 0.7058405389403433,         \"Memory in Mb\": 2.960163116455078,         \"Time in s\": 923.50335       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7053846762077963,         \"MicroF1\": 0.7053846762077963,         \"MacroF1\": 0.6965736948063982,         \"Memory in Mb\": 3.0555009841918945,         \"Time in s\": 1005.753677       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6927613941018766,         \"MicroF1\": 0.6927613941018766,         \"MacroF1\": 0.6842255816736498,         \"Memory in Mb\": 3.150838851928711,         \"Time in s\": 1092.707972       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6890737577063062,         \"MicroF1\": 0.6890737577063062,         \"MacroF1\": 0.6845669389392289,         \"Memory in Mb\": 3.246176719665528,         \"Time in s\": 1184.483965       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6873332852714296,         \"MicroF1\": 0.6873332852714296,         \"MacroF1\": 0.68390545518227,         \"Memory in Mb\": 3.341514587402344,         \"Time in s\": 1281.216395       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.682960991666083,         \"MicroF1\": 0.682960991666083,         \"MacroF1\": 0.6781566371919944,         \"Memory in Mb\": 3.43685245513916,         \"Time in s\": 1383.039909       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.686185061619119,         \"MicroF1\": 0.686185061619119,         \"MacroF1\": 0.6843713776162116,         \"Memory in Mb\": 3.532190322875977,         \"Time in s\": 1489.909884       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6928784365684001,         \"MicroF1\": 0.6928784365684001,         \"MacroF1\": 0.6911392400672977,         \"Memory in Mb\": 3.627528190612793,         \"Time in s\": 1601.996709       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6913500612784622,         \"MicroF1\": 0.6913500612784622,         \"MacroF1\": 0.687359772989117,         \"Memory in Mb\": 3.72286605834961,         \"Time in s\": 1719.445985       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6819810194205267,         \"MicroF1\": 0.6819810194205267,         \"MacroF1\": 0.674915944935936,         \"Memory in Mb\": 3.818203926086426,         \"Time in s\": 1842.197498       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6726515105092223,         \"MicroF1\": 0.6726515105092223,         \"MacroF1\": 0.6670192172011686,         \"Memory in Mb\": 3.913541793823242,         \"Time in s\": 1970.358299       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6695163508100676,         \"MicroF1\": 0.6695163508100676,         \"MacroF1\": 0.6664051037977977,         \"Memory in Mb\": 4.008879661560059,         \"Time in s\": 2103.939399       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6650131310183834,         \"MicroF1\": 0.6650131310183834,         \"MacroF1\": 0.6608988619616458,         \"Memory in Mb\": 4.1063079833984375,         \"Time in s\": 2242.845385       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6568431853160804,         \"MicroF1\": 0.6568431853160804,         \"MacroF1\": 0.6531382897719189,         \"Memory in Mb\": 4.201645851135254,         \"Time in s\": 2386.822189       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6556180714166342,         \"MicroF1\": 0.6556180714166342,         \"MacroF1\": 0.6538448358590968,         \"Memory in Mb\": 4.29698371887207,         \"Time in s\": 2536.044428       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6614194672912468,         \"MicroF1\": 0.6614194672912468,         \"MacroF1\": 0.6603186829199909,         \"Memory in Mb\": 4.392321586608887,         \"Time in s\": 2690.5476860000003       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6669686151222891,         \"MicroF1\": 0.6669686151222891,         \"MacroF1\": 0.6662293616554571,         \"Memory in Mb\": 4.487659454345703,         \"Time in s\": 2850.3140810000004       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6579921773142112,         \"MicroF1\": 0.6579921773142112,         \"MacroF1\": 0.6554177118629491,         \"Memory in Mb\": 4.58299732208252,         \"Time in s\": 3015.4823350000006       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6622580809886126,         \"MicroF1\": 0.6622580809886126,         \"MacroF1\": 0.6609360990360078,         \"Memory in Mb\": 4.678335189819336,         \"Time in s\": 3186.2814100000005       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6562453103896754,         \"MicroF1\": 0.6562453103896754,         \"MacroF1\": 0.6545704957554572,         \"Memory in Mb\": 4.773673057556152,         \"Time in s\": 3362.6238980000007       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6525319868621011,         \"MicroF1\": 0.6525319868621011,         \"MacroF1\": 0.6515767870317885,         \"Memory in Mb\": 4.869010925292969,         \"Time in s\": 3544.6906370000006       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3555555555555555,         \"MicroF1\": 0.3555555555555555,         \"MacroF1\": 0.2537942449707155,         \"Memory in Mb\": 0.4170856475830078,         \"Time in s\": 0.290301       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4945054945054945,         \"MicroF1\": 0.4945054945054945,         \"MacroF1\": 0.5043329927491418,         \"Memory in Mb\": 0.4170818328857422,         \"Time in s\": 0.82046       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5328467153284672,         \"MicroF1\": 0.5328467153284672,         \"MacroF1\": 0.5564033878668025,         \"Memory in Mb\": 0.4171772003173828,         \"Time in s\": 1.675423       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6010928961748634,         \"MicroF1\": 0.6010928961748634,         \"MacroF1\": 0.622766496539645,         \"Memory in Mb\": 0.4171772003173828,         \"Time in s\": 2.801183       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6375545851528385,         \"MicroF1\": 0.6375545851528385,         \"MacroF1\": 0.6539827168809461,         \"Memory in Mb\": 0.4172000885009765,         \"Time in s\": 4.271522       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6509090909090909,         \"MicroF1\": 0.6509090909090909,         \"MacroF1\": 0.6671561759164943,         \"Memory in Mb\": 0.4172496795654297,         \"Time in s\": 5.954744       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.67601246105919,         \"MicroF1\": 0.67601246105919,         \"MacroF1\": 0.6756614325426025,         \"Memory in Mb\": 0.4172496795654297,         \"Time in s\": 7.864603       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7029972752043597,         \"MicroF1\": 0.7029972752043597,         \"MacroF1\": 0.6993447851636564,         \"Memory in Mb\": 0.4172229766845703,         \"Time in s\": 10.008665       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7142857142857143,         \"MicroF1\": 0.7142857142857143,         \"MacroF1\": 0.7108606838045498,         \"Memory in Mb\": 0.4171428680419922,         \"Time in s\": 12.399438       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7145969498910676,         \"MicroF1\": 0.7145969498910676,         \"MacroF1\": 0.7090365931960759,         \"Memory in Mb\": 0.4172191619873047,         \"Time in s\": 15.01004       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7207920792079208,         \"MicroF1\": 0.7207920792079208,         \"MacroF1\": 0.7126631585949761,         \"Memory in Mb\": 0.4172191619873047,         \"Time in s\": 17.873655       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7223230490018149,         \"MicroF1\": 0.7223230490018149,         \"MacroF1\": 0.7157730164623107,         \"Memory in Mb\": 0.4171123504638672,         \"Time in s\": 20.946971       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7286432160804021,         \"MicroF1\": 0.7286432160804021,         \"MacroF1\": 0.7216745323124732,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 24.255884       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7278382581648523,         \"MicroF1\": 0.7278382581648523,         \"MacroF1\": 0.7229105183087501,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 27.838412       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7314949201741655,         \"MicroF1\": 0.7314949201741654,         \"MacroF1\": 0.7263583447448078,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 31.647636       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7333333333333333,         \"MicroF1\": 0.7333333333333333,         \"MacroF1\": 0.729431071218305,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 35.743157       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7387964148527529,         \"MicroF1\": 0.7387964148527529,         \"MacroF1\": 0.7349287389986899,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 40.06309       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7376058041112454,         \"MicroF1\": 0.7376058041112454,         \"MacroF1\": 0.7356226390109741,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 44.599844       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7445589919816724,         \"MicroF1\": 0.7445589919816724,         \"MacroF1\": 0.7409366047432264,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 49.398729       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7453754080522307,         \"MicroF1\": 0.7453754080522307,         \"MacroF1\": 0.7408438328939173,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 54.404894       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7471502590673575,         \"MicroF1\": 0.7471502590673575,         \"MacroF1\": 0.7416651838589269,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 59.665949       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7467853610286844,         \"MicroF1\": 0.7467853610286844,         \"MacroF1\": 0.7416356251822,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 65.211169       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7492904446546831,         \"MicroF1\": 0.7492904446546831,         \"MacroF1\": 0.7430778844390783,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 70.961377       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7515865820489573,         \"MicroF1\": 0.7515865820489573,         \"MacroF1\": 0.7451256886686588,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 76.969446       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7536988685813751,         \"MicroF1\": 0.7536988685813751,         \"MacroF1\": 0.7468312166689606,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 83.201851       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7564853556485356,         \"MicroF1\": 0.7564853556485356,         \"MacroF1\": 0.7503479321738041,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 89.604352       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7566478646253022,         \"MicroF1\": 0.7566478646253022,         \"MacroF1\": 0.7509717522131719,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 96.307026       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7614607614607615,         \"MicroF1\": 0.7614607614607615,         \"MacroF1\": 0.7547643483779538,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 103.262462       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7614403600900225,         \"MicroF1\": 0.7614403600900225,         \"MacroF1\": 0.7551060921605869,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 110.41488900000002       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7621464829586657,         \"MicroF1\": 0.7621464829586658,         \"MacroF1\": 0.7562209880685912,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 117.799886       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7642105263157895,         \"MicroF1\": 0.7642105263157895,         \"MacroF1\": 0.7575332274919562,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 125.46176800000002       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7688647178789939,         \"MicroF1\": 0.768864717878994,         \"MacroF1\": 0.760438686053582,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 133.360363       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7705998681608438,         \"MicroF1\": 0.7705998681608438,         \"MacroF1\": 0.7612069012840872,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 141.48549400000002       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7709532949456174,         \"MicroF1\": 0.7709532949456174,         \"MacroF1\": 0.7622701654854867,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 149.83563600000002       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7712865133623369,         \"MicroF1\": 0.771286513362337,         \"MacroF1\": 0.7617247271717752,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 158.439217       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7709969788519637,         \"MicroF1\": 0.7709969788519637,         \"MacroF1\": 0.7615629120572474,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 167.22864700000002       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.770135214579659,         \"MicroF1\": 0.770135214579659,         \"MacroF1\": 0.7627316365695143,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 176.30742800000002       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7727532913566113,         \"MicroF1\": 0.7727532913566113,         \"MacroF1\": 0.7649467707214076,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 185.609237       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7741215839375348,         \"MicroF1\": 0.7741215839375348,         \"MacroF1\": 0.7649332326562149,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 195.107308       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7754214246873301,         \"MicroF1\": 0.7754214246873301,         \"MacroF1\": 0.7664700790631908,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 204.88888000000003       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7740053050397878,         \"MicroF1\": 0.7740053050397878,         \"MacroF1\": 0.7655121135276625,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 214.87796100000003       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7742102537545313,         \"MicroF1\": 0.7742102537545313,         \"MacroF1\": 0.7648034036287765,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 225.10774000000004       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7754172989377845,         \"MicroF1\": 0.7754172989377845,         \"MacroF1\": 0.7656013068970459,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 235.56491900000003       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7770637666831438,         \"MicroF1\": 0.7770637666831438,         \"MacroF1\": 0.7660878232247856,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 246.31694000000005       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7762203963267279,         \"MicroF1\": 0.7762203963267279,         \"MacroF1\": 0.7654829214385931,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 257.28426500000006       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7768321513002364,         \"MicroF1\": 0.7768321513002364,         \"MacroF1\": 0.7653071619305024,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 268.5154150000001       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7778806108283203,         \"MicroF1\": 0.7778806108283203,         \"MacroF1\": 0.7659351904174982,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 279.94414300000005       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7797915722700498,         \"MicroF1\": 0.7797915722700498,         \"MacroF1\": 0.7668192864082087,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 291.65328600000004       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7767421216156236,         \"MicroF1\": 0.7767421216156236,         \"MacroF1\": 0.7637794374955548,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 303.618395       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7759895606785558,         \"MicroF1\": 0.7759895606785558,         \"MacroF1\": 0.763026662835187,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 315.80512400000003       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.776093546990039,         \"MicroF1\": 0.776093546990039,         \"MacroF1\": 0.7631372452021826,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 328.06738900000005       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6218009478672986,         \"MicroF1\": 0.6218009478672986,         \"MacroF1\": 0.5852663107194211,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 7.68277       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6153481762198011,         \"MicroF1\": 0.6153481762198011,         \"MacroF1\": 0.5806436317780949,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 22.565114       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6071992421850332,         \"MicroF1\": 0.6071992421850332,         \"MacroF1\": 0.572248584718361,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 43.997682       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043097324177126,         \"MicroF1\": 0.6043097324177126,         \"MacroF1\": 0.5697573109597247,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 71.858443       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088274294373934,         \"MicroF1\": 0.6088274294373934,         \"MacroF1\": 0.5727379077413696,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 105.92484       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6026835043409629,         \"MicroF1\": 0.6026835043409629,         \"MacroF1\": 0.568251333238805,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 146.287253       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.600189419564335,         \"MicroF1\": 0.600189419564335,         \"MacroF1\": 0.5659762112716077,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 192.863981       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.60258079791642,         \"MicroF1\": 0.60258079791642,         \"MacroF1\": 0.5679781484640408,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 245.806734       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5998105861306956,         \"MicroF1\": 0.5998105861306956,         \"MacroF1\": 0.5649597336877693,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 305.14044       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5998674116867128,         \"MicroF1\": 0.5998674116867128,         \"MacroF1\": 0.5650173260529011,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 370.680891       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5974171330176495,         \"MicroF1\": 0.5974171330176495,         \"MacroF1\": 0.5633067089377387,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 442.338443       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001894088864336,         \"MicroF1\": 0.6001894088864336,         \"MacroF1\": 0.5684760329567131,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 520.121563       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6120783856632913,         \"MicroF1\": 0.6120783856632913,         \"MacroF1\": 0.5935956771555828,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 604.039429       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6024487587093282,         \"MicroF1\": 0.6024487587093282,         \"MacroF1\": 0.5842148300149193,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 694.113241       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5677757434181451,         \"MicroF1\": 0.5677757434181451,         \"MacroF1\": 0.5509250187877572,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 790.19156       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5419354838709678,         \"MicroF1\": 0.5419354838709678,         \"MacroF1\": 0.5257359157219258,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 892.361186       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5233691716338923,         \"MicroF1\": 0.5233691716338923,         \"MacroF1\": 0.5068581838352059,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 1000.471748       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5121271110643447,         \"MicroF1\": 0.5121271110643447,         \"MacroF1\": 0.4929289906509415,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 1114.494528       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5120370831879579,         \"MicroF1\": 0.5120370831879579,         \"MacroF1\": 0.4920970323041603,         \"Memory in Mb\": 1.3099584579467771,         \"Time in s\": 1234.20565       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5173066906577016,         \"MicroF1\": 0.5173066906577016,         \"MacroF1\": 0.4973447169836249,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1358.925583       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5229312288613304,         \"MicroF1\": 0.5229312288613304,         \"MacroF1\": 0.5026343687424488,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1488.370808       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5301536739701261,         \"MicroF1\": 0.5301536739701261,         \"MacroF1\": 0.5095132087733324,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1622.41448       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5351422571746202,         \"MicroF1\": 0.5351422571746202,         \"MacroF1\": 0.5135975374357353,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1760.8970379999998       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5403069881229531,         \"MicroF1\": 0.5403069881229531,         \"MacroF1\": 0.5180803411538233,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1903.591145       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5441493995984696,         \"MicroF1\": 0.5441493995984696,         \"MacroF1\": 0.5209012984387186,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2050.469487       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5475869604807867,         \"MicroF1\": 0.5475869604807867,         \"MacroF1\": 0.5230407124785976,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2201.55681       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5442460804601733,         \"MicroF1\": 0.5442460804601733,         \"MacroF1\": 0.5199893698637053,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2356.711105       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5439848479724017,         \"MicroF1\": 0.5439848479724017,         \"MacroF1\": 0.5225387960194383,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2516.62263       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5449825294713124,         \"MicroF1\": 0.5449825294713124,         \"MacroF1\": 0.5260472440529832,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2681.546079       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5469238296663405,         \"MicroF1\": 0.5469238296663405,         \"MacroF1\": 0.5300194392617626,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2851.622305       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5492286543455017,         \"MicroF1\": 0.5492286543455017,         \"MacroF1\": 0.5337692045397758,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3026.797274       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5448196265277737,         \"MicroF1\": 0.5448196265277737,         \"MacroF1\": 0.5298516474077153,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3207.119826       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.539357763939507,         \"MicroF1\": 0.539357763939507,         \"MacroF1\": 0.5246413689313029,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3392.401024       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5352756037099964,         \"MicroF1\": 0.5352756037099964,         \"MacroF1\": 0.5204658240271913,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3582.6817720000004       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5307232338537298,         \"MicroF1\": 0.5307232338537298,         \"MacroF1\": 0.5158458403074864,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3778.309285       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5287912666052874,         \"MicroF1\": 0.5287912666052874,         \"MacroF1\": 0.5138605376143625,         \"Memory in Mb\": 1.8479537963867188,         \"Time in s\": 3978.822433       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5245322617798367,         \"MicroF1\": 0.5245322617798367,         \"MacroF1\": 0.5100329616180462,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4184.1075280000005       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5244847608841927,         \"MicroF1\": 0.5244847608841927,         \"MacroF1\": 0.5114466799524962,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4393.646320000001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5269650098341548,         \"MicroF1\": 0.5269650098341548,         \"MacroF1\": 0.5145630920489553,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4606.675677000001       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5290608205686688,         \"MicroF1\": 0.5290608205686688,         \"MacroF1\": 0.5171452370879218,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4823.052294000001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5316318281556762,         \"MicroF1\": 0.5316318281556762,         \"MacroF1\": 0.5200714653059241,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 5042.794587000001       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5332912448422809,         \"MicroF1\": 0.5332912448422809,         \"MacroF1\": 0.521951703681177,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5266.308108000001       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5350937080185875,         \"MicroF1\": 0.5350937080185875,         \"MacroF1\": 0.5236272112757866,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5493.659660000001       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5374168693368917,         \"MicroF1\": 0.5374168693368917,         \"MacroF1\": 0.5257977177437826,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5724.562244000002       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5359540394368568,         \"MicroF1\": 0.5359540394368568,         \"MacroF1\": 0.5247049329892776,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5959.275286000002       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5333196088522902,         \"MicroF1\": 0.5333196088522902,         \"MacroF1\": 0.5224640186909637,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 6197.987866000002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5314017448771937,         \"MicroF1\": 0.5314017448771937,         \"MacroF1\": 0.5209076603734537,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 6440.583835000002       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5322271982954209,         \"MicroF1\": 0.5322271982954209,         \"MacroF1\": 0.5219695808096345,         \"Memory in Mb\": 2.081958770751953,         \"Time in s\": 6687.224874000002       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5377345727924551,         \"MicroF1\": 0.5377345727924551,         \"MacroF1\": 0.5274876060436412,         \"Memory in Mb\": 2.3156700134277344,         \"Time in s\": 6937.746409000002       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5370366863008769,         \"MicroF1\": 0.5370366863008769,         \"MacroF1\": 0.5270872650003847,         \"Memory in Mb\": 2.519227981567383,         \"Time in s\": 7191.466386000002       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5373058073305959,         \"MicroF1\": 0.5373058073305959,         \"MacroF1\": 0.5273644947479657,         \"Memory in Mb\": 2.519227981567383,         \"Time in s\": 7445.3631460000015       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 0.2240447998046875,         \"Time in s\": 0.863228       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9423312883435584,         \"MicroF1\": 0.9423312883435584,         \"MacroF1\": 0.7661667470992702,         \"Memory in Mb\": 0.3196687698364258,         \"Time in s\": 3.107641       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8830744071954211,         \"MicroF1\": 0.883074407195421,         \"MacroF1\": 0.8761191747044462,         \"Memory in Mb\": 0.415292739868164,         \"Time in s\": 7.048775       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8902513795217658,         \"MicroF1\": 0.8902513795217658,         \"MacroF1\": 0.8767853151263398,         \"Memory in Mb\": 0.5114049911499023,         \"Time in s\": 13.087732       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8891613536047082,         \"MicroF1\": 0.8891613536047082,         \"MacroF1\": 0.8807858055314012,         \"Memory in Mb\": 0.6185035705566406,         \"Time in s\": 21.551525       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.848385778504291,         \"MicroF1\": 0.848385778504291,         \"MacroF1\": 0.8522513926518692,         \"Memory in Mb\": 0.7141275405883789,         \"Time in s\": 32.816222999999994       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8563922942206655,         \"MicroF1\": 0.8563922942206655,         \"MacroF1\": 0.8440193478447516,         \"Memory in Mb\": 0.8097515106201172,         \"Time in s\": 47.080319       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8482991112473184,         \"MicroF1\": 0.8482991112473184,         \"MacroF1\": 0.8269786301577753,         \"Memory in Mb\": 0.9053754806518556,         \"Time in s\": 64.636989       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8392808499046581,         \"MicroF1\": 0.8392808499046581,         \"MacroF1\": 0.8374924160046072,         \"Memory in Mb\": 1.0009994506835938,         \"Time in s\": 85.706576       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8323118411375338,         \"MicroF1\": 0.8323118411375338,         \"MacroF1\": 0.8182261307945194,         \"Memory in Mb\": 1.1217241287231443,         \"Time in s\": 110.709782       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8159126365054602,         \"MicroF1\": 0.8159126365054602,         \"MacroF1\": 0.8260965842218733,         \"Memory in Mb\": 1.2173480987548828,         \"Time in s\": 139.812165       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8149131767109296,         \"MicroF1\": 0.8149131767109296,         \"MacroF1\": 0.8221314665977922,         \"Memory in Mb\": 1.312972068786621,         \"Time in s\": 173.369773       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8125589289081652,         \"MicroF1\": 0.8125589289081652,         \"MacroF1\": 0.797613058026624,         \"Memory in Mb\": 1.4085960388183594,         \"Time in s\": 211.780209       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7907546839432674,         \"MicroF1\": 0.7907546839432674,         \"MacroF1\": 0.7936708037520236,         \"Memory in Mb\": 1.5042200088500977,         \"Time in s\": 255.273991       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7886909625755842,         \"MicroF1\": 0.7886909625755842,         \"MacroF1\": 0.7694478218498494,         \"Memory in Mb\": 1.599843978881836,         \"Time in s\": 304.294734       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7635973647924008,         \"MicroF1\": 0.7635973647924008,         \"MacroF1\": 0.75687960152136,         \"Memory in Mb\": 1.6954679489135742,         \"Time in s\": 359.144129       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.75155010814708,         \"MicroF1\": 0.7515501081470799,         \"MacroF1\": 0.7521509466338959,         \"Memory in Mb\": 1.7910919189453125,         \"Time in s\": 420.221142       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7611330518861501,         \"MicroF1\": 0.7611330518861501,         \"MacroF1\": 0.7576671162861806,         \"Memory in Mb\": 1.8881807327270508,         \"Time in s\": 487.76956500000006       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7617081666881693,         \"MicroF1\": 0.7617081666881692,         \"MacroF1\": 0.7593340838982119,         \"Memory in Mb\": 1.983804702758789,         \"Time in s\": 562.1432000000001       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7655349920333374,         \"MicroF1\": 0.7655349920333374,         \"MacroF1\": 0.7610505848438686,         \"Memory in Mb\": 2.079428672790528,         \"Time in s\": 643.5514560000001       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7644449632310026,         \"MicroF1\": 0.7644449632310025,         \"MacroF1\": 0.7639417799779614,         \"Memory in Mb\": 2.223102569580078,         \"Time in s\": 732.3349550000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7624512534818941,         \"MicroF1\": 0.7624512534818941,         \"MacroF1\": 0.7625605608371232,         \"Memory in Mb\": 2.3187265396118164,         \"Time in s\": 828.9274100000001       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7605243525524885,         \"MicroF1\": 0.7605243525524885,         \"MacroF1\": 0.7588384348689571,         \"Memory in Mb\": 2.4143505096435547,         \"Time in s\": 933.484588       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.753344908589521,         \"MicroF1\": 0.753344908589521,         \"MacroF1\": 0.7499438215834663,         \"Memory in Mb\": 2.509974479675293,         \"Time in s\": 1046.19484       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7450730463770958,         \"MicroF1\": 0.7450730463770959,         \"MacroF1\": 0.7369660419615974,         \"Memory in Mb\": 2.6055984497070312,         \"Time in s\": 1167.344916       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7240501555576506,         \"MicroF1\": 0.7240501555576506,         \"MacroF1\": 0.7111305646829175,         \"Memory in Mb\": 2.701222419738769,         \"Time in s\": 1296.919782       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7166591012256015,         \"MicroF1\": 0.7166591012256015,         \"MacroF1\": 0.7122511515574346,         \"Memory in Mb\": 2.796846389770508,         \"Time in s\": 1434.776076       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.710146196270682,         \"MicroF1\": 0.710146196270682,         \"MacroF1\": 0.6963016796632095,         \"Memory in Mb\": 2.892470359802246,         \"Time in s\": 1580.7280859999998       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7005324993660722,         \"MicroF1\": 0.7005324993660722,         \"MacroF1\": 0.6925666211338901,         \"Memory in Mb\": 2.9880943298339844,         \"Time in s\": 1735.0271709999995       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7043876133671052,         \"MicroF1\": 0.7043876133671052,         \"MacroF1\": 0.7007845610449206,         \"Memory in Mb\": 3.0837182998657227,         \"Time in s\": 1897.652612       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7004032576895707,         \"MicroF1\": 0.7004032576895707,         \"MacroF1\": 0.6915775762792657,         \"Memory in Mb\": 3.179342269897461,         \"Time in s\": 2069.0860809999995       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6877058598238223,         \"MicroF1\": 0.6877058598238223,         \"MacroF1\": 0.6789768292873962,         \"Memory in Mb\": 3.274966239929199,         \"Time in s\": 2249.389177       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6838743222164451,         \"MicroF1\": 0.6838743222164451,         \"MacroF1\": 0.6791243465680947,         \"Memory in Mb\": 3.370590209960937,         \"Time in s\": 2438.693149       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6822146925239708,         \"MicroF1\": 0.6822146925239708,         \"MacroF1\": 0.6786558938530485,         \"Memory in Mb\": 3.466214179992676,         \"Time in s\": 2637.684102       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6777085230058127,         \"MicroF1\": 0.6777085230058127,         \"MacroF1\": 0.6725285130045525,         \"Memory in Mb\": 3.561838150024414,         \"Time in s\": 2845.828808       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6807380676788997,         \"MicroF1\": 0.6807380676788997,         \"MacroF1\": 0.6786761142186741,         \"Memory in Mb\": 3.657462120056152,         \"Time in s\": 3062.994215       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6873799271281882,         \"MicroF1\": 0.6873799271281882,         \"MacroF1\": 0.6854839306484398,         \"Memory in Mb\": 3.75308609008789,         \"Time in s\": 3290.055422       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6858027478552539,         \"MicroF1\": 0.6858027478552539,         \"MacroF1\": 0.6816808496509055,         \"Memory in Mb\": 3.848710060119629,         \"Time in s\": 3526.69202       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6765759537426937,         \"MicroF1\": 0.6765759537426937,         \"MacroF1\": 0.6694713281964946,         \"Memory in Mb\": 3.944334030151367,         \"Time in s\": 3772.997519       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6673815797536614,         \"MicroF1\": 0.6673815797536614,         \"MacroF1\": 0.6617321933140904,         \"Memory in Mb\": 4.0399580001831055,         \"Time in s\": 4029.133223       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6643151790518323,         \"MicroF1\": 0.6643151790518323,         \"MacroF1\": 0.661178029358405,         \"Memory in Mb\": 4.135581970214844,         \"Time in s\": 4295.086238       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6598774438284214,         \"MicroF1\": 0.6598774438284214,         \"MacroF1\": 0.655734247886306,         \"Memory in Mb\": 4.32945728302002,         \"Time in s\": 4570.827071       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6518269395200365,         \"MicroF1\": 0.6518269395200365,         \"MacroF1\": 0.6481085155228206,         \"Memory in Mb\": 4.425081253051758,         \"Time in s\": 4856.254143       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6507158375577963,         \"MicroF1\": 0.6507158375577963,         \"MacroF1\": 0.6489368995854258,         \"Memory in Mb\": 4.520705223083496,         \"Time in s\": 5151.869359       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6566806470940683,         \"MicroF1\": 0.6566806470940683,         \"MacroF1\": 0.6555764711123695,         \"Memory in Mb\": 4.616329193115234,         \"Time in s\": 5457.498716       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.662279533223211,         \"MicroF1\": 0.662279533223211,         \"MacroF1\": 0.6615432060687808,         \"Memory in Mb\": 4.711953163146973,         \"Time in s\": 5772.982264       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6534028683181226,         \"MicroF1\": 0.6534028683181226,         \"MacroF1\": 0.6508089832432514,         \"Memory in Mb\": 4.807577133178711,         \"Time in s\": 6098.679956       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6577643874789358,         \"MicroF1\": 0.6577643874789358,         \"MacroF1\": 0.6564201177589184,         \"Memory in Mb\": 4.903201103210449,         \"Time in s\": 6434.678037       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6518433294982742,         \"MicroF1\": 0.6518433294982742,         \"MacroF1\": 0.6501496360982542,         \"Memory in Mb\": 4.998825073242188,         \"Time in s\": 6781.324361       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6482180499044071,         \"MicroF1\": 0.6482180499044071,         \"MacroF1\": 0.6472493759146578,         \"Memory in Mb\": 5.094449043273926,         \"Time in s\": 7138.730487       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3777777777777777,         \"MicroF1\": 0.3777777777777777,         \"MacroF1\": 0.2811210847975554,         \"Memory in Mb\": 0.4234571456909179,         \"Time in s\": 0.325579       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5164835164835165,         \"MicroF1\": 0.5164835164835165,         \"MacroF1\": 0.5335477748411618,         \"Memory in Mb\": 0.4235143661499023,         \"Time in s\": 1.056326       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5474452554744526,         \"MicroF1\": 0.5474452554744526,         \"MacroF1\": 0.5743273066802479,         \"Memory in Mb\": 0.4236364364624023,         \"Time in s\": 2.202996       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6120218579234973,         \"MicroF1\": 0.6120218579234973,         \"MacroF1\": 0.6355989308336889,         \"Memory in Mb\": 0.4237203598022461,         \"Time in s\": 3.699294       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6375545851528385,         \"MicroF1\": 0.6375545851528385,         \"MacroF1\": 0.6557923943920432,         \"Memory in Mb\": 0.4237203598022461,         \"Time in s\": 5.564336       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6509090909090909,         \"MicroF1\": 0.6509090909090909,         \"MacroF1\": 0.66910740948952,         \"Memory in Mb\": 0.4237699508666992,         \"Time in s\": 7.749814       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.67601246105919,         \"MicroF1\": 0.67601246105919,         \"MacroF1\": 0.678427291711157,         \"Memory in Mb\": 0.4238309860229492,         \"Time in s\": 10.278631       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7002724795640327,         \"MicroF1\": 0.7002724795640327,         \"MacroF1\": 0.6988359939675117,         \"Memory in Mb\": 0.4238042831420898,         \"Time in s\": 13.125556       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.711864406779661,         \"MicroF1\": 0.711864406779661,         \"MacroF1\": 0.7104564330601258,         \"Memory in Mb\": 0.4237241744995117,         \"Time in s\": 16.369918000000002       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7124183006535948,         \"MicroF1\": 0.7124183006535948,         \"MacroF1\": 0.7087721216219991,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 19.921878000000003       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7207920792079208,         \"MicroF1\": 0.7207920792079208,         \"MacroF1\": 0.7145025942185106,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 23.844357       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7223230490018149,         \"MicroF1\": 0.7223230490018149,         \"MacroF1\": 0.7174926871575792,         \"Memory in Mb\": 0.4236936569213867,         \"Time in s\": 28.111685       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7269681742043551,         \"MicroF1\": 0.7269681742043551,         \"MacroF1\": 0.7216367248754637,         \"Memory in Mb\": 0.4237165451049804,         \"Time in s\": 32.752989       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7262830482115086,         \"MicroF1\": 0.7262830482115085,         \"MacroF1\": 0.7230014848259525,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 37.712808       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7300435413642961,         \"MicroF1\": 0.7300435413642961,         \"MacroF1\": 0.7265684058467008,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 43.006145       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7319727891156462,         \"MicroF1\": 0.7319727891156461,         \"MacroF1\": 0.7296570819427115,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 48.68780100000001       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.737516005121639,         \"MicroF1\": 0.737516005121639,         \"MacroF1\": 0.7350906419548328,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 54.69172       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7363966142684402,         \"MicroF1\": 0.7363966142684402,         \"MacroF1\": 0.7359651798179677,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 60.98272       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7422680412371134,         \"MicroF1\": 0.7422680412371134,         \"MacroF1\": 0.7398886847335938,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 67.641769       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7421109902067464,         \"MicroF1\": 0.7421109902067464,         \"MacroF1\": 0.738912026501458,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 74.649906       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7419689119170985,         \"MicroF1\": 0.7419689119170985,         \"MacroF1\": 0.7379593683174607,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 81.98079       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7418397626112759,         \"MicroF1\": 0.741839762611276,         \"MacroF1\": 0.7380802548116379,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 89.699811       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7436140018921475,         \"MicroF1\": 0.7436140018921475,         \"MacroF1\": 0.7390703652035102,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 97.738161       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7461468721668177,         \"MicroF1\": 0.7461468721668177,         \"MacroF1\": 0.7413714574148674,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 106.141078       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7476066144473456,         \"MicroF1\": 0.7476066144473456,         \"MacroF1\": 0.742441565911322,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 114.875735       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7506276150627615,         \"MicroF1\": 0.7506276150627615,         \"MacroF1\": 0.7460917536510117,         \"Memory in Mb\": 0.4234342575073242,         \"Time in s\": 123.973121       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7510072522159549,         \"MicroF1\": 0.7510072522159549,         \"MacroF1\": 0.7470578866974922,         \"Memory in Mb\": 0.4235563278198242,         \"Time in s\": 133.391788       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.756021756021756,         \"MicroF1\": 0.7560217560217559,         \"MacroF1\": 0.7510482446555896,         \"Memory in Mb\": 0.4236173629760742,         \"Time in s\": 143.113173       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7569392348087022,         \"MicroF1\": 0.7569392348087022,         \"MacroF1\": 0.7522366633133313,         \"Memory in Mb\": 0.4236173629760742,         \"Time in s\": 153.228885       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7585206671501088,         \"MicroF1\": 0.7585206671501088,         \"MacroF1\": 0.7544196711061472,         \"Memory in Mb\": 0.4236783981323242,         \"Time in s\": 163.64661999999998       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7614035087719299,         \"MicroF1\": 0.7614035087719299,         \"MacroF1\": 0.7567964121564391,         \"Memory in Mb\": 0.4236783981323242,         \"Time in s\": 174.36664399999998       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7654656696125085,         \"MicroF1\": 0.7654656696125085,         \"MacroF1\": 0.7591802078998249,         \"Memory in Mb\": 0.4236783981323242,         \"Time in s\": 185.463757       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7673038892551087,         \"MicroF1\": 0.7673038892551087,         \"MacroF1\": 0.7600352016074767,         \"Memory in Mb\": 0.4237394332885742,         \"Time in s\": 196.90308       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7677543186180422,         \"MicroF1\": 0.7677543186180422,         \"MacroF1\": 0.7612494392404334,         \"Memory in Mb\": 0.4237394332885742,         \"Time in s\": 208.647576       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7675574891236793,         \"MicroF1\": 0.7675574891236793,         \"MacroF1\": 0.7602773300593106,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 220.786107       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.76797583081571,         \"MicroF1\": 0.76797583081571,         \"MacroF1\": 0.7607906010792568,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 233.2194       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7677836566725456,         \"MicroF1\": 0.7677836566725456,         \"MacroF1\": 0.7627036277641847,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 245.952092       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7710360618202633,         \"MicroF1\": 0.7710360618202633,         \"MacroF1\": 0.7657334796773966,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 259.02703       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7724484104852203,         \"MicroF1\": 0.7724484104852203,         \"MacroF1\": 0.7657758298578787,         \"Memory in Mb\": 0.4237356185913086,         \"Time in s\": 272.394107       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7737901033170201,         \"MicroF1\": 0.77379010331702,         \"MacroF1\": 0.767302943564198,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 286.067762       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7724137931034483,         \"MicroF1\": 0.7724137931034483,         \"MacroF1\": 0.7666353585191567,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 300.095471       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7731745209735889,         \"MicroF1\": 0.7731745209735889,         \"MacroF1\": 0.7666634536176192,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 314.417396       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7738998482549317,         \"MicroF1\": 0.7738998482549316,         \"MacroF1\": 0.7665909326930368,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 329.067854       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7750865051903114,         \"MicroF1\": 0.7750865051903113,         \"MacroF1\": 0.7662611838286661,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 344.01511700000003       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7747704204929918,         \"MicroF1\": 0.7747704204929918,         \"MacroF1\": 0.7660645062500586,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 359.290159       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7754137115839244,         \"MicroF1\": 0.7754137115839244,         \"MacroF1\": 0.7658988206988366,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 374.882405       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7760296159185562,         \"MicroF1\": 0.7760296159185563,         \"MacroF1\": 0.7660708746783081,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 390.75768       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.777979157227005,         \"MicroF1\": 0.7779791572270048,         \"MacroF1\": 0.7670029065892423,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 407.002801       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7749667110519307,         \"MicroF1\": 0.7749667110519308,         \"MacroF1\": 0.7639707440456852,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 423.546299       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7742496737712049,         \"MicroF1\": 0.7742496737712049,         \"MacroF1\": 0.7632394528829524,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 440.393364       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7743611953226505,         \"MicroF1\": 0.7743611953226506,         \"MacroF1\": 0.7633622232911937,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 457.310729       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6161137440758294,         \"MicroF1\": 0.6161137440758294,         \"MacroF1\": 0.581384151333148,         \"Memory in Mb\": 0.6645784378051758,         \"Time in s\": 11.249192       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6120322122216959,         \"MicroF1\": 0.6120322122216959,         \"MacroF1\": 0.5792161554760864,         \"Memory in Mb\": 0.6646394729614258,         \"Time in s\": 32.358705       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6049889485317335,         \"MicroF1\": 0.6049889485317335,         \"MacroF1\": 0.5721633809277145,         \"Memory in Mb\": 0.6647005081176758,         \"Time in s\": 62.851539       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.603125739995264,         \"MicroF1\": 0.603125739995264,         \"MacroF1\": 0.5703574432462961,         \"Memory in Mb\": 0.6647005081176758,         \"Time in s\": 102.700179       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6061754120098504,         \"MicroF1\": 0.6061754120098504,         \"MacroF1\": 0.5722430970062696,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 151.914202       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5995264404104184,         \"MicroF1\": 0.5995264404104184,         \"MacroF1\": 0.5671511237518188,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 210.432187       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5972128264104992,         \"MicroF1\": 0.5972128264104992,         \"MacroF1\": 0.5650210504998666,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 278.267755       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5989108559251806,         \"MicroF1\": 0.5989108559251806,         \"MacroF1\": 0.566418690076869,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 355.204938       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5962327685993897,         \"MicroF1\": 0.5962327685993897,         \"MacroF1\": 0.5633780031885509,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 441.186739       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5964579979164694,         \"MicroF1\": 0.5964579979164694,         \"MacroF1\": 0.5634236596216465,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 536.283653       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.594317692638829,         \"MicroF1\": 0.594317692638829,         \"MacroF1\": 0.5620068495149612,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 640.2689049999999       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5975061163286244,         \"MicroF1\": 0.5975061163286244,         \"MacroF1\": 0.567518061449456,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 753.0441599999999       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6097472135207984,         \"MicroF1\": 0.6097472135207984,         \"MacroF1\": 0.5927729676671933,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 874.528885       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001488195900697,         \"MicroF1\": 0.6001488195900697,         \"MacroF1\": 0.5832911478837771,         \"Memory in Mb\": 0.6645174026489258,         \"Time in s\": 1004.55011       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5673969316244712,         \"MicroF1\": 0.5673969316244712,         \"MacroF1\": 0.5522471754341495,         \"Memory in Mb\": 0.8876123428344727,         \"Time in s\": 1142.6522839999998       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5712340929269014,         \"MicroF1\": 0.5712340929269014,         \"MacroF1\": 0.5590383236849579,         \"Memory in Mb\": 1.4319400787353516,         \"Time in s\": 1288.8770269999998       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5741184335134533,         \"MicroF1\": 0.5741184335134533,         \"MacroF1\": 0.5632919959429028,         \"Memory in Mb\": 1.8629226684570312,         \"Time in s\": 1445.4718369999998       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5867312042931552,         \"MicroF1\": 0.5867312042931552,         \"MacroF1\": 0.5723846445183198,         \"Memory in Mb\": 0.4819307327270508,         \"Time in s\": 1609.073978       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5966704879629168,         \"MicroF1\": 0.5966704879629168,         \"MacroF1\": 0.5796820575913003,         \"Memory in Mb\": 0.6649179458618164,         \"Time in s\": 1780.2710459999998       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5984658364505895,         \"MicroF1\": 0.5984658364505895,         \"MacroF1\": 0.5810209140208816,         \"Memory in Mb\": 0.6650400161743164,         \"Time in s\": 1958.819581       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001803833145434,         \"MicroF1\": 0.6001803833145434,         \"MacroF1\": 0.5822125955100945,         \"Memory in Mb\": 1.2073478698730469,         \"Time in s\": 2144.726031       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6020403770823468,         \"MicroF1\": 0.6020403770823468,         \"MacroF1\": 0.5837921358595156,         \"Memory in Mb\": 1.321575164794922,         \"Time in s\": 2339.531046       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6047268085807221,         \"MicroF1\": 0.6047268085807221,         \"MacroF1\": 0.5859785990228289,         \"Memory in Mb\": 1.321636199951172,         \"Time in s\": 2543.839083       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6069131515605887,         \"MicroF1\": 0.6069131515605887,         \"MacroF1\": 0.587737290445056,         \"Memory in Mb\": 1.321758270263672,         \"Time in s\": 2757.206681       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6094927838175689,         \"MicroF1\": 0.6094927838175689,         \"MacroF1\": 0.5895162861993263,         \"Memory in Mb\": 1.321758270263672,         \"Time in s\": 2979.334505       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6105991622655254,         \"MicroF1\": 0.6105991622655254,         \"MacroF1\": 0.5896134687358237,         \"Memory in Mb\": 1.321941375732422,         \"Time in s\": 3211.082358       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6106064326049595,         \"MicroF1\": 0.6106064326049595,         \"MacroF1\": 0.5910741826972655,         \"Memory in Mb\": 1.321941375732422,         \"Time in s\": 3451.544855       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6099029323231981,         \"MicroF1\": 0.6099029323231981,         \"MacroF1\": 0.5935355609859342,         \"Memory in Mb\": 1.321941375732422,         \"Time in s\": 3700.712954       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088887437546942,         \"MicroF1\": 0.6088887437546942,         \"MacroF1\": 0.5952474102625339,         \"Memory in Mb\": 1.321453094482422,         \"Time in s\": 3958.532225       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088891694813598,         \"MicroF1\": 0.6088891694813598,         \"MacroF1\": 0.5975058139751561,         \"Memory in Mb\": 1.321697235107422,         \"Time in s\": 4224.837575       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6095921796242554,         \"MicroF1\": 0.6095921796242554,         \"MacroF1\": 0.5998546240309938,         \"Memory in Mb\": 1.321758270263672,         \"Time in s\": 4499.473663       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043917019324673,         \"MicroF1\": 0.6043917019324673,         \"MacroF1\": 0.595080118632132,         \"Memory in Mb\": 0.6649713516235352,         \"Time in s\": 4783.331389999999       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6034378856142566,         \"MicroF1\": 0.6034378856142566,         \"MacroF1\": 0.5941773754098104,         \"Memory in Mb\": 0.6650934219360352,         \"Time in s\": 5073.360361999999       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6029022644347269,         \"MicroF1\": 0.6029022644347269,         \"MacroF1\": 0.5935512429191343,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 5369.406481999999       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6013690846613815,         \"MicroF1\": 0.6013690846613815,         \"MacroF1\": 0.5919623858291095,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 5671.388488999999       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6010259108246745,         \"MicroF1\": 0.6010259108246745,         \"MacroF1\": 0.5912597483191937,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 5979.127636999999       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6003429653707353,         \"MicroF1\": 0.6003429653707353,         \"MacroF1\": 0.5902279082897147,         \"Memory in Mb\": 0.6648492813110352,         \"Time in s\": 6292.481400999999       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5961322800109652,         \"MicroF1\": 0.5961322800109652,         \"MacroF1\": 0.5867765456240649,         \"Memory in Mb\": 0.6648492813110352,         \"Time in s\": 6611.499413       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5939829541315591,         \"MicroF1\": 0.5939829541315591,         \"MacroF1\": 0.585290407267574,         \"Memory in Mb\": 0.6650323867797852,         \"Time in s\": 6936.132393       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5925803167688629,         \"MicroF1\": 0.5925803167688629,         \"MacroF1\": 0.5844470095695741,         \"Memory in Mb\": 0.6650934219360352,         \"Time in s\": 7266.407125       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5911306155445202,         \"MicroF1\": 0.5911306155445202,         \"MacroF1\": 0.5835517912214992,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 7602.391688       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.58959211742689,         \"MicroF1\": 0.58959211742689,         \"MacroF1\": 0.58246410272577,         \"Memory in Mb\": 1.1046571731567385,         \"Time in s\": 7943.862096       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5875746030347744,         \"MicroF1\": 0.5875746030347744,         \"MacroF1\": 0.5808874407233396,         \"Memory in Mb\": 1.3207244873046875,         \"Time in s\": 8291.951918       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5862083808621914,         \"MicroF1\": 0.5862083808621914,         \"MacroF1\": 0.5791892600330408,         \"Memory in Mb\": 1.3209075927734375,         \"Time in s\": 8644.890712       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5879332477535302,         \"MicroF1\": 0.5879332477535302,         \"MacroF1\": 0.5810233099134106,         \"Memory in Mb\": 1.3210525512695312,         \"Time in s\": 9004.012781000001       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5928152341739578,         \"MicroF1\": 0.5928152341739578,         \"MacroF1\": 0.5858160887305829,         \"Memory in Mb\": 1.3216018676757812,         \"Time in s\": 9370.107000000002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5979327436481231,         \"MicroF1\": 0.5979327436481231,         \"MacroF1\": 0.5906079347867982,         \"Memory in Mb\": 1.3215408325195312,         \"Time in s\": 9743.028377000002       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6027383747311934,         \"MicroF1\": 0.6027383747311934,         \"MacroF1\": 0.594893758427483,         \"Memory in Mb\": 1.3217239379882812,         \"Time in s\": 10122.858893000002       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6077923583866417,         \"MicroF1\": 0.6077923583866417,         \"MacroF1\": 0.5993180348311721,         \"Memory in Mb\": 1.3217239379882812,         \"Time in s\": 10509.572003000005       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.612985094414667,         \"MicroF1\": 0.612985094414667,         \"MacroF1\": 0.6039181082054342,         \"Memory in Mb\": 0.1438255310058593,         \"Time in s\": 10901.200853000002       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6133366132420005,         \"MicroF1\": 0.6133366132420005,         \"MacroF1\": 0.604218855594392,         \"Memory in Mb\": 0.1438255310058593,         \"Time in s\": 11292.868844000002       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 0.230626106262207,         \"Time in s\": 0.871514       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943558282208589,         \"MicroF1\": 0.943558282208589,         \"MacroF1\": 0.7669956277713079,         \"Memory in Mb\": 0.3262500762939453,         \"Time in s\": 3.583779       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8863450531479967,         \"MicroF1\": 0.8863450531479967,         \"MacroF1\": 0.8786592421362933,         \"Memory in Mb\": 0.4218740463256836,         \"Time in s\": 8.686347999999999       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.891477621091355,         \"MicroF1\": 0.891477621091355,         \"MacroF1\": 0.8818548670971931,         \"Memory in Mb\": 0.5179252624511719,         \"Time in s\": 16.685395       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.889651790093183,         \"MicroF1\": 0.889651790093183,         \"MacroF1\": 0.8812768038030504,         \"Memory in Mb\": 0.6251459121704102,         \"Time in s\": 28.245741       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8414384961176952,         \"MicroF1\": 0.8414384961176952,         \"MacroF1\": 0.8420581397672002,         \"Memory in Mb\": 0.7206478118896484,         \"Time in s\": 43.571154       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8500875656742557,         \"MicroF1\": 0.8500875656742557,         \"MacroF1\": 0.8345582037188519,         \"Memory in Mb\": 0.8163328170776367,         \"Time in s\": 63.099422       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8406374501992032,         \"MicroF1\": 0.8406374501992032,         \"MacroF1\": 0.8151418555553325,         \"Memory in Mb\": 0.911895751953125,         \"Time in s\": 87.33095300000001       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8321983110868973,         \"MicroF1\": 0.8321983110868973,         \"MacroF1\": 0.8307198315203921,         \"Memory in Mb\": 1.0075807571411133,         \"Time in s\": 116.498805       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.826182887962736,         \"MicroF1\": 0.826182887962736,         \"MacroF1\": 0.8123767856033619,         \"Memory in Mb\": 1.128366470336914,         \"Time in s\": 151.118073       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.809226654780477,         \"MicroF1\": 0.809226654780477,         \"MacroF1\": 0.8196273526663149,         \"Memory in Mb\": 1.2239294052124023,         \"Time in s\": 191.820305       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8081716036772216,         \"MicroF1\": 0.8081716036772216,         \"MacroF1\": 0.815232111826365,         \"Memory in Mb\": 1.3194313049316406,         \"Time in s\": 239.061616       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8057703186875353,         \"MicroF1\": 0.8057703186875353,         \"MacroF1\": 0.7903391475861199,         \"Memory in Mb\": 1.415055274963379,         \"Time in s\": 293.29488000000003       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7860269655051655,         \"MicroF1\": 0.7860269655051656,         \"MacroF1\": 0.7895763142947654,         \"Memory in Mb\": 1.5108013153076172,         \"Time in s\": 355.22640600000005       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.784441902271613,         \"MicroF1\": 0.784441902271613,         \"MacroF1\": 0.7657785418705475,         \"Memory in Mb\": 1.6062421798706057,         \"Time in s\": 425.240619       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7585414432357898,         \"MicroF1\": 0.7585414432357898,         \"MacroF1\": 0.751418836389106,         \"Memory in Mb\": 1.7020492553710938,         \"Time in s\": 503.467226       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7473684210526316,         \"MicroF1\": 0.7473684210526316,         \"MacroF1\": 0.7484284412750404,         \"Memory in Mb\": 1.797490119934082,         \"Time in s\": 590.6999010000001       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7565027917744791,         \"MicroF1\": 0.7565027917744791,         \"MacroF1\": 0.7526701844923946,         \"Memory in Mb\": 1.8947620391845703,         \"Time in s\": 687.248946       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7577086827506129,         \"MicroF1\": 0.7577086827506129,         \"MacroF1\": 0.755735065870518,         \"Memory in Mb\": 1.9903860092163088,         \"Time in s\": 793.498598       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7617355068023042,         \"MicroF1\": 0.7617355068023042,         \"MacroF1\": 0.7576049653668414,         \"Memory in Mb\": 2.085948944091797,         \"Time in s\": 909.902095       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7604762460604646,         \"MicroF1\": 0.7604762460604646,         \"MacroF1\": 0.7596175662696861,         \"Memory in Mb\": 2.2296838760375977,         \"Time in s\": 1036.556796       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.756991643454039,         \"MicroF1\": 0.7569916434540391,         \"MacroF1\": 0.7575313939177277,         \"Memory in Mb\": 2.325368881225586,         \"Time in s\": 1173.436632       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7558350207822658,         \"MicroF1\": 0.7558350207822658,         \"MacroF1\": 0.7548436696787698,         \"Memory in Mb\": 2.420870780944824,         \"Time in s\": 1320.145727       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.748340312531917,         \"MicroF1\": 0.7483403125319169,         \"MacroF1\": 0.744390859626019,         \"Memory in Mb\": 2.5164337158203125,         \"Time in s\": 1476.992513       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7393862143347387,         \"MicroF1\": 0.7393862143347387,         \"MacroF1\": 0.7315892779928432,         \"Memory in Mb\": 2.612057685852051,         \"Time in s\": 1644.1937280000002       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7196191194494201,         \"MicroF1\": 0.7196191194494201,         \"MacroF1\": 0.7089541376321258,         \"Memory in Mb\": 2.707803726196289,         \"Time in s\": 1822.193822       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7123921924648207,         \"MicroF1\": 0.7123921924648208,         \"MacroF1\": 0.7092068316988943,         \"Memory in Mb\": 2.8033666610717773,         \"Time in s\": 2011.0989090000005       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7062943184802591,         \"MicroF1\": 0.7062943184802591,         \"MacroF1\": 0.6946713230955313,         \"Memory in Mb\": 2.898990631103516,         \"Time in s\": 2211.8042590000005       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6967289324655566,         \"MicroF1\": 0.6967289324655566,         \"MacroF1\": 0.690232830798306,         \"Memory in Mb\": 2.994553565979004,         \"Time in s\": 2423.5715250000003       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7007108423890841,         \"MicroF1\": 0.7007108423890841,         \"MacroF1\": 0.6983689907908355,         \"Memory in Mb\": 3.090177536010742,         \"Time in s\": 2646.6754960000003       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6969241717403337,         \"MicroF1\": 0.6969241717403337,         \"MacroF1\": 0.6892508246262707,         \"Memory in Mb\": 3.1858625411987305,         \"Time in s\": 2881.7592360000003       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6836461126005362,         \"MicroF1\": 0.6836461126005362,         \"MacroF1\": 0.6755391962059192,         \"Memory in Mb\": 3.2815475463867188,         \"Time in s\": 3128.5577150000004       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6793433855752804,         \"MicroF1\": 0.6793433855752804,         \"MacroF1\": 0.6754035266161623,         \"Memory in Mb\": 3.377110481262207,         \"Time in s\": 3387.355816       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6769519140653161,         \"MicroF1\": 0.6769519140653161,         \"MacroF1\": 0.6742482232309566,         \"Memory in Mb\": 3.4728565216064453,         \"Time in s\": 3658.0697       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6728762518383641,         \"MicroF1\": 0.6728762518383641,         \"MacroF1\": 0.6689356443053495,         \"Memory in Mb\": 3.5684194564819336,         \"Time in s\": 3940.688111       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6762442976782188,         \"MicroF1\": 0.6762442976782188,         \"MacroF1\": 0.6753292472514647,         \"Memory in Mb\": 3.663982391357422,         \"Time in s\": 4235.610853       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6830076184166942,         \"MicroF1\": 0.6830076184166942,         \"MacroF1\": 0.6822311287838643,         \"Memory in Mb\": 3.75966739654541,         \"Time in s\": 4542.8267670000005       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6818035218989873,         \"MicroF1\": 0.6818035218989873,         \"MacroF1\": 0.6788656596145114,         \"Memory in Mb\": 3.8552303314208975,         \"Time in s\": 4862.152597       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6816039218150964,         \"MicroF1\": 0.6816039218150964,         \"MacroF1\": 0.6801525397911032,         \"Memory in Mb\": 0.2705574035644531,         \"Time in s\": 5190.397888       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6858263373981249,         \"MicroF1\": 0.6858263373981249,         \"MacroF1\": 0.685191280018575,         \"Memory in Mb\": 0.4621334075927734,         \"Time in s\": 5522.880902000001       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6896634184253004,         \"MicroF1\": 0.6896634184253004,         \"MacroF1\": 0.6890226069872224,         \"Memory in Mb\": 0.6535873413085938,         \"Time in s\": 5860.018685000001       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6925007295010213,         \"MicroF1\": 0.6925007295010213,         \"MacroF1\": 0.6918635442211969,         \"Memory in Mb\": 0.9691534042358398,         \"Time in s\": 6202.345681000001       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6990252522373597,         \"MicroF1\": 0.6990252522373597,         \"MacroF1\": 0.6986638608261282,         \"Memory in Mb\": 0.2649049758911133,         \"Time in s\": 6547.073149000001       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7038605091638349,         \"MicroF1\": 0.7038605091638349,         \"MacroF1\": 0.7032543903990934,         \"Memory in Mb\": 0.579315185546875,         \"Time in s\": 6893.121988000001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.710114930007081,         \"MicroF1\": 0.7101149300070809,         \"MacroF1\": 0.70950849929648,         \"Memory in Mb\": 0.2349414825439453,         \"Time in s\": 7240.035665000001       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.715351414717323,         \"MicroF1\": 0.715351414717323,         \"MacroF1\": 0.7146010079934133,         \"Memory in Mb\": 0.3305654525756836,         \"Time in s\": 7588.155090000001       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7179139504563233,         \"MicroF1\": 0.7179139504563233,         \"MacroF1\": 0.7169858006379833,         \"Memory in Mb\": 0.4260063171386719,         \"Time in s\": 7937.751954000001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7223612316805392,         \"MicroF1\": 0.7223612316805392,         \"MacroF1\": 0.7214649429496548,         \"Memory in Mb\": 0.5217523574829102,         \"Time in s\": 8289.115139000001       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7219248661897854,         \"MicroF1\": 0.7219248661897855,         \"MacroF1\": 0.7206428236711905,         \"Memory in Mb\": 0.6287288665771484,         \"Time in s\": 8642.591702000002       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7231236825334575,         \"MicroF1\": 0.7231236825334575,         \"MacroF1\": 0.7218249685926471,         \"Memory in Mb\": 0.7244749069213867,         \"Time in s\": 8998.461289       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4222222222222222,         \"MicroF1\": 0.4222222222222222,         \"MacroF1\": 0.3590236094437775,         \"Memory in Mb\": 0.9685115814208984,         \"Time in s\": 1.326052       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5604395604395604,         \"MicroF1\": 0.5604395604395604,         \"MacroF1\": 0.5746538615446178,         \"Memory in Mb\": 1.0556058883666992,         \"Time in s\": 4.053487       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5766423357664233,         \"MicroF1\": 0.5766423357664233,         \"MacroF1\": 0.598257695340355,         \"Memory in Mb\": 1.344954490661621,         \"Time in s\": 8.154789999999998       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6229508196721312,         \"MicroF1\": 0.6229508196721312,         \"MacroF1\": 0.6451744040758778,         \"Memory in Mb\": 1.4133405685424805,         \"Time in s\": 13.553012999999998       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6506550218340611,         \"MicroF1\": 0.6506550218340611,         \"MacroF1\": 0.6680655280025949,         \"Memory in Mb\": 1.5576086044311523,         \"Time in s\": 20.188933       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6727272727272727,         \"MicroF1\": 0.6727272727272727,         \"MacroF1\": 0.6900672130049011,         \"Memory in Mb\": 1.7550430297851562,         \"Time in s\": 28.051384       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7040498442367601,         \"MicroF1\": 0.7040498442367601,         \"MacroF1\": 0.7087861936875776,         \"Memory in Mb\": 1.832967758178711,         \"Time in s\": 37.15395       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7302452316076294,         \"MicroF1\": 0.7302452316076294,         \"MacroF1\": 0.7285991575377422,         \"Memory in Mb\": 1.971024513244629,         \"Time in s\": 47.432602       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7457627118644068,         \"MicroF1\": 0.7457627118644068,         \"MacroF1\": 0.7430362907281778,         \"Memory in Mb\": 1.991847038269043,         \"Time in s\": 58.97377399999999       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7342047930283224,         \"MicroF1\": 0.7342047930283224,         \"MacroF1\": 0.7271744800226857,         \"Memory in Mb\": 1.8101978302001955,         \"Time in s\": 71.823928       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7405940594059406,         \"MicroF1\": 0.7405940594059406,         \"MacroF1\": 0.7304322149686578,         \"Memory in Mb\": 1.7132930755615234,         \"Time in s\": 85.827474       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7368421052631579,         \"MicroF1\": 0.7368421052631579,         \"MacroF1\": 0.7267508109083203,         \"Memory in Mb\": 1.5079193115234375,         \"Time in s\": 101.049314       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7403685092127303,         \"MicroF1\": 0.7403685092127302,         \"MacroF1\": 0.7318978254380314,         \"Memory in Mb\": 1.6471452713012695,         \"Time in s\": 117.420156       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7325038880248833,         \"MicroF1\": 0.7325038880248833,         \"MacroF1\": 0.7248107612258207,         \"Memory in Mb\": 1.7740907669067385,         \"Time in s\": 135.017443       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7242380261248186,         \"MicroF1\": 0.7242380261248187,         \"MacroF1\": 0.7153272190465999,         \"Memory in Mb\": 1.913142204284668,         \"Time in s\": 153.656893       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7251700680272108,         \"MicroF1\": 0.725170068027211,         \"MacroF1\": 0.7148466398758337,         \"Memory in Mb\": 2.0619029998779297,         \"Time in s\": 173.429455       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7259923175416133,         \"MicroF1\": 0.7259923175416134,         \"MacroF1\": 0.7134712280209221,         \"Memory in Mb\": 2.0208959579467773,         \"Time in s\": 194.315292       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.727932285368803,         \"MicroF1\": 0.727932285368803,         \"MacroF1\": 0.7177600265828429,         \"Memory in Mb\": 2.224555015563965,         \"Time in s\": 216.352158       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7353951890034365,         \"MicroF1\": 0.7353951890034366,         \"MacroF1\": 0.7262567978322628,         \"Memory in Mb\": 2.300021171569824,         \"Time in s\": 239.599524       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7431991294885746,         \"MicroF1\": 0.7431991294885745,         \"MacroF1\": 0.7345004589126253,         \"Memory in Mb\": 2.4412155151367188,         \"Time in s\": 263.99359400000003       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7471502590673575,         \"MicroF1\": 0.7471502590673575,         \"MacroF1\": 0.7368855656689403,         \"Memory in Mb\": 2.474191665649414,         \"Time in s\": 289.66420500000004       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7546983184965381,         \"MicroF1\": 0.754698318496538,         \"MacroF1\": 0.7446216664767904,         \"Memory in Mb\": 2.5655078887939453,         \"Time in s\": 316.44421900000003       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.760643330179754,         \"MicroF1\": 0.760643330179754,         \"MacroF1\": 0.7502594177262459,         \"Memory in Mb\": 2.798956871032715,         \"Time in s\": 344.45448600000003       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7624660018132366,         \"MicroF1\": 0.7624660018132366,         \"MacroF1\": 0.7523020427630668,         \"Memory in Mb\": 2.48898983001709,         \"Time in s\": 373.71735       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7650130548302873,         \"MicroF1\": 0.7650130548302874,         \"MacroF1\": 0.7555087521342715,         \"Memory in Mb\": 2.3284912109375,         \"Time in s\": 404.061966       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7690376569037657,         \"MicroF1\": 0.7690376569037657,         \"MacroF1\": 0.7603504370239861,         \"Memory in Mb\": 2.0560731887817383,         \"Time in s\": 435.510035       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7719580983078163,         \"MicroF1\": 0.7719580983078163,         \"MacroF1\": 0.7638249032322542,         \"Memory in Mb\": 2.11933708190918,         \"Time in s\": 467.969641       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7746697746697747,         \"MicroF1\": 0.7746697746697747,         \"MacroF1\": 0.7668828628349821,         \"Memory in Mb\": 2.277647018432617,         \"Time in s\": 501.448754       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7771942985746436,         \"MicroF1\": 0.7771942985746436,         \"MacroF1\": 0.7696789046658701,         \"Memory in Mb\": 2.3871631622314453,         \"Time in s\": 535.887669       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7817258883248731,         \"MicroF1\": 0.7817258883248731,         \"MacroF1\": 0.7754511149783997,         \"Memory in Mb\": 2.3104944229125977,         \"Time in s\": 571.357393       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7866666666666666,         \"MicroF1\": 0.7866666666666666,         \"MacroF1\": 0.7797171864703156,         \"Memory in Mb\": 2.4089183807373047,         \"Time in s\": 607.784244       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7912984364377974,         \"MicroF1\": 0.7912984364377974,         \"MacroF1\": 0.7836430453045393,         \"Memory in Mb\": 2.5425024032592773,         \"Time in s\": 645.2286509999999       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7963085036255768,         \"MicroF1\": 0.7963085036255768,         \"MacroF1\": 0.7883976288226553,         \"Memory in Mb\": 2.6389265060424805,         \"Time in s\": 683.7451019999999       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7978246960972489,         \"MicroF1\": 0.7978246960972489,         \"MacroF1\": 0.790949738475821,         \"Memory in Mb\": 2.283763885498047,         \"Time in s\": 723.3862519999999       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.798011187072716,         \"MicroF1\": 0.7980111870727161,         \"MacroF1\": 0.7914720525222512,         \"Memory in Mb\": 2.519012451171875,         \"Time in s\": 764.1312649999999       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7981873111782477,         \"MicroF1\": 0.7981873111782477,         \"MacroF1\": 0.7919320984228655,         \"Memory in Mb\": 2.307619094848633,         \"Time in s\": 806.0160599999999       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.798941798941799,         \"MicroF1\": 0.7989417989417988,         \"MacroF1\": 0.7945012991620244,         \"Memory in Mb\": 2.40640926361084,         \"Time in s\": 848.960292       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8019461934745278,         \"MicroF1\": 0.8019461934745278,         \"MacroF1\": 0.797056036319667,         \"Memory in Mb\": 2.447686195373535,         \"Time in s\": 893.037184       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8047964305633017,         \"MicroF1\": 0.8047964305633019,         \"MacroF1\": 0.7993493873930555,         \"Memory in Mb\": 2.5208606719970703,         \"Time in s\": 938.202728       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8069603045133225,         \"MicroF1\": 0.8069603045133223,         \"MacroF1\": 0.8019867749609348,         \"Memory in Mb\": 2.8025121688842773,         \"Time in s\": 984.592034       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8084880636604774,         \"MicroF1\": 0.8084880636604774,         \"MacroF1\": 0.8043300839686539,         \"Memory in Mb\": 2.9287471771240234,         \"Time in s\": 1032.221691       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8114966338684619,         \"MicroF1\": 0.8114966338684619,         \"MacroF1\": 0.8071482324590065,         \"Memory in Mb\": 2.977842330932617,         \"Time in s\": 1081.048247       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8148710166919575,         \"MicroF1\": 0.8148710166919576,         \"MacroF1\": 0.8107088256390683,         \"Memory in Mb\": 3.110445022583008,         \"Time in s\": 1130.994965       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8161146811665843,         \"MicroF1\": 0.8161146811665844,         \"MacroF1\": 0.8110472160986095,         \"Memory in Mb\": 3.3117494583129883,         \"Time in s\": 1182.226115       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8173030449492509,         \"MicroF1\": 0.8173030449492509,         \"MacroF1\": 0.8127793203399477,         \"Memory in Mb\": 2.7790603637695312,         \"Time in s\": 1234.703432       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8193853427895981,         \"MicroF1\": 0.8193853427895981,         \"MacroF1\": 0.8144282151100146,         \"Memory in Mb\": 2.8652515411376958,         \"Time in s\": 1288.356269       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8199907450254512,         \"MicroF1\": 0.8199907450254512,         \"MacroF1\": 0.8150157846003385,         \"Memory in Mb\": 2.925917625427246,         \"Time in s\": 1343.2838700000002       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8205709107385591,         \"MicroF1\": 0.8205709107385591,         \"MacroF1\": 0.8153449009635614,         \"Memory in Mb\": 2.785597801208496,         \"Time in s\": 1399.325285       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8175765645805593,         \"MicroF1\": 0.8175765645805593,         \"MacroF1\": 0.813116129924445,         \"Memory in Mb\": 2.868098258972168,         \"Time in s\": 1456.6402850000002       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8186167899086559,         \"MicroF1\": 0.8186167899086559,         \"MacroF1\": 0.8144518819207099,         \"Memory in Mb\": 3.062863349914551,         \"Time in s\": 1515.2003170000005       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8185361628410567,         \"MicroF1\": 0.8185361628410566,         \"MacroF1\": 0.8145347387119569,         \"Memory in Mb\": 3.063481330871582,         \"Time in s\": 1574.1800910000002       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6682464454976303,         \"MicroF1\": 0.6682464454976303,         \"MacroF1\": 0.6049011732627783,         \"Memory in Mb\": 7.181946754455566,         \"Time in s\": 32.418226       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6944576030317385,         \"MicroF1\": 0.6944576030317385,         \"MacroF1\": 0.6288311688548281,         \"Memory in Mb\": 9.89784336090088,         \"Time in s\": 94.873564       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6984527944426903,         \"MicroF1\": 0.6984527944426903,         \"MacroF1\": 0.625371849015863,         \"Memory in Mb\": 13.448436737060549,         \"Time in s\": 186.837042       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.706369879232773,         \"MicroF1\": 0.706369879232773,         \"MacroF1\": 0.6266042661686886,         \"Memory in Mb\": 17.43436622619629,         \"Time in s\": 307.272577       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7107406705815495,         \"MicroF1\": 0.7107406705815495,         \"MacroF1\": 0.6273487761971507,         \"Memory in Mb\": 20.93905258178711,         \"Time in s\": 452.99825       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7108129439621153,         \"MicroF1\": 0.7108129439621153,         \"MacroF1\": 0.6274052515282983,         \"Memory in Mb\": 25.022296905517575,         \"Time in s\": 622.602665       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7127587606548504,         \"MicroF1\": 0.7127587606548504,         \"MacroF1\": 0.6273117178459473,         \"Memory in Mb\": 28.81925773620605,         \"Time in s\": 816.020547       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7164673848703682,         \"MicroF1\": 0.7164673848703682,         \"MacroF1\": 0.6293431255193823,         \"Memory in Mb\": 32.80279922485352,         \"Time in s\": 1032.257355       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.721666842049879,         \"MicroF1\": 0.721666842049879,         \"MacroF1\": 0.63170101976307,         \"Memory in Mb\": 32.88048076629639,         \"Time in s\": 1271.699652       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.724405720238659,         \"MicroF1\": 0.724405720238659,         \"MacroF1\": 0.6339052025360064,         \"Memory in Mb\": 29.71586036682129,         \"Time in s\": 1533.827375       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7244080929832114,         \"MicroF1\": 0.7244080929832114,         \"MacroF1\": 0.6334336343217646,         \"Memory in Mb\": 33.71169948577881,         \"Time in s\": 1818.162347       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7225949017441402,         \"MicroF1\": 0.7225949017441402,         \"MacroF1\": 0.6332595599893077,         \"Memory in Mb\": 29.64934635162353,         \"Time in s\": 2125.062078       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7416769869600058,         \"MicroF1\": 0.7416769869600057,         \"MacroF1\": 0.7385871869253197,         \"Memory in Mb\": 11.750191688537598,         \"Time in s\": 2443.236189       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7472096326861936,         \"MicroF1\": 0.7472096326861937,         \"MacroF1\": 0.7473000008879964,         \"Memory in Mb\": 7.712667465209961,         \"Time in s\": 2772.229259       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7404507860344719,         \"MicroF1\": 0.7404507860344719,         \"MacroF1\": 0.7427443120881612,         \"Memory in Mb\": 5.854048728942871,         \"Time in s\": 3118.280673       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.73666765315182,         \"MicroF1\": 0.73666765315182,         \"MacroF1\": 0.7407696345938622,         \"Memory in Mb\": 9.543391227722168,         \"Time in s\": 3480.1200929999995       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7295972369227341,         \"MicroF1\": 0.7295972369227341,         \"MacroF1\": 0.7347001031972082,         \"Memory in Mb\": 14.625198364257812,         \"Time in s\": 3856.632275       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.739780081022781,         \"MicroF1\": 0.7397800810227809,         \"MacroF1\": 0.7407912307996387,         \"Memory in Mb\": 5.110816955566406,         \"Time in s\": 4245.133706999999       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7434581069630664,         \"MicroF1\": 0.7434581069630664,         \"MacroF1\": 0.7402037922066672,         \"Memory in Mb\": 3.8148155212402335,         \"Time in s\": 4646.574114999999       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.745111037454425,         \"MicroF1\": 0.7451110374544251,         \"MacroF1\": 0.7386209934273732,         \"Memory in Mb\": 7.313493728637695,         \"Time in s\": 5063.578879       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7462006764374295,         \"MicroF1\": 0.7462006764374295,         \"MacroF1\": 0.7365944363606786,         \"Memory in Mb\": 12.210733413696287,         \"Time in s\": 5495.973683       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7483965391072274,         \"MicroF1\": 0.7483965391072274,         \"MacroF1\": 0.7360584061499352,         \"Memory in Mb\": 11.241872787475586,         \"Time in s\": 5944.2105440000005       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7495779635195784,         \"MicroF1\": 0.7495779635195785,         \"MacroF1\": 0.7345443205753824,         \"Memory in Mb\": 12.262273788452148,         \"Time in s\": 6407.867088000001       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7508582251509293,         \"MicroF1\": 0.7508582251509293,         \"MacroF1\": 0.7336140903014292,         \"Memory in Mb\": 15.815716743469238,         \"Time in s\": 6885.995097000001       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7510890564036516,         \"MicroF1\": 0.7510890564036516,         \"MacroF1\": 0.7317409587301968,         \"Memory in Mb\": 20.072275161743164,         \"Time in s\": 7378.034229000001       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7520670187579676,         \"MicroF1\": 0.7520670187579677,         \"MacroF1\": 0.7304776676466566,         \"Memory in Mb\": 23.249674797058105,         \"Time in s\": 7884.702304       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7487285609063169,         \"MicroF1\": 0.7487285609063169,         \"MacroF1\": 0.7285292321096271,         \"Memory in Mb\": 2.702430725097656,         \"Time in s\": 8406.670172       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7464741096492712,         \"MicroF1\": 0.7464741096492712,         \"MacroF1\": 0.7309964825863351,         \"Memory in Mb\": 6.2935638427734375,         \"Time in s\": 8940.901067       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7457793162002416,         \"MicroF1\": 0.7457793162002416,         \"MacroF1\": 0.7347045068936117,         \"Memory in Mb\": 9.350909233093262,         \"Time in s\": 9487.04409       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.745036143817671,         \"MicroF1\": 0.745036143817671,         \"MacroF1\": 0.7375864352537521,         \"Memory in Mb\": 14.599569320678713,         \"Time in s\": 10044.836672       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7451962731021842,         \"MicroF1\": 0.7451962731021842,         \"MacroF1\": 0.7406480970104784,         \"Memory in Mb\": 19.12519836425781,         \"Time in s\": 10615.117300000002       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7402858749371134,         \"MicroF1\": 0.7402858749371134,         \"MacroF1\": 0.7370798749337869,         \"Memory in Mb\": 6.808139801025391,         \"Time in s\": 11202.653786000004       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7366200820730623,         \"MicroF1\": 0.7366200820730623,         \"MacroF1\": 0.7333315604235389,         \"Memory in Mb\": 5.8602495193481445,         \"Time in s\": 11807.700361000005       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.733921956382475,         \"MicroF1\": 0.7339219563824751,         \"MacroF1\": 0.7303171015411175,         \"Memory in Mb\": 9.36469554901123,         \"Time in s\": 12429.747970000002       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7304039611461349,         \"MicroF1\": 0.7304039611461349,         \"MacroF1\": 0.7265687877692525,         \"Memory in Mb\": 14.848862648010254,         \"Time in s\": 13069.446785000002       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7276864395633302,         \"MicroF1\": 0.7276864395633302,         \"MacroF1\": 0.7236022807953257,         \"Memory in Mb\": 19.807891845703125,         \"Time in s\": 13727.939023000004       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7250134370760922,         \"MicroF1\": 0.7250134370760921,         \"MacroF1\": 0.7209989950382084,         \"Memory in Mb\": 16.71243381500244,         \"Time in s\": 14405.601845000005       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7235028783612032,         \"MicroF1\": 0.7235028783612032,         \"MacroF1\": 0.7198278735760195,         \"Memory in Mb\": 8.331427574157715,         \"Time in s\": 15101.835691000002       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.723623825364835,         \"MicroF1\": 0.723623825364835,         \"MacroF1\": 0.7203262236880287,         \"Memory in Mb\": 6.9819841384887695,         \"Time in s\": 15814.868539000005       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7240464973129098,         \"MicroF1\": 0.7240464973129098,         \"MacroF1\": 0.7211005399097123,         \"Memory in Mb\": 10.71219539642334,         \"Time in s\": 16543.112989       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7245409400623629,         \"MicroF1\": 0.7245409400623629,         \"MacroF1\": 0.721844297210525,         \"Memory in Mb\": 10.330558776855469,         \"Time in s\": 17285.760894000003       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7248765529525828,         \"MicroF1\": 0.7248765529525828,         \"MacroF1\": 0.7223628081683402,         \"Memory in Mb\": 13.299851417541504,         \"Time in s\": 18041.694028       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7254167859581122,         \"MicroF1\": 0.7254167859581122,         \"MacroF1\": 0.7228420559832612,         \"Memory in Mb\": 15.662115097045898,         \"Time in s\": 18810.181113       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7263844349267159,         \"MicroF1\": 0.7263844349267159,         \"MacroF1\": 0.7236482152790997,         \"Memory in Mb\": 19.25161361694336,         \"Time in s\": 19591.516438       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7265304404553968,         \"MicroF1\": 0.7265304404553967,         \"MacroF1\": 0.7240124567772878,         \"Memory in Mb\": 14.065608024597168,         \"Time in s\": 20387.990038000004       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7304374678332476,         \"MicroF1\": 0.7304374678332476,         \"MacroF1\": 0.7281756207358935,         \"Memory in Mb\": 7.354809761047363,         \"Time in s\": 21197.413376000004       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7344603171404969,         \"MicroF1\": 0.7344603171404969,         \"MacroF1\": 0.7322565876518081,         \"Memory in Mb\": 7.006095886230469,         \"Time in s\": 22016.972025000003       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7380590684001815,         \"MicroF1\": 0.7380590684001815,         \"MacroF1\": 0.7356981427827818,         \"Memory in Mb\": 10.14159107208252,         \"Time in s\": 22847.182754       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7420134124422627,         \"MicroF1\": 0.7420134124422627,         \"MacroF1\": 0.7394134340953542,         \"Memory in Mb\": 13.563420295715332,         \"Time in s\": 23688.037606       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7451466883842497,         \"MicroF1\": 0.7451466883842497,         \"MacroF1\": 0.7430487162081567,         \"Memory in Mb\": 0.3614501953125,         \"Time in s\": 24535.706056000003       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7453781671618067,         \"MicroF1\": 0.7453781671618067,         \"MacroF1\": 0.7433023109254195,         \"Memory in Mb\": 0.3617935180664062,         \"Time in s\": 25383.518073000003       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 0.3354053497314453,         \"Time in s\": 3.23067       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9730061349693252,         \"MicroF1\": 0.9730061349693252,         \"MacroF1\": 0.8116978142719798,         \"Memory in Mb\": 0.988037109375,         \"Time in s\": 11.21298       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9730171708912512,         \"MicroF1\": 0.9730171708912512,         \"MacroF1\": 0.9579161898493525,         \"Memory in Mb\": 2.195523262023926,         \"Time in s\": 25.427007       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9693439607602696,         \"MicroF1\": 0.9693439607602696,         \"MacroF1\": 0.9069773132409142,         \"Memory in Mb\": 3.526730537414551,         \"Time in s\": 46.453054       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9666503187837177,         \"MicroF1\": 0.9666503187837177,         \"MacroF1\": 0.9303026980117672,         \"Memory in Mb\": 5.496582984924316,         \"Time in s\": 74.431187       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9660809154066204,         \"MicroF1\": 0.9660809154066204,         \"MacroF1\": 0.955517866483744,         \"Memory in Mb\": 2.29970645904541,         \"Time in s\": 107.969459       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9691768826619964,         \"MicroF1\": 0.9691768826619964,         \"MacroF1\": 0.9674134048328416,         \"Memory in Mb\": 3.376467704772949,         \"Time in s\": 146.96126800000002       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9672080907140668,         \"MicroF1\": 0.9672080907140668,         \"MacroF1\": 0.9546197483047236,         \"Memory in Mb\": 4.62060546875,         \"Time in s\": 192.073824       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9684009806592208,         \"MicroF1\": 0.968400980659221,         \"MacroF1\": 0.9654409635782653,         \"Memory in Mb\": 3.119338035583496,         \"Time in s\": 243.354323       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9644520715861732,         \"MicroF1\": 0.9644520715861732,         \"MacroF1\": 0.95030552665756,         \"Memory in Mb\": 4.705347061157227,         \"Time in s\": 301.433133       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9661243592600848,         \"MicroF1\": 0.9661243592600848,         \"MacroF1\": 0.9659906155964958,         \"Memory in Mb\": 1.508072853088379,         \"Time in s\": 365.412759       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677221654749744,         \"MicroF1\": 0.9677221654749744,         \"MacroF1\": 0.96768641848376,         \"Memory in Mb\": 2.487558364868164,         \"Time in s\": 434.672843       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9685083914765228,         \"MicroF1\": 0.9685083914765228,         \"MacroF1\": 0.9677400809149086,         \"Memory in Mb\": 2.8771514892578125,         \"Time in s\": 509.854138       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9690071791279986,         \"MicroF1\": 0.9690071791279986,         \"MacroF1\": 0.968698427792926,         \"Memory in Mb\": 4.140267372131348,         \"Time in s\": 591.7133210000001       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9671514953423762,         \"MicroF1\": 0.9671514953423762,         \"MacroF1\": 0.9635575047511442,         \"Memory in Mb\": 5.121949195861816,         \"Time in s\": 681.1937680000001       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9675195342423778,         \"MicroF1\": 0.9675195342423778,         \"MacroF1\": 0.9673223823066148,         \"Memory in Mb\": 2.1385393142700195,         \"Time in s\": 777.2102420000001       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968565248738284,         \"MicroF1\": 0.968565248738284,         \"MacroF1\": 0.9688652926813892,         \"Memory in Mb\": 2.7864933013916016,         \"Time in s\": 879.0640510000001       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9686776521857552,         \"MicroF1\": 0.9686776521857552,         \"MacroF1\": 0.9682274153773373,         \"Memory in Mb\": 3.314570426940918,         \"Time in s\": 987.062921       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9682621597213262,         \"MicroF1\": 0.9682621597213262,         \"MacroF1\": 0.9674704101631952,         \"Memory in Mb\": 4.690197944641113,         \"Time in s\": 1101.141854       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.96727540139723,         \"MicroF1\": 0.96727540139723,         \"MacroF1\": 0.9662379529396136,         \"Memory in Mb\": 5.223731994628906,         \"Time in s\": 1221.487909       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677833547332788,         \"MicroF1\": 0.9677833547332788,         \"MacroF1\": 0.9678822443058488,         \"Memory in Mb\": 4.885932922363281,         \"Time in s\": 1347.980617       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9686908077994428,         \"MicroF1\": 0.9686908077994428,         \"MacroF1\": 0.9690861219789196,         \"Memory in Mb\": 6.402636528015137,         \"Time in s\": 1480.694289       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9683470105509964,         \"MicroF1\": 0.9683470105509964,         \"MacroF1\": 0.9680699356268632,         \"Memory in Mb\": 6.928671836853027,         \"Time in s\": 1620.259773       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.96864467367991,         \"MicroF1\": 0.96864467367991,         \"MacroF1\": 0.9687197530276812,         \"Memory in Mb\": 5.552419662475586,         \"Time in s\": 1766.078849       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9684282772820864,         \"MicroF1\": 0.9684282772820866,         \"MacroF1\": 0.9682582636163196,         \"Memory in Mb\": 2.695918083190918,         \"Time in s\": 1917.758924       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9673800320543038,         \"MicroF1\": 0.9673800320543038,         \"MacroF1\": 0.9668238422002586,         \"Memory in Mb\": 3.239151954650879,         \"Time in s\": 2074.190769       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9676804357694052,         \"MicroF1\": 0.9676804357694052,         \"MacroF1\": 0.9678040910458204,         \"Memory in Mb\": 4.023995399475098,         \"Time in s\": 2235.420867       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677842948437364,         \"MicroF1\": 0.9677842948437364,         \"MacroF1\": 0.9678364439490078,         \"Memory in Mb\": 4.695375442504883,         \"Time in s\": 2402.164192       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677119432000676,         \"MicroF1\": 0.9677119432000676,         \"MacroF1\": 0.9677086079179034,         \"Memory in Mb\": 5.258674621582031,         \"Time in s\": 2574.870699       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968706593675954,         \"MicroF1\": 0.968706593675954,         \"MacroF1\": 0.9690716756618885,         \"Memory in Mb\": 6.001680374145508,         \"Time in s\": 2753.36713       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9688463667272872,         \"MicroF1\": 0.9688463667272872,         \"MacroF1\": 0.9689334511448672,         \"Memory in Mb\": 5.217698097229004,         \"Time in s\": 2937.769964       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9687476062811184,         \"MicroF1\": 0.9687476062811184,         \"MacroF1\": 0.968764477893114,         \"Memory in Mb\": 5.266051292419434,         \"Time in s\": 3127.802403       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9687291094109782,         \"MicroF1\": 0.9687291094109782,         \"MacroF1\": 0.9687736841624996,         \"Memory in Mb\": 6.279603958129883,         \"Time in s\": 3323.370395       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9695047220820416,         \"MicroF1\": 0.9695047220820416,         \"MacroF1\": 0.9697384724636318,         \"Memory in Mb\": 4.041820526123047,         \"Time in s\": 3524.041026       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9682750892919672,         \"MicroF1\": 0.9682750892919672,         \"MacroF1\": 0.9680357071263168,         \"Memory in Mb\": 2.1731691360473637,         \"Time in s\": 3729.110149       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9686797848437394,         \"MicroF1\": 0.9686797848437394,         \"MacroF1\": 0.9688099431838716,         \"Memory in Mb\": 2.4900379180908203,         \"Time in s\": 3938.33843       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9692613448161644,         \"MicroF1\": 0.9692613448161644,         \"MacroF1\": 0.9694122553904638,         \"Memory in Mb\": 2.7789316177368164,         \"Time in s\": 4151.996270000001       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9694897761723538,         \"MicroF1\": 0.9694897761723538,         \"MacroF1\": 0.969571649124791,         \"Memory in Mb\": 3.946505546569824,         \"Time in s\": 4370.227344000001       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9694550939601534,         \"MicroF1\": 0.9694550939601534,         \"MacroF1\": 0.9694916672888816,         \"Memory in Mb\": 4.345325469970703,         \"Time in s\": 4594.050341000001       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9695447024940254,         \"MicroF1\": 0.9695447024940254,         \"MacroF1\": 0.9695954968773725,         \"Memory in Mb\": 3.909954071044922,         \"Time in s\": 4823.361799000001       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9692114545345848,         \"MicroF1\": 0.9692114545345848,         \"MacroF1\": 0.9692084456743588,         \"Memory in Mb\": 1.764338493347168,         \"Time in s\": 5057.2303470000015       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9696527575138604,         \"MicroF1\": 0.9696527575138604,         \"MacroF1\": 0.9697329621491684,         \"Memory in Mb\": 1.7167367935180664,         \"Time in s\": 5295.013901000001       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9696745140511884,         \"MicroF1\": 0.9696745140511884,         \"MacroF1\": 0.9697082565052514,         \"Memory in Mb\": 2.814372062683105,         \"Time in s\": 5537.319837000001       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968748259149908,         \"MicroF1\": 0.968748259149908,         \"MacroF1\": 0.968705960089485,         \"Memory in Mb\": 2.951136589050293,         \"Time in s\": 5784.484584000001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9690070265264992,         \"MicroF1\": 0.9690070265264992,         \"MacroF1\": 0.9690448168177233,         \"Memory in Mb\": 3.5441465377807617,         \"Time in s\": 6036.117893000001       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9690946874833484,         \"MicroF1\": 0.9690946874833484,         \"MacroF1\": 0.9691164520527108,         \"Memory in Mb\": 4.379698753356934,         \"Time in s\": 6292.729193000001       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968761408083442,         \"MicroF1\": 0.968761408083442,         \"MacroF1\": 0.9687617227117352,         \"Memory in Mb\": 3.8120603561401367,         \"Time in s\": 6554.348831000001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9689526630240516,         \"MicroF1\": 0.9689526630240516,         \"MacroF1\": 0.9689629146490384,         \"Memory in Mb\": 2.019772529602051,         \"Time in s\": 6819.891372000001       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9692861787804512,         \"MicroF1\": 0.9692861787804512,         \"MacroF1\": 0.9692901573177236,         \"Memory in Mb\": 1.256450653076172,         \"Time in s\": 7089.584863000001       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9691161331437816,         \"MicroF1\": 0.9691161331437816,         \"MacroF1\": 0.9691108096285476,         \"Memory in Mb\": 1.6354646682739258,         \"Time in s\": 7363.046142000001       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5333333333333333,         \"MicroF1\": 0.5333333333333333,         \"MacroF1\": 0.5005728607232367,         \"Memory in Mb\": 0.8510866165161133,         \"Time in s\": 0.941842       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6153846153846154,         \"MicroF1\": 0.6153846153846154,         \"MacroF1\": 0.596131344383025,         \"Memory in Mb\": 1.5052366256713867,         \"Time in s\": 2.918201       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6496350364963503,         \"MicroF1\": 0.6496350364963503,         \"MacroF1\": 0.6567305057749026,         \"Memory in Mb\": 2.146304130554199,         \"Time in s\": 6.147886       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6994535519125683,         \"MicroF1\": 0.6994535519125683,         \"MacroF1\": 0.7070190759413217,         \"Memory in Mb\": 2.7665939331054688,         \"Time in s\": 10.824064       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7379912663755459,         \"MicroF1\": 0.7379912663755459,         \"MacroF1\": 0.7433871451842025,         \"Memory in Mb\": 3.2484235763549805,         \"Time in s\": 16.931166       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7490909090909091,         \"MicroF1\": 0.7490909090909091,         \"MacroF1\": 0.7566070103930901,         \"Memory in Mb\": 3.776392936706543,         \"Time in s\": 24.729994       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7694704049844237,         \"MicroF1\": 0.7694704049844237,         \"MacroF1\": 0.7681721604320974,         \"Memory in Mb\": 4.142314910888672,         \"Time in s\": 34.173162000000005       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.784741144414169,         \"MicroF1\": 0.7847411444141691,         \"MacroF1\": 0.7789718513534348,         \"Memory in Mb\": 4.497910499572754,         \"Time in s\": 45.384105000000005       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7990314769975787,         \"MicroF1\": 0.7990314769975787,         \"MacroF1\": 0.7943771701942021,         \"Memory in Mb\": 4.869846343994141,         \"Time in s\": 58.265676000000006       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7973856209150327,         \"MicroF1\": 0.7973856209150327,         \"MacroF1\": 0.7916511033189314,         \"Memory in Mb\": 5.3911848068237305,         \"Time in s\": 73.08883800000001       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.805940594059406,         \"MicroF1\": 0.805940594059406,         \"MacroF1\": 0.8010859843658406,         \"Memory in Mb\": 5.806554794311523,         \"Time in s\": 89.87625100000001       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8076225045372051,         \"MicroF1\": 0.8076225045372051,         \"MacroF1\": 0.8036838079612314,         \"Memory in Mb\": 6.295863151550293,         \"Time in s\": 108.5993       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8174204355108877,         \"MicroF1\": 0.8174204355108878,         \"MacroF1\": 0.8156009215135775,         \"Memory in Mb\": 6.727802276611328,         \"Time in s\": 129.48595400000002       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8211508553654744,         \"MicroF1\": 0.8211508553654744,         \"MacroF1\": 0.8207645722848749,         \"Memory in Mb\": 7.18087100982666,         \"Time in s\": 152.525841       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8229317851959361,         \"MicroF1\": 0.8229317851959362,         \"MacroF1\": 0.8226135245892084,         \"Memory in Mb\": 7.561182022094727,         \"Time in s\": 177.86541200000002       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8231292517006803,         \"MicroF1\": 0.8231292517006803,         \"MacroF1\": 0.8228959515200417,         \"Memory in Mb\": 7.975464820861816,         \"Time in s\": 205.499576       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8309859154929577,         \"MicroF1\": 0.8309859154929577,         \"MacroF1\": 0.8306123687436626,         \"Memory in Mb\": 8.301925659179688,         \"Time in s\": 235.39408200000003       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8343409915356711,         \"MicroF1\": 0.834340991535671,         \"MacroF1\": 0.835521648488366,         \"Memory in Mb\": 8.722038269042969,         \"Time in s\": 267.718494       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8407789232531501,         \"MicroF1\": 0.8407789232531501,         \"MacroF1\": 0.8414965916969209,         \"Memory in Mb\": 9.057206153869627,         \"Time in s\": 302.46008700000004       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8443960826985855,         \"MicroF1\": 0.8443960826985855,         \"MacroF1\": 0.8446110045111287,         \"Memory in Mb\": 9.38282871246338,         \"Time in s\": 339.623661       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8466321243523316,         \"MicroF1\": 0.8466321243523316,         \"MacroF1\": 0.8462590694093756,         \"Memory in Mb\": 9.696897506713867,         \"Time in s\": 379.342347       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8516320474777448,         \"MicroF1\": 0.8516320474777448,         \"MacroF1\": 0.8504483916737715,         \"Memory in Mb\": 9.949009895324709,         \"Time in s\": 421.625642       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8571428571428571,         \"MicroF1\": 0.8571428571428571,         \"MacroF1\": 0.8557487568785946,         \"Memory in Mb\": 10.2299222946167,         \"Time in s\": 466.542637       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8603807796917498,         \"MicroF1\": 0.8603807796917498,         \"MacroF1\": 0.8594481550185353,         \"Memory in Mb\": 10.524299621582031,         \"Time in s\": 514.218423       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8624891209747607,         \"MicroF1\": 0.8624891209747607,         \"MacroF1\": 0.8612253786789881,         \"Memory in Mb\": 10.737759590148926,         \"Time in s\": 564.599929       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8652719665271966,         \"MicroF1\": 0.8652719665271966,         \"MacroF1\": 0.8642881992026393,         \"Memory in Mb\": 11.010127067565918,         \"Time in s\": 617.836337       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8670427074939565,         \"MicroF1\": 0.8670427074939565,         \"MacroF1\": 0.8663181473795101,         \"Memory in Mb\": 11.261144638061523,         \"Time in s\": 674.05967       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8694638694638694,         \"MicroF1\": 0.8694638694638694,         \"MacroF1\": 0.8687259920464652,         \"Memory in Mb\": 11.505732536315918,         \"Time in s\": 733.385389       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8709677419354839,         \"MicroF1\": 0.8709677419354839,         \"MacroF1\": 0.870193396369452,         \"Memory in Mb\": 11.826444625854492,         \"Time in s\": 796.067675       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8745467730239304,         \"MicroF1\": 0.8745467730239304,         \"MacroF1\": 0.874089581073643,         \"Memory in Mb\": 12.086430549621582,         \"Time in s\": 861.584672       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8771929824561403,         \"MicroF1\": 0.8771929824561403,         \"MacroF1\": 0.8759011931352845,         \"Memory in Mb\": 12.29430866241455,         \"Time in s\": 930.204519       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8796736913664174,         \"MicroF1\": 0.8796736913664174,         \"MacroF1\": 0.877566397675441,         \"Memory in Mb\": 12.500163078308104,         \"Time in s\": 1001.8141389999998       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8826631509558339,         \"MicroF1\": 0.8826631509558339,         \"MacroF1\": 0.8803270226288138,         \"Memory in Mb\": 12.740474700927734,         \"Time in s\": 1076.488215       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8841970569417786,         \"MicroF1\": 0.8841970569417786,         \"MacroF1\": 0.8822041640143002,         \"Memory in Mb\": 12.987508773803713,         \"Time in s\": 1154.350794       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.886886264760721,         \"MicroF1\": 0.886886264760721,         \"MacroF1\": 0.8850836875294148,         \"Memory in Mb\": 13.252826690673828,         \"Time in s\": 1235.463166       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.888821752265861,         \"MicroF1\": 0.888821752265861,         \"MacroF1\": 0.8870702351165313,         \"Memory in Mb\": 13.500110626220703,         \"Time in s\": 1319.86391       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8912404467960023,         \"MicroF1\": 0.8912404467960025,         \"MacroF1\": 0.8905987472429445,         \"Memory in Mb\": 13.767583847045898,         \"Time in s\": 1407.541035       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8929593589009731,         \"MicroF1\": 0.892959358900973,         \"MacroF1\": 0.8920318510221457,         \"Memory in Mb\": 14.030475616455078,         \"Time in s\": 1498.676265       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.894032348020078,         \"MicroF1\": 0.894032348020078,         \"MacroF1\": 0.8925886559949978,         \"Memory in Mb\": 14.271255493164062,         \"Time in s\": 1593.100327       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8945078847199565,         \"MicroF1\": 0.8945078847199565,         \"MacroF1\": 0.8931986390525462,         \"Memory in Mb\": 14.574835777282717,         \"Time in s\": 1691.005218       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.896551724137931,         \"MicroF1\": 0.896551724137931,         \"MacroF1\": 0.8956464025201587,         \"Memory in Mb\": 14.834091186523438,         \"Time in s\": 1792.408733       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8964267219057483,         \"MicroF1\": 0.8964267219057483,         \"MacroF1\": 0.8951782213786073,         \"Memory in Mb\": 15.134613037109377,         \"Time in s\": 1897.156299       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8973191704602934,         \"MicroF1\": 0.8973191704602934,         \"MacroF1\": 0.8961901832930852,         \"Memory in Mb\": 15.326050758361816,         \"Time in s\": 2005.31409       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8986653484923381,         \"MicroF1\": 0.8986653484923381,         \"MacroF1\": 0.8970310627029995,         \"Memory in Mb\": 15.549851417541504,         \"Time in s\": 2116.877653       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8994683421942967,         \"MicroF1\": 0.8994683421942967,         \"MacroF1\": 0.8980105869909577,         \"Memory in Mb\": 15.81621551513672,         \"Time in s\": 2232.114727       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.900709219858156,         \"MicroF1\": 0.900709219858156,         \"MacroF1\": 0.8989778942952686,         \"Memory in Mb\": 15.957537651062012,         \"Time in s\": 2350.8625340000003       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9000462748727441,         \"MicroF1\": 0.9000462748727441,         \"MacroF1\": 0.8982611856050026,         \"Memory in Mb\": 16.206623077392578,         \"Time in s\": 2473.2186990000005       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9012233801540552,         \"MicroF1\": 0.9012233801540552,         \"MacroF1\": 0.8993036839855942,         \"Memory in Mb\": 16.400617599487305,         \"Time in s\": 2599.1257530000003       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9014647137150466,         \"MicroF1\": 0.9014647137150466,         \"MacroF1\": 0.8999821457114682,         \"Memory in Mb\": 16.693093299865723,         \"Time in s\": 2728.6827460000004       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9016963897346671,         \"MicroF1\": 0.9016963897346671,         \"MacroF1\": 0.9003174232892135,         \"Memory in Mb\": 16.988688468933105,         \"Time in s\": 2861.834306       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9016890428757036,         \"MicroF1\": 0.9016890428757036,         \"MacroF1\": 0.9003808534937335,         \"Memory in Mb\": 17.050235748291016,         \"Time in s\": 2997.696193       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6511848341232227,         \"MicroF1\": 0.6511848341232227,         \"MacroF1\": 0.5805974192561721,         \"Memory in Mb\": 27.882014274597168,         \"Time in s\": 41.422615       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6830885836096636,         \"MicroF1\": 0.6830885836096636,         \"MacroF1\": 0.6159001145696381,         \"Memory in Mb\": 53.9009017944336,         \"Time in s\": 137.16292       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6889801073571203,         \"MicroF1\": 0.6889801073571203,         \"MacroF1\": 0.6135176771695448,         \"Memory in Mb\": 79.45620250701904,         \"Time in s\": 291.10856       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6954771489462468,         \"MicroF1\": 0.6954771489462468,         \"MacroF1\": 0.6159765684907534,         \"Memory in Mb\": 104.90542316436768,         \"Time in s\": 501.70617       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7003220306876302,         \"MicroF1\": 0.7003220306876302,         \"MacroF1\": 0.6217575035584229,         \"Memory in Mb\": 130.7021541595459,         \"Time in s\": 768.289754       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7021310181531176,         \"MicroF1\": 0.7021310181531176,         \"MacroF1\": 0.622391174421368,         \"Memory in Mb\": 156.0168752670288,         \"Time in s\": 1090.319759       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7027465836828576,         \"MicroF1\": 0.7027465836828576,         \"MacroF1\": 0.6232948240709647,         \"Memory in Mb\": 180.8397483825684,         \"Time in s\": 1466.44078       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7040369361903634,         \"MicroF1\": 0.7040369361903634,         \"MacroF1\": 0.6235946437988805,         \"Memory in Mb\": 205.63252925872803,         \"Time in s\": 1896.370643       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7105124697463959,         \"MicroF1\": 0.7105124697463959,         \"MacroF1\": 0.6284709935917355,         \"Memory in Mb\": 229.19151210784912,         \"Time in s\": 2381.431795       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7140827729898664,         \"MicroF1\": 0.7140827729898664,         \"MacroF1\": 0.6302854833117341,         \"Memory in Mb\": 253.17632389068604,         \"Time in s\": 2925.98814       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.71562634524322,         \"MicroF1\": 0.7156263452432199,         \"MacroF1\": 0.6305326785921538,         \"Memory in Mb\": 277.4567346572876,         \"Time in s\": 3530.852036       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7145450240707126,         \"MicroF1\": 0.7145450240707125,         \"MacroF1\": 0.6284185449457835,         \"Memory in Mb\": 301.7114896774292,         \"Time in s\": 4201.052974       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7057623661397247,         \"MicroF1\": 0.7057623661397247,         \"MacroF1\": 0.6885364031919957,         \"Memory in Mb\": 327.2237205505371,         \"Time in s\": 4936.820951       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6967462625989312,         \"MicroF1\": 0.6967462625989312,         \"MacroF1\": 0.69194472505998,         \"Memory in Mb\": 352.6018476486206,         \"Time in s\": 5738.465999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.676684134099375,         \"MicroF1\": 0.676684134099375,         \"MacroF1\": 0.673854549025314,         \"Memory in Mb\": 384.9730758666992,         \"Time in s\": 6613.285946       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6698431488606097,         \"MicroF1\": 0.6698431488606097,         \"MacroF1\": 0.668750254945471,         \"Memory in Mb\": 415.2214603424072,         \"Time in s\": 7559.921071       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6646983454960727,         \"MicroF1\": 0.6646983454960727,         \"MacroF1\": 0.6646134205077884,         \"Memory in Mb\": 444.32067584991455,         \"Time in s\": 8589.520858       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6620192560635555,         \"MicroF1\": 0.6620192560635555,         \"MacroF1\": 0.6605985532750915,         \"Memory in Mb\": 472.75781440734863,         \"Time in s\": 9705.665905       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6597717190848826,         \"MicroF1\": 0.6597717190848826,         \"MacroF1\": 0.6570293922418718,         \"Memory in Mb\": 499.4876089096069,         \"Time in s\": 10901.076562       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6539608882996354,         \"MicroF1\": 0.6539608882996354,         \"MacroF1\": 0.6496192149174075,         \"Memory in Mb\": 528.8777961730957,         \"Time in s\": 12166.701144       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6547463359639233,         \"MicroF1\": 0.6547463359639233,         \"MacroF1\": 0.6484047117859243,         \"Memory in Mb\": 557.1920728683472,         \"Time in s\": 13501.384366       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6583444535319185,         \"MicroF1\": 0.6583444535319185,         \"MacroF1\": 0.6499882024630633,         \"Memory in Mb\": 584.0554361343384,         \"Time in s\": 14901.095396       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6611767612302878,         \"MicroF1\": 0.6611767612302878,         \"MacroF1\": 0.6506059068013808,         \"Memory in Mb\": 610.3706150054932,         \"Time in s\": 16366.700533       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6659827171210986,         \"MicroF1\": 0.6659827171210986,         \"MacroF1\": 0.6532433614752314,         \"Memory in Mb\": 635.6853046417236,         \"Time in s\": 17901.739193       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6702526610856472,         \"MicroF1\": 0.6702526610856472,         \"MacroF1\": 0.6554263220708306,         \"Memory in Mb\": 660.4025926589966,         \"Time in s\": 19504.786084       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6745947914769623,         \"MicroF1\": 0.6745947914769623,         \"MacroF1\": 0.6575507550972549,         \"Memory in Mb\": 684.36501121521,         \"Time in s\": 21172.086014       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6705482094630143,         \"MicroF1\": 0.6705482094630143,         \"MacroF1\": 0.6539581966383304,         \"Memory in Mb\": 712.6770572662354,         \"Time in s\": 22902.746936       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6644231744850678,         \"MicroF1\": 0.6644231744850678,         \"MacroF1\": 0.6512239029866641,         \"Memory in Mb\": 743.5559530258179,         \"Time in s\": 24691.477434       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6622799856317148,         \"MicroF1\": 0.6622799856317148,         \"MacroF1\": 0.6527566844616065,         \"Memory in Mb\": 772.5478630065918,         \"Time in s\": 26538.585641       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6621736797247388,         \"MicroF1\": 0.6621736797247388,         \"MacroF1\": 0.6557760097374935,         \"Memory in Mb\": 800.5439138412476,         \"Time in s\": 28440.416189000003       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6623797159004124,         \"MicroF1\": 0.6623797159004124,         \"MacroF1\": 0.6584479912704261,         \"Memory in Mb\": 827.4998264312744,         \"Time in s\": 30418.714512       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6575123553608949,         \"MicroF1\": 0.6575123553608949,         \"MacroF1\": 0.6541419435809196,         \"Memory in Mb\": 857.7161102294922,         \"Time in s\": 32439.386127       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6519069073377909,         \"MicroF1\": 0.6519069073377909,         \"MacroF1\": 0.6481893367707658,         \"Memory in Mb\": 888.8327789306641,         \"Time in s\": 34499.720344       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.647550343982397,         \"MicroF1\": 0.647550343982397,         \"MacroF1\": 0.643407015045196,         \"Memory in Mb\": 919.6311988830566,         \"Time in s\": 36599.09766       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6444438431775752,         \"MicroF1\": 0.6444438431775752,         \"MacroF1\": 0.6400224052225335,         \"Memory in Mb\": 949.7819452285768,         \"Time in s\": 38735.650911       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6425358411153492,         \"MicroF1\": 0.6425358411153492,         \"MacroF1\": 0.6377821595167165,         \"Memory in Mb\": 979.4456567764282,         \"Time in s\": 40896.763765       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6414476209976709,         \"MicroF1\": 0.6414476209976709,         \"MacroF1\": 0.6370415360917451,         \"Memory in Mb\": 1009.0255756378174,         \"Time in s\": 43085.847267       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6409898572033793,         \"MicroF1\": 0.6409898572033793,         \"MacroF1\": 0.636858231937463,         \"Memory in Mb\": 1037.841980934143,         \"Time in s\": 45303.144751       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6414782798727631,         \"MicroF1\": 0.6414782798727631,         \"MacroF1\": 0.637272014233453,         \"Memory in Mb\": 1065.1163549423218,         \"Time in s\": 47540.369251       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6428419233409882,         \"MicroF1\": 0.6428419233409882,         \"MacroF1\": 0.6385110475108609,         \"Memory in Mb\": 1091.8334274291992,         \"Time in s\": 49803.522006       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6441159487238711,         \"MicroF1\": 0.6441159487238711,         \"MacroF1\": 0.6396283228479406,         \"Memory in Mb\": 1118.1560363769531,         \"Time in s\": 52086.93226       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.645058735992424,         \"MicroF1\": 0.645058735992424,         \"MacroF1\": 0.6403851797193834,         \"Memory in Mb\": 1144.4119939804075,         \"Time in s\": 54391.61903       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6469266853128373,         \"MicroF1\": 0.6469266853128373,         \"MacroF1\": 0.6418265850265934,         \"Memory in Mb\": 1169.9601306915283,         \"Time in s\": 56719.958571       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6487742935238792,         \"MicroF1\": 0.6487742935238792,         \"MacroF1\": 0.643191402092947,         \"Memory in Mb\": 1194.640343666077,         \"Time in s\": 59073.094705       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6459521454576065,         \"MicroF1\": 0.6459521454576065,         \"MacroF1\": 0.6406800374556137,         \"Memory in Mb\": 1224.6073780059814,         \"Time in s\": 61451.967815       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6443643849716932,         \"MicroF1\": 0.6443643849716932,         \"MacroF1\": 0.6398250343320808,         \"Memory in Mb\": 1254.4350862503052,         \"Time in s\": 63857.884093       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6446172754931394,         \"MicroF1\": 0.6446172754931394,         \"MacroF1\": 0.6406945505071863,         \"Memory in Mb\": 1282.3891849517822,         \"Time in s\": 66293.298766       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6461222798745241,         \"MicroF1\": 0.6461222798745241,         \"MacroF1\": 0.6426238276925219,         \"Memory in Mb\": 1309.4736614227295,         \"Time in s\": 68755.018108       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6489186943161394,         \"MicroF1\": 0.6489186943161394,         \"MacroF1\": 0.6457243405011626,         \"Memory in Mb\": 1334.444143295288,         \"Time in s\": 71244.151045       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6470577094263149,         \"MicroF1\": 0.6470577094263149,         \"MacroF1\": 0.6443966707674731,         \"Memory in Mb\": 1363.999231338501,         \"Time in s\": 73759.934152       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6469809071470471,         \"MicroF1\": 0.6469809071470471,         \"MacroF1\": 0.6443518314696601,         \"Memory in Mb\": 1365.409776687622,         \"Time in s\": 76295.692169       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9901719901719902,         \"MicroF1\": 0.9901719901719902,         \"MacroF1\": 0.8308395677472984,         \"Memory in Mb\": 0.1227684020996093,         \"Time in s\": 1.485322       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9914110429447852,         \"MicroF1\": 0.9914110429447852,         \"MacroF1\": 0.960934413925625,         \"Memory in Mb\": 0.4158411026000976,         \"Time in s\": 6.729082       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9893704006541292,         \"MicroF1\": 0.9893704006541292,         \"MacroF1\": 0.9580466011674303,         \"Memory in Mb\": 1.2467107772827148,         \"Time in s\": 20.14849       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9889638258736972,         \"MicroF1\": 0.9889638258736972,         \"MacroF1\": 0.9786672150923964,         \"Memory in Mb\": 2.28104305267334,         \"Time in s\": 50.264957       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.988719960765081,         \"MicroF1\": 0.988719960765081,         \"MacroF1\": 0.9803510904896324,         \"Memory in Mb\": 3.352717399597168,         \"Time in s\": 91.643431       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9885574172456068,         \"MicroF1\": 0.9885574172456068,         \"MacroF1\": 0.983046879237058,         \"Memory in Mb\": 4.983606338500977,         \"Time in s\": 148.278076       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9852889667250436,         \"MicroF1\": 0.9852889667250436,         \"MacroF1\": 0.9737767108051044,         \"Memory in Mb\": 6.963967323303223,         \"Time in s\": 227.073424       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9825314128102972,         \"MicroF1\": 0.9825314128102972,         \"MacroF1\": 0.9734338986941852,         \"Memory in Mb\": 9.8344087600708,         \"Time in s\": 324.702985       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.982293652955598,         \"MicroF1\": 0.982293652955598,         \"MacroF1\": 0.9788760747631072,         \"Memory in Mb\": 12.7888765335083,         \"Time in s\": 446.356435       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9806325079676392,         \"MicroF1\": 0.9806325079676392,         \"MacroF1\": 0.9749453255203756,         \"Memory in Mb\": 16.445659637451172,         \"Time in s\": 594.71846       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9801649208825496,         \"MicroF1\": 0.9801649208825496,         \"MacroF1\": 0.9779116862524244,         \"Memory in Mb\": 20.943636894226078,         \"Time in s\": 768.8230350000001       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9801838610827376,         \"MicroF1\": 0.9801838610827376,         \"MacroF1\": 0.978782474664832,         \"Memory in Mb\": 24.856953620910645,         \"Time in s\": 967.611442       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9768055817461814,         \"MicroF1\": 0.9768055817461814,         \"MacroF1\": 0.9702080932270808,         \"Memory in Mb\": 28.10527801513672,         \"Time in s\": 1191.0213970000002       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9746104009805638,         \"MicroF1\": 0.9746104009805638,         \"MacroF1\": 0.9718234131704068,         \"Memory in Mb\": 32.14579772949219,         \"Time in s\": 1440.171835       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9697663016832816,         \"MicroF1\": 0.9697663016832816,         \"MacroF1\": 0.9621279568251032,         \"Memory in Mb\": 36.40912055969238,         \"Time in s\": 1717.813161       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9656810173127012,         \"MicroF1\": 0.9656810173127012,         \"MacroF1\": 0.9634765255010708,         \"Memory in Mb\": 42.20043754577637,         \"Time in s\": 2023.330442       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9653929343907716,         \"MicroF1\": 0.9653929343907716,         \"MacroF1\": 0.9646253117338192,         \"Memory in Mb\": 46.97204208374024,         \"Time in s\": 2355.811343       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9635026555903582,         \"MicroF1\": 0.9635026555903582,         \"MacroF1\": 0.96110342818104,         \"Memory in Mb\": 50.8284969329834,         \"Time in s\": 2716.693574       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9610372855115468,         \"MicroF1\": 0.9610372855115468,         \"MacroF1\": 0.9585597537512924,         \"Memory in Mb\": 55.062747955322266,         \"Time in s\": 3108.019641       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9593087388160314,         \"MicroF1\": 0.9593087388160314,         \"MacroF1\": 0.9577319445930262,         \"Memory in Mb\": 59.75967216491699,         \"Time in s\": 3529.961458       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9598459203922026,         \"MicroF1\": 0.9598459203922026,         \"MacroF1\": 0.960171378024828,         \"Memory in Mb\": 65.88526916503906,         \"Time in s\": 3981.552133       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.959108635097493,         \"MicroF1\": 0.959108635097493,         \"MacroF1\": 0.9586518345557712,         \"Memory in Mb\": 71.85272026062012,         \"Time in s\": 4465.222941       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9573697111797932,         \"MicroF1\": 0.9573697111797932,         \"MacroF1\": 0.956135316427552,         \"Memory in Mb\": 78.18439388275146,         \"Time in s\": 4984.801354       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9555714431620876,         \"MicroF1\": 0.9555714431620876,         \"MacroF1\": 0.9546392488298882,         \"Memory in Mb\": 85.86389446258545,         \"Time in s\": 5537.4752260000005       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9486224139621532,         \"MicroF1\": 0.9486224139621532,         \"MacroF1\": 0.9433099305923252,         \"Memory in Mb\": 94.35744285583496,         \"Time in s\": 6127.477763000001       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943150749505044,         \"MicroF1\": 0.943150749505044,         \"MacroF1\": 0.9403442056943528,         \"Memory in Mb\": 104.1574821472168,         \"Time in s\": 6756.480909000001       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9408987743985474,         \"MicroF1\": 0.9408987743985474,         \"MacroF1\": 0.9399975161043574,         \"Memory in Mb\": 113.0038013458252,         \"Time in s\": 7421.284759000001       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9380197846450145,         \"MicroF1\": 0.9380197846450145,         \"MacroF1\": 0.936341059397272,         \"Memory in Mb\": 121.46645069122314,         \"Time in s\": 8125.715779000001       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9322965091708224,         \"MicroF1\": 0.9322965091708224,         \"MacroF1\": 0.9294143034054052,         \"Memory in Mb\": 131.1031150817871,         \"Time in s\": 8872.899296000001       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9326742380913472,         \"MicroF1\": 0.9326742380913472,         \"MacroF1\": 0.9327603226303838,         \"Memory in Mb\": 137.88959789276123,         \"Time in s\": 9652.703167       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.927571756147703,         \"MicroF1\": 0.927571756147703,         \"MacroF1\": 0.9249549620362734,         \"Memory in Mb\": 145.5888376235962,         \"Time in s\": 10475.658214       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9247797778628878,         \"MicroF1\": 0.9247797778628878,         \"MacroF1\": 0.92370720847711,         \"Memory in Mb\": 154.53871536254883,         \"Time in s\": 11346.213643       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9238654088984624,         \"MicroF1\": 0.9238654088984624,         \"MacroF1\": 0.9233692422863464,         \"Memory in Mb\": 161.3583574295044,         \"Time in s\": 12266.045404       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9202653017086008,         \"MicroF1\": 0.9202653017086008,         \"MacroF1\": 0.9191663953636944,         \"Memory in Mb\": 170.12918186187744,         \"Time in s\": 13231.481182       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.916310666013026,         \"MicroF1\": 0.916310666013026,         \"MacroF1\": 0.9150341930556872,         \"Memory in Mb\": 179.0350112915039,         \"Time in s\": 14245.599713       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9161162933206236,         \"MicroF1\": 0.9161162933206236,         \"MacroF1\": 0.9160540991607554,         \"Memory in Mb\": 184.834698677063,         \"Time in s\": 15311.95464       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9145412388208016,         \"MicroF1\": 0.9145412388208016,         \"MacroF1\": 0.91429667624259,         \"Memory in Mb\": 191.58009719848636,         \"Time in s\": 16433.239395       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9105979487841064,         \"MicroF1\": 0.9105979487841064,         \"MacroF1\": 0.909716370830996,         \"Memory in Mb\": 200.08039951324463,         \"Time in s\": 17613.909715       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9068568914587392,         \"MicroF1\": 0.9068568914587392,         \"MacroF1\": 0.9060681758481206,         \"Memory in Mb\": 210.5000762939453,         \"Time in s\": 18848.20155       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9031190636681168,         \"MicroF1\": 0.9031190636681168,         \"MacroF1\": 0.9023660107991418,         \"Memory in Mb\": 221.55222129821777,         \"Time in s\": 20131.794746000003       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9005799007592515,         \"MicroF1\": 0.9005799007592515,         \"MacroF1\": 0.9001704241319546,         \"Memory in Mb\": 231.28063201904297,         \"Time in s\": 21466.799965       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8989203384884739,         \"MicroF1\": 0.8989203384884739,         \"MacroF1\": 0.8987537815839572,         \"Memory in Mb\": 248.11264038085935,         \"Time in s\": 22840.808564000003       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.893746793592886,         \"MicroF1\": 0.8937467935928861,         \"MacroF1\": 0.892807745348426,         \"Memory in Mb\": 267.53482723236084,         \"Time in s\": 24265.711089000004       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8894212021614395,         \"MicroF1\": 0.8894212021614395,         \"MacroF1\": 0.8884694521151855,         \"Memory in Mb\": 281.7739496231079,         \"Time in s\": 25739.620728000005       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8911705430579008,         \"MicroF1\": 0.8911705430579007,         \"MacroF1\": 0.8908032768807751,         \"Memory in Mb\": 288.0978307723999,         \"Time in s\": 27256.627666000004       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8911387009111739,         \"MicroF1\": 0.8911387009111739,         \"MacroF1\": 0.8906428613252552,         \"Memory in Mb\": 296.0527210235596,         \"Time in s\": 28820.747713000004       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8886049543676662,         \"MicroF1\": 0.8886049543676662,         \"MacroF1\": 0.8879368647002966,         \"Memory in Mb\": 307.6826677322388,         \"Time in s\": 30435.23145800001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8895470561201042,         \"MicroF1\": 0.8895470561201042,         \"MacroF1\": 0.889061241536932,         \"Memory in Mb\": 313.4344787597656,         \"Time in s\": 32089.799369000008       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8862488119653844,         \"MicroF1\": 0.8862488119653844,         \"MacroF1\": 0.8855123768505595,         \"Memory in Mb\": 324.9442596435547,         \"Time in s\": 33786.733744000005       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8810726015981175,         \"MicroF1\": 0.8810726015981175,         \"MacroF1\": 0.8799282628097613,         \"Memory in Mb\": 338.1390075683594,         \"Time in s\": 35528.434162000005       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3555555555555555,         \"MicroF1\": 0.3555555555555555,         \"MacroF1\": 0.2468487394957983,         \"Memory in Mb\": 2.5926971435546875,         \"Time in s\": 5.672061       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5274725274725275,         \"MicroF1\": 0.5274725274725275,         \"MacroF1\": 0.5392220990960486,         \"Memory in Mb\": 2.5963096618652344,         \"Time in s\": 17.740863       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5401459854014599,         \"MicroF1\": 0.5401459854014599,         \"MacroF1\": 0.5661177456005042,         \"Memory in Mb\": 2.5979232788085938,         \"Time in s\": 35.937815       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5956284153005464,         \"MicroF1\": 0.5956284153005464,         \"MacroF1\": 0.6144104879239446,         \"Memory in Mb\": 2.6004638671875,         \"Time in s\": 59.975895       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6200873362445415,         \"MicroF1\": 0.6200873362445415,         \"MacroF1\": 0.6319742698014011,         \"Memory in Mb\": 2.6008224487304688,         \"Time in s\": 89.681265       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6327272727272727,         \"MicroF1\": 0.6327272727272727,         \"MacroF1\": 0.6440706793955739,         \"Memory in Mb\": 2.601276397705078,         \"Time in s\": 125.043898       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6573208722741433,         \"MicroF1\": 0.6573208722741433,         \"MacroF1\": 0.6535377647060517,         \"Memory in Mb\": 2.6028709411621094,         \"Time in s\": 166.036362       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6784741144414169,         \"MicroF1\": 0.6784741144414169,         \"MacroF1\": 0.6717418242612484,         \"Memory in Mb\": 2.6031723022460938,         \"Time in s\": 212.735146       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6900726392251816,         \"MicroF1\": 0.6900726392251816,         \"MacroF1\": 0.6823551618652942,         \"Memory in Mb\": 2.603717803955078,         \"Time in s\": 265.175489       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6971677559912854,         \"MicroF1\": 0.6971677559912854,         \"MacroF1\": 0.686858403065277,         \"Memory in Mb\": 2.60379409790039,         \"Time in s\": 323.486791       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.699009900990099,         \"MicroF1\": 0.699009900990099,         \"MacroF1\": 0.6869845800125663,         \"Memory in Mb\": 2.604084014892578,         \"Time in s\": 387.600808       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6987295825771325,         \"MicroF1\": 0.6987295825771325,         \"MacroF1\": 0.6895132041566728,         \"Memory in Mb\": 2.6040496826171875,         \"Time in s\": 457.323817       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7035175879396985,         \"MicroF1\": 0.7035175879396985,         \"MacroF1\": 0.6939747146282641,         \"Memory in Mb\": 2.6041183471679688,         \"Time in s\": 532.682096       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6998444790046656,         \"MicroF1\": 0.6998444790046656,         \"MacroF1\": 0.6913714585468268,         \"Memory in Mb\": 2.6053123474121094,         \"Time in s\": 613.490084       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7024673439767779,         \"MicroF1\": 0.7024673439767779,         \"MacroF1\": 0.6944906634267102,         \"Memory in Mb\": 2.6058921813964844,         \"Time in s\": 699.880084       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7020408163265306,         \"MicroF1\": 0.7020408163265306,         \"MacroF1\": 0.69548275919944,         \"Memory in Mb\": 2.605987548828125,         \"Time in s\": 791.65329       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.706786171574904,         \"MicroF1\": 0.706786171574904,         \"MacroF1\": 0.6991539785967766,         \"Memory in Mb\": 2.6064224243164062,         \"Time in s\": 888.981823       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7085852478839177,         \"MicroF1\": 0.7085852478839177,         \"MacroF1\": 0.70309750989463,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 991.647497       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.715922107674685,         \"MicroF1\": 0.7159221076746849,         \"MacroF1\": 0.7073525059690206,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 1099.573439       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7170837867247007,         \"MicroF1\": 0.7170837867247007,         \"MacroF1\": 0.707165908654469,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 1212.915424       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7160621761658031,         \"MicroF1\": 0.716062176165803,         \"MacroF1\": 0.7063689525089133,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 1331.559339       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7151335311572701,         \"MicroF1\": 0.7151335311572701,         \"MacroF1\": 0.7047830593764105,         \"Memory in Mb\": 2.6064910888671875,         \"Time in s\": 1455.3800170000002       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7152317880794702,         \"MicroF1\": 0.7152317880794702,         \"MacroF1\": 0.7037726227430311,         \"Memory in Mb\": 2.606658935546875,         \"Time in s\": 1584.327081       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.71441523118767,         \"MicroF1\": 0.71441523118767,         \"MacroF1\": 0.7026447500373862,         \"Memory in Mb\": 2.6067771911621094,         \"Time in s\": 1718.245947       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7162750217580505,         \"MicroF1\": 0.7162750217580505,         \"MacroF1\": 0.7030218527348165,         \"Memory in Mb\": 2.6067771911621094,         \"Time in s\": 1857.022931       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7179916317991631,         \"MicroF1\": 0.7179916317991631,         \"MacroF1\": 0.705575475090573,         \"Memory in Mb\": 2.379610061645508,         \"Time in s\": 2000.273372       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7155519742143432,         \"MicroF1\": 0.7155519742143431,         \"MacroF1\": 0.7053749246401603,         \"Memory in Mb\": 3.185004234313965,         \"Time in s\": 2147.034729       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7156177156177156,         \"MicroF1\": 0.7156177156177156,         \"MacroF1\": 0.7041730806550314,         \"Memory in Mb\": 3.633350372314453,         \"Time in s\": 2296.192092       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7149287321830458,         \"MicroF1\": 0.7149287321830458,         \"MacroF1\": 0.7045092702498074,         \"Memory in Mb\": 4.368736267089844,         \"Time in s\": 2447.850078       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7186366932559826,         \"MicroF1\": 0.7186366932559827,         \"MacroF1\": 0.7102131417787841,         \"Memory in Mb\": 4.724300384521484,         \"Time in s\": 2601.871177       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7256140350877193,         \"MicroF1\": 0.7256140350877193,         \"MacroF1\": 0.7174099613082184,         \"Memory in Mb\": 4.89253044128418,         \"Time in s\": 2758.036552       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7273963290278722,         \"MicroF1\": 0.7273963290278722,         \"MacroF1\": 0.7183919320082559,         \"Memory in Mb\": 5.412370681762695,         \"Time in s\": 2916.512936       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7211601845748187,         \"MicroF1\": 0.7211601845748187,         \"MacroF1\": 0.7136134581802791,         \"Memory in Mb\": 6.487729072570801,         \"Time in s\": 3077.492565       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7172104926423545,         \"MicroF1\": 0.7172104926423546,         \"MacroF1\": 0.7129536273040751,         \"Memory in Mb\": 6.405126571655273,         \"Time in s\": 3241.109       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7209446861404599,         \"MicroF1\": 0.7209446861404599,         \"MacroF1\": 0.7163536024764182,         \"Memory in Mb\": 6.857941627502441,         \"Time in s\": 3407.317179       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7238670694864048,         \"MicroF1\": 0.7238670694864048,         \"MacroF1\": 0.7196892738307762,         \"Memory in Mb\": 7.034061431884766,         \"Time in s\": 3576.258732       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7260435038212816,         \"MicroF1\": 0.7260435038212816,         \"MacroF1\": 0.7238533950478148,         \"Memory in Mb\": 7.623349189758301,         \"Time in s\": 3747.864612       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7309673726388094,         \"MicroF1\": 0.7309673726388093,         \"MacroF1\": 0.7286270619416129,         \"Memory in Mb\": 8.106144905090332,         \"Time in s\": 3922.063963000001       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7361963190184049,         \"MicroF1\": 0.7361963190184049,         \"MacroF1\": 0.7329274067865035,         \"Memory in Mb\": 8.185744285583496,         \"Time in s\": 4098.8506050000005       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7389885807504079,         \"MicroF1\": 0.7389885807504077,         \"MacroF1\": 0.7360694376974826,         \"Memory in Mb\": 8.929247856140137,         \"Time in s\": 4278.2789060000005       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7411140583554376,         \"MicroF1\": 0.7411140583554376,         \"MacroF1\": 0.7396669191579938,         \"Memory in Mb\": 9.100563049316406,         \"Time in s\": 4460.600751000001       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7431382703262558,         \"MicroF1\": 0.7431382703262558,         \"MacroF1\": 0.7411378754700444,         \"Memory in Mb\": 9.223885536193848,         \"Time in s\": 4645.683986000001       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7465857359635811,         \"MicroF1\": 0.746585735963581,         \"MacroF1\": 0.744200926808846,         \"Memory in Mb\": 9.401692390441896,         \"Time in s\": 4833.458731000001       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7508650519031141,         \"MicroF1\": 0.7508650519031143,         \"MacroF1\": 0.7476945996538615,         \"Memory in Mb\": 9.481804847717283,         \"Time in s\": 5024.230846       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7549540840985983,         \"MicroF1\": 0.7549540840985983,         \"MacroF1\": 0.7524477298078486,         \"Memory in Mb\": 9.431160926818848,         \"Time in s\": 5217.969956000001       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7583924349881797,         \"MicroF1\": 0.7583924349881797,         \"MacroF1\": 0.7554386161495508,         \"Memory in Mb\": 9.549637794494627,         \"Time in s\": 5414.604524000001       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7607589079130033,         \"MicroF1\": 0.7607589079130033,         \"MacroF1\": 0.7577216433051415,         \"Memory in Mb\": 10.151451110839844,         \"Time in s\": 5614.378132000002       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7648391481649298,         \"MicroF1\": 0.7648391481649298,         \"MacroF1\": 0.7614528787516565,         \"Memory in Mb\": 9.14443588256836,         \"Time in s\": 5817.274926000002       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7652019529516201,         \"MicroF1\": 0.7652019529516201,         \"MacroF1\": 0.762166830901651,         \"Memory in Mb\": 8.801234245300293,         \"Time in s\": 6023.198429000002       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7672901261418008,         \"MicroF1\": 0.7672901261418008,         \"MacroF1\": 0.7647372124971393,         \"Memory in Mb\": 8.857858657836914,         \"Time in s\": 6232.074878000002       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7669987007362494,         \"MicroF1\": 0.7669987007362494,         \"MacroF1\": 0.7647069285577738,         \"Memory in Mb\": 8.926526069641113,         \"Time in s\": 6441.814766000002       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6388625592417062,         \"MicroF1\": 0.6388625592417062,         \"MacroF1\": 0.6031100134310133,         \"Memory in Mb\": 8.730474472045898,         \"Time in s\": 177.190345       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.659403126480341,         \"MicroF1\": 0.659403126480341,         \"MacroF1\": 0.6244477305834598,         \"Memory in Mb\": 21.138185501098636,         \"Time in s\": 477.6689290000001       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6722450268392801,         \"MicroF1\": 0.6722450268392801,         \"MacroF1\": 0.6321534006670183,         \"Memory in Mb\": 28.433568000793457,         \"Time in s\": 884.814326       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.680322045938906,         \"MicroF1\": 0.680322045938906,         \"MacroF1\": 0.6340126191391743,         \"Memory in Mb\": 39.2259521484375,         \"Time in s\": 1406.7405990000002       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6878196628149271,         \"MicroF1\": 0.6878196628149271,         \"MacroF1\": 0.6395508722492685,         \"Memory in Mb\": 32.51231288909912,         \"Time in s\": 2046.4837620000003       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6876085240726125,         \"MicroF1\": 0.6876085240726125,         \"MacroF1\": 0.641396967542699,         \"Memory in Mb\": 34.57641696929932,         \"Time in s\": 2792.1074670000003       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6924638073332431,         \"MicroF1\": 0.6924638073332431,         \"MacroF1\": 0.6467777725107727,         \"Memory in Mb\": 40.06835174560547,         \"Time in s\": 3634.422347       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6949212738250267,         \"MicroF1\": 0.6949212738250267,         \"MacroF1\": 0.6476372139610082,         \"Memory in Mb\": 43.01965808868408,         \"Time in s\": 4571.1184410000005       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6992528675155214,         \"MicroF1\": 0.6992528675155214,         \"MacroF1\": 0.6494082466298291,         \"Memory in Mb\": 45.71251583099365,         \"Time in s\": 5608.992399000001       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7013921772895161,         \"MicroF1\": 0.7013921772895161,         \"MacroF1\": 0.6506452100316108,         \"Memory in Mb\": 50.31043148040772,         \"Time in s\": 6744.395149000001       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7043478260869566,         \"MicroF1\": 0.7043478260869566,         \"MacroF1\": 0.6524912605091605,         \"Memory in Mb\": 59.27919769287109,         \"Time in s\": 7964.619750000001       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7079946334148843,         \"MicroF1\": 0.7079946334148843,         \"MacroF1\": 0.6596828376773001,         \"Memory in Mb\": 72.61201000213623,         \"Time in s\": 9269.589572       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7208421359364756,         \"MicroF1\": 0.7208421359364756,         \"MacroF1\": 0.7145906055666686,         \"Memory in Mb\": 38.78250694274902,         \"Time in s\": 10625.218377       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7285395386592708,         \"MicroF1\": 0.7285395386592708,         \"MacroF1\": 0.7256542392368915,         \"Memory in Mb\": 15.54647731781006,         \"Time in s\": 12027.231464       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7206263021655408,         \"MicroF1\": 0.7206263021655408,         \"MacroF1\": 0.7196216319492748,         \"Memory in Mb\": 14.88278579711914,         \"Time in s\": 13491.676191       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7171352471145309,         \"MicroF1\": 0.7171352471145309,         \"MacroF1\": 0.7175260611854538,         \"Memory in Mb\": 21.28149700164795,         \"Time in s\": 15025.812845       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7121051751991533,         \"MicroF1\": 0.7121051751991533,         \"MacroF1\": 0.7136617513297842,         \"Memory in Mb\": 29.336480140686035,         \"Time in s\": 16621.157643       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7205240174672489,         \"MicroF1\": 0.720524017467249,         \"MacroF1\": 0.7180961996594418,         \"Memory in Mb\": 20.976608276367188,         \"Time in s\": 18267.655245       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7261625878482779,         \"MicroF1\": 0.7261625878482779,         \"MacroF1\": 0.7198561207408494,         \"Memory in Mb\": 15.994047164916992,         \"Time in s\": 19960.279521       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7272598134381363,         \"MicroF1\": 0.7272598134381363,         \"MacroF1\": 0.7183389579277755,         \"Memory in Mb\": 17.52824878692627,         \"Time in s\": 21708.029386999995       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7281623449830891,         \"MicroF1\": 0.7281623449830891,         \"MacroF1\": 0.7167723651435352,         \"Memory in Mb\": 22.240838050842285,         \"Time in s\": 23503.558300999997       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7307477078042272,         \"MicroF1\": 0.7307477078042272,         \"MacroF1\": 0.7170791531651185,         \"Memory in Mb\": 26.114503860473636,         \"Time in s\": 25348.434525999997       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7325318071396221,         \"MicroF1\": 0.7325318071396222,         \"MacroF1\": 0.7165563330554671,         \"Memory in Mb\": 27.97449111938477,         \"Time in s\": 27240.683159999997       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7353904431203883,         \"MicroF1\": 0.7353904431203884,         \"MacroF1\": 0.7174524973348954,         \"Memory in Mb\": 37.12833023071289,         \"Time in s\": 29182.513668999996       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7367703322095533,         \"MicroF1\": 0.7367703322095533,         \"MacroF1\": 0.7168965346030137,         \"Memory in Mb\": 31.575971603393555,         \"Time in s\": 31184.058177999992       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.738371881260244,         \"MicroF1\": 0.738371881260244,         \"MacroF1\": 0.7164257197178175,         \"Memory in Mb\": 35.22733116149902,         \"Time in s\": 33213.34443999999       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7366279681526429,         \"MicroF1\": 0.7366279681526429,         \"MacroF1\": 0.7161250847684691,         \"Memory in Mb\": 17.50509262084961,         \"Time in s\": 35271.15690999999       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7354483038522678,         \"MicroF1\": 0.7354483038522677,         \"MacroF1\": 0.719616514898752,         \"Memory in Mb\": 22.40646266937256,         \"Time in s\": 37354.25785299999       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7348724814681775,         \"MicroF1\": 0.7348724814681775,         \"MacroF1\": 0.7237598149406717,         \"Memory in Mb\": 31.22674369812012,         \"Time in s\": 39459.62893099999       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7347769815966413,         \"MicroF1\": 0.7347769815966413,         \"MacroF1\": 0.7275990709197302,         \"Memory in Mb\": 35.24118995666504,         \"Time in s\": 41587.29474699999       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7351458683366427,         \"MicroF1\": 0.7351458683366427,         \"MacroF1\": 0.7308983066693725,         \"Memory in Mb\": 48.40772724151611,         \"Time in s\": 43729.93044799999       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7303423988636027,         \"MicroF1\": 0.7303423988636027,         \"MacroF1\": 0.7274356410957497,         \"Memory in Mb\": 77.28174114227295,         \"Time in s\": 45887.55412199999       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.726805750853732,         \"MicroF1\": 0.726805750853732,         \"MacroF1\": 0.723911701718825,         \"Memory in Mb\": 53.16175174713135,         \"Time in s\": 48068.300588999984       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7248976408656658,         \"MicroF1\": 0.7248976408656659,         \"MacroF1\": 0.7218080521646734,         \"Memory in Mb\": 41.53026580810547,         \"Time in s\": 50265.61973699999       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7215833761735978,         \"MicroF1\": 0.7215833761735979,         \"MacroF1\": 0.7182506744185386,         \"Memory in Mb\": 35.33352756500244,         \"Time in s\": 52485.18591199999       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7196369854004998,         \"MicroF1\": 0.7196369854004999,         \"MacroF1\": 0.7160236415660819,         \"Memory in Mb\": 44.00273513793945,         \"Time in s\": 54721.05808499999       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7175142688950884,         \"MicroF1\": 0.7175142688950884,         \"MacroF1\": 0.713988650041017,         \"Memory in Mb\": 46.12203025817871,         \"Time in s\": 56978.30571199999       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7158023276098388,         \"MicroF1\": 0.7158023276098388,         \"MacroF1\": 0.7126852582249207,         \"Memory in Mb\": 27.841010093688965,         \"Time in s\": 59249.54765999999       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7157322196051769,         \"MicroF1\": 0.715732219605177,         \"MacroF1\": 0.7129296468122535,         \"Memory in Mb\": 21.849401473999023,         \"Time in s\": 61534.70422899999       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7156656170837378,         \"MicroF1\": 0.7156656170837377,         \"MacroF1\": 0.7131576552849198,         \"Memory in Mb\": 28.021278381347656,         \"Time in s\": 63833.59664899999       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.715925626515764,         \"MicroF1\": 0.715925626515764,         \"MacroF1\": 0.7137513847694824,         \"Memory in Mb\": 36.50454139709473,         \"Time in s\": 66146.66403399999       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7161958016730176,         \"MicroF1\": 0.7161958016730177,         \"MacroF1\": 0.7143198962298327,         \"Memory in Mb\": 46.888444900512695,         \"Time in s\": 68474.34057599999       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7170260092056291,         \"MicroF1\": 0.7170260092056291,         \"MacroF1\": 0.7151715877390813,         \"Memory in Mb\": 47.08374786376953,         \"Time in s\": 70816.527322       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7181843617502098,         \"MicroF1\": 0.7181843617502098,         \"MacroF1\": 0.7162864260409335,         \"Memory in Mb\": 43.18325901031494,         \"Time in s\": 73172.526917       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7179023127591069,         \"MicroF1\": 0.7179023127591069,         \"MacroF1\": 0.716246618663062,         \"Memory in Mb\": 54.80090522766113,         \"Time in s\": 75543.857611       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7211528564076171,         \"MicroF1\": 0.7211528564076171,         \"MacroF1\": 0.719707905487922,         \"Memory in Mb\": 60.4919376373291,         \"Time in s\": 77931.086228       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7250710241582882,         \"MicroF1\": 0.7250710241582882,         \"MacroF1\": 0.7236001513027165,         \"Memory in Mb\": 35.55128765106201,         \"Time in s\": 80332.853368       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7288259316984631,         \"MicroF1\": 0.7288259316984631,         \"MacroF1\": 0.7271241427068512,         \"Memory in Mb\": 21.017152786254883,         \"Time in s\": 82746.274567       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7329107318864387,         \"MicroF1\": 0.7329107318864386,         \"MacroF1\": 0.7308784460773333,         \"Memory in Mb\": 26.768343925476078,         \"Time in s\": 85169.877802       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7359230288452433,         \"MicroF1\": 0.7359230288452432,         \"MacroF1\": 0.7343606492383059,         \"Memory in Mb\": 9.57052230834961,         \"Time in s\": 87600.592492       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7361628853104244,         \"MicroF1\": 0.7361628853104244,         \"MacroF1\": 0.7346220154259927,         \"Memory in Mb\": 9.63199520111084,         \"Time in s\": 90031.55993999999       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9901719901719902,         \"MicroF1\": 0.9901719901719902,         \"MacroF1\": 0.8308395677472984,         \"Memory in Mb\": 1.027322769165039,         \"Time in s\": 17.348128       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9877300613496932,         \"MicroF1\": 0.9877300613496932,         \"MacroF1\": 0.9320293882508496,         \"Memory in Mb\": 2.6651391983032227,         \"Time in s\": 54.406226       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.982829108748978,         \"MicroF1\": 0.982829108748978,         \"MacroF1\": 0.9464059415055076,         \"Memory in Mb\": 5.679329872131348,         \"Time in s\": 114.007104       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828326180257512,         \"MicroF1\": 0.9828326180257512,         \"MacroF1\": 0.963209755030524,         \"Memory in Mb\": 8.335807800292969,         \"Time in s\": 201.582874       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.974987739087788,         \"MicroF1\": 0.974987739087788,         \"MacroF1\": 0.9373958892668122,         \"Memory in Mb\": 12.631415367126465,         \"Time in s\": 318.920922       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.970167552104618,         \"MicroF1\": 0.970167552104618,         \"MacroF1\": 0.957381800109682,         \"Memory in Mb\": 16.891732215881348,         \"Time in s\": 465.74626       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9660245183887916,         \"MicroF1\": 0.9660245183887916,         \"MacroF1\": 0.93947544504001,         \"Memory in Mb\": 22.937668800354004,         \"Time in s\": 641.328845       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9616916947594238,         \"MicroF1\": 0.9616916947594238,         \"MacroF1\": 0.9454054748805116,         \"Memory in Mb\": 29.12161636352539,         \"Time in s\": 847.207258       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9607736311631708,         \"MicroF1\": 0.9607736311631708,         \"MacroF1\": 0.953605417859829,         \"Memory in Mb\": 28.669262886047363,         \"Time in s\": 1081.626023       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9578328021573916,         \"MicroF1\": 0.9578328021573916,         \"MacroF1\": 0.9463612240153172,         \"Memory in Mb\": 34.20732402801514,         \"Time in s\": 1345.493591       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589926454201024,         \"MicroF1\": 0.9589926454201024,         \"MacroF1\": 0.9613092683363472,         \"Memory in Mb\": 18.652557373046875,         \"Time in s\": 1636.499529       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.960776302349336,         \"MicroF1\": 0.960776302349336,         \"MacroF1\": 0.9605208703626084,         \"Memory in Mb\": 20.81053066253662,         \"Time in s\": 1952.783692       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9615312087497644,         \"MicroF1\": 0.9615312087497644,         \"MacroF1\": 0.960303314983038,         \"Memory in Mb\": 27.91543483734131,         \"Time in s\": 2294.145458       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9616529504465068,         \"MicroF1\": 0.9616529504465068,         \"MacroF1\": 0.9605387671994152,         \"Memory in Mb\": 32.60424041748047,         \"Time in s\": 2660.691575       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9597973525085798,         \"MicroF1\": 0.9597973525085798,         \"MacroF1\": 0.9561203427932812,         \"Memory in Mb\": 39.11091995239258,         \"Time in s\": 3053.193204       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589397885705532,         \"MicroF1\": 0.9589397885705532,         \"MacroF1\": 0.9571591040678328,         \"Memory in Mb\": 29.255366325378414,         \"Time in s\": 3470.643733       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.959913482335977,         \"MicroF1\": 0.959913482335977,         \"MacroF1\": 0.9605956598361812,         \"Memory in Mb\": 31.930577278137207,         \"Time in s\": 3910.602191       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9602342366880022,         \"MicroF1\": 0.9602342366880022,         \"MacroF1\": 0.95986198823556,         \"Memory in Mb\": 26.562703132629395,         \"Time in s\": 4374.151898       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9601341762353244,         \"MicroF1\": 0.9601341762353244,         \"MacroF1\": 0.959651045460586,         \"Memory in Mb\": 31.588034629821777,         \"Time in s\": 4858.190889       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9584507905380562,         \"MicroF1\": 0.9584507905380562,         \"MacroF1\": 0.9567204261955368,         \"Memory in Mb\": 39.12565612792969,         \"Time in s\": 5363.543193       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9579782887825375,         \"MicroF1\": 0.9579782887825375,         \"MacroF1\": 0.957794146577291,         \"Memory in Mb\": 44.816758155822754,         \"Time in s\": 5892.06228       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9579944289693594,         \"MicroF1\": 0.9579944289693594,         \"MacroF1\": 0.9581242571113368,         \"Memory in Mb\": 49.5586576461792,         \"Time in s\": 6445.036349       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9570499840136416,         \"MicroF1\": 0.9570499840136416,         \"MacroF1\": 0.9565283447410108,         \"Memory in Mb\": 50.18536758422852,         \"Time in s\": 7025.065903       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563885200694516,         \"MicroF1\": 0.9563885200694516,         \"MacroF1\": 0.9560487952418978,         \"Memory in Mb\": 54.40623474121094,         \"Time in s\": 7630.795018999999       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9532307088930289,         \"MicroF1\": 0.9532307088930289,         \"MacroF1\": 0.9512518567217172,         \"Memory in Mb\": 66.82855319976807,         \"Time in s\": 8267.200675       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9519185443574998,         \"MicroF1\": 0.9519185443574998,         \"MacroF1\": 0.9512557409849248,         \"Memory in Mb\": 41.17615795135498,         \"Time in s\": 8934.408603       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9528824330458464,         \"MicroF1\": 0.9528824330458464,         \"MacroF1\": 0.953398407731189,         \"Memory in Mb\": 32.87209510803223,         \"Time in s\": 9625.903537       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.953689923837871,         \"MicroF1\": 0.953689923837871,         \"MacroF1\": 0.9540175301991308,         \"Memory in Mb\": 28.078542709350582,         \"Time in s\": 10339.782646       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9542726734849124,         \"MicroF1\": 0.9542726734849124,         \"MacroF1\": 0.9545119777330118,         \"Memory in Mb\": 20.280012130737305,         \"Time in s\": 11070.86282       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.955470218155078,         \"MicroF1\": 0.955470218155078,         \"MacroF1\": 0.9559406438939212,         \"Memory in Mb\": 21.54300308227539,         \"Time in s\": 11820.445116       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9559579346880684,         \"MicroF1\": 0.9559579346880684,         \"MacroF1\": 0.9561632451269844,         \"Memory in Mb\": 26.89114284515381,         \"Time in s\": 12588.394717       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9558789735733436,         \"MicroF1\": 0.9558789735733436,         \"MacroF1\": 0.9559075747932771,         \"Memory in Mb\": 21.382742881774902,         \"Time in s\": 13375.587705000002       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563247418851668,         \"MicroF1\": 0.9563247418851668,         \"MacroF1\": 0.9565051554876024,         \"Memory in Mb\": 21.864919662475582,         \"Time in s\": 14180.68998       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.956960565207988,         \"MicroF1\": 0.956960565207988,         \"MacroF1\": 0.9571856017401092,         \"Memory in Mb\": 25.72835636138916,         \"Time in s\": 15004.794290000002       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566496253239022,         \"MicroF1\": 0.9566496253239022,         \"MacroF1\": 0.9566382966080724,         \"Memory in Mb\": 21.764866828918457,         \"Time in s\": 15848.339318000002       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.957241097569279,         \"MicroF1\": 0.9572410975692792,         \"MacroF1\": 0.957426459656079,         \"Memory in Mb\": 25.14582061767578,         \"Time in s\": 16708.048820000004       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9580655846306724,         \"MicroF1\": 0.9580655846306724,         \"MacroF1\": 0.958277362015896,         \"Memory in Mb\": 26.658535957336422,         \"Time in s\": 17588.164126000003       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9584596529703928,         \"MicroF1\": 0.9584596529703928,         \"MacroF1\": 0.9585840009788792,         \"Memory in Mb\": 30.76789283752441,         \"Time in s\": 18489.703328000003       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.958079316196342,         \"MicroF1\": 0.958079316196342,         \"MacroF1\": 0.9580713134265896,         \"Memory in Mb\": 27.786094665527344,         \"Time in s\": 19412.324967000004       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.958514614866107,         \"MicroF1\": 0.958514614866107,         \"MacroF1\": 0.9586173296332884,         \"Memory in Mb\": 25.79348850250244,         \"Time in s\": 20355.979039000005       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9577330065164106,         \"MicroF1\": 0.9577330065164106,         \"MacroF1\": 0.9576699214368118,         \"Memory in Mb\": 33.630208015441895,         \"Time in s\": 21321.132478000007       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9576305806828128,         \"MicroF1\": 0.9576305806828128,         \"MacroF1\": 0.9576693803774444,         \"Memory in Mb\": 33.920249938964844,         \"Time in s\": 22315.653729000005       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.956506868836573,         \"MicroF1\": 0.956506868836573,         \"MacroF1\": 0.9564470129227676,         \"Memory in Mb\": 31.50515556335449,         \"Time in s\": 23335.132930000003       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563812600969306,         \"MicroF1\": 0.9563812600969306,         \"MacroF1\": 0.9564135249623557,         \"Memory in Mb\": 19.79563045501709,         \"Time in s\": 24372.247222       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9569148646440436,         \"MicroF1\": 0.9569148646440436,         \"MacroF1\": 0.9569804233582648,         \"Memory in Mb\": 23.70892333984375,         \"Time in s\": 25428.663478       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9574252677572336,         \"MicroF1\": 0.9574252677572336,         \"MacroF1\": 0.957475477736454,         \"Memory in Mb\": 21.893744468688965,         \"Time in s\": 26504.246634000003       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568187744458932,         \"MicroF1\": 0.9568187744458932,         \"MacroF1\": 0.956806677474395,         \"Memory in Mb\": 28.04871368408203,         \"Time in s\": 27598.635240000003       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9567992646683348,         \"MicroF1\": 0.9567992646683348,         \"MacroF1\": 0.9568012672257532,         \"Memory in Mb\": 32.11082458496094,         \"Time in s\": 28712.463090000005       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9565304386974138,         \"MicroF1\": 0.9565304386974138,         \"MacroF1\": 0.9565268274864178,         \"Memory in Mb\": 40.13526153564453,         \"Time in s\": 29849.822929000005       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9559292122162852,         \"MicroF1\": 0.9559292122162852,         \"MacroF1\": 0.9559196349550496,         \"Memory in Mb\": 39.63601016998291,         \"Time in s\": 31009.846621000004       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5111111111111111,         \"MicroF1\": 0.5111111111111111,         \"MacroF1\": 0.4093857832988268,         \"Memory in Mb\": 0.0911636352539062,         \"Time in s\": 0.155273       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6043956043956044,         \"MicroF1\": 0.6043956043956044,         \"MacroF1\": 0.5940974230447915,         \"Memory in Mb\": 0.16827392578125,         \"Time in s\": 0.72683       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6715328467153284,         \"MicroF1\": 0.6715328467153284,         \"MacroF1\": 0.6806196928151186,         \"Memory in Mb\": 0.245431900024414,         \"Time in s\": 1.742293       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7049180327868853,         \"MicroF1\": 0.7049180327868853,         \"MacroF1\": 0.7184732466987995,         \"Memory in Mb\": 0.3220462799072265,         \"Time in s\": 3.340711       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.74235807860262,         \"MicroF1\": 0.74235807860262,         \"MacroF1\": 0.7523809662907407,         \"Memory in Mb\": 0.3991765975952148,         \"Time in s\": 5.610709       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7490909090909091,         \"MicroF1\": 0.7490909090909091,         \"MacroF1\": 0.7611097615339608,         \"Memory in Mb\": 0.4767560958862304,         \"Time in s\": 8.7745       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7663551401869159,         \"MicroF1\": 0.766355140186916,         \"MacroF1\": 0.7725898650917747,         \"Memory in Mb\": 0.5538606643676758,         \"Time in s\": 12.918764       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.784741144414169,         \"MicroF1\": 0.7847411444141691,         \"MacroF1\": 0.7844949397573193,         \"Memory in Mb\": 0.6304874420166016,         \"Time in s\": 18.189003       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7990314769975787,         \"MicroF1\": 0.7990314769975787,         \"MacroF1\": 0.7976353129150817,         \"Memory in Mb\": 0.7076187133789062,         \"Time in s\": 24.731741       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7952069716775599,         \"MicroF1\": 0.7952069716775599,         \"MacroF1\": 0.7930763833747545,         \"Memory in Mb\": 0.7847471237182617,         \"Time in s\": 32.681045       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7960396039603961,         \"MicroF1\": 0.7960396039603961,         \"MacroF1\": 0.7941234022368324,         \"Memory in Mb\": 3.003793716430664,         \"Time in s\": 61.21191       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8021778584392014,         \"MicroF1\": 0.8021778584392014,         \"MacroF1\": 0.8007250644998717,         \"Memory in Mb\": 3.2264842987060547,         \"Time in s\": 91.162029       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8090452261306532,         \"MicroF1\": 0.8090452261306531,         \"MacroF1\": 0.8095532779239047,         \"Memory in Mb\": 3.4499826431274414,         \"Time in s\": 122.67972799999998       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8164852255054432,         \"MicroF1\": 0.8164852255054433,         \"MacroF1\": 0.8176018556357175,         \"Memory in Mb\": 3.6760778427124015,         \"Time in s\": 155.77468699999997       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8214804063860668,         \"MicroF1\": 0.8214804063860668,         \"MacroF1\": 0.8221151176242331,         \"Memory in Mb\": 3.8941650390625,         \"Time in s\": 190.537277       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8272108843537415,         \"MicroF1\": 0.8272108843537415,         \"MacroF1\": 0.8281233770721121,         \"Memory in Mb\": 4.128121376037598,         \"Time in s\": 227.085503       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8361075544174136,         \"MicroF1\": 0.8361075544174136,         \"MacroF1\": 0.8364659566156888,         \"Memory in Mb\": 4.367749214172363,         \"Time in s\": 265.419762       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8403869407496977,         \"MicroF1\": 0.8403869407496977,         \"MacroF1\": 0.8412749002251585,         \"Memory in Mb\": 4.601743698120117,         \"Time in s\": 305.543518       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.845360824742268,         \"MicroF1\": 0.845360824742268,         \"MacroF1\": 0.8465057584066101,         \"Memory in Mb\": 4.840575218200684,         \"Time in s\": 347.501906       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8487486398258978,         \"MicroF1\": 0.8487486398258978,         \"MacroF1\": 0.8489576083149123,         \"Memory in Mb\": 5.074535369873047,         \"Time in s\": 391.430234       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8538860103626943,         \"MicroF1\": 0.8538860103626943,         \"MacroF1\": 0.8530581393966605,         \"Memory in Mb\": 5.3079938888549805,         \"Time in s\": 437.316456       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8585558852621167,         \"MicroF1\": 0.8585558852621167,         \"MacroF1\": 0.8570252804249208,         \"Memory in Mb\": 5.479596138000488,         \"Time in s\": 485.23685       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8628192999053926,         \"MicroF1\": 0.8628192999053927,         \"MacroF1\": 0.8611045332429007,         \"Memory in Mb\": 5.435150146484375,         \"Time in s\": 535.278485       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8631006346328196,         \"MicroF1\": 0.8631006346328196,         \"MacroF1\": 0.8616288881212748,         \"Memory in Mb\": 5.355225563049316,         \"Time in s\": 587.436372       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8668407310704961,         \"MicroF1\": 0.8668407310704961,         \"MacroF1\": 0.8650902600877293,         \"Memory in Mb\": 5.281754493713379,         \"Time in s\": 641.538536       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8719665271966527,         \"MicroF1\": 0.8719665271966527,         \"MacroF1\": 0.8702683106604537,         \"Memory in Mb\": 5.235520362854004,         \"Time in s\": 697.554251       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8759065269943593,         \"MicroF1\": 0.8759065269943593,         \"MacroF1\": 0.8740479640614998,         \"Memory in Mb\": 5.142333984375,         \"Time in s\": 755.471933       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8787878787878788,         \"MicroF1\": 0.8787878787878788,         \"MacroF1\": 0.8772603222806128,         \"Memory in Mb\": 5.092559814453125,         \"Time in s\": 815.138635       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8777194298574643,         \"MicroF1\": 0.8777194298574643,         \"MacroF1\": 0.8760741143565023,         \"Memory in Mb\": 5.055940628051758,         \"Time in s\": 876.491339       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8796229151559101,         \"MicroF1\": 0.8796229151559101,         \"MacroF1\": 0.8783130803325612,         \"Memory in Mb\": 4.964084625244141,         \"Time in s\": 939.483975       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8785964912280702,         \"MicroF1\": 0.8785964912280702,         \"MacroF1\": 0.8768931648451159,         \"Memory in Mb\": 4.951287269592285,         \"Time in s\": 1004.010152       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8769544527532291,         \"MicroF1\": 0.8769544527532291,         \"MacroF1\": 0.8748964905672628,         \"Memory in Mb\": 4.969002723693848,         \"Time in s\": 1070.168576       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8727752142386289,         \"MicroF1\": 0.8727752142386289,         \"MacroF1\": 0.8705110235515202,         \"Memory in Mb\": 5.101251602172852,         \"Time in s\": 1138.304608       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8688419705694178,         \"MicroF1\": 0.8688419705694178,         \"MacroF1\": 0.8667015278861958,         \"Memory in Mb\": 5.262187957763672,         \"Time in s\": 1208.4659419999998       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8651336233685519,         \"MicroF1\": 0.8651336233685519,         \"MacroF1\": 0.8631350462642483,         \"Memory in Mb\": 5.320252418518066,         \"Time in s\": 1280.5305069999995       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8640483383685801,         \"MicroF1\": 0.8640483383685801,         \"MacroF1\": 0.8620479268968886,         \"Memory in Mb\": 5.35189151763916,         \"Time in s\": 1354.3819709999998       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8647854203409759,         \"MicroF1\": 0.8647854203409759,         \"MacroF1\": 0.8635043959538364,         \"Memory in Mb\": 5.359102249145508,         \"Time in s\": 1430.0286029999995       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.866056096164854,         \"MicroF1\": 0.866056096164854,         \"MacroF1\": 0.864439618601765,         \"Memory in Mb\": 5.402237892150879,         \"Time in s\": 1507.5136589999995       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8683770217512549,         \"MicroF1\": 0.8683770217512549,         \"MacroF1\": 0.8664209902402824,         \"Memory in Mb\": 5.3993330001831055,         \"Time in s\": 1586.7199259999998       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8694942903752039,         \"MicroF1\": 0.8694942903752039,         \"MacroF1\": 0.867597342266498,         \"Memory in Mb\": 5.4049272537231445,         \"Time in s\": 1667.658732       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8710875331564987,         \"MicroF1\": 0.8710875331564986,         \"MacroF1\": 0.8694766742923737,         \"Memory in Mb\": 5.4121294021606445,         \"Time in s\": 1750.3169159999998       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8705334023821854,         \"MicroF1\": 0.8705334023821854,         \"MacroF1\": 0.8686918451193435,         \"Memory in Mb\": 5.405803680419922,         \"Time in s\": 1834.60998       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8715225088517956,         \"MicroF1\": 0.8715225088517956,         \"MacroF1\": 0.8698703895904014,         \"Memory in Mb\": 5.395906448364258,         \"Time in s\": 1920.517128       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8729609490855166,         \"MicroF1\": 0.8729609490855166,         \"MacroF1\": 0.870902914954928,         \"Memory in Mb\": 5.386837959289551,         \"Time in s\": 2008.106455       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8733687771870469,         \"MicroF1\": 0.8733687771870469,         \"MacroF1\": 0.8714525187304558,         \"Memory in Mb\": 5.375288963317871,         \"Time in s\": 2097.403794       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.875177304964539,         \"MicroF1\": 0.875177304964539,         \"MacroF1\": 0.8730645404016979,         \"Memory in Mb\": 5.353263854980469,         \"Time in s\": 2188.326937       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8745950948634891,         \"MicroF1\": 0.8745950948634891,         \"MacroF1\": 0.872417325547954,         \"Memory in Mb\": 5.322790145874023,         \"Time in s\": 2280.882375       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8753964657906661,         \"MicroF1\": 0.8753964657906661,         \"MacroF1\": 0.8732500176589647,         \"Memory in Mb\": 5.30323600769043,         \"Time in s\": 2374.975797       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8748335552596538,         \"MicroF1\": 0.8748335552596538,         \"MacroF1\": 0.8732733602208504,         \"Memory in Mb\": 5.278659820556641,         \"Time in s\": 2470.732765       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8742931709438887,         \"MicroF1\": 0.8742931709438887,         \"MacroF1\": 0.8727466012343671,         \"Memory in Mb\": 5.262259483337402,         \"Time in s\": 2568.119206       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8735383282806409,         \"MicroF1\": 0.8735383282806409,         \"MacroF1\": 0.8721361121313428,         \"Memory in Mb\": 5.268708229064941,         \"Time in s\": 2666.293295       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6597156398104266,         \"MicroF1\": 0.6597156398104266,         \"MacroF1\": 0.5853273709738578,         \"Memory in Mb\": 6.371035575866699,         \"Time in s\": 65.756564       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6807200378967314,         \"MicroF1\": 0.6807200378967314,         \"MacroF1\": 0.5992086579995298,         \"Memory in Mb\": 6.278300285339356,         \"Time in s\": 182.184591       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6842437638143354,         \"MicroF1\": 0.6842437638143354,         \"MacroF1\": 0.6001715208792017,         \"Memory in Mb\": 6.298460006713867,         \"Time in s\": 341.998975       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6848212171442103,         \"MicroF1\": 0.6848212171442103,         \"MacroF1\": 0.6051604277089342,         \"Memory in Mb\": 6.265153884887695,         \"Time in s\": 541.5068689999999       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6872513733661678,         \"MicroF1\": 0.6872513733661678,         \"MacroF1\": 0.611100448555976,         \"Memory in Mb\": 6.2555742263793945,         \"Time in s\": 777.52113       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6842936069455406,         \"MicroF1\": 0.6842936069455406,         \"MacroF1\": 0.6118525331169307,         \"Memory in Mb\": 6.314010620117188,         \"Time in s\": 1048.588472       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6852929238262752,         \"MicroF1\": 0.6852929238262752,         \"MacroF1\": 0.6157762907660722,         \"Memory in Mb\": 6.288516998291016,         \"Time in s\": 1352.458798       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6828459808215934,         \"MicroF1\": 0.6828459808215934,         \"MacroF1\": 0.6148503710479976,         \"Memory in Mb\": 6.31680965423584,         \"Time in s\": 1687.096094       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6851520572450805,         \"MicroF1\": 0.6851520572450805,         \"MacroF1\": 0.6155258331015067,         \"Memory in Mb\": 6.223039627075195,         \"Time in s\": 2051.649807       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6861445212614831,         \"MicroF1\": 0.6861445212614831,         \"MacroF1\": 0.6169474950376627,         \"Memory in Mb\": 6.253497123718262,         \"Time in s\": 2444.130945       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6873009040034438,         \"MicroF1\": 0.6873009040034438,         \"MacroF1\": 0.6200568175672779,         \"Memory in Mb\": 6.251482009887695,         \"Time in s\": 2863.128904       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6866072133217583,         \"MicroF1\": 0.6866072133217583,         \"MacroF1\": 0.623883491026523,         \"Memory in Mb\": 6.276742935180664,         \"Time in s\": 3309.082968       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7020470605376266,         \"MicroF1\": 0.7020470605376266,         \"MacroF1\": 0.6991473808978487,         \"Memory in Mb\": 6.26933479309082,         \"Time in s\": 3781.277509       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7077724413177299,         \"MicroF1\": 0.7077724413177299,         \"MacroF1\": 0.7078402863830927,         \"Memory in Mb\": 6.244691848754883,         \"Time in s\": 4278.402760999999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7016857124818486,         \"MicroF1\": 0.7016857124818486,         \"MacroF1\": 0.704840832390747,         \"Memory in Mb\": 6.350223541259766,         \"Time in s\": 4805.403565999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6992009470257473,         \"MicroF1\": 0.6992009470257473,         \"MacroF1\": 0.7048178275842342,         \"Memory in Mb\": 6.243149757385254,         \"Time in s\": 5357.683726999999       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6922734109520361,         \"MicroF1\": 0.6922734109520361,         \"MacroF1\": 0.6995766929659905,         \"Memory in Mb\": 6.218992233276367,         \"Time in s\": 5935.240105999998       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6974272636397116,         \"MicroF1\": 0.6974272636397116,         \"MacroF1\": 0.7006862112488368,         \"Memory in Mb\": 6.24652099609375,         \"Time in s\": 6538.276384999998       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.699845486716842,         \"MicroF1\": 0.699845486716842,         \"MacroF1\": 0.6985118222305657,         \"Memory in Mb\": 6.205791473388672,         \"Time in s\": 7167.459726999999       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7016904209479615,         \"MicroF1\": 0.7016904209479615,         \"MacroF1\": 0.6971610909052677,         \"Memory in Mb\": 6.218420028686523,         \"Time in s\": 7825.840650999999       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7039909808342728,         \"MicroF1\": 0.7039909808342728,         \"MacroF1\": 0.6964197759629052,         \"Memory in Mb\": 6.236072540283203,         \"Time in s\": 8511.079801999998       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7076320433902974,         \"MicroF1\": 0.7076320433902974,         \"MacroF1\": 0.697368621848442,         \"Memory in Mb\": 6.279313087463379,         \"Time in s\": 9222.986801999998       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7098447729237863,         \"MicroF1\": 0.7098447729237863,         \"MacroF1\": 0.6967477548491564,         \"Memory in Mb\": 6.2948198318481445,         \"Time in s\": 9960.927248999997       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7127017322337529,         \"MicroF1\": 0.712701732233753,         \"MacroF1\": 0.6972185032799825,         \"Memory in Mb\": 6.224791526794434,         \"Time in s\": 10724.419586999997       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7145346414636918,         \"MicroF1\": 0.7145346414636918,         \"MacroF1\": 0.6967850611237018,         \"Memory in Mb\": 6.263523101806641,         \"Time in s\": 11512.986172999998       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7156437807321071,         \"MicroF1\": 0.7156437807321071,         \"MacroF1\": 0.6955595874776194,         \"Memory in Mb\": 6.272575378417969,         \"Time in s\": 12326.628602999996       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7130931921012942,         \"MicroF1\": 0.7130931921012942,         \"MacroF1\": 0.6943090782068162,         \"Memory in Mb\": 6.22489070892334,         \"Time in s\": 13165.433758999998       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7117732607298678,         \"MicroF1\": 0.7117732607298677,         \"MacroF1\": 0.6978751959025926,         \"Memory in Mb\": 6.217726707458496,         \"Time in s\": 14028.837757999998       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7122097769650263,         \"MicroF1\": 0.7122097769650264,         \"MacroF1\": 0.7026862643890369,         \"Memory in Mb\": 6.243690490722656,         \"Time in s\": 14916.319239       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7113545250797058,         \"MicroF1\": 0.7113545250797058,         \"MacroF1\": 0.7052714328980031,         \"Memory in Mb\": 6.277059555053711,         \"Time in s\": 15827.904481999998       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7111959676187567,         \"MicroF1\": 0.7111959676187566,         \"MacroF1\": 0.7078689284492299,         \"Memory in Mb\": 6.295280456542969,         \"Time in s\": 16762.400180999997       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7067562368678051,         \"MicroF1\": 0.7067562368678051,         \"MacroF1\": 0.704703743720216,         \"Memory in Mb\": 6.183221817016602,         \"Time in s\": 17721.950532       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7030734353028956,         \"MicroF1\": 0.7030734353028956,         \"MacroF1\": 0.7010614031639846,         \"Memory in Mb\": 6.343389511108398,         \"Time in s\": 18710.094933       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6998022449377489,         \"MicroF1\": 0.6998022449377489,         \"MacroF1\": 0.6976694331042329,         \"Memory in Mb\": 6.273009300231934,         \"Time in s\": 19725.980479       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6967179847939609,         \"MicroF1\": 0.6967179847939609,         \"MacroF1\": 0.6945045780432343,         \"Memory in Mb\": 6.264690399169922,         \"Time in s\": 20767.047301       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6941470472182033,         \"MicroF1\": 0.6941470472182033,         \"MacroF1\": 0.6917813776610243,         \"Memory in Mb\": 6.265054702758789,         \"Time in s\": 21835.556239       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.691996621535154,         \"MicroF1\": 0.691996621535154,         \"MacroF1\": 0.6898060776768534,         \"Memory in Mb\": 6.200959205627441,         \"Time in s\": 22932.108791       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6904328756199068,         \"MicroF1\": 0.6904328756199068,         \"MacroF1\": 0.6882031611963276,         \"Memory in Mb\": 6.413609504699707,         \"Time in s\": 24054.967875       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6916446106403128,         \"MicroF1\": 0.6916446106403128,         \"MacroF1\": 0.6892941373261507,         \"Memory in Mb\": 6.310104370117188,         \"Time in s\": 25204.026441       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.692535334643339,         \"MicroF1\": 0.692535334643339,         \"MacroF1\": 0.6900712004452627,         \"Memory in Mb\": 6.22797966003418,         \"Time in s\": 26378.286294       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6935904838895947,         \"MicroF1\": 0.6935904838895947,         \"MacroF1\": 0.6909354899104013,         \"Memory in Mb\": 6.220904350280762,         \"Time in s\": 27574.213319       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6941895334941715,         \"MicroF1\": 0.6941895334941715,         \"MacroF1\": 0.691322114366645,         \"Memory in Mb\": 6.22946834564209,         \"Time in s\": 28791.926615000004       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6950690422181602,         \"MicroF1\": 0.6950690422181602,         \"MacroF1\": 0.6917362410920441,         \"Memory in Mb\": 6.2850341796875,         \"Time in s\": 30030.670852000003       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6964466349568473,         \"MicroF1\": 0.6964466349568473,         \"MacroF1\": 0.6926338572817136,         \"Memory in Mb\": 6.233500480651856,         \"Time in s\": 31290.345387       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6963530377322755,         \"MicroF1\": 0.6963530377322755,         \"MacroF1\": 0.6929015597977773,         \"Memory in Mb\": 6.243911743164063,         \"Time in s\": 32571.46119       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7006073082861555,         \"MicroF1\": 0.7006073082861555,         \"MacroF1\": 0.697843135408715,         \"Memory in Mb\": 6.247167587280273,         \"Time in s\": 33874.398319       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7046805424029337,         \"MicroF1\": 0.7046805424029337,         \"MacroF1\": 0.7023003034160373,         \"Memory in Mb\": 6.246943473815918,         \"Time in s\": 35198.416197       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7083867658373942,         \"MicroF1\": 0.7083867658373942,         \"MacroF1\": 0.7061355873839065,         \"Memory in Mb\": 6.210485458374023,         \"Time in s\": 36536.506394       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7126567844925884,         \"MicroF1\": 0.7126567844925883,         \"MacroF1\": 0.7104085577951368,         \"Memory in Mb\": 6.270394325256348,         \"Time in s\": 37890.628371       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7128544101214038,         \"MicroF1\": 0.7128544101214038,         \"MacroF1\": 0.7110869129037599,         \"Memory in Mb\": 6.247260093688965,         \"Time in s\": 39264.666928       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7131152194069673,         \"MicroF1\": 0.7131152194069672,         \"MacroF1\": 0.7113808258412672,         \"Memory in Mb\": 6.272693634033203,         \"Time in s\": 40639.937472       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 1.0294876098632812,         \"Time in s\": 8.610537       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9251533742331288,         \"MicroF1\": 0.9251533742331288,         \"MacroF1\": 0.8588670451436246,         \"Memory in Mb\": 5.3865966796875,         \"Time in s\": 60.879099       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9247751430907604,         \"MicroF1\": 0.9247751430907604,         \"MacroF1\": 0.888226412135106,         \"Memory in Mb\": 6.246823310852051,         \"Time in s\": 137.71097600000002       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.927038626609442,         \"MicroF1\": 0.927038626609442,         \"MacroF1\": 0.893336805209695,         \"Memory in Mb\": 6.212030410766602,         \"Time in s\": 236.990146       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9298675821481118,         \"MicroF1\": 0.9298675821481118,         \"MacroF1\": 0.911424130088645,         \"Memory in Mb\": 6.329436302185059,         \"Time in s\": 359.011082       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9239885574172456,         \"MicroF1\": 0.9239885574172456,         \"MacroF1\": 0.912155547295492,         \"Memory in Mb\": 6.208271026611328,         \"Time in s\": 503.2068       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9169877408056042,         \"MicroF1\": 0.9169877408056042,         \"MacroF1\": 0.8816257260944811,         \"Memory in Mb\": 6.284844398498535,         \"Time in s\": 667.387655       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.908979466748391,         \"MicroF1\": 0.908979466748391,         \"MacroF1\": 0.9011431783951356,         \"Memory in Mb\": 6.232160568237305,         \"Time in s\": 850.70223       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.913375102152002,         \"MicroF1\": 0.913375102152002,         \"MacroF1\": 0.9125908871445696,         \"Memory in Mb\": 6.234919548034668,         \"Time in s\": 1054.346916       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9141946555528316,         \"MicroF1\": 0.9141946555528316,         \"MacroF1\": 0.9054789816810688,         \"Memory in Mb\": 6.308258056640625,         \"Time in s\": 1277.35851       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9057276576777356,         \"MicroF1\": 0.9057276576777356,         \"MacroF1\": 0.9087691557812896,         \"Memory in Mb\": 6.274983406066895,         \"Time in s\": 1518.241674       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.908682328907048,         \"MicroF1\": 0.908682328907048,         \"MacroF1\": 0.9101970481905532,         \"Memory in Mb\": 6.210176467895508,         \"Time in s\": 1774.8436390000002       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9092966245521404,         \"MicroF1\": 0.9092966245521404,         \"MacroF1\": 0.9045962329696908,         \"Memory in Mb\": 6.311163902282715,         \"Time in s\": 2048.2991580000003       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9110488530905272,         \"MicroF1\": 0.9110488530905272,         \"MacroF1\": 0.9114244990736602,         \"Memory in Mb\": 6.304790496826172,         \"Time in s\": 2337.0378760000003       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9089720542572316,         \"MicroF1\": 0.9089720542572316,         \"MacroF1\": 0.9032533666541098,         \"Memory in Mb\": 6.217576026916504,         \"Time in s\": 2640.3521840000003       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.907767734027884,         \"MicroF1\": 0.907767734027884,         \"MacroF1\": 0.9071900335968284,         \"Memory in Mb\": 6.258184432983398,         \"Time in s\": 2958.617579       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9105984138428262,         \"MicroF1\": 0.9105984138428262,         \"MacroF1\": 0.9126270814361048,         \"Memory in Mb\": 6.1720380783081055,         \"Time in s\": 3289.9253120000003       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9120250578782514,         \"MicroF1\": 0.9120250578782514,         \"MacroF1\": 0.9125633522308232,         \"Memory in Mb\": 6.2164154052734375,         \"Time in s\": 3633.832057000001       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9133015094826474,         \"MicroF1\": 0.9133015094826474,         \"MacroF1\": 0.9136330015220732,         \"Memory in Mb\": 6.260525703430176,         \"Time in s\": 3992.050824       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9148179924010296,         \"MicroF1\": 0.9148179924010296,         \"MacroF1\": 0.9154195917586072,         \"Memory in Mb\": 6.291139602661133,         \"Time in s\": 4363.477247000001       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9166569394186996,         \"MicroF1\": 0.9166569394186996,         \"MacroF1\": 0.917708642296068,         \"Memory in Mb\": 6.216147422790527,         \"Time in s\": 4747.269196000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9179944289693592,         \"MicroF1\": 0.9179944289693592,         \"MacroF1\": 0.9193727618453186,         \"Memory in Mb\": 6.204837799072266,         \"Time in s\": 5142.613476000001       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9180432697431524,         \"MicroF1\": 0.9180432697431524,         \"MacroF1\": 0.9181590265081532,         \"Memory in Mb\": 6.197464942932129,         \"Time in s\": 5549.1165660000015       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9166581554488816,         \"MicroF1\": 0.9166581554488816,         \"MacroF1\": 0.9168210748678532,         \"Memory in Mb\": 6.232473373413086,         \"Time in s\": 5968.023607000002       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9154819099911756,         \"MicroF1\": 0.9154819099911756,         \"MacroF1\": 0.9145218669909496,         \"Memory in Mb\": 6.197787284851074,         \"Time in s\": 6399.011787000002       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9126048835674556,         \"MicroF1\": 0.9126048835674556,         \"MacroF1\": 0.9111025938131312,         \"Memory in Mb\": 6.2185258865356445,         \"Time in s\": 6842.990199000003       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9106672719019518,         \"MicroF1\": 0.9106672719019518,         \"MacroF1\": 0.911227786024665,         \"Memory in Mb\": 6.261377334594727,         \"Time in s\": 7300.041981000002       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.910356298695614,         \"MicroF1\": 0.910356298695614,         \"MacroF1\": 0.9101104800687124,         \"Memory in Mb\": 6.227293968200684,         \"Time in s\": 7769.979179000002       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9092215366410278,         \"MicroF1\": 0.9092215366410278,         \"MacroF1\": 0.909418612123662,         \"Memory in Mb\": 6.287784576416016,         \"Time in s\": 8252.975566000001       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9110221423318898,         \"MicroF1\": 0.9110221423318898,         \"MacroF1\": 0.9118339797691072,         \"Memory in Mb\": 6.31197452545166,         \"Time in s\": 8747.696417000001       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.912627500593026,         \"MicroF1\": 0.912627500593026,         \"MacroF1\": 0.9131272841889786,         \"Memory in Mb\": 6.279851913452148,         \"Time in s\": 9255.008595       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9129069322098812,         \"MicroF1\": 0.9129069322098812,         \"MacroF1\": 0.913006147591119,         \"Memory in Mb\": 6.346117973327637,         \"Time in s\": 9774.692327       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9136150932184506,         \"MicroF1\": 0.9136150932184506,         \"MacroF1\": 0.9138210444048112,         \"Memory in Mb\": 6.238006591796875,         \"Time in s\": 10306.725428000002       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9137769447047798,         \"MicroF1\": 0.9137769447047795,         \"MacroF1\": 0.913931693448659,         \"Memory in Mb\": 6.288792610168457,         \"Time in s\": 10851.686584       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.91217872400028,         \"MicroF1\": 0.91217872400028,         \"MacroF1\": 0.9118234284090696,         \"Memory in Mb\": 6.252389907836914,         \"Time in s\": 11409.551357       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9133247089262612,         \"MicroF1\": 0.9133247089262612,         \"MacroF1\": 0.9136581918124824,         \"Memory in Mb\": 6.268362998962402,         \"Time in s\": 11980.223294       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9134150380920836,         \"MicroF1\": 0.9134150380920836,         \"MacroF1\": 0.9134700562544148,         \"Memory in Mb\": 6.304409027099609,         \"Time in s\": 12563.090191       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9127910726956072,         \"MicroF1\": 0.9127910726956072,         \"MacroF1\": 0.9127632118282708,         \"Memory in Mb\": 6.222077369689941,         \"Time in s\": 13158.500652       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9127019043429074,         \"MicroF1\": 0.9127019043429074,         \"MacroF1\": 0.9127365496247324,         \"Memory in Mb\": 6.263586044311523,         \"Time in s\": 13766.752283999998       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9113303511244562,         \"MicroF1\": 0.9113303511244562,         \"MacroF1\": 0.9110956080213112,         \"Memory in Mb\": 6.256609916687012,         \"Time in s\": 14388.121762       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.910384408441442,         \"MicroF1\": 0.910384408441442,         \"MacroF1\": 0.910332436025812,         \"Memory in Mb\": 6.208023071289063,         \"Time in s\": 15023.088924       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9113510358914504,         \"MicroF1\": 0.9113510358914504,         \"MacroF1\": 0.9114963483082136,         \"Memory in Mb\": 6.223179817199707,         \"Time in s\": 15670.063673       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9113606566721768,         \"MicroF1\": 0.9113606566721768,         \"MacroF1\": 0.9113826667045092,         \"Memory in Mb\": 6.23558235168457,         \"Time in s\": 16331.265293       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9114255473232687,         \"MicroF1\": 0.911425547323269,         \"MacroF1\": 0.9114384409485988,         \"Memory in Mb\": 6.255133628845215,         \"Time in s\": 17006.553944       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9126858761370444,         \"MicroF1\": 0.9126858761370444,         \"MacroF1\": 0.9127656000580756,         \"Memory in Mb\": 6.270404815673828,         \"Time in s\": 17693.792261       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9125059945649278,         \"MicroF1\": 0.9125059945649276,         \"MacroF1\": 0.9124701420883568,         \"Memory in Mb\": 6.180520057678223,         \"Time in s\": 18393.849908       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.912594524119948,         \"MicroF1\": 0.912594524119948,         \"MacroF1\": 0.9125632790621416,         \"Memory in Mb\": 6.265439987182617,         \"Time in s\": 19106.227212       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.913547464637696,         \"MicroF1\": 0.913547464637696,         \"MacroF1\": 0.9135225066457016,         \"Memory in Mb\": 6.303133964538574,         \"Time in s\": 19831.701162       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9111099994997748,         \"MicroF1\": 0.9111099994997748,         \"MacroF1\": 0.910917465793804,         \"Memory in Mb\": 6.225028991699219,         \"Time in s\": 20571.760391       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9104858081278494,         \"MicroF1\": 0.9104858081278494,         \"MacroF1\": 0.910327982122686,         \"Memory in Mb\": 6.325108528137207,         \"Time in s\": 21326.45228       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3111111111111111,         \"MicroF1\": 0.3111111111111111,         \"MacroF1\": 0.245764972655729,         \"Memory in Mb\": 4.105147361755371,         \"Time in s\": 2.153154       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4835164835164835,         \"MicroF1\": 0.4835164835164835,         \"MacroF1\": 0.4934752395581889,         \"Memory in Mb\": 4.108363151550293,         \"Time in s\": 6.907408       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5328467153284672,         \"MicroF1\": 0.5328467153284672,         \"MacroF1\": 0.5528821792646677,         \"Memory in Mb\": 4.108027458190918,         \"Time in s\": 14.639156       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5956284153005464,         \"MicroF1\": 0.5956284153005464,         \"MacroF1\": 0.614143164890895,         \"Memory in Mb\": 4.108977317810059,         \"Time in s\": 25.443956       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.62882096069869,         \"MicroF1\": 0.62882096069869,         \"MacroF1\": 0.6441389332893815,         \"Memory in Mb\": 3.881842613220215,         \"Time in s\": 39.254234       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.64,         \"MicroF1\": 0.64,         \"MacroF1\": 0.6559607038460422,         \"Memory in Mb\": 3.996514320373535,         \"Time in s\": 55.768073       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6697819314641744,         \"MicroF1\": 0.6697819314641744,         \"MacroF1\": 0.6706320385346652,         \"Memory in Mb\": 4.112936019897461,         \"Time in s\": 74.877199       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6948228882833788,         \"MicroF1\": 0.6948228882833788,         \"MacroF1\": 0.6897433526546475,         \"Memory in Mb\": 4.112924575805664,         \"Time in s\": 96.687005       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.711864406779661,         \"MicroF1\": 0.711864406779661,         \"MacroF1\": 0.706570530482581,         \"Memory in Mb\": 4.117301940917969,         \"Time in s\": 121.290167       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7145969498910676,         \"MicroF1\": 0.7145969498910676,         \"MacroF1\": 0.7071122267088654,         \"Memory in Mb\": 4.116390228271484,         \"Time in s\": 148.551711       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7247524752475247,         \"MicroF1\": 0.7247524752475247,         \"MacroF1\": 0.7147973207987898,         \"Memory in Mb\": 4.115703582763672,         \"Time in s\": 178.365171       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7295825771324864,         \"MicroF1\": 0.7295825771324864,         \"MacroF1\": 0.7210771168277493,         \"Memory in Mb\": 4.115436553955078,         \"Time in s\": 210.796119       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7336683417085427,         \"MicroF1\": 0.7336683417085426,         \"MacroF1\": 0.7250288715672424,         \"Memory in Mb\": 4.115207672119141,         \"Time in s\": 245.953277       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7325038880248833,         \"MicroF1\": 0.7325038880248833,         \"MacroF1\": 0.7258924883659029,         \"Memory in Mb\": 4.118658065795898,         \"Time in s\": 283.80303000000004       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.737300435413643,         \"MicroF1\": 0.737300435413643,         \"MacroF1\": 0.7302536378735861,         \"Memory in Mb\": 4.118425369262695,         \"Time in s\": 324.296282       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7387755102040816,         \"MicroF1\": 0.7387755102040816,         \"MacroF1\": 0.7329631379486719,         \"Memory in Mb\": 4.118097305297852,         \"Time in s\": 367.43284       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7439180537772087,         \"MicroF1\": 0.7439180537772088,         \"MacroF1\": 0.7387105187530085,         \"Memory in Mb\": 4.117616653442383,         \"Time in s\": 413.28289       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7460701330108828,         \"MicroF1\": 0.7460701330108827,         \"MacroF1\": 0.7425025596154723,         \"Memory in Mb\": 4.117326736450195,         \"Time in s\": 461.953497       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7514318442153494,         \"MicroF1\": 0.7514318442153494,         \"MacroF1\": 0.7467163857842193,         \"Memory in Mb\": 4.117303848266602,         \"Time in s\": 513.2937440000001       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.750816104461371,         \"MicroF1\": 0.750816104461371,         \"MacroF1\": 0.7453933609147309,         \"Memory in Mb\": 4.117105484008789,         \"Time in s\": 567.431634       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7512953367875648,         \"MicroF1\": 0.7512953367875648,         \"MacroF1\": 0.7451117895470661,         \"Memory in Mb\": 4.116701126098633,         \"Time in s\": 624.2209760000001       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7507418397626113,         \"MicroF1\": 0.7507418397626113,         \"MacroF1\": 0.744963080481548,         \"Memory in Mb\": 4.116399765014648,         \"Time in s\": 683.8404210000001       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7511825922421949,         \"MicroF1\": 0.7511825922421949,         \"MacroF1\": 0.7446315489945475,         \"Memory in Mb\": 4.117582321166992,         \"Time in s\": 746.2097300000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7533998186763372,         \"MicroF1\": 0.7533998186763373,         \"MacroF1\": 0.7466082689908061,         \"Memory in Mb\": 4.117956161499023,         \"Time in s\": 811.1743250000002       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7563098346388164,         \"MicroF1\": 0.7563098346388164,         \"MacroF1\": 0.7491651771194966,         \"Memory in Mb\": 4.117490768432617,         \"Time in s\": 878.6809830000002       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7589958158995815,         \"MicroF1\": 0.7589958158995815,         \"MacroF1\": 0.7526420027035883,         \"Memory in Mb\": 4.117303848266602,         \"Time in s\": 948.8627130000002       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.75825946817083,         \"MicroF1\": 0.7582594681708301,         \"MacroF1\": 0.7524016178277559,         \"Memory in Mb\": 4.11713981628418,         \"Time in s\": 1021.5806660000002       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7637917637917638,         \"MicroF1\": 0.7637917637917638,         \"MacroF1\": 0.75666252908711,         \"Memory in Mb\": 4.117353439331055,         \"Time in s\": 1096.9757510000002       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7636909227306826,         \"MicroF1\": 0.7636909227306825,         \"MacroF1\": 0.7569484848610158,         \"Memory in Mb\": 4.11726188659668,         \"Time in s\": 1175.015685       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7650471356055112,         \"MicroF1\": 0.7650471356055112,         \"MacroF1\": 0.7590436403579585,         \"Memory in Mb\": 4.11729621887207,         \"Time in s\": 1255.693831       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.767719298245614,         \"MicroF1\": 0.767719298245614,         \"MacroF1\": 0.761211289695921,         \"Memory in Mb\": 4.117136001586914,         \"Time in s\": 1338.965745       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7722637661454793,         \"MicroF1\": 0.7722637661454793,         \"MacroF1\": 0.764056696643358,         \"Memory in Mb\": 4.117197036743164,         \"Time in s\": 1424.822234       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7732366512854317,         \"MicroF1\": 0.7732366512854317,         \"MacroF1\": 0.764234133414765,         \"Memory in Mb\": 4.117246627807617,         \"Time in s\": 1513.291691       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7735124760076776,         \"MicroF1\": 0.7735124760076776,         \"MacroF1\": 0.7653316001442944,         \"Memory in Mb\": 4.117277145385742,         \"Time in s\": 1604.2857379999998       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7737725295214419,         \"MicroF1\": 0.7737725295214419,         \"MacroF1\": 0.7647353044337893,         \"Memory in Mb\": 4.11713981628418,         \"Time in s\": 1697.9838939999995       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7734138972809668,         \"MicroF1\": 0.7734138972809667,         \"MacroF1\": 0.7645730180903108,         \"Memory in Mb\": 4.116628646850586,         \"Time in s\": 1794.3974679999997       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7724867724867724,         \"MicroF1\": 0.7724867724867724,         \"MacroF1\": 0.7656182355666586,         \"Memory in Mb\": 4.116819381713867,         \"Time in s\": 1893.301291       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7750429307384087,         \"MicroF1\": 0.7750429307384087,         \"MacroF1\": 0.7677424040514297,         \"Memory in Mb\": 4.116933822631836,         \"Time in s\": 1994.662727       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7763524818739542,         \"MicroF1\": 0.7763524818739542,         \"MacroF1\": 0.7677176136548693,         \"Memory in Mb\": 4.116861343383789,         \"Time in s\": 2098.663831       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7775965198477434,         \"MicroF1\": 0.7775965198477434,         \"MacroF1\": 0.7691578918725354,         \"Memory in Mb\": 4.11646842956543,         \"Time in s\": 2205.226802       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7761273209549071,         \"MicroF1\": 0.7761273209549071,         \"MacroF1\": 0.7681560201617949,         \"Memory in Mb\": 4.116430282592773,         \"Time in s\": 2314.321902       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7762817193164163,         \"MicroF1\": 0.7762817193164163,         \"MacroF1\": 0.7674170460709655,         \"Memory in Mb\": 4.116365432739258,         \"Time in s\": 2425.9767019999995       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7769347496206374,         \"MicroF1\": 0.7769347496206374,         \"MacroF1\": 0.7672843880004774,         \"Memory in Mb\": 4.116201400756836,         \"Time in s\": 2540.253238       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7790410281759763,         \"MicroF1\": 0.7790410281759763,         \"MacroF1\": 0.7681802739952505,         \"Memory in Mb\": 4.116155624389648,         \"Time in s\": 2657.1841359999994       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.778153697438376,         \"MicroF1\": 0.7781536974383759,         \"MacroF1\": 0.767530439166732,         \"Memory in Mb\": 4.116151809692383,         \"Time in s\": 2776.607733       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7787234042553192,         \"MicroF1\": 0.778723404255319,         \"MacroF1\": 0.7673415220519754,         \"Memory in Mb\": 4.116128921508789,         \"Time in s\": 2898.5867789999998       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7797316057380842,         \"MicroF1\": 0.7797316057380842,         \"MacroF1\": 0.7679341969633587,         \"Memory in Mb\": 4.116201400756836,         \"Time in s\": 3023.0016299999997       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7816039873130947,         \"MicroF1\": 0.7816039873130947,         \"MacroF1\": 0.7687944234581563,         \"Memory in Mb\": 4.11619758605957,         \"Time in s\": 3150.038225       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7785175321793165,         \"MicroF1\": 0.7785175321793165,         \"MacroF1\": 0.7657018899401807,         \"Memory in Mb\": 4.116170883178711,         \"Time in s\": 3279.4760369999995       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7777294475859069,         \"MicroF1\": 0.7777294475859068,         \"MacroF1\": 0.7649119672933203,         \"Memory in Mb\": 4.116254806518555,         \"Time in s\": 3411.201767       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7778258986574275,         \"MicroF1\": 0.7778258986574276,         \"MacroF1\": 0.765010539660814,         \"Memory in Mb\": 4.116277694702148,         \"Time in s\": 3543.54869       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360189573459716,         \"MicroF1\": 0.6360189573459716,         \"MacroF1\": 0.5970323052762562,         \"Memory in Mb\": 6.48949146270752,         \"Time in s\": 90.340432       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.62482235907153,         \"MicroF1\": 0.62482235907153,         \"MacroF1\": 0.5890580890213498,         \"Memory in Mb\": 6.490170478820801,         \"Time in s\": 257.284509       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6157246605620461,         \"MicroF1\": 0.6157246605620461,         \"MacroF1\": 0.5802533923244894,         \"Memory in Mb\": 6.491124153137207,         \"Time in s\": 490.807794       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6107032914989344,         \"MicroF1\": 0.6107032914989344,         \"MacroF1\": 0.574850135712032,         \"Memory in Mb\": 6.49120044708252,         \"Time in s\": 783.3577379999999       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.614889183557492,         \"MicroF1\": 0.614889183557492,         \"MacroF1\": 0.5777842549225517,         \"Memory in Mb\": 6.491948127746582,         \"Time in s\": 1129.937274       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.608997632202052,         \"MicroF1\": 0.608997632202052,         \"MacroF1\": 0.5733157350789626,         \"Memory in Mb\": 6.490735054016113,         \"Time in s\": 1525.7763839999998       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6057367068055743,         \"MicroF1\": 0.6057367068055743,         \"MacroF1\": 0.5703382690867537,         \"Memory in Mb\": 6.490704536437988,         \"Time in s\": 1967.928226       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6069610512608027,         \"MicroF1\": 0.6069610512608027,         \"MacroF1\": 0.5711427916016896,         \"Memory in Mb\": 6.490643501281738,         \"Time in s\": 2456.105543       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6039145532989583,         \"MicroF1\": 0.6039145532989583,         \"MacroF1\": 0.5678102867297488,         \"Memory in Mb\": 6.491009712219238,         \"Time in s\": 2990.761064       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6034662373330808,         \"MicroF1\": 0.6034662373330808,         \"MacroF1\": 0.567425153452482,         \"Memory in Mb\": 6.491185188293457,         \"Time in s\": 3571.580702       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6005165733964701,         \"MicroF1\": 0.6005165733964701,         \"MacroF1\": 0.56512832395729,         \"Memory in Mb\": 6.491345405578613,         \"Time in s\": 4198.711386999999       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6031883829216321,         \"MicroF1\": 0.6031883829216321,         \"MacroF1\": 0.5703828979306638,         \"Memory in Mb\": 6.491543769836426,         \"Time in s\": 4874.277407999999       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6152108982297662,         \"MicroF1\": 0.6152108982297662,         \"MacroF1\": 0.5959760515786451,         \"Memory in Mb\": 5.97607421875,         \"Time in s\": 5593.125681999999       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6060339579246432,         \"MicroF1\": 0.6060339579246432,         \"MacroF1\": 0.5869142505177357,         \"Memory in Mb\": 6.496403694152832,         \"Time in s\": 6355.050274999999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5713744554580465,         \"MicroF1\": 0.5713744554580465,         \"MacroF1\": 0.5537658591956377,         \"Memory in Mb\": 6.4967546463012695,         \"Time in s\": 7160.569073999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.545546019532406,         \"MicroF1\": 0.545546019532406,         \"MacroF1\": 0.5286479939306438,         \"Memory in Mb\": 6.381303787231445,         \"Time in s\": 8010.073855999999       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.526767311013314,         \"MicroF1\": 0.526767311013314,         \"MacroF1\": 0.509587529402725,         \"Memory in Mb\": 6.497265815734863,         \"Time in s\": 8901.233847       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.517756615983585,         \"MicroF1\": 0.517756615983585,         \"MacroF1\": 0.4976462434137419,         \"Memory in Mb\": 4.685893058776856,         \"Time in s\": 9829.81162       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5296815032647162,         \"MicroF1\": 0.5296815032647162,         \"MacroF1\": 0.5080882715573688,         \"Memory in Mb\": 10.369908332824709,         \"Time in s\": 10791.950057       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.539750935176855,         \"MicroF1\": 0.539750935176855,         \"MacroF1\": 0.5184934777423561,         \"Memory in Mb\": 10.92272663116455,         \"Time in s\": 11801.930673       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5468771138669674,         \"MicroF1\": 0.5468771138669674,         \"MacroF1\": 0.525970977438283,         \"Memory in Mb\": 10.920933723449709,         \"Time in s\": 12856.592968       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5551633593043778,         \"MicroF1\": 0.5551633593043778,         \"MacroF1\": 0.5340735310276195,         \"Memory in Mb\": 12.231526374816896,         \"Time in s\": 13957.460176       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5615761518507844,         \"MicroF1\": 0.5615761518507844,         \"MacroF1\": 0.5396852076547556,         \"Memory in Mb\": 12.87682819366455,         \"Time in s\": 15100.290122       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5679280274632048,         \"MicroF1\": 0.5679280274632048,         \"MacroF1\": 0.5455634192548013,         \"Memory in Mb\": 13.528642654418944,         \"Time in s\": 16285.362621       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5727868479866661,         \"MicroF1\": 0.5727868479866661,         \"MacroF1\": 0.5496374434570932,         \"Memory in Mb\": 13.632143020629885,         \"Time in s\": 17508.053461       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5754143143325442,         \"MicroF1\": 0.5754143143325442,         \"MacroF1\": 0.5513680135969626,         \"Memory in Mb\": 13.630533218383787,         \"Time in s\": 18766.96928       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5772859598049875,         \"MicroF1\": 0.5772859598049875,         \"MacroF1\": 0.5551350356863173,         \"Memory in Mb\": 13.627862930297852,         \"Time in s\": 20061.957755000003       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.577772516657084,         \"MicroF1\": 0.577772516657084,         \"MacroF1\": 0.5590861332292512,         \"Memory in Mb\": 13.626611709594728,         \"Time in s\": 21394.440888000005       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.578225516768442,         \"MicroF1\": 0.578225516768442,         \"MacroF1\": 0.5625516131192055,         \"Memory in Mb\": 12.769641876220703,         \"Time in s\": 22755.311286000004       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5795637488557088,         \"MicroF1\": 0.5795637488557088,         \"MacroF1\": 0.5663363640160616,         \"Memory in Mb\": 12.768932342529297,         \"Time in s\": 24150.336726000005       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5811211241790133,         \"MicroF1\": 0.5811211241790133,         \"MacroF1\": 0.5696723582178381,         \"Memory in Mb\": 12.768062591552734,         \"Time in s\": 25577.802283000005       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.575804208221124,         \"MicroF1\": 0.575804208221124,         \"MacroF1\": 0.5647934119551398,         \"Memory in Mb\": 12.981294631958008,         \"Time in s\": 27039.45595800001       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5701495107182828,         \"MicroF1\": 0.5701495107182828,         \"MacroF1\": 0.559068023359177,         \"Memory in Mb\": 12.981256484985352,         \"Time in s\": 28537.493337000007       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5657744478177311,         \"MicroF1\": 0.5657744478177311,         \"MacroF1\": 0.5542573482740075,         \"Memory in Mb\": 12.983362197875977,         \"Time in s\": 30069.70246600001       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5611894261208366,         \"MicroF1\": 0.5611894261208366,         \"MacroF1\": 0.5493152777162592,         \"Memory in Mb\": 13.52482795715332,         \"Time in s\": 31635.746830000007       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.558779429172695,         \"MicroF1\": 0.558779429172695,         \"MacroF1\": 0.5463982360776033,         \"Memory in Mb\": 13.526559829711914,         \"Time in s\": 33235.41425300001       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5546825010877633,         \"MicroF1\": 0.5546825010877633,         \"MacroF1\": 0.5426283860139581,         \"Memory in Mb\": 14.304903030395508,         \"Time in s\": 34865.73115700001       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5542153662122761,         \"MicroF1\": 0.5542153662122761,         \"MacroF1\": 0.5429626632180721,         \"Memory in Mb\": 15.152182579040527,         \"Time in s\": 36525.24862800001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5541364155112547,         \"MicroF1\": 0.5541364155112547,         \"MacroF1\": 0.5435420562964655,         \"Memory in Mb\": 15.252725601196287,         \"Time in s\": 38211.297236000006       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5542981604678141,         \"MicroF1\": 0.5542981604678141,         \"MacroF1\": 0.544391400018036,         \"Memory in Mb\": 15.251314163208008,         \"Time in s\": 39923.86574200001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.554151749624668,         \"MicroF1\": 0.554151749624668,         \"MacroF1\": 0.5448486588729107,         \"Memory in Mb\": 13.424749374389648,         \"Time in s\": 41664.47056700001       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5536290049829767,         \"MicroF1\": 0.5536290049829767,         \"MacroF1\": 0.5448029815059025,         \"Memory in Mb\": 13.64866542816162,         \"Time in s\": 43429.160590000014       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5541436342414165,         \"MicroF1\": 0.5541436342414165,         \"MacroF1\": 0.5454957405719211,         \"Memory in Mb\": 14.18911075592041,         \"Time in s\": 45215.90786900002       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5553020683124207,         \"MicroF1\": 0.5553020683124207,         \"MacroF1\": 0.546961663735647,         \"Memory in Mb\": 15.156656265258787,         \"Time in s\": 47024.86244100002       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5579662871693428,         \"MicroF1\": 0.5579662871693428,         \"MacroF1\": 0.5498636684303295,         \"Memory in Mb\": 14.218542098999023,         \"Time in s\": 48856.78116300002       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5627586206896552,         \"MicroF1\": 0.5627586206896552,         \"MacroF1\": 0.5545030394801858,         \"Memory in Mb\": 14.845645904541016,         \"Time in s\": 50711.77001700002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5677701436602124,         \"MicroF1\": 0.5677701436602124,         \"MacroF1\": 0.5591808574875289,         \"Memory in Mb\": 15.233248710632324,         \"Time in s\": 52589.43568900003       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5730463432438297,         \"MicroF1\": 0.5730463432438297,         \"MacroF1\": 0.5639878919164368,         \"Memory in Mb\": 15.890131950378418,         \"Time in s\": 54487.48381000003       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5791894555785324,         \"MicroF1\": 0.5791894555785324,         \"MacroF1\": 0.5695807960578061,         \"Memory in Mb\": 16.1916446685791,         \"Time in s\": 56406.77001200003       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5794427924771303,         \"MicroF1\": 0.5794427924771303,         \"MacroF1\": 0.5701512686040561,         \"Memory in Mb\": 15.30721950531006,         \"Time in s\": 58342.70756100003       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5794652487369197,         \"MicroF1\": 0.5794652487369197,         \"MacroF1\": 0.5701984940722999,         \"Memory in Mb\": 15.30735683441162,         \"Time in s\": 60279.413314000034       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 2.100947380065918,         \"Time in s\": 5.705318       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943558282208589,         \"MicroF1\": 0.943558282208589,         \"MacroF1\": 0.7669956277713079,         \"Memory in Mb\": 3.048105239868164,         \"Time in s\": 25.352994       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8912510220768601,         \"MicroF1\": 0.8912510220768601,         \"MacroF1\": 0.8617021305177772,         \"Memory in Mb\": 3.9921913146972656,         \"Time in s\": 63.032035       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9031269160024524,         \"MicroF1\": 0.9031269160024524,         \"MacroF1\": 0.8868998230762756,         \"Memory in Mb\": 4.944231986999512,         \"Time in s\": 123.600275       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.898970083374203,         \"MicroF1\": 0.898970083374203,         \"MacroF1\": 0.888705938214812,         \"Memory in Mb\": 5.993730545043945,         \"Time in s\": 211.611387       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8598283612586841,         \"MicroF1\": 0.8598283612586841,         \"MacroF1\": 0.8569666636755086,         \"Memory in Mb\": 6.37155818939209,         \"Time in s\": 330.620683       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8669001751313485,         \"MicroF1\": 0.8669001751313484,         \"MacroF1\": 0.8547854134985733,         \"Memory in Mb\": 7.318934440612793,         \"Time in s\": 479.663409       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8581060373889059,         \"MicroF1\": 0.8581060373889059,         \"MacroF1\": 0.8327540420876277,         \"Memory in Mb\": 8.264909744262695,         \"Time in s\": 660.474615       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8490874421138654,         \"MicroF1\": 0.8490874421138654,         \"MacroF1\": 0.8463961237855363,         \"Memory in Mb\": 8.926459312438965,         \"Time in s\": 875.371562       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8421181662172101,         \"MicroF1\": 0.84211816621721,         \"MacroF1\": 0.8299816031455575,         \"Memory in Mb\": 10.074895858764648,         \"Time in s\": 1125.854174       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8301760641854246,         \"MicroF1\": 0.8301760641854244,         \"MacroF1\": 0.8400819204125556,         \"Memory in Mb\": 11.044804573059082,         \"Time in s\": 1412.144879       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8314606741573034,         \"MicroF1\": 0.8314606741573034,         \"MacroF1\": 0.8387821748480373,         \"Memory in Mb\": 8.862092971801758,         \"Time in s\": 1728.261118       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8333019045823119,         \"MicroF1\": 0.8333019045823119,         \"MacroF1\": 0.8299513887279447,         \"Memory in Mb\": 9.715648651123049,         \"Time in s\": 2074.079546       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8255997198389073,         \"MicroF1\": 0.8255997198389075,         \"MacroF1\": 0.831498100235552,         \"Memory in Mb\": 10.513428688049316,         \"Time in s\": 2449.732459       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8241542735741134,         \"MicroF1\": 0.8241542735741134,         \"MacroF1\": 0.813971923025991,         \"Memory in Mb\": 11.555256843566896,         \"Time in s\": 2856.066254       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8043511567335683,         \"MicroF1\": 0.8043511567335683,         \"MacroF1\": 0.8048077550156274,         \"Memory in Mb\": 12.298343658447266,         \"Time in s\": 3295.046229       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8002883922134102,         \"MicroF1\": 0.8002883922134101,         \"MacroF1\": 0.8062362865697692,         \"Memory in Mb\": 11.726844787597656,         \"Time in s\": 3768.473318       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8093422306959008,         \"MicroF1\": 0.8093422306959008,         \"MacroF1\": 0.813125473572493,         \"Memory in Mb\": 9.433514595031738,         \"Time in s\": 4267.304275       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8157657076506257,         \"MicroF1\": 0.8157657076506257,         \"MacroF1\": 0.8184378776785012,         \"Memory in Mb\": 10.539642333984377,         \"Time in s\": 4791.9677950000005       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8199534256649099,         \"MicroF1\": 0.81995342566491,         \"MacroF1\": 0.8213128379144453,         \"Memory in Mb\": 11.364830017089844,         \"Time in s\": 5345.1630700000005       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8247928096183028,         \"MicroF1\": 0.8247928096183028,         \"MacroF1\": 0.8275146627418534,         \"Memory in Mb\": 12.672901153564451,         \"Time in s\": 5929.400246       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8295264623955432,         \"MicroF1\": 0.8295264623955433,         \"MacroF1\": 0.8318915513040454,         \"Memory in Mb\": 13.833264350891112,         \"Time in s\": 6547.972923       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8319300863263348,         \"MicroF1\": 0.8319300863263348,         \"MacroF1\": 0.8336463894938194,         \"Memory in Mb\": 14.741169929504396,         \"Time in s\": 7204.877164       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8342355224185476,         \"MicroF1\": 0.8342355224185476,         \"MacroF1\": 0.8362542817352725,         \"Memory in Mb\": 16.02083969116211,         \"Time in s\": 7900.921468       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8343955289734287,         \"MicroF1\": 0.8343955289734286,         \"MacroF1\": 0.833886744496364,         \"Memory in Mb\": 17.251243591308594,         \"Time in s\": 8638.28045       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8258697086829452,         \"MicroF1\": 0.8258697086829452,         \"MacroF1\": 0.823298887298616,         \"Memory in Mb\": 18.484009742736816,         \"Time in s\": 9418.818077       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.825692237857467,         \"MicroF1\": 0.825692237857467,         \"MacroF1\": 0.827229896548608,         \"Memory in Mb\": 17.053231239318848,         \"Time in s\": 10241.512178       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8263153287227524,         \"MicroF1\": 0.8263153287227524,         \"MacroF1\": 0.8251000136898328,         \"Memory in Mb\": 18.097841262817383,         \"Time in s\": 11105.510711       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8257966359563857,         \"MicroF1\": 0.8257966359563859,         \"MacroF1\": 0.8251092059206939,         \"Memory in Mb\": 19.1904354095459,         \"Time in s\": 12012.672379       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8289892965111528,         \"MicroF1\": 0.8289892965111528,         \"MacroF1\": 0.8300645161883343,         \"Memory in Mb\": 17.2155818939209,         \"Time in s\": 12963.554855000002       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8324503834901558,         \"MicroF1\": 0.8324503834901558,         \"MacroF1\": 0.8328446288662702,         \"Memory in Mb\": 17.090572357177734,         \"Time in s\": 13955.688030000005       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8295672156261968,         \"MicroF1\": 0.8295672156261968,         \"MacroF1\": 0.8279815503081916,         \"Memory in Mb\": 18.284998893737797,         \"Time in s\": 14990.870602000005       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.828121518235163,         \"MicroF1\": 0.828121518235163,         \"MacroF1\": 0.8279872572314477,         \"Memory in Mb\": 18.759904861450195,         \"Time in s\": 16071.989638000005       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8300771393554899,         \"MicroF1\": 0.8300771393554899,         \"MacroF1\": 0.8300312724960401,         \"Memory in Mb\": 19.937789916992188,         \"Time in s\": 17198.739284000003       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8329714966034036,         \"MicroF1\": 0.8329714966034036,         \"MacroF1\": 0.8330653900337638,         \"Memory in Mb\": 21.17230033874512,         \"Time in s\": 18373.548754000003       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8360454823994008,         \"MicroF1\": 0.8360454823994008,         \"MacroF1\": 0.8362319050195895,         \"Memory in Mb\": 22.12139320373535,         \"Time in s\": 19591.296889000005       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8391520370983769,         \"MicroF1\": 0.8391520370983769,         \"MacroF1\": 0.8393677597260801,         \"Memory in Mb\": 22.688467979431152,         \"Time in s\": 20852.364231000003       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8400309617493389,         \"MicroF1\": 0.8400309617493388,         \"MacroF1\": 0.8398031059873,         \"Memory in Mb\": 23.806550979614254,         \"Time in s\": 22157.639176000004       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8335114072025642,         \"MicroF1\": 0.8335114072025642,         \"MacroF1\": 0.8310693286634668,         \"Memory in Mb\": 25.06292724609375,         \"Time in s\": 23499.792247000005       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8283595808566702,         \"MicroF1\": 0.8283595808566702,         \"MacroF1\": 0.826721014765785,         \"Memory in Mb\": 26.060873985290527,         \"Time in s\": 24886.958001000006       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.826747175225683,         \"MicroF1\": 0.8267471752256829,         \"MacroF1\": 0.8259678903415486,         \"Memory in Mb\": 27.245673179626465,         \"Time in s\": 26317.085564000008       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.821943390720747,         \"MicroF1\": 0.821943390720747,         \"MacroF1\": 0.8202405231953956,         \"Memory in Mb\": 28.675668716430664,         \"Time in s\": 27793.820666000007       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8182180926865417,         \"MicroF1\": 0.8182180926865417,         \"MacroF1\": 0.8170173651382093,         \"Memory in Mb\": 29.74225902557373,         \"Time in s\": 29319.213378000008       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.81878446883182,         \"MicroF1\": 0.81878446883182,         \"MacroF1\": 0.8179349229322325,         \"Memory in Mb\": 30.87984085083008,         \"Time in s\": 30892.43160700001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.821123154855929,         \"MicroF1\": 0.821123154855929,         \"MacroF1\": 0.8204502524156659,         \"Memory in Mb\": 32.019548416137695,         \"Time in s\": 32513.434007000007       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8235200085256035,         \"MicroF1\": 0.8235200085256035,         \"MacroF1\": 0.8229965581236837,         \"Memory in Mb\": 33.157379150390625,         \"Time in s\": 34181.929457000006       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.819973924380704,         \"MicroF1\": 0.819973924380704,         \"MacroF1\": 0.8189812465563673,         \"Memory in Mb\": 34.295823097229004,         \"Time in s\": 35895.74071300001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.821733135883164,         \"MicroF1\": 0.821733135883164,         \"MacroF1\": 0.8211010404575377,         \"Memory in Mb\": 35.31475067138672,         \"Time in s\": 37655.039070000006       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8188684908208694,         \"MicroF1\": 0.8188684908208694,         \"MacroF1\": 0.8180458262517715,         \"Memory in Mb\": 36.57470226287842,         \"Time in s\": 39458.702899       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.816559635276239,         \"MicroF1\": 0.816559635276239,         \"MacroF1\": 0.8159075588016685,         \"Memory in Mb\": 37.85576725006104,         \"Time in s\": 41308.014952000005       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1111111111111111,         \"MicroF1\": 0.1111111111111111,         \"MacroF1\": 0.0815018315018315,         \"Memory in Mb\": 3.4160032272338867,         \"Time in s\": 1.208286       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.2307692307692307,         \"MicroF1\": 0.2307692307692307,         \"MacroF1\": 0.2226391771283412,         \"Memory in Mb\": 4.099128723144531,         \"Time in s\": 4.553382       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4233576642335766,         \"MicroF1\": 0.4233576642335766,         \"MacroF1\": 0.4463537718619156,         \"Memory in Mb\": 4.099002838134766,         \"Time in s\": 10.351245       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5355191256830601,         \"MicroF1\": 0.5355191256830601,         \"MacroF1\": 0.5617062146473912,         \"Memory in Mb\": 4.099178314208984,         \"Time in s\": 18.765494       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5938864628820961,         \"MicroF1\": 0.5938864628820961,         \"MacroF1\": 0.6236530662596055,         \"Memory in Mb\": 4.099166870117188,         \"Time in s\": 30.180838       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6290909090909091,         \"MicroF1\": 0.6290909090909091,         \"MacroF1\": 0.6558170665459355,         \"Memory in Mb\": 4.099109649658203,         \"Time in s\": 44.412895       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.660436137071651,         \"MicroF1\": 0.660436137071651,         \"MacroF1\": 0.678574720261515,         \"Memory in Mb\": 4.098438262939453,         \"Time in s\": 61.214822       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6920980926430518,         \"MicroF1\": 0.6920980926430518,         \"MacroF1\": 0.7041680355881775,         \"Memory in Mb\": 4.0984954833984375,         \"Time in s\": 80.427396       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7167070217917676,         \"MicroF1\": 0.7167070217917676,         \"MacroF1\": 0.7259075149442813,         \"Memory in Mb\": 4.097980499267578,         \"Time in s\": 102.254145       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7254901960784313,         \"MicroF1\": 0.7254901960784313,         \"MacroF1\": 0.7325011710849479,         \"Memory in Mb\": 4.098300933837891,         \"Time in s\": 127.091256       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7386138613861386,         \"MicroF1\": 0.7386138613861386,         \"MacroF1\": 0.7428621938273078,         \"Memory in Mb\": 4.098552703857422,         \"Time in s\": 154.35838199999998       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7422867513611615,         \"MicroF1\": 0.7422867513611615,         \"MacroF1\": 0.7453719085253248,         \"Memory in Mb\": 4.098358154296875,         \"Time in s\": 184.303693       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7487437185929648,         \"MicroF1\": 0.7487437185929648,         \"MacroF1\": 0.7504522188790486,         \"Memory in Mb\": 4.098468780517578,         \"Time in s\": 216.734559       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7465007776049767,         \"MicroF1\": 0.7465007776049767,         \"MacroF1\": 0.7482323503576439,         \"Memory in Mb\": 4.098541259765625,         \"Time in s\": 252.139078       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7489114658925979,         \"MicroF1\": 0.748911465892598,         \"MacroF1\": 0.7488472102580618,         \"Memory in Mb\": 4.098594665527344,         \"Time in s\": 290.044846       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7523809523809524,         \"MicroF1\": 0.7523809523809524,         \"MacroF1\": 0.7518283723099097,         \"Memory in Mb\": 4.098430633544922,         \"Time in s\": 330.661237       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7541613316261203,         \"MicroF1\": 0.7541613316261204,         \"MacroF1\": 0.7531089046321314,         \"Memory in Mb\": 4.098361968994141,         \"Time in s\": 373.819675       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7557436517533253,         \"MicroF1\": 0.7557436517533253,         \"MacroF1\": 0.7552013614952863,         \"Memory in Mb\": 4.098308563232422,         \"Time in s\": 419.6867600000001       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7617411225658648,         \"MicroF1\": 0.7617411225658649,         \"MacroF1\": 0.7601066395856337,         \"Memory in Mb\": 4.098381042480469,         \"Time in s\": 467.91378800000007       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.763873775843308,         \"MicroF1\": 0.763873775843308,         \"MacroF1\": 0.7623480483274478,         \"Memory in Mb\": 4.098400115966797,         \"Time in s\": 518.622959       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7678756476683938,         \"MicroF1\": 0.7678756476683938,         \"MacroF1\": 0.7646598072570266,         \"Memory in Mb\": 4.098423004150391,         \"Time in s\": 571.868767       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7705242334322453,         \"MicroF1\": 0.7705242334322453,         \"MacroF1\": 0.7668271197983111,         \"Memory in Mb\": 4.098529815673828,         \"Time in s\": 627.754669       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7757805108798487,         \"MicroF1\": 0.7757805108798487,         \"MacroF1\": 0.7714920336037777,         \"Memory in Mb\": 4.098388671875,         \"Time in s\": 686.3790640000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7760652765185857,         \"MicroF1\": 0.7760652765185856,         \"MacroF1\": 0.7719206139767609,         \"Memory in Mb\": 4.098537445068359,         \"Time in s\": 747.748718       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7789382071366405,         \"MicroF1\": 0.7789382071366405,         \"MacroF1\": 0.7750313949659527,         \"Memory in Mb\": 4.098442077636719,         \"Time in s\": 811.629001       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7849372384937239,         \"MicroF1\": 0.7849372384937239,         \"MacroF1\": 0.7820003890472508,         \"Memory in Mb\": 4.098487854003906,         \"Time in s\": 878.060098       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7856567284448026,         \"MicroF1\": 0.7856567284448026,         \"MacroF1\": 0.7827470902102026,         \"Memory in Mb\": 4.098438262939453,         \"Time in s\": 947.241797       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7894327894327894,         \"MicroF1\": 0.7894327894327894,         \"MacroF1\": 0.785982924599392,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 1018.793017       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7906976744186046,         \"MicroF1\": 0.7906976744186046,         \"MacroF1\": 0.7876424482584368,         \"Memory in Mb\": 4.098438262939453,         \"Time in s\": 1093.078269       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7933284989122552,         \"MicroF1\": 0.7933284989122552,         \"MacroF1\": 0.7906471924204205,         \"Memory in Mb\": 4.098392486572266,         \"Time in s\": 1169.7117919999998       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7978947368421052,         \"MicroF1\": 0.7978947368421052,         \"MacroF1\": 0.7945020166797493,         \"Memory in Mb\": 4.098480224609375,         \"Time in s\": 1248.577134       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8028552005438477,         \"MicroF1\": 0.8028552005438477,         \"MacroF1\": 0.7982243751921434,         \"Memory in Mb\": 4.098472595214844,         \"Time in s\": 1329.680821       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8035596572181938,         \"MicroF1\": 0.8035596572181938,         \"MacroF1\": 0.7981876534181912,         \"Memory in Mb\": 4.098491668701172,         \"Time in s\": 1413.4983189999998       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8035828534868842,         \"MicroF1\": 0.8035828534868842,         \"MacroF1\": 0.798634974540431,         \"Memory in Mb\": 4.098518371582031,         \"Time in s\": 1499.905218       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048477315102548,         \"MicroF1\": 0.8048477315102549,         \"MacroF1\": 0.7997380784882049,         \"Memory in Mb\": 4.098381042480469,         \"Time in s\": 1588.836817       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8066465256797583,         \"MicroF1\": 0.8066465256797583,         \"MacroF1\": 0.80161945439383,         \"Memory in Mb\": 4.098377227783203,         \"Time in s\": 1680.249514       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8059964726631393,         \"MicroF1\": 0.8059964726631393,         \"MacroF1\": 0.8024858564723997,         \"Memory in Mb\": 4.098514556884766,         \"Time in s\": 1774.345382       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8070978820835718,         \"MicroF1\": 0.8070978820835718,         \"MacroF1\": 0.8029124203507955,         \"Memory in Mb\": 4.098423004150391,         \"Time in s\": 1871.065136       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8081427774679308,         \"MicroF1\": 0.8081427774679307,         \"MacroF1\": 0.8029834045630979,         \"Memory in Mb\": 4.098461151123047,         \"Time in s\": 1970.03779       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8069603045133225,         \"MicroF1\": 0.8069603045133223,         \"MacroF1\": 0.801927622716254,         \"Memory in Mb\": 4.098594665527344,         \"Time in s\": 2071.588776       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8053050397877984,         \"MicroF1\": 0.8053050397877984,         \"MacroF1\": 0.8006727596367825,         \"Memory in Mb\": 4.098400115966797,         \"Time in s\": 2175.772942       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8047643707923355,         \"MicroF1\": 0.8047643707923355,         \"MacroF1\": 0.7995493059800365,         \"Memory in Mb\": 4.098396301269531,         \"Time in s\": 2282.41088       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8057663125948407,         \"MicroF1\": 0.8057663125948407,         \"MacroF1\": 0.8003960406612564,         \"Memory in Mb\": 4.098434448242188,         \"Time in s\": 2391.59611       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8072170044488384,         \"MicroF1\": 0.8072170044488384,         \"MacroF1\": 0.8005625942078284,         \"Memory in Mb\": 4.098430633544922,         \"Time in s\": 2503.16971       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8066698888351861,         \"MicroF1\": 0.8066698888351861,         \"MacroF1\": 0.8002110568368,         \"Memory in Mb\": 4.098316192626953,         \"Time in s\": 2617.3694960000003       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.807565011820331,         \"MicroF1\": 0.807565011820331,         \"MacroF1\": 0.8005131307885663,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 2733.922308       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8079592781119852,         \"MicroF1\": 0.8079592781119852,         \"MacroF1\": 0.8006755955605837,         \"Memory in Mb\": 4.098320007324219,         \"Time in s\": 2852.747139       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8087902129587675,         \"MicroF1\": 0.8087902129587675,         \"MacroF1\": 0.8009921695193862,         \"Memory in Mb\": 4.098320007324219,         \"Time in s\": 2973.740681       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8060363959165557,         \"MicroF1\": 0.8060363959165557,         \"MacroF1\": 0.7987732120640717,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 3097.6149410000003       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8051326663766856,         \"MicroF1\": 0.8051326663766856,         \"MacroF1\": 0.798077892809675,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 3223.9293610000004       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8046773495019489,         \"MicroF1\": 0.8046773495019489,         \"MacroF1\": 0.7977695866822911,         \"Memory in Mb\": 4.098388671875,         \"Time in s\": 3350.8763620000004       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360189573459716,         \"MicroF1\": 0.6360189573459716,         \"MacroF1\": 0.5992691812827112,         \"Memory in Mb\": 6.474042892456055,         \"Time in s\": 88.969298       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6110847939365229,         \"MicroF1\": 0.6110847939365229,         \"MacroF1\": 0.5773210074897359,         \"Memory in Mb\": 6.473905563354492,         \"Time in s\": 256.045675       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043574360593622,         \"MicroF1\": 0.6043574360593622,         \"MacroF1\": 0.5704368753709179,         \"Memory in Mb\": 6.473470687866211,         \"Time in s\": 489.83548       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6014681506038362,         \"MicroF1\": 0.6014681506038362,         \"MacroF1\": 0.5676969561642586,         \"Memory in Mb\": 6.473196029663086,         \"Time in s\": 783.3519140000001       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6057965523773442,         \"MicroF1\": 0.6057965523773442,         \"MacroF1\": 0.5710016183775801,         \"Memory in Mb\": 6.473196029663086,         \"Time in s\": 1130.801617       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5966850828729282,         \"MicroF1\": 0.5966850828729282,         \"MacroF1\": 0.5635903588556204,         \"Memory in Mb\": 6.473356246948242,         \"Time in s\": 1527.884634       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5957245298335814,         \"MicroF1\": 0.5957245298335814,         \"MacroF1\": 0.5625002603439991,         \"Memory in Mb\": 6.473814010620117,         \"Time in s\": 1971.450931       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5982005445720374,         \"MicroF1\": 0.5982005445720374,         \"MacroF1\": 0.5646892369665863,         \"Memory in Mb\": 6.474157333374023,         \"Time in s\": 2461.351589       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.596337998526781,         \"MicroF1\": 0.596337998526781,         \"MacroF1\": 0.5627085514562804,         \"Memory in Mb\": 6.47450065612793,         \"Time in s\": 2997.75158       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5965527038545316,         \"MicroF1\": 0.5965527038545316,         \"MacroF1\": 0.5631320282838163,         \"Memory in Mb\": 6.47468376159668,         \"Time in s\": 3580.189969       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5953508394317693,         \"MicroF1\": 0.5953508394317693,         \"MacroF1\": 0.562671447170627,         \"Memory in Mb\": 6.47468376159668,         \"Time in s\": 4209.202801       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5979796385447084,         \"MicroF1\": 0.5979796385447084,         \"MacroF1\": 0.5680559575776837,         \"Memory in Mb\": 6.474340438842773,         \"Time in s\": 4886.872526       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.610767101333139,         \"MicroF1\": 0.610767101333139,         \"MacroF1\": 0.5941277335666079,         \"Memory in Mb\": 6.473836898803711,         \"Time in s\": 5609.570035       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6019752418318338,         \"MicroF1\": 0.6019752418318338,         \"MacroF1\": 0.5851264744797859,         \"Memory in Mb\": 6.473745346069336,         \"Time in s\": 6378.998584999999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5705536965717533,         \"MicroF1\": 0.5705536965717533,         \"MacroF1\": 0.5545059657048704,         \"Memory in Mb\": 6.473974227905273,         \"Time in s\": 7193.860588999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.548091151228174,         \"MicroF1\": 0.548091151228174,         \"MacroF1\": 0.5320735507355622,         \"Memory in Mb\": 6.474386215209961,         \"Time in s\": 8051.816493999999       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5307225224221492,         \"MicroF1\": 0.5307225224221492,         \"MacroF1\": 0.5138536287616571,         \"Memory in Mb\": 6.474637985229492,         \"Time in s\": 8952.059017       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5182827379386542,         \"MicroF1\": 0.5182827379386542,         \"MacroF1\": 0.4990809738484312,         \"Memory in Mb\": 6.47486686706543,         \"Time in s\": 9893.706361       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5182176145142801,         \"MicroF1\": 0.5182176145142801,         \"MacroF1\": 0.497867701567998,         \"Memory in Mb\": 8.642622947692871,         \"Time in s\": 10881.966771       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5272503432927695,         \"MicroF1\": 0.5272503432927695,         \"MacroF1\": 0.5067114684709674,         \"Memory in Mb\": 15.437758445739746,         \"Time in s\": 11917.076256       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.533032694475761,         \"MicroF1\": 0.533032694475761,         \"MacroF1\": 0.5127471323280748,         \"Memory in Mb\": 16.81709384918213,         \"Time in s\": 12999.258268       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5410442942619775,         \"MicroF1\": 0.5410442942619775,         \"MacroF1\": 0.5207771198745245,         \"Memory in Mb\": 17.041016578674316,         \"Time in s\": 14124.681759       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5459710956478775,         \"MicroF1\": 0.5459710956478775,         \"MacroF1\": 0.5251711652768184,         \"Memory in Mb\": 17.038064002990723,         \"Time in s\": 15290.808705       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5532099593576135,         \"MicroF1\": 0.5532099593576135,         \"MacroF1\": 0.5314216535856217,         \"Memory in Mb\": 17.036622047424316,         \"Time in s\": 16491.42669       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5607788173794462,         \"MicroF1\": 0.5607788173794462,         \"MacroF1\": 0.5375130024626694,         \"Memory in Mb\": 17.14900016784668,         \"Time in s\": 17723.115047       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5667091604443635,         \"MicroF1\": 0.5667091604443635,         \"MacroF1\": 0.5418496825562071,         \"Memory in Mb\": 17.261820793151855,         \"Time in s\": 18986.675501       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5692890463329943,         \"MicroF1\": 0.5692890463329943,         \"MacroF1\": 0.5455529487931667,         \"Memory in Mb\": 17.26294231414795,         \"Time in s\": 20283.404341       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5688436432509216,         \"MicroF1\": 0.5688436432509216,         \"MacroF1\": 0.5481992899375988,         \"Memory in Mb\": 17.26337718963623,         \"Time in s\": 21617.011828       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5687228553701467,         \"MicroF1\": 0.5687228553701467,         \"MacroF1\": 0.5505043481720591,         \"Memory in Mb\": 17.26330852508545,         \"Time in s\": 22980.237824       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5691467533697402,         \"MicroF1\": 0.5691467533697402,         \"MacroF1\": 0.5529220328647554,         \"Memory in Mb\": 17.262804985046387,         \"Time in s\": 24378.053405       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5703986558729189,         \"MicroF1\": 0.5703986558729189,         \"MacroF1\": 0.5556828084411201,         \"Memory in Mb\": 17.262507438659668,         \"Time in s\": 25809.039082       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5650025154626972,         \"MicroF1\": 0.5650025154626972,         \"MacroF1\": 0.5507695387439543,         \"Memory in Mb\": 17.487704277038574,         \"Time in s\": 27274.413314       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5587281545039745,         \"MicroF1\": 0.5587281545039745,         \"MacroF1\": 0.5445349362041821,         \"Memory in Mb\": 17.929275512695312,         \"Time in s\": 28775.770082       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5541876723393588,         \"MicroF1\": 0.5541876723393588,         \"MacroF1\": 0.5396635045593164,         \"Memory in Mb\": 18.030207633972168,         \"Time in s\": 30311.158157       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.549122000054114,         \"MicroF1\": 0.549122000054114,         \"MacroF1\": 0.5343517375956978,         \"Memory in Mb\": 19.681435585021973,         \"Time in s\": 31880.342243       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5473628830724714,         \"MicroF1\": 0.5473628830724714,         \"MacroF1\": 0.5321033552605493,         \"Memory in Mb\": 21.71814346313477,         \"Time in s\": 33482.866823       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5426787131120269,         \"MicroF1\": 0.5426787131120269,         \"MacroF1\": 0.52803892360078,         \"Memory in Mb\": 22.487850189208984,         \"Time in s\": 35116.535538       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5419293742367982,         \"MicroF1\": 0.5419293742367982,         \"MacroF1\": 0.5284857300708793,         \"Memory in Mb\": 23.76238250732422,         \"Time in s\": 36778.160189       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5417769467984362,         \"MicroF1\": 0.5417769467984362,         \"MacroF1\": 0.5296361895775551,         \"Memory in Mb\": 23.860816955566406,         \"Time in s\": 38464.674338       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5422240109851085,         \"MicroF1\": 0.5422240109851085,         \"MacroF1\": 0.5313111510734391,         \"Memory in Mb\": 24.196860313415527,         \"Time in s\": 40175.576413       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5444970550871925,         \"MicroF1\": 0.5444970550871925,         \"MacroF1\": 0.5344195798463859,         \"Memory in Mb\": 24.296037673950195,         \"Time in s\": 41906.99288399999       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5463461928705102,         \"MicroF1\": 0.5463461928705102,         \"MacroF1\": 0.5369578677381479,         \"Memory in Mb\": 24.84640598297119,         \"Time in s\": 43659.477485       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5482855066399454,         \"MicroF1\": 0.5482855066399454,         \"MacroF1\": 0.5392181145139481,         \"Memory in Mb\": 25.28636360168457,         \"Time in s\": 45430.571796       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5506532079288896,         \"MicroF1\": 0.5506532079288896,         \"MacroF1\": 0.5419048601727473,         \"Memory in Mb\": 25.498303413391117,         \"Time in s\": 47220.970484       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5514846692901787,         \"MicroF1\": 0.5514846692901787,         \"MacroF1\": 0.5429796926051395,         \"Memory in Mb\": 25.82335567474365,         \"Time in s\": 49033.125826       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5515388574369532,         \"MicroF1\": 0.5515388574369532,         \"MacroF1\": 0.543031483592694,         \"Memory in Mb\": 25.821112632751465,         \"Time in s\": 50867.580247       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.551953416211642,         \"MicroF1\": 0.551953416211642,         \"MacroF1\": 0.5433574148660688,         \"Memory in Mb\": 26.09889411926269,         \"Time in s\": 52723.913942       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5563359441276856,         \"MicroF1\": 0.5563359441276856,         \"MacroF1\": 0.5472854805195191,         \"Memory in Mb\": 26.366034507751465,         \"Time in s\": 54600.511787       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5623562607502464,         \"MicroF1\": 0.5623562607502464,         \"MacroF1\": 0.552981536157949,         \"Memory in Mb\": 27.10032081604004,         \"Time in s\": 56496.789585       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5634576412432054,         \"MicroF1\": 0.5634576412432054,         \"MacroF1\": 0.5545218292020726,         \"Memory in Mb\": 27.943178176879883,         \"Time in s\": 58415.671716       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5635324616345299,         \"MicroF1\": 0.5635324616345299,         \"MacroF1\": 0.5546220283668154,         \"Memory in Mb\": 27.942995071411133,         \"Time in s\": 60335.727696       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9877149877149876,         \"MicroF1\": 0.9877149877149876,         \"MacroF1\": 0.7696139476961394,         \"Memory in Mb\": 2.1207275390625,         \"Time in s\": 4.211814       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.988957055214724,         \"MicroF1\": 0.988957055214724,         \"MacroF1\": 0.9592655637573824,         \"Memory in Mb\": 2.9369373321533203,         \"Time in s\": 23.739993       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.983646770237122,         \"MicroF1\": 0.983646770237122,         \"MacroF1\": 0.9326470331192014,         \"Memory in Mb\": 4.590028762817383,         \"Time in s\": 86.527265       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828326180257512,         \"MicroF1\": 0.9828326180257512,         \"MacroF1\": 0.9594506659780556,         \"Memory in Mb\": 5.819695472717285,         \"Time in s\": 184.232691       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9705738106915156,         \"MicroF1\": 0.9705738106915156,         \"MacroF1\": 0.9304838721924584,         \"Memory in Mb\": 8.549582481384277,         \"Time in s\": 323.308574       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9607682876992236,         \"MicroF1\": 0.9607682876992236,         \"MacroF1\": 0.9455756842664336,         \"Memory in Mb\": 10.061903953552246,         \"Time in s\": 491.40663400000005       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9541155866900176,         \"MicroF1\": 0.9541155866900176,         \"MacroF1\": 0.9254688528922778,         \"Memory in Mb\": 12.678574562072754,         \"Time in s\": 687.150288       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943610174685872,         \"MicroF1\": 0.943610174685872,         \"MacroF1\": 0.9191430707434156,         \"Memory in Mb\": 16.086813926696777,         \"Time in s\": 906.458431       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9403432307273224,         \"MicroF1\": 0.9403432307273224,         \"MacroF1\": 0.9284235615798526,         \"Memory in Mb\": 18.255277633666992,         \"Time in s\": 1151.002639       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9338073057121844,         \"MicroF1\": 0.9338073057121844,         \"MacroF1\": 0.918242970538206,         \"Memory in Mb\": 21.65336036682129,         \"Time in s\": 1427.8669570000002       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9318029864051705,         \"MicroF1\": 0.9318029864051705,         \"MacroF1\": 0.9319119487505448,         \"Memory in Mb\": 22.76876544952393,         \"Time in s\": 1733.8095280000002       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9317671092951992,         \"MicroF1\": 0.9317671092951992,         \"MacroF1\": 0.9296889978700974,         \"Memory in Mb\": 24.966033935546875,         \"Time in s\": 2062.8329900000003       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9281538751650008,         \"MicroF1\": 0.9281538751650008,         \"MacroF1\": 0.919765356403914,         \"Memory in Mb\": 29.062508583068848,         \"Time in s\": 2423.731892       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9227805988443356,         \"MicroF1\": 0.9227805988443356,         \"MacroF1\": 0.9201475418022376,         \"Memory in Mb\": 32.12655830383301,         \"Time in s\": 2815.063536       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9177970256577872,         \"MicroF1\": 0.9177970256577872,         \"MacroF1\": 0.9072843264203106,         \"Memory in Mb\": 37.27707767486572,         \"Time in s\": 3238.118927       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9115979776313774,         \"MicroF1\": 0.9115979776313774,         \"MacroF1\": 0.909931232789514,         \"Memory in Mb\": 41.43412208557129,         \"Time in s\": 3706.915394       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.912905551550108,         \"MicroF1\": 0.912905551550108,         \"MacroF1\": 0.9153430596364792,         \"Memory in Mb\": 44.48411560058594,         \"Time in s\": 4207.758914       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9135230832084978,         \"MicroF1\": 0.9135230832084978,         \"MacroF1\": 0.9124682676754272,         \"Memory in Mb\": 47.44067192077637,         \"Time in s\": 4740.722969       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9121403689846472,         \"MicroF1\": 0.9121403689846472,         \"MacroF1\": 0.9121831707972876,         \"Memory in Mb\": 51.03960132598877,         \"Time in s\": 5307.755902000001       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.908689790415492,         \"MicroF1\": 0.908689790415492,         \"MacroF1\": 0.9062633734460516,         \"Memory in Mb\": 56.064818382263184,         \"Time in s\": 5918.019803000001       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.905918057663126,         \"MicroF1\": 0.905918057663126,         \"MacroF1\": 0.9058259471519292,         \"Memory in Mb\": 61.820496559143066,         \"Time in s\": 6575.962782000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9042896935933148,         \"MicroF1\": 0.9042896935933148,         \"MacroF1\": 0.9043251050138336,         \"Memory in Mb\": 64.74030494689941,         \"Time in s\": 7280.842530000002       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.901843759991474,         \"MicroF1\": 0.901843759991474,         \"MacroF1\": 0.9009662752730246,         \"Memory in Mb\": 68.81300067901611,         \"Time in s\": 8035.1031440000015       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8971504442855683,         \"MicroF1\": 0.8971504442855683,         \"MacroF1\": 0.8956423708961025,         \"Memory in Mb\": 74.25286674499512,         \"Time in s\": 8855.073479000002       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8926365329934307,         \"MicroF1\": 0.8926365329934307,         \"MacroF1\": 0.8903074227158838,         \"Memory in Mb\": 79.6785535812378,         \"Time in s\": 9744.976722000005       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8846987838220043,         \"MicroF1\": 0.8846987838220043,         \"MacroF1\": 0.8819820059100918,         \"Memory in Mb\": 85.28873825073242,         \"Time in s\": 10726.537155000004       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8791647753064004,         \"MicroF1\": 0.8791647753064004,         \"MacroF1\": 0.8795835231396919,         \"Memory in Mb\": 89.59383392333984,         \"Time in s\": 11788.438997000005       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8759520266129738,         \"MicroF1\": 0.8759520266129738,         \"MacroF1\": 0.8744149508862001,         \"Memory in Mb\": 94.86630344390868,         \"Time in s\": 12917.703791000004       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.872200152142676,         \"MicroF1\": 0.872200152142676,         \"MacroF1\": 0.8717012117300328,         \"Memory in Mb\": 100.15169906616212,         \"Time in s\": 14117.771334000005       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8736824903995425,         \"MicroF1\": 0.8736824903995425,         \"MacroF1\": 0.8749440738646468,         \"Memory in Mb\": 101.9357843399048,         \"Time in s\": 15365.994884000003       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8717482406894915,         \"MicroF1\": 0.8717482406894915,         \"MacroF1\": 0.8710221211412438,         \"Memory in Mb\": 107.46908473968506,         \"Time in s\": 16670.526324000002       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8661815396399847,         \"MicroF1\": 0.8661815396399847,         \"MacroF1\": 0.8651994621744733,         \"Memory in Mb\": 112.71690273284912,         \"Time in s\": 18043.485216       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8642204560647702,         \"MicroF1\": 0.8642204560647702,         \"MacroF1\": 0.8645487273027374,         \"Memory in Mb\": 116.56386756896973,         \"Time in s\": 19480.298866       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8619421815298104,         \"MicroF1\": 0.8619421815298104,         \"MacroF1\": 0.862314869215492,         \"Memory in Mb\": 121.52821636199953,         \"Time in s\": 20980.471725       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.859163806989285,         \"MicroF1\": 0.859163806989285,         \"MacroF1\": 0.8592780138529494,         \"Memory in Mb\": 125.80194187164308,         \"Time in s\": 22534.622977       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8591952066453326,         \"MicroF1\": 0.8591952066453326,         \"MacroF1\": 0.8604793833246808,         \"Memory in Mb\": 129.6016607284546,         \"Time in s\": 24135.768584       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8607485922490891,         \"MicroF1\": 0.8607485922490891,         \"MacroF1\": 0.8621609789956539,         \"Memory in Mb\": 132.68816757202148,         \"Time in s\": 25779.863983       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8604141133974069,         \"MicroF1\": 0.8604141133974069,         \"MacroF1\": 0.8613237595899307,         \"Memory in Mb\": 136.05841445922852,         \"Time in s\": 27480.057181       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8536861290930803,         \"MicroF1\": 0.8536861290930803,         \"MacroF1\": 0.853192144751886,         \"Memory in Mb\": 141.70578575134277,         \"Time in s\": 29254.423593       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8493167473497151,         \"MicroF1\": 0.849316747349715,         \"MacroF1\": 0.8496464102754333,         \"Memory in Mb\": 147.49746799468994,         \"Time in s\": 31089.50572       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.846296407006636,         \"MicroF1\": 0.846296407006636,         \"MacroF1\": 0.8470383589757107,         \"Memory in Mb\": 153.1935510635376,         \"Time in s\": 32973.577156       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8411438576014006,         \"MicroF1\": 0.8411438576014006,         \"MacroF1\": 0.8410396667771575,         \"Memory in Mb\": 156.28132915496826,         \"Time in s\": 34948.88082       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8365729920766117,         \"MicroF1\": 0.8365729920766117,         \"MacroF1\": 0.8367907010021001,         \"Memory in Mb\": 161.03080940246582,         \"Time in s\": 36967.519165       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8355523369171634,         \"MicroF1\": 0.8355523369171634,         \"MacroF1\": 0.8362918425397341,         \"Memory in Mb\": 166.63249397277832,         \"Time in s\": 39016.229589       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.837572852551882,         \"MicroF1\": 0.8375728525518821,         \"MacroF1\": 0.8385662484273668,         \"Memory in Mb\": 171.47760772705078,         \"Time in s\": 41092.028593       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8390259498055097,         \"MicroF1\": 0.8390259498055097,         \"MacroF1\": 0.8401126675526959,         \"Memory in Mb\": 175.70373821258545,         \"Time in s\": 43194.947864       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8376531942633637,         \"MicroF1\": 0.8376531942633637,         \"MacroF1\": 0.838676297522501,         \"Memory in Mb\": 180.87701034545896,         \"Time in s\": 45330.650988       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8390951335341879,         \"MicroF1\": 0.8390951335341879,         \"MacroF1\": 0.8403338496937821,         \"Memory in Mb\": 185.05438709259036,         \"Time in s\": 47482.84587799999       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8372767745485469,         \"MicroF1\": 0.8372767745485468,         \"MacroF1\": 0.8385640183306876,         \"Memory in Mb\": 190.1383810043335,         \"Time in s\": 49661.2352       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8347958233246727,         \"MicroF1\": 0.8347958233246727,         \"MacroF1\": 0.8360623278174891,         \"Memory in Mb\": 194.794171333313,         \"Time in s\": 51861.27850099999       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3111111111111111,         \"MicroF1\": 0.3111111111111111,         \"MacroF1\": 0.2457649726557289,         \"Memory in Mb\": 4.149084091186523,         \"Time in s\": 2.196675       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4835164835164835,         \"MicroF1\": 0.4835164835164835,         \"MacroF1\": 0.4934752395581889,         \"Memory in Mb\": 4.152299880981445,         \"Time in s\": 7.023639       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5328467153284672,         \"MicroF1\": 0.5328467153284672,         \"MacroF1\": 0.5528821792646678,         \"Memory in Mb\": 4.15202522277832,         \"Time in s\": 15.046926       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5956284153005464,         \"MicroF1\": 0.5956284153005464,         \"MacroF1\": 0.6141431648908949,         \"Memory in Mb\": 4.152608871459961,         \"Time in s\": 26.297795       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.62882096069869,         \"MicroF1\": 0.62882096069869,         \"MacroF1\": 0.6441389332893815,         \"Memory in Mb\": 4.151983261108398,         \"Time in s\": 40.50873       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.64,         \"MicroF1\": 0.64,         \"MacroF1\": 0.6559607038460421,         \"Memory in Mb\": 4.152521133422852,         \"Time in s\": 57.698206       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6666666666666666,         \"MicroF1\": 0.6666666666666666,         \"MacroF1\": 0.6673617488913626,         \"Memory in Mb\": 4.152231216430664,         \"Time in s\": 77.585785       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6948228882833788,         \"MicroF1\": 0.6948228882833788,         \"MacroF1\": 0.6911959597548878,         \"Memory in Mb\": 4.152448654174805,         \"Time in s\": 100.185488       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.711864406779661,         \"MicroF1\": 0.711864406779661,         \"MacroF1\": 0.7079630503641953,         \"Memory in Mb\": 4.152788162231445,         \"Time in s\": 125.717288       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7124183006535948,         \"MicroF1\": 0.7124183006535948,         \"MacroF1\": 0.7065500352371009,         \"Memory in Mb\": 4.152704238891602,         \"Time in s\": 154.000542       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7207920792079208,         \"MicroF1\": 0.7207920792079208,         \"MacroF1\": 0.7127593158348896,         \"Memory in Mb\": 4.152563095092773,         \"Time in s\": 184.883226       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7259528130671506,         \"MicroF1\": 0.7259528130671506,         \"MacroF1\": 0.7192025503807162,         \"Memory in Mb\": 4.152528762817383,         \"Time in s\": 218.482328       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7319932998324958,         \"MicroF1\": 0.7319932998324957,         \"MacroF1\": 0.7251188986558661,         \"Memory in Mb\": 4.152769088745117,         \"Time in s\": 254.840787       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7309486780715396,         \"MicroF1\": 0.7309486780715396,         \"MacroF1\": 0.7259740406437201,         \"Memory in Mb\": 4.152563095092773,         \"Time in s\": 294.12903800000004       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7358490566037735,         \"MicroF1\": 0.7358490566037735,         \"MacroF1\": 0.7304359912942561,         \"Memory in Mb\": 4.152692794799805,         \"Time in s\": 336.073433       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7374149659863946,         \"MicroF1\": 0.7374149659863947,         \"MacroF1\": 0.733149934717071,         \"Memory in Mb\": 4.152753829956055,         \"Time in s\": 380.701162       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7426376440460948,         \"MicroF1\": 0.7426376440460948,         \"MacroF1\": 0.7385597120510639,         \"Memory in Mb\": 4.152643203735352,         \"Time in s\": 428.175969       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7436517533252721,         \"MicroF1\": 0.7436517533252721,         \"MacroF1\": 0.7412375783772316,         \"Memory in Mb\": 4.152631759643555,         \"Time in s\": 478.460063       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7491408934707904,         \"MicroF1\": 0.7491408934707904,         \"MacroF1\": 0.7454343548790067,         \"Memory in Mb\": 4.153181076049805,         \"Time in s\": 531.417765       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7486398258977149,         \"MicroF1\": 0.7486398258977149,         \"MacroF1\": 0.7441307384051415,         \"Memory in Mb\": 4.153326034545898,         \"Time in s\": 587.1362770000001       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7492227979274612,         \"MicroF1\": 0.749222797927461,         \"MacroF1\": 0.7439306216964366,         \"Memory in Mb\": 4.153120040893555,         \"Time in s\": 645.6842       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7487636003956478,         \"MicroF1\": 0.7487636003956478,         \"MacroF1\": 0.7437900284473965,         \"Memory in Mb\": 4.153234481811523,         \"Time in s\": 707.105172       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.750236518448439,         \"MicroF1\": 0.7502365184484389,         \"MacroF1\": 0.7448138061687654,         \"Memory in Mb\": 4.153268814086914,         \"Time in s\": 771.2868930000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7524932003626473,         \"MicroF1\": 0.7524932003626473,         \"MacroF1\": 0.7468314646869904,         \"Memory in Mb\": 4.153234481811523,         \"Time in s\": 838.222518       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7554395126196692,         \"MicroF1\": 0.7554395126196692,         \"MacroF1\": 0.7493227137357602,         \"Memory in Mb\": 4.153413772583008,         \"Time in s\": 907.556087       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7581589958158996,         \"MicroF1\": 0.7581589958158996,         \"MacroF1\": 0.7527652773681007,         \"Memory in Mb\": 4.153318405151367,         \"Time in s\": 979.579718       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7574536663980661,         \"MicroF1\": 0.7574536663980661,         \"MacroF1\": 0.7525915384194216,         \"Memory in Mb\": 4.153432846069336,         \"Time in s\": 1054.216781       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7622377622377622,         \"MicroF1\": 0.7622377622377621,         \"MacroF1\": 0.7563448085202398,         \"Memory in Mb\": 4.153615951538086,         \"Time in s\": 1131.5718310000002       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7621905476369092,         \"MicroF1\": 0.7621905476369092,         \"MacroF1\": 0.7566636999776912,         \"Memory in Mb\": 4.153776168823242,         \"Time in s\": 1211.5912470000003       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7635968092820885,         \"MicroF1\": 0.7635968092820886,         \"MacroF1\": 0.7587252257765656,         \"Memory in Mb\": 4.153825759887695,         \"Time in s\": 1294.4019940000005       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7663157894736842,         \"MicroF1\": 0.7663157894736842,         \"MacroF1\": 0.7609139797315134,         \"Memory in Mb\": 4.153848648071289,         \"Time in s\": 1379.8910190000004       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7709041468388851,         \"MicroF1\": 0.7709041468388851,         \"MacroF1\": 0.7637689949207689,         \"Memory in Mb\": 4.153989791870117,         \"Time in s\": 1467.9946540000003       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7719182597231378,         \"MicroF1\": 0.7719182597231378,         \"MacroF1\": 0.7639714255563932,         \"Memory in Mb\": 4.154367446899414,         \"Time in s\": 1558.8129900000004       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7722328854766475,         \"MicroF1\": 0.7722328854766475,         \"MacroF1\": 0.7650721335080709,         \"Memory in Mb\": 4.154550552368164,         \"Time in s\": 1652.2028900000005       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7725295214418894,         \"MicroF1\": 0.7725295214418892,         \"MacroF1\": 0.764505787280341,         \"Memory in Mb\": 4.154642105102539,         \"Time in s\": 1748.3782850000002       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7716012084592145,         \"MicroF1\": 0.7716012084592145,         \"MacroF1\": 0.7634170612719108,         \"Memory in Mb\": 4.15452766418457,         \"Time in s\": 1847.3163560000005       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7713109935332157,         \"MicroF1\": 0.7713109935332157,         \"MacroF1\": 0.7652815676598499,         \"Memory in Mb\": 4.154825210571289,         \"Time in s\": 1948.702894       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.77389811104751,         \"MicroF1\": 0.77389811104751,         \"MacroF1\": 0.7674409436090757,         \"Memory in Mb\": 4.155008316040039,         \"Time in s\": 2052.533374       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7752370329057445,         \"MicroF1\": 0.7752370329057446,         \"MacroF1\": 0.7674318582149376,         \"Memory in Mb\": 4.155046463012695,         \"Time in s\": 2159.053176       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7765089722675367,         \"MicroF1\": 0.7765089722675368,         \"MacroF1\": 0.7688731808749575,         \"Memory in Mb\": 4.154977798461914,         \"Time in s\": 2268.233507       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7750663129973475,         \"MicroF1\": 0.7750663129973475,         \"MacroF1\": 0.7678921362145585,         \"Memory in Mb\": 4.154905319213867,         \"Time in s\": 2379.837789       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7752459865354738,         \"MicroF1\": 0.7752459865354739,         \"MacroF1\": 0.7671636716269125,         \"Memory in Mb\": 4.155000686645508,         \"Time in s\": 2494.085284       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7759231158320687,         \"MicroF1\": 0.7759231158320687,         \"MacroF1\": 0.7670573130332384,         \"Memory in Mb\": 4.154901504516602,         \"Time in s\": 2611.052254       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7775580820563519,         \"MicroF1\": 0.7775580820563519,         \"MacroF1\": 0.7671264358471986,         \"Memory in Mb\": 4.154878616333008,         \"Time in s\": 2730.562491       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.77670372160464,         \"MicroF1\": 0.7767037216046399,         \"MacroF1\": 0.7665050383810529,         \"Memory in Mb\": 4.15495491027832,         \"Time in s\": 2852.439553       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7773049645390071,         \"MicroF1\": 0.7773049645390071,         \"MacroF1\": 0.766340416614934,         \"Memory in Mb\": 4.15495491027832,         \"Time in s\": 2976.99213       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7783433595557612,         \"MicroF1\": 0.7783433595557612,         \"MacroF1\": 0.766965714748886,         \"Memory in Mb\": 4.155027389526367,         \"Time in s\": 3104.012504       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.780244676030811,         \"MicroF1\": 0.780244676030811,         \"MacroF1\": 0.7678552364681828,         \"Memory in Mb\": 4.155023574829102,         \"Time in s\": 3233.660984       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7776298268974701,         \"MicroF1\": 0.7776298268974701,         \"MacroF1\": 0.7652407320979201,         \"Memory in Mb\": 4.154973983764648,         \"Time in s\": 3365.640643       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7768595041322314,         \"MicroF1\": 0.7768595041322314,         \"MacroF1\": 0.764461061100325,         \"Memory in Mb\": 4.15504264831543,         \"Time in s\": 3499.962334       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7769597228237333,         \"MicroF1\": 0.7769597228237333,         \"MacroF1\": 0.7645642360301897,         \"Memory in Mb\": 4.155065536499023,         \"Time in s\": 3634.881072       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360189573459716,         \"MicroF1\": 0.6360189573459716,         \"MacroF1\": 0.5970323052762561,         \"Memory in Mb\": 6.533428192138672,         \"Time in s\": 93.097088       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.62482235907153,         \"MicroF1\": 0.62482235907153,         \"MacroF1\": 0.5890580890213498,         \"Memory in Mb\": 6.533924102783203,         \"Time in s\": 264.682132       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6157246605620461,         \"MicroF1\": 0.6157246605620461,         \"MacroF1\": 0.5802533923244892,         \"Memory in Mb\": 6.534633636474609,         \"Time in s\": 504.284209       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6107032914989344,         \"MicroF1\": 0.6107032914989344,         \"MacroF1\": 0.5748501357120321,         \"Memory in Mb\": 6.535015106201172,         \"Time in s\": 804.5259470000001       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.614889183557492,         \"MicroF1\": 0.614889183557492,         \"MacroF1\": 0.5777842549225517,         \"Memory in Mb\": 6.535823822021484,         \"Time in s\": 1159.582019       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.608997632202052,         \"MicroF1\": 0.608997632202052,         \"MacroF1\": 0.5733157350789625,         \"Memory in Mb\": 6.535648345947266,         \"Time in s\": 1564.000203       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6057367068055743,         \"MicroF1\": 0.6057367068055743,         \"MacroF1\": 0.5703382690867537,         \"Memory in Mb\": 6.535068511962891,         \"Time in s\": 2016.310233       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6069610512608027,         \"MicroF1\": 0.6069610512608027,         \"MacroF1\": 0.5711427916016896,         \"Memory in Mb\": 6.534946441650391,         \"Time in s\": 2516.339397       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6039145532989583,         \"MicroF1\": 0.6039145532989583,         \"MacroF1\": 0.5678102867297489,         \"Memory in Mb\": 6.535068511962891,         \"Time in s\": 3064.243813       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6034662373330808,         \"MicroF1\": 0.6034662373330808,         \"MacroF1\": 0.567425153452482,         \"Memory in Mb\": 6.535427093505859,         \"Time in s\": 3659.768381       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6005165733964701,         \"MicroF1\": 0.6005165733964701,         \"MacroF1\": 0.56512832395729,         \"Memory in Mb\": 6.535404205322266,         \"Time in s\": 4303.846419       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6031883829216321,         \"MicroF1\": 0.6031883829216321,         \"MacroF1\": 0.5703828979306639,         \"Memory in Mb\": 6.535358428955078,         \"Time in s\": 4997.310473       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6147009543235958,         \"MicroF1\": 0.6147009543235958,         \"MacroF1\": 0.5955104002005771,         \"Memory in Mb\": 6.534030914306641,         \"Time in s\": 5738.022631999999       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6051545694378678,         \"MicroF1\": 0.6051545694378678,         \"MacroF1\": 0.586271708420286,         \"Memory in Mb\": 6.533008575439453,         \"Time in s\": 6524.316427       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5703642906749163,         \"MicroF1\": 0.5703642906749163,         \"MacroF1\": 0.5530031721301686,         \"Memory in Mb\": 6.534244537353516,         \"Time in s\": 7355.370967999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5440662918023084,         \"MicroF1\": 0.5440662918023084,         \"MacroF1\": 0.5274181049148582,         \"Memory in Mb\": 6.532741546630859,         \"Time in s\": 8230.882624       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.524650437301543,         \"MicroF1\": 0.524650437301543,         \"MacroF1\": 0.5077439094080566,         \"Memory in Mb\": 6.533657073974609,         \"Time in s\": 9149.482306       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5142842110801283,         \"MicroF1\": 0.5142842110801283,         \"MacroF1\": 0.4945495171544722,         \"Memory in Mb\": 5.423342704772949,         \"Time in s\": 10110.1367       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5202611772915317,         \"MicroF1\": 0.5202611772915317,         \"MacroF1\": 0.499632175624185,         \"Memory in Mb\": 13.463048934936523,         \"Time in s\": 11121.939621       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5284814621904447,         \"MicroF1\": 0.5284814621904447,         \"MacroF1\": 0.5082299437323158,         \"Memory in Mb\": 14.233846664428713,         \"Time in s\": 12202.11771       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5344757609921083,         \"MicroF1\": 0.5344757609921083,         \"MacroF1\": 0.5148729059414189,         \"Memory in Mb\": 14.772774696350098,         \"Time in s\": 13344.394485       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5430674529723215,         \"MicroF1\": 0.5430674529723215,         \"MacroF1\": 0.5233933209280776,         \"Memory in Mb\": 14.684733390808104,         \"Time in s\": 14542.607494       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5502120475974801,         \"MicroF1\": 0.5502120475974801,         \"MacroF1\": 0.5298443248135049,         \"Memory in Mb\": 16.20911407470703,         \"Time in s\": 15791.070918       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5564061081955569,         \"MicroF1\": 0.5564061081955569,         \"MacroF1\": 0.5355525016331893,         \"Memory in Mb\": 16.199478149414062,         \"Time in s\": 17093.843057       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.561460661388689,         \"MicroF1\": 0.561460661388689,         \"MacroF1\": 0.5398397773012414,         \"Memory in Mb\": 16.192718505859375,         \"Time in s\": 18441.382026       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.564742305590967,         \"MicroF1\": 0.564742305590967,         \"MacroF1\": 0.5421523628031605,         \"Memory in Mb\": 15.229331016540527,         \"Time in s\": 19838.570208       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5680614499666795,         \"MicroF1\": 0.5680614499666795,         \"MacroF1\": 0.5472893783055924,         \"Memory in Mb\": 13.71937370300293,         \"Time in s\": 21280.868604       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5701288598775662,         \"MicroF1\": 0.5701288598775662,         \"MacroF1\": 0.55295508639855,         \"Memory in Mb\": 11.343052864074709,         \"Time in s\": 22768.358846       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5724128922705156,         \"MicroF1\": 0.5724128922705156,         \"MacroF1\": 0.5585792537754973,         \"Memory in Mb\": 9.387857437133787,         \"Time in s\": 24294.822849       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5749865841724802,         \"MicroF1\": 0.5749865841724802,         \"MacroF1\": 0.5636037623129485,         \"Memory in Mb\": 9.38664436340332,         \"Time in s\": 25857.719223       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5781884832747823,         \"MicroF1\": 0.5781884832747823,         \"MacroF1\": 0.5684564968293649,         \"Memory in Mb\": 9.385660171508787,         \"Time in s\": 27456.12944       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.575656239827173,         \"MicroF1\": 0.575656239827173,         \"MacroF1\": 0.5663415557568727,         \"Memory in Mb\": 7.860757827758789,         \"Time in s\": 29092.739018       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5754584325766924,         \"MicroF1\": 0.5754584325766924,         \"MacroF1\": 0.565994999425249,         \"Memory in Mb\": 7.205549240112305,         \"Time in s\": 30762.023764       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5763863743976827,         \"MicroF1\": 0.5763863743976827,         \"MacroF1\": 0.5665127709334143,         \"Memory in Mb\": 6.54947566986084,         \"Time in s\": 32461.363070000003       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5758813820720258,         \"MicroF1\": 0.5758813820720258,         \"MacroF1\": 0.56571927622701,         \"Memory in Mb\": 6.547377586364746,         \"Time in s\": 34189.07998       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5767460213073786,         \"MicroF1\": 0.5767460213073786,         \"MacroF1\": 0.5661110063916132,         \"Memory in Mb\": 6.546515464782715,         \"Time in s\": 35945.284935       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5764633615725219,         \"MicroF1\": 0.5764633615725219,         \"MacroF1\": 0.5659285794545608,         \"Memory in Mb\": 6.543356895446777,         \"Time in s\": 37730.818682000005       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.573454282652578,         \"MicroF1\": 0.573454282652578,         \"MacroF1\": 0.5636611811263741,         \"Memory in Mb\": 8.510072708129883,         \"Time in s\": 39542.33547700001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5726391957846685,         \"MicroF1\": 0.5726391957846685,         \"MacroF1\": 0.5633960246210544,         \"Memory in Mb\": 8.712862014770508,         \"Time in s\": 41378.34519600001       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5723146854802433,         \"MicroF1\": 0.5723146854802433,         \"MacroF1\": 0.5635786987292998,         \"Memory in Mb\": 10.13754653930664,         \"Time in s\": 43237.00688100001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5717981291142165,         \"MicroF1\": 0.5717981291142165,         \"MacroF1\": 0.5635967907133216,         \"Memory in Mb\": 10.13637924194336,         \"Time in s\": 45117.76335600001       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.571103244571712,         \"MicroF1\": 0.571103244571712,         \"MacroF1\": 0.5633625241299441,         \"Memory in Mb\": 10.135028839111328,         \"Time in s\": 47020.66134100001       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5712335102517233,         \"MicroF1\": 0.5712335102517233,         \"MacroF1\": 0.563808836162261,         \"Memory in Mb\": 11.334146499633787,         \"Time in s\": 48947.97157600001       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5728213847577642,         \"MicroF1\": 0.5728213847577642,         \"MacroF1\": 0.5658781423773395,         \"Memory in Mb\": 12.350201606750488,         \"Time in s\": 50897.74209700001       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.576863991245607,         \"MicroF1\": 0.576863991245607,         \"MacroF1\": 0.5703778478941884,         \"Memory in Mb\": 16.125893592834473,         \"Time in s\": 52890.49897200001       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5828512609366958,         \"MicroF1\": 0.5828512609366958,         \"MacroF1\": 0.5764029561430954,         \"Memory in Mb\": 15.266244888305664,         \"Time in s\": 54904.91240000001       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5890270194031956,         \"MicroF1\": 0.5890270194031956,         \"MacroF1\": 0.5823661991476956,         \"Memory in Mb\": 14.839654922485352,         \"Time in s\": 56940.07330000001       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5947087024286306,         \"MicroF1\": 0.5947087024286306,         \"MacroF1\": 0.5876086024291545,         \"Memory in Mb\": 12.465810775756836,         \"Time in s\": 58994.29364000001       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.600718937827339,         \"MicroF1\": 0.600718937827339,         \"MacroF1\": 0.5930357853224563,         \"Memory in Mb\": 11.884730339050291,         \"Time in s\": 61065.77177200001       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6060342051932802,         \"MicroF1\": 0.6060342051932802,         \"MacroF1\": 0.5982060206393416,         \"Memory in Mb\": 3.691446304321289,         \"Time in s\": 63151.215841000005       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6063920373909588,         \"MicroF1\": 0.6063920373909588,         \"MacroF1\": 0.5985419438128344,         \"Memory in Mb\": 3.691621780395508,         \"Time in s\": 65236.99615100001       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 2.1448841094970703,         \"Time in s\": 5.867596       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943558282208589,         \"MicroF1\": 0.943558282208589,         \"MacroF1\": 0.7669956277713079,         \"Memory in Mb\": 3.0916757583618164,         \"Time in s\": 25.808269       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8912510220768601,         \"MicroF1\": 0.8912510220768601,         \"MacroF1\": 0.8617021305177773,         \"Memory in Mb\": 4.035944938659668,         \"Time in s\": 63.939426       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9031269160024524,         \"MicroF1\": 0.9031269160024524,         \"MacroF1\": 0.8868998230762758,         \"Memory in Mb\": 4.988290786743164,         \"Time in s\": 125.34339       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.898970083374203,         \"MicroF1\": 0.898970083374203,         \"MacroF1\": 0.888705938214812,         \"Memory in Mb\": 6.037667274475098,         \"Time in s\": 214.307845       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8594196975888844,         \"MicroF1\": 0.8594196975888844,         \"MacroF1\": 0.8547805855679916,         \"Memory in Mb\": 6.993380546569824,         \"Time in s\": 335.016386       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8651488616462347,         \"MicroF1\": 0.8651488616462347,         \"MacroF1\": 0.8483773016417727,         \"Memory in Mb\": 7.939821243286133,         \"Time in s\": 488.1215580000001       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8553478394115844,         \"MicroF1\": 0.8553478394115844,         \"MacroF1\": 0.8302147847543373,         \"Memory in Mb\": 8.885003089904785,         \"Time in s\": 675.394352       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8452737673658404,         \"MicroF1\": 0.8452737673658404,         \"MacroF1\": 0.8411086163638233,         \"Memory in Mb\": 9.830622673034668,         \"Time in s\": 899.024814       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8374601618043638,         \"MicroF1\": 0.8374601618043638,         \"MacroF1\": 0.8238000521910981,         \"Memory in Mb\": 11.003908157348633,         \"Time in s\": 1161.3046       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8250501448629374,         \"MicroF1\": 0.8250501448629373,         \"MacroF1\": 0.8343531144302688,         \"Memory in Mb\": 11.974610328674316,         \"Time in s\": 1461.738376       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8232890704800817,         \"MicroF1\": 0.8232890704800817,         \"MacroF1\": 0.8292209535545839,         \"Memory in Mb\": 12.919659614562988,         \"Time in s\": 1801.820426       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8199132566471808,         \"MicroF1\": 0.819913256647181,         \"MacroF1\": 0.8044565992905442,         \"Memory in Mb\": 13.86521053314209,         \"Time in s\": 2181.861898       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7998599194536858,         \"MicroF1\": 0.7998599194536857,         \"MacroF1\": 0.8029484507582976,         \"Memory in Mb\": 14.811628341674805,         \"Time in s\": 2601.779179       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7970256577872201,         \"MicroF1\": 0.7970256577872201,         \"MacroF1\": 0.7783451709211457,         \"Memory in Mb\": 15.75713062286377,         \"Time in s\": 3063.5971010000003       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7720239007200858,         \"MicroF1\": 0.7720239007200858,         \"MacroF1\": 0.767005590841987,         \"Memory in Mb\": 16.704151153564453,         \"Time in s\": 3570.6766780000003       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7645277577505407,         \"MicroF1\": 0.7645277577505407,         \"MacroF1\": 0.766187831914561,         \"Memory in Mb\": 17.649503707885742,         \"Time in s\": 4126.519897       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.773389622769985,         \"MicroF1\": 0.7733896227699851,         \"MacroF1\": 0.770832075885354,         \"Memory in Mb\": 18.61162567138672,         \"Time in s\": 4733.5217410000005       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7737066185008385,         \"MicroF1\": 0.7737066185008385,         \"MacroF1\": 0.7718493223486268,         \"Memory in Mb\": 19.557814598083496,         \"Time in s\": 5395.069721000001       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7765657556073048,         \"MicroF1\": 0.7765657556073047,         \"MacroF1\": 0.7724710929560354,         \"Memory in Mb\": 20.503721237182617,         \"Time in s\": 6113.943535       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7730827594257033,         \"MicroF1\": 0.7730827594257033,         \"MacroF1\": 0.7727491763630034,         \"Memory in Mb\": 21.88267517089844,         \"Time in s\": 6890.839823       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7714763231197772,         \"MicroF1\": 0.7714763231197772,         \"MacroF1\": 0.7717207236627096,         \"Memory in Mb\": 22.87528133392334,         \"Time in s\": 7728.212391       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7702227432590856,         \"MicroF1\": 0.7702227432590856,         \"MacroF1\": 0.7694267539223918,         \"Memory in Mb\": 23.822596549987797,         \"Time in s\": 8626.275614       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7656010621999796,         \"MicroF1\": 0.7656010621999795,         \"MacroF1\": 0.7644081311179032,         \"Memory in Mb\": 24.768078804016117,         \"Time in s\": 9586.24664       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.757623296401608,         \"MicroF1\": 0.757623296401608,         \"MacroF1\": 0.749720417225094,         \"Memory in Mb\": 25.71299648284912,         \"Time in s\": 10618.940127       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.737154709154332,         \"MicroF1\": 0.737154709154332,         \"MacroF1\": 0.7245707699101513,         \"Memory in Mb\": 26.660439491271973,         \"Time in s\": 11726.561153       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.729822968679074,         \"MicroF1\": 0.7298229686790739,         \"MacroF1\": 0.7256689004292383,         \"Memory in Mb\": 27.605186462402344,         \"Time in s\": 12907.41343       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7229274271207213,         \"MicroF1\": 0.7229274271207213,         \"MacroF1\": 0.7092514304350318,         \"Memory in Mb\": 28.551199913024902,         \"Time in s\": 14153.769988       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7133801031189249,         \"MicroF1\": 0.7133801031189249,         \"MacroF1\": 0.7054771135814562,         \"Memory in Mb\": 29.4963436126709,         \"Time in s\": 15465.906612       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7177874009314487,         \"MicroF1\": 0.7177874009314487,         \"MacroF1\": 0.7138351093258007,         \"Memory in Mb\": 30.441871643066406,         \"Time in s\": 16835.329364       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7147149521625682,         \"MicroF1\": 0.7147149521625682,         \"MacroF1\": 0.7065885995198201,         \"Memory in Mb\": 31.388431549072266,         \"Time in s\": 18265.757575       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7031788586748372,         \"MicroF1\": 0.7031788586748372,         \"MacroF1\": 0.6954173783902821,         \"Memory in Mb\": 32.33424186706543,         \"Time in s\": 19760.458564       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7011067369828419,         \"MicroF1\": 0.7011067369828419,         \"MacroF1\": 0.6966368809795416,         \"Memory in Mb\": 33.27959156036377,         \"Time in s\": 21319.158047       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7007425564126595,         \"MicroF1\": 0.7007425564126595,         \"MacroF1\": 0.6971102154727419,         \"Memory in Mb\": 34.22630214691162,         \"Time in s\": 22941.129728       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6961972126899643,         \"MicroF1\": 0.6961972126899643,         \"MacroF1\": 0.691133802747568,         \"Memory in Mb\": 35.17108726501465,         \"Time in s\": 24623.129677       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.698781235105876,         \"MicroF1\": 0.698781235105876,         \"MacroF1\": 0.696592906911097,         \"Memory in Mb\": 36.11711597442627,         \"Time in s\": 26362.470984       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7048029148724744,         \"MicroF1\": 0.7048029148724744,         \"MacroF1\": 0.702773358939844,         \"Memory in Mb\": 37.0643196105957,         \"Time in s\": 28156.052692       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7047668193252918,         \"MicroF1\": 0.7047668193252918,         \"MacroF1\": 0.7013012225519919,         \"Memory in Mb\": 38.00920104980469,         \"Time in s\": 30004.434818       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6956822324178241,         \"MicroF1\": 0.6956822324178241,         \"MacroF1\": 0.6887843659114408,         \"Memory in Mb\": 38.955204010009766,         \"Time in s\": 31904.325566000003       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6869906244255163,         \"MicroF1\": 0.6869906244255163,         \"MacroF1\": 0.6817298949676788,         \"Memory in Mb\": 39.901418685913086,         \"Time in s\": 33880.147234000004       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6840437615830693,         \"MicroF1\": 0.6840437615830693,         \"MacroF1\": 0.6809878840610977,         \"Memory in Mb\": 40.84670162200928,         \"Time in s\": 35894.19480500001       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6798949518529326,         \"MicroF1\": 0.6798949518529326,         \"MacroF1\": 0.6760668667678135,         \"Memory in Mb\": 42.678324699401855,         \"Time in s\": 37945.35177200001       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6725759562218548,         \"MicroF1\": 0.6725759562218548,         \"MacroF1\": 0.6693298574086026,         \"Memory in Mb\": 43.72208595275879,         \"Time in s\": 40033.19473500001       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6715503314578575,         \"MicroF1\": 0.6715503314578575,         \"MacroF1\": 0.6700615486077944,         \"Memory in Mb\": 44.66869449615479,         \"Time in s\": 42156.41544100001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6768887194291628,         \"MicroF1\": 0.6768887194291628,         \"MacroF1\": 0.6760264883444682,         \"Memory in Mb\": 45.61451721191406,         \"Time in s\": 44306.462280000014       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6818884211648105,         \"MicroF1\": 0.6818884211648105,         \"MacroF1\": 0.6814185274246665,         \"Memory in Mb\": 46.56109237670898,         \"Time in s\": 46484.07667300002       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6739504563233377,         \"MicroF1\": 0.6739504563233377,         \"MacroF1\": 0.6724064481498903,         \"Memory in Mb\": 47.50611400604248,         \"Time in s\": 48682.185033000016       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.677883878874534,         \"MicroF1\": 0.677883878874534,         \"MacroF1\": 0.6774885006147249,         \"Memory in Mb\": 48.45180988311768,         \"Time in s\": 50904.928535000014       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6733530088539843,         \"MicroF1\": 0.6733530088539843,         \"MacroF1\": 0.6729949515014169,         \"Memory in Mb\": 49.39821243286133,         \"Time in s\": 53145.742009000016       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6697387126819943,         \"MicroF1\": 0.6697387126819943,         \"MacroF1\": 0.6699810213452306,         \"Memory in Mb\": 50.34487438201904,         \"Time in s\": 55411.38251600001       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3777777777777777,         \"MicroF1\": 0.3777777777777777,         \"MacroF1\": 0.2811210847975554,         \"Memory in Mb\": 4.09740161895752,         \"Time in s\": 6.997987       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5164835164835165,         \"MicroF1\": 0.5164835164835165,         \"MacroF1\": 0.5316649744849407,         \"Memory in Mb\": 4.097981452941895,         \"Time in s\": 22.017115       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5547445255474452,         \"MicroF1\": 0.5547445255474452,         \"MacroF1\": 0.5804654781117263,         \"Memory in Mb\": 4.0981035232543945,         \"Time in s\": 44.610384       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6174863387978142,         \"MicroF1\": 0.6174863387978142,         \"MacroF1\": 0.6394923756219437,         \"Memory in Mb\": 4.098713874816895,         \"Time in s\": 74.61421299999999       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6506550218340611,         \"MicroF1\": 0.6506550218340611,         \"MacroF1\": 0.66859135700569,         \"Memory in Mb\": 4.098713874816895,         \"Time in s\": 111.653321       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6618181818181819,         \"MicroF1\": 0.6618181818181819,         \"MacroF1\": 0.6795855359270878,         \"Memory in Mb\": 4.098832130432129,         \"Time in s\": 156.076644       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6853582554517134,         \"MicroF1\": 0.6853582554517134,         \"MacroF1\": 0.6872635633687633,         \"Memory in Mb\": 4.099373817443848,         \"Time in s\": 207.574915       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7111716621253406,         \"MicroF1\": 0.7111716621253404,         \"MacroF1\": 0.7098417316927395,         \"Memory in Mb\": 4.099347114562988,         \"Time in s\": 266.341739       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7215496368038741,         \"MicroF1\": 0.7215496368038742,         \"MacroF1\": 0.7201557312728714,         \"Memory in Mb\": 4.09926700592041,         \"Time in s\": 332.356571       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7211328976034859,         \"MicroF1\": 0.721132897603486,         \"MacroF1\": 0.7175330036146421,         \"Memory in Mb\": 4.099320411682129,         \"Time in s\": 405.380301       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7287128712871287,         \"MicroF1\": 0.7287128712871287,         \"MacroF1\": 0.7233455022590812,         \"Memory in Mb\": 4.099320411682129,         \"Time in s\": 485.520305       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7295825771324864,         \"MicroF1\": 0.7295825771324864,         \"MacroF1\": 0.7255599965917697,         \"Memory in Mb\": 4.099240303039551,         \"Time in s\": 572.983507       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7353433835845896,         \"MicroF1\": 0.7353433835845896,         \"MacroF1\": 0.7308494254186014,         \"Memory in Mb\": 4.0992631912231445,         \"Time in s\": 667.526521       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7340590979782271,         \"MicroF1\": 0.7340590979782271,         \"MacroF1\": 0.7314183982762247,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 768.914228       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.737300435413643,         \"MicroF1\": 0.737300435413643,         \"MacroF1\": 0.7343909641298695,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 877.069835       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7387755102040816,         \"MicroF1\": 0.7387755102040816,         \"MacroF1\": 0.7369557659594496,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 992.190131       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7439180537772087,         \"MicroF1\": 0.7439180537772088,         \"MacroF1\": 0.7419020281650245,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 1114.103609       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7436517533252721,         \"MicroF1\": 0.7436517533252721,         \"MacroF1\": 0.7432199627682998,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 1242.576589       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7502863688430699,         \"MicroF1\": 0.7502863688430699,         \"MacroF1\": 0.7482089866208982,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 1377.530874       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.750816104461371,         \"MicroF1\": 0.750816104461371,         \"MacroF1\": 0.7477650187313974,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1518.374517       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7512953367875648,         \"MicroF1\": 0.7512953367875648,         \"MacroF1\": 0.747322646811651,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1664.895359       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7507418397626113,         \"MicroF1\": 0.7507418397626113,         \"MacroF1\": 0.7469783619055548,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1817.198797       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7530747398297067,         \"MicroF1\": 0.7530747398297066,         \"MacroF1\": 0.7482363934596314,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1975.112421       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7552130553037172,         \"MicroF1\": 0.7552130553037172,         \"MacroF1\": 0.750118495060715,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2138.658016       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7571801566579635,         \"MicroF1\": 0.7571801566579635,         \"MacroF1\": 0.7516199800653577,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2307.825702       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7598326359832636,         \"MicroF1\": 0.7598326359832636,         \"MacroF1\": 0.7548841797367702,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2482.820129       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7598710717163578,         \"MicroF1\": 0.7598710717163577,         \"MacroF1\": 0.7553301531902636,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2663.447895       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7645687645687645,         \"MicroF1\": 0.7645687645687647,         \"MacroF1\": 0.7590078532621816,         \"Memory in Mb\": 4.1004838943481445,         \"Time in s\": 2849.419913       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7644411102775694,         \"MicroF1\": 0.7644411102775694,         \"MacroF1\": 0.7591993978414527,         \"Memory in Mb\": 4.100506782531738,         \"Time in s\": 3040.9965970000003       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7650471356055112,         \"MicroF1\": 0.7650471356055112,         \"MacroF1\": 0.7601575050520947,         \"Memory in Mb\": 4.100506782531738,         \"Time in s\": 3238.5768190000003       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7670175438596492,         \"MicroF1\": 0.7670175438596492,         \"MacroF1\": 0.7613339877221927,         \"Memory in Mb\": 4.100506782531738,         \"Time in s\": 3441.4807240000005       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7715839564921821,         \"MicroF1\": 0.7715839564921821,         \"MacroF1\": 0.7641396475218201,         \"Memory in Mb\": 4.100552558898926,         \"Time in s\": 3649.5015090000006       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7732366512854317,         \"MicroF1\": 0.7732366512854317,         \"MacroF1\": 0.7648275341801108,         \"Memory in Mb\": 4.100552558898926,         \"Time in s\": 3862.69427       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7735124760076776,         \"MicroF1\": 0.7735124760076776,         \"MacroF1\": 0.7657569341108763,         \"Memory in Mb\": 4.100552558898926,         \"Time in s\": 4080.891056       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7737725295214419,         \"MicroF1\": 0.7737725295214419,         \"MacroF1\": 0.7651494083475014,         \"Memory in Mb\": 4.10057544708252,         \"Time in s\": 4304.577590000001       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7740181268882175,         \"MicroF1\": 0.7740181268882175,         \"MacroF1\": 0.7654813489818475,         \"Memory in Mb\": 4.100529670715332,         \"Time in s\": 4533.710142000001       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7730746619635509,         \"MicroF1\": 0.7730746619635509,         \"MacroF1\": 0.766493027961906,         \"Memory in Mb\": 4.100529670715332,         \"Time in s\": 4767.793233000001       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7756153405838581,         \"MicroF1\": 0.7756153405838581,         \"MacroF1\": 0.7686072256536652,         \"Memory in Mb\": 4.100529670715332,         \"Time in s\": 5007.029776000001       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7769102063580591,         \"MicroF1\": 0.7769102063580591,         \"MacroF1\": 0.7685414235990152,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 5251.440116000002       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7781402936378466,         \"MicroF1\": 0.7781402936378466,         \"MacroF1\": 0.7699957723931323,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 5500.964415000001       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7761273209549071,         \"MicroF1\": 0.7761273209549071,         \"MacroF1\": 0.7684985598909853,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 5755.503987000001       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7762817193164163,         \"MicroF1\": 0.7762817193164163,         \"MacroF1\": 0.7677434418046419,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6014.862306000001       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7774405665149215,         \"MicroF1\": 0.7774405665149215,         \"MacroF1\": 0.7684788817649146,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6279.121569000001       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7790410281759763,         \"MicroF1\": 0.7790410281759763,         \"MacroF1\": 0.7689103339153599,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6548.278113000001       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7786370227162881,         \"MicroF1\": 0.7786370227162881,         \"MacroF1\": 0.7686288077529282,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6822.363214000001       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7791962174940898,         \"MicroF1\": 0.7791962174940898,         \"MacroF1\": 0.768391950800897,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 7101.096348000001       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7801943544655252,         \"MicroF1\": 0.7801943544655253,         \"MacroF1\": 0.768962628827985,         \"Memory in Mb\": 4.100525856018066,         \"Time in s\": 7384.333285000001       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7820570910738559,         \"MicroF1\": 0.7820570910738559,         \"MacroF1\": 0.7698068761587117,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 7672.298476000001       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7789613848202397,         \"MicroF1\": 0.7789613848202397,         \"MacroF1\": 0.7667173742344939,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 7965.117559000001       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7781644193127447,         \"MicroF1\": 0.7781644193127447,         \"MacroF1\": 0.7659138381656089,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 8262.647904000001       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7782589865742746,         \"MicroF1\": 0.7782589865742745,         \"MacroF1\": 0.7660163657276376,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 8561.303246000001       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6218009478672986,         \"MicroF1\": 0.6218009478672986,         \"MacroF1\": 0.5857016652718549,         \"Memory in Mb\": 6.471495628356934,         \"Time in s\": 220.837673       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6196115585030791,         \"MicroF1\": 0.6196115585030791,         \"MacroF1\": 0.5856756432415233,         \"Memory in Mb\": 10.302834510803224,         \"Time in s\": 598.297395       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.628986422481844,         \"MicroF1\": 0.628986422481844,         \"MacroF1\": 0.5949930595607559,         \"Memory in Mb\": 19.024110794067383,         \"Time in s\": 1103.516793       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6294103717736207,         \"MicroF1\": 0.6294103717736207,         \"MacroF1\": 0.5952675443708706,         \"Memory in Mb\": 19.52926254272461,         \"Time in s\": 1735.893967       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6364841826103429,         \"MicroF1\": 0.6364841826103429,         \"MacroF1\": 0.5994911272790603,         \"Memory in Mb\": 18.82306957244873,         \"Time in s\": 2497.807238       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6352012628255722,         \"MicroF1\": 0.6352012628255722,         \"MacroF1\": 0.5993891820807258,         \"Memory in Mb\": 20.00343894958496,         \"Time in s\": 3379.788115       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.638749830875389,         \"MicroF1\": 0.638749830875389,         \"MacroF1\": 0.6030343276880051,         \"Memory in Mb\": 20.9547061920166,         \"Time in s\": 4385.582643       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6405824553095774,         \"MicroF1\": 0.6405824553095774,         \"MacroF1\": 0.6028521616895871,         \"Memory in Mb\": 23.98197650909424,         \"Time in s\": 5520.259032       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6449542249815847,         \"MicroF1\": 0.6449542249815847,         \"MacroF1\": 0.6055705492028415,         \"Memory in Mb\": 24.687146186828613,         \"Time in s\": 6764.141036999999       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6485462638507434,         \"MicroF1\": 0.6485462638507434,         \"MacroF1\": 0.6081614166360887,         \"Memory in Mb\": 28.76917839050293,         \"Time in s\": 8102.806145       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6490744726646578,         \"MicroF1\": 0.6490744726646578,         \"MacroF1\": 0.6078786452761632,         \"Memory in Mb\": 30.803756713867188,         \"Time in s\": 9530.02909       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6514876489621971,         \"MicroF1\": 0.6514876489621971,         \"MacroF1\": 0.6111938480023122,         \"Memory in Mb\": 35.14385414123535,         \"Time in s\": 11044.69783       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6707947840023312,         \"MicroF1\": 0.6707947840023312,         \"MacroF1\": 0.6607574394823457,         \"Memory in Mb\": 17.51351547241211,         \"Time in s\": 12617.098737       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6821348846648176,         \"MicroF1\": 0.6821348846648176,         \"MacroF1\": 0.6733632096765088,         \"Memory in Mb\": 9.275564193725586,         \"Time in s\": 14250.678949       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6778205694803965,         \"MicroF1\": 0.6778205694803965,         \"MacroF1\": 0.670556396248407,         \"Memory in Mb\": 11.964457511901855,         \"Time in s\": 15956.730999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6754661142349808,         \"MicroF1\": 0.6754661142349808,         \"MacroF1\": 0.6690281338426608,         \"Memory in Mb\": 12.60369873046875,         \"Time in s\": 17732.973803       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6721631106902123,         \"MicroF1\": 0.6721631106902123,         \"MacroF1\": 0.6660357480506892,         \"Memory in Mb\": 12.93508529663086,         \"Time in s\": 19577.321708       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6856947440416689,         \"MicroF1\": 0.6856947440416689,         \"MacroF1\": 0.6751812770122833,         \"Memory in Mb\": 14.563780784606934,         \"Time in s\": 21465.395048       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6926680954991776,         \"MicroF1\": 0.6926680954991776,         \"MacroF1\": 0.6785701715539604,         \"Memory in Mb\": 23.61655616760254,         \"Time in s\": 23398.659989       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6942090061082438,         \"MicroF1\": 0.6942090061082438,         \"MacroF1\": 0.6784920731228882,         \"Memory in Mb\": 30.02095413208008,         \"Time in s\": 25401.280766       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6958737316798196,         \"MicroF1\": 0.6958737316798196,         \"MacroF1\": 0.6784853924286285,         \"Memory in Mb\": 31.29345321655273,         \"Time in s\": 27443.688764       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6989798114588266,         \"MicroF1\": 0.6989798114588266,         \"MacroF1\": 0.6799590657327791,         \"Memory in Mb\": 29.59604263305664,         \"Time in s\": 29526.276677       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7011981718614897,         \"MicroF1\": 0.7011981718614897,         \"MacroF1\": 0.680282364066019,         \"Memory in Mb\": 32.615909576416016,         \"Time in s\": 31645.669428       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7031527443475516,         \"MicroF1\": 0.7031527443475516,         \"MacroF1\": 0.6805566439417602,         \"Memory in Mb\": 33.91432285308838,         \"Time in s\": 33792.819539       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7051782264479716,         \"MicroF1\": 0.7051782264479716,         \"MacroF1\": 0.6809495737401271,         \"Memory in Mb\": 35.12977695465088,         \"Time in s\": 35966.301701       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7065743944636678,         \"MicroF1\": 0.7065743944636678,         \"MacroF1\": 0.6805936316849747,         \"Memory in Mb\": 38.84447956085205,         \"Time in s\": 38159.78466       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7054820946301428,         \"MicroF1\": 0.7054820946301428,         \"MacroF1\": 0.681225779493031,         \"Memory in Mb\": 34.570815086364746,         \"Time in s\": 40377.715599       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7045692833226231,         \"MicroF1\": 0.7045692833226231,         \"MacroF1\": 0.6849598194839713,         \"Memory in Mb\": 20.38253498077393,         \"Time in s\": 42611.23284999999       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7031316330862424,         \"MicroF1\": 0.7031316330862424,         \"MacroF1\": 0.6877640955933652,         \"Memory in Mb\": 22.55568027496338,         \"Time in s\": 44864.71640799999       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7032418952618454,         \"MicroF1\": 0.7032418952618454,         \"MacroF1\": 0.6917227552448634,         \"Memory in Mb\": 26.177990913391117,         \"Time in s\": 47133.482559       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7037421719871697,         \"MicroF1\": 0.7037421719871697,         \"MacroF1\": 0.6952024388211077,         \"Memory in Mb\": 25.7611780166626,         \"Time in s\": 49415.922785       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7002160338551685,         \"MicroF1\": 0.7002160338551685,         \"MacroF1\": 0.6931280234945141,         \"Memory in Mb\": 25.958494186401367,         \"Time in s\": 51714.47139399999       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6973627571957414,         \"MicroF1\": 0.6973627571957414,         \"MacroF1\": 0.6902163957562899,         \"Memory in Mb\": 18.894118309021,         \"Time in s\": 54032.337052       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6951786758766677,         \"MicroF1\": 0.6951786758766677,         \"MacroF1\": 0.6877287571005829,         \"Memory in Mb\": 18.049145698547363,         \"Time in s\": 56371.370632       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6919830081982737,         \"MicroF1\": 0.6919830081982737,         \"MacroF1\": 0.6843647347906762,         \"Memory in Mb\": 22.045016288757324,         \"Time in s\": 58731.919307       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6900697093252663,         \"MicroF1\": 0.6900697093252663,         \"MacroF1\": 0.68217396069655,         \"Memory in Mb\": 25.079078674316406,         \"Time in s\": 61114.705624       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.688720534411712,         \"MicroF1\": 0.688720534411712,         \"MacroF1\": 0.6808510434728485,         \"Memory in Mb\": 19.794261932373047,         \"Time in s\": 63520.517783       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6867695068158597,         \"MicroF1\": 0.6867695068158597,         \"MacroF1\": 0.6796002866264578,         \"Memory in Mb\": 10.854747772216797,         \"Time in s\": 65948.78476699999       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6843843333414273,         \"MicroF1\": 0.6843843333414273,         \"MacroF1\": 0.6779529807793833,         \"Memory in Mb\": 10.474969863891602,         \"Time in s\": 68395.63477799999       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6822131205757712,         \"MicroF1\": 0.6822131205757712,         \"MacroF1\": 0.6764872431583758,         \"Memory in Mb\": 14.707494735717772,         \"Time in s\": 70864.05938699999       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6795472918350849,         \"MicroF1\": 0.6795472918350849,         \"MacroF1\": 0.674587653669649,         \"Memory in Mb\": 12.672552108764648,         \"Time in s\": 73351.02096199998       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6769633153705666,         \"MicroF1\": 0.6769633153705666,         \"MacroF1\": 0.6725984110786069,         \"Memory in Mb\": 13.144417762756348,         \"Time in s\": 75857.66838799998       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6748959411544475,         \"MicroF1\": 0.6748959411544475,         \"MacroF1\": 0.6710316194917795,         \"Memory in Mb\": 14.719610214233398,         \"Time in s\": 78383.45415699997       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6743215031315241,         \"MicroF1\": 0.6743215031315241,         \"MacroF1\": 0.670959098678123,         \"Memory in Mb\": 15.027325630187988,         \"Time in s\": 80927.72302099997       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6765293882447021,         \"MicroF1\": 0.6765293882447021,         \"MacroF1\": 0.6733002712216741,         \"Memory in Mb\": 17.283148765563965,         \"Time in s\": 83488.02074099997       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6805970149253732,         \"MicroF1\": 0.6805970149253732,         \"MacroF1\": 0.6770692638556323,         \"Memory in Mb\": 17.906007766723633,         \"Time in s\": 86063.52222099998       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6848340754770205,         \"MicroF1\": 0.6848340754770205,         \"MacroF1\": 0.6808344811077705,         \"Memory in Mb\": 18.8202543258667,         \"Time in s\": 88653.18323199998       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6890524197525992,         \"MicroF1\": 0.6890524197525992,         \"MacroF1\": 0.6843657264244208,         \"Memory in Mb\": 21.50714492797852,         \"Time in s\": 91255.74433499998       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6932531936687089,         \"MicroF1\": 0.6932531936687089,         \"MacroF1\": 0.6877873898777546,         \"Memory in Mb\": 23.154582023620605,         \"Time in s\": 93870.637319       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6956002954601412,         \"MicroF1\": 0.6956002954601412,         \"MacroF1\": 0.6902433463100389,         \"Memory in Mb\": 14.128369331359863,         \"Time in s\": 96495.216564       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6958578538043787,         \"MicroF1\": 0.6958578538043787,         \"MacroF1\": 0.6905081705907102,         \"Memory in Mb\": 13.83100128173828,         \"Time in s\": 99120.191439       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 2.0028390884399414,         \"Time in s\": 23.27864       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9521472392638036,         \"MicroF1\": 0.9521472392638036,         \"MacroF1\": 0.8408896590786493,         \"Memory in Mb\": 4.076430320739746,         \"Time in s\": 76.761208       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9533932951757972,         \"MicroF1\": 0.9533932951757972,         \"MacroF1\": 0.9542235338779168,         \"Memory in Mb\": 5.6716413497924805,         \"Time in s\": 164.877928       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589209074187616,         \"MicroF1\": 0.9589209074187616,         \"MacroF1\": 0.936122253486076,         \"Memory in Mb\": 8.122180938720703,         \"Time in s\": 291.606081       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9573320255026974,         \"MicroF1\": 0.9573320255026974,         \"MacroF1\": 0.9445755787125868,         \"Memory in Mb\": 10.5212984085083,         \"Time in s\": 455.912148       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9607682876992236,         \"MicroF1\": 0.9607682876992236,         \"MacroF1\": 0.9588299190873342,         \"Memory in Mb\": 9.06541347503662,         \"Time in s\": 649.921126       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9618213660245184,         \"MicroF1\": 0.9618213660245184,         \"MacroF1\": 0.9516555143941908,         \"Memory in Mb\": 13.188368797302246,         \"Time in s\": 870.236672       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589334967821024,         \"MicroF1\": 0.9589334967821024,         \"MacroF1\": 0.9492703335352553,         \"Memory in Mb\": 13.21088695526123,         \"Time in s\": 1122.958204       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9585943884500135,         \"MicroF1\": 0.9585943884500135,         \"MacroF1\": 0.9531276848185062,         \"Memory in Mb\": 16.65507411956787,         \"Time in s\": 1406.732623       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9541554302525128,         \"MicroF1\": 0.9541554302525128,         \"MacroF1\": 0.9416377826660955,         \"Memory in Mb\": 17.091320037841797,         \"Time in s\": 1722.764314       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9529752618676176,         \"MicroF1\": 0.9529752618676176,         \"MacroF1\": 0.9549694463549354,         \"Memory in Mb\": 10.336687088012695,         \"Time in s\": 2070.686487       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9550561797752808,         \"MicroF1\": 0.9550561797752808,         \"MacroF1\": 0.95517907029875,         \"Memory in Mb\": 11.520882606506348,         \"Time in s\": 2449.358224       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568168960965492,         \"MicroF1\": 0.9568168960965492,         \"MacroF1\": 0.9575833276239932,         \"Memory in Mb\": 13.737529754638672,         \"Time in s\": 2856.6015070000003       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9574505340570828,         \"MicroF1\": 0.9574505340570828,         \"MacroF1\": 0.9570632809827344,         \"Memory in Mb\": 15.842782020568848,         \"Time in s\": 3290.691514       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9557117176009152,         \"MicroF1\": 0.9557117176009152,         \"MacroF1\": 0.9522483041543378,         \"Memory in Mb\": 20.04281044006348,         \"Time in s\": 3760.43818       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566416424084572,         \"MicroF1\": 0.9566416424084572,         \"MacroF1\": 0.9568246790885272,         \"Memory in Mb\": 11.687369346618652,         \"Time in s\": 4258.095146       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.95746214852199,         \"MicroF1\": 0.95746214852199,         \"MacroF1\": 0.9579855320572276,         \"Memory in Mb\": 12.48728847503662,         \"Time in s\": 4780.363237       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568296336647149,         \"MicroF1\": 0.9568296336647149,         \"MacroF1\": 0.9563404233689646,         \"Memory in Mb\": 15.327423095703123,         \"Time in s\": 5334.336646       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9565217391304348,         \"MicroF1\": 0.9565217391304348,         \"MacroF1\": 0.9563017119581124,         \"Memory in Mb\": 18.70553493499756,         \"Time in s\": 5920.99825       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.954528741267312,         \"MicroF1\": 0.954528741267312,         \"MacroF1\": 0.9527980459948604,         \"Memory in Mb\": 24.08679676055908,         \"Time in s\": 6539.621381999999       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.95459320649002,         \"MicroF1\": 0.95459320649002,         \"MacroF1\": 0.9549210113442088,         \"Memory in Mb\": 21.21516990661621,         \"Time in s\": 7187.28418       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9550974930362116,         \"MicroF1\": 0.9550974930362116,         \"MacroF1\": 0.955362716075958,         \"Memory in Mb\": 17.566545486450195,         \"Time in s\": 7867.802732999999       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9555579239049344,         \"MicroF1\": 0.9555579239049344,         \"MacroF1\": 0.9558253322166266,         \"Memory in Mb\": 15.710055351257324,         \"Time in s\": 8578.363562999999       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9552650393218262,         \"MicroF1\": 0.9552650393218262,         \"MacroF1\": 0.955371511778808,         \"Memory in Mb\": 18.71725273132324,         \"Time in s\": 9321.146736       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9533287577213452,         \"MicroF1\": 0.9533287577213452,         \"MacroF1\": 0.9523119157834916,         \"Memory in Mb\": 15.605277061462402,         \"Time in s\": 10099.276789999998       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9521070990855096,         \"MicroF1\": 0.9521070990855096,         \"MacroF1\": 0.9515822083565744,         \"Memory in Mb\": 11.186952590942385,         \"Time in s\": 10903.979313999998       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.953427144802542,         \"MicroF1\": 0.953427144802542,         \"MacroF1\": 0.9541201209142028,         \"Memory in Mb\": 7.581887245178223,         \"Time in s\": 11728.435273       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.953689923837871,         \"MicroF1\": 0.953689923837871,         \"MacroF1\": 0.9538275342826804,         \"Memory in Mb\": 10.15964126586914,         \"Time in s\": 12573.844722999998       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9535964838137098,         \"MicroF1\": 0.9535964838137098,         \"MacroF1\": 0.9538502960885475,         \"Memory in Mb\": 11.061944961547852,         \"Time in s\": 13441.174569       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9541629218073372,         \"MicroF1\": 0.9541629218073372,         \"MacroF1\": 0.9544632162431566,         \"Memory in Mb\": 11.249642372131348,         \"Time in s\": 14331.138910999998       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9548509527951292,         \"MicroF1\": 0.9548509527951292,         \"MacroF1\": 0.9551609875055332,         \"Memory in Mb\": 13.203255653381348,         \"Time in s\": 15243.410521999998       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9551895825354272,         \"MicroF1\": 0.955189582535427,         \"MacroF1\": 0.9553883557595892,         \"Memory in Mb\": 9.36058521270752,         \"Time in s\": 16176.429406999998       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.955953353635891,         \"MicroF1\": 0.955953353635891,         \"MacroF1\": 0.9562606797905644,         \"Memory in Mb\": 11.575583457946776,         \"Time in s\": 17130.275872       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9561675437964098,         \"MicroF1\": 0.9561675437964098,         \"MacroF1\": 0.9563487774281332,         \"Memory in Mb\": 11.42638874053955,         \"Time in s\": 18106.07239       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9549688353526156,         \"MicroF1\": 0.9549688353526156,         \"MacroF1\": 0.954852939557476,         \"Memory in Mb\": 10.249165534973145,         \"Time in s\": 19109.380901       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9552665622659496,         \"MicroF1\": 0.9552665622659496,         \"MacroF1\": 0.955472434271787,         \"Memory in Mb\": 8.168793678283691,         \"Time in s\": 20137.264239       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9560781715799934,         \"MicroF1\": 0.9560781715799934,         \"MacroF1\": 0.9563263247313608,         \"Memory in Mb\": 9.020037651062012,         \"Time in s\": 21191.151533       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563955363478036,         \"MicroF1\": 0.9563955363478036,         \"MacroF1\": 0.9565429512012836,         \"Memory in Mb\": 8.031278610229492,         \"Time in s\": 22273.408126999995       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566337753755264,         \"MicroF1\": 0.9566337753755264,         \"MacroF1\": 0.9567672375037608,         \"Memory in Mb\": 10.967172622680664,         \"Time in s\": 23379.117397999995       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563085973405232,         \"MicroF1\": 0.9563085973405232,         \"MacroF1\": 0.9563585840602682,         \"Memory in Mb\": 11.29026985168457,         \"Time in s\": 24508.785403999995       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.955580797513003,         \"MicroF1\": 0.955580797513003,         \"MacroF1\": 0.9555776398983684,         \"Memory in Mb\": 9.525394439697266,         \"Time in s\": 25660.924918999997       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9564050189670266,         \"MicroF1\": 0.9564050189670268,         \"MacroF1\": 0.9565585833577668,         \"Memory in Mb\": 10.421767234802246,         \"Time in s\": 26839.493165999997       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566778772159836,         \"MicroF1\": 0.9566778772159836,         \"MacroF1\": 0.9567660151847868,         \"Memory in Mb\": 11.633780479431152,         \"Time in s\": 28038.177978999996       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9564369672998718,         \"MicroF1\": 0.9564369672998718,         \"MacroF1\": 0.9564736297242662,         \"Memory in Mb\": 8.448995590209961,         \"Time in s\": 29257.620389999996       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9567514570510376,         \"MicroF1\": 0.9567514570510376,         \"MacroF1\": 0.9568227044222712,         \"Memory in Mb\": 7.821832656860352,         \"Time in s\": 30499.29393699999       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568924175414292,         \"MicroF1\": 0.9568924175414292,         \"MacroF1\": 0.9569505378685396,         \"Memory in Mb\": 9.859258651733398,         \"Time in s\": 31763.565401999997       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9567144719687092,         \"MicroF1\": 0.9567144719687092,         \"MacroF1\": 0.956766336746882,         \"Memory in Mb\": 11.256629943847656,         \"Time in s\": 33053.804573999994       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568503293673084,         \"MicroF1\": 0.9568503293673084,         \"MacroF1\": 0.9569026376832064,         \"Memory in Mb\": 11.690522193908691,         \"Time in s\": 34367.36625199999       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9564303936771548,         \"MicroF1\": 0.9564303936771548,         \"MacroF1\": 0.956465338137946,         \"Memory in Mb\": 12.451186180114746,         \"Time in s\": 35701.899461999994       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566155203686456,         \"MicroF1\": 0.9566155203686456,         \"MacroF1\": 0.9566498206969932,         \"Memory in Mb\": 7.4099931716918945,         \"Time in s\": 37049.10208799999       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4,         \"MicroF1\": 0.4000000000000001,         \"MacroF1\": 0.3289160825620571,         \"Memory in Mb\": 1.89190673828125,         \"Time in s\": 1.901401       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5494505494505495,         \"MicroF1\": 0.5494505494505495,         \"MacroF1\": 0.5607526488856412,         \"Memory in Mb\": 2.084074020385742,         \"Time in s\": 6.467373       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5693430656934306,         \"MicroF1\": 0.5693430656934306,         \"MacroF1\": 0.5872103411959265,         \"Memory in Mb\": 2.357966423034668,         \"Time in s\": 13.822826       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6174863387978142,         \"MicroF1\": 0.6174863387978142,         \"MacroF1\": 0.6372989403156369,         \"Memory in Mb\": 2.7369613647460938,         \"Time in s\": 24.259991       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6375545851528385,         \"MicroF1\": 0.6375545851528385,         \"MacroF1\": 0.6548159763148107,         \"Memory in Mb\": 2.862431526184082,         \"Time in s\": 37.817905       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6618181818181819,         \"MicroF1\": 0.6618181818181819,         \"MacroF1\": 0.6802187985971371,         \"Memory in Mb\": 2.982741355895996,         \"Time in s\": 54.565381       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6915887850467289,         \"MicroF1\": 0.6915887850467289,         \"MacroF1\": 0.6955507555363084,         \"Memory in Mb\": 3.080752372741699,         \"Time in s\": 74.633343       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7111716621253406,         \"MicroF1\": 0.7111716621253404,         \"MacroF1\": 0.7105739026832886,         \"Memory in Mb\": 3.232259750366211,         \"Time in s\": 98.204704       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7263922518159807,         \"MicroF1\": 0.7263922518159807,         \"MacroF1\": 0.7261041400072307,         \"Memory in Mb\": 3.505929946899414,         \"Time in s\": 125.527545       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7276688453159041,         \"MicroF1\": 0.7276688453159043,         \"MacroF1\": 0.72519869331257,         \"Memory in Mb\": 3.787288665771485,         \"Time in s\": 156.78717       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7425742574257426,         \"MicroF1\": 0.7425742574257425,         \"MacroF1\": 0.7379486431795568,         \"Memory in Mb\": 6.240692138671875,         \"Time in s\": 210.523476       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7422867513611615,         \"MicroF1\": 0.7422867513611615,         \"MacroF1\": 0.7388440561615693,         \"Memory in Mb\": 6.313092231750488,         \"Time in s\": 268.009607       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7520938023450586,         \"MicroF1\": 0.7520938023450586,         \"MacroF1\": 0.749839509127547,         \"Memory in Mb\": 6.682056427001953,         \"Time in s\": 329.26112900000004       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7573872472783826,         \"MicroF1\": 0.7573872472783826,         \"MacroF1\": 0.7582793237949303,         \"Memory in Mb\": 7.269444465637207,         \"Time in s\": 394.37227100000007       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7634252539912917,         \"MicroF1\": 0.7634252539912917,         \"MacroF1\": 0.7648953830992049,         \"Memory in Mb\": 7.531791687011719,         \"Time in s\": 463.2777280000001       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7673469387755102,         \"MicroF1\": 0.7673469387755102,         \"MacroF1\": 0.7694390547687558,         \"Memory in Mb\": 7.987269401550293,         \"Time in s\": 536.1609010000001       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7772087067861716,         \"MicroF1\": 0.7772087067861717,         \"MacroF1\": 0.7788980835102386,         \"Memory in Mb\": 8.317158699035645,         \"Time in s\": 613.0067590000001       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7823458282950423,         \"MicroF1\": 0.7823458282950423,         \"MacroF1\": 0.7854763667551727,         \"Memory in Mb\": 8.613452911376953,         \"Time in s\": 693.8523060000001       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7915234822451317,         \"MicroF1\": 0.7915234822451317,         \"MacroF1\": 0.7933203073280156,         \"Memory in Mb\": 8.694649696350098,         \"Time in s\": 778.7710210000001       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7986942328618063,         \"MicroF1\": 0.7986942328618062,         \"MacroF1\": 0.7996826842527437,         \"Memory in Mb\": 8.824880599975586,         \"Time in s\": 867.8856220000001       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8041450777202073,         \"MicroF1\": 0.8041450777202073,         \"MacroF1\": 0.8044659150084363,         \"Memory in Mb\": 9.089361190795898,         \"Time in s\": 961.208295       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8100890207715133,         \"MicroF1\": 0.8100890207715133,         \"MacroF1\": 0.8093994872208631,         \"Memory in Mb\": 9.280214309692385,         \"Time in s\": 1058.9817440000002       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8145695364238411,         \"MicroF1\": 0.814569536423841,         \"MacroF1\": 0.8133421993203876,         \"Memory in Mb\": 9.165953636169434,         \"Time in s\": 1161.0697040000002       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8213961922030825,         \"MicroF1\": 0.8213961922030824,         \"MacroF1\": 0.8206569542548617,         \"Memory in Mb\": 8.760258674621582,         \"Time in s\": 1267.2341280000005       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.824194952132289,         \"MicroF1\": 0.824194952132289,         \"MacroF1\": 0.8228781271733864,         \"Memory in Mb\": 8.742037773132324,         \"Time in s\": 1377.3471480000003       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8292887029288702,         \"MicroF1\": 0.8292887029288704,         \"MacroF1\": 0.8281638601893785,         \"Memory in Mb\": 8.87535572052002,         \"Time in s\": 1491.4919770000004       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8340048348106366,         \"MicroF1\": 0.8340048348106366,         \"MacroF1\": 0.833490204478907,         \"Memory in Mb\": 8.332135200500488,         \"Time in s\": 1609.3898390000004       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8360528360528361,         \"MicroF1\": 0.8360528360528361,         \"MacroF1\": 0.8353480055004047,         \"Memory in Mb\": 8.416248321533203,         \"Time in s\": 1730.8650650000004       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8394598649662416,         \"MicroF1\": 0.8394598649662416,         \"MacroF1\": 0.8389194005130135,         \"Memory in Mb\": 8.469959259033203,         \"Time in s\": 1855.8596220000004       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8419144307469181,         \"MicroF1\": 0.8419144307469181,         \"MacroF1\": 0.8414934007209077,         \"Memory in Mb\": 8.578604698181152,         \"Time in s\": 1984.2269550000003       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8449122807017544,         \"MicroF1\": 0.8449122807017544,         \"MacroF1\": 0.8435602800871403,         \"Memory in Mb\": 8.689190864562988,         \"Time in s\": 2115.814455       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8484024473147519,         \"MicroF1\": 0.8484024473147518,         \"MacroF1\": 0.8459519552383536,         \"Memory in Mb\": 8.800261497497559,         \"Time in s\": 2250.613689       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8503625576796309,         \"MicroF1\": 0.8503625576796308,         \"MacroF1\": 0.8475723684173131,         \"Memory in Mb\": 9.025433540344238,         \"Time in s\": 2388.852428       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8522072936660269,         \"MicroF1\": 0.8522072936660269,         \"MacroF1\": 0.8497128793769615,         \"Memory in Mb\": 8.811847686767578,         \"Time in s\": 2530.498155       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8527035425730267,         \"MicroF1\": 0.8527035425730267,         \"MacroF1\": 0.8503048238231962,         \"Memory in Mb\": 8.729784965515137,         \"Time in s\": 2675.5358680000004       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8531722054380665,         \"MicroF1\": 0.8531722054380665,         \"MacroF1\": 0.8508343416398155,         \"Memory in Mb\": 8.761359214782715,         \"Time in s\": 2823.7565440000003       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8571428571428571,         \"MicroF1\": 0.8571428571428571,         \"MacroF1\": 0.8561317791292776,         \"Memory in Mb\": 8.798370361328125,         \"Time in s\": 2975.2442650000003       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8580423583285632,         \"MicroF1\": 0.8580423583285632,         \"MacroF1\": 0.8567712479140972,         \"Memory in Mb\": 8.86152172088623,         \"Time in s\": 3129.874549       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8611266034578918,         \"MicroF1\": 0.8611266034578918,         \"MacroF1\": 0.8591986188286931,         \"Memory in Mb\": 8.932531356811523,         \"Time in s\": 3287.541657       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8618814573137574,         \"MicroF1\": 0.8618814573137574,         \"MacroF1\": 0.8601172531559075,         \"Memory in Mb\": 8.819746017456055,         \"Time in s\": 3448.3205970000004       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8636604774535809,         \"MicroF1\": 0.8636604774535809,         \"MacroF1\": 0.8623243992773615,         \"Memory in Mb\": 9.007128715515137,         \"Time in s\": 3612.136702       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8648368720870016,         \"MicroF1\": 0.8648368720870016,         \"MacroF1\": 0.8630569076841595,         \"Memory in Mb\": 9.368453979492188,         \"Time in s\": 3779.147863       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8649468892261002,         \"MicroF1\": 0.8649468892261002,         \"MacroF1\": 0.8631362872103546,         \"Memory in Mb\": 8.952109336853027,         \"Time in s\": 3949.363777       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8665348492338112,         \"MicroF1\": 0.8665348492338112,         \"MacroF1\": 0.8639071890295129,         \"Memory in Mb\": 9.146061897277832,         \"Time in s\": 4122.536804       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8680521991300145,         \"MicroF1\": 0.8680521991300145,         \"MacroF1\": 0.8658036637930728,         \"Memory in Mb\": 8.80567455291748,         \"Time in s\": 4298.84894       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8695035460992908,         \"MicroF1\": 0.8695035460992909,         \"MacroF1\": 0.8667661913422944,         \"Memory in Mb\": 8.892473220825195,         \"Time in s\": 4478.129032       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8690421101341971,         \"MicroF1\": 0.869042110134197,         \"MacroF1\": 0.8663186552920692,         \"Memory in Mb\": 8.910783767700195,         \"Time in s\": 4660.403073       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8699592206615315,         \"MicroF1\": 0.8699592206615315,         \"MacroF1\": 0.8669965232275297,         \"Memory in Mb\": 8.99278450012207,         \"Time in s\": 4845.573407999999       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.869063470927652,         \"MicroF1\": 0.8690634709276521,         \"MacroF1\": 0.8666022158227548,         \"Memory in Mb\": 9.09610366821289,         \"Time in s\": 5033.674223999999       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8686385384949978,         \"MicroF1\": 0.8686385384949978,         \"MacroF1\": 0.8662053097556822,         \"Memory in Mb\": 9.110825538635254,         \"Time in s\": 5224.692184       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8679081853616284,         \"MicroF1\": 0.8679081853616284,         \"MacroF1\": 0.8656034675726049,         \"Memory in Mb\": 9.181622505187988,         \"Time in s\": 5416.881651       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6511848341232227,         \"MicroF1\": 0.6511848341232227,         \"MacroF1\": 0.5864257754346489,         \"Memory in Mb\": 12.51792049407959,         \"Time in s\": 137.265242       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6873519658929418,         \"MicroF1\": 0.6873519658929418,         \"MacroF1\": 0.6004104483953082,         \"Memory in Mb\": 15.371862411499023,         \"Time in s\": 366.367491       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6978212819703189,         \"MicroF1\": 0.6978212819703189,         \"MacroF1\": 0.602242348585179,         \"Memory in Mb\": 17.772335052490234,         \"Time in s\": 671.574116       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7054226852948141,         \"MicroF1\": 0.7054226852948141,         \"MacroF1\": 0.6059831617919115,         \"Memory in Mb\": 20.14197826385498,         \"Time in s\": 1043.757912       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7080886531540065,         \"MicroF1\": 0.7080886531540066,         \"MacroF1\": 0.6082411118035554,         \"Memory in Mb\": 23.246225357055664,         \"Time in s\": 1476.569185       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.708602999210734,         \"MicroF1\": 0.708602999210734,         \"MacroF1\": 0.6091818949546898,         \"Memory in Mb\": 28.13547992706299,         \"Time in s\": 1970.150117       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7104586659450683,         \"MicroF1\": 0.7104586659450683,         \"MacroF1\": 0.6104104212994758,         \"Memory in Mb\": 30.16471099853516,         \"Time in s\": 2526.789716       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7130342133301764,         \"MicroF1\": 0.7130342133301764,         \"MacroF1\": 0.6119778058667307,         \"Memory in Mb\": 27.69899654388428,         \"Time in s\": 3146.1270590000004       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.717773334736399,         \"MicroF1\": 0.717773334736399,         \"MacroF1\": 0.6149023583636667,         \"Memory in Mb\": 27.04288387298584,         \"Time in s\": 3829.1831980000006       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7215645420967894,         \"MicroF1\": 0.7215645420967894,         \"MacroF1\": 0.617635708330779,         \"Memory in Mb\": 23.96706485748291,         \"Time in s\": 4572.729772000001       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7213086526043909,         \"MicroF1\": 0.721308652604391,         \"MacroF1\": 0.6182075626749539,         \"Memory in Mb\": 26.15617847442627,         \"Time in s\": 5374.612830000001       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7240943887617394,         \"MicroF1\": 0.7240943887617394,         \"MacroF1\": 0.6351065980046956,         \"Memory in Mb\": 25.05154228210449,         \"Time in s\": 6233.453892000001       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7432796678079697,         \"MicroF1\": 0.7432796678079697,         \"MacroF1\": 0.7402334392509421,         \"Memory in Mb\": 15.30208683013916,         \"Time in s\": 7142.743199000001       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7491713454643848,         \"MicroF1\": 0.7491713454643848,         \"MacroF1\": 0.7487081677599373,         \"Memory in Mb\": 11.128735542297363,         \"Time in s\": 8102.097506000001       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7424079803017867,         \"MicroF1\": 0.7424079803017867,         \"MacroF1\": 0.7445532404968841,         \"Memory in Mb\": 16.950417518615723,         \"Time in s\": 9128.379042       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7382657591003255,         \"MicroF1\": 0.7382657591003255,         \"MacroF1\": 0.7427378731329454,         \"Memory in Mb\": 18.26229953765869,         \"Time in s\": 10214.572621       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7309342097933262,         \"MicroF1\": 0.7309342097933262,         \"MacroF1\": 0.7368436311738037,         \"Memory in Mb\": 23.77636337280273,         \"Time in s\": 11358.099531000002       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7429368127531962,         \"MicroF1\": 0.7429368127531962,         \"MacroF1\": 0.7441354243297112,         \"Memory in Mb\": 12.95803928375244,         \"Time in s\": 12553.803014       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7475950755121368,         \"MicroF1\": 0.7475950755121367,         \"MacroF1\": 0.7439196968116685,         \"Memory in Mb\": 12.612845420837402,         \"Time in s\": 13796.415893       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7492305506889531,         \"MicroF1\": 0.7492305506889531,         \"MacroF1\": 0.7418613509588597,         \"Memory in Mb\": 16.95127773284912,         \"Time in s\": 15088.238885       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7509808342728298,         \"MicroF1\": 0.7509808342728299,         \"MacroF1\": 0.7400929587109365,         \"Memory in Mb\": 17.926865577697754,         \"Time in s\": 16424.025269       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7532176832680471,         \"MicroF1\": 0.7532176832680472,         \"MacroF1\": 0.7391930166872092,         \"Memory in Mb\": 20.93969821929932,         \"Time in s\": 17798.240955       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7550129699015935,         \"MicroF1\": 0.7550129699015935,         \"MacroF1\": 0.7379653286035112,         \"Memory in Mb\": 25.43882942199707,         \"Time in s\": 19212.969178       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7569743124334136,         \"MicroF1\": 0.7569743124334136,         \"MacroF1\": 0.7375346698329149,         \"Memory in Mb\": 29.94521999359131,         \"Time in s\": 20668.368585       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7580590173870222,         \"MicroF1\": 0.7580590173870221,         \"MacroF1\": 0.7363169253318035,         \"Memory in Mb\": 34.1699275970459,         \"Time in s\": 22166.950006       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7593880896011656,         \"MicroF1\": 0.7593880896011656,         \"MacroF1\": 0.7352131419868576,         \"Memory in Mb\": 32.93678665161133,         \"Time in s\": 23706.536377       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7573217354705202,         \"MicroF1\": 0.7573217354705202,         \"MacroF1\": 0.7350502568377754,         \"Memory in Mb\": 21.273219108581543,         \"Time in s\": 25286.984696       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7555382690161329,         \"MicroF1\": 0.7555382690161329,         \"MacroF1\": 0.7386915112539557,         \"Memory in Mb\": 20.747055053710938,         \"Time in s\": 26906.631088       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7544982529471312,         \"MicroF1\": 0.7544982529471312,         \"MacroF1\": 0.7426503125712552,         \"Memory in Mb\": 24.91079425811768,         \"Time in s\": 28562.795387       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7531487736355315,         \"MicroF1\": 0.7531487736355315,         \"MacroF1\": 0.7453200395899969,         \"Memory in Mb\": 32.13512706756592,         \"Time in s\": 30253.076649       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7530471971895525,         \"MicroF1\": 0.7530471971895525,         \"MacroF1\": 0.7484606399297139,         \"Memory in Mb\": 36.17057991027832,         \"Time in s\": 31977.334616       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7480986061377289,         \"MicroF1\": 0.748098606137729,         \"MacroF1\": 0.7448942365218528,         \"Memory in Mb\": 13.298456192016602,         \"Time in s\": 33736.870240000004       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7436795133010016,         \"MicroF1\": 0.7436795133010016,         \"MacroF1\": 0.7403442775964885,         \"Memory in Mb\": 15.221885681152344,         \"Time in s\": 35530.857132000005       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7404952232403977,         \"MicroF1\": 0.7404952232403977,         \"MacroF1\": 0.7368033013057004,         \"Memory in Mb\": 16.932289123535156,         \"Time in s\": 37356.72126300001       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7371411564165697,         \"MicroF1\": 0.7371411564165696,         \"MacroF1\": 0.7332530467261859,         \"Memory in Mb\": 22.237309455871586,         \"Time in s\": 39213.91358200001       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7341049585689859,         \"MicroF1\": 0.7341049585689859,         \"MacroF1\": 0.7299460315219516,         \"Memory in Mb\": 22.86026954650879,         \"Time in s\": 41100.10013700001       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7343042154027284,         \"MicroF1\": 0.7343042154027284,         \"MacroF1\": 0.7301016033872143,         \"Memory in Mb\": 21.91624164581299,         \"Time in s\": 43017.482867000006       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7327734443143021,         \"MicroF1\": 0.7327734443143021,         \"MacroF1\": 0.728948208474553,         \"Memory in Mb\": 20.388718605041504,         \"Time in s\": 44961.93393100001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7327538061821626,         \"MicroF1\": 0.7327538061821626,         \"MacroF1\": 0.7292630064673854,         \"Memory in Mb\": 15.630711555480955,         \"Time in s\": 46951.12268600001       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7331849712351145,         \"MicroF1\": 0.7331849712351144,         \"MacroF1\": 0.7301128191332076,         \"Memory in Mb\": 20.110919952392575,         \"Time in s\": 48959.16072000001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7337567848481349,         \"MicroF1\": 0.7337567848481349,         \"MacroF1\": 0.7309969621648841,         \"Memory in Mb\": 24.057676315307617,         \"Time in s\": 50985.068017000005       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7342111790038556,         \"MicroF1\": 0.7342111790038556,         \"MacroF1\": 0.731637560144403,         \"Memory in Mb\": 28.52964782714844,         \"Time in s\": 53028.942305       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7351289448763406,         \"MicroF1\": 0.7351289448763407,         \"MacroF1\": 0.7324911060941295,         \"Memory in Mb\": 28.861422538757324,         \"Time in s\": 55091.119898       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7357682457008803,         \"MicroF1\": 0.7357682457008803,         \"MacroF1\": 0.7329742877599967,         \"Memory in Mb\": 33.07672500610352,         \"Time in s\": 57170.52325500001       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7366947957659041,         \"MicroF1\": 0.736694795765904,         \"MacroF1\": 0.7341498113226347,         \"Memory in Mb\": 21.3528356552124,         \"Time in s\": 59267.07909500001       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7403602676273804,         \"MicroF1\": 0.7403602676273804,         \"MacroF1\": 0.7381372580344014,         \"Memory in Mb\": 19.381468772888184,         \"Time in s\": 61379.50627500001       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7442122866756664,         \"MicroF1\": 0.7442122866756663,         \"MacroF1\": 0.742109373234967,         \"Memory in Mb\": 21.8067569732666,         \"Time in s\": 63507.849119000006       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7475289521968157,         \"MicroF1\": 0.7475289521968157,         \"MacroF1\": 0.7453466445950636,         \"Memory in Mb\": 21.65154266357422,         \"Time in s\": 65647.81315       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7510581141410433,         \"MicroF1\": 0.7510581141410433,         \"MacroF1\": 0.7487124138061083,         \"Memory in Mb\": 22.87060165405273,         \"Time in s\": 67797.610737       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7545218659444307,         \"MicroF1\": 0.7545218659444307,         \"MacroF1\": 0.752582163258218,         \"Memory in Mb\": 10.55445957183838,         \"Time in s\": 69956.07865499999       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7547448294132117,         \"MicroF1\": 0.7547448294132117,         \"MacroF1\": 0.7528178949021433,         \"Memory in Mb\": 10.58643913269043,         \"Time in s\": 72115.038215       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 1.786503791809082,         \"Time in s\": 20.578742       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.98159509202454,         \"MicroF1\": 0.98159509202454,         \"MacroF1\": 0.9278568842209168,         \"Memory in Mb\": 6.9002227783203125,         \"Time in s\": 101.280083       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803761242845462,         \"MicroF1\": 0.9803761242845462,         \"MacroF1\": 0.9574942570636208,         \"Memory in Mb\": 9.112634658813477,         \"Time in s\": 223.840162       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9779276517473944,         \"MicroF1\": 0.9779276517473944,         \"MacroF1\": 0.9432755457272628,         \"Memory in Mb\": 10.40715503692627,         \"Time in s\": 381.844655       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.973516429622364,         \"MicroF1\": 0.973516429622364,         \"MacroF1\": 0.9361356188587968,         \"Memory in Mb\": 12.656171798706056,         \"Time in s\": 575.269036       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9726195341234164,         \"MicroF1\": 0.9726195341234164,         \"MacroF1\": 0.9612590316809274,         \"Memory in Mb\": 8.745987892150879,         \"Time in s\": 802.257021       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754816112084064,         \"MicroF1\": 0.9754816112084064,         \"MacroF1\": 0.975146989141396,         \"Memory in Mb\": 9.931495666503906,         \"Time in s\": 1061.688609       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754826846460312,         \"MicroF1\": 0.9754826846460312,         \"MacroF1\": 0.9697604489278108,         \"Memory in Mb\": 10.511832237243652,         \"Time in s\": 1352.298412       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9733042767638246,         \"MicroF1\": 0.9733042767638246,         \"MacroF1\": 0.9642745555297418,         \"Memory in Mb\": 11.800049781799316,         \"Time in s\": 1675.333833       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9722971316499142,         \"MicroF1\": 0.9722971316499142,         \"MacroF1\": 0.9666413905932107,         \"Memory in Mb\": 12.42660903930664,         \"Time in s\": 2030.177258       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9734789391575664,         \"MicroF1\": 0.9734789391575664,         \"MacroF1\": 0.9728883985144964,         \"Memory in Mb\": 9.746350288391112,         \"Time in s\": 2413.735444       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9740551583248211,         \"MicroF1\": 0.9740551583248211,         \"MacroF1\": 0.9730015599884004,         \"Memory in Mb\": 10.666529655456545,         \"Time in s\": 2823.505547       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9741655666603808,         \"MicroF1\": 0.9741655666603808,         \"MacroF1\": 0.9728266773902404,         \"Memory in Mb\": 11.775634765625,         \"Time in s\": 3261.739577       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9747855016634565,         \"MicroF1\": 0.9747855016634565,         \"MacroF1\": 0.9744326987999562,         \"Memory in Mb\": 12.58005428314209,         \"Time in s\": 3727.994683       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9751593397613988,         \"MicroF1\": 0.9751593397613988,         \"MacroF1\": 0.9747223863351728,         \"Memory in Mb\": 13.55466365814209,         \"Time in s\": 4223.159482999999       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9751800214493642,         \"MicroF1\": 0.9751800214493642,         \"MacroF1\": 0.974525548169428,         \"Memory in Mb\": 11.360074043273926,         \"Time in s\": 4747.988555       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9763518385003604,         \"MicroF1\": 0.9763518385003604,         \"MacroF1\": 0.9769458779347456,         \"Memory in Mb\": 11.155635833740234,         \"Time in s\": 5300.577354999999       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9765763311997822,         \"MicroF1\": 0.9765763311997822,         \"MacroF1\": 0.976392359672136,         \"Memory in Mb\": 12.33658504486084,         \"Time in s\": 5881.207234       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9771642368726616,         \"MicroF1\": 0.9771642368726616,         \"MacroF1\": 0.9773496343719736,         \"Memory in Mb\": 13.116165161132812,         \"Time in s\": 6492.775159       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.976590268415247,         \"MicroF1\": 0.976590268415247,         \"MacroF1\": 0.975927508407602,         \"Memory in Mb\": 13.303799629211426,         \"Time in s\": 7137.299991       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9768880588303958,         \"MicroF1\": 0.9768880588303958,         \"MacroF1\": 0.9769304999907084,         \"Memory in Mb\": 13.133574485778809,         \"Time in s\": 7814.540539       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9774930362116993,         \"MicroF1\": 0.9774930362116993,         \"MacroF1\": 0.9777587646121524,         \"Memory in Mb\": 13.50635814666748,         \"Time in s\": 8523.072866       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9767664925929872,         \"MicroF1\": 0.9767664925929872,         \"MacroF1\": 0.9763135719034828,         \"Memory in Mb\": 15.166536331176758,         \"Time in s\": 9263.702674       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9765090389132876,         \"MicroF1\": 0.9765090389132876,         \"MacroF1\": 0.9763153416047448,         \"Memory in Mb\": 16.169885635375977,         \"Time in s\": 10037.443626       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9758799882341406,         \"MicroF1\": 0.9758799882341406,         \"MacroF1\": 0.9755246287395946,         \"Memory in Mb\": 14.205968856811523,         \"Time in s\": 10844.068015       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9755821627227302,         \"MicroF1\": 0.9755821627227302,         \"MacroF1\": 0.9754319444516872,         \"Memory in Mb\": 12.997503280639648,         \"Time in s\": 11685.064117       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759418974126192,         \"MicroF1\": 0.9759418974126192,         \"MacroF1\": 0.9761027289556774,         \"Memory in Mb\": 12.962043762207031,         \"Time in s\": 12559.39796       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9760133064869124,         \"MicroF1\": 0.9760133064869124,         \"MacroF1\": 0.9760613734021468,         \"Memory in Mb\": 14.09043312072754,         \"Time in s\": 13467.395857       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754881244188996,         \"MicroF1\": 0.9754881244188996,         \"MacroF1\": 0.9753195915858492,         \"Memory in Mb\": 14.295487403869627,         \"Time in s\": 14408.853786       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759784296102624,         \"MicroF1\": 0.9759784296102624,         \"MacroF1\": 0.9761779987511396,         \"Memory in Mb\": 15.044499397277832,         \"Time in s\": 15385.688043       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9762789594370206,         \"MicroF1\": 0.9762789594370206,         \"MacroF1\": 0.9764127823145236,         \"Memory in Mb\": 15.120206832885742,         \"Time in s\": 16404.149055       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9758713136729222,         \"MicroF1\": 0.975871313672922,         \"MacroF1\": 0.975797420384815,         \"Memory in Mb\": 15.049361228942873,         \"Time in s\": 17460.942559000003       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9757112084973631,         \"MicroF1\": 0.9757112084973631,         \"MacroF1\": 0.9757165619520196,         \"Memory in Mb\": 15.162266731262209,         \"Time in s\": 18558.798501       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759930790858626,         \"MicroF1\": 0.9759930790858626,         \"MacroF1\": 0.9761084708221816,         \"Memory in Mb\": 15.711796760559082,         \"Time in s\": 19695.838422       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754884795854052,         \"MicroF1\": 0.9754884795854052,         \"MacroF1\": 0.975424480421301,         \"Memory in Mb\": 16.988737106323242,         \"Time in s\": 20872.643227       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.975624702117519,         \"MicroF1\": 0.975624702117519,         \"MacroF1\": 0.9757017096421696,         \"Memory in Mb\": 17.869779586791992,         \"Time in s\": 22084.930025       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9757535607817158,         \"MicroF1\": 0.9757535607817158,         \"MacroF1\": 0.9758249143111628,         \"Memory in Mb\": 17.579912185668945,         \"Time in s\": 23330.568569000003       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9755531187512094,         \"MicroF1\": 0.9755531187512094,         \"MacroF1\": 0.9755669148190674,         \"Memory in Mb\": 16.59157657623291,         \"Time in s\": 24610.336028       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9756772044497516,         \"MicroF1\": 0.9756772044497516,         \"MacroF1\": 0.97573890775282,         \"Memory in Mb\": 16.193113327026367,         \"Time in s\": 25925.188427       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759176420123782,         \"MicroF1\": 0.9759176420123782,         \"MacroF1\": 0.9759886766110538,         \"Memory in Mb\": 16.353660583496094,         \"Time in s\": 27266.573062       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9756680815448078,         \"MicroF1\": 0.9756680815448078,         \"MacroF1\": 0.9756766431570708,         \"Memory in Mb\": 17.00908374786377,         \"Time in s\": 28641.859399       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9758389261744966,         \"MicroF1\": 0.9758389261744966,         \"MacroF1\": 0.975891563489883,         \"Memory in Mb\": 18.364989280700684,         \"Time in s\": 30047.550521       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9753747933648748,         \"MicroF1\": 0.9753747933648748,         \"MacroF1\": 0.975363882573194,         \"Memory in Mb\": 17.298136711120605,         \"Time in s\": 31485.782589       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9753217090969862,         \"MicroF1\": 0.9753217090969862,         \"MacroF1\": 0.9753429667022142,         \"Memory in Mb\": 16.72727108001709,         \"Time in s\": 32956.927282000004       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754888610490768,         \"MicroF1\": 0.9754888610490768,         \"MacroF1\": 0.9755190387029732,         \"Memory in Mb\": 17.51059913635254,         \"Time in s\": 34461.639008000006       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9757553151809026,         \"MicroF1\": 0.9757553151809026,         \"MacroF1\": 0.9757835195290104,         \"Memory in Mb\": 18.871691703796387,         \"Time in s\": 35998.873267       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754367666232072,         \"MicroF1\": 0.9754367666232072,         \"MacroF1\": 0.9754369138844644,         \"Memory in Mb\": 17.42948341369629,         \"Time in s\": 37568.082084       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754889444926722,         \"MicroF1\": 0.9754889444926722,         \"MacroF1\": 0.9754964783302286,         \"Memory in Mb\": 17.978480339050293,         \"Time in s\": 39170.395427       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9756390375669052,         \"MicroF1\": 0.9756390375669052,         \"MacroF1\": 0.975642520227376,         \"Memory in Mb\": 19.26256561279297,         \"Time in s\": 40805.125646       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754889945585568,         \"MicroF1\": 0.9754889945585568,         \"MacroF1\": 0.9754863274548964,         \"Memory in Mb\": 18.711057662963867,         \"Time in s\": 42471.761869       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4666666666666667,         \"MicroF1\": 0.4666666666666667,         \"MacroF1\": 0.3890768588137009,         \"Memory in Mb\": 0.9137420654296876,         \"Time in s\": 0.663852       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6153846153846154,         \"MicroF1\": 0.6153846153846154,         \"MacroF1\": 0.617040786788686,         \"Memory in Mb\": 0.9906883239746094,         \"Time in s\": 2.032737       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6715328467153284,         \"MicroF1\": 0.6715328467153284,         \"MacroF1\": 0.6884491245817251,         \"Memory in Mb\": 1.067914962768555,         \"Time in s\": 4.226265       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7049180327868853,         \"MicroF1\": 0.7049180327868853,         \"MacroF1\": 0.7194266051408907,         \"Memory in Mb\": 1.1443958282470703,         \"Time in s\": 7.386208       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7292576419213974,         \"MicroF1\": 0.7292576419213974,         \"MacroF1\": 0.7448338459304749,         \"Memory in Mb\": 1.2214689254760742,         \"Time in s\": 11.723904       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7381818181818182,         \"MicroF1\": 0.7381818181818182,         \"MacroF1\": 0.7559766728000937,         \"Memory in Mb\": 1.2995519638061523,         \"Time in s\": 17.331033       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7538940809968847,         \"MicroF1\": 0.7538940809968847,         \"MacroF1\": 0.7616248500949714,         \"Memory in Mb\": 1.3766565322875977,         \"Time in s\": 24.26159       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.773841961852861,         \"MicroF1\": 0.7738419618528611,         \"MacroF1\": 0.7772939373537765,         \"Memory in Mb\": 1.4532833099365234,         \"Time in s\": 32.770568000000004       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7820823244552058,         \"MicroF1\": 0.7820823244552059,         \"MacroF1\": 0.7854200812154107,         \"Memory in Mb\": 1.530414581298828,         \"Time in s\": 42.983195       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7777777777777778,         \"MicroF1\": 0.7777777777777778,         \"MacroF1\": 0.7796254955467015,         \"Memory in Mb\": 1.6075658798217771,         \"Time in s\": 54.886431       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7861386138613862,         \"MicroF1\": 0.7861386138613862,         \"MacroF1\": 0.7886239053396241,         \"Memory in Mb\": 3.8640270233154297,         \"Time in s\": 87.00222099999999       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7858439201451906,         \"MicroF1\": 0.7858439201451906,         \"MacroF1\": 0.7889431335032357,         \"Memory in Mb\": 4.088808059692383,         \"Time in s\": 121.00394599999998       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7906197654941374,         \"MicroF1\": 0.7906197654941374,         \"MacroF1\": 0.7944387660679091,         \"Memory in Mb\": 4.304059028625488,         \"Time in s\": 157.00397999999998       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7853810264385692,         \"MicroF1\": 0.7853810264385692,         \"MacroF1\": 0.7901251252871709,         \"Memory in Mb\": 4.532710075378418,         \"Time in s\": 195.073691       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7895500725689405,         \"MicroF1\": 0.7895500725689405,         \"MacroF1\": 0.7935315861788143,         \"Memory in Mb\": 4.759090423583984,         \"Time in s\": 235.272046       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7863945578231293,         \"MicroF1\": 0.7863945578231294,         \"MacroF1\": 0.7911065855691086,         \"Memory in Mb\": 4.991429328918457,         \"Time in s\": 277.59962       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7887323943661971,         \"MicroF1\": 0.7887323943661971,         \"MacroF1\": 0.792926322670609,         \"Memory in Mb\": 5.219735145568848,         \"Time in s\": 322.071315       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7896009673518742,         \"MicroF1\": 0.7896009673518742,         \"MacroF1\": 0.7950712422059908,         \"Memory in Mb\": 5.452417373657227,         \"Time in s\": 368.82718       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7938144329896907,         \"MicroF1\": 0.7938144329896907,         \"MacroF1\": 0.7979586706142276,         \"Memory in Mb\": 5.699496269226074,         \"Time in s\": 417.885664       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.794341675734494,         \"MicroF1\": 0.794341675734494,         \"MacroF1\": 0.7973145688626199,         \"Memory in Mb\": 5.9376373291015625,         \"Time in s\": 469.16999       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7937823834196891,         \"MicroF1\": 0.7937823834196891,         \"MacroF1\": 0.7958827691316667,         \"Memory in Mb\": 6.182188987731934,         \"Time in s\": 522.9385980000001       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7912957467853611,         \"MicroF1\": 0.7912957467853611,         \"MacroF1\": 0.7931630938612351,         \"Memory in Mb\": 6.34267520904541,         \"Time in s\": 579.2700850000001       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.793755912961211,         \"MicroF1\": 0.7937559129612108,         \"MacroF1\": 0.7947921362588558,         \"Memory in Mb\": 6.295009613037109,         \"Time in s\": 638.2805060000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7941976427923844,         \"MicroF1\": 0.7941976427923844,         \"MacroF1\": 0.7951664828862093,         \"Memory in Mb\": 6.2213640213012695,         \"Time in s\": 699.725726       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7954743255004352,         \"MicroF1\": 0.7954743255004351,         \"MacroF1\": 0.7958304956922065,         \"Memory in Mb\": 6.151959419250488,         \"Time in s\": 763.5071       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.796652719665272,         \"MicroF1\": 0.796652719665272,         \"MacroF1\": 0.7972397572733622,         \"Memory in Mb\": 6.087224006652832,         \"Time in s\": 829.5043310000001       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7953263497179693,         \"MicroF1\": 0.7953263497179693,         \"MacroF1\": 0.795947547023496,         \"Memory in Mb\": 6.001987457275391,         \"Time in s\": 897.6078460000001       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7995337995337995,         \"MicroF1\": 0.7995337995337995,         \"MacroF1\": 0.799082939294124,         \"Memory in Mb\": 5.924266815185547,         \"Time in s\": 967.722432       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7981995498874719,         \"MicroF1\": 0.7981995498874719,         \"MacroF1\": 0.7978549794399667,         \"Memory in Mb\": 5.872907638549805,         \"Time in s\": 1039.926656       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7991298042059464,         \"MicroF1\": 0.7991298042059464,         \"MacroF1\": 0.799072028035076,         \"Memory in Mb\": 5.784454345703125,         \"Time in s\": 1114.0085680000002       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8007017543859649,         \"MicroF1\": 0.8007017543859649,         \"MacroF1\": 0.799801266098334,         \"Memory in Mb\": 5.781437873840332,         \"Time in s\": 1190.125915       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8042148198504419,         \"MicroF1\": 0.8042148198504419,         \"MacroF1\": 0.8016037490391381,         \"Memory in Mb\": 5.805401802062988,         \"Time in s\": 1268.322504       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048780487804879,         \"MicroF1\": 0.8048780487804877,         \"MacroF1\": 0.8013581039030082,         \"Memory in Mb\": 5.915700912475586,         \"Time in s\": 1348.933518       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048624440179143,         \"MicroF1\": 0.8048624440179143,         \"MacroF1\": 0.8017038254481382,         \"Memory in Mb\": 6.069503784179688,         \"Time in s\": 1431.999326       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048477315102548,         \"MicroF1\": 0.8048477315102549,         \"MacroF1\": 0.8009666848419111,         \"Memory in Mb\": 6.138180732727051,         \"Time in s\": 1517.316045       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.804833836858006,         \"MicroF1\": 0.804833836858006,         \"MacroF1\": 0.8009346118743482,         \"Memory in Mb\": 6.15428638458252,         \"Time in s\": 1604.689641       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048206937095826,         \"MicroF1\": 0.8048206937095828,         \"MacroF1\": 0.802987300619633,         \"Memory in Mb\": 6.14796257019043,         \"Time in s\": 1694.105126       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8065254722381225,         \"MicroF1\": 0.8065254722381225,         \"MacroF1\": 0.8041280306488863,         \"Memory in Mb\": 6.185528755187988,         \"Time in s\": 1785.64513       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8070273284997211,         \"MicroF1\": 0.8070273284997211,         \"MacroF1\": 0.8033862119520573,         \"Memory in Mb\": 6.18717098236084,         \"Time in s\": 1879.221363       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8085916258836324,         \"MicroF1\": 0.8085916258836324,         \"MacroF1\": 0.8051706679397826,         \"Memory in Mb\": 6.228180885314941,         \"Time in s\": 1974.88599       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8074270557029177,         \"MicroF1\": 0.8074270557029178,         \"MacroF1\": 0.8044133208197751,         \"Memory in Mb\": 6.244633674621582,         \"Time in s\": 2072.712055       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8073537027446919,         \"MicroF1\": 0.8073537027446919,         \"MacroF1\": 0.8036280810428232,         \"Memory in Mb\": 6.232837677001953,         \"Time in s\": 2172.610836       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.808295397066262,         \"MicroF1\": 0.808295397066262,         \"MacroF1\": 0.8041943782356388,         \"Memory in Mb\": 6.225313186645508,         \"Time in s\": 2274.502409       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8096885813148789,         \"MicroF1\": 0.809688581314879,         \"MacroF1\": 0.8043903689108628,         \"Memory in Mb\": 6.209332466125488,         \"Time in s\": 2378.336668       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8086031899468342,         \"MicroF1\": 0.8086031899468342,         \"MacroF1\": 0.8034099584264852,         \"Memory in Mb\": 6.192641258239746,         \"Time in s\": 2484.108554       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.808983451536643,         \"MicroF1\": 0.808983451536643,         \"MacroF1\": 0.8029929757635029,         \"Memory in Mb\": 6.163993835449219,         \"Time in s\": 2591.83622       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8093475242943082,         \"MicroF1\": 0.8093475242943081,         \"MacroF1\": 0.8028985652670257,         \"Memory in Mb\": 6.160528182983398,         \"Time in s\": 2701.493184       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8110557317625736,         \"MicroF1\": 0.8110557317625736,         \"MacroF1\": 0.8037088502350873,         \"Memory in Mb\": 6.127141952514648,         \"Time in s\": 2812.975729       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8078118064802485,         \"MicroF1\": 0.8078118064802485,         \"MacroF1\": 0.8004652010359966,         \"Memory in Mb\": 6.094814300537109,         \"Time in s\": 2926.384262       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8064375815571988,         \"MicroF1\": 0.8064375815571988,         \"MacroF1\": 0.7990276111502428,         \"Memory in Mb\": 6.073050498962402,         \"Time in s\": 3041.734776       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8064097011693374,         \"MicroF1\": 0.8064097011693374,         \"MacroF1\": 0.7989986920740723,         \"Memory in Mb\": 6.073922157287598,         \"Time in s\": 3157.943153       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6293838862559241,         \"MicroF1\": 0.6293838862559241,         \"MacroF1\": 0.5938169901557457,         \"Memory in Mb\": 7.681754112243652,         \"Time in s\": 78.197886       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6290857413548081,         \"MicroF1\": 0.6290857413548081,         \"MacroF1\": 0.5936238360694311,         \"Memory in Mb\": 7.563845634460449,         \"Time in s\": 217.436369       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.625197347647616,         \"MicroF1\": 0.625197347647616,         \"MacroF1\": 0.5890732389154221,         \"Memory in Mb\": 7.54627799987793,         \"Time in s\": 406.781755       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.624437603599337,         \"MicroF1\": 0.624437603599337,         \"MacroF1\": 0.5890978975177876,         \"Memory in Mb\": 7.509035110473633,         \"Time in s\": 643.136123       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6309907179390036,         \"MicroF1\": 0.6309907179390036,         \"MacroF1\": 0.5943307513870396,         \"Memory in Mb\": 7.529419898986816,         \"Time in s\": 922.055301       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6249408050513023,         \"MicroF1\": 0.6249408050513023,         \"MacroF1\": 0.5899587518293812,         \"Memory in Mb\": 7.541637420654297,         \"Time in s\": 1240.879558       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6242727641726424,         \"MicroF1\": 0.6242727641726424,         \"MacroF1\": 0.589208790087756,         \"Memory in Mb\": 7.519943237304687,         \"Time in s\": 1598.2590730000002       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6266129986977625,         \"MicroF1\": 0.6266129986977625,         \"MacroF1\": 0.5910042020201396,         \"Memory in Mb\": 7.600367546081543,         \"Time in s\": 1990.9287910000005       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6255919183415763,         \"MicroF1\": 0.6255919183415763,         \"MacroF1\": 0.5892477749449755,         \"Memory in Mb\": 7.551809310913086,         \"Time in s\": 2416.671036       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6269533099725353,         \"MicroF1\": 0.6269533099725353,         \"MacroF1\": 0.5906555376897765,         \"Memory in Mb\": 7.57810115814209,         \"Time in s\": 2875.240995       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6254842875591907,         \"MicroF1\": 0.6254842875591907,         \"MacroF1\": 0.5899069142128334,         \"Memory in Mb\": 7.574300765991211,         \"Time in s\": 3366.8452850000003       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6276536974193039,         \"MicroF1\": 0.6276536974193039,         \"MacroF1\": 0.5948280902959312,         \"Memory in Mb\": 7.593076705932617,         \"Time in s\": 3891.533291       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6419465287389816,         \"MicroF1\": 0.6419465287389816,         \"MacroF1\": 0.6240594787506325,         \"Memory in Mb\": 7.568525314331055,         \"Time in s\": 4449.097087       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6349861327200162,         \"MicroF1\": 0.6349861327200162,         \"MacroF1\": 0.6168664949740267,         \"Memory in Mb\": 7.497129440307617,         \"Time in s\": 5038.3500540000005       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6042048109097796,         \"MicroF1\": 0.6042048109097796,         \"MacroF1\": 0.5876183517420878,         \"Memory in Mb\": 7.622871398925781,         \"Time in s\": 5663.9066330000005       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5831311038768866,         \"MicroF1\": 0.5831311038768866,         \"MacroF1\": 0.5677288238088704,         \"Memory in Mb\": 7.5406084060668945,         \"Time in s\": 6323.428796       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5683805916104953,         \"MicroF1\": 0.5683805916104953,         \"MacroF1\": 0.5530005563922373,         \"Memory in Mb\": 7.511743545532227,         \"Time in s\": 7015.247243       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5655811016993739,         \"MicroF1\": 0.5655811016993739,         \"MacroF1\": 0.5465928919365096,         \"Memory in Mb\": 7.569133758544922,         \"Time in s\": 7739.601247       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5718985196630614,         \"MicroF1\": 0.5718985196630614,         \"MacroF1\": 0.5506497035356593,         \"Memory in Mb\": 8.179316520690918,         \"Time in s\": 8496.204598999999       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5817510298783086,         \"MicroF1\": 0.5817510298783086,         \"MacroF1\": 0.55937505855693,         \"Memory in Mb\": 8.13927173614502,         \"Time in s\": 9285.092110999998       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5905298759864712,         \"MicroF1\": 0.5905298759864712,         \"MacroF1\": 0.5668099949242361,         \"Memory in Mb\": 8.13715648651123,         \"Time in s\": 10104.551326       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6004907236020834,         \"MicroF1\": 0.6004907236020834,         \"MacroF1\": 0.5756153967719769,         \"Memory in Mb\": 8.254791259765625,         \"Time in s\": 10955.282648       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088854119487792,         \"MicroF1\": 0.6088854119487792,         \"MacroF1\": 0.5822871692574689,         \"Memory in Mb\": 8.217899322509766,         \"Time in s\": 11836.441737999998       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.617014560233595,         \"MicroF1\": 0.617014560233595,         \"MacroF1\": 0.5890646667396601,         \"Memory in Mb\": 8.13050651550293,         \"Time in s\": 12747.590801999995       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6237357475661957,         \"MicroF1\": 0.6237357475661957,         \"MacroF1\": 0.5942060376379845,         \"Memory in Mb\": 8.178851127624512,         \"Time in s\": 13688.250944999996       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6299763248952832,         \"MicroF1\": 0.6299763248952832,         \"MacroF1\": 0.5983574644866619,         \"Memory in Mb\": 8.215079307556152,         \"Time in s\": 14661.447404999995       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6312651257409421,         \"MicroF1\": 0.6312651257409421,         \"MacroF1\": 0.6016879522351425,         \"Memory in Mb\": 8.160200119018555,         \"Time in s\": 15669.084531999995       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6310751851726587,         \"MicroF1\": 0.6310751851726587,         \"MacroF1\": 0.6062390002054064,         \"Memory in Mb\": 8.153844833374023,         \"Time in s\": 16709.899933999994       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6313228619011854,         \"MicroF1\": 0.6313228619011854,         \"MacroF1\": 0.610710416812842,         \"Memory in Mb\": 8.221953392028809,         \"Time in s\": 17785.196262999994       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6320590927743931,         \"MicroF1\": 0.6320590927743931,         \"MacroF1\": 0.614817700164209,         \"Memory in Mb\": 8.237210273742676,         \"Time in s\": 18894.010558999995       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6331144035436077,         \"MicroF1\": 0.6331144035436077,         \"MacroF1\": 0.6184679282473909,         \"Memory in Mb\": 8.208189964294434,         \"Time in s\": 20033.816622999995       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6291616110798733,         \"MicroF1\": 0.6291616110798733,         \"MacroF1\": 0.6151628967287334,         \"Memory in Mb\": 8.149331092834473,         \"Time in s\": 21206.789184999998       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6245587855482538,         \"MicroF1\": 0.6245587855482538,         \"MacroF1\": 0.6103108800280445,         \"Memory in Mb\": 8.270771980285645,         \"Time in s\": 22409.569843999994       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6211737180736986,         \"MicroF1\": 0.6211737180736986,         \"MacroF1\": 0.6063163580543118,         \"Memory in Mb\": 8.246885299682617,         \"Time in s\": 23639.112909       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6171433209772992,         \"MicroF1\": 0.6171433209772992,         \"MacroF1\": 0.6018416894357856,         \"Memory in Mb\": 8.222872734069824,         \"Time in s\": 24895.212451       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6153360515585953,         \"MicroF1\": 0.6153360515585953,         \"MacroF1\": 0.5996210858832133,         \"Memory in Mb\": 8.711487770080566,         \"Time in s\": 26177.407049999994       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.613472908295155,         \"MicroF1\": 0.613472908295155,         \"MacroF1\": 0.5980758777202522,         \"Memory in Mb\": 8.84398365020752,         \"Time in s\": 27486.887242999997       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6139008647544048,         \"MicroF1\": 0.6139008647544048,         \"MacroF1\": 0.5993833357378361,         \"Memory in Mb\": 9.00393295288086,         \"Time in s\": 28821.579146999997       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6157395041643396,         \"MicroF1\": 0.6157395041643396,         \"MacroF1\": 0.6018873090815099,         \"Memory in Mb\": 8.895415306091309,         \"Time in s\": 30174.675792999995       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6179833802883591,         \"MicroF1\": 0.6179833802883591,         \"MacroF1\": 0.6047393094362844,         \"Memory in Mb\": 8.820836067199707,         \"Time in s\": 31551.592344999997       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6202101859337106,         \"MicroF1\": 0.6202101859337106,         \"MacroF1\": 0.60743097275183,         \"Memory in Mb\": 8.80302619934082,         \"Time in s\": 32950.21258099999       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6221054767648982,         \"MicroF1\": 0.6221054767648982,         \"MacroF1\": 0.6097047537791253,         \"Memory in Mb\": 8.807188034057617,         \"Time in s\": 34370.71930599999       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.623736428304006,         \"MicroF1\": 0.623736428304006,         \"MacroF1\": 0.6112415003179203,         \"Memory in Mb\": 8.906554222106934,         \"Time in s\": 35814.22252799999       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6259389191399608,         \"MicroF1\": 0.6259389191399608,         \"MacroF1\": 0.6133867892257391,         \"Memory in Mb\": 8.822076797485352,         \"Time in s\": 37279.498287       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6274542814453166,         \"MicroF1\": 0.6274542814453166,         \"MacroF1\": 0.6153714367024555,         \"Memory in Mb\": 8.875716209411621,         \"Time in s\": 38770.246246       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6317858980957283,         \"MicroF1\": 0.6317858980957283,         \"MacroF1\": 0.6202967225132047,         \"Memory in Mb\": 8.86828327178955,         \"Time in s\": 40284.403256       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360137817090125,         \"MicroF1\": 0.6360137817090125,         \"MacroF1\": 0.6247992459885968,         \"Memory in Mb\": 8.835649490356445,         \"Time in s\": 41820.805008       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6403811628228145,         \"MicroF1\": 0.6403811628228145,         \"MacroF1\": 0.6293790828873279,         \"Memory in Mb\": 8.924153327941895,         \"Time in s\": 43378.957976       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6455559206076185,         \"MicroF1\": 0.6455559206076185,         \"MacroF1\": 0.6346828420183047,         \"Memory in Mb\": 9.21804904937744,         \"Time in s\": 44959.107881       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.648269853595712,         \"MicroF1\": 0.648269853595712,         \"MacroF1\": 0.6377385869395499,         \"Memory in Mb\": 9.400546073913574,         \"Time in s\": 46560.782       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6485325562472799,         \"MicroF1\": 0.6485325562472799,         \"MacroF1\": 0.637999701607352,         \"Memory in Mb\": 9.406517028808594,         \"Time in s\": 48163.738895       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 1.4587059020996094,         \"Time in s\": 10.139614       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9496932515337424,         \"MicroF1\": 0.9496932515337424,         \"MacroF1\": 0.7435135353411919,         \"Memory in Mb\": 6.019382476806641,         \"Time in s\": 66.737739       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9149632052330336,         \"MicroF1\": 0.9149632052330336,         \"MacroF1\": 0.9012024099743488,         \"Memory in Mb\": 7.076447486877441,         \"Time in s\": 151.07716299999998       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9258123850398527,         \"MicroF1\": 0.9258123850398527,         \"MacroF1\": 0.913338738884437,         \"Memory in Mb\": 7.232892990112305,         \"Time in s\": 261.540164       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9230014713094654,         \"MicroF1\": 0.9230014713094654,         \"MacroF1\": 0.9086113906821328,         \"Memory in Mb\": 7.553393363952637,         \"Time in s\": 397.836215       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8961994278708623,         \"MicroF1\": 0.8961994278708623,         \"MacroF1\": 0.8992132713257572,         \"Memory in Mb\": 7.640434265136719,         \"Time in s\": 558.733108       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9001751313485113,         \"MicroF1\": 0.9001751313485113,         \"MacroF1\": 0.8860451027148403,         \"Memory in Mb\": 7.9326982498168945,         \"Time in s\": 743.600486       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8924302788844621,         \"MicroF1\": 0.8924302788844621,         \"MacroF1\": 0.8761196773917237,         \"Memory in Mb\": 8.074724197387695,         \"Time in s\": 952.077233       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8874965949332607,         \"MicroF1\": 0.8874965949332607,         \"MacroF1\": 0.8846937712308092,         \"Memory in Mb\": 8.20841121673584,         \"Time in s\": 1184.393658       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8815886246629075,         \"MicroF1\": 0.8815886246629075,         \"MacroF1\": 0.868452721773406,         \"Memory in Mb\": 8.525882720947266,         \"Time in s\": 1441.208937       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8760864720303098,         \"MicroF1\": 0.8760864720303098,         \"MacroF1\": 0.8834419600614621,         \"Memory in Mb\": 8.681946754455566,         \"Time in s\": 1719.7568239999998       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8737487231869254,         \"MicroF1\": 0.8737487231869254,         \"MacroF1\": 0.8797220914000274,         \"Memory in Mb\": 8.834684371948242,         \"Time in s\": 2018.974207       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8693192532528757,         \"MicroF1\": 0.8693192532528757,         \"MacroF1\": 0.8538682361373632,         \"Memory in Mb\": 9.067034721374512,         \"Time in s\": 2339.699668       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8607949571003327,         \"MicroF1\": 0.8607949571003327,         \"MacroF1\": 0.8654889627515672,         \"Memory in Mb\": 9.271133422851562,         \"Time in s\": 2680.904224       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8561856512502043,         \"MicroF1\": 0.8561856512502043,         \"MacroF1\": 0.84095068957581,         \"Memory in Mb\": 9.378315925598145,         \"Time in s\": 3042.663698       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8434196414891987,         \"MicroF1\": 0.8434196414891987,         \"MacroF1\": 0.8427350578509161,         \"Memory in Mb\": 9.608606338500977,         \"Time in s\": 3424.478417       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8392213410237923,         \"MicroF1\": 0.8392213410237923,         \"MacroF1\": 0.8447429510460126,         \"Memory in Mb\": 9.751982688903809,         \"Time in s\": 3824.86879       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8454310227427482,         \"MicroF1\": 0.8454310227427482,         \"MacroF1\": 0.847842289102327,         \"Memory in Mb\": 9.957889556884766,         \"Time in s\": 4243.00141       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8456973293768546,         \"MicroF1\": 0.8456973293768547,         \"MacroF1\": 0.8480563212460421,         \"Memory in Mb\": 10.19985294342041,         \"Time in s\": 4680.993142       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8469175144012747,         \"MicroF1\": 0.8469175144012746,         \"MacroF1\": 0.8472851046009279,         \"Memory in Mb\": 10.418806076049805,         \"Time in s\": 5138.9878340000005       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8469709349830746,         \"MicroF1\": 0.8469709349830746,         \"MacroF1\": 0.8501227536717817,         \"Memory in Mb\": 10.607142448425291,         \"Time in s\": 5616.664707000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8475766016713092,         \"MicroF1\": 0.8475766016713092,         \"MacroF1\": 0.8507851780426926,         \"Memory in Mb\": 10.772598266601562,         \"Time in s\": 6113.940894000001       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8459980816370031,         \"MicroF1\": 0.8459980816370031,         \"MacroF1\": 0.8471668648040658,         \"Memory in Mb\": 10.97368335723877,         \"Time in s\": 6631.342845000001       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8418956184250843,         \"MicroF1\": 0.8418956184250843,         \"MacroF1\": 0.8426049398612477,         \"Memory in Mb\": 11.192140579223633,         \"Time in s\": 7169.901201000001       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8344935778017453,         \"MicroF1\": 0.8344935778017454,         \"MacroF1\": 0.8308153568434791,         \"Memory in Mb\": 11.354521751403809,         \"Time in s\": 7729.92345       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.817384745922504,         \"MicroF1\": 0.817384745922504,         \"MacroF1\": 0.8105787344487394,         \"Memory in Mb\": 11.59365177154541,         \"Time in s\": 8312.440227000001       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8127099409895597,         \"MicroF1\": 0.8127099409895597,         \"MacroF1\": 0.8142119266109252,         \"Memory in Mb\": 11.793928146362305,         \"Time in s\": 8918.030696000002       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8079313665411888,         \"MicroF1\": 0.8079313665411888,         \"MacroF1\": 0.8037472320719128,         \"Memory in Mb\": 11.945178031921388,         \"Time in s\": 9547.170938       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8040740427689967,         \"MicroF1\": 0.8040740427689967,         \"MacroF1\": 0.8039730126613296,         \"Memory in Mb\": 12.203582763671877,         \"Time in s\": 10200.281645       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8072554947299616,         \"MicroF1\": 0.8072554947299616,         \"MacroF1\": 0.8097160881214022,         \"Memory in Mb\": 12.414502143859863,         \"Time in s\": 10877.318664       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8043014153554202,         \"MicroF1\": 0.8043014153554202,         \"MacroF1\": 0.8038043720799647,         \"Memory in Mb\": 12.561456680297852,         \"Time in s\": 11578.515438       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7996936039831483,         \"MicroF1\": 0.7996936039831483,         \"MacroF1\": 0.8010057260657798,         \"Memory in Mb\": 12.889472007751465,         \"Time in s\": 12304.325005       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7974448488449826,         \"MicroF1\": 0.7974448488449826,         \"MacroF1\": 0.7996515087686575,         \"Memory in Mb\": 12.99599838256836,         \"Time in s\": 13054.609905       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7978516329031793,         \"MicroF1\": 0.7978516329031793,         \"MacroF1\": 0.8006715750629478,         \"Memory in Mb\": 13.20394229888916,         \"Time in s\": 13829.291085       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.797674907206387,         \"MicroF1\": 0.7976749072063871,         \"MacroF1\": 0.8002875748518964,         \"Memory in Mb\": 13.36452293395996,         \"Time in s\": 14628.347686       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8007761966364813,         \"MicroF1\": 0.8007761966364813,         \"MacroF1\": 0.8043248634763072,         \"Memory in Mb\": 13.53370189666748,         \"Time in s\": 15451.756014       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8051010268300762,         \"MicroF1\": 0.8051010268300763,         \"MacroF1\": 0.8085780284871096,         \"Memory in Mb\": 13.774932861328123,         \"Time in s\": 16299.960754       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8052634973876024,         \"MicroF1\": 0.8052634973876024,         \"MacroF1\": 0.8077470357827514,         \"Memory in Mb\": 13.933537483215332,         \"Time in s\": 17172.988913       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7978756834894098,         \"MicroF1\": 0.7978756834894098,         \"MacroF1\": 0.7983136026998061,         \"Memory in Mb\": 14.138628005981444,         \"Time in s\": 18070.675966000003       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.793369691770329,         \"MicroF1\": 0.7933696917703291,         \"MacroF1\": 0.7956625263629296,         \"Memory in Mb\": 14.30509090423584,         \"Time in s\": 18993.33345       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7901596221677527,         \"MicroF1\": 0.7901596221677527,         \"MacroF1\": 0.7932579365729884,         \"Memory in Mb\": 14.447582244873049,         \"Time in s\": 19941.842904       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7861686606361249,         \"MicroF1\": 0.7861686606361248,         \"MacroF1\": 0.7888822346867281,         \"Memory in Mb\": 14.767212867736816,         \"Time in s\": 20916.572711       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.780425240836801,         \"MicroF1\": 0.780425240836801,         \"MacroF1\": 0.7838193866310822,         \"Memory in Mb\": 14.989240646362305,         \"Time in s\": 21922.215184       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7802907915993538,         \"MicroF1\": 0.7802907915993537,         \"MacroF1\": 0.7845235361146662,         \"Memory in Mb\": 15.200251579284668,         \"Time in s\": 22957.213951       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.783975162045863,         \"MicroF1\": 0.783975162045863,         \"MacroF1\": 0.7883700169311393,         \"Memory in Mb\": 15.375930786132812,         \"Time in s\": 24020.765336       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7869664837214259,         \"MicroF1\": 0.7869664837214259,         \"MacroF1\": 0.7913854757843782,         \"Memory in Mb\": 15.5132417678833,         \"Time in s\": 25114.453204       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7816427640156454,         \"MicroF1\": 0.7816427640156454,         \"MacroF1\": 0.7858184292134073,         \"Memory in Mb\": 15.77665901184082,         \"Time in s\": 26236.293864000003       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7846090997293571,         \"MicroF1\": 0.7846090997293571,         \"MacroF1\": 0.7893723685613512,         \"Memory in Mb\": 15.996115684509276,         \"Time in s\": 27388.205854000003       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7807013155920164,         \"MicroF1\": 0.7807013155920164,         \"MacroF1\": 0.785620728786203,         \"Memory in Mb\": 16.12063980102539,         \"Time in s\": 28569.915626       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7791068189617139,         \"MicroF1\": 0.7791068189617139,         \"MacroF1\": 0.7841355172773921,         \"Memory in Mb\": 16.39253330230713,         \"Time in s\": 29779.243894000003       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1777777777777777,         \"MicroF1\": 0.1777777777777777,         \"MacroF1\": 0.1526026604973973,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 0.110776       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1318681318681318,         \"MicroF1\": 0.1318681318681318,         \"MacroF1\": 0.1213108980966124,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 0.225611       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1240875912408759,         \"MicroF1\": 0.1240875912408759,         \"MacroF1\": 0.1187445506554449,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 0.343639       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1256830601092896,         \"MicroF1\": 0.1256830601092896,         \"MacroF1\": 0.1226298342307158,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 0.484524       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1266375545851528,         \"MicroF1\": 0.1266375545851528,         \"MacroF1\": 0.1250385204120806,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 0.6292090000000001       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1272727272727272,         \"MicroF1\": 0.1272727272727272,         \"MacroF1\": 0.1242790791814499,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 0.7861950000000001       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1339563862928348,         \"MicroF1\": 0.1339563862928348,         \"MacroF1\": 0.1321003659624602,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 1.0166240000000002       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1389645776566757,         \"MicroF1\": 0.1389645776566757,         \"MacroF1\": 0.1374501146297296,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 1.2507780000000002       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1404358353510895,         \"MicroF1\": 0.1404358353510895,         \"MacroF1\": 0.1403581309694754,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 1.5223060000000002       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1459694989106753,         \"MicroF1\": 0.1459694989106753,         \"MacroF1\": 0.1456314871072794,         \"Memory in Mb\": 0.0013656616210937,         \"Time in s\": 1.7974560000000002       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1386138613861386,         \"MicroF1\": 0.1386138613861386,         \"MacroF1\": 0.1383381610231494,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 2.07562       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1397459165154265,         \"MicroF1\": 0.1397459165154265,         \"MacroF1\": 0.1393865249177789,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 2.402759       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1373534338358459,         \"MicroF1\": 0.1373534338358459,         \"MacroF1\": 0.1372798104345861,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 2.771723       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1399688958009331,         \"MicroF1\": 0.1399688958009331,         \"MacroF1\": 0.1401757170901796,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 3.149556       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1378809869375907,         \"MicroF1\": 0.1378809869375907,         \"MacroF1\": 0.1380151778455332,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 3.580436       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1401360544217687,         \"MicroF1\": 0.1401360544217687,         \"MacroF1\": 0.1403108892795828,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 4.0152470000000005       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1421254801536491,         \"MicroF1\": 0.1421254801536491,         \"MacroF1\": 0.1420930265541123,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 4.453992       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1426844014510278,         \"MicroF1\": 0.1426844014510278,         \"MacroF1\": 0.1422987455304691,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 4.959761       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.138602520045819,         \"MicroF1\": 0.138602520045819,         \"MacroF1\": 0.1384535269459527,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 5.469480000000001       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1349292709466811,         \"MicroF1\": 0.1349292709466811,         \"MacroF1\": 0.1348083913046733,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 6.0005820000000005       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1336787564766839,         \"MicroF1\": 0.1336787564766839,         \"MacroF1\": 0.1334917777444527,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 6.535053       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1325420375865479,         \"MicroF1\": 0.1325420375865479,         \"MacroF1\": 0.1324936677659038,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 7.07275       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1333964049195837,         \"MicroF1\": 0.1333964049195837,         \"MacroF1\": 0.1331834965440007,         \"Memory in Mb\": 0.0013656616210937,         \"Time in s\": 7.645426       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1341795104261106,         \"MicroF1\": 0.1341795104261106,         \"MacroF1\": 0.1340282652950153,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 8.221471000000001       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.134029590948651,         \"MicroF1\": 0.134029590948651,         \"MacroF1\": 0.1340639115051912,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 8.800858000000002       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1364016736401673,         \"MicroF1\": 0.1364016736401673,         \"MacroF1\": 0.1363948420172951,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 9.430169       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1394037066881547,         \"MicroF1\": 0.1394037066881547,         \"MacroF1\": 0.1391977238389222,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 10.062783       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1414141414141414,         \"MicroF1\": 0.1414141414141414,         \"MacroF1\": 0.1411871502321015,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 10.698372       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1432858214553638,         \"MicroF1\": 0.1432858214553638,         \"MacroF1\": 0.1430255327815666,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 11.387531       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1435823060188542,         \"MicroF1\": 0.1435823060188542,         \"MacroF1\": 0.1433209000486506,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 12.080639       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1417543859649122,         \"MicroF1\": 0.1417543859649122,         \"MacroF1\": 0.1414546655929112,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 12.777602000000002       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1393609789259007,         \"MicroF1\": 0.1393609789259007,         \"MacroF1\": 0.1390762971394262,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 13.546128       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1397495056031641,         \"MicroF1\": 0.1397495056031641,         \"MacroF1\": 0.1395136668589845,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 14.318195       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1369161868202175,         \"MicroF1\": 0.1369161868202175,         \"MacroF1\": 0.1366417047439511,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 15.093811       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1361093847110006,         \"MicroF1\": 0.1361093847110006,         \"MacroF1\": 0.1359768388190307,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 15.942934       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1365558912386707,         \"MicroF1\": 0.1365558912386707,         \"MacroF1\": 0.1363322462377459,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 16.795246000000002       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1393298059964726,         \"MicroF1\": 0.1393298059964726,         \"MacroF1\": 0.1390129627439909,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 17.650687       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1419576416714367,         \"MicroF1\": 0.1419576416714367,         \"MacroF1\": 0.1414719731272364,         \"Memory in Mb\": 0.0013656616210937,         \"Time in s\": 18.510738       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1422197434467373,         \"MicroF1\": 0.1422197434467373,         \"MacroF1\": 0.1419410396611007,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 19.374685       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1413811854268624,         \"MicroF1\": 0.1413811854268624,         \"MacroF1\": 0.1411432976659866,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 20.24245       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.140053050397878,         \"MicroF1\": 0.140053050397878,         \"MacroF1\": 0.1397325871382075,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 21.182873       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1429311237700673,         \"MicroF1\": 0.1429311237700673,         \"MacroF1\": 0.1427522922982585,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 22.12686       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1461810824481537,         \"MicroF1\": 0.1461810824481537,         \"MacroF1\": 0.1459715815160596,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 23.074113       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1443400889767671,         \"MicroF1\": 0.1443400889767671,         \"MacroF1\": 0.1441662523776106,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 24.067371       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1440309328177863,         \"MicroF1\": 0.1440309328177863,         \"MacroF1\": 0.1438554349712762,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 25.063921       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1446808510638297,         \"MicroF1\": 0.1446808510638297,         \"MacroF1\": 0.1446036231777657,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 26.06363       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1453031004164738,         \"MicroF1\": 0.1453031004164738,         \"MacroF1\": 0.1452046591382179,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 27.083891999999995       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1449932034435885,         \"MicroF1\": 0.1449932034435885,         \"MacroF1\": 0.1449110985199169,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 28.107944       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1464713715046604,         \"MicroF1\": 0.1464713715046604,         \"MacroF1\": 0.146404255341296,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 29.207951       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1478903871248368,         \"MicroF1\": 0.1478903871248368,         \"MacroF1\": 0.1478868852481029,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 30.311507       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.148116067561715,         \"MicroF1\": 0.148116067561715,         \"MacroF1\": 0.1481156678425267,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 31.415921       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1582938388625592,         \"MicroF1\": 0.1582938388625592,         \"MacroF1\": 0.1376212379233521,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 0.57267       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1657981999052581,         \"MicroF1\": 0.1657981999052581,         \"MacroF1\": 0.1511045106411843,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 1.690872       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1701926113040732,         \"MicroF1\": 0.1701926113040732,         \"MacroF1\": 0.1568151235503963,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 3.298143       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1659957376272791,         \"MicroF1\": 0.1659957376272791,         \"MacroF1\": 0.1525443315605066,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 5.473684       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1708656942602765,         \"MicroF1\": 0.1708656942602765,         \"MacroF1\": 0.1567667911399358,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 8.202311       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1737963693764798,         \"MicroF1\": 0.1737963693764798,         \"MacroF1\": 0.1613756819597299,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 11.448991       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1752130970098769,         \"MicroF1\": 0.1752130970098769,         \"MacroF1\": 0.1618940790413477,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 15.242684       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1772226826092103,         \"MicroF1\": 0.1772226826092103,         \"MacroF1\": 0.163740045170864,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 19.537217       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1773124276544249,         \"MicroF1\": 0.1773124276544249,         \"MacroF1\": 0.1637492974453096,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 24.318802       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1790889288758405,         \"MicroF1\": 0.1790889288758405,         \"MacroF1\": 0.1656421076747495,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 29.683683       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1789926818768833,         \"MicroF1\": 0.1789926818768833,         \"MacroF1\": 0.1655925383533761,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 35.598037000000005       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1853050272275274,         \"MicroF1\": 0.1853050272275274,         \"MacroF1\": 0.182698099884098,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 41.981502000000006       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2479784366576819,         \"MicroF1\": 0.2479784366576819,         \"MacroF1\": 0.266039368455288,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 48.94863000000001       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2795778935263478,         \"MicroF1\": 0.2795778935263478,         \"MacroF1\": 0.2822974275171512,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 56.43945000000001       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2761537975882315,         \"MicroF1\": 0.2761537975882315,         \"MacroF1\": 0.2847375853365436,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 64.48233200000001       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2723290914471737,         \"MicroF1\": 0.2723290914471737,         \"MacroF1\": 0.2859139704285301,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 73.03679300000002       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2720739791655061,         \"MicroF1\": 0.2720739791655061,         \"MacroF1\": 0.2880143206503878,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 82.10379000000002       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2825274898721523,         \"MicroF1\": 0.2825274898721523,         \"MacroF1\": 0.2877504429321087,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 91.70347300000002       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2872451776902756,         \"MicroF1\": 0.2872451776902756,         \"MacroF1\": 0.2866739236661926,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 101.81113500000002       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2830626450116009,         \"MicroF1\": 0.2830626450116009,         \"MacroF1\": 0.2816476602425525,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 112.42818900000002       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2805411499436302,         \"MicroF1\": 0.2805411499436302,         \"MacroF1\": 0.2786296072528009,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 123.55266000000002       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2797124531875511,         \"MicroF1\": 0.2797124531875511,         \"MacroF1\": 0.2771941975793341,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 135.22034100000002       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2777205912628155,         \"MicroF1\": 0.2777205912628155,         \"MacroF1\": 0.2745878480946635,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 147.32084400000002       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2756579726157124,         \"MicroF1\": 0.2756579726157124,         \"MacroF1\": 0.2723380305202896,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 159.88729300000003       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2739497708246524,         \"MicroF1\": 0.2739497708246524,         \"MacroF1\": 0.2699690442569991,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 172.95537600000003       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2718994718630486,         \"MicroF1\": 0.2718994718630486,         \"MacroF1\": 0.2671948532388624,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 186.52082400000003       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2723860965942969,         \"MicroF1\": 0.2723860965942969,         \"MacroF1\": 0.2686965366571337,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 200.59564800000004       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2738187844556431,         \"MicroF1\": 0.2738187844556431,         \"MacroF1\": 0.2720266804437783,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 215.16150500000003       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2753812493877151,         \"MicroF1\": 0.2753812493877151,         \"MacroF1\": 0.2748698663810351,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 230.19075300000003       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2780390795163989,         \"MicroF1\": 0.2780390795163989,         \"MacroF1\": 0.2784141751235631,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 245.71900300000004       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.279670077898274,         \"MicroF1\": 0.279670077898274,         \"MacroF1\": 0.2802192251245275,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 261.76959600000004       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2808440117190968,         \"MicroF1\": 0.2808440117190968,         \"MacroF1\": 0.2811962745371706,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 278.22772000000003       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2772405085086234,         \"MicroF1\": 0.2772405085086234,         \"MacroF1\": 0.2781905182864757,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 295.19763900000004       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2739325404562293,         \"MicroF1\": 0.2739325404562293,         \"MacroF1\": 0.2754200456137155,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 312.64260700000005       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.271246516410076,         \"MicroF1\": 0.271246516410076,         \"MacroF1\": 0.273332837678202,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 330.5037730000001       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2685518874128633,         \"MicroF1\": 0.2685518874128633,         \"MacroF1\": 0.2710722002891223,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 348.8496650000001       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.277034117376059,         \"MicroF1\": 0.277034117376059,         \"MacroF1\": 0.2770619820799866,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 367.6207990000001       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2761731502479627,         \"MicroF1\": 0.2761731502479627,         \"MacroF1\": 0.2760769006623072,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 386.8573710000001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2756720005827647,         \"MicroF1\": 0.2756720005827647,         \"MacroF1\": 0.2754352632972116,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 406.52795400000014       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2740121688486943,         \"MicroF1\": 0.2740121688486943,         \"MacroF1\": 0.2735946193588542,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 426.69962200000015       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2738422450629403,         \"MicroF1\": 0.2738422450629403,         \"MacroF1\": 0.2731948869083578,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 447.37129900000014       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2729588960790061,         \"MicroF1\": 0.2729588960790061,         \"MacroF1\": 0.2720911653869048,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 468.49129600000015       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2720505648908758,         \"MicroF1\": 0.2720505648908758,         \"MacroF1\": 0.2708084959373003,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 490.06234300000017       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.271377224888621,         \"MicroF1\": 0.271377224888621,         \"MacroF1\": 0.2698631410415437,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 512.0778290000002       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2723542162082535,         \"MicroF1\": 0.2723542162082535,         \"MacroF1\": 0.2717062798322285,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 534.5781510000002       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2741327843540916,         \"MicroF1\": 0.2741327843540916,         \"MacroF1\": 0.2744946340974243,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 557.5265480000002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2753520984868328,         \"MicroF1\": 0.2753520984868328,         \"MacroF1\": 0.2765036876430403,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 580.9705880000001       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2768362696549411,         \"MicroF1\": 0.2768362696549411,         \"MacroF1\": 0.2786344091273496,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 604.9012140000001       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2782791875229499,         \"MicroF1\": 0.2782791875229499,         \"MacroF1\": 0.2805971515128955,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 629.3033230000001       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2891153241538665,         \"MicroF1\": 0.2891153241538665,         \"MacroF1\": 0.2892953202729756,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 654.1512880000001       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2897610081934642,         \"MicroF1\": 0.2897610081934642,         \"MacroF1\": 0.2897627257031321,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 679.0036960000001       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975429975429976,         \"MicroF1\": 0.9975429975429976,         \"MacroF1\": 0.966040884438882,         \"Memory in Mb\": 0.0006122589111328,         \"Time in s\": 0.255536       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975460122699388,         \"MicroF1\": 0.9975460122699388,         \"MacroF1\": 0.9879967903427672,         \"Memory in Mb\": 0.0006628036499023,         \"Time in s\": 0.794196       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975470155355682,         \"MicroF1\": 0.9975470155355682,         \"MacroF1\": 0.9931179599499376,         \"Memory in Mb\": 0.0007133483886718,         \"Time in s\": 1.53447       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975475168608215,         \"MicroF1\": 0.9975475168608215,         \"MacroF1\": 0.9950750839342832,         \"Memory in Mb\": 0.0012521743774414,         \"Time in s\": 2.469131       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975478175576264,         \"MicroF1\": 0.9975478175576264,         \"MacroF1\": 0.9960150346160552,         \"Memory in Mb\": 0.0013027191162109,         \"Time in s\": 3.675833       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975480179812016,         \"MicroF1\": 0.9975480179812016,         \"MacroF1\": 0.9965317313935652,         \"Memory in Mb\": 0.0013532638549804,         \"Time in s\": 5.030286       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975481611208408,         \"MicroF1\": 0.9975481611208408,         \"MacroF1\": 0.996842428316928,         \"Memory in Mb\": 0.00140380859375,         \"Time in s\": 6.586031       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975482684646032,         \"MicroF1\": 0.9975482684646032,         \"MacroF1\": 0.9970416021996,         \"Memory in Mb\": 0.0014543533325195,         \"Time in s\": 8.377109       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975483519476982,         \"MicroF1\": 0.9975483519476982,         \"MacroF1\": 0.9971755428551424,         \"Memory in Mb\": 0.001504898071289,         \"Time in s\": 10.331252       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975484187300808,         \"MicroF1\": 0.9975484187300808,         \"MacroF1\": 0.9972690115789392,         \"Memory in Mb\": 0.0015554428100585,         \"Time in s\": 12.525489       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975484733675062,         \"MicroF1\": 0.9975484733675062,         \"MacroF1\": 0.9973361791525124,         \"Memory in Mb\": 0.0016059875488281,         \"Time in s\": 14.940819       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975485188968336,         \"MicroF1\": 0.9975485188968336,         \"MacroF1\": 0.9973856025730918,         \"Memory in Mb\": 0.0016565322875976,         \"Time in s\": 17.495259       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548557420328,         \"MicroF1\": 0.997548557420328,         \"MacroF1\": 0.9974226798335742,         \"Memory in Mb\": 0.0017070770263671,         \"Time in s\": 20.336762       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975485904395028,         \"MicroF1\": 0.9975485904395028,         \"MacroF1\": 0.99745094204078,         \"Memory in Mb\": 0.0017576217651367,         \"Time in s\": 23.402208       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975486190554012,         \"MicroF1\": 0.9975486190554012,         \"MacroF1\": 0.9974727709453766,         \"Memory in Mb\": 0.0018081665039062,         \"Time in s\": 26.661861       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975486440937644,         \"MicroF1\": 0.9975486440937644,         \"MacroF1\": 0.997489815700999,         \"Memory in Mb\": 0.0018587112426757,         \"Time in s\": 30.164710000000003       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548666186013,         \"MicroF1\": 0.997548666186013,         \"MacroF1\": 0.9975032443691146,         \"Memory in Mb\": 0.0019092559814453,         \"Time in s\": 33.838397       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548685823233,         \"MicroF1\": 0.997548685823233,         \"MacroF1\": 0.9975139007887864,         \"Memory in Mb\": 0.0034246444702148,         \"Time in s\": 37.738436       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487033931104,         \"MicroF1\": 0.9975487033931104,         \"MacroF1\": 0.9975224052755716,         \"Memory in Mb\": 0.0034751892089843,         \"Time in s\": 41.800015       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548719205785,         \"MicroF1\": 0.997548719205785,         \"MacroF1\": 0.9975292209193424,         \"Memory in Mb\": 0.0035257339477539,         \"Time in s\": 46.105028       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487335123148,         \"MicroF1\": 0.9975487335123148,         \"MacroF1\": 0.9975346982235258,         \"Memory in Mb\": 0.0035762786865234,         \"Time in s\": 50.63279300000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548746518106,         \"MicroF1\": 0.997548746518106,         \"MacroF1\": 0.9975391057693664,         \"Memory in Mb\": 0.0036268234252929,         \"Time in s\": 55.447067       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548758392838,         \"MicroF1\": 0.997548758392838,         \"MacroF1\": 0.997542651662671,         \"Memory in Mb\": 0.0036773681640625,         \"Time in s\": 60.387128       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487692779084,         \"MicroF1\": 0.9975487692779084,         \"MacroF1\": 0.9975454987794796,         \"Memory in Mb\": 0.003727912902832,         \"Time in s\": 65.547582       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487792920874,         \"MicroF1\": 0.9975487792920874,         \"MacroF1\": 0.9975477757646256,         \"Memory in Mb\": 0.0037784576416015,         \"Time in s\": 70.981052       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487885358726,         \"MicroF1\": 0.9975487885358726,         \"MacroF1\": 0.9975495850737114,         \"Memory in Mb\": 0.003829002380371,         \"Time in s\": 76.594226       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487970948708,         \"MicroF1\": 0.9975487970948708,         \"MacroF1\": 0.9975510089260562,         \"Memory in Mb\": 0.0038795471191406,         \"Time in s\": 82.44596800000001       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488050424582,         \"MicroF1\": 0.9975488050424582,         \"MacroF1\": 0.9975521137613484,         \"Memory in Mb\": 0.0039300918579101,         \"Time in s\": 88.533094       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.99754881244189,         \"MicroF1\": 0.99754881244189,         \"MacroF1\": 0.99755295361102,         \"Memory in Mb\": 0.0039806365966796,         \"Time in s\": 94.818744       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548819347986,         \"MicroF1\": 0.997548819347986,         \"MacroF1\": 0.9975535726732964,         \"Memory in Mb\": 0.0040311813354492,         \"Time in s\": 101.331754       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548825808492,         \"MicroF1\": 0.997548825808492,         \"MacroF1\": 0.997554007297632,         \"Memory in Mb\": 0.0040817260742187,         \"Time in s\": 108.051678       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488318651856,         \"MicroF1\": 0.9975488318651856,         \"MacroF1\": 0.997554287526727,         \"Memory in Mb\": 0.0041322708129882,         \"Time in s\": 114.996681       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488375547796,         \"MicroF1\": 0.9975488375547796,         \"MacroF1\": 0.9975544383040468,         \"Memory in Mb\": 0.0041828155517578,         \"Time in s\": 122.1119       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488429096676,         \"MicroF1\": 0.9975488429096676,         \"MacroF1\": 0.9975544804262362,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 129.47010500000002       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488479585404,         \"MicroF1\": 0.9975488479585404,         \"MacroF1\": 0.99755443129941,         \"Memory in Mb\": 0.0042839050292968,         \"Time in s\": 136.988051       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488527269012,         \"MicroF1\": 0.9975488527269012,         \"MacroF1\": 0.997554305543504,         \"Memory in Mb\": 0.0043344497680664,         \"Time in s\": 144.742896       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548857237496,         \"MicroF1\": 0.997548857237496,         \"MacroF1\": 0.9975541154780816,         \"Memory in Mb\": 0.0043849945068359,         \"Time in s\": 152.648866       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488615106752,         \"MicroF1\": 0.9975488615106752,         \"MacroF1\": 0.9975538715150368,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 160.767465       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488655647036,         \"MicroF1\": 0.9975488655647036,         \"MacroF1\": 0.997553582477696,         \"Memory in Mb\": 0.004486083984375,         \"Time in s\": 169.09858       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488694160182,         \"MicroF1\": 0.9975488694160182,         \"MacroF1\": 0.9975532558614028,         \"Memory in Mb\": 0.0045366287231445,         \"Time in s\": 177.653336       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488730794524,         \"MicroF1\": 0.9975488730794524,         \"MacroF1\": 0.997552898047314,         \"Memory in Mb\": 0.004587173461914,         \"Time in s\": 186.438203       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488765684272,         \"MicroF1\": 0.9975488765684272,         \"MacroF1\": 0.9975525144785748,         \"Memory in Mb\": 0.0046377182006835,         \"Time in s\": 195.447168       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488798951148,         \"MicroF1\": 0.9975488798951148,         \"MacroF1\": 0.997552109806108,         \"Memory in Mb\": 0.0046882629394531,         \"Time in s\": 204.563635       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548883070581,         \"MicroF1\": 0.997548883070581,         \"MacroF1\": 0.9975516880097278,         \"Memory in Mb\": 0.0047388076782226,         \"Time in s\": 213.933058       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488861049076,         \"MicroF1\": 0.9975488861049076,         \"MacroF1\": 0.997551252499137,         \"Memory in Mb\": 0.0047893524169921,         \"Time in s\": 223.513668       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488890073,         \"MicroF1\": 0.9975488890073,         \"MacroF1\": 0.9975508061984416,         \"Memory in Mb\": 0.0048398971557617,         \"Time in s\": 233.322943       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.99754889178618,         \"MicroF1\": 0.99754889178618,         \"MacroF1\": 0.9975503516171184,         \"Memory in Mb\": 0.0048904418945312,         \"Time in s\": 243.357771       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488944492672,         \"MicroF1\": 0.9975488944492672,         \"MacroF1\": 0.9975498909097889,         \"Memory in Mb\": 0.0049409866333007,         \"Time in s\": 253.567103       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488970036516,         \"MicroF1\": 0.9975488970036516,         \"MacroF1\": 0.9975494259267256,         \"Memory in Mb\": 0.0049915313720703,         \"Time in s\": 264.004285       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488994558556,         \"MicroF1\": 0.9975488994558556,         \"MacroF1\": 0.9975489582566448,         \"Memory in Mb\": 0.0050420761108398,         \"Time in s\": 274.675054       }     ]   },   \"params\": [     {       \"name\": \"models\",       \"select\": {         \"type\": \"point\",         \"fields\": [           \"model\"         ]       },       \"bind\": \"legend\"     },     {       \"name\": \"Dataset\",       \"value\": \"ImageSegments\",       \"bind\": {         \"input\": \"select\",         \"options\": [           \"ImageSegments\",           \"Insects\",           \"Keystroke\"         ]       }     },     {       \"name\": \"grid\",       \"select\": \"interval\",       \"bind\": \"scales\"     }   ],   \"transform\": [     {       \"filter\": {         \"field\": \"dataset\",         \"equal\": {           \"expr\": \"Dataset\"         }       }     }   ],   \"repeat\": {     \"row\": [       \"Accuracy\",       \"MicroF1\",       \"MacroF1\",       \"Memory in Mb\",       \"Time in s\"     ]   },   \"spec\": {     \"width\": \"container\",     \"mark\": \"line\",     \"encoding\": {       \"x\": {         \"field\": \"step\",         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"title\": \"Instance\"         }       },       \"y\": {         \"field\": {           \"repeat\": \"row\"         },         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18         }       },       \"color\": {         \"field\": \"model\",         \"type\": \"ordinal\",         \"scale\": {           \"scheme\": \"category20b\"         },         \"title\": \"Models\",         \"legend\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"labelLimit\": 500         }       },       \"opacity\": {         \"condition\": {           \"param\": \"models\",           \"value\": 1         },         \"value\": 0.2       }     }   } }</p>"},{"location":"benchmarks/Multiclass%20classification/#datasets","title":"Datasets","text":"ImageSegments <p>Image segments classification.</p> <p>This dataset contains features that describe image segments into 7 classes: brickface, sky, foliage, cement, window, path, and grass.</p> <pre><code>Name  ImageSegments                                                                                               \nTask  Multi-class classification\n</code></pre> <p>Samples  2,310                                                                                                      Features  18                                                                                                          Classes  7                                                                                                            Sparse  False                                                                                                          Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/segment.csv.zip</p> <p></p> Insects <p>Insects dataset.</p> <p>This dataset has different variants, which are:</p> <ul> <li>abrupt_balanced</li> <li>abrupt_imbalanced</li> <li>gradual_balanced</li> <li>gradual_imbalanced</li> <li>incremental-abrupt_balanced</li> <li>incremental-abrupt_imbalanced</li> <li>incremental-reoccurring_balanced</li> <li>incremental-reoccurring_imbalanced</li> <li>incremental_balanced</li> <li>incremental_imbalanced</li> <li>out-of-control</li> </ul> <p>The number of samples and the difficulty change from one variant to another. The number of classes is always the same (6), except for the last variant (24).</p> <pre><code>  Name  Insects                                                                                 \n  Task  Multi-class classification\n</code></pre> <p>Samples  52,848                                                                                   Features  33                                                                                        Classes  6                                                                                          Sparse  False                                                                                        Path  /Users/mastelini/river_data/Insects/INSECTS-abrupt_balanced_norm.arff                         URL  http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff       Size  15.66 MB                                                                               Downloaded  True                                                                                      Variant  abrupt_balanced                                                                         </p> <p></p> Keystroke <p>CMU keystroke dataset.</p> <p>Users are tasked to type in a password. The task is to determine which user is typing in the password.</p> <p>The only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes have been dropped.</p> <pre><code>  Name  Keystroke                                                       \n  Task  Multi-class classification\n</code></pre> <p>Samples  20,400                                                           Features  31                                                                Classes  51                                                                 Sparse  False                                                                Path  /Users/mastelini/river_data/Keystroke/DSL-StrongPasswordData.csv        URL  http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv          Size  4.45 MB                                                        Downloaded  True                                                            </p> <p></p>"},{"location":"benchmarks/Multiclass%20classification/#parameters","title":"Parameters","text":"<pre><code>variant\n    Indicates which variant of the dataset to load.\n</code></pre>"},{"location":"benchmarks/Multiclass%20classification/#models","title":"Models","text":"Naive Bayes <p><pre>GaussianNB ()</pre></p> <p></p> Hoeffding Tree <p><pre>HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)</pre></p> <p></p> Hoeffding Adaptive Tree <p><pre>HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=True\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=42\n)</pre></p> <p></p> Adaptive Random Forest <p><pre>[]</pre></p> <p></p> Aggregated Mondrian Forest <p><pre>[]</pre></p> <p></p> Streaming Random Patches <p><pre>SRPClassifier (\n  model=HoeffdingTreeClassifier (\n    grace_period=50\n    max_depth=inf\n    split_criterion=\"info_gain\"\n    delta=0.01\n    tau=0.05\n    leaf_prediction=\"nba\"\n    nb_threshold=0\n    nominal_attributes=None\n    splitter=GaussianSplitter (\n      n_splits=10\n    )\n    binary_split=False\n    min_branch_fraction=0.01\n    max_share_to_split=0.99\n    max_size=100.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n  )\n  n_models=10\n  subspace_size=0.6\n  training_method=\"patches\"\n  lam=6\n  drift_detector=ADWIN (\n    delta=1e-05\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  warning_detector=ADWIN (\n    delta=0.0001\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  disable_detector=\"off\"\n  disable_weighted_vote=False\n  seed=None\n  metric=Accuracy (\n    cm=ConfusionMatrix (\n      classes=[]\n    )\n  )\n)</pre></p> <p></p> k-Nearest Neighbors <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)\n\n<p></p>\n\nADWIN Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nAdaBoost\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nBagging\n<p><pre>[HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n)]</pre></p>\n\n<p></p>\n\nLeveraging Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nStacking\n<p><pre>[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n\n<p></p>\n\nVoting\n<p><pre>VotingClassifier (\n  models=[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n  use_probabilities=True\n)\n\n<p></p>\n\n[baseline] Last Class\n<p><pre>NoChangeClassifier ()</pre></p>\n\n<p></p>"},{"location":"benchmarks/Multiclass%20classification/#environment","title":"Environment","text":"<pre>Python implementation: CPython\nPython version       : 3.12.4\nIPython version      : 8.18.1\n\nriver       : 0.21.2\nnumpy       : 1.26.4\nscikit-learn: 1.3.1\npandas      : 2.2.2\nscipy       : 1.13.0\n\nCompiler    : GCC 11.4.0\nOS          : Linux\nRelease     : 6.5.0-1024-azure\nMachine     : x86_64\nProcessor   : x86_64\nCPU cores   : 4\nArchitecture: 64bit\n</pre>"},{"location":"benchmarks/Regression/","title":"Regression","text":"TableChart Model Dataset MAE RMSE R2 Memory in Mb Time in s Adaptive Model Rules ChickWeights 24.1943 37.2166 0.725319 0.046977 5.25855 Adaptive Model Rules TrumpApproval 1.39847 2.43336 -1.02372 0.114429 9.38293 Adaptive Random Forest ChickWeights 26.1016 40.8094 0.669725 1.19043 56.006 Adaptive Random Forest TrumpApproval 0.800378 2.11495 -0.528761 1.28462 87.4457 Aggregated Mondrian Forest ChickWeights 25.6742 41.7123 0.65479 8.21412 127.415 Aggregated Mondrian Forest TrumpApproval 0.268533 0.349421 0.958184 16.9323 186.034 Bagging ChickWeights 23.1143 36.6311 0.733893 0.628034 38.0203 Bagging TrumpApproval 0.908203 2.23718 -0.710572 1.31579 82.0689 Exponentially Weighted Average ChickWeights 121.818 141.004 -2.94294 3.09241 55.8851 Exponentially Weighted Average TrumpApproval 40.7546 40.7905 -567.663 5.27613 141.452 Hoeffding Adaptive Tree ChickWeights 23.3739 37.6579 0.718766 0.0947332 7.99029 Hoeffding Adaptive Tree TrumpApproval 0.921313 2.23942 -0.713986 0.138225 16.7576 Hoeffding Tree ChickWeights 23.1619 36.7336 0.732402 0.0440512 6.29305 Hoeffding Tree TrumpApproval 0.956103 2.24987 -0.730022 0.148639 11.7656 Linear Regression ChickWeights 23.7587 37.0377 0.727954 0.00421047 3.21471 Linear Regression TrumpApproval 1.31455 3.91198 -4.23035 0.00497341 11.5379 Linear Regression with l1 regularization ChickWeights 23.7577 37.078 0.727361 0.00444126 9.7485 Linear Regression with l1 regularization TrumpApproval 1.15377 3.82872 -4.01007 0.0052042 13.3595 Linear Regression with l2 regularization ChickWeights 25.2738 38.5885 0.704694 0.00423336 1.22128 Linear Regression with l2 regularization TrumpApproval 1.87151 4.13052 -4.83107 0.0049963 4.15677 Passive-Aggressive Regressor, mode 1 ChickWeights 24.3423 37.596 0.71969 0.00345898 1.10187 Passive-Aggressive Regressor, mode 1 TrumpApproval 4.98403 6.97667 -15.6354 0.00443554 2.99338 Passive-Aggressive Regressor, mode 2 ChickWeights 100.624 143.066 -3.05911 0.00345898 1.16798 Passive-Aggressive Regressor, mode 2 TrumpApproval 31.0933 34.6257 -408.765 0.00443554 4.72475 River MLP ChickWeights 51.4078 80.9203 -0.298584 0.0123129 28.2295 River MLP TrumpApproval 1.58058 5.03392 -7.66066 0.0133505 32.2432 Stochastic Gradient Tree ChickWeights 68.7588 80.358 -0.280601 1.12059 22.3803 Stochastic Gradient Tree TrumpApproval 9.42975 17.9379 -108.972 3.08244 52.4507 Streaming Random Patches ChickWeights 23.7097 38.4416 0.706938 0.355182 93.4014 Streaming Random Patches TrumpApproval 0.656697 1.98434 -0.345761 1.06461 134.903 [baseline] Mean predictor ChickWeights 50.2509 71.1144 -0.00292947 0.000490189 0.302835 [baseline] Mean predictor TrumpApproval 1.56755 2.20286 -0.658483 0.000490189 1.08177 k-Nearest Neighbors ChickWeights 24.8406 39.2016 0.695236 2.88522 40.0878 k-Nearest Neighbors TrumpApproval 0.641679 1.59417 0.131425 5.03263 123.301 <p>Try reloading the page if something is buggy</p> <p>{   \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",   \"data\": {     \"values\": [       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.432219699626994,         \"RMSE\": 31.267456151778337,         \"R2\": -1257.4692714745631,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.000963       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.75760844034268,         \"RMSE\": 23.632210645041404,         \"R2\": -590.4769976066937,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.002374       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.555240079240876,         \"RMSE\": 19.34929493332969,         \"R2\": -259.0232069515881,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.004113       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.14363365913676,         \"RMSE\": 16.767243978820222,         \"R2\": -220.3452424437857,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.006175       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.841164000616114,         \"RMSE\": 17.714902804136145,         \"R2\": -60.2608923989398,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.008581       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.32598508406065,         \"RMSE\": 16.527353468164844,         \"R2\": -21.985729074745297,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.01133       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.718401993814265,         \"RMSE\": 15.52109639018614,         \"R2\": -12.587024696233003,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.014424       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.767755200283737,         \"RMSE\": 14.552446235427842,         \"R2\": -9.829280875288257,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.017858       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.977130626229444,         \"RMSE\": 13.740429605807138,         \"R2\": -7.074807888709797,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0216349999999999       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.506893871110683,         \"RMSE\": 13.098273311725844,         \"R2\": -4.124041411671393,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0257519999999999       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.252833276832352,         \"RMSE\": 12.607637144454216,         \"R2\": -2.6562249812820733,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0302089999999999       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.896359231575217,         \"RMSE\": 12.121970224209305,         \"R2\": -1.7624336939368233,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0350039999999999       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.581914741629191,         \"RMSE\": 11.688367143429067,         \"R2\": -1.080274127204615,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0401389999999999       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.347682986169337,         \"RMSE\": 11.314945909537578,         \"R2\": -0.6567859420078188,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0456129999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.47676439389405,         \"RMSE\": 11.21748999353191,         \"R2\": -0.3089959076061037,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0514269999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.552290709218319,         \"RMSE\": 11.100632967129414,         \"R2\": -0.0335718949744832,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0575809999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.503097179992549,         \"RMSE\": 10.915357728148932,         \"R2\": 0.1817591258850298,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0640729999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.420443618722296,         \"RMSE\": 10.727647067877951,         \"R2\": 0.3713230272376924,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.070904       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.54715053669462,         \"RMSE\": 10.814712106795348,         \"R2\": 0.4732913339801876,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0780719999999999       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.075852889975692,         \"RMSE\": 11.488147441481184,         \"R2\": 0.479648982578291,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0855759999999999       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.197265349840174,         \"RMSE\": 11.527376107146,         \"R2\": 0.5518657524511614,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0934159999999999       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.359957454348683,         \"RMSE\": 11.71365363090123,         \"R2\": 0.6276606533313056,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.1015909999999999       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.389343614466645,         \"RMSE\": 11.70410418267156,         \"R2\": 0.6771453727427903,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.1101019999999999       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.007684680730522,         \"RMSE\": 12.681713023454453,         \"R2\": 0.6536838584261326,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.1189499999999999       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.456356064016727,         \"RMSE\": 13.562457362384484,         \"R2\": 0.6514630282957669,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.128137       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.682222588679535,         \"RMSE\": 13.91372755183948,         \"R2\": 0.6822857451181047,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.137663       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.656490376145301,         \"RMSE\": 13.862729792291397,         \"R2\": 0.7264657185265005,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.147527       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.17087534181789,         \"RMSE\": 14.586626878398466,         \"R2\": 0.730278281446047,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.157738       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.253235573939358,         \"RMSE\": 17.040182474587255,         \"R2\": 0.6659707835095393,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.270641       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.67218268870669,         \"RMSE\": 17.597898989920818,         \"R2\": 0.6951262006904333,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.38498       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.865878827617594,         \"RMSE\": 17.684075493652397,         \"R2\": 0.7243197409220903,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.500381       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.014541487264225,         \"RMSE\": 17.788847456042067,         \"R2\": 0.7464163188501894,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.6168239999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.893125923244742,         \"RMSE\": 19.14640328452056,         \"R2\": 0.7147396000186461,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.7343709999999999       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.40252640363099,         \"RMSE\": 20.24468752454989,         \"R2\": 0.7068188127948265,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.8529599999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.78041264925886,         \"RMSE\": 20.84297745742841,         \"R2\": 0.7250508110390363,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.972583       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.908163646252072,         \"RMSE\": 20.82655299121286,         \"R2\": 0.7440434321899679,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.093238       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.78624220521945,         \"RMSE\": 22.297725224665918,         \"R2\": 0.7272822586077066,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.214927       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.56231380927385,         \"RMSE\": 23.732773749874315,         \"R2\": 0.7099846963904786,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.3375199999999998       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.109717404902195,         \"RMSE\": 24.64206848989837,         \"R2\": 0.7221580232945248,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.4604629999999998       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.287005413554732,         \"RMSE\": 24.72152256024044,         \"R2\": 0.7401560140604169,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.583729       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.806865735774078,         \"RMSE\": 25.331119330890413,         \"R2\": 0.7387809061287051,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.707315       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.912347710111163,         \"RMSE\": 27.450327347193877,         \"R2\": 0.7118740092210123,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.831218       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.68786801080465,         \"RMSE\": 28.74804692307192,         \"R2\": 0.7209603573249957,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.955435       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.02230431978895,         \"RMSE\": 29.040370094251127,         \"R2\": 0.7308604085348502,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.079964       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.47643461729765,         \"RMSE\": 29.56562239854821,         \"R2\": 0.7375811559076941,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.204806       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.368862660258834,         \"RMSE\": 31.016595939650863,         \"R2\": 0.7195863076124669,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.32996       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.093492725340727,         \"RMSE\": 32.00802507821089,         \"R2\": 0.7181912437784894,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.455434       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.883641447975457,         \"RMSE\": 33.20140091570763,         \"R2\": 0.727385103943677,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.581219       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.055940734584823,         \"RMSE\": 33.19901872731025,         \"R2\": 0.7386798629639011,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.707313       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.04665839885113,         \"RMSE\": 34.818142407426606,         \"R2\": 0.7214274205964286,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.833717       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.75015079068996,         \"RMSE\": 35.737018888500465,         \"R2\": 0.7193638350430389,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.960429       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.60149518688988,         \"RMSE\": 36.92142939550449,         \"R2\": 0.722919218201958,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 3.087448       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.75865667886776,         \"RMSE\": 37.03767126301035,         \"R2\": 0.7279537206511313,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 3.2147080000000003       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.71537559933632,         \"RMSE\": 24.27612097298636,         \"R2\": -1381.3340079163324,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.003774       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.956746822999646,         \"RMSE\": 17.85530816845139,         \"R2\": -127.17403450091604,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.008234       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.540337295823328,         \"RMSE\": 15.264267507077204,         \"R2\": -125.28803290438402,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.013346       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.92648259034571,         \"RMSE\": 13.436420463778148,         \"R2\": -97.15695382305036,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.019104       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.5495393499287236,         \"RMSE\": 12.076339439187349,         \"R2\": -48.75014684916543,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.025552       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.571266653106965,         \"RMSE\": 11.058195411086311,         \"R2\": -34.38851346579008,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.032651       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.868178209177549,         \"RMSE\": 10.265658199354172,         \"R2\": -30.51567288629301,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.040397       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.226493262391851,         \"RMSE\": 9.609365926739027,         \"R2\": -23.352843972650145,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.048786       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.806672346419344,         \"RMSE\": 9.079121174210671,         \"R2\": -18.092824435696784,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.057857       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.400421129740624,         \"RMSE\": 8.617551092451054,         \"R2\": -16.252012396913173,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.06766       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.083414123099576,         \"RMSE\": 8.223437931584808,         \"R2\": -15.946617088642816,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.0781589999999999       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.82353438841577,         \"RMSE\": 7.87966547036827,         \"R2\": -14.67643164713968,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.0893539999999999       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.573342996804622,         \"RMSE\": 7.572887494545769,         \"R2\": -13.674649599158814,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.1012429999999999       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.399764262602937,         \"RMSE\": 7.307305033384193,         \"R2\": -13.305426773388604,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.1138299999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.2435269592384794,         \"RMSE\": 7.069212717011484,         \"R2\": -12.166742621467945,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.2177469999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.1105754847518408,         \"RMSE\": 6.854541649824586,         \"R2\": -11.99216513034567,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.416803       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.9569354047226284,         \"RMSE\": 6.651479799277566,         \"R2\": -11.928129373171446,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.618037       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.8537856094930785,         \"RMSE\": 6.474036710445056,         \"R2\": -11.348131391644102,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.8214119999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.750449728962714,         \"RMSE\": 6.305826559379086,         \"R2\": -11.120053648606476,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.026924       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.663414115575528,         \"RMSE\": 6.151161672136967,         \"R2\": -10.85874486639798,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.234147       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.556259025339157,         \"RMSE\": 6.003825249623929,         \"R2\": -10.671335514866264,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.4420449999999998       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.471571610669061,         \"RMSE\": 5.868919367302693,         \"R2\": -9.950915405915524,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.6506019999999997       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.379680763039582,         \"RMSE\": 5.740715994508566,         \"R2\": -8.935993501779443,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.860033       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.293542327214648,         \"RMSE\": 5.620383847029998,         \"R2\": -8.304713733239236,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.070158       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.2170719472274363,         \"RMSE\": 5.50775327209046,         \"R2\": -7.748060324415055,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.280968       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1605380581247338,         \"RMSE\": 5.403051906918496,         \"R2\": -7.433320998258445,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.492507       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.093930365363914,         \"RMSE\": 5.302901387269021,         \"R2\": -7.093810234661742,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.70473       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0590245226095627,         \"RMSE\": 5.213512799867119,         \"R2\": -7.009651494197669,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.917634       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9976476082662875,         \"RMSE\": 5.1231852511763165,         \"R2\": -6.925804791819894,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.1312199999999994       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.950641059884997,         \"RMSE\": 5.038426259116397,         \"R2\": -6.58092298894084,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.3455269999999997       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9139787950639096,         \"RMSE\": 4.959092402037442,         \"R2\": -6.2321238970256,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.560656999999999       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8644177203659007,         \"RMSE\": 4.8815607080230725,         \"R2\": -5.87676544995844,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.776474999999999       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8242147858959743,         \"RMSE\": 4.808190620674182,         \"R2\": -5.62363098938706,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.992974       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7745110240786572,         \"RMSE\": 4.737042423784333,         \"R2\": -5.530654039159668,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 4.267744       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.73663030353679,         \"RMSE\": 4.669916427921507,         \"R2\": -5.51357997146441,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 4.544746       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.692679144669073,         \"RMSE\": 4.604703216269991,         \"R2\": -5.472066122280332,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 4.823995       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6517738073879171,         \"RMSE\": 4.542197076044804,         \"R2\": -5.293761488897717,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.105493       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6176019850850996,         \"RMSE\": 4.482676429973872,         \"R2\": -5.196305855003581,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.389141       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5865007641193465,         \"RMSE\": 4.425455260516019,         \"R2\": -5.066178353973196,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.673523       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5595678531598225,         \"RMSE\": 4.370690133148669,         \"R2\": -4.970481738375755,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.9679660000000005       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.535948345073891,         \"RMSE\": 4.318357063182573,         \"R2\": -4.892367885242343,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 6.2645230000000005       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.509480285222116,         \"RMSE\": 4.267276732370252,         \"R2\": -4.807214337073276,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 6.563154000000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4815681878661566,         \"RMSE\": 4.217864876321593,         \"R2\": -4.663732871777943,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 6.863868000000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.452683177817048,         \"RMSE\": 4.169823323470254,         \"R2\": -4.507942651608292,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 7.276079000000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.425504815240136,         \"RMSE\": 4.123417367951589,         \"R2\": -4.4087270270070205,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 7.880966000000001       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.401135420694234,         \"RMSE\": 4.078757160785335,         \"R2\": -4.379153600942964,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 8.488067000000001       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3798894262867003,         \"RMSE\": 4.035722473386745,         \"R2\": -4.310917809364017,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 9.096757       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3578157698337674,         \"RMSE\": 3.993911445090692,         \"R2\": -4.255827563021541,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 9.706153       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.334955498529068,         \"RMSE\": 3.953168904153961,         \"R2\": -4.249147855442176,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 10.316233       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3157385915327031,         \"RMSE\": 3.913934448961732,         \"R2\": -4.232086679588724,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 10.926998       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3145482000473083,         \"RMSE\": 3.911980916488244,         \"R2\": -4.230354806784151,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 11.537908000000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.519429760441792,         \"RMSE\": 31.341724959881887,         \"R2\": -1263.4547929656037,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.001889       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.93274945698016,         \"RMSE\": 23.730069634788823,         \"R2\": -595.3856524245364,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.005264       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.671976905269483,         \"RMSE\": 19.432784890847977,         \"R2\": -261.2719879213097,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.0454959999999999       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.206218788565426,         \"RMSE\": 16.83704009498573,         \"R2\": -222.1918420065333,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.086209       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.7873677371092,         \"RMSE\": 17.69725945175844,         \"R2\": -60.138926246201024,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.127302       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.358479420064798,         \"RMSE\": 16.54420972880916,         \"R2\": -22.032639310332936,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.168766       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.753598876381378,         \"RMSE\": 15.536347024393615,         \"R2\": -12.613738343052718,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.2106009999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.774706713989955,         \"RMSE\": 14.560860647391404,         \"R2\": -9.841807755380492,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.252804       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.976543403311107,         \"RMSE\": 13.74760854733656,         \"R2\": -7.083247758311314,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.295373       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.528406770561816,         \"RMSE\": 13.11078583789324,         \"R2\": -4.133835882207287,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.338308       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.271666718491515,         \"RMSE\": 12.6229442838289,         \"R2\": -2.665108536473531,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.38161       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.91845605456336,         \"RMSE\": 12.134014714075713,         \"R2\": -1.767925975098496,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.425278       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.610383809165891,         \"RMSE\": 11.700505099139123,         \"R2\": -1.084596952740374,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.469311       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.3485668448406924,         \"RMSE\": 11.31852948419668,         \"R2\": -0.6578355548574832,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.51371       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.473998962981321,         \"RMSE\": 11.222073845492618,         \"R2\": -0.3100659276219817,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.558476       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.543521830550948,         \"RMSE\": 11.096254270292285,         \"R2\": -0.0327566612108853,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.603607       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.493894355635018,         \"RMSE\": 10.908553918682982,         \"R2\": 0.1827788670738018,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.649102       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.432058292739276,         \"RMSE\": 10.739983052449066,         \"R2\": 0.3698763337697944,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.6949599999999999       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.530905166315106,         \"RMSE\": 10.805387069826963,         \"R2\": 0.4741992564876139,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.74118       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.049069109840064,         \"RMSE\": 11.46222613381468,         \"R2\": 0.4819945238144716,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.7877609999999999       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.185364391622807,         \"RMSE\": 11.520615160379734,         \"R2\": 0.5523912707049028,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.834703       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.384443509591489,         \"RMSE\": 11.759466507882768,         \"R2\": 0.6247424700583044,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.882006       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.370825288025247,         \"RMSE\": 11.706644644448966,         \"R2\": 0.6770052015955412,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.929669       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.997212264968545,         \"RMSE\": 12.688148058774216,         \"R2\": 0.6533323093865229,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 0.977694       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.45564901988644,         \"RMSE\": 13.583827871673952,         \"R2\": 0.6503637760490552,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.026082       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.687395226209604,         \"RMSE\": 13.953064893865328,         \"R2\": 0.6804867014487179,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.074833       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.660171229881424,         \"RMSE\": 13.910099225377923,         \"R2\": 0.7245931722706233,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.1239519999999998       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.16625719191718,         \"RMSE\": 14.612234985526298,         \"R2\": 0.7293304097140514,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.1734349999999998       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.250950211093048,         \"RMSE\": 17.0718306278326,         \"R2\": 0.664728869016383,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.2232849999999995       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.679670450254022,         \"RMSE\": 17.65395670255975,         \"R2\": 0.6931807697512926,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.3407639999999996       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.873729384474112,         \"RMSE\": 17.73873175202587,         \"R2\": 0.7226130148559202,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.6983979999999996       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.018541118771262,         \"RMSE\": 17.831871437600412,         \"R2\": 0.745188204067577,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 2.057199       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.899574150448762,         \"RMSE\": 19.1903382176024,         \"R2\": 0.7134289333715201,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 2.417118       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.408282768986876,         \"RMSE\": 20.289550367060546,         \"R2\": 0.7055179762102581,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 2.778154       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.788104615245372,         \"RMSE\": 20.897902847676004,         \"R2\": 0.7235998101431352,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 3.140457       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.90822201416442,         \"RMSE\": 20.86950621812891,         \"R2\": 0.7429865604297317,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 3.503137       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.78564736405168,         \"RMSE\": 22.33392717480972,         \"R2\": 0.726395986248676,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 3.866169       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.562464823979756,         \"RMSE\": 23.77146138634261,         \"R2\": 0.709038397249883,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 4.229544       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.115712915071189,         \"RMSE\": 24.692790084324347,         \"R2\": 0.7210130632693055,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 4.59326       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.290646451171162,         \"RMSE\": 24.766775019882367,         \"R2\": 0.7392038606135755,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 4.957315       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.806610158983217,         \"RMSE\": 25.37056359629737,         \"R2\": 0.7379667599208486,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 5.321708999999999       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.91167446753811,         \"RMSE\": 27.489289014578038,         \"R2\": 0.711055524573946,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 5.686440999999999       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.69453441784174,         \"RMSE\": 28.803034656505247,         \"R2\": 0.7198918720890418,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 6.051508999999999       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.02591429387984,         \"RMSE\": 29.08166628667707,         \"R2\": 0.7300944167213836,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 6.416912999999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.47687089345869,         \"RMSE\": 29.604201733284565,         \"R2\": 0.7368958634072684,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 6.782651999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.37032815671457,         \"RMSE\": 31.058772984483277,         \"R2\": 0.7188231637639817,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 7.148725999999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.096649322747314,         \"RMSE\": 32.051830787895724,         \"R2\": 0.717419357352562,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 7.515149999999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.88685610593147,         \"RMSE\": 33.24610520798377,         \"R2\": 0.7266504806846955,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 7.882236999999999       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.052957054073875,         \"RMSE\": 33.24035912136826,         \"R2\": 0.7380286507287471,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 8.25334       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.046178761536364,         \"RMSE\": 34.86098206113683,         \"R2\": 0.7207414968982613,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 8.62558       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.751953045975853,         \"RMSE\": 35.78242297978339,         \"R2\": 0.7186502822700677,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 8.998921       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.603432973098663,         \"RMSE\": 36.96472548228527,         \"R2\": 0.7222689970347711,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 9.373355       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.757667537133976,         \"RMSE\": 37.07802525541943,         \"R2\": 0.7273605875689941,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 9.748496       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.96628233331211,         \"RMSE\": 24.387937149248955,         \"R2\": -1394.0974368768457,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.003367       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.95809265443779,         \"RMSE\": 17.886947111698607,         \"R2\": -127.62867621055317,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.060679       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.43403375286247,         \"RMSE\": 15.198987179765494,         \"R2\": -124.2101566950438,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.118829       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.76952679896777,         \"RMSE\": 13.348146279436204,         \"R2\": -95.8714533526398,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.177742       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.318348711169017,         \"RMSE\": 11.969856517585775,         \"R2\": -47.87667264392048,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.237441       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.2853039116310185,         \"RMSE\": 10.94189036106609,         \"R2\": -33.648027646243705,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.297866       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.5208355911538485,         \"RMSE\": 10.138862242229528,         \"R2\": -29.74195117722151,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.359017       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.9080595636493145,         \"RMSE\": 9.487746704217276,         \"R2\": -22.740310036230184,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.420891       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.437342628193194,         \"RMSE\": 8.948953859899,         \"R2\": -17.549281500204398,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.483487       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.020740144728086,         \"RMSE\": 8.490404067975657,         \"R2\": -15.746680942149272,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.546844       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.702540763677515,         \"RMSE\": 8.09713522450445,         \"R2\": -15.430052960036054,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.610978       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.449057445346116,         \"RMSE\": 7.755193128790045,         \"R2\": -14.185073150160106,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.675884       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.201640426877581,         \"RMSE\": 7.451485247160068,         \"R2\": -13.20791735379428,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 0.741568       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.9861522146348123,         \"RMSE\": 7.180696949733205,         \"R2\": -12.814002869999907,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 0.808037       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.8260389726991693,         \"RMSE\": 6.939608203297966,         \"R2\": -11.688379207589731,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 0.926618       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.694730270614988,         \"RMSE\": 6.722171113188908,         \"R2\": -11.495217468089896,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 1.215588       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.572442774284147,         \"RMSE\": 6.524300196624447,         \"R2\": -11.438471282384336,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 1.5070640000000002       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4832798669216825,         \"RMSE\": 6.345294903725613,         \"R2\": -10.86190793294698,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 1.8008990000000002       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.371542642654472,         \"RMSE\": 6.177015076243767,         \"R2\": -10.629949316856385,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 2.0970690000000003       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.263251524870982,         \"RMSE\": 6.020874949010495,         \"R2\": -10.36170885736068,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 2.4016       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.166901825777709,         \"RMSE\": 5.8760767656227735,         \"R2\": -10.179937857653265,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 2.706944       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.102550908901192,         \"RMSE\": 5.743886676480224,         \"R2\": -9.489284487381322,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.013076       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.036030402550628,         \"RMSE\": 5.61908468135586,         \"R2\": -8.519416508014515,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.319991       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.965717807967496,         \"RMSE\": 5.50138701729293,         \"R2\": -7.914879120336785,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.627685       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8948913466896105,         \"RMSE\": 5.390446783732167,         \"R2\": -7.379388774419297,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.93618       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8304411336225568,         \"RMSE\": 5.286008256480869,         \"R2\": -7.071904701569496,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 4.245555       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7733791235095338,         \"RMSE\": 5.187623645241403,         \"R2\": -6.74573862520947,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 4.555757000000001       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7328732375480085,         \"RMSE\": 5.096231477200102,         \"R2\": -6.653340289034931,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 4.866786000000001       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6922671720641331,         \"RMSE\": 5.009032279128942,         \"R2\": -6.5765398617523605,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 5.1996410000000015       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6600221636451291,         \"RMSE\": 4.9270067527590165,         \"R2\": -6.249341959517198,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 5.545038000000002       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6169171465584515,         \"RMSE\": 4.847662648980224,         \"R2\": -5.910766757861972,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 5.892753000000002       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5787668849144931,         \"RMSE\": 4.771995268006674,         \"R2\": -5.5715350899413965,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 6.242777000000002       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.535700232104731,         \"RMSE\": 4.69925054984221,         \"R2\": -5.326885534626132,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 6.599796000000002       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5003699975160405,         \"RMSE\": 4.630081239411466,         \"R2\": -5.239062722957792,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 6.957619000000002       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4782734303433982,         \"RMSE\": 4.565354365023557,         \"R2\": -5.225160013321354,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 7.316272000000002       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4563696019956498,         \"RMSE\": 4.503833132228122,         \"R2\": -5.19161922746511,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 7.675697000000002       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4392280778003554,         \"RMSE\": 4.445645440595998,         \"R2\": -5.02903742417401,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 8.035893000000002       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4073407178561264,         \"RMSE\": 4.387021097703184,         \"R2\": -4.9346827726614455,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 8.396854000000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3782504190107006,         \"RMSE\": 4.330701361336262,         \"R2\": -4.809192109617374,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 8.758623000000002       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3571814777264213,         \"RMSE\": 4.277370073659861,         \"R2\": -4.718248073230613,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 9.121240000000002       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3328025450945626,         \"RMSE\": 4.2253925636382,         \"R2\": -4.641399853721709,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 9.484674000000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.311715211433691,         \"RMSE\": 4.175582527272098,         \"R2\": -4.560327645533724,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 9.848922000000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2897432923325247,         \"RMSE\": 4.127236925138345,         \"R2\": -4.422957957045758,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 10.213983000000002       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2672991203860131,         \"RMSE\": 4.080383024210964,         \"R2\": -4.274192362897992,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 10.579853000000002       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2421842209255052,         \"RMSE\": 4.03488734209176,         \"R2\": -4.178968845613636,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 10.965988       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.220808929344255,         \"RMSE\": 3.991045752926761,         \"R2\": -4.15028972045945,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 11.354575       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2057181063421545,         \"RMSE\": 3.9494511154557617,         \"R2\": -4.0862825167589865,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 11.745471       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.188437369603739,         \"RMSE\": 3.9086583836793856,         \"R2\": -4.0338431039290805,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 12.138778       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1710173649101312,         \"RMSE\": 3.869039281342956,         \"R2\": -4.028105053503917,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 12.545048       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1544521877618488,         \"RMSE\": 3.830602085194232,         \"R2\": -4.011663649294047,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 12.952187       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1537672749321948,         \"RMSE\": 3.8287168981917103,         \"R2\": -4.010074752320696,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 13.359495       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.6062254572366,         \"RMSE\": 31.39938120772091,         \"R2\": -1268.1112549740517,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.000711       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.412737763681047,         \"RMSE\": 23.97862157826266,         \"R2\": -607.9443275975191,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.001889       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.119104680903606,         \"RMSE\": 19.655410372524667,         \"R2\": -267.315679768846,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.003406       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.691588950452092,         \"RMSE\": 17.042779535378298,         \"R2\": -227.6797328948204,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.00525       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.128477598777668,         \"RMSE\": 17.570968714531574,         \"R2\": -59.26944361385635,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.007421       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.75565671610116,         \"RMSE\": 16.483156797846284,         \"R2\": -21.862958739409084,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.009919       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.454334080303978,         \"RMSE\": 15.644372833730271,         \"R2\": -12.803711937026078,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.012745       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.893519322025275,         \"RMSE\": 14.807378680481822,         \"R2\": -10.212022929829027,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.015896       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.219705201317108,         \"RMSE\": 14.0445461378022,         \"R2\": -7.436202462041329,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.019372       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.828389618716818,         \"RMSE\": 13.455080798744472,         \"R2\": -4.4070097733575375,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.023173       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.61456960864212,         \"RMSE\": 13.037583740326507,         \"R2\": -2.909846715773841,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.027299       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.52880743945525,         \"RMSE\": 12.69008098915324,         \"R2\": -2.0274307958032884,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.031782       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.39143583855712,         \"RMSE\": 12.35961426350804,         \"R2\": -1.3260696348061909,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.036638       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.12180315101294,         \"RMSE\": 12.009375103170282,         \"R2\": -0.866389387173786,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0418749999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.136940986261356,         \"RMSE\": 11.920551719153746,         \"R2\": -0.4782218583187949,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0474919999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.284290032332207,         \"RMSE\": 11.93362687305613,         \"R2\": -0.1945108920445761,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0534829999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.390309464431912,         \"RMSE\": 11.903488345267943,         \"R2\": 0.0269083954035856,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0598469999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.350219958465262,         \"RMSE\": 11.791481226840991,         \"R2\": 0.2404518209934976,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.06659       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.499019855105985,         \"RMSE\": 11.958125495095471,         \"R2\": 0.3560283448388185,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.073713       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.90272187978296,         \"RMSE\": 12.527163169679886,         \"R2\": 0.3812690011074207,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.081218       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.171291167504233,         \"RMSE\": 12.73748746029564,         \"R2\": 0.4528394877125938,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.08971       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.37629466014084,         \"RMSE\": 13.047657656056804,         \"R2\": 0.538024139715424,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.099295       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.440817816219347,         \"RMSE\": 13.0964165059942,         \"R2\": 0.5957634168273553,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.110134       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.906487060964151,         \"RMSE\": 13.855497684527965,         \"R2\": 0.5866088718530376,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1222419999999999       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.387009537918406,         \"RMSE\": 14.786939232799543,         \"R2\": 0.5856869069436603,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1355289999999999       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.701469010841246,         \"RMSE\": 15.270898285463774,         \"R2\": 0.6172820078624095,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1498919999999999       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.689852199892528,         \"RMSE\": 15.284847538688991,         \"R2\": 0.6674656839655615,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1772019999999999       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.168487287417785,         \"RMSE\": 16.008183102465477,         \"R2\": 0.6751444757196481,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2058519999999999       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.085867087734242,         \"RMSE\": 18.170753499240718,         \"R2\": 0.6201764868699093,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2348879999999999       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.672501856506583,         \"RMSE\": 19.05837058612535,         \"R2\": 0.6424226539377311,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2642819999999999       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.822446828447037,         \"RMSE\": 19.13937756684808,         \"R2\": 0.6770787925994421,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2940279999999999       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.055746883990931,         \"RMSE\": 19.31213644577825,         \"R2\": 0.7011272480618885,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.3241269999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.79008745873622,         \"RMSE\": 20.396105048894267,         \"R2\": 0.6762859401979866,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.3545779999999999       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.293199062265238,         \"RMSE\": 21.539399675842866,         \"R2\": 0.6681199603719434,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.3853799999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.740320816630271,         \"RMSE\": 22.31102616496048,         \"R2\": 0.6849554171717112,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.4261629999999999       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.862968645899144,         \"RMSE\": 22.29409698811668,         \"R2\": 0.7067005430463744,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.467315       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.699705023283965,         \"RMSE\": 23.67314903355933,         \"R2\": 0.6925996644733732,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.508826       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.38213993729544,         \"RMSE\": 25.048095107979137,         \"R2\": 0.6769473375050636,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.55069       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.967894830794286,         \"RMSE\": 26.15320189056989,         \"R2\": 0.6870368010887093,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.592905       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.10728249235129,         \"RMSE\": 26.204092785638924,         \"R2\": 0.7080553660644732,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.6354690000000001       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.603016925007317,         \"RMSE\": 26.772391386711117,         \"R2\": 0.7082099437723521,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.6783830000000001       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.614531201761597,         \"RMSE\": 28.786744962703725,         \"R2\": 0.6831362914484524,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.7216460000000001       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.48829335200544,         \"RMSE\": 30.38515335394973,         \"R2\": 0.6882746780375071,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.7652570000000001       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.755002868307955,         \"RMSE\": 30.52390276571354,         \"R2\": 0.7026599444855313,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.8092140000000001       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.22217092676305,         \"RMSE\": 31.08727194033441,         \"R2\": 0.7098743070293987,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.8535240000000001       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.03670858216615,         \"RMSE\": 32.44431034253017,         \"R2\": 0.6931769059461363,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.898204       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.78200415465676,         \"RMSE\": 33.496021791915204,         \"R2\": 0.6913806254796178,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.943264       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.56258004106143,         \"RMSE\": 34.768391171729405,         \"R2\": 0.7010449079513538,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.988697       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.68725373887437,         \"RMSE\": 34.77075336357408,         \"R2\": 0.7133508993505916,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.0345       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.627725892037507,         \"RMSE\": 36.32441604878253,         \"R2\": 0.6968033114915981,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.080674       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.34737619246692,         \"RMSE\": 37.30920796407717,         \"R2\": 0.6941284720923248,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.127216       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.18573737545828,         \"RMSE\": 38.51358935872805,         \"R2\": 0.698506895988072,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.174127       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.27380465992389,         \"RMSE\": 38.58852748240754,         \"R2\": 0.7046942807227952,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.2212839999999998       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.994354275814885,         \"RMSE\": 24.339467027537435,         \"R2\": -1388.5575385664913,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.002841       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.808927193108108,         \"RMSE\": 17.83271591943186,         \"R2\": -126.84988353201342,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.006663       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.864002308096952,         \"RMSE\": 15.320672400398038,         \"R2\": -126.22308256175272,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.011298       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.882777304938948,         \"RMSE\": 13.38981065066765,         \"R2\": -96.4771385394691,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.01675       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.231639558854497,         \"RMSE\": 11.98203471414171,         \"R2\": -47.97617801736401,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.023066       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.334108393931037,         \"RMSE\": 10.98237795329033,         \"R2\": -33.904913895880355,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.030179       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.563493982833803,         \"RMSE\": 10.178707085968126,         \"R2\": -29.98405233271513,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.038033       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.002122045077101,         \"RMSE\": 9.533278572445496,         \"R2\": -22.968717144675637,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.048318       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.587842803317817,         \"RMSE\": 9.003737317880292,         \"R2\": -17.777085610739057,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.072946       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.458683971614509,         \"RMSE\": 8.652080760634158,         \"R2\": -16.390543570087573,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.099467       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.239995800771734,         \"RMSE\": 8.280452519944822,         \"R2\": -16.182419642449048,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.129303       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.943592784264584,         \"RMSE\": 7.932077353220182,         \"R2\": -14.885669934585447,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.159907       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.7846302486799286,         \"RMSE\": 7.646201644169009,         \"R2\": -13.96015951270874,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.191262       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.6468171672887713,         \"RMSE\": 7.389977926170562,         \"R2\": -13.630953412847402,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.2233629999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.526112368086922,         \"RMSE\": 7.1621871014808685,         \"R2\": -12.51535851099468,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.2587739999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.5074300839639245,         \"RMSE\": 6.985469271455791,         \"R2\": -12.493228210862725,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.2965239999999999       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.434140699763514,         \"RMSE\": 6.814822943627961,         \"R2\": -12.570888751300782,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.3365219999999999       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.42722001559718,         \"RMSE\": 6.678288393486038,         \"R2\": -12.13957341395007,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.3787709999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.332029752839207,         \"RMSE\": 6.516115548498917,         \"R2\": -11.941900403072644,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.4232519999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.217390968362987,         \"RMSE\": 6.356555790563252,         \"R2\": -11.66392028459017,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.4696459999999999       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.100825681509746,         \"RMSE\": 6.206562691759863,         \"R2\": -11.47288048490914,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.516851       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.0187726323631243,         \"RMSE\": 6.072312098448126,         \"R2\": -10.723095644711892,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.5652379999999999       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.947022825868371,         \"RMSE\": 5.94849802587685,         \"R2\": -9.668265577306911,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.6161209999999999       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.867282537241402,         \"RMSE\": 5.828292237410032,         \"R2\": -9.005843404687633,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.6743809999999999       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.8281006485905213,         \"RMSE\": 5.729646774374514,         \"R2\": -8.467133754251039,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.7334769999999999       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.759113137285707,         \"RMSE\": 5.623931694381955,         \"R2\": -8.136932704030892,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.79343       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.7113951403332286,         \"RMSE\": 5.52770300084093,         \"R2\": -7.794584318722522,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.8541829999999999       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.646739535451309,         \"RMSE\": 5.432090595521053,         \"R2\": -7.695343452205655,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.94202       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5972398336076634,         \"RMSE\": 5.343168086286508,         \"R2\": -7.621065103502998,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.0306859999999998       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.533455116608919,         \"RMSE\": 5.255265792942869,         \"R2\": -7.247487071141652,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.1202       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.497138699914293,         \"RMSE\": 5.178243230235351,         \"R2\": -6.88544757519055,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.210495       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4712145738198297,         \"RMSE\": 5.107804033669319,         \"R2\": -6.528964961790648,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.30153       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.429247896498525,         \"RMSE\": 5.0347117637840935,         \"R2\": -6.262430681498119,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.3932969999999998       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.398090124502612,         \"RMSE\": 4.967521902410674,         \"R2\": -6.18160824182094,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.4857959999999997       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.360396901712673,         \"RMSE\": 4.90286744871834,         \"R2\": -6.1796262474179136,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.5790619999999995       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3150393936015323,         \"RMSE\": 4.836721469702358,         \"R2\": -6.140716883970916,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.6730619999999998       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.267679208737699,         \"RMSE\": 4.77254376860168,         \"R2\": -5.948293801048677,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.7677929999999995       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.2434173929652075,         \"RMSE\": 4.715867112708654,         \"R2\": -5.857742612566196,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.863279       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.199009654391343,         \"RMSE\": 4.656030786420359,         \"R2\": -5.714767077599112,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.959541       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.159659681172017,         \"RMSE\": 4.59898830049537,         \"R2\": -5.610494506343661,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.056608       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1249482408574707,         \"RMSE\": 4.545176313025559,         \"R2\": -5.527610390446187,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.175652       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.094058354623314,         \"RMSE\": 4.493551443258636,         \"R2\": -5.439404045425388,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.37025       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.062104039794744,         \"RMSE\": 4.442864622918497,         \"R2\": -5.284107374622643,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.565666       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.030706594140141,         \"RMSE\": 4.393695684791879,         \"R2\": -5.115247638705276,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.761867       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.003263565299311,         \"RMSE\": 4.347049681772325,         \"R2\": -5.011317673951863,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.958847       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9706878923964863,         \"RMSE\": 4.300488305941944,         \"R2\": -4.979898179552831,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.156658       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.949819924248383,         \"RMSE\": 4.257394252920471,         \"R2\": -4.91037086709413,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.355253       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9258186229947107,         \"RMSE\": 4.214725843085415,         \"R2\": -4.85305905428677,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.554625       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.900426010360992,         \"RMSE\": 4.173138113177231,         \"R2\": -4.849565137575967,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.754774       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.872733130377695,         \"RMSE\": 4.13253797119814,         \"R2\": -4.832859832721421,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.955698       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.871510887330926,         \"RMSE\": 4.130524228438989,         \"R2\": -4.8310671605777085,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 4.156775       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.624124996337724,         \"RMSE\": 28.77138517975663,         \"R2\": -1064.5628215382144,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.000572       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.0510878175865,         \"RMSE\": 20.931739283093208,         \"R2\": -463.0233071270199,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.001645       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.49930786476168,         \"RMSE\": 17.564629142555763,         \"R2\": -213.26922094451623,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.003059       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.378514545021682,         \"RMSE\": 15.405121473747096,         \"R2\": -185.84310618709696,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.004809       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.844108697295251,         \"RMSE\": 17.128215293517524,         \"R2\": -56.27037115396167,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.006892       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.889488781892217,         \"RMSE\": 15.88743125142584,         \"R2\": -20.24022051627188,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.009306       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.103343480706034,         \"RMSE\": 14.91594241381016,         \"R2\": -11.548186613409534,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.012052       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.288900850158633,         \"RMSE\": 14.011374344891149,         \"R2\": -9.0389683228034,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.015129       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.736865157066078,         \"RMSE\": 13.281093172283262,         \"R2\": -6.543957317046456,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.018712       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.618125386224052,         \"RMSE\": 12.858171267844924,         \"R2\": -3.9379074608874927,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.022717       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.580936033253089,         \"RMSE\": 12.51524762994286,         \"R2\": -2.6028352541816,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.027127       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.191573127926202,         \"RMSE\": 12.024287681643044,         \"R2\": -1.7180920054032294,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.031928       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.001452140019149,         \"RMSE\": 11.63905100750295,         \"R2\": -1.062756769701151,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0371139999999999       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.959260067984971,         \"RMSE\": 11.397763679955697,         \"R2\": -0.6811278108981134,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0426889999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.036161429677985,         \"RMSE\": 11.359538570018056,         \"R2\": -0.3423577921849861,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0486539999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.141200516910354,         \"RMSE\": 11.407680550849577,         \"R2\": -0.0915406328349714,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0550109999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.061965626679777,         \"RMSE\": 11.211626858308708,         \"R2\": 0.1367382583661435,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0617539999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.988600359846859,         \"RMSE\": 11.023879576943443,         \"R2\": 0.3361231572252737,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0688849999999999       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.115468527113427,         \"RMSE\": 11.18859440458875,         \"R2\": 0.4362434515233688,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0763959999999999       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.571784360381598,         \"RMSE\": 11.99879894105818,         \"R2\": 0.4323613497222297,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0842879999999999       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.610536559977233,         \"RMSE\": 11.962244483436113,         \"R2\": 0.5174164020157423,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0937539999999999       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.753677752144043,         \"RMSE\": 12.109970858596688,         \"R2\": 0.6020391290764042,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.1045109999999999       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.763402728464486,         \"RMSE\": 12.046916639776326,         \"R2\": 0.6579556132088519,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.123112       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.37232599699494,         \"RMSE\": 12.9382814211091,         \"R2\": 0.6395292100633578,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.147917       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.870502401884236,         \"RMSE\": 14.03783628218945,         \"R2\": 0.6266016212673495,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.173118       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.125553299295866,         \"RMSE\": 14.312481045438886,         \"R2\": 0.6638140497188074,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.198688       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.11642729851449,         \"RMSE\": 14.234872044017685,         \"R2\": 0.7115826499817814,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.225283       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.63053955101658,         \"RMSE\": 15.01159987060024,         \"R2\": 0.7143329631149452,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.252265       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.671899739762464,         \"RMSE\": 17.42953249336733,         \"R2\": 0.650531972004655,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.279616       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.1135598398273,         \"RMSE\": 17.980470366868552,         \"R2\": 0.6817264420663893,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.307329       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.368994570730054,         \"RMSE\": 18.183536514460908,         \"R2\": 0.7085274545262112,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.335405       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.47998520043724,         \"RMSE\": 18.216810890558104,         \"R2\": 0.7340681346165732,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.363842       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.490995837872443,         \"RMSE\": 19.84181186896939,         \"R2\": 0.693641639088806,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.392636       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.988870134156189,         \"RMSE\": 20.81926805033374,         \"R2\": 0.6899406322869175,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4217869999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.420579982415202,         \"RMSE\": 21.48960215237335,         \"R2\": 0.7077263415053474,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4512919999999999       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.424816444492956,         \"RMSE\": 21.37796604773129,         \"R2\": 0.7303103668220552,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.481151       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.284688005634004,         \"RMSE\": 22.70157511582256,         \"R2\": 0.7173140296578691,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.514769       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.038658536726118,         \"RMSE\": 24.042516108283174,         \"R2\": 0.7023651722582169,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.548784       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.59029009825774,         \"RMSE\": 24.916858152232297,         \"R2\": 0.7159269075042054,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.583167       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.812702077031824,         \"RMSE\": 25.07250049330081,         \"R2\": 0.7327254930224041,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.617914       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.346042839206106,         \"RMSE\": 25.68091484988461,         \"R2\": 0.7315167856134144,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.653021       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.370765923434053,         \"RMSE\": 27.689388199834635,         \"R2\": 0.7068336630361484,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.688487       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.264179516209435,         \"RMSE\": 29.30099868065636,         \"R2\": 0.7101227966068765,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.724309       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.63502154656571,         \"RMSE\": 29.559619400414903,         \"R2\": 0.7211497930314269,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.760485       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.145243718121584,         \"RMSE\": 30.130361606680754,         \"R2\": 0.7274603750306702,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.7970149999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.98075812634153,         \"RMSE\": 31.43770898148617,         \"R2\": 0.7119202509985216,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.8338999999999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.7046141289421,         \"RMSE\": 32.42665929478992,         \"R2\": 0.7107714616434232,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.8711439999999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.54126059149082,         \"RMSE\": 33.75343345950398,         \"R2\": 0.7182443212146572,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.908739       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.73603745751772,         \"RMSE\": 33.829762552174344,         \"R2\": 0.7286559632490104,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.946687       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.674609740448528,         \"RMSE\": 35.33904665998618,         \"R2\": 0.7130297805712756,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.984985       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.350956760305525,         \"RMSE\": 36.24046007710213,         \"R2\": 0.7114012814424304,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.023633       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.20743030595361,         \"RMSE\": 37.47019278346573,         \"R2\": 0.7146215025224946,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.06263       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.342328163686027,         \"RMSE\": 37.59599019491026,         \"R2\": 0.7196900586014492,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.101865       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.806898309502586,         \"RMSE\": 26.56763494383828,         \"R2\": -1654.6182189603317,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.003003       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 14.866074912822512,         \"RMSE\": 20.957300378156614,         \"R2\": -175.5777711351631,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.009504       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.772648582583251,         \"RMSE\": 17.555009093750932,         \"R2\": -166.03688377592212,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.016813       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.372925375947808,         \"RMSE\": 15.758572852966298,         \"R2\": -134.01675577859288,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.02489       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.950999863257042,         \"RMSE\": 14.807263848606526,         \"R2\": -73.79513907078027,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.033777       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.131163180965077,         \"RMSE\": 13.743973626529105,         \"R2\": -53.66614209724606,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.043434       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.532294463666167,         \"RMSE\": 12.93588512414824,         \"R2\": -49.04322437944699,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.05944       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.33219708929472,         \"RMSE\": 12.40854626547306,         \"R2\": -39.60710527883104,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.077842       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.281092452540433,         \"RMSE\": 12.043698542516514,         \"R2\": -32.597139849683785,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0986229999999999       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.889313429527772,         \"RMSE\": 11.548268653424005,         \"R2\": -29.98173855178904,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.121857       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.555718436766954,         \"RMSE\": 11.115454500430198,         \"R2\": -29.96211572526289,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.149384       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.300584612865839,         \"RMSE\": 10.768588372618428,         \"R2\": -28.278525817685868,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.177703       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.073956995660685,         \"RMSE\": 10.455941089275187,         \"R2\": -26.97505735848669,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.206834       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.879100927439736,         \"RMSE\": 10.179149173565092,         \"R2\": -26.75935065850941,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.236782       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.698392466938299,         \"RMSE\": 9.935855831167723,         \"R2\": -25.01038668400382,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.26759       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.496977203333427,         \"RMSE\": 9.674599820332077,         \"R2\": -24.881575653507443,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.299212       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.319501534649956,         \"RMSE\": 9.433800456219284,         \"R2\": -25.005937123592886,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3316429999999999       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.189316591643737,         \"RMSE\": 9.224778235838508,         \"R2\": -24.070488458586468,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3648849999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.05373584315195,         \"RMSE\": 9.02603667348878,         \"R2\": -23.83217081250324,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3989449999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.893196935767096,         \"RMSE\": 8.831009888188627,         \"R2\": -23.44247524737401,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.4338649999999999       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.787168115685009,         \"RMSE\": 8.669033337133486,         \"R2\": -23.33356914323267,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.4696089999999999       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.789860410241021,         \"RMSE\": 8.633597516354541,         \"R2\": -22.69832962851382,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.5061589999999999       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.751464501173282,         \"RMSE\": 8.532971696368575,         \"R2\": -20.952287666855003,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.5516559999999999       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.758413491181221,         \"RMSE\": 8.476961067588123,         \"R2\": -20.166616413985547,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.599555       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.682950272504451,         \"RMSE\": 8.3510488559106,         \"R2\": -19.11151854181045,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.6519079999999999       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.627995468360723,         \"RMSE\": 8.245754355787446,         \"R2\": -18.64179334000355,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.705132       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.546541731300828,         \"RMSE\": 8.130789587119862,         \"R2\": -18.02792190581397,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.7591289999999999       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.474658569482086,         \"RMSE\": 8.019262742277965,         \"R2\": -17.95054121046633,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.813877       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.409420416004319,         \"RMSE\": 7.920158789530457,         \"R2\": -17.94222667017848,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.869386       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.394854582323811,         \"RMSE\": 7.870548110777217,         \"R2\": -17.498743363524373,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.941553       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.360408122735632,         \"RMSE\": 7.801849933723111,         \"R2\": -16.900148820132806,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.016153       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.332182524169608,         \"RMSE\": 7.745335706289596,         \"R2\": -16.312002846243146,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.093294       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.286484086266954,         \"RMSE\": 7.672164501241343,         \"R2\": -15.864310422998043,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.1728       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.240017672508232,         \"RMSE\": 7.591734569529257,         \"R2\": -15.77351804027652,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.259908       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.203631741702394,         \"RMSE\": 7.526058935068808,         \"R2\": -15.917522479350382,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.347915       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.198551833398676,         \"RMSE\": 7.481861117849272,         \"R2\": -16.086729414362967,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.436748       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.200051628353664,         \"RMSE\": 7.443314903444159,         \"R2\": -15.900950117878589,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.526395       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.146415466772512,         \"RMSE\": 7.367313347205083,         \"R2\": -15.73695108767244,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.61685       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.164438314106662,         \"RMSE\": 7.352756459959702,         \"R2\": -15.745558187055655,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.708114       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.199091748701669,         \"RMSE\": 7.381485816300255,         \"R2\": -16.029304780133675,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.8001699999999998       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.184244405270293,         \"RMSE\": 7.343677512392477,         \"R2\": -16.040406471643664,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.892982       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.162940711797175,         \"RMSE\": 7.295196877254559,         \"R2\": -15.972283354719572,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.986541       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.146772746928229,         \"RMSE\": 7.251973114148485,         \"R2\": -15.742866229030533,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.080851       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.141562534384022,         \"RMSE\": 7.225910341165371,         \"R2\": -15.54014218136778,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.175912       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.113671043317916,         \"RMSE\": 7.181653170625269,         \"R2\": -15.40700562565575,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.271722       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.082725756932772,         \"RMSE\": 7.134180367835326,         \"R2\": -15.456838882747109,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.368328       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.049460198345376,         \"RMSE\": 7.092641752853287,         \"R2\": -15.403746251323405,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.491683       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.012955702794688,         \"RMSE\": 7.041188655025779,         \"R2\": -15.335641983730405,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.616037       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.992587411597517,         \"RMSE\": 7.002009756347646,         \"R2\": -15.46809971530338,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.74122       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.986581819477306,         \"RMSE\": 6.97972894589718,         \"R2\": -15.638912943338369,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.867218       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.984033991902679,         \"RMSE\": 6.9766666383395455,         \"R2\": -15.63541499061877,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.993378       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.19936706045659,         \"RMSE\": 55.118879370280126,         \"R2\": -3909.733983269086,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.001533       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.495026158423794,         \"RMSE\": 43.23165104261441,         \"R2\": -1978.396532834284,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.004589       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.680053698816124,         \"RMSE\": 39.98506660332775,         \"R2\": -1109.3949268723327,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.008788       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 29.375885022911746,         \"RMSE\": 37.29886968855784,         \"R2\": -1094.3128086885838,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.014141       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.707444751978134,         \"RMSE\": 40.753235251415205,         \"R2\": -323.21264874535376,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.020635       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.96097441162184,         \"RMSE\": 40.14945868859866,         \"R2\": -134.64726490280094,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.028271       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.25989567011213,         \"RMSE\": 39.82501544894248,         \"R2\": -88.45229320906665,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.041195       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.76307262878121,         \"RMSE\": 39.802536485586,         \"R2\": -80.01195436020778,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.054526       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.66411513705659,         \"RMSE\": 39.325402336106926,         \"R2\": -65.1420916497486,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.068227       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.19940912800194,         \"RMSE\": 40.704130728492046,         \"R2\": -48.48362457590105,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.082293       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.629161705635866,         \"RMSE\": 40.92880988729008,         \"R2\": -37.53219439908784,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.096719       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 35.29035427006805,         \"RMSE\": 41.59178542812187,         \"R2\": -31.520750879588867,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.111503       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.23638449140802,         \"RMSE\": 42.62018794050648,         \"R2\": -26.65945376164948,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.126644       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.72501013289919,         \"RMSE\": 42.91835139661213,         \"R2\": -22.83677510551845,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.142141       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.731745662210095,         \"RMSE\": 42.91744223234227,         \"R2\": -18.16084111691443,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.157996       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 37.94402632003076,         \"RMSE\": 44.39720610255875,         \"R2\": -15.5331835318136,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.174208       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 38.69858083339784,         \"RMSE\": 45.06856008203835,         \"R2\": -12.949305609255877,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.190776       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.18624064352699,         \"RMSE\": 46.68267333461602,         \"R2\": -10.905017439998025,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.2077       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.85432327682653,         \"RMSE\": 47.463811090322665,         \"R2\": -9.145320047375163,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.224978       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.36451127701117,         \"RMSE\": 48.41262940233051,         \"R2\": -8.240888884363313,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.24261       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.17342712408468,         \"RMSE\": 49.46918668675267,         \"R2\": -7.253093192412523,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.260594       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.81461612103895,         \"RMSE\": 51.73551020684679,         \"R2\": -6.2632609796369465,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.27893       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.90819615603068,         \"RMSE\": 53.07334253773936,         \"R2\": -5.6387075541588505,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.297618       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.45334973048907,         \"RMSE\": 55.82244674340302,         \"R2\": -5.710187070138379,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3166589999999999       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.05643802527038,         \"RMSE\": 58.804796990371536,         \"R2\": -5.552357606253864,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3360539999999999       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.41721923566732,         \"RMSE\": 60.72765972830183,         \"R2\": -5.052332799264802,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3558019999999999       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.23299901747073,         \"RMSE\": 63.29154255446438,         \"R2\": -4.701716347098143,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3759049999999999       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.82583967659276,         \"RMSE\": 65.36972550348784,         \"R2\": -4.417008197806662,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3963679999999999       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.851023886215806,         \"RMSE\": 70.45860717413167,         \"R2\": -4.71089374971376,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4171839999999999       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.58220488738844,         \"RMSE\": 72.62689780553444,         \"R2\": -4.192702597603254,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4383529999999999       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 58.456862484765374,         \"RMSE\": 75.26810540469758,         \"R2\": -3.994165343488624,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4598799999999999       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 59.98229295122657,         \"RMSE\": 76.97767263775137,         \"R2\": -3.748486775975887,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4817619999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 61.989108820835376,         \"RMSE\": 80.62951920841103,         \"R2\": -4.0588898392459045,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.503996       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 63.93796840595574,         \"RMSE\": 84.48840832488506,         \"R2\": -4.106322034628877,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.526584       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.15236861414519,         \"RMSE\": 85.79755918852514,         \"R2\": -3.6588935912158114,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.551564       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 66.90365663892747,         \"RMSE\": 87.9249329113371,         \"R2\": -3.562003118430529,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.5777180000000001       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.17917622540308,         \"RMSE\": 89.58756462611774,         \"R2\": -3.402382103640547,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.6050150000000001       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 70.80702754948452,         \"RMSE\": 94.96753809429286,         \"R2\": -3.643808228470034,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.6334650000000001       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 72.44730173566225,         \"RMSE\": 97.09455233033468,         \"R2\": -3.313534410167211,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.663057       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 74.29167351363806,         \"RMSE\": 99.40774027870644,         \"R2\": -3.201483315326532,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.693783       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 75.83174494284101,         \"RMSE\": 101.77506329990584,         \"R2\": -3.216760347648722,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.725638       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 78.5111288104629,         \"RMSE\": 106.99570126481906,         \"R2\": -3.3774383617967887,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.758621       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 81.63741116996734,         \"RMSE\": 112.58139375423264,         \"R2\": -3.2793958269429844,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.79391       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 82.66628549198501,         \"RMSE\": 113.50934761838604,         \"R2\": -3.1118432523868984,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.829617       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 84.40016304476833,         \"RMSE\": 116.34990208847,         \"R2\": -3.0639935553557365,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.865723       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 86.52132256561038,         \"RMSE\": 120.30772815943004,         \"R2\": -3.2188880650982723,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.902224       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 87.79244029751037,         \"RMSE\": 121.63869088166206,         \"R2\": -3.069866847809537,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.93912       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 90.43735682351402,         \"RMSE\": 126.62066541565774,         \"R2\": -2.9650251625135784,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.976398       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 91.59763342322412,         \"RMSE\": 127.6295949640952,         \"R2\": -2.862114672867245,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.014034       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 93.80067010965053,         \"RMSE\": 131.39026699356185,         \"R2\": -2.966921087845916,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.0520230000000002       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 96.52355815418714,         \"RMSE\": 136.33091173427522,         \"R2\": -3.0840934586689466,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.0903630000000002       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 99.60515399822415,         \"RMSE\": 141.80943605664237,         \"R2\": -3.0875177525301325,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.129055       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 100.62422612381133,         \"RMSE\": 143.06646930774232,         \"R2\": -3.0591132110693486,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.167982       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 48.24517612267716,         \"RMSE\": 65.52170729560882,         \"R2\": -10068.892101934754,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0014       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.96170708962665,         \"RMSE\": 54.398737007050464,         \"R2\": -1188.715138210959,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0037089999999999       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 37.75687919715097,         \"RMSE\": 48.78450375470138,         \"R2\": -1288.953469480389,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0068       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 34.906129137913965,         \"RMSE\": 44.99379649673769,         \"R2\": -1099.675197364534,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.010662       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 33.91700787894482,         \"RMSE\": 42.88559259598606,         \"R2\": -626.4029768570122,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0153429999999999       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 33.25318798467783,         \"RMSE\": 41.41783833748641,         \"R2\": -495.442160460349,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0207979999999999       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 32.454169303664,         \"RMSE\": 40.06534641626261,         \"R2\": -479.0547686921885,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0270209999999999       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.456143135335843,         \"RMSE\": 38.757475924320815,         \"R2\": -395.1605214699538,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0340139999999999       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.609503890456164,         \"RMSE\": 37.605439707525925,         \"R2\": -326.55474603918685,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0417749999999999       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.18524212396377,         \"RMSE\": 36.915721425331306,         \"R2\": -315.5882175367347,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0503299999999999       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.065472528043387,         \"RMSE\": 36.44442035805252,         \"R2\": -331.8421153382835,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0596439999999999       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.78865598017145,         \"RMSE\": 35.91292614465666,         \"R2\": -324.6366197799479,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0697149999999999       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.781114432924586,         \"RMSE\": 35.79236523689611,         \"R2\": -326.81251307944893,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.0853099999999999       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.53323842574737,         \"RMSE\": 35.35890478021234,         \"R2\": -333.95306350592915,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.1033029999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.45783145374521,         \"RMSE\": 35.09485674586195,         \"R2\": -323.506345927368,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.1237949999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.587592426740265,         \"RMSE\": 34.99099947403571,         \"R2\": -337.56135797788454,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.1466579999999999       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.592186767063264,         \"RMSE\": 34.82748458593961,         \"R2\": -353.4405109424598,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.178415       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.81187133621213,         \"RMSE\": 34.87255971663822,         \"R2\": -357.27671195740294,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.2110159999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.96085998978186,         \"RMSE\": 34.8837386376585,         \"R2\": -369.9082961036221,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.2444429999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.10861760053803,         \"RMSE\": 34.89146879370085,         \"R2\": -380.5600928496674,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.2787509999999999       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.237056214581205,         \"RMSE\": 34.87113676993804,         \"R2\": -392.72834237954817,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3138979999999999       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.396870134836657,         \"RMSE\": 34.919939008119975,         \"R2\": -386.68686883790514,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3498769999999999       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.5142090152444,         \"RMSE\": 34.936230377424984,         \"R2\": -366.9859696545672,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3866269999999999       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.60304766371323,         \"RMSE\": 34.90556469597589,         \"R2\": -357.88920799289923,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.4241359999999999       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.723498435612367,         \"RMSE\": 34.929338036322235,         \"R2\": -350.83863344189655,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.462404       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.811640107301315,         \"RMSE\": 34.91688659850233,         \"R2\": -351.20164208687333,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.5014689999999999       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.90684609870959,         \"RMSE\": 34.93305250730057,         \"R2\": -350.23597283973027,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.541291       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.83222834631613,         \"RMSE\": 34.80844611918478,         \"R2\": -356.0442181025925,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.58187       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.81214247339674,         \"RMSE\": 34.7464796172207,         \"R2\": -363.5733105101423,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.629234       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.96266506693229,         \"RMSE\": 34.82326914665847,         \"R2\": -361.1357082485383,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.6774979999999999       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.066025409450507,         \"RMSE\": 34.86865150113252,         \"R2\": -356.54586556020155,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.743837       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.17687176552783,         \"RMSE\": 34.929241693507976,         \"R2\": -351.0830657253844,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.810979       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.17965741293356,         \"RMSE\": 34.892251240403844,         \"R2\": -347.8114699445998,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.87889       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.2564554130016,         \"RMSE\": 34.924087575336145,         \"R2\": -353.970503405387,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.947564       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.205643809070587,         \"RMSE\": 34.8991435368638,         \"R2\": -362.7735100050666,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.017054       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.176512353694505,         \"RMSE\": 34.84497410939031,         \"R2\": -369.6124031875757,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.087302       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.10578554229227,         \"RMSE\": 34.74995813099662,         \"R2\": -367.37224871607793,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.158309       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.047274834607855,         \"RMSE\": 34.691812272848594,         \"R2\": -370.11801689051305,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.23401       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.10380007346799,         \"RMSE\": 34.703384372728905,         \"R2\": -372.0292841604823,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.319073       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.08555480002386,         \"RMSE\": 34.670374973731704,         \"R2\": -374.68721566223496,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.406663       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.19885148971359,         \"RMSE\": 34.750222550469864,         \"R2\": -380.56447731408525,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.496625       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.25463715565584,         \"RMSE\": 34.7657556157579,         \"R2\": -384.4513633760299,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.5889890000000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.316317159155098,         \"RMSE\": 34.79186578621207,         \"R2\": -384.3655387384781,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.693075       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.241979864666277,         \"RMSE\": 34.706915019978055,         \"R2\": -380.5804591262153,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.065505       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.224226006229813,         \"RMSE\": 34.673719949901624,         \"R2\": -381.4558834892021,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.440347       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.14134426690263,         \"RMSE\": 34.58836614994504,         \"R2\": -385.8283921715467,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.817664       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.997921544748237,         \"RMSE\": 34.45657797481166,         \"R2\": -386.1428842704396,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 3.197344       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.060400411885407,         \"RMSE\": 34.566740304864304,         \"R2\": -392.6960879419034,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 3.57811       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.96911325529305,         \"RMSE\": 34.515642414657464,         \"R2\": -399.1566023636026,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 3.959721       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.09609109084082,         \"RMSE\": 34.63142513356851,         \"R2\": -408.6269881777103,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 4.342153       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.093334457100017,         \"RMSE\": 34.62570772928248,         \"R2\": -408.7651443035993,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 4.724752       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.6439393939393945,         \"RMSE\": 12.708027567111456,         \"R2\": -206.8805289598106,         \"Memory in Mb\": 0.0208587646484375,         \"Time in s\": 0.001745       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7674242424242426,         \"RMSE\": 9.021574170013263,         \"R2\": -85.19732920009746,         \"Memory in Mb\": 0.0300941467285156,         \"Time in s\": 0.005817       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3601010101010105,         \"RMSE\": 7.4346315168437105,         \"R2\": -37.38846247874159,         \"Memory in Mb\": 0.0395355224609375,         \"Time in s\": 0.012524       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.988257575757577,         \"RMSE\": 6.459864921032004,         \"R2\": -31.8544119108943,         \"Memory in Mb\": 0.0488433837890625,         \"Time in s\": 0.023287       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.201515151515152,         \"RMSE\": 6.079045396219125,         \"R2\": -6.214006750846093,         \"Memory in Mb\": 0.0583724975585937,         \"Time in s\": 0.035467       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.2709595959595963,         \"RMSE\": 5.693634951086079,         \"R2\": -1.7279153546475992,         \"Memory in Mb\": 0.0685691833496093,         \"Time in s\": 0.049542       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.6114718614718617,         \"RMSE\": 5.706903555891601,         \"R2\": -0.8368793810695487,         \"Memory in Mb\": 0.0782623291015625,         \"Time in s\": 0.068809       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.5236742424242427,         \"RMSE\": 5.412016943708686,         \"R2\": -0.4977726858852578,         \"Memory in Mb\": 0.0879554748535156,         \"Time in s\": 0.099298       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.4695286195286204,         \"RMSE\": 5.169211114529652,         \"R2\": -0.1428260058474422,         \"Memory in Mb\": 0.0976486206054687,         \"Time in s\": 0.132347       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7553030303030317,         \"RMSE\": 5.269495069058163,         \"R2\": 0.1706792355598563,         \"Memory in Mb\": 0.1073417663574218,         \"Time in s\": 0.167959       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.1511019283746564,         \"RMSE\": 5.580125306741311,         \"R2\": 0.2837685080447375,         \"Memory in Mb\": 0.117034912109375,         \"Time in s\": 0.206371       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.315782828282829,         \"RMSE\": 5.649452649212155,         \"R2\": 0.3999904226030885,         \"Memory in Mb\": 0.1272315979003906,         \"Time in s\": 0.248032       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.6019813519813537,         \"RMSE\": 5.868270501527574,         \"R2\": 0.475635686274607,         \"Memory in Mb\": 0.1369247436523437,         \"Time in s\": 0.313422       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.745995670995673,         \"RMSE\": 5.964828521670115,         \"R2\": 0.5395766265984425,         \"Memory in Mb\": 0.1466178894042968,         \"Time in s\": 0.409887       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.050202020202021,         \"RMSE\": 6.4542180762994805,         \"R2\": 0.5666546129487657,         \"Memory in Mb\": 0.15631103515625,         \"Time in s\": 0.575073       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.420928030303032,         \"RMSE\": 6.954884488253524,         \"R2\": 0.5942812793055753,         \"Memory in Mb\": 0.1660041809082031,         \"Time in s\": 0.746583       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.757664884135474,         \"RMSE\": 7.278917476631412,         \"R2\": 0.6361362300357987,         \"Memory in Mb\": 0.1756973266601562,         \"Time in s\": 0.922657       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.192340067340069,         \"RMSE\": 7.767087259749381,         \"R2\": 0.6704396407154757,         \"Memory in Mb\": 0.1858940124511718,         \"Time in s\": 1.103506       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.571690590111645,         \"RMSE\": 8.414476478500024,         \"R2\": 0.6811438926382001,         \"Memory in Mb\": 0.195587158203125,         \"Time in s\": 1.289988       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.017651515151518,         \"RMSE\": 9.535434778453542,         \"R2\": 0.641509702161033,         \"Memory in Mb\": 0.2052803039550781,         \"Time in s\": 1.481758       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.514646464646468,         \"RMSE\": 10.15268578355149,         \"R2\": 0.652376522878304,         \"Memory in Mb\": 0.2149734497070312,         \"Time in s\": 1.688886       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.006955922865016,         \"RMSE\": 10.883499074839364,         \"R2\": 0.6785664047839641,         \"Memory in Mb\": 0.2246665954589843,         \"Time in s\": 1.905808       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.401119894598158,         \"RMSE\": 11.259257694820905,         \"R2\": 0.7012209269570091,         \"Memory in Mb\": 0.2343597412109375,         \"Time in s\": 2.129833       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.873800505050509,         \"RMSE\": 12.237701558545494,         \"R2\": 0.6775097363055258,         \"Memory in Mb\": 0.2446098327636718,         \"Time in s\": 2.3598440000000003       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.501393939393942,         \"RMSE\": 13.456617650281162,         \"R2\": 0.6568816796501455,         \"Memory in Mb\": 0.254302978515625,         \"Time in s\": 2.605392       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.999592074592076,         \"RMSE\": 14.081405883193678,         \"R2\": 0.6745818706784585,         \"Memory in Mb\": 0.2639961242675781,         \"Time in s\": 2.8696780000000004       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.403647586980924,         \"RMSE\": 14.487230370517851,         \"R2\": 0.7012657763253116,         \"Memory in Mb\": 0.2736892700195312,         \"Time in s\": 3.1592620000000005       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.82559523809524,         \"RMSE\": 15.247017337775036,         \"R2\": 0.7053028346163965,         \"Memory in Mb\": 0.2833824157714844,         \"Time in s\": 3.471445000000001       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.570794148380358,         \"RMSE\": 17.082267622288043,         \"R2\": 0.6643188025566307,         \"Memory in Mb\": 0.2930755615234375,         \"Time in s\": 3.895766000000001       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.342676767676773,         \"RMSE\": 18.20491056057454,         \"R2\": 0.6737311884314376,         \"Memory in Mb\": 0.3032722473144531,         \"Time in s\": 4.333389       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.75625610948192,         \"RMSE\": 18.5968301788559,         \"R2\": 0.6951271166039881,         \"Memory in Mb\": 0.3129653930664062,         \"Time in s\": 4.790776       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.16955492424243,         \"RMSE\": 18.94133239132977,         \"R2\": 0.7124941202708752,         \"Memory in Mb\": 0.3226585388183594,         \"Time in s\": 5.260338       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.609595959595964,         \"RMSE\": 19.7022738973151,         \"R2\": 0.6979354313341102,         \"Memory in Mb\": 0.3323516845703125,         \"Time in s\": 5.750168       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.251024955436726,         \"RMSE\": 20.7851367099449,         \"R2\": 0.6909564285254863,         \"Memory in Mb\": 0.3420448303222656,         \"Time in s\": 6.251716       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.78255411255412,         \"RMSE\": 21.481025974379733,         \"R2\": 0.7079595790244884,         \"Memory in Mb\": 0.3522415161132812,         \"Time in s\": 6.769761       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.010311447811455,         \"RMSE\": 21.53574862211497,         \"R2\": 0.7263147242326703,         \"Memory in Mb\": 0.3619346618652344,         \"Time in s\": 7.297159       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.576126126126132,         \"RMSE\": 22.56379182999173,         \"R2\": 0.720735043690873,         \"Memory in Mb\": 0.3716278076171875,         \"Time in s\": 7.841892       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.256658692185017,         \"RMSE\": 23.708044463333223,         \"R2\": 0.710588766956741,         \"Memory in Mb\": 0.38134765625,         \"Time in s\": 8.4256       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.863597513597522,         \"RMSE\": 24.650993900023582,         \"R2\": 0.7219567169230845,         \"Memory in Mb\": 0.3910675048828125,         \"Time in s\": 9.118776       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.15655303030304,         \"RMSE\": 24.89490243600041,         \"R2\": 0.7364984966983625,         \"Memory in Mb\": 0.4007606506347656,         \"Time in s\": 9.83444       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.474242424242437,         \"RMSE\": 25.235361878916876,         \"R2\": 0.7407521096740679,         \"Memory in Mb\": 0.4109573364257812,         \"Time in s\": 10.564263       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.206240981241,         \"RMSE\": 26.51959634874256,         \"R2\": 0.731081178462164,         \"Memory in Mb\": 0.4206771850585937,         \"Time in s\": 11.311639       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.061486962649766,         \"RMSE\": 27.919441407022266,         \"R2\": 0.7368140706560946,         \"Memory in Mb\": 0.430450439453125,         \"Time in s\": 12.077289       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.444800275482105,         \"RMSE\": 28.396609389438456,         \"R2\": 0.742660608098584,         \"Memory in Mb\": 0.4401702880859375,         \"Time in s\": 12.86575       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.85067340067341,         \"RMSE\": 28.917019336286597,         \"R2\": 0.7489686179689856,         \"Memory in Mb\": 0.4499168395996094,         \"Time in s\": 13.665871       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.39739789196312,         \"RMSE\": 29.705616030262235,         \"R2\": 0.7427898649120724,         \"Memory in Mb\": 2.5872955322265625,         \"Time in s\": 16.820609       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.115441650548043,         \"RMSE\": 30.73530324863436,         \"R2\": 0.7401565757784102,         \"Memory in Mb\": 2.6296463012695312,         \"Time in s\": 20.027458000000003       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.836142676767683,         \"RMSE\": 31.98623382904741,         \"R2\": 0.7469752640852343,         \"Memory in Mb\": 2.6746597290039062,         \"Time in s\": 23.267231       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.017594310451457,         \"RMSE\": 32.125858524254696,         \"R2\": 0.7553011842320496,         \"Memory in Mb\": 2.717792510986328,         \"Time in s\": 26.555547000000004       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.677242424242426,         \"RMSE\": 32.83678407493398,         \"R2\": 0.7522301799631583,         \"Memory in Mb\": 2.769092559814453,         \"Time in s\": 29.885669000000004       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.80977421271539,         \"RMSE\": 35.198755082788004,         \"R2\": 0.727753941720713,         \"Memory in Mb\": 2.8112449645996094,         \"Time in s\": 33.249092000000005       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.195600233100237,         \"RMSE\": 38.25560047694445,         \"R2\": 0.7025325582791198,         \"Memory in Mb\": 2.857513427734375,         \"Time in s\": 36.653026       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.84062860438293,         \"RMSE\": 39.201635479156685,         \"R2\": 0.6952358931227007,         \"Memory in Mb\": 2.885215759277344,         \"Time in s\": 40.087818000000006       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.554585433333335,         \"RMSE\": 9.794739803036965,         \"R2\": -224.02989290855143,         \"Memory in Mb\": 0.0335884094238281,         \"Time in s\": 0.001545       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7993247666666672,         \"RMSE\": 6.973235588114817,         \"R2\": -18.54942689237887,         \"Memory in Mb\": 0.0554847717285156,         \"Time in s\": 0.0054       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.366773144444445,         \"RMSE\": 5.705236645726316,         \"R2\": -16.642396889136542,         \"Memory in Mb\": 0.0773544311523437,         \"Time in s\": 0.012332       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1277757833333335,         \"RMSE\": 4.947712433075743,         \"R2\": -12.30953248968821,         \"Memory in Mb\": 0.0997543334960937,         \"Time in s\": 0.028826       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.046201766666667,         \"RMSE\": 4.439862929674892,         \"R2\": -5.724544452799038,         \"Memory in Mb\": 0.1216506958007812,         \"Time in s\": 0.050318       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.000865705555556,         \"RMSE\": 4.0744555355418335,         \"R2\": -3.804331488196434,         \"Memory in Mb\": 0.1435470581054687,         \"Time in s\": 0.086896       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9447764619047624,         \"RMSE\": 3.7809361134406254,         \"R2\": -3.275153002458012,         \"Memory in Mb\": 0.1659469604492187,         \"Time in s\": 0.149837       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9352969166666673,         \"RMSE\": 3.5531790499707645,         \"R2\": -2.329617982408036,         \"Memory in Mb\": 0.1878433227539062,         \"Time in s\": 0.2304459999999999       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9445764925925928,         \"RMSE\": 3.380979243961517,         \"R2\": -1.647692611170827,         \"Memory in Mb\": 0.2097396850585937,         \"Time in s\": 0.432465       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9456943733333336,         \"RMSE\": 3.232789339199984,         \"R2\": -1.427877878808435,         \"Memory in Mb\": 0.2321395874023437,         \"Time in s\": 0.648003       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9124697575757575,         \"RMSE\": 3.0919339165015143,         \"R2\": -1.3957229068060464,         \"Memory in Mb\": 0.2540359497070312,         \"Time in s\": 0.888162       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9329223611111108,         \"RMSE\": 2.985727855147271,         \"R2\": -1.2507750530936188,         \"Memory in Mb\": 0.2759323120117187,         \"Time in s\": 1.171554       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9025974717948716,         \"RMSE\": 2.873740673763463,         \"R2\": -1.11319648675526,         \"Memory in Mb\": 0.2984657287597656,         \"Time in s\": 1.484213       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8654126523809523,         \"RMSE\": 2.773524640439575,         \"R2\": -1.0608690746642817,         \"Memory in Mb\": 0.3203620910644531,         \"Time in s\": 1.81098       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8525042622222223,         \"RMSE\": 2.688069339615046,         \"R2\": -0.9037818439458584,         \"Memory in Mb\": 0.3422584533691406,         \"Time in s\": 2.170241       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8265282395833334,         \"RMSE\": 2.6077957497476296,         \"R2\": -0.880493509713772,         \"Memory in Mb\": 0.3646583557128906,         \"Time in s\": 2.558101       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8137511019607846,         \"RMSE\": 2.539210136300266,         \"R2\": -0.8840673465916704,         \"Memory in Mb\": 0.3865547180175781,         \"Time in s\": 3.13755       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7887328240740744,         \"RMSE\": 2.4696835584739105,         \"R2\": -0.7969398815662787,         \"Memory in Mb\": 0.4084510803222656,         \"Time in s\": 3.744079       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7710879228070179,         \"RMSE\": 2.4087271831437693,         \"R2\": -0.7684619785143365,         \"Memory in Mb\": 0.4303474426269531,         \"Time in s\": 4.380375       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.756179386666667,         \"RMSE\": 2.351105641867075,         \"R2\": -0.7324819925835522,         \"Memory in Mb\": 0.4527473449707031,         \"Time in s\": 5.04744       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7300392539682541,         \"RMSE\": 2.295700426816902,         \"R2\": -0.7064552265553199,         \"Memory in Mb\": 0.4746437072753906,         \"Time in s\": 5.735437       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7180258560606063,         \"RMSE\": 2.24592493832078,         \"R2\": -0.6037054809307543,         \"Memory in Mb\": 0.4965400695800781,         \"Time in s\": 6.669957       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7103659666666668,         \"RMSE\": 2.200554873752302,         \"R2\": -0.4599688187191526,         \"Memory in Mb\": 0.5189399719238281,         \"Time in s\": 7.633919000000001       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6905233472222223,         \"RMSE\": 2.1551860359584523,         \"R2\": -0.3681716631920215,         \"Memory in Mb\": 0.5408363342285156,         \"Time in s\": 8.635162000000001       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6835753693333335,         \"RMSE\": 2.11616682722306,         \"R2\": -0.2914054626850582,         \"Memory in Mb\": 2.70669937133789,         \"Time in s\": 12.111573000000002       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6741869282051286,         \"RMSE\": 2.077523623184556,         \"R2\": -0.2468444979074313,         \"Memory in Mb\": 2.7946739196777344,         \"Time in s\": 15.640183000000002       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6635047197530868,         \"RMSE\": 2.0412653603832838,         \"R2\": -0.1992917531598592,         \"Memory in Mb\": 2.8836631774902344,         \"Time in s\": 19.224183000000004       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6666769047619049,         \"RMSE\": 2.01181749557566,         \"R2\": -0.1926963577893738,         \"Memory in Mb\": 2.973125457763672,         \"Time in s\": 22.863484000000003       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.662313208045977,         \"RMSE\": 1.9804661409620816,         \"R2\": -0.1843991731259868,         \"Memory in Mb\": 3.06191635131836,         \"Time in s\": 26.560120000000005       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6595208444444446,         \"RMSE\": 1.9515625148224915,         \"R2\": -0.1373580524839326,         \"Memory in Mb\": 3.158283233642578,         \"Time in s\": 30.31408200000001       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6603871010752689,         \"RMSE\": 1.924909501402362,         \"R2\": -0.0896376201735813,         \"Memory in Mb\": 3.248737335205078,         \"Time in s\": 34.12595900000001       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6518434010416667,         \"RMSE\": 1.8967107462711992,         \"R2\": -0.0381713320833934,         \"Memory in Mb\": 3.341320037841797,         \"Time in s\": 37.99311800000001       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6481796161616163,         \"RMSE\": 1.873162681009878,         \"R2\": -0.00527242303062,         \"Memory in Mb\": 3.435527801513672,         \"Time in s\": 41.91854100000001       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6594073715686274,         \"RMSE\": 1.8574009428793896,         \"R2\": -0.0040456355040212,         \"Memory in Mb\": 3.5269508361816406,         \"Time in s\": 45.90599500000001       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6619153695238096,         \"RMSE\": 1.8376987056605067,         \"R2\": -0.0086724321908719,         \"Memory in Mb\": 3.615283966064453,         \"Time in s\": 49.957909000000015       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6538050537037038,         \"RMSE\": 1.8142062090777376,         \"R2\": -0.0046457590045041,         \"Memory in Mb\": 3.7059364318847656,         \"Time in s\": 54.07172000000001       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6437102684684685,         \"RMSE\": 1.7904191974020045,         \"R2\": 0.0221149973980568,         \"Memory in Mb\": 3.8005104064941406,         \"Time in s\": 58.24412800000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6465423666666668,         \"RMSE\": 1.7722456151874884,         \"R2\": 0.0314860408387392,         \"Memory in Mb\": 3.89126205444336,         \"Time in s\": 62.47501600000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6423591829059828,         \"RMSE\": 1.752432393946061,         \"R2\": 0.0487781645727875,         \"Memory in Mb\": 3.987659454345703,         \"Time in s\": 66.76475200000002       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6415445258333332,         \"RMSE\": 1.7335108155585357,         \"R2\": 0.0607905571401552,         \"Memory in Mb\": 4.087154388427734,         \"Time in s\": 71.11932700000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.641812437398374,         \"RMSE\": 1.7198679523833968,         \"R2\": 0.0653630129096917,         \"Memory in Mb\": 4.179523468017578,         \"Time in s\": 75.53533700000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6391550126984127,         \"RMSE\": 1.7023246638821516,         \"R2\": 0.0758317950759702,         \"Memory in Mb\": 4.276576995849609,         \"Time in s\": 80.015814       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6397551612403103,         \"RMSE\": 1.6865214638981003,         \"R2\": 0.0944734629735503,         \"Memory in Mb\": 4.372867584228516,         \"Time in s\": 84.56990800000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6401663234848486,         \"RMSE\": 1.6719359262678322,         \"R2\": 0.1144902218209267,         \"Memory in Mb\": 4.465221405029297,         \"Time in s\": 89.18993400000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6373928251851855,         \"RMSE\": 1.6559913256631793,         \"R2\": 0.1276383063389357,         \"Memory in Mb\": 4.558887481689453,         \"Time in s\": 93.877546       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6333341724637681,         \"RMSE\": 1.6410816825275083,         \"R2\": 0.1291995533352813,         \"Memory in Mb\": 4.652858734130859,         \"Time in s\": 98.626246       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.637460545390071,         \"RMSE\": 1.630772212254164,         \"R2\": 0.1328113217779126,         \"Memory in Mb\": 4.746517181396484,         \"Time in s\": 103.437785       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6446958777777775,         \"RMSE\": 1.6213030711335543,         \"R2\": 0.1338907909289651,         \"Memory in Mb\": 4.844425201416016,         \"Time in s\": 108.312072       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.643768610068027,         \"RMSE\": 1.6085965270907718,         \"R2\": 0.1308548353743899,         \"Memory in Mb\": 4.935100555419922,         \"Time in s\": 113.251739       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6420156240666665,         \"RMSE\": 1.59493855356346,         \"R2\": 0.1311681221050482,         \"Memory in Mb\": 5.030651092529297,         \"Time in s\": 118.255967       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6416785025641023,         \"RMSE\": 1.5941707450098015,         \"R2\": 0.1314249186277071,         \"Memory in Mb\": 5.032634735107422,         \"Time in s\": 123.301096       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.042756132756132,         \"RMSE\": 17.336048579080593,         \"R2\": -385.8634917094176,         \"Memory in Mb\": 0.0162086486816406,         \"Time in s\": 0.002632       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.456785613727984,         \"RMSE\": 12.282422261556867,         \"R2\": -158.770726389092,         \"Memory in Mb\": 0.0177879333496093,         \"Time in s\": 0.007319       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4353973358733074,         \"RMSE\": 10.07037651743448,         \"R2\": -69.4325218162971,         \"Memory in Mb\": 0.0230522155761718,         \"Time in s\": 0.013907       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.736909422894262,         \"RMSE\": 8.732393473100391,         \"R2\": -59.03623058514604,         \"Memory in Mb\": 0.0241050720214843,         \"Time in s\": 0.0217009999999999       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.788577579622257,         \"RMSE\": 8.074088551816661,         \"R2\": -11.726025456653014,         \"Memory in Mb\": 0.0309486389160156,         \"Time in s\": 0.0303349999999999       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.395880085598137,         \"RMSE\": 7.878422021930021,         \"R2\": -4.223121571879303,         \"Memory in Mb\": 0.0404243469238281,         \"Time in s\": 0.040093       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.889526501621088,         \"RMSE\": 7.800910386370324,         \"R2\": -2.432180745921895,         \"Memory in Mb\": 0.0467414855957031,         \"Time in s\": 0.0511699999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.072650698433535,         \"RMSE\": 7.572197783925699,         \"R2\": -1.9320509270116557,         \"Memory in Mb\": 0.0525321960449218,         \"Time in s\": 0.0635609999999999       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.410984939713907,         \"RMSE\": 7.55185413515251,         \"R2\": -1.439151418709002,         \"Memory in Mb\": 0.0535850524902343,         \"Time in s\": 0.0772419999999999       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.370948473977548,         \"RMSE\": 7.327634340090197,         \"R2\": -0.6036593212329582,         \"Memory in Mb\": 0.0551643371582031,         \"Time in s\": 0.0921019999999999       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.401973824893138,         \"RMSE\": 7.197046558152955,         \"R2\": -0.1914453698838978,         \"Memory in Mb\": 0.0551643371582031,         \"Time in s\": 0.1081669999999999       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.283071400630936,         \"RMSE\": 6.979735895990854,         \"R2\": 0.0841519683549982,         \"Memory in Mb\": 0.0551643371582031,         \"Time in s\": 0.1323959999999999       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.169649051526778,         \"RMSE\": 6.77851615807502,         \"R2\": 0.3003478880703081,         \"Memory in Mb\": 0.0556907653808593,         \"Time in s\": 0.1602449999999999       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.107721988217097,         \"RMSE\": 6.620782354691122,         \"R2\": 0.4327427443050297,         \"Memory in Mb\": 0.0556907653808593,         \"Time in s\": 0.1914819999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.386134129138624,         \"RMSE\": 6.8739888422895685,         \"R2\": 0.5084535624523276,         \"Memory in Mb\": 0.0556907653808593,         \"Time in s\": 0.2319989999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.592324836010107,         \"RMSE\": 7.0395287886899816,         \"R2\": 0.5843455987500039,         \"Memory in Mb\": 0.0562171936035156,         \"Time in s\": 0.273788       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.658423416973056,         \"RMSE\": 7.057579140031887,         \"R2\": 0.6579286220132116,         \"Memory in Mb\": 0.0562171936035156,         \"Time in s\": 0.316974       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.6782517314261085,         \"RMSE\": 7.042640058036562,         \"R2\": 0.7290497323677609,         \"Memory in Mb\": 0.0562171936035156,         \"Time in s\": 0.361531       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.896652959256127,         \"RMSE\": 7.410861778989444,         \"R2\": 0.7526693351807108,         \"Memory in Mb\": 0.0217466354370117,         \"Time in s\": 0.409543       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.507880191409123,         \"RMSE\": 8.546476599974424,         \"R2\": 0.7120144996082314,         \"Memory in Mb\": 0.0280637741088867,         \"Time in s\": 0.458317       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.703958017872014,         \"RMSE\": 8.760797449465004,         \"R2\": 0.7411581545051223,         \"Memory in Mb\": 0.0333280563354492,         \"Time in s\": 0.507954       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.934527728379076,         \"RMSE\": 9.145062262320872,         \"R2\": 0.7730513990797492,         \"Memory in Mb\": 0.0380659103393554,         \"Time in s\": 0.576578       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.025889093973978,         \"RMSE\": 9.259481324724224,         \"R2\": 0.7979290061199974,         \"Memory in Mb\": 0.0417509078979492,         \"Time in s\": 0.647861       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.701040765258382,         \"RMSE\": 10.569442782845146,         \"R2\": 0.7594412957229723,         \"Memory in Mb\": 0.0418310165405273,         \"Time in s\": 0.7217790000000001       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.201977905163474,         \"RMSE\": 11.695812678726384,         \"R2\": 0.740801257827299,         \"Memory in Mb\": 0.0418310165405273,         \"Time in s\": 0.7983520000000001       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.47608974362833,         \"RMSE\": 12.176082777300053,         \"R2\": 0.7566872347890514,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 0.889757       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.495029117947843,         \"RMSE\": 12.186858586615225,         \"R2\": 0.7886035011133373,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 0.982264       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.05089484284177,         \"RMSE\": 13.06419009031293,         \"R2\": 0.7836428997387894,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.075782       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.171875092169309,         \"RMSE\": 15.802620207207104,         \"R2\": 0.7127274179827436,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.170327       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.626867556328977,         \"RMSE\": 16.443718231711543,         \"R2\": 0.7338058453397931,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.26591       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.854283538219804,         \"RMSE\": 16.574189924013226,         \"R2\": 0.7578382368534643,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.362543       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.034558550660114,         \"RMSE\": 16.72149964752778,         \"R2\": 0.7759339138910493,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.460131       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.942839439265006,         \"RMSE\": 18.18973374364872,         \"R2\": 0.7425340708967089,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.65219       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.480189522121243,         \"RMSE\": 19.36955258798825,         \"R2\": 0.7316181626186655,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.847066       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.884428250077962,         \"RMSE\": 20.018801475409063,         \"R2\": 0.7463650656532205,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 2.044712       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.037067702603975,         \"RMSE\": 20.02507161492445,         \"R2\": 0.7633646392298079,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 2.245044       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.938689395183468,         \"RMSE\": 21.571547182252875,         \"R2\": 0.7447563988620904,         \"Memory in Mb\": 0.0393133163452148,         \"Time in s\": 2.459951       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.737065020554605,         \"RMSE\": 23.070023559587742,         \"R2\": 0.7259561921053947,         \"Memory in Mb\": 0.039839744567871,         \"Time in s\": 2.675857       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.305628841534729,         \"RMSE\": 24.020997573013894,         \"R2\": 0.7359868139097058,         \"Memory in Mb\": 0.0408926010131835,         \"Time in s\": 2.892761       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.503019064271443,         \"RMSE\": 24.118168317988548,         \"R2\": 0.7526847575357923,         \"Memory in Mb\": 0.0414190292358398,         \"Time in s\": 3.110678       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.042001004765991,         \"RMSE\": 24.757154413851225,         \"R2\": 0.7504844548860922,         \"Memory in Mb\": 0.0429983139038085,         \"Time in s\": 3.329579       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.165694044127083,         \"RMSE\": 26.934291479182736,         \"R2\": 0.7226050873941003,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 3.549505       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.958578383564387,         \"RMSE\": 28.26726815061745,         \"R2\": 0.7302155620528221,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 3.77047       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.309589456804158,         \"RMSE\": 28.5754148947933,         \"R2\": 0.7394096166099926,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.010874       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.77955786237919,         \"RMSE\": 29.119281838039548,         \"R2\": 0.7454446166142166,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.254034       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.687135400012505,         \"RMSE\": 30.600738447390604,         \"R2\": 0.7270552375925041,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.499866       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.426270300418786,         \"RMSE\": 31.61383923822668,         \"R2\": 0.7250895764829616,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.748399       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.230319490239392,         \"RMSE\": 32.829508990096734,         \"R2\": 0.7334580691909136,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.00325       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.415951878027045,         \"RMSE\": 32.83473210597698,         \"R2\": 0.7443832332812113,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.259172       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.41946931942451,         \"RMSE\": 34.477948502753435,         \"R2\": 0.726844465494657,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.516121       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.135259536350134,         \"RMSE\": 35.412182207518484,         \"R2\": 0.7244424125617825,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.774111       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.998428764364284,         \"RMSE\": 36.61317436816486,         \"R2\": 0.7275265693889857,         \"Memory in Mb\": 0.044051170349121,         \"Time in s\": 6.033148000000001       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.16185046142029,         \"RMSE\": 36.73359474841229,         \"R2\": 0.7324023432169282,         \"Memory in Mb\": 0.044051170349121,         \"Time in s\": 6.293050000000001       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.834704431652337,         \"RMSE\": 13.708514217962266,         \"R2\": -439.7934984576362,         \"Memory in Mb\": 0.0500869750976562,         \"Time in s\": 0.001817       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.4692310697037447,         \"RMSE\": 9.813795721313518,         \"R2\": -37.72035957928713,         \"Memory in Mb\": 0.0732498168945312,         \"Time in s\": 0.005553       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.530247618203559,         \"RMSE\": 8.024836796214231,         \"R2\": -33.90460110966681,         \"Memory in Mb\": 0.0858840942382812,         \"Time in s\": 0.011369       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1398752670733447,         \"RMSE\": 6.982837000856316,         \"R2\": -25.510487239912003,         \"Memory in Mb\": 0.09588623046875,         \"Time in s\": 0.023421       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.2521629689485394,         \"RMSE\": 6.362737158647257,         \"R2\": -12.810573390910957,         \"Memory in Mb\": 0.1053619384765625,         \"Time in s\": 0.037893       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.275331183116589,         \"RMSE\": 5.895687482983747,         \"R2\": -9.059182991303912,         \"Memory in Mb\": 0.1095733642578125,         \"Time in s\": 0.054815       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.181766409647037,         \"RMSE\": 5.493495699082884,         \"R2\": -8.025069637302263,         \"Memory in Mb\": 0.1116790771484375,         \"Time in s\": 0.074219       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0635226048812747,         \"RMSE\": 5.165876255053421,         \"R2\": -6.037983110569301,         \"Memory in Mb\": 0.1158905029296875,         \"Time in s\": 0.098519       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9951428730766116,         \"RMSE\": 4.906287161641783,         \"R2\": -4.575559841528811,         \"Memory in Mb\": 0.1179962158203125,         \"Time in s\": 0.130188       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8700446037321656,         \"RMSE\": 4.662539866408188,         \"R2\": -4.050299616280768,         \"Memory in Mb\": 0.015085220336914,         \"Time in s\": 0.169487       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7830718267282506,         \"RMSE\": 4.458344141345012,         \"R2\": -3.981078161152351,         \"Memory in Mb\": 0.0312490463256835,         \"Time in s\": 0.210179       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.714887283408722,         \"RMSE\": 4.280191261764102,         \"R2\": -3.625492757292576,         \"Memory in Mb\": 0.0370397567749023,         \"Time in s\": 0.273546       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.626899515259654,         \"RMSE\": 4.116599014627653,         \"R2\": -3.336325373761703,         \"Memory in Mb\": 0.044569969177246,         \"Time in s\": 0.338443       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6037708656255951,         \"RMSE\": 3.992199218884993,         \"R2\": -3.269831686495559,         \"Memory in Mb\": 0.0575590133666992,         \"Time in s\": 0.405125       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5808413297038584,         \"RMSE\": 3.882244388071726,         \"R2\": -2.971019208275212,         \"Memory in Mb\": 0.0675611495971679,         \"Time in s\": 0.4768960000000001       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5112246352788372,         \"RMSE\": 3.7620340381312185,         \"R2\": -2.9135432145577016,         \"Memory in Mb\": 0.0754575729370117,         \"Time in s\": 0.5560110000000001       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.464954049061847,         \"RMSE\": 3.6574443601858126,         \"R2\": -2.908900292165721,         \"Memory in Mb\": 0.0807218551635742,         \"Time in s\": 0.6372390000000001       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4845626481571883,         \"RMSE\": 3.5832345434246853,         \"R2\": -2.782695640732784,         \"Memory in Mb\": 0.0886182785034179,         \"Time in s\": 0.7206760000000001       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.451940332797817,         \"RMSE\": 3.496542725118452,         \"R2\": -2.72647470962537,         \"Memory in Mb\": 0.0938825607299804,         \"Time in s\": 0.8063740000000001       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4093274160891025,         \"RMSE\": 3.4133346926199284,         \"R2\": -2.65159153540002,         \"Memory in Mb\": 0.1012525558471679,         \"Time in s\": 0.8943350000000001       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3677964737960675,         \"RMSE\": 3.3343173536823296,         \"R2\": -2.5997996751089016,         \"Memory in Mb\": 0.1054639816284179,         \"Time in s\": 1.079576       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.335717224673182,         \"RMSE\": 3.2621145597551164,         \"R2\": -2.3832380441779537,         \"Memory in Mb\": 0.1112546920776367,         \"Time in s\": 1.2716070000000002       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3223220949397412,         \"RMSE\": 3.20054856097613,         \"R2\": -2.088360697350681,         \"Memory in Mb\": 0.1196775436401367,         \"Time in s\": 1.466366       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2961820395725512,         \"RMSE\": 3.1370925842333546,         \"R2\": -1.8988499404168715,         \"Memory in Mb\": 0.1275739669799804,         \"Time in s\": 1.663894       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2652762767168435,         \"RMSE\": 3.076750388249757,         \"R2\": -1.7299037995212605,         \"Memory in Mb\": 0.1323118209838867,         \"Time in s\": 1.864298       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2471740635308572,         \"RMSE\": 3.022290137612829,         \"R2\": -1.6387160551274738,         \"Memory in Mb\": 0.1375761032104492,         \"Time in s\": 2.070902       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.222508472081129,         \"RMSE\": 2.968388528244746,         \"R2\": -1.5361060189709668,         \"Memory in Mb\": 0.1396818161010742,         \"Time in s\": 2.286544       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2073384071706728,         \"RMSE\": 2.920065266046622,         \"R2\": -1.5126838513129577,         \"Memory in Mb\": 0.1444196701049804,         \"Time in s\": 2.5053300000000003       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1845779132924192,         \"RMSE\": 2.8723790540044147,         \"R2\": -1.4914188956527816,         \"Memory in Mb\": 0.1470518112182617,         \"Time in s\": 2.7271330000000003       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1745692976588702,         \"RMSE\": 2.8296294830278077,         \"R2\": -1.3910651808347,         \"Memory in Mb\": 0.1414899826049804,         \"Time in s\": 2.96944       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1708259630571385,         \"RMSE\": 2.7920061348512903,         \"R2\": -1.2924200227078335,         \"Memory in Mb\": 0.1441221237182617,         \"Time in s\": 3.214893       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1599967464968943,         \"RMSE\": 2.7528504813508814,         \"R2\": -1.186915838733254,         \"Memory in Mb\": 0.1462278366088867,         \"Time in s\": 3.481443       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1455993461288598,         \"RMSE\": 2.715465758170179,         \"R2\": -1.112620243595547,         \"Memory in Mb\": 0.0933332443237304,         \"Time in s\": 3.768351       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1331386715536065,         \"RMSE\": 2.679518493749607,         \"R2\": -1.0895638535289454,         \"Memory in Mb\": 0.1028089523315429,         \"Time in s\": 4.057625       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1287919059851137,         \"RMSE\": 2.648832972736431,         \"R2\": -1.0956110522943685,         \"Memory in Mb\": 0.1107053756713867,         \"Time in s\": 4.349483       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1090542602054634,         \"RMSE\": 2.6130484736329,         \"R2\": -1.0841769561048746,         \"Memory in Mb\": 0.1170225143432617,         \"Time in s\": 4.655905000000001       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0919225542546631,         \"RMSE\": 2.579731998640208,         \"R2\": -1.0301471378292058,         \"Memory in Mb\": 0.1207075119018554,         \"Time in s\": 4.969391000000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0729346607841277,         \"RMSE\": 2.546521266569091,         \"R2\": -0.9996439724530696,         \"Memory in Mb\": 0.1238660812377929,         \"Time in s\": 5.409378000000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0548522699101792,         \"RMSE\": 2.514796200212546,         \"R2\": -0.958866579835745,         \"Memory in Mb\": 0.1301832199096679,         \"Time in s\": 5.856313000000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0458975693179249,         \"RMSE\": 2.486381451783576,         \"R2\": -0.9321678603320388,         \"Memory in Mb\": 0.1405134201049804,         \"Time in s\": 6.306401000000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.042667475968943,         \"RMSE\": 2.463395040447954,         \"R2\": -0.9174360179218256,         \"Memory in Mb\": 0.1468305587768554,         \"Time in s\": 6.759550000000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0338402028724885,         \"RMSE\": 2.4371652901742165,         \"R2\": -0.8942452584110789,         \"Memory in Mb\": 0.1505155563354492,         \"Time in s\": 7.215791000000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0182769822752689,         \"RMSE\": 2.409744604248102,         \"R2\": -0.8486703239118398,         \"Memory in Mb\": 0.1520948410034179,         \"Time in s\": 7.680462000000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.007294910176456,         \"RMSE\": 2.3841216724611445,         \"R2\": -0.8005738256179296,         \"Memory in Mb\": 0.1552534103393554,         \"Time in s\": 8.149683000000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9984699415812968,         \"RMSE\": 2.359722022526475,         \"R2\": -0.7713409518355698,         \"Memory in Mb\": 0.1573591232299804,         \"Time in s\": 8.622145000000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9848390746890626,         \"RMSE\": 2.334975438117308,         \"R2\": -0.7628805257854674,         \"Memory in Mb\": 0.1254529953002929,         \"Time in s\": 9.108636       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9804934467335736,         \"RMSE\": 2.3136297350671566,         \"R2\": -0.7454793227879806,         \"Memory in Mb\": 0.1344251632690429,         \"Time in s\": 9.599086       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9715993160407668,         \"RMSE\": 2.291923159938466,         \"R2\": -0.7307898991199615,         \"Memory in Mb\": 0.1402158737182617,         \"Time in s\": 10.092417       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.96479276321034,         \"RMSE\": 2.271398262551761,         \"R2\": -0.7329444574748756,         \"Memory in Mb\": 0.1444272994995117,         \"Time in s\": 10.588738       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.956776427041678,         \"RMSE\": 2.250974677037298,         \"R2\": -0.7305695321170174,         \"Memory in Mb\": 0.1486387252807617,         \"Time in s\": 11.174487       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9561028857812052,         \"RMSE\": 2.249867758958838,         \"R2\": -0.7300222157865335,         \"Memory in Mb\": 0.1486387252807617,         \"Time in s\": 11.765558       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.051220648038832,         \"RMSE\": 17.336198122120386,         \"R2\": -385.8701660091343,         \"Memory in Mb\": 0.0229225158691406,         \"Time in s\": 0.002862       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.498502947359929,         \"RMSE\": 12.28528637536428,         \"R2\": -158.84524831763767,         \"Memory in Mb\": 0.0245628356933593,         \"Time in s\": 0.008031       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4668695042339137,         \"RMSE\": 10.074636808082968,         \"R2\": -69.49212762837747,         \"Memory in Mb\": 0.0298271179199218,         \"Time in s\": 0.0152       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7637805804889557,         \"RMSE\": 8.735764655686483,         \"R2\": -59.08259408516962,         \"Memory in Mb\": 0.0309410095214843,         \"Time in s\": 0.027573       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.814517498310432,         \"RMSE\": 8.074396776941786,         \"R2\": -11.726997097138026,         \"Memory in Mb\": 0.0377845764160156,         \"Time in s\": 0.040817       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.396900059747575,         \"RMSE\": 7.862006773633152,         \"R2\": -4.201378762014764,         \"Memory in Mb\": 0.0472602844238281,         \"Time in s\": 0.055065       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.8844336568547537,         \"RMSE\": 7.782255505653143,         \"R2\": -2.415785129732385,         \"Memory in Mb\": 0.0536384582519531,         \"Time in s\": 0.070503       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.068768385552718,         \"RMSE\": 7.555909217267645,         \"R2\": -1.9194502155140076,         \"Memory in Mb\": 0.0594291687011718,         \"Time in s\": 0.087235       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.319029347030655,         \"RMSE\": 7.489629607912237,         \"R2\": -1.3991215781815165,         \"Memory in Mb\": 0.0604820251464843,         \"Time in s\": 0.105314       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.231978704025333,         \"RMSE\": 7.230698639905546,         \"R2\": -0.5615110336669555,         \"Memory in Mb\": 0.0620613098144531,         \"Time in s\": 0.124657       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.279767976439616,         \"RMSE\": 7.114292598648662,         \"R2\": -0.1642036472993016,         \"Memory in Mb\": 0.0620613098144531,         \"Time in s\": 0.145348       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.161677712403324,         \"RMSE\": 6.8979209349412445,         \"R2\": 0.1054968774084013,         \"Memory in Mb\": 0.0620613098144531,         \"Time in s\": 0.183683       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.036201943040193,         \"RMSE\": 6.686446116179646,         \"R2\": 0.3192250351622916,         \"Memory in Mb\": 0.0241641998291015,         \"Time in s\": 0.233347       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.002163310161137,         \"RMSE\": 6.555243218534794,         \"R2\": 0.4439177197734564,         \"Memory in Mb\": 0.034926414489746,         \"Time in s\": 0.283952       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.269310553181931,         \"RMSE\": 6.794169336453219,         \"R2\": 0.5198027804498322,         \"Memory in Mb\": 0.041365623474121,         \"Time in s\": 0.335528       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.394431170074558,         \"RMSE\": 6.916563516446891,         \"R2\": 0.5987399306940604,         \"Memory in Mb\": 0.0471563339233398,         \"Time in s\": 0.38817       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.429782113532627,         \"RMSE\": 6.896434310822903,         \"R2\": 0.6733712331422652,         \"Memory in Mb\": 0.052016258239746,         \"Time in s\": 0.441959       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.448580123995543,         \"RMSE\": 6.86078369215091,         \"R2\": 0.7428621234581485,         \"Memory in Mb\": 0.0546483993530273,         \"Time in s\": 0.496925       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.634718338792146,         \"RMSE\": 7.17917659207716,         \"R2\": 0.7678921596594357,         \"Memory in Mb\": 0.0546483993530273,         \"Time in s\": 0.5531490000000001       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.229854791420841,         \"RMSE\": 8.435313620968111,         \"R2\": 0.7194573631198581,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.6106500000000001       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.400637324787383,         \"RMSE\": 8.615072190659467,         \"R2\": 0.7496975788166091,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.6850710000000001       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.622607300541604,         \"RMSE\": 8.982158345389516,         \"R2\": 0.781064800957145,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.7630730000000001       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.728895576419993,         \"RMSE\": 9.10264619767678,         \"R2\": 0.8047163053551843,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.8439190000000001       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.468790531655633,         \"RMSE\": 10.532848432020362,         \"R2\": 0.7611041743489119,         \"Memory in Mb\": 0.0553770065307617,         \"Time in s\": 0.926058       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.961259791220884,         \"RMSE\": 11.725202267966395,         \"R2\": 0.7394969764024641,         \"Memory in Mb\": 0.0553770065307617,         \"Time in s\": 1.009526       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.243017687832032,         \"RMSE\": 12.175095097400796,         \"R2\": 0.7567267064951951,         \"Memory in Mb\": 0.0539121627807617,         \"Time in s\": 1.109836       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.333189926829036,         \"RMSE\": 12.221129948725446,         \"R2\": 0.7874128689691341,         \"Memory in Mb\": 0.0544385910034179,         \"Time in s\": 1.300493       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.907494608974745,         \"RMSE\": 13.13418786953933,         \"R2\": 0.7813182108747583,         \"Memory in Mb\": 0.0545606613159179,         \"Time in s\": 1.494565       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.086203691627809,         \"RMSE\": 16.084282058543664,         \"R2\": 0.7023956098414756,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 1.6919570000000002       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.398286710797228,         \"RMSE\": 16.38837159928856,         \"R2\": 0.7355947540985646,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 1.900794       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.688169379844998,         \"RMSE\": 16.65705092991554,         \"R2\": 0.7554108572015372,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 2.110987       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.856066264187849,         \"RMSE\": 16.815734957180027,         \"R2\": 0.7734013139584004,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 2.322457       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.788654210226415,         \"RMSE\": 18.368645129880047,         \"R2\": 0.7374443731514406,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 2.535213       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.535989444086796,         \"RMSE\": 20.177763325541772,         \"R2\": 0.7087539856658172,         \"Memory in Mb\": 0.0658864974975586,         \"Time in s\": 2.749718       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.949331836981814,         \"RMSE\": 20.800028245688587,         \"R2\": 0.7261827687212361,         \"Memory in Mb\": 0.0713338851928711,         \"Time in s\": 2.965855       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.958714190964644,         \"RMSE\": 20.66064387908481,         \"R2\": 0.748105206776327,         \"Memory in Mb\": 0.0781774520874023,         \"Time in s\": 3.183657       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.807531574997112,         \"RMSE\": 22.01468171576837,         \"R2\": 0.7341619793955468,         \"Memory in Mb\": 0.0825719833374023,         \"Time in s\": 3.418965       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.71794187476778,         \"RMSE\": 23.73901232910809,         \"R2\": 0.7098322050491193,         \"Memory in Mb\": 0.0846776962280273,         \"Time in s\": 3.659139       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.269314924317156,         \"RMSE\": 24.65274813293709,         \"R2\": 0.7219171428567855,         \"Memory in Mb\": 0.0656805038452148,         \"Time in s\": 3.912261       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.511771919641935,         \"RMSE\": 24.834167752766053,         \"R2\": 0.7377826277560943,         \"Memory in Mb\": 0.0706624984741211,         \"Time in s\": 4.16702       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.00667707818897,         \"RMSE\": 25.401748915029017,         \"R2\": 0.7373221851710817,         \"Memory in Mb\": 0.0787420272827148,         \"Time in s\": 4.423509       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.106263610815663,         \"RMSE\": 27.4394567629727,         \"R2\": 0.7121021651653525,         \"Memory in Mb\": 0.0857076644897461,         \"Time in s\": 4.681795       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.950411373417108,         \"RMSE\": 28.951900473786843,         \"R2\": 0.7169889638801871,         \"Memory in Mb\": 0.0888662338256836,         \"Time in s\": 4.941903       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.321905164714362,         \"RMSE\": 29.29627092175635,         \"R2\": 0.7260962478080234,         \"Memory in Mb\": 0.0889272689819336,         \"Time in s\": 5.203768       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.829552469069228,         \"RMSE\": 29.855361574147427,         \"R2\": 0.732412614196017,         \"Memory in Mb\": 0.0889272689819336,         \"Time in s\": 5.467412       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.715769054600838,         \"RMSE\": 31.21095148117224,         \"R2\": 0.7160610523989874,         \"Memory in Mb\": 0.0895147323608398,         \"Time in s\": 5.743327000000001       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.54236471467993,         \"RMSE\": 32.39367117342827,         \"R2\": 0.7113596352744775,         \"Memory in Mb\": 0.0743856430053711,         \"Time in s\": 6.026441000000001       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.379374275832948,         \"RMSE\": 33.670378810622296,         \"R2\": 0.7196292071862618,         \"Memory in Mb\": 0.0787191390991211,         \"Time in s\": 6.311317000000001       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.522458105265056,         \"RMSE\": 33.639909372937744,         \"R2\": 0.7316929916628531,         \"Memory in Mb\": 0.0872030258178711,         \"Time in s\": 6.597982000000001       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.5114661084191,         \"RMSE\": 35.24478084224406,         \"R2\": 0.714558707096332,         \"Memory in Mb\": 0.0935201644897461,         \"Time in s\": 6.886526000000001       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.293418976341684,         \"RMSE\": 36.29050935662323,         \"R2\": 0.7106036021726428,         \"Memory in Mb\": 0.0934362411499023,         \"Time in s\": 7.177067000000001       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.158877831353536,         \"RMSE\": 37.47206255417766,         \"R2\": 0.7145930209145848,         \"Memory in Mb\": 0.0946111679077148,         \"Time in s\": 7.581349000000001       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.37390218951093,         \"RMSE\": 37.6579284312523,         \"R2\": 0.7187656938003131,         \"Memory in Mb\": 0.0947332382202148,         \"Time in s\": 7.990285000000001       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.828377634536296,         \"RMSE\": 13.70786256219322,         \"R2\": -439.7515918302183,         \"Memory in Mb\": 0.0568618774414062,         \"Time in s\": 0.005477       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.453811275213839,         \"RMSE\": 9.811073218407971,         \"R2\": -37.69887927291551,         \"Memory in Mb\": 0.0800857543945312,         \"Time in s\": 0.014203       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5116544078850294,         \"RMSE\": 8.021960641037959,         \"R2\": -33.879585508404254,         \"Memory in Mb\": 0.0927200317382812,         \"Time in s\": 0.025216       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1224425015381523,         \"RMSE\": 6.9797990571526345,         \"R2\": -25.487425023640156,         \"Memory in Mb\": 0.102783203125,         \"Time in s\": 0.038581       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.246653919301699,         \"RMSE\": 6.363694444016854,         \"R2\": -12.814729355257526,         \"Memory in Mb\": 0.1122589111328125,         \"Time in s\": 0.054574       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.270681160376927,         \"RMSE\": 5.896666779393501,         \"R2\": -9.06252500695684,         \"Memory in Mb\": 0.1164703369140625,         \"Time in s\": 0.096737       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.162967815650222,         \"RMSE\": 5.491011289549727,         \"R2\": -8.016908386196121,         \"Memory in Mb\": 0.1185760498046875,         \"Time in s\": 0.145193       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9648637778298337,         \"RMSE\": 5.147547754256808,         \"R2\": -5.988130255135697,         \"Memory in Mb\": 0.048110008239746,         \"Time in s\": 0.201618       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.86652787828915,         \"RMSE\": 4.875884330950751,         \"R2\": -4.506673701927233,         \"Memory in Mb\": 0.0645513534545898,         \"Time in s\": 0.259848       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.773434994745299,         \"RMSE\": 4.638841370319518,         \"R2\": -3.999091327975425,         \"Memory in Mb\": 0.0751142501831054,         \"Time in s\": 0.334681       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6594682627798778,         \"RMSE\": 4.42936028038101,         \"R2\": -3.916524330360767,         \"Memory in Mb\": 0.0809926986694336,         \"Time in s\": 0.415547       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5811297097344512,         \"RMSE\": 4.24689633509078,         \"R2\": -3.553810703437006,         \"Memory in Mb\": 0.0831594467163086,         \"Time in s\": 0.499019       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4918706813368772,         \"RMSE\": 4.083314206963185,         \"R2\": -3.2664860479391056,         \"Memory in Mb\": 0.0869779586791992,         \"Time in s\": 0.584777       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4582505621214346,         \"RMSE\": 3.950619643811522,         \"R2\": -3.181352514384196,         \"Memory in Mb\": 0.0965147018432617,         \"Time in s\": 0.672987       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4293807431017047,         \"RMSE\": 3.836527362327468,         \"R2\": -2.8780450161882043,         \"Memory in Mb\": 0.1050596237182617,         \"Time in s\": 0.763791       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3766835460490845,         \"RMSE\": 3.718390713103106,         \"R2\": -2.8232679475596494,         \"Memory in Mb\": 0.1113767623901367,         \"Time in s\": 0.862886       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3285707966483495,         \"RMSE\": 3.611463128557805,         \"R2\": -2.8112330604866624,         \"Memory in Mb\": 0.0969266891479492,         \"Time in s\": 1.077289       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3305028688272291,         \"RMSE\": 3.538102571280229,         \"R2\": -2.688007239623816,         \"Memory in Mb\": 0.1048231124877929,         \"Time in s\": 1.294249       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3086678355415842,         \"RMSE\": 3.4529556765760527,         \"R2\": -2.6341471363086995,         \"Memory in Mb\": 0.1101484298706054,         \"Time in s\": 1.513868       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.256053624567095,         \"RMSE\": 3.3666460142322228,         \"R2\": -2.552379472359031,         \"Memory in Mb\": 0.1175184249877929,         \"Time in s\": 1.736306       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2254239545780012,         \"RMSE\": 3.2887455105144454,         \"R2\": -2.5020714662192383,         \"Memory in Mb\": 0.1217298507690429,         \"Time in s\": 1.978865       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.204020924712129,         \"RMSE\": 3.2198773978896,         \"R2\": -2.2961943419959137,         \"Memory in Mb\": 0.1275205612182617,         \"Time in s\": 2.244474       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1975328241312166,         \"RMSE\": 3.1601130927415366,         \"R2\": -2.010817456858815,         \"Memory in Mb\": 0.1354780197143554,         \"Time in s\": 2.513147       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.186148143661266,         \"RMSE\": 3.1001176815841758,         \"R2\": -1.8309188655239268,         \"Memory in Mb\": 0.1433744430541992,         \"Time in s\": 2.784897       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1667856894749518,         \"RMSE\": 3.042966728214852,         \"R2\": -1.6702825792738007,         \"Memory in Mb\": 0.1362333297729492,         \"Time in s\": 3.07197       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.153194144927427,         \"RMSE\": 2.98944402729251,         \"R2\": -1.5816728306403074,         \"Memory in Mb\": 0.1415586471557617,         \"Time in s\": 3.362254       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1356058423088553,         \"RMSE\": 2.937036564746637,         \"R2\": -1.4828164968540292,         \"Memory in Mb\": 0.1436643600463867,         \"Time in s\": 3.655648       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.125648357086568,         \"RMSE\": 2.890393580385493,         \"R2\": -1.4618789770567937,         \"Memory in Mb\": 0.1484022140502929,         \"Time in s\": 3.952243       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1072323197222282,         \"RMSE\": 2.84377722554966,         \"R2\": -1.4420491211959612,         \"Memory in Mb\": 0.1510343551635742,         \"Time in s\": 4.252035       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0962221602561253,         \"RMSE\": 2.8010574809052518,         \"R2\": -1.343021715112041,         \"Memory in Mb\": 0.1547193527221679,         \"Time in s\": 4.557891000000001       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.095549207215165,         \"RMSE\": 2.765029222449673,         \"R2\": -1.2483344123018605,         \"Memory in Mb\": 0.1578779220581054,         \"Time in s\": 4.883158000000001       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.085957414095071,         \"RMSE\": 2.726589883354214,         \"R2\": -1.1453910301968575,         \"Memory in Mb\": 0.1599836349487304,         \"Time in s\": 5.354946000000001       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0751762466913892,         \"RMSE\": 2.6908702968299423,         \"R2\": -1.074523242859362,         \"Memory in Mb\": 0.1636686325073242,         \"Time in s\": 5.834119000000001       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0667684392102676,         \"RMSE\": 2.656475453821568,         \"R2\": -1.0537791659469915,         \"Memory in Mb\": 0.1663007736206054,         \"Time in s\": 6.316591000000001       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.066718890752265,         \"RMSE\": 2.6278494556992995,         \"R2\": -1.0625405514881172,         \"Memory in Mb\": 0.1547193527221679,         \"Time in s\": 6.8062700000000005       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0487756272096451,         \"RMSE\": 2.5923957614441,         \"R2\": -1.0513617944147002,         \"Memory in Mb\": 0.1594572067260742,         \"Time in s\": 7.299083       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0336933816342644,         \"RMSE\": 2.5596915816453274,         \"R2\": -0.9987276211091368,         \"Memory in Mb\": 0.1632032394409179,         \"Time in s\": 7.795134000000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0143808347523189,         \"RMSE\": 2.5263993770636084,         \"R2\": -0.968167584311768,         \"Memory in Mb\": 0.1647825241088867,         \"Time in s\": 8.298925       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0004245938094416,         \"RMSE\": 2.495691505058861,         \"R2\": -0.9292169429583496,         \"Memory in Mb\": 0.1695814132690429,         \"Time in s\": 8.809149000000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9976736219043986,         \"RMSE\": 2.469777786083391,         \"R2\": -0.9064485942635294,         \"Memory in Mb\": 0.1616849899291992,         \"Time in s\": 9.32665       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0020392091388557,         \"RMSE\": 2.450590646975973,         \"R2\": -0.8975546778436754,         \"Memory in Mb\": 0.1643171310424804,         \"Time in s\": 9.853038000000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9936292081382508,         \"RMSE\": 2.424886643827349,         \"R2\": -0.8752066007627983,         \"Memory in Mb\": 0.1680021286010742,         \"Time in s\": 10.484318000000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9794930742877992,         \"RMSE\": 2.3980423354299125,         \"R2\": -0.8307587924463844,         \"Memory in Mb\": 0.1701078414916992,         \"Time in s\": 11.125036       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9694853941742788,         \"RMSE\": 2.372794343098121,         \"R2\": -0.7835048635250907,         \"Memory in Mb\": 0.1727399826049804,         \"Time in s\": 11.769143       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9594920424525858,         \"RMSE\": 2.348266033222206,         \"R2\": -0.7541836724323567,         \"Memory in Mb\": 0.1115369796752929,         \"Time in s\": 12.432199       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9482726802907966,         \"RMSE\": 2.324135545417226,         \"R2\": -0.7465505219679065,         \"Memory in Mb\": 0.1163969039916992,         \"Time in s\": 13.10541       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9455376055826032,         \"RMSE\": 2.30345366329758,         \"R2\": -0.7301587545146957,         \"Memory in Mb\": 0.1223096847534179,         \"Time in s\": 13.781537       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9379298129457146,         \"RMSE\": 2.282181144273129,         \"R2\": -0.7161074287562055,         \"Memory in Mb\": 0.1296796798706054,         \"Time in s\": 14.460614       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.930996996530802,         \"RMSE\": 2.261860474984104,         \"R2\": -0.7184214614837348,         \"Memory in Mb\": 0.1349439620971679,         \"Time in s\": 15.148323       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9214575102921838,         \"RMSE\": 2.2404008018877137,         \"R2\": -0.714349138962711,         \"Memory in Mb\": 0.1382246017456054,         \"Time in s\": 15.950631       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9213134079227226,         \"RMSE\": 2.239416179559339,         \"R2\": -0.7139861919037982,         \"Memory in Mb\": 0.1382246017456054,         \"Time in s\": 16.757639       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.63636363636363,         \"RMSE\": 41.64569169030137,         \"R2\": -2231.5319148936137,         \"Memory in Mb\": 0.0096149444580078,         \"Time in s\": 0.001328       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.31818181818181,         \"RMSE\": 41.32960638133835,         \"R2\": -1808.0547045951903,         \"Memory in Mb\": 0.0126094818115234,         \"Time in s\": 0.003944       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.12121212121212,         \"RMSE\": 41.13871582091424,         \"R2\": -1174.393494897962,         \"Memory in Mb\": 0.015787124633789,         \"Time in s\": 0.007623       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.159090909090914,         \"RMSE\": 41.17451771534076,         \"R2\": -1333.7620984139928,         \"Memory in Mb\": 0.0188732147216796,         \"Time in s\": 0.012489       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.5090909090909,         \"RMSE\": 41.57075020645253,         \"R2\": -336.3506066081568,         \"Memory in Mb\": 0.0218257904052734,         \"Time in s\": 0.019505       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.681818181818166,         \"RMSE\": 42.82080349691271,         \"R2\": -153.29834830483878,         \"Memory in Mb\": 0.0246181488037109,         \"Time in s\": 0.027128       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.50649350649351,         \"RMSE\": 43.70978671356627,         \"R2\": -106.75487995129542,         \"Memory in Mb\": 0.0275020599365234,         \"Time in s\": 0.035372       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.21590909090909,         \"RMSE\": 44.43649707984724,         \"R2\": -99.97346126163,         \"Memory in Mb\": 0.0300197601318359,         \"Time in s\": 0.047911       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.05050505050505,         \"RMSE\": 45.309262771858165,         \"R2\": -86.8022342468144,         \"Memory in Mb\": 0.0329036712646484,         \"Time in s\": 0.072727       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.16363636363636,         \"RMSE\": 46.52487115902242,         \"R2\": -63.64797006437341,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.103163       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 47.21487603305785,         \"RMSE\": 47.67304278378361,         \"R2\": -51.27707184490422,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.146595       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.29545454545455,         \"RMSE\": 48.843054157105485,         \"R2\": -43.84882422437649,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.196283       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.44055944055945,         \"RMSE\": 50.100318941519305,         \"R2\": -37.220279564063546,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.258522       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 50.532467532467535,         \"RMSE\": 51.29137544271156,         \"R2\": -33.04474826644667,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.329566       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.690909090909095,         \"RMSE\": 52.61253451297311,         \"R2\": -27.795548438273773,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.40393       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.00568181818182,         \"RMSE\": 54.11860921749895,         \"R2\": -23.566226925646237,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.481694       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.41176470588235,         \"RMSE\": 55.733754017636336,         \"R2\": -20.33250305682894,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.681251       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.02525252525252,         \"RMSE\": 57.635786091488654,         \"R2\": -17.146924852486976,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.884966       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 55.16354936929098,         \"RMSE\": 57.0482200725598,         \"R2\": -13.656313160472004,         \"Memory in Mb\": 0.6838865280151367,         \"Time in s\": 1.131695       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.62203856749311,         \"RMSE\": 56.03531795068661,         \"R2\": -11.37998411824978,         \"Memory in Mb\": 0.6869077682495117,         \"Time in s\": 1.3969520000000002       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.77279286370195,         \"RMSE\": 55.29408706815337,         \"R2\": -9.311090357596036,         \"Memory in Mb\": 0.6899290084838867,         \"Time in s\": 1.6754760000000002       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.49661908339594,         \"RMSE\": 55.0071045368674,         \"R2\": -7.210918602421254,         \"Memory in Mb\": 0.6929502487182617,         \"Time in s\": 1.960024       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.25631812193077,         \"RMSE\": 54.71344660515688,         \"R2\": -6.055353919833875,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 2.270278       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.62511478420569,         \"RMSE\": 54.312843786153664,         \"R2\": -5.352168023774992,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 2.586688       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.4425344352617,         \"RMSE\": 54.29364548356293,         \"R2\": -4.585603291722447,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 2.915419       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.75651621106165,         \"RMSE\": 54.635705044608144,         \"R2\": -3.8989478253777694,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 3.266148       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.373839404142416,         \"RMSE\": 55.25476711535166,         \"R2\": -3.3456400671942,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 3.622985       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.87239275875638,         \"RMSE\": 55.86677247417265,         \"R2\": -2.9565197175813718,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 3.98691       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.69554478958866,         \"RMSE\": 56.2770501442128,         \"R2\": -2.6433309475704183,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 4.356941       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.85316804407712,         \"RMSE\": 57.75044402630399,         \"R2\": -2.2832890424968197,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 4.733992       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.90678041411178,         \"RMSE\": 59.01114057562677,         \"R2\": -2.0697921090482247,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 5.128946       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.00533746556472,         \"RMSE\": 60.30224520856101,         \"R2\": -1.9140207825503284,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 5.54848       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 55.99599298772852,         \"RMSE\": 60.54917173074773,         \"R2\": -1.852879941931207,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 6.172488       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.87222492302705,         \"RMSE\": 61.81275171085535,         \"R2\": -1.7331917323651345,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 6.808446       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 58.41786698150333,         \"RMSE\": 63.95254893573906,         \"R2\": -1.588502821427925,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 7.450193       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 59.7033976124885,         \"RMSE\": 65.46926983257002,         \"R2\": -1.5293357430909813,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 8.100657       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.057805647389294,         \"RMSE\": 66.17359973042984,         \"R2\": -1.4019380007417157,         \"Memory in Mb\": 1.1097631454467771,         \"Time in s\": 8.796904       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 59.7070864579051,         \"RMSE\": 66.11592086962122,         \"R2\": -1.2507954049688483,         \"Memory in Mb\": 1.1127843856811523,         \"Time in s\": 9.50192       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.122823673891816,         \"RMSE\": 66.73609937588846,         \"R2\": -1.0378169857688957,         \"Memory in Mb\": 1.1158056259155271,         \"Time in s\": 10.222461       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.39504675635191,         \"RMSE\": 66.96100690444877,         \"R2\": -0.906365593827489,         \"Memory in Mb\": 1.1188268661499023,         \"Time in s\": 10.951743       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.27126048587789,         \"RMSE\": 66.93502892662679,         \"R2\": -0.8239085862185902,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 11.696828       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.340686610373176,         \"RMSE\": 67.43825007380137,         \"R2\": -0.7390015352251049,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 12.465469       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 61.40703262301831,         \"RMSE\": 69.11306667757516,         \"R2\": -0.6127592621572406,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 13.248766       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 61.95796621360106,         \"RMSE\": 69.71422620021941,         \"R2\": -0.5510154280248158,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 14.047315       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 62.59018166487368,         \"RMSE\": 70.55352405729404,         \"R2\": -0.4943708535906215,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 14.854826       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 62.49664579133251,         \"RMSE\": 70.88193125644693,         \"R2\": -0.4644752452013045,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 15.674675       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 63.25224079915844,         \"RMSE\": 71.92080214464903,         \"R2\": -0.4228062717918979,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 16.51129       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 64.80783657170488,         \"RMSE\": 74.3681944005728,         \"R2\": -0.367764222300833,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 17.364023       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.59959781369417,         \"RMSE\": 75.30113885843834,         \"R2\": -0.3443906138479853,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 18.342072       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.79684627343133,         \"RMSE\": 76.01328745307667,         \"R2\": -0.3277190973108916,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 19.334776       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 66.6512855136148,         \"RMSE\": 77.20436469287773,         \"R2\": -0.3097569166669509,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 20.336346       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.11975592628174,         \"RMSE\": 79.56492566870935,         \"R2\": -0.2867456678376987,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 21.353617       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.75877313437184,         \"RMSE\": 80.35800679505147,         \"R2\": -0.2806007657015741,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 22.38029       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 43.8732195,         \"RMSE\": 43.87807788634269,         \"R2\": -4514.954899312423,         \"Memory in Mb\": 0.0199413299560546,         \"Time in s\": 0.002168       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.4932955,         \"RMSE\": 42.52255283421693,         \"R2\": -725.9491167623446,         \"Memory in Mb\": 0.0317363739013671,         \"Time in s\": 0.006794       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.2167785,         \"RMSE\": 42.2386240157387,         \"R2\": -966.0073736019044,         \"Memory in Mb\": 0.0438976287841796,         \"Time in s\": 0.018434       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.975705625,         \"RMSE\": 41.99760868559829,         \"R2\": -957.9655948743646,         \"Memory in Mb\": 0.0562419891357421,         \"Time in s\": 0.031286       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.37550450000001,         \"RMSE\": 41.410913785433536,         \"R2\": -583.9966399141301,         \"Memory in Mb\": 0.5381031036376953,         \"Time in s\": 0.048039       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.936110000000006,         \"RMSE\": 40.97829382197767,         \"R2\": -484.9611418859003,         \"Memory in Mb\": 0.5386066436767578,         \"Time in s\": 0.080711       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.6885472857143,         \"RMSE\": 40.72961738075088,         \"R2\": -495.1050461477588,         \"Memory in Mb\": 0.5391101837158203,         \"Time in s\": 0.166791       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.35105437500001,         \"RMSE\": 40.39801158334292,         \"R2\": -429.4078677932073,         \"Memory in Mb\": 0.5393619537353516,         \"Time in s\": 0.262676       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.00981655555555,         \"RMSE\": 40.06373388340122,         \"R2\": -370.7794659133543,         \"Memory in Mb\": 0.5396137237548828,         \"Time in s\": 0.43318       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.80633095,         \"RMSE\": 39.860362966711,         \"R2\": -368.1089073295326,         \"Memory in Mb\": 0.5077581405639648,         \"Time in s\": 0.638958       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 36.497516001377406,         \"RMSE\": 38.01945344470104,         \"R2\": -361.2329206514933,         \"Memory in Mb\": 1.3602590560913086,         \"Time in s\": 0.913553       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 33.64243104419191,         \"RMSE\": 36.40668421494773,         \"R2\": -333.65237138497804,         \"Memory in Mb\": 1.360762596130371,         \"Time in s\": 1.221179       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.222114965034955,         \"RMSE\": 34.98371838354962,         \"R2\": -312.16748668977897,         \"Memory in Mb\": 1.3610143661499023,         \"Time in s\": 1.570709       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.18205946861472,         \"RMSE\": 33.71869814960704,         \"R2\": -303.5986275675674,         \"Memory in Mb\": 1.361769676208496,         \"Time in s\": 1.939253       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 27.34275770505051,         \"RMSE\": 32.57805191350732,         \"R2\": -278.63174197976707,         \"Memory in Mb\": 1.3620214462280271,         \"Time in s\": 2.324555       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 25.81388747443183,         \"RMSE\": 31.5521424826706,         \"R2\": -274.2849072221064,         \"Memory in Mb\": 1.3630285263061523,         \"Time in s\": 2.877117       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 24.51835124153299,         \"RMSE\": 30.62414457186519,         \"R2\": -273.0482727941538,         \"Memory in Mb\": 1.3640356063842771,         \"Time in s\": 3.447       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 23.451930423400693,         \"RMSE\": 29.78792492645533,         \"R2\": -260.4155562259403,         \"Memory in Mb\": 1.3660497665405271,         \"Time in s\": 4.029196       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 22.46844053349284,         \"RMSE\": 29.014219480552867,         \"R2\": -255.5915105297988,         \"Memory in Mb\": 1.3665533065795898,         \"Time in s\": 4.629964999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 21.59490700757577,         \"RMSE\": 28.301677882839343,         \"R2\": -250.0434007116766,         \"Memory in Mb\": 0.510127067565918,         \"Time in s\": 5.253793       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.62268781294523,         \"RMSE\": 27.62086591367872,         \"R2\": -246.0239415518119,         \"Memory in Mb\": 1.3623762130737305,         \"Time in s\": 5.968102       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 19.786863931462925,         \"RMSE\": 26.990398924900397,         \"R2\": -230.60756767519212,         \"Memory in Mb\": 1.3643903732299805,         \"Time in s\": 6.700306       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 19.05732899619648,         \"RMSE\": 26.404670160589287,         \"R2\": -209.2038511633616,         \"Memory in Mb\": 1.3666563034057615,         \"Time in s\": 7.451319000000001       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 18.376512097202227,         \"RMSE\": 25.854792215140314,         \"R2\": -195.90337768575387,         \"Memory in Mb\": 1.3701810836791992,         \"Time in s\": 8.221931000000001       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 17.755044410127518,         \"RMSE\": 25.338820973360427,         \"R2\": -184.1550753065148,         \"Memory in Mb\": 1.3716917037963867,         \"Time in s\": 9.124580000000002       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 17.16611419898163,         \"RMSE\": 24.851444862058347,         \"R2\": -177.4118263333629,         \"Memory in Mb\": 1.3737058639526367,         \"Time in s\": 10.044684000000002       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 16.628565596068775,         \"RMSE\": 24.392285078947275,         \"R2\": -170.25012213753183,         \"Memory in Mb\": 1.3747129440307615,         \"Time in s\": 10.981068000000002       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 16.091244232649693,         \"RMSE\": 23.955027361350904,         \"R2\": -168.10096043791202,         \"Memory in Mb\": 1.3752164840698242,         \"Time in s\": 11.990243000000005       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 15.590768135673304,         \"RMSE\": 23.54051091957351,         \"R2\": -166.33817208986073,         \"Memory in Mb\": 1.3764753341674805,         \"Time in s\": 13.016881000000003       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 15.168708628495342,         \"RMSE\": 23.15108754841241,         \"R2\": -159.05714501634571,         \"Memory in Mb\": 0.5124959945678711,         \"Time in s\": 14.090212000000005       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 14.742446374247312,         \"RMSE\": 22.77953961802373,         \"R2\": -151.59887848495535,         \"Memory in Mb\": 3.064208030700684,         \"Time in s\": 15.285325000000004       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 14.319364852585176,         \"RMSE\": 22.42187566882095,         \"R2\": -144.08105420081068,         \"Memory in Mb\": 3.0679845809936523,         \"Time in s\": 16.529242000000004       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.916412195872256,         \"RMSE\": 22.080274918425697,         \"R2\": -138.68241285181185,         \"Memory in Mb\": 3.0712575912475586,         \"Time in s\": 17.842975000000003       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.515604789075644,         \"RMSE\": 21.753254558457893,         \"R2\": -136.71797028279042,         \"Memory in Mb\": 3.074782371520996,         \"Time in s\": 19.280557       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.16391092204058,         \"RMSE\": 21.44141764506316,         \"R2\": -136.3120101768532,         \"Memory in Mb\": 3.0773000717163086,         \"Time in s\": 20.753146       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.828283113852926,         \"RMSE\": 21.142484202016185,         \"R2\": -135.44313416922282,         \"Memory in Mb\": 3.078558921813965,         \"Time in s\": 22.284495       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.50446646701278,         \"RMSE\": 20.855361315179096,         \"R2\": -131.6825380828392,         \"Memory in Mb\": 3.0800695419311523,         \"Time in s\": 23.930702       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.187542748969031,         \"RMSE\": 20.57929219886472,         \"R2\": -129.592708960364,         \"Memory in Mb\": 3.0813283920288086,         \"Time in s\": 25.608717       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.899403743710543,         \"RMSE\": 20.31464229706916,         \"R2\": -126.82553676745258,         \"Memory in Mb\": 3.08359432220459,         \"Time in s\": 27.347366       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.634366305883283,         \"RMSE\": 20.06137952581079,         \"R2\": -124.7856004590591,         \"Memory in Mb\": 3.084601402282715,         \"Time in s\": 29.130085       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.363415331478278,         \"RMSE\": 19.815492221289517,         \"R2\": -123.0687724200615,         \"Memory in Mb\": 3.08560848236084,         \"Time in s\": 30.98707       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.106640469158773,         \"RMSE\": 19.57848368678801,         \"R2\": -121.2430978899656,         \"Memory in Mb\": 3.086615562438965,         \"Time in s\": 32.880055       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.873909665943762,         \"RMSE\": 19.35022618912736,         \"R2\": -118.20364312373844,         \"Memory in Mb\": 3.087119102478028,         \"Time in s\": 34.808534       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.65545006969969,         \"RMSE\": 19.130035299019603,         \"R2\": -114.92727947355436,         \"Memory in Mb\": 3.0873708724975586,         \"Time in s\": 36.791638       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.439309697188907,         \"RMSE\": 18.916827199314994,         \"R2\": -112.83532852765144,         \"Memory in Mb\": 3.08762264251709,         \"Time in s\": 38.832751       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.21789524284777,         \"RMSE\": 18.710158789526105,         \"R2\": -112.19133803320568,         \"Memory in Mb\": 3.087874412536621,         \"Time in s\": 40.951802       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.012578535125469,         \"RMSE\": 18.510293787577226,         \"R2\": -110.72583714230213,         \"Memory in Mb\": 3.077906608581543,         \"Time in s\": 43.146806       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.811853150109153,         \"RMSE\": 18.316579311485903,         \"R2\": -109.54344305213982,         \"Memory in Mb\": 3.0804243087768555,         \"Time in s\": 45.38444       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.61909067795052,         \"RMSE\": 18.12881604876013,         \"R2\": -109.39183420714345,         \"Memory in Mb\": 3.080927848815918,         \"Time in s\": 47.662491       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.438738635632271,         \"RMSE\": 17.946847607318464,         \"R2\": -109.00797869183796,         \"Memory in Mb\": 3.082438468933105,         \"Time in s\": 50.039238       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.429746533156267,         \"RMSE\": 17.937886241411594,         \"R2\": -108.97151968967049,         \"Memory in Mb\": 3.082438468933105,         \"Time in s\": 52.450717       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.837563210503649,         \"RMSE\": 16.830121687224917,         \"R2\": -363.61289911513376,         \"Memory in Mb\": 0.1506052017211914,         \"Time in s\": 0.01357       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.3557641651310055,         \"RMSE\": 11.925612892987612,         \"R2\": -149.62275175212707,         \"Memory in Mb\": 0.1761331558227539,         \"Time in s\": 0.033876       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.3711466349197527,         \"RMSE\": 9.780434627556833,         \"R2\": -65.4351822307151,         \"Memory in Mb\": 0.214268684387207,         \"Time in s\": 0.065705       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.6922077728217837,         \"RMSE\": 8.482083592242564,         \"R2\": -55.643739991610765,         \"Memory in Mb\": 0.2312593460083007,         \"Time in s\": 0.110591       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.74736475641488,         \"RMSE\": 7.825318026963682,         \"R2\": -10.953904022002217,         \"Memory in Mb\": 0.2869501113891601,         \"Time in s\": 0.166016       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.8724679940162905,         \"RMSE\": 7.312536888278379,         \"R2\": -3.4997438549991955,         \"Memory in Mb\": 0.3332719802856445,         \"Time in s\": 0.243978       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.0470429271529937,         \"RMSE\": 7.064245743713448,         \"R2\": -1.8145642692685129,         \"Memory in Mb\": 0.3119173049926758,         \"Time in s\": 0.346862       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.9741223361578246,         \"RMSE\": 6.690154558226259,         \"R2\": -1.288758200280824,         \"Memory in Mb\": 0.3463144302368164,         \"Time in s\": 0.647937       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.5306191185317584,         \"RMSE\": 6.892431773474538,         \"R2\": -1.031779280787943,         \"Memory in Mb\": 0.3914194107055664,         \"Time in s\": 0.960086       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.8799314967396743,         \"RMSE\": 6.981555605673833,         \"R2\": -0.4557571678865852,         \"Memory in Mb\": 0.4218912124633789,         \"Time in s\": 1.292146       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.113667008668635,         \"RMSE\": 7.033914104811044,         \"R2\": -0.1380455127293207,         \"Memory in Mb\": 0.4422159194946289,         \"Time in s\": 1.662194       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.34164975929163,         \"RMSE\": 7.058470289925444,         \"R2\": 0.0633731167085442,         \"Memory in Mb\": 0.4695367813110351,         \"Time in s\": 2.044753       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.57586761829926,         \"RMSE\": 7.15786747745719,         \"R2\": 0.2198462773680444,         \"Memory in Mb\": 0.5039682388305664,         \"Time in s\": 2.459644       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.72768375743327,         \"RMSE\": 7.245199860946492,         \"R2\": 0.3206991207112422,         \"Memory in Mb\": 0.5465364456176758,         \"Time in s\": 2.897301       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.104360720447454,         \"RMSE\": 7.731417459148682,         \"R2\": 0.3781793296599212,         \"Memory in Mb\": 0.5543031692504883,         \"Time in s\": 3.478625       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.614563299993537,         \"RMSE\": 8.384781892618234,         \"R2\": 0.4103032354466553,         \"Memory in Mb\": 0.577855110168457,         \"Time in s\": 4.083596       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.030281219875818,         \"RMSE\": 8.796345271037008,         \"R2\": 0.4686144271207236,         \"Memory in Mb\": 0.5973634719848633,         \"Time in s\": 4.712472       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.128233569544692,         \"RMSE\": 8.84845009665535,         \"R2\": 0.572286448100142,         \"Memory in Mb\": 0.6156282424926758,         \"Time in s\": 5.362321       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.65587905711115,         \"RMSE\": 9.6527323574251,         \"R2\": 0.5803946009681837,         \"Memory in Mb\": 0.637272834777832,         \"Time in s\": 6.068711       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.106977341119842,         \"RMSE\": 10.677274056234571,         \"R2\": 0.550512947323095,         \"Memory in Mb\": 0.6516351699829102,         \"Time in s\": 6.792503       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.51605472684967,         \"RMSE\": 11.121858780588036,         \"R2\": 0.582840670024279,         \"Memory in Mb\": 0.6455926895141602,         \"Time in s\": 7.540065       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.8763674823035235,         \"RMSE\": 11.54794620868086,         \"R2\": 0.6381207504866175,         \"Memory in Mb\": 0.654301643371582,         \"Time in s\": 8.314509000000001       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.048654689630025,         \"RMSE\": 11.785882981718466,         \"R2\": 0.6726179175042853,         \"Memory in Mb\": 0.6542215347290039,         \"Time in s\": 9.228486       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.558470564817128,         \"RMSE\": 12.694815113306078,         \"R2\": 0.6529678969258632,         \"Memory in Mb\": 0.7006998062133789,         \"Time in s\": 10.165772       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.011287699636805,         \"RMSE\": 13.865710758190522,         \"R2\": 0.6357023625954032,         \"Memory in Mb\": 0.7181978225708008,         \"Time in s\": 11.12726       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.454493871269731,         \"RMSE\": 14.39909947248495,         \"R2\": 0.6597325750664246,         \"Memory in Mb\": 0.7397470474243164,         \"Time in s\": 12.112744       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.455634964453314,         \"RMSE\": 14.370566123736594,         \"R2\": 0.7060577585099084,         \"Memory in Mb\": 0.6961946487426758,         \"Time in s\": 13.208544       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.98259297559382,         \"RMSE\": 15.278989711680778,         \"R2\": 0.7040656028742478,         \"Memory in Mb\": 0.7122316360473633,         \"Time in s\": 14.327932       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.896304985778038,         \"RMSE\": 17.680267148091307,         \"R2\": 0.6404050215106214,         \"Memory in Mb\": 0.7230386734008789,         \"Time in s\": 15.472301       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.34830207391465,         \"RMSE\": 18.238325787402868,         \"R2\": 0.6725323523293205,         \"Memory in Mb\": 0.7481813430786133,         \"Time in s\": 16.699404       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.700671911575691,         \"RMSE\": 18.698639858183288,         \"R2\": 0.6917798823884449,         \"Memory in Mb\": 0.750828742980957,         \"Time in s\": 17.953966       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.012928806619971,         \"RMSE\": 19.028065448277463,         \"R2\": 0.7098550919958697,         \"Memory in Mb\": 0.779423713684082,         \"Time in s\": 19.24314       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.590729727774807,         \"RMSE\": 20.061815233276363,         \"R2\": 0.6868102538385266,         \"Memory in Mb\": 0.8219194412231445,         \"Time in s\": 20.584825       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.29572445199132,         \"RMSE\": 21.688967498502105,         \"R2\": 0.6634948622954009,         \"Memory in Mb\": 0.8368387222290039,         \"Time in s\": 21.949532       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.850252347511734,         \"RMSE\": 22.377982941031117,         \"R2\": 0.6830616430184708,         \"Memory in Mb\": 0.8398981094360352,         \"Time in s\": 23.337733       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.995508749414425,         \"RMSE\": 22.434927630401365,         \"R2\": 0.7029833246789492,         \"Memory in Mb\": 0.8451242446899414,         \"Time in s\": 24.808931       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.855647843034443,         \"RMSE\": 23.972462409994428,         \"R2\": 0.6847772413527866,         \"Memory in Mb\": 0.8440675735473633,         \"Time in s\": 26.305221       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.648428200057216,         \"RMSE\": 25.832735423225586,         \"R2\": 0.6563908585574095,         \"Memory in Mb\": 0.8621377944946289,         \"Time in s\": 27.821712       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.477960681723363,         \"RMSE\": 27.01651731063008,         \"R2\": 0.6660339910533338,         \"Memory in Mb\": 0.8826723098754883,         \"Time in s\": 29.421277       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.794784005292485,         \"RMSE\": 27.277386650758192,         \"R2\": 0.6836500576952018,         \"Memory in Mb\": 0.8854074478149414,         \"Time in s\": 31.044846       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.2443539228967,         \"RMSE\": 27.815314781786785,         \"R2\": 0.6850336806962379,         \"Memory in Mb\": 0.915654182434082,         \"Time in s\": 32.692345       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.21783864235053,         \"RMSE\": 29.965283642676138,         \"R2\": 0.6566601868655235,         \"Memory in Mb\": 0.947678565979004,         \"Time in s\": 34.453267999999994       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.154558799374207,         \"RMSE\": 31.27949805899601,         \"R2\": 0.6696542166515442,         \"Memory in Mb\": 0.9631280899047852,         \"Time in s\": 36.23863899999999       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.65302219917293,         \"RMSE\": 31.71092492917292,         \"R2\": 0.6790841892096586,         \"Memory in Mb\": 0.979741096496582,         \"Time in s\": 38.04845199999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.17748759588543,         \"RMSE\": 32.35841629000369,         \"R2\": 0.6856630158751376,         \"Memory in Mb\": 1.0035409927368164,         \"Time in s\": 39.89288999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.994447812000203,         \"RMSE\": 33.88452895368057,         \"R2\": 0.6653322556738073,         \"Memory in Mb\": 1.0402307510375977,         \"Time in s\": 41.77183299999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.74940325928189,         \"RMSE\": 34.92971521251369,         \"R2\": 0.6643962418834424,         \"Memory in Mb\": 1.0591440200805664,         \"Time in s\": 43.68222899999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.71806819464153,         \"RMSE\": 36.27208023143736,         \"R2\": 0.6746268651566016,         \"Memory in Mb\": 1.0802621841430664,         \"Time in s\": 45.622796999999984       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.976084812890598,         \"RMSE\": 36.32299861842887,         \"R2\": 0.6871862958215178,         \"Memory in Mb\": 1.1105661392211914,         \"Time in s\": 47.62064799999998       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.812560792713985,         \"RMSE\": 37.68037385984369,         \"R2\": 0.6737446986071818,         \"Memory in Mb\": 1.1564149856567385,         \"Time in s\": 49.63871399999998       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.744158926088524,         \"RMSE\": 38.95638961509032,         \"R2\": 0.6665241448790927,         \"Memory in Mb\": 1.171940803527832,         \"Time in s\": 51.68634399999998       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.965548256363952,         \"RMSE\": 40.779089345824126,         \"R2\": 0.6619939776632806,         \"Memory in Mb\": 1.1861085891723633,         \"Time in s\": 53.83172099999997       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.10164191353107,         \"RMSE\": 40.80941552099692,         \"R2\": 0.669724624616493,         \"Memory in Mb\": 1.1904268264770508,         \"Time in s\": 56.00600499999997       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.656196028844478,         \"RMSE\": 13.301506400077992,         \"R2\": -414.0076115498352,         \"Memory in Mb\": 0.2015810012817382,         \"Time in s\": 0.057323       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.307191630717303,         \"RMSE\": 9.5148436405931,         \"R2\": -35.39725790498291,         \"Memory in Mb\": 0.2895097732543945,         \"Time in s\": 0.159522       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3916587233350866,         \"RMSE\": 7.783560456255013,         \"R2\": -31.83725667748105,         \"Memory in Mb\": 0.3228082656860351,         \"Time in s\": 0.43784       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0172424359013847,         \"RMSE\": 6.770328731809264,         \"R2\": -23.92145608895444,         \"Memory in Mb\": 0.3692712783813476,         \"Time in s\": 0.749169       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.069330341220504,         \"RMSE\": 6.141775226189047,         \"R2\": -11.868015650386663,         \"Memory in Mb\": 0.4076700210571289,         \"Time in s\": 1.082433       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.013474643057227,         \"RMSE\": 5.653544639730099,         \"R2\": -8.249866206703038,         \"Memory in Mb\": 0.4241609573364258,         \"Time in s\": 1.485703       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.894365920134237,         \"RMSE\": 5.255534318342925,         \"R2\": -7.260127227254786,         \"Memory in Mb\": 0.4439592361450195,         \"Time in s\": 2.052392       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.942363436061872,         \"RMSE\": 4.987168106592344,         \"R2\": -5.559462264629689,         \"Memory in Mb\": 0.4557695388793945,         \"Time in s\": 2.6505280000000004       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9639788846395132,         \"RMSE\": 4.758402061618727,         \"R2\": -4.244508869717663,         \"Memory in Mb\": 0.4725847244262695,         \"Time in s\": 3.3297560000000006       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9045329443413328,         \"RMSE\": 4.539431452034987,         \"R2\": -3.787127034775958,         \"Memory in Mb\": 0.5018167495727539,         \"Time in s\": 4.037089000000001       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7801675790175082,         \"RMSE\": 4.332908187325825,         \"R2\": -3.704734847036908,         \"Memory in Mb\": 0.539036750793457,         \"Time in s\": 4.871381000000001       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7262455165213564,         \"RMSE\": 4.162317120423255,         \"R2\": -3.374233717467068,         \"Memory in Mb\": 0.5583086013793945,         \"Time in s\": 5.726343000000002       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6726006855046047,         \"RMSE\": 4.010034080286883,         \"R2\": -3.11472540009772,         \"Memory in Mb\": 0.586766242980957,         \"Time in s\": 6.608691000000002       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6001254820213158,         \"RMSE\": 3.86938412403892,         \"R2\": -3.01116046378164,         \"Memory in Mb\": 0.6034517288208008,         \"Time in s\": 7.537396000000002       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5903246290151525,         \"RMSE\": 3.758572865099384,         \"R2\": -2.72204992570884,         \"Memory in Mb\": 0.6344270706176758,         \"Time in s\": 8.563678000000001       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5306703522535514,         \"RMSE\": 3.644833568467773,         \"R2\": -2.673500446315358,         \"Memory in Mb\": 0.6524057388305664,         \"Time in s\": 9.653495       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.462120415173825,         \"RMSE\": 3.538151879462345,         \"R2\": -2.658070572544154,         \"Memory in Mb\": 0.6771516799926758,         \"Time in s\": 10.778313       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4104873891633294,         \"RMSE\": 3.442715407420023,         \"R2\": -2.491830651593505,         \"Memory in Mb\": 0.712040901184082,         \"Time in s\": 12.006044       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3577274631021343,         \"RMSE\": 3.353553439657788,         \"R2\": -2.42792224294701,         \"Memory in Mb\": 0.7612085342407227,         \"Time in s\": 13.264975000000002       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.328889471148693,         \"RMSE\": 3.2750276755937477,         \"R2\": -2.361664675892684,         \"Memory in Mb\": 0.7830896377563477,         \"Time in s\": 14.608014       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2856838141339133,         \"RMSE\": 3.198005596242657,         \"R2\": -2.3114858734875385,         \"Memory in Mb\": 0.8153314590454102,         \"Time in s\": 15.987435       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2502461578606217,         \"RMSE\": 3.1277634460074983,         \"R2\": -2.1102975482726696,         \"Memory in Mb\": 0.8549776077270508,         \"Time in s\": 17.476653000000002       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2118787702501406,         \"RMSE\": 3.0607885313580625,         \"R2\": -1.824527619127544,         \"Memory in Mb\": 0.8641138076782227,         \"Time in s\": 19.005275       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1755519926992437,         \"RMSE\": 2.997482691409013,         \"R2\": -1.6465763687209671,         \"Memory in Mb\": 0.904881477355957,         \"Time in s\": 20.582478       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1542746800420942,         \"RMSE\": 2.9412002898427465,         \"R2\": -1.494663742459657,         \"Memory in Mb\": 0.9429025650024414,         \"Time in s\": 22.213321       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1232655769227813,         \"RMSE\": 2.885625631130148,         \"R2\": -1.4054721071787497,         \"Memory in Mb\": 0.9187402725219728,         \"Time in s\": 23.93785       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0927628011224122,         \"RMSE\": 2.8324064719208977,         \"R2\": -1.3090698562992058,         \"Memory in Mb\": 0.9784936904907228,         \"Time in s\": 25.72016       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0798076211233285,         \"RMSE\": 2.7860066009246958,         \"R2\": -1.2872677886963872,         \"Memory in Mb\": 0.8415918350219727,         \"Time in s\": 27.559893       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0533259806656756,         \"RMSE\": 2.7386650773118006,         \"R2\": -1.2648586320750757,         \"Memory in Mb\": 0.926945686340332,         \"Time in s\": 29.430927       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0370277841126194,         \"RMSE\": 2.695306817676886,         \"R2\": -1.1694452334238137,         \"Memory in Mb\": 1.0152063369750977,         \"Time in s\": 31.394822       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0220360797787769,         \"RMSE\": 2.6548714349996483,         \"R2\": -1.0727572654712625,         \"Memory in Mb\": 0.9687509536743164,         \"Time in s\": 33.420245       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.006223169156282,         \"RMSE\": 2.615089153799328,         \"R2\": -0.9735122270872276,         \"Memory in Mb\": 0.8030519485473633,         \"Time in s\": 35.538976       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9862189251721106,         \"RMSE\": 2.576116595691222,         \"R2\": -0.901357605879683,         \"Memory in Mb\": 0.7759256362915039,         \"Time in s\": 37.763356       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9658028732124052,         \"RMSE\": 2.5385905860617046,         \"R2\": -0.8755448750460146,         \"Memory in Mb\": 0.8428354263305664,         \"Time in s\": 40.028227       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.958070286753166,         \"RMSE\": 2.506070409170758,         \"R2\": -0.8758066430098239,         \"Memory in Mb\": 0.9465646743774414,         \"Time in s\": 42.353807       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9436099236768006,         \"RMSE\": 2.472715624364642,         \"R2\": -0.8663281360281503,         \"Memory in Mb\": 1.0379304885864258,         \"Time in s\": 44.723793       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9279645732871132,         \"RMSE\": 2.440285299254925,         \"R2\": -0.8166009677654511,         \"Memory in Mb\": 1.1119890213012695,         \"Time in s\": 47.149898       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.91590994704171,         \"RMSE\": 2.410261116608071,         \"R2\": -0.7913739428902633,         \"Memory in Mb\": 1.1737489700317385,         \"Time in s\": 49.62861       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8968362370347621,         \"RMSE\": 2.379411736746614,         \"R2\": -0.7536320120768303,         \"Memory in Mb\": 1.261582374572754,         \"Time in s\": 52.179919       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8878342141912964,         \"RMSE\": 2.351392140243472,         \"R2\": -0.7280625702741705,         \"Memory in Mb\": 1.3552255630493164,         \"Time in s\": 54.784302       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8775558321142263,         \"RMSE\": 2.3237456817971016,         \"R2\": -0.7062000372474271,         \"Memory in Mb\": 1.4321069717407229,         \"Time in s\": 57.452517       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8672496542857573,         \"RMSE\": 2.297532908897418,         \"R2\": -0.6834092983276716,         \"Memory in Mb\": 1.4874773025512695,         \"Time in s\": 60.173386       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8593706057522699,         \"RMSE\": 2.272389423762812,         \"R2\": -0.6439286158863597,         \"Memory in Mb\": 1.5595178604125977,         \"Time in s\": 62.976048       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8551106332542915,         \"RMSE\": 2.2487703297155224,         \"R2\": -0.6019328469057044,         \"Memory in Mb\": 1.619084358215332,         \"Time in s\": 65.856537       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8437512715146732,         \"RMSE\": 2.224375873084905,         \"R2\": -0.5739713521606553,         \"Memory in Mb\": 1.1261072158813477,         \"Time in s\": 68.798174       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8344220404851989,         \"RMSE\": 2.2010168562801016,         \"R2\": -0.5664083310843888,         \"Memory in Mb\": 1.167832374572754,         \"Time in s\": 71.779904       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.825939320609599,         \"RMSE\": 2.179009982884269,         \"R2\": -0.5482654902802699,         \"Memory in Mb\": 1.1199464797973633,         \"Time in s\": 74.829165       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8156984309758435,         \"RMSE\": 2.1571048007400404,         \"R2\": -0.5331573747015668,         \"Memory in Mb\": 1.1733713150024414,         \"Time in s\": 77.929016       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.806477335746804,         \"RMSE\": 2.1360065895495888,         \"R2\": -0.5325097322303367,         \"Memory in Mb\": 1.2283296585083008,         \"Time in s\": 81.05698100000001       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8008625237630099,         \"RMSE\": 2.1159877488140326,         \"R2\": -0.5292346593649373,         \"Memory in Mb\": 1.2836008071899414,         \"Time in s\": 84.241983       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.800378499538596,         \"RMSE\": 2.1149541843634605,         \"R2\": -0.5287610996295022,         \"Memory in Mb\": 1.2846193313598633,         \"Time in s\": 87.445729       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.0878895070954884,         \"RMSE\": 1.3778002085324723,         \"R2\": -1.2599207317049026,         \"Memory in Mb\": 0.1791715621948242,         \"Time in s\": 0.003809       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.15171477394762,         \"RMSE\": 1.5218208011368886,         \"R2\": -1.3974856828423898,         \"Memory in Mb\": 0.3313665390014648,         \"Time in s\": 0.017797       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.2596040860169628,         \"RMSE\": 1.630698561429495,         \"R2\": -0.8214033882315572,         \"Memory in Mb\": 0.4835615158081054,         \"Time in s\": 0.056943       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.147002532502157,         \"RMSE\": 1.5136945038262,         \"R2\": -0.7860708992998826,         \"Memory in Mb\": 0.6357030868530273,         \"Time in s\": 0.120895       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.7448650745312246,         \"RMSE\": 2.8901942810902064,         \"R2\": -0.6023944619968462,         \"Memory in Mb\": 0.795161247253418,         \"Time in s\": 0.341288       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.974173643458203,         \"RMSE\": 3.1122799656868354,         \"R2\": 0.1967701507194095,         \"Memory in Mb\": 0.949946403503418,         \"Time in s\": 0.602886       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3465039451978784,         \"RMSE\": 3.868783481489585,         \"R2\": 0.1653904369447871,         \"Memory in Mb\": 1.1044378280639648,         \"Time in s\": 0.885897       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3152944739841907,         \"RMSE\": 3.751470845606434,         \"R2\": 0.286453015670338,         \"Memory in Mb\": 1.2595434188842771,         \"Time in s\": 1.213122       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.485126688481329,         \"RMSE\": 3.8753788781661274,         \"R2\": 0.3615628965518305,         \"Memory in Mb\": 1.4176397323608398,         \"Time in s\": 1.708637       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.679180085056696,         \"RMSE\": 4.098463178184459,         \"R2\": 0.5005082908479199,         \"Memory in Mb\": 1.580409049987793,         \"Time in s\": 2.233256       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.993112128155013,         \"RMSE\": 4.501608187312601,         \"R2\": 0.5353065115430311,         \"Memory in Mb\": 1.7385053634643557,         \"Time in s\": 2.789253       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.049130101089184,         \"RMSE\": 4.474860576824222,         \"R2\": 0.624267329970883,         \"Memory in Mb\": 1.8972959518432615,         \"Time in s\": 3.530617       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.129389359320645,         \"RMSE\": 4.535626207267123,         \"R2\": 0.6870855629914132,         \"Memory in Mb\": 2.0540571212768555,         \"Time in s\": 4.307795       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.2350921629171503,         \"RMSE\": 4.614317779917637,         \"R2\": 0.7245583098520811,         \"Memory in Mb\": 2.21335506439209,         \"Time in s\": 5.238077       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.615407192454655,         \"RMSE\": 5.434402308521257,         \"R2\": 0.6928112980835472,         \"Memory in Mb\": 2.370730400085449,         \"Time in s\": 6.215347       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.842899644735678,         \"RMSE\": 5.8926781106586255,         \"R2\": 0.7087106044563829,         \"Memory in Mb\": 2.5251951217651367,         \"Time in s\": 7.308114       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.939333513046091,         \"RMSE\": 5.936527515565436,         \"R2\": 0.7578865873655871,         \"Memory in Mb\": 2.680434226989746,         \"Time in s\": 8.444739       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.1526220464224926,         \"RMSE\": 6.160116941975886,         \"R2\": 0.7926170753106898,         \"Memory in Mb\": 2.8339643478393555,         \"Time in s\": 9.747256       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.486090256229248,         \"RMSE\": 6.857164593682279,         \"R2\": 0.7881489392686998,         \"Memory in Mb\": 2.990111351013184,         \"Time in s\": 11.107311       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.095083445923365,         \"RMSE\": 8.268326900050806,         \"R2\": 0.7303274183124314,         \"Memory in Mb\": 3.147219657897949,         \"Time in s\": 12.601651       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.345901760482457,         \"RMSE\": 8.651953805757511,         \"R2\": 0.7474084291359289,         \"Memory in Mb\": 3.301150321960449,         \"Time in s\": 14.172197999999998       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.775936882313693,         \"RMSE\": 9.234098241635358,         \"R2\": 0.7685060952534608,         \"Memory in Mb\": 3.4575910568237305,         \"Time in s\": 15.876551999999998       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.050411841877211,         \"RMSE\": 9.480574702158652,         \"R2\": 0.7880472652798773,         \"Memory in Mb\": 3.6158742904663086,         \"Time in s\": 17.675130999999997       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.7396819662512994,         \"RMSE\": 10.861908099063555,         \"R2\": 0.7457953067175733,         \"Memory in Mb\": 3.77274227142334,         \"Time in s\": 19.599013       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.418933110619537,         \"RMSE\": 12.596893007879746,         \"R2\": 0.699156722346497,         \"Memory in Mb\": 3.926619529724121,         \"Time in s\": 21.625011       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.830180870941,         \"RMSE\": 13.02165358749325,         \"R2\": 0.7215679622698357,         \"Memory in Mb\": 4.082179069519043,         \"Time in s\": 23.771258       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.059624975297776,         \"RMSE\": 13.201631143135527,         \"R2\": 0.7517949935656911,         \"Memory in Mb\": 4.237311363220215,         \"Time in s\": 26.095147       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.517266870602596,         \"RMSE\": 14.029786197157003,         \"R2\": 0.750336177123377,         \"Memory in Mb\": 4.390841484069824,         \"Time in s\": 28.501657       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.872910663629112,         \"RMSE\": 17.67011178426297,         \"R2\": 0.6406578335650022,         \"Memory in Mb\": 4.544772148132324,         \"Time in s\": 31.026265       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.355957081475973,         \"RMSE\": 18.251720539867826,         \"R2\": 0.671924837978655,         \"Memory in Mb\": 4.698409080505371,         \"Time in s\": 33.677708       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.779061369126929,         \"RMSE\": 18.644503325392748,         \"R2\": 0.6934349036095702,         \"Memory in Mb\": 4.852313041687012,         \"Time in s\": 36.483969       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.97013178962945,         \"RMSE\": 18.69029492717773,         \"R2\": 0.7199342471488321,         \"Memory in Mb\": 5.009421348571777,         \"Time in s\": 39.412921       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.836385670325258,         \"RMSE\": 20.411474322578705,         \"R2\": 0.6756306209292051,         \"Memory in Mb\": 5.165541648864746,         \"Time in s\": 42.477176       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.650208752532226,         \"RMSE\": 22.152599191433616,         \"R2\": 0.6487731216482631,         \"Memory in Mb\": 5.323611259460449,         \"Time in s\": 45.685202       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.26433375884341,         \"RMSE\": 22.74111870549559,         \"R2\": 0.672536034672935,         \"Memory in Mb\": 5.478930473327637,         \"Time in s\": 49.011467       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.285084454056172,         \"RMSE\": 22.62858691877232,         \"R2\": 0.6976709876564118,         \"Memory in Mb\": 5.63400936126709,         \"Time in s\": 52.447627       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.297859574888522,         \"RMSE\": 24.603705237609702,         \"R2\": 0.6677818184200794,         \"Memory in Mb\": 5.789168357849121,         \"Time in s\": 55.986709       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.277775247208368,         \"RMSE\": 26.91758918665374,         \"R2\": 0.6267299649165277,         \"Memory in Mb\": 5.945448875427246,         \"Time in s\": 59.629264       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.148002577856595,         \"RMSE\": 27.91235298687263,         \"R2\": 0.643353503146777,         \"Memory in Mb\": 6.099112510681152,         \"Time in s\": 63.379206       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.450833155107055,         \"RMSE\": 28.053185003016477,         \"R2\": 0.6652347923635655,         \"Memory in Mb\": 6.252856254577637,         \"Time in s\": 67.23493599999999       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.938736394119786,         \"RMSE\": 28.680885446607185,         \"R2\": 0.6649461832952053,         \"Memory in Mb\": 6.407908439636231,         \"Time in s\": 71.205463       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.465286457846624,         \"RMSE\": 32.222162406640614,         \"R2\": 0.6027938253530374,         \"Memory in Mb\": 6.562827110290527,         \"Time in s\": 75.286683       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.36878629272608,         \"RMSE\": 33.403615991184594,         \"R2\": 0.6231055060350865,         \"Memory in Mb\": 6.717398643493652,         \"Time in s\": 79.474485       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.88015130963188,         \"RMSE\": 33.764210229402664,         \"R2\": 0.6360141173956066,         \"Memory in Mb\": 6.872824668884277,         \"Time in s\": 83.769797       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.57744796303998,         \"RMSE\": 34.830627929035586,         \"R2\": 0.6356250399764956,         \"Memory in Mb\": 7.029131889343262,         \"Time in s\": 88.176271       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.43571571603741,         \"RMSE\": 36.40788480688662,         \"R2\": 0.6134430891768862,         \"Memory in Mb\": 7.184878349304199,         \"Time in s\": 92.694929       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.34914238062968,         \"RMSE\": 37.807266067412606,         \"R2\": 0.6066317565796657,         \"Memory in Mb\": 7.340197563171387,         \"Time in s\": 97.332285       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.191315994328228,         \"RMSE\": 38.81894260965106,         \"R2\": 0.6271697641288401,         \"Memory in Mb\": 7.495863914489746,         \"Time in s\": 102.073572       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.34075784343543,         \"RMSE\": 38.827434948624926,         \"R2\": 0.6423969913213963,         \"Memory in Mb\": 7.652411460876465,         \"Time in s\": 106.920364       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.12545732554984,         \"RMSE\": 39.99965605849559,         \"R2\": 0.6321733437483394,         \"Memory in Mb\": 7.811335563659668,         \"Time in s\": 111.876093       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.859407485948264,         \"RMSE\": 40.9180834433101,         \"R2\": 0.6319179521646502,         \"Memory in Mb\": 7.9698591232299805,         \"Time in s\": 116.942977       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.582967433016183,         \"RMSE\": 41.65667948828452,         \"R2\": 0.6471310448579161,         \"Memory in Mb\": 8.12806224822998,         \"Time in s\": 122.12293       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.674172622955844,         \"RMSE\": 41.71227980537356,         \"R2\": 0.65479005999511,         \"Memory in Mb\": 8.21412181854248,         \"Time in s\": 127.415096       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3812789990066343,         \"RMSE\": 0.4856864156914124,         \"R2\": 0.4734504440676397,         \"Memory in Mb\": 0.3661947250366211,         \"Time in s\": 0.012803       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3903932807396207,         \"RMSE\": 0.4802129236445582,         \"R2\": 0.908098150441852,         \"Memory in Mb\": 0.7077703475952148,         \"Time in s\": 0.066318       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3553562094560649,         \"RMSE\": 0.4475448539758346,         \"R2\": 0.8908737885344612,         \"Memory in Mb\": 1.0465993881225586,         \"Time in s\": 0.161186       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3785078228066897,         \"RMSE\": 0.4818104291982039,         \"R2\": 0.8725877843301283,         \"Memory in Mb\": 1.386988639831543,         \"Time in s\": 0.310355       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3456451525369771,         \"RMSE\": 0.450476311872574,         \"R2\": 0.9301027529077078,         \"Memory in Mb\": 1.7262754440307615,         \"Time in s\": 0.520575       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3369671927041528,         \"RMSE\": 0.4421642502671299,         \"R2\": 0.9427860880043346,         \"Memory in Mb\": 2.0684385299682617,         \"Time in s\": 0.828496       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3170957614007029,         \"RMSE\": 0.4217273000916425,         \"R2\": 0.9461011323760548,         \"Memory in Mb\": 2.404311180114746,         \"Time in s\": 1.349971       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3307037984070857,         \"RMSE\": 0.4315519243898653,         \"R2\": 0.9502341257934384,         \"Memory in Mb\": 2.7412595748901367,         \"Time in s\": 1.938101       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3239117556806251,         \"RMSE\": 0.4198186275021798,         \"R2\": 0.9586532343987924,         \"Memory in Mb\": 3.0777502059936523,         \"Time in s\": 2.6707590000000003       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3216990324082377,         \"RMSE\": 0.4152317221827294,         \"R2\": 0.959406710806771,         \"Memory in Mb\": 3.414671897888184,         \"Time in s\": 3.525266       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3195426619101423,         \"RMSE\": 0.4098153846782661,         \"R2\": 0.95731130106893,         \"Memory in Mb\": 3.7566747665405273,         \"Time in s\": 4.592894       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3205833893003068,         \"RMSE\": 0.409734240531711,         \"R2\": 0.9569909127297423,         \"Memory in Mb\": 4.096526145935059,         \"Time in s\": 5.7511       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3096350275266986,         \"RMSE\": 0.3977121378847216,         \"R2\": 0.958918388812615,         \"Memory in Mb\": 4.436213493347168,         \"Time in s\": 7.098098       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3021514917324106,         \"RMSE\": 0.3881456980787764,         \"R2\": 0.9590118642619369,         \"Memory in Mb\": 4.772730827331543,         \"Time in s\": 8.655558000000001       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.302378432925199,         \"RMSE\": 0.3879302261175293,         \"R2\": 0.9597410141114792,         \"Memory in Mb\": 5.1073408126831055,         \"Time in s\": 10.324296       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3042635511277332,         \"RMSE\": 0.3880820602227424,         \"R2\": 0.9577019017103428,         \"Memory in Mb\": 5.440821647644043,         \"Time in s\": 12.208908       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3061116805473403,         \"RMSE\": 0.391104192749731,         \"R2\": 0.9546026616524738,         \"Memory in Mb\": 5.773791313171387,         \"Time in s\": 14.314788       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3092553456162419,         \"RMSE\": 0.3953517094261167,         \"R2\": 0.9532980843409116,         \"Memory in Mb\": 6.1096906661987305,         \"Time in s\": 16.588556       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3076082401740039,         \"RMSE\": 0.3960205626085123,         \"R2\": 0.9515479192781826,         \"Memory in Mb\": 6.445483207702637,         \"Time in s\": 19.05639       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3014609513967445,         \"RMSE\": 0.3890658925747077,         \"R2\": 0.951945723428325,         \"Memory in Mb\": 6.780200004577637,         \"Time in s\": 21.697867       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2971138293692631,         \"RMSE\": 0.3834166944817816,         \"R2\": 0.9518088379060108,         \"Memory in Mb\": 7.116557121276856,         \"Time in s\": 24.511965000000004       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2982326339454942,         \"RMSE\": 0.3845174635941775,         \"R2\": 0.952475485177767,         \"Memory in Mb\": 7.451247215270996,         \"Time in s\": 27.506667000000004       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2946274143925286,         \"RMSE\": 0.3801223802157071,         \"R2\": 0.9560358463571956,         \"Memory in Mb\": 7.78644847869873,         \"Time in s\": 30.697063000000004       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2933968019133882,         \"RMSE\": 0.3767220961039539,         \"R2\": 0.9578595845961764,         \"Memory in Mb\": 8.120524406433105,         \"Time in s\": 34.045785       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2879691623817681,         \"RMSE\": 0.3709865810494578,         \"R2\": 0.9600287871527096,         \"Memory in Mb\": 8.45413875579834,         \"Time in s\": 37.543006000000005       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2858512762877924,         \"RMSE\": 0.3680336140311482,         \"R2\": 0.9606162857565064,         \"Memory in Mb\": 8.791060447692871,         \"Time in s\": 41.194255000000005       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.28337361966731,         \"RMSE\": 0.3643610952909248,         \"R2\": 0.9615619008486128,         \"Memory in Mb\": 9.128493309020996,         \"Time in s\": 45.008334000000005       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2882996011532734,         \"RMSE\": 0.3724151060029248,         \"R2\": 0.9588932716362208,         \"Memory in Mb\": 9.463343620300291,         \"Time in s\": 48.98956       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.289315517198266,         \"RMSE\": 0.3729583387798602,         \"R2\": 0.957758997678865,         \"Memory in Mb\": 9.801314353942873,         \"Time in s\": 53.13082800000001       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2880445309367084,         \"RMSE\": 0.3714720231862385,         \"R2\": 0.9585805866298192,         \"Memory in Mb\": 10.13992977142334,         \"Time in s\": 57.44083900000001       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.285913070387579,         \"RMSE\": 0.3694806072400069,         \"R2\": 0.95966894305211,         \"Memory in Mb\": 10.477011680603027,         \"Time in s\": 61.917696000000014       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2840899599351511,         \"RMSE\": 0.3669642572161526,         \"R2\": 0.9609793991220156,         \"Memory in Mb\": 10.80936336517334,         \"Time in s\": 66.56801000000002       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2804371592513609,         \"RMSE\": 0.3629082548929102,         \"R2\": 0.9621252180674722,         \"Memory in Mb\": 11.1486234664917,         \"Time in s\": 71.37699000000002       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2832721366095781,         \"RMSE\": 0.3646482252308528,         \"R2\": 0.9611623224331388,         \"Memory in Mb\": 11.485033988952637,         \"Time in s\": 76.34970200000002       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2853757262809053,         \"RMSE\": 0.3664704272728199,         \"R2\": 0.959741096022156,         \"Memory in Mb\": 11.82703685760498,         \"Time in s\": 81.48889300000002       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2846995678315971,         \"RMSE\": 0.3663721647933076,         \"R2\": 0.9588788359612516,         \"Memory in Mb\": 12.1649808883667,         \"Time in s\": 86.79821800000002       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.284389692089141,         \"RMSE\": 0.365623460665911,         \"R2\": 0.9590812376103318,         \"Memory in Mb\": 12.5026273727417,         \"Time in s\": 92.27027700000002       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2812524955317089,         \"RMSE\": 0.3622785856535135,         \"R2\": 0.9593969665626948,         \"Memory in Mb\": 12.84118938446045,         \"Time in s\": 97.89633500000002       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2784736419799919,         \"RMSE\": 0.3590495394564995,         \"R2\": 0.9599459478528628,         \"Memory in Mb\": 13.18101406097412,         \"Time in s\": 103.67983800000002       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.28122680710979,         \"RMSE\": 0.3614117991183927,         \"R2\": 0.9590552347518728,         \"Memory in Mb\": 13.522452354431152,         \"Time in s\": 109.62063100000002       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2798414038154103,         \"RMSE\": 0.3599105705870861,         \"R2\": 0.958953025104572,         \"Memory in Mb\": 13.858756065368652,         \"Time in s\": 115.71824100000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2792299366421054,         \"RMSE\": 0.358810295818463,         \"R2\": 0.9588293518961978,         \"Memory in Mb\": 14.19906520843506,         \"Time in s\": 121.97670500000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2757234931419036,         \"RMSE\": 0.3557294429539717,         \"R2\": 0.9596100235239656,         \"Memory in Mb\": 14.53821849822998,         \"Time in s\": 128.39866500000002       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2725087918367814,         \"RMSE\": 0.3526976163964828,         \"R2\": 0.9604999212537026,         \"Memory in Mb\": 14.877989768981934,         \"Time in s\": 134.987331       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2707423985398595,         \"RMSE\": 0.3505322158445511,         \"R2\": 0.9608236269181288,         \"Memory in Mb\": 15.214400291442873,         \"Time in s\": 141.746125       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2714968277868111,         \"RMSE\": 0.3507195370917735,         \"R2\": 0.960138783514385,         \"Memory in Mb\": 15.551268577575684,         \"Time in s\": 148.677278       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2704179121014184,         \"RMSE\": 0.350599255928843,         \"R2\": 0.9598313134304426,         \"Memory in Mb\": 15.892088890075684,         \"Time in s\": 155.784091       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2707082259086565,         \"RMSE\": 0.3516525290937312,         \"R2\": 0.9591696518431828,         \"Memory in Mb\": 16.229090690612793,         \"Time in s\": 163.071856       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2709225398475326,         \"RMSE\": 0.3517696828828596,         \"R2\": 0.9583487676398438,         \"Memory in Mb\": 16.574454307556152,         \"Time in s\": 170.541967       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2686954199893275,         \"RMSE\": 0.349579763566054,         \"R2\": 0.9581740736545136,         \"Memory in Mb\": 16.91306972503662,         \"Time in s\": 178.198313       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.268533139095725,         \"RMSE\": 0.3494211343568523,         \"R2\": 0.9581842100200092,         \"Memory in Mb\": 16.932257652282715,         \"Time in s\": 186.03359       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.664574314574316,         \"RMSE\": 12.7079745317607,         \"R2\": -206.87879383707747,         \"Memory in Mb\": 0.0196142196655273,         \"Time in s\": 0.000715       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.767694704637076,         \"RMSE\": 9.018587183866767,         \"R2\": -85.14025986830408,         \"Memory in Mb\": 0.0211782455444335,         \"Time in s\": 0.002248       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3093367298127023,         \"RMSE\": 7.420500566500976,         \"R2\": -37.24267181629702,         \"Memory in Mb\": 0.0263471603393554,         \"Time in s\": 0.0045       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.892363968348808,         \"RMSE\": 6.441521936619904,         \"R2\": -31.668094594906044,         \"Memory in Mb\": 0.0274343490600585,         \"Time in s\": 0.007522       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.1129412159858934,         \"RMSE\": 6.114058653243701,         \"R2\": -6.297346571779499,         \"Memory in Mb\": 0.0340337753295898,         \"Time in s\": 0.011341       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.832849782567835,         \"RMSE\": 6.236602142425367,         \"R2\": -2.2730130120415795,         \"Memory in Mb\": 0.043257713317871,         \"Time in s\": 0.016081       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4069290990236856,         \"RMSE\": 6.402381882180361,         \"R2\": -1.3118663438824,         \"Memory in Mb\": 0.0494871139526367,         \"Time in s\": 0.021988       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.650377971160808,         \"RMSE\": 6.321189272940957,         \"R2\": -1.043267371916866,         \"Memory in Mb\": 0.0551328659057617,         \"Time in s\": 0.050945       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.035631404360372,         \"RMSE\": 6.4483291916176695,         \"R2\": -0.7783857772357967,         \"Memory in Mb\": 0.0562467575073242,         \"Time in s\": 0.083616       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.693189868957898,         \"RMSE\": 7.0697740144659305,         \"R2\": -0.4927792786841307,         \"Memory in Mb\": 0.0576238632202148,         \"Time in s\": 0.119642       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.274396860168236,         \"RMSE\": 7.6542276724395,         \"R2\": -0.3476225254437259,         \"Memory in Mb\": 0.0577573776245117,         \"Time in s\": 0.157993       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.216037157212015,         \"RMSE\": 7.551012267266295,         \"R2\": -0.0719037453282565,         \"Memory in Mb\": 0.0578107833862304,         \"Time in s\": 0.197604       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.030848211447775,         \"RMSE\": 7.321940337412501,         \"R2\": 0.1836709538125499,         \"Memory in Mb\": 0.058394432067871,         \"Time in s\": 0.238591       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.907406922429448,         \"RMSE\": 7.137924382310331,         \"R2\": 0.3406662269828342,         \"Memory in Mb\": 0.0584478378295898,         \"Time in s\": 0.28091       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.132506734403487,         \"RMSE\": 7.341156657504303,         \"R2\": 0.439370571581684,         \"Memory in Mb\": 0.0584478378295898,         \"Time in s\": 0.32452       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.292049153445915,         \"RMSE\": 7.468652514259996,         \"R2\": 0.5321251372519638,         \"Memory in Mb\": 0.0590581893920898,         \"Time in s\": 0.369448       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.31698748044205,         \"RMSE\": 7.461166418014795,         \"R2\": 0.6176873420824156,         \"Memory in Mb\": 0.0591115951538085,         \"Time in s\": 0.415771       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.300228902480157,         \"RMSE\": 7.425148329077998,         \"R2\": 0.6988181014109027,         \"Memory in Mb\": 0.0590581893920898,         \"Time in s\": 0.474994       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.830499581707958,         \"RMSE\": 9.648698249017793,         \"R2\": 0.5807452540036802,         \"Memory in Mb\": 0.0252714157104492,         \"Time in s\": 0.54427       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.400718854692065,         \"RMSE\": 10.45569246424029,         \"R2\": 0.5689754490886993,         \"Memory in Mb\": 0.0314207077026367,         \"Time in s\": 0.614353       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.611665150046439,         \"RMSE\": 10.61745698030736,         \"R2\": 0.6198209084753062,         \"Memory in Mb\": 0.0365362167358398,         \"Time in s\": 0.6853290000000001       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.029624246247838,         \"RMSE\": 11.197269958950692,         \"R2\": 0.6597654020329642,         \"Memory in Mb\": 0.0410680770874023,         \"Time in s\": 0.7572760000000001       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.254490759785878,         \"RMSE\": 11.350610231674398,         \"R2\": 0.6963529412438163,         \"Memory in Mb\": 0.0445928573608398,         \"Time in s\": 0.830219       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.784750145903498,         \"RMSE\": 12.258358647532567,         \"R2\": 0.6764200982742594,         \"Memory in Mb\": 0.0446996688842773,         \"Time in s\": 0.904176       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.342804112650073,         \"RMSE\": 13.247943494163705,         \"R2\": 0.6674407623884211,         \"Memory in Mb\": 0.0446996688842773,         \"Time in s\": 0.986871       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.88061114203256,         \"RMSE\": 14.075280539927816,         \"R2\": 0.6748649197186086,         \"Memory in Mb\": 0.0452032089233398,         \"Time in s\": 1.0705630000000002       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.50078499680378,         \"RMSE\": 14.855892526018591,         \"R2\": 0.6858683144490312,         \"Memory in Mb\": 0.0452299118041992,         \"Time in s\": 1.1552540000000002       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.07078824210446,         \"RMSE\": 15.77018489177999,         \"R2\": 0.6847321098344714,         \"Memory in Mb\": 0.0452566146850585,         \"Time in s\": 1.2409280000000005       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.988840488902907,         \"RMSE\": 17.80174938329892,         \"R2\": 0.6354464447499208,         \"Memory in Mb\": 0.0452566146850585,         \"Time in s\": 1.3276340000000002       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.635092222175304,         \"RMSE\": 18.61329763011445,         \"R2\": 0.6589287557789436,         \"Memory in Mb\": 0.0452833175659179,         \"Time in s\": 1.4153970000000002       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.7817306308102,         \"RMSE\": 18.65165772134248,         \"R2\": 0.6933268021215234,         \"Memory in Mb\": 0.0452833175659179,         \"Time in s\": 1.5042310000000003       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.878812775671824,         \"RMSE\": 18.699040402285984,         \"R2\": 0.7198024587207095,         \"Memory in Mb\": 0.0452833175659179,         \"Time in s\": 1.5941090000000002       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.712200605470676,         \"RMSE\": 19.934033697107445,         \"R2\": 0.690787203614232,         \"Memory in Mb\": 0.0453100204467773,         \"Time in s\": 1.685007       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.202927457133043,         \"RMSE\": 20.9603625224819,         \"R2\": 0.6857237785454591,         \"Memory in Mb\": 0.0453367233276367,         \"Time in s\": 1.776946       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.5542070698499,         \"RMSE\": 21.51079994203591,         \"R2\": 0.707149447507495,         \"Memory in Mb\": 0.0453367233276367,         \"Time in s\": 1.869942       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.642433072457155,         \"RMSE\": 21.454130101613703,         \"R2\": 0.7283852775805406,         \"Memory in Mb\": 0.0453367233276367,         \"Time in s\": 1.963987       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.50232093628697,         \"RMSE\": 22.86556238504221,         \"R2\": 0.7132152539462153,         \"Memory in Mb\": 0.0456762313842773,         \"Time in s\": 2.060696       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.245933470432924,         \"RMSE\": 24.220098655355127,         \"R2\": 0.6979521608965717,         \"Memory in Mb\": 0.045729637145996,         \"Time in s\": 2.158386       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.766409258920858,         \"RMSE\": 25.08619072251902,         \"R2\": 0.7120527209837302,         \"Memory in Mb\": 0.045729637145996,         \"Time in s\": 2.2570650000000003       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.931210335947624,         \"RMSE\": 25.166941851240068,         \"R2\": 0.7307081986676882,         \"Memory in Mb\": 0.0456495285034179,         \"Time in s\": 2.4439120000000005       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.418312975299003,         \"RMSE\": 25.73673973791796,         \"R2\": 0.7303482652633313,         \"Memory in Mb\": 0.0461797714233398,         \"Time in s\": 2.6336030000000004       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.4982370763817,         \"RMSE\": 27.78944281741256,         \"R2\": 0.7047111429028308,         \"Memory in Mb\": 0.0469236373901367,         \"Time in s\": 2.8262370000000003       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.254684132762545,         \"RMSE\": 29.056725346353637,         \"R2\": 0.7149358826261665,         \"Memory in Mb\": 0.0469770431518554,         \"Time in s\": 3.0200670000000005       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.58513038702809,         \"RMSE\": 29.35463525495672,         \"R2\": 0.7250038129485413,         \"Memory in Mb\": 0.046950340270996,         \"Time in s\": 3.2149970000000003       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.01404260598322,         \"RMSE\": 29.86038018890717,         \"R2\": 0.7323226450377984,         \"Memory in Mb\": 0.0468969345092773,         \"Time in s\": 3.430483       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.88342353555136,         \"RMSE\": 31.26600741511644,         \"R2\": 0.7150584356224581,         \"Memory in Mb\": 0.0469770431518554,         \"Time in s\": 3.648791       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.595063111922972,         \"RMSE\": 32.24616798680886,         \"R2\": 0.713982273554131,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 3.869932       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.38047446701005,         \"RMSE\": 33.43504054753495,         \"R2\": 0.7235347994633756,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 4.098743       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.53249764026729,         \"RMSE\": 33.42135235584957,         \"R2\": 0.735168024057878,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 4.328606       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.49918784329445,         \"RMSE\": 35.002118414433774,         \"R2\": 0.7184757368310433,         \"Memory in Mb\": 0.0470304489135742,         \"Time in s\": 4.559521999999999       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.19163412189557,         \"RMSE\": 35.912468657285935,         \"R2\": 0.7166015220750928,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 4.791517       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.04065682138389,         \"RMSE\": 37.100860859043735,         \"R2\": 0.7202195492645626,         \"Memory in Mb\": 0.046950340270996,         \"Time in s\": 5.02459       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.19431912937701,         \"RMSE\": 37.21658551958108,         \"R2\": 0.7253190778127725,         \"Memory in Mb\": 0.0469770431518554,         \"Time in s\": 5.258551       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.695184981652336,         \"RMSE\": 9.807184976514188,         \"R2\": -224.6021011118197,         \"Memory in Mb\": 0.0538091659545898,         \"Time in s\": 0.004347       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3994713447037435,         \"RMSE\": 7.102066178895935,         \"R2\": -19.27845129783118,         \"Memory in Mb\": 0.0761518478393554,         \"Time in s\": 0.011776       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8170744682035584,         \"RMSE\": 5.815253847056423,         \"R2\": -17.329373299766118,         \"Memory in Mb\": 0.0883970260620117,         \"Time in s\": 0.021496       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.604995404573344,         \"RMSE\": 5.081770494168446,         \"R2\": -13.040545957103586,         \"Memory in Mb\": 0.0980443954467773,         \"Time in s\": 0.033628       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.824259078948539,         \"RMSE\": 4.70488333223354,         \"R2\": -6.5512954222403845,         \"Memory in Mb\": 0.1071348190307617,         \"Time in s\": 0.048339       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.918744608116588,         \"RMSE\": 4.412336880489357,         \"R2\": -4.634185300646759,         \"Memory in Mb\": 0.1113233566284179,         \"Time in s\": 0.066047       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8761207739327503,         \"RMSE\": 4.13187920011476,         \"R2\": -4.105616799680584,         \"Memory in Mb\": 0.1133375167846679,         \"Time in s\": 0.086317       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.961232939518506,         \"RMSE\": 3.976173487274506,         \"R2\": -3.1695661963674864,         \"Memory in Mb\": 0.1174459457397461,         \"Time in s\": 0.109348       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.066134597500757,         \"RMSE\": 3.873731518767916,         \"R2\": -2.4756944369169624,         \"Memory in Mb\": 0.1194601058959961,         \"Time in s\": 0.13519       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.051125997923389,         \"RMSE\": 3.731810291394655,         \"R2\": -2.23527456693896,         \"Memory in Mb\": 0.017618179321289,         \"Time in s\": 0.169486       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0738811328897206,         \"RMSE\": 4.417664564856108,         \"R2\": -3.890594467356201,         \"Memory in Mb\": 0.0357999801635742,         \"Time in s\": 0.205189       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9726100065438288,         \"RMSE\": 4.237524240975239,         \"R2\": -3.5337340888030546,         \"Memory in Mb\": 0.0414991378784179,         \"Time in s\": 0.242476       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8594315384151243,         \"RMSE\": 4.074751007989252,         \"R2\": -3.248610147038553,         \"Memory in Mb\": 0.048842430114746,         \"Time in s\": 0.281406       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7773205119132678,         \"RMSE\": 3.936654153117972,         \"R2\": -3.1518424972300867,         \"Memory in Mb\": 0.0637884140014648,         \"Time in s\": 0.322149       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8265705896173516,         \"RMSE\": 3.8591002097544127,         \"R2\": -2.923813511442849,         \"Memory in Mb\": 0.0734968185424804,         \"Time in s\": 0.364943       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7442837931334845,         \"RMSE\": 3.739506488697679,         \"R2\": -2.866813933026025,         \"Memory in Mb\": 0.0810766220092773,         \"Time in s\": 0.409782       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6994316865849048,         \"RMSE\": 3.638004990484729,         \"R2\": -2.8674589929341425,         \"Memory in Mb\": 0.0861921310424804,         \"Time in s\": 0.456846       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6868885299887,         \"RMSE\": 3.55458556923881,         \"R2\": -2.7224500036418355,         \"Memory in Mb\": 0.0937795639038086,         \"Time in s\": 0.506202       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.637461983479605,         \"RMSE\": 3.464628975063406,         \"R2\": -2.658760364179245,         \"Memory in Mb\": 0.0988950729370117,         \"Time in s\": 0.560423       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.622197889515682,         \"RMSE\": 3.392154183911459,         \"R2\": -2.6064142473473755,         \"Memory in Mb\": 0.1061124801635742,         \"Time in s\": 0.624493       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6252883623828789,         \"RMSE\": 3.33131196963583,         \"R2\": -2.593313247083074,         \"Memory in Mb\": 0.1101140975952148,         \"Time in s\": 0.691125       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.663593439145693,         \"RMSE\": 3.2993129689970107,         \"R2\": -2.4608371725208844,         \"Memory in Mb\": 0.1157598495483398,         \"Time in s\": 0.760369       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6928806013876203,         \"RMSE\": 3.26900202016339,         \"R2\": -2.221881423949668,         \"Memory in Mb\": 0.1238431930541992,         \"Time in s\": 0.832438       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6463369530072471,         \"RMSE\": 3.2036213976345094,         \"R2\": -2.023106408032965,         \"Memory in Mb\": 0.1315031051635742,         \"Time in s\": 0.907505       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6312675040418116,         \"RMSE\": 3.1569789450171624,         \"R2\": -1.8741285299844173,         \"Memory in Mb\": 0.0784368515014648,         \"Time in s\": 0.99002       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6486177246548734,         \"RMSE\": 3.1232792518100463,         \"R2\": -1.81800645719813,         \"Memory in Mb\": 0.0835790634155273,         \"Time in s\": 1.082168       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.664948820150162,         \"RMSE\": 3.091452157271598,         \"R2\": -1.7507490735781142,         \"Memory in Mb\": 0.0873861312866211,         \"Time in s\": 1.179534       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6361907885919602,         \"RMSE\": 3.043459997537018,         \"R2\": -1.7295303491345493,         \"Memory in Mb\": 0.0885534286499023,         \"Time in s\": 1.466782       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6082012495575049,         \"RMSE\": 2.9965453347231947,         \"R2\": -1.7114709556760634,         \"Memory in Mb\": 0.0890569686889648,         \"Time in s\": 1.757406       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.622569336171024,         \"RMSE\": 2.97009213510141,         \"R2\": -1.634341750696236,         \"Memory in Mb\": 0.0909147262573242,         \"Time in s\": 2.050417       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.636890396487252,         \"RMSE\": 2.946158197159977,         \"R2\": -1.5525460315178896,         \"Memory in Mb\": 0.0923452377319336,         \"Time in s\": 2.345765       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.652159107256621,         \"RMSE\": 2.9245287804119107,         \"R2\": -1.4681901897894076,         \"Memory in Mb\": 0.0944395065307617,         \"Time in s\": 2.643466       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6570267761004454,         \"RMSE\": 2.8972896524900835,         \"R2\": -1.4050084478390592,         \"Memory in Mb\": 0.0960302352905273,         \"Time in s\": 2.943569       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6362052297782712,         \"RMSE\": 2.859601997032609,         \"R2\": -1.379870428705038,         \"Memory in Mb\": 0.0981245040893554,         \"Time in s\": 3.2460560000000003       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.608205636538717,         \"RMSE\": 2.821326923745488,         \"R2\": -1.377433396876134,         \"Memory in Mb\": 0.1015691757202148,         \"Time in s\": 3.5543920000000004       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5855230254631891,         \"RMSE\": 2.785659545407005,         \"R2\": -1.3686218528413674,         \"Memory in Mb\": 0.1027364730834961,         \"Time in s\": 3.875571       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.583695771004626,         \"RMSE\": 2.7597111871599203,         \"R2\": -1.3233016566851918,         \"Memory in Mb\": 0.1038503646850586,         \"Time in s\": 4.202987       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5704020318609786,         \"RMSE\": 2.7290361106702816,         \"R2\": -1.2965538228485634,         \"Memory in Mb\": 0.1038503646850586,         \"Time in s\": 4.532954       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5638796853366008,         \"RMSE\": 2.702190403614744,         \"R2\": -1.2616800152467116,         \"Memory in Mb\": 0.1064214706420898,         \"Time in s\": 4.876142       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5494799828615766,         \"RMSE\": 2.674411214594314,         \"R2\": -1.2354538504080876,         \"Memory in Mb\": 0.1070318222045898,         \"Time in s\": 5.221917       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.533437809889996,         \"RMSE\": 2.6465115200139584,         \"R2\": -1.213096407446464,         \"Memory in Mb\": 0.1085958480834961,         \"Time in s\": 5.570086999999999       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5202839319169328,         \"RMSE\": 2.6201051582792827,         \"R2\": -1.189291971541785,         \"Memory in Mb\": 0.1101598739624023,         \"Time in s\": 5.920738999999999       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5178574341866524,         \"RMSE\": 2.5988091386120904,         \"R2\": -1.1501373691585313,         \"Memory in Mb\": 0.1107702255249023,         \"Time in s\": 6.280333       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4962844530295305,         \"RMSE\": 2.571223801389781,         \"R2\": -1.094275733877604,         \"Memory in Mb\": 0.1112470626831054,         \"Time in s\": 6.652339       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4724252749133646,         \"RMSE\": 2.5436398469986066,         \"R2\": -1.0582196084183888,         \"Memory in Mb\": 0.1116437911987304,         \"Time in s\": 7.031402       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4596881679466962,         \"RMSE\": 2.5220256913044325,         \"R2\": -1.056635177134157,         \"Memory in Mb\": 0.1116704940795898,         \"Time in s\": 7.413564       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.452139596196528,         \"RMSE\": 2.5028075284250018,         \"R2\": -1.0425932823285438,         \"Memory in Mb\": 0.1127042770385742,         \"Time in s\": 7.802334       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4364147887122178,         \"RMSE\": 2.481230554777158,         \"R2\": -1.0285162299402342,         \"Memory in Mb\": 0.1132078170776367,         \"Time in s\": 8.193766       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4186260884044517,         \"RMSE\": 2.45780687839372,         \"R2\": -1.029053861068545,         \"Memory in Mb\": 0.1138181686401367,         \"Time in s\": 8.587828       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3997779646996389,         \"RMSE\": 2.434572696055838,         \"R2\": -1.024386017127401,         \"Memory in Mb\": 0.1144285202026367,         \"Time in s\": 8.984547       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3984653255896196,         \"RMSE\": 2.433357833975862,         \"R2\": -1.0237164038272608,         \"Memory in Mb\": 0.1144285202026367,         \"Time in s\": 9.38293       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.674710287324511,         \"RMSE\": 12.709622005759083,         \"R2\": -206.93269654300337,         \"Memory in Mb\": 0.1438665390014648,         \"Time in s\": 0.053261       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.741934273684416,         \"RMSE\": 9.017856101646904,         \"R2\": -85.12629469646626,         \"Memory in Mb\": 0.1680784225463867,         \"Time in s\": 0.14276       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.35434760029741,         \"RMSE\": 7.430504888974863,         \"R2\": -37.34585890537725,         \"Memory in Mb\": 0.2096052169799804,         \"Time in s\": 0.266148       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.9327820011330463,         \"RMSE\": 6.452362261246447,         \"R2\": -31.77814024428305,         \"Memory in Mb\": 0.2417478561401367,         \"Time in s\": 0.646479       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.2606373648191784,         \"RMSE\": 6.146136842066936,         \"R2\": -6.374120366305681,         \"Memory in Mb\": 0.3060827255249023,         \"Time in s\": 1.057843       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3521495161457717,         \"RMSE\": 5.750947689984691,         \"R2\": -1.7831107407377038,         \"Memory in Mb\": 0.3567266464233398,         \"Time in s\": 1.521792       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.707478618787897,         \"RMSE\": 5.832856917221716,         \"R2\": -0.9188552689556648,         \"Memory in Mb\": 0.3732900619506836,         \"Time in s\": 2.259814       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.60389034076398,         \"RMSE\": 5.525482549715508,         \"R2\": -0.5612341217350767,         \"Memory in Mb\": 0.4128637313842773,         \"Time in s\": 3.03269       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7646559934763437,         \"RMSE\": 5.466320467144536,         \"R2\": -0.2779732207399938,         \"Memory in Mb\": 0.4623746871948242,         \"Time in s\": 3.85418       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.880719733615897,         \"RMSE\": 5.407041915578862,         \"R2\": 0.1268195431914148,         \"Memory in Mb\": 0.5318593978881836,         \"Time in s\": 4.717509000000001       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.0896780011355176,         \"RMSE\": 5.466874267225462,         \"R2\": 0.3125459405386915,         \"Memory in Mb\": 0.5604543685913086,         \"Time in s\": 5.631386000000001       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.270943191870578,         \"RMSE\": 5.7618521847151385,         \"R2\": 0.3758777549527384,         \"Memory in Mb\": 0.1946859359741211,         \"Time in s\": 6.649151000000001       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.24701597703502,         \"RMSE\": 5.633009027852055,         \"R2\": 0.5168368848346436,         \"Memory in Mb\": 0.2288389205932617,         \"Time in s\": 7.703019000000001       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.2192007860807728,         \"RMSE\": 5.520141144427338,         \"R2\": 0.6056681999848637,         \"Memory in Mb\": 0.2577199935913086,         \"Time in s\": 8.869892000000002       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.5165819956804767,         \"RMSE\": 5.874797514643079,         \"R2\": 0.6409683688760216,         \"Memory in Mb\": 0.2627325057983398,         \"Time in s\": 10.065309000000005       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.700430602978386,         \"RMSE\": 6.068185859760413,         \"R2\": 0.6911390854727877,         \"Memory in Mb\": 0.2941198348999023,         \"Time in s\": 11.324773000000002       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.803730902742884,         \"RMSE\": 6.1218084380222,         \"R2\": 0.7426259865968339,         \"Memory in Mb\": 0.3320951461791992,         \"Time in s\": 12.629055000000005       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.006649900662983,         \"RMSE\": 6.578339511639692,         \"R2\": 0.7635979888713487,         \"Memory in Mb\": 0.2527418136596679,         \"Time in s\": 13.981910000000005       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.229383118423565,         \"RMSE\": 6.982583803939909,         \"R2\": 0.780430075854037,         \"Memory in Mb\": 0.3202161788940429,         \"Time in s\": 15.391493000000002       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.825249759558611,         \"RMSE\": 8.423350501384354,         \"R2\": 0.720252540483964,         \"Memory in Mb\": 0.3510808944702148,         \"Time in s\": 16.880127       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.088028806665401,         \"RMSE\": 8.669832171958772,         \"R2\": 0.7465054715218886,         \"Memory in Mb\": 0.3200139999389648,         \"Time in s\": 18.399834       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.462442991686406,         \"RMSE\": 9.175585237136575,         \"R2\": 0.7715339230013022,         \"Memory in Mb\": 0.3710927963256836,         \"Time in s\": 20.020863       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.563619467556412,         \"RMSE\": 9.260101660572657,         \"R2\": 0.797901929856006,         \"Memory in Mb\": 0.4192609786987304,         \"Time in s\": 21.677784000000003       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.261116867150435,         \"RMSE\": 10.599777327157994,         \"R2\": 0.7580584961853923,         \"Memory in Mb\": 0.3416013717651367,         \"Time in s\": 23.387217000000003       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.742618468073929,         \"RMSE\": 11.80224059778972,         \"R2\": 0.7360625543191792,         \"Memory in Mb\": 0.3569021224975586,         \"Time in s\": 25.127202000000004       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.039594962415952,         \"RMSE\": 12.249488193444416,         \"R2\": 0.7537446936710837,         \"Memory in Mb\": 0.3965520858764648,         \"Time in s\": 26.911059000000005       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.148800229885712,         \"RMSE\": 12.311677740983953,         \"R2\": 0.7842510327710687,         \"Memory in Mb\": 0.4258260726928711,         \"Time in s\": 28.739538000000007       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.753683144422786,         \"RMSE\": 13.244190950829555,         \"R2\": 0.7776398094561477,         \"Memory in Mb\": 0.4501142501831054,         \"Time in s\": 30.59385600000001       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.773143666519827,         \"RMSE\": 15.93975365978218,         \"R2\": 0.7077199430319765,         \"Memory in Mb\": 0.4718656539916992,         \"Time in s\": 32.508295000000004       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.312574124937234,         \"RMSE\": 16.796832021919023,         \"R2\": 0.7222505421616592,         \"Memory in Mb\": 0.3236379623413086,         \"Time in s\": 34.514388000000004       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.544591695703463,         \"RMSE\": 16.94083213248977,         \"R2\": 0.7470058810264122,         \"Memory in Mb\": 0.3676939010620117,         \"Time in s\": 36.550995       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.680039805071171,         \"RMSE\": 17.006622056031052,         \"R2\": 0.7682275557667291,         \"Memory in Mb\": 0.3629522323608398,         \"Time in s\": 38.648436       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.417098847501563,         \"RMSE\": 18.381838377902795,         \"R2\": 0.7370670774420947,         \"Memory in Mb\": 0.3571195602416992,         \"Time in s\": 40.771216       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.080869197293334,         \"RMSE\": 19.965812195666537,         \"R2\": 0.7148404591067161,         \"Memory in Mb\": 0.3998785018920898,         \"Time in s\": 42.937715       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.60940210623338,         \"RMSE\": 20.687378926969966,         \"R2\": 0.7291406299077132,         \"Memory in Mb\": 0.3288450241088867,         \"Time in s\": 45.213373       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.737814918904208,         \"RMSE\": 20.678726756260627,         \"R2\": 0.7476640805491588,         \"Memory in Mb\": 0.4024953842163086,         \"Time in s\": 47.506899       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.682108791666492,         \"RMSE\": 22.361726245252385,         \"R2\": 0.7257144517605874,         \"Memory in Mb\": 0.4475545883178711,         \"Time in s\": 49.84079500000001       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.622708961705229,         \"RMSE\": 24.146094170569185,         \"R2\": 0.6997951546356598,         \"Memory in Mb\": 0.4968290328979492,         \"Time in s\": 52.212878       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.165217959113354,         \"RMSE\": 24.88032134675199,         \"R2\": 0.7167593971826183,         \"Memory in Mb\": 0.5376424789428711,         \"Time in s\": 54.630802       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.411006646174876,         \"RMSE\": 24.97835315387625,         \"R2\": 0.7347289581181171,         \"Memory in Mb\": 0.5657072067260742,         \"Time in s\": 57.077112       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.766578325445964,         \"RMSE\": 25.376772271610328,         \"R2\": 0.7378384948653763,         \"Memory in Mb\": 0.2583265304565429,         \"Time in s\": 59.570857       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.09445226127713,         \"RMSE\": 28.12961105819035,         \"R2\": 0.6974376845026461,         \"Memory in Mb\": 0.3047628402709961,         \"Time in s\": 62.101038       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.916275460891086,         \"RMSE\": 29.341089843915015,         \"R2\": 0.7093290035354769,         \"Memory in Mb\": 0.3544912338256836,         \"Time in s\": 64.67061       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.222566694739786,         \"RMSE\": 29.549549967606488,         \"R2\": 0.7213397403469026,         \"Memory in Mb\": 0.3983259201049804,         \"Time in s\": 67.26974899999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.854950072386483,         \"RMSE\": 30.34354672604944,         \"R2\": 0.7235900637963901,         \"Memory in Mb\": 0.4324254989624023,         \"Time in s\": 69.89740799999998       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.84874733203415,         \"RMSE\": 31.79966974813451,         \"R2\": 0.7052484004379906,         \"Memory in Mb\": 0.4678411483764648,         \"Time in s\": 72.66954099999998       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.785853660032195,         \"RMSE\": 33.20181112471305,         \"R2\": 0.6967783021275082,         \"Memory in Mb\": 0.4975500106811523,         \"Time in s\": 75.49558399999998       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.52664258005787,         \"RMSE\": 34.100310164439925,         \"R2\": 0.7124234805234935,         \"Memory in Mb\": 0.5351285934448242,         \"Time in s\": 78.36005299999998       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.766026265849117,         \"RMSE\": 34.21097619783695,         \"R2\": 0.7225061795517687,         \"Memory in Mb\": 0.576685905456543,         \"Time in s\": 81.26779099999997       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.840503815170692,         \"RMSE\": 36.15896607268933,         \"R2\": 0.6995590139862528,         \"Memory in Mb\": 0.4753904342651367,         \"Time in s\": 84.25568299999998       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.59690624313325,         \"RMSE\": 37.108967777985264,         \"R2\": 0.6974029137241997,         \"Memory in Mb\": 0.5189352035522461,         \"Time in s\": 87.27382099999997       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.534320250737128,         \"RMSE\": 38.28067851879141,         \"R2\": 0.7021424273708647,         \"Memory in Mb\": 0.5585355758666992,         \"Time in s\": 90.32555299999996       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.709683411591413,         \"RMSE\": 38.44162901827647,         \"R2\": 0.7069383356385298,         \"Memory in Mb\": 0.3551816940307617,         \"Time in s\": 93.40141099999995       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.677140920600926,         \"RMSE\": 9.804891856735376,         \"R2\": -224.4966127051096,         \"Memory in Mb\": 0.2373647689819336,         \"Time in s\": 0.078317       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.42676306487335,         \"RMSE\": 7.150663284447028,         \"R2\": -19.556918338481843,         \"Memory in Mb\": 0.3270711898803711,         \"Time in s\": 0.2600639999999999       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8116457742622056,         \"RMSE\": 5.852230884873156,         \"R2\": -17.563213740222555,         \"Memory in Mb\": 0.3493108749389648,         \"Time in s\": 0.628767       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5261658032230545,         \"RMSE\": 5.084894428469453,         \"R2\": -13.057813649460384,         \"Memory in Mb\": 0.3968191146850586,         \"Time in s\": 1.163603       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.404885103917059,         \"RMSE\": 4.580518627305071,         \"R2\": -6.157363117938322,         \"Memory in Mb\": 0.4107885360717773,         \"Time in s\": 1.7740749999999998       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2872329076385731,         \"RMSE\": 4.198963935277897,         \"R2\": -4.102442140657352,         \"Memory in Mb\": 0.4562673568725586,         \"Time in s\": 2.455989       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4191481295394186,         \"RMSE\": 4.9019146331166,         \"R2\": -6.185954638838571,         \"Memory in Mb\": 0.2026891708374023,         \"Time in s\": 3.22825       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.329290869551211,         \"RMSE\": 4.594852312450113,         \"R2\": -4.568052693162012,         \"Memory in Mb\": 0.3215742111206054,         \"Time in s\": 4.121231       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2559271503392595,         \"RMSE\": 4.341680890984575,         \"R2\": -3.3661470093978725,         \"Memory in Mb\": 0.4017667770385742,         \"Time in s\": 5.147797       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.169313410896163,         \"RMSE\": 4.12134361195162,         \"R2\": -2.94593273053925,         \"Memory in Mb\": 0.4772901535034179,         \"Time in s\": 6.338072       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1066399346497042,         \"RMSE\": 3.9344660517514094,         \"R2\": -2.8792501333638807,         \"Memory in Mb\": 0.5200605392456055,         \"Time in s\": 7.615136       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0535228972416335,         \"RMSE\": 3.7704097053754206,         \"R2\": -2.589291599563797,         \"Memory in Mb\": 0.590418815612793,         \"Time in s\": 8.98983       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0002808672832586,         \"RMSE\": 3.624331376507975,         \"R2\": -2.3612477765650133,         \"Memory in Mb\": 0.677699089050293,         \"Time in s\": 10.49221       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9484528900796008,         \"RMSE\": 3.4935839886686573,         \"R2\": -2.269856713922535,         \"Memory in Mb\": 0.7459287643432617,         \"Time in s\": 12.09809       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9319658343242508,         \"RMSE\": 3.3810597344709987,         \"R2\": -2.011909605217061,         \"Memory in Mb\": 0.8300580978393555,         \"Time in s\": 13.828036       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9015525068993324,         \"RMSE\": 3.2761126415748776,         \"R2\": -1.9678527090537403,         \"Memory in Mb\": 0.8134641647338867,         \"Time in s\": 15.697273999999998       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9086073156973856,         \"RMSE\": 3.206516550071244,         \"R2\": -2.0044577988421626,         \"Memory in Mb\": 0.7938528060913086,         \"Time in s\": 17.678026       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9209686764414104,         \"RMSE\": 3.130698586248577,         \"R2\": -1.887576115168536,         \"Memory in Mb\": 0.8660383224487305,         \"Time in s\": 19.789752       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9054594388814018,         \"RMSE\": 3.0518145886207013,         \"R2\": -1.838813023440576,         \"Memory in Mb\": 0.930495262145996,         \"Time in s\": 22.025823       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9021459083449618,         \"RMSE\": 2.9892737243691805,         \"R2\": -1.8006304820861794,         \"Memory in Mb\": 0.9046812057495116,         \"Time in s\": 24.377516       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9000900027115483,         \"RMSE\": 2.937103674242639,         \"R2\": -1.7932063526136273,         \"Memory in Mb\": 0.2984609603881836,         \"Time in s\": 26.835964       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.884833385913356,         \"RMSE\": 2.873027664171451,         \"R2\": -1.6243016536608597,         \"Memory in Mb\": 0.3453207015991211,         \"Time in s\": 29.357596       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8690754265879537,         \"RMSE\": 2.8131168800056585,         \"R2\": -1.3859136859847618,         \"Memory in Mb\": 0.3807516098022461,         \"Time in s\": 31.984181       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8473225380080763,         \"RMSE\": 2.75510952719956,         \"R2\": -1.235881587238783,         \"Memory in Mb\": 0.4482488632202148,         \"Time in s\": 34.68911       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8286186581223807,         \"RMSE\": 2.701061117260836,         \"R2\": -1.1039316995995572,         \"Memory in Mb\": 0.4975194931030273,         \"Time in s\": 37.472375       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8308331247066605,         \"RMSE\": 2.676087993829765,         \"R2\": -1.0688124961584973,         \"Memory in Mb\": 0.3753881454467773,         \"Time in s\": 40.349897       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8063786739892863,         \"RMSE\": 2.6263308571208617,         \"R2\": -0.9852938090834252,         \"Memory in Mb\": 0.4063673019409179,         \"Time in s\": 43.316687       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7997929461413226,         \"RMSE\": 2.582727501713279,         \"R2\": -0.9656668153374994,         \"Memory in Mb\": 0.4374494552612304,         \"Time in s\": 46.3679       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7908850979728871,         \"RMSE\": 2.541457165367321,         \"R2\": -0.950423138286411,         \"Memory in Mb\": 0.4967718124389648,         \"Time in s\": 49.479346       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7789627943481009,         \"RMSE\": 2.500361162030882,         \"R2\": -0.8669718089652279,         \"Memory in Mb\": 0.569575309753418,         \"Time in s\": 52.654549       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7682254429218135,         \"RMSE\": 2.461677332861117,         \"R2\": -0.7820656947310429,         \"Memory in Mb\": 0.6584272384643555,         \"Time in s\": 55.885269       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.756836908225871,         \"RMSE\": 2.4246570785119217,         \"R2\": -0.6965531574296129,         \"Memory in Mb\": 0.7120962142944336,         \"Time in s\": 59.181412       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7406340846119412,         \"RMSE\": 2.388088765643962,         \"R2\": -0.6339309775627988,         \"Memory in Mb\": 0.8089780807495117,         \"Time in s\": 62.54643       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7257657440750075,         \"RMSE\": 2.3532857176647086,         \"R2\": -0.6117268738432657,         \"Memory in Mb\": 0.8398160934448242,         \"Time in s\": 65.979515       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7284794894639326,         \"RMSE\": 2.325029779726661,         \"R2\": -0.6145762958857721,         \"Memory in Mb\": 0.9275884628295898,         \"Time in s\": 69.48963400000001       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7231955460113827,         \"RMSE\": 2.297270435922827,         \"R2\": -0.6108826519065647,         \"Memory in Mb\": 0.9087285995483398,         \"Time in s\": 73.071921       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7217944839950929,         \"RMSE\": 2.2699024953355416,         \"R2\": -0.5717835473178023,         \"Memory in Mb\": 0.8719320297241211,         \"Time in s\": 76.723943       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7121024512438853,         \"RMSE\": 2.241058668519108,         \"R2\": -0.5486900768629306,         \"Memory in Mb\": 0.929518699645996,         \"Time in s\": 80.452493       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7019422114012909,         \"RMSE\": 2.213016497735788,         \"R2\": -0.5169405784918113,         \"Memory in Mb\": 1.0446271896362305,         \"Time in s\": 84.252351       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7005931807120314,         \"RMSE\": 2.188428332233621,         \"R2\": -0.4968352306391974,         \"Memory in Mb\": 1.1031560897827148,         \"Time in s\": 88.13395700000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6997484436891046,         \"RMSE\": 2.169363820936814,         \"R2\": -0.4870225063787143,         \"Memory in Mb\": 1.0328702926635742,         \"Time in s\": 92.08914500000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6949195885567419,         \"RMSE\": 2.14957176369946,         \"R2\": -0.4735678496288336,         \"Memory in Mb\": 0.7475957870483398,         \"Time in s\": 96.10836100000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6920590805093112,         \"RMSE\": 2.1264870362465933,         \"R2\": -0.439603572136809,         \"Memory in Mb\": 0.8119535446166992,         \"Time in s\": 100.182244       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6882027439938415,         \"RMSE\": 2.1038322601427426,         \"R2\": -0.4020913886240724,         \"Memory in Mb\": 0.8655576705932617,         \"Time in s\": 104.313167       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6818594219129391,         \"RMSE\": 2.085410616994055,         \"R2\": -0.38345052454273,         \"Memory in Mb\": 0.8467855453491211,         \"Time in s\": 108.503439       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6756248333192205,         \"RMSE\": 2.0637081851469703,         \"R2\": -0.377066205838211,         \"Memory in Mb\": 0.9400205612182616,         \"Time in s\": 112.753186       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6689624136970388,         \"RMSE\": 2.0428592411141837,         \"R2\": -0.3608300191067024,         \"Memory in Mb\": 0.8168668746948242,         \"Time in s\": 117.061539       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6627773066160889,         \"RMSE\": 2.022520414368002,         \"R2\": -0.3478143420231987,         \"Memory in Mb\": 0.9120321273803712,         \"Time in s\": 121.43268700000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6600305544016135,         \"RMSE\": 2.003593726688123,         \"R2\": -0.348395800771305,         \"Memory in Mb\": 0.9782476425170898,         \"Time in s\": 125.86239700000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.657029407021691,         \"RMSE\": 1.9853014454830336,         \"R2\": -0.3461726175729922,         \"Memory in Mb\": 1.0593442916870115,         \"Time in s\": 130.37428000000003       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6566965628025029,         \"RMSE\": 1.984335939466105,         \"R2\": -0.3457614763231473,         \"Memory in Mb\": 1.0646085739135742,         \"Time in s\": 134.90266400000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.95559056599966,         \"RMSE\": 17.7409835250609,         \"R2\": -404.147256051216,         \"Memory in Mb\": 0.1553668975830078,         \"Time in s\": 0.005094       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.88626580700965,         \"RMSE\": 12.566688603347808,         \"R2\": -166.25182631838038,         \"Memory in Mb\": 0.1681652069091797,         \"Time in s\": 0.018278       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.383857039198176,         \"RMSE\": 10.299865918219764,         \"R2\": -72.67921052893462,         \"Memory in Mb\": 0.2052059173583984,         \"Time in s\": 0.039075       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.446496162870555,         \"RMSE\": 8.931116231999566,         \"R2\": -61.79980671874969,         \"Memory in Mb\": 0.2209186553955078,         \"Time in s\": 0.065167       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.3513349782155037,         \"RMSE\": 8.247717183177938,         \"R2\": -12.279242202465667,         \"Memory in Mb\": 0.2687969207763672,         \"Time in s\": 0.096566       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.889627188952696,         \"RMSE\": 8.0458642201752,         \"R2\": -4.4474976461238604,         \"Memory in Mb\": 0.3383464813232422,         \"Time in s\": 0.144605       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.337751636727128,         \"RMSE\": 7.9681159743419645,         \"R2\": -2.5808890563388096,         \"Memory in Mb\": 0.3940753936767578,         \"Time in s\": 0.201306       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.489908334389532,         \"RMSE\": 7.740787033322287,         \"R2\": -2.0640641214272355,         \"Memory in Mb\": 0.4405231475830078,         \"Time in s\": 0.274421       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.7831270806190425,         \"RMSE\": 7.705843596650206,         \"R2\": -1.5396388125269618,         \"Memory in Mb\": 0.4615802764892578,         \"Time in s\": 0.372843       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.73395080514245,         \"RMSE\": 7.47334250555501,         \"R2\": -0.6680701376440403,         \"Memory in Mb\": 0.4910602569580078,         \"Time in s\": 0.5845670000000001       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.733710015085173,         \"RMSE\": 7.331306378435282,         \"R2\": -0.236312465025352,         \"Memory in Mb\": 0.5042209625244141,         \"Time in s\": 0.806575       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.565752852065114,         \"RMSE\": 7.0976416640915465,         \"R2\": 0.0529485447239657,         \"Memory in Mb\": 0.5126438140869141,         \"Time in s\": 1.039517       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.439022558662509,         \"RMSE\": 6.895745596080793,         \"R2\": 0.2759386934515202,         \"Memory in Mb\": 0.5216274261474609,         \"Time in s\": 1.284562       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.362170284876481,         \"RMSE\": 6.736533340066285,         \"R2\": 0.4127346685162743,         \"Memory in Mb\": 0.5262050628662109,         \"Time in s\": 1.543379       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.647894929983432,         \"RMSE\": 7.008861526290804,         \"R2\": 0.4889753297409128,         \"Memory in Mb\": 0.5290126800537109,         \"Time in s\": 1.82575       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.817744211127824,         \"RMSE\": 7.136288548419971,         \"R2\": 0.5728405597677722,         \"Memory in Mb\": 0.5066156387329102,         \"Time in s\": 2.145522       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.867036081233358,         \"RMSE\": 7.152118590173611,         \"R2\": 0.6487028411390494,         \"Memory in Mb\": 0.4790925979614258,         \"Time in s\": 2.491984       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.887061675652274,         \"RMSE\": 7.15502256260352,         \"R2\": 0.720333392468735,         \"Memory in Mb\": 0.3514680862426758,         \"Time in s\": 2.944156       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.070260383978678,         \"RMSE\": 7.484020932149266,         \"R2\": 0.7477619935177958,         \"Memory in Mb\": 0.3277406692504883,         \"Time in s\": 3.412471       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.671227628293087,         \"RMSE\": 8.602358503958763,         \"R2\": 0.7082361492582977,         \"Memory in Mb\": 0.3353548049926758,         \"Time in s\": 3.897275       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.872335352103937,         \"RMSE\": 8.83175934179542,         \"R2\": 0.7369479677711775,         \"Memory in Mb\": 0.372288703918457,         \"Time in s\": 4.389179       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.107145463120707,         \"RMSE\": 9.222510821361375,         \"R2\": 0.769191114739663,         \"Memory in Mb\": 0.3991250991821289,         \"Time in s\": 4.889463       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.19844823305091,         \"RMSE\": 9.33633324769997,         \"R2\": 0.7945607849348244,         \"Memory in Mb\": 0.4207086563110351,         \"Time in s\": 5.506109       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.823605404288741,         \"RMSE\": 10.586090935492884,         \"R2\": 0.758682880699561,         \"Memory in Mb\": 0.4333581924438476,         \"Time in s\": 6.131382       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.289576170484155,         \"RMSE\": 11.670233638164651,         \"R2\": 0.7419337665758028,         \"Memory in Mb\": 0.4418039321899414,         \"Time in s\": 6.777342       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.579012857443305,         \"RMSE\": 12.145524073459754,         \"R2\": 0.75790700185782,         \"Memory in Mb\": 0.4508142471313476,         \"Time in s\": 7.439278       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.564986803201262,         \"RMSE\": 12.135208564512553,         \"R2\": 0.7903915740986345,         \"Memory in Mb\": 0.4540948867797851,         \"Time in s\": 8.115354       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.103353916061925,         \"RMSE\": 13.02855032884451,         \"R2\": 0.7848217554522041,         \"Memory in Mb\": 0.4577798843383789,         \"Time in s\": 8.913327       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.2182891996096,         \"RMSE\": 15.75502466975724,         \"R2\": 0.7144552709875674,         \"Memory in Mb\": 0.4609994888305664,         \"Time in s\": 9.721339       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.685083231372472,         \"RMSE\": 16.400765556025647,         \"R2\": 0.7351946818510116,         \"Memory in Mb\": 0.4719209671020508,         \"Time in s\": 10.546217       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.903299441393282,         \"RMSE\": 16.527032528363478,         \"R2\": 0.759214288686034,         \"Memory in Mb\": 0.477086067199707,         \"Time in s\": 11.387382       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.047801743751696,         \"RMSE\": 16.62843798311862,         \"R2\": 0.778421004008311,         \"Memory in Mb\": 0.4848833084106445,         \"Time in s\": 12.279877       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.963674059851892,         \"RMSE\": 18.110346084278056,         \"R2\": 0.7447765461541069,         \"Memory in Mb\": 0.4873552322387695,         \"Time in s\": 13.19145       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.492835005144466,         \"RMSE\": 19.28081121430548,         \"R2\": 0.7340717056357994,         \"Memory in Mb\": 0.4739046096801758,         \"Time in s\": 14.120497000000002       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.898657720927194,         \"RMSE\": 19.94321338535613,         \"R2\": 0.7482768269892894,         \"Memory in Mb\": 0.4488801956176758,         \"Time in s\": 15.069160000000002       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.0617729851989,         \"RMSE\": 19.965773188137263,         \"R2\": 0.7647640178574115,         \"Memory in Mb\": 0.4161386489868164,         \"Time in s\": 16.114715       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.97304553348899,         \"RMSE\": 21.57136186484404,         \"R2\": 0.7447607843499935,         \"Memory in Mb\": 0.4060754776000976,         \"Time in s\": 17.182106       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.747411939847144,         \"RMSE\": 23.06575414024212,         \"R2\": 0.7260576137332548,         \"Memory in Mb\": 0.4295892715454101,         \"Time in s\": 18.259955       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.305030376306712,         \"RMSE\": 23.986335540211613,         \"R2\": 0.7367482003695625,         \"Memory in Mb\": 0.4628152847290039,         \"Time in s\": 19.347052       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.526268288674354,         \"RMSE\": 24.074522605416067,         \"R2\": 0.7535790611891788,         \"Memory in Mb\": 0.4899911880493164,         \"Time in s\": 20.478651000000003       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.027594657922048,         \"RMSE\": 24.671918598232004,         \"R2\": 0.7521995995009789,         \"Memory in Mb\": 0.5175485610961914,         \"Time in s\": 21.623166       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.208274238827823,         \"RMSE\": 27.03842360672758,         \"R2\": 0.7204560380476568,         \"Memory in Mb\": 0.5713090896606445,         \"Time in s\": 22.784126       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.99589357541462,         \"RMSE\": 28.364120175265192,         \"R2\": 0.7283636722963454,         \"Memory in Mb\": 0.5903940200805664,         \"Time in s\": 23.973479       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.304815327407063,         \"RMSE\": 28.547476213614512,         \"R2\": 0.739918935022724,         \"Memory in Mb\": 0.6084756851196289,         \"Time in s\": 25.225522       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.747173803776352,         \"RMSE\": 29.064129392830434,         \"R2\": 0.7464079684248853,         \"Memory in Mb\": 0.6325922012329102,         \"Time in s\": 26.517497       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.655435380807354,         \"RMSE\": 30.57773482627066,         \"R2\": 0.7274654471662664,         \"Memory in Mb\": 0.6401453018188477,         \"Time in s\": 27.826703       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.38212758204995,         \"RMSE\": 31.56694275275528,         \"R2\": 0.7259045847606268,         \"Memory in Mb\": 0.5900964736938477,         \"Time in s\": 29.161388       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.16255538075237,         \"RMSE\": 32.73116726334994,         \"R2\": 0.7350525453098611,         \"Memory in Mb\": 0.6013956069946289,         \"Time in s\": 30.57764       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.34377517847412,         \"RMSE\": 32.75647044736101,         \"R2\": 0.7456003073902285,         \"Memory in Mb\": 0.6148271560668945,         \"Time in s\": 32.006392000000005       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.397093652240404,         \"RMSE\": 34.43808497807088,         \"R2\": 0.7274757471078548,         \"Memory in Mb\": 0.6217546463012695,         \"Time in s\": 33.45442800000001       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.130535392790676,         \"RMSE\": 35.39551310036421,         \"R2\": 0.7247017706467436,         \"Memory in Mb\": 0.6181917190551758,         \"Time in s\": 34.916661000000005       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.97609679727041,         \"RMSE\": 36.53926451086616,         \"R2\": 0.7286255260499503,         \"Memory in Mb\": 0.6243486404418945,         \"Time in s\": 36.463466       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.114298050830318,         \"RMSE\": 36.631109645590286,         \"R2\": 0.7338934315030725,         \"Memory in Mb\": 0.6280336380004883,         \"Time in s\": 38.020312       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.57361785669815,         \"RMSE\": 13.877675781396096,         \"R2\": -450.7393063082519,         \"Memory in Mb\": 0.3875865936279297,         \"Time in s\": 0.030628       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.357601810962072,         \"RMSE\": 9.93598927447802,         \"R2\": -38.690592530050864,         \"Memory in Mb\": 0.5688495635986328,         \"Time in s\": 0.093522       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.120546196671925,         \"RMSE\": 8.124382016407804,         \"R2\": -34.775930157070896,         \"Memory in Mb\": 0.6946392059326172,         \"Time in s\": 0.180013       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5823668216656817,         \"RMSE\": 7.068571931029129,         \"R2\": -26.16547256881584,         \"Memory in Mb\": 0.7988948822021484,         \"Time in s\": 0.365225       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.6103510398716643,         \"RMSE\": 6.439797187103485,         \"R2\": -13.147122820254191,         \"Memory in Mb\": 0.9021625518798828,         \"Time in s\": 0.589181       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5653436103516496,         \"RMSE\": 5.96335184363353,         \"R2\": -9.29140495411716,         \"Memory in Mb\": 0.9421710968017578,         \"Time in s\": 0.842127       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4314692166818666,         \"RMSE\": 5.556159680491977,         \"R2\": -8.232140838080387,         \"Memory in Mb\": 0.9627094268798828,         \"Time in s\": 1.43468       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.270493582871441,         \"RMSE\": 5.217534738727647,         \"R2\": -6.179445803611509,         \"Memory in Mb\": 1.0016803741455078,         \"Time in s\": 2.055929       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1841879014169865,         \"RMSE\": 4.9594120506005,         \"R2\": -4.6969569828406526,         \"Memory in Mb\": 0.9622507095336914,         \"Time in s\": 2.757735       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.030794616399332,         \"RMSE\": 4.7110231793054895,         \"R2\": -4.155876544063708,         \"Memory in Mb\": 0.5397500991821289,         \"Time in s\": 3.551711       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.922882727301643,         \"RMSE\": 4.50300441964265,         \"R2\": -4.081371242371108,         \"Memory in Mb\": 0.3774957656860351,         \"Time in s\": 4.407055       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8390508968191757,         \"RMSE\": 4.321014818317665,         \"R2\": -3.714147473566389,         \"Memory in Mb\": 0.4312639236450195,         \"Time in s\": 5.281937999999999       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7379678526387643,         \"RMSE\": 4.155226166492631,         \"R2\": -3.4180849750109363,         \"Memory in Mb\": 0.4941263198852539,         \"Time in s\": 6.197258999999999       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7042826877160742,         \"RMSE\": 4.0269186303191065,         \"R2\": -3.3444224917120184,         \"Memory in Mb\": 0.5997896194458008,         \"Time in s\": 7.197467999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6796571065333832,         \"RMSE\": 3.9174008876388,         \"R2\": -3.043265693703045,         \"Memory in Mb\": 0.6906900405883789,         \"Time in s\": 8.219126999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5891460162001485,         \"RMSE\": 3.793680488164568,         \"R2\": -2.979662055693274,         \"Memory in Mb\": 0.757817268371582,         \"Time in s\": 9.288978999999998       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5335884019062007,         \"RMSE\": 3.685003453386448,         \"R2\": -2.968029890458662,         \"Memory in Mb\": 0.7969903945922852,         \"Time in s\": 10.383406999999998       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.54418408246079,         \"RMSE\": 3.606838798974545,         \"R2\": -2.832696164635261,         \"Memory in Mb\": 0.8751077651977539,         \"Time in s\": 11.515104999999998       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5054402320411853,         \"RMSE\": 3.517798427376237,         \"R2\": -2.771919367898169,         \"Memory in Mb\": 0.9264421463012696,         \"Time in s\": 12.700466999999998       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4723491084260332,         \"RMSE\": 3.435525460733128,         \"R2\": -2.699225318590951,         \"Memory in Mb\": 0.9911317825317384,         \"Time in s\": 13.924277999999996       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.429579158986208,         \"RMSE\": 3.3562143901072865,         \"R2\": -2.6472359354971333,         \"Memory in Mb\": 0.9703760147094728,         \"Time in s\": 15.190464999999998       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3992424504019558,         \"RMSE\": 3.2853835752695946,         \"R2\": -2.431676192315116,         \"Memory in Mb\": 1.0316247940063477,         \"Time in s\": 16.527614999999997       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.365864594828797,         \"RMSE\": 3.217129802398014,         \"R2\": -2.120443637805541,         \"Memory in Mb\": 1.1097803115844729,         \"Time in s\": 17.906522       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3301487592579586,         \"RMSE\": 3.151695763852486,         \"R2\": -1.9259010739386908,         \"Memory in Mb\": 1.1814966201782229,         \"Time in s\": 19.341224       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2968821746115176,         \"RMSE\": 3.0904885141585767,         \"R2\": -1.7543370405557224,         \"Memory in Mb\": 1.2276010513305664,         \"Time in s\": 20.84359       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2678501702074907,         \"RMSE\": 3.0331483120333496,         \"R2\": -1.657710331787236,         \"Memory in Mb\": 1.2783823013305664,         \"Time in s\": 22.421223       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2343399552126226,         \"RMSE\": 2.9775564396478966,         \"R2\": -1.551795811786203,         \"Memory in Mb\": 1.2796869277954102,         \"Time in s\": 24.086658       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.220715255826944,         \"RMSE\": 2.9300367331798807,         \"R2\": -1.5298738258017646,         \"Memory in Mb\": 1.2170305252075195,         \"Time in s\": 25.790581       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1924146311245054,         \"RMSE\": 2.8809984439523286,         \"R2\": -1.506393751525562,         \"Memory in Mb\": 1.0756006240844729,         \"Time in s\": 27.649708999999994       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1793821598427467,         \"RMSE\": 2.837096711415006,         \"R2\": -1.4037015974174163,         \"Memory in Mb\": 0.9519128799438475,         \"Time in s\": 29.574727       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1645342598465298,         \"RMSE\": 2.79612287562216,         \"R2\": -1.2991852345603885,         \"Memory in Mb\": 0.9679117202758788,         \"Time in s\": 31.579398       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1398425529628198,         \"RMSE\": 2.753175690936408,         \"R2\": -1.1874325743915652,         \"Memory in Mb\": 0.936314582824707,         \"Time in s\": 33.628169       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1198821044801988,         \"RMSE\": 2.7133180185933967,         \"R2\": -1.1092797015680484,         \"Memory in Mb\": 0.9601030349731444,         \"Time in s\": 35.724068       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.103543375947734,         \"RMSE\": 2.675681585437618,         \"R2\": -1.0835838801311364,         \"Memory in Mb\": 1.0132951736450195,         \"Time in s\": 37.850435       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0951788603095205,         \"RMSE\": 2.643674463536044,         \"R2\": -1.0874567422761272,         \"Memory in Mb\": 1.0955934524536133,         \"Time in s\": 40.041881       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.073603439060177,         \"RMSE\": 2.607328103868497,         \"R2\": -1.075061768905586,         \"Memory in Mb\": 1.1506471633911133,         \"Time in s\": 42.267731000000005       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.052216298294681,         \"RMSE\": 2.5725220480150237,         \"R2\": -1.0188151031858663,         \"Memory in Mb\": 1.1900300979614258,         \"Time in s\": 44.534549000000005       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0329729065757678,         \"RMSE\": 2.539265324444459,         \"R2\": -0.9882648176410448,         \"Memory in Mb\": 1.2228517532348633,         \"Time in s\": 46.85075200000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.015201035443157,         \"RMSE\": 2.5074934380267417,         \"R2\": -0.9475063222432678,         \"Memory in Mb\": 1.2825212478637695,         \"Time in s\": 49.22632000000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.007374170791078,         \"RMSE\": 2.4793027589713232,         \"R2\": -0.9211818120686948,         \"Memory in Mb\": 1.3126497268676758,         \"Time in s\": 51.66223500000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.002386830132377,         \"RMSE\": 2.4547936379778226,         \"R2\": -0.9040692252875042,         \"Memory in Mb\": 1.3594255447387695,         \"Time in s\": 54.13772100000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9929186057762196,         \"RMSE\": 2.428247138481691,         \"R2\": -0.8804076589484491,         \"Memory in Mb\": 1.397130012512207,         \"Time in s\": 56.66204300000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9756374748391698,         \"RMSE\": 2.400488599154532,         \"R2\": -0.8344958433267429,         \"Memory in Mb\": 1.4288606643676758,         \"Time in s\": 59.23948900000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.964181960731443,         \"RMSE\": 2.37450147105602,         \"R2\": -0.7860721035239808,         \"Memory in Mb\": 1.4496355056762695,         \"Time in s\": 61.87688900000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9549728240782616,         \"RMSE\": 2.3497625798451978,         \"R2\": -0.7564202624419067,         \"Memory in Mb\": 1.3498811721801758,         \"Time in s\": 64.59788000000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9412862327577896,         \"RMSE\": 2.3247867434211136,         \"R2\": -0.747529388757789,         \"Memory in Mb\": 1.1945161819458008,         \"Time in s\": 67.39612200000002       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.934469347520636,         \"RMSE\": 2.302477656767798,         \"R2\": -0.7286928787152502,         \"Memory in Mb\": 1.2202577590942385,         \"Time in s\": 70.24630900000002       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9259837543593707,         \"RMSE\": 2.280476047701142,         \"R2\": -0.7135440601163805,         \"Memory in Mb\": 1.2635469436645508,         \"Time in s\": 73.13750900000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9196316545824528,         \"RMSE\": 2.2597103614150886,         \"R2\": -0.715155968132227,         \"Memory in Mb\": 1.2794008255004885,         \"Time in s\": 76.07904100000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9087747756519629,         \"RMSE\": 2.2382775608394114,         \"R2\": -0.7111012811273396,         \"Memory in Mb\": 1.3157854080200195,         \"Time in s\": 79.06112300000002       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9082029688272106,         \"RMSE\": 2.2371845268363217,         \"R2\": -0.710571805505718,         \"Memory in Mb\": 1.3157854080200195,         \"Time in s\": 82.06893700000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.63636363636363,         \"RMSE\": 41.64569169030137,         \"R2\": -2231.5319148936137,         \"Memory in Mb\": 0.0652570724487304,         \"Time in s\": 0.004749       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.31818181818181,         \"RMSE\": 41.32960638133835,         \"R2\": -1808.0547045951903,         \"Memory in Mb\": 0.0776433944702148,         \"Time in s\": 0.02229       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.12121212121212,         \"RMSE\": 41.13871582091424,         \"R2\": -1174.393494897962,         \"Memory in Mb\": 0.0973310470581054,         \"Time in s\": 0.042479       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.159090909090914,         \"RMSE\": 41.17451771534076,         \"R2\": -1333.7620984139928,         \"Memory in Mb\": 0.1087598800659179,         \"Time in s\": 0.065964       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.5090909090909,         \"RMSE\": 41.57075020645253,         \"R2\": -336.3506066081568,         \"Memory in Mb\": 0.1316785812377929,         \"Time in s\": 0.1143329999999999       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.681818181818166,         \"RMSE\": 42.82080349691271,         \"R2\": -153.29834830483878,         \"Memory in Mb\": 0.1604146957397461,         \"Time in s\": 0.1705729999999999       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.50649350649351,         \"RMSE\": 43.70978671356627,         \"R2\": -106.75487995129542,         \"Memory in Mb\": 0.1825284957885742,         \"Time in s\": 0.2318889999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.21590909090909,         \"RMSE\": 44.43649707984724,         \"R2\": -99.97346126163,         \"Memory in Mb\": 0.2035512924194336,         \"Time in s\": 0.308411       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.05050505050505,         \"RMSE\": 45.309262771858165,         \"R2\": -86.8022342468144,         \"Memory in Mb\": 0.2153043746948242,         \"Time in s\": 0.393152       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.16363636363636,         \"RMSE\": 46.52487115902242,         \"R2\": -63.64797006437341,         \"Memory in Mb\": 0.2280874252319336,         \"Time in s\": 0.484559       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 47.21487603305785,         \"RMSE\": 47.67304278378361,         \"R2\": -51.27707184490422,         \"Memory in Mb\": 0.2377805709838867,         \"Time in s\": 0.583103       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.29545454545455,         \"RMSE\": 48.843054157105485,         \"R2\": -43.84882422437649,         \"Memory in Mb\": 0.2479772567749023,         \"Time in s\": 0.792791       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.44055944055945,         \"RMSE\": 50.100318941519305,         \"R2\": -37.220279564063546,         \"Memory in Mb\": 0.2207241058349609,         \"Time in s\": 1.018133       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 50.532467532467535,         \"RMSE\": 51.29137544271156,         \"R2\": -33.04474826644667,         \"Memory in Mb\": 0.2406787872314453,         \"Time in s\": 1.254223       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.690909090909095,         \"RMSE\": 52.61253451297311,         \"R2\": -27.795548438273773,         \"Memory in Mb\": 0.2568111419677734,         \"Time in s\": 1.5160939999999998       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.00568181818182,         \"RMSE\": 54.11860921749895,         \"R2\": -23.566226925646237,         \"Memory in Mb\": 0.2727985382080078,         \"Time in s\": 1.7876089999999998       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.41176470588235,         \"RMSE\": 55.733754017636336,         \"R2\": -20.33250305682894,         \"Memory in Mb\": 0.2873516082763672,         \"Time in s\": 2.079546       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.02525252525252,         \"RMSE\": 57.635786091488654,         \"R2\": -17.146924852486976,         \"Memory in Mb\": 0.300180435180664,         \"Time in s\": 2.3857599999999994       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 57.5645933014354,         \"RMSE\": 59.46206220864915,         \"R2\": -14.922837840066968,         \"Memory in Mb\": 0.2762508392333984,         \"Time in s\": 2.7086299999999994       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 58.69090909090908,         \"RMSE\": 60.81327606250582,         \"R2\": -13.581197962556498,         \"Memory in Mb\": 0.2926349639892578,         \"Time in s\": 3.0480419999999997       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.25541125541125,         \"RMSE\": 62.66764529032318,         \"R2\": -12.244451024360147,         \"Memory in Mb\": 0.3073635101318359,         \"Time in s\": 3.437715999999999       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 62.17355371900826,         \"RMSE\": 65.06963847478845,         \"R2\": -10.489760184397111,         \"Memory in Mb\": 0.3215885162353515,         \"Time in s\": 3.8493299999999993       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 63.93675889328063,         \"RMSE\": 67.17295239601157,         \"R2\": -9.634560128382748,         \"Memory in Mb\": 0.3348064422607422,         \"Time in s\": 4.393024       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.10606060606062,         \"RMSE\": 68.57980310513724,         \"R2\": -9.127665748505592,         \"Memory in Mb\": 0.3451099395751953,         \"Time in s\": 4.977281       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 66.61454545454548,         \"RMSE\": 70.46451073219248,         \"R2\": -8.408339126213217,         \"Memory in Mb\": 0.3548030853271484,         \"Time in s\": 5.586244       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.48951048951052,         \"RMSE\": 72.8020594498525,         \"R2\": -7.6983532427125105,         \"Memory in Mb\": 0.3655261993408203,         \"Time in s\": 6.228505       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 70.55218855218858,         \"RMSE\": 75.3669362796119,         \"R2\": -7.08492451355157,         \"Memory in Mb\": 0.3711223602294922,         \"Time in s\": 6.899176000000001       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 72.39285714285718,         \"RMSE\": 77.65033596401675,         \"R2\": -6.643510181414674,         \"Memory in Mb\": 0.3809375762939453,         \"Time in s\": 7.597348       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 73.45454545454551,         \"RMSE\": 79.15086186624424,         \"R2\": -6.206879640065647,         \"Memory in Mb\": 0.3927364349365234,         \"Time in s\": 8.345726       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 75.77878787878792,         \"RMSE\": 82.20832738177494,         \"R2\": -5.653192449779911,         \"Memory in Mb\": 0.4039859771728515,         \"Time in s\": 9.225203       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 77.92375366568919,         \"RMSE\": 84.89106353805269,         \"R2\": -5.352795814687307,         \"Memory in Mb\": 0.4136791229248047,         \"Time in s\": 10.124705       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 80.04545454545458,         \"RMSE\": 87.49376601169416,         \"R2\": -5.134510311668016,         \"Memory in Mb\": 0.4233722686767578,         \"Time in s\": 11.048248       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 80.99724517906337,         \"RMSE\": 88.57562798692558,         \"R2\": -5.105139086016474,         \"Memory in Mb\": 0.4330654144287109,         \"Time in s\": 11.989622       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 82.77807486631018,         \"RMSE\": 90.83029071422122,         \"R2\": -4.901675845817959,         \"Memory in Mb\": 0.4524784088134765,         \"Time in s\": 12.967698       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 85.1766233766234,         \"RMSE\": 93.99517810235533,         \"R2\": -4.591702735915359,         \"Memory in Mb\": 0.4681224822998047,         \"Time in s\": 14.032748       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 87.26767676767678,         \"RMSE\": 96.48964983485284,         \"R2\": -4.494054297851511,         \"Memory in Mb\": 0.484659194946289,         \"Time in s\": 15.12168       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 89.00737100737103,         \"RMSE\": 98.71879502607636,         \"R2\": -4.345544683073043,         \"Memory in Mb\": 0.4991130828857422,         \"Time in s\": 16.236767999999998       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 90.57416267942588,         \"RMSE\": 100.72635724110243,         \"R2\": -4.224084264201084,         \"Memory in Mb\": 0.5109386444091797,         \"Time in s\": 17.416859999999996       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 93.12121212121215,         \"RMSE\": 104.19735398794236,         \"R2\": -3.967717840349581,         \"Memory in Mb\": 0.5206584930419922,         \"Time in s\": 18.643557999999995       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 95.41818181818184,         \"RMSE\": 107.03565676064125,         \"R2\": -3.8710119659250095,         \"Memory in Mb\": 0.5303516387939453,         \"Time in s\": 19.910713999999995       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 97.16629711751663,         \"RMSE\": 109.07665280092142,         \"R2\": -3.843505105397095,         \"Memory in Mb\": 0.5280742645263672,         \"Time in s\": 21.225085999999997       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 98.71645021645024,         \"RMSE\": 111.1763643167196,         \"R2\": -3.72620239405422,         \"Memory in Mb\": 0.5443019866943359,         \"Time in s\": 22.607732999999996       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 101.54122621564484,         \"RMSE\": 115.2058457378686,         \"R2\": -3.48124047566686,         \"Memory in Mb\": 0.5577869415283203,         \"Time in s\": 24.015635999999997       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 103.77066115702482,         \"RMSE\": 117.90601559037044,         \"R2\": -3.4365483842712585,         \"Memory in Mb\": 0.5712184906005859,         \"Time in s\": 25.474227999999997       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 106.02424242424244,         \"RMSE\": 120.71525892518191,         \"R2\": -3.37467008920777,         \"Memory in Mb\": 0.5825443267822266,         \"Time in s\": 26.96779499999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 107.31620553359684,         \"RMSE\": 122.26004165941237,         \"R2\": -3.356924458603192,         \"Memory in Mb\": 2.7805843353271484,         \"Time in s\": 30.43480299999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 109.39651837524178,         \"RMSE\": 124.91233289427784,         \"R2\": -3.291877964737682,         \"Memory in Mb\": 2.825040817260742,         \"Time in s\": 33.93932499999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 112.36553030303028,         \"RMSE\": 129.1106745698386,         \"R2\": -3.1225038051323804,         \"Memory in Mb\": 2.876035690307617,         \"Time in s\": 37.48648499999999       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 114.52504638218922,         \"RMSE\": 131.65752925403248,         \"R2\": -3.109734667916423,         \"Memory in Mb\": 2.927671432495117,         \"Time in s\": 41.07985699999999       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 115.89999999999996,         \"RMSE\": 133.35909826820617,         \"R2\": -3.0866973064470367,         \"Memory in Mb\": 2.9773387908935547,         \"Time in s\": 44.71417799999999       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 117.86452762923346,         \"RMSE\": 135.8046463151548,         \"R2\": -3.0526234314410727,         \"Memory in Mb\": 3.02082633972168,         \"Time in s\": 48.39640499999999       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 120.54020979020974,         \"RMSE\": 139.4624607986965,         \"R2\": -2.953338846956928,         \"Memory in Mb\": 3.065652847290039,         \"Time in s\": 52.12007199999999       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 121.81833910034597,         \"RMSE\": 141.00422703423635,         \"R2\": -2.942935834251463,         \"Memory in Mb\": 3.092409133911133,         \"Time in s\": 55.88513699999999       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 43.8732195,         \"RMSE\": 43.87807788634269,         \"R2\": -4514.954899312423,         \"Memory in Mb\": 0.1445150375366211,         \"Time in s\": 0.017697       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.4932955,         \"RMSE\": 42.52255283421693,         \"R2\": -725.9491167623446,         \"Memory in Mb\": 0.2117376327514648,         \"Time in s\": 0.059669       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.2167785,         \"RMSE\": 42.2386240157387,         \"R2\": -966.0073736019044,         \"Memory in Mb\": 0.2583265304565429,         \"Time in s\": 0.116093       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.975705625,         \"RMSE\": 41.99760868559829,         \"R2\": -957.9655948743646,         \"Memory in Mb\": 0.3003568649291992,         \"Time in s\": 0.194541       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.37550450000001,         \"RMSE\": 41.410913785433536,         \"R2\": -583.9966399141301,         \"Memory in Mb\": 0.3407926559448242,         \"Time in s\": 0.295523       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.936110000000006,         \"RMSE\": 40.97829382197767,         \"R2\": -484.9611418859003,         \"Memory in Mb\": 0.3709287643432617,         \"Time in s\": 0.510712       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.6885472857143,         \"RMSE\": 40.72961738075088,         \"R2\": -495.1050461477588,         \"Memory in Mb\": 0.3974485397338867,         \"Time in s\": 0.760252       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.35105437500001,         \"RMSE\": 40.39801158334292,         \"R2\": -429.4078677932073,         \"Memory in Mb\": 0.3402233123779297,         \"Time in s\": 1.044132       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.00981655555555,         \"RMSE\": 40.06373388340122,         \"R2\": -370.7794659133543,         \"Memory in Mb\": 0.3811016082763672,         \"Time in s\": 1.367502       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.80633095,         \"RMSE\": 39.860362966711,         \"R2\": -368.1089073295326,         \"Memory in Mb\": 0.3127880096435547,         \"Time in s\": 1.794637       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.727043136363626,         \"RMSE\": 39.77723500009918,         \"R2\": -395.5019807293188,         \"Memory in Mb\": 0.3578624725341797,         \"Time in s\": 2.266575       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.56323079166665,         \"RMSE\": 39.61325406766278,         \"R2\": -395.19837684116754,         \"Memory in Mb\": 0.3875255584716797,         \"Time in s\": 2.775551       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.42014538461535,         \"RMSE\": 39.46968290441584,         \"R2\": -397.63185900832246,         \"Memory in Mb\": 0.420846939086914,         \"Time in s\": 3.358614       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.33200189285712,         \"RMSE\": 39.37942345737111,         \"R2\": -414.4560159350036,         \"Memory in Mb\": 0.4701480865478515,         \"Time in s\": 4.097303       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.18435719999999,         \"RMSE\": 39.23275803924839,         \"R2\": -404.5402138221895,         \"Memory in Mb\": 0.5117359161376953,         \"Time in s\": 4.882924       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.13568690624999,         \"RMSE\": 39.1818628962716,         \"R2\": -423.5167725219512,         \"Memory in Mb\": 0.5480670928955078,         \"Time in s\": 5.740529       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.14620944117645,         \"RMSE\": 39.18989510023786,         \"R2\": -447.7943063391533,         \"Memory in Mb\": 0.5615406036376953,         \"Time in s\": 6.72918       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.24072974999997,         \"RMSE\": 39.28395553300239,         \"R2\": -453.6543473793619,         \"Memory in Mb\": 0.5990085601806641,         \"Time in s\": 7.76074       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.29597665789471,         \"RMSE\": 39.33769921546023,         \"R2\": -470.6701690846498,         \"Memory in Mb\": 0.6312580108642578,         \"Time in s\": 8.924786000000001       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.35730624999997,         \"RMSE\": 39.39781946688104,         \"R2\": -485.4842825426507,         \"Memory in Mb\": 0.6682605743408203,         \"Time in s\": 10.154856       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.40549083333331,         \"RMSE\": 39.44465897881697,         \"R2\": -502.7799504226928,         \"Memory in Mb\": 0.6983966827392578,         \"Time in s\": 11.469727       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.49730674999998,         \"RMSE\": 39.53710368662846,         \"R2\": -495.9856416828035,         \"Memory in Mb\": 0.7316226959228516,         \"Time in s\": 12.901862       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.61474728260867,         \"RMSE\": 39.65658853240579,         \"R2\": -473.14358309219216,         \"Memory in Mb\": 0.7705020904541016,         \"Time in s\": 14.448849       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.71032456249997,         \"RMSE\": 39.75304758270976,         \"R2\": -464.4916761787406,         \"Memory in Mb\": 0.8079357147216797,         \"Time in s\": 16.094285       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.80313951999997,         \"RMSE\": 39.84667590965187,         \"R2\": -456.8750824508669,         \"Memory in Mb\": 2.9298267364501958,         \"Time in s\": 19.825027       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.87354713461536,         \"RMSE\": 39.916931033645376,         \"R2\": -459.2932847271911,         \"Memory in Mb\": 3.0076160430908203,         \"Time in s\": 23.629967       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.94649651851849,         \"RMSE\": 39.98996046818772,         \"R2\": -459.28610565666287,         \"Memory in Mb\": 3.105920791625977,         \"Time in s\": 27.509295       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.97606614285712,         \"RMSE\": 40.018487723609816,         \"R2\": -470.926187706672,         \"Memory in Mb\": 3.203706741333008,         \"Time in s\": 31.453258       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.00338510344825,         \"RMSE\": 40.044755101652726,         \"R2\": -483.2331705341176,         \"Memory in Mb\": 3.296670913696289,         \"Time in s\": 35.460995000000004       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.07393431666663,         \"RMSE\": 40.11569326301364,         \"R2\": -479.5746686678817,         \"Memory in Mb\": 3.391347885131836,         \"Time in s\": 39.544454       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.1459417741935,         \"RMSE\": 40.18827077358568,         \"R2\": -473.96334667177865,         \"Memory in Mb\": 3.4906063079833984,         \"Time in s\": 43.697410000000005       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.21943815624997,         \"RMSE\": 40.26249426545423,         \"R2\": -466.8085709746123,         \"Memory in Mb\": 3.5870800018310547,         \"Time in s\": 47.92464       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.28296777272724,         \"RMSE\": 40.32626722721455,         \"R2\": -464.9172853497744,         \"Memory in Mb\": 3.686498641967773,         \"Time in s\": 52.218165000000006       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.31998279411761,         \"RMSE\": 40.36256991107017,         \"R2\": -473.1325264408024,         \"Memory in Mb\": 3.782560348510742,         \"Time in s\": 56.58359200000001       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.31359012857138,         \"RMSE\": 40.35509446667054,         \"R2\": -485.40526703956544,         \"Memory in Mb\": 3.816682815551758,         \"Time in s\": 61.02307200000001       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.31730695833329,         \"RMSE\": 40.357915759594896,         \"R2\": -496.1610725544049,         \"Memory in Mb\": 3.917215347290039,         \"Time in s\": 65.52718500000002       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.36653568918915,         \"RMSE\": 40.40711941642496,         \"R2\": -497.0742803710164,         \"Memory in Mb\": 4.021100997924805,         \"Time in s\": 70.10250300000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.40314367105261,         \"RMSE\": 40.443256311482514,         \"R2\": -503.3712175162706,         \"Memory in Mb\": 4.113973617553711,         \"Time in s\": 74.743136       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.44545064102563,         \"RMSE\": 40.48534274444009,         \"R2\": -506.6856716110208,         \"Memory in Mb\": 4.221334457397461,         \"Time in s\": 79.45160200000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.47854825,         \"RMSE\": 40.518050685964006,         \"R2\": -512.1052117095793,         \"Memory in Mb\": 4.33137321472168,         \"Time in s\": 84.22367000000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.50894034146341,         \"RMSE\": 40.5479845946661,         \"R2\": -518.5068774177179,         \"Memory in Mb\": 4.393171310424805,         \"Time in s\": 89.06994900000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.5406558690476,         \"RMSE\": 40.57931089736599,         \"R2\": -524.140575335229,         \"Memory in Mb\": 4.501546859741211,         \"Time in s\": 93.986539       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.58371181395347,         \"RMSE\": 40.62239247493601,         \"R2\": -524.3496319016275,         \"Memory in Mb\": 4.600507736206055,         \"Time in s\": 98.97766       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.62855514772725,         \"RMSE\": 40.66738601007716,         \"R2\": -522.897851512946,         \"Memory in Mb\": 4.697576522827148,         \"Time in s\": 104.03937600000002       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.664104233333326,         \"RMSE\": 40.702738445808535,         \"R2\": -526.020768835918,         \"Memory in Mb\": 4.774164199829102,         \"Time in s\": 109.17863200000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.68274704347825,         \"RMSE\": 40.72073961991632,         \"R2\": -535.1540147256861,         \"Memory in Mb\": 4.872934341430664,         \"Time in s\": 114.38942200000002       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.70972619148935,         \"RMSE\": 40.74737437775791,         \"R2\": -540.4099749760601,         \"Memory in Mb\": 4.975519180297852,         \"Time in s\": 119.67171900000002       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.73400636458332,         \"RMSE\": 40.771242977826994,         \"R2\": -546.7118652484228,         \"Memory in Mb\": 5.07771110534668,         \"Time in s\": 125.02115000000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.74031829795916,         \"RMSE\": 40.77684015923968,         \"R2\": -557.5026042066913,         \"Memory in Mb\": 5.174932479858398,         \"Time in s\": 130.44017000000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.75359492299998,         \"RMSE\": 40.78950075300399,         \"R2\": -567.2567645513548,         \"Memory in Mb\": 5.274145126342773,         \"Time in s\": 135.92720000000003       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.75458054545452,         \"RMSE\": 40.7904615623717,         \"R2\": -567.6629514867817,         \"Memory in Mb\": 5.276128768920898,         \"Time in s\": 141.45243100000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.63636363636363,         \"RMSE\": 41.64569169030137,         \"R2\": -2231.5319148936137,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.004659       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.31818181818181,         \"RMSE\": 41.32960638133835,         \"R2\": -1808.0547045951903,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.035685       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.12121212121212,         \"RMSE\": 41.13871582091424,         \"R2\": -1174.393494897962,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.0849989999999999       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.159090909090914,         \"RMSE\": 41.17451771534076,         \"R2\": -1333.7620984139928,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.154054       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.5090909090909,         \"RMSE\": 41.57075020645253,         \"R2\": -336.3506066081568,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.236038       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.681818181818166,         \"RMSE\": 42.82080349691271,         \"R2\": -153.29834830483878,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.334153       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.46421300395698,         \"RMSE\": 43.66282826571568,         \"R2\": -106.52347713504813,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.4453709999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.359772412267546,         \"RMSE\": 43.583709810639945,         \"R2\": -96.13505707304522,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.810611       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.34760833403674,         \"RMSE\": 41.28110871337288,         \"R2\": -71.88434940071843,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 1.1795       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.27694842893514,         \"RMSE\": 39.43109665219568,         \"R2\": -45.43679588251114,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 1.551937       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 33.38449530211604,         \"RMSE\": 37.62985177124845,         \"R2\": -31.570957412576163,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 1.927681       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.760956861305427,         \"RMSE\": 36.03410144813446,         \"R2\": -23.41028390603237,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 2.306782       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 28.636527077105512,         \"RMSE\": 34.63559483719005,         \"R2\": -17.266619380249022,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 2.6893580000000004       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.848366395937333,         \"RMSE\": 33.39569685051843,         \"R2\": -13.432529594168455,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 3.0876050000000004       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.68994399106157,         \"RMSE\": 32.40192925941153,         \"R2\": -9.92165984317062,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 3.562508       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.410830110997512,         \"RMSE\": 31.4363281477476,         \"R2\": -7.289127728255313,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 4.041374       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.27797268834062,         \"RMSE\": 30.533192270092133,         \"R2\": -5.402500905628177,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 4.523515000000001       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.21718064008457,         \"RMSE\": 29.702466677215767,         \"R2\": -3.8195183008370286,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 5.01074       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.55319621821813,         \"RMSE\": 29.08035323359505,         \"R2\": -2.8083766468986493,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 5.506907       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.47717514737632,         \"RMSE\": 28.87553300521557,         \"R2\": -2.287429329651187,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 6.006767       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.952511346795177,         \"RMSE\": 28.33784041981669,         \"R2\": -1.7081998467380504,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 6.546588       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.676711476832995,         \"RMSE\": 27.952207916118613,         \"R2\": -1.120246781438643,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 7.091828       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.0995501155626,         \"RMSE\": 27.40917062551413,         \"R2\": -0.77060809797316,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 7.64039       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.480542447806847,         \"RMSE\": 27.75611161124588,         \"R2\": -0.6589532289622051,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 8.194426       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.614496901205563,         \"RMSE\": 28.041519628611827,         \"R2\": -0.4899619312470022,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 8.751968       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.565278129073285,         \"RMSE\": 27.847512109279503,         \"R2\": -0.2726896536826668,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 9.312993       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.274958424672484,         \"RMSE\": 27.62568723918364,         \"R2\": -0.0862766099689866,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 9.883614       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.45327995927562,         \"RMSE\": 27.912230969749725,         \"R2\": 0.0123677325099795,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 10.459328       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.51025107964545,         \"RMSE\": 30.009813508826145,         \"R2\": -0.0360067042108638,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 11.038549       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.665590739767385,         \"RMSE\": 30.044848619277115,         \"R2\": 0.1113341277332687,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 11.621241       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.89048226594194,         \"RMSE\": 30.334415208142868,         \"R2\": 0.1888294001405642,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 12.207835       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.89395295521016,         \"RMSE\": 30.339570241246864,         \"R2\": 0.2623598804373043,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 12.797939       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.90533819708786,         \"RMSE\": 32.054250800509514,         \"R2\": 0.2004634102060014,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 13.393067000000002       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.763742364378043,         \"RMSE\": 33.85198840163,         \"R2\": 0.1802483296948254,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 13.997035000000002       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.57154278807733,         \"RMSE\": 34.76894700117602,         \"R2\": 0.2349038768732488,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 14.642537000000004       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.36284577450351,         \"RMSE\": 34.48838301267373,         \"R2\": 0.2980969129506975,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 15.292230000000002       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.734101326034637,         \"RMSE\": 37.062399123466015,         \"R2\": 0.2465415113840201,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 15.945417000000004       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 27.11119186006235,         \"RMSE\": 39.928390080533305,         \"R2\": 0.1791051768225413,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 16.606412000000002       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 28.3590406143643,         \"RMSE\": 42.09339312084405,         \"R2\": 0.1892790232513257,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 17.270793       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 29.357663164641018,         \"RMSE\": 43.68705243766196,         \"R2\": 0.1885388580498291,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 17.938627       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.691095011752385,         \"RMSE\": 45.67489043760892,         \"R2\": 0.1507194354669618,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 18.61722       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.58469239048663,         \"RMSE\": 49.95891321104316,         \"R2\": 0.0456375461688204,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 19.304175       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.457204466549335,         \"RMSE\": 53.83603157260298,         \"R2\": 0.0214223438020899,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 19.9947       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 37.61656772325176,         \"RMSE\": 59.47905507802133,         \"R2\": -0.1290194303344141,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 20.688692000000003       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.835053793820734,         \"RMSE\": 63.30264151550531,         \"R2\": -0.2029972425035688,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 21.389042000000003       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.84375246428709,         \"RMSE\": 64.75828138125749,         \"R2\": -0.2223668969879733,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 22.096762       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.3259867290128,         \"RMSE\": 66.7403629812066,         \"R2\": -0.2252193711452539,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 22.959028000000004       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.90376331145991,         \"RMSE\": 69.27444073484561,         \"R2\": -0.1868144391113539,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 23.825387000000003       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.11725117254523,         \"RMSE\": 70.97054614693485,         \"R2\": -0.1942044281482897,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 24.695185       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.19146939493793,         \"RMSE\": 72.84904016514736,         \"R2\": -0.2194804408254527,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 25.570022       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.0255382358928,         \"RMSE\": 75.55118081102547,         \"R2\": -0.2542655857337756,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 26.453531       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.60861685612801,         \"RMSE\": 77.95895414232838,         \"R2\": -0.2353254830584423,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 27.340524       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.40782550111089,         \"RMSE\": 80.92025038917566,         \"R2\": -0.298583502299673,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 28.229463000000003       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 28.203089584036217,         \"RMSE\": 31.678254793976468,         \"R2\": -2352.839799462937,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.018592       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 17.631407237579232,         \"RMSE\": 23.536801219235823,         \"R2\": -221.7205207554288,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.0539339999999999       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.441671937224772,         \"RMSE\": 19.739075566761823,         \"R2\": -210.18539534147197,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.098078       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.19674929006134,         \"RMSE\": 17.292913087737123,         \"R2\": -161.5886474703317,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.159515       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.529407951935296,         \"RMSE\": 15.54264880746251,         \"R2\": -81.40884208187767,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.228076       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.478754286735066,         \"RMSE\": 14.272499783288554,         \"R2\": -57.95136830581733,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.332328       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.525552058981039,         \"RMSE\": 13.242333407520348,         \"R2\": -51.44233495767236,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.527456       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.729532853932534,         \"RMSE\": 12.401843141618142,         \"R2\": -39.56324503441056,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.7296090000000001       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.20494414148211,         \"RMSE\": 11.727398222866162,         \"R2\": -30.855608065765253,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.938725       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.707613016041334,         \"RMSE\": 11.135875265707485,         \"R2\": -27.80850643367628,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 1.173453       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.35235544657082,         \"RMSE\": 10.636236352263047,         \"R2\": -27.34993958822678,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 1.423387       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.997211310189409,         \"RMSE\": 10.191758203807838,         \"R2\": -25.22586832784644,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 1.696442       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.698339965696975,         \"RMSE\": 9.799142308635478,         \"R2\": -23.570888426665658,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 2.044166       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.429952698677103,         \"RMSE\": 9.448184269747657,         \"R2\": -22.91569767610472,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 2.398587       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.185436867704573,         \"RMSE\": 9.131292683908228,         \"R2\": -20.968518634865895,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 2.759636       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.989857840855361,         \"RMSE\": 8.848493522992882,         \"R2\": -20.65027251080777,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 3.127609       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.793510888989401,         \"RMSE\": 8.58729864958044,         \"R2\": -20.548263158423392,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 3.5072970000000003       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.624008920532304,         \"RMSE\": 8.350732680595982,         \"R2\": -19.54471351213204,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 3.994133       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.4723941591948395,         \"RMSE\": 8.130732570333006,         \"R2\": -19.15022282865096,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 4.488831       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.327129028584169,         \"RMSE\": 7.926491124989248,         \"R2\": -18.69184448676573,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 4.996594       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.1988914312464622,         \"RMSE\": 7.737168953530663,         \"R2\": -18.383340677896445,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 5.512642       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.090541865220404,         \"RMSE\": 7.562941397323993,         \"R2\": -17.185096454486196,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 6.041213       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.9841087219087,         \"RMSE\": 7.398658950448711,         \"R2\": -15.50384711789857,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 6.671723       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.878027938067315,         \"RMSE\": 7.243616567021465,         \"R2\": -14.455461195017085,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 7.311147       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.790174040420949,         \"RMSE\": 7.099353406661882,         \"R2\": -13.534510391092628,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 7.962598       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.702735665583147,         \"RMSE\": 6.962851553400364,         \"R2\": -13.005371203657658,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 8.621042       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.619923274637493,         \"RMSE\": 6.8334980874356335,         \"R2\": -12.440396167539378,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 9.286046       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.556848015725479,         \"RMSE\": 6.714676061099242,         \"R2\": -12.286263553450382,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 9.968405999999998       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4920466556079988,         \"RMSE\": 6.599727625727584,         \"R2\": -12.1527109643015,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 10.662936999999998       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4260558633236777,         \"RMSE\": 6.490118235625791,         \"R2\": -11.578750092830704,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 11.381531999999998       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3708694907142864,         \"RMSE\": 6.387338726311831,         \"R2\": -10.997792654674662,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 12.131809999999998       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.309077397643504,         \"RMSE\": 6.287531812954472,         \"R2\": -10.40846497658843,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 12.902857999999998       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.253417256192923,         \"RMSE\": 6.192441467899733,         \"R2\": -9.986430076809746,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 13.765244       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1933714736526424,         \"RMSE\": 6.100884478116631,         \"R2\": -9.832475924452435,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 14.638727       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1444840100167197,         \"RMSE\": 6.014053532220149,         \"R2\": -9.80279442231031,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 15.530555       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.088914979350032,         \"RMSE\": 5.930058413028094,         \"R2\": -9.733901359629304,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 16.434104       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.038014375475162,         \"RMSE\": 5.849577408393744,         \"R2\": -9.43824097776182,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 17.347853       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.990139846596363,         \"RMSE\": 5.772270602035659,         \"R2\": -9.274280741061778,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 18.296023       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.946515411702069,         \"RMSE\": 5.69815370877383,         \"R2\": -9.05697999731458,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 19.263123       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.908897117108588,         \"RMSE\": 5.627293726045093,         \"R2\": -8.897112526821198,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 20.241821       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8732261968689676,         \"RMSE\": 5.559226632327132,         \"R2\": -8.765208356441784,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 21.227083       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8347271749400444,         \"RMSE\": 5.492864392938861,         \"R2\": -8.621969919695442,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 22.223141       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8001515928803729,         \"RMSE\": 5.4291765082898245,         \"R2\": -8.383942958793503,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 23.230427       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.762610565031098,         \"RMSE\": 5.367211780988228,         \"R2\": -8.125392758933815,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 24.244373000000003       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7278213800286455,         \"RMSE\": 5.307357454879676,         \"R2\": -7.960601204549325,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 25.346781000000004       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6959197142820022,         \"RMSE\": 5.249600105314111,         \"R2\": -7.910676780558388,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 26.455778       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6672680101890094,         \"RMSE\": 5.193857129216991,         \"R2\": -7.796440957593809,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 27.578615000000003       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.637208742738251,         \"RMSE\": 5.139738169534271,         \"R2\": -7.704147396017831,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 28.708325       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6082702309133736,         \"RMSE\": 5.087224346398139,         \"R2\": -7.692803163516414,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 29.852185       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.582128560540168,         \"RMSE\": 5.036439630005545,         \"R2\": -7.663534315499042,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 31.047385       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5805783932319006,         \"RMSE\": 5.033923389051291,         \"R2\": -7.660658200179739,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 32.243154000000004       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.664574314574316,         \"RMSE\": 12.7079745317607,         \"R2\": -206.87879383707747,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.000258       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.767694704637076,         \"RMSE\": 9.018587183866767,         \"R2\": -85.14025986830408,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.000737       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3093367298127023,         \"RMSE\": 7.420500566500976,         \"R2\": -37.24267181629702,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.00134       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.892363968348808,         \"RMSE\": 6.441521936619904,         \"R2\": -31.668094594906044,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.002066       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.1129412159858934,         \"RMSE\": 6.114058653243701,         \"R2\": -6.297346571779499,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.00291       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.832849782567835,         \"RMSE\": 6.236602142425367,         \"R2\": -2.2730130120415795,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.003872       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4069290990236856,         \"RMSE\": 6.402381882180361,         \"R2\": -1.3118663438824,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.004952       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.650377971160808,         \"RMSE\": 6.321189272940957,         \"R2\": -1.043267371916866,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.006149       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.035631404360372,         \"RMSE\": 6.4483291916176695,         \"R2\": -0.7783857772357967,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.007464       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.693189868957898,         \"RMSE\": 7.0697740144659305,         \"R2\": -0.4927792786841307,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.008896       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.274396860168236,         \"RMSE\": 7.6542276724395,         \"R2\": -0.3476225254437259,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.010446       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.875758254207378,         \"RMSE\": 8.194624755054596,         \"R2\": -0.2624191661321591,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.012113       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.530760796045927,         \"RMSE\": 8.870097879563003,         \"R2\": -0.1980355424044948,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.013898       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.121466111912466,         \"RMSE\": 9.458403141043558,         \"R2\": -0.1577027852151795,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.015801       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.772438504082036,         \"RMSE\": 10.375670403553157,         \"R2\": -0.1198999930450892,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0178219999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.565827130563894,         \"RMSE\": 11.410434180005833,         \"R2\": -0.0920676568626532,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0199609999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.429958588641576,         \"RMSE\": 12.495061319237752,         \"R2\": -0.0722153171628203,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0222169999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.47731537859646,         \"RMSE\": 13.900491647656429,         \"R2\": -0.0555502703757588,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0245899999999999       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.43172675762076,         \"RMSE\": 15.229123619635446,         \"R2\": -0.0444565128716372,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.027079       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.97432098008114,         \"RMSE\": 16.22368260926648,         \"R2\": -0.0377560869847111,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.029685       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.9382196746461,         \"RMSE\": 17.489503190785292,         \"R2\": -0.0315781972827118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.032406       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.229204186206864,         \"RMSE\": 19.43725798629848,         \"R2\": -0.0252367718674193,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.035243       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.339413196393396,         \"RMSE\": 20.82023831254592,         \"R2\": -0.0216497893038387,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.041904       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.948617107030818,         \"RMSE\": 21.75817315507082,         \"R2\": -0.0194401851240946,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.048726       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.794155127707494,         \"RMSE\": 23.16724301729152,         \"R2\": -0.0169996193237813,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.055688       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.990009992534457,         \"RMSE\": 24.865985915258104,         \"R2\": -0.0147547133955299,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.062787       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.34919450213405,         \"RMSE\": 26.67620929760368,         \"R2\": -0.0128904565600072,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.070018       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.46881241431745,         \"RMSE\": 28.248013022827838,         \"R2\": -0.011537481517321,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.077383       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.993702124162965,         \"RMSE\": 29.63814114349949,         \"R2\": -0.0105036731193923,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0848839999999999       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.586872779548436,         \"RMSE\": 32.01796640002603,         \"R2\": -0.0092202379520505,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0925169999999999       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.97345887210737,         \"RMSE\": 33.821533603903084,         \"R2\": -0.0083877019037323,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1002819999999999       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.315991788770976,         \"RMSE\": 35.461698606860665,         \"R2\": -0.0077313021586467,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1081779999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.615062978866305,         \"RMSE\": 35.981300981590465,         \"R2\": -0.0074437490312051,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1162059999999999       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.673321526932543,         \"RMSE\": 37.51836715700961,         \"R2\": -0.0069358461242559,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1243619999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 28.27694482780972,         \"RMSE\": 39.8753298933956,         \"R2\": -0.0063325109838794,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1326399999999999       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 29.55612496209691,         \"RMSE\": 41.28848705945016,         \"R2\": -0.0059801818919071,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1410409999999999       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.56167711268285,         \"RMSE\": 42.81802042618151,         \"R2\": -0.0056467231500465,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1495659999999999       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.39346669137945,         \"RMSE\": 44.18765357092498,         \"R2\": -0.0053697143301307,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1582139999999999       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 33.10612890637694,         \"RMSE\": 46.865579751152914,         \"R2\": -0.0049663660706051,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1669849999999999       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.54914638861108,         \"RMSE\": 48.61167278858254,         \"R2\": -0.0047161238549726,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1758829999999999       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 35.43263419295921,         \"RMSE\": 49.67507127970072,         \"R2\": -0.0045536938071879,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1849059999999999       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.308550382896186,         \"RMSE\": 51.2507761435036,         \"R2\": -0.0043573774895468,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1940549999999999       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 38.26330298063241,         \"RMSE\": 54.53225049728104,         \"R2\": -0.0040516612048955,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2033279999999999       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.59866234800828,         \"RMSE\": 56.08659790201894,         \"R2\": -0.0039023944795495,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2127249999999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.94697327298068,         \"RMSE\": 57.823326559810994,         \"R2\": -0.0037535911132069,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2222449999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.42384714758024,         \"RMSE\": 58.67984594201592,         \"R2\": -0.0036652347211194,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2318889999999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.72663002099646,         \"RMSE\": 60.40151056768402,         \"R2\": -0.0035345422299792,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2416599999999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.77321528369677,         \"RMSE\": 63.69509749878913,         \"R2\": -0.0033415055563215,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2515539999999999       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.99579764939489,         \"RMSE\": 65.0494992510053,         \"R2\": -0.003252609562637,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2615709999999999       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.57020777663759,         \"RMSE\": 66.07332710234044,         \"R2\": -0.0031815200825582,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2717109999999999       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 47.75825760640621,         \"RMSE\": 67.5643396193493,         \"R2\": -0.0030950009187136,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2819719999999999       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.49138874897682,         \"RMSE\": 70.24569214117749,         \"R2\": -0.0029719424061886,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2923559999999999       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 50.250899455914585,         \"RMSE\": 71.11438743304103,         \"R2\": -0.0029294686391043,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.3028349999999999       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.695184981652336,         \"RMSE\": 9.807184976514188,         \"R2\": -224.6021011118197,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.001338       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3994713447037435,         \"RMSE\": 7.102066178895935,         \"R2\": -19.27845129783118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.003825       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8170744682035584,         \"RMSE\": 5.815253847056423,         \"R2\": -17.329373299766118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.00717       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.604995404573344,         \"RMSE\": 5.081770494168446,         \"R2\": -13.040545957103586,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0113569999999999       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.824259078948539,         \"RMSE\": 4.70488333223354,         \"R2\": -6.5512954222403845,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.020929       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.918744608116588,         \"RMSE\": 4.412336880489357,         \"R2\": -4.634185300646759,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.030834       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8761207739327503,         \"RMSE\": 4.13187920011476,         \"R2\": -4.105616799680584,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.041039       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.961232939518506,         \"RMSE\": 3.976173487274506,         \"R2\": -3.1695661963674864,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.051538       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.066134597500757,         \"RMSE\": 3.873731518767916,         \"R2\": -2.4756944369169624,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.062312       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.051125997923389,         \"RMSE\": 3.731810291394655,         \"R2\": -2.23527456693896,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.073408       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.94095193468414,         \"RMSE\": 3.56902990398404,         \"R2\": -2.19210047340805,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.084777       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9366756524315063,         \"RMSE\": 3.4612902974772624,         \"R2\": -2.024876884626847,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.096419       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9250039777458068,         \"RMSE\": 3.363327951159923,         \"R2\": -1.8945640461454525,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.108333       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8726934920539136,         \"RMSE\": 3.257010428159885,         \"R2\": -1.8420037280027224,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.120517       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8907476896224935,         \"RMSE\": 3.1958821895815714,         \"R2\": -1.6910252267675163,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.133002       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.819623890420079,         \"RMSE\": 3.103812605138666,         \"R2\": -1.663886258690169,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.145758       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7396293145937214,         \"RMSE\": 3.014220627768389,         \"R2\": -1.654906383755708,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.158784       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7350691203787965,         \"RMSE\": 2.9569384317632506,         \"R2\": -1.5759385016835008,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.172076       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6987131960417108,         \"RMSE\": 2.8893997308323693,         \"R2\": -1.5446951110541192,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.185636       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.673610627740774,         \"RMSE\": 2.82935583501861,         \"R2\": -1.5089937655143242,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.199488       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6410137122925974,         \"RMSE\": 2.7701802079251965,         \"R2\": -1.484737486096575,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.213608       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6565972573555454,         \"RMSE\": 2.7427790467379385,         \"R2\": -1.391750010744973,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.227993       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.699464840115161,         \"RMSE\": 2.73946740401384,         \"R2\": -1.2626191030939884,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.242643       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7224824441896145,         \"RMSE\": 2.7219018737730583,         \"R2\": -1.182307732575659,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.25756       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7446092142173422,         \"RMSE\": 2.70580354422956,         \"R2\": -1.1113262021905803,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.272747       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7464998751860934,         \"RMSE\": 2.677192702589883,         \"R2\": -1.0705208906620065,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.288233       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7535492786865423,         \"RMSE\": 2.653885630983747,         \"R2\": -1.027170706279252,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.303987       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7201019899937544,         \"RMSE\": 2.614359234374483,         \"R2\": -1.0141103337708768,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.320009       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6887559504032663,         \"RMSE\": 2.5757257291728384,         \"R2\": -1.0033760803823184,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.336298       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.701917368353294,         \"RMSE\": 2.561424763732869,         \"R2\": -0.9592753712060648,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.3528799999999999       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7178157166185173,         \"RMSE\": 2.551346895968156,         \"R2\": -0.9142580419512064,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.369731       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7365901196485038,         \"RMSE\": 2.545046385321895,         \"R2\": -0.8692105635365064,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.386852       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7465677425181807,         \"RMSE\": 2.532051562790666,         \"R2\": -0.8368676529707118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.40424       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.731617734826669,         \"RMSE\": 2.504226186170861,         \"R2\": -0.8251107974736909,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.4218949999999999       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6973720107412231,         \"RMSE\": 2.47026789197972,         \"R2\": -0.8225927549994396,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.439849       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6698372433333928,         \"RMSE\": 2.4400355004771077,         \"R2\": -0.81732226470892,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.458072       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6732482399922957,         \"RMSE\": 2.425592833263792,         \"R2\": -0.7947920429290933,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.4765629999999999       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6653913599894004,         \"RMSE\": 2.404136439714782,         \"R2\": -0.7822814452716051,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.4953209999999999       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6644612180457288,         \"RMSE\": 2.387561393188575,         \"R2\": -0.7656652158374817,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.514347       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6556359332933146,         \"RMSE\": 2.368497267913513,         \"R2\": -0.7532954885990883,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.533661       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6452077788467467,         \"RMSE\": 2.348678653798561,         \"R2\": -0.7430103139622937,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.5532450000000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6374623223784903,         \"RMSE\": 2.3305035344735936,         \"R2\": -0.7320713255917544,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.5730930000000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6419505315856449,         \"RMSE\": 2.320208013716276,         \"R2\": -0.7138439732116804,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.6284980000000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6490002164922652,         \"RMSE\": 2.3126155324510744,         \"R2\": -0.6941855677649247,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.6842080000000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6474991175923384,         \"RMSE\": 2.299197536504521,         \"R2\": -0.6816400531907807,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.7401880000000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6301006788336792,         \"RMSE\": 2.2779225390149764,         \"R2\": -0.6777843948800273,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.7964830000000002       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6221876471839871,         \"RMSE\": 2.262378737250057,         \"R2\": -0.6690049120995847,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.8530460000000002       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6124120493571743,         \"RMSE\": 2.245866476718547,         \"R2\": -0.6619276404267609,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.9098760000000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5867001120604314,         \"RMSE\": 2.223758235975506,         \"R2\": -0.661013659831075,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.9669740000000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5681359363812415,         \"RMSE\": 2.2037391763141216,         \"R2\": -0.6587014308970958,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 1.0243380000000002       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.567554989468773,         \"RMSE\": 2.202858861923226,         \"R2\": -0.6584830635688459,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 1.081765       }     ]   },   \"params\": [     {       \"name\": \"models\",       \"select\": {         \"type\": \"point\",         \"fields\": [           \"model\"         ]       },       \"bind\": \"legend\"     },     {       \"name\": \"Dataset\",       \"value\": \"ChickWeights\",       \"bind\": {         \"input\": \"select\",         \"options\": [           \"ChickWeights\",           \"TrumpApproval\"         ]       }     },     {       \"name\": \"grid\",       \"select\": \"interval\",       \"bind\": \"scales\"     }   ],   \"transform\": [     {       \"filter\": {         \"field\": \"dataset\",         \"equal\": {           \"expr\": \"Dataset\"         }       }     }   ],   \"repeat\": {     \"row\": [       \"MAE\",       \"RMSE\",       \"R2\",       \"Memory in Mb\",       \"Time in s\"     ]   },   \"spec\": {     \"width\": \"container\",     \"mark\": \"line\",     \"encoding\": {       \"x\": {         \"field\": \"step\",         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"title\": \"Instance\"         }       },       \"y\": {         \"field\": {           \"repeat\": \"row\"         },         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18         }       },       \"color\": {         \"field\": \"model\",         \"type\": \"ordinal\",         \"scale\": {           \"scheme\": \"category20b\"         },         \"title\": \"Models\",         \"legend\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"labelLimit\": 500         }       },       \"opacity\": {         \"condition\": {           \"param\": \"models\",           \"value\": 1         },         \"value\": 0.2       }     }   } }</p>"},{"location":"benchmarks/Regression/#datasets","title":"Datasets","text":"ChickWeights <p>Chick weights along time.</p> <p>The stream contains 578 items and 3 features. The goal is to predict the weight of each chick along time, according to the diet the chick is on. The data is ordered by time and then by chick.</p> <pre><code>Name  ChickWeights                                                                                                  \nTask  Regression\n</code></pre> <p>Samples  578                                                                                                          Features  3                                                                                                              Sparse  False                                                                                                            Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/chick-weights.csv</p> <p></p> TrumpApproval <p>Donald Trump approval ratings.</p> <p>This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald Trump's approval ratings. It contains 5 features, which are approval ratings collected by 5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of this task is to see if we can reproduce FiveThirtyEight's model.</p> <pre><code>Name  TrumpApproval                                                                                                     \nTask  Regression\n</code></pre> <p>Samples  1,001                                                                                                            Features  6                                                                                                                  Sparse  False                                                                                                                Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/trump_approval.csv.gz</p> <p></p>"},{"location":"benchmarks/Regression/#models","title":"Models","text":"Linear Regression <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Linear Regression with l1 regularization <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=1.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Linear Regression with l2 regularization <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=1.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Passive-Aggressive Regressor, mode 1 <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  PARegressor (\n    C=1.\n    mode=1\n    eps=0.1\n    learn_intercept=True\n  )\n)</pre></p> <p></p> Passive-Aggressive Regressor, mode 2 <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  PARegressor (\n    C=1.\n    mode=2\n    eps=0.1\n    learn_intercept=True\n  )\n)</pre></p> <p></p> k-Nearest Neighbors <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNRegressor (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    aggregation_method=\"mean\"\n  )\n)\n\n<p></p>\n\nHoeffding Tree\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  HoeffdingTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n  )\n)</pre></p>\n\n<p></p>\n\nHoeffding Adaptive Tree\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=True\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=42\n  )\n)</pre></p>\n\n<p></p>\n\nStochastic Gradient Tree\n<p><pre>SGTRegressor (\n  delta=1e-07\n  grace_period=200\n  init_pred=0.\n  max_depth=inf\n  lambda_value=0.1\n  gamma=1.\n  nominal_attributes=[]\n  feature_quantizer=StaticQuantizer (\n    n_bins=64\n    warm_start=100\n    buckets=None\n  )\n)</pre></p>\n\n<p></p>\n\nAdaptive Random Forest\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  []\n)</pre></p>\n\n<p></p>\n\nAggregated Mondrian Forest\n<p><pre>[]</pre></p>\n\n<p></p>\n\nAdaptive Model Rules\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  AMRules (\n    n_min=200\n    delta=1e-07\n    tau=0.05\n    pred_type=\"adaptive\"\n    pred_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    fading_factor=0.99\n    anomaly_threshold=-0.75\n    m_min=30\n    ordered_rule_set=True\n    min_samples_split=5\n  )\n)</pre></p>\n\n<p></p>\n\nStreaming Random Patches\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SRPRegressor (\n    model=HoeffdingTreeRegressor (\n      grace_period=50\n      max_depth=inf\n      delta=0.01\n      tau=0.05\n      leaf_prediction=\"adaptive\"\n      leaf_model=LinearRegression (\n        optimizer=SGD (\n          lr=Constant (\n            learning_rate=0.01\n          )\n        )\n        loss=Squared ()\n        l2=0.\n        l1=0.\n        intercept_init=0.\n        intercept_lr=Constant (\n          learning_rate=0.01\n        )\n        clip_gradient=1e+12\n        initializer=Zeros ()\n      )\n      model_selector_decay=0.95\n      nominal_attributes=None\n      splitter=TEBSTSplitter (\n        digits=1\n      )\n      min_samples_split=5\n      binary_split=False\n      max_size=500.\n      memory_estimate_period=1000000\n      stop_mem_management=False\n      remove_poor_attrs=False\n      merit_preprune=True\n    )\n    n_models=10\n    subspace_size=0.6\n    training_method=\"patches\"\n    lam=6\n    drift_detector=ADWIN (\n      delta=1e-05\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    warning_detector=ADWIN (\n      delta=0.0001\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    disable_detector=\"off\"\n    disable_weighted_vote=True\n    drift_detection_criteria=\"error\"\n    aggregation_method=\"mean\"\n    seed=42\n    metric=MAE ()\n  )\n)</pre></p>\n\n<p></p>\n\nBagging\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  [HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  )]\n)</pre></p>\n\n<p></p>\n\nExponentially Weighted Average\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  [LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=True\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), KNNRegressor (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    aggregation_method=\"mean\"\n  ), AMRules (\n    n_min=200\n    delta=1e-07\n    tau=0.05\n    pred_type=\"adaptive\"\n    pred_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    fading_factor=0.99\n    anomaly_threshold=-0.75\n    m_min=30\n    ordered_rule_set=True\n    min_samples_split=5\n  )]\n)\n\n<p></p>\n\nRiver MLP\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  MLPRegressor (\n    hidden_dims=(5,)\n    activations=(, , )\n    loss=Squared ()\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.001\n      )\n    )\n    seed=42\n  )\n)\n\n<p></p>\n\n[baseline] Mean predictor\n<p><pre>StatisticRegressor (\n  statistic=Mean ()\n)</pre></p>\n\n<p></p>"},{"location":"benchmarks/Regression/#environment","title":"Environment","text":"<pre>Python implementation: CPython\nPython version       : 3.12.4\nIPython version      : 8.18.1\n\nriver       : 0.21.2\nnumpy       : 1.26.4\nscikit-learn: 1.3.1\npandas      : 2.2.2\nscipy       : 1.13.0\n\nCompiler    : GCC 11.4.0\nOS          : Linux\nRelease     : 6.5.0-1024-azure\nMachine     : x86_64\nProcessor   : x86_64\nCPU cores   : 4\nArchitecture: 64bit\n</pre>"},{"location":"examples/batch-to-online/","title":"From batch to online/stream","text":""},{"location":"examples/batch-to-online/#a-quick-overview-of-batch-learning","title":"A quick overview of batch learning","text":"<p>If you've already delved into machine learning, then you shouldn't have any difficulty in getting to use incremental learning. If you are somewhat new to machine learning, then do not worry! The point of this notebook in particular is to introduce simple notions. We'll also start to show how River fits in and explain how to use it.</p> <p>The whole point of machine learning is to learn from data. In supervised learning you want to learn how to predict a target \\(y\\) given a set of features \\(X\\). Meanwhile in an unsupervised learning there is no target, and the goal is rather to identify patterns and trends in the features \\(X\\). At this point most people tend to imagine \\(X\\) as a somewhat big table where each row is an observation and each column is a feature, and they would be quite right. Learning from tabular data is part of what's called batch learning, which basically that all of the data is available to our learning algorithm at once. Multiple libraries have been created to handle the batch learning regime, with one of the most prominent being Python's scikit-learn.</p> <p>As a simple example of batch learning let's say we want to learn to predict if a women has breast cancer or not. We'll use the breast cancer dataset available with scikit-learn. We'll learn to map a set of features to a binary decision using a logistic regression. Like many other models based on numerical weights, logistic regression is sensitive to the scale of the features. Rescaling the data so that each feature has mean 0 and variance 1 is generally considered good practice. We can apply the rescaling and fit the logistic regression sequentially in an elegant manner using a Pipeline. To measure the performance of the model we'll evaluate the average ROC AUC score using a 5 fold cross-validation. </p> <pre><code>from sklearn import datasets\nfrom sklearn import linear_model\nfrom sklearn import metrics\nfrom sklearn import model_selection\nfrom sklearn import pipeline\nfrom sklearn import preprocessing\n\n\n# Load the data\ndataset = datasets.load_breast_cancer()\nX, y = dataset.data, dataset.target\n\n# Define the steps of the model\nmodel = pipeline.Pipeline([\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LogisticRegression(solver='lbfgs'))\n])\n\n# Define a determistic cross-validation procedure\ncv = model_selection.KFold(n_splits=5, shuffle=True, random_state=42)\n\n# Compute the MSE values\nscorer = metrics.make_scorer(metrics.roc_auc_score)\nscores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n\n# Display the average score and its standard deviation\nprint(f'ROC AUC: {scores.mean():.3f} (\u00b1 {scores.std():.3f})')\n</code></pre> <pre><code>ROC AUC: 0.975 (\u00b1 0.011)\n</code></pre> <p>This might be a lot to take in if you're not accustomed to scikit-learn, but it probably isn't if you are. Batch learning basically boils down to:</p> <ol> <li>Loading (and preprocessing) the data</li> <li>Fitting a model to the data</li> <li>Computing the performance of the model on unseen data</li> </ol> <p>This is pretty standard and is maybe how most people imagine a machine learning pipeline. However, this way of proceeding has certain downsides. First of all your laptop would crash if the <code>load_boston</code> function returned a dataset who's size exceeds your available amount of RAM. Sometimes you can use some tricks to get around this. For example by optimizing the data types and by using sparse representations when applicable you can potentially save precious gigabytes of RAM. However, like many tricks this only goes so far. If your dataset weighs hundreds of gigabytes then you won't go far without some special hardware. One solution is to do out-of-core learning; that is, algorithms that can learn by being presented the data in chunks or mini-batches. If you want to go down this road then take a look at Dask and Spark's MLlib.</p> <p>Another issue with the batch learning regime is that it can't elegantly learn from new data. Indeed if new data is made available, then the model has to learn from scratch with a new dataset composed of the old data and the new data. This is particularly annoying in a real situation where you might have new incoming data every week, day, hour, minute, or even second. For example if you're building a recommendation engine for an e-commerce app, then you're probably training your model from 0 every week or so. As your app grows in popularity, so does the dataset you're training on. This will lead to longer and longer training times and might require a hardware upgrade.</p> <p>A final downside that isn't very easy to grasp concerns the manner in which features are extracted. Every time you want to train your model you first have to extract features. The trick is that some features might not be accessible at the particular point in time you are at. For example maybe that some attributes in your data warehouse get overwritten with time. In other words maybe that all the features pertaining to a particular observations are not available, whereas they were a week ago. This happens more often than not in real scenarios, and apart if you have a sophisticated data engineering pipeline then you will encounter these issues at some point. </p>"},{"location":"examples/batch-to-online/#a-hands-on-introduction-to-incremental-learning","title":"A hands-on introduction to incremental learning","text":"<p>Incremental learning is also often called online learning or stream learning, but if you google online learning a lot of the results will point to educational websites. Hence, the terms \"incremental learning\" and \"stream learning\" (from which River derives its name) are preferred. The point of incremental learning is to fit a model to a stream of data. In other words, the data isn't available in its entirety, but rather the observations are provided one by one. As an example let's stream through the dataset used previously.</p> <pre><code>for xi, yi in zip(X, y):\n    # This is where the model learns\n    pass\n</code></pre> <p>In this case we're iterating over a dataset that is already in memory, but we could just as well stream from a CSV file, a Kafka stream, an SQL query, etc. If we look at <code>xi</code> we can notice that it is a <code>numpy.ndarray</code>.</p> <pre><code>xi\n</code></pre> <pre><code>array([7.760e+00, 2.454e+01, 4.792e+01, 1.810e+02, 5.263e-02, 4.362e-02,\n       0.000e+00, 0.000e+00, 1.587e-01, 5.884e-02, 3.857e-01, 1.428e+00,\n       2.548e+00, 1.915e+01, 7.189e-03, 4.660e-03, 0.000e+00, 0.000e+00,\n       2.676e-02, 2.783e-03, 9.456e+00, 3.037e+01, 5.916e+01, 2.686e+02,\n       8.996e-02, 6.444e-02, 0.000e+00, 0.000e+00, 2.871e-01, 7.039e-02])\n</code></pre> <p>River by design works with <code>dict</code>s. We believe that <code>dict</code>s are more enjoyable to program with than <code>numpy.ndarray</code>s, at least for when single observations are concerned. <code>dict</code>'s bring the added benefit that each feature can be accessed by name rather than by position.</p> <pre><code>for xi, yi in zip(X, y):\n    xi = dict(zip(dataset.feature_names, xi))\n    pass\n\nxi\n</code></pre> <pre><code>{'mean radius': 7.76,\n 'mean texture': 24.54,\n 'mean perimeter': 47.92,\n 'mean area': 181.0,\n 'mean smoothness': 0.05263,\n 'mean compactness': 0.04362,\n 'mean concavity': 0.0,\n 'mean concave points': 0.0,\n 'mean symmetry': 0.1587,\n 'mean fractal dimension': 0.05884,\n 'radius error': 0.3857,\n 'texture error': 1.428,\n 'perimeter error': 2.548,\n 'area error': 19.15,\n 'smoothness error': 0.007189,\n 'compactness error': 0.00466,\n 'concavity error': 0.0,\n 'concave points error': 0.0,\n 'symmetry error': 0.02676,\n 'fractal dimension error': 0.002783,\n 'worst radius': 9.456,\n 'worst texture': 30.37,\n 'worst perimeter': 59.16,\n 'worst area': 268.6,\n 'worst smoothness': 0.08996,\n 'worst compactness': 0.06444,\n 'worst concavity': 0.0,\n 'worst concave points': 0.0,\n 'worst symmetry': 0.2871,\n 'worst fractal dimension': 0.07039}\n</code></pre> <p>Conveniently, River's <code>stream</code> module has an <code>iter_sklearn_dataset</code> method that we can use instead.</p> <pre><code>from river import stream\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n    pass\n</code></pre> <p>The simple fact that we are getting the data as a stream means that we can't do a lot of things the same way as in a batch setting. For example let's say we want to scale the data so that it has mean 0 and variance 1, as we did earlier. To do so we simply have to subtract the mean of each feature to each value and then divide the result by the standard deviation of the feature. The problem is that we can't possible know the values of the mean and the standard deviation before actually going through all the data! One way to proceed would be to do a first pass over the data to compute the necessary values and then scale the values during a second pass. The problem is that this defeats our purpose, which is to learn by only looking at the data once. Although this might seem rather restrictive, it reaps sizable benefits down the road.</p> <p>The way we do feature scaling in River involves computing running statistics (also know as moving statistics). The idea is that we use a data structure that estimates the mean and updates itself when it is provided with a value. The same goes for the variance (and thus the standard deviation). For example, if we denote \\(\\mu_t\\) the mean and \\(n_t\\) the count at any moment \\(t\\), then updating the mean can be done as so:</p> \\[ \\begin{cases} n_{t+1} = n_t + 1 \\\\ \\mu_{t+1} = \\mu_t + \\frac{x - \\mu_t}{n_{t+1}} \\end{cases} \\] <p>Likewise, the running variance can be computed as so:</p> \\[ \\begin{cases} n_{t+1} = n_t + 1 \\\\ \\mu_{t+1} = \\mu_t + \\frac{x - \\mu_t}{n_{t+1}} \\\\ s_{t+1} = s_t + (x - \\mu_t) \\times (x - \\mu_{t+1}) \\\\ \\sigma_{t+1} = \\frac{s_{t+1}}{n_{t+1}} \\end{cases} \\] <p>where \\(s_t\\) is a running sum of squares and \\(\\sigma_t\\) is the running variance at time \\(t\\). This might seem a tad more involved than the batch algorithms you learn in school, but it is rather elegant. Implementing this in Python is not too difficult. For example let's compute the running mean and variance of the <code>'mean area'</code> variable.</p> <pre><code>n, mean, sum_of_squares, variance = 0, 0, 0, 0\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n    n += 1\n    old_mean = mean\n    mean += (xi['mean area'] - mean) / n\n    sum_of_squares += (xi['mean area'] - old_mean) * (xi['mean area'] - mean)\n    variance = sum_of_squares / n\n\nprint(f'Running mean: {mean:.3f}')\nprint(f'Running variance: {variance:.3f}')\n</code></pre> <pre><code>Running mean: 654.889\nRunning variance: 123625.903\n</code></pre> <p>Let's compare this with <code>numpy</code>. But remember, <code>numpy</code> requires access to \"all\" the data.</p> <pre><code>import numpy as np\n\ni = list(dataset.feature_names).index('mean area')\nprint(f'True mean: {np.mean(X[:, i]):.3f}')\nprint(f'True variance: {np.var(X[:, i]):.3f}')\n</code></pre> <pre><code>True mean: 654.889\nTrue variance: 123625.903\n</code></pre> <p>The results seem to be exactly the same! The twist is that the running statistics won't be very accurate for the first few observations. In general though this doesn't matter too much. Some would even go as far as to say that this descrepancy is beneficial and acts as some sort of regularization...</p> <p>Now the idea is that we can compute the running statistics of each feature and scale them as they come along. The way to do this with River is to use the <code>StandardScaler</code> class from the <code>preprocessing</code> module, as so:</p> <pre><code>from river import preprocessing\n\nscaler = preprocessing.StandardScaler()\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n    scaler.learn_one(xi)\n</code></pre> <p>Now that we are scaling the data, we can start doing some actual machine learning. We're going to implement an online linear regression task. Because all the data isn't available at once, we are obliged to do what is called stochastic gradient descent, which is a popular research topic and has a lot of variants. SGD is commonly used to train neural networks. The idea is that at each step we compute the loss between the target prediction and the truth. We then calculate the gradient, which is simply a set of derivatives with respect to each weight from the linear regression. Once we have obtained the gradient, we can update the weights by moving them in the opposite direction of the gradient. The amount by which the weights are moved typically depends on a learning rate, which is typically set by the user. Different optimizers have different ways of managing the weight update, and some handle the learning rate implicitly. Online linear regression can be done in River with the <code>LinearRegression</code> class from the <code>linear_model</code> module. We'll be using plain and simple SGD using the <code>SGD</code> optimizer from the <code>optim</code> module. During training we'll measure the squared error between the truth and the predictions.</p> <pre><code>from river import linear_model\nfrom river import optim\n\nscaler = preprocessing.StandardScaler()\noptimizer = optim.SGD(lr=0.01)\nlog_reg = linear_model.LogisticRegression(optimizer)\n\ny_true = []\ny_pred = []\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer(), shuffle=True, seed=42):\n\n    # Scale the features\n    scaler.learn_one(xi)\n    xi_scaled = scaler.transform_one(xi)\n\n    # Test the current model on the new \"unobserved\" sample\n    yi_pred = log_reg.predict_proba_one(xi_scaled)\n    # Train the model with the new sample\n    log_reg.learn_one(xi_scaled, yi)\n\n    # Store the truth and the prediction\n    y_true.append(yi)\n    y_pred.append(yi_pred[True])\n\nprint(f'ROC AUC: {metrics.roc_auc_score(y_true, y_pred):.3f}')\n</code></pre> <pre><code>ROC AUC: 0.990\n</code></pre> <p>The ROC AUC is significantly better than the one obtained from the cross-validation of scikit-learn's logisitic regression. However to make things really comparable it would be nice to compare with the same cross-validation procedure. River has a <code>compat</code> module that contains utilities for making River compatible with other Python libraries. Because we're doing regression we'll be using the <code>SKLRegressorWrapper</code>. We'll also be using <code>Pipeline</code> to encapsulate the logic of the <code>StandardScaler</code> and the <code>LogisticRegression</code> in one single object.</p> <pre><code>from river import compat\nfrom river import compose\n\n# We define a Pipeline, exactly like we did earlier for sklearn \nmodel = compose.Pipeline(\n    ('scale', preprocessing.StandardScaler()),\n    ('log_reg', linear_model.LogisticRegression())\n)\n\n# We make the Pipeline compatible with sklearn\nmodel = compat.convert_river_to_sklearn(model)\n\n# We compute the CV scores using the same CV scheme and the same scoring\nscores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n\n# Display the average score and its standard deviation\nprint(f'ROC AUC: {scores.mean():.3f} (\u00b1 {scores.std():.3f})')\n</code></pre> <pre><code>ROC AUC: 0.964 (\u00b1 0.016)\n</code></pre> <p>This time the ROC AUC score is lower, which is what we would expect. Indeed online learning isn't as accurate as batch learning. However it all depends in what you're interested in. If you're only interested in predicting the next observation then the online learning regime would be better. That's why it's a bit hard to compare both approaches: they're both suited to different scenarios.</p>"},{"location":"examples/batch-to-online/#going-further","title":"Going further","text":"<p>Here a few resources if you want to do some reading:</p> <ul> <li>Online learning -- Wikipedia</li> <li>What is online machine learning? -- Max Pagels</li> <li>Introduction to Online Learning -- USC course</li> <li>Online Methods in Machine Learning -- MIT course</li> <li>Online Learning: A Comprehensive Survey</li> <li>Streaming 101: The world beyond batch</li> <li>Machine learning for data streams</li> <li>Data Stream Mining: A Practical Approach</li> </ul>"},{"location":"examples/bike-sharing-forecasting/","title":"Bike-sharing forecasting","text":"<p>In this tutorial we're going to forecast the number of bikes in 5 bike stations from the city of Toulouse. We'll do so by building a simple model step by step. The dataset contains 182,470 observations. Let's first take a peak at the data.</p> <pre><code>from pprint import pprint\nfrom river import datasets\n\ndataset = datasets.Bikes()\n\nfor x, y in dataset:\n    pprint(x)\n    print(f'Number of available bikes: {y}')\n    break\n</code></pre> <pre><code>{'clouds': 75,\n 'description': 'light rain',\n 'humidity': 81,\n 'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n 'pressure': 1017.0,\n 'station': 'metro-canal-du-midi',\n 'temperature': 6.54,\n 'wind': 9.3}\nNumber of available bikes: 1\n</code></pre> <p>Let's start by using a simple linear regression on the numeric features. We can select the numeric features and discard the rest of the features using a <code>Select</code>. Linear regression is very likely to go haywire if we don't scale the data, so we'll use a <code>StandardScaler</code> to do just that. We'll evaluate the model by measuring the mean absolute error. Finally we'll print the score every 20,000 observations. </p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import metrics\nfrom river import evaluate\nfrom river import preprocessing\nfrom river import optim\n\nmodel = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression(optimizer=optim.SGD(0.001))\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric, print_every=20_000)\n</code></pre> <pre><code>[20,000] MAE: 4.912763\n[40,000] MAE: 5.333578\n[60,000] MAE: 5.330969\n[80,000] MAE: 5.392334\n[100,000] MAE: 5.423078\n[120,000] MAE: 5.541239\n[140,000] MAE: 5.613038\n[160,000] MAE: 5.622441\n[180,000] MAE: 5.567836\n[182,470] MAE: 5.563905\n\n\n\n\n\nMAE: 5.563905\n</code></pre> <p>The model doesn't seem to be doing that well, but then again we didn't provide a lot of features. Generally, a good idea for this kind of problem is to look at an average of the previous values. For example, for each station we can look at the average number of bikes per hour. To do so we first have to extract the hour from the  <code>moment</code> field. We can then use a <code>TargetAgg</code> to aggregate the values of the target.</p> <pre><code>from river import feature_extraction\nfrom river import stats\n\ndef get_hour(x):\n    x['hour'] = x['moment'].hour\n    return x\n\nmodel = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')\nmodel += (\n    get_hour |\n    feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean())\n)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression(optimizer=optim.SGD(0.001))\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric, print_every=20_000)\n</code></pre> <pre><code>[20,000] MAE: 3.720766\n[40,000] MAE: 3.829739\n[60,000] MAE: 3.844905\n[80,000] MAE: 3.910137\n[100,000] MAE: 3.888553\n[120,000] MAE: 3.923644\n[140,000] MAE: 3.980882\n[160,000] MAE: 3.949972\n[180,000] MAE: 3.934489\n[182,470] MAE: 3.933442\n\n\n\n\n\nMAE: 3.933442\n</code></pre> <p>By adding a single feature, we've managed to significantly reduce the mean absolute error. At this point you might think that the model is getting slightly complex, and is difficult to understand and test. Pipelines have the advantage of being terse, but they aren't always to debug. Thankfully River has some ways to relieve the pain.</p> <p>The first thing we can do it to visualize the pipeline, to get an idea of how the data flows through it.</p> <pre><code>model\n</code></pre> <pre>['clouds', [...]</pre><code>Select (   clouds   humidity   pressure   temperature   wind ) </code><pre>get_hour</pre><code> def get_hour(x):     x['hour'] = x['moment'].hour     return x  </code><pre>y_mean_by_station_and_hour</pre><code>TargetAgg (   by=['station', 'hour']   how=Mean ()   target_name=\"y\" ) </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.001     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>We can also use the <code>debug_one</code> method to see what happens to one particular instance. Let's train the model on the first 10,000 observations and then call <code>debug_one</code> on the next one. To do this, we will turn the <code>Bike</code> object into a Python generator with <code>iter()</code> function. The Pythonic way to read the first 10,000 elements of a generator is to use <code>itertools.islice</code>.</p> <pre><code>import itertools\n\nmodel = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')\nmodel += (\n    get_hour |\n    feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean())\n)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n\nfor x, y in itertools.islice(dataset, 10000):\n    y_pred = model.predict_one(x)\n    model.learn_one(x, y)\n\nx, y = next(iter(dataset))\nprint(model.debug_one(x))\n</code></pre> <pre><code>0. Input\n--------\nclouds: 75 (int)\ndescription: light rain (str)\nhumidity: 81 (int)\nmoment: 2016-04-01 00:00:07 (datetime)\npressure: 1,017.00000 (float)\nstation: metro-canal-du-midi (str)\ntemperature: 6.54000 (float)\nwind: 9.30000 (float)\n\n1. Transformer union\n--------------------\n    1.0 Select\n    ----------\n    clouds: 75 (int)\n    humidity: 81 (int)\n    pressure: 1,017.00000 (float)\n    temperature: 6.54000 (float)\n    wind: 9.30000 (float)\n\n    1.1 get_hour | y_mean_by_station_and_hour\n    -----------------------------------------\n    y_mean_by_station_and_hour: 4.43243 (float)\n\nclouds: 75 (int)\nhumidity: 81 (int)\npressure: 1,017.00000 (float)\ntemperature: 6.54000 (float)\nwind: 9.30000 (float)\ny_mean_by_station_and_hour: 4.43243 (float)\n\n2. StandardScaler\n-----------------\nclouds: 0.47566 (float)\nhumidity: 0.42247 (float)\npressure: 1.05314 (float)\ntemperature: -1.22098 (float)\nwind: 2.21104 (float)\ny_mean_by_station_and_hour: -0.59098 (float)\n\n3. LinearRegression\n-------------------\nName                         Value      Weight     Contribution  \n                 Intercept    1.00000    6.58252        6.58252  \n                  pressure    1.05314    3.78529        3.98646  \n                  humidity    0.42247    1.44921        0.61225  \ny_mean_by_station_and_hour   -0.59098    0.54167       -0.32011  \n                    clouds    0.47566   -1.92255       -0.91448  \n                      wind    2.21104   -0.77720       -1.71843  \n               temperature   -1.22098    2.47030       -3.01619\n\nPrediction: 5.21201\n</code></pre> <p>The <code>debug_one</code> method shows what happens to an input set of features, step by step.</p> <p>And now comes the catch. Up until now we've been using the <code>progressive_val_score</code> method from the <code>evaluate</code> module. What this does it that it sequentially predicts the output of an observation and updates the model immediately afterwards. This way of proceeding is often used for evaluating online learning models. But in some cases it is the wrong approach.</p> <p>When evaluating a machine learning model, the goal is to simulate production conditions in order to get a trust-worthy assessment of the performance of the model. In our case, we typically want to forecast the number of bikes available in a station, say, 30 minutes ahead. Then, once the 30 minutes have passed, the true number of available bikes will be available and we will be able to update the model using the features available 30 minutes ago.</p> <p>What we really want is to evaluate the model by forecasting 30 minutes ahead and only updating the model once the true values are available. This can be done using the <code>moment</code> and <code>delay</code> parameters in the  <code>progressive_val_score</code> method. The idea is that each observation in the stream of the data is shown twice to the model: once for making a prediction, and once for updating the model when the true value is revealed. The <code>moment</code> parameter determines which variable should be used as a timestamp, while the <code>delay</code> parameter controls the duration to wait before revealing the true values to the model.</p> <pre><code>import datetime as dt\n\nevaluate.progressive_val_score(\n    dataset=dataset,\n    model=model.clone(),\n    metric=metrics.MAE(),\n    moment='moment',\n    delay=dt.timedelta(minutes=30),\n    print_every=20_000\n)\n</code></pre> <pre><code>[20,000] MAE: 20.198137\n[40,000] MAE: 12.199763\n[60,000] MAE: 9.468279\n[80,000] MAE: 8.126625\n[100,000] MAE: 7.273133\n[120,000] MAE: 6.735469\n[140,000] MAE: 6.376704\n[160,000] MAE: 6.06156\n[180,000] MAE: 5.806744\n[182,470] MAE: 5.780772\n\n\n\n\n\nMAE: 5.780772\n</code></pre> <p>The performance is a bit worse, which is to be expected. Indeed, the task is more difficult: the model is only shown the ground truth 30 minutes after making a prediction.</p> <p>The takeaway of this notebook is that the <code>progressive_val_score</code> method can be used to simulate a production scenario, and is thus extremely valuable.</p>"},{"location":"examples/building-a-simple-nowcasting-model/","title":"Building a simple nowcasting model","text":"<p>Nowcasting is a special case of forecasting. It simply consists in predicting the next value in a time series.</p> <p>We'll be using the international airline passenger data available from here. This particular dataset is included with River in the <code>datasets</code> module.</p> <pre><code>from river import datasets\n\nfor x, y in datasets.AirlinePassengers():\n    print(x, y)\n    break\n</code></pre> <pre><code>{'month': datetime.datetime(1949, 1, 1, 0, 0)} 112\n</code></pre> <p>The data is as simple as can be: it consists of a sequence of months and values representing the total number of international airline passengers per month. Our goal is going to be to predict the number of passengers for the next month at each step. Notice that because the dataset is small  -- which is usually the case for time series -- we could just fit a model from scratch each month. However for the sake of example we're going to train a single model online. Although the overall performance might be potentially weaker, training a time series model online has the benefit of being scalable if, say, you have have thousands of time series to manage.</p> <p>We'll start with a very simple model where the only feature will be the ordinal date of each month. This should be able to capture some of the underlying trend. </p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import preprocessing\n\n\ndef get_ordinal_date(x):\n    return {'ordinal_date': x['month'].toordinal()}\n\n\nmodel = compose.Pipeline(\n    ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression())\n)\n</code></pre> <p>We'll write down a function to evaluate the model. This will go through each observation in the dataset and update the model as it goes on. The prior predictions will be stored along with the true values and will be plotted together. </p> <pre><code>from river import metrics\nfrom river import utils\nimport matplotlib.pyplot as plt\n\n\ndef evaluate_model(model): \n\n    metric = utils.Rolling(metrics.MAE(), 12)\n\n    dates = []\n    y_trues = []\n    y_preds = []\n\n    for x, y in datasets.AirlinePassengers():\n\n        # Obtain the prior prediction and update the model in one go\n        y_pred = model.predict_one(x)\n        model.learn_one(x, y)\n\n        # Update the error metric\n        metric.update(y, y_pred)\n\n        # Store the true value and the prediction\n        dates.append(x['month'])\n        y_trues.append(y)\n        y_preds.append(y_pred)\n\n    # Plot the results\n    fig, ax = plt.subplots(figsize=(10, 6))\n    ax.grid(alpha=0.75)\n    ax.plot(dates, y_trues, lw=3, color='#2ecc71', alpha=0.8, label='Ground truth')\n    ax.plot(dates, y_preds, lw=3, color='#e74c3c', alpha=0.8, label='Prediction')\n    ax.legend()\n    ax.set_title(metric)\n</code></pre> <p>Let's evaluate our first model.</p> <pre><code>evaluate_model(model)\n</code></pre> <p></p> <p>The model has captured a trend but not the right one. Indeed it thinks the trend is linear whereas we can visually see that the growth of the data increases with time. In other words the second derivative of the series is positive. This is a well know problem in time series forecasting and there are thus many ways to handle it; for example by using a Box-Cox transform. However we are going to do something a bit different, and instead linearly detrend the series using a <code>TargetStandardScaler</code>.</p> <pre><code>from river import stats\n\n\nmodel = compose.Pipeline(\n    ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(intercept_lr=0)),\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>Now let's try and capture the monthly trend by one-hot encoding the month name.</p> <pre><code>import calendar\n\n\ndef get_month(x):\n    return {\n        calendar.month_name[month]: month == x['month'].month\n        for month in range(1, 13)\n    }\n\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n        ('month', compose.FuncTransformer(get_month)),\n    )),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(intercept_lr=0))\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>This seems pretty decent. We can take a look at the weights of the linear regression to get an idea of the importance of each feature.</p> <pre><code>model.regressor['lin_reg'].weights\n</code></pre> <pre><code>{'January': -0.13808091575141299,\n 'February': -0.18716063793638954,\n 'March': -0.026469206216021102,\n 'April': -0.03500685108350436,\n 'May': -0.013638742192777328,\n 'June': 0.16194267303548826,\n 'July': 0.31995865445067634,\n 'August': 0.2810396556938982,\n 'September': 0.03834350518076595,\n 'October': -0.11655850082390988,\n 'November': -0.2663497734491209,\n 'December': -0.15396048501165746,\n 'ordinal_date': 1.0234863735122575}\n</code></pre> <p>As could be expected the months of July and August have the highest weights because these are the months where people typically go on holiday abroad. The month of December has a low weight because this is a month of festivities in most of the Western world where people usually stay at home.</p> <p>Our model seems to understand which months are important, but it fails to see that the importance of each month grows multiplicatively as the years go on. In other words our model is too shy. We can fix this by increasing the learning rate of the <code>LinearRegression</code>'s optimizer.</p> <pre><code>from river import optim\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n        ('month', compose.FuncTransformer(get_month)),\n    )),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(\n        intercept_lr=0,\n        optimizer=optim.SGD(0.03)\n    ))\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>This is starting to look good! Naturally in production we would tune the learning rate, ideally in real-time.</p> <p>Before finishing, we're going to introduce a cool feature extraction trick based on radial basis function kernels. The one-hot encoding we did on the month is a good idea but if you think about it is a bit rigid. Indeed the value of each feature is going to be 0 or 1, depending on the month of each observation. We're basically saying that the month of September is as distant to the month of August as it is to the month of March. Of course this isn't true, and it would be nice if our features would reflect this. To do so we can simply calculate the distance between the month of each observation and all the months in the calendar. Instead of simply computing the distance linearly, we're going to use a so-called Gaussian radial basic function kernel. This is a bit of a mouthful but for us it boils down to a simple formula, which is:</p> \\[d(i, j) = exp(-\\frac{(i - j)^2}{2\\sigma^2})\\] <p>Intuitively this computes a similarity between two months -- denoted by \\(i\\) and \\(j\\) -- which decreases the further apart they are from each other. The \\(sigma\\) parameter can be seen as a hyperparameter than can be tuned -- in the following snippet we'll simply ignore it. The thing to take away is that this results in smoother predictions than when using a one-hot encoding scheme, which is often a desirable property. You can also see trick in action in this nice presentation.</p> <pre><code>import math\n\ndef get_month_distances(x):\n    return {\n        calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)\n        for month in range(1, 13)\n    }\n\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n        ('month_distances', compose.FuncTransformer(get_month_distances)),\n    )),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(\n        intercept_lr=0,\n        optimizer=optim.SGD(0.03)\n    ))\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>We've managed to get a good looking prediction curve with a reasonably simple model. What's more our model has the advantage of being interpretable and easy to debug. There surely are more rocks to squeeze (e.g. tune the hyperparameters, use an ensemble model, etc.) but we'll leave that as an exercice to the reader.</p> <p>As a finishing touch we'll rewrite our pipeline using the <code>|</code> operator, which is called a \"pipe\".</p> <pre><code>extract_features = compose.TransformerUnion(get_ordinal_date, get_month_distances)\n\nscale = preprocessing.StandardScaler()\n\nlearn = linear_model.LinearRegression(\n    intercept_lr=0,\n    optimizer=optim.SGD(0.03)\n)\n\nmodel = extract_features | scale | learn\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <pre><code>model\n</code></pre> <pre>TargetStandardScaler</pre><code>TargetStandardScaler (   regressor=Pipeline (     steps=OrderedDict([('TransformerUnion', TransformerUnion (   FuncTransformer (     func=\"get_ordinal_date\"   ),   FuncTransformer (     func=\"get_month_distances\"   ) )), ('StandardScaler', StandardScaler (   with_std=True )), ('LinearRegression', LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.03     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0   )   clip_gradient=1e+12   initializer=Zeros () ))])   ) ) </code><pre>get_ordinal_date</pre><code> def get_ordinal_date(x):     return {'ordinal_date': x['month'].toordinal()}  </code><pre>get_month_distances</pre><code> def get_month_distances(x):     return {         calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)         for month in range(1, 13)     }  </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.03     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0   )   clip_gradient=1e+12   initializer=Zeros () ) </code>"},{"location":"examples/content-personalization/","title":"Content personalization","text":""},{"location":"examples/content-personalization/#without-context","title":"Without context","text":"<p>This example takes inspiration from Vowpal Wabbit's excellent tutorial.</p> <p>Content personalization is about taking into account user preferences. It's a special case of recommender systems. Ideally, side-information should be taken into account in addition to the user. But we'll start with something simpler. We'll assume that each user has stable preferences that are independent of the context. We capture this by implementing a \"reward\" function.</p> <pre><code>def get_reward(user, item, context):\n\n    time_of_day = context['time_of_day']\n\n    USER_LIKED_ARTICLE = 1\n    USER_DISLIKED_ARTICLE = 0\n\n    if user == 'Tom':\n        if time_of_day == 'morning' and item == 'politics':\n            return USER_LIKED_ARTICLE\n        elif time_of_day == 'afternoon' and item == 'music':\n            return USER_LIKED_ARTICLE\n        else:\n            return USER_DISLIKED_ARTICLE\n    elif user == 'Anna':\n        if time_of_day == 'morning' and item == 'sports':\n            return USER_LIKED_ARTICLE\n        elif time_of_day == 'afternoon' and item == 'politics':\n            return USER_LIKED_ARTICLE\n        else:\n            return USER_DISLIKED_ARTICLE\n\nget_reward('Tom', 'politics', {'time_of_day': 'morning'})\n</code></pre> <pre><code>1\n</code></pre> <p>Measuring the performance of a recommendation is not straightforward, mostly because of the interactive aspect of recommender systems. In a real situation, recommendations are presented to a user, and the user gives feedback indicating whether they like what they have been recommended or not. This feedback loop can't be captured entirely by a historical dataset. Some kind of simulator is required to generate recommendations and capture feedback. We already have a reward function. Now let's implement a simulation function.</p> <pre><code>import random\nimport matplotlib.pyplot as plt\n\ndef plot_ctr(ctr):\n    plt.plot(range(1, len(ctr) + 1), ctr)\n    plt.xlabel('n_iterations', fontsize=14)\n    plt.ylabel('CTR', fontsize=14)\n    plt.ylim([0, 1])\n    plt.title(f'final CTR: {ctr[-1]:.2%}', fontsize=14)\n    plt.grid()\n\nusers = ['Tom', 'Anna']\ntimes_of_day = ['morning', 'afternoon']\nitems = {'politics', 'sports', 'music', 'food', 'finance', 'health', 'camping'}\n\ndef simulate(n, reward_func, model, seed):\n\n    rng = random.Random(seed)\n    n_clicks = 0\n    ctr = []  # click-through rate along time\n\n    for i in range(n):\n\n        # Generate a context at random\n        user = rng.choice(users)\n        context = {\n            'time_of_day': rng.choice(times_of_day)\n        }\n\n        # Make a single recommendation\n        item = model.rank(user, items=items, x=context)[0]\n\n        # Measure the reward\n        clicked = reward_func(user, item, context)\n        n_clicks += clicked\n        ctr.append(n_clicks / (i + 1))\n\n        # Update the model\n        model.learn_one(user, item, y=clicked, x=context)\n\n    plot_ctr(ctr)\n</code></pre> <p>This simulation function does quite a few things. It can be seen as a simple reinforcement learning simulation. It samples a user, and then ask the model to provide a single recommendation. The user then gives as to whether they liked the recommendation or not. Crucially, the user doesn't tell us what item they would have liked. We could model this as a multi-class classification problem if that were the case.</p> <p>The strategy parameter determines the mechanism used to generate the recommendations. The <code>'best'</code> strategy means that the items are each scored by the model, and are then ranked from the most preferred to the least preferred. Here the most preferred item is the one which gets recommended. But you could imagine all sorts of alternative ways to proceed.</p> <p>We can first evaluate a recommended which acts completely at random. It assigns a random preference to each item, regardless of the user.</p> <pre><code>from river import reco\n\nmodel = reco.RandomNormal(seed=10)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>We can see that the click-through rate (CTR) oscillates around 28.74%. In fact, this model is expected to be correct <code>100 * (2 / 7)% = 28.57%</code> of the time. Indeed, each user likes two items, and there are seven items in total.</p> <p>Let's now use the <code>Baseline</code> recommended. This one models each preference as the following sum:</p> \\[preference = \\bar{y} + b_{u} + b_{i}\\] <p>where</p> <ul> <li>\\(\\bar{y}\\) is the average CTR overall</li> <li>\\(b_{u}\\) is the average CTR per user minus \\(\\bar{y}\\) -- it's therefore called a bias</li> <li>\\(b_{i}\\) is the average CTR per item minus \\(\\bar{y}\\)</li> </ul> <p>This model is considered to be a baseline because it doesn't actually learn what items are preferred by each user. Instead it models each user and item separately. We shouldn't expect it to be a strong model. It should however do better than the random model used above.</p> <pre><code>model = reco.Baseline(seed=10)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>This baseline model seems perfect, which is surprising. The reason why it works so well is because both users have in common that they both like politics. The model therefore learns that the <code>'politics'</code> is a good item to recommend.</p> <pre><code>model.i_biases\n</code></pre> <pre><code>defaultdict(Zeros (),\n            {'politics': 0.06389451550325113,\n             'music': -0.04041254194187752,\n             'camping': -0.040319730234734,\n             'health': -0.03581829597317823,\n             'food': -0.037778771188204816,\n             'finance': -0.04029646665611086,\n             'sports': -0.03661678982763635})\n</code></pre> <p>The model is not as performant if we use a reward function where both users have different preferences.</p> <pre><code>simulate(\n    5_000,\n    reward_func=lambda user, item, context: (\n        item in {'music', 'politics'} if user == \"Tom\" else\n        item in {'food', 'sports'}\n    ),\n    model=model,\n    seed=42\n)\n</code></pre> <p></p> <p>A good recommender model should at the very least understand what kind of items each user prefers. One of the simplest and yet performant way to do this is Simon Funk's SGD method he developped for the Netflix challenge and wrote about here. It models each user and each item as latent vectors. The dot product of these two vectors is the expected preference of the user for the item.</p> <pre><code>model = reco.FunkMF(seed=10)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>We can see that this model learns what items each user enjoys very well. Of course, there are some caveats. In our simulation, we ask the model to recommend the item most likely to be preferred for each user. Indeed, we rank all the items and pick the item at the top of the list. We do this many times for only two users.</p> <p>This is of course not realistic. Users will get fed up with recommendations if they're always shown the same item. It's important to include diversity into recommendations, and to let the model explore other options instead of always focusing on the item with the highest score. This is where evaluating recommender systems gets tricky: the reward function itself is difficult to model.</p> <p>We will keep ignoring these caveats in this notebook. Instead we will focus on a different concern: making recommendations when context is involved.</p>"},{"location":"examples/content-personalization/#with-context","title":"With context","text":"<p>We'll add some context by making it so that user preferences change depending on the time the day. Very simply, preferences might change from morning to afternoon. This is captured by the following reward function.</p> <pre><code>times_of_day = ['morning', 'afternoon']\n\ndef get_reward(user, item, context):\n    if user == 'Tom':\n        if context['time_of_day'] == 'morning':\n            return item == 'politics'\n        if context['time_of_day'] == 'afternoon':\n            return item == 'music'\n    if user == 'Anna':\n        if context['time_of_day'] == 'morning':\n            return item == 'sports'\n        if context['time_of_day'] == 'afternoon':\n            return item == 'politics'\n</code></pre> <p>We have to update our simulation function to generate a random context at each step. We also want our model to use it for recommending items as well as learning.</p> <pre><code>def simulate(n, reward_func, model, seed):\n\n    rng = random.Random(seed)\n    n_clicks = 0\n    ctr = []\n\n    for i in range(n):\n\n        user = rng.choice(users)\n\n        # New: pass a context\n        context = {'time_of_day': rng.choice(times_of_day)}\n        item = model.rank(user, items, context)[0]\n\n        clicked = reward_func(user, item, context)\n        n_clicks += clicked\n        ctr.append(n_clicks / (i + 1))\n\n        # New: pass a context\n        model.learn_one(user, item, clicked, context)\n\n    plot_ctr(ctr)\n</code></pre> <p>Not all models are capable of taking into account context. For instance, the <code>FunkMF</code> model only models users and items. It completely ignores the context, even when we provide one. All recommender models inherit from the base <code>Recommender</code> class. They also have a property which indicates whether or not they are able to handle context:</p> <pre><code>model = reco.FunkMF(seed=10)\nmodel.is_contextual\n</code></pre> <pre><code>False\n</code></pre> <p>Let's see well it performs.</p> <pre><code>simulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>The performance has roughly been divided by half. This is most likely because there are now two times of day, and if the model has learnt preferences for one time of the day, then it's expected to be wrong half of the time.</p> <p>Before delving into recsys models that can handle context, a simple hack is to notice that we can append the time of day to the user. This effectively results in new users which our model can distinguish between. We could apply this trick during the simulation, but we can also override the behavior of the <code>learn_one</code> and <code>rank</code> methods of our model.</p> <pre><code>class FunkMFWithHack(reco.FunkMF):\n\n    def learn_one(self, user, item, reward, context):\n        user = f\"{user}@{context['time_of_day']}\"\n        return super().learn_one(user, item, reward, context)\n\n    def rank(self, user, items, context):\n        user = f\"{user}@{context['time_of_day']}\"\n        return super().rank(user, items, context)\n\nmodel = FunkMFWithHack(seed=29)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>We can verify that the model has learnt the correct preferences by looking at the expected preference for each <code>(user, item)</code> pair.</p> <pre><code>import pandas as pd\n\n(\n    pd.DataFrame(\n        {\n            'user': user,\n            'item': item,\n            'preference': model.predict_one(user, item)\n        }\n        for user in model.u_latents\n        for item in model.i_latents\n    )\n    .pivot(index='user', columns='item')\n    .style.highlight_max(color='lightgreen', axis='columns')\n)\n</code></pre> preference item camping finance food health music politics sports user Anna@afternoon -0.018105 0.032865 0.069222 -0.059041 0.168353 1.000000 0.195960 Anna@morning -0.117577 0.081131 0.076300 -0.136399 0.154483 0.221890 1.000000 Tom@afternoon 0.057220 -0.027115 -0.074671 -0.233071 1.000000 0.163607 0.141781 Tom@morning -0.028562 -0.005428 0.061163 -0.050107 0.063483 1.000000 0.125515"},{"location":"examples/debugging-a-pipeline/","title":"Debugging a pipeline","text":"<p>River encourages users to make use of pipelines. The biggest pain point of pipelines is that it can be hard to understand what's happening to the data, especially when the pipeline is complex. Fortunately the <code>Pipeline</code> class has a <code>debug_one</code> method that can help out.</p> <p>Let's look at a fairly complex pipeline for predicting the number of bikes in 5 bike stations from the city of Toulouse. It doesn't matter if you understand the pipeline or not; the point of this notebook is to learn how to introspect a pipeline.</p> <pre><code>import datetime as dt\nfrom river import compose\nfrom river import datasets\nfrom river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stats\nfrom river import stream\n\n\nX_y = datasets.Bikes()\nX_y = stream.simulate_qa(X_y, moment='moment', delay=dt.timedelta(minutes=30))\n\ndef add_time_features(x):\n    return {\n        **x,\n        'hour': x['moment'].hour,\n        'day': x['moment'].weekday()\n    }\n\nmodel = add_time_features\nmodel |= (\n    compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind') +\n    feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean()) +\n    feature_extraction.TargetAgg(by='station', how=stats.EWMean())\n)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n\nmetric = metrics.MAE()\n\nquestions = {}\n\nfor i, x, y in X_y:\n    # Question\n    is_question = y is None\n    if is_question:\n        y_pred = model.predict_one(x)\n        questions[i] = y_pred\n\n    # Answer\n    else:\n        metric.update(y, questions[i])\n        model.learn_one(x, y)\n\n        if i &gt;= 30000 and i % 30000 == 0:\n            print(i, metric)\n</code></pre> <pre><code>30000 MAE: 13.328051\n60000 MAE: 7.824087\n90000 MAE: 6.003909\n120000 MAE: 5.052855\n150000 MAE: 4.496826\n180000 MAE: 4.140702\n</code></pre> <p>Let's start by looking at the pipeline. You can click each cell to display the current state for each step of the pipeline.</p> <pre><code>model\n</code></pre> <pre>add_time_features</pre><code> def add_time_features(x):     return {         **x,         'hour': x['moment'].hour,         'day': x['moment'].weekday()     }  </code><pre>['clouds', [...]</pre><code>Select (   clouds   humidity   pressure   temperature   wind ) </code><pre>y_mean_by_station_and_hour</pre><code>TargetAgg (   by=['station', 'hour']   how=Mean ()   target_name=\"y\" ) </code><pre>y_ewm_0.5_by_station</pre><code>TargetAgg (   by=['station']   how=EWMean (     fading_factor=0.5   )   target_name=\"y\" ) </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>As mentioned above the <code>Pipeline</code> class has a <code>debug_one</code> method. You can use this at any point you want to visualize what happen to an input <code>x</code>. For example, let's see what happens to the last seen <code>x</code>.</p> <pre><code>print(model.debug_one(x))\n</code></pre> <pre><code>0. Input\n--------\nclouds: 88 (int)\ndescription: overcast clouds (str)\nhumidity: 84 (int)\nmoment: 2016-10-05 09:57:18 (datetime)\npressure: 1,017.34000 (float)\nstation: pomme (str)\ntemperature: 17.45000 (float)\nwind: 1.95000 (float)\n\n1. add_time_features\n--------------------\nclouds: 88 (int)\nday: 2 (int)\ndescription: overcast clouds (str)\nhour: 9 (int)\nhumidity: 84 (int)\nmoment: 2016-10-05 09:57:18 (datetime)\npressure: 1,017.34000 (float)\nstation: pomme (str)\ntemperature: 17.45000 (float)\nwind: 1.95000 (float)\n\n2. Transformer union\n--------------------\n    2.0 Select\n    ----------\n    clouds: 88 (int)\n    humidity: 84 (int)\n    pressure: 1,017.34000 (float)\n    temperature: 17.45000 (float)\n    wind: 1.95000 (float)\n\n    2.1 TargetAgg\n    -------------\n    y_mean_by_station_and_hour: 7.89396 (float)\n\n    2.2 TargetAgg1\n    --------------\n    y_ewm_0.5_by_station: 11.80372 (float)\n\nclouds: 88 (int)\nhumidity: 84 (int)\npressure: 1,017.34000 (float)\ntemperature: 17.45000 (float)\nwind: 1.95000 (float)\ny_ewm_0.5_by_station: 11.80372 (float)\ny_mean_by_station_and_hour: 7.89396 (float)\n\n3. StandardScaler\n-----------------\nclouds: 1.54778 (float)\nhumidity: 1.16366 (float)\npressure: 0.04916 (float)\ntemperature: -0.51938 (float)\nwind: -0.69426 (float)\ny_ewm_0.5_by_station: 0.19640 (float)\ny_mean_by_station_and_hour: -0.27110 (float)\n\n4. LinearRegression\n-------------------\nName                         Value      Weight     Contribution  \n                 Intercept    1.00000    9.19960        9.19960  \n      y_ewm_0.5_by_station    0.19640    9.19349        1.80562  \n                  humidity    1.16366    1.01680        1.18320  \n               temperature   -0.51938   -0.41575        0.21593  \n                      wind   -0.69426   -0.03810        0.02645  \n                  pressure    0.04916    0.18321        0.00901  \ny_mean_by_station_and_hour   -0.27110    0.19553       -0.05301  \n                    clouds    1.54778   -0.32838       -0.50827\n\nPrediction: 11.87854\n</code></pre> <p>The pipeline does quite a few things, but using <code>debug_one</code> shows what happens step by step. This is really useful for checking that the pipeline is behaving as you're expecting it too. Remember that you can <code>debug_one</code> whenever you wish, be it before, during, or after training a model.</p>"},{"location":"examples/imbalanced-learning/","title":"Working with imbalanced data","text":"<p>In machine learning it is quite usual to have to deal with imbalanced dataset. This is particularly true in online learning for tasks such as fraud detection and spam classification. In these two cases, which are binary classification problems, there are usually many more 0s than 1s, which generally hinders the performance of the classifiers we thrown at them.</p> <p>As an example we'll use the credit card dataset available in River. We'll first use a <code>collections.Counter</code> to count the number of 0s and 1s in order to get an idea of the class balance.</p> <pre><code>import collections\nfrom river import datasets\n\nX_y = datasets.CreditCard()\n\ncounts = collections.Counter(y for _, y in X_y)\n\nfor c, count in counts.items():\n    print(f'{c}: {count} ({count / sum(counts.values()):.5%})')\n</code></pre> <pre><code>0: 284315 (99.82725%)\n1: 492 (0.17275%)\n</code></pre>"},{"location":"examples/imbalanced-learning/#baseline","title":"Baseline","text":"<p>The dataset is quite unbalanced. For each 1 there are about 578 0s. Let's now train a logistic regression with default parameters and see how well it does. We'll measure the ROC AUC score.</p> <pre><code>from river import linear_model\nfrom river import metrics\nfrom river import evaluate\nfrom river import preprocessing\n\n\nX_y = datasets.CreditCard()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 89.11%\n</code></pre>"},{"location":"examples/imbalanced-learning/#importance-weighting","title":"Importance weighting","text":"<p>The performance is already quite acceptable, but as we will now see we can do even better. The first thing we can do is to add weight to the 1s by using the <code>weight_pos</code> argument of the <code>Log</code> loss function.</p> <pre><code>from river import optim\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(\n        loss=optim.losses.Log(weight_pos=5)\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 91.43%\n</code></pre>"},{"location":"examples/imbalanced-learning/#focal-loss","title":"Focal loss","text":"<p>The deep learning for object detection community has produced a special loss function for imbalanced learning called focal loss. We are doing binary classification, so we can plug the binary version of focal loss into our logistic regression and see how well it fairs.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(loss=optim.losses.BinaryFocalLoss(2, 1))\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 91.31%\n</code></pre>"},{"location":"examples/imbalanced-learning/#under-sampling-the-majority-class","title":"Under-sampling the majority class","text":"<p>Adding importance weights only works with gradient-based models (which includes neural networks). A more generic, and potentially more effective approach, is to use undersamplig and oversampling. As an example, we'll under-sample the stream so that our logistic regression encounter 20% of 1s and 80% of 0s. Under-sampling has the additional benefit of requiring less training steps, and thus reduces the total training time.</p> <pre><code>from river import imblearn\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomUnderSampler(\n        classifier=linear_model.LogisticRegression(),\n        desired_dist={0: .8, 1: .2},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 94.75%\n</code></pre> <p>The <code>RandomUnderSampler</code> class is a wrapper for classifiers. This is represented by a rectangle around the logistic regression bubble when we visualize the model.</p> <pre><code>model\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>RandomUnderSampler</pre><code>RandomUnderSampler (   classifier=LogisticRegression (     optimizer=SGD (       lr=Constant (         learning_rate=0.01       )     )     loss=Log (       weight_pos=1.       weight_neg=1.     )     l2=0.     l1=0.     intercept_init=0.     intercept_lr=Constant (       learning_rate=0.01     )     clip_gradient=1e+12     initializer=Zeros ()   )   desired_dist={0: 0.8, 1: 0.2}   seed=42 ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code>"},{"location":"examples/imbalanced-learning/#over-sampling-the-minority-class","title":"Over-sampling the minority class","text":"<p>We can also attain the same class distribution by over-sampling the minority class. This will come at cost of having to train with more samples.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomOverSampler(\n        classifier=linear_model.LogisticRegression(),\n        desired_dist={0: .8, 1: .2},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 91.71%\n</code></pre>"},{"location":"examples/imbalanced-learning/#sampling-with-a-desired-sample-size","title":"Sampling with a desired sample size","text":"<p>The downside of both <code>RandomUnderSampler</code> and <code>RandomOverSampler</code> is that you don't have any control on the amount of data the classifier trains on. The number of samples is adjusted so that the target distribution can be attained, either by under-sampling or over-sampling. However, you can do both at the same time and choose how much data the classifier will see. To do so, we can use the <code>RandomSampler</code> class. In addition to the desired class distribution, we can specify how much data to train on. The samples will both be under-sampled and over-sampled in order to fit your constraints. This is powerful because it allows you to control both the class distribution and the size of the training data (and thus the training time). In the following example we'll set it so that the model will train with 1 percent of the data.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomSampler(\n        classifier=linear_model.LogisticRegression(),\n        desired_dist={0: .8, 1: .2},\n        sampling_rate=.01,\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 94.71%\n</code></pre>"},{"location":"examples/imbalanced-learning/#hybrid-approach","title":"Hybrid approach","text":"<p>As you might have guessed by now, nothing is stopping you from mixing imbalanced learning methods together. As an example, let's combine <code>sampling.RandomUnderSampler</code> and the <code>weight_pos</code> parameter from the <code>optim.losses.Log</code> loss function.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomUnderSampler(\n        classifier=linear_model.LogisticRegression(\n            loss=optim.losses.Log(weight_pos=5)\n        ),\n        desired_dist={0: .8, 1: .2},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 96.52%\n</code></pre>"},{"location":"examples/quantile-regression-uncertainty/","title":"Handling uncertainty with quantile regression","text":"<pre><code>%matplotlib inline\n</code></pre> <p>Quantile regression is useful when you're not so much interested in the accuracy of your model, but rather you want your model to be good at ranking observations correctly. The typical way to perform quantile regression is to use a special loss function, namely the quantile loss. The quantile loss takes a parameter, \\(\\alpha\\) (alpha), which indicates which quantile the model should be targeting. In the case of \\(\\alpha = 0.5\\), then this is equivalent to asking the model to predict the median value of the target, and not the most likely value which would be the mean. </p> <p>A nice thing we can do with quantile regression is to produce a prediction interval for each prediction. Indeed, if we predict the lower and upper quantiles of the target then we will be able to obtain a \"trust region\" in between which the true value is likely to belong. Of course, the likeliness will depend on the chosen quantiles. For a slightly more detailed explanation see this blog post.</p> <p>As an example, let us take the simple nowcasting model we built in another notebook. Instead of predicting the mean value of the target distribution, we will predict the 5th, 50th, 95th quantiles. This will require training three separate models, so we will encapsulate the model building logic in a function called <code>make_model</code>. We also have to slightly adapt the training loop, but not by much. Finally, we will draw the prediction interval along with the predictions from for 50th quantile (i.e. the median) and the true values.</p> <pre><code>import calendar\nimport math\nimport matplotlib.pyplot as plt\nfrom river import compose\nfrom river import datasets\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\nfrom river import stats\n\n\ndef get_ordinal_date(x):\n    return {'ordinal_date': x['month'].toordinal()}    \n\n\ndef get_month_distances(x):\n    return {\n        calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)\n        for month in range(1, 13)\n    }\n\n\ndef make_model(alpha):\n\n    extract_features = compose.TransformerUnion(get_ordinal_date, get_month_distances)\n\n    scale = preprocessing.StandardScaler()\n\n    learn = linear_model.LinearRegression(\n        intercept_lr=0,\n        optimizer=optim.SGD(0.03),\n        loss=optim.losses.Quantile(alpha=alpha)\n    )\n\n    model = extract_features | scale | learn\n    model = preprocessing.TargetStandardScaler(regressor=model)\n\n    return model\n\nmetric = metrics.MAE()\n\nmodels = {\n    'lower': make_model(alpha=0.05),\n    'center': make_model(alpha=0.5),\n    'upper': make_model(alpha=0.95)\n}\n\ndates = []\ny_trues = []\ny_preds = {\n    'lower': [],\n    'center': [],\n    'upper': []\n}\n\nfor x, y in datasets.AirlinePassengers():\n    y_trues.append(y)\n    dates.append(x['month'])\n\n    for name, model in models.items():\n        y_preds[name].append(model.predict_one(x))\n        model.learn_one(x, y)\n\n    # Update the error metric\n    metric.update(y, y_preds['center'][-1])\n\n# Plot the results\nfig, ax = plt.subplots(figsize=(10, 6))\nax.grid(alpha=0.75)\nax.plot(dates, y_trues, lw=3, color='#2ecc71', alpha=0.8, label='Truth')\nax.plot(dates, y_preds['center'], lw=3, color='#e74c3c', alpha=0.8, label='Prediction')\nax.fill_between(dates, y_preds['lower'], y_preds['upper'], color='#e74c3c', alpha=0.3, label='Prediction interval')\nax.legend()\nax.set_title(metric);\n</code></pre> <p></p> <p>An important thing to note is that the prediction interval we obtained should not be confused with a confidence interval. Simply put, a prediction interval represents uncertainty for where the true value lies, whereas a confidence interval encapsulates the uncertainty on the prediction. You can find out more by reading this CrossValidated post.</p>"},{"location":"examples/sentence-classification/","title":"Sentence classification","text":"<p>In this tutorial we will try to predict whether an SMS is a spam or not. To train our model, we will use the <code>SMSSpam</code> dataset. This dataset is unbalanced, there is only 13.4% spam. Let's look at the data:</p> <pre><code>from river import datasets\n\ndatasets.SMSSpam()\n</code></pre> <pre><code>SMS Spam Collection dataset.\n\nThe data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent\n13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.\n\n      Name  SMSSpam                                                                              \n      Task  Binary classification                                                                \n   Samples  5,574                                                                                \n  Features  1                                                                                    \n    Sparse  False                                                                                \n      Path  /Users/max/river_data/SMSSpam/SMSSpamCollection                                      \n       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\n      Size  466.71 KB                                                                            \nDownloaded  True\n</code></pre> <pre><code>from pprint import pprint\n\nX_y = datasets.SMSSpam()\n\nfor x, y in X_y:\n    pprint(x)\n    print(f'Spam: {y}')\n    break\n</code></pre> <pre><code>{'body': 'Go until jurong point, crazy.. Available only in bugis n great world '\n         'la e buffet... Cine there got amore wat...\\n'}\nSpam: False\n</code></pre> <p>Let's start by building a simple model like a Naive Bayes classifier. We will first preprocess the sentences with a TF-IDF transform that our model can consume. Then, we will measure the accuracy of our model with the AUC metric. This is the right metric to use when the classes are not balanced. In addition, the Naive Bayes models can perform very well on unbalanced datasets and can be used for both binary and multi-class classification problems.</p> <pre><code>from river import feature_extraction\nfrom river import naive_bayes\nfrom river import metrics\n\nX_y = datasets.SMSSpam()\n\nmodel = (\n    feature_extraction.TFIDF(on='body') | \n    naive_bayes.BernoulliNB(alpha=0)\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    if y_pred is not None:\n        metric.update(y_pred=y_pred, y_true=y)\n        cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 93.00%\n</code></pre> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,809     17  \n True     102    645\n</code></pre> <p>The results are quite good with this first model.</p> <p>Since we are working with an imbalanced dataset, we can use the <code>imblearn</code> module to rebalance the classes of our dataset. For more information about the <code>imblearn</code> module, you can find a dedicated tutorial here.</p> <pre><code>from river import imblearn\n\nX_y = datasets.SMSSpam()\n\nmodel = (\n    feature_extraction.TFIDF(on='body') | \n    imblearn.RandomUnderSampler(\n        classifier=naive_bayes.BernoulliNB(alpha=0),\n        desired_dist={0: .5, 1: .5},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    if y_pred is not None:\n        metric.update(y_pred=y_pred, y_true=y)\n        cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 94.61%\n</code></pre> <p>The <code>imblearn</code> module improved our results. Not bad! We can visualize the pipeline to understand how the data is processed.</p> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,570    255  \n True      41    706\n</code></pre> <pre><code>model\n</code></pre> <pre>TFIDF</pre><code>TFIDF (   normalize=True   on=\"body\"   strip_accents=True   lowercase=True   preprocessor=None   tokenizer=None   ngram_range=(1, 1) ) </code><pre>RandomUnderSampler</pre><code>RandomUnderSampler (   classifier=BernoulliNB (     alpha=0     true_threshold=0.   )   desired_dist={0: 0.5, 1: 0.5}   seed=42 ) </code><pre>BernoulliNB</pre><code>BernoulliNB (   alpha=0   true_threshold=0. ) </code> <p>Now let's try to use logistic regression to classify messages. We will use different tips to make my model perform better. As in the previous example, we rebalance the classes of our dataset. The logistics regression will be fed from a TF-IDF.</p> <pre><code>from river import linear_model\nfrom river import optim\nfrom river import preprocessing\n\nX_y = datasets.SMSSpam()\n\nmodel = (\n    feature_extraction.TFIDF(on='body') | \n    preprocessing.Normalizer() | \n    imblearn.RandomUnderSampler(\n        classifier=linear_model.LogisticRegression(\n            optimizer=optim.SGD(.9), \n            loss=optim.losses.Log()\n        ),\n        desired_dist={0: .5, 1: .5},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    metric.update(y_pred=y_pred, y_true=y)\n    cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 93.80%\n</code></pre> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,584    243  \n True      55    692\n</code></pre> <pre><code>model\n</code></pre> <pre>TFIDF</pre><code>TFIDF (   normalize=True   on=\"body\"   strip_accents=True   lowercase=True   preprocessor=None   tokenizer=None   ngram_range=(1, 1) ) </code><pre>Normalizer</pre><code>Normalizer (   order=2 ) </code><pre>RandomUnderSampler</pre><code>RandomUnderSampler (   classifier=LogisticRegression (     optimizer=SGD (       lr=Constant (         learning_rate=0.9       )     )     loss=Log (       weight_pos=1.       weight_neg=1.     )     l2=0.     l1=0.     intercept_init=0.     intercept_lr=Constant (       learning_rate=0.01     )     clip_gradient=1e+12     initializer=Zeros ()   )   desired_dist={0: 0.5, 1: 0.5}   seed=42 ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.9     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>The results of the logistic regression are quite good but still inferior to the naive Bayes model.</p> <p>Let's try to use word embeddings to improve our logistic regression. Word embeddings allow you to represent a word as a vector. Embeddings are developed to build semantically rich vectors. For instance, the vector which represents the word python should be close to the vector which represents the word programming. We will use spaCy to convert our sentence to vectors. spaCy converts a sentence to a vector by calculating the average of the embeddings of the words in the sentence.</p> <p>You can download pre-trained embeddings in many languages. We will use English pre-trained embeddings as our SMS are in English.</p> <p>The command below allows you to download the pre-trained embeddings that spaCy makes available. More informations about spaCy and its installation may be found here here.</p> <pre><code>python -m spacy download en_core_web_sm\n</code></pre> <p>Here, we create a custom transformer to convert an input sentence to a dict of floats. We will integrate this transformer into our pipeline.</p> <pre><code>import spacy\n\nfrom river.base import Transformer\n\nclass Embeddings(Transformer):\n    \"\"\"My custom transformer, word embedding using spaCy.\"\"\"\n\n    def __init__(self, on: str):\n        self.on = on\n        self.embeddings = spacy.load('en_core_web_sm')\n\n    def transform_one(self, x, y=None):\n        return {dimension: xi for dimension, xi in enumerate(self.embeddings(x[self.on]).vector)}\n</code></pre> <p>Let's train our logistic regression:</p> <pre><code>X_y = datasets.SMSSpam()\n\nmodel = (\n    Embeddings(on='body') | \n    preprocessing.Normalizer() |\n    imblearn.RandomOverSampler(\n        classifier=linear_model.LogisticRegression(\n            optimizer=optim.SGD(.5), \n            loss=optim.losses.Log()\n        ),\n        desired_dist={0: .5, 1: .5},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    metric.update(y_pred=y_pred, y_true=y)\n    cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 91.31%\n</code></pre> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,537    290  \n True      85    662\n</code></pre> <pre><code>model\n</code></pre> <pre>Embeddings</pre><code>Embeddings (   on=\"body\" ) </code><pre>Normalizer</pre><code>Normalizer (   order=2 ) </code><pre>RandomOverSampler</pre><code>RandomOverSampler (   classifier=LogisticRegression (     optimizer=SGD (       lr=Constant (         learning_rate=0.5       )     )     loss=Log (       weight_pos=1.       weight_neg=1.     )     l2=0.     l1=0.     intercept_init=0.     intercept_lr=Constant (       learning_rate=0.01     )     clip_gradient=1e+12     initializer=Zeros ()   )   desired_dist={0: 0.5, 1: 0.5}   seed=42 ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.5     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>The results of the logistic regression using spaCy embeddings are lower than those obtained with TF-IDF values. We could surely improve the results by cleaning up the text. We could also use embeddings more suited to our dataset. However, on this problem, the logistic regression is not better than the Naive Bayes model. No free lunch today.</p>"},{"location":"examples/the-art-of-using-pipelines/","title":"The art of using pipelines","text":"<p>Pipelines are a natural way to think about a machine learning system. Indeed with some practice a data scientist can visualise data \"flowing\" through a series of steps. The input is typically some raw data which has to be processed in some manner. The goal is to represent the data in such a way that is can be ingested by a machine learning algorithm. Along the way some steps will extract features, while others will normalize the data and remove undesirable elements. Pipelines are simple, and yet they are a powerful way of designing sophisticated machine learning systems.</p> <p>Both scikit-learn and pandas make it possible to use pipelines. However it's quite rare to see pipelines being used in practice (at least on Kaggle). Sometimes you get to see people using scikit-learn's <code>pipeline</code> module, however the <code>pipe</code> method from <code>pandas</code> is sadly underappreciated. A big reason why pipelines are not given much love is that it's easier to think of batch learning in terms of a script or a notebook. Indeed many people doing data science seem to prefer a procedural style to a declarative style. Moreover in practice pipelines can be a bit rigid if one wishes to do non-orthodox operations.</p> <p>Although pipelines may be a bit of an odd fit for batch learning, they make complete sense when they are used for online learning. Indeed the UNIX philosophy has advocated the use of pipelines for data processing for many decades. If you can visualise data as a stream of observations then using pipelines should make a lot of sense to you. We'll attempt to convince you by writing a machine learning algorithm in a procedural way and then converting it to a declarative pipeline in small steps. Hopefully by the end you'll be convinced, or not!</p> <p>In this notebook we'll manipulate data from the Kaggle Recruit Restaurants Visitor Forecasting competition. The data is directly available through River's <code>datasets</code> module.</p> <pre><code>from pprint import pprint\nfrom river import datasets\n\nfor x, y in datasets.Restaurants():\n    pprint(x)\n    pprint(y)\n    break\n</code></pre> <pre><code>{'area_name': 'T\u014dky\u014d-to Nerima-ku Toyotamakita',\n 'date': datetime.datetime(2016, 1, 1, 0, 0),\n 'genre_name': 'Izakaya',\n 'is_holiday': True,\n 'latitude': 35.7356234,\n 'longitude': 139.6516577,\n 'store_id': 'air_04341b588bde96cd'}\n10\n</code></pre> <p>We'll start by building and running a model using a procedural coding style. The performance of the model doesn't matter, we're simply interested in the design of the model.</p> <pre><code>from river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stats\nfrom river import utils\n\nmeans = (\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)),\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)),\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21))\n)\n\nscaler = preprocessing.StandardScaler()\nlin_reg = linear_model.LinearRegression()\nmetric = metrics.MAE()\n\nfor x, y in datasets.Restaurants():\n\n    # Derive date features\n    x['weekday'] = x['date'].weekday()\n    x['is_weekend'] = x['date'].weekday() in (5, 6)\n\n    # Process the rolling means of the target  \n    for mean in means:\n        x = {**x, **mean.transform_one(x)}\n        mean.learn_one(x, y)\n\n    # Remove the key/value pairs that aren't features\n    for key in ['store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude']:\n        x.pop(key)\n\n    # Rescale the data\n    scaler.learn_one(x)\n    x = scaler.transform_one(x)\n\n    # Fit the linear regression\n    y_pred = lin_reg.predict_one(x)\n    lin_reg.learn_one(x, y)\n\n    # Update the metric using the out-of-fold prediction\n    metric.update(y, y_pred)\n\nprint(metric)\n</code></pre> <pre><code>MAE: 8.316538\n</code></pre> <p>We're not using many features. We can print the last <code>x</code> to get an idea of the features (don't forget they've been scaled!)</p> <pre><code>pprint(x)\n</code></pre> <pre><code>{'is_holiday': -0.23103573677646685,\n 'is_weekend': 1.6249280076334165,\n 'weekday': 1.0292832579142892,\n 'y_mean_by_store_id': -1.3980979075298516}\n</code></pre> <p>The above chunk of code is quite explicit but it's a bit verbose. The whole point of libraries such as River is to make life easier for users. Moreover there's too much space for users to mess up the order in which things are done, which increases the chance of there being target leakage. We'll now rewrite our model in a declarative fashion using a pipeline \u00e0 la sklearn.  </p> <pre><code>from river import compose\n\n\ndef get_date_features(x):\n    weekday =  x['date'].weekday()\n    return {'weekday': weekday, 'is_weekend': weekday in (5, 6)}\n\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('date_features', compose.FuncTransformer(get_date_features)),\n        ('last_7_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7))),\n        ('last_14_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14))),\n        ('last_21_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21)))\n    )),\n    ('drop_non_features', compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude')),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression())\n)\n\nmetric = metrics.MAE()\n\nfor x, y in datasets.Restaurants():\n\n    # Make a prediction without using the target\n    y_pred = model.predict_one(x)\n\n    # Update the model using the target\n    model.learn_one(x, y)\n\n    # Update the metric using the out-of-fold prediction\n    metric.update(y, y_pred)\n\nprint(metric)\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>We use a <code>Pipeline</code> to arrange each step in a sequential order. A <code>TransformerUnion</code> is used to merge multiple feature extractors into a single transformer. The <code>for</code> loop is now much shorter and is thus easier to grok: we get the out-of-fold prediction, we fit the model, and finally we update the metric. This way of evaluating a model is typical of online learning, and so we put it wrapped it inside a function called <code>progressive_val_score</code> part of the <code>evaluate</code> module. We can use it to replace the <code>for</code> loop.</p> <pre><code>from river import evaluate\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('date_features', compose.FuncTransformer(get_date_features)),\n        ('last_7_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7))),\n        ('last_14_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14))),\n        ('last_21_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21)))\n    )),\n    ('drop_non_features', compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude')),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression())\n)\n\nevaluate.progressive_val_score(dataset=datasets.Restaurants(), model=model, metric=metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Notice that you couldn't have used the <code>progressive_val_score</code> method if you wrote the model in a procedural manner.</p> <p>Our code is getting shorter, but it's still a bit difficult on the eyes. Indeed there is a lot of boilerplate code associated with pipelines that can get tedious to write. However River has some special tricks up it's sleeve to save you from a lot of pain.</p> <p>The first trick is that the name of each step in the pipeline can be omitted. If no name is given for a step then River automatically infers one.</p> <pre><code>model = compose.Pipeline(\n    compose.TransformerUnion(\n        compose.FuncTransformer(get_date_features),\n        feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)),\n        feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)),\n        feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21))\n    ),\n    compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude'),\n    preprocessing.StandardScaler(),\n    linear_model.LinearRegression()\n)\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Under the hood a <code>Pipeline</code> inherits from <code>collections.OrderedDict</code>. Indeed this makes sense because if you think about it a <code>Pipeline</code> is simply a sequence of steps where each step has a name. The reason we mention this is because it means you can manipulate a <code>Pipeline</code> the same way you would manipulate an ordinary <code>dict</code>. For instance we can print the name of each step by using the <code>keys</code> method.</p> <pre><code>for name in model.steps:\n    print(name)\n</code></pre> <pre><code>TransformerUnion\nDiscard\nStandardScaler\nLinearRegression\n</code></pre> <p>The first step is a <code>FeatureUnion</code> and it's string representation contains the string representation of each of it's elements. Not having to write names saves up some time and space and is certainly less tedious.</p> <p>The next trick is that we can use mathematical operators to compose our pipeline. For example we can use the <code>+</code> operator to merge <code>Transformer</code>s into a <code>TransformerUnion</code>. </p> <pre><code>model = compose.Pipeline(\n    compose.FuncTransformer(get_date_features) + \\\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)) + \\\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)) + \\\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21)),\n\n    compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude'),\n    preprocessing.StandardScaler(),\n    linear_model.LinearRegression()\n)\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Likewhise we can use the <code>|</code> operator to assemble steps into a <code>Pipeline</code>. </p> <pre><code>model = (\n    compose.FuncTransformer(get_date_features) +\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)) +\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)) +\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21))\n)\n\nto_discard = ['store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude']\n\nmodel = model | compose.Discard(*to_discard) | preprocessing.StandardScaler()\n\nmodel |= linear_model.LinearRegression()\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Hopefully you'll agree that this is a powerful way to express machine learning pipelines. For some people this should be quite remeniscent of the UNIX pipe operator. One final trick we want to mention is that functions are automatically wrapped with a <code>FuncTransformer</code>, which can be quite handy.</p> <pre><code>model = get_date_features\n\nfor n in [7, 14, 21]:\n    model += feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), n))\n\nmodel |= compose.Discard(*to_discard)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Naturally some may prefer the procedural style we first used because they find it easier to work with. It all depends on your style and you should use what you feel comfortable with. However we encourage you to use operators because we believe that this will increase the readability of your code, which is very important. To each their own!</p> <p>Before finishing we can take an interactive look at our pipeline.</p> <pre><code>model\n</code></pre> <pre>get_date_features</pre><code> def get_date_features(x):     weekday =  x['date'].weekday()     return {'weekday': weekday, 'is_weekend': weekday in (5, 6)}  </code><pre>y_mean_by_store_id</pre><code>TargetAgg (   by=['store_id']   how=Rolling (     obj=Mean ()     window_size=7   )   target_name=\"y\" ) </code><pre>y_mean_by_store_id</pre><code>TargetAgg (   by=['store_id']   how=Rolling (     obj=Mean ()     window_size=14   )   target_name=\"y\" ) </code><pre>y_mean_by_store_id</pre><code>TargetAgg (   by=['store_id']   how=Rolling (     obj=Mean ()     window_size=21   )   target_name=\"y\" ) </code><pre>~['area_name', [...]</pre><code>Discard (   area_name   date   genre_name   latitude   longitude   store_id ) </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/","title":"Part 1","text":"<p>Table of contents of this tutorial series on matrix factorization for recommender systems:</p> <ul> <li>Part 1 - Traditional Matrix Factorization methods for Recommender Systems</li> <li>Part 2 - Factorization Machines and Field-aware Factorization Machines</li> <li>Part 3 - Large scale learning and better predictive power with multiple pass learning</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#introduction","title":"Introduction","text":"<p>A recommender system is a software tool designed to generate and suggest items or entities to the users. Popular large scale examples include:</p> <ul> <li>Amazon (suggesting products)</li> <li>Facebook (suggesting posts in users' news feeds)</li> <li>Spotify (suggesting music)</li> </ul> <p>Social recommendation from graph (mostly used by social networks) are not covered in River. We focus on the general case, item recommendation. This problem can be represented with the user-item matrix:</p> \\[ \\normalsize \\begin{matrix}     &amp; \\begin{matrix} _1 &amp; _\\cdots &amp; _\\cdots &amp; _\\cdots &amp; _I \\end{matrix} \\\\     \\begin{matrix} _1 \\\\ _\\vdots \\\\ _\\vdots \\\\ _\\vdots \\\\ _U \\end{matrix} &amp;          \\begin{bmatrix}             {\\color{Red} ?} &amp; 2 &amp; \\cdots &amp; {\\color{Red} ?} &amp; {\\color{Red} ?} \\\\             {\\color{Red} ?} &amp; {\\color{Red} ?} &amp; \\cdots &amp; {\\color{Red} ?} &amp; 4.5 \\\\             \\vdots &amp; \\ddots &amp; \\ddots &amp; \\ddots &amp; \\vdots \\\\             3 &amp; {\\color{Red} ?} &amp; \\cdots &amp; {\\color{Red} ?} &amp; {\\color{Red} ?} \\\\             {\\color{Red} ?} &amp; {\\color{Red} ?} &amp; \\cdots &amp; 5 &amp; {\\color{Red} ?}         \\end{bmatrix} \\end{matrix} \\] <p>Where \\(U\\) and \\(I\\) are the number of user and item of the system, respectively. A matrix entry represents a user's preference for an item, it can be a rating, a like or dislike, etc. Because of the huge number of users and items compared to the number of observed entries, those matrices are very sparsed (usually less than 1% filled).</p> <p>Matrix Factorization (MF) is a class of collaborative filtering algorithms derived from Singular Value Decomposition (SVD). MF strength lies in its capacity to able to model high cardinality categorical variables interactions. This subfield boomed during the famous Netflix Prize contest in 2006, when numerous novel variants has been invented and became popular thanks to their attractive accuracy and scalability.</p> <p>MF approach seeks to fill the user-item matrix considering the problem as a matrix completion one. MF core idea assume a latent model learning its own representation of the users and the items in a lower latent dimensional space by factorizing the observed parts of the matrix.</p> <p>A factorized user or item is represented as a vector \\(\\mathbf{v}_u\\) or \\(\\mathbf{v}_i\\) composed of \\(k\\) latent factors, with \\(k &lt;&lt; U, I\\). Those learnt latent variables represent, for an item the various aspects describing it, and for a user its interests in terms of those aspects. The model then assume a user's choice or fondness is composed of a sum of preferences about the various aspects of the concerned item. This sum being the dot product between the latent vectors of a given user-item pair:</p> \\[ \\normalsize \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{u, f} \\cdot \\mathbf{v}_{i, f} \\] <p>MF models weights are learnt in an online fashion, often with stochastic gradient descent as it provides relatively fast running time and good accuracy. There is a great and widely popular library named surprise that implements MF models (and others) but in contrast with River doesn't follow a pure online philosophy (all the data have to be loaded in memory and the API doesn't allow you to update your model with new data).</p> <p>Notes:</p> <ul> <li>In recent years, proposed deep learning techniques for recommendation tasks claim state of the art results. However, recent work (August 2019) showed that those promises can't be taken for granted and traditional MF methods are still relevant today.</li> <li>For more information about how the business value of recommender systems is measured and why they are one of the main success stories of machine learning, see the following literature survey (December 2019).</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#lets-start","title":"Let's start","text":"<p>In this tutorial, we are going to explore MF algorithms available in River and test them on a movie recommendation problem with the MovieLens 100K dataset. This latter is a collection of movie ratings (from 1 to 5) that includes various information about both the items and the users. We can access it from the river.datasets module:</p> <pre><code>import json\n\nfrom river import datasets\n\nfor x, y in datasets.MovieLens100K():\n    print(f'x = {json.dumps(x, indent=4)}')\n    print(f'y = {y}')\n    break\n</code></pre> <pre><code>x = {\n    \"user\": \"259\",\n    \"item\": \"255\",\n    \"timestamp\": 874731910000000000,\n    \"title\": \"My Best Friend's Wedding (1997)\",\n    \"release_date\": 866764800000000000,\n    \"genres\": \"comedy, romance\",\n    \"age\": 21.0,\n    \"gender\": \"M\",\n    \"occupation\": \"student\",\n    \"zip_code\": \"48823\"\n}\ny = 4.0\n</code></pre> <p>Let's define a routine to evaluate our different models on MovieLens 100K. Mean Absolute Error and Root Mean Squared Error will be our metrics printed alongside model's computation time and memory usage:</p> <pre><code>from river import metrics\nfrom river.evaluate import progressive_val_score\n\ndef evaluate(model, unpack_user_and_item=True):\n    X_y = datasets.MovieLens100K(unpack_user_and_item)\n    metric = metrics.MAE() + metrics.RMSE()\n    _ = progressive_val_score(X_y, model, metric, print_every=25_000, show_time=True, show_memory=True)\n</code></pre>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#naive-prediction","title":"Naive prediction","text":"<p>It's good practice in machine learning to start with a naive baseline and then iterate from simple things to complex ones observing progress incrementally. Let's start by predicting the target running mean as a first shot:</p> <pre><code>from river import dummy\nfrom river import stats\n\nmodel = dummy.StatisticRegressor(stats.Mean())\nevaluate(model, unpack_user_and_item=False)\n</code></pre> <pre><code>[25,000] MAE: 0.934259\nRMSE: 1.124469 \u2013 00:00:00 \u2013 898 B\n[50,000] MAE: 0.923893\nRMSE: 1.105 \u2013 00:00:00 \u2013 898 B\n[75,000] MAE: 0.937359\nRMSE: 1.123696 \u2013 00:00:00 \u2013 898 B\n[100,000] MAE: 0.942162\nRMSE: 1.125783 \u2013 00:00:01 \u2013 898 B\n</code></pre>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#baseline-model","title":"Baseline model","text":"<p>Now we can do machine learning and explore available models in river.reco module starting with the baseline model. It extends our naive prediction by adding to the global running mean two bias terms characterizing the user and the item discrepancy from the general tendency. The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i} \\] <p>This baseline model can be viewed as a linear regression where the intercept is replaced by the target running mean with the users and the items one hot encoded.</p> <p>All machine learning models in River expect dicts as input with feature names as keys and feature values as values. Specifically, models from <code>river.reco</code> expect a <code>'user'</code> and an <code>'item'</code> entries without any type constraint on their values (i.e. can be strings or numbers), e.g.:</p> <pre><code>x = {\n    'user': 'Guido',\n    'item': \"Monty Python's Flying Circus\"\n}\n</code></pre> <p>Other entries, if exist, are simply ignored. This is quite useful as we don't need to spend time and storage doing one hot encoding.</p> <pre><code>from river import preprocessing\nfrom river import optim\nfrom river import reco\n\nbaseline_params = {\n    'optimizer': optim.SGD(0.025),\n    'l2': 0.,\n    'initializer': optim.initializers.Zeros()\n}\n\nmodel = preprocessing.PredClipper(\n    regressor=reco.Baseline(**baseline_params),\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761844\nRMSE: 0.960972 \u2013 00:00:00 \u2013 161.03 KB\n[50,000] MAE: 0.753292\nRMSE: 0.951223 \u2013 00:00:00 \u2013 216.34 KB\n[75,000] MAE: 0.754177\nRMSE: 0.953376 \u2013 00:00:01 \u2013 254.81 KB\n[100,000] MAE: 0.754651\nRMSE: 0.954148 \u2013 00:00:01 \u2013 278.41 KB\n</code></pre> <p>We won two tenth of MAE compared to our naive prediction (0.7546 vs 0.9421) meaning that significant information has been learnt by the model.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#funk-matrix-factorization-funkmf","title":"Funk Matrix Factorization (FunkMF)","text":"<p>It's the pure form of matrix factorization consisting of only learning the users and items latent representations as discussed in introduction. Simon Funk popularized its stochastic gradient descent optimization in 2006 during the Netflix Prize. The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle \\] <p>Note: FunkMF is sometimes referred as Probabilistic Matrix Factorization which is an extended probabilistic version.</p> <pre><code>funk_mf_params = {\n    'n_factors': 10,\n    'optimizer': optim.SGD(0.05),\n    'l2': 0.1,\n    'initializer': optim.initializers.Normal(mu=0., sigma=0.1, seed=73)\n}\n\nmodel = preprocessing.PredClipper(\n    regressor=reco.FunkMF(**funk_mf_params),\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 1.070136\nRMSE: 1.397014 \u2013 00:00:00 \u2013 557.99 KB\n[50,000] MAE: 0.99174\nRMSE: 1.290666 \u2013 00:00:01 \u2013 690.31 KB\n[75,000] MAE: 0.961072\nRMSE: 1.250842 \u2013 00:00:01 \u2013 813.07 KB\n[100,000] MAE: 0.944883\nRMSE: 1.227688 \u2013 00:00:02 \u2013 914.17 KB\n</code></pre> <p>Results are equivalent to our naive prediction (0.9448 vs 0.9421). By only focusing on the users preferences and the items characteristics, the model is limited in his ability to capture different views of the problem. Despite its poor performance alone, this algorithm is quite useful combined in other models or when we need to build dense representations for other tasks.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#biased-matrix-factorization-biasedmf","title":"Biased Matrix Factorization (BiasedMF)","text":"<p>It's the combination of the Baseline model and FunkMF. The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i} + \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle \\] <p>Note: Biased Matrix Factorization name is used by some people but some others refer to it by SVD or Funk SVD. It's the case of Yehuda Koren and Robert Bell in Recommender Systems Handbook (Chapter 5 Advances in Collaborative Filtering) and of <code>surprise</code> library. Nevertheless, SVD could be confused with the original Singular Value Decomposition from which it's derived from, and Funk SVD could also be misleading because of the biased part of the model equation which doesn't come from Simon Funk's work. For those reasons, we chose to side with Biased Matrix Factorization which fits more naturally to it.</p> <pre><code>biased_mf_params = {\n    'n_factors': 10,\n    'bias_optimizer': optim.SGD(0.025),\n    'latent_optimizer': optim.SGD(0.05),\n    'weight_initializer': optim.initializers.Zeros(),\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.1, seed=73),\n    'l2_bias': 0.,\n    'l2_latent': 0.\n}\n\nmodel = preprocessing.PredClipper(\n    regressor=reco.BiasedMF(**biased_mf_params),\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761818\nRMSE: 0.961057 \u2013 00:00:00 \u2013 643.81 KB\n[50,000] MAE: 0.751667\nRMSE: 0.949443 \u2013 00:00:01 \u2013 817.72 KB\n[75,000] MAE: 0.749653\nRMSE: 0.948723 \u2013 00:00:01 \u2013 964.02 KB\n[100,000] MAE: 0.748559\nRMSE: 0.947854 \u2013 00:00:02 \u2013 1.05 MB\n</code></pre> <p>Results improved (0.7485 vs 0.7546) demonstrating that users and items latent representations bring additional information.</p> <p>To conclude this first tutorial about factorization models, let's review the important parameters to tune when dealing with this family of methods:</p> <ul> <li><code>n_factors</code>: the number of latent factors. The more you set, the more items aspects and users preferences you are going to learn. Too many will cause overfitting, <code>l2</code> regularization could help.</li> <li><code>*_optimizer</code>: the optimizers. Classic stochastic gradient descent performs well, finding the good learning rate will make the difference.</li> <li><code>initializer</code>: the latent weights initialization. Latent vectors have to be initialized with non-constant values. We generally sample them from a zero-mean normal distribution with small standard deviation.</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/","title":"Part 2","text":"<p>As seen in Part 1, strength of Matrix Factorization (MF) lies in its ability to deal with sparse and high cardinality categorical variables. In this second tutorial we will have a look at Factorization Machines (FM) algorithm and study how it generalizes the power of MF.</p> <p>Table of contents of this tutorial series on matrix factorization for recommender systems:</p> <ul> <li>Part 1 - Traditional Matrix Factorization methods for Recommender Systems</li> <li>Part 2 - Factorization Machines and Field-aware Factorization Machines</li> <li>Part 3 - Large scale learning and better predictive power with multiple pass learning</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#factorization-machines","title":"Factorization Machines","text":"<p>Steffen Rendel came up in 2010 with Factorization Machines, an algorithm able to handle any real valued feature vector, combining the advantages of general predictors with factorization models. It became quite popular in the field of online advertising, notably after winning several Kaggle competitions. The modeling technique starts with a linear regression to capture the effects of each variable individually:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j} \\] <p>Then are added interaction terms to learn features relations. Instead of learning a single and specific weight per interaction (as in polynomial regression), a set of latent factors is learnt per feature (as in MF). An interaction is calculated by multiplying involved features product with their latent vectors dot product. The degree of factorization \u2014 or model order \u2014 represents the maximum number of features per interaction considered. The model equation for a factorization machine of degree \\(d\\) = 2 is defined as:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'} \\] <p>Where \\(\\normalsize \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle\\) is the dot product of \\(j\\) and \\(j'\\) latent vectors:</p> \\[ \\normalsize \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{j, f} \\cdot \\mathbf{v}_{j', f} \\] <p>Higher-order FM will be covered in a following section, just note that factorization models express their power in sparse settings, which is also where higher-order interactions are hard to estimate.</p> <p>Strong emphasis must be placed on feature engineering as it allows FM to mimic most factorization models and significantly impact its performance. High cardinality categorical variables one hot encoding is the most frequent step before feeding the model with data. For more efficiency, River FM implementation considers string values as categorical variables and automatically one hot encode them. FM models have their own module river.facto.</p> <p>## Mimic Biased Matrix Factorization (BiasedMF)</p> <p>Let's start with a simple example where we want to reproduce the Biased Matrix Factorization model we trained in the previous tutorial. For a fair comparison with Part 1 example, let's set the same evaluation framework:</p> <pre><code>from river import datasets\nfrom river import metrics\nfrom river.evaluate import progressive_val_score\n\ndef evaluate(model):\n    X_y = datasets.MovieLens100K()\n    metric = metrics.MAE() + metrics.RMSE()\n    _ = progressive_val_score(X_y, model, metric, print_every=25_000, show_time=True, show_memory=True)\n</code></pre> <p>In order to build an equivalent model we need to use the same hyper-parameters. As we can't replace FM intercept by the global running mean we won't be able to build the exact same model:</p> <pre><code>from river import compose\nfrom river import facto\nfrom river import preprocessing\nfrom river import optim\nfrom river import stats\n\nfm_params = {\n    'n_factors': 10,\n    'weight_optimizer': optim.SGD(0.025),\n    'latent_optimizer': optim.SGD(0.05),\n    'sample_normalization': False,\n    'l1_weight': 0.,\n    'l2_weight': 0.,\n    'l1_latent': 0.,\n    'l2_latent': 0.,\n    'intercept': 3,\n    'intercept_lr': .01,\n    'weight_initializer': optim.initializers.Zeros(),\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.1, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor |= facto.FMRegressor(**fm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761778\nRMSE: 0.960803 \u2013 00:00:01 \u2013 778.29 KB\n[50,000] MAE: 0.751986\nRMSE: 0.949941 \u2013 00:00:02 \u2013 908.2 KB\n[75,000] MAE: 0.750044\nRMSE: 0.948911 \u2013 00:00:03 \u2013 1.03 MB\n[100,000] MAE: 0.748609\nRMSE: 0.947994 \u2013 00:00:05 \u2013 1.15 MB\n</code></pre> <p>Both MAE are very close to each other (0.7486 vs 0.7485) showing that we almost reproduced [reco.BiasedMF](../../../api/reco/BiasedMF) algorithm. The cost is a naturally slower running time as FM implementation offers more flexibility.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#feature-engineering-for-fm-models","title":"Feature engineering for FM models","text":"<p>Let's study the basics of how to properly encode data for FM models. We are going to keep using MovieLens 100K as it provides various feature types:</p> <pre><code>import json\n\nfor x, y in datasets.MovieLens100K():\n    print(f'x = {json.dumps(x, indent=4)}\\ny = {y}')\n    break\n</code></pre> <pre><code>x = {\n    \"user\": \"259\",\n    \"item\": \"255\",\n    \"timestamp\": 874731910000000000,\n    \"title\": \"My Best Friend's Wedding (1997)\",\n    \"release_date\": 866764800000000000,\n    \"genres\": \"comedy, romance\",\n    \"age\": 21.0,\n    \"gender\": \"M\",\n    \"occupation\": \"student\",\n    \"zip_code\": \"48823\"\n}\ny = 4.0\n</code></pre> <p>The features we are going to add to our model don't improve its predictive power. Nevertheless, they are useful to illustrate different methods of data encoding:</p> <ol> <li>Set-categorical variables</li> </ol> <p>We have seen that categorical variables are one hot encoded automatically if set to strings, in the other hand, set-categorical variables must be encoded explicitly by the user. A good way of doing so is to assign them a value of \\(1/m\\), where \\(m\\) is the number of elements of the sample set. It gives the feature a constant \"weight\" across all samples preserving model's stability. Let's create a routine to encode movies genres this way:</p> <pre><code>def split_genres(x):\n    genres = x['genres'].split(', ')\n    return {f'genre_{genre}': 1 / len(genres) for genre in genres}\n</code></pre> <ol> <li>Numerical variables</li> </ol> <p>In practice, transforming numerical features into categorical ones works better in most cases. Feature binning is the natural way, but finding good bins is sometimes more an art than a science. Let's encode users age with something simple:</p> <pre><code>def bin_age(x):\n    if x['age'] &lt;= 18:\n        return {'age_0-18': 1}\n    elif x['age'] &lt;= 32:\n        return {'age_19-32': 1}\n    elif x['age'] &lt; 55:\n        return {'age_33-54': 1}\n    else:\n        return {'age_55-100': 1}\n</code></pre> <p>Let's put everything together:</p> <pre><code>fm_params = {\n    'n_factors': 14,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.05, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.FMRegressor(**fm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.759838\nRMSE: 0.961281 \u2013 00:00:03 \u2013 895.78 KB\n[50,000] MAE: 0.751307\nRMSE: 0.951391 \u2013 00:00:08 \u2013 1.02 MB\n[75,000] MAE: 0.750361\nRMSE: 0.951393 \u2013 00:00:12 \u2013 1.18 MB\n[100,000] MAE: 0.749994\nRMSE: 0.951435 \u2013 00:00:16 \u2013 1.33 MB\n</code></pre> <p>Note that using more variables involves factorizing a larger latent space, then increasing the number of latent factors \\(k\\) often helps capturing more information.</p> <p>Some other feature engineering tips from 3 idiots' winning solution for Kaggle Criteo display ads competition in 2014:</p> <ul> <li>Infrequent modalities often bring noise and little information, transforming them into a special tag can help</li> <li>In some cases, sample-wise normalization seems to make the optimization problem easier to be solved</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#higher-order-factorization-machines-hofm","title":"Higher-Order Factorization Machines (HOFM)","text":"<p>The model equation generalized to any order \\(d \\geq 2\\) is defined as:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{l=2}^{d} \\sum_{j_1=1}^{p} \\cdots \\sum_{j_l=j_{l-1}+1}^{p} \\left(\\prod_{j'=1}^{l} x_{j_{j'}} \\right) \\left(\\sum_{f=1}^{k_l} \\prod_{j'=1}^{l} v_{j_{j'}, f}^{(l)} \\right) \\] <pre><code>hofm_params = {\n    'degree': 3,\n    'n_factors': 12,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.05, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.HOFMRegressor(**hofm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761297\nRMSE: 0.962054 \u2013 00:00:15 \u2013 1.67 MB\n[50,000] MAE: 0.751865\nRMSE: 0.951499 \u2013 00:00:31 \u2013 1.97 MB\n[75,000] MAE: 0.750853\nRMSE: 0.951526 \u2013 00:00:47 \u2013 2.3 MB\n[100,000] MAE: 0.750607\nRMSE: 0.951982 \u2013 00:01:03 \u2013 2.6 MB\n</code></pre> <p>As said previously, high-order interactions are often hard to estimate due to too much sparsity, that's why we won't spend too much time here.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#field-aware-factorization-machines-ffm","title":"Field-aware Factorization Machines (FFM)","text":"<p>Field-aware variant of FM (FFM) improved the original method by adding the notion of \"fields\". A \"field\" is a group of features that belong to a specific domain (e.g. the \"users\" field, the \"items\" field, or the \"movie genres\" field).</p> <p>FFM restricts itself to pairwise interactions and factorizes separated latent spaces \u2014 one per combination of fields (e.g. users/items, users/movie genres, or items/movie genres) \u2014 instead of a common one shared by all fields. Therefore, each feature has one latent vector per field it can interact with \u2014 so that it can learn the specific effect with each different field.</p> <p>The model equation is defined by:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_{j, f_{j'}}, \\mathbf{v}_{j', f_{j}} \\rangle x_{j} x_{j'} \\] <p>Where \\(f_j\\) and \\(f_{j'}\\) are the fields corresponding to \\(j\\) and \\(j'\\) features, respectively.</p> <pre><code>ffm_params = {\n    'n_factors': 8,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.05, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.FFMRegressor(**ffm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.757718\nRMSE: 0.958158 \u2013 00:00:06 \u2013 2.04 MB\n[50,000] MAE: 0.749502\nRMSE: 0.948065 \u2013 00:00:12 \u2013 2.41 MB\n[75,000] MAE: 0.749275\nRMSE: 0.948918 \u2013 00:00:18 \u2013 2.82 MB\n[100,000] MAE: 0.749542\nRMSE: 0.949769 \u2013 00:00:24 \u2013 3.19 MB\n</code></pre> <p>Note that FFM usually needs to learn smaller number of latent factors \\(k\\) than FM as each latent vector only deals with one field.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#field-weighted-factorization-machines-fwfm","title":"Field-weighted Factorization Machines (FwFM)","text":"<p>Field-weighted Factorization Machines (FwFM) address FFM memory issues caused by its large number of parameters, which is in the order of feature number times field number. As FFM, FwFM is an extension of FM restricted to pairwise interactions, but instead of factorizing separated latent spaces, it learns a specific weight \\(r_{f_j, f_{j'}}\\) for each field combination modelling the interaction strength.</p> <p>The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} r_{f_j, f_{j'}} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'} \\] <pre><code>fwfm_params = {\n    'n_factors': 10,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'seed': 73,\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.FwFMRegressor(**fwfm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761539\nRMSE: 0.962241 \u2013 00:00:07 \u2013 792.94 KB\n[50,000] MAE: 0.754089\nRMSE: 0.953181 \u2013 00:00:15 \u2013 922.85 KB\n[75,000] MAE: 0.754806\nRMSE: 0.954979 \u2013 00:00:22 \u2013 1.04 MB\n[100,000] MAE: 0.755404\nRMSE: 0.95604 \u2013 00:00:30 \u2013 1.17 MB\n</code></pre>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-3/","title":"Part 3","text":"<p>To do.</p>"},{"location":"faq/","title":"Frequently Asked Questions","text":""},{"location":"faq/#do-all-classifiers-support-multi-class-classification","title":"Do all classifiers support multi-class classification?","text":"<p>No, they don't. Although binary classification can be seen as a special case of multi-class classification, there are many optimizations that can be performed if we know that there are only two classes. It would be annoying to have to check whether this is the case in an online setting. All in all we find that separating both cases leads to much cleaner code. Note that the <code>multiclass</code> module contains wrapper models that enable you to perform multi-class classification with binary classifiers.</p>"},{"location":"faq/#how-do-i-know-if-a-classifier-supports-multi-class-classification","title":"How do I know if a classifier supports multi-class classification?","text":"<p>Each classifier in River inherits from the <code>base.Classifier</code> class. Each classifier therefore has a <code>_multiclass</code> property which indicates whether or not it can process a non-boolean target value.</p> <pre><code>&gt;&gt;&gt; from river import linear_model\n\n&gt;&gt;&gt; classifier = linear_model.LogisticRegression()\n&gt;&gt;&gt; classifier._multiclass\nFalse\n</code></pre>"},{"location":"faq/#why-doesnt-river-do-any-input-validation","title":"Why doesn't river do any input validation?","text":"<p>Python encourages a coding style called EAFP, which stands for \"Easier to Ask for Forgiveness than Permission\". The idea is to assume that runtime errors don't occur, and instead use try/expects to catch errors. The great benefit is that we don't have to drown our code with <code>if</code> statements, which is symptomatic of the LBYL style, which stands for \"look before you leap\". This makes our implementations much more readable than, say, scikit-learn, which does a lot of input validation. The catch is that users have to be careful to use sane inputs. As always, there is no free lunch!</p>"},{"location":"faq/#what-about-reinforcement-learning","title":"What about reinforcement learning?","text":"<p>Reinforcement learning works in an online manner because of the nature of the task. Reinforcement learning can be therefore be seen as a subcase of online machine learning. However, we prefer not to support it because there are already many existing opensource libraries dedicated to it.</p>"},{"location":"faq/#what-are-the-differences-between-scikit-learns-online-learning-algorithm-which-have-a-partial_fit-method-and-their-equivalents-in-river","title":"What are the differences between scikit-learn's online learning algorithm which have a partial_fit method and their equivalents in River?","text":"<p>The algorithms from <code>sklearn</code> that support incremental learning are mostly meant for mini-batch learning. In a pure streaming context where the observations arrive one by one, then River is much faster than <code>sklearn</code>. This is mostly because <code>sklearn</code> incurs a lot of overhead by performing data checks. Also, sklearn assumes that you're always using the same number of features. This is not the case with River because it use dictionaries which allows you to drop and add features as you wish.</p>"},{"location":"faq/#how-do-i-save-and-load-models","title":"How do I save and load models?","text":"<pre><code>&gt;&gt;&gt; from river import ensemble\n&gt;&gt;&gt; import pickle\n\n&gt;&gt;&gt; model = ensemble.AdaptiveRandomForestClassifier()\n\n# save\n&gt;&gt;&gt; with open('model.pkl', 'wb') as f:\n...     pickle.dump(model, f)\n\n# load\n&gt;&gt;&gt; with open('model.pkl', 'rb') as f:\n...     model = pickle.load(f)\n</code></pre> <p>We also encourage you to try out dill and cloudpickle.</p>"},{"location":"faq/#what-about-neural-networks","title":"What about neural networks?","text":"<p>There are many great open-source libraries for building neural network models. We don't feel that we can bring anything of value to the existing Python ecosystem. However, we are open to implementing compatibility wrappers for popular libraries such as PyTorch and Keras.</p>"},{"location":"faq/#who-are-the-authors-of-this-library","title":"Who are the authors of this library?","text":"<p>We are research engineers, graduate students, PhDs and machine learning researchers. The members of the develompent team are mainly located in France, Brazil and New Zealand.</p>"},{"location":"introduction/basic-concepts/","title":"Basic concepts","text":"<p>Here are some concepts to give you a feel for what problems River addresses.</p>"},{"location":"introduction/basic-concepts/#data-streams","title":"Data streams","text":"<p>River is a library to build online machine learning models. Such models operate on data streams. But a data stream is a bit of a vague concept.</p> <p>In general, a data stream is a sequence of individual elements. In the case of machine learning, each element is a bunch of features. We call these samples, or observations. Each sample might follow a fixed structure and always contain the same features. But features can also appear and disappear over time. That depends on the use case.</p>"},{"location":"introduction/basic-concepts/#reactive-and-proactive-data-streams","title":"Reactive and proactive data streams","text":"<p>The origin of a data stream can vary, and usually it doesn't matter. You should be able to use River regardless of where your data comes from. It is however important to keep in mind the difference between reactive and proactive data streams.</p> <p>Reactive data streams are ones where the data comes to you. For instance, when a user visits your website, that's out of your control. You have no influence on the event. It just happens and you have to react to it.</p> <p>Proactive data streams are ones where you have control on the data stream. For example, you might be reading the data from a file. You decide at which speed you want to read the data, in what order, etc.</p> <p>If you consider data analysis as a whole, you're realize that the general approach is to turn reactive streams into proactive datasets. Events are usually logged into a database and are processed offline. Be it for building KPIs or training models.</p> <p>The challenge for machine learning is to ensure models you train offline on proactive datasets will perform correctly in production on reactive data streams.</p>"},{"location":"introduction/basic-concepts/#online-processing","title":"Online processing","text":"<p>Online processing is the act of processing a data stream one element at a time. In the case of machine learning, that means training a model by teaching it one sample at a time. This is completely opposite to the traditional way of doing machine learning, which is to train a model on whole batches of data at a time.</p> <p>An online model is therefore a stateful, dynamic object. It keeps learning and doesn't have to revisit past data. It's a different way of doing things, and therefore has its own set of pros and cons.</p>"},{"location":"introduction/basic-concepts/#tasks","title":"Tasks","text":"<p>Machine learning encompasses many different tasks: classification, regression, anomaly detection, time series forecasting, etc. The ideology behind River is to be a generic machine learning approach which allows these tasks to be performed in a streaming manner. Indeed, many batch machine learning algorithms have online equivalents.</p> <p>Note that River also supports some more basic tasks. For instance, you might just want to calculate a running average of a data stream. These are usually smaller parts of a whole stream processing pipeline.</p>"},{"location":"introduction/basic-concepts/#dictionaries-everywhere","title":"Dictionaries everywhere","text":"<p>River is a Python library. It is composed of a bunch of classes which implement various online processing algorithms. Most of these classes are machine learning models which can process a single sample, be it for learning or for inference.</p> <p>We made the choice to use dictionaries as the basic building block. First of all, online processing is different to batch processing, in that vectorization doesn't bring any speed-up. Therefore numeric processing libraries such as NumPy and PyTorch actually bring too much overhead. Using native Python data structures is faster.</p> <p>Dictionaries are therefore a perfect fit. They're native to Python and have excellent support in the standard library. They allow the naming of each feature. They can hold any kind of data type. They allow transparent support of JSON payloads, allowing seamless integration with web apps.</p>"},{"location":"introduction/basic-concepts/#datasets","title":"Datasets","text":"<p>In production, you're almost always going to face data streams which you have to react to, such as users visiting your website. The advantage of online machine learning is that you can design models that make predictions as well as learn from this data stream as it flows.</p> <p>But of course, when you're developping a model, you don't usually have access to a real-time feed on which to evaluate your model. You usually have an offline dataset which you want to evaluate your model on. River provides some datasets which can be read in online manner, one sample at a time. It is however crucial to keep in mind that the goal is to reproduce a production scenario as closely as possible, in order to ensure your model will perform just as well in production.</p>"},{"location":"introduction/basic-concepts/#model-evaluation","title":"Model evaluation","text":"<p>Online model evaluation differs from its traditional batch counterpart. In the latter, you usually perform cross-validation, whereby your training dataset is split into a learning and an evaluation dataset. This is fine, but it doesn't exactly reflect the data generation process that occurs in production.</p> <p>Online model evaluation involves learning and inference in the same order as what would happen in production. Indeed, if you know the order in which your data arrives, then you can process it the exact same order. This allows you to replay a production scenario and evaluate your model with higher fidelity than cross-validation.</p> <p>This is what makes online machine learning powerful. By replaying datasets in the correct order, you ensure you are designing models which will perform as expected in production.</p>"},{"location":"introduction/basic-concepts/#concept-drift","title":"Concept drift","text":"<p>The main reason why an offline model might not perform as expected in production is because of concept drift. But this is true for all machine learning models, be they offline or online.</p> <p>The advantage of online models over offline models is that they can cope with drift. Indeed, because they can keep learning, they usually adapt to concept drift in a seamless manner. As opposed to batch models which have to be retrained from scratch.</p>"},{"location":"introduction/installation/","title":"Installation","text":"<p>River is meant to work with Python 3.8 and above. Installation can be done via <code>pip</code>:</p> <pre><code>pip install river\n</code></pre> <p>You can install the latest development version from GitHub, as so:</p> <pre><code>pip install git+https://github.com/online-ml/river --upgrade\npip install git+ssh://git@github.com/online-ml/river.git --upgrade  # using SSH\n</code></pre> <p>This method requires having Cython and Rust installed on your machine.</p> <p>Feel welcome to open an issue on GitHub if you are having any trouble.</p>"},{"location":"introduction/next-steps/","title":"Next steps","text":"<p>The Recipes \ud83c\udf71 section is made up of small tutorials. Each one explains how to perform mundane tasks, such as measuring the performance of a model, selecting hyperparameters, etc.</p> <p>The Examples \ud83c\udf36\ufe0f section contains more involved notebooks with less explanations. Each notebook addresses a particular machine learning problem.</p> <p>The API \ud83d\udcda section references all the modules, classes, and functions in River. It is automatically generated from the codebase's Python docstrings.</p> <p>Feel welcome to open a discussion if you have a question. Before that you can check out the FAQ \ud83d\ude4b, which has answers to recurring questions.</p> <p>The released versions are listed in the Releases \ud83c\udfd7 section. Changes that will be part of the next release are listed in the unreleased section of the documentation's development version, which you may find here.</p> <p>We recommend checking out Awesome Online Machine Learning if you want to go deeper. There you will find online machine learning related content: research papers, alternative and complementary software, blog posts, etc.</p>"},{"location":"introduction/related-projects/","title":"Related projects","text":"<p>Here is a list of projects which are more or less coupled with River:</p> <ul> <li>deep-river interfaces PyTorch models with River.</li> <li>light-river implements fast algorithms in rust. </li> <li>river-extra regroups experimental features which have yet to prove themselves to make it into the main River repository. Between us we call this \"the arena\".</li> <li>Beaver is an MLOps tool for covering the whole lifecycle of online machine learning models.</li> </ul>"},{"location":"introduction/why-use-river/","title":"Why use River?","text":""},{"location":"introduction/why-use-river/#processing-one-sample-at-a-time","title":"Processing one sample at a time","text":"<p>All the tools in the library can be updated with a single observation at a time. They can therefore be used to process streaming data. Depending on your use case, this might be more convenient than using a batch model.</p>"},{"location":"introduction/why-use-river/#adapting-to-drift","title":"Adapting to drift","text":"<p>In the streaming setting, data can evolve. Adaptive methods are specifically designed to be robust against concept drift in dynamic environments. Many of River's models can cope with concept drift.</p>"},{"location":"introduction/why-use-river/#general-purpose","title":"General purpose","text":"<p>River supports different machine learning tasks, including regression, classification, and unsupervised learning. It can also be used for ad hoc tasks, such as computing online metrics, as well as concept drift detection.</p>"},{"location":"introduction/why-use-river/#user-experience","title":"User experience","text":"<p>River is not the only library allowing you to do online machine learning. But it might just the simplest one to use in the Python ecosystem. River plays nicely with Python dictionaries, therefore making it easy to use in the context of web applications where JSON payloads are aplenty.</p>"},{"location":"introduction/getting-started/binary-classification/","title":"Binary classification","text":"<p>Classification is about predicting an outcome from a fixed list of classes. The prediction is a probability distribution that assigns a probability to each possible outcome.</p> <p>A labeled classification sample is made up of a bunch of features and a class. The class is a boolean in the case of binary classification. We'll use the phishing dataset as an example.</p> <pre><code>from river import datasets\n\ndataset = datasets.Phishing()\ndataset\n</code></pre> <pre><code>Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n    Name  Phishing                                                          \n    Task  Binary classification                                             \n Samples  1,250                                                             \nFeatures  9                                                                 \n  Sparse  False                                                             \n    Path  /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz\n</code></pre> <p>This dataset is a streaming dataset which can be looped over.</p> <pre><code>for x, y in dataset:\n    pass\n</code></pre> <p>Let's take a look at the first sample.</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'empty_server_form_handler': 0.0,\n 'popup_window': 0.0,\n 'https': 0.0,\n 'request_from_other_domain': 0.0,\n 'anchor_from_other_domain': 0.0,\n 'is_popular': 0.5,\n 'long_url': 1.0,\n 'age_of_domain': 1,\n 'ip_in_url': 1}\n</code></pre> <pre><code>y\n</code></pre> <pre><code>True\n</code></pre> <p>A binary classifier's goal is to learn to predict a binary target <code>y</code> from some given features <code>x</code>. We'll try to do this with a logistic regression.</p> <pre><code>from river import linear_model\n\nmodel = linear_model.LogisticRegression()\nmodel.predict_proba_one(x)\n</code></pre> <pre><code>{False: 0.5, True: 0.5}\n</code></pre> <p>The model hasn't been trained on any data, and therefore outputs a default probability of 50% for each class.</p> <p>The model can be trained on the sample, which will update the model's state.</p> <pre><code>model.learn_one(x, y)\n</code></pre> <p>If we try to make a prediction on the same sample, we can see that the probabilities are different, because the model has learned something.</p> <pre><code>model.predict_proba_one(x)\n</code></pre> <pre><code>{False: 0.494687699901455, True: 0.505312300098545}\n</code></pre> <p>Note that there is also a <code>predict_one</code> if you're only interested in the most likely class rather than the probability distribution.</p> <pre><code>model.predict_one(x)\n</code></pre> <pre><code>True\n</code></pre> <p>Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.</p> <pre><code>from river import metrics\n\nmodel = linear_model.LogisticRegression()\n\nmetric = metrics.ROCAUC()\n\nfor x, y in dataset:\n    y_pred = model.predict_proba_one(x)\n    model.learn_one(x, y)\n    metric.update(y, y_pred)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 89.36%\n</code></pre> <p>This is a common way to evaluate an online model. In fact, there is a dedicated <code>evaluate.progressive_val_score</code> function that does this for you.</p> <pre><code>from river import evaluate\n\nmodel = linear_model.LogisticRegression()\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>ROCAUC: 89.36%\n</code></pre> <p>A common way to improve the performance of a logistic regression is to scale the data. This can be done by using a <code>preprocessing.StandardScaler</code>. In particular, we can define a pipeline to organise our model into a sequence of steps:</p> <pre><code>from river import compose\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    linear_model.LogisticRegression()\n)\n\nmodel\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <pre><code>metric = metrics.ROCAUC()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>ROCAUC: 95.07%\n</code></pre>"},{"location":"introduction/getting-started/concept-drift-detection/","title":"Concept drift","text":"<p>In online machine learning, it is assumed that data can change over time. When building machine learning models, we assume data has a probability distribution, which is usually fixed, i.e., stationary. Changes in the data distribution give rise to the phenomenon called Concept drift. Such drifts can be either virtual or real. In virtual drifts, only the distribution of the features, \\(P(X)\\), changes, whereas the relationship between \\(X\\) (features) and the target, \\(y\\), remains unchanged. The joint probability of \\(P(X, y)\\) changes in real concept drifts. Consequently, non-supervised online machine learning problems might face only virtual concept drifts.</p> <p>Real concept drits can be further divided in abrupt (happen instantly at a given point) or gradual (one \"concept\" changes to another gradually). There are other possible divisions, but they can be fit into abrupt or gradual drifts.</p>"},{"location":"introduction/getting-started/concept-drift-detection/#examples-of-concept-drift","title":"Examples of concept drift","text":"<p>Concept drifts might happen in the electricity demand across the year, in the stock market, in buying preferences, and in the likelihood of a new movie's success, among others.</p> <p>Let us consider the movie example: two movies made at different epochs can have similar features such as famous actors/directors, storyline, production budget, marketing campaigns, etc., yet it is not certain that both will be similarly successful. What the target audience considers is worth watching (and their money worth spending) is constantly changing, and production companies must adapt accordingly to avoid \"box office flops\".</p> <p>Prior to the pandemic, the usage of hand sanitizers and facial masks was not widespread. When the cases of COVID-19 started increasing, there was a lack of such products for the end consumer. Imagine a batch-learning model deciding how much of each product a supermarket should stock during those times. What a mess!</p>"},{"location":"introduction/getting-started/concept-drift-detection/#impact-of-drift-on-learning","title":"Impact of drift on learning","text":"<p>Concept drift can have a significant impact on predictive performance if not handled properly. Most batch learning models will fail in the presence of concept drift as they are essentially trained on different data. On the other hand, stream learning methods continuously update themselves and adapt to new concepts. Furthermore, drift-aware methods use change detection methods (a.k.a. drift detectors) to trigger mitigation mechanisms if a change in performance is detected.</p>"},{"location":"introduction/getting-started/concept-drift-detection/#detecting-concept-drift","title":"Detecting concept drift","text":"<p>Multiple drift detection methods have been proposed. The goal of a drift detector is to signal an alarm in the presence of drift. A good drift detector maximizes the number of true positives while keeping the number of false positives to a minimum. It must also be resource-wise efficient to work in the context of infinite data streams.</p> <p>For this example, we will generate a synthetic data stream by concatenating 3 distributions of 1000 samples each:</p> <ul> <li>\\(dist_a\\): \\(\\mu=0.8\\), \\(\\sigma=0.05\\)</li> <li>\\(dist_b\\): \\(\\mu=0.4\\), \\(\\sigma=0.02\\)</li> <li>\\(dist_c\\): \\(\\mu=0.6\\), \\(\\sigma=0.1\\).</li> </ul> <pre><code>import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import gridspec\n\n# Generate data for 3 distributions\nrandom_state = np.random.RandomState(seed=42)\ndist_a = random_state.normal(0.8, 0.05, 1000)\ndist_b = random_state.normal(0.4, 0.02, 1000)\ndist_c = random_state.normal(0.6, 0.1, 1000)\n\n# Concatenate data to simulate a data stream with 2 drifts\nstream = np.concatenate((dist_a, dist_b, dist_c))\n\n# Auxiliary function to plot the data\ndef plot_data(dist_a, dist_b, dist_c, drifts=None):\n    fig = plt.figure(figsize=(7,3), tight_layout=True)\n    gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])\n    ax1, ax2 = plt.subplot(gs[0]), plt.subplot(gs[1])\n    ax1.grid()\n    ax1.plot(stream, label='Stream')\n    ax2.grid(axis='y')\n    ax2.hist(dist_a, label=r'$dist_a$')\n    ax2.hist(dist_b, label=r'$dist_b$')\n    ax2.hist(dist_c, label=r'$dist_c$')\n    if drifts is not None:\n        for drift_detected in drifts:\n            ax1.axvline(drift_detected, color='red')\n    plt.show()\n\nplot_data(dist_a, dist_b, dist_c)\n</code></pre> <p></p>"},{"location":"introduction/getting-started/concept-drift-detection/#drift-detection-test","title":"Drift detection test","text":"<p>We will use the ADaptive WINdowing (<code>ADWIN</code>) drift detection method. Remember that the goal is to indicate that drift has occurred after samples 1000 and 2000 in the synthetic data stream.</p> <pre><code>from river import drift\n\ndrift_detector = drift.ADWIN()\ndrifts = []\n\nfor i, val in enumerate(stream):\n    drift_detector.update(val)   # Data is processed one sample at a time\n    if drift_detector.drift_detected:\n        # The drift detector indicates after each sample if there is a drift in the data\n        print(f'Change detected at index {i}')\n        drifts.append(i)\n\nplot_data(dist_a, dist_b, dist_c, drifts)\n</code></pre> <pre><code>Change detected at index 1055\nChange detected at index 2079\n</code></pre> <p></p> <p>We see that <code>ADWIN</code> successfully indicates the presence of drift (red vertical lines) close to the begining of a new data distribution.</p> <p>We conclude this example with some remarks regarding concept drift detectors and their usage:</p> <ul> <li>In practice, drift detectors provide stream learning methods with robustness against concept drift. Drift detectors monitor the model usually through a performance metric.</li> <li>Drift detectors work on univariate data. This is why they are used to monitor a model's performance and not the data itself. Remember that concept drift is defined as a change in the relationship between data and the target to learn (in supervised learning).</li> <li>Drift detectors define their expectations regarding input data. It is important to know these expectations to feed a given drift detector with the correct data.</li> </ul>"},{"location":"introduction/getting-started/multiclass-classification/","title":"Multi-class classification","text":"<p>Classification is about predicting an outcome from a fixed list of classes. The prediction is a probability distribution that assigns a probability to each possible outcome.</p> <p>A labeled classification sample is made up of a bunch of features and a class. The class is a usually a string or a number in the case of multiclass classification. We'll use the image segments dataset as an example.</p> <pre><code>from river import datasets\n\ndataset = datasets.ImageSegments()\ndataset\n</code></pre> <pre><code>Image segments classification.\n\nThis dataset contains features that describe image segments into 7 classes: brickface, sky,\nfoliage, cement, window, path, and grass.\n\n    Name  ImageSegments                                                     \n    Task  Multi-class classification                                        \n Samples  2,310                                                             \nFeatures  18                                                                \n Classes  7                                                                 \n  Sparse  False                                                             \n    Path  /Users/max/projects/online-ml/river/river/datasets/segment.csv.zip\n</code></pre> <p>This dataset is a streaming dataset which can be looped over.</p> <pre><code>for x, y in dataset:\n    pass\n</code></pre> <p>Let's take a look at the first sample.</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'region-centroid-col': 218,\n 'region-centroid-row': 178,\n 'short-line-density-5': 0.11111111,\n 'short-line-density-2': 0.0,\n 'vedge-mean': 0.8333326999999999,\n 'vegde-sd': 0.54772234,\n 'hedge-mean': 1.1111094,\n 'hedge-sd': 0.5443307,\n 'intensity-mean': 59.629630000000006,\n 'rawred-mean': 52.44444300000001,\n 'rawblue-mean': 75.22222,\n 'rawgreen-mean': 51.22222,\n 'exred-mean': -21.555555,\n 'exblue-mean': 46.77778,\n 'exgreen-mean': -25.222220999999998,\n 'value-mean': 75.22222,\n 'saturation-mean': 0.31899637,\n 'hue-mean': -2.0405545}\n</code></pre> <pre><code>y\n</code></pre> <pre><code>'path'\n</code></pre> <p>A multiclass classifier's goal is to learn how to predict a class <code>y</code> from a bunch of features <code>x</code>. We'll attempt to do this with a decision tree.</p> <pre><code>from river import tree\n\nmodel = tree.HoeffdingTreeClassifier()\nmodel.predict_proba_one(x)\n</code></pre> <pre><code>{}\n</code></pre> <p>The reason why the output dictionary is empty is because the model hasn't seen any data yet. It isn't aware of the dataset whatsoever. If this were a binary classifier, then it would output a probability of 50% for <code>True</code> and <code>False</code> because the classes are implicit. But in this case we're doing multiclass classification.</p> <p>Likewise, the <code>predict_one</code> method initially returns <code>None</code> because the model hasn't seen any labeled data yet.</p> <pre><code>print(model.predict_one(x))\n</code></pre> <pre><code>None\n</code></pre> <p>If we update the model and try again, then we see that a probability of 100% is assigned to the <code>'path'</code> class because that's the only one the model is aware of.</p> <pre><code>model.learn_one(x, y)\nmodel.predict_proba_one(x)\n</code></pre> <pre><code>{'path': 1.0}\n</code></pre> <p>This is a strength of online classifiers: they're able to deal with new classes appearing in the data stream.</p> <p>Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.</p> <pre><code>from river import metrics\n\nmodel = tree.HoeffdingTreeClassifier()\n\nmetric = metrics.ClassificationReport()\n\nfor x, y in dataset:\n    y_pred = model.predict_one(x)\n    model.learn_one(x, y)\n    if y_pred is not None:\n        metric.update(y, y_pred)\n\nmetric\n</code></pre> <pre><code>            Precision   Recall   F1       Support\n\nbrickface      77.13%   84.85%   80.81%       330  \n   cement      78.92%   83.94%   81.35%       330  \n  foliage      65.69%   20.30%   31.02%       330  \n    grass     100.00%   96.97%   98.46%       330  \n     path      90.63%   91.19%   90.91%       329  \n      sky      99.08%   98.18%   98.63%       330  \n   window      43.50%   67.88%   53.02%       330\n\n    Macro      79.28%   77.62%   76.31%            \n    Micro      77.61%   77.61%   77.61%            \n Weighted      79.27%   77.61%   76.31%\n\n                  77.61% accuracy\n</code></pre> <p>This is a common way to evaluate an online model. In fact, there is a dedicated <code>evaluate.progressive_val_score</code> function that does this for you.</p> <pre><code>from river import evaluate\n\nmodel = tree.HoeffdingTreeClassifier()\nmetric = metrics.ClassificationReport()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>            Precision   Recall   F1       Support\n\nbrickface      77.13%   84.85%   80.81%       330  \n   cement      78.92%   83.94%   81.35%       330  \n  foliage      65.69%   20.30%   31.02%       330  \n    grass     100.00%   96.97%   98.46%       330  \n     path      90.63%   91.19%   90.91%       329  \n      sky      99.08%   98.18%   98.63%       330  \n   window      43.50%   67.88%   53.02%       330\n\n    Macro      79.28%   77.62%   76.31%            \n    Micro      77.61%   77.61%   77.61%            \n Weighted      79.27%   77.61%   76.31%\n\n                  77.61% accuracy\n</code></pre>"},{"location":"introduction/getting-started/regression/","title":"Regression","text":"<p>Regression is about predicting a numeric output for a given sample. A labeled regression sample is made up of a bunch of features and a number. The number is usually continuous, but it may also be discrete. We'll use the Trump approval rating dataset as an example.</p> <pre><code>from river import datasets\n\ndataset = datasets.TrumpApproval()\ndataset\n</code></pre> <pre><code>Donald Trump approval ratings.\n\nThis dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald\nTrump's approval ratings. It contains 5 features, which are approval ratings collected by\n5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\nthis task is to see if we can reproduce FiveThirtyEight's model.\n\n    Name  TrumpApproval                                                           \n    Task  Regression                                                              \n Samples  1,001                                                                   \nFeatures  6                                                                       \n  Sparse  False                                                                   \n    Path  /Users/max/projects/online-ml/river/river/datasets/trump_approval.csv.gz\n</code></pre> <p>This dataset is a streaming dataset which can be looped over.</p> <pre><code>for x, y in dataset:\n    pass\n</code></pre> <p>Let's take a look at the first sample.</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'ordinal_date': 736389,\n 'gallup': 43.843213,\n 'ipsos': 46.19925042857143,\n 'morning_consult': 48.318749,\n 'rasmussen': 44.104692,\n 'you_gov': 43.636914000000004}\n</code></pre> <p>A regression model's goal is to learn to predict a numeric target <code>y</code> from a bunch of features <code>x</code>. We'll attempt to do this with a nearest neighbors model.</p> <pre><code>from river import neighbors\n\nmodel = neighbors.KNNRegressor()\nmodel.predict_one(x)\n</code></pre> <pre><code>0.0\n</code></pre> <p>The model hasn't been trained on any data, and therefore outputs a default value of 0.</p> <p>The model can be trained on the sample, which will update the model's state.</p> <pre><code>model.learn_one(x, y)\n</code></pre> <p>If we try to make a prediction on the same sample, we can see that the output is different, because the model has learned something.</p> <pre><code>model.predict_one(x)\n</code></pre> <pre><code>43.75505\n</code></pre> <p>Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.</p> <pre><code>from river import metrics\n\nmodel = neighbors.KNNRegressor()\n\nmetric = metrics.MAE()\n\nfor x, y in dataset:\n    y_pred = model.predict_one(x)\n    model.learn_one(x, y)\n    metric.update(y, y_pred)\n\nmetric\n</code></pre> <pre><code>MAE: 0.310353\n</code></pre> <p>This is a common way to evaluate an online model. In fact, there is a dedicated <code>evaluate.progressive_val_score</code> function that does this for you.</p> <pre><code>from river import evaluate\n\nmodel = neighbors.KNNRegressor()\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.310353\n</code></pre>"},{"location":"license/license/","title":"License","text":"<p>River is free and open-source software licensed under the 3-clause BSD license.</p>"},{"location":"recipes/active-learning/","title":"Active learning","text":"<p>Active learning is a training regime, where the goal is to fit a model on the most discriminative samples. It is usually applied in situations where a limited amount of labeled data is available. In such a case, a human might be asked to annotate a sample. Doing this is expensive, so it's important to ask for labels for the most samples that will have the most impact.</p> <p>Online active learning is active learning done in a streaming fashion. Every time a prediction is made, an active learning strategy decides whether a label should be asked for or not. In case the strategy decides a yes, then the system could ask for a human to intervene. This is well summarized in the following schema from Online Active Learning Methods for Fast Label-Efficient Spam Filtering.</p>"},{"location":"recipes/active-learning/#online-active-learning","title":"Online active learning","text":"<p>River's online active learning strategies are located in the <code>active</code> module. The latter contains wrapper models. These wrappers enrich the <code>predict_one</code> and <code>predict_proba_one</code> methods to include a boolean in the output.</p> <p>The returned boolean indicates whether or not a label should be asked for. In a production system, we could feed this to a web interface, and get the human to annotate the sample. Offline, we can simply use the label in the dataset.</p> <p>We'll implement this basic flow. We'll apply a TFIDF followed by logistic regression to a datasets of spam/ham received by SMS.</p> <pre><code>from river import active\nfrom river import datasets\nfrom river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\n\ndataset = datasets.SMSSpam()\nmetric = metrics.Accuracy()\nmodel = (\n    feature_extraction.TFIDF(on='body') |\n    linear_model.LogisticRegression()\n)\nmodel = active.EntropySampler(model, seed=42)\n\nn_samples_used = 0\nfor x, y in dataset:\n    y_pred, ask = model.predict_one(x)\n    metric.update(y, y_pred)\n    if ask:\n        n_samples_used += 1\n        model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>Accuracy: 86.60%\n</code></pre> <p>The performance is reasonable, even though all the dataset wasn't used for training. We can check how many samples were actually used.</p> <pre><code>print(f\"{n_samples_used} / {dataset.n_samples} = {n_samples_used / dataset.n_samples:.2%}\")\n</code></pre> <pre><code>1921 / 5574 = 34.46%\n</code></pre> <p>Note that the above logic can be succinctly reproduced with the <code>progressive_val_score</code> function from the <code>evaluate</code> module. It recognises when an active learning model is provided, and will automatically display the number of samples used.</p> <pre><code>from river import evaluate\n\nevaluate.progressive_val_score(\n    dataset=dataset,\n    model=model.clone(),\n    metric=metric.clone(),\n    print_every=1000\n)\n</code></pre> <pre><code>[1,000] Accuracy: 84.80% \u2013 661 samples used\n[2,000] Accuracy: 86.00% \u2013 1,057 samples used\n[3,000] Accuracy: 86.37% \u2013 1,339 samples used\n[4,000] Accuracy: 86.65% \u2013 1,568 samples used\n[5,000] Accuracy: 86.54% \u2013 1,790 samples used\n[5,574] Accuracy: 86.60% \u2013 1,921 samples used\n\n\n\n\n\nAccuracy: 86.60%\n</code></pre>"},{"location":"recipes/active-learning/#reduce-training-time","title":"Reduce training time","text":"<p>Active learning is primarly used to label data in an efficient manner. However, in an online setting, active learning can also be used simply to speed up training. The point is that you can achieve a very good performance without training on an entire dataset. Active learning is a powerful way to decide which samples to train on.</p>"},{"location":"recipes/active-learning/#_1","title":"Active learning","text":""},{"location":"recipes/active-learning/#production-considerations","title":"Production considerations","text":"<p>In production, you might want to deploy a system where humans may annotate samples queried by an active learning strategy. You have several options at your disposal, all of which go beyond the scope of River.</p> <p>The general idea is to have some kind of queue in which queried samples are fed into. Then you would have a user interface which displays the elements in the queue one-by-one. Each time a sample is labeled, the label would be used to update the model. You might have one or more threads/processes doing inference. You'll want to update the model in each one each time the model learns.</p>"},{"location":"recipes/bandits-101/","title":"Multi-armed bandits","text":"<p>River has a <code>bandit</code> module. It contains several multi-armed bandit policies, bandit environments, and utilities to benchmark policies on bandit problems.</p> <p>Bandit environments in River implement the Gym interface. You can thus load them with <code>gym.make</code>. Note that Gym is intended for reinforcement learning algorithms, while bandit policies are the simplest form of reinforcement learing. Bandit policies learn by receiving a reward after each step, while reinforcement learning algorithms have to learn from feedback that may arrive at the end of a (long) sequence of steps.</p> <pre><code>import gym\n\nfor k in gym.envs.registry:\n    if k.startswith('river_bandits'):\n        print(k)\n</code></pre> <p>River's bandit module offers the <code>bandit.evaluate</code> function to benchmark several policies on a given environment. It takes as input a list of bandit policies, a bandit environment (the problem to solve), and a reward object.</p> <pre><code>import gym\nfrom river import bandit\nimport pandas as pd\nfrom tqdm import tqdm\nfrom river import stats\n\npolicies=[\n    bandit.EpsilonGreedy(epsilon=0.1),\n    bandit.EpsilonGreedy(epsilon=0.01),\n    bandit.EpsilonGreedy(epsilon=0),\n]\n\nenv = gym.make(\n    'river_bandits/KArmedTestbed-v0',\n    max_episode_steps=1000\n)\n\ntrace = bandit.evaluate(\n    policies=policies,\n    env=env,\n    reward_stat=stats.Mean(),\n    n_episodes=(n_episodes := 2000),\n)\n</code></pre> <p>The <code>bandit.evaluate</code> function returns a generator containing the results at each step of the benchmark. This can be wrapped with a <code>pandas.DataFrame</code> to gather all the results.</p> <pre><code>trace_df = pd.DataFrame(tqdm(\n    trace, position=0, total=(\n        n_episodes *\n        len(policies) *\n        env._max_episode_steps\n    )\n))\ntrace_df.sample(5, random_state=42)\n</code></pre> <pre><code>  0%|                                                                                                                                                                   | 0/6000000 [00:00&lt;?, ?it/s]/Users/max/Library/Caches/pypoetry/virtualenvs/river--dXL33ck-py3.11/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n  if not isinstance(terminated, (bool, np.bool8)):\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6000000/6000000 [00:25&lt;00:00, 236810.21it/s]\n</code></pre> episode step policy_idx arm reward reward_stat 1324896 441 632 0 2 0.226086 0.499848 3566176 1188 725 1 6 2.363962 0.935468 1109043 369 681 0 5 2.780757 1.467402 4286042 1428 680 2 1 2.039255 1.603312 5395174 1798 391 1 8 1.625523 1.232745 <p>It is then straightforward to plot the average reward each policy obtains at each step, by averaging over episodes.</p> <pre><code>policy_names = {\n    0: '\u03b5 = 0.1',\n    1: '\u03b5 = 0.01',\n    2: '\u03b5 = 0 (greedy)'\n}\n\n(\n    trace_df\n    .assign(policy=trace_df.policy_idx.map(policy_names))\n    .groupby(['step', 'policy'])\n    ['reward'].mean()\n    .unstack()\n    .plot()\n)\n</code></pre> <pre><code>&lt;Axes: xlabel='step'&gt;\n</code></pre> <p></p>"},{"location":"recipes/bandits-101/#controlling-the-evaluation-loop","title":"Controlling the evaluation loop","text":"<p>The <code>bandit.evaluate</code> function is useful for benchmarking. But in practice, you'll want to have control over your bandit policy. Indeed you'll want the freedom to pull arms (with the <code>pull</code> method) and update the policy (with the <code>update</code> method) at your discretion.</p> <p>As an example, the following is a possible reimplementation of the <code>bandit.evaluate</code> function. Here we'll be measuring the rate at which each policy selects the optimal arm.</p> <p>Note how the <code>pull</code> and <code>update</code> methods are used.</p> <pre><code>import copy\n\npolicies=[\n    bandit.EpsilonGreedy(epsilon=0.1),\n    bandit.EpsilonGreedy(epsilon=0.01),\n    bandit.EpsilonGreedy(epsilon=0),\n]\n\nenv = gym.make(\n    'river_bandits/KArmedTestbed-v0',\n    max_episode_steps=1000\n)\nn_episodes = 2000\n\ntrace = []\n\nwith tqdm(total=len(policies) * n_episodes * env._max_episode_steps, position=0) as progress:\n    for policy in policies:\n        for episode in range(n_episodes):\n            episode_policy = policy.clone()\n            episode_env = copy.deepcopy(env)\n            episode_env.reset()\n            step = 0\n            while True:\n                action = episode_policy.pull(range(episode_env.action_space.n))\n                observation, reward, terminated, truncated, info = episode_env.step(action)\n                best_action = observation\n                episode_policy.update(action, reward)\n\n                trace.append({\n                    \"episode\": episode,\n                    \"step\": step,\n                    \"policy\": f\"\u03b5 = {policy.epsilon}\",\n                    \"is_action_optimal\": action == best_action\n                })\n                step += 1\n                progress.update()\n\n                if terminated or truncated:\n                    break\n\ntrace_df = pd.DataFrame(trace)\n</code></pre> <pre><code>  0%|                                                                                                                                                                   | 0/6000000 [00:00&lt;?, ?it/s]/Users/max/Library/Caches/pypoetry/virtualenvs/river--dXL33ck-py3.11/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n  if not isinstance(terminated, (bool, np.bool8)):\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6000000/6000000 [00:26&lt;00:00, 228987.26it/s]\n</code></pre> <pre><code>colors = {\n    '\u03b5 = 0.1': 'tab:blue',\n    '\u03b5 = 0.01': 'tab:red',\n    '\u03b5 = 0': 'tab:green'\n}\n\n(\n    trace_df\n    .groupby(['step', 'policy'])\n    ['is_action_optimal'].mean()\n    .unstack()\n    .plot()\n)\n</code></pre> <pre><code>&lt;Axes: xlabel='step'&gt;\n</code></pre> <p></p>"},{"location":"recipes/bandits-101/#handling-drift","title":"Handling drift","text":"<p>The environment used above is a toy situation used for introducing bandits. It is stationary, meaning that the expected reward of each arm does not change over time.</p> <p>In practice, arms are dynamic, and their performance can vary over time. A simple example of this is the Candy Cane Contest that was hosted on Kaggle in 2020. The expected reward of each arm diminishes each time it is pulled.</p> <p>The way bandit policies in River deal with drift depends on the method. For the <code>bandit.EpsilonGreedy</code> policy, it makes sense to use a rolling average as the reward object. What this means is that the empirical reward the policy calculates for each arm is a rolling average, rather than a global one.</p> <pre><code>from river import proba, utils\n\npolicies=[\n    bandit.EpsilonGreedy(\n        epsilon=0.1,\n        seed=42\n    ),\n    bandit.EpsilonGreedy(\n        epsilon=0.3,\n        reward_obj=utils.Rolling(stats.Mean(), window_size=50),\n        seed=42\n    ),\n    bandit.ThompsonSampling(\n        reward_obj=proba.Beta(),\n        seed=42\n    )\n]\n\nenv = gym.make('river_bandits/CandyCaneContest-v0')\n\ntrace = bandit.evaluate(\n    policies=policies,\n    env=env,\n    n_episodes=(n_episodes := 30),\n    seed=42\n)\n\ntrace_df = pd.DataFrame(tqdm(\n    trace, position=0, total=(\n        n_episodes *\n        len(policies) *\n        env._max_episode_steps\n    )\n))\n</code></pre> <pre><code>  0%|                                                                                                                                                                    | 0/180000 [00:00&lt;?, ?it/s]/Users/max/Library/Caches/pypoetry/virtualenvs/river--dXL33ck-py3.11/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n  if not isinstance(terminated, (bool, np.bool8)):\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 180000/180000 [00:11&lt;00:00, 15839.35it/s]\n</code></pre> <p>We can compare the performance of each policy by checking the average reward at the end of each episode.</p> <pre><code>(\n    trace_df\n    .groupby(['policy_idx', 'episode'])\n    .last()\n    .groupby('policy_idx')\n    .reward_stat.mean()\n)\n</code></pre> <pre><code>policy_idx\n0    736.1\n1    817.0\n2    854.0\nName: reward_stat, dtype: float64\n</code></pre> <p>We see that using a rolling average gives a boost to the epsilon greedy strategy. However, we see that the <code>bandit.ThompsonSampling</code> policy performs even better, even though no particular care was given to drift. A natural next step would thus be to see how it could be improved to handle drift. For instance, its <code>dist</code> parameter could be wrapped with a <code>utils.Rolling</code>:</p> <pre><code>policy = bandit.ThompsonSampling(\n    reward_obj=utils.Rolling(proba.Beta(), window_size=50),\n    seed=42\n)\n</code></pre> <p>Bandits can be used for several tasks. They can be used for content personalization, as well as online model selection (see <code>model_selection.BanditRegressor</code>). The policies in River are therefore designed to be flexible, so that they can be used in conjunction with other River modules. For instance, the <code>reward_obj</code> in <code>bandit.EpsilonGreedy</code> can be a metric, a probability distribution, or a statistic. This works because objects in River adher to a coherent get/update interface.</p>"},{"location":"recipes/cloning-and-mutating/","title":"Cloning and mutating","text":"<p>Sometimes you might want to reset a model, or edit (what we call mutate) its attributes. This can be useful in an online environment. Indeed, if you detect a drift, then you might want to mutate a model's attributes. Or if you see that a model's performance is plummeting, then you might to reset it to its \"factory settings\".</p> <p>Anyway, this is not to convince you, but rather to say that a model's attributes don't have be to set in stone throughout its lifetime. In particular, if you're developping your own model, then you might want to have good tools to do this. This is what this recipe is about.</p>"},{"location":"recipes/cloning-and-mutating/#cloning","title":"Cloning","text":"<p>The first thing you can do is clone a model. This creates a deep copy of the model. The resulting model is entirely independent of the original model. The clone is fresh, in the sense that it is as if it hasn't seen any data.</p> <p>For instance, say you have a linear regression model which you have trained on some data.</p> <pre><code>from river import datasets, linear_model, optim, preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression(\n        optimizer=optim.SGD(3e-2)\n    )\n)\n\nfor x, y in datasets.TrumpApproval():\n    model.predict_one(x)\n    model.learn_one(x, y)\n\nmodel[-1].weights\n</code></pre> <pre><code>{'ordinal_date': 20.59955380229643,\n 'gallup': 0.39114944304212645,\n 'ipsos': 0.4101918314868111,\n 'morning_consult': 0.12042970179504908,\n 'rasmussen': 0.18951231512561392,\n 'you_gov': 0.04991712783831687}\n</code></pre> <p>For whatever reason, we may want to clone this model. This can be done with the <code>clone</code> method.</p> <pre><code>clone = model.clone()\nclone[-1].weights\n</code></pre> <pre><code>{}\n</code></pre> <p>As we can see, there are no weights because the clone is fresh copy that has not seen any data. However, the learning rate we specified is preserved.</p> <pre><code>clone[-1].optimizer.learning_rate\n</code></pre> <pre><code>0.03\n</code></pre> <p>You may also specify parameters you want changed. For instance, let's say we want to clone the model, but we want to change the optimizer:</p> <pre><code>clone = model.clone({\"LinearRegression\": {\"optimizer\": optim.Adam()}})\nclone[-1].optimizer\n</code></pre> <pre><code>Adam({'lr': Constant({'learning_rate': 0.1}), 'n_iterations': 0, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'm': None, 'v': None})\n</code></pre> <p>The first key indicates that we want to specify a different parameter for the <code>LinearRegression</code> part of the pipeline. Then the second key accesses the linear regression's <code>optimizer</code> parameter.</p> <p>Finally, note that the <code>clone</code> method isn't reserved to models. Indeed, every object in River has it. That's because they all inherit from the <code>Base</code> class in the <code>base</code> module.</p>"},{"location":"recipes/cloning-and-mutating/#mutating-attributes","title":"Mutating attributes","text":"<p>Cloning a model can be useful, but the fact that it essentially resets the model may not be desired. Instead, you might want to change a attribute while preserving the model's state. For example, let's change the <code>l2</code> attribute, and the optimizer's <code>lr</code> attribute.</p> <pre><code>model.mutate({\n    \"LinearRegression\": {\n        \"l2\": 0.1,\n        \"optimizer\": {\n            \"lr\": optim.schedulers.Constant(25e-3)\n        }\n    }\n})\n\nprint(repr(model))\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.025\n      )\n    )\n    loss=Squared ()\n    l2=0.1\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre> <p>We can see the attributes we specified have changed. However, the model's state is preserved:</p> <pre><code>model[-1].weights\n</code></pre> <pre><code>{'ordinal_date': 20.59955380229643,\n 'gallup': 0.39114944304212645,\n 'ipsos': 0.4101918314868111,\n 'morning_consult': 0.12042970179504908,\n 'rasmussen': 0.18951231512561392,\n 'you_gov': 0.04991712783831687}\n</code></pre> <p>In other words, the <code>mutate</code> method does not create a deep copy of the model. It just sets attributes. At this point you may ask:</p> <p>Why can't I just change the attribute directly, without calling <code>mutate</code>?</p> <p>The answer is that you're free to do proceed as such, but it's not the way we recommend. The <code>mutate</code> method is safer, in that it prevents you from mutating attributes you shouldn't be touching. We call these immutable attributes. For instance, there's no reason you should be modifying the weights.</p> <pre><code>try:\n    model.mutate({\n        \"LinearRegression\": {\n            \"weights\": \"this makes no sense\"\n        }\n    })\nexcept ValueError as e:\n    print(e)\n</code></pre> <pre><code>'weights' is not a mutable attribute of LinearRegression\n</code></pre> <p>All attributes are immutable by default. Under the hood, each model can specify a set of mutable attributes via the <code>_mutable_attributes</code> property. In theory this can be overriden. But the general idea is that we will progressively add more and more mutable attributes with time.</p> <p>And that concludes this recipe. Arguably, this recipe caters to advanced users, and in particular users who are developping their own models. And yet, one could also argue that modifying parameters of a model on-the-fly is a great tool to have at your disposal when you're doing online machine learning.</p>"},{"location":"recipes/feature-extraction/","title":"Feature extraction","text":"<p>To do.</p>"},{"location":"recipes/hyperparameter-tuning/","title":"Hyperparameter tuning","text":"<p>To do.</p>"},{"location":"recipes/mini-batching/","title":"Mini-batching","text":"<p>In its purest form, online machine learning encompasses models which learn with one sample at a time. This is the design which is used in River.</p> <p>The main downside of single-instance processing is that it doesn't scale to big data, at least not in the sense of traditional batch learning. Indeed, processing one sample at a time means that we are unable to fully take advantage of vectorisation and other computational tools that are taken for granted in batch learning. On top of this, processing a large dataset in River essentially involves a Python <code>for</code> loop, which might be too slow for some usecases. However, this doesn't mean that River is slow. In fact, for processing a single instance, River is actually a couple of orders of magnitude faster than libraries such as scikit-learn, PyTorch, and Tensorflow. The reason why is because River is designed from the ground up to process a single instance, whereas the majority of other libraries choose to care about batches of data. Both approaches offer different compromises, and the best choice depends on your usecase.</p> <p>In order to propose the best of both worlds, River offers some limited support for mini-batch learning. Some of River's estimators implement <code>*_many</code> methods on top of their <code>*_one</code> counterparts. For instance, <code>preprocessing.StandardScaler</code> has a <code>learn_many</code> method as well as a <code>transform_many</code> method, in addition to <code>learn_one</code> and <code>transform_one</code>. Each mini-batch method takes as input a <code>pandas.DataFrame</code>. Supervised estimators also take as input a <code>pandas.Series</code> of target values. We choose to use <code>pandas.DataFrames</code> over <code>numpy.ndarrays</code> because of the simple fact that the former allows us to name each feature. This in turn allows us to offer a uniform interface for both single instance and mini-batch learning.</p> <p>As an example, we will build a simple pipeline that scales the data and trains a logistic regression. Indeed, the <code>compose.Pipeline</code> class can be applied to mini-batches, as long as each step is able to do so.</p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>For this example, we will use <code>datasets.Higgs</code>.</p> <pre><code>from river import datasets\n\ndataset = datasets.Higgs()\nif not dataset.is_downloaded:\n    dataset.download()\ndataset\n</code></pre> <pre><code>Higgs dataset.\n\nThe data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22)\nare kinematic properties measured by the particle detectors in the accelerator. The last seven\nfeatures are functions of the first 21 features; these are high-level features derived by\nphysicists to help discriminate between the two classes.\n\n      Name  Higgs                                                                       \n      Task  Binary classification                                                       \n   Samples  11,000,000                                                                  \n  Features  28                                                                          \n    Sparse  False                                                                       \n      Path  /Users/max/river_data/Higgs/HIGGS.csv.gz                                    \n       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\n      Size  2.62 GB                                                                     \nDownloaded  True\n</code></pre> <p>The easiest way to read the data in a mini-batch fashion is to use the <code>read_csv</code> from <code>pandas</code>.</p> <pre><code>import pandas as pd\n\nnames = [\n    'target', 'lepton pT', 'lepton eta', 'lepton phi',\n    'missing energy magnitude', 'missing energy phi',\n    'jet 1 pt', 'jet 1 eta', 'jet 1 phi', 'jet 1 b-tag',\n    'jet 2 pt', 'jet 2 eta', 'jet 2 phi', 'jet 2 b-tag',\n    'jet 3 pt', 'jet 3 eta', 'jet 3 phi', 'jet 3 b-tag',\n    'jet 4 pt', 'jet 4 eta', 'jet 4 phi', 'jet 4 b-tag',\n    'm_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'\n]\n\nfor x in pd.read_csv(dataset.path, names=names, chunksize=8096, nrows=3e5):\n    y = x.pop('target')\n    y_pred = model.predict_proba_many(x)\n    model.learn_many(x, y)\n</code></pre> <p>If you are familiar with scikit-learn, you might be aware that some of their estimators have a <code>partial_fit</code> method, which is similar to river's <code>learn_many</code> method. Here are some advantages that river has over scikit-learn:</p> <ul> <li>We guarantee that river's is just as fast, if not faster than scikit-learn. The differences are negligeable, but are slightly in favor of river.</li> <li>We take as input dataframes, which allows us to name each feature. The benefit is that you can add/remove/permute features between batches and everything will keep working.</li> <li>Estimators that support mini-batches also support single instance learning. This means that you can enjoy the best of both worlds. For instance, you can train with mini-batches and use <code>predict_one</code> to make predictions.</li> </ul> <p>Note that you can check which estimators can process mini-batches programmatically:</p> <pre><code>import importlib\nimport inspect\n\ndef can_mini_batch(obj):\n    return hasattr(obj, 'learn_many')\n\nfor module in importlib.import_module('river.api').__all__:\n    if module in ['datasets', 'synth']:\n        continue\n    for name, obj in inspect.getmembers(importlib.import_module(f'river.{module}'), can_mini_batch):\n        print(name)\n</code></pre> <pre><code>LocalOutlierFactor\nOneClassSVM\nMiniBatchClassifier\nMiniBatchRegressor\nMiniBatchSupervisedTransformer\nMiniBatchTransformer\nSKL2RiverClassifier\nSKL2RiverRegressor\nFuncTransformer\nPipeline\nSelect\nTransformerProduct\nTransformerUnion\nBagOfWords\nTFIDF\nLinearRegression\nLogisticRegression\nPerceptron\nOneVsRestClassifier\nBernoulliNB\nComplementNB\nMultinomialNB\nMLPRegressor\nOneHotEncoder\nOrdinalEncoder\nStandardScaler\n</code></pre> <p>Because mini-batch learning isn't treated as a first-class citizen, some of the river's functionalities require some work in order to play nicely with mini-batches. For instance, the objects from the <code>metrics</code> module have an <code>update</code> method that take as input a single pair <code>(y_true, y_pred)</code>. This might change in the future, depending on the demand.</p> <p>We plan to promote more models to the mini-batch regime. However, we will only be doing so for the methods that benefit the most from it, as well as those that are most popular. Indeed, River's core philosophy will remain to cater to single instance learning.</p>"},{"location":"recipes/model-evaluation/","title":"Model evaluation","text":"<p>To do.</p>"},{"location":"recipes/on-hoeffding-trees/","title":"Incremental decision trees in river: the Hoeffding Tree case","text":"<p>Decision trees (DT) are popular learning models due to their inherently simplicity, flexibility and self-explainable structure. Moreover, when aggregated in ensembles, high predictive power might be achieved. Bagging and gradient boosting-based tree ensembles are very popular solutions in competition platforms such as Kaggle, and also among researchers.</p> <p>Although fairly lightweight, traditional batch DTs cannot cope with data stream mining/online learning requirements, as they do multiple passes over the data and have to be retrained from scratch every time a new observation appears.</p> <p>The data stream literature has plenty of incremental DT (iDT) families that are better suited to online learning. Nonetheless, Hoeffding Trees (HT) are historically the most popular family of iDTs to date. In fact, HTs have some nice properties:</p> <ul> <li>one-pass learning regime;</li> <li>theoretical guarantees to converge to the batch DT model given enough observations and a stationary data distribution;</li> <li>small memory and running time footprint (in most cases);</li> <li>some of their variations can deal with non-stationary distributions.</li> </ul> <p>And the previous list goes on and on. Besides that, HTs also have the same advantages as batch DTs (<code>C4.5</code>/<code>J48</code>, <code>CART</code>, <code>M5</code>, etc.) do. We can inspect the structure of a HT to understand how decisions were made, which is a nice feature to have in online learning tasks.</p> <p>In River, HTs are first-class citizens, so we have multiple realizations of this framework that are suited to different learning tasks and scenarios.</p> <p>This brief introduction to HT does not aims at being extensive nor delving into algorithmic or implementation details of the HTs. Instead, we intend to provide a high-level overview of the HTs as they are envisioned in River, as well as their shared properties and important hyperparameters.</p> <p>In this guide, we are going to:</p> <ol> <li>summarize the differences accross the multiple HT versions available;</li> <li>learn how to inspect tree models;</li> <li>learn how to manage the memory usage of HTs;</li> <li>compare numerical tree splitters and understand their impact on the iDT induction process.</li> </ol> <p>Well, without further ado, let's go! </p> <p>First things first, we are going to start with some imports.</p> <pre><code>import matplotlib.pyplot as plt\nimport datetime as dt\n\nfrom river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing  # we are going to use that later\nfrom river.datasets import synth  # we are going to use some synthetic datasets too\nfrom river import tree\n</code></pre>"},{"location":"recipes/on-hoeffding-trees/#1-trees-trees-everywhere-gardening-101-with-river","title":"1. Trees, trees everywhere: gardening 101 with river","text":"<p>At first glance, the amount of iDT algorithms in River might seem too much to handle, but in reality the distinction among them is easy to grasp. To facilitate our lives, here's a neat table listing the available HT models and summarizing their differences:</p> Name Acronym Task Non-stationary? Comments Source Hoeffding Tree Classifier HTC Classification No Basic HT for classification tasks [1] Hoeffding Adaptive Tree Classifier HATC Classification Yes Modifies HTC by adding an instance of ADWIN to each node to detect and react to drift detection [2] Extremely Fast Decision Tree Classifier EFDT Classification No Deploys split decisions as soon as possible and periodically revisit decisions and redo them if necessary. Not as fast in practice as the name implies, but it tends to converge faster than HTC to the model generated by a batch DT [3] Hoeffding Tree Regressor HTR Regression No Basic HT for regression tasks. It is an adaptation of the FIRT/FIMT algorithm that bears some semblance to HTC [4] Hoeffding Adaptive Tree Regressor HATR Regression Yes Modifies HTR by adding an instance of ADWIN to each node to detect and react to drift detection - incremental Structured-Output Prediction Tree Regressor iSOUPT Multi-target regression No Multi-target version of HTR [5] Label Combination Hoeffding Tree Classifier LCHTC Multi-label classification No Creates a numerical code for each combination of the binary labels and uses HTC to learn from this encoded representation. At prediction time, decodes the modified representation to obtain the original label set - <p>As we can see, although their application fields might overlap sometimes, the HT variations have specific situations in which they are better suited to work. Moreover, in River we provide a standardized API access to all the HT variants since they share many properties in common.</p>"},{"location":"recipes/on-hoeffding-trees/#2-how-to-inspect-tree-models","title":"2. How to inspect tree models?","text":"<p>We provide a handful of tools to inspect trained HTs in River. Here, we will provide some examples of how to access their inner structures, get useful information, and plot the iDT structure.</p> <p>Firstly, let's pick a toy dataset from which our tree will learn from. Here we are going to focus on the classification case, but the same operations apply to other learning tasks. We will select the <code>Phishing</code> dataset from the <code>datasets</code> module to exemplify the HTs' capabilities.</p> <pre><code>dataset = datasets.Phishing()\ndataset\n</code></pre> <pre><code>Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n    Name  Phishing                                                          \n    Task  Binary classification                                             \n Samples  1,250                                                             \nFeatures  9                                                                 \n  Sparse  False                                                             \n    Path  /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz\n</code></pre> <p>We are going to train an instance of <code>HoeffdingTreeClassifier</code> using this dataset. As everything else in River, training an iDT is a piece of cake!</p> <pre><code>%%time\n\nmodel = tree.HoeffdingTreeClassifier(grace_period=50)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel\n</code></pre> <pre><code>CPU times: user 37.6 ms, sys: 569 \u00b5s, total: 38.2 ms\nWall time: 39.1 ms\n</code></pre> <pre>HoeffdingTreeClassifier</pre><code>HoeffdingTreeClassifier (   grace_period=50   max_depth=inf   split_criterion=\"info_gain\"   delta=1e-07   tau=0.05   leaf_prediction=\"nba\"   nb_threshold=0   nominal_attributes=None   splitter=GaussianSplitter (     n_splits=10   )   binary_split=False   min_branch_fraction=0.01   max_share_to_split=0.99   max_size=100.   memory_estimate_period=1000000   stop_mem_management=False   remove_poor_attrs=False   merit_preprune=True ) </code> <p>That's it! We are not going to enter into details about some of the available parameters of HTC here. The user can refer to the documentation page for more information about that. Let's talk about model inspection :D</p> <p>At any time, we can easily get some statistics about our trained model by using the <code>summary</code> property:</p> <pre><code>model.summary\n</code></pre> <pre><code>{'n_nodes': 5,\n 'n_branches': 2,\n 'n_leaves': 3,\n 'n_active_leaves': 3,\n 'n_inactive_leaves': 0,\n 'height': 3,\n 'total_observed_weight': 1250.0}\n</code></pre> <p>This property show us the internal structure of the tree, including data concerning the memory-management routines that we are going to check later in this guide. We can also get a representation of the tree model as a <code>pandas.DataFrame</code> object:</p> <pre><code>model.to_dataframe().iloc[:5, :5]\n</code></pre> parent is_leaf depth stats feature node 0 &lt;NA&gt; False 0 {True: 260.0, False: 390.0} empty_server_form_handler 1 0 True 1 {True: 443.4163997711022, False: 59.8769131081... NaN 2 0 False 1 {True: 71.58360022889781, False: 404.123086891... popup_window 3 2 True 2 {False: 31.426538522574834, True: 33.0} NaN 4 2 True 2 {False: 250.57346147742516, True: 6.0} NaN <p>Hmm, maybe not the clearest of the representations. What about drawing the tree structure instead?</p> <pre><code>model.draw()\n</code></pre> <p></p> <p>Much better, huh?</p> <p>Lastly, we can check how the tree predicts one specific instance by using the <code>debug_one</code> method:</p> <pre><code>x, y = next(iter(dataset))  # Let's select the first example in the stream\nx, y\n</code></pre> <pre><code>({'empty_server_form_handler': 0.0,\n  'popup_window': 0.0,\n  'https': 0.0,\n  'request_from_other_domain': 0.0,\n  'anchor_from_other_domain': 0.0,\n  'is_popular': 0.5,\n  'long_url': 1.0,\n  'age_of_domain': 1,\n  'ip_in_url': 1},\n True)\n</code></pre> <pre><code>print(model.debug_one(x))\n</code></pre> <pre><code>empty_server_form_handler \u2264 0.5454545454545454\nClass True:\n    P(False) = 0.1\n    P(True) = 0.9\n</code></pre> <p>Our tree got this one right! The method <code>debug_one</code> is especially useful when we are dealing with a big tree model where drawing might not be the wisest of the choices (we will end up with a tree chart that has too much information to visually understand).</p> <p>Some additional hints:</p> <ul> <li>the <code>max_depth</code> parameter is our friend when building HTs that need to be constantly inspected. This parameter, which is available for every HT variant, triggers a pre-pruning mechanism that stops tree growth when the given depth is reached.</li> <li>we can also limit the depth when using the <code>draw</code> method.</li> <li>in the case of tree ensembles, individual trees can be accessed using the <code>[index]</code> operator. Then, the same set of inspection tools are available to play with!</li> </ul>"},{"location":"recipes/on-hoeffding-trees/#3-advanced-gardening-with-river-grab-your-pruning-shears-and-lets-limit-memory-usage","title":"3. Advanced gardening with river: grab your pruning shears and let's limit memory usage","text":"<p>Online learning is well-suited to highly scalable processing centers with petabytes of data arriving intermittently, but it can also work with Internet of Things (IoT) devices operating at low power and with limited processing capability. Hence, making sure our trees are not going to use too much memory is a nice feature that can impact on both energy usage and the running time. HTs have memory-management routines that put the user in the control of computational resources that are available.</p> <p>In this brief guide, we are going to use a regression tree, since this kind of iDT typically spends more memory than the classification counterparts. However, the user can control the memory usage in the exact same way in River, regardless of the HT variant!</p> <p>We will rely on the <code>Friedman</code> synthetic dataset (data generator) from the <code>synth</code> module in our evaluation. Since data generators can produce instances indefinitely, we will select a sample of size 10K for our tests.</p> <p>We are almost ready to go. Let's first define a simple function that plots the results obtained from a given dataset, metric and </p> <pre><code>def plot_performance(dataset, metric, models):\n    metric_name = metric.__class__.__name__\n\n    # To make the generated data reusable\n    dataset = list(dataset)\n    fig, ax = plt.subplots(figsize=(10, 5), nrows=3, dpi=300)\n    for model_name, model in models.items():\n        step = []\n        error = []\n        r_time = []\n        memory = []\n\n        for checkpoint in evaluate.iter_progressive_val_score(\n            dataset, model, metric, measure_time=True, measure_memory=True, step=100\n        ):\n            step.append(checkpoint[\"Step\"])\n            error.append(checkpoint[metric_name].get())\n\n            # Convert timedelta object into seconds\n            r_time.append(checkpoint[\"Time\"].total_seconds())\n            # Make sure the memory measurements are in MB\n            raw_memory = checkpoint[\"Memory\"]\n            memory.append(raw_memory * 2**-20)\n\n        ax[0].plot(step, error, label=model_name)\n        ax[1].plot(step, r_time, label=model_name)\n        ax[2].plot(step, memory, label=model_name)\n\n    ax[0].set_ylabel(metric_name)\n    ax[1].set_ylabel('Time (seconds)')\n    ax[2].set_ylabel('Memory (MB)')\n    ax[2].set_xlabel('Instances')\n\n    ax[0].grid(True)\n    ax[1].grid(True)\n    ax[2].grid(True)\n\n    ax[0].legend(\n        loc='upper center', bbox_to_anchor=(0.5, 1.25),\n        ncol=3, fancybox=True, shadow=True\n    )\n    plt.tight_layout()\n    plt.close()\n\n    return fig\n</code></pre> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"Unbounded HTR\": (\n            preprocessing.StandardScaler() |\n            tree.HoeffdingTreeRegressor(splitter=tree.splitter.EBSTSplitter())\n        )\n    }\n)\n</code></pre> <p></p> <p>In our example we use the <code>EBSTSplitter</code>, which is going to discussed later. For now, is enough to know that it is a mechanism to evaluate split candidates in the trees.</p> <p>As we can see, our tree uses almost 10 MB to keep its structure. Let's say we wanted to limit our memory usage to 5 MB. How could we do that?</p> <p>Note that we are using a illustration case here. In real applications, data may be unbounded, so the trees might grow indefinitely.</p> <p>HTs expose some parameters related to memory management. The user can refer to the documentation for more details on that matter. Here, we are going to focus on two parameters:</p> <ul> <li><code>max_size</code>: determines the maximum amount of memory (in MB) that the HT can use.</li> <li><code>memory_estimate_period</code>: intervals after which the memory-management is triggered.</li> </ul> <p>We are going to limit our HTR to 5 MB and perform memory checks at intervals of 500 instances.</p> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"Restricted HTR\": (\n            preprocessing.StandardScaler()\n            | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.EBSTSplitter(),\n                max_size=5,\n                memory_estimate_period=500\n            )\n        )\n    }\n)\n</code></pre> <p></p> <p>Note that as soon the memory usage reaches the limit that we determined (at the memory check intervals), HTR starts managing its resource usage to reduce the size. As a consequence, the running time also decreases. For more accurate management, the intervals between memory checks should be decreased. This action, however, has costs since the tree stops the learning process to estimate its size and alter its own structure. Too frequent memory checks might end up result in a slow learning process. Besides, by using fewer resources, the predictive performance can be negatively impacted. So, use this tool with caution!</p> <p>But how that works at all?</p> <p>HTs monitor the incoming feature values to perform split attempts. To do so, they rely on a class of algorithms called Attribute Observers (AO) or Splitters (spoiler alert!). Each leaf node in an HT keeps one AO per incoming feature. After pre-determined intervals (<code>grace_period</code> parameter), leaves query their AOs for split candidates. Well, there are costs to monitor input features (mainly the numerical ones). In fact, AOs correspond to one of the most time and memory-consuming portions of the HTs. To manage memory usage, an HT firstly determines its least promising leaves, w.r.t. how likely they will be split. Then, these leaves' AOs are removed, and the tree nodes are said to be \"deactivated.\" That's it! The deactivated leaves do not perform split attempts anymore, but they continue to be updated to provide responses. They will be kept as leaves as long as there are not available resources to enable tree growth. These leaves can be activated again (meaning that new AOs will be created for them) if there is available memory, so don't worry!</p> <p>Hint: another indirect way to bound memory usage is to limit the tree depth. By default, the trees can grow indefinitely, but the <code>max_depth</code> parameter can control this behavior.</p> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"HTR with at most 5 levels\": (\n            preprocessing.StandardScaler()\n            | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.EBSTSplitter(),\n                max_depth=5\n            )\n        )\n    }\n)\n</code></pre> <p></p>"},{"location":"recipes/on-hoeffding-trees/#4-branching-and-growth-splitters-the-heart-of-the-trees","title":"4. Branching and growth: splitters, the heart of the trees","text":"<p>As previously stated, one of the core operations of iDT is, well, to grow. Plants and gardening-related jokes apart, growth in HTs is guided by their AOs or splitters, as mentioned in the end of Section 3. </p> <p>Nominal features can be easily monitored, since the feature partitions are well-defined beforehand. Numerical features, on the other hand, do not have an explicit best cut point. Still, numerical features are typically split by using a binary test: \\(\\le\\) or \\(&gt;\\). Therefore, numerical splitters must somehow summarize the incoming feature values and be able to evaluate the merit of split point candidates.</p> <p>There are diverse strategies to monitor numerical features and choices related to them, including which data structure will be used to keep a summary of the incoming feature and also how many split points are going to be evaluated during split attempts. Again, this guide does not intend to be an exhaustive delve into the iDT subject. In fact, each of the following aspects of the iDTs could be considered a separate research area: AOs, intervals between split attempts, split heuristics (e.g., info gain, variance reduction, and so on), tree depth and max size, and much more!</p> <p>Let's focus a bit into the AO matter. River provides a handful of splitters for classification and regression trees, which can be chosen using the parameter <code>splitter</code>. We will list the available tree splitters in the following sections and compare some of their chacteristics.</p> <p>Some notation:</p> <ul> <li>\\(n\\): Number of observations seen so far.</li> <li>\\(c\\): the number of classes.</li> <li>\\(s\\): the number of split points to evaluate (which means that this is a user-given parameter).</li> <li>\\(h\\): the number of histogram bins or hash slots. Tipically, \\(h \\ll n\\).</li> </ul>"},{"location":"recipes/on-hoeffding-trees/#41-classification-tree-splitters","title":"4.1. Classification tree splitters","text":"<p>The following table summarizes the available classification splitters. The user might refer to the documentation of each splitter for more details about their functioning.</p> Splitter Description Insertion Memory Split candidate query Works with Naive Bayes leaves? Exhaustive Keeps all the observed input values and class counts in a Binary Search Tree (BST) \\(O(\\log n)\\) (average) or \\(O(n)\\) (worst case) \\(O(n)\\) \\(O(n)\\) No Histogram Builds a histogram for each class in order to discretize the input feature \\(O(\\log h)\\) \\(O(c h)\\) \\(O(c h)\\) Yes Gaussian Approximates the class distributions using Gaussian distributions \\(O(1)\\) \\(O(c)\\) \\(O(cs)\\) Yes <p>Note that some of the splitters have configurable parameters that directly impact not only on their time and memory costs, but also on the final predictive performance. Examples:</p> <ul> <li>The number of split points can be configured in the Gaussian splitter. Increasing this number makes this splitter slower, but it also potentially increases the quality of the obtained query points, implying enhanced tree accuracy. </li> <li>The number of stored bins can be selected in the Histogram splitter. Increasing this number increases the memory footprint and running time of this splitter, but it also potentially makes its split candidates more accurate and positively impacts on the tree's final predictive performance.</li> </ul> <p>Next, we provide a brief comparison of the classification splitters using 10K instances of the Random RBF synthetic dataset. Note that the tree equiped with the Exhaustive splitter does not use Naive Bayes leaves.</p> <pre><code>plot_performance(\n    synth.RandomRBF(seed_model=7, seed_sample=42).take(10_000),\n    metrics.Accuracy(),\n    {\n        \"HTC + Exhaustive splitter\": tree.HoeffdingTreeClassifier(\n            splitter=tree.splitter.ExhaustiveSplitter(),\n            leaf_prediction=\"mc\"\n        ),\n        \"HTC + Histogram splitter\": tree.HoeffdingTreeClassifier(\n            splitter=tree.splitter.HistogramSplitter()\n        ),\n        \"HTC + Gaussian splitter\": tree.HoeffdingTreeClassifier(\n            splitter=tree.splitter.GaussianSplitter()\n        )\n    }\n)\n</code></pre> <p></p>"},{"location":"recipes/on-hoeffding-trees/#42-regression-tree-splitters","title":"4.2 Regression tree splitters","text":"<p>The available regression tree splitters are summarized in the next table. The TE-BST costs are expressed in terms of \\(n^*\\) because the number of stored elements can be smaller than or equal to \\(n\\).</p> Splitter Description Insertion Memory Split candidate query Extended Binary Search Tree (E-BST) Stores all the observations and target statistics in a BST \\(O(\\log n)\\) (average) or \\(O(n)\\) (worst case) \\(O(n)\\) \\(O(n)\\) Truncated E-BST (TE-BST) Rounds the incoming data before passing it to the BST \\(O(\\log n^*)\\) (average) or \\(O(n^*)\\) (worst case) \\(O(n^*)\\) \\(O(n^*)\\) Quantization Observer (QO) Uses a hash-like structure to quantize the incoming data \\(O(1)\\) \\(O(h)\\) \\(O(h \\log h)\\) <p>E-BST is an exhaustive algorithm, i.e., it works as batch solutions usually do, which might be prohibitive in real-world online scenarios. TE-BST and QO apply approximations to alleviate the costs involved in monitoring numerical data and performing split attempts. The number of desired decimal places to round the data (TE-BST) and the quantization radius (QO) are directly related to the running time, memory footprint, and error of the resulting tree model.</p> <p>We present a brief comparison of the available regression tree splitters using the 10K instances of the Friedman synthetic dataset.</p> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"HTR + E-BST\": (\n            preprocessing.StandardScaler() | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.EBSTSplitter()\n            )\n        ),\n        \"HTR + TE-BST\": (\n            preprocessing.StandardScaler() | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.TEBSTSplitter()\n            )\n        ),\n        \"HTR + QO\": (\n            preprocessing.StandardScaler() | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.QOSplitter()\n            )\n        ),\n\n    }\n)\n</code></pre> <p></p>"},{"location":"recipes/on-hoeffding-trees/#wrapping-up","title":"Wrapping up","text":"<p>This guide provides a walkthrough in the HTs available in River. We discussed about model inspection, memory management, and feature splits. Keep in mind that each HT variant has specific details and capabilities that are out-of-the-scope of this introductory material. The user is advised to check the documentation page of the tree models for detailed information.</p>"},{"location":"recipes/pipelines/","title":"Pipelines","text":"<p>Pipelines are an integral part of River. We encourage their usage and apply them in many of their examples.</p> <p>The <code>compose.Pipeline</code> contains all the logic for building and applying pipelines. A pipeline is essentially a list of estimators that are applied in sequence. The only requirement is that the first <code>n - 1</code> steps be transformers. The last step can be a regressor, a classifier, a clusterer, a transformer, etc.</p> <p>Here is an example:</p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import preprocessing\nfrom river import feature_extraction\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    feature_extraction.PolynomialExtender(),\n    linear_model.LinearRegression()\n)\n</code></pre> <p>You can also use the <code>|</code> operator, as so:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    feature_extraction.PolynomialExtender() |\n    linear_model.LinearRegression()\n)\n</code></pre> <p>Or, equally:</p> <pre><code>model = preprocessing.StandardScaler() \nmodel |= feature_extraction.PolynomialExtender()\nmodel |= linear_model.LinearRegression()\n</code></pre> <p>A pipeline, as any River estimator, has a <code>_repr_html_</code> method, which can be used to visualize it in Jupyter-like notebooks:</p> <pre><code>model\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>PolynomialExtender</pre><code>PolynomialExtender (   degree=2   interaction_only=False   include_bias=False   bias_name=\"bias\" ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p><code>compose.Pipeline</code> implements a <code>learn_one</code> method which in sequence calls the <code>learn_one</code> of each component and a <code>predict_one</code> (resp <code>predict_proba_one</code>) method which calls <code>transform_one</code> on the first <code>n - 1</code> steps and <code>predict_one</code> (resp <code>predict_proba_one</code>) on the last step.</p> <p>Here is a small example to illustrate the previous point:</p> <pre><code>from river import datasets\n\ndataset = datasets.TrumpApproval()\nx, y = next(iter(dataset))\nx, y\n</code></pre> <pre><code>({'ordinal_date': 736389,\n  'gallup': 43.843213,\n  'ipsos': 46.19925042857143,\n  'morning_consult': 48.318749,\n  'rasmussen': 44.104692,\n  'you_gov': 43.636914000000004},\n 43.75505)\n</code></pre> <p>We can predict the target value of a new sample by calling the <code>predict_one</code> method, however, by default, <code>predict_one</code> does not update any model parameter, therefore the predictions will be 0 and the model parameters will remain the default values (0 for <code>StandardScaler</code> component):</p> <pre><code>for (x, y) in dataset.take(2):\n    print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n    print(f\"{model['StandardScaler'].means = }\")\n</code></pre> <pre><code>model.predict_one(x)=0.00, y=43.76\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 0.0, 'gallup': 0.0, 'ipsos': 0.0, 'morning_consult': 0.0, 'rasmussen': 0.0, 'you_gov': 0.0})\nmodel.predict_one(x)=0.00, y=43.71\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 0.0, 'gallup': 0.0, 'ipsos': 0.0, 'morning_consult': 0.0, 'rasmussen': 0.0, 'you_gov': 0.0})\n</code></pre> <p><code>learn_one</code> updates pipeline stateful steps, parameters and the prediction change:</p> <pre><code>for (x, y) in dataset.take(2):\n    model.learn_one(x, y)\n\n    print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n    print(f\"{model['StandardScaler'].means = }\")\n</code></pre> <pre><code>model.predict_one(x)=0.88, y=43.76\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.0, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 44.104692, 'you_gov': 43.636914000000004})\nmodel.predict_one(x)=9.44, y=43.71\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.5, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 45.104692, 'you_gov': 42.636914000000004})\n</code></pre> <p>Each component of the pipeline has been updated with the new data point. </p> <p>A pipeline is a very powerful tool that can be used to chain together multiple steps in a machine learning workflow.</p> <p>Notice that it is also possible to call <code>transform_one</code> with a pipeline, this method will run <code>transform_one</code> of each transformer in it, and return the result of the last transformer (which is thus the penultimate step if the last step is a predictor or clusterer, while it is the last step if the last step is a transformer):</p> <pre><code>model.transform_one(x)\n</code></pre> <pre><code>{'ordinal_date': 1.0,\n 'gallup': 0.0,\n 'ipsos': 0.0,\n 'morning_consult': 0.0,\n 'rasmussen': 1.0,\n 'you_gov': -1.0,\n 'ordinal_date*ordinal_date': 1.0,\n 'gallup*ordinal_date': 0.0,\n 'ipsos*ordinal_date': 0.0,\n 'morning_consult*ordinal_date': 0.0,\n 'ordinal_date*rasmussen': 1.0,\n 'ordinal_date*you_gov': -1.0,\n 'gallup*gallup': 0.0,\n 'gallup*ipsos': 0.0,\n 'gallup*morning_consult': 0.0,\n 'gallup*rasmussen': 0.0,\n 'gallup*you_gov': -0.0,\n 'ipsos*ipsos': 0.0,\n 'ipsos*morning_consult': 0.0,\n 'ipsos*rasmussen': 0.0,\n 'ipsos*you_gov': -0.0,\n 'morning_consult*morning_consult': 0.0,\n 'morning_consult*rasmussen': 0.0,\n 'morning_consult*you_gov': -0.0,\n 'rasmussen*rasmussen': 1.0,\n 'rasmussen*you_gov': -1.0,\n 'you_gov*you_gov': 1.0}\n</code></pre> <p>In many cases, you might want to connect a step to multiple steps. For instance, you might to extract different kinds of features from a single input. An elegant way to do this is to use a <code>compose.TransformerUnion</code>. Essentially, the latter is a list of transformers who's results will be merged into a single <code>dict</code> when <code>transform_one</code> is called.</p> <p>As an example let's say that we want to apply a <code>feature_extraction.RBFSampler</code> as well as the <code>feature_extraction.PolynomialExtender</code>. This may be done as so:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    (feature_extraction.PolynomialExtender() + feature_extraction.RBFSampler()) |\n    linear_model.LinearRegression()\n)\n\nmodel\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>PolynomialExtender</pre><code>PolynomialExtender (   degree=2   interaction_only=False   include_bias=False   bias_name=\"bias\" ) </code><pre>RBFSampler</pre><code>RBFSampler (   gamma=1.   n_components=100   seed=None ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>Note that the <code>+</code> symbol acts as a shorthand notation for creating a <code>compose.TransformerUnion</code>, which means that we could have declared the above pipeline as so:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    compose.TransformerUnion(\n        feature_extraction.PolynomialExtender(),\n        feature_extraction.RBFSampler()\n    ) |\n    linear_model.LinearRegression()\n)\n</code></pre> <p>Pipelines provide the benefit of removing a lot of cruft by taking care of tedious details for you. They also enable to clearly define what steps your model is made of.</p> <p>Finally, having your model in a single object means that you can move it around more easily.</p> <p>Note that you can include user-defined functions in a pipeline by using a <code>compose.FuncTransformer</code>.</p>"},{"location":"recipes/pipelines/#learning-during-predict","title":"Learning during predict","text":"<p>In online machine learning, we can update the unsupervised parts of our model when a sample arrives. We don't really have to wait for the ground truth to arrive in order to update unsupervised estimators that don't depend on it.</p> <p>In other words, in a pipeline, <code>learn_one</code> updates the supervised parts, whilst <code>predict_one</code> (or <code>predict_proba_one</code> for that matter) can update the unsupervised parts, which often yields better results. </p> <p>In river, we can achieve this behavior using a dedicated context manager: <code>compose.learn_during_predict</code>.</p> <p>Here is the same example as before, with the only difference of activating the such learning during predict behavior:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    feature_extraction.PolynomialExtender() |\n    linear_model.LinearRegression()\n)\n</code></pre> <pre><code>with compose.learn_during_predict():\n    for (x, y) in dataset.take(2):\n\n        print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n        print(f\"{model['StandardScaler'].means = }\")\n</code></pre> <pre><code>model.predict_one(x)=0.00, y=43.76\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.0, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 44.104692, 'you_gov': 43.636914000000004})\nmodel.predict_one(x)=0.00, y=43.71\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.5, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 45.104692, 'you_gov': 42.636914000000004})\n</code></pre> <p>Calling <code>predict_one</code> within this context will update each transformer of the pipeline. For instance here we can see that the mean of each feature of the standard scaler step have been updated.</p> <p>On the other hand, the supervised part of our pipeline, the linear regression, has not been updated or learned anything yet. Hence the prediction on any sample will be nil because each weight is still equal to 0.</p> <pre><code>model.predict_one(x), model[\"LinearRegression\"].weights\n</code></pre> <pre><code>(0.0, {})\n</code></pre>"},{"location":"recipes/pipelines/#performance-comparison","title":"Performance Comparison","text":"<p>One may wonder what is the advantage of learning during predict. Let's compare the performance of a pipeline with and without learning during predict, in two scenarios: one in which the flow of data stays the same, we just update </p> <pre><code>from contextlib import nullcontext\nfrom river import metrics\n\nimport pandas as pd\n</code></pre> <pre><code>def score_pipeline(learn_during_predict: bool, n_learning_samples: int | None = None) -&gt; float:\n    \"\"\"Scores a pipeline on the TrumpApproval dataset.\n\n    Parameters\n    ----------\n    learn_during_predict : bool\n        Whether or not to learn the unsupervided components during the prediction step.\n        If False it will only learn when `learn_one` is explicitly called.\n    n_learning_samples : int | None \n        Number of samples used to `learn_one`.\n\n    Return\n    ------\n    MAE : float\n        Mean absolute error of the pipeline on the dataset\n    \"\"\"\n\n    dataset = datasets.TrumpApproval()\n\n    model = (\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression()\n        )\n\n    metric = metrics.MAE()\n\n    ctx = compose.learn_during_predict if learn_during_predict else nullcontext\n    n_learning_samples = n_learning_samples or dataset.n_samples\n\n    with ctx():\n        for _idx, (x, y) in enumerate(dataset):\n            y_pred = model.predict_one(x)\n\n            metric.update(y, y_pred)\n\n            if _idx &lt; n_learning_samples:\n                model.learn_one(x, y)\n\n    return metric.get()\n</code></pre> <pre><code>max_samples = datasets.TrumpApproval().n_samples\n\nresults = [\n    {\n        \"learn_during_predict\": learn_during_predict,\n        \"pct_learning_samples\": round(100*n_learning_samples/max_samples, 0),\n        \"mae\": score_pipeline(learn_during_predict=learn_during_predict, n_learning_samples=n_learning_samples)\n    }\n    for learn_during_predict in (True, False)\n    for n_learning_samples in range(max_samples, max_samples//10, -(max_samples//10))\n]\n</code></pre> <pre><code>(pd.DataFrame(results)\n .pivot(columns=\"learn_during_predict\", index=\"pct_learning_samples\", values=\"mae\")\n .sort_index(ascending=False)\n .style.format_index('{0}%')\n)\n</code></pre> learn_during_predict False True pct_learning_samples 100.0% 1.314548 1.347434 90.0% 1.629333 1.355274 80.0% 2.712125 1.371599 70.0% 4.840620 1.440773 60.0% 8.918634 1.498240 50.0% 15.112753 1.878434 40.0% 26.387331 2.105553 30.0% 42.997083 3.654709 20.0% 90.703102 3.504950 10.0% 226.836953 4.803600 <p>As we can see from the resulting table above, the scores are comparable only in the case in which the percentage of learning samples above 90%. After that the score starts to degrade quite fast as the percentage of learning samples decreases, and it is very remarkable (one order of magnitude or more) when less than 50% of the samples are used for learning.</p> <p>Although a simple case, this examplify how powerful it can be to learn unsupervised components during predict.</p>"},{"location":"recipes/reading-data/","title":"Reading data","text":"<p>In River, the features of a sample are stored inside a dictionary, which in Python is called a <code>dict</code> and is a native data structure. In other words, we don't use any sophisticated data structure, such as a <code>numpy.ndarray</code> or a <code>pandas.DataFrame</code>.</p> <p>The main advantage of using plain <code>dict</code>s is that it removes the overhead that comes with using the aforementioned data structures. This is important in a streaming context because we want to be able to process many individual samples in rapid succession. Another advantage is that <code>dict</code>s allow us to give names to our features. Finally, <code>dict</code>s are not typed, and can therefore store heterogeneous data.</p> <p>Another advantage which we haven't mentioned is that <code>dict</code>s play nicely with Python's standard library. Indeed, Python contains many tools that allow manipulating <code>dict</code>s. For instance, the <code>csv.DictReader</code> can be used to read a CSV file and convert each row to a <code>dict</code>. In fact, the <code>stream.iter_csv</code> method from River is just a wrapper on top of <code>csv.DictReader</code> that adds a few bells and whistles.</p> <p>River provides some out-of-the-box datasets to get you started.</p> <pre><code>from river import datasets\n\ndataset = datasets.Bikes()\ndataset\n</code></pre> <pre><code>Bike sharing station information from the city of Toulouse.\n\nThe goal is to predict the number of bikes in 5 different bike stations from the city of\nToulouse.\n\n      Name  Bikes                                                         \n      Task  Regression                                                    \n   Samples  182,470                                                       \n  Features  8                                                             \n    Sparse  False                                                         \n      Path  /Users/max/river_data/Bikes/toulouse_bikes.csv                \n       URL  https://maxhalford.github.io/files/datasets/toulouse_bikes.zip\n      Size  12.52 MB                                                      \nDownloaded  True\n</code></pre> <p>Note that when we say \"loaded\", we don't mean that the actual data is read from the disk. On the contrary, the dataset is a streaming data that can be iterated over one sample at a time. In Python lingo, it's a generator.</p> <p>Let's take a look at the first sample:</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n 'station': 'metro-canal-du-midi',\n 'clouds': 75,\n 'description': 'light rain',\n 'humidity': 81,\n 'pressure': 1017.0,\n 'temperature': 6.54,\n 'wind': 9.3}\n</code></pre> <p>Each dataset is iterable, which means we can also do:</p> <pre><code>for x, y in dataset:\n    break\nx\n</code></pre> <pre><code>{'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n 'station': 'metro-canal-du-midi',\n 'clouds': 75,\n 'description': 'light rain',\n 'humidity': 81,\n 'pressure': 1017.0,\n 'temperature': 6.54,\n 'wind': 9.3}\n</code></pre> <p>As we can see, the values have different types.</p> <p>Under the hood, calling <code>for x, y in dataset</code> simply iterates over a file and parses each value appropriately. We can do this ourselves by using <code>stream.iter_csv</code>:</p> <pre><code>from river import stream\n\nX_y = stream.iter_csv(dataset.path)\nx, y = next(X_y)\nx, y\n</code></pre> <pre><code>({'moment': '2016-04-01 00:00:07',\n  'bikes': '1',\n  'station': 'metro-canal-du-midi',\n  'clouds': '75',\n  'description': 'light rain',\n  'humidity': '81',\n  'pressure': '1017.0',\n  'temperature': '6.54',\n  'wind': '9.3'},\n None)\n</code></pre> <p>There are a couple things that are wrong. First of all, the numeric features have not been casted into numbers. Indeed, by default, <code>stream.iter_csv</code> assumes that everything is a string. A related issue is that the <code>moment</code> field hasn't been parsed into a <code>datetime</code>. Finally, the target field, which is <code>bikes</code>, hasn't been separated from the rest of the features. We can remedy to these issues by setting a few parameters:</p> <pre><code>X_y = stream.iter_csv(\n    dataset.path,\n    converters={\n        'bikes': int,\n        'clouds': int,\n        'humidity': int,\n        'pressure': float,\n        'temperature': float,\n        'wind': float\n    },\n    parse_dates={'moment': '%Y-%m-%d %H:%M:%S'},\n    target='bikes'\n)\nx, y = next(X_y)\nx, y\n</code></pre> <pre><code>({'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n  'station': 'metro-canal-du-midi',\n  'clouds': 75,\n  'description': 'light rain',\n  'humidity': 81,\n  'pressure': 1017.0,\n  'temperature': 6.54,\n  'wind': 9.3},\n 1)\n</code></pre> <p>That's much better. We invite you to take a look at the <code>stream</code> module to see for yourself what other methods are available. Note that River is first and foremost a machine learning library, and therefore isn't as much concerned about reading data as it is about statistical algorithms. We do however believe that the fact that we use dictionary gives you, the user, a lot of freedom and flexibility.</p> <p>The <code>stream</code> module provides helper functions to read data from different formats. For instance, you can use the <code>stream.iter_sklearn_dataset</code> function to turn any scikit-learn dataset into a stream.</p> <pre><code>from sklearn import datasets\n\ndataset = datasets.load_diabetes()\n\nfor x, y in stream.iter_sklearn_dataset(dataset):\n    break\n\nx, y\n</code></pre> <pre><code>({'age': 0.038075906433423026,\n  'sex': 0.05068011873981862,\n  'bmi': 0.061696206518683294,\n  'bp': 0.0218723855140367,\n  's1': -0.04422349842444599,\n  's2': -0.03482076283769895,\n  's3': -0.04340084565202491,\n  's4': -0.002592261998183278,\n  's5': 0.019907486170462722,\n  's6': -0.01764612515980379},\n 151.0)\n</code></pre> <p>To conclude, let us shortly mention the difference between proactive learning and reactive learning in the specific context of online machine learning. When we loop over a data with a <code>for</code> loop, we have the control over the data and the order in which it arrives. We are proactive in the sense that we, the user, are asking for the data to arrive.</p> <p>In contract, in a reactive situation, we don't have control on the data arrival. A typical example of such a situation is a web server, where web requests arrive in an arbitrary order. This is a situation where River shines. For instance, in a Flask application, you could define a route to make predictions with a River model as so:</p> <pre><code>import flask\n\napp = flask.Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef predict():\n    payload = flask.request.json\n    river_model = load_model()\n    return river_model.predict_proba_one(payload)\n</code></pre> <p>Likewise, a model can be updated whenever a request arrives as so:</p> <pre><code>@app.route('/', methods=['POST'])\ndef learn():\n    payload = flask.request.json\n    river_model = load_model()\n    river_model.learn_one(payload['features'], payload['target'])\n    return {}, 201\n</code></pre> <p>To summarize, River can be used in many different ways. The fact that it uses dictionaries to represent features provides a lot of flexibility and space for creativity.</p>"},{"location":"recipes/rolling-computations/","title":"Rolling computations","text":"<p>You might wonder which classes in River can be wrapped with a <code>utils.Rolling</code>. This can be answered with a bit of metaprogramming.</p> <pre><code>import importlib\nimport inspect\nfrom river.utils.rolling import Rollable\n\nfor submodule in importlib.import_module(\"river.api\").__all__:\n    for _, obj in inspect.getmembers(\n        importlib.import_module(f\"river.{submodule}\"), lambda x: isinstance(x, Rollable)\n    ):\n        print(f'{submodule}.{obj.__name__}')\n</code></pre> <pre><code>[covariance.EmpiricalCovariance](../../api/covariance/EmpiricalCovariance)\n[metrics.Accuracy](../../api/metrics/Accuracy)\n[metrics.AdjustedMutualInfo](../../api/metrics/AdjustedMutualInfo)\n[metrics.AdjustedRand](../../api/metrics/AdjustedRand)\n[metrics.BalancedAccuracy](../../api/metrics/BalancedAccuracy)\n[metrics.ClassificationReport](../../api/metrics/ClassificationReport)\n[metrics.CohenKappa](../../api/metrics/CohenKappa)\n[metrics.Completeness](../../api/metrics/Completeness)\n[metrics.ConfusionMatrix](../../api/metrics/ConfusionMatrix)\n[metrics.CrossEntropy](../../api/metrics/CrossEntropy)\n[metrics.F1](../../api/metrics/F1)\n[metrics.FBeta](../../api/metrics/FBeta)\n[metrics.FowlkesMallows](../../api/metrics/FowlkesMallows)\n[metrics.GeometricMean](../../api/metrics/GeometricMean)\n[metrics.Homogeneity](../../api/metrics/Homogeneity)\n[metrics.Jaccard](../../api/metrics/Jaccard)\n[metrics.LogLoss](../../api/metrics/LogLoss)\n[metrics.MAE](../../api/metrics/MAE)\n[metrics.MAPE](../../api/metrics/MAPE)\n[metrics.MCC](../../api/metrics/MCC)\n[metrics.MSE](../../api/metrics/MSE)\n[metrics.MacroF1](../../api/metrics/MacroF1)\n[metrics.MacroFBeta](../../api/metrics/MacroFBeta)\n[metrics.MacroJaccard](../../api/metrics/MacroJaccard)\n[metrics.MacroPrecision](../../api/metrics/MacroPrecision)\n[metrics.MacroRecall](../../api/metrics/MacroRecall)\n[metrics.MicroF1](../../api/metrics/MicroF1)\n[metrics.MicroFBeta](../../api/metrics/MicroFBeta)\n[metrics.MicroJaccard](../../api/metrics/MicroJaccard)\n[metrics.MicroPrecision](../../api/metrics/MicroPrecision)\n[metrics.MicroRecall](../../api/metrics/MicroRecall)\n[metrics.MultiFBeta](../../api/metrics/MultiFBeta)\n[metrics.MutualInfo](../../api/metrics/MutualInfo)\n[metrics.NormalizedMutualInfo](../../api/metrics/NormalizedMutualInfo)\n[metrics.Precision](../../api/metrics/Precision)\n[metrics.R2](../../api/metrics/R2)\n[metrics.RMSE](../../api/metrics/RMSE)\n[metrics.RMSLE](../../api/metrics/RMSLE)\n[metrics.ROCAUC](../../api/metrics/ROCAUC)\n[metrics.Rand](../../api/metrics/Rand)\n[metrics.Recall](../../api/metrics/Recall)\n[metrics.RollingROCAUC](../../api/metrics/RollingROCAUC)\n[metrics.SMAPE](../../api/metrics/SMAPE)\n[metrics.Silhouette](../../api/metrics/Silhouette)\n[metrics.VBeta](../../api/metrics/VBeta)\n[metrics.WeightedF1](../../api/metrics/WeightedF1)\n[metrics.WeightedFBeta](../../api/metrics/WeightedFBeta)\n[metrics.WeightedJaccard](../../api/metrics/WeightedJaccard)\n[metrics.WeightedPrecision](../../api/metrics/WeightedPrecision)\n[metrics.WeightedRecall](../../api/metrics/WeightedRecall)\n[proba.Beta](../../api/proba/Beta)\n[proba.Gaussian](../../api/proba/Gaussian)\n[proba.Multinomial](../../api/proba/Multinomial)\n[proba.MultivariateGaussian](../../api/proba/MultivariateGaussian)\n[stats.BayesianMean](../../api/stats/BayesianMean)\n[stats.Cov](../../api/stats/Cov)\n[stats.KolmogorovSmirnov](../../api/stats/KolmogorovSmirnov)\n[stats.Mean](../../api/stats/Mean)\n[stats.PearsonCorr](../../api/stats/PearsonCorr)\n[stats.SEM](../../api/stats/SEM)\n[stats.Sum](../../api/stats/Sum)\n[stats.Var](../../api/stats/Var)\n</code></pre>"},{"location":"releases/0.0.2/","title":"0.0.2 - 2019-02-13","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.0.2/#compat","title":"compat","text":"<ul> <li>Added <code>sklearn</code> wrappers.</li> </ul>"},{"location":"releases/0.0.2/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.HedgeClassifier</code>.</li> </ul>"},{"location":"releases/0.0.2/#feature_selection","title":"feature_selection","text":"<ul> <li>Added <code>feature_selection.RandomDiscarder</code>.</li> </ul>"},{"location":"releases/0.0.2/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added <code>feature_extraction.TargetEncoder</code>.</li> </ul>"},{"location":"releases/0.0.2/#impute","title":"impute","text":"<ul> <li>Added <code>impute.NumericImputer</code>.</li> </ul>"},{"location":"releases/0.0.2/#optim","title":"optim","text":"<ul> <li>Added <code>optim.AbsoluteLoss</code>.</li> <li>Added <code>optim.HingeLoss</code>.</li> <li>Added <code>optim.EpsilonInsensitiveHingeLoss</code>.</li> </ul>"},{"location":"releases/0.0.2/#stats","title":"stats","text":"<ul> <li>Added <code>stats.NUnique</code>.</li> <li>Added <code>stats.Min</code>.</li> <li>Added <code>stats.Max</code>.</li> <li>Added <code>stats.PeakToPeak</code>.</li> <li>Added <code>stats.Kurtosis</code>.</li> <li>Added <code>stats.Skew</code>.</li> <li>Added <code>stats.Sum</code>.</li> <li>Added <code>stats.EWMean</code>.</li> <li>Made sure the running statistics produce the same results as <code>pandas.DataFrame.rolling</code> method.</li> </ul>"},{"location":"releases/0.0.3/","title":"0.0.3 - 2019-03-21","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.0.3/#base","title":"base","text":"<ul> <li>Calling <code>fit_one</code> now returns the calling instance, not the out-of-fold prediction/transform; <code>fit_predict_one</code>, <code>fit_predict_proba_one</code>, and <code>fit_transform_one</code> are available to reproduce the previous behavior.</li> <li>Binary classifiers now output a <code>dict</code> with probabilities for <code>False</code> and <code>True</code> when calling <code>predict_proba_one</code>, which solves the interface issues of having multi-class classifiers do binary classification.</li> </ul>"},{"location":"releases/0.0.3/#compat","title":"compat","text":"<ul> <li>Added <code>compat.convert_river_to_sklearn</code>.</li> </ul>"},{"location":"releases/0.0.3/#compose","title":"compose","text":"<ul> <li>Added <code>compose.BoxCoxTransformRegressor</code>.</li> <li>Added <code>compose.TargetModifierRegressor</code>.</li> </ul>"},{"location":"releases/0.0.3/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_restaurants</code>.</li> <li>Added <code>datasets.load_airline</code>.</li> </ul>"},{"location":"releases/0.0.3/#dist","title":"dist","text":"<ul> <li>Added <code>dist.Multinomial</code>.</li> <li>Added <code>dist.Normal</code>.</li> </ul>"},{"location":"releases/0.0.3/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.BaggingRegressor</code>.</li> </ul>"},{"location":"releases/0.0.3/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added <code>feature_extraction.TargetGroupBy</code>.</li> </ul>"},{"location":"releases/0.0.3/#impute","title":"impute","text":"<ul> <li>Added <code>impute.CategoricalImputer</code>.</li> </ul>"},{"location":"releases/0.0.3/#linear_model","title":"linear_model","text":"<ul> <li>Added <code>linear_model.FMRegressor</code>.</li> <li>Removed all the passive-aggressive estimators.</li> </ul>"},{"location":"releases/0.0.3/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.Accuracy</code>.</li> <li>Added <code>metrics.MAE</code>.</li> <li>Added <code>metrics.MSE</code>.</li> <li>Added <code>metrics.RMSE</code>.</li> <li>Added <code>metrics.RMSLE</code>.</li> <li>Added <code>metrics.SMAPE</code>.</li> <li>Added <code>metrics.Precision</code>.</li> <li>Added <code>metrics.Recall</code>.</li> <li>Added <code>metrics.F1</code>.</li> </ul>"},{"location":"releases/0.0.3/#model_selection","title":"model_selection","text":"<ul> <li><code>model_selection.online_score</code> can now be passed a <code>metrics.Metric</code> instead of an <code>sklearn</code> metric; it also checks that the provided metric can be used with the accompanying model.</li> </ul>"},{"location":"releases/0.0.3/#naive_bayes","title":"naive_bayes","text":"<ul> <li>Added <code>naive_bayes.GaussianNB</code>.</li> </ul>"},{"location":"releases/0.0.3/#optim","title":"optim","text":"<ul> <li>Added <code>optim.PassiveAggressiveI</code>.</li> <li>Added <code>optim.PassiveAggressiveII</code>.</li> </ul>"},{"location":"releases/0.0.3/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.Discarder</code>.</li> <li>Added <code>preprocessing.PolynomialExtender</code>.</li> <li>Added <code>preprocessing.FuncTransformer</code>.</li> </ul>"},{"location":"releases/0.0.3/#reco","title":"reco","text":"<ul> <li>Added <code>reco.SVD</code>.</li> </ul>"},{"location":"releases/0.0.3/#stats","title":"stats","text":"<ul> <li>Added <code>stats.Mode</code>.</li> <li>Added <code>stats.Quantile</code>.</li> <li>Added <code>stats.RollingQuantile</code>.</li> <li>Added <code>stats.Entropy</code>.</li> <li>Added <code>stats.RollingMin</code>.</li> <li>Added <code>stats.RollingMax</code>.</li> <li>Added <code>stats.RollingMode</code>.</li> <li>Added <code>stats.RollingSum</code>.</li> <li>Added <code>stats.RollingPeakToPeak</code>.</li> </ul>"},{"location":"releases/0.0.3/#stream","title":"stream","text":"<ul> <li>Added <code>stream.iter_csv</code>.</li> </ul>"},{"location":"releases/0.0.3/#tree","title":"tree","text":"<ul> <li>Added <code>tree.MondrianTreeClassifier</code>.</li> <li>Added <code>tree.MondrianTreeRegressor</code>.</li> </ul>"},{"location":"releases/0.1.0/","title":"0.1.0 - 2019-05-08","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.1.0/#base","title":"base","text":"<ul> <li>Removed the <code>fit_predict_one</code> estimator method.</li> <li>Removed the <code>fit_predict_proba_one</code> estimator method.</li> <li>Removed the <code>fit_transform_one</code> estimator method.</li> </ul>"},{"location":"releases/0.1.0/#compat","title":"compat","text":"<ul> <li>Added <code>compat.convert_sklearn_to_river</code>.</li> <li><code>compat.convert_river_to_sklearn</code> now returns an <code>sklearn.pipeline.Pipeline</code> when provided with a <code>compose.Pipeline</code>.</li> </ul>"},{"location":"releases/0.1.0/#compose","title":"compose","text":"<ul> <li>Added <code>compose.Discard</code>.</li> <li>Added <code>compose.Select</code>.</li> <li>Added <code>compose.SplitRegressor</code>.</li> <li>The <code>draw</code> method of <code>compose.Pipeline</code> now works properly for arbitrary amounts of nesting, including multiple nested <code>compose.FeatureUnion</code>.</li> </ul>"},{"location":"releases/0.1.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_electricity</code>.</li> </ul>"},{"location":"releases/0.1.0/#dummy","title":"dummy","text":"<ul> <li>Added <code>dummy.NoChangeClassifier</code>.</li> <li>Added <code>dummy.PriorClassifier</code>.</li> <li>Added <code>dummy.StatisticRegressor</code>.</li> </ul>"},{"location":"releases/0.1.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added <code>feature_extraction.Differ</code>.</li> <li>Renamed <code>feature_extraction.GroupBy</code> to <code>feature_extraction.Agg</code>.</li> <li>Renamed <code>feature_extraction.TargetGroupBy</code> to <code>feature_extraction.TargetAgg</code>.</li> </ul>"},{"location":"releases/0.1.0/#feature_selection","title":"feature_selection","text":"<ul> <li>Added <code>feature_selection.SelectKBest</code>.</li> <li>Added <code>feature_selection.VarianceThreshold</code>.</li> </ul>"},{"location":"releases/0.1.0/#impute","title":"impute","text":"<ul> <li>Added <code>impute.StatImputer</code>.</li> <li>Removed <code>impute.CategoricalImputer</code>.</li> <li>Removed <code>impute.NumericImputer</code>.</li> </ul>"},{"location":"releases/0.1.0/#linear_model","title":"linear_model","text":"<ul> <li>Added <code>linear_model.PAClassifier</code>.</li> <li>Added <code>linear_model.PARegressor</code>.</li> <li>Added <code>linear_model.SoftmaxRegression</code>.</li> </ul>"},{"location":"releases/0.1.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.ConfusionMatrix</code>.</li> <li>Added <code>metrics.CrossEntropy</code>.</li> <li>Added <code>metrics.MacroF1</code>.</li> <li>Added <code>metrics.MacroPrecision</code>.</li> <li>Added <code>metrics.MacroRecall</code>.</li> <li>Added <code>metrics.MicroF1</code>.</li> <li>Added <code>metrics.MicroPrecision</code>.</li> <li>Added <code>metrics.MicroRecall</code>.</li> <li>Each metric now has a <code>bigger_is_better</code> property to indicate if a high value is better than a low one or not.</li> </ul>"},{"location":"releases/0.1.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.OptimalLR</code>.</li> <li>Added <code>optim.CrossEntropy</code>.</li> <li>Removed <code>optim.PassiveAggressiveI</code>.</li> <li>Removed <code>optim.PassiveAggressiveII</code>.</li> </ul>"},{"location":"releases/0.1.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Removed <code>preprocessing.Discarder</code>.</li> <li>Added <code>on</code> and <code>sparse</code> parameters to <code>preprocessing.OneHotEncoder</code>.</li> </ul>"},{"location":"releases/0.1.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.Covariance</code>.</li> <li>Added <code>stats.PearsonCorrelation</code>.</li> <li>Added <code>stats.SmoothMean</code>.</li> </ul>"},{"location":"releases/0.1.0/#utils","title":"utils","text":"<ul> <li>Added <code>utils.check_estimator</code>.</li> <li>Added <code>utils.Histogram</code>.</li> <li>Added <code>utils.SortedWindow</code>.</li> <li>Added <code>utils.Window</code>.</li> </ul>"},{"location":"releases/0.10.0/","title":"0.10.0 - 2022-02-04","text":""},{"location":"releases/0.10.0/#base","title":"base","text":"<ul> <li>Introduce <code>base.MiniBatchTransformer</code>. Add support for mini-batches to <code>compose.TransformerUnion</code>, <code>compose.Select</code>, and <code>preprocessing.OneHotEncoder</code>.</li> </ul>"},{"location":"releases/0.10.0/#checks","title":"checks","text":"<ul> <li>Created this module to store estimator unit testing, rather than having it in the <code>utils</code> module.</li> </ul>"},{"location":"releases/0.10.0/#compose","title":"compose","text":"<ul> <li>Split <code>compose.Renamer</code> into <code>compose.Prefixer</code> and <code>compose.Suffixer</code> that respectively prepend and append a string to the features' name.</li> <li>Changed <code>compose.Renamer</code> to allow feature renaming following a mapping.</li> </ul>"},{"location":"releases/0.10.0/#evaluate","title":"evaluate","text":"<ul> <li>Refactored <code>evaluate.progressive_validation</code> to work with <code>api.anomaly.base.AnomalyDetector</code>s.</li> </ul>"},{"location":"releases/0.10.0/#facto","title":"facto","text":"<ul> <li>Added <code>debug_one</code> method to <code>BaseFM</code>.</li> </ul>"},{"location":"releases/0.10.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Make the <code>by</code> parameter in <code>feature_extraction.Agg</code> and <code>feature_extraction.TargetAgg</code> to be optional, allowing to calculate aggregates over the whole data.</li> <li>Removed <code>feature_extraction.Lagger</code> and <code>feature_extraction.TargetLagger</code>. Their functionality can be reproduced by combining <code>feature_extraction.Agg</code> and <code>stats.Shift</code>.</li> <li><code>feature_extraction.Agg</code> and <code>feature_extraction.Target</code> now have a <code>state</code> property. It returns a <code>pandas.Series</code> representing the current aggregates values within each group.</li> </ul>"},{"location":"releases/0.10.0/#metrics","title":"metrics","text":"<ul> <li><code>metrics.ROCAUC</code> works with <code>base.AnomalyDetectors</code>s.</li> </ul>"},{"location":"releases/0.10.0/#misc","title":"misc","text":"<ul> <li>Created this module to store some stuff that was in the <code>utils</code> module but wasn't necessarily shared between modules.</li> <li>Implement <code>misc.CovMatrix</code>.</li> </ul>"},{"location":"releases/0.10.0/#reco","title":"reco","text":"<ul> <li>Renamed the <code>Recommender</code> base class into <code>Ranker</code>.</li> <li>Added a <code>rank</code> method to each recommender.</li> <li>Removed <code>reco.SurpriseWrapper</code> as it wasn't really useful.</li> <li>Added an <code>is_contextual</code> property to each ranker to indicate if a model makes use of contextual features or not.</li> </ul>"},{"location":"releases/0.10.0/#stats","title":"stats","text":"<ul> <li><code>stats.Mean</code>, <code>stats.Var</code>, and <code>stats.Cov</code> each now have an <code>update_many</code> method which accepts numpy arrays.</li> </ul>"},{"location":"releases/0.10.0/#utils","title":"utils","text":"<ul> <li>Removed <code>utils.Window</code> and use <code>collections.deque</code> instead where necessary.</li> </ul>"},{"location":"releases/0.10.1/","title":"0.10.1 - 2022-02-05","text":""},{"location":"releases/0.10.1/#evaluate","title":"evaluate","text":"<p><code>evaluate.progressive_val_score</code> can now handle models which use <code>**kwargs</code> in their <code>learn_one</code> and <code>predict_one</code> methods. For instance, this is useful for <code>reco.Ranker</code> models which require passing a user and an item.</p>"},{"location":"releases/0.11.0/","title":"0.11.0 - 2022-05-28","text":"<ul> <li>Moved all metrics in <code>metrics.cluster</code> except <code>metrics.Silhouette</code> to river-extra.</li> </ul>"},{"location":"releases/0.11.0/#anomaly","title":"anomaly","text":"<ul> <li>There is now a <code>anomaly.base.SupervisedAnomalyDetector</code> base class for supervised anomaly detection.</li> <li>Added <code>api.anomaly.GaussianScorer</code>, which is the first supervised anomaly detector.</li> <li>There is now a <code>anomaly.base.AnomalyFilter</code> base class for anomaly filtering methods. These allow to classify anomaly scores. They can also prevent models from learning on anomalous data, for instance by putting them as an initial step of a pipeline.</li> <li>Added <code>anomaly.ConstantFilter</code> and <code>QuantileFilter</code>, which are the first anomaly filters.</li> <li>Removed <code>anomaly.ConstantThresholder</code> and <code>anomaly.QuantileThresholder</code>, as they overlap with the new anomaly filtering mechanism.</li> </ul>"},{"location":"releases/0.11.0/#base","title":"base","text":"<ul> <li>Fixed an issue where the <code>_raw_memory_usage</code> property would spin into an infinite loop if a model's property was an <code>itertools.count</code>.</li> </ul>"},{"location":"releases/0.11.0/#dataset","title":"dataset","text":"<ul> <li>Added the <code>datasets.WaterFlow</code> dataset.</li> </ul>"},{"location":"releases/0.11.0/#dist","title":"dist","text":"<ul> <li>A <code>revert</code> method has been added to <code>stats.Gaussian</code>.</li> <li>A <code>revert</code> method has been added to <code>stats.Multinomial</code>.</li> <li>Added <code>dist.TimeRolling</code> to measure probability distributions over windows of time.</li> </ul>"},{"location":"releases/0.11.0/#drift","title":"drift","text":"<ul> <li>Add the <code>PeriodicTrigger</code> detector, a baseline capable of producing drift signals in regular or random intervals.</li> <li>The numpy usage was removed in <code>drift.KSWIN</code> in favor of <code>collections.deque</code>. Appending or deleting elements to numpy arrays imply creating another object.</li> <li>Added the seed parameter to <code>drift.KSWIN</code> to control reproducibility.</li> <li>The Kolmogorov-Smirnov test mode was changed to the default (<code>\"auto\"</code>) to suppress warnings (<code>drift.KSWIN</code>).</li> <li>Unnecessary usage of numpy was also removed in other concept drift detectors.</li> </ul>"},{"location":"releases/0.11.0/#ensemble","title":"ensemble","text":"<ul> <li>Streamline <code>SRP{Classifier,Regressor}</code>, remove unneeded numpy usage, make SRP variants robust against missing features, and fix bugs.</li> <li>Remove unneeded numpy usage <code>AdaptiveRandomForest{Classifier,Regressor}</code>.</li> </ul>"},{"location":"releases/0.11.0/#evaluate","title":"evaluate","text":"<ul> <li>Added a <code>iter_progressive_val_score</code> function, which does the same as <code>progressive_val_score</code>, except that it yields rather than prints results at each step, which give more control to the user.</li> </ul>"},{"location":"releases/0.11.0/#imblearn","title":"imblearn","text":"<ul> <li>Added <code>imblearn.ChebyshevUnderSampler</code> and <code>imblearn.ChebyshevOverSampler</code> for imbalanced regression.</li> </ul>"},{"location":"releases/0.11.0/#linear_model","title":"linear_model","text":"<ul> <li><code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code> now correctly apply the <code>l2</code> regularization when their <code>learn_many</code> method is used.</li> <li>Added <code>l1</code> regularization (implementation with cumulative penalty, see paper) for <code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code></li> </ul>"},{"location":"releases/0.11.0/#neighbors","title":"neighbors","text":"<ul> <li><code>neighbors.KNNADWINClassifier</code> and <code>neighbors.SAMKNNClassifier</code> have been deprecated.</li> <li>Introduced <code>neighbors.NearestNeighbors</code> for searching nearest neighbors.</li> <li>Vastly refactored and simplified the nearest neighbors logic.</li> </ul>"},{"location":"releases/0.11.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.Rolling</code> to measure a probability distribution over a window.</li> </ul>"},{"location":"releases/0.11.0/#rules","title":"rules","text":"<ul> <li>AMRules's <code>debug_one</code> explicitly indicates the prediction strategy used by each rule.</li> <li>Fix bug in <code>debug_one</code> (AMRules) where prediction explanations were incorrectly displayed when <code>ordered_rule_set=True</code>.</li> </ul>"},{"location":"releases/0.11.0/#time_series","title":"time_series","text":"<ul> <li>Added an <code>iter_evaluate</code> function to trace the evaluation at each sample in a dataset.</li> </ul>"},{"location":"releases/0.11.0/#tree","title":"tree","text":"<ul> <li>Fix bug in Naive Bayes-based leaf prediction.</li> <li>Remove unneeded numpy usage in <code>HoeffdingAdaptiveTree{Classifier,Regressor}</code>.</li> </ul>"},{"location":"releases/0.11.0/#stats","title":"stats","text":"<ul> <li>A <code>revert</code> method has been added to <code>stats.Var</code>.</li> </ul>"},{"location":"releases/0.11.1/","title":"0.11.1 - 2022-06-06","text":"<p>A small release to introduce benchmarks.</p>"},{"location":"releases/0.11.1/#anomaly","title":"anomaly","text":"<ul> <li>Fixed a bug where anomaly filters were never updated.</li> </ul>"},{"location":"releases/0.12.0/","title":"0.12.0 - 2022-09-02","text":"<ul> <li>Moved all the public modules imports from <code>river/__init__.py</code> to <code>river/api.py</code> and removed unnecessary dependencies between modules enabling faster cherry-picked import times (~3x).</li> <li>Adding wheels for Python 3.11.</li> </ul>"},{"location":"releases/0.12.0/#base","title":"base","text":"<ul> <li>Introduced an <code>mutate</code> method to the <code>base.Base</code> class. This allows setting attributes in a controlled manner, which paves the way for online AutoML. See the recipe for more information.</li> </ul>"},{"location":"releases/0.12.0/#compat","title":"compat","text":"<ul> <li>Moved the PyTorch wrappers to river-extra.</li> </ul>"},{"location":"releases/0.12.0/#covariance","title":"covariance","text":"<ul> <li>Created a new <code>covariance</code> module to hold everything related to covariance and inversion covariance matrix estimation.</li> <li>Moved <code>misc.CovarianceMatrix</code> to <code>covariance.EmpiricalCovariance</code>.</li> <li>Added <code>covariance.EmpiricalPrecision</code> to estimate the inverse covariance matrix.</li> </ul>"},{"location":"releases/0.12.0/#compose","title":"compose","text":"<ul> <li>Moved <code>utils.pure_inference_mode</code> to <code>compose.pure_inference_mode</code> and <code>utils.warm_up_mode</code> to <code>compose.warm_up_mode</code>.</li> <li>Pipeline parts can now be accessed by integer positions as well as by name.</li> </ul>"},{"location":"releases/0.12.0/#datasets","title":"datasets","text":"<ul> <li>Imports <code>synth</code>, enabling `from river import datasets; datasets.synth.</li> </ul>"},{"location":"releases/0.12.0/#drift","title":"drift","text":"<ul> <li>Refactor the concept drift detectors to match the remaining of River's API. Warnings are only issued by detectors that support this feature.</li> <li>Drifts can be assessed via the property <code>drift_detected</code>. Warning signals can be acessed by the property <code>warning_detected</code>. The <code>update</code> now returns <code>self</code>.</li> <li>Ensure all detectors automatically reset their inner states after a concept drift detection.</li> <li>Streamline <code>DDM</code>, <code>EDDM</code>, <code>HDDM_A</code>, and <code>HDDM_W</code>. Make the configurable parameters names match their respective papers.</li> <li>Fix bugs in <code>EDDM</code> and <code>HDDM_W</code>.</li> <li>Enable two-sided tests in <code>PageHinkley</code>.</li> <li>Improve documentation and update tests.</li> </ul>"},{"location":"releases/0.12.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added a <code>tokenizer_pattern</code> parameter to <code>feature_extraction.BagOfWords</code> and <code>feature_extraction.TFIDF</code> to override the default pattern used for tokenizing text.</li> <li>Added a <code>stop_words</code> parameter to <code>feature_extraction.BagOfWords</code> and <code>feature_extraction.TFIDF</code> for removing stop words once the text has been tokenized.</li> </ul>"},{"location":"releases/0.12.0/#linear_model","title":"linear_model","text":"<ul> <li>After long ado, we've finally implemented <code>linear_model.BayesianLinearRegression</code>.</li> </ul>"},{"location":"releases/0.12.0/#metrics","title":"metrics","text":"<ul> <li>Removed dependency to <code>optim</code>.</li> <li>Removed <code>metrics.Rolling</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>metrics.TimeRolling</code>, due to the addition of <code>utils.Rolling</code>.</li> </ul>"},{"location":"releases/0.12.0/#proba","title":"proba","text":"<ul> <li>Removed <code>proba.Rolling</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>proba.TimeRolling</code>, due to the addition of <code>utils.Rolling</code>.</li> </ul>"},{"location":"releases/0.12.0/#rule","title":"rule","text":"<ul> <li>The default <code>splitter</code> was changed to <code>tree.splitter.TEBST</code> for memory and running time efficiency.</li> </ul>"},{"location":"releases/0.12.0/#stats","title":"stats","text":"<ul> <li>Removed <code>stats.RollingMean</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>stats.RollingVar</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>stats.RollingCov</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>stats.RollingPearsonCorr</code>, due to the addition of <code>utils.Rolling</code>.</li> </ul>"},{"location":"releases/0.12.0/#stream","title":"stream","text":"<ul> <li><code>stream.iter_array</code> now handles text data.</li> <li>Added <code>stream.TwitterLiveStream</code>, to listen to a filtered live stream of Tweets.</li> </ul>"},{"location":"releases/0.12.0/#time_series","title":"time_series","text":"<ul> <li>Added <code>time_series.HorizonAggMetric</code>.</li> <li>Fixed a bug in <code>time_series.SNARIMAX</code> where the number of seasonal components was not correct when <code>sp</code> or <code>sq</code> were specified.</li> <li>Fixed the differencing logic in <code>time_series.SNARIMAX</code> when <code>d</code> or <code>sd</code> were specified.</li> </ul>"},{"location":"releases/0.12.0/#tree","title":"tree","text":"<ul> <li>Rename <code>split_confidence</code> and <code>tie_threshold</code> to <code>delta</code> and <code>tau</code>, respectively. This way, the parameters are not misleading and match what the research papers have used for decades.</li> <li>Refactor <code>HoeffdingAdaptiveTree{Classifier,Regressor}</code> to allow the usage of any drift detector. Expose the significance level of the test used to switch between subtrees as a user-defined parameter.</li> <li>Correct test used to switch between foreground and background subtrees in <code>HoeffdingAdaptiveTreeRegressor</code>. Due to the continuous and unbounded nature of the monitored errors, a z-test is now performed to decide which subtree to keep.</li> <li>The default <code>leaf_prediction</code> value was changed to <code>\"adaptive\"</code>, as this often results in the smallest errors in practice.</li> <li>The default <code>splitter</code> was changed to <code>tree.splitter.TEBST</code> for memory and running time efficiency.</li> </ul>"},{"location":"releases/0.12.0/#utils","title":"utils","text":"<ul> <li>Removed dependencies to <code>anomaly</code> and <code>compose</code>.</li> <li>Added <code>utils.Rolling</code> and <code>utils.TimeRolling</code>, which are generic wrappers for computing over a window (of time).</li> <li>Use binary search to speed-up element removal in <code>utils.SortedWindow</code>.</li> </ul>"},{"location":"releases/0.12.1/","title":"0.12.1 - 2022-09-02","text":""},{"location":"releases/0.12.1/#base","title":"base","text":"<ul> <li>Fix the way the <code>clone</code> method handles positional arguments.</li> </ul>"},{"location":"releases/0.13.0/","title":"0.13.0 - 2022-09-15","text":""},{"location":"releases/0.13.0/#compose","title":"compose","text":"<ul> <li><code>compose.TransformerUnion</code> parts can now be accessed by index as well as by name.</li> </ul>"},{"location":"releases/0.13.0/#stats","title":"stats","text":"<ul> <li>Added the <code>LossyCount</code> for tracking frequent itemsets. This implementation also supports a forgetting factor to reduce the influence of old elements.</li> <li>The following statistics are now implemented in Rust:</li> <li><code>Quantile</code></li> <li><code>EWMean</code></li> <li><code>EWVar</code></li> <li><code>IQR</code></li> <li><code>Kurtosis</code></li> <li><code>PeaktoPeak</code></li> <li><code>Skew</code></li> <li><code>RollingQuantile</code></li> <li><code>RollingIQR</code></li> </ul>"},{"location":"releases/0.13.0/#stream","title":"stream","text":"<ul> <li>Implemented <code>stream.TwitchChatStream</code>.</li> </ul>"},{"location":"releases/0.14.0/","title":"0.14.0 - 2022-10-26","text":"<ul> <li>Introducing the <code>bandit</code> module for running multi-armed bandits</li> <li>Introducing the <code>sketch</code> module with summarization tools and data sketches working in a streaming fashion!</li> </ul>"},{"location":"releases/0.14.0/#bandit","title":"bandit","text":"<ul> <li>Added <code>bandit.EpsilonGreedy</code>.</li> <li>Added <code>bandit.UCB</code>.</li> <li>Added <code>bandit.ThomsonSampling</code>.</li> <li>Added a <code>bandit.base</code> module.</li> <li>Added <code>bandit.envs.CandyCaneContest</code>, which implements the Gym interface.</li> <li>Added <code>bandit.envs.KArmedTestbed</code>, which implements the Gym interface.</li> <li>Added <code>bandit.evaluate</code> for basic benchmarking of bandit policies on a Gym environment.</li> </ul>"},{"location":"releases/0.14.0/#drift","title":"drift","text":"<ul> <li>Exposed more parameters in ADWIN: <code>clock</code>, <code>max_buckets</code>, <code>min_window_length</code>, and <code>grace_period</code>.</li> </ul>"},{"location":"releases/0.14.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.BanditRegressor</code>, which is a generic model selection method that works with any bandit policy.</li> <li>Removed <code>model_selection.EpsilonGreedyRegressor</code> due to the addition of <code>model_selection.BanditRegressor</code>.</li> <li>Removed <code>model_selection.UCBRegressor</code> due to the addition of <code>model_selection.BanditRegressor</code>.</li> </ul>"},{"location":"releases/0.14.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.Beta</code>.</li> <li>Added a <code>sample</code> method to each distribution.</li> <li>Added a <code>mode</code> property to each distribution.</li> <li>Replaced the <code>pmf</code> and <code>pdf</code> methods with a <code>__call__</code> method.</li> </ul>"},{"location":"releases/0.14.0/#sketch","title":"sketch","text":"<ul> <li>Moved <code>misc.Histogram</code> to <code>sketch.Histogram</code>.</li> <li>Moved <code>stats.LossyCount</code> to <code>sketch.HeavyHitters</code> and update its API to better match <code>collections.Counter</code>.</li> <li>Added missing return <code>self</code> in <code>HeavyHitters</code>.</li> <li>Added the Count-Min Sketch (<code>sketch.Counter</code>) algorithm for approximate element counting.</li> <li>Added an implementation of Bloom filter (<code>sketch.Set</code>) to provide approximate set-like operations.</li> </ul>"},{"location":"releases/0.15.0/","title":"0.15.0 - 2023-01-29","text":""},{"location":"releases/0.15.0/#active","title":"active","text":"<ul> <li>Created this module dedicated to online active learning.</li> <li>Added <code>api.active.EntropySampler</code>.</li> </ul>"},{"location":"releases/0.15.0/#base","title":"base","text":"<ul> <li>Fixed an issue where an estimator that has attribute a pipeline could not be cloned.</li> <li>Added a <code>base.DriftAndWarningDetector</code> to clarify the difference between drift detectors that have a <code>warning_detected</code> property and those that don't.</li> <li>Added <code>MultiLabelClassifier</code>.</li> <li>Added <code>MultiTargetRegressor</code>.</li> <li>Added <code>drift.BinaryDriftDetector</code>.</li> <li>Added <code>drift.BinaryDriftAndWarningDetector</code>.</li> </ul>"},{"location":"releases/0.15.0/#conf","title":"conf","text":"<ul> <li>Introduced this new module to perform conformal predictions.</li> <li>Added a <code>conf.Interval</code> dataclass to represent predictive intervals.</li> <li>Added <code>conf.RegressionJackknife</code>.</li> </ul>"},{"location":"releases/0.15.0/#datasets","title":"datasets","text":"<ul> <li>Removed unnecessary Numpy usage in the <code>synth</code> submodule.</li> <li>Changed <code>np.random.RandomState</code> to <code>np.random.default_rng</code> where necessary.</li> </ul>"},{"location":"releases/0.15.0/#drift","title":"drift","text":"<ul> <li>Added <code>drift.DriftRetrainingClassifier</code>.</li> <li>Renamed <code>drift.PeriodicTrigger</code> to <code>drift.DummyDriftDetector</code> to clarify it is a naive baseline.</li> <li>Created a <code>binary</code> submodule to organize all drift detectors which only apply to binary inputs.</li> </ul>"},{"location":"releases/0.15.0/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.ADWINBoostingClassifier</code>.</li> <li>Added <code>ensemble.BOLEClassifier</code>.</li> </ul>"},{"location":"releases/0.15.0/#evaluate","title":"evaluate","text":"<ul> <li><code>evaluate.progressive_val_score</code> and <code>evaluate.iter_progressive_val_score</code> will now also produce a report once the last sample has been processed, in addition to every <code>print_every</code> steps.</li> </ul>"},{"location":"releases/0.15.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li><code>feature_extraction.BagOfWords</code> now outputs a dictionary, and not a <code>collections.Counter</code>.</li> </ul>"},{"location":"releases/0.15.0/#forest","title":"forest","text":"<ul> <li>Created this new module to host all models based on an ensemble of decision trees.</li> <li>Moved <code>ensemble.AdaptiveRandomForestClassifier</code> to <code>forest.ARFClassifier</code>.</li> <li>Moved <code>ensemble.AdaptiveRandomForestRegressor</code> to <code>forest.ARFRegressor</code>.</li> <li>Added <code>forest.AMFClassifier</code>.</li> <li>Added <code>forest.OXTRegressor</code>.</li> </ul>"},{"location":"releases/0.15.0/#linear_model","title":"linear_model","text":"<ul> <li>Renamed <code>use_dist</code> to <code>with_dist</code> in <code>linear_model.BayesianLinearRegression</code>'s <code>predict_one</code> method.</li> </ul>"},{"location":"releases/0.15.0/#multiclass","title":"multiclass","text":"<ul> <li>Added a <code>coding_method</code> method to <code>multiclass.OCC</code> to control how the codes are randomly generated.</li> </ul>"},{"location":"releases/0.15.0/#multioutput","title":"multioutput","text":"<ul> <li>Added <code>MultiClassEncoder</code> to convert multi-label tasks into multi-class problems.</li> </ul>"},{"location":"releases/0.15.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>preprocessing.AdaptiveStandardScaler</code>.</li> </ul>"},{"location":"releases/0.15.0/#rules","title":"rules","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>rules.AMRules</code>.</li> </ul>"},{"location":"releases/0.15.0/#sketch","title":"sketch","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>sketch.HeavyHitters</code>.</li> </ul>"},{"location":"releases/0.15.0/#stats","title":"stats","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>stats.Entropy</code>.</li> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>stats.EWMean</code>.</li> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>stats.EWVar</code>.</li> </ul>"},{"location":"releases/0.15.0/#stream","title":"stream","text":"<ul> <li>Upgraded <code>stream.iter_sql</code> to SQLAlchemy 2.0.</li> </ul>"},{"location":"releases/0.15.0/#tree","title":"tree","text":"<ul> <li>Remove <code>LabelCombinationHoeffdingTreeClassifier</code>. New code should use <code>multioutput.MulticlassEncoder</code> instead.</li> </ul>"},{"location":"releases/0.15.0/#utils","title":"utils","text":"<ul> <li>Removed artifacts from the merger.</li> </ul>"},{"location":"releases/0.16.0/","title":"0.16.0 - 2023-05-08","text":"<p>Added wheels for Python 3.11.</p>"},{"location":"releases/0.16.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li><code>feature_extraction.Agg</code> and <code>feature_extraction.TargetAgg</code> can now be passed an optional <code>t</code> in its <code>learn_one</code> method, which allows it to work with <code>utils.TimeRolling</code>.</li> </ul>"},{"location":"releases/0.16.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.MAPE</code>.</li> <li>Added <code>metrics.RollingROCAUC</code>.</li> </ul>"},{"location":"releases/0.16.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.GaussianRandomProjector</code>.</li> <li>Added <code>preprocessing.SparseRandomProjector</code>.</li> </ul>"},{"location":"releases/0.16.0/#stats","title":"stats","text":"<ul> <li>Fixed randomness issue with the first few outputs of <code>stats.Quantile</code>.</li> </ul>"},{"location":"releases/0.17.0/","title":"0.17.0 - 2023-05-27","text":""},{"location":"releases/0.17.0/#bandit","title":"bandit","text":"<ul> <li>Bandit policies now return a single arm when the <code>pull</code> method is called, instead of yielding or one more arms at a time. This is simpler to understand. We will move back to multi-armed pulls in the future.</li> <li>Added <code>bandit.Exp3</code>.</li> <li><code>bandit.UCB</code> and <code>bandit.Exp3</code> have an extra <code>reward_scaler</code> parameter, which can be any object that inherits from <code>compose.TargetTransformRegressor</code>. This allows scaling rewards before updating arms.</li> </ul>"},{"location":"releases/0.17.0/#compose","title":"compose","text":"<ul> <li><code>compose.TransformerProduct</code> now correctly returns a <code>compose.TransformerUnion</code> when a transformer is added to it.</li> <li>Fixed <code>compose.TransformerProduct</code>'s <code>transform_many</code> behavior.</li> <li><code>compose.TransformerUnion</code> and <code>compose.TransformerProduct</code> will now clone the provided estimators, so that shallow copies aren't shared in different places.</li> </ul>"},{"location":"releases/0.17.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.BanditClassifier</code>, which is the classification equivalent to <code>bandit.BanditRegressor</code>. Both are methods to perform online model selection via a bandit policy.</li> </ul>"},{"location":"releases/0.17.0/#multioutput","title":"multioutput","text":"<ul> <li><code>metrics.multioutput.MacroAverage</code> and <code>metrics.multioutput.MicroAverage</code> now loop over the keys of <code>y_true</code> instead of <code>y_pred</code>. This ensures a <code>KeyError</code> is correctly raised if <code>y_pred</code> is missing an output that is present in <code>y_true</code>.</li> </ul>"},{"location":"releases/0.17.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.TargetMinMaxScaler</code>, which operates the same as <code>preprocessing.TargetStandardScaler</code>, but instead uses min-max scaling.</li> </ul>"},{"location":"releases/0.18.0/","title":"0.18.0 - 2023-06-26","text":""},{"location":"releases/0.18.0/#bandit","title":"bandit","text":"<ul> <li>Added <code>bandit.BayesUCB</code>.</li> <li>Added <code>bandit.evaluate_offline</code>, for evaluating bandits on historical (logged) data.</li> </ul>"},{"location":"releases/0.18.0/#cluster","title":"cluster","text":"<ul> <li><code>DBStream</code> will now only recluster on demand, rather than at every call to <code>learn_one</code>.</li> </ul>"},{"location":"releases/0.18.0/#compat","title":"compat","text":"<ul> <li>The <code>predict_many</code> method scikit-learn models wrapped with <code>compat.convert_sklearn_to_river</code> raised an exception if the model had not been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River.</li> <li><code>compat.SKL2RiverRegressor</code> and <code>compat.SKL2RiverClassifier</code> didn't check whether features were ordered in the same way at each method call. They now store the list of feature names at the first function call, and align subsequent inputs in the same order.</li> </ul>"},{"location":"releases/0.18.0/#compose","title":"compose","text":"<ul> <li><code>compose.TransformerProduct</code> will now preserve the density of sparse columns.</li> <li>Added a <code>transform_many</code> method to <code>compose.FuncTransformer</code>, allowing it to be used in mini-batch pipelines.</li> <li>The <code>compose.pure_inference_mode</code> now works with mini-batching.</li> </ul>"},{"location":"releases/0.18.0/#neighbors","title":"neighbors","text":"<ul> <li>Added <code>neighbors.SWINN</code> to power-up approximate nearest neighbor search. SWINN uses graphs to speed up nearest neighbor search in large sliding windows of data.</li> <li>Renamed <code>neighbors.NearestNeighbors</code> to <code>neighbors.LazySearch</code>.</li> <li>Standardize and create base classes for generic nearest neighbor search utilities.</li> <li>The user can now select the nearest neighbor search engine to use in <code>neighbors.KNNClassifier</code> and <code>neighbors.KNNRegressor</code>.</li> </ul>"},{"location":"releases/0.18.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Rename <code>sparse</code> parameter to <code>drop_zeros</code> in <code>preprocessing.OneHotEncoder</code>.</li> <li>The <code>transform_many</code> method of <code>preprocessing.OneHotEncoder</code> will now return a sparse dataframe, rather than a dense one, which will consume much less memory.</li> </ul>"},{"location":"releases/0.18.0/#proba","title":"proba","text":"<ul> <li>Added a <code>cdf</code> method to <code>proba.Beta</code>.</li> </ul>"},{"location":"releases/0.18.0/#tree","title":"tree","text":"<ul> <li>Expose the <code>min_branch_fraction</code> parameter to avoid splits where most of the data goes to a single branch. Affects   classification trees.</li> <li>Added the <code>max_share_to_split</code> parameter to Hoeffding Tree classifiers. This parameters avoids splitting when the majority   class has most of the data.</li> </ul>"},{"location":"releases/0.18.0/#utils","title":"utils","text":"<ul> <li>Fixed <code>utils.math.minkowski_distance</code>.</li> </ul>"},{"location":"releases/0.19.0/","title":"0.19.0 - 2023-08-02","text":"<p>Calling <code>learn_one</code> in a pipeline will now update each part of the pipeline in turn. Before the unsupervised parts of the pipeline were updated during <code>predict_one</code>. This is more intuitive for new users. The old behavior, which yields better results, can be restored by calling <code>learn_one</code> with the new <code>compose.learn_during_predict</code> context manager.</p>"},{"location":"releases/0.19.0/#bandit","title":"bandit","text":"<ul> <li>Added a <code>bandit.datasets</code> submodule, which is meant to contain contextual bandit datasets.</li> <li>Added <code>bandit.base.ContextualPolicy</code>.</li> <li>Added <code>bandit.datasets.NewsArticles</code>.</li> <li>Added <code>bandit.LinUCBDisjoint</code>, which is River's first contextual bandit policy.</li> <li>Added <code>bandit.RandomPolicy</code>.</li> </ul>"},{"location":"releases/0.19.0/#compose","title":"compose","text":"<ul> <li>Removed the <code>compose.warm_up_mode</code> context manager.</li> <li>Removed the <code>compose.pure_inference_mode</code> context manager.</li> <li>The last step of a pipeline will be correctly updated if it is unsupervised, which wasn't the case before.</li> <li>Fixed an edge-case where <code>compose.TransformerProduct</code> would not work when chained more than twice.</li> </ul>"},{"location":"releases/0.19.0/#drift","title":"drift","text":"<ul> <li>Added a <code>datasets</code> submodule, which contains datasets that are useful for concept drift experiments.</li> <li>Fix bugs in <code>drift.binary.HDDM_A</code> and <code>drift.binary.HDDM_W</code>.</li> </ul>"},{"location":"releases/0.19.0/#linear_model","title":"linear_model","text":"<ul> <li>Added a <code>predict_many</code> method to <code>linear_model.BayesianLinearRegression</code>.</li> <li>Added a <code>smoothing</code> parameter to <code>linear_model.BayesianLinearRegression</code>, which allows it to cope with concept drift.</li> </ul>"},{"location":"releases/0.19.0/#forest","title":"forest","text":"<ul> <li>Fixed issue with <code>forest.ARFClassifier</code> which couldn't be passed a <code>CrossEntropy</code> metric.</li> <li>Fixed a bug in <code>forest.AMFClassifier</code> which slightly improves predictive accurary.</li> <li>Added <code>forest.AMFRegressor</code>.</li> </ul>"},{"location":"releases/0.19.0/#multioutput","title":"multioutput","text":"<ul> <li>Added <code>metrics.multioutput.SampleAverage</code>, which is equivalent to using <code>average='samples'</code> in scikit-learn.</li> </ul>"},{"location":"releases/0.19.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.OrdinalEncoder</code>, to map string features to integers.</li> <li>The <code>transform_many</code> method of <code>preprocessing.StandardScaler</code> now uses the dtype of the input for the output.</li> </ul>"},{"location":"releases/0.19.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.MultivariateGaussian</code>.</li> </ul>"},{"location":"releases/0.19.0/#stream","title":"stream","text":"<ul> <li><code>stream.iter_arff</code> now supports sparse data.</li> <li><code>stream.iter_arff</code> now supports multi-output targets.</li> <li><code>stream.iter_arff</code> now supports missing values indicated with question marks.</li> </ul>"},{"location":"releases/0.19.0/#utils","title":"utils","text":"<ul> <li>Added <code>utils.random.exponential</code> to retrieve random samples following an exponential distribution.</li> </ul>"},{"location":"releases/0.2.0/","title":"0.2.0 - 2019-05-27","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.2.0/#compose","title":"compose","text":"<ul> <li><code>compose.Pipeline</code> now has a <code>debug_one</code>.</li> <li><code>compose.Discard</code> and <code>compose.Select</code> now take variadic inputs, which means you don't have to provide a list of features to exclude/include.</li> </ul>"},{"location":"releases/0.2.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_bikes</code></li> </ul>"},{"location":"releases/0.2.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Classes that inherit from <code>feature_extraction.VectorizerMixin</code> can now directly be passed <code>str</code> instances instead of <code>dict</code> instances.</li> <li><code>feature_extraction.Agg</code> and <code>feature_extraction.TargetAgg</code> can now aggregate on multiple attributes.</li> </ul>"},{"location":"releases/0.2.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>RollingAccuracy</code></li> <li>Added <code>RollingCrossEntropy</code></li> <li>Added <code>RollingF1</code></li> <li>Added <code>RollingLogLoss</code></li> <li>Added <code>RollingMacroF1</code></li> <li>Added <code>RollingMacroPrecision</code></li> <li>Added <code>RollingMacroRecall</code></li> <li>Added <code>RollingMAE</code></li> <li>Added <code>RollingMicroF1</code></li> <li>Added <code>RollingMicroPrecision</code></li> <li>Added <code>RollingMicroRecall</code></li> <li>Added <code>RollingMSE</code></li> <li>Added <code>RollingPrecision</code></li> <li>Added <code>RollingRecall</code></li> <li>Added <code>RollingRMSE</code></li> <li>Added <code>RollingRMSLE</code></li> <li>Added <code>RollingSMAPE</code></li> </ul>"},{"location":"releases/0.2.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.online_qa_score</code>.</li> </ul>"},{"location":"releases/0.2.0/#proba","title":"proba","text":"<p>The <code>dist</code> module has been renamed to <code>proba</code> and is now public, for the moment it contains a single distribution called <code>proba.Gaussian</code>.</p>"},{"location":"releases/0.2.0/#naive_bayes","title":"naive_bayes","text":"<ul> <li>Added <code>naive_bayes.BernoulliNB</code>.</li> <li>Added <code>naive_bayes.ComplementNB</code>.</li> </ul>"},{"location":"releases/0.2.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.AdaBound</code>.</li> </ul>"},{"location":"releases/0.2.0/#tree","title":"tree","text":"<ul> <li>Added <code>tree.DecisionTreeClassifier</code>.</li> <li>Removed <code>tree.MondrianTreeClassifier</code> and <code>tree.MondrianTreeRegressor</code> because their performance wasn't good enough.</li> </ul>"},{"location":"releases/0.2.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.AutoCorrelation</code>.</li> <li>Added <code>stats.EWVar</code>.</li> <li>Rename <code>stats.Variance</code> to <code>stats.Var</code> and <code>stats.RollingVariance</code> to <code>stats.RollingVar</code>.</li> </ul>"},{"location":"releases/0.2.0/#stream","title":"stream","text":"<ul> <li>Added <code>stream.simulate_qa</code>.</li> </ul>"},{"location":"releases/0.2.0/#utils","title":"utils","text":"<ul> <li>Added <code>utils.SDFT</code>.</li> <li>Added <code>utils.Skyline</code>.</li> <li>Renamed the <code>window_size</code> parameter to <code>size</code> in <code>utils.Window</code> and <code>utils.SortedWindow</code>.</li> </ul>"},{"location":"releases/0.20.0/","title":"0.20.0 - 2023-11-09","text":"<ul> <li>River's mini-batch methods now support pandas v2. In particular, River conforms to pandas' new sparse API.</li> <li>Dropped support for Python 3.8.</li> <li>Added support for Python 3.12.</li> </ul>"},{"location":"releases/0.20.0/#anomaly","title":"anomaly","text":"<ul> <li>Added <code>api.anomaly.LocalOutlierFactor</code>, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation.</li> <li>Made <code>score_one</code> method of <code>api.anomaly.LocalOutlierFactor</code> stateless</li> <li>Defined default score for uninitialized detector</li> <li>Implementation of the <code>api.anomaly.StandardAbsoluteDeviation</code> algorithm, which is a uni-variate anomaly detection algorithm, based on the implementation in PySAD (Python Streaming Anomaly Detection)</li> </ul>"},{"location":"releases/0.20.0/#covariance","title":"covariance","text":"<ul> <li>Added <code>_from_state</code> method to <code>covariance.EmpiricalCovariance</code> to warm start from previous knowledge.</li> </ul>"},{"location":"releases/0.20.0/#clustering","title":"clustering","text":"<ul> <li>Add fixes to <code>cluster.DBSTREAM</code> algorithm, including:</li> <li>Addition of the <code>-</code> sign before the <code>fading_factor</code> in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed.</li> <li>The new <code>micro_cluster</code> is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (<code>len = 0</code>), a new micro cluster is added with key 0.</li> <li><code>cluster_is_up_to_date</code> is set to <code>True</code> at the end of the <code>self._recluster()</code> function.</li> <li>Shared density graph update timestamps are initialized with the current timestamp value</li> <li><code>neighbour_neighbours</code> are appended correctly to the <code>seed_set</code> when generating cluster labels</li> <li>When building weighted adjacency matrix the algorithm accounts for possibly orphaned entries in shared density graph</li> </ul>"},{"location":"releases/0.20.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.WebTraffic</code>, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs.</li> </ul>"},{"location":"releases/0.20.0/#drift","title":"drift","text":"<ul> <li>Add <code>drift.NoDrift</code> to allow disabling the drift detection capabilities of models. This detector does nothing and always returns <code>False</code> when queried whether or not a concept drift was detected.</li> </ul>"},{"location":"releases/0.20.0/#evaluate","title":"evaluate","text":"<ul> <li>Added a <code>yield_predictions</code> parameter to <code>evaluate.iter_progressive_val_score</code>, which allows including predictions in the output.</li> </ul>"},{"location":"releases/0.20.0/#forest","title":"forest","text":"<ul> <li>Simplify inner the structures of <code>forest.ARFClassifier</code> and <code>forest.ARFRegressor</code> by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole.</li> </ul>"},{"location":"releases/0.20.0/#metrics","title":"metrics","text":"<ul> <li><code>metrics.ConfusionMatrix</code> may now be used with <code>evaluate.progressive_val_score</code> and <code>evaluate.iter_progressive_val_score</code>.</li> </ul>"},{"location":"releases/0.20.0/#proba","title":"proba","text":"<ul> <li>Added <code>_from_state</code> method to <code>proba.MultivariateGaussian</code> to warm start from previous knowledge.</li> </ul>"},{"location":"releases/0.20.0/#tree","title":"tree","text":"<ul> <li>Fix a bug in <code>tree.splitter.NominalSplitterClassif</code> that generated a mismatch between the number of existing tree branches and the number of tracked branches.</li> <li>Fix a bug in <code>tree.ExtremelyFastDecisionTreeClassifier</code> where the split re-evaluation failed when the current branch's feature was not available as a split option. The fix also enables the tree to pre-prune a leaf via the tie-breaking mechanism.</li> </ul>"},{"location":"releases/0.20.0/#stats","title":"stats","text":"<ul> <li>Implementation of the incremental Kolmogorov-Smirnov statistics (at <code>stats.KolmogorovSmirnov</code>), with the option to calculate either the original KS or Kuiper's test.</li> </ul>"},{"location":"releases/0.20.0/#utils","title":"utils","text":"<ul> <li>Removed <code>utils.dict2numpy</code> and <code>utils.numpy2dict</code> functions. They were not used anywhere in the library.</li> <li><code>utils.TimeRolling</code> now works correctly if two samples with the same timestamp are added in a row.</li> </ul>"},{"location":"releases/0.20.1/","title":"0.20.1 - 2023-11-09","text":"<p>Dummy release to make wheels available. No actual difference with v0.20.0.</p>"},{"location":"releases/0.21.0/","title":"0.21.0 - 2023-12-04","text":"<ul> <li>The <code>learn_one</code> and <code>learn_many</code> methods of each estimator don't not return anything anymore. This is to emphasize that the estimators are stateful.</li> <li>The <code>update</code> and <code>revert</code> method of classes that have also cease to return anything.</li> <li><code>sample_weight</code> has been renamed to <code>w</code>.</li> </ul>"},{"location":"releases/0.21.0/#covariance","title":"covariance","text":"<ul> <li>Fixed an issue where <code>update_many</code> would reset <code>covariance.EmpiricalCovariance</code> each time it was called.</li> </ul>"},{"location":"releases/0.21.1/","title":"0.21.1 - 2024-03-28","text":"<p>This release should fix some of the installation issues when building the River wheel from scratch.</p>"},{"location":"releases/0.21.1/#anomaly","title":"anomaly","text":"<ul> <li>Added <code>PredictiveAnomalyDetection</code>, a semi-supervised technique that employs a predictive model for anomaly detection.</li> </ul>"},{"location":"releases/0.21.1/#drift","title":"drift","text":"<ul> <li>Added <code>FHDDM</code> drift detector.</li> <li>Added a <code>iter_polars</code> function to iterate over the rows of a polars DataFrame.</li> </ul>"},{"location":"releases/0.21.1/#neighbors","title":"neighbors","text":"<ul> <li>Simplified <code>neighbors.SWINN</code> to avoid recursion limit and pickling issues.</li> </ul>"},{"location":"releases/0.21.2/","title":"0.21.2 - 2024-07-08","text":"<p>This release makes Polars an optional dependency instead of a required one.</p>"},{"location":"releases/0.21.2/#cluster","title":"cluster","text":"<ul> <li>Added <code>ODAC</code> (Online Divisive-Agglomerative Clustering) for clustering time series.</li> </ul>"},{"location":"releases/0.21.2/#forest","title":"forest","text":"<ul> <li>Fix error in <code>forest.ARFClassifer</code> and <code>forest.ARFRegressor</code> where the algorithms would crash in case the number of features available for learning went below the value of the <code>max_features</code> parameter (#1560).</li> </ul>"},{"location":"releases/0.3.0/","title":"0.3.0 - 2019-06-23","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.3.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.load_chick_weights</code>.</li> </ul>"},{"location":"releases/0.3.0/#decomposition","title":"decomposition","text":"<ul> <li>Added <code>decomposition.LDA</code>.</li> </ul>"},{"location":"releases/0.3.0/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.HedgeRegressor</code>.</li> <li>Added <code>ensemble.StackingBinaryClassifier</code>.</li> </ul>"},{"location":"releases/0.3.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.FBeta</code></li> <li>Added <code>metrics.MacroFBeta</code></li> <li>Added <code>metrics.MicroFBeta</code></li> <li>Added <code>metrics.MultiFBeta</code></li> <li>Added <code>metrics.RollingFBeta</code></li> <li>Added <code>metrics.RollingMacroFBeta</code></li> <li>Added <code>metrics.RollingMicroFBeta</code></li> <li>Added <code>metrics.RollingMultiFBeta</code></li> <li>Added <code>metrics.Jaccard</code></li> <li>Added <code>metrics.RollingConfusionMatrix</code></li> <li>Added <code>metrics.RegressionMultiOutput</code></li> <li>Added <code>metrics.MCC</code></li> <li>Added <code>metrics.RollingMCC</code></li> <li>Added <code>metrics.ROCAUC</code></li> <li>Renamed <code>metrics.F1Score</code> to <code>metrics.F1</code>.</li> </ul>"},{"location":"releases/0.3.0/#multioutput","title":"multioutput","text":"<ul> <li>Added <code>multioutput.ClassifierChain</code>.</li> <li>Added <code>multioutput.RegressorChain</code>.</li> </ul>"},{"location":"releases/0.3.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.QuantileLoss</code></li> <li>Added <code>optim.MiniBatcher</code>.</li> </ul>"},{"location":"releases/0.3.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.Normalizer</code>.</li> </ul>"},{"location":"releases/0.3.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.Multinomial</code>.</li> </ul>"},{"location":"releases/0.4.1/","title":"0.4.1 - 2019-10-23","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.4.1/#base","title":"base","text":"<ul> <li>Tests are now much more extensive, thanks mostly to the newly added estimator tags.</li> </ul>"},{"location":"releases/0.4.1/#compose","title":"compose","text":"<ul> <li>Added <code>compose.Renamer</code>.</li> </ul>"},{"location":"releases/0.4.1/#datasets","title":"datasets","text":"<ul> <li>Added <code>fetch_kdd99_http</code>.</li> <li>Added <code>fetch_sms</code>.</li> <li>Added <code>fetch_trec07p</code>.</li> </ul>"},{"location":"releases/0.4.1/#ensemble","title":"ensemble","text":"<ul> <li>Removed <code>ensemble.HedgeBinaryClassifier</code> because its performance was subpar.</li> <li>Removed <code>ensemble.GroupRegressor</code>, as this should be a special case of <code>ensemble.StackingRegressor</code>.</li> </ul>"},{"location":"releases/0.4.1/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Fixed a bug where <code>feature_extraction.CountVectorizer</code> and <code>feature_extraction.TFIDFVectorizer</code> couldn't be pickled.</li> </ul>"},{"location":"releases/0.4.1/#linear_model","title":"linear_model","text":"<ul> <li><code>linear_model.LogisticRegression</code> and <code>linear_model.LinearRegression</code> now have an <code>intercept_lr</code> parameter.</li> </ul>"},{"location":"releases/0.4.1/#metrics","title":"metrics","text":"<ul> <li>Metrics can now be composed using the <code>+</code> operator, which is useful for evaluating multiple metrics at the same time.</li> <li>Added <code>metrics.Rolling</code>, which eliminates the need for a specific rolling implementation for each metric.</li> <li>Each metric can now be passed a <code>sample_weight</code> argument.</li> <li>Added <code>metrics.WeightedF1</code>.</li> <li>Added <code>metrics.WeightedFBeta</code>.</li> <li>Added <code>metrics.WeightedPrecision</code>.</li> <li>Added <code>metrics.WeightedRecall</code>.</li> </ul>"},{"location":"releases/0.4.1/#neighbors","title":"neighbors","text":"<ul> <li>Added <code>neighbors.KNeighborsRegressor</code>.</li> <li>Added <code>neighbors.KNeighborsClassifier</code>.</li> </ul>"},{"location":"releases/0.4.1/#optim","title":"optim","text":"<ul> <li>Added <code>optim.AdaMax</code>.</li> <li>The <code>optim</code> module has been reorganized into submodules; namely <code>optim.schedulers</code>, <code>optim.initializers</code>, and <code>optim.losses</code>. The top-level now only contains optimizers. Some classes have been renamed accordingly. See the documentation for details.</li> <li>Renamed <code>optim.VanillaSGD</code> to <code>optim.SGD</code>.</li> </ul>"},{"location":"releases/0.4.1/#stats","title":"stats","text":"<ul> <li>Added <code>stats.IQR</code>.</li> <li>Added <code>stats.RollingIQR</code>.</li> <li>Cythonized <code>stats.Mean</code> and <code>stats.Var</code>.</li> </ul>"},{"location":"releases/0.4.1/#stream","title":"stream","text":"<ul> <li>Added <code>stream.shuffle</code>.</li> <li><code>stream.iter_csv</code> now has <code>fraction</code> and <code>seed</code> parameters to sample rows, deterministically or not.</li> <li>Renamed <code>stream.iter_numpy</code> to <code>stream.iter_array</code>.</li> <li><code>stream.iter_csv</code> can now read from gzipped files.</li> </ul>"},{"location":"releases/0.4.1/#time_series","title":"time_series","text":"<ul> <li><code>time_series.Detrender</code> now has a <code>window_size</code> parameter for detrending with a rolling mean.</li> </ul>"},{"location":"releases/0.4.1/#tree","title":"tree","text":"<ul> <li>Added <code>tree.RandomForestClassifier</code>.</li> </ul>"},{"location":"releases/0.4.1/#utils","title":"utils","text":"<ul> <li>Fixed a bug where <code>utils.dot</code> could take longer than necessary.</li> </ul>"},{"location":"releases/0.4.3/","title":"0.4.3 - 2019-10-27","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.4.3/#base","title":"base","text":"<ul> <li>Model that inherit from <code>base.Wrapper</code> (e.g. <code>tree.RandomForestClassifier</code>) can now be pickled.</li> </ul>"},{"location":"releases/0.4.3/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_credit_card</code>.</li> </ul>"},{"location":"releases/0.4.3/#utils","title":"utils","text":"<ul> <li>Added the <code>utils.math</code> sub-module.</li> </ul>"},{"location":"releases/0.4.3/#tree","title":"tree","text":"<ul> <li>Fixed the <code>debug_one</code> method of <code>tree.DecisionTreeClassifier</code>.</li> </ul>"},{"location":"releases/0.4.4/","title":"0.4.4 - 2019-11-11","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul> <p>This release was mainly made to provide access to <code>wheels &lt;https://pythonwheels.com/&gt;</code>_ for Windows and MacOS.</p>"},{"location":"releases/0.4.4/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.AdaBoostClassifier</code>.</li> </ul>"},{"location":"releases/0.4.4/#linear_model","title":"linear_model","text":"<ul> <li>Added a <code>clip_gradient</code> parameter to <code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code>. Gradient clipping was already implemented, but the maximum absolute value can now be set by the user.</li> <li>The <code>intercept_lr</code> parameter of <code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code> can now be passed an instance of <code>optim.schedulers.Scheduler</code> as well as a <code>float</code>.</li> </ul>"},{"location":"releases/0.4.4/#metrics","title":"metrics","text":"<ul> <li>Fixed <code>metrics.SMAPE</code>, the implementation was missing a multiplication by 2.</li> </ul>"},{"location":"releases/0.4.4/#optim","title":"optim","text":"<ul> <li>Added <code>optim.schedulers.Optimal</code> produces results that are identical to <code>sklearn.linear_model.SGDRegressor</code> and <code>sklearn.linear_model.SGDClassifier</code> when setting their <code>learning_rate</code> parameter to <code>'optimal'</code>.</li> </ul>"},{"location":"releases/0.4.4/#time_series","title":"time_series","text":"<ul> <li>Added <code>time_series.SNARIMAX</code>, a generic model which encompasses well-known time series models such as ARIMA and NARX.</li> </ul>"},{"location":"releases/0.5.0/","title":"0.5.0 - 2020-03-13","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.5.0/#compat","title":"compat","text":"<ul> <li>Added <code>compat.PyTorch2CremeRegressor</code>.</li> <li><code>compat.SKL2CremeRegressor</code> and <code>compat.SKL2CremeClassifier</code> now have an optional <code>batch_size</code> parameter in order to perform mini-batching.</li> </ul>"},{"location":"releases/0.5.0/#compose","title":"compose","text":"<ul> <li>Renamed <code>compose.Whitelister</code> to <code>compose.Select</code>.</li> <li>Renamed <code>compose.Blacklister</code> to <code>compose.Discard</code>.</li> </ul>"},{"location":"releases/0.5.0/#facto","title":"facto","text":"<ul> <li>Added <code>facto.FFMClassifier</code>.</li> <li>Added <code>facto.FFMRegressor</code>.</li> <li>Added <code>facto.FwFMClassifier</code>.</li> <li>Added <code>facto.FwFMRegressor</code>.</li> <li>Added <code>facto.HOFMClassifier</code>.</li> <li>Added <code>facto.HOFMRegressor</code>.</li> <li>Refactored <code>facto.FMClassifier</code>.</li> <li>Refactored <code>facto.FMRegressor</code>.</li> </ul>"},{"location":"releases/0.5.0/#feature_selection","title":"feature_selection","text":"<ul> <li>Added <code>feature_selection.PoissonInclusion</code>.</li> <li>Removed <code>feature_selection.RandomDiscarder</code> as it didn't make much sense.</li> </ul>"},{"location":"releases/0.5.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Renamed <code>feature_extraction.CountVectorizer</code> to <code>feature_extraction.BagOfWords</code>.</li> <li>Renamed <code>feature_extraction.TFIDFVectorizer</code> to <code>feature_extraction.TFIDF</code>.</li> <li>Added <code>preprocessor</code> and <code>ngram_range</code> parameters to <code>feature_extraction.BagOfWords</code>.</li> <li>Added <code>preprocessor</code> and <code>ngram_range</code> parameters to <code>feature_extraction.TFIDF</code>.</li> </ul>"},{"location":"releases/0.5.0/#datasets","title":"datasets","text":"<ul> <li>The <code>datasets</code> module has been overhauled. Each dataset is now a class (e.g. <code>fetch_electricity</code> has become <code>datasets.Elec2</code>).</li> <li>Added <code>datasets.TrumpApproval</code>.</li> <li>Added <code>datasets.MaliciousURL</code>.</li> <li>Added <code>datasets.gen.SEA</code>.</li> <li>Added <code>datasets.Higgs</code>.</li> <li>Added <code>datasets.MovieLens100K</code>.</li> <li>Added <code>datasets.Bananas</code>.</li> <li>Added <code>datasets.Taxis</code>.</li> <li>Added <code>datasets.ImageSegments</code>.</li> <li>Added <code>datasets.SMTP</code></li> </ul>"},{"location":"releases/0.5.0/#impute","title":"impute","text":"<ul> <li>Added <code>impute.PreviousImputer</code>.</li> </ul>"},{"location":"releases/0.5.0/#linear_model","title":"linear_model","text":"<ul> <li><code>linear_model.FMClassifier</code> has been moved to the <code>facto</code> module.</li> <li><code>linear_model.FMRegressor</code> has been  moved to the <code>facto</code> module.</li> <li>Added <code>linear_model.ALMAClassifier</code>.</li> </ul>"},{"location":"releases/0.5.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.ClassificationReport</code>.</li> <li>Added <code>metrics.TimeRolling</code>.</li> <li>The implementation of <code>metrics.ROCAUC</code> was incorrect. Using the trapezoidal rule instead of Simpson's rule seems to be more robust.</li> <li><code>metrics.PerClass</code> has been removed; it is recommended that you use <code>metrics.ClassificationReport</code> instead as it gives a better overview.</li> </ul>"},{"location":"releases/0.5.0/#meta","title":"meta","text":"<ul> <li>Moved <code>meta.TransformedTargetRegressor</code> and <code>meta.BoxCoxRegressor</code> to this module (they were previously in the <code>compose</code> module).</li> <li>Added <code>meta.PredClipper</code></li> </ul>"},{"location":"releases/0.5.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.expand_param_grid</code> to generate a list of models from a grid of parameters.</li> <li>Added the <code>model_selection.successive_halving</code> method for selecting hyperparameters.</li> <li>The <code>online_score</code> and <code>online_qa_score</code> methods have been merged into a single method named <code>model_selection.progressive_val_score</code>.</li> </ul>"},{"location":"releases/0.5.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.RBFSampler</code>.</li> <li>Added <code>preprocessing.MaxAbsScaler</code>.</li> <li>Added <code>preprocessing.RobustScaler</code>.</li> <li>Added <code>preprocessing.Binarizer</code>.</li> <li>Added <code>with_mean</code> and <code>with_std</code> parameters to <code>preprocessing.StandardScaler</code>.</li> </ul>"},{"location":"releases/0.5.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.losses.BinaryFocalLoss</code>.</li> <li>Added the <code>optim.AMSGrad</code> optimizer.</li> <li>Added the <code>optim.Nadam</code> optimizer.</li> <li>Added <code>optim.losses.Poisson</code>.</li> <li>Fixed a performance bug in <code>optim.NesterovMomentum</code>.</li> </ul>"},{"location":"releases/0.5.0/#reco","title":"reco","text":"<ul> <li>Added <code>reco.FunkMF</code>.</li> <li>Renamed <code>reco.SVD</code> to <code>reco.BiasedMF</code>.</li> <li>Renamed <code>reco.SGDBaseline</code> to <code>reco.Baseline</code>.</li> <li>Models now expect a <code>dict</code> input with <code>user</code> and <code>item</code> fields.</li> </ul>"},{"location":"releases/0.5.0/#sampling","title":"sampling","text":"<ul> <li>Added <code>sampling.RandomUnderSampler</code>.</li> <li>Added <code>sampling.RandomOverSampler</code>.</li> <li>Added <code>sampling.RandomSampler</code>.</li> <li>Added <code>sampling.HardSamplingClassifier</code>.</li> <li>Added <code>sampling.HardSamplingRegressor</code>.</li> </ul>"},{"location":"releases/0.5.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.AbsMax</code>.</li> <li>Added <code>stats.RollingAbsMax</code>.</li> </ul>"},{"location":"releases/0.5.0/#stream","title":"stream","text":"<ul> <li>Added <code>stream.iter_libsvm</code>.</li> <li><code>stream.iter_csv</code> now supports reading from '.zip' files.</li> <li>Added <code>stream.Cache</code>.</li> <li>Added a <code>drop</code> parameter to <code>stream.iter_csv</code> to discard fields.</li> </ul>"},{"location":"releases/0.5.1/","title":"0.5.1 - 2020-03-29","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.5.1/#compose","title":"compose","text":"<ul> <li><code>compose.Pipeline</code> and <code>compose.TransformerUnion</code> now variadic arguments as input instead of a list. This doesn't change anything when using the shorthand operators <code>|</code> and <code>+</code>.</li> </ul>"},{"location":"releases/0.5.1/#model_selection","title":"model_selection","text":"<ul> <li>Removed <code>model_selection.successive_halving</code></li> <li>Added <code>model_selection.SuccessiveHalvingRegressor</code> and <code>model_selection.SuccessiveHalvingClassifier</code></li> </ul>"},{"location":"releases/0.5.1/#stream","title":"stream","text":"<ul> <li>Added a <code>copy</code> parameter to <code>stream.simulate_qa</code> in order to handle unwanted feature modifications.</li> </ul>"},{"location":"releases/0.5.1/#tree","title":"tree","text":"<ul> <li>Added a <code>curtail_under</code> parameter to <code>tree.DecisionTreeClassifier</code>.</li> <li>The speed and accuracy of both <code>tree.DecisionTreeClassifier</code> and <code>tree.RandomForestClassifier</code> has been slightly improved for numerical attributes.</li> <li>The esthetics of the <code>tree.DecisionTreeClassifier.draw</code> method have been improved.</li> </ul>"},{"location":"releases/0.6.0/","title":"0.6.0 - 2020-06-09","text":""},{"location":"releases/0.6.0/#base","title":"base","text":"<ul> <li>Added a new base class called <code>SupervisedTransformer</code> from which supervised transformers inherit from. Before this, supervised transformers has a <code>is_supervised</code> property.</li> </ul>"},{"location":"releases/0.6.0/#compose","title":"compose","text":"<ul> <li>Added <code>compose.SelectType</code>, which allows selecting feature subsets based on their type.</li> <li>Added a <code>score_one</code> method to <code>compose.Pipeline</code> so that estimators from the <code>anomaly</code> module can be pipelined.</li> <li>Added <code>compose.Grouper</code>, which allows applying transformers within different subgroups.</li> </ul>"},{"location":"releases/0.6.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.Music</code>, which is a dataset for multi-output binary classification.</li> <li>Added <code>datasets.synth.Friedman</code>, which is synthetic regression dataset.</li> <li>The <code>datasets.gen</code> module has been renamed to <code>datasets.synth</code></li> <li>Each dataset now has a <code>__repr__</code> method which displays some descriptive information.</li> <li>Added <code>datasets.Insects</code>, which has 10 variants.</li> </ul>"},{"location":"releases/0.6.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li><code>feature_extraction.Differ</code> has been deprecated. We might put it back in a future if we find a better design.</li> </ul>"},{"location":"releases/0.6.0/#impute","title":"impute","text":"<ul> <li><code>impute.StatImputer</code> has been completely refactored.</li> </ul>"},{"location":"releases/0.6.0/#metrics","title":"metrics","text":"<ul> <li>In <code>metrics.SMAPE</code>, instead of raising a <code>ZeroDivisionError</code>, the convention is now to use 0 when both <code>y_true</code> and <code>y_pred</code> are equal to 0.</li> </ul>"},{"location":"releases/0.6.0/#model_selection","title":"model_selection","text":"<ul> <li>Added the possibility to configure how the progress is printed in <code>model_selection.progressive_val_score</code>. For instance, the progress can now be printed to a file by providing the <code>file</code> argument.</li> </ul>"},{"location":"releases/0.6.0/#multiclass","title":"multiclass","text":"<ul> <li>Added <code>multiclass.OutputCodeClassifier</code>.</li> <li>Added <code>multiclass.OneVsOneClassifier</code>.</li> </ul>"},{"location":"releases/0.6.0/#multioutput","title":"multioutput","text":"<ul> <li>Fixed a bug where <code>multioutput.ClassifierChain</code> and <code>multioutput.RegressorChain</code> could not be pickled.</li> </ul>"},{"location":"releases/0.6.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.Shift</code>, which can be used to compute statistics over a shifted version of a variable.</li> <li>Added <code>stats.Link</code>, which can be used to compose univariate statistics. Univariate statistics can now be composed via the <code>|</code> operator.</li> <li>Renamed <code>stats.Covariance</code> to <code>stats.Cov</code>.</li> <li>Renamed <code>stats.PearsonCorrelation</code> to <code>stats.PearsonCorr</code>.</li> <li>Renamed <code>stats.AutoCorrelation</code> to <code>stats.AutoCorr</code>.</li> <li>Added <code>stats.RollingCov</code>, which computes covariance between two variables over a window.</li> <li>Added <code>stats.RollingPearsonCorr</code>, which computes the Pearson correlation over a window.</li> </ul>"},{"location":"releases/0.6.0/#stream","title":"stream","text":"<ul> <li>Added a <code>stream.iter_sql</code> utility method to work with SQLAlchemy.</li> <li>The <code>target_name</code> parameter of <code>stream.iter_csv</code> has been renamed to <code>target</code>. It can now be passed a list of values in order to support multi-output scenarios.</li> <li>Added <code>stream.iter_arff</code> for handling ARFF files.</li> </ul>"},{"location":"releases/0.6.0/#tree","title":"tree","text":"<ul> <li>Cancelled the behavior where <code>tree.DecisionTreeRegressor</code> would raise an exception when no split was found.</li> </ul>"},{"location":"releases/0.6.1/","title":"0.6.1 - 2020-06-10","text":""},{"location":"releases/0.6.1/#compose","title":"compose","text":"<ul> <li>Fixed a bug that occurred when part of a <code>compose.Transformer</code> was a <code>compose.Pipeline</code> and wasn't properly handled.</li> </ul>"},{"location":"releases/0.7.0/","title":"0.7.0 - 2021-04-16","text":"<p>Alas, no release notes for this one.</p>"},{"location":"releases/0.7.1/","title":"0.7.1 - 2021-06-13","text":"<p>Fixed an issue where scikit-learn was imported in <code>sam_knn.py</code> but wasn't specified as a dependency.</p>"},{"location":"releases/0.7.1/#expert","title":"expert","text":"<ul> <li>Each expert model will now raise a <code>NotEnoughModels</code> exception if only a single model is passed.</li> </ul>"},{"location":"releases/0.7.1/#stream","title":"stream","text":"<ul> <li>Added <code>drop_nones</code> parameter to <code>stream.iter_csv</code>.</li> </ul>"},{"location":"releases/0.8.0/","title":"0.8.0 - 2021-08-31","text":""},{"location":"releases/0.8.0/#base","title":"base","text":"<ul> <li>The <code>predict_many</code> and <code>predict_proba_many</code> methods have been removed from <code>base.Classifier</code>. They're part of <code>base.MiniBatchClassifier</code>.</li> </ul>"},{"location":"releases/0.8.0/#ensemble","title":"ensemble","text":"<ul> <li>Implemented <code>ensemble.VotingClassifier</code>.</li> <li>Implemented <code>ensemble.SRPRegressor</code>.</li> </ul>"},{"location":"releases/0.8.0/#meta","title":"meta","text":"<ul> <li>Renamed <code>meta.TransformedTargetRegressor</code> to <code>meta.TargetTransformRegressor</code>.</li> <li>Added <code>meta.TargetStandardScaler</code>.</li> </ul>"},{"location":"releases/0.8.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added a <code>with_std</code> parameter to <code>StandardScaler</code>.</li> </ul>"},{"location":"releases/0.8.0/#rules","title":"rules","text":"<ul> <li>Added <code>rules.AMRules</code></li> </ul>"},{"location":"releases/0.8.0/#stats","title":"stats","text":"<ul> <li>Make <code>stats.RollingQuantile</code> match the default behavior of Numpy's <code>quantile</code> function.</li> </ul>"},{"location":"releases/0.8.0/#tree","title":"tree","text":"<ul> <li>Unifed base class structure applied to all tree models.</li> <li>Bug fixes.</li> <li>Added <code>tree.SGTClassifier</code> and <code>tree.SGTRegressor</code>.</li> </ul>"},{"location":"releases/0.9.0/","title":"0.9.0 - 2021-11-30","text":"<ul> <li>Wheels for Python 3.6 have been dropped.</li> <li>Wheels for Python 3.9 have been added.</li> </ul>"},{"location":"releases/0.9.0/#anomaly","title":"anomaly","text":"<ul> <li>Moved <code>api.anomaly.base.AnomalyDetector</code> to <code>anomaly.AnomalyDetector</code>.</li> <li>Implemented <code>anomaly.ConstantThresholder</code>.</li> <li>Implemented <code>anomaly.QuantileThresholder</code>.</li> <li>Implemented <code>api.anomaly.OneClassSVM</code>.</li> </ul>"},{"location":"releases/0.9.0/#base","title":"base","text":"<ul> <li>Renamed <code>base.WrapperMixin</code> to <code>base.Wrapper</code>.</li> <li>Introduced <code>base.WrapperEnsemble</code>.</li> <li>Clarified the difference between a <code>base.typing.Dataset</code> and a <code>base.typing.Stream</code>. A <code>Stream</code> is an instance of a <code>Dataset</code> and is stateful. A <code>Dataset</code> is stateless. It's essentially the same difference between an <code>Iterable</code> and an <code>Iterator</code> in the Python standard library.</li> </ul>"},{"location":"releases/0.9.0/#compat","title":"compat","text":"<ul> <li>Added <code>compat.PyTorch2RiverClassifier</code></li> <li>Implemented median absolute deviation in <code>stats.MAD</code>.</li> <li>Refactored <code>compat.PyTorch2RiverRegressor</code></li> <li>Fixed an issue where some statistics could not be printed if they had not seen any data yet.</li> </ul>"},{"location":"releases/0.9.0/#compose","title":"compose","text":"<ul> <li>You can now use a <code>list</code> as a shorthand to build a <code>TransformerUnion</code>.</li> <li>Fixed a visualization issue when using a pipeline with multiple feature unions.</li> <li>The prejudiced terms <code>blacklist</code> and <code>whitelist</code> have both been renamed to <code>keys</code>.</li> <li>Removed <code>learn_unsupervised</code> parameter from pipeline methods.</li> <li>Implemented <code>compose.TransformerProduct</code>.</li> </ul>"},{"location":"releases/0.9.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.Keystroke</code>.</li> </ul>"},{"location":"releases/0.9.0/#ensemble","title":"ensemble","text":"<ul> <li>Bug fixes in <code>ensemble.SRPClassifier</code> and <code>ensemble.SRPRegressor</code>.</li> <li>Some estimators have been moved into the <code>ensemble</code> module.</li> </ul>"},{"location":"releases/0.9.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Implemented <code>feature_extraction.Lagger</code>.</li> <li>Implemented <code>feature_extraction.TargetLagger</code>.</li> </ul>"},{"location":"releases/0.9.0/#meta","title":"meta","text":"<p>This module has been deleted.</p> <ul> <li>Move <code>meta.PredClipper</code> to the <code>preprocessing</code> module.</li> <li>Removed <code>meta.BoxCoxRegressor</code>.</li> <li>Moved <code>meta.TargetTransformRegressor</code> to <code>compose.TargetTransformRegressor</code>.</li> <li>Moved <code>meta.TargetStandardScaler</code> to <code>preprocessing.TargetStandardScaler</code>.</li> </ul>"},{"location":"releases/0.9.0/#model_selection","title":"model_selection","text":"<ul> <li>This new module replaces the <code>expert</code> module.</li> <li>Implemented <code>model_selection.GreedyRegressor</code>.</li> <li>Added <code>ModelSelector</code> base class.</li> </ul>"},{"location":"releases/0.9.0/#optim","title":"optim","text":"<ul> <li><code>optim.Adam</code> and <code>optim.RMSProp</code> now work with <code>utils.VectorDict</code>s as well as <code>numpy.ndarray</code>s.</li> <li>Added <code>optim.losses.Huber</code>.</li> </ul>"},{"location":"releases/0.9.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Enabled <code>preprocessing.OneHotEncoder</code> to one-hot encode values that are list or sets.</li> </ul>"},{"location":"releases/0.9.0/#reco","title":"reco","text":"<ul> <li>Added a <code>debug_one</code> method to <code>reco.FMRegressor</code>.</li> </ul>"},{"location":"releases/0.9.0/#selection","title":"selection","text":"<ul> <li>This new module replaces the <code>expert</code> module.</li> <li>Implemented <code>selection.GreedyExpertRegressor</code>.</li> </ul>"},{"location":"releases/0.9.0/#stats","title":"stats","text":"<ul> <li>Fixed an issue where some statistics could not be printed if they had not seen any data yet.</li> <li>Implemented median absolute deviation in <code>stats.MAD</code>.</li> <li>The <code>stats.Mean</code> and <code>stats.Var</code> implementations have been made more numerically stable.</li> </ul>"},{"location":"releases/0.9.0/#time_series","title":"time_series","text":"<ul> <li><code>time_series.Detrender</code> and <code>time_series.GroupDetrender</code> have been removed as they overlap with <code>preprocessing.TargetStandardScaler</code>.</li> <li>Implemented a <code>time_series.evaluate</code> method, which performs progressive validation for time series scenarios.</li> <li>Implemented <code>time_series.HorizonMetric</code> class to evaluate the performance of a forecasting model at each time step along a horizon.</li> <li>Implemented <code>time_series.HoltWinters</code>.</li> </ul>"},{"location":"releases/0.9.0/#utils","title":"utils","text":"<ul> <li>Moved <code>model_selection.expand_param_grid</code> to <code>utils.expand_param_grid</code>.</li> <li>Added <code>utils.poisson</code>.</li> <li>Added the <code>utils.log_method_calls</code> context manager.</li> <li>Added the <code>utils.warm_up_mode</code> context manager.</li> <li>Added the <code>utils.pure_inference_model</code> context manager.</li> </ul>"},{"location":"releases/unreleased/","title":"Unreleased","text":"<ul> <li>The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged.</li> </ul>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"api/overview/","title":"Overview","text":""},{"location":"api/overview/#active","title":"active","text":"<p>Online active learning.</p> <ul> <li>EntropySampler</li> </ul>"},{"location":"api/overview/#base","title":"base","text":"<ul> <li>ActiveLearningClassifier</li> </ul>"},{"location":"api/overview/#anomaly","title":"anomaly","text":"<p>Anomaly detection.</p> <p>Estimators in the <code>anomaly</code> module have a bespoke API. Each anomaly detector has a <code>score_one</code> method instead of a <code>predict_one</code> method. This method returns an anomaly score. Normal observations should have a low score, whereas anomalous observations should have a high score. The range of the scores is relative to each estimator.</p> <p>Anomaly detectors are usually unsupervised, in that they analyze the distribution of the features they are shown. But River also has a notion of supervised anomaly detectors. These analyze the distribution of a target variable, and optionally include the distribution of the features as well. They are useful for detecting labelling anomalies, which can be detrimental if they learned by a model.</p> <ul> <li>GaussianScorer</li> <li>HalfSpaceTrees</li> <li>LocalOutlierFactor</li> <li>OneClassSVM</li> <li>PredictiveAnomalyDetection</li> <li>QuantileFilter</li> <li>StandardAbsoluteDeviation</li> <li>ThresholdFilter</li> </ul>"},{"location":"api/overview/#base_1","title":"base","text":"<ul> <li>AnomalyDetector</li> <li>AnomalyFilter</li> <li>SupervisedAnomalyDetector</li> </ul>"},{"location":"api/overview/#bandit","title":"bandit","text":"<p>Multi-armed bandit (MAB) policies.</p> <p>The bandit policies in River have a generic API. This allows them to be used in a variety of situations. For instance, they can be used for model selection (see <code>model_selection.BanditRegressor</code>).</p> <p>Classes</p> <ul> <li>BayesUCB</li> <li>EpsilonGreedy</li> <li>Exp3</li> <li>LinUCBDisjoint</li> <li>RandomPolicy</li> <li>ThompsonSampling</li> <li>UCB</li> </ul> <p>Functions</p> <ul> <li>evaluate</li> <li>evaluate_offline</li> </ul>"},{"location":"api/overview/#base_2","title":"base","text":"<ul> <li>ContextualPolicy</li> <li>Policy</li> </ul>"},{"location":"api/overview/#datasets","title":"datasets","text":"<ul> <li>BanditDataset</li> <li>NewsArticles</li> </ul>"},{"location":"api/overview/#envs","title":"envs","text":"<ul> <li>CandyCaneContest</li> <li>KArmedTestbed</li> </ul>"},{"location":"api/overview/#base_3","title":"base","text":"<p>Base interfaces.</p> <p>Every estimator in River is a class, and as such inherits from at least one base interface. These are used to categorize, organize, and standardize the many estimators that River contains.</p> <p>This module contains mixin classes, which are all suffixed by <code>Mixin</code>. Their purpose is to provide additional functionality to an estimator, and thus need to be used in conjunction with a non-mixin base class.</p> <p>This module also contains utilities for type hinting and tagging estimators.</p> <ul> <li>Base</li> <li>BinaryDriftAndWarningDetector</li> <li>BinaryDriftDetector</li> <li>Classifier</li> <li>Clusterer</li> <li>DriftAndWarningDetector</li> <li>DriftDetector</li> <li>Ensemble</li> <li>Estimator</li> <li>MiniBatchClassifier</li> <li>MiniBatchRegressor</li> <li>MiniBatchSupervisedTransformer</li> <li>MiniBatchTransformer</li> <li>MultiLabelClassifier</li> <li>MultiTargetRegressor</li> <li>Regressor</li> <li>SupervisedTransformer</li> <li>Transformer</li> <li>Wrapper</li> <li>WrapperEnsemble</li> </ul>"},{"location":"api/overview/#cluster","title":"cluster","text":"<p>Unsupervised clustering.</p> <ul> <li>CluStream</li> <li>DBSTREAM</li> <li>DenStream</li> <li>KMeans</li> <li>ODAC</li> <li>STREAMKMeans</li> <li>TextClust</li> </ul>"},{"location":"api/overview/#compat","title":"compat","text":"<p>Compatibility tools.</p> <p>This module contains adapters for making River estimators compatible with other libraries, and vice-versa whenever possible. The relevant adapters will only be usable if you have installed the necessary library. For instance, you have to install scikit-learn in order to use the <code>compat.convert_sklearn_to_river</code> function.</p> <p>Classes</p> <ul> <li>River2SKLClassifier</li> <li>River2SKLClusterer</li> <li>River2SKLRegressor</li> <li>River2SKLTransformer</li> <li>SKL2RiverClassifier</li> <li>SKL2RiverRegressor</li> </ul> <p>Functions</p> <ul> <li>convert_river_to_sklearn</li> <li>convert_sklearn_to_river</li> </ul>"},{"location":"api/overview/#compose","title":"compose","text":"<p>Model composition.</p> <p>This module contains utilities for merging multiple modeling steps into a single pipeline. Although pipelines are not the only way to process a stream of data, we highly encourage you to use them.</p> <p>Classes</p> <ul> <li>Discard</li> <li>FuncTransformer</li> <li>Grouper</li> <li>Pipeline</li> <li>Prefixer</li> <li>Renamer</li> <li>Select</li> <li>SelectType</li> <li>Suffixer</li> <li>TargetTransformRegressor</li> <li>TransformerProduct</li> <li>TransformerUnion</li> </ul> <p>Functions</p> <ul> <li>learn_during_predict</li> </ul>"},{"location":"api/overview/#conf","title":"conf","text":"<p>Conformal predictions. This modules contains wrappers to enable conformal predictions on any regressor or classifier.</p> <ul> <li>Interval</li> <li>RegressionJackknife</li> </ul>"},{"location":"api/overview/#covariance","title":"covariance","text":"<p>Online estimation of covariance and precision matrices.</p> <ul> <li>EmpiricalCovariance</li> <li>EmpiricalPrecision</li> </ul>"},{"location":"api/overview/#datasets_1","title":"datasets","text":"<p>Datasets.</p> <p>This module contains a collection of datasets for multiple tasks: classification, regression, etc. The data corresponds to popular datasets and are conveniently wrapped to easily iterate over the data in a stream fashion. All datasets have fixed size. Please refer to <code>river.synth</code> if you are interested in infinite synthetic data generators.</p> <p>Regression</p> Name Samples Features AirlinePassengers 144 1 Bikes 182,470 8 ChickWeights 578 3 MovieLens100K 100,000 10 Restaurants 252,108 7 Taxis 1,458,644 8 TrumpApproval 1,001 6 WaterFlow 1,268 1 <p>Binary classification</p> Name Samples Features Sparse Bananas 5,300 2 CreditCard 284,807 30 Elec2 45,312 8 Higgs 11,000,000 28 HTTP 567,498 3 MaliciousURL 2,396,130 3,231,961 \u2714\ufe0f Phishing 1,250 9 SMSSpam 5,574 1 SMTP 95,156 3 TREC07 75,419 5 <p>Multi-class classification</p> Name Samples Features Classes ImageSegments 2,310 18 7 Insects 52,848 33 6 Keystroke 20,400 31 51 <p>Multi-output binary classification</p> Name Samples Features Outputs Music 593 72 6 <p>Multi-output regression</p> Name Samples Features Outputs SolarFlare 1,066 10 3 WebTraffic 44,160 3 2"},{"location":"api/overview/#base_4","title":"base","text":"<ul> <li>Dataset</li> <li>FileDataset</li> <li>RemoteDataset</li> <li>SyntheticDataset</li> </ul>"},{"location":"api/overview/#synth","title":"synth","text":"<p>Synthetic datasets.</p> <p>Each synthetic dataset is a stream generator. The benefit of using a generator is that they do not store the data and each data sample is generated on the fly. Except for a couple of methods, the majority of these methods are infinite data generators.</p> <p>Binary classification</p> Name Features Agrawal 9 AnomalySine 2 ConceptDriftStream 9 Hyperplane 10 Mixed 4 SEA 3 Sine 2 STAGGER 3 <p>Regression</p> Name Features Friedman 10 FriedmanDrift 10 Mv 10 Planes2D 10 <p>Multi-class classification</p> Name Features Classes LED 7 10 LEDDrift 7 10 RandomRBF 10 2 RandomRBFDrift 10 2 RandomTree 10 2 Waveform 21 3 <p>Multi-output binary classification</p> Name Features Outputs Logical 2 3"},{"location":"api/overview/#drift","title":"drift","text":"<p>Concept Drift Detection.</p> <p>This module contains concept drift detection methods. The purpose of a drift detector is to raise an alarm if the data distribution changes. A good drift detector method is the one that maximizes the true positives while keeping the number of false positives to a minimum.</p> <ul> <li>ADWIN</li> <li>DriftRetrainingClassifier</li> <li>DummyDriftDetector</li> <li>KSWIN</li> <li>NoDrift</li> <li>PageHinkley</li> </ul>"},{"location":"api/overview/#binary","title":"binary","text":"<p>Drift detection for binary data.</p> <ul> <li>DDM</li> <li>EDDM</li> <li>FHDDM</li> <li>HDDM_A</li> <li>HDDM_W</li> </ul>"},{"location":"api/overview/#datasets_2","title":"datasets","text":"<ul> <li>AirlinePassengers</li> <li>Apple</li> <li>Bitcoin</li> <li>BrentSpotPrice</li> <li>Occupancy</li> <li>RunLog</li> <li>UKCoalEmploy</li> </ul>"},{"location":"api/overview/#dummy","title":"dummy","text":"<p>Dummy estimators.</p> <p>This module is here for testing purposes, as well as providing baseline performances.</p> <ul> <li>NoChangeClassifier</li> <li>PriorClassifier</li> <li>StatisticRegressor</li> </ul>"},{"location":"api/overview/#ensemble","title":"ensemble","text":"<p>Ensemble learning.</p> <p>Broadly speaking, there are two kinds of ensemble approaches. There are those that copy a single model several times and aggregate the predictions of said copies. This includes bagging as well as boosting. Then there are those that are composed of an arbitrary list of models, and can therefore aggregate predictions from different kinds of models.</p> <ul> <li>ADWINBaggingClassifier</li> <li>ADWINBoostingClassifier</li> <li>AdaBoostClassifier</li> <li>BOLEClassifier</li> <li>BaggingClassifier</li> <li>BaggingRegressor</li> <li>EWARegressor</li> <li>LeveragingBaggingClassifier</li> <li>SRPClassifier</li> <li>SRPRegressor</li> <li>StackingClassifier</li> <li>VotingClassifier</li> </ul>"},{"location":"api/overview/#evaluate","title":"evaluate","text":"<p>Model evaluation.</p> <p>This module provides utilities to evaluate an online model. The goal is to reproduce a real-world scenario with high fidelity. The core function of this module is <code>progressive_val_score</code>, which allows to evaluate a model via progressive validation.</p> <p>This module also exposes \"tracks\". A track is a predefined combination of a dataset and one or more metrics. This allows a principled manner to compare models with each other. For instance, the <code>RegressionTrack</code> contains several datasets and metrics to evaluate regression models. There is also a bare <code>Track</code> class to implement a custom track. The <code>benchmarks</code> directory at the root of the River repository uses these tracks.</p> <p>Classes</p> <ul> <li>BinaryClassificationTrack</li> <li>MultiClassClassificationTrack</li> <li>RegressionTrack</li> <li>Track</li> </ul> <p>Functions</p> <ul> <li>iter_progressive_val_score</li> <li>progressive_val_score</li> </ul>"},{"location":"api/overview/#facto","title":"facto","text":"<p>Factorization machines.</p> <ul> <li>FFMClassifier</li> <li>FFMRegressor</li> <li>FMClassifier</li> <li>FMRegressor</li> <li>FwFMClassifier</li> <li>FwFMRegressor</li> <li>HOFMClassifier</li> <li>HOFMRegressor</li> </ul>"},{"location":"api/overview/#feature_extraction","title":"feature_extraction","text":"<p>Feature extraction.</p> <p>This module can be used to extract information from raw features. This includes encoding categorical data as well as looking at interactions between existing features. This differs from the <code>preprocessing</code> module, in that the latter's purpose is rather to clean the data so that it may be processed by a particular machine learning algorithm.</p> <ul> <li>Agg</li> <li>BagOfWords</li> <li>PolynomialExtender</li> <li>RBFSampler</li> <li>TFIDF</li> <li>TargetAgg</li> </ul>"},{"location":"api/overview/#feature_selection","title":"feature_selection","text":"<p>Feature selection.</p> <ul> <li>PoissonInclusion</li> <li>SelectKBest</li> <li>VarianceThreshold</li> </ul>"},{"location":"api/overview/#forest","title":"forest","text":"<p>This module implements forest-based classifiers and regressors.</p> <ul> <li>AMFClassifier</li> <li>AMFRegressor</li> <li>ARFClassifier</li> <li>ARFRegressor</li> <li>OXTRegressor</li> </ul>"},{"location":"api/overview/#imblearn","title":"imblearn","text":"<p>Sampling methods.</p> <ul> <li>ChebyshevOverSampler</li> <li>ChebyshevUnderSampler</li> <li>HardSamplingClassifier</li> <li>HardSamplingRegressor</li> <li>RandomOverSampler</li> <li>RandomSampler</li> <li>RandomUnderSampler</li> </ul>"},{"location":"api/overview/#linear_model","title":"linear_model","text":"<p>Linear models.</p> <ul> <li>ALMAClassifier</li> <li>BayesianLinearRegression</li> <li>LinearRegression</li> <li>LogisticRegression</li> <li>PAClassifier</li> <li>PARegressor</li> <li>Perceptron</li> <li>SoftmaxRegression</li> </ul>"},{"location":"api/overview/#base_5","title":"base","text":"<ul> <li>GLM</li> </ul>"},{"location":"api/overview/#metrics","title":"metrics","text":"<p>Evaluation metrics.</p> <p>All the metrics are updated one sample at a time. This way we can track performance of predictive methods over time.</p> <p>Note that all metrics have a <code>revert</code> method, enabling them to be wrapped in <code>utils.Rolling</code>. This allows computirng rolling metrics:</p> <pre><code>from river import metrics, utils\n\ny_true = [True, False, True, True]\ny_pred = [False, False, True, True]\n\nmetric = utils.Rolling(metrics.Accuracy(), window_size=3)\n\nfor yt, yp in zip(y_true, y_pred):\n    print(metric.update(yt, yp))\n</code></pre> <pre><code>Accuracy: 0.00%\nAccuracy: 50.00%\nAccuracy: 66.67%\nAccuracy: 100.00%\n</code></pre> <ul> <li>Accuracy</li> <li>AdjustedMutualInfo</li> <li>AdjustedRand</li> <li>BalancedAccuracy</li> <li>ClassificationReport</li> <li>CohenKappa</li> <li>Completeness</li> <li>ConfusionMatrix</li> <li>CrossEntropy</li> <li>F1</li> <li>FBeta</li> <li>FowlkesMallows</li> <li>GeometricMean</li> <li>Homogeneity</li> <li>Jaccard</li> <li>LogLoss</li> <li>MAE</li> <li>MAPE</li> <li>MCC</li> <li>MSE</li> <li>MacroF1</li> <li>MacroFBeta</li> <li>MacroJaccard</li> <li>MacroPrecision</li> <li>MacroRecall</li> <li>MicroF1</li> <li>MicroFBeta</li> <li>MicroJaccard</li> <li>MicroPrecision</li> <li>MicroRecall</li> <li>MultiFBeta</li> <li>MutualInfo</li> <li>NormalizedMutualInfo</li> <li>Precision</li> <li>R2</li> <li>RMSE</li> <li>RMSLE</li> <li>ROCAUC</li> <li>Rand</li> <li>Recall</li> <li>RollingROCAUC</li> <li>SMAPE</li> <li>Silhouette</li> <li>VBeta</li> <li>WeightedF1</li> <li>WeightedFBeta</li> <li>WeightedJaccard</li> <li>WeightedPrecision</li> <li>WeightedRecall</li> </ul>"},{"location":"api/overview/#base_6","title":"base","text":"<ul> <li>BinaryMetric</li> <li>ClassificationMetric</li> <li>Metric</li> <li>Metrics</li> <li>MultiClassMetric</li> <li>RegressionMetric</li> <li>WrapperMetric</li> </ul>"},{"location":"api/overview/#multioutput","title":"multioutput","text":"<p>Metrics for multi-output learning.</p> <ul> <li>ExactMatch</li> <li>MacroAverage</li> <li>MicroAverage</li> <li>MultiLabelConfusionMatrix</li> <li>PerOutput</li> <li>SampleAverage</li> </ul>"},{"location":"api/overview/#base_7","title":"base","text":"<ul> <li>MultiOutputClassificationMetric</li> <li>MultiOutputRegressionMetric</li> </ul>"},{"location":"api/overview/#misc","title":"misc","text":"<p>Miscellaneous.</p> <p>This module essentially regroups some implementations that have nowhere else to go.</p> <ul> <li>SDFT</li> <li>Skyline</li> </ul>"},{"location":"api/overview/#model_selection","title":"model_selection","text":"<p>Model selection.</p> <p>This module regroups a variety of methods that may be used for performing model selection. An model selector is provided with a list of models. These are called \"experts\" in the expert learning literature. The model selector's goal is to perform at least as well as the best model. Indeed, initially, the best model is not known. The performance of each model becomes more apparent as time goes by. Different strategies are possible, each one offering a different tradeoff in terms of accuracy and computational performance.</p> <p>Model selection can be used for tuning the hyperparameters of a model. This may be done by creating a copy of the model for each set of hyperparameters, and treating each copy as a separate model. The <code>utils.expand_param_grid</code> function can be used for this purpose.</p> <ul> <li>BanditClassifier</li> <li>BanditRegressor</li> <li>GreedyRegressor</li> <li>SuccessiveHalvingClassifier</li> <li>SuccessiveHalvingRegressor</li> </ul>"},{"location":"api/overview/#base_8","title":"base","text":"<ul> <li>ModelSelectionClassifier</li> <li>ModelSelectionRegressor</li> </ul>"},{"location":"api/overview/#multiclass","title":"multiclass","text":"<p>Multi-class classification.</p> <ul> <li>OneVsOneClassifier</li> <li>OneVsRestClassifier</li> <li>OutputCodeClassifier</li> </ul>"},{"location":"api/overview/#multioutput_1","title":"multioutput","text":"<p>Multi-output models.</p> <ul> <li>ClassifierChain</li> <li>MonteCarloClassifierChain</li> <li>MultiClassEncoder</li> <li>ProbabilisticClassifierChain</li> <li>RegressorChain</li> </ul>"},{"location":"api/overview/#naive_bayes","title":"naive_bayes","text":"<p>Naive Bayes algorithms.</p> <ul> <li>BernoulliNB</li> <li>ComplementNB</li> <li>GaussianNB</li> <li>MultinomialNB</li> </ul>"},{"location":"api/overview/#neighbors","title":"neighbors","text":"<p>Neighbors-based learning.</p> <p>Also known as lazy methods. In these methods, generalisation of the training data is delayed until a query is received.</p> <ul> <li>KNNClassifier</li> <li>KNNRegressor</li> <li>LazySearch</li> <li>SWINN</li> </ul>"},{"location":"api/overview/#neural_net","title":"neural_net","text":"<p>Neural networks.</p> <ul> <li>MLPRegressor</li> </ul>"},{"location":"api/overview/#activations","title":"activations","text":"<ul> <li>Identity</li> <li>ReLU</li> <li>Sigmoid</li> </ul>"},{"location":"api/overview/#optim","title":"optim","text":"<p>Stochastic optimization.</p> <ul> <li>AMSGrad</li> <li>AdaBound</li> <li>AdaDelta</li> <li>AdaGrad</li> <li>AdaMax</li> <li>Adam</li> <li>Averager</li> <li>FTRLProximal</li> <li>Momentum</li> <li>Nadam</li> <li>NesterovMomentum</li> <li>RMSProp</li> <li>SGD</li> </ul>"},{"location":"api/overview/#base_9","title":"base","text":"<ul> <li>Initializer</li> <li>Loss</li> <li>Optimizer</li> <li>Scheduler</li> </ul>"},{"location":"api/overview/#initializers","title":"initializers","text":"<p>Weight initializers.</p> <ul> <li>Constant</li> <li>Normal</li> <li>Zeros</li> </ul>"},{"location":"api/overview/#losses","title":"losses","text":"<p>Loss functions.</p> <p>Each loss function is intended to work with both single values as well as numpy vectors.</p> <ul> <li>Absolute</li> <li>BinaryFocalLoss</li> <li>BinaryLoss</li> <li>Cauchy</li> <li>CrossEntropy</li> <li>EpsilonInsensitiveHinge</li> <li>Hinge</li> <li>Huber</li> <li>Log</li> <li>MultiClassLoss</li> <li>Poisson</li> <li>Quantile</li> <li>RegressionLoss</li> <li>Squared</li> </ul>"},{"location":"api/overview/#schedulers","title":"schedulers","text":"<p>Learning rate schedulers.</p> <ul> <li>Constant</li> <li>InverseScaling</li> <li>Optimal</li> </ul>"},{"location":"api/overview/#preprocessing","title":"preprocessing","text":"<p>Feature preprocessing.</p> <p>The purpose of this module is to modify an existing set of features so that they can be processed by a machine learning algorithm. This may be done by scaling numeric parts of the data or by one-hot encoding categorical features. The difference with the <code>feature_extraction</code> module is that the latter extracts new information from the data</p> <ul> <li>AdaptiveStandardScaler</li> <li>Binarizer</li> <li>FeatureHasher</li> <li>GaussianRandomProjector</li> <li>LDA</li> <li>MaxAbsScaler</li> <li>MinMaxScaler</li> <li>Normalizer</li> <li>OneHotEncoder</li> <li>OrdinalEncoder</li> <li>PredClipper</li> <li>PreviousImputer</li> <li>RobustScaler</li> <li>SparseRandomProjector</li> <li>StandardScaler</li> <li>StatImputer</li> <li>TargetMinMaxScaler</li> <li>TargetStandardScaler</li> </ul>"},{"location":"api/overview/#proba","title":"proba","text":"<p>Probability distributions.</p> <ul> <li>Beta</li> <li>Gaussian</li> <li>Multinomial</li> <li>MultivariateGaussian</li> </ul>"},{"location":"api/overview/#base_10","title":"base","text":"<ul> <li>BinaryDistribution</li> <li>ContinuousDistribution</li> <li>DiscreteDistribution</li> <li>Distribution</li> </ul>"},{"location":"api/overview/#reco","title":"reco","text":"<p>Recommender systems module.</p> <p>Recommender systems (recsys for short) is a large topic. This module is far from comprehensive. It simply provides models which can contribute towards building a recommender system.</p> <p>A typical recommender system is made up of a retrieval phase, followed by a ranking phase. The output of the retrieval phase is a shortlist of the catalogue of items. The items in the shortlist are then usually ranked according to the expected preference the user will have for each item. This module focuses on the ranking phase.</p> <p>Models which inherit from the <code>Ranker</code> class have a <code>rank</code> method. This allows sorting a set of items for a given user. Each model also has a <code>learn_one(user, item, y, context)</code> which allows learning user preferences. The <code>y</code> parameter is a reward value, the nature of which depends is specific to each and every recommendation task. Typically the reward is a number or a boolean value. It is up to the user to determine how to translate a user session into training data.</p> <ul> <li>Baseline</li> <li>BiasedMF</li> <li>FunkMF</li> <li>RandomNormal</li> </ul>"},{"location":"api/overview/#base_11","title":"base","text":"<ul> <li>Ranker</li> </ul>"},{"location":"api/overview/#rules","title":"rules","text":"<p>Decision rules-based algorithms.</p> <ul> <li>AMRules</li> </ul>"},{"location":"api/overview/#sketch","title":"sketch","text":"<p>Data containers and collections for sequential data.</p> <p>This module has summary and sketch structures that operate with constrained amounts of memory and processing time.</p> <ul> <li>Counter</li> <li>HeavyHitters</li> <li>Histogram</li> <li>Set</li> </ul>"},{"location":"api/overview/#stats","title":"stats","text":"<p>Running statistics</p> <ul> <li>AbsMax</li> <li>AutoCorr</li> <li>BayesianMean</li> <li>Count</li> <li>Cov</li> <li>EWMean</li> <li>EWVar</li> <li>Entropy</li> <li>IQR</li> <li>KolmogorovSmirnov</li> <li>Kurtosis</li> <li>Link</li> <li>MAD</li> <li>Max</li> <li>Mean</li> <li>Min</li> <li>Mode</li> <li>NUnique</li> <li>PeakToPeak</li> <li>PearsonCorr</li> <li>Quantile</li> <li>RollingAbsMax</li> <li>RollingIQR</li> <li>RollingMax</li> <li>RollingMin</li> <li>RollingMode</li> <li>RollingPeakToPeak</li> <li>RollingQuantile</li> <li>SEM</li> <li>Shift</li> <li>Skew</li> <li>Sum</li> <li>Var</li> </ul>"},{"location":"api/overview/#base_12","title":"base","text":"<ul> <li>Bivariate</li> <li>Univariate</li> </ul>"},{"location":"api/overview/#stream","title":"stream","text":"<p>Streaming utilities.</p> <p>The module includes tools to iterate over data streams.</p> <p>Classes</p> <ul> <li>Cache</li> <li>TwitchChatStream</li> <li>TwitterLiveStream</li> </ul> <p>Functions</p> <ul> <li>iter_arff</li> <li>iter_array</li> <li>iter_csv</li> <li>iter_libsvm</li> <li>iter_pandas</li> <li>iter_polars</li> <li>iter_sklearn_dataset</li> <li>iter_sql</li> <li>shuffle</li> <li>simulate_qa</li> </ul>"},{"location":"api/overview/#time_series","title":"time_series","text":"<p>Time series forecasting.</p> <p>Classes</p> <ul> <li>ForecastingMetric</li> <li>HoltWinters</li> <li>HorizonAggMetric</li> <li>HorizonMetric</li> <li>SNARIMAX</li> </ul> <p>Functions</p> <ul> <li>evaluate</li> <li>iter_evaluate</li> </ul>"},{"location":"api/overview/#base_13","title":"base","text":"<ul> <li>Forecaster</li> </ul>"},{"location":"api/overview/#tree","title":"tree","text":"<p>This module implements incremental Decision Tree (iDT) algorithms for handling classification and regression tasks.</p> <p>Each family of iDT will be presented in a dedicated section.</p> <p>At any moment, iDT might face situations where an input feature previously used to make a split decision is missing in an incoming sample. In this case, the most traversed path is selected to pass down the instance. Moreover, in the case of nominal features, if a new category arises and the feature is used in a decision node, a new branch is created to accommodate the new value.</p> <p>1. Hoeffding Trees</p> <p>This family of iDT algorithms use the Hoeffding Bound to determine whether or not the incrementally computed best split candidates would be equivalent to the ones obtained in a batch-processing fashion.</p> <p>All the available Hoeffding Tree (HT) implementation share some common functionalities:</p> <ul> <li> <p>Set the maximum tree depth allowed (<code>max_depth</code>).</p> </li> <li> <p>Handle Active and Inactive nodes: Active learning nodes update their own internal state to improve predictions and monitor input features to perform split attempts. Inactive learning nodes do not update their internal state and only keep the predictors; they are used to save memory in the tree (<code>max_size</code>).</p> </li> <li> <p>Enable/disable memory management.</p> </li> <li> <p>Define strategies to sort leaves according to how likely they are going to be split. This enables deactivating non-promising leaves to save memory.</p> </li> <li> <p>Disabling \u2018poor\u2019 attributes to save memory and speed up tree construction. A poor attribute is an input feature whose split merit is much smaller than the current best candidate. Once a feature is disabled, the tree stops saving statistics necessary to split such a feature.</p> </li> <li> <p>Define properties to access leaf prediction strategies, split criteria, and other relevant characteristics.</p> </li> </ul> <p>2. Stochastic Gradient Trees</p> <p>Stochastic Gradient Trees (SGT) directly optimize a loss function, rather than relying on split heuristics to guide the tree growth. F-tests are performed do decide whether a leaf should be expanded or its prediction value should be updated.</p> <p>SGTs can deal with binary classification and single-target regression. They also support dynamic and static feature quantizers to deal with numerical inputs.</p> <ul> <li>ExtremelyFastDecisionTreeClassifier</li> <li>HoeffdingAdaptiveTreeClassifier</li> <li>HoeffdingAdaptiveTreeRegressor</li> <li>HoeffdingTreeClassifier</li> <li>HoeffdingTreeRegressor</li> <li>SGTClassifier</li> <li>SGTRegressor</li> <li>iSOUPTreeRegressor</li> </ul>"},{"location":"api/overview/#base_14","title":"base","text":"<p>This module defines generic branch and leaf implementations. These should be used in River by each tree-based model. Using these classes makes the code more DRY. The only exception for not doing so would be for performance, whereby a tree-based model uses a bespoke implementation.</p> <p>This module defines a bunch of methods to ease the manipulation and diagnostic of trees. Its intention is to provide utilities for walking over a tree and visualizing it.</p> <ul> <li>Branch</li> <li>Leaf</li> </ul>"},{"location":"api/overview/#splitter","title":"splitter","text":"<p>This module implements the Attribute Observers (AO) (or tree splitters) that are used by the Hoeffding Trees (HT). It also implements the feature quantizers (FQ) used by Stochastic Gradient Trees (SGT). AOs are a core aspect of the HTs construction, and might represent one of the major bottlenecks when building the trees. The same holds for SGTs and FQs. The correct choice and setup of a splitter might result in significant differences in the running time and memory usage of the incremental decision trees.</p> <p>AOs for classification and regression trees can be differentiated by using the property <code>is_target_class</code> (<code>True</code> for splitters designed to classification tasks). An error will be raised if one tries to use a classification splitter in a regression tree and vice-versa. Lastly, AOs cannot be used in SGT and FQs cannot be used in Hoeffding Trees. So, care must be taken when choosing the correct feature splitter.</p> <ul> <li>DynamicQuantizer</li> <li>EBSTSplitter</li> <li>ExhaustiveSplitter</li> <li>GaussianSplitter</li> <li>HistogramSplitter</li> <li>QOSplitter</li> <li>Quantizer</li> <li>Splitter</li> <li>StaticQuantizer</li> <li>TEBSTSplitter</li> </ul>"},{"location":"api/overview/#utils","title":"utils","text":"<p>Shared utility classes and functions</p> <p>Classes</p> <ul> <li>Rolling</li> <li>SortedWindow</li> <li>TimeRolling</li> <li>VectorDict</li> </ul> <p>Functions</p> <ul> <li>expand_param_grid</li> <li>log_method_calls</li> </ul>"},{"location":"api/overview/#math","title":"math","text":"<p>Mathematical utility functions (intended for internal purposes).</p> <p>A lot of this is experimental and has a high probability of changing in the future.</p> <ul> <li>argmax</li> <li>chain_dot</li> <li>clamp</li> <li>dot</li> <li>dotvecmat</li> <li>log_sum_2_exp</li> <li>matmul2d</li> <li>minkowski_distance</li> <li>norm</li> <li>outer</li> <li>prod</li> <li>sherman_morrison</li> <li>sigmoid</li> <li>sign</li> <li>softmax</li> <li>woodbury_matrix</li> </ul>"},{"location":"api/overview/#norm","title":"norm","text":"<ul> <li>normalize_values_in_dict</li> <li>scale_values_in_dict</li> </ul>"},{"location":"api/overview/#pretty","title":"pretty","text":"<p>Helper functions for making things readable by humans.</p> <ul> <li>humanize_bytes</li> <li>print_table</li> </ul>"},{"location":"api/overview/#random","title":"random","text":"<ul> <li>exponential</li> <li>poisson</li> </ul>"},{"location":"api/active/EntropySampler/","title":"EntropySampler","text":"<p>Active learning classifier based on entropy measures.</p> <p>The entropy sampler selects samples for labeling based on the entropy of the prediction. The higher the entropy, the more likely the sample will be selected for labeling. The entropy measure is normalized to [0, 1] and then raised to the power of the discount factor.</p>"},{"location":"api/active/EntropySampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to wrap.</p> </li> <li> <p>discount_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>3</code></p> <p>The discount factor to apply to the entropy measure. A value of 1 won't affect the entropy. The higher the discount factor, the more the entropy will be discounted, and the less likely samples will be selected for labeling. A value of 0 will select all samples for labeling. The discount factor is thus a way to control how many samples are selected for labeling.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/active/EntropySampler/#examples","title":"Examples","text":"<p><pre><code>from river import active\nfrom river import datasets\nfrom river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\n\ndataset = datasets.SMSSpam()\nmetric = metrics.Accuracy()\nmodel = (\n    feature_extraction.TFIDF(on='body') |\n    linear_model.LogisticRegression()\n)\nmodel = active.EntropySampler(model, seed=42)\n\nn_samples_used = 0\nfor x, y in dataset:\n    y_pred, ask = model.predict_one(x)\n    metric.update(y, y_pred)\n    if ask:\n        n_samples_used += 1\n        model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>Accuracy: 86.60%\n</code></pre></p> <p><pre><code>dataset.n_samples, n_samples_used\n</code></pre> <pre><code>(5574, 1921)\n</code></pre></p> <p><pre><code>print(f\"{n_samples_used / dataset.n_samples:.2%}\")\n</code></pre> <pre><code>34.46%\n</code></pre></p>"},{"location":"api/active/EntropySampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/active/base/ActiveLearningClassifier/","title":"ActiveLearningClassifier","text":"<p>Base class for active learning classifiers.</p>"},{"location":"api/active/base/ActiveLearningClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to wrap.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/active/base/ActiveLearningClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for <code>x</code> and indicate whether a label is needed.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/anomaly/GaussianScorer/","title":"GaussianScorer","text":"<p>Univariate Gaussian anomaly detector.</p> <p>This is a supervised anomaly detector. It fits a Gaussian distribution to the target values. The anomaly score is then computed as so: </p> \\[score = 2 \\mid CDF(y) - 0.5 \\mid\\] <p>This makes it so that the anomaly score is between 0 and 1.</p>"},{"location":"api/anomaly/GaussianScorer/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Default \u2192 <code>None</code></p> <p>Set this to fit the Gaussian distribution over a window of recent values.</p> </li> <li> <p>grace_period</p> <p>Default \u2192 <code>100</code></p> <p>Number of samples before which a 0 is always returned. This is handy because the Gaussian distribution needs time to stabilize, and will likely produce overly high anomaly score for the first samples.</p> </li> </ul>"},{"location":"api/anomaly/GaussianScorer/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import anomaly\n\nrng = random.Random(42)\ndetector = anomaly.GaussianScorer()\n\nfor y in (rng.gauss(0, 1) for _ in range(100)):\n    detector.learn_one(None, y)\n\ndetector.score_one(None, -3)\n</code></pre> <pre><code>0.999477...\n</code></pre></p> <p><pre><code>detector.score_one(None, 3)\n</code></pre> <pre><code>0.999153...\n</code></pre></p> <p><pre><code>detector.score_one(None, 0)\n</code></pre> <pre><code>0.052665...\n</code></pre></p> <p><pre><code>detector.score_one(None, 0.5)\n</code></pre> <pre><code>0.383717...\n</code></pre></p>"},{"location":"api/anomaly/GaussianScorer/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/HalfSpaceTrees/","title":"HalfSpaceTrees","text":"<p>Half-Space Trees (HST).</p> <p>Half-space trees are an online variant of isolation forests. They work well when anomalies are spread out. However, they do not work well if anomalies are packed together in windows. </p> <p>By default, this implementation assumes that each feature has values that are comprised between 0 and 1. If this isn't the case, then you can manually specify the limits via the <code>limits</code> argument. If you do not know the limits in advance, then you can use a <code>preprocessing.MinMaxScaler</code> as an initial preprocessing step. </p> <p>The current implementation builds the trees the first time the <code>learn_one</code> method is called. Therefore, the first <code>learn_one</code> call might be slow, whereas subsequent calls will be very fast in comparison. In general, the computation time of both <code>learn_one</code> and <code>score_one</code> scales linearly with the number of trees, and exponentially with the height of each tree. </p> <p>Note that high scores indicate anomalies, whereas low scores indicate normal observations.</p>"},{"location":"api/anomaly/HalfSpaceTrees/#parameters","title":"Parameters","text":"<ul> <li> <p>n_trees</p> <p>Default \u2192 <code>10</code></p> <p>Number of trees to use.</p> </li> <li> <p>height</p> <p>Default \u2192 <code>8</code></p> <p>Height of each tree. Note that a tree of height <code>h</code> is made up of <code>h + 1</code> levels and therefore contains <code>2 ** (h + 1) - 1</code> nodes.</p> </li> <li> <p>window_size</p> <p>Default \u2192 <code>250</code></p> <p>Number of observations to use for calculating the mass at each node in each tree.</p> </li> <li> <p>limits</p> <p>Type \u2192 dict[base.typing.FeatureName, tuple[float, float]] | None</p> <p>Default \u2192 <code>None</code></p> <p>Specifies the range of each feature. By default each feature is assumed to be in range <code>[0, 1]</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number seed.</p> </li> </ul>"},{"location":"api/anomaly/HalfSpaceTrees/#attributes","title":"Attributes","text":"<ul> <li> <p>size_limit</p> <p>This is the threshold under which the node search stops during the scoring phase.  The value .1 is a magic constant indicated in the original paper.</p> </li> </ul>"},{"location":"api/anomaly/HalfSpaceTrees/#examples","title":"Examples","text":"<p><pre><code>from river import anomaly\n\nX = [0.5, 0.45, 0.43, 0.44, 0.445, 0.45, 0.0]\nhst = anomaly.HalfSpaceTrees(\n    n_trees=5,\n    height=3,\n    window_size=3,\n    seed=42\n)\n\nfor x in X[:3]:\n    hst.learn_one({'x': x})  # Warming up\n\nfor x in X:\n    features = {'x': x}\n    hst.learn_one(features)\n    print(f'Anomaly score for x={x:.3f}: {hst.score_one(features):.3f}')\n</code></pre> <pre><code>Anomaly score for x=0.500: 0.107\nAnomaly score for x=0.450: 0.071\nAnomaly score for x=0.430: 0.107\nAnomaly score for x=0.440: 0.107\nAnomaly score for x=0.445: 0.107\nAnomaly score for x=0.450: 0.071\nAnomaly score for x=0.000: 0.853\n</code></pre></p> <p>The feature values are all comprised between 0 and 1. This is what is assumed by the model by default. In the following example, we construct a pipeline that scales the data online and ensures that the values of each feature are comprised between 0 and 1.</p> <p><pre><code>from river import compose\nfrom river import datasets\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.MinMaxScaler(),\n    anomaly.HalfSpaceTrees(seed=42)\n)\n\nauc = metrics.ROCAUC()\n\nfor x, y in datasets.CreditCard().take(2500):\n    score = model.score_one(x)\n    model.learn_one(x)\n    auc.update(y, score)\n\nauc\n</code></pre> <pre><code>ROCAUC: 91.15%\n</code></pre></p> <p>You can also use the <code>evaluate.progressive_val_score</code> function to evaluate the model on a data stream.</p> <p><pre><code>from river import evaluate\n\nmodel = model.clone()\n\nevaluate.progressive_val_score(\n    dataset=datasets.CreditCard().take(2500),\n    model=model,\n    metric=metrics.ROCAUC(),\n    print_every=1000\n)\n</code></pre> <pre><code>[1,000] ROCAUC: 88.43%\n[2,000] ROCAUC: 89.28%\n[2,500] ROCAUC: 91.15%\nROCAUC: 91.15%\n</code></pre></p>"},{"location":"api/anomaly/HalfSpaceTrees/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p> <ol> <li> <p>Tan, S.C., Ting, K.M. and Liu, T.F., 2011, June. Fast anomaly detection for streaming data. In Twenty-Second International Joint Conference on Artificial Intelligence. \u21a9</p> </li> </ol>"},{"location":"api/anomaly/LocalOutlierFactor/","title":"LocalOutlierFactor","text":"<p>Incremental Local Outlier Factor (Incremental LOF).</p> <p>The Incremental Local Outlier Factor (ILOF) is an online version of the Local Outlier Factor (LOF), proposed by Pokrajac et al. (2017),  and is used to identify outliers based on density of local neighbors. </p> <p>The algorithm take into account the following elements:     - <code>NewPoints</code>: new points;</p> <pre><code>- `kNN(p)`: the k-nearest neighboors of `p` (the k-closest points to `p`);\n\n- `RkNN(p)`: the reverse-k-nearest neighboors of `p` (points that have `p` as one of their neighboors);\n\n- `set_upd_lrd`: Set of points that need to have the local reachability distance updated;\n\n- `set_upd_lof`: Set of points that need to have the local outlier factor updated.\n</code></pre> <p>This current implementation within <code>River</code>, based on the original one in the paper, follows the following steps:     1) Insert new data points (<code>NewPoints</code>) and calculate its distance to existing points;     2) Update the nreaest neighboors and reverse nearest neighboors of all the points;     3) Define sets of affected points that required updates;     4) Calculate the reachability-distance from new point to neighboors (<code>NewPoints</code> -&gt; <code>kNN(NewPoints)</code>)        and from rev-neighboors to new point (<code>RkNN(NewPoints)</code> -&gt; <code>NewPoints</code>);     5) Update the reachability-distance for affected points: <code>RkNN(RkNN(NewPoints))</code> -&gt; <code>RkNN(NewPoints)</code>     6) Update local reachability distance of affected points: <code>lrd(set_upd_lrd)</code>;     7) Update local outlier factor: <code>lof(set_upd_lof)</code>. </p> <p>The incremental LOF algorithm is expected to provide equivalent detection performance as the iterated static LOF algroithm (applied after insertion of each data record), while requiring significantly less computational time. Moreover, the insertion of a new data point as well as deletion of an old data point influence only a limited number of their closest neighbors, which means that the number of updates per such insertion/deletion does not depend on the total number of instances learned/in the data set.</p>"},{"location":"api/anomaly/LocalOutlierFactor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_neighbors</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of nearest neighbors to use for density estimation.</p> </li> <li> <p>distance_func</p> <p>Type \u2192 DistanceFunc | None</p> <p>Default \u2192 <code>None</code></p> <p>Distance function to be used. By default, the Euclidean distance is used.</p> </li> </ul>"},{"location":"api/anomaly/LocalOutlierFactor/#attributes","title":"Attributes","text":"<ul> <li> <p>x_list</p> <p>A list of stored observations.</p> </li> <li> <p>x_batch</p> <p>A buffer to hold incoming observations until it's time to update the model.</p> </li> <li> <p>x_scores</p> <p>A buffer to hold incoming observations until it's time to score them.</p> </li> <li> <p>dist_dict</p> <p>A dictionary to hold distances between observations.</p> </li> <li> <p>neighborhoods</p> <p>A dictionary to hold neighborhoods for each observation.</p> </li> <li> <p>rev_neighborhoods</p> <p>A dictionary to hold reverse neighborhoods for each observation.</p> </li> <li> <p>k_dist</p> <p>A dictionary to hold k-distances for each observation.</p> </li> <li> <p>reach_dist</p> <p>A dictionary to hold reachability distances for each observation.</p> </li> <li> <p>lof</p> <p>A dictionary to hold Local Outlier Factors for each observation.</p> </li> <li> <p>local_reach</p> <p>A dictionary to hold local reachability distances for each observation.</p> </li> </ul>"},{"location":"api/anomaly/LocalOutlierFactor/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import anomaly\nfrom river import datasets\n\ncc_df = pd.DataFrame(datasets.CreditCard())\n\nlof = anomaly.LocalOutlierFactor(n_neighbors=20)\n\nfor x, _ in datasets.CreditCard().take(200):\n    lof.learn_one(x)\n\nlof.learn_many(cc_df[201:401])\n\nscores = []\nfor x in cc_df[0][401:406]:\n    scores.append(lof.score_one(x))\n\n[round(score, 3) for score in scores]\n</code></pre> <pre><code>[1.802, 1.936, 1.566, 1.181, 1.272]\n</code></pre></p> <p><pre><code>X = [0.5, 0.45, 0.43, 0.44, 0.445, 0.45, 0.0]\nlof = anomaly.LocalOutlierFactor()\n\nfor x in X[:3]:\n    lof.learn_one({'x': x})  # Warming up\n\nfor x in X:\n    features = {'x': x}\n    print(\n        f'Anomaly score for x={x:.3f}: {lof.score_one(features):.3f}')\n    lof.learn_one(features)\n</code></pre> <pre><code>Anomaly score for x=0.500: 0.000\nAnomaly score for x=0.450: 0.000\nAnomaly score for x=0.430: 0.000\nAnomaly score for x=0.440: 1.020\nAnomaly score for x=0.445: 1.032\nAnomaly score for x=0.450: 0.000\nAnomaly score for x=0.000: 0.980\n</code></pre></p>"},{"location":"api/anomaly/LocalOutlierFactor/#methods","title":"Methods","text":"learn learn_many learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p> David Pokrajac, Aleksandar Lazarevic, and Longin Jan Latecki (2007). Incremental Local Outlier Detection for Data Streams. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). 504-515. DOI: 10.1109/CIDM.2007.368917.</p>"},{"location":"api/anomaly/OneClassSVM/","title":"OneClassSVM","text":"<p>One-class SVM for anomaly detection.</p> <p>This is a stochastic implementation of the one-class SVM algorithm, and will not exactly match its batch formulation. </p> <p>It is encouraged to scale the data upstream with <code>preprocessing.StandardScaler</code>, as well as use <code>feature_extraction.RBFSampler</code> to capture non-linearities.</p>"},{"location":"api/anomaly/OneClassSVM/#parameters","title":"Parameters","text":"<ul> <li> <p>nu</p> <p>Default \u2192 <code>0.1</code></p> <p>An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. You can think of it as the expected fraction of anomalies.</p> </li> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.base.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/anomaly/OneClassSVM/#attributes","title":"Attributes","text":"<ul> <li>weights</li> </ul>"},{"location":"api/anomaly/OneClassSVM/#examples","title":"Examples","text":"<p><pre><code>from river import anomaly\nfrom river import compose\nfrom river import datasets\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = anomaly.QuantileFilter(\n    anomaly.OneClassSVM(nu=0.2),\n    q=0.995\n)\n\nauc = metrics.ROCAUC()\n\nfor x, y in datasets.CreditCard().take(2500):\n    score = model.score_one(x)\n    is_anomaly = model.classify(score)\n    model.learn_one(x)\n    auc.update(y, is_anomaly)\n\nauc\n</code></pre> <pre><code>ROCAUC: 74.68%\n</code></pre></p> <p>You can also use the <code>evaluate.progressive_val_score</code> function to evaluate the model on a data stream.</p> <p><pre><code>from river import evaluate\n\nmodel = model.clone()\n\nevaluate.progressive_val_score(\n    dataset=datasets.CreditCard().take(2500),\n    model=model,\n    metric=metrics.ROCAUC(),\n    print_every=1000\n)\n</code></pre> <pre><code>[1,000] ROCAUC: 74.40%\n[2,000] ROCAUC: 74.60%\n[2,500] ROCAUC: 74.68%\nROCAUC: 74.68%\n</code></pre></p>"},{"location":"api/anomaly/OneClassSVM/#methods","title":"Methods","text":"learn_many learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/PredictiveAnomalyDetection/","title":"PredictiveAnomalyDetection","text":"<p>Predictive Anomaly Detection.</p> <p>This semi-supervised technique to anomaly detection employs a predictive model to learn the normal behavior of a dataset. It forecasts future data points and compares these predictions with actual values to determine anomalies. An anomaly score is calculated based on the deviation of the prediction from the actual value, with higher scores indicating a higher probability of an anomaly. </p> <p>The actual anomaly score is calculated by comparing the squared-error to a dynamic threshold. If the error is larger than this threshold, the score will be 1.0; else, the score will be linearly distributed within the range (0.0, 1.0), with a higher score indicating a higher squared error compared to the threshold.</p>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#parameters","title":"Parameters","text":"<ul> <li> <p>predictive_model</p> <p>Type \u2192 base.Estimator | None</p> <p>Default \u2192 <code>None</code></p> <p>The underlying model that learns the normal behavior of the data and makes predictions on future behavior. This can be an estimator of any type, depending on the type of problem (e.g. some Forecaster for Time-Series Data).</p> </li> <li> <p>horizon</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>When a Forecaster is used as a predictive model, this is the horizon of its forecasts.</p> </li> <li> <p>n_std</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>3.0</code></p> <p>Number of Standard Deviations to calculate the threshold. A larger number of standard deviation will result in a higher threshold, resulting in the model being less sensitive.</p> </li> <li> <p>warmup_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Duration for the model to warm up. Since the model starts with zero knowledge, the first instances will have very high anomaly scores, resulting in bad predictions (or high error). As such, a warm-up period is necessary to discard the first seen instances. While the model is within the warm-up period, no score will be calculated and the score_one method will return 0.0.</p> </li> </ul>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#attributes","title":"Attributes","text":"<ul> <li> <p>dynamic_mae (stats.Mean)</p> <p>The running mean of the (squared) errors from the predictions of the model to update the dynamic threshold.</p> </li> <li> <p>dynamic_se_variance (stats.Var)</p> <p>The running variance of the (squared) errors from the predictions of the model to update the dynamic threshold.</p> </li> <li> <p>iter (int)</p> <p>The number of iterations (data points) passed.</p> </li> </ul>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import time_series\nfrom river import anomaly\nfrom river import preprocessing\nfrom river import linear_model\nfrom river import optim\n\nperiod = 12\npredictive_model = time_series.SNARIMAX(\n    p=period,\n    d=1,\n    q=period,\n    m=period,\n    sd=1,\n    regressor=(\n        preprocessing.StandardScaler()\n        | linear_model.LinearRegression(\n            optimizer=optim.SGD(0.005),\n        )\n    ),\n)\n\nPAD = anomaly.PredictiveAnomalyDetection(\n    predictive_model,\n    horizon=1,\n    n_std=3.5,\n    warmup_period=15\n)\n\nscores = []\n\nfor t, (x, y) in enumerate(datasets.AirlinePassengers()):\n    score = PAD.score_one(None, y)\n    PAD = PAD.learn_one(None, y)\n    scores.append(score)\n\nprint(scores[-1])\n</code></pre> <pre><code>0.05329236123455621\n</code></pre></p>"},{"location":"api/anomaly/PredictiveAnomalyDetection/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict | None' </li> <li>y     \u2014 'base.typing.Target | float' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p> <ol> <li> <p>Laptev N, Amizadeh S, Flint I. Generic and scalable framework for Automated Time-series Anomaly Detection. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015. doi:10.1145/2783258.2788611.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/anomaly/QuantileFilter/","title":"QuantileFilter","text":"<p>Threshold anomaly filter.</p>"},{"location":"api/anomaly/QuantileFilter/#parameters","title":"Parameters","text":"<ul> <li> <p>anomaly_detector</p> <p>An anomaly detector.</p> </li> <li> <p>q</p> <p>Type \u2192 float</p> <p>The quantile level above which to classify an anomaly score as anomalous.</p> </li> <li> <p>protect_anomaly_detector</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the anomaly detector should be updated when the anomaly score is anomalous. If the data contains sporadic anomalies, then the anomaly detector should likely not be updated. Indeed, if it learns the anomaly score, then it will slowly start to consider anomalous anomaly scores as normal. This might be desirable, for instance in the case of drift.</p> </li> </ul>"},{"location":"api/anomaly/QuantileFilter/#attributes","title":"Attributes","text":"<ul> <li>q</li> </ul>"},{"location":"api/anomaly/QuantileFilter/#examples","title":"Examples","text":"<p><pre><code>from river import anomaly\nfrom river import compose\nfrom river import datasets\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.MinMaxScaler(),\n    anomaly.QuantileFilter(\n        anomaly.HalfSpaceTrees(seed=42),\n        q=0.95\n    )\n)\n\nreport = metrics.ClassificationReport()\n\nfor x, y in datasets.CreditCard().take(2000):\n    score = model.score_one(x)\n    is_anomaly = model['QuantileFilter'].classify(score)\n    model.learn_one(x)\n    report.update(y, is_anomaly)\n\nreport\n</code></pre> <pre><code>               Precision   Recall   F1       Support\n&lt;BLANKLINE&gt;\n       0      99.95%   94.49%   97.14%      1998\n       1       0.90%   50.00%    1.77%         2\n&lt;BLANKLINE&gt;\n   Macro      50.42%   72.25%   49.46%\n   Micro      94.45%   94.45%   94.45%\nWeighted      99.85%   94.45%   97.05%\n&lt;BLANKLINE&gt;\n                 94.45% accuracy\n</code></pre></p>"},{"location":"api/anomaly/QuantileFilter/#methods","title":"Methods","text":"classify <p>Classify an anomaly score as anomalous or not.</p> <p>Parameters</p> <ul> <li>score     \u2014 'float' </li> </ul> <p>Returns</p> <p>bool:     A boolean value indicating whether the anomaly score is anomalous or not.</p> <p></p> learn_one <p>Update the anomaly filter and the underlying anomaly detector.</p> <p>Parameters</p> <ul> <li>args </li> <li>learn_kwargs </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/StandardAbsoluteDeviation/","title":"StandardAbsoluteDeviation","text":"<p>Standard Absolute Deviation (SAD).</p> <p>SAD is the model that calculates the anomaly score by using the deviation from the mean/median, divided by the standard deviation of all the points seen within the data stream. The idea of this model is based on the \\(3 \\times \\sigma\\) rule described in <sup>1</sup>. </p> <p>This implementation is adapted from the implementation within PySAD (Python Streaming Anomaly Detection) <sup>2</sup>. </p> <p>As a univariate anomaly detection algorithm, this implementation is adapted to <code>River</code> in a similar way as that of the <code>GaussianScorer</code> algorithm, with the variable taken into the account at the learning phase and scoring phase under variable <code>y</code>, ignoring <code>x</code>.</p>"},{"location":"api/anomaly/StandardAbsoluteDeviation/#parameters","title":"Parameters","text":"<ul> <li> <p>sub_stat</p> <p>Type \u2192 stats.base.Univariate | None</p> <p>Default \u2192 <code>None</code></p> <p>The statistic to be subtracted, then divided by the standard deviation for scoring. Defaults to <code>stats.Mean</code>()`.</p> </li> </ul>"},{"location":"api/anomaly/StandardAbsoluteDeviation/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import anomaly\nfrom river import stats\nfrom river import stream\n\nrng = random.Random(42)\n\nmodel = anomaly.StandardAbsoluteDeviation(sub_stat=stats.Mean())\n\nfor _ in range(150):\n    y = rng.gauss(0, 1)\n    model.learn_one(None, y)\n\nmodel.score_one(None, 2)\n</code></pre> <pre><code>2.057...\n</code></pre></p> <p><pre><code>model.score_one(None, 0)\n</code></pre> <pre><code>0.084...\n</code></pre></p> <p><pre><code>model.score_one(None, 1)\n</code></pre> <pre><code>0.986...\n</code></pre></p>"},{"location":"api/anomaly/StandardAbsoluteDeviation/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p> <ol> <li> <p>Hochenbaum, J., Vallis, O.S., Kejariwal, A., 2017. Automatic Anomaly Detection in the Cloud Via Statistical Learning. https://doi.org/10.48550/ARXIV.1704.07706.\u00a0\u21a9</p> </li> <li> <p>Yilmaz, S.F., Kozat, S.S., 2020. PySAD: A Streaming Anomaly Detection Framework in Python. https://doi.org/10.48550/ARXIV.2009.02572.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/anomaly/ThresholdFilter/","title":"ThresholdFilter","text":"<p>Threshold anomaly filter.</p>"},{"location":"api/anomaly/ThresholdFilter/#parameters","title":"Parameters","text":"<ul> <li> <p>anomaly_detector</p> <p>An anomaly detector.</p> </li> <li> <p>threshold</p> <p>Type \u2192 float</p> <p>A threshold above which to classify an anomaly score as anomalous.</p> </li> <li> <p>protect_anomaly_detector</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the anomaly detector should be updated when the anomaly score is anomalous. If the data contains sporadic anomalies, then the anomaly detector should likely not be updated. Indeed, if it learns the anomaly score, then it will slowly start to consider anomalous anomaly scores as normal. This might be desirable, for instance in the case of drift.</p> </li> </ul>"},{"location":"api/anomaly/ThresholdFilter/#examples","title":"Examples","text":"<p>Anomaly filters can be used as part of a pipeline. For instance, we might want to filter out anomalous observations so as not to corrupt a supervised model. As an example, let's take the <code>datasets.WaterFlow</code> dataset. Some of the samples have anomalous target variables because of human interventions. We don't want our model to learn these values.</p> <p><pre><code>from river import datasets\nfrom river import metrics\nfrom river import time_series\n\ndataset = datasets.WaterFlow()\nmetric = metrics.SMAPE()\n\nperiod = 24  # 24 samples per day\n\nmodel = (\n    anomaly.ThresholdFilter(\n        anomaly.GaussianScorer(\n            window_size=period * 7,  # 7 days\n            grace_period=30\n        ),\n        threshold=0.995\n    ) |\n    time_series.HoltWinters(\n        alpha=0.3,\n        beta=0.1,\n        multiplicative=False\n    )\n)\n\ntime_series.evaluate(\n    dataset,\n    model,\n    metric,\n    horizon=period\n)\n</code></pre> <pre><code>+1  SMAPE: 4.220171\n+2  SMAPE: 4.322648\n+3  SMAPE: 4.418546\n+4  SMAPE: 4.504986\n+5  SMAPE: 4.57924\n+6  SMAPE: 4.64123\n+7  SMAPE: 4.694042\n+8  SMAPE: 4.740753\n+9  SMAPE: 4.777291\n+10 SMAPE: 4.804558\n+11 SMAPE: 4.828114\n+12 SMAPE: 4.849823\n+13 SMAPE: 4.865871\n+14 SMAPE: 4.871972\n+15 SMAPE: 4.866274\n+16 SMAPE: 4.842614\n+17 SMAPE: 4.806214\n+18 SMAPE: 4.763355\n+19 SMAPE: 4.713455\n+20 SMAPE: 4.672062\n+21 SMAPE: 4.659102\n+22 SMAPE: 4.693496\n+23 SMAPE: 4.773707\n+24 SMAPE: 4.880654\n</code></pre></p>"},{"location":"api/anomaly/ThresholdFilter/#methods","title":"Methods","text":"classify <p>Classify an anomaly score as anomalous or not.</p> <p>Parameters</p> <ul> <li>score     \u2014 'float' </li> </ul> <p>Returns</p> <p>bool:     A boolean value indicating whether the anomaly score is anomalous or not.</p> <p></p> learn_one <p>Update the anomaly filter and the underlying anomaly detector.</p> <p>Parameters</p> <ul> <li>args </li> <li>learn_kwargs </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/base/AnomalyDetector/","title":"AnomalyDetector","text":"<p>An anomaly detector.</p>"},{"location":"api/anomaly/base/AnomalyDetector/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/base/AnomalyFilter/","title":"AnomalyFilter","text":"<p>Anomaly filter base class.</p> <p>An anomaly filter has the ability to classify an anomaly score as anomalous or not. It can then be used to filter anomalies, in particular as part of a pipeline.</p>"},{"location":"api/anomaly/base/AnomalyFilter/#parameters","title":"Parameters","text":"<ul> <li> <p>anomaly_detector</p> <p>Type \u2192 AnomalyDetector</p> <p>An anomaly detector wrapped by the anomaly filter.</p> </li> <li> <p>protect_anomaly_detector</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the anomaly detector should be updated when the anomaly score is anomalous. If the data contains sporadic anomalies, then the anomaly detector should likely not be updated. Indeed, if it learns the anomaly score, then it will slowly start to consider anomalous anomaly scores as normal. This might be desirable, for instance in the case of drift.</p> </li> </ul>"},{"location":"api/anomaly/base/AnomalyFilter/#methods","title":"Methods","text":"classify <p>Classify an anomaly score as anomalous or not.</p> <p>Parameters</p> <ul> <li>score     \u2014 'float' </li> </ul> <p>Returns</p> <p>bool:     A boolean value indicating whether the anomaly score is anomalous or not.</p> <p></p> learn_one <p>Update the anomaly filter and the underlying anomaly detector.</p> <p>Parameters</p> <ul> <li>args </li> <li>learn_kwargs </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds to a normal observation.</p> <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p>Returns</p> <p>An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/anomaly/base/SupervisedAnomalyDetector/","title":"SupervisedAnomalyDetector","text":"<p>A supervised anomaly detector.</p>"},{"location":"api/anomaly/base/SupervisedAnomalyDetector/#methods","title":"Methods","text":"learn_one <p>Update the model.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> score_one <p>Return an outlier score.</p> <p>A high score is indicative of an anomaly. A low score corresponds a normal observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p>Returns</p> <p>float:     An anomaly score. A high score is indicative of an anomaly. A low score corresponds a</p> <p></p>"},{"location":"api/bandit/BayesUCB/","title":"BayesUCB","text":"<p>Bayes-UCB bandit policy.</p> <p>Bayes-UCB is a Bayesian algorithm for the multi-armed bandit problem. It uses the posterior distribution of the reward of each arm to compute an upper confidence bound (UCB) on the expected reward of each arm. The arm with the highest UCB is then pulled. The posterior distribution is updated after each pull. The algorithm is described in [^1].</p>"},{"location":"api/bandit/BayesUCB/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object that is used to update the posterior distribution.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>Number of initial observations per arm before using the posterior distribution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/BayesUCB/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/BayesUCB/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.BayesUCB(seed=123)\n\nmetric = stats.Sum()\nwhile True:\n    action = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(action)\n    policy.update(action, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 841.\n</code></pre></p>"},{"location":"api/bandit/BayesUCB/#methods","title":"Methods","text":"compute_index <p>the p-th quantile of the beta distribution for the arm</p> <p>Parameters</p> <ul> <li>arm_id </li> </ul> <p></p> pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Rewrite update function</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/EpsilonGreedy/","title":"EpsilonGreedy","text":"<p>\\(\\varepsilon\\)-greedy bandit policy.</p> <p>Performs arm selection by using an \\(\\varepsilon\\)-greedy bandit strategy. An arm is selected at each step. The best arm is selected (1 - \\(\\varepsilon\\))% of the time. </p> <p>Selection bias is a common problem when using bandits. This bias can be mitigated by using burn-in phase. Each model is given the chance to learn during the first <code>burn_in</code> steps.</p>"},{"location":"api/bandit/EpsilonGreedy/#parameters","title":"Parameters","text":"<ul> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>The probability of exploring.</p> </li> <li> <p>decay</p> <p>Default \u2192 <code>0.0</code></p> <p>The decay rate of epsilon.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/EpsilonGreedy/#attributes","title":"Attributes","text":"<ul> <li> <p>current_epsilon</p> <p>The value of epsilon after factoring in the decay rate.</p> </li> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/EpsilonGreedy/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.EpsilonGreedy(epsilon=0.9, seed=101)\n\nmetric = stats.Sum()\nwhile True:\n    arm = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(arm)\n    policy.update(arm, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 775.\n</code></pre></p>"},{"location":"api/bandit/EpsilonGreedy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>\u03b5-Greedy Algorithm - The Multi-Armed Bandit Problem and Its Solutions - Lilian Weng \u21a9</p> </li> </ol>"},{"location":"api/bandit/Exp3/","title":"Exp3","text":"<p>Exp3 bandit policy.</p> <p>This policy works by maintaining a weight for each arm. These weights are used to randomly decide which arm to pull. The weights are increased or decreased, depending on the reward. An egalitarianism factor \\(\\gamma \\in [0, 1]\\) is included, to tune the desire to pick an arm uniformly at random. That is, if \\(\\gamma = 1\\), the arms are picked uniformly at random.</p>"},{"location":"api/bandit/Exp3/#parameters","title":"Parameters","text":"<ul> <li> <p>gamma</p> <p>Type \u2192 float</p> <p>The egalitarianism factor. Setting this to 0 leads to what is called the EXP3 policy.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/Exp3/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/Exp3/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.Exp3(gamma=0.5, seed=42)\n\nmetric = stats.Sum()\nwhile True:\n    action = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(action)\n    policy.update(action, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 799.\n</code></pre></p>"},{"location":"api/bandit/Exp3/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>Auer, P., Cesa-Bianchi, N., Freund, Y. and Schapire, R.E., 2002. The nonstochastic multiarmed bandit problem. SIAM journal on computing, 32(1), pp.48-77. \u21a9</p> </li> <li> <p>Adversarial Bandits and the Exp3 Algorithm \u2014 Jeremy Kun \u21a9</p> </li> </ol>"},{"location":"api/bandit/LinUCBDisjoint/","title":"LinUCBDisjoint","text":"<p>LinUCB, disjoint variant.</p> <p>Although it works, as of yet it is too slow to realistically be used in practice. </p> <p>The way this works is that each arm is assigned a <code>linear_model.BayesianLinearRegression</code> instance. This instance is updated every time the arm is pulled. The context is used as features for the regression. The reward is used as the target. The posterior distribution is used to compute the upper confidence bound. The arm with the highest upper confidence bound is pulled.</p>"},{"location":"api/bandit/LinUCBDisjoint/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Parameter used in each Bayesian linear regression.</p> </li> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Parameter used in each Bayesian linear regression.</p> </li> <li> <p>smoothing</p> <p>Type \u2192 float | None</p> <p>Default \u2192 <code>None</code></p> <p>Parameter used in each Bayesian linear regression.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of time steps during which each arm is pulled once.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/LinUCBDisjoint/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/LinUCBDisjoint/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> <li>context     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Rewrite update function</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>context </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>A Contextual-Bandit Approach to Personalized News Article Recommendation [^2:] Contextual Bandits Analysis of LinUCB Disjoint Algorithm with Dataset \u21a9</p> </li> </ol>"},{"location":"api/bandit/RandomPolicy/","title":"RandomPolicy","text":"<p>Random bandit policy.</p> <p>This policy simply pulls a random arm at each time step. It is useful as a baseline.</p>"},{"location":"api/bandit/RandomPolicy/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object that is used to update the posterior distribution.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>Number of initial observations per arm before using the posterior distribution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/RandomPolicy/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/RandomPolicy/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.RandomPolicy(seed=123)\n\nmetric = stats.Sum()\nwhile True:\n    action = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(action)\n    policy.update(action, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 755.\n</code></pre></p>"},{"location":"api/bandit/RandomPolicy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/ThompsonSampling/","title":"ThompsonSampling","text":"<p>Thompson sampling.</p> <p>Thompson sampling is often used with a Beta distribution. However, any probability distribution can be used, as long it makes sense with the reward shape. For instance, a Beta distribution is meant to be used with binary rewards, while a Gaussian distribution is meant to be used with continuous rewards. </p> <p>The randomness of a distribution is controlled by its seed. The seed should not set within the distribution, but should rather be defined in the policy parametrization. In other words, you should do this: </p> <pre><code>policy = ThompsonSampling(dist=proba.Beta(1, 1), seed=42) \n</code></pre> <p>and not this: </p> <pre><code>policy = ThompsonSampling(dist=proba.Beta(1, 1, seed=42)) \n</code></pre>"},{"location":"api/bandit/ThompsonSampling/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Type \u2192 proba.base.Distribution | None</p> <p>Default \u2192 <code>None</code></p> <p>A distribution to sample from.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/ThompsonSampling/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/ThompsonSampling/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import proba\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.ThompsonSampling(reward_obj=proba.Beta(), seed=101)\n\nmetric = stats.Sum()\nwhile True:\n    arm = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(arm)\n    policy.update(arm, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 820.\n</code></pre></p>"},{"location":"api/bandit/ThompsonSampling/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>An Empirical Evaluation of Thompson Sampling \u21a9</p> </li> </ol>"},{"location":"api/bandit/UCB/","title":"UCB","text":"<p>Upper Confidence Bound (UCB) bandit policy.</p> <p>Due to the nature of this algorithm, it's recommended to scale the target so that it exhibits sub-gaussian properties. This can be done by passing a <code>preprocessing.TargetStandardScaler</code> instance to the <code>reward_scaler</code> argument.</p>"},{"location":"api/bandit/UCB/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Type \u2192 float</p> <p>The confidence level. Setting this to 1 leads to what is called the UCB1 policy.</p> </li> <li> <p>reward_obj</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/bandit/UCB/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/UCB/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\nfrom river import preprocessing\nfrom river import stats\n\nenv = gym.make(\n    'river_bandits/CandyCaneContest-v0'\n)\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\npolicy = bandit.UCB(\n    delta=100,\n    reward_scaler=preprocessing.TargetStandardScaler(None),\n    seed=42\n)\n\nmetric = stats.Sum()\nwhile True:\n    arm = policy.pull(range(env.action_space.n))\n    observation, reward, terminated, truncated, info = env.step(arm)\n    policy.update(arm, reward)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 744.\n</code></pre></p>"},{"location":"api/bandit/UCB/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p> <ol> <li> <p>Lai, T. L., &amp; Robbins, H. (1985). Asymptotically efficient adaptive allocation rules. Advances in applied mathematics, 6(1), 4-22. \u21a9</p> </li> <li> <p>Upper Confidence Bounds - The Multi-Armed Bandit Problem and Its Solutions - Lilian Weng \u21a9</p> </li> <li> <p>The Upper Confidence Bound Algorithm - Bandit Algorithms \u21a9</p> </li> </ol>"},{"location":"api/bandit/evaluate-offline/","title":"evaluate_offline","text":"<p>Evaluate a policy on historical logs using replay.</p> <p>This is a high-level utility function for evaluating a policy using the replay methodology. This methodology is an off-policy evaluation method. It does not require an environment, and is instead data-driven. </p> <p>At each step, an arm is pulled from the provided policy. If the arm is the same as the arm that was pulled in the historical data, the reward is used to update the policy. If the arm is different, the reward is ignored. This is the off-policy aspect of the evaluation.</p>"},{"location":"api/bandit/evaluate-offline/#parameters","title":"Parameters","text":"<ul> <li> <p>policy</p> <p>Type \u2192 bandit.base.Policy</p> <p>The policy to evaluate.</p> </li> <li> <p>history</p> <p>Type \u2192 History | bandit.datasets.BanditDataset</p> <p>The history of the bandit problem. This is a generator that yields tuples of the form <code>(arms, context, arm, reward)</code>.</p> </li> <li> <p>reward_stat</p> <p>Type \u2192 stats.base.Univariate | None</p> <p>Default \u2192 <code>None</code></p> <p>The reward statistic to use. Defaults to <code>stats.Sum</code>.</p> </li> </ul>"},{"location":"api/bandit/evaluate-offline/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import bandit\n\nrng = random.Random(42)\narms = ['A', 'B', 'C']\nclicks = [\n    (\n        arms,\n        # no context\n        None,\n        # random arm\n        rng.choice(arms),\n        # reward\n        rng.random() &gt; 0.5\n    )\n    for _ in range(1000)\n]\n\ntotal_reward, n_samples_used = bandit.evaluate_offline(\n    policy=bandit.EpsilonGreedy(0.1, seed=42),\n    history=clicks,\n)\n\ntotal_reward\n</code></pre> <pre><code>Sum: 172.\n</code></pre></p> <p><pre><code>n_samples_used\n</code></pre> <pre><code>321\n</code></pre></p> <p>This also works out of the box with datasets that inherit from <code>river.bandit.BanditDataset</code>.</p> <p><pre><code>news = bandit.datasets.NewsArticles()\ntotal_reward, n_samples_used = bandit.evaluate_offline(\n    policy=bandit.RandomPolicy(seed=42),\n    history=news,\n)\n\ntotal_reward, n_samples_used\n</code></pre> <pre><code>(Sum: 105., 1027)\n</code></pre></p> <p>As expected, the policy succeeds in roughly 10% of cases. Indeed, there are 10 arms and 10000 samples, so the expected number of successes is 1000.</p> <ol> <li> <p>Offline Evaluation of Multi-Armed Bandit Algorithms in Python using Replay \u21a9</p> </li> <li> <p>Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms \u21a9</p> </li> <li> <p>Understanding Inverse Propensity Score for Contextual Bandits \u21a9</p> </li> </ol>"},{"location":"api/bandit/evaluate/","title":"evaluate","text":"<p>Benchmark a list of policies on a given Gym environment.</p> <p>This is a high-level utility function for benchmarking a list of policies on a given Gym environment. For example, it can be used to populate a <code>pandas.DataFrame</code> with the contents of each step of each episode.</p>"},{"location":"api/bandit/evaluate/#parameters","title":"Parameters","text":"<ul> <li> <p>policies</p> <p>Type \u2192 list[bandit.base.Policy]</p> <p>A list of policies to evaluate. The policy will be reset before each episode.</p> </li> <li> <p>env</p> <p>Type \u2192 gym.Env</p> <p>The Gym environment to use. One copy will be made for each policy at the beginning of each episode.</p> </li> <li> <p>reward_stat</p> <p>Type \u2192 stats.base.Univariate | None</p> <p>Default \u2192 <code>None</code></p> <p>A univariate statistic to keep track of the rewards. This statistic will be reset before each episode. Note that this is not the same as the reward object used by the policies. It's just a statistic to keep track of each policy's performance. If <code>None</code>, <code>stats.Sum</code> is used.</p> </li> <li> <p>n_episodes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>20</code></p> <p>The number of episodes to run.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility. A random number generator will be used to seed differently the environment before each episode.</p> </li> </ul>"},{"location":"api/bandit/evaluate/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import bandit\n\ntrace = bandit.evaluate(\n    policies=[\n        bandit.UCB(delta=1, seed=42),\n        bandit.EpsilonGreedy(epsilon=0.1, seed=42),\n    ],\n    env=gym.make(\n        'river_bandits/CandyCaneContest-v0',\n        max_episode_steps=100\n    ),\n    n_episodes=5,\n    seed=42\n)\n\nfor step in trace:\n    print(step)\n    break\n</code></pre> <pre><code>{'episode': 0, 'step': 0, 'policy_idx': 0, 'arm': 81, 'reward': 0.0, 'reward_stat': 0.0}\n</code></pre></p> <p>The return type of this function is a generator. Each step of the generator is a dictionary. You can pass the generator to a <code>pandas.DataFrame</code> to get a nice representation of the results.</p> <p><pre><code>import pandas as pd\n\ntrace = bandit.evaluate(\n    policies=[\n        bandit.UCB(delta=1, seed=42),\n        bandit.EpsilonGreedy(epsilon=0.1, seed=42),\n    ],\n    env=gym.make(\n        'river_bandits/CandyCaneContest-v0',\n        max_episode_steps=100\n    ),\n    n_episodes=5,\n    seed=42\n)\n\ntrace_df = pd.DataFrame(trace)\ntrace_df.sample(5, random_state=42)\n</code></pre> <pre><code>     episode  step  policy_idx  arm  reward  reward_stat\n521        2    60           1   25     0.0         36.0\n737        3    68           1   40     1.0         20.0\n740        3    70           0   58     0.0         36.0\n660        3    30           0   31     1.0         16.0\n411        2     5           1   35     1.0          5.0\n</code></pre></p> <p>The length of the dataframe is the number of policies times the number of episodes times the maximum number of steps per episode.</p> <p><pre><code>len(trace_df)\n</code></pre> <pre><code>1000\n</code></pre></p> <p><pre><code>(\n    trace_df.policy_idx.nunique() *\n    trace_df.episode.nunique() *\n    trace_df.step.nunique()\n)\n</code></pre> <pre><code>1000\n</code></pre></p>"},{"location":"api/bandit/base/ContextualPolicy/","title":"ContextualPolicy","text":"<p>Contextual bandit policy base class.</p>"},{"location":"api/bandit/base/ContextualPolicy/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Type \u2192 RewardObj | None</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Type \u2192 compose.TargetTransformRegressor | None</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> </ul>"},{"location":"api/bandit/base/ContextualPolicy/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/base/ContextualPolicy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> <li>context     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>context </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/base/Policy/","title":"Policy","text":"<p>Bandit policy base class.</p>"},{"location":"api/bandit/base/Policy/#parameters","title":"Parameters","text":"<ul> <li> <p>reward_obj</p> <p>Type \u2192 RewardObj | None</p> <p>Default \u2192 <code>None</code></p> <p>The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.</p> </li> <li> <p>reward_scaler</p> <p>Type \u2192 compose.TargetTransformRegressor | None</p> <p>Default \u2192 <code>None</code></p> <p>A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.</p> </li> <li> <p>burn_in</p> <p>Default \u2192 <code>0</code></p> <p>The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.</p> </li> </ul>"},{"location":"api/bandit/base/Policy/#attributes","title":"Attributes","text":"<ul> <li> <p>ranking</p> <p>Return the list of arms in descending order of performance.</p> </li> </ul>"},{"location":"api/bandit/base/Policy/#methods","title":"Methods","text":"pull <p>Pull arm(s).</p> <p>This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call <code>next(policy.pull(arms))</code>.</p> <p>Parameters</p> <ul> <li>arm_ids     \u2014 'list[ArmID]' </li> </ul> <p>Returns</p> <p>ArmID:     A single arm.</p> <p></p> update <p>Update an arm's state.</p> <p>Parameters</p> <ul> <li>arm_id </li> <li>reward_args </li> <li>reward_kwargs </li> </ul> <p></p>"},{"location":"api/bandit/datasets/BanditDataset/","title":"BanditDataset","text":"<p>Base class for bandit datasets.</p>"},{"location":"api/bandit/datasets/BanditDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>n_features</p> <p>Number of features in the dataset.</p> </li> <li> <p>n_samples</p> <p>Default \u2192 <code>None</code></p> <p>Number of samples in the dataset.</p> </li> <li> <p>n_classes</p> <p>Default \u2192 <code>None</code></p> <p>Number of classes in the dataset, only applies to classification datasets.</p> </li> <li> <p>n_outputs</p> <p>Default \u2192 <code>None</code></p> <p>Number of outputs the target is made of, only applies to multi-output datasets.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the dataset is sparse or not.</p> </li> </ul>"},{"location":"api/bandit/datasets/BanditDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>arms</p> <p>The list of arms that can be pulled.</p> </li> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/bandit/datasets/BanditDataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/bandit/datasets/NewsArticles/","title":"NewsArticles","text":"<p>News articles bandit dataset.</p> <p>This is a personalization dataset. It contains 10000 observations. There are 10 arms, and the reward is binary. There are 100 features, which turns this into a contextual bandit problem.</p>"},{"location":"api/bandit/datasets/NewsArticles/#attributes","title":"Attributes","text":"<ul> <li> <p>arms</p> <p>The list of arms that can be pulled.</p> </li> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/bandit/datasets/NewsArticles/#examples","title":"Examples","text":"<p><pre><code>from river import bandit\n\ndataset = bandit.datasets.NewsArticles()\ncontext, arm, reward = next(iter(dataset))\n\nlen(context)\n</code></pre> <pre><code>100\n</code></pre></p> <p><pre><code>arm, reward\n</code></pre> <pre><code>(2, False)\n</code></pre></p>"},{"location":"api/bandit/datasets/NewsArticles/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Machine Learning for Personalization homework \u21a9</p> </li> <li> <p>Contextual Bandits Analysis of LinUCB Disjoint Algorithm with Dataset \u21a9</p> </li> </ol>"},{"location":"api/bandit/envs/CandyCaneContest/","title":"CandyCaneContest","text":"<p>Candy cane contest Kaggle competition.</p>"},{"location":"api/bandit/envs/CandyCaneContest/#parameters","title":"Parameters","text":"<ul> <li> <p>n_machines</p> <p>Default \u2192 <code>100</code></p> <p>Number of vending machines.</p> </li> <li> <p>reward_decay</p> <p>Default \u2192 <code>0.03</code></p> <p>The multiplicate rate at which the expected reward of each vending machine decays.</p> </li> </ul>"},{"location":"api/bandit/envs/CandyCaneContest/#attributes","title":"Attributes","text":"<ul> <li> <p>np_random</p> <p>Returns the environment's internal :attr:<code>_np_random</code> that if not set will initialise with a random seed.  Returns:     Instances of <code>np.random.Generator</code></p> </li> <li> <p>render_mode</p> </li> <li> <p>spec</p> </li> <li> <p>unwrapped</p> <p>Returns the base non-wrapped environment.  Returns:     Env: The base non-wrapped :class:<code>gymnasium.Env</code> instance</p> </li> </ul>"},{"location":"api/bandit/envs/CandyCaneContest/#examples","title":"Examples","text":"<p><pre><code>import gymnasium as gym\nfrom river import stats\n\nenv = gym.make('river_bandits/CandyCaneContest-v0')\n_ = env.reset(seed=42)\n_ = env.action_space.seed(123)\n\nmetric = stats.Sum()\nwhile True:\n    arm = env.action_space.sample()\n    observation, reward, terminated, truncated, info = env.step(arm)\n    metric.update(reward)\n    if terminated or truncated:\n        break\n\nmetric\n</code></pre> <pre><code>Sum: 734.\n</code></pre></p>"},{"location":"api/bandit/envs/CandyCaneContest/#methods","title":"Methods","text":"close <p>After the user has finished using the environment, close contains the code necessary to \"clean up\" the environment.</p> <p>This is critical for closing rendering windows, database or HTTP connections. Calling <code>close</code> on an already closed environment has no effect and won't raise an error.</p> <p></p> get_wrapper_attr <p>Gets the attribute <code>name</code> from the environment.</p> <p>Parameters</p> <ul> <li>name     \u2014 'str' </li> </ul> <p></p> render <p>Compute the render frames as specified by :attr:<code>render_mode</code> during the initialization of the environment.</p> <p>The environment's :attr:<code>metadata</code> render modes (<code>env.metadata[\"render_modes\"]</code>) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through <code>gymnasium.make</code> which automatically applies a wrapper to collect rendered frames.  Note:     As the :attr:<code>render_mode</code> is known during <code>__init__</code>, the objects used to render the environment state     should be initialised in <code>__init__</code>.  By convention, if the :attr:<code>render_mode</code> is:  - None (default): no render is computed. - \"human\": The environment is continuously rendered in the current display or terminal, usually for human consumption.   This rendering should occur during :meth:<code>step</code> and :meth:<code>render</code> doesn't need to be called. Returns <code>None</code>. - \"rgb_array\": Return a single frame representing the current state of the environment.   A frame is a <code>np.ndarray</code> with shape <code>(x, y, 3)</code> representing RGB values for an x-by-y pixel image. - \"ansi\": Return a strings (<code>str</code>) or <code>StringIO.StringIO</code> containing a terminal-style text representation   for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors). - \"rgb_array_list\" and \"ansi_list\": List based version of render modes are possible (except Human) through the   wrapper, :py:class:<code>gymnasium.wrappers.RenderCollection</code> that is automatically applied during <code>gymnasium.make(..., render_mode=\"rgb_array_list\")</code>.   The frames collected are popped after :meth:<code>render</code> is called or :meth:<code>reset</code>.  Note:     Make sure that your class's :attr:<code>metadata</code> <code>\"render_modes\"</code> key includes the list of supported modes.  .. versionchanged:: 0.25.0      The render function was changed to no longer accept parameters, rather these parameters should be specified     in the environment initialised, i.e., <code>gymnasium.make(\"CartPole-v1\", render_mode=\"human\")</code></p> <p></p> reset <p>Resets the environment to an initial internal state, returning an initial observation and info.</p> <p>This method generates a new starting state often with some randomness to ensure that the agent explores the state space and learns a generalised policy about the environment. This randomness can be controlled with the <code>seed</code> parameter otherwise if the environment already has a random number generator and :meth:<code>reset</code> is called with <code>seed=None</code>, the RNG is not reset.  Therefore, :meth:<code>reset</code> should (in the typical use case) be called with a seed right after initialization and then never again.  For Custom environments, the first line of :meth:<code>reset</code> should be <code>super().reset(seed=seed)</code> which implements the seeding correctly.  .. versionchanged:: v0.25      The <code>return_info</code> parameter was removed and now info is expected to be returned.  Args:     seed (optional int): The seed that is used to initialize the environment's PRNG (<code>np_random</code>).         If the environment does not already have a PRNG and <code>seed=None</code> (the default option) is passed,         a seed will be chosen from some source of entropy (e.g. timestamp or /dev/urandom).         However, if the environment already has a PRNG and <code>seed=None</code> is passed, the PRNG will not be reset.         If you pass an integer, the PRNG will be reset even if it already exists.         Usually, you want to pass an integer right after the environment has been initialized and then never again.         Please refer to the minimal example above to see this paradigm in action.     options (optional dict): Additional information to specify how the environment is reset (optional,         depending on the specific environment)  Returns:     observation (ObsType): Observation of the initial state. This will be an element of :attr:<code>observation_space</code>         (typically a numpy array) and is analogous to the observation returned by :meth:<code>step</code>.     info (dictionary):  This dictionary contains auxiliary information complementing <code>observation</code>. It should be analogous to         the <code>info</code> returned by :meth:<code>step</code>.</p> <p>Parameters</p> <ul> <li>seed     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> <li>options     \u2014 'dict[str, Any] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> step <p>Run one timestep of the environment's dynamics using the agent actions.</p> <p>When the end of an episode is reached (<code>terminated or truncated</code>), it is necessary to call :meth:<code>reset</code> to reset this environment's state for the next episode.  .. versionchanged:: 0.26      The Step API was changed removing <code>done</code> in favor of <code>terminated</code> and <code>truncated</code> to make it clearer     to users when the environment had terminated or truncated which is critical for reinforcement learning     bootstrapping algorithms.  Args:     action (ActType): an action provided by the agent to update the environment state.  Returns:     observation (ObsType): An element of the environment's :attr:<code>observation_space</code> as the next observation due to the agent actions.         An example is a numpy array containing the positions and velocities of the pole in CartPole.     reward (SupportsFloat): The reward as a result of taking the action.     terminated (bool): Whether the agent reaches the terminal state (as defined under the MDP of the task)         which can be positive or negative. An example is reaching the goal state or moving into the lava from         the Sutton and Barton, Gridworld. If true, the user needs to call :meth:<code>reset</code>.     truncated (bool): Whether the truncation condition outside the scope of the MDP is satisfied.         Typically, this is a timelimit, but could also be used to indicate an agent physically going out of bounds.         Can be used to end the episode prematurely before a terminal state is reached.         If true, the user needs to call :meth:<code>reset</code>.     info (dict): Contains auxiliary diagnostic information (helpful for debugging, learning, and logging).         This might, for instance, contain: metrics that describe the agent's performance state, variables that are         hidden from observations, or individual reward terms that are combined to produce the total reward.         In OpenAI Gym &lt;v26, it contains \"TimeLimit.truncated\" to distinguish truncation and termination,         however this is deprecated in favour of returning terminated and truncated variables.     done (bool): (Deprecated) A boolean value for if the episode has ended, in which case further :meth:<code>step</code> calls will         return undefined results. This was removed in OpenAI Gym v26 in favor of terminated and truncated attributes.         A done signal may be emitted for different reasons: Maybe the task underlying the environment was solved successfully,         a certain timelimit was exceeded, or the physics simulation has entered an invalid state.</p> <p>Parameters</p> <ul> <li>machine_index </li> </ul> <p></p> <ol> <li> <p>Santa 2020 - The Candy Cane Contest \u21a9</p> </li> </ol>"},{"location":"api/bandit/envs/KArmedTestbed/","title":"KArmedTestbed","text":"<p>k-armed testbed.</p> <p>This is a simple environment that can be used to test bandit algorithms. It is based on the 10 armed testbed described in the book \"Reinforcement Learning: An Introduction\" by Sutton and Barto.</p>"},{"location":"api/bandit/envs/KArmedTestbed/#parameters","title":"Parameters","text":"<ul> <li> <p>k</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of arms.</p> </li> </ul>"},{"location":"api/bandit/envs/KArmedTestbed/#attributes","title":"Attributes","text":"<ul> <li> <p>np_random</p> <p>Returns the environment's internal :attr:<code>_np_random</code> that if not set will initialise with a random seed.  Returns:     Instances of <code>np.random.Generator</code></p> </li> <li> <p>render_mode</p> </li> <li> <p>spec</p> </li> <li> <p>unwrapped</p> <p>Returns the base non-wrapped environment.  Returns:     Env: The base non-wrapped :class:<code>gymnasium.Env</code> instance</p> </li> </ul>"},{"location":"api/bandit/envs/KArmedTestbed/#methods","title":"Methods","text":"close <p>After the user has finished using the environment, close contains the code necessary to \"clean up\" the environment.</p> <p>This is critical for closing rendering windows, database or HTTP connections. Calling <code>close</code> on an already closed environment has no effect and won't raise an error.</p> <p></p> get_wrapper_attr <p>Gets the attribute <code>name</code> from the environment.</p> <p>Parameters</p> <ul> <li>name     \u2014 'str' </li> </ul> <p></p> render <p>Compute the render frames as specified by :attr:<code>render_mode</code> during the initialization of the environment.</p> <p>The environment's :attr:<code>metadata</code> render modes (<code>env.metadata[\"render_modes\"]</code>) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through <code>gymnasium.make</code> which automatically applies a wrapper to collect rendered frames.  Note:     As the :attr:<code>render_mode</code> is known during <code>__init__</code>, the objects used to render the environment state     should be initialised in <code>__init__</code>.  By convention, if the :attr:<code>render_mode</code> is:  - None (default): no render is computed. - \"human\": The environment is continuously rendered in the current display or terminal, usually for human consumption.   This rendering should occur during :meth:<code>step</code> and :meth:<code>render</code> doesn't need to be called. Returns <code>None</code>. - \"rgb_array\": Return a single frame representing the current state of the environment.   A frame is a <code>np.ndarray</code> with shape <code>(x, y, 3)</code> representing RGB values for an x-by-y pixel image. - \"ansi\": Return a strings (<code>str</code>) or <code>StringIO.StringIO</code> containing a terminal-style text representation   for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors). - \"rgb_array_list\" and \"ansi_list\": List based version of render modes are possible (except Human) through the   wrapper, :py:class:<code>gymnasium.wrappers.RenderCollection</code> that is automatically applied during <code>gymnasium.make(..., render_mode=\"rgb_array_list\")</code>.   The frames collected are popped after :meth:<code>render</code> is called or :meth:<code>reset</code>.  Note:     Make sure that your class's :attr:<code>metadata</code> <code>\"render_modes\"</code> key includes the list of supported modes.  .. versionchanged:: 0.25.0      The render function was changed to no longer accept parameters, rather these parameters should be specified     in the environment initialised, i.e., <code>gymnasium.make(\"CartPole-v1\", render_mode=\"human\")</code></p> <p></p> reset <p>Resets the environment to an initial internal state, returning an initial observation and info.</p> <p>This method generates a new starting state often with some randomness to ensure that the agent explores the state space and learns a generalised policy about the environment. This randomness can be controlled with the <code>seed</code> parameter otherwise if the environment already has a random number generator and :meth:<code>reset</code> is called with <code>seed=None</code>, the RNG is not reset.  Therefore, :meth:<code>reset</code> should (in the typical use case) be called with a seed right after initialization and then never again.  For Custom environments, the first line of :meth:<code>reset</code> should be <code>super().reset(seed=seed)</code> which implements the seeding correctly.  .. versionchanged:: v0.25      The <code>return_info</code> parameter was removed and now info is expected to be returned.  Args:     seed (optional int): The seed that is used to initialize the environment's PRNG (<code>np_random</code>).         If the environment does not already have a PRNG and <code>seed=None</code> (the default option) is passed,         a seed will be chosen from some source of entropy (e.g. timestamp or /dev/urandom).         However, if the environment already has a PRNG and <code>seed=None</code> is passed, the PRNG will not be reset.         If you pass an integer, the PRNG will be reset even if it already exists.         Usually, you want to pass an integer right after the environment has been initialized and then never again.         Please refer to the minimal example above to see this paradigm in action.     options (optional dict): Additional information to specify how the environment is reset (optional,         depending on the specific environment)  Returns:     observation (ObsType): Observation of the initial state. This will be an element of :attr:<code>observation_space</code>         (typically a numpy array) and is analogous to the observation returned by :meth:<code>step</code>.     info (dictionary):  This dictionary contains auxiliary information complementing <code>observation</code>. It should be analogous to         the <code>info</code> returned by :meth:<code>step</code>.</p> <p>Parameters</p> <ul> <li>seed     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> <li>options     \u2014 'dict[str, Any] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> step <p>Run one timestep of the environment's dynamics using the agent actions.</p> <p>When the end of an episode is reached (<code>terminated or truncated</code>), it is necessary to call :meth:<code>reset</code> to reset this environment's state for the next episode.  .. versionchanged:: 0.26      The Step API was changed removing <code>done</code> in favor of <code>terminated</code> and <code>truncated</code> to make it clearer     to users when the environment had terminated or truncated which is critical for reinforcement learning     bootstrapping algorithms.  Args:     action (ActType): an action provided by the agent to update the environment state.  Returns:     observation (ObsType): An element of the environment's :attr:<code>observation_space</code> as the next observation due to the agent actions.         An example is a numpy array containing the positions and velocities of the pole in CartPole.     reward (SupportsFloat): The reward as a result of taking the action.     terminated (bool): Whether the agent reaches the terminal state (as defined under the MDP of the task)         which can be positive or negative. An example is reaching the goal state or moving into the lava from         the Sutton and Barton, Gridworld. If true, the user needs to call :meth:<code>reset</code>.     truncated (bool): Whether the truncation condition outside the scope of the MDP is satisfied.         Typically, this is a timelimit, but could also be used to indicate an agent physically going out of bounds.         Can be used to end the episode prematurely before a terminal state is reached.         If true, the user needs to call :meth:<code>reset</code>.     info (dict): Contains auxiliary diagnostic information (helpful for debugging, learning, and logging).         This might, for instance, contain: metrics that describe the agent's performance state, variables that are         hidden from observations, or individual reward terms that are combined to produce the total reward.         In OpenAI Gym &lt;v26, it contains \"TimeLimit.truncated\" to distinguish truncation and termination,         however this is deprecated in favour of returning terminated and truncated variables.     done (bool): (Deprecated) A boolean value for if the episode has ended, in which case further :meth:<code>step</code> calls will         return undefined results. This was removed in OpenAI Gym v26 in favor of terminated and truncated attributes.         A done signal may be emitted for different reasons: Maybe the task underlying the environment was solved successfully,         a certain timelimit was exceeded, or the physics simulation has entered an invalid state.</p> <p>Parameters</p> <ul> <li>arm </li> </ul> <p></p>"},{"location":"api/base/Base/","title":"Base","text":"<p>Base class that is inherited by the majority of classes in River.</p> <p>This base class allows us to handle the following tasks in a uniform manner: </p> <ul> <li> <p>Getting and setting parameters</p> </li> <li> <p>Displaying information</p> </li> <li> <p>Mutating/cloning</p> </li> </ul>"},{"location":"api/base/Base/#methods","title":"Methods","text":"clone <p>Return a fresh estimator with the same parameters.</p> <p>The clone has the same parameters but has not been updated with any data.  This works by looking at the parameters from the class signature. Each parameter is either  - recursively cloned if its a class. - deep-copied via <code>copy.deepcopy</code> if not.  If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.</p> <p>Parameters</p> <ul> <li>new_params     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> <li>include_attributes     \u2014 defaults to <code>False</code> </li> </ul> <p></p> mutate <p>Modify attributes.</p> <p>This changes parameters inplace. Although you can change attributes yourself, this is the recommended way to proceed. By default, all attributes are immutable, meaning they shouldn't be mutated. Calling <code>mutate</code> on an immutable attribute raises a <code>ValueError</code>. Mutable attributes are specified via the <code>_mutable_attributes</code> property, and are thus specified on a per-estimator basis.</p> <p>Parameters</p> <ul> <li>new_attrs     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/base/BinaryDriftAndWarningDetector/","title":"BinaryDriftAndWarningDetector","text":"<p>A binary drift detector that is also capable of issuing warnings.</p>"},{"location":"api/base/BinaryDriftAndWarningDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/BinaryDriftAndWarningDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p>"},{"location":"api/base/BinaryDriftDetector/","title":"BinaryDriftDetector","text":"<p>A drift detector for binary data.</p>"},{"location":"api/base/BinaryDriftDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/BinaryDriftDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p>"},{"location":"api/base/Classifier/","title":"Classifier","text":"<p>A classifier.</p>"},{"location":"api/base/Classifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/base/Clusterer/","title":"Clusterer","text":"<p>A clustering model.</p>"},{"location":"api/base/Clusterer/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p>"},{"location":"api/base/DriftAndWarningDetector/","title":"DriftAndWarningDetector","text":"<p>A drift detector that is also capable of issuing warnings.</p>"},{"location":"api/base/DriftAndWarningDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/DriftAndWarningDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/base/DriftDetector/","title":"DriftDetector","text":"<p>A drift detector.</p>"},{"location":"api/base/DriftDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/base/DriftDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/base/Ensemble/","title":"Ensemble","text":"<p>An ensemble is a model which is composed of a list of models.</p>"},{"location":"api/base/Ensemble/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 Iterator[Estimator]</p> </li> </ul>"},{"location":"api/base/Ensemble/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/base/Ensemble/#methods","title":"Methods","text":"append <p>S.append(value) -- append value to the end of the sequence</p> <p>Parameters</p> <ul> <li>item </li> </ul> <p></p> clear <p>S.clear() -&gt; None -- remove all items from S</p> <p></p> copy count <p>S.count(value) -&gt; integer -- return number of occurrences of value</p> <p>Parameters</p> <ul> <li>item </li> </ul> <p></p> extend <p>S.extend(iterable) -- extend sequence by appending elements from the iterable</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> index <p>S.index(value, [start, [stop]]) -&gt; integer -- return first index of value. Raises ValueError if the value is not present.</p> <p>Supporting start and stop arguments is optional, but recommended.</p> <p>Parameters</p> <ul> <li>item </li> <li>args </li> </ul> <p></p> insert <p>S.insert(index, value) -- insert value before index</p> <p>Parameters</p> <ul> <li>i </li> <li>item </li> </ul> <p></p> pop <p>S.pop([index]) -&gt; item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.</p> <p>Parameters</p> <ul> <li>i     \u2014 defaults to <code>-1</code> </li> </ul> <p></p> remove <p>S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.</p> <p>Parameters</p> <ul> <li>item </li> </ul> <p></p> reverse <p>S.reverse() -- reverse IN PLACE</p> <p></p> sort"},{"location":"api/base/Estimator/","title":"Estimator","text":"<p>An estimator.</p>"},{"location":"api/base/Estimator/#methods","title":"Methods","text":""},{"location":"api/base/MiniBatchClassifier/","title":"MiniBatchClassifier","text":"<p>A classifier that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchClassifier/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and boolean targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Predict the outcome probabilities for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A dataframe with probabilities of <code>True</code> and <code>False</code> for each sample.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/base/MiniBatchRegressor/","title":"MiniBatchRegressor","text":"<p>A regressor that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchRegressor/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and real-valued targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted outcomes.</p> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/base/MiniBatchSupervisedTransformer/","title":"MiniBatchSupervisedTransformer","text":"<p>A supervised transformer that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchSupervisedTransformer/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/MiniBatchTransformer/","title":"MiniBatchTransformer","text":"<p>A transform that can operate on mini-batches.</p>"},{"location":"api/base/MiniBatchTransformer/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/MultiLabelClassifier/","title":"MultiLabelClassifier","text":"<p>Multi-label classifier.</p>"},{"location":"api/base/MultiLabelClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'dict[FeatureName, bool]' </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, dict[bool, float]]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/base/MultiTargetRegressor/","title":"MultiTargetRegressor","text":"<p>Multi-target regressor.</p>"},{"location":"api/base/MultiTargetRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'dict[FeatureName, RegTarget]' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the outputs of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[FeatureName, RegTarget]:     The predictions.</p> <p></p>"},{"location":"api/base/Regressor/","title":"Regressor","text":"<p>A regressor.</p>"},{"location":"api/base/Regressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/base/SupervisedTransformer/","title":"SupervisedTransformer","text":"<p>A supervised transformer.</p>"},{"location":"api/base/SupervisedTransformer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code> and a target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/Transformer/","title":"Transformer","text":"<p>A transformer.</p>"},{"location":"api/base/Transformer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/base/Wrapper/","title":"Wrapper","text":"<p>A wrapper model.</p>"},{"location":"api/base/WrapperEnsemble/","title":"WrapperEnsemble","text":"<p>A wrapper ensemble is an ensemble composed of multiple copies of the same model.</p>"},{"location":"api/base/WrapperEnsemble/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>The model to copy.</p> </li> <li> <p>n_models</p> <p>The number of copies to make.</p> </li> <li> <p>seed</p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/base/WrapperEnsemble/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/base/WrapperEnsemble/#methods","title":"Methods","text":""},{"location":"api/cluster/CluStream/","title":"CluStream","text":"<p>CluStream</p> <p>The CluStream algorithm <sup>1</sup> maintains statistical information about the data using micro-clusters. These micro-clusters are temporal extensions of cluster feature vectors. The micro-clusters are stored at snapshots in time following a pyramidal pattern. This pattern allows to recall summary statistics from different time horizons. </p> <p>Training with a new point <code>p</code> is performed in two main tasks: </p> <ul> <li> <p>Determinate the closest micro-cluster to <code>p</code>. </p> </li> <li> <p>Check whether <code>p</code> fits (memory) into the closest micro-cluster: </p> <ul> <li> <p>if <code>p</code> fits, put into micro-cluster</p> </li> <li> <p>if <code>p</code> does not fit, free some space to insert a new micro-cluster.</p> </li> </ul> <p>This is done in two ways, delete an old micro-cluster or merge the       two micro-clusters closest to each other. </p> </li> </ul> <p>This implementation is an improved version from the original algorithm. Instead of calculating the traditional cluster feature vector of the number of observations, linear sum and sum of squares of data points and time stamps, this implementation adopts the use of Welford's algorithm <sup>2</sup> to calculate the incremental variance, facilitated through <code>stats.Var</code> available within River. </p> <p>Since River does not support an actual \"off-line\" phase of the clustering algorithm (as data points are assumed to arrive continuously, one at a time), a <code>time_gap</code> parameter is introduced. After each <code>time_gap</code>, an incremental K-Means clustering algorithm will be initialized and applied on currently available micro-clusters to form the final solution, i.e. macro-clusters.</p>"},{"location":"api/cluster/CluStream/#parameters","title":"Parameters","text":"<ul> <li> <p>n_macro_clusters</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of clusters (k) for the k-means algorithm.</p> </li> <li> <p>max_micro_clusters</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>The maximum number of micro-clusters to use.</p> </li> <li> <p>micro_cluster_r_factor</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>Multiplier for the micro-cluster radius. When deciding to add a new data point to a micro-cluster, the maximum boundary is defined as a factor of the <code>micro_cluster_r_factor</code> of the RMS deviation of the data points in the micro-cluster from the centroid.</p> </li> <li> <p>time_window</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>If the current time is <code>T</code> and the time window is <code>h</code>, we only consider the data that arrived within the period <code>(T-h,T)</code>.</p> </li> <li> <p>time_gap</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>An incremental k-means is applied on the current set of micro-clusters after each <code>time_gap</code> to form the final macro-cluster solution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for generating initial centroid positions.</p> </li> <li> <p>kwargs</p> <p>Other parameters passed to the incremental kmeans at <code>cluster.KMeans</code>.</p> </li> </ul>"},{"location":"api/cluster/CluStream/#attributes","title":"Attributes","text":"<ul> <li> <p>centers (dict)</p> <p>Central positions of each cluster.</p> </li> </ul>"},{"location":"api/cluster/CluStream/#examples","title":"Examples","text":"<p>In the following example, <code>max_micro_clusters</code> is set relatively low due to the limited number of training points. Moreover, all points are learnt before any predictions are made. The <code>halflife</code> is set at 0.4, to show that you can pass <code>cluster.KMeans</code> parameters via keyword arguments.</p> <p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 2],\n    [1, 4],\n    [1, 0],\n    [-4, 2],\n    [-4, 4],\n    [-4, 0],\n    [5, 0],\n    [5, 2],\n    [5, 4]\n]\n\nclustream = cluster.CluStream(\n    n_macro_clusters=3,\n    max_micro_clusters=5,\n    time_gap=3,\n    seed=0,\n    halflife=0.4\n)\n\nfor x, _ in stream.iter_array(X):\n    clustream.learn_one(x)\n\nclustream.predict_one({0: 1, 1: 1})\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>clustream.predict_one({0: -4, 1: 3})\n</code></pre> <pre><code>2\n</code></pre></p> <p><pre><code>clustream.predict_one({0: 4, 1: 3.5})\n</code></pre> <pre><code>0\n</code></pre></p>"},{"location":"api/cluster/CluStream/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Aggarwal, C.C., Philip, S.Y., Han, J. and Wang, J., 2003, A framework for clustering evolving data streams. In Proceedings 2003 VLDB conference (pp. 81-92). Morgan Kaufmann.\u00a0\u21a9</p> </li> <li> <p>Chan, T.F., Golub, G.H. and LeVeque, R.J., 1982. Updating formulae and a pairwise algorithm for computing sample variances. In COMPSTAT 1982 5th Symposium held at Toulouse 1982 (pp. 30-41). Physica, Heidelberg. https://doi.org/10.1007/978-3-642-51461-6_3.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/DBSTREAM/","title":"DBSTREAM","text":"<p>DBSTREAM</p> <p>DBSTREAM <sup>1</sup> is a clustering algorithm for evolving data streams. It is the first micro-cluster-based online clustering component that explicitely captures the density between micro-clusters via a shared density graph. The density information in the graph is then exploited for reclustering based on actual density between adjacent micro clusters. </p> <p>The algorithm is divided into two parts: </p> <p>Online micro-cluster maintenance (learning) </p> <p>For a new point <code>p</code>: </p> <ul> <li> <p>Find all micro clusters for which <code>p</code> falls within the fixed radius (clustering threshold). If no neighbor is found, a new micro cluster with a weight of 1 is created for <code>p</code>. </p> </li> <li> <p>If no neighbor is found, a new micro cluster with a weight of 1 is created for <code>p</code>. If one or more neighbors of <code>p</code> are found, we update the micro clusters by applying the appropriate fading, increasing their weight and then we try to move them closer to <code>p</code> using the Gaussian neighborhood function. </p> </li> <li> <p>Next, the shared density graph is updated. To prevent collapsing micro clusters, we will restrict the movement for micro clusters in case they come closer than \\(r\\) (clustering threshold) to each other. Finishing this process, the time stamp is also increased by 1. </p> </li> <li> <p>Finally, the cleanup will be processed. It is executed every <code>t_gap</code> time steps, removing weak micro clusters and weak entries in the shared density graph to recover memory and improve the clustering algorithm's processing speed. </p> </li> </ul> <p>Offline generation of macro clusters (clustering) </p> <p>The offline generation of macro clusters is generated through the two following steps: </p> <ul> <li> <p>The connectivity graph <code>C</code> is constructed using shared density entries between strong micro clusters. The edges in this connectivity graph with a connectivity value greater than the intersection threshold (\\(\\alpha\\)) are used to find connected components representing the final cluster. </p> </li> <li> <p>After the connectivity graph is generated, a variant of the DBSCAN algorithm proposed by Ester et al. is applied to form all macro clusters from \\(\\alpha\\)-connected micro clusters.</p> </li> </ul>"},{"location":"api/cluster/DBSTREAM/#parameters","title":"Parameters","text":"<ul> <li> <p>clustering_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>DBStream represents each micro cluster by a leader (a data point defining the micro cluster's center) and the density in an area of a user-specified radius \\(r\\) (<code>clustering_threshold</code>) around the center.</p> </li> <li> <p>fading_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>Parameter that controls the importance of historical data to current cluster. Note that <code>fading_factor</code> has to be different from <code>0</code>.</p> </li> <li> <p>cleanup_interval</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>2</code></p> <p>The time interval between two consecutive time points when the cleanup process is  conducted.</p> </li> <li> <p>intersection_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.3</code></p> <p>The intersection factor related to the area of the overlap of the micro clusters relative to the area cover by micro clusters. This parameter is used to determine whether a micro cluster or a shared density is weak.</p> </li> <li> <p>minimum_weight</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>The minimum weight for a cluster to be not \"noisy\".</p> </li> </ul>"},{"location":"api/cluster/DBSTREAM/#attributes","title":"Attributes","text":"<ul> <li> <p>n_clusters</p> <p>Number of clusters generated by the algorithm.</p> </li> <li> <p>clusters</p> <p>A set of final clusters of type <code>DBStreamMicroCluster</code>. However, these are either micro clusters, or macro clusters that are generated by merging all \\(\\alpha\\)-connected micro clusters. This set is generated through the offline phase of the algorithm.</p> </li> <li> <p>centers</p> <p>Final clusters' centers.</p> </li> <li> <p>micro_clusters</p> <p>Micro clusters generated by the algorithm. Instead of updating directly the new instance points into a nearest micro cluster, through each iteration, the weight and center will be modified so that the clusters are closer to the new points, using the Gaussian neighborhood function.</p> </li> </ul>"},{"location":"api/cluster/DBSTREAM/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 0.5], [1, 0.625], [1, 0.75], [1, 1.125], [1, 1.5], [1, 1.75],\n    [4, 1.5], [4, 2.25], [4, 2.5], [4, 3], [4, 3.25], [4, 3.5]\n]\n\ndbstream = cluster.DBSTREAM(\n    clustering_threshold=1.5,\n    fading_factor=0.05,\n    cleanup_interval=4,\n    intersection_factor=0.5,\n    minimum_weight=1\n)\n\nfor x, _ in stream.iter_array(X):\n    dbstream.learn_one(x)\n\ndbstream.predict_one({0: 1, 1: 2})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>dbstream.predict_one({0: 5, 1: 2})\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>dbstream._n_clusters\n</code></pre> <pre><code>2\n</code></pre></p>"},{"location":"api/cluster/DBSTREAM/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Michael Hahsler and Matthew Bolanos (2016, pp 1449-1461). Clustering Data Streams Based on   Shared Density between Micro-Clusters, IEEE Transactions on Knowledge and Data Engineering 28(6) .   In Proceedings of the Sixth SIAM International Conference on Data Mining,   April 20\u201322, 2006, Bethesda, MD, USA.\u00a0\u21a9</p> </li> <li> <p>Ester et al (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases   with Noise. In KDD-96 Proceedings, AAAI.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/DenStream/","title":"DenStream","text":"<p>DenStream</p> <p>DenStream <sup>1</sup> is a clustering algorithm for evolving data streams. DenStream can discover clusters with arbitrary shape and is robust against noise (outliers). </p> <p>\"Dense\" micro-clusters (named core-micro-clusters) summarise the clusters of arbitrary shape. A pruning strategy based on the concepts of potential and outlier micro-clusters guarantees the precision of the weights of the micro-clusters with limited memory. </p> <p>The algorithm is divided into two parts: </p> <p>Online micro-cluster maintenance (learning) </p> <p>For a new point <code>p</code>: </p> <ul> <li> <p>Try to merge <code>p</code> into either the nearest <code>p-micro-cluster</code> (potential), <code>o-micro-cluster</code> (outlier), or create a new <code>o-micro-cluster</code> and insert it into the outlier buffer. </p> </li> <li> <p>For each <code>T_p</code> iterations, consider the weights of all potential and outlier micro-clusters. If their weights are smaller than a certain threshold (different for each type of micro-clusters), the micro-cluster is deleted. </p> </li> </ul> <p>Offline generation of clusters on-demand (clustering) </p> <p>A variant of the DBSCAN algorithm <sup>2</sup> is used, such that all density-connected p-micro-clusters determine the final clusters. Moreover, in order for the algorithm to always be able to generate clusters, a certain number of points must be passed through the algorithm with a suitable streaming speed (number of points passed through within a unit time), indicated by <code>n_samples_init</code> and <code>stream_speed</code>.</p>"},{"location":"api/cluster/DenStream/#parameters","title":"Parameters","text":"<ul> <li> <p>decaying_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.25</code></p> <p>Parameter that controls the importance of historical data to current cluster. Note that <code>decaying_factor</code> has to be different from <code>0</code>.</p> </li> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.75</code></p> <p>Parameter to determine the threshold of outlier relative to core micro-clusters. The value of <code>beta</code> must be within the range <code>(0,1]</code>.</p> </li> <li> <p>mu</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>2</code></p> <p>Parameter to determine the threshold of outliers relative to core micro-cluster. As <code>beta * mu</code> must be greater than 1, <code>mu</code> must be within the range <code>(1/beta, inf)</code>.</p> </li> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.02</code></p> <p>Defines the epsilon neighborhood</p> </li> <li> <p>n_samples_init</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>Number of points to to initiqalize the online process</p> </li> <li> <p>stream_speed</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>Number of points arrived in unit time</p> </li> </ul>"},{"location":"api/cluster/DenStream/#attributes","title":"Attributes","text":"<ul> <li> <p>n_clusters</p> <p>Number of clusters generated by the algorithm.</p> </li> <li> <p>clusters</p> <p>A set of final clusters of type <code>MicroCluster</code>, which means that these cluster include all the required information, including number of points, creation time, weight, (weighted) linear sum, (weighted) square sum, center and radius.</p> </li> <li> <p>p_micro_clusters</p> <p>The potential core-icro-clusters that are generated by the algorithm. When a generate cluster request arrives, these p-micro-clusters will go through a variant of the DBSCAN algorithm to determine the final clusters.</p> </li> <li> <p>o_micro_clusters</p> <p>The outlier micro-clusters.</p> </li> </ul>"},{"location":"api/cluster/DenStream/#examples","title":"Examples","text":"<p>The following example uses the default parameters of the algorithm to test its functionality. The set of evolving points <code>X</code> are designed so that clusters are easily identifiable.</p> <p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [-1, -0.5], [-1, -0.625], [-1, -0.75], [-1, -1], [-1, -1.125],\n    [-1, -1.25], [-1.5, -0.5], [-1.5, -0.625], [-1.5, -0.75], [-1.5, -1],\n    [-1.5, -1.125], [-1.5, -1.25], [1, 1.5], [1, 1.75], [1, 2],\n    [4, 1.25], [4, 1.5], [4, 2.25], [4, 2.5], [4, 3],\n    [4, 3.25], [4, 3.5], [4, 3.75], [4, 4],\n]\n\ndenstream = cluster.DenStream(decaying_factor=0.01,\n                              beta=0.5,\n                              mu=2.5,\n                              epsilon=0.5,\n                              n_samples_init=10)\n\nfor x, _ in stream.iter_array(X):\n    denstream.learn_one(x)\n\ndenstream.predict_one({0: -1, 1: -2})\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>denstream.predict_one({0: 5, 1: 4})\n</code></pre> <pre><code>2\n</code></pre></p> <p><pre><code>denstream.predict_one({0: 1, 1: 1})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>denstream.n_clusters\n</code></pre> <pre><code>3\n</code></pre></p>"},{"location":"api/cluster/DenStream/#methods","title":"Methods","text":"BufferItem learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Feng et al (2006, pp 328-339). Density-Based Clustering over an Evolving Data Stream with   Noise. In Proceedings of the Sixth SIAM International Conference on Data Mining,   April 20\u201322, 2006, Bethesda, MD, USA.\u00a0\u21a9</p> </li> <li> <p>Ester et al (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial   Databases with Noise. In KDD-96 Proceedings, AAAI.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/KMeans/","title":"KMeans","text":"<p>Incremental k-means.</p> <p>The most common way to implement batch k-means is to use Lloyd's algorithm, which consists in assigning all the data points to a set of cluster centers and then moving the centers accordingly. This requires multiple passes over the data and thus isn't applicable in a streaming setting. </p> <p>In this implementation we start by finding the cluster that is closest to the current observation. We then move the cluster's central position towards the new observation. The <code>halflife</code> parameter determines by how much to move the cluster toward the new observation. You will get better results if you scale your data appropriately.</p>"},{"location":"api/cluster/KMeans/#parameters","title":"Parameters","text":"<ul> <li> <p>n_clusters</p> <p>Default \u2192 <code>5</code></p> <p>Maximum number of clusters to assign.</p> </li> <li> <p>halflife</p> <p>Default \u2192 <code>0.5</code></p> <p>Amount by which to move the cluster centers, a reasonable value if between 0 and 1.</p> </li> <li> <p>mu</p> <p>Default \u2192 <code>0</code></p> <p>Mean of the normal distribution used to instantiate cluster positions.</p> </li> <li> <p>sigma</p> <p>Default \u2192 <code>1</code></p> <p>Standard deviation of the normal distribution used to instantiate cluster positions.</p> </li> <li> <p>p</p> <p>Default \u2192 <code>2</code></p> <p>Power parameter for the Minkowski metric. When <code>p=1</code>, this corresponds to the Manhattan distance, while <code>p=2</code> corresponds to the Euclidean distance.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for generating initial centroid positions.</p> </li> </ul>"},{"location":"api/cluster/KMeans/#attributes","title":"Attributes","text":"<ul> <li> <p>centers (dict)</p> <p>Central positions of each cluster.</p> </li> </ul>"},{"location":"api/cluster/KMeans/#examples","title":"Examples","text":"<p>In the following example the cluster assignments are exactly the same as when using <code>sklearn</code>'s batch implementation. However changing the <code>halflife</code> parameter will produce different outputs.</p> <p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 2],\n    [1, 4],\n    [1, 0],\n    [-4, 2],\n    [-4, 4],\n    [-4, 0]\n]\n\nk_means = cluster.KMeans(n_clusters=2, halflife=0.1, sigma=3, seed=42)\n\nfor i, (x, _) in enumerate(stream.iter_array(X)):\n    k_means.learn_one(x)\n    print(f'{X[i]} is assigned to cluster {k_means.predict_one(x)}')\n</code></pre> <pre><code>[1, 2] is assigned to cluster 1\n[1, 4] is assigned to cluster 1\n[1, 0] is assigned to cluster 0\n[-4, 2] is assigned to cluster 1\n[-4, 4] is assigned to cluster 1\n[-4, 0] is assigned to cluster 0\n</code></pre></p> <p><pre><code>k_means.predict_one({0: 0, 1: 0})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>k_means.predict_one({0: 4, 1: 4})\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/cluster/KMeans/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> learn_predict_one <p>Equivalent to <code>k_means.learn_one(x).predict_one(x)</code>, but faster.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>Sequential k-Means Clustering \u21a9</p> </li> <li> <p>Sculley, D., 2010, April. Web-scale k-means clustering. In Proceedings of the 19th international conference on World wide web (pp. 1177-1178 \u21a9</p> </li> </ol>"},{"location":"api/cluster/ODAC/","title":"ODAC","text":"<p>The Online Divisive-Agglomerative Clustering (ODAC)<sup>1</sup> aims at continuously maintaining a hierarchical cluster structure from evolving time series data streams.</p> <p>ODAC continuosly monitors the evolution of clusters' diameters and split or merge them by gathering more data or reacting to concept drift. Such changes are supported by a confidence level that comes from the Hoeffding bound. ODAC relies on keeping the linear correlation between series to evaluate whether or not the time series hierarchy has changed. </p> <p>The distance between time-series a and b is given by <code>rnomc(a, b) = sqrt((1 - corr(a, b)) / 2)</code>, where <code>corr(a, b)</code> is the Pearson Correlation coefficient. </p> <p>In the following topics, \u03b5 stands for the Hoeffding bound and considers clusters cj with descendants ck and cs. </p> <p>The Merge Operator </p> <p>The Splitting Criteria guarantees that cluster's diameters monotonically decrease. </p> <ul> <li> <p>If diameter (ck) - diameter (cj) &gt; \u03b5 OR diameter (cs) - diameter (cj ) &gt; \u03b5:</p> <ul> <li>There is a change in the correlation structure, so merge clusters ck and cs into cj. </li> </ul> </li> </ul> <p>Splitting Criteria </p> <p>Consider: </p> <ul> <li> <p>d0: the minimum distance;</p> </li> <li> <p>d1: the farthest distance;</p> </li> <li> <p>d_avg: the average distance;</p> </li> <li> <p>d2: the second farthest distance.</p> </li> </ul> <p>Then: </p> <ul> <li> <p>if d1 - d2 &gt; \u03b5k or t &gt; \u03b5k then</p> <ul> <li> <p>if (d1 - d0)|(d1 - d_avg) - (d_avg - d0) &gt; \u03b5k then</p> <ul> <li>Split the cluster</li> </ul> </li> </ul> </li> </ul>"},{"location":"api/cluster/ODAC/#parameters","title":"Parameters","text":"<ul> <li> <p>confidence_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.9</code></p> <p>The confidence level that user wants to work.</p> </li> <li> <p>n_min</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>Number of minimum observations to gather before checking whether or not clusters must be split or merged.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> </ul>"},{"location":"api/cluster/ODAC/#attributes","title":"Attributes","text":"<ul> <li> <p>structure_changed (bool)</p> <p>This variable is true when the structure changed, produced by splitting or aggregation.</p> </li> </ul>"},{"location":"api/cluster/ODAC/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river.datasets import synth\n\nmodel = cluster.ODAC()\n\ndataset = synth.FriedmanDrift(drift_type='gra', position=(150, 200), seed=42)\n\nfor i, (x, _) in enumerate(dataset.take(500)):\n    model.learn_one(x)\n    if model.structure_changed:\n        print(f\"Structure changed at observation {i + 1}\")\n</code></pre> <pre><code>Structure changed at observation 1\nStructure changed at observation 100\nStructure changed at observation 200\nStructure changed at observation 300\n</code></pre></p> <p><pre><code>print(model.draw(n_decimal_places=2))\n</code></pre> <pre><code>ROOT d1=0.79 d2=0.76 [NOT ACTIVE]\n\u251c\u2500\u2500 CH1_LVL_1 d1=0.74 d2=0.72 [NOT ACTIVE]\n\u2502   \u251c\u2500\u2500 CH1_LVL_2 d1=&lt;Not calculated&gt; [3]\n\u2502   \u2514\u2500\u2500 CH2_LVL_2 d1=0.73 [2, 4]\n\u2514\u2500\u2500 CH2_LVL_1 d1=0.81 d2=0.78 [NOT ACTIVE]\n    \u251c\u2500\u2500 CH1_LVL_2 d1=0.73 d2=0.67 [NOT ACTIVE]\n    \u2502   \u251c\u2500\u2500 CH1_LVL_3 d1=0.72 [0, 9]\n    \u2502   \u2514\u2500\u2500 CH2_LVL_3 d1=&lt;Not calculated&gt; [1]\n    \u2514\u2500\u2500 CH2_LVL_2 d1=0.74 d2=0.73 [NOT ACTIVE]\n        \u251c\u2500\u2500 CH1_LVL_3 d1=0.71 [5, 6]\n        \u2514\u2500\u2500 CH2_LVL_3 d1=0.71 [7, 8]\n</code></pre></p> <p>You can acess some properties of the clustering model directly:</p> <p><pre><code>model.n_clusters\n</code></pre> <pre><code>11\n</code></pre></p> <p><pre><code>model.n_active_clusters\n</code></pre> <pre><code>6\n</code></pre></p> <p><pre><code>model.height\n</code></pre> <pre><code>3\n</code></pre></p> <p>These properties are also available in a summarized form:</p> <p><pre><code>model.summary\n</code></pre> <pre><code>{'n_clusters': 11, 'n_active_clusters': 6, 'height': 3}\n</code></pre></p>"},{"location":"api/cluster/ODAC/#methods","title":"Methods","text":"draw <p>Method to draw the hierarchical cluster's structure.</p> <p>Parameters</p> <ul> <li>n_decimal_places     \u2014 'int'     \u2014 defaults to <code>2</code> </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> predict_one <p>This algorithm does not predict anything. It builds a hierarchical cluster's structure.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>Hierarchical clustering of time-series data streams. \u21a9</p> </li> </ol>"},{"location":"api/cluster/STREAMKMeans/","title":"STREAMKMeans","text":"<p>STREAMKMeans</p> <p>STREAMKMeans is an alternative version of the original algorithm STREAMLSEARCH proposed by O'Callaghan et al. <sup>1</sup>, by replacing the k-medians using <code>LSEARCH</code> by the k-means algorithm. </p> <p>However, instead of using the traditional k-means, which requires a total reclustering each time the temporary chunk of data points is full, the implementation of this algorithm uses an increamental k-means. </p> <p>At first, the cluster centers are initialized with a <code>KMeans</code> instance. For a new point <code>p</code>: </p> <ul> <li> <p>If the size of chunk is less than the maximum size allowed, add the new point to the temporary chunk. </p> </li> <li> <p>When the size of chunk reaches the maximum value size allowed </p> <ul> <li>A new incremental <code>KMeans</code> instance is created. The latter will process all points in the</li> </ul> <p>temporary chunk. The centers of this new instance then become the new centers. </p> <ul> <li>All points are deleted from the temporary chunk so that new points can be added.</li> </ul> </li> <li> <p>When a prediction request arrives, the centers of the algorithm will be exactly the same as the centers of the original <code>KMeans</code> at the time of retrieval.</p> </li> </ul>"},{"location":"api/cluster/STREAMKMeans/#parameters","title":"Parameters","text":"<ul> <li> <p>chunk_size</p> <p>Default \u2192 <code>10</code></p> <p>Maximum size allowed for the temporary data chunk.</p> </li> <li> <p>n_clusters</p> <p>Default \u2192 <code>2</code></p> <p>Number of clusters generated by the algorithm.</p> </li> <li> <p>kwargs</p> <p>Other parameters passed to the incremental kmeans at <code>cluster.KMeans</code>.</p> </li> </ul>"},{"location":"api/cluster/STREAMKMeans/#attributes","title":"Attributes","text":"<ul> <li> <p>centers</p> <p>Cluster centers generated from running the incremental <code>KMeans</code> algorithm through centers of each chunk.</p> </li> </ul>"},{"location":"api/cluster/STREAMKMeans/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river import stream\n\nX = [\n    [1, 0.5], [1, 0.625], [1, 0.75], [1, 1.125], [1, 1.5], [1, 1.75],\n    [4, 1.5], [4, 2.25], [4, 2.5], [4, 3], [4, 3.25], [4, 3.5]\n]\n\nstreamkmeans = cluster.STREAMKMeans(chunk_size=3, n_clusters=2, halflife=0.5, sigma=1.5, seed=0)\n\nfor x, _ in stream.iter_array(X):\n    streamkmeans.learn_one(x)\n\nstreamkmeans.predict_one({0: 1, 1: 0})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>streamkmeans.predict_one({0: 5, 1: 2})\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/cluster/STREAMKMeans/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> <ol> <li> <p>O'Callaghan et al. (2002). Streaming-data algorithms for high-quality clustering.   In Proceedings 18th International Conference on Data Engineering, Feb 26 - March 1,   San Jose, CA, USA. DOI: 10.1109/ICDE.2002.994785.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/cluster/TextClust/","title":"TextClust","text":"<p>textClust, a clustering algorithm for text data.</p> <p>textClust <sup>1</sup><sup>2</sup> is a stream clustering algorithm for textual data that can identify and track topics over time in a stream of texts. The algorithm uses a widely popular two-phase clustering approach where the stream is first summarised in real-time. </p> <p>The result is many small preliminary clusters in the stream called <code>micro-clusters</code>. Micro-clusters maintain enough information to update and efficiently calculate the cosine similarity between them over time, based on the TF-IDF vector of their texts. Upon request, the miro-clusters can be reclustered to generate the final result using any distance-based clustering algorithm, such as hierarchical clustering. To keep the micro-clusters up-to-date, our algorithm applies a fading strategy where micro-clusters that are not updated regularly lose relevance and are eventually removed.</p>"},{"location":"api/cluster/TextClust/#parameters","title":"Parameters","text":"<ul> <li> <p>radius</p> <p>Default \u2192 <code>0.3</code></p> <p>Distance threshold to merge two micro-clusters. Must be within the range <code>(0, 1]</code></p> </li> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.0005</code></p> <p>Fading factor of micro-clusters</p> </li> <li> <p>tgap</p> <p>Default \u2192 <code>100</code></p> <p>Time between outlier removal</p> </li> <li> <p>term_fading</p> <p>Default \u2192 <code>True</code></p> <p>Determines whether individual terms should also be faded</p> </li> <li> <p>real_time_fading</p> <p>Default \u2192 <code>True</code></p> <p>Parameter that specifies whether natural time or the number of observations should be used for fading</p> </li> <li> <p>micro_distance</p> <p>Default \u2192 <code>tfidf_cosine_distance</code></p> <p>Distance metric used for clustering macro-clusters</p> </li> <li> <p>macro_distance</p> <p>Default \u2192 <code>tfidf_cosine_distance</code></p> <p>Distance metric used for clustering macro-clusters</p> </li> <li> <p>num_macro</p> <p>Default \u2192 <code>3</code></p> <p>Number of macro clusters that should be identified during the reclustering phase</p> </li> <li> <p>min_weight</p> <p>Default \u2192 <code>0</code></p> <p>Minimum weight of micro clusters to be used for reclustering</p> </li> <li> <p>auto_r</p> <p>Default \u2192 <code>False</code></p> <p>Parameter that specifies if  <code>radius</code> should be automatically updated</p> </li> <li> <p>auto_merge</p> <p>Default \u2192 <code>True</code></p> <p>Determines, if close observations shall be merged together</p> </li> <li> <p>sigma</p> <p>Default \u2192 <code>1</code></p> <p>Parameter that influences the automated trheshold adaption technique</p> </li> </ul>"},{"location":"api/cluster/TextClust/#attributes","title":"Attributes","text":"<ul> <li> <p>micro_clusters</p> <p>Micro-clusters generated by the algorithm. Micro-clusters are of type <code>textclust.microcluster</code></p> </li> </ul>"},{"location":"api/cluster/TextClust/#examples","title":"Examples","text":"<p><pre><code>from river import compose\nfrom river import feature_extraction\nfrom river import metrics\nfrom river import cluster\n\ncorpus = [\n   {\"text\":'This is the first document.',\"idd\":1, \"cluster\": 1, \"cluster\":1},\n   {\"text\":'This document is the second document.',\"idd\":2,\"cluster\": 1},\n   {\"text\":'And this is super unrelated.',\"idd\":3,\"cluster\": 2},\n   {\"text\":'Is this the first document?',\"idd\":4,\"cluster\": 1},\n   {\"text\":'This is super unrelated as well',\"idd\":5,\"cluster\": 2},\n   {\"text\":'Test text',\"idd\":6,\"cluster\": 5}\n]\n\nstopwords = [ 'stop', 'the', 'to', 'and', 'a', 'in', 'it', 'is', 'I']\n\nmetric = metrics.AdjustedRand()\n\nmodel = compose.Pipeline(\n    feature_extraction.BagOfWords(lowercase=True, ngram_range=(1, 2), stop_words=stopwords),\n    cluster.TextClust(real_time_fading=False, fading_factor=0.001, tgap=100, auto_r=True,\n    radius=0.9)\n)\n\nfor x in corpus:\n    y_pred = model.predict_one(x[\"text\"])\n    y = x[\"cluster\"]\n    metric.update(y,y_pred)\n    model.learn_one(x[\"text\"])\n\nprint(metric)\n</code></pre> <pre><code>AdjustedRand: -0.17647058823529413\n</code></pre></p>"},{"location":"api/cluster/TextClust/#methods","title":"Methods","text":"distances get_assignment get_macroclusters learn_one <p>Update the model with a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>t     \u2014 defaults to <code>None</code> </li> <li>w     \u2014 defaults to <code>None</code> </li> </ul> <p></p> microcluster predict_one <p>Predicts the cluster number for a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>w     \u2014 defaults to <code>None</code> </li> <li>type     \u2014 defaults to <code>micro</code> </li> </ul> <p>Returns</p> <p>int:     A cluster number.</p> <p></p> showclusters tfcontainer updateMacroClusters <ol> <li> <p>Assenmacher, D. und Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In: Asian Conference on Intelligent Information and Database Systems (Accepted)\u00a0\u21a9</p> </li> <li> <p>Carnein, M., Assenmacher, D., Trautmann, H. (2017). Stream Clustering of Chat Messages with Applications to Twitch Streams. In: Advances in Conceptual Modeling. ER 2017.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/compat/River2SKLClassifier/","title":"River2SKLClassifier","text":"<p>Compatibility layer from River to scikit-learn for classification.</p>"},{"location":"api/compat/River2SKLClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Classifier</p> </li> </ul>"},{"location":"api/compat/River2SKLClassifier/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> <li>classes     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> predict <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Predicted target values for each row of <code>X</code>.</p> <p></p> predict_proba <p>Predicts the target probability of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Predicted target values for each row of <code>X</code>.</p> <p></p> score <p>Return the mean accuracy on the given test data and labels.</p> <p>In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> <li>sample_weight     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>float</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> set_partial_fit_request <p>Request metadata passed to the <code>partial_fit</code> method.</p> <p>Note that this method is only relevant if <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>). Please see :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.  The options for each parameter are:  - <code>True</code>: metadata is requested, and passed to <code>partial_fit</code> if provided. The request is ignored if metadata is not provided.  - <code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>partial_fit</code>.  - <code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.  - <code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.  The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the existing request. This allows you to change the request for some parameters and not others.  .. versionadded:: 1.3  .. note::     This method is only relevant if this estimator is used as a     sub-estimator of a meta-estimator, e.g. used inside a     :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p> <p>Parameters</p> <ul> <li>classes     \u2014 Union[bool, NoneType, str]     \u2014 defaults to <code>$UNCHANGED$</code> </li> </ul> <p>Returns</p> <p>River2SKLClassifier:     object</p> <p></p> set_score_request <p>Request metadata passed to the <code>score</code> method.</p> <p>Note that this method is only relevant if <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>). Please see :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.  The options for each parameter are:  - <code>True</code>: metadata is requested, and passed to <code>score</code> if provided. The request is ignored if metadata is not provided.  - <code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>score</code>.  - <code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.  - <code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.  The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the existing request. This allows you to change the request for some parameters and not others.  .. versionadded:: 1.3  .. note::     This method is only relevant if this estimator is used as a     sub-estimator of a meta-estimator, e.g. used inside a     :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p> <p>Parameters</p> <ul> <li>sample_weight     \u2014 Union[bool, NoneType, str]     \u2014 defaults to <code>$UNCHANGED$</code> </li> </ul> <p>Returns</p> <p>River2SKLClassifier:     object</p> <p></p>"},{"location":"api/compat/River2SKLClusterer/","title":"River2SKLClusterer","text":"<p>Compatibility layer from River to scikit-learn for clustering.</p>"},{"location":"api/compat/River2SKLClusterer/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Clusterer</p> </li> </ul>"},{"location":"api/compat/River2SKLClusterer/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> fit_predict <p>Perform clustering on <code>X</code> and returns cluster labels.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>ndarray of shape (n_samples,), dtype=np.int64</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> predict <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Transformed output.</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p>"},{"location":"api/compat/River2SKLRegressor/","title":"River2SKLRegressor","text":"<p>Compatibility layer from River to scikit-learn for regression.</p>"},{"location":"api/compat/River2SKLRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Regressor</p> </li> </ul>"},{"location":"api/compat/River2SKLRegressor/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> </ul> <p>Returns</p> <p>self</p> <p></p> predict <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>np.ndarray:     Predicted target values for each row of <code>X</code>.</p> <p></p> score <p>Return the coefficient of determination of the prediction.</p> <p>The coefficient of determination :math:<code>R^2</code> is defined as :math:<code>(1 - \\frac{u}{v})</code>, where :math:<code>u</code> is the residual sum of squares <code>((y_true - y_pred)** 2).sum()</code> and :math:<code>v</code> is the total sum of squares <code>((y_true - y_true.mean()) ** 2).sum()</code>. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of <code>y</code>, disregarding the input features, would get a :math:<code>R^2</code> score of 0.0.</p> <p>Parameters</p> <ul> <li>X </li> <li>y </li> <li>sample_weight     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>float</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> set_score_request <p>Request metadata passed to the <code>score</code> method.</p> <p>Note that this method is only relevant if <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>). Please see :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.  The options for each parameter are:  - <code>True</code>: metadata is requested, and passed to <code>score</code> if provided. The request is ignored if metadata is not provided.  - <code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>score</code>.  - <code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.  - <code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.  The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the existing request. This allows you to change the request for some parameters and not others.  .. versionadded:: 1.3  .. note::     This method is only relevant if this estimator is used as a     sub-estimator of a meta-estimator, e.g. used inside a     :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p> <p>Parameters</p> <ul> <li>sample_weight     \u2014 Union[bool, NoneType, str]     \u2014 defaults to <code>$UNCHANGED$</code> </li> </ul> <p>Returns</p> <p>River2SKLRegressor:     object</p> <p></p>"},{"location":"api/compat/River2SKLTransformer/","title":"River2SKLTransformer","text":"<p>Compatibility layer from River to scikit-learn for transformation.</p>"},{"location":"api/compat/River2SKLTransformer/#parameters","title":"Parameters","text":"<ul> <li> <p>river_estimator</p> <p>Type \u2192 base.Transformer</p> </li> </ul>"},{"location":"api/compat/River2SKLTransformer/#methods","title":"Methods","text":"fit <p>Fits to an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> fit_transform <p>Fit to data, then transform it.</p> <p>Fits transformer to <code>X</code> and <code>y</code> with optional parameters <code>fit_params</code> and returns a transformed version of <code>X</code>.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> <li>fit_params </li> </ul> <p>Returns</p> <p>ndarray array of shape (n_samples, n_features_new)</p> <p></p> get_metadata_routing <p>Get metadata routing of this object.</p> <p>Please check :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing mechanism works.</p> <p>Returns</p> <p>MetadataRequest</p> <p></p> get_params <p>Get parameters for this estimator.</p> <p>Parameters</p> <ul> <li>deep     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>dict</p> <p></p> partial_fit <p>Fits incrementally on a portion of a dataset.</p> <p>Parameters</p> <ul> <li>X </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>self</p> <p></p> set_output <p>Set output container.</p> <p>See :ref:<code>sphx_glr_auto_examples_miscellaneous_plot_set_output.py</code> for an example on how to use the API.</p> <p>Parameters</p> <ul> <li>transform     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> set_params <p>Set the parameters of this estimator.</p> <p>The method works on simple estimators as well as on nested objects (such as :class:<code>~sklearn.pipeline.Pipeline</code>). The latter have parameters of the form <code>&lt;component&gt;__&lt;parameter&gt;</code> so that it's possible to update each component of a nested object.</p> <p>Parameters</p> <ul> <li>params </li> </ul> <p>Returns</p> <p>estimator instance</p> <p></p> transform <p>Predicts the target of an entire dataset contained in memory.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>Transformed output.</p> <p></p>"},{"location":"api/compat/SKL2RiverClassifier/","title":"SKL2RiverClassifier","text":"<p>Compatibility layer from scikit-learn to River for classification.</p>"},{"location":"api/compat/SKL2RiverClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 sklearn_base.ClassifierMixin</p> <p>A scikit-learn regressor which has a <code>partial_fit</code> method.</p> </li> <li> <p>classes</p> <p>Type \u2192 list</p> </li> </ul>"},{"location":"api/compat/SKL2RiverClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import compat\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stream\nfrom sklearn import linear_model\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.load_breast_cancer(),\n    shuffle=True,\n    seed=42\n)\n\nmodel = preprocessing.StandardScaler()\nmodel |= compat.convert_sklearn_to_river(\n    estimator=linear_model.SGDClassifier(\n        loss='log_loss',\n        eta0=0.01,\n        learning_rate='constant'\n    ),\n    classes=[False, True]\n)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.198029\n</code></pre></p>"},{"location":"api/compat/SKL2RiverClassifier/#methods","title":"Methods","text":"learn_many learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_many predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_many predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/compat/SKL2RiverRegressor/","title":"SKL2RiverRegressor","text":"<p>Compatibility layer from scikit-learn to River for regression.</p>"},{"location":"api/compat/SKL2RiverRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 sklearn_base.BaseEstimator</p> <p>A scikit-learn transformer which has a <code>partial_fit</code> method.</p> </li> </ul>"},{"location":"api/compat/SKL2RiverRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import compat\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stream\nfrom sklearn import linear_model\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.load_diabetes(),\n    shuffle=True,\n    seed=42\n)\n\nscaler = preprocessing.StandardScaler()\nsgd_reg = compat.convert_sklearn_to_river(linear_model.SGDRegressor())\nmodel = scaler | sgd_reg\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 84.501421\n</code></pre></p>"},{"location":"api/compat/SKL2RiverRegressor/#methods","title":"Methods","text":"learn_many learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_many predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/compat/convert-river-to-sklearn/","title":"convert_river_to_sklearn","text":"<p>Wraps a river estimator to make it compatible with scikit-learn.</p>"},{"location":"api/compat/convert-river-to-sklearn/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 base.Estimator</p> </li> </ul>"},{"location":"api/compat/convert-sklearn-to-river/","title":"convert_sklearn_to_river","text":"<p>Wraps a scikit-learn estimator to make it compatible with river.</p>"},{"location":"api/compat/convert-sklearn-to-river/#parameters","title":"Parameters","text":"<ul> <li> <p>estimator</p> <p>Type \u2192 sklearn_base.BaseEstimator</p> </li> <li> <p>classes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>Class names necessary for classifiers.</p> </li> </ul>"},{"location":"api/compose/Discard/","title":"Discard","text":"<p>Removes features.</p> <p>This can be used in a pipeline when you want to remove certain features. The <code>transform_one</code> method is pure, and therefore returns a fresh new dictionary instead of removing the specified keys from the input.</p>"},{"location":"api/compose/Discard/#parameters","title":"Parameters","text":"<ul> <li> <p>keys</p> <p>Type \u2192 tuple[base.typing.FeatureName]</p> <p>Key(s) to discard.</p> </li> </ul>"},{"location":"api/compose/Discard/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12, 'c': 13}\ncompose.Discard('a', 'b').transform_one(x)\n</code></pre> <pre><code>{'c': 13}\n</code></pre></p> <p>You can chain a discarder with any estimator in order to apply said estimator to the desired features.</p> <p><pre><code>from river import feature_extraction as fx\n\nx = {'sales': 10, 'shop': 'Ikea', 'country': 'Sweden'}\n\npipeline = (\n    compose.Discard('shop', 'country') |\n    fx.PolynomialExtender()\n)\npipeline.transform_one(x)\n</code></pre> <pre><code>{'sales': 10, 'sales*sales': 100}\n</code></pre></p>"},{"location":"api/compose/Discard/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/FuncTransformer/","title":"FuncTransformer","text":"<p>Wraps a function to make it usable in a pipeline.</p> <p>There is often a need to apply an arbitrary transformation to a set of features. For instance, this could involve parsing a date and then extracting the hour from said date. If you're processing a stream of data, then you can do this yourself by calling the necessary code at your leisure. On the other hand, if you want to do this as part of a pipeline, then you need to follow a simple convention. </p> <p>To use a function as part of a pipeline, take as input a <code>dict</code> of features and output a <code>dict</code>. Once you have initialized this class with your function, then you can use it like you would use any other (unsupervised) transformer. </p> <p>It is up to you if you want your function to be pure or not. By pure we refer to a function that doesn't modify its input. However, we recommend writing pure functions because this reduces the chances of inserting bugs into your pipeline.</p>"},{"location":"api/compose/FuncTransformer/#parameters","title":"Parameters","text":"<ul> <li> <p>func</p> <p>Type \u2192 typing.Callable[[dict], dict]</p> <p>A function that takes as input a <code>dict</code> and outputs a <code>dict</code>.</p> </li> </ul>"},{"location":"api/compose/FuncTransformer/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nimport datetime as dt\nfrom river import compose\n\nx = {'date': '2019-02-14'}\n\ndef parse_date(x):\n    date = dt.datetime.strptime(x['date'], '%Y-%m-%d')\n    x['is_weekend'] = date.day in (5, 6)\n    x['hour'] = date.hour\n    return x\n\nt = compose.FuncTransformer(parse_date)\npprint(t.transform_one(x))\n</code></pre> <pre><code>{'date': '2019-02-14', 'hour': 0, 'is_weekend': False}\n</code></pre></p> <p>The above example is not pure because it modifies the input. The following example is pure and produces the same output:</p> <p><pre><code>def parse_date(x):\n    date = dt.datetime.strptime(x['date'], '%Y-%m-%d')\n    return {'is_weekend': date.day in (5, 6), 'hour': date.hour}\n\nt = compose.FuncTransformer(parse_date)\npprint(t.transform_one(x))\n</code></pre> <pre><code>{'hour': 0, 'is_weekend': False}\n</code></pre></p> <p>The previous example doesn't include the <code>date</code> feature because it returns a new <code>dict</code>. However, a common usecase is to add a feature to an existing set of features. You can do this in a pure way by unpacking the input <code>dict</code> into the output <code>dict</code>:</p> <p><pre><code>def parse_date(x):\n    date = dt.datetime.strptime(x['date'], '%Y-%m-%d')\n    return {'is_weekend': date.day in (5, 6), 'hour': date.hour, **x}\n\nt = compose.FuncTransformer(parse_date)\npprint(t.transform_one(x))\n</code></pre> <pre><code>{'date': '2019-02-14', 'hour': 0, 'is_weekend': False}\n</code></pre></p> <p>You can add <code>FuncTransformer</code> to a pipeline just like you would with any other transformer.</p> <p><pre><code>from river import naive_bayes\n\npipeline = compose.FuncTransformer(parse_date) | naive_bayes.MultinomialNB()\npipeline\n</code></pre> <pre><code>Pipeline (\n  FuncTransformer (\n    func=\"parse_date\"\n  ),\n  MultinomialNB (\n    alpha=1.\n  )\n)\n</code></pre></p> <p>If you provide a function without wrapping it, then the pipeline will do it for you:</p> <pre><code>pipeline = parse_date | naive_bayes.MultinomialNB()\n</code></pre>"},{"location":"api/compose/FuncTransformer/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Grouper/","title":"Grouper","text":"<p>Applies a transformer within different groups.</p> <p>This transformer allows you to split your data into groups and apply a transformer within each group. This happens in a streaming manner, which means that the groups are discovered online. A separate copy of the provided transformer is made whenever a new group appears. The groups are defined according to one or more keys.</p>"},{"location":"api/compose/Grouper/#parameters","title":"Parameters","text":"<ul> <li> <p>transformer</p> <p>Type \u2192 base.Transformer</p> </li> <li> <p>by</p> <p>Type \u2192 base.typing.FeatureName | list[base.typing.FeatureName]</p> <p>The field on which to group the data. This can either by a single value, or a list of values.</p> </li> </ul>"},{"location":"api/compose/Grouper/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Pipeline/","title":"Pipeline","text":"<p>A pipeline of estimators.</p> <p>Pipelines allow you to chain different steps into a sequence. Typically, when doing supervised learning, a pipeline contains one or more transformation steps, whilst it's a regressor or a classifier. It is highly recommended to use pipelines with River. Indeed, in an online learning setting, it is very practical to have a model defined as a single object. Take a look at the user guide for further information and practical examples. </p> <p>One special thing to take notice to is the way transformers are handled. It is usual to predict something for a sample and wait for the ground truth to arrive. In such a scenario, the features are seen before the ground truth arrives. Therefore, the unsupervised parts of the pipeline are updated when <code>predict_one</code> and <code>predict_proba_one</code> are called. Usually the unsupervised parts of the pipeline are all the steps that precede the final step, which is a supervised model. However, some transformers are supervised and are therefore also updated during calls to <code>learn_one</code>.</p>"},{"location":"api/compose/Pipeline/#parameters","title":"Parameters","text":"<ul> <li> <p>steps</p> <p>Ideally, a list of (name, estimator) tuples. A name is automatically inferred if none is provided.</p> </li> </ul>"},{"location":"api/compose/Pipeline/#examples","title":"Examples","text":"<p>The recommended way to declare a pipeline is to use the <code>|</code> operator. The latter allows you to chain estimators in a very terse manner:</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nscaler = preprocessing.StandardScaler()\nlog_reg = linear_model.LinearRegression()\nmodel = scaler | log_reg\n</code></pre> <p>This results in a pipeline that stores each step inside a dictionary.</p> <p><pre><code>model\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre></p> <p>You can access parts of a pipeline in the same manner as a dictionary:</p> <p><pre><code>model['LinearRegression']\n</code></pre> <pre><code>LinearRegression (\n  optimizer=SGD (\n    lr=Constant (\n      learning_rate=0.01\n    )\n  )\n  loss=Squared ()\n  l2=0.\n  l1=0.\n  intercept_init=0.\n  intercept_lr=Constant (\n    learning_rate=0.01\n  )\n  clip_gradient=1e+12\n  initializer=Zeros ()\n)\n</code></pre></p> <p>Note that you can also declare a pipeline by using the <code>compose.Pipeline</code> constructor method, which is slightly more verbose:</p> <pre><code>from river import compose\n\nmodel = compose.Pipeline(scaler, log_reg)\n</code></pre> <p>By using a <code>compose.TransformerUnion</code>, you can define complex pipelines that apply different steps to different parts of the data. For instance, we can extract word counts from text data, and extract polynomial features from numeric data.</p> <pre><code>from river import feature_extraction as fx\n\ntfidf = fx.TFIDF('text')\ncounts = fx.BagOfWords('text')\ntext_part = compose.Select('text') | (tfidf + counts)\n\nnum_part = compose.Select('a', 'b') | fx.PolynomialExtender()\n\nmodel = text_part + num_part\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n</code></pre> <p>The following shows an example of using <code>debug_one</code> to visualize how the information flows and changes throughout the pipeline.</p> <p><pre><code>from river import compose\nfrom river import naive_bayes\n\ndataset = [\n    ('A positive comment', True),\n    ('A negative comment', False),\n    ('A happy comment', True),\n    ('A lovely comment', True),\n    ('A harsh comment', False)\n]\n\ntfidf = fx.TFIDF() | compose.Prefixer('tfidf_')\ncounts = fx.BagOfWords() | compose.Prefixer('count_')\nmnb = naive_bayes.MultinomialNB()\nmodel = (tfidf + counts) | mnb\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nx = dataset[0][0]\nreport = model.debug_one(dataset[0][0])\nprint(report)\n</code></pre> <pre><code>0. Input\n--------\nA positive comment\n1. Transformer union\n--------------------\n    1.0 TFIDF | Prefixer\n    --------------------\n    tfidf_comment: 0.43017 (float)\n    tfidf_positive: 0.90275 (float)\n    1.1 BagOfWords | Prefixer\n    -------------------------\n    count_comment: 1 (int)\n    count_positive: 1 (int)\ncount_comment: 1 (int)\ncount_positive: 1 (int)\ntfidf_comment: 0.43017 (float)\ntfidf_positive: 0.90275 (float)\n2. MultinomialNB\n----------------\nFalse: 0.19221\nTrue: 0.80779\n</code></pre></p>"},{"location":"api/compose/Pipeline/#methods","title":"Methods","text":"debug_one <p>Displays the state of a set of features as it goes through the pipeline.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>show_types     \u2014 defaults to <code>True</code> </li> <li>n_decimals     \u2014 defaults to <code>5</code> </li> </ul> <p></p> forecast <p>Return a forecast.</p> <p>Only works if each estimator has a <code>transform_one</code> method and the final estimator has a <code>forecast</code> method. This is the case of time series models from the <code>time_series</code> module.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_many <p>Fit to a mini-batch.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series | None'     \u2014 defaults to <code>None</code> </li> <li>params </li> </ul> <p></p> learn_one <p>Fit to a single instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> <li>params </li> </ul> <p></p> predict_many <p>Call transform_many, and then predict_many on the final step.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_one <p>Call <code>transform_one</code> on the first steps and <code>predict_one</code> on the last step.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p> predict_proba_many <p>Call transform_many, and then predict_proba_many on the final step.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Call <code>transform_one</code> on the first steps and <code>predict_proba_one</code> on the last step.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p> score_one <p>Call <code>transform_one</code> on the first steps and <code>score_one</code> on the last step.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p> transform_many <p>Apply each transformer in the pipeline to some features.</p> <p>The final step in the pipeline will be applied if it is a transformer. If not, then it will be ignored and the output from the penultimate step will be returned. Note that the steps that precede the final step are assumed to all be transformers.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Apply each transformer in the pipeline to some features.</p> <p>The final step in the pipeline will be applied if it is a transformer. If not, then it will be ignored and the output from the penultimate step will be returned. Note that the steps that precede the final step are assumed to all be transformers.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>params </li> </ul> <p></p>"},{"location":"api/compose/Prefixer/","title":"Prefixer","text":"<p>Prepends a prefix on features names.</p>"},{"location":"api/compose/Prefixer/#parameters","title":"Parameters","text":"<ul> <li> <p>prefix</p> <p>Type \u2192 str</p> </li> </ul>"},{"location":"api/compose/Prefixer/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12}\ncompose.Prefixer('prefix_').transform_one(x)\n</code></pre> <pre><code>{'prefix_a': 42, 'prefix_b': 12}\n</code></pre></p>"},{"location":"api/compose/Prefixer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Renamer/","title":"Renamer","text":"<p>Renames features following substitution rules.</p>"},{"location":"api/compose/Renamer/#parameters","title":"Parameters","text":"<ul> <li> <p>mapping</p> <p>Type \u2192 dict[str, str]</p> <p>Dictionnary describing substitution rules. Keys in <code>mapping</code> that are not a feature's name are silently ignored.</p> </li> </ul>"},{"location":"api/compose/Renamer/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nmapping = {'a': 'v', 'c': 'o'}\nx = {'a': 42, 'b': 12}\ncompose.Renamer(mapping).transform_one(x)\n</code></pre> <pre><code>{'b': 12, 'v': 42}\n</code></pre></p>"},{"location":"api/compose/Renamer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Select/","title":"Select","text":"<p>Selects features.</p> <p>This can be used in a pipeline when you want to select certain features. The <code>transform_one</code> method is pure, and therefore returns a fresh new dictionary instead of filtering the specified keys from the input.</p>"},{"location":"api/compose/Select/#parameters","title":"Parameters","text":"<ul> <li> <p>keys</p> <p>Type \u2192 tuple[base.typing.FeatureName]</p> <p>Key(s) to keep.</p> </li> </ul>"},{"location":"api/compose/Select/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12, 'c': 13}\ncompose.Select('c').transform_one(x)\n</code></pre> <pre><code>{'c': 13}\n</code></pre></p> <p>You can chain a selector with any estimator in order to apply said estimator to the desired features.</p> <p><pre><code>from river import feature_extraction as fx\n\nx = {'sales': 10, 'shop': 'Ikea', 'country': 'Sweden'}\n\npipeline = (\n    compose.Select('sales') |\n    fx.PolynomialExtender()\n)\npipeline.transform_one(x)\n</code></pre> <pre><code>{'sales': 10, 'sales*sales': 100}\n</code></pre></p> <p>This transformer also supports mini-batch processing:</p> <p><pre><code>import random\nfrom river import compose\n\nrandom.seed(42)\nX = [{\"x_1\": random.uniform(8, 12), \"x_2\": random.uniform(8, 12)} for _ in range(6)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x_1': 10.557707193831535, 'x_2': 8.100043020890668}\n{'x_1': 9.100117273476478, 'x_2': 8.892842952595291}\n{'x_1': 10.94588485665605, 'x_2': 10.706797949691644}\n{'x_1': 11.568718270819382, 'x_2': 8.347755330517664}\n{'x_1': 9.687687278741082, 'x_2': 8.119188877752281}\n{'x_1': 8.874551899214413, 'x_2': 10.021421152413449}\n</code></pre></p> <pre><code>import pandas as pd\nX = pd.DataFrame.from_dict(X)\n</code></pre> <p>You can then call <code>transform_many</code> to transform a mini-batch of features:</p> <p><pre><code>compose.Select('x_2').transform_many(X)\n</code></pre> <pre><code>    x_2\n0   8.100043\n1   8.892843\n2  10.706798\n3   8.347755\n4   8.119189\n5  10.021421\n</code></pre></p>"},{"location":"api/compose/Select/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/SelectType/","title":"SelectType","text":"<p>Selects features based on their type.</p> <p>This is practical when you want to apply different preprocessing steps to different kinds of features. For instance, a common usecase is to apply a <code>preprocessing.StandardScaler</code> to numeric features and a <code>preprocessing.OneHotEncoder</code> to categorical features.</p>"},{"location":"api/compose/SelectType/#parameters","title":"Parameters","text":"<ul> <li> <p>types</p> <p>Type \u2192 tuple[type]</p> <p>Python types which you want to select. Under the hood, the <code>isinstance</code> method will be used to check if a value is of a given type.</p> </li> </ul>"},{"location":"api/compose/SelectType/#examples","title":"Examples","text":"<pre><code>import numbers\nfrom river import compose\nfrom river import linear_model\nfrom river import preprocessing\n\nnum = compose.SelectType(numbers.Number) | preprocessing.StandardScaler()\ncat = compose.SelectType(str) | preprocessing.OneHotEncoder()\nmodel = (num + cat) | linear_model.LogisticRegression()\n</code></pre>"},{"location":"api/compose/SelectType/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/Suffixer/","title":"Suffixer","text":"<p>Appends a suffix on features names.</p>"},{"location":"api/compose/Suffixer/#parameters","title":"Parameters","text":"<ul> <li> <p>suffix</p> <p>Type \u2192 str</p> </li> </ul>"},{"location":"api/compose/Suffixer/#examples","title":"Examples","text":"<p><pre><code>from river import compose\n\nx = {'a': 42, 'b': 12}\ncompose.Suffixer('_suffix').transform_one(x)\n</code></pre> <pre><code>{'a_suffix': 42, 'b_suffix': 12}\n</code></pre></p>"},{"location":"api/compose/Suffixer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/compose/TargetTransformRegressor/","title":"TargetTransformRegressor","text":"<p>Modifies the target before training.</p> <p>The user is expected to check that <code>func</code> and <code>inverse_func</code> are coherent with each other.</p>"},{"location":"api/compose/TargetTransformRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regression model to wrap.</p> </li> <li> <p>func</p> <p>Type \u2192 typing.Callable</p> <p>A function modifying the target before training.</p> </li> <li> <p>inverse_func</p> <p>Type \u2192 typing.Callable</p> <p>A function to return to the target's original space.</p> </li> </ul>"},{"location":"api/compose/TargetTransformRegressor/#examples","title":"Examples","text":"<p><pre><code>import math\nfrom river import compose\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    compose.TargetTransformRegressor(\n        regressor=linear_model.LinearRegression(intercept_lr=0.15),\n        func=math.log,\n        inverse_func=math.exp\n    )\n)\nmetric = metrics.MSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MSE: 10.999752\n</code></pre></p>"},{"location":"api/compose/TargetTransformRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/compose/TransformerProduct/","title":"TransformerProduct","text":"<p>Computes interactions between the outputs of a set transformers.</p> <p>This is for when you want to add interaction terms between groups of features. It may also be used an alternative to <code>feature_extraction.PolynomialExtender</code> when the latter is overkill.</p>"},{"location":"api/compose/TransformerProduct/#parameters","title":"Parameters","text":"<ul> <li> <p>transformers</p> <p>Ideally, a list of (name, estimator) tuples. A name is automatically inferred if none is provided.</p> </li> </ul>"},{"location":"api/compose/TransformerProduct/#examples","title":"Examples","text":"<p>Let's say we have a certain set of features with two groups. In practice these may be different namespaces, such one for items and the other for users.</p> <pre><code>x = dict(\n    a=0, b=1,  # group 1\n    x=2, y=3   # group 2\n)\n</code></pre> <p>We might want to add interaction terms between groups <code>('a', 'b')</code> and <code>('x', 'y')</code>, as so:</p> <p><pre><code>from pprint import pprint\nfrom river.compose import Select, TransformerProduct\n\nproduct = TransformerProduct(\n    Select('a', 'b'),\n    Select('x', 'y')\n)\npprint(product.transform_one(x))\n</code></pre> <pre><code>{'a*x': 0, 'a*y': 0, 'b*x': 2, 'b*y': 3}\n</code></pre></p> <p>This can also be done with the following shorthand:</p> <p><pre><code>product = Select('a', 'b') * Select('x', 'y')\npprint(product.transform_one(x))\n</code></pre> <pre><code>{'a*x': 0, 'a*y': 0, 'b*x': 2, 'b*y': 3}\n</code></pre></p> <p>If you want to include the original terms, you can do something like this:</p> <p><pre><code>group_1 = Select('a', 'b')\ngroup_2 = Select('x', 'y')\nproduct = group_1 + group_2 + group_1 * group_2\npprint(product.transform_one(x))\n</code></pre> <pre><code>{'a': 0, 'a*x': 0, 'a*y': 0, 'b': 1, 'b*x': 2, 'b*y': 3, 'x': 2, 'y': 3}\n</code></pre></p>"},{"location":"api/compose/TransformerProduct/#methods","title":"Methods","text":"learn_many <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_many <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/compose/TransformerUnion/","title":"TransformerUnion","text":"<p>Packs multiple transformers into a single one.</p> <p>Pipelines allow you to apply steps sequentially. Therefore, the output of a step becomes the input of the next one. In many cases, you may want to pass the output of a step to multiple steps. This simple transformer allows you to do so. In other words, it enables you to apply particular steps to different parts of an input. A typical example is when you want to scale numeric features and one-hot encode categorical features. </p> <p>This transformer is essentially a list of transformers. Whenever it is updated, it loops through each transformer and updates them. Meanwhile, calling <code>transform_one</code> collects the output of each transformer and merges them into a single dictionary.</p>"},{"location":"api/compose/TransformerUnion/#parameters","title":"Parameters","text":"<ul> <li> <p>transformers</p> <p>Ideally, a list of (name, estimator) tuples. A name is automatically inferred if none is provided.</p> </li> </ul>"},{"location":"api/compose/TransformerUnion/#examples","title":"Examples","text":"<p>Take the following dataset:</p> <pre><code>X = [\n    {'place': 'Taco Bell', 'revenue': 42},\n    {'place': 'Burger King', 'revenue': 16},\n    {'place': 'Burger King', 'revenue': 24},\n    {'place': 'Taco Bell', 'revenue': 58},\n    {'place': 'Burger King', 'revenue': 20},\n    {'place': 'Taco Bell', 'revenue': 50}\n]\n</code></pre> <p>As an example, let's assume we want to compute two aggregates of a dataset. We therefore define two <code>feature_extraction.Agg</code>s and initialize a <code>TransformerUnion</code> with them:</p> <pre><code>from river import compose\nfrom river import feature_extraction\nfrom river import stats\n\nmean = feature_extraction.Agg(\n    on='revenue', by='place',\n    how=stats.Mean()\n)\ncount = feature_extraction.Agg(\n    on='revenue', by='place',\n    how=stats.Count()\n)\nagg = compose.TransformerUnion(mean, count)\n</code></pre> <p>We can now update each transformer and obtain their output with a single function call:</p> <p><pre><code>from pprint import pprint\nfor x in X:\n    agg.learn_one(x)\n    pprint(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_count_by_place': 1, 'revenue_mean_by_place': 42.0}\n{'revenue_count_by_place': 1, 'revenue_mean_by_place': 16.0}\n{'revenue_count_by_place': 2, 'revenue_mean_by_place': 20.0}\n{'revenue_count_by_place': 2, 'revenue_mean_by_place': 50.0}\n{'revenue_count_by_place': 3, 'revenue_mean_by_place': 20.0}\n{'revenue_count_by_place': 3, 'revenue_mean_by_place': 50.0}\n</code></pre></p> <p>Note that you can use the <code>+</code> operator as a shorthand notation:</p> <p>agg = mean + count</p> <p>This allows you to build complex pipelines in a very terse manner. For instance, we can create a pipeline that scales each feature and fits a logistic regression as so:</p> <pre><code>from river import linear_model as lm\nfrom river import preprocessing as pp\n\nmodel = (\n    (mean + count) |\n    pp.StandardScaler() |\n    lm.LogisticRegression()\n)\n</code></pre> <p>Whice is equivalent to the following code:</p> <pre><code>model = compose.Pipeline(\n    compose.TransformerUnion(mean, count),\n    pp.StandardScaler(),\n    lm.LogisticRegression()\n)\n</code></pre> <p>Note that you access any part of a <code>TransformerUnion</code> by name:</p> <p><pre><code>model['TransformerUnion']['Agg']\n</code></pre> <pre><code>Agg (\n    on=\"revenue\"\n    by=['place']\n    how=Mean ()\n)\n</code></pre></p> <p><pre><code>model['TransformerUnion']['Agg1']\n</code></pre> <pre><code>Agg (\n    on=\"revenue\"\n    by=['place']\n    how=Count ()\n)\n</code></pre></p> <p>You can also manually provide a name for each step:</p> <pre><code>agg = compose.TransformerUnion(\n    ('Mean revenue by place', mean),\n    ('# by place', count)\n)\n</code></pre> <p>Mini-batch example:</p> <pre><code>X = pd.DataFrame([\n    {\"place\": 2, \"revenue\": 42},\n    {\"place\": 3, \"revenue\": 16},\n    {\"place\": 3, \"revenue\": 24},\n    {\"place\": 2, \"revenue\": 58},\n    {\"place\": 3, \"revenue\": 20},\n    {\"place\": 2, \"revenue\": 50},\n])\n</code></pre> <p>Since we need a transformer with mini-batch support to demonstrate, we shall use a <code>StandardScaler</code>.</p> <p><pre><code>from river import compose\nfrom river import preprocessing\n\nagg = (\n    compose.Select(\"place\") +\n    (compose.Select(\"revenue\") | preprocessing.StandardScaler())\n)\n\nagg.learn_many(X)\nagg.transform_many(X)\n</code></pre> <pre><code>   place   revenue\n0      2  0.441250\n1      3 -1.197680\n2      3 -0.693394\n3      2  1.449823\n4      3 -0.945537\n5      2  0.945537\n</code></pre></p>"},{"location":"api/compose/TransformerUnion/#methods","title":"Methods","text":"learn_many <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Update each transformer.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_many <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Passes the data through each transformer and packs the results together.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/compose/learn-during-predict/","title":"learn_during_predict","text":"<p>A context manager for fitting unsupervised steps during prediction.</p> <p>Usually, unsupervised parts of a pipeline are updated during <code>learn_one</code>. However, in the case of online learning, it is possible to update them before, during the prediction step. This context manager allows you to do so. </p> <p>This usually brings a slight performance improvement. But it is not done by default because it is not intuitive and is more difficult to test. It also means that you have to call <code>predict_one</code> before <code>learn_one</code> in order for the whole pipeline to be updated.</p>"},{"location":"api/compose/learn-during-predict/#examples","title":"Examples","text":"<p>Let's first see what methods are called if we just call <code>predict_one</code>.</p> <p><pre><code>import io\nimport logging\nfrom river import compose\nfrom river import datasets\nfrom river import linear_model\nfrom river import preprocessing\nfrom river import utils\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    linear_model.LinearRegression()\n)\n\nclass_condition = lambda x: x.__class__.__name__ in ('StandardScaler', 'LinearRegression')\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\nlogs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition):\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_one(x)\n\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.transform_one\nLinearRegression.predict_one\n</code></pre></p> <p>Now let's use the context manager and see what methods get called.</p> <p><pre><code>logs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition), compose.learn_during_predict():\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_one(x)\n\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.learn_one\nStandardScaler.transform_one\nLinearRegression.predict_one\n</code></pre></p> <p>We can see that the scaler did not get updated before transforming the data.</p> <p>This also works when working with mini-batches.</p> <p><pre><code>logs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition):\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_many(pd.DataFrame([x]))\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.transform_many\nLinearRegression.predict_many\n</code></pre></p> <p><pre><code>logs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition), compose.learn_during_predict():\n    for x, y in datasets.TrumpApproval().take(1):\n        _ = model.predict_many(pd.DataFrame([x]))\nprint(logs.getvalue())\n</code></pre> <pre><code>StandardScaler.learn_many\nStandardScaler.transform_many\nLinearRegression.predict_many\n</code></pre></p>"},{"location":"api/conf/Interval/","title":"Interval","text":"<p>An object to represent a (prediction) interval.</p> <p>Users are not expected to use this class as-is. Instead, they should use the <code>with_interval</code> parameter of the <code>predict_one</code> method of any regressor or classifier wrapped with a conformal prediction method.</p>"},{"location":"api/conf/Interval/#parameters","title":"Parameters","text":"<ul> <li> <p>lower</p> <p>Type \u2192 float</p> <p>The lower bound of the interval.</p> </li> <li> <p>upper</p> <p>Type \u2192 float</p> <p>The upper bound of the interval.</p> </li> </ul>"},{"location":"api/conf/Interval/#attributes","title":"Attributes","text":"<ul> <li> <p>center</p> <p>The center of the interval.</p> </li> <li> <p>width</p> <p>The width of the interval.</p> </li> </ul>"},{"location":"api/conf/RegressionJackknife/","title":"RegressionJackknife","text":"<p>Jackknife method for regression.</p> <p>This is a conformal prediction method for regression. It is based on the jackknife method. The idea is to compute the quantiles of the residuals of the regressor. The prediction interval is then computed as the prediction of the regressor plus the quantiles of the residuals. </p> <p>This works naturally online, as the quantiles of the residuals are updated at each iteration. Each residual is produced before the regressor is updated, which ensures the predicted intervals are not optimistic. </p> <p>Note that the produced intervals are marginal and not conditional. This means that the intervals are not adjusted for the features <code>x</code>. This is a limitation of the jackknife method. However, the jackknife method is very simple and efficient. It is also very robust to outliers.</p>"},{"location":"api/conf/RegressionJackknife/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>The regressor to be wrapped.</p> </li> <li> <p>confidence_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The confidence level of the prediction intervals.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The size of the window used to compute the quantiles of the residuals. If <code>None</code>, the quantiles are computed over the whole history. It is advised to set this if you expect the model's performance to change over time.</p> </li> </ul>"},{"location":"api/conf/RegressionJackknife/#examples","title":"Examples","text":"<pre><code>from river import conf\nfrom river import datasets\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stats\n\ndataset = datasets.TrumpApproval()\n\nmodel = conf.RegressionJackknife(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression(intercept_lr=.1)\n    ),\n    confidence_level=0.9\n)\n\nvalidity = stats.Mean()\nefficiency = stats.Mean()\n\nfor x, y in dataset:\n    interval = model.predict_one(x, with_interval=True)\n    validity.update(y in interval)\n    efficiency.update(interval.width)\n    model.learn_one(x, y)\n</code></pre> <p>The interval's validity is the proportion of times the true value is within the interval. We specified a confidence level of 90%, so we expect the validity to be around 90%.</p> <p><pre><code>validity\n</code></pre> <pre><code>Mean: 0.939061\n</code></pre></p> <p>The interval's efficiency is the average width of the intervals.</p> <p><pre><code>efficiency\n</code></pre> <pre><code>Mean: 4.078361\n</code></pre></p> <p>Lowering the confidence lowering will mechanically improve the efficiency.</p>"},{"location":"api/conf/RegressionJackknife/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>with_interval     \u2014 defaults to <code>False</code> </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Barber, Rina Foygel, Emmanuel J. Candes, Aaditya Ramdas, and Ryan J. Tibshirani. \"Predictive inference with the jackknife+.\" The Annals of Statistics 49, no. 1 (2021): 486-507. \u21a9</p> </li> </ol>"},{"location":"api/covariance/EmpiricalCovariance/","title":"EmpiricalCovariance","text":"<p>Empirical covariance matrix.</p>"},{"location":"api/covariance/EmpiricalCovariance/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom.</p> </li> </ul>"},{"location":"api/covariance/EmpiricalCovariance/#attributes","title":"Attributes","text":"<ul> <li>matrix</li> </ul>"},{"location":"api/covariance/EmpiricalCovariance/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nimport pandas as pd\nfrom river import covariance\n\nnp.random.seed(42)\nX = pd.DataFrame(np.random.random((8, 3)), columns=[\"red\", \"green\", \"blue\"])\nX\n</code></pre> <pre><code>        red     green      blue\n0  0.374540  0.950714  0.731994\n1  0.598658  0.156019  0.155995\n2  0.058084  0.866176  0.601115\n3  0.708073  0.020584  0.969910\n4  0.832443  0.212339  0.181825\n5  0.183405  0.304242  0.524756\n6  0.431945  0.291229  0.611853\n7  0.139494  0.292145  0.366362\n</code></pre></p> <p><pre><code>cov = covariance.EmpiricalCovariance()\nfor x in X.to_dict(orient=\"records\"):\n    cov.update(x)\ncov\n</code></pre> <pre><code>        blue     green    red\n blue    0.076    0.020   -0.010\ngreen    0.020    0.113   -0.053\n  red   -0.010   -0.053    0.079\n</code></pre></p> <p>There is also an <code>update_many</code> method to process mini-batches. The results are identical.</p> <p><pre><code>cov = covariance.EmpiricalCovariance()\ncov.update_many(X)\ncov\n</code></pre> <pre><code>        blue     green    red\n blue    0.076    0.020   -0.010\ngreen    0.020    0.113   -0.053\n  red   -0.010   -0.053    0.079\n</code></pre></p> <p>The covariances are stored in a dictionary, meaning any one of them can be accessed as such:</p> <p><pre><code>cov[\"blue\", \"green\"]\n</code></pre> <pre><code>Cov: 0.020292\n</code></pre></p> <p>Diagonal entries are variances:</p> <p><pre><code>cov[\"blue\", \"blue\"]\n</code></pre> <pre><code>Var: 0.076119\n</code></pre></p>"},{"location":"api/covariance/EmpiricalCovariance/#methods","title":"Methods","text":"revert <p>Downdate with a single sample.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> update <p>Update with a single sample.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> update_many <p>Update with a dataframe of samples.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p>"},{"location":"api/covariance/EmpiricalPrecision/","title":"EmpiricalPrecision","text":"<p>Empirical precision matrix.</p> <p>The precision matrix is the inverse of the covariance matrix. </p> <p>This implementation leverages the Sherman-Morrison formula. The resulting inverse covariance matrix is not guaranteed to be identical to a batch computation. However, the difference shrinks with the number of observations.</p>"},{"location":"api/covariance/EmpiricalPrecision/#attributes","title":"Attributes","text":"<ul> <li>matrix</li> </ul>"},{"location":"api/covariance/EmpiricalPrecision/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nimport pandas as pd\nfrom river import covariance\n\nnp.random.seed(42)\nX = pd.DataFrame(np.random.random((1000, 3)))\nX.head()\n</code></pre> <pre><code>          0         1         2\n0  0.374540  0.950714  0.731994\n1  0.598658  0.156019  0.155995\n2  0.058084  0.866176  0.601115\n3  0.708073  0.020584  0.969910\n4  0.832443  0.212339  0.181825\n</code></pre></p> <p><pre><code>prec = covariance.EmpiricalPrecision()\nfor x in X.to_dict(orient=\"records\"):\n    prec.update(x)\n\nprec\n</code></pre> <pre><code>    0        1        2\n0   12.026   -0.122   -0.214\n1   -0.122   11.276   -0.026\n2   -0.214   -0.026   11.632\n</code></pre></p> <p><pre><code>pd.DataFrame(np.linalg.inv(np.cov(X.T, ddof=1)))\n</code></pre> <pre><code>           0          1          2\n0  12.159791  -0.124966  -0.218671\n1  -0.124966  11.393394  -0.026662\n2  -0.218671  -0.026662  11.756907\n</code></pre></p>"},{"location":"api/covariance/EmpiricalPrecision/#methods","title":"Methods","text":"update <p>Update with a single sample.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> update_many <p>Update with a dataframe of samples.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> <ol> <li> <p>Online Estimation of the Inverse Covariance Matrix - Markus Thill \u21a9</p> </li> <li> <p>Fast rank-one updates to matrix inverse? - Tim Vieira \u21a9</p> </li> <li> <p>Woodbury matrix identity \u21a9</p> </li> </ol>"},{"location":"api/datasets/AirlinePassengers/","title":"AirlinePassengers","text":"<p>Monthly number of international airline passengers.</p> <p>The stream contains 144 items and only one single feature, which is the month. The goal is to predict the number of passengers each month by capturing the trend and the seasonality of the data.</p>"},{"location":"api/datasets/AirlinePassengers/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/AirlinePassengers/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>International airline passengers: monthly totals in thousands. Jan 49 \u2013 Dec 60 \u21a9</p> </li> </ol>"},{"location":"api/datasets/Bananas/","title":"Bananas","text":"<p>Bananas dataset.</p> <p>An artificial dataset where instances belongs to several clusters with a banana shape. There are two attributes that correspond to the x and y axis, respectively.</p>"},{"location":"api/datasets/Bananas/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Bananas/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>OpenML page \u21a9</p> </li> </ol>"},{"location":"api/datasets/Bikes/","title":"Bikes","text":"<p>Bike sharing station information from the city of Toulouse.</p> <p>The goal is to predict the number of bikes in 5 different bike stations from the city of Toulouse.</p>"},{"location":"api/datasets/Bikes/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Bikes/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>A short introduction and conclusion to the OpenBikes 2016 Challenge \u21a9</p> </li> </ol>"},{"location":"api/datasets/ChickWeights/","title":"ChickWeights","text":"<p>Chick weights along time.</p> <p>The stream contains 578 items and 3 features. The goal is to predict the weight of each chick along time, according to the diet the chick is on. The data is ordered by time and then by chick.</p>"},{"location":"api/datasets/ChickWeights/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/ChickWeights/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Chick weight dataset overview \u21a9</p> </li> </ol>"},{"location":"api/datasets/CreditCard/","title":"CreditCard","text":"<p>Credit card frauds.</p> <p>The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. </p> <p>It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.</p>"},{"location":"api/datasets/CreditCard/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/CreditCard/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\u00a0\u21a9</p> </li> <li> <p>Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\u00a0\u21a9</p> </li> <li> <p>Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\u00a0\u21a9</p> </li> <li> <p>Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\u00a0\u21a9</p> </li> <li> <p>Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Ael; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\u00a0\u21a9</p> </li> <li> <p>Carcillo, Fabrizio; Le Borgne, Yann-Ael; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing\u00a0\u21a9</p> </li> <li> <p>Bertrand Lebichot, Yann-Ael Le Borgne, Liyun He, Frederic Oble, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019\u00a0\u21a9</p> </li> <li> <p>Fabrizio Carcillo, Yann-Ael Le Borgne, Olivier Caelen, Frederic Oble, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/Elec2/","title":"Elec2","text":"<p>Electricity prices in New South Wales.</p> <p>This is a binary classification task, where the goal is to predict if the price of electricity will go up or down. </p> <p>This data was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.</p>"},{"location":"api/datasets/Elec2/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Elec2/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>SPLICE-2 Comparative Evaluation: Electricity Pricing \u21a9</p> </li> <li> <p>DataHub description \u21a9</p> </li> </ol>"},{"location":"api/datasets/HTTP/","title":"HTTP","text":"<p>HTTP dataset of the KDD 1999 cup.</p> <p>The goal is to predict whether or not an HTTP connection is anomalous or not. The dataset only contains 2,211 (0.4%) positive labels.</p>"},{"location":"api/datasets/HTTP/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/HTTP/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>HTTP (KDDCUP99) dataset \u21a9</p> </li> </ol>"},{"location":"api/datasets/Higgs/","title":"Higgs","text":"<p>Higgs dataset.</p> <p>The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes.</p>"},{"location":"api/datasets/Higgs/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Higgs/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/ImageSegments/","title":"ImageSegments","text":"<p>Image segments classification.</p> <p>This dataset contains features that describe image segments into 7 classes: brickface, sky, foliage, cement, window, path, and grass.</p>"},{"location":"api/datasets/ImageSegments/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/ImageSegments/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/Insects/","title":"Insects","text":"<p>Insects dataset.</p> <p>This dataset has different variants, which are: </p> <ul> <li> <p>abrupt_balanced</p> </li> <li> <p>abrupt_imbalanced</p> </li> <li> <p>gradual_balanced</p> </li> <li> <p>gradual_imbalanced</p> </li> <li> <p>incremental-abrupt_balanced</p> </li> <li> <p>incremental-abrupt_imbalanced</p> </li> <li> <p>incremental-reoccurring_balanced</p> </li> <li> <p>incremental-reoccurring_imbalanced</p> </li> <li> <p>incremental_balanced</p> </li> <li> <p>incremental_imbalanced</p> </li> <li> <p>out-of-control</p> </li> </ul> <p>The number of samples and the difficulty change from one variant to another. The number of classes is always the same (6), except for the last variant (24).</p>"},{"location":"api/datasets/Insects/#parameters","title":"Parameters","text":"<ul> <li> <p>variant</p> <p>Default \u2192 <code>abrupt_balanced</code></p> <p>Indicates which variant of the dataset to load.</p> </li> </ul>"},{"location":"api/datasets/Insects/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Insects/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>USP DS repository \u21a9</p> </li> <li> <p>Souza, V., Reis, D.M.D., Maletzke, A.G. and Batista, G.E., 2020. Challenges in Benchmarking Stream Learning Algorithms with Real-world Data. arXiv preprint arXiv:2005.00113. \u21a9</p> </li> </ol>"},{"location":"api/datasets/Keystroke/","title":"Keystroke","text":"<p>CMU keystroke dataset.</p> <p>Users are tasked to type in a password. The task is to determine which user is typing in the password. </p> <p>The only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes have been dropped.</p>"},{"location":"api/datasets/Keystroke/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Keystroke/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Keystroke Dynamics - Benchmark Data Set \u21a9</p> </li> </ol>"},{"location":"api/datasets/MaliciousURL/","title":"MaliciousURL","text":"<p>Malicious URLs dataset.</p> <p>This dataset contains features about URLs that are classified as malicious or not.</p>"},{"location":"api/datasets/MaliciousURL/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/MaliciousURL/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Detecting Malicious URLs \u21a9</p> </li> <li> <p>Identifying Suspicious URLs: An Application of Large-Scale Online Learning \u21a9</p> </li> </ol>"},{"location":"api/datasets/MovieLens100K/","title":"MovieLens100K","text":"<p>MovieLens 100K dataset.</p> <p>MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota. This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. User and movie information are provided. The data was collected through the MovieLens web site (movielens.umn.edu) during the seven-month period from September 19th, 1997 through April 22nd, 1998.</p>"},{"location":"api/datasets/MovieLens100K/#parameters","title":"Parameters","text":"<ul> <li> <p>unpack_user_and_item</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not the user and item should be extracted from the context and included as extra keyword arguments.</p> </li> </ul>"},{"location":"api/datasets/MovieLens100K/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/MovieLens100K/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>The MovieLens Datasets: History and Context \u21a9</p> </li> </ol>"},{"location":"api/datasets/Music/","title":"Music","text":"<p>Multi-label music mood prediction.</p> <p>The goal is to predict to which kinds of moods a song pertains to.</p>"},{"location":"api/datasets/Music/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Music/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Read, J., Reutemann, P., Pfahringer, B. and Holmes, G., 2016. MEKA: a multi-label/multi-target extension to WEKA. The Journal of Machine Learning Research, 17(1), pp.667-671. \u21a9</p> </li> </ol>"},{"location":"api/datasets/Phishing/","title":"Phishing","text":"<p>Phishing websites.</p> <p>This dataset contains features from web pages that are classified as phishing or not.</p>"},{"location":"api/datasets/Phishing/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Phishing/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/Restaurants/","title":"Restaurants","text":"<p>Data from the Kaggle Recruit Restaurants challenge.</p> <p>The goal is to predict the number of visitors in each of 829 Japanese restaurants over a priod of roughly 16 weeks. The data is ordered by date and then by restaurant ID.</p>"},{"location":"api/datasets/Restaurants/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Restaurants/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Recruit Restaurant Visitor Forecasting \u21a9</p> </li> </ol>"},{"location":"api/datasets/SMSSpam/","title":"SMSSpam","text":"<p>SMS Spam Collection dataset.</p> <p>The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent 13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.</p>"},{"location":"api/datasets/SMSSpam/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/SMSSpam/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Almeida, T.A., Hidalgo, J.M.G. and Yamakami, A., 2011, September. Contributions to the study of SMS spam filtering: new collection and results. In Proceedings of the 11th ACM symposium on Document engineering (pp. 259-262). \u21a9</p> </li> </ol>"},{"location":"api/datasets/SMTP/","title":"SMTP","text":"<p>SMTP dataset from the KDD 1999 cup.</p> <p>The goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only contains 2,211 (0.4%) positive labels.</p>"},{"location":"api/datasets/SMTP/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/SMTP/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>SMTP (KDDCUP99) dataset \u21a9</p> </li> </ol>"},{"location":"api/datasets/SolarFlare/","title":"SolarFlare","text":"<p>Solar flare multi-output regression.</p>"},{"location":"api/datasets/SolarFlare/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/SolarFlare/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>UCI page \u21a9</p> </li> </ol>"},{"location":"api/datasets/TREC07/","title":"TREC07","text":"<p>TREC's 2007 Spam Track dataset.</p> <p>The data contains 75,419 chronologically ordered items, i.e. 3 months of emails delivered to a particular server in 2007. Spam messages represent 66.6% of the dataset. The goal is to predict whether an email is a spam or not. </p> <p>The available raw features are: sender, recipients, date, subject, body.</p>"},{"location":"api/datasets/TREC07/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/TREC07/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>TREC 2007 Spam Track Overview \u21a9</p> </li> <li> <p>Code ran to parse the dataset \u21a9</p> </li> </ol>"},{"location":"api/datasets/Taxis/","title":"Taxis","text":"<p>Taxi ride durations in New York City.</p> <p>The goal is to predict the duration of taxi rides in New York City.</p>"},{"location":"api/datasets/Taxis/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/Taxis/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>New York City Taxi Trip Duration competition on Kaggle \u21a9</p> </li> </ol>"},{"location":"api/datasets/TrumpApproval/","title":"TrumpApproval","text":"<p>Donald Trump approval ratings.</p> <p>This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald Trump's approval ratings. It contains 5 features, which are approval ratings collected by 5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of this task is to see if we can reproduce FiveThirtyEight's model.</p>"},{"location":"api/datasets/TrumpApproval/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/TrumpApproval/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Trump Approval Ratings \u21a9</p> </li> </ol>"},{"location":"api/datasets/WaterFlow/","title":"WaterFlow","text":"<p>Water flow through a pipeline branch.</p> <p>The series includes hourly values for about 2 months, March 2022 to May 2022. The values are expressed in liters per second. There are four anomalous segments in the series: </p> <ul> <li>3 \"low value moments\": this is due to water losses or human intervention for maintenance * A small peak in the water inflow after the first 2 segments: this is due to a pumping     operation into the main pipeline, when more water pressure is needed </li> </ul> <p>This dataset is well suited for time series forecasting models, as well as anomaly detection methods. Ideally, the goal is to build a time series forecasting model that is robust to the anomalous segments. </p> <p>This data has been kindly donated by the Tecnojest s.r.l. company (www.invidea.it) from Italy.</p>"},{"location":"api/datasets/WaterFlow/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/WaterFlow/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/WebTraffic/","title":"WebTraffic","text":"<p>Web sessions information from an events company based in South Africa.</p> <p>The goal is to predict the number of web sessions in 4 different regions in South Africa. </p> <p>The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and '2023-09-15 23:45:00' for each region. Two types of sessions are captured <code>sessionsA</code> and <code>sessionsB</code>. The <code>isMissing</code> flag is equal to 1 if any of the servers failed to capture sessions, otherwise if all servers functioned properly this flag is equal to 0. </p> <p>Things to consider: </p> <ul> <li>region <code>R5</code> captures sessions in backup mode. Strictly speaking, <code>R5</code> is not necessary to predict. * Can <code>sessionsA</code> and <code>sessionsB</code> events be predicted accurately for each region over the next day (next 96 intervals)? * What is the best way to deal with the missing values? * How can model selection be used (a multi-model approach)? * Can dependence (correlation) between regions be utilised for more accurate predictions? * Can both <code>sessionA</code> and <code>sessionB</code> be predicted simultaneously with one model? </li> </ul> <p>This dataset is well suited for time series forecasting models, as well as anomaly detection methods. Ideally, the goal is to build a time series forecasting model that is robust to the anomalous events and generalise well on normal operating conditions.</p>"},{"location":"api/datasets/WebTraffic/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/WebTraffic/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/Dataset/","title":"Dataset","text":"<p>Base class for all datasets.</p> <p>All datasets inherit from this class, be they stored in a file or generated on the fly.</p>"},{"location":"api/datasets/base/Dataset/#parameters","title":"Parameters","text":"<ul> <li> <p>task</p> <p>Type of task the dataset is meant for. Should be one of the following:      - \"Regression\"     - \"Binary classification\"     - \"Multi-class classification\"     - \"Multi-output binary classification\"     - \"Multi-output regression\"</p> </li> <li> <p>n_features</p> <p>Number of features in the dataset.</p> </li> <li> <p>n_samples</p> <p>Default \u2192 <code>None</code></p> <p>Number of samples in the dataset.</p> </li> <li> <p>n_classes</p> <p>Default \u2192 <code>None</code></p> <p>Number of classes in the dataset, only applies to classification datasets.</p> </li> <li> <p>n_outputs</p> <p>Default \u2192 <code>None</code></p> <p>Number of outputs the target is made of, only applies to multi-output datasets.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the dataset is sparse or not.</p> </li> </ul>"},{"location":"api/datasets/base/Dataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/base/Dataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/FileDataset/","title":"FileDataset","text":"<p>Base class for datasets that are stored in a local file.</p> <p>Small datasets that are part of the river package inherit from this class.</p>"},{"location":"api/datasets/base/FileDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>filename</p> <p>The file's name.</p> </li> <li> <p>directory</p> <p>Default \u2192 <code>None</code></p> <p>The directory where the file is contained. Defaults to the location of the <code>datasets</code> module.</p> </li> <li> <p>desc</p> <p>Extra dataset parameters to pass as keyword arguments.</p> </li> </ul>"},{"location":"api/datasets/base/FileDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/base/FileDataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/RemoteDataset/","title":"RemoteDataset","text":"<p>Base class for datasets that are stored in a remote file.</p> <p>Medium and large datasets that are not part of the river package inherit from this class. </p> <p>The filename doesn't have to be provided if unpack is False. Indeed in the latter case the filename will be inferred from the URL.</p>"},{"location":"api/datasets/base/RemoteDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>url</p> <p>The URL the dataset is located at.</p> </li> <li> <p>size</p> <p>The expected download size.</p> </li> <li> <p>unpack</p> <p>Default \u2192 <code>True</code></p> <p>Whether to unpack the download or not.</p> </li> <li> <p>filename</p> <p>Default \u2192 <code>None</code></p> <p>An optional name to given to the file if the file is unpacked.</p> </li> <li> <p>desc</p> <p>Extra dataset parameters to pass as keyword arguments.</p> </li> </ul>"},{"location":"api/datasets/base/RemoteDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>is_downloaded</p> <p>Indicate whether or the data has been correctly downloaded.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/datasets/base/RemoteDataset/#methods","title":"Methods","text":"download take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/base/SyntheticDataset/","title":"SyntheticDataset","text":"<p>A synthetic dataset.</p>"},{"location":"api/datasets/base/SyntheticDataset/#parameters","title":"Parameters","text":"<ul> <li> <p>task</p> <p>Type of task the dataset is meant for. Should be one of: - \"Regression\" - \"Binary classification\" - \"Multi-class classification\" - \"Multi-output binary classification\" - \"Multi-output regression\"</p> </li> <li> <p>n_features</p> <p>Number of features in the dataset.</p> </li> <li> <p>n_samples</p> <p>Default \u2192 <code>None</code></p> <p>Number of samples in the dataset.</p> </li> <li> <p>n_classes</p> <p>Default \u2192 <code>None</code></p> <p>Number of classes in the dataset, only applies to classification datasets.</p> </li> <li> <p>n_outputs</p> <p>Default \u2192 <code>None</code></p> <p>Number of outputs the target is made of, only applies to multi-output datasets.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the dataset is sparse or not.</p> </li> </ul>"},{"location":"api/datasets/base/SyntheticDataset/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/base/SyntheticDataset/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Agrawal/","title":"Agrawal","text":"<p>Agrawal stream generator.</p> <p>The generator was introduced by Agrawal et al. <sup>1</sup>, and was a common source of data for early work on scaling up decision tree learners. The generator produces a stream containing nine features, six numeric and three categorical. There are 10 functions defined for generating binary class labels from the features. Presumably these determine whether the loan should be approved. Classification functions are listed in the original paper <sup>1</sup>. </p> <p>Feature | Description | Values </p> <ul> <li> <p><code>salary</code> | salary | uniformly distributed from 20k to 150k </p> </li> <li> <p><code>commission</code> | commission | 0 if <code>salary</code> &lt; 75k else uniformly distributed from 10k to 75k </p> </li> <li> <p><code>age</code> | age | uniformly distributed from 20 to 80 </p> </li> <li> <p><code>elevel</code> | education level | uniformly chosen from 0 to 4 </p> </li> <li> <p><code>car</code> | car maker | uniformly chosen from 1 to 20 </p> </li> <li> <p><code>zipcode</code> | zip code of the town | uniformly chosen from 0 to 8 </p> </li> <li> <p><code>hvalue</code> | house value | uniformly distributed from 50k x zipcode to 100k x zipcode </p> </li> <li> <p><code>hyears</code> | years house owned | uniformly distributed from 1 to 30 </p> </li> <li> <p><code>loan</code> | total loan amount | uniformly distributed from 0 to 500k</p> </li> </ul>"},{"location":"api/datasets/synth/Agrawal/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>The classification function to use for the generation. Valid values are from 0 to 9.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, the class distribution will converge to a uniform distribution.</p> </li> <li> <p>perturbation</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability that noise will happen in the generation. Each new sample will be perturbed by the magnitude of <code>perturbation</code>. Valid values are in the range [0.0 to 1.0].</p> </li> </ul>"},{"location":"api/datasets/synth/Agrawal/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Agrawal/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Agrawal(\n    classification_function=0,\n    seed=42\n)\n\ndataset\n</code></pre> <pre><code>Synthetic data generator\n&lt;BLANKLINE&gt;\n    Name  Agrawal\n    Task  Binary classification\n Samples  \u221e\nFeatures  9\n Outputs  1\n Classes  2\n  Sparse  False\n&lt;BLANKLINE&gt;\nConfiguration\n-------------\nclassification_function  0\n                   seed  42\n        balance_classes  False\n           perturbation  0.0\n</code></pre></p> <p><pre><code>for x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[103125.4837, 0, 21, 2, 8, 3, 319768.9642, 4, 338349.7437] 1\n[135983.3438, 0, 25, 4, 14, 0, 423837.7755, 7, 116330.4466] 1\n[98262.4347, 0, 55, 1, 18, 6, 144088.1244, 19, 139095.3541] 0\n[133009.0417, 0, 68, 1, 14, 5, 233361.4025, 7, 478606.5361] 1\n[63757.2908, 16955.9382, 26, 2, 12, 4, 522851.3093, 24, 229712.4398] 1\n</code></pre></p>"},{"location":"api/datasets/synth/Agrawal/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function randomly.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Agrawal/#notes","title":"Notes","text":"<p>The sample generation works as follows: The 9 features are generated with the random generator, initialized with the seed passed by the user. Then, the classification function decides, as a function of all the attributes, whether to classify the instance as class 0 or class 1. The next step is to verify if the classes should be balanced, and if so, balance the classes. Finally, add noise if <code>perturbation</code> &gt; 0.0.</p> <ol> <li> <p>Rakesh Agrawal, Tomasz Imielinksi, and Arun Swami. \"Database Mining:   A Performance Perspective\", IEEE Transactions on Knowledge and   Data Engineering, 5(6), December 1993.\u00a0\u21a9\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/AnomalySine/","title":"AnomalySine","text":"<p>Simulate a stream with anomalies in sine waves.</p> <p>The amount of data generated by this generator is finite. </p> <p>The data generated corresponds to sine and cosine functions. Anomalies are induced by replacing the cosine values with values from a different a sine function. The <code>contextual</code> flag can be used to introduce contextual anomalies which are values in the normal global range, but abnormal compared to the seasonal pattern. Contextual attributes are introduced by replacing cosine entries with sine values. </p> <p>The target indicates whether or not the instances are anomalous.</p>"},{"location":"api/datasets/synth/AnomalySine/#parameters","title":"Parameters","text":"<ul> <li> <p>n_samples</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10000</code></p> <p>The number of samples to generate. This generator creates a batch of data affected by contextual anomalies and noise.</p> </li> <li> <p>n_anomalies</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2500</code></p> <p>Number of anomalies. Can't be larger than <code>n_samples</code>.</p> </li> <li> <p>contextual</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, will add contextual anomalies.</p> </li> <li> <p>n_contextual</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2500</code></p> <p>Number of contextual anomalies. Can't be larger than <code>n_samples</code>.</p> </li> <li> <p>shift</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>4</code></p> <p>Shift in number of samples applied when retrieving contextual anomalies.</p> </li> <li> <p>noise</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>Amount of noise.</p> </li> <li> <p>replace</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, anomalies are randomly sampled with replacement.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/AnomalySine/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/AnomalySine/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.AnomalySine(\n    seed=12345,\n    n_samples=100,\n    n_anomalies=25,\n    contextual=True,\n    n_contextual=10\n)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'sine': -0.7119, 'cosine': 0.8777} False\n{'sine': 0.8792, 'cosine': -0.0290} False\n{'sine': 0.0440, 'cosine': 3.0852} True\n{'sine': 0.5520, 'cosine': 3.4515} True\n{'sine': 0.8037, 'cosine': 0.4027} False\n</code></pre></p>"},{"location":"api/datasets/synth/AnomalySine/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/ConceptDriftStream/","title":"ConceptDriftStream","text":"<p>Generates a stream with concept drift.</p> <p>A stream generator that adds concept drift or change by joining two streams. This is done by building a weighted combination of two pure distributions that characterizes the target concepts before and after the change. </p> <p>The sigmoid function is an elegant and practical solution to define the probability that each new instance of the stream belongs to the new concept after the drift. The sigmoid function introduces a gradual, smooth transition whose duration is controlled with two parameters: </p> <ul> <li> <p>\\(p\\), the position of the change.</p> </li> <li> <p>\\(w\\), the width of the transition.</p> </li> </ul> <p>The sigmoid function at sample \\(t\\) is </p> \\[f(t) = 1/(1+e^{-4(t-p)/w})\\]"},{"location":"api/datasets/synth/ConceptDriftStream/#parameters","title":"Parameters","text":"<ul> <li> <p>stream</p> <p>Type \u2192 datasets.base.SyntheticDataset | None</p> <p>Default \u2192 <code>None</code></p> <p>Original stream</p> </li> <li> <p>drift_stream</p> <p>Type \u2192 datasets.base.SyntheticDataset | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift stream</p> </li> <li> <p>position</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5000</code></p> <p>Central position of the concept drift change.</p> </li> <li> <p>width</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>Width of concept drift change.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>alpha</p> <p>Type \u2192 float | None</p> <p>Default \u2192 <code>None</code></p> <p>Angle of change used to estimate the width of concept drift change. If set, it will override the width parameter. Valid values are in the range (0.0, 90.0].</p> </li> </ul>"},{"location":"api/datasets/synth/ConceptDriftStream/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/ConceptDriftStream/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.ConceptDriftStream(\n    stream=synth.SEA(seed=42, variant=0),\n    drift_stream=synth.SEA(seed=42, variant=1),\n    seed=1, position=5, width=2\n)\n\nfor x, y in dataset.take(10):\n    print(x, y)\n</code></pre> <pre><code>{0: 6.3942, 1: 0.2501, 2: 2.7502} False\n{0: 2.2321, 1: 7.3647, 2: 6.7669} True\n{0: 8.9217, 1: 0.8693, 2: 4.2192} True\n{0: 0.2979, 1: 2.1863, 2: 5.0535} False\n{0: 6.3942, 1: 0.2501, 2: 2.7502} False\n{0: 2.2321, 1: 7.3647, 2: 6.7669} True\n{0: 8.9217, 1: 0.8693, 2: 4.2192} True\n{0: 0.2979, 1: 2.1863, 2: 5.0535} False\n{0: 0.2653, 1: 1.9883, 2: 6.4988} False\n{0: 5.4494, 1: 2.2044, 2: 5.8926} False\n</code></pre></p>"},{"location":"api/datasets/synth/ConceptDriftStream/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/ConceptDriftStream/#notes","title":"Notes","text":"<p>An optional way to estimate the width of the transition \\(w\\) is based on the angle \\(\\alpha\\), \\(w = 1/ tan(\\alpha)\\). Since width corresponds to the number of samples for the transition, the width is rounded to the nearest smaller integer. Notice that larger values of \\(\\alpha\\) result in smaller widths. For \\(\\alpha &gt; 45.0\\), the width is smaller than 1 so values are rounded to 1 to avoid division by zero errors.</p>"},{"location":"api/datasets/synth/Friedman/","title":"Friedman","text":"<p>Friedman synthetic dataset.</p> <p>Each observation is composed of 10 features. Each feature value is sampled uniformly in [0, 1]. The target is defined by the following function: </p> \\[y = 10 sin(\\pi x_0 x_1) + 20 (x_2 - 0.5)^2 + 10 x_3 + 5 x_4 + \\epsilon\\] <p>In the last expression, \\(\\epsilon \\sim \\mathcal{N}(0, 1)\\), is the noise. Therefore, only the first 5 features are relevant.</p>"},{"location":"api/datasets/synth/Friedman/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Friedman/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Friedman/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Friedman(seed=42)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 7.04\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 18.16\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] 8.90\n</code></pre></p>"},{"location":"api/datasets/synth/Friedman/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Friedman, J.H., 1991. Multivariate adaptive regression splines. The annals of statistics, pp.1-67. \u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/FriedmanDrift/","title":"FriedmanDrift","text":"<p>Friedman synthetic dataset with concept drifts.</p> <p>Each observation is composed of 10 features. Each feature value is sampled uniformly in [0, 1]. Only the first 5 features are relevant. The target is defined by different functions depending on the type of the drift. </p> <p>The three available modes of operation of the data generator are described in <sup>1</sup>.</p>"},{"location":"api/datasets/synth/FriedmanDrift/#parameters","title":"Parameters","text":"<ul> <li> <p>drift_type</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>lea</code></p> <p>The variant of concept drift. - <code>'lea'</code>: Local Expanding Abrupt drift. The concept drift appears in two distinct regions of the instance space, while the remaining regions are left unaltered. There are three points of abrupt change in the training dataset. At every consecutive change the regions of drift are expanded. - <code>'gra'</code>: Global Recurring Abrupt drift. The concept drift appears over the whole instance space. There are two points of concept drift. At the second point of drift the old concept reoccurs. - <code>'gsg'</code>: Global and Slow Gradual drift. The concept drift affects all the instance space. However, the change is gradual and not abrupt. After each one of the two change points covered by this variant, and during a window of length <code>transition_window</code>, examples from both old and the new concepts are generated with equal probability. After the transition period, only the examples from the new concept are generated.</p> </li> <li> <p>position</p> <p>Type \u2192 tuple[int, ...]</p> <p>Default \u2192 <code>(50000, 100000, 150000)</code></p> <p>The amount of monitored instances after which each concept drift occurs. A tuple with at least two element must be passed, where each number is greater than the preceding one. If <code>drift_type='lea'</code>, then the tuple must have three elements.</p> </li> <li> <p>transition_window</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10000</code></p> <p>The length of the transition window between two concepts. Only applicable when  <code>drift_type='gsg'</code>. If set to zero, the drifts will be abrupt. Anytime  <code>transition_window &gt; 0</code>, it defines a window in which instances of the new  concept are gradually introduced among the examples from the old concept.  During this transition phase, both old and new concepts appear with equal probability.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/FriedmanDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/FriedmanDrift/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.FriedmanDrift(\n    drift_type='lea',\n    position=(1, 2, 3),\n    seed=42\n)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 7.04\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 18.16\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] -2.65\n</code></pre></p> <p><pre><code>dataset = synth.FriedmanDrift(\n    drift_type='gra',\n    position=(2, 3),\n    seed=42\n)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 8.96\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 18.16\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] 8.90\n</code></pre></p> <p><pre><code>dataset = synth.FriedmanDrift(\n    drift_type='gsg',\n    position=(1, 4),\n    transition_window=2,\n    seed=42\n)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[0.63, 0.02, 0.27, 0.22, 0.73, 0.67, 0.89, 0.08, 0.42, 0.02] 7.66\n[0.02, 0.19, 0.64, 0.54, 0.22, 0.58, 0.80, 0.00, 0.80, 0.69] 8.33\n[0.34, 0.15, 0.95, 0.33, 0.09, 0.09, 0.84, 0.60, 0.80, 0.72] 8.92\n[0.37, 0.55, 0.82, 0.61, 0.86, 0.57, 0.70, 0.04, 0.22, 0.28] 17.32\n[0.07, 0.23, 0.10, 0.27, 0.63, 0.36, 0.37, 0.20, 0.26, 0.93] 6.05\n</code></pre></p>"},{"location":"api/datasets/synth/FriedmanDrift/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Ikonomovska, E., Gama, J. and D\u017eeroski, S., 2011. Learning model trees from evolving data streams. Data mining and knowledge discovery, 23(1), pp.128-168.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Hyperplane/","title":"Hyperplane","text":"<p>Hyperplane stream generator.</p> <p>Generates a problem of prediction class of a rotation hyperplane. It was used as testbed for CVFDT and VFDT in <sup>1</sup>. </p> <p>A hyperplane in d-dimensional space is the set of points \\(x\\) that satisfy </p> \\[\\sum^{d}_{i=1} w_i x_i = w_0 = \\sum^{d}_{i=1} w_i\\] <p>where \\(x_i\\) is the i-th coordinate of \\(x\\). </p> <ul> <li> <p>Examples for which \\(\\sum^{d}_{i=1} w_i x_i &gt; w_0\\), are labeled positive.</p> </li> <li> <p>Examples for which \\(\\sum^{d}_{i=1} w_i x_i \\leq w_0\\), are labeled negative.</p> </li> </ul> <p>Hyperplanes are useful for simulating time-changing concepts because we can change the orientation and position of the hyperplane in a smooth manner by changing the relative size of the weights. We introduce change to this dataset by adding drift to each weighted feature \\(w_i = w_i + d \\sigma\\), where \\(\\sigma\\) is the probability that the direction of change is reversed and \\(d\\) is the change applied to each example.</p>"},{"location":"api/datasets/synth/Hyperplane/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>n_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of attributes to generate. Higher than 2.</p> </li> <li> <p>n_drift_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of attributes with drift. Higher than 2.</p> </li> <li> <p>mag_change</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>Magnitude of the change for every example. From 0.0 to 1.0.</p> </li> <li> <p>noise_percentage</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Percentage of noise to add to the data. From 0.0 to 1.0.</p> </li> <li> <p>sigma</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Probability that the direction of change is reversed. From 0.0 to 1.0.</p> </li> </ul>"},{"location":"api/datasets/synth/Hyperplane/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Hyperplane/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Hyperplane(seed=42, n_features=2)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 0.2750, 1: 0.2232} 0\n{0: 0.0869, 1: 0.4219} 1\n{0: 0.0265, 1: 0.1988} 0\n{0: 0.5892, 1: 0.8094} 0\n{0: 0.3402, 1: 0.1554} 0\n</code></pre></p>"},{"location":"api/datasets/synth/Hyperplane/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Hyperplane/#notes","title":"Notes","text":"<p>The sample generation works as follows: The features are generated with the random number generator, initialized with the seed passed by the user. Then the classification function decides, as a function of the sum of the weighted features and the sum of the weights, whether the instance belongs to class 0 or class 1. The last step is to add noise and generate drift.</p> <ol> <li> <p>G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams.   In KDD'01, pages 97-106, San Francisco, CA, 2001. ACM Press.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/LED/","title":"LED","text":"<p>LED stream generator.</p> <p>This data source originates from the CART book <sup>1</sup>. An implementation in C was donated to the UCI <sup>2</sup> machine learning repository by David Aha. The goal is to predict the digit displayed on a seven-segment LED display, where each attribute has a 10% chance of being inverted. It has an optimal Bayes classification rate of 74%. The particular configuration of the generator used for experiments (LED) produces 24 binary attributes, 17 of which are irrelevant.</p>"},{"location":"api/datasets/synth/LED/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>noise_percentage</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability that noise will happen in the generation. At each new sample generated, a random number is generated, and if it is equal or less than the noise_percentage, the led value  will be switched</p> </li> <li> <p>irrelevant_features</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 17 non-relevant attributes to the stream.</p> </li> </ul>"},{"location":"api/datasets/synth/LED/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/LED/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.LED(seed = 112, noise_percentage = 0.28, irrelevant_features= False)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 1, 1: 0, 2: 1, 3: 0, 4: 0, 5: 1, 6: 0} 7\n{0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 0} 8\n{0: 1, 1: 1, 2: 1, 3: 1, 4: 0, 5: 1, 6: 0} 9\n{0: 0, 1: 0, 2: 1, 3: 0, 4: 0, 5: 1, 6: 0} 1\n{0: 0, 1: 1, 2: 1, 3: 0, 4: 0, 5: 0, 6: 0} 1\n</code></pre></p>"},{"location":"api/datasets/synth/LED/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/LED/#notes","title":"Notes","text":"<p>An instance is generated based on the parameters passed. If <code>has_noise</code> is set then the total number of attributes will be 24, otherwise there will be 7 attributes.</p> <ol> <li> <p>Leo Breiman, Jerome Friedman, R. Olshen, and Charles J. Stone.   Classification and Regression Trees. Wadsworth and Brooks,   Monterey, CA,1984.\u00a0\u21a9</p> </li> <li> <p>A. Asuncion and D. J. Newman. UCI Machine Learning Repository   [http://www.ics.uci.edu/~mlearn/mlrepository.html].   University of California, Irvine, School of Information and   Computer Sciences,2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/LEDDrift/","title":"LEDDrift","text":"<p>LED stream generator with concept drift.</p> <p>This class is an extension of the <code>LED</code> generator whose purpose is to add concept drift to the stream.</p>"},{"location":"api/datasets/synth/LEDDrift/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>noise_percentage</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability that noise will happen in the generation. At each new sample generated, a random number is generated, and if it is equal or less than the noise_percentage, the led value  will be switched</p> </li> <li> <p>irrelevant_features</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 17 non-relevant attributes to the stream.</p> </li> <li> <p>n_drift_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>The number of attributes that have drift.</p> </li> </ul>"},{"location":"api/datasets/synth/LEDDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/LEDDrift/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.LEDDrift(seed = 112, noise_percentage = 0.28,\n                         irrelevant_features= True, n_drift_features=4)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1] 7\n[1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0] 6\n[0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1] 1\n[1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1] 6\n[1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0] 7\n</code></pre></p>"},{"location":"api/datasets/synth/LEDDrift/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/LEDDrift/#notes","title":"Notes","text":"<p>An instance is generated based on the parameters passed. If <code>has_noise</code> is set then the total number of attributes will be 24, otherwise there will be 7 attributes.</p>"},{"location":"api/datasets/synth/Logical/","title":"Logical","text":"<p>Logical functions stream generator.</p> <p>Make a toy dataset with three labels that represent the logical functions: <code>OR</code>, <code>XOR</code>, <code>AND</code> (functions of the 2D input). </p> <p>Data is generated in 'tiles' which contain the complete set of logical operations results. The tiles are repeated <code>n_tiles</code> times. Optionally, the generated data can be shuffled.</p>"},{"location":"api/datasets/synth/Logical/#parameters","title":"Parameters","text":"<ul> <li> <p>n_tiles</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Number of tiles to generate.</p> </li> <li> <p>shuffle</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If set, generated data will be shuffled.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Logical/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Logical/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Logical(n_tiles=2, shuffle=True, seed=42)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'A': 1, 'B': 1} {'OR': 1, 'XOR': 0, 'AND': 1}\n{'A': 0, 'B': 0} {'OR': 0, 'XOR': 0, 'AND': 0}\n{'A': 1, 'B': 0} {'OR': 1, 'XOR': 1, 'AND': 0}\n{'A': 1, 'B': 1} {'OR': 1, 'XOR': 0, 'AND': 1}\n{'A': 1, 'B': 0} {'OR': 1, 'XOR': 1, 'AND': 0}\n</code></pre></p>"},{"location":"api/datasets/synth/Logical/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Mixed/","title":"Mixed","text":"<p>Mixed data stream generator.</p> <p>This generator is an implementation of a data stream with abrupt concept drift and boolean noise-free examples as described in <sup>1</sup>. </p> <p>It has four relevant attributes, two boolean attributes \\(v, w\\) and two numeric attributes \\(x, y\\) uniformly distributed from 0 to 1. The examples are labeled depending on the classification function chosen from below. </p> <ul> <li> <p><code>function 0</code>:   if \\(v\\) and \\(w\\) are true or \\(v\\) and \\(z\\) are true or \\(w\\) and \\(z\\) are true   then 0 else 1, where \\(z\\) is \\(y &lt; 0.5 + 0.3 sin(3 \\pi  x)\\) </p> </li> <li> <p><code>function 1</code>:    The opposite of <code>function 0</code>. </p> </li> </ul> <p>Concept drift can be introduced by changing the classification function. This can be done manually or using <code>ConceptDriftStream</code>.</p>"},{"location":"api/datasets/synth/Mixed/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Which of the two classification functions to use for the generation. Valid options are 0 or 1.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to balance classes or not. If balanced, the class distribution will converge to a uniform distribution.</p> </li> </ul>"},{"location":"api/datasets/synth/Mixed/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Mixed/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\ndataset = synth.Mixed(seed = 42, classification_function=1, balance_classes = True)\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: True, 1: False, 2: 0.2750, 3: 0.2232} 1\n{0: False, 1: False, 2: 0.2186, 3: 0.5053} 0\n{0: False, 1: True, 2: 0.8094, 3: 0.0064} 1\n{0: False, 1: False, 2: 0.1010, 3: 0.2779} 0\n{0: True, 1: False, 2: 0.37018, 3: 0.2095} 1\n</code></pre></p>"},{"location":"api/datasets/synth/Mixed/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Mixed/#notes","title":"Notes","text":"<p>The sample generation works as follows: The two numeric attributes are generated with the random  generator initialized with the seed passed by the user (optional). The boolean attributes are either 0 or 1 based on the comparison of the random number generator and 0.5, the classification function decides whether to classify the instance as class 0 or class 1. The next step is to verify if the classes should be balanced, and if so, balance the classes.</p> <p>The generated sample will have 4 relevant features and 1 label (it is a binary-classification task).</p> <ol> <li> <p>Gama, Joao, et al. \"Learning with drift detection.\" Advances in   artificial intelligence-SBIA 2004. Springer Berlin Heidelberg,   2004. 286-295\"\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Mv/","title":"Mv","text":"<p>Mv artificial dataset.</p> <p>Artificial dataset composed of both nominal and numeric features, whose features present co-dependencies. Originally described in <sup>1</sup>. </p> <p>The features are generated using the following expressions: </p> <ul> <li> <p>\\(x_1\\): uniformly distributed over <code>[-5, 5]</code>.</p> </li> <li> <p>\\(x_2\\): uniformly distributed over <code>[-15, -10]</code>.</p> </li> <li> <p>\\(x_3\\):</p> <ul> <li> <p>if \\(x_1 &gt; 0\\), \\(x_3 \\leftarrow\\) <code>'green'</code> </p> </li> <li> <p>else \\(x_3 \\leftarrow\\) <code>'red'</code> with probability \\(0.4\\) and \\(x_3 \\leftarrow\\) <code>'brown'</code>     with probability \\(0.6\\). </p> </li> </ul> </li> <li> <p>\\(x_4\\):</p> <ul> <li> <p>if \\(x_3 =\\) <code>'green'</code>, \\(x_4 \\leftarrow x_1 + 2 x_2\\) </p> </li> <li> <p>else \\(x_4 = \\frac{x_1}{2}\\) with probability \\(0.3\\) and \\(x_4 = \\frac{x_2}{2}\\)     with probability \\(0.7\\). </p> </li> </ul> </li> <li> <p>\\(x_5\\): uniformly distributed over <code>[-1, 1]</code>.</p> </li> <li> <p>\\(x_6 \\leftarrow x_4 \\times \\epsilon\\), where \\(\\epsilon\\) is uniformly distributed</p> </li> </ul> <p>over <code>[0, 5]</code>. </p> <ul> <li> <p>\\(x_7\\): <code>'yes'</code> with probability \\(0.3\\), and <code>'no'</code> with probability \\(0.7\\).</p> </li> <li> <p>\\(x_8\\): <code>'normal'</code> if \\(x_5 &lt; 0.5\\) else <code>'large'</code>.</p> </li> <li> <p>\\(x_9\\): uniformly distributed over <code>[100, 500]</code>.</p> </li> <li> <p>\\(x_{10}\\): uniformly distributed integer over the interval <code>[1000, 1200]</code>.</p> </li> </ul> <p>The target value is generated using the following rules: </p> <ul> <li> <p>if \\(x_2 &gt; 2\\), \\(y \\leftarrow 35 - 0.5 x_4\\)</p> </li> <li> <p>else if \\(-2 \\le x_4 \\le 2\\), \\(y \\leftarrow 10 - 2 x_1\\)</p> </li> <li> <p>else if \\(x_7 =\\) <code>'yes'</code>, \\(y \\leftarrow 3 - \\frac{x_1}{x_4}\\)</p> </li> <li> <p>else if \\(x_8 =\\) <code>'normal'</code>, \\(y \\leftarrow x_6 + x_1\\)</p> </li> <li> <p>else \\(y \\leftarrow \\frac{x_1}{2}\\).</p> </li> </ul>"},{"location":"api/datasets/synth/Mv/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Mv/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Mv/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Mv(seed=42)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[1.39, -14.87, 'green', -28.35, -0.44, -31.64, 'no', 'normal', 370.67, 1178.43] -30.25\n[-4.13, -12.89, 'red', -2.06, 0.01, -0.27, 'yes', 'normal', 359.95, 1108.98] 1.00\n[-2.79, -12.05, 'brown', -1.39, 0.61, -4.87, 'no', 'large', 162.19, 1191.44] 15.59\n[-1.63, -14.53, 'red', -7.26, 0.20, -29.33, 'no', 'normal', 314.49, 1194.62] -30.96\n[-1.21, -12.23, 'brown', -6.11, 0.72, -17.66, 'no', 'large', 118.32, 1045.57] -0.60\n</code></pre></p>"},{"location":"api/datasets/synth/Mv/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Mv in Lu\u00eds Torgo regression datasets \u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Planes2D/","title":"Planes2D","text":"<p>2D Planes synthetic dataset.</p> <p>This dataset is described in <sup>1</sup> and was adapted from <sup>2</sup>. The features are generated using the following probabilities: </p> \\[P(x_1 = -1) = P(x_1 = 1) = \\frac{1}{2}\\] \\[P(x_m = -1) = P(x_m = 0) = P(x_m = 1) = \\frac{1}{3}, m=2,\\ldots, 10\\] <p>The target value is defined by the following rule: </p> \\[\\text{if}~x_1 = 1, y \\leftarrow 3 + 3x_2 + 2x_3 + x_4 + \\epsilon\\] \\[\\text{if}~x_1 = -1, y \\leftarrow -3 + 3x_5 + 2x_6 + x_7 + \\epsilon\\] <p>In the expressions, \\(\\epsilon \\sim \\mathcal{N}(0, 1)\\), is the noise.</p>"},{"location":"api/datasets/synth/Planes2D/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/Planes2D/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Planes2D/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Planes2D(seed=42)\n\nfor x, y in dataset.take(5):\n    print(list(x.values()), y)\n</code></pre> <pre><code>[-1, -1, 1, 0, -1, -1, -1, 1, -1, 1] -9.07\n[1, -1, -1, -1, -1, -1, 1, 1, -1, 1] -4.25\n[-1, 1, 1, 1, 1, 0, -1, 0, 1, 0] -0.95\n[-1, 1, 0, 0, 0, -1, -1, 0, -1, -1] -6.10\n[1, -1, 0, 0, 1, 0, -1, 1, 0, 1] 1.60\n</code></pre></p>"},{"location":"api/datasets/synth/Planes2D/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>2DPlanes in Lu\u00eds Torgo regression datasets \u21a9</p> </li> <li> <p>Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A., 1984. Classification and regression trees. CRC press.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/RandomRBF/","title":"RandomRBF","text":"<p>Random Radial Basis Function generator.</p> <p>Produces a radial basis function stream. A number of centroids, having a random central position, a standard deviation, a class label and weight are generated. A new sample is created by choosing one of the centroids at random, taking into account their weights, and offsetting the attributes in a random direction from the centroid's center. The offset length is drawn from a Gaussian distribution. </p> <p>This process will create a normally distributed hypersphere of samples on the surrounds of each centroid.</p>"},{"location":"api/datasets/synth/RandomRBF/#parameters","title":"Parameters","text":"<ul> <li> <p>seed_model</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Model's random seed to generate centroids.</p> </li> <li> <p>seed_sample</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Sample's random seed.</p> </li> <li> <p>n_classes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of class labels to generate.</p> </li> <li> <p>n_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of numerical features to generate.</p> </li> <li> <p>n_centroids</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>The number of centroids to generate.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBF/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBF/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\ndataset = synth.RandomRBF(seed_model=42, seed_sample=42,\n                          n_classes=4, n_features=4, n_centroids=20)\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 1.0989, 1: 0.3840, 2: 0.7759, 3: 0.6592} 2\n{0: 0.2366, 1: 1.3233, 2: 0.5691, 3: 0.2083} 0\n{0: 1.3540, 1: -0.3306, 2: 0.1683, 3: 0.8865} 0\n{0: 0.2585, 1: -0.2217, 2: 0.4739, 3: 0.6522} 0\n{0: 0.1295, 1: 0.5953, 2: 0.1774, 3: 0.6673} 1\n</code></pre></p>"},{"location":"api/datasets/synth/RandomRBF/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/RandomRBFDrift/","title":"RandomRBFDrift","text":"<p>Random Radial Basis Function generator with concept drift.</p> <p>This class is an extension from the <code>RandomRBF</code> generator. Concept drift can be introduced in instances of this class. </p> <p>The drift is created by adding a \"speed\" to certain centroids. As the samples are generated each of the moving centroids' centers is changed by an amount determined by its speed.</p>"},{"location":"api/datasets/synth/RandomRBFDrift/#parameters","title":"Parameters","text":"<ul> <li> <p>seed_model</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Model's random seed to generate centroids.</p> </li> <li> <p>seed_sample</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Sample's random seed.</p> </li> <li> <p>n_classes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of class labels to generate.</p> </li> <li> <p>n_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of numerical features to generate.</p> </li> <li> <p>n_centroids</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>The number of centroids to generate.</p> </li> <li> <p>change_speed</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The concept drift speed.</p> </li> <li> <p>n_drift_centroids</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>The number of centroids that will drift.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBFDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomRBFDrift/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\ndataset = synth.RandomRBFDrift(seed_model=42, seed_sample=42,\n                               n_classes=4, n_features=4, n_centroids=20,\n                               change_speed=0.87, n_drift_centroids=10)\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 1.0989, 1: 0.3840, 2: 0.7759, 3: 0.6592} 2\n{0: 1.1496, 1: 1.9014, 2: 1.5393, 3: 0.3210} 0\n{0: 0.7146, 1: -0.2414, 2: 0.8933, 3: 1.6633} 0\n{0: 0.3797, 1: -0.1027, 2: 0.8717, 3: 1.1635} 0\n{0: 0.1295, 1: 0.5953, 2: 0.1774, 3: 0.6673} 1\n</code></pre></p>"},{"location":"api/datasets/synth/RandomRBFDrift/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/RandomTree/","title":"RandomTree","text":"<p>Random Tree generator.</p> <p>This generator is based on <sup>1</sup>. The generator creates a random tree by splitting features at random and setting labels at its leaves. </p> <p>The tree structure is composed of node objects, which can be either inner nodes or leaf nodes. The choice comes as a function of the parameters passed to its initializer. </p> <p>Since the concepts are generated and classified according to a tree structure, in theory, it should favor decision tree learners.</p>"},{"location":"api/datasets/synth/RandomTree/#parameters","title":"Parameters","text":"<ul> <li> <p>seed_tree</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Seed for random generation of tree.</p> </li> <li> <p>seed_sample</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Seed for random generation of instances.</p> </li> <li> <p>n_classes</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2</code></p> <p>The number of classes to generate.</p> </li> <li> <p>n_num_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of numerical features to generate.</p> </li> <li> <p>n_cat_features</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of categorical features to generate.</p> </li> <li> <p>n_categories_per_feature</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of values to generate per categorical feature.</p> </li> <li> <p>max_tree_depth</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The maximum depth of the tree concept.</p> </li> <li> <p>first_leaf_level</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>3</code></p> <p>The first level of the tree above <code>max_tree_depth</code> that can have leaves.</p> </li> <li> <p>fraction_leaves_per_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.15</code></p> <p>The fraction of leaves per level from <code>first_leaf_level</code> onwards.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomTree/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/RandomTree/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.RandomTree(seed_tree=42, seed_sample=42, n_classes=2,\n                           n_num_features=2, n_cat_features=2,\n                           n_categories_per_feature=2, max_tree_depth=6,\n                           first_leaf_level=3, fraction_leaves_per_level=0.15)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'x_num_0': 0.6394, 'x_num_1': 0.0250, 'x_cat_0': 1, 'x_cat_1': 0} 0\n{'x_num_0': 0.2232, 'x_num_1': 0.7364, 'x_cat_0': 0, 'x_cat_1': 1} 1\n{'x_num_0': 0.0317, 'x_num_1': 0.0936, 'x_cat_0': 0, 'x_cat_1': 0} 0\n{'x_num_0': 0.5612, 'x_num_1': 0.7160, 'x_cat_0': 1, 'x_cat_1': 0} 0\n{'x_num_0': 0.4492, 'x_num_1': 0.2781, 'x_cat_0': 0, 'x_cat_1': 0} 0\n</code></pre></p>"},{"location":"api/datasets/synth/RandomTree/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Domingos, Pedro, and Geoff Hulten. \"Mining high-speed data streams.\"   In Proceedings of the sixth ACM SIGKDD international conference on   Knowledge discovery and data mining, pp. 71-80. 2000.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/SEA/","title":"SEA","text":"<p>SEA synthetic dataset.</p> <p>Implementation of the data stream with abrupt drift described in <sup>1</sup>. Each observation is composed of 3 features. Only the first two features are relevant. The target is binary, and is positive if the sum of the features exceeds a certain threshold. There are 4 thresholds to choose from. Concept drift can be introduced by switching the threshold anytime during the stream. </p> <ul> <li> <p>Variant 0: <code>True</code> if \\(att1 + att2 &gt; 8\\) </p> </li> <li> <p>Variant 1: <code>True</code> if \\(att1 + att2 &gt; 9\\) </p> </li> <li> <p>Variant 2: <code>True</code> if \\(att1 + att2 &gt; 7\\) </p> </li> <li> <p>Variant 3: <code>True</code> if \\(att1 + att2 &gt; 9.5\\)</p> </li> </ul>"},{"location":"api/datasets/synth/SEA/#parameters","title":"Parameters","text":"<ul> <li> <p>variant</p> <p>Default \u2192 <code>0</code></p> <p>Determines the classification function to use. Possible choices are 0, 1, 2, 3.</p> </li> <li> <p>noise</p> <p>Default \u2192 <code>0.0</code></p> <p>Determines the amount of observations for which the target sign will be flipped.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed number used for reproducibility.</p> </li> </ul>"},{"location":"api/datasets/synth/SEA/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/SEA/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.SEA(variant=0, seed=42)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 6.39426, 1: 0.25010, 2: 2.75029} False\n{0: 2.23210, 1: 7.36471, 2: 6.76699} True\n{0: 8.92179, 1: 0.86938, 2: 4.21921} True\n{0: 0.29797, 1: 2.18637, 2: 5.05355} False\n{0: 0.26535, 1: 1.98837, 2: 6.49884} False\n</code></pre></p>"},{"location":"api/datasets/synth/SEA/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>A Streaming Ensemble Algorithm (SEA) for Large-Scale Classification \u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/STAGGER/","title":"STAGGER","text":"<p>STAGGER concepts stream generator.</p> <p>This generator is an implementation of the dara stream with abrupt concept drift, as described in <sup>1</sup>. </p> <p>The STAGGER concepts are boolean functions <code>f</code> with three features describing objects: size (small, medium and large), shape (circle, square and triangle) and colour (red, blue and green). </p> <p><code>f</code> options: </p> <ol> <li> <p><code>True</code> if the size is small and the color is red. </p> </li> <li> <p><code>True</code> if the color is green or the shape is a circle. </p> </li> <li> <p><code>True</code> if the size is medium or large </p> </li> </ol> <p>Concept drift can be introduced by changing the classification function. This can be done manually or using <code>datasets.synth.ConceptDriftStream</code>. </p> <p>One important feature is the possibility to balance classes, which means the class distribution will tend to a uniform one.</p>"},{"location":"api/datasets/synth/STAGGER/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Classification functions to use. From 0 to 2.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to balance classes or not. If balanced, the class distribution will converge to an uniform distribution.</p> </li> </ul>"},{"location":"api/datasets/synth/STAGGER/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/STAGGER/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.STAGGER(classification_function = 2, seed = 112,\n                     balance_classes = False)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{'size': 1, 'color': 2, 'shape': 2} 1\n{'size': 2, 'color': 1, 'shape': 2} 1\n{'size': 1, 'color': 1, 'shape': 2} 1\n{'size': 0, 'color': 1, 'shape': 0} 0\n{'size': 2, 'color': 1, 'shape': 0} 1\n</code></pre></p>"},{"location":"api/datasets/synth/STAGGER/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function at random.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/STAGGER/#notes","title":"Notes","text":"<p>The sample generation works as follows: The 3 attributes are generated with the random number generator. The classification function defines whether to classify the instance as class 0 or class 1. Finally, data is balanced, if this option is set by the user.</p> <ol> <li> <p>Schlimmer, J. C., &amp; Granger, R. H. (1986). Incremental learning   from noisy data. Machine learning, 1(3), 317-354.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Sine/","title":"Sine","text":"<p>Sine generator.</p> <p>This generator is an implementation of the dara stream with abrupt concept drift, as described in Gama, Joao, et al. <sup>1</sup>. </p> <p>It generates up to 4 relevant numerical features, that vary from 0 to 1, where only 2 of them are relevant to the classification task and the other 2 are optionally added by as noise. A classification function is chosen among four options: </p> <ol> <li> <p><code>SINE1</code>. Abrupt concept drift, noise-free examples. It has two relevant    attributes. Each attributes has values uniformly distributed in [0, 1].    In the first context all points below the curve \\(y = sin(x)\\) are    classified as positive. </p> </li> <li> <p><code>Reversed SINE1</code>. The reversed classification of <code>SINE1</code>. </p> </li> <li> <p><code>SINE2</code>. The same two relevant attributes. The classification function    is \\(y &lt; 0.5 + 0.3 sin(3 \\pi  x)\\). </p> </li> <li> <p><code>Reversed SINE2</code>. The reversed classification of <code>SINE2</code>. </p> </li> </ol> <p>Concept drift can be introduced by changing the classification function. This can be done manually or using <code>ConceptDriftStream</code>. </p> <p>Two important features are the possibility to balance classes, which means the class distribution will tend to a uniform one, and the possibility to add noise, which will, add two non relevant attributes.</p>"},{"location":"api/datasets/synth/Sine/#parameters","title":"Parameters","text":"<ul> <li> <p>classification_function</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Classification functions to use. From 0 to 3.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>balance_classes</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to balance classes or not. If balanced, the class distribution will converge to an uniform distribution.</p> </li> <li> <p>has_noise</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 2 non relevant features to the stream.</p> </li> </ul>"},{"location":"api/datasets/synth/Sine/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Sine/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Sine(classification_function = 2, seed = 112,\n                     balance_classes = False, has_noise = True)\n\nfor x, y in dataset.take(5):\n    print(x, y)\n</code></pre> <pre><code>{0: 0.4812, 1: 0.6660, 2: 0.6198, 3: 0.6994} 1\n{0: 0.9022, 1: 0.7518, 2: 0.1625, 3: 0.2209} 0\n{0: 0.4547, 1: 0.3901, 2: 0.9629, 3: 0.7287} 0\n{0: 0.4683, 1: 0.3515, 2: 0.2273, 3: 0.6027} 0\n{0: 0.9238, 1: 0.1673, 2: 0.4522, 3: 0.3447} 0\n</code></pre></p>"},{"location":"api/datasets/synth/Sine/#methods","title":"Methods","text":"generate_drift <p>Generate drift by switching the classification function at random.</p> <p></p> take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Sine/#notes","title":"Notes","text":"<p>The sample generation works as follows: The two attributes are generated with the random number generator. The classification function defines whether to classify the instance as class 0 or class 1. Finally, data is balanced and noise is added, if these options are set by the user.</p> <p>The generated sample will have 2 relevant features, and an additional two noise features if <code>has_noise</code> is set.</p> <ol> <li> <p>Gama, Joao, et al.'s 'Learning with drift detection.'   Advances in artificial intelligence-SBIA 2004.   Springer Berlin Heidelberg, 2004. 286-295.\"\u00a0\u21a9</p> </li> </ol>"},{"location":"api/datasets/synth/Waveform/","title":"Waveform","text":"<p>Waveform stream generator.</p> <p>Generates samples with 21 numeric features and 3 classes, based on a random differentiation of some base waveforms. Supports noise addition, in this case the samples will have 40 features.</p>"},{"location":"api/datasets/synth/Waveform/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>has_noise</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Adds 19 unrelated features to the stream.</p> </li> </ul>"},{"location":"api/datasets/synth/Waveform/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> </ul>"},{"location":"api/datasets/synth/Waveform/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\n\ndataset = synth.Waveform(seed=42, has_noise=True)\n\nfor x, y in dataset:\n    break\n\nx\n</code></pre> <pre><code>{0: -0.0397, 1: -0.7484, 2: 0.2974, 3: 0.3574, 4: -0.0735, 5: -0.3647, 6: 1.5631,     7: 2.5291, 8: 4.1599, 9: 4.9587, 10: 4.52587, 11: 4.0097, 12: 3.6705, 13: 1.7033,     14: 1.4898, 15: 1.9743, 16: 0.0898, 17: 2.319, 18: 0.2552, 19: -0.4775, 20: -0.71339,     21: 0.3770, 22: 0.3671, 23: 1.6579, 24: 0.7828, 25: 0.5855, 26: -0.5807, 27: 0.7112,     28: -0.0271, 29: 0.2968, 30: -0.4997, 31: 0.1302, 32: 0.3578, 33: -0.1900, 34: -0.3771,     35: 1.3560, 36: 0.7124, 37: -0.6245, 38: 0.1346, 39: 0.3550}\n</code></pre></p> <p><pre><code>y\n</code></pre> <pre><code>2\n</code></pre></p>"},{"location":"api/datasets/synth/Waveform/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/datasets/synth/Waveform/#notes","title":"Notes","text":"<p>An instance is generated based on the parameters passed. The generator will randomly choose one of the hard coded waveforms, as well as random multipliers. For each feature, the actual value generated will be a a combination of the hard coded functions, with the multipliers and a random value.</p> <p>If noise is added then the features 21 to 40 will be replaced with a random normal value.</p>"},{"location":"api/drift/ADWIN/","title":"ADWIN","text":"<p>Adaptive Windowing method for concept drift detection<sup>1</sup>.</p> <p>ADWIN (ADaptive WINdowing) is a popular drift detection method with mathematical guarantees. ADWIN efficiently keeps a variable-length window of recent items; such that it holds that there has no been change in the data distribution. This window is further divided into two sub-windows \\((W_0, W_1)\\) used to determine if a change has happened. ADWIN compares the average of \\(W_0\\) and \\(W_1\\) to confirm that they correspond to the same distribution. Concept drift is detected if the distribution equality no longer holds. Upon detecting a drift, \\(W_0\\) is replaced by \\(W_1\\) and a new \\(W_1\\) is initialized. ADWIN uses a significance value \\(\\delta=\\in(0,1)\\) to determine if the two sub-windows correspond to the same distribution.</p>"},{"location":"api/drift/ADWIN/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Default \u2192 <code>0.002</code></p> <p>Significance value.</p> </li> <li> <p>clock</p> <p>Default \u2192 <code>32</code></p> <p>How often ADWIN should check for changes. 1 means every new data point, default is 32. Higher values speed up processing, but may also lead to increased delay in change detection.</p> </li> <li> <p>max_buckets</p> <p>Default \u2192 <code>5</code></p> <p>The maximum number of buckets of each size that ADWIN should keep before merging buckets. The idea of data buckets comes from the compression algorithm introduced in the ADWIN2, the second iteration of the ADWIN algorithm presented in the original research paper. This is the ADWIN version available in River.</p> </li> <li> <p>min_window_length</p> <p>Default \u2192 <code>5</code></p> <p>The minimum length allowed for a subwindow when checking for concept drift. Subwindows whose size is smaller than this value will be ignored during concept drift evaluation. Lower values may decrease delay in change detection but may also lead to more false positives.</p> </li> <li> <p>grace_period</p> <p>Default \u2192 <code>10</code></p> <p>ADWIN does not perform any change detection until at least this many data points have arrived.</p> </li> </ul>"},{"location":"api/drift/ADWIN/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>estimation</p> <p>Estimate of mean value in the window.</p> </li> <li> <p>n_detections</p> <p>The total number of detected changes.</p> </li> <li> <p>total</p> <p>The sum of the stored elements.</p> </li> <li> <p>variance</p> <p>The sample variance within the stored (adaptive) window.</p> </li> <li> <p>width</p> <p>Window size</p> </li> </ul>"},{"location":"api/drift/ADWIN/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(12345)\nadwin = drift.ADWIN()\n\ndata_stream = rng.choices([0, 1], k=1000) + rng.choices(range(4, 8), k=1000)\n\nfor i, val in enumerate(data_stream):\n    adwin.update(val)\n    if adwin.drift_detected:\n        print(f\"Change detected at index {i}, input value: {val}\")\n</code></pre> <pre><code>Change detected at index 1023, input value: 4\n</code></pre></p>"},{"location":"api/drift/ADWIN/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Apart from adding the element value to the window, by inserting it in the correct bucket, it will also update the relevant statistics, in this case the total sum of all values, the window width and the total variance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p>Returns</p> <p>None:     self</p> <p></p> <ol> <li> <p>Albert Bifet and Ricard Gavalda. \"Learning from time-changing data with adaptive windowing.\" In Proceedings of the 2007 SIAM international conference on data mining, pp. 443-448. Society for Industrial and Applied Mathematics, 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/DriftRetrainingClassifier/","title":"DriftRetrainingClassifier","text":"<p>Drift retraining classifier.</p> <p>This classifier is a wrapper for any classifier. It monitors the incoming data for concept drifts and warnings in the model's accurary. In case a warning is detected, a background model starts to train. If a drift is detected, the model will be replaced by the background model, and the background model will be reset.</p>"},{"location":"api/drift/DriftRetrainingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier and background classifier class.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftAndWarningDetector | base.BinaryDriftAndWarningDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Algorithm to track warnings and concept drifts. Attention! If the parameter train_in_background is True, the drift_detector must have a warning tracker.</p> </li> <li> <p>train_in_background</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Parameter to determine if a background model will be used.</p> </li> </ul>"},{"location":"api/drift/DriftRetrainingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import drift\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Elec2().take(3000)\n\nmodel = drift.DriftRetrainingClassifier(\n    model=tree.HoeffdingTreeClassifier(),\n    drift_detector=drift.binary.DDM()\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 86.46%\n</code></pre></p>"},{"location":"api/drift/DriftRetrainingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/drift/DummyDriftDetector/","title":"DummyDriftDetector","text":"<p>Baseline drift detector that generates pseudo drift detection signals.</p> <p>There are two approaches<sup>1</sup>: </p> <ul> <li> <p><code>fixed</code> where the drift signal is generated every <code>t_0</code> samples.</p> </li> <li> <p><code>random</code> corresponds to a pseudo-random drift detection strategy.</p> </li> </ul>"},{"location":"api/drift/DummyDriftDetector/#parameters","title":"Parameters","text":"<ul> <li> <p>trigger_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>fixed</code></p> <p>The trigger method to use. * <code>fixed</code> * <code>random</code></p> </li> <li> <p>t_0</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>300</code></p> <p>Reference point to define triggers.</p> </li> <li> <p>w</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Auxiliary parameter whose purpose is twofold: - if <code>trigger_method=\"fixed\"</code>, the periodic drift signals will only start after an initial warm-up period randomly defined between <code>[0, w]</code>. Useful to avoid that all ensemble members are reset at the same time when periodic triggers are used as the adaptation strategy. - if <code>trigger_method=\"random\"</code>, <code>w</code> defines the probability bounds of triggering a drift. The chance of triggering a drift is \\(0.5\\) after observing <code>t_0</code> instances and becomes \\(1\\) after monitoring <code>t_0 + w / 2</code> instances. A sigmoid function is used to produce values between <code>[0, 1]</code> that are used as the reset probabilities.</p> </li> <li> <p>dynamic_cloning</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether to change the <code>seed</code> and <code>w</code> values each time <code>clone()</code> is called.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/drift/DummyDriftDetector/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/DummyDriftDetector/#examples","title":"Examples","text":"<pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\n</code></pre> <p>The observed values will not affect the periodic triggers.</p> <pre><code>data = [rng.gauss(0, 1) for _ in range(1000)]\n</code></pre> <p>Let's start with the fixed drift signals:</p> <p><pre><code>ptrigger = DummyDriftDetector(t_0=500, seed=42)\nfor i, v in enumerate(data):\n    ptrigger.update(v)\n    if ptrigger.drift_detected:\n        print(f\"Drift detected at instance {i}.\")\n</code></pre> <pre><code>Drift detected at instance 499.\nDrift detected at instance 999.\n</code></pre></p> <p>Now, the random drift signals:</p> <p><pre><code>rtrigger = DummyDriftDetector(\n    trigger_method=\"random\",\n    t_0=500,\n    w=100,\n    dynamic_cloning=True,\n    seed=42\n)\nfor i, v in enumerate(data):\n    rtrigger.update(v)\n    if rtrigger.drift_detected:\n        print(f\"Drift detected at instance {i}.\")\n</code></pre> <pre><code>Drift detected at instance 368.\nDrift detected at instance 817.\n</code></pre></p> <p>Remember to set a w &gt; 0 value if random triggers are used:</p> <p><pre><code>try:\n    DummyDriftDetector(trigger_method=\"random\")\nexcept ValueError as ve:\n    print(ve)\n</code></pre> <pre><code>The 'w' value must be greater than zero when 'trigger_method' is 'random'.\n</code></pre></p> <p>Since we set <code>dynamic_cloning</code> to <code>True</code>, a clone of the periodic trigger will have its internal paramenters changed:</p> <p><pre><code>rtrigger = rtrigger.clone()\nfor i, v in enumerate(data):\n    rtrigger.update(v)\n    if rtrigger.drift_detected:\n        print(f\"Drift detected at instance {i}.\")\n</code></pre> <pre><code>Drift detected at instance 429.\nDrift detected at instance 728.\n</code></pre></p>"},{"location":"api/drift/DummyDriftDetector/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/drift/DummyDriftDetector/#notes","title":"Notes","text":"<p>When used in ensembles, a naive implementation of periodic drift signals would make all ensemble members reset at the same time. To avoid that, the <code>dynamic_cloning</code> parameter can be set to <code>True</code>. In this case, every time the <code>clone</code> method of this detector is called in an ensemble a new <code>seed</code> is defined. If <code>dynamic_cloning=True</code> and <code>trigger_method=\"fixed\"</code>, a new <code>w</code> between <code>[0, t_0]</code> will also be created for the new cloned instance.</p> <ol> <li> <p>Heitor Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer, and Albert Bifet. On Ensemble Techniques for Data Stream Regression. IJCNN'20. International Joint Conference on Neural Networks. 2020.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/KSWIN/","title":"KSWIN","text":"<p>Kolmogorov-Smirnov Windowing method for concept drift detection.</p>"},{"location":"api/drift/KSWIN/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.005</code></p> <p>Probability for the test statistic of the Kolmogorov-Smirnov-Test. The alpha parameter is very sensitive, therefore should be set below 0.01.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>Size of the sliding window.</p> </li> <li> <p>stat_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>Size of the statistic window.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> <li> <p>window</p> <p>Type \u2192 typing.Iterable | None</p> <p>Default \u2192 <code>None</code></p> <p>Already collected data to avoid cold start.</p> </li> </ul>"},{"location":"api/drift/KSWIN/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/KSWIN/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(12345)\nkswin = drift.KSWIN(alpha=0.0001, seed=42)\n\ndata_stream = rng.choices([0, 1], k=1000) + rng.choices(range(4, 8), k=1000)\n\nfor i, val in enumerate(data_stream):\n    kswin.update(val)\n    if kswin.drift_detected:\n        print(f\"Change detected at index {i}, input value: {val}\")\n</code></pre> <pre><code>Change detected at index 1016, input value: 6\n</code></pre></p>"},{"location":"api/drift/KSWIN/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Adds an element on top of the sliding window and removes the oldest one from the window. Afterwards, the KS-test is performed.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p>Returns</p> <p>None:     self</p> <p></p>"},{"location":"api/drift/KSWIN/#notes","title":"Notes","text":"<p>KSWIN (Kolmogorov-Smirnov Windowing) is a concept change detection method based on the Kolmogorov-Smirnov (KS) statistical test. KS-test is a statistical test with no assumption of underlying data distribution. KSWIN can monitor data or performance distributions. Note that the detector accepts one dimensional input as array.</p> <p>KSWIN maintains a sliding window \\(\\Psi\\) of fixed size \\(n\\) (window_size). The last \\(r\\) (stat_size) samples of \\(\\Psi\\) are assumed to represent the last concept considered as \\(R\\). From the first \\(n-r\\) samples of \\(\\Psi\\), \\(r\\) samples are uniformly drawn, representing an approximated last concept \\(W\\).</p> <p>The KS-test is performed on the windows \\(R\\) and \\(W\\) of the same size. KS -test compares the distance of the empirical cumulative data distribution \\(dist(R,W)\\).</p> <p>A concept drift is detected by KSWIN if:</p> \\[ dist(R,W) &gt; \\sqrt{-\\frac{ln\\alpha}{r}} \\] <p>The difference in empirical data distributions between the windows \\(R\\) and \\(W\\) is too large since \\(R\\) and \\(W\\) come from the same distribution.</p> <ol> <li> <p>Christoph Raab, Moritz Heusinger, Frank-Michael Schleif, Reactive Soft Prototype Computing for Concept Drift Streams, Neurocomputing, 2020,\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/NoDrift/","title":"NoDrift","text":"<p>Dummy class used to turn off concept drift detection capabilities of adaptive models. It always signals that no concept drift was detected. Examples --------</p> <p>from river import drift &gt;&gt;&gt; from river import evaluate &gt;&gt;&gt; from river import forest &gt;&gt;&gt; from river import metrics &gt;&gt;&gt; from river.datasets import synth </p> <p>dataset = datasets.synth.ConceptDriftStream( ...     seed=8, ...     position=500, ...     width=40, ... ).take(700) </p> <p>We can turn off the warning detection capabilities of Adaptive Random Forest (ARF) or other similar models. Thus, the base models will reset immediately after identifying a drift, bypassing the background model building phase: </p> <p>adaptive_model = forest.ARFClassifier( ...     leaf_prediction=\"mc\", ...     warning_detector=drift.NoDrift(), ...     seed=8 ... ) </p> <p>We can also turn off the concept drift handling capabilities completely: </p> <p>stationary_model = forest.ARFClassifier( ...     leaf_prediction=\"mc\", ...     warning_detector=drift.NoDrift(), ...     drift_detector=drift.NoDrift(), ...     seed=8 ... ) </p> <p>Let's put that to test: </p> <p>for x, y in dataset: ...     adaptive_model.learn_one(x, y) ...     stationary_model.learn_one(x, y) </p> <p>The adaptive model: </p> <p>adaptive_model.n_drifts_detected() 2 </p> <p>adaptive_model.n_warnings_detected() 0 </p> <p>The stationary one: </p> <p>stationary_model.n_drifts_detected() 0 </p> <p>stationary_model.n_warnings_detected() 0</p>"},{"location":"api/drift/NoDrift/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/NoDrift/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p>"},{"location":"api/drift/PageHinkley/","title":"PageHinkley","text":"<p>Page-Hinkley method for concept drift detection.</p> <p>This change detection method works by computing the observed values and their mean up to the current moment. Page-Hinkley does not signal warning zones, only change detections. </p> <p>This detector implements the CUSUM control chart for detecting changes. This implementation also supports the two-sided Page-Hinkley test to detect increasing and decreasing changes in the mean of the input values.</p>"},{"location":"api/drift/PageHinkley/#parameters","title":"Parameters","text":"<ul> <li> <p>min_instances</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum number of instances before detecting change.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.005</code></p> <p>The delta factor for the Page-Hinkley test.</p> </li> <li> <p>threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>50.0</code></p> <p>The change detection threshold (lambda).</p> </li> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.9999</code></p> <p>The forgetting factor, used to weight the observed value and the mean.</p> </li> <li> <p>mode</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>both</code></p> <p>Whether to consider increases (\"up\"), decreases (\"down\") or both (\"both\") when monitoring the fading mean.</p> </li> </ul>"},{"location":"api/drift/PageHinkley/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/PageHinkley/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(12345)\nph = drift.PageHinkley()\n\ndata_stream = rng.choices([0, 1], k=1000) + rng.choices(range(4, 8), k=1000)\n\nfor i, val in enumerate(data_stream):\n    ph.update(val)\n    if ph.drift_detected:\n        print(f\"Change detected at index {i}, input value: {val}\")\n</code></pre> <pre><code>Change detected at index 1006, input value: 5\n</code></pre></p>"},{"location":"api/drift/PageHinkley/#methods","title":"Methods","text":"update <p>Update the detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'int | float' </li> </ul> <p></p> <ol> <li> <p>E. S. Page. 1954. Continuous Inspection Schemes. Biometrika 41, 1/2 (1954), 100-115.\u00a0\u21a9</p> </li> <li> <p>Sebasti\u00e3o, R., &amp; Fernandes, J. M. (2017, June). Supporting the Page-Hinkley test with empirical mode decomposition for change detection. In International Symposium on Methodologies for Intelligent Systems (pp. 492-498). Springer, Cham.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/DDM/","title":"DDM","text":"<p>Drift Detection Method.</p> <p>DDM (Drift Detection Method) is a concept change detection method based on the PAC learning model premise, that the learner's error rate will decrease as the number of analysed samples increase, as long as the data distribution is stationary. </p> <p>If the algorithm detects an increase in the error rate, that surpasses a calculated threshold, either change is detected or the algorithm will warn the user that change may occur in the near future, which is called the warning zone. </p> <p>The detection threshold is calculated in function of two statistics, obtained when \\((p_i + s_i)\\) is minimum: </p> <ul> <li> <p>\\(p_{min}\\): The minimum recorded error rate. </p> </li> <li> <p>\\(s_{min}\\): The minimum recorded standard deviation. </p> </li> </ul> <p>At instant \\(i\\), the detection algorithm uses: </p> <ul> <li> <p>\\(p_i\\): The error rate at instant \\(i\\). </p> </li> <li> <p>\\(s_i\\): The standard deviation at instant \\(i\\). </p> </li> </ul> <p>The conditions for entering the warning zone and detecting change are as follows [see implementation note below]: </p> <ul> <li> <p>if \\(p_i + s_i \\geq p_{min} + w_l * s_{min}\\) -&gt; Warning zone </p> </li> <li> <p>if \\(p_i + s_i \\geq p_{min} + d_l * s_{min}\\) -&gt; Change detected </p> </li> </ul> <p>In the above expressions, \\(w_l\\) and \\(d_l\\) represent, respectively, the warning and drift thresholds. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul>"},{"location":"api/drift/binary/DDM/#parameters","title":"Parameters","text":"<ul> <li> <p>warm_start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum required number of analyzed samples so change can be detected. Warm start parameter for the drift detector.</p> </li> <li> <p>warning_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>2.0</code></p> <p>Threshold to decide if the detector is in a warning zone. The default value gives 95\\% of confidence level to the warning assessment.</p> </li> <li> <p>drift_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>3.0</code></p> <p>Threshold to decide if a drift was detected. The default value gives a 99\\% of confidence level to the drift assessment.</p> </li> </ul>"},{"location":"api/drift/binary/DDM/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/DDM/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nddm = drift.binary.DDM()\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    ddm.update(x)\n    if ddm.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if ddm.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 1084\nChange detected at index 1334\nWarning detected at index 1492\n</code></pre></p>"},{"location":"api/drift/binary/DDM/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p> <ol> <li> <p>Jo\u00e3o Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/EDDM/","title":"EDDM","text":"<p>Early Drift Detection Method.</p> <p>EDDM (Early Drift Detection Method) aims to improve the detection rate of gradual concept drift in DDM, while keeping a good performance against abrupt concept drift. </p> <p>This method works by keeping track of the average distance between two errors instead of only the error rate. For this, it is necessary to keep track of the running average distance and the running standard deviation, as well as the maximum distance and the maximum standard deviation. </p> <p>The algorithm works similarly to the DDM algorithm, by keeping track of statistics only. It works with the running average distance (\\(p_i'\\)) and the running standard deviation (\\(s_i'\\)), as well as \\(p'_{max}\\) and \\(s'_{max}\\), which are the values of \\(p_i'\\) and \\(s_i'\\) when \\((p_i' + 2 * s_i')\\) reaches its maximum. </p> <p>Like DDM, there are two threshold values that define the borderline between no change, warning zone, and drift detected. These are as follows: </p> <ul> <li> <p>if \\((p_i' + 2 * s_i') / (p'_{max} + 2 * s'_{max}) &lt; \\alpha\\) -&gt; Warning zone </p> </li> <li> <p>if \\((p_i' + 2 * s_i') / (p'_{max} + 2 * s'_{max}) &lt; \\beta\\) -&gt; Change detected </p> </li> </ul> <p>\\(\\alpha\\) and \\(\\beta\\) are set to 0.95 and 0.9, respectively. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\\\neq y'\\)</p> </li> </ul>"},{"location":"api/drift/binary/EDDM/#parameters","title":"Parameters","text":"<ul> <li> <p>warm_start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum required number of monitored errors/failures so change can be detected. Warm start parameter for the drift detector.</p> </li> <li> <p>alpha</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>Threshold for triggering a warning. Must be between 0 and 1. The smaller the value, the more conservative the detector becomes.</p> </li> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.9</code></p> <p>Threshold for triggering a drift. Must be between 0 and 1. The smaller the value, the more conservative the detector becomes.</p> </li> </ul>"},{"location":"api/drift/binary/EDDM/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/EDDM/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\neddm = drift.binary.EDDM(alpha=0.8, beta=0.75)\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    eddm.update(x)\n    if eddm.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if eddm.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 1059\nChange detected at index 1278\n</code></pre></p>"},{"location":"api/drift/binary/EDDM/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BinaryDriftDetector:     self</p> <p></p> <ol> <li> <p>Early Drift Detection Method. Manuel Baena-Garcia, Jose Del Campo-Avila, Ra\u00fal Fidalgo, Albert Bifet, Ricard Gavalda, Rafael Morales-Bueno. In Fourth International Workshop on Knowledge Discovery from Data Streams, 2006.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/FHDDM/","title":"FHDDM","text":"<p>Fast Hoeffding Drift Detection Method.</p> <p>FHDDM is a drift detection method based on the Hoeffding's inequality which uses the input average as estimator. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul> <p>Implementation based on MOA.</p>"},{"location":"api/drift/binary/FHDDM/#parameters","title":"Parameters","text":"<ul> <li> <p>sliding_window_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>The minimum required number of analyzed samples so change can be detected.</p> </li> <li> <p>confidence_level</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-06</code></p> <p>Confidence level used to determine the epsilon coefficient in Hoeffding\u2019s inequality. The default value gives a 99\\% of confidence level to the drift assessment.</p> </li> <li> <p>short_window_size</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The size of the short window size that it is used in a Stacking version of FHDDM <sup>2</sup>.</p> </li> </ul>"},{"location":"api/drift/binary/FHDDM/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/FHDDM/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nfhddm = drift.binary.FHDDM()\nfhddm_s = drift.binary.FHDDM(short_window_size = 20)\ndata_stream = rng.choices([0, 1], k=250)\ndata_stream = data_stream + rng.choices([0, 1], k=250, weights=[0.9, 0.1])\nfor i, x in enumerate(data_stream):\n    fhddm.update(x)\n    fhddm_s.update(x)\n    if fhddm.drift_detected or fhddm_s.drift_detected:\n        print(f\"Change detected at index {i}\")\n</code></pre> <pre><code>Change detected at index 279\nChange detected at index 315\n</code></pre></p>"},{"location":"api/drift/binary/FHDDM/#methods","title":"Methods","text":"update <p>Update the detector with a single boolean input.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p> <ol> <li> <p>A. Pesaranghader, H.L. Viktor, Fast Hoeffding Drift Detection Method for Evolving Data Streams. In the Proceedings of ECML-PKDD 2016.\u00a0\u21a9</p> </li> <li> <p>Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/HDDM-A/","title":"HDDM_A","text":"<p>Drift Detection Method based on Hoeffding's bounds with moving average-test.</p> <p>HDDM_A is a drift detection method based on the Hoeffding's inequality which uses the input average as estimator. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul> <p>Implementation based on MOA.</p>"},{"location":"api/drift/binary/HDDM-A/#parameters","title":"Parameters","text":"<ul> <li> <p>drift_confidence</p> <p>Default \u2192 <code>0.001</code></p> <p>Confidence to the drift</p> </li> <li> <p>warning_confidence</p> <p>Default \u2192 <code>0.005</code></p> <p>Confidence to the warning</p> </li> <li> <p>two_sided_test</p> <p>Default \u2192 <code>False</code></p> <p>If <code>True</code>, will monitor error increments and decrements (two-sided). By default will only monitor increments (one-sided).</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-A/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-A/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nhddm_a = drift.binary.HDDM_A()\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    hddm_a.update(x)\n    if hddm_a.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if hddm_a.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 451\nChange detected at index 1206\n</code></pre></p>"},{"location":"api/drift/binary/HDDM-A/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BinaryDriftDetector:     self</p> <p></p> <ol> <li> <p>Fr\u00edas-Blanco I, del Campo-\u00c1vila J, Ramos-Jimenez G, et al. Online and non-parametric drift detection methods based on Hoeffding's bounds. IEEE Transactions on Knowledge and Data Engineering, 2014, 27(3): 810-823.\u00a0\u21a9</p> </li> <li> <p>Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer. MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604, 2010.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/binary/HDDM-W/","title":"HDDM_W","text":"<p>Drift Detection Method based on Hoeffding's bounds with moving weighted average-test.</p> <p>HDDM_W is an online drift detection method based on McDiarmid's bounds. HDDM_W uses the Exponentially Weighted Moving Average (EWMA) statistic as estimator. </p> <p>Input: <code>x</code> is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values. </p> <p>For example, if a classifier's prediction \\(y'\\) is right or wrong w.r.t. the true target label \\(y\\): </p> <ul> <li> <p>0: Correct, \\(y=y'\\)</p> </li> <li> <p>1: Error, \\(y \\neq y'\\)</p> </li> </ul> <p>Implementation based on MOA.</p>"},{"location":"api/drift/binary/HDDM-W/#parameters","title":"Parameters","text":"<ul> <li> <p>drift_confidence</p> <p>Default \u2192 <code>0.001</code></p> <p>Confidence to the drift</p> </li> <li> <p>warning_confidence</p> <p>Default \u2192 <code>0.005</code></p> <p>Confidence to the warning</p> </li> <li> <p>lambda_val</p> <p>Default \u2192 <code>0.05</code></p> <p>The weight given to recent data. Smaller values mean less weight given to recent data.</p> </li> <li> <p>two_sided_test</p> <p>Default \u2192 <code>False</code></p> <p>If True, will monitor error increments and decrements (two-sided). By default will only monitor increments (one-sided).</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-W/#attributes","title":"Attributes","text":"<ul> <li> <p>drift_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> <li> <p>warning_detected</p> <p>Whether or not a drift is detected following the last update.</p> </li> </ul>"},{"location":"api/drift/binary/HDDM-W/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import drift\n\nrng = random.Random(42)\nhddm_w = drift.binary.HDDM_W()\n\ndata_stream = rng.choices([0, 1], k=1000)\ndata_stream = data_stream + rng.choices([0, 1], k=1000, weights=[0.3, 0.7])\n\nprint_warning = True\nfor i, x in enumerate(data_stream):\n    hddm_w.update(x)\n    if hddm_w.warning_detected and print_warning:\n        print(f\"Warning detected at index {i}\")\n        print_warning = False\n    if hddm_w.drift_detected:\n        print(f\"Change detected at index {i}\")\n        print_warning = True\n</code></pre> <pre><code>Warning detected at index 451\nChange detected at index 1077\n</code></pre></p>"},{"location":"api/drift/binary/HDDM-W/#methods","title":"Methods","text":"update <p>Update the change detector with a single data point.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BinaryDriftDetector:     self</p> <p></p> <ol> <li> <p>Fr\u00edas-Blanco I, del Campo-\u00c1vila J, Ramos-Jimenez G, et al. Online and non-parametric drift  detection methods based on Hoeffding\u2019s bounds. IEEE Transactions on Knowledge and Data  Engineering, 2014, 27(3): 810-823.\u00a0\u21a9</p> </li> <li> <p>Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer. MOA: Massive Online Analysis;  Journal of Machine Learning Research 11: 1601-1604, 2010.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/AirlinePassengers/","title":"AirlinePassengers","text":"<p>JFK Airline Passengers</p> <p>This dataset gives the number of passengers arriving and departing at JFK. The data is obtained from New York State's official Kaggle page for this dataset.</p>"},{"location":"api/drift/datasets/AirlinePassengers/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/AirlinePassengers/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://www.kaggle.com/new-york-state/nys-air-passenger-traffic,-port-authority-of-ny-nj#air-passenger-traffic-per-month-port-authority-of-ny-nj-beginning-1977.csv\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/Apple/","title":"Apple","text":"<p>Apple Stock</p> <p>This dataset concerns the daily close price and volume of Apple stock around the year 2000. The dataset is sampled every 3 observations to reduce the length of the time series. This dataset is retrieved from Yahoo Finance.</p>"},{"location":"api/drift/datasets/Apple/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/Apple/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://finance.yahoo.com/quote/AAPL/history?period1=850348800&amp;period2=1084579200&amp;interval=1d&amp;filter=history&amp;frequency=1d\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/Bitcoin/","title":"Bitcoin","text":"<p>Bitcoin Market Price</p> <p>This is a regression task, where the goal is to predict the average USD market price across major bitcoin exchanges. This data was collected from the official Blockchain website. There is only one feature given, the day of exchange, which is in increments of three. The first 500 lines have been removed because they are not interesting.</p>"},{"location":"api/drift/datasets/Bitcoin/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/Bitcoin/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://www.blockchain.com/fr/explorer/charts/market-price?timespan=all\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/BrentSpotPrice/","title":"BrentSpotPrice","text":"<p>Brent Spot Price</p> <p>This is the USD price for Brent Crude oil, measured daily. We include the time series     from 2000 onwards. The data is sampled at every 10 original observations to reduce the length of the series. </p> <p>The data is obtained from the U.S. Energy Information Administration. Since the data is in the public domain,     we distribute it as part of this repository. </p> <p>Since the original data has observations only on trading days, there are arguably gaps in this time     series (on non-trading days). However we consider these to be consecutive, and thus also consider     the sampled time series to have consecutive observations.</p>"},{"location":"api/drift/datasets/BrentSpotPrice/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/BrentSpotPrice/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>U.S. Energy Information Administration (Sep. 2019)\u00a0\u21a9</p> </li> <li> <p>https://www.eia.gov/opendata/v1/qb.php?sdid=PET.RBRTE.D\u00a0\u21a9</p> </li> </ol>"},{"location":"api/drift/datasets/Occupancy/","title":"Occupancy","text":"<p>Room occupancy data.</p> <p>Dataset on detecting room occupancy based on several variables. The dataset contains temperature, humidity, light, and CO2 variables. </p> <p>The data is sampled at every 16 observations to reduce the length of the series.</p>"},{"location":"api/drift/datasets/Occupancy/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/Occupancy/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p> Candanedo, Luis M., and V\u00e9ronique Feldheim. \"Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models.\" Energy and Buildings 112 (2016): 28-39.</p>"},{"location":"api/drift/datasets/RunLog/","title":"RunLog","text":"<p>Interval Training Running Pace.</p> <p>This dataset shows the pace of a runner during an interval training session, where a mobile application provides instructions on when to run and when to walk.</p>"},{"location":"api/drift/datasets/RunLog/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/RunLog/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/drift/datasets/UKCoalEmploy/","title":"UKCoalEmploy","text":"<p>Historic Employment in UK Coal Mines</p> <p>This is historic data obtained from the UK government.     We use the employment column for the number of workers employed in the British coal mines     Missing values in the data are replaced with the value of the preceding year.</p>"},{"location":"api/drift/datasets/UKCoalEmploy/#attributes","title":"Attributes","text":"<ul> <li> <p>desc</p> <p>Return the description from the docstring.</p> </li> <li> <p>path</p> </li> </ul>"},{"location":"api/drift/datasets/UKCoalEmploy/#methods","title":"Methods","text":"take <p>Iterate over the k samples.</p> <p>Parameters</p> <ul> <li>k     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>https://www.gov.uk/government/statistical-data-sets/historical-coal-data-coal-production-availability-and-consumption\u00a0\u21a9</p> </li> </ol>"},{"location":"api/dummy/NoChangeClassifier/","title":"NoChangeClassifier","text":"<p>Dummy classifier which returns the last class seen.</p> <p>The predict_one method will output the last class seen whilst predict_proba_one will return 1 for the last class seen and 0 for the others.</p>"},{"location":"api/dummy/NoChangeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>last_class</p> <p>The last class seen.</p> </li> <li> <p>classes</p> <p>The set of classes seen.</p> </li> </ul>"},{"location":"api/dummy/NoChangeClassifier/#examples","title":"Examples","text":"<p>Taken from example 2.1 from this page.</p> <p><pre><code>import pprint\nfrom river import dummy\n\nsentences = [\n    ('glad happy glad', '+'),\n    ('glad glad joyful', '+'),\n    ('glad pleasant', '+'),\n    ('miserable sad glad', '\u2212')\n]\n\nmodel = dummy.NoChangeClassifier()\n\nfor sentence, label in sentences:\n    model.learn_one(sentence, label)\n\nnew_sentence = 'glad sad miserable pleasant glad'\nmodel.predict_one(new_sentence)\n</code></pre> <pre><code>'\u2212'\n</code></pre></p> <p><pre><code>pprint.pprint(model.predict_proba_one(new_sentence))\n</code></pre> <pre><code>{'+': 0, '\u2212': 1}\n</code></pre></p>"},{"location":"api/dummy/NoChangeClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/dummy/PriorClassifier/","title":"PriorClassifier","text":"<p>Dummy classifier which uses the prior distribution.</p> <p>The <code>predict_one</code> method will output the most common class whilst <code>predict_proba_one</code> will return the normalized class counts.</p>"},{"location":"api/dummy/PriorClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>counts (collections.Counter)</p> <p>Class counts.</p> </li> <li> <p>n (int)</p> <p>Total number of seen instances.</p> </li> </ul>"},{"location":"api/dummy/PriorClassifier/#examples","title":"Examples","text":"<p>Taken from example 2.1 from this page</p> <p><pre><code>from river import dummy\n\nsentences = [\n    ('glad happy glad', '+'),\n    ('glad glad joyful', '+'),\n    ('glad pleasant', '+'),\n    ('miserable sad glad', '\u2212')\n]\n\nmodel = dummy.PriorClassifier()\n\nfor sentence, label in sentences:\n    model.learn_one(sentence, label)\n\nnew_sentence = 'glad sad miserable pleasant glad'\nmodel.predict_one(new_sentence)\n</code></pre> <pre><code>'+'\n</code></pre> <pre><code>model.predict_proba_one(new_sentence)\n</code></pre> <pre><code>{'+': 0.75, '\u2212': 0.25}\n</code></pre></p>"},{"location":"api/dummy/PriorClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Krichevsky\u2013Trofimov estimator \u21a9</p> </li> </ol>"},{"location":"api/dummy/StatisticRegressor/","title":"StatisticRegressor","text":"<p>Dummy regressor that uses a univariate statistic to make predictions.</p>"},{"location":"api/dummy/StatisticRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>statistic</p> <p>Type \u2192 stats.base.Univariate</p> </li> </ul>"},{"location":"api/dummy/StatisticRegressor/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nfrom river import dummy\nfrom river import stats\n\nsentences = [\n    ('glad happy glad', 3),\n    ('glad glad joyful', 3),\n    ('glad pleasant', 2),\n    ('miserable sad glad', -3)\n]\n\nmodel = dummy.StatisticRegressor(stats.Mean())\n\nfor sentence, score in sentences:\n    model.learn_one(sentence, score)\n\nnew_sentence = 'glad sad miserable pleasant glad'\nmodel.predict_one(new_sentence)\n</code></pre> <pre><code>1.25\n</code></pre></p>"},{"location":"api/dummy/StatisticRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/ensemble/ADWINBaggingClassifier/","title":"ADWINBaggingClassifier","text":"<p>ADWIN Bagging classifier.</p> <p>ADWIN Bagging <sup>1</sup> is the online bagging method of Oza and Russell <sup>2</sup> with the addition of the <code>ADWIN</code> algorithm as a change detector. If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.</p>"},{"location":"api/ensemble/ADWINBaggingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to bag.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/ADWINBaggingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/ADWINBaggingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = ensemble.ADWINBaggingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.65%\n</code></pre></p>"},{"location":"api/ensemble/ADWINBaggingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Averages the predictions of each classifier.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p> <ol> <li> <p>Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavald\u00e0. \"New ensemble methods for evolving data streams.\" In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.\u00a0\u21a9</p> </li> <li> <p>Oza, N., Russell, S. \"Online bagging and boosting.\" In: Artificial Intelligence and Statistics 2001, pp. 105\u2013112. Morgan Kaufmann, 2001.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/ADWINBoostingClassifier/","title":"ADWINBoostingClassifier","text":"<p>ADWIN Boosting classifier.</p> <p>ADWIN Boosting <sup>1</sup> is the online boosting method of Oza and Russell <sup>2</sup> with the addition of the <code>ADWIN</code> algorithm as a change detector. If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.</p>"},{"location":"api/ensemble/ADWINBoostingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to boost.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/ADWINBoostingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/ADWINBoostingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\nmodel = ensemble.ADWINBoostingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.61%\n</code></pre></p>"},{"location":"api/ensemble/ADWINBoostingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavald\u00e0. \"New ensemble methods for evolving data streams.\" In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.\u00a0\u21a9</p> </li> <li> <p>Oza, N., Russell, S. \"Online bagging and boosting.\" In: Artificial Intelligence and Statistics 2001, pp. 105\u2013112. Morgan Kaufmann, 2001.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/AdaBoostClassifier/","title":"AdaBoostClassifier","text":"<p>Boosting for classification.</p> <p>For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter lambda. The lambda parameter is updated when the weaks learners fit successively the same observation.</p>"},{"location":"api/ensemble/AdaBoostClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to boost.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/AdaBoostClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/AdaBoostClassifier/#examples","title":"Examples","text":"<p>In the following example three tree classifiers are boosted together. The performance is slightly better than when using a single tree.</p> <p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Phishing()\n\nmetric = metrics.LogLoss()\n\nmodel = ensemble.AdaBoostClassifier(\n    model=(\n        tree.HoeffdingTreeClassifier(\n            split_criterion='gini',\n            delta=1e-5,\n            grace_period=2000\n        )\n    ),\n    n_models=5,\n    seed=42\n)\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.370805\n</code></pre></p> <p><pre><code>print(model)\n</code></pre> <pre><code>AdaBoostClassifier(HoeffdingTreeClassifier)\n</code></pre></p>"},{"location":"api/ensemble/AdaBoostClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/BOLEClassifier/","title":"BOLEClassifier","text":"<p>Boosting Online Learning Ensemble (BOLE).</p> <p>A modified version of Oza Online Boosting Algorithm <sup>1</sup>. For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter lambda. The first model to be trained will be the one with worst <code>correct_weight / (correct_weight + wrong_weight)</code>. The worst model not yet trained will receive lambda values for training from the models that incorrectly classified an instance, and the best model's not yet trained will receive lambda values for training from the models that correctly classified an instance. For more details, see <sup>2</sup>.</p>"},{"location":"api/ensemble/BOLEClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to boost.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> <li> <p>error_bound</p> <p>Default \u2192 <code>0.5</code></p> <p>Error bound percentage for allowing models to vote.</p> </li> </ul>"},{"location":"api/ensemble/BOLEClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/BOLEClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import drift\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Elec2().take(3000)\n\nmodel = ensemble.BOLEClassifier(\n    model=drift.DriftRetrainingClassifier(\n        model=tree.HoeffdingTreeClassifier(),\n        drift_detector=drift.binary.DDM()\n    ),\n    n_models=10,\n    seed=42\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 93.63%\n</code></pre></p>"},{"location":"api/ensemble/BOLEClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> <li> <p>R. S. M. d. Barros, S. Garrido T. de Carvalho Santos and P. M. Gon\u00e7alves J\u00fanior, \"A Boosting-like Online Learning Ensemble,\" 2016 International Joint Conference on Neural Networks (IJCNN), 2016, pp. 1871-1878, doi: 10.1109/IJCNN.2016.7727427.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/BaggingClassifier/","title":"BaggingClassifier","text":"<p>Online bootstrap aggregation for classification.</p> <p>For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter 1. <code>k</code> thus has a 36% chance of being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6% chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do <code>scipy.stats.utils.random.poisson(1).pmf(k)</code> to obtain more detailed values.</p>"},{"location":"api/ensemble/BaggingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to bag.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/BaggingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/BaggingClassifier/#examples","title":"Examples","text":"<p>In the following example three logistic regressions are bagged together. The performance is slightly better than when using a single logistic regression.</p> <p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = ensemble.BaggingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.65%\n</code></pre></p> <p><pre><code>print(model)\n</code></pre> <pre><code>BaggingClassifier(StandardScaler | LogisticRegression)\n</code></pre></p>"},{"location":"api/ensemble/BaggingClassifier/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Averages the predictions of each classifier.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/BaggingRegressor/","title":"BaggingRegressor","text":"<p>Online bootstrap aggregation for regression.</p> <p>For each incoming observation, each model's <code>learn_one</code> method is called <code>k</code> times where <code>k</code> is sampled from a Poisson distribution of parameter 1. <code>k</code> thus has a 36% chance of being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6% chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do <code>scipy.stats.utils.random.poisson(1).pmf(k)</code> for more detailed values.</p>"},{"location":"api/ensemble/BaggingRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Regressor</p> <p>The regressor to bag.</p> </li> <li> <p>n_models</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/BaggingRegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/BaggingRegressor/#examples","title":"Examples","text":"<p>In the following example three logistic regressions are bagged together. The performance is slightly better than when using a single logistic regression.</p> <p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = preprocessing.StandardScaler()\nmodel |= ensemble.BaggingRegressor(\n    model=linear_model.LinearRegression(intercept_lr=0.1),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.677586\n</code></pre></p>"},{"location":"api/ensemble/BaggingRegressor/#methods","title":"Methods","text":"learn_one predict_one <p>Averages the predictions of each regressor.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p> <ol> <li> <p>Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/EWARegressor/","title":"EWARegressor","text":"<p>Exponentially Weighted Average regressor.</p>"},{"location":"api/ensemble/EWARegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Regressor]</p> <p>The regressors to hedge.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function that has to be minimized. Defaults to <code>optim.losses.Squared</code>.</p> </li> <li> <p>learning_rate</p> <p>Default \u2192 <code>0.5</code></p> <p>The learning rate by which the model weights are multiplied at each iteration.</p> </li> </ul>"},{"location":"api/ensemble/EWARegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/EWARegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\nfrom river import stream\n\noptimizers = [\n    optim.SGD(0.01),\n    optim.RMSProp(),\n    optim.AdaGrad()\n]\n\nfor optimizer in optimizers:\n\n    dataset = datasets.TrumpApproval()\n    metric = metrics.MAE()\n    model = (\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression(\n            optimizer=optimizer,\n            intercept_lr=.1\n        )\n    )\n\n    print(optimizer, evaluate.progressive_val_score(dataset, model, metric))\n</code></pre> <pre><code>SGD MAE: 0.558735\nRMSProp MAE: 0.522449\nAdaGrad MAE: 0.477289\n</code></pre></p> <p><pre><code>dataset = datasets.TrumpApproval()\nmetric = metrics.MAE()\nhedge = (\n    preprocessing.StandardScaler() |\n    ensemble.EWARegressor(\n        [\n            linear_model.LinearRegression(optimizer=o, intercept_lr=.1)\n            for o in optimizers\n        ],\n        learning_rate=0.005\n    )\n)\n\nevaluate.progressive_val_score(dataset, hedge, metric)\n</code></pre> <pre><code>MAE: 0.496298\n</code></pre></p>"},{"location":"api/ensemble/EWARegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> learn_predict_one predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Online Learning from Experts: Weighed Majority and Hedge \u21a9</p> </li> <li> <p>Wikipedia page on the multiplicative weight update method \u21a9</p> </li> <li> <p>Kivinen, J. and Warmuth, M.K., 1997. Exponentiated gradient versus gradient descent for linear predictors. information and computation, 132(1), pp.1-63. \u21a9</p> </li> </ol>"},{"location":"api/ensemble/LeveragingBaggingClassifier/","title":"LeveragingBaggingClassifier","text":"<p>Leveraging Bagging ensemble classifier.</p> <p>Leveraging Bagging [^1] is an improvement over the Oza Bagging algorithm. The bagging performance is leveraged by increasing the re-sampling. It uses a poisson distribution to simulate the re-sampling process. To increase re-sampling it uses a higher <code>w</code> value of the Poisson distribution (agerage number of events), 6 by default, increasing the input space diversity, by attributing a different range of weights to the data samples. </p> <p>To deal with concept drift, Leveraging Bagging uses the ADWIN algorithm to monitor the performance of each member of the enemble If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.</p>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier to bag.</p> </li> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of models in the ensemble.</p> </li> <li> <p>w</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>6</code></p> <p>Indicates the average number of events. This is the lambda parameter of the Poisson distribution used to compute the re-sampling weight.</p> </li> <li> <p>adwin_delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.002</code></p> <p>The delta parameter for the ADWIN change detector.</p> </li> <li> <p>bagging_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>bag</code></p> <p>The bagging method to use. Can be one of the following: * 'bag' - Leveraging Bagging using ADWIN. * 'me' - Assigns \\(weight=1\\) if sample is misclassified,   otherwise \\(weight=error/(1-error)\\). * 'half' - Use resampling without replacement for half of the instances. * 'wt' - Resample without taking out all instances. * 'subag' - Resampling without replacement.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>bagging_methods</p> <p>Valid bagging_method options.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = ensemble.LeveragingBaggingClassifier(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.55%\n</code></pre></p>"},{"location":"api/ensemble/LeveragingBaggingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Averages the predictions of each classifier.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p></p>"},{"location":"api/ensemble/SRPClassifier/","title":"SRPClassifier","text":"<p>Streaming Random Patches ensemble classifier.</p> <p>The Streaming Random Patches (SRP) <sup>1</sup> is an ensemble method that simulates bagging or random subspaces. The default algorithm uses both bagging and random subspaces, namely Random Patches. The default base estimator is a Hoeffding Tree, but other base estimators can be used (differently from random forest variations).</p>"},{"location":"api/ensemble/SRPClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Estimator | None</p> <p>Default \u2192 <code>None</code></p> <p>The base estimator.</p> </li> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of members in the ensemble.</p> </li> <li> <p>subspace_size</p> <p>Type \u2192 int | float | str</p> <p>Default \u2192 <code>0.6</code></p> <p>Number of features per subset for each classifier where <code>M</code> is the total number of features. A negative value means <code>M - subspace_size</code>. Only applies when using random subspaces or random patches. * If <code>int</code> indicates the number of features to use. Valid range [2, M].  * If <code>float</code> indicates the percentage of features to use, Valid range (0., 1.].  * 'sqrt' - <code>sqrt(M)+1</code> * 'rmsqrt' - Residual from <code>M-(sqrt(M)+1)</code></p> </li> <li> <p>training_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>patches</code></p> <p>The training method to use. * 'subspaces' - Random subspaces. * 'resampling' - Resampling. * 'patches' - Random patches.</p> </li> <li> <p>lam</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>Lambda value for resampling.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift detector.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning detector.</p> </li> <li> <p>disable_detector</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>off</code></p> <p>Option to disable drift detectors: * If <code>'off'</code>, detectors are enabled. * If <code>'drift'</code>, disables concept drift detection and the background learner. * If <code>'warning'</code>, disables the background learner and ensemble members are  reset if drift is detected.</p> </li> <li> <p>disable_weighted_vote</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disables weighted voting.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> <li> <p>metric</p> <p>Type \u2192 ClassificationMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The metric to track members performance within the ensemble. This implementation assumes that larger values are better when using weighted votes.</p> </li> </ul>"},{"location":"api/ensemble/SRPClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/SRPClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import ensemble\nfrom river import evaluate\nfrom river import metrics\nfrom river.datasets import synth\nfrom river import tree\n\ndataset = synth.ConceptDriftStream(\n    seed=42,\n    position=500,\n    width=50\n).take(1000)\n\nbase_model = tree.HoeffdingTreeClassifier(\n    grace_period=50, delta=0.01,\n    nominal_attributes=['age', 'car', 'zipcode']\n)\nmodel = ensemble.SRPClassifier(\n    model=base_model, n_models=3, seed=42,\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 71.97%\n</code></pre></p>"},{"location":"api/ensemble/SRPClassifier/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> reset"},{"location":"api/ensemble/SRPClassifier/#notes","title":"Notes","text":"<p>This implementation uses <code>n_models=10</code> as default given the impact on processing time. The optimal number of models depends on the data and resources available.</p> <ol> <li> <p>Heitor Murilo Gomes, Jesse Read, Albert Bifet.   Streaming Random Patches for Evolving Data Stream Classification.   IEEE International Conference on Data Mining (ICDM), 2019.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/SRPRegressor/","title":"SRPRegressor","text":"<p>Streaming Random Patches ensemble regressor.</p> <p>The Streaming Random Patches <sup>1</sup> ensemble method for regression trains each base learner on a subset of features and instances from the original data, namely a random patch. This strategy to enforce diverse base models is similar to the one in the random forest, yet it is not restricted to using decision trees as base learner. </p> <p>This method is an adaptation of <sup>2</sup> for regression.</p>"},{"location":"api/ensemble/SRPRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The base estimator.</p> </li> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of members in the ensemble.</p> </li> <li> <p>subspace_size</p> <p>Type \u2192 int | float | str</p> <p>Default \u2192 <code>0.6</code></p> <p>Number of features per subset for each classifier where <code>M</code> is the total number of features. A negative value means <code>M - subspace_size</code>. Only applies when using random subspaces or random patches. * If <code>int</code> indicates the number of features to use. Valid range [2, M].  * If <code>float</code> indicates the percentage of features to use, Valid range (0., 1.].  * 'sqrt' - <code>sqrt(M)+1</code> * 'rmsqrt' - Residual from <code>M-(sqrt(M)+1)</code></p> </li> <li> <p>training_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>patches</code></p> <p>The training method to use. * 'subspaces' - Random subspaces. * 'resampling' - Resampling. * 'patches' - Random patches.</p> </li> <li> <p>lam</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>Lambda value for bagging.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift detector.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning detector.</p> </li> <li> <p>disable_detector</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>off</code></p> <p>Option to disable drift detectors: * If <code>'off'</code>, detectors are enabled. * If <code>'drift'</code>, disables concept drift detection and the background learner. * If <code>'warning'</code>, disables the background learner and ensemble members are  reset if drift is detected.</p> </li> <li> <p>disable_weighted_vote</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, disables weighted voting.</p> </li> <li> <p>drift_detection_criteria</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>error</code></p> <p>The criteria used to track drifts. * 'error' - absolute error. * 'prediction' - predicted target values.</p> </li> <li> <p>aggregation_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>mean</code></p> <p>The method to use to aggregate predictions in the ensemble. * 'mean' * 'median'</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> <li> <p>metric</p> <p>Type \u2192 RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The metric to track members performance within the ensemble.</p> </li> </ul>"},{"location":"api/ensemble/SRPRegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/SRPRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import ensemble\nfrom river import evaluate\nfrom river import metrics\nfrom river.datasets import synth\nfrom river import tree\n\ndataset = synth.FriedmanDrift(\n    drift_type='gsg',\n    position=(350, 750),\n    transition_window=200,\n    seed=42\n).take(1000)\n\nbase_model = tree.HoeffdingTreeRegressor(grace_period=50)\nmodel = ensemble.SRPRegressor(\n    model=base_model,\n    training_method=\"patches\",\n    n_models=3,\n    seed=42\n)\n\nmetric = metrics.R2()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>R2: 0.571117\n</code></pre></p>"},{"location":"api/ensemble/SRPRegressor/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> reset"},{"location":"api/ensemble/SRPRegressor/#notes","title":"Notes","text":"<p>This implementation uses <code>n_models=10</code> as default given the impact on processing time. The optimal number of models depends on the data and resources available.</p> <ol> <li> <p>Heitor Gomes, Jacob Montiel, Saulo Martiello Mastelini,   Bernhard Pfahringer, and Albert Bifet.   On Ensemble Techniques for Data Stream Regression.   IJCNN'20. International Joint Conference on Neural Networks. 2020.\u00a0\u21a9</p> </li> <li> <p>Heitor Murilo Gomes, Jesse Read, Albert Bifet.   Streaming Random Patches for Evolving Data Stream Classification.   IEEE International Conference on Data Mining (ICDM), 2019.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/ensemble/StackingClassifier/","title":"StackingClassifier","text":"<p>Stacking for binary classification.</p>"},{"location":"api/ensemble/StackingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Classifier]</p> </li> <li> <p>meta_classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>include_features</p> <p>Default \u2192 <code>True</code></p> <p>Indicates whether or not the original features should be provided to the meta-model along with the predictions from each model.</p> </li> </ul>"},{"location":"api/ensemble/StackingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/StackingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import compose\nfrom river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model as lm\nfrom river import metrics\nfrom river import preprocessing as pp\n\ndataset = datasets.Phishing()\n\nmodel = compose.Pipeline(\n    ('scale', pp.StandardScaler()),\n    ('stack', ensemble.StackingClassifier(\n        [\n            lm.LogisticRegression(),\n            lm.PAClassifier(mode=1, C=0.01),\n            lm.PAClassifier(mode=2, C=0.01),\n        ],\n        meta_classifier=lm.LogisticRegression()\n    ))\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.14%\n</code></pre></p>"},{"location":"api/ensemble/StackingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>A Kaggler's Guide to Model Stacking in Practice \u21a9</p> </li> </ol>"},{"location":"api/ensemble/VotingClassifier/","title":"VotingClassifier","text":"<p>Voting classifier.</p> <p>A classification is made by aggregating the predictions of each model in the ensemble. The probabilities for each class are summed up if <code>use_probabilities</code> is set to <code>True</code>. If not, the probabilities are ignored and each prediction is weighted the same. In this case, it's important that you use an odd number of classifiers. A random class will be picked if the number of classifiers is even.</p>"},{"location":"api/ensemble/VotingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Classifier]</p> <p>The classifiers.</p> </li> <li> <p>use_probabilities</p> <p>Default \u2192 <code>True</code></p> <p>Whether or to weight each prediction with its associated probability.</p> </li> </ul>"},{"location":"api/ensemble/VotingClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/ensemble/VotingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import ensemble\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import naive_bayes\nfrom river import preprocessing\nfrom river import tree\n\ndataset = datasets.Phishing()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    ensemble.VotingClassifier([\n        linear_model.LogisticRegression(),\n        tree.HoeffdingTreeClassifier(),\n        naive_bayes.GaussianNB()\n    ])\n)\n\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.94%\n</code></pre></p>"},{"location":"api/ensemble/VotingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/evaluate/BinaryClassificationTrack/","title":"BinaryClassificationTrack","text":"<p>This track evaluates a model's performance on binary classification tasks. These do not include synthetic datasets.</p>"},{"location":"api/evaluate/BinaryClassificationTrack/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/MultiClassClassificationTrack/","title":"MultiClassClassificationTrack","text":"<p>This track evaluates a model's performance on multi-class classification tasks. These do not include synthetic datasets.</p>"},{"location":"api/evaluate/MultiClassClassificationTrack/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/RegressionTrack/","title":"RegressionTrack","text":"<p>This track evaluates a model's performance on regression tasks. These do not include synthetic datasets.</p>"},{"location":"api/evaluate/RegressionTrack/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/Track/","title":"Track","text":"<p>A track evaluate a model's performance.</p> <p>The following metrics are recorded: </p> <ul> <li> <p>Time, which should be interpreted with wisdom. Indeed time can depend on the architecture</p> <p>and local resource situations. Comparison via FLOPS should be preferred. - The model's memory footprint.</p> </li> <li> <p>The model's predictive performance on the track's dataset.</p> </li> </ul>"},{"location":"api/evaluate/Track/#parameters","title":"Parameters","text":"<ul> <li> <p>name</p> <p>Type \u2192 str</p> <p>The name of the track.</p> </li> <li> <p>datasets</p> <p>The datasets that compose the track.</p> </li> <li> <p>metric</p> <p>The metric(s) used to track performance.</p> </li> </ul>"},{"location":"api/evaluate/Track/#methods","title":"Methods","text":"run"},{"location":"api/evaluate/iter-progressive-val-score/","title":"iter_progressive_val_score","text":"<p>Evaluates the performance of a model on a streaming dataset and yields results.</p> <p>This does exactly the same as <code>evaluate.progressive_val_score</code>. The only difference is that this function returns an iterator, yielding results at every step. This can be useful if you want to have control over what you do with the results. For instance, you might want to plot the results.</p>"},{"location":"api/evaluate/iter-progressive-val-score/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>The stream of observations against which the model will be evaluated.</p> </li> <li> <p>model</p> <p>The model to evaluate.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>The metric used to evaluate the model's predictions.</p> </li> <li> <p>moment</p> <p>Type \u2192 str | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The attribute used for measuring time. If a callable is passed, then it is expected to take as input a <code>dict</code> of features. If <code>None</code>, then the observations are implicitly timestamped in the order in which they arrive.</p> </li> <li> <p>delay</p> <p>Type \u2192 str | int | dt.timedelta | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The amount to wait before revealing the target associated with each observation to the model. This value is expected to be able to sum with the <code>moment</code> value. For instance, if <code>moment</code> is a <code>datetime.date</code>, then <code>delay</code> is expected to be a <code>datetime.timedelta</code>. If a callable is passed, then it is expected to take as input a <code>dict</code> of features and the target. If a <code>str</code> is passed, then it will be used to access the relevant field from the features. If <code>None</code> is passed, then no delay will be used, which leads to doing standard online validation.</p> </li> <li> <p>step</p> <p>Default \u2192 <code>1</code></p> <p>Iteration number at which to yield results. This only takes into account the predictions, and not the training steps.</p> </li> <li> <p>measure_time</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to measure the elapsed time.</p> </li> <li> <p>measure_memory</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to measure the memory usage of the model.</p> </li> <li> <p>yield_predictions</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to include predictions. If step is 1, then this is equivalent to yielding the predictions at every iterations. Otherwise, not all predictions will be yielded.</p> </li> </ul>"},{"location":"api/evaluate/iter-progressive-val-score/#examples","title":"Examples","text":"<p>Take the following model:</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>We can evaluate it on the <code>Phishing</code> dataset as so:</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nsteps = evaluate.iter_progressive_val_score(\n    model=model,\n    dataset=datasets.Phishing(),\n    metric=metrics.ROCAUC(),\n    step=200\n)\n\nfor step in steps:\n    print(step)\n</code></pre> <pre><code>{'ROCAUC': ROCAUC: 90.20%, 'Step': 200}\n{'ROCAUC': ROCAUC: 92.25%, 'Step': 400}\n{'ROCAUC': ROCAUC: 93.23%, 'Step': 600}\n{'ROCAUC': ROCAUC: 94.05%, 'Step': 800}\n{'ROCAUC': ROCAUC: 94.79%, 'Step': 1000}\n{'ROCAUC': ROCAUC: 95.07%, 'Step': 1200}\n{'ROCAUC': ROCAUC: 95.07%, 'Step': 1250}\n</code></pre></p> <p>The <code>yield_predictions</code> parameter can be used to include the predictions in the results:</p> <p><pre><code>import itertools\n\nsteps = evaluate.iter_progressive_val_score(\n    model=model,\n    dataset=datasets.Phishing(),\n    metric=metrics.ROCAUC(),\n    step=1,\n    yield_predictions=True\n)\n\nfor step in itertools.islice(steps, 100, 105):\n   print(step)\n</code></pre> <pre><code>{'ROCAUC': ROCAUC: 94.68%, 'Step': 101, 'Prediction': {False: 0.966..., True: 0.033...}}\n{'ROCAUC': ROCAUC: 94.75%, 'Step': 102, 'Prediction': {False: 0.035..., True: 0.964...}}\n{'ROCAUC': ROCAUC: 94.82%, 'Step': 103, 'Prediction': {False: 0.043..., True: 0.956...}}\n{'ROCAUC': ROCAUC: 94.89%, 'Step': 104, 'Prediction': {False: 0.816..., True: 0.183...}}\n{'ROCAUC': ROCAUC: 94.96%, 'Step': 105, 'Prediction': {False: 0.041..., True: 0.958...}}\n</code></pre></p> <ol> <li> <p>Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation \u21a9</p> </li> <li> <p>Grzenda, M., Gomes, H.M. and Bifet, A., 2019. Delayed labelling evaluation for data streams. Data Mining and Knowledge Discovery, pp.1-30 \u21a9</p> </li> </ol>"},{"location":"api/evaluate/progressive-val-score/","title":"progressive_val_score","text":"<p>Evaluates the performance of a model on a streaming dataset.</p> <p>This method is the canonical way to evaluate a model's performance. When used correctly, it allows you to exactly assess how a model would have performed in a production scenario. </p> <p><code>dataset</code> is converted into a stream of questions and answers. At each step the model is either asked to predict an observation, or is either updated. The target is only revealed to the model after a certain amount of time, which is determined by the <code>delay</code> parameter. Note that under the hood this uses the <code>stream.simulate_qa</code> function to go through the data in arrival order. </p> <p>By default, there is no delay, which means that the samples are processed one after the other. When there is no delay, this function essentially performs progressive validation. When there is a delay, then we refer to it as delayed progressive validation. </p> <p>It is recommended to use this method when you want to determine a model's performance on a dataset. In particular, it is advised to use the <code>delay</code> parameter in order to get a reliable assessment. Indeed, in a production scenario, it is often the case that ground truths are made available after a certain amount of time. By using this method, you can reproduce this scenario and therefore truthfully assess what would have been the performance of a model on a given dataset.</p>"},{"location":"api/evaluate/progressive-val-score/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>The stream of observations against which the model will be evaluated.</p> </li> <li> <p>model</p> <p>The model to evaluate.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>The metric used to evaluate the model's predictions.</p> </li> <li> <p>moment</p> <p>Type \u2192 str | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The attribute used for measuring time. If a callable is passed, then it is expected to take as input a <code>dict</code> of features. If <code>None</code>, then the observations are implicitly timestamped in the order in which they arrive.</p> </li> <li> <p>delay</p> <p>Type \u2192 str | int | dt.timedelta | typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>The amount to wait before revealing the target associated with each observation to the model. This value is expected to be able to sum with the <code>moment</code> value. For instance, if <code>moment</code> is a <code>datetime.date</code>, then <code>delay</code> is expected to be a <code>datetime.timedelta</code>. If a callable is passed, then it is expected to take as input a <code>dict</code> of features and the target. If a <code>str</code> is passed, then it will be used to access the relevant field from the features. If <code>None</code> is passed, then no delay will be used, which leads to doing standard online validation.</p> </li> <li> <p>print_every</p> <p>Default \u2192 <code>0</code></p> <p>Iteration number at which to print the current metric. This only takes into account the predictions, and not the training steps.</p> </li> <li> <p>show_time</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to display the elapsed time.</p> </li> <li> <p>show_memory</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to display the memory usage of the model.</p> </li> <li> <p>print_kwargs</p> <p>Extra keyword arguments are passed to the <code>print</code> function. For instance, this allows providing a <code>file</code> argument, which indicates where to output progress.</p> </li> </ul>"},{"location":"api/evaluate/progressive-val-score/#examples","title":"Examples","text":"<p>Take the following model:</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>We can evaluate it on the <code>Phishing</code> dataset as so:</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nevaluate.progressive_val_score(\n    model=model,\n    dataset=datasets.Phishing(),\n    metric=metrics.ROCAUC(),\n    print_every=200\n)\n</code></pre> <pre><code>[200] ROCAUC: 90.20%\n[400] ROCAUC: 92.25%\n[600] ROCAUC: 93.23%\n[800] ROCAUC: 94.05%\n[1,000] ROCAUC: 94.79%\n[1,200] ROCAUC: 95.07%\n[1,250] ROCAUC: 95.07%\nROCAUC: 95.07%\n</code></pre></p> <p>We haven't specified a delay, therefore this is strictly equivalent to the following piece of code:</p> <p><pre><code>model = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n\nmetric = metrics.ROCAUC()\n\nfor x, y in datasets.Phishing():\n    y_pred = model.predict_proba_one(x)\n    metric.update(y, y_pred)\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 95.07%\n</code></pre></p> <p>When <code>print_every</code> is specified, the current state is printed at regular intervals. Under the hood, Python's <code>print</code> method is being used. You can pass extra keyword arguments to modify its behavior. For instance, you may use the <code>file</code> argument if you want to log the progress to a file of your choice.</p> <p><pre><code>with open('progress.log', 'w') as f:\n    metric = evaluate.progressive_val_score(\n        model=model,\n        dataset=datasets.Phishing(),\n        metric=metrics.ROCAUC(),\n        print_every=200,\n        file=f\n    )\n\nwith open('progress.log') as f:\n    for line in f.read().splitlines():\n        print(line)\n</code></pre> <pre><code>[200] ROCAUC: 94.00%\n[400] ROCAUC: 94.70%\n[600] ROCAUC: 95.17%\n[800] ROCAUC: 95.42%\n[1,000] ROCAUC: 95.82%\n[1,200] ROCAUC: 96.00%\n[1,250] ROCAUC: 96.04%\n</code></pre></p> <p>Note that the performance is slightly better than above because we haven't used a fresh copy of the model. Instead, we've reused the existing model which has already done a full pass on the data.</p> <pre><code>import os; os.remove('progress.log')\n</code></pre> <ol> <li> <p>Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation \u21a9</p> </li> <li> <p>Grzenda, M., Gomes, H.M. and Bifet, A., 2019. Delayed labelling evaluation for data streams. Data Mining and Knowledge Discovery, pp.1-30 \u21a9</p> </li> </ol>"},{"location":"api/facto/FFMClassifier/","title":"FFMClassifier","text":"<p>Field-aware Factorization Machine for binary classification.</p> <p>The model equation is defined by: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_{j, f_{j'}}, \\mathbf{v}_{j', f_j} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_{j, f_{j'}}\\) is the latent vector corresponding to \\(j\\) feature for \\(f_{j'}\\) field, and \\(\\mathbf{v}_{j', f_j}\\) is the latent vector corresponding to \\(j'\\) feature for \\(f_j\\) field. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FFMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FFMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FFMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, True),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, True),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, True),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, True),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, False)\n)\n\nmodel = facto.FFMClassifier(\n    n_factors=10,\n    intercept=.5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/FFMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Juan, Y., Zhuang, Y., Chin, W.S. and Lin, C.J., 2016, September. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 43-50). \u21a9</p> </li> </ol>"},{"location":"api/facto/FFMRegressor/","title":"FFMRegressor","text":"<p>Field-aware Factorization Machine for regression.</p> <p>The model equation is defined by: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_{j, f_{j'}}, \\mathbf{v}_{j', f_j} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_{j, f_{j'}}\\) is the latent vector corresponding to \\(j\\) feature for \\(f_{j'}\\) field, and \\(\\mathbf{v}_{j', f_j}\\) is the latent vector corresponding to \\(j'\\) feature for \\(f_j\\) field. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FFMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FFMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FFMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, 8),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, 5),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, 8),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, 2)\n)\n\nmodel = facto.FFMRegressor(\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>5.319945\n</code></pre></p> <p><pre><code>report = model.debug_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n\nprint(report)\n</code></pre> <pre><code>Name                                       Value      Weight     Contribution\n                               Intercept    1.00000    5.23501        5.23501\n                                user_Bob    1.00000    0.11438        0.11438\n                                    time    0.14000    0.03186        0.00446\n    item_Harry Potter(time) - time(item)    0.14000    0.03153        0.00441\n             user_Bob(time) - time(user)    0.14000    0.02864        0.00401\n                       item_Harry Potter    1.00000    0.00000        0.00000\nuser_Bob(item) - item_Harry Potter(user)    1.00000   -0.04232       -0.04232\n</code></pre></p>"},{"location":"api/facto/FFMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Juan, Y., Zhuang, Y., Chin, W.S. and Lin, C.J., 2016, September. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 43-50). \u21a9</p> </li> </ol>"},{"location":"api/facto/FMClassifier/","title":"FMClassifier","text":"<p>Factorization Machine for binary classification.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/FMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, True),\n    ({'user': 'Alice', 'item': 'Terminator'}, True),\n    ({'user': 'Alice', 'item': 'Star Wars'}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, True),\n    ({'user': 'Bob', 'item': 'Superman'}, True),\n    ({'user': 'Bob', 'item': 'Terminator'}, True),\n    ({'user': 'Bob', 'item': 'Star Wars'}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, False)\n)\n\nmodel = facto.FMClassifier(\n    n_factors=10,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/FMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> <li> <p>Rendle, S., 2012, May. Factorization Machines with libFM. In ACM Transactions on Intelligent Systems and Technology 3, 3, Article 57, 22 pages. \u21a9</p> </li> </ol>"},{"location":"api/facto/FMRegressor/","title":"FMRegressor","text":"<p>Factorization Machine for regression.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/FMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/FMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = facto.FMRegressor(\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>5.236504\n</code></pre></p> <p><pre><code>report = model.debug_one({'Bob': 1, 'Harry Potter': 1})\n\nprint(report)\n</code></pre> <pre><code>Name                 Value      Weight     Contribution\n         Intercept    1.00000    5.23426        5.23426\nBob - Harry Potter    1.00000    0.00224        0.00224\n      Harry Potter    1.00000    0.00000        0.00000\n               Bob    1.00000    0.00000        0.00000\n</code></pre></p>"},{"location":"api/facto/FMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> <li> <p>Rendle, S., 2012, May. Factorization Machines with libFM. In ACM Transactions on Intelligent Systems and Technology 3, 3, Article 57, 22 pages. \u21a9</p> </li> </ol>"},{"location":"api/facto/FwFMClassifier/","title":"FwFMClassifier","text":"<p>Field-weighted Factorization Machine for binary classification.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} r_{f_j, f_{j'}} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(f_j\\) and \\(f_{j'}\\) are \\(j\\) and \\(j'\\) fields, respectively, and \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FwFMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>int_weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the field pairs interaction weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FwFMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> <li> <p>interaction_weights</p> <p>The current interaction strengths of field pairs.</p> </li> </ul>"},{"location":"api/facto/FwFMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, True),\n    ({'user': 'Alice', 'item': 'Terminator'}, True),\n    ({'user': 'Alice', 'item': 'Star Wars'}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, True),\n    ({'user': 'Bob', 'item': 'Superman'}, True),\n    ({'user': 'Bob', 'item': 'Terminator'}, True),\n    ({'user': 'Bob', 'item': 'Star Wars'}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, False)\n)\n\nmodel = facto.FwFMClassifier(\n    n_factors=10,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/FwFMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, and Quan Lu, 2018, April. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, (pp. 1349\u20131357). \u21a9</p> </li> </ol>"},{"location":"api/facto/FwFMRegressor/","title":"FwFMRegressor","text":"<p>Field-weighted Factorization Machine for regression.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} r_{f_j, f_{j'}} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'}\\] <p>Where \\(f_j\\) and \\(f_{j'}\\) are \\(j\\) and \\(j'\\) fields, respectively, and \\(\\mathbf{v}_j\\) and \\(\\mathbf{v}_{j'}\\) are \\(j\\) and \\(j'\\) latent vectors, respectively. </p> <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: <code>feature_name.split('_')[0]</code>.</p>"},{"location":"api/facto/FwFMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>int_weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the field pairs interaction weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/FwFMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> <li> <p>interaction_weights</p> <p>The current interaction strengths of field pairs.</p> </li> </ul>"},{"location":"api/facto/FwFMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter '}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = facto.FwFMRegressor(\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'Bob': 1, 'Harry Potter': 1})\n</code></pre> <pre><code>5.236501\n</code></pre></p> <p><pre><code>report = model.debug_one({'Bob': 1, 'Harry Potter': 1})\n\nprint(report)\n</code></pre> <pre><code>Name                                    Value      Weight     Contribution\n                            Intercept    1.00000    5.23426        5.23426\nBob(Harry Potter) - Harry Potter(Bob)    1.00000    0.00224        0.00224\n                         Harry Potter    1.00000    0.00000        0.00000\n                                  Bob    1.00000    0.00000        0.00000\n</code></pre></p>"},{"location":"api/facto/FwFMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, and Quan Lu, 2018, April. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, (pp. 1349\u20131357). \u21a9</p> </li> </ol>"},{"location":"api/facto/HOFMClassifier/","title":"HOFMClassifier","text":"<p>Higher-Order Factorization Machine for binary classification.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{l=2}^{d} \\sum_{j_1=1}^{p} \\cdots \\sum_{j_l=j_{l-1}+1}^{p} \\left(\\prod_{j'=1}^{l} x_{j_{j'}} \\right) \\left(\\sum_{f=1}^{k_l} \\prod_{j'=1}^{l} v_{j_{j'}, f}^{(l)} \\right)\\] <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/HOFMClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>degree</p> <p>Default \u2192 <code>3</code></p> <p>Polynomial degree or model order.</p> </li> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/HOFMClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/HOFMClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, True),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, True),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, True),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, False),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, True),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, True),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, True),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, False)\n)\n\nmodel = facto.HOFMClassifier(\n    degree=3,\n    n_factors=10,\n    intercept=.5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/facto/HOFMClassifier/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> </ol>"},{"location":"api/facto/HOFMRegressor/","title":"HOFMRegressor","text":"<p>Higher-Order Factorization Machine for regression.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{l=2}^{d} \\sum_{j_1=1}^{p} \\cdots \\sum_{j_l=j_{l-1}+1}^{p} \\left(\\prod_{j'=1}^{l} x_{j_{j'}} \\right) \\left(\\sum_{f=1}^{k_l} \\prod_{j'=1}^{l} v_{j_{j'}, f}^{(l)} \\right)\\] <p>For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.</p>"},{"location":"api/facto/HOFMRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>degree</p> <p>Default \u2192 <code>3</code></p> <p>Polynomial degree or model order.</p> </li> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>weight_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the feature weights. Note thatthe intercept is handled separately.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>sample_normalization</p> <p>Default \u2192 <code>False</code></p> <p>Whether to divide each element of <code>x</code> by <code>x</code>'s L2-norm.</p> </li> <li> <p>l1_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0.</p> </li> <li> <p>l2_weight</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>l1_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push latent weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>intercept</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. An instance of <code>optim.schedulers.Constant</code> is used if a <code>float</code> is passed. No intercept will be used if this is set to 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme. Defaults to <code>optim.initializers.Zeros</code>()`.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme. Defaults to <code>optim.initializers.Normal</code>(mu=.0, sigma=.1, random_state=self.random_state)`.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Randomization seed used for reproducibility.</p> </li> </ul>"},{"location":"api/facto/HOFMRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights assigned to the features.</p> </li> <li> <p>latents</p> <p>The current latent weights assigned to the features.</p> </li> </ul>"},{"location":"api/facto/HOFMRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import facto\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman', 'time': .12}, 8),\n    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, 5),\n    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, 8),\n    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, 2)\n)\n\nmodel = facto.HOFMRegressor(\n    degree=3,\n    n_factors=10,\n    intercept=5,\n    seed=42,\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n</code></pre> <pre><code>5.311745\n</code></pre></p> <p><pre><code>report = model.debug_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})\n\nprint(report)\n</code></pre> <pre><code>Name                                  Value      Weight     Contribution\n                          Intercept    1.00000    5.23495        5.23495\n                           user_Bob    1.00000    0.11436        0.11436\n                               time    0.14000    0.03185        0.00446\n                    user_Bob - time    0.14000    0.00884        0.00124\nuser_Bob - item_Harry Potter - time    0.14000    0.00117        0.00016\n                  item_Harry Potter    1.00000    0.00000        0.00000\n           item_Harry Potter - time    0.14000   -0.00695       -0.00097\n       user_Bob - item_Harry Potter    1.00000   -0.04246       -0.04246\n</code></pre></p>"},{"location":"api/facto/HOFMRegressor/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the FM regressor.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. \u21a9</p> </li> </ol>"},{"location":"api/feature-extraction/Agg/","title":"Agg","text":"<p>Computes a streaming aggregate.</p> <p>This transformer allows to compute an aggregate statistic, very much like the groupby method from <code>pandas</code>, but on a streaming dataset. This makes use of the streaming statistics from the <code>stats</code> module. </p> <p>When <code>learn_one</code> is called, the running statistic <code>how</code> of group <code>by</code> is updated with the value of <code>on</code>. Meanwhile, the output of <code>transform_one</code> is a single-element dictionary, where the key is the name of the aggregate and the value is the current value of the statistic for the relevant group. The key is automatically inferred from the parameters. </p> <p>Note that you can use a <code>compose.TransformerUnion</code> to extract many aggregate statistics in a concise manner.</p>"},{"location":"api/feature-extraction/Agg/#parameters","title":"Parameters","text":"<ul> <li> <p>on</p> <p>Type \u2192 str</p> <p>The feature on which to compute the aggregate statistic.</p> </li> <li> <p>by</p> <p>Type \u2192 str | list[str] | None</p> <p>The feature by which to group the data. All the data is included in the aggregate if this is <code>None</code>.</p> </li> <li> <p>how</p> <p>Type \u2192 stats.base.Univariate | utils.Rolling | utils.TimeRolling</p> <p>The statistic to compute.</p> </li> </ul>"},{"location":"api/feature-extraction/Agg/#attributes","title":"Attributes","text":"<ul> <li> <p>state</p> <p>Return the current values for each group as a series.</p> </li> </ul>"},{"location":"api/feature-extraction/Agg/#examples","title":"Examples","text":"<p>Consider the following dataset:</p> <pre><code>X = [\n    {'country': 'France', 'place': 'Taco Bell', 'revenue': 42},\n    {'country': 'Sweden', 'place': 'Burger King', 'revenue': 16},\n    {'country': 'France', 'place': 'Burger King', 'revenue': 24},\n    {'country': 'Sweden', 'place': 'Taco Bell', 'revenue': 58},\n    {'country': 'Sweden', 'place': 'Burger King', 'revenue': 20},\n    {'country': 'France', 'place': 'Taco Bell', 'revenue': 50},\n    {'country': 'France', 'place': 'Burger King', 'revenue': 10},\n    {'country': 'Sweden', 'place': 'Taco Bell', 'revenue': 80}\n]\n</code></pre> <p>As an example, we can calculate the average (how) revenue (on) for each place (by):</p> <p><pre><code>from river import feature_extraction as fx\nfrom river import stats\n\nagg = fx.Agg(\n    on='revenue',\n    by='place',\n    how=stats.Mean()\n)\n\nfor x in X:\n    agg.learn_one(x)\n    print(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_mean_by_place': 42.0}\n{'revenue_mean_by_place': 16.0}\n{'revenue_mean_by_place': 20.0}\n{'revenue_mean_by_place': 50.0}\n{'revenue_mean_by_place': 20.0}\n{'revenue_mean_by_place': 50.0}\n{'revenue_mean_by_place': 17.5}\n{'revenue_mean_by_place': 57.5}\n</code></pre></p> <p>You can compute an aggregate over multiple keys by passing a tuple to the <code>by</code> argument. For instance, we can compute the maximum (how) revenue (on) per place as well as per day (by):</p> <p><pre><code>agg = fx.Agg(\n    on='revenue',\n    by=['place', 'country'],\n    how=stats.Max()\n)\n\nfor x in X:\n    agg.learn_one(x)\n    print(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_max_by_place_and_country': 42}\n{'revenue_max_by_place_and_country': 16}\n{'revenue_max_by_place_and_country': 24}\n{'revenue_max_by_place_and_country': 58}\n{'revenue_max_by_place_and_country': 20}\n{'revenue_max_by_place_and_country': 50}\n{'revenue_max_by_place_and_country': 24}\n{'revenue_max_by_place_and_country': 80}\n</code></pre></p> <p>You can use a <code>compose.TransformerUnion</code> in order to calculate multiple aggregates in one go. The latter can be constructed by using the <code>+</code> operator:</p> <p><pre><code>agg = (\n    fx.Agg(on='revenue', by='place', how=stats.Mean()) +\n    fx.Agg(on='revenue', by=['place', 'country'], how=stats.Max())\n)\n\nimport pprint\nfor x in X:\n    agg.learn_one(x)\n    pprint.pprint(agg.transform_one(x))\n</code></pre> <pre><code>{'revenue_max_by_place_and_country': 42, 'revenue_mean_by_place': 42.0}\n{'revenue_max_by_place_and_country': 16, 'revenue_mean_by_place': 16.0}\n{'revenue_max_by_place_and_country': 24, 'revenue_mean_by_place': 20.0}\n{'revenue_max_by_place_and_country': 58, 'revenue_mean_by_place': 50.0}\n{'revenue_max_by_place_and_country': 20, 'revenue_mean_by_place': 20.0}\n{'revenue_max_by_place_and_country': 50, 'revenue_mean_by_place': 50.0}\n{'revenue_max_by_place_and_country': 24, 'revenue_mean_by_place': 17.5}\n{'revenue_max_by_place_and_country': 80, 'revenue_mean_by_place': 57.5}\n</code></pre></p> <p>The <code>state</code> property returns a <code>pandas.Series</code>, which can be useful for visualizing the current state.</p> <p><pre><code>agg[0].state\n</code></pre> <pre><code>Taco Bell      57.5\nBurger King    17.5\nName: revenue_mean_by_place, dtype: float64\n</code></pre></p> <p><pre><code>agg[1].state\n</code></pre> <pre><code>place        country\nTaco Bell    France     50\nBurger King  Sweden     20\n             France     24\nTaco Bell    Sweden     80\nName: revenue_max_by_place_and_country, dtype: int64\n</code></pre></p> <p>This transformer can also be used in conjunction with <code>utils.TimeRolling</code>. The latter requires a <code>t</code> argument, which is a timestamp that indicates when the current row was observed. For instance, we can calculate the average (how) revenue (on) for each place (by) over the last 7 days (t):</p> <p><pre><code>import datetime as dt\nimport random\nimport string\nfrom river import utils\n\nagg = fx.Agg(\n    on=\"value\",\n    by=\"group\",\n    how=utils.TimeRolling(stats.Mean(), dt.timedelta(days=7))\n)\n\nfor day in range(366):\n    g = random.choice(string.ascii_lowercase)\n    x = {\n        \"group\": g,\n        \"value\": string.ascii_lowercase.index(g) + random.random(),\n    }\n    t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day)\n    agg.learn_one(x, t=t)\n\nlen(agg.state)\n</code></pre> <pre><code>26\n</code></pre></p>"},{"location":"api/feature-extraction/Agg/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>t     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Streaming groupbys in pandas for big datasets \u21a9</p> </li> </ol>"},{"location":"api/feature-extraction/BagOfWords/","title":"BagOfWords","text":"<p>Counts tokens in sentences.</p> <p>This transformer can be used to counts tokens in a given piece of text. It takes care of normalizing the text before tokenizing it. In mini-batch settings, this transformers allows to convert a series of pandas of text into sparse dataframe. </p> <p>Note that the parameters are identical to those of <code>feature_extraction.TFIDF</code>.</p>"},{"location":"api/feature-extraction/BagOfWords/#parameters","title":"Parameters","text":"<ul> <li> <p>on</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>None</code></p> <p>The name of the feature that contains the text to vectorize. If <code>None</code>, then each <code>learn_one</code> and <code>transform_one</code> will assume that each <code>x</code> that is provided is a <code>str</code>, andnot a <code>dict</code>.</p> </li> <li> <p>strip_accents</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to strip accent characters.</p> </li> <li> <p>lowercase</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to convert all characters to lowercase.</p> </li> <li> <p>preprocessor</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional preprocessing function which overrides the <code>strip_accents</code> and <code>lowercase</code> steps, while preserving the tokenizing and n-grams generation steps.</p> </li> <li> <p>stop_words</p> <p>Type \u2192 set[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional set of tokens to remove.</p> </li> <li> <p>tokenizer_pattern</p> <p>Default \u2192 <code>(?u)\\b\\w[\\w\\-]+\\b</code></p> <p>The tokenization pattern which is used when no <code>tokenizer</code> function is passed. A single capture group may optionally be specified.</p> </li> <li> <p>tokenizer</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>A function used to convert preprocessed text into a <code>dict</code> of tokens. By default, a regex formula that works well in most cases is used.</p> </li> <li> <p>ngram_range</p> <p>Default \u2192 <code>(1, 1)</code></p> <p>The lower and upper boundary of the range n-grams to be extracted. All values of n such that <code>min_n &lt;= n &lt;= max_n</code> will be used. For example an <code>ngram_range</code> of <code>(1, 1)</code> means only unigrams, <code>(1, 2)</code> means unigrams and bigrams, and <code>(2, 2)</code> means only bigrams.</p> </li> </ul>"},{"location":"api/feature-extraction/BagOfWords/#examples","title":"Examples","text":"<p>By default, <code>BagOfWords</code> will take as input a sentence, preprocess it, tokenize the preprocessed text, and then return a <code>collections.Counter</code> containing the number of occurrences of each token.</p> <p><pre><code>from river import feature_extraction as fx\n\ncorpus = [\n    'This is the first document.',\n    'This document is the second document.',\n    'And this is the third one.',\n    'Is this the first document?',\n]\n\nbow = fx.BagOfWords()\n\nfor sentence in corpus:\n    print(bow.transform_one(sentence))\n</code></pre> <pre><code>{'this': 1, 'is': 1, 'the': 1, 'first': 1, 'document': 1}\n{'this': 1, 'document': 2, 'is': 1, 'the': 1, 'second': 1}\n{'and': 1, 'this': 1, 'is': 1, 'the': 1, 'third': 1, 'one': 1}\n{'is': 1, 'this': 1, 'the': 1, 'first': 1, 'document': 1}\n</code></pre></p> <p>Note that <code>learn_one</code> does not have to be called because <code>BagOfWords</code> is stateless. You can call it but it won't do anything.</p> <p>In the above example, a string is passed to <code>transform_one</code>. You can also indicate which field to access if the string is stored in a dictionary:</p> <p><pre><code>bow = fx.BagOfWords(on='sentence')\n\nfor sentence in corpus:\n    x = {'sentence': sentence}\n    print(bow.transform_one(x))\n</code></pre> <pre><code>{'this': 1, 'is': 1, 'the': 1, 'first': 1, 'document': 1}\n{'this': 1, 'document': 2, 'is': 1, 'the': 1, 'second': 1}\n{'and': 1, 'this': 1, 'is': 1, 'the': 1, 'third': 1, 'one': 1}\n{'is': 1, 'this': 1, 'the': 1, 'first': 1, 'document': 1}\n</code></pre></p> <p>The <code>ngram_range</code> parameter can be used to extract n-grams (including unigrams):</p> <p><pre><code>ngrammer = fx.BagOfWords(ngram_range=(1, 2))\n\nngrams = ngrammer.transform_one('I love the smell of napalm in the morning')\nfor ngram, count in ngrams.items():\n    print(ngram, count)\n</code></pre> <pre><code>love 1\nthe 2\nsmell 1\nof 1\nnapalm 1\nin 1\nmorning 1\n('love', 'the') 1\n('the', 'smell') 1\n('smell', 'of') 1\n('of', 'napalm') 1\n('napalm', 'in') 1\n('in', 'the') 1\n('the', 'morning') 1\n</code></pre></p> <p><code>BagOfWord</code> allows to build a term-frequency pandas sparse dataframe with the <code>transform_many</code> method.</p> <p><pre><code>import pandas as pd\nX = pd.Series(['Hello world', 'Hello River'], index = ['river', 'rocks'])\nbow = fx.BagOfWords()\nbow.transform_many(X=X)\n</code></pre> <pre><code>       hello  world  river\nriver      1      1      0\nrocks      1      0      1\n</code></pre></p>"},{"location":"api/feature-extraction/BagOfWords/#methods","title":"Methods","text":"learn_many learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> process_text transform_many <p>Transform pandas series of string into term-frequency pandas sparse dataframe.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.Series' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-extraction/PolynomialExtender/","title":"PolynomialExtender","text":"<p>Polynomial feature extender.</p> <p>Generate features consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. </p> <p>Be aware that the number of outputted features scales polynomially in the number of input features and exponentially in the degree. High degrees can cause overfitting.</p>"},{"location":"api/feature-extraction/PolynomialExtender/#parameters","title":"Parameters","text":"<ul> <li> <p>degree</p> <p>Default \u2192 <code>2</code></p> <p>The maximum degree of the polynomial features.</p> </li> <li> <p>interaction_only</p> <p>Default \u2192 <code>False</code></p> <p>If <code>True</code> then only combinations that include an element at most once will be computed.</p> </li> <li> <p>include_bias</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to include a dummy feature which is always equal to 1.</p> </li> <li> <p>bias_name</p> <p>Default \u2192 <code>bias</code></p> <p>Name to give to the bias feature.</p> </li> </ul>"},{"location":"api/feature-extraction/PolynomialExtender/#examples","title":"Examples","text":"<p><pre><code>from river import feature_extraction as fx\n\nX = [\n    {'x': 0, 'y': 1},\n    {'x': 2, 'y': 3},\n    {'x': 4, 'y': 5}\n]\n\npoly = fx.PolynomialExtender(degree=2, include_bias=True)\nfor x in X:\n    print(poly.transform_one(x))\n</code></pre> <pre><code>{'x': 0, 'y': 1, 'x*x': 0, 'x*y': 0, 'y*y': 1, 'bias': 1}\n{'x': 2, 'y': 3, 'x*x': 4, 'x*y': 6, 'y*y': 9, 'bias': 1}\n{'x': 4, 'y': 5, 'x*x': 16, 'x*y': 20, 'y*y': 25, 'bias': 1}\n</code></pre></p> <p><pre><code>X = [\n    {'x': 0, 'y': 1, 'z': 2},\n    {'x': 2, 'y': 3, 'z': 2},\n    {'x': 4, 'y': 5, 'z': 2}\n]\n\npoly = fx.PolynomialExtender(degree=3, interaction_only=True)\nfor x in X:\n    print(poly.transform_one(x))\n</code></pre> <pre><code>{'x': 0, 'y': 1, 'z': 2, 'x*y': 0, 'x*z': 0, 'y*z': 2, 'x*y*z': 0}\n{'x': 2, 'y': 3, 'z': 2, 'x*y': 6, 'x*z': 4, 'y*z': 6, 'x*y*z': 12}\n{'x': 4, 'y': 5, 'z': 2, 'x*y': 20, 'x*z': 8, 'y*z': 10, 'x*y*z': 40}\n</code></pre></p> <p>Polynomial features are typically used for a linear model to capture interactions between features. This may done by setting up a pipeline, as so:</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model as lm\nfrom river import metrics\nfrom river import preprocessing as pp\n\ndataset = datasets.Phishing()\n\nmodel = (\n    fx.PolynomialExtender() |\n    pp.StandardScaler() |\n    lm.LogisticRegression()\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 88.88%\n</code></pre></p>"},{"location":"api/feature-extraction/PolynomialExtender/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-extraction/RBFSampler/","title":"RBFSampler","text":"<p>Extracts random features which approximate an RBF kernel.</p> <p>This is a powerful way to give non-linear capacity to linear classifiers. This method is also called \"random Fourier features\" in the literature.</p>"},{"location":"api/feature-extraction/RBFSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>gamma</p> <p>Default \u2192 <code>1.0</code></p> <p>RBF kernel parameter in <code>(-gamma * x^2)</code>.</p> </li> <li> <p>n_components</p> <p>Default \u2192 <code>100</code></p> <p>Number of samples per original feature. Equals the dimensionality of the computed feature space.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number seed.</p> </li> </ul>"},{"location":"api/feature-extraction/RBFSampler/#examples","title":"Examples","text":"<p><pre><code>from river import feature_extraction as fx\nfrom river import linear_model as lm\nfrom river import optim\nfrom river import stream\n\nX = [[0, 0], [1, 1], [1, 0], [0, 1]]\nY = [0, 0, 1, 1]\n\nmodel = lm.LogisticRegression(optimizer=optim.SGD(.1))\n\nfor x, y in stream.iter_array(X, Y):\n    model.learn_one(x, y)\n    y_pred = model.predict_one(x)\n    print(y, int(y_pred))\n</code></pre> <pre><code>0 0\n0 0\n1 0\n1 1\n</code></pre></p> <p><pre><code>model = (\n    fx.RBFSampler(seed=3) |\n    lm.LogisticRegression(optimizer=optim.SGD(.1))\n)\n\nfor x, y in stream.iter_array(X, Y):\n    model.learn_one(x, y)\n    y_pred = model.predict_one(x)\n    print(y, int(y_pred))\n</code></pre> <pre><code>0 0\n0 0\n1 1\n1 1\n</code></pre></p>"},{"location":"api/feature-extraction/RBFSampler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Rahimi, A. and Recht, B., 2008. Random features for large-scale kernel machines. In Advances in neural information processing systems (pp. 1177-1184 \u21a9</p> </li> </ol>"},{"location":"api/feature-extraction/TFIDF/","title":"TFIDF","text":"<p>Computes TF-IDF values from sentences.</p> <p>The TF-IDF formula is the same one as scikit-learn. The only difference is the fact that the document frequencies are determined online, whereas in a batch setting they can be determined by performing an initial pass through the data. </p> <p>Note that the parameters are identical to those of <code>feature_extraction.BagOfWords</code>.</p>"},{"location":"api/feature-extraction/TFIDF/#parameters","title":"Parameters","text":"<ul> <li> <p>normalize</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not the TF-IDF values by their L2 norm.</p> </li> <li> <p>on</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>None</code></p> <p>The name of the feature that contains the text to vectorize. If <code>None</code>, then the input is treated as a document instead of a set of features.</p> </li> <li> <p>strip_accents</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to strip accent characters.</p> </li> <li> <p>lowercase</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not to convert all characters to lowercase.</p> </li> <li> <p>preprocessor</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional preprocessing function which overrides the <code>strip_accents</code> and <code>lowercase</code> steps, while preserving the tokenizing and n-grams generation steps.</p> </li> <li> <p>tokenizer</p> <p>Type \u2192 typing.Callable | None</p> <p>Default \u2192 <code>None</code></p> <p>A function used to convert preprocessed text into a <code>dict</code> of tokens. By default, a regex formula that works well in most cases is used.</p> </li> <li> <p>ngram_range</p> <p>Default \u2192 <code>(1, 1)</code></p> <p>The lower and upper boundary of the range n-grams to be extracted. All values of n such that <code>min_n &lt;= n &lt;= max_n</code> will be used. For example an <code>ngram_range</code> of <code>(1, 1)</code> means only unigrams, <code>(1, 2)</code> means unigrams and bigrams, and <code>(2, 2)</code> means only bigrams. Only works if <code>tokenizer</code> is not set to <code>False</code>.</p> </li> </ul>"},{"location":"api/feature-extraction/TFIDF/#attributes","title":"Attributes","text":"<ul> <li> <p>dfs (collections.defaultdict))</p> <p>Document counts.</p> </li> <li> <p>n (int)</p> <p>Number of scanned documents.</p> </li> </ul>"},{"location":"api/feature-extraction/TFIDF/#examples","title":"Examples","text":"<p><pre><code>from river import feature_extraction\n\ntfidf = feature_extraction.TFIDF()\n\ncorpus = [\n    'This is the first document.',\n    'This document is the second document.',\n    'And this is the third one.',\n    'Is this the first document?',\n]\n\nfor sentence in corpus:\n    tfidf.learn_one(sentence)\n    print(tfidf.transform_one(sentence))\n</code></pre> <pre><code>{'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447}\n{'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469}\n{'and': 0.497, 'this': 0.293, 'is': 0.293, 'the': 0.293, 'third': 0.497, 'one': 0.497}\n{'is': 0.384, 'this': 0.384, 'the': 0.384, 'first': 0.580, 'document': 0.469}\n</code></pre></p> <p>In the above example, a string is passed to <code>transform_one</code>. You can also indicate which field to access if the string is stored in a dictionary:</p> <p><pre><code>tfidf = feature_extraction.TFIDF(on='sentence')\n\nfor sentence in corpus:\n    x = {'sentence': sentence}\n    tfidf.learn_one(x)\n    print(tfidf.transform_one(x))\n</code></pre> <pre><code>{'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447}\n{'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469}\n{'and': 0.497, 'this': 0.293, 'is': 0.293, 'the': 0.293, 'third': 0.497, 'one': 0.497}\n{'is': 0.384, 'this': 0.384, 'the': 0.384, 'first': 0.580, 'document': 0.469}\n</code></pre></p>"},{"location":"api/feature-extraction/TFIDF/#methods","title":"Methods","text":"learn_many learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> process_text transform_many <p>Transform pandas series of string into term-frequency pandas sparse dataframe.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.Series' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-extraction/TargetAgg/","title":"TargetAgg","text":"<p>Computes a streaming aggregate of the target values.</p> <p>This transformer is identical to <code>feature_extraction.Agg</code>, the only difference is that it operates on the target rather than on a feature. At each step, the running statistic <code>how</code> of target values in group <code>by</code> is updated with the target. It is therefore a supervised transformer.</p>"},{"location":"api/feature-extraction/TargetAgg/#parameters","title":"Parameters","text":"<ul> <li> <p>by</p> <p>Type \u2192 str | list[str] | None</p> <p>The feature by which to group the target values. All the data is included in the aggregate if this is <code>None</code>.</p> </li> <li> <p>how</p> <p>Type \u2192 stats.base.Univariate | utils.Rolling | utils.TimeRolling</p> <p>The statistic to compute.</p> </li> <li> <p>target_name</p> <p>Default \u2192 <code>y</code></p> <p>The target name which is used in the result.</p> </li> </ul>"},{"location":"api/feature-extraction/TargetAgg/#attributes","title":"Attributes","text":"<ul> <li> <p>state</p> <p>Return the current values for each group as a series.</p> </li> <li> <p>target_name</p> </li> </ul>"},{"location":"api/feature-extraction/TargetAgg/#examples","title":"Examples","text":"<p>Consider the following dataset, where the second value of each value is the target:</p> <pre><code>dataset = [\n    ({'country': 'France', 'place': 'Taco Bell'}, 42),\n    ({'country': 'Sweden', 'place': 'Burger King'}, 16),\n    ({'country': 'France', 'place': 'Burger King'}, 24),\n    ({'country': 'Sweden', 'place': 'Taco Bell'}, 58),\n    ({'country': 'Sweden', 'place': 'Burger King'}, 20),\n    ({'country': 'France', 'place': 'Taco Bell'}, 50),\n    ({'country': 'France', 'place': 'Burger King'}, 10),\n    ({'country': 'Sweden', 'place': 'Taco Bell'}, 80)\n]\n</code></pre> <p>As an example, let's perform a target encoding of the <code>place</code> feature. Instead of simply updating a running average, we use a <code>stats.BayesianMean</code> which allows us to incorporate some prior knowledge. This makes subsequent models less prone to overfitting. Indeed, it dampens the fact that too few samples might have been seen within a group.</p> <p><pre><code>from river import feature_extraction\nfrom river import stats\n\nagg = feature_extraction.TargetAgg(\n    by='place',\n    how=stats.BayesianMean(\n        prior=3,\n        prior_weight=1\n    )\n)\n\nfor x, y in dataset:\n    print(agg.transform_one(x))\n    agg.learn_one(x, y)\n</code></pre> <pre><code>{'y_bayes_mean_by_place': 3.0}\n{'y_bayes_mean_by_place': 3.0}\n{'y_bayes_mean_by_place': 9.5}\n{'y_bayes_mean_by_place': 22.5}\n{'y_bayes_mean_by_place': 14.333}\n{'y_bayes_mean_by_place': 34.333}\n{'y_bayes_mean_by_place': 15.75}\n{'y_bayes_mean_by_place': 38.25}\n</code></pre></p> <p>Just like with <code>feature_extraction.Agg</code>, we can specify multiple features on which to group the data:</p> <p><pre><code>agg = feature_extraction.TargetAgg(\n    by=['place', 'country'],\n    how=stats.BayesianMean(\n        prior=3,\n        prior_weight=1\n    )\n)\n\nfor x, y in dataset:\n    print(agg.transform_one(x))\n    agg.learn_one(x, y)\n</code></pre> <pre><code>{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 3.0}\n{'y_bayes_mean_by_place_and_country': 9.5}\n{'y_bayes_mean_by_place_and_country': 22.5}\n{'y_bayes_mean_by_place_and_country': 13.5}\n{'y_bayes_mean_by_place_and_country': 30.5}\n</code></pre></p> <p><pre><code>agg.state\n</code></pre> <pre><code>place        country\nTaco Bell    France     31.666667\nBurger King  Sweden     13.000000\n             France     12.333333\nTaco Bell    Sweden     47.000000\nName: y_bayes_mean_by_place_and_country, dtype: float64\n</code></pre></p> <p>This transformer can also be used in conjunction with <code>utils.TimeRolling</code>. The latter requires a <code>t</code> argument, which is a timestamp that indicates when the current row was observed. For instance, we can calculate the average (how) revenue (on) for each place (by) over the last 7 days (t):</p> <pre><code>import datetime as dt\nimport random\nimport string\nfrom river import utils\n\nagg = feature_extraction.TargetAgg(\n    by=\"group\",\n    how=utils.TimeRolling(stats.Mean(), dt.timedelta(days=7))\n)\n\nfor day in range(366):\n    g = random.choice(string.ascii_lowercase)\n    x = {\"group\": g}\n    y = string.ascii_lowercase.index(g) + random.random()\n    t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day)\n    agg.learn_one(x, y, t=t)\n</code></pre>"},{"location":"api/feature-extraction/TargetAgg/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code> and a target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> <li>t     \u2014 defaults to <code>None</code> </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p> 1. Streaming groupbys in pandas for big datasets</p>"},{"location":"api/feature-selection/PoissonInclusion/","title":"PoissonInclusion","text":"<p>Randomly selects features with an inclusion trial.</p> <p>When a new feature is encountered, it is selected with probability <code>p</code>. The number of times a feature needs to beseen before it is added to the model follows a geometric distribution with expected value <code>1 / p</code>. This feature selection method is meant to be used when you have a very large amount of sparse features.</p>"},{"location":"api/feature-selection/PoissonInclusion/#parameters","title":"Parameters","text":"<ul> <li> <p>p</p> <p>Type \u2192 float</p> <p>Probability of including a feature the first time it is encountered.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed value used for reproducibility.</p> </li> </ul>"},{"location":"api/feature-selection/PoissonInclusion/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import feature_selection\nfrom river import stream\n\nselector = feature_selection.PoissonInclusion(p=0.1, seed=42)\n\ndataset = iter(datasets.TrumpApproval())\n\nfeature_names = next(dataset)[0].keys()\nn = 0\n\nwhile True:\n    x, y = next(dataset)\n    xt = selector.transform_one(x)\n    if xt.keys() == feature_names:\n        break\n    n += 1\n\nn\n</code></pre> <pre><code>12\n</code></pre></p>"},{"location":"api/feature-selection/PoissonInclusion/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D. and Chikkerur, S., 2013, August. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222-1230) \u21a9</p> </li> </ol>"},{"location":"api/feature-selection/SelectKBest/","title":"SelectKBest","text":"<p>Removes all but the \\(k\\) highest scoring features.</p>"},{"location":"api/feature-selection/SelectKBest/#parameters","title":"Parameters","text":"<ul> <li> <p>similarity</p> <p>Type \u2192 stats.base.Bivariate</p> </li> <li> <p>k</p> <p>Default \u2192 <code>10</code></p> <p>The number of features to keep.</p> </li> </ul>"},{"location":"api/feature-selection/SelectKBest/#attributes","title":"Attributes","text":"<ul> <li> <p>similarities (dict)</p> <p>The similarity instances used for each feature.</p> </li> <li> <p>leaderboard (dict)</p> <p>The actual similarity measures.</p> </li> </ul>"},{"location":"api/feature-selection/SelectKBest/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nfrom river import feature_selection\nfrom river import stats\nfrom river import stream\nfrom sklearn import datasets\n\nX, y = datasets.make_regression(\n    n_samples=100,\n    n_features=10,\n    n_informative=2,\n    random_state=42\n)\n\nselector = feature_selection.SelectKBest(\n    similarity=stats.PearsonCorr(),\n    k=2\n)\n\nfor xi, yi, in stream.iter_array(X, y):\n    selector.learn_one(xi, yi)\n\npprint(selector.leaderboard)\n</code></pre> <pre><code>Counter({9: 0.7898,\n        7: 0.5444,\n        8: 0.1062,\n        2: 0.0638,\n        4: 0.0538,\n        5: 0.0271,\n        1: -0.0312,\n        6: -0.0657,\n        3: -0.1501,\n        0: -0.1895})\n</code></pre></p> <p><pre><code>selector.transform_one(xi)\n</code></pre> <pre><code>{7: -1.2795, 9: -1.8408}\n</code></pre></p>"},{"location":"api/feature-selection/SelectKBest/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code> and a target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.Target' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/feature-selection/VarianceThreshold/","title":"VarianceThreshold","text":"<p>Removes low-variance features.</p>"},{"location":"api/feature-selection/VarianceThreshold/#parameters","title":"Parameters","text":"<ul> <li> <p>threshold</p> <p>Default \u2192 <code>0</code></p> <p>Only features with a variance above the threshold will be kept.</p> </li> <li> <p>min_samples</p> <p>Default \u2192 <code>2</code></p> <p>The minimum number of samples required to perform selection.</p> </li> </ul>"},{"location":"api/feature-selection/VarianceThreshold/#attributes","title":"Attributes","text":"<ul> <li> <p>variances (dict)</p> <p>The variance of each feature.</p> </li> </ul>"},{"location":"api/feature-selection/VarianceThreshold/#examples","title":"Examples","text":"<p><pre><code>from river import feature_selection\nfrom river import stream\n\nX = [\n    [0, 2, 0, 3],\n    [0, 1, 4, 3],\n    [0, 1, 1, 3]\n]\n\nselector = feature_selection.VarianceThreshold()\n\nfor x, _ in stream.iter_array(X):\n    selector.learn_one(x)\n    print(selector.transform_one(x))\n</code></pre> <pre><code>{0: 0, 1: 2, 2: 0, 3: 3}\n{1: 1, 2: 4}\n{1: 1, 2: 1}\n</code></pre></p>"},{"location":"api/feature-selection/VarianceThreshold/#methods","title":"Methods","text":"check_feature learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/forest/AMFClassifier/","title":"AMFClassifier","text":"<p>Aggregated Mondrian Forest classifier for online learning.</p> <p>This implementation is truly online<sup>1</sup>, in the sense that a single pass is performed, and that predictions can be produced anytime. </p> <p>Each node in a tree predicts according to the distribution of the labels it contains. This distribution is regularized using a \"Jeffreys\" prior with parameter <code>dirichlet</code>. For each class with <code>count</code> labels in the node and <code>n_samples</code> samples in it, the prediction of a node is given by </p> <p>\\(\\frac{count + dirichlet}{n_{samples} + dirichlet \\times n_{classes}}\\). </p> <p>The prediction for a sample is computed as the aggregated predictions of all the subtrees along the path leading to the leaf node containing the sample. The aggregation weights are exponential weights with learning rate <code>step</code> and log-loss when <code>use_aggregation</code> is <code>True</code>. </p> <p>This computation is performed exactly thanks to a context tree weighting algorithm. More details can be found in the paper cited in the references below. </p> <p>The final predictions are the average class probabilities predicted by each of the <code>n_estimators</code> trees in the forest.</p>"},{"location":"api/forest/AMFClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_estimators</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of trees in the forest.</p> </li> <li> <p>step</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Step-size for the aggregation weights. Default is 1 for classification with the log-loss, which is usually the best choice.</p> </li> <li> <p>use_aggregation</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Controls if aggregation is used in the trees. It is highly recommended to leave it as <code>True</code>.</p> </li> <li> <p>dirichlet</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>Regularization level of the class frequencies used for predictions in each node. A rule of thumb is to set this to <code>1 / n_classes</code>, where <code>n_classes</code> is the expected number of classes which might appear. Default is <code>dirichlet = 0.5</code>, which works well for binary classification problems.</p> </li> <li> <p>split_pure</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Controls if nodes that contains only sample of the same class should be split (\"pure\" nodes). Default is <code>False</code>, namely pure nodes are not split, but <code>True</code> can be sometimes better.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/AMFClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/forest/AMFClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import forest\nfrom river import metrics\n\ndataset = datasets.Bananas().take(500)\n\nmodel = forest.AMFClassifier(\n    n_estimators=10,\n    use_aggregation=True,\n    dirichlet=0.5,\n    seed=1\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 85.37%\n</code></pre></p>"},{"location":"api/forest/AMFClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/forest/AMFClassifier/#notes","title":"Notes","text":"<p>Only log_loss used for the computation of the aggregation weights is supported for now, namely the log-loss for multi-class classification.</p> <ol> <li> <p>Mourtada, J., Ga\u00efffas, S., &amp; Scornet, E. (2021). AMF: Aggregated Mondrian forests for online learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/AMFRegressor/","title":"AMFRegressor","text":"<p>Aggregated Mondrian Forest regressor for online learning.</p> <p>This algorithm is truly online, in the sense that a single pass is performed, and that predictions can be produced anytime. </p> <p>Each node in a tree predicts according to the average of the labels it contains. The prediction for a sample is computed as the aggregated predictions of all the subtrees along the path leading to the leaf node containing the sample. The aggregation weights are exponential weights with learning rate <code>step</code> using a squared loss when <code>use_aggregation</code> is <code>True</code>. </p> <p>This computation is performed exactly thanks to a context tree weighting algorithm. More details can be found in the original paper<sup>1</sup>. </p> <p>The final predictions are the average of the predictions of each of the <code>n_estimators</code> trees in the forest.</p>"},{"location":"api/forest/AMFRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_estimators</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of trees in the forest.</p> </li> <li> <p>step</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Step-size for the aggregation weights.</p> </li> <li> <p>use_aggregation</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Controls if aggregation is used in the trees. It is highly recommended to leave it as <code>True</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/AMFRegressor/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/forest/AMFRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import forest\nfrom river import metrics\n\ndataset = datasets.TrumpApproval()\nmodel = forest.AMFRegressor(seed=42)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.268533\n</code></pre></p>"},{"location":"api/forest/AMFRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Mourtada, J., Ga\u00efffas, S., &amp; Scornet, E. (2021). AMF: Aggregated Mondrian forests for online learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/ARFClassifier/","title":"ARFClassifier","text":"<p>Adaptive Random Forest classifier.</p> <p>The 3 most important aspects of Adaptive Random Forest <sup>1</sup> are: </p> <ol> <li> <p>inducing diversity through re-sampling </p> </li> <li> <p>inducing diversity through randomly selecting subsets of features for    node splits </p> </li> <li> <p>drift detectors per base tree, which cause selective resets in response    to drifts </p> </li> </ol> <p>It also allows training background trees, which start training if a warning is detected and replace the active tree if the warning escalates to a drift.</p>"},{"location":"api/forest/ARFClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of trees in the ensemble.</p> </li> <li> <p>max_features</p> <p>Type \u2192 bool | str | int</p> <p>Default \u2192 <code>sqrt</code></p> <p>Max number of attributes for each node split. - If <code>int</code>, then consider <code>max_features</code> at each split. - If <code>float</code>, then <code>max_features</code> is a percentage and   <code>int(max_features * n_features)</code> features are considered per split. - If \"sqrt\", then <code>max_features=sqrt(n_features)</code>. - If \"log2\", then <code>max_features=log2(n_features)</code>. - If None, then <code>max_features=n_features</code>.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.MultiClassMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>Metric used to track trees performance within the ensemble. Defaults to <code>metrics.Accuracy</code>()`.</p> </li> <li> <p>disable_weighted_vote</p> <p>Default \u2192 <code>False</code></p> <p>If <code>True</code>, disables the weighted vote prediction.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift Detection method. Set to None to disable Drift detection. Defaults to <code>drift.ADWIN</code>(delta=0.001)`.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning Detection method. Set to None to disable warning detection. Defaults to <code>drift.ADWIN</code>(delta=0.01)`.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>[Tree parameter] Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>[Tree parameter] Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Hellinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>[Tree parameter] Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>[Tree parameter] Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>[Tree parameter] Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>[Tree parameter] Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>[Tree parameter] Maximum memory (MiB) consumed by the tree.</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2000000</code></p> <p>[Tree parameter] Number of instances between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>[Tree parameter] If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/ARFClassifier/#attributes","title":"Attributes","text":"<ul> <li>models</li> </ul>"},{"location":"api/forest/ARFClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import evaluate\nfrom river import forest\nfrom river import metrics\nfrom river.datasets import synth\n\ndataset = synth.ConceptDriftStream(\n    seed=42,\n    position=500,\n    width=40\n).take(1000)\n\nmodel = forest.ARFClassifier(seed=8, leaf_prediction=\"mc\")\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 71.17%\n</code></pre></p> <p>The total number of warnings and drifts detected, respectively <pre><code>model.n_warnings_detected(), model.n_drifts_detected()\n</code></pre> <pre><code>(2, 1)\n</code></pre></p> <p>The number of warnings detected by tree number 2 <pre><code>model.n_warnings_detected(2)\n</code></pre> <pre><code>1\n</code></pre></p> <p>And the corresponding number of actual concept drift detected <pre><code>model.n_drifts_detected(2)\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/forest/ARFClassifier/#methods","title":"Methods","text":"learn_one n_drifts_detected <p>Get the total number of concept drifts detected, or such number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drifts detected.</p> <p></p> n_warnings_detected <p>Get the total number of concept drift warnings detected, or the number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drift warnings detected.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal,  Fabricio Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem.  Adaptive random forests for evolving data stream classification.  In Machine Learning, DOI: 10.1007/s10994-017-5642-8, Springer, 2017.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/ARFRegressor/","title":"ARFRegressor","text":"<p>Adaptive Random Forest regressor.</p> <p>The 3 most important aspects of Adaptive Random Forest <sup>1</sup> are: </p> <ol> <li> <p>inducing diversity through re-sampling </p> </li> <li> <p>inducing diversity through randomly selecting subsets of features for    node splits </p> </li> <li> <p>drift detectors per base tree, which cause selective resets in response    to drifts </p> </li> </ol> <p>Notice that this implementation is slightly different from the original algorithm proposed in <sup>2</sup>. The <code>HoeffdingTreeRegressor</code> is used as base learner, instead of <code>FIMT-DD</code>. It also adds a new strategy to monitor the predictions and check for concept drifts. The deviations of the predictions to the target are monitored and normalized in the [0, 1] range to fulfill ADWIN's requirements. We assume that the data subjected to the normalization follows a normal distribution, and thus, lies within the interval of the mean \\(\\pm3\\sigma\\).</p>"},{"location":"api/forest/ARFRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of trees in the ensemble.</p> </li> <li> <p>max_features</p> <p>Default \u2192 <code>sqrt</code></p> <p>Max number of attributes for each node split. - If <code>int</code>, then consider <code>max_features</code> at each split. - If <code>float</code>, then <code>max_features</code> is a percentage and   <code>int(max_features * n_features)</code> features are considered per split. - If \"sqrt\", then <code>max_features=sqrt(n_features)</code>. - If \"log2\", then <code>max_features=log2(n_features)</code>. - If None, then <code>max_features=n_features</code>.</p> </li> <li> <p>aggregation_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>median</code></p> <p>The method to use to aggregate predictions in the ensemble. - 'mean' - 'median' - If selected will disable the weighted vote.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>6</code></p> <p>The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>Metric used to track trees performance within the ensemble. Depending, on the configuration, this metric is also used to weight predictions from the members of the ensemble. Defaults to <code>metrics.MSE</code>()`.</p> </li> <li> <p>disable_weighted_vote</p> <p>Default \u2192 <code>True</code></p> <p>If <code>True</code>, disables the weighted vote prediction, i.e. does not assign weights to individual tree's predictions and uses the arithmetic mean instead. Otherwise will use the <code>metric</code> value to weight predictions.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Drift Detection method. Set to None to disable Drift detection. Defaults to <code>drift.ADWIN</code>(0.001)`.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>Warning Detection method. Set to None to disable warning detection. Defaults to <code>drift.ADWIN</code>(0.01)`.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>[Tree parameter] Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>[Tree parameter] Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>[Tree parameter] Prediction mechanism used at leaves. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided, an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters  is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>[Tree parameter] The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters.By default, <code>tree.splitter.EBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>[Tree parameter] The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>[Tree parameter] Maximum memory (MiB) consumed by the tree.</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2000000</code></p> <p>[Tree parameter] Number of instances between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>[Tree parameter] If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/forest/ARFRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>models</p> </li> <li> <p>valid_aggregation_method</p> <p>Valid aggregation_method values.</p> </li> </ul>"},{"location":"api/forest/ARFRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import forest\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    forest.ARFRegressor(seed=42)\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.788619\n</code></pre></p>"},{"location":"api/forest/ARFRegressor/#methods","title":"Methods","text":"learn_one n_drifts_detected <p>Get the total number of concept drifts detected, or such number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drifts detected.</p> <p></p> n_warnings_detected <p>Get the total number of concept drift warnings detected, or the number on an individual tree basis (optionally).</p> <p>Parameters</p> <ul> <li>tree_id     \u2014 'int | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>int:     The number of concept drift warnings detected.</p> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p> <ol> <li> <p>Gomes, H.M., Bifet, A., Read, J., Barddal, J.P., Enembreck, F.,   Pfharinger, B., Holmes, G. and Abdessalem, T., 2017. Adaptive random   forests for evolving data stream classification. Machine Learning,   106(9-10), pp.1469-1495.\u00a0\u21a9</p> </li> <li> <p>Gomes, H.M., Barddal, J.P., Boiko, L.E., Bifet, A., 2018.   Adaptive random forests for data stream regression. ESANN 2018.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/forest/OXTRegressor/","title":"OXTRegressor","text":"<p>Online Extra Trees regressor.</p> <p>The online Extra Trees<sup>1</sup> ensemble takes some steps further into randomization when compared to Adaptive Random Forests (ARF). A subspace of the feature space is considered at each split attempt, as ARF does, and online bagging or subbagging can also be (optionally) used. Nonetheless, Extra Trees randomizes the split candidates evaluated by each leaf node (just a single split is tested by numerical feature, which brings significant speedups to the ensemble), and might also randomize the maximum depth of the forest members, as well as the size of the feature subspace processed by each of its trees' leaves. </p> <p>On the other hand, OXT suffers from a cold-start problem. As the splits are random, the predictive performance in small samples is usually worse than using a deterministic split approach, such as the one used by ARF.</p>"},{"location":"api/forest/OXTRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_models</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of trees in the ensemble.</p> </li> <li> <p>max_features</p> <p>Type \u2192 bool | str | int</p> <p>Default \u2192 <code>sqrt</code></p> <p>Max number of attributes for each node split. - If int, then consider <code>max_features</code> at each split. - If float, then <code>max_features</code> is a percentage and <code>int(max_features * n_features)</code> features are considered per split. - If \"sqrt\", then <code>max_features=sqrt(n_features)</code>. - If \"log2\", then <code>max_features=log2(n_features)</code>. - If \"random\", then <code>max_features</code> will assume a different random number in the interval <code>[2, n_features]</code> for each tree leaf. - If None, then <code>max_features=n_features</code>.</p> </li> <li> <p>resampling_strategy</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>subbagging</code></p> <p>The chosen instance resampling strategy: - If <code>None</code>, no resampling will be done and the trees will process all instances. - If <code>'baggging'</code>, online bagging will be performed (sampling with replacement). - If <code>'subbagging'</code>, online subbagging will be performed (sampling without replacement).</p> </li> <li> <p>resampling_rate</p> <p>Type \u2192 int | float</p> <p>Default \u2192 <code>0.5</code></p> <p>Only valid if <code>resampling_strategy</code> is not None. Controls the parameters of the resampling strategy.. - If <code>resampling_strategy='bagging'</code>, must be an integer greater than or equal to 1 that parameterizes the poisson distribution used to simulate bagging in online learning settings. It acts as the lambda parameter of Oza Bagging and Leveraging Bagging. - If <code>resampling_strategy='subbagging'</code>, must be a float in the interval \\((0, 1]\\) that controls the chance of each instance being used by a tree for learning.</p> </li> <li> <p>detection_mode</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>all</code></p> <p>The concept drift detection mode in which the forest operates. Valid values are: - \"all\": creates both warning and concept drift detectors. If a warning is detected, an alternate tree starts being trained in the background. If the warning trigger escalates to a concept drift, the affected tree is replaced by the alternate tree. - \"drop\": only the concept drift detectors are created. If a drift is detected, the affected tree is dropped and replaced by a new tree. - \"off\": disables the concept drift adaptation capabilities. The forest will act as if the processed stream is stationary.</p> </li> <li> <p>warning_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The detector that will be used to trigger concept drift warnings. Defaults to <code>drift.ADWIN</code>(0.01)`.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The detector used to detect concept drifts. Defaults to <code>drift.ADWIN</code>(0.001)`.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth the ensemble members might reach. If <code>None</code>, the trees will grow indefinitely.</p> </li> <li> <p>randomize_tree_depth</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not randomize the maximum depth of each tree in the ensemble. If <code>max_depth</code> is provided, it is going to act as an upper bound to generate the maximum depth for each tree.</p> </li> <li> <p>track_metric</p> <p>Type \u2192 metrics.base.RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The performance metric used to weight predictions. Defaults to <code>metrics.MAE</code>()`.</p> </li> <li> <p>disable_weighted_vote</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Defines whether or not to use predictions weighted by each trees' prediction performance.</p> </li> <li> <p>split_buffer_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>Defines the size of the buffer used by the tree splitters when determining the feature range and a random split point in this interval.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed to support reproducibility.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>[Tree parameter] Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>[Tree parameter] Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>[Tree parameter] Prediction mechanism used at leaves. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided, an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters  is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>[Tree parameter] The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>[Tree parameter] The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>500</code></p> <p>[Tree parameter] Maximum memory (MiB) consumed by the tree.</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2000000</code></p> <p>[Tree parameter] Number of instances between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>[Tree parameter] If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>[Tree parameter] If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/forest/OXTRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>instances_per_tree</p> <p>The number of instances processed by each one of the current forest members.  Each time a concept drift is detected, the count corresponding to the affected tree is reset.</p> </li> <li> <p>models</p> </li> <li> <p>n_drifts</p> <p>The number of concept drifts detected per ensemble member.</p> </li> <li> <p>n_tree_swaps</p> <p>The number of performed alternate tree swaps.  Not applicable if the warning detectors are disabled.</p> </li> <li> <p>n_warnings</p> <p>The number of warnings detected per ensemble member.</p> </li> <li> <p>total_instances</p> <p>The total number of instances processed by the ensemble.</p> </li> </ul>"},{"location":"api/forest/OXTRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import forest\n\ndataset = datasets.synth.Friedman(seed=42).take(5000)\n\nmodel = forest.OXTRegressor(n_models=3, seed=42)\n\nmetric = metrics.RMSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>RMSE: 3.127311\n</code></pre></p>"},{"location":"api/forest/OXTRegressor/#methods","title":"Methods","text":"learn_one predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/forest/OXTRegressor/#notes","title":"Notes","text":"<p>As the Online Extra Trees change the way in which Hoeffding Trees perform split attempts and monitor numerical input features, some of the parameters of the vanilla Hoeffding Tree algorithms are not available.</p> <ol> <li> <p>Mastelini, S. M., Nakano, F. K., Vens, C., &amp; de Leon Ferreira, A. C. P. (2022). Online Extra Trees Regressor. IEEE Transactions on Neural Networks and Learning Systems.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/imblearn/ChebyshevOverSampler/","title":"ChebyshevOverSampler","text":"<p>Over-sampling for imbalanced regression using Chebyshev's inequality.</p> <p>Chebyshev's inequality can be used to define the probability of target observations being frequent values (w.r.t. the distribution mean). </p> <p>Let \\(Y\\) be a random variable with finite expected value \\(\\overline{y}\\) and non-zero variance \\(\\sigma^2\\). For any real number \\(t &gt; 0\\), the Chebyshev's inequality states that, for a wide class of unimodal probability distributions: \\(Pr(|y-\\overline{y}| \\ge t\\sigma) \\le \\dfrac{1}{t^2}\\). </p> <p>Taking \\(t=\\dfrac{|y-\\overline{y}|}{\\sigma}\\), and assuming \\(t &gt; 1\\), the Chebyshev\u2019s inequality for an observation \\(y\\) becomes: \\(P(|y - \\overline{y}|=t) = \\dfrac{\\sigma^2}{|y-\\overline{y}|}\\). </p> <p>Alternatively, one can use \\(t\\) directly to estimate a frequency weight \\(\\kappa = \\lceil t\\rceil\\) and define an over-sampling strategy for extreme and rare target values<sup>1</sup>. Each incoming instance is used \\(\\kappa\\) times to update the underlying regressor. Frequent target values contribute only once to the underlying regressor, whereas rares cases are used multiple times for training.</p>"},{"location":"api/imblearn/ChebyshevOverSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>The regression model that will receive the biased sample.</p> </li> </ul>"},{"location":"api/imblearn/ChebyshevOverSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import metrics\nfrom river import preprocessing\nfrom river import rules\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.ChebyshevOverSampler(\n        regressor=rules.AMRules(\n            n_min=50, delta=0.01\n        )\n    )\n)\n\nevaluate.progressive_val_score(\n    datasets.TrumpApproval(),\n    model,\n    metrics.MAE(),\n    print_every=500\n)\n</code></pre> <pre><code>[500] MAE: 1.673902\n[1,000] MAE: 1.743046\n[1,001] MAE: 1.741335\nMAE: 1.741335\n</code></pre></p>"},{"location":"api/imblearn/ChebyshevOverSampler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Aminian, Ehsan, Rita P. Ribeiro, and Jo\u00e3o Gama. \"Chebyshev approaches for imbalanced data streams regression models.\" Data Mining and Knowledge Discovery 35.6 (2021): 2389-2466.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/imblearn/ChebyshevUnderSampler/","title":"ChebyshevUnderSampler","text":"<p>Under-sampling for imbalanced regression using Chebyshev's inequality.</p> <p>Chebyshev's inequality can be used to define the probability of target observations being frequent values (w.r.t. the distribution mean). </p> <p>Let \\(Y\\) be a random variable with finite expected value \\(\\overline{y}\\) and non-zero variance \\(\\sigma^2\\). For any real number \\(t &gt; 0\\), the Chebyshev's inequality states that, for a wide class of unimodal probability distributions: \\(Pr(|y-\\overline{y}| \\ge t\\sigma) \\le \\dfrac{1}{t^2}\\). </p> <p>Taking \\(t=\\dfrac{|y-\\overline{y}|}{\\sigma}\\), and assuming \\(t &gt; 1\\), the Chebyshev\u2019s inequality for an observation \\(y\\) becomes: \\(P(|y - \\overline{y}|=t) = \\dfrac{\\sigma^2}{|y-\\overline{y}|}\\). The reciprocal of this probability is used for under-sampling<sup>1</sup> the most frequent cases. Extreme valued or rare cases have higher probabilities of selection, whereas the most frequent cases are likely to be discarded. Still, frequent cases have a small chance of being selected (controlled via the <code>sp</code> parameter) in case few rare instances were observed.</p>"},{"location":"api/imblearn/ChebyshevUnderSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>The regression model that will receive the biased sample.</p> </li> <li> <p>sp</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.15</code></p> <p>Second chance probability. Even if an example is not initially selected for training, it still has a small chance of being selected in case the number of rare case observed so far is small.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed to support reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/ChebyshevUnderSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import metrics\nfrom river import preprocessing\nfrom river import rules\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.ChebyshevUnderSampler(\n        regressor=rules.AMRules(\n            n_min=50, delta=0.01,\n        ),\n        seed=42\n    )\n)\n\nevaluate.progressive_val_score(\n    datasets.TrumpApproval(),\n    model,\n    metrics.MAE(),\n    print_every=500\n)\n</code></pre> <pre><code>[500] MAE: 1.787162\n[1,000] MAE: 1.515711\n[1,001] MAE: 1.515236\nMAE: 1.515236\n</code></pre></p>"},{"location":"api/imblearn/ChebyshevUnderSampler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Aminian, Ehsan, Rita P. Ribeiro, and Jo\u00e3o Gama. \"Chebyshev approaches for imbalanced data streams regression models.\" Data Mining and Knowledge Discovery 35.6 (2021): 2389-2466.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/imblearn/HardSamplingClassifier/","title":"HardSamplingClassifier","text":"<p>Hard sampling classifier.</p> <p>This wrapper enables a model to retrain on past samples who's output was hard to predict. This works by storing the hardest samples in a buffer of a fixed size. When a new sample arrives, the wrapped model is either trained on one of the buffered samples with a probability p or on the new sample with a probability (1 - p). </p> <p>The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the lowest loss in the buffer, then the sample takes its place.</p>"},{"location":"api/imblearn/HardSamplingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>size</p> <p>Type \u2192 int</p> <p>Size of the buffer.</p> </li> <li> <p>p</p> <p>Type \u2192 float</p> <p>Probability of updating the model with a sample from the buffer instead of a new incoming sample.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | optim.losses.MultiClassLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>Criterion used to evaluate the hardness of a sample.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed.</p> </li> </ul>"},{"location":"api/imblearn/HardSamplingClassifier/#attributes","title":"Attributes","text":"<ul> <li>classifier</li> </ul>"},{"location":"api/imblearn/HardSamplingClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.HardSamplingClassifier(\n        classifier=linear_model.LogisticRegression(),\n        p=0.1,\n        size=40,\n        seed=42,\n    )\n)\n\nevaluate.progressive_val_score(\n    dataset=datasets.Phishing(),\n    model=model,\n    metric=metrics.ROCAUC(),\n    print_every=500,\n)\n</code></pre> <pre><code>[500] ROCAUC: 92.78%\n[1,000] ROCAUC: 94.76%\n[1,250] ROCAUC: 95.06%\nROCAUC: 95.06%\n</code></pre></p>"},{"location":"api/imblearn/HardSamplingClassifier/#methods","title":"Methods","text":"learn_one predict_one predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/imblearn/HardSamplingRegressor/","title":"HardSamplingRegressor","text":"<p>Hard sampling regressor.</p> <p>This wrapper enables a model to retrain on past samples who's output was hard to predict. This works by storing the hardest samples in a buffer of a fixed size. When a new sample arrives, the wrapped model is either trained on one of the buffered samples with a probability p or on the new sample with a probability (1 - p). </p> <p>The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the lowest loss in the buffer, then the sample takes its place.</p>"},{"location":"api/imblearn/HardSamplingRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> </li> <li> <p>size</p> <p>Type \u2192 int</p> <p>Size of the buffer.</p> </li> <li> <p>p</p> <p>Type \u2192 float</p> <p>Probability of updating the model with a sample from the buffer instead of a new incoming sample.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>Criterion used to evaluate the hardness of a sample.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed.</p> </li> </ul>"},{"location":"api/imblearn/HardSamplingRegressor/#attributes","title":"Attributes","text":"<ul> <li>regressor</li> </ul>"},{"location":"api/imblearn/HardSamplingRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.HardSamplingRegressor(\n        regressor=linear_model.LinearRegression(),\n        p=.2,\n        size=30,\n        seed=42,\n    )\n)\n\nevaluate.progressive_val_score(\n    datasets.TrumpApproval(),\n    model,\n    metrics.MAE(),\n    print_every=500\n)\n</code></pre> <pre><code>[500] MAE: 2.274021\n[1,000] MAE: 1.392399\n[1,001] MAE: 1.391246\nMAE: 1.391246\n</code></pre></p>"},{"location":"api/imblearn/HardSamplingRegressor/#methods","title":"Methods","text":"learn_one predict_one"},{"location":"api/imblearn/RandomOverSampler/","title":"RandomOverSampler","text":"<p>Random over-sampling.</p> <p>This is a wrapper for classifiers. It will train the provided classifier by over-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution. The implementation is a discrete version of reverse rejection sampling. </p> <p>See Working with imbalanced data for example usage.</p>"},{"location":"api/imblearn/RandomOverSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>desired_dist</p> <p>Type \u2192 dict</p> <p>The desired class distribution. The keys are the classes whilst the values are the desired class percentages. The values must sum up to 1.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/RandomOverSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = imblearn.RandomOverSampler(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    desired_dist={False: 0.4, True: 0.6},\n    seed=42\n)\n\ndataset = datasets.CreditCard().take(3000)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.0457...\n</code></pre></p>"},{"location":"api/imblearn/RandomOverSampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/imblearn/RandomSampler/","title":"RandomSampler","text":"<p>Random sampling by mixing under-sampling and over-sampling.</p> <p>This is a wrapper for classifiers. It will train the provided classifier by both under-sampling and over-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution. </p> <p>See Working with imbalanced data for example usage.</p>"},{"location":"api/imblearn/RandomSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>desired_dist</p> <p>Type \u2192 dict</p> <p>The desired class distribution. The keys are the classes whilst the values are the desired class percentages. The values must sum up to 1. If set to <code>None</code>, then the observations will be sampled uniformly at random, which is stricly equivalent to using <code>ensemble.BaggingClassifier</code>.</p> </li> <li> <p>sampling_rate</p> <p>Default \u2192 <code>1.0</code></p> <p>The desired ratio of data to sample.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/RandomSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = imblearn.RandomSampler(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    desired_dist={False: 0.4, True: 0.6},\n    sampling_rate=0.8,\n    seed=42\n)\n\ndataset = datasets.CreditCard().take(3000)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.09...\n</code></pre></p>"},{"location":"api/imblearn/RandomSampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/imblearn/RandomUnderSampler/","title":"RandomUnderSampler","text":"<p>Random under-sampling.</p> <p>This is a wrapper for classifiers. It will train the provided classifier by under-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution. The implementation is a discrete version of rejection sampling. </p> <p>See Working with imbalanced data for example usage.</p>"},{"location":"api/imblearn/RandomUnderSampler/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>desired_dist</p> <p>Type \u2192 dict</p> <p>The desired class distribution. The keys are the classes whilst the values are the desired class percentages. The values must sum up to 1.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/imblearn/RandomUnderSampler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import imblearn\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\nmodel = imblearn.RandomUnderSampler(\n    (\n        preprocessing.StandardScaler() |\n        linear_model.LogisticRegression()\n    ),\n    desired_dist={False: 0.4, True: 0.6},\n    seed=42\n)\n\ndataset = datasets.CreditCard().take(3000)\n\nmetric = metrics.LogLoss()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>LogLoss: 0.0336...\n</code></pre></p>"},{"location":"api/imblearn/RandomUnderSampler/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Under-sampling a dataset with desired ratios \u21a9</p> </li> <li> <p>Wikipedia article on rejection sampling \u21a9</p> </li> </ol>"},{"location":"api/linear-model/ALMAClassifier/","title":"ALMAClassifier","text":"<p>Approximate Large Margin Algorithm (ALMA).</p>"},{"location":"api/linear-model/ALMAClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>p</p> <p>Default \u2192 <code>2</code></p> </li> <li> <p>alpha</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>B</p> <p>Default \u2192 <code>1.1111111111111112</code></p> </li> <li> <p>C</p> <p>Default \u2192 <code>1.4142135623730951</code></p> </li> </ul>"},{"location":"api/linear-model/ALMAClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>w (collections.defaultdict)</p> <p>The current weights.</p> </li> <li> <p>k (int)</p> <p>The number of instances seen during training.</p> </li> </ul>"},{"location":"api/linear-model/ALMAClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.ALMAClassifier()\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 82.56%\n</code></pre></p>"},{"location":"api/linear-model/ALMAClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Gentile, Claudio. \"A new approximate maximal margin classification algorithm.\" Journal of Machine Learning Research 2.Dec (2001): 213-242 \u21a9</p> </li> </ol>"},{"location":"api/linear-model/BayesianLinearRegression/","title":"BayesianLinearRegression","text":"<p>Bayesian linear regression.</p> <p>An advantage of Bayesian linear regression over standard linear regression is that features do not have to scaled beforehand. Another attractive property is that this flavor of linear regression is somewhat insensitive to its hyperparameters. Finally, this model can output instead a predictive distribution rather than just a point estimate. </p> <p>The downside is that the learning step runs in <code>O(n^2)</code> time, whereas the learning step of standard linear regression takes <code>O(n)</code> time.</p>"},{"location":"api/linear-model/BayesianLinearRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1</code></p> <p>Prior parameter.</p> </li> <li> <p>beta</p> <p>Default \u2192 <code>1</code></p> <p>Noise parameter.</p> </li> <li> <p>smoothing</p> <p>Type \u2192 float | None</p> <p>Default \u2192 <code>None</code></p> <p>Smoothing allows the model to gradually \"forget\" the past, and focus on the more recent data. It thus enables the model to deal with concept drift. Due to the current implementation, activating smoothing may slow down the model.</p> </li> </ul>"},{"location":"api/linear-model/BayesianLinearRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\n\ndataset = datasets.TrumpApproval()\nmodel = linear_model.BayesianLinearRegression()\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.586...\n</code></pre></p> <p><pre><code>x, _ = next(iter(dataset))\nmodel.predict_one(x)\n</code></pre> <pre><code>43.852...\n</code></pre></p> <p><pre><code>model.predict_one(x, with_dist=True)\n</code></pre> <pre><code>\ud835\udca9(\u03bc=43.85..., \u03c3=1.00...)\n</code></pre></p> <p>The <code>smoothing</code> parameter can be set to make the model robust to drift. The parameter is expected to be between 0 and 1. To exemplify, let's generate some simulation data with an abrupt concept drift right in the middle.</p> <pre><code>import itertools\nimport random\n\ndef random_data(coefs, n, seed=42):\n    rng = random.Random(seed)\n    for _ in range(n):\n        x = {i: rng.random() for i, c in enumerate(coefs)}\n        y = sum(c * xi for c, xi in zip(coefs, x.values()))\n        yield x, y\n</code></pre> <p>Here's how the model performs without any smoothing:</p> <p><pre><code>model = linear_model.BayesianLinearRegression()\ndataset = itertools.chain(\n    random_data([0.1, 3], 100),\n    random_data([10, -2], 100)\n)\nmetric = metrics.MAE()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.284...\n</code></pre></p> <p>And here's how it performs with some smoothing:</p> <p><pre><code>model = linear_model.BayesianLinearRegression(smoothing=0.8)\ndataset = itertools.chain(\n    random_data([0.1, 3], 100),\n    random_data([10, -2], 100)\n)\nmetric = metrics.MAE()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.159...\n</code></pre></p> <p>Smoothing allows the model to gradually \"forget\" the past, and focus on the more recent data.</p> <p>Note how this works better than standard linear regression, even when using an aggressive learning rate.</p> <p><pre><code>from river import optim\nmodel = linear_model.LinearRegression(optimizer=optim.SGD(0.5))\ndataset = itertools.chain(\n    random_data([0.1, 3], 100),\n    random_data([10, -2], 100)\n)\nmetric = metrics.MAE()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.242...\n</code></pre></p>"},{"location":"api/linear-model/BayesianLinearRegression/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_many predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>with_dist     \u2014 defaults to <code>False</code> </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p> <ol> <li> <p>Pattern Recognition and Machine Learning, page 52 \u2014 Christopher M. Bishop \u21a9</p> </li> <li> <p>Bayesian/Streaming Algorithms \u2014 Vincent Warmerdam \u21a9</p> </li> <li> <p>Bayesian linear regression for practitioners \u2014 Max Halford \u21a9</p> </li> </ol>"},{"location":"api/linear-model/LinearRegression/","title":"LinearRegression","text":"<p>Linear regression.</p> <p>This estimator supports learning with mini-batches. On top of the single instance methods, it provides the following methods: <code>learn_many</code>, <code>predict_many</code>, <code>predict_proba_many</code>. Each method takes as input a <code>pandas.DataFrame</code> where each column represents a feature. </p> <p>It is generally a good idea to scale the data beforehand in order for the optimizer to converge. You can do this online with a <code>preprocessing.StandardScaler</code>.</p>"},{"location":"api/linear-model/LinearRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights. Note that the intercept updates are handled separately.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.RegressionLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>l1</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>intercept_init</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 optim.base.Scheduler | float</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.base.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/LinearRegression/#attributes","title":"Attributes","text":"<ul> <li> <p>weights (dict)</p> <p>The current weights.</p> </li> </ul>"},{"location":"api/linear-model/LinearRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression(intercept_lr=.1)\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.558735\n</code></pre></p> <p><pre><code>model['LinearRegression'].intercept\n</code></pre> <pre><code>35.617670\n</code></pre></p> <p>You can call the <code>debug_one</code> method to break down a prediction. This works even if the linear regression is part of a pipeline.</p> <p><pre><code>x, y = next(iter(dataset))\nreport = model.debug_one(x)\nprint(report)\n</code></pre> <pre><code>0. Input\n--------\ngallup: 43.84321 (float)\nipsos: 46.19925 (float)\nmorning_consult: 48.31875 (float)\nordinal_date: 736389 (int)\nrasmussen: 44.10469 (float)\nyou_gov: 43.63691 (float)\n&lt;BLANKLINE&gt;\n1. StandardScaler\n-----------------\ngallup: 1.18810 (float)\nipsos: 2.10348 (float)\nmorning_consult: 2.73545 (float)\nordinal_date: -1.73032 (float)\nrasmussen: 1.26872 (float)\nyou_gov: 1.48391 (float)\n&lt;BLANKLINE&gt;\n2. LinearRegression\n-------------------\nName              Value      Weight      Contribution\n      Intercept    1.00000    35.61767       35.61767\n          ipsos    2.10348     0.62689        1.31866\nmorning_consult    2.73545     0.24180        0.66144\n         gallup    1.18810     0.43568        0.51764\n      rasmussen    1.26872     0.28118        0.35674\n        you_gov    1.48391     0.03123        0.04634\n   ordinal_date   -1.73032     3.45162       -5.97242\n&lt;BLANKLINE&gt;\nPrediction: 32.54607\n</code></pre></p>"},{"location":"api/linear-model/LinearRegression/#methods","title":"Methods","text":"debug_one <p>Debugs the output of the linear regression.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>decimals     \u2014 'int'     \u2014 defaults to <code>5</code> </li> </ul> <p>Returns</p> <p>str:     A table which explains the output.</p> <p></p> learn_many <p>Update the model with a mini-batch of features <code>X</code> and real-valued targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> <li>w     \u2014 'float | pd.Series'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X </li> </ul> <p>Returns</p> <p>The predicted outcomes.</p> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/linear-model/LogisticRegression/","title":"LogisticRegression","text":"<p>Logistic regression.</p> <p>This estimator supports learning with mini-batches. On top of the single instance methods, it provides the following methods: <code>learn_many</code>, <code>predict_many</code>, <code>predict_proba_many</code>. Each method takes as input a <code>pandas.DataFrame</code> where each column represents a feature. </p> <p>It is generally a good idea to scale the data beforehand in order for the optimizer to converge. You can do this online with a <code>preprocessing.StandardScaler</code>.</p>"},{"location":"api/linear-model/LogisticRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights. Note that the intercept is handled separately.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.BinaryLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for. Defaults to <code>optim.losses.Log</code>.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>l1</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>intercept_init</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Type \u2192 float | optim.base.Scheduler</p> <p>Default \u2192 <code>0.01</code></p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.base.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/LogisticRegression/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights.</p> </li> </ul>"},{"location":"api/linear-model/LogisticRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer=optim.SGD(.1))\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 88.96%\n</code></pre></p>"},{"location":"api/linear-model/LogisticRegression/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and boolean targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> <li>w     \u2014 'float | pd.Series'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Predict the outcome probabilities for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A dataframe with probabilities of <code>True</code> and <code>False</code> for each sample.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/linear-model/PAClassifier/","title":"PAClassifier","text":"<p>Passive-aggressive learning for classification.</p>"},{"location":"api/linear-model/PAClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>C</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>mode</p> <p>Default \u2192 <code>1</code></p> </li> <li> <p>learn_intercept</p> <p>Default \u2192 <code>True</code></p> </li> </ul>"},{"location":"api/linear-model/PAClassifier/#examples","title":"Examples","text":"<p>The following example is taken from this blog post.</p> <p><pre><code>from river import linear_model\nfrom river import metrics\nfrom river import stream\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn import model_selection\n\nnp.random.seed(1000)\nX, y = datasets.make_classification(\n    n_samples=5000,\n    n_features=4,\n    n_informative=2,\n    n_redundant=0,\n    n_repeated=0,\n    n_classes=2,\n    n_clusters_per_class=2\n)\n\nX_train, X_test, y_train, y_test = model_selection.train_test_split(\n    X,\n    y,\n    test_size=0.35,\n    random_state=1000\n)\n\nmodel = linear_model.PAClassifier(\n    C=0.01,\n    mode=1\n)\n\nfor xi, yi in stream.iter_array(X_train, y_train):\n    model.learn_one(xi, yi)\n\nmetric = metrics.Accuracy() + metrics.LogLoss()\n\nfor xi, yi in stream.iter_array(X_test, y_test):\n    metric.update(yi, model.predict_proba_one(xi))\n\nprint(metric)\n</code></pre> <pre><code>Accuracy: 88.46%\nLogLoss: 0.325727...\n</code></pre></p>"},{"location":"api/linear-model/PAClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S. and Singer, Y., 2006. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7(Mar), pp.551-585 \u21a9</p> </li> </ol>"},{"location":"api/linear-model/PARegressor/","title":"PARegressor","text":"<p>Passive-aggressive learning for regression.</p>"},{"location":"api/linear-model/PARegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>C</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>mode</p> <p>Default \u2192 <code>1</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>learn_intercept</p> <p>Default \u2192 <code>True</code></p> </li> </ul>"},{"location":"api/linear-model/PARegressor/#examples","title":"Examples","text":"<p>The following example is taken from this blog post.</p> <p><pre><code>from river import linear_model\nfrom river import metrics\nfrom river import stream\nimport numpy as np\nfrom sklearn import datasets\n\nnp.random.seed(1000)\nX, y = datasets.make_regression(n_samples=500, n_features=4)\n\nmodel = linear_model.PARegressor(\n    C=0.01,\n    mode=2,\n    eps=0.1,\n    learn_intercept=False\n)\nmetric = metrics.MAE() + metrics.MSE()\n\nfor xi, yi in stream.iter_array(X, y):\n    y_pred = model.predict_one(xi)\n    model.learn_one(xi, yi)\n    metric.update(yi, y_pred)\n\nprint(metric)\n</code></pre> <pre><code>MAE: 9.809402\nMSE: 472.393532\n</code></pre></p>"},{"location":"api/linear-model/PARegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S. and Singer, Y., 2006. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7(Mar), pp.551-585. \u21a9</p> </li> </ol>"},{"location":"api/linear-model/Perceptron/","title":"Perceptron","text":"<p>Perceptron classifier.</p> <p>In this implementation, the Perceptron is viewed as a special case of the logistic regression. The loss function that is used is the Hinge loss with a threshold set to 0, whilst the learning rate of the stochastic gradient descent procedure is set to 1 for both the weights and the intercept.</p>"},{"location":"api/linear-model/Perceptron/#parameters","title":"Parameters","text":"<ul> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/Perceptron/#attributes","title":"Attributes","text":"<ul> <li> <p>weights</p> <p>The current weights.</p> </li> </ul>"},{"location":"api/linear-model/Perceptron/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model as lm\nfrom river import metrics\nfrom river import preprocessing as pp\n\ndataset = datasets.Phishing()\n\nmodel = pp.StandardScaler() | lm.Perceptron()\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 85.84%\n</code></pre></p>"},{"location":"api/linear-model/Perceptron/#methods","title":"Methods","text":"learn_many <p>Update the model with a mini-batch of features <code>X</code> and boolean targets <code>y</code>.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> <li>w     \u2014 'float | pd.Series'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Predict the outcome probabilities for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A dataframe with probabilities of <code>True</code> and <code>False</code> for each sample.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/linear-model/SoftmaxRegression/","title":"SoftmaxRegression","text":"<p>Softmax regression is a generalization of logistic regression to multiple classes.</p> <p>Softmax regression is also known as \"multinomial logistic regression\". There are a set weights for each class, hence the <code>weights</code> attribute is a nested <code>collections.defaultdict</code>. The main advantage of using this instead of a one-vs-all logistic regression is that the probabilities will be calibrated. Moreover softmax regression is more robust to outliers.</p>"},{"location":"api/linear-model/SoftmaxRegression/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used to tune the weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.MultiClassLoss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> </ul>"},{"location":"api/linear-model/SoftmaxRegression/#attributes","title":"Attributes","text":"<ul> <li>weights (collections.defaultdict)</li> </ul>"},{"location":"api/linear-model/SoftmaxRegression/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nmodel = preprocessing.StandardScaler()\nmodel |= linear_model.SoftmaxRegression()\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 81.88%\n</code></pre></p>"},{"location":"api/linear-model/SoftmaxRegression/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Course on classification stochastic gradient descent \u21a9</p> </li> <li> <p>Binary vs. Multi-Class Logistic Regression \u21a9</p> </li> </ol>"},{"location":"api/linear-model/base/GLM/","title":"GLM","text":"<p>Generalized Linear Model.</p> <p>This serves as a base class for linear and logistic regression.</p>"},{"location":"api/linear-model/base/GLM/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>The sequential optimizer used for updating the weights. Note that the intercept updates are handled separately.</p> </li> <li> <p>loss</p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>l1</p> <p>Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.</p> </li> <li> <p>intercept_init</p> <p>Initial intercept value.</p> </li> <li> <p>intercept_lr</p> <p>Learning rate scheduler used for updating the intercept. A <code>optim.schedulers.Constant</code> is used if a <code>float</code> is provided. The intercept is not updated when this is set to 0.</p> </li> <li> <p>clip_gradient</p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>initializer</p> <p>Weights initialization scheme.</p> </li> </ul>"},{"location":"api/linear-model/base/GLM/#attributes","title":"Attributes","text":"<ul> <li>weights</li> </ul>"},{"location":"api/linear-model/base/GLM/#methods","title":"Methods","text":"learn_many learn_one"},{"location":"api/metrics/Accuracy/","title":"Accuracy","text":"<p>Accuracy score, which is the percentage of exact matches.</p>"},{"location":"api/metrics/Accuracy/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Accuracy/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Accuracy/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, True, True, True]\ny_pred = [True, True, False, True, True]\n\nmetric = metrics.Accuracy()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>Accuracy: 60.00%\n</code></pre></p>"},{"location":"api/metrics/Accuracy/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/AdjustedMutualInfo/","title":"AdjustedMutualInfo","text":"<p>Adjusted Mutual Information between two clusterings.</p> <p>Adjusted Mutual Information (AMI) is an adjustment of the Mutual Information score that accounts for chance. It corrects the effect of agreement solely due to chance between clusterings, similar to the way the Adjusted Rand Index corrects the Rand Index. It is closely related to variation of information. The adjusted measure, however, is no longer metrical. </p> <p>For two clusterings \\(U\\) and \\(V\\), the Adjusted Mutual Information is calculated as: </p> \\[ AMI(U, V) = \\frac{MI(U, V) - E(MI(U, V))}{avg(H(U), H(V)) - E(MI(U, V))} \\] <p>This metric is independent of the permutation of the class or cluster label values; furthermore, it is also symmetric. This can be useful to measure the agreement of two label assignments strategies on the same dataset, regardless of the ground truth. </p> <p>However, due to the complexity of the Expected Mutual Info Score, the computation of this metric is an order of magnitude slower than most other metrics, in general.</p>"},{"location":"api/metrics/AdjustedMutualInfo/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>average_method</p> <p>Default \u2192 <code>arithmetic</code></p> <p>This parameter defines how to compute the normalizer in the denominator. Possible options include <code>min</code>, <code>max</code>, <code>arithmetic</code> and <code>geometric</code>.</p> </li> </ul>"},{"location":"api/metrics/AdjustedMutualInfo/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/AdjustedMutualInfo/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.AdjustedMutualInfo()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.0\n0.105891\n0.298792\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>AdjustedMutualInfo: 0.298792\n</code></pre></p>"},{"location":"api/metrics/AdjustedMutualInfo/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, March 17). Mutual information.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Mutual_information&amp;oldid=1012714929\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/AdjustedRand/","title":"AdjustedRand","text":"<p>Adjusted Rand Index.</p> <p>The Adjusted Rand Index is the corrected-for-chance version of the Rand Index <sup>1</sup> <sup>2</sup>. Such a correction for chance establishes a baseline by using the expected similarity of all pair-wise comparisions between clusterings specified by a random model. </p> <p>Traditionally, the Rand Index was corrected using the Permutation Model for Clustering. However, the premises of the permutation model are frequently violated; in many clustering scenarios, either the number of clusters or the size distribution of those clusters vary drastically. Variations of the adjusted Rand Index account for different models of random clusterings. </p> <p>Though the Rand Index may only yield a value between 0 and 1, the Adjusted Rand index can yield negative values if the index is less than the expected index.</p>"},{"location":"api/metrics/AdjustedRand/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/AdjustedRand/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/AdjustedRand/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0, 0, 1, 1, 1]\ny_pred = [0, 0, 1, 1, 2, 2]\n\nmetric = metrics.AdjustedRand()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.0\n0.09090909090909091\n0.24242424242424243\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>AdjustedRand: 0.242424\n</code></pre></p>"},{"location":"api/metrics/AdjustedRand/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, January 13). Rand index.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Rand_index&amp;oldid=1000098911\u00a0\u21a9</p> </li> <li> <p>W. M. Rand (1971). \"Objective criteria for the evaluation of clustering methods\".   Journal of the American Statistical Association. American Statistical Association.   66 (336): 846\u2013850. arXiv:1704.01036. doi:10.2307/2284239. JSTOR 2284239.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/BalancedAccuracy/","title":"BalancedAccuracy","text":"<p>Balanced accuracy.</p> <p>Balanced accuracy is the average of recall obtained on each class. It is used to deal with imbalanced datasets in binary and multi-class classification problems.</p>"},{"location":"api/metrics/BalancedAccuracy/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/BalancedAccuracy/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/BalancedAccuracy/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\ny_true = [True, False, True, True, False, True]\ny_pred = [True, False, True, True, True, False]\n\nmetric = metrics.BalancedAccuracy()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>BalancedAccuracy: 62.50%\n</code></pre></p> <p><pre><code>y_true = [0, 1, 0, 0, 1, 0]\ny_pred = [0, 1, 0, 0, 0, 1]\nmetric = metrics.BalancedAccuracy()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>BalancedAccuracy: 62.50%\n</code></pre></p>"},{"location":"api/metrics/BalancedAccuracy/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/ClassificationReport/","title":"ClassificationReport","text":"<p>A report for monitoring a classifier.</p> <p>This class maintains a set of metrics and updates each of them every time <code>update</code> is called. You can print this class at any time during a model's lifetime to get a tabular visualization of various metrics. </p> <p>You can wrap a <code>metrics.ClassificationReport</code> with <code>utils.Rolling</code> in order to obtain a classification report over a window of observations. You can also wrap it with <code>utils.TimeRolling</code> to obtain a report over a period of time.</p>"},{"location":"api/metrics/ClassificationReport/#parameters","title":"Parameters","text":"<ul> <li> <p>decimals</p> <p>Default \u2192 <code>2</code></p> <p>The number of decimals to display in each cell.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/ClassificationReport/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/ClassificationReport/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['pear', 'apple', 'banana', 'banana', 'banana']\ny_pred = ['apple', 'pear', 'banana', 'banana', 'apple']\n\nreport = metrics.ClassificationReport()\n\nfor yt, yp in zip(y_true, y_pred):\n    report.update(yt, yp)\n\nprint(report)\n</code></pre> <pre><code>               Precision   Recall   F1       Support\n&lt;BLANKLINE&gt;\n   apple       0.00%    0.00%    0.00%         1\n  banana     100.00%   66.67%   80.00%         3\n    pear       0.00%    0.00%    0.00%         1\n&lt;BLANKLINE&gt;\n   Macro      33.33%   22.22%   26.67%\n   Micro      40.00%   40.00%   40.00%\nWeighted      60.00%   40.00%   48.00%\n&lt;BLANKLINE&gt;\n                 40.00% accuracy\n</code></pre></p>"},{"location":"api/metrics/ClassificationReport/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/CohenKappa/","title":"CohenKappa","text":"<p>Cohen's Kappa score.</p> <p>Cohen's Kappa expresses the level of agreement between two annotators on a classification problem. It is defined as </p> \\[ \\kappa = (p_o - p_e) / (1 - p_e) \\] <p>where \\(p_o\\) is the empirical probability of agreement on the label assigned to any sample (prequential accuracy), and \\(p_e\\) is the expected agreement when both annotators assign labels randomly.</p>"},{"location":"api/metrics/CohenKappa/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/CohenKappa/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/CohenKappa/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']\ny_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']\n\nmetric = metrics.CohenKappa()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>CohenKappa: 42.86%\n</code></pre></p>"},{"location":"api/metrics/CohenKappa/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>J. Cohen (1960). \"A coefficient of agreement for nominal scales\". Educational and Psychological Measurement 20(1):37-46. doi:10.1177/001316446002000104.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Completeness/","title":"Completeness","text":"<p>Completeness Score.</p> <p>Completeness <sup>1</sup> is symmetrical to homogeneity. In order to satisfy the completeness criteria, a clustering must assign all of those datapoints that are members of a single class to a single cluster. To evaluate completeness, we examine the distribution cluster assignments within each class. In a perfectly complete clustering solution, each of these distributions will be completely skewed to a single cluster. </p> <p>We can evaluate this degree of skew by calculating the conditional entropy of the proposed cluster distribution given the class of the component data points. However, in the worst case scenario, each class is represented by every cluster with a distribution equal to the distribution of cluster sizes. Therefore, symmetric to the claculation above, we define completeness as: </p> \\[ c = \\begin{cases} 1 if H(K) = 0, \\\\ 1 - \\frac{H(K|C)}{H(K)} otherwise. \\end{cases}. \\]"},{"location":"api/metrics/Completeness/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Completeness/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Completeness/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.Completeness()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n1.0\n0.3836885465963443\n0.5880325916843805\n0.6666666666666667\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>Completeness: 66.67%\n</code></pre></p>"},{"location":"api/metrics/Completeness/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Andrew Rosenberg and Julia Hirschberg (2007).   V-Measure: A conditional entropy-based external cluster evaluation measure.   Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language   Processing and Computational Natural Language Learning, pp. 410 - 420,   Prague, June 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/ConfusionMatrix/","title":"ConfusionMatrix","text":"<p>Confusion Matrix for binary and multi-class classification.</p>"},{"location":"api/metrics/ConfusionMatrix/#parameters","title":"Parameters","text":"<ul> <li> <p>classes</p> <p>Default \u2192 <code>None</code></p> <p>The initial set of classes. This is optional and serves only for displaying purposes.</p> </li> </ul>"},{"location":"api/metrics/ConfusionMatrix/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>classes</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>total_false_negatives</p> </li> <li> <p>total_false_positives</p> </li> <li> <p>total_true_negatives</p> </li> <li> <p>total_true_positives</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/ConfusionMatrix/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']\ny_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']\n\ncm = metrics.ConfusionMatrix()\n\nfor yt, yp in zip(y_true, y_pred):\n    cm.update(yt, yp)\n\ncm\n</code></pre> <pre><code>       ant  bird   cat\n ant     2     0     0\nbird     0     0     1\n cat     1     0     2\n</code></pre></p> <p><pre><code>cm['bird']['cat']\n</code></pre> <pre><code>1.0\n</code></pre></p>"},{"location":"api/metrics/ConfusionMatrix/#methods","title":"Methods","text":"false_negatives false_positives get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> support true_negatives true_positives update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/ConfusionMatrix/#notes","title":"Notes","text":"<p>This confusion matrix is a 2D matrix of shape <code>(n_classes, n_classes)</code>, corresponding to a single-target (binary and multi-class) classification task.</p> <p>Each row represents <code>true</code> (actual) class-labels, while each column corresponds to the <code>predicted</code> class-labels. For example, an entry in position <code>[1, 2]</code> means that the true class-label is 1, and the predicted class-label is 2 (incorrect prediction).</p> <p>This structure is used to keep updated statistics about a single-output classifier's performance and to compute multiple evaluation metrics.</p>"},{"location":"api/metrics/CrossEntropy/","title":"CrossEntropy","text":"<p>Multiclass generalization of the logarithmic loss.</p>"},{"location":"api/metrics/CrossEntropy/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/CrossEntropy/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2]\ny_pred = [\n    {0: 0.29450637, 1: 0.34216758, 2: 0.36332605},\n    {0: 0.21290077, 1: 0.32728332, 2: 0.45981591},\n    {0: 0.42860913, 1: 0.33380113, 2: 0.23758974},\n    {0: 0.44941979, 1: 0.32962558, 2: 0.22095463}\n]\n\nmetric = metrics.CrossEntropy()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.222454\n1.169691\n1.258864\n1.321597\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>CrossEntropy: 1.321598\n</code></pre></p>"},{"location":"api/metrics/CrossEntropy/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/F1/","title":"F1","text":"<p>Binary F1 score.</p>"},{"location":"api/metrics/F1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/F1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/F1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [False, False, False, True, True, True]\ny_pred = [False, False, True, True, False, False]\n\nmetric = metrics.F1()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>F1: 40.00%\n</code></pre></p>"},{"location":"api/metrics/F1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/FBeta/","title":"FBeta","text":"<p>Binary F-Beta score.</p> <p>The FBeta score is a weighted harmonic mean between precision and recall. The higher the <code>beta</code> value, the higher the recall will be taken into account. When <code>beta</code> equals 1, precision and recall and equivalently weighted, which results in the F1 score (see <code>metrics.F1</code>).</p>"},{"location":"api/metrics/FBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Weight of precision in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/FBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>precision (metrics.Precision)</p> </li> <li> <p>recall (metrics.Recall)</p> </li> </ul>"},{"location":"api/metrics/FBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [False, False, False, True, True, True]\ny_pred = [False, False, True, True, False, False]\n\nmetric = metrics.FBeta(beta=2)\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>FBeta: 35.71%\n</code></pre></p>"},{"location":"api/metrics/FBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/FowlkesMallows/","title":"FowlkesMallows","text":"<p>Fowlkes-Mallows Index.</p> <p>The Fowlkes-Mallows Index <sup>1</sup> <sup>2</sup> is an external evaluation method that is used to determine the similarity between two clusterings, and also a metric to measure confusion matrices. The measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher value for the Fowlkes-Mallows index indicates a greater similarity between the clusters and the benchmark classifications. </p> <p>The Fowlkes-Mallows Index, for two cluster algorithms, is defined as: </p> \\[ FM = \\sqrt{PPV \\times TPR} = \\sqrt{\\frac{TP}{TP+FP} \\times \\frac{TP}{TP+FN}} \\] <p>where </p> <ul> <li> <p>TP, FP, FN are respectively the number of true positives, false positives and false negatives; </p> </li> <li> <p>TPR is the True Positive Rate (or Sensitivity/Recall), PPV is the Positive Predictive Rate (or Precision).</p> </li> </ul>"},{"location":"api/metrics/FowlkesMallows/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/FowlkesMallows/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/FowlkesMallows/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0, 0, 1, 1, 1]\ny_pred = [0, 0, 1, 1, 2, 2]\n\nmetric = metrics.FowlkesMallows()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>FowlkesMallows: 0.00%\nFowlkesMallows: 100.00%\nFowlkesMallows: 57.74%\nFowlkesMallows: 40.82%\nFowlkesMallows: 35.36%\nFowlkesMallows: 47.14%\n</code></pre></p>"},{"location":"api/metrics/FowlkesMallows/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2020, December 22).   Fowlkes\u2013Mallows index. In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Fowlkes%E2%80%93Mallows_index&amp;oldid=995714222\u00a0\u21a9</p> </li> <li> <p>E. B. Fowkles and C. L. Mallows (1983).   \u201cA method for comparing two hierarchical clusterings\u201d.   Journal of the American Statistical Association\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/GeometricMean/","title":"GeometricMean","text":"<p>Geometric mean score.</p> <p>The geometric mean is a good indicator of a classifier's performance in the presence of class imbalance because it is independent of the distribution of examples between classes. This implementation computes the geometric mean of class-wise sensitivity (recall). </p> \\[ gm = \\sqrt[n]{s_1\\cdot s_2\\cdot s_3\\cdot \\ldots\\cdot s_n} \\] <p>where \\(s_i\\) is the sensitivity (recall) of class \\(i\\) and \\(n\\) is the number of classes.</p>"},{"location":"api/metrics/GeometricMean/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/GeometricMean/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/GeometricMean/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird', 'bird']\ny_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat', 'bird']\n\nmetric = metrics.GeometricMean()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>GeometricMean: 69.34%\n</code></pre></p>"},{"location":"api/metrics/GeometricMean/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Barandela, R. et al. \u201cStrategies for learning in class imbalance problems\u201d, Pattern Recognition, 36(3), (2003), pp 849-851.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Homogeneity/","title":"Homogeneity","text":"<p>Homogeneity Score.</p> <p>Homogeneity metric <sup>1</sup> of a cluster labeling given a ground truth. </p> <p>In order to satisfy the homogeneity criteria, a clustering must assign only those data points that are members of a single class to a single cluster. That is, the class distribution within each cluster should be skewed to a single class, that is, zero entropy. We determine how close a given clustering is to this ideal by examining the conditional entropy of the class distribution given the proposed clustering. </p> <p>However, in an imperfect situation, the size of this value is dependent on the size of the dataset and the distribution of class sizes. Therefore, instead of taking the raw conditional entropy, we normalize by the maximum reduction in entropy the clustering information could provide. </p> <p>As such, we define homogeneity as: </p> \\[ h = \\begin{cases} 1 if H(C) = 0, \\\\ 1 - \\frac{H(C|K)}{H(C)} otherwise. \\end{cases}. \\]"},{"location":"api/metrics/Homogeneity/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Homogeneity/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Homogeneity/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.Homogeneity()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.311278\n0.37515\n0.42062\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>Homogeneity: 42.06%\n</code></pre></p>"},{"location":"api/metrics/Homogeneity/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Andrew Rosenberg and Julia Hirschberg (2007).   V-Measure: A conditional entropy-based external cluster evaluation measure.   Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language   Processing and Computational Natural Language Learning, pp. 410 - 420,   Prague, June 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Jaccard/","title":"Jaccard","text":"<p>Jaccard score.</p>"},{"location":"api/metrics/Jaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/Jaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Jaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [False, True, True]\ny_pred = [True, True, True]\n\nmetric = metrics.Jaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>Jaccard: 0.00%\nJaccard: 50.00%\nJaccard: 66.67%\n</code></pre></p>"},{"location":"api/metrics/Jaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Jaccard index \u21a9</p> </li> </ol>"},{"location":"api/metrics/LogLoss/","title":"LogLoss","text":"<p>Binary logarithmic loss.</p>"},{"location":"api/metrics/LogLoss/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/LogLoss/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, False, True]\ny_pred = [0.9,  0.1,   0.2,   0.65]\n\nmetric = metrics.LogLoss()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.105360\n0.105360\n0.144621\n0.216161\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>LogLoss: 0.216162\n</code></pre></p>"},{"location":"api/metrics/LogLoss/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MAE/","title":"MAE","text":"<p>Mean absolute error.</p>"},{"location":"api/metrics/MAE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MAE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.MAE()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.5\n0.5\n0.333\n0.5\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>MAE: 0.5\n</code></pre></p>"},{"location":"api/metrics/MAE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MAPE/","title":"MAPE","text":"<p>Mean absolute percentage error.</p>"},{"location":"api/metrics/MAPE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MAPE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.MAPE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>MAPE: 32.738095\n</code></pre></p>"},{"location":"api/metrics/MAPE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MCC/","title":"MCC","text":"<p>Matthews correlation coefficient.</p>"},{"location":"api/metrics/MCC/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/MCC/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MCC/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, True, True, False]\ny_pred = [True, False, True, True]\n\nmcc = metrics.MCC()\n\nfor yt, yp in zip(y_true, y_pred):\n    mcc.update(yt, yp)\n\nmcc\n</code></pre> <pre><code>MCC: -0.333333\n</code></pre></p>"},{"location":"api/metrics/MCC/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia article \u21a9</p> </li> </ol>"},{"location":"api/metrics/MSE/","title":"MSE","text":"<p>Mean squared error.</p>"},{"location":"api/metrics/MSE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MSE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.MSE()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.25\n0.25\n0.1666\n0.375\n</code></pre></p>"},{"location":"api/metrics/MSE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/MacroF1/","title":"MacroF1","text":"<p>Macro-average F1 score.</p> <p>This works by computing the F1 score per class, and then performs a global average.</p>"},{"location":"api/metrics/MacroF1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroF1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroF1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroF1()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroF1: 100.00%\nMacroF1: 33.33%\nMacroF1: 55.56%\nMacroF1: 55.56%\nMacroF1: 48.89%\n</code></pre></p>"},{"location":"api/metrics/MacroF1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroFBeta/","title":"MacroFBeta","text":"<p>Macro-average F-Beta score.</p> <p>This works by computing the F-Beta score per class, and then performs a global average.</p>"},{"location":"api/metrics/MacroFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Weight of precision in harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroFBeta(beta=.8)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroFBeta: 100.00%\nMacroFBeta: 31.06%\nMacroFBeta: 54.04%\nMacroFBeta: 54.04%\nMacroFBeta: 48.60%\n</code></pre></p>"},{"location":"api/metrics/MacroFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroJaccard/","title":"MacroJaccard","text":"<p>Macro-average Jaccard score.</p>"},{"location":"api/metrics/MacroJaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroJaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroJaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroJaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroJaccard: 100.00%\nMacroJaccard: 25.00%\nMacroJaccard: 50.00%\nMacroJaccard: 50.00%\nMacroJaccard: 38.89%\n</code></pre></p>"},{"location":"api/metrics/MacroJaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroPrecision/","title":"MacroPrecision","text":"<p>Macro-average precision score.</p>"},{"location":"api/metrics/MacroPrecision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroPrecision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroPrecision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroPrecision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroPrecision: 100.00%\nMacroPrecision: 25.00%\nMacroPrecision: 50.00%\nMacroPrecision: 50.00%\nMacroPrecision: 50.00%\n</code></pre></p>"},{"location":"api/metrics/MacroPrecision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MacroRecall/","title":"MacroRecall","text":"<p>Macro-average recall score.</p>"},{"location":"api/metrics/MacroRecall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MacroRecall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MacroRecall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MacroRecall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MacroRecall: 100.00%\nMacroRecall: 50.00%\nMacroRecall: 66.67%\nMacroRecall: 66.67%\nMacroRecall: 55.56%\n</code></pre></p>"},{"location":"api/metrics/MacroRecall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MicroF1/","title":"MicroF1","text":"<p>Micro-average F1 score.</p> <p>This computes the F1 score by merging all the predictions and true labels, and then computes a global F1 score.</p>"},{"location":"api/metrics/MicroF1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroF1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroF1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 0]\ny_pred = [0, 1, 1, 2, 1]\n\nmetric = metrics.MicroF1()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>MicroF1: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroF1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem? \u21a9</p> </li> </ol>"},{"location":"api/metrics/MicroFBeta/","title":"MicroFBeta","text":"<p>Micro-average F-Beta score.</p> <p>This computes the F-Beta score by merging all the predictions and true labels, and then computes a global F-Beta score.</p>"},{"location":"api/metrics/MicroFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Weight of precision in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 0]\ny_pred = [0, 1, 1, 2, 1]\n\nmetric = metrics.MicroFBeta(beta=2)\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>MicroFBeta: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p> 1. Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem?</p>"},{"location":"api/metrics/MicroJaccard/","title":"MicroJaccard","text":"<p>Micro-average Jaccard score.</p>"},{"location":"api/metrics/MicroJaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroJaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroJaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MicroJaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MicroJaccard: 100.00%\nMicroJaccard: 33.33%\nMicroJaccard: 50.00%\nMicroJaccard: 60.00%\nMicroJaccard: 42.86%\n</code></pre></p>"},{"location":"api/metrics/MicroJaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MicroPrecision/","title":"MicroPrecision","text":"<p>Micro-average precision score.</p> <p>The micro-average precision score is exactly equivalent to the micro-average recall as well as the micro-average F1 score.</p>"},{"location":"api/metrics/MicroPrecision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroPrecision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroPrecision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MicroPrecision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MicroPrecision: 100.00%\nMicroPrecision: 50.00%\nMicroPrecision: 66.67%\nMicroPrecision: 75.00%\nMicroPrecision: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroPrecision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem? \u21a9</p> </li> </ol>"},{"location":"api/metrics/MicroRecall/","title":"MicroRecall","text":"<p>Micro-average recall score.</p> <p>The micro-average recall is exactly equivalent to the micro-average precision as well as the micro-average F1 score.</p>"},{"location":"api/metrics/MicroRecall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MicroRecall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MicroRecall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MicroRecall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MicroRecall: 100.00%\nMicroRecall: 50.00%\nMicroRecall: 66.67%\nMicroRecall: 75.00%\nMicroRecall: 60.00%\n</code></pre></p>"},{"location":"api/metrics/MicroRecall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem? \u21a9</p> </li> </ol>"},{"location":"api/metrics/MultiFBeta/","title":"MultiFBeta","text":"<p>Multi-class F-Beta score with different betas per class.</p> <p>The multiclass F-Beta score is the arithmetic average of the binary F-Beta scores of each class. The mean can be weighted by providing class weights.</p>"},{"location":"api/metrics/MultiFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>betas</p> <p>Weight of precision in the harmonic mean of each class.</p> </li> <li> <p>weights</p> <p>Class weights. If not provided then uniform weights will be used.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MultiFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MultiFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.MultiFBeta(\n    betas={0: 0.25, 1: 1, 2: 4},\n    weights={0: 1, 1: 1, 2: 2}\n)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>MultiFBeta: 100.00%\nMultiFBeta: 25.76%\nMultiFBeta: 62.88%\nMultiFBeta: 62.88%\nMultiFBeta: 46.88%\n</code></pre></p>"},{"location":"api/metrics/MultiFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/MutualInfo/","title":"MutualInfo","text":"<p>Mutual Information between two clusterings.</p> <p>The Mutual Information <sup>1</sup> is a measure of the similarity between two labels of the same data. Where \\(|U_i|\\) is the number of samples in cluster \\(U_i\\) and \\(|V_j|\\) is the number of the samples in cluster \\(V_j\\), the Mutual Information between clusterings \\(U\\) and \\(V\\) can be calculated as: </p> \\[ MI(U,V) = \\sum_{i=1}^{|U|} \\sum_{v=1}^{|V|} \\frac{|U_i \\cup V_j|}{N} \\log \\frac{N |U_i \\cup V_j|}{|U_i| |V_j|} \\] <p>This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won't change the score. </p> <p>This metric is furthermore symmetric: switching <code>y_true</code> and <code>y_pred</code> will return the same score value. This can be useful to measure the agreement of two independent label assignments strategies on the same dataset when the real ground truth is not known. </p> <p>The Mutual Information can be equivalently expressed as: </p> \\[ MI(U,V) = H(U) - H(U | V) = H(V) - H(V | U) \\] <p>where \\(H(U)\\) and \\(H(V)\\) are the marginal entropies, \\(H(U | V)\\) and \\(H(V | U)\\) are the conditional entropies.</p>"},{"location":"api/metrics/MutualInfo/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/MutualInfo/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/MutualInfo/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.MutualInfo()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.0\n0.0\n0.0\n0.215761\n0.395752\n0.462098\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>MutualInfo: 0.462098\n</code></pre></p>"},{"location":"api/metrics/MutualInfo/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, March 17). Mutual information.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Mutual_information&amp;oldid=1012714929\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/NormalizedMutualInfo/","title":"NormalizedMutualInfo","text":"<p>Normalized Mutual Information between two clusterings.</p> <p>Normalized Mutual Information (NMI) is a normalized version of the Mutual Information (MI) score to scale the results between the range of 0 (no mutual information) and 1 (perfectly mutual information). In the formula, the mutual information will be normalized by a generalized mean of the entropy of true and predicted labels, defined by the <code>average_method</code>. </p> <p>We note that this measure is not adjusted for chance (i.e corrected the effect of result agreement solely due to chance); as a result, the Adjusted Mutual Info Score will mostly be preferred. However, this metric is still symmetric, which means that switching true and predicted labels will not alter the score value. This fact can be useful when the metric is used to measure the agreement between two indepedent label solutions on the same dataset, when the ground truth remains unknown. </p> <p>Another advantage of the metric is that as it is based on the calculation of entropy-related measures, it is independent of the permutation of class/cluster labels.</p>"},{"location":"api/metrics/NormalizedMutualInfo/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>average_method</p> <p>Default \u2192 <code>arithmetic</code></p> <p>This parameter defines how to compute the normalizer in the denominator. Possible options include <code>min</code>, <code>max</code>, <code>arithmetic</code> and <code>geometric</code>.</p> </li> </ul>"},{"location":"api/metrics/NormalizedMutualInfo/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/NormalizedMutualInfo/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.NormalizedMutualInfo()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.343711\n0.458065\n0.515803\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>NormalizedMutualInfo: 0.515804\n</code></pre></p>"},{"location":"api/metrics/NormalizedMutualInfo/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, March 17). Mutual information.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Mutual_information&amp;oldid=1012714929\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Precision/","title":"Precision","text":"<p>Binary precision score.</p>"},{"location":"api/metrics/Precision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/Precision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Precision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, True, True, True]\ny_pred = [True, True, False, True, True]\n\nmetric = metrics.Precision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>Precision: 100.00%\nPrecision: 50.00%\nPrecision: 50.00%\nPrecision: 66.67%\nPrecision: 75.00%\n</code></pre></p>"},{"location":"api/metrics/Precision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/R2/","title":"R2","text":"<p>Coefficient of determination (\\(R^2\\)) score</p> <p>The coefficient of determination, denoted \\(R^2\\) or \\(r^2\\), is the proportion of the variance in the dependent variable that is predictable from the independent variable(s). <sup>1</sup> </p> <p>Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of \\(y\\), disregarding the input features, would get a \\(R^2\\) score of 0.0. </p> <p>\\(R^2\\) is not defined when less than 2 samples have been observed. This implementation returns 0.0 in this case.</p>"},{"location":"api/metrics/R2/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/R2/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.R2()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.0\n0.9183\n0.9230\n0.9486\n</code></pre></p>"},{"location":"api/metrics/R2/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Coefficient of determination (Wikipedia) \u21a9</p> </li> </ol>"},{"location":"api/metrics/RMSE/","title":"RMSE","text":"<p>Root mean squared error.</p>"},{"location":"api/metrics/RMSE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/RMSE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.RMSE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>0.5\n0.5\n0.408248\n0.612372\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>RMSE: 0.612372\n</code></pre></p>"},{"location":"api/metrics/RMSE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/RMSLE/","title":"RMSLE","text":"<p>Root mean squared logarithmic error.</p>"},{"location":"api/metrics/RMSLE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/RMSLE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [3, -0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, 8]\n\nmetric = metrics.RMSLE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>RMSLE: 0.357826\n</code></pre></p>"},{"location":"api/metrics/RMSLE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/ROCAUC/","title":"ROCAUC","text":"<p>Receiving Operating Characteristic Area Under the Curve.</p> <p>This metric is an approximation of the true ROC AUC. Computing the true ROC AUC would require storing all the predictions and ground truths, which isn't desirable. The approximation error is not significant as long as the predicted probabilities are well calibrated. In any case, this metric can still be used to reliably compare models between each other.</p>"},{"location":"api/metrics/ROCAUC/#parameters","title":"Parameters","text":"<ul> <li> <p>n_thresholds</p> <p>Default \u2192 <code>10</code></p> <p>The number of thresholds used for discretizing the ROC curve. A higher value will lead to more accurate results, but will also cost more time and memory.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/ROCAUC/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/ROCAUC/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [ 0,  0,   1,  1]\ny_pred = [.1, .4, .35, .8]\n\nmetric = metrics.ROCAUC()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 87.50%\n</code></pre></p> <p>The true ROC AUC is in fact 0.75. We can improve the accuracy by increasing the amount of thresholds. This comes at the cost more computation time and more memory usage.</p> <p><pre><code>metric = metrics.ROCAUC(n_thresholds=20)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 75.00%\n</code></pre></p>"},{"location":"api/metrics/ROCAUC/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/Rand/","title":"Rand","text":"<p>Rand Index.</p> <p>The Rand Index <sup>1</sup> <sup>2</sup> is a measure of the similarity between two data clusterings. Given a set of elements <code>S</code> and two partitions of <code>S</code> to compare, <code>X</code> and <code>Y</code>, define the following: </p> <ul> <li> <p>a, the number of pairs of elements in <code>S</code> that are in the same subset in <code>X</code> and in the same subset in <code>Y</code> </p> </li> <li> <p>b, the number of pairs of elements in <code>S</code> that are in the different subset in <code>X</code> and in different subsets in <code>Y</code> </p> </li> <li> <p>c, the number of pairs of elements in <code>S</code> that are in the same subset in <code>X</code> and in different subsets in <code>Y</code> </p> </li> <li> <p>d, the number of pairs of elements in <code>S</code> that are in the different subset in <code>X</code> and in the same subset in <code>Y</code> </p> </li> </ul> <p>The Rand index, R, is </p> \\[ R = \frac{a+b}{a+b+c+d} = \frac{a+b}{\frac{n(n-1)}{2}}. \\]"},{"location":"api/metrics/Rand/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/Rand/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Rand/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0, 0, 1, 1, 1]\ny_pred = [0, 0, 1, 1, 2, 2]\n\nmetric = metrics.Rand()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>Rand: 0.666667\n</code></pre></p>"},{"location":"api/metrics/Rand/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Wikipedia contributors. (2021, January 13). Rand index.   In Wikipedia, The Free Encyclopedia,   from https://en.wikipedia.org/w/index.php?title=Rand_index&amp;oldid=1000098911\u00a0\u21a9</p> </li> <li> <p>W. M. Rand (1971). \"Objective criteria for the evaluation of clustering methods\".   Journal of the American Statistical Association. American Statistical Association.   66 (336): 846\u2013850. arXiv:1704.01036. doi:10.2307/2284239. JSTOR 2284239.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/Recall/","title":"Recall","text":"<p>Binary recall score.</p>"},{"location":"api/metrics/Recall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/Recall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/Recall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [True, False, True, True, True]\ny_pred = [True, True, False, True, True]\n\nmetric = metrics.Recall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>Recall: 100.00%\nRecall: 100.00%\nRecall: 50.00%\nRecall: 66.67%\nRecall: 75.00%\n</code></pre></p>"},{"location":"api/metrics/Recall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/RollingROCAUC/","title":"RollingROCAUC","text":"<p>Rolling version of the Receiving Operating Characteristic Area Under the Curve.</p> <p>The RollingROCAUC calculates the metric using the instances in its window of size S. It keeps a queue of the instances, when an instance is added and the queue length is equal to S, the last instance is removed. The metric has a tree with ordered instances, in order to calculate the AUC efficiently. It was implemented based on the algorithm presented in Brzezinski and Stefanowski, 2017. </p> <p>The difference between this metric and the standard ROCAUC is that the latter calculates an approximation of the real metric considering all data from the beginning of the stream, while the RollingROCAUC calculates the exact value considering only the last S instances. This approach may be beneficial if it's necessary to evaluate the model's performance over time, since calculating the metric using the entire stream may hide the current performance of the classifier.</p>"},{"location":"api/metrics/RollingROCAUC/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Default \u2192 <code>1000</code></p> <p>The max length of the window.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/RollingROCAUC/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/RollingROCAUC/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [ 0,  1,  0,  1,  0,  1,  0,  0,   1,  1]\ny_pred = [.3, .5, .5, .7, .1, .3, .1, .4, .35, .8]\n\nmetric = metrics.RollingROCAUC(window_size=4)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>RollingROCAUC: 75.00%\n</code></pre></p>"},{"location":"api/metrics/RollingROCAUC/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/SMAPE/","title":"SMAPE","text":"<p>Symmetric mean absolute percentage error.</p>"},{"location":"api/metrics/SMAPE/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/SMAPE/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 0.07533, 0.07533, 0.07533, 0.07533, 0.07533, 0.07533, 0.0672, 0.0672]\ny_pred = [0, 0.102, 0.107, 0.047, 0.1, 0.032, 0.047, 0.108, 0.089]\n\nmetric = metrics.SMAPE()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>SMAPE: 37.869392\n</code></pre></p>"},{"location":"api/metrics/SMAPE/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/Silhouette/","title":"Silhouette","text":"<p>Silhouette coefficient <sup>1</sup>, roughly speaking, is the ratio between cohesion and the average distances from the points to their second-closest centroid. It rewards the clustering algorithm where points are very close to their assigned centroids and far from any other centroids, that is, clustering results with good cohesion and good separation.</p> <p>It rewards clusterings where points are very close to their assigned centroids and far from any other centroids, that is clusterings with good cohesion and good separation. <sup>2</sup> </p> <p>The definition of Silhouette coefficient for online clustering evaluation is different from that of batch learning. It does not store information and calculate pairwise distances between all points at the same time, since the practice is too expensive for an incremental metric.</p>"},{"location":"api/metrics/Silhouette/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicates if a high value is better than a low one or not.</p> </li> </ul>"},{"location":"api/metrics/Silhouette/#examples","title":"Examples","text":"<p><pre><code>from river import cluster\nfrom river import stream\nfrom river import metrics\n\nX = [\n    [1, 2],\n    [1, 4],\n    [1, 0],\n    [4, 2],\n    [4, 4],\n    [4, 0],\n    [-2, 2],\n    [-2, 4],\n    [-2, 0]\n]\n\nk_means = cluster.KMeans(n_clusters=3, halflife=0.4, sigma=3, seed=0)\nmetric = metrics.Silhouette()\n\nfor x, _ in stream.iter_array(X):\n    k_means.learn_one(x)\n    y_pred = k_means.predict_one(x)\n    metric.update(x, y_pred, k_means.centers)\n\nmetric\n</code></pre> <pre><code>Silhouette: 0.32145\n</code></pre></p>"},{"location":"api/metrics/Silhouette/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>x </li> <li>y_pred </li> <li>centers </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>x </li> <li>y_pred </li> <li>centers </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Rousseeuw, P. (1987). Silhouettes: a graphical aid to the intepretation and validation   of cluster analysis 20, 53 - 65. DOI: 10.1016/0377-0427(87)90125-7\u00a0\u21a9</p> </li> <li> <p>Bifet, A. et al. (2018). \"Machine Learning for Data Streams\".   DOI: 10.7551/mitpress/10654.001.0001.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/VBeta/","title":"VBeta","text":"<p>VBeta.</p> <p>VBeta (or V-Measure) <sup>1</sup> is an external entropy-based cluster evaluation measure. It provides an elegant solution to many problems that affect previously defined cluster evaluation measures including </p> <ul> <li> <p>Dependance of clustering algorithm or dataset, </p> </li> <li> <p>The \"problem of matching\", where the clustering of only a portion of data points are evaluated, and </p> </li> <li> <p>Accurate evaluation and combination of two desirable aspects of clustering, homogeneity and completeness. </p> </li> </ul> <p>Based upon the calculations of homogeneity and completeness, a clustering solution's V-measure is calculated by computing the weighted harmonic mean of homogeneity and completeness, </p> \\[ V_{\\beta} = \\frac{(1 + \\beta) \\times h \\times c}{\\beta \\times h + c}. \\]"},{"location":"api/metrics/VBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Weight of Homogeneity in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/VBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/VBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [1, 1, 2, 2, 3, 3]\ny_pred = [1, 1, 1, 2, 2, 2]\n\nmetric = metrics.VBeta(beta=1.0)\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric.get())\n</code></pre> <pre><code>1.0\n1.0\n0.0\n0.3437110184854507\n0.4580652856440158\n0.5158037429793888\n</code></pre></p> <p><pre><code>metric\n</code></pre> <pre><code>VBeta: 51.58%\n</code></pre></p>"},{"location":"api/metrics/VBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p> <ol> <li> <p>Andrew Rosenberg and Julia Hirschberg (2007).   V-Measure: A conditional entropy-based external cluster evaluation measure.   Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language   Processing and Computational Natural Language Learning, pp. 410 - 420,   Prague, June 2007.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/metrics/WeightedF1/","title":"WeightedF1","text":"<p>Weighted-average F1 score.</p> <p>This works by computing the F1 score per class, and then performs a global weighted average by using the support of each class.</p>"},{"location":"api/metrics/WeightedF1/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedF1/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedF1/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedF1()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedF1: 100.00%\nWeightedF1: 33.33%\nWeightedF1: 55.56%\nWeightedF1: 66.67%\nWeightedF1: 61.33%\n</code></pre></p>"},{"location":"api/metrics/WeightedF1/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedFBeta/","title":"WeightedFBeta","text":"<p>Weighted-average F-Beta score.</p> <p>This works by computing the F-Beta score per class, and then performs a global weighted average according to the support of each class.</p>"},{"location":"api/metrics/WeightedFBeta/#parameters","title":"Parameters","text":"<ul> <li> <p>beta</p> <p>Weight of precision in the harmonic mean.</p> </li> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedFBeta/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedFBeta/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedFBeta(beta=0.8)\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedFBeta: 100.00%\nWeightedFBeta: 31.06%\nWeightedFBeta: 54.04%\nWeightedFBeta: 65.53%\nWeightedFBeta: 62.63%\n</code></pre></p>"},{"location":"api/metrics/WeightedFBeta/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedJaccard/","title":"WeightedJaccard","text":"<p>Weighted average Jaccard score.</p>"},{"location":"api/metrics/WeightedJaccard/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedJaccard/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedJaccard/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedJaccard()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedJaccard: 100.00%\nWeightedJaccard: 25.00%\nWeightedJaccard: 50.00%\nWeightedJaccard: 62.50%\nWeightedJaccard: 50.00%\n</code></pre></p>"},{"location":"api/metrics/WeightedJaccard/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedPrecision/","title":"WeightedPrecision","text":"<p>Weighted-average precision score.</p> <p>This uses the support of each label to compute an average score, whereas <code>metrics.MacroPrecision</code> ignores the support.</p>"},{"location":"api/metrics/WeightedPrecision/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedPrecision/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedPrecision/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedPrecision()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedPrecision: 100.00%\nWeightedPrecision: 25.00%\nWeightedPrecision: 50.00%\nWeightedPrecision: 62.50%\nWeightedPrecision: 70.00%\n</code></pre></p>"},{"location":"api/metrics/WeightedPrecision/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/WeightedRecall/","title":"WeightedRecall","text":"<p>Weighted-average recall score.</p> <p>This uses the support of each label to compute an average score, whereas <code>MacroRecall</code> ignores the support.</p>"},{"location":"api/metrics/WeightedRecall/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/WeightedRecall/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/WeightedRecall/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [0, 1, 2, 2, 2]\ny_pred = [0, 0, 2, 2, 1]\n\nmetric = metrics.WeightedRecall()\n\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n    print(metric)\n</code></pre> <pre><code>WeightedRecall: 100.00%\nWeightedRecall: 50.00%\nWeightedRecall: 66.67%\nWeightedRecall: 75.00%\nWeightedRecall: 60.00%\n</code></pre></p>"},{"location":"api/metrics/WeightedRecall/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/BinaryMetric/","title":"BinaryMetric","text":"<p>Mother class for all binary classification metrics.</p>"},{"location":"api/metrics/base/BinaryMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> <li> <p>pos_val</p> <p>Default \u2192 <code>True</code></p> <p>Value to treat as \"positive\".</p> </li> </ul>"},{"location":"api/metrics/base/BinaryMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/BinaryMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'bool' </li> <li>y_pred     \u2014 'bool | float | dict[bool, float]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/ClassificationMetric/","title":"ClassificationMetric","text":"<p>Mother class for all classification metrics.</p>"},{"location":"api/metrics/base/ClassificationMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/base/ClassificationMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/ClassificationMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/Metric/","title":"Metric","text":"<p>Mother class for all metrics.</p>"},{"location":"api/metrics/base/Metric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/Metric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/Metrics/","title":"Metrics","text":"<p>A container class for handling multiple metrics at once.</p>"},{"location":"api/metrics/base/Metrics/#parameters","title":"Parameters","text":"<ul> <li> <p>metrics</p> </li> <li> <p>str_sep</p> <p>Default \u2192 </p> </li> </ul>"},{"location":"api/metrics/base/Metrics/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/Metrics/#methods","title":"Methods","text":"is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/MultiClassMetric/","title":"MultiClassMetric","text":"<p>Mother class for all multi-class classification metrics.</p>"},{"location":"api/metrics/base/MultiClassMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/base/MultiClassMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> <p>Indicates if labels are required, rather than probabilities.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/MultiClassMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/RegressionMetric/","title":"RegressionMetric","text":"<p>Mother class for all regression metrics.</p>"},{"location":"api/metrics/base/RegressionMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/RegressionMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'numbers.Number' </li> <li>y_pred     \u2014 'numbers.Number' </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/base/WrapperMetric/","title":"WrapperMetric","text":""},{"location":"api/metrics/base/WrapperMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/base/WrapperMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/ExactMatch/","title":"ExactMatch","text":"<p>Exact match score.</p> <p>This is the most strict multi-label metric, defined as the number of samples that have all their labels correctly classified, divided by the total number of samples.</p>"},{"location":"api/metrics/multioutput/ExactMatch/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/ExactMatch/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [\n    {0: False, 1: True, 2: True},\n    {0: True, 1: True, 2: False},\n    {0: True, 1: True, 2: False},\n]\n\ny_pred = [\n    {0: True, 1: True, 2: True},\n    {0: True, 1: False, 2: False},\n    {0: True, 1: True, 2: False},\n]\n\nmetric = metrics.multioutput.ExactMatch()\nfor yt, yp in zip(y_true, y_pred):\n    metric.update(yt, yp)\n\nmetric\n</code></pre> <pre><code>ExactMatch: 33.33%\n</code></pre></p>"},{"location":"api/metrics/multioutput/ExactMatch/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/MacroAverage/","title":"MacroAverage","text":"<p>Macro-average wrapper.</p> <p>A copy of the provided metric is made for each output. The arithmetic average of all the metrics is returned.</p>"},{"location":"api/metrics/multioutput/MacroAverage/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/MacroAverage/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/MacroAverage/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/MicroAverage/","title":"MicroAverage","text":"<p>Micro-average wrapper.</p> <p>The provided metric is updated with the value of each output.</p>"},{"location":"api/metrics/multioutput/MicroAverage/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/MicroAverage/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/MicroAverage/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/MultiLabelConfusionMatrix/","title":"MultiLabelConfusionMatrix","text":"<p>Multi-label confusion matrix.</p> <p>Under the hood, this stores one <code>metrics.ConfusionMatrix</code> for each output.</p>"},{"location":"api/metrics/multioutput/MultiLabelConfusionMatrix/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ncm = metrics.multioutput.MultiLabelConfusionMatrix()\n\ny_true = [\n    {0: False, 1: True, 2: True},\n    {0: True, 1: True, 2: False}\n]\n\ny_pred = [\n    {0: True, 1: True, 2: True},\n    {0: True, 1: False, 2: False}\n]\n\nfor yt, yp in zip(y_true, y_pred):\n    cm.update(yt, yp)\n\ncm\n</code></pre> <pre><code>0\n            False   True\n    False       0      1\n     True       0      1\n&lt;BLANKLINE&gt;\n1\n            False   True\n    False       0      0\n     True       1      1\n&lt;BLANKLINE&gt;\n2\n            False   True\n    False       1      0\n     True       0      1\n</code></pre></p>"},{"location":"api/metrics/multioutput/MultiLabelConfusionMatrix/#methods","title":"Methods","text":"revert update"},{"location":"api/metrics/multioutput/PerOutput/","title":"PerOutput","text":"<p>Per-output wrapper.</p> <p>A copy of the metric is maintained for each output.</p>"},{"location":"api/metrics/multioutput/PerOutput/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/PerOutput/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/PerOutput/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/SampleAverage/","title":"SampleAverage","text":"<p>Sample-average wrapper.</p> <p>The provided metric is evaluate on each sample. The arithmetic average over all the samples is returned. This is equivalent to using <code>average='samples'</code> in scikit-learn.</p>"},{"location":"api/metrics/multioutput/SampleAverage/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>A classification or a regression metric.</p> </li> </ul>"},{"location":"api/metrics/multioutput/SampleAverage/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>metric</p> <p>Gives access to the wrapped metric.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/SampleAverage/#examples","title":"Examples","text":"<p><pre><code>from river import metrics\n\ny_true = [\n    {0: False, 1: True, 2: True},\n    {0: True, 1: True, 2: False}\n]\ny_pred = [\n    {0: True, 1: True, 2: True},\n    {0: True, 1: False, 2: False}\n]\n\nsample_jaccard = metrics.multioutput.SampleAverage(metrics.Jaccard())\n\nfor yt, yp in zip(y_true, y_pred):\n    sample_jaccard.update(yt, yp)\n\nsample_jaccard\n</code></pre> <pre><code>SampleAverage(Jaccard): 58.33%\n</code></pre></p>"},{"location":"api/metrics/multioutput/SampleAverage/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/","title":"MultiOutputClassificationMetric","text":"<p>Mother class for all multi-output classification metrics.</p>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>cm</p> <p>Type \u2192 MultiLabelConfusionMatrix | None</p> <p>Default \u2192 <code>None</code></p> <p>This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.</p> </li> </ul>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>requires_labels</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/base/MultiOutputClassificationMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, base.typing.ClfTarget]' </li> <li>y_pred     \u2014 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/metrics/multioutput/base/MultiOutputRegressionMetric/","title":"MultiOutputRegressionMetric","text":"<p>Mother class for all multi-output regression metrics.</p>"},{"location":"api/metrics/multioutput/base/MultiOutputRegressionMetric/#attributes","title":"Attributes","text":"<ul> <li> <p>bigger_is_better</p> <p>Indicate if a high value is better than a low one or not.</p> </li> <li> <p>works_with_weights</p> <p>Indicate whether the model takes into consideration the effect of sample weights</p> </li> </ul>"},{"location":"api/metrics/multioutput/base/MultiOutputRegressionMetric/#methods","title":"Methods","text":"get <p>Return the current value of the metric.</p> <p></p> is_better_than <p>Indicate if the current metric is better than another one.</p> <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> revert <p>Revert the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, float | int]' </li> <li>y_pred     \u2014 'dict[str | int, float | int]' </li> </ul> <p></p> update <p>Update the metric.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'dict[str | int, float | int]' </li> <li>y_pred     \u2014 'dict[str | int, float | int]' </li> </ul> <p></p> works_with <p>Indicates whether or not a metric can work with a given model.</p> <p>Parameters</p> <ul> <li>model     \u2014 'base.Estimator' </li> </ul> <p></p>"},{"location":"api/misc/SDFT/","title":"SDFT","text":"<p>Sliding Discrete Fourier Transform (SDFT).</p> <p>Initially, the coefficients are all equal to 0, up until enough values have been seen. A call to <code>numpy.fft.fft</code> is triggered once <code>window_size</code> values have been seen. Subsequent values will update the coefficients online. This is much faster than recomputing an FFT from scratch for every new value.</p>"},{"location":"api/misc/SDFT/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>The size of the window.</p> </li> </ul>"},{"location":"api/misc/SDFT/#attributes","title":"Attributes","text":"<ul> <li>window_size</li> </ul>"},{"location":"api/misc/SDFT/#examples","title":"Examples","text":"<pre><code>import numpy as np\nfrom river import misc\n\nX = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\nwindow_size = 5\nsdft = misc.SDFT(window_size)\n\nfor i, x in enumerate(X):\n    sdft.update(x)\n    if i + 1 &gt;= window_size:\n        assert np.allclose(sdft.coefficients, np.fft.fft(X[i+1 - window_size:i+1]))\n</code></pre>"},{"location":"api/misc/SDFT/#methods","title":"Methods","text":"update <ol> <li> <p>Jacobsen, E. asample_average.pynd Lyons, R., 2003. The sliding DFT. IEEE Signal Processing Magazine, 20(2), pp.74-80. \u21a9</p> </li> <li> <p>Understanding and Implementing the Sliding DFT \u21a9</p> </li> </ol>"},{"location":"api/misc/Skyline/","title":"Skyline","text":"<p>A skyline is set of points which is not dominated by any other point.</p> <p>This implementation uses a block nested loop. Identical observations are all part of the skyline if applicable.</p>"},{"location":"api/misc/Skyline/#parameters","title":"Parameters","text":"<ul> <li> <p>minimize</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list of features for which the values need to be minimized. Can be omitted as long as <code>maximize</code> is specified.</p> </li> <li> <p>maximize</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list of features for which the values need to be maximized. Can be omitted as long as <code>minimize</code> is specified.</p> </li> </ul>"},{"location":"api/misc/Skyline/#examples","title":"Examples","text":"<p>Here is an example taken from this blog post.</p> <p><pre><code>import random\nfrom river import misc\n\ncity_prices = {\n    'Bordeaux': 4045,\n    'Lyon': 4547,\n    'Toulouse': 3278\n}\n\ndef random_house():\n    city = random.choice(['Bordeaux', 'Lyon', 'Toulouse'])\n    size = round(random.gauss(200, 50))\n    price = round(random.uniform(0.8, 1.2) * city_prices[city] * size)\n    return {'city': city, 'size': size, 'price': price}\n\nskyline = misc.Skyline(minimize=['price'], maximize=['size'])\n\nrandom.seed(42)\n\nfor _ in range(100):\n    house = random_house()\n    skyline.update(house)\n\nprint(len(skyline))\n</code></pre> <pre><code>13\n</code></pre></p> <p><pre><code>print(skyline[0])\n</code></pre> <pre><code>{'city': 'Toulouse', 'size': 280, 'price': 763202}\n</code></pre></p> <p>Here is another example using the kart data from Mario Kart: Double Dash!!.</p> <p><pre><code>import collections\nfrom river import misc\n\nKart = collections.namedtuple(\n     'Kart',\n     'name speed off_road acceleration weight turbo'\n)\n\nkarts = [\n    Kart('Red Fire', 5, 4, 4, 5, 2),\n    Kart('Green Fire', 7, 3, 3, 4, 2),\n    Kart('Heart Coach', 4, 6, 6, 5, 2),\n    Kart('Bloom Coach', 6, 4, 5, 3, 2),\n    Kart('Turbo Yoshi', 4, 5, 6, 6, 2),\n    Kart('Turbo Birdo', 6, 4, 4, 7, 2),\n    Kart('Goo-Goo Buggy', 1, 9, 9, 2, 3),\n    Kart('Rattle Buggy', 2, 9, 8, 2, 3),\n    Kart('Toad Kart', 3, 9, 7, 2, 3),\n    Kart('Toadette Kart', 1, 9, 9, 2, 3),\n    Kart('Koopa Dasher', 2, 8, 8, 3, 3),\n    Kart('Para-Wing', 1, 8, 9, 3, 3),\n    Kart('DK Jumbo', 8, 2, 2, 8, 1),\n    Kart('Barrel Train', 8, 7, 3, 5, 3),\n    Kart('Koopa King', 9, 1, 1, 9, 1),\n    Kart('Bullet Blaster', 8, 1, 4, 1, 3),\n    Kart('Wario Car', 7, 3, 3, 7, 1),\n    Kart('Waluigi Racer', 5, 9, 5, 6, 2),\n    Kart('Piranha Pipes', 8, 7, 2, 9, 1),\n    Kart('Boo Pipes', 2, 9, 8, 9, 1),\n    Kart('Parade Kart', 7, 3, 4, 7, 3)\n]\n\nskyline = misc.Skyline(\n    maximize=['speed', 'off_road', 'acceleration', 'turbo'],\n    minimize=['weight']\n)\n\nfor kart in karts:\n    skyline.update(kart._asdict())\n\nbest_cart_names = [kart['name'] for kart in skyline]\nfor name in best_cart_names:\n    print(f'- {name}')\n</code></pre> <pre><code>- Green Fire\n- Heart Coach\n- Bloom Coach\n- Goo-Goo Buggy\n- Rattle Buggy\n- Toad Kart\n- Toadette Kart\n- Barrel Train\n- Koopa King\n- Bullet Blaster\n- Waluigi Racer\n- Parade Kart\n</code></pre></p> <p><pre><code>for name in sorted(set(kart.name for kart in karts) - set(best_cart_names)):\n    print(f'- {name}')\n</code></pre> <pre><code>- Boo Pipes\n- DK Jumbo\n- Koopa Dasher\n- Para-Wing\n- Piranha Pipes\n- Red Fire\n- Turbo Birdo\n- Turbo Yoshi\n- Wario Car\n</code></pre></p>"},{"location":"api/misc/Skyline/#methods","title":"Methods","text":"<ol> <li> <p>Skyline queries in Python \u21a9</p> </li> <li> <p>Borzsony, S., Kossmann, D. and Stocker, K., 2001, April. The skyline operator. In Proceedings 17th international conference on data engineering (pp. 421-430). IEEE. \u21a9</p> </li> <li> <p>Tao, Y. and Papadias, D., 2006. Maintaining sliding window skylines on data streams. IEEE Transactions on Knowledge and Data Engineering, 18(3), pp.377-391. \u21a9</p> </li> </ol>"},{"location":"api/model-selection/BanditClassifier/","title":"BanditClassifier","text":"<p>Bandit-based model selection for classification.</p> <p>Each model is associated with an arm. At each <code>learn_one</code> call, the policy decides which arm/model to pull. The reward is the performance of the model on the provided sample. The <code>predict_one</code> and <code>predict_proba_one</code> methods use the current best model.</p>"},{"location":"api/model-selection/BanditClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to select from.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.ClassificationMetric</p> <p>The metric that is used to measure the performance of each model.</p> </li> <li> <p>policy</p> <p>Type \u2192 bandit.base.Policy</p> <p>The bandit policy to use.</p> </li> </ul>"},{"location":"api/model-selection/BanditClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/BanditClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import bandit\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import model_selection\nfrom river import optim\nfrom river import preprocessing\n\nmodels = [\n    linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [0.0001, 0.001, 1e-05, 0.01]\n]\n\ndataset = datasets.Phishing()\nmodel = (\n    preprocessing.StandardScaler() |\n    model_selection.BanditClassifier(\n        models,\n        metric=metrics.Accuracy(),\n        policy=bandit.EpsilonGreedy(\n            epsilon=0.1,\n            decay=0.001,\n            burn_in=20,\n            seed=42\n        )\n    )\n)\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 88.96%\n</code></pre></p>"},{"location":"api/model-selection/BanditClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/model-selection/BanditRegressor/","title":"BanditRegressor","text":"<p>Bandit-based model selection for regression.</p> <p>Each model is associated with an arm. At each <code>learn_one</code> call, the policy decides which arm/model to pull. The reward is the performance of the model on the provided sample. The <code>predict_one</code> method uses the current best model.</p>"},{"location":"api/model-selection/BanditRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to select from.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>The metric that is used to measure the performance of each model.</p> </li> <li> <p>policy</p> <p>Type \u2192 bandit.base.Policy</p> <p>The bandit policy to use.</p> </li> </ul>"},{"location":"api/model-selection/BanditRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/BanditRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import bandit\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import model_selection\nfrom river import optim\nfrom river import preprocessing\n\nmodels = [\n    linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [0.0001, 0.001, 1e-05, 0.01]\n]\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    model_selection.BanditRegressor(\n        models,\n        metric=metrics.MAE(),\n        policy=bandit.EpsilonGreedy(\n            epsilon=0.1,\n            decay=0.001,\n            burn_in=100,\n            seed=42\n        )\n    )\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 3.134089\n</code></pre></p> <p>Here's another example using the UCB policy. The latter is more sensitive to the target scale, and usually works better when the target is rescaled.</p> <p><pre><code>models = [\n    linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [0.0001, 0.001, 1e-05, 0.01]\n]\n\nmodel = (\n    preprocessing.StandardScaler() |\n    preprocessing.TargetStandardScaler(\n        model_selection.BanditRegressor(\n            models,\n            metric=metrics.MAE(),\n            policy=bandit.UCB(\n                delta=1,\n                burn_in=100\n            )\n        )\n    )\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.875333\n</code></pre></p>"},{"location":"api/model-selection/BanditRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/model-selection/GreedyRegressor/","title":"GreedyRegressor","text":"<p>Greedy selection regressor.</p> <p>This selection method simply updates each model at each time step. The current best model is used to make predictions. It's greedy in the sense that updating each model can be costly. On the other hand, bandit-like algorithms are more temperate in that only update a subset of the models at each step.</p>"},{"location":"api/model-selection/GreedyRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 list[base.Regressor]</p> <p>The models to select from.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric | None</p> <p>Default \u2192 <code>None</code></p> <p>The metric that is used to measure the performance of each model.</p> </li> </ul>"},{"location":"api/model-selection/GreedyRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/GreedyRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import model_selection\nfrom river import optim\nfrom river import preprocessing\n\nmodels = [\n    linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))\n    for lr in [1e-5, 1e-4, 1e-3, 1e-2]\n]\n\ndataset = datasets.TrumpApproval()\nmetric = metrics.MAE()\nmodel = (\n    preprocessing.StandardScaler() |\n    model_selection.GreedyRegressor(models, metric)\n)\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.319678\n</code></pre></p>"},{"location":"api/model-selection/GreedyRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/","title":"SuccessiveHalvingClassifier","text":"<p>Successive halving algorithm for classification.</p> <p>Successive halving is a method for performing model selection without having to train each model on all the dataset. At certain points in time (called \"rungs\"), the worst performing will be discarded and the best ones will keep competing between each other. The rung values are designed so that at most <code>budget</code> model updates will be performed in total. </p> <p>If you have <code>k</code> combinations of hyperparameters and that your dataset contains <code>n</code> observations, then the maximal budget you can allocate is: </p> \\[\\frac{2kn}{eta}\\] <p>It is recommended that you check this beforehand. This bound can't be checked by the function because the size of the dataset is not known. In fact it is potentially infinite, in which case the algorithm will terminate once all the budget has been spent. </p> <p>If you have a budget of <code>B</code>, and that your dataset contains <code>n</code> observations, then the number of hyperparameter combinations that will spend all the budget and go through all the data is: </p> \\[\\left\\lceil\\left\\lfloor\\frac{B}{2n}\\right\\rfloor \\times eta \\right\\rceil\\]"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to compare.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>Metric used for comparing models with.</p> </li> <li> <p>budget</p> <p>Type \u2192 int</p> <p>Total number of model updates you wish to allocate.</p> </li> <li> <p>eta</p> <p>Default \u2192 <code>2</code></p> <p>Rate of elimination. At every rung, <code>math.ceil(k / eta)</code> models are kept, where <code>k</code> is the number of models that have reached the rung. A higher <code>eta</code> value will focus on less models but will allocate more iterations to the best models.</p> </li> <li> <p>verbose</p> <p>Default \u2192 <code>False</code></p> <p>Whether to display progress or not.</p> </li> <li> <p>print_kwargs</p> <p>Extra keyword arguments are passed to the <code>print</code> function. For instance, this allows providing a <code>file</code> argument, which indicates where to output progress.</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#examples","title":"Examples","text":"<p>As an example, let's use successive halving to tune the optimizer of a logistic regression. We'll first define the model.</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>Let's now define a grid of parameters which we would like to compare. We'll try different optimizers with various learning rates.</p> <pre><code>from river import utils\nfrom river import optim\n\nmodels = utils.expand_param_grid(model, {\n    'LogisticRegression': {\n        'optimizer': [\n            (optim.SGD, {'lr': [.1, .01, .005]}),\n            (optim.Adam, {'beta_1': [.01, .001], 'lr': [.1, .01, .001]}),\n            (optim.Adam, {'beta_1': [.1], 'lr': [.001]}),\n        ]\n    }\n})\n</code></pre> <p>We can check how many models we've created.</p> <p><pre><code>len(models)\n</code></pre> <pre><code>10\n</code></pre></p> <p>We can now pass these models to a <code>SuccessiveHalvingClassifier</code>. We also need to pick a metric to compare the models, and a budget which indicates how many iterations to run before picking the best model and discarding the rest.</p> <pre><code>from river import model_selection\n\nsh = model_selection.SuccessiveHalvingClassifier(\n    models,\n    metric=metrics.Accuracy(),\n    budget=2000,\n    eta=2,\n    verbose=True\n)\n</code></pre> <p>A <code>SuccessiveHalvingClassifier</code> is also a classifier with a <code>learn_one</code> and a <code>predict_proba_one</code> method. We can therefore evaluate it like any other classifier with <code>evaluate.progressive_val_score</code>.</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nevaluate.progressive_val_score(\n    dataset=datasets.Phishing(),\n    model=sh,\n    metric=metrics.ROCAUC()\n)\n</code></pre> <pre><code>[1] 5 removed       5 left  50 iterations   budget used: 500        budget left: 1500       best Accuracy: 80.00%\n[2] 2 removed       3 left  100 iterations  budget used: 1000       budget left: 1000       best Accuracy: 84.00%\n[3] 1 removed       2 left  166 iterations  budget used: 1498       budget left: 502        best Accuracy: 86.14%\n[4] 1 removed       1 left  250 iterations  budget used: 1998       budget left: 2  best Accuracy: 84.80%\nROCAUC: 95.22%\n</code></pre></p> <p>We can now view the best model.</p> <p><pre><code>sh.best_model\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LogisticRegression (\n    optimizer=Adam (\n      lr=Constant (\n        learning_rate=0.01\n      )\n      beta_1=0.01\n      beta_2=0.999\n      eps=1e-08\n    )\n    loss=Log (\n      weight_pos=1.\n      weight_neg=1.\n    )\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre></p>"},{"location":"api/model-selection/SuccessiveHalvingClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Jamieson, K. and Talwalkar, A., 2016, May. Non-stochastic best arm identification and hyperparameter optimization. In Artificial Intelligence and Statistics (pp. 240-248). \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Hardt, M., Recht, B. and Talwalkar, A., 2018. Massively parallel hyperparameter tuning. arXiv preprint arXiv:1810.05934. \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2017. Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1), pp.6765-6816. \u21a9</p> </li> </ol>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/","title":"SuccessiveHalvingRegressor","text":"<p>Successive halving algorithm for regression.</p> <p>Successive halving is a method for performing model selection without having to train each model on all the dataset. At certain points in time (called \"rungs\"), the worst performing will be discarded and the best ones will keep competing between each other. The rung values are designed so that at most <code>budget</code> model updates will be performed in total. </p> <p>If you have <code>k</code> combinations of hyperparameters and that your dataset contains <code>n</code> observations, then the maximal budget you can allocate is: </p> \\[\\frac{2kn}{eta}\\] <p>It is recommended that you check this beforehand. This bound can't be checked by the function because the size of the dataset is not known. In fact it is potentially infinite, in which case the algorithm will terminate once all the budget has been spent. </p> <p>If you have a budget of <code>B</code>, and that your dataset contains <code>n</code> observations, then the number of hyperparameter combinations that will spend all the budget and go through all the data is: </p> \\[\\left\\lceil\\left\\lfloor\\frac{B}{2n}\\right\\rfloor \\times eta \\right\\rceil\\]"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>The models to compare.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> <p>Metric used for comparing models with.</p> </li> <li> <p>budget</p> <p>Type \u2192 int</p> <p>Total number of model updates you wish to allocate.</p> </li> <li> <p>eta</p> <p>Default \u2192 <code>2</code></p> <p>Rate of elimination. At every rung, <code>math.ceil(k / eta)</code> models are kept, where <code>k</code> is the number of models that have reached the rung. A higher <code>eta</code> value will focus on less models but will allocate more iterations to the best models.</p> </li> <li> <p>verbose</p> <p>Default \u2192 <code>False</code></p> <p>Whether to display progress or not.</p> </li> <li> <p>print_kwargs</p> <p>Extra keyword arguments are passed to the <code>print</code> function. For instance, this allows providing a <code>file</code> argument, which indicates where to output progress.</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#examples","title":"Examples","text":"<p>As an example, let's use successive halving to tune the optimizer of a linear regression. We'll first define the model.</p> <pre><code>from river import linear_model\nfrom river import preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression(intercept_lr=.1)\n)\n</code></pre> <p>Let's now define a grid of parameters which we would like to compare. We'll try different optimizers with various learning rates.</p> <pre><code>from river import optim\nfrom river import utils\n\nmodels = utils.expand_param_grid(model, {\n    'LinearRegression': {\n        'optimizer': [\n            (optim.SGD, {'lr': [.1, .01, .005]}),\n            (optim.Adam, {'beta_1': [.01, .001], 'lr': [.1, .01, .001]}),\n            (optim.Adam, {'beta_1': [.1], 'lr': [.001]}),\n        ]\n    }\n})\n</code></pre> <p>We can check how many models we've created.</p> <p><pre><code>len(models)\n</code></pre> <pre><code>10\n</code></pre></p> <p>We can now pass these models to a <code>SuccessiveHalvingRegressor</code>. We also need to pick a metric to compare the models, and a budget which indicates how many iterations to run before picking the best model and discarding the rest.</p> <pre><code>from river import model_selection\n\nsh = model_selection.SuccessiveHalvingRegressor(\n    models,\n    metric=metrics.MAE(),\n    budget=2000,\n    eta=2,\n    verbose=True\n)\n</code></pre> <p>A <code>SuccessiveHalvingRegressor</code> is also a regressor with a <code>learn_one</code> and a <code>predict_one</code> method. We can therefore evaluate it like any other classifier with <code>evaluate.progressive_val_score</code>.</p> <p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\n\nevaluate.progressive_val_score(\n    dataset=datasets.TrumpApproval(),\n    model=sh,\n    metric=metrics.MAE()\n)\n</code></pre> <pre><code>[1] 5 removed       5 left  50 iterations   budget used: 500        budget left: 1500       best MAE: 4.419643\n[2] 2 removed       3 left  100 iterations  budget used: 1000       budget left: 1000       best MAE: 2.392266\n[3] 1 removed       2 left  166 iterations  budget used: 1498       budget left: 502        best MAE: 1.541383\n[4] 1 removed       1 left  250 iterations  budget used: 1998       budget left: 2  best MAE: 1.112122\nMAE: 0.490688\n</code></pre></p> <p>We can now view the best model.</p> <p><pre><code>sh.best_model\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=Adam (\n      lr=Constant (\n        learning_rate=0.1\n      )\n      beta_1=0.01\n      beta_2=0.999\n      eps=1e-08\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.1\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre></p>"},{"location":"api/model-selection/SuccessiveHalvingRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p> <ol> <li> <p>Jamieson, K. and Talwalkar, A., 2016, May. Non-stochastic best arm identification and hyperparameter optimization. In Artificial Intelligence and Statistics (pp. 240-248). \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Hardt, M., Recht, B. and Talwalkar, A., 2018. Massively parallel hyperparameter tuning. arXiv preprint arXiv:1810.05934. \u21a9</p> </li> <li> <p>Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2017. Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1), pp.6765-6816. \u21a9</p> </li> </ol>"},{"location":"api/model-selection/base/ModelSelectionClassifier/","title":"ModelSelectionClassifier","text":"<p>A model selector for classification.</p>"},{"location":"api/model-selection/base/ModelSelectionClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 Iterator[base.Estimator]</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/model-selection/base/ModelSelectionRegressor/","title":"ModelSelectionRegressor","text":"<p>A model selector for regression.</p>"},{"location":"api/model-selection/base/ModelSelectionRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>models</p> <p>Type \u2192 Iterator[base.Estimator]</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.Metric</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>best_model</p> <p>The current best model.</p> </li> <li> <p>models</p> </li> </ul>"},{"location":"api/model-selection/base/ModelSelectionRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/multiclass/OneVsOneClassifier/","title":"OneVsOneClassifier","text":"<p>One-vs-One (OvO) multiclass strategy.</p> <p>This strategy consists in fitting one binary classifier for each pair of classes. Because we are in a streaming context, the number of classes isn't known from the start, hence new classifiers are instantiated on the fly. </p> <p>The number of classifiers is <code>k * (k - 1) / 2</code>, where <code>k</code> is the number of classes. However, each call to <code>learn_one</code> only requires training <code>k - 1</code> models. Indeed, only the models that pertain to the given label have to be trained. Meanwhile, making a prediction requires going through each and every model.</p>"},{"location":"api/multiclass/OneVsOneClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>A binary classifier, although a multi-class classifier will work too.</p> </li> </ul>"},{"location":"api/multiclass/OneVsOneClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>classifiers (dict)</p> <p>A mapping between pairs of classes and classifiers. The keys are tuples which contain a pair of classes. Each pair is sorted in lexicographical order.</p> </li> </ul>"},{"location":"api/multiclass/OneVsOneClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multiclass\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nscaler = preprocessing.StandardScaler()\novo = multiclass.OneVsOneClassifier(linear_model.LogisticRegression())\nmodel = scaler | ovo\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 80.76%\n</code></pre></p>"},{"location":"api/multiclass/OneVsOneClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/multiclass/OneVsRestClassifier/","title":"OneVsRestClassifier","text":"<p>One-vs-the-rest (OvR) multiclass strategy.</p> <p>This strategy consists in fitting one binary classifier per class. Because we are in a streaming context, the number of classes isn't known from the start. Hence, new classifiers are instantiated on the fly. Likewise, the predicted probabilities will only include the classes seen up to a given point in time. </p> <p>Note that this classifier supports mini-batches as well as single instances. </p> <p>The computational complexity for both learning and predicting grows linearly with the number of classes. If you have a very large number of classes, then you might want to consider using an <code>multiclass.OutputCodeClassifier</code> instead.</p>"},{"location":"api/multiclass/OneVsRestClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>A binary classifier, although a multi-class classifier will work too.</p> </li> </ul>"},{"location":"api/multiclass/OneVsRestClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>classifiers (dict)</p> <p>A mapping between classes and classifiers.</p> </li> </ul>"},{"location":"api/multiclass/OneVsRestClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multiclass\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nscaler = preprocessing.StandardScaler()\novr = multiclass.OneVsRestClassifier(linear_model.LogisticRegression())\nmodel = scaler | ovr\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 77.46%\n</code></pre></p> <p>This estimator also also supports mini-batching.</p> <pre><code>for X in pd.read_csv(dataset.path, chunksize=64):\n    y = X.pop('category')\n    y_pred = model.predict_many(X)\n    model.learn_many(X, y)\n</code></pre>"},{"location":"api/multiclass/OneVsRestClassifier/#methods","title":"Methods","text":"learn_many learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_many predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/multiclass/OutputCodeClassifier/","title":"OutputCodeClassifier","text":"<p>Output-code multiclass strategy.</p> <p>This also referred to as \"error-correcting output codes\". </p> <p>This class allows to learn a multi-class classification problem with a binary classifier. Each class is converted to a code of 0s and 1s. The length of the code is called  the code size. A copy of the classifier made for code. The codes associated with the classes are stored in a code book. </p> <p>When a new sample arrives, the label's code is retrieved from the code book. Then, each classifier is trained on the relevant part of code, which is either a 0 or a 1. </p> <p>For predicting, each classifier outputs a probability. These are then compared to each code in the code book, and the label which is the \"closest\" is chosen as the most likely class. Closeness is determined in terms of Manhattan distance. </p> <p>One specificity of online learning is that we don't how many classes there are initially. Therefore, a random procedure generates random codes on the fly whenever a previously unseed label appears.</p>"},{"location":"api/multiclass/OutputCodeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>classifier</p> <p>Type \u2192 base.Classifier</p> <p>A binary classifier, although a multi-class classifier will work too.</p> </li> <li> <p>code_size</p> <p>Type \u2192 int</p> <p>The code size, which dictates how many copies of the provided classifiers to train. Must be strictly positive.</p> </li> <li> <p>coding_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>random</code></p> <p>The method used to generate the codes. Can be either 'exact' or 'random'. The 'exact' method generates all possible codes of a given size in memory, and streams them in a random order. The 'random' method generates random codes of a given size on the fly. The 'exact' method necessarily generates different codes for each class, but requires more memory. The 'random' method can generate duplicate codes for different classes, but requires less memory.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>A random seed number that can be set for reproducibility.</p> </li> </ul>"},{"location":"api/multiclass/OutputCodeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multiclass\nfrom river import preprocessing\n\ndataset = datasets.ImageSegments()\n\nscaler = preprocessing.StandardScaler()\nooc = multiclass.OutputCodeClassifier(\n    classifier=linear_model.LogisticRegression(),\n    code_size=10,\n    coding_method='random',\n    seed=1\n)\nmodel = scaler | ooc\n\nmetric = metrics.MacroF1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MacroF1: 79.58%\n</code></pre></p>"},{"location":"api/multiclass/OutputCodeClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Dietterich, T.G. and Bakiri, G., 1994. Solving multiclass learning problems via error-correcting output codes. Journal of artificial intelligence research, 2, pp.263-286. \u21a9</p> </li> <li> <p>James, G. and Hastie, T., 1998. The error coding method and PICTs. Journal of Computational and Graphical statistics, 7(3), pp.377-387. \u21a9</p> </li> </ol>"},{"location":"api/multioutput/ClassifierChain/","title":"ClassifierChain","text":"<p>A multi-output model that arranges classifiers into a chain.</p> <p>This will create one model per output. The prediction of the first output will be used as a feature in the second model. The prediction for the second output will be used as a feature for the third model, etc. This \"chain model\" is therefore capable of capturing dependencies between outputs.</p>"},{"location":"api/multioutput/ClassifierChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>A classifier model used for each label.</p> </li> <li> <p>order</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list with the targets order in which to construct the chain. If <code>None</code> then the order will be inferred from the order of the keys in the target.</p> </li> </ul>"},{"location":"api/multioutput/ClassifierChain/#examples","title":"Examples","text":"<p><pre><code>from river import feature_selection\nfrom river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river import preprocessing\nfrom river import stream\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.fetch_openml('yeast', version=4, parser='auto', as_frame=False),\n    shuffle=True,\n    seed=42\n)\n\nmodel = feature_selection.VarianceThreshold(threshold=0.01)\nmodel |= preprocessing.StandardScaler()\nmodel |= multioutput.ClassifierChain(\n    model=linear_model.LogisticRegression(),\n    order=list(range(14))\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n    # Convert y values to booleans\n    y = {i: yi == 'TRUE' for i, yi in y.items()}\n    y_pred = model.predict_one(x)\n    metric.update(y, y_pred)\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 41.81%\n</code></pre></p>"},{"location":"api/multioutput/ClassifierChain/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Multi-Output Chain Models and their Application in Data Streams \u21a9</p> </li> </ol>"},{"location":"api/multioutput/MonteCarloClassifierChain/","title":"MonteCarloClassifierChain","text":"<p>Monte Carlo Sampling Classifier Chains.</p> <p>Probabilistic Classifier Chains using Monte Carlo sampling, as described in <sup>1</sup>. </p> <p>m samples are taken from the posterior distribution. Therefore we need a probabilistic interpretation of the output, and thus, this is a particular variety of ProbabilisticClassifierChain.</p>"},{"location":"api/multioutput/MonteCarloClassifierChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> </li> <li> <p>m</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>Number of samples to take from the posterior distribution.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/multioutput/MonteCarloClassifierChain/#examples","title":"Examples","text":"<p><pre><code>from river import feature_selection\nfrom river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river import preprocessing\nfrom river.datasets import synth\n\ndataset = synth.Logical(seed=42, n_tiles=100)\n\nmodel = multioutput.MonteCarloClassifierChain(\n    model=linear_model.LogisticRegression(),\n    m=10,\n    seed=42\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n   y_pred = model.predict_one(x)\n   y_pred = {k: y_pred.get(k, 0) for k in y}\n   metric.update(y, y_pred)\n   model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 51.79%\n</code></pre></p>"},{"location":"api/multioutput/MonteCarloClassifierChain/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Read, J., Martino, L., &amp; Luengo, D. (2014). Efficient monte carlo   methods for multi-dimensional learning with classifier chains.   Pattern Recognition, 47(3), 1535-1546.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/multioutput/MultiClassEncoder/","title":"MultiClassEncoder","text":"<p>Convert a multi-label task into multiclass.</p> <p>Assigns a class to each unique combination of labels, and proceeds with training the supplied multi-class classifier. </p> <p>The transformation is done by converting the label set, which could be seen as a binary number, into an integer representing a class. At prediction time, the predicted integer is converted back to a binary number which is the predicted label set.</p>"},{"location":"api/multioutput/MultiClassEncoder/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> <p>The classifier used for learning.</p> </li> </ul>"},{"location":"api/multioutput/MultiClassEncoder/#examples","title":"Examples","text":"<p><pre><code>from river import forest\nfrom river import metrics\nfrom river import multioutput\nfrom river.datasets import synth\n\ndataset = synth.Logical(seed=42, n_tiles=100)\n\nmodel = multioutput.MultiClassEncoder(\n    model=forest.ARFClassifier(seed=7)\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n   y_pred = model.predict_one(x)\n   y_pred = {k: y_pred.get(k, 0) for k in y}\n   metric.update(y, y_pred)\n   model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 95.10%\n</code></pre></p>"},{"location":"api/multioutput/MultiClassEncoder/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'dict[FeatureName, bool]' </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, dict[bool, float]]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/multioutput/ProbabilisticClassifierChain/","title":"ProbabilisticClassifierChain","text":"<p>Probabilistic Classifier Chains.</p> <p>The Probabilistic Classifier Chains (PCC) <sup>1</sup> is a Bayes-optimal method based on the Classifier Chains (CC). </p> <p>Consider the concept of chaining classifiers as searching a path in a binary tree whose leaf nodes are associated with a label \\(y \\in Y\\). While CC searches only a single path in the aforementioned binary tree, PCC looks at each of the \\(2^l\\) paths, where \\(l\\) is the number of labels. This limits the applicability of the method to data sets with a small to moderate number of labels. The authors recommend no more than about 15 labels for real-world applications.</p>"},{"location":"api/multioutput/ProbabilisticClassifierChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Classifier</p> </li> </ul>"},{"location":"api/multioutput/ProbabilisticClassifierChain/#examples","title":"Examples","text":"<p><pre><code>from river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river.datasets import synth\n\ndataset = synth.Logical(seed=42, n_tiles=100)\n\nmodel = multioutput.ProbabilisticClassifierChain(\n    model=linear_model.LogisticRegression()\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.Jaccard())\n\nfor x, y in dataset:\n   y_pred = model.predict_one(x)\n   y_pred = {k: y_pred.get(k, 0) for k in y}\n   metric.update(y, y_pred)\n   model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>MicroAverage(Jaccard): 51.84%\n</code></pre></p>"},{"location":"api/multioutput/ProbabilisticClassifierChain/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and the labels <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the labels of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[FeatureName, bool]:     The predicted labels.</p> <p></p> predict_proba_one <p>Predict the probability of each label appearing given dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Cheng, W., H\u00fcllermeier, E., &amp; Dembczynski, K. J. (2010).   Bayes optimal multilabel classification via probabilistic classifier   chains. In Proceedings of the 27th international conference on   machine learning (ICML-10) (pp. 279-286).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/multioutput/RegressorChain/","title":"RegressorChain","text":"<p>A multi-output model that arranges regressors into a chain.</p> <p>This will create one model per output. The prediction of the first output will be used as a feature in the second output. The prediction for the second output will be used as a feature for the third, etc. This \"chain model\" is therefore capable of capturing dependencies between outputs.</p>"},{"location":"api/multioutput/RegressorChain/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Regressor</p> <p>The regression model used to make predictions for each target.</p> </li> <li> <p>order</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>A list with the targets order in which to construct the chain. If <code>None</code> then the order will be inferred from the order of the keys in the target.</p> </li> </ul>"},{"location":"api/multioutput/RegressorChain/#examples","title":"Examples","text":"<p><pre><code>from river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import multioutput\nfrom river import preprocessing\nfrom river import stream\n\nfrom sklearn import datasets\n\ndataset = stream.iter_sklearn_dataset(\n    dataset=datasets.load_linnerud(),\n    shuffle=True,\n    seed=42\n)\n\nmodel = multioutput.RegressorChain(\n    model=(\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression(intercept_lr=0.3)\n    ),\n    order=[0, 1, 2]\n)\n\nmetric = metrics.multioutput.MicroAverage(metrics.MAE())\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MicroAverage(MAE): 12.733525\n</code></pre></p>"},{"location":"api/multioutput/RegressorChain/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the outputs of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The predictions.</p> <p></p>"},{"location":"api/naive-bayes/BernoulliNB/","title":"BernoulliNB","text":"<p>Bernoulli Naive Bayes.</p> <p>Bernoulli Naive Bayes model learns from occurrences between features such as word counts and discrete classes. The input vector must contain positive values, such as counts or TF-IDF values.</p>"},{"location":"api/naive-bayes/BernoulliNB/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1.0</code></p> <p>Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).</p> </li> <li> <p>true_threshold</p> <p>Default \u2192 <code>0.0</code></p> <p>Threshold for binarizing (mapping to booleans) features.</p> </li> </ul>"},{"location":"api/naive-bayes/BernoulliNB/#attributes","title":"Attributes","text":"<ul> <li> <p>class_counts (collections.Counter)</p> <p>Number of times each class has been seen.</p> </li> <li> <p>feature_counts (collections.defaultdict)</p> <p>Total frequencies per feature and class.</p> </li> </ul>"},{"location":"api/naive-bayes/BernoulliNB/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import compose\nfrom river import feature_extraction\nfrom river import naive_bayes\n\ndocs = [\n    (\"Chinese Beijing Chinese\", \"yes\"),\n    (\"Chinese Chinese Shanghai\", \"yes\"),\n    (\"Chinese Macao\", \"yes\"),\n    (\"Tokyo Japan Chinese\", \"no\")\n]\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.BernoulliNB(alpha=1))\n)\n\nfor sentence, label in docs:\n    model.learn_one(sentence, label)\n\nmodel[\"nb\"].p_class(\"yes\")\n</code></pre> <pre><code>0.75\n</code></pre> <pre><code>model[\"nb\"].p_class(\"no\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model.predict_proba_one(\"test\")\n</code></pre> <pre><code>{'yes': 0.883..., 'no': 0.116...}\n</code></pre></p> <p><pre><code>model.predict_one(\"test\")\n</code></pre> <pre><code>'yes'\n</code></pre></p> <p>You can train the model and make predictions in mini-batch mode using the class methods <code>learn_many</code> and <code>predict_many</code>.</p> <p><pre><code>df_docs = pd.DataFrame(docs, columns = [\"docs\", \"y\"])\n\nX = pd.Series([\n   \"Chinese Beijing Chinese\",\n   \"Chinese Chinese Shanghai\",\n   \"Chinese Macao\",\n   \"Tokyo Japan Chinese\"\n])\n\ny = pd.Series([\"yes\", \"yes\", \"yes\", \"no\"])\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.BernoulliNB(alpha=1))\n)\n\nmodel.learn_many(X, y)\n\nunseen = pd.Series([\"Taiwanese Taipei\", \"Chinese Shanghai\"])\n\nmodel.predict_proba_many(unseen)\n</code></pre> <pre><code>         no       yes\n0  0.116846  0.883154\n1  0.047269  0.952731\n</code></pre></p> <p><pre><code>model.predict_many(unseen)\n</code></pre> <pre><code>0    yes\n1    yes\ndtype: object\n</code></pre></p>"},{"location":"api/naive-bayes/BernoulliNB/#methods","title":"Methods","text":"joint_log_likelihood <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     Mapping between classes and joint log likelihood.</p> <p></p> joint_log_likelihood_many <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     Input samples joint log likelihood.</p> <p></p> learn_many <p>Learn from a batch of count vectors.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Updates the model with a single observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class p_class_many p_feature_given_class predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Return probabilities using the log-likelihoods in mini-batchs setting.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>The Bernoulli model \u21a9</p> </li> </ol>"},{"location":"api/naive-bayes/ComplementNB/","title":"ComplementNB","text":"<p>Naive Bayes classifier for multinomial models.</p> <p>Complement Naive Bayes model learns from occurrences between features such as word counts and discrete classes. ComplementNB is suitable for imbalance dataset. The input vector must contain positive values, such as counts or TF-IDF values.</p>"},{"location":"api/naive-bayes/ComplementNB/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1.0</code></p> <p>Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).</p> </li> </ul>"},{"location":"api/naive-bayes/ComplementNB/#attributes","title":"Attributes","text":"<ul> <li> <p>class_dist (proba.Multinomial)</p> <p>Class prior probability distribution.</p> </li> <li> <p>feature_counts (collections.defaultdict)</p> <p>Total frequencies per feature and class.</p> </li> <li> <p>class_totals (collections.Counter)</p> <p>Total frequencies per class.</p> </li> </ul>"},{"location":"api/naive-bayes/ComplementNB/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import compose\nfrom river import feature_extraction\nfrom river import naive_bayes\n\ndocs = [\n    (\"Chinese Beijing Chinese\", \"yes\"),\n    (\"Chinese Chinese Shanghai\", \"yes\"),\n    (\"Chinese Macao\", \"maybe\"),\n    (\"Tokyo Japan Chinese\", \"no\")\n]\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.ComplementNB(alpha=1))\n)\n\nfor sentence, label in docs:\n    model.learn_one(sentence, label)\n\nmodel[\"nb\"].p_class(\"yes\")\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"no\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"maybe\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model.predict_proba_one(\"test\")\n</code></pre> <pre><code>{'yes': 0.275, 'maybe': 0.375, 'no': 0.35}\n</code></pre></p> <p><pre><code>model.predict_one(\"test\")\n</code></pre> <pre><code>'maybe'\n</code></pre></p> <p>You can train the model and make predictions in mini-batch mode using the class methods <code>learn_many</code> and <code>predict_many</code>.</p> <p><pre><code>df_docs = pd.DataFrame(docs, columns = [\"docs\", \"y\"])\n\nX = pd.Series([\n   \"Chinese Beijing Chinese\",\n   \"Chinese Chinese Shanghai\",\n   \"Chinese Macao\",\n   \"Tokyo Japan Chinese\"\n])\n\ny = pd.Series([\"yes\", \"yes\", \"maybe\", \"no\"])\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.ComplementNB(alpha=1))\n)\n\nmodel.learn_many(X, y)\n\nunseen = pd.Series([\"Taiwanese Taipei\", \"Chinese Shanghai\"])\n\nmodel.predict_proba_many(unseen)\n</code></pre> <pre><code>      maybe        no       yes\n0  0.415129  0.361624  0.223247\n1  0.248619  0.216575  0.534807\n</code></pre></p> <p><pre><code>model.predict_many(unseen)\n</code></pre> <pre><code>0    maybe\n1      yes\ndtype: object\n</code></pre></p>"},{"location":"api/naive-bayes/ComplementNB/#methods","title":"Methods","text":"joint_log_likelihood <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     Mapping between classes and joint log likelihood.</p> <p></p> joint_log_likelihood_many <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     Input samples joint log likelihood.</p> <p></p> learn_many <p>Learn from a batch of count vectors.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Updates the model with a single observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class p_class_many predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Return probabilities using the log-likelihoods in mini-batchs setting.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>Rennie, J.D., Shih, L., Teevan, J. and Karger, D.R., 2003. Tackling the poor assumptions of naive bayes text classifiers. In Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 616-623) \u21a9</p> </li> <li> <p>StackExchange discussion \u21a9</p> </li> </ol>"},{"location":"api/naive-bayes/GaussianNB/","title":"GaussianNB","text":"<p>Gaussian Naive Bayes.</p> <p>A Gaussian distribution \\(G_{cf}\\) is maintained for each class \\(c\\) and each feature \\(f\\). Each Gaussian is updated using the amount associated with each feature; the details can be be found in <code>proba.Gaussian</code>. The joint log-likelihood is then obtained by summing the log probabilities of each feature associated with each class.</p>"},{"location":"api/naive-bayes/GaussianNB/#examples","title":"Examples","text":"<p><pre><code>from river import naive_bayes\nfrom river import stream\nimport numpy as np\n\nX = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\nY = np.array([1, 1, 1, 2, 2, 2])\n\nmodel = naive_bayes.GaussianNB()\n\nfor x, y in stream.iter_array(X, Y):\n    model.learn_one(x, y)\n\nmodel.predict_one({0: -0.8, 1: -1})\n</code></pre> <pre><code>1\n</code></pre></p>"},{"location":"api/naive-bayes/GaussianNB/#methods","title":"Methods","text":"joint_log_likelihood joint_log_likelihood_many learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p>"},{"location":"api/naive-bayes/MultinomialNB/","title":"MultinomialNB","text":"<p>Naive Bayes classifier for multinomial models.</p> <p>Multinomial Naive Bayes model learns from occurrences between features such as word counts and discrete classes. The input vector must contain positive values, such as counts or TF-IDF values.</p>"},{"location":"api/naive-bayes/MultinomialNB/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>1.0</code></p> <p>Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).</p> </li> </ul>"},{"location":"api/naive-bayes/MultinomialNB/#attributes","title":"Attributes","text":"<ul> <li> <p>class_dist (proba.Multinomial)</p> <p>Class prior probability distribution.</p> </li> <li> <p>feature_counts (collections.defaultdict)</p> <p>Total frequencies per feature and class.</p> </li> <li> <p>class_totals (collections.Counter)</p> <p>Total frequencies per class.</p> </li> </ul>"},{"location":"api/naive-bayes/MultinomialNB/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import compose\nfrom river import feature_extraction\nfrom river import naive_bayes\n\ndocs = [\n    (\"Chinese Beijing Chinese\", \"yes\"),\n    (\"Chinese Chinese Shanghai\", \"yes\"),\n    (\"Chinese Macao\", \"maybe\"),\n    (\"Tokyo Japan Chinese\", \"no\")\n]\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.MultinomialNB(alpha=1))\n)\n\nfor sentence, label in docs:\n    model.learn_one(sentence, label)\n\nmodel[\"nb\"].p_class(\"yes\")\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"no\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model[\"nb\"].p_class(\"maybe\")\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>model.predict_proba_one(\"test\")\n</code></pre> <pre><code>{'yes': 0.413, 'maybe': 0.310, 'no': 0.275}\n</code></pre></p> <p><pre><code>model.predict_one(\"test\")\n</code></pre> <pre><code>'yes'\n</code></pre></p> <p>You can train the model and make predictions in mini-batch mode using the class methods <code>learn_many</code> and <code>predict_many</code>.</p> <p><pre><code>df_docs = pd.DataFrame(docs, columns = [\"docs\", \"y\"])\n\nX = pd.Series([\n   \"Chinese Beijing Chinese\",\n   \"Chinese Chinese Shanghai\",\n   \"Chinese Macao\",\n   \"Tokyo Japan Chinese\"\n])\n\ny = pd.Series([\"yes\", \"yes\", \"maybe\", \"no\"])\n\nmodel = compose.Pipeline(\n    (\"tokenize\", feature_extraction.BagOfWords(lowercase=False)),\n    (\"nb\", naive_bayes.MultinomialNB(alpha=1))\n)\n\nmodel.learn_many(X, y)\n\nunseen = pd.Series([\"Taiwanese Taipei\", \"Chinese Shanghai\"])\n\nmodel.predict_proba_many(unseen)\n</code></pre> <pre><code>      maybe        no       yes\n0  0.373272  0.294931  0.331797\n1  0.160396  0.126733  0.712871\n</code></pre></p> <p><pre><code>model.predict_many(unseen)\n</code></pre> <pre><code>0    maybe\n1      yes\ndtype: object\n</code></pre></p>"},{"location":"api/naive-bayes/MultinomialNB/#methods","title":"Methods","text":"joint_log_likelihood <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>float:     Mapping between classes and joint log likelihood.</p> <p></p> joint_log_likelihood_many <p>Computes the joint log likelihood of input features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     Input samples joint log likelihood.</p> <p></p> learn_many <p>Learn from a batch of count vectors.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.Series' </li> </ul> <p></p> learn_one <p>Updates the model with a single observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> p_class p_class_many p_feature_given_class predict_many <p>Predict the outcome for each given sample.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.Series:     The predicted labels.</p> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_many <p>Return probabilities using the log-likelihoods in mini-batchs setting.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> predict_proba_one <p>Return probabilities using the log-likelihoods.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> <ol> <li> <p>Naive Bayes text classification \u21a9</p> </li> </ol>"},{"location":"api/neighbors/KNNClassifier/","title":"KNNClassifier","text":"<p>K-Nearest Neighbors (KNN) for classification.</p> <p>Samples are stored using a first-in, first-out strategy. The strategy to perform search queries in the data buffer is defined by the <code>engine</code> parameter.</p>"},{"location":"api/neighbors/KNNClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>n_neighbors</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of nearest neighbors to search for.</p> </li> <li> <p>engine</p> <p>Type \u2192 BaseNN | None</p> <p>Default \u2192 <code>None</code></p> <p>The search engine used to store the instances and perform search queries. Depending on the choose engine, search will be exact or approximate. Please, consult the documentation of each available search engine for more details on its usage. By default, use the <code>SWINN</code> search engine for approximate search queries.</p> </li> <li> <p>weighted</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>Weight the contribution of each neighbor by its inverse distance.</p> </li> <li> <p>cleanup_every</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>This determines at which rate old classes are cleaned up. Classes that have been seen in the past but that are not present in the current window are dropped. Classes are never dropped when this is set to 0.</p> </li> <li> <p>softmax</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to use softmax normalization to normalize the neighbors contributions. Votes are divided by the total number of votes if this is <code>False</code>.</p> </li> </ul>"},{"location":"api/neighbors/KNNClassifier/#examples","title":"Examples","text":"<pre><code>import functools\nfrom river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import neighbors\nfrom river import preprocessing\nfrom river import utils\n\ndataset = datasets.Phishing()\n</code></pre> <p>To select a custom distance metric which takes one or several parameter, you can wrap your chosen distance using <code>functools.partial</code>:</p> <p><pre><code>l1_dist = functools.partial(utils.math.minkowski_distance, p=1)\n\nmodel = (\n    preprocessing.StandardScaler() |\n    neighbors.KNNClassifier(\n        engine=neighbors.SWINN(\n            dist_func=l1_dist,\n            seed=42\n        )\n    )\n)\n\nevaluate.progressive_val_score(dataset, model, metrics.Accuracy())\n</code></pre> <pre><code>Accuracy: 89.59%\n</code></pre></p>"},{"location":"api/neighbors/KNNClassifier/#methods","title":"Methods","text":"clean_up_classes <p>Clean up classes added to the window.</p> <p>Classes that are added (and removed) from the window may no longer be valid. This method cleans up the window and and ensures only known classes are added, and we do not consider \"None\" a class. It is called every <code>cleanup_every</code> step, or can be called manually.</p> <p></p> learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p>"},{"location":"api/neighbors/KNNClassifier/#notes","title":"Notes","text":"<p>Note that since the window is moving and we keep track of all classes that are added at some point, a class might be returned in a result (with a value of 0) if it is no longer in the window. You can call model.clean_up_classes(), or set <code>cleanup_every</code> to a non-zero value.</p>"},{"location":"api/neighbors/KNNRegressor/","title":"KNNRegressor","text":"<p>K-Nearest Neighbors regressor.</p> <p>Samples are stored using a first-in, first-out strategy. The strategy to perform search queries in the data buffer is defined by the <code>engine</code> parameter. Predictions are obtained by aggregating the values of the closest n_neighbors stored samples with respect to a query sample.</p>"},{"location":"api/neighbors/KNNRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>n_neighbors</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The number of nearest neighbors to search for.</p> </li> <li> <p>engine</p> <p>Type \u2192 BaseNN | None</p> <p>Default \u2192 <code>None</code></p> <p>The search engine used to store the instances and perform search queries. Depending on the choose engine, search will be exact or approximate. Please, consult the documentation of each available search engine for more details on its usage. By default, use the <code>SWINN</code> search engine for approximate search queries.</p> </li> <li> <p>aggregation_method</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>mean</code></p> <p>The method to aggregate the target values of neighbors.     | 'mean'     | 'median'     | 'weighted_mean'</p> </li> </ul>"},{"location":"api/neighbors/KNNRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import neighbors\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = neighbors.KNNRegressor()\nevaluate.progressive_val_score(dataset, model, metrics.RMSE())\n</code></pre> <pre><code>RMSE: 1.427743\n</code></pre></p>"},{"location":"api/neighbors/KNNRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/neighbors/LazySearch/","title":"LazySearch","text":"<p>Exact nearest neighbors using a lazy search estrategy.</p>"},{"location":"api/neighbors/LazySearch/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>50</code></p> <p>Size of the sliding window use to search neighbors with.</p> </li> <li> <p>min_distance_keep</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The minimum distance (similarity) to consider adding a point to the window. E.g., a value of 0.0 will add even exact duplicates.</p> </li> <li> <p>dist_func</p> <p>Type \u2192 DistanceFunc | FunctionWrapper | None</p> <p>Default \u2192 <code>None</code></p> <p>A distance function which accepts two input items to compare. If not set, use the Minkowski distance with <code>p=2</code>.</p> </li> </ul>"},{"location":"api/neighbors/LazySearch/#methods","title":"Methods","text":"append <p>Add a point to the window, optionally with extra metadata.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>extra     \u2014 'typing.Any | None'     \u2014 defaults to <code>None</code> </li> <li>kwargs </li> </ul> <p></p> search <p>Find the <code>n_neighbors</code> closest points to <code>item</code>, along with their distances.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>n_neighbors     \u2014 'int' </li> <li>kwargs </li> </ul> <p></p> update <p>Update the window with a new point, only added if &gt; min distance.</p> <p>If min distance is 0, we do not need to do the calculation. The item (and extra metadata) will not be added to the window if it is too close to an existing point.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>n_neighbors     \u2014 'int'     \u2014 defaults to <code>1</code> </li> <li>extra     \u2014 'typing.Any | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>A boolean (true/false) to indicate if the point was added.</p> <p></p>"},{"location":"api/neighbors/LazySearch/#notes","title":"Notes","text":"<p>Updates are by default stored by the FIFO (first in first out) method, which means that when the size limit is reached, old samples are dumped to give room for new samples. This is circular, meaning that older points are dumped first. This also gives the implementation a temporal aspect, because older samples are replaced with newer ones.</p> <p>The parameter <code>min_dinstance_keep</code> controls the addition of new items to the window - items that are far enough away (&gt; min_distance_keep) are added to the window. Thus a value of 0 indicates that we add all points, and increasing from 0 makes it less likely we will keep a new item.</p>"},{"location":"api/neighbors/SWINN/","title":"SWINN","text":"<p>Sliding WIndow-based Nearest Neighbor (SWINN) search using Graphs.</p> <p>Extends the NNDescent algorithm<sup>1</sup> to handle vertex addition and removal in a FIFO data ingestion policy. SWINN builds and keeps a directed graph where edges connect the nearest neighbors. Any distance metric can be used to build the graph. By using a directed graph, the user must set the desired number of neighbors. More neighbors imply more accurate search queries at the cost of increased running time and memory usage. Note that although the number of directed neighbors is limited by the user, there is no direct control on the number of reverse neighbors, i.e., the number of vertices that have an edge to a given vertex. </p> <p>The basic idea of SWINN and NNDescent is that \"the neighbor of my neighbors might as well be my neighbor\". Hence, the connections are constantly revisited to improve the graph structure. The algorithm for creating and maintaining the search graph can be described in general lines as follows: </p> <ul> <li> <p>Start with a random neighborhood graph; </p> </li> <li> <p>For each node in the search graph: refine the current neighborhood by checking if there are better neighborhood options among the neighbors of the current neighbors; </p> </li> <li> <p>If the total number of neighborhood changes is smaller than a given stopping criterion, then stop. </p> </li> </ul> <p>SWINN adds strategies to remove vertices from the search graph and pruning redundant edges. SWINN is more efficient when the selected <code>maxlen</code> is greater than 500. For small sized data windows, using the lazy/exhaustive search, i.e., <code>neighbors.LazySearch</code> might be a better idea.</p>"},{"location":"api/neighbors/SWINN/#parameters","title":"Parameters","text":"<ul> <li> <p>graph_k</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>20</code></p> <p>The maximum number of direct nearest neighbors each node has.</p> </li> <li> <p>dist_func</p> <p>Type \u2192 DistanceFunc | FunctionWrapper | None</p> <p>Default \u2192 <code>None</code></p> <p>The distance function used to compare two items. If not set, use the Minkowski distance with <code>p=2</code>.</p> </li> <li> <p>maxlen</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000</code></p> <p>The maximum size of the data buffer.</p> </li> <li> <p>warm_up</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>500</code></p> <p>How many data instances to observe before starting the search graph.</p> </li> <li> <p>max_candidates</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum number of vertices to consider when performing local neighborhood joins. If not set SWINN will use <code>min(50, max(50, self.graph_k))</code>.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0001</code></p> <p>Early stop parameter for the neighborhood refinement procedure. NNDescent will stop running if the maximum number of iterations is reached or the number of edge changes after an iteration is smaller than or equal to <code>delta * graph_k * n_nodes</code>. In the last expression, <code>n_nodes</code> refers to the number of graph nodes involved in the (local) neighborhood refinement.</p> </li> <li> <p>prune_prob</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>The probability of removing redundant edges. Must be between <code>0</code> and <code>1</code>. If set to zero, no edge will be pruned. When set to one, every potentially redundant edge will be dropped.</p> </li> <li> <p>n_iters</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The maximum number of NNDescent iterations to perform to refine the search index.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/neighbors/SWINN/#methods","title":"Methods","text":"append <p>Add a new item to the search index.</p> <p>Data is stored using the FIFO strategy. Both the data buffer and the search graph are updated. The addition of a new item will trigger the removal of the oldest item, if the maximum size was reached. All edges of the removed node are also dropped and safety procedures are applied to ensure its neighbors keep accessible. The addition of a new item also trigger local neighborhood refinement procedures, to ensure the search index is effective and the node degree constraints are met.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>kwargs </li> </ul> <p></p> connectivity <p>Get a list with the size of each connected component in the search graph.</p> <p>This metric provides an overview of reachability in the search index by using Kruskal's algorithm to build a forest of connected components.  We want our search index to have a single connected component, i.e., the case where we get a list containing a single number which is equal to <code>maxlen</code>. If that is not the case, not every node in the search graph can be reached from any given starting point. You may want to try increasing <code>graph_k</code> to improve connectivity. However, keep in mind the following aspects: 1) computing this metric is a costly operation (\\(O(E\\log V)\\)), where \\(E\\) and \\(V\\) are, respectively, the number of edges and vertices in the search graph; 2) often, connectivity comes at the price of increased computational costs. Tweaking the <code>sample_rate</code> might help in such situations. The best possible scenario is to decrease the value of <code>graph_k</code> while keeping a single connected component.</p> <p>Returns</p> <p>list[int]:     A list of the number of elements in each connected component of the graph.</p> <p></p> search <p>Search the underlying nearest neighbor graph given a query item.</p> <p>In case not enough samples were observed, i.e., the number of stored samples is smaller than <code>warm_up</code>, then the search switches to a brute force strategy.</p> <p>Parameters</p> <ul> <li>item     \u2014 'typing.Any' </li> <li>n_neighbors     \u2014 'int' </li> <li>epsilon     \u2014 'float'     \u2014 defaults to <code>0.1</code> </li> <li>kwargs </li> </ul> <p>Returns</p> <p>tuple[list, list]:     neighbors, dists</p> <p></p>"},{"location":"api/neighbors/SWINN/#notes","title":"Notes","text":"<p>There is an accuracy/speed trade-off between <code>graph_k</code> and <code>sample_rate</code>. To ensure a single connected component, and thus an effective search index, one can increase <code>graph_k</code>. The <code>connectivity</code> method is a helper to determine whether the search index has a single connected component. However, search accuracy might come at the cost of increased memory usage and slow processing. To alleviate that, one can rely on decreasing the <code>sample_rate</code> to avoid exploring all the undirected edges of a node during search queries and local graph refinements. Moreover, the edge pruning procedures also help decreasing the computational costs. Note that, anything that limits the number of explored neighbors or prunes edges might have a negative impact on search accuracy.</p> <ol> <li> <p>Dong, W., Moses, C., &amp; Li, K. (2011, March). Efficient k-nearest neighbor graph construction for generic similarity measures. In Proceedings of the 20th international conference on World wide web (pp. 577-586).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/neural-net/MLPRegressor/","title":"MLPRegressor","text":"<p>Multi-layer Perceptron for regression.</p> <p>This model is still work in progress. Here are some features that still need implementing: </p> <ul> <li> <p><code>learn_one</code> and <code>predict_one</code> just cast the input <code>dict</code> to a single row dataframe and then</p> <p>call <code>learn_many</code> and <code>predict_many</code> respectively. This is very inefficient. - Not all of the optimizers in the <code>optim</code> module can be used as they are not all vectorised.</p> </li> <li> <p>Emerging and disappearing features are not supported. Each instance/batch has to have the</p> <p>same features. - The gradient haven't been numerically checked.</p> </li> </ul>"},{"location":"api/neural-net/MLPRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>hidden_dims</p> <p>The dimensions of the hidden layers. For example, specifying <code>(10, 20)</code> means that there are two hidden layers with 10 and 20 neurons, respectively. Note that the number of layers the network contains is equal to the number of hidden layers plus two (to account for the input and output layers).</p> </li> <li> <p>activations</p> <p>The activation functions to use at each layer, including the input and output layers. Therefore you need to specify three activation if you specify one hidden layer.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>Loss function. Defaults to <code>optim.losses.Squared</code>.</p> </li> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>Optimizer. Defaults to <code>optim.SGD</code> with the learning rate set to <code>0.01</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/neural-net/MLPRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>n_layers</p> <p>Return the number of layers in the network.  The number of layers is equal to the number of hidden layers plus 2. The 2 accounts for the input layer and the output layer.</p> </li> </ul>"},{"location":"api/neural-net/MLPRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import neural_net as nn\nfrom river import optim\nfrom river import preprocessing as pp\nfrom river import metrics\n\nmodel = (\n    pp.StandardScaler() |\n    nn.MLPRegressor(\n        hidden_dims=(5,),\n        activations=(\n            nn.activations.ReLU,\n            nn.activations.ReLU,\n            nn.activations.Identity\n        ),\n        optimizer=optim.SGD(1e-3),\n        seed=42\n    )\n)\n\ndataset = datasets.TrumpApproval()\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.580578\n</code></pre></p> <p>You can also use this to process mini-batches of data.</p> <p><pre><code>model = (\n    pp.StandardScaler() |\n    nn.MLPRegressor(\n        hidden_dims=(10,),\n        activations=(\n            nn.activations.ReLU,\n            nn.activations.ReLU,\n            nn.activations.ReLU\n        ),\n        optimizer=optim.SGD(1e-4),\n        seed=42\n    )\n)\n\ndataset = datasets.TrumpApproval()\nbatch_size = 32\n\nfor epoch in range(10):\n    for xb in pd.read_csv(dataset.path, chunksize=batch_size):\n        yb = xb.pop('five_thirty_eight')\n        y_pred = model.predict_many(xb)\n        model.learn_many(xb, yb)\n\nmodel.predict_many(xb)\n</code></pre> <pre><code>      five_thirty_eight\n992           39.405231\n993           46.447481\n994           42.121865\n995           40.251148\n996           40.836378\n997           40.893153\n998           40.949927\n999           48.416504\n1000          42.077830\n</code></pre></p>"},{"location":"api/neural-net/MLPRegressor/#methods","title":"Methods","text":"call <p>Make predictions.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_many <p>Train the network.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> </ul> <p></p> predict_many predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/neural-net/activations/Identity/","title":"Identity","text":"<p>Identity activation function.</p>"},{"location":"api/neural-net/activations/Identity/#methods","title":"Methods","text":"apply <p>Apply the activation function to a layer output z.</p> <ul> <li>z </li> </ul> <p></p> gradient <p>Return the gradient with respect to a layer output z.</p> <ul> <li>z </li> </ul> <p></p>"},{"location":"api/neural-net/activations/ReLU/","title":"ReLU","text":"<p>Rectified Linear Unit (ReLU) activation function.</p>"},{"location":"api/neural-net/activations/ReLU/#methods","title":"Methods","text":"apply <p>Apply the activation function to a layer output z.</p> <ul> <li>z </li> </ul> <p></p> gradient <p>Return the gradient with respect to a layer output z.</p> <ul> <li>z </li> </ul> <p></p>"},{"location":"api/neural-net/activations/Sigmoid/","title":"Sigmoid","text":"<p>Sigmoid activation function.</p>"},{"location":"api/neural-net/activations/Sigmoid/#methods","title":"Methods","text":"apply <p>Apply the activation function to a layer output z.</p> <ul> <li>z </li> </ul> <p></p> gradient <p>Return the gradient with respect to a layer output z.</p> <ul> <li>z </li> </ul> <p></p>"},{"location":"api/optim/AMSGrad/","title":"AMSGrad","text":"<p>AMSGrad optimizer.</p>"},{"location":"api/optim/AMSGrad/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Type \u2192 int | float | optim.base.Scheduler</p> <p>Default \u2192 <code>0.1</code></p> <p>The learning rate.</p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> <li> <p>correct_bias</p> <p>Default \u2192 <code>True</code></p> </li> </ul>"},{"location":"api/optim/AMSGrad/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>v (collections.defaultdict)</p> </li> <li> <p>v_hat (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AMSGrad/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AMSGrad()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.60%\n</code></pre></p>"},{"location":"api/optim/AMSGrad/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Reddi, S.J., Kale, S. and Kumar, S., 2019. On the convergence of adam and beyond. arXiv preprint arXiv:1904.09237 \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaBound/","title":"AdaBound","text":"<p>AdaBound optimizer.</p>"},{"location":"api/optim/AdaBound/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.001</code></p> <p>The learning rate.</p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> <li> <p>gamma</p> <p>Default \u2192 <code>0.001</code></p> </li> <li> <p>final_lr</p> <p>Default \u2192 <code>0.1</code></p> </li> </ul>"},{"location":"api/optim/AdaBound/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>s (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AdaBound/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaBound()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.06%\n</code></pre></p>"},{"location":"api/optim/AdaBound/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Luo, L., Xiong, Y., Liu, Y. and Sun, X., 2019. Adaptive gradient methods with dynamic bound of learning rate. arXiv preprint arXiv:1902.09843 \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaDelta/","title":"AdaDelta","text":"<p>AdaDelta optimizer.</p>"},{"location":"api/optim/AdaDelta/#parameters","title":"Parameters","text":"<ul> <li> <p>rho</p> <p>Default \u2192 <code>0.95</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/AdaDelta/#attributes","title":"Attributes","text":"<ul> <li> <p>g2 (collections.defaultdict)</p> </li> <li> <p>s2 (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AdaDelta/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaDelta()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 80.56%\n</code></pre></p>"},{"location":"api/optim/AdaDelta/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Zeiler, M.D., 2012. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701. \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaGrad/","title":"AdaGrad","text":"<p>AdaGrad optimizer.</p>"},{"location":"api/optim/AdaGrad/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/AdaGrad/#attributes","title":"Attributes","text":"<ul> <li>g2 (collections.defaultdict)</li> </ul>"},{"location":"api/optim/AdaGrad/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaGrad()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 88.01%\n</code></pre></p>"},{"location":"api/optim/AdaGrad/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Duchi, J., Hazan, E. and Singer, Y., 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(Jul), pp.2121-2159. \u21a9</p> </li> </ol>"},{"location":"api/optim/AdaMax/","title":"AdaMax","text":"<p>AdaMax optimizer.</p>"},{"location":"api/optim/AdaMax/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/AdaMax/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>v (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/AdaMax/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.AdaMax()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.61%\n</code></pre></p>"},{"location":"api/optim/AdaMax/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. \u21a9</p> </li> <li> <p>Ruder, S., 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. \u21a9</p> </li> </ol>"},{"location":"api/optim/Adam/","title":"Adam","text":"<p>Adam optimizer.</p>"},{"location":"api/optim/Adam/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/Adam/#attributes","title":"Attributes","text":"<ul> <li> <p>m (collections.defaultdict)</p> </li> <li> <p>v (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/Adam/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Adam()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.52%\n</code></pre></p>"},{"location":"api/optim/Adam/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. \u21a9</p> </li> </ol>"},{"location":"api/optim/Averager/","title":"Averager","text":"<p>Averaged stochastic gradient descent.</p> <p>This is a wrapper that can be applied to any stochastic gradient descent optimiser. Note that this implementation differs than what may be found elsewhere. Essentially, the average of the weights is usually only used at the end of the optimisation, once all the data has been seen. However, in this implementation the optimiser returns the current averaged weights.</p>"},{"location":"api/optim/Averager/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer</p> <p>An optimizer for which the produced weights will be averaged.</p> </li> <li> <p>start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Indicates the number of iterations to wait before starting the average. Essentially, nothing happens differently before the number of iterations reaches this value.</p> </li> </ul>"},{"location":"api/optim/Averager/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/Averager/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Averager(optim.SGD(0.01), 100)\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.97%\n</code></pre></p>"},{"location":"api/optim/Averager/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Bottou, L., 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010 (pp. 177-186). Physica-Verlag HD. \u21a9</p> </li> <li> <p>Stochastic Algorithms for One-Pass Learning slides by L\u00e9on Bottou \u21a9</p> </li> <li> <p>Xu, W., 2011. Towards optimal one pass large scale learning with averaged stochastic gradient descent. arXiv preprint arXiv:1107.2490. \u21a9</p> </li> </ol>"},{"location":"api/optim/FTRLProximal/","title":"FTRLProximal","text":"<p>FTRL-Proximal optimizer.</p>"},{"location":"api/optim/FTRLProximal/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>0.05</code></p> </li> <li> <p>beta</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>l1</p> <p>Default \u2192 <code>0.0</code></p> </li> <li> <p>l2</p> <p>Default \u2192 <code>1.0</code></p> </li> </ul>"},{"location":"api/optim/FTRLProximal/#attributes","title":"Attributes","text":"<ul> <li> <p>z (collections.defaultdict)</p> </li> <li> <p>n (collections.defaultdict)</p> </li> </ul>"},{"location":"api/optim/FTRLProximal/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.FTRLProximal()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.56%\n</code></pre></p>"},{"location":"api/optim/FTRLProximal/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D. and Chikkerur, S., 2013, August. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222-1230) \u21a9</p> </li> <li> <p>Tensorflow's <code>FtrlOptimizer</code> \u21a9</p> </li> </ol>"},{"location":"api/optim/Momentum/","title":"Momentum","text":"<p>Momentum optimizer.</p>"},{"location":"api/optim/Momentum/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>rho</p> <p>Default \u2192 <code>0.9</code></p> </li> </ul>"},{"location":"api/optim/Momentum/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/Momentum/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Momentum()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 84.09%\n</code></pre></p>"},{"location":"api/optim/Momentum/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p>"},{"location":"api/optim/Nadam/","title":"Nadam","text":"<p>Nadam optimizer.</p>"},{"location":"api/optim/Nadam/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>beta_1</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>beta_2</p> <p>Default \u2192 <code>0.999</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/Nadam/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/Nadam/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.Nadam()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 86.60%\n</code></pre></p>"},{"location":"api/optim/Nadam/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Nadam: A combination of adam and nesterov \u21a9</p> </li> </ol>"},{"location":"api/optim/NesterovMomentum/","title":"NesterovMomentum","text":"<p>Nesterov Momentum optimizer.</p>"},{"location":"api/optim/NesterovMomentum/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>rho</p> <p>Default \u2192 <code>0.9</code></p> </li> </ul>"},{"location":"api/optim/NesterovMomentum/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/NesterovMomentum/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.NesterovMomentum()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 84.22%\n</code></pre></p>"},{"location":"api/optim/NesterovMomentum/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p>"},{"location":"api/optim/RMSProp/","title":"RMSProp","text":"<p>RMSProp optimizer.</p>"},{"location":"api/optim/RMSProp/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.1</code></p> </li> <li> <p>rho</p> <p>Default \u2192 <code>0.9</code></p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> </li> </ul>"},{"location":"api/optim/RMSProp/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/RMSProp/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.RMSProp()\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.24%\n</code></pre></p>"},{"location":"api/optim/RMSProp/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Divide the gradient by a running average of itsrecent magnitude \u21a9</p> </li> </ol>"},{"location":"api/optim/SGD/","title":"SGD","text":"<p>Plain stochastic gradient descent.</p>"},{"location":"api/optim/SGD/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Default \u2192 <code>0.01</code></p> </li> </ul>"},{"location":"api/optim/SGD/#attributes","title":"Attributes","text":"<ul> <li>learning_rate</li> </ul>"},{"location":"api/optim/SGD/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\n\ndataset = datasets.Phishing()\noptimizer = optim.SGD(0.1)\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(optimizer)\n)\nmetric = metrics.F1()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>F1: 87.85%\n</code></pre></p>"},{"location":"api/optim/SGD/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p> <ol> <li> <p>Robbins, H. and Monro, S., 1951. A stochastic approximation method. The annals of mathematical statistics, pp.400-407 \u21a9</p> </li> </ol>"},{"location":"api/optim/base/Initializer/","title":"Initializer","text":"<p>An initializer is used to set initial weights in a model.</p>"},{"location":"api/optim/base/Initializer/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/base/Loss/","title":"Loss","text":"<p>Base class for all loss functions.</p>"},{"location":"api/optim/base/Loss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/base/Optimizer/","title":"Optimizer","text":"<p>Optimizer interface.</p> <p>Every optimizer inherits from this base interface.</p>"},{"location":"api/optim/base/Optimizer/#parameters","title":"Parameters","text":"<ul> <li> <p>lr</p> <p>Type \u2192 int | float | Scheduler</p> </li> </ul>"},{"location":"api/optim/base/Optimizer/#attributes","title":"Attributes","text":"<ul> <li> <p>learning_rate (float)</p> <p>Returns the current learning rate value.</p> </li> </ul>"},{"location":"api/optim/base/Optimizer/#methods","title":"Methods","text":"look_ahead <p>Updates a weight vector before a prediction is made.</p> <p>Parameters:     w (dict): A dictionary of weight parameters. The weights are modified in-place.  Returns:     The updated weights.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict' </li> </ul> <p></p> step <p>Updates a weight vector given a gradient.</p> <p>Parameters</p> <ul> <li>w     \u2014 'dict | VectorLike' </li> <li>g     \u2014 'dict | VectorLike' </li> </ul> <p>Returns</p> <p>dict | VectorLike:     The updated weights.</p> <p></p>"},{"location":"api/optim/base/Scheduler/","title":"Scheduler","text":"<p>Can be used to program the learning rate schedule of an <code>optim.base.Optimizer</code>.</p>"},{"location":"api/optim/base/Scheduler/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/optim/initializers/Constant/","title":"Constant","text":"<p>Constant initializer which always returns the same value.</p>"},{"location":"api/optim/initializers/Constant/#parameters","title":"Parameters","text":"<ul> <li> <p>value</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/optim/initializers/Constant/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ninit = optim.initializers.Constant(value=3.14)\n\ninit(shape=1)\n</code></pre> <pre><code>3.14\n</code></pre></p> <p><pre><code>init(shape=2)\n</code></pre> <pre><code>array([3.14, 3.14])\n</code></pre></p>"},{"location":"api/optim/initializers/Constant/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/initializers/Normal/","title":"Normal","text":"<p>Random normal initializer which simulate a normal distribution with specified parameters.</p>"},{"location":"api/optim/initializers/Normal/#parameters","title":"Parameters","text":"<ul> <li> <p>mu</p> <p>Default \u2192 <code>0.0</code></p> <p>The mean of the normal distribution</p> </li> <li> <p>sigma</p> <p>Default \u2192 <code>1.0</code></p> <p>The standard deviation of the normal distribution</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed that can be set for reproducibility.</p> </li> </ul>"},{"location":"api/optim/initializers/Normal/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ninit = optim.initializers.Normal(mu=0, sigma=1, seed=42)\n\ninit(shape=1)\n</code></pre> <pre><code>0.496714\n</code></pre></p> <p><pre><code>init(shape=2)\n</code></pre> <pre><code>array([-0.1382643 ,  0.64768854])\n</code></pre></p>"},{"location":"api/optim/initializers/Normal/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/initializers/Zeros/","title":"Zeros","text":"<p>Constant initializer which always returns zeros.</p>"},{"location":"api/optim/initializers/Zeros/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ninit = optim.initializers.Zeros()\n\ninit(shape=1)\n</code></pre> <pre><code>0.0\n</code></pre></p> <p><pre><code>init(shape=2)\n</code></pre> <pre><code>array([0., 0.])\n</code></pre></p>"},{"location":"api/optim/initializers/Zeros/#methods","title":"Methods","text":"call <p>Returns a fresh set of weights.</p> <p>Parameters</p> <ul> <li>shape     \u2014 defaults to <code>1</code> </li> </ul> <p></p>"},{"location":"api/optim/losses/Absolute/","title":"Absolute","text":"<p>Absolute loss, also known as the mean absolute error or L1 loss.</p> <p>Mathematically, it is defined as </p> \\[L = |p_i - y_i|\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[\\frac{\\partial L}{\\partial p_i} = sgn(p_i - y_i)\\]"},{"location":"api/optim/losses/Absolute/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Absolute()\nloss(-42, 42)\n</code></pre> <pre><code>84\n</code></pre> <pre><code>loss.gradient(1, 2)\n</code></pre> <pre><code>1\n</code></pre> <pre><code>loss.gradient(2, 1)\n</code></pre> <pre><code>-1\n</code></pre></p>"},{"location":"api/optim/losses/Absolute/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/BinaryFocalLoss/","title":"BinaryFocalLoss","text":"<p>Binary focal loss.</p> <p>This implements the \"star\" algorithm from the appendix of the focal loss paper.</p>"},{"location":"api/optim/losses/BinaryFocalLoss/#parameters","title":"Parameters","text":"<ul> <li> <p>gamma</p> <p>Default \u2192 <code>2</code></p> </li> <li> <p>beta</p> <p>Default \u2192 <code>1</code></p> </li> </ul>"},{"location":"api/optim/losses/BinaryFocalLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p> 1. Lin, T.Y., Goyal, P., Girshick, R., He, K. and Doll\u00e1r, P., 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988)</p>"},{"location":"api/optim/losses/BinaryLoss/","title":"BinaryLoss","text":"<p>A loss appropriate for binary classification tasks.</p>"},{"location":"api/optim/losses/BinaryLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Cauchy/","title":"Cauchy","text":"<p>Cauchy loss function.</p>"},{"location":"api/optim/losses/Cauchy/#parameters","title":"Parameters","text":"<ul> <li> <p>C</p> <p>Default \u2192 <code>80</code></p> </li> </ul>"},{"location":"api/optim/losses/Cauchy/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>\"Effect of MAE\" Kaggle discussion \u21a9</p> </li> <li> <p>Paris Madness Kaggle kernel \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/CrossEntropy/","title":"CrossEntropy","text":"<p>Cross entropy loss.</p> <p>This is a generalization of logistic loss to multiple classes.</p>"},{"location":"api/optim/losses/CrossEntropy/#parameters","title":"Parameters","text":"<ul> <li> <p>class_weight</p> <p>Type \u2192 dict[base.typing.ClfTarget, float] | None</p> <p>Default \u2192 <code>None</code></p> <p>A dictionary that indicates what weight to associate with each class.</p> </li> </ul>"},{"location":"api/optim/losses/CrossEntropy/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\ny_true = [0, 1, 2, 2]\ny_pred = [\n    {0: 0.29450637, 1: 0.34216758, 2: 0.36332605},\n    {0: 0.21290077, 1: 0.32728332, 2: 0.45981591},\n    {0: 0.42860913, 1: 0.33380113, 2: 0.23758974},\n    {0: 0.44941979, 1: 0.32962558, 2: 0.22095463}\n]\n\nloss = optim.losses.CrossEntropy()\n\nfor yt, yp in zip(y_true, y_pred):\n    print(loss(yt, yp))\n</code></pre> <pre><code>1.222454\n1.116929\n1.437209\n1.509797\n</code></pre></p> <p><pre><code>for yt, yp in zip(y_true, y_pred):\n    print(loss.gradient(yt, yp))\n</code></pre> <pre><code>{0: -0.70549363, 1: 0.34216758, 2: 0.36332605}\n{0: 0.21290077, 1: -0.67271668, 2: 0.45981591}\n{0: 0.42860913, 1: 0.33380113, 2: -0.76241026}\n{0: 0.44941979, 1: 0.32962558, 2: -0.77904537}\n</code></pre></p>"},{"location":"api/optim/losses/CrossEntropy/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>What is Softmax regression and how is it related to Logistic regression? \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/EpsilonInsensitiveHinge/","title":"EpsilonInsensitiveHinge","text":"<p>Epsilon-insensitive hinge loss.</p>"},{"location":"api/optim/losses/EpsilonInsensitiveHinge/#parameters","title":"Parameters","text":"<ul> <li> <p>eps</p> <p>Default \u2192 <code>0.1</code></p> </li> </ul>"},{"location":"api/optim/losses/EpsilonInsensitiveHinge/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Hinge/","title":"Hinge","text":"<p>Computes the hinge loss.</p> <p>Mathematically, it is defined as </p> \\[L = max(0, 1 - p_i * y_i)\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[ \\\\frac{\\\\partial L}{\\\\partial y_i} = \\\\left\\{ \\\\begin{array}{ll}     \\\\ 0  &amp;   p_iy_i \\geqslant 1  \\\\\\\\     \\\\ - y_i &amp; p_iy_i &lt; 1 \\\\end{array} \\\\right. \\]"},{"location":"api/optim/losses/Hinge/#parameters","title":"Parameters","text":"<ul> <li> <p>threshold</p> <p>Default \u2192 <code>1.0</code></p> <p>Margin threshold. 1 yield the loss used in SVMs, whilst 0 is equivalent to the loss used in the Perceptron algorithm.</p> </li> </ul>"},{"location":"api/optim/losses/Hinge/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Hinge(threshold=1)\nloss(1, .2)\n</code></pre> <pre><code>0.8\n</code></pre></p> <p><pre><code>loss.gradient(1, .2)\n</code></pre> <pre><code>-1\n</code></pre></p>"},{"location":"api/optim/losses/Hinge/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Huber/","title":"Huber","text":"<p>Huber loss.</p> <p>Variant of the squared loss that is robust to outliers.</p>"},{"location":"api/optim/losses/Huber/#parameters","title":"Parameters","text":"<ul> <li> <p>epsilon</p> <p>Default \u2192 <code>0.1</code></p> </li> </ul>"},{"location":"api/optim/losses/Huber/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p> 1. Huber loss function - Wikipedia</p>"},{"location":"api/optim/losses/Log/","title":"Log","text":"<p>Logarithmic loss.</p> <p>This loss function expects each provided <code>y_pred</code> to be a logit. In other words if must be the raw output of a linear model or a neural network.</p>"},{"location":"api/optim/losses/Log/#parameters","title":"Parameters","text":"<ul> <li> <p>weight_pos</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>weight_neg</p> <p>Default \u2192 <code>1.0</code></p> </li> </ul>"},{"location":"api/optim/losses/Log/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>Logit Wikipedia page \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/MultiClassLoss/","title":"MultiClassLoss","text":"<p>A loss appropriate for multi-class classification tasks.</p>"},{"location":"api/optim/losses/MultiClassLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Poisson/","title":"Poisson","text":"<p>Poisson loss.</p> <p>The Poisson loss is usually more suited for regression with count data than the squared loss. </p> <p>Mathematically, it is defined as </p> \\[L = exp(p_i) - y_i \\times p_i\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[\\frac{\\partial L}{\\partial p_i} = exp(p_i) - y_i\\]"},{"location":"api/optim/losses/Poisson/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Quantile/","title":"Quantile","text":"<p>Quantile loss.</p>"},{"location":"api/optim/losses/Quantile/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Default \u2192 <code>0.5</code></p> <p>Desired quantile to attain.</p> </li> </ul>"},{"location":"api/optim/losses/Quantile/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Quantile(0.5)\nloss(1, 3)\n</code></pre> <pre><code>1.0\n</code></pre></p> <p><pre><code>loss.gradient(1, 3)\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>loss.gradient(3, 1)\n</code></pre> <pre><code>-0.5\n</code></pre></p>"},{"location":"api/optim/losses/Quantile/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p> <ol> <li> <p>Wikipedia article on quantile regression \u21a9</p> </li> <li> <p>Derivative from WolframAlpha \u21a9</p> </li> </ol>"},{"location":"api/optim/losses/RegressionLoss/","title":"RegressionLoss","text":"<p>A loss appropriate for regression tasks.</p>"},{"location":"api/optim/losses/RegressionLoss/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/losses/Squared/","title":"Squared","text":"<p>Squared loss, also known as the L2 loss.</p> <p>Mathematically, it is defined as </p> \\[L = (p_i - y_i) ^ 2\\] <p>Its gradient w.r.t. to \\(p_i\\) is </p> \\[\\frac{\\partial L}{\\partial p_i} = 2 (p_i - y_i)\\] <p>One thing to note is that this convention is consistent with Vowpal Wabbit and PyTorch, but not with scikit-learn. Indeed, scikit-learn divides the loss by 2, making the 2 disappear in the gradient.</p>"},{"location":"api/optim/losses/Squared/#examples","title":"Examples","text":"<p><pre><code>from river import optim\n\nloss = optim.losses.Squared()\nloss(-4, 5)\n</code></pre> <pre><code>81\n</code></pre> <pre><code>loss.gradient(-4, 5)\n</code></pre> <pre><code>18\n</code></pre> <pre><code>loss.gradient(5, -4)\n</code></pre> <pre><code>-18\n</code></pre></p>"},{"location":"api/optim/losses/Squared/#methods","title":"Methods","text":"call <p>Returns the loss.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The loss(es).</p> <p></p> gradient <p>Return the gradient with respect to y_pred.</p> <p>Parameters</p> <ul> <li>y_true </li> <li>y_pred </li> </ul> <p>Returns</p> <p>The gradient(s).</p> <p></p> mean_func <p>Mean function.</p> <p>This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.</p> <p>Parameters</p> <ul> <li>y_pred </li> </ul> <p>Returns</p> <p>The adjusted prediction(s).</p> <p></p>"},{"location":"api/optim/schedulers/Constant/","title":"Constant","text":"<p>Always uses the same learning rate.</p>"},{"location":"api/optim/schedulers/Constant/#parameters","title":"Parameters","text":"<ul> <li> <p>learning_rate</p> <p>Type \u2192 int | float</p> </li> </ul>"},{"location":"api/optim/schedulers/Constant/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/optim/schedulers/InverseScaling/","title":"InverseScaling","text":"<p>Reduces the learning rate using a power schedule.</p> <p>Assuming an initial learning rate \\(\\eta\\), the learning rate at step \\(t\\) is: </p> \\[\\\\frac{eta}{(t + 1) ^ p}\\] <p>where \\(p\\) is a user-defined parameter.</p>"},{"location":"api/optim/schedulers/InverseScaling/#parameters","title":"Parameters","text":"<ul> <li> <p>learning_rate</p> <p>Type \u2192 float</p> </li> <li> <p>power</p> <p>Default \u2192 <code>0.5</code></p> </li> </ul>"},{"location":"api/optim/schedulers/InverseScaling/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p>"},{"location":"api/optim/schedulers/Optimal/","title":"Optimal","text":"<p>Optimal learning schedule as proposed by L\u00e9on Bottou.</p>"},{"location":"api/optim/schedulers/Optimal/#parameters","title":"Parameters","text":"<ul> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss</p> </li> <li> <p>alpha</p> <p>Default \u2192 <code>0.0001</code></p> </li> </ul>"},{"location":"api/optim/schedulers/Optimal/#methods","title":"Methods","text":"get <p>Returns the learning rate at a given iteration.</p> <p>Parameters</p> <ul> <li>t     \u2014 'int' </li> </ul> <p></p> <ol> <li> <p>Bottou, L., 2012. Stochastic gradient descent tricks. In Neural networks: Tricks of the trade (pp. 421-436). Springer, Berlin, Heidelberg. \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/AdaptiveStandardScaler/","title":"AdaptiveStandardScaler","text":"<p>Scales data using exponentially weighted moving average and variance.</p> <p>Under the hood, a exponentially weighted running mean and variance are maintained for each feature. This can potentially provide better results for drifting data in comparison to <code>preprocessing.StandardScaler</code>. Indeed, the latter computes a global mean and variance for each feature, whereas this scaler weights data in proportion to their recency.</p>"},{"location":"api/preprocessing/AdaptiveStandardScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.3</code></p> <p>This parameter is passed to <code>stats.EWVar</code>. It is expected to be in [0, 1]. More weight is assigned to recent samples the closer <code>fading_factor</code> is to 1.</p> </li> </ul>"},{"location":"api/preprocessing/AdaptiveStandardScaler/#examples","title":"Examples","text":"<p>Consider the following series which contains a positive trend.</p> <p><pre><code>import random\n\nrandom.seed(42)\nX = [\n    {'x': random.uniform(4 + i, 6 + i)}\n    for i in range(8)\n]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 5.278}\n{'x': 5.050}\n{'x': 6.550}\n{'x': 7.446}\n{'x': 9.472}\n{'x': 10.353}\n{'x': 11.784}\n{'x': 11.173}\n</code></pre></p> <p>This scaler works well with this kind of data because it uses statistics that assign higher weight to more recent data.</p> <p><pre><code>from river import preprocessing\n\nscaler = preprocessing.AdaptiveStandardScaler(fading_factor=.6)\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 0.0}\n{'x': -0.816}\n{'x': 0.812}\n{'x': 0.695}\n{'x': 0.754}\n{'x': 0.598}\n{'x': 0.651}\n{'x': 0.124}\n</code></pre></p>"},{"location":"api/preprocessing/AdaptiveStandardScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/Binarizer/","title":"Binarizer","text":"<p>Binarizes the data to 0 or 1 according to a threshold.</p>"},{"location":"api/preprocessing/Binarizer/#parameters","title":"Parameters","text":"<ul> <li> <p>threshold</p> <p>Default \u2192 <code>0.0</code></p> <p>Values above this are replaced by 1 and the others by 0.</p> </li> <li> <p>dtype</p> <p>Default \u2192 <code>&lt;class 'bool'&gt;</code></p> <p>The desired data type to apply.</p> </li> </ul>"},{"location":"api/preprocessing/Binarizer/#examples","title":"Examples","text":"<p><pre><code>import river\nimport numpy as np\n\nrng = np.random.RandomState(42)\nX = [{'x1': v, 'x2': int(v)} for v in rng.uniform(low=-4, high=4, size=6)]\n\nbinarizer = river.preprocessing.Binarizer()\nfor x in X:\n    binarizer.learn_one(x)\n    print(binarizer.transform_one(x))\n</code></pre> <pre><code>{'x1': False, 'x2': False}\n{'x1': True, 'x2': True}\n{'x1': True, 'x2': True}\n{'x1': True, 'x2': False}\n{'x1': False, 'x2': False}\n{'x1': False, 'x2': False}\n</code></pre></p>"},{"location":"api/preprocessing/Binarizer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/FeatureHasher/","title":"FeatureHasher","text":"<p>Implements the hashing trick.</p> <p>Each pair of (name, value) features is hashed into a random integer. A module operator is then used to make sure the hash is in a certain range. We use the Murmurhash implementation from scikit-learn.</p>"},{"location":"api/preprocessing/FeatureHasher/#parameters","title":"Parameters","text":"<ul> <li> <p>n_features</p> <p>Default \u2192 <code>1048576</code></p> <p>The number by which each hash will be moduloed by.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Set the seed to produce identical results.</p> </li> </ul>"},{"location":"api/preprocessing/FeatureHasher/#examples","title":"Examples","text":"<p><pre><code>import river\n\nhasher = river.preprocessing.FeatureHasher(n_features=10, seed=42)\n\nX = [\n    {'dog': 1, 'cat': 2, 'elephant': 4},\n    {'dog': 2, 'run': 5}\n]\nfor x in X:\n    print(hasher.transform_one(x))\n</code></pre> <pre><code>Counter({1: 4, 9: 2, 8: 1})\nCounter({4: 5, 8: 2})\n</code></pre></p>"},{"location":"api/preprocessing/FeatureHasher/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Wikipedia article on feature vectorization using the hashing trick \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/GaussianRandomProjector/","title":"GaussianRandomProjector","text":"<p>Gaussian random projector.</p> <p>This transformer reduces the dimensionality of inputs through Gaussian random projection. </p> <p>The components of the random projections matrix are drawn from <code>N(0, 1 / n_components)</code>.</p>"},{"location":"api/preprocessing/GaussianRandomProjector/#parameters","title":"Parameters","text":"<ul> <li> <p>n_components</p> <p>Default \u2192 <code>10</code></p> <p>Number of components to project the data onto.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/preprocessing/GaussianRandomProjector/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = preprocessing.GaussianRandomProjector(\n    n_components=3,\n    seed=42\n)\n\nfor x, y in dataset:\n    x = model.transform_one(x)\n    print(x)\n    break\n</code></pre> <pre><code>{0: -61289.371..., 1: 141312.510..., 2: 279165.993...}\n</code></pre></p> <p><pre><code>model = (\n    preprocessing.GaussianRandomProjector(\n        n_components=5,\n        seed=42\n    ) |\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression()\n)\nevaluate.progressive_val_score(dataset, model, metrics.MAE())\n</code></pre> <pre><code>MAE: 0.933...\n</code></pre></p>"},{"location":"api/preprocessing/GaussianRandomProjector/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Gaussian random projection \u21a9</p> </li> <li> <p>scikit-learn random projections module \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/LDA/","title":"LDA","text":"<p>Online Latent Dirichlet Allocation with Infinite Vocabulary.</p> <p>Latent Dirichlet allocation (LDA) is a probabilistic approach for exploring topics in document collections. The key advantage of this variant is that it assumes an infinite vocabulary, meaning that the set of tokens does not have to known in advance, as opposed to the implementation from sklearn The results produced by this implementation are identical to those from the original implementation proposed by the method's authors. </p> <p>This class takes as input token counts. Therefore, it requires you to tokenize beforehand. You can do so by using a <code>feature_extraction.BagOfWords</code> instance, as shown in the example below.</p>"},{"location":"api/preprocessing/LDA/#parameters","title":"Parameters","text":"<ul> <li> <p>n_components</p> <p>Default \u2192 <code>10</code></p> <p>Number of topics of the latent Drichlet allocation.</p> </li> <li> <p>number_of_documents</p> <p>Default \u2192 <code>1000000.0</code></p> <p>Estimated number of documents.</p> </li> <li> <p>alpha_theta</p> <p>Default \u2192 <code>0.5</code></p> <p>Hyper-parameter of the Dirichlet distribution of topics.</p> </li> <li> <p>alpha_beta</p> <p>Default \u2192 <code>100.0</code></p> <p>Hyper-parameter of the Dirichlet process of distribution over words.</p> </li> <li> <p>tau</p> <p>Default \u2192 <code>64.0</code></p> <p>Learning inertia to prevent premature convergence.</p> </li> <li> <p>kappa</p> <p>Default \u2192 <code>0.75</code></p> <p>The learning rate kappa controls how quickly new parameters estimates replace the old ones. kappa \u2208 (0.5, 1] is required for convergence.</p> </li> <li> <p>vocab_prune_interval</p> <p>Default \u2192 <code>10</code></p> <p>Interval at which to refresh the words topics distribution.</p> </li> <li> <p>number_of_samples</p> <p>Default \u2192 <code>10</code></p> <p>Number of iteration to computes documents topics distribution.</p> </li> <li> <p>ranking_smooth_factor</p> <p>Default \u2192 <code>1e-12</code></p> </li> <li> <p>burn_in_sweeps</p> <p>Default \u2192 <code>5</code></p> <p>Number of iteration necessaries while analyzing a document before updating document topics distribution.</p> </li> <li> <p>maximum_size_vocabulary</p> <p>Default \u2192 <code>4000</code></p> <p>Maximum size of the stored vocabulary.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number seed used for reproducibility.</p> </li> </ul>"},{"location":"api/preprocessing/LDA/#attributes","title":"Attributes","text":"<ul> <li> <p>counter (int)</p> <p>The current number of observed documents.</p> </li> <li> <p>truncation_size_prime (int)</p> <p>Number of distincts words stored in the vocabulary. Updated before processing a document.</p> </li> <li> <p>truncation_size (int)</p> <p>Number of distincts words stored in the vocabulary. Updated after processing a document.</p> </li> <li> <p>word_to_index (dict)</p> <p>Words as keys and indexes as values.</p> </li> <li> <p>index_to_word (dict)</p> <p>Indexes as keys and words as values.</p> </li> <li> <p>nu_1 (dict)</p> <p>Weights of the words. Component of the variational inference.</p> </li> <li> <p>nu_2 (dict)</p> <p>Weights of the words. Component of the variational inference.</p> </li> </ul>"},{"location":"api/preprocessing/LDA/#examples","title":"Examples","text":"<p><pre><code>from river import compose\nfrom river import feature_extraction\nfrom river import preprocessing\n\nX = [\n   'weather cold',\n   'weather hot dry',\n   'weather cold rainy',\n   'weather hot',\n   'weather cold humid',\n]\n\nlda = compose.Pipeline(\n    feature_extraction.BagOfWords(),\n    preprocessing.LDA(\n        n_components=2,\n        number_of_documents=60,\n        seed=42\n    )\n)\n\nfor x in X:\n    lda.learn_one(x)\n    topics = lda.transform_one(x)\n    print(topics)\n</code></pre> <pre><code>{0: 0.5, 1: 2.5}\n{0: 2.499..., 1: 1.5}\n{0: 0.5, 1: 3.5}\n{0: 0.5, 1: 2.5}\n{0: 1.5, 1: 2.5}\n</code></pre></p>"},{"location":"api/preprocessing/LDA/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> learn_transform_one <p>Equivalent to <code>lda.learn_one(x).transform_one(x)</code>s, but faster.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     Component attributions for the input document.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Zhai, K. and Boyd-Graber, J., 2013, February. Online latent Dirichlet allocation with infinite vocabulary. In International Conference on Machine Learning (pp. 561-569). \u21a9</p> </li> <li> <p>PyInfVoc on GitHub \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/MaxAbsScaler/","title":"MaxAbsScaler","text":"<p>Scales the data to a [-1, 1] range based on absolute maximum.</p> <p>Under the hood a running absolute max is maintained. This scaler is meant for data that is already centered at zero or sparse data. It does not shift/center the data, and thus does not destroy any sparsity.</p>"},{"location":"api/preprocessing/MaxAbsScaler/#attributes","title":"Attributes","text":"<ul> <li> <p>abs_max (dict)</p> <p>Mapping between features and instances of <code>stats.AbsMax</code>.</p> </li> </ul>"},{"location":"api/preprocessing/MaxAbsScaler/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12)} for _ in range(5)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 10.557707}\n{'x': 8.100043}\n{'x': 9.100117}\n{'x': 8.892842}\n{'x': 10.945884}\n</code></pre></p> <p><pre><code>scaler = preprocessing.MaxAbsScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 1.0}\n{'x': 0.767216}\n{'x': 0.861940}\n{'x': 0.842308}\n{'x': 1.0}\n</code></pre></p>"},{"location":"api/preprocessing/MaxAbsScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/MinMaxScaler/","title":"MinMaxScaler","text":"<p>Scales the data to a fixed range from 0 to 1.</p> <p>Under the hood a running min and a running peak to peak (max - min) are maintained.</p>"},{"location":"api/preprocessing/MinMaxScaler/#attributes","title":"Attributes","text":"<ul> <li> <p>min (dict)</p> <p>Mapping between features and instances of <code>stats.Min</code>.</p> </li> <li> <p>max (dict)</p> <p>Mapping between features and instances of <code>stats.Max</code>.</p> </li> </ul>"},{"location":"api/preprocessing/MinMaxScaler/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12)} for _ in range(5)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 10.557707}\n{'x': 8.100043}\n{'x': 9.100117}\n{'x': 8.892842}\n{'x': 10.945884}\n</code></pre></p> <p><pre><code>scaler = preprocessing.MinMaxScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 0.0}\n{'x': 0.0}\n{'x': 0.406920}\n{'x': 0.322582}\n{'x': 1.0}\n</code></pre></p>"},{"location":"api/preprocessing/MinMaxScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/Normalizer/","title":"Normalizer","text":"<p>Scales a set of features so that it has unit norm.</p> <p>This is particularly useful when used after a <code>feature_extraction.TFIDF</code>.</p>"},{"location":"api/preprocessing/Normalizer/#parameters","title":"Parameters","text":"<ul> <li> <p>order</p> <p>Default \u2192 <code>2</code></p> <p>Order of the norm (e.g. 2 corresponds to the \\(L^2\\) norm).</p> </li> </ul>"},{"location":"api/preprocessing/Normalizer/#examples","title":"Examples","text":"<p><pre><code>from river import preprocessing\nfrom river import stream\n\nscaler = preprocessing.Normalizer(order=2)\n\nX = [[4, 1, 2, 2],\n     [1, 3, 9, 3],\n     [5, 7, 5, 1]]\n\nfor x, _ in stream.iter_array(X):\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{0: 0.8, 1: 0.2, 2: 0.4, 3: 0.4}\n{0: 0.1, 1: 0.3, 2: 0.9, 3: 0.3}\n{0: 0.5, 1: 0.7, 2: 0.5, 3: 0.1}\n</code></pre></p>"},{"location":"api/preprocessing/Normalizer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/OneHotEncoder/","title":"OneHotEncoder","text":"<p>One-hot encoding.</p> <p>This transformer will encode every feature it is provided with. If a list or set is provided, this transformer will encode every entry in the list/set. You can apply it to a subset of features by composing it  with <code>compose.Select</code> or <code>compose.SelectType</code>.</p>"},{"location":"api/preprocessing/OneHotEncoder/#parameters","title":"Parameters","text":"<ul> <li> <p>drop_zeros</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not 0s should be made explicit or not.</p> </li> <li> <p>drop_first</p> <p>Default \u2192 <code>False</code></p> <p>Whether to get <code>k - 1</code> dummies out of <code>k</code> categorical levels by removing the first key. This is useful in some statistical models where perfectly collinear features cause problems.</p> </li> </ul>"},{"location":"api/preprocessing/OneHotEncoder/#examples","title":"Examples","text":"<p>Let us first create an example dataset.</p> <p><pre><code>from pprint import pprint\nimport random\nimport string\n\nrandom.seed(42)\nalphabet = list(string.ascii_lowercase)\nX = [\n    {\n        'c1': random.choice(alphabet),\n        'c2': random.choice(alphabet),\n    }\n    for _ in range(4)\n]\npprint(X)\n</code></pre> <pre><code>[{'c1': 'u', 'c2': 'd'},\n    {'c1': 'a', 'c2': 'x'},\n    {'c1': 'i', 'c2': 'h'},\n    {'c1': 'h', 'c2': 'e'}]\n</code></pre></p> <p>e can now apply one-hot encoding. All the provided are one-hot encoded, there is therefore no need to specify which features to encode.</p> <p><pre><code>from river import preprocessing\n\noh = preprocessing.OneHotEncoder()\nfor x in X[:2]:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1, 'c2_d': 1}\n{'c1_a': 1, 'c1_u': 0, 'c2_d': 0, 'c2_x': 1}\n</code></pre></p> <p>The <code>drop_zeros</code> parameter can be set to <code>True</code> if you don't want the past features to be included in the output. Otherwise, all the past features will be included in the output.</p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=True)\nfor x in X:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1, 'c2_d': 1}\n{'c1_a': 1, 'c2_x': 1}\n{'c1_i': 1, 'c2_h': 1}\n{'c1_h': 1, 'c2_e': 1}\n</code></pre></p> <p>You can encode only <code>k - 1</code> features out of <code>k</code> by setting <code>drop_first</code> to <code>True</code>.</p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_first=True, drop_zeros=True)\nfor x in X:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c2_d': 1}\n{'c2_x': 1}\n{'c2_h': 1}\n{'c2_e': 1}\n</code></pre></p> <p>A subset of the features can be one-hot encoded by piping a <code>compose.Select</code> into the <code>OneHotEncoder</code>.</p> <p><pre><code>from river import compose\n\npp = compose.Select('c1') | preprocessing.OneHotEncoder()\n\nfor x in X:\n    pp.learn_one(x)\n    pprint(pp.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1}\n{'c1_a': 1, 'c1_u': 0}\n{'c1_a': 0, 'c1_i': 1, 'c1_u': 0}\n{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0}\n</code></pre></p> <p>You can preserve the <code>c2</code> feature by using a union:</p> <p><pre><code>pp = compose.Select('c1') | preprocessing.OneHotEncoder()\npp += compose.Select('c2')\n\nfor x in X:\n    pp.learn_one(x)\n    pprint(pp.transform_one(x))\n</code></pre> <pre><code>{'c1_u': 1, 'c2': 'd'}\n{'c1_a': 1, 'c1_u': 0, 'c2': 'x'}\n{'c1_a': 0, 'c1_i': 1, 'c1_u': 0, 'c2': 'h'}\n{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0, 'c2': 'e'}\n</code></pre></p> <p>Similar to the above examples, we can also pass values as a list. This will one-hot encode all of the entries individually.</p> <p><pre><code>X = [{'c1': ['u', 'a'], 'c2': ['d']},\n    {'c1': ['a', 'b'], 'c2': ['x']},\n    {'c1': ['i'], 'c2': ['h', 'z']},\n    {'c1': ['h', 'b'], 'c2': ['e']}]\n\noh = preprocessing.OneHotEncoder(drop_zeros=True)\nfor x in X:\n    oh.learn_one(x)\n    pprint(oh.transform_one(x))\n</code></pre> <pre><code>{'c1_a': 1, 'c1_u': 1, 'c2_d': 1}\n{'c1_a': 1, 'c1_b': 1, 'c2_x': 1}\n{'c1_i': 1, 'c2_h': 1, 'c2_z': 1}\n{'c1_b': 1, 'c1_h': 1, 'c2_e': 1}\n</code></pre></p> <p>Processing mini-batches is also possible.</p> <p><pre><code>from pprint import pprint\nimport random\nimport string\n\nrandom.seed(42)\nalphabet = list(string.ascii_lowercase)\nX = pd.DataFrame(\n    {\n        'c1': random.choice(alphabet),\n        'c2': random.choice(alphabet),\n    }\n    for _ in range(3)\n)\nX\n</code></pre> <pre><code>  c1 c2\n0  u  d\n1  a  x\n2  i  h\n</code></pre></p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=True)\ndf = oh.transform_many(X)\ndf.sort_index(axis=\"columns\")\n</code></pre> <pre><code>   c1_a  c1_i  c1_u  c2_d  c2_h  c2_x\n0     0     0     1     1     0     0\n1     1     0     0     0     0     1\n2     0     1     0     0     1     0\n</code></pre></p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=True, drop_first=True)\ndf = oh.transform_many(X)\ndf.sort_index(axis=\"columns\")\n</code></pre> <pre><code>   c1_i  c1_u  c2_d  c2_h  c2_x\n0     0     1     1     0     0\n1     0     0     0     0     1\n2     1     0     0     1     0\n</code></pre></p> <p>Here's an example where the zeros are kept:</p> <p><pre><code>oh = preprocessing.OneHotEncoder(drop_zeros=False)\nX_init = pd.DataFrame([{\"c1\": \"Oranges\", \"c2\": \"Apples\"}])\noh.learn_many(X_init)\noh.learn_many(X)\n\ndf = oh.transform_many(X)\ndf.sort_index(axis=\"columns\")\n</code></pre> <pre><code>   c1_Oranges  c1_a  c1_i  c1_u  c2_Apples  c2_d  c2_h  c2_x\n0           0     0     0     1          0     1     0     0\n1           0     1     0     0          0     0     0     1\n2           0     0     1     0          0     0     1     0\n</code></pre></p> <p><pre><code>df.dtypes.sort_index()\n</code></pre> <pre><code>c1_Oranges    Sparse[uint8, 0]\nc1_a          Sparse[uint8, 0]\nc1_i          Sparse[uint8, 0]\nc1_u          Sparse[uint8, 0]\nc2_Apples     Sparse[uint8, 0]\nc2_d          Sparse[uint8, 0]\nc2_h          Sparse[uint8, 0]\nc2_x          Sparse[uint8, 0]\ndtype: object\n</code></pre></p>"},{"location":"api/preprocessing/OneHotEncoder/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/OrdinalEncoder/","title":"OrdinalEncoder","text":"<p>Ordinal encoder.</p> <p>This transformer maps each feature to integers. It can useful when a feature has string values (i.e. categorical variables).</p>"},{"location":"api/preprocessing/OrdinalEncoder/#parameters","title":"Parameters","text":"<ul> <li> <p>unknown_value</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>0</code></p> <p>The value to use for unknown categories seen during <code>transform_one</code>. Unknown categories will be mapped to an integer once they are seen during <code>learn_one</code>. This value can be set to <code>None</code> in order to categories to <code>None</code> if they've never been seen before.</p> </li> <li> <p>none_value</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>-1</code></p> <p>The value to encode <code>None</code> with.</p> </li> </ul>"},{"location":"api/preprocessing/OrdinalEncoder/#attributes","title":"Attributes","text":"<ul> <li> <p>categories</p> <p>A dict of dicts. The outer dict maps each feature to its inner dict. The inner dict maps each category to its code.</p> </li> </ul>"},{"location":"api/preprocessing/OrdinalEncoder/#examples","title":"Examples","text":"<p><pre><code>from river import preprocessing\n\nX = [\n    {\"country\": \"France\", \"place\": \"Taco Bell\"},\n    {\"country\": None, \"place\": None},\n    {\"country\": \"Sweden\", \"place\": \"Burger King\"},\n    {\"country\": \"France\", \"place\": \"Burger King\"},\n    {\"country\": \"Russia\", \"place\": \"Starbucks\"},\n    {\"country\": \"Russia\", \"place\": \"Starbucks\"},\n    {\"country\": \"Sweden\", \"place\": \"Taco Bell\"},\n    {\"country\": None, \"place\": None},\n]\n\nencoder = preprocessing.OrdinalEncoder()\nfor x in X:\n    print(encoder.transform_one(x))\n    encoder.learn_one(x)\n</code></pre> <pre><code>{'country': 0, 'place': 0}\n{'country': -1, 'place': -1}\n{'country': 0, 'place': 0}\n{'country': 1, 'place': 2}\n{'country': 0, 'place': 0}\n{'country': 3, 'place': 3}\n{'country': 2, 'place': 1}\n{'country': -1, 'place': -1}\n</code></pre></p> <p><pre><code>xb1 = pd.DataFrame(X[0:4], index=[0, 1, 2, 3])\nxb2 = pd.DataFrame(X[4:8], index=[4, 5, 6, 7])\n\nencoder = preprocessing.OrdinalEncoder()\nencoder.transform_many(xb1)\n</code></pre> <pre><code>   country  place\n0        0      0\n1       -1     -1\n2        0      0\n3        0      0\n</code></pre></p> <p><pre><code>encoder.learn_many(xb1)\nencoder.transform_many(xb2)\n</code></pre> <pre><code>   country  place\n4        0      0\n5        0      0\n6        2      1\n7       -1     -1\n</code></pre></p>"},{"location":"api/preprocessing/OrdinalEncoder/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_many</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_many</code> can override this method.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> <li>y     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Transform a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p>Returns</p> <p>pd.DataFrame:     A new DataFrame.</p> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/PredClipper/","title":"PredClipper","text":"<p>Clips the target after predicting.</p>"},{"location":"api/preprocessing/PredClipper/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regressor model for which to clip the predictions.</p> </li> <li> <p>y_min</p> <p>Type \u2192 float</p> <p>minimum value.</p> </li> <li> <p>y_max</p> <p>Type \u2192 float</p> <p>maximum value.</p> </li> </ul>"},{"location":"api/preprocessing/PredClipper/#examples","title":"Examples","text":"<p><pre><code>from river import linear_model\nfrom river import preprocessing\n\ndataset = (\n    ({'a': 2, 'b': 4}, 80),\n    ({'a': 3, 'b': 5}, 100),\n    ({'a': 4, 'b': 6}, 120)\n)\n\nmodel = preprocessing.PredClipper(\n    regressor=linear_model.LinearRegression(),\n    y_min=0,\n    y_max=200\n)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel.predict_one({'a': -100, 'b': -200})\n</code></pre> <pre><code>0\n</code></pre></p> <p><pre><code>model.predict_one({'a': 50, 'b': 60})\n</code></pre> <pre><code>200\n</code></pre></p>"},{"location":"api/preprocessing/PredClipper/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>kwargs </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>kwargs </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/preprocessing/PreviousImputer/","title":"PreviousImputer","text":"<p>Imputes missing values by using the most recent value.</p>"},{"location":"api/preprocessing/PreviousImputer/#examples","title":"Examples","text":"<p><pre><code>from river import preprocessing\n\nimputer = preprocessing.PreviousImputer()\n\nimputer.learn_one({'x': 1, 'y': 2})\nimputer.transform_one({'y': None})\n</code></pre> <pre><code>{'y': 2}\n</code></pre></p> <p><pre><code>imputer.transform_one({'x': None})\n</code></pre> <pre><code>{'x': 1}\n</code></pre></p>"},{"location":"api/preprocessing/PreviousImputer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/RobustScaler/","title":"RobustScaler","text":"<p>Scale features using statistics that are robust to outliers.</p> <p>This Scaler removes the median and scales the data according to the interquantile range.</p>"},{"location":"api/preprocessing/RobustScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>with_centering</p> <p>Default \u2192 <code>True</code></p> <p>Whether to centre the data before scaling.</p> </li> <li> <p>with_scaling</p> <p>Default \u2192 <code>True</code></p> <p>Whether to scale data to IQR.</p> </li> <li> <p>q_inf</p> <p>Default \u2192 <code>0.25</code></p> <p>Desired inferior quantile, must be between 0 and 1.</p> </li> <li> <p>q_sup</p> <p>Default \u2192 <code>0.75</code></p> <p>Desired superior quantile, must be between 0 and 1.</p> </li> </ul>"},{"location":"api/preprocessing/RobustScaler/#attributes","title":"Attributes","text":"<ul> <li> <p>median (dict)</p> <p>Mapping between features and instances of <code>stats.Quantile</code>(0.5)`.</p> </li> <li> <p>iqr (dict)</p> <p>Mapping between features and instances of <code>stats.IQR</code>.</p> </li> </ul>"},{"location":"api/preprocessing/RobustScaler/#examples","title":"Examples","text":"<p><pre><code>from pprint import pprint\nimport random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12)} for _ in range(5)]\npprint(X)\n</code></pre> <pre><code>[{'x': 10.557707},\n    {'x': 8.100043},\n    {'x': 9.100117},\n    {'x': 8.892842},\n    {'x': 10.945884}]\n</code></pre></p> <p><pre><code>scaler = preprocessing.RobustScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>    {'x': 0.0}\n    {'x': -1.0}\n    {'x': 0.0}\n    {'x': -0.12449923287875722}\n    {'x': 1.1086595155704708}\n</code></pre></p>"},{"location":"api/preprocessing/RobustScaler/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/SparseRandomProjector/","title":"SparseRandomProjector","text":"<p>Sparse random projector.</p> <p>This transformer reduces the dimensionality of inputs by projecting them onto a sparse random projection matrix. </p> <p>Ping Li et al. recommend using a minimum density of <code>1 / sqrt(n_features)</code>. The transformer is not aware of how many features will be seen, so the user must specify the density manually.</p>"},{"location":"api/preprocessing/SparseRandomProjector/#parameters","title":"Parameters","text":"<ul> <li> <p>n_components</p> <p>Default \u2192 <code>10</code></p> <p>Number of components to project the data onto.</p> </li> <li> <p>density</p> <p>Default \u2192 <code>0.1</code></p> <p>Density of the random projection matrix. The density is defined as the ratio of non-zero components in the matrix. It is equal to <code>1 - sparsity</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/preprocessing/SparseRandomProjector/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = preprocessing.SparseRandomProjector(\n    n_components=3,\n    seed=42\n)\n\nfor x, y in dataset:\n    x = model.transform_one(x)\n    print(x)\n    break\n</code></pre> <pre><code>{0: 92.89572746525327, 1: 1344540.5692342375, 2: 0}\n</code></pre></p> <p><pre><code>model = (\n    preprocessing.SparseRandomProjector(\n        n_components=5,\n        seed=42\n    ) |\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression()\n)\nevaluate.progressive_val_score(dataset, model, metrics.MAE())\n</code></pre> <pre><code>MAE: 1.292572\n</code></pre></p>"},{"location":"api/preprocessing/SparseRandomProjector/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>D. Achlioptas. 2003. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences 66 (2003) 671-687\u00a0\u21a9</p> </li> <li> <p>Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006. Very sparse random projections. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'06). ACM, New York, NY, USA, 287-296.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/preprocessing/StandardScaler/","title":"StandardScaler","text":"<p>Scales the data so that it has zero mean and unit variance.</p> <p>Under the hood, a running mean and a running variance are maintained. The scaling is slightly different than when scaling the data in batch because the exact means and variances are not known in advance. However, this doesn't have a detrimental impact on performance in the long run. </p> <p>This transformer supports mini-batches as well as single instances. In the mini-batch case, the number of columns and the ordering of the columns are allowed to change between subsequent calls. In other words, this transformer will keep working even if you add and/or remove features every time you call <code>learn_many</code> and <code>transform_many</code>.</p>"},{"location":"api/preprocessing/StandardScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>with_std</p> <p>Default \u2192 <code>True</code></p> <p>Whether or not each feature should be divided by its standard deviation.</p> </li> </ul>"},{"location":"api/preprocessing/StandardScaler/#examples","title":"Examples","text":"<p><pre><code>import random\nfrom river import preprocessing\n\nrandom.seed(42)\nX = [{'x': random.uniform(8, 12), 'y': random.uniform(8, 12)} for _ in range(6)]\nfor x in X:\n    print(x)\n</code></pre> <pre><code>{'x': 10.557, 'y': 8.100}\n{'x': 9.100, 'y': 8.892}\n{'x': 10.945, 'y': 10.706}\n{'x': 11.568, 'y': 8.347}\n{'x': 9.687, 'y': 8.119}\n{'x': 8.874, 'y': 10.021}\n</code></pre></p> <p><pre><code>scaler = preprocessing.StandardScaler()\n\nfor x in X:\n    scaler.learn_one(x)\n    print(scaler.transform_one(x))\n</code></pre> <pre><code>{'x': 0.0, 'y': 0.0}\n{'x': -0.999, 'y': 0.999}\n{'x': 0.937, 'y': 1.350}\n{'x': 1.129, 'y': -0.651}\n{'x': -0.776, 'y': -0.729}\n{'x': -1.274, 'y': 0.992}\n</code></pre></p> <p>This transformer also supports mini-batch updates. You can call <code>learn_many</code> and provide a <code>pandas.DataFrame</code>:</p> <pre><code>import pandas as pd\nX = pd.DataFrame.from_dict(X)\n\nscaler = preprocessing.StandardScaler()\nscaler.learn_many(X[:3])\nscaler.learn_many(X[3:])\n</code></pre> <p>You can then call <code>transform_many</code> to scale a mini-batch of features:</p> <p><pre><code>scaler.transform_many(X)\n</code></pre> <pre><code>    x         y\n0  0.444600 -0.933384\n1 -1.044259 -0.138809\n2  0.841106  1.679208\n3  1.477301 -0.685117\n4 -0.444084 -0.914195\n5 -1.274664  0.992296\n</code></pre></p>"},{"location":"api/preprocessing/StandardScaler/#methods","title":"Methods","text":"learn_many <p>Update with a mini-batch of features.</p> <p>Note that the update formulas for mean and variance are slightly different than in the single instance case, but they produce exactly the same result.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_many <p>Scale a mini-batch of features.</p> <p>Parameters</p> <ul> <li>X     \u2014 'pd.DataFrame' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p> <ol> <li> <p>Welford's Method (and Friends) \u21a9</p> </li> <li> <p>Batch updates for simple statistics \u21a9</p> </li> </ol>"},{"location":"api/preprocessing/StatImputer/","title":"StatImputer","text":"<p>Replaces missing values with a statistic.</p> <p>This transformer allows you to replace missing values with the value of a running statistic. During a call to <code>learn_one</code>, for each feature, a statistic is updated whenever a numeric feature is observed. When <code>transform_one</code> is called, each feature with a <code>None</code> value is replaced with the current value of the corresponding statistic.</p>"},{"location":"api/preprocessing/StatImputer/#parameters","title":"Parameters","text":"<ul> <li> <p>imputers</p> <p>A list of tuples where each tuple has two elements. The first elements is a feature name and the second value is an instance of <code>stats.base.Univariate</code>. The second value can also be an arbitrary value, such as -1, in which case the missing values will be replaced with it.</p> </li> </ul>"},{"location":"api/preprocessing/StatImputer/#examples","title":"Examples","text":"<pre><code>from river import preprocessing\nfrom river import stats\n</code></pre> <p>For numeric data, we can use a <code>stats.Mean</code>()` to replace missing values by the running average of the previously seen values:</p> <p><pre><code>X = [\n    {'temperature': 1},\n    {'temperature': 8},\n    {'temperature': 3},\n    {'temperature': None},\n    {'temperature': 4}\n]\n\nimp = preprocessing.StatImputer(('temperature', stats.Mean()))\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'temperature': 1}\n{'temperature': 8}\n{'temperature': 3}\n{'temperature': 4.0}\n{'temperature': 4}\n</code></pre></p> <p>For discrete/categorical data, a common practice is to <code>stats.Mode</code> to replace missing values by the most commonly seen value:</p> <p><pre><code>X = [\n    {'weather': 'sunny'},\n    {'weather': 'rainy'},\n    {'weather': 'sunny'},\n    {'weather': None},\n    {'weather': 'rainy'},\n    {'weather': 'rainy'},\n    {'weather': None}\n]\n\nimp = preprocessing.StatImputer(('weather', stats.Mode()))\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny'}\n{'weather': 'rainy'}\n{'weather': 'sunny'}\n{'weather': 'sunny'}\n{'weather': 'rainy'}\n{'weather': 'rainy'}\n{'weather': 'rainy'}\n</code></pre></p> <p>You can also choose to replace missing values with a constant value, as so:</p> <p><pre><code>imp = preprocessing.StatImputer(('weather', 'missing'))\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny'}\n{'weather': 'rainy'}\n{'weather': 'sunny'}\n{'weather': 'missing'}\n{'weather': 'rainy'}\n{'weather': 'rainy'}\n{'weather': 'missing'}\n</code></pre></p> <p>Multiple imputers can be defined by providing a tuple for each feature which you want to impute:</p> <p><pre><code>X = [\n    {'weather': 'sunny', 'temperature': 8},\n    {'weather': 'rainy', 'temperature': 3},\n    {'weather': 'sunny', 'temperature': None},\n    {'weather': None, 'temperature': 4},\n    {'weather': 'snowy', 'temperature': -4},\n    {'weather': 'snowy', 'temperature': -3},\n    {'weather': 'snowy', 'temperature': -3},\n    {'weather': None, 'temperature': None}\n]\n\nimp = preprocessing.StatImputer(\n    ('temperature', stats.Mean()),\n    ('weather', stats.Mode())\n)\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny', 'temperature': 8}\n{'weather': 'rainy', 'temperature': 3}\n{'weather': 'sunny', 'temperature': 5.5}\n{'weather': 'sunny', 'temperature': 4}\n{'weather': 'snowy', 'temperature': -4}\n{'weather': 'snowy', 'temperature': -3}\n{'weather': 'snowy', 'temperature': -3}\n{'weather': 'snowy', 'temperature': 0.8333}\n</code></pre></p> <p>A sophisticated way to go about imputation is condition the statistics on a given feature. For instance, we might want to replace a missing temperature with the average temperature of a particular weather condition. As an example, consider the following dataset where the temperature is missing, but not the weather condition:</p> <pre><code>X = [\n    {'weather': 'sunny', 'temperature': 8},\n    {'weather': 'rainy', 'temperature': 3},\n    {'weather': 'sunny', 'temperature': None},\n    {'weather': 'rainy', 'temperature': 4},\n    {'weather': 'sunny', 'temperature': 10},\n    {'weather': 'sunny', 'temperature': None},\n    {'weather': 'sunny', 'temperature': 12},\n    {'weather': 'rainy', 'temperature': None}\n]\n</code></pre> <p>Each missing temperature can be replaced with the average temperature of the corresponding weather condition as so:</p> <p><pre><code>from river import compose\n\nimp = compose.Grouper(\n    preprocessing.StatImputer(('temperature', stats.Mean())),\n    by='weather'\n)\n\nfor x in X:\n    imp.learn_one(x)\n    print(imp.transform_one(x))\n</code></pre> <pre><code>{'weather': 'sunny', 'temperature': 8}\n{'weather': 'rainy', 'temperature': 3}\n{'weather': 'sunny', 'temperature': 8.0}\n{'weather': 'rainy', 'temperature': 4}\n{'weather': 'sunny', 'temperature': 10}\n{'weather': 'sunny', 'temperature': 9.0}\n{'weather': 'sunny', 'temperature': 12}\n{'weather': 'rainy', 'temperature': 3.5}\n</code></pre></p> <p>Note that you can also create a <code>Grouper</code> with the <code>*</code> operator:</p> <pre><code>imp = preprocessing.StatImputer(('temperature', stats.Mean())) * 'weather'\n</code></pre>"},{"location":"api/preprocessing/StatImputer/#methods","title":"Methods","text":"learn_one <p>Update with a set of features <code>x</code>.</p> <p>A lot of transformers don't actually have to do anything during the <code>learn_one</code> step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the <code>learn_one</code> can override this method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p></p> transform_one <p>Transform a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict:     The transformed values.</p> <p></p>"},{"location":"api/preprocessing/TargetMinMaxScaler/","title":"TargetMinMaxScaler","text":"<p>Applies min-max scaling to the target.</p>"},{"location":"api/preprocessing/TargetMinMaxScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regression model to wrap.</p> </li> </ul>"},{"location":"api/preprocessing/TargetMinMaxScaler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    preprocessing.TargetMinMaxScaler(\n        regressor=linear_model.LinearRegression(intercept_lr=0.15)\n    )\n)\nmetric = metrics.MSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MSE: 2.018905\n</code></pre></p>"},{"location":"api/preprocessing/TargetMinMaxScaler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/preprocessing/TargetStandardScaler/","title":"TargetStandardScaler","text":"<p>Applies standard scaling to the target.</p>"},{"location":"api/preprocessing/TargetStandardScaler/#parameters","title":"Parameters","text":"<ul> <li> <p>regressor</p> <p>Type \u2192 base.Regressor</p> <p>Regression model to wrap.</p> </li> </ul>"},{"location":"api/preprocessing/TargetStandardScaler/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\nmodel = (\n    preprocessing.StandardScaler() |\n    preprocessing.TargetStandardScaler(\n        regressor=linear_model.LinearRegression(intercept_lr=0.15)\n    )\n)\nmetric = metrics.MSE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MSE: 2.005999\n</code></pre></p>"},{"location":"api/preprocessing/TargetStandardScaler/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>The prediction.</p> <p></p>"},{"location":"api/proba/Beta/","title":"Beta","text":"<p>Beta distribution for binary data.</p> <p>A Beta distribution is very similar to a Bernoulli distribution in that it counts occurrences of boolean events. The differences lies in what is being measured. A Binomial distribution models the probability of an event occurring, whereas a Beta distribution models the probability distribution itself. In other words, it's a probability distribution over probability distributions.</p>"},{"location":"api/proba/Beta/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Initial alpha parameter.</p> </li> <li> <p>beta</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Initial beta parameter.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/Beta/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/Beta/#examples","title":"Examples","text":"<p><pre><code>from river import proba\n\nsuccesses = 81\nfailures = 219\nbeta = proba.Beta(successes, failures)\n\nbeta(.21), beta(.35)\n</code></pre> <pre><code>(0.867..., 0.165...)\n</code></pre></p> <p><pre><code>for success in range(100):\n    beta.update(True)\nfor failure in range(200):\n    beta.update(False)\n\nbeta(.21), beta(.35)\n</code></pre> <pre><code>(2.525...e-05, 0.841...)\n</code></pre></p> <p><pre><code>beta.cdf(.35)\n</code></pre> <pre><code>0.994168...\n</code></pre></p>"},{"location":"api/proba/Beta/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>p     \u2014 'float' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>What is the intuition behind beta distribution? \u21a9</p> </li> </ol>"},{"location":"api/proba/Gaussian/","title":"Gaussian","text":"<p>Normal distribution with parameters mu and sigma.</p>"},{"location":"api/proba/Gaussian/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/Gaussian/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>mu</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> <li> <p>sigma</p> </li> </ul>"},{"location":"api/proba/Gaussian/#examples","title":"Examples","text":"<p><pre><code>from river import proba\n\np = proba.Gaussian()\np.update(6)\np.update(7)\n\np\n</code></pre> <pre><code>\ud835\udca9(\u03bc=6.500, \u03c3=0.707)\n</code></pre></p> <p><pre><code>p(6.5)\n</code></pre> <pre><code>0.564189\n</code></pre></p> <p><pre><code>p.revert(7)\np\n</code></pre> <pre><code>\ud835\udca9(\u03bc=6.000, \u03c3=0.000)\n</code></pre></p>"},{"location":"api/proba/Gaussian/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p>"},{"location":"api/proba/Multinomial/","title":"Multinomial","text":"<p>Multinomial distribution for categorical data.</p>"},{"location":"api/proba/Multinomial/#parameters","title":"Parameters","text":"<ul> <li> <p>events</p> <p>Type \u2192 dict | list | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional list of events that already occurred.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/Multinomial/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/Multinomial/#examples","title":"Examples","text":"<p><pre><code>from river import proba\n\np = proba.Multinomial(['green'] * 3)\np.update('red')\np('red')\n</code></pre> <pre><code>0.25\n</code></pre></p> <p><pre><code>p.update('red')\np.update('red')\np('green')\n</code></pre> <pre><code>0.5\n</code></pre></p> <p><pre><code>p.revert('red')\np.revert('red')\np('red')\n</code></pre> <pre><code>0.25\n</code></pre></p> <p>You can wrap this with a <code>utils.Rolling</code> to measure a distribution over a window:</p> <p><pre><code>from river import utils\n\nX = ['red', 'green', 'green', 'blue', 'blue']\n\ndist = utils.Rolling(\n    proba.Multinomial(),\n    window_size=3\n)\n\nfor x in X:\n    dist.update(x)\n    print(dist)\n    print()\n</code></pre> <pre><code>P(red) = 1.000\n&lt;BLANKLINE&gt;\nP(red) = 0.500\nP(green) = 0.500\n&lt;BLANKLINE&gt;\nP(green) = 0.667\nP(red) = 0.333\n&lt;BLANKLINE&gt;\nP(green) = 0.667\nP(blue) = 0.333\nP(red) = 0.000\n&lt;BLANKLINE&gt;\nP(blue) = 0.667\nP(green) = 0.333\nP(red) = 0.000\n&lt;BLANKLINE&gt;\n</code></pre></p> <p>You can wrap this with a <code>utils.Rolling</code> to measure a distribution over a window of time:</p> <p><pre><code>import datetime as dt\n\nX = ['red', 'green', 'green', 'blue']\ndays = [1, 2, 3, 4]\n\ndist = utils.TimeRolling(\n    proba.Multinomial(),\n    period=dt.timedelta(days=2)\n)\n\nfor x, day in zip(X, days):\n    dist.update(x, t=dt.datetime(2019, 1, day))\n    print(dist)\n    print()\n</code></pre> <pre><code>P(red) = 1.000\n&lt;BLANKLINE&gt;\nP(red) = 0.500\nP(green) = 0.500\n&lt;BLANKLINE&gt;\nP(green) = 1.000\nP(red) = 0.000\n&lt;BLANKLINE&gt;\nP(green) = 0.500\nP(blue) = 0.500\nP(red) = 0.000\n&lt;BLANKLINE&gt;\n</code></pre></p>"},{"location":"api/proba/Multinomial/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p>"},{"location":"api/proba/MultivariateGaussian/","title":"MultivariateGaussian","text":"<p>Multivariate normal distribution with parameters mu and var.</p>"},{"location":"api/proba/MultivariateGaussian/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/MultivariateGaussian/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>mu</p> <p>The mean value of the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> <li> <p>sigma</p> <p>The standard deviation of the distribution.</p> </li> <li> <p>var</p> <p>The variance of the distribution.</p> </li> </ul>"},{"location":"api/proba/MultivariateGaussian/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nimport pandas as pd\nfrom river import proba\n\nnp.random.seed(42)\nX = pd.DataFrame(\n    np.random.random((8, 3)),\n    columns=[\"red\", \"green\", \"blue\"]\n)\nX\n</code></pre> <pre><code>        red     green      blue\n0  0.374540  0.950714  0.731994\n1  0.598658  0.156019  0.155995\n2  0.058084  0.866176  0.601115\n3  0.708073  0.020584  0.969910\n4  0.832443  0.212339  0.181825\n5  0.183405  0.304242  0.524756\n6  0.431945  0.291229  0.611853\n7  0.139494  0.292145  0.366362\n</code></pre></p> <p><pre><code>p = proba.MultivariateGaussian(seed=42)\np.n_samples\n</code></pre> <pre><code>0.0\n</code></pre></p> <p><pre><code>for x in X.to_dict(orient=\"records\"):\n    p.update(x)\np.var\n</code></pre> <pre><code>           blue     green       red\nblue   0.076119  0.020292 -0.010128\ngreen  0.020292  0.112931 -0.053268\nred   -0.010128 -0.053268  0.078961\n</code></pre></p> <p>Retrieving current state in nice format is simple <pre><code>p\n</code></pre> <pre><code>\ud835\udca9(\n    \u03bc=(0.518, 0.387, 0.416),\n    \u03c3^2=(\n        [ 0.076  0.020 -0.010]\n        [ 0.020  0.113 -0.053]\n        [-0.010 -0.053  0.079]\n    )\n)\n</code></pre></p> <p>To retrieve number of samples and mode:</p> <p><pre><code>p.n_samples\n</code></pre> <pre><code>8.0\n</code></pre> <pre><code>p.mode\n</code></pre> <pre><code>{'blue': 0.5179..., 'green': 0.3866..., 'red': 0.4158...}\n</code></pre></p> <p>To retrieve the PDF and CDF:</p> <p><pre><code>p(x)\n</code></pre> <pre><code>0.97967...\n</code></pre> <pre><code>p.cdf(x)\n</code></pre> <pre><code>0.00787...\n</code></pre></p> <p>To sample data from distribution:</p> <p><pre><code>p.sample()\n</code></pre> <pre><code>{'blue': -0.179..., 'green': -0.051..., 'red': 0.376...}\n</code></pre></p> <p>MultivariateGaussian works with <code>utils.Rolling</code>:</p> <p><pre><code>from river import utils\n\np = utils.Rolling(MultivariateGaussian(), window_size=5)\nfor x in X.to_dict(orient=\"records\"):\n    p.update(x)\np.var\n</code></pre> <pre><code>           blue     green       red\nblue   0.087062 -0.022873  0.007765\ngreen -0.022873  0.014279 -0.025181\nred    0.007765 -0.025181  0.095066\n</code></pre></p> <p>MultivariateGaussian works with <code>utils.TimeRolling</code>:</p> <p><pre><code>from datetime import datetime as dt, timedelta as td\nX.index = [dt(2023, 3, 28, 0, 0, 0) + td(seconds=x) for x in range(8)]\np = utils.TimeRolling(MultivariateGaussian(), period=td(seconds=5))\nfor t, x in X.iterrows():\n    p.update(x.to_dict(), t=t)\np.var\n</code></pre> <pre><code>           blue     green       red\nblue   0.087062 -0.022873  0.007765\ngreen -0.022873  0.014279 -0.025181\nred    0.007765 -0.025181  0.095066\n</code></pre></p> <p>Variance on diagonal is consistent with <code>proba.Gaussian</code>.</p> <p><pre><code>multi = proba.MultivariateGaussian()\nsingle = proba.Gaussian()\nfor x in X.to_dict(orient='records'):\n    multi.update(x)\n    single.update(x['blue'])\nmulti.mu['blue'] == single.mu\n</code></pre> <pre><code>True\n</code></pre> <pre><code>multi.sigma['blue']['blue'] == single.sigma\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/proba/MultivariateGaussian/#methods","title":"Methods","text":"call <p>PDF(x) method.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict[str, float]' </li> </ul> <p></p>"},{"location":"api/proba/base/BinaryDistribution/","title":"BinaryDistribution","text":"<p>A probability distribution for discrete values.</p>"},{"location":"api/proba/base/BinaryDistribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/BinaryDistribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/BinaryDistribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'bool' </li> </ul> <p></p>"},{"location":"api/proba/base/ContinuousDistribution/","title":"ContinuousDistribution","text":"<p>A probability distribution for continuous values.</p>"},{"location":"api/proba/base/ContinuousDistribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/ContinuousDistribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/ContinuousDistribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> cdf <p>Cumulative density function, i.e. P(X &lt;= x).</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/proba/base/DiscreteDistribution/","title":"DiscreteDistribution","text":"<p>A probability distribution for discrete values.</p>"},{"location":"api/proba/base/DiscreteDistribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/DiscreteDistribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/DiscreteDistribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> revert <p>Reverts the parameters of the distribution for a given observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p> update <p>Updates the parameters of the distribution given a new observation.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Hashable' </li> </ul> <p></p>"},{"location":"api/proba/base/Distribution/","title":"Distribution","text":"<p>General distribution.</p>"},{"location":"api/proba/base/Distribution/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generator seed for reproducibility.</p> </li> </ul>"},{"location":"api/proba/base/Distribution/#attributes","title":"Attributes","text":"<ul> <li> <p>mode</p> <p>The most likely value in the distribution.</p> </li> <li> <p>n_samples</p> <p>The number of observed samples.</p> </li> </ul>"},{"location":"api/proba/base/Distribution/#methods","title":"Methods","text":"call <p>Probability mass/density function.</p> <p>Parameters</p> <ul> <li>x     \u2014 'typing.Any' </li> </ul> <p></p> sample <p>Sample a random value from the distribution.</p> <p></p>"},{"location":"api/reco/Baseline/","title":"Baseline","text":"<p>Baseline for recommender systems.</p> <p>A first-order approximation of the bias involved in target. The model equation is defined as: </p> \\[\\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i}\\] <p>Where \\(bu_{u}\\) and \\(bi_{i}\\) are respectively the user and item biases. </p> <p>This model expects a dict input with a <code>user</code> and an <code>item</code> entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.</p>"},{"location":"api/reco/Baseline/#parameters","title":"Parameters","text":"<ul> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>regularization amount used to push weights towards 0.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/Baseline/#attributes","title":"Attributes","text":"<ul> <li> <p>global_mean (stats.Mean)</p> <p>The target arithmetic mean.</p> </li> <li> <p>u_biases (collections.defaultdict)</p> <p>The user bias weights.</p> </li> <li> <p>i_biases (collections.defaultdict)</p> <p>The item bias weights.</p> </li> <li> <p>u_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user bias weights.</p> </li> <li> <p>i_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item bias weights.</p> </li> </ul>"},{"location":"api/reco/Baseline/#examples","title":"Examples","text":"<p><pre><code>from river import optim\nfrom river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.Baseline(optimizer=optim.SGD(0.005))\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>6.538120\n</code></pre></p>"},{"location":"api/reco/Baseline/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Matrix factorization techniques for recommender systems \u21a9</p> </li> </ol>"},{"location":"api/reco/BiasedMF/","title":"BiasedMF","text":"<p>Biased Matrix Factorization for recommender systems.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i} + \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle\\] <p>Where \\(bu_{u}\\) and \\(bi_{i}\\) are respectively the user and item biases. The last term being simply the dot product between the latent vectors of the given user-item pair: </p> \\[\\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{u, f} \\cdot \\mathbf{v}_{i, f}\\] <p>where \\(k\\) is the number of latent factors. </p> <p>This model expects a dict input with a <code>user</code> and an <code>item</code> entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.</p>"},{"location":"api/reco/BiasedMF/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>bias_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the bias weights.</p> </li> <li> <p>latent_optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent weights.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2_bias</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push bias weights towards 0.</p> </li> <li> <p>l2_latent</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push latent weights towards 0.</p> </li> <li> <p>weight_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Weights initialization scheme.</p> </li> <li> <p>latent_initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/BiasedMF/#attributes","title":"Attributes","text":"<ul> <li> <p>global_mean (stats.Mean)</p> <p>The target arithmetic mean.</p> </li> <li> <p>u_biases (collections.defaultdict)</p> <p>The user bias weights.</p> </li> <li> <p>i_biases (collections.defaultdict)</p> <p>The item bias weights.</p> </li> <li> <p>u_latents (collections.defaultdict)</p> <p>The user latent vectors randomly initialized.</p> </li> <li> <p>i_latents (collections.defaultdict)</p> <p>The item latent vectors randomly initialized.</p> </li> <li> <p>u_bias_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user bias weights.</p> </li> <li> <p>i_bias_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item bias weights.</p> </li> <li> <p>u_latent_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user latent weights.</p> </li> <li> <p>i_latent_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item latent weights.</p> </li> </ul>"},{"location":"api/reco/BiasedMF/#examples","title":"Examples","text":"<p><pre><code>from river import optim\nfrom river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.BiasedMF(\n    n_factors=10,\n    bias_optimizer=optim.SGD(0.025),\n    latent_optimizer=optim.SGD(0.025),\n    latent_initializer=optim.initializers.Normal(mu=0., sigma=0.1, seed=71)\n)\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>6.489025\n</code></pre></p>"},{"location":"api/reco/BiasedMF/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Paterek, A., 2007, August. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD cup and workshop (Vol. 2007, pp. 5-8) \u21a9</p> </li> <li> <p>Matrix factorization techniques for recommender systems \u21a9</p> </li> </ol>"},{"location":"api/reco/FunkMF/","title":"FunkMF","text":"<p>Funk Matrix Factorization for recommender systems.</p> <p>The model equation is defined as: </p> \\[\\hat{y}(x) = \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{u, f} \\cdot \\mathbf{v}_{i, f}\\] <p>where \\(k\\) is the number of latent factors. </p> <p>This model expects a dict input with a <code>user</code> and an <code>item</code> entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.</p>"},{"location":"api/reco/FunkMF/#parameters","title":"Parameters","text":"<ul> <li> <p>n_factors</p> <p>Default \u2192 <code>10</code></p> <p>Dimensionality of the factorization or number of latent factors.</p> </li> <li> <p>optimizer</p> <p>Type \u2192 optim.base.Optimizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The sequential optimizer used for updating the latent factors.</p> </li> <li> <p>loss</p> <p>Type \u2192 optim.losses.Loss | None</p> <p>Default \u2192 <code>None</code></p> <p>The loss function to optimize for.</p> </li> <li> <p>l2</p> <p>Default \u2192 <code>0.0</code></p> <p>Amount of L2 regularization used to push weights towards 0.</p> </li> <li> <p>initializer</p> <p>Type \u2192 optim.initializers.Initializer | None</p> <p>Default \u2192 <code>None</code></p> <p>Latent factors initialization scheme.</p> </li> <li> <p>clip_gradient</p> <p>Default \u2192 <code>1000000000000.0</code></p> <p>Clips the absolute value of each gradient value.</p> </li> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/FunkMF/#attributes","title":"Attributes","text":"<ul> <li> <p>u_latents (collections.defaultdict)</p> <p>The user latent vectors randomly initialized.</p> </li> <li> <p>i_latents (collections.defaultdict)</p> <p>The item latent vectors randomly initialized.</p> </li> <li> <p>u_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the user latent weights.</p> </li> <li> <p>i_optimizer (optim.base.Optimizer)</p> <p>The sequential optimizer used for updating the item latent weights.</p> </li> </ul>"},{"location":"api/reco/FunkMF/#examples","title":"Examples","text":"<p><pre><code>from river import optim\nfrom river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.FunkMF(\n    n_factors=10,\n    optimizer=optim.SGD(0.1),\n    initializer=optim.initializers.Normal(mu=0., sigma=0.1, seed=11),\n)\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>1.866272\n</code></pre></p>"},{"location":"api/reco/FunkMF/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Netflix update: Try this at home \u21a9</p> </li> <li> <p>Matrix factorization techniques for recommender systems \u21a9</p> </li> </ol>"},{"location":"api/reco/RandomNormal/","title":"RandomNormal","text":"<p>Predicts random values sampled from a normal distribution.</p> <p>The parameters of the normal distribution are fitted with running statistics. They parameters are independent of the user, the item, or the context, and are instead fitted globally. This recommender therefore acts as a dummy model that any serious model should easily outperform.</p>"},{"location":"api/reco/RandomNormal/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/RandomNormal/#attributes","title":"Attributes","text":"<ul> <li> <p>mean</p> <p>stats.Mean</p> </li> <li> <p>variance</p> <p>stats.Var</p> </li> </ul>"},{"location":"api/reco/RandomNormal/#examples","title":"Examples","text":"<p><pre><code>from river import reco\n\ndataset = (\n    ({'user': 'Alice', 'item': 'Superman'}, 8),\n    ({'user': 'Alice', 'item': 'Terminator'}, 9),\n    ({'user': 'Alice', 'item': 'Star Wars'}, 8),\n    ({'user': 'Alice', 'item': 'Notting Hill'}, 2),\n    ({'user': 'Alice', 'item': 'Harry Potter'}, 5),\n    ({'user': 'Bob', 'item': 'Superman'}, 8),\n    ({'user': 'Bob', 'item': 'Terminator'}, 9),\n    ({'user': 'Bob', 'item': 'Star Wars'}, 8),\n    ({'user': 'Bob', 'item': 'Notting Hill'}, 2)\n)\n\nmodel = reco.RandomNormal(seed=42)\n\nfor x, y in dataset:\n    model.learn_one(**x, y=y)\n\nmodel.predict_one(user='Bob', item='Harry Potter')\n</code></pre> <pre><code>6.147299621751425\n</code></pre></p>"},{"location":"api/reco/RandomNormal/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/reco/base/Ranker/","title":"Ranker","text":"<p>Base class for ranking models.</p>"},{"location":"api/reco/base/Ranker/#parameters","title":"Parameters","text":"<ul> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random number generation seed. Set this for reproducibility.</p> </li> </ul>"},{"location":"api/reco/base/Ranker/#attributes","title":"Attributes","text":"<ul> <li>is_contextual</li> </ul>"},{"location":"api/reco/base/Ranker/#methods","title":"Methods","text":"learn_one <p>Fits a <code>user</code>-<code>item</code> pair and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>y     \u2014 'Reward' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> predict_one <p>Predicts the target value of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>item     \u2014 'ID' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p>Returns</p> <p>Reward:     The predicted preference from the user for the item.</p> <p></p> rank <p>Rank models by decreasing order of preference for a given user.</p> <p>Parameters</p> <ul> <li>user     \u2014 'ID' </li> <li>items     \u2014 'set[ID]' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/rules/AMRules/","title":"AMRules","text":"<p>Adaptive Model Rules.</p> <p>AMRules<sup>1</sup> is a rule-based algorithm for incremental regression tasks. AMRules relies on the Hoeffding bound to build its rule set, similarly to Hoeffding Trees. The Variance-Ratio heuristic is used to evaluate rules' splits. Moreover, this rule-based regressor has additional capacities not usually found in decision trees. </p> <p>Firstly, each created decision rule has a built-in drift detection mechanism. Every time a drift is detected, the affected decision rule is removed. In addition, AMRules' rules also have anomaly detection capabilities. After a warm-up period, each rule tests whether or not the incoming instances are anomalies. Anomalous instances are not used for training. </p> <p>Every time no rule is covering an incoming example, a default rule is used to learn from it. A rule covers an instance when all of the rule's literals (tests joined by the logical operation <code>and</code>) match the input case. The default rule is also applied for predicting examples not covered by any rules from the rule set.</p>"},{"location":"api/rules/AMRules/#parameters","title":"Parameters","text":"<ul> <li> <p>n_min</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>The total weight that must be observed by a rule between expansion attempts.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>The split test significance. The split confidence is given by <code>1 - delta</code>.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>The tie-breaking threshold.</p> </li> <li> <p>pred_type</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>The prediction strategy used by the decision rules. Can be either: - <code>\"mean\"</code>: outputs the target mean within the partitions defined by the decision rules. - <code>\"model\"</code>: always use instances of the model passed <code>pred_model</code> to make predictions. - <code>\"adaptive\"</code>: dynamically selects between \"mean\" and \"model\" for each incoming example. The most accurate option at the moment will be used.</p> </li> <li> <p>pred_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model that will be replicated for every rule when <code>pred_type</code> is either <code>\"model\"</code> or <code>\"adaptive\"</code>.</p> </li> <li> <p>splitter</p> <p>Type \u2192 spl.Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The drift detection model that is used by each rule. Care must be taken to avoid the triggering of too many false alarms or delaying too much the concept drift detection. By default, <code>drift.ADWIN</code> is used if <code>drift_detector</code> is <code>None</code>.</p> </li> <li> <p>fading_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>The exponential decaying factor applied to the learning models' absolute errors, that are monitored if <code>pred_type='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>anomaly_threshold</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>-0.75</code></p> <p>The threshold below which instances will be considered anomalies by the rules.</p> </li> <li> <p>m_min</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>30</code></p> <p>The minimum total weight a rule must observe before it starts to skip anomalous instances during training.</p> </li> <li> <p>ordered_rule_set</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If <code>True</code>, only the first rule that covers an instance will be used for training or prediction. If <code>False</code>, all the rules covering an instance will be updated during training, and the predictions for an instance will be the average prediction of all rules covering that example.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples each partition of a binary split candidate must have to be considered valid.</p> </li> </ul>"},{"location":"api/rules/AMRules/#attributes","title":"Attributes","text":"<ul> <li> <p>n_drifts_detected</p> <p>The number of detected concept drifts.</p> </li> </ul>"},{"location":"api/rules/AMRules/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import drift\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing\nfrom river import rules\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    rules.AMRules(\n        delta=0.01,\n        n_min=50,\n        drift_detector=drift.ADWIN()\n    )\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.119553\n</code></pre></p>"},{"location":"api/rules/AMRules/#methods","title":"Methods","text":"anomaly_score <p>Aggregated anomaly score computed using all the rules that cover the input instance.</p> <p>Returns the mean anomaly score, the standard deviation of the score, and the proportion of rules that cover the instance (support). If the support is zero, it means that the default rule was used (not other rule covered <code>x</code>).</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>tuple[float, float, float]:     mean_anomaly_score, std_anomaly_score, support</p> <p></p> debug_one <p>Return an explanation of how <code>x</code> is predicted</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>str:     A representation of the rules that cover the input and their prediction.</p> <p></p> learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 'int'     \u2014 defaults to <code>1</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/rules/AMRules/#notes","title":"Notes","text":"<p>AMRules treats all the non-numerical inputs as nominal features. All instances of <code>numbers.Number</code> will be treated as continuous, even if they represent integer categories. When using nominal features, <code>pred_type</code> should be set to \"mean\", otherwise errors will be thrown while trying to update the underlying rules' prediction models. Prediction strategies other than \"mean\" can be used, as long as the prediction model passed to <code>pred_model</code> supports nominal features.</p> <ol> <li> <p>Duarte, J., Gama, J. and Bifet, A., 2016. Adaptive model rules from high-speed data streams. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(3), pp.1-22.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/sketch/Counter/","title":"Counter","text":"<p>Counting using the Count-Min Sketch (CMS) algorithm.</p> <p>Contrary to an exhaustive approach, e.g., using a <code>collections.Counter</code>, CMS uses a limited and fixed amount of memory. The CMS algorithm uses a sketch structure consisting of a matrix \\(w \\times d\\). </p> <p>These dimensions are obtained via: </p> <ul> <li> <p>\\(w = \\lceil \\frac{e}{\\epsilon} \\rceil\\), where \\(e\\) is the Euler number.</p> </li> <li> <p>\\(d = \\lceil \\ln\\left(\\frac{1}{\\delta} \\right) \\rceil\\).</p> </li> </ul> <p>Decreasing the values of \\(\\epsilon\\) (<code>epsilon</code>) and \\(\\delta\\) (<code>delta</code>) increase the accuracy of the algorithm, at the cost of increased memory usage. The values of <code>w</code> and <code>d</code> control the hash tables' capability and the amount of hash collisions, respectively. </p> <p>CMS works by keeping <code>d</code> hash tables with <code>w</code> slots each. Elements are mapped to a slot in each hash table. These tables store the counting estimates. This implementation assumes the turnstile case described in the paper, i.e., count values and updates can be negative. </p> <p>The count values obtained by CMS are always overestimates. Suppose \\(c_i\\) and \\(\\hat{c}_i\\) are the ground truth and estimated count values, respectively, for a given element \\(i\\). CMS guarantees that \\(c_i \\le \\hat{c}_i\\) and, with probability \\(1 - \\delta\\), \\(\\hat{c}_i \\le c_i + \\epsilon||\\mathbf{c}||_1\\). In the expression, \\(||\\mathbf{c}||_1 = \\sum_i |c_i|\\).</p>"},{"location":"api/sketch/Counter/#parameters","title":"Parameters","text":"<ul> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>The approximation error parameter. The error in answering a query is within a factor of <code>epsilon</code> with probability <code>delta</code>.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>A query estimates have a probability of <code>1 - delta</code> of having errors which are a factor of <code>epsilon</code>. See the CMS description above for more details.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/sketch/Counter/#attributes","title":"Attributes","text":"<ul> <li> <p>n_slots</p> <p>The number of slots in each hash table.</p> </li> <li> <p>n_tables</p> <p>The number of stored hash tables.</p> </li> </ul>"},{"location":"api/sketch/Counter/#examples","title":"Examples","text":"<pre><code>import collections\nfrom river import sketch\n\ncms = sketch.Counter(epsilon=0.005, seed=0)\n\nrng = random.Random(7)\n\ncounter = collections.Counter()\n</code></pre> <p>We can check the number of slots per hash table: <pre><code>cms.n_slots\n</code></pre> <pre><code>544\n</code></pre></p> <p>And the number of hash tables: <pre><code>cms.n_tables\n</code></pre> <pre><code>3\n</code></pre></p> <p>Let's compare the sketch against a brute force approach:</p> <pre><code>vals = []\nfor _ in range(10000):\n    v = rng.randint(-1000, 1000)\n    cms.update(v)\n    counter[v] += 1\n    vals.append(v)\n</code></pre> <p>Now, we can compare the estimates of CMS against the exhaustive counting strategy:</p> <p><pre><code>counter[7]\n</code></pre> <pre><code>5\n</code></pre> <pre><code>cms[7]\n</code></pre> <pre><code>12\n</code></pre> <pre><code>counter[532]\n</code></pre> <pre><code>4\n</code></pre> <pre><code>cms[532]\n</code></pre> <pre><code>15\n</code></pre></p> <p>Keep in mind that CMS is an approximate sketch algorithm. Couting estimates for unseen values might not be always reliable:</p> <p><pre><code>cms[1001]\n</code></pre> <pre><code>9\n</code></pre></p> <p>We can check the number of elements stored by each approach:</p> <p><pre><code>len(counter), len(cms)\n</code></pre> <pre><code>(1982, 1632)\n</code></pre></p> <p>And also retrieve the total sum of counts:</p> <p><pre><code>cms.total()\n</code></pre> <pre><code>10000\n</code></pre></p> <p>We can decrease the error by allocating more memory in the CMS:</p> <p><pre><code>cms_a = sketch.Counter(epsilon=0.001, delta=0.01, seed=0)\nfor v in vals:\n    cms_a.update(v)\n\ncms_a[7]\n</code></pre> <pre><code>5\n</code></pre> <pre><code>cms_a[532]\n</code></pre> <pre><code>4\n</code></pre></p> <p>We can also obtain estimates of the dot product between two instances of <code>river.collections.Counter</code>. This could be useful, for instance, to estimate the cosine distance between the data monitored in two different counter sketch instances. Suppose we create another CMS instance (the number of slots and hash tables must match) that monitors another sample of the same data generating process:</p> <pre><code>cms_b = sketch.Counter(epsilon=0.001, delta=0.01, seed=7)\n\nfor _ in range(10000):\n    v = rng.randint(-1000, 1000)\n    cms_b.update(v)\n</code></pre> <p>Now, we can define a cosine distance function:</p> <pre><code>def cosine_dist(cms_a, cms_b):\n    num = cms_a @ cms_b\n    den = math.sqrt(cms_a @ cms_a) * math.sqrt(cms_b @ cms_b)\n    return num / den\n</code></pre> <p>And use it to calculate the cosine distance between the elements monitored in <code>cms_a</code> and <code>cms_b</code>:</p> <p><pre><code>cosine_dist(cms_a, cms_b)\n</code></pre> <pre><code>0.175363...\n</code></pre></p>"},{"location":"api/sketch/Counter/#methods","title":"Methods","text":"total <p>Return the total count.</p> <p></p> update <ol> <li> <p>Cormode, G., &amp; Muthukrishnan, S. (2005). An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1), 58-75. \u21a9</p> </li> <li> <p>Count-Min Sketch \u21a9</p> </li> <li> <p>Hash functions family generator in Python \u21a9</p> </li> </ol>"},{"location":"api/sketch/HeavyHitters/","title":"HeavyHitters","text":"<p>Find the Heavy Hitters using the Lossy Count with Forgetting factor algorithm<sup>1</sup>.</p> <p>Keep track of the most frequent item(set)s in a data stream and apply a forgetting factor to discard previous frequent items that do not often appear anymore. This is an approximation algorithm designed to work with a limited amount of memory rather than accounting for every possible solution (thus using an unbounded memory footprint). Any hashable type can be passed as input, hence tuples or frozensets can also be monitored. </p> <p>Considering a data stream where <code>n</code> elements were observed so far, the Lossy Count algorithm has the following properties: </p> <ul> <li>All item(set)s whose true frequency exceeds <code>support * n</code> are output. There are no</li> </ul> <p>false negatives; </p> <ul> <li> <p>No item(set) whose true frequency is less than <code>(support - epsilon) * n</code> is outputted;</p> </li> <li> <p>Estimated frequencies are less than the true frequencies by at most <code>epsilon * n</code>.</p> </li> </ul>"},{"location":"api/sketch/HeavyHitters/#parameters","title":"Parameters","text":"<ul> <li> <p>support</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.001</code></p> <p>The support threshold used to determine if an item is frequent. The value of <code>support</code> must be in \\([0, 1]\\). Elements whose frequency is higher than <code>support</code> times the number of observations seen so far are outputted.</p> </li> <li> <p>epsilon</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.005</code></p> <p>Error parameter to control the accuracy-memory tradeoff. The value of <code>epsilon</code> must be in \\((0, 1]\\) and typically <code>epsilon</code> \\(\\ll\\) <code>support</code>. The smaller the <code>epsilon</code>, the more accurate the estimates will be, but the count sketch will have an increased memory footprint.</p> </li> <li> <p>fading_factor</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.999</code></p> <p>Forgetting factor applied to the frequency estimates to reduce the impact of old items. The value of <code>fading_factor</code> must be in \\((0, 1]\\).</p> </li> </ul>"},{"location":"api/sketch/HeavyHitters/#examples","title":"Examples","text":"<pre><code>import random\nimport string\nfrom river import sketch\n\nrng = random.Random(42)\nhh = sketch.HeavyHitters()\n</code></pre> <p>We will feed the counter with printable ASCII characters:</p> <pre><code>for _ in range(10_000):\n    hh.update(rng.choice(string.printable))\n</code></pre> <p>We can retrieve estimates of the <code>n</code> top elements and their frequencies. Let's try <code>n=3</code> <pre><code>hh.most_common(3)\n</code></pre> <pre><code>[(',', 122.099142...), ('[', 116.049510...), ('W', 115.013402...)]\n</code></pre></p> <p>We can also access estimates of individual elements:</p> <p><pre><code>hh['A']\n</code></pre> <pre><code>99.483575...\n</code></pre></p> <p>Unobserved elements are handled just fine: <pre><code>hh[(1, 2, 3)]\n</code></pre> <pre><code>0.0\n</code></pre></p>"},{"location":"api/sketch/HeavyHitters/#methods","title":"Methods","text":"most_common update <ol> <li> <p>Veloso, B., Tabassum, S., Martins, C., Espanha, R., Azevedo, R., &amp; Gama, J. (2020). Interconnect bypass fraud detection: a case study. Annals of Telecommunications, 75(9), 583-596.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/sketch/Histogram/","title":"Histogram","text":"<p>Streaming histogram.</p>"},{"location":"api/sketch/Histogram/#parameters","title":"Parameters","text":"<ul> <li> <p>max_bins</p> <p>Default \u2192 <code>256</code></p> <p>Maximal number of bins.</p> </li> </ul>"},{"location":"api/sketch/Histogram/#attributes","title":"Attributes","text":"<ul> <li> <p>n</p> <p>Total number of seen values.</p> </li> </ul>"},{"location":"api/sketch/Histogram/#examples","title":"Examples","text":"<p><pre><code>from river import sketch\nimport numpy as np\n\nnp.random.seed(42)\n\nvalues = np.hstack((\n    np.random.normal(-3, 1, 1000),\n    np.random.normal(3, 1, 1000),\n))\n\nhist = sketch.Histogram(max_bins=15)\n\nfor x in values:\n    hist.update(x)\n\nfor bin in hist:\n    print(bin)\n</code></pre> <pre><code>[-6.24127, -6.24127]: 1\n[-5.69689, -5.19881]: 8\n[-5.12390, -4.43014]: 57\n[-4.42475, -3.72574]: 158\n[-3.71984, -3.01642]: 262\n[-3.01350, -2.50668]: 206\n[-2.50329, -0.81020]: 294\n[-0.80954, 0.29677]: 19\n[0.40896, 0.82733]: 7\n[0.84661, 1.25147]: 24\n[1.26029, 2.30758]: 178\n[2.31081, 3.05701]: 284\n[3.05963, 3.69695]: 242\n[3.69822, 5.64434]: 258\n[6.13775, 6.19311]: 2\n</code></pre></p>"},{"location":"api/sketch/Histogram/#methods","title":"Methods","text":"cdf <p>Cumulative distribution function.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> iter_cdf <p>Yields CDF values for a sorted iterable of values.</p> <p>This is faster than calling <code>cdf</code> with many values.</p> <p>Parameters</p> <ul> <li>X </li> <li>verbose     \u2014 defaults to <code>False</code> </li> </ul> <p></p> <ol> <li> <p>Ben-Haim, Y. and Tom-Tov, E., 2010. A streaming parallel decision tree algorithm. Journal of Machine Learning Research, 11(Feb), pp.849-872. \u21a9</p> </li> <li> <p>Go implementation \u21a9</p> </li> </ol>"},{"location":"api/sketch/Set/","title":"Set","text":"<p>Approximate tracking of observed items using Bloom filters.</p> <p>Bloom filters enable using a limited amount of memory to check whether a given item was already observed in a stream. They can be used similarly to Python's built-in sets with the difference that items are not explicitly stored. For that reason, element removal and set difference are not currently supported. </p> <p>Bloom filters store a bit array and map incoming items to <code>k</code> index positions in the such array. The selected positions are set to <code>True</code>. Therefore, a binary code representation is created for each item. Membership works by projecting the query item and checking if every position of its binary code is <code>True</code>. If that is not the case, the item was not observed yet. A nice property of Bloom filters is that they do not yield false negatives: unobserved items might be signalized as observed, but observed items are never signalized as unobserved. </p> <p>If more than one item has the same binary code, i.e., hash collisions happen, the accuracy of the Bloom filter decreases, and false positives are produced. For instance, a previously unobserved item is signalized as observed. Increasing the size of the binary array and the value of <code>k</code> increase the filter's accuracy as hash collisions are avoided. Nonetheless, even using an increased number of hash functions, hash collisions will frequently happen if the array capacity is too small. The length of the bit array and the number of hash functions are inferred automatically from the supplied <code>capacity</code> and <code>fp_rate</code>.</p>"},{"location":"api/sketch/Set/#parameters","title":"Parameters","text":"<ul> <li> <p>capacity</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2048</code></p> <p>The maximum capacity of the Bloom filter, i.e., the maximum number of distinct items to store given the selected <code>fp_rate</code>.</p> </li> <li> <p>fp_rate</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The allowed rate of false positives. The probability of obtaining a true positive is <code>1 - fp_rate</code>.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/sketch/Set/#attributes","title":"Attributes","text":"<ul> <li> <p>n_bits</p> <p>Return the size of the binary array used by the Bloom filter.</p> </li> <li> <p>n_hash</p> <p>Return the number of used hash functions.</p> </li> </ul>"},{"location":"api/sketch/Set/#examples","title":"Examples","text":"<pre><code>import random\nfrom river import sketch\n\nrng = random.Random(42)\ns_set = sketch.Set(capacity=100, seed=0)\n</code></pre> <p>We can retrieve the number of selected hash functions:</p> <p><pre><code>s_set.n_hash\n</code></pre> <pre><code>7\n</code></pre></p> <p>And the size of the binary array used by the Bloom filter: <pre><code>s_set.n_bits\n</code></pre> <pre><code>959\n</code></pre></p> <p>We can add new items and check for membership using the same calls used by Python's standard sets: <pre><code>for _ in range(1000):\n    s_set.add(rng.randint(0, 200))\n\n1 in s_set\n</code></pre> <pre><code>True\n</code></pre></p> <p>False positives might happen if the capacity is not large enough: <pre><code>-10 in s_set\n</code></pre> <pre><code>True\n</code></pre></p> <p>Iterables can also be supplied to perform multiple updates with a single call to <code>update</code>: <pre><code>s_set = s_set.update([1, 2, 3, 4, 5, 6, 7])\n</code></pre></p> <p>We can also combine instances of <code>sketch.Set</code> using the intersection and union operations, as long as they share the same hash functions and capability. In other words, all they hyperparameters match. Let's create two instances that will monitor different portions of a stream of random numbers:</p> <p><pre><code>s1 = sketch.Set(seed=8)\ns2 = sketch.Set(seed=8)\n\nfor _ in range(1000):\n    s1.add(rng.randint(0, 5000))\n\nfor _ in range(1000):\n    s2.add(rng.randint(0, 5000))\n\n43 in s1\n</code></pre> <pre><code>True\n</code></pre> <pre><code>43 in s2\n</code></pre> <pre><code>False\n</code></pre></p> <p>We can get the intersection between the two instances by using:</p> <p><pre><code>s_intersection = s1 &amp; s2\n43 in s_intersection\n</code></pre> <pre><code>False\n</code></pre></p> <p>We can also obtain the set union:</p> <p><pre><code>s_union = s1 | s2\n\n43 in s_union\n</code></pre> <pre><code>True\n</code></pre></p> <p>The same effect of the non-inplace dunder methods can be achieved via explicit method calls:</p> <p><pre><code>43 in s1.intersection(s2)\n</code></pre> <pre><code>False\n</code></pre></p> <p><pre><code>43 in s1.union(s2)\n</code></pre> <pre><code>True\n</code></pre></p>"},{"location":"api/sketch/Set/#methods","title":"Methods","text":"add intersection <p>Set intersection.</p> <p>Return a new instance that results from the set intersection between the current <code>Set</code> object and <code>other</code>. Dunder operators can be used to replace the method call, i.e., <code>a &amp;= b</code> and <code>a &amp; b</code> for inplace and non-inplace intersections, respectively.</p> <p>Parameters</p> <ul> <li>other     \u2014 'Set' </li> </ul> <p></p> union <p>Set union.</p> <p>Return a new instance that results from the set union between the current <code>Set</code> object and <code>other</code>. Dunder operators can be used to replace the method call, i.e., <code>a |= b</code> and <code>a | b</code> for inplace and non-inplace unions, respectively.</p> <p>Parameters</p> <ul> <li>other     \u2014 'Set' </li> </ul> <p></p> update"},{"location":"api/sketch/Set/#notes","title":"Notes","text":"<p>This implementation uses an integer to represent the binary array. Bitwise operations are performed in the integer to reflect the Bloom filter updates.</p> <ol> <li> <p>Florian Hartmann's blog article on Bloom Filters.\u00a0\u21a9</p> </li> <li> <p>Wikipedia entry on Bloom filters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/AbsMax/","title":"AbsMax","text":"<p>Running absolute max.</p>"},{"location":"api/stats/AbsMax/#attributes","title":"Attributes","text":"<ul> <li> <p>abs_max (float)</p> <p>The current absolute max.</p> </li> </ul>"},{"location":"api/stats/AbsMax/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 5, -6]\nabs_max = stats.AbsMax()\nfor x in X:\n    abs_max.update(x)\n    print(abs_max.get())\n</code></pre> <pre><code>1\n4\n4\n4\n5\n6\n</code></pre></p>"},{"location":"api/stats/AbsMax/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/AutoCorr/","title":"AutoCorr","text":"<p>Measures the serial correlation.</p> <p>This method computes the Pearson correlation between the current value and the value seen <code>n</code> steps before.</p>"},{"location":"api/stats/AutoCorr/#parameters","title":"Parameters","text":"<ul> <li> <p>lag</p> <p>Type \u2192 int</p> </li> </ul>"},{"location":"api/stats/AutoCorr/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/AutoCorr/#examples","title":"Examples","text":"<p>The following examples are taken from the pandas documentation.</p> <p><pre><code>from river import stats\n\nauto_corr = stats.AutoCorr(lag=1)\nfor x in [0.25, 0.5, 0.2, -0.05]:\n    auto_corr.update(x)\n    print(auto_corr.get())\n</code></pre> <pre><code>0\n0\n-1.0\n0.103552\n</code></pre></p> <p><pre><code>auto_corr = stats.AutoCorr(lag=2)\nfor x in [0.25, 0.5, 0.2, -0.05]:\n    auto_corr.update(x)\n    print(auto_corr.get())\n</code></pre> <pre><code>0\n0\n0\n-1.0\n</code></pre></p> <p><pre><code>auto_corr = stats.AutoCorr(lag=1)\nfor x in [1, 0, 0, 0]:\n    auto_corr.update(x)\n    print(auto_corr.get())\n</code></pre> <pre><code>0\n0\n0\n0\n</code></pre></p>"},{"location":"api/stats/AutoCorr/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/BayesianMean/","title":"BayesianMean","text":"<p>Estimates a mean using outside information.</p>"},{"location":"api/stats/BayesianMean/#parameters","title":"Parameters","text":"<ul> <li> <p>prior</p> <p>Type \u2192 float</p> </li> <li> <p>prior_weight</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/stats/BayesianMean/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/BayesianMean/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Additive smoothing \u21a9</p> </li> <li> <p>Bayesian average \u21a9</p> </li> <li> <p>Practical example of Bayes estimators \u21a9</p> </li> </ol>"},{"location":"api/stats/Count/","title":"Count","text":"<p>A simple counter.</p>"},{"location":"api/stats/Count/#attributes","title":"Attributes","text":"<ul> <li> <p>n (int)</p> <p>The current number of observations.</p> </li> </ul>"},{"location":"api/stats/Count/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/stats/Cov/","title":"Cov","text":"<p>Covariance.</p>"},{"location":"api/stats/Cov/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom.</p> </li> </ul>"},{"location":"api/stats/Cov/#attributes","title":"Attributes","text":"<ul> <li>n</li> </ul>"},{"location":"api/stats/Cov/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nx = [-2.1,  -1,  4.3]\ny = [   3, 1.1, 0.12]\n\ncov = stats.Cov()\n\nfor xi, yi in zip(x, y):\n    cov.update(xi, yi)\n    print(cov.get())\n</code></pre> <pre><code>0.0\n-1.044999\n-4.286\n</code></pre></p> <p>This class has a <code>revert</code> method, and can thus be wrapped by <code>utils.Rolling</code>:</p> <p><pre><code>from river import utils\n\nx = [-2.1,  -1, 4.3, 1, -2.1,  -1, 4.3]\ny = [   3, 1.1, .12, 1,    3, 1.1, .12]\n\nrcov = utils.Rolling(stats.Cov(), window_size=3)\n\nfor xi, yi in zip(x, y):\n    rcov.update(xi, yi)\n    print(rcov.get())\n</code></pre> <pre><code>0.0\n-1.045\n-4.286\n-1.382\n-4.589\n-1.415\n-4.286\n</code></pre></p>"},{"location":"api/stats/Cov/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many"},{"location":"api/stats/Cov/#notes","title":"Notes","text":"<p>The outcomes of the incremental and parallel updates are consistent with numpy's batch processing when \\(\\text{ddof} \\le 1\\).</p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> <li> <p>Schubert, E. and Gertz, M., 2018, July. Numerically stable parallel computation of (co-) variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (pp. 1-12).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/EWMean/","title":"EWMean","text":"<p>Exponentially weighted mean.</p>"},{"location":"api/stats/EWMean/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.5</code></p> <p>The closer <code>fading_factor</code> is to 1 the more the statistic will adapt to recent values.</p> </li> </ul>"},{"location":"api/stats/EWMean/#attributes","title":"Attributes","text":"<ul> <li> <p>mean (float)</p> <p>The running exponentially weighted mean.</p> </li> </ul>"},{"location":"api/stats/EWMean/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, 3, 5, 4, 6, 8, 7, 9, 11]\newm = stats.EWMean(fading_factor=0.5)\nfor x in X:\n    ewm.update(x)\n    print(ewm.get())\n</code></pre> <pre><code>1.0\n2.0\n3.5\n3.75\n4.875\n6.4375\n6.71875\n7.859375\n9.4296875\n</code></pre></p>"},{"location":"api/stats/EWMean/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. \u21a9</p> </li> <li> <p>Exponential Moving Average on Streaming Data \u21a9</p> </li> </ol>"},{"location":"api/stats/EWVar/","title":"EWVar","text":"<p>Exponentially weighted variance.</p> <p>To calculate the variance we use the fact that Var(X) = Mean(x^2) - Mean(x)^2 and internally we use the exponentially weighted mean of x/x^2 to calculate this.</p>"},{"location":"api/stats/EWVar/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>0.5</code></p> <p>The closer <code>fading_factor</code> is to 1 the more the statistic will adapt to recent values.</p> </li> </ul>"},{"location":"api/stats/EWVar/#attributes","title":"Attributes","text":"<ul> <li> <p>variance (float)</p> <p>The running exponentially weighted variance.</p> </li> </ul>"},{"location":"api/stats/EWVar/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, 3, 5, 4, 6, 8, 7, 9, 11]\newv = stats.EWVar(fading_factor=0.5)\nfor x in X:\n    ewv.update(x)\n    print(ewv.get())\n</code></pre> <pre><code>0.0\n1.0\n2.75\n1.4375\n1.984375\n3.43359375\n1.7958984375\n2.198974609375\n3.56536865234375\n</code></pre></p>"},{"location":"api/stats/EWVar/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. \u21a9</p> </li> <li> <p>Exponential Moving Average on Streaming Data \u21a9</p> </li> </ol>"},{"location":"api/stats/Entropy/","title":"Entropy","text":"<p>Running entropy.</p>"},{"location":"api/stats/Entropy/#parameters","title":"Parameters","text":"<ul> <li> <p>fading_factor</p> <p>Default \u2192 <code>1</code></p> <p>Fading factor.</p> </li> <li> <p>eps</p> <p>Default \u2192 <code>1e-08</code></p> <p>Small value that will be added to the denominator to avoid division by zero.</p> </li> </ul>"},{"location":"api/stats/Entropy/#attributes","title":"Attributes","text":"<ul> <li> <p>entropy (float)</p> <p>The running entropy.</p> </li> <li> <p>n (int)</p> <p>The current number of observations.</p> </li> <li> <p>counter (collections.Counter)</p> <p>Count the number of times the values have occurred</p> </li> </ul>"},{"location":"api/stats/Entropy/#examples","title":"Examples","text":"<p><pre><code>import math\nimport random\nimport numpy as np\nfrom scipy.stats import entropy\nfrom river import stats\n\ndef entropy_list(labels, base=None):\n    value,counts = np.unique(labels, return_counts=True)\n    return entropy(counts, base=base)\n\nSEED = 42 * 1337\nrandom.seed(SEED)\n\nentro = stats.Entropy(fading_factor=1)\n\nlist_animal = []\nfor animal, num_val in zip(['cat', 'dog', 'bird'],[301, 401, 601]):\n    list_animal += [animal for i in range(num_val)]\nrandom.shuffle(list_animal)\n\nfor animal in list_animal:\n    entro.update(animal)\n\nprint(f'{entro.get():.6f}')\n</code></pre> <pre><code>1.058093\n</code></pre> <pre><code>print(f'{entropy_list(list_animal):.6f}')\n</code></pre> <pre><code>1.058093\n</code></pre></p>"},{"location":"api/stats/Entropy/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Sovdat, B., 2014. Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams. arXiv preprint arXiv:1403.6348. \u21a9</p> </li> </ol>"},{"location":"api/stats/IQR/","title":"IQR","text":"<p>Computes the interquartile range.</p>"},{"location":"api/stats/IQR/#parameters","title":"Parameters","text":"<ul> <li> <p>q_inf</p> <p>Default \u2192 <code>0.25</code></p> <p>Desired inferior quantile, must be between 0 and 1. Defaults to <code>0.25</code>.</p> </li> <li> <p>q_sup</p> <p>Default \u2192 <code>0.75</code></p> <p>Desired superior quantile, must be between 0 and 1. Defaults to <code>0.75</code>.</p> </li> </ul>"},{"location":"api/stats/IQR/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/IQR/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\niqr = stats.IQR(q_inf=0.25, q_sup=0.75)\n\nfor i in range(0, 1001):\n    iqr.update(i)\n    if i % 100 == 0:\n        print(iqr.get())\n</code></pre> <pre><code>0.0\n50.0\n100.0\n150.0\n200.0\n250.0\n300.0\n350.0\n400.0\n450.0\n500.0\n</code></pre></p>"},{"location":"api/stats/IQR/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/KolmogorovSmirnov/","title":"KolmogorovSmirnov","text":"<p>Incremental Kolmogorov-Smirnov statistics.</p> <p>The two-sample Kolmogorov-Smirnov test quantifies the distance between the empirical functions of two samples, with the null distribution of this statistic is calculated under the null hypothesis that the samples are drawn from the same distribution. The formula can be described as </p> \\[ D_{n, m} = \\sup_x \\| F_{1, n}(x) - F_{2, m}(x) \\|. \\] <p>This implementation is the incremental version of the previously mentioned statistics, with the change being in the ability to insert and remove an observation thorugh time. This can be done using a randomized tree called Treap (or Cartesian Tree) <sup>2</sup> with bulk operation and lazy propagation. </p> <p>The implemented algorithm is able to perform the insertion and removal operations in O(logN) with high probability and calculate the Kolmogorov-Smirnov test in O(1), where N is the number of sample observations. This is a significant improvement compared to the O(N logN) cost of non-incremental implementation. </p> <p>This implementation also supports the calculation of the Kuiper statistics. Different from the orginial Kolmogorov-Smirnov statistics, Kuiper's test <sup>3</sup> calculates the sum of the absolute sizes of the most positive and most negative differences between the two cumulative distribution functions taken into account. As such, Kuiper's test is very sensitive in the tails as at the median. </p> <p>Last but not least, this implementation is also based on the original implementation within the supplementary material of the authors of paper <sup>1</sup>, at the following Github repository.</p>"},{"location":"api/stats/KolmogorovSmirnov/#parameters","title":"Parameters","text":"<ul> <li> <p>statistic</p> <p>Default \u2192 <code>ks</code></p> <p>The method used to calculate the statistic, can be either \"ks\" or \"kuiper\". The default value is set as \"ks\".</p> </li> </ul>"},{"location":"api/stats/KolmogorovSmirnov/#examples","title":"Examples","text":"<p><pre><code>import numpy as np\nfrom river import stats\n\nstream_a = [1, 1, 2, 2, 3, 3, 4, 4]\nstream_b = [1, 1, 1, 1, 2, 2, 2, 2]\n\nincremental_ks = stats.KolmogorovSmirnov(statistic=\"ks\")\nfor a, b in zip(stream_a, stream_b):\n    incremental_ks.update(a, b)\n\nincremental_ks\n</code></pre> <pre><code>KolmogorovSmirnov: 0.5\n</code></pre></p> <p><pre><code>incremental_ks.n_samples\n</code></pre> <pre><code>8\n</code></pre></p>"},{"location":"api/stats/KolmogorovSmirnov/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p> <ol> <li> <p>dos Reis, D.M. et al. (2016) \u2018Fast unsupervised online drift detection using incremental Kolmogorov-Smirnov test\u2019, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/2939672.2939836.\u00a0\u21a9</p> </li> <li> <p>C. R. Aragon and R. G. Seidel. Randomized search trees. In FOCS, pages 540\u2013545. IEEE, 1989.\u00a0\u21a9</p> </li> <li> <p>Kuiper, N. H. (1960). \"Tests concerning random points on a circle\". Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series A. 63: 38\u201347.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/Kurtosis/","title":"Kurtosis","text":"<p>Running kurtosis using Welford's algorithm.</p>"},{"location":"api/stats/Kurtosis/#parameters","title":"Parameters","text":"<ul> <li> <p>bias</p> <p>Default \u2192 <code>False</code></p> <p>If <code>False</code>, then the calculations are corrected for statistical bias.</p> </li> </ul>"},{"location":"api/stats/Kurtosis/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Kurtosis/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nimport scipy.stats\nimport numpy as np\n\nnp.random.seed(42)\nX = np.random.normal(loc=0, scale=1, size=10)\n\nkurtosis = stats.Kurtosis(bias=False)\nfor x in X:\n    kurtosis.update(x)\n    print(kurtosis.get())\n</code></pre> <pre><code>-3.0\n-2.0\n-1.5\n1.4130027920707047\n0.15367976585756438\n0.46142633246812653\n-1.620647789230658\n-1.3540178492487054\n-1.2310268787102745\n-0.9490372374384453\n</code></pre></p> <p><pre><code>for i in range(2, len(X)+1):\n    print(scipy.stats.kurtosis(X[:i], bias=False))\n</code></pre> <pre><code>-2.0\n-1.4999999999999998\n1.4130027920707082\n0.15367976585756082\n0.46142633246812403\n-1.620647789230658\n-1.3540178492487063\n-1.2310268787102738\n-0.9490372374384459\n</code></pre></p> <p><pre><code>kurtosis = stats.Kurtosis(bias=True)\nfor x in X:\n    kurtosis.update(x)\n    print(kurtosis.get())\n</code></pre> <pre><code>-3.0\n-2.0\n-1.5\n-1.011599627723906\n-0.9615800585356089\n-0.6989395431537853\n-1.4252699121794408\n-1.311437071070812\n-1.246289111322894\n-1.082283689864171\n</code></pre></p> <p><pre><code>for i in range(2, len(X)+1):\n    print(scipy.stats.kurtosis(X[:i], bias=True))\n</code></pre> <pre><code>-2.0\n-1.4999999999999998\n-1.0115996277239057\n-0.9615800585356098\n-0.6989395431537861\n-1.425269912179441\n-1.3114370710708125\n-1.2462891113228936\n-1.0822836898641714\n</code></pre></p>"},{"location":"api/stats/Kurtosis/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> </ol>"},{"location":"api/stats/Link/","title":"Link","text":"<p>A link joins two univariate statistics as a sequence.</p> <p>This can be used to pipe the output of one statistic to the input of another. This can be used, for instance, to calculate the mean of the variance of a variable. It can also be used to compute shifted statistics by piping statistics with an instance of <code>stats.Shift</code>. </p> <p>Note that a link is not meant to be instantiated via this class definition. Instead, users can link statistics together via the <code>|</code> operator.</p>"},{"location":"api/stats/Link/#parameters","title":"Parameters","text":"<ul> <li> <p>left</p> <p>Type \u2192 stats.base.Univariate</p> </li> <li> <p>right</p> <p>Type \u2192 stats.base.Univariate</p> <p>The output from <code>left</code>'s <code>get</code> method is passed to <code>right</code>'s <code>update</code> method if <code>left</code>'s <code>get</code> method doesn't produce <code>None.</code></p> </li> </ul>"},{"location":"api/stats/Link/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Link/#examples","title":"Examples","text":"<pre><code>from river import stats\nstat = stats.Shift(1) | stats.Mean()\n</code></pre> <p>No values have been seen, therefore <code>get</code> defaults to the initial value of <code>stats.Mean</code>, which is 0.</p> <p><pre><code>stat.get()\n</code></pre> <pre><code>0.\n</code></pre></p> <p>Let us now call <code>update</code>.</p> <pre><code>stat.update(1)\n</code></pre> <p>The output from <code>get</code> will still be 0. The reason is that <code>stats.Shift</code> has not enough values, and therefore outputs its default value, which is <code>None</code>. The <code>stats.Mean</code> instance is therefore not updated.</p> <p><pre><code>stat.get()\n</code></pre> <pre><code>0.0\n</code></pre></p> <p>On the next call to <code>update</code>, the <code>stats.Shift</code> instance has seen enough values, and therefore the mean can be updated. The mean is therefore equal to 1, because that's the only value from the past.</p> <p><pre><code>stat.update(3)\nstat.get()\n</code></pre> <pre><code>1.0\n</code></pre></p> <p>On the subsequent call to update, the mean will be updated with the value 3.</p> <p><pre><code>stat.update(4)\nstat.get()\n</code></pre> <pre><code>2.0\n</code></pre></p> <p>Note that composing statistics returns a new statistic with its own name.</p> <p><pre><code>stat.name\n</code></pre> <pre><code>'mean_of_shift_1'\n</code></pre></p>"},{"location":"api/stats/Link/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/MAD/","title":"MAD","text":"<p>Median Absolute Deviation (MAD).</p> <p>The median absolute deviation is the median of the absolute differences between each data point and the data's overall median. In an online setting, the median of the data is unknown beforehand. Therefore, both the median of the data and the median of the differences of the data with respect to the latter are updated online. To be precise, the median of the data is updated before the median of the differences. As a consequence, this online version of the MAD does not coincide exactly with its batch counterpart.</p>"},{"location":"api/stats/MAD/#attributes","title":"Attributes","text":"<ul> <li> <p>median (stats.Median)</p> <p>The median of the data.</p> </li> </ul>"},{"location":"api/stats/MAD/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [4, 2, 5, 3, 0, 4]\n\nmad = stats.MAD()\nfor x in X:\n    mad.update(x)\n    print(mad.get())\n</code></pre> <pre><code>0.0\n2.0\n1.0\n1.0\n1.0\n1.0\n</code></pre></p>"},{"location":"api/stats/MAD/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Median absolute deviation article on Wikipedia \u21a9</p> </li> </ol>"},{"location":"api/stats/Max/","title":"Max","text":"<p>Running max.</p>"},{"location":"api/stats/Max/#attributes","title":"Attributes","text":"<ul> <li> <p>max (float)</p> <p>The current max.</p> </li> </ul>"},{"location":"api/stats/Max/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 5, -6]\nmaximum = stats.Max()\nfor x in X:\n    maximum.update(x)\n    print(maximum.get())\n</code></pre> <pre><code>1\n1\n3\n3\n5\n5\n</code></pre></p>"},{"location":"api/stats/Max/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Mean/","title":"Mean","text":"<p>Running mean.</p>"},{"location":"api/stats/Mean/#attributes","title":"Attributes","text":"<ul> <li> <p>n (float)</p> <p>The current sum of weights. If each passed weight was 1, then this is equal to the number of seen observations.</p> </li> </ul>"},{"location":"api/stats/Mean/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [-5, -3, -1, 1, 3, 5]\nmean = stats.Mean()\nfor x in X:\n    mean.update(x)\n    print(mean.get())\n</code></pre> <pre><code>-5.0\n-4.0\n-3.0\n-2.0\n-1.0\n0.0\n</code></pre></p> <p>You can calculate a rolling average by wrapping a <code>utils.Rolling</code> around:</p> <p><pre><code>from river import utils\n\nX = [1, 2, 3, 4, 5, 6]\nrmean = utils.Rolling(stats.Mean(), window_size=2)\n\nfor x in X:\n    rmean.update(x)\n    print(rmean.get())\n</code></pre> <pre><code>1.0\n1.5\n2.5\n3.5\n4.5\n5.5\n</code></pre></p>"},{"location":"api/stats/Mean/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many <ol> <li> <p>West, D. H. D. (1979). Updating mean and variance estimates: An improved method. Communications of the ACM, 22(9), 532-535. \u21a9</p> </li> <li> <p>Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. \u21a9</p> </li> <li> <p>Chan, T.F., Golub, G.H. and LeVeque, R.J., 1983. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician, 37(3), pp.242-247. \u21a9</p> </li> </ol>"},{"location":"api/stats/Min/","title":"Min","text":"<p>Running min.</p>"},{"location":"api/stats/Min/#attributes","title":"Attributes","text":"<ul> <li> <p>min (float)</p> <p>The current min.</p> </li> </ul>"},{"location":"api/stats/Min/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Mode/","title":"Mode","text":"<p>Running mode.</p> <p>The mode is simply the most common value. An approximate mode can be computed by setting the number of first unique values to count.</p>"},{"location":"api/stats/Mode/#parameters","title":"Parameters","text":"<ul> <li> <p>k</p> <p>Default \u2192 <code>25</code></p> <p>Only the first <code>k</code> unique values will be included. If <code>k</code> equals -1, the exact mode is computed.</p> </li> </ul>"},{"location":"api/stats/Mode/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Mode/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = ['sunny', 'cloudy', 'cloudy', 'rainy', 'rainy', 'rainy']\nmode = stats.Mode(k=2)\nfor x in X:\n    mode.update(x)\n    print(mode.get())\n</code></pre> <pre><code>sunny\nsunny\ncloudy\ncloudy\ncloudy\ncloudy\n</code></pre></p> <p><pre><code>mode = stats.Mode(k=-1)\nfor x in X:\n    mode.update(x)\n    print(mode.get())\n</code></pre> <pre><code>sunny\nsunny\ncloudy\ncloudy\ncloudy\nrainy\n</code></pre></p>"},{"location":"api/stats/Mode/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/NUnique/","title":"NUnique","text":"<p>Approximate number of unique values counter.</p> <p>This is basically an implementation of the HyperLogLog algorithm. Adapted from <code>hypy</code>. The code is a bit too terse but it will do for now.</p>"},{"location":"api/stats/NUnique/#parameters","title":"Parameters","text":"<ul> <li> <p>error_rate</p> <p>Default \u2192 <code>0.01</code></p> <p>Desired error rate. Memory usage is inversely proportional to this value.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Set the seed to produce identical results.</p> </li> </ul>"},{"location":"api/stats/NUnique/#attributes","title":"Attributes","text":"<ul> <li> <p>n_bits (int)</p> </li> <li> <p>n_buckets (int)</p> </li> <li> <p>buckets (list)</p> </li> </ul>"},{"location":"api/stats/NUnique/#examples","title":"Examples","text":"<p><pre><code>import string\nfrom river import stats\n\nalphabet = string.ascii_lowercase\nn_unique = stats.NUnique(error_rate=0.2, seed=42)\n\nn_unique.update('a')\nn_unique.get()\n</code></pre> <pre><code>1\n</code></pre></p> <p><pre><code>n_unique.update('b')\nn_unique.get()\n</code></pre> <pre><code>2\n</code></pre></p> <p><pre><code>for letter in alphabet:\n    n_unique.update(letter)\nn_unique.get()\n</code></pre> <pre><code>31\n</code></pre></p> <p>Lowering the <code>error_rate</code> parameter will increase the precision.</p> <p><pre><code>n_unique = stats.NUnique(error_rate=0.01, seed=42)\nfor letter in alphabet:\n    n_unique.update(letter)\nn_unique.get()\n</code></pre> <pre><code>26\n</code></pre></p>"},{"location":"api/stats/NUnique/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>My favorite algorithm (and data structure): HyperLogLog \u21a9</p> </li> <li> <p>Flajolet, P., Fusy, \u00c9., Gandouet, O. and Meunier, F., 2007, June. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. \u21a9</p> </li> </ol>"},{"location":"api/stats/PeakToPeak/","title":"PeakToPeak","text":"<p>Running peak to peak (max - min).</p>"},{"location":"api/stats/PeakToPeak/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/PeakToPeak/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 4]\nptp = stats.PeakToPeak()\nfor x in X:\n    ptp.update(x)\n    print(ptp.get())\n</code></pre> <pre><code>0.\n5.\n7.\n7.\n7.\n8.\n</code></pre></p>"},{"location":"api/stats/PeakToPeak/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/PearsonCorr/","title":"PearsonCorr","text":"<p>Online Pearson correlation.</p>"},{"location":"api/stats/PearsonCorr/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom.</p> </li> </ul>"},{"location":"api/stats/PearsonCorr/#attributes","title":"Attributes","text":"<ul> <li> <p>var_x (stats.Var)</p> <p>Running variance of <code>x</code>.</p> </li> <li> <p>var_y (stats.Var)</p> <p>Running variance of <code>y</code>.</p> </li> <li> <p>cov_xy (stats.Cov)</p> <p>Running covariance of <code>x</code> and <code>y</code>.</p> </li> </ul>"},{"location":"api/stats/PearsonCorr/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nx = [0, 0, 0, 1, 1, 1, 1]\ny = [0, 1, 2, 3, 4, 5, 6]\n\npearson = stats.PearsonCorr()\n\nfor xi, yi in zip(x, y):\n    pearson.update(xi, yi)\n    print(pearson.get())\n</code></pre> <pre><code>0\n0\n0\n0.774596\n0.866025\n0.878310\n0.866025\n</code></pre></p> <p>You can also do this in a rolling fashion:</p> <p><pre><code>from river import utils\n\nx = [0, 0, 0, 1, 1, 1, 1]\ny = [0, 1, 2, 3, 4, 5, 6]\n\npearson = utils.Rolling(stats.PearsonCorr(), window_size=4)\n\nfor xi, yi in zip(x, y):\n    pearson.update(xi, yi)\n    print(pearson.get())\n</code></pre> <pre><code>0\n0\n0\n0.7745966692414834\n0.8944271909999159\n0.7745966692414832\n-4.712160915387242e-09\n</code></pre></p>"},{"location":"api/stats/PearsonCorr/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p>"},{"location":"api/stats/Quantile/","title":"Quantile","text":"<p>Running quantile.</p> <p>Uses the P\u00b2 algorithm, which is also known as the \"Piecewise-Parabolic quantile estimator\". The code is inspired by LiveStat's implementation <sup>2</sup>.</p>"},{"location":"api/stats/Quantile/#parameters","title":"Parameters","text":"<ul> <li> <p>q</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>Determines which quantile to compute, must be comprised between 0 and 1.</p> </li> </ul>"},{"location":"api/stats/Quantile/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Quantile/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nimport numpy as np\n\nnp.random.seed(42 * 1337)\nmu, sigma = 0, 1\ns = np.random.normal(mu, sigma, 500)\n\nmedian = stats.Quantile(0.5)\nfor x in s:\n   _ = median.update(x)\nprint(f'The estimated value of the 50th (median) quantile is {median.get():.4f}')\n</code></pre> <pre><code>The estimated value of the 50th (median) quantile is -0.0275\n</code></pre></p> <p><pre><code>print(f'The real value of the 50th (median) quantile is {np.median(s):.4f}')\n</code></pre> <pre><code>The real value of the 50th (median) quantile is -0.0135\n</code></pre></p> <p><pre><code>percentile_17 = stats.Quantile(0.17)\nfor x in s:\n   _ = percentile_17.update(x)\nprint(f'The estimated value of the 17th quantile is {percentile_17.get():.4f}')\n</code></pre> <pre><code>The estimated value of the 17th quantile is -0.8652\n</code></pre></p> <p><pre><code>print(f'The real value of the 17th quantile is {np.percentile(s,17):.4f}')\n</code></pre> <pre><code>The real value of the 17th quantile is -0.9072\n</code></pre></p>"},{"location":"api/stats/Quantile/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>The P\u00b2 Algorithm for Dynamic Univariateal Computing Calculation of Quantiles and Editor Histograms Without Storing Observations \u21a9</p> </li> <li> <p>LiveStats \u21a9</p> </li> <li> <p>P\u00b2 quantile estimator: estimating the median without storing values \u21a9</p> </li> </ol>"},{"location":"api/stats/RollingAbsMax/","title":"RollingAbsMax","text":"<p>Running absolute max over a window.</p>"},{"location":"api/stats/RollingAbsMax/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingAbsMax/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingAbsMax/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_absmax = stats.RollingAbsMax(window_size=2)\nfor x in X:\n    rolling_absmax.update(x)\n    print(rolling_absmax.get())\n</code></pre> <pre><code>1\n4\n4\n3\n2\n2\n</code></pre></p>"},{"location":"api/stats/RollingAbsMax/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingIQR/","title":"RollingIQR","text":"<p>Computes the rolling interquartile range.</p>"},{"location":"api/stats/RollingIQR/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the window.</p> </li> <li> <p>q_inf</p> <p>Default \u2192 <code>0.25</code></p> <p>Desired inferior quantile, must be between 0 and 1. Defaults to <code>0.25</code>.</p> </li> <li> <p>q_sup</p> <p>Default \u2192 <code>0.75</code></p> <p>Desired superior quantile, must be between 0 and 1. Defaults to <code>0.75</code>.</p> </li> </ul>"},{"location":"api/stats/RollingIQR/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingIQR/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nrolling_iqr = stats.RollingIQR(\n    q_inf=0.25,\n    q_sup=0.75,\n    window_size=101\n)\n\nfor i in range(0, 1001):\n    rolling_iqr.update(i)\n    if i % 100 == 0:\n        print(rolling_iqr.get())\n</code></pre> <pre><code>0.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n50.0\n</code></pre></p>"},{"location":"api/stats/RollingIQR/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingMax/","title":"RollingMax","text":"<p>Running max over a window.</p>"},{"location":"api/stats/RollingMax/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingMax/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingMax/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_max = stats.RollingMax(window_size=2)\nfor x in X:\n    rolling_max.update(x)\n    print(rolling_max.get())\n</code></pre> <pre><code>1\n1\n3\n3\n2\n2\n</code></pre></p>"},{"location":"api/stats/RollingMax/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingMin/","title":"RollingMin","text":"<p>Running min over a window.</p>"},{"location":"api/stats/RollingMin/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingMin/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingMin/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_min = stats.RollingMin(2)\nfor x in X:\n    rolling_min.update(x)\n    print(rolling_min.get())\n</code></pre> <pre><code>1\n-4\n-4\n-2\n-2\n1\n</code></pre></p>"},{"location":"api/stats/RollingMin/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingMode/","title":"RollingMode","text":"<p>Running mode over a window.</p> <p>The mode is the most common value.</p>"},{"location":"api/stats/RollingMode/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingMode/#attributes","title":"Attributes","text":"<ul> <li> <p>counts (collections.defaultdict)</p> <p>Value counts.</p> </li> </ul>"},{"location":"api/stats/RollingMode/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = ['sunny', 'sunny', 'sunny', 'rainy', 'rainy', 'rainy', 'rainy']\nrolling_mode = stats.RollingMode(window_size=2)\nfor x in X:\n    rolling_mode.update(x)\n    print(rolling_mode.get())\n</code></pre> <pre><code>sunny\nsunny\nsunny\nsunny\nrainy\nrainy\nrainy\n</code></pre></p> <p><pre><code>rolling_mode = stats.RollingMode(window_size=5)\nfor x in X:\n    rolling_mode.update(x)\n    print(rolling_mode.get())\n</code></pre> <pre><code>sunny\nsunny\nsunny\nsunny\nsunny\nrainy\nrainy\n</code></pre></p>"},{"location":"api/stats/RollingMode/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingPeakToPeak/","title":"RollingPeakToPeak","text":"<p>Running peak to peak (max - min) over a window.</p>"},{"location":"api/stats/RollingPeakToPeak/#parameters","title":"Parameters","text":"<ul> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the rolling window.</p> </li> </ul>"},{"location":"api/stats/RollingPeakToPeak/#attributes","title":"Attributes","text":"<ul> <li> <p>max (stats.RollingMax)</p> <p>The running rolling max.</p> </li> <li> <p>min (stats.RollingMin)</p> <p>The running rolling min.</p> </li> </ul>"},{"location":"api/stats/RollingPeakToPeak/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [1, -4, 3, -2, 2, 1]\nptp = stats.RollingPeakToPeak(window_size=2)\nfor x in X:\n    ptp.update(x)\n    print(ptp.get())\n</code></pre> <pre><code>0\n5\n7\n5\n4\n1\n</code></pre></p>"},{"location":"api/stats/RollingPeakToPeak/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/RollingQuantile/","title":"RollingQuantile","text":"<p>Running quantile over a window.</p>"},{"location":"api/stats/RollingQuantile/#parameters","title":"Parameters","text":"<ul> <li> <p>q</p> <p>Type \u2192 float</p> <p>Determines which quantile to compute, must be comprised between 0 and 1.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the window.</p> </li> </ul>"},{"location":"api/stats/RollingQuantile/#attributes","title":"Attributes","text":"<ul> <li> <p>name</p> </li> <li> <p>window_size</p> </li> </ul>"},{"location":"api/stats/RollingQuantile/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nrolling_quantile = stats.RollingQuantile(\n    q=.5,\n    window_size=101,\n)\n\nfor i in range(1001):\n    rolling_quantile.update(i)\n    if i % 100 == 0:\n        print(rolling_quantile.get())\n</code></pre> <pre><code>0.0\n50.0\n150.0\n250.0\n350.0\n450.0\n550.0\n650.0\n750.0\n850.0\n950.0\n</code></pre></p>"},{"location":"api/stats/RollingQuantile/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Left sorted \u21a9</p> </li> </ol>"},{"location":"api/stats/SEM/","title":"SEM","text":"<p>Running standard error of the mean using Welford's algorithm.</p>"},{"location":"api/stats/SEM/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom. The divisor used in calculations is <code>n - ddof</code>, where <code>n</code> is the number of seen elements.</p> </li> </ul>"},{"location":"api/stats/SEM/#attributes","title":"Attributes","text":"<ul> <li> <p>n (int)</p> <p>Number of observations.</p> </li> </ul>"},{"location":"api/stats/SEM/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [3, 5, 4, 7, 10, 12]\n\nsem = stats.SEM()\nfor x in X:\n    sem.update(x)\n    print(sem.get())\n</code></pre> <pre><code>0.0\n1.0\n0.577350\n0.853912\n1.240967\n1.447219\n</code></pre></p> <p><pre><code>from river import utils\n\nX = [1, 4, 2, -4, -8, 0]\n\nrolling_sem = utils.Rolling(stats.SEM(ddof=1), window_size=3)\nfor x in X:\n    rolling_sem.update(x)\n    print(rolling_sem.get())\n</code></pre> <pre><code>0.0\n1.5\n0.881917\n2.403700\n2.905932\n2.309401\n</code></pre></p>"},{"location":"api/stats/SEM/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> </ol>"},{"location":"api/stats/Shift/","title":"Shift","text":"<p>Shifts a data stream by returning past values.</p> <p>This can be used to compute statistics over past data. For instance, if you're computing daily averages, then shifting by 7 will be equivalent to computing averages from a week ago. </p> <p>Shifting values is useful when you're calculating an average over a target value. Indeed, in this case it's important to shift the values in order not to introduce leakage. The recommended way to do this is to <code>feature_extraction.TargetAgg</code>, which already takes care of shifting the target values once.</p>"},{"location":"api/stats/Shift/#parameters","title":"Parameters","text":"<ul> <li> <p>amount</p> <p>Default \u2192 <code>1</code></p> <p>Shift amount. The <code>get</code> method will return the <code>t - amount</code> value, where <code>t</code> is the current moment.</p> </li> <li> <p>fill_value</p> <p>Default \u2192 <code>None</code></p> <p>This value will be returned by the <code>get</code> method if not enough values have been observed.</p> </li> </ul>"},{"location":"api/stats/Shift/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Shift/#examples","title":"Examples","text":"<p>It is rare to have to use <code>Shift</code> by itself. A more common usage is to compose it with other statistics. This can be done via the <code>|</code> operator.</p> <p><pre><code>from river import stats\n\nstat = stats.Shift(1) | stats.Mean()\n\nfor i in range(5):\n    stat.update(i)\n    print(stat.get())\n</code></pre> <pre><code>0.0\n0.0\n0.5\n1.0\n1.5\n</code></pre></p> <p>A common usecase for using <code>Shift</code> is when computing statistics on shifted data. For instance, say you have a dataset which records the amount of sales for a set of shops. You might then have a <code>shop</code> field and a <code>sales</code> field. Let's say you want to look at the average amount of sales per shop. You can do this by using a <code>feature_extraction.Agg</code>. When you call <code>transform_one</code>, you're expecting it to return the average amount of sales, without including today's sales. You can do this by prepending an instance of <code>stats.Mean</code> with an instance of <code>stats.Shift</code>.</p> <pre><code>from river import feature_extraction\n\nagg = feature_extraction.Agg(\n    on='sales',\n    how=stats.Shift(1) | stats.Mean(),\n    by='shop'\n)\n</code></pre> <p>Let's define a little example dataset.</p> <pre><code>X = iter([\n    {'shop': 'Ikea', 'sales': 10},\n    {'shop': 'Ikea', 'sales': 15},\n    {'shop': 'Ikea', 'sales': 20}\n])\n</code></pre> <p>Now let's call the <code>learn_one</code> method to update our feature extractor.</p> <pre><code>x = next(X)\nagg.learn_one(x)\n</code></pre> <p>At this point, the average defaults to the initial value of <code>stats.Mean</code>, which is 0.</p> <p><pre><code>agg.transform_one(x)\n</code></pre> <pre><code>{'sales_mean_of_shift_1_by_shop': 0.0}\n</code></pre></p> <p>We can now update our feature extractor with the next data point and check the output.</p> <p><pre><code>agg.learn_one(next(X))\nagg.transform_one(x)\n</code></pre> <pre><code>{'sales_mean_of_shift_1_by_shop': 10.0}\n</code></pre></p> <p><pre><code>agg.learn_one(next(X))\nagg.transform_one(x)\n</code></pre> <pre><code>{'sales_mean_of_shift_1_by_shop': 12.5}\n</code></pre></p>"},{"location":"api/stats/Shift/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Skew/","title":"Skew","text":"<p>Running skew using Welford's algorithm.</p>"},{"location":"api/stats/Skew/#parameters","title":"Parameters","text":"<ul> <li> <p>bias</p> <p>Default \u2192 <code>False</code></p> <p>If <code>False</code>, then the calculations are corrected for statistical bias.</p> </li> </ul>"},{"location":"api/stats/Skew/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/Skew/#examples","title":"Examples","text":"<p><pre><code>from river import stats\nimport numpy as np\n\nnp.random.seed(42)\nX = np.random.normal(loc=0, scale=1, size=10)\n\nskew = stats.Skew(bias=False)\nfor x in X:\n    skew.update(x)\n    print(skew.get())\n</code></pre> <pre><code>0.0\n0.0\n-1.4802398132849872\n0.5127437186677888\n0.7803466510704751\n1.056115628922055\n0.5057840774320389\n0.3478402420400934\n0.4536710660918704\n0.4123070197493227\n</code></pre></p> <p><pre><code>skew = stats.Skew(bias=True)\nfor x in X:\n    skew.update(x)\n    print(skew.get())\n</code></pre> <pre><code>0.0\n0.0\n-0.6043053732501439\n0.2960327239981376\n0.5234724473423674\n0.7712778043924866\n0.39022088752624845\n0.278892645224261\n0.37425953513864063\n0.3476878073823696\n</code></pre></p>"},{"location":"api/stats/Skew/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> </ol>"},{"location":"api/stats/Sum/","title":"Sum","text":"<p>Running sum.</p>"},{"location":"api/stats/Sum/#attributes","title":"Attributes","text":"<ul> <li> <p>sum (float)</p> <p>The running sum.</p> </li> </ul>"},{"location":"api/stats/Sum/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [-5, -3, -1, 1, 3, 5]\nmean = stats.Sum()\nfor x in X:\n    mean.update(x)\n    print(mean.get())\n</code></pre> <pre><code>-5.0\n-8.0\n-9.0\n-8.0\n-5.0\n0.0\n</code></pre></p> <p><pre><code>from river import utils\n\nX = [1, -4, 3, -2, 2, 1]\nrolling_sum = utils.Rolling(stats.Sum(), window_size=2)\nfor x in X:\n    rolling_sum.update(x)\n    print(rolling_sum.get())\n</code></pre> <pre><code>1.0\n-3.0\n-1.0\n1.0\n0.0\n3.0\n</code></pre></p>"},{"location":"api/stats/Sum/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stats/Var/","title":"Var","text":"<p>Running variance using Welford's algorithm.</p>"},{"location":"api/stats/Var/#parameters","title":"Parameters","text":"<ul> <li> <p>ddof</p> <p>Default \u2192 <code>1</code></p> <p>Delta Degrees of Freedom. The divisor used in calculations is <code>n - ddof</code>, where <code>n</code> represents the number of seen elements.</p> </li> </ul>"},{"location":"api/stats/Var/#attributes","title":"Attributes","text":"<ul> <li> <p>mean</p> <p>It is necessary to calculate the mean of the data in order to calculate its variance.</p> </li> </ul>"},{"location":"api/stats/Var/#examples","title":"Examples","text":"<p><pre><code>from river import stats\n\nX = [3, 5, 4, 7, 10, 12]\n\nvar = stats.Var()\nfor x in X:\n    var.update(x)\n    print(var.get())\n</code></pre> <pre><code>0.0\n2.0\n1.0\n2.916666\n7.7\n12.56666\n</code></pre></p> <p>You can measure a rolling variance by using a <code>utils.Rolling</code> wrapper:</p> <p><pre><code>from river import utils\n\nX = [1, 4, 2, -4, -8, 0]\nrvar = utils.Rolling(stats.Var(ddof=1), window_size=3)\nfor x in X:\n    rvar.update(x)\n    print(rvar.get())\n</code></pre> <pre><code>0.0\n4.5\n2.333333\n17.333333\n25.333333\n16.0\n</code></pre></p>"},{"location":"api/stats/Var/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> revert update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> update_many"},{"location":"api/stats/Var/#notes","title":"Notes","text":"<p>The outcomes of the incremental and parallel updates are consistent with numpy's batch processing when \\(\\text{ddof} \\le 1\\).</p> <ol> <li> <p>Wikipedia article on algorithms for calculating variance \u21a9</p> </li> <li> <p>Chan, T.F., Golub, G.H. and LeVeque, R.J., 1983. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician, 37(3), pp.242-247. \u21a9</p> </li> <li> <p>Schubert, E. and Gertz, M., 2018, July. Numerically stable parallel computation of (co-)variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (pp. 1-12).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/stats/base/Bivariate/","title":"Bivariate","text":"<p>A bivariate statistic measures a relationship between two variables.</p>"},{"location":"api/stats/base/Bivariate/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> </ul> <p></p>"},{"location":"api/stats/base/Univariate/","title":"Univariate","text":"<p>A univariate statistic measures a property of a variable.</p>"},{"location":"api/stats/base/Univariate/#attributes","title":"Attributes","text":"<ul> <li>name</li> </ul>"},{"location":"api/stats/base/Univariate/#methods","title":"Methods","text":"get <p>Return the current value of the statistic.</p> <p></p> update <p>Update and return the called instance.</p> <p>Parameters</p> <ul> <li>x     \u2014 'numbers.Number' </li> </ul> <p></p>"},{"location":"api/stream/Cache/","title":"Cache","text":"<p>Utility for caching iterables.</p> <p>This can be used to save a stream of data to the disk in order to iterate over it faster the following time. This can save time depending on the nature of stream. The more processing happens in a stream, the more time will be saved. Even in the case where no processing is done apart from reading the data, the cache will save some time because it is using the pickle binary protocol. It can thus improve the speed in common cases such as reading from a CSV file.</p>"},{"location":"api/stream/Cache/#parameters","title":"Parameters","text":"<ul> <li> <p>directory</p> <p>Default \u2192 <code>None</code></p> <p>The path where to store the pickled data streams. If not provided, then it will be automatically inferred whenever possible, if not an exception will be raised.</p> </li> </ul>"},{"location":"api/stream/Cache/#attributes","title":"Attributes","text":"<ul> <li> <p>keys (set)</p> <p>The set of keys that are being cached.</p> </li> </ul>"},{"location":"api/stream/Cache/#examples","title":"Examples","text":"<pre><code>import time\nfrom river import datasets\nfrom river import stream\n\ndataset = datasets.Phishing()\ncache = stream.Cache()\n</code></pre> <p>The cache can be used by wrapping it around an iterable. Because this is the first time are iterating over the data, nothing is cached.</p> <p><pre><code>tic = time.time()\nfor x, y in cache(dataset, key='phishing'):\n    pass\ntoc = time.time()\nprint(toc - tic)  # doctest: +SKIP\n</code></pre> <pre><code>0.012813\n</code></pre></p> <p>If we do the same thing again, we can see the loop is now faster.</p> <p><pre><code>tic = time.time()\nfor x, y in cache(dataset, key='phishing'):\n    pass\ntoc = time.time()\nprint(toc - tic)  # doctest: +SKIP\n</code></pre> <pre><code>0.001927\n</code></pre></p> <p>We can see an overview of the cache. The first line indicates the location of the cache.</p> <p><pre><code>cache  # doctest: +SKIP\n</code></pre> <pre><code>/tmp\nphishing - 125.2KiB\n</code></pre></p> <p>Finally, we can clear the stream from the cache.</p> <p><pre><code>cache.clear('phishing')\ncache  # doctest: +SKIP\n</code></pre> <pre><code>/tmp\n</code></pre></p> <p>There is also a <code>clear_all</code> method to remove all the items in the cache.</p> <pre><code>cache.clear_all()\n</code></pre>"},{"location":"api/stream/Cache/#methods","title":"Methods","text":"call <p>Call self as a function.</p> <p>Parameters</p> <ul> <li>stream </li> <li>key     \u2014 defaults to <code>None</code> </li> </ul> <p></p> clear <p>Delete the cached stream associated with the given key.</p> <p>Parameters</p> <ul> <li>key     \u2014 'str' </li> </ul> <p></p> clear_all <p>Delete all the cached streams.</p> <p></p>"},{"location":"api/stream/TwitchChatStream/","title":"TwitchChatStream","text":"<p>Twitch chat stream client.</p> <p>This client gives access to a live stream of chat messages in Twitch channels using IRC protocol. You need to have a Twitch account and receive an OAuth token from https://twitchapps.com/tmi/.</p>"},{"location":"api/stream/TwitchChatStream/#parameters","title":"Parameters","text":"<ul> <li> <p>nickname</p> <p>Type \u2192 str</p> <p>The nickname of your account.</p> </li> <li> <p>token</p> <p>Type \u2192 str</p> <p>OAuth token which has been generated.</p> </li> <li> <p>channels</p> <p>Type \u2192 list[str]</p> <p>A list of channel names like <code>[\"asmongold\", \"shroud\"]</code> you want to collect messages from.</p> </li> <li> <p>buffer_size</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>2048</code></p> <p>Size of buffer in bytes used for receiving responses from Twitch with IRC (default 2 KiB).</p> </li> <li> <p>timeout</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>60</code></p> <p>A timeout value in seconds for waiting response from Twitch (default 60s). It can be useful if all requested channels are offline or chat is not active enough.</p> </li> </ul>"},{"location":"api/stream/TwitchChatStream/#examples","title":"Examples","text":"<p>The live stream is instantiated by passing your Twitch account nickname, OAuth token and list of channels. Other parameters are optional.</p> <pre><code>from river import stream\n\ntwitch_chat = stream.TwitchChatStream(\n    nickname=\"twitch_user1\",\n    token=\"oauth:okrip6j6fjio8n5xpy2oum1lph4fbve\",\n    channels=[\"asmongold\", \"shroud\"]\n)\n</code></pre> <p>The stream can be iterated over like this:</p> <pre><code>for item in twitch_chat:\n    print(item)\n</code></pre> <p>Here's a single stream item example: <pre><code>{\n    'dt': datetime.datetime(2022, 9, 14, 10, 33, 37, 989560),\n    'channel': 'asmongold',\n    'username': 'moojiejaa',\n    'msg': 'damn this chat mod are wild'\n}\n</code></pre></p> <ol> <li> <p>Twitch IRC doc \u21a9</p> </li> </ol>"},{"location":"api/stream/TwitterLiveStream/","title":"TwitterLiveStream","text":"<p>Twitter API v2 live stream client.</p> <p>This client gives access to a live stream of Tweets. That is, Tweets that have just been     published. This is different to <code>stream.TwitterRecentStream</code>, which also covers Tweets that     have been published over recent days, and not necessarily in real-time. </p> <p>A list of filtering rules has to be provided. For instance, this allows focusing on a subset of     topics and/or users. </p> <p>Note</p> <p>Using this requires having the <code>requests</code>         package installed.</p>"},{"location":"api/stream/TwitterLiveStream/#parameters","title":"Parameters","text":"<ul> <li> <p>rules</p> <p>See the documentation[^2] for a comprehensive overview of filtering rules.</p> </li> <li> <p>bearer_token</p> <p>A bearer token that is available in each account's developer portal.</p> </li> </ul>"},{"location":"api/stream/TwitterLiveStream/#examples","title":"Examples","text":"<p>The live stream is instantiated by passing a list of filtering rules, as well as a bearer     token. For instance, we can listen to all the breaking news Tweets from the BBC and CNN.</p> <pre><code>from river import stream\n\ntweets = stream.TwitterLiveStream(\n    rules=[\"from:BBCBreaking\", \"from:cnnbrk\"],\n    bearer_token=\"&lt;insert_bearer_token&gt;\"\n)\n</code></pre> <pre><code>The stream can then be iterated over, possibly in an infinite loop. This will listen to the\nlive feed of Tweets and produce a Tweet right after it's been published.\n\n```py\nimport logging\n\nwhile True:\n    try:\n        for tweet in tweets:\n            print(tweet)\n    except requests.exceptions.RequestException as e:\n        logging.warning(str(e))\n        time.sleep(10)\n```\n\nHere's a Tweet example:\n\n```py\n{\n    'data': {\n        'author_id': '428333',\n        'created_at': '2022-08-26T12:59:48.000Z',\n        'id': '1563149212774445058',\n        'text': \"Ukraine's Zaporizhzhia nuclear power plant, which is currently held by\n</code></pre> <p>Russian forces, has been reconnected to Ukraine's electricity grid, according to the country's nuclear operator https://t.co/xfylkBs4JR\"         },         'includes': {             'users': [                 {                     'created_at': '2007-01-02T01:48:14.000Z',                     'id': '428333',                     'name': 'CNN Breaking News',                     'username': 'cnnbrk'                 }             ]         },         'matching_rules': [{'id': '1563148866333151233', 'tag': 'from:cnnbrk'}]     }     ```     [^1]: Filtered stream introduction     [^2]: Building rules for filtered stream     [^3]: Stream Tweets in real-time</p>"},{"location":"api/stream/iter-arff/","title":"iter_arff","text":"<p>Iterates over rows from an ARFF file.</p>"},{"location":"api/stream/iter-arff/#parameters","title":"Parameters","text":"<ul> <li> <p>filepath_or_buffer</p> <p>Either a string indicating the location of a file, or a buffer object that has a <code>read</code> method.</p> </li> <li> <p>target</p> <p>Type \u2192 str | list[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>Name(s) of the target field. If <code>None</code>, then the target field is ignored. If a list of names is passed, then a dictionary is returned instead of a single value.</p> </li> <li> <p>compression</p> <p>Default \u2192 <code>infer</code></p> <p>For on-the-fly decompression of on-disk data. If this is set to 'infer' and <code>filepath_or_buffer</code> is a path, then the decompression method is inferred for the following extensions: '.gz', '.zip'.</p> </li> <li> <p>sparse</p> <p>Default \u2192 <code>False</code></p> <p>Whether the data is sparse or not.</p> </li> </ul>"},{"location":"api/stream/iter-arff/#examples","title":"Examples","text":"<p><pre><code>cars = '''\n@relation CarData\n@attribute make {Toyota, Honda, Ford, Chevrolet}\n@attribute model string\n@attribute year numeric\n@attribute price numeric\n@attribute mpg numeric\n@data\nToyota, Corolla, 2018, 15000, 30.5\nHonda, Civic, 2019, 16000, 32.2\nFord, Mustang, 2020, 25000, 25.0\nChevrolet, Malibu, 2017, 18000, 28.9\nToyota, Camry, 2019, 22000, 29.8\n'''\nwith open('cars.arff', mode='w') as f:\n    _ = f.write(cars)\n\nfrom river import stream\n\nfor x, y in stream.iter_arff('cars.arff', target='price'):\n    print(x, y)\n</code></pre> <pre><code>{'make': 'Toyota', 'model': ' Corolla', 'year': 2018.0, 'mpg': 30.5} 15000.0\n{'make': 'Honda', 'model': ' Civic', 'year': 2019.0, 'mpg': 32.2} 16000.0\n{'make': 'Ford', 'model': ' Mustang', 'year': 2020.0, 'mpg': 25.0} 25000.0\n{'make': 'Chevrolet', 'model': ' Malibu', 'year': 2017.0, 'mpg': 28.9} 18000.0\n{'make': 'Toyota', 'model': ' Camry', 'year': 2019.0, 'mpg': 29.8} 22000.0\n</code></pre></p> <p>Finally, let's delete the example file.</p> <pre><code>import os; os.remove('cars.arff')\n</code></pre> <p>ARFF files support sparse data. Let's create a sparse ARFF file.</p> <pre><code>sparse = '''\n% traindata\n@RELATION \"traindata: -C 6\"\n@ATTRIBUTE y0 {0, 1}\n@ATTRIBUTE y1 {0, 1}\n@ATTRIBUTE y2 {0, 1}\n@ATTRIBUTE y3 {0, 1}\n@ATTRIBUTE y4 {0, 1}\n@ATTRIBUTE y5 {0, 1}\n@ATTRIBUTE X0 NUMERIC\n@ATTRIBUTE X1 NUMERIC\n@ATTRIBUTE X2 NUMERIC\n@DATA\n{ 3 1,6 0.863382,8 0.820094 }\n{ 2 1,6 0.659761 }\n{ 0 1,3 1,6 0.437881,8 0.818882 }\n{ 2 1,6 0.676477,7 0.724635,8 0.755123 }\n'''\n\nwith open('sparse.arff', mode='w') as f:\n    _ = f.write(sparse)\n</code></pre> <p>In addition, we'll specify that there are several target fields.</p> <p><pre><code>arff_stream = stream.iter_arff(\n    'sparse.arff',\n    target=['y0', 'y1', 'y2', 'y3', 'y4', 'y5'],\n    sparse=True\n)\n\nfor x, y in arff_stream:\n    print(x)\n    print(y)\n</code></pre> <pre><code>{'X0': '0.863382', 'X2': '0.820094'}\n{'y0': 0, 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0}\n{'X0': '0.659761'}\n{'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0}\n{'X0': '0.437881', 'X2': '0.818882'}\n{'y0': '1', 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0}\n{'X0': '0.676477', 'X1': '0.724635', 'X2': '0.755123'}\n{'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0}\n</code></pre></p> <p>This function can also deal with missing features in non-sparse data. These are indicated with a question mark.</p> <p><pre><code>data = '''\n@relation giveMeLoan-weka.filters.unsupervised.attribute.Remove-R1\n@attribute RevolvingUtilizationOfUnsecuredLines numeric\n@attribute age numeric\n@attribute NumberOfTime30-59DaysPastDueNotWorse numeric\n@attribute DebtRatio numeric\n@attribute MonthlyIncome numeric\n@attribute NumberOfOpenCreditLinesAndLoans numeric\n@attribute NumberOfTimes90DaysLate numeric\n@attribute NumberRealEstateLoansOrLines numeric\n@attribute NumberOfTime60-89DaysPastDueNotWorse numeric\n@attribute NumberOfDependents numeric\n@attribute isFraud {0,1}\n@data\n0.213179,74,0,0.375607,3500,3,0,1,0,1,0\n0.305682,57,0,5710,?,8,0,3,0,0,0\n0.754464,39,0,0.20994,3500,8,0,0,0,0,0\n0.116951,27,0,46,?,2,0,0,0,0,0\n0.189169,57,0,0.606291,23684,9,0,4,0,2,0\n'''\n\nwith open('data.arff', mode='w') as f:\n    _ = f.write(data)\n\nfor x, y in stream.iter_arff('data.arff', target='isFraud'):\n    print(len(x))\n</code></pre> <pre><code>10\n9\n10\n9\n10\n</code></pre></p> <ol> <li> <p>ARFF format description from Weka \u21a9</p> </li> </ol>"},{"location":"api/stream/iter-array/","title":"iter_array","text":"<p>Iterates over the rows from an array of features and an array of targets.</p> <p>This method is intended to work with <code>numpy</code> arrays, but should also work with Python lists.</p>"},{"location":"api/stream/iter-array/#parameters","title":"Parameters","text":"<ul> <li> <p>X</p> <p>Type \u2192 np.ndarray</p> <p>A 2D array of features. This can also be a 1D array of strings, which can be the case if you're working with text.</p> </li> <li> <p>y</p> <p>Type \u2192 np.ndarray | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional array of targets.</p> </li> <li> <p>feature_names</p> <p>Type \u2192 list[base.typing.FeatureName] | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional list of feature names. The features will be labeled with integers if no names are provided.</p> </li> <li> <p>target_names</p> <p>Type \u2192 list[base.typing.FeatureName] | None</p> <p>Default \u2192 <code>None</code></p> <p>An optional list of output names. The outputs will be labeled with integers if no names are provided. Only applies if there are multiple outputs, i.e. if <code>y</code> is a 2D array.</p> </li> <li> <p>shuffle</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>Indicates whether or not to shuffle the input arrays before iterating over them.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for shuffling the data.</p> </li> </ul>"},{"location":"api/stream/iter-array/#examples","title":"Examples","text":"<p><pre><code>from river import stream\nimport numpy as np\n\nX = np.array([[1, 2, 3], [11, 12, 13]])\nY = np.array([True, False])\n\ndataset = stream.iter_array(\n    X, Y,\n    feature_names=['x1', 'x2', 'x3']\n)\nfor x, y in dataset:\n    print(x, y)\n</code></pre> <pre><code>{'x1': 1, 'x2': 2, 'x3': 3} True\n{'x1': 11, 'x2': 12, 'x3': 13} False\n</code></pre></p> <p>This also works with a array of texts:</p> <p><pre><code>X = [\"foo\", \"bar\"]\ndataset = stream.iter_array(\n    X, Y,\n    feature_names=['x1', 'x2', 'x3']\n)\nfor x, y in dataset:\n    print(x, y)\n</code></pre> <pre><code>foo True\nbar False\n</code></pre></p>"},{"location":"api/stream/iter-csv/","title":"iter_csv","text":"<p>Iterates over rows from a CSV file.</p> <p>Reading CSV files can be quite slow. If, for whatever reason, you're going to loop through the same file multiple times, then we recommend that you to use the <code>stream.Cache</code> utility.</p>"},{"location":"api/stream/iter-csv/#parameters","title":"Parameters","text":"<ul> <li> <p>filepath_or_buffer</p> <p>Either a string indicating the location of a file, or a buffer object that has a <code>read</code> method.</p> </li> <li> <p>target</p> <p>Type \u2192 str | list[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>A single target column is assumed if a string is passed. A multiple output scenario is assumed if a list of strings is passed. A <code>None</code> value will be assigned to each <code>y</code> if this parameter is omitted.</p> </li> <li> <p>converters</p> <p>Type \u2192 dict | None</p> <p>Default \u2192 <code>None</code></p> <p>All values in the CSV are interpreted as strings by default. You can use this parameter to cast values to the desired type. This should be a <code>dict</code> mapping feature names to callables used to parse their associated values. Note that a callable may be a type, such as <code>float</code> and <code>int</code>.</p> </li> <li> <p>parse_dates</p> <p>Type \u2192 dict | None</p> <p>Default \u2192 <code>None</code></p> <p>A <code>dict</code> mapping feature names to a format passed to the <code>datetime.datetime.strptime</code> method.</p> </li> <li> <p>drop</p> <p>Type \u2192 list[str] | None</p> <p>Default \u2192 <code>None</code></p> <p>Fields to ignore.</p> </li> <li> <p>drop_nones</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to drop fields where the value is a <code>None</code>.</p> </li> <li> <p>fraction</p> <p>Default \u2192 <code>1.0</code></p> <p>Sampling fraction.</p> </li> <li> <p>compression</p> <p>Default \u2192 <code>infer</code></p> <p>For on-the-fly decompression of on-disk data. If this is set to 'infer' and <code>filepath_or_buffer</code> is a path, then the decompression method is inferred for the following extensions: '.gz', '.zip'.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>If specified, the sampling will be deterministic.</p> </li> <li> <p>field_size_limit</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>If not <code>None</code>, this will be passed to the <code>csv.field_size_limit</code> function.</p> </li> <li> <p>kwargs</p> <p>All other keyword arguments are passed to the underlying <code>csv.DictReader</code>.</p> </li> </ul>"},{"location":"api/stream/iter-csv/#examples","title":"Examples","text":"<p>Although this function is designed to handle different kinds of inputs, the most common use case is to read a file on the disk. We'll first create a little CSV file to illustrate.</p> <pre><code>tv_shows = '''name,year,rating\nPlanet Earth II,2016,9.5\nPlanet Earth,2006,9.4\nBand of Brothers,2001,9.4\nBreaking Bad,2008,9.4\nChernobyl,2019,9.4\n'''\nwith open('tv_shows.csv', mode='w') as f:\n    _ = f.write(tv_shows)\n</code></pre> <p>We can now go through the rows one by one. We can use the <code>converters</code> parameter to cast the <code>rating</code> field value as a <code>float</code>. We can also convert the <code>year</code> to a <code>datetime</code> via the <code>parse_dates</code> parameter.</p> <p><pre><code>from river import stream\n\nparams = {\n    'converters': {'rating': float},\n    'parse_dates': {'year': '%Y'}\n}\nfor x, y in stream.iter_csv('tv_shows.csv', **params):\n    print(x, y)\n</code></pre> <pre><code>{'name': 'Planet Earth II', 'year': datetime.datetime(2016, 1, 1, 0, 0), 'rating': 9.5} None\n{'name': 'Planet Earth', 'year': datetime.datetime(2006, 1, 1, 0, 0), 'rating': 9.4} None\n{'name': 'Band of Brothers', 'year': datetime.datetime(2001, 1, 1, 0, 0), 'rating': 9.4} None\n{'name': 'Breaking Bad', 'year': datetime.datetime(2008, 1, 1, 0, 0), 'rating': 9.4} None\n{'name': 'Chernobyl', 'year': datetime.datetime(2019, 1, 1, 0, 0), 'rating': 9.4} None\n</code></pre></p> <p>The value of <code>y</code> is always <code>None</code> because we haven't provided a value for the <code>target</code> parameter. Here is an example where a <code>target</code> is provided:</p> <p><pre><code>dataset = stream.iter_csv('tv_shows.csv', target='rating', **params)\nfor x, y in dataset:\n    print(x, y)\n</code></pre> <pre><code>{'name': 'Planet Earth II', 'year': datetime.datetime(2016, 1, 1, 0, 0)} 9.5\n{'name': 'Planet Earth', 'year': datetime.datetime(2006, 1, 1, 0, 0)} 9.4\n{'name': 'Band of Brothers', 'year': datetime.datetime(2001, 1, 1, 0, 0)} 9.4\n{'name': 'Breaking Bad', 'year': datetime.datetime(2008, 1, 1, 0, 0)} 9.4\n{'name': 'Chernobyl', 'year': datetime.datetime(2019, 1, 1, 0, 0)} 9.4\n</code></pre></p> <p>Finally, let's delete the example file.</p> <pre><code>import os; os.remove('tv_shows.csv')\n</code></pre>"},{"location":"api/stream/iter-libsvm/","title":"iter_libsvm","text":"<p>Iterates over a dataset in LIBSVM format.</p> <p>The LIBSVM format is a popular way in the machine learning community to store sparse datasets. Only numerical feature values are supported. The feature names will be considered as strings.</p>"},{"location":"api/stream/iter-libsvm/#parameters","title":"Parameters","text":"<ul> <li> <p>filepath_or_buffer</p> <p>Type \u2192 str</p> <p>Either a string indicating the location of a file, or a buffer object that has a <code>read</code> method.</p> </li> <li> <p>target_type</p> <p>Default \u2192 <code>&lt;class 'float'&gt;</code></p> <p>The type of the target value.</p> </li> <li> <p>compression</p> <p>Default \u2192 <code>infer</code></p> <p>For on-the-fly decompression of on-disk data. If this is set to 'infer' and <code>filepath_or_buffer</code> is a path, then the decompression method is inferred for the following extensions: '.gz', '.zip'.</p> </li> </ul>"},{"location":"api/stream/iter-libsvm/#examples","title":"Examples","text":"<p><pre><code>import io\nfrom river import stream\n\ndata = io.StringIO('''+1 x:-134.26 y:0.2563\n1 x:-12 z:0.3\n-1 y:.25\n''')\n\nfor x, y in stream.iter_libsvm(data, target_type=int):\n    print(y, x)\n</code></pre> <pre><code>1 {'x': -134.26, 'y': 0.2563}\n1 {'x': -12.0, 'z': 0.3}\n-1 {'y': 0.25}\n</code></pre></p> <ol> <li> <p>LIBSVM documentation \u21a9</p> </li> </ol>"},{"location":"api/stream/iter-pandas/","title":"iter_pandas","text":"<p>Iterates over the rows of a <code>pandas.DataFrame</code>.</p>"},{"location":"api/stream/iter-pandas/#parameters","title":"Parameters","text":"<ul> <li> <p>X</p> <p>Type \u2192 pd.DataFrame</p> <p>A dataframe of features.</p> </li> <li> <p>y</p> <p>Type \u2192 pd.Series | pd.DataFrame | None</p> <p>Default \u2192 <code>None</code></p> <p>A series or a dataframe with one column per target.</p> </li> <li> <p>kwargs</p> <p>Extra keyword arguments are passed to the underlying call to <code>stream.iter_array</code>.</p> </li> </ul>"},{"location":"api/stream/iter-pandas/#examples","title":"Examples","text":"<p><pre><code>import pandas as pd\nfrom river import stream\n\nX = pd.DataFrame({\n    'x1': [1, 2, 3, 4],\n    'x2': ['blue', 'yellow', 'yellow', 'blue'],\n    'y': [True, False, False, True]\n})\ny = X.pop('y')\n\nfor xi, yi in stream.iter_pandas(X, y):\n    print(xi, yi)\n</code></pre> <pre><code>{'x1': 1, 'x2': 'blue'} True\n{'x1': 2, 'x2': 'yellow'} False\n{'x1': 3, 'x2': 'yellow'} False\n{'x1': 4, 'x2': 'blue'} True\n</code></pre></p>"},{"location":"api/stream/iter-polars/","title":"iter_polars","text":"<p>Iterates over the rows of a <code>polars.DataFrame</code>.</p>"},{"location":"api/stream/iter-polars/#parameters","title":"Parameters","text":"<ul> <li> <p>X</p> <p>Type \u2192 pl.DataFrame</p> <p>A dataframe of features.</p> </li> <li> <p>y</p> <p>Type \u2192 pl.Series | pl.DataFrame | None</p> <p>Default \u2192 <code>None</code></p> <p>A series or a dataframe with one column per target.</p> </li> <li> <p>kwargs</p> <p>Extra keyword arguments are passed to the underlying call to <code>stream.iter_array</code>.</p> </li> </ul>"},{"location":"api/stream/iter-polars/#examples","title":"Examples","text":"<p><pre><code>import polars as pl\nfrom river import stream\n\nX = pl.DataFrame({\n    'x1': [1, 2, 3, 4],\n    'x2': ['blue', 'yellow', 'yellow', 'blue'],\n    'y': [True, False, False, True]\n})\ny = X.get_column('y')\nX=X.drop(\"y\")\n\nfor xi, yi in stream.iter_polars(X, y):\n    print(xi, yi)\n</code></pre> <pre><code>{'x1': 1, 'x2': 'blue'} True\n{'x1': 2, 'x2': 'yellow'} False\n{'x1': 3, 'x2': 'yellow'} False\n{'x1': 4, 'x2': 'blue'} True\n</code></pre></p>"},{"location":"api/stream/iter-sklearn-dataset/","title":"iter_sklearn_dataset","text":"<p>Iterates rows from one of the datasets provided by scikit-learn.</p> <p>This allows you to use any dataset from scikit-learn's <code>datasets</code> module. For instance, you can use the <code>fetch_openml</code> function to get access to all of the datasets from the OpenML website.</p>"},{"location":"api/stream/iter-sklearn-dataset/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 sklearn.utils.Bunch</p> <p>A scikit-learn dataset.</p> </li> <li> <p>kwargs</p> <p>Extra keyword arguments are passed to the underlying call to <code>stream.iter_array</code>.</p> </li> </ul>"},{"location":"api/stream/iter-sklearn-dataset/#examples","title":"Examples","text":"<p><pre><code>import pprint\nfrom sklearn import datasets\nfrom river import stream\n\ndataset = datasets.load_diabetes()\n\nfor xi, yi in stream.iter_sklearn_dataset(dataset):\n    pprint.pprint(xi)\n    print(yi)\n    break\n</code></pre> <pre><code>{'age': 0.038075906433423026,\n 'bmi': 0.061696206518683294,\n 'bp': 0.0218723855140367,\n 's1': -0.04422349842444599,\n 's2': -0.03482076283769895,\n 's3': -0.04340084565202491,\n 's4': -0.002592261998183278,\n 's5': 0.019907486170462722,\n 's6': -0.01764612515980379,\n 'sex': 0.05068011873981862}\n151.0\n</code></pre></p>"},{"location":"api/stream/iter-sql/","title":"iter_sql","text":"<p>Iterates over the results from an SQL query.</p> <p>By default, SQLAlchemy prefetches results. Therefore, even though you can iterate over the resulting rows one by one, the results are in fact loaded in batch. You can modify this behavior by configuring the connection you pass to <code>iter_sql</code>. For instance, you can set the <code>stream_results</code> parameter to <code>True</code>, as explained in SQLAlchemy's documentation. Note, however, that this isn't available for all database engines.</p>"},{"location":"api/stream/iter-sql/#parameters","title":"Parameters","text":"<ul> <li> <p>query</p> <p>Type \u2192 str | sqlalchemy.TextClause | sqlalchemy.Select</p> <p>SQL query to be executed.</p> </li> <li> <p>conn</p> <p>Type \u2192 sqlalchemy.Connection</p> <p>An SQLAlchemy construct which has an <code>execute</code> method. In other words you can pass an engine, a connection, or a session.</p> </li> <li> <p>target_name</p> <p>Type \u2192 str | None</p> <p>Default \u2192 <code>None</code></p> <p>The name of the target field. If this is <code>None</code>, then <code>y</code> will also be <code>None</code>.</p> </li> </ul>"},{"location":"api/stream/iter-sql/#examples","title":"Examples","text":"<p>As an example we'll create an in-memory database with SQLAlchemy.</p> <pre><code>import datetime as dt\nimport sqlalchemy\n\nengine = sqlalchemy.create_engine('sqlite://')\n\nmetadata = sqlalchemy.MetaData()\n\nt_sales = sqlalchemy.Table('sales', metadata,\n    sqlalchemy.Column('shop', sqlalchemy.String, primary_key=True),\n    sqlalchemy.Column('date', sqlalchemy.Date, primary_key=True),\n    sqlalchemy.Column('amount', sqlalchemy.Integer)\n)\n\nmetadata.create_all(engine)\n\nsales = [\n    {'shop': 'Hema', 'date': dt.date(2016, 8, 2), 'amount': 20},\n    {'shop': 'Ikea', 'date': dt.date(2016, 8, 2), 'amount': 18},\n    {'shop': 'Hema', 'date': dt.date(2016, 8, 3), 'amount': 22},\n    {'shop': 'Ikea', 'date': dt.date(2016, 8, 3), 'amount': 14},\n    {'shop': 'Hema', 'date': dt.date(2016, 8, 4), 'amount': 12},\n    {'shop': 'Ikea', 'date': dt.date(2016, 8, 4), 'amount': 16}\n]\n\nwith engine.connect() as conn:\n    _ = conn.execute(t_sales.insert(), sales)\n    conn.commit()\n</code></pre> <p>We can now query the database. We will set <code>amount</code> to be the target field.</p> <p><pre><code>from river import stream\n\nwith engine.connect() as conn:\n    query = sqlalchemy.sql.select(t_sales)\n    dataset = stream.iter_sql(query, conn, target_name='amount')\n    for x, y in dataset:\n        print(x, y)\n</code></pre> <pre><code>{'shop': 'Hema', 'date': datetime.date(2016, 8, 2)} 20\n{'shop': 'Ikea', 'date': datetime.date(2016, 8, 2)} 18\n{'shop': 'Hema', 'date': datetime.date(2016, 8, 3)} 22\n{'shop': 'Ikea', 'date': datetime.date(2016, 8, 3)} 14\n{'shop': 'Hema', 'date': datetime.date(2016, 8, 4)} 12\n{'shop': 'Ikea', 'date': datetime.date(2016, 8, 4)} 16\n</code></pre></p> <p>This also with raw SQL queries.</p> <p><pre><code>with engine.connect() as conn:\n    query = \"SELECT * FROM sales WHERE shop = 'Hema'\"\n    dataset = stream.iter_sql(query, conn, target_name='amount')\n    for x, y in dataset:\n        print(x, y)\n</code></pre> <pre><code>{'shop': 'Hema', 'date': '2016-08-02'} 20\n{'shop': 'Hema', 'date': '2016-08-03'} 22\n{'shop': 'Hema', 'date': '2016-08-04'} 12\n</code></pre></p>"},{"location":"api/stream/shuffle/","title":"shuffle","text":"<p>Shuffles a stream of data.</p> <p>This works by maintaining a buffer of elements. The first <code>buffer_size</code> elements are stored in memory. Once the buffer is full, a random element inside the buffer is yielded. Every time an element is yielded, the next element in the stream replaces it and the buffer is sampled again. Increasing <code>buffer_size</code> will improve the quality of the shuffling. </p> <p>If you really want to stream over your dataset in a \"good\" random order, the best way is to split your dataset into smaller datasets and loop over them in a round-robin fashion. You may do this by using the <code>roundrobin</code> recipe from the <code>itertools</code> module.</p>"},{"location":"api/stream/shuffle/#parameters","title":"Parameters","text":"<ul> <li> <p>stream</p> <p>Type \u2192 typing.Iterator</p> <p>The stream to shuffle.</p> </li> <li> <p>buffer_size</p> <p>Type \u2192 int</p> <p>The size of the buffer which contains the elements help in memory. Increasing this will increase randomness but will incur more memory usage.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed used for sampling.</p> </li> </ul>"},{"location":"api/stream/shuffle/#examples","title":"Examples","text":"<p><pre><code>from river import stream\n\nfor i in stream.shuffle(range(15), buffer_size=5, seed=42):\n    print(i)\n</code></pre> <pre><code>0\n5\n2\n1\n8\n9\n6\n4\n11\n12\n10\n7\n14\n13\n3\n</code></pre></p> <ol> <li> <p>Visualizing TensorFlow's streaming shufflers \u21a9</p> </li> </ol>"},{"location":"api/stream/simulate-qa/","title":"simulate_qa","text":"<p>Simulate a time-ordered question and answer session.</p> <p>This method allows looping through a dataset in the order in which it arrived. Indeed, it usually is the case that labels arrive after features. Being able to go through a dataset in arrival order enables assessing a model's performance in a reliable manner. For instance, the <code>evaluate.progressive_val_score</code> is a high-level method that can be used to score a model on a dataset. Under the hood it uses this method to determine the correct arrival order.</p>"},{"location":"api/stream/simulate-qa/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>A stream of (features, target) tuples.</p> </li> <li> <p>moment</p> <p>Type \u2192 str | typing.Callable[[dict], dt.datetime] | None</p> <p>The attribute used for measuring time. If a callable is passed, then it is expected to take as input a <code>dict</code> of features. If <code>None</code>, then the observations are implicitly timestamped in the order in which they arrive. If a <code>str</code> is passed, then it will be used to obtain the time from the input features.</p> </li> <li> <p>delay</p> <p>Type \u2192 str | int | dt.timedelta | typing.Callable | None</p> <p>The amount of time to wait before revealing the target associated with each observation to the model. This value is expected to be able to sum with the <code>moment</code> value. For instance, if <code>moment</code> is a <code>datetime.date</code>, then <code>delay</code> is expected to be a <code>datetime.timedelta</code>. If a callable is passed, then it is expected to take as input a <code>dict</code> of features and the target. If a <code>str</code> is passed, then it will be used to access the relevant field from the features. If <code>None</code> is passed, then no delay will be used, which leads to doing standard online validation. If a scalar is passed, such an <code>int</code> or a <code>datetime.timedelta</code>, then the delay is constant.</p> </li> <li> <p>copy</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If <code>True</code>, then a separate copy of the features are yielded the second time around. This ensures that inadvertent modifications in downstream code don't have any effect.</p> </li> </ul>"},{"location":"api/stream/simulate-qa/#examples","title":"Examples","text":"<p>The arrival delay isn't usually indicated in a dataset, but it might be able to be inferred from the features. As an example, we'll simulate the departure and arrival time of taxi trips. Let's first create a time table which records the departure time and the duration of seconds of several taxi trips.</p> <pre><code>import datetime as dt\ntime_table = [\n    (dt.datetime(2020, 1, 1, 20,  0, 0),  900),\n    (dt.datetime(2020, 1, 1, 20, 10, 0), 1800),\n    (dt.datetime(2020, 1, 1, 20, 20, 0),  300),\n    (dt.datetime(2020, 1, 1, 20, 45, 0),  400),\n    (dt.datetime(2020, 1, 1, 20, 50, 0),  240),\n    (dt.datetime(2020, 1, 1, 20, 55, 0),  450)\n]\n</code></pre> <p>We can now create a streaming dataset where the features are the departure dates and the targets are the durations.</p> <pre><code>dataset = (\n    ({'date': date}, duration)\n    for date, duration in time_table\n)\n</code></pre> <p>Now, we can use <code>simulate_qa</code> to iterate over the events in the order in which they are meant to occur.</p> <p><pre><code>delay = lambda _, y: dt.timedelta(seconds=y)\n\nfor i, x, y in simulate_qa(dataset, moment='date', delay=delay):\n    if y is None:\n        print(f'{x[\"date\"]} - trip #{i} departs')\n    else:\n        arrival_date = x['date'] + dt.timedelta(seconds=y)\n        print(f'{arrival_date} - trip #{i} arrives after {y} seconds')\n</code></pre> <pre><code>2020-01-01 20:00:00 - trip #0 departs\n2020-01-01 20:10:00 - trip #1 departs\n2020-01-01 20:15:00 - trip #0 arrives after 900 seconds\n2020-01-01 20:20:00 - trip #2 departs\n2020-01-01 20:25:00 - trip #2 arrives after 300 seconds\n2020-01-01 20:40:00 - trip #1 arrives after 1800 seconds\n2020-01-01 20:45:00 - trip #3 departs\n2020-01-01 20:50:00 - trip #4 departs\n2020-01-01 20:51:40 - trip #3 arrives after 400 seconds\n2020-01-01 20:54:00 - trip #4 arrives after 240 seconds\n2020-01-01 20:55:00 - trip #5 departs\n2020-01-01 21:02:30 - trip #5 arrives after 450 seconds\n</code></pre></p> <p>This function is extremely practical because it provides a reliable way to evaluate the performance of a model in a real scenario. Indeed, it allows to make predictions and perform model updates in exactly the same manner that would happen live. For instance, it is used in <code>evaluate.progressive_val_score</code>, which is a higher level function for evaluating models in an online manner.</p>"},{"location":"api/time-series/ForecastingMetric/","title":"ForecastingMetric","text":""},{"location":"api/time-series/ForecastingMetric/#methods","title":"Methods","text":"get <p>Return the current performance along the horizon.</p> <p>Returns</p> <p>list[float]:     The current performance.</p> <p></p> update <p>Update the metric at each step along the horizon.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'list[Number]' </li> <li>y_pred     \u2014 'list[Number]' </li> </ul> <p></p>"},{"location":"api/time-series/HoltWinters/","title":"HoltWinters","text":"<p>Holt-Winters forecaster.</p> <p>This is a standard implementation of the Holt-Winters forecasting method. Certain parametrisations result in special cases, such as simple exponential smoothing. </p> <p>Optimal parameters and initialisation values can be determined in a batch setting. However, in an online setting, it is necessary to wait and observe enough values. The first <code>k = max(2, seasonality)</code> values are indeed used to initialize the components. </p> <p>Level initialization </p> \\[l = \\frac{1}{k} \\sum_{i=1}{k} y_i\\] <p>Trend initialization </p> \\[t = \\frac{1}{k - 1} \\sum_{i=2}{k} y_i - y_{i-1}\\] <p>Trend initialization </p> \\[s_i = \\frac{y_i}{k}\\]"},{"location":"api/time-series/HoltWinters/#parameters","title":"Parameters","text":"<ul> <li> <p>alpha</p> <p>Smoothing parameter for the level.</p> </li> <li> <p>beta</p> <p>Default \u2192 <code>None</code></p> <p>Smoothing parameter for the trend.</p> </li> <li> <p>gamma</p> <p>Default \u2192 <code>None</code></p> <p>Smoothing parameter for the seasonality.</p> </li> <li> <p>seasonality</p> <p>Default \u2192 <code>0</code></p> <p>The number of periods in a season. For instance, this should be 4 for quarterly data, and 12 for yearly data.</p> </li> <li> <p>multiplicative</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not to use a multiplicative formulation.</p> </li> </ul>"},{"location":"api/time-series/HoltWinters/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import metrics\nfrom river import time_series\n\ndataset = datasets.AirlinePassengers()\n\nmodel = time_series.HoltWinters(\n    alpha=0.3,\n    beta=0.1,\n    gamma=0.6,\n    seasonality=12,\n    multiplicative=True\n)\n\nmetric = metrics.MAE()\n\ntime_series.evaluate(\n    dataset,\n    model,\n    metric,\n    horizon=12\n)\n</code></pre> <pre><code>+1  MAE: 25.899087\n+2  MAE: 26.26131\n+3  MAE: 25.735903\n+4  MAE: 25.625678\n+5  MAE: 26.093842\n+6  MAE: 26.90249\n+7  MAE: 28.634398\n+8  MAE: 29.284769\n+9  MAE: 31.018351\n+10 MAE: 32.252349\n+11 MAE: 33.518946\n+12 MAE: 33.975057\n</code></pre></p>"},{"location":"api/time-series/HoltWinters/#methods","title":"Methods","text":"forecast <p>Makes forecast at each step of the given horizon.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Updates the model.</p> <p>Parameters</p> <ul> <li>y     \u2014 'float' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>Exponential smoothing \u2014 Wikipedia \u21a9</p> </li> <li> <p>Exponential smoothing \u2014 Forecasting: Principles and Practice \u21a9</p> </li> <li> <p>What is Exponential Smoothing? \u2014 Engineering statistics handbook \u21a9</p> </li> </ol>"},{"location":"api/time-series/HorizonAggMetric/","title":"HorizonAggMetric","text":"<p>Same as <code>HorizonMetric</code>, but aggregates the result based on an provided function.</p> <p>This allows, for instance, to measure the average performance of a forecasting model along the horizon.</p>"},{"location":"api/time-series/HorizonAggMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> <li> <p>agg_func</p> <p>Type \u2192 typing.Callable[[list[float]], float]</p> <p>A function that takes as input a list of floats and outputs a single float. You may want to <code>min</code>, <code>max</code>, as well as <code>statistics.mean</code> and <code>statistics.median</code>.</p> </li> </ul>"},{"location":"api/time-series/HorizonAggMetric/#examples","title":"Examples","text":"<p>This is used internally by the <code>time_series.evaluate</code> function when you pass an <code>agg_func</code>.</p> <p><pre><code>import statistics\nfrom river import datasets\nfrom river import metrics\nfrom river import time_series\n\nmetric = time_series.evaluate(\n    dataset=datasets.AirlinePassengers(),\n    model=time_series.HoltWinters(alpha=0.1),\n    metric=metrics.MAE(),\n    agg_func=statistics.mean,\n    horizon=4\n)\n\nmetric\n</code></pre> <pre><code>mean(MAE): 42.901748\n</code></pre></p>"},{"location":"api/time-series/HorizonAggMetric/#methods","title":"Methods","text":"get <p>Return the current performance along the horizon.</p> <p>Returns</p> <p>list[float]:     The current performance.</p> <p></p> update <p>Update the metric at each step along the horizon.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'list[Number]' </li> <li>y_pred     \u2014 'list[Number]' </li> </ul> <p></p>"},{"location":"api/time-series/HorizonMetric/","title":"HorizonMetric","text":"<p>Measures performance at each time step ahead.</p> <p>This allows to measure the performance of a model at each time step along the horizon. A copy of the provided regression metric is made for each time step. At each time step ahead, the metric is thus evaluated on each prediction for said time step, and not for the time steps before or after that.</p>"},{"location":"api/time-series/HorizonMetric/#parameters","title":"Parameters","text":"<ul> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> </ul>"},{"location":"api/time-series/HorizonMetric/#examples","title":"Examples","text":"<p>This is used internally by the <code>time_series.evaluate</code> function.</p> <p><pre><code>from river import datasets\nfrom river import metrics\nfrom river import time_series\n\nmetric = time_series.evaluate(\n    dataset=datasets.AirlinePassengers(),\n    model=time_series.HoltWinters(alpha=0.1),\n    metric=metrics.MAE(),\n    horizon=4\n)\n\nmetric\n</code></pre> <pre><code>+1 MAE: 40.931286\n+2 MAE: 42.667998\n+3 MAE: 44.158092\n+4 MAE: 43.849617\n</code></pre></p>"},{"location":"api/time-series/HorizonMetric/#methods","title":"Methods","text":"get <p>Return the current performance along the horizon.</p> <p>Returns</p> <p>list[float]:     The current performance.</p> <p></p> update <p>Update the metric at each step along the horizon.</p> <p>Parameters</p> <ul> <li>y_true     \u2014 'list[Number]' </li> <li>y_pred     \u2014 'list[Number]' </li> </ul> <p></p>"},{"location":"api/time-series/SNARIMAX/","title":"SNARIMAX","text":"<p>SNARIMAX model.</p> <p>SNARIMAX stands for (S)easonal (N)on-linear (A)uto(R)egressive (I)ntegrated (M)oving-(A)verage with e(X)ogenous inputs model. </p> <p>This model generalizes many established time series models in a single interface that can be trained online. It assumes that the provided training data is ordered in time and is uniformly spaced. It is made up of the following components: </p> <ul> <li> <p>S (Seasonal)</p> </li> <li> <p>N (Non-linear): Any online regression model can be used, not necessarily a linear regression</p> <p>as is done in textbooks. - AR (Autoregressive): Lags of the target variable are used as features.</p> </li> <li> <p>I (Integrated): The model can be fitted on a differenced version of a time series. In this</p> <p>context, integration is the reverse of differencing. - MA (Moving average): Lags of the errors are used as features.</p> </li> <li> <p>X (Exogenous): Users can provide additional features. Care has to be taken to include</p> <p>features that will be available both at training and prediction time. </p> </li> </ul> <p>Each of these components can be switched on and off by specifying the appropriate parameters. Classical time series models such as AR, MA, ARMA, and ARIMA can thus be seen as special parametrizations of the SNARIMAX model. </p> <p>This model is tailored for time series that are homoskedastic. In other words, it might not work well if the variance of the time series varies widely along time.</p>"},{"location":"api/time-series/SNARIMAX/#parameters","title":"Parameters","text":"<ul> <li> <p>p</p> <p>Type \u2192 int</p> <p>Order of the autoregressive part. This is the number of past target values that will be included as features.</p> </li> <li> <p>d</p> <p>Type \u2192 int</p> <p>Differencing order.</p> </li> <li> <p>q</p> <p>Type \u2192 int</p> <p>Order of the moving average part. This is the number of past error terms that will be included as features.</p> </li> <li> <p>m</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>Season length used for extracting seasonal features. If you believe your data has a seasonal pattern, then set this accordingly. For instance, if the data seems to exhibit a yearly seasonality, and that your data is spaced by month, then you should set this to 12. Note that for this parameter to have any impact you should also set at least one of the <code>p</code>, <code>d</code>, and <code>q</code> parameters.</p> </li> <li> <p>sp</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Seasonal order of the autoregressive part. This is the number of past target values that will be included as features.</p> </li> <li> <p>sd</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Seasonal differencing order.</p> </li> <li> <p>sq</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Seasonal order of the moving average part. This is the number of past error terms that will be included as features.</p> </li> <li> <p>regressor</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The online regression model to use. By default, a <code>preprocessing.StandardScaler</code> piped with a <code>linear_model.LinearRegression</code> will be used.</p> </li> </ul>"},{"location":"api/time-series/SNARIMAX/#attributes","title":"Attributes","text":"<ul> <li> <p>differencer (Differencer)</p> </li> <li> <p>y_trues (collections.deque)</p> <p>The <code>p</code> past target values.</p> </li> <li> <p>errors (collections.deque)</p> <p>The <code>q</code> past error values.</p> </li> </ul>"},{"location":"api/time-series/SNARIMAX/#examples","title":"Examples","text":"<p><pre><code>import datetime as dt\nfrom river import datasets\nfrom river import time_series\nfrom river import utils\n\nperiod = 12\nmodel = time_series.SNARIMAX(\n    p=period,\n    d=1,\n    q=period,\n    m=period,\n    sd=1\n)\n\nfor t, (x, y) in enumerate(datasets.AirlinePassengers()):\n    model.learn_one(y)\n\nhorizon = 12\nfuture = [\n    {'month': dt.date(year=1961, month=m, day=1)}\n    for m in range(1, horizon + 1)\n]\nforecast = model.forecast(horizon=horizon)\nfor x, y_pred in zip(future, forecast):\n    print(x['month'], f'{y_pred:.3f}')\n</code></pre> <pre><code>1961-01-01 494.542\n1961-02-01 450.825\n1961-03-01 484.972\n1961-04-01 576.401\n1961-05-01 559.489\n1961-06-01 612.251\n1961-07-01 722.410\n1961-08-01 674.604\n1961-09-01 575.716\n1961-10-01 562.808\n1961-11-01 477.049\n1961-12-01 515.191\n</code></pre></p> <p>Classic ARIMA models learn solely on the time series values. You can also include features built at each step.</p> <p><pre><code>import calendar\nimport math\nfrom river import compose\nfrom river import linear_model\nfrom river import optim\nfrom river import preprocessing\n\ndef get_month_distances(x):\n    return {\n        calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)\n        for month in range(1, 13)\n    }\n\ndef get_ordinal_date(x):\n    return {'ordinal_date': x['month'].toordinal()}\n\nextract_features = compose.TransformerUnion(\n    get_ordinal_date,\n    get_month_distances\n)\n\nmodel = (\n    extract_features |\n    time_series.SNARIMAX(\n        p=1,\n        d=0,\n        q=0,\n        m=12,\n        sp=3,\n        sq=6,\n        regressor=(\n            preprocessing.StandardScaler() |\n            linear_model.LinearRegression(\n                intercept_init=110,\n                optimizer=optim.SGD(0.01),\n                intercept_lr=0.3\n            )\n        )\n    )\n)\n\nfor x, y in datasets.AirlinePassengers():\n    model.learn_one(x, y)\n\nforecast = model.forecast(horizon=horizon)\nfor x, y_pred in zip(future, forecast):\n    print(x['month'], f'{y_pred:.3f}')\n</code></pre> <pre><code>1961-01-01 444.821\n1961-02-01 432.612\n1961-03-01 457.739\n1961-04-01 465.544\n1961-05-01 476.575\n1961-06-01 516.255\n1961-07-01 565.405\n1961-08-01 572.470\n1961-09-01 512.645\n1961-10-01 475.919\n1961-11-01 438.033\n1961-12-01 456.892\n</code></pre></p>"},{"location":"api/time-series/SNARIMAX/#methods","title":"Methods","text":"forecast <p>Makes forecast at each step of the given horizon.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Updates the model.</p> <p>Parameters</p> <ul> <li>y     \u2014 'float' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> <ol> <li> <p>ARMA - Wikipedia \u21a9</p> </li> <li> <p>NARX - Wikipedia \u21a9</p> </li> <li> <p>ARIMA - Forecasting: Principles and Practice \u21a9</p> </li> <li> <p>Anava, O., Hazan, E., Mannor, S. and Shamir, O., 2013, June. Online learning for time series prediction. In Conference on learning theory (pp. 172-184) \u21a9</p> </li> </ol>"},{"location":"api/time-series/evaluate/","title":"evaluate","text":"<p>Evaluates the performance of a forecaster on a time series dataset.</p> <p>To understand why this method is useful, it's important to understand the difference between nowcasting and forecasting. Nowcasting is about predicting a value at the next time step. This can be seen as a special case of regression, where the value to predict is the value at the next time step. In this case, the <code>evaluate.progressive_val_score</code> function may be used to evaluate a model via progressive validation. </p> <p>Forecasting models can also be evaluated via progressive validation. This is the purpose of this function. At each time step <code>t</code>, the forecaster is asked to predict the values at <code>t + 1</code>, <code>t + 2</code>, ..., <code>t + horizon</code>. The performance at each time step is measured and returned.</p>"},{"location":"api/time-series/evaluate/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>A sequential time series.</p> </li> <li> <p>model</p> <p>Type \u2192 time_series.base.Forecaster</p> <p>A forecaster.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> <li> <p>horizon</p> <p>Type \u2192 int</p> </li> <li> <p>agg_func</p> <p>Type \u2192 typing.Callable[[list[float]], float] | None</p> <p>Default \u2192 <code>None</code></p> </li> <li> <p>grace_period</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Initial period during which the metric is not updated. This is to fairly evaluate models which need a warming up period to start producing meaningful forecasts. The value of this parameter is equal to the horizon by default.</p> </li> </ul>"},{"location":"api/time-series/iter-evaluate/","title":"iter_evaluate","text":"<p>Evaluates the performance of a forecaster on a time series dataset and yields results.</p> <p>This does exactly the same as <code>evaluate.progressive_val_score</code>. The only difference is that this function returns an iterator, yielding results at every step. This can be useful if you want to have control over what you do with the results. For instance, you might want to plot the results.</p>"},{"location":"api/time-series/iter-evaluate/#parameters","title":"Parameters","text":"<ul> <li> <p>dataset</p> <p>Type \u2192 base.typing.Dataset</p> <p>A sequential time series.</p> </li> <li> <p>model</p> <p>Type \u2192 time_series.base.Forecaster</p> <p>A forecaster.</p> </li> <li> <p>metric</p> <p>Type \u2192 metrics.base.RegressionMetric</p> <p>A regression metric.</p> </li> <li> <p>horizon</p> <p>Type \u2192 int</p> </li> <li> <p>agg_func</p> <p>Type \u2192 typing.Callable[[list[float]], float] | None</p> <p>Default \u2192 <code>None</code></p> </li> <li> <p>grace_period</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Initial period during which the metric is not updated. This is to fairly evaluate models which need a warming up period to start producing meaningful forecasts. The value of this parameter is equal to the horizon by default.</p> </li> </ul>"},{"location":"api/time-series/base/Forecaster/","title":"Forecaster","text":""},{"location":"api/time-series/base/Forecaster/#methods","title":"Methods","text":"forecast <p>Makes forecast at each step of the given horizon.</p> <p>Parameters</p> <ul> <li>horizon     \u2014 'int' </li> <li>xs     \u2014 'list[dict] | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p> learn_one <p>Updates the model.</p> <p>Parameters</p> <ul> <li>y     \u2014 'float' </li> <li>x     \u2014 'dict | None'     \u2014 defaults to <code>None</code> </li> </ul> <p></p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/","title":"ExtremelyFastDecisionTreeClassifier","text":"<p>Extremely Fast Decision Tree (EFDT) classifier.</p> <p>Also referred to as the Hoeffding AnyTime Tree (HATT) classifier. In practice, despite the name, EFDTs are typically slower than a vanilla Hoeffding Tree to process data. The speed differences come from the mechanism of split re-evaluation present in EFDT. Nonetheless, EFDT has theoretical properties that ensure it converges faster than the vanilla Hoeffding Tree to the structure that would be created by a batch decision tree model (such as Classification and Regression Trees - CART). Keep in mind that such propositions hold when processing a stationary data stream. When dealing with non-stationary data, EFDT is somewhat robust to concept drifts as it continually revisits and updates its internal decision tree structure. Still, in such cases, the Hoeffind Adaptive Tree might be a better option, as it was specifically designed to handle non-stationarity.</p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>min_samples_reevaluate</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>20</code></p> <p>Number of instances a node should observe before reevaluating the best split.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Helinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ngen = synth.Agrawal(classification_function=0, seed=42)\ndataset = iter(gen.take(1000))\n\nmodel = tree.ExtremelyFastDecisionTreeClassifier(\n    grace_period=100,\n    delta=1e-5,\n    nominal_attributes=['elevel', 'car', 'zipcode'],\n    min_samples_reevaluate=100\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 87.29%\n</code></pre></p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Incrementally train the model</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/ExtremelyFastDecisionTreeClassifier/#notes","title":"Notes","text":"<p>The Extremely Fast Decision Tree (EFDT) <sup>1</sup> constructs a tree incrementally. The EFDT seeks to select and deploy a split as soon as it is confident the split is useful, and then revisits that decision, replacing the split if it subsequently becomes evident that a better split is available. The EFDT learns rapidly from a stationary distribution and eventually it learns the asymptotic batch tree if the distribution from which the data are drawn is stationary.</p> <ol> <li> <p>C. Manapragada, G. Webb, and M. Salehi. Extremely Fast Decision Tree. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining (KDD '18). ACM, New York, NY, USA, 1953-1962. DOI: https://doi.org/10.1145/3219819.3220005\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/","title":"HoeffdingAdaptiveTreeClassifier","text":"<p>Hoeffding Adaptive Tree classifier.</p>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Helinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>bootstrap_sampling</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform bootstrap sampling in the leaf nodes.</p> </li> <li> <p>drift_window_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>300</code></p> <p>Minimum number of examples an alternate tree must observe before being considered as a potential replacement to the current one.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The drift detector used to build the tree. If <code>None</code> then <code>drift.ADWIN</code> is used.</p> </li> <li> <p>switch_significance</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>The significance level to assess whether alternate subtrees are significantly better than their main subtree counterparts.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_alternate_trees</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_pruned_alternate_trees</p> </li> <li> <p>n_switch_alternate_trees</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ngen = synth.ConceptDriftStream(stream=synth.SEA(seed=42, variant=0),\n                               drift_stream=synth.SEA(seed=42, variant=1),\n                               seed=1, position=500, width=50)\ndataset = iter(gen.take(1000))\n\nmodel = tree.HoeffdingAdaptiveTreeClassifier(\n    grace_period=100,\n    delta=1e-5,\n    leaf_prediction='nb',\n    nb_threshold=10,\n    seed=0\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 91.49%\n</code></pre></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the model on instance x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeClassifier/#notes","title":"Notes","text":"<p>The Hoeffding Adaptive Tree <sup>1</sup> uses a drift detector to monitor performance of branches in the tree and to replace them with new branches when their accuracy decreases.</p> <p>The bootstrap sampling strategy is an improvement over the original Hoeffding Adaptive Tree algorithm. It is enabled by default since, in general, it results in better performance.</p> <ol> <li> <p>Bifet, Albert, and Ricard Gavald\u00e0. \"Adaptive learning from evolving data streams.\"    In International Symposium on Intelligent Data Analysis, pp. 249-260. Springer, Berlin,    Heidelberg, 2009.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/","title":"HoeffdingAdaptiveTreeRegressor","text":"<p>Hoeffding Adaptive Tree regressor (HATR).</p> <p>This class implements a regression version of the Hoeffding Adaptive Tree Classifier. Hence, it also uses an ADWIN concept-drift detector instance at each decision node to monitor possible changes in the data distribution. If a drift is detected in a node, an alternate tree begins to be induced in the background. When enough information is gathered, HATR swaps the node where the change was detected by its alternate tree.</p>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>Prediction mechanism used at leafs. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>bootstrap_sampling</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform bootstrap sampling in the leaf nodes.</p> </li> <li> <p>drift_window_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>300</code></p> <p>Minimum number of examples an alternate tree must observe before being considered as a potential replacement to the current one.</p> </li> <li> <p>drift_detector</p> <p>Type \u2192 base.DriftDetector | None</p> <p>Default \u2192 <code>None</code></p> <p>The drift detector used to build the tree. If <code>None</code> then <code>drift.ADWIN</code> is used. Only detectors that support arbitrarily valued continuous data can be used for regression.</p> </li> <li> <p>switch_significance</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>The significance level to assess whether alternate subtrees are significantly better than their main subtree counterparts.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> <li> <p>seed</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>Random seed for reproducibility.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_alternate_trees</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_pruned_alternate_trees</p> </li> <li> <p>n_switch_alternate_trees</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    tree.HoeffdingAdaptiveTreeRegressor(\n        grace_period=50,\n        model_selector_decay=0.3,\n        seed=0\n    )\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.823026\n</code></pre></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the tree model on sample x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the target value using one of the leaf prediction strategies.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>Predicted target value.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingAdaptiveTreeRegressor/#notes","title":"Notes","text":"<p>The Hoeffding Adaptive Tree <sup>1</sup> uses drift detectors to monitor performance of branches in the tree and to replace them with new branches when their accuracy decreases.</p> <p>The bootstrap sampling strategy is an improvement over the original Hoeffding Adaptive Tree algorithm. It is enabled by default since, in general, it results in better performance.</p> <p>To cope with ADWIN's requirements of bounded input data, HATR uses a novel error normalization strategy based on the empiral rule of Gaussian distributions. We assume the deviations of the predictions from the expected values follow a normal distribution. Hence, we subject these errors to a min-max normalization assuming that most of the data lies in the \\(\\left[-3\\sigma, 3\\sigma\\right]\\) range. These normalized errors are passed to the ADWIN instances. This is the same strategy used by Adaptive Random Forest Regressor.</p> <ol> <li> <p>Bifet, Albert, and Ricard Gavald\u00e0. \"Adaptive learning from evolving data streams.\" In International Symposium on Intelligent Data Analysis, pp. 249-260. Springer, Berlin, Heidelberg, 2009.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingTreeClassifier/","title":"HoeffdingTreeClassifier","text":"<p>Hoeffding Tree or Very Fast Decision Tree classifier.</p>"},{"location":"api/tree/HoeffdingTreeClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>split_criterion</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>info_gain</code></p> <p>Split criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Helinger Distance</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>nba</code></p> <p>Prediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive</p> </li> <li> <p>nb_threshold</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>0</code></p> <p>Number of instances a leaf should observe before allowing Naive Bayes.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.GaussianSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>min_branch_fraction</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.01</code></p> <p>The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than <code>min_branch_fraction</code>. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.</p> </li> <li> <p>max_share_to_split</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.99</code></p> <p>Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>100.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeClassifier/#examples","title":"Examples","text":"<p><pre><code>from river.datasets import synth\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ngen = synth.Agrawal(classification_function=0, seed=42)\ndataset = iter(gen.take(1000))\n\nmodel = tree.HoeffdingTreeClassifier(\n    grace_period=100,\n    delta=1e-5,\n    nominal_attributes=['elevel', 'car', 'zipcode']\n)\n\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 84.58%\n</code></pre></p>"},{"location":"api/tree/HoeffdingTreeClassifier/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the model on instance x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>A dictionary that associates a probability which each label.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingTreeClassifier/#notes","title":"Notes","text":"<p>A Hoeffding Tree <sup>1</sup> is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).</p> <p>A theoretically appealing feature of Hoeffding Trees not shared by other incremental decision tree learners is that it has sound guarantees of performance. Using the Hoeffding bound one can show that its output is asymptotically nearly identical to that of a non-incremental learner using infinitely many examples. Implementation based on MOA <sup>2</sup>.</p> <ol> <li> <p>G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams.    In KDD\u201901, pages 97\u2013106, San Francisco, CA, 2001. ACM Press.\u00a0\u21a9</p> </li> <li> <p>Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer.    MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604, 2010.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/HoeffdingTreeRegressor/","title":"HoeffdingTreeRegressor","text":"<p>Hoeffding Tree regressor.</p>"},{"location":"api/tree/HoeffdingTreeRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Significance level to calculate the Hoeffding bound. The significance level is given by <code>1 - delta</code>. Values closer to zero imply longer split decision delays.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>Prediction mechanism used at leafs. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model used to provide responses if <code>leaf_prediction='model'</code>. If not provided an instance of <code>linear_model.LinearRegression</code> with the default hyperparameters is used.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/HoeffdingTreeRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\nfrom river import preprocessing\n\ndataset = datasets.TrumpApproval()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    tree.HoeffdingTreeRegressor(\n        grace_period=100,\n        model_selector_decay=0.9\n    )\n)\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.793345\n</code></pre></p>"},{"location":"api/tree/HoeffdingTreeRegressor/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Train the tree model on sample x and corresponding target y.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the target value using one of the leaf prediction strategies.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>Predicted target value.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p>"},{"location":"api/tree/HoeffdingTreeRegressor/#notes","title":"Notes","text":"<p>The Hoeffding Tree Regressor (HTR) is an adaptation of the incremental tree algorithm of the same name for classification. Similarly to its classification counterpart, HTR uses the Hoeffding bound to control its split decisions. Differently from the classification algorithm, HTR relies on calculating the reduction of variance in the target space to decide among the split candidates. The smallest the variance at its leaf nodes, the more homogeneous the partitions are. At its leaf nodes, HTR fits either linear models or uses the target average as the predictor.</p>"},{"location":"api/tree/SGTClassifier/","title":"SGTClassifier","text":"<p>Stochastic Gradient Tree<sup>1</sup> for binary classification.</p> <p>Binary decision tree classifier that minimizes the binary cross-entropy to guide its growth. </p> <p>Stochastic Gradient Trees (SGT) directly minimize a loss function to guide tree growth and update their predictions. Thus, they differ from other incrementally tree learners that do not directly optimize the loss, but data impurity-related heuristics.</p>"},{"location":"api/tree/SGTClassifier/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Define the significance level of the F-tests performed to decide upon creating splits or updating predictions.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Interval between split attempts or prediction updates.</p> </li> <li> <p>init_pred</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial value predicted by the tree.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth the tree might reach. If set to <code>None</code>, the trees will grow indefinitely.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Positive float value used to impose a penalty over the tree's predictions and force them to become smaller. The greater the lambda value, the more constrained are the predictions.</p> </li> <li> <p>gamma</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Positive float value used to impose a penalty over the tree's splits and force them to be avoided when possible. The greater the gamma value, the smaller the chance of a split occurring.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List with identifiers of the nominal attributes. If None, all features containing numbers are assumed to be numeric.</p> </li> <li> <p>feature_quantizer</p> <p>Type \u2192 tree.splitter.Quantizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The algorithm used to quantize numeric features. Either a static quantizer (as in the original implementation) or a dynamic quantizer can be used. The correct choice and setup of the feature quantizer is a crucial step to determine the performance of SGTs. Feature quantizers are akin to the attribute observers used in Hoeffding Trees. By default, an instance of <code>tree.splitter.StaticQuantizer</code> (with default parameters) is used if this parameter is not set.</p> </li> </ul>"},{"location":"api/tree/SGTClassifier/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_node_updates</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_observations</p> </li> <li> <p>n_splits</p> </li> </ul>"},{"location":"api/tree/SGTClassifier/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.Phishing()\nmodel = tree.SGTClassifier(\n    feature_quantizer=tree.splitter.StaticQuantizer(\n        n_bins=32, warm_start=10\n    )\n)\nmetric = metrics.Accuracy()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>Accuracy: 82.24%\n</code></pre></p>"},{"location":"api/tree/SGTClassifier/#methods","title":"Methods","text":"learn_one <p>Update the model with a set of features <code>x</code> and a label <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.ClfTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the label of a set of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>kwargs </li> </ul> <p>Returns</p> <p>base.typing.ClfTarget | None:     The predicted label.</p> <p></p> predict_proba_one <p>Predict the probability of each label for a dictionary of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>dict[base.typing.ClfTarget, float]:     A dictionary that associates a probability which each label.</p> <p></p> <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/SGTRegressor/","title":"SGTRegressor","text":"<p>Stochastic Gradient Tree for regression.</p> <p>Incremental decision tree regressor that minimizes the mean square error to guide its growth. </p> <p>Stochastic Gradient Trees (SGT) directly minimize a loss function to guide tree growth and update their predictions. Thus, they differ from other incrementally tree learners that do not directly optimize the loss, but a data impurity-related heuristic.</p>"},{"location":"api/tree/SGTRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Define the significance level of the F-tests performed to decide upon creating splits or updating predictions.</p> </li> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Interval between split attempts or prediction updates.</p> </li> <li> <p>init_pred</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.0</code></p> <p>Initial value predicted by the tree.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth the tree might reach. If set to <code>None</code>, the trees will grow indefinitely.</p> </li> <li> <p>lambda_value</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.1</code></p> <p>Positive float value used to impose a penalty over the tree's predictions and force them to become smaller. The greater the lambda value, the more constrained are the predictions.</p> </li> <li> <p>gamma</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> <p>Positive float value used to impose a penalty over the tree's splits and force them to be avoided when possible. The greater the gamma value, the smaller the chance of a split occurring.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List with identifiers of the nominal attributes. If None, all features containing numbers are assumed to be numeric.</p> </li> <li> <p>feature_quantizer</p> <p>Type \u2192 tree.splitter.Quantizer | None</p> <p>Default \u2192 <code>None</code></p> <p>The algorithm used to quantize numeric features. Either a static quantizer (as in the original implementation) or a dynamic quantizer can be used. The correct choice and setup of the feature quantizer is a crucial step to determine the performance of SGTs. Feature quantizers are akin to the attribute observers used in Hoeffding Trees. By default, an instance of <code>tree.splitter.StaticQuantizer</code> (with default parameters) is used if this parameter is not set.</p> </li> </ul>"},{"location":"api/tree/SGTRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_node_updates</p> </li> <li> <p>n_nodes</p> </li> <li> <p>n_observations</p> </li> <li> <p>n_splits</p> </li> </ul>"},{"location":"api/tree/SGTRegressor/#examples","title":"Examples","text":"<p><pre><code>from river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import tree\n\ndataset = datasets.TrumpApproval()\nmodel = tree.SGTRegressor(\n    delta=0.01,\n    lambda_value=0.01,\n    grace_period=20,\n    feature_quantizer=tree.splitter.DynamicQuantizer(std_prop=0.1)\n)\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 1.721818\n</code></pre></p>"},{"location":"api/tree/SGTRegressor/#methods","title":"Methods","text":"learn_one <p>Fits to a set of features <code>x</code> and a real-valued target <code>y</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> <li>y     \u2014 'base.typing.RegTarget' </li> <li>w     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the output of features <code>x</code>.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>base.typing.RegTarget:     The prediction.</p> <p></p>"},{"location":"api/tree/SGTRegressor/#notes","title":"Notes","text":"<p>This implementation enhances the original proposal <sup>1</sup> by using an incremental strategy to discretize numerical features dynamically, rather than relying on a calibration set and parameterized number of bins. The strategy used is an adaptation of the Quantization Observer (QO) <sup>2</sup>. Different bin size setting policies are available for selection. They directly related to number of split candidates the tree is going to explore, and thus, how accurate its split decisions are going to be. Besides, the number of stored bins per feature is directly related to the tree's memory usage and runtime.</p> <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> <li> <p>Mastelini, S.M. and de Leon Ferreira, A.C.P., 2021. Using dynamical quantization to perform split attempts in online tree regressors. Pattern Recognition Letters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/iSOUPTreeRegressor/","title":"iSOUPTreeRegressor","text":"<p>Incremental Structured Output Prediction Tree (iSOUP-Tree) for multi-target regression.</p> <p>This is an implementation of the iSOUP-Tree proposed by A. Osojnik, P. Panov, and S. D\u017eeroski <sup>1</sup>.</p>"},{"location":"api/tree/iSOUPTreeRegressor/#parameters","title":"Parameters","text":"<ul> <li> <p>grace_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>200</code></p> <p>Number of instances a leaf should observe between split attempts.</p> </li> <li> <p>max_depth</p> <p>Type \u2192 int | None</p> <p>Default \u2192 <code>None</code></p> <p>The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</p> </li> <li> <p>delta</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1e-07</code></p> <p>Allowed error in split decision, a value closer to 0 takes longer to decide.</p> </li> <li> <p>tau</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.05</code></p> <p>Threshold below which a split will be forced to break ties.</p> </li> <li> <p>leaf_prediction</p> <p>Type \u2192 str</p> <p>Default \u2192 <code>adaptive</code></p> <p>Prediction mechanism used at leafs. - 'mean' - Target mean - 'model' - Uses the model defined in <code>leaf_model</code> - 'adaptive' - Chooses between 'mean' and 'model' dynamically</p> </li> <li> <p>leaf_model</p> <p>Type \u2192 base.Regressor | dict | None</p> <p>Default \u2192 <code>None</code></p> <p>The regression model(s) used to provide responses if <code>leaf_prediction='model'</code>. It can be either a regressor (in which case it is going to be replicated to all the targets) or a dictionary whose keys are target identifiers, and the values are instances of <code>base.Regressor</code>.<code>If not provided, instances of [</code>linear_model.LinearRegression`](../../linear-model/LinearRegression) with the default hyperparameters are used for all the targets. If a dictionary is passed and not all target models are specified, copies from the first model match in the dictionary will be used to the remaining targets.</p> </li> <li> <p>model_selector_decay</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.95</code></p> <p>The exponential decaying factor applied to the learning models' squared errors, that are monitored if <code>leaf_prediction='adaptive'</code>. Must be between <code>0</code> and <code>1</code>. The closer to <code>1</code>, the more importance is going to be given to past observations. On the other hand, if its value approaches <code>0</code>, the recent observed errors are going to have more influence on the final decision.</p> </li> <li> <p>nominal_attributes</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.</p> </li> <li> <p>splitter</p> <p>Type \u2192 Splitter | None</p> <p>Default \u2192 <code>None</code></p> <p>The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the <code>tree.splitter</code> module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property <code>is_target_class</code>. This is an advanced option. Special care must be taken when choosing different splitters. By default, <code>tree.splitter.TEBSTSplitter</code> is used if <code>splitter</code> is <code>None</code>.</p> </li> <li> <p>min_samples_split</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>5</code></p> <p>The minimum number of samples every branch resulting from a split candidate must have to be considered valid.</p> </li> <li> <p>binary_split</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, only allow binary splits.</p> </li> <li> <p>max_size</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>500.0</code></p> <p>The max size of the tree, in mebibytes (MiB).</p> </li> <li> <p>memory_estimate_period</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1000000</code></p> <p>Interval (number of processed instances) between memory consumption checks.</p> </li> <li> <p>stop_mem_management</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, stop growing as soon as memory limit is hit.</p> </li> <li> <p>remove_poor_attrs</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>False</code></p> <p>If True, disable poor attributes to reduce memory usage.</p> </li> <li> <p>merit_preprune</p> <p>Type \u2192 bool</p> <p>Default \u2192 <code>True</code></p> <p>If True, enable merit-based tree pre-pruning.</p> </li> </ul>"},{"location":"api/tree/iSOUPTreeRegressor/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>leaf_prediction</p> <p>Return the prediction strategy used by the tree at its leaves.</p> </li> <li> <p>max_size</p> <p>Max allowed size tree can reach (in MiB).</p> </li> <li> <p>n_active_leaves</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_inactive_leaves</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> <li> <p>split_criterion</p> <p>Return a string with the name of the split criterion being used by the tree.</p> </li> <li> <p>summary</p> <p>Collect metrics corresponding to the current status of the tree in a string buffer.</p> </li> </ul>"},{"location":"api/tree/iSOUPTreeRegressor/#examples","title":"Examples","text":"<p><pre><code>import numbers\nfrom river import compose\nfrom river import datasets\nfrom river import evaluate\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import tree\n\ndataset = datasets.SolarFlare()\n\nnum = compose.SelectType(numbers.Number) | preprocessing.MinMaxScaler()\ncat = compose.SelectType(str) | preprocessing.OneHotEncoder()\n\nmodel = tree.iSOUPTreeRegressor(\n    grace_period=100,\n    leaf_prediction='model',\n    leaf_model={\n        'c-class-flares': linear_model.LinearRegression(l2=0.02),\n        'm-class-flares': linear_model.PARegressor(),\n        'x-class-flares': linear_model.LinearRegression(l2=0.1)\n    }\n)\n\npipeline = (num + cat) | model\nmetric = metrics.multioutput.MicroAverage(metrics.MAE())\n\nevaluate.progressive_val_score(dataset, pipeline, metric)\n</code></pre> <pre><code>MicroAverage(MAE): 0.426177\n</code></pre></p>"},{"location":"api/tree/iSOUPTreeRegressor/#methods","title":"Methods","text":"debug_one <p>Print an explanation of how <code>x</code> is predicted.</p> <p>Parameters</p> <ul> <li>x     \u2014 'dict' </li> </ul> <p>Returns</p> <p>str | None:     A representation of the path followed by the tree to predict <code>x</code>; <code>None</code> if</p> <p></p> draw <p>Draw the tree using the <code>graphviz</code> library.</p> <p>Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.</p> <p>Parameters</p> <ul> <li>max_depth     \u2014 'int | None'     \u2014 defaults to <code>None</code>      The maximum depth a tree can reach. If <code>None</code>, the tree will grow indefinitely.</li> </ul> <p></p> learn_one <p>Incrementally train the model with one sample.</p> <p>Training tasks:  * If the tree is empty, create a leaf node as the root. * If the tree is already initialized, find the corresponding leaf for   the instance and update the leaf node statistics. * If growth is allowed and the number of instances that the leaf has   observed between split attempts exceed the grace period then attempt   to split.</p> <p>Parameters</p> <ul> <li>x </li> <li>y </li> <li>w     \u2014 'float'     \u2014 defaults to <code>1.0</code> </li> </ul> <p></p> predict_one <p>Predict the target value using one of the leaf prediction strategies.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p>Returns</p> <p>Predicted target value.</p> <p></p> to_dataframe <p>Return a representation of the current tree structure organized in a <code>pandas.DataFrame</code> object.</p> <p>In case the tree is empty or it only contains a single node (a leaf), <code>None</code> is returned.</p> <p>Returns</p> <p>df</p> <p></p> <ol> <li> <p>Alja\u017e Osojnik, Pan\u010de Panov, and Sa\u0161o D\u017eeroski. \"Tree-based methods for online multi-target regression.\" Journal of Intelligent Information Systems 50.2 (2018): 315-339.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/base/Branch/","title":"Branch","text":"<p>A generic tree branch.</p>"},{"location":"api/tree/base/Branch/#parameters","title":"Parameters","text":"<ul> <li> <p>children</p> <p>Child branches and/or leaves.</p> </li> </ul>"},{"location":"api/tree/base/Branch/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> <p>Distance to the deepest descendant.</p> </li> <li> <p>n_branches</p> <p>Number of branches, including thyself.</p> </li> <li> <p>n_leaves</p> <p>Number of leaves.</p> </li> <li> <p>n_nodes</p> <p>Number of descendants, including thyself.</p> </li> <li> <p>repr_split</p> <p>String representation of the split.</p> </li> </ul>"},{"location":"api/tree/base/Branch/#methods","title":"Methods","text":"iter_bfs <p>Iterate over nodes in breadth-first order.</p> <p></p> iter_branches <p>Iterate over branches in depth-first order.</p> <p></p> iter_dfs <p>Iterate over nodes in depth-first order.</p> <p></p> iter_edges <p>Iterate over edges in depth-first order.</p> <p></p> iter_leaves <p>Iterate over leaves from the left-most one to the right-most one.</p> <p></p> most_common_path <p>Return a tuple with the branch index and the child node related to the most traversed path.</p> <p>Used in case the split feature is missing from an instance.</p> <p></p> next <p>Move to the next node down the tree.</p> <p>Parameters</p> <ul> <li>x </li> </ul> <p></p> to_dataframe <p>Build a DataFrame containing one record for each node.</p> <p></p> traverse <p>Return the leaf corresponding to the given input.</p> <p>Parameters</p> <ul> <li>x </li> <li>until_leaf     \u2014 defaults to <code>True</code> </li> </ul> <p></p> walk <p>Iterate over the nodes of the path induced by x.</p> <p>Parameters</p> <ul> <li>x </li> <li>until_leaf     \u2014 defaults to <code>True</code> </li> </ul> <p></p>"},{"location":"api/tree/base/Leaf/","title":"Leaf","text":"<p>A generic tree node.</p>"},{"location":"api/tree/base/Leaf/#parameters","title":"Parameters","text":"<ul> <li> <p>kwargs</p> <p>Each provided keyword argument is stored in the leaf as an attribute.</p> </li> </ul>"},{"location":"api/tree/base/Leaf/#attributes","title":"Attributes","text":"<ul> <li> <p>height</p> </li> <li> <p>n_branches</p> </li> <li> <p>n_leaves</p> </li> <li> <p>n_nodes</p> </li> </ul>"},{"location":"api/tree/base/Leaf/#methods","title":"Methods","text":"iter_branches iter_dfs iter_edges iter_leaves walk"},{"location":"api/tree/splitter/DynamicQuantizer/","title":"DynamicQuantizer","text":"<p>Adapted version of the Quantizer Observer (QO)<sup>1</sup> that is applied to Stochastic Gradient Trees (SGT).</p> <p>This feature quantizer starts by partitioning the inputs using the passed <code>radius</code> value. As more splits are created in the SGTs, new feature quantizers will use <code>std * std_prop</code> as the quantization radius. In the expression, <code>std</code> represents the standard deviation of the input data, which is calculated incrementally.</p>"},{"location":"api/tree/splitter/DynamicQuantizer/#parameters","title":"Parameters","text":"<ul> <li> <p>radius</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.5</code></p> <p>The initial quantization radius.</p> </li> <li> <p>std_prop</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.25</code></p> <p>The proportion of the standard deviation that is going to be used to define the radius value for new quantizer instances following the initial one.</p> </li> </ul>"},{"location":"api/tree/splitter/DynamicQuantizer/#methods","title":"Methods","text":"update <ol> <li> <p>Mastelini, S.M. and de Leon Ferreira, A.C.P., 2021. Using dynamical quantization to perform split attempts in online tree regressors. Pattern Recognition Letters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/EBSTSplitter/","title":"EBSTSplitter","text":"<p>iSOUP-Tree's Extended Binary Search Tree (E-BST).</p> <p>This class implements the Extended Binary Search Tree<sup>1</sup> (E-BST) structure, using the variant employed by Osojnik et al.<sup>2</sup> in the iSOUP-Tree algorithm. This structure is employed to observe the target space distribution. </p> <p>Proposed along with Fast Incremental Model Tree with Drift Detection<sup>1</sup> (FIMT-DD), E-BST was the first attribute observer (AO) proposed for incremental Hoeffding Tree regressors. This AO works by storing all observations between splits in an extended binary search tree structure. E-BST stores the input feature realizations and statistics of the target(s) that enable calculating the split heuristic at any time. To alleviate time and memory costs, E-BST implements a memory management routine, where the worst split candidates are pruned from the binary tree. </p> <p>In this variant, only the left branch statistics are stored and the complete split-enabling statistics are calculated with an in-order traversal of the binary search tree.</p>"},{"location":"api/tree/splitter/EBSTSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/EBSTSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool'     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Not implemented in regression splitters.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> remove_bad_splits <p>Remove bad splits.</p> <p>Based on FIMT-DD's <sup>1</sup> procedure to remove bad split candidates from the E-BST. This mechanism is triggered every time a split attempt fails. The rationale is to remove points whose split merit is much worse than the best candidate overall (for which the growth decision already failed).  Let \\(m_1\\) be the merit of the best split point and \\(m_2\\) be the merit of the second best split candidate. The ratio \\(r = m_2/m_1\\) along with the Hoeffding bound (\\(\\epsilon\\)) are used to decide upon creating a split. A split occurs when \\(r &lt; 1 - \\epsilon\\). A split candidate, with merit \\(m_i\\), is considered badr if \\(m_i / m_1 &lt; r - 2\\epsilon\\). The rationale is the following: if the merit ratio for this point is smaller than the lower bound of \\(r\\), then the true merit of that split relative to the best one is small. Hence, this candidate can be safely removed.  To avoid excessive and costly manipulations of the E-BST to update the stored statistics, only the nodes whose children are all bad split points are pruned, as defined in <sup>1</sup>.</p> <p>Parameters</p> <ul> <li>criterion </li> <li>last_check_ratio     \u2014 'float' </li> <li>last_check_vr     \u2014 'float' </li> <li>last_check_e     \u2014 'float' </li> <li>pre_split_dist     \u2014 'list | dict' </li> </ul> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>Ikonomovska, E., Gama, J., &amp; D\u017eeroski, S. (2011). Learning model trees from evolving data streams. Data mining and knowledge discovery, 23(1), 128-168.\u00a0\u21a9\u21a9\u21a9\u21a9</p> </li> <li> <p>Osojnik, Alja\u017e. 2017. Structured output prediction on Data Streams (Doctoral Dissertation) \u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/ExhaustiveSplitter/","title":"ExhaustiveSplitter","text":"<p>Numeric attribute observer for classification tasks that is based on a Binary Search Tree.</p> <p>This algorithm<sup>1</sup> is also referred to as exhaustive attribute observer, since it ends up storing all the observations between split attempts<sup>2</sup>. </p> <p>This splitter cannot perform probability density estimations, so it does not work well when coupled with tree leaves using naive bayes models.</p>"},{"location":"api/tree/splitter/ExhaustiveSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/ExhaustiveSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>The underlying data structure used to monitor the input does not allow probability density estimations. Hence, it always returns zero for any given input.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>Domingos, P. and Hulten, G., 2000, August. Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 71-80).\u00a0\u21a9</p> </li> <li> <p>Pfahringer, B., Holmes, G. and Kirkby, R., 2008, May. Handling numeric attributes in hoeffding trees. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 296-307). Springer, Berlin, Heidelberg.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/GaussianSplitter/","title":"GaussianSplitter","text":"<p>Numeric attribute observer for classification tasks that is based on Gaussian estimators.</p> <p>The distribution of each class is approximated using a Gaussian distribution. Hence, the probability density function can be easily calculated.</p>"},{"location":"api/tree/splitter/GaussianSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>n_splits</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>10</code></p> <p>The number of partitions to consider when querying for split candidates.</p> </li> </ul>"},{"location":"api/tree/splitter/GaussianSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/GaussianSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/tree/splitter/HistogramSplitter/","title":"HistogramSplitter","text":"<p>Numeric attribute observer for classification tasks that discretizes features using histograms.</p>"},{"location":"api/tree/splitter/HistogramSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>n_bins</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>256</code></p> <p>The maximum number of bins in the histogram.</p> </li> <li> <p>n_splits</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>32</code></p> <p>The number of split points to evaluate when querying for the best split candidate.</p> </li> </ul>"},{"location":"api/tree/splitter/HistogramSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/HistogramSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/tree/splitter/QOSplitter/","title":"QOSplitter","text":"<p>Quantization observer (QO).</p> <p>This splitter utilizes a hash-based quantization algorithm to keep track of the target statistics and evaluate split candidates. QO, relies on the radius parameter to define discretization intervals for each incoming feature. Split candidates are defined as the midpoints between two consecutive hash slots. Both binary splits and multi-way splits can be created by this attribute observer. This class implements the algorithm described in <sup>1</sup>. </p> <p>The smaller the quantization radius, the more hash slots will be created to accommodate the discretized data. Hence, both the running time and memory consumption increase, but the resulting splits ought to be closer to the ones obtained by a batch exhaustive approach. On the other hand, if the radius is too large, fewer slots will be created, less memory and running time will be required, but at the cost of coarse split suggestions. </p> <p>QO assumes that all features have the same range. It is always advised to scale the features to apply this splitter. That can be done using the <code>preprocessing</code> module. A good \"rule of thumb\" is to scale data using <code>preprocessing.StandardScaler</code> and define the radius as a proportion of the features' standard deviation. For instance, the default radius value would correspond to one quarter of the normalized features' standard deviation (since the scaled data has zero mean and unit variance). If the features come from normal distributions, by following the empirical rule, roughly <code>32</code> hash slots will be created.</p>"},{"location":"api/tree/splitter/QOSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>radius</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>0.25</code></p> <p>The quantization radius. QO discretizes the incoming feature in intervals of equal length that are defined by this parameter.</p> </li> <li> <p>allow_multiway_splits</p> <p>Default \u2192 <code>False</code></p> <p>Whether or not allow that multiway splits are evaluated. Numeric multi-way splits use the same quantization strategy of QO to create multiple tree branches. The same quantization radius is used, and each stored slot represents the split enabling statistics of one branch.</p> </li> </ul>"},{"location":"api/tree/splitter/QOSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/QOSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool'     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p> <ol> <li> <p>Mastelini, S.M. and de Leon Ferreira, A.C.P., 2021. Using dynamical quantization to perform split attempts in online tree regressors. Pattern Recognition Letters.\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/Quantizer/","title":"Quantizer","text":"<p>Base class for the feature quantizers used in Stochastic Gradient Trees<sup>1</sup>.</p>"},{"location":"api/tree/splitter/Quantizer/#methods","title":"Methods","text":"update <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/Splitter/","title":"Splitter","text":"<p>Base class for the tree splitters.</p> <p>Each Attribute Observer (AO) or Splitter monitors one input feature and finds the best split point for this attribute. AOs can also perform other tasks related to the monitored feature, such as estimating its probability density function (classification case). </p> <p>This class should not be instantiated, as none of its methods are implemented.</p>"},{"location":"api/tree/splitter/Splitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/Splitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool' </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Get the probability for an attribute value given a class.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p>Returns</p> <p>float:     Probability for an attribute value given a class.</p> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/tree/splitter/StaticQuantizer/","title":"StaticQuantizer","text":"<p>Quantization strategy originally used in Stochastic Gradient Trees (SGT)<sup>1</sup>.</p> <p>Firstly, a buffer of size <code>warm_start</code> is stored. The data stored in the buffer is then used to quantize the input feature into <code>n_bins</code> intervals. These intervals will be replicated to every new quantizer. Feature values lying outside of the limits defined by the initial buffer will be mapped to the head or tail of the list of intervals.</p>"},{"location":"api/tree/splitter/StaticQuantizer/#parameters","title":"Parameters","text":"<ul> <li> <p>n_bins</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>64</code></p> <p>The number of bins (intervals) to divide the input feature.</p> </li> <li> <p>warm_start</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>100</code></p> <p>The number of observations used to initialize the quantization intervals.</p> </li> <li> <p>buckets</p> <p>Type \u2192 list | None</p> <p>Default \u2192 <code>None</code></p> <p>This parameter is only used internally by the quantizer, so it must not be set. Once the intervals are defined, new instances of this quantizer will receive the quantization information via this parameter.</p> </li> </ul>"},{"location":"api/tree/splitter/StaticQuantizer/#methods","title":"Methods","text":"update <ol> <li> <p>Gouk, H., Pfahringer, B., &amp; Frank, E. (2019, October). Stochastic Gradient Trees. In Asian Conference on Machine Learning (pp. 1094-1109).\u00a0\u21a9</p> </li> </ol>"},{"location":"api/tree/splitter/TEBSTSplitter/","title":"TEBSTSplitter","text":"<p>Truncated E-BST.</p> <p>Variation of E-BST that rounds the incoming feature values before passing them to the binary search tree (BST). By doing so, the attribute observer might reduce its processing time and memory usage since small variations in the input values will end up being mapped to the same BST node.</p>"},{"location":"api/tree/splitter/TEBSTSplitter/#parameters","title":"Parameters","text":"<ul> <li> <p>digits</p> <p>Type \u2192 int</p> <p>Default \u2192 <code>1</code></p> <p>The number of decimal places used to round the input feature values.</p> </li> </ul>"},{"location":"api/tree/splitter/TEBSTSplitter/#attributes","title":"Attributes","text":"<ul> <li> <p>is_numeric</p> <p>Determine whether or not the splitter works with numerical features.</p> </li> <li> <p>is_target_class</p> <p>Check on which kind of learning task the splitter is designed to work.  If <code>True</code>, the splitter works with classification trees, otherwise it is designed for regression trees.</p> </li> </ul>"},{"location":"api/tree/splitter/TEBSTSplitter/#methods","title":"Methods","text":"best_evaluated_split_suggestion <p>Get the best split suggestion given a criterion and the target's statistics.</p> <p>Parameters</p> <ul> <li>criterion     \u2014 'SplitCriterion' </li> <li>pre_split_dist     \u2014 'list | dict' </li> <li>att_idx     \u2014 'base.typing.FeatureName' </li> <li>binary_only     \u2014 'bool'     \u2014 defaults to <code>True</code> </li> </ul> <p>Returns</p> <p>BranchFactory:     Suggestion of the best attribute split.</p> <p></p> cond_proba <p>Not implemented in regression splitters.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.ClfTarget' </li> </ul> <p></p> remove_bad_splits <p>Remove bad splits.</p> <p>Based on FIMT-DD's [^1] procedure to remove bad split candidates from the E-BST. This mechanism is triggered every time a split attempt fails. The rationale is to remove points whose split merit is much worse than the best candidate overall (for which the growth decision already failed).  Let \\(m_1\\) be the merit of the best split point and \\(m_2\\) be the merit of the second best split candidate. The ratio \\(r = m_2/m_1\\) along with the Hoeffding bound (\\(\\epsilon\\)) are used to decide upon creating a split. A split occurs when \\(r &lt; 1 - \\epsilon\\). A split candidate, with merit \\(m_i\\), is considered badr if \\(m_i / m_1 &lt; r - 2\\epsilon\\). The rationale is the following: if the merit ratio for this point is smaller than the lower bound of \\(r\\), then the true merit of that split relative to the best one is small. Hence, this candidate can be safely removed.  To avoid excessive and costly manipulations of the E-BST to update the stored statistics, only the nodes whose children are all bad split points are pruned, as defined in [^1].</p> <p>Parameters</p> <ul> <li>criterion </li> <li>last_check_ratio     \u2014 'float' </li> <li>last_check_vr     \u2014 'float' </li> <li>last_check_e     \u2014 'float' </li> <li>pre_split_dist     \u2014 'list | dict' </li> </ul> <p></p> update <p>Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.</p> <p>Parameters</p> <ul> <li>att_val </li> <li>target_val     \u2014 'base.typing.Target' </li> <li>w     \u2014 'float' </li> </ul> <p></p>"},{"location":"api/utils/Rolling/","title":"Rolling","text":"<p>A generic wrapper for performing rolling computations.</p> <p>This can be wrapped around any object which implements both an <code>update</code> and a <code>revert</code> method. Inputs to <code>update</code> are stored in a queue. Elements of the queue are popped when the window is full.</p>"},{"location":"api/utils/Rolling/#parameters","title":"Parameters","text":"<ul> <li> <p>obj</p> <p>Type \u2192 Rollable</p> <p>An object that implements both an <code>update</code> method and a <code>rolling</code>method.</p> </li> <li> <p>window_size</p> <p>Type \u2192 int</p> <p>Size of the window.</p> </li> </ul>"},{"location":"api/utils/Rolling/#attributes","title":"Attributes","text":"<ul> <li>window_size</li> </ul>"},{"location":"api/utils/Rolling/#examples","title":"Examples","text":"<p>For instance, here is how you can compute a rolling average over a window of size 3:</p> <p><pre><code>from river import stats, utils\n\nX = [1, 3, 5, 7]\nrmean = utils.Rolling(stats.Mean(), window_size=3)\n\nfor x in X:\n    rmean.update(x)\n    print(rmean.get())\n</code></pre> <pre><code>1.0\n2.0\n3.0\n5.0\n</code></pre></p>"},{"location":"api/utils/Rolling/#methods","title":"Methods","text":"update"},{"location":"api/utils/SortedWindow/","title":"SortedWindow","text":"<p>Sorted running window data structure.</p>"},{"location":"api/utils/SortedWindow/#parameters","title":"Parameters","text":"<ul> <li> <p>size</p> <p>Type \u2192 int</p> <p>Size of the window to compute the rolling quantile.</p> </li> </ul>"},{"location":"api/utils/SortedWindow/#attributes","title":"Attributes","text":"<ul> <li>size</li> </ul>"},{"location":"api/utils/SortedWindow/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nwindow = utils.SortedWindow(size=3)\n\nfor i in reversed(range(9)):\n    print(window.append(i))\n</code></pre> <pre><code>[8]\n[7, 8]\n[6, 7, 8]\n[5, 6, 7]\n[4, 5, 6]\n[3, 4, 5]\n[2, 3, 4]\n[1, 2, 3]\n[0, 1, 2]\n</code></pre></p>"},{"location":"api/utils/SortedWindow/#methods","title":"Methods","text":"<ol> <li> <p>Left sorted inserts in Python \u21a9</p> </li> </ol>"},{"location":"api/utils/TimeRolling/","title":"TimeRolling","text":"<p>A generic wrapper for performing time rolling computations.</p> <p>This can be wrapped around any object which implements both an <code>update</code> and a <code>revert</code> method. Inputs to <code>update</code> are stored in a queue. Elements of the queue are popped when they are too old.</p>"},{"location":"api/utils/TimeRolling/#parameters","title":"Parameters","text":"<ul> <li> <p>obj</p> <p>Type \u2192 Rollable</p> <p>An object that implements both an <code>update</code> method and a <code>rolling</code>method.</p> </li> <li> <p>period</p> <p>Type \u2192 dt.timedelta</p> <p>A duration of time, expressed as a <code>datetime.timedelta</code>.</p> </li> </ul>"},{"location":"api/utils/TimeRolling/#examples","title":"Examples","text":"<p>For instance, here is how you can compute a rolling average over a period of 3 days:</p> <p><pre><code>from river import stats, utils\n\nX = {\n    dt.datetime(2019, 1, 1): 1,\n    dt.datetime(2019, 1, 2): 5,\n    dt.datetime(2019, 1, 3): 9,\n    dt.datetime(2019, 1, 4): 13\n}\n\nrmean = utils.TimeRolling(stats.Mean(), period=dt.timedelta(days=3))\nfor t, x in X.items():\n    rmean.update(x, t=t)\n    print(rmean.get())\n</code></pre> <pre><code>1.0\n3.0\n5.0\n9.0\n</code></pre></p>"},{"location":"api/utils/TimeRolling/#methods","title":"Methods","text":"update"},{"location":"api/utils/VectorDict/","title":"VectorDict","text":""},{"location":"api/utils/VectorDict/#methods","title":"Methods","text":"abs clear get <p>Parameters</p> <ul> <li>key </li> <li>args </li> <li>kwargs </li> </ul> <p></p> items <p></p> keys <p></p> max <p></p> maximum <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> min <p></p> minimum <p>Parameters</p> <ul> <li>other </li> </ul> <p></p> pop <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p></p> popitem <p></p> setdefault <p>Parameters</p> <ul> <li>key </li> <li>args </li> <li>kwargs </li> </ul> <p></p> to_dict <p></p> to_numpy <p>Parameters</p> <ul> <li>fields </li> </ul> <p></p> update <p>Parameters</p> <ul> <li>args </li> <li>kwargs </li> </ul> <p></p> values <p></p> with_mask <p>Parameters</p> <ul> <li>mask </li> <li>copy     \u2014 defaults to <code>False</code> </li> </ul> <p></p>"},{"location":"api/utils/expand-param-grid/","title":"expand_param_grid","text":"<p>Expands a grid of parameters.</p> <p>This method can be used to generate a list of model parametrizations from a dictionary where each parameter is associated with a list of possible parameters. In other words, it expands a grid of parameters. </p> <p>Typically, this method can be used to create copies of a given model with different parameter choices. The models can then be used as part of a model selection process, such as a <code>selection.SuccessiveHalvingClassifier</code> or a <code>selection.EWARegressor</code>. </p> <p>The syntax for the parameter grid is quite flexible. It allows nesting parameters and can therefore be used to generate parameters for a pipeline.</p>"},{"location":"api/utils/expand-param-grid/#parameters","title":"Parameters","text":"<ul> <li> <p>model</p> <p>Type \u2192 base.Estimator</p> </li> <li> <p>grid</p> <p>Type \u2192 dict</p> <p>The grid of parameters to expand. The provided dictionary can be nested. The only requirement is that the values at the leaves need to be lists.</p> </li> </ul>"},{"location":"api/utils/expand-param-grid/#examples","title":"Examples","text":"<p>As an initial example, we can expand a grid of parameters for a single model.</p> <p><pre><code>from river import linear_model\nfrom river import optim\nfrom river import utils\n\nmodel = linear_model.LinearRegression()\n\ngrid = {'optimizer': [optim.SGD(.1), optim.SGD(.01), optim.SGD(.001)]}\nmodels = utils.expand_param_grid(model, grid)\nlen(models)\n</code></pre> <pre><code>3\n</code></pre></p> <p><pre><code>models[0]\n</code></pre> <pre><code>LinearRegression (\n  optimizer=SGD (\n    lr=Constant (\n      learning_rate=0.1\n    )\n  )\n  loss=Squared ()\n  l2=0.\n  l1=0.\n  intercept_init=0.\n  intercept_lr=Constant (\n    learning_rate=0.01\n  )\n  clip_gradient=1e+12\n  initializer=Zeros ()\n)\n</code></pre></p> <p>You can expand parameters for multiple choices like so:</p> <p><pre><code>grid = {\n    'optimizer': [\n        (optim.SGD, {'lr': [.1, .01, .001]}),\n        (optim.Adam, {'lr': [.1, .01, .01]})\n    ]\n}\nmodels = utils.expand_param_grid(model, grid)\nlen(models)\n</code></pre> <pre><code>6\n</code></pre></p> <p>You may specify a grid of parameters for a pipeline via nesting:</p> <p><pre><code>from river import feature_extraction\n\nmodel = (\n    feature_extraction.BagOfWords() |\n    linear_model.LinearRegression()\n)\n\ngrid = {\n    'BagOfWords': {\n        'strip_accents': [False, True]\n    },\n    'LinearRegression': {\n        'optimizer': [\n            (optim.SGD, {'lr': [.1, .01]}),\n            (optim.Adam, {'lr': [.1, .01]})\n        ]\n    }\n}\n\nmodels = utils.expand_param_grid(model, grid)\nlen(models)\n</code></pre> <pre><code>8\n</code></pre></p>"},{"location":"api/utils/log-method-calls/","title":"log_method_calls","text":"<p>A context manager to log method calls.</p> <p>All method calls will be logged by default. This behavior can be overriden by passing filtering functions.</p>"},{"location":"api/utils/log-method-calls/#parameters","title":"Parameters","text":"<ul> <li> <p>class_condition</p> <p>Type \u2192 typing.Callable[[typing.Any], bool] | None</p> <p>Default \u2192 <code>None</code></p> <p>A function which determines if a class should be logged or not.</p> </li> <li> <p>method_condition</p> <p>Type \u2192 typing.Callable[[typing.Any], bool] | None</p> <p>Default \u2192 <code>None</code></p> <p>A function which determines if a method should be logged or not.</p> </li> </ul>"},{"location":"api/utils/log-method-calls/#examples","title":"Examples","text":"<p><pre><code>import io\nimport logging\nfrom river import anomaly\nfrom river import compose\nfrom river import datasets\nfrom river import preprocessing\nfrom river import utils\n\nmodel = compose.Pipeline(\n    preprocessing.MinMaxScaler(),\n    anomaly.HalfSpaceTrees(seed=42)\n)\n\nclass_condition = lambda x: x.__class__.__name__ in ('MinMaxScaler', 'HalfSpaceTrees')\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\nlogs = io.StringIO()\nsh = logging.StreamHandler(logs)\nsh.setLevel(logging.DEBUG)\nlogger.addHandler(sh)\n\nwith utils.log_method_calls(class_condition):\n    for x, y in datasets.CreditCard().take(1):\n        score = model.score_one(x)\n        model.learn_one(x)\n\nprint(logs.getvalue())\n</code></pre> <pre><code>MinMaxScaler.transform_one\nHalfSpaceTrees.score_one\nMinMaxScaler.learn_one\nMinMaxScaler.transform_one\nHalfSpaceTrees.learn_one\n</code></pre></p> <pre><code>logs.close()\n</code></pre>"},{"location":"api/utils/math/argmax/","title":"argmax","text":"<p>Argmax function.</p>"},{"location":"api/utils/math/argmax/#parameters","title":"Parameters","text":"<ul> <li> <p>lst</p> <p>Type \u2192 list</p> </li> </ul>"},{"location":"api/utils/math/chain-dot/","title":"chain_dot","text":"<p>Returns the dot product of multiple vectors represented as dicts.</p>"},{"location":"api/utils/math/chain-dot/#parameters","title":"Parameters","text":"<ul> <li>xs</li> </ul>"},{"location":"api/utils/math/chain-dot/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nx = {'x0': 1, 'x1': 2, 'x2': 1}\ny = {'x1': 21, 'x2': 3}\nz = {'x1': 2, 'x2': 1 / 3}\n\nutils.math.chain_dot(x, y, z)\n</code></pre> <pre><code>85.0\n</code></pre></p>"},{"location":"api/utils/math/clamp/","title":"clamp","text":"<p>Clamp a number.</p> <p>This is a synonym of clipping.</p>"},{"location":"api/utils/math/clamp/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 float</p> </li> <li> <p>minimum</p> <p>Default \u2192 <code>0.0</code></p> </li> <li> <p>maximum</p> <p>Default \u2192 <code>1.0</code></p> </li> </ul>"},{"location":"api/utils/math/dot/","title":"dot","text":"<p>Returns the dot product of two vectors represented as dicts.</p>"},{"location":"api/utils/math/dot/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 dict</p> </li> <li> <p>y</p> <p>Type \u2192 dict</p> </li> </ul>"},{"location":"api/utils/math/dot/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nx = {'x0': 1, 'x1': 2}\ny = {'x1': 21, 'x2': 3}\n\nutils.math.dot(x, y)\n</code></pre> <pre><code>42\n</code></pre></p>"},{"location":"api/utils/math/dotvecmat/","title":"dotvecmat","text":"<p>Vector times matrix from left side, i.e. transpose(x)A.</p>"},{"location":"api/utils/math/dotvecmat/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> </li> <li> <p>A</p> </li> </ul>"},{"location":"api/utils/math/dotvecmat/#examples","title":"Examples","text":"<p><pre><code>from river import utils\n\nx = {0: 4, 1: 5}\n\nA = {\n    (0, 0): 0, (0, 1): 1,\n    (1, 0): 2, (1, 1): 3\n}\n\nC = utils.math.dotvecmat(x, A)\nprint(C)\n</code></pre> <pre><code>{0: 10.0, 1: 19.0}\n</code></pre></p>"},{"location":"api/utils/math/log-sum-2-exp/","title":"log_sum_2_exp","text":"<p>Computation of log( (e^a + e^b) / 2) in an overflow-proof way</p>"},{"location":"api/utils/math/log-sum-2-exp/#parameters","title":"Parameters","text":"<ul> <li> <p>a</p> <p>Type \u2192 float</p> <p>First number</p> </li> <li> <p>b</p> <p>Type \u2192 float</p> <p>Second number</p> </li> </ul>"},{"location":"api/utils/math/matmul2d/","title":"matmul2d","text":"<p>Multiplication for 2D matrices.</p>"},{"location":"api/utils/math/matmul2d/#parameters","title":"Parameters","text":"<ul> <li> <p>A</p> </li> <li> <p>B</p> </li> </ul>"},{"location":"api/utils/math/matmul2d/#examples","title":"Examples","text":"<p><pre><code>import pprint\nfrom river import utils\n\nA = {\n    (0, 0): 2, (0, 1): 0, (0, 2): 4,\n    (1, 0): 5, (1, 1): 6, (1, 2): 0\n}\n\nB = {\n    (0, 0): 1, (0, 1): 1, (0, 2): 0, (0, 3): 0,\n    (1, 0): 2, (1, 1): 0, (1, 2): 1, (1, 3): 3,\n    (2, 0): 4, (2, 1): 0, (2, 2): 0, (2, 3): 0\n}\n\nC = utils.math.matmul2d(A, B)\npprint.pprint(C)\n</code></pre> <pre><code>{(0, 0): 18.0,\n    (0, 1): 2.0,\n    (0, 2): 0.0,\n    (0, 3): 0.0,\n    (1, 0): 17.0,\n    (1, 1): 5.0,\n    (1, 2): 6.0,\n    (1, 3): 18.0}\n</code></pre></p>"},{"location":"api/utils/math/minkowski-distance/","title":"minkowski_distance","text":"<p>Minkowski distance.</p>"},{"location":"api/utils/math/minkowski-distance/#parameters","title":"Parameters","text":"<ul> <li> <p>a</p> <p>Type \u2192 dict</p> </li> <li> <p>b</p> <p>Type \u2192 dict</p> </li> <li> <p>p</p> <p>Type \u2192 int</p> <p>Parameter for the Minkowski distance. When <code>p=1</code>, this is equivalent to using the Manhattan distance. When <code>p=2</code>, this is equivalent to using the Euclidean distance.</p> </li> </ul>"},{"location":"api/utils/math/norm/","title":"norm","text":"<p>Compute the norm of a dictionaries values.</p>"},{"location":"api/utils/math/norm/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 dict</p> </li> <li> <p>order</p> <p>Default \u2192 <code>None</code></p> </li> </ul>"},{"location":"api/utils/math/outer/","title":"outer","text":"<p>Outer-product between two vectors.</p>"},{"location":"api/utils/math/outer/#parameters","title":"Parameters","text":"<ul> <li> <p>u</p> <p>Type \u2192 dict</p> </li> <li> <p>v</p> <p>Type \u2192 dict</p> </li> </ul>"},{"location":"api/utils/math/outer/#examples","title":"Examples","text":"<p><pre><code>import pprint\nfrom river import utils\n\nu = dict(enumerate((1, 2, 3)))\nv = dict(enumerate((2, 4, 8)))\n\nuTv = utils.math.outer(u, v)\npprint.pprint(uTv)\n</code></pre> <pre><code>{(0, 0): 2,\n    (0, 1): 4,\n    (0, 2): 8,\n    (1, 0): 4,\n    (1, 1): 8,\n    (1, 2): 16,\n    (2, 0): 6,\n    (2, 1): 12,\n    (2, 2): 24}\n</code></pre></p>"},{"location":"api/utils/math/prod/","title":"prod","text":"<p>Product function.</p>"},{"location":"api/utils/math/prod/#parameters","title":"Parameters","text":"<ul> <li>iterable</li> </ul>"},{"location":"api/utils/math/sherman-morrison/","title":"sherman_morrison","text":"<p>Sherman-Morrison formula.</p> <p>This is an inplace function.</p>"},{"location":"api/utils/math/sherman-morrison/#parameters","title":"Parameters","text":"<ul> <li> <p>A</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>u</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>v</p> <p>Type \u2192 np.ndarray</p> </li> </ul> <ol> <li> <p>Fast rank-one updates to matrix inverse? \u2014 Tim Vieira \u21a9</p> </li> </ol>"},{"location":"api/utils/math/sigmoid/","title":"sigmoid","text":"<p>Sigmoid function.</p>"},{"location":"api/utils/math/sigmoid/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/utils/math/sign/","title":"sign","text":"<p>Sign function.</p>"},{"location":"api/utils/math/sign/#parameters","title":"Parameters","text":"<ul> <li> <p>x</p> <p>Type \u2192 float</p> </li> </ul>"},{"location":"api/utils/math/softmax/","title":"softmax","text":"<p>Normalizes a dictionary of predicted probabilities, in-place.</p>"},{"location":"api/utils/math/softmax/#parameters","title":"Parameters","text":"<ul> <li> <p>y_pred</p> <p>Type \u2192 dict</p> </li> </ul>"},{"location":"api/utils/math/woodbury-matrix/","title":"woodbury_matrix","text":"<p>Woodbury matrix identity.</p> <p>This is an inplace function.</p>"},{"location":"api/utils/math/woodbury-matrix/#parameters","title":"Parameters","text":"<ul> <li> <p>A</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>U</p> <p>Type \u2192 np.ndarray</p> </li> <li> <p>V</p> <p>Type \u2192 np.ndarray</p> </li> </ul> <ol> <li> <p>Matrix inverse mini-batch updates \u2014 Max Halford \u21a9</p> </li> </ol>"},{"location":"api/utils/norm/normalize-values-in-dict/","title":"normalize_values_in_dict","text":"<p>Normalize the values in a dictionary using the given factor.</p> <p>For each element in the dictionary, applies <code>value/factor</code>.</p>"},{"location":"api/utils/norm/normalize-values-in-dict/#parameters","title":"Parameters","text":"<ul> <li> <p>dictionary</p> <p>Dictionary to normalize.</p> </li> <li> <p>factor</p> <p>Default \u2192 <code>None</code></p> <p>Normalization factor value. If not set, use the sum of values.</p> </li> <li> <p>inplace</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform operation in-place</p> </li> <li> <p>raise_error</p> <p>Default \u2192 <code>False</code></p> <p>In case the normalization factor is either <code>0</code> or <code>None</code>: - <code>True</code>: raise an error. - <code>False</code>: return gracefully (if <code>inplace=False</code>, a copy of) <code>dictionary</code>.</p> </li> </ul>"},{"location":"api/utils/norm/scale-values-in-dict/","title":"scale_values_in_dict","text":"<p>Scale the values in a dictionary.</p> <p>For each element in the dictionary, applies <code>value * multiplier</code>.</p>"},{"location":"api/utils/norm/scale-values-in-dict/#parameters","title":"Parameters","text":"<ul> <li> <p>dictionary</p> <p>Dictionary to scale.</p> </li> <li> <p>multiplier</p> <p>Scaling value.</p> </li> <li> <p>inplace</p> <p>Default \u2192 <code>True</code></p> <p>If True, perform operation in-place</p> </li> </ul>"},{"location":"api/utils/pretty/humanize-bytes/","title":"humanize_bytes","text":"<p>Returns a human-friendly byte size.</p>"},{"location":"api/utils/pretty/humanize-bytes/#parameters","title":"Parameters","text":"<ul> <li> <p>n_bytes</p> <p>Type \u2192 int</p> </li> </ul>"},{"location":"api/utils/pretty/print-table/","title":"print_table","text":"<p>Pretty-prints a table.</p>"},{"location":"api/utils/pretty/print-table/#parameters","title":"Parameters","text":"<ul> <li> <p>headers</p> <p>Type \u2192 list[str]</p> <p>The column names.</p> </li> <li> <p>columns</p> <p>Type \u2192 list[list[str]]</p> <p>The column values.</p> </li> <li> <p>order</p> <p>Type \u2192 list[int] | None</p> <p>Default \u2192 <code>None</code></p> <p>Order in which to print the column the values. Defaults to the order in which the values are given.</p> </li> </ul>"},{"location":"api/utils/random/exponential/","title":"exponential","text":"<p>Sample a random value from a Poisson distribution.</p>"},{"location":"api/utils/random/exponential/#parameters","title":"Parameters","text":"<ul> <li> <p>rate</p> <p>Type \u2192 float</p> <p>Default \u2192 <code>1.0</code></p> </li> <li> <p>rng</p> <p>Default \u2192 <code>&lt;module 'random' from '/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/random.py'&gt;</code></p> </li> </ul> <ol> <li> <p>Wikipedia article \u21a9</p> </li> </ol>"},{"location":"api/utils/random/poisson/","title":"poisson","text":"<p>Sample a random value from a Poisson distribution.</p>"},{"location":"api/utils/random/poisson/#parameters","title":"Parameters","text":"<ul> <li> <p>rate</p> <p>Type \u2192 float</p> </li> <li> <p>rng</p> <p>Default \u2192 <code>&lt;module 'random' from '/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/random.py'&gt;</code></p> </li> </ul> <p>[^1] Wikipedia article</p>"},{"location":"benchmarks/Binary%20classification/","title":"Binary classification","text":"TableChart Model Dataset Accuracy F1 Memory in Mb Time in s ADWIN Bagging Bananas 0.625967 0.448218 0.400658 942.73 ADWIN Bagging Elec2 0.823773 0.776587 0.598438 8970.15 ADWIN Bagging Phishing 0.893515 0.879201 1.31008 568.218 ADWIN Bagging SMTP 0.999685 0 0.164217 8006.78 ALMA Bananas 0.506415 0.482595 0.0029211 68.9731 ALMA Elec2 0.906427 0.889767 0.00435829 836.498 ALMA Phishing 0.8256 0.810764 0.0045805 29.7613 ALMA SMTP 0.764986 0.00178548 0.00309372 1361.61 AdaBoost Bananas 0.677864 0.645041 0.453154 876.714 AdaBoost Elec2 0.880581 0.858687 13.5424 10153.7 AdaBoost Phishing 0.878303 0.863555 0.873312 552.609 AdaBoost SMTP 0.999443 0.404494 1.33633 6617.5 Adaptive Random Forest Bananas 0.88696 0.871707 15.3551 2603.02 Adaptive Random Forest Elec2 0.876608 0.852391 22.3949 12397.6 Adaptive Random Forest Phishing 0.907926 0.896116 4.10291 743.377 Adaptive Random Forest SMTP 0.999685 0 0.327095 11543.4 Aggregated Mondrian Forest Bananas 0.889413 0.874249 17.2377 2954.75 Aggregated Mondrian Forest Elec2 0.849904 0.819731 287.315 18206.6 Aggregated Mondrian Forest Phishing 0.904724 0.892112 3.39106 807.573 Aggregated Mondrian Forest SMTP 0.999863 0.734694 0.211749 5848.87 Bagging Bananas 0.634082 0.459437 0.703124 1170.85 Bagging Elec2 0.840436 0.80208 2.28896 13164.5 Bagging Phishing 0.893515 0.879201 1.38826 633.136 Bagging SMTP 0.999685 0 0.207971 8814.84 Hoeffding Adaptive Tree Bananas 0.616531 0.42825 0.0618467 163.516 Hoeffding Adaptive Tree Elec2 0.821258 0.787344 0.435328 2980.69 Hoeffding Adaptive Tree Phishing 0.874299 0.856095 0.142962 77.865 Hoeffding Adaptive Tree SMTP 0.999685 0 0.0241137 2094.95 Hoeffding Tree Bananas 0.642197 0.503405 0.0594654 93.5302 Hoeffding Tree Elec2 0.795635 0.750834 0.938466 1485.98 Hoeffding Tree Phishing 0.879904 0.860595 0.132719 54.2758 Hoeffding Tree SMTP 0.999685 0 0.0170441 1543.56 Leveraging Bagging Bananas 0.828269 0.802689 3.23571 2747.95 Leveraging Bagging Elec2 0.892653 0.871966 7.56535 18763.3 Leveraging Bagging Phishing 0.894315 0.877323 4.0114 1619.65 Leveraging Bagging SMTP 0.999674 0 0.164603 17549.6 Logistic regression Bananas 0.543208 0.197015 0.00424099 82.0689 Logistic regression Elec2 0.822144 0.777086 0.005373 953.54 Logistic regression Phishing 0.8872 0.871233 0.00556469 29.2066 Logistic regression SMTP 0.999769 0.421053 0.00438309 1531.37 Naive Bayes Bananas 0.61521 0.413912 0.0140247 97.154 Naive Bayes Elec2 0.728741 0.603785 0.0510378 1230.66 Naive Bayes Phishing 0.884708 0.871429 0.05723 38.528 Naive Bayes SMTP 0.993484 0.0490798 0.0201406 1826.47 Stacking Bananas 0.876203 0.859649 19.1946 5236.84 Stacking Elec2 0.885458 0.864157 40.7547 22944.4 Stacking Phishing 0.895116 0.882722 8.72124 2411.41 Stacking SMTP 0.999685 0 4.88868 24733.2 Streaming Random Patches Bananas 0.871674 0.854265 10.5381 3551.41 Streaming Random Patches Elec2 0.868884 0.843009 107.322 22969 Streaming Random Patches Phishing 0.913531 0.901996 6.59559 1436.69 Streaming Random Patches SMTP 0.999685 0 0.17817 18142.3 Voting Bananas 0.872617 0.849162 4.58403 2790.97 Voting Elec2 0.84368 0.797958 5.7575 13925.5 Voting Phishing 0.896717 0.884512 4.8203 1436.72 Voting SMTP 0.999779 0.487805 4.60205 18069.8 Vowpal Wabbit logistic regression Bananas 0.551321 0 0.000646591 88.7248 Vowpal Wabbit logistic regression Elec2 0.697475 0.459592 0.000646591 937.011 Vowpal Wabbit logistic regression Phishing 0.7736 0.669778 0.000646591 27.8334 Vowpal Wabbit logistic regression SMTP 0.999695 0.121212 0.000646591 1631.37 [baseline] Last Class Bananas 0.50953 0.452957 0.000510216 30.809 [baseline] Last Class Elec2 0.853303 0.827229 0.000510216 341.39 [baseline] Last Class Phishing 0.515612 0.447489 0.000510216 11.9196 [baseline] Last Class SMTP 0.999601 0.366667 0.000510216 532.359 k-Nearest Neighbors Bananas 0.885073 0.870838 4.50996 2974.33 k-Nearest Neighbors Elec2 0.853148 0.823642 4.76604 13503.4 k-Nearest Neighbors Phishing 0.881505 0.867145 4.59643 1552.65 k-Nearest Neighbors SMTP 0.999821 0.666667 4.51822 17961.1 sklearn SGDClassifier Bananas 0.546415 0.205026 0.00557804 621.426 sklearn SGDClassifier Elec2 0.819099 0.772892 0.00680161 4291.77 sklearn SGDClassifier Phishing 0.8896 0.876122 0.00701618 167.984 sklearn SGDClassifier SMTP 0.999706 0.363636 0.00574303 7118.18 <p>Try reloading the page if something is buggy</p> <p>{   \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",   \"data\": {     \"values\": [       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.490566037735849,         \"F1\": 0.3414634146341463,         \"Memory in Mb\": 0.0041875839233398,         \"Time in s\": 0.00989       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5141509433962265,         \"F1\": 0.3832335329341317,         \"Memory in Mb\": 0.0041875839233398,         \"Time in s\": 0.123413       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5220125786163522,         \"F1\": 0.4242424242424242,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 0.315017       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165094339622641,         \"F1\": 0.4023323615160349,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 0.5849610000000001       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5320754716981132,         \"F1\": 0.3641025641025641,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 0.937213       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.3287671232876712,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 1.342505       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5525606469002695,         \"F1\": 0.3054393305439331,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 1.895068       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5530660377358491,         \"F1\": 0.2808349146110057,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 2.518365       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5555555555555556,         \"F1\": 0.2587412587412587,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 3.1930270000000003       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5622641509433962,         \"F1\": 0.2418300653594771,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 3.938137       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5608919382504288,         \"F1\": 0.2242424242424242,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 4.7351090000000005       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5613207547169812,         \"F1\": 0.2206703910614525,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 5.600857       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5645863570391872,         \"F1\": 0.20844327176781,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 6.476079       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5646900269541779,         \"F1\": 0.1965174129353233,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 7.428853       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5647798742138365,         \"F1\": 0.1858823529411764,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 8.473991       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5660377358490566,         \"F1\": 0.1785714285714285,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 9.59319       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.562708102108768,         \"F1\": 0.1705263157894736,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 10.745503       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5587002096436059,         \"F1\": 0.1679841897233201,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 11.962335       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5516385302879842,         \"F1\": 0.1662049861495844,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 13.252336       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5495283018867925,         \"F1\": 0.1688424717145344,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 14.603624       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5485175202156334,         \"F1\": 0.1809290953545232,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 15.981958       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5484562607204116,         \"F1\": 0.1967963386727688,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 17.395643       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5471698113207547,         \"F1\": 0.1999999999999999,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 18.850781       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5479559748427673,         \"F1\": 0.2166212534059945,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 20.422045       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5452830188679245,         \"F1\": 0.2260757867694284,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 22.049363       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5395500725689405,         \"F1\": 0.2285714285714285,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 23.763248       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5391334730957372,         \"F1\": 0.230005837711617,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 25.51638       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5411051212938005,         \"F1\": 0.2261363636363636,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 27.316788000000003       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5403383214053351,         \"F1\": 0.2214876033057851,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 29.124189       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5437106918238994,         \"F1\": 0.2203116603976356,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 31.016333000000003       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5450395617772368,         \"F1\": 0.21604614577871,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 32.984057       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5439268867924528,         \"F1\": 0.2127226463104325,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 35.003757       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5457404230989137,         \"F1\": 0.2082710513203786,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 37.068178       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5480022197558269,         \"F1\": 0.2042012701514411,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 39.232173       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.546900269541779,         \"F1\": 0.1991424487851357,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 41.450117000000006       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5463836477987422,         \"F1\": 0.1945090739879013,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 43.72876300000001       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5474247832738399,         \"F1\": 0.1906064751481988,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 46.072390000000006       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.547914597815293,         \"F1\": 0.1866904868244752,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 48.42327300000001       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.548137397194001,         \"F1\": 0.1828521434820647,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 50.870554000000006       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5474056603773585,         \"F1\": 0.1788617886178861,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 53.39424700000001       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5476300046019328,         \"F1\": 0.176716917922948,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 55.939767       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5498652291105122,         \"F1\": 0.1820408163265306,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 58.584779000000005       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5467310223782361,         \"F1\": 0.1814580031695721,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 61.26661800000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5465265866209262,         \"F1\": 0.1880998080614203,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 64.04445700000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5467505241090147,         \"F1\": 0.1908682634730538,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 66.91140200000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5469647251845775,         \"F1\": 0.1911387770047601,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 69.84398600000002       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5469690887193898,         \"F1\": 0.1976537504443654,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 72.84582100000002       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5448113207547169,         \"F1\": 0.1958333333333333,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 75.85667200000002       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5429341547939931,         \"F1\": 0.1941615750169721,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 78.94956300000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5432075471698113,         \"F1\": 0.1970149253731343,         \"Memory in Mb\": 0.0042409896850585,         \"Time in s\": 82.06889500000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7980132450331126,         \"F1\": 0.7834319526627219,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 0.687155       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8134657836644592,         \"F1\": 0.7488855869242199,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 2.092465       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8024282560706402,         \"F1\": 0.7300150829562596,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 4.064074       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8192604856512141,         \"F1\": 0.7598093142647598,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 6.824807       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8289183222958058,         \"F1\": 0.7613181398213735,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 10.234028       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8226637233259749,         \"F1\": 0.7528205128205128,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 14.344314       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8229265216020183,         \"F1\": 0.7589611504614724,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 19.167838       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8261589403973509,         \"F1\": 0.7617246596066566,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 24.744494       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8318616629874908,         \"F1\": 0.7833096254148886,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 31.081721       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8375275938189846,         \"F1\": 0.7975797579757975,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 38.163875       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8377483443708609,         \"F1\": 0.802008081302804,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 45.915004       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400478292862399,         \"F1\": 0.8089220964729151,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 54.352834       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8432671081677704,         \"F1\": 0.8127789046653143,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 63.489549       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8419268369599495,         \"F1\": 0.8117547648108159,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 73.399178       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8437821927888153,         \"F1\": 0.8167141500474834,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 84.03825400000001       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8447157836644592,         \"F1\": 0.8189204408334004,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 95.414959       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8464485131801065,         \"F1\": 0.8201110519510155,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 107.55183300000002       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8411822418444935,         \"F1\": 0.812780106982796,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 120.38661500000002       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397234808876496,         \"F1\": 0.8069954529555788,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 133.99787400000002       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8419426048565122,         \"F1\": 0.80987785448752,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 148.356557       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8451066961000736,         \"F1\": 0.8115849370244869,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 163.518734       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8428155729480232,         \"F1\": 0.8097637986520129,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 179.395561       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8393799788847298,         \"F1\": 0.805689404934688,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 196.009478       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8402777777777778,         \"F1\": 0.8036632935722765,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 213.342445       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8394701986754967,         \"F1\": 0.8009198423127463,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 231.348647       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8357106469689252,         \"F1\": 0.7954545454545454,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 250.064916       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8330471752105306,         \"F1\": 0.791441119395363,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 269.489469       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8298249763481551,         \"F1\": 0.7872875092387287,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 289.628629       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8304407398949532,         \"F1\": 0.787745962170661,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 310.508458       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8308682855040471,         \"F1\": 0.7889638709085066,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 332.12320800000003       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8277077547532579,         \"F1\": 0.7843678980437593,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 354.49968       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8270212472406181,         \"F1\": 0.7820039121930016,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 377.655941       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8260418757107498,         \"F1\": 0.780872129766168,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 401.520333       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8258992338657317,         \"F1\": 0.7797807251673304,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 426.09085       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.821286660359508,         \"F1\": 0.7731294287201249,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 451.387139       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8188619082658818,         \"F1\": 0.7700093428838368,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 477.46355       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8168963665652408,         \"F1\": 0.7682024169184289,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 504.189667       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8143952596723597,         \"F1\": 0.7647795037915042,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 531.635688       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8142016188373804,         \"F1\": 0.7627822944896115,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 559.807066       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8154801324503311,         \"F1\": 0.7629984051036682,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 588.709839       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.815161794002046,         \"F1\": 0.7614481273017858,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 618.344034       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8151476926311363,         \"F1\": 0.7609596955073744,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 648.6306599999999       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8162379998973254,         \"F1\": 0.7631274195149389,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 679.6980239999999       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8169275536825206,         \"F1\": 0.7661946562439931,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 711.4719719999999       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8186656855531028,         \"F1\": 0.7707241432780277,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 743.966698       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8201602840963624,         \"F1\": 0.7745390006918749,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 777.181242       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8211920529801324,         \"F1\": 0.7763613934089174,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 811.063029       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8216979396615158,         \"F1\": 0.7772863051470587,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 845.685827       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8211695274136145,         \"F1\": 0.7754109027129481,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 881.122534       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8221412803532009,         \"F1\": 0.7771292633675417,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 917.330239       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8221442443502824,         \"F1\": 0.7770862722319033,         \"Memory in Mb\": 0.0053730010986328,         \"Time in s\": 953.539999       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6,         \"F1\": 0.6428571428571429,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.005087       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.7499999999999999,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.014273       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8,         \"F1\": 0.8,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.080154       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.81,         \"F1\": 0.8041237113402061,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.160529       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8,         \"F1\": 0.7933884297520661,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.244823       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8066666666666666,         \"F1\": 0.8079470198675497,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.373717       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8171428571428572,         \"F1\": 0.8072289156626506,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.564558       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.815,         \"F1\": 0.8042328042328043,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.765703       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8133333333333334,         \"F1\": 0.7980769230769231,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 0.969796       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.82,         \"F1\": 0.8068669527896996,         \"Memory in Mb\": 0.005324363708496,         \"Time in s\": 1.176844       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8218181818181818,         \"F1\": 0.8078431372549019,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 1.38745       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8161764705882353,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 1.62648       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.84,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 1.940615       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8514285714285714,         \"F1\": 0.8278145695364238,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 2.281543       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.848,         \"F1\": 0.8213166144200628,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 2.625835       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.85,         \"F1\": 0.8214285714285715,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 2.973623       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8564705882352941,         \"F1\": 0.825214899713467,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 3.357502       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.86,         \"F1\": 0.8273972602739726,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 3.744344       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8568421052631578,         \"F1\": 0.8247422680412371,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 4.182096       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.858,         \"F1\": 0.8297362110311751,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 4.631479       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8251748251748252,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 5.084116       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8581818181818182,         \"F1\": 0.827433628318584,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 5.539997       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8608695652173913,         \"F1\": 0.8305084745762712,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 6.065522       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.865,         \"F1\": 0.8329896907216495,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 6.594884       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672,         \"F1\": 0.8323232323232322,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 7.192367999999999       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8707692307692307,         \"F1\": 0.8390804597701149,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 7.814115999999999       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8711111111111111,         \"F1\": 0.8426763110307414,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 8.439065999999999       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8757142857142857,         \"F1\": 0.8465608465608465,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 9.067184       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8772413793103448,         \"F1\": 0.8514190317195326,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 9.744984       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8786666666666667,         \"F1\": 0.8539325842696629,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 10.426391       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8549141965678626,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 11.153806       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8567164179104476,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 11.884597       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8579626972740315,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 12.619003       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8811764705882353,         \"F1\": 0.8587412587412586,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 13.411056       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8845714285714286,         \"F1\": 0.8622100954979536,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 14.234524       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844444444444445,         \"F1\": 0.8617021276595744,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 15.105192999999998       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8864864864864865,         \"F1\": 0.8655569782330347,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 15.990264999999996       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8852631578947369,         \"F1\": 0.8655980271270037,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 16.878196999999997       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8861538461538462,         \"F1\": 0.8664259927797834,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 17.769031       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.887,         \"F1\": 0.8675263774912075,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 18.72316       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8868292682926829,         \"F1\": 0.8678815489749431,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 19.680949       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8885714285714286,         \"F1\": 0.8704318936877077,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 20.642059       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8874418604651163,         \"F1\": 0.8703108252947481,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 21.642509       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.889090909090909,         \"F1\": 0.8723849372384936,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 22.64645       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8897777777777778,         \"F1\": 0.8742393509127788,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 23.715816       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8895652173913043,         \"F1\": 0.8738828202581926,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 24.78868       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8885106382978724,         \"F1\": 0.872444011684518,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 25.864657       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8891666666666667,         \"F1\": 0.8729703915950333,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 26.968066       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.889795918367347,         \"F1\": 0.8737137511693172,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 28.075126       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8872,         \"F1\": 0.8712328767123287,         \"Memory in Mb\": 0.0055646896362304,         \"Time in s\": 29.206647       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1.174944       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 3.465965       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 6.937403       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 11.610183       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 17.462392       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 24.519273       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 32.784706       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996715712033633,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 42.234241       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997080632918784,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 52.882453       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 64.622668       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999761142693355,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 77.568109       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997810474689088,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 91.771967       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997978899713004,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 107.109486       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999774791682306,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 123.681834       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997898055701524,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 141.369945       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999802942722018,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 160.23044       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999814534326605,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 180.239632       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999824837975127,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 201.318948       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998340570290676,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 223.519273       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998423541776142,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 246.976714       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498611215374,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 271.56812399999995       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999856685616013,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 297.295844       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998629166761864,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 324.2115329999999       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686284813452,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 352.27523699999995       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998738833420916,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 381.597104       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998787339827804,         \"F1\": 0.7272727272727273,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 412.116627       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998443004223352,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 443.867429       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498611215374,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 476.838798       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999855038324243,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 510.9819989999999       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997022245577158,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 546.274013       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997118302171444,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 582.6678519999999       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997208355228586,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 620.2082039999999       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999697447411583,         \"F1\": 0.4571428571428571,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 658.8625569999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997063460171248,         \"F1\": 0.4571428571428571,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 698.5852799999999       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997147361309212,         \"F1\": 0.4571428571428571,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 739.3620329999999       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934664564724,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 781.2563779999999       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999701751146838,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 824.198222       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997095998008684,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 868.202086       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997170459598204,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 913.268811       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999724119810825,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 959.416173       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997308485959268,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1006.608919       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1054.85163       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997433672658838,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1104.06085       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997491998280228,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1154.258062       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997547731651778,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1205.371532       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999760104183326,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1257.4462130000002       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997540277948592,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1310.5048250000002       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997591522157996,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1364.5437910000005       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997640674767015,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1419.4942320000002       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997687861271676,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1475.4318390000003       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997688007062088,         \"F1\": 0.4210526315789474,         \"Memory in Mb\": 0.0043830871582031,         \"Time in s\": 1531.3705140000004       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7047619047619048,         \"F1\": 0.6990291262135924,         \"Memory in Mb\": 0.8133068084716797,         \"Time in s\": 0.833499       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7867298578199052,         \"F1\": 0.7668393782383419,         \"Memory in Mb\": 1.3378009796142578,         \"Time in s\": 2.8663       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8233438485804416,         \"F1\": 0.806896551724138,         \"Memory in Mb\": 1.855398178100586,         \"Time in s\": 6.250927       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8392434988179669,         \"F1\": 0.8229166666666667,         \"Memory in Mb\": 2.3226680755615234,         \"Time in s\": 11.143336       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8412098298676749,         \"F1\": 0.8181818181818182,         \"Memory in Mb\": 2.776212692260742,         \"Time in s\": 17.797124       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8488188976377953,         \"F1\": 0.8267148014440434,         \"Memory in Mb\": 3.173288345336914,         \"Time in s\": 26.396562       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8596491228070176,         \"F1\": 0.8359621451104102,         \"Memory in Mb\": 3.5500621795654297,         \"Time in s\": 36.969223       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8677685950413223,         \"F1\": 0.8461538461538461,         \"Memory in Mb\": 3.917997360229492,         \"Time in s\": 49.692848       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8730325288562435,         \"F1\": 0.8515337423312884,         \"Memory in Mb\": 4.238534927368164,         \"Time in s\": 64.631677       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8772426817752597,         \"F1\": 0.8549107142857144,         \"Memory in Mb\": 4.491437911987305,         \"Time in s\": 81.765253       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8772532188841202,         \"F1\": 0.8557013118062564,         \"Memory in Mb\": 4.809717178344727,         \"Time in s\": 101.295253       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8772619984264359,         \"F1\": 0.8566176470588236,         \"Memory in Mb\": 5.171953201293945,         \"Time in s\": 123.161687       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8779956427015251,         \"F1\": 0.8561643835616438,         \"Memory in Mb\": 5.501619338989258,         \"Time in s\": 147.513883       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8813216453135536,         \"F1\": 0.860759493670886,         \"Memory in Mb\": 5.80189323425293,         \"Time in s\": 174.53874199999998       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8785399622404028,         \"F1\": 0.8579838116261957,         \"Memory in Mb\": 6.17225456237793,         \"Time in s\": 204.250002       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8790560471976401,         \"F1\": 0.8585231193926847,         \"Memory in Mb\": 6.45002555847168,         \"Time in s\": 237.091398       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8806218767351471,         \"F1\": 0.8613797549967763,         \"Memory in Mb\": 6.703157424926758,         \"Time in s\": 272.83416       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8783429470372313,         \"F1\": 0.8602409638554217,         \"Memory in Mb\": 7.075212478637695,         \"Time in s\": 311.419605       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8777943368107303,         \"F1\": 0.8607021517553795,         \"Memory in Mb\": 7.409914016723633,         \"Time in s\": 352.79492       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8791882963662104,         \"F1\": 0.8636847710330138,         \"Memory in Mb\": 7.730207443237305,         \"Time in s\": 397.065386       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8782022471910113,         \"F1\": 0.8626457171819564,         \"Memory in Mb\": 8.068941116333008,         \"Time in s\": 444.302777       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8777348777348777,         \"F1\": 0.8621190130624092,         \"Memory in Mb\": 8.392999649047852,         \"Time in s\": 494.454577       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8781288469429627,         \"F1\": 0.8624363131079205,         \"Memory in Mb\": 8.738908767700195,         \"Time in s\": 547.433225       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8784899724734565,         \"F1\": 0.8635761589403974,         \"Memory in Mb\": 9.069158554077148,         \"Time in s\": 603.367304       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8799546998867497,         \"F1\": 0.8654822335025381,         \"Memory in Mb\": 9.38022804260254,         \"Time in s\": 661.971994       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8820326678765881,         \"F1\": 0.8676171079429736,         \"Memory in Mb\": 9.675683975219728,         \"Time in s\": 723.088894       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8836071303739951,         \"F1\": 0.86905230043256,         \"Memory in Mb\": 10.005556106567385,         \"Time in s\": 786.780009       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8840579710144928,         \"F1\": 0.8691019786910198,         \"Memory in Mb\": 10.283010482788086,         \"Time in s\": 853.0146269999999       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8831760494630654,         \"F1\": 0.8683535020168683,         \"Memory in Mb\": 10.632661819458008,         \"Time in s\": 921.667133       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8858131487889274,         \"F1\": 0.8707725169099323,         \"Memory in Mb\": 10.90281867980957,         \"Time in s\": 992.810764       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8852359208523592,         \"F1\": 0.8696854476322157,         \"Memory in Mb\": 11.200468063354492,         \"Time in s\": 1066.389204       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8849896785608965,         \"F1\": 0.87017310252996,         \"Memory in Mb\": 11.512235641479492,         \"Time in s\": 1142.442462       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8864741206748642,         \"F1\": 0.8712293220888745,         \"Memory in Mb\": 11.797895431518556,         \"Time in s\": 1221.036812       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8878712184290869,         \"F1\": 0.8721518987341771,         \"Memory in Mb\": 12.102933883666992,         \"Time in s\": 1302.125963       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8878403882448099,         \"F1\": 0.8725490196078431,         \"Memory in Mb\": 12.41331672668457,         \"Time in s\": 1385.838182       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.889646133682831,         \"F1\": 0.8746650788925276,         \"Memory in Mb\": 12.665735244750977,         \"Time in s\": 1472.135343       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8885488395817394,         \"F1\": 0.8730758059831543,         \"Memory in Mb\": 13.002767562866213,         \"Time in s\": 1561.047711       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8872609883287808,         \"F1\": 0.8714609286523215,         \"Memory in Mb\": 13.407987594604492,         \"Time in s\": 1652.580672       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8874909266876361,         \"F1\": 0.8717241379310345,         \"Memory in Mb\": 13.751871109008787,         \"Time in s\": 1746.8148660000002       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8886529841943854,         \"F1\": 0.8731864588930682,         \"Memory in Mb\": 13.96497917175293,         \"Time in s\": 1843.750561       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8895281933256617,         \"F1\": 0.8742138364779874,         \"Memory in Mb\": 14.240518569946287,         \"Time in s\": 1943.403214       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8890137047854415,         \"F1\": 0.8735926305015352,         \"Memory in Mb\": 14.605810165405272,         \"Time in s\": 2045.776976       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8894009216589862,         \"F1\": 0.874439461883408,         \"Memory in Mb\": 14.917993545532228,         \"Time in s\": 2150.6554650000003       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8893416255629423,         \"F1\": 0.8748180494905387,         \"Memory in Mb\": 15.239774703979492,         \"Time in s\": 2258.064088       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8880268400083875,         \"F1\": 0.8729176582579724,         \"Memory in Mb\": 15.67698097229004,         \"Time in s\": 2367.913167       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8888205128205128,         \"F1\": 0.8733644859813083,         \"Memory in Mb\": 15.96486473083496,         \"Time in s\": 2480.267593       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.889580405541056,         \"F1\": 0.8746010031919745,         \"Memory in Mb\": 16.210702896118164,         \"Time in s\": 2595.134509       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8891291527422842,         \"F1\": 0.8740509155873157,         \"Memory in Mb\": 16.543100357055664,         \"Time in s\": 2712.434229       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8894665896398999,         \"F1\": 0.8743982494529539,         \"Memory in Mb\": 16.87101936340332,         \"Time in s\": 2832.294496       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.889413096810719,         \"F1\": 0.8742489270386266,         \"Memory in Mb\": 17.23769187927246,         \"Time in s\": 2954.746773       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8662983425414365,         \"F1\": 0.8638920134983127,         \"Memory in Mb\": 5.093213081359863,         \"Time in s\": 9.961559       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895637769188294,         \"F1\": 0.863013698630137,         \"Memory in Mb\": 9.274415016174316,         \"Time in s\": 34.997891       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8737578211262422,         \"F1\": 0.8433074463225217,         \"Memory in Mb\": 14.81954288482666,         \"Time in s\": 77.180768       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8746894838531604,         \"F1\": 0.8451568894952252,         \"Memory in Mb\": 20.35789203643799,         \"Time in s\": 135.799753       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.869728416869066,         \"F1\": 0.8295782784517621,         \"Memory in Mb\": 25.320820808410645,         \"Time in s\": 209.048681       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8658693652253909,         \"F1\": 0.8254728273880776,         \"Memory in Mb\": 30.94210529327393,         \"Time in s\": 297.509476       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8613783314934553,         \"F1\": 0.8220287507592631,         \"Memory in Mb\": 36.922226905822754,         \"Time in s\": 401.254404       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8563543535255967,         \"F1\": 0.8144715736945286,         \"Memory in Mb\": 42.8322229385376,         \"Time in s\": 518.853069       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8547773825585674,         \"F1\": 0.8211480362537765,         \"Memory in Mb\": 49.13461780548096,         \"Time in s\": 650.61595       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8564963020200905,         \"F1\": 0.8276776246023331,         \"Memory in Mb\": 54.27480792999268,         \"Time in s\": 797.031608       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8559959859508279,         \"F1\": 0.830478440637921,         \"Memory in Mb\": 59.58850955963135,         \"Time in s\": 957.298151       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.858522675006899,         \"F1\": 0.8360690684289065,         \"Memory in Mb\": 64.43849277496338,         \"Time in s\": 1132.655012       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8588774730406725,         \"F1\": 0.8365138697619515,         \"Memory in Mb\": 69.77676105499268,         \"Time in s\": 1321.3849659999998       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8572892848695104,         \"F1\": 0.8352148579752368,         \"Memory in Mb\": 75.08023929595947,         \"Time in s\": 1522.77491       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8577525940098609,         \"F1\": 0.8380665158750105,         \"Memory in Mb\": 79.94311618804932,         \"Time in s\": 1737.870186       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8584339427388755,         \"F1\": 0.8393863494051347,         \"Memory in Mb\": 84.43613529205322,         \"Time in s\": 1968.10555       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8584507499513019,         \"F1\": 0.8387335404645658,         \"Memory in Mb\": 89.24470615386963,         \"Time in s\": 2211.432474       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8561354019746121,         \"F1\": 0.8352296670880741,         \"Memory in Mb\": 95.6551637649536,         \"Time in s\": 2468.910492       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8563295183872655,         \"F1\": 0.8333445649976414,         \"Memory in Mb\": 100.85075855255128,         \"Time in s\": 2740.76049       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8570009382416248,         \"F1\": 0.834176,         \"Memory in Mb\": 106.8406229019165,         \"Time in s\": 3026.823297       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.858712220762155,         \"F1\": 0.8348082595870207,         \"Memory in Mb\": 111.7458429336548,         \"Time in s\": 3325.548438       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8587125583262255,         \"F1\": 0.8363361618040218,         \"Memory in Mb\": 117.0202569961548,         \"Time in s\": 3636.553219       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8564572635216202,         \"F1\": 0.8339348176114596,         \"Memory in Mb\": 123.3725290298462,         \"Time in s\": 3960.554229       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8540219840868325,         \"F1\": 0.8286917098445596,         \"Memory in Mb\": 130.42929553985596,         \"Time in s\": 4298.210438       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8531944015188309,         \"F1\": 0.8264160793526494,         \"Memory in Mb\": 136.64212131500244,         \"Time in s\": 4650.500753       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8528550201655699,         \"F1\": 0.8255134917438581,         \"Memory in Mb\": 142.6701021194458,         \"Time in s\": 5016.675492       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8532766444544376,         \"F1\": 0.8247130647130647,         \"Memory in Mb\": 148.4442949295044,         \"Time in s\": 5397.142957       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8514605589939686,         \"F1\": 0.8225487425826504,         \"Memory in Mb\": 154.72937488555908,         \"Time in s\": 5792.939295       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8521676245575306,         \"F1\": 0.8231490756761678,         \"Memory in Mb\": 160.280930519104,         \"Time in s\": 6204.791143       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530851024688179,         \"F1\": 0.8247069669432372,         \"Memory in Mb\": 165.12001132965088,         \"Time in s\": 6630.671498000001       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8528752002848495,         \"F1\": 0.8239904583404327,         \"Memory in Mb\": 171.1938066482544,         \"Time in s\": 7068.974646000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8532303128557138,         \"F1\": 0.8236415633937083,         \"Memory in Mb\": 176.66365909576416,         \"Time in s\": 7519.88705       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8538649362812323,         \"F1\": 0.8241355713883187,         \"Memory in Mb\": 181.78493976593015,         \"Time in s\": 7981.874679       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8542349771126189,         \"F1\": 0.8238110186783865,         \"Memory in Mb\": 187.08849048614505,         \"Time in s\": 8454.454599       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8525655176763695,         \"F1\": 0.8216125462662648,         \"Memory in Mb\": 193.5201120376587,         \"Time in s\": 8938.242097       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.852245899126169,         \"F1\": 0.821432541594101,         \"Memory in Mb\": 199.6366205215454,         \"Time in s\": 9433.534304       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.852003221860923,         \"F1\": 0.8214247147330909,         \"Memory in Mb\": 205.8111581802368,         \"Time in s\": 9940.639789       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.851715223516426,         \"F1\": 0.8209965286300361,         \"Memory in Mb\": 212.10033893585205,         \"Time in s\": 10459.964952       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8513287861206238,         \"F1\": 0.8197137660019906,         \"Memory in Mb\": 218.64550113677976,         \"Time in s\": 10993.026606       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8508788873865173,         \"F1\": 0.8179735920237133,         \"Memory in Mb\": 225.19258975982663,         \"Time in s\": 11538.003929       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8496432898102032,         \"F1\": 0.8159741671883752,         \"Memory in Mb\": 232.33557987213132,         \"Time in s\": 12096.169427       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8497279966360937,         \"F1\": 0.8155126798735239,         \"Memory in Mb\": 238.56606006622317,         \"Time in s\": 12664.877691       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8494493929203994,         \"F1\": 0.8154906093686098,         \"Memory in Mb\": 244.89648151397705,         \"Time in s\": 13243.508414       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8492336251661942,         \"F1\": 0.8164773421277635,         \"Memory in Mb\": 251.12543201446533,         \"Time in s\": 13830.859859       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8486104638328141,         \"F1\": 0.8170174918470204,         \"Memory in Mb\": 257.83575916290283,         \"Time in s\": 14427.278119       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8490941811637672,         \"F1\": 0.8186928820595613,         \"Memory in Mb\": 264.1331262588501,         \"Time in s\": 15032.883602       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8493929217256523,         \"F1\": 0.8194385787087872,         \"Memory in Mb\": 270.1314744949341,         \"Time in s\": 15648.679676       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8493802745648125,         \"F1\": 0.8194995590828924,         \"Memory in Mb\": 276.0468301773072,         \"Time in s\": 16273.986894       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8493681436262474,         \"F1\": 0.8189620164063134,         \"Memory in Mb\": 282.1419038772583,         \"Time in s\": 16909.074578       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8499083864985982,         \"F1\": 0.8197651300267741,         \"Memory in Mb\": 287.208477973938,         \"Time in s\": 17554.066457       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8499039968219637,         \"F1\": 0.8197312269727252,         \"Memory in Mb\": 287.3145227432251,         \"Time in s\": 18206.640571       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6666666666666666,         \"F1\": 0.6923076923076924,         \"Memory in Mb\": 0.2663440704345703,         \"Time in s\": 0.180038       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7755102040816326,         \"F1\": 0.7555555555555555,         \"Memory in Mb\": 0.4029140472412109,         \"Time in s\": 0.591649       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7972972972972973,         \"F1\": 0.7945205479452055,         \"Memory in Mb\": 0.5196552276611328,         \"Time in s\": 1.289716       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8181818181818182,         \"F1\": 0.8125,         \"Memory in Mb\": 0.6383838653564453,         \"Time in s\": 2.331468       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8225806451612904,         \"F1\": 0.819672131147541,         \"Memory in Mb\": 0.7669887542724609,         \"Time in s\": 3.724154       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8456375838926175,         \"F1\": 0.847682119205298,         \"Memory in Mb\": 0.9175167083740234,         \"Time in s\": 5.520175       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867816091954023,         \"F1\": 0.8606060606060606,         \"Memory in Mb\": 1.0086803436279297,         \"Time in s\": 7.749843999999999       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.864321608040201,         \"F1\": 0.8571428571428572,         \"Memory in Mb\": 1.1245098114013672,         \"Time in s\": 10.53336       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8660714285714286,         \"F1\": 0.8557692307692308,         \"Memory in Mb\": 1.2114391326904297,         \"Time in s\": 13.795268       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554216867469879,         \"F1\": 0.8448275862068965,         \"Memory in Mb\": 1.322244644165039,         \"Time in s\": 17.57486       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8540145985401459,         \"F1\": 0.84251968503937,         \"Memory in Mb\": 1.3987751007080078,         \"Time in s\": 21.876977       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561872909698997,         \"F1\": 0.8413284132841329,         \"Memory in Mb\": 1.489828109741211,         \"Time in s\": 26.743447       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672839506172839,         \"F1\": 0.8501742160278746,         \"Memory in Mb\": 1.5769939422607422,         \"Time in s\": 32.2729       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8681948424068768,         \"F1\": 0.8486842105263156,         \"Memory in Mb\": 1.638784408569336,         \"Time in s\": 38.477964       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8689839572192514,         \"F1\": 0.8482972136222912,         \"Memory in Mb\": 1.7178211212158203,         \"Time in s\": 45.357054       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671679197994987,         \"F1\": 0.8436578171091446,         \"Memory in Mb\": 1.7941875457763672,         \"Time in s\": 52.888585       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8702830188679245,         \"F1\": 0.8433048433048433,         \"Memory in Mb\": 1.8353633880615237,         \"Time in s\": 61.095765       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8730512249443207,         \"F1\": 0.8455284552845528,         \"Memory in Mb\": 1.909624099731445,         \"Time in s\": 70.024579       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8755274261603375,         \"F1\": 0.8506329113924052,         \"Memory in Mb\": 1.988790512084961,         \"Time in s\": 79.720297       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.875751503006012,         \"F1\": 0.8530805687203792,         \"Memory in Mb\": 2.063833236694336,         \"Time in s\": 90.078634       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8778625954198473,         \"F1\": 0.8525345622119817,         \"Memory in Mb\": 2.144712448120117,         \"Time in s\": 101.258101       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8779599271402551,         \"F1\": 0.8533916849015317,         \"Memory in Mb\": 2.199640274047852,         \"Time in s\": 113.251819       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8780487804878049,         \"F1\": 0.8535564853556484,         \"Memory in Mb\": 2.2528209686279297,         \"Time in s\": 125.935841       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8797996661101837,         \"F1\": 0.8536585365853657,         \"Memory in Mb\": 2.283121109008789,         \"Time in s\": 139.44840100000002       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8814102564102564,         \"F1\": 0.852589641434263,         \"Memory in Mb\": 2.343900680541992,         \"Time in s\": 153.77905700000002       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.884437596302003,         \"F1\": 0.8587570621468926,         \"Memory in Mb\": 2.418844223022461,         \"Time in s\": 168.92061400000003       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.884272997032641,         \"F1\": 0.8617021276595745,         \"Memory in Mb\": 2.468423843383789,         \"Time in s\": 184.940001       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8884120171673819,         \"F1\": 0.8650519031141869,         \"Memory in Mb\": 2.478273391723633,         \"Time in s\": 201.76583       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8895027624309392,         \"F1\": 0.8684210526315789,         \"Memory in Mb\": 2.52436637878418,         \"Time in s\": 219.457713       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8918558077436582,         \"F1\": 0.8716323296354993,         \"Memory in Mb\": 2.5813236236572266,         \"Time in s\": 238.014124       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8914728682170543,         \"F1\": 0.8707692307692307,         \"Memory in Mb\": 2.6200389862060547,         \"Time in s\": 257.461391       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8898623279098874,         \"F1\": 0.8702064896755163,         \"Memory in Mb\": 2.657014846801758,         \"Time in s\": 277.779634       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8907766990291263,         \"F1\": 0.872159090909091,         \"Memory in Mb\": 2.706361770629883,         \"Time in s\": 298.980548       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8928150765606596,         \"F1\": 0.8741355463347164,         \"Memory in Mb\": 2.730466842651367,         \"Time in s\": 321.097396       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8958810068649885,         \"F1\": 0.8771929824561403,         \"Memory in Mb\": 2.753351211547852,         \"Time in s\": 344.186724       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8976640711902113,         \"F1\": 0.8786279683377309,         \"Memory in Mb\": 2.807779312133789,         \"Time in s\": 368.101507       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9004329004329005,         \"F1\": 0.8829516539440204,         \"Memory in Mb\": 2.8523120880126958,         \"Time in s\": 392.980624       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9009483667017912,         \"F1\": 0.8850855745721271,         \"Memory in Mb\": 2.913583755493164,         \"Time in s\": 418.83123200000006       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9024640657084188,         \"F1\": 0.8867699642431467,         \"Memory in Mb\": 2.943540573120117,         \"Time in s\": 445.632777       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9009009009009008,         \"F1\": 0.8850174216027874,         \"Memory in Mb\": 2.9903697967529297,         \"Time in s\": 473.399028       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8994140625,         \"F1\": 0.8836158192090395,         \"Memory in Mb\": 3.035707473754883,         \"Time in s\": 502.224676       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9008579599618683,         \"F1\": 0.8857142857142858,         \"Memory in Mb\": 3.069150924682617,         \"Time in s\": 532.049603       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9013035381750466,         \"F1\": 0.8869936034115138,         \"Memory in Mb\": 3.114839553833008,         \"Time in s\": 562.838704       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9035486806187444,         \"F1\": 0.8898128898128899,         \"Memory in Mb\": 3.132375717163086,         \"Time in s\": 594.67778       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.905693950177936,         \"F1\": 0.8933601609657947,         \"Memory in Mb\": 3.1889095306396484,         \"Time in s\": 627.518257       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9060052219321147,         \"F1\": 0.893491124260355,         \"Memory in Mb\": 3.220029830932617,         \"Time in s\": 661.4048929999999       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9045996592844976,         \"F1\": 0.8916827852998066,         \"Memory in Mb\": 3.270620346069336,         \"Time in s\": 696.4079739999999       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9040867389491244,         \"F1\": 0.8909952606635072,         \"Memory in Mb\": 3.311410903930664,         \"Time in s\": 732.4743999999998       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9044117647058824,         \"F1\": 0.8911627906976743,         \"Memory in Mb\": 3.344022750854492,         \"Time in s\": 769.4892029999999       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9047237790232184,         \"F1\": 0.8921124206708976,         \"Memory in Mb\": 3.391061782836914,         \"Time in s\": 807.5726659999999       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 2.745403       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 8.183125       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 16.539666       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 27.755785000000003       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 41.777067       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 58.637769000000006       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0440750122070312,         \"Time in s\": 78.268206       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686198515404,         \"F1\": 0.9090909090909092,         \"Memory in Mb\": 0.0923185348510742,         \"Time in s\": 101.443914       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998832184981898,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972318649291992,         \"Time in s\": 131.805417       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998948972620736,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972814559936523,         \"Time in s\": 169.246217       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999904452512899,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972814559936523,         \"Time in s\": 213.148727       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124151521788,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0972814559936523,         \"Time in s\": 263.357684       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999191527205108,         \"F1\": 0.9230769230769232,         \"Memory in Mb\": 0.0973081588745117,         \"Time in s\": 319.49775       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998873916144289,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1091413497924804,         \"Time in s\": 381.401703       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999894899103139,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1091642379760742,         \"Time in s\": 448.60874       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999014681249384,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1091642379760742,         \"Time in s\": 520.91477       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999072642967544,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1096677780151367,         \"Time in s\": 598.09858       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124164306776,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1113195419311523,         \"Time in s\": 680.064697       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999170262197146,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 766.82968       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999211750177356,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 858.2478070000001       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999924928682248,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 954.233503       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999283410963812,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 1054.7914       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999931456772071,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 1159.67034       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999343128024348,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1112737655639648,         \"Time in s\": 1268.4432900000002       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999369403455668,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1298818588256836,         \"Time in s\": 1381.268586       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999393657659116,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 0.1299276351928711,         \"Time in s\": 1498.4984390000002       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999941611521993,         \"F1\": 0.9032258064516128,         \"Memory in Mb\": 0.1434888839721679,         \"Time in s\": 1620.0599740000002       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999436968639152,         \"F1\": 0.9032258064516128,         \"Memory in Mb\": 0.1434888839721679,         \"Time in s\": 1745.8256190000002       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999456383865472,         \"F1\": 0.9032258064516128,         \"Memory in Mb\": 0.1440382003784179,         \"Time in s\": 1875.6542580000005       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248349068998,         \"F1\": 0.7619047619047621,         \"Memory in Mb\": 0.1476888656616211,         \"Time in s\": 2010.272303       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999830485489558,         \"F1\": 0.7619047619047621,         \"Memory in Mb\": 0.1510839462280273,         \"Time in s\": 2149.2802330000004       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998357829050004,         \"F1\": 0.7619047619047621,         \"Memory in Mb\": 0.1510610580444336,         \"Time in s\": 2292.3763450000006       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089111118188,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.1511411666870117,         \"Time in s\": 2439.4913830000005       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998145314601012,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.1534147262573242,         \"Time in s\": 2590.5360980000005       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998198306408024,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.1576833724975586,         \"Time in s\": 2745.6113380000006       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248354182784,         \"F1\": 0.75,         \"Memory in Mb\": 0.1762075424194336,         \"Time in s\": 2905.2725530000007       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998295696634,         \"F1\": 0.75,         \"Memory in Mb\": 0.1762075424194336,         \"Time in s\": 3069.514522000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99983405473428,         \"F1\": 0.75,         \"Memory in Mb\": 0.1762075424194336,         \"Time in s\": 3238.428366000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998383097984264,         \"F1\": 0.75,         \"Memory in Mb\": 0.1761388778686523,         \"Time in s\": 3411.935267000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99984235210657,         \"F1\": 0.75,         \"Memory in Mb\": 0.1760702133178711,         \"Time in s\": 3590.1136440000014       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998461972264232,         \"F1\": 0.75,         \"Memory in Mb\": 0.1782979965209961,         \"Time in s\": 3773.084966000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498592430404,         \"F1\": 0.75,         \"Memory in Mb\": 0.1782979965209961,         \"Time in s\": 3960.4867140000015       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998533509312216,         \"F1\": 0.75,         \"Memory in Mb\": 0.1782979965209961,         \"Time in s\": 4152.338698000001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998566839044082,         \"F1\": 0.75,         \"Memory in Mb\": 0.1783208847045898,         \"Time in s\": 4348.642178000001       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998598687437232,         \"F1\": 0.75,         \"Memory in Mb\": 0.1783208847045898,         \"Time in s\": 4549.410423000001       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999862915110182,         \"F1\": 0.75,         \"Memory in Mb\": 0.1783208847045898,         \"Time in s\": 4754.622131000001       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998546511627908,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.1783475875854492,         \"Time in s\": 4964.315109000001       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998576792967168,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.1972723007202148,         \"Time in s\": 5178.776489000001       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998605838320144,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.2118177413940429,         \"Time in s\": 5398.511461000001       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998633721846788,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.2117490768432617,         \"Time in s\": 5623.669278000001       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998633807997478,         \"F1\": 0.7346938775510204,         \"Memory in Mb\": 0.2117490768432617,         \"Time in s\": 5848.865968000001       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.5242718446601942,         \"Memory in Mb\": 0.0028944015502929,         \"Time in s\": 0.039715       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5330188679245284,         \"F1\": 0.5217391304347825,         \"Memory in Mb\": 0.0028944015502929,         \"Time in s\": 0.180531       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5188679245283019,         \"F1\": 0.5173501577287066,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 0.3338649999999999       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5330188679245284,         \"F1\": 0.5330188679245282,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 0.4937739999999999       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5207547169811321,         \"F1\": 0.5115384615384615,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 0.7446539999999999       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.5303514376996804,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 1.03169       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.522911051212938,         \"F1\": 0.512396694214876,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 1.379859       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5235849056603774,         \"F1\": 0.5061124694376529,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 1.737155       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5157232704402516,         \"F1\": 0.5,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 2.173505       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5160377358490567,         \"F1\": 0.4975514201762978,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 2.70321       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5154373927958834,         \"F1\": 0.495985727029438,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 3.270309       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165094339622641,         \"F1\": 0.4979591836734694,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 3.844268       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5195936139332366,         \"F1\": 0.4977238239757208,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 4.501151       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5195417789757413,         \"F1\": 0.4968242766407903,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 5.229491       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5226415094339623,         \"F1\": 0.4983476536682089,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 6.030342       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5194575471698113,         \"F1\": 0.4947303161810291,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 6.8837410000000006       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5205327413984462,         \"F1\": 0.4965034965034965,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 7.813207       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5193920335429769,         \"F1\": 0.4964305326743548,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 8.751116       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.519364448857994,         \"F1\": 0.4989648033126293,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 9.762632       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5174528301886793,         \"F1\": 0.4997555012224939,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 10.806008       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5197663971248877,         \"F1\": 0.5002337540906966,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 11.968014       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5175814751286449,         \"F1\": 0.4975435462259938,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 13.16512       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5176374077112387,         \"F1\": 0.4957118353344769,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 14.408045       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5196540880503144,         \"F1\": 0.5008169934640523,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 15.661105       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.520377358490566,         \"F1\": 0.5037094884810621,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 17.014893999999998       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.521044992743106,         \"F1\": 0.5041322314049587,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 18.454389       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5213137665967854,         \"F1\": 0.5032632342277013,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 19.942263       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5175202156334232,         \"F1\": 0.4985994397759103,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 21.473074       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5152895250487963,         \"F1\": 0.4969615124915597,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 23.106855       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5132075471698113,         \"F1\": 0.4931237721021611,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 24.747764       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5130858186244674,         \"F1\": 0.4927076727964489,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 26.464385       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5103183962264151,         \"F1\": 0.490959239963224,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 28.215584       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5091480846197828,         \"F1\": 0.4891401368640284,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 30.068049       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5097114317425083,         \"F1\": 0.4876775877065816,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 31.96012       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5118598382749326,         \"F1\": 0.4908630868709586,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 33.864206       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.510482180293501,         \"F1\": 0.4893384363039912,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 35.803291       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.50790413054564,         \"F1\": 0.485881726158764,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 37.844614       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.506454816285998,         \"F1\": 0.4844398340248962,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 39.968017       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5050798258345428,         \"F1\": 0.4828109201213346,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 42.128298       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5068396226415094,         \"F1\": 0.4848484848484848,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 44.30306       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5080533824206167,         \"F1\": 0.4858104858104858,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 46.485881       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5080862533692723,         \"F1\": 0.4847058823529412,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 48.746465       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5063624396665204,         \"F1\": 0.4837081229921982,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 51.058035       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5051457975986278,         \"F1\": 0.4829749103942652,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 53.475871000000005       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5048218029350104,         \"F1\": 0.482017543859649,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 55.900409       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5036915504511895,         \"F1\": 0.4802405498281787,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 58.409701000000005       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5038137294259334,         \"F1\": 0.4811083123425693,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 60.970617       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5029481132075472,         \"F1\": 0.4799506477483035,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 63.61249900000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5040431266846361,         \"F1\": 0.4810636583400483,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 66.28843800000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5064150943396226,         \"F1\": 0.4825949367088608,         \"Memory in Mb\": 0.0029211044311523,         \"Time in s\": 68.97313600000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9072847682119204,         \"F1\": 0.90561797752809,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 0.679052       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9166666666666666,         \"F1\": 0.8967874231032126,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 1.978643       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9175864606328182,         \"F1\": 0.898458748866727,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 3.929769       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9268763796909492,         \"F1\": 0.9098945936756204,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 6.478699       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9271523178807948,         \"F1\": 0.9076664801343034,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 9.702945       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9269683590875644,         \"F1\": 0.907437631149452,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 13.508006       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9274676758120468,         \"F1\": 0.9089108910891088,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 17.915655       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.925496688741722,         \"F1\": 0.9066390041493776,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 22.910275       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9251900907530046,         \"F1\": 0.9100294985250738,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 28.53571       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9266004415011038,         \"F1\": 0.9135128105085184,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 34.833569000000004       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9293598233995584,         \"F1\": 0.9182535996284256,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 41.779447000000005       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.931383370125092,         \"F1\": 0.9217208814270724,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 49.40882500000001       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9313975208014944,         \"F1\": 0.9218568665377176,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 57.61408300000001       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9290444654683064,         \"F1\": 0.9191665169750316,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 66.44160400000001       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9298013245033112,         \"F1\": 0.9209872453205236,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 75.85703300000002       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9305325607064018,         \"F1\": 0.9222213640225536,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 85.90938600000001       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9308531359563692,         \"F1\": 0.922279792746114,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 96.62160000000002       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9293598233995584,         \"F1\": 0.9203319502074688,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 107.965593       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9279656093877076,         \"F1\": 0.9176298658163944,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 119.984123       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9266004415011038,         \"F1\": 0.9160141449861076,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 132.63841200000002       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9265741616734994,         \"F1\": 0.915143048047136,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 146.00166900000002       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9262994180212724,         \"F1\": 0.915589266218468,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 159.938555       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.923217199347346,         \"F1\": 0.9122710823555212,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 174.54734100000002       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9225073583517291,         \"F1\": 0.9101955977189148,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 189.73533200000003       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9217218543046356,         \"F1\": 0.9087540528022232,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 205.619545       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9186619120393956,         \"F1\": 0.905063918343078,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 222.161849       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9173003025100156,         \"F1\": 0.902885123133791,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 239.399015       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9144985808893094,         \"F1\": 0.8997643144322751,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 257.26822000000004       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.91424982872802,         \"F1\": 0.8992622401073105,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 275.765482       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9138337012509198,         \"F1\": 0.89909521757863,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 294.848784       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9110232856227302,         \"F1\": 0.8955049132343716,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 314.617946       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9101476269315674,         \"F1\": 0.8940927755417328,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 335.076303       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9094922737306844,         \"F1\": 0.8931701539676272,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 356.063392       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9083235943383976,         \"F1\": 0.8913093680240166,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 377.566828       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9062125512456638,         \"F1\": 0.8888722815933038,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 399.67833300000007       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9052918812852588,         \"F1\": 0.8879294706671989,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 422.4669390000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9050474315374978,         \"F1\": 0.8877050626212737,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 445.7904010000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9050772626931568,         \"F1\": 0.8877901387172092,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 469.7481780000001       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904539536989868,         \"F1\": 0.8866104144955793,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 494.2109750000001       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9048013245033112,         \"F1\": 0.8860483551327785,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 519.3392310000002       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9045119259139612,         \"F1\": 0.8854217139903738,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 545.0643520000001       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9042625880374224,         \"F1\": 0.8846092933388237,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 571.4397520000001       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904409877303763,         \"F1\": 0.88502624266749,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 598.3711440000001       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904926750953241,         \"F1\": 0.8863636363636365,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 626.0397580000001       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9055187637969097,         \"F1\": 0.8878667908709827,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 654.3634580000002       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9061090315769268,         \"F1\": 0.8892285916489738,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 683.2521370000002       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9063923723639096,         \"F1\": 0.889810361032786,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 712.7682970000002       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9067098969830758,         \"F1\": 0.8902534693104661,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 742.9022130000002       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9062711177186106,         \"F1\": 0.8894732648019762,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 773.7300850000001       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064238410596026,         \"F1\": 0.8897844569823977,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 805.1132940000001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064265536723164,         \"F1\": 0.8897670549084858,         \"Memory in Mb\": 0.0043582916259765,         \"Time in s\": 836.4982200000001       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.56,         \"F1\": 0.5217391304347826,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.003459       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7,         \"F1\": 0.6341463414634146,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.050212       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7066666666666667,         \"F1\": 0.676470588235294,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.100001       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.72,         \"F1\": 0.702127659574468,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.153128       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.72,         \"F1\": 0.7058823529411765,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.228063       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7133333333333334,         \"F1\": 0.7189542483660132,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.334445       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7314285714285714,         \"F1\": 0.718562874251497,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.511774       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.735,         \"F1\": 0.7225130890052356,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.692607       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7244444444444444,         \"F1\": 0.701923076923077,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 0.876779       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.724,         \"F1\": 0.7038626609442059,         \"Memory in Mb\": 0.0043668746948242,         \"Time in s\": 1.156005       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7345454545454545,         \"F1\": 0.7137254901960783,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 1.438242       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7366666666666667,         \"F1\": 0.7127272727272725,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 1.723391       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7476923076923077,         \"F1\": 0.7172413793103447,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 2.078902       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7542857142857143,         \"F1\": 0.7225806451612904,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 2.437997       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7573333333333333,         \"F1\": 0.723404255319149,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 2.800319       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.7257142857142856,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 3.165567       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.7197802197802199,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 3.585508999999999       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7622222222222222,         \"F1\": 0.7206266318537858,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 4.009149999999999       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7663157894736842,         \"F1\": 0.7272727272727272,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 4.435539999999999       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.768,         \"F1\": 0.7327188940092165,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 4.959426999999999       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7714285714285715,         \"F1\": 0.7321428571428573,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 5.485955999999999       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7709090909090909,         \"F1\": 0.7341772151898734,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 6.028689999999999       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7739130434782608,         \"F1\": 0.7379032258064516,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 6.595545999999999       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.78,         \"F1\": 0.7401574803149605,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 7.165257999999999       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7744,         \"F1\": 0.7314285714285715,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 7.741693999999999       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7815384615384615,         \"F1\": 0.7427536231884059,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 8.363988999999998       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837037037037037,         \"F1\": 0.75,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 9.010548999999996       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.79,         \"F1\": 0.7545909849749582,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 9.660288999999995       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7917241379310345,         \"F1\": 0.7606973058637084,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 10.349524999999996       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.792,         \"F1\": 0.7621951219512195,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 11.041959999999996       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.792258064516129,         \"F1\": 0.7614814814814814,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 11.737615999999996       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.795,         \"F1\": 0.7670454545454546,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 12.526707999999996       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.793939393939394,         \"F1\": 0.7671232876712327,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 13.319008999999996       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7976470588235294,         \"F1\": 0.7706666666666667,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 14.117527999999997       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8022857142857143,         \"F1\": 0.7744458930899608,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 14.959668999999996       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8011111111111111,         \"F1\": 0.7737041719342603,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 15.804425999999996       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8054054054054054,         \"F1\": 0.7804878048780488,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 16.651646999999997       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8073684210526316,         \"F1\": 0.7849588719153936,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 17.502014999999997       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8102564102564103,         \"F1\": 0.7880870561282932,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 18.418237999999995       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.811,         \"F1\": 0.7892976588628764,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 19.337672999999995       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8146341463414634,         \"F1\": 0.7943722943722944,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 20.260238999999995       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8161904761904762,         \"F1\": 0.7970557308096741,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 21.242111999999995       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.815813953488372,         \"F1\": 0.7983706720977597,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 22.243638999999995       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8190909090909091,         \"F1\": 0.8023833167825224,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 23.247954999999997       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8213333333333334,         \"F1\": 0.8061716489874637,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 24.273946999999996       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8226086956521739,         \"F1\": 0.8071833648393195,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 25.351544999999994       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8212765957446808,         \"F1\": 0.8059149722735675,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 26.431981999999994       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8233333333333334,         \"F1\": 0.8076225045372051,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 27.515220999999997       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8244897959183674,         \"F1\": 0.8088888888888888,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 28.636604999999992       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8256,         \"F1\": 0.810763888888889,         \"Memory in Mb\": 0.0045804977416992,         \"Time in s\": 29.761263999999997       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.720966894377299,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1.027868       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7769311613242249,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 3.106358       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7509196006305833,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 6.233245       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7900683131897005,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 10.302873       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7826589595375723,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 15.393504       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7699246803293046,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 21.578682       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7722393213722694,         \"F1\": 0.0,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 28.779608       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7791644771413557,         \"F1\": 0.0041469194312796,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 37.113003       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.783207800548841,         \"F1\": 0.004824443848834,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 46.3898       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7891224382553862,         \"F1\": 0.0044653932026792,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 56.715322       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7832131084889887,         \"F1\": 0.0039508340649692,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 68.217717       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7821422315641969,         \"F1\": 0.0036050470658922,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 80.77427399999999       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7877440478596548,         \"F1\": 0.0034162080091098,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 94.432579       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.78188574431349,         \"F1\": 0.0034299434059338,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 109.06303       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7857418111753371,         \"F1\": 0.0032594524119947,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 124.719605       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7871452968996322,         \"F1\": 0.0030764497769573,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 141.369597       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7866835646502427,         \"F1\": 0.0028897558156335,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 159.093537       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7860979739592456,         \"F1\": 0.002722199537226,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 177.829964       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7771939043615345,         \"F1\": 0.0024764735017335,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 197.576045       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7831581713084603,         \"F1\": 0.0024175027196905,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 218.31617       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.779496033831294,         \"F1\": 0.0022644927536231,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 240.067789       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7831175655663307,         \"F1\": 0.0021978021978021,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 262.83473100000003       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7791130708949257,         \"F1\": 0.0020644095788604,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 286.65613700000006       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7808066211245402,         \"F1\": 0.0019938191606021,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 311.45044200000007       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7799684708355229,         \"F1\": 0.001906941266209,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 337.28748800000005       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7778810784591131,         \"F1\": 0.0018165304268846,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 364.149506       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7807944570950351,         \"F1\": 0.0021263400372109,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 392.065506       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7777193904361535,         \"F1\": 0.0020222446916076,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 421.04388200000005       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7785891604906953,         \"F1\": 0.0019603038470963,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 451.0228320000001       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7758801891749869,         \"F1\": 0.0026502455374542,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 482.06238400000007       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.774159646059702,         \"F1\": 0.0025454817698585,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 514.138052       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7746157383079348,         \"F1\": 0.0024711098190275,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 547.227498       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7704899759550311,         \"F1\": 0.0023534297778085,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 581.3499069999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.771274458285679,         \"F1\": 0.002292186341266,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 616.580127       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7721942797087306,         \"F1\": 0.0022358124547905,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 652.800029       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7705085537455479,         \"F1\": 0.0024111675126903,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 690.043858       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7685872945988553,         \"F1\": 0.0023267205486162,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 728.276014       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7687999557485411,         \"F1\": 0.0022677090171271,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 767.526171       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7657140547313958,         \"F1\": 0.0021806496040399,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 807.759105       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7665002627430373,         \"F1\": 0.0021333932180552,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 848.897716       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7657101111210797,         \"F1\": 0.0020744622775412,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 890.953712       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7636313590070816,         \"F1\": 0.0020073956682514,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 933.942273       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7647777682728617,         \"F1\": 0.0019703411801306,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 977.888636       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7652868676252806,         \"F1\": 0.0019298156518206,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1022.739679       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7642552694575816,         \"F1\": 0.0018787699001285,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1068.558668       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7644680024674998,         \"F1\": 0.001839659178931,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1115.337297       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7635312664214399,         \"F1\": 0.0018876828692779,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1162.988807       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7650091960063058,         \"F1\": 0.0018600325505696,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1211.563326       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7647859984771628,         \"F1\": 0.001820415965048,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1260.984755       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7649710982658959,         \"F1\": 0.0017854751595768,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1311.295723       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"ALMA\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.7649859178611963,         \"F1\": 0.0017854751595768,         \"Memory in Mb\": 0.0030937194824218,         \"Time in s\": 1361.607838       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5283018867924528,         \"F1\": 0.4680851063829788,         \"Memory in Mb\": 0.0055513381958007,         \"Time in s\": 0.50714       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5377358490566038,         \"F1\": 0.4673913043478261,         \"Memory in Mb\": 0.0055513381958007,         \"Time in s\": 1.544995       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5345911949685535,         \"F1\": 0.4861111111111111,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 3.028568       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5188679245283019,         \"F1\": 0.4659685863874345,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 4.996646       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5264150943396226,         \"F1\": 0.4256292906178489,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 7.439068000000001       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5235849056603774,         \"F1\": 0.3878787878787879,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 10.329429       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5363881401617251,         \"F1\": 0.3629629629629629,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 13.751562000000002       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5400943396226415,         \"F1\": 0.3389830508474576,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 17.710995       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5440251572327044,         \"F1\": 0.3149606299212598,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 22.189814       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5518867924528302,         \"F1\": 0.2962962962962963,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 27.154882       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5523156089193825,         \"F1\": 0.2790055248618784,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 32.629664       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5542452830188679,         \"F1\": 0.2758620689655172,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 38.64774       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5566037735849056,         \"F1\": 0.2611850060459492,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 45.182197       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.557277628032345,         \"F1\": 0.2474226804123711,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 52.237503       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5578616352201258,         \"F1\": 0.2350380848748639,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 59.74126700000001       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5595518867924528,         \"F1\": 0.2259067357512953,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 67.72058500000001       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5566037735849056,         \"F1\": 0.2158979391560353,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 76.17010700000002       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5545073375262054,         \"F1\": 0.2115027829313543,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 85.08376800000002       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5496524329692155,         \"F1\": 0.2008810572687224,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 94.42289500000004       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5466981132075471,         \"F1\": 0.1944677284157585,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 104.22299500000004       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.550314465408805,         \"F1\": 0.2036595067621321,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 114.52672400000004       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5493138936535163,         \"F1\": 0.2103681442524417,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 125.29025300000004       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5479901558654635,         \"F1\": 0.21173104434907,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 136.49837800000003       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5483490566037735,         \"F1\": 0.2262626262626262,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 148.22051100000004       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460377358490566,         \"F1\": 0.2322910019144863,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 160.35879600000004       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5395500725689405,         \"F1\": 0.2304426925409338,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 173.00042100000005       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5394828791055206,         \"F1\": 0.2310385064177363,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 186.18470300000004       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5411051212938005,         \"F1\": 0.2305084745762711,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 199.85251500000004       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5396877033181522,         \"F1\": 0.227198252321136,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 214.04388100000003       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5430817610062894,         \"F1\": 0.2283590015932023,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 228.72034700000003       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5444309190505173,         \"F1\": 0.2247540134645261,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 243.83191300000004       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5445165094339622,         \"F1\": 0.224786753637732,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 259.480064       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5463121783876501,         \"F1\": 0.2201474201474201,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 275.54602900000003       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.548834628190899,         \"F1\": 0.2167630057803468,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 292.10262700000004       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.547978436657682,         \"F1\": 0.2123062470643494,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 309.14156900000006       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5474318658280922,         \"F1\": 0.2074346030289123,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 326.6693460000001       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5484446710861806,         \"F1\": 0.2033288349077822,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 344.5959390000001       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5489076464746773,         \"F1\": 0.1992066989863376,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 363.0622570000001       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5491049830672472,         \"F1\": 0.1951640759930915,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 382.0101650000001       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5483490566037735,         \"F1\": 0.1909590198563582,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 401.4423870000001       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.548550391164289,         \"F1\": 0.1885856079404466,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 421.3360060000001       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.550763701707098,         \"F1\": 0.1935483870967742,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 441.6814400000001       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5482667836770513,         \"F1\": 0.1934978456717587,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 462.3969810000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5490994854202401,         \"F1\": 0.1976344906524227,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 483.6474930000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.550104821802935,         \"F1\": 0.1998508575689783,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 505.4107300000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5504511894995898,         \"F1\": 0.2,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 527.6413840000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5503813729425934,         \"F1\": 0.2062367115520907,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 550.3366250000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5479559748427673,         \"F1\": 0.2041522491349481,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 573.5279610000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5462071621101271,         \"F1\": 0.2023688663282571,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 597.2511250000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5464150943396227,         \"F1\": 0.205026455026455,         \"Memory in Mb\": 0.0055780410766601,         \"Time in s\": 621.4261850000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8002207505518764,         \"F1\": 0.7868080094228505,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4.395754       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8140176600441501,         \"F1\": 0.7501853224610822,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 13.314942       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8005886681383371,         \"F1\": 0.7262626262626262,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 26.594138       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8189845474613686,         \"F1\": 0.7586460632818247,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 44.068779       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8278145695364238,         \"F1\": 0.7588126159554731,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 65.924464       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8211920529801324,         \"F1\": 0.7498713329902212,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 92.07692       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8222958057395143,         \"F1\": 0.7575822757582275,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 122.546282       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8253311258278145,         \"F1\": 0.7598634294385433,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 157.279906       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8303899926416483,         \"F1\": 0.780789348549691,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 196.124663       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8364238410596027,         \"F1\": 0.7958677685950413,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 238.938569       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8371462974111981,         \"F1\": 0.8011273128293102,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 285.711115       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8393119941133186,         \"F1\": 0.8079586676926458,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 336.134661       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8422482594668025,         \"F1\": 0.8114088509947219,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 390.124895       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8409019236833807,         \"F1\": 0.810445237647943,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 447.475874       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8427520235467255,         \"F1\": 0.8154098643862832,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 507.886031       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8438189845474614,         \"F1\": 0.8177720540888602,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 571.200551       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.845214907154915,         \"F1\": 0.8184587267742918,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 637.3575030000001       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397105714986509,         \"F1\": 0.8108264582428716,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 706.2352450000001       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8384454513767864,         \"F1\": 0.8053202660133008,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 777.6642180000001       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.840728476821192,         \"F1\": 0.8082646824342281,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 851.7523750000001       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.843950383685483,         \"F1\": 0.8100326316462987,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 928.4036970000002       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8412101143889223,         \"F1\": 0.8075636894266431,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1007.5500400000002       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8373644303675977,         \"F1\": 0.8028848950154133,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1089.2694250000002       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8382542310522443,         \"F1\": 0.8008155405788072,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1173.5296240000002       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8376600441501104,         \"F1\": 0.7982441700960219,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1260.4177460000003       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8337578536254033,         \"F1\": 0.7924748277689453,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1349.7468710000005       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8313302264737144,         \"F1\": 0.7887569117345894,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1441.5955620000002       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8278539892778304,         \"F1\": 0.7842711060613546,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1535.8662290000002       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8282712948161681,         \"F1\": 0.784486052732136,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1632.5910050000002       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8285504047093452,         \"F1\": 0.785431439359057,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1731.7068200000003       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.825357829523606,         \"F1\": 0.7809192013935414,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1833.2277320000003       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8246412803532008,         \"F1\": 0.7785135488368041,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 1936.9708820000003       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8228644056458626,         \"F1\": 0.7766343315056937,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2042.9068730000004       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8227827554863005,         \"F1\": 0.775599128540305,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2150.889688       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8180384736676127,         \"F1\": 0.7686632988533396,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2260.9522580000003       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8156119695854795,         \"F1\": 0.765426320305796,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2373.012802       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8136746017540719,         \"F1\": 0.7636955205811137,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2487.1515040000004       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8108516323922389,         \"F1\": 0.7597934341571375,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2603.2748090000005       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.811031867323258,         \"F1\": 0.7582811425261557,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2721.3856460000006       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8123344370860928,         \"F1\": 0.7585643792821898,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2841.498866000001       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8119312981209282,         \"F1\": 0.7567887480852249,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 2963.634415000001       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8118364343529907,         \"F1\": 0.7562636165577343,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3087.755988000001       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8128497356127111,         \"F1\": 0.7583601232890332,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3213.7594090000007       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8136162954043749,         \"F1\": 0.7616297722168751,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3341.6530200000007       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8154034829531518,         \"F1\": 0.7662732919254659,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3471.3422760000008       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8169929935694404,         \"F1\": 0.7702641645832705,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3602.9648230000007       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8180216993095675,         \"F1\": 0.7720681236579698,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3737.0799900000006       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8185936350257542,         \"F1\": 0.7730894238789657,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 3873.415774000001       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8179708969680587,         \"F1\": 0.7710051290770494,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4011.612716000001       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8190949227373069,         \"F1\": 0.7729350807680585,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4151.679177000001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8190986935028248,         \"F1\": 0.7728922505749037,         \"Memory in Mb\": 0.0068016052246093,         \"Time in s\": 4291.771713000001       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.68,         \"F1\": 0.6923076923076923,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 0.149754       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 0.457736       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8266666666666667,         \"F1\": 0.8219178082191781,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 0.892069       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.83,         \"F1\": 0.8210526315789473,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 1.419094       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.816,         \"F1\": 0.8067226890756303,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 2.091236       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.82,         \"F1\": 0.8187919463087249,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 2.916232       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8285714285714286,         \"F1\": 0.8170731707317075,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 3.840025       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.825,         \"F1\": 0.8128342245989306,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 4.910046       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8222222222222222,         \"F1\": 0.8058252427184465,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 6.121922       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.824,         \"F1\": 0.8103448275862069,         \"Memory in Mb\": 0.0068025588989257,         \"Time in s\": 7.479490999999999       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8254545454545454,         \"F1\": 0.8110236220472441,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 8.920382       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8366666666666667,         \"F1\": 0.8191881918819188,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 10.509974       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8461538461538461,         \"F1\": 0.8251748251748252,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 12.191812       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8514285714285714,         \"F1\": 0.8289473684210525,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 13.999137       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8506666666666667,         \"F1\": 0.8271604938271606,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 15.959285       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8525,         \"F1\": 0.8269794721407624,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 18.058664       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8564705882352941,         \"F1\": 0.828169014084507,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 20.312993       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.86,         \"F1\": 0.8301886792452831,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 22.675489       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8589473684210527,         \"F1\": 0.830379746835443,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 25.19503       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.858,         \"F1\": 0.8329411764705883,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 27.784241       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8283752860411898,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 30.514065       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8618181818181818,         \"F1\": 0.8354978354978354,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 33.400870999999995       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8626086956521739,         \"F1\": 0.8364389233954452,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 36.397646       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8666666666666667,         \"F1\": 0.8387096774193549,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 39.57675499999999       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672,         \"F1\": 0.8362919132149901,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 42.828696       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8707692307692307,         \"F1\": 0.8432835820895522,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 46.253182       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8725925925925926,         \"F1\": 0.8485915492957746,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 49.816151       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8771428571428571,         \"F1\": 0.8522336769759451,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 53.516545       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8786206896551724,         \"F1\": 0.8566775244299674,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 57.35818       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88,         \"F1\": 0.8589341692789968,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 61.281034000000005       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8812903225806452,         \"F1\": 0.8597560975609757,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 65.347537       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88125,         \"F1\": 0.8613138686131386,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 69.566336       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8812121212121212,         \"F1\": 0.8623595505617978,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 73.91498000000001       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8823529411764706,         \"F1\": 0.8630136986301369,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 78.39968800000001       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8857142857142857,         \"F1\": 0.8663101604278075,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 83.02084100000002       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844444444444445,         \"F1\": 0.8645833333333334,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 87.71921500000002       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8864864864864865,         \"F1\": 0.8682559598494354,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 92.55798800000002       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8863157894736842,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 97.51738800000004       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8871794871794871,         \"F1\": 0.8702830188679245,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 102.59954100000004       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.888,         \"F1\": 0.871264367816092,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 107.87282600000005       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878048780487805,         \"F1\": 0.8715083798882682,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 113.28564700000004       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8895238095238095,         \"F1\": 0.8739130434782609,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 118.79277100000004       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8883720930232558,         \"F1\": 0.8736842105263158,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 124.46348200000004       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89,         \"F1\": 0.8756423432682425,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 130.26843700000003       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8915555555555555,         \"F1\": 0.8784860557768924,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 136.21796400000002       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8913043478260869,         \"F1\": 0.878048780487805,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 142.31432400000003       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8902127659574468,         \"F1\": 0.876555023923445,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 148.52290000000002       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8908333333333334,         \"F1\": 0.8769953051643193,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 154.887447       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8914285714285715,         \"F1\": 0.8776448942042319,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 161.410896       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896,         \"F1\": 0.8761220825852785,         \"Memory in Mb\": 0.0070161819458007,         \"Time in s\": 167.984219       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.996847083552286,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 9.012274       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9984235417761428,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 26.992092       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.998949027850762,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 53.749217       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992117708880714,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 89.545782       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993694167104572,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 133.365466       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474513925381,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 185.067425       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995495833646124,         \"F1\": 0.0,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 243.739666       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774566473988,         \"F1\": 0.5217391304347826,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 308.57406100000003       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993577392421324,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 378.971453       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999421965317919,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 454.798992       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474513925381,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 535.937031       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995183044315992,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 622.51799       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995553579368608,         \"F1\": 0.5925925925925927,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 714.221122       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995495833646124,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 811.098386       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796111403048,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 912.878884       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058854440356,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1019.269091       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99962906865321,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1129.962426       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999649675950254,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1244.872652       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996681140581354,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1363.91764       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847083552286,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1487.072194       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997222430748,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1614.171257       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999713371232026,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1745.093316       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997258333523726,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 1880.714485       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2020.289668       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997477666841827,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2163.67936       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997574679655604,         \"F1\": 0.5714285714285714,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2310.817497       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997275257390864,         \"F1\": 0.5333333333333333,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2461.472155       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.5333333333333333,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2615.677493       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997463170674252,         \"F1\": 0.5333333333333333,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2773.449537       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999597127342792,         \"F1\": 0.4102564102564102,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 2934.793173       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99961012323496,         \"F1\": 0.4102564102564102,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3099.649087       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996223068838676,         \"F1\": 0.4102564102564102,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3268.0999800000004       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996019044889248,         \"F1\": 0.3902439024390244,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3440.0722140000003       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996136131804272,         \"F1\": 0.3902439024390244,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3615.503218       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996246528038436,         \"F1\": 0.3902439024390244,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3794.437889       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058854440356,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 3977.094501000001       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996165371887916,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4163.174128000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996266283154024,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4352.606393000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996362019483408,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4545.416162000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999645296899632,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4741.562007000001       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999653948194763,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 4941.079189000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996621875234591,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5143.951781000001       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996700436275648,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5350.231536       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996775426360291,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5559.929647       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847083552286,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5773.003953       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996915625214192,         \"F1\": 0.3720930232558139,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 5989.467769000001       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996869444661844,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6209.264779000001       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934664564724,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6432.452666000001       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997222430748,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6658.918178000001       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057277982132,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 6888.546542000001       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"sklearn SGDClassifier\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057463533566,         \"F1\": 0.3636363636363636,         \"Memory in Mb\": 0.0057430267333984,         \"Time in s\": 7118.179378000001       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.16057       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5283018867924528,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.37729       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5314465408805031,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.7064710000000001       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5400943396226415,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.0774430000000002       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5547169811320755,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.492379       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5550314465408805,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.9966470000000005       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5660377358490566,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.539797       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5636792452830188,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.1757850000000003       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5649895178197065,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.855114       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5707547169811321,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.635951       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5686106346483705,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 5.458947       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5644654088050315,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 6.34328       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5682148040638607,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.308669       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5680592991913747,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 8.359952       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5679245283018868,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 9.451883       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5683962264150944,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 10.590847       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5643729189789123,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 11.83715       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.560272536687631,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 13.126962       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5551142005958292,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 14.497203999999998       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5509433962264151,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.938437999999998       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5512129380053908,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 17.424999       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5506003430531733,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 19.022886       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.551681706316653,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 20.666828       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5487421383647799,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 22.355416       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5467924528301886,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 24.051772       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5471698113207547,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 25.858309       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5489168413696716,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 27.751459       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5505390835579514,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 29.665552       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5487963565387117,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 31.686176       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5509433962264151,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 33.740652       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5517346317711503,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 35.89104       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5498231132075472,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 38.079414       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5514579759862779,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 40.353903       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5535516093229744,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 42.668922       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5522911051212938,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 45.086801       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5516247379454927,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 47.540759       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5525242223355431,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 50.094246       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5528798411122146,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 52.697211       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5529753265602322,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 55.369587       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5523584905660377,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 58.109436       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5526921306948919,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 60.894093       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5530098831985625,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 63.717346       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5508995173321632,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 66.643891       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5497427101200686,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 69.6601       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5505241090146751,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 72.725555       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5518867924528302,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 75.798736       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5509835407466881,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 78.970205       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5511006289308176,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 82.150165       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5514054678475163,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 85.416127       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5513207547169812,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 88.72481       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6799116997792495,         \"F1\": 0.5482866043613708,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.820242       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7190949227373068,         \"F1\": 0.4904904904904904,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.329863       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6986754966887417,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.585071       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7047461368653422,         \"F1\": 0.4478844169246646,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.424633       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7024282560706402,         \"F1\": 0.4118673647469459,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 10.992865       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7041942604856513,         \"F1\": 0.4165457184325108,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.263433       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6986754966887417,         \"F1\": 0.4048582995951417,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 20.287068       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.695364238410596,         \"F1\": 0.3953997809419496,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 26.004014       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6873926907039489,         \"F1\": 0.4084474355999072,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 32.433812       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6864238410596026,         \"F1\": 0.4240827082911007,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 39.59982599999999       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.687537627934979,         \"F1\": 0.4433321415802646,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 47.447315       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6938925680647535,         \"F1\": 0.4717460317460317,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 55.964905       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6932416369502462,         \"F1\": 0.4715518502267076,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 65.217817       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6944970040996531,         \"F1\": 0.4755717959128434,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 75.258293       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6942604856512141,         \"F1\": 0.4842993670100534,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 85.993354       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6935016556291391,         \"F1\": 0.4860613071139387,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 97.389046       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6929619529931178,         \"F1\": 0.480957084842498,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 109.49806       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6904586705911209,         \"F1\": 0.4713028906577293,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 122.273049       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6921691646334379,         \"F1\": 0.4645852278468223,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 135.723897       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.694205298013245,         \"F1\": 0.4685911575716888,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 150.008391       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6967307894460212,         \"F1\": 0.467515688445921,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 164.94785199999998       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6958157736303432,         \"F1\": 0.4737435986459509,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 180.578389       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6933966791438718,         \"F1\": 0.4696604963891426,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 196.972492       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6968359087564385,         \"F1\": 0.4670978172999191,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 214.037594       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6977041942604857,         \"F1\": 0.4643667370726746,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 231.758696       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6952368823229751,         \"F1\": 0.4573285962657797,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 250.247632       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6978170223203336,         \"F1\": 0.4597281099254495,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 269.425119       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6976505834121728,         \"F1\": 0.4612250632200056,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 289.286378       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6983329527289336,         \"F1\": 0.4614757439869547,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 309.833587       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6959896983075791,         \"F1\": 0.4576304561864129,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 331.075152       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.695649077832372,         \"F1\": 0.4559572301425662,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 353.085242       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6952262693156733,         \"F1\": 0.4515207945375543,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 375.713328       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6939260151180681,         \"F1\": 0.4465678863017841,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 399.011341       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6941630957018569,         \"F1\": 0.4433020150091591,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 423.007885       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6917376222011984,         \"F1\": 0.4368266405484819,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 447.820686       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6893549178317391,         \"F1\": 0.4316805025802109,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 473.259187       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.688353916830738,         \"F1\": 0.4290944860374884,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 499.4094079999999       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6863599395840595,         \"F1\": 0.4245363461948412,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 526.1781749999999       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6869304352748061,         \"F1\": 0.4212013394725827,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 553.6489789999999       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6911147902869758,         \"F1\": 0.4267718148299877,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 581.8684239999999       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6919722177354224,         \"F1\": 0.4269831730769231,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 610.7708879999999       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6944181646168401,         \"F1\": 0.431171118285882,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 640.2659799999999       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6937727809435803,         \"F1\": 0.4308206106870229,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 670.4759659999999       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6930814770218744,         \"F1\": 0.4344288818009522,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 701.3646249999998       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6924208977189109,         \"F1\": 0.4391771019677997,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 733.0001779999998       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6933966791438718,         \"F1\": 0.4472227028897733,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 765.3741459999998       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6956225635244939,         \"F1\": 0.4550767290309018,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 798.4329459999998       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6962150478292862,         \"F1\": 0.4576097220511558,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 832.1587539999998       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6963103122043519,         \"F1\": 0.4557564992733731,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 866.6068249999998       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.697439293598234,         \"F1\": 0.4596278189560006,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 901.80814       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.6974752824858758,         \"F1\": 0.4595915792793503,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 937.011341       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.52,         \"F1\": 0.3333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.00395       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.56,         \"F1\": 0.2142857142857142,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.0784219999999999       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5866666666666667,         \"F1\": 0.3404255319148936,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.1562489999999999       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6,         \"F1\": 0.375,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.2373179999999999       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.64,         \"F1\": 0.4705882352941176,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.4181319999999999       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.62,         \"F1\": 0.4466019417475728,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.6021909999999999       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6342857142857142,         \"F1\": 0.4181818181818181,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 0.7890869999999999       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.63,         \"F1\": 0.4126984126984127,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.012096       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6488888888888888,         \"F1\": 0.4316546762589928,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.2378889999999998       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.648,         \"F1\": 0.4358974358974359,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.4692909999999997       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6618181818181819,         \"F1\": 0.4561403508771929,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.7531039999999996       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6733333333333333,         \"F1\": 0.4615384615384615,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.040874       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.683076923076923,         \"F1\": 0.4663212435233161,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.33165       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6942857142857143,         \"F1\": 0.4780487804878048,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 2.715045       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7013333333333334,         \"F1\": 0.4909090909090909,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.101519       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.705,         \"F1\": 0.4913793103448276,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.491013       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7105882352941176,         \"F1\": 0.4896265560165975,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.92315       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7222222222222222,         \"F1\": 0.5098039215686275,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.358171       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7157894736842105,         \"F1\": 0.5054945054945055,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 4.796033       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.718,         \"F1\": 0.5252525252525252,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 5.248043999999999       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7257142857142858,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 5.702586999999999       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7218181818181818,         \"F1\": 0.5233644859813085,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 6.159983       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7217391304347827,         \"F1\": 0.5209580838323353,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 6.620335       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7283333333333334,         \"F1\": 0.5275362318840581,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.113508       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7376,         \"F1\": 0.5340909090909091,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.613357       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7369230769230769,         \"F1\": 0.5415549597855228,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 8.116107       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7333333333333333,         \"F1\": 0.5477386934673367,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 8.713363       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.74,         \"F1\": 0.5560975609756097,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 9.313647       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.743448275862069,         \"F1\": 0.5753424657534246,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 9.917033       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7453333333333333,         \"F1\": 0.5820568927789934,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 10.613639       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7470967741935484,         \"F1\": 0.5847457627118644,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 11.313363       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.74625,         \"F1\": 0.5915492957746479,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 12.015877       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7490909090909091,         \"F1\": 0.602687140115163,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 12.766805       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7541176470588236,         \"F1\": 0.6122448979591837,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 13.520246       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7554285714285714,         \"F1\": 0.6123188405797102,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 14.2887       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7566666666666667,         \"F1\": 0.6123893805309735,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.059985       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.76,         \"F1\": 0.6237288135593221,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 15.877357000000002       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7589473684210526,         \"F1\": 0.6288492706645057,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 16.697524       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7610256410256411,         \"F1\": 0.631911532385466,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 17.562951       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.761,         \"F1\": 0.6328725038402457,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 18.43148       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7609756097560976,         \"F1\": 0.635958395245171,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 19.325245       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7638095238095238,         \"F1\": 0.6436781609195402,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 20.222005       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7665116279069767,         \"F1\": 0.651872399445215,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 21.121639       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.77,         \"F1\": 0.6594885598923284,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 22.071389       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.768,         \"F1\": 0.6597131681877444,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 23.024294       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7695652173913043,         \"F1\": 0.6615581098339719,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 23.980116       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7702127659574468,         \"F1\": 0.6633416458852868,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 24.939110000000003       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7741666666666667,         \"F1\": 0.6691086691086692,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 25.901192       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7771428571428571,         \"F1\": 0.6746126340882003,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 26.865956       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7736,         \"F1\": 0.6697782963827306,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 27.833412       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1.287853       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 3.780599       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 7.576109       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 12.534125       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 18.771881       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 26.322128       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 35.214625       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774566473988,         \"F1\": 0.0,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 45.441498       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999299351900508,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 56.927386       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993694167104572,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 69.74153799999999       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999426742464052,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 83.765543       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474513925381,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 99.065492       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999514935931121,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 115.581943       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995120486449968,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 133.361343       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995445787353302,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 152.36548100000002       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999573042564372,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 172.57866       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995981577076444,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 193.969825       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996204822794418,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 216.600052       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996404568963132,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 240.511883       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996584340514976,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 265.709607       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996746990966644,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 292.142637       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996894855013616,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 319.878347       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999702986131737,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 348.79444399999994       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997153617095814,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 378.9697039999999       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999726747241198,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 410.4053809999999       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372569626904,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 443.057614       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997080632918784,         \"F1\": 0.1176470588235294,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 477.025323       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997184896028828,         \"F1\": 0.1176470588235294,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 512.247669       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997281968579556,         \"F1\": 0.1176470588235294,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 548.612896       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796111403048,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 586.233337       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995931720712626,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 625.057298       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058854440356,         \"F1\": 0.1428571428571428,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 665.0820319999999       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999585980668482,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 706.1803269999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995981577076444,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 748.3509649999999       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996096389159972,         \"F1\": 0.1333333333333333,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 791.6670189999999       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912886086296,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 836.0877649999999       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996023348624504,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 881.7116259999999       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127997344912,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 928.538605       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996227279464274,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 976.409207       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321597477666,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1025.3623619999998       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996411314612358,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1075.4142359999998       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999649675950254,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1126.58116       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996578230211784,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1178.778759       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999665599770697,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1231.992839       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996730308869036,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1286.303482       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801389111014,         \"F1\": 0.125,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1341.617815       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996757639114052,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1397.839199       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996825188299177,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1455.075933       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996889980374704,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1513.261041       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999695218076721,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1572.312523       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Vowpal Wabbit logistic regression\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999695237294548,         \"F1\": 0.1212121212121212,         \"Memory in Mb\": 0.0006465911865234,         \"Time in s\": 1631.370354       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5333333333333333,         \"F1\": 0.4615384615384615,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.089081       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5592417061611374,         \"F1\": 0.5026737967914437,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.244558       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.555205047318612,         \"F1\": 0.5154639175257733,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.453937       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5626477541371159,         \"F1\": 0.5066666666666667,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 0.76271       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5689981096408318,         \"F1\": 0.4818181818181818,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 1.109688       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5716535433070866,         \"F1\": 0.4645669291338582,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 1.619873       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5870445344129555,         \"F1\": 0.4555160142348755,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 2.197835       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5962219598583235,         \"F1\": 0.4554140127388535,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 2.834188       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6002098635886673,         \"F1\": 0.4454148471615721,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 3.5547570000000004       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6090651558073654,         \"F1\": 0.4405405405405405,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 4.339157       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6068669527896996,         \"F1\": 0.4260651629072681,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 5.220598       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6136900078678206,         \"F1\": 0.433679354094579,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 6.139897       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6143790849673203,         \"F1\": 0.419672131147541,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 7.157157999999999       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6142953472690492,         \"F1\": 0.4127310061601643,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 8.301379999999998       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6135934550031467,         \"F1\": 0.4061895551257253,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 9.487101       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6141592920353982,         \"F1\": 0.4010989010989011,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 10.730798       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.614658523042754,         \"F1\": 0.4037800687285223,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 12.063669       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6151022548505506,         \"F1\": 0.4080645161290322,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 13.448557       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6100347739692003,         \"F1\": 0.4048521607278241,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 14.939018       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608305804624823,         \"F1\": 0.4071428571428571,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 16.439687       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6089887640449438,         \"F1\": 0.4089673913043478,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 18.077419       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6096096096096096,         \"F1\": 0.4098573281452659,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 19.752885       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6101764464505539,         \"F1\": 0.4084682440846824,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 21.52049       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6114825009830909,         \"F1\": 0.4153846153846153,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 23.348549       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6100415251038127,         \"F1\": 0.41273450824332,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 25.279207       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6076225045372051,         \"F1\": 0.4070213933077345,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 27.31295       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085284865431667,         \"F1\": 0.4092827004219409,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 29.37924       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6083586113919784,         \"F1\": 0.4065372829417773,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 31.545651       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.60624796615685,         \"F1\": 0.4062806673209028,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 33.733231       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6071091538219566,         \"F1\": 0.4077761972498815,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 36.007059       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6063926940639269,         \"F1\": 0.4049700874367234,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 38.312687       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6048363314656443,         \"F1\": 0.4060283687943262,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 40.720334       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6065198741778668,         \"F1\": 0.4053586862575626,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 43.213056       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6086594504579517,         \"F1\": 0.4090528080469404,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 45.745916       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085198166621731,         \"F1\": 0.4078303425774878,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 48.41046399999999       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6070773263433814,         \"F1\": 0.4049225883287018,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 51.18183799999999       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6067329762815609,         \"F1\": 0.4027885360185902,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 54.01538599999999       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6088899925502855,         \"F1\": 0.405436013590034,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 56.94241999999999       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6106944108395839,         \"F1\": 0.4078027235921972,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 59.88324999999999       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.611936777541873,         \"F1\": 0.4118698605648909,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 62.92792699999999       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6131185270425776,         \"F1\": 0.4128536500174642,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 66.00451999999999       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6137946528869916,         \"F1\": 0.413510747185261,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 69.16846599999998       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6122448979591837,         \"F1\": 0.4115884115884116,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 72.34300699999999       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6126956894702981,         \"F1\": 0.4124918672739102,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 75.66876099999999       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6143845669951772,         \"F1\": 0.4130226619853175,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 79.08375899999999       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6153846153846154,         \"F1\": 0.4131455399061033,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 82.54849799999998       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6163420999799237,         \"F1\": 0.4168446750076289,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 86.09394899999998       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6150973068606251,         \"F1\": 0.4141232794733692,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 89.70897599999998       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6146735990756788,         \"F1\": 0.4133685136323659,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 93.40231399999998       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6152104170598226,         \"F1\": 0.4139120436907157,         \"Memory in Mb\": 0.0140247344970703,         \"Time in s\": 97.15397799999998       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8187845303867404,         \"F1\": 0.8284518828451883,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 0.90253       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8023191606847045,         \"F1\": 0.7475317348377998,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 2.668728       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.784688995215311,         \"F1\": 0.706177800100452,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 5.38565       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8032017664918576,         \"F1\": 0.7356321839080461,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 8.965856       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7979686465003312,         \"F1\": 0.7073872721458268,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 13.460125       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7937442502299908,         \"F1\": 0.6972724817715366,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 18.947959       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7982967986122063,         \"F1\": 0.7065840789171829,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 25.368016       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.790396025941769,         \"F1\": 0.6875128574367414,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 32.74734       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7841285416411137,         \"F1\": 0.6888260254596887,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 41.092102       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7897118887294403,         \"F1\": 0.7086710506193606,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 50.330249       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.793176116407426,         \"F1\": 0.7240594457089301,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 60.487048       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7960629196946003,         \"F1\": 0.7361656551231703,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 71.57583       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.792137216608644,         \"F1\": 0.7295027624309391,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 83.57396899999999       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7820704880548766,         \"F1\": 0.7260111022997621,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 96.505859       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7858562072264331,         \"F1\": 0.7383564107174968,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 110.378561       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7866850638151086,         \"F1\": 0.7435727317963178,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 125.162828       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.785728199467567,         \"F1\": 0.738593155893536,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 140.864825       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7806463481940271,         \"F1\": 0.7274666666666666,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 157.49451299999998       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7788880497298554,         \"F1\": 0.7181158346911569,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 175.04662       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7728903361112645,         \"F1\": 0.7138983522213725,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 193.534389       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7701445466491459,         \"F1\": 0.7094931242941608,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 212.921569       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7628317696051378,         \"F1\": 0.702236220472441,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 233.287368       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7537553390603254,         \"F1\": 0.6903626817934946,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 254.638983       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7508163546888654,         \"F1\": 0.6836389115964032,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 276.932282       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7509823833281822,         \"F1\": 0.6798001589644601,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 300.189673       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7457015495648482,         \"F1\": 0.668217569513681,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 324.33764       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7466170638976329,         \"F1\": 0.665839982747466,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 349.45202499999994       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7447865336854969,         \"F1\": 0.6611180904522613,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 375.51598899999993       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7448711605069843,         \"F1\": 0.6581322996888865,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 402.576751       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.741123661650539,         \"F1\": 0.650402464473815,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 430.58128099999993       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7390065871461634,         \"F1\": 0.6440019426906265,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 459.5402439999999       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7358145631402849,         \"F1\": 0.6343280019097637,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 489.4018749999999       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7320466936481921,         \"F1\": 0.6243023964732918,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 520.249024       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7297990455475116,         \"F1\": 0.6158319870759289,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 552.052505       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7256930209088902,         \"F1\": 0.6059617649723658,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 584.838155       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7215391690939752,         \"F1\": 0.596427301813011,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 618.5052350000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7176695205990274,         \"F1\": 0.5867248908296943,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 653.1230730000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7142359194818021,         \"F1\": 0.5779493779493778,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 688.6949970000001       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7138369229898395,         \"F1\": 0.5724554949469323,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 725.19325       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7174866856149452,         \"F1\": 0.5752924583091347,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 762.649856       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7169740207295733,         \"F1\": 0.5716148486206756,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 801.028112       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7183516858952459,         \"F1\": 0.573859795618116,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 840.263393       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7206407064198989,         \"F1\": 0.5799529121154812,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 880.2889349999999       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7217720693374808,         \"F1\": 0.5866964784795975,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 921.221106       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7228776766660944,         \"F1\": 0.5923065819861432,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 962.947955       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.724127174565087,         \"F1\": 0.5973170817134251,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1005.542302       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7260280406754186,         \"F1\": 0.6013259517462921,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1049.006993       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7277117299422816,         \"F1\": 0.6045222270465248,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1093.33419       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7273894532921857,         \"F1\": 0.6015933631814591,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1138.520645       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7287136581381487,         \"F1\": 0.6038234630387828,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1184.586595       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7287413652314008,         \"F1\": 0.6037845330582509,         \"Memory in Mb\": 0.0510377883911132,         \"Time in s\": 1230.65543       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.005899       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7636363636363637,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.034194       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.8048780487804877,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.070237       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.819047619047619,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.1191769999999999       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8145161290322581,         \"F1\": 0.8217054263565893,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.172458       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.830188679245283,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.294112       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8323699421965318,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.432822       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8341708542713567,         \"F1\": 0.83248730964467,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.620751       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8240740740740741,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 0.8126760000000001       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.825,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 1.097418       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8244274809160306,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 1.386748       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8394648829431438,         \"F1\": 0.8285714285714285,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 1.708109       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845679012345679,         \"F1\": 0.8299319727891157,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 2.034368       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8510028653295129,         \"F1\": 0.8322580645161292,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 2.472974       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8529411764705882,         \"F1\": 0.8318042813455658,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 2.916035       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8546365914786967,         \"F1\": 0.8313953488372093,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 3.458949       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561320754716981,         \"F1\": 0.8291316526610645,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 4.00711       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8596881959910914,         \"F1\": 0.8310991957104559,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 4.560221       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8565400843881856,         \"F1\": 0.8291457286432161,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 5.117465       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8577154308617234,         \"F1\": 0.8337236533957845,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 5.6788810000000005       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8587786259541985,         \"F1\": 0.8310502283105022,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 6.3168120000000005       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8579234972677595,         \"F1\": 0.8311688311688311,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 6.9590250000000005       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8606271777003485,         \"F1\": 0.8340248962655602,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 7.670201       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8647746243739566,         \"F1\": 0.8363636363636363,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 8.386169       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669871794871795,         \"F1\": 0.8356435643564357,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 9.138945       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8705701078582434,         \"F1\": 0.8426966292134833,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 9.901064000000002       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.870919881305638,         \"F1\": 0.8465608465608465,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 10.713223       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8755364806866953,         \"F1\": 0.8502581755593803,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 11.569231       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8784530386740331,         \"F1\": 0.8562091503267973,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 12.458796       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798397863818425,         \"F1\": 0.8584905660377359,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 13.352328       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798449612403101,         \"F1\": 0.8580152671755725,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 14.337352       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798498122653317,         \"F1\": 0.8596491228070174,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 15.326948       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798543689320388,         \"F1\": 0.860759493670886,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 16.325159       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798586572438163,         \"F1\": 0.8602739726027396,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 17.375421       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8832951945080092,         \"F1\": 0.8636363636363635,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 18.429913       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8809788654060067,         \"F1\": 0.8608582574772432,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 19.528877       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8820346320346321,         \"F1\": 0.8635794743429286,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 20.632714       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8819810326659642,         \"F1\": 0.8650602409638554,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 21.817705       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8829568788501027,         \"F1\": 0.8661971830985915,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 23.016963       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8808808808808809,         \"F1\": 0.8643101482326111,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 24.246233       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880859375,         \"F1\": 0.8647450110864746,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 25.480751       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.882745471877979,         \"F1\": 0.8673139158576052,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 26.819711       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8817504655493482,         \"F1\": 0.8672936259143157,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 28.162914       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8835304822565969,         \"F1\": 0.8693877551020409,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 29.538844       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8861209964412812,         \"F1\": 0.8735177865612648,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 30.932979       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8859878154917319,         \"F1\": 0.8731848983543079,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 32.425237       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8850085178875639,         \"F1\": 0.8717948717948718,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 33.921729       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8865721434528774,         \"F1\": 0.8731343283582089,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 35.452877       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.886437908496732,         \"F1\": 0.8728270814272644,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 36.988201       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8847077662129704,         \"F1\": 0.8714285714285714,         \"Memory in Mb\": 0.057229995727539,         \"Time in s\": 38.528021       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 1.286863       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 3.863138       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 7.731956       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 13.024672       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 19.659339000000003       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 27.654251       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0107755661010742,         \"Time in s\": 36.976608       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372397030808,         \"F1\": 0.7777777777777778,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 47.719054       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997664369963798,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 59.951688       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997897945241474,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 73.68853899999999       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089050257978,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 88.86509       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248303043572,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 105.535247       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999838305441022,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 123.661746       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998498554859052,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 143.18039199999998       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999859865470852,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 164.12799199999998       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686241665844,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 186.627363       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998763523956724,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 210.517492       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998832219075702,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 235.832236       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998893682929528,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 262.657063       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998949000236474,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 290.942762       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998999049096642,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 320.716469       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999904454795175,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 352.027934       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999908609029428,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 384.764181       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124170699132,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 419.010574       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999159204607558,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 454.738206       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999191543545486,         \"F1\": 0.8333333333333333,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 491.833824       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999026858699884,         \"F1\": 0.8275862068965517,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 530.367488       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999061614398588,         \"F1\": 0.8275862068965517,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 570.267553       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998912767730946,         \"F1\": 0.7999999999999999,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 611.572363       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993869221741492,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 654.167087       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9988473013289938,         \"F1\": 0.2916666666666666,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 698.17064       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9986369981115034,         \"F1\": 0.2522522522522523,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 743.490298       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9979139463040224,         \"F1\": 0.1761006289308176,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 790.115891       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9979443903494536,         \"F1\": 0.1739130434782608,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 838.025404       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9977478830100296,         \"F1\": 0.1573033707865168,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 887.160342       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9967302611411972,         \"F1\": 0.125,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 937.511304       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9964777730436016,         \"F1\": 0.1142857142857143,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 989.136144       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9964045192427364,         \"F1\": 0.1095890410958904,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1041.952402       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9958230031260106,         \"F1\": 0.0935672514619883,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1095.894331       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9956515456062218,         \"F1\": 0.0881542699724517,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1151.054816       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9951936633257288,         \"F1\": 0.0786240786240786,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1207.299045       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9946700031279324,         \"F1\": 0.0698689956331877,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1264.68116       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9945862052109302,         \"F1\": 0.0673684210526315,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1323.182614       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9945539883675102,         \"F1\": 0.0655737704918032,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1382.690018       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9939860335847912,         \"F1\": 0.0585009140767824,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1443.251394       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9938540274398254,         \"F1\": 0.0561403508771929,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1504.7859910000002       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9938618067978532,         \"F1\": 0.0550774526678141,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1567.3680330000002       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9939677917300724,         \"F1\": 0.0548885077186964,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1630.9013820000002       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.993543958990198,         \"F1\": 0.0504731861198738,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1695.428307       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.993483904192372,         \"F1\": 0.0490797546012269,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1760.949483       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.993484315064894,         \"F1\": 0.0490797546012269,         \"Memory in Mb\": 0.0201406478881835,         \"Time in s\": 1826.472109       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.4952380952380952,         \"F1\": 0.208955223880597,         \"Memory in Mb\": 0.0192251205444335,         \"Time in s\": 0.143993       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5213270142180095,         \"F1\": 0.3129251700680272,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 0.331364       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5299684542586751,         \"F1\": 0.4063745019920318,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 0.6339969999999999       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5437352245862884,         \"F1\": 0.4238805970149253,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 1.026482       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.553875236294896,         \"F1\": 0.4099999999999999,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 1.502748       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5590551181102362,         \"F1\": 0.4017094017094017,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 2.038539       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5762483130904184,         \"F1\": 0.3984674329501916,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 2.585217       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5867768595041323,         \"F1\": 0.4047619047619047,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 3.2443470000000003       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5918153200419727,         \"F1\": 0.3987635239567234,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 4.029044000000001       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6015108593012276,         \"F1\": 0.3971428571428571,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 4.857172       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6,         \"F1\": 0.3852242744063324,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 5.757943       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6073957513768686,         \"F1\": 0.3966142684401451,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 6.7312840000000005       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085693536673928,         \"F1\": 0.384,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 7.793338       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6089008766014835,         \"F1\": 0.3790149892933619,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 8.86628       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6085588420390182,         \"F1\": 0.3742454728370221,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 10.05345       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6094395280235988,         \"F1\": 0.370722433460076,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 11.283728       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6102165463631316,         \"F1\": 0.3754448398576512,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 12.570083       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.610907184058731,         \"F1\": 0.3816666666666667,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 13.875162       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6060606060606061,         \"F1\": 0.3799843627834245,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 15.24589       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6045304388862671,         \"F1\": 0.3838235294117647,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 16.723857       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6053932584269663,         \"F1\": 0.3868715083798882,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 18.213202       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6061776061776062,         \"F1\": 0.388814913448735,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 19.838043       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.606893721789085,         \"F1\": 0.388250319284802,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 21.528239       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608336610302792,         \"F1\": 0.3963636363636363,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 23.295102       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6070215175537939,         \"F1\": 0.3944153577661431,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 25.108421       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6047186932849364,         \"F1\": 0.3892316320807628,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 26.99552       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6057322614470465,         \"F1\": 0.3922413793103448,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 28.904989       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6056622851365016,         \"F1\": 0.3899895724713243,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 30.87684       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6036446469248291,         \"F1\": 0.3903903903903904,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 32.946938       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6045926391947153,         \"F1\": 0.3924601256645723,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 35.112766       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6039573820395738,         \"F1\": 0.3900609470229723,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 37.308874       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6024771453848422,         \"F1\": 0.391696750902527,         \"Memory in Mb\": 0.0192480087280273,         \"Time in s\": 39.588614       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030883614526737,         \"F1\": 0.3933566433566433,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 41.900558       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6069941715237303,         \"F1\": 0.4035383319292334,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 44.30438       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6079805877595039,         \"F1\": 0.4079804560260586,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 46.776579       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6107470511140236,         \"F1\": 0.4146629877808436,         \"Memory in Mb\": 0.0348339080810546,         \"Time in s\": 49.299973       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6123437898495282,         \"F1\": 0.4180704441041348,         \"Memory in Mb\": 0.0440921783447265,         \"Time in s\": 51.9923       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6143531164638689,         \"F1\": 0.4246017043349389,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 54.748055       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.617227195741592,         \"F1\": 0.4321608040201005,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 57.554127       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6218447747110167,         \"F1\": 0.4439819632327437,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 60.441345       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6239355581127733,         \"F1\": 0.4513096037609133,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 63.387968       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6259267580319029,         \"F1\": 0.4567699836867862,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 66.368103       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6276058810621022,         \"F1\": 0.4638230647709321,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 69.45485500000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6283508470941453,         \"F1\": 0.4695439240893787,         \"Memory in Mb\": 0.0502567291259765,         \"Time in s\": 72.676145       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6288530090165653,         \"F1\": 0.471641791044776,         \"Memory in Mb\": 0.0594196319580078,         \"Time in s\": 75.94439200000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6311794871794871,         \"F1\": 0.475801749271137,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 79.31100500000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6336077092953222,         \"F1\": 0.484026010743568,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 82.69585900000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6361313151169649,         \"F1\": 0.4905037159372419,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 86.19871000000002       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6383593298671288,         \"F1\": 0.495703544575725,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 89.82165300000003       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6421966408756369,         \"F1\": 0.5034049240440022,         \"Memory in Mb\": 0.0594654083251953,         \"Time in s\": 93.53024900000004       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530386740331491,         \"F1\": 0.8513966480446927,         \"Memory in Mb\": 0.1757516860961914,         \"Time in s\": 1.081486       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8663721700717836,         \"F1\": 0.8393094289508632,         \"Memory in Mb\": 0.2084512710571289,         \"Time in s\": 3.208731       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8365844681634156,         \"F1\": 0.809278350515464,         \"Memory in Mb\": 0.2330217361450195,         \"Time in s\": 6.394793       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8459839911675407,         \"F1\": 0.8210391276459269,         \"Memory in Mb\": 0.2330217361450195,         \"Time in s\": 10.694791       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8511812762199161,         \"F1\": 0.8157463094587206,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 16.02834       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404783808647655,         \"F1\": 0.8020095912308747,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 22.571918       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8334647531935025,         \"F1\": 0.7966884867154409,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 30.238204       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8330343590451221,         \"F1\": 0.7912353347135956,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 38.961308       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8344167790997179,         \"F1\": 0.8013537374926426,         \"Memory in Mb\": 0.2329683303833007,         \"Time in s\": 48.732242       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8403797328623468,         \"F1\": 0.8129849974133472,         \"Memory in Mb\": 0.2980508804321289,         \"Time in s\": 59.636444       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8398394380331159,         \"F1\": 0.8171402383134739,         \"Memory in Mb\": 0.2981653213500976,         \"Time in s\": 71.55266999999999       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.840493054916751,         \"F1\": 0.8200124558854057,         \"Memory in Mb\": 0.2981653213500976,         \"Time in s\": 84.613385       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404517279442982,         \"F1\": 0.8184014690248381,         \"Memory in Mb\": 0.3811311721801758,         \"Time in s\": 98.771271       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397066939998423,         \"F1\": 0.8184983483617534,         \"Memory in Mb\": 0.3811311721801758,         \"Time in s\": 114.088656       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8422253293104717,         \"F1\": 0.8228684732319893,         \"Memory in Mb\": 0.3811311721801758,         \"Time in s\": 130.484857       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8440841669541221,         \"F1\": 0.8259128023417038,         \"Memory in Mb\": 0.3823747634887695,         \"Time in s\": 148.034702       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8445555483410169,         \"F1\": 0.8246153846153847,         \"Memory in Mb\": 0.3824014663696289,         \"Time in s\": 166.630313       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8382903047770895,         \"F1\": 0.8146221441124781,         \"Memory in Mb\": 0.4081621170043945,         \"Time in s\": 186.33286       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8345436588624876,         \"F1\": 0.8052516411378555,         \"Memory in Mb\": 0.4081621170043945,         \"Time in s\": 207.149801       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8332689442022186,         \"F1\": 0.8030253635000325,         \"Memory in Mb\": 0.408848762512207,         \"Time in s\": 229.10312700000003       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8340604467805519,         \"F1\": 0.8008327550312283,         \"Memory in Mb\": 0.4101419448852539,         \"Time in s\": 252.195562       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8288595655009784,         \"F1\": 0.7951228302000121,         \"Memory in Mb\": 0.4740419387817383,         \"Time in s\": 276.349221       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8238230071507414,         \"F1\": 0.787570163763671,         \"Memory in Mb\": 0.4986543655395508,         \"Time in s\": 301.796373       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8251391252357081,         \"F1\": 0.7858028169014086,         \"Memory in Mb\": 0.498814582824707,         \"Time in s\": 328.42960500000004       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8245838668373879,         \"F1\": 0.7828843106180666,         \"Memory in Mb\": 0.4754457473754883,         \"Time in s\": 356.18046400000003       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.81761834005519,         \"F1\": 0.7712703652433182,         \"Memory in Mb\": 0.5000581741333008,         \"Time in s\": 385.033694       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8151342954090184,         \"F1\": 0.7656509121061359,         \"Memory in Mb\": 0.5002222061157227,         \"Time in s\": 415.059878       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8133401663578665,         \"F1\": 0.7649540828989824,         \"Memory in Mb\": 0.5574884414672852,         \"Time in s\": 446.384667       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8142199215925094,         \"F1\": 0.7659329592864336,         \"Memory in Mb\": 0.5574884414672852,         \"Time in s\": 478.875266       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8130909893667906,         \"F1\": 0.7650758416574177,         \"Memory in Mb\": 0.5574884414672852,         \"Time in s\": 512.642228       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.810646252447926,         \"F1\": 0.7611605137878379,         \"Memory in Mb\": 0.5575571060180664,         \"Time in s\": 547.6069200000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8084233037839329,         \"F1\": 0.755846667838931,         \"Memory in Mb\": 0.5575571060180664,         \"Time in s\": 583.7938770000001       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8039602635715958,         \"F1\": 0.7488322262695523,         \"Memory in Mb\": 0.5575571060180664,         \"Time in s\": 621.109455       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8052787066194851,         \"F1\": 0.7498540328634582,         \"Memory in Mb\": 0.6720895767211914,         \"Time in s\": 659.6567610000001       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.802863540319783,         \"F1\": 0.7460285215130216,         \"Memory in Mb\": 0.6720895767211914,         \"Time in s\": 699.5069490000001       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8010731258623333,         \"F1\": 0.7451889089623752,         \"Memory in Mb\": 0.6838197708129883,         \"Time in s\": 740.492735       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8010500880045345,         \"F1\": 0.7469934367768125,         \"Memory in Mb\": 0.7644319534301758,         \"Time in s\": 782.6537000000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.799663055160194,         \"F1\": 0.7444893120438633,         \"Memory in Mb\": 0.7656755447387695,         \"Time in s\": 825.979857       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7997056576005434,         \"F1\": 0.7438746335637508,         \"Memory in Mb\": 0.796971321105957,         \"Time in s\": 870.4262610000001       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.798283617097602,         \"F1\": 0.7418420680887131,         \"Memory in Mb\": 0.8215837478637695,         \"Time in s\": 916.052417       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7980347287656482,         \"F1\": 0.741577678263865,         \"Memory in Mb\": 0.8528566360473633,         \"Time in s\": 962.729846       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7942761031247536,         \"F1\": 0.7384913476314559,         \"Memory in Mb\": 0.8296480178833008,         \"Time in s\": 1010.431184       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.791975768154632,         \"F1\": 0.7385131646876614,         \"Memory in Mb\": 0.8296480178833008,         \"Time in s\": 1059.228611       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7917617841105787,         \"F1\": 0.7414904549842734,         \"Memory in Mb\": 0.8308916091918945,         \"Time in s\": 1109.092208       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7937158134857367,         \"F1\": 0.7465187775031646,         \"Memory in Mb\": 0.8308916091918945,         \"Time in s\": 1159.935967       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7945770845830834,         \"F1\": 0.749744219357479,         \"Memory in Mb\": 0.8553438186645508,         \"Time in s\": 1211.819823       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7952373124163359,         \"F1\": 0.7509355271802782,         \"Memory in Mb\": 0.8799333572387695,         \"Time in s\": 1264.739744       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7953871271874353,         \"F1\": 0.7515912897822445,         \"Memory in Mb\": 0.881199836730957,         \"Time in s\": 1318.691004       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7949676750839096,         \"F1\": 0.7499038303016982,         \"Memory in Mb\": 0.881199836730957,         \"Time in s\": 1373.745004       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7956246274752202,         \"F1\": 0.7508745492707605,         \"Memory in Mb\": 0.9384660720825196,         \"Time in s\": 1429.8385990000002       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.7956346141113637,         \"F1\": 0.7508341405661393,         \"Memory in Mb\": 0.9384660720825196,         \"Time in s\": 1485.976427       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.6428571428571429,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.007366       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7346938775510203,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.021904       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.7894736842105262,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.108104       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.8080808080808081,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.262464       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8145161290322581,         \"F1\": 0.8130081300813008,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.426996       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.8235294117647058,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.670297       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8263473053892215,         \"Memory in Mb\": 0.0684270858764648,         \"Time in s\": 0.944361       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8341708542713567,         \"F1\": 0.8272251308900525,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 1.225091       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8190476190476189,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 1.620606       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.8205128205128206,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 2.072395       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8203125000000001,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 2.536963       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8394648829431438,         \"F1\": 0.8248175182481753,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 3.035956       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845679012345679,         \"F1\": 0.8263888888888888,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 3.5418380000000003       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8510028653295129,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 4.130076000000001       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8529411764705882,         \"F1\": 0.8286604361370716,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 4.770647       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8546365914786967,         \"F1\": 0.8284023668639053,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 5.418701       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561320754716981,         \"F1\": 0.8262108262108262,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 6.103302       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8596881959910914,         \"F1\": 0.8283378746594006,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 6.878701       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8565400843881856,         \"F1\": 0.826530612244898,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 7.659851000000001       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8577154308617234,         \"F1\": 0.8313539192399049,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 8.471725000000001       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8587786259541985,         \"F1\": 0.8287037037037036,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 9.290161       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8579234972677595,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 10.200081       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8606271777003485,         \"F1\": 0.8319327731092437,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 11.144835       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8647746243739566,         \"F1\": 0.834355828220859,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 12.149797       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669871794871795,         \"F1\": 0.8336673346693387,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 13.191129       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8705701078582434,         \"F1\": 0.8409090909090909,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 14.297317       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.870919881305638,         \"F1\": 0.8449197860962566,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 15.408972       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8755364806866953,         \"F1\": 0.8486956521739131,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 16.598807       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8784530386740331,         \"F1\": 0.8547854785478548,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 17.82593       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798397863818425,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 19.123649       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798449612403101,         \"F1\": 0.8567026194144837,         \"Memory in Mb\": 0.0684499740600586,         \"Time in s\": 20.439518       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798498122653317,         \"F1\": 0.8584070796460177,         \"Memory in Mb\": 0.0060701370239257,         \"Time in s\": 21.870793       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8786407766990292,         \"F1\": 0.8575498575498576,         \"Memory in Mb\": 0.1326732635498047,         \"Time in s\": 23.308925       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798586572438163,         \"F1\": 0.8579387186629527,         \"Memory in Mb\": 0.1326961517333984,         \"Time in s\": 24.793982       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8810068649885584,         \"F1\": 0.8583106267029972,         \"Memory in Mb\": 0.1326961517333984,         \"Time in s\": 26.327422       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.882091212458287,         \"F1\": 0.8590425531914893,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 27.867454       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8831168831168831,         \"F1\": 0.8611825192802056,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 29.49699       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880927291886196,         \"F1\": 0.8599752168525404,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 31.132602       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8819301848049281,         \"F1\": 0.8609431680773881,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 32.858381       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8828828828828829,         \"F1\": 0.8621908127208481,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 34.600804000000004       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8818359375,         \"F1\": 0.8613974799541809,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 36.37479200000001       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8836987607244995,         \"F1\": 0.8641425389755011,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 38.236126000000006       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8845437616387337,         \"F1\": 0.8658008658008659,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 40.114172       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844404003639672,         \"F1\": 0.8656084656084656,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 41.998405000000005       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8816725978647687,         \"F1\": 0.8630278063851698,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 43.96255500000001       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807658833768495,         \"F1\": 0.8614762386248735,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 45.93298000000001       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.879045996592845,         \"F1\": 0.8594059405940594,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 47.92952100000001       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807339449541285,         \"F1\": 0.8610301263362489,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 50.02063300000001       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880718954248366,         \"F1\": 0.8609523809523809,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 52.11693600000001       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8799039231385108,         \"F1\": 0.8605947955390334,         \"Memory in Mb\": 0.1327190399169922,         \"Time in s\": 54.27575100000001       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 1.086226       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 3.167363       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 6.335451       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 10.587829       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 15.968691       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 22.515101       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170211791992187,         \"Time in s\": 30.177580000000003       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.0262222290039062,         \"Time in s\": 38.907943       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 48.927066       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 60.172540000000005       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 72.66801500000001       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 86.48422800000002       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 101.518721       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 117.73184500000002       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 135.114956       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 153.67141       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 173.49434000000002       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 194.59387200000003       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 216.90021000000004       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 240.42103000000003       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 265.208452       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 291.209442       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 318.43843100000004       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 346.89099000000004       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 376.5294080000001       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 407.49804200000005       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 439.6062170000001       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 472.95335100000005       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 507.474024       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 543.2100710000001       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 580.098547       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 618.1800870000001       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 657.2504170000001       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 697.447209       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 738.7766780000001       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 781.207926       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 824.7260210000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 869.3947840000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 915.176721       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 961.985028       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1009.890756       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1058.848202       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1108.7919539999998       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1159.7893379999998       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1211.840415       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1264.8087919999998       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1318.8158339999998       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1373.8298589999995       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1429.7685149999998       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1486.6476859999998       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.0170440673828125,         \"Time in s\": 1543.5553739999998       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5714285714285714,         \"F1\": 0.628099173553719,         \"Memory in Mb\": 0.0256843566894531,         \"Time in s\": 0.216494       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5592417061611374,         \"F1\": 0.5903083700440529,         \"Memory in Mb\": 0.0257682800292968,         \"Time in s\": 0.463954       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5615141955835962,         \"F1\": 0.5947521865889213,         \"Memory in Mb\": 0.0258293151855468,         \"Time in s\": 0.862573       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5555555555555556,         \"F1\": 0.5822222222222222,         \"Memory in Mb\": 0.0258293151855468,         \"Time in s\": 1.389833       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.555765595463138,         \"F1\": 0.5506692160611854,         \"Memory in Mb\": 0.0258293151855468,         \"Time in s\": 1.964112       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5543307086614173,         \"F1\": 0.5291181364392679,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 2.693957       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5708502024291497,         \"F1\": 0.5167173252279634,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 3.480128       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5761511216056671,         \"F1\": 0.510231923601637,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 4.453125999999999       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5844700944386149,         \"F1\": 0.505,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 5.580188       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5920679886685553,         \"F1\": 0.4953271028037382,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 6.755147       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.590557939914163,         \"F1\": 0.478688524590164,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 8.08575       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971675845790716,         \"F1\": 0.4807302231237322,         \"Memory in Mb\": 0.0258903503417968,         \"Time in s\": 9.558451000000002       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599128540305011,         \"F1\": 0.4661508704061895,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 11.087295       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5994605529332434,         \"F1\": 0.458029197080292,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 12.740385000000002       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5997482693517936,         \"F1\": 0.4517241379310345,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 14.490633000000004       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6011799410029498,         \"F1\": 0.4459016393442623,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 16.383274000000004       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6018878400888396,         \"F1\": 0.445475638051044,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 18.381747000000004       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030414263240692,         \"F1\": 0.4470416362308254,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 20.420329       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986090412319921,         \"F1\": 0.443526170798898,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 22.615668000000003       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5960358659745163,         \"F1\": 0.4427083333333333,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 24.891681       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5968539325842697,         \"F1\": 0.4425108763206961,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 27.295309000000003       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5975975975975976,         \"F1\": 0.4423305588585017,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 29.792211       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5982765695527288,         \"F1\": 0.4396107613050944,         \"Memory in Mb\": 0.0259513854980468,         \"Time in s\": 32.414577       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5973259929217459,         \"F1\": 0.4398249452954048,         \"Memory in Mb\": 0.0302915573120117,         \"Time in s\": 35.17394       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5956964892412231,         \"F1\": 0.4436363636363636,         \"Memory in Mb\": 0.0567083358764648,         \"Time in s\": 38.067898       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5985480943738657,         \"F1\": 0.4497512437810945,         \"Memory in Mb\": 0.0569524765014648,         \"Time in s\": 41.158639       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.600139811254806,         \"F1\": 0.4536771728748806,         \"Memory in Mb\": 0.0571966171264648,         \"Time in s\": 44.452629       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5979103471520054,         \"F1\": 0.4525011473152822,         \"Memory in Mb\": 0.0573034286499023,         \"Time in s\": 47.888765       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971363488447771,         \"F1\": 0.4497777777777778,         \"Memory in Mb\": 0.0574254989624023,         \"Time in s\": 51.476908       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008178672538534,         \"F1\": 0.4499349804941482,         \"Memory in Mb\": 0.0574865341186523,         \"Time in s\": 55.178717       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6024353120243531,         \"F1\": 0.4470787468247248,         \"Memory in Mb\": 0.0576086044311523,         \"Time in s\": 59.035765       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6012975523444412,         \"F1\": 0.444991789819376,         \"Memory in Mb\": 0.0576696395874023,         \"Time in s\": 63.084021       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.603946239633972,         \"F1\": 0.4431041415359871,         \"Memory in Mb\": 0.0577306747436523,         \"Time in s\": 67.283017       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.607826810990841,         \"F1\": 0.4452296819787986,         \"Memory in Mb\": 0.0577306747436523,         \"Time in s\": 71.628079       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6071717444055001,         \"F1\": 0.441976254308694,         \"Memory in Mb\": 0.0577306747436523,         \"Time in s\": 76.17092       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6062909567496724,         \"F1\": 0.4378742514970059,         \"Memory in Mb\": 0.0577917098999023,         \"Time in s\": 80.84267399999999       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.606988013261923,         \"F1\": 0.4353242946134115,         \"Memory in Mb\": 0.0578527450561523,         \"Time in s\": 85.696272       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6088899925502855,         \"F1\": 0.4360902255639098,         \"Memory in Mb\": 0.0578527450561523,         \"Time in s\": 90.658573       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6082748608758771,         \"F1\": 0.4341139461726669,         \"Memory in Mb\": 0.0579137802124023,         \"Time in s\": 95.895015       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6105213493748526,         \"F1\": 0.4370951244459598,         \"Memory in Mb\": 0.0579137802124023,         \"Time in s\": 101.21739399999998       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6119677790563867,         \"F1\": 0.4372496662216288,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 106.75825799999998       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.614243990114581,         \"F1\": 0.4387054593004249,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 112.41943399999998       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6126837831906956,         \"F1\": 0.4355612408058842,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 118.261679       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.613339052112374,         \"F1\": 0.4360337816703159,         \"Memory in Mb\": 0.0579748153686523,         \"Time in s\": 124.266912       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6148039421262319,         \"F1\": 0.4352905010759299,         \"Memory in Mb\": 0.0641164779663086,         \"Time in s\": 130.389994       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6157948717948718,         \"F1\": 0.4332829046898639,         \"Memory in Mb\": 0.0641164779663086,         \"Time in s\": 136.677955       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6167436257779563,         \"F1\": 0.434705359786793,         \"Memory in Mb\": 0.0641164779663086,         \"Time in s\": 143.134937       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6158836249262827,         \"F1\": 0.4313154831199068,         \"Memory in Mb\": 0.0641775131225586,         \"Time in s\": 149.736273       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6160215674947044,         \"F1\": 0.4296338672768878,         \"Memory in Mb\": 0.0642385482788086,         \"Time in s\": 156.525598       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6165314210228345,         \"F1\": 0.4282498593134496,         \"Memory in Mb\": 0.0618467330932617,         \"Time in s\": 163.516222       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8386740331491712,         \"F1\": 0.8370535714285713,         \"Memory in Mb\": 0.1553249359130859,         \"Time in s\": 2.212895       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8823854224185533,         \"F1\": 0.857334226389819,         \"Memory in Mb\": 0.2904033660888672,         \"Time in s\": 6.521798       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8715495031284505,         \"F1\": 0.8438478747203579,         \"Memory in Mb\": 0.1283740997314453,         \"Time in s\": 13.845606       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.875241512558653,         \"F1\": 0.8472972972972973,         \"Memory in Mb\": 0.2500133514404297,         \"Time in s\": 22.913433       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8737028041510267,         \"F1\": 0.8396860986547084,         \"Memory in Mb\": 0.3712940216064453,         \"Time in s\": 34.075607999999995       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8656853725850966,         \"F1\": 0.8300744878957169,         \"Memory in Mb\": 0.4407672882080078,         \"Time in s\": 47.709684       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8646901119697209,         \"F1\": 0.8296943231441047,         \"Memory in Mb\": 0.2620487213134765,         \"Time in s\": 63.50523799999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.864771629639851,         \"F1\": 0.8289703315881326,         \"Memory in Mb\": 0.2866535186767578,         \"Time in s\": 81.25362299999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8572304673126456,         \"F1\": 0.8312064965197217,         \"Memory in Mb\": 0.2866878509521484,         \"Time in s\": 101.144105       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8580417264598742,         \"F1\": 0.8370501773948302,         \"Memory in Mb\": 0.2865924835205078,         \"Time in s\": 123.033084       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8544907175112895,         \"F1\": 0.8369320737741789,         \"Memory in Mb\": 0.3109416961669922,         \"Time in s\": 147.021637       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8583386992916935,         \"F1\": 0.8434322895485971,         \"Memory in Mb\": 0.371591567993164,         \"Time in s\": 172.950216       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8529336842999066,         \"F1\": 0.8357982555934774,         \"Memory in Mb\": 0.4628047943115234,         \"Time in s\": 201.490822       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533469999211543,         \"F1\": 0.8362099330750264,         \"Memory in Mb\": 0.1895275115966797,         \"Time in s\": 232.642586       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8551769813820002,         \"F1\": 0.8395303326810176,         \"Memory in Mb\": 0.1939334869384765,         \"Time in s\": 265.763258       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.855122456019317,         \"F1\": 0.8397435897435896,         \"Memory in Mb\": 0.1697406768798828,         \"Time in s\": 300.834382       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8537757288487761,         \"F1\": 0.8365984617617181,         \"Memory in Mb\": 0.1694965362548828,         \"Time in s\": 338.213883       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8506776231066413,         \"F1\": 0.8316626339440029,         \"Memory in Mb\": 0.1640834808349609,         \"Time in s\": 377.804328       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8495904258409341,         \"F1\": 0.8278704873346187,         \"Memory in Mb\": 0.1691112518310547,         \"Time in s\": 419.462025       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8498261493459904,         \"F1\": 0.827752104830031,         \"Memory in Mb\": 0.198678970336914,         \"Time in s\": 463.148728       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8522996057818659,         \"F1\": 0.8287211995611362,         \"Memory in Mb\": 0.2572231292724609,         \"Time in s\": 508.959572       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8470222266820531,         \"F1\": 0.8238895627563102,         \"Memory in Mb\": 0.315378189086914,         \"Time in s\": 557.675148       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8434035609732687,         \"F1\": 0.8200121352529097,         \"Memory in Mb\": 0.3239650726318359,         \"Time in s\": 609.8923100000001       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8450535804626776,         \"F1\": 0.8196563353139554,         \"Memory in Mb\": 0.3222179412841797,         \"Time in s\": 664.4541280000001       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8452911828336792,         \"F1\": 0.8184455958549223,         \"Memory in Mb\": 0.4409503936767578,         \"Time in s\": 721.910691       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8424962852897474,         \"F1\": 0.8143700590413289,         \"Memory in Mb\": 0.4440975189208984,         \"Time in s\": 782.4177400000001       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8403990024937655,         \"F1\": 0.8111089607122121,         \"Memory in Mb\": 0.5018138885498047,         \"Time in s\": 845.9758730000001       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8367169945204399,         \"F1\": 0.8072053621299572,         \"Memory in Mb\": 0.5608501434326172,         \"Time in s\": 913.002536       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8375137974346287,         \"F1\": 0.8080226649278229,         \"Memory in Mb\": 0.3154354095458984,         \"Time in s\": 983.555265       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8378527539644579,         \"F1\": 0.8096081565645656,         \"Memory in Mb\": 0.315774917602539,         \"Time in s\": 1056.640488       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8349652839594089,         \"F1\": 0.8051456678017405,         \"Memory in Mb\": 0.187021255493164,         \"Time in s\": 1131.521461       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8328791693973991,         \"F1\": 0.8007566722868775,         \"Memory in Mb\": 0.2230243682861328,         \"Time in s\": 1208.700577       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8303843194969395,         \"F1\": 0.7968267959453503,         \"Memory in Mb\": 0.104043960571289,         \"Time in s\": 1288.239999       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8300490211992338,         \"F1\": 0.7958984755740965,         \"Memory in Mb\": 0.2261257171630859,         \"Time in s\": 1369.046902       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8277460657857391,         \"F1\": 0.7934815486993346,         \"Memory in Mb\": 0.378305435180664,         \"Time in s\": 1451.951614       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8227502682814656,         \"F1\": 0.7867025790502896,         \"Memory in Mb\": 0.1292285919189453,         \"Time in s\": 1536.962592       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8198442767220548,         \"F1\": 0.7850354180756772,         \"Memory in Mb\": 0.1289234161376953,         \"Time in s\": 1623.2849270000002       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8166264850262875,         \"F1\": 0.7809127190699289,         \"Memory in Mb\": 0.1940555572509765,         \"Time in s\": 1710.9311020000002       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8170265757224124,         \"F1\": 0.7804530172852923,         \"Memory in Mb\": 0.3470821380615234,         \"Time in s\": 1800.2335220000002       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8175446342338365,         \"F1\": 0.7795559111822364,         \"Memory in Mb\": 0.4078502655029297,         \"Time in s\": 1890.9592170000003       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.816610580158837,         \"F1\": 0.7771817349208426,         \"Memory in Mb\": 0.4166545867919922,         \"Time in s\": 1983.256251       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8155107618722242,         \"F1\": 0.7753456221198157,         \"Memory in Mb\": 0.1291065216064453,         \"Time in s\": 2077.01254       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8146674538593834,         \"F1\": 0.7751479289940829,         \"Memory in Mb\": 0.2005825042724609,         \"Time in s\": 2172.116472       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8157188370167825,         \"F1\": 0.7778516995282448,         \"Memory in Mb\": 0.169626235961914,         \"Time in s\": 2268.811937       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8171650028207706,         \"F1\": 0.7812151452891106,         \"Memory in Mb\": 0.2508678436279297,         \"Time in s\": 2366.696648       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8187402519496101,         \"F1\": 0.7846022241231821,         \"Memory in Mb\": 0.2554492950439453,         \"Time in s\": 2465.8461540000003       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8196848359597003,         \"F1\": 0.7859015113490603,         \"Memory in Mb\": 0.3113727569580078,         \"Time in s\": 2566.2285070000003       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8203141168625107,         \"F1\": 0.7864093592827466,         \"Memory in Mb\": 0.2909717559814453,         \"Time in s\": 2667.8448940000003       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8199265649989863,         \"F1\": 0.7853959731543624,         \"Memory in Mb\": 0.4365749359130859,         \"Time in s\": 2771.03508       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8212543323252169,         \"F1\": 0.7873854475750336,         \"Memory in Mb\": 0.4353275299072265,         \"Time in s\": 2875.842657       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8212575312837942,         \"F1\": 0.7873440987265327,         \"Memory in Mb\": 0.4353275299072265,         \"Time in s\": 2980.68937       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.6428571428571429,         \"Memory in Mb\": 0.0747642517089843,         \"Time in s\": 0.008848       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7346938775510203,         \"Memory in Mb\": 0.0748252868652343,         \"Time in s\": 0.123836       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.7894736842105262,         \"Memory in Mb\": 0.0748252868652343,         \"Time in s\": 0.332733       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.8080808080808081,         \"Memory in Mb\": 0.0748863220214843,         \"Time in s\": 0.575353       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8225806451612904,         \"F1\": 0.819672131147541,         \"Memory in Mb\": 0.0748863220214843,         \"Time in s\": 0.909775       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.825503355704698,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.0749092102050781,         \"Time in s\": 1.284417       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8242424242424242,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 1.765942       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8291457286432161,         \"F1\": 0.8191489361702128,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 2.25515       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8155339805825242,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 2.805996       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.817391304347826,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 3.45663       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8174603174603176,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 4.129749       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8361204013377926,         \"F1\": 0.8178438661710038,         \"Memory in Mb\": 0.0749702453613281,         \"Time in s\": 4.871246       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8425925925925926,         \"F1\": 0.8197879858657244,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 5.6743250000000005       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8481375358166189,         \"F1\": 0.822742474916388,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 6.528728000000001       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8502673796791443,         \"F1\": 0.8227848101265823,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 7.451788       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8521303258145363,         \"F1\": 0.8228228228228228,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 8.382915       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8537735849056604,         \"F1\": 0.8208092485549133,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 9.397859       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8574610244988864,         \"F1\": 0.8232044198895027,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 10.451359       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8565400843881856,         \"F1\": 0.8238341968911918,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 11.619284       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8557114228456913,         \"F1\": 0.8260869565217391,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 12.854081       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8568702290076335,         \"F1\": 0.823529411764706,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 14.155744       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561020036429873,         \"F1\": 0.8240534521158129,         \"Memory in Mb\": 0.0750312805175781,         \"Time in s\": 15.508673       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554006968641115,         \"F1\": 0.8230277185501066,         \"Memory in Mb\": 0.1140928268432617,         \"Time in s\": 16.956902       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8547579298831386,         \"F1\": 0.8176100628930818,         \"Memory in Mb\": 0.1419858932495117,         \"Time in s\": 18.498415       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8573717948717948,         \"F1\": 0.8172484599589321,         \"Memory in Mb\": 0.1422224044799804,         \"Time in s\": 20.046891       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8597842835130971,         \"F1\": 0.8233009708737864,         \"Memory in Mb\": 0.1423902511596679,         \"Time in s\": 21.659211       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8590504451038575,         \"F1\": 0.8263254113345521,         \"Memory in Mb\": 0.1424512863159179,         \"Time in s\": 23.289464       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8640915593705293,         \"F1\": 0.8306595365418894,         \"Memory in Mb\": 0.1425123214721679,         \"Time in s\": 25.025232       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8646408839779005,         \"F1\": 0.8344594594594595,         \"Memory in Mb\": 0.1425733566284179,         \"Time in s\": 26.77059       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8664886515353805,         \"F1\": 0.8371335504885993,         \"Memory in Mb\": 0.1426343917846679,         \"Time in s\": 28.602532       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8643410852713178,         \"F1\": 0.8330683624801273,         \"Memory in Mb\": 0.1426572799682617,         \"Time in s\": 30.506622       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8635794743429287,         \"F1\": 0.8340943683409437,         \"Memory in Mb\": 0.1426572799682617,         \"Time in s\": 32.425334       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8628640776699029,         \"F1\": 0.8345534407027819,         \"Memory in Mb\": 0.1426572799682617,         \"Time in s\": 34.352118       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8645465253239105,         \"F1\": 0.8364153627311521,         \"Memory in Mb\": 0.1427183151245117,         \"Time in s\": 36.371837       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672768878718535,         \"F1\": 0.838888888888889,         \"Memory in Mb\": 0.1427183151245117,         \"Time in s\": 38.48177       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8665183537263627,         \"F1\": 0.8378378378378378,         \"Memory in Mb\": 0.1427793502807617,         \"Time in s\": 40.637242       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8668831168831169,         \"F1\": 0.8400520156046815,         \"Memory in Mb\": 0.1427793502807617,         \"Time in s\": 42.878637       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8661749209694415,         \"F1\": 0.8410513141426783,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 45.126806       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.86652977412731,         \"F1\": 0.8414634146341464,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 47.480371       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8638638638638638,         \"F1\": 0.8392434988179669,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 49.860964       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8623046875,         \"F1\": 0.8377445339470656,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 52.359728       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8636796949475691,         \"F1\": 0.8402234636871508,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 54.917309       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8649906890130353,         \"F1\": 0.8429035752979415,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 57.530035       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671519563239308,         \"F1\": 0.8456659619450316,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 60.237019       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8701067615658363,         \"F1\": 0.8507157464212679,         \"Memory in Mb\": 0.1428403854370117,         \"Time in s\": 62.977514       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8720626631853786,         \"F1\": 0.852852852852853,         \"Memory in Mb\": 0.1429014205932617,         \"Time in s\": 65.816825       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8713798977853492,         \"F1\": 0.8521057786483839,         \"Memory in Mb\": 0.1429014205932617,         \"Time in s\": 68.70086099999999       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.872393661384487,         \"F1\": 0.8530259365994236,         \"Memory in Mb\": 0.1429014205932617,         \"Time in s\": 71.69850399999999       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8733660130718954,         \"F1\": 0.8541862652869238,         \"Memory in Mb\": 0.1429624557495117,         \"Time in s\": 74.74610599999998       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8742994395516414,         \"F1\": 0.8560953253895509,         \"Memory in Mb\": 0.1429624557495117,         \"Time in s\": 77.86495099999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0237245559692382,         \"Time in s\": 1.538486       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0237855911254882,         \"Time in s\": 4.672632       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0238466262817382,         \"Time in s\": 9.280899       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0238466262817382,         \"Time in s\": 15.518128       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0238466262817382,         \"Time in s\": 23.359252       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0239076614379882,         \"Time in s\": 32.724381       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0239076614379882,         \"Time in s\": 43.566998       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.0331544876098632,         \"Time in s\": 56.059492       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.023930549621582,         \"Time in s\": 70.315874       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.023930549621582,         \"Time in s\": 86.215201       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 103.810178       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 123.100461       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 144.232391       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 167.059054       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 191.625058       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 217.971038       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 246.1385       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 276.028821       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 307.727155       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 341.140461       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.023991584777832,         \"Time in s\": 376.24707       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 412.982235       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 451.376836       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 491.342412       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 532.9409959999999       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 576.05932       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 620.875381       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 667.2134759999999       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 715.0678459999999       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 764.41064       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 815.214443       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 867.5634849999999       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 921.307598       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 976.50125       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1033.066349       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1090.838953       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1149.858677       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1210.07507       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1271.441242       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1333.994434       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1397.762471       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1462.67416       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.024052619934082,         \"Time in s\": 1528.680001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1595.853878       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1664.0432529999998       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1733.3243249999998       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1803.716354       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1875.203937       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 1947.740223       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 2021.343945       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.024113655090332,         \"Time in s\": 2094.94949       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.638095238095238,         \"F1\": 0.5777777777777778,         \"Memory in Mb\": 0.6023197174072266,         \"Time in s\": 1.25138       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7535545023696683,         \"F1\": 0.711111111111111,         \"Memory in Mb\": 1.087297439575195,         \"Time in s\": 3.920593       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7760252365930599,         \"F1\": 0.7380073800738007,         \"Memory in Mb\": 1.471883773803711,         \"Time in s\": 8.005582       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8085106382978723,         \"F1\": 0.7768595041322315,         \"Memory in Mb\": 1.8271961212158203,         \"Time in s\": 13.704046000000002       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8204158790170132,         \"F1\": 0.7845804988662132,         \"Memory in Mb\": 2.2761096954345703,         \"Time in s\": 21.017212       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8362204724409449,         \"F1\": 0.8052434456928838,         \"Memory in Mb\": 2.6539440155029297,         \"Time in s\": 30.07026       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8434547908232118,         \"F1\": 0.8110749185667754,         \"Memory in Mb\": 3.06672477722168,         \"Time in s\": 40.88011       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8512396694214877,         \"F1\": 0.8220338983050847,         \"Memory in Mb\": 3.4897289276123047,         \"Time in s\": 53.580909000000005       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8583420776495279,         \"F1\": 0.8301886792452831,         \"Memory in Mb\": 3.93476676940918,         \"Time in s\": 68.252368       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8659112370160529,         \"F1\": 0.8378995433789953,         \"Memory in Mb\": 4.283300399780273,         \"Time in s\": 84.83204500000001       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8695278969957082,         \"F1\": 0.8429752066115702,         \"Memory in Mb\": 4.800313949584961,         \"Time in s\": 103.574646       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8693941778127459,         \"F1\": 0.8442776735459662,         \"Memory in Mb\": 5.391313552856445,         \"Time in s\": 124.497156       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8714596949891068,         \"F1\": 0.8454148471615721,         \"Memory in Mb\": 5.846994400024414,         \"Time in s\": 147.830368       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8759271746459879,         \"F1\": 0.8518518518518519,         \"Memory in Mb\": 6.193078994750977,         \"Time in s\": 173.44391299999998       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8753933291378225,         \"F1\": 0.8520179372197308,         \"Memory in Mb\": 6.296388626098633,         \"Time in s\": 201.557429       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8755162241887906,         \"F1\": 0.8523442967109867,         \"Memory in Mb\": 6.211141586303711,         \"Time in s\": 232.060182       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8767351471404775,         \"F1\": 0.8550913838120104,         \"Memory in Mb\": 6.65928840637207,         \"Time in s\": 264.912691       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8730991085474568,         \"F1\": 0.8522588522588523,         \"Memory in Mb\": 6.686662673950195,         \"Time in s\": 300.275827       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8708395429706905,         \"F1\": 0.8507462686567164,         \"Memory in Mb\": 7.210599899291992,         \"Time in s\": 338.470819       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8725814063237376,         \"F1\": 0.8540540540540541,         \"Memory in Mb\": 7.48176383972168,         \"Time in s\": 378.991608       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8723595505617977,         \"F1\": 0.8539094650205761,         \"Memory in Mb\": 7.915548324584961,         \"Time in s\": 421.86917       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8751608751608752,         \"F1\": 0.8574228319451249,         \"Memory in Mb\": 8.423246383666992,         \"Time in s\": 467.302026       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8740254411161263,         \"F1\": 0.8560712611345522,         \"Memory in Mb\": 8.870996475219727,         \"Time in s\": 515.200386       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.874557609123083,         \"F1\": 0.857779759251003,         \"Memory in Mb\": 9.376256942749023,         \"Time in s\": 565.505493       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8761796904492262,         \"F1\": 0.8600682593856656,         \"Memory in Mb\": 9.769472122192385,         \"Time in s\": 618.233402       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8780399274047187,         \"F1\": 0.8621821164889254,         \"Memory in Mb\": 10.359186172485352,         \"Time in s\": 673.368122       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8797623208668298,         \"F1\": 0.8638163103721298,         \"Memory in Mb\": 10.741575241088867,         \"Time in s\": 730.824809       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8800134816312774,         \"F1\": 0.8637059724349159,         \"Memory in Mb\": 11.09235954284668,         \"Time in s\": 790.5343869999999       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8805727302310445,         \"F1\": 0.8648250460405157,         \"Memory in Mb\": 11.562868118286133,         \"Time in s\": 852.458144       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8826675055048757,         \"F1\": 0.8667381207574134,         \"Memory in Mb\": 10.152639389038086,         \"Time in s\": 916.443171       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.882496194824962,         \"F1\": 0.8663434903047091,         \"Memory in Mb\": 10.670488357543944,         \"Time in s\": 982.391476       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8826304924800944,         \"F1\": 0.867244829886591,         \"Memory in Mb\": 11.057397842407228,         \"Time in s\": 1050.1851049999998       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8839004861309694,         \"F1\": 0.8680961663417803,         \"Memory in Mb\": 10.33408546447754,         \"Time in s\": 1119.7824649999998       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8850957535387177,         \"F1\": 0.8689043698543382,         \"Memory in Mb\": 10.692270278930664,         \"Time in s\": 1191.1920959999998       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8846050148287948,         \"F1\": 0.8687116564417178,         \"Memory in Mb\": 11.112970352172852,         \"Time in s\": 1264.4750769999998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8859764089121888,         \"F1\": 0.870420017873101,         \"Memory in Mb\": 11.59941291809082,         \"Time in s\": 1339.6069449999998       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.884723284876307,         \"F1\": 0.8687572590011614,         \"Memory in Mb\": 12.03856086730957,         \"Time in s\": 1416.6409169999995       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8840327787434815,         \"F1\": 0.867892503536068,         \"Memory in Mb\": 12.43459129333496,         \"Time in s\": 1495.7064749999995       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.884587466731188,         \"F1\": 0.868558831634059,         \"Memory in Mb\": 12.796384811401367,         \"Time in s\": 1576.7285749999996       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8858221278603444,         \"F1\": 0.8701019860440149,         \"Memory in Mb\": 13.12009620666504,         \"Time in s\": 1659.7498909999997       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8872266973532796,         \"F1\": 0.8716605552645365,         \"Memory in Mb\": 13.362188339233398,         \"Time in s\": 1744.8641249999996       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869916872612896,         \"F1\": 0.8713225888974162,         \"Memory in Mb\": 13.906320571899414,         \"Time in s\": 1831.981592       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8872064955014264,         \"F1\": 0.8719481813652217,         \"Memory in Mb\": 14.321008682250977,         \"Time in s\": 1921.07186       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8876259918507399,         \"F1\": 0.8728155339805825,         \"Memory in Mb\": 14.677774429321287,         \"Time in s\": 2012.226564       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8867687146152233,         \"F1\": 0.8716119828815977,         \"Memory in Mb\": 15.041936874389648,         \"Time in s\": 2105.5104569999994       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886974358974359,         \"F1\": 0.8715318256003731,         \"Memory in Mb\": 15.36302375793457,         \"Time in s\": 2200.9404059999997       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8877735394499097,         \"F1\": 0.8726941471191073,         \"Memory in Mb\": 14.241693496704102,         \"Time in s\": 2298.464636999999       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886966778061726,         \"F1\": 0.8716804284757868,         \"Memory in Mb\": 14.559698104858398,         \"Time in s\": 2397.961828999999       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869632197188523,         \"F1\": 0.8716939890710382,         \"Memory in Mb\": 15.019205093383787,         \"Time in s\": 2499.520506999999       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886959803736554,         \"F1\": 0.8717070036410367,         \"Memory in Mb\": 15.355104446411133,         \"Time in s\": 2603.016254999998       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8674033149171271,         \"F1\": 0.8669623059866962,         \"Memory in Mb\": 3.022599220275879,         \"Time in s\": 14.706798       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8956377691882937,         \"F1\": 0.8737474949899798,         \"Memory in Mb\": 3.453568458557129,         \"Time in s\": 43.63985       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.889216047110784,         \"F1\": 0.8638625056535504,         \"Memory in Mb\": 5.134407997131348,         \"Time in s\": 89.85880599999999       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8901462876069556,         \"F1\": 0.8665325285043594,         \"Memory in Mb\": 5.045891761779785,         \"Time in s\": 149.36791399999998       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8924707440936189,         \"F1\": 0.8628555336524922,         \"Memory in Mb\": 6.377499580383301,         \"Time in s\": 220.195834       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8870285188592456,         \"F1\": 0.8556652562294312,         \"Memory in Mb\": 8.556572914123535,         \"Time in s\": 302.539101       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.884245387162908,         \"F1\": 0.8540175019888624,         \"Memory in Mb\": 10.355942726135254,         \"Time in s\": 396.160169       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835380157306472,         \"F1\": 0.8516174402250353,         \"Memory in Mb\": 10.061070442199709,         \"Time in s\": 501.0892999999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8847050165583221,         \"F1\": 0.8605341246290802,         \"Memory in Mb\": 12.516213417053224,         \"Time in s\": 615.7123809999999       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8869632409758251,         \"F1\": 0.8668400520156047,         \"Memory in Mb\": 14.3945894241333,         \"Time in s\": 740.068354       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8839939789262419,         \"F1\": 0.8666974169741698,         \"Memory in Mb\": 15.028592109680176,         \"Time in s\": 874.678214       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.886119032287738,         \"F1\": 0.8712830110210023,         \"Memory in Mb\": 18.58602237701416,         \"Time in s\": 1018.727325       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8851150547677676,         \"F1\": 0.869464544138929,         \"Memory in Mb\": 18.284192085266117,         \"Time in s\": 1172.329585       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8825987542379563,         \"F1\": 0.8672550592850139,         \"Memory in Mb\": 18.56262683868408,         \"Time in s\": 1335.5183499999998       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835087202884686,         \"F1\": 0.8699794661190965,         \"Memory in Mb\": 22.36763858795166,         \"Time in s\": 1507.9316749999998       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883270093135564,         \"F1\": 0.870085995085995,         \"Memory in Mb\": 23.97218418121338,         \"Time in s\": 1690.157865       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8826050256476852,         \"F1\": 0.8679713743245216,         \"Memory in Mb\": 24.89116382598877,         \"Time in s\": 1881.87418       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8806034218433801,         \"F1\": 0.8649885583524027,         \"Memory in Mb\": 9.630642890930176,         \"Time in s\": 2082.713846       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880787776680416,         \"F1\": 0.862742474916388,         \"Memory in Mb\": 9.825531959533691,         \"Time in s\": 2290.8714669999995       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.881505601854407,         \"F1\": 0.8635178946030132,         \"Memory in Mb\": 13.432568550109863,         \"Time in s\": 2506.1422499999994       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835742444152431,         \"F1\": 0.864335150364427,         \"Memory in Mb\": 11.236374855041504,         \"Time in s\": 2728.16983       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8847022226682053,         \"F1\": 0.8666744024135531,         \"Memory in Mb\": 10.915810585021973,         \"Time in s\": 2956.9763619999994       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8845803138647598,         \"F1\": 0.866618601297765,         \"Memory in Mb\": 6.771607398986816,         \"Time in s\": 3192.2184919999995       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8842845973416732,         \"F1\": 0.8643665768194071,         \"Memory in Mb\": 9.905537605285645,         \"Time in s\": 3433.744552       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8832178021104684,         \"F1\": 0.8619303648796786,         \"Memory in Mb\": 11.60939121246338,         \"Time in s\": 3682.104731       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8819783485459562,         \"F1\": 0.8599637316139432,         \"Memory in Mb\": 7.878331184387207,         \"Time in s\": 3937.9632889999993       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805854216916724,         \"F1\": 0.8573800107416631,         \"Memory in Mb\": 10.65384006500244,         \"Time in s\": 4201.066475999999       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8791343083533725,         \"F1\": 0.8556497175141242,         \"Memory in Mb\": 11.591797828674316,         \"Time in s\": 4470.752341999999       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8801431127012522,         \"F1\": 0.8566616596112704,         \"Memory in Mb\": 13.86082935333252,         \"Time in s\": 4746.937765999999       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.881195040288458,         \"F1\": 0.8584082438061829,         \"Memory in Mb\": 14.463074684143066,         \"Time in s\": 5029.888654999999       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8798646964571836,         \"F1\": 0.8561807331628303,         \"Memory in Mb\": 15.367924690246582,         \"Time in s\": 5319.910836999999       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8796523058880342,         \"F1\": 0.8551019560612982,         \"Memory in Mb\": 16.377129554748535,         \"Time in s\": 5616.458601999999       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.879285547044854,         \"F1\": 0.8544934080554772,         \"Memory in Mb\": 16.05477237701416,         \"Time in s\": 5919.470834999999       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8791351491737818,         \"F1\": 0.8535347574648885,         \"Memory in Mb\": 17.57622241973877,         \"Time in s\": 6228.255318999999       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8775111167176511,         \"F1\": 0.851267519338286,         \"Memory in Mb\": 17.546963691711426,         \"Time in s\": 6543.393541999999       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8771117583933773,         \"F1\": 0.8510701545778836,         \"Memory in Mb\": 17.15481662750244,         \"Time in s\": 6864.899302999999       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8770621401509502,         \"F1\": 0.8512864927285193,         \"Memory in Mb\": 13.577618598937988,         \"Time in s\": 7192.851994       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8757080198681267,         \"F1\": 0.849643346568748,         \"Memory in Mb\": 12.363858222961426,         \"Time in s\": 7526.949055999999       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8754139189992358,         \"F1\": 0.848624484181568,         \"Memory in Mb\": 12.55275058746338,         \"Time in s\": 7866.609101999999       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.875134523579569,         \"F1\": 0.8474427699672971,         \"Memory in Mb\": 12.88097858428955,         \"Time in s\": 8211.574848999999       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8743034055727554,         \"F1\": 0.8459838363846282,         \"Memory in Mb\": 15.634392738342283,         \"Time in s\": 8562.247513999999       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8741163175737826,         \"F1\": 0.8451642099818981,         \"Memory in Mb\": 17.75814151763916,         \"Time in s\": 8918.936239999999       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8743743101368175,         \"F1\": 0.8458873913591133,         \"Memory in Mb\": 18.55082416534424,         \"Time in s\": 9281.578229       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8744951458746206,         \"F1\": 0.847381104908331,         \"Memory in Mb\": 20.39273166656494,         \"Time in s\": 9650.394165999998       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8750521229365449,         \"F1\": 0.8493434283686265,         \"Memory in Mb\": 20.04684543609619,         \"Time in s\": 10025.208226999996       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8757768446310737,         \"F1\": 0.8512911843276937,         \"Memory in Mb\": 22.40410327911377,         \"Time in s\": 10405.883388999997       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8760010333247223,         \"F1\": 0.8517603458925264,         \"Memory in Mb\": 17.905674934387207,         \"Time in s\": 10792.424187999995       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8758249591832041,         \"F1\": 0.8516320474777449,         \"Memory in Mb\": 17.979458808898926,         \"Time in s\": 11185.171687999997       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8758362804946725,         \"F1\": 0.8511557571829769,         \"Memory in Mb\": 19.483532905578613,         \"Time in s\": 11584.749531999996       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8765977173889048,         \"F1\": 0.8524053440354862,         \"Memory in Mb\": 22.38176822662353,         \"Time in s\": 11990.855977999996       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8766083291033082,         \"F1\": 0.8523906328378699,         \"Memory in Mb\": 22.39494037628174,         \"Time in s\": 12397.578789999996       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.625,         \"F1\": 0.7096774193548387,         \"Memory in Mb\": 0.4178829193115234,         \"Time in s\": 0.504078       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7450980392156864,         \"Memory in Mb\": 0.6195468902587891,         \"Time in s\": 1.506996       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.7999999999999999,         \"Memory in Mb\": 0.8261966705322266,         \"Time in s\": 2.945496       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.797979797979798,         \"F1\": 0.8039215686274509,         \"Memory in Mb\": 0.9074077606201172,         \"Time in s\": 4.810448       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7903225806451613,         \"F1\": 0.7968749999999999,         \"Memory in Mb\": 1.0524044036865234,         \"Time in s\": 7.208508       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8120805369127517,         \"F1\": 0.8227848101265823,         \"Memory in Mb\": 1.155344009399414,         \"Time in s\": 10.051324       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8372093023255814,         \"Memory in Mb\": 1.2272701263427734,         \"Time in s\": 13.451327       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8442211055276382,         \"F1\": 0.8426395939086295,         \"Memory in Mb\": 1.3437442779541016,         \"Time in s\": 17.363237       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8465116279069769,         \"Memory in Mb\": 1.4417095184326172,         \"Time in s\": 21.777532       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554216867469879,         \"F1\": 0.85,         \"Memory in Mb\": 1.652822494506836,         \"Time in s\": 26.610844       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8540145985401459,         \"F1\": 0.8473282442748092,         \"Memory in Mb\": 1.7137775421142578,         \"Time in s\": 32.103705       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8595317725752508,         \"F1\": 0.85,         \"Memory in Mb\": 1.6836071014404297,         \"Time in s\": 38.177642       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8672839506172839,         \"F1\": 0.8542372881355932,         \"Memory in Mb\": 1.8154468536376955,         \"Time in s\": 44.724647       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8681948424068768,         \"F1\": 0.8525641025641026,         \"Memory in Mb\": 1.9355945587158203,         \"Time in s\": 51.783982       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663101604278075,         \"F1\": 0.8484848484848485,         \"Memory in Mb\": 2.1126270294189453,         \"Time in s\": 59.453597       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8696741854636592,         \"F1\": 0.8505747126436781,         \"Memory in Mb\": 2.2513599395751958,         \"Time in s\": 67.701448       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8702830188679245,         \"F1\": 0.8467966573816157,         \"Memory in Mb\": 2.4080867767333984,         \"Time in s\": 76.525873       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8775055679287305,         \"F1\": 0.8533333333333333,         \"Memory in Mb\": 2.413846969604492,         \"Time in s\": 85.91210000000001       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.879746835443038,         \"F1\": 0.85785536159601,         \"Memory in Mb\": 2.540945053100586,         \"Time in s\": 95.911251       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8817635270541082,         \"F1\": 0.8624708624708626,         \"Memory in Mb\": 2.727457046508789,         \"Time in s\": 106.551347       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8835877862595419,         \"F1\": 0.8623024830699774,         \"Memory in Mb\": 2.780088424682617,         \"Time in s\": 117.8137       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8816029143897997,         \"F1\": 0.8602150537634409,         \"Memory in Mb\": 2.84419059753418,         \"Time in s\": 129.668358       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8832752613240418,         \"F1\": 0.8618556701030927,         \"Memory in Mb\": 2.9667911529541016,         \"Time in s\": 142.149346       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8864774624373957,         \"F1\": 0.8634538152610441,         \"Memory in Mb\": 2.9313793182373047,         \"Time in s\": 155.146824       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878205128205128,         \"F1\": 0.8622047244094488,         \"Memory in Mb\": 3.1180286407470703,         \"Time in s\": 168.863765       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8906009244992296,         \"F1\": 0.8672897196261682,         \"Memory in Mb\": 3.1772937774658203,         \"Time in s\": 183.176428       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8931750741839762,         \"F1\": 0.8732394366197184,         \"Memory in Mb\": 3.270914077758789,         \"Time in s\": 198.116157       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8969957081545065,         \"F1\": 0.8762886597938143,         \"Memory in Mb\": 3.2819652557373047,         \"Time in s\": 213.702702       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8950276243093923,         \"F1\": 0.8762214983713356,         \"Memory in Mb\": 3.465627670288086,         \"Time in s\": 229.894704       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.897196261682243,         \"F1\": 0.8791208791208791,         \"Memory in Mb\": 3.637697219848633,         \"Time in s\": 246.719197       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8979328165374677,         \"F1\": 0.8793893129770992,         \"Memory in Mb\": 3.6838626861572266,         \"Time in s\": 264.274398       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8961201501877347,         \"F1\": 0.8784773060029282,         \"Memory in Mb\": 3.7807750701904297,         \"Time in s\": 282.500107       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8968446601941747,         \"F1\": 0.8801128349788435,         \"Memory in Mb\": 3.913633346557617,         \"Time in s\": 301.464554       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8987043580683156,         \"F1\": 0.8818681318681318,         \"Memory in Mb\": 4.011789321899414,         \"Time in s\": 321.06457099999994       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9016018306636157,         \"F1\": 0.8847184986595175,         \"Memory in Mb\": 4.15968132019043,         \"Time in s\": 341.449663       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9010011123470524,         \"F1\": 0.8836601307189543,         \"Memory in Mb\": 3.946676254272461,         \"Time in s\": 362.64137199999993       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9036796536796536,         \"F1\": 0.8877679697351829,         \"Memory in Mb\": 4.049928665161133,         \"Time in s\": 384.6765929999999       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9030558482613276,         \"F1\": 0.8883495145631068,         \"Memory in Mb\": 3.684160232543945,         \"Time in s\": 407.5276099999999       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.904517453798768,         \"F1\": 0.8899408284023669,         \"Memory in Mb\": 3.787748336791992,         \"Time in s\": 431.1052179999999       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9049049049049048,         \"F1\": 0.8904267589388698,         \"Memory in Mb\": 4.052656173706055,         \"Time in s\": 455.43358799999993       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9033203125,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 4.062379837036133,         \"Time in s\": 480.5827999999999       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9046711153479504,         \"F1\": 0.8908296943231442,         \"Memory in Mb\": 4.190084457397461,         \"Time in s\": 506.4991779999999       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9059590316573556,         \"F1\": 0.8928950159066809,         \"Memory in Mb\": 4.285711288452148,         \"Time in s\": 533.2628339999999       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9062784349408554,         \"F1\": 0.8934850051706308,         \"Memory in Mb\": 4.370790481567383,         \"Time in s\": 560.8485399999998       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9065836298932384,         \"F1\": 0.8948948948948948,         \"Memory in Mb\": 3.93486213684082,         \"Time in s\": 589.2233909999999       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9077458659704092,         \"F1\": 0.896078431372549,         \"Memory in Mb\": 4.19316291809082,         \"Time in s\": 618.4066789999998       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9063032367972744,         \"F1\": 0.8942307692307692,         \"Memory in Mb\": 4.349401473999023,         \"Time in s\": 648.4320089999999       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9065888240200168,         \"F1\": 0.8943396226415095,         \"Memory in Mb\": 4.34752082824707,         \"Time in s\": 679.2709969999999       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9068627450980392,         \"F1\": 0.8944444444444444,         \"Memory in Mb\": 4.031515121459961,         \"Time in s\": 710.9105619999998       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9079263410728584,         \"F1\": 0.896115627822945,         \"Memory in Mb\": 4.102910995483398,         \"Time in s\": 743.3769359999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1703529357910156,         \"Time in s\": 12.381202       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1715736389160156,         \"Time in s\": 37.073822       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1727943420410156,         \"Time in s\": 74.106824       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1727943420410156,         \"Time in s\": 122.30955       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1727943420410156,         \"Time in s\": 180.539768       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1740150451660156,         \"Time in s\": 247.174942       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1740150451660156,         \"Time in s\": 321.611977       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.23138427734375,         \"Time in s\": 404.612477       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.1771812438964843,         \"Time in s\": 498.19936       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691703796386718,         \"Time in s\": 601.949625       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704368591308593,         \"Time in s\": 714.510104       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782646179199218,         \"Time in s\": 835.601872       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.1626014709472656,         \"Time in s\": 964.845183       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.170440673828125,         \"Time in s\": 1103.12758       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782646179199218,         \"Time in s\": 1249.157139       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781692504882812,         \"Time in s\": 1402.52966       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.1626205444335937,         \"Time in s\": 1563.348194       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704368591308593,         \"Time in s\": 1731.617157       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704330444335937,         \"Time in s\": 1907.150595       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781158447265625,         \"Time in s\": 2090.214351       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.1704559326171875,         \"Time in s\": 2280.326256       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.163818359375,         \"Time in s\": 2477.410916       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.1715812683105468,         \"Time in s\": 2681.481315       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.171630859375,         \"Time in s\": 2892.603814       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.1794776916503906,         \"Time in s\": 3110.724932       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.3225936889648437,         \"Time in s\": 3336.815763       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.3238716125488281,         \"Time in s\": 3571.848368       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.2926750183105469,         \"Time in s\": 3815.616702       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.3244895935058594,         \"Time in s\": 4068.203899       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.2933502197265625,         \"Time in s\": 4329.847041999999       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.2782058715820312,         \"Time in s\": 4599.769264       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.3094024658203125,         \"Time in s\": 4878.106527       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.3095321655273437,         \"Time in s\": 5164.770925       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.2940139770507812,         \"Time in s\": 5459.39874       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.293975830078125,         \"Time in s\": 5761.313045999999       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.3251571655273437,         \"Time in s\": 6070.487373999999       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101234436035156,         \"Time in s\": 6386.879354       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101387023925781,         \"Time in s\": 6710.520715       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101615905761719,         \"Time in s\": 7041.446151       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101768493652344,         \"Time in s\": 7379.414547       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.3100128173828125,         \"Time in s\": 7724.224697000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.3101425170898437,         \"Time in s\": 8075.760258       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.3100852966308594,         \"Time in s\": 8434.038373       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.3107223510742187,         \"Time in s\": 8799.103777999999       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.310821533203125,         \"Time in s\": 9170.869249       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.2950706481933594,         \"Time in s\": 9549.506616       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.2951812744140625,         \"Time in s\": 9935.081315999998       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.2957954406738281,         \"Time in s\": 10327.647623999996       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.3115119934082031,         \"Time in s\": 10728.119291999998       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.3115577697753906,         \"Time in s\": 11135.737891999996       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.3270950317382812,         \"Time in s\": 11543.384409999995       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5428571428571428,         \"F1\": 0.4,         \"Memory in Mb\": 0.2255392074584961,         \"Time in s\": 2.569769       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5592417061611374,         \"F1\": 0.4685714285714286,         \"Memory in Mb\": 0.6304416656494141,         \"Time in s\": 8.011061999999999       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.637223974763407,         \"F1\": 0.5724907063197027,         \"Memory in Mb\": 0.9710559844970704,         \"Time in s\": 16.523663       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6926713947990544,         \"F1\": 0.6448087431693988,         \"Memory in Mb\": 1.262800216674805,         \"Time in s\": 28.175313       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7145557655954632,         \"F1\": 0.6621923937360179,         \"Memory in Mb\": 1.5703105926513672,         \"Time in s\": 43.218913       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7448818897637796,         \"F1\": 0.7000000000000001,         \"Memory in Mb\": 1.467294692993164,         \"Time in s\": 61.573557       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7624831309041835,         \"F1\": 0.7170418006430868,         \"Memory in Mb\": 1.877767562866211,         \"Time in s\": 83.22394800000001       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7827626918536009,         \"F1\": 0.7430167597765364,         \"Memory in Mb\": 2.3253536224365234,         \"Time in s\": 108.413447       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7964323189926548,         \"F1\": 0.7599009900990098,         \"Memory in Mb\": 1.7426891326904297,         \"Time in s\": 137.183902       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8054768649669499,         \"F1\": 0.7674943566591422,         \"Memory in Mb\": 1.7942829132080078,         \"Time in s\": 169.50541299999998       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8103004291845494,         \"F1\": 0.7747196738022425,         \"Memory in Mb\": 1.8575687408447263,         \"Time in s\": 205.462561       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8151062155782848,         \"F1\": 0.7822057460611677,         \"Memory in Mb\": 1.917165756225586,         \"Time in s\": 244.587795       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8191721132897604,         \"F1\": 0.7851596203623814,         \"Memory in Mb\": 2.1873340606689453,         \"Time in s\": 286.663235       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8240053944706676,         \"F1\": 0.7916999201915402,         \"Memory in Mb\": 2.2810306549072266,         \"Time in s\": 331.700902       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8231592196349906,         \"F1\": 0.7916975537435137,         \"Memory in Mb\": 2.585817337036133,         \"Time in s\": 379.560946       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8271386430678466,         \"F1\": 0.7961029923451634,         \"Memory in Mb\": 2.885595321655273,         \"Time in s\": 430.279509       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8334258745141588,         \"F1\": 0.8046875,         \"Memory in Mb\": 2.8240184783935547,         \"Time in s\": 483.724395       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8332459360251704,         \"F1\": 0.8060975609756097,         \"Memory in Mb\": 3.138376235961914,         \"Time in s\": 539.732848       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8340784898161947,         \"F1\": 0.8084862385321101,         \"Memory in Mb\": 3.5751514434814453,         \"Time in s\": 598.378334       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8367154318074563,         \"F1\": 0.813778256189451,         \"Memory in Mb\": 3.890401840209961,         \"Time in s\": 659.469374       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8382022471910112,         \"F1\": 0.8157625383828044,         \"Memory in Mb\": 4.414094924926758,         \"Time in s\": 723.240852       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8404118404118404,         \"F1\": 0.8185365853658537,         \"Memory in Mb\": 4.828973770141602,         \"Time in s\": 789.4489060000001       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8432498974148543,         \"F1\": 0.8216619981325864,         \"Memory in Mb\": 4.724649429321289,         \"Time in s\": 858.0992190000001       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8450648839952811,         \"F1\": 0.8247330960854093,         \"Memory in Mb\": 4.20762825012207,         \"Time in s\": 929.163006       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.846734616836542,         \"F1\": 0.8270868824531515,         \"Memory in Mb\": 4.517709732055664,         \"Time in s\": 1002.552295       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8500907441016334,         \"F1\": 0.8306683066830667,         \"Memory in Mb\": 4.757001876831055,         \"Time in s\": 1078.240465       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8521495980426425,         \"F1\": 0.8324752475247525,         \"Memory in Mb\": 4.690572738647461,         \"Time in s\": 1156.1945970000002       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.854061341422312,         \"F1\": 0.8339087073264287,         \"Memory in Mb\": 4.873067855834961,         \"Time in s\": 1236.185934       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8538887081028311,         \"F1\": 0.8340110905730129,         \"Memory in Mb\": 5.244169235229492,         \"Time in s\": 1318.3478       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8565586662472475,         \"F1\": 0.836441893830703,         \"Memory in Mb\": 5.473237991333008,         \"Time in s\": 1402.707418       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8575342465753425,         \"F1\": 0.8371607515657619,         \"Memory in Mb\": 5.716192245483398,         \"Time in s\": 1489.296647       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8593335299321734,         \"F1\": 0.8400938652363393,         \"Memory in Mb\": 6.05610466003418,         \"Time in s\": 1578.3688909999998       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8615956534172148,         \"F1\": 0.8419333768778575,         \"Memory in Mb\": 6.433168411254883,         \"Time in s\": 1669.799251       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8631695809048016,         \"F1\": 0.8430436166825852,         \"Memory in Mb\": 6.670698165893555,         \"Time in s\": 1763.5519829999998       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8638447020760313,         \"F1\": 0.8444718201416692,         \"Memory in Mb\": 7.050989151000977,         \"Time in s\": 1859.626818       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8657929226736566,         \"F1\": 0.84688995215311,         \"Memory in Mb\": 7.316404342651367,         \"Time in s\": 1958.012503       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8653404743687835,         \"F1\": 0.846064139941691,         \"Memory in Mb\": 7.61528205871582,         \"Time in s\": 2058.675528       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8644151974174323,         \"F1\": 0.8449744463373083,         \"Memory in Mb\": 7.967977523803711,         \"Time in s\": 2161.579262       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8654730220179047,         \"F1\": 0.8462389380530975,         \"Memory in Mb\": 7.394952774047852,         \"Time in s\": 2266.595873       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8674215616890776,         \"F1\": 0.8486806677436726,         \"Memory in Mb\": 7.571531295776367,         \"Time in s\": 2373.458397       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8688147295742232,         \"F1\": 0.8503937007874016,         \"Memory in Mb\": 7.877435684204102,         \"Time in s\": 2482.305033       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8683441923163334,         \"F1\": 0.8496664956387892,         \"Memory in Mb\": 8.180627822875977,         \"Time in s\": 2593.165336       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8689927583936801,         \"F1\": 0.8508618536097925,         \"Memory in Mb\": 8.39448356628418,         \"Time in s\": 2705.945127       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8691829294445635,         \"F1\": 0.8516536964980544,         \"Memory in Mb\": 8.710580825805664,         \"Time in s\": 2820.674173       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8689452715453974,         \"F1\": 0.8510131108462455,         \"Memory in Mb\": 9.014997482299805,         \"Time in s\": 2937.453009       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8703589743589744,         \"F1\": 0.8523364485981308,         \"Memory in Mb\": 9.167715072631836,         \"Time in s\": 3056.177406       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8713109817305762,         \"F1\": 0.8538864827900615,         \"Memory in Mb\": 9.482858657836914,         \"Time in s\": 3176.936292       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8714369962649892,         \"F1\": 0.8539526574363555,         \"Memory in Mb\": 9.87147331237793,         \"Time in s\": 3299.754349       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8717504332755632,         \"F1\": 0.8543944031482291,         \"Memory in Mb\": 10.20412254333496,         \"Time in s\": 3424.594329       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8716739007359879,         \"F1\": 0.8542648949849978,         \"Memory in Mb\": 10.538087844848633,         \"Time in s\": 3551.414099       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8828729281767956,         \"F1\": 0.8811659192825113,         \"Memory in Mb\": 5.258722305297852,         \"Time in s\": 37.408806       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9039204859193816,         \"F1\": 0.8804945054945055,         \"Memory in Mb\": 8.443174362182617,         \"Time in s\": 104.985856       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8873757821126242,         \"F1\": 0.8602739726027397,         \"Memory in Mb\": 12.445928573608398,         \"Time in s\": 198.514402       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.884902014904775,         \"F1\": 0.8576305906452714,         \"Memory in Mb\": 16.533422470092773,         \"Time in s\": 314.268209       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8812099801280636,         \"F1\": 0.8452243958573072,         \"Memory in Mb\": 19.26629447937012,         \"Time in s\": 451.77035       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8756209751609936,         \"F1\": 0.8372652864708715,         \"Memory in Mb\": 24.12981605529785,         \"Time in s\": 609.66041       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8719444882510645,         \"F1\": 0.8340825500612996,         \"Memory in Mb\": 28.348302841186523,         \"Time in s\": 788.9253299999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8691872498965089,         \"F1\": 0.8308351177730193,         \"Memory in Mb\": 31.66439247131348,         \"Time in s\": 988.709629       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8690052741322213,         \"F1\": 0.8387681159420289,         \"Memory in Mb\": 35.27585411071777,         \"Time in s\": 1209.125777       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.869742797218236,         \"F1\": 0.844162704701532,         \"Memory in Mb\": 38.39363670349121,         \"Time in s\": 1448.169528       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8681384846964375,         \"F1\": 0.8455934195064629,         \"Memory in Mb\": 42.49019813537598,         \"Time in s\": 1706.33653       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8687333272008095,         \"F1\": 0.849265870920038,         \"Memory in Mb\": 46.91076469421387,         \"Time in s\": 1983.228319       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8694064702386006,         \"F1\": 0.8495402073958129,         \"Memory in Mb\": 41.51856422424317,         \"Time in s\": 2278.772125       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8672238429393676,         \"F1\": 0.8479320931912588,         \"Memory in Mb\": 46.98099327087402,         \"Time in s\": 2591.81768       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8682758113179778,         \"F1\": 0.8513289036544851,         \"Memory in Mb\": 50.757638931274414,         \"Time in s\": 2922.748705       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8687823387374957,         \"F1\": 0.8527863777089782,         \"Memory in Mb\": 43.74130058288574,         \"Time in s\": 3269.777293       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8686448931887539,         \"F1\": 0.8518708354689903,         \"Memory in Mb\": 49.06788444519043,         \"Time in s\": 3632.343799       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8649659655362728,         \"F1\": 0.8467427616926504,         \"Memory in Mb\": 54.357858657836914,         \"Time in s\": 4011.129855       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.865392435949573,         \"F1\": 0.8447987139125192,         \"Memory in Mb\": 52.38222694396973,         \"Time in s\": 4407.893822       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8652795408135107,         \"F1\": 0.8448286822198208,         \"Memory in Mb\": 59.36540412902832,         \"Time in s\": 4822.718697       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.867700394218134,         \"F1\": 0.8459136822773186,         \"Memory in Mb\": 57.10729789733887,         \"Time in s\": 5251.994155       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8692489087351363,         \"F1\": 0.84882236918436,         \"Memory in Mb\": 51.34463310241699,         \"Time in s\": 5694.600867       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8691750251955656,         \"F1\": 0.8490085299656586,         \"Memory in Mb\": 53.35045051574707,         \"Time in s\": 6149.875029       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8700271351699398,         \"F1\": 0.8478682170542635,         \"Memory in Mb\": 59.89077186584473,         \"Time in s\": 6616.750619       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8694865115457636,         \"F1\": 0.8459614382490881,         \"Memory in Mb\": 65.61615180969238,         \"Time in s\": 7094.935599       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.868860114625345,         \"F1\": 0.8444690599667689,         \"Memory in Mb\": 72.22853660583496,         \"Time in s\": 7585.744803       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8682392379706472,         \"F1\": 0.8428648042513772,         \"Memory in Mb\": 80.47726249694824,         \"Time in s\": 8090.441156999999       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8672290771474751,         \"F1\": 0.8417888012025554,         \"Memory in Mb\": 73.03231239318848,         \"Time in s\": 8608.413273       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8684581128915617,         \"F1\": 0.8430802760624773,         \"Memory in Mb\": 79.16955757141113,         \"Time in s\": 9138.877569       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8698259685786821,         \"F1\": 0.8451234459814394,         \"Memory in Mb\": 84.2670955657959,         \"Time in s\": 9681.232951       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689335944454335,         \"F1\": 0.8434616202423985,         \"Memory in Mb\": 93.53372383117676,         \"Time in s\": 10236.036265       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8688558518160808,         \"F1\": 0.8423322551215061,         \"Memory in Mb\": 92.7535800933838,         \"Time in s\": 10801.143387       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689166137070609,         \"F1\": 0.8421603769785332,         \"Memory in Mb\": 90.79977607727052,         \"Time in s\": 11375.205452       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8684868356978216,         \"F1\": 0.8408063818917749,         \"Memory in Mb\": 97.95510292053224,         \"Time in s\": 11957.401901       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8668832192752847,         \"F1\": 0.8384553561177235,         \"Memory in Mb\": 105.25788688659668,         \"Time in s\": 12547.475367       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8664724819868159,         \"F1\": 0.8381943154374885,         \"Memory in Mb\": 94.53887367248537,         \"Time in s\": 13145.300695000002       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8661436114674383,         \"F1\": 0.8380553650701988,         \"Memory in Mb\": 102.01883506774902,         \"Time in s\": 13750.733881000002       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8648444534812793,         \"F1\": 0.8364441632394811,         \"Memory in Mb\": 100.427827835083,         \"Time in s\": 14363.910035000004       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8647723091727281,         \"F1\": 0.8357849876271651,         \"Memory in Mb\": 107.87262153625488,         \"Time in s\": 14984.863075000005       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8642346643119292,         \"F1\": 0.83420946219167,         \"Memory in Mb\": 112.83228874206544,         \"Time in s\": 15613.594311000004       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8632655808318751,         \"F1\": 0.8326689289361843,         \"Memory in Mb\": 120.66686058044434,         \"Time in s\": 16250.422859000002       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8627631336889964,         \"F1\": 0.8316135689410551,         \"Memory in Mb\": 126.15458106994627,         \"Time in s\": 16895.166705000003       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8634391765279668,         \"F1\": 0.8328620797989319,         \"Memory in Mb\": 107.22049903869627,         \"Time in s\": 17548.303432000004       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8644356922459423,         \"F1\": 0.8353041570157259,         \"Memory in Mb\": 103.5422191619873,         \"Time in s\": 18208.490072000004       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.865436974171552,         \"F1\": 0.8378745788758201,         \"Memory in Mb\": 97.78764533996582,         \"Time in s\": 18874.504490000003       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8666586682663467,         \"F1\": 0.8403940603728063,         \"Memory in Mb\": 102.76390266418456,         \"Time in s\": 19545.753018000003       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8673821657546793,         \"F1\": 0.8415055151702265,         \"Memory in Mb\": 107.51249122619627,         \"Time in s\": 20221.607860000004       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8677075907742544,         \"F1\": 0.841885392332005,         \"Memory in Mb\": 102.9560489654541,         \"Time in s\": 20902.251232       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8679071024711104,         \"F1\": 0.8415905775568644,         \"Memory in Mb\": 103.86480903625488,         \"Time in s\": 21587.715975000003       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8688933530541513,         \"F1\": 0.843053830501308,         \"Memory in Mb\": 107.29028511047365,         \"Time in s\": 22278.011723000003       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8688839354682086,         \"F1\": 0.8430092751631743,         \"Memory in Mb\": 107.32242012023926,         \"Time in s\": 22968.976341       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8333333333333334,         \"F1\": 0.8333333333333334,         \"Memory in Mb\": 0.7029104232788086,         \"Time in s\": 1.141902       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8372093023255814,         \"Memory in Mb\": 0.9397382736206056,         \"Time in s\": 3.355867       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8783783783783784,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 0.9708013534545898,         \"Time in s\": 6.532426       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8888888888888888,         \"F1\": 0.8817204301075269,         \"Memory in Mb\": 1.056624412536621,         \"Time in s\": 10.815831       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8790322580645161,         \"F1\": 0.8739495798319329,         \"Memory in Mb\": 1.3782567977905271,         \"Time in s\": 16.293882       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8791946308724832,         \"F1\": 0.8783783783783784,         \"Memory in Mb\": 1.379134178161621,         \"Time in s\": 22.890072       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.896551724137931,         \"F1\": 0.888888888888889,         \"Memory in Mb\": 1.4786596298217771,         \"Time in s\": 30.52314       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8944723618090452,         \"F1\": 0.8864864864864866,         \"Memory in Mb\": 1.6607275009155271,         \"Time in s\": 39.247513       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8973214285714286,         \"F1\": 0.8866995073891626,         \"Memory in Mb\": 1.686568260192871,         \"Time in s\": 49.014513       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.891566265060241,         \"F1\": 0.88,         \"Memory in Mb\": 1.9668035507202148,         \"Time in s\": 59.910523       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8905109489051095,         \"F1\": 0.8780487804878049,         \"Memory in Mb\": 2.071291923522949,         \"Time in s\": 71.88595       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896321070234113,         \"F1\": 0.8754716981132077,         \"Memory in Mb\": 2.2423620223999023,         \"Time in s\": 85.00905599999999       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8888888888888888,         \"F1\": 0.8723404255319148,         \"Memory in Mb\": 2.4750547409057617,         \"Time in s\": 99.146325       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8853868194842407,         \"F1\": 0.8666666666666667,         \"Memory in Mb\": 2.532855033874512,         \"Time in s\": 114.354375       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8850267379679144,         \"F1\": 0.8652037617554859,         \"Memory in Mb\": 2.8150205612182617,         \"Time in s\": 130.79065599999998       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8822055137844611,         \"F1\": 0.8613569321533923,         \"Memory in Mb\": 2.795191764831543,         \"Time in s\": 148.41625799999997       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844339622641509,         \"F1\": 0.8611898016997167,         \"Memory in Mb\": 2.962000846862793,         \"Time in s\": 167.06847699999997       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.888641425389755,         \"F1\": 0.8648648648648649,         \"Memory in Mb\": 3.03415584564209,         \"Time in s\": 186.853211       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.890295358649789,         \"F1\": 0.8686868686868687,         \"Memory in Mb\": 3.071761131286621,         \"Time in s\": 207.829       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8917835671342685,         \"F1\": 0.8726415094339622,         \"Memory in Mb\": 3.1551198959350586,         \"Time in s\": 229.951047       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8950381679389313,         \"F1\": 0.8741418764302059,         \"Memory in Mb\": 3.1928510665893555,         \"Time in s\": 253.214946       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8943533697632058,         \"F1\": 0.8739130434782608,         \"Memory in Mb\": 3.2878904342651367,         \"Time in s\": 277.566695       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8937282229965157,         \"F1\": 0.8726513569937369,         \"Memory in Mb\": 3.441771507263184,         \"Time in s\": 303.140017       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8964941569282137,         \"F1\": 0.8739837398373984,         \"Memory in Mb\": 3.515273094177246,         \"Time in s\": 329.715755       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8958333333333334,         \"F1\": 0.8707753479125249,         \"Memory in Mb\": 3.5807180404663086,         \"Time in s\": 357.461609       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8983050847457628,         \"F1\": 0.8754716981132076,         \"Memory in Mb\": 3.695376396179199,         \"Time in s\": 386.398038       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8961424332344213,         \"F1\": 0.8754448398576512,         \"Memory in Mb\": 3.755015373229981,         \"Time in s\": 416.468493       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.899856938483548,         \"F1\": 0.8784722222222222,         \"Memory in Mb\": 3.790995597839356,         \"Time in s\": 447.643877       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.899171270718232,         \"F1\": 0.8797364085667215,         \"Memory in Mb\": 3.939352989196777,         \"Time in s\": 479.929216       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9012016021361816,         \"F1\": 0.8825396825396825,         \"Memory in Mb\": 3.942519187927246,         \"Time in s\": 513.493128       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9018087855297158,         \"F1\": 0.8827160493827161,         \"Memory in Mb\": 4.2751874923706055,         \"Time in s\": 548.2965389999999       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.899874843554443,         \"F1\": 0.8816568047337278,         \"Memory in Mb\": 4.513812065124512,         \"Time in s\": 584.3583229999999       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8992718446601942,         \"F1\": 0.8819345661450925,         \"Memory in Mb\": 4.773520469665527,         \"Time in s\": 621.611368       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.901060070671378,         \"F1\": 0.8836565096952909,         \"Memory in Mb\": 4.815379142761231,         \"Time in s\": 660.1796979999999       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.902745995423341,         \"F1\": 0.884979702300406,         \"Memory in Mb\": 4.980830192565918,         \"Time in s\": 699.8371419999999       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9043381535038932,         \"F1\": 0.8862433862433862,         \"Memory in Mb\": 5.134486198425293,         \"Time in s\": 740.7850809999999       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9069264069264068,         \"F1\": 0.8903061224489796,         \"Memory in Mb\": 5.209948539733887,         \"Time in s\": 782.8443949999998       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9083245521601686,         \"F1\": 0.8932515337423312,         \"Memory in Mb\": 5.338950157165527,         \"Time in s\": 826.1813729999999       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9106776180698152,         \"F1\": 0.895808383233533,         \"Memory in Mb\": 5.382990837097168,         \"Time in s\": 870.7373769999999       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9109109109109108,         \"F1\": 0.896149358226371,         \"Memory in Mb\": 5.44773006439209,         \"Time in s\": 916.477949       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9111328125,         \"F1\": 0.896942242355606,         \"Memory in Mb\": 5.591532707214356,         \"Time in s\": 963.445117       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.911344137273594,         \"F1\": 0.8976897689768977,         \"Memory in Mb\": 5.678961753845215,         \"Time in s\": 1011.481541       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9115456238361266,         \"F1\": 0.8986125933831376,         \"Memory in Mb\": 5.788058280944824,         \"Time in s\": 1060.652982       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9117379435850772,         \"F1\": 0.8990634755463061,         \"Memory in Mb\": 5.880267143249512,         \"Time in s\": 1110.965738       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9119217081850534,         \"F1\": 0.9003021148036253,         \"Memory in Mb\": 6.120665550231934,         \"Time in s\": 1162.442095       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9129677980852916,         \"F1\": 0.90138067061144,         \"Memory in Mb\": 6.185591697692871,         \"Time in s\": 1215.005575       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9114139693356048,         \"F1\": 0.8996138996138997,         \"Memory in Mb\": 6.431841850280762,         \"Time in s\": 1268.8167280000002       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9124270225187656,         \"F1\": 0.9004739336492891,         \"Memory in Mb\": 6.484606742858887,         \"Time in s\": 1323.7082160000002       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9133986928104576,         \"F1\": 0.9014869888475836,         \"Memory in Mb\": 6.481654167175293,         \"Time in s\": 1379.6419000000003       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.9135308246597278,         \"F1\": 0.9019963702359348,         \"Memory in Mb\": 6.595587730407715,         \"Time in s\": 1436.6903440000003       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1670236587524414,         \"Time in s\": 31.246172       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1682443618774414,         \"Time in s\": 90.057064       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1694650650024414,         \"Time in s\": 168.92668600000002       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1694650650024414,         \"Time in s\": 266.339332       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1694650650024414,         \"Time in s\": 379.700681       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1706857681274414,         \"Time in s\": 507.500932       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1706857681274414,         \"Time in s\": 650.046105       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.2171335220336914,         \"Time in s\": 806.74928       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.1745767593383789,         \"Time in s\": 979.905317       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.1744394302368164,         \"Time in s\": 1169.37771       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757745742797851,         \"Time in s\": 1374.513378       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757287979125976,         \"Time in s\": 1595.365052       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757516860961914,         \"Time in s\": 1830.528112       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757287979125976,         \"Time in s\": 2079.072293       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757287979125976,         \"Time in s\": 2341.33087       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.1756372451782226,         \"Time in s\": 2616.3107910000003       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757745742797851,         \"Time in s\": 2903.8369350000003       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.1756601333618164,         \"Time in s\": 3203.0985050000004       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.1756830215454101,         \"Time in s\": 3513.3936680000006       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757059097290039,         \"Time in s\": 3834.7595300000007       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.1757745742797851,         \"Time in s\": 4167.168481000001       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 4510.027218000001       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 4863.234695000001       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 5226.731618000001       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770639419555664,         \"Time in s\": 5600.511358000001       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769037246704101,         \"Time in s\": 5984.651066       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691675186157226,         \"Time in s\": 6379.477192       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770639419555664,         \"Time in s\": 6785.903036000001       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770410537719726,         \"Time in s\": 7201.6518080000005       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 7626.427031       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 8059.133738       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 8499.283325       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769266128540039,         \"Time in s\": 8946.957028       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769952774047851,         \"Time in s\": 9401.959764       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 9864.111715       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691446304321289,         \"Time in s\": 10332.853073       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769037246704101,         \"Time in s\": 10808.168746       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.1691217422485351,         \"Time in s\": 11290.14581       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 11778.656001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 12273.787996       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769952774047851,         \"Time in s\": 12775.472063       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.1769723892211914,         \"Time in s\": 13283.764208       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.1770181655883789,         \"Time in s\": 13798.661938       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782617568969726,         \"Time in s\": 14320.191281       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.1703653335571289,         \"Time in s\": 14848.294436       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.1702966690063476,         \"Time in s\": 15383.005183       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781930923461914,         \"Time in s\": 15923.647685       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781473159790039,         \"Time in s\": 16470.415157       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782159805297851,         \"Time in s\": 17023.687732       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.1782159805297851,         \"Time in s\": 17582.958979       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1781702041625976,         \"Time in s\": 18142.251025       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7238095238095238,         \"F1\": 0.6881720430107527,         \"Memory in Mb\": 0.1032800674438476,         \"Time in s\": 0.213787       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8056872037914692,         \"F1\": 0.7807486631016043,         \"Memory in Mb\": 0.1952676773071289,         \"Time in s\": 0.888466       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.807570977917981,         \"F1\": 0.7859649122807018,         \"Memory in Mb\": 0.286778450012207,         \"Time in s\": 2.29757       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8297872340425532,         \"F1\": 0.8115183246073298,         \"Memory in Mb\": 0.3787660598754883,         \"Time in s\": 4.640547       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.831758034026465,         \"F1\": 0.8061002178649236,         \"Memory in Mb\": 2.6361207962036133,         \"Time in s\": 29.527472000000003       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8472440944881889,         \"F1\": 0.8245931283905967,         \"Memory in Mb\": 3.060887336730957,         \"Time in s\": 56.29478       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8529014844804319,         \"F1\": 0.8278041074249604,         \"Memory in Mb\": 3.5180253982543945,         \"Time in s\": 85.033958       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8559622195985832,         \"F1\": 0.8328767123287671,         \"Memory in Mb\": 3.9749040603637695,         \"Time in s\": 116.009539       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8604407135362014,         \"F1\": 0.8372093023255813,         \"Memory in Mb\": 4.4283952713012695,         \"Time in s\": 149.38786199999998       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8706326723323891,         \"F1\": 0.8476084538375974,         \"Memory in Mb\": 4.592366218566895,         \"Time in s\": 185.280189       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.871244635193133,         \"F1\": 0.8484848484848485,         \"Memory in Mb\": 4.394963264465332,         \"Time in s\": 223.461408       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8693941778127459,         \"F1\": 0.8477064220183486,         \"Memory in Mb\": 4.242337226867676,         \"Time in s\": 263.595386       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8714596949891068,         \"F1\": 0.8488471391972673,         \"Memory in Mb\": 4.1376237869262695,         \"Time in s\": 305.593042       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8759271746459879,         \"F1\": 0.8548895899053628,         \"Memory in Mb\": 4.233838081359863,         \"Time in s\": 349.622856       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8735053492762744,         \"F1\": 0.8527472527472527,         \"Memory in Mb\": 4.485638618469238,         \"Time in s\": 396.123591       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8755162241887906,         \"F1\": 0.854982817869416,         \"Memory in Mb\": 4.566784858703613,         \"Time in s\": 444.689218       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8778456413103831,         \"F1\": 0.858611825192802,         \"Memory in Mb\": 4.580937385559082,         \"Time in s\": 495.109217       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8778185631882538,         \"F1\": 0.8598917618761276,         \"Memory in Mb\": 4.553791999816895,         \"Time in s\": 547.400313       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.877297565822156,         \"F1\": 0.8605307735742519,         \"Memory in Mb\": 4.4779558181762695,         \"Time in s\": 601.303554       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8787163756488909,         \"F1\": 0.8635156664896441,         \"Memory in Mb\": 4.453892707824707,         \"Time in s\": 656.840699       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8782022471910113,         \"F1\": 0.8630621526023244,         \"Memory in Mb\": 4.4562273025512695,         \"Time in s\": 714.0315049999999       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8777348777348777,         \"F1\": 0.862782859894078,         \"Memory in Mb\": 4.439526557922363,         \"Time in s\": 772.745195       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8785391875256463,         \"F1\": 0.8635944700460828,         \"Memory in Mb\": 4.450131416320801,         \"Time in s\": 833.024947       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8788832088084939,         \"F1\": 0.864793678665496,         \"Memory in Mb\": 4.448788642883301,         \"Time in s\": 894.808032       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8784446961117403,         \"F1\": 0.8647058823529411,         \"Memory in Mb\": 4.491581916809082,         \"Time in s\": 958.170481       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.879491833030853,         \"F1\": 0.8659127625201939,         \"Memory in Mb\": 4.482541084289551,         \"Time in s\": 1022.970177       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8808109052778749,         \"F1\": 0.867056530214425,         \"Memory in Mb\": 4.4542436599731445,         \"Time in s\": 1089.131713       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8813616447590158,         \"F1\": 0.8673700075357951,         \"Memory in Mb\": 4.489590644836426,         \"Time in s\": 1156.687087       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8805727302310445,         \"F1\": 0.8665939658306071,         \"Memory in Mb\": 4.4426774978637695,         \"Time in s\": 1225.526022       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8820383768480654,         \"F1\": 0.8677248677248678,         \"Memory in Mb\": 4.4409685134887695,         \"Time in s\": 1295.666774       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.882496194824962,         \"F1\": 0.8678082191780822,         \"Memory in Mb\": 4.441540718078613,         \"Time in s\": 1367.284144       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8832202890002949,         \"F1\": 0.8693931398416888,         \"Memory in Mb\": 4.4570817947387695,         \"Time in s\": 1440.244405       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8850443237060337,         \"F1\": 0.8709055876685934,         \"Memory in Mb\": 4.465977668762207,         \"Time in s\": 1514.504066       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8856508465167916,         \"F1\": 0.8710888610763454,         \"Memory in Mb\": 4.4596757888793945,         \"Time in s\": 1590.040367       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8864923159881369,         \"F1\": 0.8724628900333233,         \"Memory in Mb\": 4.477154731750488,         \"Time in s\": 1666.899992       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8875491480996068,         \"F1\": 0.8737120989108037,         \"Memory in Mb\": 4.4705095291137695,         \"Time in s\": 1745.0344980000002       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8867635807192042,         \"F1\": 0.8724870763928776,         \"Memory in Mb\": 4.45665454864502,         \"Time in s\": 1824.4605280000003       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8852743978147505,         \"F1\": 0.8706606942889138,         \"Memory in Mb\": 4.454602241516113,         \"Time in s\": 1905.1708120000003       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8857972417130414,         \"F1\": 0.8712493180578287,         \"Memory in Mb\": 4.461682319641113,         \"Time in s\": 1987.1975480000003       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886765746638358,         \"F1\": 0.8724760892667376,         \"Memory in Mb\": 4.4584245681762695,         \"Time in s\": 2070.530861       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8876869965477561,         \"F1\": 0.8735751295336789,         \"Memory in Mb\": 4.494175910949707,         \"Time in s\": 2155.1880220000003       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869916872612896,         \"F1\": 0.8725614390676463,         \"Memory in Mb\": 4.517621040344238,         \"Time in s\": 2241.198533       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869870528856704,         \"F1\": 0.8729336294103133,         \"Memory in Mb\": 4.495129585266113,         \"Time in s\": 2328.452784       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.886982629208664,         \"F1\": 0.873286847799952,         \"Memory in Mb\": 4.453595161437988,         \"Time in s\": 2416.918556       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8857202767875865,         \"F1\": 0.8715531463587085,         \"Memory in Mb\": 4.469174385070801,         \"Time in s\": 2506.628209       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8861538461538462,         \"F1\": 0.871616932685635,         \"Memory in Mb\": 4.478787422180176,         \"Time in s\": 2597.661861       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8869704878538446,         \"F1\": 0.8728832693610296,         \"Memory in Mb\": 4.4154863357543945,         \"Time in s\": 2689.897866       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.885983880479654,         \"F1\": 0.8716814159292035,         \"Memory in Mb\": 4.439602851867676,         \"Time in s\": 2783.355406       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.885422684382823,         \"F1\": 0.8711842390127733,         \"Memory in Mb\": 4.50291919708252,         \"Time in s\": 2878.218251       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8850726552179656,         \"F1\": 0.8708377518557794,         \"Memory in Mb\": 4.509961128234863,         \"Time in s\": 2974.330637       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8784530386740331,         \"F1\": 0.8711943793911008,         \"Memory in Mb\": 4.434150695800781,         \"Time in s\": 37.114054       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8801766979569299,         \"F1\": 0.8453314326443336,         \"Memory in Mb\": 4.643096923828125,         \"Time in s\": 93.709907       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8568273831431726,         \"F1\": 0.8160756501182034,         \"Memory in Mb\": 4.667282104492188,         \"Time in s\": 164.56349699999998       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8746894838531604,         \"F1\": 0.8411476557032889,         \"Memory in Mb\": 4.594398498535156,         \"Time in s\": 248.378172       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8783395893133142,         \"F1\": 0.8399651466744118,         \"Memory in Mb\": 4.710762023925781,         \"Time in s\": 344.82085099999995       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8745170193192272,         \"F1\": 0.8360576923076923,         \"Memory in Mb\": 4.698677062988281,         \"Time in s\": 452.381486       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8747831572307208,         \"F1\": 0.8384865744507731,         \"Memory in Mb\": 4.6694183349609375,         \"Time in s\": 569.8523869999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8723609769559818,         \"F1\": 0.8348509194786646,         \"Memory in Mb\": 4.666007995605469,         \"Time in s\": 697.1091419999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8718263215994113,         \"F1\": 0.8430695299594534,         \"Memory in Mb\": 4.7265625,         \"Time in s\": 834.9178869999998       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8738271332376643,         \"F1\": 0.8493475682087781,         \"Memory in Mb\": 4.708610534667969,         \"Time in s\": 981.8103579999998       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8720521826392373,         \"F1\": 0.8501234277653698,         \"Memory in Mb\": 4.625167846679688,         \"Time in s\": 1137.002296       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8740686229417717,         \"F1\": 0.8545628386274301,         \"Memory in Mb\": 4.637184143066406,         \"Time in s\": 1300.798705       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8742464124989386,         \"F1\": 0.8546756942400157,         \"Memory in Mb\": 4.6933135986328125,         \"Time in s\": 1473.412993       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.872664196168099,         \"F1\": 0.8527937289217027,         \"Memory in Mb\": 4.810676574707031,         \"Time in s\": 1655.581963       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8748252262859666,         \"F1\": 0.8573824096587573,         \"Memory in Mb\": 4.703468322753906,         \"Time in s\": 1846.50108       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8750603656433252,         \"F1\": 0.85826093762229,         \"Memory in Mb\": 4.719985961914063,         \"Time in s\": 2046.373626       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8755924939938965,         \"F1\": 0.8581371242410781,         \"Memory in Mb\": 4.7149505615234375,         \"Time in s\": 2254.774116       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.872079475072055,         \"F1\": 0.8535112359550563,         \"Memory in Mb\": 4.6830902099609375,         \"Time in s\": 2471.471716       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8723058153721025,         \"F1\": 0.8517669274345832,         \"Memory in Mb\": 4.657257080078125,         \"Time in s\": 2696.3467009999995       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.87234394834152,         \"F1\": 0.8515118443859535,         \"Memory in Mb\": 4.7351837158203125,         \"Time in s\": 2929.434657       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8734822601839685,         \"F1\": 0.8509505232522138,         \"Memory in Mb\": 4.845870971679688,         \"Time in s\": 3171.2751989999992       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8722091214690683,         \"F1\": 0.8505193966782089,         \"Memory in Mb\": 4.855270385742188,         \"Time in s\": 3421.524358999999       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8678312616979411,         \"F1\": 0.8451765234989881,         \"Memory in Mb\": 4.8942718505859375,         \"Time in s\": 3679.924087999999       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8677735363105368,         \"F1\": 0.8427672955974842,         \"Memory in Mb\": 4.7196807861328125,         \"Time in s\": 3945.793201999999       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8669256920835356,         \"F1\": 0.840444679724722,         \"Memory in Mb\": 4.809005737304688,         \"Time in s\": 4218.904371999999       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8647845468053492,         \"F1\": 0.8373257061136934,         \"Memory in Mb\": 4.794342041015625,         \"Time in s\": 4499.001777999999       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8644372674870201,         \"F1\": 0.8359715077166601,         \"Memory in Mb\": 4.758956909179688,         \"Time in s\": 4786.067247999999       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8619860448614342,         \"F1\": 0.8330710914032329,         \"Memory in Mb\": 4.846771240234375,         \"Time in s\": 5079.937268999999       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8623301488219846,         \"F1\": 0.8333410127632125,         \"Memory in Mb\": 4.699310302734375,         \"Time in s\": 5380.101847999999       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8632767945840538,         \"F1\": 0.8350350705851016,         \"Memory in Mb\": 4.794769287109375,         \"Time in s\": 5686.612909       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.862061598718177,         \"F1\": 0.8333620096352374,         \"Memory in Mb\": 4.6817474365234375,         \"Time in s\": 5999.306036       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8618191852643924,         \"F1\": 0.8323989624299222,         \"Memory in Mb\": 4.8116455078125,         \"Time in s\": 6318.119808       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8607218115529987,         \"F1\": 0.8308417289567761,         \"Memory in Mb\": 4.769432067871094,         \"Time in s\": 6643.155825       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8599162419244879,         \"F1\": 0.8291562735083342,         \"Memory in Mb\": 4.83782958984375,         \"Time in s\": 6975.21929       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8578006244283958,         \"F1\": 0.8263832736513803,         \"Memory in Mb\": 4.765548706054688,         \"Time in s\": 7313.109579       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8558332055802544,         \"F1\": 0.8246307623452186,         \"Memory in Mb\": 4.726959228515625,         \"Time in s\": 7656.831982       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8543897855075923,         \"F1\": 0.8232354325861008,         \"Memory in Mb\": 4.798057556152344,         \"Time in s\": 8006.181245       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533128068086095,         \"F1\": 0.8218066337332393,         \"Memory in Mb\": 4.773887634277344,         \"Time in s\": 8361.39601       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8518099227351201,         \"F1\": 0.8192737815822173,         \"Memory in Mb\": 4.808341979980469,         \"Time in s\": 8722.632877       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8522310218273131,         \"F1\": 0.8186651315566692,         \"Memory in Mb\": 4.722572326660156,         \"Time in s\": 9089.688296       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8505586216179836,         \"F1\": 0.8161859664227292,         \"Memory in Mb\": 4.720252990722656,         \"Time in s\": 9462.293988       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8507792173661665,         \"F1\": 0.81590039556449,         \"Memory in Mb\": 4.766929626464844,         \"Time in s\": 9840.72504       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8507841979618553,         \"F1\": 0.8163523204751524,         \"Memory in Mb\": 4.769111633300781,         \"Time in s\": 10225.058019       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.850889295838246,         \"F1\": 0.8178809976101478,         \"Memory in Mb\": 4.736076354980469,         \"Time in s\": 10615.291715       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8509161372611543,         \"F1\": 0.8193329766363474,         \"Memory in Mb\": 4.725471496582031,         \"Time in s\": 11011.540552       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8518536292741452,         \"F1\": 0.8217770336585647,         \"Memory in Mb\": 4.700096130371094,         \"Time in s\": 11414.574328       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8529156196425636,         \"F1\": 0.8235028885444553,         \"Memory in Mb\": 4.746559143066406,         \"Time in s\": 11823.240959       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8525536367190195,         \"F1\": 0.8231074817920989,         \"Memory in Mb\": 4.826316833496094,         \"Time in s\": 12236.954316       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8525217939765278,         \"F1\": 0.8226754421602882,         \"Memory in Mb\": 4.775764465332031,         \"Time in s\": 12655.735001       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.853131415704541,         \"F1\": 0.8236541468974475,         \"Memory in Mb\": 4.767349243164063,         \"Time in s\": 13079.48614       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8531482421487057,         \"F1\": 0.8236416644579911,         \"Memory in Mb\": 4.766036987304688,         \"Time in s\": 13503.439196       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.0411081314086914,         \"Time in s\": 0.04635       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7551020408163265,         \"F1\": 0.7777777777777778,         \"Memory in Mb\": 0.0695962905883789,         \"Time in s\": 0.16308       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7972972972972973,         \"F1\": 0.8235294117647058,         \"Memory in Mb\": 0.0986146926879882,         \"Time in s\": 0.336872       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.797979797979798,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.1271295547485351,         \"Time in s\": 0.6777850000000001       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8064516129032258,         \"F1\": 0.8208955223880596,         \"Memory in Mb\": 0.155644416809082,         \"Time in s\": 1.226658       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.834355828220859,         \"Memory in Mb\": 0.1846628189086914,         \"Time in s\": 1.947513       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8426966292134832,         \"Memory in Mb\": 0.2131776809692382,         \"Time in s\": 2.9471350000000003       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8391959798994975,         \"F1\": 0.8415841584158417,         \"Memory in Mb\": 0.2421960830688476,         \"Time in s\": 4.26255       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8392857142857143,         \"F1\": 0.8363636363636364,         \"Memory in Mb\": 0.2707109451293945,         \"Time in s\": 5.830504       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8232931726907631,         \"F1\": 0.8225806451612903,         \"Memory in Mb\": 0.2992258071899414,         \"Time in s\": 7.819846       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8248175182481752,         \"F1\": 0.8208955223880596,         \"Memory in Mb\": 0.3284578323364258,         \"Time in s\": 10.199689       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8260869565217391,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 0.3569726943969726,         \"Time in s\": 12.972732       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8364197530864198,         \"F1\": 0.8250825082508251,         \"Memory in Mb\": 0.385991096496582,         \"Time in s\": 16.225203999999998       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8452722063037249,         \"F1\": 0.83125,         \"Memory in Mb\": 0.4145059585571289,         \"Time in s\": 19.962148       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.839572192513369,         \"F1\": 0.8235294117647058,         \"Memory in Mb\": 0.4430208206176758,         \"Time in s\": 24.257676       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8421052631578947,         \"F1\": 0.8225352112676055,         \"Memory in Mb\": 0.4720392227172851,         \"Time in s\": 29.175886       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8443396226415094,         \"F1\": 0.819672131147541,         \"Memory in Mb\": 0.500554084777832,         \"Time in s\": 34.714831       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8463251670378619,         \"F1\": 0.8198433420365536,         \"Memory in Mb\": 0.5295724868774414,         \"Time in s\": 40.875928       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8438818565400844,         \"F1\": 0.8177339901477833,         \"Memory in Mb\": 0.5580873489379883,         \"Time in s\": 47.66810099999999       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845691382765531,         \"F1\": 0.8229885057471266,         \"Memory in Mb\": 2.675789833068848,         \"Time in s\": 79.03492       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8454198473282443,         \"F1\": 0.8187919463087249,         \"Memory in Mb\": 2.7769289016723637,         \"Time in s\": 111.488727       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.848816029143898,         \"F1\": 0.8237791932059448,         \"Memory in Mb\": 2.8829355239868164,         \"Time in s\": 145.039549       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8519163763066202,         \"F1\": 0.8268839103869654,         \"Memory in Mb\": 2.989964485168457,         \"Time in s\": 179.782132       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8514190317195326,         \"F1\": 0.8230616302186878,         \"Memory in Mb\": 3.098984718322754,         \"Time in s\": 215.642079       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8525641025641025,         \"F1\": 0.8210116731517509,         \"Memory in Mb\": 3.2059221267700195,         \"Time in s\": 252.521109       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8582434514637904,         \"F1\": 0.8302583025830258,         \"Memory in Mb\": 3.3169260025024414,         \"Time in s\": 290.529559       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8620178041543026,         \"F1\": 0.8382608695652174,         \"Memory in Mb\": 3.429127693176269,         \"Time in s\": 329.62297       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669527896995708,         \"F1\": 0.8421052631578948,         \"Memory in Mb\": 3.5458459854125977,         \"Time in s\": 369.665809       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8674033149171271,         \"F1\": 0.8456591639871384,         \"Memory in Mb\": 3.659161567687988,         \"Time in s\": 410.977113       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8678237650200267,         \"F1\": 0.8465116279069768,         \"Memory in Mb\": 3.769242286682129,         \"Time in s\": 453.550102       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8669250645994832,         \"F1\": 0.8446455505279035,         \"Memory in Mb\": 3.881718635559082,         \"Time in s\": 497.265773       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8648310387984981,         \"F1\": 0.8434782608695652,         \"Memory in Mb\": 3.994263648986816,         \"Time in s\": 542.189116       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8628640776699029,         \"F1\": 0.8423988842398884,         \"Memory in Mb\": 4.110844612121582,         \"Time in s\": 588.325742       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8657243816254417,         \"F1\": 0.8451086956521738,         \"Memory in Mb\": 4.225159645080566,         \"Time in s\": 635.696811       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.868421052631579,         \"F1\": 0.847277556440903,         \"Memory in Mb\": 4.342709541320801,         \"Time in s\": 684.216091       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8698553948832035,         \"F1\": 0.8482490272373541,         \"Memory in Mb\": 4.455658912658691,         \"Time in s\": 733.952375       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8712121212121212,         \"F1\": 0.8514357053682896,         \"Memory in Mb\": 4.573666572570801,         \"Time in s\": 785.013527       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8735511064278187,         \"F1\": 0.8561151079136691,         \"Memory in Mb\": 4.697152137756348,         \"Time in s\": 837.27657       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8757700205338809,         \"F1\": 0.8581477139507622,         \"Memory in Mb\": 4.820996284484863,         \"Time in s\": 890.836474       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8758758758758759,         \"F1\": 0.858447488584475,         \"Memory in Mb\": 4.93715763092041,         \"Time in s\": 945.663339       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8759765625,         \"F1\": 0.8590455049944505,         \"Memory in Mb\": 4.905686378479004,         \"Time in s\": 1001.665911       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8779790276453765,         \"F1\": 0.8617710583153347,         \"Memory in Mb\": 4.881028175354004,         \"Time in s\": 1058.849613       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8780260707635009,         \"F1\": 0.86282722513089,         \"Memory in Mb\": 4.857575416564941,         \"Time in s\": 1117.1299479999998       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8789808917197452,         \"F1\": 0.8641470888661901,         \"Memory in Mb\": 4.821175575256348,         \"Time in s\": 1176.4409139999998       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798932384341637,         \"F1\": 0.8662041625371655,         \"Memory in Mb\": 4.749619483947754,         \"Time in s\": 1236.7626339999997       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807658833768495,         \"F1\": 0.8668610301263362,         \"Memory in Mb\": 4.722535133361816,         \"Time in s\": 1298.0588499999997       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.879045996592845,         \"F1\": 0.8645038167938931,         \"Memory in Mb\": 4.706612586975098,         \"Time in s\": 1360.3228199999996       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8807339449541285,         \"F1\": 0.865979381443299,         \"Memory in Mb\": 4.686341285705566,         \"Time in s\": 1423.5043029999997       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8815359477124183,         \"F1\": 0.8666053357865686,         \"Memory in Mb\": 4.653275489807129,         \"Time in s\": 1487.6393369999996       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8815052041633307,         \"F1\": 0.867145421903052,         \"Memory in Mb\": 4.596428871154785,         \"Time in s\": 1552.6498929999996       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.559709548950195,         \"Time in s\": 49.463009       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.594751358032227,         \"Time in s\": 126.299444       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.435243606567383,         \"Time in s\": 223.803561       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.493677139282227,         \"Time in s\": 340.763146       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.534708023071289,         \"Time in s\": 475.19475       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.455095291137695,         \"Time in s\": 625.35715       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.479013442993164,         \"Time in s\": 790.6914730000001       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686198515404,         \"F1\": 0.9,         \"Memory in Mb\": 4.445444107055664,         \"Time in s\": 971.128522       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998832184981898,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.544534683227539,         \"Time in s\": 1166.447296       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998948972620736,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.52708625793457,         \"Time in s\": 1376.022994       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999904452512899,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.493997573852539,         \"Time in s\": 1599.513782       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124151521788,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.490983963012695,         \"Time in s\": 1835.532511       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999191527205108,         \"F1\": 0.9166666666666666,         \"Memory in Mb\": 4.531465530395508,         \"Time in s\": 2083.498651       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998873916144289,         \"F1\": 0.88,         \"Memory in Mb\": 4.54191780090332,         \"Time in s\": 2343.113276       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999894899103139,         \"F1\": 0.88,         \"Memory in Mb\": 4.488824844360352,         \"Time in s\": 2613.833594       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999014681249384,         \"F1\": 0.88,         \"Memory in Mb\": 4.459695816040039,         \"Time in s\": 2894.8346570000003       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999072642967544,         \"F1\": 0.88,         \"Memory in Mb\": 4.475152969360352,         \"Time in s\": 3186.5040240000003       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999124164306776,         \"F1\": 0.88,         \"Memory in Mb\": 4.543954849243164,         \"Time in s\": 3487.9588300000005       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999170262197146,         \"F1\": 0.88,         \"Memory in Mb\": 4.482622146606445,         \"Time in s\": 3798.7831540000006       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999211750177356,         \"F1\": 0.88,         \"Memory in Mb\": 4.496248245239258,         \"Time in s\": 4119.269013000001       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999924928682248,         \"F1\": 0.88,         \"Memory in Mb\": 4.471353530883789,         \"Time in s\": 4448.958874000001       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999283410963812,         \"F1\": 0.88,         \"Memory in Mb\": 4.53770637512207,         \"Time in s\": 4788.142489000001       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999931456772071,         \"F1\": 0.88,         \"Memory in Mb\": 4.51286506652832,         \"Time in s\": 5135.940338       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999343128024348,         \"F1\": 0.88,         \"Memory in Mb\": 4.49894905090332,         \"Time in s\": 5492.9415070000005       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999369403455668,         \"F1\": 0.88,         \"Memory in Mb\": 4.555765151977539,         \"Time in s\": 5859.118968000001       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999393657659116,         \"F1\": 0.88,         \"Memory in Mb\": 4.430139541625977,         \"Time in s\": 6234.600581000001       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999221486959906,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 4.466188430786133,         \"Time in s\": 6619.789592000001       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999249291518872,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 4.526651382446289,         \"Time in s\": 7013.956607000001       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9999275178487298,         \"F1\": 0.8571428571428571,         \"Memory in Mb\": 4.485139846801758,         \"Time in s\": 7418.775951000001       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997898018882796,         \"F1\": 0.7391304347826089,         \"Memory in Mb\": 4.452577590942383,         \"Time in s\": 7831.9045620000015       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997965825874696,         \"F1\": 0.7391304347826089,         \"Memory in Mb\": 4.485406875610352,         \"Time in s\": 8252.946705000002       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998029394860004,         \"F1\": 0.7391304347826089,         \"Memory in Mb\": 4.502649307250977,         \"Time in s\": 8681.511861000003       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997770629637888,         \"F1\": 0.7083333333333334,         \"Memory in Mb\": 4.495584487915039,         \"Time in s\": 9116.499033000002       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997836200367846,         \"F1\": 0.7083333333333334,         \"Memory in Mb\": 4.500345230102539,         \"Time in s\": 9557.922914000002       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997898024142694,         \"F1\": 0.7083333333333334,         \"Memory in Mb\": 4.572656631469727,         \"Time in s\": 10005.892137000004       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997664472243712,         \"F1\": 0.68,         \"Memory in Mb\": 4.537866592407227,         \"Time in s\": 10460.064213000003       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997727595512002,         \"F1\": 0.68,         \"Memory in Mb\": 4.469621658325195,         \"Time in s\": 10920.643886000003       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997787396457068,         \"F1\": 0.68,         \"Memory in Mb\": 4.537904739379883,         \"Time in s\": 11387.046035000005       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997844130645684,         \"F1\": 0.68,         \"Memory in Mb\": 4.493074417114258,         \"Time in s\": 11859.048710000005       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99978980280876,         \"F1\": 0.68,         \"Memory in Mb\": 4.520692825317383,         \"Time in s\": 12336.641580000003       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997949296352312,         \"F1\": 0.68,         \"Memory in Mb\": 4.566102981567383,         \"Time in s\": 12820.170836000005       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997998123240538,         \"F1\": 0.68,         \"Memory in Mb\": 4.500688552856445,         \"Time in s\": 13309.287446000002       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998044679082956,         \"F1\": 0.68,         \"Memory in Mb\": 4.506959915161133,         \"Time in s\": 13804.219559000005       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089118725442,         \"F1\": 0.68,         \"Memory in Mb\": 4.503435134887695,         \"Time in s\": 14304.793113000003       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998131583249644,         \"F1\": 0.68,         \"Memory in Mb\": 4.498682022094727,         \"Time in s\": 14810.923265000005       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998172201469092,         \"F1\": 0.68,         \"Memory in Mb\": 4.491861343383789,         \"Time in s\": 15322.783751000004       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998099284436494,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.496858596801758,         \"Time in s\": 15840.351229000004       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998138883110912,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.480546951293945,         \"Time in s\": 16363.581709000004       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999817686549557,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.533571243286133,         \"Time in s\": 16892.331870000005       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998213328568876,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.517786026000977,         \"Time in s\": 17426.665113000006       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998213441227471,         \"F1\": 0.6666666666666666,         \"Memory in Mb\": 4.518220901489258,         \"Time in s\": 17961.110841000005       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.4857142857142857,         \"F1\": 0.4599999999999999,         \"Memory in Mb\": 0.1797952651977539,         \"Time in s\": 0.693272       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165876777251185,         \"F1\": 0.4574468085106383,         \"Memory in Mb\": 0.1805887222290039,         \"Time in s\": 2.027128       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5205047318611987,         \"F1\": 0.4722222222222222,         \"Memory in Mb\": 0.1812677383422851,         \"Time in s\": 4.089008       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460992907801419,         \"F1\": 0.4838709677419355,         \"Memory in Mb\": 0.1813135147094726,         \"Time in s\": 6.917919       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.55765595463138,         \"F1\": 0.455813953488372,         \"Memory in Mb\": 0.1813364028930664,         \"Time in s\": 10.429995       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5543307086614173,         \"F1\": 0.4259634888438134,         \"Memory in Mb\": 0.1819925308227539,         \"Time in s\": 14.687229       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5748987854251012,         \"F1\": 0.4220183486238532,         \"Memory in Mb\": 0.1820383071899414,         \"Time in s\": 19.647457       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5785123966942148,         \"F1\": 0.4232633279483037,         \"Memory in Mb\": 0.1819696426391601,         \"Time in s\": 25.366911       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5844700944386149,         \"F1\": 0.4193548387096774,         \"Memory in Mb\": 0.1819467544555664,         \"Time in s\": 31.800242       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5920679886685553,         \"F1\": 0.4146341463414634,         \"Memory in Mb\": 0.1819467544555664,         \"Time in s\": 39.029576       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.590557939914163,         \"F1\": 0.4015056461731493,         \"Memory in Mb\": 0.1819238662719726,         \"Time in s\": 46.984262       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971675845790716,         \"F1\": 0.4101382488479262,         \"Memory in Mb\": 0.1819238662719726,         \"Time in s\": 55.672123       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599128540305011,         \"F1\": 0.3973799126637554,         \"Memory in Mb\": 0.1825342178344726,         \"Time in s\": 65.02100899999999       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5994605529332434,         \"F1\": 0.3926380368098159,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 75.177128       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5997482693517936,         \"F1\": 0.3896353166986563,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 86.053176       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6011799410029498,         \"F1\": 0.3876811594202898,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 97.727606       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6013325930038868,         \"F1\": 0.3904923599320882,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 110.067211       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030414263240692,         \"F1\": 0.396812749003984,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 123.213825       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986090412319921,         \"F1\": 0.3961136023916292,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 137.051202       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5969797074091553,         \"F1\": 0.3994374120956399,         \"Memory in Mb\": 0.1824884414672851,         \"Time in s\": 151.568436       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597752808988764,         \"F1\": 0.4013377926421405,         \"Memory in Mb\": 0.1824426651000976,         \"Time in s\": 166.814875       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5988845988845989,         \"F1\": 0.4033184428844926,         \"Memory in Mb\": 0.1824426651000976,         \"Time in s\": 182.823981       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5995075913007797,         \"F1\": 0.4019607843137255,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 199.616425       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008651199370821,         \"F1\": 0.4088526499708794,         \"Memory in Mb\": 0.1824655532836914,         \"Time in s\": 217.084375       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6002265005662514,         \"F1\": 0.4073866815892558,         \"Memory in Mb\": 0.1830987930297851,         \"Time in s\": 235.279245       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5985480943738657,         \"F1\": 0.4028077753779697,         \"Memory in Mb\": 0.1830987930297851,         \"Time in s\": 254.250965       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599790283117791,         \"F1\": 0.4051948051948052,         \"Memory in Mb\": 0.1830987930297851,         \"Time in s\": 273.857236       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599932591843613,         \"F1\": 0.4026170105686965,         \"Memory in Mb\": 0.1831216812133789,         \"Time in s\": 294.204326       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5977871786527823,         \"F1\": 0.4023210831721469,         \"Memory in Mb\": 0.1831216812133789,         \"Time in s\": 315.311898       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986159169550173,         \"F1\": 0.4042950513538749,         \"Memory in Mb\": 0.1831216812133789,         \"Time in s\": 337.189575       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5981735159817352,         \"F1\": 0.4021739130434782,         \"Memory in Mb\": 0.1785964965820312,         \"Time in s\": 359.75124       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5959893836626364,         \"F1\": 0.4022687609075043,         \"Memory in Mb\": 0.2364349365234375,         \"Time in s\": 383.144231       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597369173577352,         \"F1\": 0.4023769100169779,         \"Memory in Mb\": 0.2806434631347656,         \"Time in s\": 407.324287       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008881487649181,         \"F1\": 0.4087171052631579,         \"Memory in Mb\": 0.3000526428222656,         \"Time in s\": 432.314941       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6012402264761392,         \"F1\": 0.4086365453818472,         \"Memory in Mb\": 0.3464546203613281,         \"Time in s\": 458.107983       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6023591087811271,         \"F1\": 0.4104158569762923,         \"Memory in Mb\": 0.3760719299316406,         \"Time in s\": 484.645149       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6052027543993879,         \"F1\": 0.4145234493192133,         \"Memory in Mb\": 0.4113121032714844,         \"Time in s\": 512.014837       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608393344921778,         \"F1\": 0.4195804195804196,         \"Memory in Mb\": 0.4392280578613281,         \"Time in s\": 540.239956       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6121461408178079,         \"F1\": 0.4260651629072682,         \"Memory in Mb\": 0.4532661437988281,         \"Time in s\": 569.3761920000001       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6157112526539278,         \"F1\": 0.4329968673860076,         \"Memory in Mb\": 0.4546051025390625,         \"Time in s\": 599.333749       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6186421173762946,         \"F1\": 0.4384954252795662,         \"Memory in Mb\": 0.4373931884765625,         \"Time in s\": 630.119       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6212087171422153,         \"F1\": 0.4420913302448709,         \"Memory in Mb\": 0.4377059936523437,         \"Time in s\": 661.732786       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6214614878209348,         \"F1\": 0.4437278297323443,         \"Memory in Mb\": 0.4275894165039062,         \"Time in s\": 694.0925080000001       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6219172206733863,         \"F1\": 0.4454230890217049,         \"Memory in Mb\": 0.3975372314453125,         \"Time in s\": 727.2725200000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6227720696162717,         \"F1\": 0.4449244060475162,         \"Memory in Mb\": 0.42584228515625,         \"Time in s\": 761.2761580000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6235897435897436,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 0.393829345703125,         \"Time in s\": 796.1318860000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6251756675366392,         \"F1\": 0.4491000295072292,         \"Memory in Mb\": 0.39398193359375,         \"Time in s\": 831.6856570000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.624139964615687,         \"F1\": 0.4467592592592592,         \"Memory in Mb\": 0.39410400390625,         \"Time in s\": 867.9313910000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6248796456768727,         \"F1\": 0.4469051675184554,         \"Memory in Mb\": 0.394500732421875,         \"Time in s\": 904.957506       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6259671636157765,         \"F1\": 0.4482182628062361,         \"Memory in Mb\": 0.4006576538085937,         \"Time in s\": 942.730038       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8651933701657458,         \"F1\": 0.8685344827586208,         \"Memory in Mb\": 1.5650663375854492,         \"Time in s\": 11.845084       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895637769188294,         \"F1\": 0.8684210526315789,         \"Memory in Mb\": 1.8734617233276367,         \"Time in s\": 34.886970000000005       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8778064041221936,         \"F1\": 0.8547681539807523,         \"Memory in Mb\": 1.7035398483276367,         \"Time in s\": 70.08374500000001       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835219431410434,         \"F1\": 0.8607260726072606,         \"Memory in Mb\": 1.6641263961791992,         \"Time in s\": 115.953507       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8878339589313314,         \"F1\": 0.8599007170435742,         \"Memory in Mb\": 2.069842338562012,         \"Time in s\": 171.720075       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.886292548298068,         \"F1\": 0.8580615525953147,         \"Memory in Mb\": 2.326838493347168,         \"Time in s\": 235.88433300000003       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8845607948273143,         \"F1\": 0.8556782334384859,         \"Memory in Mb\": 1.8882322311401367,         \"Time in s\": 307.801578       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8835380157306472,         \"F1\": 0.8526021655606008,         \"Memory in Mb\": 1.7675046920776367,         \"Time in s\": 386.842642       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8854409419845456,         \"F1\": 0.8617115783239561,         \"Memory in Mb\": 1.8750486373901367,         \"Time in s\": 473.251889       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8863009162159179,         \"F1\": 0.8664765361680064,         \"Memory in Mb\": 1.8668012619018557,         \"Time in s\": 566.6143930000001       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883492222779729,         \"F1\": 0.8657337805019083,         \"Memory in Mb\": 1.624751091003418,         \"Time in s\": 666.8526760000001       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8851071658541072,         \"F1\": 0.8689539397754694,         \"Memory in Mb\": 1.836443901062012,         \"Time in s\": 773.0944300000001       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8819733378619343,         \"F1\": 0.8645224171539961,         \"Memory in Mb\": 1.461909294128418,         \"Time in s\": 884.893217       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8788930063865016,         \"F1\": 0.8610709117221419,         \"Memory in Mb\": 1.412806510925293,         \"Time in s\": 1002.145707       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880197218338362,         \"F1\": 0.863970588235294,         \"Memory in Mb\": 1.521845817565918,         \"Time in s\": 1125.032122       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8799586064160055,         \"F1\": 0.8644437519476471,         \"Memory in Mb\": 1.922499656677246,         \"Time in s\": 1253.0070850000002       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805272384910071,         \"F1\": 0.8643667993513195,         \"Memory in Mb\": 1.924330711364746,         \"Time in s\": 1386.9590420000002       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8794382780401054,         \"F1\": 0.8622670589883704,         \"Memory in Mb\": 1.6739492416381836,         \"Time in s\": 1526.6043270000002       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8781153779120432,         \"F1\": 0.8583389601620527,         \"Memory in Mb\": 2.050276756286621,         \"Time in s\": 1671.5850450000005       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8772559192008389,         \"F1\": 0.8570510348373829,         \"Memory in Mb\": 2.0607213973999023,         \"Time in s\": 1821.619573       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8782128777923784,         \"F1\": 0.8564702967230379,         \"Memory in Mb\": 1.4914274215698242,         \"Time in s\": 1976.682617       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8703025437760273,         \"F1\": 0.8482357776081723,         \"Memory in Mb\": 0.7451009750366211,         \"Time in s\": 2137.4066430000003       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8626001823679033,         \"F1\": 0.8387314820030418,         \"Memory in Mb\": 0.7786626815795898,         \"Time in s\": 2303.79277       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8638642321666743,         \"F1\": 0.8378259916721456,         \"Memory in Mb\": 0.8927946090698242,         \"Time in s\": 2475.479733       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8620689655172413,         \"F1\": 0.8337590464027246,         \"Memory in Mb\": 1.0149259567260742,         \"Time in s\": 2652.48421       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8552748885586924,         \"F1\": 0.8239789332369494,         \"Memory in Mb\": 0.7698392868041992,         \"Time in s\": 2835.224884       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8513143371080496,         \"F1\": 0.8171902488062327,         \"Memory in Mb\": 0.8274068832397461,         \"Time in s\": 3023.118045       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8471242165017543,         \"F1\": 0.8121670057153928,         \"Memory in Mb\": 0.9264287948608398,         \"Time in s\": 3216.225907       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8469912077037263,         \"F1\": 0.8116213683223992,         \"Memory in Mb\": 1.0318632125854492,         \"Time in s\": 3414.151818       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8476397218440708,         \"F1\": 0.8134600657687283,         \"Memory in Mb\": 0.828364372253418,         \"Time in s\": 3617.194246       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8442585009791703,         \"F1\": 0.8083260297984224,         \"Memory in Mb\": 0.9404935836791992,         \"Time in s\": 3825.52125       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8416405091235211,         \"F1\": 0.8035935828877006,         \"Memory in Mb\": 0.932948112487793,         \"Time in s\": 4039.267659       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8389804997156906,         \"F1\": 0.7997670742866649,         \"Memory in Mb\": 0.8770322799682617,         \"Time in s\": 4258.308337       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8374508976398403,         \"F1\": 0.7964054812344976,         \"Memory in Mb\": 1.024897575378418,         \"Time in s\": 4482.681616999999       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8326973414488,         \"F1\": 0.7888725275599952,         \"Memory in Mb\": 0.9494352340698242,         \"Time in s\": 4711.951208999999       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.827318718381113,         \"F1\": 0.7808219178082191,         \"Memory in Mb\": 0.861109733581543,         \"Time in s\": 4945.765051999999       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8271531278899794,         \"F1\": 0.7806964420893262,         \"Memory in Mb\": 1.093031883239746,         \"Time in s\": 5184.141968999998       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8255439044935661,         \"F1\": 0.7779174678302028,         \"Memory in Mb\": 1.133570671081543,         \"Time in s\": 5427.007607999998       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8253191067840263,         \"F1\": 0.7766196163590301,         \"Memory in Mb\": 1.1872129440307615,         \"Time in s\": 5674.462564999998       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8264576837109192,         \"F1\": 0.7764070110569915,         \"Memory in Mb\": 1.431788444519043,         \"Time in s\": 5926.318084999998       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8245255081437609,         \"F1\": 0.7721616331096196,         \"Memory in Mb\": 1.357222557067871,         \"Time in s\": 6182.753490999998       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8241833328953247,         \"F1\": 0.7704974271012006,         \"Memory in Mb\": 1.1449995040893557,         \"Time in s\": 6443.434381999998       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8233950252842878,         \"F1\": 0.7698534823041413,         \"Memory in Mb\": 1.147334098815918,         \"Time in s\": 6708.424011999998       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8222160901086221,         \"F1\": 0.7699250073044834,         \"Memory in Mb\": 0.7468709945678711,         \"Time in s\": 6977.570913999998       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8227329588658049,         \"F1\": 0.7725140860587366,         \"Memory in Mb\": 0.9336042404174804,         \"Time in s\": 7251.011506999998       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8235872825434913,         \"F1\": 0.7755388654820785,         \"Memory in Mb\": 1.097620964050293,         \"Time in s\": 7528.4976689999985       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8245696437378174,         \"F1\": 0.7776785714285713,         \"Memory in Mb\": 1.5453977584838867,         \"Time in s\": 7809.843107999998       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8251431462276082,         \"F1\": 0.7787605469886529,         \"Memory in Mb\": 0.8941831588745117,         \"Time in s\": 8094.859004999998       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8243867276372401,         \"F1\": 0.7766701042740918,         \"Memory in Mb\": 0.744959831237793,         \"Time in s\": 8383.886548999999       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.823770944170953,         \"F1\": 0.7766305716444221,         \"Memory in Mb\": 0.5983161926269531,         \"Time in s\": 8676.996437       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8237734766392267,         \"F1\": 0.7765871128395959,         \"Memory in Mb\": 0.5984382629394531,         \"Time in s\": 8970.151376       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7083333333333334,         \"F1\": 0.7407407407407408,         \"Memory in Mb\": 0.6633157730102539,         \"Time in s\": 0.427424       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8163265306122449,         \"F1\": 0.8085106382978724,         \"Memory in Mb\": 0.6639947891235352,         \"Time in s\": 1.324595       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8513513513513513,         \"F1\": 0.8493150684931507,         \"Memory in Mb\": 0.6639490127563477,         \"Time in s\": 2.554164       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8585858585858586,         \"F1\": 0.8541666666666666,         \"Memory in Mb\": 0.6645593643188477,         \"Time in s\": 4.28613       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8548387096774194,         \"F1\": 0.85,         \"Memory in Mb\": 0.6645593643188477,         \"Time in s\": 6.454494       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8523489932885906,         \"F1\": 0.8533333333333335,         \"Memory in Mb\": 0.6645593643188477,         \"Time in s\": 8.992416       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8620689655172413,         \"F1\": 0.8536585365853658,         \"Memory in Mb\": 0.6651926040649414,         \"Time in s\": 11.94422       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8592964824120602,         \"F1\": 0.8510638297872339,         \"Memory in Mb\": 0.6653299331665039,         \"Time in s\": 15.477702       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8405797101449276,         \"Memory in Mb\": 0.7024993896484375,         \"Time in s\": 19.458772       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8473895582329317,         \"F1\": 0.8347826086956521,         \"Memory in Mb\": 0.730194091796875,         \"Time in s\": 23.970287       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467153284671532,         \"F1\": 0.8333333333333335,         \"Memory in Mb\": 0.7302398681640625,         \"Time in s\": 28.914006       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8528428093645485,         \"F1\": 0.837037037037037,         \"Memory in Mb\": 0.7302398681640625,         \"Time in s\": 34.304573       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8611111111111112,         \"F1\": 0.8421052631578947,         \"Memory in Mb\": 0.7308502197265625,         \"Time in s\": 40.225779       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8653295128939829,         \"F1\": 0.8438538205980067,         \"Memory in Mb\": 0.7308731079101562,         \"Time in s\": 46.580822       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663101604278075,         \"F1\": 0.8427672955974843,         \"Memory in Mb\": 0.7674179077148438,         \"Time in s\": 53.398646       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671679197994987,         \"F1\": 0.8417910447761194,         \"Memory in Mb\": 0.804534912109375,         \"Time in s\": 60.683253       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8679245283018868,         \"F1\": 0.839080459770115,         \"Memory in Mb\": 0.8596954345703125,         \"Time in s\": 68.459501       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8708240534521158,         \"F1\": 0.8406593406593408,         \"Memory in Mb\": 0.8597640991210938,         \"Time in s\": 76.689807       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.869198312236287,         \"F1\": 0.8402061855670103,         \"Memory in Mb\": 0.859832763671875,         \"Time in s\": 85.340536       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8677354709418837,         \"F1\": 0.8413461538461539,         \"Memory in Mb\": 0.8598556518554688,         \"Time in s\": 94.431883       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8683206106870229,         \"F1\": 0.8384074941451991,         \"Memory in Mb\": 0.8598556518554688,         \"Time in s\": 103.968058       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8670309653916212,         \"F1\": 0.8381374722838136,         \"Memory in Mb\": 0.8599014282226562,         \"Time in s\": 113.947374       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867595818815331,         \"F1\": 0.8382978723404255,         \"Memory in Mb\": 0.8599014282226562,         \"Time in s\": 124.337957       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8697829716193656,         \"F1\": 0.8381742738589212,         \"Memory in Mb\": 0.8599014282226562,         \"Time in s\": 135.24069899999998       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8717948717948718,         \"F1\": 0.8373983739837398,         \"Memory in Mb\": 0.8966064453125,         \"Time in s\": 146.56138699999997       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8767334360554699,         \"F1\": 0.846153846153846,         \"Memory in Mb\": 0.897308349609375,         \"Time in s\": 158.32837399999997       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8753709198813057,         \"F1\": 0.8478260869565216,         \"Memory in Mb\": 0.92486572265625,         \"Time in s\": 170.52005599999995       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798283261802575,         \"F1\": 0.8515901060070671,         \"Memory in Mb\": 0.8633918762207031,         \"Time in s\": 183.100014       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8825966850828729,         \"F1\": 0.8576214405360134,         \"Memory in Mb\": 0.9612770080566406,         \"Time in s\": 196.144193       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8865153538050734,         \"F1\": 0.8631239935587761,         \"Memory in Mb\": 0.9975471496582032,         \"Time in s\": 209.598306       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8875968992248062,         \"F1\": 0.863849765258216,         \"Memory in Mb\": 1.052570343017578,         \"Time in s\": 223.56015       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8873591989987485,         \"F1\": 0.8652694610778443,         \"Memory in Mb\": 1.1531257629394531,         \"Time in s\": 237.949979       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8871359223300971,         \"F1\": 0.8661870503597122,         \"Memory in Mb\": 1.1537437438964844,         \"Time in s\": 252.78040299999995       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8881036513545347,         \"F1\": 0.8671328671328671,         \"Memory in Mb\": 1.1632118225097656,         \"Time in s\": 267.983484       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901601830663616,         \"F1\": 0.8688524590163934,         \"Memory in Mb\": 1.1906776428222656,         \"Time in s\": 283.604953       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8887652947719689,         \"F1\": 0.8670212765957446,         \"Memory in Mb\": 1.2457008361816406,         \"Time in s\": 299.652639       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896103896103896,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 1.2457923889160156,         \"Time in s\": 316.05876399999994       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8893572181243414,         \"F1\": 0.8708487084870848,         \"Memory in Mb\": 1.2458381652832031,         \"Time in s\": 332.972637       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901437371663244,         \"F1\": 0.8718562874251498,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 350.27117       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878878878878879,         \"F1\": 0.8697674418604652,         \"Memory in Mb\": 1.2458610534667969,         \"Time in s\": 368.008026       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876953125,         \"F1\": 0.8700564971751412,         \"Memory in Mb\": 1.2459068298339844,         \"Time in s\": 386.172169       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8894184938036225,         \"F1\": 0.8725274725274725,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 404.74234       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901303538175046,         \"F1\": 0.8742004264392325,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 423.719952       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89171974522293,         \"F1\": 0.8761706555671176,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 443.13918       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932384341637011,         \"F1\": 0.8790322580645162,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 462.955181       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8938207136640557,         \"F1\": 0.8794466403162056,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 483.090208       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8926746166950597,         \"F1\": 0.877906976744186,         \"Memory in Mb\": 1.2458839416503906,         \"Time in s\": 503.748339       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932443703085905,         \"F1\": 0.8783269961977186,         \"Memory in Mb\": 1.2550315856933594,         \"Time in s\": 524.8299360000001       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929738562091504,         \"F1\": 0.8779123951537745,         \"Memory in Mb\": 1.3099861145019531,         \"Time in s\": 546.3067460000001       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8935148118494796,         \"F1\": 0.8792007266121706,         \"Memory in Mb\": 1.310077667236328,         \"Time in s\": 568.2182720000001       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1599369049072265,         \"Time in s\": 9.565839       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1605472564697265,         \"Time in s\": 28.660555       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1610889434814453,         \"Time in s\": 57.169533       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.161111831665039,         \"Time in s\": 95.025921       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.161111831665039,         \"Time in s\": 141.315828       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.161722183227539,         \"Time in s\": 195.517174       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1617450714111328,         \"Time in s\": 256.578558       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.2173633575439453,         \"Time in s\": 324.084886       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.162454605102539,         \"Time in s\": 398.342181       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.162271499633789,         \"Time in s\": 479.30544       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629047393798828,         \"Time in s\": 566.848605       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629962921142578,         \"Time in s\": 660.867353       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631336212158203,         \"Time in s\": 761.190417       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.1628131866455078,         \"Time in s\": 867.1613560000001       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.1630420684814453,         \"Time in s\": 978.380996       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629276275634765,         \"Time in s\": 1094.7710920000002       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.163064956665039,         \"Time in s\": 1216.3368180000002       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.162973403930664,         \"Time in s\": 1342.8439390000003       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.162973403930664,         \"Time in s\": 1474.4175280000004       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.1629962921142578,         \"Time in s\": 1611.5765880000004       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.1628589630126953,         \"Time in s\": 1753.4950250000004       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 1900.0594340000005       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 2051.0940990000004       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636295318603515,         \"Time in s\": 2206.5822620000004       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 2366.582792       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.1516590118408203,         \"Time in s\": 2531.0740060000003       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.163583755493164,         \"Time in s\": 2700.0024150000004       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.163583755493164,         \"Time in s\": 2873.4051700000005       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635379791259765,         \"Time in s\": 3051.2280980000005       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 3233.6982610000005       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1634693145751953,         \"Time in s\": 3420.4241430000006       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.163400650024414,         \"Time in s\": 3611.3532100000007       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 3806.5756970000007       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 4005.990633000001       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 4209.692892000001       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636066436767578,         \"Time in s\": 4417.671552000001       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.1634464263916015,         \"Time in s\": 4629.924287000001       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636066436767578,         \"Time in s\": 4846.389066000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636066436767578,         \"Time in s\": 5067.145737000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 5292.119512000001       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636524200439453,         \"Time in s\": 5520.9977020000015       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.1633319854736328,         \"Time in s\": 5753.467598000001       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.1635608673095703,         \"Time in s\": 5989.673013000001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.1642627716064453,         \"Time in s\": 6229.466851000001       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.164285659790039,         \"Time in s\": 6472.919364000001       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.164102554321289,         \"Time in s\": 6719.889077000002       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.1641483306884765,         \"Time in s\": 6970.567347000002       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.1640567779541015,         \"Time in s\": 7224.925889000002       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.1523609161376953,         \"Time in s\": 7483.091298000002       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.164194107055664,         \"Time in s\": 7744.930013000002       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.1642169952392578,         \"Time in s\": 8006.777295000002       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5523809523809524,         \"F1\": 0.5252525252525252,         \"Memory in Mb\": 0.1663923263549804,         \"Time in s\": 0.661448       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5829383886255924,         \"F1\": 0.5555555555555555,         \"Memory in Mb\": 0.1665983200073242,         \"Time in s\": 2.064295       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6025236593059937,         \"F1\": 0.5827814569536425,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 4.087538       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6099290780141844,         \"F1\": 0.5758354755784061,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 6.767182       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5841209829867675,         \"F1\": 0.5089285714285714,         \"Memory in Mb\": 0.1665983200073242,         \"Time in s\": 10.090322       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5748031496062992,         \"F1\": 0.4981412639405205,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 14.036758       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.582995951417004,         \"F1\": 0.4892561983471074,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 18.750104       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5749704840613932,         \"F1\": 0.4812680115273775,         \"Memory in Mb\": 0.1665296554565429,         \"Time in s\": 24.116907       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5760755508919203,         \"F1\": 0.482051282051282,         \"Memory in Mb\": 0.1665296554565429,         \"Time in s\": 30.255333       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5873465533522191,         \"F1\": 0.4828402366863905,         \"Memory in Mb\": 0.1665296554565429,         \"Time in s\": 37.077733       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5931330472103005,         \"F1\": 0.4925053533190577,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 44.554975       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5979543666404405,         \"F1\": 0.5034013605442177,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 52.671883       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6005809731299927,         \"F1\": 0.4990892531876139,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 61.596702       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6089008766014835,         \"F1\": 0.5117845117845117,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 71.17423       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6091881686595343,         \"F1\": 0.5121759622937941,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 81.390284       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6135693215339233,         \"F1\": 0.5194424064563462,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 92.365155       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6185452526374237,         \"F1\": 0.5354969574036511,         \"Memory in Mb\": 0.1665754318237304,         \"Time in s\": 104.03539999999998       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6208704771893025,         \"F1\": 0.5467084639498432,         \"Memory in Mb\": 0.1665983200073242,         \"Time in s\": 116.33010199999998       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.620963735717834,         \"F1\": 0.5561372891215823,         \"Memory in Mb\": 0.1666212081909179,         \"Time in s\": 129.40978099999998       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6252949504483247,         \"F1\": 0.56941431670282,         \"Memory in Mb\": 0.1666212081909179,         \"Time in s\": 143.16628799999998       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6242696629213483,         \"F1\": 0.5721596724667348,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 157.57341499999998       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6229086229086229,         \"F1\": 0.5763855421686748,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 172.619666       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.62330734509643,         \"F1\": 0.5796703296703297,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 188.342899       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6244593000393236,         \"F1\": 0.5860424794104898,         \"Memory in Mb\": 0.1666440963745117,         \"Time in s\": 204.771397       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6266515666289165,         \"F1\": 0.591828312009905,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 221.901573       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6250453720508167,         \"F1\": 0.5921831819976313,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 239.674138       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6249563089828731,         \"F1\": 0.5927893738140417,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 258.142834       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6248736097067745,         \"F1\": 0.5924569754668619,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 277.299077       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6260982753010088,         \"F1\": 0.5958494548012664,         \"Memory in Mb\": 0.1666898727416992,         \"Time in s\": 297.096451       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.62378106322743,         \"F1\": 0.5934738273283481,         \"Memory in Mb\": 0.1541042327880859,         \"Time in s\": 317.58276       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6246575342465753,         \"F1\": 0.5937397034596376,         \"Memory in Mb\": 0.1971263885498047,         \"Time in s\": 338.83455200000003       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6234149218519611,         \"F1\": 0.5931825422108953,         \"Memory in Mb\": 0.2317180633544922,         \"Time in s\": 360.725819       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6211038032599371,         \"F1\": 0.5894019212891229,         \"Memory in Mb\": 0.2662029266357422,         \"Time in s\": 383.343908       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6194837635303914,         \"F1\": 0.5866747060596926,         \"Memory in Mb\": 0.3123226165771484,         \"Time in s\": 406.679113       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6238878403882449,         \"F1\": 0.5915080527086384,         \"Memory in Mb\": 0.3201503753662109,         \"Time in s\": 430.743987       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6277850589777195,         \"F1\": 0.5970488081725313,         \"Memory in Mb\": 0.3261775970458984,         \"Time in s\": 455.494794       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6322366743177761,         \"F1\": 0.6009961261759823,         \"Memory in Mb\": 0.3539028167724609,         \"Time in s\": 480.941019       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6354606406754407,         \"F1\": 0.6034575904916262,         \"Memory in Mb\": 0.3679637908935547,         \"Time in s\": 507.154231       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6399709654004355,         \"F1\": 0.6073878627968339,         \"Memory in Mb\": 0.3758831024169922,         \"Time in s\": 534.089651       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.644963434772352,         \"F1\": 0.6130110568269478,         \"Memory in Mb\": 0.3759059906005859,         \"Time in s\": 561.67777       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6508630609896433,         \"F1\": 0.6185567010309279,         \"Memory in Mb\": 0.3759288787841797,         \"Time in s\": 589.949243       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6535609975286453,         \"F1\": 0.620384047267356,         \"Memory in Mb\": 0.3758831024169922,         \"Time in s\": 618.987429       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6570111915734036,         \"F1\": 0.6243691420331651,         \"Memory in Mb\": 0.3760662078857422,         \"Time in s\": 648.691271       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6607334334119666,         \"F1\": 0.6288127639605818,         \"Memory in Mb\": 0.3761119842529297,         \"Time in s\": 679.16585       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6630320821975257,         \"F1\": 0.6303197607545433,         \"Memory in Mb\": 0.4315700531005859,         \"Time in s\": 710.3587739999999       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6670769230769231,         \"F1\": 0.6330544879041374,         \"Memory in Mb\": 0.4394893646240234,         \"Time in s\": 742.192598       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6707488456133307,         \"F1\": 0.6378091872791519,         \"Memory in Mb\": 0.4457454681396484,         \"Time in s\": 774.746403       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6734814232356988,         \"F1\": 0.6407094959982694,         \"Memory in Mb\": 0.4518642425537109,         \"Time in s\": 807.9427459999999       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.674369343346813,         \"F1\": 0.6412051771695311,         \"Memory in Mb\": 0.4518413543701172,         \"Time in s\": 841.965614       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6778637478769579,         \"F1\": 0.64504054897068,         \"Memory in Mb\": 0.4531536102294922,         \"Time in s\": 876.7139659999999       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9337016574585636,         \"F1\": 0.933184855233853,         \"Memory in Mb\": 1.423478126525879,         \"Time in s\": 13.145088       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9491993373826616,         \"F1\": 0.937837837837838,         \"Memory in Mb\": 2.051041603088379,         \"Time in s\": 36.742593       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9385351490614648,         \"F1\": 0.9243316719528772,         \"Memory in Mb\": 2.3655481338500977,         \"Time in s\": 75.07794200000001       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9359646701628483,         \"F1\": 0.920980926430518,         \"Memory in Mb\": 2.6522607803344727,         \"Time in s\": 124.641449       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9361890041951866,         \"F1\": 0.9185226952354102,         \"Memory in Mb\": 3.339066505432129,         \"Time in s\": 184.399421       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9332106715731372,         \"F1\": 0.914487632508834,         \"Memory in Mb\": 3.582810401916504,         \"Time in s\": 253.628197       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9309257214950324,         \"F1\": 0.9124350259896042,         \"Memory in Mb\": 3.74349308013916,         \"Time in s\": 332.298993       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9232785980405686,         \"F1\": 0.9024903542616626,         \"Memory in Mb\": 3.99596118927002,         \"Time in s\": 420.603134       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9207653624432725,         \"F1\": 0.9042962962962964,         \"Memory in Mb\": 4.062603950500488,         \"Time in s\": 517.241068       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9214041284910034,         \"F1\": 0.9072191816523326,         \"Memory in Mb\": 4.2443437576293945,         \"Time in s\": 621.271616       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9173105870546914,         \"F1\": 0.9037158214536104,         \"Memory in Mb\": 4.387467384338379,         \"Time in s\": 732.039898       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.916842976727072,         \"F1\": 0.9044195390145908,         \"Memory in Mb\": 4.416756629943848,         \"Time in s\": 849.200167       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9150887322747728,         \"F1\": 0.9024580569644948,         \"Memory in Mb\": 4.712822914123535,         \"Time in s\": 973.870605       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9128755026413308,         \"F1\": 0.9002077124537162,         \"Memory in Mb\": 5.243111610412598,         \"Time in s\": 1105.403187       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9123555817205092,         \"F1\": 0.900890405259216,         \"Memory in Mb\": 5.419106483459473,         \"Time in s\": 1243.898383       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9112107623318386,         \"F1\": 0.9002402914502752,         \"Memory in Mb\": 5.619416236877441,         \"Time in s\": 1388.762519       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9125381468735796,         \"F1\": 0.9014414282578476,         \"Memory in Mb\": 5.888123512268066,         \"Time in s\": 1539.2398159999998       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9096093702091128,         \"F1\": 0.8977808599167822,         \"Memory in Mb\": 6.072480201721191,         \"Time in s\": 1695.9498239999998       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9093708243769244,         \"F1\": 0.8958611481975968,         \"Memory in Mb\": 6.119706153869629,         \"Time in s\": 1858.647702       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9071140791434406,         \"F1\": 0.892972972972973,         \"Memory in Mb\": 6.420571327209473,         \"Time in s\": 2027.885605       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.907910643889619,         \"F1\": 0.8927784577723377,         \"Memory in Mb\": 6.732544898986816,         \"Time in s\": 2202.439499       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9079323666649942,         \"F1\": 0.8936540133294696,         \"Memory in Mb\": 6.836274147033691,         \"Time in s\": 2383.16175       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9073283102174018,         \"F1\": 0.8931673582295988,         \"Memory in Mb\": 7.145352363586426,         \"Time in s\": 2570.030805       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9069585613760752,         \"F1\": 0.8912424063222407,         \"Memory in Mb\": 7.368103981018066,         \"Time in s\": 2762.468029       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9053379840169544,         \"F1\": 0.8884611382790553,         \"Memory in Mb\": 7.513260841369629,         \"Time in s\": 2961.014701       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9031203566121844,         \"F1\": 0.885441767068273,         \"Memory in Mb\": 7.7879228591918945,         \"Time in s\": 3165.900418       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9015984628592452,         \"F1\": 0.8830361047669955,         \"Memory in Mb\": 7.954785346984863,         \"Time in s\": 3377.578704       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8990026412267907,         \"F1\": 0.8799775133514476,         \"Memory in Mb\": 8.00295352935791,         \"Time in s\": 3596.265534       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8993263045712329,         \"F1\": 0.8800942925789926,         \"Memory in Mb\": 8.124005317687988,         \"Time in s\": 3821.215918       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8986717686449097,         \"F1\": 0.8798324461122262,         \"Memory in Mb\": 8.133870124816895,         \"Time in s\": 4052.049016       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8958874844222895,         \"F1\": 0.8761436801084379,         \"Memory in Mb\": 8.60555362701416,         \"Time in s\": 4289.013778       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8951398709944466,         \"F1\": 0.8747011787981206,         \"Memory in Mb\": 8.944867134094238,         \"Time in s\": 4531.544758       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8927986085560424,         \"F1\": 0.8719485396939551,         \"Memory in Mb\": 9.235833168029783,         \"Time in s\": 4780.469894       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8921533616855502,         \"F1\": 0.8705882352941176,         \"Memory in Mb\": 9.317421913146973,         \"Time in s\": 5034.96976       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8903465892964143,         \"F1\": 0.8684499262229957,         \"Memory in Mb\": 9.565300941467283,         \"Time in s\": 5295.565511       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8890387858347386,         \"F1\": 0.867226767435888,         \"Memory in Mb\": 9.898663520812988,         \"Time in s\": 5561.886477999999       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8882789892902956,         \"F1\": 0.8666547979348406,         \"Memory in Mb\": 10.141366004943848,         \"Time in s\": 5833.648399       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8878496528887211,         \"F1\": 0.8660444783679699,         \"Memory in Mb\": 10.462204933166504,         \"Time in s\": 6110.893153       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8864800611326522,         \"F1\": 0.8639185750636134,         \"Memory in Mb\": 10.841256141662598,         \"Time in s\": 6393.894783       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8857584370429648,         \"F1\": 0.8622387861040862,         \"Memory in Mb\": 11.13858127593994,         \"Time in s\": 6682.129445       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8846412706959214,         \"F1\": 0.8604643589827087,         \"Memory in Mb\": 11.587563514709473,         \"Time in s\": 6975.521303       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883682426217445,         \"F1\": 0.8588377878420617,         \"Memory in Mb\": 12.028901100158691,         \"Time in s\": 7273.546291       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8819210924865878,         \"F1\": 0.8569117830036083,         \"Memory in Mb\": 12.1774263381958,         \"Time in s\": 7576.403824       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880741539773725,         \"F1\": 0.8567122792211707,         \"Memory in Mb\": 12.330445289611816,         \"Time in s\": 7883.760394       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880423851455763,         \"F1\": 0.8574603081781235,         \"Memory in Mb\": 12.583298683166504,         \"Time in s\": 8195.49812       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8811517696460708,         \"F1\": 0.8591977712710009,         \"Memory in Mb\": 12.884881019592283,         \"Time in s\": 8511.394385       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8815199267278834,         \"F1\": 0.8597403319525146,         \"Memory in Mb\": 13.200516700744627,         \"Time in s\": 8831.520838       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8809069377055212,         \"F1\": 0.8591399896646449,         \"Memory in Mb\": 13.322876930236816,         \"Time in s\": 9156.403803       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880476651724371,         \"F1\": 0.8583404527979496,         \"Memory in Mb\": 13.499638557434082,         \"Time in s\": 9485.877502       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805713150400671,         \"F1\": 0.8587024655244463,         \"Memory in Mb\": 13.542492866516112,         \"Time in s\": 9819.626928       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8805808744013595,         \"F1\": 0.8586874200203704,         \"Memory in Mb\": 13.542401313781738,         \"Time in s\": 10153.705154       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6666666666666666,         \"F1\": 0.7142857142857143,         \"Memory in Mb\": 0.6517477035522461,         \"Time in s\": 0.344782       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7551020408163265,         \"F1\": 0.7391304347826088,         \"Memory in Mb\": 0.6519079208374023,         \"Time in s\": 1.052047       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7972972972972973,         \"F1\": 0.7945205479452055,         \"Memory in Mb\": 0.6519308090209961,         \"Time in s\": 2.04852       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.7999999999999999,         \"Memory in Mb\": 0.6519536972045898,         \"Time in s\": 3.481699       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8064516129032258,         \"F1\": 0.8000000000000002,         \"Memory in Mb\": 0.6519804000854492,         \"Time in s\": 5.345952       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.8211920529801323,         \"Memory in Mb\": 0.6519804000854492,         \"Time in s\": 7.607799       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8313253012048192,         \"Memory in Mb\": 0.6519804000854492,         \"Time in s\": 10.226605       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8341708542713567,         \"F1\": 0.8253968253968254,         \"Memory in Mb\": 0.6889629364013672,         \"Time in s\": 13.384675       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8303571428571429,         \"F1\": 0.8173076923076923,         \"Memory in Mb\": 0.6891918182373047,         \"Time in s\": 16.995274       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8273092369477911,         \"F1\": 0.8154506437768241,         \"Memory in Mb\": 0.6892147064208984,         \"Time in s\": 21.029962       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8321167883211679,         \"F1\": 0.8188976377952757,         \"Memory in Mb\": 0.6892833709716797,         \"Time in s\": 25.500591       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8394648829431438,         \"F1\": 0.823529411764706,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 30.459705       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.845679012345679,         \"F1\": 0.8263888888888888,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 35.865097       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8510028653295129,         \"F1\": 0.8289473684210527,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 41.618205       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8502673796791443,         \"F1\": 0.8260869565217391,         \"Memory in Mb\": 0.6892795562744141,         \"Time in s\": 47.877466       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.849624060150376,         \"F1\": 0.8235294117647061,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 54.483198       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8561320754716981,         \"F1\": 0.8271954674220963,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 61.544972       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8530066815144766,         \"F1\": 0.8225806451612903,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 69.002264       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8523206751054853,         \"F1\": 0.8241206030150755,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 77.051638       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8557114228456913,         \"F1\": 0.8317757009345793,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 85.50066       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8530534351145038,         \"F1\": 0.8253968253968255,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 94.362743       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8579234972677595,         \"F1\": 0.832618025751073,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 103.688841       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8588850174216028,         \"F1\": 0.8336755646817249,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 113.608321       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8631051752921536,         \"F1\": 0.8360000000000001,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 123.905415       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8621794871794872,         \"F1\": 0.83203125,         \"Memory in Mb\": 0.6893062591552734,         \"Time in s\": 134.725616       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8659476117103235,         \"F1\": 0.8391866913123845,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 146.008333       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8679525222551929,         \"F1\": 0.8446771378708552,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 157.728693       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8726752503576538,         \"F1\": 0.848381601362862,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 169.816185       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8756906077348067,         \"F1\": 0.8543689320388349,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 182.229315       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.87716955941255,         \"F1\": 0.8566978193146417,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 195.113196       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8785529715762274,         \"F1\": 0.8575757575757577,         \"Memory in Mb\": 0.6893291473388672,         \"Time in s\": 208.501766       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8785982478097623,         \"F1\": 0.8592162554426704,         \"Memory in Mb\": 0.7275295257568359,         \"Time in s\": 222.496546       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798543689320388,         \"F1\": 0.8619246861924686,         \"Memory in Mb\": 0.7627391815185547,         \"Time in s\": 236.948041       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798586572438163,         \"F1\": 0.8614130434782608,         \"Memory in Mb\": 0.7627849578857422,         \"Time in s\": 251.813013       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8787185354691075,         \"F1\": 0.8594164456233422,         \"Memory in Mb\": 0.7628536224365234,         \"Time in s\": 267.105354       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8787541713014461,         \"F1\": 0.8589909443725743,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 282.965748       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8809523809523809,         \"F1\": 0.8628428927680798,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 299.167544       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798735511064278,         \"F1\": 0.8629807692307693,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 315.934336       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8819301848049281,         \"F1\": 0.8651817116060961,         \"Memory in Mb\": 0.7628765106201172,         \"Time in s\": 333.021735       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8828828828828829,         \"F1\": 0.8662857142857143,         \"Memory in Mb\": 0.7645549774169922,         \"Time in s\": 350.749086       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8828125,         \"F1\": 0.8666666666666666,         \"Memory in Mb\": 0.8362636566162109,         \"Time in s\": 368.847305       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8846520495710201,         \"F1\": 0.8691891891891892,         \"Memory in Mb\": 0.8363094329833984,         \"Time in s\": 387.397742       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8836126629422719,         \"F1\": 0.8691099476439791,         \"Memory in Mb\": 0.8378963470458984,         \"Time in s\": 406.476047       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844404003639672,         \"F1\": 0.8702757916241062,         \"Memory in Mb\": 0.8380107879638672,         \"Time in s\": 425.926155       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8861209964412812,         \"F1\": 0.8732673267326733,         \"Memory in Mb\": 0.8731288909912109,         \"Time in s\": 445.763528       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8842471714534378,         \"F1\": 0.8707482993197277,         \"Memory in Mb\": 0.8731517791748047,         \"Time in s\": 466.112685       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8816013628620102,         \"F1\": 0.8677450047573739,         \"Memory in Mb\": 0.8732662200927734,         \"Time in s\": 487.01292       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798999165971643,         \"F1\": 0.8654205607476635,         \"Memory in Mb\": 0.8732662200927734,         \"Time in s\": 508.389566       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.880718954248366,         \"F1\": 0.8660550458715598,         \"Memory in Mb\": 0.8732891082763672,         \"Time in s\": 530.180451       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8783026421136909,         \"F1\": 0.8635547576301617,         \"Memory in Mb\": 0.8733119964599609,         \"Time in s\": 552.608585       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.14459228515625,         \"Time in s\": 4.671696       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446609497070312,         \"Time in s\": 14.150102       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446151733398437,         \"Time in s\": 28.360088       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446380615234375,         \"Time in s\": 47.155736000000005       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446380615234375,         \"Time in s\": 70.60316700000001       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1446151733398437,         \"Time in s\": 98.660415       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.144683837890625,         \"Time in s\": 131.464682       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996715496288512,         \"F1\": 0.761904761904762,         \"Memory in Mb\": 0.3174581527709961,         \"Time in s\": 185.699411       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997080462454748,         \"F1\": 0.8,         \"Memory in Mb\": 0.3083944320678711,         \"Time in s\": 248.358611       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372431551842,         \"F1\": 0.8,         \"Memory in Mb\": 0.3005514144897461,         \"Time in s\": 315.58373       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997611312822472,         \"F1\": 0.8,         \"Memory in Mb\": 0.2926855087280273,         \"Time in s\": 387.343573       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997810378804468,         \"F1\": 0.8,         \"Memory in Mb\": 0.2926855087280273,         \"Time in s\": 463.526447       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997978818012774,         \"F1\": 0.8,         \"Memory in Mb\": 0.2926855087280273,         \"Time in s\": 544.0157879999999       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998123193573816,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579168319702148,         \"Time in s\": 629.1407149999999       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248318385652,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579168319702148,         \"Time in s\": 718.5008859999999       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998357802082308,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579168319702148,         \"Time in s\": 812.0251739999999       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998454404945905,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579626083374023,         \"Time in s\": 909.687376       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998540273844628,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3579854965209961,         \"Time in s\": 1011.417335       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999861710366191,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1116.7962549999995       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686250295594,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1225.6397989999998       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998748811370802,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1337.9609139999998       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998805684939688,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1453.6581889999998       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998857612867847,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1572.7472669999995       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998905213373912,         \"F1\": 0.8148148148148148,         \"Memory in Mb\": 0.3580083847045898,         \"Time in s\": 1695.2891979999995       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998738806911338,         \"F1\": 0.7857142857142857,         \"Memory in Mb\": 0.3903570175170898,         \"Time in s\": 1822.117106       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998787315318228,         \"F1\": 0.7857142857142857,         \"Memory in Mb\": 0.3928442001342773,         \"Time in s\": 1952.439332       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998637602179836,         \"F1\": 0.787878787878788,         \"Memory in Mb\": 0.4802007675170898,         \"Time in s\": 2088.556257       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686260158024,         \"F1\": 0.787878787878788,         \"Memory in Mb\": 0.4802465438842773,         \"Time in s\": 2228.050331       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998550356974596,         \"F1\": 0.7647058823529411,         \"Memory in Mb\": 0.5258626937866211,         \"Time in s\": 2371.062704       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999281823118289,         \"F1\": 0.4383561643835616,         \"Memory in Mb\": 0.8453359603881836,         \"Time in s\": 2524.551865       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993049905071876,         \"F1\": 0.4383561643835616,         \"Memory in Mb\": 0.8887395858764648,         \"Time in s\": 2681.973264       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993267099105017,         \"F1\": 0.4383561643835616,         \"Memory in Mb\": 0.8967199325561523,         \"Time in s\": 2843.114587       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993152648173508,         \"F1\": 0.4266666666666667,         \"Memory in Mb\": 1.0689306259155271,         \"Time in s\": 3009.18915       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993354043986956,         \"F1\": 0.4266666666666667,         \"Memory in Mb\": 1.0783147811889648,         \"Time in s\": 3178.956821       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993393790162752,         \"F1\": 0.4210526315789473,         \"Memory in Mb\": 1.0915288925170898,         \"Time in s\": 3352.423608       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993577298670208,         \"F1\": 0.45,         \"Memory in Mb\": 1.0735387802124023,         \"Time in s\": 3530.715962       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993750887658004,         \"F1\": 0.45,         \"Memory in Mb\": 1.0788640975952148,         \"Time in s\": 3712.739099       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993915340256938,         \"F1\": 0.45,         \"Memory in Mb\": 1.0906057357788086,         \"Time in s\": 3898.148799       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994071359275628,         \"F1\": 0.45,         \"Memory in Mb\": 1.0906057357788086,         \"Time in s\": 4086.957815       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99942195772409,         \"F1\": 0.45,         \"Memory in Mb\": 1.090651512145996,         \"Time in s\": 4279.139143       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994232395990874,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1481237411499023,         \"Time in s\": 4474.636732       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994369721614011,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1493444442749023,         \"Time in s\": 4673.509203       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999450065992081,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1612462997436523,         \"Time in s\": 4875.758715       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994625646415306,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.161269187927246,         \"Time in s\": 5081.435613       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745077889624,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1612234115600586,         \"Time in s\": 5290.523743       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994859316631824,         \"F1\": 0.4444444444444444,         \"Memory in Mb\": 1.1584348678588867,         \"Time in s\": 5502.994331999999       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999463327370304,         \"F1\": 0.4285714285714285,         \"Memory in Mb\": 1.2966947555541992,         \"Time in s\": 5719.643149       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745081724928,         \"F1\": 0.4285714285714285,         \"Memory in Mb\": 1.3124494552612305,         \"Time in s\": 5939.715090999999       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994316110074428,         \"F1\": 0.4044943820224719,         \"Memory in Mb\": 1.3362340927124023,         \"Time in s\": 6163.415711999999       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994429789067673,         \"F1\": 0.4044943820224719,         \"Memory in Mb\": 1.3363256454467771,         \"Time in s\": 6390.433574999999       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994430140297408,         \"F1\": 0.4044943820224719,         \"Memory in Mb\": 1.3363256454467771,         \"Time in s\": 6617.502892999999       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.4857142857142857,         \"F1\": 0.4599999999999999,         \"Memory in Mb\": 0.2237319946289062,         \"Time in s\": 0.813651       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5165876777251185,         \"F1\": 0.4574468085106383,         \"Memory in Mb\": 0.2245254516601562,         \"Time in s\": 2.392298       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5205047318611987,         \"F1\": 0.4722222222222222,         \"Memory in Mb\": 0.2251434326171875,         \"Time in s\": 4.879886       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460992907801419,         \"F1\": 0.4838709677419355,         \"Memory in Mb\": 0.225250244140625,         \"Time in s\": 8.257922       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.55765595463138,         \"F1\": 0.455813953488372,         \"Memory in Mb\": 0.2252731323242187,         \"Time in s\": 12.416081000000002       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5543307086614173,         \"F1\": 0.4259634888438134,         \"Memory in Mb\": 0.2257461547851562,         \"Time in s\": 17.551695000000002       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5748987854251012,         \"F1\": 0.4220183486238532,         \"Memory in Mb\": 0.2259750366210937,         \"Time in s\": 23.418389       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5785123966942148,         \"F1\": 0.4232633279483037,         \"Memory in Mb\": 0.2259063720703125,         \"Time in s\": 30.181971       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5844700944386149,         \"F1\": 0.4193548387096774,         \"Memory in Mb\": 0.2258834838867187,         \"Time in s\": 37.806045       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5920679886685553,         \"F1\": 0.4146341463414634,         \"Memory in Mb\": 0.2256393432617187,         \"Time in s\": 46.336236       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.590557939914163,         \"F1\": 0.4015056461731493,         \"Memory in Mb\": 0.225738525390625,         \"Time in s\": 55.794626       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5971675845790716,         \"F1\": 0.4101382488479262,         \"Memory in Mb\": 0.226043701171875,         \"Time in s\": 66.093431       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599128540305011,         \"F1\": 0.3973799126637554,         \"Memory in Mb\": 0.226348876953125,         \"Time in s\": 77.304266       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5994605529332434,         \"F1\": 0.3926380368098159,         \"Memory in Mb\": 0.2263031005859375,         \"Time in s\": 89.41731899999999       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5997482693517936,         \"F1\": 0.3896353166986563,         \"Memory in Mb\": 0.2262802124023437,         \"Time in s\": 102.388462       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6011799410029498,         \"F1\": 0.3876811594202898,         \"Memory in Mb\": 0.2263412475585937,         \"Time in s\": 116.266249       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6013325930038868,         \"F1\": 0.3904923599320882,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 130.90050499999998       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6030414263240692,         \"F1\": 0.396812749003984,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 146.406164       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986090412319921,         \"F1\": 0.3961136023916292,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 162.81699799999998       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5969797074091553,         \"F1\": 0.3994374120956399,         \"Memory in Mb\": 0.2263641357421875,         \"Time in s\": 180.02605599999998       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597752808988764,         \"F1\": 0.4013377926421405,         \"Memory in Mb\": 0.226318359375,         \"Time in s\": 198.114263       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5988845988845989,         \"F1\": 0.4033184428844926,         \"Memory in Mb\": 0.22637939453125,         \"Time in s\": 217.043548       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5995075913007797,         \"F1\": 0.4019607843137255,         \"Memory in Mb\": 0.2264022827148437,         \"Time in s\": 236.778245       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008651199370821,         \"F1\": 0.4088526499708794,         \"Memory in Mb\": 0.2267684936523437,         \"Time in s\": 257.426783       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6002265005662514,         \"F1\": 0.4073866815892558,         \"Memory in Mb\": 0.2269744873046875,         \"Time in s\": 278.871455       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5985480943738657,         \"F1\": 0.4028077753779697,         \"Memory in Mb\": 0.2269744873046875,         \"Time in s\": 301.18545300000005       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599790283117791,         \"F1\": 0.4051948051948052,         \"Memory in Mb\": 0.2269744873046875,         \"Time in s\": 324.33878000000004       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.599932591843613,         \"F1\": 0.4026170105686965,         \"Memory in Mb\": 0.2269973754882812,         \"Time in s\": 348.42370100000005       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5977871786527823,         \"F1\": 0.4023210831721469,         \"Memory in Mb\": 0.2269973754882812,         \"Time in s\": 373.346007       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5986159169550173,         \"F1\": 0.4042950513538749,         \"Memory in Mb\": 0.2269973754882812,         \"Time in s\": 399.176002       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5981735159817352,         \"F1\": 0.4021739130434782,         \"Memory in Mb\": 0.2248907089233398,         \"Time in s\": 425.805579       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5959893836626364,         \"F1\": 0.4022687609075043,         \"Memory in Mb\": 0.2988729476928711,         \"Time in s\": 453.430877       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.597369173577352,         \"F1\": 0.4023769100169779,         \"Memory in Mb\": 0.3531064987182617,         \"Time in s\": 482.040674       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6008881487649181,         \"F1\": 0.4087171052631579,         \"Memory in Mb\": 0.3826017379760742,         \"Time in s\": 511.8008850000001       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6012402264761392,         \"F1\": 0.4086365453818472,         \"Memory in Mb\": 0.4367246627807617,         \"Time in s\": 542.835536       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6023591087811271,         \"F1\": 0.4104158569762923,         \"Memory in Mb\": 0.4704160690307617,         \"Time in s\": 575.071901       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6052027543993879,         \"F1\": 0.4145234493192133,         \"Memory in Mb\": 0.5176496505737305,         \"Time in s\": 608.725741       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.608393344921778,         \"F1\": 0.4195804195804196,         \"Memory in Mb\": 0.5480222702026367,         \"Time in s\": 643.745138       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6121461408178079,         \"F1\": 0.4260651629072682,         \"Memory in Mb\": 0.5632429122924805,         \"Time in s\": 680.30634       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6157112526539278,         \"F1\": 0.4329968673860076,         \"Memory in Mb\": 0.5676107406616211,         \"Time in s\": 718.216367       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6193325661680092,         \"F1\": 0.438560760353021,         \"Memory in Mb\": 0.5822668075561523,         \"Time in s\": 757.397991       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6218827229835991,         \"F1\": 0.4421610871726881,         \"Memory in Mb\": 0.5884695053100586,         \"Time in s\": 797.943115       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6219003730524468,         \"F1\": 0.4429356611703847,         \"Memory in Mb\": 0.6275625228881836,         \"Time in s\": 839.803567       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.623203945957538,         \"F1\": 0.4455664247396655,         \"Memory in Mb\": 0.6328649520874023,         \"Time in s\": 883.024854       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6250786328370728,         \"F1\": 0.446096654275093,         \"Memory in Mb\": 0.6821584701538086,         \"Time in s\": 927.682473       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6266666666666667,         \"F1\": 0.4468085106382978,         \"Memory in Mb\": 0.6950826644897461,         \"Time in s\": 973.720669       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.629592451314997,         \"F1\": 0.4530091906314853,         \"Memory in Mb\": 0.7119512557983398,         \"Time in s\": 1021.080455       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6298407705917043,         \"F1\": 0.4527753560011624,         \"Memory in Mb\": 0.6960439682006836,         \"Time in s\": 1069.7402539999998       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6321971885230118,         \"F1\": 0.456459874786568,         \"Memory in Mb\": 0.6964941024780273,         \"Time in s\": 1119.6924109999998       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6340819022457067,         \"F1\": 0.4594368553108447,         \"Memory in Mb\": 0.7031240463256836,         \"Time in s\": 1170.8531239999998       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8629834254143647,         \"F1\": 0.8663793103448276,         \"Memory in Mb\": 1.7490100860595703,         \"Time in s\": 16.056337       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8890115958034235,         \"F1\": 0.8680236375574525,         \"Memory in Mb\": 2.496591567993164,         \"Time in s\": 50.652304       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.87523003312477,         \"F1\": 0.8521587440034889,         \"Memory in Mb\": 1.8562908172607424,         \"Time in s\": 106.697102       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8868341153739995,         \"F1\": 0.8653972422849641,         \"Memory in Mb\": 2.5584278106689453,         \"Time in s\": 176.150463       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8880547582247736,         \"F1\": 0.8593619972260749,         \"Memory in Mb\": 3.1707210540771484,         \"Time in s\": 258.677392       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8829806807727691,         \"F1\": 0.8518863530507685,         \"Memory in Mb\": 2.113290786743164,         \"Time in s\": 353.604927       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8814067181832519,         \"F1\": 0.8497802636835796,         \"Memory in Mb\": 2.472631454467773,         \"Time in s\": 459.993548       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.883262039464606,         \"F1\": 0.8516310066643283,         \"Memory in Mb\": 2.354246139526367,         \"Time in s\": 576.649537       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8828652029927634,         \"F1\": 0.8585394756332394,         \"Memory in Mb\": 2.1453304290771484,         \"Time in s\": 702.348431       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8839827795562424,         \"F1\": 0.8639129871811472,         \"Memory in Mb\": 2.1982364654541016,         \"Time in s\": 836.637311       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.880983442047165,         \"F1\": 0.8635840809753854,         \"Memory in Mb\": 2.4484920501708984,         \"Time in s\": 979.832141       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.881151687977187,         \"F1\": 0.8654446990210373,         \"Memory in Mb\": 2.578580856323242,         \"Time in s\": 1131.438926       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8799354674365288,         \"F1\": 0.8634344214796214,         \"Memory in Mb\": 2.730459213256836,         \"Time in s\": 1291.261447       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8768430182133564,         \"F1\": 0.8601361031518624,         \"Memory in Mb\": 2.090116500854492,         \"Time in s\": 1459.3451100000002       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8789462064905438,         \"F1\": 0.8639483913654784,         \"Memory in Mb\": 1.877275466918945,         \"Time in s\": 1635.065473       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.878854777509486,         \"F1\": 0.86444341516134,         \"Memory in Mb\": 2.105062484741211,         \"Time in s\": 1818.351758       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8775404194532822,         \"F1\": 0.86187197890728,         \"Memory in Mb\": 2.440736770629883,         \"Time in s\": 2009.388651       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8765560802109523,         \"F1\": 0.8599262403451395,         \"Memory in Mb\": 2.627225875854492,         \"Time in s\": 2209.910977       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8758496485214663,         \"F1\": 0.8567214213878646,         \"Memory in Mb\": 2.5119991302490234,         \"Time in s\": 2419.733571       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8760969148407749,         \"F1\": 0.8567600331780769,         \"Memory in Mb\": 2.716485977172852,         \"Time in s\": 2638.279319       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8772141918528252,         \"F1\": 0.8562284588872477,         \"Memory in Mb\": 3.019651412963867,         \"Time in s\": 2865.7383750000004       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8739651798705534,         \"F1\": 0.8535106134826219,         \"Memory in Mb\": 2.721925735473633,         \"Time in s\": 3103.6766270000003       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8716225944233815,         \"F1\": 0.8503663925714606,         \"Memory in Mb\": 2.4018421173095703,         \"Time in s\": 3351.3122810000004       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.872556684910086,         \"F1\": 0.8492300995701616,         \"Memory in Mb\": 2.248655319213867,         \"Time in s\": 3607.2754260000006       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.870722769217184,         \"F1\": 0.845275840202917,         \"Memory in Mb\": 2.611169815063477,         \"Time in s\": 3871.072358000001       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8645722776480578,         \"F1\": 0.8365611230658879,         \"Memory in Mb\": 1.8957767486572263,         \"Time in s\": 4144.250301000001       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8614120436613385,         \"F1\": 0.8315276811450154,         \"Memory in Mb\": 1.5607776641845703,         \"Time in s\": 4424.237452000001       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8560334292584855,         \"F1\": 0.8249113050148624,         \"Memory in Mb\": 1.3715801239013672,         \"Time in s\": 4711.1951020000015       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8558596277547292,         \"F1\": 0.824277295717136,         \"Memory in Mb\": 1.611249923706055,         \"Time in s\": 5004.571280000002       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8564332756907906,         \"F1\": 0.8258035714285713,         \"Memory in Mb\": 2.025979995727539,         \"Time in s\": 5304.465246000002       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8535517179989318,         \"F1\": 0.8215385950449082,         \"Memory in Mb\": 1.848848342895508,         \"Time in s\": 5611.580754000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8515746266082578,         \"F1\": 0.8178624338624338,         \"Memory in Mb\": 2.0671520233154297,         \"Time in s\": 5927.3319900000015       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.849048399504967,         \"F1\": 0.8140885684860969,         \"Memory in Mb\": 1.3224430084228516,         \"Time in s\": 6250.652578000001       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8473849949680226,         \"F1\": 0.8106344410876132,         \"Memory in Mb\": 1.549489974975586,         \"Time in s\": 6580.126329000001       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8429783342268756,         \"F1\": 0.8039377830281552,         \"Memory in Mb\": 1.5209712982177734,         \"Time in s\": 6916.182134000001       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8411773723746743,         \"F1\": 0.8020785572367416,         \"Memory in Mb\": 1.995222091674805,         \"Time in s\": 7258.669481000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8415023418155783,         \"F1\": 0.8033751526590429,         \"Memory in Mb\": 1.8286800384521484,         \"Time in s\": 7608.320925000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.839689778371627,         \"F1\": 0.8006357692446627,         \"Memory in Mb\": 2.242650985717773,         \"Time in s\": 7966.087012000001       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8395550901423598,         \"F1\": 0.7993487417265422,         \"Memory in Mb\": 2.1107349395751958,         \"Time in s\": 8331.986249000001       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400618118601507,         \"F1\": 0.7984280447937677,         \"Memory in Mb\": 1.8943347930908203,         \"Time in s\": 8703.460619000001       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.839278503163279,         \"F1\": 0.796356938190749,         \"Memory in Mb\": 1.3389415740966797,         \"Time in s\": 9080.992051       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8389267036345956,         \"F1\": 0.7946940006029546,         \"Memory in Mb\": 1.607133865356445,         \"Time in s\": 9463.837927       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8382832353620658,         \"F1\": 0.7942655607079877,         \"Memory in Mb\": 1.8687000274658203,         \"Time in s\": 9853.704286       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8387477109098663,         \"F1\": 0.7967495098969203,         \"Memory in Mb\": 1.466756820678711,         \"Time in s\": 10249.501094       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400009811376291,         \"F1\": 0.8001225677953119,         \"Memory in Mb\": 2.0175647735595703,         \"Time in s\": 10651.197686       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8407918416316736,         \"F1\": 0.8026413635146792,         \"Memory in Mb\": 2.1117191314697266,         \"Time in s\": 11058.632461       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8411732932528593,         \"F1\": 0.8035781708344224,         \"Memory in Mb\": 2.033967971801758,         \"Time in s\": 11470.822587       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8416538275806563,         \"F1\": 0.804308286915994,         \"Memory in Mb\": 1.7070560455322266,         \"Time in s\": 11887.700533       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8406280269411844,         \"F1\": 0.8019483246087955,         \"Memory in Mb\": 2.28169059753418,         \"Time in s\": 12309.209906       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404379787633282,         \"F1\": 0.802124397722295,         \"Memory in Mb\": 2.2888126373291016,         \"Time in s\": 12736.77001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8404360971949416,         \"F1\": 0.8020804817957842,         \"Memory in Mb\": 2.2889575958251958,         \"Time in s\": 13164.474026       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7083333333333334,         \"F1\": 0.7407407407407408,         \"Memory in Mb\": 0.7072525024414062,         \"Time in s\": 0.45657       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8163265306122449,         \"F1\": 0.8085106382978724,         \"Memory in Mb\": 0.7079315185546875,         \"Time in s\": 1.426682       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8513513513513513,         \"F1\": 0.8493150684931507,         \"Memory in Mb\": 0.708251953125,         \"Time in s\": 2.873238       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8585858585858586,         \"F1\": 0.8541666666666666,         \"Memory in Mb\": 0.70849609375,         \"Time in s\": 4.790442       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8548387096774194,         \"F1\": 0.85,         \"Memory in Mb\": 0.70849609375,         \"Time in s\": 7.239611999999999       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8523489932885906,         \"F1\": 0.8533333333333335,         \"Memory in Mb\": 0.708740234375,         \"Time in s\": 10.202642999999998       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8620689655172413,         \"F1\": 0.8536585365853658,         \"Memory in Mb\": 0.7091293334960938,         \"Time in s\": 13.59528       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8592964824120602,         \"F1\": 0.8510638297872339,         \"Memory in Mb\": 0.7092666625976562,         \"Time in s\": 17.527801       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8405797101449276,         \"Memory in Mb\": 0.7491827011108398,         \"Time in s\": 22.029145       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8473895582329317,         \"F1\": 0.8347826086956521,         \"Memory in Mb\": 0.7771825790405273,         \"Time in s\": 27.026807       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467153284671532,         \"F1\": 0.8333333333333335,         \"Memory in Mb\": 0.7774114608764648,         \"Time in s\": 32.501577       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8528428093645485,         \"F1\": 0.837037037037037,         \"Memory in Mb\": 0.7775945663452148,         \"Time in s\": 38.42215899999999       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8611111111111112,         \"F1\": 0.8421052631578947,         \"Memory in Mb\": 0.7779607772827148,         \"Time in s\": 44.92146699999999       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8653295128939829,         \"F1\": 0.8438538205980067,         \"Memory in Mb\": 0.7781057357788086,         \"Time in s\": 51.897795       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663101604278075,         \"F1\": 0.8427672955974843,         \"Memory in Mb\": 0.8172750473022461,         \"Time in s\": 59.36314       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8671679197994987,         \"F1\": 0.8417910447761194,         \"Memory in Mb\": 0.8571996688842773,         \"Time in s\": 67.416022       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8679245283018868,         \"F1\": 0.839080459770115,         \"Memory in Mb\": 0.9128484725952148,         \"Time in s\": 76.017673       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8708240534521158,         \"F1\": 0.8406593406593408,         \"Memory in Mb\": 0.913100242614746,         \"Time in s\": 85.092057       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.869198312236287,         \"F1\": 0.8402061855670103,         \"Memory in Mb\": 0.9133520126342772,         \"Time in s\": 94.603797       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8677354709418837,         \"F1\": 0.8413461538461539,         \"Memory in Mb\": 0.9135580062866212,         \"Time in s\": 104.609638       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8683206106870229,         \"F1\": 0.8384074941451991,         \"Memory in Mb\": 0.9136190414428712,         \"Time in s\": 115.080656       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8670309653916212,         \"F1\": 0.8381374722838136,         \"Memory in Mb\": 0.9137258529663086,         \"Time in s\": 126.050962       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867595818815331,         \"F1\": 0.8382978723404255,         \"Memory in Mb\": 0.9137868881225586,         \"Time in s\": 137.397676       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8697829716193656,         \"F1\": 0.8381742738589212,         \"Memory in Mb\": 0.9139089584350586,         \"Time in s\": 149.31562       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8717948717948718,         \"F1\": 0.8373983739837398,         \"Memory in Mb\": 0.9536046981811525,         \"Time in s\": 161.695664       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8767334360554699,         \"F1\": 0.846153846153846,         \"Memory in Mb\": 0.9540624618530272,         \"Time in s\": 174.565593       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8753709198813057,         \"F1\": 0.8478260869565216,         \"Memory in Mb\": 0.9818639755249025,         \"Time in s\": 187.898512       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798283261802575,         \"F1\": 0.8515901060070671,         \"Memory in Mb\": 0.9230222702026368,         \"Time in s\": 201.735875       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8825966850828729,         \"F1\": 0.8576214405360134,         \"Memory in Mb\": 1.021204948425293,         \"Time in s\": 216.085388       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8865153538050734,         \"F1\": 0.8631239935587761,         \"Memory in Mb\": 1.0604047775268557,         \"Time in s\": 230.90612       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8875968992248062,         \"F1\": 0.863849765258216,         \"Memory in Mb\": 1.1157331466674805,         \"Time in s\": 246.307883       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8873591989987485,         \"F1\": 0.8652694610778443,         \"Memory in Mb\": 1.2215375900268557,         \"Time in s\": 262.241262       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8871359223300971,         \"F1\": 0.8661870503597122,         \"Memory in Mb\": 1.2229490280151367,         \"Time in s\": 278.553882       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8881036513545347,         \"F1\": 0.8671328671328671,         \"Memory in Mb\": 1.235407829284668,         \"Time in s\": 295.406724       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901601830663616,         \"F1\": 0.8688524590163934,         \"Memory in Mb\": 1.263422966003418,         \"Time in s\": 312.749904       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8887652947719689,         \"F1\": 0.8670212765957446,         \"Memory in Mb\": 1.318751335144043,         \"Time in s\": 330.632511       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896103896103896,         \"F1\": 0.8695652173913043,         \"Memory in Mb\": 1.318964958190918,         \"Time in s\": 348.945693       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8893572181243414,         \"F1\": 0.8708487084870848,         \"Memory in Mb\": 1.3194990158081057,         \"Time in s\": 367.701988       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901437371663244,         \"F1\": 0.8718562874251498,         \"Memory in Mb\": 1.319605827331543,         \"Time in s\": 386.96933700000005       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878878878878879,         \"F1\": 0.8697674418604652,         \"Memory in Mb\": 1.3197660446166992,         \"Time in s\": 406.7503420000001       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876953125,         \"F1\": 0.8700564971751412,         \"Memory in Mb\": 1.3200559616088867,         \"Time in s\": 426.99481600000007       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8894184938036225,         \"F1\": 0.8725274725274725,         \"Memory in Mb\": 1.320155143737793,         \"Time in s\": 447.8295280000001       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901303538175046,         \"F1\": 0.8742004264392325,         \"Memory in Mb\": 1.320277214050293,         \"Time in s\": 469.1965740000001       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89171974522293,         \"F1\": 0.8761706555671176,         \"Memory in Mb\": 1.320643424987793,         \"Time in s\": 491.1150510000001       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932384341637011,         \"F1\": 0.8790322580645162,         \"Memory in Mb\": 1.320704460144043,         \"Time in s\": 513.5552500000001       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8938207136640557,         \"F1\": 0.8794466403162056,         \"Memory in Mb\": 1.320704460144043,         \"Time in s\": 536.4428780000001       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8926746166950597,         \"F1\": 0.877906976744186,         \"Memory in Mb\": 1.320765495300293,         \"Time in s\": 559.8515450000001       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932443703085905,         \"F1\": 0.8783269961977186,         \"Memory in Mb\": 1.3328428268432615,         \"Time in s\": 583.8125340000001       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929738562091504,         \"F1\": 0.8779123951537745,         \"Memory in Mb\": 1.3880414962768557,         \"Time in s\": 608.2234330000001       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8935148118494796,         \"F1\": 0.8792007266121706,         \"Memory in Mb\": 1.3882551193237305,         \"Time in s\": 633.1359570000001       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2038736343383789,         \"Time in s\": 10.878823       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2044839859008789,         \"Time in s\": 32.501535000000004       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2050256729125976,         \"Time in s\": 64.818606       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2050485610961914,         \"Time in s\": 107.076722       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2050485610961914,         \"Time in s\": 158.807432       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2056589126586914,         \"Time in s\": 218.4459       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.2056818008422851,         \"Time in s\": 285.193275       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 0.2613000869750976,         \"Time in s\": 359.039643       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 0.2063913345336914,         \"Time in s\": 440.617697       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 0.2062082290649414,         \"Time in s\": 529.79805       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 0.2068414688110351,         \"Time in s\": 626.4074929999999       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069330215454101,         \"Time in s\": 729.8228539999999       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070703506469726,         \"Time in s\": 839.3276679999999       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 0.2067499160766601,         \"Time in s\": 954.865641       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069787979125976,         \"Time in s\": 1076.4115539999998       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 0.2068643569946289,         \"Time in s\": 1203.4939569999997       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070016860961914,         \"Time in s\": 1337.1471509999997       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069101333618164,         \"Time in s\": 1476.3596139999995       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069101333618164,         \"Time in s\": 1621.0863639999998       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069330215454101,         \"Time in s\": 1771.0710339999998       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 0.2067956924438476,         \"Time in s\": 1926.306088       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 0.2074975967407226,         \"Time in s\": 2086.761849       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 0.2069787979125976,         \"Time in s\": 2252.424258       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 0.2072610855102539,         \"Time in s\": 2423.176177       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 0.2072534561157226,         \"Time in s\": 2599.130792       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 0.1954126358032226,         \"Time in s\": 2780.435567       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073373794555664,         \"Time in s\": 2966.651736       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073373794555664,         \"Time in s\": 3157.874809       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073526382446289,         \"Time in s\": 3353.943675       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070703506469726,         \"Time in s\": 3555.07983       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070398330688476,         \"Time in s\": 3761.219357       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 0.2070322036743164,         \"Time in s\": 3972.252634       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 0.2071924209594726,         \"Time in s\": 4188.261246       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073450088500976,         \"Time in s\": 4409.191759       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073144912719726,         \"Time in s\": 4634.878548000001       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073602676391601,         \"Time in s\": 4864.934707       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 0.2072000503540039,         \"Time in s\": 5099.2875650000005       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073602676391601,         \"Time in s\": 5337.886074000001       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 0.2073602676391601,         \"Time in s\": 5580.709613000001       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 0.2074670791625976,         \"Time in s\": 5827.7179830000005       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 0.2074670791625976,         \"Time in s\": 6079.015463000001       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 0.2071466445922851,         \"Time in s\": 6334.716663000001       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 0.2075586318969726,         \"Time in s\": 6594.692589000001       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079553604125976,         \"Time in s\": 6858.666542000001       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 0.2080392837524414,         \"Time in s\": 7126.604303000001       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 0.2078561782836914,         \"Time in s\": 7398.595214000001       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079019546508789,         \"Time in s\": 7674.616155000001       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 0.2078104019165039,         \"Time in s\": 7954.656032000001       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 0.1961145401000976,         \"Time in s\": 8238.690655       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079477310180664,         \"Time in s\": 8526.751857000001       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 0.2079706192016601,         \"Time in s\": 8814.843001000001       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5142857142857142,         \"F1\": 0.4516129032258064,         \"Memory in Mb\": 0.1802501678466797,         \"Time in s\": 1.958268       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5402843601895735,         \"F1\": 0.4756756756756757,         \"Memory in Mb\": 0.1808605194091797,         \"Time in s\": 6.1304110000000005       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5394321766561514,         \"F1\": 0.4930555555555555,         \"Memory in Mb\": 0.1814937591552734,         \"Time in s\": 12.559627       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5531914893617021,         \"F1\": 0.4932975871313673,         \"Memory in Mb\": 0.1814708709716797,         \"Time in s\": 21.158430000000003       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5614366729678639,         \"F1\": 0.4703196347031963,         \"Memory in Mb\": 0.1814708709716797,         \"Time in s\": 31.954501       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5763779527559055,         \"F1\": 0.4836852207293666,         \"Memory in Mb\": 0.4110956192016601,         \"Time in s\": 45.100937       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5991902834008097,         \"F1\": 0.4940374787052811,         \"Memory in Mb\": 0.5197267532348633,         \"Time in s\": 60.647662       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6210153482880756,         \"F1\": 0.5201793721973094,         \"Memory in Mb\": 0.6145830154418945,         \"Time in s\": 78.646382       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6411332633788038,         \"F1\": 0.5464190981432361,         \"Memory in Mb\": 0.681065559387207,         \"Time in s\": 99.167462       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6515580736543909,         \"F1\": 0.555956678700361,         \"Memory in Mb\": 0.7228097915649414,         \"Time in s\": 122.156435       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6626609442060086,         \"F1\": 0.5732899022801302,         \"Memory in Mb\": 0.8111352920532227,         \"Time in s\": 147.677601       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6766325727773407,         \"F1\": 0.5958702064896755,         \"Memory in Mb\": 0.8519144058227539,         \"Time in s\": 175.66982000000002       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6877269426289034,         \"F1\": 0.6062271062271062,         \"Memory in Mb\": 0.9361848831176758,         \"Time in s\": 206.112203       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6999325691166555,         \"F1\": 0.6238377007607777,         \"Memory in Mb\": 0.978398323059082,         \"Time in s\": 239.08437400000005       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7073631214600378,         \"F1\": 0.6375681995323461,         \"Memory in Mb\": 1.0816278457641602,         \"Time in s\": 274.51056400000004       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7162241887905605,         \"F1\": 0.6496722505462491,         \"Memory in Mb\": 1.146012306213379,         \"Time in s\": 312.20930000000004       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7262631871182677,         \"F1\": 0.6662153012863914,         \"Memory in Mb\": 1.231095314025879,         \"Time in s\": 352.32891700000005       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7320398531725223,         \"F1\": 0.677602523659306,         \"Memory in Mb\": 1.3021745681762695,         \"Time in s\": 394.808348       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7391952309985097,         \"F1\": 0.6902654867256638,         \"Memory in Mb\": 1.3571443557739258,         \"Time in s\": 439.684188       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7456347333647947,         \"F1\": 0.7020453289110005,         \"Memory in Mb\": 1.439896583557129,         \"Time in s\": 486.825513       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.750561797752809,         \"F1\": 0.7080483955812729,         \"Memory in Mb\": 1.4615755081176758,         \"Time in s\": 536.150148       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7554697554697555,         \"F1\": 0.715,         \"Memory in Mb\": 1.4801912307739258,         \"Time in s\": 587.578093       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7599507591300779,         \"F1\": 0.7202295552367289,         \"Memory in Mb\": 1.5264062881469729,         \"Time in s\": 641.095023       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7624852536374361,         \"F1\": 0.7257039055404179,         \"Memory in Mb\": 1.5866899490356443,         \"Time in s\": 696.665992       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7678369195922989,         \"F1\": 0.7331887201735358,         \"Memory in Mb\": 1.6338167190551758,         \"Time in s\": 754.18395       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7731397459165155,         \"F1\": 0.7396917950853811,         \"Memory in Mb\": 1.718327522277832,         \"Time in s\": 813.521765       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.777350576721426,         \"F1\": 0.7440739252711932,         \"Memory in Mb\": 1.7761125564575195,         \"Time in s\": 874.768076       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7812605325244355,         \"F1\": 0.7479611650485437,         \"Memory in Mb\": 1.876938819885254,         \"Time in s\": 937.838653       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7845753335502766,         \"F1\": 0.7526158445440957,         \"Memory in Mb\": 1.974156379699707,         \"Time in s\": 1002.618853       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7892418999685435,         \"F1\": 0.7572463768115942,         \"Memory in Mb\": 2.007943153381348,         \"Time in s\": 1069.033932       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7923896499238965,         \"F1\": 0.7605337078651686,         \"Memory in Mb\": 2.0704050064086914,         \"Time in s\": 1137.092928       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7938661161899144,         \"F1\": 0.7636117686844774,         \"Memory in Mb\": 2.141594886779785,         \"Time in s\": 1206.880705       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7966828710323134,         \"F1\": 0.7657331136738056,         \"Memory in Mb\": 2.2472352981567383,         \"Time in s\": 1278.374701       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7998889814043852,         \"F1\": 0.7685393258426965,         \"Memory in Mb\": 2.2915468215942383,         \"Time in s\": 1351.505796       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8021029927204099,         \"F1\": 0.7717661691542288,         \"Memory in Mb\": 2.3504953384399414,         \"Time in s\": 1426.3168569999998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8055045871559633,         \"F1\": 0.7761013880506941,         \"Memory in Mb\": 2.397225379943848,         \"Time in s\": 1502.8248519999995       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8071920428462127,         \"F1\": 0.7776470588235294,         \"Memory in Mb\": 2.4447336196899414,         \"Time in s\": 1580.9903339999996       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8085423392103303,         \"F1\": 0.7788930312589618,         \"Memory in Mb\": 2.513848304748535,         \"Time in s\": 1660.8236829999996       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8107911928381321,         \"F1\": 0.7816862088218872,         \"Memory in Mb\": 2.6076173782348637,         \"Time in s\": 1742.3313809999995       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8136352913422977,         \"F1\": 0.7852093529091897,         \"Memory in Mb\": 2.653599739074707,         \"Time in s\": 1825.5059619999995       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8161104718066743,         \"F1\": 0.7881198621055423,         \"Memory in Mb\": 2.711110115051269,         \"Time in s\": 1910.383718       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8173444169849472,         \"F1\": 0.7894327894327894,         \"Memory in Mb\": 2.7411813735961914,         \"Time in s\": 1996.892937       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8183015141540487,         \"F1\": 0.7910146390711761,         \"Memory in Mb\": 2.7629594802856445,         \"Time in s\": 2085.0686209999994       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8205018228608192,         \"F1\": 0.7941971969510695,         \"Memory in Mb\": 2.818455696105957,         \"Time in s\": 2174.887267       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8209268190396309,         \"F1\": 0.7942168674698795,         \"Memory in Mb\": 2.852097511291504,         \"Time in s\": 2266.3018959999995       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.822974358974359,         \"F1\": 0.795932844644124,         \"Memory in Mb\": 2.940415382385254,         \"Time in s\": 2359.381272       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.825135514956836,         \"F1\": 0.7990772779700116,         \"Memory in Mb\": 2.986912727355957,         \"Time in s\": 2454.120045       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.825437389424022,         \"F1\": 0.7995485327313769,         \"Memory in Mb\": 3.072648048400879,         \"Time in s\": 2550.44047       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8266897746967071,         \"F1\": 0.8008849557522125,         \"Memory in Mb\": 3.1882104873657227,         \"Time in s\": 2648.361542       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8282694848084544,         \"F1\": 0.8026886383347789,         \"Memory in Mb\": 3.2357072830200195,         \"Time in s\": 2747.950666       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895027624309392,         \"F1\": 0.8873873873873873,         \"Memory in Mb\": 2.537948608398437,         \"Time in s\": 35.61841       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9127553837658752,         \"F1\": 0.8941018766756033,         \"Memory in Mb\": 3.267250061035156,         \"Time in s\": 98.669065       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9013617960986382,         \"F1\": 0.8815207780725023,         \"Memory in Mb\": 2.908538818359375,         \"Time in s\": 185.051304       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.905051062655258,         \"F1\": 0.8859416445623343,         \"Memory in Mb\": 4.239933013916016,         \"Time in s\": 291.140366       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9059395009935968,         \"F1\": 0.8829026937877955,         \"Memory in Mb\": 4.5028228759765625,         \"Time in s\": 414.050546       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904691812327507,         \"F1\": 0.8806451612903227,         \"Memory in Mb\": 5.411556243896484,         \"Time in s\": 555.0527619999999       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.904746885349314,         \"F1\": 0.8810086682427108,         \"Memory in Mb\": 3.64324951171875,         \"Time in s\": 712.2276919999999       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9038222712846696,         \"F1\": 0.8793908980792524,         \"Memory in Mb\": 4.176555633544922,         \"Time in s\": 885.4512659999999       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9062921623942108,         \"F1\": 0.8879107981220656,         \"Memory in Mb\": 4.873016357421875,         \"Time in s\": 1073.942042       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.907384921072966,         \"F1\": 0.8915600361897376,         \"Memory in Mb\": 6.068294525146484,         \"Time in s\": 1277.3056459999998       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9066733567486202,         \"F1\": 0.8924855491329481,         \"Memory in Mb\": 5.883171081542969,         \"Time in s\": 1496.0059789999998       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9090240088308345,         \"F1\": 0.8966238110170377,         \"Memory in Mb\": 7.123630523681641,         \"Time in s\": 1728.5474849999998       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.908805298463106,         \"F1\": 0.8963720571208027,         \"Memory in Mb\": 4.904956817626953,         \"Time in s\": 1974.076492       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9071197666167312,         \"F1\": 0.8948214285714287,         \"Memory in Mb\": 4.745685577392578,         \"Time in s\": 2233.08693       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.908234601515932,         \"F1\": 0.8972732515034187,         \"Memory in Mb\": 5.919612884521484,         \"Time in s\": 2504.250556       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9082442221455674,         \"F1\": 0.8976293103448276,         \"Memory in Mb\": 4.272552490234375,         \"Time in s\": 2787.365554       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9089669501980392,         \"F1\": 0.8979027090008739,         \"Memory in Mb\": 4.651363372802734,         \"Time in s\": 3081.637323       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9085668731219722,         \"F1\": 0.8971653217463273,         \"Memory in Mb\": 5.967304229736328,         \"Time in s\": 3386.795808       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9075698599895428,         \"F1\": 0.8943488943488943,         \"Memory in Mb\": 5.553913116455078,         \"Time in s\": 3703.059282       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9077211766653788,         \"F1\": 0.8943911066195048,         \"Memory in Mb\": 7.001399993896484,         \"Time in s\": 4030.541025       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9078580814717476,         \"F1\": 0.8932854446947099,         \"Memory in Mb\": 7.953182220458984,         \"Time in s\": 4368.505934999999       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9081832321509208,         \"F1\": 0.8944636678200691,         \"Memory in Mb\": 8.54180908203125,         \"Time in s\": 4717.761576999999       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064644622546432,         \"F1\": 0.8926111631494847,         \"Memory in Mb\": 7.284095764160156,         \"Time in s\": 5078.966551999999       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064066596145886,         \"F1\": 0.8909840895698291,         \"Memory in Mb\": 8.78485107421875,         \"Time in s\": 5451.144687999999       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9054704401960352,         \"F1\": 0.8890386110391294,         \"Memory in Mb\": 9.895774841308594,         \"Time in s\": 5834.807108999999       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9032052642751008,         \"F1\": 0.8860113988601139,         \"Memory in Mb\": 9.921958923339844,         \"Time in s\": 6231.782156999999       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9009443604104492,         \"F1\": 0.8825098191339766,         \"Memory in Mb\": 6.414276123046875,         \"Time in s\": 6640.471452999998       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8975046320022076,         \"F1\": 0.87847059923343,         \"Memory in Mb\": 7.025360107421875,         \"Time in s\": 7059.615553999998       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8978418909146272,         \"F1\": 0.878705712219812,         \"Memory in Mb\": 8.249675750732422,         \"Time in s\": 7487.941528999998       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8983038375216159,         \"F1\": 0.879888753693725,         \"Memory in Mb\": 7.590415954589844,         \"Time in s\": 7924.652190999998       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8965640021363718,         \"F1\": 0.8772448763997466,         \"Memory in Mb\": 7.862815856933594,         \"Time in s\": 8369.790676999999       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8963126487530613,         \"F1\": 0.8762962962962964,         \"Memory in Mb\": 9.08489990234375,         \"Time in s\": 8823.391086       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8952403251162324,         \"F1\": 0.8748901493968203,         \"Memory in Mb\": 2.6490402221679688,         \"Time in s\": 9284.744377       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8947505113138331,         \"F1\": 0.8736357966947302,         \"Memory in Mb\": 3.22769546508789,         \"Time in s\": 9752.904185       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8935948784256835,         \"F1\": 0.8721291594027135,         \"Memory in Mb\": 3.8703384399414062,         \"Time in s\": 10228.075712       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8929940211559099,         \"F1\": 0.8717194736455194,         \"Memory in Mb\": 4.073085784912109,         \"Time in s\": 10710.354293       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8932311088571343,         \"F1\": 0.872338148742643,         \"Memory in Mb\": 4.776435852050781,         \"Time in s\": 11199.826533       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8924390739826299,         \"F1\": 0.8711865585974188,         \"Memory in Mb\": 4.868198394775391,         \"Time in s\": 11696.46579       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8922537005066086,         \"F1\": 0.8703735231025913,         \"Memory in Mb\": 5.445720672607422,         \"Time in s\": 12200.310087       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8919948122188802,         \"F1\": 0.8690619563762879,         \"Memory in Mb\": 4.9837493896484375,         \"Time in s\": 12711.187581       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8920985327769552,         \"F1\": 0.8688996467355751,         \"Memory in Mb\": 5.313899993896484,         \"Time in s\": 13229.1012       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8916979842842501,         \"F1\": 0.8676919125437441,         \"Memory in Mb\": 5.129566192626953,         \"Time in s\": 13754.199791       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8912647277767796,         \"F1\": 0.8674924924924926,         \"Memory in Mb\": 5.3661651611328125,         \"Time in s\": 14286.524658       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8915535709806086,         \"F1\": 0.8687813021702837,         \"Memory in Mb\": 5.776020050048828,         \"Time in s\": 14825.184636       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8920012754789178,         \"F1\": 0.8703512852978417,         \"Memory in Mb\": 6.964508056640625,         \"Time in s\": 15370.438083       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.89250149970006,         \"F1\": 0.8717728547713092,         \"Memory in Mb\": 8.029548645019531,         \"Time in s\": 15922.831901       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8927925600619995,         \"F1\": 0.8723184068469779,         \"Memory in Mb\": 8.723072052001953,         \"Time in s\": 16481.133162000002       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8927495573389749,         \"F1\": 0.8722821622213702,         \"Memory in Mb\": 8.793426513671875,         \"Time in s\": 17045.039295000002       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8920775797986169,         \"F1\": 0.8710467526175545,         \"Memory in Mb\": 6.634971618652344,         \"Time in s\": 17614.470389000002       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8926687123336056,         \"F1\": 0.8720122143834896,         \"Memory in Mb\": 7.5638427734375,         \"Time in s\": 18188.843118       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8926529981682152,         \"F1\": 0.8719663069228745,         \"Memory in Mb\": 7.565349578857422,         \"Time in s\": 18763.342135       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.75,         \"F1\": 0.75,         \"Memory in Mb\": 0.6626491546630859,         \"Time in s\": 1.23946       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8163265306122449,         \"F1\": 0.8,         \"Memory in Mb\": 0.6635112762451172,         \"Time in s\": 3.937992       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8378378378378378,         \"F1\": 0.8333333333333334,         \"Memory in Mb\": 0.6635112762451172,         \"Time in s\": 8.007437       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8484848484848485,         \"F1\": 0.8421052631578947,         \"Memory in Mb\": 0.6476030349731445,         \"Time in s\": 13.398751       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467741935483871,         \"F1\": 0.8403361344537815,         \"Memory in Mb\": 0.9203081130981444,         \"Time in s\": 19.997869       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8456375838926175,         \"F1\": 0.8456375838926175,         \"Memory in Mb\": 0.9203310012817384,         \"Time in s\": 27.874049       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.867816091954023,         \"F1\": 0.8588957055214724,         \"Memory in Mb\": 1.0861825942993164,         \"Time in s\": 37.283539       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8693467336683417,         \"F1\": 0.8617021276595744,         \"Memory in Mb\": 1.2813997268676758,         \"Time in s\": 47.998757       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8660714285714286,         \"F1\": 0.8557692307692308,         \"Memory in Mb\": 1.3089113235473633,         \"Time in s\": 59.952353       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8554216867469879,         \"F1\": 0.8434782608695653,         \"Memory in Mb\": 1.3089799880981443,         \"Time in s\": 73.11976999999999       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8576642335766423,         \"F1\": 0.844621513944223,         \"Memory in Mb\": 1.2476167678833008,         \"Time in s\": 87.72028699999998       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.862876254180602,         \"F1\": 0.8464419475655431,         \"Memory in Mb\": 1.4594087600708008,         \"Time in s\": 103.60422699999998       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8703703703703703,         \"F1\": 0.851063829787234,         \"Memory in Mb\": 1.4950456619262695,         \"Time in s\": 120.70292199999996       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8710601719197708,         \"F1\": 0.8494983277591974,         \"Memory in Mb\": 1.5330171585083008,         \"Time in s\": 138.98074599999998       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8716577540106952,         \"F1\": 0.8481012658227849,         \"Memory in Mb\": 1.809849739074707,         \"Time in s\": 158.64485       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8696741854636592,         \"F1\": 0.8433734939759037,         \"Memory in Mb\": 2.068051338195801,         \"Time in s\": 179.646811       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8702830188679245,         \"F1\": 0.8405797101449276,         \"Memory in Mb\": 2.104710578918457,         \"Time in s\": 201.850953       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8752783964365256,         \"F1\": 0.845303867403315,         \"Memory in Mb\": 2.104527473449707,         \"Time in s\": 225.239967       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8776371308016878,         \"F1\": 0.8505154639175259,         \"Memory in Mb\": 2.132199287414551,         \"Time in s\": 249.917589       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.875751503006012,         \"F1\": 0.8502415458937198,         \"Memory in Mb\": 2.1503801345825195,         \"Time in s\": 275.75512899999995       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8778625954198473,         \"F1\": 0.8497652582159624,         \"Memory in Mb\": 2.187130928039551,         \"Time in s\": 302.922289       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8743169398907104,         \"F1\": 0.8463251670378619,         \"Memory in Mb\": 2.2971315383911133,         \"Time in s\": 331.41425       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8763066202090593,         \"F1\": 0.8479657387580299,         \"Memory in Mb\": 2.406788825988769,         \"Time in s\": 361.219435       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8764607679465777,         \"F1\": 0.8451882845188285,         \"Memory in Mb\": 2.406834602355957,         \"Time in s\": 392.26267       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8782051282051282,         \"F1\": 0.8442622950819672,         \"Memory in Mb\": 2.352017402648926,         \"Time in s\": 424.555734       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8813559322033898,         \"F1\": 0.850485436893204,         \"Memory in Mb\": 2.279099464416504,         \"Time in s\": 458.242359       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798219584569733,         \"F1\": 0.8513761467889909,         \"Memory in Mb\": 2.54854679107666,         \"Time in s\": 493.209676       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8841201716738197,         \"F1\": 0.8550983899821109,         \"Memory in Mb\": 2.565995216369629,         \"Time in s\": 529.352808       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8812154696132597,         \"F1\": 0.8537414965986394,         \"Memory in Mb\": 2.870518684387207,         \"Time in s\": 566.738792       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8825100133511349,         \"F1\": 0.8557377049180328,         \"Memory in Mb\": 2.941006660461426,         \"Time in s\": 605.3090109999999       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8837209302325582,         \"F1\": 0.856687898089172,         \"Memory in Mb\": 3.0508241653442383,         \"Time in s\": 645.053635       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8836045056320401,         \"F1\": 0.8584474885844748,         \"Memory in Mb\": 3.1606874465942383,         \"Time in s\": 686.031259       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8810679611650486,         \"F1\": 0.8567251461988304,         \"Memory in Mb\": 3.270321846008301,         \"Time in s\": 728.1933819999999       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8833922261484098,         \"F1\": 0.8591749644381224,         \"Memory in Mb\": 3.2882280349731445,         \"Time in s\": 771.57935       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8844393592677345,         \"F1\": 0.8595271210013908,         \"Memory in Mb\": 3.2632036209106445,         \"Time in s\": 816.1995999999999       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8832035595105673,         \"F1\": 0.8575305291723202,         \"Memory in Mb\": 3.380833625793457,         \"Time in s\": 861.997768       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8841991341991342,         \"F1\": 0.8597640891218873,         \"Memory in Mb\": 3.4813432693481445,         \"Time in s\": 908.973065       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8851422550052687,         \"F1\": 0.8628930817610063,         \"Memory in Mb\": 3.5117311477661133,         \"Time in s\": 957.120781       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8870636550308009,         \"F1\": 0.8651960784313726,         \"Memory in Mb\": 3.5666399002075195,         \"Time in s\": 1006.3541349999998       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8878878878878879,         \"F1\": 0.8663484486873507,         \"Memory in Mb\": 3.645543098449707,         \"Time in s\": 1056.728559       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876953125,         \"F1\": 0.8667439165701043,         \"Memory in Mb\": 3.735753059387207,         \"Time in s\": 1108.282035       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8894184938036225,         \"F1\": 0.8693693693693694,         \"Memory in Mb\": 3.808384895324707,         \"Time in s\": 1160.949301       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8901303538175046,         \"F1\": 0.87117903930131,         \"Memory in Mb\": 3.94530200958252,         \"Time in s\": 1214.7027389999998       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.89171974522293,         \"F1\": 0.873269435569755,         \"Memory in Mb\": 3.945347785949707,         \"Time in s\": 1269.5439849999998       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8905693950177936,         \"F1\": 0.8730650154798762,         \"Memory in Mb\": 3.972836494445801,         \"Time in s\": 1325.4243339999998       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8920800696257616,         \"F1\": 0.8747474747474747,         \"Memory in Mb\": 3.945645332336426,         \"Time in s\": 1382.2374609999997       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8909710391822828,         \"F1\": 0.8732673267326733,         \"Memory in Mb\": 3.973248481750488,         \"Time in s\": 1440.0925429999998       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8924103419516264,         \"F1\": 0.8746355685131195,         \"Memory in Mb\": 3.947278022766113,         \"Time in s\": 1498.9929549999997       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8937908496732027,         \"F1\": 0.8761904761904762,         \"Memory in Mb\": 3.982327461242676,         \"Time in s\": 1558.8210809999996       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8943154523618895,         \"F1\": 0.8773234200743495,         \"Memory in Mb\": 4.0114030838012695,         \"Time in s\": 1619.6535709999996       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1602087020874023,         \"Time in s\": 31.58816       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1608190536499023,         \"Time in s\": 89.620857       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1614294052124023,         \"Time in s\": 167.42750999999998       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1614294052124023,         \"Time in s\": 263.688726       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1614294052124023,         \"Time in s\": 375.655092       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1620397567749023,         \"Time in s\": 502.190419       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.1620397567749023,         \"Time in s\": 643.105063       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992117191092426,         \"F1\": 0.0,         \"Memory in Mb\": 0.2446889877319336,         \"Time in s\": 797.8947989999999       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9991825294873292,         \"F1\": 0.0,         \"Memory in Mb\": 0.1627492904663086,         \"Time in s\": 968.143197       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992642808345158,         \"F1\": 0.0,         \"Memory in Mb\": 0.1625890731811523,         \"Time in s\": 1153.4864309999998       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993311675902924,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631536483764648,         \"Time in s\": 1353.5382149999998       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993869060652508,         \"F1\": 0.0,         \"Memory in Mb\": 0.1632680892944336,         \"Time in s\": 1568.364428       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994340690435768,         \"F1\": 0.0,         \"Memory in Mb\": 0.1632909774780273,         \"Time in s\": 1796.946914       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994369580721444,         \"F1\": 0.0,         \"Memory in Mb\": 0.1630849838256836,         \"Time in s\": 2038.09448       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744955156952,         \"F1\": 0.0,         \"Memory in Mb\": 0.1630849838256836,         \"Time in s\": 2291.954796       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999507340624692,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631536483764648,         \"Time in s\": 2557.874515       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995363214837713,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631765365600586,         \"Time in s\": 2835.65121       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999562082153388,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631536483764648,         \"Time in s\": 3124.961925       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995851310985728,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631078720092773,         \"Time in s\": 3424.787068       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058750886782,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631307601928711,         \"Time in s\": 3734.997034       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996246434112408,         \"F1\": 0.0,         \"Memory in Mb\": 0.1631994247436523,         \"Time in s\": 4055.587677       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999641705481906,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638784408569336,         \"Time in s\": 4386.458848       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996572838603546,         \"F1\": 0.0,         \"Memory in Mb\": 0.1636495590209961,         \"Time in s\": 4726.906731       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671564012174,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638555526733398,         \"Time in s\": 5077.03597       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847017278344,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639471054077148,         \"Time in s\": 5436.872312       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996968288295572,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 5806.300992       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996691319579604,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639471054077148,         \"Time in s\": 6185.544045999999       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996809488955202,         \"F1\": 0.0,         \"Memory in Mb\": 0.1641073226928711,         \"Time in s\": 6574.848143999999       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996919508571014,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637639999389648,         \"Time in s\": 6975.121680999999       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995445707579392,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638784408569336,         \"Time in s\": 7384.584095999999       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995592622728504,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639013290405273,         \"Time in s\": 7802.5953629999985       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999573035553001,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 8228.594131999998       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995541259275772,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639013290405273,         \"Time in s\": 8661.618187999999       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672400735692,         \"F1\": 0.0,         \"Memory in Mb\": 0.1639928817749023,         \"Time in s\": 9101.660233       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796048285388,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638326644897461,         \"Time in s\": 9548.69733       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999562088545696,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638097763061523,         \"Time in s\": 10002.655236       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995739241585002,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 10463.105481       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995851368357004,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637639999389648,         \"Time in s\": 10929.695224       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995957744960656,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638097763061523,         \"Time in s\": 11402.447204       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996058802664248,         \"F1\": 0.0,         \"Memory in Mb\": 0.1638784408569336,         \"Time in s\": 11881.476782       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996154930660582,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637411117553711,         \"Time in s\": 12366.666901       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996246481076008,         \"F1\": 0.0,         \"Memory in Mb\": 0.1637182235717773,         \"Time in s\": 12858.057531       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999633377328054,         \"F1\": 0.0,         \"Memory in Mb\": 0.1521596908569336,         \"Time in s\": 13355.582794       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996417097610204,         \"F1\": 0.0,         \"Memory in Mb\": 0.1528844833374023,         \"Time in s\": 13859.323941       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496718593082,         \"F1\": 0.0,         \"Memory in Mb\": 0.1643285751342773,         \"Time in s\": 14369.189455       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996572877754548,         \"F1\": 0.0,         \"Memory in Mb\": 0.1646032333374023,         \"Time in s\": 14885.224126       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996533989266548,         \"F1\": 0.0,         \"Memory in Mb\": 0.1645116806030273,         \"Time in s\": 15407.418989999998       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996606198614016,         \"F1\": 0.0,         \"Memory in Mb\": 0.1643285751342773,         \"Time in s\": 15935.791256       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996675460609572,         \"F1\": 0.0,         \"Memory in Mb\": 0.1645116806030273,         \"Time in s\": 16470.041814999997       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996741952096186,         \"F1\": 0.0,         \"Memory in Mb\": 0.1645345687866211,         \"Time in s\": 17009.813748999997       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996742157532448,         \"F1\": 0.0,         \"Memory in Mb\": 0.1646032333374023,         \"Time in s\": 17549.605714999998       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6095238095238096,         \"F1\": 0.577319587628866,         \"Memory in Mb\": 0.7777948379516602,         \"Time in s\": 2.119535       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7109004739336493,         \"F1\": 0.6702702702702703,         \"Memory in Mb\": 1.3802881240844729,         \"Time in s\": 6.931057       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7602523659305994,         \"F1\": 0.7361111111111112,         \"Memory in Mb\": 1.8119163513183596,         \"Time in s\": 15.160032       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7943262411347518,         \"F1\": 0.772845953002611,         \"Memory in Mb\": 2.401026725769043,         \"Time in s\": 27.407145       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8052930056710775,         \"F1\": 0.7775377969762419,         \"Memory in Mb\": 5.0262651443481445,         \"Time in s\": 65.121823       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8236220472440945,         \"F1\": 0.7992831541218638,         \"Memory in Mb\": 5.88111686706543,         \"Time in s\": 107.288216       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8299595141700404,         \"F1\": 0.8025078369905957,         \"Memory in Mb\": 6.734616279602051,         \"Time in s\": 153.798119       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8347107438016529,         \"F1\": 0.8087431693989071,         \"Memory in Mb\": 7.555168151855469,         \"Time in s\": 204.76644700000003       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8426023084994754,         \"F1\": 0.8166259168704157,         \"Memory in Mb\": 8.384669303894043,         \"Time in s\": 260.019764       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8536355051935789,         \"F1\": 0.8275862068965517,         \"Memory in Mb\": 8.926264762878418,         \"Time in s\": 319.31365700000003       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8532188841201717,         \"F1\": 0.8274470232088799,         \"Memory in Mb\": 9.188977241516112,         \"Time in s\": 382.58132300000005       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8536585365853658,         \"F1\": 0.8290441176470588,         \"Memory in Mb\": 9.45701789855957,         \"Time in s\": 449.3987790000001       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8576615831517792,         \"F1\": 0.8321917808219177,         \"Memory in Mb\": 9.84501838684082,         \"Time in s\": 519.6596460000001       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8590694538098449,         \"F1\": 0.8345209817893903,         \"Memory in Mb\": 10.364198684692385,         \"Time in s\": 593.3590610000001       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8565135305223411,         \"F1\": 0.8321060382916053,         \"Memory in Mb\": 10.46892547607422,         \"Time in s\": 670.9077340000001       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8595870206489675,         \"F1\": 0.8354080221300139,         \"Memory in Mb\": 10.96640968322754,         \"Time in s\": 752.0270200000001       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8634092171016102,         \"F1\": 0.8410852713178295,         \"Memory in Mb\": 10.118447303771973,         \"Time in s\": 836.636461       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8626114315679078,         \"F1\": 0.8417874396135265,         \"Memory in Mb\": 10.3862943649292,         \"Time in s\": 924.5557       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8614008941877794,         \"F1\": 0.8415672913117546,         \"Memory in Mb\": 10.646858215332031,         \"Time in s\": 1015.887245       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8640868334119868,         \"F1\": 0.8459893048128343,         \"Memory in Mb\": 10.9229736328125,         \"Time in s\": 1110.420791       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8642696629213483,         \"F1\": 0.8462321792260691,         \"Memory in Mb\": 11.325839042663574,         \"Time in s\": 1208.24484       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.864006864006864,         \"F1\": 0.8461911693352742,         \"Memory in Mb\": 11.659860610961914,         \"Time in s\": 1308.9666100000002       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8641772671317193,         \"F1\": 0.8465461288827074,         \"Memory in Mb\": 11.19693660736084,         \"Time in s\": 1412.476276       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8651199370821864,         \"F1\": 0.8484312859036677,         \"Memory in Mb\": 11.452000617980955,         \"Time in s\": 1518.7765900000002       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.864477161192903,         \"F1\": 0.8480744815911976,         \"Memory in Mb\": 11.787381172180176,         \"Time in s\": 1627.8671150000002       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8653357531760436,         \"F1\": 0.849125660837739,         \"Memory in Mb\": 12.11353874206543,         \"Time in s\": 1739.675061       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8678783642083188,         \"F1\": 0.8515318146111548,         \"Memory in Mb\": 12.35980987548828,         \"Time in s\": 1854.131846       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8695652173913043,         \"F1\": 0.8529076396807297,         \"Memory in Mb\": 12.722334861755373,         \"Time in s\": 1971.385777       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8682069638789457,         \"F1\": 0.8515939904727006,         \"Memory in Mb\": 13.0479736328125,         \"Time in s\": 2091.3191060000004       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8700849323686694,         \"F1\": 0.8530771967271433,         \"Memory in Mb\": 13.308364868164062,         \"Time in s\": 2213.9114950000003       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8700152207001522,         \"F1\": 0.8523002421307506,         \"Memory in Mb\": 13.608009338378906,         \"Time in s\": 2339.1031470000003       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.871129460336184,         \"F1\": 0.8544788544788545,         \"Memory in Mb\": 13.70766258239746,         \"Time in s\": 2466.937686       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.872748069774092,         \"F1\": 0.8557536466774717,         \"Memory in Mb\": 14.051713943481444,         \"Time in s\": 2598.213051       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8742714404662781,         \"F1\": 0.856872037914692,         \"Memory in Mb\": 14.26829433441162,         \"Time in s\": 2732.2090540000004       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8751685090320841,         \"F1\": 0.8583664729275007,         \"Memory in Mb\": 14.518733978271484,         \"Time in s\": 2868.9613940000004       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8762778505897771,         \"F1\": 0.8596908442330559,         \"Memory in Mb\": 14.742842674255373,         \"Time in s\": 3008.2640120000005       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8747768426421831,         \"F1\": 0.8578048074138431,         \"Memory in Mb\": 15.053866386413574,         \"Time in s\": 3150.2484420000005       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8733548547305686,         \"F1\": 0.8560135516657256,         \"Memory in Mb\": 15.453622817993164,         \"Time in s\": 3294.8873180000005       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.874183401887249,         \"F1\": 0.8569069895432032,         \"Memory in Mb\": 15.755488395690918,         \"Time in s\": 3442.0965550000005       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8749705119131871,         \"F1\": 0.8579849946409432,         \"Memory in Mb\": 15.976973533630373,         \"Time in s\": 3591.8798750000005       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8759493670886076,         \"F1\": 0.8590849673202615,         \"Memory in Mb\": 16.313834190368652,         \"Time in s\": 3744.3343260000006       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8755335879577623,         \"F1\": 0.8585291113381002,         \"Memory in Mb\": 16.729196548461914,         \"Time in s\": 3899.4355060000007       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8757954794821154,         \"F1\": 0.8592039800995025,         \"Memory in Mb\": 17.032727241516113,         \"Time in s\": 4057.1530940000007       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8756165558653227,         \"F1\": 0.8594961240310078,         \"Memory in Mb\": 17.45319175720215,         \"Time in s\": 4217.5274930000005       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8754455860767456,         \"F1\": 0.8590412909349787,         \"Memory in Mb\": 17.6948184967041,         \"Time in s\": 4380.594611       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8756923076923077,         \"F1\": 0.8588070829450141,         \"Memory in Mb\": 17.917430877685547,         \"Time in s\": 4546.383704000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8761292913069665,         \"F1\": 0.8596770525358198,         \"Memory in Mb\": 18.09793186187744,         \"Time in s\": 4714.847995000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8757617456261058,         \"F1\": 0.8591800356506238,         \"Memory in Mb\": 18.51348114013672,         \"Time in s\": 4886.014522000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.876372039283651,         \"F1\": 0.8598865124399825,         \"Memory in Mb\": 18.89274883270264,         \"Time in s\": 5059.990176000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8762030571806001,         \"F1\": 0.8596491228070176,         \"Memory in Mb\": 19.194592475891117,         \"Time in s\": 5236.837813000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9116022099447514,         \"F1\": 0.908256880733945,         \"Memory in Mb\": 7.041282653808594,         \"Time in s\": 59.400144       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.906129210381005,         \"F1\": 0.8855989232839839,         \"Memory in Mb\": 9.07800579071045,         \"Time in s\": 148.928403       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9002576370997424,         \"F1\": 0.8772088808337108,         \"Memory in Mb\": 9.477606773376465,         \"Time in s\": 264.671315       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9064311344189898,         \"F1\": 0.8849677638276212,         \"Memory in Mb\": 10.383838653564451,         \"Time in s\": 404.188666       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.90527710311327,         \"F1\": 0.8788477831121152,         \"Memory in Mb\": 11.437847137451172,         \"Time in s\": 565.129871       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9000919963201472,         \"F1\": 0.8723854289071681,         \"Memory in Mb\": 14.209432601928713,         \"Time in s\": 747.817107       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.897019397571361,         \"F1\": 0.8704622098789923,         \"Memory in Mb\": 15.688876152038574,         \"Time in s\": 951.56122       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8965089002345799,         \"F1\": 0.8694744169857292,         \"Memory in Mb\": 19.837779998779297,         \"Time in s\": 1176.633371       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8980743284680486,         \"F1\": 0.8778839088905216,         \"Memory in Mb\": 22.41482448577881,         \"Time in s\": 1420.714573       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9000993487139861,         \"F1\": 0.8828478964401294,         \"Memory in Mb\": 25.97023296356201,         \"Time in s\": 1683.800751       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8982438534872053,         \"F1\": 0.8827203331020125,         \"Memory in Mb\": 28.783666610717773,         \"Time in s\": 1965.440637       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9006531137889798,         \"F1\": 0.8870765370138016,         \"Memory in Mb\": 30.882869720458984,         \"Time in s\": 2263.780185       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9021822195805383,         \"F1\": 0.8888030888030888,         \"Memory in Mb\": 33.277831077575684,         \"Time in s\": 2578.868179       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.901285184893164,         \"F1\": 0.8879541793449078,         \"Memory in Mb\": 35.50911808013916,         \"Time in s\": 2911.085292       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.901905953344617,         \"F1\": 0.8899529431189631,         \"Memory in Mb\": 38.6168327331543,         \"Time in s\": 3259.512573       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9030010348395998,         \"F1\": 0.8917128773875539,         \"Memory in Mb\": 41.26064682006836,         \"Time in s\": 3624.142031       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.903902344003636,         \"F1\": 0.8922382408620941,         \"Memory in Mb\": 43.65532207489014,         \"Time in s\": 4004.812957       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9002882197829153,         \"F1\": 0.8879239040529363,         \"Memory in Mb\": 45.1539192199707,         \"Time in s\": 4403.507418       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8997850461860222,         \"F1\": 0.8856176646111001,         \"Memory in Mb\": 37.54582214355469,         \"Time in s\": 4816.829749       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9001048622992439,         \"F1\": 0.8858908082209053,         \"Memory in Mb\": 41.9152717590332,         \"Time in s\": 5243.603799       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9014454664914586,         \"F1\": 0.886177381169186,         \"Memory in Mb\": 44.45838737487793,         \"Time in s\": 5683.033974000001       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.9011088254477948,         \"F1\": 0.8867826986041704,         \"Memory in Mb\": 48.213175773620605,         \"Time in s\": 6135.158415000001       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8992657292316553,         \"F1\": 0.8848411696933121,         \"Memory in Mb\": 47.78572177886963,         \"Time in s\": 6598.876461000001       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8986800349537782,         \"F1\": 0.8824376967821121,         \"Memory in Mb\": 49.35674381256104,         \"Time in s\": 7073.276106000001       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.898361958585368,         \"F1\": 0.8812912541254125,         \"Memory in Mb\": 47.91073036193848,         \"Time in s\": 7558.681153000001       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8961579282530249,         \"F1\": 0.8784656663022956,         \"Memory in Mb\": 53.37149906158447,         \"Time in s\": 8055.088432000001       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8945259801316381,         \"F1\": 0.8760211436809228,         \"Memory in Mb\": 52.06687641143799,         \"Time in s\": 8562.621137000002       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8922221784207829,         \"F1\": 0.8734610756271406,         \"Memory in Mb\": 20.63737678527832,         \"Time in s\": 9080.309208000002       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8928177216153466,         \"F1\": 0.873801201039706,         \"Memory in Mb\": 17.83812713623047,         \"Time in s\": 9607.290172000005       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8932263880201626,         \"F1\": 0.874632797649905,         \"Memory in Mb\": 19.30546188354492,         \"Time in s\": 10142.799192000002       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8915435285739719,         \"F1\": 0.8721564677243349,         \"Memory in Mb\": 18.23539447784424,         \"Time in s\": 10686.882271000002       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.891345590010693,         \"F1\": 0.8712498978173793,         \"Memory in Mb\": 21.229859352111816,         \"Time in s\": 11240.330868000005       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8905575810281968,         \"F1\": 0.8700246285850481,         \"Memory in Mb\": 25.24380111694336,         \"Time in s\": 11801.274420000003       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.890335356945752,         \"F1\": 0.8690697674418605,         \"Memory in Mb\": 28.836254119873047,         \"Time in s\": 12369.623812000003       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8895266328171813,         \"F1\": 0.8679458664756663,         \"Memory in Mb\": 27.00519371032715,         \"Time in s\": 12944.308752000004       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8879963207113292,         \"F1\": 0.86634224872855,         \"Memory in Mb\": 30.17805576324463,         \"Time in s\": 13524.938061000004       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8870260433757943,         \"F1\": 0.8654563541407612,         \"Memory in Mb\": 32.107930183410645,         \"Time in s\": 14111.446119000002       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8857582711244082,         \"F1\": 0.8638770636486346,         \"Memory in Mb\": 32.29031944274902,         \"Time in s\": 14703.865141000002       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8850083491353692,         \"F1\": 0.8622665175090681,         \"Memory in Mb\": 36.271653175354,         \"Time in s\": 15302.291934000004       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8850133833715058,         \"F1\": 0.8612988050461006,         \"Memory in Mb\": 35.55355262756348,         \"Time in s\": 15906.592759000005       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8836182527931081,         \"F1\": 0.859061715515274,         \"Memory in Mb\": 38.75662517547608,         \"Time in s\": 16517.021041000004       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8830516937794013,         \"F1\": 0.8577547628180539,         \"Memory in Mb\": 41.063425064086914,         \"Time in s\": 17133.624997000003       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8831788895448828,         \"F1\": 0.8582198822393221,         \"Memory in Mb\": 42.255154609680176,         \"Time in s\": 17756.348401000003       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8836765923287259,         \"F1\": 0.8598797328740216,         \"Memory in Mb\": 43.78993701934815,         \"Time in s\": 18385.013164000004       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8840295322426354,         \"F1\": 0.8614708467623791,         \"Memory in Mb\": 40.85312080383301,         \"Time in s\": 19019.639174000004       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8847030593881223,         \"F1\": 0.8632106356933413,         \"Memory in Mb\": 39.29591369628906,         \"Time in s\": 19660.085711000003       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8851130786031328,         \"F1\": 0.8639675212724542,         \"Memory in Mb\": 42.33915042877197,         \"Time in s\": 20306.976876000004       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8848391473313864,         \"F1\": 0.8636091290375292,         \"Memory in Mb\": 42.20731544494629,         \"Time in s\": 20960.03932700001       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8848467100669024,         \"F1\": 0.8632350580555408,         \"Memory in Mb\": 44.13865566253662,         \"Time in s\": 21618.107174000004       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8854720854765006,         \"F1\": 0.8642027012878233,         \"Memory in Mb\": 40.63082981109619,         \"Time in s\": 22281.08467600001       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8854582772395224,         \"F1\": 0.8641574621787154,         \"Memory in Mb\": 40.75471591949463,         \"Time in s\": 22944.429270000004       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6666666666666666,         \"F1\": 0.7142857142857143,         \"Memory in Mb\": 0.6122617721557617,         \"Time in s\": 0.640752       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7755102040816326,         \"F1\": 0.7659574468085107,         \"Memory in Mb\": 0.7524843215942383,         \"Time in s\": 1.992597       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8243243243243243,         \"F1\": 0.8266666666666667,         \"Memory in Mb\": 0.9228668212890624,         \"Time in s\": 4.151733       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8282828282828283,         \"F1\": 0.8282828282828283,         \"Memory in Mb\": 1.193608283996582,         \"Time in s\": 7.194986       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8306451612903226,         \"F1\": 0.8292682926829269,         \"Memory in Mb\": 1.3295679092407229,         \"Time in s\": 11.208747       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8389261744966443,         \"F1\": 0.8441558441558442,         \"Memory in Mb\": 1.3798675537109375,         \"Time in s\": 16.195196       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8563218390804598,         \"F1\": 0.8520710059171597,         \"Memory in Mb\": 1.4546594619750977,         \"Time in s\": 22.422243       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8542713567839196,         \"F1\": 0.8497409326424871,         \"Memory in Mb\": 1.6083984375,         \"Time in s\": 29.888728       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8526785714285714,         \"F1\": 0.8436018957345972,         \"Memory in Mb\": 1.7997064590454102,         \"Time in s\": 38.482186       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8433734939759037,         \"F1\": 0.8354430379746836,         \"Memory in Mb\": 1.9343080520629885,         \"Time in s\": 48.311454       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8467153284671532,         \"F1\": 0.8372093023255813,         \"Memory in Mb\": 2.053934097290039,         \"Time in s\": 59.483297       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8461538461538461,         \"F1\": 0.8333333333333334,         \"Memory in Mb\": 2.12460994720459,         \"Time in s\": 72.133375       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8518518518518519,         \"F1\": 0.8356164383561644,         \"Memory in Mb\": 2.201033592224121,         \"Time in s\": 86.30699200000001       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8595988538681948,         \"F1\": 0.8414239482200646,         \"Memory in Mb\": 2.2356014251708984,         \"Time in s\": 102.050203       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8556149732620321,         \"F1\": 0.8353658536585366,         \"Memory in Mb\": 2.328523635864258,         \"Time in s\": 119.413833       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8571428571428571,         \"F1\": 0.8347826086956521,         \"Memory in Mb\": 2.316814422607422,         \"Time in s\": 138.615081       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8608490566037735,         \"F1\": 0.8356545961002786,         \"Memory in Mb\": 2.353947639465332,         \"Time in s\": 159.65345200000002       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8619153674832962,         \"F1\": 0.8351063829787234,         \"Memory in Mb\": 2.429127693176269,         \"Time in s\": 182.47163200000003       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8649789029535865,         \"F1\": 0.8407960199004976,         \"Memory in Mb\": 2.579917907714844,         \"Time in s\": 207.23725       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8637274549098196,         \"F1\": 0.841860465116279,         \"Memory in Mb\": 4.818408012390137,         \"Time in s\": 255.437855       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8645038167938931,         \"F1\": 0.8397291196388261,         \"Memory in Mb\": 5.000759124755859,         \"Time in s\": 305.327991       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8652094717668488,         \"F1\": 0.8418803418803419,         \"Memory in Mb\": 5.177936553955078,         \"Time in s\": 356.788916       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8658536585365854,         \"F1\": 0.8425357873210634,         \"Memory in Mb\": 5.324765205383301,         \"Time in s\": 409.723896       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8697829716193656,         \"F1\": 0.8446215139442231,         \"Memory in Mb\": 5.557343482971191,         \"Time in s\": 464.11455       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8685897435897436,         \"F1\": 0.8404669260700389,         \"Memory in Mb\": 5.701066970825195,         \"Time in s\": 520.0238599999999       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8721109399075501,         \"F1\": 0.847145488029466,         \"Memory in Mb\": 5.875107765197754,         \"Time in s\": 577.5076929999999       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8753709198813057,         \"F1\": 0.8541666666666667,         \"Memory in Mb\": 5.993474006652832,         \"Time in s\": 636.4901669999999       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798283261802575,         \"F1\": 0.8576271186440678,         \"Memory in Mb\": 6.097118377685547,         \"Time in s\": 696.8595059999999       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8798342541436464,         \"F1\": 0.8603531300160514,         \"Memory in Mb\": 6.2616376876831055,         \"Time in s\": 758.6517739999999       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8811748998664887,         \"F1\": 0.8624420401854715,         \"Memory in Mb\": 6.510566711425781,         \"Time in s\": 822.0041369999999       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8824289405684754,         \"F1\": 0.8631578947368422,         \"Memory in Mb\": 6.659415245056152,         \"Time in s\": 886.863173       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8823529411764706,         \"F1\": 0.8645533141210374,         \"Memory in Mb\": 6.793304443359375,         \"Time in s\": 953.229371       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8822815533980582,         \"F1\": 0.8654646324549237,         \"Memory in Mb\": 7.087222099304199,         \"Time in s\": 1021.107018       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8833922261484098,         \"F1\": 0.8660351826792964,         \"Memory in Mb\": 7.324291229248047,         \"Time in s\": 1090.559446       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.88558352402746,         \"F1\": 0.8677248677248677,         \"Memory in Mb\": 7.470724105834961,         \"Time in s\": 1161.497406       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8854282536151279,         \"F1\": 0.8670967741935484,         \"Memory in Mb\": 7.810632705688477,         \"Time in s\": 1233.983859       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8874458874458875,         \"F1\": 0.8706467661691542,         \"Memory in Mb\": 7.971014976501465,         \"Time in s\": 1307.96073       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8872497365648051,         \"F1\": 0.8718562874251498,         \"Memory in Mb\": 8.080266952514648,         \"Time in s\": 1383.548543       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8891170431211499,         \"F1\": 0.8738317757009346,         \"Memory in Mb\": 8.205357551574707,         \"Time in s\": 1460.7186840000002       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8888888888888888,         \"F1\": 0.8737201365187712,         \"Memory in Mb\": 8.39128303527832,         \"Time in s\": 1539.50166       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.888671875,         \"F1\": 0.8738938053097345,         \"Memory in Mb\": 8.406519889831543,         \"Time in s\": 1619.9124450000002       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8903717826501429,         \"F1\": 0.8762109795479011,         \"Memory in Mb\": 8.400672912597656,         \"Time in s\": 1701.87919       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8910614525139665,         \"F1\": 0.877742946708464,         \"Memory in Mb\": 8.453279495239258,         \"Time in s\": 1785.406003       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8926296633303002,         \"F1\": 0.8795918367346939,         \"Memory in Mb\": 8.421560287475586,         \"Time in s\": 1870.444315       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932384341637011,         \"F1\": 0.8811881188118813,         \"Memory in Mb\": 8.383057594299316,         \"Time in s\": 1956.980231       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929503916449086,         \"F1\": 0.8806983511154219,         \"Memory in Mb\": 8.433841705322266,         \"Time in s\": 2045.031177       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8909710391822828,         \"F1\": 0.8783269961977185,         \"Memory in Mb\": 8.58321475982666,         \"Time in s\": 2134.506504       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8932443703085905,         \"F1\": 0.8803738317757008,         \"Memory in Mb\": 8.605175971984863,         \"Time in s\": 2225.421876       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8946078431372549,         \"F1\": 0.8817598533455545,         \"Memory in Mb\": 8.70528507232666,         \"Time in s\": 2317.752916       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8951160928742994,         \"F1\": 0.88272157564906,         \"Memory in Mb\": 8.721240997314453,         \"Time in s\": 2411.411116       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.776657104492188,         \"Time in s\": 62.937451       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.703582763671875,         \"Time in s\": 162.906939       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.683967590332031,         \"Time in s\": 291.999561       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.6548919677734375,         \"Time in s\": 449.905783       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.674896240234375,         \"Time in s\": 634.264648       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.68939208984375,         \"Time in s\": 844.6132379999999       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.6727142333984375,         \"Time in s\": 1077.972428       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992774091834724,         \"F1\": 0.0,         \"Memory in Mb\": 4.755153656005859,         \"Time in s\": 1334.293209       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992409202382344,         \"F1\": 0.0,         \"Memory in Mb\": 4.668712615966797,         \"Time in s\": 1613.416001       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993168322034788,         \"F1\": 0.0,         \"Memory in Mb\": 4.707424163818359,         \"Time in s\": 1913.210948       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999378941333843,         \"F1\": 0.0,         \"Memory in Mb\": 4.680248260498047,         \"Time in s\": 2233.020054       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994306984891612,         \"F1\": 0.0,         \"Memory in Mb\": 4.695384979248047,         \"Time in s\": 2572.124593       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994744926833212,         \"F1\": 0.0,         \"Memory in Mb\": 4.721019744873047,         \"Time in s\": 2930.571035       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999474494200668,         \"F1\": 0.0,         \"Memory in Mb\": 4.747562408447266,         \"Time in s\": 3309.032834       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999509529147982,         \"F1\": 0.0,         \"Memory in Mb\": 4.741054534912109,         \"Time in s\": 3705.221814       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999540184583046,         \"F1\": 0.0,         \"Memory in Mb\": 4.678241729736328,         \"Time in s\": 4115.910057       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995672333848532,         \"F1\": 0.0,         \"Memory in Mb\": 4.619670867919922,         \"Time in s\": 4539.977119       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995912766764956,         \"F1\": 0.0,         \"Memory in Mb\": 4.749675750732422,         \"Time in s\": 4977.188868       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996127890253348,         \"F1\": 0.0,         \"Memory in Mb\": 4.678524017333984,         \"Time in s\": 5426.058059       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996321500827662,         \"F1\": 0.0,         \"Memory in Mb\": 4.705173492431641,         \"Time in s\": 5886.859448       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996496671838246,         \"F1\": 0.0,         \"Memory in Mb\": 4.729236602783203,         \"Time in s\": 6359.258672       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996655917831124,         \"F1\": 0.0,         \"Memory in Mb\": 4.729305267333984,         \"Time in s\": 6843.735511       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996801316029976,         \"F1\": 0.0,         \"Memory in Mb\": 4.741458892822266,         \"Time in s\": 7339.76837       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996934597446958,         \"F1\": 0.0,         \"Memory in Mb\": 4.677211761474609,         \"Time in s\": 7847.750223999999       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997057216126456,         \"F1\": 0.0,         \"Memory in Mb\": 4.833148956298828,         \"Time in s\": 8367.044639       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99971704024092,         \"F1\": 0.0,         \"Memory in Mb\": 4.807292938232422,         \"Time in s\": 8898.416265       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996885947839628,         \"F1\": 0.0,         \"Memory in Mb\": 4.893611907958984,         \"Time in s\": 9441.1614       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996997166075484,         \"F1\": 0.0,         \"Memory in Mb\": 4.877178192138672,         \"Time in s\": 9993.506038       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999710071394919,         \"F1\": 0.0,         \"Memory in Mb\": 4.888896942138672,         \"Time in s\": 10554.524537       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995620872672494,         \"F1\": 0.0,         \"Memory in Mb\": 4.783634185791016,         \"Time in s\": 11123.611551       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995762137238948,         \"F1\": 0.0,         \"Memory in Mb\": 4.831531524658203,         \"Time in s\": 11701.029088       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999589457262501,         \"F1\": 0.0,         \"Memory in Mb\": 4.854015350341797,         \"Time in s\": 12286.755546       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700500015924,         \"F1\": 0.0,         \"Memory in Mb\": 4.858226776123047,         \"Time in s\": 12880.441843       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995826957852274,         \"F1\": 0.0,         \"Memory in Mb\": 4.846561431884766,         \"Time in s\": 13482.274686       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995946189418052,         \"F1\": 0.0,         \"Memory in Mb\": 4.872089385986328,         \"Time in s\": 14092.292627       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995766855941728,         \"F1\": 0.0,         \"Memory in Mb\": 4.843868255615234,         \"Time in s\": 14710.448755       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995881266865502,         \"F1\": 0.0,         \"Memory in Mb\": 4.835132598876953,         \"Time in s\": 15336.827122       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995989656078436,         \"F1\": 0.0,         \"Memory in Mb\": 4.892154693603516,         \"Time in s\": 15971.964918999998       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99960924867953,         \"F1\": 0.0,         \"Memory in Mb\": 4.812671661376953,         \"Time in s\": 16613.982513       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996190175908776,         \"F1\": 0.0,         \"Memory in Mb\": 4.880641937255859,         \"Time in s\": 17262.391145999998       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996283099638564,         \"F1\": 0.0,         \"Memory in Mb\": 4.831180572509766,         \"Time in s\": 17916.095854       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996371598373476,         \"F1\": 0.0,         \"Memory in Mb\": 4.851375579833984,         \"Time in s\": 18574.918078       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996455980837856,         \"F1\": 0.0,         \"Memory in Mb\": 4.851016998291016,         \"Time in s\": 19239.025055       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996536527689864,         \"F1\": 0.0,         \"Memory in Mb\": 4.869503021240234,         \"Time in s\": 19908.351772       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999661349463998,         \"F1\": 0.0,         \"Memory in Mb\": 4.886287689208984,         \"Time in s\": 20582.843626       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996687115162732,         \"F1\": 0.0,         \"Memory in Mb\": 4.888690948486328,         \"Time in s\": 21262.535161       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99966457960644,         \"F1\": 0.0,         \"Memory in Mb\": 4.888484954833984,         \"Time in s\": 21947.343422       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999671567607808,         \"F1\": 0.0,         \"Memory in Mb\": 4.876293182373047,         \"Time in s\": 22637.362347       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996782703815712,         \"F1\": 0.0,         \"Memory in Mb\": 4.905620574951172,         \"Time in s\": 23332.514825       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847050415664,         \"F1\": 0.0,         \"Memory in Mb\": 4.880191802978516,         \"Time in s\": 24032.848442       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Stacking\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996847249224948,         \"F1\": 0.0,         \"Memory in Mb\": 4.888683319091797,         \"Time in s\": 24733.238041       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.6761904761904762,         \"F1\": 0.6136363636363638,         \"Memory in Mb\": 0.1434221267700195,         \"Time in s\": 0.374142       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7772511848341233,         \"F1\": 0.7374301675977653,         \"Memory in Mb\": 0.2354059219360351,         \"Time in s\": 1.3677169999999998       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7886435331230284,         \"F1\": 0.7527675276752769,         \"Memory in Mb\": 0.3270235061645508,         \"Time in s\": 3.238746       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.7990543735224587,         \"F1\": 0.7658402203856748,         \"Memory in Mb\": 0.419011116027832,         \"Time in s\": 6.2520690000000005       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8015122873345936,         \"F1\": 0.7575057736720554,         \"Memory in Mb\": 2.719620704650879,         \"Time in s\": 30.609494       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8173228346456692,         \"F1\": 0.7777777777777779,         \"Memory in Mb\": 3.159085273742676,         \"Time in s\": 56.745796       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8259109311740891,         \"F1\": 0.7839195979899498,         \"Memory in Mb\": 3.6036806106567374,         \"Time in s\": 84.9251       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8299881936245572,         \"F1\": 0.7913043478260869,         \"Memory in Mb\": 4.0666093826293945,         \"Time in s\": 115.117686       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8352570828961176,         \"F1\": 0.7963683527885861,         \"Memory in Mb\": 4.521588325500488,         \"Time in s\": 147.5017       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8470254957507082,         \"F1\": 0.8094117647058824,         \"Memory in Mb\": 4.660099983215332,         \"Time in s\": 182.008963       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8497854077253219,         \"F1\": 0.8132337246531482,         \"Memory in Mb\": 4.474972724914551,         \"Time in s\": 218.357961       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8489378442171518,         \"F1\": 0.8135922330097087,         \"Memory in Mb\": 4.3258256912231445,         \"Time in s\": 256.411001       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8482207697893972,         \"F1\": 0.8112014453477868,         \"Memory in Mb\": 4.1847429275512695,         \"Time in s\": 296.011707       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8530006743088334,         \"F1\": 0.8180300500834724,         \"Memory in Mb\": 4.276310920715332,         \"Time in s\": 337.212047       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8539962240402769,         \"F1\": 0.8198757763975156,         \"Memory in Mb\": 4.522702217102051,         \"Time in s\": 380.364724       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8584070796460177,         \"F1\": 0.8250728862973761,         \"Memory in Mb\": 4.59923267364502,         \"Time in s\": 425.089112       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8622987229317046,         \"F1\": 0.8315217391304348,         \"Memory in Mb\": 4.609700202941895,         \"Time in s\": 471.420384       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8610382800209754,         \"F1\": 0.8319594166138238,         \"Memory in Mb\": 4.583279609680176,         \"Time in s\": 519.2335119999999       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8584202682563339,         \"F1\": 0.8302561048243002,         \"Memory in Mb\": 4.509037971496582,         \"Time in s\": 568.4848139999999       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8612553091080698,         \"F1\": 0.8355704697986577,         \"Memory in Mb\": 4.487088203430176,         \"Time in s\": 619.150273       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8624719101123596,         \"F1\": 0.8370607028753994,         \"Memory in Mb\": 4.479489326477051,         \"Time in s\": 671.27983       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8614328614328615,         \"F1\": 0.8357905439755974,         \"Memory in Mb\": 4.476758003234863,         \"Time in s\": 724.8282939999999       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8621255642183012,         \"F1\": 0.8364167478091528,         \"Memory in Mb\": 4.495999336242676,         \"Time in s\": 779.843051       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8623672827369249,         \"F1\": 0.8375116063138347,         \"Memory in Mb\": 4.492741584777832,         \"Time in s\": 836.3752579999999       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8618346545866364,         \"F1\": 0.8374777975133214,         \"Memory in Mb\": 4.535428047180176,         \"Time in s\": 894.373804       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8627949183303085,         \"F1\": 0.8384615384615384,         \"Memory in Mb\": 4.529454231262207,         \"Time in s\": 953.727688       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8661307235232436,         \"F1\": 0.842061855670103,         \"Memory in Mb\": 4.489285469055176,         \"Time in s\": 1014.523259       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8678800134816312,         \"F1\": 0.8437001594896333,         \"Memory in Mb\": 4.522076606750488,         \"Time in s\": 1076.832167       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8662544744549301,         \"F1\": 0.8419838523644751,         \"Memory in Mb\": 4.4861345291137695,         \"Time in s\": 1140.498025       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8678829820698333,         \"F1\": 0.8432835820895522,         \"Memory in Mb\": 4.490513801574707,         \"Time in s\": 1205.597432       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8684931506849315,         \"F1\": 0.8433647570703406,         \"Memory in Mb\": 4.50365161895752,         \"Time in s\": 1272.097546       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8690651725154822,         \"F1\": 0.8449720670391062,         \"Memory in Mb\": 4.519848823547363,         \"Time in s\": 1340.030829       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8687446382613668,         \"F1\": 0.8439306358381503,         \"Memory in Mb\": 4.534294128417969,         \"Time in s\": 1409.423343       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8701082431307244,         \"F1\": 0.8451356717405691,         \"Memory in Mb\": 4.515525817871094,         \"Time in s\": 1480.1764939999998       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8705850633593961,         \"F1\": 0.8462524023062139,         \"Memory in Mb\": 4.521697998046875,         \"Time in s\": 1552.3097799999998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8718217562254259,         \"F1\": 0.847900466562986,         \"Memory in Mb\": 4.52362060546875,         \"Time in s\": 1625.8426229999998       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8704412139760265,         \"F1\": 0.845873786407767,         \"Memory in Mb\": 4.511474609375,         \"Time in s\": 1700.7302089999998       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8698783213310156,         \"F1\": 0.8450620934358367,         \"Memory in Mb\": 4.530387878417969,         \"Time in s\": 1777.041184       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8707960319380595,         \"F1\": 0.8461981566820277,         \"Memory in Mb\": 4.540306091308594,         \"Time in s\": 1854.755673       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8723755602736495,         \"F1\": 0.8485018202184262,         \"Memory in Mb\": 4.5452880859375,         \"Time in s\": 1933.828162       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8734177215189873,         \"F1\": 0.8498088476242489,         \"Memory in Mb\": 4.5819854736328125,         \"Time in s\": 2014.179242       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8732869018198157,         \"F1\": 0.8494394020288306,         \"Memory in Mb\": 4.578292846679688,         \"Time in s\": 2095.838517       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8720649550142637,         \"F1\": 0.8482166102577455,         \"Memory in Mb\": 4.539161682128906,         \"Time in s\": 2178.6750019999995       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8719708342268926,         \"F1\": 0.8485156051763512,         \"Memory in Mb\": 4.509727478027344,         \"Time in s\": 2262.6244259999994       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8712518347661984,         \"F1\": 0.8472636815920398,         \"Memory in Mb\": 4.5496673583984375,         \"Time in s\": 2347.803564       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8717948717948718,         \"F1\": 0.8474493531852575,         \"Memory in Mb\": 4.560760498046875,         \"Time in s\": 2434.119985       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8725155591246737,         \"F1\": 0.8487735175041676,         \"Memory in Mb\": 4.513671875,         \"Time in s\": 2521.5491019999995       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8718301552978179,         \"F1\": 0.8480186480186479,         \"Memory in Mb\": 4.541267395019531,         \"Time in s\": 2610.1395069999994       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8725207009435779,         \"F1\": 0.848927430397079,         \"Memory in Mb\": 4.582290649414063,         \"Time in s\": 2699.957936       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.8726174749952821,         \"F1\": 0.8491620111731844,         \"Memory in Mb\": 4.584030151367188,         \"Time in s\": 2790.9651129999997       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8795580110497238,         \"F1\": 0.880351262349067,         \"Memory in Mb\": 4.715929985046387,         \"Time in s\": 35.551681       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8807288790723358,         \"F1\": 0.8536585365853658,         \"Memory in Mb\": 4.9170331954956055,         \"Time in s\": 87.613259       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689731321310269,         \"F1\": 0.8344186046511628,         \"Memory in Mb\": 4.988085746765137,         \"Time in s\": 154.923184       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8793817278498481,         \"F1\": 0.8493622888659084,         \"Memory in Mb\": 4.879870414733887,         \"Time in s\": 235.92708       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8792227864870833,         \"F1\": 0.8405712620227338,         \"Memory in Mb\": 5.017077445983887,         \"Time in s\": 328.79801       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689972401103956,         \"F1\": 0.8260869565217391,         \"Memory in Mb\": 4.985064506530762,         \"Time in s\": 432.967134       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8680018924459865,         \"F1\": 0.8269588587967748,         \"Memory in Mb\": 4.949084281921387,         \"Time in s\": 548.642546       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8643576652407893,         \"F1\": 0.8194010655888295,         \"Memory in Mb\": 4.962946891784668,         \"Time in s\": 674.769885       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8671654605666625,         \"F1\": 0.8317016317016317,         \"Memory in Mb\": 5.020190238952637,         \"Time in s\": 811.067026       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8711778341980351,         \"F1\": 0.8417627118644068,         \"Memory in Mb\": 5.0752363204956055,         \"Time in s\": 957.595858       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8706472654290015,         \"F1\": 0.845165165165165,         \"Memory in Mb\": 4.979113578796387,         \"Time in s\": 1113.746382       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8737006715113605,         \"F1\": 0.8516156922079325,         \"Memory in Mb\": 4.9885969161987305,         \"Time in s\": 1279.290866       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8733972998216863,         \"F1\": 0.8507059176930009,         \"Memory in Mb\": 5.106616020202637,         \"Time in s\": 1454.661274       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.873294961759836,         \"F1\": 0.8513551012857274,         \"Memory in Mb\": 5.2120466232299805,         \"Time in s\": 1640.606202       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8755611156082125,         \"F1\": 0.8560973534167304,         \"Memory in Mb\": 5.144991874694824,         \"Time in s\": 1836.835898       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8765781303897896,         \"F1\": 0.8583643416989946,         \"Memory in Mb\": 5.178118705749512,         \"Time in s\": 2042.985996       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8766963184208818,         \"F1\": 0.8575500712624708,         \"Memory in Mb\": 5.108157157897949,         \"Time in s\": 2257.366998       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8711596247010487,         \"F1\": 0.8493366798135532,         \"Memory in Mb\": 5.1558027267456055,         \"Time in s\": 2480.044725       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8687038865973392,         \"F1\": 0.8429683157309616,         \"Memory in Mb\": 5.140070915222168,         \"Time in s\": 2710.925816       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8689773166289531,         \"F1\": 0.8433623647400369,         \"Memory in Mb\": 5.172907829284668,         \"Time in s\": 2950.660411       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8696977660972405,         \"F1\": 0.8421320766732471,         \"Memory in Mb\": 5.328249931335449,         \"Time in s\": 3199.829749       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8659876574180925,         \"F1\": 0.8380132209351688,         \"Memory in Mb\": 5.355593681335449,         \"Time in s\": 3457.907085       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8617843259586313,         \"F1\": 0.8322851153039832,         \"Memory in Mb\": 5.442904472351074,         \"Time in s\": 3724.572015       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.862668445016787,         \"F1\": 0.8307064293003741,         \"Memory in Mb\": 5.347712516784668,         \"Time in s\": 3998.841393       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8610093160845953,         \"F1\": 0.8268045774647886,         \"Memory in Mb\": 5.363558769226074,         \"Time in s\": 4280.436632       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.85434090426661,         \"F1\": 0.8163571160948456,         \"Memory in Mb\": 5.3763532638549805,         \"Time in s\": 4569.013267       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8534401700666366,         \"F1\": 0.8138145936120489,         \"Memory in Mb\": 5.330439567565918,         \"Time in s\": 4864.708005       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8518941932431899,         \"F1\": 0.8121030257564391,         \"Memory in Mb\": 5.456484794616699,         \"Time in s\": 5167.517117       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530811098846725,         \"F1\": 0.8130931628897928,         \"Memory in Mb\": 5.321070671081543,         \"Time in s\": 5477.642376000001       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8524964126715479,         \"F1\": 0.8125672074430782,         \"Memory in Mb\": 5.426630973815918,         \"Time in s\": 5794.594214000001       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8496350364963504,         \"F1\": 0.8078795323233702,         \"Memory in Mb\": 5.366648674011231,         \"Time in s\": 6118.251751000001       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8475043979165948,         \"F1\": 0.8032575319300432,         \"Memory in Mb\": 5.4148359298706055,         \"Time in s\": 6448.591708000001       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8460046158477439,         \"F1\": 0.8002429711905589,         \"Memory in Mb\": 5.3892927169799805,         \"Time in s\": 6785.693940000001       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8462162776352953,         \"F1\": 0.7992881657556884,         \"Memory in Mb\": 5.532000541687012,         \"Time in s\": 7130.317230000001       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8429783342268756,         \"F1\": 0.7938387644403958,         \"Memory in Mb\": 5.507189750671387,         \"Time in s\": 7481.445887000001       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8419745515866932,         \"F1\": 0.7926122646064703,         \"Memory in Mb\": 5.485476493835449,         \"Time in s\": 7839.281994000001       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8421884788639957,         \"F1\": 0.7931492922499414,         \"Memory in Mb\": 5.629275321960449,         \"Time in s\": 8203.623919000001       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8400092950300636,         \"F1\": 0.7894656371837016,         \"Memory in Mb\": 5.587969779968262,         \"Time in s\": 8574.868737       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8398947159878867,         \"F1\": 0.7879287722586691,         \"Memory in Mb\": 5.669405937194824,         \"Time in s\": 8954.118864       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8408896492728828,         \"F1\": 0.7879523389232127,         \"Memory in Mb\": 5.650286674499512,         \"Time in s\": 9340.118817       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8397092475434109,         \"F1\": 0.7854569040069184,         \"Memory in Mb\": 5.652537345886231,         \"Time in s\": 9731.872155       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8398202412551575,         \"F1\": 0.7854402083993381,         \"Memory in Mb\": 5.638819694519043,         \"Time in s\": 10129.488521       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8406704828400544,         \"F1\": 0.787685992816829,         \"Memory in Mb\": 5.6699628829956055,         \"Time in s\": 10532.813227       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.841381732433585,         \"F1\": 0.7910235647949235,         \"Memory in Mb\": 5.623560905456543,         \"Time in s\": 10941.028498       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8422085408030612,         \"F1\": 0.7943480067772769,         \"Memory in Mb\": 5.627467155456543,         \"Time in s\": 11353.937417       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8431673665266947,         \"F1\": 0.7973835947671896,         \"Memory in Mb\": 5.641619682312012,         \"Time in s\": 11771.547134       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8438505436697118,         \"F1\": 0.7987529888919157,         \"Memory in Mb\": 5.640711784362793,         \"Time in s\": 12193.871152999998       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.843999356129418,         \"F1\": 0.7991592160577892,         \"Memory in Mb\": 5.725451469421387,         \"Time in s\": 12620.815871999996       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8432635775910616,         \"F1\": 0.7972256221950225,         \"Memory in Mb\": 5.7456769943237305,         \"Time in s\": 13052.460535999997       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8436830835117773,         \"F1\": 0.7980031379261162,         \"Memory in Mb\": 5.754740715026856,         \"Time in s\": 13488.904282999996       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8436803425216834,         \"F1\": 0.7979576118892089,         \"Memory in Mb\": 5.757502555847168,         \"Time in s\": 13925.545040999996       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5833333333333334,         \"F1\": 0.7058823529411764,         \"Memory in Mb\": 0.1740083694458007,         \"Time in s\": 0.162813       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7346938775510204,         \"F1\": 0.7636363636363637,         \"Memory in Mb\": 0.2024965286254882,         \"Time in s\": 0.520257       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.7837837837837838,         \"F1\": 0.8048780487804877,         \"Memory in Mb\": 0.2315149307250976,         \"Time in s\": 1.0500919999999998       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8080808080808081,         \"F1\": 0.819047619047619,         \"Memory in Mb\": 0.2600297927856445,         \"Time in s\": 1.7634529999999995       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8145161290322581,         \"F1\": 0.8217054263565893,         \"Memory in Mb\": 0.2885446548461914,         \"Time in s\": 2.765045       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8187919463087249,         \"F1\": 0.830188679245283,         \"Memory in Mb\": 0.3175630569458008,         \"Time in s\": 4.083864999999999       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8390804597701149,         \"F1\": 0.8390804597701148,         \"Memory in Mb\": 0.3460779190063476,         \"Time in s\": 5.803779       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8391959798994975,         \"F1\": 0.8383838383838383,         \"Memory in Mb\": 0.3750925064086914,         \"Time in s\": 7.866028       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8348214285714286,         \"F1\": 0.8294930875576038,         \"Memory in Mb\": 0.4036073684692383,         \"Time in s\": 10.317012       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8313253012048193,         \"F1\": 0.8264462809917356,         \"Memory in Mb\": 0.4321222305297851,         \"Time in s\": 13.231482       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8357664233576643,         \"F1\": 0.8288973384030419,         \"Memory in Mb\": 0.4616479873657226,         \"Time in s\": 16.680039999999998       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.842809364548495,         \"F1\": 0.8327402135231317,         \"Memory in Mb\": 0.4901628494262695,         \"Time in s\": 20.639261       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8549382716049383,         \"F1\": 0.8417508417508418,         \"Memory in Mb\": 0.5191812515258789,         \"Time in s\": 25.174847       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8624641833810889,         \"F1\": 0.8471337579617835,         \"Memory in Mb\": 0.5476961135864258,         \"Time in s\": 30.349829       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8609625668449198,         \"F1\": 0.8433734939759037,         \"Memory in Mb\": 0.5762109756469727,         \"Time in s\": 36.11991       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8621553884711779,         \"F1\": 0.8424068767908309,         \"Memory in Mb\": 0.605229377746582,         \"Time in s\": 42.598398       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8632075471698113,         \"F1\": 0.839779005524862,         \"Memory in Mb\": 0.6337442398071289,         \"Time in s\": 49.849208       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8663697104677061,         \"F1\": 0.8412698412698413,         \"Memory in Mb\": 0.6627893447875977,         \"Time in s\": 57.823258       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8649789029535865,         \"F1\": 0.8407960199004976,         \"Memory in Mb\": 0.6913042068481445,         \"Time in s\": 66.560459       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8657314629258517,         \"F1\": 0.8445475638051043,         \"Memory in Mb\": 2.87209415435791,         \"Time in s\": 96.818717       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8683206106870229,         \"F1\": 0.8442437923250564,         \"Memory in Mb\": 2.980504035949707,         \"Time in s\": 127.963431       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8688524590163934,         \"F1\": 0.8461538461538463,         \"Memory in Mb\": 3.08364200592041,         \"Time in s\": 159.977649       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8710801393728222,         \"F1\": 0.848360655737705,         \"Memory in Mb\": 3.1883134841918945,         \"Time in s\": 192.908804       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8747913188647746,         \"F1\": 0.8502994011976048,         \"Memory in Mb\": 3.300492286682129,         \"Time in s\": 226.71997       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8733974358974359,         \"F1\": 0.8460038986354775,         \"Memory in Mb\": 3.412938117980957,         \"Time in s\": 261.370264       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8767334360554699,         \"F1\": 0.8523985239852399,         \"Memory in Mb\": 3.522160530090332,         \"Time in s\": 296.927054       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8783382789317508,         \"F1\": 0.8571428571428572,         \"Memory in Mb\": 3.6335840225219727,         \"Time in s\": 333.391974       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.882689556509299,         \"F1\": 0.8605442176870748,         \"Memory in Mb\": 3.7482118606567374,         \"Time in s\": 370.807982       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8839779005524862,         \"F1\": 0.864516129032258,         \"Memory in Mb\": 3.861550331115722,         \"Time in s\": 409.129822       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8851802403204272,         \"F1\": 0.8664596273291927,         \"Memory in Mb\": 3.975522041320801,         \"Time in s\": 448.343177       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8863049095607235,         \"F1\": 0.8670694864048338,         \"Memory in Mb\": 4.095002174377441,         \"Time in s\": 488.481904       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.886107634543179,         \"F1\": 0.8683068017366136,         \"Memory in Mb\": 4.146827697753906,         \"Time in s\": 529.573688       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8859223300970874,         \"F1\": 0.8690807799442897,         \"Memory in Mb\": 4.390903472900391,         \"Time in s\": 571.6173799999999       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8869257950530035,         \"F1\": 0.8695652173913044,         \"Memory in Mb\": 4.504707336425781,         \"Time in s\": 614.6492939999999       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8890160183066361,         \"F1\": 0.8711819389110226,         \"Memory in Mb\": 4.624469757080078,         \"Time in s\": 658.6172529999999       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8876529477196885,         \"F1\": 0.869340232858991,         \"Memory in Mb\": 4.741554260253906,         \"Time in s\": 703.5317429999999       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8896103896103896,         \"F1\": 0.8728179551122195,         \"Memory in Mb\": 4.862430572509766,         \"Time in s\": 749.5245539999999       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8904109589041096,         \"F1\": 0.8752997601918464,         \"Memory in Mb\": 4.984291076660156,         \"Time in s\": 796.5006999999998       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8921971252566735,         \"F1\": 0.8771929824561404,         \"Memory in Mb\": 5.102375030517578,         \"Time in s\": 844.5149469999998       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8928928928928929,         \"F1\": 0.8779931584948689,         \"Memory in Mb\": 5.219093322753906,         \"Time in s\": 893.5084249999998       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.892578125,         \"F1\": 0.8780487804878048,         \"Memory in Mb\": 5.178688049316406,         \"Time in s\": 943.5715689999996       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.894184938036225,         \"F1\": 0.8802588996763754,         \"Memory in Mb\": 5.151969909667969,         \"Time in s\": 994.6247309999998       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8929236499068901,         \"F1\": 0.8798328108672936,         \"Memory in Mb\": 5.117225646972656,         \"Time in s\": 1046.6512989999997       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8944494995450409,         \"F1\": 0.8816326530612245,         \"Memory in Mb\": 5.075950622558594,         \"Time in s\": 1099.6701919999996       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8959074733096085,         \"F1\": 0.884272997032641,         \"Memory in Mb\": 5.007194519042969,         \"Time in s\": 1153.6019869999996       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.896431679721497,         \"F1\": 0.8845780795344327,         \"Memory in Mb\": 4.982025146484375,         \"Time in s\": 1208.4750359999996       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8952299829642248,         \"F1\": 0.8829686013320648,         \"Memory in Mb\": 4.96966552734375,         \"Time in s\": 1264.1689839999997       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.896580483736447,         \"F1\": 0.8841121495327102,         \"Memory in Mb\": 4.9371490478515625,         \"Time in s\": 1320.7795169999995       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8970588235294118,         \"F1\": 0.8844036697247706,         \"Memory in Mb\": 4.8813018798828125,         \"Time in s\": 1378.3046599999998       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.8967173738991193,         \"F1\": 0.8845120859444942,         \"Memory in Mb\": 4.820304870605469,         \"Time in s\": 1436.7224909999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.661611557006836,         \"Time in s\": 43.527964       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.557134628295898,         \"Time in s\": 113.03315       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.496244430541992,         \"Time in s\": 201.747221       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.508665084838867,         \"Time in s\": 310.754596       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.565656661987305,         \"Time in s\": 436.825284       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.554738998413086,         \"Time in s\": 579.964386       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 4.492513656616211,         \"Time in s\": 739.485002       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372397030808,         \"F1\": 0.7777777777777778,         \"Memory in Mb\": 4.532373428344727,         \"Time in s\": 915.25293       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997664369963798,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.528841018676758,         \"Time in s\": 1107.42481       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997897945241474,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.520586013793945,         \"Time in s\": 1314.036215       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998089050257978,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.519166946411133,         \"Time in s\": 1534.186276       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248303043572,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.512857437133789,         \"Time in s\": 1768.1243410000002       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999838305441022,         \"F1\": 0.8181818181818181,         \"Memory in Mb\": 4.568696975708008,         \"Time in s\": 2014.7470960000005       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998123193573816,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.55253791809082,         \"Time in s\": 2273.6909760000003       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998248318385652,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.554193496704102,         \"Time in s\": 2543.9053730000005       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998357802082308,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.487833023071289,         \"Time in s\": 2824.6447140000005       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998454404945905,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.52525520324707,         \"Time in s\": 3116.2234200000003       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998540273844628,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.608850479125977,         \"Time in s\": 3418.6303260000004       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999861710366191,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.462549209594727,         \"Time in s\": 3731.216163000001       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686250295594,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.517663955688477,         \"Time in s\": 4053.885436000001       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998748811370802,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.596040725708008,         \"Time in s\": 4386.480402       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998805684939688,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.581964492797852,         \"Time in s\": 4729.507439       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998857612867847,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.56077766418457,         \"Time in s\": 5082.623411       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998905213373912,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.554697036743164,         \"Time in s\": 5446.283888999999       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998949005759448,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.589784622192383,         \"Time in s\": 5821.612630999999       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998989429431856,         \"F1\": 0.782608695652174,         \"Memory in Mb\": 4.514947891235352,         \"Time in s\": 6206.523862999999       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998637602179836,         \"F1\": 0.72,         \"Memory in Mb\": 4.571069717407227,         \"Time in s\": 6600.877267999999       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998686260158024,         \"F1\": 0.72,         \"Memory in Mb\": 4.544900894165039,         \"Time in s\": 7002.824473       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9998731562352772,         \"F1\": 0.72,         \"Memory in Mb\": 4.490015029907227,         \"Time in s\": 7412.266105       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997197358510396,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 4.520219802856445,         \"Time in s\": 7829.032212999999       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997287767832926,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 4.567926406860352,         \"Time in s\": 8253.169596       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372526480006,         \"F1\": 0.5294117647058824,         \"Memory in Mb\": 4.602060317993164,         \"Time in s\": 8684.574786       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997133666677284,         \"F1\": 0.5,         \"Memory in Mb\": 4.518564224243164,         \"Time in s\": 9122.220249       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997217971901516,         \"F1\": 0.5,         \"Memory in Mb\": 4.562410354614258,         \"Time in s\": 9566.245368       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997297459612036,         \"F1\": 0.5,         \"Memory in Mb\": 4.587350845336914,         \"Time in s\": 10016.770375       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997226560789408,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.58268928527832,         \"Time in s\": 10473.685786       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997301519670502,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.552003860473633,         \"Time in s\": 10937.213874       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997372533292768,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.568490982055664,         \"Time in s\": 11407.187031       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997439905141748,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.501398086547852,         \"Time in s\": 11883.434676       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997503908354024,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.530572891235352,         \"Time in s\": 12366.124718       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999756478941837,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.565195083618164,         \"Time in s\": 12855.350972       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999762277134814,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.555276870727539,         \"Time in s\": 13350.750206       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997678056411008,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.523683547973633,         \"Time in s\": 13852.638924       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997730828486464,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.51640510559082,         \"Time in s\": 14360.452684       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997781255108952,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.554742813110352,         \"Time in s\": 14874.156807       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997829489244549,         \"F1\": 0.5128205128205129,         \"Memory in Mb\": 4.55351448059082,         \"Time in s\": 15393.589182       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997763864042932,         \"F1\": 0.5,         \"Memory in Mb\": 4.576028823852539,         \"Time in s\": 15919.905447       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997700973254656,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.509759902954102,         \"Time in s\": 16451.368555       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997747892671,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.633722305297852,         \"Time in s\": 16987.639193000003       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997792935290964,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.606340408325195,         \"Time in s\": 17528.67073       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"Voting\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9997793074457464,         \"F1\": 0.4878048780487804,         \"Memory in Mb\": 4.602045059204102,         \"Time in s\": 18069.786262       },       {         \"step\": 106,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5333333333333333,         \"F1\": 0.5242718446601942,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.004468       },       {         \"step\": 212,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5876777251184834,         \"F1\": 0.5538461538461539,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.067972       },       {         \"step\": 318,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5457413249211357,         \"F1\": 0.5102040816326531,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.134988       },       {         \"step\": 424,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5460992907801419,         \"F1\": 0.5025906735751295,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.20522       },       {         \"step\": 530,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5671077504725898,         \"F1\": 0.5096359743040686,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.337716       },       {         \"step\": 636,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5464566929133858,         \"F1\": 0.4875444839857651,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.474055       },       {         \"step\": 742,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5573549257759784,         \"F1\": 0.4875,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.646583       },       {         \"step\": 848,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5501770956316411,         \"F1\": 0.4816326530612245,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.822555       },       {         \"step\": 954,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5487932843651626,         \"F1\": 0.4794188861985472,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.00209       },       {         \"step\": 1060,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5448536355051936,         \"F1\": 0.4679911699779249,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.292978       },       {         \"step\": 1166,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.534763948497854,         \"F1\": 0.4590818363273453,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.5875979999999998       },       {         \"step\": 1272,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5287175452399685,         \"F1\": 0.456935630099728,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.885535       },       {         \"step\": 1378,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5286855482933914,         \"F1\": 0.4523206751054852,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.211477       },       {         \"step\": 1484,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5252865812542145,         \"F1\": 0.4491392801251955,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.547239       },       {         \"step\": 1590,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5204531151667715,         \"F1\": 0.4437956204379563,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.88734       },       {         \"step\": 1696,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5227138643067847,         \"F1\": 0.4455106237148732,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.258534       },       {         \"step\": 1802,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.524153248195447,         \"F1\": 0.4523961661341854,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.633124       },       {         \"step\": 1908,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5233350812794966,         \"F1\": 0.456664674237896,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.01125       },       {         \"step\": 2014,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5171385991058122,         \"F1\": 0.4563758389261745,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.505139000000001       },       {         \"step\": 2120,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5143935818782445,         \"F1\": 0.4581358609794628,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.002779       },       {         \"step\": 2226,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5114606741573033,         \"F1\": 0.4545910687405921,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.503925000000001       },       {         \"step\": 2332,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.510939510939511,         \"F1\": 0.4550669216061185,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.074663000000001       },       {         \"step\": 2438,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5104636848584325,         \"F1\": 0.4530032095369097,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.648598000000001       },       {         \"step\": 2544,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5084545812033032,         \"F1\": 0.4546247818499127,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.226634000000001       },       {         \"step\": 2650,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5096262740656852,         \"F1\": 0.458072590738423,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.863299       },       {         \"step\": 2756,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5092558983666061,         \"F1\": 0.4574638844301765,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.503527       },       {         \"step\": 2862,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5103110800419434,         \"F1\": 0.4563445867287544,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.147193       },       {         \"step\": 2968,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5133131108864173,         \"F1\": 0.457957957957958,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.82546       },       {         \"step\": 3074,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099251545720794,         \"F1\": 0.4563176895306859,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.507099       },       {         \"step\": 3180,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5102233406731677,         \"F1\": 0.4538758330410382,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.191893       },       {         \"step\": 3286,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5095890410958904,         \"F1\": 0.4522271336280176,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.975438       },       {         \"step\": 3392,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5107637864936597,         \"F1\": 0.4558871761233191,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 12.764918       },       {         \"step\": 3498,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5124392336288247,         \"F1\": 0.4557931694861155,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 13.557573       },       {         \"step\": 3604,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5134610047182903,         \"F1\": 0.4544039838157485,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 14.3795       },       {         \"step\": 3710,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5122674575357239,         \"F1\": 0.4546276756104914,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 15.204998       },       {         \"step\": 3816,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.510615989515072,         \"F1\": 0.4536142815335089,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 16.116361       },       {         \"step\": 3922,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5090538128028564,         \"F1\": 0.4507845934379457,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 17.035489000000002       },       {         \"step\": 4028,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5108020859200397,         \"F1\": 0.452473596442468,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 17.958008000000003       },       {         \"step\": 4134,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5102830873457537,         \"F1\": 0.4517876489707476,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 18.927027       },       {         \"step\": 4240,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5102618542108988,         \"F1\": 0.4525316455696203,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 19.900154000000004       },       {         \"step\": 4346,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5074798619102416,         \"F1\": 0.4490216271884655,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 20.87662300000001       },       {         \"step\": 4452,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099977533138621,         \"F1\": 0.4513207547169811,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 21.913356000000007       },       {         \"step\": 4558,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099846390168971,         \"F1\": 0.4539007092198581,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 22.953869000000008       },       {         \"step\": 4664,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099721209521767,         \"F1\": 0.4553039332538737,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 23.99911400000001       },       {         \"step\": 4770,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5110085971901867,         \"F1\": 0.4556489262371615,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 25.08372100000001       },       {         \"step\": 4876,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5109743589743589,         \"F1\": 0.4539624370132845,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 26.17171900000001       },       {         \"step\": 4982,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099377635013049,         \"F1\": 0.453792794808682,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 27.26320500000001       },       {         \"step\": 5088,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5099272655789266,         \"F1\": 0.4536489151873767,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 28.44143600000001       },       {         \"step\": 5194,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5097246293086848,         \"F1\": 0.4531786941580756,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 29.62357400000001       },       {         \"step\": 5300,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Bananas\",         \"Accuracy\": 0.5095301000188714,         \"F1\": 0.4529572721532309,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 30.80903600000001       },       {         \"step\": 906,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530386740331491,         \"F1\": 0.8500563697857948,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.224121       },       {         \"step\": 1812,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8619547211485368,         \"F1\": 0.8287671232876712,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.785464       },       {         \"step\": 2718,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8450496871549503,         \"F1\": 0.80958842152872,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.64751       },       {         \"step\": 3624,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8418437758763456,         \"F1\": 0.8056968463886063,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.805953       },       {         \"step\": 4530,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8388165157871494,         \"F1\": 0.7960893854748604,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.158177       },       {         \"step\": 5436,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8413983440662374,         \"F1\": 0.7995348837209302,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.857693       },       {         \"step\": 6342,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8370919413341744,         \"F1\": 0.7958094485076103,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.811494       },       {         \"step\": 7248,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8359321098385539,         \"F1\": 0.7948231233822259,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.005109       },       {         \"step\": 8154,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8352753587636453,         \"F1\": 0.8021799970540581,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 12.510532       },       {         \"step\": 9060,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8358538470029805,         \"F1\": 0.8069081937410726,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 15.278308       },       {         \"step\": 9966,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8372303060712494,         \"F1\": 0.8118765947575969,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 18.289259       },       {         \"step\": 10872,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8368135406126391,         \"F1\": 0.8140461215932915,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 21.565546       },       {         \"step\": 11778,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8374798335739153,         \"F1\": 0.8150724637681159,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 25.041396       },       {         \"step\": 12684,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8384451628163684,         \"F1\": 0.8161177420802298,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 28.814916       },       {         \"step\": 13590,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.842004562513798,         \"F1\": 0.8223417459660736,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 32.85712       },       {         \"step\": 14496,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8448430493273542,         \"F1\": 0.8264794383149447,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 37.134508       },       {         \"step\": 15402,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8460489578598792,         \"F1\": 0.8270983738058776,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 41.682175       },       {         \"step\": 16308,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.844851904090268,         \"F1\": 0.8251313243019076,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 46.494991       },       {         \"step\": 17214,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8443618195549875,         \"F1\": 0.8222177981286084,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 51.515798       },       {         \"step\": 18120,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8450797505381091,         \"F1\": 0.8227792158595871,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 56.800748       },       {         \"step\": 19026,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8462023653088042,         \"F1\": 0.8224083515416363,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 62.372633       },       {         \"step\": 19932,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.847523957653906,         \"F1\": 0.8255753888538139,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 68.180409       },       {         \"step\": 20838,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.84661899505687,         \"F1\": 0.8249917862227577,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 74.270057       },       {         \"step\": 21744,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8452835395299637,         \"F1\": 0.8209495422610177,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 80.612623       },       {         \"step\": 22650,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8444081416398075,         \"F1\": 0.8188733552631579,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 87.17507       },       {         \"step\": 23556,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8451284228401613,         \"F1\": 0.8194595664654062,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 93.968638       },       {         \"step\": 24462,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8464903315481788,         \"F1\": 0.8198781599270878,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 100.983267       },       {         \"step\": 25368,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8462963692986951,         \"F1\": 0.8199492034172247,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 108.278888       },       {         \"step\": 26274,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8477524454763445,         \"F1\": 0.8213168944876262,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 115.769594       },       {         \"step\": 27180,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8495529636851982,         \"F1\": 0.8240457851026293,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 123.465792       },       {         \"step\": 28086,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8509880719245149,         \"F1\": 0.825107610012955,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 131.36678899999998       },       {         \"step\": 28992,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8521265220240765,         \"F1\": 0.8258237516759436,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 139.55273799999998       },       {         \"step\": 29898,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8531959728400843,         \"F1\": 0.8268160833366216,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 147.964309       },       {         \"step\": 30804,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8537480115573158,         \"F1\": 0.8267107743201139,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 156.664426       },       {         \"step\": 31710,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8530385694913116,         \"F1\": 0.8259895444361464,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 165.606267       },       {         \"step\": 32616,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8536869538555879,         \"F1\": 0.8269760696156635,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 174.782391       },       {         \"step\": 33522,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8541511291429253,         \"F1\": 0.8276032300151628,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 184.217189       },       {         \"step\": 34428,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8549684840386905,         \"F1\": 0.8286724084685859,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 193.875362       },       {         \"step\": 35334,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8555175048821215,         \"F1\": 0.8284321962695346,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 203.791365       },       {         \"step\": 36240,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8545213720025387,         \"F1\": 0.8259146744155329,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 213.957306       },       {         \"step\": 37146,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.854354556467896,         \"F1\": 0.8252696854208386,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 224.377202       },       {         \"step\": 38052,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8545636119944285,         \"F1\": 0.8247736052181622,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 234.998191       },       {         \"step\": 38958,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8548142824139435,         \"F1\": 0.8254213223038459,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 245.897409       },       {         \"step\": 39864,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8546521837292728,         \"F1\": 0.8262981172802495,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 257.034489       },       {         \"step\": 40770,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8540067207927592,         \"F1\": 0.8267652366261132,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 268.379106       },       {         \"step\": 41676,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8537012597480504,         \"F1\": 0.8274320002264302,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 279.987419       },       {         \"step\": 42582,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8536201592259458,         \"F1\": 0.8277177368086459,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 291.808183       },       {         \"step\": 43488,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.853473451836181,         \"F1\": 0.8276626818845675,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 303.899029       },       {         \"step\": 44394,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533777847858897,         \"F1\": 0.8271686890948196,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 316.239245       },       {         \"step\": 45300,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533521711296055,         \"F1\": 0.8273155007928462,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 328.813976       },       {         \"step\": 45312,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Elec2\",         \"Accuracy\": 0.8533027300214076,         \"F1\": 0.8272294856132872,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 341.389555       },       {         \"step\": 25,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.625,         \"F1\": 0.64,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.001863       },       {         \"step\": 50,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.6530612244897959,         \"F1\": 0.6222222222222223,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.005016       },       {         \"step\": 75,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5675675675675675,         \"F1\": 0.5555555555555556,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.009415       },       {         \"step\": 100,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5555555555555556,         \"F1\": 0.5416666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.115037       },       {         \"step\": 125,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5241935483870968,         \"F1\": 0.5123966942148761,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.222127       },       {         \"step\": 150,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5234899328859061,         \"F1\": 0.5298013245033113,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.330326       },       {         \"step\": 175,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5229885057471264,         \"F1\": 0.496969696969697,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.439628       },       {         \"step\": 200,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.507537688442211,         \"F1\": 0.4787234042553192,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.550035       },       {         \"step\": 225,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5,         \"F1\": 0.4509803921568627,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.6616070000000001       },       {         \"step\": 250,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5180722891566265,         \"F1\": 0.4782608695652174,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.774476       },       {         \"step\": 275,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5218978102189781,         \"F1\": 0.4738955823293172,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 0.8884620000000001       },       {         \"step\": 300,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5217391304347826,         \"F1\": 0.460377358490566,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.0035580000000002       },       {         \"step\": 325,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5216049382716049,         \"F1\": 0.4483985765124554,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.151113       },       {         \"step\": 350,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5329512893982808,         \"F1\": 0.4511784511784511,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.299965       },       {         \"step\": 375,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5267379679144385,         \"F1\": 0.4380952380952381,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.45006       },       {         \"step\": 400,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5263157894736842,         \"F1\": 0.4324324324324324,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.6013830000000002       },       {         \"step\": 425,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5424528301886793,         \"F1\": 0.436046511627907,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.7539290000000003       },       {         \"step\": 450,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5367483296213809,         \"F1\": 0.4222222222222222,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 1.9077010000000003       },       {         \"step\": 475,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5358649789029536,         \"F1\": 0.4329896907216494,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.0627030000000004       },       {         \"step\": 500,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5370741482965932,         \"F1\": 0.4460431654676259,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.2669650000000003       },       {         \"step\": 525,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5400763358778626,         \"F1\": 0.4382284382284382,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.491531       },       {         \"step\": 550,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5391621129326047,         \"F1\": 0.4415011037527593,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.717762       },       {         \"step\": 575,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5418118466898955,         \"F1\": 0.4416135881104034,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 2.945135       },       {         \"step\": 600,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5509181969949917,         \"F1\": 0.443064182194617,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.175983       },       {         \"step\": 625,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5560897435897436,         \"F1\": 0.4358452138492871,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.407996       },       {         \"step\": 650,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.551617873651772,         \"F1\": 0.4393063583815029,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.641123       },       {         \"step\": 675,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5459940652818991,         \"F1\": 0.4436363636363636,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 3.875357       },       {         \"step\": 700,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5464949928469242,         \"F1\": 0.4389380530973452,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.110698999999999       },       {         \"step\": 725,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5441988950276243,         \"F1\": 0.4463087248322148,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.380075       },       {         \"step\": 750,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5367156208277704,         \"F1\": 0.4412238325281803,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.650483       },       {         \"step\": 775,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5310077519379846,         \"F1\": 0.4336973478939157,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 4.940408       },       {         \"step\": 800,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5294117647058824,         \"F1\": 0.4388059701492537,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.231503       },       {         \"step\": 825,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5266990291262136,         \"F1\": 0.4396551724137931,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.523716       },       {         \"step\": 850,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5241460541813898,         \"F1\": 0.4341736694677871,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 5.817038       },       {         \"step\": 875,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.522883295194508,         \"F1\": 0.4311050477489768,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.111637       },       {         \"step\": 900,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5272525027808677,         \"F1\": 0.4340878828229028,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.407366       },       {         \"step\": 925,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5227272727272727,         \"F1\": 0.4338896020539153,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 6.766113       },       {         \"step\": 950,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5205479452054794,         \"F1\": 0.438964241676942,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.126579       },       {         \"step\": 975,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5174537987679672,         \"F1\": 0.4337349397590361,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.488418999999999       },       {         \"step\": 1000,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5185185185185185,         \"F1\": 0.4361078546307151,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 7.851253       },       {         \"step\": 1025,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.517578125,         \"F1\": 0.4386363636363636,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.215067       },       {         \"step\": 1050,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5138226882745471,         \"F1\": 0.4370860927152318,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.579858       },       {         \"step\": 1075,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5111731843575419,         \"F1\": 0.4372990353697749,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 8.945611       },       {         \"step\": 1100,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5122838944494995,         \"F1\": 0.4393305439330544,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.312328       },       {         \"step\": 1125,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5124555160142349,         \"F1\": 0.4453441295546558,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 9.680001999999998       },       {         \"step\": 1150,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5143603133159269,         \"F1\": 0.4464285714285714,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.125450999999998       },       {         \"step\": 1175,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5187393526405452,         \"F1\": 0.4509232264334305,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 10.572148       },       {         \"step\": 1200,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5187656380316931,         \"F1\": 0.448901623686724,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.020091999999998       },       {         \"step\": 1225,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5171568627450981,         \"F1\": 0.4471468662301216,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.469231       },       {         \"step\": 1250,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Phishing\",         \"Accuracy\": 0.5156124899919936,         \"F1\": 0.4474885844748858,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 11.919638999999998       },       {         \"step\": 1903,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 0.335236       },       {         \"step\": 3806,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 1.143886       },       {         \"step\": 5709,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 2.36402       },       {         \"step\": 7612,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 4.028138       },       {         \"step\": 9515,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 6.117771       },       {         \"step\": 11418,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 8.657701       },       {         \"step\": 13321,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 1.0,         \"F1\": 0.0,         \"Memory in Mb\": 0.0004835128784179,         \"Time in s\": 11.631       },       {         \"step\": 15224,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9985548183669448,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 15.05037       },       {         \"step\": 17127,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9984818404764684,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 18.876176       },       {         \"step\": 19030,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9986336644069578,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 23.061029       },       {         \"step\": 20933,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9987578826676858,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 27.619004       },       {         \"step\": 22836,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9988613969783228,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 32.587888       },       {         \"step\": 24739,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9989489853666425,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 37.966612       },       {         \"step\": 26642,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9989489884013364,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 43.747016       },       {         \"step\": 28545,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9990190582959642,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 49.923315       },       {         \"step\": 30448,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999080369166092,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 56.476617       },       {         \"step\": 32351,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9991344667697064,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 63.442318       },       {         \"step\": 34254,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999182553352991,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 70.796392       },       {         \"step\": 36157,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992255780506694,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 78.554987       },       {         \"step\": 38060,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992643001655324,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 86.688101       },       {         \"step\": 39963,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9992993343676492,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 95.30254       },       {         \"step\": 41866,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993311835662247,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 104.31052400000002       },       {         \"step\": 43769,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993602632059952,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 113.72316700000002       },       {         \"step\": 45672,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9993869194893916,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 123.549629       },       {         \"step\": 47575,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994114432252912,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 133.72455100000002       },       {         \"step\": 49478,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99943408048184,         \"F1\": 0.0,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 144.361296       },       {         \"step\": 51381,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99941611521993,         \"F1\": 0.0625,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 155.342577       },       {         \"step\": 53284,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994369686391532,         \"F1\": 0.0625,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 166.71976       },       {         \"step\": 55187,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994563838654732,         \"F1\": 0.0625,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 178.568204       },       {         \"step\": 57090,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994394717020793,         \"F1\": 0.36,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 190.760423       },       {         \"step\": 58993,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994575535665852,         \"F1\": 0.36,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 203.318295       },       {         \"step\": 60896,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745052960012,         \"F1\": 0.36,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 216.324675       },       {         \"step\": 62799,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994585814834868,         \"F1\": 0.3703703703703703,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 229.751949       },       {         \"step\": 64702,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745058036196,         \"F1\": 0.3703703703703703,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 243.519605       },       {         \"step\": 66605,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99948952014894,         \"F1\": 0.3703703703703703,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 257.632512       },       {         \"step\": 68508,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994745062548352,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 272.164854       },       {         \"step\": 70411,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9994887089902004,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 287.040678       },       {         \"step\": 72314,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995021642028404,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 302.255295       },       {         \"step\": 74217,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995149293952786,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 317.85437       },       {         \"step\": 76120,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99952705631971,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 333.81575100000003       },       {         \"step\": 78023,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.99953859167927,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 350.111597       },       {         \"step\": 79926,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999549577729121,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 366.757049       },       {         \"step\": 81829,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995600527936648,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 383.769929       },       {         \"step\": 83732,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995700517132244,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 401.126049       },       {         \"step\": 85635,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995796062311698,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 418.855408       },       {         \"step\": 87538,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999588745330546,         \"F1\": 0.3793103448275862,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 436.927356       },       {         \"step\": 89441,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995751341681576,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 455.364423       },       {         \"step\": 91344,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9995839856365568,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 474.187698       },       {         \"step\": 93247,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999592475816657,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 493.377496       },       {         \"step\": 95150,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.999600626385984,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 512.867881       },       {         \"step\": 95156,         \"track\": \"Binary classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"SMTP\",         \"Accuracy\": 0.9996006515684936,         \"F1\": 0.3666666666666666,         \"Memory in Mb\": 0.0005102157592773,         \"Time in s\": 532.358985       }     ]   },   \"params\": [     {       \"name\": \"models\",       \"select\": {         \"type\": \"point\",         \"fields\": [           \"model\"         ]       },       \"bind\": \"legend\"     },     {       \"name\": \"Dataset\",       \"value\": \"Bananas\",       \"bind\": {         \"input\": \"select\",         \"options\": [           \"Bananas\",           \"Elec2\",           \"Phishing\",           \"SMTP\"         ]       }     },     {       \"name\": \"grid\",       \"select\": \"interval\",       \"bind\": \"scales\"     }   ],   \"transform\": [     {       \"filter\": {         \"field\": \"dataset\",         \"equal\": {           \"expr\": \"Dataset\"         }       }     }   ],   \"repeat\": {     \"row\": [       \"Accuracy\",       \"F1\",       \"Memory in Mb\",       \"Time in s\"     ]   },   \"spec\": {     \"width\": \"container\",     \"mark\": \"line\",     \"encoding\": {       \"x\": {         \"field\": \"step\",         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"title\": \"Instance\"         }       },       \"y\": {         \"field\": {           \"repeat\": \"row\"         },         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18         }       },       \"color\": {         \"field\": \"model\",         \"type\": \"ordinal\",         \"scale\": {           \"scheme\": \"category20b\"         },         \"title\": \"Models\",         \"legend\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"labelLimit\": 500         }       },       \"opacity\": {         \"condition\": {           \"param\": \"models\",           \"value\": 1         },         \"value\": 0.2       }     }   } }</p>"},{"location":"benchmarks/Binary%20classification/#datasets","title":"Datasets","text":"Bananas <p>Bananas dataset.</p> <p>An artificial dataset where instances belongs to several clusters with a banana shape. There are two attributes that correspond to the x and y axis, respectively.</p> <pre><code>Name  Bananas                                                                                                \nTask  Binary classification\n</code></pre> <p>Samples  5,300                                                                                                 Features  2                                                                                                       Sparse  False                                                                                                     Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/banana.zip</p> <p></p> Elec2 <p>Electricity prices in New South Wales.</p> <p>This is a binary classification task, where the goal is to predict if the price of electricity will go up or down.</p> <p>This data was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.</p> <pre><code>  Name  Elec2                                                      \n  Task  Binary classification\n</code></pre> <p>Samples  45,312                                                      Features  8                                                             Sparse  False                                                           Path  /Users/mastelini/river_data/Elec2/electricity.csv                URL  https://maxhalford.github.io/files/datasets/electricity.zip       Size  2.95 MB                                                   Downloaded  True                                                       </p> <p></p> Phishing <p>Phishing websites.</p> <p>This dataset contains features from web pages that are classified as phishing or not.</p> <pre><code>Name  Phishing                                                                                                    \nTask  Binary classification\n</code></pre> <p>Samples  1,250                                                                                                      Features  9                                                                                                            Sparse  False                                                                                                          Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/phishing.csv.gz</p> <p></p> SMTP <p>SMTP dataset from the KDD 1999 cup.</p> <p>The goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only contains 2,211 (0.4%) positive labels.</p> <pre><code>  Name  SMTP                                                \n  Task  Binary classification\n</code></pre> <p>Samples  95,156                                               Features  3                                                      Sparse  False                                                    Path  /Users/mastelini/river_data/SMTP/smtp.csv                 URL  https://maxhalford.github.io/files/datasets/smtp.zip       Size  5.23 MB                                            Downloaded  True                                                </p> <p></p>"},{"location":"benchmarks/Binary%20classification/#models","title":"Models","text":"Logistic regression <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LogisticRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.005\n      )\n    )\n    loss=Log (\n      weight_pos=1.\n      weight_neg=1.\n    )\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Aggregated Mondrian Forest <p><pre>[]</pre></p> <p></p> ALMA <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  ALMAClassifier (\n    p=2\n    alpha=0.9\n    B=1.111111\n    C=1.414214\n  )\n)</pre></p> <p></p> sklearn SGDClassifier <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SKL2RiverClassifier (\n    estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log_loss',\n                penalty=None)\n    classes=[False, True]\n  )\n)</pre></p> <p></p> Vowpal Wabbit logistic regression <p><pre>VW2RiverClassifier ()</pre></p> <p></p> Naive Bayes <p><pre>GaussianNB ()</pre></p> <p></p> Hoeffding Tree <p><pre>HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)</pre></p> <p></p> Hoeffding Adaptive Tree <p><pre>HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=True\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=42\n)</pre></p> <p></p> Adaptive Random Forest <p><pre>[]</pre></p> <p></p> Streaming Random Patches <p><pre>SRPClassifier (\n  model=HoeffdingTreeClassifier (\n    grace_period=50\n    max_depth=inf\n    split_criterion=\"info_gain\"\n    delta=0.01\n    tau=0.05\n    leaf_prediction=\"nba\"\n    nb_threshold=0\n    nominal_attributes=None\n    splitter=GaussianSplitter (\n      n_splits=10\n    )\n    binary_split=False\n    min_branch_fraction=0.01\n    max_share_to_split=0.99\n    max_size=100.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n  )\n  n_models=10\n  subspace_size=0.6\n  training_method=\"patches\"\n  lam=6\n  drift_detector=ADWIN (\n    delta=1e-05\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  warning_detector=ADWIN (\n    delta=0.0001\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  disable_detector=\"off\"\n  disable_weighted_vote=False\n  seed=None\n  metric=Accuracy (\n    cm=ConfusionMatrix (\n      classes=[]\n    )\n  )\n)</pre></p> <p></p> k-Nearest Neighbors <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)\n\n<p></p>\n\nADWIN Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nAdaBoost\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nBagging\n<p><pre>[HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n)]</pre></p>\n\n<p></p>\n\nLeveraging Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nStacking\n<p><pre>[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n\n<p></p>\n\nVoting\n<p><pre>VotingClassifier (\n  models=[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n  use_probabilities=True\n)\n\n<p></p>\n\n[baseline] Last Class\n<p><pre>NoChangeClassifier ()</pre></p>\n\n<p></p>"},{"location":"benchmarks/Binary%20classification/#environment","title":"Environment","text":"<pre>Python implementation: CPython\nPython version       : 3.12.4\nIPython version      : 8.18.1\n\nriver       : 0.21.2\nnumpy       : 1.26.4\nscikit-learn: 1.3.1\npandas      : 2.2.2\nscipy       : 1.13.0\n\nCompiler    : GCC 11.4.0\nOS          : Linux\nRelease     : 6.5.0-1024-azure\nMachine     : x86_64\nProcessor   : x86_64\nCPU cores   : 4\nArchitecture: 64bit\n</pre>"},{"location":"benchmarks/Multiclass%20classification/","title":"Multiclass classification","text":"TableChart Model Dataset Accuracy MicroF1 MacroF1 Memory in Mb Time in s ADWIN Bagging ImageSegments 0.777826 0.777826 0.765011 4.11628 3543.55 ADWIN Bagging Insects 0.579465 0.579465 0.570198 15.3074 60279.4 ADWIN Bagging Keystroke 0.81656 0.81656 0.815908 37.8558 41308 AdaBoost ImageSegments 0.804677 0.804677 0.79777 4.09839 3350.88 AdaBoost Insects 0.563532 0.563532 0.554622 27.943 60335.7 AdaBoost Keystroke 0.834796 0.834796 0.836062 194.794 51861.3 Adaptive Random Forest ImageSegments 0.818536 0.818536 0.814535 3.06348 1574.18 Adaptive Random Forest Insects 0.745378 0.745378 0.743302 0.361794 25383.5 Adaptive Random Forest Keystroke 0.969116 0.969116 0.969111 1.63546 7363.05 Aggregated Mondrian Forest ImageSegments 0.901689 0.901689 0.900381 17.0502 2997.7 Aggregated Mondrian Forest Insects 0.646981 0.646981 0.644352 1365.41 76295.7 Aggregated Mondrian Forest Keystroke 0.881073 0.881073 0.879928 338.139 35528.4 Bagging ImageSegments 0.77696 0.77696 0.764564 4.15507 3634.88 Bagging Insects 0.606392 0.606392 0.598542 3.69162 65237 Bagging Keystroke 0.669739 0.669739 0.669981 50.3449 55411.4 Hoeffding Adaptive Tree ImageSegments 0.774361 0.774361 0.763362 0.423797 457.311 Hoeffding Adaptive Tree Insects 0.613337 0.613337 0.604219 0.143826 11292.9 Hoeffding Adaptive Tree Keystroke 0.723124 0.723124 0.721825 0.724475 8998.46 Hoeffding Tree ImageSegments 0.776094 0.776094 0.763137 0.417154 328.067 Hoeffding Tree Insects 0.537306 0.537306 0.527364 2.51923 7445.36 Hoeffding Tree Keystroke 0.648218 0.648218 0.647249 5.09445 7138.73 Leveraging Bagging ImageSegments 0.778259 0.778259 0.766016 4.1005 8561.3 Leveraging Bagging Insects 0.695858 0.695858 0.690508 13.831 99120.2 Leveraging Bagging Keystroke 0.956616 0.956616 0.95665 7.40999 37049.1 Naive Bayes ImageSegments 0.731919 0.731919 0.730419 0.390004 248.959 Naive Bayes Insects 0.506897 0.506897 0.493019 0.611693 4263.77 Naive Bayes Keystroke 0.652532 0.652532 0.651577 4.86901 3544.69 Stacking ImageSegments 0.867908 0.867908 0.865603 9.18162 5416.88 Stacking Insects 0.754745 0.754745 0.752818 10.5864 72115 Stacking Keystroke 0.975489 0.975489 0.975486 18.7111 42471.8 Streaming Random Patches ImageSegments 0.766999 0.766999 0.764707 8.92653 6441.81 Streaming Random Patches Insects 0.736163 0.736163 0.734622 9.632 90031.6 Streaming Random Patches Keystroke 0.955929 0.955929 0.95592 39.636 31009.8 Voting ImageSegments 0.80641 0.80641 0.798999 6.07392 3157.94 Voting Insects 0.648533 0.648533 0.638 9.40652 48163.7 Voting Keystroke 0.779107 0.779107 0.784136 16.3925 29779.2 [baseline] Last Class ImageSegments 0.148116 0.148116 0.148116 0.00136948 31.4159 [baseline] Last Class Insects 0.289761 0.289761 0.289763 0.00138664 679.004 [baseline] Last Class Keystroke 0.997549 0.997549 0.997549 0.00504208 274.675 k-Nearest Neighbors ImageSegments 0.873538 0.873538 0.872136 5.26871 2666.29 k-Nearest Neighbors Insects 0.713115 0.713115 0.711381 6.27269 40639.9 k-Nearest Neighbors Keystroke 0.910486 0.910486 0.910328 6.32511 21326.5 <p>Try reloading the page if something is buggy</p> <p>{   \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",   \"data\": {     \"values\": [       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4666666666666667,         \"MicroF1\": 0.4666666666666667,         \"MacroF1\": 0.4009102009102009,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 0.450679       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5604395604395604,         \"MicroF1\": 0.5604395604395604,         \"MacroF1\": 0.5279334700387331,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 1.152847       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5474452554744526,         \"MicroF1\": 0.5474452554744526,         \"MacroF1\": 0.5191892873237387,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 2.278305       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5573770491803278,         \"MicroF1\": 0.5573770491803278,         \"MacroF1\": 0.5225713529323662,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 3.449742       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5545851528384279,         \"MicroF1\": 0.5545851528384279,         \"MacroF1\": 0.5217226223148511,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 4.939578       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.56,         \"MicroF1\": 0.56,         \"MacroF1\": 0.5450388711329709,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 6.667081       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5825545171339563,         \"MicroF1\": 0.5825545171339563,         \"MacroF1\": 0.5566705826058684,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 8.548779999999999       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5940054495912807,         \"MicroF1\": 0.5940054495912807,         \"MacroF1\": 0.5613773296963412,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 10.607026       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5980629539951574,         \"MicroF1\": 0.5980629539951574,         \"MacroF1\": 0.5624927052752284,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 12.811145       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.599128540305011,         \"MicroF1\": 0.599128540305011,         \"MacroF1\": 0.5669821167583783,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 15.144022       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6099009900990099,         \"MicroF1\": 0.6099009900990099,         \"MacroF1\": 0.592228619098681,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 17.683543       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6116152450090744,         \"MicroF1\": 0.6116152450090744,         \"MacroF1\": 0.5983340184133136,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 20.357047       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6180904522613065,         \"MicroF1\": 0.6180904522613065,         \"MacroF1\": 0.611527101723203,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 23.213992       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6158631415241057,         \"MicroF1\": 0.6158631415241057,         \"MacroF1\": 0.6113311881078581,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 26.205369       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6182873730043541,         \"MicroF1\": 0.6182873730043541,         \"MacroF1\": 0.6150189987146761,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 29.350024       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.617687074829932,         \"MicroF1\": 0.617687074829932,         \"MacroF1\": 0.6157912419016742,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 32.567265       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6274007682458387,         \"MicroF1\": 0.6274007682458387,         \"MacroF1\": 0.6216325704223051,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 36.027093       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6324062877871826,         \"MicroF1\": 0.6324062877871826,         \"MacroF1\": 0.6280704917469789,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 39.646129       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6426116838487973,         \"MicroF1\": 0.6426116838487973,         \"MacroF1\": 0.6349558095046656,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 43.417442       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6485310119695321,         \"MicroF1\": 0.6485310119695321,         \"MacroF1\": 0.6384515982514894,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 47.360213       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6507772020725389,         \"MicroF1\": 0.6507772020725389,         \"MacroF1\": 0.6399118827528387,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 51.459671       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6508407517309595,         \"MicroF1\": 0.6508407517309595,         \"MacroF1\": 0.6387857120889422,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 55.677121       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6537369914853358,         \"MicroF1\": 0.6537369914853358,         \"MacroF1\": 0.6398811322847953,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 60.129657       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.658204895738894,         \"MicroF1\": 0.658204895738894,         \"MacroF1\": 0.6463297068165035,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 64.716333       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6640557006092254,         \"MicroF1\": 0.6640557006092254,         \"MacroF1\": 0.6508930463144657,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 69.425449       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6702928870292887,         \"MicroF1\": 0.6702928870292887,         \"MacroF1\": 0.6599370641329333,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 74.368592       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6736502820306205,         \"MicroF1\": 0.6736502820306205,         \"MacroF1\": 0.669511465798708,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 79.42749       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6822066822066822,         \"MicroF1\": 0.6822066822066822,         \"MacroF1\": 0.6790074545382362,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 84.676077       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6841710427606902,         \"MicroF1\": 0.6841710427606902,         \"MacroF1\": 0.6834974476087327,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 90.04929600000001       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6874546773023931,         \"MicroF1\": 0.6874546773023931,         \"MacroF1\": 0.687676692272135,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 95.54439700000002       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6919298245614035,         \"MicroF1\": 0.6919298245614035,         \"MacroF1\": 0.6930786661709784,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 101.25523300000002       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.698844323589395,         \"MicroF1\": 0.698844323589395,         \"MacroF1\": 0.6985606658027719,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 107.09626300000002       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7027027027027027,         \"MicroF1\": 0.7027027027027027,         \"MacroF1\": 0.7017787722939461,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 113.17857300000004       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7056941778630839,         \"MicroF1\": 0.7056941778630839,         \"MacroF1\": 0.7062915374924865,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 119.38367200000005       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7078931013051585,         \"MicroF1\": 0.7078931013051585,         \"MacroF1\": 0.7081385387673028,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 125.72760100000004       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7093655589123867,         \"MicroF1\": 0.7093655589123867,         \"MacroF1\": 0.7109488618373111,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 132.27559300000004       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7101704879482658,         \"MicroF1\": 0.7101704879482658,         \"MacroF1\": 0.7132092257742534,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 138.94755600000005       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7143674871207785,         \"MicroF1\": 0.7143674871207784,         \"MacroF1\": 0.7178399485500211,         \"Memory in Mb\": 0.3899507522583008,         \"Time in s\": 145.68584300000003       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7172336865588399,         \"MicroF1\": 0.7172336865588399,         \"MacroF1\": 0.7191260584555579,         \"Memory in Mb\": 0.3899774551391601,         \"Time in s\": 152.67811600000005       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7199564980967917,         \"MicroF1\": 0.7199564980967917,         \"MacroF1\": 0.7217017555070446,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 159.82058900000004       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7204244031830239,         \"MicroF1\": 0.7204244031830238,         \"MacroF1\": 0.7234495525792994,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 167.13449700000004       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7219057483169342,         \"MicroF1\": 0.7219057483169342,         \"MacroF1\": 0.7238483512148008,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 174.57489300000003       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.723823975720789,         \"MicroF1\": 0.723823975720789,         \"MacroF1\": 0.7251399238639739,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 182.21825900000005       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.726643598615917,         \"MicroF1\": 0.726643598615917,         \"MacroF1\": 0.7268553573885639,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 189.97396200000009       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7269212179797003,         \"MicroF1\": 0.7269212179797003,         \"MacroF1\": 0.7276782991451582,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 197.92708900000005       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7286052009456265,         \"MicroF1\": 0.7286052009456266,         \"MacroF1\": 0.7283656039279267,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 206.04766600000005       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7306802406293382,         \"MicroF1\": 0.7306802406293383,         \"MacroF1\": 0.7303992643507475,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 214.36632800000004       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.733574988672406,         \"MicroF1\": 0.733574988672406,         \"MacroF1\": 0.7322842940126589,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 222.77231300000005       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7314691522414558,         \"MicroF1\": 0.7314691522414558,         \"MacroF1\": 0.7300322879925133,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 231.40748800000003       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7316224445411048,         \"MicroF1\": 0.7316224445411048,         \"MacroF1\": 0.7300416811383057,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 240.14309800000004       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7319185794716327,         \"MicroF1\": 0.7319185794716329,         \"MacroF1\": 0.7304188192194185,         \"Memory in Mb\": 0.3900041580200195,         \"Time in s\": 248.95897400000004       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.623696682464455,         \"MicroF1\": 0.623696682464455,         \"MacroF1\": 0.5870724729616662,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 4.116407       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6148744670772146,         \"MicroF1\": 0.6148744670772146,         \"MacroF1\": 0.5800776869595596,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 12.008893       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6065677297126618,         \"MicroF1\": 0.6065677297126618,         \"MacroF1\": 0.5714781230184183,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 23.636521       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043097324177126,         \"MicroF1\": 0.6043097324177126,         \"MacroF1\": 0.5697541737710122,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 38.735534       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088274294373934,         \"MicroF1\": 0.6088274294373934,         \"MacroF1\": 0.5727560614138387,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 57.253764       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6023677979479084,         \"MicroF1\": 0.6023677979479084,         \"MacroF1\": 0.5679597008529512,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 79.038555       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5995129211202814,         \"MicroF1\": 0.5995129211202814,         \"MacroF1\": 0.5652603100832261,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 104.109779       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6019888717888008,         \"MicroF1\": 0.6019888717888008,         \"MacroF1\": 0.5673514925692325,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 132.296427       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5993896664211301,         \"MicroF1\": 0.5993896664211301,         \"MacroF1\": 0.5644951651039589,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 163.68164199999998       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5994885879344635,         \"MicroF1\": 0.5994885879344635,         \"MacroF1\": 0.564565538599863,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 198.252114       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5972449418854929,         \"MicroF1\": 0.5972449418854929,         \"MacroF1\": 0.5631227877868952,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 235.999104       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001894088864336,         \"MicroF1\": 0.6001894088864336,         \"MacroF1\": 0.5684733590606373,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 276.973484       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6120783856632913,         \"MicroF1\": 0.6120783856632913,         \"MacroF1\": 0.5935173038317552,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 321.087465       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6024487587093282,         \"MicroF1\": 0.6024487587093282,         \"MacroF1\": 0.5841270876002982,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 368.414891       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5676494728202538,         \"MicroF1\": 0.5676494728202538,         \"MacroF1\": 0.5507155080701159,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 418.926748       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5418762947617638,         \"MicroF1\": 0.5418762947617638,         \"MacroF1\": 0.5256197352354142,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 472.672831       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5232020500250683,         \"MicroF1\": 0.5232020500250683,         \"MacroF1\": 0.5066898143269706,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 529.5973250000001       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5118640500868101,         \"MicroF1\": 0.5118640500868101,         \"MacroF1\": 0.4926543583964285,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 589.87103       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5103922643672432,         \"MicroF1\": 0.5103922643672432,         \"MacroF1\": 0.4900586962359796,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 653.257514       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5115772527108291,         \"MicroF1\": 0.5115772527108291,         \"MacroF1\": 0.4910837640903744,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 719.720849       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5140022547914318,         \"MicroF1\": 0.5140022547914318,         \"MacroF1\": 0.4932541888231957,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 789.2473650000001       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5154319659076234,         \"MicroF1\": 0.5154319659076234,         \"MacroF1\": 0.4943013417599926,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 861.9200270000001       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5184254951208466,         \"MicroF1\": 0.5184254951208466,         \"MacroF1\": 0.4965832238311332,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 937.628382       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5225111470623052,         \"MicroF1\": 0.5225111470623052,         \"MacroF1\": 0.499893079239698,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1016.433905       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5257396113489148,         \"MicroF1\": 0.5257396113489148,         \"MacroF1\": 0.5022487669255871,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1098.325454       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5301402294663996,         \"MicroF1\": 0.5301402294663996,         \"MacroF1\": 0.5051550433324518,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1183.302333       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5277261407877661,         \"MicroF1\": 0.5277261407877661,         \"MacroF1\": 0.5036945145235058,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1271.323869       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5204450908107011,         \"MicroF1\": 0.5204450908107011,         \"MacroF1\": 0.4989008712312768,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1362.446785       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5147111648107632,         \"MicroF1\": 0.5147111648107632,         \"MacroF1\": 0.495826840073632,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1456.7074690000002       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5105590454244137,         \"MicroF1\": 0.5105590454244137,         \"MacroF1\": 0.4941101813344875,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1553.9918670000002       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5075607148312204,         \"MicroF1\": 0.5075607148312204,         \"MacroF1\": 0.4931947798921405,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1654.355087       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5044538486579266,         \"MicroF1\": 0.5044538486579266,         \"MacroF1\": 0.4905626123916189,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1757.6376       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5020231296811777,         \"MicroF1\": 0.5020231296811777,         \"MacroF1\": 0.487879842488124,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1863.925375       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4998746622844887,         \"MicroF1\": 0.4998746622844887,         \"MacroF1\": 0.4853435061152475,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 1973.177917       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4967937444194918,         \"MicroF1\": 0.4967937444194918,         \"MacroF1\": 0.4819418474093529,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2085.445724       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4955938445350519,         \"MicroF1\": 0.4955938445350519,         \"MacroF1\": 0.4801892436835747,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2200.821931       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4940237004427836,         \"MicroF1\": 0.4940237004427836,         \"MacroF1\": 0.478380783820526,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2319.178703       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.493508111745209,         \"MicroF1\": 0.493508111745209,         \"MacroF1\": 0.4785213801670671,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2440.443075       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4936988563242114,         \"MicroF1\": 0.4936988563242114,         \"MacroF1\": 0.4794201499427274,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2564.583087       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4938800634484718,         \"MicroF1\": 0.4938800634484718,         \"MacroF1\": 0.4802377497532935,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2691.651665       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4943757939715902,         \"MicroF1\": 0.4943757939715902,         \"MacroF1\": 0.4812132921167227,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2821.601336       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.494036211133909,         \"MicroF1\": 0.494036211133909,         \"MacroF1\": 0.4812388919618418,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 2954.377766       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4944832294580131,         \"MicroF1\": 0.4944832294580131,         \"MacroF1\": 0.4818441874360225,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3089.8310679999995       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4945225232981082,         \"MicroF1\": 0.4945225232981082,         \"MacroF1\": 0.4820791268335544,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3227.9665449999998       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4956333256171216,         \"MicroF1\": 0.4956333256171216,         \"MacroF1\": 0.4833168636021498,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3368.688097999999       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4970869788986104,         \"MicroF1\": 0.4970869788986104,         \"MacroF1\": 0.4846703771634363,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3511.887438999999       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.4987608551107171,         \"MicroF1\": 0.4987608551107171,         \"MacroF1\": 0.4862426724473749,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3657.6494079999993       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5009568528419516,         \"MicroF1\": 0.5009568528419516,         \"MacroF1\": 0.4881725476999718,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3806.011259       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5034497419940862,         \"MicroF1\": 0.5034497419940862,         \"MacroF1\": 0.4903712806540024,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 3956.893516       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5068467205818292,         \"MicroF1\": 0.5068467205818292,         \"MacroF1\": 0.4930025316136313,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 4110.278735       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5068972694760346,         \"MicroF1\": 0.5068972694760346,         \"MacroF1\": 0.4930190627831494,         \"Memory in Mb\": 0.6116933822631836,         \"Time in s\": 4263.766907       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9852579852579852,         \"MicroF1\": 0.9852579852579852,         \"MacroF1\": 0.6962686567164179,         \"Memory in Mb\": 0.1935644149780273,         \"Time in s\": 0.780775       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.947239263803681,         \"MicroF1\": 0.947239263803681,         \"MacroF1\": 0.7418606503288051,         \"Memory in Mb\": 0.2889022827148437,         \"Time in s\": 2.463269       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.884709730171709,         \"MicroF1\": 0.884709730171709,         \"MacroF1\": 0.8705899666065842,         \"Memory in Mb\": 0.3842401504516601,         \"Time in s\": 5.15507       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8933169834457388,         \"MicroF1\": 0.8933169834457388,         \"MacroF1\": 0.8791291775937072,         \"Memory in Mb\": 0.4795780181884765,         \"Time in s\": 8.960951       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8921039725355566,         \"MicroF1\": 0.8921039725355566,         \"MacroF1\": 0.8831785360852743,         \"Memory in Mb\": 0.575160026550293,         \"Time in s\": 14.051639       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.851655087862689,         \"MicroF1\": 0.851655087862689,         \"MacroF1\": 0.8581984289516641,         \"Memory in Mb\": 0.6704978942871094,         \"Time in s\": 20.582882       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8598949211908932,         \"MicroF1\": 0.8598949211908932,         \"MacroF1\": 0.8469962214365346,         \"Memory in Mb\": 0.7658357620239258,         \"Time in s\": 28.649143       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8513637756665645,         \"MicroF1\": 0.8513637756665645,         \"MacroF1\": 0.8281280134770846,         \"Memory in Mb\": 0.8611736297607422,         \"Time in s\": 38.532046       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8422773086352493,         \"MicroF1\": 0.8422773086352493,         \"MacroF1\": 0.8409307955747314,         \"Memory in Mb\": 0.9565114974975586,         \"Time in s\": 50.273206       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8367246874233881,         \"MicroF1\": 0.8367246874233881,         \"MacroF1\": 0.8249418657104467,         \"Memory in Mb\": 1.0523834228515625,         \"Time in s\": 63.882498       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8203699576554491,         \"MicroF1\": 0.8203699576554491,         \"MacroF1\": 0.8300896799820437,         \"Memory in Mb\": 1.147721290588379,         \"Time in s\": 79.531469       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8192032686414709,         \"MicroF1\": 0.8192032686414709,         \"MacroF1\": 0.8269731591910484,         \"Memory in Mb\": 1.243059158325195,         \"Time in s\": 97.310117       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8172732415613804,         \"MicroF1\": 0.8172732415613804,         \"MacroF1\": 0.8027823390848743,         \"Memory in Mb\": 1.3383970260620115,         \"Time in s\": 117.35519       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7961828051129399,         \"MicroF1\": 0.7961828051129399,         \"MacroF1\": 0.8002006091139847,         \"Memory in Mb\": 1.433734893798828,         \"Time in s\": 139.817583       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.793920575257395,         \"MicroF1\": 0.793920575257395,         \"MacroF1\": 0.7746960355921345,         \"Memory in Mb\": 1.5290727615356443,         \"Time in s\": 164.727582       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7688064960931515,         \"MicroF1\": 0.7688064960931515,         \"MacroF1\": 0.7622487598340326,         \"Memory in Mb\": 1.624410629272461,         \"Time in s\": 192.151707       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7568853640951694,         \"MicroF1\": 0.7568853640951694,         \"MacroF1\": 0.757813781660983,         \"Memory in Mb\": 1.7197484970092771,         \"Time in s\": 222.243586       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7669889690862045,         \"MicroF1\": 0.7669889690862046,         \"MacroF1\": 0.7643943615019536,         \"Memory in Mb\": 1.8150863647460935,         \"Time in s\": 255.230678       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7676428847890595,         \"MicroF1\": 0.7676428847890595,         \"MacroF1\": 0.7655695901071293,         \"Memory in Mb\": 1.9104242324829104,         \"Time in s\": 291.218411       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7714180659394534,         \"MicroF1\": 0.7714180659394533,         \"MacroF1\": 0.7672011803374248,         \"Memory in Mb\": 2.0057621002197266,         \"Time in s\": 330.398823       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7702813120112058,         \"MicroF1\": 0.7702813120112058,         \"MacroF1\": 0.7699263138193525,         \"Memory in Mb\": 2.1021223068237305,         \"Time in s\": 372.827664       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7680222841225627,         \"MicroF1\": 0.7680222841225627,         \"MacroF1\": 0.7682287234686136,         \"Memory in Mb\": 2.197460174560547,         \"Time in s\": 418.63015       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7659597143770649,         \"MicroF1\": 0.7659597143770649,         \"MacroF1\": 0.7643546547243014,         \"Memory in Mb\": 2.2927980422973637,         \"Time in s\": 468.011111       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7586559084873864,         \"MicroF1\": 0.7586559084873864,         \"MacroF1\": 0.7552148692020618,         \"Memory in Mb\": 2.38813591003418,         \"Time in s\": 521.0847249999999       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7505637807628199,         \"MicroF1\": 0.7505637807628199,         \"MacroF1\": 0.7430512224080145,         \"Memory in Mb\": 2.483473777770996,         \"Time in s\": 577.917349       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7290468558499105,         \"MicroF1\": 0.7290468558499106,         \"MacroF1\": 0.715756093271779,         \"Memory in Mb\": 2.5788116455078125,         \"Time in s\": 638.790947       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7217430776214253,         \"MicroF1\": 0.7217430776214253,         \"MacroF1\": 0.7173640789896896,         \"Memory in Mb\": 2.674149513244629,         \"Time in s\": 703.666983       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7151361288628206,         \"MicroF1\": 0.7151361288628206,         \"MacroF1\": 0.7011862635194489,         \"Memory in Mb\": 2.7694873809814453,         \"Time in s\": 772.6431349999999       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.705603921900093,         \"MicroF1\": 0.705603921900093,         \"MacroF1\": 0.6976881379682607,         \"Memory in Mb\": 2.8648252487182617,         \"Time in s\": 845.8350979999999       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7094533867146009,         \"MicroF1\": 0.7094533867146009,         \"MacroF1\": 0.7058405389403433,         \"Memory in Mb\": 2.960163116455078,         \"Time in s\": 923.50335       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7053846762077963,         \"MicroF1\": 0.7053846762077963,         \"MacroF1\": 0.6965736948063982,         \"Memory in Mb\": 3.0555009841918945,         \"Time in s\": 1005.753677       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6927613941018766,         \"MicroF1\": 0.6927613941018766,         \"MacroF1\": 0.6842255816736498,         \"Memory in Mb\": 3.150838851928711,         \"Time in s\": 1092.707972       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6890737577063062,         \"MicroF1\": 0.6890737577063062,         \"MacroF1\": 0.6845669389392289,         \"Memory in Mb\": 3.246176719665528,         \"Time in s\": 1184.483965       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6873332852714296,         \"MicroF1\": 0.6873332852714296,         \"MacroF1\": 0.68390545518227,         \"Memory in Mb\": 3.341514587402344,         \"Time in s\": 1281.216395       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.682960991666083,         \"MicroF1\": 0.682960991666083,         \"MacroF1\": 0.6781566371919944,         \"Memory in Mb\": 3.43685245513916,         \"Time in s\": 1383.039909       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.686185061619119,         \"MicroF1\": 0.686185061619119,         \"MacroF1\": 0.6843713776162116,         \"Memory in Mb\": 3.532190322875977,         \"Time in s\": 1489.909884       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6928784365684001,         \"MicroF1\": 0.6928784365684001,         \"MacroF1\": 0.6911392400672977,         \"Memory in Mb\": 3.627528190612793,         \"Time in s\": 1601.996709       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6913500612784622,         \"MicroF1\": 0.6913500612784622,         \"MacroF1\": 0.687359772989117,         \"Memory in Mb\": 3.72286605834961,         \"Time in s\": 1719.445985       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6819810194205267,         \"MicroF1\": 0.6819810194205267,         \"MacroF1\": 0.674915944935936,         \"Memory in Mb\": 3.818203926086426,         \"Time in s\": 1842.197498       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6726515105092223,         \"MicroF1\": 0.6726515105092223,         \"MacroF1\": 0.6670192172011686,         \"Memory in Mb\": 3.913541793823242,         \"Time in s\": 1970.358299       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6695163508100676,         \"MicroF1\": 0.6695163508100676,         \"MacroF1\": 0.6664051037977977,         \"Memory in Mb\": 4.008879661560059,         \"Time in s\": 2103.939399       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6650131310183834,         \"MicroF1\": 0.6650131310183834,         \"MacroF1\": 0.6608988619616458,         \"Memory in Mb\": 4.1063079833984375,         \"Time in s\": 2242.845385       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6568431853160804,         \"MicroF1\": 0.6568431853160804,         \"MacroF1\": 0.6531382897719189,         \"Memory in Mb\": 4.201645851135254,         \"Time in s\": 2386.822189       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6556180714166342,         \"MicroF1\": 0.6556180714166342,         \"MacroF1\": 0.6538448358590968,         \"Memory in Mb\": 4.29698371887207,         \"Time in s\": 2536.044428       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6614194672912468,         \"MicroF1\": 0.6614194672912468,         \"MacroF1\": 0.6603186829199909,         \"Memory in Mb\": 4.392321586608887,         \"Time in s\": 2690.5476860000003       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6669686151222891,         \"MicroF1\": 0.6669686151222891,         \"MacroF1\": 0.6662293616554571,         \"Memory in Mb\": 4.487659454345703,         \"Time in s\": 2850.3140810000004       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6579921773142112,         \"MicroF1\": 0.6579921773142112,         \"MacroF1\": 0.6554177118629491,         \"Memory in Mb\": 4.58299732208252,         \"Time in s\": 3015.4823350000006       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6622580809886126,         \"MicroF1\": 0.6622580809886126,         \"MacroF1\": 0.6609360990360078,         \"Memory in Mb\": 4.678335189819336,         \"Time in s\": 3186.2814100000005       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6562453103896754,         \"MicroF1\": 0.6562453103896754,         \"MacroF1\": 0.6545704957554572,         \"Memory in Mb\": 4.773673057556152,         \"Time in s\": 3362.6238980000007       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Naive Bayes\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6525319868621011,         \"MicroF1\": 0.6525319868621011,         \"MacroF1\": 0.6515767870317885,         \"Memory in Mb\": 4.869010925292969,         \"Time in s\": 3544.6906370000006       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3555555555555555,         \"MicroF1\": 0.3555555555555555,         \"MacroF1\": 0.2537942449707155,         \"Memory in Mb\": 0.4170856475830078,         \"Time in s\": 0.290301       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4945054945054945,         \"MicroF1\": 0.4945054945054945,         \"MacroF1\": 0.5043329927491418,         \"Memory in Mb\": 0.4170818328857422,         \"Time in s\": 0.82046       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5328467153284672,         \"MicroF1\": 0.5328467153284672,         \"MacroF1\": 0.5564033878668025,         \"Memory in Mb\": 0.4171772003173828,         \"Time in s\": 1.675423       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6010928961748634,         \"MicroF1\": 0.6010928961748634,         \"MacroF1\": 0.622766496539645,         \"Memory in Mb\": 0.4171772003173828,         \"Time in s\": 2.801183       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6375545851528385,         \"MicroF1\": 0.6375545851528385,         \"MacroF1\": 0.6539827168809461,         \"Memory in Mb\": 0.4172000885009765,         \"Time in s\": 4.271522       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6509090909090909,         \"MicroF1\": 0.6509090909090909,         \"MacroF1\": 0.6671561759164943,         \"Memory in Mb\": 0.4172496795654297,         \"Time in s\": 5.954744       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.67601246105919,         \"MicroF1\": 0.67601246105919,         \"MacroF1\": 0.6756614325426025,         \"Memory in Mb\": 0.4172496795654297,         \"Time in s\": 7.864603       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7029972752043597,         \"MicroF1\": 0.7029972752043597,         \"MacroF1\": 0.6993447851636564,         \"Memory in Mb\": 0.4172229766845703,         \"Time in s\": 10.008665       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7142857142857143,         \"MicroF1\": 0.7142857142857143,         \"MacroF1\": 0.7108606838045498,         \"Memory in Mb\": 0.4171428680419922,         \"Time in s\": 12.399438       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7145969498910676,         \"MicroF1\": 0.7145969498910676,         \"MacroF1\": 0.7090365931960759,         \"Memory in Mb\": 0.4172191619873047,         \"Time in s\": 15.01004       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7207920792079208,         \"MicroF1\": 0.7207920792079208,         \"MacroF1\": 0.7126631585949761,         \"Memory in Mb\": 0.4172191619873047,         \"Time in s\": 17.873655       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7223230490018149,         \"MicroF1\": 0.7223230490018149,         \"MacroF1\": 0.7157730164623107,         \"Memory in Mb\": 0.4171123504638672,         \"Time in s\": 20.946971       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7286432160804021,         \"MicroF1\": 0.7286432160804021,         \"MacroF1\": 0.7216745323124732,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 24.255884       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7278382581648523,         \"MicroF1\": 0.7278382581648523,         \"MacroF1\": 0.7229105183087501,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 27.838412       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7314949201741655,         \"MicroF1\": 0.7314949201741654,         \"MacroF1\": 0.7263583447448078,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 31.647636       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7333333333333333,         \"MicroF1\": 0.7333333333333333,         \"MacroF1\": 0.729431071218305,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 35.743157       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7387964148527529,         \"MicroF1\": 0.7387964148527529,         \"MacroF1\": 0.7349287389986899,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 40.06309       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7376058041112454,         \"MicroF1\": 0.7376058041112454,         \"MacroF1\": 0.7356226390109741,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 44.599844       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7445589919816724,         \"MicroF1\": 0.7445589919816724,         \"MacroF1\": 0.7409366047432264,         \"Memory in Mb\": 0.4171352386474609,         \"Time in s\": 49.398729       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7453754080522307,         \"MicroF1\": 0.7453754080522307,         \"MacroF1\": 0.7408438328939173,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 54.404894       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7471502590673575,         \"MicroF1\": 0.7471502590673575,         \"MacroF1\": 0.7416651838589269,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 59.665949       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7467853610286844,         \"MicroF1\": 0.7467853610286844,         \"MacroF1\": 0.7416356251822,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 65.211169       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7492904446546831,         \"MicroF1\": 0.7492904446546831,         \"MacroF1\": 0.7430778844390783,         \"Memory in Mb\": 0.4171085357666015,         \"Time in s\": 70.961377       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7515865820489573,         \"MicroF1\": 0.7515865820489573,         \"MacroF1\": 0.7451256886686588,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 76.969446       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7536988685813751,         \"MicroF1\": 0.7536988685813751,         \"MacroF1\": 0.7468312166689606,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 83.201851       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7564853556485356,         \"MicroF1\": 0.7564853556485356,         \"MacroF1\": 0.7503479321738041,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 89.604352       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7566478646253022,         \"MicroF1\": 0.7566478646253022,         \"MacroF1\": 0.7509717522131719,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 96.307026       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7614607614607615,         \"MicroF1\": 0.7614607614607615,         \"MacroF1\": 0.7547643483779538,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 103.262462       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7614403600900225,         \"MicroF1\": 0.7614403600900225,         \"MacroF1\": 0.7551060921605869,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 110.41488900000002       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7621464829586657,         \"MicroF1\": 0.7621464829586658,         \"MacroF1\": 0.7562209880685912,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 117.799886       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7642105263157895,         \"MicroF1\": 0.7642105263157895,         \"MacroF1\": 0.7575332274919562,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 125.46176800000002       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7688647178789939,         \"MicroF1\": 0.768864717878994,         \"MacroF1\": 0.760438686053582,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 133.360363       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7705998681608438,         \"MicroF1\": 0.7705998681608438,         \"MacroF1\": 0.7612069012840872,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 141.48549400000002       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7709532949456174,         \"MicroF1\": 0.7709532949456174,         \"MacroF1\": 0.7622701654854867,         \"Memory in Mb\": 0.4171581268310547,         \"Time in s\": 149.83563600000002       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7712865133623369,         \"MicroF1\": 0.771286513362337,         \"MacroF1\": 0.7617247271717752,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 158.439217       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7709969788519637,         \"MicroF1\": 0.7709969788519637,         \"MacroF1\": 0.7615629120572474,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 167.22864700000002       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.770135214579659,         \"MicroF1\": 0.770135214579659,         \"MacroF1\": 0.7627316365695143,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 176.30742800000002       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7727532913566113,         \"MicroF1\": 0.7727532913566113,         \"MacroF1\": 0.7649467707214076,         \"Memory in Mb\": 0.4171810150146484,         \"Time in s\": 185.609237       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7741215839375348,         \"MicroF1\": 0.7741215839375348,         \"MacroF1\": 0.7649332326562149,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 195.107308       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7754214246873301,         \"MicroF1\": 0.7754214246873301,         \"MacroF1\": 0.7664700790631908,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 204.88888000000003       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7740053050397878,         \"MicroF1\": 0.7740053050397878,         \"MacroF1\": 0.7655121135276625,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 214.87796100000003       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7742102537545313,         \"MicroF1\": 0.7742102537545313,         \"MacroF1\": 0.7648034036287765,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 225.10774000000004       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7754172989377845,         \"MicroF1\": 0.7754172989377845,         \"MacroF1\": 0.7656013068970459,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 235.56491900000003       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7770637666831438,         \"MicroF1\": 0.7770637666831438,         \"MacroF1\": 0.7660878232247856,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 246.31694000000005       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7762203963267279,         \"MicroF1\": 0.7762203963267279,         \"MacroF1\": 0.7654829214385931,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 257.28426500000006       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7768321513002364,         \"MicroF1\": 0.7768321513002364,         \"MacroF1\": 0.7653071619305024,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 268.5154150000001       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7778806108283203,         \"MicroF1\": 0.7778806108283203,         \"MacroF1\": 0.7659351904174982,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 279.94414300000005       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7797915722700498,         \"MicroF1\": 0.7797915722700498,         \"MacroF1\": 0.7668192864082087,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 291.65328600000004       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7767421216156236,         \"MicroF1\": 0.7767421216156236,         \"MacroF1\": 0.7637794374955548,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 303.618395       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7759895606785558,         \"MicroF1\": 0.7759895606785558,         \"MacroF1\": 0.763026662835187,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 315.80512400000003       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.776093546990039,         \"MicroF1\": 0.776093546990039,         \"MacroF1\": 0.7631372452021826,         \"Memory in Mb\": 0.417154312133789,         \"Time in s\": 328.06738900000005       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6218009478672986,         \"MicroF1\": 0.6218009478672986,         \"MacroF1\": 0.5852663107194211,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 7.68277       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6153481762198011,         \"MicroF1\": 0.6153481762198011,         \"MacroF1\": 0.5806436317780949,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 22.565114       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6071992421850332,         \"MicroF1\": 0.6071992421850332,         \"MacroF1\": 0.572248584718361,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 43.997682       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043097324177126,         \"MicroF1\": 0.6043097324177126,         \"MacroF1\": 0.5697573109597247,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 71.858443       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088274294373934,         \"MicroF1\": 0.6088274294373934,         \"MacroF1\": 0.5727379077413696,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 105.92484       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6026835043409629,         \"MicroF1\": 0.6026835043409629,         \"MacroF1\": 0.568251333238805,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 146.287253       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.600189419564335,         \"MicroF1\": 0.600189419564335,         \"MacroF1\": 0.5659762112716077,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 192.863981       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.60258079791642,         \"MicroF1\": 0.60258079791642,         \"MacroF1\": 0.5679781484640408,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 245.806734       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5998105861306956,         \"MicroF1\": 0.5998105861306956,         \"MacroF1\": 0.5649597336877693,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 305.14044       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5998674116867128,         \"MicroF1\": 0.5998674116867128,         \"MacroF1\": 0.5650173260529011,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 370.680891       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5974171330176495,         \"MicroF1\": 0.5974171330176495,         \"MacroF1\": 0.5633067089377387,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 442.338443       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001894088864336,         \"MicroF1\": 0.6001894088864336,         \"MacroF1\": 0.5684760329567131,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 520.121563       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6120783856632913,         \"MicroF1\": 0.6120783856632913,         \"MacroF1\": 0.5935956771555828,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 604.039429       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6024487587093282,         \"MicroF1\": 0.6024487587093282,         \"MacroF1\": 0.5842148300149193,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 694.113241       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5677757434181451,         \"MicroF1\": 0.5677757434181451,         \"MacroF1\": 0.5509250187877572,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 790.19156       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5419354838709678,         \"MicroF1\": 0.5419354838709678,         \"MacroF1\": 0.5257359157219258,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 892.361186       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5233691716338923,         \"MicroF1\": 0.5233691716338923,         \"MacroF1\": 0.5068581838352059,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 1000.471748       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5121271110643447,         \"MicroF1\": 0.5121271110643447,         \"MacroF1\": 0.4929289906509415,         \"Memory in Mb\": 0.6579360961914062,         \"Time in s\": 1114.494528       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5120370831879579,         \"MicroF1\": 0.5120370831879579,         \"MacroF1\": 0.4920970323041603,         \"Memory in Mb\": 1.3099584579467771,         \"Time in s\": 1234.20565       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5173066906577016,         \"MicroF1\": 0.5173066906577016,         \"MacroF1\": 0.4973447169836249,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1358.925583       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5229312288613304,         \"MicroF1\": 0.5229312288613304,         \"MacroF1\": 0.5026343687424488,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1488.370808       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5301536739701261,         \"MicroF1\": 0.5301536739701261,         \"MacroF1\": 0.5095132087733324,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1622.41448       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5351422571746202,         \"MicroF1\": 0.5351422571746202,         \"MacroF1\": 0.5135975374357353,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1760.8970379999998       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5403069881229531,         \"MicroF1\": 0.5403069881229531,         \"MacroF1\": 0.5180803411538233,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 1903.591145       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5441493995984696,         \"MicroF1\": 0.5441493995984696,         \"MacroF1\": 0.5209012984387186,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2050.469487       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5475869604807867,         \"MicroF1\": 0.5475869604807867,         \"MacroF1\": 0.5230407124785976,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2201.55681       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5442460804601733,         \"MicroF1\": 0.5442460804601733,         \"MacroF1\": 0.5199893698637053,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2356.711105       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5439848479724017,         \"MicroF1\": 0.5439848479724017,         \"MacroF1\": 0.5225387960194383,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2516.62263       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5449825294713124,         \"MicroF1\": 0.5449825294713124,         \"MacroF1\": 0.5260472440529832,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2681.546079       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5469238296663405,         \"MicroF1\": 0.5469238296663405,         \"MacroF1\": 0.5300194392617626,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 2851.622305       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5492286543455017,         \"MicroF1\": 0.5492286543455017,         \"MacroF1\": 0.5337692045397758,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3026.797274       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5448196265277737,         \"MicroF1\": 0.5448196265277737,         \"MacroF1\": 0.5298516474077153,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3207.119826       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.539357763939507,         \"MicroF1\": 0.539357763939507,         \"MacroF1\": 0.5246413689313029,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3392.401024       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5352756037099964,         \"MicroF1\": 0.5352756037099964,         \"MacroF1\": 0.5204658240271913,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3582.6817720000004       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5307232338537298,         \"MicroF1\": 0.5307232338537298,         \"MacroF1\": 0.5158458403074864,         \"Memory in Mb\": 1.310713768005371,         \"Time in s\": 3778.309285       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5287912666052874,         \"MicroF1\": 0.5287912666052874,         \"MacroF1\": 0.5138605376143625,         \"Memory in Mb\": 1.8479537963867188,         \"Time in s\": 3978.822433       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5245322617798367,         \"MicroF1\": 0.5245322617798367,         \"MacroF1\": 0.5100329616180462,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4184.1075280000005       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5244847608841927,         \"MicroF1\": 0.5244847608841927,         \"MacroF1\": 0.5114466799524962,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4393.646320000001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5269650098341548,         \"MicroF1\": 0.5269650098341548,         \"MacroF1\": 0.5145630920489553,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4606.675677000001       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5290608205686688,         \"MicroF1\": 0.5290608205686688,         \"MacroF1\": 0.5171452370879218,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 4823.052294000001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5316318281556762,         \"MicroF1\": 0.5316318281556762,         \"MacroF1\": 0.5200714653059241,         \"Memory in Mb\": 1.9625730514526367,         \"Time in s\": 5042.794587000001       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5332912448422809,         \"MicroF1\": 0.5332912448422809,         \"MacroF1\": 0.521951703681177,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5266.308108000001       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5350937080185875,         \"MicroF1\": 0.5350937080185875,         \"MacroF1\": 0.5236272112757866,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5493.659660000001       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5374168693368917,         \"MicroF1\": 0.5374168693368917,         \"MacroF1\": 0.5257977177437826,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5724.562244000002       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5359540394368568,         \"MicroF1\": 0.5359540394368568,         \"MacroF1\": 0.5247049329892776,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 5959.275286000002       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5333196088522902,         \"MicroF1\": 0.5333196088522902,         \"MacroF1\": 0.5224640186909637,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 6197.987866000002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5314017448771937,         \"MicroF1\": 0.5314017448771937,         \"MacroF1\": 0.5209076603734537,         \"Memory in Mb\": 1.9633283615112305,         \"Time in s\": 6440.583835000002       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5322271982954209,         \"MicroF1\": 0.5322271982954209,         \"MacroF1\": 0.5219695808096345,         \"Memory in Mb\": 2.081958770751953,         \"Time in s\": 6687.224874000002       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5377345727924551,         \"MicroF1\": 0.5377345727924551,         \"MacroF1\": 0.5274876060436412,         \"Memory in Mb\": 2.3156700134277344,         \"Time in s\": 6937.746409000002       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5370366863008769,         \"MicroF1\": 0.5370366863008769,         \"MacroF1\": 0.5270872650003847,         \"Memory in Mb\": 2.519227981567383,         \"Time in s\": 7191.466386000002       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5373058073305959,         \"MicroF1\": 0.5373058073305959,         \"MacroF1\": 0.5273644947479657,         \"Memory in Mb\": 2.519227981567383,         \"Time in s\": 7445.3631460000015       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 0.2240447998046875,         \"Time in s\": 0.863228       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9423312883435584,         \"MicroF1\": 0.9423312883435584,         \"MacroF1\": 0.7661667470992702,         \"Memory in Mb\": 0.3196687698364258,         \"Time in s\": 3.107641       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8830744071954211,         \"MicroF1\": 0.883074407195421,         \"MacroF1\": 0.8761191747044462,         \"Memory in Mb\": 0.415292739868164,         \"Time in s\": 7.048775       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8902513795217658,         \"MicroF1\": 0.8902513795217658,         \"MacroF1\": 0.8767853151263398,         \"Memory in Mb\": 0.5114049911499023,         \"Time in s\": 13.087732       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8891613536047082,         \"MicroF1\": 0.8891613536047082,         \"MacroF1\": 0.8807858055314012,         \"Memory in Mb\": 0.6185035705566406,         \"Time in s\": 21.551525       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.848385778504291,         \"MicroF1\": 0.848385778504291,         \"MacroF1\": 0.8522513926518692,         \"Memory in Mb\": 0.7141275405883789,         \"Time in s\": 32.816222999999994       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8563922942206655,         \"MicroF1\": 0.8563922942206655,         \"MacroF1\": 0.8440193478447516,         \"Memory in Mb\": 0.8097515106201172,         \"Time in s\": 47.080319       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8482991112473184,         \"MicroF1\": 0.8482991112473184,         \"MacroF1\": 0.8269786301577753,         \"Memory in Mb\": 0.9053754806518556,         \"Time in s\": 64.636989       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8392808499046581,         \"MicroF1\": 0.8392808499046581,         \"MacroF1\": 0.8374924160046072,         \"Memory in Mb\": 1.0009994506835938,         \"Time in s\": 85.706576       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8323118411375338,         \"MicroF1\": 0.8323118411375338,         \"MacroF1\": 0.8182261307945194,         \"Memory in Mb\": 1.1217241287231443,         \"Time in s\": 110.709782       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8159126365054602,         \"MicroF1\": 0.8159126365054602,         \"MacroF1\": 0.8260965842218733,         \"Memory in Mb\": 1.2173480987548828,         \"Time in s\": 139.812165       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8149131767109296,         \"MicroF1\": 0.8149131767109296,         \"MacroF1\": 0.8221314665977922,         \"Memory in Mb\": 1.312972068786621,         \"Time in s\": 173.369773       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8125589289081652,         \"MicroF1\": 0.8125589289081652,         \"MacroF1\": 0.797613058026624,         \"Memory in Mb\": 1.4085960388183594,         \"Time in s\": 211.780209       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7907546839432674,         \"MicroF1\": 0.7907546839432674,         \"MacroF1\": 0.7936708037520236,         \"Memory in Mb\": 1.5042200088500977,         \"Time in s\": 255.273991       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7886909625755842,         \"MicroF1\": 0.7886909625755842,         \"MacroF1\": 0.7694478218498494,         \"Memory in Mb\": 1.599843978881836,         \"Time in s\": 304.294734       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7635973647924008,         \"MicroF1\": 0.7635973647924008,         \"MacroF1\": 0.75687960152136,         \"Memory in Mb\": 1.6954679489135742,         \"Time in s\": 359.144129       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.75155010814708,         \"MicroF1\": 0.7515501081470799,         \"MacroF1\": 0.7521509466338959,         \"Memory in Mb\": 1.7910919189453125,         \"Time in s\": 420.221142       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7611330518861501,         \"MicroF1\": 0.7611330518861501,         \"MacroF1\": 0.7576671162861806,         \"Memory in Mb\": 1.8881807327270508,         \"Time in s\": 487.76956500000006       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7617081666881693,         \"MicroF1\": 0.7617081666881692,         \"MacroF1\": 0.7593340838982119,         \"Memory in Mb\": 1.983804702758789,         \"Time in s\": 562.1432000000001       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7655349920333374,         \"MicroF1\": 0.7655349920333374,         \"MacroF1\": 0.7610505848438686,         \"Memory in Mb\": 2.079428672790528,         \"Time in s\": 643.5514560000001       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7644449632310026,         \"MicroF1\": 0.7644449632310025,         \"MacroF1\": 0.7639417799779614,         \"Memory in Mb\": 2.223102569580078,         \"Time in s\": 732.3349550000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7624512534818941,         \"MicroF1\": 0.7624512534818941,         \"MacroF1\": 0.7625605608371232,         \"Memory in Mb\": 2.3187265396118164,         \"Time in s\": 828.9274100000001       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7605243525524885,         \"MicroF1\": 0.7605243525524885,         \"MacroF1\": 0.7588384348689571,         \"Memory in Mb\": 2.4143505096435547,         \"Time in s\": 933.484588       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.753344908589521,         \"MicroF1\": 0.753344908589521,         \"MacroF1\": 0.7499438215834663,         \"Memory in Mb\": 2.509974479675293,         \"Time in s\": 1046.19484       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7450730463770958,         \"MicroF1\": 0.7450730463770959,         \"MacroF1\": 0.7369660419615974,         \"Memory in Mb\": 2.6055984497070312,         \"Time in s\": 1167.344916       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7240501555576506,         \"MicroF1\": 0.7240501555576506,         \"MacroF1\": 0.7111305646829175,         \"Memory in Mb\": 2.701222419738769,         \"Time in s\": 1296.919782       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7166591012256015,         \"MicroF1\": 0.7166591012256015,         \"MacroF1\": 0.7122511515574346,         \"Memory in Mb\": 2.796846389770508,         \"Time in s\": 1434.776076       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.710146196270682,         \"MicroF1\": 0.710146196270682,         \"MacroF1\": 0.6963016796632095,         \"Memory in Mb\": 2.892470359802246,         \"Time in s\": 1580.7280859999998       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7005324993660722,         \"MicroF1\": 0.7005324993660722,         \"MacroF1\": 0.6925666211338901,         \"Memory in Mb\": 2.9880943298339844,         \"Time in s\": 1735.0271709999995       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7043876133671052,         \"MicroF1\": 0.7043876133671052,         \"MacroF1\": 0.7007845610449206,         \"Memory in Mb\": 3.0837182998657227,         \"Time in s\": 1897.652612       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7004032576895707,         \"MicroF1\": 0.7004032576895707,         \"MacroF1\": 0.6915775762792657,         \"Memory in Mb\": 3.179342269897461,         \"Time in s\": 2069.0860809999995       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6877058598238223,         \"MicroF1\": 0.6877058598238223,         \"MacroF1\": 0.6789768292873962,         \"Memory in Mb\": 3.274966239929199,         \"Time in s\": 2249.389177       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6838743222164451,         \"MicroF1\": 0.6838743222164451,         \"MacroF1\": 0.6791243465680947,         \"Memory in Mb\": 3.370590209960937,         \"Time in s\": 2438.693149       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6822146925239708,         \"MicroF1\": 0.6822146925239708,         \"MacroF1\": 0.6786558938530485,         \"Memory in Mb\": 3.466214179992676,         \"Time in s\": 2637.684102       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6777085230058127,         \"MicroF1\": 0.6777085230058127,         \"MacroF1\": 0.6725285130045525,         \"Memory in Mb\": 3.561838150024414,         \"Time in s\": 2845.828808       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6807380676788997,         \"MicroF1\": 0.6807380676788997,         \"MacroF1\": 0.6786761142186741,         \"Memory in Mb\": 3.657462120056152,         \"Time in s\": 3062.994215       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6873799271281882,         \"MicroF1\": 0.6873799271281882,         \"MacroF1\": 0.6854839306484398,         \"Memory in Mb\": 3.75308609008789,         \"Time in s\": 3290.055422       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6858027478552539,         \"MicroF1\": 0.6858027478552539,         \"MacroF1\": 0.6816808496509055,         \"Memory in Mb\": 3.848710060119629,         \"Time in s\": 3526.69202       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6765759537426937,         \"MicroF1\": 0.6765759537426937,         \"MacroF1\": 0.6694713281964946,         \"Memory in Mb\": 3.944334030151367,         \"Time in s\": 3772.997519       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6673815797536614,         \"MicroF1\": 0.6673815797536614,         \"MacroF1\": 0.6617321933140904,         \"Memory in Mb\": 4.0399580001831055,         \"Time in s\": 4029.133223       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6643151790518323,         \"MicroF1\": 0.6643151790518323,         \"MacroF1\": 0.661178029358405,         \"Memory in Mb\": 4.135581970214844,         \"Time in s\": 4295.086238       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6598774438284214,         \"MicroF1\": 0.6598774438284214,         \"MacroF1\": 0.655734247886306,         \"Memory in Mb\": 4.32945728302002,         \"Time in s\": 4570.827071       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6518269395200365,         \"MicroF1\": 0.6518269395200365,         \"MacroF1\": 0.6481085155228206,         \"Memory in Mb\": 4.425081253051758,         \"Time in s\": 4856.254143       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6507158375577963,         \"MicroF1\": 0.6507158375577963,         \"MacroF1\": 0.6489368995854258,         \"Memory in Mb\": 4.520705223083496,         \"Time in s\": 5151.869359       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6566806470940683,         \"MicroF1\": 0.6566806470940683,         \"MacroF1\": 0.6555764711123695,         \"Memory in Mb\": 4.616329193115234,         \"Time in s\": 5457.498716       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.662279533223211,         \"MicroF1\": 0.662279533223211,         \"MacroF1\": 0.6615432060687808,         \"Memory in Mb\": 4.711953163146973,         \"Time in s\": 5772.982264       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6534028683181226,         \"MicroF1\": 0.6534028683181226,         \"MacroF1\": 0.6508089832432514,         \"Memory in Mb\": 4.807577133178711,         \"Time in s\": 6098.679956       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6577643874789358,         \"MicroF1\": 0.6577643874789358,         \"MacroF1\": 0.6564201177589184,         \"Memory in Mb\": 4.903201103210449,         \"Time in s\": 6434.678037       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6518433294982742,         \"MicroF1\": 0.6518433294982742,         \"MacroF1\": 0.6501496360982542,         \"Memory in Mb\": 4.998825073242188,         \"Time in s\": 6781.324361       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6482180499044071,         \"MicroF1\": 0.6482180499044071,         \"MacroF1\": 0.6472493759146578,         \"Memory in Mb\": 5.094449043273926,         \"Time in s\": 7138.730487       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3777777777777777,         \"MicroF1\": 0.3777777777777777,         \"MacroF1\": 0.2811210847975554,         \"Memory in Mb\": 0.4234571456909179,         \"Time in s\": 0.325579       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5164835164835165,         \"MicroF1\": 0.5164835164835165,         \"MacroF1\": 0.5335477748411618,         \"Memory in Mb\": 0.4235143661499023,         \"Time in s\": 1.056326       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5474452554744526,         \"MicroF1\": 0.5474452554744526,         \"MacroF1\": 0.5743273066802479,         \"Memory in Mb\": 0.4236364364624023,         \"Time in s\": 2.202996       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6120218579234973,         \"MicroF1\": 0.6120218579234973,         \"MacroF1\": 0.6355989308336889,         \"Memory in Mb\": 0.4237203598022461,         \"Time in s\": 3.699294       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6375545851528385,         \"MicroF1\": 0.6375545851528385,         \"MacroF1\": 0.6557923943920432,         \"Memory in Mb\": 0.4237203598022461,         \"Time in s\": 5.564336       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6509090909090909,         \"MicroF1\": 0.6509090909090909,         \"MacroF1\": 0.66910740948952,         \"Memory in Mb\": 0.4237699508666992,         \"Time in s\": 7.749814       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.67601246105919,         \"MicroF1\": 0.67601246105919,         \"MacroF1\": 0.678427291711157,         \"Memory in Mb\": 0.4238309860229492,         \"Time in s\": 10.278631       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7002724795640327,         \"MicroF1\": 0.7002724795640327,         \"MacroF1\": 0.6988359939675117,         \"Memory in Mb\": 0.4238042831420898,         \"Time in s\": 13.125556       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.711864406779661,         \"MicroF1\": 0.711864406779661,         \"MacroF1\": 0.7104564330601258,         \"Memory in Mb\": 0.4237241744995117,         \"Time in s\": 16.369918000000002       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7124183006535948,         \"MicroF1\": 0.7124183006535948,         \"MacroF1\": 0.7087721216219991,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 19.921878000000003       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7207920792079208,         \"MicroF1\": 0.7207920792079208,         \"MacroF1\": 0.7145025942185106,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 23.844357       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7223230490018149,         \"MicroF1\": 0.7223230490018149,         \"MacroF1\": 0.7174926871575792,         \"Memory in Mb\": 0.4236936569213867,         \"Time in s\": 28.111685       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7269681742043551,         \"MicroF1\": 0.7269681742043551,         \"MacroF1\": 0.7216367248754637,         \"Memory in Mb\": 0.4237165451049804,         \"Time in s\": 32.752989       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7262830482115086,         \"MicroF1\": 0.7262830482115085,         \"MacroF1\": 0.7230014848259525,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 37.712808       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7300435413642961,         \"MicroF1\": 0.7300435413642961,         \"MacroF1\": 0.7265684058467008,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 43.006145       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7319727891156462,         \"MicroF1\": 0.7319727891156461,         \"MacroF1\": 0.7296570819427115,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 48.68780100000001       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.737516005121639,         \"MicroF1\": 0.737516005121639,         \"MacroF1\": 0.7350906419548328,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 54.69172       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7363966142684402,         \"MicroF1\": 0.7363966142684402,         \"MacroF1\": 0.7359651798179677,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 60.98272       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7422680412371134,         \"MicroF1\": 0.7422680412371134,         \"MacroF1\": 0.7398886847335938,         \"Memory in Mb\": 0.4237775802612304,         \"Time in s\": 67.641769       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7421109902067464,         \"MicroF1\": 0.7421109902067464,         \"MacroF1\": 0.738912026501458,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 74.649906       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7419689119170985,         \"MicroF1\": 0.7419689119170985,         \"MacroF1\": 0.7379593683174607,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 81.98079       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7418397626112759,         \"MicroF1\": 0.741839762611276,         \"MacroF1\": 0.7380802548116379,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 89.699811       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7436140018921475,         \"MicroF1\": 0.7436140018921475,         \"MacroF1\": 0.7390703652035102,         \"Memory in Mb\": 0.4237508773803711,         \"Time in s\": 97.738161       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7461468721668177,         \"MicroF1\": 0.7461468721668177,         \"MacroF1\": 0.7413714574148674,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 106.141078       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7476066144473456,         \"MicroF1\": 0.7476066144473456,         \"MacroF1\": 0.742441565911322,         \"Memory in Mb\": 0.4238004684448242,         \"Time in s\": 114.875735       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7506276150627615,         \"MicroF1\": 0.7506276150627615,         \"MacroF1\": 0.7460917536510117,         \"Memory in Mb\": 0.4234342575073242,         \"Time in s\": 123.973121       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7510072522159549,         \"MicroF1\": 0.7510072522159549,         \"MacroF1\": 0.7470578866974922,         \"Memory in Mb\": 0.4235563278198242,         \"Time in s\": 133.391788       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.756021756021756,         \"MicroF1\": 0.7560217560217559,         \"MacroF1\": 0.7510482446555896,         \"Memory in Mb\": 0.4236173629760742,         \"Time in s\": 143.113173       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7569392348087022,         \"MicroF1\": 0.7569392348087022,         \"MacroF1\": 0.7522366633133313,         \"Memory in Mb\": 0.4236173629760742,         \"Time in s\": 153.228885       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7585206671501088,         \"MicroF1\": 0.7585206671501088,         \"MacroF1\": 0.7544196711061472,         \"Memory in Mb\": 0.4236783981323242,         \"Time in s\": 163.64661999999998       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7614035087719299,         \"MicroF1\": 0.7614035087719299,         \"MacroF1\": 0.7567964121564391,         \"Memory in Mb\": 0.4236783981323242,         \"Time in s\": 174.36664399999998       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7654656696125085,         \"MicroF1\": 0.7654656696125085,         \"MacroF1\": 0.7591802078998249,         \"Memory in Mb\": 0.4236783981323242,         \"Time in s\": 185.463757       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7673038892551087,         \"MicroF1\": 0.7673038892551087,         \"MacroF1\": 0.7600352016074767,         \"Memory in Mb\": 0.4237394332885742,         \"Time in s\": 196.90308       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7677543186180422,         \"MicroF1\": 0.7677543186180422,         \"MacroF1\": 0.7612494392404334,         \"Memory in Mb\": 0.4237394332885742,         \"Time in s\": 208.647576       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7675574891236793,         \"MicroF1\": 0.7675574891236793,         \"MacroF1\": 0.7602773300593106,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 220.786107       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.76797583081571,         \"MicroF1\": 0.76797583081571,         \"MacroF1\": 0.7607906010792568,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 233.2194       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7677836566725456,         \"MicroF1\": 0.7677836566725456,         \"MacroF1\": 0.7627036277641847,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 245.952092       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7710360618202633,         \"MicroF1\": 0.7710360618202633,         \"MacroF1\": 0.7657334796773966,         \"Memory in Mb\": 0.4237623214721679,         \"Time in s\": 259.02703       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7724484104852203,         \"MicroF1\": 0.7724484104852203,         \"MacroF1\": 0.7657758298578787,         \"Memory in Mb\": 0.4237356185913086,         \"Time in s\": 272.394107       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7737901033170201,         \"MicroF1\": 0.77379010331702,         \"MacroF1\": 0.767302943564198,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 286.067762       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7724137931034483,         \"MicroF1\": 0.7724137931034483,         \"MacroF1\": 0.7666353585191567,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 300.095471       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7731745209735889,         \"MicroF1\": 0.7731745209735889,         \"MacroF1\": 0.7666634536176192,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 314.417396       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7738998482549317,         \"MicroF1\": 0.7738998482549316,         \"MacroF1\": 0.7665909326930368,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 329.067854       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7750865051903114,         \"MicroF1\": 0.7750865051903113,         \"MacroF1\": 0.7662611838286661,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 344.01511700000003       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7747704204929918,         \"MicroF1\": 0.7747704204929918,         \"MacroF1\": 0.7660645062500586,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 359.290159       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7754137115839244,         \"MicroF1\": 0.7754137115839244,         \"MacroF1\": 0.7658988206988366,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 374.882405       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7760296159185562,         \"MicroF1\": 0.7760296159185563,         \"MacroF1\": 0.7660708746783081,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 390.75768       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.777979157227005,         \"MicroF1\": 0.7779791572270048,         \"MacroF1\": 0.7670029065892423,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 407.002801       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7749667110519307,         \"MicroF1\": 0.7749667110519308,         \"MacroF1\": 0.7639707440456852,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 423.546299       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7742496737712049,         \"MicroF1\": 0.7742496737712049,         \"MacroF1\": 0.7632394528829524,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 440.393364       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7743611953226505,         \"MicroF1\": 0.7743611953226506,         \"MacroF1\": 0.7633622232911937,         \"Memory in Mb\": 0.4237966537475586,         \"Time in s\": 457.310729       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6161137440758294,         \"MicroF1\": 0.6161137440758294,         \"MacroF1\": 0.581384151333148,         \"Memory in Mb\": 0.6645784378051758,         \"Time in s\": 11.249192       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6120322122216959,         \"MicroF1\": 0.6120322122216959,         \"MacroF1\": 0.5792161554760864,         \"Memory in Mb\": 0.6646394729614258,         \"Time in s\": 32.358705       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6049889485317335,         \"MicroF1\": 0.6049889485317335,         \"MacroF1\": 0.5721633809277145,         \"Memory in Mb\": 0.6647005081176758,         \"Time in s\": 62.851539       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.603125739995264,         \"MicroF1\": 0.603125739995264,         \"MacroF1\": 0.5703574432462961,         \"Memory in Mb\": 0.6647005081176758,         \"Time in s\": 102.700179       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6061754120098504,         \"MicroF1\": 0.6061754120098504,         \"MacroF1\": 0.5722430970062696,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 151.914202       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5995264404104184,         \"MicroF1\": 0.5995264404104184,         \"MacroF1\": 0.5671511237518188,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 210.432187       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5972128264104992,         \"MicroF1\": 0.5972128264104992,         \"MacroF1\": 0.5650210504998666,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 278.267755       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5989108559251806,         \"MicroF1\": 0.5989108559251806,         \"MacroF1\": 0.566418690076869,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 355.204938       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5962327685993897,         \"MicroF1\": 0.5962327685993897,         \"MacroF1\": 0.5633780031885509,         \"Memory in Mb\": 0.6647615432739258,         \"Time in s\": 441.186739       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5964579979164694,         \"MicroF1\": 0.5964579979164694,         \"MacroF1\": 0.5634236596216465,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 536.283653       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.594317692638829,         \"MicroF1\": 0.594317692638829,         \"MacroF1\": 0.5620068495149612,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 640.2689049999999       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5975061163286244,         \"MicroF1\": 0.5975061163286244,         \"MacroF1\": 0.567518061449456,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 753.0441599999999       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6097472135207984,         \"MicroF1\": 0.6097472135207984,         \"MacroF1\": 0.5927729676671933,         \"Memory in Mb\": 0.6648225784301758,         \"Time in s\": 874.528885       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001488195900697,         \"MicroF1\": 0.6001488195900697,         \"MacroF1\": 0.5832911478837771,         \"Memory in Mb\": 0.6645174026489258,         \"Time in s\": 1004.55011       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5673969316244712,         \"MicroF1\": 0.5673969316244712,         \"MacroF1\": 0.5522471754341495,         \"Memory in Mb\": 0.8876123428344727,         \"Time in s\": 1142.6522839999998       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5712340929269014,         \"MicroF1\": 0.5712340929269014,         \"MacroF1\": 0.5590383236849579,         \"Memory in Mb\": 1.4319400787353516,         \"Time in s\": 1288.8770269999998       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5741184335134533,         \"MicroF1\": 0.5741184335134533,         \"MacroF1\": 0.5632919959429028,         \"Memory in Mb\": 1.8629226684570312,         \"Time in s\": 1445.4718369999998       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5867312042931552,         \"MicroF1\": 0.5867312042931552,         \"MacroF1\": 0.5723846445183198,         \"Memory in Mb\": 0.4819307327270508,         \"Time in s\": 1609.073978       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5966704879629168,         \"MicroF1\": 0.5966704879629168,         \"MacroF1\": 0.5796820575913003,         \"Memory in Mb\": 0.6649179458618164,         \"Time in s\": 1780.2710459999998       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5984658364505895,         \"MicroF1\": 0.5984658364505895,         \"MacroF1\": 0.5810209140208816,         \"Memory in Mb\": 0.6650400161743164,         \"Time in s\": 1958.819581       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6001803833145434,         \"MicroF1\": 0.6001803833145434,         \"MacroF1\": 0.5822125955100945,         \"Memory in Mb\": 1.2073478698730469,         \"Time in s\": 2144.726031       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6020403770823468,         \"MicroF1\": 0.6020403770823468,         \"MacroF1\": 0.5837921358595156,         \"Memory in Mb\": 1.321575164794922,         \"Time in s\": 2339.531046       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6047268085807221,         \"MicroF1\": 0.6047268085807221,         \"MacroF1\": 0.5859785990228289,         \"Memory in Mb\": 1.321636199951172,         \"Time in s\": 2543.839083       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6069131515605887,         \"MicroF1\": 0.6069131515605887,         \"MacroF1\": 0.587737290445056,         \"Memory in Mb\": 1.321758270263672,         \"Time in s\": 2757.206681       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6094927838175689,         \"MicroF1\": 0.6094927838175689,         \"MacroF1\": 0.5895162861993263,         \"Memory in Mb\": 1.321758270263672,         \"Time in s\": 2979.334505       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6105991622655254,         \"MicroF1\": 0.6105991622655254,         \"MacroF1\": 0.5896134687358237,         \"Memory in Mb\": 1.321941375732422,         \"Time in s\": 3211.082358       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6106064326049595,         \"MicroF1\": 0.6106064326049595,         \"MacroF1\": 0.5910741826972655,         \"Memory in Mb\": 1.321941375732422,         \"Time in s\": 3451.544855       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6099029323231981,         \"MicroF1\": 0.6099029323231981,         \"MacroF1\": 0.5935355609859342,         \"Memory in Mb\": 1.321941375732422,         \"Time in s\": 3700.712954       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088887437546942,         \"MicroF1\": 0.6088887437546942,         \"MacroF1\": 0.5952474102625339,         \"Memory in Mb\": 1.321453094482422,         \"Time in s\": 3958.532225       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088891694813598,         \"MicroF1\": 0.6088891694813598,         \"MacroF1\": 0.5975058139751561,         \"Memory in Mb\": 1.321697235107422,         \"Time in s\": 4224.837575       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6095921796242554,         \"MicroF1\": 0.6095921796242554,         \"MacroF1\": 0.5998546240309938,         \"Memory in Mb\": 1.321758270263672,         \"Time in s\": 4499.473663       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043917019324673,         \"MicroF1\": 0.6043917019324673,         \"MacroF1\": 0.595080118632132,         \"Memory in Mb\": 0.6649713516235352,         \"Time in s\": 4783.331389999999       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6034378856142566,         \"MicroF1\": 0.6034378856142566,         \"MacroF1\": 0.5941773754098104,         \"Memory in Mb\": 0.6650934219360352,         \"Time in s\": 5073.360361999999       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6029022644347269,         \"MicroF1\": 0.6029022644347269,         \"MacroF1\": 0.5935512429191343,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 5369.406481999999       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6013690846613815,         \"MicroF1\": 0.6013690846613815,         \"MacroF1\": 0.5919623858291095,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 5671.388488999999       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6010259108246745,         \"MicroF1\": 0.6010259108246745,         \"MacroF1\": 0.5912597483191937,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 5979.127636999999       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6003429653707353,         \"MicroF1\": 0.6003429653707353,         \"MacroF1\": 0.5902279082897147,         \"Memory in Mb\": 0.6648492813110352,         \"Time in s\": 6292.481400999999       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5961322800109652,         \"MicroF1\": 0.5961322800109652,         \"MacroF1\": 0.5867765456240649,         \"Memory in Mb\": 0.6648492813110352,         \"Time in s\": 6611.499413       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5939829541315591,         \"MicroF1\": 0.5939829541315591,         \"MacroF1\": 0.585290407267574,         \"Memory in Mb\": 0.6650323867797852,         \"Time in s\": 6936.132393       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5925803167688629,         \"MicroF1\": 0.5925803167688629,         \"MacroF1\": 0.5844470095695741,         \"Memory in Mb\": 0.6650934219360352,         \"Time in s\": 7266.407125       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5911306155445202,         \"MicroF1\": 0.5911306155445202,         \"MacroF1\": 0.5835517912214992,         \"Memory in Mb\": 0.6651544570922852,         \"Time in s\": 7602.391688       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.58959211742689,         \"MicroF1\": 0.58959211742689,         \"MacroF1\": 0.58246410272577,         \"Memory in Mb\": 1.1046571731567385,         \"Time in s\": 7943.862096       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5875746030347744,         \"MicroF1\": 0.5875746030347744,         \"MacroF1\": 0.5808874407233396,         \"Memory in Mb\": 1.3207244873046875,         \"Time in s\": 8291.951918       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5862083808621914,         \"MicroF1\": 0.5862083808621914,         \"MacroF1\": 0.5791892600330408,         \"Memory in Mb\": 1.3209075927734375,         \"Time in s\": 8644.890712       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5879332477535302,         \"MicroF1\": 0.5879332477535302,         \"MacroF1\": 0.5810233099134106,         \"Memory in Mb\": 1.3210525512695312,         \"Time in s\": 9004.012781000001       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5928152341739578,         \"MicroF1\": 0.5928152341739578,         \"MacroF1\": 0.5858160887305829,         \"Memory in Mb\": 1.3216018676757812,         \"Time in s\": 9370.107000000002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5979327436481231,         \"MicroF1\": 0.5979327436481231,         \"MacroF1\": 0.5906079347867982,         \"Memory in Mb\": 1.3215408325195312,         \"Time in s\": 9743.028377000002       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6027383747311934,         \"MicroF1\": 0.6027383747311934,         \"MacroF1\": 0.594893758427483,         \"Memory in Mb\": 1.3217239379882812,         \"Time in s\": 10122.858893000002       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6077923583866417,         \"MicroF1\": 0.6077923583866417,         \"MacroF1\": 0.5993180348311721,         \"Memory in Mb\": 1.3217239379882812,         \"Time in s\": 10509.572003000005       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.612985094414667,         \"MicroF1\": 0.612985094414667,         \"MacroF1\": 0.6039181082054342,         \"Memory in Mb\": 0.1438255310058593,         \"Time in s\": 10901.200853000002       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6133366132420005,         \"MicroF1\": 0.6133366132420005,         \"MacroF1\": 0.604218855594392,         \"Memory in Mb\": 0.1438255310058593,         \"Time in s\": 11292.868844000002       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 0.230626106262207,         \"Time in s\": 0.871514       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943558282208589,         \"MicroF1\": 0.943558282208589,         \"MacroF1\": 0.7669956277713079,         \"Memory in Mb\": 0.3262500762939453,         \"Time in s\": 3.583779       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8863450531479967,         \"MicroF1\": 0.8863450531479967,         \"MacroF1\": 0.8786592421362933,         \"Memory in Mb\": 0.4218740463256836,         \"Time in s\": 8.686347999999999       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.891477621091355,         \"MicroF1\": 0.891477621091355,         \"MacroF1\": 0.8818548670971931,         \"Memory in Mb\": 0.5179252624511719,         \"Time in s\": 16.685395       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.889651790093183,         \"MicroF1\": 0.889651790093183,         \"MacroF1\": 0.8812768038030504,         \"Memory in Mb\": 0.6251459121704102,         \"Time in s\": 28.245741       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8414384961176952,         \"MicroF1\": 0.8414384961176952,         \"MacroF1\": 0.8420581397672002,         \"Memory in Mb\": 0.7206478118896484,         \"Time in s\": 43.571154       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8500875656742557,         \"MicroF1\": 0.8500875656742557,         \"MacroF1\": 0.8345582037188519,         \"Memory in Mb\": 0.8163328170776367,         \"Time in s\": 63.099422       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8406374501992032,         \"MicroF1\": 0.8406374501992032,         \"MacroF1\": 0.8151418555553325,         \"Memory in Mb\": 0.911895751953125,         \"Time in s\": 87.33095300000001       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8321983110868973,         \"MicroF1\": 0.8321983110868973,         \"MacroF1\": 0.8307198315203921,         \"Memory in Mb\": 1.0075807571411133,         \"Time in s\": 116.498805       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.826182887962736,         \"MicroF1\": 0.826182887962736,         \"MacroF1\": 0.8123767856033619,         \"Memory in Mb\": 1.128366470336914,         \"Time in s\": 151.118073       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.809226654780477,         \"MicroF1\": 0.809226654780477,         \"MacroF1\": 0.8196273526663149,         \"Memory in Mb\": 1.2239294052124023,         \"Time in s\": 191.820305       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8081716036772216,         \"MicroF1\": 0.8081716036772216,         \"MacroF1\": 0.815232111826365,         \"Memory in Mb\": 1.3194313049316406,         \"Time in s\": 239.061616       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8057703186875353,         \"MicroF1\": 0.8057703186875353,         \"MacroF1\": 0.7903391475861199,         \"Memory in Mb\": 1.415055274963379,         \"Time in s\": 293.29488000000003       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7860269655051655,         \"MicroF1\": 0.7860269655051656,         \"MacroF1\": 0.7895763142947654,         \"Memory in Mb\": 1.5108013153076172,         \"Time in s\": 355.22640600000005       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.784441902271613,         \"MicroF1\": 0.784441902271613,         \"MacroF1\": 0.7657785418705475,         \"Memory in Mb\": 1.6062421798706057,         \"Time in s\": 425.240619       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7585414432357898,         \"MicroF1\": 0.7585414432357898,         \"MacroF1\": 0.751418836389106,         \"Memory in Mb\": 1.7020492553710938,         \"Time in s\": 503.467226       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7473684210526316,         \"MicroF1\": 0.7473684210526316,         \"MacroF1\": 0.7484284412750404,         \"Memory in Mb\": 1.797490119934082,         \"Time in s\": 590.6999010000001       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7565027917744791,         \"MicroF1\": 0.7565027917744791,         \"MacroF1\": 0.7526701844923946,         \"Memory in Mb\": 1.8947620391845703,         \"Time in s\": 687.248946       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7577086827506129,         \"MicroF1\": 0.7577086827506129,         \"MacroF1\": 0.755735065870518,         \"Memory in Mb\": 1.9903860092163088,         \"Time in s\": 793.498598       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7617355068023042,         \"MicroF1\": 0.7617355068023042,         \"MacroF1\": 0.7576049653668414,         \"Memory in Mb\": 2.085948944091797,         \"Time in s\": 909.902095       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7604762460604646,         \"MicroF1\": 0.7604762460604646,         \"MacroF1\": 0.7596175662696861,         \"Memory in Mb\": 2.2296838760375977,         \"Time in s\": 1036.556796       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.756991643454039,         \"MicroF1\": 0.7569916434540391,         \"MacroF1\": 0.7575313939177277,         \"Memory in Mb\": 2.325368881225586,         \"Time in s\": 1173.436632       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7558350207822658,         \"MicroF1\": 0.7558350207822658,         \"MacroF1\": 0.7548436696787698,         \"Memory in Mb\": 2.420870780944824,         \"Time in s\": 1320.145727       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.748340312531917,         \"MicroF1\": 0.7483403125319169,         \"MacroF1\": 0.744390859626019,         \"Memory in Mb\": 2.5164337158203125,         \"Time in s\": 1476.992513       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7393862143347387,         \"MicroF1\": 0.7393862143347387,         \"MacroF1\": 0.7315892779928432,         \"Memory in Mb\": 2.612057685852051,         \"Time in s\": 1644.1937280000002       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7196191194494201,         \"MicroF1\": 0.7196191194494201,         \"MacroF1\": 0.7089541376321258,         \"Memory in Mb\": 2.707803726196289,         \"Time in s\": 1822.193822       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7123921924648207,         \"MicroF1\": 0.7123921924648208,         \"MacroF1\": 0.7092068316988943,         \"Memory in Mb\": 2.8033666610717773,         \"Time in s\": 2011.0989090000005       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7062943184802591,         \"MicroF1\": 0.7062943184802591,         \"MacroF1\": 0.6946713230955313,         \"Memory in Mb\": 2.898990631103516,         \"Time in s\": 2211.8042590000005       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6967289324655566,         \"MicroF1\": 0.6967289324655566,         \"MacroF1\": 0.690232830798306,         \"Memory in Mb\": 2.994553565979004,         \"Time in s\": 2423.5715250000003       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7007108423890841,         \"MicroF1\": 0.7007108423890841,         \"MacroF1\": 0.6983689907908355,         \"Memory in Mb\": 3.090177536010742,         \"Time in s\": 2646.6754960000003       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6969241717403337,         \"MicroF1\": 0.6969241717403337,         \"MacroF1\": 0.6892508246262707,         \"Memory in Mb\": 3.1858625411987305,         \"Time in s\": 2881.7592360000003       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6836461126005362,         \"MicroF1\": 0.6836461126005362,         \"MacroF1\": 0.6755391962059192,         \"Memory in Mb\": 3.2815475463867188,         \"Time in s\": 3128.5577150000004       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6793433855752804,         \"MicroF1\": 0.6793433855752804,         \"MacroF1\": 0.6754035266161623,         \"Memory in Mb\": 3.377110481262207,         \"Time in s\": 3387.355816       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6769519140653161,         \"MicroF1\": 0.6769519140653161,         \"MacroF1\": 0.6742482232309566,         \"Memory in Mb\": 3.4728565216064453,         \"Time in s\": 3658.0697       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6728762518383641,         \"MicroF1\": 0.6728762518383641,         \"MacroF1\": 0.6689356443053495,         \"Memory in Mb\": 3.5684194564819336,         \"Time in s\": 3940.688111       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6762442976782188,         \"MicroF1\": 0.6762442976782188,         \"MacroF1\": 0.6753292472514647,         \"Memory in Mb\": 3.663982391357422,         \"Time in s\": 4235.610853       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6830076184166942,         \"MicroF1\": 0.6830076184166942,         \"MacroF1\": 0.6822311287838643,         \"Memory in Mb\": 3.75966739654541,         \"Time in s\": 4542.8267670000005       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6818035218989873,         \"MicroF1\": 0.6818035218989873,         \"MacroF1\": 0.6788656596145114,         \"Memory in Mb\": 3.8552303314208975,         \"Time in s\": 4862.152597       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6816039218150964,         \"MicroF1\": 0.6816039218150964,         \"MacroF1\": 0.6801525397911032,         \"Memory in Mb\": 0.2705574035644531,         \"Time in s\": 5190.397888       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6858263373981249,         \"MicroF1\": 0.6858263373981249,         \"MacroF1\": 0.685191280018575,         \"Memory in Mb\": 0.4621334075927734,         \"Time in s\": 5522.880902000001       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6896634184253004,         \"MicroF1\": 0.6896634184253004,         \"MacroF1\": 0.6890226069872224,         \"Memory in Mb\": 0.6535873413085938,         \"Time in s\": 5860.018685000001       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6925007295010213,         \"MicroF1\": 0.6925007295010213,         \"MacroF1\": 0.6918635442211969,         \"Memory in Mb\": 0.9691534042358398,         \"Time in s\": 6202.345681000001       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6990252522373597,         \"MicroF1\": 0.6990252522373597,         \"MacroF1\": 0.6986638608261282,         \"Memory in Mb\": 0.2649049758911133,         \"Time in s\": 6547.073149000001       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7038605091638349,         \"MicroF1\": 0.7038605091638349,         \"MacroF1\": 0.7032543903990934,         \"Memory in Mb\": 0.579315185546875,         \"Time in s\": 6893.121988000001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.710114930007081,         \"MicroF1\": 0.7101149300070809,         \"MacroF1\": 0.70950849929648,         \"Memory in Mb\": 0.2349414825439453,         \"Time in s\": 7240.035665000001       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.715351414717323,         \"MicroF1\": 0.715351414717323,         \"MacroF1\": 0.7146010079934133,         \"Memory in Mb\": 0.3305654525756836,         \"Time in s\": 7588.155090000001       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7179139504563233,         \"MicroF1\": 0.7179139504563233,         \"MacroF1\": 0.7169858006379833,         \"Memory in Mb\": 0.4260063171386719,         \"Time in s\": 7937.751954000001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7223612316805392,         \"MicroF1\": 0.7223612316805392,         \"MacroF1\": 0.7214649429496548,         \"Memory in Mb\": 0.5217523574829102,         \"Time in s\": 8289.115139000001       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7219248661897854,         \"MicroF1\": 0.7219248661897855,         \"MacroF1\": 0.7206428236711905,         \"Memory in Mb\": 0.6287288665771484,         \"Time in s\": 8642.591702000002       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7231236825334575,         \"MicroF1\": 0.7231236825334575,         \"MacroF1\": 0.7218249685926471,         \"Memory in Mb\": 0.7244749069213867,         \"Time in s\": 8998.461289       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4222222222222222,         \"MicroF1\": 0.4222222222222222,         \"MacroF1\": 0.3590236094437775,         \"Memory in Mb\": 0.9685115814208984,         \"Time in s\": 1.326052       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5604395604395604,         \"MicroF1\": 0.5604395604395604,         \"MacroF1\": 0.5746538615446178,         \"Memory in Mb\": 1.0556058883666992,         \"Time in s\": 4.053487       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5766423357664233,         \"MicroF1\": 0.5766423357664233,         \"MacroF1\": 0.598257695340355,         \"Memory in Mb\": 1.344954490661621,         \"Time in s\": 8.154789999999998       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6229508196721312,         \"MicroF1\": 0.6229508196721312,         \"MacroF1\": 0.6451744040758778,         \"Memory in Mb\": 1.4133405685424805,         \"Time in s\": 13.553012999999998       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6506550218340611,         \"MicroF1\": 0.6506550218340611,         \"MacroF1\": 0.6680655280025949,         \"Memory in Mb\": 1.5576086044311523,         \"Time in s\": 20.188933       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6727272727272727,         \"MicroF1\": 0.6727272727272727,         \"MacroF1\": 0.6900672130049011,         \"Memory in Mb\": 1.7550430297851562,         \"Time in s\": 28.051384       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7040498442367601,         \"MicroF1\": 0.7040498442367601,         \"MacroF1\": 0.7087861936875776,         \"Memory in Mb\": 1.832967758178711,         \"Time in s\": 37.15395       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7302452316076294,         \"MicroF1\": 0.7302452316076294,         \"MacroF1\": 0.7285991575377422,         \"Memory in Mb\": 1.971024513244629,         \"Time in s\": 47.432602       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7457627118644068,         \"MicroF1\": 0.7457627118644068,         \"MacroF1\": 0.7430362907281778,         \"Memory in Mb\": 1.991847038269043,         \"Time in s\": 58.97377399999999       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7342047930283224,         \"MicroF1\": 0.7342047930283224,         \"MacroF1\": 0.7271744800226857,         \"Memory in Mb\": 1.8101978302001955,         \"Time in s\": 71.823928       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7405940594059406,         \"MicroF1\": 0.7405940594059406,         \"MacroF1\": 0.7304322149686578,         \"Memory in Mb\": 1.7132930755615234,         \"Time in s\": 85.827474       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7368421052631579,         \"MicroF1\": 0.7368421052631579,         \"MacroF1\": 0.7267508109083203,         \"Memory in Mb\": 1.5079193115234375,         \"Time in s\": 101.049314       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7403685092127303,         \"MicroF1\": 0.7403685092127302,         \"MacroF1\": 0.7318978254380314,         \"Memory in Mb\": 1.6471452713012695,         \"Time in s\": 117.420156       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7325038880248833,         \"MicroF1\": 0.7325038880248833,         \"MacroF1\": 0.7248107612258207,         \"Memory in Mb\": 1.7740907669067385,         \"Time in s\": 135.017443       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7242380261248186,         \"MicroF1\": 0.7242380261248187,         \"MacroF1\": 0.7153272190465999,         \"Memory in Mb\": 1.913142204284668,         \"Time in s\": 153.656893       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7251700680272108,         \"MicroF1\": 0.725170068027211,         \"MacroF1\": 0.7148466398758337,         \"Memory in Mb\": 2.0619029998779297,         \"Time in s\": 173.429455       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7259923175416133,         \"MicroF1\": 0.7259923175416134,         \"MacroF1\": 0.7134712280209221,         \"Memory in Mb\": 2.0208959579467773,         \"Time in s\": 194.315292       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.727932285368803,         \"MicroF1\": 0.727932285368803,         \"MacroF1\": 0.7177600265828429,         \"Memory in Mb\": 2.224555015563965,         \"Time in s\": 216.352158       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7353951890034365,         \"MicroF1\": 0.7353951890034366,         \"MacroF1\": 0.7262567978322628,         \"Memory in Mb\": 2.300021171569824,         \"Time in s\": 239.599524       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7431991294885746,         \"MicroF1\": 0.7431991294885745,         \"MacroF1\": 0.7345004589126253,         \"Memory in Mb\": 2.4412155151367188,         \"Time in s\": 263.99359400000003       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7471502590673575,         \"MicroF1\": 0.7471502590673575,         \"MacroF1\": 0.7368855656689403,         \"Memory in Mb\": 2.474191665649414,         \"Time in s\": 289.66420500000004       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7546983184965381,         \"MicroF1\": 0.754698318496538,         \"MacroF1\": 0.7446216664767904,         \"Memory in Mb\": 2.5655078887939453,         \"Time in s\": 316.44421900000003       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.760643330179754,         \"MicroF1\": 0.760643330179754,         \"MacroF1\": 0.7502594177262459,         \"Memory in Mb\": 2.798956871032715,         \"Time in s\": 344.45448600000003       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7624660018132366,         \"MicroF1\": 0.7624660018132366,         \"MacroF1\": 0.7523020427630668,         \"Memory in Mb\": 2.48898983001709,         \"Time in s\": 373.71735       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7650130548302873,         \"MicroF1\": 0.7650130548302874,         \"MacroF1\": 0.7555087521342715,         \"Memory in Mb\": 2.3284912109375,         \"Time in s\": 404.061966       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7690376569037657,         \"MicroF1\": 0.7690376569037657,         \"MacroF1\": 0.7603504370239861,         \"Memory in Mb\": 2.0560731887817383,         \"Time in s\": 435.510035       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7719580983078163,         \"MicroF1\": 0.7719580983078163,         \"MacroF1\": 0.7638249032322542,         \"Memory in Mb\": 2.11933708190918,         \"Time in s\": 467.969641       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7746697746697747,         \"MicroF1\": 0.7746697746697747,         \"MacroF1\": 0.7668828628349821,         \"Memory in Mb\": 2.277647018432617,         \"Time in s\": 501.448754       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7771942985746436,         \"MicroF1\": 0.7771942985746436,         \"MacroF1\": 0.7696789046658701,         \"Memory in Mb\": 2.3871631622314453,         \"Time in s\": 535.887669       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7817258883248731,         \"MicroF1\": 0.7817258883248731,         \"MacroF1\": 0.7754511149783997,         \"Memory in Mb\": 2.3104944229125977,         \"Time in s\": 571.357393       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7866666666666666,         \"MicroF1\": 0.7866666666666666,         \"MacroF1\": 0.7797171864703156,         \"Memory in Mb\": 2.4089183807373047,         \"Time in s\": 607.784244       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7912984364377974,         \"MicroF1\": 0.7912984364377974,         \"MacroF1\": 0.7836430453045393,         \"Memory in Mb\": 2.5425024032592773,         \"Time in s\": 645.2286509999999       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7963085036255768,         \"MicroF1\": 0.7963085036255768,         \"MacroF1\": 0.7883976288226553,         \"Memory in Mb\": 2.6389265060424805,         \"Time in s\": 683.7451019999999       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7978246960972489,         \"MicroF1\": 0.7978246960972489,         \"MacroF1\": 0.790949738475821,         \"Memory in Mb\": 2.283763885498047,         \"Time in s\": 723.3862519999999       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.798011187072716,         \"MicroF1\": 0.7980111870727161,         \"MacroF1\": 0.7914720525222512,         \"Memory in Mb\": 2.519012451171875,         \"Time in s\": 764.1312649999999       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7981873111782477,         \"MicroF1\": 0.7981873111782477,         \"MacroF1\": 0.7919320984228655,         \"Memory in Mb\": 2.307619094848633,         \"Time in s\": 806.0160599999999       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.798941798941799,         \"MicroF1\": 0.7989417989417988,         \"MacroF1\": 0.7945012991620244,         \"Memory in Mb\": 2.40640926361084,         \"Time in s\": 848.960292       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8019461934745278,         \"MicroF1\": 0.8019461934745278,         \"MacroF1\": 0.797056036319667,         \"Memory in Mb\": 2.447686195373535,         \"Time in s\": 893.037184       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8047964305633017,         \"MicroF1\": 0.8047964305633019,         \"MacroF1\": 0.7993493873930555,         \"Memory in Mb\": 2.5208606719970703,         \"Time in s\": 938.202728       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8069603045133225,         \"MicroF1\": 0.8069603045133223,         \"MacroF1\": 0.8019867749609348,         \"Memory in Mb\": 2.8025121688842773,         \"Time in s\": 984.592034       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8084880636604774,         \"MicroF1\": 0.8084880636604774,         \"MacroF1\": 0.8043300839686539,         \"Memory in Mb\": 2.9287471771240234,         \"Time in s\": 1032.221691       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8114966338684619,         \"MicroF1\": 0.8114966338684619,         \"MacroF1\": 0.8071482324590065,         \"Memory in Mb\": 2.977842330932617,         \"Time in s\": 1081.048247       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8148710166919575,         \"MicroF1\": 0.8148710166919576,         \"MacroF1\": 0.8107088256390683,         \"Memory in Mb\": 3.110445022583008,         \"Time in s\": 1130.994965       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8161146811665843,         \"MicroF1\": 0.8161146811665844,         \"MacroF1\": 0.8110472160986095,         \"Memory in Mb\": 3.3117494583129883,         \"Time in s\": 1182.226115       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8173030449492509,         \"MicroF1\": 0.8173030449492509,         \"MacroF1\": 0.8127793203399477,         \"Memory in Mb\": 2.7790603637695312,         \"Time in s\": 1234.703432       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8193853427895981,         \"MicroF1\": 0.8193853427895981,         \"MacroF1\": 0.8144282151100146,         \"Memory in Mb\": 2.8652515411376958,         \"Time in s\": 1288.356269       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8199907450254512,         \"MicroF1\": 0.8199907450254512,         \"MacroF1\": 0.8150157846003385,         \"Memory in Mb\": 2.925917625427246,         \"Time in s\": 1343.2838700000002       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8205709107385591,         \"MicroF1\": 0.8205709107385591,         \"MacroF1\": 0.8153449009635614,         \"Memory in Mb\": 2.785597801208496,         \"Time in s\": 1399.325285       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8175765645805593,         \"MicroF1\": 0.8175765645805593,         \"MacroF1\": 0.813116129924445,         \"Memory in Mb\": 2.868098258972168,         \"Time in s\": 1456.6402850000002       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8186167899086559,         \"MicroF1\": 0.8186167899086559,         \"MacroF1\": 0.8144518819207099,         \"Memory in Mb\": 3.062863349914551,         \"Time in s\": 1515.2003170000005       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8185361628410567,         \"MicroF1\": 0.8185361628410566,         \"MacroF1\": 0.8145347387119569,         \"Memory in Mb\": 3.063481330871582,         \"Time in s\": 1574.1800910000002       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6682464454976303,         \"MicroF1\": 0.6682464454976303,         \"MacroF1\": 0.6049011732627783,         \"Memory in Mb\": 7.181946754455566,         \"Time in s\": 32.418226       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6944576030317385,         \"MicroF1\": 0.6944576030317385,         \"MacroF1\": 0.6288311688548281,         \"Memory in Mb\": 9.89784336090088,         \"Time in s\": 94.873564       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6984527944426903,         \"MicroF1\": 0.6984527944426903,         \"MacroF1\": 0.625371849015863,         \"Memory in Mb\": 13.448436737060549,         \"Time in s\": 186.837042       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.706369879232773,         \"MicroF1\": 0.706369879232773,         \"MacroF1\": 0.6266042661686886,         \"Memory in Mb\": 17.43436622619629,         \"Time in s\": 307.272577       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7107406705815495,         \"MicroF1\": 0.7107406705815495,         \"MacroF1\": 0.6273487761971507,         \"Memory in Mb\": 20.93905258178711,         \"Time in s\": 452.99825       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7108129439621153,         \"MicroF1\": 0.7108129439621153,         \"MacroF1\": 0.6274052515282983,         \"Memory in Mb\": 25.022296905517575,         \"Time in s\": 622.602665       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7127587606548504,         \"MicroF1\": 0.7127587606548504,         \"MacroF1\": 0.6273117178459473,         \"Memory in Mb\": 28.81925773620605,         \"Time in s\": 816.020547       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7164673848703682,         \"MicroF1\": 0.7164673848703682,         \"MacroF1\": 0.6293431255193823,         \"Memory in Mb\": 32.80279922485352,         \"Time in s\": 1032.257355       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.721666842049879,         \"MicroF1\": 0.721666842049879,         \"MacroF1\": 0.63170101976307,         \"Memory in Mb\": 32.88048076629639,         \"Time in s\": 1271.699652       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.724405720238659,         \"MicroF1\": 0.724405720238659,         \"MacroF1\": 0.6339052025360064,         \"Memory in Mb\": 29.71586036682129,         \"Time in s\": 1533.827375       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7244080929832114,         \"MicroF1\": 0.7244080929832114,         \"MacroF1\": 0.6334336343217646,         \"Memory in Mb\": 33.71169948577881,         \"Time in s\": 1818.162347       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7225949017441402,         \"MicroF1\": 0.7225949017441402,         \"MacroF1\": 0.6332595599893077,         \"Memory in Mb\": 29.64934635162353,         \"Time in s\": 2125.062078       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7416769869600058,         \"MicroF1\": 0.7416769869600057,         \"MacroF1\": 0.7385871869253197,         \"Memory in Mb\": 11.750191688537598,         \"Time in s\": 2443.236189       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7472096326861936,         \"MicroF1\": 0.7472096326861937,         \"MacroF1\": 0.7473000008879964,         \"Memory in Mb\": 7.712667465209961,         \"Time in s\": 2772.229259       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7404507860344719,         \"MicroF1\": 0.7404507860344719,         \"MacroF1\": 0.7427443120881612,         \"Memory in Mb\": 5.854048728942871,         \"Time in s\": 3118.280673       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.73666765315182,         \"MicroF1\": 0.73666765315182,         \"MacroF1\": 0.7407696345938622,         \"Memory in Mb\": 9.543391227722168,         \"Time in s\": 3480.1200929999995       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7295972369227341,         \"MicroF1\": 0.7295972369227341,         \"MacroF1\": 0.7347001031972082,         \"Memory in Mb\": 14.625198364257812,         \"Time in s\": 3856.632275       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.739780081022781,         \"MicroF1\": 0.7397800810227809,         \"MacroF1\": 0.7407912307996387,         \"Memory in Mb\": 5.110816955566406,         \"Time in s\": 4245.133706999999       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7434581069630664,         \"MicroF1\": 0.7434581069630664,         \"MacroF1\": 0.7402037922066672,         \"Memory in Mb\": 3.8148155212402335,         \"Time in s\": 4646.574114999999       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.745111037454425,         \"MicroF1\": 0.7451110374544251,         \"MacroF1\": 0.7386209934273732,         \"Memory in Mb\": 7.313493728637695,         \"Time in s\": 5063.578879       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7462006764374295,         \"MicroF1\": 0.7462006764374295,         \"MacroF1\": 0.7365944363606786,         \"Memory in Mb\": 12.210733413696287,         \"Time in s\": 5495.973683       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7483965391072274,         \"MicroF1\": 0.7483965391072274,         \"MacroF1\": 0.7360584061499352,         \"Memory in Mb\": 11.241872787475586,         \"Time in s\": 5944.2105440000005       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7495779635195784,         \"MicroF1\": 0.7495779635195785,         \"MacroF1\": 0.7345443205753824,         \"Memory in Mb\": 12.262273788452148,         \"Time in s\": 6407.867088000001       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7508582251509293,         \"MicroF1\": 0.7508582251509293,         \"MacroF1\": 0.7336140903014292,         \"Memory in Mb\": 15.815716743469238,         \"Time in s\": 6885.995097000001       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7510890564036516,         \"MicroF1\": 0.7510890564036516,         \"MacroF1\": 0.7317409587301968,         \"Memory in Mb\": 20.072275161743164,         \"Time in s\": 7378.034229000001       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7520670187579676,         \"MicroF1\": 0.7520670187579677,         \"MacroF1\": 0.7304776676466566,         \"Memory in Mb\": 23.249674797058105,         \"Time in s\": 7884.702304       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7487285609063169,         \"MicroF1\": 0.7487285609063169,         \"MacroF1\": 0.7285292321096271,         \"Memory in Mb\": 2.702430725097656,         \"Time in s\": 8406.670172       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7464741096492712,         \"MicroF1\": 0.7464741096492712,         \"MacroF1\": 0.7309964825863351,         \"Memory in Mb\": 6.2935638427734375,         \"Time in s\": 8940.901067       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7457793162002416,         \"MicroF1\": 0.7457793162002416,         \"MacroF1\": 0.7347045068936117,         \"Memory in Mb\": 9.350909233093262,         \"Time in s\": 9487.04409       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.745036143817671,         \"MicroF1\": 0.745036143817671,         \"MacroF1\": 0.7375864352537521,         \"Memory in Mb\": 14.599569320678713,         \"Time in s\": 10044.836672       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7451962731021842,         \"MicroF1\": 0.7451962731021842,         \"MacroF1\": 0.7406480970104784,         \"Memory in Mb\": 19.12519836425781,         \"Time in s\": 10615.117300000002       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7402858749371134,         \"MicroF1\": 0.7402858749371134,         \"MacroF1\": 0.7370798749337869,         \"Memory in Mb\": 6.808139801025391,         \"Time in s\": 11202.653786000004       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7366200820730623,         \"MicroF1\": 0.7366200820730623,         \"MacroF1\": 0.7333315604235389,         \"Memory in Mb\": 5.8602495193481445,         \"Time in s\": 11807.700361000005       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.733921956382475,         \"MicroF1\": 0.7339219563824751,         \"MacroF1\": 0.7303171015411175,         \"Memory in Mb\": 9.36469554901123,         \"Time in s\": 12429.747970000002       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7304039611461349,         \"MicroF1\": 0.7304039611461349,         \"MacroF1\": 0.7265687877692525,         \"Memory in Mb\": 14.848862648010254,         \"Time in s\": 13069.446785000002       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7276864395633302,         \"MicroF1\": 0.7276864395633302,         \"MacroF1\": 0.7236022807953257,         \"Memory in Mb\": 19.807891845703125,         \"Time in s\": 13727.939023000004       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7250134370760922,         \"MicroF1\": 0.7250134370760921,         \"MacroF1\": 0.7209989950382084,         \"Memory in Mb\": 16.71243381500244,         \"Time in s\": 14405.601845000005       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7235028783612032,         \"MicroF1\": 0.7235028783612032,         \"MacroF1\": 0.7198278735760195,         \"Memory in Mb\": 8.331427574157715,         \"Time in s\": 15101.835691000002       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.723623825364835,         \"MicroF1\": 0.723623825364835,         \"MacroF1\": 0.7203262236880287,         \"Memory in Mb\": 6.9819841384887695,         \"Time in s\": 15814.868539000005       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7240464973129098,         \"MicroF1\": 0.7240464973129098,         \"MacroF1\": 0.7211005399097123,         \"Memory in Mb\": 10.71219539642334,         \"Time in s\": 16543.112989       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7245409400623629,         \"MicroF1\": 0.7245409400623629,         \"MacroF1\": 0.721844297210525,         \"Memory in Mb\": 10.330558776855469,         \"Time in s\": 17285.760894000003       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7248765529525828,         \"MicroF1\": 0.7248765529525828,         \"MacroF1\": 0.7223628081683402,         \"Memory in Mb\": 13.299851417541504,         \"Time in s\": 18041.694028       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7254167859581122,         \"MicroF1\": 0.7254167859581122,         \"MacroF1\": 0.7228420559832612,         \"Memory in Mb\": 15.662115097045898,         \"Time in s\": 18810.181113       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7263844349267159,         \"MicroF1\": 0.7263844349267159,         \"MacroF1\": 0.7236482152790997,         \"Memory in Mb\": 19.25161361694336,         \"Time in s\": 19591.516438       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7265304404553968,         \"MicroF1\": 0.7265304404553967,         \"MacroF1\": 0.7240124567772878,         \"Memory in Mb\": 14.065608024597168,         \"Time in s\": 20387.990038000004       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7304374678332476,         \"MicroF1\": 0.7304374678332476,         \"MacroF1\": 0.7281756207358935,         \"Memory in Mb\": 7.354809761047363,         \"Time in s\": 21197.413376000004       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7344603171404969,         \"MicroF1\": 0.7344603171404969,         \"MacroF1\": 0.7322565876518081,         \"Memory in Mb\": 7.006095886230469,         \"Time in s\": 22016.972025000003       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7380590684001815,         \"MicroF1\": 0.7380590684001815,         \"MacroF1\": 0.7356981427827818,         \"Memory in Mb\": 10.14159107208252,         \"Time in s\": 22847.182754       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7420134124422627,         \"MicroF1\": 0.7420134124422627,         \"MacroF1\": 0.7394134340953542,         \"Memory in Mb\": 13.563420295715332,         \"Time in s\": 23688.037606       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7451466883842497,         \"MicroF1\": 0.7451466883842497,         \"MacroF1\": 0.7430487162081567,         \"Memory in Mb\": 0.3614501953125,         \"Time in s\": 24535.706056000003       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7453781671618067,         \"MicroF1\": 0.7453781671618067,         \"MacroF1\": 0.7433023109254195,         \"Memory in Mb\": 0.3617935180664062,         \"Time in s\": 25383.518073000003       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 0.3354053497314453,         \"Time in s\": 3.23067       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9730061349693252,         \"MicroF1\": 0.9730061349693252,         \"MacroF1\": 0.8116978142719798,         \"Memory in Mb\": 0.988037109375,         \"Time in s\": 11.21298       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9730171708912512,         \"MicroF1\": 0.9730171708912512,         \"MacroF1\": 0.9579161898493525,         \"Memory in Mb\": 2.195523262023926,         \"Time in s\": 25.427007       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9693439607602696,         \"MicroF1\": 0.9693439607602696,         \"MacroF1\": 0.9069773132409142,         \"Memory in Mb\": 3.526730537414551,         \"Time in s\": 46.453054       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9666503187837177,         \"MicroF1\": 0.9666503187837177,         \"MacroF1\": 0.9303026980117672,         \"Memory in Mb\": 5.496582984924316,         \"Time in s\": 74.431187       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9660809154066204,         \"MicroF1\": 0.9660809154066204,         \"MacroF1\": 0.955517866483744,         \"Memory in Mb\": 2.29970645904541,         \"Time in s\": 107.969459       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9691768826619964,         \"MicroF1\": 0.9691768826619964,         \"MacroF1\": 0.9674134048328416,         \"Memory in Mb\": 3.376467704772949,         \"Time in s\": 146.96126800000002       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9672080907140668,         \"MicroF1\": 0.9672080907140668,         \"MacroF1\": 0.9546197483047236,         \"Memory in Mb\": 4.62060546875,         \"Time in s\": 192.073824       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9684009806592208,         \"MicroF1\": 0.968400980659221,         \"MacroF1\": 0.9654409635782653,         \"Memory in Mb\": 3.119338035583496,         \"Time in s\": 243.354323       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9644520715861732,         \"MicroF1\": 0.9644520715861732,         \"MacroF1\": 0.95030552665756,         \"Memory in Mb\": 4.705347061157227,         \"Time in s\": 301.433133       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9661243592600848,         \"MicroF1\": 0.9661243592600848,         \"MacroF1\": 0.9659906155964958,         \"Memory in Mb\": 1.508072853088379,         \"Time in s\": 365.412759       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677221654749744,         \"MicroF1\": 0.9677221654749744,         \"MacroF1\": 0.96768641848376,         \"Memory in Mb\": 2.487558364868164,         \"Time in s\": 434.672843       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9685083914765228,         \"MicroF1\": 0.9685083914765228,         \"MacroF1\": 0.9677400809149086,         \"Memory in Mb\": 2.8771514892578125,         \"Time in s\": 509.854138       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9690071791279986,         \"MicroF1\": 0.9690071791279986,         \"MacroF1\": 0.968698427792926,         \"Memory in Mb\": 4.140267372131348,         \"Time in s\": 591.7133210000001       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9671514953423762,         \"MicroF1\": 0.9671514953423762,         \"MacroF1\": 0.9635575047511442,         \"Memory in Mb\": 5.121949195861816,         \"Time in s\": 681.1937680000001       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9675195342423778,         \"MicroF1\": 0.9675195342423778,         \"MacroF1\": 0.9673223823066148,         \"Memory in Mb\": 2.1385393142700195,         \"Time in s\": 777.2102420000001       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968565248738284,         \"MicroF1\": 0.968565248738284,         \"MacroF1\": 0.9688652926813892,         \"Memory in Mb\": 2.7864933013916016,         \"Time in s\": 879.0640510000001       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9686776521857552,         \"MicroF1\": 0.9686776521857552,         \"MacroF1\": 0.9682274153773373,         \"Memory in Mb\": 3.314570426940918,         \"Time in s\": 987.062921       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9682621597213262,         \"MicroF1\": 0.9682621597213262,         \"MacroF1\": 0.9674704101631952,         \"Memory in Mb\": 4.690197944641113,         \"Time in s\": 1101.141854       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.96727540139723,         \"MicroF1\": 0.96727540139723,         \"MacroF1\": 0.9662379529396136,         \"Memory in Mb\": 5.223731994628906,         \"Time in s\": 1221.487909       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677833547332788,         \"MicroF1\": 0.9677833547332788,         \"MacroF1\": 0.9678822443058488,         \"Memory in Mb\": 4.885932922363281,         \"Time in s\": 1347.980617       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9686908077994428,         \"MicroF1\": 0.9686908077994428,         \"MacroF1\": 0.9690861219789196,         \"Memory in Mb\": 6.402636528015137,         \"Time in s\": 1480.694289       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9683470105509964,         \"MicroF1\": 0.9683470105509964,         \"MacroF1\": 0.9680699356268632,         \"Memory in Mb\": 6.928671836853027,         \"Time in s\": 1620.259773       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.96864467367991,         \"MicroF1\": 0.96864467367991,         \"MacroF1\": 0.9687197530276812,         \"Memory in Mb\": 5.552419662475586,         \"Time in s\": 1766.078849       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9684282772820864,         \"MicroF1\": 0.9684282772820866,         \"MacroF1\": 0.9682582636163196,         \"Memory in Mb\": 2.695918083190918,         \"Time in s\": 1917.758924       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9673800320543038,         \"MicroF1\": 0.9673800320543038,         \"MacroF1\": 0.9668238422002586,         \"Memory in Mb\": 3.239151954650879,         \"Time in s\": 2074.190769       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9676804357694052,         \"MicroF1\": 0.9676804357694052,         \"MacroF1\": 0.9678040910458204,         \"Memory in Mb\": 4.023995399475098,         \"Time in s\": 2235.420867       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677842948437364,         \"MicroF1\": 0.9677842948437364,         \"MacroF1\": 0.9678364439490078,         \"Memory in Mb\": 4.695375442504883,         \"Time in s\": 2402.164192       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9677119432000676,         \"MicroF1\": 0.9677119432000676,         \"MacroF1\": 0.9677086079179034,         \"Memory in Mb\": 5.258674621582031,         \"Time in s\": 2574.870699       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968706593675954,         \"MicroF1\": 0.968706593675954,         \"MacroF1\": 0.9690716756618885,         \"Memory in Mb\": 6.001680374145508,         \"Time in s\": 2753.36713       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9688463667272872,         \"MicroF1\": 0.9688463667272872,         \"MacroF1\": 0.9689334511448672,         \"Memory in Mb\": 5.217698097229004,         \"Time in s\": 2937.769964       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9687476062811184,         \"MicroF1\": 0.9687476062811184,         \"MacroF1\": 0.968764477893114,         \"Memory in Mb\": 5.266051292419434,         \"Time in s\": 3127.802403       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9687291094109782,         \"MicroF1\": 0.9687291094109782,         \"MacroF1\": 0.9687736841624996,         \"Memory in Mb\": 6.279603958129883,         \"Time in s\": 3323.370395       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9695047220820416,         \"MicroF1\": 0.9695047220820416,         \"MacroF1\": 0.9697384724636318,         \"Memory in Mb\": 4.041820526123047,         \"Time in s\": 3524.041026       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9682750892919672,         \"MicroF1\": 0.9682750892919672,         \"MacroF1\": 0.9680357071263168,         \"Memory in Mb\": 2.1731691360473637,         \"Time in s\": 3729.110149       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9686797848437394,         \"MicroF1\": 0.9686797848437394,         \"MacroF1\": 0.9688099431838716,         \"Memory in Mb\": 2.4900379180908203,         \"Time in s\": 3938.33843       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9692613448161644,         \"MicroF1\": 0.9692613448161644,         \"MacroF1\": 0.9694122553904638,         \"Memory in Mb\": 2.7789316177368164,         \"Time in s\": 4151.996270000001       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9694897761723538,         \"MicroF1\": 0.9694897761723538,         \"MacroF1\": 0.969571649124791,         \"Memory in Mb\": 3.946505546569824,         \"Time in s\": 4370.227344000001       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9694550939601534,         \"MicroF1\": 0.9694550939601534,         \"MacroF1\": 0.9694916672888816,         \"Memory in Mb\": 4.345325469970703,         \"Time in s\": 4594.050341000001       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9695447024940254,         \"MicroF1\": 0.9695447024940254,         \"MacroF1\": 0.9695954968773725,         \"Memory in Mb\": 3.909954071044922,         \"Time in s\": 4823.361799000001       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9692114545345848,         \"MicroF1\": 0.9692114545345848,         \"MacroF1\": 0.9692084456743588,         \"Memory in Mb\": 1.764338493347168,         \"Time in s\": 5057.2303470000015       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9696527575138604,         \"MicroF1\": 0.9696527575138604,         \"MacroF1\": 0.9697329621491684,         \"Memory in Mb\": 1.7167367935180664,         \"Time in s\": 5295.013901000001       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9696745140511884,         \"MicroF1\": 0.9696745140511884,         \"MacroF1\": 0.9697082565052514,         \"Memory in Mb\": 2.814372062683105,         \"Time in s\": 5537.319837000001       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968748259149908,         \"MicroF1\": 0.968748259149908,         \"MacroF1\": 0.968705960089485,         \"Memory in Mb\": 2.951136589050293,         \"Time in s\": 5784.484584000001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9690070265264992,         \"MicroF1\": 0.9690070265264992,         \"MacroF1\": 0.9690448168177233,         \"Memory in Mb\": 3.5441465377807617,         \"Time in s\": 6036.117893000001       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9690946874833484,         \"MicroF1\": 0.9690946874833484,         \"MacroF1\": 0.9691164520527108,         \"Memory in Mb\": 4.379698753356934,         \"Time in s\": 6292.729193000001       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.968761408083442,         \"MicroF1\": 0.968761408083442,         \"MacroF1\": 0.9687617227117352,         \"Memory in Mb\": 3.8120603561401367,         \"Time in s\": 6554.348831000001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9689526630240516,         \"MicroF1\": 0.9689526630240516,         \"MacroF1\": 0.9689629146490384,         \"Memory in Mb\": 2.019772529602051,         \"Time in s\": 6819.891372000001       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9692861787804512,         \"MicroF1\": 0.9692861787804512,         \"MacroF1\": 0.9692901573177236,         \"Memory in Mb\": 1.256450653076172,         \"Time in s\": 7089.584863000001       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9691161331437816,         \"MicroF1\": 0.9691161331437816,         \"MacroF1\": 0.9691108096285476,         \"Memory in Mb\": 1.6354646682739258,         \"Time in s\": 7363.046142000001       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5333333333333333,         \"MicroF1\": 0.5333333333333333,         \"MacroF1\": 0.5005728607232367,         \"Memory in Mb\": 0.8510866165161133,         \"Time in s\": 0.941842       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6153846153846154,         \"MicroF1\": 0.6153846153846154,         \"MacroF1\": 0.596131344383025,         \"Memory in Mb\": 1.5052366256713867,         \"Time in s\": 2.918201       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6496350364963503,         \"MicroF1\": 0.6496350364963503,         \"MacroF1\": 0.6567305057749026,         \"Memory in Mb\": 2.146304130554199,         \"Time in s\": 6.147886       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6994535519125683,         \"MicroF1\": 0.6994535519125683,         \"MacroF1\": 0.7070190759413217,         \"Memory in Mb\": 2.7665939331054688,         \"Time in s\": 10.824064       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7379912663755459,         \"MicroF1\": 0.7379912663755459,         \"MacroF1\": 0.7433871451842025,         \"Memory in Mb\": 3.2484235763549805,         \"Time in s\": 16.931166       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7490909090909091,         \"MicroF1\": 0.7490909090909091,         \"MacroF1\": 0.7566070103930901,         \"Memory in Mb\": 3.776392936706543,         \"Time in s\": 24.729994       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7694704049844237,         \"MicroF1\": 0.7694704049844237,         \"MacroF1\": 0.7681721604320974,         \"Memory in Mb\": 4.142314910888672,         \"Time in s\": 34.173162000000005       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.784741144414169,         \"MicroF1\": 0.7847411444141691,         \"MacroF1\": 0.7789718513534348,         \"Memory in Mb\": 4.497910499572754,         \"Time in s\": 45.384105000000005       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7990314769975787,         \"MicroF1\": 0.7990314769975787,         \"MacroF1\": 0.7943771701942021,         \"Memory in Mb\": 4.869846343994141,         \"Time in s\": 58.265676000000006       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7973856209150327,         \"MicroF1\": 0.7973856209150327,         \"MacroF1\": 0.7916511033189314,         \"Memory in Mb\": 5.3911848068237305,         \"Time in s\": 73.08883800000001       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.805940594059406,         \"MicroF1\": 0.805940594059406,         \"MacroF1\": 0.8010859843658406,         \"Memory in Mb\": 5.806554794311523,         \"Time in s\": 89.87625100000001       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8076225045372051,         \"MicroF1\": 0.8076225045372051,         \"MacroF1\": 0.8036838079612314,         \"Memory in Mb\": 6.295863151550293,         \"Time in s\": 108.5993       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8174204355108877,         \"MicroF1\": 0.8174204355108878,         \"MacroF1\": 0.8156009215135775,         \"Memory in Mb\": 6.727802276611328,         \"Time in s\": 129.48595400000002       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8211508553654744,         \"MicroF1\": 0.8211508553654744,         \"MacroF1\": 0.8207645722848749,         \"Memory in Mb\": 7.18087100982666,         \"Time in s\": 152.525841       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8229317851959361,         \"MicroF1\": 0.8229317851959362,         \"MacroF1\": 0.8226135245892084,         \"Memory in Mb\": 7.561182022094727,         \"Time in s\": 177.86541200000002       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8231292517006803,         \"MicroF1\": 0.8231292517006803,         \"MacroF1\": 0.8228959515200417,         \"Memory in Mb\": 7.975464820861816,         \"Time in s\": 205.499576       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8309859154929577,         \"MicroF1\": 0.8309859154929577,         \"MacroF1\": 0.8306123687436626,         \"Memory in Mb\": 8.301925659179688,         \"Time in s\": 235.39408200000003       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8343409915356711,         \"MicroF1\": 0.834340991535671,         \"MacroF1\": 0.835521648488366,         \"Memory in Mb\": 8.722038269042969,         \"Time in s\": 267.718494       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8407789232531501,         \"MicroF1\": 0.8407789232531501,         \"MacroF1\": 0.8414965916969209,         \"Memory in Mb\": 9.057206153869627,         \"Time in s\": 302.46008700000004       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8443960826985855,         \"MicroF1\": 0.8443960826985855,         \"MacroF1\": 0.8446110045111287,         \"Memory in Mb\": 9.38282871246338,         \"Time in s\": 339.623661       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8466321243523316,         \"MicroF1\": 0.8466321243523316,         \"MacroF1\": 0.8462590694093756,         \"Memory in Mb\": 9.696897506713867,         \"Time in s\": 379.342347       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8516320474777448,         \"MicroF1\": 0.8516320474777448,         \"MacroF1\": 0.8504483916737715,         \"Memory in Mb\": 9.949009895324709,         \"Time in s\": 421.625642       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8571428571428571,         \"MicroF1\": 0.8571428571428571,         \"MacroF1\": 0.8557487568785946,         \"Memory in Mb\": 10.2299222946167,         \"Time in s\": 466.542637       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8603807796917498,         \"MicroF1\": 0.8603807796917498,         \"MacroF1\": 0.8594481550185353,         \"Memory in Mb\": 10.524299621582031,         \"Time in s\": 514.218423       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8624891209747607,         \"MicroF1\": 0.8624891209747607,         \"MacroF1\": 0.8612253786789881,         \"Memory in Mb\": 10.737759590148926,         \"Time in s\": 564.599929       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8652719665271966,         \"MicroF1\": 0.8652719665271966,         \"MacroF1\": 0.8642881992026393,         \"Memory in Mb\": 11.010127067565918,         \"Time in s\": 617.836337       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8670427074939565,         \"MicroF1\": 0.8670427074939565,         \"MacroF1\": 0.8663181473795101,         \"Memory in Mb\": 11.261144638061523,         \"Time in s\": 674.05967       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8694638694638694,         \"MicroF1\": 0.8694638694638694,         \"MacroF1\": 0.8687259920464652,         \"Memory in Mb\": 11.505732536315918,         \"Time in s\": 733.385389       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8709677419354839,         \"MicroF1\": 0.8709677419354839,         \"MacroF1\": 0.870193396369452,         \"Memory in Mb\": 11.826444625854492,         \"Time in s\": 796.067675       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8745467730239304,         \"MicroF1\": 0.8745467730239304,         \"MacroF1\": 0.874089581073643,         \"Memory in Mb\": 12.086430549621582,         \"Time in s\": 861.584672       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8771929824561403,         \"MicroF1\": 0.8771929824561403,         \"MacroF1\": 0.8759011931352845,         \"Memory in Mb\": 12.29430866241455,         \"Time in s\": 930.204519       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8796736913664174,         \"MicroF1\": 0.8796736913664174,         \"MacroF1\": 0.877566397675441,         \"Memory in Mb\": 12.500163078308104,         \"Time in s\": 1001.8141389999998       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8826631509558339,         \"MicroF1\": 0.8826631509558339,         \"MacroF1\": 0.8803270226288138,         \"Memory in Mb\": 12.740474700927734,         \"Time in s\": 1076.488215       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8841970569417786,         \"MicroF1\": 0.8841970569417786,         \"MacroF1\": 0.8822041640143002,         \"Memory in Mb\": 12.987508773803713,         \"Time in s\": 1154.350794       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.886886264760721,         \"MicroF1\": 0.886886264760721,         \"MacroF1\": 0.8850836875294148,         \"Memory in Mb\": 13.252826690673828,         \"Time in s\": 1235.463166       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.888821752265861,         \"MicroF1\": 0.888821752265861,         \"MacroF1\": 0.8870702351165313,         \"Memory in Mb\": 13.500110626220703,         \"Time in s\": 1319.86391       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8912404467960023,         \"MicroF1\": 0.8912404467960025,         \"MacroF1\": 0.8905987472429445,         \"Memory in Mb\": 13.767583847045898,         \"Time in s\": 1407.541035       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8929593589009731,         \"MicroF1\": 0.892959358900973,         \"MacroF1\": 0.8920318510221457,         \"Memory in Mb\": 14.030475616455078,         \"Time in s\": 1498.676265       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.894032348020078,         \"MicroF1\": 0.894032348020078,         \"MacroF1\": 0.8925886559949978,         \"Memory in Mb\": 14.271255493164062,         \"Time in s\": 1593.100327       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8945078847199565,         \"MicroF1\": 0.8945078847199565,         \"MacroF1\": 0.8931986390525462,         \"Memory in Mb\": 14.574835777282717,         \"Time in s\": 1691.005218       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.896551724137931,         \"MicroF1\": 0.896551724137931,         \"MacroF1\": 0.8956464025201587,         \"Memory in Mb\": 14.834091186523438,         \"Time in s\": 1792.408733       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8964267219057483,         \"MicroF1\": 0.8964267219057483,         \"MacroF1\": 0.8951782213786073,         \"Memory in Mb\": 15.134613037109377,         \"Time in s\": 1897.156299       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8973191704602934,         \"MicroF1\": 0.8973191704602934,         \"MacroF1\": 0.8961901832930852,         \"Memory in Mb\": 15.326050758361816,         \"Time in s\": 2005.31409       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8986653484923381,         \"MicroF1\": 0.8986653484923381,         \"MacroF1\": 0.8970310627029995,         \"Memory in Mb\": 15.549851417541504,         \"Time in s\": 2116.877653       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8994683421942967,         \"MicroF1\": 0.8994683421942967,         \"MacroF1\": 0.8980105869909577,         \"Memory in Mb\": 15.81621551513672,         \"Time in s\": 2232.114727       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.900709219858156,         \"MicroF1\": 0.900709219858156,         \"MacroF1\": 0.8989778942952686,         \"Memory in Mb\": 15.957537651062012,         \"Time in s\": 2350.8625340000003       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9000462748727441,         \"MicroF1\": 0.9000462748727441,         \"MacroF1\": 0.8982611856050026,         \"Memory in Mb\": 16.206623077392578,         \"Time in s\": 2473.2186990000005       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9012233801540552,         \"MicroF1\": 0.9012233801540552,         \"MacroF1\": 0.8993036839855942,         \"Memory in Mb\": 16.400617599487305,         \"Time in s\": 2599.1257530000003       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9014647137150466,         \"MicroF1\": 0.9014647137150466,         \"MacroF1\": 0.8999821457114682,         \"Memory in Mb\": 16.693093299865723,         \"Time in s\": 2728.6827460000004       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9016963897346671,         \"MicroF1\": 0.9016963897346671,         \"MacroF1\": 0.9003174232892135,         \"Memory in Mb\": 16.988688468933105,         \"Time in s\": 2861.834306       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.9016890428757036,         \"MicroF1\": 0.9016890428757036,         \"MacroF1\": 0.9003808534937335,         \"Memory in Mb\": 17.050235748291016,         \"Time in s\": 2997.696193       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6511848341232227,         \"MicroF1\": 0.6511848341232227,         \"MacroF1\": 0.5805974192561721,         \"Memory in Mb\": 27.882014274597168,         \"Time in s\": 41.422615       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6830885836096636,         \"MicroF1\": 0.6830885836096636,         \"MacroF1\": 0.6159001145696381,         \"Memory in Mb\": 53.9009017944336,         \"Time in s\": 137.16292       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6889801073571203,         \"MicroF1\": 0.6889801073571203,         \"MacroF1\": 0.6135176771695448,         \"Memory in Mb\": 79.45620250701904,         \"Time in s\": 291.10856       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6954771489462468,         \"MicroF1\": 0.6954771489462468,         \"MacroF1\": 0.6159765684907534,         \"Memory in Mb\": 104.90542316436768,         \"Time in s\": 501.70617       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7003220306876302,         \"MicroF1\": 0.7003220306876302,         \"MacroF1\": 0.6217575035584229,         \"Memory in Mb\": 130.7021541595459,         \"Time in s\": 768.289754       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7021310181531176,         \"MicroF1\": 0.7021310181531176,         \"MacroF1\": 0.622391174421368,         \"Memory in Mb\": 156.0168752670288,         \"Time in s\": 1090.319759       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7027465836828576,         \"MicroF1\": 0.7027465836828576,         \"MacroF1\": 0.6232948240709647,         \"Memory in Mb\": 180.8397483825684,         \"Time in s\": 1466.44078       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7040369361903634,         \"MicroF1\": 0.7040369361903634,         \"MacroF1\": 0.6235946437988805,         \"Memory in Mb\": 205.63252925872803,         \"Time in s\": 1896.370643       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7105124697463959,         \"MicroF1\": 0.7105124697463959,         \"MacroF1\": 0.6284709935917355,         \"Memory in Mb\": 229.19151210784912,         \"Time in s\": 2381.431795       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7140827729898664,         \"MicroF1\": 0.7140827729898664,         \"MacroF1\": 0.6302854833117341,         \"Memory in Mb\": 253.17632389068604,         \"Time in s\": 2925.98814       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.71562634524322,         \"MicroF1\": 0.7156263452432199,         \"MacroF1\": 0.6305326785921538,         \"Memory in Mb\": 277.4567346572876,         \"Time in s\": 3530.852036       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7145450240707126,         \"MicroF1\": 0.7145450240707125,         \"MacroF1\": 0.6284185449457835,         \"Memory in Mb\": 301.7114896774292,         \"Time in s\": 4201.052974       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7057623661397247,         \"MicroF1\": 0.7057623661397247,         \"MacroF1\": 0.6885364031919957,         \"Memory in Mb\": 327.2237205505371,         \"Time in s\": 4936.820951       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6967462625989312,         \"MicroF1\": 0.6967462625989312,         \"MacroF1\": 0.69194472505998,         \"Memory in Mb\": 352.6018476486206,         \"Time in s\": 5738.465999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.676684134099375,         \"MicroF1\": 0.676684134099375,         \"MacroF1\": 0.673854549025314,         \"Memory in Mb\": 384.9730758666992,         \"Time in s\": 6613.285946       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6698431488606097,         \"MicroF1\": 0.6698431488606097,         \"MacroF1\": 0.668750254945471,         \"Memory in Mb\": 415.2214603424072,         \"Time in s\": 7559.921071       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6646983454960727,         \"MicroF1\": 0.6646983454960727,         \"MacroF1\": 0.6646134205077884,         \"Memory in Mb\": 444.32067584991455,         \"Time in s\": 8589.520858       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6620192560635555,         \"MicroF1\": 0.6620192560635555,         \"MacroF1\": 0.6605985532750915,         \"Memory in Mb\": 472.75781440734863,         \"Time in s\": 9705.665905       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6597717190848826,         \"MicroF1\": 0.6597717190848826,         \"MacroF1\": 0.6570293922418718,         \"Memory in Mb\": 499.4876089096069,         \"Time in s\": 10901.076562       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6539608882996354,         \"MicroF1\": 0.6539608882996354,         \"MacroF1\": 0.6496192149174075,         \"Memory in Mb\": 528.8777961730957,         \"Time in s\": 12166.701144       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6547463359639233,         \"MicroF1\": 0.6547463359639233,         \"MacroF1\": 0.6484047117859243,         \"Memory in Mb\": 557.1920728683472,         \"Time in s\": 13501.384366       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6583444535319185,         \"MicroF1\": 0.6583444535319185,         \"MacroF1\": 0.6499882024630633,         \"Memory in Mb\": 584.0554361343384,         \"Time in s\": 14901.095396       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6611767612302878,         \"MicroF1\": 0.6611767612302878,         \"MacroF1\": 0.6506059068013808,         \"Memory in Mb\": 610.3706150054932,         \"Time in s\": 16366.700533       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6659827171210986,         \"MicroF1\": 0.6659827171210986,         \"MacroF1\": 0.6532433614752314,         \"Memory in Mb\": 635.6853046417236,         \"Time in s\": 17901.739193       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6702526610856472,         \"MicroF1\": 0.6702526610856472,         \"MacroF1\": 0.6554263220708306,         \"Memory in Mb\": 660.4025926589966,         \"Time in s\": 19504.786084       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6745947914769623,         \"MicroF1\": 0.6745947914769623,         \"MacroF1\": 0.6575507550972549,         \"Memory in Mb\": 684.36501121521,         \"Time in s\": 21172.086014       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6705482094630143,         \"MicroF1\": 0.6705482094630143,         \"MacroF1\": 0.6539581966383304,         \"Memory in Mb\": 712.6770572662354,         \"Time in s\": 22902.746936       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6644231744850678,         \"MicroF1\": 0.6644231744850678,         \"MacroF1\": 0.6512239029866641,         \"Memory in Mb\": 743.5559530258179,         \"Time in s\": 24691.477434       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6622799856317148,         \"MicroF1\": 0.6622799856317148,         \"MacroF1\": 0.6527566844616065,         \"Memory in Mb\": 772.5478630065918,         \"Time in s\": 26538.585641       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6621736797247388,         \"MicroF1\": 0.6621736797247388,         \"MacroF1\": 0.6557760097374935,         \"Memory in Mb\": 800.5439138412476,         \"Time in s\": 28440.416189000003       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6623797159004124,         \"MicroF1\": 0.6623797159004124,         \"MacroF1\": 0.6584479912704261,         \"Memory in Mb\": 827.4998264312744,         \"Time in s\": 30418.714512       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6575123553608949,         \"MicroF1\": 0.6575123553608949,         \"MacroF1\": 0.6541419435809196,         \"Memory in Mb\": 857.7161102294922,         \"Time in s\": 32439.386127       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6519069073377909,         \"MicroF1\": 0.6519069073377909,         \"MacroF1\": 0.6481893367707658,         \"Memory in Mb\": 888.8327789306641,         \"Time in s\": 34499.720344       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.647550343982397,         \"MicroF1\": 0.647550343982397,         \"MacroF1\": 0.643407015045196,         \"Memory in Mb\": 919.6311988830566,         \"Time in s\": 36599.09766       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6444438431775752,         \"MicroF1\": 0.6444438431775752,         \"MacroF1\": 0.6400224052225335,         \"Memory in Mb\": 949.7819452285768,         \"Time in s\": 38735.650911       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6425358411153492,         \"MicroF1\": 0.6425358411153492,         \"MacroF1\": 0.6377821595167165,         \"Memory in Mb\": 979.4456567764282,         \"Time in s\": 40896.763765       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6414476209976709,         \"MicroF1\": 0.6414476209976709,         \"MacroF1\": 0.6370415360917451,         \"Memory in Mb\": 1009.0255756378174,         \"Time in s\": 43085.847267       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6409898572033793,         \"MicroF1\": 0.6409898572033793,         \"MacroF1\": 0.636858231937463,         \"Memory in Mb\": 1037.841980934143,         \"Time in s\": 45303.144751       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6414782798727631,         \"MicroF1\": 0.6414782798727631,         \"MacroF1\": 0.637272014233453,         \"Memory in Mb\": 1065.1163549423218,         \"Time in s\": 47540.369251       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6428419233409882,         \"MicroF1\": 0.6428419233409882,         \"MacroF1\": 0.6385110475108609,         \"Memory in Mb\": 1091.8334274291992,         \"Time in s\": 49803.522006       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6441159487238711,         \"MicroF1\": 0.6441159487238711,         \"MacroF1\": 0.6396283228479406,         \"Memory in Mb\": 1118.1560363769531,         \"Time in s\": 52086.93226       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.645058735992424,         \"MicroF1\": 0.645058735992424,         \"MacroF1\": 0.6403851797193834,         \"Memory in Mb\": 1144.4119939804075,         \"Time in s\": 54391.61903       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6469266853128373,         \"MicroF1\": 0.6469266853128373,         \"MacroF1\": 0.6418265850265934,         \"Memory in Mb\": 1169.9601306915283,         \"Time in s\": 56719.958571       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6487742935238792,         \"MicroF1\": 0.6487742935238792,         \"MacroF1\": 0.643191402092947,         \"Memory in Mb\": 1194.640343666077,         \"Time in s\": 59073.094705       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6459521454576065,         \"MicroF1\": 0.6459521454576065,         \"MacroF1\": 0.6406800374556137,         \"Memory in Mb\": 1224.6073780059814,         \"Time in s\": 61451.967815       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6443643849716932,         \"MicroF1\": 0.6443643849716932,         \"MacroF1\": 0.6398250343320808,         \"Memory in Mb\": 1254.4350862503052,         \"Time in s\": 63857.884093       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6446172754931394,         \"MicroF1\": 0.6446172754931394,         \"MacroF1\": 0.6406945505071863,         \"Memory in Mb\": 1282.3891849517822,         \"Time in s\": 66293.298766       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6461222798745241,         \"MicroF1\": 0.6461222798745241,         \"MacroF1\": 0.6426238276925219,         \"Memory in Mb\": 1309.4736614227295,         \"Time in s\": 68755.018108       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6489186943161394,         \"MicroF1\": 0.6489186943161394,         \"MacroF1\": 0.6457243405011626,         \"Memory in Mb\": 1334.444143295288,         \"Time in s\": 71244.151045       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6470577094263149,         \"MicroF1\": 0.6470577094263149,         \"MacroF1\": 0.6443966707674731,         \"Memory in Mb\": 1363.999231338501,         \"Time in s\": 73759.934152       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6469809071470471,         \"MicroF1\": 0.6469809071470471,         \"MacroF1\": 0.6443518314696601,         \"Memory in Mb\": 1365.409776687622,         \"Time in s\": 76295.692169       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9901719901719902,         \"MicroF1\": 0.9901719901719902,         \"MacroF1\": 0.8308395677472984,         \"Memory in Mb\": 0.1227684020996093,         \"Time in s\": 1.485322       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9914110429447852,         \"MicroF1\": 0.9914110429447852,         \"MacroF1\": 0.960934413925625,         \"Memory in Mb\": 0.4158411026000976,         \"Time in s\": 6.729082       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9893704006541292,         \"MicroF1\": 0.9893704006541292,         \"MacroF1\": 0.9580466011674303,         \"Memory in Mb\": 1.2467107772827148,         \"Time in s\": 20.14849       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9889638258736972,         \"MicroF1\": 0.9889638258736972,         \"MacroF1\": 0.9786672150923964,         \"Memory in Mb\": 2.28104305267334,         \"Time in s\": 50.264957       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.988719960765081,         \"MicroF1\": 0.988719960765081,         \"MacroF1\": 0.9803510904896324,         \"Memory in Mb\": 3.352717399597168,         \"Time in s\": 91.643431       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9885574172456068,         \"MicroF1\": 0.9885574172456068,         \"MacroF1\": 0.983046879237058,         \"Memory in Mb\": 4.983606338500977,         \"Time in s\": 148.278076       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9852889667250436,         \"MicroF1\": 0.9852889667250436,         \"MacroF1\": 0.9737767108051044,         \"Memory in Mb\": 6.963967323303223,         \"Time in s\": 227.073424       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9825314128102972,         \"MicroF1\": 0.9825314128102972,         \"MacroF1\": 0.9734338986941852,         \"Memory in Mb\": 9.8344087600708,         \"Time in s\": 324.702985       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.982293652955598,         \"MicroF1\": 0.982293652955598,         \"MacroF1\": 0.9788760747631072,         \"Memory in Mb\": 12.7888765335083,         \"Time in s\": 446.356435       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9806325079676392,         \"MicroF1\": 0.9806325079676392,         \"MacroF1\": 0.9749453255203756,         \"Memory in Mb\": 16.445659637451172,         \"Time in s\": 594.71846       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9801649208825496,         \"MicroF1\": 0.9801649208825496,         \"MacroF1\": 0.9779116862524244,         \"Memory in Mb\": 20.943636894226078,         \"Time in s\": 768.8230350000001       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9801838610827376,         \"MicroF1\": 0.9801838610827376,         \"MacroF1\": 0.978782474664832,         \"Memory in Mb\": 24.856953620910645,         \"Time in s\": 967.611442       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9768055817461814,         \"MicroF1\": 0.9768055817461814,         \"MacroF1\": 0.9702080932270808,         \"Memory in Mb\": 28.10527801513672,         \"Time in s\": 1191.0213970000002       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9746104009805638,         \"MicroF1\": 0.9746104009805638,         \"MacroF1\": 0.9718234131704068,         \"Memory in Mb\": 32.14579772949219,         \"Time in s\": 1440.171835       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9697663016832816,         \"MicroF1\": 0.9697663016832816,         \"MacroF1\": 0.9621279568251032,         \"Memory in Mb\": 36.40912055969238,         \"Time in s\": 1717.813161       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9656810173127012,         \"MicroF1\": 0.9656810173127012,         \"MacroF1\": 0.9634765255010708,         \"Memory in Mb\": 42.20043754577637,         \"Time in s\": 2023.330442       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9653929343907716,         \"MicroF1\": 0.9653929343907716,         \"MacroF1\": 0.9646253117338192,         \"Memory in Mb\": 46.97204208374024,         \"Time in s\": 2355.811343       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9635026555903582,         \"MicroF1\": 0.9635026555903582,         \"MacroF1\": 0.96110342818104,         \"Memory in Mb\": 50.8284969329834,         \"Time in s\": 2716.693574       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9610372855115468,         \"MicroF1\": 0.9610372855115468,         \"MacroF1\": 0.9585597537512924,         \"Memory in Mb\": 55.062747955322266,         \"Time in s\": 3108.019641       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9593087388160314,         \"MicroF1\": 0.9593087388160314,         \"MacroF1\": 0.9577319445930262,         \"Memory in Mb\": 59.75967216491699,         \"Time in s\": 3529.961458       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9598459203922026,         \"MicroF1\": 0.9598459203922026,         \"MacroF1\": 0.960171378024828,         \"Memory in Mb\": 65.88526916503906,         \"Time in s\": 3981.552133       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.959108635097493,         \"MicroF1\": 0.959108635097493,         \"MacroF1\": 0.9586518345557712,         \"Memory in Mb\": 71.85272026062012,         \"Time in s\": 4465.222941       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9573697111797932,         \"MicroF1\": 0.9573697111797932,         \"MacroF1\": 0.956135316427552,         \"Memory in Mb\": 78.18439388275146,         \"Time in s\": 4984.801354       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9555714431620876,         \"MicroF1\": 0.9555714431620876,         \"MacroF1\": 0.9546392488298882,         \"Memory in Mb\": 85.86389446258545,         \"Time in s\": 5537.4752260000005       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9486224139621532,         \"MicroF1\": 0.9486224139621532,         \"MacroF1\": 0.9433099305923252,         \"Memory in Mb\": 94.35744285583496,         \"Time in s\": 6127.477763000001       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943150749505044,         \"MicroF1\": 0.943150749505044,         \"MacroF1\": 0.9403442056943528,         \"Memory in Mb\": 104.1574821472168,         \"Time in s\": 6756.480909000001       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9408987743985474,         \"MicroF1\": 0.9408987743985474,         \"MacroF1\": 0.9399975161043574,         \"Memory in Mb\": 113.0038013458252,         \"Time in s\": 7421.284759000001       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9380197846450145,         \"MicroF1\": 0.9380197846450145,         \"MacroF1\": 0.936341059397272,         \"Memory in Mb\": 121.46645069122314,         \"Time in s\": 8125.715779000001       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9322965091708224,         \"MicroF1\": 0.9322965091708224,         \"MacroF1\": 0.9294143034054052,         \"Memory in Mb\": 131.1031150817871,         \"Time in s\": 8872.899296000001       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9326742380913472,         \"MicroF1\": 0.9326742380913472,         \"MacroF1\": 0.9327603226303838,         \"Memory in Mb\": 137.88959789276123,         \"Time in s\": 9652.703167       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.927571756147703,         \"MicroF1\": 0.927571756147703,         \"MacroF1\": 0.9249549620362734,         \"Memory in Mb\": 145.5888376235962,         \"Time in s\": 10475.658214       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9247797778628878,         \"MicroF1\": 0.9247797778628878,         \"MacroF1\": 0.92370720847711,         \"Memory in Mb\": 154.53871536254883,         \"Time in s\": 11346.213643       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9238654088984624,         \"MicroF1\": 0.9238654088984624,         \"MacroF1\": 0.9233692422863464,         \"Memory in Mb\": 161.3583574295044,         \"Time in s\": 12266.045404       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9202653017086008,         \"MicroF1\": 0.9202653017086008,         \"MacroF1\": 0.9191663953636944,         \"Memory in Mb\": 170.12918186187744,         \"Time in s\": 13231.481182       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.916310666013026,         \"MicroF1\": 0.916310666013026,         \"MacroF1\": 0.9150341930556872,         \"Memory in Mb\": 179.0350112915039,         \"Time in s\": 14245.599713       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9161162933206236,         \"MicroF1\": 0.9161162933206236,         \"MacroF1\": 0.9160540991607554,         \"Memory in Mb\": 184.834698677063,         \"Time in s\": 15311.95464       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9145412388208016,         \"MicroF1\": 0.9145412388208016,         \"MacroF1\": 0.91429667624259,         \"Memory in Mb\": 191.58009719848636,         \"Time in s\": 16433.239395       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9105979487841064,         \"MicroF1\": 0.9105979487841064,         \"MacroF1\": 0.909716370830996,         \"Memory in Mb\": 200.08039951324463,         \"Time in s\": 17613.909715       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9068568914587392,         \"MicroF1\": 0.9068568914587392,         \"MacroF1\": 0.9060681758481206,         \"Memory in Mb\": 210.5000762939453,         \"Time in s\": 18848.20155       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9031190636681168,         \"MicroF1\": 0.9031190636681168,         \"MacroF1\": 0.9023660107991418,         \"Memory in Mb\": 221.55222129821777,         \"Time in s\": 20131.794746000003       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9005799007592515,         \"MicroF1\": 0.9005799007592515,         \"MacroF1\": 0.9001704241319546,         \"Memory in Mb\": 231.28063201904297,         \"Time in s\": 21466.799965       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8989203384884739,         \"MicroF1\": 0.8989203384884739,         \"MacroF1\": 0.8987537815839572,         \"Memory in Mb\": 248.11264038085935,         \"Time in s\": 22840.808564000003       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.893746793592886,         \"MicroF1\": 0.8937467935928861,         \"MacroF1\": 0.892807745348426,         \"Memory in Mb\": 267.53482723236084,         \"Time in s\": 24265.711089000004       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8894212021614395,         \"MicroF1\": 0.8894212021614395,         \"MacroF1\": 0.8884694521151855,         \"Memory in Mb\": 281.7739496231079,         \"Time in s\": 25739.620728000005       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8911705430579008,         \"MicroF1\": 0.8911705430579007,         \"MacroF1\": 0.8908032768807751,         \"Memory in Mb\": 288.0978307723999,         \"Time in s\": 27256.627666000004       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8911387009111739,         \"MicroF1\": 0.8911387009111739,         \"MacroF1\": 0.8906428613252552,         \"Memory in Mb\": 296.0527210235596,         \"Time in s\": 28820.747713000004       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8886049543676662,         \"MicroF1\": 0.8886049543676662,         \"MacroF1\": 0.8879368647002966,         \"Memory in Mb\": 307.6826677322388,         \"Time in s\": 30435.23145800001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8895470561201042,         \"MicroF1\": 0.8895470561201042,         \"MacroF1\": 0.889061241536932,         \"Memory in Mb\": 313.4344787597656,         \"Time in s\": 32089.799369000008       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8862488119653844,         \"MicroF1\": 0.8862488119653844,         \"MacroF1\": 0.8855123768505595,         \"Memory in Mb\": 324.9442596435547,         \"Time in s\": 33786.733744000005       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8810726015981175,         \"MicroF1\": 0.8810726015981175,         \"MacroF1\": 0.8799282628097613,         \"Memory in Mb\": 338.1390075683594,         \"Time in s\": 35528.434162000005       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3555555555555555,         \"MicroF1\": 0.3555555555555555,         \"MacroF1\": 0.2468487394957983,         \"Memory in Mb\": 2.5926971435546875,         \"Time in s\": 5.672061       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5274725274725275,         \"MicroF1\": 0.5274725274725275,         \"MacroF1\": 0.5392220990960486,         \"Memory in Mb\": 2.5963096618652344,         \"Time in s\": 17.740863       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5401459854014599,         \"MicroF1\": 0.5401459854014599,         \"MacroF1\": 0.5661177456005042,         \"Memory in Mb\": 2.5979232788085938,         \"Time in s\": 35.937815       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5956284153005464,         \"MicroF1\": 0.5956284153005464,         \"MacroF1\": 0.6144104879239446,         \"Memory in Mb\": 2.6004638671875,         \"Time in s\": 59.975895       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6200873362445415,         \"MicroF1\": 0.6200873362445415,         \"MacroF1\": 0.6319742698014011,         \"Memory in Mb\": 2.6008224487304688,         \"Time in s\": 89.681265       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6327272727272727,         \"MicroF1\": 0.6327272727272727,         \"MacroF1\": 0.6440706793955739,         \"Memory in Mb\": 2.601276397705078,         \"Time in s\": 125.043898       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6573208722741433,         \"MicroF1\": 0.6573208722741433,         \"MacroF1\": 0.6535377647060517,         \"Memory in Mb\": 2.6028709411621094,         \"Time in s\": 166.036362       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6784741144414169,         \"MicroF1\": 0.6784741144414169,         \"MacroF1\": 0.6717418242612484,         \"Memory in Mb\": 2.6031723022460938,         \"Time in s\": 212.735146       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6900726392251816,         \"MicroF1\": 0.6900726392251816,         \"MacroF1\": 0.6823551618652942,         \"Memory in Mb\": 2.603717803955078,         \"Time in s\": 265.175489       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6971677559912854,         \"MicroF1\": 0.6971677559912854,         \"MacroF1\": 0.686858403065277,         \"Memory in Mb\": 2.60379409790039,         \"Time in s\": 323.486791       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.699009900990099,         \"MicroF1\": 0.699009900990099,         \"MacroF1\": 0.6869845800125663,         \"Memory in Mb\": 2.604084014892578,         \"Time in s\": 387.600808       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6987295825771325,         \"MicroF1\": 0.6987295825771325,         \"MacroF1\": 0.6895132041566728,         \"Memory in Mb\": 2.6040496826171875,         \"Time in s\": 457.323817       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7035175879396985,         \"MicroF1\": 0.7035175879396985,         \"MacroF1\": 0.6939747146282641,         \"Memory in Mb\": 2.6041183471679688,         \"Time in s\": 532.682096       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6998444790046656,         \"MicroF1\": 0.6998444790046656,         \"MacroF1\": 0.6913714585468268,         \"Memory in Mb\": 2.6053123474121094,         \"Time in s\": 613.490084       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7024673439767779,         \"MicroF1\": 0.7024673439767779,         \"MacroF1\": 0.6944906634267102,         \"Memory in Mb\": 2.6058921813964844,         \"Time in s\": 699.880084       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7020408163265306,         \"MicroF1\": 0.7020408163265306,         \"MacroF1\": 0.69548275919944,         \"Memory in Mb\": 2.605987548828125,         \"Time in s\": 791.65329       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.706786171574904,         \"MicroF1\": 0.706786171574904,         \"MacroF1\": 0.6991539785967766,         \"Memory in Mb\": 2.6064224243164062,         \"Time in s\": 888.981823       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7085852478839177,         \"MicroF1\": 0.7085852478839177,         \"MacroF1\": 0.70309750989463,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 991.647497       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.715922107674685,         \"MicroF1\": 0.7159221076746849,         \"MacroF1\": 0.7073525059690206,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 1099.573439       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7170837867247007,         \"MicroF1\": 0.7170837867247007,         \"MacroF1\": 0.707165908654469,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 1212.915424       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7160621761658031,         \"MicroF1\": 0.716062176165803,         \"MacroF1\": 0.7063689525089133,         \"Memory in Mb\": 2.6064682006835938,         \"Time in s\": 1331.559339       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7151335311572701,         \"MicroF1\": 0.7151335311572701,         \"MacroF1\": 0.7047830593764105,         \"Memory in Mb\": 2.6064910888671875,         \"Time in s\": 1455.3800170000002       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7152317880794702,         \"MicroF1\": 0.7152317880794702,         \"MacroF1\": 0.7037726227430311,         \"Memory in Mb\": 2.606658935546875,         \"Time in s\": 1584.327081       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.71441523118767,         \"MicroF1\": 0.71441523118767,         \"MacroF1\": 0.7026447500373862,         \"Memory in Mb\": 2.6067771911621094,         \"Time in s\": 1718.245947       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7162750217580505,         \"MicroF1\": 0.7162750217580505,         \"MacroF1\": 0.7030218527348165,         \"Memory in Mb\": 2.6067771911621094,         \"Time in s\": 1857.022931       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7179916317991631,         \"MicroF1\": 0.7179916317991631,         \"MacroF1\": 0.705575475090573,         \"Memory in Mb\": 2.379610061645508,         \"Time in s\": 2000.273372       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7155519742143432,         \"MicroF1\": 0.7155519742143431,         \"MacroF1\": 0.7053749246401603,         \"Memory in Mb\": 3.185004234313965,         \"Time in s\": 2147.034729       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7156177156177156,         \"MicroF1\": 0.7156177156177156,         \"MacroF1\": 0.7041730806550314,         \"Memory in Mb\": 3.633350372314453,         \"Time in s\": 2296.192092       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7149287321830458,         \"MicroF1\": 0.7149287321830458,         \"MacroF1\": 0.7045092702498074,         \"Memory in Mb\": 4.368736267089844,         \"Time in s\": 2447.850078       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7186366932559826,         \"MicroF1\": 0.7186366932559827,         \"MacroF1\": 0.7102131417787841,         \"Memory in Mb\": 4.724300384521484,         \"Time in s\": 2601.871177       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7256140350877193,         \"MicroF1\": 0.7256140350877193,         \"MacroF1\": 0.7174099613082184,         \"Memory in Mb\": 4.89253044128418,         \"Time in s\": 2758.036552       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7273963290278722,         \"MicroF1\": 0.7273963290278722,         \"MacroF1\": 0.7183919320082559,         \"Memory in Mb\": 5.412370681762695,         \"Time in s\": 2916.512936       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7211601845748187,         \"MicroF1\": 0.7211601845748187,         \"MacroF1\": 0.7136134581802791,         \"Memory in Mb\": 6.487729072570801,         \"Time in s\": 3077.492565       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7172104926423545,         \"MicroF1\": 0.7172104926423546,         \"MacroF1\": 0.7129536273040751,         \"Memory in Mb\": 6.405126571655273,         \"Time in s\": 3241.109       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7209446861404599,         \"MicroF1\": 0.7209446861404599,         \"MacroF1\": 0.7163536024764182,         \"Memory in Mb\": 6.857941627502441,         \"Time in s\": 3407.317179       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7238670694864048,         \"MicroF1\": 0.7238670694864048,         \"MacroF1\": 0.7196892738307762,         \"Memory in Mb\": 7.034061431884766,         \"Time in s\": 3576.258732       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7260435038212816,         \"MicroF1\": 0.7260435038212816,         \"MacroF1\": 0.7238533950478148,         \"Memory in Mb\": 7.623349189758301,         \"Time in s\": 3747.864612       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7309673726388094,         \"MicroF1\": 0.7309673726388093,         \"MacroF1\": 0.7286270619416129,         \"Memory in Mb\": 8.106144905090332,         \"Time in s\": 3922.063963000001       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7361963190184049,         \"MicroF1\": 0.7361963190184049,         \"MacroF1\": 0.7329274067865035,         \"Memory in Mb\": 8.185744285583496,         \"Time in s\": 4098.8506050000005       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7389885807504079,         \"MicroF1\": 0.7389885807504077,         \"MacroF1\": 0.7360694376974826,         \"Memory in Mb\": 8.929247856140137,         \"Time in s\": 4278.2789060000005       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7411140583554376,         \"MicroF1\": 0.7411140583554376,         \"MacroF1\": 0.7396669191579938,         \"Memory in Mb\": 9.100563049316406,         \"Time in s\": 4460.600751000001       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7431382703262558,         \"MicroF1\": 0.7431382703262558,         \"MacroF1\": 0.7411378754700444,         \"Memory in Mb\": 9.223885536193848,         \"Time in s\": 4645.683986000001       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7465857359635811,         \"MicroF1\": 0.746585735963581,         \"MacroF1\": 0.744200926808846,         \"Memory in Mb\": 9.401692390441896,         \"Time in s\": 4833.458731000001       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7508650519031141,         \"MicroF1\": 0.7508650519031143,         \"MacroF1\": 0.7476945996538615,         \"Memory in Mb\": 9.481804847717283,         \"Time in s\": 5024.230846       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7549540840985983,         \"MicroF1\": 0.7549540840985983,         \"MacroF1\": 0.7524477298078486,         \"Memory in Mb\": 9.431160926818848,         \"Time in s\": 5217.969956000001       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7583924349881797,         \"MicroF1\": 0.7583924349881797,         \"MacroF1\": 0.7554386161495508,         \"Memory in Mb\": 9.549637794494627,         \"Time in s\": 5414.604524000001       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7607589079130033,         \"MicroF1\": 0.7607589079130033,         \"MacroF1\": 0.7577216433051415,         \"Memory in Mb\": 10.151451110839844,         \"Time in s\": 5614.378132000002       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7648391481649298,         \"MicroF1\": 0.7648391481649298,         \"MacroF1\": 0.7614528787516565,         \"Memory in Mb\": 9.14443588256836,         \"Time in s\": 5817.274926000002       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7652019529516201,         \"MicroF1\": 0.7652019529516201,         \"MacroF1\": 0.762166830901651,         \"Memory in Mb\": 8.801234245300293,         \"Time in s\": 6023.198429000002       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7672901261418008,         \"MicroF1\": 0.7672901261418008,         \"MacroF1\": 0.7647372124971393,         \"Memory in Mb\": 8.857858657836914,         \"Time in s\": 6232.074878000002       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7669987007362494,         \"MicroF1\": 0.7669987007362494,         \"MacroF1\": 0.7647069285577738,         \"Memory in Mb\": 8.926526069641113,         \"Time in s\": 6441.814766000002       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6388625592417062,         \"MicroF1\": 0.6388625592417062,         \"MacroF1\": 0.6031100134310133,         \"Memory in Mb\": 8.730474472045898,         \"Time in s\": 177.190345       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.659403126480341,         \"MicroF1\": 0.659403126480341,         \"MacroF1\": 0.6244477305834598,         \"Memory in Mb\": 21.138185501098636,         \"Time in s\": 477.6689290000001       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6722450268392801,         \"MicroF1\": 0.6722450268392801,         \"MacroF1\": 0.6321534006670183,         \"Memory in Mb\": 28.433568000793457,         \"Time in s\": 884.814326       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.680322045938906,         \"MicroF1\": 0.680322045938906,         \"MacroF1\": 0.6340126191391743,         \"Memory in Mb\": 39.2259521484375,         \"Time in s\": 1406.7405990000002       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6878196628149271,         \"MicroF1\": 0.6878196628149271,         \"MacroF1\": 0.6395508722492685,         \"Memory in Mb\": 32.51231288909912,         \"Time in s\": 2046.4837620000003       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6876085240726125,         \"MicroF1\": 0.6876085240726125,         \"MacroF1\": 0.641396967542699,         \"Memory in Mb\": 34.57641696929932,         \"Time in s\": 2792.1074670000003       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6924638073332431,         \"MicroF1\": 0.6924638073332431,         \"MacroF1\": 0.6467777725107727,         \"Memory in Mb\": 40.06835174560547,         \"Time in s\": 3634.422347       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6949212738250267,         \"MicroF1\": 0.6949212738250267,         \"MacroF1\": 0.6476372139610082,         \"Memory in Mb\": 43.01965808868408,         \"Time in s\": 4571.1184410000005       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6992528675155214,         \"MicroF1\": 0.6992528675155214,         \"MacroF1\": 0.6494082466298291,         \"Memory in Mb\": 45.71251583099365,         \"Time in s\": 5608.992399000001       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7013921772895161,         \"MicroF1\": 0.7013921772895161,         \"MacroF1\": 0.6506452100316108,         \"Memory in Mb\": 50.31043148040772,         \"Time in s\": 6744.395149000001       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7043478260869566,         \"MicroF1\": 0.7043478260869566,         \"MacroF1\": 0.6524912605091605,         \"Memory in Mb\": 59.27919769287109,         \"Time in s\": 7964.619750000001       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7079946334148843,         \"MicroF1\": 0.7079946334148843,         \"MacroF1\": 0.6596828376773001,         \"Memory in Mb\": 72.61201000213623,         \"Time in s\": 9269.589572       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7208421359364756,         \"MicroF1\": 0.7208421359364756,         \"MacroF1\": 0.7145906055666686,         \"Memory in Mb\": 38.78250694274902,         \"Time in s\": 10625.218377       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7285395386592708,         \"MicroF1\": 0.7285395386592708,         \"MacroF1\": 0.7256542392368915,         \"Memory in Mb\": 15.54647731781006,         \"Time in s\": 12027.231464       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7206263021655408,         \"MicroF1\": 0.7206263021655408,         \"MacroF1\": 0.7196216319492748,         \"Memory in Mb\": 14.88278579711914,         \"Time in s\": 13491.676191       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7171352471145309,         \"MicroF1\": 0.7171352471145309,         \"MacroF1\": 0.7175260611854538,         \"Memory in Mb\": 21.28149700164795,         \"Time in s\": 15025.812845       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7121051751991533,         \"MicroF1\": 0.7121051751991533,         \"MacroF1\": 0.7136617513297842,         \"Memory in Mb\": 29.336480140686035,         \"Time in s\": 16621.157643       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7205240174672489,         \"MicroF1\": 0.720524017467249,         \"MacroF1\": 0.7180961996594418,         \"Memory in Mb\": 20.976608276367188,         \"Time in s\": 18267.655245       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7261625878482779,         \"MicroF1\": 0.7261625878482779,         \"MacroF1\": 0.7198561207408494,         \"Memory in Mb\": 15.994047164916992,         \"Time in s\": 19960.279521       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7272598134381363,         \"MicroF1\": 0.7272598134381363,         \"MacroF1\": 0.7183389579277755,         \"Memory in Mb\": 17.52824878692627,         \"Time in s\": 21708.029386999995       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7281623449830891,         \"MicroF1\": 0.7281623449830891,         \"MacroF1\": 0.7167723651435352,         \"Memory in Mb\": 22.240838050842285,         \"Time in s\": 23503.558300999997       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7307477078042272,         \"MicroF1\": 0.7307477078042272,         \"MacroF1\": 0.7170791531651185,         \"Memory in Mb\": 26.114503860473636,         \"Time in s\": 25348.434525999997       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7325318071396221,         \"MicroF1\": 0.7325318071396222,         \"MacroF1\": 0.7165563330554671,         \"Memory in Mb\": 27.97449111938477,         \"Time in s\": 27240.683159999997       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7353904431203883,         \"MicroF1\": 0.7353904431203884,         \"MacroF1\": 0.7174524973348954,         \"Memory in Mb\": 37.12833023071289,         \"Time in s\": 29182.513668999996       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7367703322095533,         \"MicroF1\": 0.7367703322095533,         \"MacroF1\": 0.7168965346030137,         \"Memory in Mb\": 31.575971603393555,         \"Time in s\": 31184.058177999992       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.738371881260244,         \"MicroF1\": 0.738371881260244,         \"MacroF1\": 0.7164257197178175,         \"Memory in Mb\": 35.22733116149902,         \"Time in s\": 33213.34443999999       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7366279681526429,         \"MicroF1\": 0.7366279681526429,         \"MacroF1\": 0.7161250847684691,         \"Memory in Mb\": 17.50509262084961,         \"Time in s\": 35271.15690999999       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7354483038522678,         \"MicroF1\": 0.7354483038522677,         \"MacroF1\": 0.719616514898752,         \"Memory in Mb\": 22.40646266937256,         \"Time in s\": 37354.25785299999       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7348724814681775,         \"MicroF1\": 0.7348724814681775,         \"MacroF1\": 0.7237598149406717,         \"Memory in Mb\": 31.22674369812012,         \"Time in s\": 39459.62893099999       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7347769815966413,         \"MicroF1\": 0.7347769815966413,         \"MacroF1\": 0.7275990709197302,         \"Memory in Mb\": 35.24118995666504,         \"Time in s\": 41587.29474699999       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7351458683366427,         \"MicroF1\": 0.7351458683366427,         \"MacroF1\": 0.7308983066693725,         \"Memory in Mb\": 48.40772724151611,         \"Time in s\": 43729.93044799999       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7303423988636027,         \"MicroF1\": 0.7303423988636027,         \"MacroF1\": 0.7274356410957497,         \"Memory in Mb\": 77.28174114227295,         \"Time in s\": 45887.55412199999       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.726805750853732,         \"MicroF1\": 0.726805750853732,         \"MacroF1\": 0.723911701718825,         \"Memory in Mb\": 53.16175174713135,         \"Time in s\": 48068.300588999984       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7248976408656658,         \"MicroF1\": 0.7248976408656659,         \"MacroF1\": 0.7218080521646734,         \"Memory in Mb\": 41.53026580810547,         \"Time in s\": 50265.61973699999       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7215833761735978,         \"MicroF1\": 0.7215833761735979,         \"MacroF1\": 0.7182506744185386,         \"Memory in Mb\": 35.33352756500244,         \"Time in s\": 52485.18591199999       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7196369854004998,         \"MicroF1\": 0.7196369854004999,         \"MacroF1\": 0.7160236415660819,         \"Memory in Mb\": 44.00273513793945,         \"Time in s\": 54721.05808499999       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7175142688950884,         \"MicroF1\": 0.7175142688950884,         \"MacroF1\": 0.713988650041017,         \"Memory in Mb\": 46.12203025817871,         \"Time in s\": 56978.30571199999       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7158023276098388,         \"MicroF1\": 0.7158023276098388,         \"MacroF1\": 0.7126852582249207,         \"Memory in Mb\": 27.841010093688965,         \"Time in s\": 59249.54765999999       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7157322196051769,         \"MicroF1\": 0.715732219605177,         \"MacroF1\": 0.7129296468122535,         \"Memory in Mb\": 21.849401473999023,         \"Time in s\": 61534.70422899999       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7156656170837378,         \"MicroF1\": 0.7156656170837377,         \"MacroF1\": 0.7131576552849198,         \"Memory in Mb\": 28.021278381347656,         \"Time in s\": 63833.59664899999       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.715925626515764,         \"MicroF1\": 0.715925626515764,         \"MacroF1\": 0.7137513847694824,         \"Memory in Mb\": 36.50454139709473,         \"Time in s\": 66146.66403399999       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7161958016730176,         \"MicroF1\": 0.7161958016730177,         \"MacroF1\": 0.7143198962298327,         \"Memory in Mb\": 46.888444900512695,         \"Time in s\": 68474.34057599999       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7170260092056291,         \"MicroF1\": 0.7170260092056291,         \"MacroF1\": 0.7151715877390813,         \"Memory in Mb\": 47.08374786376953,         \"Time in s\": 70816.527322       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7181843617502098,         \"MicroF1\": 0.7181843617502098,         \"MacroF1\": 0.7162864260409335,         \"Memory in Mb\": 43.18325901031494,         \"Time in s\": 73172.526917       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7179023127591069,         \"MicroF1\": 0.7179023127591069,         \"MacroF1\": 0.716246618663062,         \"Memory in Mb\": 54.80090522766113,         \"Time in s\": 75543.857611       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7211528564076171,         \"MicroF1\": 0.7211528564076171,         \"MacroF1\": 0.719707905487922,         \"Memory in Mb\": 60.4919376373291,         \"Time in s\": 77931.086228       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7250710241582882,         \"MicroF1\": 0.7250710241582882,         \"MacroF1\": 0.7236001513027165,         \"Memory in Mb\": 35.55128765106201,         \"Time in s\": 80332.853368       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7288259316984631,         \"MicroF1\": 0.7288259316984631,         \"MacroF1\": 0.7271241427068512,         \"Memory in Mb\": 21.017152786254883,         \"Time in s\": 82746.274567       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7329107318864387,         \"MicroF1\": 0.7329107318864386,         \"MacroF1\": 0.7308784460773333,         \"Memory in Mb\": 26.768343925476078,         \"Time in s\": 85169.877802       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7359230288452433,         \"MicroF1\": 0.7359230288452432,         \"MacroF1\": 0.7343606492383059,         \"Memory in Mb\": 9.57052230834961,         \"Time in s\": 87600.592492       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7361628853104244,         \"MicroF1\": 0.7361628853104244,         \"MacroF1\": 0.7346220154259927,         \"Memory in Mb\": 9.63199520111084,         \"Time in s\": 90031.55993999999       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9901719901719902,         \"MicroF1\": 0.9901719901719902,         \"MacroF1\": 0.8308395677472984,         \"Memory in Mb\": 1.027322769165039,         \"Time in s\": 17.348128       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9877300613496932,         \"MicroF1\": 0.9877300613496932,         \"MacroF1\": 0.9320293882508496,         \"Memory in Mb\": 2.6651391983032227,         \"Time in s\": 54.406226       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.982829108748978,         \"MicroF1\": 0.982829108748978,         \"MacroF1\": 0.9464059415055076,         \"Memory in Mb\": 5.679329872131348,         \"Time in s\": 114.007104       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828326180257512,         \"MicroF1\": 0.9828326180257512,         \"MacroF1\": 0.963209755030524,         \"Memory in Mb\": 8.335807800292969,         \"Time in s\": 201.582874       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.974987739087788,         \"MicroF1\": 0.974987739087788,         \"MacroF1\": 0.9373958892668122,         \"Memory in Mb\": 12.631415367126465,         \"Time in s\": 318.920922       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.970167552104618,         \"MicroF1\": 0.970167552104618,         \"MacroF1\": 0.957381800109682,         \"Memory in Mb\": 16.891732215881348,         \"Time in s\": 465.74626       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9660245183887916,         \"MicroF1\": 0.9660245183887916,         \"MacroF1\": 0.93947544504001,         \"Memory in Mb\": 22.937668800354004,         \"Time in s\": 641.328845       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9616916947594238,         \"MicroF1\": 0.9616916947594238,         \"MacroF1\": 0.9454054748805116,         \"Memory in Mb\": 29.12161636352539,         \"Time in s\": 847.207258       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9607736311631708,         \"MicroF1\": 0.9607736311631708,         \"MacroF1\": 0.953605417859829,         \"Memory in Mb\": 28.669262886047363,         \"Time in s\": 1081.626023       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9578328021573916,         \"MicroF1\": 0.9578328021573916,         \"MacroF1\": 0.9463612240153172,         \"Memory in Mb\": 34.20732402801514,         \"Time in s\": 1345.493591       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589926454201024,         \"MicroF1\": 0.9589926454201024,         \"MacroF1\": 0.9613092683363472,         \"Memory in Mb\": 18.652557373046875,         \"Time in s\": 1636.499529       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.960776302349336,         \"MicroF1\": 0.960776302349336,         \"MacroF1\": 0.9605208703626084,         \"Memory in Mb\": 20.81053066253662,         \"Time in s\": 1952.783692       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9615312087497644,         \"MicroF1\": 0.9615312087497644,         \"MacroF1\": 0.960303314983038,         \"Memory in Mb\": 27.91543483734131,         \"Time in s\": 2294.145458       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9616529504465068,         \"MicroF1\": 0.9616529504465068,         \"MacroF1\": 0.9605387671994152,         \"Memory in Mb\": 32.60424041748047,         \"Time in s\": 2660.691575       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9597973525085798,         \"MicroF1\": 0.9597973525085798,         \"MacroF1\": 0.9561203427932812,         \"Memory in Mb\": 39.11091995239258,         \"Time in s\": 3053.193204       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589397885705532,         \"MicroF1\": 0.9589397885705532,         \"MacroF1\": 0.9571591040678328,         \"Memory in Mb\": 29.255366325378414,         \"Time in s\": 3470.643733       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.959913482335977,         \"MicroF1\": 0.959913482335977,         \"MacroF1\": 0.9605956598361812,         \"Memory in Mb\": 31.930577278137207,         \"Time in s\": 3910.602191       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9602342366880022,         \"MicroF1\": 0.9602342366880022,         \"MacroF1\": 0.95986198823556,         \"Memory in Mb\": 26.562703132629395,         \"Time in s\": 4374.151898       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9601341762353244,         \"MicroF1\": 0.9601341762353244,         \"MacroF1\": 0.959651045460586,         \"Memory in Mb\": 31.588034629821777,         \"Time in s\": 4858.190889       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9584507905380562,         \"MicroF1\": 0.9584507905380562,         \"MacroF1\": 0.9567204261955368,         \"Memory in Mb\": 39.12565612792969,         \"Time in s\": 5363.543193       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9579782887825375,         \"MicroF1\": 0.9579782887825375,         \"MacroF1\": 0.957794146577291,         \"Memory in Mb\": 44.816758155822754,         \"Time in s\": 5892.06228       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9579944289693594,         \"MicroF1\": 0.9579944289693594,         \"MacroF1\": 0.9581242571113368,         \"Memory in Mb\": 49.5586576461792,         \"Time in s\": 6445.036349       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9570499840136416,         \"MicroF1\": 0.9570499840136416,         \"MacroF1\": 0.9565283447410108,         \"Memory in Mb\": 50.18536758422852,         \"Time in s\": 7025.065903       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563885200694516,         \"MicroF1\": 0.9563885200694516,         \"MacroF1\": 0.9560487952418978,         \"Memory in Mb\": 54.40623474121094,         \"Time in s\": 7630.795018999999       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9532307088930289,         \"MicroF1\": 0.9532307088930289,         \"MacroF1\": 0.9512518567217172,         \"Memory in Mb\": 66.82855319976807,         \"Time in s\": 8267.200675       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9519185443574998,         \"MicroF1\": 0.9519185443574998,         \"MacroF1\": 0.9512557409849248,         \"Memory in Mb\": 41.17615795135498,         \"Time in s\": 8934.408603       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9528824330458464,         \"MicroF1\": 0.9528824330458464,         \"MacroF1\": 0.953398407731189,         \"Memory in Mb\": 32.87209510803223,         \"Time in s\": 9625.903537       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.953689923837871,         \"MicroF1\": 0.953689923837871,         \"MacroF1\": 0.9540175301991308,         \"Memory in Mb\": 28.078542709350582,         \"Time in s\": 10339.782646       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9542726734849124,         \"MicroF1\": 0.9542726734849124,         \"MacroF1\": 0.9545119777330118,         \"Memory in Mb\": 20.280012130737305,         \"Time in s\": 11070.86282       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.955470218155078,         \"MicroF1\": 0.955470218155078,         \"MacroF1\": 0.9559406438939212,         \"Memory in Mb\": 21.54300308227539,         \"Time in s\": 11820.445116       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9559579346880684,         \"MicroF1\": 0.9559579346880684,         \"MacroF1\": 0.9561632451269844,         \"Memory in Mb\": 26.89114284515381,         \"Time in s\": 12588.394717       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9558789735733436,         \"MicroF1\": 0.9558789735733436,         \"MacroF1\": 0.9559075747932771,         \"Memory in Mb\": 21.382742881774902,         \"Time in s\": 13375.587705000002       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563247418851668,         \"MicroF1\": 0.9563247418851668,         \"MacroF1\": 0.9565051554876024,         \"Memory in Mb\": 21.864919662475582,         \"Time in s\": 14180.68998       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.956960565207988,         \"MicroF1\": 0.956960565207988,         \"MacroF1\": 0.9571856017401092,         \"Memory in Mb\": 25.72835636138916,         \"Time in s\": 15004.794290000002       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566496253239022,         \"MicroF1\": 0.9566496253239022,         \"MacroF1\": 0.9566382966080724,         \"Memory in Mb\": 21.764866828918457,         \"Time in s\": 15848.339318000002       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.957241097569279,         \"MicroF1\": 0.9572410975692792,         \"MacroF1\": 0.957426459656079,         \"Memory in Mb\": 25.14582061767578,         \"Time in s\": 16708.048820000004       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9580655846306724,         \"MicroF1\": 0.9580655846306724,         \"MacroF1\": 0.958277362015896,         \"Memory in Mb\": 26.658535957336422,         \"Time in s\": 17588.164126000003       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9584596529703928,         \"MicroF1\": 0.9584596529703928,         \"MacroF1\": 0.9585840009788792,         \"Memory in Mb\": 30.76789283752441,         \"Time in s\": 18489.703328000003       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.958079316196342,         \"MicroF1\": 0.958079316196342,         \"MacroF1\": 0.9580713134265896,         \"Memory in Mb\": 27.786094665527344,         \"Time in s\": 19412.324967000004       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.958514614866107,         \"MicroF1\": 0.958514614866107,         \"MacroF1\": 0.9586173296332884,         \"Memory in Mb\": 25.79348850250244,         \"Time in s\": 20355.979039000005       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9577330065164106,         \"MicroF1\": 0.9577330065164106,         \"MacroF1\": 0.9576699214368118,         \"Memory in Mb\": 33.630208015441895,         \"Time in s\": 21321.132478000007       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9576305806828128,         \"MicroF1\": 0.9576305806828128,         \"MacroF1\": 0.9576693803774444,         \"Memory in Mb\": 33.920249938964844,         \"Time in s\": 22315.653729000005       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.956506868836573,         \"MicroF1\": 0.956506868836573,         \"MacroF1\": 0.9564470129227676,         \"Memory in Mb\": 31.50515556335449,         \"Time in s\": 23335.132930000003       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563812600969306,         \"MicroF1\": 0.9563812600969306,         \"MacroF1\": 0.9564135249623557,         \"Memory in Mb\": 19.79563045501709,         \"Time in s\": 24372.247222       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9569148646440436,         \"MicroF1\": 0.9569148646440436,         \"MacroF1\": 0.9569804233582648,         \"Memory in Mb\": 23.70892333984375,         \"Time in s\": 25428.663478       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9574252677572336,         \"MicroF1\": 0.9574252677572336,         \"MacroF1\": 0.957475477736454,         \"Memory in Mb\": 21.893744468688965,         \"Time in s\": 26504.246634000003       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568187744458932,         \"MicroF1\": 0.9568187744458932,         \"MacroF1\": 0.956806677474395,         \"Memory in Mb\": 28.04871368408203,         \"Time in s\": 27598.635240000003       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9567992646683348,         \"MicroF1\": 0.9567992646683348,         \"MacroF1\": 0.9568012672257532,         \"Memory in Mb\": 32.11082458496094,         \"Time in s\": 28712.463090000005       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9565304386974138,         \"MicroF1\": 0.9565304386974138,         \"MacroF1\": 0.9565268274864178,         \"Memory in Mb\": 40.13526153564453,         \"Time in s\": 29849.822929000005       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9559292122162852,         \"MicroF1\": 0.9559292122162852,         \"MacroF1\": 0.9559196349550496,         \"Memory in Mb\": 39.63601016998291,         \"Time in s\": 31009.846621000004       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5111111111111111,         \"MicroF1\": 0.5111111111111111,         \"MacroF1\": 0.4093857832988268,         \"Memory in Mb\": 0.0911636352539062,         \"Time in s\": 0.155273       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6043956043956044,         \"MicroF1\": 0.6043956043956044,         \"MacroF1\": 0.5940974230447915,         \"Memory in Mb\": 0.16827392578125,         \"Time in s\": 0.72683       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6715328467153284,         \"MicroF1\": 0.6715328467153284,         \"MacroF1\": 0.6806196928151186,         \"Memory in Mb\": 0.245431900024414,         \"Time in s\": 1.742293       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7049180327868853,         \"MicroF1\": 0.7049180327868853,         \"MacroF1\": 0.7184732466987995,         \"Memory in Mb\": 0.3220462799072265,         \"Time in s\": 3.340711       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.74235807860262,         \"MicroF1\": 0.74235807860262,         \"MacroF1\": 0.7523809662907407,         \"Memory in Mb\": 0.3991765975952148,         \"Time in s\": 5.610709       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7490909090909091,         \"MicroF1\": 0.7490909090909091,         \"MacroF1\": 0.7611097615339608,         \"Memory in Mb\": 0.4767560958862304,         \"Time in s\": 8.7745       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7663551401869159,         \"MicroF1\": 0.766355140186916,         \"MacroF1\": 0.7725898650917747,         \"Memory in Mb\": 0.5538606643676758,         \"Time in s\": 12.918764       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.784741144414169,         \"MicroF1\": 0.7847411444141691,         \"MacroF1\": 0.7844949397573193,         \"Memory in Mb\": 0.6304874420166016,         \"Time in s\": 18.189003       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7990314769975787,         \"MicroF1\": 0.7990314769975787,         \"MacroF1\": 0.7976353129150817,         \"Memory in Mb\": 0.7076187133789062,         \"Time in s\": 24.731741       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7952069716775599,         \"MicroF1\": 0.7952069716775599,         \"MacroF1\": 0.7930763833747545,         \"Memory in Mb\": 0.7847471237182617,         \"Time in s\": 32.681045       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7960396039603961,         \"MicroF1\": 0.7960396039603961,         \"MacroF1\": 0.7941234022368324,         \"Memory in Mb\": 3.003793716430664,         \"Time in s\": 61.21191       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8021778584392014,         \"MicroF1\": 0.8021778584392014,         \"MacroF1\": 0.8007250644998717,         \"Memory in Mb\": 3.2264842987060547,         \"Time in s\": 91.162029       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8090452261306532,         \"MicroF1\": 0.8090452261306531,         \"MacroF1\": 0.8095532779239047,         \"Memory in Mb\": 3.4499826431274414,         \"Time in s\": 122.67972799999998       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8164852255054432,         \"MicroF1\": 0.8164852255054433,         \"MacroF1\": 0.8176018556357175,         \"Memory in Mb\": 3.6760778427124015,         \"Time in s\": 155.77468699999997       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8214804063860668,         \"MicroF1\": 0.8214804063860668,         \"MacroF1\": 0.8221151176242331,         \"Memory in Mb\": 3.8941650390625,         \"Time in s\": 190.537277       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8272108843537415,         \"MicroF1\": 0.8272108843537415,         \"MacroF1\": 0.8281233770721121,         \"Memory in Mb\": 4.128121376037598,         \"Time in s\": 227.085503       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8361075544174136,         \"MicroF1\": 0.8361075544174136,         \"MacroF1\": 0.8364659566156888,         \"Memory in Mb\": 4.367749214172363,         \"Time in s\": 265.419762       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8403869407496977,         \"MicroF1\": 0.8403869407496977,         \"MacroF1\": 0.8412749002251585,         \"Memory in Mb\": 4.601743698120117,         \"Time in s\": 305.543518       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.845360824742268,         \"MicroF1\": 0.845360824742268,         \"MacroF1\": 0.8465057584066101,         \"Memory in Mb\": 4.840575218200684,         \"Time in s\": 347.501906       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8487486398258978,         \"MicroF1\": 0.8487486398258978,         \"MacroF1\": 0.8489576083149123,         \"Memory in Mb\": 5.074535369873047,         \"Time in s\": 391.430234       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8538860103626943,         \"MicroF1\": 0.8538860103626943,         \"MacroF1\": 0.8530581393966605,         \"Memory in Mb\": 5.3079938888549805,         \"Time in s\": 437.316456       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8585558852621167,         \"MicroF1\": 0.8585558852621167,         \"MacroF1\": 0.8570252804249208,         \"Memory in Mb\": 5.479596138000488,         \"Time in s\": 485.23685       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8628192999053926,         \"MicroF1\": 0.8628192999053927,         \"MacroF1\": 0.8611045332429007,         \"Memory in Mb\": 5.435150146484375,         \"Time in s\": 535.278485       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8631006346328196,         \"MicroF1\": 0.8631006346328196,         \"MacroF1\": 0.8616288881212748,         \"Memory in Mb\": 5.355225563049316,         \"Time in s\": 587.436372       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8668407310704961,         \"MicroF1\": 0.8668407310704961,         \"MacroF1\": 0.8650902600877293,         \"Memory in Mb\": 5.281754493713379,         \"Time in s\": 641.538536       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8719665271966527,         \"MicroF1\": 0.8719665271966527,         \"MacroF1\": 0.8702683106604537,         \"Memory in Mb\": 5.235520362854004,         \"Time in s\": 697.554251       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8759065269943593,         \"MicroF1\": 0.8759065269943593,         \"MacroF1\": 0.8740479640614998,         \"Memory in Mb\": 5.142333984375,         \"Time in s\": 755.471933       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8787878787878788,         \"MicroF1\": 0.8787878787878788,         \"MacroF1\": 0.8772603222806128,         \"Memory in Mb\": 5.092559814453125,         \"Time in s\": 815.138635       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8777194298574643,         \"MicroF1\": 0.8777194298574643,         \"MacroF1\": 0.8760741143565023,         \"Memory in Mb\": 5.055940628051758,         \"Time in s\": 876.491339       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8796229151559101,         \"MicroF1\": 0.8796229151559101,         \"MacroF1\": 0.8783130803325612,         \"Memory in Mb\": 4.964084625244141,         \"Time in s\": 939.483975       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8785964912280702,         \"MicroF1\": 0.8785964912280702,         \"MacroF1\": 0.8768931648451159,         \"Memory in Mb\": 4.951287269592285,         \"Time in s\": 1004.010152       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8769544527532291,         \"MicroF1\": 0.8769544527532291,         \"MacroF1\": 0.8748964905672628,         \"Memory in Mb\": 4.969002723693848,         \"Time in s\": 1070.168576       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8727752142386289,         \"MicroF1\": 0.8727752142386289,         \"MacroF1\": 0.8705110235515202,         \"Memory in Mb\": 5.101251602172852,         \"Time in s\": 1138.304608       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8688419705694178,         \"MicroF1\": 0.8688419705694178,         \"MacroF1\": 0.8667015278861958,         \"Memory in Mb\": 5.262187957763672,         \"Time in s\": 1208.4659419999998       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8651336233685519,         \"MicroF1\": 0.8651336233685519,         \"MacroF1\": 0.8631350462642483,         \"Memory in Mb\": 5.320252418518066,         \"Time in s\": 1280.5305069999995       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8640483383685801,         \"MicroF1\": 0.8640483383685801,         \"MacroF1\": 0.8620479268968886,         \"Memory in Mb\": 5.35189151763916,         \"Time in s\": 1354.3819709999998       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8647854203409759,         \"MicroF1\": 0.8647854203409759,         \"MacroF1\": 0.8635043959538364,         \"Memory in Mb\": 5.359102249145508,         \"Time in s\": 1430.0286029999995       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.866056096164854,         \"MicroF1\": 0.866056096164854,         \"MacroF1\": 0.864439618601765,         \"Memory in Mb\": 5.402237892150879,         \"Time in s\": 1507.5136589999995       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8683770217512549,         \"MicroF1\": 0.8683770217512549,         \"MacroF1\": 0.8664209902402824,         \"Memory in Mb\": 5.3993330001831055,         \"Time in s\": 1586.7199259999998       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8694942903752039,         \"MicroF1\": 0.8694942903752039,         \"MacroF1\": 0.867597342266498,         \"Memory in Mb\": 5.4049272537231445,         \"Time in s\": 1667.658732       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8710875331564987,         \"MicroF1\": 0.8710875331564986,         \"MacroF1\": 0.8694766742923737,         \"Memory in Mb\": 5.4121294021606445,         \"Time in s\": 1750.3169159999998       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8705334023821854,         \"MicroF1\": 0.8705334023821854,         \"MacroF1\": 0.8686918451193435,         \"Memory in Mb\": 5.405803680419922,         \"Time in s\": 1834.60998       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8715225088517956,         \"MicroF1\": 0.8715225088517956,         \"MacroF1\": 0.8698703895904014,         \"Memory in Mb\": 5.395906448364258,         \"Time in s\": 1920.517128       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8729609490855166,         \"MicroF1\": 0.8729609490855166,         \"MacroF1\": 0.870902914954928,         \"Memory in Mb\": 5.386837959289551,         \"Time in s\": 2008.106455       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8733687771870469,         \"MicroF1\": 0.8733687771870469,         \"MacroF1\": 0.8714525187304558,         \"Memory in Mb\": 5.375288963317871,         \"Time in s\": 2097.403794       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.875177304964539,         \"MicroF1\": 0.875177304964539,         \"MacroF1\": 0.8730645404016979,         \"Memory in Mb\": 5.353263854980469,         \"Time in s\": 2188.326937       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8745950948634891,         \"MicroF1\": 0.8745950948634891,         \"MacroF1\": 0.872417325547954,         \"Memory in Mb\": 5.322790145874023,         \"Time in s\": 2280.882375       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8753964657906661,         \"MicroF1\": 0.8753964657906661,         \"MacroF1\": 0.8732500176589647,         \"Memory in Mb\": 5.30323600769043,         \"Time in s\": 2374.975797       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8748335552596538,         \"MicroF1\": 0.8748335552596538,         \"MacroF1\": 0.8732733602208504,         \"Memory in Mb\": 5.278659820556641,         \"Time in s\": 2470.732765       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8742931709438887,         \"MicroF1\": 0.8742931709438887,         \"MacroF1\": 0.8727466012343671,         \"Memory in Mb\": 5.262259483337402,         \"Time in s\": 2568.119206       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8735383282806409,         \"MicroF1\": 0.8735383282806409,         \"MacroF1\": 0.8721361121313428,         \"Memory in Mb\": 5.268708229064941,         \"Time in s\": 2666.293295       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6597156398104266,         \"MicroF1\": 0.6597156398104266,         \"MacroF1\": 0.5853273709738578,         \"Memory in Mb\": 6.371035575866699,         \"Time in s\": 65.756564       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6807200378967314,         \"MicroF1\": 0.6807200378967314,         \"MacroF1\": 0.5992086579995298,         \"Memory in Mb\": 6.278300285339356,         \"Time in s\": 182.184591       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6842437638143354,         \"MicroF1\": 0.6842437638143354,         \"MacroF1\": 0.6001715208792017,         \"Memory in Mb\": 6.298460006713867,         \"Time in s\": 341.998975       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6848212171442103,         \"MicroF1\": 0.6848212171442103,         \"MacroF1\": 0.6051604277089342,         \"Memory in Mb\": 6.265153884887695,         \"Time in s\": 541.5068689999999       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6872513733661678,         \"MicroF1\": 0.6872513733661678,         \"MacroF1\": 0.611100448555976,         \"Memory in Mb\": 6.2555742263793945,         \"Time in s\": 777.52113       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6842936069455406,         \"MicroF1\": 0.6842936069455406,         \"MacroF1\": 0.6118525331169307,         \"Memory in Mb\": 6.314010620117188,         \"Time in s\": 1048.588472       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6852929238262752,         \"MicroF1\": 0.6852929238262752,         \"MacroF1\": 0.6157762907660722,         \"Memory in Mb\": 6.288516998291016,         \"Time in s\": 1352.458798       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6828459808215934,         \"MicroF1\": 0.6828459808215934,         \"MacroF1\": 0.6148503710479976,         \"Memory in Mb\": 6.31680965423584,         \"Time in s\": 1687.096094       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6851520572450805,         \"MicroF1\": 0.6851520572450805,         \"MacroF1\": 0.6155258331015067,         \"Memory in Mb\": 6.223039627075195,         \"Time in s\": 2051.649807       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6861445212614831,         \"MicroF1\": 0.6861445212614831,         \"MacroF1\": 0.6169474950376627,         \"Memory in Mb\": 6.253497123718262,         \"Time in s\": 2444.130945       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6873009040034438,         \"MicroF1\": 0.6873009040034438,         \"MacroF1\": 0.6200568175672779,         \"Memory in Mb\": 6.251482009887695,         \"Time in s\": 2863.128904       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6866072133217583,         \"MicroF1\": 0.6866072133217583,         \"MacroF1\": 0.623883491026523,         \"Memory in Mb\": 6.276742935180664,         \"Time in s\": 3309.082968       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7020470605376266,         \"MicroF1\": 0.7020470605376266,         \"MacroF1\": 0.6991473808978487,         \"Memory in Mb\": 6.26933479309082,         \"Time in s\": 3781.277509       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7077724413177299,         \"MicroF1\": 0.7077724413177299,         \"MacroF1\": 0.7078402863830927,         \"Memory in Mb\": 6.244691848754883,         \"Time in s\": 4278.402760999999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7016857124818486,         \"MicroF1\": 0.7016857124818486,         \"MacroF1\": 0.704840832390747,         \"Memory in Mb\": 6.350223541259766,         \"Time in s\": 4805.403565999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6992009470257473,         \"MicroF1\": 0.6992009470257473,         \"MacroF1\": 0.7048178275842342,         \"Memory in Mb\": 6.243149757385254,         \"Time in s\": 5357.683726999999       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6922734109520361,         \"MicroF1\": 0.6922734109520361,         \"MacroF1\": 0.6995766929659905,         \"Memory in Mb\": 6.218992233276367,         \"Time in s\": 5935.240105999998       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6974272636397116,         \"MicroF1\": 0.6974272636397116,         \"MacroF1\": 0.7006862112488368,         \"Memory in Mb\": 6.24652099609375,         \"Time in s\": 6538.276384999998       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.699845486716842,         \"MicroF1\": 0.699845486716842,         \"MacroF1\": 0.6985118222305657,         \"Memory in Mb\": 6.205791473388672,         \"Time in s\": 7167.459726999999       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7016904209479615,         \"MicroF1\": 0.7016904209479615,         \"MacroF1\": 0.6971610909052677,         \"Memory in Mb\": 6.218420028686523,         \"Time in s\": 7825.840650999999       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7039909808342728,         \"MicroF1\": 0.7039909808342728,         \"MacroF1\": 0.6964197759629052,         \"Memory in Mb\": 6.236072540283203,         \"Time in s\": 8511.079801999998       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7076320433902974,         \"MicroF1\": 0.7076320433902974,         \"MacroF1\": 0.697368621848442,         \"Memory in Mb\": 6.279313087463379,         \"Time in s\": 9222.986801999998       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7098447729237863,         \"MicroF1\": 0.7098447729237863,         \"MacroF1\": 0.6967477548491564,         \"Memory in Mb\": 6.2948198318481445,         \"Time in s\": 9960.927248999997       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7127017322337529,         \"MicroF1\": 0.712701732233753,         \"MacroF1\": 0.6972185032799825,         \"Memory in Mb\": 6.224791526794434,         \"Time in s\": 10724.419586999997       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7145346414636918,         \"MicroF1\": 0.7145346414636918,         \"MacroF1\": 0.6967850611237018,         \"Memory in Mb\": 6.263523101806641,         \"Time in s\": 11512.986172999998       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7156437807321071,         \"MicroF1\": 0.7156437807321071,         \"MacroF1\": 0.6955595874776194,         \"Memory in Mb\": 6.272575378417969,         \"Time in s\": 12326.628602999996       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7130931921012942,         \"MicroF1\": 0.7130931921012942,         \"MacroF1\": 0.6943090782068162,         \"Memory in Mb\": 6.22489070892334,         \"Time in s\": 13165.433758999998       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7117732607298678,         \"MicroF1\": 0.7117732607298677,         \"MacroF1\": 0.6978751959025926,         \"Memory in Mb\": 6.217726707458496,         \"Time in s\": 14028.837757999998       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7122097769650263,         \"MicroF1\": 0.7122097769650264,         \"MacroF1\": 0.7026862643890369,         \"Memory in Mb\": 6.243690490722656,         \"Time in s\": 14916.319239       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7113545250797058,         \"MicroF1\": 0.7113545250797058,         \"MacroF1\": 0.7052714328980031,         \"Memory in Mb\": 6.277059555053711,         \"Time in s\": 15827.904481999998       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7111959676187567,         \"MicroF1\": 0.7111959676187566,         \"MacroF1\": 0.7078689284492299,         \"Memory in Mb\": 6.295280456542969,         \"Time in s\": 16762.400180999997       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7067562368678051,         \"MicroF1\": 0.7067562368678051,         \"MacroF1\": 0.704703743720216,         \"Memory in Mb\": 6.183221817016602,         \"Time in s\": 17721.950532       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7030734353028956,         \"MicroF1\": 0.7030734353028956,         \"MacroF1\": 0.7010614031639846,         \"Memory in Mb\": 6.343389511108398,         \"Time in s\": 18710.094933       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6998022449377489,         \"MicroF1\": 0.6998022449377489,         \"MacroF1\": 0.6976694331042329,         \"Memory in Mb\": 6.273009300231934,         \"Time in s\": 19725.980479       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6967179847939609,         \"MicroF1\": 0.6967179847939609,         \"MacroF1\": 0.6945045780432343,         \"Memory in Mb\": 6.264690399169922,         \"Time in s\": 20767.047301       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6941470472182033,         \"MicroF1\": 0.6941470472182033,         \"MacroF1\": 0.6917813776610243,         \"Memory in Mb\": 6.265054702758789,         \"Time in s\": 21835.556239       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.691996621535154,         \"MicroF1\": 0.691996621535154,         \"MacroF1\": 0.6898060776768534,         \"Memory in Mb\": 6.200959205627441,         \"Time in s\": 22932.108791       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6904328756199068,         \"MicroF1\": 0.6904328756199068,         \"MacroF1\": 0.6882031611963276,         \"Memory in Mb\": 6.413609504699707,         \"Time in s\": 24054.967875       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6916446106403128,         \"MicroF1\": 0.6916446106403128,         \"MacroF1\": 0.6892941373261507,         \"Memory in Mb\": 6.310104370117188,         \"Time in s\": 25204.026441       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.692535334643339,         \"MicroF1\": 0.692535334643339,         \"MacroF1\": 0.6900712004452627,         \"Memory in Mb\": 6.22797966003418,         \"Time in s\": 26378.286294       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6935904838895947,         \"MicroF1\": 0.6935904838895947,         \"MacroF1\": 0.6909354899104013,         \"Memory in Mb\": 6.220904350280762,         \"Time in s\": 27574.213319       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6941895334941715,         \"MicroF1\": 0.6941895334941715,         \"MacroF1\": 0.691322114366645,         \"Memory in Mb\": 6.22946834564209,         \"Time in s\": 28791.926615000004       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6950690422181602,         \"MicroF1\": 0.6950690422181602,         \"MacroF1\": 0.6917362410920441,         \"Memory in Mb\": 6.2850341796875,         \"Time in s\": 30030.670852000003       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6964466349568473,         \"MicroF1\": 0.6964466349568473,         \"MacroF1\": 0.6926338572817136,         \"Memory in Mb\": 6.233500480651856,         \"Time in s\": 31290.345387       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6963530377322755,         \"MicroF1\": 0.6963530377322755,         \"MacroF1\": 0.6929015597977773,         \"Memory in Mb\": 6.243911743164063,         \"Time in s\": 32571.46119       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7006073082861555,         \"MicroF1\": 0.7006073082861555,         \"MacroF1\": 0.697843135408715,         \"Memory in Mb\": 6.247167587280273,         \"Time in s\": 33874.398319       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7046805424029337,         \"MicroF1\": 0.7046805424029337,         \"MacroF1\": 0.7023003034160373,         \"Memory in Mb\": 6.246943473815918,         \"Time in s\": 35198.416197       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7083867658373942,         \"MicroF1\": 0.7083867658373942,         \"MacroF1\": 0.7061355873839065,         \"Memory in Mb\": 6.210485458374023,         \"Time in s\": 36536.506394       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7126567844925884,         \"MicroF1\": 0.7126567844925883,         \"MacroF1\": 0.7104085577951368,         \"Memory in Mb\": 6.270394325256348,         \"Time in s\": 37890.628371       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7128544101214038,         \"MicroF1\": 0.7128544101214038,         \"MacroF1\": 0.7110869129037599,         \"Memory in Mb\": 6.247260093688965,         \"Time in s\": 39264.666928       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7131152194069673,         \"MicroF1\": 0.7131152194069672,         \"MacroF1\": 0.7113808258412672,         \"Memory in Mb\": 6.272693634033203,         \"Time in s\": 40639.937472       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 1.0294876098632812,         \"Time in s\": 8.610537       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9251533742331288,         \"MicroF1\": 0.9251533742331288,         \"MacroF1\": 0.8588670451436246,         \"Memory in Mb\": 5.3865966796875,         \"Time in s\": 60.879099       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9247751430907604,         \"MicroF1\": 0.9247751430907604,         \"MacroF1\": 0.888226412135106,         \"Memory in Mb\": 6.246823310852051,         \"Time in s\": 137.71097600000002       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.927038626609442,         \"MicroF1\": 0.927038626609442,         \"MacroF1\": 0.893336805209695,         \"Memory in Mb\": 6.212030410766602,         \"Time in s\": 236.990146       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9298675821481118,         \"MicroF1\": 0.9298675821481118,         \"MacroF1\": 0.911424130088645,         \"Memory in Mb\": 6.329436302185059,         \"Time in s\": 359.011082       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9239885574172456,         \"MicroF1\": 0.9239885574172456,         \"MacroF1\": 0.912155547295492,         \"Memory in Mb\": 6.208271026611328,         \"Time in s\": 503.2068       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9169877408056042,         \"MicroF1\": 0.9169877408056042,         \"MacroF1\": 0.8816257260944811,         \"Memory in Mb\": 6.284844398498535,         \"Time in s\": 667.387655       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.908979466748391,         \"MicroF1\": 0.908979466748391,         \"MacroF1\": 0.9011431783951356,         \"Memory in Mb\": 6.232160568237305,         \"Time in s\": 850.70223       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.913375102152002,         \"MicroF1\": 0.913375102152002,         \"MacroF1\": 0.9125908871445696,         \"Memory in Mb\": 6.234919548034668,         \"Time in s\": 1054.346916       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9141946555528316,         \"MicroF1\": 0.9141946555528316,         \"MacroF1\": 0.9054789816810688,         \"Memory in Mb\": 6.308258056640625,         \"Time in s\": 1277.35851       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9057276576777356,         \"MicroF1\": 0.9057276576777356,         \"MacroF1\": 0.9087691557812896,         \"Memory in Mb\": 6.274983406066895,         \"Time in s\": 1518.241674       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.908682328907048,         \"MicroF1\": 0.908682328907048,         \"MacroF1\": 0.9101970481905532,         \"Memory in Mb\": 6.210176467895508,         \"Time in s\": 1774.8436390000002       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9092966245521404,         \"MicroF1\": 0.9092966245521404,         \"MacroF1\": 0.9045962329696908,         \"Memory in Mb\": 6.311163902282715,         \"Time in s\": 2048.2991580000003       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9110488530905272,         \"MicroF1\": 0.9110488530905272,         \"MacroF1\": 0.9114244990736602,         \"Memory in Mb\": 6.304790496826172,         \"Time in s\": 2337.0378760000003       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9089720542572316,         \"MicroF1\": 0.9089720542572316,         \"MacroF1\": 0.9032533666541098,         \"Memory in Mb\": 6.217576026916504,         \"Time in s\": 2640.3521840000003       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.907767734027884,         \"MicroF1\": 0.907767734027884,         \"MacroF1\": 0.9071900335968284,         \"Memory in Mb\": 6.258184432983398,         \"Time in s\": 2958.617579       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9105984138428262,         \"MicroF1\": 0.9105984138428262,         \"MacroF1\": 0.9126270814361048,         \"Memory in Mb\": 6.1720380783081055,         \"Time in s\": 3289.9253120000003       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9120250578782514,         \"MicroF1\": 0.9120250578782514,         \"MacroF1\": 0.9125633522308232,         \"Memory in Mb\": 6.2164154052734375,         \"Time in s\": 3633.832057000001       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9133015094826474,         \"MicroF1\": 0.9133015094826474,         \"MacroF1\": 0.9136330015220732,         \"Memory in Mb\": 6.260525703430176,         \"Time in s\": 3992.050824       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9148179924010296,         \"MicroF1\": 0.9148179924010296,         \"MacroF1\": 0.9154195917586072,         \"Memory in Mb\": 6.291139602661133,         \"Time in s\": 4363.477247000001       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9166569394186996,         \"MicroF1\": 0.9166569394186996,         \"MacroF1\": 0.917708642296068,         \"Memory in Mb\": 6.216147422790527,         \"Time in s\": 4747.269196000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9179944289693592,         \"MicroF1\": 0.9179944289693592,         \"MacroF1\": 0.9193727618453186,         \"Memory in Mb\": 6.204837799072266,         \"Time in s\": 5142.613476000001       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9180432697431524,         \"MicroF1\": 0.9180432697431524,         \"MacroF1\": 0.9181590265081532,         \"Memory in Mb\": 6.197464942932129,         \"Time in s\": 5549.1165660000015       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9166581554488816,         \"MicroF1\": 0.9166581554488816,         \"MacroF1\": 0.9168210748678532,         \"Memory in Mb\": 6.232473373413086,         \"Time in s\": 5968.023607000002       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9154819099911756,         \"MicroF1\": 0.9154819099911756,         \"MacroF1\": 0.9145218669909496,         \"Memory in Mb\": 6.197787284851074,         \"Time in s\": 6399.011787000002       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9126048835674556,         \"MicroF1\": 0.9126048835674556,         \"MacroF1\": 0.9111025938131312,         \"Memory in Mb\": 6.2185258865356445,         \"Time in s\": 6842.990199000003       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9106672719019518,         \"MicroF1\": 0.9106672719019518,         \"MacroF1\": 0.911227786024665,         \"Memory in Mb\": 6.261377334594727,         \"Time in s\": 7300.041981000002       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.910356298695614,         \"MicroF1\": 0.910356298695614,         \"MacroF1\": 0.9101104800687124,         \"Memory in Mb\": 6.227293968200684,         \"Time in s\": 7769.979179000002       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9092215366410278,         \"MicroF1\": 0.9092215366410278,         \"MacroF1\": 0.909418612123662,         \"Memory in Mb\": 6.287784576416016,         \"Time in s\": 8252.975566000001       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9110221423318898,         \"MicroF1\": 0.9110221423318898,         \"MacroF1\": 0.9118339797691072,         \"Memory in Mb\": 6.31197452545166,         \"Time in s\": 8747.696417000001       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.912627500593026,         \"MicroF1\": 0.912627500593026,         \"MacroF1\": 0.9131272841889786,         \"Memory in Mb\": 6.279851913452148,         \"Time in s\": 9255.008595       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9129069322098812,         \"MicroF1\": 0.9129069322098812,         \"MacroF1\": 0.913006147591119,         \"Memory in Mb\": 6.346117973327637,         \"Time in s\": 9774.692327       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9136150932184506,         \"MicroF1\": 0.9136150932184506,         \"MacroF1\": 0.9138210444048112,         \"Memory in Mb\": 6.238006591796875,         \"Time in s\": 10306.725428000002       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9137769447047798,         \"MicroF1\": 0.9137769447047795,         \"MacroF1\": 0.913931693448659,         \"Memory in Mb\": 6.288792610168457,         \"Time in s\": 10851.686584       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.91217872400028,         \"MicroF1\": 0.91217872400028,         \"MacroF1\": 0.9118234284090696,         \"Memory in Mb\": 6.252389907836914,         \"Time in s\": 11409.551357       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9133247089262612,         \"MicroF1\": 0.9133247089262612,         \"MacroF1\": 0.9136581918124824,         \"Memory in Mb\": 6.268362998962402,         \"Time in s\": 11980.223294       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9134150380920836,         \"MicroF1\": 0.9134150380920836,         \"MacroF1\": 0.9134700562544148,         \"Memory in Mb\": 6.304409027099609,         \"Time in s\": 12563.090191       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9127910726956072,         \"MicroF1\": 0.9127910726956072,         \"MacroF1\": 0.9127632118282708,         \"Memory in Mb\": 6.222077369689941,         \"Time in s\": 13158.500652       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9127019043429074,         \"MicroF1\": 0.9127019043429074,         \"MacroF1\": 0.9127365496247324,         \"Memory in Mb\": 6.263586044311523,         \"Time in s\": 13766.752283999998       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9113303511244562,         \"MicroF1\": 0.9113303511244562,         \"MacroF1\": 0.9110956080213112,         \"Memory in Mb\": 6.256609916687012,         \"Time in s\": 14388.121762       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.910384408441442,         \"MicroF1\": 0.910384408441442,         \"MacroF1\": 0.910332436025812,         \"Memory in Mb\": 6.208023071289063,         \"Time in s\": 15023.088924       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9113510358914504,         \"MicroF1\": 0.9113510358914504,         \"MacroF1\": 0.9114963483082136,         \"Memory in Mb\": 6.223179817199707,         \"Time in s\": 15670.063673       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9113606566721768,         \"MicroF1\": 0.9113606566721768,         \"MacroF1\": 0.9113826667045092,         \"Memory in Mb\": 6.23558235168457,         \"Time in s\": 16331.265293       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9114255473232687,         \"MicroF1\": 0.911425547323269,         \"MacroF1\": 0.9114384409485988,         \"Memory in Mb\": 6.255133628845215,         \"Time in s\": 17006.553944       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9126858761370444,         \"MicroF1\": 0.9126858761370444,         \"MacroF1\": 0.9127656000580756,         \"Memory in Mb\": 6.270404815673828,         \"Time in s\": 17693.792261       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9125059945649278,         \"MicroF1\": 0.9125059945649276,         \"MacroF1\": 0.9124701420883568,         \"Memory in Mb\": 6.180520057678223,         \"Time in s\": 18393.849908       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.912594524119948,         \"MicroF1\": 0.912594524119948,         \"MacroF1\": 0.9125632790621416,         \"Memory in Mb\": 6.265439987182617,         \"Time in s\": 19106.227212       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.913547464637696,         \"MicroF1\": 0.913547464637696,         \"MacroF1\": 0.9135225066457016,         \"Memory in Mb\": 6.303133964538574,         \"Time in s\": 19831.701162       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9111099994997748,         \"MicroF1\": 0.9111099994997748,         \"MacroF1\": 0.910917465793804,         \"Memory in Mb\": 6.225028991699219,         \"Time in s\": 20571.760391       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9104858081278494,         \"MicroF1\": 0.9104858081278494,         \"MacroF1\": 0.910327982122686,         \"Memory in Mb\": 6.325108528137207,         \"Time in s\": 21326.45228       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3111111111111111,         \"MicroF1\": 0.3111111111111111,         \"MacroF1\": 0.245764972655729,         \"Memory in Mb\": 4.105147361755371,         \"Time in s\": 2.153154       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4835164835164835,         \"MicroF1\": 0.4835164835164835,         \"MacroF1\": 0.4934752395581889,         \"Memory in Mb\": 4.108363151550293,         \"Time in s\": 6.907408       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5328467153284672,         \"MicroF1\": 0.5328467153284672,         \"MacroF1\": 0.5528821792646677,         \"Memory in Mb\": 4.108027458190918,         \"Time in s\": 14.639156       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5956284153005464,         \"MicroF1\": 0.5956284153005464,         \"MacroF1\": 0.614143164890895,         \"Memory in Mb\": 4.108977317810059,         \"Time in s\": 25.443956       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.62882096069869,         \"MicroF1\": 0.62882096069869,         \"MacroF1\": 0.6441389332893815,         \"Memory in Mb\": 3.881842613220215,         \"Time in s\": 39.254234       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.64,         \"MicroF1\": 0.64,         \"MacroF1\": 0.6559607038460422,         \"Memory in Mb\": 3.996514320373535,         \"Time in s\": 55.768073       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6697819314641744,         \"MicroF1\": 0.6697819314641744,         \"MacroF1\": 0.6706320385346652,         \"Memory in Mb\": 4.112936019897461,         \"Time in s\": 74.877199       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6948228882833788,         \"MicroF1\": 0.6948228882833788,         \"MacroF1\": 0.6897433526546475,         \"Memory in Mb\": 4.112924575805664,         \"Time in s\": 96.687005       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.711864406779661,         \"MicroF1\": 0.711864406779661,         \"MacroF1\": 0.706570530482581,         \"Memory in Mb\": 4.117301940917969,         \"Time in s\": 121.290167       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7145969498910676,         \"MicroF1\": 0.7145969498910676,         \"MacroF1\": 0.7071122267088654,         \"Memory in Mb\": 4.116390228271484,         \"Time in s\": 148.551711       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7247524752475247,         \"MicroF1\": 0.7247524752475247,         \"MacroF1\": 0.7147973207987898,         \"Memory in Mb\": 4.115703582763672,         \"Time in s\": 178.365171       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7295825771324864,         \"MicroF1\": 0.7295825771324864,         \"MacroF1\": 0.7210771168277493,         \"Memory in Mb\": 4.115436553955078,         \"Time in s\": 210.796119       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7336683417085427,         \"MicroF1\": 0.7336683417085426,         \"MacroF1\": 0.7250288715672424,         \"Memory in Mb\": 4.115207672119141,         \"Time in s\": 245.953277       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7325038880248833,         \"MicroF1\": 0.7325038880248833,         \"MacroF1\": 0.7258924883659029,         \"Memory in Mb\": 4.118658065795898,         \"Time in s\": 283.80303000000004       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.737300435413643,         \"MicroF1\": 0.737300435413643,         \"MacroF1\": 0.7302536378735861,         \"Memory in Mb\": 4.118425369262695,         \"Time in s\": 324.296282       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7387755102040816,         \"MicroF1\": 0.7387755102040816,         \"MacroF1\": 0.7329631379486719,         \"Memory in Mb\": 4.118097305297852,         \"Time in s\": 367.43284       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7439180537772087,         \"MicroF1\": 0.7439180537772088,         \"MacroF1\": 0.7387105187530085,         \"Memory in Mb\": 4.117616653442383,         \"Time in s\": 413.28289       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7460701330108828,         \"MicroF1\": 0.7460701330108827,         \"MacroF1\": 0.7425025596154723,         \"Memory in Mb\": 4.117326736450195,         \"Time in s\": 461.953497       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7514318442153494,         \"MicroF1\": 0.7514318442153494,         \"MacroF1\": 0.7467163857842193,         \"Memory in Mb\": 4.117303848266602,         \"Time in s\": 513.2937440000001       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.750816104461371,         \"MicroF1\": 0.750816104461371,         \"MacroF1\": 0.7453933609147309,         \"Memory in Mb\": 4.117105484008789,         \"Time in s\": 567.431634       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7512953367875648,         \"MicroF1\": 0.7512953367875648,         \"MacroF1\": 0.7451117895470661,         \"Memory in Mb\": 4.116701126098633,         \"Time in s\": 624.2209760000001       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7507418397626113,         \"MicroF1\": 0.7507418397626113,         \"MacroF1\": 0.744963080481548,         \"Memory in Mb\": 4.116399765014648,         \"Time in s\": 683.8404210000001       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7511825922421949,         \"MicroF1\": 0.7511825922421949,         \"MacroF1\": 0.7446315489945475,         \"Memory in Mb\": 4.117582321166992,         \"Time in s\": 746.2097300000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7533998186763372,         \"MicroF1\": 0.7533998186763373,         \"MacroF1\": 0.7466082689908061,         \"Memory in Mb\": 4.117956161499023,         \"Time in s\": 811.1743250000002       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7563098346388164,         \"MicroF1\": 0.7563098346388164,         \"MacroF1\": 0.7491651771194966,         \"Memory in Mb\": 4.117490768432617,         \"Time in s\": 878.6809830000002       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7589958158995815,         \"MicroF1\": 0.7589958158995815,         \"MacroF1\": 0.7526420027035883,         \"Memory in Mb\": 4.117303848266602,         \"Time in s\": 948.8627130000002       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.75825946817083,         \"MicroF1\": 0.7582594681708301,         \"MacroF1\": 0.7524016178277559,         \"Memory in Mb\": 4.11713981628418,         \"Time in s\": 1021.5806660000002       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7637917637917638,         \"MicroF1\": 0.7637917637917638,         \"MacroF1\": 0.75666252908711,         \"Memory in Mb\": 4.117353439331055,         \"Time in s\": 1096.9757510000002       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7636909227306826,         \"MicroF1\": 0.7636909227306825,         \"MacroF1\": 0.7569484848610158,         \"Memory in Mb\": 4.11726188659668,         \"Time in s\": 1175.015685       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7650471356055112,         \"MicroF1\": 0.7650471356055112,         \"MacroF1\": 0.7590436403579585,         \"Memory in Mb\": 4.11729621887207,         \"Time in s\": 1255.693831       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.767719298245614,         \"MicroF1\": 0.767719298245614,         \"MacroF1\": 0.761211289695921,         \"Memory in Mb\": 4.117136001586914,         \"Time in s\": 1338.965745       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7722637661454793,         \"MicroF1\": 0.7722637661454793,         \"MacroF1\": 0.764056696643358,         \"Memory in Mb\": 4.117197036743164,         \"Time in s\": 1424.822234       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7732366512854317,         \"MicroF1\": 0.7732366512854317,         \"MacroF1\": 0.764234133414765,         \"Memory in Mb\": 4.117246627807617,         \"Time in s\": 1513.291691       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7735124760076776,         \"MicroF1\": 0.7735124760076776,         \"MacroF1\": 0.7653316001442944,         \"Memory in Mb\": 4.117277145385742,         \"Time in s\": 1604.2857379999998       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7737725295214419,         \"MicroF1\": 0.7737725295214419,         \"MacroF1\": 0.7647353044337893,         \"Memory in Mb\": 4.11713981628418,         \"Time in s\": 1697.9838939999995       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7734138972809668,         \"MicroF1\": 0.7734138972809667,         \"MacroF1\": 0.7645730180903108,         \"Memory in Mb\": 4.116628646850586,         \"Time in s\": 1794.3974679999997       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7724867724867724,         \"MicroF1\": 0.7724867724867724,         \"MacroF1\": 0.7656182355666586,         \"Memory in Mb\": 4.116819381713867,         \"Time in s\": 1893.301291       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7750429307384087,         \"MicroF1\": 0.7750429307384087,         \"MacroF1\": 0.7677424040514297,         \"Memory in Mb\": 4.116933822631836,         \"Time in s\": 1994.662727       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7763524818739542,         \"MicroF1\": 0.7763524818739542,         \"MacroF1\": 0.7677176136548693,         \"Memory in Mb\": 4.116861343383789,         \"Time in s\": 2098.663831       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7775965198477434,         \"MicroF1\": 0.7775965198477434,         \"MacroF1\": 0.7691578918725354,         \"Memory in Mb\": 4.11646842956543,         \"Time in s\": 2205.226802       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7761273209549071,         \"MicroF1\": 0.7761273209549071,         \"MacroF1\": 0.7681560201617949,         \"Memory in Mb\": 4.116430282592773,         \"Time in s\": 2314.321902       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7762817193164163,         \"MicroF1\": 0.7762817193164163,         \"MacroF1\": 0.7674170460709655,         \"Memory in Mb\": 4.116365432739258,         \"Time in s\": 2425.9767019999995       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7769347496206374,         \"MicroF1\": 0.7769347496206374,         \"MacroF1\": 0.7672843880004774,         \"Memory in Mb\": 4.116201400756836,         \"Time in s\": 2540.253238       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7790410281759763,         \"MicroF1\": 0.7790410281759763,         \"MacroF1\": 0.7681802739952505,         \"Memory in Mb\": 4.116155624389648,         \"Time in s\": 2657.1841359999994       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.778153697438376,         \"MicroF1\": 0.7781536974383759,         \"MacroF1\": 0.767530439166732,         \"Memory in Mb\": 4.116151809692383,         \"Time in s\": 2776.607733       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7787234042553192,         \"MicroF1\": 0.778723404255319,         \"MacroF1\": 0.7673415220519754,         \"Memory in Mb\": 4.116128921508789,         \"Time in s\": 2898.5867789999998       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7797316057380842,         \"MicroF1\": 0.7797316057380842,         \"MacroF1\": 0.7679341969633587,         \"Memory in Mb\": 4.116201400756836,         \"Time in s\": 3023.0016299999997       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7816039873130947,         \"MicroF1\": 0.7816039873130947,         \"MacroF1\": 0.7687944234581563,         \"Memory in Mb\": 4.11619758605957,         \"Time in s\": 3150.038225       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7785175321793165,         \"MicroF1\": 0.7785175321793165,         \"MacroF1\": 0.7657018899401807,         \"Memory in Mb\": 4.116170883178711,         \"Time in s\": 3279.4760369999995       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7777294475859069,         \"MicroF1\": 0.7777294475859068,         \"MacroF1\": 0.7649119672933203,         \"Memory in Mb\": 4.116254806518555,         \"Time in s\": 3411.201767       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7778258986574275,         \"MicroF1\": 0.7778258986574276,         \"MacroF1\": 0.765010539660814,         \"Memory in Mb\": 4.116277694702148,         \"Time in s\": 3543.54869       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360189573459716,         \"MicroF1\": 0.6360189573459716,         \"MacroF1\": 0.5970323052762562,         \"Memory in Mb\": 6.48949146270752,         \"Time in s\": 90.340432       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.62482235907153,         \"MicroF1\": 0.62482235907153,         \"MacroF1\": 0.5890580890213498,         \"Memory in Mb\": 6.490170478820801,         \"Time in s\": 257.284509       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6157246605620461,         \"MicroF1\": 0.6157246605620461,         \"MacroF1\": 0.5802533923244894,         \"Memory in Mb\": 6.491124153137207,         \"Time in s\": 490.807794       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6107032914989344,         \"MicroF1\": 0.6107032914989344,         \"MacroF1\": 0.574850135712032,         \"Memory in Mb\": 6.49120044708252,         \"Time in s\": 783.3577379999999       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.614889183557492,         \"MicroF1\": 0.614889183557492,         \"MacroF1\": 0.5777842549225517,         \"Memory in Mb\": 6.491948127746582,         \"Time in s\": 1129.937274       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.608997632202052,         \"MicroF1\": 0.608997632202052,         \"MacroF1\": 0.5733157350789626,         \"Memory in Mb\": 6.490735054016113,         \"Time in s\": 1525.7763839999998       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6057367068055743,         \"MicroF1\": 0.6057367068055743,         \"MacroF1\": 0.5703382690867537,         \"Memory in Mb\": 6.490704536437988,         \"Time in s\": 1967.928226       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6069610512608027,         \"MicroF1\": 0.6069610512608027,         \"MacroF1\": 0.5711427916016896,         \"Memory in Mb\": 6.490643501281738,         \"Time in s\": 2456.105543       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6039145532989583,         \"MicroF1\": 0.6039145532989583,         \"MacroF1\": 0.5678102867297488,         \"Memory in Mb\": 6.491009712219238,         \"Time in s\": 2990.761064       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6034662373330808,         \"MicroF1\": 0.6034662373330808,         \"MacroF1\": 0.567425153452482,         \"Memory in Mb\": 6.491185188293457,         \"Time in s\": 3571.580702       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6005165733964701,         \"MicroF1\": 0.6005165733964701,         \"MacroF1\": 0.56512832395729,         \"Memory in Mb\": 6.491345405578613,         \"Time in s\": 4198.711386999999       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6031883829216321,         \"MicroF1\": 0.6031883829216321,         \"MacroF1\": 0.5703828979306638,         \"Memory in Mb\": 6.491543769836426,         \"Time in s\": 4874.277407999999       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6152108982297662,         \"MicroF1\": 0.6152108982297662,         \"MacroF1\": 0.5959760515786451,         \"Memory in Mb\": 5.97607421875,         \"Time in s\": 5593.125681999999       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6060339579246432,         \"MicroF1\": 0.6060339579246432,         \"MacroF1\": 0.5869142505177357,         \"Memory in Mb\": 6.496403694152832,         \"Time in s\": 6355.050274999999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5713744554580465,         \"MicroF1\": 0.5713744554580465,         \"MacroF1\": 0.5537658591956377,         \"Memory in Mb\": 6.4967546463012695,         \"Time in s\": 7160.569073999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.545546019532406,         \"MicroF1\": 0.545546019532406,         \"MacroF1\": 0.5286479939306438,         \"Memory in Mb\": 6.381303787231445,         \"Time in s\": 8010.073855999999       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.526767311013314,         \"MicroF1\": 0.526767311013314,         \"MacroF1\": 0.509587529402725,         \"Memory in Mb\": 6.497265815734863,         \"Time in s\": 8901.233847       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.517756615983585,         \"MicroF1\": 0.517756615983585,         \"MacroF1\": 0.4976462434137419,         \"Memory in Mb\": 4.685893058776856,         \"Time in s\": 9829.81162       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5296815032647162,         \"MicroF1\": 0.5296815032647162,         \"MacroF1\": 0.5080882715573688,         \"Memory in Mb\": 10.369908332824709,         \"Time in s\": 10791.950057       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.539750935176855,         \"MicroF1\": 0.539750935176855,         \"MacroF1\": 0.5184934777423561,         \"Memory in Mb\": 10.92272663116455,         \"Time in s\": 11801.930673       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5468771138669674,         \"MicroF1\": 0.5468771138669674,         \"MacroF1\": 0.525970977438283,         \"Memory in Mb\": 10.920933723449709,         \"Time in s\": 12856.592968       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5551633593043778,         \"MicroF1\": 0.5551633593043778,         \"MacroF1\": 0.5340735310276195,         \"Memory in Mb\": 12.231526374816896,         \"Time in s\": 13957.460176       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5615761518507844,         \"MicroF1\": 0.5615761518507844,         \"MacroF1\": 0.5396852076547556,         \"Memory in Mb\": 12.87682819366455,         \"Time in s\": 15100.290122       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5679280274632048,         \"MicroF1\": 0.5679280274632048,         \"MacroF1\": 0.5455634192548013,         \"Memory in Mb\": 13.528642654418944,         \"Time in s\": 16285.362621       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5727868479866661,         \"MicroF1\": 0.5727868479866661,         \"MacroF1\": 0.5496374434570932,         \"Memory in Mb\": 13.632143020629885,         \"Time in s\": 17508.053461       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5754143143325442,         \"MicroF1\": 0.5754143143325442,         \"MacroF1\": 0.5513680135969626,         \"Memory in Mb\": 13.630533218383787,         \"Time in s\": 18766.96928       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5772859598049875,         \"MicroF1\": 0.5772859598049875,         \"MacroF1\": 0.5551350356863173,         \"Memory in Mb\": 13.627862930297852,         \"Time in s\": 20061.957755000003       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.577772516657084,         \"MicroF1\": 0.577772516657084,         \"MacroF1\": 0.5590861332292512,         \"Memory in Mb\": 13.626611709594728,         \"Time in s\": 21394.440888000005       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.578225516768442,         \"MicroF1\": 0.578225516768442,         \"MacroF1\": 0.5625516131192055,         \"Memory in Mb\": 12.769641876220703,         \"Time in s\": 22755.311286000004       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5795637488557088,         \"MicroF1\": 0.5795637488557088,         \"MacroF1\": 0.5663363640160616,         \"Memory in Mb\": 12.768932342529297,         \"Time in s\": 24150.336726000005       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5811211241790133,         \"MicroF1\": 0.5811211241790133,         \"MacroF1\": 0.5696723582178381,         \"Memory in Mb\": 12.768062591552734,         \"Time in s\": 25577.802283000005       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.575804208221124,         \"MicroF1\": 0.575804208221124,         \"MacroF1\": 0.5647934119551398,         \"Memory in Mb\": 12.981294631958008,         \"Time in s\": 27039.45595800001       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5701495107182828,         \"MicroF1\": 0.5701495107182828,         \"MacroF1\": 0.559068023359177,         \"Memory in Mb\": 12.981256484985352,         \"Time in s\": 28537.493337000007       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5657744478177311,         \"MicroF1\": 0.5657744478177311,         \"MacroF1\": 0.5542573482740075,         \"Memory in Mb\": 12.983362197875977,         \"Time in s\": 30069.70246600001       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5611894261208366,         \"MicroF1\": 0.5611894261208366,         \"MacroF1\": 0.5493152777162592,         \"Memory in Mb\": 13.52482795715332,         \"Time in s\": 31635.746830000007       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.558779429172695,         \"MicroF1\": 0.558779429172695,         \"MacroF1\": 0.5463982360776033,         \"Memory in Mb\": 13.526559829711914,         \"Time in s\": 33235.41425300001       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5546825010877633,         \"MicroF1\": 0.5546825010877633,         \"MacroF1\": 0.5426283860139581,         \"Memory in Mb\": 14.304903030395508,         \"Time in s\": 34865.73115700001       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5542153662122761,         \"MicroF1\": 0.5542153662122761,         \"MacroF1\": 0.5429626632180721,         \"Memory in Mb\": 15.152182579040527,         \"Time in s\": 36525.24862800001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5541364155112547,         \"MicroF1\": 0.5541364155112547,         \"MacroF1\": 0.5435420562964655,         \"Memory in Mb\": 15.252725601196287,         \"Time in s\": 38211.297236000006       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5542981604678141,         \"MicroF1\": 0.5542981604678141,         \"MacroF1\": 0.544391400018036,         \"Memory in Mb\": 15.251314163208008,         \"Time in s\": 39923.86574200001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.554151749624668,         \"MicroF1\": 0.554151749624668,         \"MacroF1\": 0.5448486588729107,         \"Memory in Mb\": 13.424749374389648,         \"Time in s\": 41664.47056700001       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5536290049829767,         \"MicroF1\": 0.5536290049829767,         \"MacroF1\": 0.5448029815059025,         \"Memory in Mb\": 13.64866542816162,         \"Time in s\": 43429.160590000014       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5541436342414165,         \"MicroF1\": 0.5541436342414165,         \"MacroF1\": 0.5454957405719211,         \"Memory in Mb\": 14.18911075592041,         \"Time in s\": 45215.90786900002       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5553020683124207,         \"MicroF1\": 0.5553020683124207,         \"MacroF1\": 0.546961663735647,         \"Memory in Mb\": 15.156656265258787,         \"Time in s\": 47024.86244100002       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5579662871693428,         \"MicroF1\": 0.5579662871693428,         \"MacroF1\": 0.5498636684303295,         \"Memory in Mb\": 14.218542098999023,         \"Time in s\": 48856.78116300002       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5627586206896552,         \"MicroF1\": 0.5627586206896552,         \"MacroF1\": 0.5545030394801858,         \"Memory in Mb\": 14.845645904541016,         \"Time in s\": 50711.77001700002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5677701436602124,         \"MicroF1\": 0.5677701436602124,         \"MacroF1\": 0.5591808574875289,         \"Memory in Mb\": 15.233248710632324,         \"Time in s\": 52589.43568900003       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5730463432438297,         \"MicroF1\": 0.5730463432438297,         \"MacroF1\": 0.5639878919164368,         \"Memory in Mb\": 15.890131950378418,         \"Time in s\": 54487.48381000003       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5791894555785324,         \"MicroF1\": 0.5791894555785324,         \"MacroF1\": 0.5695807960578061,         \"Memory in Mb\": 16.1916446685791,         \"Time in s\": 56406.77001200003       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5794427924771303,         \"MicroF1\": 0.5794427924771303,         \"MacroF1\": 0.5701512686040561,         \"Memory in Mb\": 15.30721950531006,         \"Time in s\": 58342.70756100003       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5794652487369197,         \"MicroF1\": 0.5794652487369197,         \"MacroF1\": 0.5701984940722999,         \"Memory in Mb\": 15.30735683441162,         \"Time in s\": 60279.413314000034       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 2.100947380065918,         \"Time in s\": 5.705318       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943558282208589,         \"MicroF1\": 0.943558282208589,         \"MacroF1\": 0.7669956277713079,         \"Memory in Mb\": 3.048105239868164,         \"Time in s\": 25.352994       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8912510220768601,         \"MicroF1\": 0.8912510220768601,         \"MacroF1\": 0.8617021305177772,         \"Memory in Mb\": 3.9921913146972656,         \"Time in s\": 63.032035       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9031269160024524,         \"MicroF1\": 0.9031269160024524,         \"MacroF1\": 0.8868998230762756,         \"Memory in Mb\": 4.944231986999512,         \"Time in s\": 123.600275       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.898970083374203,         \"MicroF1\": 0.898970083374203,         \"MacroF1\": 0.888705938214812,         \"Memory in Mb\": 5.993730545043945,         \"Time in s\": 211.611387       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8598283612586841,         \"MicroF1\": 0.8598283612586841,         \"MacroF1\": 0.8569666636755086,         \"Memory in Mb\": 6.37155818939209,         \"Time in s\": 330.620683       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8669001751313485,         \"MicroF1\": 0.8669001751313484,         \"MacroF1\": 0.8547854134985733,         \"Memory in Mb\": 7.318934440612793,         \"Time in s\": 479.663409       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8581060373889059,         \"MicroF1\": 0.8581060373889059,         \"MacroF1\": 0.8327540420876277,         \"Memory in Mb\": 8.264909744262695,         \"Time in s\": 660.474615       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8490874421138654,         \"MicroF1\": 0.8490874421138654,         \"MacroF1\": 0.8463961237855363,         \"Memory in Mb\": 8.926459312438965,         \"Time in s\": 875.371562       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8421181662172101,         \"MicroF1\": 0.84211816621721,         \"MacroF1\": 0.8299816031455575,         \"Memory in Mb\": 10.074895858764648,         \"Time in s\": 1125.854174       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8301760641854246,         \"MicroF1\": 0.8301760641854244,         \"MacroF1\": 0.8400819204125556,         \"Memory in Mb\": 11.044804573059082,         \"Time in s\": 1412.144879       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8314606741573034,         \"MicroF1\": 0.8314606741573034,         \"MacroF1\": 0.8387821748480373,         \"Memory in Mb\": 8.862092971801758,         \"Time in s\": 1728.261118       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8333019045823119,         \"MicroF1\": 0.8333019045823119,         \"MacroF1\": 0.8299513887279447,         \"Memory in Mb\": 9.715648651123049,         \"Time in s\": 2074.079546       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8255997198389073,         \"MicroF1\": 0.8255997198389075,         \"MacroF1\": 0.831498100235552,         \"Memory in Mb\": 10.513428688049316,         \"Time in s\": 2449.732459       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8241542735741134,         \"MicroF1\": 0.8241542735741134,         \"MacroF1\": 0.813971923025991,         \"Memory in Mb\": 11.555256843566896,         \"Time in s\": 2856.066254       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8043511567335683,         \"MicroF1\": 0.8043511567335683,         \"MacroF1\": 0.8048077550156274,         \"Memory in Mb\": 12.298343658447266,         \"Time in s\": 3295.046229       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8002883922134102,         \"MicroF1\": 0.8002883922134101,         \"MacroF1\": 0.8062362865697692,         \"Memory in Mb\": 11.726844787597656,         \"Time in s\": 3768.473318       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8093422306959008,         \"MicroF1\": 0.8093422306959008,         \"MacroF1\": 0.813125473572493,         \"Memory in Mb\": 9.433514595031738,         \"Time in s\": 4267.304275       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8157657076506257,         \"MicroF1\": 0.8157657076506257,         \"MacroF1\": 0.8184378776785012,         \"Memory in Mb\": 10.539642333984377,         \"Time in s\": 4791.9677950000005       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8199534256649099,         \"MicroF1\": 0.81995342566491,         \"MacroF1\": 0.8213128379144453,         \"Memory in Mb\": 11.364830017089844,         \"Time in s\": 5345.1630700000005       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8247928096183028,         \"MicroF1\": 0.8247928096183028,         \"MacroF1\": 0.8275146627418534,         \"Memory in Mb\": 12.672901153564451,         \"Time in s\": 5929.400246       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8295264623955432,         \"MicroF1\": 0.8295264623955433,         \"MacroF1\": 0.8318915513040454,         \"Memory in Mb\": 13.833264350891112,         \"Time in s\": 6547.972923       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8319300863263348,         \"MicroF1\": 0.8319300863263348,         \"MacroF1\": 0.8336463894938194,         \"Memory in Mb\": 14.741169929504396,         \"Time in s\": 7204.877164       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8342355224185476,         \"MicroF1\": 0.8342355224185476,         \"MacroF1\": 0.8362542817352725,         \"Memory in Mb\": 16.02083969116211,         \"Time in s\": 7900.921468       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8343955289734287,         \"MicroF1\": 0.8343955289734286,         \"MacroF1\": 0.833886744496364,         \"Memory in Mb\": 17.251243591308594,         \"Time in s\": 8638.28045       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8258697086829452,         \"MicroF1\": 0.8258697086829452,         \"MacroF1\": 0.823298887298616,         \"Memory in Mb\": 18.484009742736816,         \"Time in s\": 9418.818077       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.825692237857467,         \"MicroF1\": 0.825692237857467,         \"MacroF1\": 0.827229896548608,         \"Memory in Mb\": 17.053231239318848,         \"Time in s\": 10241.512178       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8263153287227524,         \"MicroF1\": 0.8263153287227524,         \"MacroF1\": 0.8251000136898328,         \"Memory in Mb\": 18.097841262817383,         \"Time in s\": 11105.510711       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8257966359563857,         \"MicroF1\": 0.8257966359563859,         \"MacroF1\": 0.8251092059206939,         \"Memory in Mb\": 19.1904354095459,         \"Time in s\": 12012.672379       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8289892965111528,         \"MicroF1\": 0.8289892965111528,         \"MacroF1\": 0.8300645161883343,         \"Memory in Mb\": 17.2155818939209,         \"Time in s\": 12963.554855000002       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8324503834901558,         \"MicroF1\": 0.8324503834901558,         \"MacroF1\": 0.8328446288662702,         \"Memory in Mb\": 17.090572357177734,         \"Time in s\": 13955.688030000005       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8295672156261968,         \"MicroF1\": 0.8295672156261968,         \"MacroF1\": 0.8279815503081916,         \"Memory in Mb\": 18.284998893737797,         \"Time in s\": 14990.870602000005       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.828121518235163,         \"MicroF1\": 0.828121518235163,         \"MacroF1\": 0.8279872572314477,         \"Memory in Mb\": 18.759904861450195,         \"Time in s\": 16071.989638000005       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8300771393554899,         \"MicroF1\": 0.8300771393554899,         \"MacroF1\": 0.8300312724960401,         \"Memory in Mb\": 19.937789916992188,         \"Time in s\": 17198.739284000003       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8329714966034036,         \"MicroF1\": 0.8329714966034036,         \"MacroF1\": 0.8330653900337638,         \"Memory in Mb\": 21.17230033874512,         \"Time in s\": 18373.548754000003       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8360454823994008,         \"MicroF1\": 0.8360454823994008,         \"MacroF1\": 0.8362319050195895,         \"Memory in Mb\": 22.12139320373535,         \"Time in s\": 19591.296889000005       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8391520370983769,         \"MicroF1\": 0.8391520370983769,         \"MacroF1\": 0.8393677597260801,         \"Memory in Mb\": 22.688467979431152,         \"Time in s\": 20852.364231000003       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8400309617493389,         \"MicroF1\": 0.8400309617493388,         \"MacroF1\": 0.8398031059873,         \"Memory in Mb\": 23.806550979614254,         \"Time in s\": 22157.639176000004       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8335114072025642,         \"MicroF1\": 0.8335114072025642,         \"MacroF1\": 0.8310693286634668,         \"Memory in Mb\": 25.06292724609375,         \"Time in s\": 23499.792247000005       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8283595808566702,         \"MicroF1\": 0.8283595808566702,         \"MacroF1\": 0.826721014765785,         \"Memory in Mb\": 26.060873985290527,         \"Time in s\": 24886.958001000006       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.826747175225683,         \"MicroF1\": 0.8267471752256829,         \"MacroF1\": 0.8259678903415486,         \"Memory in Mb\": 27.245673179626465,         \"Time in s\": 26317.085564000008       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.821943390720747,         \"MicroF1\": 0.821943390720747,         \"MacroF1\": 0.8202405231953956,         \"Memory in Mb\": 28.675668716430664,         \"Time in s\": 27793.820666000007       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8182180926865417,         \"MicroF1\": 0.8182180926865417,         \"MacroF1\": 0.8170173651382093,         \"Memory in Mb\": 29.74225902557373,         \"Time in s\": 29319.213378000008       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.81878446883182,         \"MicroF1\": 0.81878446883182,         \"MacroF1\": 0.8179349229322325,         \"Memory in Mb\": 30.87984085083008,         \"Time in s\": 30892.43160700001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.821123154855929,         \"MicroF1\": 0.821123154855929,         \"MacroF1\": 0.8204502524156659,         \"Memory in Mb\": 32.019548416137695,         \"Time in s\": 32513.434007000007       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8235200085256035,         \"MicroF1\": 0.8235200085256035,         \"MacroF1\": 0.8229965581236837,         \"Memory in Mb\": 33.157379150390625,         \"Time in s\": 34181.929457000006       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.819973924380704,         \"MicroF1\": 0.819973924380704,         \"MacroF1\": 0.8189812465563673,         \"Memory in Mb\": 34.295823097229004,         \"Time in s\": 35895.74071300001       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.821733135883164,         \"MicroF1\": 0.821733135883164,         \"MacroF1\": 0.8211010404575377,         \"Memory in Mb\": 35.31475067138672,         \"Time in s\": 37655.039070000006       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8188684908208694,         \"MicroF1\": 0.8188684908208694,         \"MacroF1\": 0.8180458262517715,         \"Memory in Mb\": 36.57470226287842,         \"Time in s\": 39458.702899       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"ADWIN Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.816559635276239,         \"MicroF1\": 0.816559635276239,         \"MacroF1\": 0.8159075588016685,         \"Memory in Mb\": 37.85576725006104,         \"Time in s\": 41308.014952000005       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1111111111111111,         \"MicroF1\": 0.1111111111111111,         \"MacroF1\": 0.0815018315018315,         \"Memory in Mb\": 3.4160032272338867,         \"Time in s\": 1.208286       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.2307692307692307,         \"MicroF1\": 0.2307692307692307,         \"MacroF1\": 0.2226391771283412,         \"Memory in Mb\": 4.099128723144531,         \"Time in s\": 4.553382       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4233576642335766,         \"MicroF1\": 0.4233576642335766,         \"MacroF1\": 0.4463537718619156,         \"Memory in Mb\": 4.099002838134766,         \"Time in s\": 10.351245       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5355191256830601,         \"MicroF1\": 0.5355191256830601,         \"MacroF1\": 0.5617062146473912,         \"Memory in Mb\": 4.099178314208984,         \"Time in s\": 18.765494       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5938864628820961,         \"MicroF1\": 0.5938864628820961,         \"MacroF1\": 0.6236530662596055,         \"Memory in Mb\": 4.099166870117188,         \"Time in s\": 30.180838       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6290909090909091,         \"MicroF1\": 0.6290909090909091,         \"MacroF1\": 0.6558170665459355,         \"Memory in Mb\": 4.099109649658203,         \"Time in s\": 44.412895       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.660436137071651,         \"MicroF1\": 0.660436137071651,         \"MacroF1\": 0.678574720261515,         \"Memory in Mb\": 4.098438262939453,         \"Time in s\": 61.214822       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6920980926430518,         \"MicroF1\": 0.6920980926430518,         \"MacroF1\": 0.7041680355881775,         \"Memory in Mb\": 4.0984954833984375,         \"Time in s\": 80.427396       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7167070217917676,         \"MicroF1\": 0.7167070217917676,         \"MacroF1\": 0.7259075149442813,         \"Memory in Mb\": 4.097980499267578,         \"Time in s\": 102.254145       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7254901960784313,         \"MicroF1\": 0.7254901960784313,         \"MacroF1\": 0.7325011710849479,         \"Memory in Mb\": 4.098300933837891,         \"Time in s\": 127.091256       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7386138613861386,         \"MicroF1\": 0.7386138613861386,         \"MacroF1\": 0.7428621938273078,         \"Memory in Mb\": 4.098552703857422,         \"Time in s\": 154.35838199999998       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7422867513611615,         \"MicroF1\": 0.7422867513611615,         \"MacroF1\": 0.7453719085253248,         \"Memory in Mb\": 4.098358154296875,         \"Time in s\": 184.303693       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7487437185929648,         \"MicroF1\": 0.7487437185929648,         \"MacroF1\": 0.7504522188790486,         \"Memory in Mb\": 4.098468780517578,         \"Time in s\": 216.734559       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7465007776049767,         \"MicroF1\": 0.7465007776049767,         \"MacroF1\": 0.7482323503576439,         \"Memory in Mb\": 4.098541259765625,         \"Time in s\": 252.139078       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7489114658925979,         \"MicroF1\": 0.748911465892598,         \"MacroF1\": 0.7488472102580618,         \"Memory in Mb\": 4.098594665527344,         \"Time in s\": 290.044846       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7523809523809524,         \"MicroF1\": 0.7523809523809524,         \"MacroF1\": 0.7518283723099097,         \"Memory in Mb\": 4.098430633544922,         \"Time in s\": 330.661237       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7541613316261203,         \"MicroF1\": 0.7541613316261204,         \"MacroF1\": 0.7531089046321314,         \"Memory in Mb\": 4.098361968994141,         \"Time in s\": 373.819675       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7557436517533253,         \"MicroF1\": 0.7557436517533253,         \"MacroF1\": 0.7552013614952863,         \"Memory in Mb\": 4.098308563232422,         \"Time in s\": 419.6867600000001       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7617411225658648,         \"MicroF1\": 0.7617411225658649,         \"MacroF1\": 0.7601066395856337,         \"Memory in Mb\": 4.098381042480469,         \"Time in s\": 467.91378800000007       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.763873775843308,         \"MicroF1\": 0.763873775843308,         \"MacroF1\": 0.7623480483274478,         \"Memory in Mb\": 4.098400115966797,         \"Time in s\": 518.622959       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7678756476683938,         \"MicroF1\": 0.7678756476683938,         \"MacroF1\": 0.7646598072570266,         \"Memory in Mb\": 4.098423004150391,         \"Time in s\": 571.868767       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7705242334322453,         \"MicroF1\": 0.7705242334322453,         \"MacroF1\": 0.7668271197983111,         \"Memory in Mb\": 4.098529815673828,         \"Time in s\": 627.754669       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7757805108798487,         \"MicroF1\": 0.7757805108798487,         \"MacroF1\": 0.7714920336037777,         \"Memory in Mb\": 4.098388671875,         \"Time in s\": 686.3790640000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7760652765185857,         \"MicroF1\": 0.7760652765185856,         \"MacroF1\": 0.7719206139767609,         \"Memory in Mb\": 4.098537445068359,         \"Time in s\": 747.748718       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7789382071366405,         \"MicroF1\": 0.7789382071366405,         \"MacroF1\": 0.7750313949659527,         \"Memory in Mb\": 4.098442077636719,         \"Time in s\": 811.629001       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7849372384937239,         \"MicroF1\": 0.7849372384937239,         \"MacroF1\": 0.7820003890472508,         \"Memory in Mb\": 4.098487854003906,         \"Time in s\": 878.060098       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7856567284448026,         \"MicroF1\": 0.7856567284448026,         \"MacroF1\": 0.7827470902102026,         \"Memory in Mb\": 4.098438262939453,         \"Time in s\": 947.241797       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7894327894327894,         \"MicroF1\": 0.7894327894327894,         \"MacroF1\": 0.785982924599392,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 1018.793017       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7906976744186046,         \"MicroF1\": 0.7906976744186046,         \"MacroF1\": 0.7876424482584368,         \"Memory in Mb\": 4.098438262939453,         \"Time in s\": 1093.078269       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7933284989122552,         \"MicroF1\": 0.7933284989122552,         \"MacroF1\": 0.7906471924204205,         \"Memory in Mb\": 4.098392486572266,         \"Time in s\": 1169.7117919999998       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7978947368421052,         \"MicroF1\": 0.7978947368421052,         \"MacroF1\": 0.7945020166797493,         \"Memory in Mb\": 4.098480224609375,         \"Time in s\": 1248.577134       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8028552005438477,         \"MicroF1\": 0.8028552005438477,         \"MacroF1\": 0.7982243751921434,         \"Memory in Mb\": 4.098472595214844,         \"Time in s\": 1329.680821       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8035596572181938,         \"MicroF1\": 0.8035596572181938,         \"MacroF1\": 0.7981876534181912,         \"Memory in Mb\": 4.098491668701172,         \"Time in s\": 1413.4983189999998       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8035828534868842,         \"MicroF1\": 0.8035828534868842,         \"MacroF1\": 0.798634974540431,         \"Memory in Mb\": 4.098518371582031,         \"Time in s\": 1499.905218       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048477315102548,         \"MicroF1\": 0.8048477315102549,         \"MacroF1\": 0.7997380784882049,         \"Memory in Mb\": 4.098381042480469,         \"Time in s\": 1588.836817       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8066465256797583,         \"MicroF1\": 0.8066465256797583,         \"MacroF1\": 0.80161945439383,         \"Memory in Mb\": 4.098377227783203,         \"Time in s\": 1680.249514       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8059964726631393,         \"MicroF1\": 0.8059964726631393,         \"MacroF1\": 0.8024858564723997,         \"Memory in Mb\": 4.098514556884766,         \"Time in s\": 1774.345382       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8070978820835718,         \"MicroF1\": 0.8070978820835718,         \"MacroF1\": 0.8029124203507955,         \"Memory in Mb\": 4.098423004150391,         \"Time in s\": 1871.065136       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8081427774679308,         \"MicroF1\": 0.8081427774679307,         \"MacroF1\": 0.8029834045630979,         \"Memory in Mb\": 4.098461151123047,         \"Time in s\": 1970.03779       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8069603045133225,         \"MicroF1\": 0.8069603045133223,         \"MacroF1\": 0.801927622716254,         \"Memory in Mb\": 4.098594665527344,         \"Time in s\": 2071.588776       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8053050397877984,         \"MicroF1\": 0.8053050397877984,         \"MacroF1\": 0.8006727596367825,         \"Memory in Mb\": 4.098400115966797,         \"Time in s\": 2175.772942       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8047643707923355,         \"MicroF1\": 0.8047643707923355,         \"MacroF1\": 0.7995493059800365,         \"Memory in Mb\": 4.098396301269531,         \"Time in s\": 2282.41088       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8057663125948407,         \"MicroF1\": 0.8057663125948407,         \"MacroF1\": 0.8003960406612564,         \"Memory in Mb\": 4.098434448242188,         \"Time in s\": 2391.59611       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8072170044488384,         \"MicroF1\": 0.8072170044488384,         \"MacroF1\": 0.8005625942078284,         \"Memory in Mb\": 4.098430633544922,         \"Time in s\": 2503.16971       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8066698888351861,         \"MicroF1\": 0.8066698888351861,         \"MacroF1\": 0.8002110568368,         \"Memory in Mb\": 4.098316192626953,         \"Time in s\": 2617.3694960000003       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.807565011820331,         \"MicroF1\": 0.807565011820331,         \"MacroF1\": 0.8005131307885663,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 2733.922308       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8079592781119852,         \"MicroF1\": 0.8079592781119852,         \"MacroF1\": 0.8006755955605837,         \"Memory in Mb\": 4.098320007324219,         \"Time in s\": 2852.747139       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8087902129587675,         \"MicroF1\": 0.8087902129587675,         \"MacroF1\": 0.8009921695193862,         \"Memory in Mb\": 4.098320007324219,         \"Time in s\": 2973.740681       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8060363959165557,         \"MicroF1\": 0.8060363959165557,         \"MacroF1\": 0.7987732120640717,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 3097.6149410000003       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8051326663766856,         \"MicroF1\": 0.8051326663766856,         \"MacroF1\": 0.798077892809675,         \"Memory in Mb\": 4.0983428955078125,         \"Time in s\": 3223.9293610000004       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8046773495019489,         \"MicroF1\": 0.8046773495019489,         \"MacroF1\": 0.7977695866822911,         \"Memory in Mb\": 4.098388671875,         \"Time in s\": 3350.8763620000004       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360189573459716,         \"MicroF1\": 0.6360189573459716,         \"MacroF1\": 0.5992691812827112,         \"Memory in Mb\": 6.474042892456055,         \"Time in s\": 88.969298       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6110847939365229,         \"MicroF1\": 0.6110847939365229,         \"MacroF1\": 0.5773210074897359,         \"Memory in Mb\": 6.473905563354492,         \"Time in s\": 256.045675       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6043574360593622,         \"MicroF1\": 0.6043574360593622,         \"MacroF1\": 0.5704368753709179,         \"Memory in Mb\": 6.473470687866211,         \"Time in s\": 489.83548       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6014681506038362,         \"MicroF1\": 0.6014681506038362,         \"MacroF1\": 0.5676969561642586,         \"Memory in Mb\": 6.473196029663086,         \"Time in s\": 783.3519140000001       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6057965523773442,         \"MicroF1\": 0.6057965523773442,         \"MacroF1\": 0.5710016183775801,         \"Memory in Mb\": 6.473196029663086,         \"Time in s\": 1130.801617       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5966850828729282,         \"MicroF1\": 0.5966850828729282,         \"MacroF1\": 0.5635903588556204,         \"Memory in Mb\": 6.473356246948242,         \"Time in s\": 1527.884634       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5957245298335814,         \"MicroF1\": 0.5957245298335814,         \"MacroF1\": 0.5625002603439991,         \"Memory in Mb\": 6.473814010620117,         \"Time in s\": 1971.450931       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5982005445720374,         \"MicroF1\": 0.5982005445720374,         \"MacroF1\": 0.5646892369665863,         \"Memory in Mb\": 6.474157333374023,         \"Time in s\": 2461.351589       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.596337998526781,         \"MicroF1\": 0.596337998526781,         \"MacroF1\": 0.5627085514562804,         \"Memory in Mb\": 6.47450065612793,         \"Time in s\": 2997.75158       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5965527038545316,         \"MicroF1\": 0.5965527038545316,         \"MacroF1\": 0.5631320282838163,         \"Memory in Mb\": 6.47468376159668,         \"Time in s\": 3580.189969       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5953508394317693,         \"MicroF1\": 0.5953508394317693,         \"MacroF1\": 0.562671447170627,         \"Memory in Mb\": 6.47468376159668,         \"Time in s\": 4209.202801       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5979796385447084,         \"MicroF1\": 0.5979796385447084,         \"MacroF1\": 0.5680559575776837,         \"Memory in Mb\": 6.474340438842773,         \"Time in s\": 4886.872526       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.610767101333139,         \"MicroF1\": 0.610767101333139,         \"MacroF1\": 0.5941277335666079,         \"Memory in Mb\": 6.473836898803711,         \"Time in s\": 5609.570035       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6019752418318338,         \"MicroF1\": 0.6019752418318338,         \"MacroF1\": 0.5851264744797859,         \"Memory in Mb\": 6.473745346069336,         \"Time in s\": 6378.998584999999       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5705536965717533,         \"MicroF1\": 0.5705536965717533,         \"MacroF1\": 0.5545059657048704,         \"Memory in Mb\": 6.473974227905273,         \"Time in s\": 7193.860588999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.548091151228174,         \"MicroF1\": 0.548091151228174,         \"MacroF1\": 0.5320735507355622,         \"Memory in Mb\": 6.474386215209961,         \"Time in s\": 8051.816493999999       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5307225224221492,         \"MicroF1\": 0.5307225224221492,         \"MacroF1\": 0.5138536287616571,         \"Memory in Mb\": 6.474637985229492,         \"Time in s\": 8952.059017       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5182827379386542,         \"MicroF1\": 0.5182827379386542,         \"MacroF1\": 0.4990809738484312,         \"Memory in Mb\": 6.47486686706543,         \"Time in s\": 9893.706361       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5182176145142801,         \"MicroF1\": 0.5182176145142801,         \"MacroF1\": 0.497867701567998,         \"Memory in Mb\": 8.642622947692871,         \"Time in s\": 10881.966771       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5272503432927695,         \"MicroF1\": 0.5272503432927695,         \"MacroF1\": 0.5067114684709674,         \"Memory in Mb\": 15.437758445739746,         \"Time in s\": 11917.076256       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.533032694475761,         \"MicroF1\": 0.533032694475761,         \"MacroF1\": 0.5127471323280748,         \"Memory in Mb\": 16.81709384918213,         \"Time in s\": 12999.258268       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5410442942619775,         \"MicroF1\": 0.5410442942619775,         \"MacroF1\": 0.5207771198745245,         \"Memory in Mb\": 17.041016578674316,         \"Time in s\": 14124.681759       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5459710956478775,         \"MicroF1\": 0.5459710956478775,         \"MacroF1\": 0.5251711652768184,         \"Memory in Mb\": 17.038064002990723,         \"Time in s\": 15290.808705       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5532099593576135,         \"MicroF1\": 0.5532099593576135,         \"MacroF1\": 0.5314216535856217,         \"Memory in Mb\": 17.036622047424316,         \"Time in s\": 16491.42669       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5607788173794462,         \"MicroF1\": 0.5607788173794462,         \"MacroF1\": 0.5375130024626694,         \"Memory in Mb\": 17.14900016784668,         \"Time in s\": 17723.115047       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5667091604443635,         \"MicroF1\": 0.5667091604443635,         \"MacroF1\": 0.5418496825562071,         \"Memory in Mb\": 17.261820793151855,         \"Time in s\": 18986.675501       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5692890463329943,         \"MicroF1\": 0.5692890463329943,         \"MacroF1\": 0.5455529487931667,         \"Memory in Mb\": 17.26294231414795,         \"Time in s\": 20283.404341       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5688436432509216,         \"MicroF1\": 0.5688436432509216,         \"MacroF1\": 0.5481992899375988,         \"Memory in Mb\": 17.26337718963623,         \"Time in s\": 21617.011828       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5687228553701467,         \"MicroF1\": 0.5687228553701467,         \"MacroF1\": 0.5505043481720591,         \"Memory in Mb\": 17.26330852508545,         \"Time in s\": 22980.237824       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5691467533697402,         \"MicroF1\": 0.5691467533697402,         \"MacroF1\": 0.5529220328647554,         \"Memory in Mb\": 17.262804985046387,         \"Time in s\": 24378.053405       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5703986558729189,         \"MicroF1\": 0.5703986558729189,         \"MacroF1\": 0.5556828084411201,         \"Memory in Mb\": 17.262507438659668,         \"Time in s\": 25809.039082       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5650025154626972,         \"MicroF1\": 0.5650025154626972,         \"MacroF1\": 0.5507695387439543,         \"Memory in Mb\": 17.487704277038574,         \"Time in s\": 27274.413314       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5587281545039745,         \"MicroF1\": 0.5587281545039745,         \"MacroF1\": 0.5445349362041821,         \"Memory in Mb\": 17.929275512695312,         \"Time in s\": 28775.770082       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5541876723393588,         \"MicroF1\": 0.5541876723393588,         \"MacroF1\": 0.5396635045593164,         \"Memory in Mb\": 18.030207633972168,         \"Time in s\": 30311.158157       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.549122000054114,         \"MicroF1\": 0.549122000054114,         \"MacroF1\": 0.5343517375956978,         \"Memory in Mb\": 19.681435585021973,         \"Time in s\": 31880.342243       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5473628830724714,         \"MicroF1\": 0.5473628830724714,         \"MacroF1\": 0.5321033552605493,         \"Memory in Mb\": 21.71814346313477,         \"Time in s\": 33482.866823       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5426787131120269,         \"MicroF1\": 0.5426787131120269,         \"MacroF1\": 0.52803892360078,         \"Memory in Mb\": 22.487850189208984,         \"Time in s\": 35116.535538       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5419293742367982,         \"MicroF1\": 0.5419293742367982,         \"MacroF1\": 0.5284857300708793,         \"Memory in Mb\": 23.76238250732422,         \"Time in s\": 36778.160189       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5417769467984362,         \"MicroF1\": 0.5417769467984362,         \"MacroF1\": 0.5296361895775551,         \"Memory in Mb\": 23.860816955566406,         \"Time in s\": 38464.674338       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5422240109851085,         \"MicroF1\": 0.5422240109851085,         \"MacroF1\": 0.5313111510734391,         \"Memory in Mb\": 24.196860313415527,         \"Time in s\": 40175.576413       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5444970550871925,         \"MicroF1\": 0.5444970550871925,         \"MacroF1\": 0.5344195798463859,         \"Memory in Mb\": 24.296037673950195,         \"Time in s\": 41906.99288399999       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5463461928705102,         \"MicroF1\": 0.5463461928705102,         \"MacroF1\": 0.5369578677381479,         \"Memory in Mb\": 24.84640598297119,         \"Time in s\": 43659.477485       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5482855066399454,         \"MicroF1\": 0.5482855066399454,         \"MacroF1\": 0.5392181145139481,         \"Memory in Mb\": 25.28636360168457,         \"Time in s\": 45430.571796       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5506532079288896,         \"MicroF1\": 0.5506532079288896,         \"MacroF1\": 0.5419048601727473,         \"Memory in Mb\": 25.498303413391117,         \"Time in s\": 47220.970484       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5514846692901787,         \"MicroF1\": 0.5514846692901787,         \"MacroF1\": 0.5429796926051395,         \"Memory in Mb\": 25.82335567474365,         \"Time in s\": 49033.125826       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5515388574369532,         \"MicroF1\": 0.5515388574369532,         \"MacroF1\": 0.543031483592694,         \"Memory in Mb\": 25.821112632751465,         \"Time in s\": 50867.580247       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.551953416211642,         \"MicroF1\": 0.551953416211642,         \"MacroF1\": 0.5433574148660688,         \"Memory in Mb\": 26.09889411926269,         \"Time in s\": 52723.913942       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5563359441276856,         \"MicroF1\": 0.5563359441276856,         \"MacroF1\": 0.5472854805195191,         \"Memory in Mb\": 26.366034507751465,         \"Time in s\": 54600.511787       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5623562607502464,         \"MicroF1\": 0.5623562607502464,         \"MacroF1\": 0.552981536157949,         \"Memory in Mb\": 27.10032081604004,         \"Time in s\": 56496.789585       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5634576412432054,         \"MicroF1\": 0.5634576412432054,         \"MacroF1\": 0.5545218292020726,         \"Memory in Mb\": 27.943178176879883,         \"Time in s\": 58415.671716       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5635324616345299,         \"MicroF1\": 0.5635324616345299,         \"MacroF1\": 0.5546220283668154,         \"Memory in Mb\": 27.942995071411133,         \"Time in s\": 60335.727696       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9877149877149876,         \"MicroF1\": 0.9877149877149876,         \"MacroF1\": 0.7696139476961394,         \"Memory in Mb\": 2.1207275390625,         \"Time in s\": 4.211814       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.988957055214724,         \"MicroF1\": 0.988957055214724,         \"MacroF1\": 0.9592655637573824,         \"Memory in Mb\": 2.9369373321533203,         \"Time in s\": 23.739993       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.983646770237122,         \"MicroF1\": 0.983646770237122,         \"MacroF1\": 0.9326470331192014,         \"Memory in Mb\": 4.590028762817383,         \"Time in s\": 86.527265       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828326180257512,         \"MicroF1\": 0.9828326180257512,         \"MacroF1\": 0.9594506659780556,         \"Memory in Mb\": 5.819695472717285,         \"Time in s\": 184.232691       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9705738106915156,         \"MicroF1\": 0.9705738106915156,         \"MacroF1\": 0.9304838721924584,         \"Memory in Mb\": 8.549582481384277,         \"Time in s\": 323.308574       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9607682876992236,         \"MicroF1\": 0.9607682876992236,         \"MacroF1\": 0.9455756842664336,         \"Memory in Mb\": 10.061903953552246,         \"Time in s\": 491.40663400000005       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9541155866900176,         \"MicroF1\": 0.9541155866900176,         \"MacroF1\": 0.9254688528922778,         \"Memory in Mb\": 12.678574562072754,         \"Time in s\": 687.150288       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943610174685872,         \"MicroF1\": 0.943610174685872,         \"MacroF1\": 0.9191430707434156,         \"Memory in Mb\": 16.086813926696777,         \"Time in s\": 906.458431       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9403432307273224,         \"MicroF1\": 0.9403432307273224,         \"MacroF1\": 0.9284235615798526,         \"Memory in Mb\": 18.255277633666992,         \"Time in s\": 1151.002639       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9338073057121844,         \"MicroF1\": 0.9338073057121844,         \"MacroF1\": 0.918242970538206,         \"Memory in Mb\": 21.65336036682129,         \"Time in s\": 1427.8669570000002       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9318029864051705,         \"MicroF1\": 0.9318029864051705,         \"MacroF1\": 0.9319119487505448,         \"Memory in Mb\": 22.76876544952393,         \"Time in s\": 1733.8095280000002       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9317671092951992,         \"MicroF1\": 0.9317671092951992,         \"MacroF1\": 0.9296889978700974,         \"Memory in Mb\": 24.966033935546875,         \"Time in s\": 2062.8329900000003       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9281538751650008,         \"MicroF1\": 0.9281538751650008,         \"MacroF1\": 0.919765356403914,         \"Memory in Mb\": 29.062508583068848,         \"Time in s\": 2423.731892       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9227805988443356,         \"MicroF1\": 0.9227805988443356,         \"MacroF1\": 0.9201475418022376,         \"Memory in Mb\": 32.12655830383301,         \"Time in s\": 2815.063536       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9177970256577872,         \"MicroF1\": 0.9177970256577872,         \"MacroF1\": 0.9072843264203106,         \"Memory in Mb\": 37.27707767486572,         \"Time in s\": 3238.118927       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9115979776313774,         \"MicroF1\": 0.9115979776313774,         \"MacroF1\": 0.909931232789514,         \"Memory in Mb\": 41.43412208557129,         \"Time in s\": 3706.915394       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.912905551550108,         \"MicroF1\": 0.912905551550108,         \"MacroF1\": 0.9153430596364792,         \"Memory in Mb\": 44.48411560058594,         \"Time in s\": 4207.758914       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9135230832084978,         \"MicroF1\": 0.9135230832084978,         \"MacroF1\": 0.9124682676754272,         \"Memory in Mb\": 47.44067192077637,         \"Time in s\": 4740.722969       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9121403689846472,         \"MicroF1\": 0.9121403689846472,         \"MacroF1\": 0.9121831707972876,         \"Memory in Mb\": 51.03960132598877,         \"Time in s\": 5307.755902000001       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.908689790415492,         \"MicroF1\": 0.908689790415492,         \"MacroF1\": 0.9062633734460516,         \"Memory in Mb\": 56.064818382263184,         \"Time in s\": 5918.019803000001       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.905918057663126,         \"MicroF1\": 0.905918057663126,         \"MacroF1\": 0.9058259471519292,         \"Memory in Mb\": 61.820496559143066,         \"Time in s\": 6575.962782000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9042896935933148,         \"MicroF1\": 0.9042896935933148,         \"MacroF1\": 0.9043251050138336,         \"Memory in Mb\": 64.74030494689941,         \"Time in s\": 7280.842530000002       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.901843759991474,         \"MicroF1\": 0.901843759991474,         \"MacroF1\": 0.9009662752730246,         \"Memory in Mb\": 68.81300067901611,         \"Time in s\": 8035.1031440000015       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8971504442855683,         \"MicroF1\": 0.8971504442855683,         \"MacroF1\": 0.8956423708961025,         \"Memory in Mb\": 74.25286674499512,         \"Time in s\": 8855.073479000002       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8926365329934307,         \"MicroF1\": 0.8926365329934307,         \"MacroF1\": 0.8903074227158838,         \"Memory in Mb\": 79.6785535812378,         \"Time in s\": 9744.976722000005       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8846987838220043,         \"MicroF1\": 0.8846987838220043,         \"MacroF1\": 0.8819820059100918,         \"Memory in Mb\": 85.28873825073242,         \"Time in s\": 10726.537155000004       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8791647753064004,         \"MicroF1\": 0.8791647753064004,         \"MacroF1\": 0.8795835231396919,         \"Memory in Mb\": 89.59383392333984,         \"Time in s\": 11788.438997000005       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8759520266129738,         \"MicroF1\": 0.8759520266129738,         \"MacroF1\": 0.8744149508862001,         \"Memory in Mb\": 94.86630344390868,         \"Time in s\": 12917.703791000004       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.872200152142676,         \"MicroF1\": 0.872200152142676,         \"MacroF1\": 0.8717012117300328,         \"Memory in Mb\": 100.15169906616212,         \"Time in s\": 14117.771334000005       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8736824903995425,         \"MicroF1\": 0.8736824903995425,         \"MacroF1\": 0.8749440738646468,         \"Memory in Mb\": 101.9357843399048,         \"Time in s\": 15365.994884000003       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8717482406894915,         \"MicroF1\": 0.8717482406894915,         \"MacroF1\": 0.8710221211412438,         \"Memory in Mb\": 107.46908473968506,         \"Time in s\": 16670.526324000002       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8661815396399847,         \"MicroF1\": 0.8661815396399847,         \"MacroF1\": 0.8651994621744733,         \"Memory in Mb\": 112.71690273284912,         \"Time in s\": 18043.485216       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8642204560647702,         \"MicroF1\": 0.8642204560647702,         \"MacroF1\": 0.8645487273027374,         \"Memory in Mb\": 116.56386756896973,         \"Time in s\": 19480.298866       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8619421815298104,         \"MicroF1\": 0.8619421815298104,         \"MacroF1\": 0.862314869215492,         \"Memory in Mb\": 121.52821636199953,         \"Time in s\": 20980.471725       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.859163806989285,         \"MicroF1\": 0.859163806989285,         \"MacroF1\": 0.8592780138529494,         \"Memory in Mb\": 125.80194187164308,         \"Time in s\": 22534.622977       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8591952066453326,         \"MicroF1\": 0.8591952066453326,         \"MacroF1\": 0.8604793833246808,         \"Memory in Mb\": 129.6016607284546,         \"Time in s\": 24135.768584       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8607485922490891,         \"MicroF1\": 0.8607485922490891,         \"MacroF1\": 0.8621609789956539,         \"Memory in Mb\": 132.68816757202148,         \"Time in s\": 25779.863983       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8604141133974069,         \"MicroF1\": 0.8604141133974069,         \"MacroF1\": 0.8613237595899307,         \"Memory in Mb\": 136.05841445922852,         \"Time in s\": 27480.057181       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8536861290930803,         \"MicroF1\": 0.8536861290930803,         \"MacroF1\": 0.853192144751886,         \"Memory in Mb\": 141.70578575134277,         \"Time in s\": 29254.423593       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8493167473497151,         \"MicroF1\": 0.849316747349715,         \"MacroF1\": 0.8496464102754333,         \"Memory in Mb\": 147.49746799468994,         \"Time in s\": 31089.50572       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.846296407006636,         \"MicroF1\": 0.846296407006636,         \"MacroF1\": 0.8470383589757107,         \"Memory in Mb\": 153.1935510635376,         \"Time in s\": 32973.577156       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8411438576014006,         \"MicroF1\": 0.8411438576014006,         \"MacroF1\": 0.8410396667771575,         \"Memory in Mb\": 156.28132915496826,         \"Time in s\": 34948.88082       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8365729920766117,         \"MicroF1\": 0.8365729920766117,         \"MacroF1\": 0.8367907010021001,         \"Memory in Mb\": 161.03080940246582,         \"Time in s\": 36967.519165       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8355523369171634,         \"MicroF1\": 0.8355523369171634,         \"MacroF1\": 0.8362918425397341,         \"Memory in Mb\": 166.63249397277832,         \"Time in s\": 39016.229589       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.837572852551882,         \"MicroF1\": 0.8375728525518821,         \"MacroF1\": 0.8385662484273668,         \"Memory in Mb\": 171.47760772705078,         \"Time in s\": 41092.028593       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8390259498055097,         \"MicroF1\": 0.8390259498055097,         \"MacroF1\": 0.8401126675526959,         \"Memory in Mb\": 175.70373821258545,         \"Time in s\": 43194.947864       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8376531942633637,         \"MicroF1\": 0.8376531942633637,         \"MacroF1\": 0.838676297522501,         \"Memory in Mb\": 180.87701034545896,         \"Time in s\": 45330.650988       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8390951335341879,         \"MicroF1\": 0.8390951335341879,         \"MacroF1\": 0.8403338496937821,         \"Memory in Mb\": 185.05438709259036,         \"Time in s\": 47482.84587799999       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8372767745485469,         \"MicroF1\": 0.8372767745485468,         \"MacroF1\": 0.8385640183306876,         \"Memory in Mb\": 190.1383810043335,         \"Time in s\": 49661.2352       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"AdaBoost\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8347958233246727,         \"MicroF1\": 0.8347958233246727,         \"MacroF1\": 0.8360623278174891,         \"Memory in Mb\": 194.794171333313,         \"Time in s\": 51861.27850099999       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3111111111111111,         \"MicroF1\": 0.3111111111111111,         \"MacroF1\": 0.2457649726557289,         \"Memory in Mb\": 4.149084091186523,         \"Time in s\": 2.196675       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4835164835164835,         \"MicroF1\": 0.4835164835164835,         \"MacroF1\": 0.4934752395581889,         \"Memory in Mb\": 4.152299880981445,         \"Time in s\": 7.023639       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5328467153284672,         \"MicroF1\": 0.5328467153284672,         \"MacroF1\": 0.5528821792646678,         \"Memory in Mb\": 4.15202522277832,         \"Time in s\": 15.046926       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5956284153005464,         \"MicroF1\": 0.5956284153005464,         \"MacroF1\": 0.6141431648908949,         \"Memory in Mb\": 4.152608871459961,         \"Time in s\": 26.297795       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.62882096069869,         \"MicroF1\": 0.62882096069869,         \"MacroF1\": 0.6441389332893815,         \"Memory in Mb\": 4.151983261108398,         \"Time in s\": 40.50873       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.64,         \"MicroF1\": 0.64,         \"MacroF1\": 0.6559607038460421,         \"Memory in Mb\": 4.152521133422852,         \"Time in s\": 57.698206       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6666666666666666,         \"MicroF1\": 0.6666666666666666,         \"MacroF1\": 0.6673617488913626,         \"Memory in Mb\": 4.152231216430664,         \"Time in s\": 77.585785       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6948228882833788,         \"MicroF1\": 0.6948228882833788,         \"MacroF1\": 0.6911959597548878,         \"Memory in Mb\": 4.152448654174805,         \"Time in s\": 100.185488       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.711864406779661,         \"MicroF1\": 0.711864406779661,         \"MacroF1\": 0.7079630503641953,         \"Memory in Mb\": 4.152788162231445,         \"Time in s\": 125.717288       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7124183006535948,         \"MicroF1\": 0.7124183006535948,         \"MacroF1\": 0.7065500352371009,         \"Memory in Mb\": 4.152704238891602,         \"Time in s\": 154.000542       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7207920792079208,         \"MicroF1\": 0.7207920792079208,         \"MacroF1\": 0.7127593158348896,         \"Memory in Mb\": 4.152563095092773,         \"Time in s\": 184.883226       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7259528130671506,         \"MicroF1\": 0.7259528130671506,         \"MacroF1\": 0.7192025503807162,         \"Memory in Mb\": 4.152528762817383,         \"Time in s\": 218.482328       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7319932998324958,         \"MicroF1\": 0.7319932998324957,         \"MacroF1\": 0.7251188986558661,         \"Memory in Mb\": 4.152769088745117,         \"Time in s\": 254.840787       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7309486780715396,         \"MicroF1\": 0.7309486780715396,         \"MacroF1\": 0.7259740406437201,         \"Memory in Mb\": 4.152563095092773,         \"Time in s\": 294.12903800000004       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7358490566037735,         \"MicroF1\": 0.7358490566037735,         \"MacroF1\": 0.7304359912942561,         \"Memory in Mb\": 4.152692794799805,         \"Time in s\": 336.073433       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7374149659863946,         \"MicroF1\": 0.7374149659863947,         \"MacroF1\": 0.733149934717071,         \"Memory in Mb\": 4.152753829956055,         \"Time in s\": 380.701162       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7426376440460948,         \"MicroF1\": 0.7426376440460948,         \"MacroF1\": 0.7385597120510639,         \"Memory in Mb\": 4.152643203735352,         \"Time in s\": 428.175969       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7436517533252721,         \"MicroF1\": 0.7436517533252721,         \"MacroF1\": 0.7412375783772316,         \"Memory in Mb\": 4.152631759643555,         \"Time in s\": 478.460063       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7491408934707904,         \"MicroF1\": 0.7491408934707904,         \"MacroF1\": 0.7454343548790067,         \"Memory in Mb\": 4.153181076049805,         \"Time in s\": 531.417765       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7486398258977149,         \"MicroF1\": 0.7486398258977149,         \"MacroF1\": 0.7441307384051415,         \"Memory in Mb\": 4.153326034545898,         \"Time in s\": 587.1362770000001       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7492227979274612,         \"MicroF1\": 0.749222797927461,         \"MacroF1\": 0.7439306216964366,         \"Memory in Mb\": 4.153120040893555,         \"Time in s\": 645.6842       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7487636003956478,         \"MicroF1\": 0.7487636003956478,         \"MacroF1\": 0.7437900284473965,         \"Memory in Mb\": 4.153234481811523,         \"Time in s\": 707.105172       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.750236518448439,         \"MicroF1\": 0.7502365184484389,         \"MacroF1\": 0.7448138061687654,         \"Memory in Mb\": 4.153268814086914,         \"Time in s\": 771.2868930000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7524932003626473,         \"MicroF1\": 0.7524932003626473,         \"MacroF1\": 0.7468314646869904,         \"Memory in Mb\": 4.153234481811523,         \"Time in s\": 838.222518       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7554395126196692,         \"MicroF1\": 0.7554395126196692,         \"MacroF1\": 0.7493227137357602,         \"Memory in Mb\": 4.153413772583008,         \"Time in s\": 907.556087       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7581589958158996,         \"MicroF1\": 0.7581589958158996,         \"MacroF1\": 0.7527652773681007,         \"Memory in Mb\": 4.153318405151367,         \"Time in s\": 979.579718       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7574536663980661,         \"MicroF1\": 0.7574536663980661,         \"MacroF1\": 0.7525915384194216,         \"Memory in Mb\": 4.153432846069336,         \"Time in s\": 1054.216781       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7622377622377622,         \"MicroF1\": 0.7622377622377621,         \"MacroF1\": 0.7563448085202398,         \"Memory in Mb\": 4.153615951538086,         \"Time in s\": 1131.5718310000002       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7621905476369092,         \"MicroF1\": 0.7621905476369092,         \"MacroF1\": 0.7566636999776912,         \"Memory in Mb\": 4.153776168823242,         \"Time in s\": 1211.5912470000003       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7635968092820885,         \"MicroF1\": 0.7635968092820886,         \"MacroF1\": 0.7587252257765656,         \"Memory in Mb\": 4.153825759887695,         \"Time in s\": 1294.4019940000005       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7663157894736842,         \"MicroF1\": 0.7663157894736842,         \"MacroF1\": 0.7609139797315134,         \"Memory in Mb\": 4.153848648071289,         \"Time in s\": 1379.8910190000004       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7709041468388851,         \"MicroF1\": 0.7709041468388851,         \"MacroF1\": 0.7637689949207689,         \"Memory in Mb\": 4.153989791870117,         \"Time in s\": 1467.9946540000003       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7719182597231378,         \"MicroF1\": 0.7719182597231378,         \"MacroF1\": 0.7639714255563932,         \"Memory in Mb\": 4.154367446899414,         \"Time in s\": 1558.8129900000004       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7722328854766475,         \"MicroF1\": 0.7722328854766475,         \"MacroF1\": 0.7650721335080709,         \"Memory in Mb\": 4.154550552368164,         \"Time in s\": 1652.2028900000005       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7725295214418894,         \"MicroF1\": 0.7725295214418892,         \"MacroF1\": 0.764505787280341,         \"Memory in Mb\": 4.154642105102539,         \"Time in s\": 1748.3782850000002       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7716012084592145,         \"MicroF1\": 0.7716012084592145,         \"MacroF1\": 0.7634170612719108,         \"Memory in Mb\": 4.15452766418457,         \"Time in s\": 1847.3163560000005       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7713109935332157,         \"MicroF1\": 0.7713109935332157,         \"MacroF1\": 0.7652815676598499,         \"Memory in Mb\": 4.154825210571289,         \"Time in s\": 1948.702894       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.77389811104751,         \"MicroF1\": 0.77389811104751,         \"MacroF1\": 0.7674409436090757,         \"Memory in Mb\": 4.155008316040039,         \"Time in s\": 2052.533374       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7752370329057445,         \"MicroF1\": 0.7752370329057446,         \"MacroF1\": 0.7674318582149376,         \"Memory in Mb\": 4.155046463012695,         \"Time in s\": 2159.053176       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7765089722675367,         \"MicroF1\": 0.7765089722675368,         \"MacroF1\": 0.7688731808749575,         \"Memory in Mb\": 4.154977798461914,         \"Time in s\": 2268.233507       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7750663129973475,         \"MicroF1\": 0.7750663129973475,         \"MacroF1\": 0.7678921362145585,         \"Memory in Mb\": 4.154905319213867,         \"Time in s\": 2379.837789       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7752459865354738,         \"MicroF1\": 0.7752459865354739,         \"MacroF1\": 0.7671636716269125,         \"Memory in Mb\": 4.155000686645508,         \"Time in s\": 2494.085284       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7759231158320687,         \"MicroF1\": 0.7759231158320687,         \"MacroF1\": 0.7670573130332384,         \"Memory in Mb\": 4.154901504516602,         \"Time in s\": 2611.052254       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7775580820563519,         \"MicroF1\": 0.7775580820563519,         \"MacroF1\": 0.7671264358471986,         \"Memory in Mb\": 4.154878616333008,         \"Time in s\": 2730.562491       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.77670372160464,         \"MicroF1\": 0.7767037216046399,         \"MacroF1\": 0.7665050383810529,         \"Memory in Mb\": 4.15495491027832,         \"Time in s\": 2852.439553       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7773049645390071,         \"MicroF1\": 0.7773049645390071,         \"MacroF1\": 0.766340416614934,         \"Memory in Mb\": 4.15495491027832,         \"Time in s\": 2976.99213       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7783433595557612,         \"MicroF1\": 0.7783433595557612,         \"MacroF1\": 0.766965714748886,         \"Memory in Mb\": 4.155027389526367,         \"Time in s\": 3104.012504       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.780244676030811,         \"MicroF1\": 0.780244676030811,         \"MacroF1\": 0.7678552364681828,         \"Memory in Mb\": 4.155023574829102,         \"Time in s\": 3233.660984       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7776298268974701,         \"MicroF1\": 0.7776298268974701,         \"MacroF1\": 0.7652407320979201,         \"Memory in Mb\": 4.154973983764648,         \"Time in s\": 3365.640643       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7768595041322314,         \"MicroF1\": 0.7768595041322314,         \"MacroF1\": 0.764461061100325,         \"Memory in Mb\": 4.15504264831543,         \"Time in s\": 3499.962334       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7769597228237333,         \"MicroF1\": 0.7769597228237333,         \"MacroF1\": 0.7645642360301897,         \"Memory in Mb\": 4.155065536499023,         \"Time in s\": 3634.881072       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360189573459716,         \"MicroF1\": 0.6360189573459716,         \"MacroF1\": 0.5970323052762561,         \"Memory in Mb\": 6.533428192138672,         \"Time in s\": 93.097088       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.62482235907153,         \"MicroF1\": 0.62482235907153,         \"MacroF1\": 0.5890580890213498,         \"Memory in Mb\": 6.533924102783203,         \"Time in s\": 264.682132       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6157246605620461,         \"MicroF1\": 0.6157246605620461,         \"MacroF1\": 0.5802533923244892,         \"Memory in Mb\": 6.534633636474609,         \"Time in s\": 504.284209       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6107032914989344,         \"MicroF1\": 0.6107032914989344,         \"MacroF1\": 0.5748501357120321,         \"Memory in Mb\": 6.535015106201172,         \"Time in s\": 804.5259470000001       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.614889183557492,         \"MicroF1\": 0.614889183557492,         \"MacroF1\": 0.5777842549225517,         \"Memory in Mb\": 6.535823822021484,         \"Time in s\": 1159.582019       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.608997632202052,         \"MicroF1\": 0.608997632202052,         \"MacroF1\": 0.5733157350789625,         \"Memory in Mb\": 6.535648345947266,         \"Time in s\": 1564.000203       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6057367068055743,         \"MicroF1\": 0.6057367068055743,         \"MacroF1\": 0.5703382690867537,         \"Memory in Mb\": 6.535068511962891,         \"Time in s\": 2016.310233       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6069610512608027,         \"MicroF1\": 0.6069610512608027,         \"MacroF1\": 0.5711427916016896,         \"Memory in Mb\": 6.534946441650391,         \"Time in s\": 2516.339397       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6039145532989583,         \"MicroF1\": 0.6039145532989583,         \"MacroF1\": 0.5678102867297489,         \"Memory in Mb\": 6.535068511962891,         \"Time in s\": 3064.243813       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6034662373330808,         \"MicroF1\": 0.6034662373330808,         \"MacroF1\": 0.567425153452482,         \"Memory in Mb\": 6.535427093505859,         \"Time in s\": 3659.768381       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6005165733964701,         \"MicroF1\": 0.6005165733964701,         \"MacroF1\": 0.56512832395729,         \"Memory in Mb\": 6.535404205322266,         \"Time in s\": 4303.846419       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6031883829216321,         \"MicroF1\": 0.6031883829216321,         \"MacroF1\": 0.5703828979306639,         \"Memory in Mb\": 6.535358428955078,         \"Time in s\": 4997.310473       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6147009543235958,         \"MicroF1\": 0.6147009543235958,         \"MacroF1\": 0.5955104002005771,         \"Memory in Mb\": 6.534030914306641,         \"Time in s\": 5738.022631999999       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6051545694378678,         \"MicroF1\": 0.6051545694378678,         \"MacroF1\": 0.586271708420286,         \"Memory in Mb\": 6.533008575439453,         \"Time in s\": 6524.316427       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5703642906749163,         \"MicroF1\": 0.5703642906749163,         \"MacroF1\": 0.5530031721301686,         \"Memory in Mb\": 6.534244537353516,         \"Time in s\": 7355.370967999999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5440662918023084,         \"MicroF1\": 0.5440662918023084,         \"MacroF1\": 0.5274181049148582,         \"Memory in Mb\": 6.532741546630859,         \"Time in s\": 8230.882624       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.524650437301543,         \"MicroF1\": 0.524650437301543,         \"MacroF1\": 0.5077439094080566,         \"Memory in Mb\": 6.533657073974609,         \"Time in s\": 9149.482306       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5142842110801283,         \"MicroF1\": 0.5142842110801283,         \"MacroF1\": 0.4945495171544722,         \"Memory in Mb\": 5.423342704772949,         \"Time in s\": 10110.1367       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5202611772915317,         \"MicroF1\": 0.5202611772915317,         \"MacroF1\": 0.499632175624185,         \"Memory in Mb\": 13.463048934936523,         \"Time in s\": 11121.939621       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5284814621904447,         \"MicroF1\": 0.5284814621904447,         \"MacroF1\": 0.5082299437323158,         \"Memory in Mb\": 14.233846664428713,         \"Time in s\": 12202.11771       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5344757609921083,         \"MicroF1\": 0.5344757609921083,         \"MacroF1\": 0.5148729059414189,         \"Memory in Mb\": 14.772774696350098,         \"Time in s\": 13344.394485       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5430674529723215,         \"MicroF1\": 0.5430674529723215,         \"MacroF1\": 0.5233933209280776,         \"Memory in Mb\": 14.684733390808104,         \"Time in s\": 14542.607494       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5502120475974801,         \"MicroF1\": 0.5502120475974801,         \"MacroF1\": 0.5298443248135049,         \"Memory in Mb\": 16.20911407470703,         \"Time in s\": 15791.070918       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5564061081955569,         \"MicroF1\": 0.5564061081955569,         \"MacroF1\": 0.5355525016331893,         \"Memory in Mb\": 16.199478149414062,         \"Time in s\": 17093.843057       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.561460661388689,         \"MicroF1\": 0.561460661388689,         \"MacroF1\": 0.5398397773012414,         \"Memory in Mb\": 16.192718505859375,         \"Time in s\": 18441.382026       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.564742305590967,         \"MicroF1\": 0.564742305590967,         \"MacroF1\": 0.5421523628031605,         \"Memory in Mb\": 15.229331016540527,         \"Time in s\": 19838.570208       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5680614499666795,         \"MicroF1\": 0.5680614499666795,         \"MacroF1\": 0.5472893783055924,         \"Memory in Mb\": 13.71937370300293,         \"Time in s\": 21280.868604       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5701288598775662,         \"MicroF1\": 0.5701288598775662,         \"MacroF1\": 0.55295508639855,         \"Memory in Mb\": 11.343052864074709,         \"Time in s\": 22768.358846       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5724128922705156,         \"MicroF1\": 0.5724128922705156,         \"MacroF1\": 0.5585792537754973,         \"Memory in Mb\": 9.387857437133787,         \"Time in s\": 24294.822849       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5749865841724802,         \"MicroF1\": 0.5749865841724802,         \"MacroF1\": 0.5636037623129485,         \"Memory in Mb\": 9.38664436340332,         \"Time in s\": 25857.719223       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5781884832747823,         \"MicroF1\": 0.5781884832747823,         \"MacroF1\": 0.5684564968293649,         \"Memory in Mb\": 9.385660171508787,         \"Time in s\": 27456.12944       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.575656239827173,         \"MicroF1\": 0.575656239827173,         \"MacroF1\": 0.5663415557568727,         \"Memory in Mb\": 7.860757827758789,         \"Time in s\": 29092.739018       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5754584325766924,         \"MicroF1\": 0.5754584325766924,         \"MacroF1\": 0.565994999425249,         \"Memory in Mb\": 7.205549240112305,         \"Time in s\": 30762.023764       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5763863743976827,         \"MicroF1\": 0.5763863743976827,         \"MacroF1\": 0.5665127709334143,         \"Memory in Mb\": 6.54947566986084,         \"Time in s\": 32461.363070000003       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5758813820720258,         \"MicroF1\": 0.5758813820720258,         \"MacroF1\": 0.56571927622701,         \"Memory in Mb\": 6.547377586364746,         \"Time in s\": 34189.07998       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5767460213073786,         \"MicroF1\": 0.5767460213073786,         \"MacroF1\": 0.5661110063916132,         \"Memory in Mb\": 6.546515464782715,         \"Time in s\": 35945.284935       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5764633615725219,         \"MicroF1\": 0.5764633615725219,         \"MacroF1\": 0.5659285794545608,         \"Memory in Mb\": 6.543356895446777,         \"Time in s\": 37730.818682000005       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.573454282652578,         \"MicroF1\": 0.573454282652578,         \"MacroF1\": 0.5636611811263741,         \"Memory in Mb\": 8.510072708129883,         \"Time in s\": 39542.33547700001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5726391957846685,         \"MicroF1\": 0.5726391957846685,         \"MacroF1\": 0.5633960246210544,         \"Memory in Mb\": 8.712862014770508,         \"Time in s\": 41378.34519600001       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5723146854802433,         \"MicroF1\": 0.5723146854802433,         \"MacroF1\": 0.5635786987292998,         \"Memory in Mb\": 10.13754653930664,         \"Time in s\": 43237.00688100001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5717981291142165,         \"MicroF1\": 0.5717981291142165,         \"MacroF1\": 0.5635967907133216,         \"Memory in Mb\": 10.13637924194336,         \"Time in s\": 45117.76335600001       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.571103244571712,         \"MicroF1\": 0.571103244571712,         \"MacroF1\": 0.5633625241299441,         \"Memory in Mb\": 10.135028839111328,         \"Time in s\": 47020.66134100001       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5712335102517233,         \"MicroF1\": 0.5712335102517233,         \"MacroF1\": 0.563808836162261,         \"Memory in Mb\": 11.334146499633787,         \"Time in s\": 48947.97157600001       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5728213847577642,         \"MicroF1\": 0.5728213847577642,         \"MacroF1\": 0.5658781423773395,         \"Memory in Mb\": 12.350201606750488,         \"Time in s\": 50897.74209700001       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.576863991245607,         \"MicroF1\": 0.576863991245607,         \"MacroF1\": 0.5703778478941884,         \"Memory in Mb\": 16.125893592834473,         \"Time in s\": 52890.49897200001       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5828512609366958,         \"MicroF1\": 0.5828512609366958,         \"MacroF1\": 0.5764029561430954,         \"Memory in Mb\": 15.266244888305664,         \"Time in s\": 54904.91240000001       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5890270194031956,         \"MicroF1\": 0.5890270194031956,         \"MacroF1\": 0.5823661991476956,         \"Memory in Mb\": 14.839654922485352,         \"Time in s\": 56940.07330000001       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5947087024286306,         \"MicroF1\": 0.5947087024286306,         \"MacroF1\": 0.5876086024291545,         \"Memory in Mb\": 12.465810775756836,         \"Time in s\": 58994.29364000001       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.600718937827339,         \"MicroF1\": 0.600718937827339,         \"MacroF1\": 0.5930357853224563,         \"Memory in Mb\": 11.884730339050291,         \"Time in s\": 61065.77177200001       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6060342051932802,         \"MicroF1\": 0.6060342051932802,         \"MacroF1\": 0.5982060206393416,         \"Memory in Mb\": 3.691446304321289,         \"Time in s\": 63151.215841000005       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6063920373909588,         \"MicroF1\": 0.6063920373909588,         \"MacroF1\": 0.5985419438128344,         \"Memory in Mb\": 3.691621780395508,         \"Time in s\": 65236.99615100001       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 2.1448841094970703,         \"Time in s\": 5.867596       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.943558282208589,         \"MicroF1\": 0.943558282208589,         \"MacroF1\": 0.7669956277713079,         \"Memory in Mb\": 3.0916757583618164,         \"Time in s\": 25.808269       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8912510220768601,         \"MicroF1\": 0.8912510220768601,         \"MacroF1\": 0.8617021305177773,         \"Memory in Mb\": 4.035944938659668,         \"Time in s\": 63.939426       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9031269160024524,         \"MicroF1\": 0.9031269160024524,         \"MacroF1\": 0.8868998230762758,         \"Memory in Mb\": 4.988290786743164,         \"Time in s\": 125.34339       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.898970083374203,         \"MicroF1\": 0.898970083374203,         \"MacroF1\": 0.888705938214812,         \"Memory in Mb\": 6.037667274475098,         \"Time in s\": 214.307845       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8594196975888844,         \"MicroF1\": 0.8594196975888844,         \"MacroF1\": 0.8547805855679916,         \"Memory in Mb\": 6.993380546569824,         \"Time in s\": 335.016386       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8651488616462347,         \"MicroF1\": 0.8651488616462347,         \"MacroF1\": 0.8483773016417727,         \"Memory in Mb\": 7.939821243286133,         \"Time in s\": 488.1215580000001       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8553478394115844,         \"MicroF1\": 0.8553478394115844,         \"MacroF1\": 0.8302147847543373,         \"Memory in Mb\": 8.885003089904785,         \"Time in s\": 675.394352       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8452737673658404,         \"MicroF1\": 0.8452737673658404,         \"MacroF1\": 0.8411086163638233,         \"Memory in Mb\": 9.830622673034668,         \"Time in s\": 899.024814       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8374601618043638,         \"MicroF1\": 0.8374601618043638,         \"MacroF1\": 0.8238000521910981,         \"Memory in Mb\": 11.003908157348633,         \"Time in s\": 1161.3046       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8250501448629374,         \"MicroF1\": 0.8250501448629373,         \"MacroF1\": 0.8343531144302688,         \"Memory in Mb\": 11.974610328674316,         \"Time in s\": 1461.738376       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8232890704800817,         \"MicroF1\": 0.8232890704800817,         \"MacroF1\": 0.8292209535545839,         \"Memory in Mb\": 12.919659614562988,         \"Time in s\": 1801.820426       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8199132566471808,         \"MicroF1\": 0.819913256647181,         \"MacroF1\": 0.8044565992905442,         \"Memory in Mb\": 13.86521053314209,         \"Time in s\": 2181.861898       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7998599194536858,         \"MicroF1\": 0.7998599194536857,         \"MacroF1\": 0.8029484507582976,         \"Memory in Mb\": 14.811628341674805,         \"Time in s\": 2601.779179       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7970256577872201,         \"MicroF1\": 0.7970256577872201,         \"MacroF1\": 0.7783451709211457,         \"Memory in Mb\": 15.75713062286377,         \"Time in s\": 3063.5971010000003       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7720239007200858,         \"MicroF1\": 0.7720239007200858,         \"MacroF1\": 0.767005590841987,         \"Memory in Mb\": 16.704151153564453,         \"Time in s\": 3570.6766780000003       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7645277577505407,         \"MicroF1\": 0.7645277577505407,         \"MacroF1\": 0.766187831914561,         \"Memory in Mb\": 17.649503707885742,         \"Time in s\": 4126.519897       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.773389622769985,         \"MicroF1\": 0.7733896227699851,         \"MacroF1\": 0.770832075885354,         \"Memory in Mb\": 18.61162567138672,         \"Time in s\": 4733.5217410000005       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7737066185008385,         \"MicroF1\": 0.7737066185008385,         \"MacroF1\": 0.7718493223486268,         \"Memory in Mb\": 19.557814598083496,         \"Time in s\": 5395.069721000001       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7765657556073048,         \"MicroF1\": 0.7765657556073047,         \"MacroF1\": 0.7724710929560354,         \"Memory in Mb\": 20.503721237182617,         \"Time in s\": 6113.943535       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7730827594257033,         \"MicroF1\": 0.7730827594257033,         \"MacroF1\": 0.7727491763630034,         \"Memory in Mb\": 21.88267517089844,         \"Time in s\": 6890.839823       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7714763231197772,         \"MicroF1\": 0.7714763231197772,         \"MacroF1\": 0.7717207236627096,         \"Memory in Mb\": 22.87528133392334,         \"Time in s\": 7728.212391       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7702227432590856,         \"MicroF1\": 0.7702227432590856,         \"MacroF1\": 0.7694267539223918,         \"Memory in Mb\": 23.822596549987797,         \"Time in s\": 8626.275614       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7656010621999796,         \"MicroF1\": 0.7656010621999795,         \"MacroF1\": 0.7644081311179032,         \"Memory in Mb\": 24.768078804016117,         \"Time in s\": 9586.24664       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.757623296401608,         \"MicroF1\": 0.757623296401608,         \"MacroF1\": 0.749720417225094,         \"Memory in Mb\": 25.71299648284912,         \"Time in s\": 10618.940127       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.737154709154332,         \"MicroF1\": 0.737154709154332,         \"MacroF1\": 0.7245707699101513,         \"Memory in Mb\": 26.660439491271973,         \"Time in s\": 11726.561153       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.729822968679074,         \"MicroF1\": 0.7298229686790739,         \"MacroF1\": 0.7256689004292383,         \"Memory in Mb\": 27.605186462402344,         \"Time in s\": 12907.41343       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7229274271207213,         \"MicroF1\": 0.7229274271207213,         \"MacroF1\": 0.7092514304350318,         \"Memory in Mb\": 28.551199913024902,         \"Time in s\": 14153.769988       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7133801031189249,         \"MicroF1\": 0.7133801031189249,         \"MacroF1\": 0.7054771135814562,         \"Memory in Mb\": 29.4963436126709,         \"Time in s\": 15465.906612       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7177874009314487,         \"MicroF1\": 0.7177874009314487,         \"MacroF1\": 0.7138351093258007,         \"Memory in Mb\": 30.441871643066406,         \"Time in s\": 16835.329364       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7147149521625682,         \"MicroF1\": 0.7147149521625682,         \"MacroF1\": 0.7065885995198201,         \"Memory in Mb\": 31.388431549072266,         \"Time in s\": 18265.757575       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7031788586748372,         \"MicroF1\": 0.7031788586748372,         \"MacroF1\": 0.6954173783902821,         \"Memory in Mb\": 32.33424186706543,         \"Time in s\": 19760.458564       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7011067369828419,         \"MicroF1\": 0.7011067369828419,         \"MacroF1\": 0.6966368809795416,         \"Memory in Mb\": 33.27959156036377,         \"Time in s\": 21319.158047       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7007425564126595,         \"MicroF1\": 0.7007425564126595,         \"MacroF1\": 0.6971102154727419,         \"Memory in Mb\": 34.22630214691162,         \"Time in s\": 22941.129728       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6961972126899643,         \"MicroF1\": 0.6961972126899643,         \"MacroF1\": 0.691133802747568,         \"Memory in Mb\": 35.17108726501465,         \"Time in s\": 24623.129677       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.698781235105876,         \"MicroF1\": 0.698781235105876,         \"MacroF1\": 0.696592906911097,         \"Memory in Mb\": 36.11711597442627,         \"Time in s\": 26362.470984       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7048029148724744,         \"MicroF1\": 0.7048029148724744,         \"MacroF1\": 0.702773358939844,         \"Memory in Mb\": 37.0643196105957,         \"Time in s\": 28156.052692       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7047668193252918,         \"MicroF1\": 0.7047668193252918,         \"MacroF1\": 0.7013012225519919,         \"Memory in Mb\": 38.00920104980469,         \"Time in s\": 30004.434818       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6956822324178241,         \"MicroF1\": 0.6956822324178241,         \"MacroF1\": 0.6887843659114408,         \"Memory in Mb\": 38.955204010009766,         \"Time in s\": 31904.325566000003       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6869906244255163,         \"MicroF1\": 0.6869906244255163,         \"MacroF1\": 0.6817298949676788,         \"Memory in Mb\": 39.901418685913086,         \"Time in s\": 33880.147234000004       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6840437615830693,         \"MicroF1\": 0.6840437615830693,         \"MacroF1\": 0.6809878840610977,         \"Memory in Mb\": 40.84670162200928,         \"Time in s\": 35894.19480500001       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6798949518529326,         \"MicroF1\": 0.6798949518529326,         \"MacroF1\": 0.6760668667678135,         \"Memory in Mb\": 42.678324699401855,         \"Time in s\": 37945.35177200001       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6725759562218548,         \"MicroF1\": 0.6725759562218548,         \"MacroF1\": 0.6693298574086026,         \"Memory in Mb\": 43.72208595275879,         \"Time in s\": 40033.19473500001       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6715503314578575,         \"MicroF1\": 0.6715503314578575,         \"MacroF1\": 0.6700615486077944,         \"Memory in Mb\": 44.66869449615479,         \"Time in s\": 42156.41544100001       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6768887194291628,         \"MicroF1\": 0.6768887194291628,         \"MacroF1\": 0.6760264883444682,         \"Memory in Mb\": 45.61451721191406,         \"Time in s\": 44306.462280000014       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6818884211648105,         \"MicroF1\": 0.6818884211648105,         \"MacroF1\": 0.6814185274246665,         \"Memory in Mb\": 46.56109237670898,         \"Time in s\": 46484.07667300002       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6739504563233377,         \"MicroF1\": 0.6739504563233377,         \"MacroF1\": 0.6724064481498903,         \"Memory in Mb\": 47.50611400604248,         \"Time in s\": 48682.185033000016       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.677883878874534,         \"MicroF1\": 0.677883878874534,         \"MacroF1\": 0.6774885006147249,         \"Memory in Mb\": 48.45180988311768,         \"Time in s\": 50904.928535000014       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6733530088539843,         \"MicroF1\": 0.6733530088539843,         \"MacroF1\": 0.6729949515014169,         \"Memory in Mb\": 49.39821243286133,         \"Time in s\": 53145.742009000016       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.6697387126819943,         \"MicroF1\": 0.6697387126819943,         \"MacroF1\": 0.6699810213452306,         \"Memory in Mb\": 50.34487438201904,         \"Time in s\": 55411.38251600001       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.3777777777777777,         \"MicroF1\": 0.3777777777777777,         \"MacroF1\": 0.2811210847975554,         \"Memory in Mb\": 4.09740161895752,         \"Time in s\": 6.997987       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5164835164835165,         \"MicroF1\": 0.5164835164835165,         \"MacroF1\": 0.5316649744849407,         \"Memory in Mb\": 4.097981452941895,         \"Time in s\": 22.017115       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5547445255474452,         \"MicroF1\": 0.5547445255474452,         \"MacroF1\": 0.5804654781117263,         \"Memory in Mb\": 4.0981035232543945,         \"Time in s\": 44.610384       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6174863387978142,         \"MicroF1\": 0.6174863387978142,         \"MacroF1\": 0.6394923756219437,         \"Memory in Mb\": 4.098713874816895,         \"Time in s\": 74.61421299999999       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6506550218340611,         \"MicroF1\": 0.6506550218340611,         \"MacroF1\": 0.66859135700569,         \"Memory in Mb\": 4.098713874816895,         \"Time in s\": 111.653321       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6618181818181819,         \"MicroF1\": 0.6618181818181819,         \"MacroF1\": 0.6795855359270878,         \"Memory in Mb\": 4.098832130432129,         \"Time in s\": 156.076644       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6853582554517134,         \"MicroF1\": 0.6853582554517134,         \"MacroF1\": 0.6872635633687633,         \"Memory in Mb\": 4.099373817443848,         \"Time in s\": 207.574915       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7111716621253406,         \"MicroF1\": 0.7111716621253404,         \"MacroF1\": 0.7098417316927395,         \"Memory in Mb\": 4.099347114562988,         \"Time in s\": 266.341739       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7215496368038741,         \"MicroF1\": 0.7215496368038742,         \"MacroF1\": 0.7201557312728714,         \"Memory in Mb\": 4.09926700592041,         \"Time in s\": 332.356571       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7211328976034859,         \"MicroF1\": 0.721132897603486,         \"MacroF1\": 0.7175330036146421,         \"Memory in Mb\": 4.099320411682129,         \"Time in s\": 405.380301       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7287128712871287,         \"MicroF1\": 0.7287128712871287,         \"MacroF1\": 0.7233455022590812,         \"Memory in Mb\": 4.099320411682129,         \"Time in s\": 485.520305       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7295825771324864,         \"MicroF1\": 0.7295825771324864,         \"MacroF1\": 0.7255599965917697,         \"Memory in Mb\": 4.099240303039551,         \"Time in s\": 572.983507       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7353433835845896,         \"MicroF1\": 0.7353433835845896,         \"MacroF1\": 0.7308494254186014,         \"Memory in Mb\": 4.0992631912231445,         \"Time in s\": 667.526521       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7340590979782271,         \"MicroF1\": 0.7340590979782271,         \"MacroF1\": 0.7314183982762247,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 768.914228       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.737300435413643,         \"MicroF1\": 0.737300435413643,         \"MacroF1\": 0.7343909641298695,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 877.069835       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7387755102040816,         \"MicroF1\": 0.7387755102040816,         \"MacroF1\": 0.7369557659594496,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 992.190131       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7439180537772087,         \"MicroF1\": 0.7439180537772088,         \"MacroF1\": 0.7419020281650245,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 1114.103609       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7436517533252721,         \"MicroF1\": 0.7436517533252721,         \"MacroF1\": 0.7432199627682998,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 1242.576589       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7502863688430699,         \"MicroF1\": 0.7502863688430699,         \"MacroF1\": 0.7482089866208982,         \"Memory in Mb\": 4.099850654602051,         \"Time in s\": 1377.530874       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.750816104461371,         \"MicroF1\": 0.750816104461371,         \"MacroF1\": 0.7477650187313974,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1518.374517       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7512953367875648,         \"MicroF1\": 0.7512953367875648,         \"MacroF1\": 0.747322646811651,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1664.895359       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7507418397626113,         \"MicroF1\": 0.7507418397626113,         \"MacroF1\": 0.7469783619055548,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1817.198797       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7530747398297067,         \"MicroF1\": 0.7530747398297066,         \"MacroF1\": 0.7482363934596314,         \"Memory in Mb\": 4.099823951721191,         \"Time in s\": 1975.112421       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7552130553037172,         \"MicroF1\": 0.7552130553037172,         \"MacroF1\": 0.750118495060715,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2138.658016       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7571801566579635,         \"MicroF1\": 0.7571801566579635,         \"MacroF1\": 0.7516199800653577,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2307.825702       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7598326359832636,         \"MicroF1\": 0.7598326359832636,         \"MacroF1\": 0.7548841797367702,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2482.820129       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7598710717163578,         \"MicroF1\": 0.7598710717163577,         \"MacroF1\": 0.7553301531902636,         \"Memory in Mb\": 4.0998735427856445,         \"Time in s\": 2663.447895       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7645687645687645,         \"MicroF1\": 0.7645687645687647,         \"MacroF1\": 0.7590078532621816,         \"Memory in Mb\": 4.1004838943481445,         \"Time in s\": 2849.419913       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7644411102775694,         \"MicroF1\": 0.7644411102775694,         \"MacroF1\": 0.7591993978414527,         \"Memory in Mb\": 4.100506782531738,         \"Time in s\": 3040.9965970000003       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7650471356055112,         \"MicroF1\": 0.7650471356055112,         \"MacroF1\": 0.7601575050520947,         \"Memory in Mb\": 4.100506782531738,         \"Time in s\": 3238.5768190000003       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7670175438596492,         \"MicroF1\": 0.7670175438596492,         \"MacroF1\": 0.7613339877221927,         \"Memory in Mb\": 4.100506782531738,         \"Time in s\": 3441.4807240000005       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7715839564921821,         \"MicroF1\": 0.7715839564921821,         \"MacroF1\": 0.7641396475218201,         \"Memory in Mb\": 4.100552558898926,         \"Time in s\": 3649.5015090000006       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7732366512854317,         \"MicroF1\": 0.7732366512854317,         \"MacroF1\": 0.7648275341801108,         \"Memory in Mb\": 4.100552558898926,         \"Time in s\": 3862.69427       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7735124760076776,         \"MicroF1\": 0.7735124760076776,         \"MacroF1\": 0.7657569341108763,         \"Memory in Mb\": 4.100552558898926,         \"Time in s\": 4080.891056       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7737725295214419,         \"MicroF1\": 0.7737725295214419,         \"MacroF1\": 0.7651494083475014,         \"Memory in Mb\": 4.10057544708252,         \"Time in s\": 4304.577590000001       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7740181268882175,         \"MicroF1\": 0.7740181268882175,         \"MacroF1\": 0.7654813489818475,         \"Memory in Mb\": 4.100529670715332,         \"Time in s\": 4533.710142000001       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7730746619635509,         \"MicroF1\": 0.7730746619635509,         \"MacroF1\": 0.766493027961906,         \"Memory in Mb\": 4.100529670715332,         \"Time in s\": 4767.793233000001       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7756153405838581,         \"MicroF1\": 0.7756153405838581,         \"MacroF1\": 0.7686072256536652,         \"Memory in Mb\": 4.100529670715332,         \"Time in s\": 5007.029776000001       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7769102063580591,         \"MicroF1\": 0.7769102063580591,         \"MacroF1\": 0.7685414235990152,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 5251.440116000002       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7781402936378466,         \"MicroF1\": 0.7781402936378466,         \"MacroF1\": 0.7699957723931323,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 5500.964415000001       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7761273209549071,         \"MicroF1\": 0.7761273209549071,         \"MacroF1\": 0.7684985598909853,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 5755.503987000001       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7762817193164163,         \"MicroF1\": 0.7762817193164163,         \"MacroF1\": 0.7677434418046419,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6014.862306000001       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7774405665149215,         \"MicroF1\": 0.7774405665149215,         \"MacroF1\": 0.7684788817649146,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6279.121569000001       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7790410281759763,         \"MicroF1\": 0.7790410281759763,         \"MacroF1\": 0.7689103339153599,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6548.278113000001       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7786370227162881,         \"MicroF1\": 0.7786370227162881,         \"MacroF1\": 0.7686288077529282,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 6822.363214000001       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7791962174940898,         \"MicroF1\": 0.7791962174940898,         \"MacroF1\": 0.768391950800897,         \"Memory in Mb\": 4.100502967834473,         \"Time in s\": 7101.096348000001       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7801943544655252,         \"MicroF1\": 0.7801943544655253,         \"MacroF1\": 0.768962628827985,         \"Memory in Mb\": 4.100525856018066,         \"Time in s\": 7384.333285000001       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7820570910738559,         \"MicroF1\": 0.7820570910738559,         \"MacroF1\": 0.7698068761587117,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 7672.298476000001       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7789613848202397,         \"MicroF1\": 0.7789613848202397,         \"MacroF1\": 0.7667173742344939,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 7965.117559000001       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7781644193127447,         \"MicroF1\": 0.7781644193127447,         \"MacroF1\": 0.7659138381656089,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 8262.647904000001       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7782589865742746,         \"MicroF1\": 0.7782589865742745,         \"MacroF1\": 0.7660163657276376,         \"Memory in Mb\": 4.100499153137207,         \"Time in s\": 8561.303246000001       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6218009478672986,         \"MicroF1\": 0.6218009478672986,         \"MacroF1\": 0.5857016652718549,         \"Memory in Mb\": 6.471495628356934,         \"Time in s\": 220.837673       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6196115585030791,         \"MicroF1\": 0.6196115585030791,         \"MacroF1\": 0.5856756432415233,         \"Memory in Mb\": 10.302834510803224,         \"Time in s\": 598.297395       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.628986422481844,         \"MicroF1\": 0.628986422481844,         \"MacroF1\": 0.5949930595607559,         \"Memory in Mb\": 19.024110794067383,         \"Time in s\": 1103.516793       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6294103717736207,         \"MicroF1\": 0.6294103717736207,         \"MacroF1\": 0.5952675443708706,         \"Memory in Mb\": 19.52926254272461,         \"Time in s\": 1735.893967       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6364841826103429,         \"MicroF1\": 0.6364841826103429,         \"MacroF1\": 0.5994911272790603,         \"Memory in Mb\": 18.82306957244873,         \"Time in s\": 2497.807238       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6352012628255722,         \"MicroF1\": 0.6352012628255722,         \"MacroF1\": 0.5993891820807258,         \"Memory in Mb\": 20.00343894958496,         \"Time in s\": 3379.788115       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.638749830875389,         \"MicroF1\": 0.638749830875389,         \"MacroF1\": 0.6030343276880051,         \"Memory in Mb\": 20.9547061920166,         \"Time in s\": 4385.582643       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6405824553095774,         \"MicroF1\": 0.6405824553095774,         \"MacroF1\": 0.6028521616895871,         \"Memory in Mb\": 23.98197650909424,         \"Time in s\": 5520.259032       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6449542249815847,         \"MicroF1\": 0.6449542249815847,         \"MacroF1\": 0.6055705492028415,         \"Memory in Mb\": 24.687146186828613,         \"Time in s\": 6764.141036999999       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6485462638507434,         \"MicroF1\": 0.6485462638507434,         \"MacroF1\": 0.6081614166360887,         \"Memory in Mb\": 28.76917839050293,         \"Time in s\": 8102.806145       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6490744726646578,         \"MicroF1\": 0.6490744726646578,         \"MacroF1\": 0.6078786452761632,         \"Memory in Mb\": 30.803756713867188,         \"Time in s\": 9530.02909       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6514876489621971,         \"MicroF1\": 0.6514876489621971,         \"MacroF1\": 0.6111938480023122,         \"Memory in Mb\": 35.14385414123535,         \"Time in s\": 11044.69783       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6707947840023312,         \"MicroF1\": 0.6707947840023312,         \"MacroF1\": 0.6607574394823457,         \"Memory in Mb\": 17.51351547241211,         \"Time in s\": 12617.098737       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6821348846648176,         \"MicroF1\": 0.6821348846648176,         \"MacroF1\": 0.6733632096765088,         \"Memory in Mb\": 9.275564193725586,         \"Time in s\": 14250.678949       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6778205694803965,         \"MicroF1\": 0.6778205694803965,         \"MacroF1\": 0.670556396248407,         \"Memory in Mb\": 11.964457511901855,         \"Time in s\": 15956.730999       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6754661142349808,         \"MicroF1\": 0.6754661142349808,         \"MacroF1\": 0.6690281338426608,         \"Memory in Mb\": 12.60369873046875,         \"Time in s\": 17732.973803       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6721631106902123,         \"MicroF1\": 0.6721631106902123,         \"MacroF1\": 0.6660357480506892,         \"Memory in Mb\": 12.93508529663086,         \"Time in s\": 19577.321708       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6856947440416689,         \"MicroF1\": 0.6856947440416689,         \"MacroF1\": 0.6751812770122833,         \"Memory in Mb\": 14.563780784606934,         \"Time in s\": 21465.395048       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6926680954991776,         \"MicroF1\": 0.6926680954991776,         \"MacroF1\": 0.6785701715539604,         \"Memory in Mb\": 23.61655616760254,         \"Time in s\": 23398.659989       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6942090061082438,         \"MicroF1\": 0.6942090061082438,         \"MacroF1\": 0.6784920731228882,         \"Memory in Mb\": 30.02095413208008,         \"Time in s\": 25401.280766       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6958737316798196,         \"MicroF1\": 0.6958737316798196,         \"MacroF1\": 0.6784853924286285,         \"Memory in Mb\": 31.29345321655273,         \"Time in s\": 27443.688764       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6989798114588266,         \"MicroF1\": 0.6989798114588266,         \"MacroF1\": 0.6799590657327791,         \"Memory in Mb\": 29.59604263305664,         \"Time in s\": 29526.276677       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7011981718614897,         \"MicroF1\": 0.7011981718614897,         \"MacroF1\": 0.680282364066019,         \"Memory in Mb\": 32.615909576416016,         \"Time in s\": 31645.669428       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7031527443475516,         \"MicroF1\": 0.7031527443475516,         \"MacroF1\": 0.6805566439417602,         \"Memory in Mb\": 33.91432285308838,         \"Time in s\": 33792.819539       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7051782264479716,         \"MicroF1\": 0.7051782264479716,         \"MacroF1\": 0.6809495737401271,         \"Memory in Mb\": 35.12977695465088,         \"Time in s\": 35966.301701       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7065743944636678,         \"MicroF1\": 0.7065743944636678,         \"MacroF1\": 0.6805936316849747,         \"Memory in Mb\": 38.84447956085205,         \"Time in s\": 38159.78466       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7054820946301428,         \"MicroF1\": 0.7054820946301428,         \"MacroF1\": 0.681225779493031,         \"Memory in Mb\": 34.570815086364746,         \"Time in s\": 40377.715599       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7045692833226231,         \"MicroF1\": 0.7045692833226231,         \"MacroF1\": 0.6849598194839713,         \"Memory in Mb\": 20.38253498077393,         \"Time in s\": 42611.23284999999       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7031316330862424,         \"MicroF1\": 0.7031316330862424,         \"MacroF1\": 0.6877640955933652,         \"Memory in Mb\": 22.55568027496338,         \"Time in s\": 44864.71640799999       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7032418952618454,         \"MicroF1\": 0.7032418952618454,         \"MacroF1\": 0.6917227552448634,         \"Memory in Mb\": 26.177990913391117,         \"Time in s\": 47133.482559       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7037421719871697,         \"MicroF1\": 0.7037421719871697,         \"MacroF1\": 0.6952024388211077,         \"Memory in Mb\": 25.7611780166626,         \"Time in s\": 49415.922785       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7002160338551685,         \"MicroF1\": 0.7002160338551685,         \"MacroF1\": 0.6931280234945141,         \"Memory in Mb\": 25.958494186401367,         \"Time in s\": 51714.47139399999       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6973627571957414,         \"MicroF1\": 0.6973627571957414,         \"MacroF1\": 0.6902163957562899,         \"Memory in Mb\": 18.894118309021,         \"Time in s\": 54032.337052       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6951786758766677,         \"MicroF1\": 0.6951786758766677,         \"MacroF1\": 0.6877287571005829,         \"Memory in Mb\": 18.049145698547363,         \"Time in s\": 56371.370632       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6919830081982737,         \"MicroF1\": 0.6919830081982737,         \"MacroF1\": 0.6843647347906762,         \"Memory in Mb\": 22.045016288757324,         \"Time in s\": 58731.919307       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6900697093252663,         \"MicroF1\": 0.6900697093252663,         \"MacroF1\": 0.68217396069655,         \"Memory in Mb\": 25.079078674316406,         \"Time in s\": 61114.705624       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.688720534411712,         \"MicroF1\": 0.688720534411712,         \"MacroF1\": 0.6808510434728485,         \"Memory in Mb\": 19.794261932373047,         \"Time in s\": 63520.517783       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6867695068158597,         \"MicroF1\": 0.6867695068158597,         \"MacroF1\": 0.6796002866264578,         \"Memory in Mb\": 10.854747772216797,         \"Time in s\": 65948.78476699999       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6843843333414273,         \"MicroF1\": 0.6843843333414273,         \"MacroF1\": 0.6779529807793833,         \"Memory in Mb\": 10.474969863891602,         \"Time in s\": 68395.63477799999       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6822131205757712,         \"MicroF1\": 0.6822131205757712,         \"MacroF1\": 0.6764872431583758,         \"Memory in Mb\": 14.707494735717772,         \"Time in s\": 70864.05938699999       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6795472918350849,         \"MicroF1\": 0.6795472918350849,         \"MacroF1\": 0.674587653669649,         \"Memory in Mb\": 12.672552108764648,         \"Time in s\": 73351.02096199998       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6769633153705666,         \"MicroF1\": 0.6769633153705666,         \"MacroF1\": 0.6725984110786069,         \"Memory in Mb\": 13.144417762756348,         \"Time in s\": 75857.66838799998       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6748959411544475,         \"MicroF1\": 0.6748959411544475,         \"MacroF1\": 0.6710316194917795,         \"Memory in Mb\": 14.719610214233398,         \"Time in s\": 78383.45415699997       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6743215031315241,         \"MicroF1\": 0.6743215031315241,         \"MacroF1\": 0.670959098678123,         \"Memory in Mb\": 15.027325630187988,         \"Time in s\": 80927.72302099997       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6765293882447021,         \"MicroF1\": 0.6765293882447021,         \"MacroF1\": 0.6733002712216741,         \"Memory in Mb\": 17.283148765563965,         \"Time in s\": 83488.02074099997       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6805970149253732,         \"MicroF1\": 0.6805970149253732,         \"MacroF1\": 0.6770692638556323,         \"Memory in Mb\": 17.906007766723633,         \"Time in s\": 86063.52222099998       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6848340754770205,         \"MicroF1\": 0.6848340754770205,         \"MacroF1\": 0.6808344811077705,         \"Memory in Mb\": 18.8202543258667,         \"Time in s\": 88653.18323199998       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6890524197525992,         \"MicroF1\": 0.6890524197525992,         \"MacroF1\": 0.6843657264244208,         \"Memory in Mb\": 21.50714492797852,         \"Time in s\": 91255.74433499998       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6932531936687089,         \"MicroF1\": 0.6932531936687089,         \"MacroF1\": 0.6877873898777546,         \"Memory in Mb\": 23.154582023620605,         \"Time in s\": 93870.637319       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6956002954601412,         \"MicroF1\": 0.6956002954601412,         \"MacroF1\": 0.6902433463100389,         \"Memory in Mb\": 14.128369331359863,         \"Time in s\": 96495.216564       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6958578538043787,         \"MicroF1\": 0.6958578538043787,         \"MacroF1\": 0.6905081705907102,         \"Memory in Mb\": 13.83100128173828,         \"Time in s\": 99120.191439       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 2.0028390884399414,         \"Time in s\": 23.27864       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9521472392638036,         \"MicroF1\": 0.9521472392638036,         \"MacroF1\": 0.8408896590786493,         \"Memory in Mb\": 4.076430320739746,         \"Time in s\": 76.761208       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9533932951757972,         \"MicroF1\": 0.9533932951757972,         \"MacroF1\": 0.9542235338779168,         \"Memory in Mb\": 5.6716413497924805,         \"Time in s\": 164.877928       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589209074187616,         \"MicroF1\": 0.9589209074187616,         \"MacroF1\": 0.936122253486076,         \"Memory in Mb\": 8.122180938720703,         \"Time in s\": 291.606081       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9573320255026974,         \"MicroF1\": 0.9573320255026974,         \"MacroF1\": 0.9445755787125868,         \"Memory in Mb\": 10.5212984085083,         \"Time in s\": 455.912148       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9607682876992236,         \"MicroF1\": 0.9607682876992236,         \"MacroF1\": 0.9588299190873342,         \"Memory in Mb\": 9.06541347503662,         \"Time in s\": 649.921126       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9618213660245184,         \"MicroF1\": 0.9618213660245184,         \"MacroF1\": 0.9516555143941908,         \"Memory in Mb\": 13.188368797302246,         \"Time in s\": 870.236672       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9589334967821024,         \"MicroF1\": 0.9589334967821024,         \"MacroF1\": 0.9492703335352553,         \"Memory in Mb\": 13.21088695526123,         \"Time in s\": 1122.958204       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9585943884500135,         \"MicroF1\": 0.9585943884500135,         \"MacroF1\": 0.9531276848185062,         \"Memory in Mb\": 16.65507411956787,         \"Time in s\": 1406.732623       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9541554302525128,         \"MicroF1\": 0.9541554302525128,         \"MacroF1\": 0.9416377826660955,         \"Memory in Mb\": 17.091320037841797,         \"Time in s\": 1722.764314       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9529752618676176,         \"MicroF1\": 0.9529752618676176,         \"MacroF1\": 0.9549694463549354,         \"Memory in Mb\": 10.336687088012695,         \"Time in s\": 2070.686487       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9550561797752808,         \"MicroF1\": 0.9550561797752808,         \"MacroF1\": 0.95517907029875,         \"Memory in Mb\": 11.520882606506348,         \"Time in s\": 2449.358224       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568168960965492,         \"MicroF1\": 0.9568168960965492,         \"MacroF1\": 0.9575833276239932,         \"Memory in Mb\": 13.737529754638672,         \"Time in s\": 2856.6015070000003       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9574505340570828,         \"MicroF1\": 0.9574505340570828,         \"MacroF1\": 0.9570632809827344,         \"Memory in Mb\": 15.842782020568848,         \"Time in s\": 3290.691514       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9557117176009152,         \"MicroF1\": 0.9557117176009152,         \"MacroF1\": 0.9522483041543378,         \"Memory in Mb\": 20.04281044006348,         \"Time in s\": 3760.43818       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566416424084572,         \"MicroF1\": 0.9566416424084572,         \"MacroF1\": 0.9568246790885272,         \"Memory in Mb\": 11.687369346618652,         \"Time in s\": 4258.095146       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.95746214852199,         \"MicroF1\": 0.95746214852199,         \"MacroF1\": 0.9579855320572276,         \"Memory in Mb\": 12.48728847503662,         \"Time in s\": 4780.363237       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568296336647149,         \"MicroF1\": 0.9568296336647149,         \"MacroF1\": 0.9563404233689646,         \"Memory in Mb\": 15.327423095703123,         \"Time in s\": 5334.336646       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9565217391304348,         \"MicroF1\": 0.9565217391304348,         \"MacroF1\": 0.9563017119581124,         \"Memory in Mb\": 18.70553493499756,         \"Time in s\": 5920.99825       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.954528741267312,         \"MicroF1\": 0.954528741267312,         \"MacroF1\": 0.9527980459948604,         \"Memory in Mb\": 24.08679676055908,         \"Time in s\": 6539.621381999999       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.95459320649002,         \"MicroF1\": 0.95459320649002,         \"MacroF1\": 0.9549210113442088,         \"Memory in Mb\": 21.21516990661621,         \"Time in s\": 7187.28418       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9550974930362116,         \"MicroF1\": 0.9550974930362116,         \"MacroF1\": 0.955362716075958,         \"Memory in Mb\": 17.566545486450195,         \"Time in s\": 7867.802732999999       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9555579239049344,         \"MicroF1\": 0.9555579239049344,         \"MacroF1\": 0.9558253322166266,         \"Memory in Mb\": 15.710055351257324,         \"Time in s\": 8578.363562999999       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9552650393218262,         \"MicroF1\": 0.9552650393218262,         \"MacroF1\": 0.955371511778808,         \"Memory in Mb\": 18.71725273132324,         \"Time in s\": 9321.146736       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9533287577213452,         \"MicroF1\": 0.9533287577213452,         \"MacroF1\": 0.9523119157834916,         \"Memory in Mb\": 15.605277061462402,         \"Time in s\": 10099.276789999998       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9521070990855096,         \"MicroF1\": 0.9521070990855096,         \"MacroF1\": 0.9515822083565744,         \"Memory in Mb\": 11.186952590942385,         \"Time in s\": 10903.979313999998       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.953427144802542,         \"MicroF1\": 0.953427144802542,         \"MacroF1\": 0.9541201209142028,         \"Memory in Mb\": 7.581887245178223,         \"Time in s\": 11728.435273       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.953689923837871,         \"MicroF1\": 0.953689923837871,         \"MacroF1\": 0.9538275342826804,         \"Memory in Mb\": 10.15964126586914,         \"Time in s\": 12573.844722999998       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9535964838137098,         \"MicroF1\": 0.9535964838137098,         \"MacroF1\": 0.9538502960885475,         \"Memory in Mb\": 11.061944961547852,         \"Time in s\": 13441.174569       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9541629218073372,         \"MicroF1\": 0.9541629218073372,         \"MacroF1\": 0.9544632162431566,         \"Memory in Mb\": 11.249642372131348,         \"Time in s\": 14331.138910999998       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9548509527951292,         \"MicroF1\": 0.9548509527951292,         \"MacroF1\": 0.9551609875055332,         \"Memory in Mb\": 13.203255653381348,         \"Time in s\": 15243.410521999998       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9551895825354272,         \"MicroF1\": 0.955189582535427,         \"MacroF1\": 0.9553883557595892,         \"Memory in Mb\": 9.36058521270752,         \"Time in s\": 16176.429406999998       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.955953353635891,         \"MicroF1\": 0.955953353635891,         \"MacroF1\": 0.9562606797905644,         \"Memory in Mb\": 11.575583457946776,         \"Time in s\": 17130.275872       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9561675437964098,         \"MicroF1\": 0.9561675437964098,         \"MacroF1\": 0.9563487774281332,         \"Memory in Mb\": 11.42638874053955,         \"Time in s\": 18106.07239       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9549688353526156,         \"MicroF1\": 0.9549688353526156,         \"MacroF1\": 0.954852939557476,         \"Memory in Mb\": 10.249165534973145,         \"Time in s\": 19109.380901       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9552665622659496,         \"MicroF1\": 0.9552665622659496,         \"MacroF1\": 0.955472434271787,         \"Memory in Mb\": 8.168793678283691,         \"Time in s\": 20137.264239       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9560781715799934,         \"MicroF1\": 0.9560781715799934,         \"MacroF1\": 0.9563263247313608,         \"Memory in Mb\": 9.020037651062012,         \"Time in s\": 21191.151533       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563955363478036,         \"MicroF1\": 0.9563955363478036,         \"MacroF1\": 0.9565429512012836,         \"Memory in Mb\": 8.031278610229492,         \"Time in s\": 22273.408126999995       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566337753755264,         \"MicroF1\": 0.9566337753755264,         \"MacroF1\": 0.9567672375037608,         \"Memory in Mb\": 10.967172622680664,         \"Time in s\": 23379.117397999995       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9563085973405232,         \"MicroF1\": 0.9563085973405232,         \"MacroF1\": 0.9563585840602682,         \"Memory in Mb\": 11.29026985168457,         \"Time in s\": 24508.785403999995       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.955580797513003,         \"MicroF1\": 0.955580797513003,         \"MacroF1\": 0.9555776398983684,         \"Memory in Mb\": 9.525394439697266,         \"Time in s\": 25660.924918999997       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9564050189670266,         \"MicroF1\": 0.9564050189670268,         \"MacroF1\": 0.9565585833577668,         \"Memory in Mb\": 10.421767234802246,         \"Time in s\": 26839.493165999997       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566778772159836,         \"MicroF1\": 0.9566778772159836,         \"MacroF1\": 0.9567660151847868,         \"Memory in Mb\": 11.633780479431152,         \"Time in s\": 28038.177978999996       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9564369672998718,         \"MicroF1\": 0.9564369672998718,         \"MacroF1\": 0.9564736297242662,         \"Memory in Mb\": 8.448995590209961,         \"Time in s\": 29257.620389999996       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9567514570510376,         \"MicroF1\": 0.9567514570510376,         \"MacroF1\": 0.9568227044222712,         \"Memory in Mb\": 7.821832656860352,         \"Time in s\": 30499.29393699999       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568924175414292,         \"MicroF1\": 0.9568924175414292,         \"MacroF1\": 0.9569505378685396,         \"Memory in Mb\": 9.859258651733398,         \"Time in s\": 31763.565401999997       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9567144719687092,         \"MicroF1\": 0.9567144719687092,         \"MacroF1\": 0.956766336746882,         \"Memory in Mb\": 11.256629943847656,         \"Time in s\": 33053.804573999994       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9568503293673084,         \"MicroF1\": 0.9568503293673084,         \"MacroF1\": 0.9569026376832064,         \"Memory in Mb\": 11.690522193908691,         \"Time in s\": 34367.36625199999       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9564303936771548,         \"MicroF1\": 0.9564303936771548,         \"MacroF1\": 0.956465338137946,         \"Memory in Mb\": 12.451186180114746,         \"Time in s\": 35701.899461999994       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Leveraging Bagging\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9566155203686456,         \"MicroF1\": 0.9566155203686456,         \"MacroF1\": 0.9566498206969932,         \"Memory in Mb\": 7.4099931716918945,         \"Time in s\": 37049.10208799999       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4,         \"MicroF1\": 0.4000000000000001,         \"MacroF1\": 0.3289160825620571,         \"Memory in Mb\": 1.89190673828125,         \"Time in s\": 1.901401       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5494505494505495,         \"MicroF1\": 0.5494505494505495,         \"MacroF1\": 0.5607526488856412,         \"Memory in Mb\": 2.084074020385742,         \"Time in s\": 6.467373       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.5693430656934306,         \"MicroF1\": 0.5693430656934306,         \"MacroF1\": 0.5872103411959265,         \"Memory in Mb\": 2.357966423034668,         \"Time in s\": 13.822826       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6174863387978142,         \"MicroF1\": 0.6174863387978142,         \"MacroF1\": 0.6372989403156369,         \"Memory in Mb\": 2.7369613647460938,         \"Time in s\": 24.259991       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6375545851528385,         \"MicroF1\": 0.6375545851528385,         \"MacroF1\": 0.6548159763148107,         \"Memory in Mb\": 2.862431526184082,         \"Time in s\": 37.817905       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6618181818181819,         \"MicroF1\": 0.6618181818181819,         \"MacroF1\": 0.6802187985971371,         \"Memory in Mb\": 2.982741355895996,         \"Time in s\": 54.565381       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6915887850467289,         \"MicroF1\": 0.6915887850467289,         \"MacroF1\": 0.6955507555363084,         \"Memory in Mb\": 3.080752372741699,         \"Time in s\": 74.633343       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7111716621253406,         \"MicroF1\": 0.7111716621253404,         \"MacroF1\": 0.7105739026832886,         \"Memory in Mb\": 3.232259750366211,         \"Time in s\": 98.204704       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7263922518159807,         \"MicroF1\": 0.7263922518159807,         \"MacroF1\": 0.7261041400072307,         \"Memory in Mb\": 3.505929946899414,         \"Time in s\": 125.527545       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7276688453159041,         \"MicroF1\": 0.7276688453159043,         \"MacroF1\": 0.72519869331257,         \"Memory in Mb\": 3.787288665771485,         \"Time in s\": 156.78717       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7425742574257426,         \"MicroF1\": 0.7425742574257425,         \"MacroF1\": 0.7379486431795568,         \"Memory in Mb\": 6.240692138671875,         \"Time in s\": 210.523476       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7422867513611615,         \"MicroF1\": 0.7422867513611615,         \"MacroF1\": 0.7388440561615693,         \"Memory in Mb\": 6.313092231750488,         \"Time in s\": 268.009607       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7520938023450586,         \"MicroF1\": 0.7520938023450586,         \"MacroF1\": 0.749839509127547,         \"Memory in Mb\": 6.682056427001953,         \"Time in s\": 329.26112900000004       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7573872472783826,         \"MicroF1\": 0.7573872472783826,         \"MacroF1\": 0.7582793237949303,         \"Memory in Mb\": 7.269444465637207,         \"Time in s\": 394.37227100000007       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7634252539912917,         \"MicroF1\": 0.7634252539912917,         \"MacroF1\": 0.7648953830992049,         \"Memory in Mb\": 7.531791687011719,         \"Time in s\": 463.2777280000001       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7673469387755102,         \"MicroF1\": 0.7673469387755102,         \"MacroF1\": 0.7694390547687558,         \"Memory in Mb\": 7.987269401550293,         \"Time in s\": 536.1609010000001       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7772087067861716,         \"MicroF1\": 0.7772087067861717,         \"MacroF1\": 0.7788980835102386,         \"Memory in Mb\": 8.317158699035645,         \"Time in s\": 613.0067590000001       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7823458282950423,         \"MicroF1\": 0.7823458282950423,         \"MacroF1\": 0.7854763667551727,         \"Memory in Mb\": 8.613452911376953,         \"Time in s\": 693.8523060000001       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7915234822451317,         \"MicroF1\": 0.7915234822451317,         \"MacroF1\": 0.7933203073280156,         \"Memory in Mb\": 8.694649696350098,         \"Time in s\": 778.7710210000001       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7986942328618063,         \"MicroF1\": 0.7986942328618062,         \"MacroF1\": 0.7996826842527437,         \"Memory in Mb\": 8.824880599975586,         \"Time in s\": 867.8856220000001       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8041450777202073,         \"MicroF1\": 0.8041450777202073,         \"MacroF1\": 0.8044659150084363,         \"Memory in Mb\": 9.089361190795898,         \"Time in s\": 961.208295       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8100890207715133,         \"MicroF1\": 0.8100890207715133,         \"MacroF1\": 0.8093994872208631,         \"Memory in Mb\": 9.280214309692385,         \"Time in s\": 1058.9817440000002       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8145695364238411,         \"MicroF1\": 0.814569536423841,         \"MacroF1\": 0.8133421993203876,         \"Memory in Mb\": 9.165953636169434,         \"Time in s\": 1161.0697040000002       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8213961922030825,         \"MicroF1\": 0.8213961922030824,         \"MacroF1\": 0.8206569542548617,         \"Memory in Mb\": 8.760258674621582,         \"Time in s\": 1267.2341280000005       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.824194952132289,         \"MicroF1\": 0.824194952132289,         \"MacroF1\": 0.8228781271733864,         \"Memory in Mb\": 8.742037773132324,         \"Time in s\": 1377.3471480000003       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8292887029288702,         \"MicroF1\": 0.8292887029288704,         \"MacroF1\": 0.8281638601893785,         \"Memory in Mb\": 8.87535572052002,         \"Time in s\": 1491.4919770000004       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8340048348106366,         \"MicroF1\": 0.8340048348106366,         \"MacroF1\": 0.833490204478907,         \"Memory in Mb\": 8.332135200500488,         \"Time in s\": 1609.3898390000004       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8360528360528361,         \"MicroF1\": 0.8360528360528361,         \"MacroF1\": 0.8353480055004047,         \"Memory in Mb\": 8.416248321533203,         \"Time in s\": 1730.8650650000004       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8394598649662416,         \"MicroF1\": 0.8394598649662416,         \"MacroF1\": 0.8389194005130135,         \"Memory in Mb\": 8.469959259033203,         \"Time in s\": 1855.8596220000004       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8419144307469181,         \"MicroF1\": 0.8419144307469181,         \"MacroF1\": 0.8414934007209077,         \"Memory in Mb\": 8.578604698181152,         \"Time in s\": 1984.2269550000003       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8449122807017544,         \"MicroF1\": 0.8449122807017544,         \"MacroF1\": 0.8435602800871403,         \"Memory in Mb\": 8.689190864562988,         \"Time in s\": 2115.814455       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8484024473147519,         \"MicroF1\": 0.8484024473147518,         \"MacroF1\": 0.8459519552383536,         \"Memory in Mb\": 8.800261497497559,         \"Time in s\": 2250.613689       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8503625576796309,         \"MicroF1\": 0.8503625576796308,         \"MacroF1\": 0.8475723684173131,         \"Memory in Mb\": 9.025433540344238,         \"Time in s\": 2388.852428       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8522072936660269,         \"MicroF1\": 0.8522072936660269,         \"MacroF1\": 0.8497128793769615,         \"Memory in Mb\": 8.811847686767578,         \"Time in s\": 2530.498155       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8527035425730267,         \"MicroF1\": 0.8527035425730267,         \"MacroF1\": 0.8503048238231962,         \"Memory in Mb\": 8.729784965515137,         \"Time in s\": 2675.5358680000004       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8531722054380665,         \"MicroF1\": 0.8531722054380665,         \"MacroF1\": 0.8508343416398155,         \"Memory in Mb\": 8.761359214782715,         \"Time in s\": 2823.7565440000003       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8571428571428571,         \"MicroF1\": 0.8571428571428571,         \"MacroF1\": 0.8561317791292776,         \"Memory in Mb\": 8.798370361328125,         \"Time in s\": 2975.2442650000003       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8580423583285632,         \"MicroF1\": 0.8580423583285632,         \"MacroF1\": 0.8567712479140972,         \"Memory in Mb\": 8.86152172088623,         \"Time in s\": 3129.874549       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8611266034578918,         \"MicroF1\": 0.8611266034578918,         \"MacroF1\": 0.8591986188286931,         \"Memory in Mb\": 8.932531356811523,         \"Time in s\": 3287.541657       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8618814573137574,         \"MicroF1\": 0.8618814573137574,         \"MacroF1\": 0.8601172531559075,         \"Memory in Mb\": 8.819746017456055,         \"Time in s\": 3448.3205970000004       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8636604774535809,         \"MicroF1\": 0.8636604774535809,         \"MacroF1\": 0.8623243992773615,         \"Memory in Mb\": 9.007128715515137,         \"Time in s\": 3612.136702       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8648368720870016,         \"MicroF1\": 0.8648368720870016,         \"MacroF1\": 0.8630569076841595,         \"Memory in Mb\": 9.368453979492188,         \"Time in s\": 3779.147863       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8649468892261002,         \"MicroF1\": 0.8649468892261002,         \"MacroF1\": 0.8631362872103546,         \"Memory in Mb\": 8.952109336853027,         \"Time in s\": 3949.363777       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8665348492338112,         \"MicroF1\": 0.8665348492338112,         \"MacroF1\": 0.8639071890295129,         \"Memory in Mb\": 9.146061897277832,         \"Time in s\": 4122.536804       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8680521991300145,         \"MicroF1\": 0.8680521991300145,         \"MacroF1\": 0.8658036637930728,         \"Memory in Mb\": 8.80567455291748,         \"Time in s\": 4298.84894       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8695035460992908,         \"MicroF1\": 0.8695035460992909,         \"MacroF1\": 0.8667661913422944,         \"Memory in Mb\": 8.892473220825195,         \"Time in s\": 4478.129032       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8690421101341971,         \"MicroF1\": 0.869042110134197,         \"MacroF1\": 0.8663186552920692,         \"Memory in Mb\": 8.910783767700195,         \"Time in s\": 4660.403073       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8699592206615315,         \"MicroF1\": 0.8699592206615315,         \"MacroF1\": 0.8669965232275297,         \"Memory in Mb\": 8.99278450012207,         \"Time in s\": 4845.573407999999       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.869063470927652,         \"MicroF1\": 0.8690634709276521,         \"MacroF1\": 0.8666022158227548,         \"Memory in Mb\": 9.09610366821289,         \"Time in s\": 5033.674223999999       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8686385384949978,         \"MicroF1\": 0.8686385384949978,         \"MacroF1\": 0.8662053097556822,         \"Memory in Mb\": 9.110825538635254,         \"Time in s\": 5224.692184       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8679081853616284,         \"MicroF1\": 0.8679081853616284,         \"MacroF1\": 0.8656034675726049,         \"Memory in Mb\": 9.181622505187988,         \"Time in s\": 5416.881651       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6511848341232227,         \"MicroF1\": 0.6511848341232227,         \"MacroF1\": 0.5864257754346489,         \"Memory in Mb\": 12.51792049407959,         \"Time in s\": 137.265242       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6873519658929418,         \"MicroF1\": 0.6873519658929418,         \"MacroF1\": 0.6004104483953082,         \"Memory in Mb\": 15.371862411499023,         \"Time in s\": 366.367491       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6978212819703189,         \"MicroF1\": 0.6978212819703189,         \"MacroF1\": 0.602242348585179,         \"Memory in Mb\": 17.772335052490234,         \"Time in s\": 671.574116       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7054226852948141,         \"MicroF1\": 0.7054226852948141,         \"MacroF1\": 0.6059831617919115,         \"Memory in Mb\": 20.14197826385498,         \"Time in s\": 1043.757912       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7080886531540065,         \"MicroF1\": 0.7080886531540066,         \"MacroF1\": 0.6082411118035554,         \"Memory in Mb\": 23.246225357055664,         \"Time in s\": 1476.569185       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.708602999210734,         \"MicroF1\": 0.708602999210734,         \"MacroF1\": 0.6091818949546898,         \"Memory in Mb\": 28.13547992706299,         \"Time in s\": 1970.150117       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7104586659450683,         \"MicroF1\": 0.7104586659450683,         \"MacroF1\": 0.6104104212994758,         \"Memory in Mb\": 30.16471099853516,         \"Time in s\": 2526.789716       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7130342133301764,         \"MicroF1\": 0.7130342133301764,         \"MacroF1\": 0.6119778058667307,         \"Memory in Mb\": 27.69899654388428,         \"Time in s\": 3146.1270590000004       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.717773334736399,         \"MicroF1\": 0.717773334736399,         \"MacroF1\": 0.6149023583636667,         \"Memory in Mb\": 27.04288387298584,         \"Time in s\": 3829.1831980000006       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7215645420967894,         \"MicroF1\": 0.7215645420967894,         \"MacroF1\": 0.617635708330779,         \"Memory in Mb\": 23.96706485748291,         \"Time in s\": 4572.729772000001       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7213086526043909,         \"MicroF1\": 0.721308652604391,         \"MacroF1\": 0.6182075626749539,         \"Memory in Mb\": 26.15617847442627,         \"Time in s\": 5374.612830000001       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7240943887617394,         \"MicroF1\": 0.7240943887617394,         \"MacroF1\": 0.6351065980046956,         \"Memory in Mb\": 25.05154228210449,         \"Time in s\": 6233.453892000001       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7432796678079697,         \"MicroF1\": 0.7432796678079697,         \"MacroF1\": 0.7402334392509421,         \"Memory in Mb\": 15.30208683013916,         \"Time in s\": 7142.743199000001       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7491713454643848,         \"MicroF1\": 0.7491713454643848,         \"MacroF1\": 0.7487081677599373,         \"Memory in Mb\": 11.128735542297363,         \"Time in s\": 8102.097506000001       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7424079803017867,         \"MicroF1\": 0.7424079803017867,         \"MacroF1\": 0.7445532404968841,         \"Memory in Mb\": 16.950417518615723,         \"Time in s\": 9128.379042       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7382657591003255,         \"MicroF1\": 0.7382657591003255,         \"MacroF1\": 0.7427378731329454,         \"Memory in Mb\": 18.26229953765869,         \"Time in s\": 10214.572621       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7309342097933262,         \"MicroF1\": 0.7309342097933262,         \"MacroF1\": 0.7368436311738037,         \"Memory in Mb\": 23.77636337280273,         \"Time in s\": 11358.099531000002       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7429368127531962,         \"MicroF1\": 0.7429368127531962,         \"MacroF1\": 0.7441354243297112,         \"Memory in Mb\": 12.95803928375244,         \"Time in s\": 12553.803014       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7475950755121368,         \"MicroF1\": 0.7475950755121367,         \"MacroF1\": 0.7439196968116685,         \"Memory in Mb\": 12.612845420837402,         \"Time in s\": 13796.415893       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7492305506889531,         \"MicroF1\": 0.7492305506889531,         \"MacroF1\": 0.7418613509588597,         \"Memory in Mb\": 16.95127773284912,         \"Time in s\": 15088.238885       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7509808342728298,         \"MicroF1\": 0.7509808342728299,         \"MacroF1\": 0.7400929587109365,         \"Memory in Mb\": 17.926865577697754,         \"Time in s\": 16424.025269       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7532176832680471,         \"MicroF1\": 0.7532176832680472,         \"MacroF1\": 0.7391930166872092,         \"Memory in Mb\": 20.93969821929932,         \"Time in s\": 17798.240955       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7550129699015935,         \"MicroF1\": 0.7550129699015935,         \"MacroF1\": 0.7379653286035112,         \"Memory in Mb\": 25.43882942199707,         \"Time in s\": 19212.969178       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7569743124334136,         \"MicroF1\": 0.7569743124334136,         \"MacroF1\": 0.7375346698329149,         \"Memory in Mb\": 29.94521999359131,         \"Time in s\": 20668.368585       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7580590173870222,         \"MicroF1\": 0.7580590173870221,         \"MacroF1\": 0.7363169253318035,         \"Memory in Mb\": 34.1699275970459,         \"Time in s\": 22166.950006       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7593880896011656,         \"MicroF1\": 0.7593880896011656,         \"MacroF1\": 0.7352131419868576,         \"Memory in Mb\": 32.93678665161133,         \"Time in s\": 23706.536377       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7573217354705202,         \"MicroF1\": 0.7573217354705202,         \"MacroF1\": 0.7350502568377754,         \"Memory in Mb\": 21.273219108581543,         \"Time in s\": 25286.984696       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7555382690161329,         \"MicroF1\": 0.7555382690161329,         \"MacroF1\": 0.7386915112539557,         \"Memory in Mb\": 20.747055053710938,         \"Time in s\": 26906.631088       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7544982529471312,         \"MicroF1\": 0.7544982529471312,         \"MacroF1\": 0.7426503125712552,         \"Memory in Mb\": 24.91079425811768,         \"Time in s\": 28562.795387       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7531487736355315,         \"MicroF1\": 0.7531487736355315,         \"MacroF1\": 0.7453200395899969,         \"Memory in Mb\": 32.13512706756592,         \"Time in s\": 30253.076649       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7530471971895525,         \"MicroF1\": 0.7530471971895525,         \"MacroF1\": 0.7484606399297139,         \"Memory in Mb\": 36.17057991027832,         \"Time in s\": 31977.334616       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7480986061377289,         \"MicroF1\": 0.748098606137729,         \"MacroF1\": 0.7448942365218528,         \"Memory in Mb\": 13.298456192016602,         \"Time in s\": 33736.870240000004       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7436795133010016,         \"MicroF1\": 0.7436795133010016,         \"MacroF1\": 0.7403442775964885,         \"Memory in Mb\": 15.221885681152344,         \"Time in s\": 35530.857132000005       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7404952232403977,         \"MicroF1\": 0.7404952232403977,         \"MacroF1\": 0.7368033013057004,         \"Memory in Mb\": 16.932289123535156,         \"Time in s\": 37356.72126300001       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7371411564165697,         \"MicroF1\": 0.7371411564165696,         \"MacroF1\": 0.7332530467261859,         \"Memory in Mb\": 22.237309455871586,         \"Time in s\": 39213.91358200001       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7341049585689859,         \"MicroF1\": 0.7341049585689859,         \"MacroF1\": 0.7299460315219516,         \"Memory in Mb\": 22.86026954650879,         \"Time in s\": 41100.10013700001       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7343042154027284,         \"MicroF1\": 0.7343042154027284,         \"MacroF1\": 0.7301016033872143,         \"Memory in Mb\": 21.91624164581299,         \"Time in s\": 43017.482867000006       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7327734443143021,         \"MicroF1\": 0.7327734443143021,         \"MacroF1\": 0.728948208474553,         \"Memory in Mb\": 20.388718605041504,         \"Time in s\": 44961.93393100001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7327538061821626,         \"MicroF1\": 0.7327538061821626,         \"MacroF1\": 0.7292630064673854,         \"Memory in Mb\": 15.630711555480955,         \"Time in s\": 46951.12268600001       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7331849712351145,         \"MicroF1\": 0.7331849712351144,         \"MacroF1\": 0.7301128191332076,         \"Memory in Mb\": 20.110919952392575,         \"Time in s\": 48959.16072000001       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7337567848481349,         \"MicroF1\": 0.7337567848481349,         \"MacroF1\": 0.7309969621648841,         \"Memory in Mb\": 24.057676315307617,         \"Time in s\": 50985.068017000005       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7342111790038556,         \"MicroF1\": 0.7342111790038556,         \"MacroF1\": 0.731637560144403,         \"Memory in Mb\": 28.52964782714844,         \"Time in s\": 53028.942305       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7351289448763406,         \"MicroF1\": 0.7351289448763407,         \"MacroF1\": 0.7324911060941295,         \"Memory in Mb\": 28.861422538757324,         \"Time in s\": 55091.119898       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7357682457008803,         \"MicroF1\": 0.7357682457008803,         \"MacroF1\": 0.7329742877599967,         \"Memory in Mb\": 33.07672500610352,         \"Time in s\": 57170.52325500001       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7366947957659041,         \"MicroF1\": 0.736694795765904,         \"MacroF1\": 0.7341498113226347,         \"Memory in Mb\": 21.3528356552124,         \"Time in s\": 59267.07909500001       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7403602676273804,         \"MicroF1\": 0.7403602676273804,         \"MacroF1\": 0.7381372580344014,         \"Memory in Mb\": 19.381468772888184,         \"Time in s\": 61379.50627500001       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7442122866756664,         \"MicroF1\": 0.7442122866756663,         \"MacroF1\": 0.742109373234967,         \"Memory in Mb\": 21.8067569732666,         \"Time in s\": 63507.849119000006       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7475289521968157,         \"MicroF1\": 0.7475289521968157,         \"MacroF1\": 0.7453466445950636,         \"Memory in Mb\": 21.65154266357422,         \"Time in s\": 65647.81315       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7510581141410433,         \"MicroF1\": 0.7510581141410433,         \"MacroF1\": 0.7487124138061083,         \"Memory in Mb\": 22.87060165405273,         \"Time in s\": 67797.610737       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7545218659444307,         \"MicroF1\": 0.7545218659444307,         \"MacroF1\": 0.752582163258218,         \"Memory in Mb\": 10.55445957183838,         \"Time in s\": 69956.07865499999       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.7547448294132117,         \"MicroF1\": 0.7547448294132117,         \"MacroF1\": 0.7528178949021433,         \"Memory in Mb\": 10.58643913269043,         \"Time in s\": 72115.038215       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803439803439804,         \"MicroF1\": 0.9803439803439804,         \"MacroF1\": 0.4950372208436724,         \"Memory in Mb\": 1.786503791809082,         \"Time in s\": 20.578742       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.98159509202454,         \"MicroF1\": 0.98159509202454,         \"MacroF1\": 0.9278568842209168,         \"Memory in Mb\": 6.9002227783203125,         \"Time in s\": 101.280083       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9803761242845462,         \"MicroF1\": 0.9803761242845462,         \"MacroF1\": 0.9574942570636208,         \"Memory in Mb\": 9.112634658813477,         \"Time in s\": 223.840162       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9779276517473944,         \"MicroF1\": 0.9779276517473944,         \"MacroF1\": 0.9432755457272628,         \"Memory in Mb\": 10.40715503692627,         \"Time in s\": 381.844655       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.973516429622364,         \"MicroF1\": 0.973516429622364,         \"MacroF1\": 0.9361356188587968,         \"Memory in Mb\": 12.656171798706056,         \"Time in s\": 575.269036       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9726195341234164,         \"MicroF1\": 0.9726195341234164,         \"MacroF1\": 0.9612590316809274,         \"Memory in Mb\": 8.745987892150879,         \"Time in s\": 802.257021       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754816112084064,         \"MicroF1\": 0.9754816112084064,         \"MacroF1\": 0.975146989141396,         \"Memory in Mb\": 9.931495666503906,         \"Time in s\": 1061.688609       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754826846460312,         \"MicroF1\": 0.9754826846460312,         \"MacroF1\": 0.9697604489278108,         \"Memory in Mb\": 10.511832237243652,         \"Time in s\": 1352.298412       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9733042767638246,         \"MicroF1\": 0.9733042767638246,         \"MacroF1\": 0.9642745555297418,         \"Memory in Mb\": 11.800049781799316,         \"Time in s\": 1675.333833       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9722971316499142,         \"MicroF1\": 0.9722971316499142,         \"MacroF1\": 0.9666413905932107,         \"Memory in Mb\": 12.42660903930664,         \"Time in s\": 2030.177258       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9734789391575664,         \"MicroF1\": 0.9734789391575664,         \"MacroF1\": 0.9728883985144964,         \"Memory in Mb\": 9.746350288391112,         \"Time in s\": 2413.735444       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9740551583248211,         \"MicroF1\": 0.9740551583248211,         \"MacroF1\": 0.9730015599884004,         \"Memory in Mb\": 10.666529655456545,         \"Time in s\": 2823.505547       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9741655666603808,         \"MicroF1\": 0.9741655666603808,         \"MacroF1\": 0.9728266773902404,         \"Memory in Mb\": 11.775634765625,         \"Time in s\": 3261.739577       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9747855016634565,         \"MicroF1\": 0.9747855016634565,         \"MacroF1\": 0.9744326987999562,         \"Memory in Mb\": 12.58005428314209,         \"Time in s\": 3727.994683       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9751593397613988,         \"MicroF1\": 0.9751593397613988,         \"MacroF1\": 0.9747223863351728,         \"Memory in Mb\": 13.55466365814209,         \"Time in s\": 4223.159482999999       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9751800214493642,         \"MicroF1\": 0.9751800214493642,         \"MacroF1\": 0.974525548169428,         \"Memory in Mb\": 11.360074043273926,         \"Time in s\": 4747.988555       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9763518385003604,         \"MicroF1\": 0.9763518385003604,         \"MacroF1\": 0.9769458779347456,         \"Memory in Mb\": 11.155635833740234,         \"Time in s\": 5300.577354999999       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9765763311997822,         \"MicroF1\": 0.9765763311997822,         \"MacroF1\": 0.976392359672136,         \"Memory in Mb\": 12.33658504486084,         \"Time in s\": 5881.207234       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9771642368726616,         \"MicroF1\": 0.9771642368726616,         \"MacroF1\": 0.9773496343719736,         \"Memory in Mb\": 13.116165161132812,         \"Time in s\": 6492.775159       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.976590268415247,         \"MicroF1\": 0.976590268415247,         \"MacroF1\": 0.975927508407602,         \"Memory in Mb\": 13.303799629211426,         \"Time in s\": 7137.299991       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9768880588303958,         \"MicroF1\": 0.9768880588303958,         \"MacroF1\": 0.9769304999907084,         \"Memory in Mb\": 13.133574485778809,         \"Time in s\": 7814.540539       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9774930362116993,         \"MicroF1\": 0.9774930362116993,         \"MacroF1\": 0.9777587646121524,         \"Memory in Mb\": 13.50635814666748,         \"Time in s\": 8523.072866       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9767664925929872,         \"MicroF1\": 0.9767664925929872,         \"MacroF1\": 0.9763135719034828,         \"Memory in Mb\": 15.166536331176758,         \"Time in s\": 9263.702674       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9765090389132876,         \"MicroF1\": 0.9765090389132876,         \"MacroF1\": 0.9763153416047448,         \"Memory in Mb\": 16.169885635375977,         \"Time in s\": 10037.443626       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9758799882341406,         \"MicroF1\": 0.9758799882341406,         \"MacroF1\": 0.9755246287395946,         \"Memory in Mb\": 14.205968856811523,         \"Time in s\": 10844.068015       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9755821627227302,         \"MicroF1\": 0.9755821627227302,         \"MacroF1\": 0.9754319444516872,         \"Memory in Mb\": 12.997503280639648,         \"Time in s\": 11685.064117       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759418974126192,         \"MicroF1\": 0.9759418974126192,         \"MacroF1\": 0.9761027289556774,         \"Memory in Mb\": 12.962043762207031,         \"Time in s\": 12559.39796       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9760133064869124,         \"MicroF1\": 0.9760133064869124,         \"MacroF1\": 0.9760613734021468,         \"Memory in Mb\": 14.09043312072754,         \"Time in s\": 13467.395857       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754881244188996,         \"MicroF1\": 0.9754881244188996,         \"MacroF1\": 0.9753195915858492,         \"Memory in Mb\": 14.295487403869627,         \"Time in s\": 14408.853786       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759784296102624,         \"MicroF1\": 0.9759784296102624,         \"MacroF1\": 0.9761779987511396,         \"Memory in Mb\": 15.044499397277832,         \"Time in s\": 15385.688043       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9762789594370206,         \"MicroF1\": 0.9762789594370206,         \"MacroF1\": 0.9764127823145236,         \"Memory in Mb\": 15.120206832885742,         \"Time in s\": 16404.149055       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9758713136729222,         \"MicroF1\": 0.975871313672922,         \"MacroF1\": 0.975797420384815,         \"Memory in Mb\": 15.049361228942873,         \"Time in s\": 17460.942559000003       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9757112084973631,         \"MicroF1\": 0.9757112084973631,         \"MacroF1\": 0.9757165619520196,         \"Memory in Mb\": 15.162266731262209,         \"Time in s\": 18558.798501       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759930790858626,         \"MicroF1\": 0.9759930790858626,         \"MacroF1\": 0.9761084708221816,         \"Memory in Mb\": 15.711796760559082,         \"Time in s\": 19695.838422       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754884795854052,         \"MicroF1\": 0.9754884795854052,         \"MacroF1\": 0.975424480421301,         \"Memory in Mb\": 16.988737106323242,         \"Time in s\": 20872.643227       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.975624702117519,         \"MicroF1\": 0.975624702117519,         \"MacroF1\": 0.9757017096421696,         \"Memory in Mb\": 17.869779586791992,         \"Time in s\": 22084.930025       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9757535607817158,         \"MicroF1\": 0.9757535607817158,         \"MacroF1\": 0.9758249143111628,         \"Memory in Mb\": 17.579912185668945,         \"Time in s\": 23330.568569000003       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9755531187512094,         \"MicroF1\": 0.9755531187512094,         \"MacroF1\": 0.9755669148190674,         \"Memory in Mb\": 16.59157657623291,         \"Time in s\": 24610.336028       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9756772044497516,         \"MicroF1\": 0.9756772044497516,         \"MacroF1\": 0.97573890775282,         \"Memory in Mb\": 16.193113327026367,         \"Time in s\": 25925.188427       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9759176420123782,         \"MicroF1\": 0.9759176420123782,         \"MacroF1\": 0.9759886766110538,         \"Memory in Mb\": 16.353660583496094,         \"Time in s\": 27266.573062       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9756680815448078,         \"MicroF1\": 0.9756680815448078,         \"MacroF1\": 0.9756766431570708,         \"Memory in Mb\": 17.00908374786377,         \"Time in s\": 28641.859399       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9758389261744966,         \"MicroF1\": 0.9758389261744966,         \"MacroF1\": 0.975891563489883,         \"Memory in Mb\": 18.364989280700684,         \"Time in s\": 30047.550521       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9753747933648748,         \"MicroF1\": 0.9753747933648748,         \"MacroF1\": 0.975363882573194,         \"Memory in Mb\": 17.298136711120605,         \"Time in s\": 31485.782589       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9753217090969862,         \"MicroF1\": 0.9753217090969862,         \"MacroF1\": 0.9753429667022142,         \"Memory in Mb\": 16.72727108001709,         \"Time in s\": 32956.927282000004       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754888610490768,         \"MicroF1\": 0.9754888610490768,         \"MacroF1\": 0.9755190387029732,         \"Memory in Mb\": 17.51059913635254,         \"Time in s\": 34461.639008000006       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9757553151809026,         \"MicroF1\": 0.9757553151809026,         \"MacroF1\": 0.9757835195290104,         \"Memory in Mb\": 18.871691703796387,         \"Time in s\": 35998.873267       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754367666232072,         \"MicroF1\": 0.9754367666232072,         \"MacroF1\": 0.9754369138844644,         \"Memory in Mb\": 17.42948341369629,         \"Time in s\": 37568.082084       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754889444926722,         \"MicroF1\": 0.9754889444926722,         \"MacroF1\": 0.9754964783302286,         \"Memory in Mb\": 17.978480339050293,         \"Time in s\": 39170.395427       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9756390375669052,         \"MicroF1\": 0.9756390375669052,         \"MacroF1\": 0.975642520227376,         \"Memory in Mb\": 19.26256561279297,         \"Time in s\": 40805.125646       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Stacking\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9754889945585568,         \"MicroF1\": 0.9754889945585568,         \"MacroF1\": 0.9754863274548964,         \"Memory in Mb\": 18.711057662963867,         \"Time in s\": 42471.761869       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.4666666666666667,         \"MicroF1\": 0.4666666666666667,         \"MacroF1\": 0.3890768588137009,         \"Memory in Mb\": 0.9137420654296876,         \"Time in s\": 0.663852       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6153846153846154,         \"MicroF1\": 0.6153846153846154,         \"MacroF1\": 0.617040786788686,         \"Memory in Mb\": 0.9906883239746094,         \"Time in s\": 2.032737       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.6715328467153284,         \"MicroF1\": 0.6715328467153284,         \"MacroF1\": 0.6884491245817251,         \"Memory in Mb\": 1.067914962768555,         \"Time in s\": 4.226265       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7049180327868853,         \"MicroF1\": 0.7049180327868853,         \"MacroF1\": 0.7194266051408907,         \"Memory in Mb\": 1.1443958282470703,         \"Time in s\": 7.386208       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7292576419213974,         \"MicroF1\": 0.7292576419213974,         \"MacroF1\": 0.7448338459304749,         \"Memory in Mb\": 1.2214689254760742,         \"Time in s\": 11.723904       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7381818181818182,         \"MicroF1\": 0.7381818181818182,         \"MacroF1\": 0.7559766728000937,         \"Memory in Mb\": 1.2995519638061523,         \"Time in s\": 17.331033       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7538940809968847,         \"MicroF1\": 0.7538940809968847,         \"MacroF1\": 0.7616248500949714,         \"Memory in Mb\": 1.3766565322875977,         \"Time in s\": 24.26159       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.773841961852861,         \"MicroF1\": 0.7738419618528611,         \"MacroF1\": 0.7772939373537765,         \"Memory in Mb\": 1.4532833099365234,         \"Time in s\": 32.770568000000004       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7820823244552058,         \"MicroF1\": 0.7820823244552059,         \"MacroF1\": 0.7854200812154107,         \"Memory in Mb\": 1.530414581298828,         \"Time in s\": 42.983195       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7777777777777778,         \"MicroF1\": 0.7777777777777778,         \"MacroF1\": 0.7796254955467015,         \"Memory in Mb\": 1.6075658798217771,         \"Time in s\": 54.886431       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7861386138613862,         \"MicroF1\": 0.7861386138613862,         \"MacroF1\": 0.7886239053396241,         \"Memory in Mb\": 3.8640270233154297,         \"Time in s\": 87.00222099999999       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7858439201451906,         \"MicroF1\": 0.7858439201451906,         \"MacroF1\": 0.7889431335032357,         \"Memory in Mb\": 4.088808059692383,         \"Time in s\": 121.00394599999998       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7906197654941374,         \"MicroF1\": 0.7906197654941374,         \"MacroF1\": 0.7944387660679091,         \"Memory in Mb\": 4.304059028625488,         \"Time in s\": 157.00397999999998       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7853810264385692,         \"MicroF1\": 0.7853810264385692,         \"MacroF1\": 0.7901251252871709,         \"Memory in Mb\": 4.532710075378418,         \"Time in s\": 195.073691       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7895500725689405,         \"MicroF1\": 0.7895500725689405,         \"MacroF1\": 0.7935315861788143,         \"Memory in Mb\": 4.759090423583984,         \"Time in s\": 235.272046       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7863945578231293,         \"MicroF1\": 0.7863945578231294,         \"MacroF1\": 0.7911065855691086,         \"Memory in Mb\": 4.991429328918457,         \"Time in s\": 277.59962       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7887323943661971,         \"MicroF1\": 0.7887323943661971,         \"MacroF1\": 0.792926322670609,         \"Memory in Mb\": 5.219735145568848,         \"Time in s\": 322.071315       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7896009673518742,         \"MicroF1\": 0.7896009673518742,         \"MacroF1\": 0.7950712422059908,         \"Memory in Mb\": 5.452417373657227,         \"Time in s\": 368.82718       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7938144329896907,         \"MicroF1\": 0.7938144329896907,         \"MacroF1\": 0.7979586706142276,         \"Memory in Mb\": 5.699496269226074,         \"Time in s\": 417.885664       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.794341675734494,         \"MicroF1\": 0.794341675734494,         \"MacroF1\": 0.7973145688626199,         \"Memory in Mb\": 5.9376373291015625,         \"Time in s\": 469.16999       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7937823834196891,         \"MicroF1\": 0.7937823834196891,         \"MacroF1\": 0.7958827691316667,         \"Memory in Mb\": 6.182188987731934,         \"Time in s\": 522.9385980000001       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7912957467853611,         \"MicroF1\": 0.7912957467853611,         \"MacroF1\": 0.7931630938612351,         \"Memory in Mb\": 6.34267520904541,         \"Time in s\": 579.2700850000001       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.793755912961211,         \"MicroF1\": 0.7937559129612108,         \"MacroF1\": 0.7947921362588558,         \"Memory in Mb\": 6.295009613037109,         \"Time in s\": 638.2805060000001       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7941976427923844,         \"MicroF1\": 0.7941976427923844,         \"MacroF1\": 0.7951664828862093,         \"Memory in Mb\": 6.2213640213012695,         \"Time in s\": 699.725726       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7954743255004352,         \"MicroF1\": 0.7954743255004351,         \"MacroF1\": 0.7958304956922065,         \"Memory in Mb\": 6.151959419250488,         \"Time in s\": 763.5071       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.796652719665272,         \"MicroF1\": 0.796652719665272,         \"MacroF1\": 0.7972397572733622,         \"Memory in Mb\": 6.087224006652832,         \"Time in s\": 829.5043310000001       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7953263497179693,         \"MicroF1\": 0.7953263497179693,         \"MacroF1\": 0.795947547023496,         \"Memory in Mb\": 6.001987457275391,         \"Time in s\": 897.6078460000001       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7995337995337995,         \"MicroF1\": 0.7995337995337995,         \"MacroF1\": 0.799082939294124,         \"Memory in Mb\": 5.924266815185547,         \"Time in s\": 967.722432       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7981995498874719,         \"MicroF1\": 0.7981995498874719,         \"MacroF1\": 0.7978549794399667,         \"Memory in Mb\": 5.872907638549805,         \"Time in s\": 1039.926656       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.7991298042059464,         \"MicroF1\": 0.7991298042059464,         \"MacroF1\": 0.799072028035076,         \"Memory in Mb\": 5.784454345703125,         \"Time in s\": 1114.0085680000002       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8007017543859649,         \"MicroF1\": 0.8007017543859649,         \"MacroF1\": 0.799801266098334,         \"Memory in Mb\": 5.781437873840332,         \"Time in s\": 1190.125915       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8042148198504419,         \"MicroF1\": 0.8042148198504419,         \"MacroF1\": 0.8016037490391381,         \"Memory in Mb\": 5.805401802062988,         \"Time in s\": 1268.322504       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048780487804879,         \"MicroF1\": 0.8048780487804877,         \"MacroF1\": 0.8013581039030082,         \"Memory in Mb\": 5.915700912475586,         \"Time in s\": 1348.933518       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048624440179143,         \"MicroF1\": 0.8048624440179143,         \"MacroF1\": 0.8017038254481382,         \"Memory in Mb\": 6.069503784179688,         \"Time in s\": 1431.999326       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048477315102548,         \"MicroF1\": 0.8048477315102549,         \"MacroF1\": 0.8009666848419111,         \"Memory in Mb\": 6.138180732727051,         \"Time in s\": 1517.316045       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.804833836858006,         \"MicroF1\": 0.804833836858006,         \"MacroF1\": 0.8009346118743482,         \"Memory in Mb\": 6.15428638458252,         \"Time in s\": 1604.689641       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8048206937095826,         \"MicroF1\": 0.8048206937095828,         \"MacroF1\": 0.802987300619633,         \"Memory in Mb\": 6.14796257019043,         \"Time in s\": 1694.105126       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8065254722381225,         \"MicroF1\": 0.8065254722381225,         \"MacroF1\": 0.8041280306488863,         \"Memory in Mb\": 6.185528755187988,         \"Time in s\": 1785.64513       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8070273284997211,         \"MicroF1\": 0.8070273284997211,         \"MacroF1\": 0.8033862119520573,         \"Memory in Mb\": 6.18717098236084,         \"Time in s\": 1879.221363       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8085916258836324,         \"MicroF1\": 0.8085916258836324,         \"MacroF1\": 0.8051706679397826,         \"Memory in Mb\": 6.228180885314941,         \"Time in s\": 1974.88599       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8074270557029177,         \"MicroF1\": 0.8074270557029178,         \"MacroF1\": 0.8044133208197751,         \"Memory in Mb\": 6.244633674621582,         \"Time in s\": 2072.712055       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8073537027446919,         \"MicroF1\": 0.8073537027446919,         \"MacroF1\": 0.8036280810428232,         \"Memory in Mb\": 6.232837677001953,         \"Time in s\": 2172.610836       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.808295397066262,         \"MicroF1\": 0.808295397066262,         \"MacroF1\": 0.8041943782356388,         \"Memory in Mb\": 6.225313186645508,         \"Time in s\": 2274.502409       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8096885813148789,         \"MicroF1\": 0.809688581314879,         \"MacroF1\": 0.8043903689108628,         \"Memory in Mb\": 6.209332466125488,         \"Time in s\": 2378.336668       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8086031899468342,         \"MicroF1\": 0.8086031899468342,         \"MacroF1\": 0.8034099584264852,         \"Memory in Mb\": 6.192641258239746,         \"Time in s\": 2484.108554       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.808983451536643,         \"MicroF1\": 0.808983451536643,         \"MacroF1\": 0.8029929757635029,         \"Memory in Mb\": 6.163993835449219,         \"Time in s\": 2591.83622       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8093475242943082,         \"MicroF1\": 0.8093475242943081,         \"MacroF1\": 0.8028985652670257,         \"Memory in Mb\": 6.160528182983398,         \"Time in s\": 2701.493184       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8110557317625736,         \"MicroF1\": 0.8110557317625736,         \"MacroF1\": 0.8037088502350873,         \"Memory in Mb\": 6.127141952514648,         \"Time in s\": 2812.975729       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8078118064802485,         \"MicroF1\": 0.8078118064802485,         \"MacroF1\": 0.8004652010359966,         \"Memory in Mb\": 6.094814300537109,         \"Time in s\": 2926.384262       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8064375815571988,         \"MicroF1\": 0.8064375815571988,         \"MacroF1\": 0.7990276111502428,         \"Memory in Mb\": 6.073050498962402,         \"Time in s\": 3041.734776       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.8064097011693374,         \"MicroF1\": 0.8064097011693374,         \"MacroF1\": 0.7989986920740723,         \"Memory in Mb\": 6.073922157287598,         \"Time in s\": 3157.943153       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6293838862559241,         \"MicroF1\": 0.6293838862559241,         \"MacroF1\": 0.5938169901557457,         \"Memory in Mb\": 7.681754112243652,         \"Time in s\": 78.197886       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6290857413548081,         \"MicroF1\": 0.6290857413548081,         \"MacroF1\": 0.5936238360694311,         \"Memory in Mb\": 7.563845634460449,         \"Time in s\": 217.436369       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.625197347647616,         \"MicroF1\": 0.625197347647616,         \"MacroF1\": 0.5890732389154221,         \"Memory in Mb\": 7.54627799987793,         \"Time in s\": 406.781755       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.624437603599337,         \"MicroF1\": 0.624437603599337,         \"MacroF1\": 0.5890978975177876,         \"Memory in Mb\": 7.509035110473633,         \"Time in s\": 643.136123       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6309907179390036,         \"MicroF1\": 0.6309907179390036,         \"MacroF1\": 0.5943307513870396,         \"Memory in Mb\": 7.529419898986816,         \"Time in s\": 922.055301       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6249408050513023,         \"MicroF1\": 0.6249408050513023,         \"MacroF1\": 0.5899587518293812,         \"Memory in Mb\": 7.541637420654297,         \"Time in s\": 1240.879558       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6242727641726424,         \"MicroF1\": 0.6242727641726424,         \"MacroF1\": 0.589208790087756,         \"Memory in Mb\": 7.519943237304687,         \"Time in s\": 1598.2590730000002       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6266129986977625,         \"MicroF1\": 0.6266129986977625,         \"MacroF1\": 0.5910042020201396,         \"Memory in Mb\": 7.600367546081543,         \"Time in s\": 1990.9287910000005       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6255919183415763,         \"MicroF1\": 0.6255919183415763,         \"MacroF1\": 0.5892477749449755,         \"Memory in Mb\": 7.551809310913086,         \"Time in s\": 2416.671036       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6269533099725353,         \"MicroF1\": 0.6269533099725353,         \"MacroF1\": 0.5906555376897765,         \"Memory in Mb\": 7.57810115814209,         \"Time in s\": 2875.240995       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6254842875591907,         \"MicroF1\": 0.6254842875591907,         \"MacroF1\": 0.5899069142128334,         \"Memory in Mb\": 7.574300765991211,         \"Time in s\": 3366.8452850000003       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6276536974193039,         \"MicroF1\": 0.6276536974193039,         \"MacroF1\": 0.5948280902959312,         \"Memory in Mb\": 7.593076705932617,         \"Time in s\": 3891.533291       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6419465287389816,         \"MicroF1\": 0.6419465287389816,         \"MacroF1\": 0.6240594787506325,         \"Memory in Mb\": 7.568525314331055,         \"Time in s\": 4449.097087       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6349861327200162,         \"MicroF1\": 0.6349861327200162,         \"MacroF1\": 0.6168664949740267,         \"Memory in Mb\": 7.497129440307617,         \"Time in s\": 5038.3500540000005       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6042048109097796,         \"MicroF1\": 0.6042048109097796,         \"MacroF1\": 0.5876183517420878,         \"Memory in Mb\": 7.622871398925781,         \"Time in s\": 5663.9066330000005       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5831311038768866,         \"MicroF1\": 0.5831311038768866,         \"MacroF1\": 0.5677288238088704,         \"Memory in Mb\": 7.5406084060668945,         \"Time in s\": 6323.428796       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5683805916104953,         \"MicroF1\": 0.5683805916104953,         \"MacroF1\": 0.5530005563922373,         \"Memory in Mb\": 7.511743545532227,         \"Time in s\": 7015.247243       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5655811016993739,         \"MicroF1\": 0.5655811016993739,         \"MacroF1\": 0.5465928919365096,         \"Memory in Mb\": 7.569133758544922,         \"Time in s\": 7739.601247       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5718985196630614,         \"MicroF1\": 0.5718985196630614,         \"MacroF1\": 0.5506497035356593,         \"Memory in Mb\": 8.179316520690918,         \"Time in s\": 8496.204598999999       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5817510298783086,         \"MicroF1\": 0.5817510298783086,         \"MacroF1\": 0.55937505855693,         \"Memory in Mb\": 8.13927173614502,         \"Time in s\": 9285.092110999998       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.5905298759864712,         \"MicroF1\": 0.5905298759864712,         \"MacroF1\": 0.5668099949242361,         \"Memory in Mb\": 8.13715648651123,         \"Time in s\": 10104.551326       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6004907236020834,         \"MicroF1\": 0.6004907236020834,         \"MacroF1\": 0.5756153967719769,         \"Memory in Mb\": 8.254791259765625,         \"Time in s\": 10955.282648       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6088854119487792,         \"MicroF1\": 0.6088854119487792,         \"MacroF1\": 0.5822871692574689,         \"Memory in Mb\": 8.217899322509766,         \"Time in s\": 11836.441737999998       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.617014560233595,         \"MicroF1\": 0.617014560233595,         \"MacroF1\": 0.5890646667396601,         \"Memory in Mb\": 8.13050651550293,         \"Time in s\": 12747.590801999995       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6237357475661957,         \"MicroF1\": 0.6237357475661957,         \"MacroF1\": 0.5942060376379845,         \"Memory in Mb\": 8.178851127624512,         \"Time in s\": 13688.250944999996       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6299763248952832,         \"MicroF1\": 0.6299763248952832,         \"MacroF1\": 0.5983574644866619,         \"Memory in Mb\": 8.215079307556152,         \"Time in s\": 14661.447404999995       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6312651257409421,         \"MicroF1\": 0.6312651257409421,         \"MacroF1\": 0.6016879522351425,         \"Memory in Mb\": 8.160200119018555,         \"Time in s\": 15669.084531999995       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6310751851726587,         \"MicroF1\": 0.6310751851726587,         \"MacroF1\": 0.6062390002054064,         \"Memory in Mb\": 8.153844833374023,         \"Time in s\": 16709.899933999994       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6313228619011854,         \"MicroF1\": 0.6313228619011854,         \"MacroF1\": 0.610710416812842,         \"Memory in Mb\": 8.221953392028809,         \"Time in s\": 17785.196262999994       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6320590927743931,         \"MicroF1\": 0.6320590927743931,         \"MacroF1\": 0.614817700164209,         \"Memory in Mb\": 8.237210273742676,         \"Time in s\": 18894.010558999995       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6331144035436077,         \"MicroF1\": 0.6331144035436077,         \"MacroF1\": 0.6184679282473909,         \"Memory in Mb\": 8.208189964294434,         \"Time in s\": 20033.816622999995       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6291616110798733,         \"MicroF1\": 0.6291616110798733,         \"MacroF1\": 0.6151628967287334,         \"Memory in Mb\": 8.149331092834473,         \"Time in s\": 21206.789184999998       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6245587855482538,         \"MicroF1\": 0.6245587855482538,         \"MacroF1\": 0.6103108800280445,         \"Memory in Mb\": 8.270771980285645,         \"Time in s\": 22409.569843999994       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6211737180736986,         \"MicroF1\": 0.6211737180736986,         \"MacroF1\": 0.6063163580543118,         \"Memory in Mb\": 8.246885299682617,         \"Time in s\": 23639.112909       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6171433209772992,         \"MicroF1\": 0.6171433209772992,         \"MacroF1\": 0.6018416894357856,         \"Memory in Mb\": 8.222872734069824,         \"Time in s\": 24895.212451       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6153360515585953,         \"MicroF1\": 0.6153360515585953,         \"MacroF1\": 0.5996210858832133,         \"Memory in Mb\": 8.711487770080566,         \"Time in s\": 26177.407049999994       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.613472908295155,         \"MicroF1\": 0.613472908295155,         \"MacroF1\": 0.5980758777202522,         \"Memory in Mb\": 8.84398365020752,         \"Time in s\": 27486.887242999997       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6139008647544048,         \"MicroF1\": 0.6139008647544048,         \"MacroF1\": 0.5993833357378361,         \"Memory in Mb\": 9.00393295288086,         \"Time in s\": 28821.579146999997       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6157395041643396,         \"MicroF1\": 0.6157395041643396,         \"MacroF1\": 0.6018873090815099,         \"Memory in Mb\": 8.895415306091309,         \"Time in s\": 30174.675792999995       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6179833802883591,         \"MicroF1\": 0.6179833802883591,         \"MacroF1\": 0.6047393094362844,         \"Memory in Mb\": 8.820836067199707,         \"Time in s\": 31551.592344999997       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6202101859337106,         \"MicroF1\": 0.6202101859337106,         \"MacroF1\": 0.60743097275183,         \"Memory in Mb\": 8.80302619934082,         \"Time in s\": 32950.21258099999       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6221054767648982,         \"MicroF1\": 0.6221054767648982,         \"MacroF1\": 0.6097047537791253,         \"Memory in Mb\": 8.807188034057617,         \"Time in s\": 34370.71930599999       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.623736428304006,         \"MicroF1\": 0.623736428304006,         \"MacroF1\": 0.6112415003179203,         \"Memory in Mb\": 8.906554222106934,         \"Time in s\": 35814.22252799999       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6259389191399608,         \"MicroF1\": 0.6259389191399608,         \"MacroF1\": 0.6133867892257391,         \"Memory in Mb\": 8.822076797485352,         \"Time in s\": 37279.498287       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6274542814453166,         \"MicroF1\": 0.6274542814453166,         \"MacroF1\": 0.6153714367024555,         \"Memory in Mb\": 8.875716209411621,         \"Time in s\": 38770.246246       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6317858980957283,         \"MicroF1\": 0.6317858980957283,         \"MacroF1\": 0.6202967225132047,         \"Memory in Mb\": 8.86828327178955,         \"Time in s\": 40284.403256       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6360137817090125,         \"MicroF1\": 0.6360137817090125,         \"MacroF1\": 0.6247992459885968,         \"Memory in Mb\": 8.835649490356445,         \"Time in s\": 41820.805008       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6403811628228145,         \"MicroF1\": 0.6403811628228145,         \"MacroF1\": 0.6293790828873279,         \"Memory in Mb\": 8.924153327941895,         \"Time in s\": 43378.957976       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6455559206076185,         \"MicroF1\": 0.6455559206076185,         \"MacroF1\": 0.6346828420183047,         \"Memory in Mb\": 9.21804904937744,         \"Time in s\": 44959.107881       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.648269853595712,         \"MicroF1\": 0.648269853595712,         \"MacroF1\": 0.6377385869395499,         \"Memory in Mb\": 9.400546073913574,         \"Time in s\": 46560.782       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.6485325562472799,         \"MicroF1\": 0.6485325562472799,         \"MacroF1\": 0.637999701607352,         \"Memory in Mb\": 9.406517028808594,         \"Time in s\": 48163.738895       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9828009828009828,         \"MicroF1\": 0.9828009828009828,         \"MacroF1\": 0.6067632850241546,         \"Memory in Mb\": 1.4587059020996094,         \"Time in s\": 10.139614       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9496932515337424,         \"MicroF1\": 0.9496932515337424,         \"MacroF1\": 0.7435135353411919,         \"Memory in Mb\": 6.019382476806641,         \"Time in s\": 66.737739       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9149632052330336,         \"MicroF1\": 0.9149632052330336,         \"MacroF1\": 0.9012024099743488,         \"Memory in Mb\": 7.076447486877441,         \"Time in s\": 151.07716299999998       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9258123850398527,         \"MicroF1\": 0.9258123850398527,         \"MacroF1\": 0.913338738884437,         \"Memory in Mb\": 7.232892990112305,         \"Time in s\": 261.540164       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9230014713094654,         \"MicroF1\": 0.9230014713094654,         \"MacroF1\": 0.9086113906821328,         \"Memory in Mb\": 7.553393363952637,         \"Time in s\": 397.836215       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8961994278708623,         \"MicroF1\": 0.8961994278708623,         \"MacroF1\": 0.8992132713257572,         \"Memory in Mb\": 7.640434265136719,         \"Time in s\": 558.733108       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9001751313485113,         \"MicroF1\": 0.9001751313485113,         \"MacroF1\": 0.8860451027148403,         \"Memory in Mb\": 7.9326982498168945,         \"Time in s\": 743.600486       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8924302788844621,         \"MicroF1\": 0.8924302788844621,         \"MacroF1\": 0.8761196773917237,         \"Memory in Mb\": 8.074724197387695,         \"Time in s\": 952.077233       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8874965949332607,         \"MicroF1\": 0.8874965949332607,         \"MacroF1\": 0.8846937712308092,         \"Memory in Mb\": 8.20841121673584,         \"Time in s\": 1184.393658       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8815886246629075,         \"MicroF1\": 0.8815886246629075,         \"MacroF1\": 0.868452721773406,         \"Memory in Mb\": 8.525882720947266,         \"Time in s\": 1441.208937       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8760864720303098,         \"MicroF1\": 0.8760864720303098,         \"MacroF1\": 0.8834419600614621,         \"Memory in Mb\": 8.681946754455566,         \"Time in s\": 1719.7568239999998       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8737487231869254,         \"MicroF1\": 0.8737487231869254,         \"MacroF1\": 0.8797220914000274,         \"Memory in Mb\": 8.834684371948242,         \"Time in s\": 2018.974207       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8693192532528757,         \"MicroF1\": 0.8693192532528757,         \"MacroF1\": 0.8538682361373632,         \"Memory in Mb\": 9.067034721374512,         \"Time in s\": 2339.699668       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8607949571003327,         \"MicroF1\": 0.8607949571003327,         \"MacroF1\": 0.8654889627515672,         \"Memory in Mb\": 9.271133422851562,         \"Time in s\": 2680.904224       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8561856512502043,         \"MicroF1\": 0.8561856512502043,         \"MacroF1\": 0.84095068957581,         \"Memory in Mb\": 9.378315925598145,         \"Time in s\": 3042.663698       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8434196414891987,         \"MicroF1\": 0.8434196414891987,         \"MacroF1\": 0.8427350578509161,         \"Memory in Mb\": 9.608606338500977,         \"Time in s\": 3424.478417       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8392213410237923,         \"MicroF1\": 0.8392213410237923,         \"MacroF1\": 0.8447429510460126,         \"Memory in Mb\": 9.751982688903809,         \"Time in s\": 3824.86879       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8454310227427482,         \"MicroF1\": 0.8454310227427482,         \"MacroF1\": 0.847842289102327,         \"Memory in Mb\": 9.957889556884766,         \"Time in s\": 4243.00141       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8456973293768546,         \"MicroF1\": 0.8456973293768547,         \"MacroF1\": 0.8480563212460421,         \"Memory in Mb\": 10.19985294342041,         \"Time in s\": 4680.993142       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8469175144012747,         \"MicroF1\": 0.8469175144012746,         \"MacroF1\": 0.8472851046009279,         \"Memory in Mb\": 10.418806076049805,         \"Time in s\": 5138.9878340000005       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8469709349830746,         \"MicroF1\": 0.8469709349830746,         \"MacroF1\": 0.8501227536717817,         \"Memory in Mb\": 10.607142448425291,         \"Time in s\": 5616.664707000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8475766016713092,         \"MicroF1\": 0.8475766016713092,         \"MacroF1\": 0.8507851780426926,         \"Memory in Mb\": 10.772598266601562,         \"Time in s\": 6113.940894000001       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8459980816370031,         \"MicroF1\": 0.8459980816370031,         \"MacroF1\": 0.8471668648040658,         \"Memory in Mb\": 10.97368335723877,         \"Time in s\": 6631.342845000001       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8418956184250843,         \"MicroF1\": 0.8418956184250843,         \"MacroF1\": 0.8426049398612477,         \"Memory in Mb\": 11.192140579223633,         \"Time in s\": 7169.901201000001       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8344935778017453,         \"MicroF1\": 0.8344935778017454,         \"MacroF1\": 0.8308153568434791,         \"Memory in Mb\": 11.354521751403809,         \"Time in s\": 7729.92345       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.817384745922504,         \"MicroF1\": 0.817384745922504,         \"MacroF1\": 0.8105787344487394,         \"Memory in Mb\": 11.59365177154541,         \"Time in s\": 8312.440227000001       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8127099409895597,         \"MicroF1\": 0.8127099409895597,         \"MacroF1\": 0.8142119266109252,         \"Memory in Mb\": 11.793928146362305,         \"Time in s\": 8918.030696000002       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8079313665411888,         \"MicroF1\": 0.8079313665411888,         \"MacroF1\": 0.8037472320719128,         \"Memory in Mb\": 11.945178031921388,         \"Time in s\": 9547.170938       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8040740427689967,         \"MicroF1\": 0.8040740427689967,         \"MacroF1\": 0.8039730126613296,         \"Memory in Mb\": 12.203582763671877,         \"Time in s\": 10200.281645       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8072554947299616,         \"MicroF1\": 0.8072554947299616,         \"MacroF1\": 0.8097160881214022,         \"Memory in Mb\": 12.414502143859863,         \"Time in s\": 10877.318664       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8043014153554202,         \"MicroF1\": 0.8043014153554202,         \"MacroF1\": 0.8038043720799647,         \"Memory in Mb\": 12.561456680297852,         \"Time in s\": 11578.515438       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7996936039831483,         \"MicroF1\": 0.7996936039831483,         \"MacroF1\": 0.8010057260657798,         \"Memory in Mb\": 12.889472007751465,         \"Time in s\": 12304.325005       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7974448488449826,         \"MicroF1\": 0.7974448488449826,         \"MacroF1\": 0.7996515087686575,         \"Memory in Mb\": 12.99599838256836,         \"Time in s\": 13054.609905       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7978516329031793,         \"MicroF1\": 0.7978516329031793,         \"MacroF1\": 0.8006715750629478,         \"Memory in Mb\": 13.20394229888916,         \"Time in s\": 13829.291085       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.797674907206387,         \"MicroF1\": 0.7976749072063871,         \"MacroF1\": 0.8002875748518964,         \"Memory in Mb\": 13.36452293395996,         \"Time in s\": 14628.347686       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8007761966364813,         \"MicroF1\": 0.8007761966364813,         \"MacroF1\": 0.8043248634763072,         \"Memory in Mb\": 13.53370189666748,         \"Time in s\": 15451.756014       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8051010268300762,         \"MicroF1\": 0.8051010268300763,         \"MacroF1\": 0.8085780284871096,         \"Memory in Mb\": 13.774932861328123,         \"Time in s\": 16299.960754       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.8052634973876024,         \"MicroF1\": 0.8052634973876024,         \"MacroF1\": 0.8077470357827514,         \"Memory in Mb\": 13.933537483215332,         \"Time in s\": 17172.988913       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7978756834894098,         \"MicroF1\": 0.7978756834894098,         \"MacroF1\": 0.7983136026998061,         \"Memory in Mb\": 14.138628005981444,         \"Time in s\": 18070.675966000003       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.793369691770329,         \"MicroF1\": 0.7933696917703291,         \"MacroF1\": 0.7956625263629296,         \"Memory in Mb\": 14.30509090423584,         \"Time in s\": 18993.33345       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7901596221677527,         \"MicroF1\": 0.7901596221677527,         \"MacroF1\": 0.7932579365729884,         \"Memory in Mb\": 14.447582244873049,         \"Time in s\": 19941.842904       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7861686606361249,         \"MicroF1\": 0.7861686606361248,         \"MacroF1\": 0.7888822346867281,         \"Memory in Mb\": 14.767212867736816,         \"Time in s\": 20916.572711       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.780425240836801,         \"MicroF1\": 0.780425240836801,         \"MacroF1\": 0.7838193866310822,         \"Memory in Mb\": 14.989240646362305,         \"Time in s\": 21922.215184       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7802907915993538,         \"MicroF1\": 0.7802907915993537,         \"MacroF1\": 0.7845235361146662,         \"Memory in Mb\": 15.200251579284668,         \"Time in s\": 22957.213951       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.783975162045863,         \"MicroF1\": 0.783975162045863,         \"MacroF1\": 0.7883700169311393,         \"Memory in Mb\": 15.375930786132812,         \"Time in s\": 24020.765336       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7869664837214259,         \"MicroF1\": 0.7869664837214259,         \"MacroF1\": 0.7913854757843782,         \"Memory in Mb\": 15.5132417678833,         \"Time in s\": 25114.453204       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7816427640156454,         \"MicroF1\": 0.7816427640156454,         \"MacroF1\": 0.7858184292134073,         \"Memory in Mb\": 15.77665901184082,         \"Time in s\": 26236.293864000003       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7846090997293571,         \"MicroF1\": 0.7846090997293571,         \"MacroF1\": 0.7893723685613512,         \"Memory in Mb\": 15.996115684509276,         \"Time in s\": 27388.205854000003       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7807013155920164,         \"MicroF1\": 0.7807013155920164,         \"MacroF1\": 0.785620728786203,         \"Memory in Mb\": 16.12063980102539,         \"Time in s\": 28569.915626       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"Voting\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.7791068189617139,         \"MicroF1\": 0.7791068189617139,         \"MacroF1\": 0.7841355172773921,         \"Memory in Mb\": 16.39253330230713,         \"Time in s\": 29779.243894000003       },       {         \"step\": 46,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1777777777777777,         \"MicroF1\": 0.1777777777777777,         \"MacroF1\": 0.1526026604973973,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 0.110776       },       {         \"step\": 92,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1318681318681318,         \"MicroF1\": 0.1318681318681318,         \"MacroF1\": 0.1213108980966124,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 0.225611       },       {         \"step\": 138,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1240875912408759,         \"MicroF1\": 0.1240875912408759,         \"MacroF1\": 0.1187445506554449,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 0.343639       },       {         \"step\": 184,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1256830601092896,         \"MicroF1\": 0.1256830601092896,         \"MacroF1\": 0.1226298342307158,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 0.484524       },       {         \"step\": 230,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1266375545851528,         \"MicroF1\": 0.1266375545851528,         \"MacroF1\": 0.1250385204120806,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 0.6292090000000001       },       {         \"step\": 276,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1272727272727272,         \"MicroF1\": 0.1272727272727272,         \"MacroF1\": 0.1242790791814499,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 0.7861950000000001       },       {         \"step\": 322,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1339563862928348,         \"MicroF1\": 0.1339563862928348,         \"MacroF1\": 0.1321003659624602,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 1.0166240000000002       },       {         \"step\": 368,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1389645776566757,         \"MicroF1\": 0.1389645776566757,         \"MacroF1\": 0.1374501146297296,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 1.2507780000000002       },       {         \"step\": 414,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1404358353510895,         \"MicroF1\": 0.1404358353510895,         \"MacroF1\": 0.1403581309694754,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 1.5223060000000002       },       {         \"step\": 460,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1459694989106753,         \"MicroF1\": 0.1459694989106753,         \"MacroF1\": 0.1456314871072794,         \"Memory in Mb\": 0.0013656616210937,         \"Time in s\": 1.7974560000000002       },       {         \"step\": 506,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1386138613861386,         \"MicroF1\": 0.1386138613861386,         \"MacroF1\": 0.1383381610231494,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 2.07562       },       {         \"step\": 552,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1397459165154265,         \"MicroF1\": 0.1397459165154265,         \"MacroF1\": 0.1393865249177789,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 2.402759       },       {         \"step\": 598,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1373534338358459,         \"MicroF1\": 0.1373534338358459,         \"MacroF1\": 0.1372798104345861,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 2.771723       },       {         \"step\": 644,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1399688958009331,         \"MicroF1\": 0.1399688958009331,         \"MacroF1\": 0.1401757170901796,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 3.149556       },       {         \"step\": 690,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1378809869375907,         \"MicroF1\": 0.1378809869375907,         \"MacroF1\": 0.1380151778455332,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 3.580436       },       {         \"step\": 736,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1401360544217687,         \"MicroF1\": 0.1401360544217687,         \"MacroF1\": 0.1403108892795828,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 4.0152470000000005       },       {         \"step\": 782,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1421254801536491,         \"MicroF1\": 0.1421254801536491,         \"MacroF1\": 0.1420930265541123,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 4.453992       },       {         \"step\": 828,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1426844014510278,         \"MicroF1\": 0.1426844014510278,         \"MacroF1\": 0.1422987455304691,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 4.959761       },       {         \"step\": 874,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.138602520045819,         \"MicroF1\": 0.138602520045819,         \"MacroF1\": 0.1384535269459527,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 5.469480000000001       },       {         \"step\": 920,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1349292709466811,         \"MicroF1\": 0.1349292709466811,         \"MacroF1\": 0.1348083913046733,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 6.0005820000000005       },       {         \"step\": 966,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1336787564766839,         \"MicroF1\": 0.1336787564766839,         \"MacroF1\": 0.1334917777444527,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 6.535053       },       {         \"step\": 1012,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1325420375865479,         \"MicroF1\": 0.1325420375865479,         \"MacroF1\": 0.1324936677659038,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 7.07275       },       {         \"step\": 1058,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1333964049195837,         \"MicroF1\": 0.1333964049195837,         \"MacroF1\": 0.1331834965440007,         \"Memory in Mb\": 0.0013656616210937,         \"Time in s\": 7.645426       },       {         \"step\": 1104,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1341795104261106,         \"MicroF1\": 0.1341795104261106,         \"MacroF1\": 0.1340282652950153,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 8.221471000000001       },       {         \"step\": 1150,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.134029590948651,         \"MicroF1\": 0.134029590948651,         \"MacroF1\": 0.1340639115051912,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 8.800858000000002       },       {         \"step\": 1196,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1364016736401673,         \"MicroF1\": 0.1364016736401673,         \"MacroF1\": 0.1363948420172951,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 9.430169       },       {         \"step\": 1242,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1394037066881547,         \"MicroF1\": 0.1394037066881547,         \"MacroF1\": 0.1391977238389222,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 10.062783       },       {         \"step\": 1288,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1414141414141414,         \"MicroF1\": 0.1414141414141414,         \"MacroF1\": 0.1411871502321015,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 10.698372       },       {         \"step\": 1334,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1432858214553638,         \"MicroF1\": 0.1432858214553638,         \"MacroF1\": 0.1430255327815666,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 11.387531       },       {         \"step\": 1380,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1435823060188542,         \"MicroF1\": 0.1435823060188542,         \"MacroF1\": 0.1433209000486506,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 12.080639       },       {         \"step\": 1426,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1417543859649122,         \"MicroF1\": 0.1417543859649122,         \"MacroF1\": 0.1414546655929112,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 12.777602000000002       },       {         \"step\": 1472,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1393609789259007,         \"MicroF1\": 0.1393609789259007,         \"MacroF1\": 0.1390762971394262,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 13.546128       },       {         \"step\": 1518,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1397495056031641,         \"MicroF1\": 0.1397495056031641,         \"MacroF1\": 0.1395136668589845,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 14.318195       },       {         \"step\": 1564,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1369161868202175,         \"MicroF1\": 0.1369161868202175,         \"MacroF1\": 0.1366417047439511,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 15.093811       },       {         \"step\": 1610,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1361093847110006,         \"MicroF1\": 0.1361093847110006,         \"MacroF1\": 0.1359768388190307,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 15.942934       },       {         \"step\": 1656,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1365558912386707,         \"MicroF1\": 0.1365558912386707,         \"MacroF1\": 0.1363322462377459,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 16.795246000000002       },       {         \"step\": 1702,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1393298059964726,         \"MicroF1\": 0.1393298059964726,         \"MacroF1\": 0.1390129627439909,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 17.650687       },       {         \"step\": 1748,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1419576416714367,         \"MicroF1\": 0.1419576416714367,         \"MacroF1\": 0.1414719731272364,         \"Memory in Mb\": 0.0013656616210937,         \"Time in s\": 18.510738       },       {         \"step\": 1794,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1422197434467373,         \"MicroF1\": 0.1422197434467373,         \"MacroF1\": 0.1419410396611007,         \"Memory in Mb\": 0.0013647079467773,         \"Time in s\": 19.374685       },       {         \"step\": 1840,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1413811854268624,         \"MicroF1\": 0.1413811854268624,         \"MacroF1\": 0.1411432976659866,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 20.24245       },       {         \"step\": 1886,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.140053050397878,         \"MicroF1\": 0.140053050397878,         \"MacroF1\": 0.1397325871382075,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 21.182873       },       {         \"step\": 1932,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1429311237700673,         \"MicroF1\": 0.1429311237700673,         \"MacroF1\": 0.1427522922982585,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 22.12686       },       {         \"step\": 1978,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1461810824481537,         \"MicroF1\": 0.1461810824481537,         \"MacroF1\": 0.1459715815160596,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 23.074113       },       {         \"step\": 2024,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1443400889767671,         \"MicroF1\": 0.1443400889767671,         \"MacroF1\": 0.1441662523776106,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 24.067371       },       {         \"step\": 2070,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1440309328177863,         \"MicroF1\": 0.1440309328177863,         \"MacroF1\": 0.1438554349712762,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 25.063921       },       {         \"step\": 2116,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1446808510638297,         \"MicroF1\": 0.1446808510638297,         \"MacroF1\": 0.1446036231777657,         \"Memory in Mb\": 0.0013637542724609,         \"Time in s\": 26.06363       },       {         \"step\": 2162,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1453031004164738,         \"MicroF1\": 0.1453031004164738,         \"MacroF1\": 0.1452046591382179,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 27.083891999999995       },       {         \"step\": 2208,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1449932034435885,         \"MicroF1\": 0.1449932034435885,         \"MacroF1\": 0.1449110985199169,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 28.107944       },       {         \"step\": 2254,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1464713715046604,         \"MicroF1\": 0.1464713715046604,         \"MacroF1\": 0.146404255341296,         \"Memory in Mb\": 0.0013666152954101,         \"Time in s\": 29.207951       },       {         \"step\": 2300,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.1478903871248368,         \"MicroF1\": 0.1478903871248368,         \"MacroF1\": 0.1478868852481029,         \"Memory in Mb\": 0.0013675689697265,         \"Time in s\": 30.311507       },       {         \"step\": 2310,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"ImageSegments\",         \"Accuracy\": 0.148116067561715,         \"MicroF1\": 0.148116067561715,         \"MacroF1\": 0.1481156678425267,         \"Memory in Mb\": 0.0013694763183593,         \"Time in s\": 31.415921       },       {         \"step\": 1056,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1582938388625592,         \"MicroF1\": 0.1582938388625592,         \"MacroF1\": 0.1376212379233521,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 0.57267       },       {         \"step\": 2112,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1657981999052581,         \"MicroF1\": 0.1657981999052581,         \"MacroF1\": 0.1511045106411843,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 1.690872       },       {         \"step\": 3168,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1701926113040732,         \"MicroF1\": 0.1701926113040732,         \"MacroF1\": 0.1568151235503963,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 3.298143       },       {         \"step\": 4224,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1659957376272791,         \"MicroF1\": 0.1659957376272791,         \"MacroF1\": 0.1525443315605066,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 5.473684       },       {         \"step\": 5280,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1708656942602765,         \"MicroF1\": 0.1708656942602765,         \"MacroF1\": 0.1567667911399358,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 8.202311       },       {         \"step\": 6336,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1737963693764798,         \"MicroF1\": 0.1737963693764798,         \"MacroF1\": 0.1613756819597299,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 11.448991       },       {         \"step\": 7392,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1752130970098769,         \"MicroF1\": 0.1752130970098769,         \"MacroF1\": 0.1618940790413477,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 15.242684       },       {         \"step\": 8448,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1772226826092103,         \"MicroF1\": 0.1772226826092103,         \"MacroF1\": 0.163740045170864,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 19.537217       },       {         \"step\": 9504,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1773124276544249,         \"MicroF1\": 0.1773124276544249,         \"MacroF1\": 0.1637492974453096,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 24.318802       },       {         \"step\": 10560,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1790889288758405,         \"MicroF1\": 0.1790889288758405,         \"MacroF1\": 0.1656421076747495,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 29.683683       },       {         \"step\": 11616,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1789926818768833,         \"MicroF1\": 0.1789926818768833,         \"MacroF1\": 0.1655925383533761,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 35.598037000000005       },       {         \"step\": 12672,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.1853050272275274,         \"MicroF1\": 0.1853050272275274,         \"MacroF1\": 0.182698099884098,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 41.981502000000006       },       {         \"step\": 13728,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2479784366576819,         \"MicroF1\": 0.2479784366576819,         \"MacroF1\": 0.266039368455288,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 48.94863000000001       },       {         \"step\": 14784,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2795778935263478,         \"MicroF1\": 0.2795778935263478,         \"MacroF1\": 0.2822974275171512,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 56.43945000000001       },       {         \"step\": 15840,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2761537975882315,         \"MicroF1\": 0.2761537975882315,         \"MacroF1\": 0.2847375853365436,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 64.48233200000001       },       {         \"step\": 16896,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2723290914471737,         \"MicroF1\": 0.2723290914471737,         \"MacroF1\": 0.2859139704285301,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 73.03679300000002       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2720739791655061,         \"MicroF1\": 0.2720739791655061,         \"MacroF1\": 0.2880143206503878,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 82.10379000000002       },       {         \"step\": 19008,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2825274898721523,         \"MicroF1\": 0.2825274898721523,         \"MacroF1\": 0.2877504429321087,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 91.70347300000002       },       {         \"step\": 20064,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2872451776902756,         \"MicroF1\": 0.2872451776902756,         \"MacroF1\": 0.2866739236661926,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 101.81113500000002       },       {         \"step\": 21120,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2830626450116009,         \"MicroF1\": 0.2830626450116009,         \"MacroF1\": 0.2816476602425525,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 112.42818900000002       },       {         \"step\": 22176,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2805411499436302,         \"MicroF1\": 0.2805411499436302,         \"MacroF1\": 0.2786296072528009,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 123.55266000000002       },       {         \"step\": 23232,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2797124531875511,         \"MicroF1\": 0.2797124531875511,         \"MacroF1\": 0.2771941975793341,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 135.22034100000002       },       {         \"step\": 24288,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2777205912628155,         \"MicroF1\": 0.2777205912628155,         \"MacroF1\": 0.2745878480946635,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 147.32084400000002       },       {         \"step\": 25344,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2756579726157124,         \"MicroF1\": 0.2756579726157124,         \"MacroF1\": 0.2723380305202896,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 159.88729300000003       },       {         \"step\": 26400,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2739497708246524,         \"MicroF1\": 0.2739497708246524,         \"MacroF1\": 0.2699690442569991,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 172.95537600000003       },       {         \"step\": 27456,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2718994718630486,         \"MicroF1\": 0.2718994718630486,         \"MacroF1\": 0.2671948532388624,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 186.52082400000003       },       {         \"step\": 28512,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2723860965942969,         \"MicroF1\": 0.2723860965942969,         \"MacroF1\": 0.2686965366571337,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 200.59564800000004       },       {         \"step\": 29568,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2738187844556431,         \"MicroF1\": 0.2738187844556431,         \"MacroF1\": 0.2720266804437783,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 215.16150500000003       },       {         \"step\": 30624,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2753812493877151,         \"MicroF1\": 0.2753812493877151,         \"MacroF1\": 0.2748698663810351,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 230.19075300000003       },       {         \"step\": 31680,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2780390795163989,         \"MicroF1\": 0.2780390795163989,         \"MacroF1\": 0.2784141751235631,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 245.71900300000004       },       {         \"step\": 32736,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.279670077898274,         \"MicroF1\": 0.279670077898274,         \"MacroF1\": 0.2802192251245275,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 261.76959600000004       },       {         \"step\": 33792,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2808440117190968,         \"MicroF1\": 0.2808440117190968,         \"MacroF1\": 0.2811962745371706,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 278.22772000000003       },       {         \"step\": 34848,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2772405085086234,         \"MicroF1\": 0.2772405085086234,         \"MacroF1\": 0.2781905182864757,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 295.19763900000004       },       {         \"step\": 35904,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2739325404562293,         \"MicroF1\": 0.2739325404562293,         \"MacroF1\": 0.2754200456137155,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 312.64260700000005       },       {         \"step\": 36960,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.271246516410076,         \"MicroF1\": 0.271246516410076,         \"MacroF1\": 0.273332837678202,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 330.5037730000001       },       {         \"step\": 38016,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2685518874128633,         \"MicroF1\": 0.2685518874128633,         \"MacroF1\": 0.2710722002891223,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 348.8496650000001       },       {         \"step\": 39072,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.277034117376059,         \"MicroF1\": 0.277034117376059,         \"MacroF1\": 0.2770619820799866,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 367.6207990000001       },       {         \"step\": 40128,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2761731502479627,         \"MicroF1\": 0.2761731502479627,         \"MacroF1\": 0.2760769006623072,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 386.8573710000001       },       {         \"step\": 41184,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2756720005827647,         \"MicroF1\": 0.2756720005827647,         \"MacroF1\": 0.2754352632972116,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 406.52795400000014       },       {         \"step\": 42240,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2740121688486943,         \"MicroF1\": 0.2740121688486943,         \"MacroF1\": 0.2735946193588542,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 426.69962200000015       },       {         \"step\": 43296,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2738422450629403,         \"MicroF1\": 0.2738422450629403,         \"MacroF1\": 0.2731948869083578,         \"Memory in Mb\": 0.0013856887817382,         \"Time in s\": 447.37129900000014       },       {         \"step\": 44352,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2729588960790061,         \"MicroF1\": 0.2729588960790061,         \"MacroF1\": 0.2720911653869048,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 468.49129600000015       },       {         \"step\": 45408,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2720505648908758,         \"MicroF1\": 0.2720505648908758,         \"MacroF1\": 0.2708084959373003,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 490.06234300000017       },       {         \"step\": 46464,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.271377224888621,         \"MicroF1\": 0.271377224888621,         \"MacroF1\": 0.2698631410415437,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 512.0778290000002       },       {         \"step\": 47520,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2723542162082535,         \"MicroF1\": 0.2723542162082535,         \"MacroF1\": 0.2717062798322285,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 534.5781510000002       },       {         \"step\": 48576,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2741327843540916,         \"MicroF1\": 0.2741327843540916,         \"MacroF1\": 0.2744946340974243,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 557.5265480000002       },       {         \"step\": 49632,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2753520984868328,         \"MicroF1\": 0.2753520984868328,         \"MacroF1\": 0.2765036876430403,         \"Memory in Mb\": 0.0013818740844726,         \"Time in s\": 580.9705880000001       },       {         \"step\": 50688,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2768362696549411,         \"MicroF1\": 0.2768362696549411,         \"MacroF1\": 0.2786344091273496,         \"Memory in Mb\": 0.0013837814331054,         \"Time in s\": 604.9012140000001       },       {         \"step\": 51744,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2782791875229499,         \"MicroF1\": 0.2782791875229499,         \"MacroF1\": 0.2805971515128955,         \"Memory in Mb\": 0.0013885498046875,         \"Time in s\": 629.3033230000001       },       {         \"step\": 52800,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2891153241538665,         \"MicroF1\": 0.2891153241538665,         \"MacroF1\": 0.2892953202729756,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 654.1512880000001       },       {         \"step\": 52848,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Insects\",         \"Accuracy\": 0.2897610081934642,         \"MicroF1\": 0.2897610081934642,         \"MacroF1\": 0.2897627257031321,         \"Memory in Mb\": 0.0013866424560546,         \"Time in s\": 679.0036960000001       },       {         \"step\": 408,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975429975429976,         \"MicroF1\": 0.9975429975429976,         \"MacroF1\": 0.966040884438882,         \"Memory in Mb\": 0.0006122589111328,         \"Time in s\": 0.255536       },       {         \"step\": 816,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975460122699388,         \"MicroF1\": 0.9975460122699388,         \"MacroF1\": 0.9879967903427672,         \"Memory in Mb\": 0.0006628036499023,         \"Time in s\": 0.794196       },       {         \"step\": 1224,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975470155355682,         \"MicroF1\": 0.9975470155355682,         \"MacroF1\": 0.9931179599499376,         \"Memory in Mb\": 0.0007133483886718,         \"Time in s\": 1.53447       },       {         \"step\": 1632,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975475168608215,         \"MicroF1\": 0.9975475168608215,         \"MacroF1\": 0.9950750839342832,         \"Memory in Mb\": 0.0012521743774414,         \"Time in s\": 2.469131       },       {         \"step\": 2040,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975478175576264,         \"MicroF1\": 0.9975478175576264,         \"MacroF1\": 0.9960150346160552,         \"Memory in Mb\": 0.0013027191162109,         \"Time in s\": 3.675833       },       {         \"step\": 2448,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975480179812016,         \"MicroF1\": 0.9975480179812016,         \"MacroF1\": 0.9965317313935652,         \"Memory in Mb\": 0.0013532638549804,         \"Time in s\": 5.030286       },       {         \"step\": 2856,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975481611208408,         \"MicroF1\": 0.9975481611208408,         \"MacroF1\": 0.996842428316928,         \"Memory in Mb\": 0.00140380859375,         \"Time in s\": 6.586031       },       {         \"step\": 3264,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975482684646032,         \"MicroF1\": 0.9975482684646032,         \"MacroF1\": 0.9970416021996,         \"Memory in Mb\": 0.0014543533325195,         \"Time in s\": 8.377109       },       {         \"step\": 3672,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975483519476982,         \"MicroF1\": 0.9975483519476982,         \"MacroF1\": 0.9971755428551424,         \"Memory in Mb\": 0.001504898071289,         \"Time in s\": 10.331252       },       {         \"step\": 4080,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975484187300808,         \"MicroF1\": 0.9975484187300808,         \"MacroF1\": 0.9972690115789392,         \"Memory in Mb\": 0.0015554428100585,         \"Time in s\": 12.525489       },       {         \"step\": 4488,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975484733675062,         \"MicroF1\": 0.9975484733675062,         \"MacroF1\": 0.9973361791525124,         \"Memory in Mb\": 0.0016059875488281,         \"Time in s\": 14.940819       },       {         \"step\": 4896,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975485188968336,         \"MicroF1\": 0.9975485188968336,         \"MacroF1\": 0.9973856025730918,         \"Memory in Mb\": 0.0016565322875976,         \"Time in s\": 17.495259       },       {         \"step\": 5304,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548557420328,         \"MicroF1\": 0.997548557420328,         \"MacroF1\": 0.9974226798335742,         \"Memory in Mb\": 0.0017070770263671,         \"Time in s\": 20.336762       },       {         \"step\": 5712,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975485904395028,         \"MicroF1\": 0.9975485904395028,         \"MacroF1\": 0.99745094204078,         \"Memory in Mb\": 0.0017576217651367,         \"Time in s\": 23.402208       },       {         \"step\": 6120,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975486190554012,         \"MicroF1\": 0.9975486190554012,         \"MacroF1\": 0.9974727709453766,         \"Memory in Mb\": 0.0018081665039062,         \"Time in s\": 26.661861       },       {         \"step\": 6528,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975486440937644,         \"MicroF1\": 0.9975486440937644,         \"MacroF1\": 0.997489815700999,         \"Memory in Mb\": 0.0018587112426757,         \"Time in s\": 30.164710000000003       },       {         \"step\": 6936,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548666186013,         \"MicroF1\": 0.997548666186013,         \"MacroF1\": 0.9975032443691146,         \"Memory in Mb\": 0.0019092559814453,         \"Time in s\": 33.838397       },       {         \"step\": 7344,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548685823233,         \"MicroF1\": 0.997548685823233,         \"MacroF1\": 0.9975139007887864,         \"Memory in Mb\": 0.0034246444702148,         \"Time in s\": 37.738436       },       {         \"step\": 7752,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487033931104,         \"MicroF1\": 0.9975487033931104,         \"MacroF1\": 0.9975224052755716,         \"Memory in Mb\": 0.0034751892089843,         \"Time in s\": 41.800015       },       {         \"step\": 8160,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548719205785,         \"MicroF1\": 0.997548719205785,         \"MacroF1\": 0.9975292209193424,         \"Memory in Mb\": 0.0035257339477539,         \"Time in s\": 46.105028       },       {         \"step\": 8568,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487335123148,         \"MicroF1\": 0.9975487335123148,         \"MacroF1\": 0.9975346982235258,         \"Memory in Mb\": 0.0035762786865234,         \"Time in s\": 50.63279300000001       },       {         \"step\": 8976,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548746518106,         \"MicroF1\": 0.997548746518106,         \"MacroF1\": 0.9975391057693664,         \"Memory in Mb\": 0.0036268234252929,         \"Time in s\": 55.447067       },       {         \"step\": 9384,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548758392838,         \"MicroF1\": 0.997548758392838,         \"MacroF1\": 0.997542651662671,         \"Memory in Mb\": 0.0036773681640625,         \"Time in s\": 60.387128       },       {         \"step\": 9792,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487692779084,         \"MicroF1\": 0.9975487692779084,         \"MacroF1\": 0.9975454987794796,         \"Memory in Mb\": 0.003727912902832,         \"Time in s\": 65.547582       },       {         \"step\": 10200,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487792920874,         \"MicroF1\": 0.9975487792920874,         \"MacroF1\": 0.9975477757646256,         \"Memory in Mb\": 0.0037784576416015,         \"Time in s\": 70.981052       },       {         \"step\": 10608,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487885358726,         \"MicroF1\": 0.9975487885358726,         \"MacroF1\": 0.9975495850737114,         \"Memory in Mb\": 0.003829002380371,         \"Time in s\": 76.594226       },       {         \"step\": 11016,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975487970948708,         \"MicroF1\": 0.9975487970948708,         \"MacroF1\": 0.9975510089260562,         \"Memory in Mb\": 0.0038795471191406,         \"Time in s\": 82.44596800000001       },       {         \"step\": 11424,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488050424582,         \"MicroF1\": 0.9975488050424582,         \"MacroF1\": 0.9975521137613484,         \"Memory in Mb\": 0.0039300918579101,         \"Time in s\": 88.533094       },       {         \"step\": 11832,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.99754881244189,         \"MicroF1\": 0.99754881244189,         \"MacroF1\": 0.99755295361102,         \"Memory in Mb\": 0.0039806365966796,         \"Time in s\": 94.818744       },       {         \"step\": 12240,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548819347986,         \"MicroF1\": 0.997548819347986,         \"MacroF1\": 0.9975535726732964,         \"Memory in Mb\": 0.0040311813354492,         \"Time in s\": 101.331754       },       {         \"step\": 12648,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548825808492,         \"MicroF1\": 0.997548825808492,         \"MacroF1\": 0.997554007297632,         \"Memory in Mb\": 0.0040817260742187,         \"Time in s\": 108.051678       },       {         \"step\": 13056,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488318651856,         \"MicroF1\": 0.9975488318651856,         \"MacroF1\": 0.997554287526727,         \"Memory in Mb\": 0.0041322708129882,         \"Time in s\": 114.996681       },       {         \"step\": 13464,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488375547796,         \"MicroF1\": 0.9975488375547796,         \"MacroF1\": 0.9975544383040468,         \"Memory in Mb\": 0.0041828155517578,         \"Time in s\": 122.1119       },       {         \"step\": 13872,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488429096676,         \"MicroF1\": 0.9975488429096676,         \"MacroF1\": 0.9975544804262362,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 129.47010500000002       },       {         \"step\": 14280,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488479585404,         \"MicroF1\": 0.9975488479585404,         \"MacroF1\": 0.99755443129941,         \"Memory in Mb\": 0.0042839050292968,         \"Time in s\": 136.988051       },       {         \"step\": 14688,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488527269012,         \"MicroF1\": 0.9975488527269012,         \"MacroF1\": 0.997554305543504,         \"Memory in Mb\": 0.0043344497680664,         \"Time in s\": 144.742896       },       {         \"step\": 15096,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548857237496,         \"MicroF1\": 0.997548857237496,         \"MacroF1\": 0.9975541154780816,         \"Memory in Mb\": 0.0043849945068359,         \"Time in s\": 152.648866       },       {         \"step\": 15504,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488615106752,         \"MicroF1\": 0.9975488615106752,         \"MacroF1\": 0.9975538715150368,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 160.767465       },       {         \"step\": 15912,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488655647036,         \"MicroF1\": 0.9975488655647036,         \"MacroF1\": 0.997553582477696,         \"Memory in Mb\": 0.004486083984375,         \"Time in s\": 169.09858       },       {         \"step\": 16320,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488694160182,         \"MicroF1\": 0.9975488694160182,         \"MacroF1\": 0.9975532558614028,         \"Memory in Mb\": 0.0045366287231445,         \"Time in s\": 177.653336       },       {         \"step\": 16728,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488730794524,         \"MicroF1\": 0.9975488730794524,         \"MacroF1\": 0.997552898047314,         \"Memory in Mb\": 0.004587173461914,         \"Time in s\": 186.438203       },       {         \"step\": 17136,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488765684272,         \"MicroF1\": 0.9975488765684272,         \"MacroF1\": 0.9975525144785748,         \"Memory in Mb\": 0.0046377182006835,         \"Time in s\": 195.447168       },       {         \"step\": 17544,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488798951148,         \"MicroF1\": 0.9975488798951148,         \"MacroF1\": 0.997552109806108,         \"Memory in Mb\": 0.0046882629394531,         \"Time in s\": 204.563635       },       {         \"step\": 17952,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.997548883070581,         \"MicroF1\": 0.997548883070581,         \"MacroF1\": 0.9975516880097278,         \"Memory in Mb\": 0.0047388076782226,         \"Time in s\": 213.933058       },       {         \"step\": 18360,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488861049076,         \"MicroF1\": 0.9975488861049076,         \"MacroF1\": 0.997551252499137,         \"Memory in Mb\": 0.0047893524169921,         \"Time in s\": 223.513668       },       {         \"step\": 18768,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488890073,         \"MicroF1\": 0.9975488890073,         \"MacroF1\": 0.9975508061984416,         \"Memory in Mb\": 0.0048398971557617,         \"Time in s\": 233.322943       },       {         \"step\": 19176,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.99754889178618,         \"MicroF1\": 0.99754889178618,         \"MacroF1\": 0.9975503516171184,         \"Memory in Mb\": 0.0048904418945312,         \"Time in s\": 243.357771       },       {         \"step\": 19584,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488944492672,         \"MicroF1\": 0.9975488944492672,         \"MacroF1\": 0.9975498909097889,         \"Memory in Mb\": 0.0049409866333007,         \"Time in s\": 253.567103       },       {         \"step\": 19992,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488970036516,         \"MicroF1\": 0.9975488970036516,         \"MacroF1\": 0.9975494259267256,         \"Memory in Mb\": 0.0049915313720703,         \"Time in s\": 264.004285       },       {         \"step\": 20400,         \"track\": \"Multiclass classification\",         \"model\": \"[baseline] Last Class\",         \"dataset\": \"Keystroke\",         \"Accuracy\": 0.9975488994558556,         \"MicroF1\": 0.9975488994558556,         \"MacroF1\": 0.9975489582566448,         \"Memory in Mb\": 0.0050420761108398,         \"Time in s\": 274.675054       }     ]   },   \"params\": [     {       \"name\": \"models\",       \"select\": {         \"type\": \"point\",         \"fields\": [           \"model\"         ]       },       \"bind\": \"legend\"     },     {       \"name\": \"Dataset\",       \"value\": \"ImageSegments\",       \"bind\": {         \"input\": \"select\",         \"options\": [           \"ImageSegments\",           \"Insects\",           \"Keystroke\"         ]       }     },     {       \"name\": \"grid\",       \"select\": \"interval\",       \"bind\": \"scales\"     }   ],   \"transform\": [     {       \"filter\": {         \"field\": \"dataset\",         \"equal\": {           \"expr\": \"Dataset\"         }       }     }   ],   \"repeat\": {     \"row\": [       \"Accuracy\",       \"MicroF1\",       \"MacroF1\",       \"Memory in Mb\",       \"Time in s\"     ]   },   \"spec\": {     \"width\": \"container\",     \"mark\": \"line\",     \"encoding\": {       \"x\": {         \"field\": \"step\",         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"title\": \"Instance\"         }       },       \"y\": {         \"field\": {           \"repeat\": \"row\"         },         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18         }       },       \"color\": {         \"field\": \"model\",         \"type\": \"ordinal\",         \"scale\": {           \"scheme\": \"category20b\"         },         \"title\": \"Models\",         \"legend\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"labelLimit\": 500         }       },       \"opacity\": {         \"condition\": {           \"param\": \"models\",           \"value\": 1         },         \"value\": 0.2       }     }   } }</p>"},{"location":"benchmarks/Multiclass%20classification/#datasets","title":"Datasets","text":"ImageSegments <p>Image segments classification.</p> <p>This dataset contains features that describe image segments into 7 classes: brickface, sky, foliage, cement, window, path, and grass.</p> <pre><code>Name  ImageSegments                                                                                               \nTask  Multi-class classification\n</code></pre> <p>Samples  2,310                                                                                                      Features  18                                                                                                          Classes  7                                                                                                            Sparse  False                                                                                                          Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/segment.csv.zip</p> <p></p> Insects <p>Insects dataset.</p> <p>This dataset has different variants, which are:</p> <ul> <li>abrupt_balanced</li> <li>abrupt_imbalanced</li> <li>gradual_balanced</li> <li>gradual_imbalanced</li> <li>incremental-abrupt_balanced</li> <li>incremental-abrupt_imbalanced</li> <li>incremental-reoccurring_balanced</li> <li>incremental-reoccurring_imbalanced</li> <li>incremental_balanced</li> <li>incremental_imbalanced</li> <li>out-of-control</li> </ul> <p>The number of samples and the difficulty change from one variant to another. The number of classes is always the same (6), except for the last variant (24).</p> <pre><code>  Name  Insects                                                                                 \n  Task  Multi-class classification\n</code></pre> <p>Samples  52,848                                                                                   Features  33                                                                                        Classes  6                                                                                          Sparse  False                                                                                        Path  /Users/mastelini/river_data/Insects/INSECTS-abrupt_balanced_norm.arff                         URL  http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff       Size  15.66 MB                                                                               Downloaded  True                                                                                      Variant  abrupt_balanced                                                                         </p> <p></p> Keystroke <p>CMU keystroke dataset.</p> <p>Users are tasked to type in a password. The task is to determine which user is typing in the password.</p> <p>The only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes have been dropped.</p> <pre><code>  Name  Keystroke                                                       \n  Task  Multi-class classification\n</code></pre> <p>Samples  20,400                                                           Features  31                                                                Classes  51                                                                 Sparse  False                                                                Path  /Users/mastelini/river_data/Keystroke/DSL-StrongPasswordData.csv        URL  http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv          Size  4.45 MB                                                        Downloaded  True                                                            </p> <p></p>"},{"location":"benchmarks/Multiclass%20classification/#parameters","title":"Parameters","text":"<pre><code>variant\n    Indicates which variant of the dataset to load.\n</code></pre>"},{"location":"benchmarks/Multiclass%20classification/#models","title":"Models","text":"Naive Bayes <p><pre>GaussianNB ()</pre></p> <p></p> Hoeffding Tree <p><pre>HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)</pre></p> <p></p> Hoeffding Adaptive Tree <p><pre>HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=True\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=42\n)</pre></p> <p></p> Adaptive Random Forest <p><pre>[]</pre></p> <p></p> Aggregated Mondrian Forest <p><pre>[]</pre></p> <p></p> Streaming Random Patches <p><pre>SRPClassifier (\n  model=HoeffdingTreeClassifier (\n    grace_period=50\n    max_depth=inf\n    split_criterion=\"info_gain\"\n    delta=0.01\n    tau=0.05\n    leaf_prediction=\"nba\"\n    nb_threshold=0\n    nominal_attributes=None\n    splitter=GaussianSplitter (\n      n_splits=10\n    )\n    binary_split=False\n    min_branch_fraction=0.01\n    max_share_to_split=0.99\n    max_size=100.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n  )\n  n_models=10\n  subspace_size=0.6\n  training_method=\"patches\"\n  lam=6\n  drift_detector=ADWIN (\n    delta=1e-05\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  warning_detector=ADWIN (\n    delta=0.0001\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  disable_detector=\"off\"\n  disable_weighted_vote=False\n  seed=None\n  metric=Accuracy (\n    cm=ConfusionMatrix (\n      classes=[]\n    )\n  )\n)</pre></p> <p></p> k-Nearest Neighbors <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)\n\n<p></p>\n\nADWIN Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nAdaBoost\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nBagging\n<p><pre>[HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n), HoeffdingAdaptiveTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  bootstrap_sampling=False\n  drift_window_threshold=300\n  drift_detector=ADWIN (\n    delta=0.002\n    clock=32\n    max_buckets=5\n    min_window_length=5\n    grace_period=10\n  )\n  switch_significance=0.05\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n  seed=None\n)]</pre></p>\n\n<p></p>\n\nLeveraging Bagging\n<p><pre>[HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n)]</pre></p>\n\n<p></p>\n\nStacking\n<p><pre>[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n\n<p></p>\n\nVoting\n<p><pre>VotingClassifier (\n  models=[Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SoftmaxRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=CrossEntropy (\n      class_weight={}\n    )\n    l2=0\n  )\n), GaussianNB (), HoeffdingTreeClassifier (\n  grace_period=200\n  max_depth=inf\n  split_criterion=\"info_gain\"\n  delta=1e-07\n  tau=0.05\n  leaf_prediction=\"nba\"\n  nb_threshold=0\n  nominal_attributes=None\n  splitter=GaussianSplitter (\n    n_splits=10\n  )\n  binary_split=False\n  min_branch_fraction=0.01\n  max_share_to_split=0.99\n  max_size=100.\n  memory_estimate_period=1000000\n  stop_mem_management=False\n  remove_poor_attrs=False\n  merit_preprune=True\n), Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNClassifier (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    weighted=True\n    cleanup_every=0\n    softmax=False\n  )\n)]\n  use_probabilities=True\n)\n\n<p></p>\n\n[baseline] Last Class\n<p><pre>NoChangeClassifier ()</pre></p>\n\n<p></p>"},{"location":"benchmarks/Multiclass%20classification/#environment","title":"Environment","text":"<pre>Python implementation: CPython\nPython version       : 3.12.4\nIPython version      : 8.18.1\n\nriver       : 0.21.2\nnumpy       : 1.26.4\nscikit-learn: 1.3.1\npandas      : 2.2.2\nscipy       : 1.13.0\n\nCompiler    : GCC 11.4.0\nOS          : Linux\nRelease     : 6.5.0-1024-azure\nMachine     : x86_64\nProcessor   : x86_64\nCPU cores   : 4\nArchitecture: 64bit\n</pre>"},{"location":"benchmarks/Regression/","title":"Regression","text":"TableChart Model Dataset MAE RMSE R2 Memory in Mb Time in s Adaptive Model Rules ChickWeights 24.1943 37.2166 0.725319 0.046977 5.25855 Adaptive Model Rules TrumpApproval 1.39847 2.43336 -1.02372 0.114429 9.38293 Adaptive Random Forest ChickWeights 26.1016 40.8094 0.669725 1.19043 56.006 Adaptive Random Forest TrumpApproval 0.800378 2.11495 -0.528761 1.28462 87.4457 Aggregated Mondrian Forest ChickWeights 25.6742 41.7123 0.65479 8.21412 127.415 Aggregated Mondrian Forest TrumpApproval 0.268533 0.349421 0.958184 16.9323 186.034 Bagging ChickWeights 23.1143 36.6311 0.733893 0.628034 38.0203 Bagging TrumpApproval 0.908203 2.23718 -0.710572 1.31579 82.0689 Exponentially Weighted Average ChickWeights 121.818 141.004 -2.94294 3.09241 55.8851 Exponentially Weighted Average TrumpApproval 40.7546 40.7905 -567.663 5.27613 141.452 Hoeffding Adaptive Tree ChickWeights 23.3739 37.6579 0.718766 0.0947332 7.99029 Hoeffding Adaptive Tree TrumpApproval 0.921313 2.23942 -0.713986 0.138225 16.7576 Hoeffding Tree ChickWeights 23.1619 36.7336 0.732402 0.0440512 6.29305 Hoeffding Tree TrumpApproval 0.956103 2.24987 -0.730022 0.148639 11.7656 Linear Regression ChickWeights 23.7587 37.0377 0.727954 0.00421047 3.21471 Linear Regression TrumpApproval 1.31455 3.91198 -4.23035 0.00497341 11.5379 Linear Regression with l1 regularization ChickWeights 23.7577 37.078 0.727361 0.00444126 9.7485 Linear Regression with l1 regularization TrumpApproval 1.15377 3.82872 -4.01007 0.0052042 13.3595 Linear Regression with l2 regularization ChickWeights 25.2738 38.5885 0.704694 0.00423336 1.22128 Linear Regression with l2 regularization TrumpApproval 1.87151 4.13052 -4.83107 0.0049963 4.15677 Passive-Aggressive Regressor, mode 1 ChickWeights 24.3423 37.596 0.71969 0.00345898 1.10187 Passive-Aggressive Regressor, mode 1 TrumpApproval 4.98403 6.97667 -15.6354 0.00443554 2.99338 Passive-Aggressive Regressor, mode 2 ChickWeights 100.624 143.066 -3.05911 0.00345898 1.16798 Passive-Aggressive Regressor, mode 2 TrumpApproval 31.0933 34.6257 -408.765 0.00443554 4.72475 River MLP ChickWeights 51.4078 80.9203 -0.298584 0.0123129 28.2295 River MLP TrumpApproval 1.58058 5.03392 -7.66066 0.0133505 32.2432 Stochastic Gradient Tree ChickWeights 68.7588 80.358 -0.280601 1.12059 22.3803 Stochastic Gradient Tree TrumpApproval 9.42975 17.9379 -108.972 3.08244 52.4507 Streaming Random Patches ChickWeights 23.7097 38.4416 0.706938 0.355182 93.4014 Streaming Random Patches TrumpApproval 0.656697 1.98434 -0.345761 1.06461 134.903 [baseline] Mean predictor ChickWeights 50.2509 71.1144 -0.00292947 0.000490189 0.302835 [baseline] Mean predictor TrumpApproval 1.56755 2.20286 -0.658483 0.000490189 1.08177 k-Nearest Neighbors ChickWeights 24.8406 39.2016 0.695236 2.88522 40.0878 k-Nearest Neighbors TrumpApproval 0.641679 1.59417 0.131425 5.03263 123.301 <p>Try reloading the page if something is buggy</p> <p>{   \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",   \"data\": {     \"values\": [       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.432219699626994,         \"RMSE\": 31.267456151778337,         \"R2\": -1257.4692714745631,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.000963       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.75760844034268,         \"RMSE\": 23.632210645041404,         \"R2\": -590.4769976066937,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.002374       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.555240079240876,         \"RMSE\": 19.34929493332969,         \"R2\": -259.0232069515881,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.004113       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.14363365913676,         \"RMSE\": 16.767243978820222,         \"R2\": -220.3452424437857,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.006175       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.841164000616114,         \"RMSE\": 17.714902804136145,         \"R2\": -60.2608923989398,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.008581       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.32598508406065,         \"RMSE\": 16.527353468164844,         \"R2\": -21.985729074745297,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.01133       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.718401993814265,         \"RMSE\": 15.52109639018614,         \"R2\": -12.587024696233003,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.014424       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.767755200283737,         \"RMSE\": 14.552446235427842,         \"R2\": -9.829280875288257,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.017858       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.977130626229444,         \"RMSE\": 13.740429605807138,         \"R2\": -7.074807888709797,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0216349999999999       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.506893871110683,         \"RMSE\": 13.098273311725844,         \"R2\": -4.124041411671393,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0257519999999999       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.252833276832352,         \"RMSE\": 12.607637144454216,         \"R2\": -2.6562249812820733,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0302089999999999       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.896359231575217,         \"RMSE\": 12.121970224209305,         \"R2\": -1.7624336939368233,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0350039999999999       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.581914741629191,         \"RMSE\": 11.688367143429067,         \"R2\": -1.080274127204615,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0401389999999999       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.347682986169337,         \"RMSE\": 11.314945909537578,         \"R2\": -0.6567859420078188,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0456129999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.47676439389405,         \"RMSE\": 11.21748999353191,         \"R2\": -0.3089959076061037,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0514269999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.552290709218319,         \"RMSE\": 11.100632967129414,         \"R2\": -0.0335718949744832,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0575809999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.503097179992549,         \"RMSE\": 10.915357728148932,         \"R2\": 0.1817591258850298,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0640729999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.420443618722296,         \"RMSE\": 10.727647067877951,         \"R2\": 0.3713230272376924,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.070904       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.54715053669462,         \"RMSE\": 10.814712106795348,         \"R2\": 0.4732913339801876,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0780719999999999       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.075852889975692,         \"RMSE\": 11.488147441481184,         \"R2\": 0.479648982578291,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0855759999999999       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.197265349840174,         \"RMSE\": 11.527376107146,         \"R2\": 0.5518657524511614,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.0934159999999999       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.359957454348683,         \"RMSE\": 11.71365363090123,         \"R2\": 0.6276606533313056,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.1015909999999999       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.389343614466645,         \"RMSE\": 11.70410418267156,         \"R2\": 0.6771453727427903,         \"Memory in Mb\": 0.0041303634643554,         \"Time in s\": 0.1101019999999999       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.007684680730522,         \"RMSE\": 12.681713023454453,         \"R2\": 0.6536838584261326,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.1189499999999999       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.456356064016727,         \"RMSE\": 13.562457362384484,         \"R2\": 0.6514630282957669,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.128137       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.682222588679535,         \"RMSE\": 13.91372755183948,         \"R2\": 0.6822857451181047,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.137663       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.656490376145301,         \"RMSE\": 13.862729792291397,         \"R2\": 0.7264657185265005,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.147527       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.17087534181789,         \"RMSE\": 14.586626878398466,         \"R2\": 0.730278281446047,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.157738       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.253235573939358,         \"RMSE\": 17.040182474587255,         \"R2\": 0.6659707835095393,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.270641       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.67218268870669,         \"RMSE\": 17.597898989920818,         \"R2\": 0.6951262006904333,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.38498       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.865878827617594,         \"RMSE\": 17.684075493652397,         \"R2\": 0.7243197409220903,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.500381       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.014541487264225,         \"RMSE\": 17.788847456042067,         \"R2\": 0.7464163188501894,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.6168239999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.893125923244742,         \"RMSE\": 19.14640328452056,         \"R2\": 0.7147396000186461,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.7343709999999999       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.40252640363099,         \"RMSE\": 20.24468752454989,         \"R2\": 0.7068188127948265,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.8529599999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.78041264925886,         \"RMSE\": 20.84297745742841,         \"R2\": 0.7250508110390363,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 0.972583       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.908163646252072,         \"RMSE\": 20.82655299121286,         \"R2\": 0.7440434321899679,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.093238       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.78624220521945,         \"RMSE\": 22.297725224665918,         \"R2\": 0.7272822586077066,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.214927       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.56231380927385,         \"RMSE\": 23.732773749874315,         \"R2\": 0.7099846963904786,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.3375199999999998       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.109717404902195,         \"RMSE\": 24.64206848989837,         \"R2\": 0.7221580232945248,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.4604629999999998       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.287005413554732,         \"RMSE\": 24.72152256024044,         \"R2\": 0.7401560140604169,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.583729       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.806865735774078,         \"RMSE\": 25.331119330890413,         \"R2\": 0.7387809061287051,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.707315       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.912347710111163,         \"RMSE\": 27.450327347193877,         \"R2\": 0.7118740092210123,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.831218       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.68786801080465,         \"RMSE\": 28.74804692307192,         \"R2\": 0.7209603573249957,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 1.955435       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.02230431978895,         \"RMSE\": 29.040370094251127,         \"R2\": 0.7308604085348502,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.079964       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.47643461729765,         \"RMSE\": 29.56562239854821,         \"R2\": 0.7375811559076941,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.204806       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.368862660258834,         \"RMSE\": 31.016595939650863,         \"R2\": 0.7195863076124669,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.32996       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.093492725340727,         \"RMSE\": 32.00802507821089,         \"R2\": 0.7181912437784894,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.455434       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.883641447975457,         \"RMSE\": 33.20140091570763,         \"R2\": 0.727385103943677,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.581219       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.055940734584823,         \"RMSE\": 33.19901872731025,         \"R2\": 0.7386798629639011,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.707313       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.04665839885113,         \"RMSE\": 34.818142407426606,         \"R2\": 0.7214274205964286,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.833717       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.75015079068996,         \"RMSE\": 35.737018888500465,         \"R2\": 0.7193638350430389,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 2.960429       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.60149518688988,         \"RMSE\": 36.92142939550449,         \"R2\": 0.722919218201958,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 3.087448       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.75865667886776,         \"RMSE\": 37.03767126301035,         \"R2\": 0.7279537206511313,         \"Memory in Mb\": 0.0042104721069335,         \"Time in s\": 3.2147080000000003       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.71537559933632,         \"RMSE\": 24.27612097298636,         \"R2\": -1381.3340079163324,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.003774       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.956746822999646,         \"RMSE\": 17.85530816845139,         \"R2\": -127.17403450091604,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.008234       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.540337295823328,         \"RMSE\": 15.264267507077204,         \"R2\": -125.28803290438402,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.013346       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.92648259034571,         \"RMSE\": 13.436420463778148,         \"R2\": -97.15695382305036,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.019104       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.5495393499287236,         \"RMSE\": 12.076339439187349,         \"R2\": -48.75014684916543,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.025552       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.571266653106965,         \"RMSE\": 11.058195411086311,         \"R2\": -34.38851346579008,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.032651       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.868178209177549,         \"RMSE\": 10.265658199354172,         \"R2\": -30.51567288629301,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.040397       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.226493262391851,         \"RMSE\": 9.609365926739027,         \"R2\": -23.352843972650145,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.048786       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.806672346419344,         \"RMSE\": 9.079121174210671,         \"R2\": -18.092824435696784,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.057857       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.400421129740624,         \"RMSE\": 8.617551092451054,         \"R2\": -16.252012396913173,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.06766       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.083414123099576,         \"RMSE\": 8.223437931584808,         \"R2\": -15.946617088642816,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.0781589999999999       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.82353438841577,         \"RMSE\": 7.87966547036827,         \"R2\": -14.67643164713968,         \"Memory in Mb\": 0.0048131942749023,         \"Time in s\": 0.0893539999999999       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.573342996804622,         \"RMSE\": 7.572887494545769,         \"R2\": -13.674649599158814,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.1012429999999999       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.399764262602937,         \"RMSE\": 7.307305033384193,         \"R2\": -13.305426773388604,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.1138299999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.2435269592384794,         \"RMSE\": 7.069212717011484,         \"R2\": -12.166742621467945,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.2177469999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.1105754847518408,         \"RMSE\": 6.854541649824586,         \"R2\": -11.99216513034567,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.416803       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.9569354047226284,         \"RMSE\": 6.651479799277566,         \"R2\": -11.928129373171446,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.618037       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.8537856094930785,         \"RMSE\": 6.474036710445056,         \"R2\": -11.348131391644102,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 0.8214119999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.750449728962714,         \"RMSE\": 6.305826559379086,         \"R2\": -11.120053648606476,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.026924       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.663414115575528,         \"RMSE\": 6.151161672136967,         \"R2\": -10.85874486639798,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.234147       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.556259025339157,         \"RMSE\": 6.003825249623929,         \"R2\": -10.671335514866264,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.4420449999999998       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.471571610669061,         \"RMSE\": 5.868919367302693,         \"R2\": -9.950915405915524,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.6506019999999997       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.379680763039582,         \"RMSE\": 5.740715994508566,         \"R2\": -8.935993501779443,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 1.860033       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.293542327214648,         \"RMSE\": 5.620383847029998,         \"R2\": -8.304713733239236,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.070158       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.2170719472274363,         \"RMSE\": 5.50775327209046,         \"R2\": -7.748060324415055,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.280968       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1605380581247338,         \"RMSE\": 5.403051906918496,         \"R2\": -7.433320998258445,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.492507       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.093930365363914,         \"RMSE\": 5.302901387269021,         \"R2\": -7.093810234661742,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.70473       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0590245226095627,         \"RMSE\": 5.213512799867119,         \"R2\": -7.009651494197669,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 2.917634       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9976476082662875,         \"RMSE\": 5.1231852511763165,         \"R2\": -6.925804791819894,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.1312199999999994       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.950641059884997,         \"RMSE\": 5.038426259116397,         \"R2\": -6.58092298894084,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.3455269999999997       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9139787950639096,         \"RMSE\": 4.959092402037442,         \"R2\": -6.2321238970256,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.560656999999999       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8644177203659007,         \"RMSE\": 4.8815607080230725,         \"R2\": -5.87676544995844,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.776474999999999       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8242147858959743,         \"RMSE\": 4.808190620674182,         \"R2\": -5.62363098938706,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 3.992974       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7745110240786572,         \"RMSE\": 4.737042423784333,         \"R2\": -5.530654039159668,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 4.267744       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.73663030353679,         \"RMSE\": 4.669916427921507,         \"R2\": -5.51357997146441,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 4.544746       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.692679144669073,         \"RMSE\": 4.604703216269991,         \"R2\": -5.472066122280332,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 4.823995       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6517738073879171,         \"RMSE\": 4.542197076044804,         \"R2\": -5.293761488897717,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.105493       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6176019850850996,         \"RMSE\": 4.482676429973872,         \"R2\": -5.196305855003581,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.389141       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5865007641193465,         \"RMSE\": 4.425455260516019,         \"R2\": -5.066178353973196,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.673523       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5595678531598225,         \"RMSE\": 4.370690133148669,         \"R2\": -4.970481738375755,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 5.9679660000000005       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.535948345073891,         \"RMSE\": 4.318357063182573,         \"R2\": -4.892367885242343,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 6.2645230000000005       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.509480285222116,         \"RMSE\": 4.267276732370252,         \"R2\": -4.807214337073276,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 6.563154000000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4815681878661566,         \"RMSE\": 4.217864876321593,         \"R2\": -4.663732871777943,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 6.863868000000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.452683177817048,         \"RMSE\": 4.169823323470254,         \"R2\": -4.507942651608292,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 7.276079000000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.425504815240136,         \"RMSE\": 4.123417367951589,         \"R2\": -4.4087270270070205,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 7.880966000000001       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.401135420694234,         \"RMSE\": 4.078757160785335,         \"R2\": -4.379153600942964,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 8.488067000000001       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3798894262867003,         \"RMSE\": 4.035722473386745,         \"R2\": -4.310917809364017,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 9.096757       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3578157698337674,         \"RMSE\": 3.993911445090692,         \"R2\": -4.255827563021541,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 9.706153       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.334955498529068,         \"RMSE\": 3.953168904153961,         \"R2\": -4.249147855442176,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 10.316233       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3157385915327031,         \"RMSE\": 3.913934448961732,         \"R2\": -4.232086679588724,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 10.926998       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Linear Regression\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3145482000473083,         \"RMSE\": 3.911980916488244,         \"R2\": -4.230354806784151,         \"Memory in Mb\": 0.0049734115600585,         \"Time in s\": 11.537908000000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.519429760441792,         \"RMSE\": 31.341724959881887,         \"R2\": -1263.4547929656037,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.001889       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.93274945698016,         \"RMSE\": 23.730069634788823,         \"R2\": -595.3856524245364,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.005264       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.671976905269483,         \"RMSE\": 19.432784890847977,         \"R2\": -261.2719879213097,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.0454959999999999       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.206218788565426,         \"RMSE\": 16.83704009498573,         \"R2\": -222.1918420065333,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.086209       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.7873677371092,         \"RMSE\": 17.69725945175844,         \"R2\": -60.138926246201024,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.127302       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.358479420064798,         \"RMSE\": 16.54420972880916,         \"R2\": -22.032639310332936,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.168766       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.753598876381378,         \"RMSE\": 15.536347024393615,         \"R2\": -12.613738343052718,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.2106009999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.774706713989955,         \"RMSE\": 14.560860647391404,         \"R2\": -9.841807755380492,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.252804       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.976543403311107,         \"RMSE\": 13.74760854733656,         \"R2\": -7.083247758311314,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.295373       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.528406770561816,         \"RMSE\": 13.11078583789324,         \"R2\": -4.133835882207287,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.338308       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.271666718491515,         \"RMSE\": 12.6229442838289,         \"R2\": -2.665108536473531,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.38161       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.91845605456336,         \"RMSE\": 12.134014714075713,         \"R2\": -1.767925975098496,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.425278       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.610383809165891,         \"RMSE\": 11.700505099139123,         \"R2\": -1.084596952740374,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.469311       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.3485668448406924,         \"RMSE\": 11.31852948419668,         \"R2\": -0.6578355548574832,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.51371       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.473998962981321,         \"RMSE\": 11.222073845492618,         \"R2\": -0.3100659276219817,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.558476       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.543521830550948,         \"RMSE\": 11.096254270292285,         \"R2\": -0.0327566612108853,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.603607       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.493894355635018,         \"RMSE\": 10.908553918682982,         \"R2\": 0.1827788670738018,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.649102       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.432058292739276,         \"RMSE\": 10.739983052449066,         \"R2\": 0.3698763337697944,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.6949599999999999       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.530905166315106,         \"RMSE\": 10.805387069826963,         \"R2\": 0.4741992564876139,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.74118       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.049069109840064,         \"RMSE\": 11.46222613381468,         \"R2\": 0.4819945238144716,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.7877609999999999       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.185364391622807,         \"RMSE\": 11.520615160379734,         \"R2\": 0.5523912707049028,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.834703       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.384443509591489,         \"RMSE\": 11.759466507882768,         \"R2\": 0.6247424700583044,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.882006       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.370825288025247,         \"RMSE\": 11.706644644448966,         \"R2\": 0.6770052015955412,         \"Memory in Mb\": 0.0043611526489257,         \"Time in s\": 0.929669       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.997212264968545,         \"RMSE\": 12.688148058774216,         \"R2\": 0.6533323093865229,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 0.977694       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.45564901988644,         \"RMSE\": 13.583827871673952,         \"R2\": 0.6503637760490552,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.026082       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.687395226209604,         \"RMSE\": 13.953064893865328,         \"R2\": 0.6804867014487179,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.074833       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.660171229881424,         \"RMSE\": 13.910099225377923,         \"R2\": 0.7245931722706233,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.1239519999999998       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.16625719191718,         \"RMSE\": 14.612234985526298,         \"R2\": 0.7293304097140514,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.1734349999999998       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.250950211093048,         \"RMSE\": 17.0718306278326,         \"R2\": 0.664728869016383,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.2232849999999995       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.679670450254022,         \"RMSE\": 17.65395670255975,         \"R2\": 0.6931807697512926,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.3407639999999996       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.873729384474112,         \"RMSE\": 17.73873175202587,         \"R2\": 0.7226130148559202,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 1.6983979999999996       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.018541118771262,         \"RMSE\": 17.831871437600412,         \"R2\": 0.745188204067577,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 2.057199       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.899574150448762,         \"RMSE\": 19.1903382176024,         \"R2\": 0.7134289333715201,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 2.417118       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.408282768986876,         \"RMSE\": 20.289550367060546,         \"R2\": 0.7055179762102581,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 2.778154       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.788104615245372,         \"RMSE\": 20.897902847676004,         \"R2\": 0.7235998101431352,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 3.140457       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.90822201416442,         \"RMSE\": 20.86950621812891,         \"R2\": 0.7429865604297317,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 3.503137       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.78564736405168,         \"RMSE\": 22.33392717480972,         \"R2\": 0.726395986248676,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 3.866169       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.562464823979756,         \"RMSE\": 23.77146138634261,         \"R2\": 0.709038397249883,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 4.229544       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.115712915071189,         \"RMSE\": 24.692790084324347,         \"R2\": 0.7210130632693055,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 4.59326       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.290646451171162,         \"RMSE\": 24.766775019882367,         \"R2\": 0.7392038606135755,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 4.957315       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.806610158983217,         \"RMSE\": 25.37056359629737,         \"R2\": 0.7379667599208486,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 5.321708999999999       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.91167446753811,         \"RMSE\": 27.489289014578038,         \"R2\": 0.711055524573946,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 5.686440999999999       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.69453441784174,         \"RMSE\": 28.803034656505247,         \"R2\": 0.7198918720890418,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 6.051508999999999       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.02591429387984,         \"RMSE\": 29.08166628667707,         \"R2\": 0.7300944167213836,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 6.416912999999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.47687089345869,         \"RMSE\": 29.604201733284565,         \"R2\": 0.7368958634072684,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 6.782651999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.37032815671457,         \"RMSE\": 31.058772984483277,         \"R2\": 0.7188231637639817,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 7.148725999999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.096649322747314,         \"RMSE\": 32.051830787895724,         \"R2\": 0.717419357352562,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 7.515149999999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.88685610593147,         \"RMSE\": 33.24610520798377,         \"R2\": 0.7266504806846955,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 7.882236999999999       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.052957054073875,         \"RMSE\": 33.24035912136826,         \"R2\": 0.7380286507287471,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 8.25334       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.046178761536364,         \"RMSE\": 34.86098206113683,         \"R2\": 0.7207414968982613,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 8.62558       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.751953045975853,         \"RMSE\": 35.78242297978339,         \"R2\": 0.7186502822700677,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 8.998921       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.603432973098663,         \"RMSE\": 36.96472548228527,         \"R2\": 0.7222689970347711,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 9.373355       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.757667537133976,         \"RMSE\": 37.07802525541943,         \"R2\": 0.7273605875689941,         \"Memory in Mb\": 0.0044412612915039,         \"Time in s\": 9.748496       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.96628233331211,         \"RMSE\": 24.387937149248955,         \"R2\": -1394.0974368768457,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.003367       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.95809265443779,         \"RMSE\": 17.886947111698607,         \"R2\": -127.62867621055317,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.060679       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.43403375286247,         \"RMSE\": 15.198987179765494,         \"R2\": -124.2101566950438,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.118829       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.76952679896777,         \"RMSE\": 13.348146279436204,         \"R2\": -95.8714533526398,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.177742       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.318348711169017,         \"RMSE\": 11.969856517585775,         \"R2\": -47.87667264392048,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.237441       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.2853039116310185,         \"RMSE\": 10.94189036106609,         \"R2\": -33.648027646243705,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.297866       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.5208355911538485,         \"RMSE\": 10.138862242229528,         \"R2\": -29.74195117722151,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.359017       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.9080595636493145,         \"RMSE\": 9.487746704217276,         \"R2\": -22.740310036230184,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.420891       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.437342628193194,         \"RMSE\": 8.948953859899,         \"R2\": -17.549281500204398,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.483487       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.020740144728086,         \"RMSE\": 8.490404067975657,         \"R2\": -15.746680942149272,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.546844       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.702540763677515,         \"RMSE\": 8.09713522450445,         \"R2\": -15.430052960036054,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.610978       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.449057445346116,         \"RMSE\": 7.755193128790045,         \"R2\": -14.185073150160106,         \"Memory in Mb\": 0.0050439834594726,         \"Time in s\": 0.675884       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.201640426877581,         \"RMSE\": 7.451485247160068,         \"R2\": -13.20791735379428,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 0.741568       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.9861522146348123,         \"RMSE\": 7.180696949733205,         \"R2\": -12.814002869999907,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 0.808037       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.8260389726991693,         \"RMSE\": 6.939608203297966,         \"R2\": -11.688379207589731,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 0.926618       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.694730270614988,         \"RMSE\": 6.722171113188908,         \"R2\": -11.495217468089896,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 1.215588       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.572442774284147,         \"RMSE\": 6.524300196624447,         \"R2\": -11.438471282384336,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 1.5070640000000002       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4832798669216825,         \"RMSE\": 6.345294903725613,         \"R2\": -10.86190793294698,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 1.8008990000000002       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.371542642654472,         \"RMSE\": 6.177015076243767,         \"R2\": -10.629949316856385,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 2.0970690000000003       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.263251524870982,         \"RMSE\": 6.020874949010495,         \"R2\": -10.36170885736068,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 2.4016       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.166901825777709,         \"RMSE\": 5.8760767656227735,         \"R2\": -10.179937857653265,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 2.706944       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.102550908901192,         \"RMSE\": 5.743886676480224,         \"R2\": -9.489284487381322,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.013076       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.036030402550628,         \"RMSE\": 5.61908468135586,         \"R2\": -8.519416508014515,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.319991       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.965717807967496,         \"RMSE\": 5.50138701729293,         \"R2\": -7.914879120336785,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.627685       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8948913466896105,         \"RMSE\": 5.390446783732167,         \"R2\": -7.379388774419297,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 3.93618       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8304411336225568,         \"RMSE\": 5.286008256480869,         \"R2\": -7.071904701569496,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 4.245555       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7733791235095338,         \"RMSE\": 5.187623645241403,         \"R2\": -6.74573862520947,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 4.555757000000001       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7328732375480085,         \"RMSE\": 5.096231477200102,         \"R2\": -6.653340289034931,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 4.866786000000001       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6922671720641331,         \"RMSE\": 5.009032279128942,         \"R2\": -6.5765398617523605,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 5.1996410000000015       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6600221636451291,         \"RMSE\": 4.9270067527590165,         \"R2\": -6.249341959517198,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 5.545038000000002       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6169171465584515,         \"RMSE\": 4.847662648980224,         \"R2\": -5.910766757861972,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 5.892753000000002       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5787668849144931,         \"RMSE\": 4.771995268006674,         \"R2\": -5.5715350899413965,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 6.242777000000002       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.535700232104731,         \"RMSE\": 4.69925054984221,         \"R2\": -5.326885534626132,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 6.599796000000002       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5003699975160405,         \"RMSE\": 4.630081239411466,         \"R2\": -5.239062722957792,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 6.957619000000002       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4782734303433982,         \"RMSE\": 4.565354365023557,         \"R2\": -5.225160013321354,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 7.316272000000002       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4563696019956498,         \"RMSE\": 4.503833132228122,         \"R2\": -5.19161922746511,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 7.675697000000002       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4392280778003554,         \"RMSE\": 4.445645440595998,         \"R2\": -5.02903742417401,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 8.035893000000002       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4073407178561264,         \"RMSE\": 4.387021097703184,         \"R2\": -4.9346827726614455,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 8.396854000000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3782504190107006,         \"RMSE\": 4.330701361336262,         \"R2\": -4.809192109617374,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 8.758623000000002       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3571814777264213,         \"RMSE\": 4.277370073659861,         \"R2\": -4.718248073230613,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 9.121240000000002       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3328025450945626,         \"RMSE\": 4.2253925636382,         \"R2\": -4.641399853721709,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 9.484674000000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.311715211433691,         \"RMSE\": 4.175582527272098,         \"R2\": -4.560327645533724,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 9.848922000000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2897432923325247,         \"RMSE\": 4.127236925138345,         \"R2\": -4.422957957045758,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 10.213983000000002       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2672991203860131,         \"RMSE\": 4.080383024210964,         \"R2\": -4.274192362897992,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 10.579853000000002       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2421842209255052,         \"RMSE\": 4.03488734209176,         \"R2\": -4.178968845613636,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 10.965988       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.220808929344255,         \"RMSE\": 3.991045752926761,         \"R2\": -4.15028972045945,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 11.354575       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2057181063421545,         \"RMSE\": 3.9494511154557617,         \"R2\": -4.0862825167589865,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 11.745471       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.188437369603739,         \"RMSE\": 3.9086583836793856,         \"R2\": -4.0338431039290805,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 12.138778       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1710173649101312,         \"RMSE\": 3.869039281342956,         \"R2\": -4.028105053503917,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 12.545048       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1544521877618488,         \"RMSE\": 3.830602085194232,         \"R2\": -4.011663649294047,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 12.952187       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l1 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1537672749321948,         \"RMSE\": 3.8287168981917103,         \"R2\": -4.010074752320696,         \"Memory in Mb\": 0.0052042007446289,         \"Time in s\": 13.359495       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.6062254572366,         \"RMSE\": 31.39938120772091,         \"R2\": -1268.1112549740517,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.000711       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.412737763681047,         \"RMSE\": 23.97862157826266,         \"R2\": -607.9443275975191,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.001889       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.119104680903606,         \"RMSE\": 19.655410372524667,         \"R2\": -267.315679768846,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.003406       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.691588950452092,         \"RMSE\": 17.042779535378298,         \"R2\": -227.6797328948204,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.00525       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.128477598777668,         \"RMSE\": 17.570968714531574,         \"R2\": -59.26944361385635,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.007421       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.75565671610116,         \"RMSE\": 16.483156797846284,         \"R2\": -21.862958739409084,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.009919       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.454334080303978,         \"RMSE\": 15.644372833730271,         \"R2\": -12.803711937026078,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.012745       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.893519322025275,         \"RMSE\": 14.807378680481822,         \"R2\": -10.212022929829027,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.015896       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.219705201317108,         \"RMSE\": 14.0445461378022,         \"R2\": -7.436202462041329,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.019372       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.828389618716818,         \"RMSE\": 13.455080798744472,         \"R2\": -4.4070097733575375,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.023173       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.61456960864212,         \"RMSE\": 13.037583740326507,         \"R2\": -2.909846715773841,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.027299       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.52880743945525,         \"RMSE\": 12.69008098915324,         \"R2\": -2.0274307958032884,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.031782       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.39143583855712,         \"RMSE\": 12.35961426350804,         \"R2\": -1.3260696348061909,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.036638       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.12180315101294,         \"RMSE\": 12.009375103170282,         \"R2\": -0.866389387173786,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0418749999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.136940986261356,         \"RMSE\": 11.920551719153746,         \"R2\": -0.4782218583187949,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0474919999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.284290032332207,         \"RMSE\": 11.93362687305613,         \"R2\": -0.1945108920445761,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0534829999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.390309464431912,         \"RMSE\": 11.903488345267943,         \"R2\": 0.0269083954035856,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.0598469999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.350219958465262,         \"RMSE\": 11.791481226840991,         \"R2\": 0.2404518209934976,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.06659       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.499019855105985,         \"RMSE\": 11.958125495095471,         \"R2\": 0.3560283448388185,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.073713       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.90272187978296,         \"RMSE\": 12.527163169679886,         \"R2\": 0.3812690011074207,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.081218       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.171291167504233,         \"RMSE\": 12.73748746029564,         \"R2\": 0.4528394877125938,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.08971       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.37629466014084,         \"RMSE\": 13.047657656056804,         \"R2\": 0.538024139715424,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.099295       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.440817816219347,         \"RMSE\": 13.0964165059942,         \"R2\": 0.5957634168273553,         \"Memory in Mb\": 0.0041532516479492,         \"Time in s\": 0.110134       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.906487060964151,         \"RMSE\": 13.855497684527965,         \"R2\": 0.5866088718530376,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1222419999999999       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.387009537918406,         \"RMSE\": 14.786939232799543,         \"R2\": 0.5856869069436603,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1355289999999999       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.701469010841246,         \"RMSE\": 15.270898285463774,         \"R2\": 0.6172820078624095,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1498919999999999       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.689852199892528,         \"RMSE\": 15.284847538688991,         \"R2\": 0.6674656839655615,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.1772019999999999       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.168487287417785,         \"RMSE\": 16.008183102465477,         \"R2\": 0.6751444757196481,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2058519999999999       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.085867087734242,         \"RMSE\": 18.170753499240718,         \"R2\": 0.6201764868699093,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2348879999999999       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.672501856506583,         \"RMSE\": 19.05837058612535,         \"R2\": 0.6424226539377311,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2642819999999999       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.822446828447037,         \"RMSE\": 19.13937756684808,         \"R2\": 0.6770787925994421,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.2940279999999999       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.055746883990931,         \"RMSE\": 19.31213644577825,         \"R2\": 0.7011272480618885,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.3241269999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.79008745873622,         \"RMSE\": 20.396105048894267,         \"R2\": 0.6762859401979866,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.3545779999999999       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.293199062265238,         \"RMSE\": 21.539399675842866,         \"R2\": 0.6681199603719434,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.3853799999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.740320816630271,         \"RMSE\": 22.31102616496048,         \"R2\": 0.6849554171717112,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.4261629999999999       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.862968645899144,         \"RMSE\": 22.29409698811668,         \"R2\": 0.7067005430463744,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.467315       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.699705023283965,         \"RMSE\": 23.67314903355933,         \"R2\": 0.6925996644733732,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.508826       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.38213993729544,         \"RMSE\": 25.048095107979137,         \"R2\": 0.6769473375050636,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.55069       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.967894830794286,         \"RMSE\": 26.15320189056989,         \"R2\": 0.6870368010887093,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.592905       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.10728249235129,         \"RMSE\": 26.204092785638924,         \"R2\": 0.7080553660644732,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.6354690000000001       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.603016925007317,         \"RMSE\": 26.772391386711117,         \"R2\": 0.7082099437723521,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.6783830000000001       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.614531201761597,         \"RMSE\": 28.786744962703725,         \"R2\": 0.6831362914484524,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.7216460000000001       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.48829335200544,         \"RMSE\": 30.38515335394973,         \"R2\": 0.6882746780375071,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.7652570000000001       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.755002868307955,         \"RMSE\": 30.52390276571354,         \"R2\": 0.7026599444855313,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.8092140000000001       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.22217092676305,         \"RMSE\": 31.08727194033441,         \"R2\": 0.7098743070293987,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.8535240000000001       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.03670858216615,         \"RMSE\": 32.44431034253017,         \"R2\": 0.6931769059461363,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.898204       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.78200415465676,         \"RMSE\": 33.496021791915204,         \"R2\": 0.6913806254796178,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.943264       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.56258004106143,         \"RMSE\": 34.768391171729405,         \"R2\": 0.7010449079513538,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 0.988697       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.68725373887437,         \"RMSE\": 34.77075336357408,         \"R2\": 0.7133508993505916,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.0345       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.627725892037507,         \"RMSE\": 36.32441604878253,         \"R2\": 0.6968033114915981,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.080674       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.34737619246692,         \"RMSE\": 37.30920796407717,         \"R2\": 0.6941284720923248,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.127216       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.18573737545828,         \"RMSE\": 38.51358935872805,         \"R2\": 0.698506895988072,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.174127       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.27380465992389,         \"RMSE\": 38.58852748240754,         \"R2\": 0.7046942807227952,         \"Memory in Mb\": 0.0042333602905273,         \"Time in s\": 1.2212839999999998       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.994354275814885,         \"RMSE\": 24.339467027537435,         \"R2\": -1388.5575385664913,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.002841       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.808927193108108,         \"RMSE\": 17.83271591943186,         \"R2\": -126.84988353201342,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.006663       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.864002308096952,         \"RMSE\": 15.320672400398038,         \"R2\": -126.22308256175272,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.011298       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.882777304938948,         \"RMSE\": 13.38981065066765,         \"R2\": -96.4771385394691,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.01675       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.231639558854497,         \"RMSE\": 11.98203471414171,         \"R2\": -47.97617801736401,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.023066       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.334108393931037,         \"RMSE\": 10.98237795329033,         \"R2\": -33.904913895880355,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.030179       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.563493982833803,         \"RMSE\": 10.178707085968126,         \"R2\": -29.98405233271513,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.038033       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.002122045077101,         \"RMSE\": 9.533278572445496,         \"R2\": -22.968717144675637,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.048318       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.587842803317817,         \"RMSE\": 9.003737317880292,         \"R2\": -17.777085610739057,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.072946       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.458683971614509,         \"RMSE\": 8.652080760634158,         \"R2\": -16.390543570087573,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.099467       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.239995800771734,         \"RMSE\": 8.280452519944822,         \"R2\": -16.182419642449048,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.129303       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.943592784264584,         \"RMSE\": 7.932077353220182,         \"R2\": -14.885669934585447,         \"Memory in Mb\": 0.004836082458496,         \"Time in s\": 0.159907       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.7846302486799286,         \"RMSE\": 7.646201644169009,         \"R2\": -13.96015951270874,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.191262       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.6468171672887713,         \"RMSE\": 7.389977926170562,         \"R2\": -13.630953412847402,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.2233629999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.526112368086922,         \"RMSE\": 7.1621871014808685,         \"R2\": -12.51535851099468,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.2587739999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.5074300839639245,         \"RMSE\": 6.985469271455791,         \"R2\": -12.493228210862725,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.2965239999999999       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.434140699763514,         \"RMSE\": 6.814822943627961,         \"R2\": -12.570888751300782,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.3365219999999999       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.42722001559718,         \"RMSE\": 6.678288393486038,         \"R2\": -12.13957341395007,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.3787709999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.332029752839207,         \"RMSE\": 6.516115548498917,         \"R2\": -11.941900403072644,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.4232519999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.217390968362987,         \"RMSE\": 6.356555790563252,         \"R2\": -11.66392028459017,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.4696459999999999       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.100825681509746,         \"RMSE\": 6.206562691759863,         \"R2\": -11.47288048490914,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.516851       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.0187726323631243,         \"RMSE\": 6.072312098448126,         \"R2\": -10.723095644711892,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.5652379999999999       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.947022825868371,         \"RMSE\": 5.94849802587685,         \"R2\": -9.668265577306911,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.6161209999999999       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.867282537241402,         \"RMSE\": 5.828292237410032,         \"R2\": -9.005843404687633,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.6743809999999999       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.8281006485905213,         \"RMSE\": 5.729646774374514,         \"R2\": -8.467133754251039,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.7334769999999999       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.759113137285707,         \"RMSE\": 5.623931694381955,         \"R2\": -8.136932704030892,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.79343       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.7113951403332286,         \"RMSE\": 5.52770300084093,         \"R2\": -7.794584318722522,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.8541829999999999       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.646739535451309,         \"RMSE\": 5.432090595521053,         \"R2\": -7.695343452205655,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 0.94202       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5972398336076634,         \"RMSE\": 5.343168086286508,         \"R2\": -7.621065103502998,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.0306859999999998       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.533455116608919,         \"RMSE\": 5.255265792942869,         \"R2\": -7.247487071141652,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.1202       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.497138699914293,         \"RMSE\": 5.178243230235351,         \"R2\": -6.88544757519055,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.210495       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4712145738198297,         \"RMSE\": 5.107804033669319,         \"R2\": -6.528964961790648,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.30153       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.429247896498525,         \"RMSE\": 5.0347117637840935,         \"R2\": -6.262430681498119,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.3932969999999998       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.398090124502612,         \"RMSE\": 4.967521902410674,         \"R2\": -6.18160824182094,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.4857959999999997       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.360396901712673,         \"RMSE\": 4.90286744871834,         \"R2\": -6.1796262474179136,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.5790619999999995       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3150393936015323,         \"RMSE\": 4.836721469702358,         \"R2\": -6.140716883970916,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.6730619999999998       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.267679208737699,         \"RMSE\": 4.77254376860168,         \"R2\": -5.948293801048677,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.7677929999999995       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.2434173929652075,         \"RMSE\": 4.715867112708654,         \"R2\": -5.857742612566196,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.863279       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.199009654391343,         \"RMSE\": 4.656030786420359,         \"R2\": -5.714767077599112,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 1.959541       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.159659681172017,         \"RMSE\": 4.59898830049537,         \"R2\": -5.610494506343661,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.056608       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1249482408574707,         \"RMSE\": 4.545176313025559,         \"R2\": -5.527610390446187,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.175652       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.094058354623314,         \"RMSE\": 4.493551443258636,         \"R2\": -5.439404045425388,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.37025       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.062104039794744,         \"RMSE\": 4.442864622918497,         \"R2\": -5.284107374622643,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.565666       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.030706594140141,         \"RMSE\": 4.393695684791879,         \"R2\": -5.115247638705276,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.761867       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.003263565299311,         \"RMSE\": 4.347049681772325,         \"R2\": -5.011317673951863,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 2.958847       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9706878923964863,         \"RMSE\": 4.300488305941944,         \"R2\": -4.979898179552831,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.156658       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.949819924248383,         \"RMSE\": 4.257394252920471,         \"R2\": -4.91037086709413,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.355253       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9258186229947107,         \"RMSE\": 4.214725843085415,         \"R2\": -4.85305905428677,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.554625       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.900426010360992,         \"RMSE\": 4.173138113177231,         \"R2\": -4.849565137575967,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.754774       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.872733130377695,         \"RMSE\": 4.13253797119814,         \"R2\": -4.832859832721421,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 3.955698       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Linear Regression with l2 regularization\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.871510887330926,         \"RMSE\": 4.130524228438989,         \"R2\": -4.8310671605777085,         \"Memory in Mb\": 0.0049962997436523,         \"Time in s\": 4.156775       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.624124996337724,         \"RMSE\": 28.77138517975663,         \"R2\": -1064.5628215382144,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.000572       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.0510878175865,         \"RMSE\": 20.931739283093208,         \"R2\": -463.0233071270199,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.001645       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.49930786476168,         \"RMSE\": 17.564629142555763,         \"R2\": -213.26922094451623,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.003059       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.378514545021682,         \"RMSE\": 15.405121473747096,         \"R2\": -185.84310618709696,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.004809       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.844108697295251,         \"RMSE\": 17.128215293517524,         \"R2\": -56.27037115396167,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.006892       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.889488781892217,         \"RMSE\": 15.88743125142584,         \"R2\": -20.24022051627188,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.009306       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.103343480706034,         \"RMSE\": 14.91594241381016,         \"R2\": -11.548186613409534,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.012052       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.288900850158633,         \"RMSE\": 14.011374344891149,         \"R2\": -9.0389683228034,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.015129       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.736865157066078,         \"RMSE\": 13.281093172283262,         \"R2\": -6.543957317046456,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.018712       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.618125386224052,         \"RMSE\": 12.858171267844924,         \"R2\": -3.9379074608874927,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.022717       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.580936033253089,         \"RMSE\": 12.51524762994286,         \"R2\": -2.6028352541816,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.027127       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.191573127926202,         \"RMSE\": 12.024287681643044,         \"R2\": -1.7180920054032294,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.031928       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.001452140019149,         \"RMSE\": 11.63905100750295,         \"R2\": -1.062756769701151,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0371139999999999       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.959260067984971,         \"RMSE\": 11.397763679955697,         \"R2\": -0.6811278108981134,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0426889999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.036161429677985,         \"RMSE\": 11.359538570018056,         \"R2\": -0.3423577921849861,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0486539999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.141200516910354,         \"RMSE\": 11.407680550849577,         \"R2\": -0.0915406328349714,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0550109999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.061965626679777,         \"RMSE\": 11.211626858308708,         \"R2\": 0.1367382583661435,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0617539999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.988600359846859,         \"RMSE\": 11.023879576943443,         \"R2\": 0.3361231572252737,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0688849999999999       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.115468527113427,         \"RMSE\": 11.18859440458875,         \"R2\": 0.4362434515233688,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0763959999999999       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.571784360381598,         \"RMSE\": 11.99879894105818,         \"R2\": 0.4323613497222297,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0842879999999999       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.610536559977233,         \"RMSE\": 11.962244483436113,         \"R2\": 0.5174164020157423,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.0937539999999999       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.753677752144043,         \"RMSE\": 12.109970858596688,         \"R2\": 0.6020391290764042,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.1045109999999999       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.763402728464486,         \"RMSE\": 12.046916639776326,         \"R2\": 0.6579556132088519,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.123112       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.37232599699494,         \"RMSE\": 12.9382814211091,         \"R2\": 0.6395292100633578,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.147917       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.870502401884236,         \"RMSE\": 14.03783628218945,         \"R2\": 0.6266016212673495,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.173118       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.125553299295866,         \"RMSE\": 14.312481045438886,         \"R2\": 0.6638140497188074,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.198688       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.11642729851449,         \"RMSE\": 14.234872044017685,         \"R2\": 0.7115826499817814,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.225283       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.63053955101658,         \"RMSE\": 15.01159987060024,         \"R2\": 0.7143329631149452,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.252265       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.671899739762464,         \"RMSE\": 17.42953249336733,         \"R2\": 0.650531972004655,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.279616       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.1135598398273,         \"RMSE\": 17.980470366868552,         \"R2\": 0.6817264420663893,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.307329       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.368994570730054,         \"RMSE\": 18.183536514460908,         \"R2\": 0.7085274545262112,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.335405       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.47998520043724,         \"RMSE\": 18.216810890558104,         \"R2\": 0.7340681346165732,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.363842       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.490995837872443,         \"RMSE\": 19.84181186896939,         \"R2\": 0.693641639088806,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.392636       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.988870134156189,         \"RMSE\": 20.81926805033374,         \"R2\": 0.6899406322869175,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4217869999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.420579982415202,         \"RMSE\": 21.48960215237335,         \"R2\": 0.7077263415053474,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4512919999999999       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.424816444492956,         \"RMSE\": 21.37796604773129,         \"R2\": 0.7303103668220552,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.481151       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.284688005634004,         \"RMSE\": 22.70157511582256,         \"R2\": 0.7173140296578691,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.514769       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.038658536726118,         \"RMSE\": 24.042516108283174,         \"R2\": 0.7023651722582169,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.548784       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.59029009825774,         \"RMSE\": 24.916858152232297,         \"R2\": 0.7159269075042054,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.583167       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.812702077031824,         \"RMSE\": 25.07250049330081,         \"R2\": 0.7327254930224041,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.617914       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.346042839206106,         \"RMSE\": 25.68091484988461,         \"R2\": 0.7315167856134144,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.653021       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.370765923434053,         \"RMSE\": 27.689388199834635,         \"R2\": 0.7068336630361484,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.688487       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.264179516209435,         \"RMSE\": 29.30099868065636,         \"R2\": 0.7101227966068765,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.724309       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.63502154656571,         \"RMSE\": 29.559619400414903,         \"R2\": 0.7211497930314269,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.760485       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.145243718121584,         \"RMSE\": 30.130361606680754,         \"R2\": 0.7274603750306702,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.7970149999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.98075812634153,         \"RMSE\": 31.43770898148617,         \"R2\": 0.7119202509985216,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.8338999999999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.7046141289421,         \"RMSE\": 32.42665929478992,         \"R2\": 0.7107714616434232,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.8711439999999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.54126059149082,         \"RMSE\": 33.75343345950398,         \"R2\": 0.7182443212146572,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.908739       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.73603745751772,         \"RMSE\": 33.829762552174344,         \"R2\": 0.7286559632490104,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.946687       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.674609740448528,         \"RMSE\": 35.33904665998618,         \"R2\": 0.7130297805712756,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.984985       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.350956760305525,         \"RMSE\": 36.24046007710213,         \"R2\": 0.7114012814424304,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.023633       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.20743030595361,         \"RMSE\": 37.47019278346573,         \"R2\": 0.7146215025224946,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.06263       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.342328163686027,         \"RMSE\": 37.59599019491026,         \"R2\": 0.7196900586014492,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.101865       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.806898309502586,         \"RMSE\": 26.56763494383828,         \"R2\": -1654.6182189603317,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.003003       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 14.866074912822512,         \"RMSE\": 20.957300378156614,         \"R2\": -175.5777711351631,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.009504       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.772648582583251,         \"RMSE\": 17.555009093750932,         \"R2\": -166.03688377592212,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.016813       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.372925375947808,         \"RMSE\": 15.758572852966298,         \"R2\": -134.01675577859288,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.02489       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.950999863257042,         \"RMSE\": 14.807263848606526,         \"R2\": -73.79513907078027,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.033777       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.131163180965077,         \"RMSE\": 13.743973626529105,         \"R2\": -53.66614209724606,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.043434       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.532294463666167,         \"RMSE\": 12.93588512414824,         \"R2\": -49.04322437944699,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.05944       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.33219708929472,         \"RMSE\": 12.40854626547306,         \"R2\": -39.60710527883104,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.077842       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.281092452540433,         \"RMSE\": 12.043698542516514,         \"R2\": -32.597139849683785,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0986229999999999       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.889313429527772,         \"RMSE\": 11.548268653424005,         \"R2\": -29.98173855178904,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.121857       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.555718436766954,         \"RMSE\": 11.115454500430198,         \"R2\": -29.96211572526289,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.149384       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.300584612865839,         \"RMSE\": 10.768588372618428,         \"R2\": -28.278525817685868,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.177703       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.073956995660685,         \"RMSE\": 10.455941089275187,         \"R2\": -26.97505735848669,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.206834       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.879100927439736,         \"RMSE\": 10.179149173565092,         \"R2\": -26.75935065850941,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.236782       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.698392466938299,         \"RMSE\": 9.935855831167723,         \"R2\": -25.01038668400382,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.26759       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.496977203333427,         \"RMSE\": 9.674599820332077,         \"R2\": -24.881575653507443,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.299212       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.319501534649956,         \"RMSE\": 9.433800456219284,         \"R2\": -25.005937123592886,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3316429999999999       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.189316591643737,         \"RMSE\": 9.224778235838508,         \"R2\": -24.070488458586468,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3648849999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.05373584315195,         \"RMSE\": 9.02603667348878,         \"R2\": -23.83217081250324,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3989449999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.893196935767096,         \"RMSE\": 8.831009888188627,         \"R2\": -23.44247524737401,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.4338649999999999       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.787168115685009,         \"RMSE\": 8.669033337133486,         \"R2\": -23.33356914323267,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.4696089999999999       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.789860410241021,         \"RMSE\": 8.633597516354541,         \"R2\": -22.69832962851382,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.5061589999999999       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.751464501173282,         \"RMSE\": 8.532971696368575,         \"R2\": -20.952287666855003,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.5516559999999999       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.758413491181221,         \"RMSE\": 8.476961067588123,         \"R2\": -20.166616413985547,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.599555       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.682950272504451,         \"RMSE\": 8.3510488559106,         \"R2\": -19.11151854181045,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.6519079999999999       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.627995468360723,         \"RMSE\": 8.245754355787446,         \"R2\": -18.64179334000355,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.705132       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.546541731300828,         \"RMSE\": 8.130789587119862,         \"R2\": -18.02792190581397,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.7591289999999999       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.474658569482086,         \"RMSE\": 8.019262742277965,         \"R2\": -17.95054121046633,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.813877       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.409420416004319,         \"RMSE\": 7.920158789530457,         \"R2\": -17.94222667017848,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.869386       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.394854582323811,         \"RMSE\": 7.870548110777217,         \"R2\": -17.498743363524373,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.941553       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.360408122735632,         \"RMSE\": 7.801849933723111,         \"R2\": -16.900148820132806,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.016153       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.332182524169608,         \"RMSE\": 7.745335706289596,         \"R2\": -16.312002846243146,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.093294       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.286484086266954,         \"RMSE\": 7.672164501241343,         \"R2\": -15.864310422998043,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.1728       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.240017672508232,         \"RMSE\": 7.591734569529257,         \"R2\": -15.77351804027652,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.259908       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.203631741702394,         \"RMSE\": 7.526058935068808,         \"R2\": -15.917522479350382,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.347915       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.198551833398676,         \"RMSE\": 7.481861117849272,         \"R2\": -16.086729414362967,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.436748       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.200051628353664,         \"RMSE\": 7.443314903444159,         \"R2\": -15.900950117878589,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.526395       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.146415466772512,         \"RMSE\": 7.367313347205083,         \"R2\": -15.73695108767244,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.61685       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.164438314106662,         \"RMSE\": 7.352756459959702,         \"R2\": -15.745558187055655,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.708114       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.199091748701669,         \"RMSE\": 7.381485816300255,         \"R2\": -16.029304780133675,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.8001699999999998       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.184244405270293,         \"RMSE\": 7.343677512392477,         \"R2\": -16.040406471643664,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.892982       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.162940711797175,         \"RMSE\": 7.295196877254559,         \"R2\": -15.972283354719572,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.986541       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.146772746928229,         \"RMSE\": 7.251973114148485,         \"R2\": -15.742866229030533,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.080851       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.141562534384022,         \"RMSE\": 7.225910341165371,         \"R2\": -15.54014218136778,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.175912       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.113671043317916,         \"RMSE\": 7.181653170625269,         \"R2\": -15.40700562565575,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.271722       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.082725756932772,         \"RMSE\": 7.134180367835326,         \"R2\": -15.456838882747109,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.368328       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.049460198345376,         \"RMSE\": 7.092641752853287,         \"R2\": -15.403746251323405,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.491683       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.012955702794688,         \"RMSE\": 7.041188655025779,         \"R2\": -15.335641983730405,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.616037       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.992587411597517,         \"RMSE\": 7.002009756347646,         \"R2\": -15.46809971530338,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.74122       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.986581819477306,         \"RMSE\": 6.97972894589718,         \"R2\": -15.638912943338369,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.867218       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 1\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.984033991902679,         \"RMSE\": 6.9766666383395455,         \"R2\": -15.63541499061877,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.993378       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.19936706045659,         \"RMSE\": 55.118879370280126,         \"R2\": -3909.733983269086,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.001533       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.495026158423794,         \"RMSE\": 43.23165104261441,         \"R2\": -1978.396532834284,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.004589       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.680053698816124,         \"RMSE\": 39.98506660332775,         \"R2\": -1109.3949268723327,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.008788       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 29.375885022911746,         \"RMSE\": 37.29886968855784,         \"R2\": -1094.3128086885838,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.014141       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.707444751978134,         \"RMSE\": 40.753235251415205,         \"R2\": -323.21264874535376,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.020635       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.96097441162184,         \"RMSE\": 40.14945868859866,         \"R2\": -134.64726490280094,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.028271       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.25989567011213,         \"RMSE\": 39.82501544894248,         \"R2\": -88.45229320906665,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.041195       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.76307262878121,         \"RMSE\": 39.802536485586,         \"R2\": -80.01195436020778,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.054526       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.66411513705659,         \"RMSE\": 39.325402336106926,         \"R2\": -65.1420916497486,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.068227       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.19940912800194,         \"RMSE\": 40.704130728492046,         \"R2\": -48.48362457590105,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.082293       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.629161705635866,         \"RMSE\": 40.92880988729008,         \"R2\": -37.53219439908784,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.096719       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 35.29035427006805,         \"RMSE\": 41.59178542812187,         \"R2\": -31.520750879588867,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.111503       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.23638449140802,         \"RMSE\": 42.62018794050648,         \"R2\": -26.65945376164948,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.126644       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.72501013289919,         \"RMSE\": 42.91835139661213,         \"R2\": -22.83677510551845,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.142141       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.731745662210095,         \"RMSE\": 42.91744223234227,         \"R2\": -18.16084111691443,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.157996       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 37.94402632003076,         \"RMSE\": 44.39720610255875,         \"R2\": -15.5331835318136,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.174208       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 38.69858083339784,         \"RMSE\": 45.06856008203835,         \"R2\": -12.949305609255877,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.190776       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.18624064352699,         \"RMSE\": 46.68267333461602,         \"R2\": -10.905017439998025,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.2077       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.85432327682653,         \"RMSE\": 47.463811090322665,         \"R2\": -9.145320047375163,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.224978       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.36451127701117,         \"RMSE\": 48.41262940233051,         \"R2\": -8.240888884363313,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.24261       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.17342712408468,         \"RMSE\": 49.46918668675267,         \"R2\": -7.253093192412523,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.260594       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.81461612103895,         \"RMSE\": 51.73551020684679,         \"R2\": -6.2632609796369465,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.27893       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.90819615603068,         \"RMSE\": 53.07334253773936,         \"R2\": -5.6387075541588505,         \"Memory in Mb\": 0.0034055709838867,         \"Time in s\": 0.297618       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.45334973048907,         \"RMSE\": 55.82244674340302,         \"R2\": -5.710187070138379,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3166589999999999       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.05643802527038,         \"RMSE\": 58.804796990371536,         \"R2\": -5.552357606253864,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3360539999999999       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.41721923566732,         \"RMSE\": 60.72765972830183,         \"R2\": -5.052332799264802,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3558019999999999       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.23299901747073,         \"RMSE\": 63.29154255446438,         \"R2\": -4.701716347098143,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3759049999999999       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.82583967659276,         \"RMSE\": 65.36972550348784,         \"R2\": -4.417008197806662,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.3963679999999999       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.851023886215806,         \"RMSE\": 70.45860717413167,         \"R2\": -4.71089374971376,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4171839999999999       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.58220488738844,         \"RMSE\": 72.62689780553444,         \"R2\": -4.192702597603254,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4383529999999999       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 58.456862484765374,         \"RMSE\": 75.26810540469758,         \"R2\": -3.994165343488624,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4598799999999999       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 59.98229295122657,         \"RMSE\": 76.97767263775137,         \"R2\": -3.748486775975887,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.4817619999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 61.989108820835376,         \"RMSE\": 80.62951920841103,         \"R2\": -4.0588898392459045,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.503996       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 63.93796840595574,         \"RMSE\": 84.48840832488506,         \"R2\": -4.106322034628877,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.526584       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.15236861414519,         \"RMSE\": 85.79755918852514,         \"R2\": -3.6588935912158114,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.551564       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 66.90365663892747,         \"RMSE\": 87.9249329113371,         \"R2\": -3.562003118430529,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.5777180000000001       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.17917622540308,         \"RMSE\": 89.58756462611774,         \"R2\": -3.402382103640547,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.6050150000000001       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 70.80702754948452,         \"RMSE\": 94.96753809429286,         \"R2\": -3.643808228470034,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.6334650000000001       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 72.44730173566225,         \"RMSE\": 97.09455233033468,         \"R2\": -3.313534410167211,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.663057       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 74.29167351363806,         \"RMSE\": 99.40774027870644,         \"R2\": -3.201483315326532,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.693783       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 75.83174494284101,         \"RMSE\": 101.77506329990584,         \"R2\": -3.216760347648722,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.725638       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 78.5111288104629,         \"RMSE\": 106.99570126481906,         \"R2\": -3.3774383617967887,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.758621       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 81.63741116996734,         \"RMSE\": 112.58139375423264,         \"R2\": -3.2793958269429844,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.79391       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 82.66628549198501,         \"RMSE\": 113.50934761838604,         \"R2\": -3.1118432523868984,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.829617       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 84.40016304476833,         \"RMSE\": 116.34990208847,         \"R2\": -3.0639935553557365,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.865723       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 86.52132256561038,         \"RMSE\": 120.30772815943004,         \"R2\": -3.2188880650982723,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.902224       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 87.79244029751037,         \"RMSE\": 121.63869088166206,         \"R2\": -3.069866847809537,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.93912       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 90.43735682351402,         \"RMSE\": 126.62066541565774,         \"R2\": -2.9650251625135784,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 0.976398       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 91.59763342322412,         \"RMSE\": 127.6295949640952,         \"R2\": -2.862114672867245,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.014034       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 93.80067010965053,         \"RMSE\": 131.39026699356185,         \"R2\": -2.966921087845916,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.0520230000000002       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 96.52355815418714,         \"RMSE\": 136.33091173427522,         \"R2\": -3.0840934586689466,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.0903630000000002       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 99.60515399822415,         \"RMSE\": 141.80943605664237,         \"R2\": -3.0875177525301325,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.129055       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"ChickWeights\",         \"MAE\": 100.62422612381133,         \"RMSE\": 143.06646930774232,         \"R2\": -3.0591132110693486,         \"Memory in Mb\": 0.0034589767456054,         \"Time in s\": 1.167982       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 48.24517612267716,         \"RMSE\": 65.52170729560882,         \"R2\": -10068.892101934754,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0014       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.96170708962665,         \"RMSE\": 54.398737007050464,         \"R2\": -1188.715138210959,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0037089999999999       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 37.75687919715097,         \"RMSE\": 48.78450375470138,         \"R2\": -1288.953469480389,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0068       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 34.906129137913965,         \"RMSE\": 44.99379649673769,         \"R2\": -1099.675197364534,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.010662       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 33.91700787894482,         \"RMSE\": 42.88559259598606,         \"R2\": -626.4029768570122,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0153429999999999       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 33.25318798467783,         \"RMSE\": 41.41783833748641,         \"R2\": -495.442160460349,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0207979999999999       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 32.454169303664,         \"RMSE\": 40.06534641626261,         \"R2\": -479.0547686921885,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0270209999999999       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.456143135335843,         \"RMSE\": 38.757475924320815,         \"R2\": -395.1605214699538,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0340139999999999       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.609503890456164,         \"RMSE\": 37.605439707525925,         \"R2\": -326.55474603918685,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0417749999999999       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.18524212396377,         \"RMSE\": 36.915721425331306,         \"R2\": -315.5882175367347,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0503299999999999       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.065472528043387,         \"RMSE\": 36.44442035805252,         \"R2\": -331.8421153382835,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0596439999999999       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.78865598017145,         \"RMSE\": 35.91292614465666,         \"R2\": -324.6366197799479,         \"Memory in Mb\": 0.0043020248413085,         \"Time in s\": 0.0697149999999999       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.781114432924586,         \"RMSE\": 35.79236523689611,         \"R2\": -326.81251307944893,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.0853099999999999       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.53323842574737,         \"RMSE\": 35.35890478021234,         \"R2\": -333.95306350592915,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.1033029999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.45783145374521,         \"RMSE\": 35.09485674586195,         \"R2\": -323.506345927368,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.1237949999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.587592426740265,         \"RMSE\": 34.99099947403571,         \"R2\": -337.56135797788454,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.1466579999999999       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.592186767063264,         \"RMSE\": 34.82748458593961,         \"R2\": -353.4405109424598,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.178415       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.81187133621213,         \"RMSE\": 34.87255971663822,         \"R2\": -357.27671195740294,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.2110159999999999       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.96085998978186,         \"RMSE\": 34.8837386376585,         \"R2\": -369.9082961036221,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.2444429999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.10861760053803,         \"RMSE\": 34.89146879370085,         \"R2\": -380.5600928496674,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.2787509999999999       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.237056214581205,         \"RMSE\": 34.87113676993804,         \"R2\": -392.72834237954817,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3138979999999999       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.396870134836657,         \"RMSE\": 34.919939008119975,         \"R2\": -386.68686883790514,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3498769999999999       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.5142090152444,         \"RMSE\": 34.936230377424984,         \"R2\": -366.9859696545672,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.3866269999999999       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.60304766371323,         \"RMSE\": 34.90556469597589,         \"R2\": -357.88920799289923,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.4241359999999999       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.723498435612367,         \"RMSE\": 34.929338036322235,         \"R2\": -350.83863344189655,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.462404       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.811640107301315,         \"RMSE\": 34.91688659850233,         \"R2\": -351.20164208687333,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.5014689999999999       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.90684609870959,         \"RMSE\": 34.93305250730057,         \"R2\": -350.23597283973027,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.541291       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.83222834631613,         \"RMSE\": 34.80844611918478,         \"R2\": -356.0442181025925,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.58187       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.81214247339674,         \"RMSE\": 34.7464796172207,         \"R2\": -363.5733105101423,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.629234       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.96266506693229,         \"RMSE\": 34.82326914665847,         \"R2\": -361.1357082485383,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.6774979999999999       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.066025409450507,         \"RMSE\": 34.86865150113252,         \"R2\": -356.54586556020155,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.743837       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.17687176552783,         \"RMSE\": 34.929241693507976,         \"R2\": -351.0830657253844,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.810979       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.17965741293356,         \"RMSE\": 34.892251240403844,         \"R2\": -347.8114699445998,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.87889       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.2564554130016,         \"RMSE\": 34.924087575336145,         \"R2\": -353.970503405387,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 0.947564       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.205643809070587,         \"RMSE\": 34.8991435368638,         \"R2\": -362.7735100050666,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.017054       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.176512353694505,         \"RMSE\": 34.84497410939031,         \"R2\": -369.6124031875757,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.087302       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.10578554229227,         \"RMSE\": 34.74995813099662,         \"R2\": -367.37224871607793,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.158309       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.047274834607855,         \"RMSE\": 34.691812272848594,         \"R2\": -370.11801689051305,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.23401       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.10380007346799,         \"RMSE\": 34.703384372728905,         \"R2\": -372.0292841604823,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.319073       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.08555480002386,         \"RMSE\": 34.670374973731704,         \"R2\": -374.68721566223496,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.406663       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.19885148971359,         \"RMSE\": 34.750222550469864,         \"R2\": -380.56447731408525,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.496625       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.25463715565584,         \"RMSE\": 34.7657556157579,         \"R2\": -384.4513633760299,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.5889890000000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.316317159155098,         \"RMSE\": 34.79186578621207,         \"R2\": -384.3655387384781,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 1.693075       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.241979864666277,         \"RMSE\": 34.706915019978055,         \"R2\": -380.5804591262153,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.065505       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.224226006229813,         \"RMSE\": 34.673719949901624,         \"R2\": -381.4558834892021,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.440347       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.14134426690263,         \"RMSE\": 34.58836614994504,         \"R2\": -385.8283921715467,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 2.817664       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.997921544748237,         \"RMSE\": 34.45657797481166,         \"R2\": -386.1428842704396,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 3.197344       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.060400411885407,         \"RMSE\": 34.566740304864304,         \"R2\": -392.6960879419034,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 3.57811       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 30.96911325529305,         \"RMSE\": 34.515642414657464,         \"R2\": -399.1566023636026,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 3.959721       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.09609109084082,         \"RMSE\": 34.63142513356851,         \"R2\": -408.6269881777103,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 4.342153       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Passive-Aggressive Regressor, mode 2\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.093334457100017,         \"RMSE\": 34.62570772928248,         \"R2\": -408.7651443035993,         \"Memory in Mb\": 0.0044355392456054,         \"Time in s\": 4.724752       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.6439393939393945,         \"RMSE\": 12.708027567111456,         \"R2\": -206.8805289598106,         \"Memory in Mb\": 0.0208587646484375,         \"Time in s\": 0.001745       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7674242424242426,         \"RMSE\": 9.021574170013263,         \"R2\": -85.19732920009746,         \"Memory in Mb\": 0.0300941467285156,         \"Time in s\": 0.005817       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3601010101010105,         \"RMSE\": 7.4346315168437105,         \"R2\": -37.38846247874159,         \"Memory in Mb\": 0.0395355224609375,         \"Time in s\": 0.012524       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.988257575757577,         \"RMSE\": 6.459864921032004,         \"R2\": -31.8544119108943,         \"Memory in Mb\": 0.0488433837890625,         \"Time in s\": 0.023287       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.201515151515152,         \"RMSE\": 6.079045396219125,         \"R2\": -6.214006750846093,         \"Memory in Mb\": 0.0583724975585937,         \"Time in s\": 0.035467       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.2709595959595963,         \"RMSE\": 5.693634951086079,         \"R2\": -1.7279153546475992,         \"Memory in Mb\": 0.0685691833496093,         \"Time in s\": 0.049542       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.6114718614718617,         \"RMSE\": 5.706903555891601,         \"R2\": -0.8368793810695487,         \"Memory in Mb\": 0.0782623291015625,         \"Time in s\": 0.068809       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.5236742424242427,         \"RMSE\": 5.412016943708686,         \"R2\": -0.4977726858852578,         \"Memory in Mb\": 0.0879554748535156,         \"Time in s\": 0.099298       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.4695286195286204,         \"RMSE\": 5.169211114529652,         \"R2\": -0.1428260058474422,         \"Memory in Mb\": 0.0976486206054687,         \"Time in s\": 0.132347       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7553030303030317,         \"RMSE\": 5.269495069058163,         \"R2\": 0.1706792355598563,         \"Memory in Mb\": 0.1073417663574218,         \"Time in s\": 0.167959       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.1511019283746564,         \"RMSE\": 5.580125306741311,         \"R2\": 0.2837685080447375,         \"Memory in Mb\": 0.117034912109375,         \"Time in s\": 0.206371       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.315782828282829,         \"RMSE\": 5.649452649212155,         \"R2\": 0.3999904226030885,         \"Memory in Mb\": 0.1272315979003906,         \"Time in s\": 0.248032       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.6019813519813537,         \"RMSE\": 5.868270501527574,         \"R2\": 0.475635686274607,         \"Memory in Mb\": 0.1369247436523437,         \"Time in s\": 0.313422       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.745995670995673,         \"RMSE\": 5.964828521670115,         \"R2\": 0.5395766265984425,         \"Memory in Mb\": 0.1466178894042968,         \"Time in s\": 0.409887       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.050202020202021,         \"RMSE\": 6.4542180762994805,         \"R2\": 0.5666546129487657,         \"Memory in Mb\": 0.15631103515625,         \"Time in s\": 0.575073       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.420928030303032,         \"RMSE\": 6.954884488253524,         \"R2\": 0.5942812793055753,         \"Memory in Mb\": 0.1660041809082031,         \"Time in s\": 0.746583       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.757664884135474,         \"RMSE\": 7.278917476631412,         \"R2\": 0.6361362300357987,         \"Memory in Mb\": 0.1756973266601562,         \"Time in s\": 0.922657       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.192340067340069,         \"RMSE\": 7.767087259749381,         \"R2\": 0.6704396407154757,         \"Memory in Mb\": 0.1858940124511718,         \"Time in s\": 1.103506       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.571690590111645,         \"RMSE\": 8.414476478500024,         \"R2\": 0.6811438926382001,         \"Memory in Mb\": 0.195587158203125,         \"Time in s\": 1.289988       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.017651515151518,         \"RMSE\": 9.535434778453542,         \"R2\": 0.641509702161033,         \"Memory in Mb\": 0.2052803039550781,         \"Time in s\": 1.481758       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.514646464646468,         \"RMSE\": 10.15268578355149,         \"R2\": 0.652376522878304,         \"Memory in Mb\": 0.2149734497070312,         \"Time in s\": 1.688886       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.006955922865016,         \"RMSE\": 10.883499074839364,         \"R2\": 0.6785664047839641,         \"Memory in Mb\": 0.2246665954589843,         \"Time in s\": 1.905808       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.401119894598158,         \"RMSE\": 11.259257694820905,         \"R2\": 0.7012209269570091,         \"Memory in Mb\": 0.2343597412109375,         \"Time in s\": 2.129833       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.873800505050509,         \"RMSE\": 12.237701558545494,         \"R2\": 0.6775097363055258,         \"Memory in Mb\": 0.2446098327636718,         \"Time in s\": 2.3598440000000003       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.501393939393942,         \"RMSE\": 13.456617650281162,         \"R2\": 0.6568816796501455,         \"Memory in Mb\": 0.254302978515625,         \"Time in s\": 2.605392       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.999592074592076,         \"RMSE\": 14.081405883193678,         \"R2\": 0.6745818706784585,         \"Memory in Mb\": 0.2639961242675781,         \"Time in s\": 2.8696780000000004       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.403647586980924,         \"RMSE\": 14.487230370517851,         \"R2\": 0.7012657763253116,         \"Memory in Mb\": 0.2736892700195312,         \"Time in s\": 3.1592620000000005       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.82559523809524,         \"RMSE\": 15.247017337775036,         \"R2\": 0.7053028346163965,         \"Memory in Mb\": 0.2833824157714844,         \"Time in s\": 3.471445000000001       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.570794148380358,         \"RMSE\": 17.082267622288043,         \"R2\": 0.6643188025566307,         \"Memory in Mb\": 0.2930755615234375,         \"Time in s\": 3.895766000000001       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.342676767676773,         \"RMSE\": 18.20491056057454,         \"R2\": 0.6737311884314376,         \"Memory in Mb\": 0.3032722473144531,         \"Time in s\": 4.333389       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.75625610948192,         \"RMSE\": 18.5968301788559,         \"R2\": 0.6951271166039881,         \"Memory in Mb\": 0.3129653930664062,         \"Time in s\": 4.790776       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.16955492424243,         \"RMSE\": 18.94133239132977,         \"R2\": 0.7124941202708752,         \"Memory in Mb\": 0.3226585388183594,         \"Time in s\": 5.260338       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.609595959595964,         \"RMSE\": 19.7022738973151,         \"R2\": 0.6979354313341102,         \"Memory in Mb\": 0.3323516845703125,         \"Time in s\": 5.750168       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.251024955436726,         \"RMSE\": 20.7851367099449,         \"R2\": 0.6909564285254863,         \"Memory in Mb\": 0.3420448303222656,         \"Time in s\": 6.251716       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.78255411255412,         \"RMSE\": 21.481025974379733,         \"R2\": 0.7079595790244884,         \"Memory in Mb\": 0.3522415161132812,         \"Time in s\": 6.769761       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.010311447811455,         \"RMSE\": 21.53574862211497,         \"R2\": 0.7263147242326703,         \"Memory in Mb\": 0.3619346618652344,         \"Time in s\": 7.297159       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.576126126126132,         \"RMSE\": 22.56379182999173,         \"R2\": 0.720735043690873,         \"Memory in Mb\": 0.3716278076171875,         \"Time in s\": 7.841892       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.256658692185017,         \"RMSE\": 23.708044463333223,         \"R2\": 0.710588766956741,         \"Memory in Mb\": 0.38134765625,         \"Time in s\": 8.4256       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.863597513597522,         \"RMSE\": 24.650993900023582,         \"R2\": 0.7219567169230845,         \"Memory in Mb\": 0.3910675048828125,         \"Time in s\": 9.118776       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.15655303030304,         \"RMSE\": 24.89490243600041,         \"R2\": 0.7364984966983625,         \"Memory in Mb\": 0.4007606506347656,         \"Time in s\": 9.83444       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.474242424242437,         \"RMSE\": 25.235361878916876,         \"R2\": 0.7407521096740679,         \"Memory in Mb\": 0.4109573364257812,         \"Time in s\": 10.564263       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.206240981241,         \"RMSE\": 26.51959634874256,         \"R2\": 0.731081178462164,         \"Memory in Mb\": 0.4206771850585937,         \"Time in s\": 11.311639       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.061486962649766,         \"RMSE\": 27.919441407022266,         \"R2\": 0.7368140706560946,         \"Memory in Mb\": 0.430450439453125,         \"Time in s\": 12.077289       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.444800275482105,         \"RMSE\": 28.396609389438456,         \"R2\": 0.742660608098584,         \"Memory in Mb\": 0.4401702880859375,         \"Time in s\": 12.86575       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.85067340067341,         \"RMSE\": 28.917019336286597,         \"R2\": 0.7489686179689856,         \"Memory in Mb\": 0.4499168395996094,         \"Time in s\": 13.665871       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.39739789196312,         \"RMSE\": 29.705616030262235,         \"R2\": 0.7427898649120724,         \"Memory in Mb\": 2.5872955322265625,         \"Time in s\": 16.820609       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.115441650548043,         \"RMSE\": 30.73530324863436,         \"R2\": 0.7401565757784102,         \"Memory in Mb\": 2.6296463012695312,         \"Time in s\": 20.027458000000003       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.836142676767683,         \"RMSE\": 31.98623382904741,         \"R2\": 0.7469752640852343,         \"Memory in Mb\": 2.6746597290039062,         \"Time in s\": 23.267231       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.017594310451457,         \"RMSE\": 32.125858524254696,         \"R2\": 0.7553011842320496,         \"Memory in Mb\": 2.717792510986328,         \"Time in s\": 26.555547000000004       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.677242424242426,         \"RMSE\": 32.83678407493398,         \"R2\": 0.7522301799631583,         \"Memory in Mb\": 2.769092559814453,         \"Time in s\": 29.885669000000004       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.80977421271539,         \"RMSE\": 35.198755082788004,         \"R2\": 0.727753941720713,         \"Memory in Mb\": 2.8112449645996094,         \"Time in s\": 33.249092000000005       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.195600233100237,         \"RMSE\": 38.25560047694445,         \"R2\": 0.7025325582791198,         \"Memory in Mb\": 2.857513427734375,         \"Time in s\": 36.653026       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.84062860438293,         \"RMSE\": 39.201635479156685,         \"R2\": 0.6952358931227007,         \"Memory in Mb\": 2.885215759277344,         \"Time in s\": 40.087818000000006       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.554585433333335,         \"RMSE\": 9.794739803036965,         \"R2\": -224.02989290855143,         \"Memory in Mb\": 0.0335884094238281,         \"Time in s\": 0.001545       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7993247666666672,         \"RMSE\": 6.973235588114817,         \"R2\": -18.54942689237887,         \"Memory in Mb\": 0.0554847717285156,         \"Time in s\": 0.0054       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.366773144444445,         \"RMSE\": 5.705236645726316,         \"R2\": -16.642396889136542,         \"Memory in Mb\": 0.0773544311523437,         \"Time in s\": 0.012332       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1277757833333335,         \"RMSE\": 4.947712433075743,         \"R2\": -12.30953248968821,         \"Memory in Mb\": 0.0997543334960937,         \"Time in s\": 0.028826       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.046201766666667,         \"RMSE\": 4.439862929674892,         \"R2\": -5.724544452799038,         \"Memory in Mb\": 0.1216506958007812,         \"Time in s\": 0.050318       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.000865705555556,         \"RMSE\": 4.0744555355418335,         \"R2\": -3.804331488196434,         \"Memory in Mb\": 0.1435470581054687,         \"Time in s\": 0.086896       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9447764619047624,         \"RMSE\": 3.7809361134406254,         \"R2\": -3.275153002458012,         \"Memory in Mb\": 0.1659469604492187,         \"Time in s\": 0.149837       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9352969166666673,         \"RMSE\": 3.5531790499707645,         \"R2\": -2.329617982408036,         \"Memory in Mb\": 0.1878433227539062,         \"Time in s\": 0.2304459999999999       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9445764925925928,         \"RMSE\": 3.380979243961517,         \"R2\": -1.647692611170827,         \"Memory in Mb\": 0.2097396850585937,         \"Time in s\": 0.432465       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9456943733333336,         \"RMSE\": 3.232789339199984,         \"R2\": -1.427877878808435,         \"Memory in Mb\": 0.2321395874023437,         \"Time in s\": 0.648003       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9124697575757575,         \"RMSE\": 3.0919339165015143,         \"R2\": -1.3957229068060464,         \"Memory in Mb\": 0.2540359497070312,         \"Time in s\": 0.888162       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9329223611111108,         \"RMSE\": 2.985727855147271,         \"R2\": -1.2507750530936188,         \"Memory in Mb\": 0.2759323120117187,         \"Time in s\": 1.171554       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9025974717948716,         \"RMSE\": 2.873740673763463,         \"R2\": -1.11319648675526,         \"Memory in Mb\": 0.2984657287597656,         \"Time in s\": 1.484213       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8654126523809523,         \"RMSE\": 2.773524640439575,         \"R2\": -1.0608690746642817,         \"Memory in Mb\": 0.3203620910644531,         \"Time in s\": 1.81098       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8525042622222223,         \"RMSE\": 2.688069339615046,         \"R2\": -0.9037818439458584,         \"Memory in Mb\": 0.3422584533691406,         \"Time in s\": 2.170241       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8265282395833334,         \"RMSE\": 2.6077957497476296,         \"R2\": -0.880493509713772,         \"Memory in Mb\": 0.3646583557128906,         \"Time in s\": 2.558101       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8137511019607846,         \"RMSE\": 2.539210136300266,         \"R2\": -0.8840673465916704,         \"Memory in Mb\": 0.3865547180175781,         \"Time in s\": 3.13755       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7887328240740744,         \"RMSE\": 2.4696835584739105,         \"R2\": -0.7969398815662787,         \"Memory in Mb\": 0.4084510803222656,         \"Time in s\": 3.744079       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7710879228070179,         \"RMSE\": 2.4087271831437693,         \"R2\": -0.7684619785143365,         \"Memory in Mb\": 0.4303474426269531,         \"Time in s\": 4.380375       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.756179386666667,         \"RMSE\": 2.351105641867075,         \"R2\": -0.7324819925835522,         \"Memory in Mb\": 0.4527473449707031,         \"Time in s\": 5.04744       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7300392539682541,         \"RMSE\": 2.295700426816902,         \"R2\": -0.7064552265553199,         \"Memory in Mb\": 0.4746437072753906,         \"Time in s\": 5.735437       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7180258560606063,         \"RMSE\": 2.24592493832078,         \"R2\": -0.6037054809307543,         \"Memory in Mb\": 0.4965400695800781,         \"Time in s\": 6.669957       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7103659666666668,         \"RMSE\": 2.200554873752302,         \"R2\": -0.4599688187191526,         \"Memory in Mb\": 0.5189399719238281,         \"Time in s\": 7.633919000000001       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6905233472222223,         \"RMSE\": 2.1551860359584523,         \"R2\": -0.3681716631920215,         \"Memory in Mb\": 0.5408363342285156,         \"Time in s\": 8.635162000000001       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6835753693333335,         \"RMSE\": 2.11616682722306,         \"R2\": -0.2914054626850582,         \"Memory in Mb\": 2.70669937133789,         \"Time in s\": 12.111573000000002       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6741869282051286,         \"RMSE\": 2.077523623184556,         \"R2\": -0.2468444979074313,         \"Memory in Mb\": 2.7946739196777344,         \"Time in s\": 15.640183000000002       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6635047197530868,         \"RMSE\": 2.0412653603832838,         \"R2\": -0.1992917531598592,         \"Memory in Mb\": 2.8836631774902344,         \"Time in s\": 19.224183000000004       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6666769047619049,         \"RMSE\": 2.01181749557566,         \"R2\": -0.1926963577893738,         \"Memory in Mb\": 2.973125457763672,         \"Time in s\": 22.863484000000003       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.662313208045977,         \"RMSE\": 1.9804661409620816,         \"R2\": -0.1843991731259868,         \"Memory in Mb\": 3.06191635131836,         \"Time in s\": 26.560120000000005       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6595208444444446,         \"RMSE\": 1.9515625148224915,         \"R2\": -0.1373580524839326,         \"Memory in Mb\": 3.158283233642578,         \"Time in s\": 30.31408200000001       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6603871010752689,         \"RMSE\": 1.924909501402362,         \"R2\": -0.0896376201735813,         \"Memory in Mb\": 3.248737335205078,         \"Time in s\": 34.12595900000001       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6518434010416667,         \"RMSE\": 1.8967107462711992,         \"R2\": -0.0381713320833934,         \"Memory in Mb\": 3.341320037841797,         \"Time in s\": 37.99311800000001       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6481796161616163,         \"RMSE\": 1.873162681009878,         \"R2\": -0.00527242303062,         \"Memory in Mb\": 3.435527801513672,         \"Time in s\": 41.91854100000001       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6594073715686274,         \"RMSE\": 1.8574009428793896,         \"R2\": -0.0040456355040212,         \"Memory in Mb\": 3.5269508361816406,         \"Time in s\": 45.90599500000001       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6619153695238096,         \"RMSE\": 1.8376987056605067,         \"R2\": -0.0086724321908719,         \"Memory in Mb\": 3.615283966064453,         \"Time in s\": 49.957909000000015       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6538050537037038,         \"RMSE\": 1.8142062090777376,         \"R2\": -0.0046457590045041,         \"Memory in Mb\": 3.7059364318847656,         \"Time in s\": 54.07172000000001       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6437102684684685,         \"RMSE\": 1.7904191974020045,         \"R2\": 0.0221149973980568,         \"Memory in Mb\": 3.8005104064941406,         \"Time in s\": 58.24412800000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6465423666666668,         \"RMSE\": 1.7722456151874884,         \"R2\": 0.0314860408387392,         \"Memory in Mb\": 3.89126205444336,         \"Time in s\": 62.47501600000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6423591829059828,         \"RMSE\": 1.752432393946061,         \"R2\": 0.0487781645727875,         \"Memory in Mb\": 3.987659454345703,         \"Time in s\": 66.76475200000002       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6415445258333332,         \"RMSE\": 1.7335108155585357,         \"R2\": 0.0607905571401552,         \"Memory in Mb\": 4.087154388427734,         \"Time in s\": 71.11932700000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.641812437398374,         \"RMSE\": 1.7198679523833968,         \"R2\": 0.0653630129096917,         \"Memory in Mb\": 4.179523468017578,         \"Time in s\": 75.53533700000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6391550126984127,         \"RMSE\": 1.7023246638821516,         \"R2\": 0.0758317950759702,         \"Memory in Mb\": 4.276576995849609,         \"Time in s\": 80.015814       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6397551612403103,         \"RMSE\": 1.6865214638981003,         \"R2\": 0.0944734629735503,         \"Memory in Mb\": 4.372867584228516,         \"Time in s\": 84.56990800000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6401663234848486,         \"RMSE\": 1.6719359262678322,         \"R2\": 0.1144902218209267,         \"Memory in Mb\": 4.465221405029297,         \"Time in s\": 89.18993400000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6373928251851855,         \"RMSE\": 1.6559913256631793,         \"R2\": 0.1276383063389357,         \"Memory in Mb\": 4.558887481689453,         \"Time in s\": 93.877546       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6333341724637681,         \"RMSE\": 1.6410816825275083,         \"R2\": 0.1291995533352813,         \"Memory in Mb\": 4.652858734130859,         \"Time in s\": 98.626246       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.637460545390071,         \"RMSE\": 1.630772212254164,         \"R2\": 0.1328113217779126,         \"Memory in Mb\": 4.746517181396484,         \"Time in s\": 103.437785       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6446958777777775,         \"RMSE\": 1.6213030711335543,         \"R2\": 0.1338907909289651,         \"Memory in Mb\": 4.844425201416016,         \"Time in s\": 108.312072       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.643768610068027,         \"RMSE\": 1.6085965270907718,         \"R2\": 0.1308548353743899,         \"Memory in Mb\": 4.935100555419922,         \"Time in s\": 113.251739       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6420156240666665,         \"RMSE\": 1.59493855356346,         \"R2\": 0.1311681221050482,         \"Memory in Mb\": 5.030651092529297,         \"Time in s\": 118.255967       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"k-Nearest Neighbors\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6416785025641023,         \"RMSE\": 1.5941707450098015,         \"R2\": 0.1314249186277071,         \"Memory in Mb\": 5.032634735107422,         \"Time in s\": 123.301096       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.042756132756132,         \"RMSE\": 17.336048579080593,         \"R2\": -385.8634917094176,         \"Memory in Mb\": 0.0162086486816406,         \"Time in s\": 0.002632       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.456785613727984,         \"RMSE\": 12.282422261556867,         \"R2\": -158.770726389092,         \"Memory in Mb\": 0.0177879333496093,         \"Time in s\": 0.007319       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4353973358733074,         \"RMSE\": 10.07037651743448,         \"R2\": -69.4325218162971,         \"Memory in Mb\": 0.0230522155761718,         \"Time in s\": 0.013907       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.736909422894262,         \"RMSE\": 8.732393473100391,         \"R2\": -59.03623058514604,         \"Memory in Mb\": 0.0241050720214843,         \"Time in s\": 0.0217009999999999       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.788577579622257,         \"RMSE\": 8.074088551816661,         \"R2\": -11.726025456653014,         \"Memory in Mb\": 0.0309486389160156,         \"Time in s\": 0.0303349999999999       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.395880085598137,         \"RMSE\": 7.878422021930021,         \"R2\": -4.223121571879303,         \"Memory in Mb\": 0.0404243469238281,         \"Time in s\": 0.040093       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.889526501621088,         \"RMSE\": 7.800910386370324,         \"R2\": -2.432180745921895,         \"Memory in Mb\": 0.0467414855957031,         \"Time in s\": 0.0511699999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.072650698433535,         \"RMSE\": 7.572197783925699,         \"R2\": -1.9320509270116557,         \"Memory in Mb\": 0.0525321960449218,         \"Time in s\": 0.0635609999999999       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.410984939713907,         \"RMSE\": 7.55185413515251,         \"R2\": -1.439151418709002,         \"Memory in Mb\": 0.0535850524902343,         \"Time in s\": 0.0772419999999999       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.370948473977548,         \"RMSE\": 7.327634340090197,         \"R2\": -0.6036593212329582,         \"Memory in Mb\": 0.0551643371582031,         \"Time in s\": 0.0921019999999999       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.401973824893138,         \"RMSE\": 7.197046558152955,         \"R2\": -0.1914453698838978,         \"Memory in Mb\": 0.0551643371582031,         \"Time in s\": 0.1081669999999999       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.283071400630936,         \"RMSE\": 6.979735895990854,         \"R2\": 0.0841519683549982,         \"Memory in Mb\": 0.0551643371582031,         \"Time in s\": 0.1323959999999999       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.169649051526778,         \"RMSE\": 6.77851615807502,         \"R2\": 0.3003478880703081,         \"Memory in Mb\": 0.0556907653808593,         \"Time in s\": 0.1602449999999999       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.107721988217097,         \"RMSE\": 6.620782354691122,         \"R2\": 0.4327427443050297,         \"Memory in Mb\": 0.0556907653808593,         \"Time in s\": 0.1914819999999999       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.386134129138624,         \"RMSE\": 6.8739888422895685,         \"R2\": 0.5084535624523276,         \"Memory in Mb\": 0.0556907653808593,         \"Time in s\": 0.2319989999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.592324836010107,         \"RMSE\": 7.0395287886899816,         \"R2\": 0.5843455987500039,         \"Memory in Mb\": 0.0562171936035156,         \"Time in s\": 0.273788       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.658423416973056,         \"RMSE\": 7.057579140031887,         \"R2\": 0.6579286220132116,         \"Memory in Mb\": 0.0562171936035156,         \"Time in s\": 0.316974       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.6782517314261085,         \"RMSE\": 7.042640058036562,         \"R2\": 0.7290497323677609,         \"Memory in Mb\": 0.0562171936035156,         \"Time in s\": 0.361531       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.896652959256127,         \"RMSE\": 7.410861778989444,         \"R2\": 0.7526693351807108,         \"Memory in Mb\": 0.0217466354370117,         \"Time in s\": 0.409543       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.507880191409123,         \"RMSE\": 8.546476599974424,         \"R2\": 0.7120144996082314,         \"Memory in Mb\": 0.0280637741088867,         \"Time in s\": 0.458317       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.703958017872014,         \"RMSE\": 8.760797449465004,         \"R2\": 0.7411581545051223,         \"Memory in Mb\": 0.0333280563354492,         \"Time in s\": 0.507954       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.934527728379076,         \"RMSE\": 9.145062262320872,         \"R2\": 0.7730513990797492,         \"Memory in Mb\": 0.0380659103393554,         \"Time in s\": 0.576578       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.025889093973978,         \"RMSE\": 9.259481324724224,         \"R2\": 0.7979290061199974,         \"Memory in Mb\": 0.0417509078979492,         \"Time in s\": 0.647861       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.701040765258382,         \"RMSE\": 10.569442782845146,         \"R2\": 0.7594412957229723,         \"Memory in Mb\": 0.0418310165405273,         \"Time in s\": 0.7217790000000001       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.201977905163474,         \"RMSE\": 11.695812678726384,         \"R2\": 0.740801257827299,         \"Memory in Mb\": 0.0418310165405273,         \"Time in s\": 0.7983520000000001       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.47608974362833,         \"RMSE\": 12.176082777300053,         \"R2\": 0.7566872347890514,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 0.889757       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.495029117947843,         \"RMSE\": 12.186858586615225,         \"R2\": 0.7886035011133373,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 0.982264       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.05089484284177,         \"RMSE\": 13.06419009031293,         \"R2\": 0.7836428997387894,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.075782       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.171875092169309,         \"RMSE\": 15.802620207207104,         \"R2\": 0.7127274179827436,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.170327       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.626867556328977,         \"RMSE\": 16.443718231711543,         \"R2\": 0.7338058453397931,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.26591       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.854283538219804,         \"RMSE\": 16.574189924013226,         \"R2\": 0.7578382368534643,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.362543       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.034558550660114,         \"RMSE\": 16.72149964752778,         \"R2\": 0.7759339138910493,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.460131       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.942839439265006,         \"RMSE\": 18.18973374364872,         \"R2\": 0.7425340708967089,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.65219       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.480189522121243,         \"RMSE\": 19.36955258798825,         \"R2\": 0.7316181626186655,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 1.847066       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.884428250077962,         \"RMSE\": 20.018801475409063,         \"R2\": 0.7463650656532205,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 2.044712       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.037067702603975,         \"RMSE\": 20.02507161492445,         \"R2\": 0.7633646392298079,         \"Memory in Mb\": 0.0423574447631835,         \"Time in s\": 2.245044       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.938689395183468,         \"RMSE\": 21.571547182252875,         \"R2\": 0.7447563988620904,         \"Memory in Mb\": 0.0393133163452148,         \"Time in s\": 2.459951       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.737065020554605,         \"RMSE\": 23.070023559587742,         \"R2\": 0.7259561921053947,         \"Memory in Mb\": 0.039839744567871,         \"Time in s\": 2.675857       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.305628841534729,         \"RMSE\": 24.020997573013894,         \"R2\": 0.7359868139097058,         \"Memory in Mb\": 0.0408926010131835,         \"Time in s\": 2.892761       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.503019064271443,         \"RMSE\": 24.118168317988548,         \"R2\": 0.7526847575357923,         \"Memory in Mb\": 0.0414190292358398,         \"Time in s\": 3.110678       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.042001004765991,         \"RMSE\": 24.757154413851225,         \"R2\": 0.7504844548860922,         \"Memory in Mb\": 0.0429983139038085,         \"Time in s\": 3.329579       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.165694044127083,         \"RMSE\": 26.934291479182736,         \"R2\": 0.7226050873941003,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 3.549505       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.958578383564387,         \"RMSE\": 28.26726815061745,         \"R2\": 0.7302155620528221,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 3.77047       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.309589456804158,         \"RMSE\": 28.5754148947933,         \"R2\": 0.7394096166099926,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.010874       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.77955786237919,         \"RMSE\": 29.119281838039548,         \"R2\": 0.7454446166142166,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.254034       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.687135400012505,         \"RMSE\": 30.600738447390604,         \"R2\": 0.7270552375925041,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.499866       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.426270300418786,         \"RMSE\": 31.61383923822668,         \"R2\": 0.7250895764829616,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 4.748399       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.230319490239392,         \"RMSE\": 32.829508990096734,         \"R2\": 0.7334580691909136,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.00325       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.415951878027045,         \"RMSE\": 32.83473210597698,         \"R2\": 0.7443832332812113,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.259172       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.41946931942451,         \"RMSE\": 34.477948502753435,         \"R2\": 0.726844465494657,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.516121       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.135259536350134,         \"RMSE\": 35.412182207518484,         \"R2\": 0.7244424125617825,         \"Memory in Mb\": 0.0435247421264648,         \"Time in s\": 5.774111       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.998428764364284,         \"RMSE\": 36.61317436816486,         \"R2\": 0.7275265693889857,         \"Memory in Mb\": 0.044051170349121,         \"Time in s\": 6.033148000000001       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.16185046142029,         \"RMSE\": 36.73359474841229,         \"R2\": 0.7324023432169282,         \"Memory in Mb\": 0.044051170349121,         \"Time in s\": 6.293050000000001       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.834704431652337,         \"RMSE\": 13.708514217962266,         \"R2\": -439.7934984576362,         \"Memory in Mb\": 0.0500869750976562,         \"Time in s\": 0.001817       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.4692310697037447,         \"RMSE\": 9.813795721313518,         \"R2\": -37.72035957928713,         \"Memory in Mb\": 0.0732498168945312,         \"Time in s\": 0.005553       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.530247618203559,         \"RMSE\": 8.024836796214231,         \"R2\": -33.90460110966681,         \"Memory in Mb\": 0.0858840942382812,         \"Time in s\": 0.011369       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1398752670733447,         \"RMSE\": 6.982837000856316,         \"R2\": -25.510487239912003,         \"Memory in Mb\": 0.09588623046875,         \"Time in s\": 0.023421       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.2521629689485394,         \"RMSE\": 6.362737158647257,         \"R2\": -12.810573390910957,         \"Memory in Mb\": 0.1053619384765625,         \"Time in s\": 0.037893       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.275331183116589,         \"RMSE\": 5.895687482983747,         \"R2\": -9.059182991303912,         \"Memory in Mb\": 0.1095733642578125,         \"Time in s\": 0.054815       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.181766409647037,         \"RMSE\": 5.493495699082884,         \"R2\": -8.025069637302263,         \"Memory in Mb\": 0.1116790771484375,         \"Time in s\": 0.074219       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0635226048812747,         \"RMSE\": 5.165876255053421,         \"R2\": -6.037983110569301,         \"Memory in Mb\": 0.1158905029296875,         \"Time in s\": 0.098519       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9951428730766116,         \"RMSE\": 4.906287161641783,         \"R2\": -4.575559841528811,         \"Memory in Mb\": 0.1179962158203125,         \"Time in s\": 0.130188       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8700446037321656,         \"RMSE\": 4.662539866408188,         \"R2\": -4.050299616280768,         \"Memory in Mb\": 0.015085220336914,         \"Time in s\": 0.169487       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7830718267282506,         \"RMSE\": 4.458344141345012,         \"R2\": -3.981078161152351,         \"Memory in Mb\": 0.0312490463256835,         \"Time in s\": 0.210179       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.714887283408722,         \"RMSE\": 4.280191261764102,         \"R2\": -3.625492757292576,         \"Memory in Mb\": 0.0370397567749023,         \"Time in s\": 0.273546       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.626899515259654,         \"RMSE\": 4.116599014627653,         \"R2\": -3.336325373761703,         \"Memory in Mb\": 0.044569969177246,         \"Time in s\": 0.338443       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6037708656255951,         \"RMSE\": 3.992199218884993,         \"R2\": -3.269831686495559,         \"Memory in Mb\": 0.0575590133666992,         \"Time in s\": 0.405125       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5808413297038584,         \"RMSE\": 3.882244388071726,         \"R2\": -2.971019208275212,         \"Memory in Mb\": 0.0675611495971679,         \"Time in s\": 0.4768960000000001       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5112246352788372,         \"RMSE\": 3.7620340381312185,         \"R2\": -2.9135432145577016,         \"Memory in Mb\": 0.0754575729370117,         \"Time in s\": 0.5560110000000001       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.464954049061847,         \"RMSE\": 3.6574443601858126,         \"R2\": -2.908900292165721,         \"Memory in Mb\": 0.0807218551635742,         \"Time in s\": 0.6372390000000001       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4845626481571883,         \"RMSE\": 3.5832345434246853,         \"R2\": -2.782695640732784,         \"Memory in Mb\": 0.0886182785034179,         \"Time in s\": 0.7206760000000001       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.451940332797817,         \"RMSE\": 3.496542725118452,         \"R2\": -2.72647470962537,         \"Memory in Mb\": 0.0938825607299804,         \"Time in s\": 0.8063740000000001       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4093274160891025,         \"RMSE\": 3.4133346926199284,         \"R2\": -2.65159153540002,         \"Memory in Mb\": 0.1012525558471679,         \"Time in s\": 0.8943350000000001       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3677964737960675,         \"RMSE\": 3.3343173536823296,         \"R2\": -2.5997996751089016,         \"Memory in Mb\": 0.1054639816284179,         \"Time in s\": 1.079576       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.335717224673182,         \"RMSE\": 3.2621145597551164,         \"R2\": -2.3832380441779537,         \"Memory in Mb\": 0.1112546920776367,         \"Time in s\": 1.2716070000000002       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3223220949397412,         \"RMSE\": 3.20054856097613,         \"R2\": -2.088360697350681,         \"Memory in Mb\": 0.1196775436401367,         \"Time in s\": 1.466366       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2961820395725512,         \"RMSE\": 3.1370925842333546,         \"R2\": -1.8988499404168715,         \"Memory in Mb\": 0.1275739669799804,         \"Time in s\": 1.663894       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2652762767168435,         \"RMSE\": 3.076750388249757,         \"R2\": -1.7299037995212605,         \"Memory in Mb\": 0.1323118209838867,         \"Time in s\": 1.864298       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2471740635308572,         \"RMSE\": 3.022290137612829,         \"R2\": -1.6387160551274738,         \"Memory in Mb\": 0.1375761032104492,         \"Time in s\": 2.070902       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.222508472081129,         \"RMSE\": 2.968388528244746,         \"R2\": -1.5361060189709668,         \"Memory in Mb\": 0.1396818161010742,         \"Time in s\": 2.286544       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2073384071706728,         \"RMSE\": 2.920065266046622,         \"R2\": -1.5126838513129577,         \"Memory in Mb\": 0.1444196701049804,         \"Time in s\": 2.5053300000000003       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1845779132924192,         \"RMSE\": 2.8723790540044147,         \"R2\": -1.4914188956527816,         \"Memory in Mb\": 0.1470518112182617,         \"Time in s\": 2.7271330000000003       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1745692976588702,         \"RMSE\": 2.8296294830278077,         \"R2\": -1.3910651808347,         \"Memory in Mb\": 0.1414899826049804,         \"Time in s\": 2.96944       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1708259630571385,         \"RMSE\": 2.7920061348512903,         \"R2\": -1.2924200227078335,         \"Memory in Mb\": 0.1441221237182617,         \"Time in s\": 3.214893       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1599967464968943,         \"RMSE\": 2.7528504813508814,         \"R2\": -1.186915838733254,         \"Memory in Mb\": 0.1462278366088867,         \"Time in s\": 3.481443       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1455993461288598,         \"RMSE\": 2.715465758170179,         \"R2\": -1.112620243595547,         \"Memory in Mb\": 0.0933332443237304,         \"Time in s\": 3.768351       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1331386715536065,         \"RMSE\": 2.679518493749607,         \"R2\": -1.0895638535289454,         \"Memory in Mb\": 0.1028089523315429,         \"Time in s\": 4.057625       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1287919059851137,         \"RMSE\": 2.648832972736431,         \"R2\": -1.0956110522943685,         \"Memory in Mb\": 0.1107053756713867,         \"Time in s\": 4.349483       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1090542602054634,         \"RMSE\": 2.6130484736329,         \"R2\": -1.0841769561048746,         \"Memory in Mb\": 0.1170225143432617,         \"Time in s\": 4.655905000000001       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0919225542546631,         \"RMSE\": 2.579731998640208,         \"R2\": -1.0301471378292058,         \"Memory in Mb\": 0.1207075119018554,         \"Time in s\": 4.969391000000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0729346607841277,         \"RMSE\": 2.546521266569091,         \"R2\": -0.9996439724530696,         \"Memory in Mb\": 0.1238660812377929,         \"Time in s\": 5.409378000000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0548522699101792,         \"RMSE\": 2.514796200212546,         \"R2\": -0.958866579835745,         \"Memory in Mb\": 0.1301832199096679,         \"Time in s\": 5.856313000000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0458975693179249,         \"RMSE\": 2.486381451783576,         \"R2\": -0.9321678603320388,         \"Memory in Mb\": 0.1405134201049804,         \"Time in s\": 6.306401000000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.042667475968943,         \"RMSE\": 2.463395040447954,         \"R2\": -0.9174360179218256,         \"Memory in Mb\": 0.1468305587768554,         \"Time in s\": 6.759550000000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0338402028724885,         \"RMSE\": 2.4371652901742165,         \"R2\": -0.8942452584110789,         \"Memory in Mb\": 0.1505155563354492,         \"Time in s\": 7.215791000000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0182769822752689,         \"RMSE\": 2.409744604248102,         \"R2\": -0.8486703239118398,         \"Memory in Mb\": 0.1520948410034179,         \"Time in s\": 7.680462000000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.007294910176456,         \"RMSE\": 2.3841216724611445,         \"R2\": -0.8005738256179296,         \"Memory in Mb\": 0.1552534103393554,         \"Time in s\": 8.149683000000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9984699415812968,         \"RMSE\": 2.359722022526475,         \"R2\": -0.7713409518355698,         \"Memory in Mb\": 0.1573591232299804,         \"Time in s\": 8.622145000000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9848390746890626,         \"RMSE\": 2.334975438117308,         \"R2\": -0.7628805257854674,         \"Memory in Mb\": 0.1254529953002929,         \"Time in s\": 9.108636       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9804934467335736,         \"RMSE\": 2.3136297350671566,         \"R2\": -0.7454793227879806,         \"Memory in Mb\": 0.1344251632690429,         \"Time in s\": 9.599086       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9715993160407668,         \"RMSE\": 2.291923159938466,         \"R2\": -0.7307898991199615,         \"Memory in Mb\": 0.1402158737182617,         \"Time in s\": 10.092417       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.96479276321034,         \"RMSE\": 2.271398262551761,         \"R2\": -0.7329444574748756,         \"Memory in Mb\": 0.1444272994995117,         \"Time in s\": 10.588738       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.956776427041678,         \"RMSE\": 2.250974677037298,         \"R2\": -0.7305695321170174,         \"Memory in Mb\": 0.1486387252807617,         \"Time in s\": 11.174487       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Hoeffding Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9561028857812052,         \"RMSE\": 2.249867758958838,         \"R2\": -0.7300222157865335,         \"Memory in Mb\": 0.1486387252807617,         \"Time in s\": 11.765558       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.051220648038832,         \"RMSE\": 17.336198122120386,         \"R2\": -385.8701660091343,         \"Memory in Mb\": 0.0229225158691406,         \"Time in s\": 0.002862       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.498502947359929,         \"RMSE\": 12.28528637536428,         \"R2\": -158.84524831763767,         \"Memory in Mb\": 0.0245628356933593,         \"Time in s\": 0.008031       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4668695042339137,         \"RMSE\": 10.074636808082968,         \"R2\": -69.49212762837747,         \"Memory in Mb\": 0.0298271179199218,         \"Time in s\": 0.0152       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7637805804889557,         \"RMSE\": 8.735764655686483,         \"R2\": -59.08259408516962,         \"Memory in Mb\": 0.0309410095214843,         \"Time in s\": 0.027573       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.814517498310432,         \"RMSE\": 8.074396776941786,         \"R2\": -11.726997097138026,         \"Memory in Mb\": 0.0377845764160156,         \"Time in s\": 0.040817       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.396900059747575,         \"RMSE\": 7.862006773633152,         \"R2\": -4.201378762014764,         \"Memory in Mb\": 0.0472602844238281,         \"Time in s\": 0.055065       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.8844336568547537,         \"RMSE\": 7.782255505653143,         \"R2\": -2.415785129732385,         \"Memory in Mb\": 0.0536384582519531,         \"Time in s\": 0.070503       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.068768385552718,         \"RMSE\": 7.555909217267645,         \"R2\": -1.9194502155140076,         \"Memory in Mb\": 0.0594291687011718,         \"Time in s\": 0.087235       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.319029347030655,         \"RMSE\": 7.489629607912237,         \"R2\": -1.3991215781815165,         \"Memory in Mb\": 0.0604820251464843,         \"Time in s\": 0.105314       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.231978704025333,         \"RMSE\": 7.230698639905546,         \"R2\": -0.5615110336669555,         \"Memory in Mb\": 0.0620613098144531,         \"Time in s\": 0.124657       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.279767976439616,         \"RMSE\": 7.114292598648662,         \"R2\": -0.1642036472993016,         \"Memory in Mb\": 0.0620613098144531,         \"Time in s\": 0.145348       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.161677712403324,         \"RMSE\": 6.8979209349412445,         \"R2\": 0.1054968774084013,         \"Memory in Mb\": 0.0620613098144531,         \"Time in s\": 0.183683       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.036201943040193,         \"RMSE\": 6.686446116179646,         \"R2\": 0.3192250351622916,         \"Memory in Mb\": 0.0241641998291015,         \"Time in s\": 0.233347       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.002163310161137,         \"RMSE\": 6.555243218534794,         \"R2\": 0.4439177197734564,         \"Memory in Mb\": 0.034926414489746,         \"Time in s\": 0.283952       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.269310553181931,         \"RMSE\": 6.794169336453219,         \"R2\": 0.5198027804498322,         \"Memory in Mb\": 0.041365623474121,         \"Time in s\": 0.335528       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.394431170074558,         \"RMSE\": 6.916563516446891,         \"R2\": 0.5987399306940604,         \"Memory in Mb\": 0.0471563339233398,         \"Time in s\": 0.38817       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.429782113532627,         \"RMSE\": 6.896434310822903,         \"R2\": 0.6733712331422652,         \"Memory in Mb\": 0.052016258239746,         \"Time in s\": 0.441959       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.448580123995543,         \"RMSE\": 6.86078369215091,         \"R2\": 0.7428621234581485,         \"Memory in Mb\": 0.0546483993530273,         \"Time in s\": 0.496925       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.634718338792146,         \"RMSE\": 7.17917659207716,         \"R2\": 0.7678921596594357,         \"Memory in Mb\": 0.0546483993530273,         \"Time in s\": 0.5531490000000001       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.229854791420841,         \"RMSE\": 8.435313620968111,         \"R2\": 0.7194573631198581,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.6106500000000001       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.400637324787383,         \"RMSE\": 8.615072190659467,         \"R2\": 0.7496975788166091,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.6850710000000001       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.622607300541604,         \"RMSE\": 8.982158345389516,         \"R2\": 0.781064800957145,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.7630730000000001       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.728895576419993,         \"RMSE\": 9.10264619767678,         \"R2\": 0.8047163053551843,         \"Memory in Mb\": 0.0552968978881835,         \"Time in s\": 0.8439190000000001       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.468790531655633,         \"RMSE\": 10.532848432020362,         \"R2\": 0.7611041743489119,         \"Memory in Mb\": 0.0553770065307617,         \"Time in s\": 0.926058       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.961259791220884,         \"RMSE\": 11.725202267966395,         \"R2\": 0.7394969764024641,         \"Memory in Mb\": 0.0553770065307617,         \"Time in s\": 1.009526       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.243017687832032,         \"RMSE\": 12.175095097400796,         \"R2\": 0.7567267064951951,         \"Memory in Mb\": 0.0539121627807617,         \"Time in s\": 1.109836       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.333189926829036,         \"RMSE\": 12.221129948725446,         \"R2\": 0.7874128689691341,         \"Memory in Mb\": 0.0544385910034179,         \"Time in s\": 1.300493       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.907494608974745,         \"RMSE\": 13.13418786953933,         \"R2\": 0.7813182108747583,         \"Memory in Mb\": 0.0545606613159179,         \"Time in s\": 1.494565       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.086203691627809,         \"RMSE\": 16.084282058543664,         \"R2\": 0.7023956098414756,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 1.6919570000000002       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.398286710797228,         \"RMSE\": 16.38837159928856,         \"R2\": 0.7355947540985646,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 1.900794       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.688169379844998,         \"RMSE\": 16.65705092991554,         \"R2\": 0.7554108572015372,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 2.110987       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.856066264187849,         \"RMSE\": 16.815734957180027,         \"R2\": 0.7734013139584004,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 2.322457       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.788654210226415,         \"RMSE\": 18.368645129880047,         \"R2\": 0.7374443731514406,         \"Memory in Mb\": 0.0561399459838867,         \"Time in s\": 2.535213       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.535989444086796,         \"RMSE\": 20.177763325541772,         \"R2\": 0.7087539856658172,         \"Memory in Mb\": 0.0658864974975586,         \"Time in s\": 2.749718       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.949331836981814,         \"RMSE\": 20.800028245688587,         \"R2\": 0.7261827687212361,         \"Memory in Mb\": 0.0713338851928711,         \"Time in s\": 2.965855       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.958714190964644,         \"RMSE\": 20.66064387908481,         \"R2\": 0.748105206776327,         \"Memory in Mb\": 0.0781774520874023,         \"Time in s\": 3.183657       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.807531574997112,         \"RMSE\": 22.01468171576837,         \"R2\": 0.7341619793955468,         \"Memory in Mb\": 0.0825719833374023,         \"Time in s\": 3.418965       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.71794187476778,         \"RMSE\": 23.73901232910809,         \"R2\": 0.7098322050491193,         \"Memory in Mb\": 0.0846776962280273,         \"Time in s\": 3.659139       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.269314924317156,         \"RMSE\": 24.65274813293709,         \"R2\": 0.7219171428567855,         \"Memory in Mb\": 0.0656805038452148,         \"Time in s\": 3.912261       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.511771919641935,         \"RMSE\": 24.834167752766053,         \"R2\": 0.7377826277560943,         \"Memory in Mb\": 0.0706624984741211,         \"Time in s\": 4.16702       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.00667707818897,         \"RMSE\": 25.401748915029017,         \"R2\": 0.7373221851710817,         \"Memory in Mb\": 0.0787420272827148,         \"Time in s\": 4.423509       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.106263610815663,         \"RMSE\": 27.4394567629727,         \"R2\": 0.7121021651653525,         \"Memory in Mb\": 0.0857076644897461,         \"Time in s\": 4.681795       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.950411373417108,         \"RMSE\": 28.951900473786843,         \"R2\": 0.7169889638801871,         \"Memory in Mb\": 0.0888662338256836,         \"Time in s\": 4.941903       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.321905164714362,         \"RMSE\": 29.29627092175635,         \"R2\": 0.7260962478080234,         \"Memory in Mb\": 0.0889272689819336,         \"Time in s\": 5.203768       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.829552469069228,         \"RMSE\": 29.855361574147427,         \"R2\": 0.732412614196017,         \"Memory in Mb\": 0.0889272689819336,         \"Time in s\": 5.467412       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.715769054600838,         \"RMSE\": 31.21095148117224,         \"R2\": 0.7160610523989874,         \"Memory in Mb\": 0.0895147323608398,         \"Time in s\": 5.743327000000001       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.54236471467993,         \"RMSE\": 32.39367117342827,         \"R2\": 0.7113596352744775,         \"Memory in Mb\": 0.0743856430053711,         \"Time in s\": 6.026441000000001       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.379374275832948,         \"RMSE\": 33.670378810622296,         \"R2\": 0.7196292071862618,         \"Memory in Mb\": 0.0787191390991211,         \"Time in s\": 6.311317000000001       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.522458105265056,         \"RMSE\": 33.639909372937744,         \"R2\": 0.7316929916628531,         \"Memory in Mb\": 0.0872030258178711,         \"Time in s\": 6.597982000000001       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.5114661084191,         \"RMSE\": 35.24478084224406,         \"R2\": 0.714558707096332,         \"Memory in Mb\": 0.0935201644897461,         \"Time in s\": 6.886526000000001       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.293418976341684,         \"RMSE\": 36.29050935662323,         \"R2\": 0.7106036021726428,         \"Memory in Mb\": 0.0934362411499023,         \"Time in s\": 7.177067000000001       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.158877831353536,         \"RMSE\": 37.47206255417766,         \"R2\": 0.7145930209145848,         \"Memory in Mb\": 0.0946111679077148,         \"Time in s\": 7.581349000000001       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.37390218951093,         \"RMSE\": 37.6579284312523,         \"R2\": 0.7187656938003131,         \"Memory in Mb\": 0.0947332382202148,         \"Time in s\": 7.990285000000001       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.828377634536296,         \"RMSE\": 13.70786256219322,         \"R2\": -439.7515918302183,         \"Memory in Mb\": 0.0568618774414062,         \"Time in s\": 0.005477       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.453811275213839,         \"RMSE\": 9.811073218407971,         \"R2\": -37.69887927291551,         \"Memory in Mb\": 0.0800857543945312,         \"Time in s\": 0.014203       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5116544078850294,         \"RMSE\": 8.021960641037959,         \"R2\": -33.879585508404254,         \"Memory in Mb\": 0.0927200317382812,         \"Time in s\": 0.025216       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1224425015381523,         \"RMSE\": 6.9797990571526345,         \"R2\": -25.487425023640156,         \"Memory in Mb\": 0.102783203125,         \"Time in s\": 0.038581       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.246653919301699,         \"RMSE\": 6.363694444016854,         \"R2\": -12.814729355257526,         \"Memory in Mb\": 0.1122589111328125,         \"Time in s\": 0.054574       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.270681160376927,         \"RMSE\": 5.896666779393501,         \"R2\": -9.06252500695684,         \"Memory in Mb\": 0.1164703369140625,         \"Time in s\": 0.096737       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.162967815650222,         \"RMSE\": 5.491011289549727,         \"R2\": -8.016908386196121,         \"Memory in Mb\": 0.1185760498046875,         \"Time in s\": 0.145193       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9648637778298337,         \"RMSE\": 5.147547754256808,         \"R2\": -5.988130255135697,         \"Memory in Mb\": 0.048110008239746,         \"Time in s\": 0.201618       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.86652787828915,         \"RMSE\": 4.875884330950751,         \"R2\": -4.506673701927233,         \"Memory in Mb\": 0.0645513534545898,         \"Time in s\": 0.259848       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.773434994745299,         \"RMSE\": 4.638841370319518,         \"R2\": -3.999091327975425,         \"Memory in Mb\": 0.0751142501831054,         \"Time in s\": 0.334681       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6594682627798778,         \"RMSE\": 4.42936028038101,         \"R2\": -3.916524330360767,         \"Memory in Mb\": 0.0809926986694336,         \"Time in s\": 0.415547       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5811297097344512,         \"RMSE\": 4.24689633509078,         \"R2\": -3.553810703437006,         \"Memory in Mb\": 0.0831594467163086,         \"Time in s\": 0.499019       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4918706813368772,         \"RMSE\": 4.083314206963185,         \"R2\": -3.2664860479391056,         \"Memory in Mb\": 0.0869779586791992,         \"Time in s\": 0.584777       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4582505621214346,         \"RMSE\": 3.950619643811522,         \"R2\": -3.181352514384196,         \"Memory in Mb\": 0.0965147018432617,         \"Time in s\": 0.672987       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4293807431017047,         \"RMSE\": 3.836527362327468,         \"R2\": -2.8780450161882043,         \"Memory in Mb\": 0.1050596237182617,         \"Time in s\": 0.763791       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3766835460490845,         \"RMSE\": 3.718390713103106,         \"R2\": -2.8232679475596494,         \"Memory in Mb\": 0.1113767623901367,         \"Time in s\": 0.862886       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3285707966483495,         \"RMSE\": 3.611463128557805,         \"R2\": -2.8112330604866624,         \"Memory in Mb\": 0.0969266891479492,         \"Time in s\": 1.077289       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3305028688272291,         \"RMSE\": 3.538102571280229,         \"R2\": -2.688007239623816,         \"Memory in Mb\": 0.1048231124877929,         \"Time in s\": 1.294249       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3086678355415842,         \"RMSE\": 3.4529556765760527,         \"R2\": -2.6341471363086995,         \"Memory in Mb\": 0.1101484298706054,         \"Time in s\": 1.513868       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.256053624567095,         \"RMSE\": 3.3666460142322228,         \"R2\": -2.552379472359031,         \"Memory in Mb\": 0.1175184249877929,         \"Time in s\": 1.736306       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2254239545780012,         \"RMSE\": 3.2887455105144454,         \"R2\": -2.5020714662192383,         \"Memory in Mb\": 0.1217298507690429,         \"Time in s\": 1.978865       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.204020924712129,         \"RMSE\": 3.2198773978896,         \"R2\": -2.2961943419959137,         \"Memory in Mb\": 0.1275205612182617,         \"Time in s\": 2.244474       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1975328241312166,         \"RMSE\": 3.1601130927415366,         \"R2\": -2.010817456858815,         \"Memory in Mb\": 0.1354780197143554,         \"Time in s\": 2.513147       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.186148143661266,         \"RMSE\": 3.1001176815841758,         \"R2\": -1.8309188655239268,         \"Memory in Mb\": 0.1433744430541992,         \"Time in s\": 2.784897       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1667856894749518,         \"RMSE\": 3.042966728214852,         \"R2\": -1.6702825792738007,         \"Memory in Mb\": 0.1362333297729492,         \"Time in s\": 3.07197       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.153194144927427,         \"RMSE\": 2.98944402729251,         \"R2\": -1.5816728306403074,         \"Memory in Mb\": 0.1415586471557617,         \"Time in s\": 3.362254       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1356058423088553,         \"RMSE\": 2.937036564746637,         \"R2\": -1.4828164968540292,         \"Memory in Mb\": 0.1436643600463867,         \"Time in s\": 3.655648       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.125648357086568,         \"RMSE\": 2.890393580385493,         \"R2\": -1.4618789770567937,         \"Memory in Mb\": 0.1484022140502929,         \"Time in s\": 3.952243       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1072323197222282,         \"RMSE\": 2.84377722554966,         \"R2\": -1.4420491211959612,         \"Memory in Mb\": 0.1510343551635742,         \"Time in s\": 4.252035       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0962221602561253,         \"RMSE\": 2.8010574809052518,         \"R2\": -1.343021715112041,         \"Memory in Mb\": 0.1547193527221679,         \"Time in s\": 4.557891000000001       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.095549207215165,         \"RMSE\": 2.765029222449673,         \"R2\": -1.2483344123018605,         \"Memory in Mb\": 0.1578779220581054,         \"Time in s\": 4.883158000000001       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.085957414095071,         \"RMSE\": 2.726589883354214,         \"R2\": -1.1453910301968575,         \"Memory in Mb\": 0.1599836349487304,         \"Time in s\": 5.354946000000001       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0751762466913892,         \"RMSE\": 2.6908702968299423,         \"R2\": -1.074523242859362,         \"Memory in Mb\": 0.1636686325073242,         \"Time in s\": 5.834119000000001       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0667684392102676,         \"RMSE\": 2.656475453821568,         \"R2\": -1.0537791659469915,         \"Memory in Mb\": 0.1663007736206054,         \"Time in s\": 6.316591000000001       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.066718890752265,         \"RMSE\": 2.6278494556992995,         \"R2\": -1.0625405514881172,         \"Memory in Mb\": 0.1547193527221679,         \"Time in s\": 6.8062700000000005       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0487756272096451,         \"RMSE\": 2.5923957614441,         \"R2\": -1.0513617944147002,         \"Memory in Mb\": 0.1594572067260742,         \"Time in s\": 7.299083       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0336933816342644,         \"RMSE\": 2.5596915816453274,         \"R2\": -0.9987276211091368,         \"Memory in Mb\": 0.1632032394409179,         \"Time in s\": 7.795134000000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0143808347523189,         \"RMSE\": 2.5263993770636084,         \"R2\": -0.968167584311768,         \"Memory in Mb\": 0.1647825241088867,         \"Time in s\": 8.298925       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0004245938094416,         \"RMSE\": 2.495691505058861,         \"R2\": -0.9292169429583496,         \"Memory in Mb\": 0.1695814132690429,         \"Time in s\": 8.809149000000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9976736219043986,         \"RMSE\": 2.469777786083391,         \"R2\": -0.9064485942635294,         \"Memory in Mb\": 0.1616849899291992,         \"Time in s\": 9.32665       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0020392091388557,         \"RMSE\": 2.450590646975973,         \"R2\": -0.8975546778436754,         \"Memory in Mb\": 0.1643171310424804,         \"Time in s\": 9.853038000000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9936292081382508,         \"RMSE\": 2.424886643827349,         \"R2\": -0.8752066007627983,         \"Memory in Mb\": 0.1680021286010742,         \"Time in s\": 10.484318000000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9794930742877992,         \"RMSE\": 2.3980423354299125,         \"R2\": -0.8307587924463844,         \"Memory in Mb\": 0.1701078414916992,         \"Time in s\": 11.125036       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9694853941742788,         \"RMSE\": 2.372794343098121,         \"R2\": -0.7835048635250907,         \"Memory in Mb\": 0.1727399826049804,         \"Time in s\": 11.769143       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9594920424525858,         \"RMSE\": 2.348266033222206,         \"R2\": -0.7541836724323567,         \"Memory in Mb\": 0.1115369796752929,         \"Time in s\": 12.432199       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9482726802907966,         \"RMSE\": 2.324135545417226,         \"R2\": -0.7465505219679065,         \"Memory in Mb\": 0.1163969039916992,         \"Time in s\": 13.10541       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9455376055826032,         \"RMSE\": 2.30345366329758,         \"R2\": -0.7301587545146957,         \"Memory in Mb\": 0.1223096847534179,         \"Time in s\": 13.781537       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9379298129457146,         \"RMSE\": 2.282181144273129,         \"R2\": -0.7161074287562055,         \"Memory in Mb\": 0.1296796798706054,         \"Time in s\": 14.460614       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.930996996530802,         \"RMSE\": 2.261860474984104,         \"R2\": -0.7184214614837348,         \"Memory in Mb\": 0.1349439620971679,         \"Time in s\": 15.148323       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9214575102921838,         \"RMSE\": 2.2404008018877137,         \"R2\": -0.714349138962711,         \"Memory in Mb\": 0.1382246017456054,         \"Time in s\": 15.950631       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Hoeffding Adaptive Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9213134079227226,         \"RMSE\": 2.239416179559339,         \"R2\": -0.7139861919037982,         \"Memory in Mb\": 0.1382246017456054,         \"Time in s\": 16.757639       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.63636363636363,         \"RMSE\": 41.64569169030137,         \"R2\": -2231.5319148936137,         \"Memory in Mb\": 0.0096149444580078,         \"Time in s\": 0.001328       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.31818181818181,         \"RMSE\": 41.32960638133835,         \"R2\": -1808.0547045951903,         \"Memory in Mb\": 0.0126094818115234,         \"Time in s\": 0.003944       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.12121212121212,         \"RMSE\": 41.13871582091424,         \"R2\": -1174.393494897962,         \"Memory in Mb\": 0.015787124633789,         \"Time in s\": 0.007623       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.159090909090914,         \"RMSE\": 41.17451771534076,         \"R2\": -1333.7620984139928,         \"Memory in Mb\": 0.0188732147216796,         \"Time in s\": 0.012489       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.5090909090909,         \"RMSE\": 41.57075020645253,         \"R2\": -336.3506066081568,         \"Memory in Mb\": 0.0218257904052734,         \"Time in s\": 0.019505       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.681818181818166,         \"RMSE\": 42.82080349691271,         \"R2\": -153.29834830483878,         \"Memory in Mb\": 0.0246181488037109,         \"Time in s\": 0.027128       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.50649350649351,         \"RMSE\": 43.70978671356627,         \"R2\": -106.75487995129542,         \"Memory in Mb\": 0.0275020599365234,         \"Time in s\": 0.035372       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.21590909090909,         \"RMSE\": 44.43649707984724,         \"R2\": -99.97346126163,         \"Memory in Mb\": 0.0300197601318359,         \"Time in s\": 0.047911       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.05050505050505,         \"RMSE\": 45.309262771858165,         \"R2\": -86.8022342468144,         \"Memory in Mb\": 0.0329036712646484,         \"Time in s\": 0.072727       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.16363636363636,         \"RMSE\": 46.52487115902242,         \"R2\": -63.64797006437341,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.103163       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 47.21487603305785,         \"RMSE\": 47.67304278378361,         \"R2\": -51.27707184490422,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.146595       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.29545454545455,         \"RMSE\": 48.843054157105485,         \"R2\": -43.84882422437649,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.196283       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.44055944055945,         \"RMSE\": 50.100318941519305,         \"R2\": -37.220279564063546,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.258522       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 50.532467532467535,         \"RMSE\": 51.29137544271156,         \"R2\": -33.04474826644667,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.329566       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.690909090909095,         \"RMSE\": 52.61253451297311,         \"R2\": -27.795548438273773,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.40393       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.00568181818182,         \"RMSE\": 54.11860921749895,         \"R2\": -23.566226925646237,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.481694       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.41176470588235,         \"RMSE\": 55.733754017636336,         \"R2\": -20.33250305682894,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.681251       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.02525252525252,         \"RMSE\": 57.635786091488654,         \"R2\": -17.146924852486976,         \"Memory in Mb\": 0.2696781158447265,         \"Time in s\": 0.884966       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 55.16354936929098,         \"RMSE\": 57.0482200725598,         \"R2\": -13.656313160472004,         \"Memory in Mb\": 0.6838865280151367,         \"Time in s\": 1.131695       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.62203856749311,         \"RMSE\": 56.03531795068661,         \"R2\": -11.37998411824978,         \"Memory in Mb\": 0.6869077682495117,         \"Time in s\": 1.3969520000000002       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.77279286370195,         \"RMSE\": 55.29408706815337,         \"R2\": -9.311090357596036,         \"Memory in Mb\": 0.6899290084838867,         \"Time in s\": 1.6754760000000002       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.49661908339594,         \"RMSE\": 55.0071045368674,         \"R2\": -7.210918602421254,         \"Memory in Mb\": 0.6929502487182617,         \"Time in s\": 1.960024       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.25631812193077,         \"RMSE\": 54.71344660515688,         \"R2\": -6.055353919833875,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 2.270278       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.62511478420569,         \"RMSE\": 54.312843786153664,         \"R2\": -5.352168023774992,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 2.586688       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.4425344352617,         \"RMSE\": 54.29364548356293,         \"R2\": -4.585603291722447,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 2.915419       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.75651621106165,         \"RMSE\": 54.635705044608144,         \"R2\": -3.8989478253777694,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 3.266148       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.373839404142416,         \"RMSE\": 55.25476711535166,         \"R2\": -3.3456400671942,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 3.622985       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.87239275875638,         \"RMSE\": 55.86677247417265,         \"R2\": -2.9565197175813718,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 3.98691       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 52.69554478958866,         \"RMSE\": 56.2770501442128,         \"R2\": -2.6433309475704183,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 4.356941       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.85316804407712,         \"RMSE\": 57.75044402630399,         \"R2\": -2.2832890424968197,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 4.733992       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.90678041411178,         \"RMSE\": 59.01114057562677,         \"R2\": -2.0697921090482247,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 5.128946       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.00533746556472,         \"RMSE\": 60.30224520856101,         \"R2\": -1.9140207825503284,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 5.54848       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 55.99599298772852,         \"RMSE\": 60.54917173074773,         \"R2\": -1.852879941931207,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 6.172488       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.87222492302705,         \"RMSE\": 61.81275171085535,         \"R2\": -1.7331917323651345,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 6.808446       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 58.41786698150333,         \"RMSE\": 63.95254893573906,         \"R2\": -1.588502821427925,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 7.450193       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 59.7033976124885,         \"RMSE\": 65.46926983257002,         \"R2\": -1.5293357430909813,         \"Memory in Mb\": 0.6947126388549805,         \"Time in s\": 8.100657       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.057805647389294,         \"RMSE\": 66.17359973042984,         \"R2\": -1.4019380007417157,         \"Memory in Mb\": 1.1097631454467771,         \"Time in s\": 8.796904       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 59.7070864579051,         \"RMSE\": 66.11592086962122,         \"R2\": -1.2507954049688483,         \"Memory in Mb\": 1.1127843856811523,         \"Time in s\": 9.50192       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.122823673891816,         \"RMSE\": 66.73609937588846,         \"R2\": -1.0378169857688957,         \"Memory in Mb\": 1.1158056259155271,         \"Time in s\": 10.222461       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.39504675635191,         \"RMSE\": 66.96100690444877,         \"R2\": -0.906365593827489,         \"Memory in Mb\": 1.1188268661499023,         \"Time in s\": 10.951743       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.27126048587789,         \"RMSE\": 66.93502892662679,         \"R2\": -0.8239085862185902,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 11.696828       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.340686610373176,         \"RMSE\": 67.43825007380137,         \"R2\": -0.7390015352251049,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 12.465469       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 61.40703262301831,         \"RMSE\": 69.11306667757516,         \"R2\": -0.6127592621572406,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 13.248766       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 61.95796621360106,         \"RMSE\": 69.71422620021941,         \"R2\": -0.5510154280248158,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 14.047315       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 62.59018166487368,         \"RMSE\": 70.55352405729404,         \"R2\": -0.4943708535906215,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 14.854826       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 62.49664579133251,         \"RMSE\": 70.88193125644693,         \"R2\": -0.4644752452013045,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 15.674675       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 63.25224079915844,         \"RMSE\": 71.92080214464903,         \"R2\": -0.4228062717918979,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 16.51129       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 64.80783657170488,         \"RMSE\": 74.3681944005728,         \"R2\": -0.367764222300833,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 17.364023       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.59959781369417,         \"RMSE\": 75.30113885843834,         \"R2\": -0.3443906138479853,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 18.342072       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.79684627343133,         \"RMSE\": 76.01328745307667,         \"R2\": -0.3277190973108916,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 19.334776       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 66.6512855136148,         \"RMSE\": 77.20436469287773,         \"R2\": -0.3097569166669509,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 20.336346       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.11975592628174,         \"RMSE\": 79.56492566870935,         \"R2\": -0.2867456678376987,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 21.353617       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.75877313437184,         \"RMSE\": 80.35800679505147,         \"R2\": -0.2806007657015741,         \"Memory in Mb\": 1.120589256286621,         \"Time in s\": 22.38029       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 43.8732195,         \"RMSE\": 43.87807788634269,         \"R2\": -4514.954899312423,         \"Memory in Mb\": 0.0199413299560546,         \"Time in s\": 0.002168       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.4932955,         \"RMSE\": 42.52255283421693,         \"R2\": -725.9491167623446,         \"Memory in Mb\": 0.0317363739013671,         \"Time in s\": 0.006794       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.2167785,         \"RMSE\": 42.2386240157387,         \"R2\": -966.0073736019044,         \"Memory in Mb\": 0.0438976287841796,         \"Time in s\": 0.018434       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.975705625,         \"RMSE\": 41.99760868559829,         \"R2\": -957.9655948743646,         \"Memory in Mb\": 0.0562419891357421,         \"Time in s\": 0.031286       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.37550450000001,         \"RMSE\": 41.410913785433536,         \"R2\": -583.9966399141301,         \"Memory in Mb\": 0.5381031036376953,         \"Time in s\": 0.048039       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.936110000000006,         \"RMSE\": 40.97829382197767,         \"R2\": -484.9611418859003,         \"Memory in Mb\": 0.5386066436767578,         \"Time in s\": 0.080711       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.6885472857143,         \"RMSE\": 40.72961738075088,         \"R2\": -495.1050461477588,         \"Memory in Mb\": 0.5391101837158203,         \"Time in s\": 0.166791       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.35105437500001,         \"RMSE\": 40.39801158334292,         \"R2\": -429.4078677932073,         \"Memory in Mb\": 0.5393619537353516,         \"Time in s\": 0.262676       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.00981655555555,         \"RMSE\": 40.06373388340122,         \"R2\": -370.7794659133543,         \"Memory in Mb\": 0.5396137237548828,         \"Time in s\": 0.43318       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.80633095,         \"RMSE\": 39.860362966711,         \"R2\": -368.1089073295326,         \"Memory in Mb\": 0.5077581405639648,         \"Time in s\": 0.638958       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 36.497516001377406,         \"RMSE\": 38.01945344470104,         \"R2\": -361.2329206514933,         \"Memory in Mb\": 1.3602590560913086,         \"Time in s\": 0.913553       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 33.64243104419191,         \"RMSE\": 36.40668421494773,         \"R2\": -333.65237138497804,         \"Memory in Mb\": 1.360762596130371,         \"Time in s\": 1.221179       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 31.222114965034955,         \"RMSE\": 34.98371838354962,         \"R2\": -312.16748668977897,         \"Memory in Mb\": 1.3610143661499023,         \"Time in s\": 1.570709       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 29.18205946861472,         \"RMSE\": 33.71869814960704,         \"R2\": -303.5986275675674,         \"Memory in Mb\": 1.361769676208496,         \"Time in s\": 1.939253       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 27.34275770505051,         \"RMSE\": 32.57805191350732,         \"R2\": -278.63174197976707,         \"Memory in Mb\": 1.3620214462280271,         \"Time in s\": 2.324555       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 25.81388747443183,         \"RMSE\": 31.5521424826706,         \"R2\": -274.2849072221064,         \"Memory in Mb\": 1.3630285263061523,         \"Time in s\": 2.877117       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 24.51835124153299,         \"RMSE\": 30.62414457186519,         \"R2\": -273.0482727941538,         \"Memory in Mb\": 1.3640356063842771,         \"Time in s\": 3.447       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 23.451930423400693,         \"RMSE\": 29.78792492645533,         \"R2\": -260.4155562259403,         \"Memory in Mb\": 1.3660497665405271,         \"Time in s\": 4.029196       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 22.46844053349284,         \"RMSE\": 29.014219480552867,         \"R2\": -255.5915105297988,         \"Memory in Mb\": 1.3665533065795898,         \"Time in s\": 4.629964999999999       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 21.59490700757577,         \"RMSE\": 28.301677882839343,         \"R2\": -250.0434007116766,         \"Memory in Mb\": 0.510127067565918,         \"Time in s\": 5.253793       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 20.62268781294523,         \"RMSE\": 27.62086591367872,         \"R2\": -246.0239415518119,         \"Memory in Mb\": 1.3623762130737305,         \"Time in s\": 5.968102       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 19.786863931462925,         \"RMSE\": 26.990398924900397,         \"R2\": -230.60756767519212,         \"Memory in Mb\": 1.3643903732299805,         \"Time in s\": 6.700306       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 19.05732899619648,         \"RMSE\": 26.404670160589287,         \"R2\": -209.2038511633616,         \"Memory in Mb\": 1.3666563034057615,         \"Time in s\": 7.451319000000001       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 18.376512097202227,         \"RMSE\": 25.854792215140314,         \"R2\": -195.90337768575387,         \"Memory in Mb\": 1.3701810836791992,         \"Time in s\": 8.221931000000001       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 17.755044410127518,         \"RMSE\": 25.338820973360427,         \"R2\": -184.1550753065148,         \"Memory in Mb\": 1.3716917037963867,         \"Time in s\": 9.124580000000002       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 17.16611419898163,         \"RMSE\": 24.851444862058347,         \"R2\": -177.4118263333629,         \"Memory in Mb\": 1.3737058639526367,         \"Time in s\": 10.044684000000002       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 16.628565596068775,         \"RMSE\": 24.392285078947275,         \"R2\": -170.25012213753183,         \"Memory in Mb\": 1.3747129440307615,         \"Time in s\": 10.981068000000002       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 16.091244232649693,         \"RMSE\": 23.955027361350904,         \"R2\": -168.10096043791202,         \"Memory in Mb\": 1.3752164840698242,         \"Time in s\": 11.990243000000005       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 15.590768135673304,         \"RMSE\": 23.54051091957351,         \"R2\": -166.33817208986073,         \"Memory in Mb\": 1.3764753341674805,         \"Time in s\": 13.016881000000003       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 15.168708628495342,         \"RMSE\": 23.15108754841241,         \"R2\": -159.05714501634571,         \"Memory in Mb\": 0.5124959945678711,         \"Time in s\": 14.090212000000005       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 14.742446374247312,         \"RMSE\": 22.77953961802373,         \"R2\": -151.59887848495535,         \"Memory in Mb\": 3.064208030700684,         \"Time in s\": 15.285325000000004       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 14.319364852585176,         \"RMSE\": 22.42187566882095,         \"R2\": -144.08105420081068,         \"Memory in Mb\": 3.0679845809936523,         \"Time in s\": 16.529242000000004       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.916412195872256,         \"RMSE\": 22.080274918425697,         \"R2\": -138.68241285181185,         \"Memory in Mb\": 3.0712575912475586,         \"Time in s\": 17.842975000000003       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.515604789075644,         \"RMSE\": 21.753254558457893,         \"R2\": -136.71797028279042,         \"Memory in Mb\": 3.074782371520996,         \"Time in s\": 19.280557       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.16391092204058,         \"RMSE\": 21.44141764506316,         \"R2\": -136.3120101768532,         \"Memory in Mb\": 3.0773000717163086,         \"Time in s\": 20.753146       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.828283113852926,         \"RMSE\": 21.142484202016185,         \"R2\": -135.44313416922282,         \"Memory in Mb\": 3.078558921813965,         \"Time in s\": 22.284495       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.50446646701278,         \"RMSE\": 20.855361315179096,         \"R2\": -131.6825380828392,         \"Memory in Mb\": 3.0800695419311523,         \"Time in s\": 23.930702       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 12.187542748969031,         \"RMSE\": 20.57929219886472,         \"R2\": -129.592708960364,         \"Memory in Mb\": 3.0813283920288086,         \"Time in s\": 25.608717       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.899403743710543,         \"RMSE\": 20.31464229706916,         \"R2\": -126.82553676745258,         \"Memory in Mb\": 3.08359432220459,         \"Time in s\": 27.347366       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.634366305883283,         \"RMSE\": 20.06137952581079,         \"R2\": -124.7856004590591,         \"Memory in Mb\": 3.084601402282715,         \"Time in s\": 29.130085       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.363415331478278,         \"RMSE\": 19.815492221289517,         \"R2\": -123.0687724200615,         \"Memory in Mb\": 3.08560848236084,         \"Time in s\": 30.98707       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.106640469158773,         \"RMSE\": 19.57848368678801,         \"R2\": -121.2430978899656,         \"Memory in Mb\": 3.086615562438965,         \"Time in s\": 32.880055       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.873909665943762,         \"RMSE\": 19.35022618912736,         \"R2\": -118.20364312373844,         \"Memory in Mb\": 3.087119102478028,         \"Time in s\": 34.808534       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.65545006969969,         \"RMSE\": 19.130035299019603,         \"R2\": -114.92727947355436,         \"Memory in Mb\": 3.0873708724975586,         \"Time in s\": 36.791638       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.439309697188907,         \"RMSE\": 18.916827199314994,         \"R2\": -112.83532852765144,         \"Memory in Mb\": 3.08762264251709,         \"Time in s\": 38.832751       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.21789524284777,         \"RMSE\": 18.710158789526105,         \"R2\": -112.19133803320568,         \"Memory in Mb\": 3.087874412536621,         \"Time in s\": 40.951802       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 10.012578535125469,         \"RMSE\": 18.510293787577226,         \"R2\": -110.72583714230213,         \"Memory in Mb\": 3.077906608581543,         \"Time in s\": 43.146806       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.811853150109153,         \"RMSE\": 18.316579311485903,         \"R2\": -109.54344305213982,         \"Memory in Mb\": 3.0804243087768555,         \"Time in s\": 45.38444       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.61909067795052,         \"RMSE\": 18.12881604876013,         \"R2\": -109.39183420714345,         \"Memory in Mb\": 3.080927848815918,         \"Time in s\": 47.662491       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.438738635632271,         \"RMSE\": 17.946847607318464,         \"R2\": -109.00797869183796,         \"Memory in Mb\": 3.082438468933105,         \"Time in s\": 50.039238       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Stochastic Gradient Tree\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.429746533156267,         \"RMSE\": 17.937886241411594,         \"R2\": -108.97151968967049,         \"Memory in Mb\": 3.082438468933105,         \"Time in s\": 52.450717       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.837563210503649,         \"RMSE\": 16.830121687224917,         \"R2\": -363.61289911513376,         \"Memory in Mb\": 0.1506052017211914,         \"Time in s\": 0.01357       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.3557641651310055,         \"RMSE\": 11.925612892987612,         \"R2\": -149.62275175212707,         \"Memory in Mb\": 0.1761331558227539,         \"Time in s\": 0.033876       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.3711466349197527,         \"RMSE\": 9.780434627556833,         \"R2\": -65.4351822307151,         \"Memory in Mb\": 0.214268684387207,         \"Time in s\": 0.065705       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.6922077728217837,         \"RMSE\": 8.482083592242564,         \"R2\": -55.643739991610765,         \"Memory in Mb\": 0.2312593460083007,         \"Time in s\": 0.110591       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.74736475641488,         \"RMSE\": 7.825318026963682,         \"R2\": -10.953904022002217,         \"Memory in Mb\": 0.2869501113891601,         \"Time in s\": 0.166016       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.8724679940162905,         \"RMSE\": 7.312536888278379,         \"R2\": -3.4997438549991955,         \"Memory in Mb\": 0.3332719802856445,         \"Time in s\": 0.243978       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.0470429271529937,         \"RMSE\": 7.064245743713448,         \"R2\": -1.8145642692685129,         \"Memory in Mb\": 0.3119173049926758,         \"Time in s\": 0.346862       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.9741223361578246,         \"RMSE\": 6.690154558226259,         \"R2\": -1.288758200280824,         \"Memory in Mb\": 0.3463144302368164,         \"Time in s\": 0.647937       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.5306191185317584,         \"RMSE\": 6.892431773474538,         \"R2\": -1.031779280787943,         \"Memory in Mb\": 0.3914194107055664,         \"Time in s\": 0.960086       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.8799314967396743,         \"RMSE\": 6.981555605673833,         \"R2\": -0.4557571678865852,         \"Memory in Mb\": 0.4218912124633789,         \"Time in s\": 1.292146       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.113667008668635,         \"RMSE\": 7.033914104811044,         \"R2\": -0.1380455127293207,         \"Memory in Mb\": 0.4422159194946289,         \"Time in s\": 1.662194       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.34164975929163,         \"RMSE\": 7.058470289925444,         \"R2\": 0.0633731167085442,         \"Memory in Mb\": 0.4695367813110351,         \"Time in s\": 2.044753       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.57586761829926,         \"RMSE\": 7.15786747745719,         \"R2\": 0.2198462773680444,         \"Memory in Mb\": 0.5039682388305664,         \"Time in s\": 2.459644       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.72768375743327,         \"RMSE\": 7.245199860946492,         \"R2\": 0.3206991207112422,         \"Memory in Mb\": 0.5465364456176758,         \"Time in s\": 2.897301       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.104360720447454,         \"RMSE\": 7.731417459148682,         \"R2\": 0.3781793296599212,         \"Memory in Mb\": 0.5543031692504883,         \"Time in s\": 3.478625       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.614563299993537,         \"RMSE\": 8.384781892618234,         \"R2\": 0.4103032354466553,         \"Memory in Mb\": 0.577855110168457,         \"Time in s\": 4.083596       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.030281219875818,         \"RMSE\": 8.796345271037008,         \"R2\": 0.4686144271207236,         \"Memory in Mb\": 0.5973634719848633,         \"Time in s\": 4.712472       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.128233569544692,         \"RMSE\": 8.84845009665535,         \"R2\": 0.572286448100142,         \"Memory in Mb\": 0.6156282424926758,         \"Time in s\": 5.362321       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.65587905711115,         \"RMSE\": 9.6527323574251,         \"R2\": 0.5803946009681837,         \"Memory in Mb\": 0.637272834777832,         \"Time in s\": 6.068711       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.106977341119842,         \"RMSE\": 10.677274056234571,         \"R2\": 0.550512947323095,         \"Memory in Mb\": 0.6516351699829102,         \"Time in s\": 6.792503       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.51605472684967,         \"RMSE\": 11.121858780588036,         \"R2\": 0.582840670024279,         \"Memory in Mb\": 0.6455926895141602,         \"Time in s\": 7.540065       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.8763674823035235,         \"RMSE\": 11.54794620868086,         \"R2\": 0.6381207504866175,         \"Memory in Mb\": 0.654301643371582,         \"Time in s\": 8.314509000000001       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.048654689630025,         \"RMSE\": 11.785882981718466,         \"R2\": 0.6726179175042853,         \"Memory in Mb\": 0.6542215347290039,         \"Time in s\": 9.228486       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.558470564817128,         \"RMSE\": 12.694815113306078,         \"R2\": 0.6529678969258632,         \"Memory in Mb\": 0.7006998062133789,         \"Time in s\": 10.165772       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.011287699636805,         \"RMSE\": 13.865710758190522,         \"R2\": 0.6357023625954032,         \"Memory in Mb\": 0.7181978225708008,         \"Time in s\": 11.12726       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.454493871269731,         \"RMSE\": 14.39909947248495,         \"R2\": 0.6597325750664246,         \"Memory in Mb\": 0.7397470474243164,         \"Time in s\": 12.112744       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.455634964453314,         \"RMSE\": 14.370566123736594,         \"R2\": 0.7060577585099084,         \"Memory in Mb\": 0.6961946487426758,         \"Time in s\": 13.208544       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.98259297559382,         \"RMSE\": 15.278989711680778,         \"R2\": 0.7040656028742478,         \"Memory in Mb\": 0.7122316360473633,         \"Time in s\": 14.327932       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.896304985778038,         \"RMSE\": 17.680267148091307,         \"R2\": 0.6404050215106214,         \"Memory in Mb\": 0.7230386734008789,         \"Time in s\": 15.472301       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.34830207391465,         \"RMSE\": 18.238325787402868,         \"R2\": 0.6725323523293205,         \"Memory in Mb\": 0.7481813430786133,         \"Time in s\": 16.699404       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.700671911575691,         \"RMSE\": 18.698639858183288,         \"R2\": 0.6917798823884449,         \"Memory in Mb\": 0.750828742980957,         \"Time in s\": 17.953966       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.012928806619971,         \"RMSE\": 19.028065448277463,         \"R2\": 0.7098550919958697,         \"Memory in Mb\": 0.779423713684082,         \"Time in s\": 19.24314       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.590729727774807,         \"RMSE\": 20.061815233276363,         \"R2\": 0.6868102538385266,         \"Memory in Mb\": 0.8219194412231445,         \"Time in s\": 20.584825       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.29572445199132,         \"RMSE\": 21.688967498502105,         \"R2\": 0.6634948622954009,         \"Memory in Mb\": 0.8368387222290039,         \"Time in s\": 21.949532       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.850252347511734,         \"RMSE\": 22.377982941031117,         \"R2\": 0.6830616430184708,         \"Memory in Mb\": 0.8398981094360352,         \"Time in s\": 23.337733       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.995508749414425,         \"RMSE\": 22.434927630401365,         \"R2\": 0.7029833246789492,         \"Memory in Mb\": 0.8451242446899414,         \"Time in s\": 24.808931       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.855647843034443,         \"RMSE\": 23.972462409994428,         \"R2\": 0.6847772413527866,         \"Memory in Mb\": 0.8440675735473633,         \"Time in s\": 26.305221       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.648428200057216,         \"RMSE\": 25.832735423225586,         \"R2\": 0.6563908585574095,         \"Memory in Mb\": 0.8621377944946289,         \"Time in s\": 27.821712       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.477960681723363,         \"RMSE\": 27.01651731063008,         \"R2\": 0.6660339910533338,         \"Memory in Mb\": 0.8826723098754883,         \"Time in s\": 29.421277       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.794784005292485,         \"RMSE\": 27.277386650758192,         \"R2\": 0.6836500576952018,         \"Memory in Mb\": 0.8854074478149414,         \"Time in s\": 31.044846       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.2443539228967,         \"RMSE\": 27.815314781786785,         \"R2\": 0.6850336806962379,         \"Memory in Mb\": 0.915654182434082,         \"Time in s\": 32.692345       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.21783864235053,         \"RMSE\": 29.965283642676138,         \"R2\": 0.6566601868655235,         \"Memory in Mb\": 0.947678565979004,         \"Time in s\": 34.453267999999994       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.154558799374207,         \"RMSE\": 31.27949805899601,         \"R2\": 0.6696542166515442,         \"Memory in Mb\": 0.9631280899047852,         \"Time in s\": 36.23863899999999       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.65302219917293,         \"RMSE\": 31.71092492917292,         \"R2\": 0.6790841892096586,         \"Memory in Mb\": 0.979741096496582,         \"Time in s\": 38.04845199999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.17748759588543,         \"RMSE\": 32.35841629000369,         \"R2\": 0.6856630158751376,         \"Memory in Mb\": 1.0035409927368164,         \"Time in s\": 39.89288999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.994447812000203,         \"RMSE\": 33.88452895368057,         \"R2\": 0.6653322556738073,         \"Memory in Mb\": 1.0402307510375977,         \"Time in s\": 41.77183299999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.74940325928189,         \"RMSE\": 34.92971521251369,         \"R2\": 0.6643962418834424,         \"Memory in Mb\": 1.0591440200805664,         \"Time in s\": 43.68222899999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.71806819464153,         \"RMSE\": 36.27208023143736,         \"R2\": 0.6746268651566016,         \"Memory in Mb\": 1.0802621841430664,         \"Time in s\": 45.622796999999984       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.976084812890598,         \"RMSE\": 36.32299861842887,         \"R2\": 0.6871862958215178,         \"Memory in Mb\": 1.1105661392211914,         \"Time in s\": 47.62064799999998       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.812560792713985,         \"RMSE\": 37.68037385984369,         \"R2\": 0.6737446986071818,         \"Memory in Mb\": 1.1564149856567385,         \"Time in s\": 49.63871399999998       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.744158926088524,         \"RMSE\": 38.95638961509032,         \"R2\": 0.6665241448790927,         \"Memory in Mb\": 1.171940803527832,         \"Time in s\": 51.68634399999998       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.965548256363952,         \"RMSE\": 40.779089345824126,         \"R2\": 0.6619939776632806,         \"Memory in Mb\": 1.1861085891723633,         \"Time in s\": 53.83172099999997       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.10164191353107,         \"RMSE\": 40.80941552099692,         \"R2\": 0.669724624616493,         \"Memory in Mb\": 1.1904268264770508,         \"Time in s\": 56.00600499999997       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.656196028844478,         \"RMSE\": 13.301506400077992,         \"R2\": -414.0076115498352,         \"Memory in Mb\": 0.2015810012817382,         \"Time in s\": 0.057323       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.307191630717303,         \"RMSE\": 9.5148436405931,         \"R2\": -35.39725790498291,         \"Memory in Mb\": 0.2895097732543945,         \"Time in s\": 0.159522       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3916587233350866,         \"RMSE\": 7.783560456255013,         \"R2\": -31.83725667748105,         \"Memory in Mb\": 0.3228082656860351,         \"Time in s\": 0.43784       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0172424359013847,         \"RMSE\": 6.770328731809264,         \"R2\": -23.92145608895444,         \"Memory in Mb\": 0.3692712783813476,         \"Time in s\": 0.749169       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.069330341220504,         \"RMSE\": 6.141775226189047,         \"R2\": -11.868015650386663,         \"Memory in Mb\": 0.4076700210571289,         \"Time in s\": 1.082433       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.013474643057227,         \"RMSE\": 5.653544639730099,         \"R2\": -8.249866206703038,         \"Memory in Mb\": 0.4241609573364258,         \"Time in s\": 1.485703       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.894365920134237,         \"RMSE\": 5.255534318342925,         \"R2\": -7.260127227254786,         \"Memory in Mb\": 0.4439592361450195,         \"Time in s\": 2.052392       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.942363436061872,         \"RMSE\": 4.987168106592344,         \"R2\": -5.559462264629689,         \"Memory in Mb\": 0.4557695388793945,         \"Time in s\": 2.6505280000000004       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9639788846395132,         \"RMSE\": 4.758402061618727,         \"R2\": -4.244508869717663,         \"Memory in Mb\": 0.4725847244262695,         \"Time in s\": 3.3297560000000006       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9045329443413328,         \"RMSE\": 4.539431452034987,         \"R2\": -3.787127034775958,         \"Memory in Mb\": 0.5018167495727539,         \"Time in s\": 4.037089000000001       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7801675790175082,         \"RMSE\": 4.332908187325825,         \"R2\": -3.704734847036908,         \"Memory in Mb\": 0.539036750793457,         \"Time in s\": 4.871381000000001       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7262455165213564,         \"RMSE\": 4.162317120423255,         \"R2\": -3.374233717467068,         \"Memory in Mb\": 0.5583086013793945,         \"Time in s\": 5.726343000000002       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6726006855046047,         \"RMSE\": 4.010034080286883,         \"R2\": -3.11472540009772,         \"Memory in Mb\": 0.586766242980957,         \"Time in s\": 6.608691000000002       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6001254820213158,         \"RMSE\": 3.86938412403892,         \"R2\": -3.01116046378164,         \"Memory in Mb\": 0.6034517288208008,         \"Time in s\": 7.537396000000002       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5903246290151525,         \"RMSE\": 3.758572865099384,         \"R2\": -2.72204992570884,         \"Memory in Mb\": 0.6344270706176758,         \"Time in s\": 8.563678000000001       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5306703522535514,         \"RMSE\": 3.644833568467773,         \"R2\": -2.673500446315358,         \"Memory in Mb\": 0.6524057388305664,         \"Time in s\": 9.653495       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.462120415173825,         \"RMSE\": 3.538151879462345,         \"R2\": -2.658070572544154,         \"Memory in Mb\": 0.6771516799926758,         \"Time in s\": 10.778313       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4104873891633294,         \"RMSE\": 3.442715407420023,         \"R2\": -2.491830651593505,         \"Memory in Mb\": 0.712040901184082,         \"Time in s\": 12.006044       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3577274631021343,         \"RMSE\": 3.353553439657788,         \"R2\": -2.42792224294701,         \"Memory in Mb\": 0.7612085342407227,         \"Time in s\": 13.264975000000002       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.328889471148693,         \"RMSE\": 3.2750276755937477,         \"R2\": -2.361664675892684,         \"Memory in Mb\": 0.7830896377563477,         \"Time in s\": 14.608014       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2856838141339133,         \"RMSE\": 3.198005596242657,         \"R2\": -2.3114858734875385,         \"Memory in Mb\": 0.8153314590454102,         \"Time in s\": 15.987435       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2502461578606217,         \"RMSE\": 3.1277634460074983,         \"R2\": -2.1102975482726696,         \"Memory in Mb\": 0.8549776077270508,         \"Time in s\": 17.476653000000002       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2118787702501406,         \"RMSE\": 3.0607885313580625,         \"R2\": -1.824527619127544,         \"Memory in Mb\": 0.8641138076782227,         \"Time in s\": 19.005275       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1755519926992437,         \"RMSE\": 2.997482691409013,         \"R2\": -1.6465763687209671,         \"Memory in Mb\": 0.904881477355957,         \"Time in s\": 20.582478       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1542746800420942,         \"RMSE\": 2.9412002898427465,         \"R2\": -1.494663742459657,         \"Memory in Mb\": 0.9429025650024414,         \"Time in s\": 22.213321       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1232655769227813,         \"RMSE\": 2.885625631130148,         \"R2\": -1.4054721071787497,         \"Memory in Mb\": 0.9187402725219728,         \"Time in s\": 23.93785       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0927628011224122,         \"RMSE\": 2.8324064719208977,         \"R2\": -1.3090698562992058,         \"Memory in Mb\": 0.9784936904907228,         \"Time in s\": 25.72016       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0798076211233285,         \"RMSE\": 2.7860066009246958,         \"R2\": -1.2872677886963872,         \"Memory in Mb\": 0.8415918350219727,         \"Time in s\": 27.559893       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0533259806656756,         \"RMSE\": 2.7386650773118006,         \"R2\": -1.2648586320750757,         \"Memory in Mb\": 0.926945686340332,         \"Time in s\": 29.430927       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0370277841126194,         \"RMSE\": 2.695306817676886,         \"R2\": -1.1694452334238137,         \"Memory in Mb\": 1.0152063369750977,         \"Time in s\": 31.394822       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0220360797787769,         \"RMSE\": 2.6548714349996483,         \"R2\": -1.0727572654712625,         \"Memory in Mb\": 0.9687509536743164,         \"Time in s\": 33.420245       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.006223169156282,         \"RMSE\": 2.615089153799328,         \"R2\": -0.9735122270872276,         \"Memory in Mb\": 0.8030519485473633,         \"Time in s\": 35.538976       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9862189251721106,         \"RMSE\": 2.576116595691222,         \"R2\": -0.901357605879683,         \"Memory in Mb\": 0.7759256362915039,         \"Time in s\": 37.763356       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9658028732124052,         \"RMSE\": 2.5385905860617046,         \"R2\": -0.8755448750460146,         \"Memory in Mb\": 0.8428354263305664,         \"Time in s\": 40.028227       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.958070286753166,         \"RMSE\": 2.506070409170758,         \"R2\": -0.8758066430098239,         \"Memory in Mb\": 0.9465646743774414,         \"Time in s\": 42.353807       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9436099236768006,         \"RMSE\": 2.472715624364642,         \"R2\": -0.8663281360281503,         \"Memory in Mb\": 1.0379304885864258,         \"Time in s\": 44.723793       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9279645732871132,         \"RMSE\": 2.440285299254925,         \"R2\": -0.8166009677654511,         \"Memory in Mb\": 1.1119890213012695,         \"Time in s\": 47.149898       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.91590994704171,         \"RMSE\": 2.410261116608071,         \"R2\": -0.7913739428902633,         \"Memory in Mb\": 1.1737489700317385,         \"Time in s\": 49.62861       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8968362370347621,         \"RMSE\": 2.379411736746614,         \"R2\": -0.7536320120768303,         \"Memory in Mb\": 1.261582374572754,         \"Time in s\": 52.179919       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8878342141912964,         \"RMSE\": 2.351392140243472,         \"R2\": -0.7280625702741705,         \"Memory in Mb\": 1.3552255630493164,         \"Time in s\": 54.784302       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8775558321142263,         \"RMSE\": 2.3237456817971016,         \"R2\": -0.7062000372474271,         \"Memory in Mb\": 1.4321069717407229,         \"Time in s\": 57.452517       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8672496542857573,         \"RMSE\": 2.297532908897418,         \"R2\": -0.6834092983276716,         \"Memory in Mb\": 1.4874773025512695,         \"Time in s\": 60.173386       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8593706057522699,         \"RMSE\": 2.272389423762812,         \"R2\": -0.6439286158863597,         \"Memory in Mb\": 1.5595178604125977,         \"Time in s\": 62.976048       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8551106332542915,         \"RMSE\": 2.2487703297155224,         \"R2\": -0.6019328469057044,         \"Memory in Mb\": 1.619084358215332,         \"Time in s\": 65.856537       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8437512715146732,         \"RMSE\": 2.224375873084905,         \"R2\": -0.5739713521606553,         \"Memory in Mb\": 1.1261072158813477,         \"Time in s\": 68.798174       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8344220404851989,         \"RMSE\": 2.2010168562801016,         \"R2\": -0.5664083310843888,         \"Memory in Mb\": 1.167832374572754,         \"Time in s\": 71.779904       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.825939320609599,         \"RMSE\": 2.179009982884269,         \"R2\": -0.5482654902802699,         \"Memory in Mb\": 1.1199464797973633,         \"Time in s\": 74.829165       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8156984309758435,         \"RMSE\": 2.1571048007400404,         \"R2\": -0.5331573747015668,         \"Memory in Mb\": 1.1733713150024414,         \"Time in s\": 77.929016       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.806477335746804,         \"RMSE\": 2.1360065895495888,         \"R2\": -0.5325097322303367,         \"Memory in Mb\": 1.2283296585083008,         \"Time in s\": 81.05698100000001       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8008625237630099,         \"RMSE\": 2.1159877488140326,         \"R2\": -0.5292346593649373,         \"Memory in Mb\": 1.2836008071899414,         \"Time in s\": 84.241983       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Adaptive Random Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.800378499538596,         \"RMSE\": 2.1149541843634605,         \"R2\": -0.5287610996295022,         \"Memory in Mb\": 1.2846193313598633,         \"Time in s\": 87.445729       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.0878895070954884,         \"RMSE\": 1.3778002085324723,         \"R2\": -1.2599207317049026,         \"Memory in Mb\": 0.1791715621948242,         \"Time in s\": 0.003809       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.15171477394762,         \"RMSE\": 1.5218208011368886,         \"R2\": -1.3974856828423898,         \"Memory in Mb\": 0.3313665390014648,         \"Time in s\": 0.017797       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.2596040860169628,         \"RMSE\": 1.630698561429495,         \"R2\": -0.8214033882315572,         \"Memory in Mb\": 0.4835615158081054,         \"Time in s\": 0.056943       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.147002532502157,         \"RMSE\": 1.5136945038262,         \"R2\": -0.7860708992998826,         \"Memory in Mb\": 0.6357030868530273,         \"Time in s\": 0.120895       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.7448650745312246,         \"RMSE\": 2.8901942810902064,         \"R2\": -0.6023944619968462,         \"Memory in Mb\": 0.795161247253418,         \"Time in s\": 0.341288       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.974173643458203,         \"RMSE\": 3.1122799656868354,         \"R2\": 0.1967701507194095,         \"Memory in Mb\": 0.949946403503418,         \"Time in s\": 0.602886       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3465039451978784,         \"RMSE\": 3.868783481489585,         \"R2\": 0.1653904369447871,         \"Memory in Mb\": 1.1044378280639648,         \"Time in s\": 0.885897       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3152944739841907,         \"RMSE\": 3.751470845606434,         \"R2\": 0.286453015670338,         \"Memory in Mb\": 1.2595434188842771,         \"Time in s\": 1.213122       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.485126688481329,         \"RMSE\": 3.8753788781661274,         \"R2\": 0.3615628965518305,         \"Memory in Mb\": 1.4176397323608398,         \"Time in s\": 1.708637       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.679180085056696,         \"RMSE\": 4.098463178184459,         \"R2\": 0.5005082908479199,         \"Memory in Mb\": 1.580409049987793,         \"Time in s\": 2.233256       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.993112128155013,         \"RMSE\": 4.501608187312601,         \"R2\": 0.5353065115430311,         \"Memory in Mb\": 1.7385053634643557,         \"Time in s\": 2.789253       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.049130101089184,         \"RMSE\": 4.474860576824222,         \"R2\": 0.624267329970883,         \"Memory in Mb\": 1.8972959518432615,         \"Time in s\": 3.530617       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.129389359320645,         \"RMSE\": 4.535626207267123,         \"R2\": 0.6870855629914132,         \"Memory in Mb\": 2.0540571212768555,         \"Time in s\": 4.307795       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.2350921629171503,         \"RMSE\": 4.614317779917637,         \"R2\": 0.7245583098520811,         \"Memory in Mb\": 2.21335506439209,         \"Time in s\": 5.238077       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.615407192454655,         \"RMSE\": 5.434402308521257,         \"R2\": 0.6928112980835472,         \"Memory in Mb\": 2.370730400085449,         \"Time in s\": 6.215347       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.842899644735678,         \"RMSE\": 5.8926781106586255,         \"R2\": 0.7087106044563829,         \"Memory in Mb\": 2.5251951217651367,         \"Time in s\": 7.308114       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.939333513046091,         \"RMSE\": 5.936527515565436,         \"R2\": 0.7578865873655871,         \"Memory in Mb\": 2.680434226989746,         \"Time in s\": 8.444739       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.1526220464224926,         \"RMSE\": 6.160116941975886,         \"R2\": 0.7926170753106898,         \"Memory in Mb\": 2.8339643478393555,         \"Time in s\": 9.747256       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.486090256229248,         \"RMSE\": 6.857164593682279,         \"R2\": 0.7881489392686998,         \"Memory in Mb\": 2.990111351013184,         \"Time in s\": 11.107311       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.095083445923365,         \"RMSE\": 8.268326900050806,         \"R2\": 0.7303274183124314,         \"Memory in Mb\": 3.147219657897949,         \"Time in s\": 12.601651       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.345901760482457,         \"RMSE\": 8.651953805757511,         \"R2\": 0.7474084291359289,         \"Memory in Mb\": 3.301150321960449,         \"Time in s\": 14.172197999999998       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.775936882313693,         \"RMSE\": 9.234098241635358,         \"R2\": 0.7685060952534608,         \"Memory in Mb\": 3.4575910568237305,         \"Time in s\": 15.876551999999998       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.050411841877211,         \"RMSE\": 9.480574702158652,         \"R2\": 0.7880472652798773,         \"Memory in Mb\": 3.6158742904663086,         \"Time in s\": 17.675130999999997       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.7396819662512994,         \"RMSE\": 10.861908099063555,         \"R2\": 0.7457953067175733,         \"Memory in Mb\": 3.77274227142334,         \"Time in s\": 19.599013       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.418933110619537,         \"RMSE\": 12.596893007879746,         \"R2\": 0.699156722346497,         \"Memory in Mb\": 3.926619529724121,         \"Time in s\": 21.625011       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.830180870941,         \"RMSE\": 13.02165358749325,         \"R2\": 0.7215679622698357,         \"Memory in Mb\": 4.082179069519043,         \"Time in s\": 23.771258       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.059624975297776,         \"RMSE\": 13.201631143135527,         \"R2\": 0.7517949935656911,         \"Memory in Mb\": 4.237311363220215,         \"Time in s\": 26.095147       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.517266870602596,         \"RMSE\": 14.029786197157003,         \"R2\": 0.750336177123377,         \"Memory in Mb\": 4.390841484069824,         \"Time in s\": 28.501657       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.872910663629112,         \"RMSE\": 17.67011178426297,         \"R2\": 0.6406578335650022,         \"Memory in Mb\": 4.544772148132324,         \"Time in s\": 31.026265       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.355957081475973,         \"RMSE\": 18.251720539867826,         \"R2\": 0.671924837978655,         \"Memory in Mb\": 4.698409080505371,         \"Time in s\": 33.677708       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.779061369126929,         \"RMSE\": 18.644503325392748,         \"R2\": 0.6934349036095702,         \"Memory in Mb\": 4.852313041687012,         \"Time in s\": 36.483969       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.97013178962945,         \"RMSE\": 18.69029492717773,         \"R2\": 0.7199342471488321,         \"Memory in Mb\": 5.009421348571777,         \"Time in s\": 39.412921       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.836385670325258,         \"RMSE\": 20.411474322578705,         \"R2\": 0.6756306209292051,         \"Memory in Mb\": 5.165541648864746,         \"Time in s\": 42.477176       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.650208752532226,         \"RMSE\": 22.152599191433616,         \"R2\": 0.6487731216482631,         \"Memory in Mb\": 5.323611259460449,         \"Time in s\": 45.685202       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.26433375884341,         \"RMSE\": 22.74111870549559,         \"R2\": 0.672536034672935,         \"Memory in Mb\": 5.478930473327637,         \"Time in s\": 49.011467       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.285084454056172,         \"RMSE\": 22.62858691877232,         \"R2\": 0.6976709876564118,         \"Memory in Mb\": 5.63400936126709,         \"Time in s\": 52.447627       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.297859574888522,         \"RMSE\": 24.603705237609702,         \"R2\": 0.6677818184200794,         \"Memory in Mb\": 5.789168357849121,         \"Time in s\": 55.986709       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.277775247208368,         \"RMSE\": 26.91758918665374,         \"R2\": 0.6267299649165277,         \"Memory in Mb\": 5.945448875427246,         \"Time in s\": 59.629264       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.148002577856595,         \"RMSE\": 27.91235298687263,         \"R2\": 0.643353503146777,         \"Memory in Mb\": 6.099112510681152,         \"Time in s\": 63.379206       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.450833155107055,         \"RMSE\": 28.053185003016477,         \"R2\": 0.6652347923635655,         \"Memory in Mb\": 6.252856254577637,         \"Time in s\": 67.23493599999999       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.938736394119786,         \"RMSE\": 28.680885446607185,         \"R2\": 0.6649461832952053,         \"Memory in Mb\": 6.407908439636231,         \"Time in s\": 71.205463       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.465286457846624,         \"RMSE\": 32.222162406640614,         \"R2\": 0.6027938253530374,         \"Memory in Mb\": 6.562827110290527,         \"Time in s\": 75.286683       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.36878629272608,         \"RMSE\": 33.403615991184594,         \"R2\": 0.6231055060350865,         \"Memory in Mb\": 6.717398643493652,         \"Time in s\": 79.474485       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.88015130963188,         \"RMSE\": 33.764210229402664,         \"R2\": 0.6360141173956066,         \"Memory in Mb\": 6.872824668884277,         \"Time in s\": 83.769797       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.57744796303998,         \"RMSE\": 34.830627929035586,         \"R2\": 0.6356250399764956,         \"Memory in Mb\": 7.029131889343262,         \"Time in s\": 88.176271       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.43571571603741,         \"RMSE\": 36.40788480688662,         \"R2\": 0.6134430891768862,         \"Memory in Mb\": 7.184878349304199,         \"Time in s\": 92.694929       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.34914238062968,         \"RMSE\": 37.807266067412606,         \"R2\": 0.6066317565796657,         \"Memory in Mb\": 7.340197563171387,         \"Time in s\": 97.332285       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.191315994328228,         \"RMSE\": 38.81894260965106,         \"R2\": 0.6271697641288401,         \"Memory in Mb\": 7.495863914489746,         \"Time in s\": 102.073572       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.34075784343543,         \"RMSE\": 38.827434948624926,         \"R2\": 0.6423969913213963,         \"Memory in Mb\": 7.652411460876465,         \"Time in s\": 106.920364       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.12545732554984,         \"RMSE\": 39.99965605849559,         \"R2\": 0.6321733437483394,         \"Memory in Mb\": 7.811335563659668,         \"Time in s\": 111.876093       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.859407485948264,         \"RMSE\": 40.9180834433101,         \"R2\": 0.6319179521646502,         \"Memory in Mb\": 7.9698591232299805,         \"Time in s\": 116.942977       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.582967433016183,         \"RMSE\": 41.65667948828452,         \"R2\": 0.6471310448579161,         \"Memory in Mb\": 8.12806224822998,         \"Time in s\": 122.12293       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.674172622955844,         \"RMSE\": 41.71227980537356,         \"R2\": 0.65479005999511,         \"Memory in Mb\": 8.21412181854248,         \"Time in s\": 127.415096       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3812789990066343,         \"RMSE\": 0.4856864156914124,         \"R2\": 0.4734504440676397,         \"Memory in Mb\": 0.3661947250366211,         \"Time in s\": 0.012803       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3903932807396207,         \"RMSE\": 0.4802129236445582,         \"R2\": 0.908098150441852,         \"Memory in Mb\": 0.7077703475952148,         \"Time in s\": 0.066318       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3553562094560649,         \"RMSE\": 0.4475448539758346,         \"R2\": 0.8908737885344612,         \"Memory in Mb\": 1.0465993881225586,         \"Time in s\": 0.161186       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3785078228066897,         \"RMSE\": 0.4818104291982039,         \"R2\": 0.8725877843301283,         \"Memory in Mb\": 1.386988639831543,         \"Time in s\": 0.310355       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3456451525369771,         \"RMSE\": 0.450476311872574,         \"R2\": 0.9301027529077078,         \"Memory in Mb\": 1.7262754440307615,         \"Time in s\": 0.520575       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3369671927041528,         \"RMSE\": 0.4421642502671299,         \"R2\": 0.9427860880043346,         \"Memory in Mb\": 2.0684385299682617,         \"Time in s\": 0.828496       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3170957614007029,         \"RMSE\": 0.4217273000916425,         \"R2\": 0.9461011323760548,         \"Memory in Mb\": 2.404311180114746,         \"Time in s\": 1.349971       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3307037984070857,         \"RMSE\": 0.4315519243898653,         \"R2\": 0.9502341257934384,         \"Memory in Mb\": 2.7412595748901367,         \"Time in s\": 1.938101       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3239117556806251,         \"RMSE\": 0.4198186275021798,         \"R2\": 0.9586532343987924,         \"Memory in Mb\": 3.0777502059936523,         \"Time in s\": 2.6707590000000003       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3216990324082377,         \"RMSE\": 0.4152317221827294,         \"R2\": 0.959406710806771,         \"Memory in Mb\": 3.414671897888184,         \"Time in s\": 3.525266       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3195426619101423,         \"RMSE\": 0.4098153846782661,         \"R2\": 0.95731130106893,         \"Memory in Mb\": 3.7566747665405273,         \"Time in s\": 4.592894       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3205833893003068,         \"RMSE\": 0.409734240531711,         \"R2\": 0.9569909127297423,         \"Memory in Mb\": 4.096526145935059,         \"Time in s\": 5.7511       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3096350275266986,         \"RMSE\": 0.3977121378847216,         \"R2\": 0.958918388812615,         \"Memory in Mb\": 4.436213493347168,         \"Time in s\": 7.098098       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3021514917324106,         \"RMSE\": 0.3881456980787764,         \"R2\": 0.9590118642619369,         \"Memory in Mb\": 4.772730827331543,         \"Time in s\": 8.655558000000001       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.302378432925199,         \"RMSE\": 0.3879302261175293,         \"R2\": 0.9597410141114792,         \"Memory in Mb\": 5.1073408126831055,         \"Time in s\": 10.324296       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3042635511277332,         \"RMSE\": 0.3880820602227424,         \"R2\": 0.9577019017103428,         \"Memory in Mb\": 5.440821647644043,         \"Time in s\": 12.208908       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3061116805473403,         \"RMSE\": 0.391104192749731,         \"R2\": 0.9546026616524738,         \"Memory in Mb\": 5.773791313171387,         \"Time in s\": 14.314788       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3092553456162419,         \"RMSE\": 0.3953517094261167,         \"R2\": 0.9532980843409116,         \"Memory in Mb\": 6.1096906661987305,         \"Time in s\": 16.588556       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3076082401740039,         \"RMSE\": 0.3960205626085123,         \"R2\": 0.9515479192781826,         \"Memory in Mb\": 6.445483207702637,         \"Time in s\": 19.05639       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.3014609513967445,         \"RMSE\": 0.3890658925747077,         \"R2\": 0.951945723428325,         \"Memory in Mb\": 6.780200004577637,         \"Time in s\": 21.697867       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2971138293692631,         \"RMSE\": 0.3834166944817816,         \"R2\": 0.9518088379060108,         \"Memory in Mb\": 7.116557121276856,         \"Time in s\": 24.511965000000004       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2982326339454942,         \"RMSE\": 0.3845174635941775,         \"R2\": 0.952475485177767,         \"Memory in Mb\": 7.451247215270996,         \"Time in s\": 27.506667000000004       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2946274143925286,         \"RMSE\": 0.3801223802157071,         \"R2\": 0.9560358463571956,         \"Memory in Mb\": 7.78644847869873,         \"Time in s\": 30.697063000000004       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2933968019133882,         \"RMSE\": 0.3767220961039539,         \"R2\": 0.9578595845961764,         \"Memory in Mb\": 8.120524406433105,         \"Time in s\": 34.045785       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2879691623817681,         \"RMSE\": 0.3709865810494578,         \"R2\": 0.9600287871527096,         \"Memory in Mb\": 8.45413875579834,         \"Time in s\": 37.543006000000005       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2858512762877924,         \"RMSE\": 0.3680336140311482,         \"R2\": 0.9606162857565064,         \"Memory in Mb\": 8.791060447692871,         \"Time in s\": 41.194255000000005       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.28337361966731,         \"RMSE\": 0.3643610952909248,         \"R2\": 0.9615619008486128,         \"Memory in Mb\": 9.128493309020996,         \"Time in s\": 45.008334000000005       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2882996011532734,         \"RMSE\": 0.3724151060029248,         \"R2\": 0.9588932716362208,         \"Memory in Mb\": 9.463343620300291,         \"Time in s\": 48.98956       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.289315517198266,         \"RMSE\": 0.3729583387798602,         \"R2\": 0.957758997678865,         \"Memory in Mb\": 9.801314353942873,         \"Time in s\": 53.13082800000001       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2880445309367084,         \"RMSE\": 0.3714720231862385,         \"R2\": 0.9585805866298192,         \"Memory in Mb\": 10.13992977142334,         \"Time in s\": 57.44083900000001       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.285913070387579,         \"RMSE\": 0.3694806072400069,         \"R2\": 0.95966894305211,         \"Memory in Mb\": 10.477011680603027,         \"Time in s\": 61.917696000000014       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2840899599351511,         \"RMSE\": 0.3669642572161526,         \"R2\": 0.9609793991220156,         \"Memory in Mb\": 10.80936336517334,         \"Time in s\": 66.56801000000002       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2804371592513609,         \"RMSE\": 0.3629082548929102,         \"R2\": 0.9621252180674722,         \"Memory in Mb\": 11.1486234664917,         \"Time in s\": 71.37699000000002       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2832721366095781,         \"RMSE\": 0.3646482252308528,         \"R2\": 0.9611623224331388,         \"Memory in Mb\": 11.485033988952637,         \"Time in s\": 76.34970200000002       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2853757262809053,         \"RMSE\": 0.3664704272728199,         \"R2\": 0.959741096022156,         \"Memory in Mb\": 11.82703685760498,         \"Time in s\": 81.48889300000002       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2846995678315971,         \"RMSE\": 0.3663721647933076,         \"R2\": 0.9588788359612516,         \"Memory in Mb\": 12.1649808883667,         \"Time in s\": 86.79821800000002       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.284389692089141,         \"RMSE\": 0.365623460665911,         \"R2\": 0.9590812376103318,         \"Memory in Mb\": 12.5026273727417,         \"Time in s\": 92.27027700000002       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2812524955317089,         \"RMSE\": 0.3622785856535135,         \"R2\": 0.9593969665626948,         \"Memory in Mb\": 12.84118938446045,         \"Time in s\": 97.89633500000002       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2784736419799919,         \"RMSE\": 0.3590495394564995,         \"R2\": 0.9599459478528628,         \"Memory in Mb\": 13.18101406097412,         \"Time in s\": 103.67983800000002       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.28122680710979,         \"RMSE\": 0.3614117991183927,         \"R2\": 0.9590552347518728,         \"Memory in Mb\": 13.522452354431152,         \"Time in s\": 109.62063100000002       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2798414038154103,         \"RMSE\": 0.3599105705870861,         \"R2\": 0.958953025104572,         \"Memory in Mb\": 13.858756065368652,         \"Time in s\": 115.71824100000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2792299366421054,         \"RMSE\": 0.358810295818463,         \"R2\": 0.9588293518961978,         \"Memory in Mb\": 14.19906520843506,         \"Time in s\": 121.97670500000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2757234931419036,         \"RMSE\": 0.3557294429539717,         \"R2\": 0.9596100235239656,         \"Memory in Mb\": 14.53821849822998,         \"Time in s\": 128.39866500000002       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2725087918367814,         \"RMSE\": 0.3526976163964828,         \"R2\": 0.9604999212537026,         \"Memory in Mb\": 14.877989768981934,         \"Time in s\": 134.987331       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2707423985398595,         \"RMSE\": 0.3505322158445511,         \"R2\": 0.9608236269181288,         \"Memory in Mb\": 15.214400291442873,         \"Time in s\": 141.746125       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2714968277868111,         \"RMSE\": 0.3507195370917735,         \"R2\": 0.960138783514385,         \"Memory in Mb\": 15.551268577575684,         \"Time in s\": 148.677278       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2704179121014184,         \"RMSE\": 0.350599255928843,         \"R2\": 0.9598313134304426,         \"Memory in Mb\": 15.892088890075684,         \"Time in s\": 155.784091       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2707082259086565,         \"RMSE\": 0.3516525290937312,         \"R2\": 0.9591696518431828,         \"Memory in Mb\": 16.229090690612793,         \"Time in s\": 163.071856       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2709225398475326,         \"RMSE\": 0.3517696828828596,         \"R2\": 0.9583487676398438,         \"Memory in Mb\": 16.574454307556152,         \"Time in s\": 170.541967       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.2686954199893275,         \"RMSE\": 0.349579763566054,         \"R2\": 0.9581740736545136,         \"Memory in Mb\": 16.91306972503662,         \"Time in s\": 178.198313       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Aggregated Mondrian Forest\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.268533139095725,         \"RMSE\": 0.3494211343568523,         \"R2\": 0.9581842100200092,         \"Memory in Mb\": 16.932257652282715,         \"Time in s\": 186.03359       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.664574314574316,         \"RMSE\": 12.7079745317607,         \"R2\": -206.87879383707747,         \"Memory in Mb\": 0.0196142196655273,         \"Time in s\": 0.000715       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.767694704637076,         \"RMSE\": 9.018587183866767,         \"R2\": -85.14025986830408,         \"Memory in Mb\": 0.0211782455444335,         \"Time in s\": 0.002248       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3093367298127023,         \"RMSE\": 7.420500566500976,         \"R2\": -37.24267181629702,         \"Memory in Mb\": 0.0263471603393554,         \"Time in s\": 0.0045       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.892363968348808,         \"RMSE\": 6.441521936619904,         \"R2\": -31.668094594906044,         \"Memory in Mb\": 0.0274343490600585,         \"Time in s\": 0.007522       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.1129412159858934,         \"RMSE\": 6.114058653243701,         \"R2\": -6.297346571779499,         \"Memory in Mb\": 0.0340337753295898,         \"Time in s\": 0.011341       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.832849782567835,         \"RMSE\": 6.236602142425367,         \"R2\": -2.2730130120415795,         \"Memory in Mb\": 0.043257713317871,         \"Time in s\": 0.016081       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4069290990236856,         \"RMSE\": 6.402381882180361,         \"R2\": -1.3118663438824,         \"Memory in Mb\": 0.0494871139526367,         \"Time in s\": 0.021988       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.650377971160808,         \"RMSE\": 6.321189272940957,         \"R2\": -1.043267371916866,         \"Memory in Mb\": 0.0551328659057617,         \"Time in s\": 0.050945       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.035631404360372,         \"RMSE\": 6.4483291916176695,         \"R2\": -0.7783857772357967,         \"Memory in Mb\": 0.0562467575073242,         \"Time in s\": 0.083616       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.693189868957898,         \"RMSE\": 7.0697740144659305,         \"R2\": -0.4927792786841307,         \"Memory in Mb\": 0.0576238632202148,         \"Time in s\": 0.119642       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.274396860168236,         \"RMSE\": 7.6542276724395,         \"R2\": -0.3476225254437259,         \"Memory in Mb\": 0.0577573776245117,         \"Time in s\": 0.157993       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.216037157212015,         \"RMSE\": 7.551012267266295,         \"R2\": -0.0719037453282565,         \"Memory in Mb\": 0.0578107833862304,         \"Time in s\": 0.197604       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.030848211447775,         \"RMSE\": 7.321940337412501,         \"R2\": 0.1836709538125499,         \"Memory in Mb\": 0.058394432067871,         \"Time in s\": 0.238591       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.907406922429448,         \"RMSE\": 7.137924382310331,         \"R2\": 0.3406662269828342,         \"Memory in Mb\": 0.0584478378295898,         \"Time in s\": 0.28091       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.132506734403487,         \"RMSE\": 7.341156657504303,         \"R2\": 0.439370571581684,         \"Memory in Mb\": 0.0584478378295898,         \"Time in s\": 0.32452       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.292049153445915,         \"RMSE\": 7.468652514259996,         \"R2\": 0.5321251372519638,         \"Memory in Mb\": 0.0590581893920898,         \"Time in s\": 0.369448       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.31698748044205,         \"RMSE\": 7.461166418014795,         \"R2\": 0.6176873420824156,         \"Memory in Mb\": 0.0591115951538085,         \"Time in s\": 0.415771       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.300228902480157,         \"RMSE\": 7.425148329077998,         \"R2\": 0.6988181014109027,         \"Memory in Mb\": 0.0590581893920898,         \"Time in s\": 0.474994       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.830499581707958,         \"RMSE\": 9.648698249017793,         \"R2\": 0.5807452540036802,         \"Memory in Mb\": 0.0252714157104492,         \"Time in s\": 0.54427       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.400718854692065,         \"RMSE\": 10.45569246424029,         \"R2\": 0.5689754490886993,         \"Memory in Mb\": 0.0314207077026367,         \"Time in s\": 0.614353       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.611665150046439,         \"RMSE\": 10.61745698030736,         \"R2\": 0.6198209084753062,         \"Memory in Mb\": 0.0365362167358398,         \"Time in s\": 0.6853290000000001       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.029624246247838,         \"RMSE\": 11.197269958950692,         \"R2\": 0.6597654020329642,         \"Memory in Mb\": 0.0410680770874023,         \"Time in s\": 0.7572760000000001       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.254490759785878,         \"RMSE\": 11.350610231674398,         \"R2\": 0.6963529412438163,         \"Memory in Mb\": 0.0445928573608398,         \"Time in s\": 0.830219       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.784750145903498,         \"RMSE\": 12.258358647532567,         \"R2\": 0.6764200982742594,         \"Memory in Mb\": 0.0446996688842773,         \"Time in s\": 0.904176       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.342804112650073,         \"RMSE\": 13.247943494163705,         \"R2\": 0.6674407623884211,         \"Memory in Mb\": 0.0446996688842773,         \"Time in s\": 0.986871       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.88061114203256,         \"RMSE\": 14.075280539927816,         \"R2\": 0.6748649197186086,         \"Memory in Mb\": 0.0452032089233398,         \"Time in s\": 1.0705630000000002       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.50078499680378,         \"RMSE\": 14.855892526018591,         \"R2\": 0.6858683144490312,         \"Memory in Mb\": 0.0452299118041992,         \"Time in s\": 1.1552540000000002       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.07078824210446,         \"RMSE\": 15.77018489177999,         \"R2\": 0.6847321098344714,         \"Memory in Mb\": 0.0452566146850585,         \"Time in s\": 1.2409280000000005       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.988840488902907,         \"RMSE\": 17.80174938329892,         \"R2\": 0.6354464447499208,         \"Memory in Mb\": 0.0452566146850585,         \"Time in s\": 1.3276340000000002       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.635092222175304,         \"RMSE\": 18.61329763011445,         \"R2\": 0.6589287557789436,         \"Memory in Mb\": 0.0452833175659179,         \"Time in s\": 1.4153970000000002       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.7817306308102,         \"RMSE\": 18.65165772134248,         \"R2\": 0.6933268021215234,         \"Memory in Mb\": 0.0452833175659179,         \"Time in s\": 1.5042310000000003       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.878812775671824,         \"RMSE\": 18.699040402285984,         \"R2\": 0.7198024587207095,         \"Memory in Mb\": 0.0452833175659179,         \"Time in s\": 1.5941090000000002       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.712200605470676,         \"RMSE\": 19.934033697107445,         \"R2\": 0.690787203614232,         \"Memory in Mb\": 0.0453100204467773,         \"Time in s\": 1.685007       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.202927457133043,         \"RMSE\": 20.9603625224819,         \"R2\": 0.6857237785454591,         \"Memory in Mb\": 0.0453367233276367,         \"Time in s\": 1.776946       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.5542070698499,         \"RMSE\": 21.51079994203591,         \"R2\": 0.707149447507495,         \"Memory in Mb\": 0.0453367233276367,         \"Time in s\": 1.869942       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.642433072457155,         \"RMSE\": 21.454130101613703,         \"R2\": 0.7283852775805406,         \"Memory in Mb\": 0.0453367233276367,         \"Time in s\": 1.963987       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.50232093628697,         \"RMSE\": 22.86556238504221,         \"R2\": 0.7132152539462153,         \"Memory in Mb\": 0.0456762313842773,         \"Time in s\": 2.060696       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.245933470432924,         \"RMSE\": 24.220098655355127,         \"R2\": 0.6979521608965717,         \"Memory in Mb\": 0.045729637145996,         \"Time in s\": 2.158386       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.766409258920858,         \"RMSE\": 25.08619072251902,         \"R2\": 0.7120527209837302,         \"Memory in Mb\": 0.045729637145996,         \"Time in s\": 2.2570650000000003       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.931210335947624,         \"RMSE\": 25.166941851240068,         \"R2\": 0.7307081986676882,         \"Memory in Mb\": 0.0456495285034179,         \"Time in s\": 2.4439120000000005       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.418312975299003,         \"RMSE\": 25.73673973791796,         \"R2\": 0.7303482652633313,         \"Memory in Mb\": 0.0461797714233398,         \"Time in s\": 2.6336030000000004       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.4982370763817,         \"RMSE\": 27.78944281741256,         \"R2\": 0.7047111429028308,         \"Memory in Mb\": 0.0469236373901367,         \"Time in s\": 2.8262370000000003       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.254684132762545,         \"RMSE\": 29.056725346353637,         \"R2\": 0.7149358826261665,         \"Memory in Mb\": 0.0469770431518554,         \"Time in s\": 3.0200670000000005       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.58513038702809,         \"RMSE\": 29.35463525495672,         \"R2\": 0.7250038129485413,         \"Memory in Mb\": 0.046950340270996,         \"Time in s\": 3.2149970000000003       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.01404260598322,         \"RMSE\": 29.86038018890717,         \"R2\": 0.7323226450377984,         \"Memory in Mb\": 0.0468969345092773,         \"Time in s\": 3.430483       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.88342353555136,         \"RMSE\": 31.26600741511644,         \"R2\": 0.7150584356224581,         \"Memory in Mb\": 0.0469770431518554,         \"Time in s\": 3.648791       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.595063111922972,         \"RMSE\": 32.24616798680886,         \"R2\": 0.713982273554131,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 3.869932       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.38047446701005,         \"RMSE\": 33.43504054753495,         \"R2\": 0.7235347994633756,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 4.098743       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.53249764026729,         \"RMSE\": 33.42135235584957,         \"R2\": 0.735168024057878,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 4.328606       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.49918784329445,         \"RMSE\": 35.002118414433774,         \"R2\": 0.7184757368310433,         \"Memory in Mb\": 0.0470304489135742,         \"Time in s\": 4.559521999999999       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.19163412189557,         \"RMSE\": 35.912468657285935,         \"R2\": 0.7166015220750928,         \"Memory in Mb\": 0.0470037460327148,         \"Time in s\": 4.791517       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.04065682138389,         \"RMSE\": 37.100860859043735,         \"R2\": 0.7202195492645626,         \"Memory in Mb\": 0.046950340270996,         \"Time in s\": 5.02459       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.19431912937701,         \"RMSE\": 37.21658551958108,         \"R2\": 0.7253190778127725,         \"Memory in Mb\": 0.0469770431518554,         \"Time in s\": 5.258551       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.695184981652336,         \"RMSE\": 9.807184976514188,         \"R2\": -224.6021011118197,         \"Memory in Mb\": 0.0538091659545898,         \"Time in s\": 0.004347       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3994713447037435,         \"RMSE\": 7.102066178895935,         \"R2\": -19.27845129783118,         \"Memory in Mb\": 0.0761518478393554,         \"Time in s\": 0.011776       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8170744682035584,         \"RMSE\": 5.815253847056423,         \"R2\": -17.329373299766118,         \"Memory in Mb\": 0.0883970260620117,         \"Time in s\": 0.021496       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.604995404573344,         \"RMSE\": 5.081770494168446,         \"R2\": -13.040545957103586,         \"Memory in Mb\": 0.0980443954467773,         \"Time in s\": 0.033628       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.824259078948539,         \"RMSE\": 4.70488333223354,         \"R2\": -6.5512954222403845,         \"Memory in Mb\": 0.1071348190307617,         \"Time in s\": 0.048339       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.918744608116588,         \"RMSE\": 4.412336880489357,         \"R2\": -4.634185300646759,         \"Memory in Mb\": 0.1113233566284179,         \"Time in s\": 0.066047       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8761207739327503,         \"RMSE\": 4.13187920011476,         \"R2\": -4.105616799680584,         \"Memory in Mb\": 0.1133375167846679,         \"Time in s\": 0.086317       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.961232939518506,         \"RMSE\": 3.976173487274506,         \"R2\": -3.1695661963674864,         \"Memory in Mb\": 0.1174459457397461,         \"Time in s\": 0.109348       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.066134597500757,         \"RMSE\": 3.873731518767916,         \"R2\": -2.4756944369169624,         \"Memory in Mb\": 0.1194601058959961,         \"Time in s\": 0.13519       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.051125997923389,         \"RMSE\": 3.731810291394655,         \"R2\": -2.23527456693896,         \"Memory in Mb\": 0.017618179321289,         \"Time in s\": 0.169486       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.0738811328897206,         \"RMSE\": 4.417664564856108,         \"R2\": -3.890594467356201,         \"Memory in Mb\": 0.0357999801635742,         \"Time in s\": 0.205189       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9726100065438288,         \"RMSE\": 4.237524240975239,         \"R2\": -3.5337340888030546,         \"Memory in Mb\": 0.0414991378784179,         \"Time in s\": 0.242476       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8594315384151243,         \"RMSE\": 4.074751007989252,         \"R2\": -3.248610147038553,         \"Memory in Mb\": 0.048842430114746,         \"Time in s\": 0.281406       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7773205119132678,         \"RMSE\": 3.936654153117972,         \"R2\": -3.1518424972300867,         \"Memory in Mb\": 0.0637884140014648,         \"Time in s\": 0.322149       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8265705896173516,         \"RMSE\": 3.8591002097544127,         \"R2\": -2.923813511442849,         \"Memory in Mb\": 0.0734968185424804,         \"Time in s\": 0.364943       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7442837931334845,         \"RMSE\": 3.739506488697679,         \"R2\": -2.866813933026025,         \"Memory in Mb\": 0.0810766220092773,         \"Time in s\": 0.409782       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6994316865849048,         \"RMSE\": 3.638004990484729,         \"R2\": -2.8674589929341425,         \"Memory in Mb\": 0.0861921310424804,         \"Time in s\": 0.456846       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6868885299887,         \"RMSE\": 3.55458556923881,         \"R2\": -2.7224500036418355,         \"Memory in Mb\": 0.0937795639038086,         \"Time in s\": 0.506202       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.637461983479605,         \"RMSE\": 3.464628975063406,         \"R2\": -2.658760364179245,         \"Memory in Mb\": 0.0988950729370117,         \"Time in s\": 0.560423       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.622197889515682,         \"RMSE\": 3.392154183911459,         \"R2\": -2.6064142473473755,         \"Memory in Mb\": 0.1061124801635742,         \"Time in s\": 0.624493       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6252883623828789,         \"RMSE\": 3.33131196963583,         \"R2\": -2.593313247083074,         \"Memory in Mb\": 0.1101140975952148,         \"Time in s\": 0.691125       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.663593439145693,         \"RMSE\": 3.2993129689970107,         \"R2\": -2.4608371725208844,         \"Memory in Mb\": 0.1157598495483398,         \"Time in s\": 0.760369       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6928806013876203,         \"RMSE\": 3.26900202016339,         \"R2\": -2.221881423949668,         \"Memory in Mb\": 0.1238431930541992,         \"Time in s\": 0.832438       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6463369530072471,         \"RMSE\": 3.2036213976345094,         \"R2\": -2.023106408032965,         \"Memory in Mb\": 0.1315031051635742,         \"Time in s\": 0.907505       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6312675040418116,         \"RMSE\": 3.1569789450171624,         \"R2\": -1.8741285299844173,         \"Memory in Mb\": 0.0784368515014648,         \"Time in s\": 0.99002       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6486177246548734,         \"RMSE\": 3.1232792518100463,         \"R2\": -1.81800645719813,         \"Memory in Mb\": 0.0835790634155273,         \"Time in s\": 1.082168       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.664948820150162,         \"RMSE\": 3.091452157271598,         \"R2\": -1.7507490735781142,         \"Memory in Mb\": 0.0873861312866211,         \"Time in s\": 1.179534       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6361907885919602,         \"RMSE\": 3.043459997537018,         \"R2\": -1.7295303491345493,         \"Memory in Mb\": 0.0885534286499023,         \"Time in s\": 1.466782       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6082012495575049,         \"RMSE\": 2.9965453347231947,         \"R2\": -1.7114709556760634,         \"Memory in Mb\": 0.0890569686889648,         \"Time in s\": 1.757406       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.622569336171024,         \"RMSE\": 2.97009213510141,         \"R2\": -1.634341750696236,         \"Memory in Mb\": 0.0909147262573242,         \"Time in s\": 2.050417       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.636890396487252,         \"RMSE\": 2.946158197159977,         \"R2\": -1.5525460315178896,         \"Memory in Mb\": 0.0923452377319336,         \"Time in s\": 2.345765       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.652159107256621,         \"RMSE\": 2.9245287804119107,         \"R2\": -1.4681901897894076,         \"Memory in Mb\": 0.0944395065307617,         \"Time in s\": 2.643466       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6570267761004454,         \"RMSE\": 2.8972896524900835,         \"R2\": -1.4050084478390592,         \"Memory in Mb\": 0.0960302352905273,         \"Time in s\": 2.943569       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6362052297782712,         \"RMSE\": 2.859601997032609,         \"R2\": -1.379870428705038,         \"Memory in Mb\": 0.0981245040893554,         \"Time in s\": 3.2460560000000003       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.608205636538717,         \"RMSE\": 2.821326923745488,         \"R2\": -1.377433396876134,         \"Memory in Mb\": 0.1015691757202148,         \"Time in s\": 3.5543920000000004       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5855230254631891,         \"RMSE\": 2.785659545407005,         \"R2\": -1.3686218528413674,         \"Memory in Mb\": 0.1027364730834961,         \"Time in s\": 3.875571       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.583695771004626,         \"RMSE\": 2.7597111871599203,         \"R2\": -1.3233016566851918,         \"Memory in Mb\": 0.1038503646850586,         \"Time in s\": 4.202987       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5704020318609786,         \"RMSE\": 2.7290361106702816,         \"R2\": -1.2965538228485634,         \"Memory in Mb\": 0.1038503646850586,         \"Time in s\": 4.532954       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5638796853366008,         \"RMSE\": 2.702190403614744,         \"R2\": -1.2616800152467116,         \"Memory in Mb\": 0.1064214706420898,         \"Time in s\": 4.876142       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5494799828615766,         \"RMSE\": 2.674411214594314,         \"R2\": -1.2354538504080876,         \"Memory in Mb\": 0.1070318222045898,         \"Time in s\": 5.221917       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.533437809889996,         \"RMSE\": 2.6465115200139584,         \"R2\": -1.213096407446464,         \"Memory in Mb\": 0.1085958480834961,         \"Time in s\": 5.570086999999999       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5202839319169328,         \"RMSE\": 2.6201051582792827,         \"R2\": -1.189291971541785,         \"Memory in Mb\": 0.1101598739624023,         \"Time in s\": 5.920738999999999       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5178574341866524,         \"RMSE\": 2.5988091386120904,         \"R2\": -1.1501373691585313,         \"Memory in Mb\": 0.1107702255249023,         \"Time in s\": 6.280333       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4962844530295305,         \"RMSE\": 2.571223801389781,         \"R2\": -1.094275733877604,         \"Memory in Mb\": 0.1112470626831054,         \"Time in s\": 6.652339       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4724252749133646,         \"RMSE\": 2.5436398469986066,         \"R2\": -1.0582196084183888,         \"Memory in Mb\": 0.1116437911987304,         \"Time in s\": 7.031402       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4596881679466962,         \"RMSE\": 2.5220256913044325,         \"R2\": -1.056635177134157,         \"Memory in Mb\": 0.1116704940795898,         \"Time in s\": 7.413564       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.452139596196528,         \"RMSE\": 2.5028075284250018,         \"R2\": -1.0425932823285438,         \"Memory in Mb\": 0.1127042770385742,         \"Time in s\": 7.802334       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4364147887122178,         \"RMSE\": 2.481230554777158,         \"R2\": -1.0285162299402342,         \"Memory in Mb\": 0.1132078170776367,         \"Time in s\": 8.193766       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4186260884044517,         \"RMSE\": 2.45780687839372,         \"R2\": -1.029053861068545,         \"Memory in Mb\": 0.1138181686401367,         \"Time in s\": 8.587828       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3997779646996389,         \"RMSE\": 2.434572696055838,         \"R2\": -1.024386017127401,         \"Memory in Mb\": 0.1144285202026367,         \"Time in s\": 8.984547       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Adaptive Model Rules\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3984653255896196,         \"RMSE\": 2.433357833975862,         \"R2\": -1.0237164038272608,         \"Memory in Mb\": 0.1144285202026367,         \"Time in s\": 9.38293       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.674710287324511,         \"RMSE\": 12.709622005759083,         \"R2\": -206.93269654300337,         \"Memory in Mb\": 0.1438665390014648,         \"Time in s\": 0.053261       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.741934273684416,         \"RMSE\": 9.017856101646904,         \"R2\": -85.12629469646626,         \"Memory in Mb\": 0.1680784225463867,         \"Time in s\": 0.14276       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.35434760029741,         \"RMSE\": 7.430504888974863,         \"R2\": -37.34585890537725,         \"Memory in Mb\": 0.2096052169799804,         \"Time in s\": 0.266148       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.9327820011330463,         \"RMSE\": 6.452362261246447,         \"R2\": -31.77814024428305,         \"Memory in Mb\": 0.2417478561401367,         \"Time in s\": 0.646479       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.2606373648191784,         \"RMSE\": 6.146136842066936,         \"R2\": -6.374120366305681,         \"Memory in Mb\": 0.3060827255249023,         \"Time in s\": 1.057843       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3521495161457717,         \"RMSE\": 5.750947689984691,         \"R2\": -1.7831107407377038,         \"Memory in Mb\": 0.3567266464233398,         \"Time in s\": 1.521792       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.707478618787897,         \"RMSE\": 5.832856917221716,         \"R2\": -0.9188552689556648,         \"Memory in Mb\": 0.3732900619506836,         \"Time in s\": 2.259814       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.60389034076398,         \"RMSE\": 5.525482549715508,         \"R2\": -0.5612341217350767,         \"Memory in Mb\": 0.4128637313842773,         \"Time in s\": 3.03269       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.7646559934763437,         \"RMSE\": 5.466320467144536,         \"R2\": -0.2779732207399938,         \"Memory in Mb\": 0.4623746871948242,         \"Time in s\": 3.85418       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.880719733615897,         \"RMSE\": 5.407041915578862,         \"R2\": 0.1268195431914148,         \"Memory in Mb\": 0.5318593978881836,         \"Time in s\": 4.717509000000001       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.0896780011355176,         \"RMSE\": 5.466874267225462,         \"R2\": 0.3125459405386915,         \"Memory in Mb\": 0.5604543685913086,         \"Time in s\": 5.631386000000001       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.270943191870578,         \"RMSE\": 5.7618521847151385,         \"R2\": 0.3758777549527384,         \"Memory in Mb\": 0.1946859359741211,         \"Time in s\": 6.649151000000001       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.24701597703502,         \"RMSE\": 5.633009027852055,         \"R2\": 0.5168368848346436,         \"Memory in Mb\": 0.2288389205932617,         \"Time in s\": 7.703019000000001       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.2192007860807728,         \"RMSE\": 5.520141144427338,         \"R2\": 0.6056681999848637,         \"Memory in Mb\": 0.2577199935913086,         \"Time in s\": 8.869892000000002       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.5165819956804767,         \"RMSE\": 5.874797514643079,         \"R2\": 0.6409683688760216,         \"Memory in Mb\": 0.2627325057983398,         \"Time in s\": 10.065309000000005       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.700430602978386,         \"RMSE\": 6.068185859760413,         \"R2\": 0.6911390854727877,         \"Memory in Mb\": 0.2941198348999023,         \"Time in s\": 11.324773000000002       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.803730902742884,         \"RMSE\": 6.1218084380222,         \"R2\": 0.7426259865968339,         \"Memory in Mb\": 0.3320951461791992,         \"Time in s\": 12.629055000000005       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.006649900662983,         \"RMSE\": 6.578339511639692,         \"R2\": 0.7635979888713487,         \"Memory in Mb\": 0.2527418136596679,         \"Time in s\": 13.981910000000005       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.229383118423565,         \"RMSE\": 6.982583803939909,         \"R2\": 0.780430075854037,         \"Memory in Mb\": 0.3202161788940429,         \"Time in s\": 15.391493000000002       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.825249759558611,         \"RMSE\": 8.423350501384354,         \"R2\": 0.720252540483964,         \"Memory in Mb\": 0.3510808944702148,         \"Time in s\": 16.880127       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.088028806665401,         \"RMSE\": 8.669832171958772,         \"R2\": 0.7465054715218886,         \"Memory in Mb\": 0.3200139999389648,         \"Time in s\": 18.399834       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.462442991686406,         \"RMSE\": 9.175585237136575,         \"R2\": 0.7715339230013022,         \"Memory in Mb\": 0.3710927963256836,         \"Time in s\": 20.020863       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.563619467556412,         \"RMSE\": 9.260101660572657,         \"R2\": 0.797901929856006,         \"Memory in Mb\": 0.4192609786987304,         \"Time in s\": 21.677784000000003       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.261116867150435,         \"RMSE\": 10.599777327157994,         \"R2\": 0.7580584961853923,         \"Memory in Mb\": 0.3416013717651367,         \"Time in s\": 23.387217000000003       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.742618468073929,         \"RMSE\": 11.80224059778972,         \"R2\": 0.7360625543191792,         \"Memory in Mb\": 0.3569021224975586,         \"Time in s\": 25.127202000000004       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.039594962415952,         \"RMSE\": 12.249488193444416,         \"R2\": 0.7537446936710837,         \"Memory in Mb\": 0.3965520858764648,         \"Time in s\": 26.911059000000005       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.148800229885712,         \"RMSE\": 12.311677740983953,         \"R2\": 0.7842510327710687,         \"Memory in Mb\": 0.4258260726928711,         \"Time in s\": 28.739538000000007       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.753683144422786,         \"RMSE\": 13.244190950829555,         \"R2\": 0.7776398094561477,         \"Memory in Mb\": 0.4501142501831054,         \"Time in s\": 30.59385600000001       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.773143666519827,         \"RMSE\": 15.93975365978218,         \"R2\": 0.7077199430319765,         \"Memory in Mb\": 0.4718656539916992,         \"Time in s\": 32.508295000000004       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.312574124937234,         \"RMSE\": 16.796832021919023,         \"R2\": 0.7222505421616592,         \"Memory in Mb\": 0.3236379623413086,         \"Time in s\": 34.514388000000004       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.544591695703463,         \"RMSE\": 16.94083213248977,         \"R2\": 0.7470058810264122,         \"Memory in Mb\": 0.3676939010620117,         \"Time in s\": 36.550995       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.680039805071171,         \"RMSE\": 17.006622056031052,         \"R2\": 0.7682275557667291,         \"Memory in Mb\": 0.3629522323608398,         \"Time in s\": 38.648436       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.417098847501563,         \"RMSE\": 18.381838377902795,         \"R2\": 0.7370670774420947,         \"Memory in Mb\": 0.3571195602416992,         \"Time in s\": 40.771216       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.080869197293334,         \"RMSE\": 19.965812195666537,         \"R2\": 0.7148404591067161,         \"Memory in Mb\": 0.3998785018920898,         \"Time in s\": 42.937715       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.60940210623338,         \"RMSE\": 20.687378926969966,         \"R2\": 0.7291406299077132,         \"Memory in Mb\": 0.3288450241088867,         \"Time in s\": 45.213373       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.737814918904208,         \"RMSE\": 20.678726756260627,         \"R2\": 0.7476640805491588,         \"Memory in Mb\": 0.4024953842163086,         \"Time in s\": 47.506899       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.682108791666492,         \"RMSE\": 22.361726245252385,         \"R2\": 0.7257144517605874,         \"Memory in Mb\": 0.4475545883178711,         \"Time in s\": 49.84079500000001       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.622708961705229,         \"RMSE\": 24.146094170569185,         \"R2\": 0.6997951546356598,         \"Memory in Mb\": 0.4968290328979492,         \"Time in s\": 52.212878       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.165217959113354,         \"RMSE\": 24.88032134675199,         \"R2\": 0.7167593971826183,         \"Memory in Mb\": 0.5376424789428711,         \"Time in s\": 54.630802       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.411006646174876,         \"RMSE\": 24.97835315387625,         \"R2\": 0.7347289581181171,         \"Memory in Mb\": 0.5657072067260742,         \"Time in s\": 57.077112       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.766578325445964,         \"RMSE\": 25.376772271610328,         \"R2\": 0.7378384948653763,         \"Memory in Mb\": 0.2583265304565429,         \"Time in s\": 59.570857       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.09445226127713,         \"RMSE\": 28.12961105819035,         \"R2\": 0.6974376845026461,         \"Memory in Mb\": 0.3047628402709961,         \"Time in s\": 62.101038       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.916275460891086,         \"RMSE\": 29.341089843915015,         \"R2\": 0.7093290035354769,         \"Memory in Mb\": 0.3544912338256836,         \"Time in s\": 64.67061       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.222566694739786,         \"RMSE\": 29.549549967606488,         \"R2\": 0.7213397403469026,         \"Memory in Mb\": 0.3983259201049804,         \"Time in s\": 67.26974899999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.854950072386483,         \"RMSE\": 30.34354672604944,         \"R2\": 0.7235900637963901,         \"Memory in Mb\": 0.4324254989624023,         \"Time in s\": 69.89740799999998       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.84874733203415,         \"RMSE\": 31.79966974813451,         \"R2\": 0.7052484004379906,         \"Memory in Mb\": 0.4678411483764648,         \"Time in s\": 72.66954099999998       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.785853660032195,         \"RMSE\": 33.20181112471305,         \"R2\": 0.6967783021275082,         \"Memory in Mb\": 0.4975500106811523,         \"Time in s\": 75.49558399999998       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.52664258005787,         \"RMSE\": 34.100310164439925,         \"R2\": 0.7124234805234935,         \"Memory in Mb\": 0.5351285934448242,         \"Time in s\": 78.36005299999998       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.766026265849117,         \"RMSE\": 34.21097619783695,         \"R2\": 0.7225061795517687,         \"Memory in Mb\": 0.576685905456543,         \"Time in s\": 81.26779099999997       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.840503815170692,         \"RMSE\": 36.15896607268933,         \"R2\": 0.6995590139862528,         \"Memory in Mb\": 0.4753904342651367,         \"Time in s\": 84.25568299999998       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.59690624313325,         \"RMSE\": 37.108967777985264,         \"R2\": 0.6974029137241997,         \"Memory in Mb\": 0.5189352035522461,         \"Time in s\": 87.27382099999997       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.534320250737128,         \"RMSE\": 38.28067851879141,         \"R2\": 0.7021424273708647,         \"Memory in Mb\": 0.5585355758666992,         \"Time in s\": 90.32555299999996       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.709683411591413,         \"RMSE\": 38.44162901827647,         \"R2\": 0.7069383356385298,         \"Memory in Mb\": 0.3551816940307617,         \"Time in s\": 93.40141099999995       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.677140920600926,         \"RMSE\": 9.804891856735376,         \"R2\": -224.4966127051096,         \"Memory in Mb\": 0.2373647689819336,         \"Time in s\": 0.078317       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.42676306487335,         \"RMSE\": 7.150663284447028,         \"R2\": -19.556918338481843,         \"Memory in Mb\": 0.3270711898803711,         \"Time in s\": 0.2600639999999999       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8116457742622056,         \"RMSE\": 5.852230884873156,         \"R2\": -17.563213740222555,         \"Memory in Mb\": 0.3493108749389648,         \"Time in s\": 0.628767       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5261658032230545,         \"RMSE\": 5.084894428469453,         \"R2\": -13.057813649460384,         \"Memory in Mb\": 0.3968191146850586,         \"Time in s\": 1.163603       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.404885103917059,         \"RMSE\": 4.580518627305071,         \"R2\": -6.157363117938322,         \"Memory in Mb\": 0.4107885360717773,         \"Time in s\": 1.7740749999999998       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2872329076385731,         \"RMSE\": 4.198963935277897,         \"R2\": -4.102442140657352,         \"Memory in Mb\": 0.4562673568725586,         \"Time in s\": 2.455989       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4191481295394186,         \"RMSE\": 4.9019146331166,         \"R2\": -6.185954638838571,         \"Memory in Mb\": 0.2026891708374023,         \"Time in s\": 3.22825       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.329290869551211,         \"RMSE\": 4.594852312450113,         \"R2\": -4.568052693162012,         \"Memory in Mb\": 0.3215742111206054,         \"Time in s\": 4.121231       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2559271503392595,         \"RMSE\": 4.341680890984575,         \"R2\": -3.3661470093978725,         \"Memory in Mb\": 0.4017667770385742,         \"Time in s\": 5.147797       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.169313410896163,         \"RMSE\": 4.12134361195162,         \"R2\": -2.94593273053925,         \"Memory in Mb\": 0.4772901535034179,         \"Time in s\": 6.338072       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1066399346497042,         \"RMSE\": 3.9344660517514094,         \"R2\": -2.8792501333638807,         \"Memory in Mb\": 0.5200605392456055,         \"Time in s\": 7.615136       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0535228972416335,         \"RMSE\": 3.7704097053754206,         \"R2\": -2.589291599563797,         \"Memory in Mb\": 0.590418815612793,         \"Time in s\": 8.98983       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0002808672832586,         \"RMSE\": 3.624331376507975,         \"R2\": -2.3612477765650133,         \"Memory in Mb\": 0.677699089050293,         \"Time in s\": 10.49221       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9484528900796008,         \"RMSE\": 3.4935839886686573,         \"R2\": -2.269856713922535,         \"Memory in Mb\": 0.7459287643432617,         \"Time in s\": 12.09809       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9319658343242508,         \"RMSE\": 3.3810597344709987,         \"R2\": -2.011909605217061,         \"Memory in Mb\": 0.8300580978393555,         \"Time in s\": 13.828036       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9015525068993324,         \"RMSE\": 3.2761126415748776,         \"R2\": -1.9678527090537403,         \"Memory in Mb\": 0.8134641647338867,         \"Time in s\": 15.697273999999998       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9086073156973856,         \"RMSE\": 3.206516550071244,         \"R2\": -2.0044577988421626,         \"Memory in Mb\": 0.7938528060913086,         \"Time in s\": 17.678026       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9209686764414104,         \"RMSE\": 3.130698586248577,         \"R2\": -1.887576115168536,         \"Memory in Mb\": 0.8660383224487305,         \"Time in s\": 19.789752       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9054594388814018,         \"RMSE\": 3.0518145886207013,         \"R2\": -1.838813023440576,         \"Memory in Mb\": 0.930495262145996,         \"Time in s\": 22.025823       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9021459083449618,         \"RMSE\": 2.9892737243691805,         \"R2\": -1.8006304820861794,         \"Memory in Mb\": 0.9046812057495116,         \"Time in s\": 24.377516       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9000900027115483,         \"RMSE\": 2.937103674242639,         \"R2\": -1.7932063526136273,         \"Memory in Mb\": 0.2984609603881836,         \"Time in s\": 26.835964       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.884833385913356,         \"RMSE\": 2.873027664171451,         \"R2\": -1.6243016536608597,         \"Memory in Mb\": 0.3453207015991211,         \"Time in s\": 29.357596       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8690754265879537,         \"RMSE\": 2.8131168800056585,         \"R2\": -1.3859136859847618,         \"Memory in Mb\": 0.3807516098022461,         \"Time in s\": 31.984181       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8473225380080763,         \"RMSE\": 2.75510952719956,         \"R2\": -1.235881587238783,         \"Memory in Mb\": 0.4482488632202148,         \"Time in s\": 34.68911       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8286186581223807,         \"RMSE\": 2.701061117260836,         \"R2\": -1.1039316995995572,         \"Memory in Mb\": 0.4975194931030273,         \"Time in s\": 37.472375       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8308331247066605,         \"RMSE\": 2.676087993829765,         \"R2\": -1.0688124961584973,         \"Memory in Mb\": 0.3753881454467773,         \"Time in s\": 40.349897       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.8063786739892863,         \"RMSE\": 2.6263308571208617,         \"R2\": -0.9852938090834252,         \"Memory in Mb\": 0.4063673019409179,         \"Time in s\": 43.316687       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7997929461413226,         \"RMSE\": 2.582727501713279,         \"R2\": -0.9656668153374994,         \"Memory in Mb\": 0.4374494552612304,         \"Time in s\": 46.3679       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7908850979728871,         \"RMSE\": 2.541457165367321,         \"R2\": -0.950423138286411,         \"Memory in Mb\": 0.4967718124389648,         \"Time in s\": 49.479346       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7789627943481009,         \"RMSE\": 2.500361162030882,         \"R2\": -0.8669718089652279,         \"Memory in Mb\": 0.569575309753418,         \"Time in s\": 52.654549       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7682254429218135,         \"RMSE\": 2.461677332861117,         \"R2\": -0.7820656947310429,         \"Memory in Mb\": 0.6584272384643555,         \"Time in s\": 55.885269       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.756836908225871,         \"RMSE\": 2.4246570785119217,         \"R2\": -0.6965531574296129,         \"Memory in Mb\": 0.7120962142944336,         \"Time in s\": 59.181412       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7406340846119412,         \"RMSE\": 2.388088765643962,         \"R2\": -0.6339309775627988,         \"Memory in Mb\": 0.8089780807495117,         \"Time in s\": 62.54643       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7257657440750075,         \"RMSE\": 2.3532857176647086,         \"R2\": -0.6117268738432657,         \"Memory in Mb\": 0.8398160934448242,         \"Time in s\": 65.979515       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7284794894639326,         \"RMSE\": 2.325029779726661,         \"R2\": -0.6145762958857721,         \"Memory in Mb\": 0.9275884628295898,         \"Time in s\": 69.48963400000001       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7231955460113827,         \"RMSE\": 2.297270435922827,         \"R2\": -0.6108826519065647,         \"Memory in Mb\": 0.9087285995483398,         \"Time in s\": 73.071921       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7217944839950929,         \"RMSE\": 2.2699024953355416,         \"R2\": -0.5717835473178023,         \"Memory in Mb\": 0.8719320297241211,         \"Time in s\": 76.723943       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7121024512438853,         \"RMSE\": 2.241058668519108,         \"R2\": -0.5486900768629306,         \"Memory in Mb\": 0.929518699645996,         \"Time in s\": 80.452493       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7019422114012909,         \"RMSE\": 2.213016497735788,         \"R2\": -0.5169405784918113,         \"Memory in Mb\": 1.0446271896362305,         \"Time in s\": 84.252351       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.7005931807120314,         \"RMSE\": 2.188428332233621,         \"R2\": -0.4968352306391974,         \"Memory in Mb\": 1.1031560897827148,         \"Time in s\": 88.13395700000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6997484436891046,         \"RMSE\": 2.169363820936814,         \"R2\": -0.4870225063787143,         \"Memory in Mb\": 1.0328702926635742,         \"Time in s\": 92.08914500000002       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6949195885567419,         \"RMSE\": 2.14957176369946,         \"R2\": -0.4735678496288336,         \"Memory in Mb\": 0.7475957870483398,         \"Time in s\": 96.10836100000002       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6920590805093112,         \"RMSE\": 2.1264870362465933,         \"R2\": -0.439603572136809,         \"Memory in Mb\": 0.8119535446166992,         \"Time in s\": 100.182244       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6882027439938415,         \"RMSE\": 2.1038322601427426,         \"R2\": -0.4020913886240724,         \"Memory in Mb\": 0.8655576705932617,         \"Time in s\": 104.313167       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6818594219129391,         \"RMSE\": 2.085410616994055,         \"R2\": -0.38345052454273,         \"Memory in Mb\": 0.8467855453491211,         \"Time in s\": 108.503439       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6756248333192205,         \"RMSE\": 2.0637081851469703,         \"R2\": -0.377066205838211,         \"Memory in Mb\": 0.9400205612182616,         \"Time in s\": 112.753186       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6689624136970388,         \"RMSE\": 2.0428592411141837,         \"R2\": -0.3608300191067024,         \"Memory in Mb\": 0.8168668746948242,         \"Time in s\": 117.061539       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6627773066160889,         \"RMSE\": 2.022520414368002,         \"R2\": -0.3478143420231987,         \"Memory in Mb\": 0.9120321273803712,         \"Time in s\": 121.43268700000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6600305544016135,         \"RMSE\": 2.003593726688123,         \"R2\": -0.348395800771305,         \"Memory in Mb\": 0.9782476425170898,         \"Time in s\": 125.86239700000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.657029407021691,         \"RMSE\": 1.9853014454830336,         \"R2\": -0.3461726175729922,         \"Memory in Mb\": 1.0593442916870115,         \"Time in s\": 130.37428000000003       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Streaming Random Patches\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.6566965628025029,         \"RMSE\": 1.984335939466105,         \"R2\": -0.3457614763231473,         \"Memory in Mb\": 1.0646085739135742,         \"Time in s\": 134.90266400000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.95559056599966,         \"RMSE\": 17.7409835250609,         \"R2\": -404.147256051216,         \"Memory in Mb\": 0.1553668975830078,         \"Time in s\": 0.005094       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.88626580700965,         \"RMSE\": 12.566688603347808,         \"R2\": -166.25182631838038,         \"Memory in Mb\": 0.1681652069091797,         \"Time in s\": 0.018278       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.383857039198176,         \"RMSE\": 10.299865918219764,         \"R2\": -72.67921052893462,         \"Memory in Mb\": 0.2052059173583984,         \"Time in s\": 0.039075       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.446496162870555,         \"RMSE\": 8.931116231999566,         \"R2\": -61.79980671874969,         \"Memory in Mb\": 0.2209186553955078,         \"Time in s\": 0.065167       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.3513349782155037,         \"RMSE\": 8.247717183177938,         \"R2\": -12.279242202465667,         \"Memory in Mb\": 0.2687969207763672,         \"Time in s\": 0.096566       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.889627188952696,         \"RMSE\": 8.0458642201752,         \"R2\": -4.4474976461238604,         \"Memory in Mb\": 0.3383464813232422,         \"Time in s\": 0.144605       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.337751636727128,         \"RMSE\": 7.9681159743419645,         \"R2\": -2.5808890563388096,         \"Memory in Mb\": 0.3940753936767578,         \"Time in s\": 0.201306       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.489908334389532,         \"RMSE\": 7.740787033322287,         \"R2\": -2.0640641214272355,         \"Memory in Mb\": 0.4405231475830078,         \"Time in s\": 0.274421       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.7831270806190425,         \"RMSE\": 7.705843596650206,         \"R2\": -1.5396388125269618,         \"Memory in Mb\": 0.4615802764892578,         \"Time in s\": 0.372843       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.73395080514245,         \"RMSE\": 7.47334250555501,         \"R2\": -0.6680701376440403,         \"Memory in Mb\": 0.4910602569580078,         \"Time in s\": 0.5845670000000001       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.733710015085173,         \"RMSE\": 7.331306378435282,         \"R2\": -0.236312465025352,         \"Memory in Mb\": 0.5042209625244141,         \"Time in s\": 0.806575       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.565752852065114,         \"RMSE\": 7.0976416640915465,         \"R2\": 0.0529485447239657,         \"Memory in Mb\": 0.5126438140869141,         \"Time in s\": 1.039517       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.439022558662509,         \"RMSE\": 6.895745596080793,         \"R2\": 0.2759386934515202,         \"Memory in Mb\": 0.5216274261474609,         \"Time in s\": 1.284562       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.362170284876481,         \"RMSE\": 6.736533340066285,         \"R2\": 0.4127346685162743,         \"Memory in Mb\": 0.5262050628662109,         \"Time in s\": 1.543379       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.647894929983432,         \"RMSE\": 7.008861526290804,         \"R2\": 0.4889753297409128,         \"Memory in Mb\": 0.5290126800537109,         \"Time in s\": 1.82575       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.817744211127824,         \"RMSE\": 7.136288548419971,         \"R2\": 0.5728405597677722,         \"Memory in Mb\": 0.5066156387329102,         \"Time in s\": 2.145522       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.867036081233358,         \"RMSE\": 7.152118590173611,         \"R2\": 0.6487028411390494,         \"Memory in Mb\": 0.4790925979614258,         \"Time in s\": 2.491984       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.887061675652274,         \"RMSE\": 7.15502256260352,         \"R2\": 0.720333392468735,         \"Memory in Mb\": 0.3514680862426758,         \"Time in s\": 2.944156       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.070260383978678,         \"RMSE\": 7.484020932149266,         \"R2\": 0.7477619935177958,         \"Memory in Mb\": 0.3277406692504883,         \"Time in s\": 3.412471       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.671227628293087,         \"RMSE\": 8.602358503958763,         \"R2\": 0.7082361492582977,         \"Memory in Mb\": 0.3353548049926758,         \"Time in s\": 3.897275       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.872335352103937,         \"RMSE\": 8.83175934179542,         \"R2\": 0.7369479677711775,         \"Memory in Mb\": 0.372288703918457,         \"Time in s\": 4.389179       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.107145463120707,         \"RMSE\": 9.222510821361375,         \"R2\": 0.769191114739663,         \"Memory in Mb\": 0.3991250991821289,         \"Time in s\": 4.889463       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.19844823305091,         \"RMSE\": 9.33633324769997,         \"R2\": 0.7945607849348244,         \"Memory in Mb\": 0.4207086563110351,         \"Time in s\": 5.506109       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.823605404288741,         \"RMSE\": 10.586090935492884,         \"R2\": 0.758682880699561,         \"Memory in Mb\": 0.4333581924438476,         \"Time in s\": 6.131382       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.289576170484155,         \"RMSE\": 11.670233638164651,         \"R2\": 0.7419337665758028,         \"Memory in Mb\": 0.4418039321899414,         \"Time in s\": 6.777342       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.579012857443305,         \"RMSE\": 12.145524073459754,         \"R2\": 0.75790700185782,         \"Memory in Mb\": 0.4508142471313476,         \"Time in s\": 7.439278       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.564986803201262,         \"RMSE\": 12.135208564512553,         \"R2\": 0.7903915740986345,         \"Memory in Mb\": 0.4540948867797851,         \"Time in s\": 8.115354       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.103353916061925,         \"RMSE\": 13.02855032884451,         \"R2\": 0.7848217554522041,         \"Memory in Mb\": 0.4577798843383789,         \"Time in s\": 8.913327       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.2182891996096,         \"RMSE\": 15.75502466975724,         \"R2\": 0.7144552709875674,         \"Memory in Mb\": 0.4609994888305664,         \"Time in s\": 9.721339       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.685083231372472,         \"RMSE\": 16.400765556025647,         \"R2\": 0.7351946818510116,         \"Memory in Mb\": 0.4719209671020508,         \"Time in s\": 10.546217       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.903299441393282,         \"RMSE\": 16.527032528363478,         \"R2\": 0.759214288686034,         \"Memory in Mb\": 0.477086067199707,         \"Time in s\": 11.387382       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.047801743751696,         \"RMSE\": 16.62843798311862,         \"R2\": 0.778421004008311,         \"Memory in Mb\": 0.4848833084106445,         \"Time in s\": 12.279877       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.963674059851892,         \"RMSE\": 18.110346084278056,         \"R2\": 0.7447765461541069,         \"Memory in Mb\": 0.4873552322387695,         \"Time in s\": 13.19145       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.492835005144466,         \"RMSE\": 19.28081121430548,         \"R2\": 0.7340717056357994,         \"Memory in Mb\": 0.4739046096801758,         \"Time in s\": 14.120497000000002       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.898657720927194,         \"RMSE\": 19.94321338535613,         \"R2\": 0.7482768269892894,         \"Memory in Mb\": 0.4488801956176758,         \"Time in s\": 15.069160000000002       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.0617729851989,         \"RMSE\": 19.965773188137263,         \"R2\": 0.7647640178574115,         \"Memory in Mb\": 0.4161386489868164,         \"Time in s\": 16.114715       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.97304553348899,         \"RMSE\": 21.57136186484404,         \"R2\": 0.7447607843499935,         \"Memory in Mb\": 0.4060754776000976,         \"Time in s\": 17.182106       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 13.747411939847144,         \"RMSE\": 23.06575414024212,         \"R2\": 0.7260576137332548,         \"Memory in Mb\": 0.4295892715454101,         \"Time in s\": 18.259955       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.305030376306712,         \"RMSE\": 23.986335540211613,         \"R2\": 0.7367482003695625,         \"Memory in Mb\": 0.4628152847290039,         \"Time in s\": 19.347052       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.526268288674354,         \"RMSE\": 24.074522605416067,         \"R2\": 0.7535790611891788,         \"Memory in Mb\": 0.4899911880493164,         \"Time in s\": 20.478651000000003       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.027594657922048,         \"RMSE\": 24.671918598232004,         \"R2\": 0.7521995995009789,         \"Memory in Mb\": 0.5175485610961914,         \"Time in s\": 21.623166       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.208274238827823,         \"RMSE\": 27.03842360672758,         \"R2\": 0.7204560380476568,         \"Memory in Mb\": 0.5713090896606445,         \"Time in s\": 22.784126       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.99589357541462,         \"RMSE\": 28.364120175265192,         \"R2\": 0.7283636722963454,         \"Memory in Mb\": 0.5903940200805664,         \"Time in s\": 23.973479       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.304815327407063,         \"RMSE\": 28.547476213614512,         \"R2\": 0.739918935022724,         \"Memory in Mb\": 0.6084756851196289,         \"Time in s\": 25.225522       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.747173803776352,         \"RMSE\": 29.064129392830434,         \"R2\": 0.7464079684248853,         \"Memory in Mb\": 0.6325922012329102,         \"Time in s\": 26.517497       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 18.655435380807354,         \"RMSE\": 30.57773482627066,         \"R2\": 0.7274654471662664,         \"Memory in Mb\": 0.6401453018188477,         \"Time in s\": 27.826703       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.38212758204995,         \"RMSE\": 31.56694275275528,         \"R2\": 0.7259045847606268,         \"Memory in Mb\": 0.5900964736938477,         \"Time in s\": 29.161388       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.16255538075237,         \"RMSE\": 32.73116726334994,         \"R2\": 0.7350525453098611,         \"Memory in Mb\": 0.6013956069946289,         \"Time in s\": 30.57764       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.34377517847412,         \"RMSE\": 32.75647044736101,         \"R2\": 0.7456003073902285,         \"Memory in Mb\": 0.6148271560668945,         \"Time in s\": 32.006392000000005       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.397093652240404,         \"RMSE\": 34.43808497807088,         \"R2\": 0.7274757471078548,         \"Memory in Mb\": 0.6217546463012695,         \"Time in s\": 33.45442800000001       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.130535392790676,         \"RMSE\": 35.39551310036421,         \"R2\": 0.7247017706467436,         \"Memory in Mb\": 0.6181917190551758,         \"Time in s\": 34.916661000000005       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.97609679727041,         \"RMSE\": 36.53926451086616,         \"R2\": 0.7286255260499503,         \"Memory in Mb\": 0.6243486404418945,         \"Time in s\": 36.463466       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.114298050830318,         \"RMSE\": 36.631109645590286,         \"R2\": 0.7338934315030725,         \"Memory in Mb\": 0.6280336380004883,         \"Time in s\": 38.020312       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.57361785669815,         \"RMSE\": 13.877675781396096,         \"R2\": -450.7393063082519,         \"Memory in Mb\": 0.3875865936279297,         \"Time in s\": 0.030628       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.357601810962072,         \"RMSE\": 9.93598927447802,         \"R2\": -38.690592530050864,         \"Memory in Mb\": 0.5688495635986328,         \"Time in s\": 0.093522       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.120546196671925,         \"RMSE\": 8.124382016407804,         \"R2\": -34.775930157070896,         \"Memory in Mb\": 0.6946392059326172,         \"Time in s\": 0.180013       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5823668216656817,         \"RMSE\": 7.068571931029129,         \"R2\": -26.16547256881584,         \"Memory in Mb\": 0.7988948822021484,         \"Time in s\": 0.365225       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.6103510398716643,         \"RMSE\": 6.439797187103485,         \"R2\": -13.147122820254191,         \"Memory in Mb\": 0.9021625518798828,         \"Time in s\": 0.589181       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.5653436103516496,         \"RMSE\": 5.96335184363353,         \"R2\": -9.29140495411716,         \"Memory in Mb\": 0.9421710968017578,         \"Time in s\": 0.842127       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4314692166818666,         \"RMSE\": 5.556159680491977,         \"R2\": -8.232140838080387,         \"Memory in Mb\": 0.9627094268798828,         \"Time in s\": 1.43468       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.270493582871441,         \"RMSE\": 5.217534738727647,         \"R2\": -6.179445803611509,         \"Memory in Mb\": 1.0016803741455078,         \"Time in s\": 2.055929       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1841879014169865,         \"RMSE\": 4.9594120506005,         \"R2\": -4.6969569828406526,         \"Memory in Mb\": 0.9622507095336914,         \"Time in s\": 2.757735       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.030794616399332,         \"RMSE\": 4.7110231793054895,         \"R2\": -4.155876544063708,         \"Memory in Mb\": 0.5397500991821289,         \"Time in s\": 3.551711       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.922882727301643,         \"RMSE\": 4.50300441964265,         \"R2\": -4.081371242371108,         \"Memory in Mb\": 0.3774957656860351,         \"Time in s\": 4.407055       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8390508968191757,         \"RMSE\": 4.321014818317665,         \"R2\": -3.714147473566389,         \"Memory in Mb\": 0.4312639236450195,         \"Time in s\": 5.281937999999999       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7379678526387643,         \"RMSE\": 4.155226166492631,         \"R2\": -3.4180849750109363,         \"Memory in Mb\": 0.4941263198852539,         \"Time in s\": 6.197258999999999       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7042826877160742,         \"RMSE\": 4.0269186303191065,         \"R2\": -3.3444224917120184,         \"Memory in Mb\": 0.5997896194458008,         \"Time in s\": 7.197467999999999       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6796571065333832,         \"RMSE\": 3.9174008876388,         \"R2\": -3.043265693703045,         \"Memory in Mb\": 0.6906900405883789,         \"Time in s\": 8.219126999999999       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5891460162001485,         \"RMSE\": 3.793680488164568,         \"R2\": -2.979662055693274,         \"Memory in Mb\": 0.757817268371582,         \"Time in s\": 9.288978999999998       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5335884019062007,         \"RMSE\": 3.685003453386448,         \"R2\": -2.968029890458662,         \"Memory in Mb\": 0.7969903945922852,         \"Time in s\": 10.383406999999998       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.54418408246079,         \"RMSE\": 3.606838798974545,         \"R2\": -2.832696164635261,         \"Memory in Mb\": 0.8751077651977539,         \"Time in s\": 11.515104999999998       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5054402320411853,         \"RMSE\": 3.517798427376237,         \"R2\": -2.771919367898169,         \"Memory in Mb\": 0.9264421463012696,         \"Time in s\": 12.700466999999998       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.4723491084260332,         \"RMSE\": 3.435525460733128,         \"R2\": -2.699225318590951,         \"Memory in Mb\": 0.9911317825317384,         \"Time in s\": 13.924277999999996       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.429579158986208,         \"RMSE\": 3.3562143901072865,         \"R2\": -2.6472359354971333,         \"Memory in Mb\": 0.9703760147094728,         \"Time in s\": 15.190464999999998       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3992424504019558,         \"RMSE\": 3.2853835752695946,         \"R2\": -2.431676192315116,         \"Memory in Mb\": 1.0316247940063477,         \"Time in s\": 16.527614999999997       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.365864594828797,         \"RMSE\": 3.217129802398014,         \"R2\": -2.120443637805541,         \"Memory in Mb\": 1.1097803115844729,         \"Time in s\": 17.906522       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.3301487592579586,         \"RMSE\": 3.151695763852486,         \"R2\": -1.9259010739386908,         \"Memory in Mb\": 1.1814966201782229,         \"Time in s\": 19.341224       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2968821746115176,         \"RMSE\": 3.0904885141585767,         \"R2\": -1.7543370405557224,         \"Memory in Mb\": 1.2276010513305664,         \"Time in s\": 20.84359       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2678501702074907,         \"RMSE\": 3.0331483120333496,         \"R2\": -1.657710331787236,         \"Memory in Mb\": 1.2783823013305664,         \"Time in s\": 22.421223       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.2343399552126226,         \"RMSE\": 2.9775564396478966,         \"R2\": -1.551795811786203,         \"Memory in Mb\": 1.2796869277954102,         \"Time in s\": 24.086658       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.220715255826944,         \"RMSE\": 2.9300367331798807,         \"R2\": -1.5298738258017646,         \"Memory in Mb\": 1.2170305252075195,         \"Time in s\": 25.790581       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1924146311245054,         \"RMSE\": 2.8809984439523286,         \"R2\": -1.506393751525562,         \"Memory in Mb\": 1.0756006240844729,         \"Time in s\": 27.649708999999994       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1793821598427467,         \"RMSE\": 2.837096711415006,         \"R2\": -1.4037015974174163,         \"Memory in Mb\": 0.9519128799438475,         \"Time in s\": 29.574727       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1645342598465298,         \"RMSE\": 2.79612287562216,         \"R2\": -1.2991852345603885,         \"Memory in Mb\": 0.9679117202758788,         \"Time in s\": 31.579398       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1398425529628198,         \"RMSE\": 2.753175690936408,         \"R2\": -1.1874325743915652,         \"Memory in Mb\": 0.936314582824707,         \"Time in s\": 33.628169       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.1198821044801988,         \"RMSE\": 2.7133180185933967,         \"R2\": -1.1092797015680484,         \"Memory in Mb\": 0.9601030349731444,         \"Time in s\": 35.724068       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.103543375947734,         \"RMSE\": 2.675681585437618,         \"R2\": -1.0835838801311364,         \"Memory in Mb\": 1.0132951736450195,         \"Time in s\": 37.850435       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0951788603095205,         \"RMSE\": 2.643674463536044,         \"R2\": -1.0874567422761272,         \"Memory in Mb\": 1.0955934524536133,         \"Time in s\": 40.041881       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.073603439060177,         \"RMSE\": 2.607328103868497,         \"R2\": -1.075061768905586,         \"Memory in Mb\": 1.1506471633911133,         \"Time in s\": 42.267731000000005       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.052216298294681,         \"RMSE\": 2.5725220480150237,         \"R2\": -1.0188151031858663,         \"Memory in Mb\": 1.1900300979614258,         \"Time in s\": 44.534549000000005       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.0329729065757678,         \"RMSE\": 2.539265324444459,         \"R2\": -0.9882648176410448,         \"Memory in Mb\": 1.2228517532348633,         \"Time in s\": 46.85075200000001       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.015201035443157,         \"RMSE\": 2.5074934380267417,         \"R2\": -0.9475063222432678,         \"Memory in Mb\": 1.2825212478637695,         \"Time in s\": 49.22632000000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.007374170791078,         \"RMSE\": 2.4793027589713232,         \"R2\": -0.9211818120686948,         \"Memory in Mb\": 1.3126497268676758,         \"Time in s\": 51.66223500000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.002386830132377,         \"RMSE\": 2.4547936379778226,         \"R2\": -0.9040692252875042,         \"Memory in Mb\": 1.3594255447387695,         \"Time in s\": 54.13772100000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9929186057762196,         \"RMSE\": 2.428247138481691,         \"R2\": -0.8804076589484491,         \"Memory in Mb\": 1.397130012512207,         \"Time in s\": 56.66204300000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9756374748391698,         \"RMSE\": 2.400488599154532,         \"R2\": -0.8344958433267429,         \"Memory in Mb\": 1.4288606643676758,         \"Time in s\": 59.23948900000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.964181960731443,         \"RMSE\": 2.37450147105602,         \"R2\": -0.7860721035239808,         \"Memory in Mb\": 1.4496355056762695,         \"Time in s\": 61.87688900000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9549728240782616,         \"RMSE\": 2.3497625798451978,         \"R2\": -0.7564202624419067,         \"Memory in Mb\": 1.3498811721801758,         \"Time in s\": 64.59788000000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9412862327577896,         \"RMSE\": 2.3247867434211136,         \"R2\": -0.747529388757789,         \"Memory in Mb\": 1.1945161819458008,         \"Time in s\": 67.39612200000002       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.934469347520636,         \"RMSE\": 2.302477656767798,         \"R2\": -0.7286928787152502,         \"Memory in Mb\": 1.2202577590942385,         \"Time in s\": 70.24630900000002       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9259837543593707,         \"RMSE\": 2.280476047701142,         \"R2\": -0.7135440601163805,         \"Memory in Mb\": 1.2635469436645508,         \"Time in s\": 73.13750900000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9196316545824528,         \"RMSE\": 2.2597103614150886,         \"R2\": -0.715155968132227,         \"Memory in Mb\": 1.2794008255004885,         \"Time in s\": 76.07904100000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9087747756519629,         \"RMSE\": 2.2382775608394114,         \"R2\": -0.7111012811273396,         \"Memory in Mb\": 1.3157854080200195,         \"Time in s\": 79.06112300000002       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Bagging\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 0.9082029688272106,         \"RMSE\": 2.2371845268363217,         \"R2\": -0.710571805505718,         \"Memory in Mb\": 1.3157854080200195,         \"Time in s\": 82.06893700000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.63636363636363,         \"RMSE\": 41.64569169030137,         \"R2\": -2231.5319148936137,         \"Memory in Mb\": 0.0652570724487304,         \"Time in s\": 0.004749       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.31818181818181,         \"RMSE\": 41.32960638133835,         \"R2\": -1808.0547045951903,         \"Memory in Mb\": 0.0776433944702148,         \"Time in s\": 0.02229       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.12121212121212,         \"RMSE\": 41.13871582091424,         \"R2\": -1174.393494897962,         \"Memory in Mb\": 0.0973310470581054,         \"Time in s\": 0.042479       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.159090909090914,         \"RMSE\": 41.17451771534076,         \"R2\": -1333.7620984139928,         \"Memory in Mb\": 0.1087598800659179,         \"Time in s\": 0.065964       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.5090909090909,         \"RMSE\": 41.57075020645253,         \"R2\": -336.3506066081568,         \"Memory in Mb\": 0.1316785812377929,         \"Time in s\": 0.1143329999999999       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.681818181818166,         \"RMSE\": 42.82080349691271,         \"R2\": -153.29834830483878,         \"Memory in Mb\": 0.1604146957397461,         \"Time in s\": 0.1705729999999999       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.50649350649351,         \"RMSE\": 43.70978671356627,         \"R2\": -106.75487995129542,         \"Memory in Mb\": 0.1825284957885742,         \"Time in s\": 0.2318889999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.21590909090909,         \"RMSE\": 44.43649707984724,         \"R2\": -99.97346126163,         \"Memory in Mb\": 0.2035512924194336,         \"Time in s\": 0.308411       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.05050505050505,         \"RMSE\": 45.309262771858165,         \"R2\": -86.8022342468144,         \"Memory in Mb\": 0.2153043746948242,         \"Time in s\": 0.393152       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.16363636363636,         \"RMSE\": 46.52487115902242,         \"R2\": -63.64797006437341,         \"Memory in Mb\": 0.2280874252319336,         \"Time in s\": 0.484559       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 47.21487603305785,         \"RMSE\": 47.67304278378361,         \"R2\": -51.27707184490422,         \"Memory in Mb\": 0.2377805709838867,         \"Time in s\": 0.583103       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.29545454545455,         \"RMSE\": 48.843054157105485,         \"R2\": -43.84882422437649,         \"Memory in Mb\": 0.2479772567749023,         \"Time in s\": 0.792791       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.44055944055945,         \"RMSE\": 50.100318941519305,         \"R2\": -37.220279564063546,         \"Memory in Mb\": 0.2207241058349609,         \"Time in s\": 1.018133       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 50.532467532467535,         \"RMSE\": 51.29137544271156,         \"R2\": -33.04474826644667,         \"Memory in Mb\": 0.2406787872314453,         \"Time in s\": 1.254223       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.690909090909095,         \"RMSE\": 52.61253451297311,         \"R2\": -27.795548438273773,         \"Memory in Mb\": 0.2568111419677734,         \"Time in s\": 1.5160939999999998       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 53.00568181818182,         \"RMSE\": 54.11860921749895,         \"R2\": -23.566226925646237,         \"Memory in Mb\": 0.2727985382080078,         \"Time in s\": 1.7876089999999998       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 54.41176470588235,         \"RMSE\": 55.733754017636336,         \"R2\": -20.33250305682894,         \"Memory in Mb\": 0.2873516082763672,         \"Time in s\": 2.079546       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 56.02525252525252,         \"RMSE\": 57.635786091488654,         \"R2\": -17.146924852486976,         \"Memory in Mb\": 0.300180435180664,         \"Time in s\": 2.3857599999999994       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 57.5645933014354,         \"RMSE\": 59.46206220864915,         \"R2\": -14.922837840066968,         \"Memory in Mb\": 0.2762508392333984,         \"Time in s\": 2.7086299999999994       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 58.69090909090908,         \"RMSE\": 60.81327606250582,         \"R2\": -13.581197962556498,         \"Memory in Mb\": 0.2926349639892578,         \"Time in s\": 3.0480419999999997       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 60.25541125541125,         \"RMSE\": 62.66764529032318,         \"R2\": -12.244451024360147,         \"Memory in Mb\": 0.3073635101318359,         \"Time in s\": 3.437715999999999       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 62.17355371900826,         \"RMSE\": 65.06963847478845,         \"R2\": -10.489760184397111,         \"Memory in Mb\": 0.3215885162353515,         \"Time in s\": 3.8493299999999993       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 63.93675889328063,         \"RMSE\": 67.17295239601157,         \"R2\": -9.634560128382748,         \"Memory in Mb\": 0.3348064422607422,         \"Time in s\": 4.393024       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 65.10606060606062,         \"RMSE\": 68.57980310513724,         \"R2\": -9.127665748505592,         \"Memory in Mb\": 0.3451099395751953,         \"Time in s\": 4.977281       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 66.61454545454548,         \"RMSE\": 70.46451073219248,         \"R2\": -8.408339126213217,         \"Memory in Mb\": 0.3548030853271484,         \"Time in s\": 5.586244       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 68.48951048951052,         \"RMSE\": 72.8020594498525,         \"R2\": -7.6983532427125105,         \"Memory in Mb\": 0.3655261993408203,         \"Time in s\": 6.228505       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 70.55218855218858,         \"RMSE\": 75.3669362796119,         \"R2\": -7.08492451355157,         \"Memory in Mb\": 0.3711223602294922,         \"Time in s\": 6.899176000000001       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 72.39285714285718,         \"RMSE\": 77.65033596401675,         \"R2\": -6.643510181414674,         \"Memory in Mb\": 0.3809375762939453,         \"Time in s\": 7.597348       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 73.45454545454551,         \"RMSE\": 79.15086186624424,         \"R2\": -6.206879640065647,         \"Memory in Mb\": 0.3927364349365234,         \"Time in s\": 8.345726       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 75.77878787878792,         \"RMSE\": 82.20832738177494,         \"R2\": -5.653192449779911,         \"Memory in Mb\": 0.4039859771728515,         \"Time in s\": 9.225203       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 77.92375366568919,         \"RMSE\": 84.89106353805269,         \"R2\": -5.352795814687307,         \"Memory in Mb\": 0.4136791229248047,         \"Time in s\": 10.124705       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 80.04545454545458,         \"RMSE\": 87.49376601169416,         \"R2\": -5.134510311668016,         \"Memory in Mb\": 0.4233722686767578,         \"Time in s\": 11.048248       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 80.99724517906337,         \"RMSE\": 88.57562798692558,         \"R2\": -5.105139086016474,         \"Memory in Mb\": 0.4330654144287109,         \"Time in s\": 11.989622       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 82.77807486631018,         \"RMSE\": 90.83029071422122,         \"R2\": -4.901675845817959,         \"Memory in Mb\": 0.4524784088134765,         \"Time in s\": 12.967698       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 85.1766233766234,         \"RMSE\": 93.99517810235533,         \"R2\": -4.591702735915359,         \"Memory in Mb\": 0.4681224822998047,         \"Time in s\": 14.032748       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 87.26767676767678,         \"RMSE\": 96.48964983485284,         \"R2\": -4.494054297851511,         \"Memory in Mb\": 0.484659194946289,         \"Time in s\": 15.12168       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 89.00737100737103,         \"RMSE\": 98.71879502607636,         \"R2\": -4.345544683073043,         \"Memory in Mb\": 0.4991130828857422,         \"Time in s\": 16.236767999999998       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 90.57416267942588,         \"RMSE\": 100.72635724110243,         \"R2\": -4.224084264201084,         \"Memory in Mb\": 0.5109386444091797,         \"Time in s\": 17.416859999999996       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 93.12121212121215,         \"RMSE\": 104.19735398794236,         \"R2\": -3.967717840349581,         \"Memory in Mb\": 0.5206584930419922,         \"Time in s\": 18.643557999999995       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 95.41818181818184,         \"RMSE\": 107.03565676064125,         \"R2\": -3.8710119659250095,         \"Memory in Mb\": 0.5303516387939453,         \"Time in s\": 19.910713999999995       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 97.16629711751663,         \"RMSE\": 109.07665280092142,         \"R2\": -3.843505105397095,         \"Memory in Mb\": 0.5280742645263672,         \"Time in s\": 21.225085999999997       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 98.71645021645024,         \"RMSE\": 111.1763643167196,         \"R2\": -3.72620239405422,         \"Memory in Mb\": 0.5443019866943359,         \"Time in s\": 22.607732999999996       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 101.54122621564484,         \"RMSE\": 115.2058457378686,         \"R2\": -3.48124047566686,         \"Memory in Mb\": 0.5577869415283203,         \"Time in s\": 24.015635999999997       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 103.77066115702482,         \"RMSE\": 117.90601559037044,         \"R2\": -3.4365483842712585,         \"Memory in Mb\": 0.5712184906005859,         \"Time in s\": 25.474227999999997       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 106.02424242424244,         \"RMSE\": 120.71525892518191,         \"R2\": -3.37467008920777,         \"Memory in Mb\": 0.5825443267822266,         \"Time in s\": 26.96779499999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 107.31620553359684,         \"RMSE\": 122.26004165941237,         \"R2\": -3.356924458603192,         \"Memory in Mb\": 2.7805843353271484,         \"Time in s\": 30.43480299999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 109.39651837524178,         \"RMSE\": 124.91233289427784,         \"R2\": -3.291877964737682,         \"Memory in Mb\": 2.825040817260742,         \"Time in s\": 33.93932499999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 112.36553030303028,         \"RMSE\": 129.1106745698386,         \"R2\": -3.1225038051323804,         \"Memory in Mb\": 2.876035690307617,         \"Time in s\": 37.48648499999999       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 114.52504638218922,         \"RMSE\": 131.65752925403248,         \"R2\": -3.109734667916423,         \"Memory in Mb\": 2.927671432495117,         \"Time in s\": 41.07985699999999       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 115.89999999999996,         \"RMSE\": 133.35909826820617,         \"R2\": -3.0866973064470367,         \"Memory in Mb\": 2.9773387908935547,         \"Time in s\": 44.71417799999999       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 117.86452762923346,         \"RMSE\": 135.8046463151548,         \"R2\": -3.0526234314410727,         \"Memory in Mb\": 3.02082633972168,         \"Time in s\": 48.39640499999999       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 120.54020979020974,         \"RMSE\": 139.4624607986965,         \"R2\": -2.953338846956928,         \"Memory in Mb\": 3.065652847290039,         \"Time in s\": 52.12007199999999       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"ChickWeights\",         \"MAE\": 121.81833910034597,         \"RMSE\": 141.00422703423635,         \"R2\": -2.942935834251463,         \"Memory in Mb\": 3.092409133911133,         \"Time in s\": 55.88513699999999       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 43.8732195,         \"RMSE\": 43.87807788634269,         \"R2\": -4514.954899312423,         \"Memory in Mb\": 0.1445150375366211,         \"Time in s\": 0.017697       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.4932955,         \"RMSE\": 42.52255283421693,         \"R2\": -725.9491167623446,         \"Memory in Mb\": 0.2117376327514648,         \"Time in s\": 0.059669       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 42.2167785,         \"RMSE\": 42.2386240157387,         \"R2\": -966.0073736019044,         \"Memory in Mb\": 0.2583265304565429,         \"Time in s\": 0.116093       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.975705625,         \"RMSE\": 41.99760868559829,         \"R2\": -957.9655948743646,         \"Memory in Mb\": 0.3003568649291992,         \"Time in s\": 0.194541       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 41.37550450000001,         \"RMSE\": 41.410913785433536,         \"R2\": -583.9966399141301,         \"Memory in Mb\": 0.3407926559448242,         \"Time in s\": 0.295523       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.936110000000006,         \"RMSE\": 40.97829382197767,         \"R2\": -484.9611418859003,         \"Memory in Mb\": 0.3709287643432617,         \"Time in s\": 0.510712       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.6885472857143,         \"RMSE\": 40.72961738075088,         \"R2\": -495.1050461477588,         \"Memory in Mb\": 0.3974485397338867,         \"Time in s\": 0.760252       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.35105437500001,         \"RMSE\": 40.39801158334292,         \"R2\": -429.4078677932073,         \"Memory in Mb\": 0.3402233123779297,         \"Time in s\": 1.044132       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.00981655555555,         \"RMSE\": 40.06373388340122,         \"R2\": -370.7794659133543,         \"Memory in Mb\": 0.3811016082763672,         \"Time in s\": 1.367502       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.80633095,         \"RMSE\": 39.860362966711,         \"R2\": -368.1089073295326,         \"Memory in Mb\": 0.3127880096435547,         \"Time in s\": 1.794637       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.727043136363626,         \"RMSE\": 39.77723500009918,         \"R2\": -395.5019807293188,         \"Memory in Mb\": 0.3578624725341797,         \"Time in s\": 2.266575       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.56323079166665,         \"RMSE\": 39.61325406766278,         \"R2\": -395.19837684116754,         \"Memory in Mb\": 0.3875255584716797,         \"Time in s\": 2.775551       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.42014538461535,         \"RMSE\": 39.46968290441584,         \"R2\": -397.63185900832246,         \"Memory in Mb\": 0.420846939086914,         \"Time in s\": 3.358614       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.33200189285712,         \"RMSE\": 39.37942345737111,         \"R2\": -414.4560159350036,         \"Memory in Mb\": 0.4701480865478515,         \"Time in s\": 4.097303       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.18435719999999,         \"RMSE\": 39.23275803924839,         \"R2\": -404.5402138221895,         \"Memory in Mb\": 0.5117359161376953,         \"Time in s\": 4.882924       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.13568690624999,         \"RMSE\": 39.1818628962716,         \"R2\": -423.5167725219512,         \"Memory in Mb\": 0.5480670928955078,         \"Time in s\": 5.740529       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.14620944117645,         \"RMSE\": 39.18989510023786,         \"R2\": -447.7943063391533,         \"Memory in Mb\": 0.5615406036376953,         \"Time in s\": 6.72918       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.24072974999997,         \"RMSE\": 39.28395553300239,         \"R2\": -453.6543473793619,         \"Memory in Mb\": 0.5990085601806641,         \"Time in s\": 7.76074       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.29597665789471,         \"RMSE\": 39.33769921546023,         \"R2\": -470.6701690846498,         \"Memory in Mb\": 0.6312580108642578,         \"Time in s\": 8.924786000000001       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.35730624999997,         \"RMSE\": 39.39781946688104,         \"R2\": -485.4842825426507,         \"Memory in Mb\": 0.6682605743408203,         \"Time in s\": 10.154856       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.40549083333331,         \"RMSE\": 39.44465897881697,         \"R2\": -502.7799504226928,         \"Memory in Mb\": 0.6983966827392578,         \"Time in s\": 11.469727       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.49730674999998,         \"RMSE\": 39.53710368662846,         \"R2\": -495.9856416828035,         \"Memory in Mb\": 0.7316226959228516,         \"Time in s\": 12.901862       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.61474728260867,         \"RMSE\": 39.65658853240579,         \"R2\": -473.14358309219216,         \"Memory in Mb\": 0.7705020904541016,         \"Time in s\": 14.448849       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.71032456249997,         \"RMSE\": 39.75304758270976,         \"R2\": -464.4916761787406,         \"Memory in Mb\": 0.8079357147216797,         \"Time in s\": 16.094285       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.80313951999997,         \"RMSE\": 39.84667590965187,         \"R2\": -456.8750824508669,         \"Memory in Mb\": 2.9298267364501958,         \"Time in s\": 19.825027       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.87354713461536,         \"RMSE\": 39.916931033645376,         \"R2\": -459.2932847271911,         \"Memory in Mb\": 3.0076160430908203,         \"Time in s\": 23.629967       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.94649651851849,         \"RMSE\": 39.98996046818772,         \"R2\": -459.28610565666287,         \"Memory in Mb\": 3.105920791625977,         \"Time in s\": 27.509295       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 39.97606614285712,         \"RMSE\": 40.018487723609816,         \"R2\": -470.926187706672,         \"Memory in Mb\": 3.203706741333008,         \"Time in s\": 31.453258       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.00338510344825,         \"RMSE\": 40.044755101652726,         \"R2\": -483.2331705341176,         \"Memory in Mb\": 3.296670913696289,         \"Time in s\": 35.460995000000004       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.07393431666663,         \"RMSE\": 40.11569326301364,         \"R2\": -479.5746686678817,         \"Memory in Mb\": 3.391347885131836,         \"Time in s\": 39.544454       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.1459417741935,         \"RMSE\": 40.18827077358568,         \"R2\": -473.96334667177865,         \"Memory in Mb\": 3.4906063079833984,         \"Time in s\": 43.697410000000005       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.21943815624997,         \"RMSE\": 40.26249426545423,         \"R2\": -466.8085709746123,         \"Memory in Mb\": 3.5870800018310547,         \"Time in s\": 47.92464       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.28296777272724,         \"RMSE\": 40.32626722721455,         \"R2\": -464.9172853497744,         \"Memory in Mb\": 3.686498641967773,         \"Time in s\": 52.218165000000006       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.31998279411761,         \"RMSE\": 40.36256991107017,         \"R2\": -473.1325264408024,         \"Memory in Mb\": 3.782560348510742,         \"Time in s\": 56.58359200000001       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.31359012857138,         \"RMSE\": 40.35509446667054,         \"R2\": -485.40526703956544,         \"Memory in Mb\": 3.816682815551758,         \"Time in s\": 61.02307200000001       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.31730695833329,         \"RMSE\": 40.357915759594896,         \"R2\": -496.1610725544049,         \"Memory in Mb\": 3.917215347290039,         \"Time in s\": 65.52718500000002       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.36653568918915,         \"RMSE\": 40.40711941642496,         \"R2\": -497.0742803710164,         \"Memory in Mb\": 4.021100997924805,         \"Time in s\": 70.10250300000001       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.40314367105261,         \"RMSE\": 40.443256311482514,         \"R2\": -503.3712175162706,         \"Memory in Mb\": 4.113973617553711,         \"Time in s\": 74.743136       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.44545064102563,         \"RMSE\": 40.48534274444009,         \"R2\": -506.6856716110208,         \"Memory in Mb\": 4.221334457397461,         \"Time in s\": 79.45160200000001       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.47854825,         \"RMSE\": 40.518050685964006,         \"R2\": -512.1052117095793,         \"Memory in Mb\": 4.33137321472168,         \"Time in s\": 84.22367000000001       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.50894034146341,         \"RMSE\": 40.5479845946661,         \"R2\": -518.5068774177179,         \"Memory in Mb\": 4.393171310424805,         \"Time in s\": 89.06994900000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.5406558690476,         \"RMSE\": 40.57931089736599,         \"R2\": -524.140575335229,         \"Memory in Mb\": 4.501546859741211,         \"Time in s\": 93.986539       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.58371181395347,         \"RMSE\": 40.62239247493601,         \"R2\": -524.3496319016275,         \"Memory in Mb\": 4.600507736206055,         \"Time in s\": 98.97766       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.62855514772725,         \"RMSE\": 40.66738601007716,         \"R2\": -522.897851512946,         \"Memory in Mb\": 4.697576522827148,         \"Time in s\": 104.03937600000002       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.664104233333326,         \"RMSE\": 40.702738445808535,         \"R2\": -526.020768835918,         \"Memory in Mb\": 4.774164199829102,         \"Time in s\": 109.17863200000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.68274704347825,         \"RMSE\": 40.72073961991632,         \"R2\": -535.1540147256861,         \"Memory in Mb\": 4.872934341430664,         \"Time in s\": 114.38942200000002       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.70972619148935,         \"RMSE\": 40.74737437775791,         \"R2\": -540.4099749760601,         \"Memory in Mb\": 4.975519180297852,         \"Time in s\": 119.67171900000002       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.73400636458332,         \"RMSE\": 40.771242977826994,         \"R2\": -546.7118652484228,         \"Memory in Mb\": 5.07771110534668,         \"Time in s\": 125.02115000000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.74031829795916,         \"RMSE\": 40.77684015923968,         \"R2\": -557.5026042066913,         \"Memory in Mb\": 5.174932479858398,         \"Time in s\": 130.44017000000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.75359492299998,         \"RMSE\": 40.78950075300399,         \"R2\": -567.2567645513548,         \"Memory in Mb\": 5.274145126342773,         \"Time in s\": 135.92720000000003       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"Exponentially Weighted Average\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 40.75458054545452,         \"RMSE\": 40.7904615623717,         \"R2\": -567.6629514867817,         \"Memory in Mb\": 5.276128768920898,         \"Time in s\": 141.45243100000002       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.63636363636363,         \"RMSE\": 41.64569169030137,         \"R2\": -2231.5319148936137,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.004659       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.31818181818181,         \"RMSE\": 41.32960638133835,         \"R2\": -1808.0547045951903,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.035685       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.12121212121212,         \"RMSE\": 41.13871582091424,         \"R2\": -1174.393494897962,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.0849989999999999       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.159090909090914,         \"RMSE\": 41.17451771534076,         \"R2\": -1333.7620984139928,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.154054       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.5090909090909,         \"RMSE\": 41.57075020645253,         \"R2\": -336.3506066081568,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.236038       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.681818181818166,         \"RMSE\": 42.82080349691271,         \"R2\": -153.29834830483878,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.334153       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.46421300395698,         \"RMSE\": 43.66282826571568,         \"R2\": -106.52347713504813,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.4453709999999999       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.359772412267546,         \"RMSE\": 43.583709810639945,         \"R2\": -96.13505707304522,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 0.810611       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.34760833403674,         \"RMSE\": 41.28110871337288,         \"R2\": -71.88434940071843,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 1.1795       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.27694842893514,         \"RMSE\": 39.43109665219568,         \"R2\": -45.43679588251114,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 1.551937       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 33.38449530211604,         \"RMSE\": 37.62985177124845,         \"R2\": -31.570957412576163,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 1.927681       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.760956861305427,         \"RMSE\": 36.03410144813446,         \"R2\": -23.41028390603237,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 2.306782       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 28.636527077105512,         \"RMSE\": 34.63559483719005,         \"R2\": -17.266619380249022,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 2.6893580000000004       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.848366395937333,         \"RMSE\": 33.39569685051843,         \"R2\": -13.432529594168455,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 3.0876050000000004       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.68994399106157,         \"RMSE\": 32.40192925941153,         \"R2\": -9.92165984317062,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 3.562508       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.410830110997512,         \"RMSE\": 31.4363281477476,         \"R2\": -7.289127728255313,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 4.041374       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.27797268834062,         \"RMSE\": 30.533192270092133,         \"R2\": -5.402500905628177,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 4.523515000000001       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.21718064008457,         \"RMSE\": 29.702466677215767,         \"R2\": -3.8195183008370286,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 5.01074       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.55319621821813,         \"RMSE\": 29.08035323359505,         \"R2\": -2.8083766468986493,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 5.506907       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.47717514737632,         \"RMSE\": 28.87553300521557,         \"R2\": -2.287429329651187,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 6.006767       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.952511346795177,         \"RMSE\": 28.33784041981669,         \"R2\": -1.7081998467380504,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 6.546588       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.676711476832995,         \"RMSE\": 27.952207916118613,         \"R2\": -1.120246781438643,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 7.091828       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.0995501155626,         \"RMSE\": 27.40917062551413,         \"R2\": -0.77060809797316,         \"Memory in Mb\": 0.0121526718139648,         \"Time in s\": 7.64039       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.480542447806847,         \"RMSE\": 27.75611161124588,         \"R2\": -0.6589532289622051,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 8.194426       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.614496901205563,         \"RMSE\": 28.041519628611827,         \"R2\": -0.4899619312470022,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 8.751968       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.565278129073285,         \"RMSE\": 27.847512109279503,         \"R2\": -0.2726896536826668,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 9.312993       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.274958424672484,         \"RMSE\": 27.62568723918364,         \"R2\": -0.0862766099689866,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 9.883614       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.45327995927562,         \"RMSE\": 27.912230969749725,         \"R2\": 0.0123677325099795,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 10.459328       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.51025107964545,         \"RMSE\": 30.009813508826145,         \"R2\": -0.0360067042108638,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 11.038549       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.665590739767385,         \"RMSE\": 30.044848619277115,         \"R2\": 0.1113341277332687,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 11.621241       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.89048226594194,         \"RMSE\": 30.334415208142868,         \"R2\": 0.1888294001405642,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 12.207835       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 21.89395295521016,         \"RMSE\": 30.339570241246864,         \"R2\": 0.2623598804373043,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 12.797939       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.90533819708786,         \"RMSE\": 32.054250800509514,         \"R2\": 0.2004634102060014,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 13.393067000000002       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.763742364378043,         \"RMSE\": 33.85198840163,         \"R2\": 0.1802483296948254,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 13.997035000000002       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.57154278807733,         \"RMSE\": 34.76894700117602,         \"R2\": 0.2349038768732488,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 14.642537000000004       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 24.36284577450351,         \"RMSE\": 34.48838301267373,         \"R2\": 0.2980969129506975,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 15.292230000000002       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.734101326034637,         \"RMSE\": 37.062399123466015,         \"R2\": 0.2465415113840201,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 15.945417000000004       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 27.11119186006235,         \"RMSE\": 39.928390080533305,         \"R2\": 0.1791051768225413,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 16.606412000000002       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 28.3590406143643,         \"RMSE\": 42.09339312084405,         \"R2\": 0.1892790232513257,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 17.270793       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 29.357663164641018,         \"RMSE\": 43.68705243766196,         \"R2\": 0.1885388580498291,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 17.938627       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.691095011752385,         \"RMSE\": 45.67489043760892,         \"R2\": 0.1507194354669618,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 18.61722       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 32.58469239048663,         \"RMSE\": 49.95891321104316,         \"R2\": 0.0456375461688204,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 19.304175       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.457204466549335,         \"RMSE\": 53.83603157260298,         \"R2\": 0.0214223438020899,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 19.9947       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 37.61656772325176,         \"RMSE\": 59.47905507802133,         \"R2\": -0.1290194303344141,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 20.688692000000003       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.835053793820734,         \"RMSE\": 63.30264151550531,         \"R2\": -0.2029972425035688,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 21.389042000000003       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.84375246428709,         \"RMSE\": 64.75828138125749,         \"R2\": -0.2223668969879733,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 22.096762       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.3259867290128,         \"RMSE\": 66.7403629812066,         \"R2\": -0.2252193711452539,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 22.959028000000004       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 43.90376331145991,         \"RMSE\": 69.27444073484561,         \"R2\": -0.1868144391113539,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 23.825387000000003       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.11725117254523,         \"RMSE\": 70.97054614693485,         \"R2\": -0.1942044281482897,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 24.695185       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.19146939493793,         \"RMSE\": 72.84904016514736,         \"R2\": -0.2194804408254527,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 25.570022       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 48.0255382358928,         \"RMSE\": 75.55118081102547,         \"R2\": -0.2542655857337756,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 26.453531       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.60861685612801,         \"RMSE\": 77.95895414232838,         \"R2\": -0.2353254830584423,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 27.340524       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"ChickWeights\",         \"MAE\": 51.40782550111089,         \"RMSE\": 80.92025038917566,         \"R2\": -0.298583502299673,         \"Memory in Mb\": 0.012312889099121,         \"Time in s\": 28.229463000000003       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 28.203089584036217,         \"RMSE\": 31.678254793976468,         \"R2\": -2352.839799462937,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.018592       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 17.631407237579232,         \"RMSE\": 23.536801219235823,         \"R2\": -221.7205207554288,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.0539339999999999       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 13.441671937224772,         \"RMSE\": 19.739075566761823,         \"R2\": -210.18539534147197,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.098078       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 11.19674929006134,         \"RMSE\": 17.292913087737123,         \"R2\": -161.5886474703317,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.159515       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 9.529407951935296,         \"RMSE\": 15.54264880746251,         \"R2\": -81.40884208187767,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.228076       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 8.478754286735066,         \"RMSE\": 14.272499783288554,         \"R2\": -57.95136830581733,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.332328       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 7.525552058981039,         \"RMSE\": 13.242333407520348,         \"R2\": -51.44233495767236,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.527456       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.729532853932534,         \"RMSE\": 12.401843141618142,         \"R2\": -39.56324503441056,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.7296090000000001       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 6.20494414148211,         \"RMSE\": 11.727398222866162,         \"R2\": -30.855608065765253,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 0.938725       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.707613016041334,         \"RMSE\": 11.135875265707485,         \"R2\": -27.80850643367628,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 1.173453       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 5.35235544657082,         \"RMSE\": 10.636236352263047,         \"R2\": -27.34993958822678,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 1.423387       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.997211310189409,         \"RMSE\": 10.191758203807838,         \"R2\": -25.22586832784644,         \"Memory in Mb\": 0.0131101608276367,         \"Time in s\": 1.696442       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.698339965696975,         \"RMSE\": 9.799142308635478,         \"R2\": -23.570888426665658,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 2.044166       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.429952698677103,         \"RMSE\": 9.448184269747657,         \"R2\": -22.91569767610472,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 2.398587       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 4.185436867704573,         \"RMSE\": 9.131292683908228,         \"R2\": -20.968518634865895,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 2.759636       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.989857840855361,         \"RMSE\": 8.848493522992882,         \"R2\": -20.65027251080777,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 3.127609       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.793510888989401,         \"RMSE\": 8.58729864958044,         \"R2\": -20.548263158423392,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 3.5072970000000003       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.624008920532304,         \"RMSE\": 8.350732680595982,         \"R2\": -19.54471351213204,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 3.994133       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.4723941591948395,         \"RMSE\": 8.130732570333006,         \"R2\": -19.15022282865096,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 4.488831       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.327129028584169,         \"RMSE\": 7.926491124989248,         \"R2\": -18.69184448676573,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 4.996594       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.1988914312464622,         \"RMSE\": 7.737168953530663,         \"R2\": -18.383340677896445,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 5.512642       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 3.090541865220404,         \"RMSE\": 7.562941397323993,         \"R2\": -17.185096454486196,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 6.041213       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.9841087219087,         \"RMSE\": 7.398658950448711,         \"R2\": -15.50384711789857,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 6.671723       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.878027938067315,         \"RMSE\": 7.243616567021465,         \"R2\": -14.455461195017085,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 7.311147       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.790174040420949,         \"RMSE\": 7.099353406661882,         \"R2\": -13.534510391092628,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 7.962598       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.702735665583147,         \"RMSE\": 6.962851553400364,         \"R2\": -13.005371203657658,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 8.621042       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.619923274637493,         \"RMSE\": 6.8334980874356335,         \"R2\": -12.440396167539378,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 9.286046       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.556848015725479,         \"RMSE\": 6.714676061099242,         \"R2\": -12.286263553450382,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 9.968405999999998       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4920466556079988,         \"RMSE\": 6.599727625727584,         \"R2\": -12.1527109643015,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 10.662936999999998       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.4260558633236777,         \"RMSE\": 6.490118235625791,         \"R2\": -11.578750092830704,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 11.381531999999998       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3708694907142864,         \"RMSE\": 6.387338726311831,         \"R2\": -10.997792654674662,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 12.131809999999998       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.309077397643504,         \"RMSE\": 6.287531812954472,         \"R2\": -10.40846497658843,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 12.902857999999998       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.253417256192923,         \"RMSE\": 6.192441467899733,         \"R2\": -9.986430076809746,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 13.765244       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1933714736526424,         \"RMSE\": 6.100884478116631,         \"R2\": -9.832475924452435,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 14.638727       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.1444840100167197,         \"RMSE\": 6.014053532220149,         \"R2\": -9.80279442231031,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 15.530555       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.088914979350032,         \"RMSE\": 5.930058413028094,         \"R2\": -9.733901359629304,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 16.434104       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.038014375475162,         \"RMSE\": 5.849577408393744,         \"R2\": -9.43824097776182,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 17.347853       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.990139846596363,         \"RMSE\": 5.772270602035659,         \"R2\": -9.274280741061778,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 18.296023       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.946515411702069,         \"RMSE\": 5.69815370877383,         \"R2\": -9.05697999731458,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 19.263123       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.908897117108588,         \"RMSE\": 5.627293726045093,         \"R2\": -8.897112526821198,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 20.241821       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8732261968689676,         \"RMSE\": 5.559226632327132,         \"R2\": -8.765208356441784,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 21.227083       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8347271749400444,         \"RMSE\": 5.492864392938861,         \"R2\": -8.621969919695442,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 22.223141       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8001515928803729,         \"RMSE\": 5.4291765082898245,         \"R2\": -8.383942958793503,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 23.230427       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.762610565031098,         \"RMSE\": 5.367211780988228,         \"R2\": -8.125392758933815,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 24.244373000000003       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7278213800286455,         \"RMSE\": 5.307357454879676,         \"R2\": -7.960601204549325,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 25.346781000000004       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6959197142820022,         \"RMSE\": 5.249600105314111,         \"R2\": -7.910676780558388,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 26.455778       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6672680101890094,         \"RMSE\": 5.193857129216991,         \"R2\": -7.796440957593809,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 27.578615000000003       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.637208742738251,         \"RMSE\": 5.139738169534271,         \"R2\": -7.704147396017831,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 28.708325       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6082702309133736,         \"RMSE\": 5.087224346398139,         \"R2\": -7.692803163516414,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 29.852185       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.582128560540168,         \"RMSE\": 5.036439630005545,         \"R2\": -7.663534315499042,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 31.047385       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"River MLP\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5805783932319006,         \"RMSE\": 5.033923389051291,         \"R2\": -7.660658200179739,         \"Memory in Mb\": 0.013350486755371,         \"Time in s\": 32.243154000000004       },       {         \"step\": 11,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.664574314574316,         \"RMSE\": 12.7079745317607,         \"R2\": -206.87879383707747,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.000258       },       {         \"step\": 22,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.767694704637076,         \"RMSE\": 9.018587183866767,         \"R2\": -85.14025986830408,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.000737       },       {         \"step\": 33,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.3093367298127023,         \"RMSE\": 7.420500566500976,         \"R2\": -37.24267181629702,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.00134       },       {         \"step\": 44,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 1.892363968348808,         \"RMSE\": 6.441521936619904,         \"R2\": -31.668094594906044,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.002066       },       {         \"step\": 55,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.1129412159858934,         \"RMSE\": 6.114058653243701,         \"R2\": -6.297346571779499,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.00291       },       {         \"step\": 66,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 2.832849782567835,         \"RMSE\": 6.236602142425367,         \"R2\": -2.2730130120415795,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.003872       },       {         \"step\": 77,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.4069290990236856,         \"RMSE\": 6.402381882180361,         \"R2\": -1.3118663438824,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.004952       },       {         \"step\": 88,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 3.650377971160808,         \"RMSE\": 6.321189272940957,         \"R2\": -1.043267371916866,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.006149       },       {         \"step\": 99,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.035631404360372,         \"RMSE\": 6.4483291916176695,         \"R2\": -0.7783857772357967,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.007464       },       {         \"step\": 110,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 4.693189868957898,         \"RMSE\": 7.0697740144659305,         \"R2\": -0.4927792786841307,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.008896       },       {         \"step\": 121,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.274396860168236,         \"RMSE\": 7.6542276724395,         \"R2\": -0.3476225254437259,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.010446       },       {         \"step\": 132,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 5.875758254207378,         \"RMSE\": 8.194624755054596,         \"R2\": -0.2624191661321591,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.012113       },       {         \"step\": 143,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 6.530760796045927,         \"RMSE\": 8.870097879563003,         \"R2\": -0.1980355424044948,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.013898       },       {         \"step\": 154,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.121466111912466,         \"RMSE\": 9.458403141043558,         \"R2\": -0.1577027852151795,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.015801       },       {         \"step\": 165,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 7.772438504082036,         \"RMSE\": 10.375670403553157,         \"R2\": -0.1198999930450892,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0178219999999999       },       {         \"step\": 176,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 8.565827130563894,         \"RMSE\": 11.410434180005833,         \"R2\": -0.0920676568626532,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0199609999999999       },       {         \"step\": 187,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 9.429958588641576,         \"RMSE\": 12.495061319237752,         \"R2\": -0.0722153171628203,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0222169999999999       },       {         \"step\": 198,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 10.47731537859646,         \"RMSE\": 13.900491647656429,         \"R2\": -0.0555502703757588,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0245899999999999       },       {         \"step\": 209,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.43172675762076,         \"RMSE\": 15.229123619635446,         \"R2\": -0.0444565128716372,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.027079       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 11.97432098008114,         \"RMSE\": 16.22368260926648,         \"R2\": -0.0377560869847111,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.029685       },       {         \"step\": 231,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 12.9382196746461,         \"RMSE\": 17.489503190785292,         \"R2\": -0.0315781972827118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.032406       },       {         \"step\": 242,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 14.229204186206864,         \"RMSE\": 19.43725798629848,         \"R2\": -0.0252367718674193,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.035243       },       {         \"step\": 253,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.339413196393396,         \"RMSE\": 20.82023831254592,         \"R2\": -0.0216497893038387,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.041904       },       {         \"step\": 264,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 15.948617107030818,         \"RMSE\": 21.75817315507082,         \"R2\": -0.0194401851240946,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.048726       },       {         \"step\": 275,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 16.794155127707494,         \"RMSE\": 23.16724301729152,         \"R2\": -0.0169996193237813,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.055688       },       {         \"step\": 286,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 17.990009992534457,         \"RMSE\": 24.865985915258104,         \"R2\": -0.0147547133955299,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.062787       },       {         \"step\": 297,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 19.34919450213405,         \"RMSE\": 26.67620929760368,         \"R2\": -0.0128904565600072,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.070018       },       {         \"step\": 308,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.46881241431745,         \"RMSE\": 28.248013022827838,         \"R2\": -0.011537481517321,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.077383       },       {         \"step\": 319,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 20.993702124162965,         \"RMSE\": 29.63814114349949,         \"R2\": -0.0105036731193923,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0848839999999999       },       {         \"step\": 330,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 22.586872779548436,         \"RMSE\": 32.01796640002603,         \"R2\": -0.0092202379520505,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0925169999999999       },       {         \"step\": 341,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 23.97345887210737,         \"RMSE\": 33.821533603903084,         \"R2\": -0.0083877019037323,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1002819999999999       },       {         \"step\": 352,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.315991788770976,         \"RMSE\": 35.461698606860665,         \"R2\": -0.0077313021586467,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1081779999999999       },       {         \"step\": 363,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 25.615062978866305,         \"RMSE\": 35.981300981590465,         \"R2\": -0.0074437490312051,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1162059999999999       },       {         \"step\": 374,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 26.673321526932543,         \"RMSE\": 37.51836715700961,         \"R2\": -0.0069358461242559,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1243619999999999       },       {         \"step\": 385,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 28.27694482780972,         \"RMSE\": 39.8753298933956,         \"R2\": -0.0063325109838794,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1326399999999999       },       {         \"step\": 396,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 29.55612496209691,         \"RMSE\": 41.28848705945016,         \"R2\": -0.0059801818919071,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1410409999999999       },       {         \"step\": 407,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 30.56167711268285,         \"RMSE\": 42.81802042618151,         \"R2\": -0.0056467231500465,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1495659999999999       },       {         \"step\": 418,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 31.39346669137945,         \"RMSE\": 44.18765357092498,         \"R2\": -0.0053697143301307,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1582139999999999       },       {         \"step\": 429,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 33.10612890637694,         \"RMSE\": 46.865579751152914,         \"R2\": -0.0049663660706051,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1669849999999999       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 34.54914638861108,         \"RMSE\": 48.61167278858254,         \"R2\": -0.0047161238549726,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1758829999999999       },       {         \"step\": 451,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 35.43263419295921,         \"RMSE\": 49.67507127970072,         \"R2\": -0.0045536938071879,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1849059999999999       },       {         \"step\": 462,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 36.308550382896186,         \"RMSE\": 51.2507761435036,         \"R2\": -0.0043573774895468,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.1940549999999999       },       {         \"step\": 473,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 38.26330298063241,         \"RMSE\": 54.53225049728104,         \"R2\": -0.0040516612048955,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2033279999999999       },       {         \"step\": 484,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 39.59866234800828,         \"RMSE\": 56.08659790201894,         \"R2\": -0.0039023944795495,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2127249999999999       },       {         \"step\": 495,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 40.94697327298068,         \"RMSE\": 57.823326559810994,         \"R2\": -0.0037535911132069,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2222449999999999       },       {         \"step\": 506,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 41.42384714758024,         \"RMSE\": 58.67984594201592,         \"R2\": -0.0036652347211194,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2318889999999999       },       {         \"step\": 517,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 42.72663002099646,         \"RMSE\": 60.40151056768402,         \"R2\": -0.0035345422299792,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2416599999999999       },       {         \"step\": 528,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 44.77321528369677,         \"RMSE\": 63.69509749878913,         \"R2\": -0.0033415055563215,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2515539999999999       },       {         \"step\": 539,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 45.99579764939489,         \"RMSE\": 65.0494992510053,         \"R2\": -0.003252609562637,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2615709999999999       },       {         \"step\": 550,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 46.57020777663759,         \"RMSE\": 66.07332710234044,         \"R2\": -0.0031815200825582,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2717109999999999       },       {         \"step\": 561,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 47.75825760640621,         \"RMSE\": 67.5643396193493,         \"R2\": -0.0030950009187136,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2819719999999999       },       {         \"step\": 572,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 49.49138874897682,         \"RMSE\": 70.24569214117749,         \"R2\": -0.0029719424061886,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.2923559999999999       },       {         \"step\": 578,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"ChickWeights\",         \"MAE\": 50.250899455914585,         \"RMSE\": 71.11438743304103,         \"R2\": -0.0029294686391043,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.3028349999999999       },       {         \"step\": 20,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.695184981652336,         \"RMSE\": 9.807184976514188,         \"R2\": -224.6021011118197,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.001338       },       {         \"step\": 40,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.3994713447037435,         \"RMSE\": 7.102066178895935,         \"R2\": -19.27845129783118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.003825       },       {         \"step\": 60,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8170744682035584,         \"RMSE\": 5.815253847056423,         \"R2\": -17.329373299766118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.00717       },       {         \"step\": 80,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.604995404573344,         \"RMSE\": 5.081770494168446,         \"R2\": -13.040545957103586,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.0113569999999999       },       {         \"step\": 100,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.824259078948539,         \"RMSE\": 4.70488333223354,         \"R2\": -6.5512954222403845,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.020929       },       {         \"step\": 120,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.918744608116588,         \"RMSE\": 4.412336880489357,         \"R2\": -4.634185300646759,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.030834       },       {         \"step\": 140,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8761207739327503,         \"RMSE\": 4.13187920011476,         \"R2\": -4.105616799680584,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.041039       },       {         \"step\": 160,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.961232939518506,         \"RMSE\": 3.976173487274506,         \"R2\": -3.1695661963674864,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.051538       },       {         \"step\": 180,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.066134597500757,         \"RMSE\": 3.873731518767916,         \"R2\": -2.4756944369169624,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.062312       },       {         \"step\": 200,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 2.051125997923389,         \"RMSE\": 3.731810291394655,         \"R2\": -2.23527456693896,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.073408       },       {         \"step\": 220,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.94095193468414,         \"RMSE\": 3.56902990398404,         \"R2\": -2.19210047340805,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.084777       },       {         \"step\": 240,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9366756524315063,         \"RMSE\": 3.4612902974772624,         \"R2\": -2.024876884626847,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.096419       },       {         \"step\": 260,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.9250039777458068,         \"RMSE\": 3.363327951159923,         \"R2\": -1.8945640461454525,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.108333       },       {         \"step\": 280,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8726934920539136,         \"RMSE\": 3.257010428159885,         \"R2\": -1.8420037280027224,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.120517       },       {         \"step\": 300,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.8907476896224935,         \"RMSE\": 3.1958821895815714,         \"R2\": -1.6910252267675163,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.133002       },       {         \"step\": 320,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.819623890420079,         \"RMSE\": 3.103812605138666,         \"R2\": -1.663886258690169,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.145758       },       {         \"step\": 340,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7396293145937214,         \"RMSE\": 3.014220627768389,         \"R2\": -1.654906383755708,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.158784       },       {         \"step\": 360,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7350691203787965,         \"RMSE\": 2.9569384317632506,         \"R2\": -1.5759385016835008,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.172076       },       {         \"step\": 380,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6987131960417108,         \"RMSE\": 2.8893997308323693,         \"R2\": -1.5446951110541192,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.185636       },       {         \"step\": 400,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.673610627740774,         \"RMSE\": 2.82935583501861,         \"R2\": -1.5089937655143242,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.199488       },       {         \"step\": 420,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6410137122925974,         \"RMSE\": 2.7701802079251965,         \"R2\": -1.484737486096575,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.213608       },       {         \"step\": 440,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6565972573555454,         \"RMSE\": 2.7427790467379385,         \"R2\": -1.391750010744973,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.227993       },       {         \"step\": 460,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.699464840115161,         \"RMSE\": 2.73946740401384,         \"R2\": -1.2626191030939884,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.242643       },       {         \"step\": 480,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7224824441896145,         \"RMSE\": 2.7219018737730583,         \"R2\": -1.182307732575659,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.25756       },       {         \"step\": 500,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7446092142173422,         \"RMSE\": 2.70580354422956,         \"R2\": -1.1113262021905803,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.272747       },       {         \"step\": 520,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7464998751860934,         \"RMSE\": 2.677192702589883,         \"R2\": -1.0705208906620065,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.288233       },       {         \"step\": 540,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7535492786865423,         \"RMSE\": 2.653885630983747,         \"R2\": -1.027170706279252,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.303987       },       {         \"step\": 560,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7201019899937544,         \"RMSE\": 2.614359234374483,         \"R2\": -1.0141103337708768,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.320009       },       {         \"step\": 580,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6887559504032663,         \"RMSE\": 2.5757257291728384,         \"R2\": -1.0033760803823184,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.336298       },       {         \"step\": 600,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.701917368353294,         \"RMSE\": 2.561424763732869,         \"R2\": -0.9592753712060648,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.3528799999999999       },       {         \"step\": 620,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7178157166185173,         \"RMSE\": 2.551346895968156,         \"R2\": -0.9142580419512064,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.369731       },       {         \"step\": 640,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7365901196485038,         \"RMSE\": 2.545046385321895,         \"R2\": -0.8692105635365064,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.386852       },       {         \"step\": 660,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.7465677425181807,         \"RMSE\": 2.532051562790666,         \"R2\": -0.8368676529707118,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.40424       },       {         \"step\": 680,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.731617734826669,         \"RMSE\": 2.504226186170861,         \"R2\": -0.8251107974736909,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.4218949999999999       },       {         \"step\": 700,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6973720107412231,         \"RMSE\": 2.47026789197972,         \"R2\": -0.8225927549994396,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.439849       },       {         \"step\": 720,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6698372433333928,         \"RMSE\": 2.4400355004771077,         \"R2\": -0.81732226470892,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.458072       },       {         \"step\": 740,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6732482399922957,         \"RMSE\": 2.425592833263792,         \"R2\": -0.7947920429290933,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.4765629999999999       },       {         \"step\": 760,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6653913599894004,         \"RMSE\": 2.404136439714782,         \"R2\": -0.7822814452716051,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.4953209999999999       },       {         \"step\": 780,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6644612180457288,         \"RMSE\": 2.387561393188575,         \"R2\": -0.7656652158374817,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.514347       },       {         \"step\": 800,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6556359332933146,         \"RMSE\": 2.368497267913513,         \"R2\": -0.7532954885990883,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.533661       },       {         \"step\": 820,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6452077788467467,         \"RMSE\": 2.348678653798561,         \"R2\": -0.7430103139622937,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.5532450000000001       },       {         \"step\": 840,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6374623223784903,         \"RMSE\": 2.3305035344735936,         \"R2\": -0.7320713255917544,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.5730930000000001       },       {         \"step\": 860,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6419505315856449,         \"RMSE\": 2.320208013716276,         \"R2\": -0.7138439732116804,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.6284980000000001       },       {         \"step\": 880,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6490002164922652,         \"RMSE\": 2.3126155324510744,         \"R2\": -0.6941855677649247,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.6842080000000001       },       {         \"step\": 900,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6474991175923384,         \"RMSE\": 2.299197536504521,         \"R2\": -0.6816400531907807,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.7401880000000002       },       {         \"step\": 920,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6301006788336792,         \"RMSE\": 2.2779225390149764,         \"R2\": -0.6777843948800273,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.7964830000000002       },       {         \"step\": 940,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6221876471839871,         \"RMSE\": 2.262378737250057,         \"R2\": -0.6690049120995847,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.8530460000000002       },       {         \"step\": 960,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.6124120493571743,         \"RMSE\": 2.245866476718547,         \"R2\": -0.6619276404267609,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.9098760000000002       },       {         \"step\": 980,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5867001120604314,         \"RMSE\": 2.223758235975506,         \"R2\": -0.661013659831075,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 0.9669740000000002       },       {         \"step\": 1000,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.5681359363812415,         \"RMSE\": 2.2037391763141216,         \"R2\": -0.6587014308970958,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 1.0243380000000002       },       {         \"step\": 1001,         \"track\": \"Regression\",         \"model\": \"[baseline] Mean predictor\",         \"dataset\": \"TrumpApproval\",         \"MAE\": 1.567554989468773,         \"RMSE\": 2.202858861923226,         \"R2\": -0.6584830635688459,         \"Memory in Mb\": 0.0004901885986328,         \"Time in s\": 1.081765       }     ]   },   \"params\": [     {       \"name\": \"models\",       \"select\": {         \"type\": \"point\",         \"fields\": [           \"model\"         ]       },       \"bind\": \"legend\"     },     {       \"name\": \"Dataset\",       \"value\": \"ChickWeights\",       \"bind\": {         \"input\": \"select\",         \"options\": [           \"ChickWeights\",           \"TrumpApproval\"         ]       }     },     {       \"name\": \"grid\",       \"select\": \"interval\",       \"bind\": \"scales\"     }   ],   \"transform\": [     {       \"filter\": {         \"field\": \"dataset\",         \"equal\": {           \"expr\": \"Dataset\"         }       }     }   ],   \"repeat\": {     \"row\": [       \"MAE\",       \"RMSE\",       \"R2\",       \"Memory in Mb\",       \"Time in s\"     ]   },   \"spec\": {     \"width\": \"container\",     \"mark\": \"line\",     \"encoding\": {       \"x\": {         \"field\": \"step\",         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"title\": \"Instance\"         }       },       \"y\": {         \"field\": {           \"repeat\": \"row\"         },         \"type\": \"quantitative\",         \"axis\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18         }       },       \"color\": {         \"field\": \"model\",         \"type\": \"ordinal\",         \"scale\": {           \"scheme\": \"category20b\"         },         \"title\": \"Models\",         \"legend\": {           \"titleFontSize\": 18,           \"labelFontSize\": 18,           \"labelLimit\": 500         }       },       \"opacity\": {         \"condition\": {           \"param\": \"models\",           \"value\": 1         },         \"value\": 0.2       }     }   } }</p>"},{"location":"benchmarks/Regression/#datasets","title":"Datasets","text":"ChickWeights <p>Chick weights along time.</p> <p>The stream contains 578 items and 3 features. The goal is to predict the weight of each chick along time, according to the diet the chick is on. The data is ordered by time and then by chick.</p> <pre><code>Name  ChickWeights                                                                                                  \nTask  Regression\n</code></pre> <p>Samples  578                                                                                                          Features  3                                                                                                              Sparse  False                                                                                                            Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/chick-weights.csv</p> <p></p> TrumpApproval <p>Donald Trump approval ratings.</p> <p>This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald Trump's approval ratings. It contains 5 features, which are approval ratings collected by 5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of this task is to see if we can reproduce FiveThirtyEight's model.</p> <pre><code>Name  TrumpApproval                                                                                                     \nTask  Regression\n</code></pre> <p>Samples  1,001                                                                                                            Features  6                                                                                                                  Sparse  False                                                                                                                Path  /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/trump_approval.csv.gz</p> <p></p>"},{"location":"benchmarks/Regression/#models","title":"Models","text":"Linear Regression <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Linear Regression with l1 regularization <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=1.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Linear Regression with l2 regularization <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=1.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)</pre></p> <p></p> Passive-Aggressive Regressor, mode 1 <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  PARegressor (\n    C=1.\n    mode=1\n    eps=0.1\n    learn_intercept=True\n  )\n)</pre></p> <p></p> Passive-Aggressive Regressor, mode 2 <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  PARegressor (\n    C=1.\n    mode=2\n    eps=0.1\n    learn_intercept=True\n  )\n)</pre></p> <p></p> k-Nearest Neighbors <p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  KNNRegressor (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    aggregation_method=\"mean\"\n  )\n)\n\n<p></p>\n\nHoeffding Tree\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  HoeffdingTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n  )\n)</pre></p>\n\n<p></p>\n\nHoeffding Adaptive Tree\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=True\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=42\n  )\n)</pre></p>\n\n<p></p>\n\nStochastic Gradient Tree\n<p><pre>SGTRegressor (\n  delta=1e-07\n  grace_period=200\n  init_pred=0.\n  max_depth=inf\n  lambda_value=0.1\n  gamma=1.\n  nominal_attributes=[]\n  feature_quantizer=StaticQuantizer (\n    n_bins=64\n    warm_start=100\n    buckets=None\n  )\n)</pre></p>\n\n<p></p>\n\nAdaptive Random Forest\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  []\n)</pre></p>\n\n<p></p>\n\nAggregated Mondrian Forest\n<p><pre>[]</pre></p>\n\n<p></p>\n\nAdaptive Model Rules\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  AMRules (\n    n_min=200\n    delta=1e-07\n    tau=0.05\n    pred_type=\"adaptive\"\n    pred_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    fading_factor=0.99\n    anomaly_threshold=-0.75\n    m_min=30\n    ordered_rule_set=True\n    min_samples_split=5\n  )\n)</pre></p>\n\n<p></p>\n\nStreaming Random Patches\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  SRPRegressor (\n    model=HoeffdingTreeRegressor (\n      grace_period=50\n      max_depth=inf\n      delta=0.01\n      tau=0.05\n      leaf_prediction=\"adaptive\"\n      leaf_model=LinearRegression (\n        optimizer=SGD (\n          lr=Constant (\n            learning_rate=0.01\n          )\n        )\n        loss=Squared ()\n        l2=0.\n        l1=0.\n        intercept_init=0.\n        intercept_lr=Constant (\n          learning_rate=0.01\n        )\n        clip_gradient=1e+12\n        initializer=Zeros ()\n      )\n      model_selector_decay=0.95\n      nominal_attributes=None\n      splitter=TEBSTSplitter (\n        digits=1\n      )\n      min_samples_split=5\n      binary_split=False\n      max_size=500.\n      memory_estimate_period=1000000\n      stop_mem_management=False\n      remove_poor_attrs=False\n      merit_preprune=True\n    )\n    n_models=10\n    subspace_size=0.6\n    training_method=\"patches\"\n    lam=6\n    drift_detector=ADWIN (\n      delta=1e-05\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    warning_detector=ADWIN (\n      delta=0.0001\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    disable_detector=\"off\"\n    disable_weighted_vote=True\n    drift_detection_criteria=\"error\"\n    aggregation_method=\"mean\"\n    seed=42\n    metric=MAE ()\n  )\n)</pre></p>\n\n<p></p>\n\nBagging\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  [HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=False\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  )]\n)</pre></p>\n\n<p></p>\n\nExponentially Weighted Average\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  [LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.01\n      )\n    )\n    loss=Squared ()\n    l2=0.\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  ), HoeffdingAdaptiveTreeRegressor (\n    grace_period=200\n    max_depth=inf\n    delta=1e-07\n    tau=0.05\n    leaf_prediction=\"adaptive\"\n    leaf_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    model_selector_decay=0.95\n    nominal_attributes=None\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    min_samples_split=5\n    bootstrap_sampling=True\n    drift_window_threshold=300\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    switch_significance=0.05\n    binary_split=False\n    max_size=500.\n    memory_estimate_period=1000000\n    stop_mem_management=False\n    remove_poor_attrs=False\n    merit_preprune=True\n    seed=None\n  ), KNNRegressor (\n    n_neighbors=5\n    engine=SWINN (\n      graph_k=20\n      dist_func=FunctionWrapper (\n        distance_function=functools.partial(, p=2)\n      )\n      maxlen=1000\n      warm_up=500\n      max_candidates=50\n      delta=0.0001\n      prune_prob=0.\n      n_iters=10\n      seed=None\n    )\n    aggregation_method=\"mean\"\n  ), AMRules (\n    n_min=200\n    delta=1e-07\n    tau=0.05\n    pred_type=\"adaptive\"\n    pred_model=LinearRegression (\n      optimizer=SGD (\n        lr=Constant (\n          learning_rate=0.01\n        )\n      )\n      loss=Squared ()\n      l2=0.\n      l1=0.\n      intercept_init=0.\n      intercept_lr=Constant (\n        learning_rate=0.01\n      )\n      clip_gradient=1e+12\n      initializer=Zeros ()\n    )\n    splitter=TEBSTSplitter (\n      digits=1\n    )\n    drift_detector=ADWIN (\n      delta=0.002\n      clock=32\n      max_buckets=5\n      min_window_length=5\n      grace_period=10\n    )\n    fading_factor=0.99\n    anomaly_threshold=-0.75\n    m_min=30\n    ordered_rule_set=True\n    min_samples_split=5\n  )]\n)\n\n<p></p>\n\nRiver MLP\n<p><pre>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  MLPRegressor (\n    hidden_dims=(5,)\n    activations=(, , )\n    loss=Squared ()\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.001\n      )\n    )\n    seed=42\n  )\n)\n\n<p></p>\n\n[baseline] Mean predictor\n<p><pre>StatisticRegressor (\n  statistic=Mean ()\n)</pre></p>\n\n<p></p>"},{"location":"benchmarks/Regression/#environment","title":"Environment","text":"<pre>Python implementation: CPython\nPython version       : 3.12.4\nIPython version      : 8.18.1\n\nriver       : 0.21.2\nnumpy       : 1.26.4\nscikit-learn: 1.3.1\npandas      : 2.2.2\nscipy       : 1.13.0\n\nCompiler    : GCC 11.4.0\nOS          : Linux\nRelease     : 6.5.0-1024-azure\nMachine     : x86_64\nProcessor   : x86_64\nCPU cores   : 4\nArchitecture: 64bit\n</pre>"},{"location":"examples/batch-to-online/","title":"From batch to online/stream","text":""},{"location":"examples/batch-to-online/#a-quick-overview-of-batch-learning","title":"A quick overview of batch learning","text":"<p>If you've already delved into machine learning, then you shouldn't have any difficulty in getting to use incremental learning. If you are somewhat new to machine learning, then do not worry! The point of this notebook in particular is to introduce simple notions. We'll also start to show how River fits in and explain how to use it.</p> <p>The whole point of machine learning is to learn from data. In supervised learning you want to learn how to predict a target \\(y\\) given a set of features \\(X\\). Meanwhile in an unsupervised learning there is no target, and the goal is rather to identify patterns and trends in the features \\(X\\). At this point most people tend to imagine \\(X\\) as a somewhat big table where each row is an observation and each column is a feature, and they would be quite right. Learning from tabular data is part of what's called batch learning, which basically that all of the data is available to our learning algorithm at once. Multiple libraries have been created to handle the batch learning regime, with one of the most prominent being Python's scikit-learn.</p> <p>As a simple example of batch learning let's say we want to learn to predict if a women has breast cancer or not. We'll use the breast cancer dataset available with scikit-learn. We'll learn to map a set of features to a binary decision using a logistic regression. Like many other models based on numerical weights, logistic regression is sensitive to the scale of the features. Rescaling the data so that each feature has mean 0 and variance 1 is generally considered good practice. We can apply the rescaling and fit the logistic regression sequentially in an elegant manner using a Pipeline. To measure the performance of the model we'll evaluate the average ROC AUC score using a 5 fold cross-validation. </p> <pre><code>from sklearn import datasets\nfrom sklearn import linear_model\nfrom sklearn import metrics\nfrom sklearn import model_selection\nfrom sklearn import pipeline\nfrom sklearn import preprocessing\n\n\n# Load the data\ndataset = datasets.load_breast_cancer()\nX, y = dataset.data, dataset.target\n\n# Define the steps of the model\nmodel = pipeline.Pipeline([\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LogisticRegression(solver='lbfgs'))\n])\n\n# Define a determistic cross-validation procedure\ncv = model_selection.KFold(n_splits=5, shuffle=True, random_state=42)\n\n# Compute the MSE values\nscorer = metrics.make_scorer(metrics.roc_auc_score)\nscores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n\n# Display the average score and its standard deviation\nprint(f'ROC AUC: {scores.mean():.3f} (\u00b1 {scores.std():.3f})')\n</code></pre> <pre><code>ROC AUC: 0.975 (\u00b1 0.011)\n</code></pre> <p>This might be a lot to take in if you're not accustomed to scikit-learn, but it probably isn't if you are. Batch learning basically boils down to:</p> <ol> <li>Loading (and preprocessing) the data</li> <li>Fitting a model to the data</li> <li>Computing the performance of the model on unseen data</li> </ol> <p>This is pretty standard and is maybe how most people imagine a machine learning pipeline. However, this way of proceeding has certain downsides. First of all your laptop would crash if the <code>load_boston</code> function returned a dataset who's size exceeds your available amount of RAM. Sometimes you can use some tricks to get around this. For example by optimizing the data types and by using sparse representations when applicable you can potentially save precious gigabytes of RAM. However, like many tricks this only goes so far. If your dataset weighs hundreds of gigabytes then you won't go far without some special hardware. One solution is to do out-of-core learning; that is, algorithms that can learn by being presented the data in chunks or mini-batches. If you want to go down this road then take a look at Dask and Spark's MLlib.</p> <p>Another issue with the batch learning regime is that it can't elegantly learn from new data. Indeed if new data is made available, then the model has to learn from scratch with a new dataset composed of the old data and the new data. This is particularly annoying in a real situation where you might have new incoming data every week, day, hour, minute, or even second. For example if you're building a recommendation engine for an e-commerce app, then you're probably training your model from 0 every week or so. As your app grows in popularity, so does the dataset you're training on. This will lead to longer and longer training times and might require a hardware upgrade.</p> <p>A final downside that isn't very easy to grasp concerns the manner in which features are extracted. Every time you want to train your model you first have to extract features. The trick is that some features might not be accessible at the particular point in time you are at. For example maybe that some attributes in your data warehouse get overwritten with time. In other words maybe that all the features pertaining to a particular observations are not available, whereas they were a week ago. This happens more often than not in real scenarios, and apart if you have a sophisticated data engineering pipeline then you will encounter these issues at some point. </p>"},{"location":"examples/batch-to-online/#a-hands-on-introduction-to-incremental-learning","title":"A hands-on introduction to incremental learning","text":"<p>Incremental learning is also often called online learning or stream learning, but if you google online learning a lot of the results will point to educational websites. Hence, the terms \"incremental learning\" and \"stream learning\" (from which River derives its name) are preferred. The point of incremental learning is to fit a model to a stream of data. In other words, the data isn't available in its entirety, but rather the observations are provided one by one. As an example let's stream through the dataset used previously.</p> <pre><code>for xi, yi in zip(X, y):\n    # This is where the model learns\n    pass\n</code></pre> <p>In this case we're iterating over a dataset that is already in memory, but we could just as well stream from a CSV file, a Kafka stream, an SQL query, etc. If we look at <code>xi</code> we can notice that it is a <code>numpy.ndarray</code>.</p> <pre><code>xi\n</code></pre> <pre><code>array([7.760e+00, 2.454e+01, 4.792e+01, 1.810e+02, 5.263e-02, 4.362e-02,\n       0.000e+00, 0.000e+00, 1.587e-01, 5.884e-02, 3.857e-01, 1.428e+00,\n       2.548e+00, 1.915e+01, 7.189e-03, 4.660e-03, 0.000e+00, 0.000e+00,\n       2.676e-02, 2.783e-03, 9.456e+00, 3.037e+01, 5.916e+01, 2.686e+02,\n       8.996e-02, 6.444e-02, 0.000e+00, 0.000e+00, 2.871e-01, 7.039e-02])\n</code></pre> <p>River by design works with <code>dict</code>s. We believe that <code>dict</code>s are more enjoyable to program with than <code>numpy.ndarray</code>s, at least for when single observations are concerned. <code>dict</code>'s bring the added benefit that each feature can be accessed by name rather than by position.</p> <pre><code>for xi, yi in zip(X, y):\n    xi = dict(zip(dataset.feature_names, xi))\n    pass\n\nxi\n</code></pre> <pre><code>{'mean radius': 7.76,\n 'mean texture': 24.54,\n 'mean perimeter': 47.92,\n 'mean area': 181.0,\n 'mean smoothness': 0.05263,\n 'mean compactness': 0.04362,\n 'mean concavity': 0.0,\n 'mean concave points': 0.0,\n 'mean symmetry': 0.1587,\n 'mean fractal dimension': 0.05884,\n 'radius error': 0.3857,\n 'texture error': 1.428,\n 'perimeter error': 2.548,\n 'area error': 19.15,\n 'smoothness error': 0.007189,\n 'compactness error': 0.00466,\n 'concavity error': 0.0,\n 'concave points error': 0.0,\n 'symmetry error': 0.02676,\n 'fractal dimension error': 0.002783,\n 'worst radius': 9.456,\n 'worst texture': 30.37,\n 'worst perimeter': 59.16,\n 'worst area': 268.6,\n 'worst smoothness': 0.08996,\n 'worst compactness': 0.06444,\n 'worst concavity': 0.0,\n 'worst concave points': 0.0,\n 'worst symmetry': 0.2871,\n 'worst fractal dimension': 0.07039}\n</code></pre> <p>Conveniently, River's <code>stream</code> module has an <code>iter_sklearn_dataset</code> method that we can use instead.</p> <pre><code>from river import stream\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n    pass\n</code></pre> <p>The simple fact that we are getting the data as a stream means that we can't do a lot of things the same way as in a batch setting. For example let's say we want to scale the data so that it has mean 0 and variance 1, as we did earlier. To do so we simply have to subtract the mean of each feature to each value and then divide the result by the standard deviation of the feature. The problem is that we can't possible know the values of the mean and the standard deviation before actually going through all the data! One way to proceed would be to do a first pass over the data to compute the necessary values and then scale the values during a second pass. The problem is that this defeats our purpose, which is to learn by only looking at the data once. Although this might seem rather restrictive, it reaps sizable benefits down the road.</p> <p>The way we do feature scaling in River involves computing running statistics (also know as moving statistics). The idea is that we use a data structure that estimates the mean and updates itself when it is provided with a value. The same goes for the variance (and thus the standard deviation). For example, if we denote \\(\\mu_t\\) the mean and \\(n_t\\) the count at any moment \\(t\\), then updating the mean can be done as so:</p> \\[ \\begin{cases} n_{t+1} = n_t + 1 \\\\ \\mu_{t+1} = \\mu_t + \\frac{x - \\mu_t}{n_{t+1}} \\end{cases} \\] <p>Likewise, the running variance can be computed as so:</p> \\[ \\begin{cases} n_{t+1} = n_t + 1 \\\\ \\mu_{t+1} = \\mu_t + \\frac{x - \\mu_t}{n_{t+1}} \\\\ s_{t+1} = s_t + (x - \\mu_t) \\times (x - \\mu_{t+1}) \\\\ \\sigma_{t+1} = \\frac{s_{t+1}}{n_{t+1}} \\end{cases} \\] <p>where \\(s_t\\) is a running sum of squares and \\(\\sigma_t\\) is the running variance at time \\(t\\). This might seem a tad more involved than the batch algorithms you learn in school, but it is rather elegant. Implementing this in Python is not too difficult. For example let's compute the running mean and variance of the <code>'mean area'</code> variable.</p> <pre><code>n, mean, sum_of_squares, variance = 0, 0, 0, 0\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n    n += 1\n    old_mean = mean\n    mean += (xi['mean area'] - mean) / n\n    sum_of_squares += (xi['mean area'] - old_mean) * (xi['mean area'] - mean)\n    variance = sum_of_squares / n\n\nprint(f'Running mean: {mean:.3f}')\nprint(f'Running variance: {variance:.3f}')\n</code></pre> <pre><code>Running mean: 654.889\nRunning variance: 123625.903\n</code></pre> <p>Let's compare this with <code>numpy</code>. But remember, <code>numpy</code> requires access to \"all\" the data.</p> <pre><code>import numpy as np\n\ni = list(dataset.feature_names).index('mean area')\nprint(f'True mean: {np.mean(X[:, i]):.3f}')\nprint(f'True variance: {np.var(X[:, i]):.3f}')\n</code></pre> <pre><code>True mean: 654.889\nTrue variance: 123625.903\n</code></pre> <p>The results seem to be exactly the same! The twist is that the running statistics won't be very accurate for the first few observations. In general though this doesn't matter too much. Some would even go as far as to say that this descrepancy is beneficial and acts as some sort of regularization...</p> <p>Now the idea is that we can compute the running statistics of each feature and scale them as they come along. The way to do this with River is to use the <code>StandardScaler</code> class from the <code>preprocessing</code> module, as so:</p> <pre><code>from river import preprocessing\n\nscaler = preprocessing.StandardScaler()\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n    scaler.learn_one(xi)\n</code></pre> <p>Now that we are scaling the data, we can start doing some actual machine learning. We're going to implement an online linear regression task. Because all the data isn't available at once, we are obliged to do what is called stochastic gradient descent, which is a popular research topic and has a lot of variants. SGD is commonly used to train neural networks. The idea is that at each step we compute the loss between the target prediction and the truth. We then calculate the gradient, which is simply a set of derivatives with respect to each weight from the linear regression. Once we have obtained the gradient, we can update the weights by moving them in the opposite direction of the gradient. The amount by which the weights are moved typically depends on a learning rate, which is typically set by the user. Different optimizers have different ways of managing the weight update, and some handle the learning rate implicitly. Online linear regression can be done in River with the <code>LinearRegression</code> class from the <code>linear_model</code> module. We'll be using plain and simple SGD using the <code>SGD</code> optimizer from the <code>optim</code> module. During training we'll measure the squared error between the truth and the predictions.</p> <pre><code>from river import linear_model\nfrom river import optim\n\nscaler = preprocessing.StandardScaler()\noptimizer = optim.SGD(lr=0.01)\nlog_reg = linear_model.LogisticRegression(optimizer)\n\ny_true = []\ny_pred = []\n\nfor xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer(), shuffle=True, seed=42):\n\n    # Scale the features\n    scaler.learn_one(xi)\n    xi_scaled = scaler.transform_one(xi)\n\n    # Test the current model on the new \"unobserved\" sample\n    yi_pred = log_reg.predict_proba_one(xi_scaled)\n    # Train the model with the new sample\n    log_reg.learn_one(xi_scaled, yi)\n\n    # Store the truth and the prediction\n    y_true.append(yi)\n    y_pred.append(yi_pred[True])\n\nprint(f'ROC AUC: {metrics.roc_auc_score(y_true, y_pred):.3f}')\n</code></pre> <pre><code>ROC AUC: 0.990\n</code></pre> <p>The ROC AUC is significantly better than the one obtained from the cross-validation of scikit-learn's logisitic regression. However to make things really comparable it would be nice to compare with the same cross-validation procedure. River has a <code>compat</code> module that contains utilities for making River compatible with other Python libraries. Because we're doing regression we'll be using the <code>SKLRegressorWrapper</code>. We'll also be using <code>Pipeline</code> to encapsulate the logic of the <code>StandardScaler</code> and the <code>LogisticRegression</code> in one single object.</p> <pre><code>from river import compat\nfrom river import compose\n\n# We define a Pipeline, exactly like we did earlier for sklearn \nmodel = compose.Pipeline(\n    ('scale', preprocessing.StandardScaler()),\n    ('log_reg', linear_model.LogisticRegression())\n)\n\n# We make the Pipeline compatible with sklearn\nmodel = compat.convert_river_to_sklearn(model)\n\n# We compute the CV scores using the same CV scheme and the same scoring\nscores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n\n# Display the average score and its standard deviation\nprint(f'ROC AUC: {scores.mean():.3f} (\u00b1 {scores.std():.3f})')\n</code></pre> <pre><code>ROC AUC: 0.964 (\u00b1 0.016)\n</code></pre> <p>This time the ROC AUC score is lower, which is what we would expect. Indeed online learning isn't as accurate as batch learning. However it all depends in what you're interested in. If you're only interested in predicting the next observation then the online learning regime would be better. That's why it's a bit hard to compare both approaches: they're both suited to different scenarios.</p>"},{"location":"examples/batch-to-online/#going-further","title":"Going further","text":"<p>Here a few resources if you want to do some reading:</p> <ul> <li>Online learning -- Wikipedia</li> <li>What is online machine learning? -- Max Pagels</li> <li>Introduction to Online Learning -- USC course</li> <li>Online Methods in Machine Learning -- MIT course</li> <li>Online Learning: A Comprehensive Survey</li> <li>Streaming 101: The world beyond batch</li> <li>Machine learning for data streams</li> <li>Data Stream Mining: A Practical Approach</li> </ul>"},{"location":"examples/bike-sharing-forecasting/","title":"Bike-sharing forecasting","text":"<p>In this tutorial we're going to forecast the number of bikes in 5 bike stations from the city of Toulouse. We'll do so by building a simple model step by step. The dataset contains 182,470 observations. Let's first take a peak at the data.</p> <pre><code>from pprint import pprint\nfrom river import datasets\n\ndataset = datasets.Bikes()\n\nfor x, y in dataset:\n    pprint(x)\n    print(f'Number of available bikes: {y}')\n    break\n</code></pre> <pre><code>{'clouds': 75,\n 'description': 'light rain',\n 'humidity': 81,\n 'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n 'pressure': 1017.0,\n 'station': 'metro-canal-du-midi',\n 'temperature': 6.54,\n 'wind': 9.3}\nNumber of available bikes: 1\n</code></pre> <p>Let's start by using a simple linear regression on the numeric features. We can select the numeric features and discard the rest of the features using a <code>Select</code>. Linear regression is very likely to go haywire if we don't scale the data, so we'll use a <code>StandardScaler</code> to do just that. We'll evaluate the model by measuring the mean absolute error. Finally we'll print the score every 20,000 observations. </p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import metrics\nfrom river import evaluate\nfrom river import preprocessing\nfrom river import optim\n\nmodel = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression(optimizer=optim.SGD(0.001))\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric, print_every=20_000)\n</code></pre> <pre><code>[20,000] MAE: 4.912763\n[40,000] MAE: 5.333578\n[60,000] MAE: 5.330969\n[80,000] MAE: 5.392334\n[100,000] MAE: 5.423078\n[120,000] MAE: 5.541239\n[140,000] MAE: 5.613038\n[160,000] MAE: 5.622441\n[180,000] MAE: 5.567836\n[182,470] MAE: 5.563905\n\n\n\n\n\nMAE: 5.563905\n</code></pre> <p>The model doesn't seem to be doing that well, but then again we didn't provide a lot of features. Generally, a good idea for this kind of problem is to look at an average of the previous values. For example, for each station we can look at the average number of bikes per hour. To do so we first have to extract the hour from the  <code>moment</code> field. We can then use a <code>TargetAgg</code> to aggregate the values of the target.</p> <pre><code>from river import feature_extraction\nfrom river import stats\n\ndef get_hour(x):\n    x['hour'] = x['moment'].hour\n    return x\n\nmodel = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')\nmodel += (\n    get_hour |\n    feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean())\n)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression(optimizer=optim.SGD(0.001))\n\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric, print_every=20_000)\n</code></pre> <pre><code>[20,000] MAE: 3.720766\n[40,000] MAE: 3.829739\n[60,000] MAE: 3.844905\n[80,000] MAE: 3.910137\n[100,000] MAE: 3.888553\n[120,000] MAE: 3.923644\n[140,000] MAE: 3.980882\n[160,000] MAE: 3.949972\n[180,000] MAE: 3.934489\n[182,470] MAE: 3.933442\n\n\n\n\n\nMAE: 3.933442\n</code></pre> <p>By adding a single feature, we've managed to significantly reduce the mean absolute error. At this point you might think that the model is getting slightly complex, and is difficult to understand and test. Pipelines have the advantage of being terse, but they aren't always to debug. Thankfully River has some ways to relieve the pain.</p> <p>The first thing we can do it to visualize the pipeline, to get an idea of how the data flows through it.</p> <pre><code>model\n</code></pre> <pre>['clouds', [...]</pre><code>Select (   clouds   humidity   pressure   temperature   wind ) </code><pre>get_hour</pre><code> def get_hour(x):     x['hour'] = x['moment'].hour     return x  </code><pre>y_mean_by_station_and_hour</pre><code>TargetAgg (   by=['station', 'hour']   how=Mean ()   target_name=\"y\" ) </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.001     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>We can also use the <code>debug_one</code> method to see what happens to one particular instance. Let's train the model on the first 10,000 observations and then call <code>debug_one</code> on the next one. To do this, we will turn the <code>Bike</code> object into a Python generator with <code>iter()</code> function. The Pythonic way to read the first 10,000 elements of a generator is to use <code>itertools.islice</code>.</p> <pre><code>import itertools\n\nmodel = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')\nmodel += (\n    get_hour |\n    feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean())\n)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n\nfor x, y in itertools.islice(dataset, 10000):\n    y_pred = model.predict_one(x)\n    model.learn_one(x, y)\n\nx, y = next(iter(dataset))\nprint(model.debug_one(x))\n</code></pre> <pre><code>0. Input\n--------\nclouds: 75 (int)\ndescription: light rain (str)\nhumidity: 81 (int)\nmoment: 2016-04-01 00:00:07 (datetime)\npressure: 1,017.00000 (float)\nstation: metro-canal-du-midi (str)\ntemperature: 6.54000 (float)\nwind: 9.30000 (float)\n\n1. Transformer union\n--------------------\n    1.0 Select\n    ----------\n    clouds: 75 (int)\n    humidity: 81 (int)\n    pressure: 1,017.00000 (float)\n    temperature: 6.54000 (float)\n    wind: 9.30000 (float)\n\n    1.1 get_hour | y_mean_by_station_and_hour\n    -----------------------------------------\n    y_mean_by_station_and_hour: 4.43243 (float)\n\nclouds: 75 (int)\nhumidity: 81 (int)\npressure: 1,017.00000 (float)\ntemperature: 6.54000 (float)\nwind: 9.30000 (float)\ny_mean_by_station_and_hour: 4.43243 (float)\n\n2. StandardScaler\n-----------------\nclouds: 0.47566 (float)\nhumidity: 0.42247 (float)\npressure: 1.05314 (float)\ntemperature: -1.22098 (float)\nwind: 2.21104 (float)\ny_mean_by_station_and_hour: -0.59098 (float)\n\n3. LinearRegression\n-------------------\nName                         Value      Weight     Contribution  \n                 Intercept    1.00000    6.58252        6.58252  \n                  pressure    1.05314    3.78529        3.98646  \n                  humidity    0.42247    1.44921        0.61225  \ny_mean_by_station_and_hour   -0.59098    0.54167       -0.32011  \n                    clouds    0.47566   -1.92255       -0.91448  \n                      wind    2.21104   -0.77720       -1.71843  \n               temperature   -1.22098    2.47030       -3.01619\n\nPrediction: 5.21201\n</code></pre> <p>The <code>debug_one</code> method shows what happens to an input set of features, step by step.</p> <p>And now comes the catch. Up until now we've been using the <code>progressive_val_score</code> method from the <code>evaluate</code> module. What this does it that it sequentially predicts the output of an observation and updates the model immediately afterwards. This way of proceeding is often used for evaluating online learning models. But in some cases it is the wrong approach.</p> <p>When evaluating a machine learning model, the goal is to simulate production conditions in order to get a trust-worthy assessment of the performance of the model. In our case, we typically want to forecast the number of bikes available in a station, say, 30 minutes ahead. Then, once the 30 minutes have passed, the true number of available bikes will be available and we will be able to update the model using the features available 30 minutes ago.</p> <p>What we really want is to evaluate the model by forecasting 30 minutes ahead and only updating the model once the true values are available. This can be done using the <code>moment</code> and <code>delay</code> parameters in the  <code>progressive_val_score</code> method. The idea is that each observation in the stream of the data is shown twice to the model: once for making a prediction, and once for updating the model when the true value is revealed. The <code>moment</code> parameter determines which variable should be used as a timestamp, while the <code>delay</code> parameter controls the duration to wait before revealing the true values to the model.</p> <pre><code>import datetime as dt\n\nevaluate.progressive_val_score(\n    dataset=dataset,\n    model=model.clone(),\n    metric=metrics.MAE(),\n    moment='moment',\n    delay=dt.timedelta(minutes=30),\n    print_every=20_000\n)\n</code></pre> <pre><code>[20,000] MAE: 20.198137\n[40,000] MAE: 12.199763\n[60,000] MAE: 9.468279\n[80,000] MAE: 8.126625\n[100,000] MAE: 7.273133\n[120,000] MAE: 6.735469\n[140,000] MAE: 6.376704\n[160,000] MAE: 6.06156\n[180,000] MAE: 5.806744\n[182,470] MAE: 5.780772\n\n\n\n\n\nMAE: 5.780772\n</code></pre> <p>The performance is a bit worse, which is to be expected. Indeed, the task is more difficult: the model is only shown the ground truth 30 minutes after making a prediction.</p> <p>The takeaway of this notebook is that the <code>progressive_val_score</code> method can be used to simulate a production scenario, and is thus extremely valuable.</p>"},{"location":"examples/building-a-simple-nowcasting-model/","title":"Building a simple nowcasting model","text":"<p>Nowcasting is a special case of forecasting. It simply consists in predicting the next value in a time series.</p> <p>We'll be using the international airline passenger data available from here. This particular dataset is included with River in the <code>datasets</code> module.</p> <pre><code>from river import datasets\n\nfor x, y in datasets.AirlinePassengers():\n    print(x, y)\n    break\n</code></pre> <pre><code>{'month': datetime.datetime(1949, 1, 1, 0, 0)} 112\n</code></pre> <p>The data is as simple as can be: it consists of a sequence of months and values representing the total number of international airline passengers per month. Our goal is going to be to predict the number of passengers for the next month at each step. Notice that because the dataset is small  -- which is usually the case for time series -- we could just fit a model from scratch each month. However for the sake of example we're going to train a single model online. Although the overall performance might be potentially weaker, training a time series model online has the benefit of being scalable if, say, you have have thousands of time series to manage.</p> <p>We'll start with a very simple model where the only feature will be the ordinal date of each month. This should be able to capture some of the underlying trend. </p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import preprocessing\n\n\ndef get_ordinal_date(x):\n    return {'ordinal_date': x['month'].toordinal()}\n\n\nmodel = compose.Pipeline(\n    ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression())\n)\n</code></pre> <p>We'll write down a function to evaluate the model. This will go through each observation in the dataset and update the model as it goes on. The prior predictions will be stored along with the true values and will be plotted together. </p> <pre><code>from river import metrics\nfrom river import utils\nimport matplotlib.pyplot as plt\n\n\ndef evaluate_model(model): \n\n    metric = utils.Rolling(metrics.MAE(), 12)\n\n    dates = []\n    y_trues = []\n    y_preds = []\n\n    for x, y in datasets.AirlinePassengers():\n\n        # Obtain the prior prediction and update the model in one go\n        y_pred = model.predict_one(x)\n        model.learn_one(x, y)\n\n        # Update the error metric\n        metric.update(y, y_pred)\n\n        # Store the true value and the prediction\n        dates.append(x['month'])\n        y_trues.append(y)\n        y_preds.append(y_pred)\n\n    # Plot the results\n    fig, ax = plt.subplots(figsize=(10, 6))\n    ax.grid(alpha=0.75)\n    ax.plot(dates, y_trues, lw=3, color='#2ecc71', alpha=0.8, label='Ground truth')\n    ax.plot(dates, y_preds, lw=3, color='#e74c3c', alpha=0.8, label='Prediction')\n    ax.legend()\n    ax.set_title(metric)\n</code></pre> <p>Let's evaluate our first model.</p> <pre><code>evaluate_model(model)\n</code></pre> <p></p> <p>The model has captured a trend but not the right one. Indeed it thinks the trend is linear whereas we can visually see that the growth of the data increases with time. In other words the second derivative of the series is positive. This is a well know problem in time series forecasting and there are thus many ways to handle it; for example by using a Box-Cox transform. However we are going to do something a bit different, and instead linearly detrend the series using a <code>TargetStandardScaler</code>.</p> <pre><code>from river import stats\n\n\nmodel = compose.Pipeline(\n    ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(intercept_lr=0)),\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>Now let's try and capture the monthly trend by one-hot encoding the month name.</p> <pre><code>import calendar\n\n\ndef get_month(x):\n    return {\n        calendar.month_name[month]: month == x['month'].month\n        for month in range(1, 13)\n    }\n\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n        ('month', compose.FuncTransformer(get_month)),\n    )),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(intercept_lr=0))\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>This seems pretty decent. We can take a look at the weights of the linear regression to get an idea of the importance of each feature.</p> <pre><code>model.regressor['lin_reg'].weights\n</code></pre> <pre><code>{'January': -0.13808091575141299,\n 'February': -0.18716063793638954,\n 'March': -0.026469206216021102,\n 'April': -0.03500685108350436,\n 'May': -0.013638742192777328,\n 'June': 0.16194267303548826,\n 'July': 0.31995865445067634,\n 'August': 0.2810396556938982,\n 'September': 0.03834350518076595,\n 'October': -0.11655850082390988,\n 'November': -0.2663497734491209,\n 'December': -0.15396048501165746,\n 'ordinal_date': 1.0234863735122575}\n</code></pre> <p>As could be expected the months of July and August have the highest weights because these are the months where people typically go on holiday abroad. The month of December has a low weight because this is a month of festivities in most of the Western world where people usually stay at home.</p> <p>Our model seems to understand which months are important, but it fails to see that the importance of each month grows multiplicatively as the years go on. In other words our model is too shy. We can fix this by increasing the learning rate of the <code>LinearRegression</code>'s optimizer.</p> <pre><code>from river import optim\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n        ('month', compose.FuncTransformer(get_month)),\n    )),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(\n        intercept_lr=0,\n        optimizer=optim.SGD(0.03)\n    ))\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>This is starting to look good! Naturally in production we would tune the learning rate, ideally in real-time.</p> <p>Before finishing, we're going to introduce a cool feature extraction trick based on radial basis function kernels. The one-hot encoding we did on the month is a good idea but if you think about it is a bit rigid. Indeed the value of each feature is going to be 0 or 1, depending on the month of each observation. We're basically saying that the month of September is as distant to the month of August as it is to the month of March. Of course this isn't true, and it would be nice if our features would reflect this. To do so we can simply calculate the distance between the month of each observation and all the months in the calendar. Instead of simply computing the distance linearly, we're going to use a so-called Gaussian radial basic function kernel. This is a bit of a mouthful but for us it boils down to a simple formula, which is:</p> \\[d(i, j) = exp(-\\frac{(i - j)^2}{2\\sigma^2})\\] <p>Intuitively this computes a similarity between two months -- denoted by \\(i\\) and \\(j\\) -- which decreases the further apart they are from each other. The \\(sigma\\) parameter can be seen as a hyperparameter than can be tuned -- in the following snippet we'll simply ignore it. The thing to take away is that this results in smoother predictions than when using a one-hot encoding scheme, which is often a desirable property. You can also see trick in action in this nice presentation.</p> <pre><code>import math\n\ndef get_month_distances(x):\n    return {\n        calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)\n        for month in range(1, 13)\n    }\n\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('ordinal_date', compose.FuncTransformer(get_ordinal_date)),\n        ('month_distances', compose.FuncTransformer(get_month_distances)),\n    )),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression(\n        intercept_lr=0,\n        optimizer=optim.SGD(0.03)\n    ))\n)\n\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <p>We've managed to get a good looking prediction curve with a reasonably simple model. What's more our model has the advantage of being interpretable and easy to debug. There surely are more rocks to squeeze (e.g. tune the hyperparameters, use an ensemble model, etc.) but we'll leave that as an exercice to the reader.</p> <p>As a finishing touch we'll rewrite our pipeline using the <code>|</code> operator, which is called a \"pipe\".</p> <pre><code>extract_features = compose.TransformerUnion(get_ordinal_date, get_month_distances)\n\nscale = preprocessing.StandardScaler()\n\nlearn = linear_model.LinearRegression(\n    intercept_lr=0,\n    optimizer=optim.SGD(0.03)\n)\n\nmodel = extract_features | scale | learn\nmodel = preprocessing.TargetStandardScaler(regressor=model)\n\nevaluate_model(model)\n</code></pre> <p></p> <pre><code>model\n</code></pre> <pre>TargetStandardScaler</pre><code>TargetStandardScaler (   regressor=Pipeline (     steps=OrderedDict([('TransformerUnion', TransformerUnion (   FuncTransformer (     func=\"get_ordinal_date\"   ),   FuncTransformer (     func=\"get_month_distances\"   ) )), ('StandardScaler', StandardScaler (   with_std=True )), ('LinearRegression', LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.03     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0   )   clip_gradient=1e+12   initializer=Zeros () ))])   ) ) </code><pre>get_ordinal_date</pre><code> def get_ordinal_date(x):     return {'ordinal_date': x['month'].toordinal()}  </code><pre>get_month_distances</pre><code> def get_month_distances(x):     return {         calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)         for month in range(1, 13)     }  </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.03     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0   )   clip_gradient=1e+12   initializer=Zeros () ) </code>"},{"location":"examples/content-personalization/","title":"Content personalization","text":""},{"location":"examples/content-personalization/#without-context","title":"Without context","text":"<p>This example takes inspiration from Vowpal Wabbit's excellent tutorial.</p> <p>Content personalization is about taking into account user preferences. It's a special case of recommender systems. Ideally, side-information should be taken into account in addition to the user. But we'll start with something simpler. We'll assume that each user has stable preferences that are independent of the context. We capture this by implementing a \"reward\" function.</p> <pre><code>def get_reward(user, item, context):\n\n    time_of_day = context['time_of_day']\n\n    USER_LIKED_ARTICLE = 1\n    USER_DISLIKED_ARTICLE = 0\n\n    if user == 'Tom':\n        if time_of_day == 'morning' and item == 'politics':\n            return USER_LIKED_ARTICLE\n        elif time_of_day == 'afternoon' and item == 'music':\n            return USER_LIKED_ARTICLE\n        else:\n            return USER_DISLIKED_ARTICLE\n    elif user == 'Anna':\n        if time_of_day == 'morning' and item == 'sports':\n            return USER_LIKED_ARTICLE\n        elif time_of_day == 'afternoon' and item == 'politics':\n            return USER_LIKED_ARTICLE\n        else:\n            return USER_DISLIKED_ARTICLE\n\nget_reward('Tom', 'politics', {'time_of_day': 'morning'})\n</code></pre> <pre><code>1\n</code></pre> <p>Measuring the performance of a recommendation is not straightforward, mostly because of the interactive aspect of recommender systems. In a real situation, recommendations are presented to a user, and the user gives feedback indicating whether they like what they have been recommended or not. This feedback loop can't be captured entirely by a historical dataset. Some kind of simulator is required to generate recommendations and capture feedback. We already have a reward function. Now let's implement a simulation function.</p> <pre><code>import random\nimport matplotlib.pyplot as plt\n\ndef plot_ctr(ctr):\n    plt.plot(range(1, len(ctr) + 1), ctr)\n    plt.xlabel('n_iterations', fontsize=14)\n    plt.ylabel('CTR', fontsize=14)\n    plt.ylim([0, 1])\n    plt.title(f'final CTR: {ctr[-1]:.2%}', fontsize=14)\n    plt.grid()\n\nusers = ['Tom', 'Anna']\ntimes_of_day = ['morning', 'afternoon']\nitems = {'politics', 'sports', 'music', 'food', 'finance', 'health', 'camping'}\n\ndef simulate(n, reward_func, model, seed):\n\n    rng = random.Random(seed)\n    n_clicks = 0\n    ctr = []  # click-through rate along time\n\n    for i in range(n):\n\n        # Generate a context at random\n        user = rng.choice(users)\n        context = {\n            'time_of_day': rng.choice(times_of_day)\n        }\n\n        # Make a single recommendation\n        item = model.rank(user, items=items, x=context)[0]\n\n        # Measure the reward\n        clicked = reward_func(user, item, context)\n        n_clicks += clicked\n        ctr.append(n_clicks / (i + 1))\n\n        # Update the model\n        model.learn_one(user, item, y=clicked, x=context)\n\n    plot_ctr(ctr)\n</code></pre> <p>This simulation function does quite a few things. It can be seen as a simple reinforcement learning simulation. It samples a user, and then ask the model to provide a single recommendation. The user then gives as to whether they liked the recommendation or not. Crucially, the user doesn't tell us what item they would have liked. We could model this as a multi-class classification problem if that were the case.</p> <p>The strategy parameter determines the mechanism used to generate the recommendations. The <code>'best'</code> strategy means that the items are each scored by the model, and are then ranked from the most preferred to the least preferred. Here the most preferred item is the one which gets recommended. But you could imagine all sorts of alternative ways to proceed.</p> <p>We can first evaluate a recommended which acts completely at random. It assigns a random preference to each item, regardless of the user.</p> <pre><code>from river import reco\n\nmodel = reco.RandomNormal(seed=10)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>We can see that the click-through rate (CTR) oscillates around 28.74%. In fact, this model is expected to be correct <code>100 * (2 / 7)% = 28.57%</code> of the time. Indeed, each user likes two items, and there are seven items in total.</p> <p>Let's now use the <code>Baseline</code> recommended. This one models each preference as the following sum:</p> \\[preference = \\bar{y} + b_{u} + b_{i}\\] <p>where</p> <ul> <li>\\(\\bar{y}\\) is the average CTR overall</li> <li>\\(b_{u}\\) is the average CTR per user minus \\(\\bar{y}\\) -- it's therefore called a bias</li> <li>\\(b_{i}\\) is the average CTR per item minus \\(\\bar{y}\\)</li> </ul> <p>This model is considered to be a baseline because it doesn't actually learn what items are preferred by each user. Instead it models each user and item separately. We shouldn't expect it to be a strong model. It should however do better than the random model used above.</p> <pre><code>model = reco.Baseline(seed=10)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>This baseline model seems perfect, which is surprising. The reason why it works so well is because both users have in common that they both like politics. The model therefore learns that the <code>'politics'</code> is a good item to recommend.</p> <pre><code>model.i_biases\n</code></pre> <pre><code>defaultdict(Zeros (),\n            {'politics': 0.06389451550325113,\n             'music': -0.04041254194187752,\n             'camping': -0.040319730234734,\n             'health': -0.03581829597317823,\n             'food': -0.037778771188204816,\n             'finance': -0.04029646665611086,\n             'sports': -0.03661678982763635})\n</code></pre> <p>The model is not as performant if we use a reward function where both users have different preferences.</p> <pre><code>simulate(\n    5_000,\n    reward_func=lambda user, item, context: (\n        item in {'music', 'politics'} if user == \"Tom\" else\n        item in {'food', 'sports'}\n    ),\n    model=model,\n    seed=42\n)\n</code></pre> <p></p> <p>A good recommender model should at the very least understand what kind of items each user prefers. One of the simplest and yet performant way to do this is Simon Funk's SGD method he developped for the Netflix challenge and wrote about here. It models each user and each item as latent vectors. The dot product of these two vectors is the expected preference of the user for the item.</p> <pre><code>model = reco.FunkMF(seed=10)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>We can see that this model learns what items each user enjoys very well. Of course, there are some caveats. In our simulation, we ask the model to recommend the item most likely to be preferred for each user. Indeed, we rank all the items and pick the item at the top of the list. We do this many times for only two users.</p> <p>This is of course not realistic. Users will get fed up with recommendations if they're always shown the same item. It's important to include diversity into recommendations, and to let the model explore other options instead of always focusing on the item with the highest score. This is where evaluating recommender systems gets tricky: the reward function itself is difficult to model.</p> <p>We will keep ignoring these caveats in this notebook. Instead we will focus on a different concern: making recommendations when context is involved.</p>"},{"location":"examples/content-personalization/#with-context","title":"With context","text":"<p>We'll add some context by making it so that user preferences change depending on the time the day. Very simply, preferences might change from morning to afternoon. This is captured by the following reward function.</p> <pre><code>times_of_day = ['morning', 'afternoon']\n\ndef get_reward(user, item, context):\n    if user == 'Tom':\n        if context['time_of_day'] == 'morning':\n            return item == 'politics'\n        if context['time_of_day'] == 'afternoon':\n            return item == 'music'\n    if user == 'Anna':\n        if context['time_of_day'] == 'morning':\n            return item == 'sports'\n        if context['time_of_day'] == 'afternoon':\n            return item == 'politics'\n</code></pre> <p>We have to update our simulation function to generate a random context at each step. We also want our model to use it for recommending items as well as learning.</p> <pre><code>def simulate(n, reward_func, model, seed):\n\n    rng = random.Random(seed)\n    n_clicks = 0\n    ctr = []\n\n    for i in range(n):\n\n        user = rng.choice(users)\n\n        # New: pass a context\n        context = {'time_of_day': rng.choice(times_of_day)}\n        item = model.rank(user, items, context)[0]\n\n        clicked = reward_func(user, item, context)\n        n_clicks += clicked\n        ctr.append(n_clicks / (i + 1))\n\n        # New: pass a context\n        model.learn_one(user, item, clicked, context)\n\n    plot_ctr(ctr)\n</code></pre> <p>Not all models are capable of taking into account context. For instance, the <code>FunkMF</code> model only models users and items. It completely ignores the context, even when we provide one. All recommender models inherit from the base <code>Recommender</code> class. They also have a property which indicates whether or not they are able to handle context:</p> <pre><code>model = reco.FunkMF(seed=10)\nmodel.is_contextual\n</code></pre> <pre><code>False\n</code></pre> <p>Let's see well it performs.</p> <pre><code>simulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>The performance has roughly been divided by half. This is most likely because there are now two times of day, and if the model has learnt preferences for one time of the day, then it's expected to be wrong half of the time.</p> <p>Before delving into recsys models that can handle context, a simple hack is to notice that we can append the time of day to the user. This effectively results in new users which our model can distinguish between. We could apply this trick during the simulation, but we can also override the behavior of the <code>learn_one</code> and <code>rank</code> methods of our model.</p> <pre><code>class FunkMFWithHack(reco.FunkMF):\n\n    def learn_one(self, user, item, reward, context):\n        user = f\"{user}@{context['time_of_day']}\"\n        return super().learn_one(user, item, reward, context)\n\n    def rank(self, user, items, context):\n        user = f\"{user}@{context['time_of_day']}\"\n        return super().rank(user, items, context)\n\nmodel = FunkMFWithHack(seed=29)\nsimulate(5_000, get_reward, model, seed=42)\n</code></pre> <p></p> <p>We can verify that the model has learnt the correct preferences by looking at the expected preference for each <code>(user, item)</code> pair.</p> <pre><code>import pandas as pd\n\n(\n    pd.DataFrame(\n        {\n            'user': user,\n            'item': item,\n            'preference': model.predict_one(user, item)\n        }\n        for user in model.u_latents\n        for item in model.i_latents\n    )\n    .pivot(index='user', columns='item')\n    .style.highlight_max(color='lightgreen', axis='columns')\n)\n</code></pre> preference item camping finance food health music politics sports user Anna@afternoon -0.018105 0.032865 0.069222 -0.059041 0.168353 1.000000 0.195960 Anna@morning -0.117577 0.081131 0.076300 -0.136399 0.154483 0.221890 1.000000 Tom@afternoon 0.057220 -0.027115 -0.074671 -0.233071 1.000000 0.163607 0.141781 Tom@morning -0.028562 -0.005428 0.061163 -0.050107 0.063483 1.000000 0.125515"},{"location":"examples/debugging-a-pipeline/","title":"Debugging a pipeline","text":"<p>River encourages users to make use of pipelines. The biggest pain point of pipelines is that it can be hard to understand what's happening to the data, especially when the pipeline is complex. Fortunately the <code>Pipeline</code> class has a <code>debug_one</code> method that can help out.</p> <p>Let's look at a fairly complex pipeline for predicting the number of bikes in 5 bike stations from the city of Toulouse. It doesn't matter if you understand the pipeline or not; the point of this notebook is to learn how to introspect a pipeline.</p> <pre><code>import datetime as dt\nfrom river import compose\nfrom river import datasets\nfrom river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stats\nfrom river import stream\n\n\nX_y = datasets.Bikes()\nX_y = stream.simulate_qa(X_y, moment='moment', delay=dt.timedelta(minutes=30))\n\ndef add_time_features(x):\n    return {\n        **x,\n        'hour': x['moment'].hour,\n        'day': x['moment'].weekday()\n    }\n\nmodel = add_time_features\nmodel |= (\n    compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind') +\n    feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean()) +\n    feature_extraction.TargetAgg(by='station', how=stats.EWMean())\n)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n\nmetric = metrics.MAE()\n\nquestions = {}\n\nfor i, x, y in X_y:\n    # Question\n    is_question = y is None\n    if is_question:\n        y_pred = model.predict_one(x)\n        questions[i] = y_pred\n\n    # Answer\n    else:\n        metric.update(y, questions[i])\n        model.learn_one(x, y)\n\n        if i &gt;= 30000 and i % 30000 == 0:\n            print(i, metric)\n</code></pre> <pre><code>30000 MAE: 13.328051\n60000 MAE: 7.824087\n90000 MAE: 6.003909\n120000 MAE: 5.052855\n150000 MAE: 4.496826\n180000 MAE: 4.140702\n</code></pre> <p>Let's start by looking at the pipeline. You can click each cell to display the current state for each step of the pipeline.</p> <pre><code>model\n</code></pre> <pre>add_time_features</pre><code> def add_time_features(x):     return {         **x,         'hour': x['moment'].hour,         'day': x['moment'].weekday()     }  </code><pre>['clouds', [...]</pre><code>Select (   clouds   humidity   pressure   temperature   wind ) </code><pre>y_mean_by_station_and_hour</pre><code>TargetAgg (   by=['station', 'hour']   how=Mean ()   target_name=\"y\" ) </code><pre>y_ewm_0.5_by_station</pre><code>TargetAgg (   by=['station']   how=EWMean (     fading_factor=0.5   )   target_name=\"y\" ) </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>As mentioned above the <code>Pipeline</code> class has a <code>debug_one</code> method. You can use this at any point you want to visualize what happen to an input <code>x</code>. For example, let's see what happens to the last seen <code>x</code>.</p> <pre><code>print(model.debug_one(x))\n</code></pre> <pre><code>0. Input\n--------\nclouds: 88 (int)\ndescription: overcast clouds (str)\nhumidity: 84 (int)\nmoment: 2016-10-05 09:57:18 (datetime)\npressure: 1,017.34000 (float)\nstation: pomme (str)\ntemperature: 17.45000 (float)\nwind: 1.95000 (float)\n\n1. add_time_features\n--------------------\nclouds: 88 (int)\nday: 2 (int)\ndescription: overcast clouds (str)\nhour: 9 (int)\nhumidity: 84 (int)\nmoment: 2016-10-05 09:57:18 (datetime)\npressure: 1,017.34000 (float)\nstation: pomme (str)\ntemperature: 17.45000 (float)\nwind: 1.95000 (float)\n\n2. Transformer union\n--------------------\n    2.0 Select\n    ----------\n    clouds: 88 (int)\n    humidity: 84 (int)\n    pressure: 1,017.34000 (float)\n    temperature: 17.45000 (float)\n    wind: 1.95000 (float)\n\n    2.1 TargetAgg\n    -------------\n    y_mean_by_station_and_hour: 7.89396 (float)\n\n    2.2 TargetAgg1\n    --------------\n    y_ewm_0.5_by_station: 11.80372 (float)\n\nclouds: 88 (int)\nhumidity: 84 (int)\npressure: 1,017.34000 (float)\ntemperature: 17.45000 (float)\nwind: 1.95000 (float)\ny_ewm_0.5_by_station: 11.80372 (float)\ny_mean_by_station_and_hour: 7.89396 (float)\n\n3. StandardScaler\n-----------------\nclouds: 1.54778 (float)\nhumidity: 1.16366 (float)\npressure: 0.04916 (float)\ntemperature: -0.51938 (float)\nwind: -0.69426 (float)\ny_ewm_0.5_by_station: 0.19640 (float)\ny_mean_by_station_and_hour: -0.27110 (float)\n\n4. LinearRegression\n-------------------\nName                         Value      Weight     Contribution  \n                 Intercept    1.00000    9.19960        9.19960  \n      y_ewm_0.5_by_station    0.19640    9.19349        1.80562  \n                  humidity    1.16366    1.01680        1.18320  \n               temperature   -0.51938   -0.41575        0.21593  \n                      wind   -0.69426   -0.03810        0.02645  \n                  pressure    0.04916    0.18321        0.00901  \ny_mean_by_station_and_hour   -0.27110    0.19553       -0.05301  \n                    clouds    1.54778   -0.32838       -0.50827\n\nPrediction: 11.87854\n</code></pre> <p>The pipeline does quite a few things, but using <code>debug_one</code> shows what happens step by step. This is really useful for checking that the pipeline is behaving as you're expecting it too. Remember that you can <code>debug_one</code> whenever you wish, be it before, during, or after training a model.</p>"},{"location":"examples/imbalanced-learning/","title":"Working with imbalanced data","text":"<p>In machine learning it is quite usual to have to deal with imbalanced dataset. This is particularly true in online learning for tasks such as fraud detection and spam classification. In these two cases, which are binary classification problems, there are usually many more 0s than 1s, which generally hinders the performance of the classifiers we thrown at them.</p> <p>As an example we'll use the credit card dataset available in River. We'll first use a <code>collections.Counter</code> to count the number of 0s and 1s in order to get an idea of the class balance.</p> <pre><code>import collections\nfrom river import datasets\n\nX_y = datasets.CreditCard()\n\ncounts = collections.Counter(y for _, y in X_y)\n\nfor c, count in counts.items():\n    print(f'{c}: {count} ({count / sum(counts.values()):.5%})')\n</code></pre> <pre><code>0: 284315 (99.82725%)\n1: 492 (0.17275%)\n</code></pre>"},{"location":"examples/imbalanced-learning/#baseline","title":"Baseline","text":"<p>The dataset is quite unbalanced. For each 1 there are about 578 0s. Let's now train a logistic regression with default parameters and see how well it does. We'll measure the ROC AUC score.</p> <pre><code>from river import linear_model\nfrom river import metrics\nfrom river import evaluate\nfrom river import preprocessing\n\n\nX_y = datasets.CreditCard()\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression()\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 89.11%\n</code></pre>"},{"location":"examples/imbalanced-learning/#importance-weighting","title":"Importance weighting","text":"<p>The performance is already quite acceptable, but as we will now see we can do even better. The first thing we can do is to add weight to the 1s by using the <code>weight_pos</code> argument of the <code>Log</code> loss function.</p> <pre><code>from river import optim\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(\n        loss=optim.losses.Log(weight_pos=5)\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 91.43%\n</code></pre>"},{"location":"examples/imbalanced-learning/#focal-loss","title":"Focal loss","text":"<p>The deep learning for object detection community has produced a special loss function for imbalanced learning called focal loss. We are doing binary classification, so we can plug the binary version of focal loss into our logistic regression and see how well it fairs.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    linear_model.LogisticRegression(loss=optim.losses.BinaryFocalLoss(2, 1))\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 91.31%\n</code></pre>"},{"location":"examples/imbalanced-learning/#under-sampling-the-majority-class","title":"Under-sampling the majority class","text":"<p>Adding importance weights only works with gradient-based models (which includes neural networks). A more generic, and potentially more effective approach, is to use undersamplig and oversampling. As an example, we'll under-sample the stream so that our logistic regression encounter 20% of 1s and 80% of 0s. Under-sampling has the additional benefit of requiring less training steps, and thus reduces the total training time.</p> <pre><code>from river import imblearn\n\nmodel = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomUnderSampler(\n        classifier=linear_model.LogisticRegression(),\n        desired_dist={0: .8, 1: .2},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 94.75%\n</code></pre> <p>The <code>RandomUnderSampler</code> class is a wrapper for classifiers. This is represented by a rectangle around the logistic regression bubble when we visualize the model.</p> <pre><code>model\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>RandomUnderSampler</pre><code>RandomUnderSampler (   classifier=LogisticRegression (     optimizer=SGD (       lr=Constant (         learning_rate=0.01       )     )     loss=Log (       weight_pos=1.       weight_neg=1.     )     l2=0.     l1=0.     intercept_init=0.     intercept_lr=Constant (       learning_rate=0.01     )     clip_gradient=1e+12     initializer=Zeros ()   )   desired_dist={0: 0.8, 1: 0.2}   seed=42 ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code>"},{"location":"examples/imbalanced-learning/#over-sampling-the-minority-class","title":"Over-sampling the minority class","text":"<p>We can also attain the same class distribution by over-sampling the minority class. This will come at cost of having to train with more samples.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomOverSampler(\n        classifier=linear_model.LogisticRegression(),\n        desired_dist={0: .8, 1: .2},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 91.71%\n</code></pre>"},{"location":"examples/imbalanced-learning/#sampling-with-a-desired-sample-size","title":"Sampling with a desired sample size","text":"<p>The downside of both <code>RandomUnderSampler</code> and <code>RandomOverSampler</code> is that you don't have any control on the amount of data the classifier trains on. The number of samples is adjusted so that the target distribution can be attained, either by under-sampling or over-sampling. However, you can do both at the same time and choose how much data the classifier will see. To do so, we can use the <code>RandomSampler</code> class. In addition to the desired class distribution, we can specify how much data to train on. The samples will both be under-sampled and over-sampled in order to fit your constraints. This is powerful because it allows you to control both the class distribution and the size of the training data (and thus the training time). In the following example we'll set it so that the model will train with 1 percent of the data.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomSampler(\n        classifier=linear_model.LogisticRegression(),\n        desired_dist={0: .8, 1: .2},\n        sampling_rate=.01,\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 94.71%\n</code></pre>"},{"location":"examples/imbalanced-learning/#hybrid-approach","title":"Hybrid approach","text":"<p>As you might have guessed by now, nothing is stopping you from mixing imbalanced learning methods together. As an example, let's combine <code>sampling.RandomUnderSampler</code> and the <code>weight_pos</code> parameter from the <code>optim.losses.Log</code> loss function.</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    imblearn.RandomUnderSampler(\n        classifier=linear_model.LogisticRegression(\n            loss=optim.losses.Log(weight_pos=5)\n        ),\n        desired_dist={0: .8, 1: .2},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(X_y, model, metric)\n</code></pre> <pre><code>ROCAUC: 96.52%\n</code></pre>"},{"location":"examples/quantile-regression-uncertainty/","title":"Handling uncertainty with quantile regression","text":"<pre><code>%matplotlib inline\n</code></pre> <p>Quantile regression is useful when you're not so much interested in the accuracy of your model, but rather you want your model to be good at ranking observations correctly. The typical way to perform quantile regression is to use a special loss function, namely the quantile loss. The quantile loss takes a parameter, \\(\\alpha\\) (alpha), which indicates which quantile the model should be targeting. In the case of \\(\\alpha = 0.5\\), then this is equivalent to asking the model to predict the median value of the target, and not the most likely value which would be the mean. </p> <p>A nice thing we can do with quantile regression is to produce a prediction interval for each prediction. Indeed, if we predict the lower and upper quantiles of the target then we will be able to obtain a \"trust region\" in between which the true value is likely to belong. Of course, the likeliness will depend on the chosen quantiles. For a slightly more detailed explanation see this blog post.</p> <p>As an example, let us take the simple nowcasting model we built in another notebook. Instead of predicting the mean value of the target distribution, we will predict the 5th, 50th, 95th quantiles. This will require training three separate models, so we will encapsulate the model building logic in a function called <code>make_model</code>. We also have to slightly adapt the training loop, but not by much. Finally, we will draw the prediction interval along with the predictions from for 50th quantile (i.e. the median) and the true values.</p> <pre><code>import calendar\nimport math\nimport matplotlib.pyplot as plt\nfrom river import compose\nfrom river import datasets\nfrom river import linear_model\nfrom river import metrics\nfrom river import optim\nfrom river import preprocessing\nfrom river import stats\n\n\ndef get_ordinal_date(x):\n    return {'ordinal_date': x['month'].toordinal()}    \n\n\ndef get_month_distances(x):\n    return {\n        calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)\n        for month in range(1, 13)\n    }\n\n\ndef make_model(alpha):\n\n    extract_features = compose.TransformerUnion(get_ordinal_date, get_month_distances)\n\n    scale = preprocessing.StandardScaler()\n\n    learn = linear_model.LinearRegression(\n        intercept_lr=0,\n        optimizer=optim.SGD(0.03),\n        loss=optim.losses.Quantile(alpha=alpha)\n    )\n\n    model = extract_features | scale | learn\n    model = preprocessing.TargetStandardScaler(regressor=model)\n\n    return model\n\nmetric = metrics.MAE()\n\nmodels = {\n    'lower': make_model(alpha=0.05),\n    'center': make_model(alpha=0.5),\n    'upper': make_model(alpha=0.95)\n}\n\ndates = []\ny_trues = []\ny_preds = {\n    'lower': [],\n    'center': [],\n    'upper': []\n}\n\nfor x, y in datasets.AirlinePassengers():\n    y_trues.append(y)\n    dates.append(x['month'])\n\n    for name, model in models.items():\n        y_preds[name].append(model.predict_one(x))\n        model.learn_one(x, y)\n\n    # Update the error metric\n    metric.update(y, y_preds['center'][-1])\n\n# Plot the results\nfig, ax = plt.subplots(figsize=(10, 6))\nax.grid(alpha=0.75)\nax.plot(dates, y_trues, lw=3, color='#2ecc71', alpha=0.8, label='Truth')\nax.plot(dates, y_preds['center'], lw=3, color='#e74c3c', alpha=0.8, label='Prediction')\nax.fill_between(dates, y_preds['lower'], y_preds['upper'], color='#e74c3c', alpha=0.3, label='Prediction interval')\nax.legend()\nax.set_title(metric);\n</code></pre> <p></p> <p>An important thing to note is that the prediction interval we obtained should not be confused with a confidence interval. Simply put, a prediction interval represents uncertainty for where the true value lies, whereas a confidence interval encapsulates the uncertainty on the prediction. You can find out more by reading this CrossValidated post.</p>"},{"location":"examples/sentence-classification/","title":"Sentence classification","text":"<p>In this tutorial we will try to predict whether an SMS is a spam or not. To train our model, we will use the <code>SMSSpam</code> dataset. This dataset is unbalanced, there is only 13.4% spam. Let's look at the data:</p> <pre><code>from river import datasets\n\ndatasets.SMSSpam()\n</code></pre> <pre><code>SMS Spam Collection dataset.\n\nThe data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent\n13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.\n\n      Name  SMSSpam                                                                              \n      Task  Binary classification                                                                \n   Samples  5,574                                                                                \n  Features  1                                                                                    \n    Sparse  False                                                                                \n      Path  /Users/max/river_data/SMSSpam/SMSSpamCollection                                      \n       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\n      Size  466.71 KB                                                                            \nDownloaded  True\n</code></pre> <pre><code>from pprint import pprint\n\nX_y = datasets.SMSSpam()\n\nfor x, y in X_y:\n    pprint(x)\n    print(f'Spam: {y}')\n    break\n</code></pre> <pre><code>{'body': 'Go until jurong point, crazy.. Available only in bugis n great world '\n         'la e buffet... Cine there got amore wat...\\n'}\nSpam: False\n</code></pre> <p>Let's start by building a simple model like a Naive Bayes classifier. We will first preprocess the sentences with a TF-IDF transform that our model can consume. Then, we will measure the accuracy of our model with the AUC metric. This is the right metric to use when the classes are not balanced. In addition, the Naive Bayes models can perform very well on unbalanced datasets and can be used for both binary and multi-class classification problems.</p> <pre><code>from river import feature_extraction\nfrom river import naive_bayes\nfrom river import metrics\n\nX_y = datasets.SMSSpam()\n\nmodel = (\n    feature_extraction.TFIDF(on='body') | \n    naive_bayes.BernoulliNB(alpha=0)\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    if y_pred is not None:\n        metric.update(y_pred=y_pred, y_true=y)\n        cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 93.00%\n</code></pre> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,809     17  \n True     102    645\n</code></pre> <p>The results are quite good with this first model.</p> <p>Since we are working with an imbalanced dataset, we can use the <code>imblearn</code> module to rebalance the classes of our dataset. For more information about the <code>imblearn</code> module, you can find a dedicated tutorial here.</p> <pre><code>from river import imblearn\n\nX_y = datasets.SMSSpam()\n\nmodel = (\n    feature_extraction.TFIDF(on='body') | \n    imblearn.RandomUnderSampler(\n        classifier=naive_bayes.BernoulliNB(alpha=0),\n        desired_dist={0: .5, 1: .5},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    if y_pred is not None:\n        metric.update(y_pred=y_pred, y_true=y)\n        cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 94.61%\n</code></pre> <p>The <code>imblearn</code> module improved our results. Not bad! We can visualize the pipeline to understand how the data is processed.</p> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,570    255  \n True      41    706\n</code></pre> <pre><code>model\n</code></pre> <pre>TFIDF</pre><code>TFIDF (   normalize=True   on=\"body\"   strip_accents=True   lowercase=True   preprocessor=None   tokenizer=None   ngram_range=(1, 1) ) </code><pre>RandomUnderSampler</pre><code>RandomUnderSampler (   classifier=BernoulliNB (     alpha=0     true_threshold=0.   )   desired_dist={0: 0.5, 1: 0.5}   seed=42 ) </code><pre>BernoulliNB</pre><code>BernoulliNB (   alpha=0   true_threshold=0. ) </code> <p>Now let's try to use logistic regression to classify messages. We will use different tips to make my model perform better. As in the previous example, we rebalance the classes of our dataset. The logistics regression will be fed from a TF-IDF.</p> <pre><code>from river import linear_model\nfrom river import optim\nfrom river import preprocessing\n\nX_y = datasets.SMSSpam()\n\nmodel = (\n    feature_extraction.TFIDF(on='body') | \n    preprocessing.Normalizer() | \n    imblearn.RandomUnderSampler(\n        classifier=linear_model.LogisticRegression(\n            optimizer=optim.SGD(.9), \n            loss=optim.losses.Log()\n        ),\n        desired_dist={0: .5, 1: .5},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    metric.update(y_pred=y_pred, y_true=y)\n    cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 93.80%\n</code></pre> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,584    243  \n True      55    692\n</code></pre> <pre><code>model\n</code></pre> <pre>TFIDF</pre><code>TFIDF (   normalize=True   on=\"body\"   strip_accents=True   lowercase=True   preprocessor=None   tokenizer=None   ngram_range=(1, 1) ) </code><pre>Normalizer</pre><code>Normalizer (   order=2 ) </code><pre>RandomUnderSampler</pre><code>RandomUnderSampler (   classifier=LogisticRegression (     optimizer=SGD (       lr=Constant (         learning_rate=0.9       )     )     loss=Log (       weight_pos=1.       weight_neg=1.     )     l2=0.     l1=0.     intercept_init=0.     intercept_lr=Constant (       learning_rate=0.01     )     clip_gradient=1e+12     initializer=Zeros ()   )   desired_dist={0: 0.5, 1: 0.5}   seed=42 ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.9     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>The results of the logistic regression are quite good but still inferior to the naive Bayes model.</p> <p>Let's try to use word embeddings to improve our logistic regression. Word embeddings allow you to represent a word as a vector. Embeddings are developed to build semantically rich vectors. For instance, the vector which represents the word python should be close to the vector which represents the word programming. We will use spaCy to convert our sentence to vectors. spaCy converts a sentence to a vector by calculating the average of the embeddings of the words in the sentence.</p> <p>You can download pre-trained embeddings in many languages. We will use English pre-trained embeddings as our SMS are in English.</p> <p>The command below allows you to download the pre-trained embeddings that spaCy makes available. More informations about spaCy and its installation may be found here here.</p> <pre><code>python -m spacy download en_core_web_sm\n</code></pre> <p>Here, we create a custom transformer to convert an input sentence to a dict of floats. We will integrate this transformer into our pipeline.</p> <pre><code>import spacy\n\nfrom river.base import Transformer\n\nclass Embeddings(Transformer):\n    \"\"\"My custom transformer, word embedding using spaCy.\"\"\"\n\n    def __init__(self, on: str):\n        self.on = on\n        self.embeddings = spacy.load('en_core_web_sm')\n\n    def transform_one(self, x, y=None):\n        return {dimension: xi for dimension, xi in enumerate(self.embeddings(x[self.on]).vector)}\n</code></pre> <p>Let's train our logistic regression:</p> <pre><code>X_y = datasets.SMSSpam()\n\nmodel = (\n    Embeddings(on='body') | \n    preprocessing.Normalizer() |\n    imblearn.RandomOverSampler(\n        classifier=linear_model.LogisticRegression(\n            optimizer=optim.SGD(.5), \n            loss=optim.losses.Log()\n        ),\n        desired_dist={0: .5, 1: .5},\n        seed=42\n    )\n)\n\nmetric = metrics.ROCAUC()\ncm = metrics.ConfusionMatrix()\n\nfor x, y in X_y:\n\n    y_pred = model.predict_one(x)\n\n    metric.update(y_pred=y_pred, y_true=y)\n    cm.update(y_pred=y_pred, y_true=y)\n\n    model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 91.31%\n</code></pre> <p>The confusion matrix:</p> <pre><code>cm\n</code></pre> <pre><code>        False   True  \nFalse   4,537    290  \n True      85    662\n</code></pre> <pre><code>model\n</code></pre> <pre>Embeddings</pre><code>Embeddings (   on=\"body\" ) </code><pre>Normalizer</pre><code>Normalizer (   order=2 ) </code><pre>RandomOverSampler</pre><code>RandomOverSampler (   classifier=LogisticRegression (     optimizer=SGD (       lr=Constant (         learning_rate=0.5       )     )     loss=Log (       weight_pos=1.       weight_neg=1.     )     l2=0.     l1=0.     intercept_init=0.     intercept_lr=Constant (       learning_rate=0.01     )     clip_gradient=1e+12     initializer=Zeros ()   )   desired_dist={0: 0.5, 1: 0.5}   seed=42 ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.5     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>The results of the logistic regression using spaCy embeddings are lower than those obtained with TF-IDF values. We could surely improve the results by cleaning up the text. We could also use embeddings more suited to our dataset. However, on this problem, the logistic regression is not better than the Naive Bayes model. No free lunch today.</p>"},{"location":"examples/the-art-of-using-pipelines/","title":"The art of using pipelines","text":"<p>Pipelines are a natural way to think about a machine learning system. Indeed with some practice a data scientist can visualise data \"flowing\" through a series of steps. The input is typically some raw data which has to be processed in some manner. The goal is to represent the data in such a way that is can be ingested by a machine learning algorithm. Along the way some steps will extract features, while others will normalize the data and remove undesirable elements. Pipelines are simple, and yet they are a powerful way of designing sophisticated machine learning systems.</p> <p>Both scikit-learn and pandas make it possible to use pipelines. However it's quite rare to see pipelines being used in practice (at least on Kaggle). Sometimes you get to see people using scikit-learn's <code>pipeline</code> module, however the <code>pipe</code> method from <code>pandas</code> is sadly underappreciated. A big reason why pipelines are not given much love is that it's easier to think of batch learning in terms of a script or a notebook. Indeed many people doing data science seem to prefer a procedural style to a declarative style. Moreover in practice pipelines can be a bit rigid if one wishes to do non-orthodox operations.</p> <p>Although pipelines may be a bit of an odd fit for batch learning, they make complete sense when they are used for online learning. Indeed the UNIX philosophy has advocated the use of pipelines for data processing for many decades. If you can visualise data as a stream of observations then using pipelines should make a lot of sense to you. We'll attempt to convince you by writing a machine learning algorithm in a procedural way and then converting it to a declarative pipeline in small steps. Hopefully by the end you'll be convinced, or not!</p> <p>In this notebook we'll manipulate data from the Kaggle Recruit Restaurants Visitor Forecasting competition. The data is directly available through River's <code>datasets</code> module.</p> <pre><code>from pprint import pprint\nfrom river import datasets\n\nfor x, y in datasets.Restaurants():\n    pprint(x)\n    pprint(y)\n    break\n</code></pre> <pre><code>{'area_name': 'T\u014dky\u014d-to Nerima-ku Toyotamakita',\n 'date': datetime.datetime(2016, 1, 1, 0, 0),\n 'genre_name': 'Izakaya',\n 'is_holiday': True,\n 'latitude': 35.7356234,\n 'longitude': 139.6516577,\n 'store_id': 'air_04341b588bde96cd'}\n10\n</code></pre> <p>We'll start by building and running a model using a procedural coding style. The performance of the model doesn't matter, we're simply interested in the design of the model.</p> <pre><code>from river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\nfrom river import preprocessing\nfrom river import stats\nfrom river import utils\n\nmeans = (\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)),\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)),\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21))\n)\n\nscaler = preprocessing.StandardScaler()\nlin_reg = linear_model.LinearRegression()\nmetric = metrics.MAE()\n\nfor x, y in datasets.Restaurants():\n\n    # Derive date features\n    x['weekday'] = x['date'].weekday()\n    x['is_weekend'] = x['date'].weekday() in (5, 6)\n\n    # Process the rolling means of the target  \n    for mean in means:\n        x = {**x, **mean.transform_one(x)}\n        mean.learn_one(x, y)\n\n    # Remove the key/value pairs that aren't features\n    for key in ['store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude']:\n        x.pop(key)\n\n    # Rescale the data\n    scaler.learn_one(x)\n    x = scaler.transform_one(x)\n\n    # Fit the linear regression\n    y_pred = lin_reg.predict_one(x)\n    lin_reg.learn_one(x, y)\n\n    # Update the metric using the out-of-fold prediction\n    metric.update(y, y_pred)\n\nprint(metric)\n</code></pre> <pre><code>MAE: 8.316538\n</code></pre> <p>We're not using many features. We can print the last <code>x</code> to get an idea of the features (don't forget they've been scaled!)</p> <pre><code>pprint(x)\n</code></pre> <pre><code>{'is_holiday': -0.23103573677646685,\n 'is_weekend': 1.6249280076334165,\n 'weekday': 1.0292832579142892,\n 'y_mean_by_store_id': -1.3980979075298516}\n</code></pre> <p>The above chunk of code is quite explicit but it's a bit verbose. The whole point of libraries such as River is to make life easier for users. Moreover there's too much space for users to mess up the order in which things are done, which increases the chance of there being target leakage. We'll now rewrite our model in a declarative fashion using a pipeline \u00e0 la sklearn.  </p> <pre><code>from river import compose\n\n\ndef get_date_features(x):\n    weekday =  x['date'].weekday()\n    return {'weekday': weekday, 'is_weekend': weekday in (5, 6)}\n\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('date_features', compose.FuncTransformer(get_date_features)),\n        ('last_7_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7))),\n        ('last_14_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14))),\n        ('last_21_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21)))\n    )),\n    ('drop_non_features', compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude')),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression())\n)\n\nmetric = metrics.MAE()\n\nfor x, y in datasets.Restaurants():\n\n    # Make a prediction without using the target\n    y_pred = model.predict_one(x)\n\n    # Update the model using the target\n    model.learn_one(x, y)\n\n    # Update the metric using the out-of-fold prediction\n    metric.update(y, y_pred)\n\nprint(metric)\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>We use a <code>Pipeline</code> to arrange each step in a sequential order. A <code>TransformerUnion</code> is used to merge multiple feature extractors into a single transformer. The <code>for</code> loop is now much shorter and is thus easier to grok: we get the out-of-fold prediction, we fit the model, and finally we update the metric. This way of evaluating a model is typical of online learning, and so we put it wrapped it inside a function called <code>progressive_val_score</code> part of the <code>evaluate</code> module. We can use it to replace the <code>for</code> loop.</p> <pre><code>from river import evaluate\n\nmodel = compose.Pipeline(\n    ('features', compose.TransformerUnion(\n        ('date_features', compose.FuncTransformer(get_date_features)),\n        ('last_7_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7))),\n        ('last_14_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14))),\n        ('last_21_mean', feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21)))\n    )),\n    ('drop_non_features', compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude')),\n    ('scale', preprocessing.StandardScaler()),\n    ('lin_reg', linear_model.LinearRegression())\n)\n\nevaluate.progressive_val_score(dataset=datasets.Restaurants(), model=model, metric=metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Notice that you couldn't have used the <code>progressive_val_score</code> method if you wrote the model in a procedural manner.</p> <p>Our code is getting shorter, but it's still a bit difficult on the eyes. Indeed there is a lot of boilerplate code associated with pipelines that can get tedious to write. However River has some special tricks up it's sleeve to save you from a lot of pain.</p> <p>The first trick is that the name of each step in the pipeline can be omitted. If no name is given for a step then River automatically infers one.</p> <pre><code>model = compose.Pipeline(\n    compose.TransformerUnion(\n        compose.FuncTransformer(get_date_features),\n        feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)),\n        feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)),\n        feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21))\n    ),\n    compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude'),\n    preprocessing.StandardScaler(),\n    linear_model.LinearRegression()\n)\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Under the hood a <code>Pipeline</code> inherits from <code>collections.OrderedDict</code>. Indeed this makes sense because if you think about it a <code>Pipeline</code> is simply a sequence of steps where each step has a name. The reason we mention this is because it means you can manipulate a <code>Pipeline</code> the same way you would manipulate an ordinary <code>dict</code>. For instance we can print the name of each step by using the <code>keys</code> method.</p> <pre><code>for name in model.steps:\n    print(name)\n</code></pre> <pre><code>TransformerUnion\nDiscard\nStandardScaler\nLinearRegression\n</code></pre> <p>The first step is a <code>FeatureUnion</code> and it's string representation contains the string representation of each of it's elements. Not having to write names saves up some time and space and is certainly less tedious.</p> <p>The next trick is that we can use mathematical operators to compose our pipeline. For example we can use the <code>+</code> operator to merge <code>Transformer</code>s into a <code>TransformerUnion</code>. </p> <pre><code>model = compose.Pipeline(\n    compose.FuncTransformer(get_date_features) + \\\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)) + \\\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)) + \\\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21)),\n\n    compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude'),\n    preprocessing.StandardScaler(),\n    linear_model.LinearRegression()\n)\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Likewhise we can use the <code>|</code> operator to assemble steps into a <code>Pipeline</code>. </p> <pre><code>model = (\n    compose.FuncTransformer(get_date_features) +\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 7)) +\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 14)) +\n    feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), 21))\n)\n\nto_discard = ['store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude']\n\nmodel = model | compose.Discard(*to_discard) | preprocessing.StandardScaler()\n\nmodel |= linear_model.LinearRegression()\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Hopefully you'll agree that this is a powerful way to express machine learning pipelines. For some people this should be quite remeniscent of the UNIX pipe operator. One final trick we want to mention is that functions are automatically wrapped with a <code>FuncTransformer</code>, which can be quite handy.</p> <pre><code>model = get_date_features\n\nfor n in [7, 14, 21]:\n    model += feature_extraction.TargetAgg(by='store_id', how=utils.Rolling(stats.Mean(), n))\n\nmodel |= compose.Discard(*to_discard)\nmodel |= preprocessing.StandardScaler()\nmodel |= linear_model.LinearRegression()\n\nevaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())\n</code></pre> <pre><code>MAE: 8.413859\n</code></pre> <p>Naturally some may prefer the procedural style we first used because they find it easier to work with. It all depends on your style and you should use what you feel comfortable with. However we encourage you to use operators because we believe that this will increase the readability of your code, which is very important. To each their own!</p> <p>Before finishing we can take an interactive look at our pipeline.</p> <pre><code>model\n</code></pre> <pre>get_date_features</pre><code> def get_date_features(x):     weekday =  x['date'].weekday()     return {'weekday': weekday, 'is_weekend': weekday in (5, 6)}  </code><pre>y_mean_by_store_id</pre><code>TargetAgg (   by=['store_id']   how=Rolling (     obj=Mean ()     window_size=7   )   target_name=\"y\" ) </code><pre>y_mean_by_store_id</pre><code>TargetAgg (   by=['store_id']   how=Rolling (     obj=Mean ()     window_size=14   )   target_name=\"y\" ) </code><pre>y_mean_by_store_id</pre><code>TargetAgg (   by=['store_id']   how=Rolling (     obj=Mean ()     window_size=21   )   target_name=\"y\" ) </code><pre>~['area_name', [...]</pre><code>Discard (   area_name   date   genre_name   latitude   longitude   store_id ) </code><pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/","title":"Part 1","text":"<p>Table of contents of this tutorial series on matrix factorization for recommender systems:</p> <ul> <li>Part 1 - Traditional Matrix Factorization methods for Recommender Systems</li> <li>Part 2 - Factorization Machines and Field-aware Factorization Machines</li> <li>Part 3 - Large scale learning and better predictive power with multiple pass learning</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#introduction","title":"Introduction","text":"<p>A recommender system is a software tool designed to generate and suggest items or entities to the users. Popular large scale examples include:</p> <ul> <li>Amazon (suggesting products)</li> <li>Facebook (suggesting posts in users' news feeds)</li> <li>Spotify (suggesting music)</li> </ul> <p>Social recommendation from graph (mostly used by social networks) are not covered in River. We focus on the general case, item recommendation. This problem can be represented with the user-item matrix:</p> \\[ \\normalsize \\begin{matrix}     &amp; \\begin{matrix} _1 &amp; _\\cdots &amp; _\\cdots &amp; _\\cdots &amp; _I \\end{matrix} \\\\     \\begin{matrix} _1 \\\\ _\\vdots \\\\ _\\vdots \\\\ _\\vdots \\\\ _U \\end{matrix} &amp;          \\begin{bmatrix}             {\\color{Red} ?} &amp; 2 &amp; \\cdots &amp; {\\color{Red} ?} &amp; {\\color{Red} ?} \\\\             {\\color{Red} ?} &amp; {\\color{Red} ?} &amp; \\cdots &amp; {\\color{Red} ?} &amp; 4.5 \\\\             \\vdots &amp; \\ddots &amp; \\ddots &amp; \\ddots &amp; \\vdots \\\\             3 &amp; {\\color{Red} ?} &amp; \\cdots &amp; {\\color{Red} ?} &amp; {\\color{Red} ?} \\\\             {\\color{Red} ?} &amp; {\\color{Red} ?} &amp; \\cdots &amp; 5 &amp; {\\color{Red} ?}         \\end{bmatrix} \\end{matrix} \\] <p>Where \\(U\\) and \\(I\\) are the number of user and item of the system, respectively. A matrix entry represents a user's preference for an item, it can be a rating, a like or dislike, etc. Because of the huge number of users and items compared to the number of observed entries, those matrices are very sparsed (usually less than 1% filled).</p> <p>Matrix Factorization (MF) is a class of collaborative filtering algorithms derived from Singular Value Decomposition (SVD). MF strength lies in its capacity to able to model high cardinality categorical variables interactions. This subfield boomed during the famous Netflix Prize contest in 2006, when numerous novel variants has been invented and became popular thanks to their attractive accuracy and scalability.</p> <p>MF approach seeks to fill the user-item matrix considering the problem as a matrix completion one. MF core idea assume a latent model learning its own representation of the users and the items in a lower latent dimensional space by factorizing the observed parts of the matrix.</p> <p>A factorized user or item is represented as a vector \\(\\mathbf{v}_u\\) or \\(\\mathbf{v}_i\\) composed of \\(k\\) latent factors, with \\(k &lt;&lt; U, I\\). Those learnt latent variables represent, for an item the various aspects describing it, and for a user its interests in terms of those aspects. The model then assume a user's choice or fondness is composed of a sum of preferences about the various aspects of the concerned item. This sum being the dot product between the latent vectors of a given user-item pair:</p> \\[ \\normalsize \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{u, f} \\cdot \\mathbf{v}_{i, f} \\] <p>MF models weights are learnt in an online fashion, often with stochastic gradient descent as it provides relatively fast running time and good accuracy. There is a great and widely popular library named surprise that implements MF models (and others) but in contrast with River doesn't follow a pure online philosophy (all the data have to be loaded in memory and the API doesn't allow you to update your model with new data).</p> <p>Notes:</p> <ul> <li>In recent years, proposed deep learning techniques for recommendation tasks claim state of the art results. However, recent work (August 2019) showed that those promises can't be taken for granted and traditional MF methods are still relevant today.</li> <li>For more information about how the business value of recommender systems is measured and why they are one of the main success stories of machine learning, see the following literature survey (December 2019).</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#lets-start","title":"Let's start","text":"<p>In this tutorial, we are going to explore MF algorithms available in River and test them on a movie recommendation problem with the MovieLens 100K dataset. This latter is a collection of movie ratings (from 1 to 5) that includes various information about both the items and the users. We can access it from the river.datasets module:</p> <pre><code>import json\n\nfrom river import datasets\n\nfor x, y in datasets.MovieLens100K():\n    print(f'x = {json.dumps(x, indent=4)}')\n    print(f'y = {y}')\n    break\n</code></pre> <pre><code>x = {\n    \"user\": \"259\",\n    \"item\": \"255\",\n    \"timestamp\": 874731910000000000,\n    \"title\": \"My Best Friend's Wedding (1997)\",\n    \"release_date\": 866764800000000000,\n    \"genres\": \"comedy, romance\",\n    \"age\": 21.0,\n    \"gender\": \"M\",\n    \"occupation\": \"student\",\n    \"zip_code\": \"48823\"\n}\ny = 4.0\n</code></pre> <p>Let's define a routine to evaluate our different models on MovieLens 100K. Mean Absolute Error and Root Mean Squared Error will be our metrics printed alongside model's computation time and memory usage:</p> <pre><code>from river import metrics\nfrom river.evaluate import progressive_val_score\n\ndef evaluate(model, unpack_user_and_item=True):\n    X_y = datasets.MovieLens100K(unpack_user_and_item)\n    metric = metrics.MAE() + metrics.RMSE()\n    _ = progressive_val_score(X_y, model, metric, print_every=25_000, show_time=True, show_memory=True)\n</code></pre>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#naive-prediction","title":"Naive prediction","text":"<p>It's good practice in machine learning to start with a naive baseline and then iterate from simple things to complex ones observing progress incrementally. Let's start by predicting the target running mean as a first shot:</p> <pre><code>from river import dummy\nfrom river import stats\n\nmodel = dummy.StatisticRegressor(stats.Mean())\nevaluate(model, unpack_user_and_item=False)\n</code></pre> <pre><code>[25,000] MAE: 0.934259\nRMSE: 1.124469 \u2013 00:00:00 \u2013 898 B\n[50,000] MAE: 0.923893\nRMSE: 1.105 \u2013 00:00:00 \u2013 898 B\n[75,000] MAE: 0.937359\nRMSE: 1.123696 \u2013 00:00:00 \u2013 898 B\n[100,000] MAE: 0.942162\nRMSE: 1.125783 \u2013 00:00:01 \u2013 898 B\n</code></pre>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#baseline-model","title":"Baseline model","text":"<p>Now we can do machine learning and explore available models in river.reco module starting with the baseline model. It extends our naive prediction by adding to the global running mean two bias terms characterizing the user and the item discrepancy from the general tendency. The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i} \\] <p>This baseline model can be viewed as a linear regression where the intercept is replaced by the target running mean with the users and the items one hot encoded.</p> <p>All machine learning models in River expect dicts as input with feature names as keys and feature values as values. Specifically, models from <code>river.reco</code> expect a <code>'user'</code> and an <code>'item'</code> entries without any type constraint on their values (i.e. can be strings or numbers), e.g.:</p> <pre><code>x = {\n    'user': 'Guido',\n    'item': \"Monty Python's Flying Circus\"\n}\n</code></pre> <p>Other entries, if exist, are simply ignored. This is quite useful as we don't need to spend time and storage doing one hot encoding.</p> <pre><code>from river import preprocessing\nfrom river import optim\nfrom river import reco\n\nbaseline_params = {\n    'optimizer': optim.SGD(0.025),\n    'l2': 0.,\n    'initializer': optim.initializers.Zeros()\n}\n\nmodel = preprocessing.PredClipper(\n    regressor=reco.Baseline(**baseline_params),\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761844\nRMSE: 0.960972 \u2013 00:00:00 \u2013 161.03 KB\n[50,000] MAE: 0.753292\nRMSE: 0.951223 \u2013 00:00:00 \u2013 216.34 KB\n[75,000] MAE: 0.754177\nRMSE: 0.953376 \u2013 00:00:01 \u2013 254.81 KB\n[100,000] MAE: 0.754651\nRMSE: 0.954148 \u2013 00:00:01 \u2013 278.41 KB\n</code></pre> <p>We won two tenth of MAE compared to our naive prediction (0.7546 vs 0.9421) meaning that significant information has been learnt by the model.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#funk-matrix-factorization-funkmf","title":"Funk Matrix Factorization (FunkMF)","text":"<p>It's the pure form of matrix factorization consisting of only learning the users and items latent representations as discussed in introduction. Simon Funk popularized its stochastic gradient descent optimization in 2006 during the Netflix Prize. The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle \\] <p>Note: FunkMF is sometimes referred as Probabilistic Matrix Factorization which is an extended probabilistic version.</p> <pre><code>funk_mf_params = {\n    'n_factors': 10,\n    'optimizer': optim.SGD(0.05),\n    'l2': 0.1,\n    'initializer': optim.initializers.Normal(mu=0., sigma=0.1, seed=73)\n}\n\nmodel = preprocessing.PredClipper(\n    regressor=reco.FunkMF(**funk_mf_params),\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 1.070136\nRMSE: 1.397014 \u2013 00:00:00 \u2013 557.99 KB\n[50,000] MAE: 0.99174\nRMSE: 1.290666 \u2013 00:00:01 \u2013 690.31 KB\n[75,000] MAE: 0.961072\nRMSE: 1.250842 \u2013 00:00:01 \u2013 813.07 KB\n[100,000] MAE: 0.944883\nRMSE: 1.227688 \u2013 00:00:02 \u2013 914.17 KB\n</code></pre> <p>Results are equivalent to our naive prediction (0.9448 vs 0.9421). By only focusing on the users preferences and the items characteristics, the model is limited in his ability to capture different views of the problem. Despite its poor performance alone, this algorithm is quite useful combined in other models or when we need to build dense representations for other tasks.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-1/#biased-matrix-factorization-biasedmf","title":"Biased Matrix Factorization (BiasedMF)","text":"<p>It's the combination of the Baseline model and FunkMF. The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = \\bar{y} + bu_{u} + bi_{i} + \\langle \\mathbf{v}_u, \\mathbf{v}_i \\rangle \\] <p>Note: Biased Matrix Factorization name is used by some people but some others refer to it by SVD or Funk SVD. It's the case of Yehuda Koren and Robert Bell in Recommender Systems Handbook (Chapter 5 Advances in Collaborative Filtering) and of <code>surprise</code> library. Nevertheless, SVD could be confused with the original Singular Value Decomposition from which it's derived from, and Funk SVD could also be misleading because of the biased part of the model equation which doesn't come from Simon Funk's work. For those reasons, we chose to side with Biased Matrix Factorization which fits more naturally to it.</p> <pre><code>biased_mf_params = {\n    'n_factors': 10,\n    'bias_optimizer': optim.SGD(0.025),\n    'latent_optimizer': optim.SGD(0.05),\n    'weight_initializer': optim.initializers.Zeros(),\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.1, seed=73),\n    'l2_bias': 0.,\n    'l2_latent': 0.\n}\n\nmodel = preprocessing.PredClipper(\n    regressor=reco.BiasedMF(**biased_mf_params),\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761818\nRMSE: 0.961057 \u2013 00:00:00 \u2013 643.81 KB\n[50,000] MAE: 0.751667\nRMSE: 0.949443 \u2013 00:00:01 \u2013 817.72 KB\n[75,000] MAE: 0.749653\nRMSE: 0.948723 \u2013 00:00:01 \u2013 964.02 KB\n[100,000] MAE: 0.748559\nRMSE: 0.947854 \u2013 00:00:02 \u2013 1.05 MB\n</code></pre> <p>Results improved (0.7485 vs 0.7546) demonstrating that users and items latent representations bring additional information.</p> <p>To conclude this first tutorial about factorization models, let's review the important parameters to tune when dealing with this family of methods:</p> <ul> <li><code>n_factors</code>: the number of latent factors. The more you set, the more items aspects and users preferences you are going to learn. Too many will cause overfitting, <code>l2</code> regularization could help.</li> <li><code>*_optimizer</code>: the optimizers. Classic stochastic gradient descent performs well, finding the good learning rate will make the difference.</li> <li><code>initializer</code>: the latent weights initialization. Latent vectors have to be initialized with non-constant values. We generally sample them from a zero-mean normal distribution with small standard deviation.</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/","title":"Part 2","text":"<p>As seen in Part 1, strength of Matrix Factorization (MF) lies in its ability to deal with sparse and high cardinality categorical variables. In this second tutorial we will have a look at Factorization Machines (FM) algorithm and study how it generalizes the power of MF.</p> <p>Table of contents of this tutorial series on matrix factorization for recommender systems:</p> <ul> <li>Part 1 - Traditional Matrix Factorization methods for Recommender Systems</li> <li>Part 2 - Factorization Machines and Field-aware Factorization Machines</li> <li>Part 3 - Large scale learning and better predictive power with multiple pass learning</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#factorization-machines","title":"Factorization Machines","text":"<p>Steffen Rendel came up in 2010 with Factorization Machines, an algorithm able to handle any real valued feature vector, combining the advantages of general predictors with factorization models. It became quite popular in the field of online advertising, notably after winning several Kaggle competitions. The modeling technique starts with a linear regression to capture the effects of each variable individually:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j} \\] <p>Then are added interaction terms to learn features relations. Instead of learning a single and specific weight per interaction (as in polynomial regression), a set of latent factors is learnt per feature (as in MF). An interaction is calculated by multiplying involved features product with their latent vectors dot product. The degree of factorization \u2014 or model order \u2014 represents the maximum number of features per interaction considered. The model equation for a factorization machine of degree \\(d\\) = 2 is defined as:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'} \\] <p>Where \\(\\normalsize \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle\\) is the dot product of \\(j\\) and \\(j'\\) latent vectors:</p> \\[ \\normalsize \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle = \\sum_{f=1}^{k} \\mathbf{v}_{j, f} \\cdot \\mathbf{v}_{j', f} \\] <p>Higher-order FM will be covered in a following section, just note that factorization models express their power in sparse settings, which is also where higher-order interactions are hard to estimate.</p> <p>Strong emphasis must be placed on feature engineering as it allows FM to mimic most factorization models and significantly impact its performance. High cardinality categorical variables one hot encoding is the most frequent step before feeding the model with data. For more efficiency, River FM implementation considers string values as categorical variables and automatically one hot encode them. FM models have their own module river.facto.</p> <p>## Mimic Biased Matrix Factorization (BiasedMF)</p> <p>Let's start with a simple example where we want to reproduce the Biased Matrix Factorization model we trained in the previous tutorial. For a fair comparison with Part 1 example, let's set the same evaluation framework:</p> <pre><code>from river import datasets\nfrom river import metrics\nfrom river.evaluate import progressive_val_score\n\ndef evaluate(model):\n    X_y = datasets.MovieLens100K()\n    metric = metrics.MAE() + metrics.RMSE()\n    _ = progressive_val_score(X_y, model, metric, print_every=25_000, show_time=True, show_memory=True)\n</code></pre> <p>In order to build an equivalent model we need to use the same hyper-parameters. As we can't replace FM intercept by the global running mean we won't be able to build the exact same model:</p> <pre><code>from river import compose\nfrom river import facto\nfrom river import preprocessing\nfrom river import optim\nfrom river import stats\n\nfm_params = {\n    'n_factors': 10,\n    'weight_optimizer': optim.SGD(0.025),\n    'latent_optimizer': optim.SGD(0.05),\n    'sample_normalization': False,\n    'l1_weight': 0.,\n    'l2_weight': 0.,\n    'l1_latent': 0.,\n    'l2_latent': 0.,\n    'intercept': 3,\n    'intercept_lr': .01,\n    'weight_initializer': optim.initializers.Zeros(),\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.1, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor |= facto.FMRegressor(**fm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761778\nRMSE: 0.960803 \u2013 00:00:01 \u2013 778.29 KB\n[50,000] MAE: 0.751986\nRMSE: 0.949941 \u2013 00:00:02 \u2013 908.2 KB\n[75,000] MAE: 0.750044\nRMSE: 0.948911 \u2013 00:00:03 \u2013 1.03 MB\n[100,000] MAE: 0.748609\nRMSE: 0.947994 \u2013 00:00:05 \u2013 1.15 MB\n</code></pre> <p>Both MAE are very close to each other (0.7486 vs 0.7485) showing that we almost reproduced [reco.BiasedMF](../../../api/reco/BiasedMF) algorithm. The cost is a naturally slower running time as FM implementation offers more flexibility.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#feature-engineering-for-fm-models","title":"Feature engineering for FM models","text":"<p>Let's study the basics of how to properly encode data for FM models. We are going to keep using MovieLens 100K as it provides various feature types:</p> <pre><code>import json\n\nfor x, y in datasets.MovieLens100K():\n    print(f'x = {json.dumps(x, indent=4)}\\ny = {y}')\n    break\n</code></pre> <pre><code>x = {\n    \"user\": \"259\",\n    \"item\": \"255\",\n    \"timestamp\": 874731910000000000,\n    \"title\": \"My Best Friend's Wedding (1997)\",\n    \"release_date\": 866764800000000000,\n    \"genres\": \"comedy, romance\",\n    \"age\": 21.0,\n    \"gender\": \"M\",\n    \"occupation\": \"student\",\n    \"zip_code\": \"48823\"\n}\ny = 4.0\n</code></pre> <p>The features we are going to add to our model don't improve its predictive power. Nevertheless, they are useful to illustrate different methods of data encoding:</p> <ol> <li>Set-categorical variables</li> </ol> <p>We have seen that categorical variables are one hot encoded automatically if set to strings, in the other hand, set-categorical variables must be encoded explicitly by the user. A good way of doing so is to assign them a value of \\(1/m\\), where \\(m\\) is the number of elements of the sample set. It gives the feature a constant \"weight\" across all samples preserving model's stability. Let's create a routine to encode movies genres this way:</p> <pre><code>def split_genres(x):\n    genres = x['genres'].split(', ')\n    return {f'genre_{genre}': 1 / len(genres) for genre in genres}\n</code></pre> <ol> <li>Numerical variables</li> </ol> <p>In practice, transforming numerical features into categorical ones works better in most cases. Feature binning is the natural way, but finding good bins is sometimes more an art than a science. Let's encode users age with something simple:</p> <pre><code>def bin_age(x):\n    if x['age'] &lt;= 18:\n        return {'age_0-18': 1}\n    elif x['age'] &lt;= 32:\n        return {'age_19-32': 1}\n    elif x['age'] &lt; 55:\n        return {'age_33-54': 1}\n    else:\n        return {'age_55-100': 1}\n</code></pre> <p>Let's put everything together:</p> <pre><code>fm_params = {\n    'n_factors': 14,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.05, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.FMRegressor(**fm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.759838\nRMSE: 0.961281 \u2013 00:00:03 \u2013 895.78 KB\n[50,000] MAE: 0.751307\nRMSE: 0.951391 \u2013 00:00:08 \u2013 1.02 MB\n[75,000] MAE: 0.750361\nRMSE: 0.951393 \u2013 00:00:12 \u2013 1.18 MB\n[100,000] MAE: 0.749994\nRMSE: 0.951435 \u2013 00:00:16 \u2013 1.33 MB\n</code></pre> <p>Note that using more variables involves factorizing a larger latent space, then increasing the number of latent factors \\(k\\) often helps capturing more information.</p> <p>Some other feature engineering tips from 3 idiots' winning solution for Kaggle Criteo display ads competition in 2014:</p> <ul> <li>Infrequent modalities often bring noise and little information, transforming them into a special tag can help</li> <li>In some cases, sample-wise normalization seems to make the optimization problem easier to be solved</li> </ul>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#higher-order-factorization-machines-hofm","title":"Higher-Order Factorization Machines (HOFM)","text":"<p>The model equation generalized to any order \\(d \\geq 2\\) is defined as:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{l=2}^{d} \\sum_{j_1=1}^{p} \\cdots \\sum_{j_l=j_{l-1}+1}^{p} \\left(\\prod_{j'=1}^{l} x_{j_{j'}} \\right) \\left(\\sum_{f=1}^{k_l} \\prod_{j'=1}^{l} v_{j_{j'}, f}^{(l)} \\right) \\] <pre><code>hofm_params = {\n    'degree': 3,\n    'n_factors': 12,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.05, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.HOFMRegressor(**hofm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761297\nRMSE: 0.962054 \u2013 00:00:15 \u2013 1.67 MB\n[50,000] MAE: 0.751865\nRMSE: 0.951499 \u2013 00:00:31 \u2013 1.97 MB\n[75,000] MAE: 0.750853\nRMSE: 0.951526 \u2013 00:00:47 \u2013 2.3 MB\n[100,000] MAE: 0.750607\nRMSE: 0.951982 \u2013 00:01:03 \u2013 2.6 MB\n</code></pre> <p>As said previously, high-order interactions are often hard to estimate due to too much sparsity, that's why we won't spend too much time here.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#field-aware-factorization-machines-ffm","title":"Field-aware Factorization Machines (FFM)","text":"<p>Field-aware variant of FM (FFM) improved the original method by adding the notion of \"fields\". A \"field\" is a group of features that belong to a specific domain (e.g. the \"users\" field, the \"items\" field, or the \"movie genres\" field).</p> <p>FFM restricts itself to pairwise interactions and factorizes separated latent spaces \u2014 one per combination of fields (e.g. users/items, users/movie genres, or items/movie genres) \u2014 instead of a common one shared by all fields. Therefore, each feature has one latent vector per field it can interact with \u2014 so that it can learn the specific effect with each different field.</p> <p>The model equation is defined by:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} \\langle \\mathbf{v}_{j, f_{j'}}, \\mathbf{v}_{j', f_{j}} \\rangle x_{j} x_{j'} \\] <p>Where \\(f_j\\) and \\(f_{j'}\\) are the fields corresponding to \\(j\\) and \\(j'\\) features, respectively.</p> <pre><code>ffm_params = {\n    'n_factors': 8,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'latent_initializer': optim.initializers.Normal(mu=0., sigma=0.05, seed=73),\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.FFMRegressor(**ffm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.757718\nRMSE: 0.958158 \u2013 00:00:06 \u2013 2.04 MB\n[50,000] MAE: 0.749502\nRMSE: 0.948065 \u2013 00:00:12 \u2013 2.41 MB\n[75,000] MAE: 0.749275\nRMSE: 0.948918 \u2013 00:00:18 \u2013 2.82 MB\n[100,000] MAE: 0.749542\nRMSE: 0.949769 \u2013 00:00:24 \u2013 3.19 MB\n</code></pre> <p>Note that FFM usually needs to learn smaller number of latent factors \\(k\\) than FM as each latent vector only deals with one field.</p>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-2/#field-weighted-factorization-machines-fwfm","title":"Field-weighted Factorization Machines (FwFM)","text":"<p>Field-weighted Factorization Machines (FwFM) address FFM memory issues caused by its large number of parameters, which is in the order of feature number times field number. As FFM, FwFM is an extension of FM restricted to pairwise interactions, but instead of factorizing separated latent spaces, it learns a specific weight \\(r_{f_j, f_{j'}}\\) for each field combination modelling the interaction strength.</p> <p>The model equation is defined as:</p> \\[ \\normalsize \\hat{y}(x) = w_{0} + \\sum_{j=1}^{p} w_{j} x_{j}  + \\sum_{j=1}^{p} \\sum_{j'=j+1}^{p} r_{f_j, f_{j'}} \\langle \\mathbf{v}_j, \\mathbf{v}_{j'} \\rangle x_{j} x_{j'} \\] <pre><code>fwfm_params = {\n    'n_factors': 10,\n    'weight_optimizer': optim.SGD(0.01),\n    'latent_optimizer': optim.SGD(0.025),\n    'intercept': 3,\n    'seed': 73,\n}\n\nregressor = compose.Select('user', 'item')\nregressor += (\n    compose.Select('genres') |\n    compose.FuncTransformer(split_genres)\n)\nregressor += (\n    compose.Select('age') |\n    compose.FuncTransformer(bin_age)\n)\nregressor |= facto.FwFMRegressor(**fwfm_params)\n\nmodel = preprocessing.PredClipper(\n    regressor=regressor,\n    y_min=1,\n    y_max=5\n)\n\nevaluate(model)\n</code></pre> <pre><code>[25,000] MAE: 0.761539\nRMSE: 0.962241 \u2013 00:00:07 \u2013 792.94 KB\n[50,000] MAE: 0.754089\nRMSE: 0.953181 \u2013 00:00:15 \u2013 922.85 KB\n[75,000] MAE: 0.754806\nRMSE: 0.954979 \u2013 00:00:22 \u2013 1.04 MB\n[100,000] MAE: 0.755404\nRMSE: 0.95604 \u2013 00:00:30 \u2013 1.17 MB\n</code></pre>"},{"location":"examples/matrix-factorization-for-recommender-systems/part-3/","title":"Part 3","text":"<p>To do.</p>"},{"location":"faq/","title":"Frequently Asked Questions","text":""},{"location":"faq/#do-all-classifiers-support-multi-class-classification","title":"Do all classifiers support multi-class classification?","text":"<p>No, they don't. Although binary classification can be seen as a special case of multi-class classification, there are many optimizations that can be performed if we know that there are only two classes. It would be annoying to have to check whether this is the case in an online setting. All in all we find that separating both cases leads to much cleaner code. Note that the <code>multiclass</code> module contains wrapper models that enable you to perform multi-class classification with binary classifiers.</p>"},{"location":"faq/#how-do-i-know-if-a-classifier-supports-multi-class-classification","title":"How do I know if a classifier supports multi-class classification?","text":"<p>Each classifier in River inherits from the <code>base.Classifier</code> class. Each classifier therefore has a <code>_multiclass</code> property which indicates whether or not it can process a non-boolean target value.</p> <pre><code>&gt;&gt;&gt; from river import linear_model\n\n&gt;&gt;&gt; classifier = linear_model.LogisticRegression()\n&gt;&gt;&gt; classifier._multiclass\nFalse\n</code></pre>"},{"location":"faq/#why-doesnt-river-do-any-input-validation","title":"Why doesn't river do any input validation?","text":"<p>Python encourages a coding style called EAFP, which stands for \"Easier to Ask for Forgiveness than Permission\". The idea is to assume that runtime errors don't occur, and instead use try/expects to catch errors. The great benefit is that we don't have to drown our code with <code>if</code> statements, which is symptomatic of the LBYL style, which stands for \"look before you leap\". This makes our implementations much more readable than, say, scikit-learn, which does a lot of input validation. The catch is that users have to be careful to use sane inputs. As always, there is no free lunch!</p>"},{"location":"faq/#what-about-reinforcement-learning","title":"What about reinforcement learning?","text":"<p>Reinforcement learning works in an online manner because of the nature of the task. Reinforcement learning can be therefore be seen as a subcase of online machine learning. However, we prefer not to support it because there are already many existing opensource libraries dedicated to it.</p>"},{"location":"faq/#what-are-the-differences-between-scikit-learns-online-learning-algorithm-which-have-a-partial_fit-method-and-their-equivalents-in-river","title":"What are the differences between scikit-learn's online learning algorithm which have a partial_fit method and their equivalents in River?","text":"<p>The algorithms from <code>sklearn</code> that support incremental learning are mostly meant for mini-batch learning. In a pure streaming context where the observations arrive one by one, then River is much faster than <code>sklearn</code>. This is mostly because <code>sklearn</code> incurs a lot of overhead by performing data checks. Also, sklearn assumes that you're always using the same number of features. This is not the case with River because it use dictionaries which allows you to drop and add features as you wish.</p>"},{"location":"faq/#how-do-i-save-and-load-models","title":"How do I save and load models?","text":"<pre><code>&gt;&gt;&gt; from river import ensemble\n&gt;&gt;&gt; import pickle\n\n&gt;&gt;&gt; model = ensemble.AdaptiveRandomForestClassifier()\n\n# save\n&gt;&gt;&gt; with open('model.pkl', 'wb') as f:\n...     pickle.dump(model, f)\n\n# load\n&gt;&gt;&gt; with open('model.pkl', 'rb') as f:\n...     model = pickle.load(f)\n</code></pre> <p>We also encourage you to try out dill and cloudpickle.</p>"},{"location":"faq/#what-about-neural-networks","title":"What about neural networks?","text":"<p>There are many great open-source libraries for building neural network models. We don't feel that we can bring anything of value to the existing Python ecosystem. However, we are open to implementing compatibility wrappers for popular libraries such as PyTorch and Keras.</p>"},{"location":"faq/#who-are-the-authors-of-this-library","title":"Who are the authors of this library?","text":"<p>We are research engineers, graduate students, PhDs and machine learning researchers. The members of the develompent team are mainly located in France, Brazil and New Zealand.</p>"},{"location":"introduction/basic-concepts/","title":"Basic concepts","text":"<p>Here are some concepts to give you a feel for what problems River addresses.</p>"},{"location":"introduction/basic-concepts/#data-streams","title":"Data streams","text":"<p>River is a library to build online machine learning models. Such models operate on data streams. But a data stream is a bit of a vague concept.</p> <p>In general, a data stream is a sequence of individual elements. In the case of machine learning, each element is a bunch of features. We call these samples, or observations. Each sample might follow a fixed structure and always contain the same features. But features can also appear and disappear over time. That depends on the use case.</p>"},{"location":"introduction/basic-concepts/#reactive-and-proactive-data-streams","title":"Reactive and proactive data streams","text":"<p>The origin of a data stream can vary, and usually it doesn't matter. You should be able to use River regardless of where your data comes from. It is however important to keep in mind the difference between reactive and proactive data streams.</p> <p>Reactive data streams are ones where the data comes to you. For instance, when a user visits your website, that's out of your control. You have no influence on the event. It just happens and you have to react to it.</p> <p>Proactive data streams are ones where you have control on the data stream. For example, you might be reading the data from a file. You decide at which speed you want to read the data, in what order, etc.</p> <p>If you consider data analysis as a whole, you're realize that the general approach is to turn reactive streams into proactive datasets. Events are usually logged into a database and are processed offline. Be it for building KPIs or training models.</p> <p>The challenge for machine learning is to ensure models you train offline on proactive datasets will perform correctly in production on reactive data streams.</p>"},{"location":"introduction/basic-concepts/#online-processing","title":"Online processing","text":"<p>Online processing is the act of processing a data stream one element at a time. In the case of machine learning, that means training a model by teaching it one sample at a time. This is completely opposite to the traditional way of doing machine learning, which is to train a model on whole batches of data at a time.</p> <p>An online model is therefore a stateful, dynamic object. It keeps learning and doesn't have to revisit past data. It's a different way of doing things, and therefore has its own set of pros and cons.</p>"},{"location":"introduction/basic-concepts/#tasks","title":"Tasks","text":"<p>Machine learning encompasses many different tasks: classification, regression, anomaly detection, time series forecasting, etc. The ideology behind River is to be a generic machine learning approach which allows these tasks to be performed in a streaming manner. Indeed, many batch machine learning algorithms have online equivalents.</p> <p>Note that River also supports some more basic tasks. For instance, you might just want to calculate a running average of a data stream. These are usually smaller parts of a whole stream processing pipeline.</p>"},{"location":"introduction/basic-concepts/#dictionaries-everywhere","title":"Dictionaries everywhere","text":"<p>River is a Python library. It is composed of a bunch of classes which implement various online processing algorithms. Most of these classes are machine learning models which can process a single sample, be it for learning or for inference.</p> <p>We made the choice to use dictionaries as the basic building block. First of all, online processing is different to batch processing, in that vectorization doesn't bring any speed-up. Therefore numeric processing libraries such as NumPy and PyTorch actually bring too much overhead. Using native Python data structures is faster.</p> <p>Dictionaries are therefore a perfect fit. They're native to Python and have excellent support in the standard library. They allow the naming of each feature. They can hold any kind of data type. They allow transparent support of JSON payloads, allowing seamless integration with web apps.</p>"},{"location":"introduction/basic-concepts/#datasets","title":"Datasets","text":"<p>In production, you're almost always going to face data streams which you have to react to, such as users visiting your website. The advantage of online machine learning is that you can design models that make predictions as well as learn from this data stream as it flows.</p> <p>But of course, when you're developping a model, you don't usually have access to a real-time feed on which to evaluate your model. You usually have an offline dataset which you want to evaluate your model on. River provides some datasets which can be read in online manner, one sample at a time. It is however crucial to keep in mind that the goal is to reproduce a production scenario as closely as possible, in order to ensure your model will perform just as well in production.</p>"},{"location":"introduction/basic-concepts/#model-evaluation","title":"Model evaluation","text":"<p>Online model evaluation differs from its traditional batch counterpart. In the latter, you usually perform cross-validation, whereby your training dataset is split into a learning and an evaluation dataset. This is fine, but it doesn't exactly reflect the data generation process that occurs in production.</p> <p>Online model evaluation involves learning and inference in the same order as what would happen in production. Indeed, if you know the order in which your data arrives, then you can process it the exact same order. This allows you to replay a production scenario and evaluate your model with higher fidelity than cross-validation.</p> <p>This is what makes online machine learning powerful. By replaying datasets in the correct order, you ensure you are designing models which will perform as expected in production.</p>"},{"location":"introduction/basic-concepts/#concept-drift","title":"Concept drift","text":"<p>The main reason why an offline model might not perform as expected in production is because of concept drift. But this is true for all machine learning models, be they offline or online.</p> <p>The advantage of online models over offline models is that they can cope with drift. Indeed, because they can keep learning, they usually adapt to concept drift in a seamless manner. As opposed to batch models which have to be retrained from scratch.</p>"},{"location":"introduction/installation/","title":"Installation","text":"<p>River is meant to work with Python 3.8 and above. Installation can be done via <code>pip</code>:</p> <pre><code>pip install river\n</code></pre> <p>You can install the latest development version from GitHub, as so:</p> <pre><code>pip install git+https://github.com/online-ml/river --upgrade\npip install git+ssh://git@github.com/online-ml/river.git --upgrade  # using SSH\n</code></pre> <p>This method requires having Cython and Rust installed on your machine.</p> <p>Feel welcome to open an issue on GitHub if you are having any trouble.</p>"},{"location":"introduction/next-steps/","title":"Next steps","text":"<p>The Recipes \ud83c\udf71 section is made up of small tutorials. Each one explains how to perform mundane tasks, such as measuring the performance of a model, selecting hyperparameters, etc.</p> <p>The Examples \ud83c\udf36\ufe0f section contains more involved notebooks with less explanations. Each notebook addresses a particular machine learning problem.</p> <p>The API \ud83d\udcda section references all the modules, classes, and functions in River. It is automatically generated from the codebase's Python docstrings.</p> <p>Feel welcome to open a discussion if you have a question. Before that you can check out the FAQ \ud83d\ude4b, which has answers to recurring questions.</p> <p>The released versions are listed in the Releases \ud83c\udfd7 section. Changes that will be part of the next release are listed in the unreleased section of the documentation's development version, which you may find here.</p> <p>We recommend checking out Awesome Online Machine Learning if you want to go deeper. There you will find online machine learning related content: research papers, alternative and complementary software, blog posts, etc.</p>"},{"location":"introduction/related-projects/","title":"Related projects","text":"<p>Here is a list of projects which are more or less coupled with River:</p> <ul> <li>deep-river interfaces PyTorch models with River.</li> <li>light-river implements fast algorithms in rust. </li> <li>river-extra regroups experimental features which have yet to prove themselves to make it into the main River repository. Between us we call this \"the arena\".</li> <li>Beaver is an MLOps tool for covering the whole lifecycle of online machine learning models.</li> </ul>"},{"location":"introduction/why-use-river/","title":"Why use River?","text":""},{"location":"introduction/why-use-river/#processing-one-sample-at-a-time","title":"Processing one sample at a time","text":"<p>All the tools in the library can be updated with a single observation at a time. They can therefore be used to process streaming data. Depending on your use case, this might be more convenient than using a batch model.</p>"},{"location":"introduction/why-use-river/#adapting-to-drift","title":"Adapting to drift","text":"<p>In the streaming setting, data can evolve. Adaptive methods are specifically designed to be robust against concept drift in dynamic environments. Many of River's models can cope with concept drift.</p>"},{"location":"introduction/why-use-river/#general-purpose","title":"General purpose","text":"<p>River supports different machine learning tasks, including regression, classification, and unsupervised learning. It can also be used for ad hoc tasks, such as computing online metrics, as well as concept drift detection.</p>"},{"location":"introduction/why-use-river/#user-experience","title":"User experience","text":"<p>River is not the only library allowing you to do online machine learning. But it might just the simplest one to use in the Python ecosystem. River plays nicely with Python dictionaries, therefore making it easy to use in the context of web applications where JSON payloads are aplenty.</p>"},{"location":"introduction/getting-started/binary-classification/","title":"Binary classification","text":"<p>Classification is about predicting an outcome from a fixed list of classes. The prediction is a probability distribution that assigns a probability to each possible outcome.</p> <p>A labeled classification sample is made up of a bunch of features and a class. The class is a boolean in the case of binary classification. We'll use the phishing dataset as an example.</p> <pre><code>from river import datasets\n\ndataset = datasets.Phishing()\ndataset\n</code></pre> <pre><code>Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n    Name  Phishing                                                          \n    Task  Binary classification                                             \n Samples  1,250                                                             \nFeatures  9                                                                 \n  Sparse  False                                                             \n    Path  /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz\n</code></pre> <p>This dataset is a streaming dataset which can be looped over.</p> <pre><code>for x, y in dataset:\n    pass\n</code></pre> <p>Let's take a look at the first sample.</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'empty_server_form_handler': 0.0,\n 'popup_window': 0.0,\n 'https': 0.0,\n 'request_from_other_domain': 0.0,\n 'anchor_from_other_domain': 0.0,\n 'is_popular': 0.5,\n 'long_url': 1.0,\n 'age_of_domain': 1,\n 'ip_in_url': 1}\n</code></pre> <pre><code>y\n</code></pre> <pre><code>True\n</code></pre> <p>A binary classifier's goal is to learn to predict a binary target <code>y</code> from some given features <code>x</code>. We'll try to do this with a logistic regression.</p> <pre><code>from river import linear_model\n\nmodel = linear_model.LogisticRegression()\nmodel.predict_proba_one(x)\n</code></pre> <pre><code>{False: 0.5, True: 0.5}\n</code></pre> <p>The model hasn't been trained on any data, and therefore outputs a default probability of 50% for each class.</p> <p>The model can be trained on the sample, which will update the model's state.</p> <pre><code>model.learn_one(x, y)\n</code></pre> <p>If we try to make a prediction on the same sample, we can see that the probabilities are different, because the model has learned something.</p> <pre><code>model.predict_proba_one(x)\n</code></pre> <pre><code>{False: 0.494687699901455, True: 0.505312300098545}\n</code></pre> <p>Note that there is also a <code>predict_one</code> if you're only interested in the most likely class rather than the probability distribution.</p> <pre><code>model.predict_one(x)\n</code></pre> <pre><code>True\n</code></pre> <p>Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.</p> <pre><code>from river import metrics\n\nmodel = linear_model.LogisticRegression()\n\nmetric = metrics.ROCAUC()\n\nfor x, y in dataset:\n    y_pred = model.predict_proba_one(x)\n    model.learn_one(x, y)\n    metric.update(y, y_pred)\n\nmetric\n</code></pre> <pre><code>ROCAUC: 89.36%\n</code></pre> <p>This is a common way to evaluate an online model. In fact, there is a dedicated <code>evaluate.progressive_val_score</code> function that does this for you.</p> <pre><code>from river import evaluate\n\nmodel = linear_model.LogisticRegression()\nmetric = metrics.ROCAUC()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>ROCAUC: 89.36%\n</code></pre> <p>A common way to improve the performance of a logistic regression is to scale the data. This can be done by using a <code>preprocessing.StandardScaler</code>. In particular, we can define a pipeline to organise our model into a sequence of steps:</p> <pre><code>from river import compose\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    linear_model.LogisticRegression()\n)\n\nmodel\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>LogisticRegression</pre><code>LogisticRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Log (     weight_pos=1.     weight_neg=1.   )   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <pre><code>metric = metrics.ROCAUC()\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>ROCAUC: 95.07%\n</code></pre>"},{"location":"introduction/getting-started/concept-drift-detection/","title":"Concept drift","text":"<p>In online machine learning, it is assumed that data can change over time. When building machine learning models, we assume data has a probability distribution, which is usually fixed, i.e., stationary. Changes in the data distribution give rise to the phenomenon called Concept drift. Such drifts can be either virtual or real. In virtual drifts, only the distribution of the features, \\(P(X)\\), changes, whereas the relationship between \\(X\\) (features) and the target, \\(y\\), remains unchanged. The joint probability of \\(P(X, y)\\) changes in real concept drifts. Consequently, non-supervised online machine learning problems might face only virtual concept drifts.</p> <p>Real concept drits can be further divided in abrupt (happen instantly at a given point) or gradual (one \"concept\" changes to another gradually). There are other possible divisions, but they can be fit into abrupt or gradual drifts.</p>"},{"location":"introduction/getting-started/concept-drift-detection/#examples-of-concept-drift","title":"Examples of concept drift","text":"<p>Concept drifts might happen in the electricity demand across the year, in the stock market, in buying preferences, and in the likelihood of a new movie's success, among others.</p> <p>Let us consider the movie example: two movies made at different epochs can have similar features such as famous actors/directors, storyline, production budget, marketing campaigns, etc., yet it is not certain that both will be similarly successful. What the target audience considers is worth watching (and their money worth spending) is constantly changing, and production companies must adapt accordingly to avoid \"box office flops\".</p> <p>Prior to the pandemic, the usage of hand sanitizers and facial masks was not widespread. When the cases of COVID-19 started increasing, there was a lack of such products for the end consumer. Imagine a batch-learning model deciding how much of each product a supermarket should stock during those times. What a mess!</p>"},{"location":"introduction/getting-started/concept-drift-detection/#impact-of-drift-on-learning","title":"Impact of drift on learning","text":"<p>Concept drift can have a significant impact on predictive performance if not handled properly. Most batch learning models will fail in the presence of concept drift as they are essentially trained on different data. On the other hand, stream learning methods continuously update themselves and adapt to new concepts. Furthermore, drift-aware methods use change detection methods (a.k.a. drift detectors) to trigger mitigation mechanisms if a change in performance is detected.</p>"},{"location":"introduction/getting-started/concept-drift-detection/#detecting-concept-drift","title":"Detecting concept drift","text":"<p>Multiple drift detection methods have been proposed. The goal of a drift detector is to signal an alarm in the presence of drift. A good drift detector maximizes the number of true positives while keeping the number of false positives to a minimum. It must also be resource-wise efficient to work in the context of infinite data streams.</p> <p>For this example, we will generate a synthetic data stream by concatenating 3 distributions of 1000 samples each:</p> <ul> <li>\\(dist_a\\): \\(\\mu=0.8\\), \\(\\sigma=0.05\\)</li> <li>\\(dist_b\\): \\(\\mu=0.4\\), \\(\\sigma=0.02\\)</li> <li>\\(dist_c\\): \\(\\mu=0.6\\), \\(\\sigma=0.1\\).</li> </ul> <pre><code>import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import gridspec\n\n# Generate data for 3 distributions\nrandom_state = np.random.RandomState(seed=42)\ndist_a = random_state.normal(0.8, 0.05, 1000)\ndist_b = random_state.normal(0.4, 0.02, 1000)\ndist_c = random_state.normal(0.6, 0.1, 1000)\n\n# Concatenate data to simulate a data stream with 2 drifts\nstream = np.concatenate((dist_a, dist_b, dist_c))\n\n# Auxiliary function to plot the data\ndef plot_data(dist_a, dist_b, dist_c, drifts=None):\n    fig = plt.figure(figsize=(7,3), tight_layout=True)\n    gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])\n    ax1, ax2 = plt.subplot(gs[0]), plt.subplot(gs[1])\n    ax1.grid()\n    ax1.plot(stream, label='Stream')\n    ax2.grid(axis='y')\n    ax2.hist(dist_a, label=r'$dist_a$')\n    ax2.hist(dist_b, label=r'$dist_b$')\n    ax2.hist(dist_c, label=r'$dist_c$')\n    if drifts is not None:\n        for drift_detected in drifts:\n            ax1.axvline(drift_detected, color='red')\n    plt.show()\n\nplot_data(dist_a, dist_b, dist_c)\n</code></pre> <p></p>"},{"location":"introduction/getting-started/concept-drift-detection/#drift-detection-test","title":"Drift detection test","text":"<p>We will use the ADaptive WINdowing (<code>ADWIN</code>) drift detection method. Remember that the goal is to indicate that drift has occurred after samples 1000 and 2000 in the synthetic data stream.</p> <pre><code>from river import drift\n\ndrift_detector = drift.ADWIN()\ndrifts = []\n\nfor i, val in enumerate(stream):\n    drift_detector.update(val)   # Data is processed one sample at a time\n    if drift_detector.drift_detected:\n        # The drift detector indicates after each sample if there is a drift in the data\n        print(f'Change detected at index {i}')\n        drifts.append(i)\n\nplot_data(dist_a, dist_b, dist_c, drifts)\n</code></pre> <pre><code>Change detected at index 1055\nChange detected at index 2079\n</code></pre> <p></p> <p>We see that <code>ADWIN</code> successfully indicates the presence of drift (red vertical lines) close to the begining of a new data distribution.</p> <p>We conclude this example with some remarks regarding concept drift detectors and their usage:</p> <ul> <li>In practice, drift detectors provide stream learning methods with robustness against concept drift. Drift detectors monitor the model usually through a performance metric.</li> <li>Drift detectors work on univariate data. This is why they are used to monitor a model's performance and not the data itself. Remember that concept drift is defined as a change in the relationship between data and the target to learn (in supervised learning).</li> <li>Drift detectors define their expectations regarding input data. It is important to know these expectations to feed a given drift detector with the correct data.</li> </ul>"},{"location":"introduction/getting-started/multiclass-classification/","title":"Multi-class classification","text":"<p>Classification is about predicting an outcome from a fixed list of classes. The prediction is a probability distribution that assigns a probability to each possible outcome.</p> <p>A labeled classification sample is made up of a bunch of features and a class. The class is a usually a string or a number in the case of multiclass classification. We'll use the image segments dataset as an example.</p> <pre><code>from river import datasets\n\ndataset = datasets.ImageSegments()\ndataset\n</code></pre> <pre><code>Image segments classification.\n\nThis dataset contains features that describe image segments into 7 classes: brickface, sky,\nfoliage, cement, window, path, and grass.\n\n    Name  ImageSegments                                                     \n    Task  Multi-class classification                                        \n Samples  2,310                                                             \nFeatures  18                                                                \n Classes  7                                                                 \n  Sparse  False                                                             \n    Path  /Users/max/projects/online-ml/river/river/datasets/segment.csv.zip\n</code></pre> <p>This dataset is a streaming dataset which can be looped over.</p> <pre><code>for x, y in dataset:\n    pass\n</code></pre> <p>Let's take a look at the first sample.</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'region-centroid-col': 218,\n 'region-centroid-row': 178,\n 'short-line-density-5': 0.11111111,\n 'short-line-density-2': 0.0,\n 'vedge-mean': 0.8333326999999999,\n 'vegde-sd': 0.54772234,\n 'hedge-mean': 1.1111094,\n 'hedge-sd': 0.5443307,\n 'intensity-mean': 59.629630000000006,\n 'rawred-mean': 52.44444300000001,\n 'rawblue-mean': 75.22222,\n 'rawgreen-mean': 51.22222,\n 'exred-mean': -21.555555,\n 'exblue-mean': 46.77778,\n 'exgreen-mean': -25.222220999999998,\n 'value-mean': 75.22222,\n 'saturation-mean': 0.31899637,\n 'hue-mean': -2.0405545}\n</code></pre> <pre><code>y\n</code></pre> <pre><code>'path'\n</code></pre> <p>A multiclass classifier's goal is to learn how to predict a class <code>y</code> from a bunch of features <code>x</code>. We'll attempt to do this with a decision tree.</p> <pre><code>from river import tree\n\nmodel = tree.HoeffdingTreeClassifier()\nmodel.predict_proba_one(x)\n</code></pre> <pre><code>{}\n</code></pre> <p>The reason why the output dictionary is empty is because the model hasn't seen any data yet. It isn't aware of the dataset whatsoever. If this were a binary classifier, then it would output a probability of 50% for <code>True</code> and <code>False</code> because the classes are implicit. But in this case we're doing multiclass classification.</p> <p>Likewise, the <code>predict_one</code> method initially returns <code>None</code> because the model hasn't seen any labeled data yet.</p> <pre><code>print(model.predict_one(x))\n</code></pre> <pre><code>None\n</code></pre> <p>If we update the model and try again, then we see that a probability of 100% is assigned to the <code>'path'</code> class because that's the only one the model is aware of.</p> <pre><code>model.learn_one(x, y)\nmodel.predict_proba_one(x)\n</code></pre> <pre><code>{'path': 1.0}\n</code></pre> <p>This is a strength of online classifiers: they're able to deal with new classes appearing in the data stream.</p> <p>Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.</p> <pre><code>from river import metrics\n\nmodel = tree.HoeffdingTreeClassifier()\n\nmetric = metrics.ClassificationReport()\n\nfor x, y in dataset:\n    y_pred = model.predict_one(x)\n    model.learn_one(x, y)\n    if y_pred is not None:\n        metric.update(y, y_pred)\n\nmetric\n</code></pre> <pre><code>            Precision   Recall   F1       Support\n\nbrickface      77.13%   84.85%   80.81%       330  \n   cement      78.92%   83.94%   81.35%       330  \n  foliage      65.69%   20.30%   31.02%       330  \n    grass     100.00%   96.97%   98.46%       330  \n     path      90.63%   91.19%   90.91%       329  \n      sky      99.08%   98.18%   98.63%       330  \n   window      43.50%   67.88%   53.02%       330\n\n    Macro      79.28%   77.62%   76.31%            \n    Micro      77.61%   77.61%   77.61%            \n Weighted      79.27%   77.61%   76.31%\n\n                  77.61% accuracy\n</code></pre> <p>This is a common way to evaluate an online model. In fact, there is a dedicated <code>evaluate.progressive_val_score</code> function that does this for you.</p> <pre><code>from river import evaluate\n\nmodel = tree.HoeffdingTreeClassifier()\nmetric = metrics.ClassificationReport()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>            Precision   Recall   F1       Support\n\nbrickface      77.13%   84.85%   80.81%       330  \n   cement      78.92%   83.94%   81.35%       330  \n  foliage      65.69%   20.30%   31.02%       330  \n    grass     100.00%   96.97%   98.46%       330  \n     path      90.63%   91.19%   90.91%       329  \n      sky      99.08%   98.18%   98.63%       330  \n   window      43.50%   67.88%   53.02%       330\n\n    Macro      79.28%   77.62%   76.31%            \n    Micro      77.61%   77.61%   77.61%            \n Weighted      79.27%   77.61%   76.31%\n\n                  77.61% accuracy\n</code></pre>"},{"location":"introduction/getting-started/regression/","title":"Regression","text":"<p>Regression is about predicting a numeric output for a given sample. A labeled regression sample is made up of a bunch of features and a number. The number is usually continuous, but it may also be discrete. We'll use the Trump approval rating dataset as an example.</p> <pre><code>from river import datasets\n\ndataset = datasets.TrumpApproval()\ndataset\n</code></pre> <pre><code>Donald Trump approval ratings.\n\nThis dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald\nTrump's approval ratings. It contains 5 features, which are approval ratings collected by\n5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\nthis task is to see if we can reproduce FiveThirtyEight's model.\n\n    Name  TrumpApproval                                                           \n    Task  Regression                                                              \n Samples  1,001                                                                   \nFeatures  6                                                                       \n  Sparse  False                                                                   \n    Path  /Users/max/projects/online-ml/river/river/datasets/trump_approval.csv.gz\n</code></pre> <p>This dataset is a streaming dataset which can be looped over.</p> <pre><code>for x, y in dataset:\n    pass\n</code></pre> <p>Let's take a look at the first sample.</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'ordinal_date': 736389,\n 'gallup': 43.843213,\n 'ipsos': 46.19925042857143,\n 'morning_consult': 48.318749,\n 'rasmussen': 44.104692,\n 'you_gov': 43.636914000000004}\n</code></pre> <p>A regression model's goal is to learn to predict a numeric target <code>y</code> from a bunch of features <code>x</code>. We'll attempt to do this with a nearest neighbors model.</p> <pre><code>from river import neighbors\n\nmodel = neighbors.KNNRegressor()\nmodel.predict_one(x)\n</code></pre> <pre><code>0.0\n</code></pre> <p>The model hasn't been trained on any data, and therefore outputs a default value of 0.</p> <p>The model can be trained on the sample, which will update the model's state.</p> <pre><code>model.learn_one(x, y)\n</code></pre> <p>If we try to make a prediction on the same sample, we can see that the output is different, because the model has learned something.</p> <pre><code>model.predict_one(x)\n</code></pre> <pre><code>43.75505\n</code></pre> <p>Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.</p> <pre><code>from river import metrics\n\nmodel = neighbors.KNNRegressor()\n\nmetric = metrics.MAE()\n\nfor x, y in dataset:\n    y_pred = model.predict_one(x)\n    model.learn_one(x, y)\n    metric.update(y, y_pred)\n\nmetric\n</code></pre> <pre><code>MAE: 0.310353\n</code></pre> <p>This is a common way to evaluate an online model. In fact, there is a dedicated <code>evaluate.progressive_val_score</code> function that does this for you.</p> <pre><code>from river import evaluate\n\nmodel = neighbors.KNNRegressor()\nmetric = metrics.MAE()\n\nevaluate.progressive_val_score(dataset, model, metric)\n</code></pre> <pre><code>MAE: 0.310353\n</code></pre>"},{"location":"license/license/","title":"License","text":"<p>River is free and open-source software licensed under the 3-clause BSD license.</p>"},{"location":"recipes/active-learning/","title":"Active learning","text":"<p>Active learning is a training regime, where the goal is to fit a model on the most discriminative samples. It is usually applied in situations where a limited amount of labeled data is available. In such a case, a human might be asked to annotate a sample. Doing this is expensive, so it's important to ask for labels for the most samples that will have the most impact.</p> <p>Online active learning is active learning done in a streaming fashion. Every time a prediction is made, an active learning strategy decides whether a label should be asked for or not. In case the strategy decides a yes, then the system could ask for a human to intervene. This is well summarized in the following schema from Online Active Learning Methods for Fast Label-Efficient Spam Filtering.</p>"},{"location":"recipes/active-learning/#online-active-learning","title":"Online active learning","text":"<p>River's online active learning strategies are located in the <code>active</code> module. The latter contains wrapper models. These wrappers enrich the <code>predict_one</code> and <code>predict_proba_one</code> methods to include a boolean in the output.</p> <p>The returned boolean indicates whether or not a label should be asked for. In a production system, we could feed this to a web interface, and get the human to annotate the sample. Offline, we can simply use the label in the dataset.</p> <p>We'll implement this basic flow. We'll apply a TFIDF followed by logistic regression to a datasets of spam/ham received by SMS.</p> <pre><code>from river import active\nfrom river import datasets\nfrom river import feature_extraction\nfrom river import linear_model\nfrom river import metrics\n\ndataset = datasets.SMSSpam()\nmetric = metrics.Accuracy()\nmodel = (\n    feature_extraction.TFIDF(on='body') |\n    linear_model.LogisticRegression()\n)\nmodel = active.EntropySampler(model, seed=42)\n\nn_samples_used = 0\nfor x, y in dataset:\n    y_pred, ask = model.predict_one(x)\n    metric.update(y, y_pred)\n    if ask:\n        n_samples_used += 1\n        model.learn_one(x, y)\n\nmetric\n</code></pre> <pre><code>Accuracy: 86.60%\n</code></pre> <p>The performance is reasonable, even though all the dataset wasn't used for training. We can check how many samples were actually used.</p> <pre><code>print(f\"{n_samples_used} / {dataset.n_samples} = {n_samples_used / dataset.n_samples:.2%}\")\n</code></pre> <pre><code>1921 / 5574 = 34.46%\n</code></pre> <p>Note that the above logic can be succinctly reproduced with the <code>progressive_val_score</code> function from the <code>evaluate</code> module. It recognises when an active learning model is provided, and will automatically display the number of samples used.</p> <pre><code>from river import evaluate\n\nevaluate.progressive_val_score(\n    dataset=dataset,\n    model=model.clone(),\n    metric=metric.clone(),\n    print_every=1000\n)\n</code></pre> <pre><code>[1,000] Accuracy: 84.80% \u2013 661 samples used\n[2,000] Accuracy: 86.00% \u2013 1,057 samples used\n[3,000] Accuracy: 86.37% \u2013 1,339 samples used\n[4,000] Accuracy: 86.65% \u2013 1,568 samples used\n[5,000] Accuracy: 86.54% \u2013 1,790 samples used\n[5,574] Accuracy: 86.60% \u2013 1,921 samples used\n\n\n\n\n\nAccuracy: 86.60%\n</code></pre>"},{"location":"recipes/active-learning/#reduce-training-time","title":"Reduce training time","text":"<p>Active learning is primarly used to label data in an efficient manner. However, in an online setting, active learning can also be used simply to speed up training. The point is that you can achieve a very good performance without training on an entire dataset. Active learning is a powerful way to decide which samples to train on.</p>"},{"location":"recipes/active-learning/#_1","title":"Active learning","text":""},{"location":"recipes/active-learning/#production-considerations","title":"Production considerations","text":"<p>In production, you might want to deploy a system where humans may annotate samples queried by an active learning strategy. You have several options at your disposal, all of which go beyond the scope of River.</p> <p>The general idea is to have some kind of queue in which queried samples are fed into. Then you would have a user interface which displays the elements in the queue one-by-one. Each time a sample is labeled, the label would be used to update the model. You might have one or more threads/processes doing inference. You'll want to update the model in each one each time the model learns.</p>"},{"location":"recipes/bandits-101/","title":"Multi-armed bandits","text":"<p>River has a <code>bandit</code> module. It contains several multi-armed bandit policies, bandit environments, and utilities to benchmark policies on bandit problems.</p> <p>Bandit environments in River implement the Gym interface. You can thus load them with <code>gym.make</code>. Note that Gym is intended for reinforcement learning algorithms, while bandit policies are the simplest form of reinforcement learing. Bandit policies learn by receiving a reward after each step, while reinforcement learning algorithms have to learn from feedback that may arrive at the end of a (long) sequence of steps.</p> <pre><code>import gym\n\nfor k in gym.envs.registry:\n    if k.startswith('river_bandits'):\n        print(k)\n</code></pre> <p>River's bandit module offers the <code>bandit.evaluate</code> function to benchmark several policies on a given environment. It takes as input a list of bandit policies, a bandit environment (the problem to solve), and a reward object.</p> <pre><code>import gym\nfrom river import bandit\nimport pandas as pd\nfrom tqdm import tqdm\nfrom river import stats\n\npolicies=[\n    bandit.EpsilonGreedy(epsilon=0.1),\n    bandit.EpsilonGreedy(epsilon=0.01),\n    bandit.EpsilonGreedy(epsilon=0),\n]\n\nenv = gym.make(\n    'river_bandits/KArmedTestbed-v0',\n    max_episode_steps=1000\n)\n\ntrace = bandit.evaluate(\n    policies=policies,\n    env=env,\n    reward_stat=stats.Mean(),\n    n_episodes=(n_episodes := 2000),\n)\n</code></pre> <p>The <code>bandit.evaluate</code> function returns a generator containing the results at each step of the benchmark. This can be wrapped with a <code>pandas.DataFrame</code> to gather all the results.</p> <pre><code>trace_df = pd.DataFrame(tqdm(\n    trace, position=0, total=(\n        n_episodes *\n        len(policies) *\n        env._max_episode_steps\n    )\n))\ntrace_df.sample(5, random_state=42)\n</code></pre> <pre><code>  0%|                                                                                                                                                                   | 0/6000000 [00:00&lt;?, ?it/s]/Users/max/Library/Caches/pypoetry/virtualenvs/river--dXL33ck-py3.11/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n  if not isinstance(terminated, (bool, np.bool8)):\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6000000/6000000 [00:25&lt;00:00, 236810.21it/s]\n</code></pre> episode step policy_idx arm reward reward_stat 1324896 441 632 0 2 0.226086 0.499848 3566176 1188 725 1 6 2.363962 0.935468 1109043 369 681 0 5 2.780757 1.467402 4286042 1428 680 2 1 2.039255 1.603312 5395174 1798 391 1 8 1.625523 1.232745 <p>It is then straightforward to plot the average reward each policy obtains at each step, by averaging over episodes.</p> <pre><code>policy_names = {\n    0: '\u03b5 = 0.1',\n    1: '\u03b5 = 0.01',\n    2: '\u03b5 = 0 (greedy)'\n}\n\n(\n    trace_df\n    .assign(policy=trace_df.policy_idx.map(policy_names))\n    .groupby(['step', 'policy'])\n    ['reward'].mean()\n    .unstack()\n    .plot()\n)\n</code></pre> <pre><code>&lt;Axes: xlabel='step'&gt;\n</code></pre> <p></p>"},{"location":"recipes/bandits-101/#controlling-the-evaluation-loop","title":"Controlling the evaluation loop","text":"<p>The <code>bandit.evaluate</code> function is useful for benchmarking. But in practice, you'll want to have control over your bandit policy. Indeed you'll want the freedom to pull arms (with the <code>pull</code> method) and update the policy (with the <code>update</code> method) at your discretion.</p> <p>As an example, the following is a possible reimplementation of the <code>bandit.evaluate</code> function. Here we'll be measuring the rate at which each policy selects the optimal arm.</p> <p>Note how the <code>pull</code> and <code>update</code> methods are used.</p> <pre><code>import copy\n\npolicies=[\n    bandit.EpsilonGreedy(epsilon=0.1),\n    bandit.EpsilonGreedy(epsilon=0.01),\n    bandit.EpsilonGreedy(epsilon=0),\n]\n\nenv = gym.make(\n    'river_bandits/KArmedTestbed-v0',\n    max_episode_steps=1000\n)\nn_episodes = 2000\n\ntrace = []\n\nwith tqdm(total=len(policies) * n_episodes * env._max_episode_steps, position=0) as progress:\n    for policy in policies:\n        for episode in range(n_episodes):\n            episode_policy = policy.clone()\n            episode_env = copy.deepcopy(env)\n            episode_env.reset()\n            step = 0\n            while True:\n                action = episode_policy.pull(range(episode_env.action_space.n))\n                observation, reward, terminated, truncated, info = episode_env.step(action)\n                best_action = observation\n                episode_policy.update(action, reward)\n\n                trace.append({\n                    \"episode\": episode,\n                    \"step\": step,\n                    \"policy\": f\"\u03b5 = {policy.epsilon}\",\n                    \"is_action_optimal\": action == best_action\n                })\n                step += 1\n                progress.update()\n\n                if terminated or truncated:\n                    break\n\ntrace_df = pd.DataFrame(trace)\n</code></pre> <pre><code>  0%|                                                                                                                                                                   | 0/6000000 [00:00&lt;?, ?it/s]/Users/max/Library/Caches/pypoetry/virtualenvs/river--dXL33ck-py3.11/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n  if not isinstance(terminated, (bool, np.bool8)):\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6000000/6000000 [00:26&lt;00:00, 228987.26it/s]\n</code></pre> <pre><code>colors = {\n    '\u03b5 = 0.1': 'tab:blue',\n    '\u03b5 = 0.01': 'tab:red',\n    '\u03b5 = 0': 'tab:green'\n}\n\n(\n    trace_df\n    .groupby(['step', 'policy'])\n    ['is_action_optimal'].mean()\n    .unstack()\n    .plot()\n)\n</code></pre> <pre><code>&lt;Axes: xlabel='step'&gt;\n</code></pre> <p></p>"},{"location":"recipes/bandits-101/#handling-drift","title":"Handling drift","text":"<p>The environment used above is a toy situation used for introducing bandits. It is stationary, meaning that the expected reward of each arm does not change over time.</p> <p>In practice, arms are dynamic, and their performance can vary over time. A simple example of this is the Candy Cane Contest that was hosted on Kaggle in 2020. The expected reward of each arm diminishes each time it is pulled.</p> <p>The way bandit policies in River deal with drift depends on the method. For the <code>bandit.EpsilonGreedy</code> policy, it makes sense to use a rolling average as the reward object. What this means is that the empirical reward the policy calculates for each arm is a rolling average, rather than a global one.</p> <pre><code>from river import proba, utils\n\npolicies=[\n    bandit.EpsilonGreedy(\n        epsilon=0.1,\n        seed=42\n    ),\n    bandit.EpsilonGreedy(\n        epsilon=0.3,\n        reward_obj=utils.Rolling(stats.Mean(), window_size=50),\n        seed=42\n    ),\n    bandit.ThompsonSampling(\n        reward_obj=proba.Beta(),\n        seed=42\n    )\n]\n\nenv = gym.make('river_bandits/CandyCaneContest-v0')\n\ntrace = bandit.evaluate(\n    policies=policies,\n    env=env,\n    n_episodes=(n_episodes := 30),\n    seed=42\n)\n\ntrace_df = pd.DataFrame(tqdm(\n    trace, position=0, total=(\n        n_episodes *\n        len(policies) *\n        env._max_episode_steps\n    )\n))\n</code></pre> <pre><code>  0%|                                                                                                                                                                    | 0/180000 [00:00&lt;?, ?it/s]/Users/max/Library/Caches/pypoetry/virtualenvs/river--dXL33ck-py3.11/lib/python3.11/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n  if not isinstance(terminated, (bool, np.bool8)):\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 180000/180000 [00:11&lt;00:00, 15839.35it/s]\n</code></pre> <p>We can compare the performance of each policy by checking the average reward at the end of each episode.</p> <pre><code>(\n    trace_df\n    .groupby(['policy_idx', 'episode'])\n    .last()\n    .groupby('policy_idx')\n    .reward_stat.mean()\n)\n</code></pre> <pre><code>policy_idx\n0    736.1\n1    817.0\n2    854.0\nName: reward_stat, dtype: float64\n</code></pre> <p>We see that using a rolling average gives a boost to the epsilon greedy strategy. However, we see that the <code>bandit.ThompsonSampling</code> policy performs even better, even though no particular care was given to drift. A natural next step would thus be to see how it could be improved to handle drift. For instance, its <code>dist</code> parameter could be wrapped with a <code>utils.Rolling</code>:</p> <pre><code>policy = bandit.ThompsonSampling(\n    reward_obj=utils.Rolling(proba.Beta(), window_size=50),\n    seed=42\n)\n</code></pre> <p>Bandits can be used for several tasks. They can be used for content personalization, as well as online model selection (see <code>model_selection.BanditRegressor</code>). The policies in River are therefore designed to be flexible, so that they can be used in conjunction with other River modules. For instance, the <code>reward_obj</code> in <code>bandit.EpsilonGreedy</code> can be a metric, a probability distribution, or a statistic. This works because objects in River adher to a coherent get/update interface.</p>"},{"location":"recipes/cloning-and-mutating/","title":"Cloning and mutating","text":"<p>Sometimes you might want to reset a model, or edit (what we call mutate) its attributes. This can be useful in an online environment. Indeed, if you detect a drift, then you might want to mutate a model's attributes. Or if you see that a model's performance is plummeting, then you might to reset it to its \"factory settings\".</p> <p>Anyway, this is not to convince you, but rather to say that a model's attributes don't have be to set in stone throughout its lifetime. In particular, if you're developping your own model, then you might want to have good tools to do this. This is what this recipe is about.</p>"},{"location":"recipes/cloning-and-mutating/#cloning","title":"Cloning","text":"<p>The first thing you can do is clone a model. This creates a deep copy of the model. The resulting model is entirely independent of the original model. The clone is fresh, in the sense that it is as if it hasn't seen any data.</p> <p>For instance, say you have a linear regression model which you have trained on some data.</p> <pre><code>from river import datasets, linear_model, optim, preprocessing\n\nmodel = (\n    preprocessing.StandardScaler() |\n    linear_model.LinearRegression(\n        optimizer=optim.SGD(3e-2)\n    )\n)\n\nfor x, y in datasets.TrumpApproval():\n    model.predict_one(x)\n    model.learn_one(x, y)\n\nmodel[-1].weights\n</code></pre> <pre><code>{'ordinal_date': 20.59955380229643,\n 'gallup': 0.39114944304212645,\n 'ipsos': 0.4101918314868111,\n 'morning_consult': 0.12042970179504908,\n 'rasmussen': 0.18951231512561392,\n 'you_gov': 0.04991712783831687}\n</code></pre> <p>For whatever reason, we may want to clone this model. This can be done with the <code>clone</code> method.</p> <pre><code>clone = model.clone()\nclone[-1].weights\n</code></pre> <pre><code>{}\n</code></pre> <p>As we can see, there are no weights because the clone is fresh copy that has not seen any data. However, the learning rate we specified is preserved.</p> <pre><code>clone[-1].optimizer.learning_rate\n</code></pre> <pre><code>0.03\n</code></pre> <p>You may also specify parameters you want changed. For instance, let's say we want to clone the model, but we want to change the optimizer:</p> <pre><code>clone = model.clone({\"LinearRegression\": {\"optimizer\": optim.Adam()}})\nclone[-1].optimizer\n</code></pre> <pre><code>Adam({'lr': Constant({'learning_rate': 0.1}), 'n_iterations': 0, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'm': None, 'v': None})\n</code></pre> <p>The first key indicates that we want to specify a different parameter for the <code>LinearRegression</code> part of the pipeline. Then the second key accesses the linear regression's <code>optimizer</code> parameter.</p> <p>Finally, note that the <code>clone</code> method isn't reserved to models. Indeed, every object in River has it. That's because they all inherit from the <code>Base</code> class in the <code>base</code> module.</p>"},{"location":"recipes/cloning-and-mutating/#mutating-attributes","title":"Mutating attributes","text":"<p>Cloning a model can be useful, but the fact that it essentially resets the model may not be desired. Instead, you might want to change a attribute while preserving the model's state. For example, let's change the <code>l2</code> attribute, and the optimizer's <code>lr</code> attribute.</p> <pre><code>model.mutate({\n    \"LinearRegression\": {\n        \"l2\": 0.1,\n        \"optimizer\": {\n            \"lr\": optim.schedulers.Constant(25e-3)\n        }\n    }\n})\n\nprint(repr(model))\n</code></pre> <pre><code>Pipeline (\n  StandardScaler (\n    with_std=True\n  ),\n  LinearRegression (\n    optimizer=SGD (\n      lr=Constant (\n        learning_rate=0.025\n      )\n    )\n    loss=Squared ()\n    l2=0.1\n    l1=0.\n    intercept_init=0.\n    intercept_lr=Constant (\n      learning_rate=0.01\n    )\n    clip_gradient=1e+12\n    initializer=Zeros ()\n  )\n)\n</code></pre> <p>We can see the attributes we specified have changed. However, the model's state is preserved:</p> <pre><code>model[-1].weights\n</code></pre> <pre><code>{'ordinal_date': 20.59955380229643,\n 'gallup': 0.39114944304212645,\n 'ipsos': 0.4101918314868111,\n 'morning_consult': 0.12042970179504908,\n 'rasmussen': 0.18951231512561392,\n 'you_gov': 0.04991712783831687}\n</code></pre> <p>In other words, the <code>mutate</code> method does not create a deep copy of the model. It just sets attributes. At this point you may ask:</p> <p>Why can't I just change the attribute directly, without calling <code>mutate</code>?</p> <p>The answer is that you're free to do proceed as such, but it's not the way we recommend. The <code>mutate</code> method is safer, in that it prevents you from mutating attributes you shouldn't be touching. We call these immutable attributes. For instance, there's no reason you should be modifying the weights.</p> <pre><code>try:\n    model.mutate({\n        \"LinearRegression\": {\n            \"weights\": \"this makes no sense\"\n        }\n    })\nexcept ValueError as e:\n    print(e)\n</code></pre> <pre><code>'weights' is not a mutable attribute of LinearRegression\n</code></pre> <p>All attributes are immutable by default. Under the hood, each model can specify a set of mutable attributes via the <code>_mutable_attributes</code> property. In theory this can be overriden. But the general idea is that we will progressively add more and more mutable attributes with time.</p> <p>And that concludes this recipe. Arguably, this recipe caters to advanced users, and in particular users who are developping their own models. And yet, one could also argue that modifying parameters of a model on-the-fly is a great tool to have at your disposal when you're doing online machine learning.</p>"},{"location":"recipes/feature-extraction/","title":"Feature extraction","text":"<p>To do.</p>"},{"location":"recipes/hyperparameter-tuning/","title":"Hyperparameter tuning","text":"<p>To do.</p>"},{"location":"recipes/mini-batching/","title":"Mini-batching","text":"<p>In its purest form, online machine learning encompasses models which learn with one sample at a time. This is the design which is used in River.</p> <p>The main downside of single-instance processing is that it doesn't scale to big data, at least not in the sense of traditional batch learning. Indeed, processing one sample at a time means that we are unable to fully take advantage of vectorisation and other computational tools that are taken for granted in batch learning. On top of this, processing a large dataset in River essentially involves a Python <code>for</code> loop, which might be too slow for some usecases. However, this doesn't mean that River is slow. In fact, for processing a single instance, River is actually a couple of orders of magnitude faster than libraries such as scikit-learn, PyTorch, and Tensorflow. The reason why is because River is designed from the ground up to process a single instance, whereas the majority of other libraries choose to care about batches of data. Both approaches offer different compromises, and the best choice depends on your usecase.</p> <p>In order to propose the best of both worlds, River offers some limited support for mini-batch learning. Some of River's estimators implement <code>*_many</code> methods on top of their <code>*_one</code> counterparts. For instance, <code>preprocessing.StandardScaler</code> has a <code>learn_many</code> method as well as a <code>transform_many</code> method, in addition to <code>learn_one</code> and <code>transform_one</code>. Each mini-batch method takes as input a <code>pandas.DataFrame</code>. Supervised estimators also take as input a <code>pandas.Series</code> of target values. We choose to use <code>pandas.DataFrames</code> over <code>numpy.ndarrays</code> because of the simple fact that the former allows us to name each feature. This in turn allows us to offer a uniform interface for both single instance and mini-batch learning.</p> <p>As an example, we will build a simple pipeline that scales the data and trains a logistic regression. Indeed, the <code>compose.Pipeline</code> class can be applied to mini-batches, as long as each step is able to do so.</p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import preprocessing\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    linear_model.LogisticRegression()\n)\n</code></pre> <p>For this example, we will use <code>datasets.Higgs</code>.</p> <pre><code>from river import datasets\n\ndataset = datasets.Higgs()\nif not dataset.is_downloaded:\n    dataset.download()\ndataset\n</code></pre> <pre><code>Higgs dataset.\n\nThe data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22)\nare kinematic properties measured by the particle detectors in the accelerator. The last seven\nfeatures are functions of the first 21 features; these are high-level features derived by\nphysicists to help discriminate between the two classes.\n\n      Name  Higgs                                                                       \n      Task  Binary classification                                                       \n   Samples  11,000,000                                                                  \n  Features  28                                                                          \n    Sparse  False                                                                       \n      Path  /Users/max/river_data/Higgs/HIGGS.csv.gz                                    \n       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\n      Size  2.62 GB                                                                     \nDownloaded  True\n</code></pre> <p>The easiest way to read the data in a mini-batch fashion is to use the <code>read_csv</code> from <code>pandas</code>.</p> <pre><code>import pandas as pd\n\nnames = [\n    'target', 'lepton pT', 'lepton eta', 'lepton phi',\n    'missing energy magnitude', 'missing energy phi',\n    'jet 1 pt', 'jet 1 eta', 'jet 1 phi', 'jet 1 b-tag',\n    'jet 2 pt', 'jet 2 eta', 'jet 2 phi', 'jet 2 b-tag',\n    'jet 3 pt', 'jet 3 eta', 'jet 3 phi', 'jet 3 b-tag',\n    'jet 4 pt', 'jet 4 eta', 'jet 4 phi', 'jet 4 b-tag',\n    'm_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'\n]\n\nfor x in pd.read_csv(dataset.path, names=names, chunksize=8096, nrows=3e5):\n    y = x.pop('target')\n    y_pred = model.predict_proba_many(x)\n    model.learn_many(x, y)\n</code></pre> <p>If you are familiar with scikit-learn, you might be aware that some of their estimators have a <code>partial_fit</code> method, which is similar to river's <code>learn_many</code> method. Here are some advantages that river has over scikit-learn:</p> <ul> <li>We guarantee that river's is just as fast, if not faster than scikit-learn. The differences are negligeable, but are slightly in favor of river.</li> <li>We take as input dataframes, which allows us to name each feature. The benefit is that you can add/remove/permute features between batches and everything will keep working.</li> <li>Estimators that support mini-batches also support single instance learning. This means that you can enjoy the best of both worlds. For instance, you can train with mini-batches and use <code>predict_one</code> to make predictions.</li> </ul> <p>Note that you can check which estimators can process mini-batches programmatically:</p> <pre><code>import importlib\nimport inspect\n\ndef can_mini_batch(obj):\n    return hasattr(obj, 'learn_many')\n\nfor module in importlib.import_module('river.api').__all__:\n    if module in ['datasets', 'synth']:\n        continue\n    for name, obj in inspect.getmembers(importlib.import_module(f'river.{module}'), can_mini_batch):\n        print(name)\n</code></pre> <pre><code>LocalOutlierFactor\nOneClassSVM\nMiniBatchClassifier\nMiniBatchRegressor\nMiniBatchSupervisedTransformer\nMiniBatchTransformer\nSKL2RiverClassifier\nSKL2RiverRegressor\nFuncTransformer\nPipeline\nSelect\nTransformerProduct\nTransformerUnion\nBagOfWords\nTFIDF\nLinearRegression\nLogisticRegression\nPerceptron\nOneVsRestClassifier\nBernoulliNB\nComplementNB\nMultinomialNB\nMLPRegressor\nOneHotEncoder\nOrdinalEncoder\nStandardScaler\n</code></pre> <p>Because mini-batch learning isn't treated as a first-class citizen, some of the river's functionalities require some work in order to play nicely with mini-batches. For instance, the objects from the <code>metrics</code> module have an <code>update</code> method that take as input a single pair <code>(y_true, y_pred)</code>. This might change in the future, depending on the demand.</p> <p>We plan to promote more models to the mini-batch regime. However, we will only be doing so for the methods that benefit the most from it, as well as those that are most popular. Indeed, River's core philosophy will remain to cater to single instance learning.</p>"},{"location":"recipes/model-evaluation/","title":"Model evaluation","text":"<p>To do.</p>"},{"location":"recipes/on-hoeffding-trees/","title":"Incremental decision trees in river: the Hoeffding Tree case","text":"<p>Decision trees (DT) are popular learning models due to their inherently simplicity, flexibility and self-explainable structure. Moreover, when aggregated in ensembles, high predictive power might be achieved. Bagging and gradient boosting-based tree ensembles are very popular solutions in competition platforms such as Kaggle, and also among researchers.</p> <p>Although fairly lightweight, traditional batch DTs cannot cope with data stream mining/online learning requirements, as they do multiple passes over the data and have to be retrained from scratch every time a new observation appears.</p> <p>The data stream literature has plenty of incremental DT (iDT) families that are better suited to online learning. Nonetheless, Hoeffding Trees (HT) are historically the most popular family of iDTs to date. In fact, HTs have some nice properties:</p> <ul> <li>one-pass learning regime;</li> <li>theoretical guarantees to converge to the batch DT model given enough observations and a stationary data distribution;</li> <li>small memory and running time footprint (in most cases);</li> <li>some of their variations can deal with non-stationary distributions.</li> </ul> <p>And the previous list goes on and on. Besides that, HTs also have the same advantages as batch DTs (<code>C4.5</code>/<code>J48</code>, <code>CART</code>, <code>M5</code>, etc.) do. We can inspect the structure of a HT to understand how decisions were made, which is a nice feature to have in online learning tasks.</p> <p>In River, HTs are first-class citizens, so we have multiple realizations of this framework that are suited to different learning tasks and scenarios.</p> <p>This brief introduction to HT does not aims at being extensive nor delving into algorithmic or implementation details of the HTs. Instead, we intend to provide a high-level overview of the HTs as they are envisioned in River, as well as their shared properties and important hyperparameters.</p> <p>In this guide, we are going to:</p> <ol> <li>summarize the differences accross the multiple HT versions available;</li> <li>learn how to inspect tree models;</li> <li>learn how to manage the memory usage of HTs;</li> <li>compare numerical tree splitters and understand their impact on the iDT induction process.</li> </ol> <p>Well, without further ado, let's go! </p> <p>First things first, we are going to start with some imports.</p> <pre><code>import matplotlib.pyplot as plt\nimport datetime as dt\n\nfrom river import datasets\nfrom river import evaluate\nfrom river import metrics\nfrom river import preprocessing  # we are going to use that later\nfrom river.datasets import synth  # we are going to use some synthetic datasets too\nfrom river import tree\n</code></pre>"},{"location":"recipes/on-hoeffding-trees/#1-trees-trees-everywhere-gardening-101-with-river","title":"1. Trees, trees everywhere: gardening 101 with river","text":"<p>At first glance, the amount of iDT algorithms in River might seem too much to handle, but in reality the distinction among them is easy to grasp. To facilitate our lives, here's a neat table listing the available HT models and summarizing their differences:</p> Name Acronym Task Non-stationary? Comments Source Hoeffding Tree Classifier HTC Classification No Basic HT for classification tasks [1] Hoeffding Adaptive Tree Classifier HATC Classification Yes Modifies HTC by adding an instance of ADWIN to each node to detect and react to drift detection [2] Extremely Fast Decision Tree Classifier EFDT Classification No Deploys split decisions as soon as possible and periodically revisit decisions and redo them if necessary. Not as fast in practice as the name implies, but it tends to converge faster than HTC to the model generated by a batch DT [3] Hoeffding Tree Regressor HTR Regression No Basic HT for regression tasks. It is an adaptation of the FIRT/FIMT algorithm that bears some semblance to HTC [4] Hoeffding Adaptive Tree Regressor HATR Regression Yes Modifies HTR by adding an instance of ADWIN to each node to detect and react to drift detection - incremental Structured-Output Prediction Tree Regressor iSOUPT Multi-target regression No Multi-target version of HTR [5] Label Combination Hoeffding Tree Classifier LCHTC Multi-label classification No Creates a numerical code for each combination of the binary labels and uses HTC to learn from this encoded representation. At prediction time, decodes the modified representation to obtain the original label set - <p>As we can see, although their application fields might overlap sometimes, the HT variations have specific situations in which they are better suited to work. Moreover, in River we provide a standardized API access to all the HT variants since they share many properties in common.</p>"},{"location":"recipes/on-hoeffding-trees/#2-how-to-inspect-tree-models","title":"2. How to inspect tree models?","text":"<p>We provide a handful of tools to inspect trained HTs in River. Here, we will provide some examples of how to access their inner structures, get useful information, and plot the iDT structure.</p> <p>Firstly, let's pick a toy dataset from which our tree will learn from. Here we are going to focus on the classification case, but the same operations apply to other learning tasks. We will select the <code>Phishing</code> dataset from the <code>datasets</code> module to exemplify the HTs' capabilities.</p> <pre><code>dataset = datasets.Phishing()\ndataset\n</code></pre> <pre><code>Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n    Name  Phishing                                                       \n    Task  Binary classification                                          \n Samples  1,250                                                          \nFeatures  9                                                              \n  Sparse  False                                                          \n    Path  /Users/mastelini/Documents/river/river/datasets/phishing.csv.gz\n</code></pre> <p>We are going to train an instance of <code>HoeffdingTreeClassifier</code> using this dataset. As everything else in River, training an iDT is a piece of cake!</p> <pre><code>%%time\n\nmodel = tree.HoeffdingTreeClassifier(grace_period=50)\n\nfor x, y in dataset:\n    model.learn_one(x, y)\n\nmodel\n</code></pre> <pre><code>CPU times: user 64.8 ms, sys: 2.75 ms, total: 67.6 ms\nWall time: 68.1 ms\n</code></pre> <pre>HoeffdingTreeClassifier</pre><code>HoeffdingTreeClassifier (   grace_period=50   max_depth=inf   split_criterion=\"info_gain\"   delta=1e-07   tau=0.05   leaf_prediction=\"nba\"   nb_threshold=0   nominal_attributes=None   splitter=GaussianSplitter (     n_splits=10   )   binary_split=False   min_branch_fraction=0.01   max_share_to_split=0.99   max_size=100.   memory_estimate_period=1000000   stop_mem_management=False   remove_poor_attrs=False   merit_preprune=True ) </code> <p>That's it! We are not going to enter into details about some of the available parameters of HTC here. The user can refer to the documentation page for more information about that. Let's talk about model inspection :D</p> <p>At any time, we can easily get some statistics about our trained model by using the <code>summary</code> property:</p> <pre><code>model.summary\n</code></pre> <pre><code>{'n_nodes': 5,\n 'n_branches': 2,\n 'n_leaves': 3,\n 'n_active_leaves': 3,\n 'n_inactive_leaves': 0,\n 'height': 3,\n 'total_observed_weight': 1250.0}\n</code></pre> <p>This property show us the internal structure of the tree, including data concerning the memory-management routines that we are going to check later in this guide. We can also get a representation of the tree model as a <code>pandas.DataFrame</code> object:</p> <pre><code>model.to_dataframe().iloc[:5, :5]\n</code></pre> parent is_leaf depth stats feature node 0 &lt;NA&gt; False 0 {True: 260.0, False: 390.0} empty_server_form_handler 1 0 True 1 {True: 443.4163997711022, False: 59.8769131081... NaN 2 0 False 1 {True: 71.58360022889781, False: 404.123086891... popup_window 3 2 True 2 {False: 31.426538522574834, True: 33.0} NaN 4 2 True 2 {False: 250.57346147742516, True: 6.0} NaN <p>Hmm, maybe not the clearest of the representations. What about drawing the tree structure instead?</p> <pre><code>model.draw()\n</code></pre> <p></p> <p>Much better, huh?</p> <p>Lastly, we can check how the tree predicts one specific instance by using the <code>debug_one</code> method:</p> <pre><code>x, y = next(iter(dataset))  # Let's select the first example in the stream\nx, y\n</code></pre> <pre><code>({'empty_server_form_handler': 0.0,\n  'popup_window': 0.0,\n  'https': 0.0,\n  'request_from_other_domain': 0.0,\n  'anchor_from_other_domain': 0.0,\n  'is_popular': 0.5,\n  'long_url': 1.0,\n  'age_of_domain': 1,\n  'ip_in_url': 1},\n True)\n</code></pre> <pre><code>print(model.debug_one(x))\n</code></pre> <pre><code>empty_server_form_handler \u2264 0.5454545454545454\nClass True:\n    P(False) = 0.1\n    P(True) = 0.9\n</code></pre> <p>Our tree got this one right! The method <code>debug_one</code> is especially useful when we are dealing with a big tree model where drawing might not be the wisest of the choices (we will end up with a tree chart that has too much information to visually understand).</p> <p>Some additional hints:</p> <ul> <li>the <code>max_depth</code> parameter is our friend when building HTs that need to be constantly inspected. This parameter, which is available for every HT variant, triggers a pre-pruning mechanism that stops tree growth when the given depth is reached.</li> <li>we can also limit the depth when using the <code>draw</code> method.</li> <li>in the case of tree ensembles, individual trees can be accessed using the <code>[index]</code> operator. Then, the same set of inspection tools are available to play with!</li> </ul>"},{"location":"recipes/on-hoeffding-trees/#3-advanced-gardening-with-river-grab-your-pruning-shears-and-lets-limit-memory-usage","title":"3. Advanced gardening with river: grab your pruning shears and let's limit memory usage","text":"<p>Online learning is well-suited to highly scalable processing centers with petabytes of data arriving intermittently, but it can also work with Internet of Things (IoT) devices operating at low power and with limited processing capability. Hence, making sure our trees are not going to use too much memory is a nice feature that can impact on both energy usage and the running time. HTs have memory-management routines that put the user in the control of computational resources that are available.</p> <p>In this brief guide, we are going to use a regression tree, since this kind of iDT typically spends more memory than the classification counterparts. However, the user can control the memory usage in the exact same way in River, regardless of the HT variant!</p> <p>We will rely on the <code>Friedman</code> synthetic dataset (data generator) from the <code>synth</code> module in our evaluation. Since data generators can produce instances indefinitely, we will select a sample of size 10K for our tests.</p> <p>We are almost ready to go. Let's first define a simple function that plots the results obtained from a given dataset, metric and </p> <pre><code>def plot_performance(dataset, metric, models):\n    metric_name = metric.__class__.__name__\n\n    # To make the generated data reusable\n    dataset = list(dataset)\n    fig, ax = plt.subplots(figsize=(10, 5), nrows=3, dpi=300)\n    for model_name, model in models.items():\n        step = []\n        error = []\n        r_time = []\n        memory = []\n\n        for checkpoint in evaluate.iter_progressive_val_score(\n            dataset, model, metric, measure_time=True, measure_memory=True, step=100\n        ):\n            step.append(checkpoint[\"Step\"])\n            error.append(checkpoint[metric_name].get())\n\n            # Convert timedelta object into seconds\n            r_time.append(checkpoint[\"Time\"].total_seconds())\n            # Make sure the memory measurements are in MiB\n            raw_memory = checkpoint[\"Memory\"]\n            memory.append(raw_memory * 2**-20)\n\n        ax[0].plot(step, error, label=model_name)\n        ax[1].plot(step, r_time, label=model_name)\n        ax[2].plot(step, memory, label=model_name)\n\n    ax[0].set_ylabel(metric_name)\n    ax[1].set_ylabel('Time (seconds)')\n    ax[2].set_ylabel('Memory (MiB)')\n    ax[2].set_xlabel('Instances')\n\n    ax[0].grid(True)\n    ax[1].grid(True)\n    ax[2].grid(True)\n\n    ax[0].legend(\n        loc='upper center', bbox_to_anchor=(0.5, 1.25),\n        ncol=3, fancybox=True, shadow=True\n    )\n    plt.tight_layout()\n    plt.close()\n\n    return fig\n</code></pre> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"Unbounded HTR\": (\n            preprocessing.StandardScaler() |\n            tree.HoeffdingTreeRegressor(splitter=tree.splitter.EBSTSplitter())\n        )\n    }\n)\n</code></pre> <p></p> <p>In our example we use the <code>EBSTSplitter</code>, which is going to discussed later. For now, is enough to know that it is a mechanism to evaluate split candidates in the trees.</p> <p>As we can see, our tree uses almost 10 MiB to keep its structure. Let's say we wanted to limit our memory usage to 5 MiB. How could we do that?</p> <p>Note that we are using a illustration case here. In real applications, data may be unbounded, so the trees might grow indefinitely.</p> <p>HTs expose some parameters related to memory management. The user can refer to the documentation for more details on that matter. Here, we are going to focus on two parameters:</p> <ul> <li><code>max_size</code>: determines the maximum amount of memory (in MiB) that the HT can use.</li> <li><code>memory_estimate_period</code>: intervals after which the memory-management is triggered.</li> </ul> <p>We are going to limit our HTR to 5 MiB and perform memory checks at intervals of 500 instances.</p> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"Restricted HTR\": (\n            preprocessing.StandardScaler()\n            | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.EBSTSplitter(),\n                max_size=5,\n                memory_estimate_period=500\n            )\n        )\n    }\n)\n</code></pre> <p></p> <p>Note that as soon the memory usage reaches the limit that we determined (at the memory check intervals), HTR starts managing its resource usage to reduce the size. As a consequence, the running time also decreases. For more accurate management, the intervals between memory checks should be decreased. This action, however, has costs since the tree stops the learning process to estimate its size and alter its own structure. Too frequent memory checks might end up result in a slow learning process. Besides, by using fewer resources, the predictive performance can be negatively impacted. So, use this tool with caution!</p> <p>But how that works at all?</p> <p>HTs monitor the incoming feature values to perform split attempts. To do so, they rely on a class of algorithms called Attribute Observers (AO) or Splitters (spoiler alert!). Each leaf node in an HT keeps one AO per incoming feature. After pre-determined intervals (<code>grace_period</code> parameter), leaves query their AOs for split candidates. Well, there are costs to monitor input features (mainly the numerical ones). In fact, AOs correspond to one of the most time and memory-consuming portions of the HTs. To manage memory usage, an HT firstly determines its least promising leaves, w.r.t. how likely they will be split. Then, these leaves' AOs are removed, and the tree nodes are said to be \"deactivated.\" That's it! The deactivated leaves do not perform split attempts anymore, but they continue to be updated to provide responses. They will be kept as leaves as long as there are not available resources to enable tree growth. These leaves can be activated again (meaning that new AOs will be created for them) if there is available memory, so don't worry!</p> <p>Hint: another indirect way to bound memory usage is to limit the tree depth. By default, the trees can grow indefinitely, but the <code>max_depth</code> parameter can control this behavior.</p> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"HTR with at most 5 levels\": (\n            preprocessing.StandardScaler()\n            | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.EBSTSplitter(),\n                max_depth=5\n            )\n        )\n    }\n)\n</code></pre> <p></p>"},{"location":"recipes/on-hoeffding-trees/#4-branching-and-growth-splitters-the-heart-of-the-trees","title":"4. Branching and growth: splitters, the heart of the trees","text":"<p>As previously stated, one of the core operations of iDT is, well, to grow. Plants and gardening-related jokes apart, growth in HTs is guided by their AOs or splitters, as mentioned in the end of Section 3. </p> <p>Nominal features can be easily monitored, since the feature partitions are well-defined beforehand. Numerical features, on the other hand, do not have an explicit best cut point. Still, numerical features are typically split by using a binary test: \\(\\le\\) or \\(&gt;\\). Therefore, numerical splitters must somehow summarize the incoming feature values and be able to evaluate the merit of split point candidates.</p> <p>There are diverse strategies to monitor numerical features and choices related to them, including which data structure will be used to keep a summary of the incoming feature and also how many split points are going to be evaluated during split attempts. Again, this guide does not intend to be an exhaustive delve into the iDT subject. In fact, each of the following aspects of the iDTs could be considered a separate research area: AOs, intervals between split attempts, split heuristics (e.g., info gain, variance reduction, and so on), tree depth and max size, and much more!</p> <p>Let's focus a bit into the AO matter. River provides a handful of splitters for classification and regression trees, which can be chosen using the parameter <code>splitter</code>. We will list the available tree splitters in the following sections and compare some of their chacteristics.</p> <p>Some notation:</p> <ul> <li>\\(n\\): Number of observations seen so far.</li> <li>\\(c\\): the number of classes.</li> <li>\\(s\\): the number of split points to evaluate (which means that this is a user-given parameter).</li> <li>\\(h\\): the number of histogram bins or hash slots. Tipically, \\(h \\ll n\\).</li> </ul>"},{"location":"recipes/on-hoeffding-trees/#41-classification-tree-splitters","title":"4.1. Classification tree splitters","text":"<p>The following table summarizes the available classification splitters. The user might refer to the documentation of each splitter for more details about their functioning.</p> Splitter Description Insertion Memory Split candidate query Works with Naive Bayes leaves? Exhaustive Keeps all the observed input values and class counts in a Binary Search Tree (BST) \\(O(\\log n)\\) (average) or \\(O(n)\\) (worst case) \\(O(n)\\) \\(O(n)\\) No Histogram Builds a histogram for each class in order to discretize the input feature \\(O(\\log h)\\) \\(O(c h)\\) \\(O(c h)\\) Yes Gaussian Approximates the class distributions using Gaussian distributions \\(O(1)\\) \\(O(c)\\) \\(O(cs)\\) Yes <p>Note that some of the splitters have configurable parameters that directly impact not only on their time and memory costs, but also on the final predictive performance. Examples:</p> <ul> <li>The number of split points can be configured in the Gaussian splitter. Increasing this number makes this splitter slower, but it also potentially increases the quality of the obtained query points, implying enhanced tree accuracy. </li> <li>The number of stored bins can be selected in the Histogram splitter. Increasing this number increases the memory footprint and running time of this splitter, but it also potentially makes its split candidates more accurate and positively impacts on the tree's final predictive performance.</li> </ul> <p>Next, we provide a brief comparison of the classification splitters using 10K instances of the Random RBF synthetic dataset. Note that the tree equiped with the Exhaustive splitter does not use Naive Bayes leaves.</p> <pre><code>plot_performance(\n    synth.RandomRBF(seed_model=7, seed_sample=42).take(10_000),\n    metrics.Accuracy(),\n    {\n        \"HTC + Exhaustive splitter\": tree.HoeffdingTreeClassifier(\n            splitter=tree.splitter.ExhaustiveSplitter(),\n            leaf_prediction=\"mc\"\n        ),\n        \"HTC + Histogram splitter\": tree.HoeffdingTreeClassifier(\n            splitter=tree.splitter.HistogramSplitter()\n        ),\n        \"HTC + Gaussian splitter\": tree.HoeffdingTreeClassifier(\n            splitter=tree.splitter.GaussianSplitter()\n        )\n    }\n)\n</code></pre> <p></p>"},{"location":"recipes/on-hoeffding-trees/#42-regression-tree-splitters","title":"4.2 Regression tree splitters","text":"<p>The available regression tree splitters are summarized in the next table. The TE-BST costs are expressed in terms of \\(n^*\\) because the number of stored elements can be smaller than or equal to \\(n\\).</p> Splitter Description Insertion Memory Split candidate query Extended Binary Search Tree (E-BST) Stores all the observations and target statistics in a BST \\(O(\\log n)\\) (average) or \\(O(n)\\) (worst case) \\(O(n)\\) \\(O(n)\\) Truncated E-BST (TE-BST) Rounds the incoming data before passing it to the BST \\(O(\\log n^*)\\) (average) or \\(O(n^*)\\) (worst case) \\(O(n^*)\\) \\(O(n^*)\\) Quantization Observer (QO) Uses a hash-like structure to quantize the incoming data \\(O(1)\\) \\(O(h)\\) \\(O(h \\log h)\\) <p>E-BST is an exhaustive algorithm, i.e., it works as batch solutions usually do, which might be prohibitive in real-world online scenarios. TE-BST and QO apply approximations to alleviate the costs involved in monitoring numerical data and performing split attempts. The number of desired decimal places to round the data (TE-BST) and the quantization radius (QO) are directly related to the running time, memory footprint, and error of the resulting tree model.</p> <p>We present a brief comparison of the available regression tree splitters using the 10K instances of the Friedman synthetic dataset.</p> <pre><code>plot_performance(\n    synth.Friedman(seed=42).take(10_000),\n    metrics.MAE(),\n    {\n        \"HTR + E-BST\": (\n            preprocessing.StandardScaler() | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.EBSTSplitter()\n            )\n        ),\n        \"HTR + TE-BST\": (\n            preprocessing.StandardScaler() | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.TEBSTSplitter()\n            )\n        ),\n        \"HTR + QO\": (\n            preprocessing.StandardScaler() | tree.HoeffdingTreeRegressor(\n                splitter=tree.splitter.QOSplitter()\n            )\n        ),\n\n    }\n)\n</code></pre> <p></p>"},{"location":"recipes/on-hoeffding-trees/#wrapping-up","title":"Wrapping up","text":"<p>This guide provides a walkthrough in the HTs available in River. We discussed about model inspection, memory management, and feature splits. Keep in mind that each HT variant has specific details and capabilities that are out-of-the-scope of this introductory material. The user is advised to check the documentation page of the tree models for detailed information.</p>"},{"location":"recipes/pipelines/","title":"Pipelines","text":"<p>Pipelines are an integral part of River. We encourage their usage and apply them in many of their examples.</p> <p>The <code>compose.Pipeline</code> contains all the logic for building and applying pipelines. A pipeline is essentially a list of estimators that are applied in sequence. The only requirement is that the first <code>n - 1</code> steps be transformers. The last step can be a regressor, a classifier, a clusterer, a transformer, etc.</p> <p>Here is an example:</p> <pre><code>from river import compose\nfrom river import linear_model\nfrom river import preprocessing\nfrom river import feature_extraction\n\nmodel = compose.Pipeline(\n    preprocessing.StandardScaler(),\n    feature_extraction.PolynomialExtender(),\n    linear_model.LinearRegression()\n)\n</code></pre> <p>You can also use the <code>|</code> operator, as so:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    feature_extraction.PolynomialExtender() |\n    linear_model.LinearRegression()\n)\n</code></pre> <p>Or, equally:</p> <pre><code>model = preprocessing.StandardScaler() \nmodel |= feature_extraction.PolynomialExtender()\nmodel |= linear_model.LinearRegression()\n</code></pre> <p>A pipeline, as any River estimator, has a <code>_repr_html_</code> method, which can be used to visualize it in Jupyter-like notebooks:</p> <pre><code>model\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>PolynomialExtender</pre><code>PolynomialExtender (   degree=2   interaction_only=False   include_bias=False   bias_name=\"bias\" ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p><code>compose.Pipeline</code> implements a <code>learn_one</code> method which in sequence calls the <code>learn_one</code> of each component and a <code>predict_one</code> (resp <code>predict_proba_one</code>) method which calls <code>transform_one</code> on the first <code>n - 1</code> steps and <code>predict_one</code> (resp <code>predict_proba_one</code>) on the last step.</p> <p>Here is a small example to illustrate the previous point:</p> <pre><code>from river import datasets\n\ndataset = datasets.TrumpApproval()\nx, y = next(iter(dataset))\nx, y\n</code></pre> <pre><code>({'ordinal_date': 736389,\n  'gallup': 43.843213,\n  'ipsos': 46.19925042857143,\n  'morning_consult': 48.318749,\n  'rasmussen': 44.104692,\n  'you_gov': 43.636914000000004},\n 43.75505)\n</code></pre> <p>We can predict the target value of a new sample by calling the <code>predict_one</code> method, however, by default, <code>predict_one</code> does not update any model parameter, therefore the predictions will be 0 and the model parameters will remain the default values (0 for <code>StandardScaler</code> component):</p> <pre><code>for (x, y) in dataset.take(2):\n    print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n    print(f\"{model['StandardScaler'].means = }\")\n</code></pre> <pre><code>model.predict_one(x)=0.00, y=43.76\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 0.0, 'gallup': 0.0, 'ipsos': 0.0, 'morning_consult': 0.0, 'rasmussen': 0.0, 'you_gov': 0.0})\nmodel.predict_one(x)=0.00, y=43.71\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 0.0, 'gallup': 0.0, 'ipsos': 0.0, 'morning_consult': 0.0, 'rasmussen': 0.0, 'you_gov': 0.0})\n</code></pre> <p><code>learn_one</code> updates pipeline stateful steps, parameters and the prediction change:</p> <pre><code>for (x, y) in dataset.take(2):\n    model.learn_one(x, y)\n\n    print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n    print(f\"{model['StandardScaler'].means = }\")\n</code></pre> <pre><code>model.predict_one(x)=0.88, y=43.76\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.0, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 44.104692, 'you_gov': 43.636914000000004})\nmodel.predict_one(x)=9.44, y=43.71\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.5, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 45.104692, 'you_gov': 42.636914000000004})\n</code></pre> <p>Each component of the pipeline has been updated with the new data point. </p> <p>A pipeline is a very powerful tool that can be used to chain together multiple steps in a machine learning workflow.</p> <p>Notice that it is also possible to call <code>transform_one</code> with a pipeline, this method will run <code>transform_one</code> of each transformer in it, and return the result of the last transformer (which is thus the penultimate step if the last step is a predictor or clusterer, while it is the last step if the last step is a transformer):</p> <pre><code>model.transform_one(x)\n</code></pre> <pre><code>{'ordinal_date': 1.0,\n 'gallup': 0.0,\n 'ipsos': 0.0,\n 'morning_consult': 0.0,\n 'rasmussen': 1.0,\n 'you_gov': -1.0,\n 'ordinal_date*ordinal_date': 1.0,\n 'gallup*ordinal_date': 0.0,\n 'ipsos*ordinal_date': 0.0,\n 'morning_consult*ordinal_date': 0.0,\n 'ordinal_date*rasmussen': 1.0,\n 'ordinal_date*you_gov': -1.0,\n 'gallup*gallup': 0.0,\n 'gallup*ipsos': 0.0,\n 'gallup*morning_consult': 0.0,\n 'gallup*rasmussen': 0.0,\n 'gallup*you_gov': -0.0,\n 'ipsos*ipsos': 0.0,\n 'ipsos*morning_consult': 0.0,\n 'ipsos*rasmussen': 0.0,\n 'ipsos*you_gov': -0.0,\n 'morning_consult*morning_consult': 0.0,\n 'morning_consult*rasmussen': 0.0,\n 'morning_consult*you_gov': -0.0,\n 'rasmussen*rasmussen': 1.0,\n 'rasmussen*you_gov': -1.0,\n 'you_gov*you_gov': 1.0}\n</code></pre> <p>In many cases, you might want to connect a step to multiple steps. For instance, you might to extract different kinds of features from a single input. An elegant way to do this is to use a <code>compose.TransformerUnion</code>. Essentially, the latter is a list of transformers who's results will be merged into a single <code>dict</code> when <code>transform_one</code> is called.</p> <p>As an example let's say that we want to apply a <code>feature_extraction.RBFSampler</code> as well as the <code>feature_extraction.PolynomialExtender</code>. This may be done as so:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    (feature_extraction.PolynomialExtender() + feature_extraction.RBFSampler()) |\n    linear_model.LinearRegression()\n)\n\nmodel\n</code></pre> <pre>StandardScaler</pre><code>StandardScaler (   with_std=True ) </code><pre>PolynomialExtender</pre><code>PolynomialExtender (   degree=2   interaction_only=False   include_bias=False   bias_name=\"bias\" ) </code><pre>RBFSampler</pre><code>RBFSampler (   gamma=1.   n_components=100   seed=None ) </code><pre>LinearRegression</pre><code>LinearRegression (   optimizer=SGD (     lr=Constant (       learning_rate=0.01     )   )   loss=Squared ()   l2=0.   l1=0.   intercept_init=0.   intercept_lr=Constant (     learning_rate=0.01   )   clip_gradient=1e+12   initializer=Zeros () ) </code> <p>Note that the <code>+</code> symbol acts as a shorthand notation for creating a <code>compose.TransformerUnion</code>, which means that we could have declared the above pipeline as so:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    compose.TransformerUnion(\n        feature_extraction.PolynomialExtender(),\n        feature_extraction.RBFSampler()\n    ) |\n    linear_model.LinearRegression()\n)\n</code></pre> <p>Pipelines provide the benefit of removing a lot of cruft by taking care of tedious details for you. They also enable to clearly define what steps your model is made of.</p> <p>Finally, having your model in a single object means that you can move it around more easily.</p> <p>Note that you can include user-defined functions in a pipeline by using a <code>compose.FuncTransformer</code>.</p>"},{"location":"recipes/pipelines/#learning-during-predict","title":"Learning during predict","text":"<p>In online machine learning, we can update the unsupervised parts of our model when a sample arrives. We don't really have to wait for the ground truth to arrive in order to update unsupervised estimators that don't depend on it.</p> <p>In other words, in a pipeline, <code>learn_one</code> updates the supervised parts, whilst <code>predict_one</code> (or <code>predict_proba_one</code> for that matter) can update the unsupervised parts, which often yields better results. </p> <p>In river, we can achieve this behavior using a dedicated context manager: <code>compose.learn_during_predict</code>.</p> <p>Here is the same example as before, with the only difference of activating the such learning during predict behavior:</p> <pre><code>model = (\n    preprocessing.StandardScaler() |\n    feature_extraction.PolynomialExtender() |\n    linear_model.LinearRegression()\n)\n</code></pre> <pre><code>with compose.learn_during_predict():\n    for (x, y) in dataset.take(2):\n\n        print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n        print(f\"{model['StandardScaler'].means = }\")\n</code></pre> <pre><code>model.predict_one(x)=0.00, y=43.76\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.0, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 44.104692, 'you_gov': 43.636914000000004})\nmodel.predict_one(x)=0.00, y=43.71\nmodel['StandardScaler'].means = defaultdict(&lt;class 'float'&gt;, {'ordinal_date': 736389.5, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 45.104692, 'you_gov': 42.636914000000004})\n</code></pre> <p>Calling <code>predict_one</code> within this context will update each transformer of the pipeline. For instance here we can see that the mean of each feature of the standard scaler step have been updated.</p> <p>On the other hand, the supervised part of our pipeline, the linear regression, has not been updated or learned anything yet. Hence the prediction on any sample will be nil because each weight is still equal to 0.</p> <pre><code>model.predict_one(x), model[\"LinearRegression\"].weights\n</code></pre> <pre><code>(0.0, {})\n</code></pre>"},{"location":"recipes/pipelines/#performance-comparison","title":"Performance Comparison","text":"<p>One may wonder what is the advantage of learning during predict. Let's compare the performance of a pipeline with and without learning during predict, in two scenarios: one in which the flow of data stays the same, we just update </p> <pre><code>from contextlib import nullcontext\nfrom river import metrics\n\nimport pandas as pd\n</code></pre> <pre><code>def score_pipeline(learn_during_predict: bool, n_learning_samples: int | None = None) -&gt; float:\n    \"\"\"Scores a pipeline on the TrumpApproval dataset.\n\n    Parameters\n    ----------\n    learn_during_predict : bool\n        Whether or not to learn the unsupervided components during the prediction step.\n        If False it will only learn when `learn_one` is explicitly called.\n    n_learning_samples : int | None \n        Number of samples used to `learn_one`.\n\n    Return\n    ------\n    MAE : float\n        Mean absolute error of the pipeline on the dataset\n    \"\"\"\n\n    dataset = datasets.TrumpApproval()\n\n    model = (\n        preprocessing.StandardScaler() |\n        linear_model.LinearRegression()\n        )\n\n    metric = metrics.MAE()\n\n    ctx = compose.learn_during_predict if learn_during_predict else nullcontext\n    n_learning_samples = n_learning_samples or dataset.n_samples\n\n    with ctx():\n        for _idx, (x, y) in enumerate(dataset):\n            y_pred = model.predict_one(x)\n\n            metric.update(y, y_pred)\n\n            if _idx &lt; n_learning_samples:\n                model.learn_one(x, y)\n\n    return metric.get()\n</code></pre> <pre><code>max_samples = datasets.TrumpApproval().n_samples\n\nresults = [\n    {\n        \"learn_during_predict\": learn_during_predict,\n        \"pct_learning_samples\": round(100*n_learning_samples/max_samples, 0),\n        \"mae\": score_pipeline(learn_during_predict=learn_during_predict, n_learning_samples=n_learning_samples)\n    }\n    for learn_during_predict in (True, False)\n    for n_learning_samples in range(max_samples, max_samples//10, -(max_samples//10))\n]\n</code></pre> <pre><code>(pd.DataFrame(results)\n .pivot(columns=\"learn_during_predict\", index=\"pct_learning_samples\", values=\"mae\")\n .sort_index(ascending=False)\n .style.format_index('{0}%')\n)\n</code></pre> learn_during_predict False True pct_learning_samples 100.0% 1.314548 1.347434 90.0% 1.629333 1.355274 80.0% 2.712125 1.371599 70.0% 4.840620 1.440773 60.0% 8.918634 1.498240 50.0% 15.112753 1.878434 40.0% 26.387331 2.105553 30.0% 42.997083 3.654709 20.0% 90.703102 3.504950 10.0% 226.836953 4.803600 <p>As we can see from the resulting table above, the scores are comparable only in the case in which the percentage of learning samples above 90%. After that the score starts to degrade quite fast as the percentage of learning samples decreases, and it is very remarkable (one order of magnitude or more) when less than 50% of the samples are used for learning.</p> <p>Although a simple case, this examplify how powerful it can be to learn unsupervised components during predict.</p>"},{"location":"recipes/reading-data/","title":"Reading data","text":"<p>In River, the features of a sample are stored inside a dictionary, which in Python is called a <code>dict</code> and is a native data structure. In other words, we don't use any sophisticated data structure, such as a <code>numpy.ndarray</code> or a <code>pandas.DataFrame</code>.</p> <p>The main advantage of using plain <code>dict</code>s is that it removes the overhead that comes with using the aforementioned data structures. This is important in a streaming context because we want to be able to process many individual samples in rapid succession. Another advantage is that <code>dict</code>s allow us to give names to our features. Finally, <code>dict</code>s are not typed, and can therefore store heterogeneous data.</p> <p>Another advantage which we haven't mentioned is that <code>dict</code>s play nicely with Python's standard library. Indeed, Python contains many tools that allow manipulating <code>dict</code>s. For instance, the <code>csv.DictReader</code> can be used to read a CSV file and convert each row to a <code>dict</code>. In fact, the <code>stream.iter_csv</code> method from River is just a wrapper on top of <code>csv.DictReader</code> that adds a few bells and whistles.</p> <p>River provides some out-of-the-box datasets to get you started.</p> <pre><code>from river import datasets\n\ndataset = datasets.Bikes()\ndataset\n</code></pre> <pre><code>Bike sharing station information from the city of Toulouse.\n\nThe goal is to predict the number of bikes in 5 different bike stations from the city of\nToulouse.\n\n      Name  Bikes                                                         \n      Task  Regression                                                    \n   Samples  182,470                                                       \n  Features  8                                                             \n    Sparse  False                                                         \n      Path  /Users/max/river_data/Bikes/toulouse_bikes.csv                \n       URL  https://maxhalford.github.io/files/datasets/toulouse_bikes.zip\n      Size  12.52 MB                                                      \nDownloaded  True\n</code></pre> <p>Note that when we say \"loaded\", we don't mean that the actual data is read from the disk. On the contrary, the dataset is a streaming data that can be iterated over one sample at a time. In Python lingo, it's a generator.</p> <p>Let's take a look at the first sample:</p> <pre><code>x, y = next(iter(dataset))\nx\n</code></pre> <pre><code>{'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n 'station': 'metro-canal-du-midi',\n 'clouds': 75,\n 'description': 'light rain',\n 'humidity': 81,\n 'pressure': 1017.0,\n 'temperature': 6.54,\n 'wind': 9.3}\n</code></pre> <p>Each dataset is iterable, which means we can also do:</p> <pre><code>for x, y in dataset:\n    break\nx\n</code></pre> <pre><code>{'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n 'station': 'metro-canal-du-midi',\n 'clouds': 75,\n 'description': 'light rain',\n 'humidity': 81,\n 'pressure': 1017.0,\n 'temperature': 6.54,\n 'wind': 9.3}\n</code></pre> <p>As we can see, the values have different types.</p> <p>Under the hood, calling <code>for x, y in dataset</code> simply iterates over a file and parses each value appropriately. We can do this ourselves by using <code>stream.iter_csv</code>:</p> <pre><code>from river import stream\n\nX_y = stream.iter_csv(dataset.path)\nx, y = next(X_y)\nx, y\n</code></pre> <pre><code>({'moment': '2016-04-01 00:00:07',\n  'bikes': '1',\n  'station': 'metro-canal-du-midi',\n  'clouds': '75',\n  'description': 'light rain',\n  'humidity': '81',\n  'pressure': '1017.0',\n  'temperature': '6.54',\n  'wind': '9.3'},\n None)\n</code></pre> <p>There are a couple things that are wrong. First of all, the numeric features have not been casted into numbers. Indeed, by default, <code>stream.iter_csv</code> assumes that everything is a string. A related issue is that the <code>moment</code> field hasn't been parsed into a <code>datetime</code>. Finally, the target field, which is <code>bikes</code>, hasn't been separated from the rest of the features. We can remedy to these issues by setting a few parameters:</p> <pre><code>X_y = stream.iter_csv(\n    dataset.path,\n    converters={\n        'bikes': int,\n        'clouds': int,\n        'humidity': int,\n        'pressure': float,\n        'temperature': float,\n        'wind': float\n    },\n    parse_dates={'moment': '%Y-%m-%d %H:%M:%S'},\n    target='bikes'\n)\nx, y = next(X_y)\nx, y\n</code></pre> <pre><code>({'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),\n  'station': 'metro-canal-du-midi',\n  'clouds': 75,\n  'description': 'light rain',\n  'humidity': 81,\n  'pressure': 1017.0,\n  'temperature': 6.54,\n  'wind': 9.3},\n 1)\n</code></pre> <p>That's much better. We invite you to take a look at the <code>stream</code> module to see for yourself what other methods are available. Note that River is first and foremost a machine learning library, and therefore isn't as much concerned about reading data as it is about statistical algorithms. We do however believe that the fact that we use dictionary gives you, the user, a lot of freedom and flexibility.</p> <p>The <code>stream</code> module provides helper functions to read data from different formats. For instance, you can use the <code>stream.iter_sklearn_dataset</code> function to turn any scikit-learn dataset into a stream.</p> <pre><code>from sklearn import datasets\n\ndataset = datasets.load_diabetes()\n\nfor x, y in stream.iter_sklearn_dataset(dataset):\n    break\n\nx, y\n</code></pre> <pre><code>({'age': 0.038075906433423026,\n  'sex': 0.05068011873981862,\n  'bmi': 0.061696206518683294,\n  'bp': 0.0218723855140367,\n  's1': -0.04422349842444599,\n  's2': -0.03482076283769895,\n  's3': -0.04340084565202491,\n  's4': -0.002592261998183278,\n  's5': 0.019907486170462722,\n  's6': -0.01764612515980379},\n 151.0)\n</code></pre> <p>To conclude, let us shortly mention the difference between proactive learning and reactive learning in the specific context of online machine learning. When we loop over a data with a <code>for</code> loop, we have the control over the data and the order in which it arrives. We are proactive in the sense that we, the user, are asking for the data to arrive.</p> <p>In contract, in a reactive situation, we don't have control on the data arrival. A typical example of such a situation is a web server, where web requests arrive in an arbitrary order. This is a situation where River shines. For instance, in a Flask application, you could define a route to make predictions with a River model as so:</p> <pre><code>import flask\n\napp = flask.Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef predict():\n    payload = flask.request.json\n    river_model = load_model()\n    return river_model.predict_proba_one(payload)\n</code></pre> <p>Likewise, a model can be updated whenever a request arrives as so:</p> <pre><code>@app.route('/', methods=['POST'])\ndef learn():\n    payload = flask.request.json\n    river_model = load_model()\n    river_model.learn_one(payload['features'], payload['target'])\n    return {}, 201\n</code></pre> <p>To summarize, River can be used in many different ways. The fact that it uses dictionaries to represent features provides a lot of flexibility and space for creativity.</p>"},{"location":"recipes/rolling-computations/","title":"Rolling computations","text":"<p>You might wonder which classes in River can be wrapped with a <code>utils.Rolling</code>. This can be answered with a bit of metaprogramming.</p> <pre><code>import importlib\nimport inspect\nfrom river.utils.rolling import Rollable\n\nfor submodule in importlib.import_module(\"river.api\").__all__:\n    for _, obj in inspect.getmembers(\n        importlib.import_module(f\"river.{submodule}\"), lambda x: isinstance(x, Rollable)\n    ):\n        print(f'{submodule}.{obj.__name__}')\n</code></pre> <pre><code>[covariance.EmpiricalCovariance](../../api/covariance/EmpiricalCovariance)\n[metrics.Accuracy](../../api/metrics/Accuracy)\n[metrics.AdjustedMutualInfo](../../api/metrics/AdjustedMutualInfo)\n[metrics.AdjustedRand](../../api/metrics/AdjustedRand)\n[metrics.BalancedAccuracy](../../api/metrics/BalancedAccuracy)\n[metrics.ClassificationReport](../../api/metrics/ClassificationReport)\n[metrics.CohenKappa](../../api/metrics/CohenKappa)\n[metrics.Completeness](../../api/metrics/Completeness)\n[metrics.ConfusionMatrix](../../api/metrics/ConfusionMatrix)\n[metrics.CrossEntropy](../../api/metrics/CrossEntropy)\n[metrics.F1](../../api/metrics/F1)\n[metrics.FBeta](../../api/metrics/FBeta)\n[metrics.FowlkesMallows](../../api/metrics/FowlkesMallows)\n[metrics.GeometricMean](../../api/metrics/GeometricMean)\n[metrics.Homogeneity](../../api/metrics/Homogeneity)\n[metrics.Jaccard](../../api/metrics/Jaccard)\n[metrics.LogLoss](../../api/metrics/LogLoss)\n[metrics.MAE](../../api/metrics/MAE)\n[metrics.MAPE](../../api/metrics/MAPE)\n[metrics.MCC](../../api/metrics/MCC)\n[metrics.MSE](../../api/metrics/MSE)\n[metrics.MacroF1](../../api/metrics/MacroF1)\n[metrics.MacroFBeta](../../api/metrics/MacroFBeta)\n[metrics.MacroJaccard](../../api/metrics/MacroJaccard)\n[metrics.MacroPrecision](../../api/metrics/MacroPrecision)\n[metrics.MacroRecall](../../api/metrics/MacroRecall)\n[metrics.MicroF1](../../api/metrics/MicroF1)\n[metrics.MicroFBeta](../../api/metrics/MicroFBeta)\n[metrics.MicroJaccard](../../api/metrics/MicroJaccard)\n[metrics.MicroPrecision](../../api/metrics/MicroPrecision)\n[metrics.MicroRecall](../../api/metrics/MicroRecall)\n[metrics.MultiFBeta](../../api/metrics/MultiFBeta)\n[metrics.MutualInfo](../../api/metrics/MutualInfo)\n[metrics.NormalizedMutualInfo](../../api/metrics/NormalizedMutualInfo)\n[metrics.Precision](../../api/metrics/Precision)\n[metrics.R2](../../api/metrics/R2)\n[metrics.RMSE](../../api/metrics/RMSE)\n[metrics.RMSLE](../../api/metrics/RMSLE)\n[metrics.ROCAUC](../../api/metrics/ROCAUC)\n[metrics.Rand](../../api/metrics/Rand)\n[metrics.Recall](../../api/metrics/Recall)\n[metrics.RollingROCAUC](../../api/metrics/RollingROCAUC)\n[metrics.SMAPE](../../api/metrics/SMAPE)\n[metrics.Silhouette](../../api/metrics/Silhouette)\n[metrics.VBeta](../../api/metrics/VBeta)\n[metrics.WeightedF1](../../api/metrics/WeightedF1)\n[metrics.WeightedFBeta](../../api/metrics/WeightedFBeta)\n[metrics.WeightedJaccard](../../api/metrics/WeightedJaccard)\n[metrics.WeightedPrecision](../../api/metrics/WeightedPrecision)\n[metrics.WeightedRecall](../../api/metrics/WeightedRecall)\n[proba.Beta](../../api/proba/Beta)\n[proba.Gaussian](../../api/proba/Gaussian)\n[proba.Multinomial](../../api/proba/Multinomial)\n[proba.MultivariateGaussian](../../api/proba/MultivariateGaussian)\n[stats.BayesianMean](../../api/stats/BayesianMean)\n[stats.Cov](../../api/stats/Cov)\n[stats.KolmogorovSmirnov](../../api/stats/KolmogorovSmirnov)\n[stats.Mean](../../api/stats/Mean)\n[stats.PearsonCorr](../../api/stats/PearsonCorr)\n[stats.SEM](../../api/stats/SEM)\n[stats.Sum](../../api/stats/Sum)\n[stats.Var](../../api/stats/Var)\n</code></pre>"},{"location":"releases/0.0.2/","title":"0.0.2 - 2019-02-13","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.0.2/#compat","title":"compat","text":"<ul> <li>Added <code>sklearn</code> wrappers.</li> </ul>"},{"location":"releases/0.0.2/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.HedgeClassifier</code>.</li> </ul>"},{"location":"releases/0.0.2/#feature_selection","title":"feature_selection","text":"<ul> <li>Added <code>feature_selection.RandomDiscarder</code>.</li> </ul>"},{"location":"releases/0.0.2/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added <code>feature_extraction.TargetEncoder</code>.</li> </ul>"},{"location":"releases/0.0.2/#impute","title":"impute","text":"<ul> <li>Added <code>impute.NumericImputer</code>.</li> </ul>"},{"location":"releases/0.0.2/#optim","title":"optim","text":"<ul> <li>Added <code>optim.AbsoluteLoss</code>.</li> <li>Added <code>optim.HingeLoss</code>.</li> <li>Added <code>optim.EpsilonInsensitiveHingeLoss</code>.</li> </ul>"},{"location":"releases/0.0.2/#stats","title":"stats","text":"<ul> <li>Added <code>stats.NUnique</code>.</li> <li>Added <code>stats.Min</code>.</li> <li>Added <code>stats.Max</code>.</li> <li>Added <code>stats.PeakToPeak</code>.</li> <li>Added <code>stats.Kurtosis</code>.</li> <li>Added <code>stats.Skew</code>.</li> <li>Added <code>stats.Sum</code>.</li> <li>Added <code>stats.EWMean</code>.</li> <li>Made sure the running statistics produce the same results as <code>pandas.DataFrame.rolling</code> method.</li> </ul>"},{"location":"releases/0.0.3/","title":"0.0.3 - 2019-03-21","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.0.3/#base","title":"base","text":"<ul> <li>Calling <code>fit_one</code> now returns the calling instance, not the out-of-fold prediction/transform; <code>fit_predict_one</code>, <code>fit_predict_proba_one</code>, and <code>fit_transform_one</code> are available to reproduce the previous behavior.</li> <li>Binary classifiers now output a <code>dict</code> with probabilities for <code>False</code> and <code>True</code> when calling <code>predict_proba_one</code>, which solves the interface issues of having multi-class classifiers do binary classification.</li> </ul>"},{"location":"releases/0.0.3/#compat","title":"compat","text":"<ul> <li>Added <code>compat.convert_river_to_sklearn</code>.</li> </ul>"},{"location":"releases/0.0.3/#compose","title":"compose","text":"<ul> <li>Added <code>compose.BoxCoxTransformRegressor</code>.</li> <li>Added <code>compose.TargetModifierRegressor</code>.</li> </ul>"},{"location":"releases/0.0.3/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_restaurants</code>.</li> <li>Added <code>datasets.load_airline</code>.</li> </ul>"},{"location":"releases/0.0.3/#dist","title":"dist","text":"<ul> <li>Added <code>dist.Multinomial</code>.</li> <li>Added <code>dist.Normal</code>.</li> </ul>"},{"location":"releases/0.0.3/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.BaggingRegressor</code>.</li> </ul>"},{"location":"releases/0.0.3/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added <code>feature_extraction.TargetGroupBy</code>.</li> </ul>"},{"location":"releases/0.0.3/#impute","title":"impute","text":"<ul> <li>Added <code>impute.CategoricalImputer</code>.</li> </ul>"},{"location":"releases/0.0.3/#linear_model","title":"linear_model","text":"<ul> <li>Added <code>linear_model.FMRegressor</code>.</li> <li>Removed all the passive-aggressive estimators.</li> </ul>"},{"location":"releases/0.0.3/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.Accuracy</code>.</li> <li>Added <code>metrics.MAE</code>.</li> <li>Added <code>metrics.MSE</code>.</li> <li>Added <code>metrics.RMSE</code>.</li> <li>Added <code>metrics.RMSLE</code>.</li> <li>Added <code>metrics.SMAPE</code>.</li> <li>Added <code>metrics.Precision</code>.</li> <li>Added <code>metrics.Recall</code>.</li> <li>Added <code>metrics.F1</code>.</li> </ul>"},{"location":"releases/0.0.3/#model_selection","title":"model_selection","text":"<ul> <li><code>model_selection.online_score</code> can now be passed a <code>metrics.Metric</code> instead of an <code>sklearn</code> metric; it also checks that the provided metric can be used with the accompanying model.</li> </ul>"},{"location":"releases/0.0.3/#naive_bayes","title":"naive_bayes","text":"<ul> <li>Added <code>naive_bayes.GaussianNB</code>.</li> </ul>"},{"location":"releases/0.0.3/#optim","title":"optim","text":"<ul> <li>Added <code>optim.PassiveAggressiveI</code>.</li> <li>Added <code>optim.PassiveAggressiveII</code>.</li> </ul>"},{"location":"releases/0.0.3/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.Discarder</code>.</li> <li>Added <code>preprocessing.PolynomialExtender</code>.</li> <li>Added <code>preprocessing.FuncTransformer</code>.</li> </ul>"},{"location":"releases/0.0.3/#reco","title":"reco","text":"<ul> <li>Added <code>reco.SVD</code>.</li> </ul>"},{"location":"releases/0.0.3/#stats","title":"stats","text":"<ul> <li>Added <code>stats.Mode</code>.</li> <li>Added <code>stats.Quantile</code>.</li> <li>Added <code>stats.RollingQuantile</code>.</li> <li>Added <code>stats.Entropy</code>.</li> <li>Added <code>stats.RollingMin</code>.</li> <li>Added <code>stats.RollingMax</code>.</li> <li>Added <code>stats.RollingMode</code>.</li> <li>Added <code>stats.RollingSum</code>.</li> <li>Added <code>stats.RollingPeakToPeak</code>.</li> </ul>"},{"location":"releases/0.0.3/#stream","title":"stream","text":"<ul> <li>Added <code>stream.iter_csv</code>.</li> </ul>"},{"location":"releases/0.0.3/#tree","title":"tree","text":"<ul> <li>Added <code>tree.MondrianTreeClassifier</code>.</li> <li>Added <code>tree.MondrianTreeRegressor</code>.</li> </ul>"},{"location":"releases/0.1.0/","title":"0.1.0 - 2019-05-08","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.1.0/#base","title":"base","text":"<ul> <li>Removed the <code>fit_predict_one</code> estimator method.</li> <li>Removed the <code>fit_predict_proba_one</code> estimator method.</li> <li>Removed the <code>fit_transform_one</code> estimator method.</li> </ul>"},{"location":"releases/0.1.0/#compat","title":"compat","text":"<ul> <li>Added <code>compat.convert_sklearn_to_river</code>.</li> <li><code>compat.convert_river_to_sklearn</code> now returns an <code>sklearn.pipeline.Pipeline</code> when provided with a <code>compose.Pipeline</code>.</li> </ul>"},{"location":"releases/0.1.0/#compose","title":"compose","text":"<ul> <li>Added <code>compose.Discard</code>.</li> <li>Added <code>compose.Select</code>.</li> <li>Added <code>compose.SplitRegressor</code>.</li> <li>The <code>draw</code> method of <code>compose.Pipeline</code> now works properly for arbitrary amounts of nesting, including multiple nested <code>compose.FeatureUnion</code>.</li> </ul>"},{"location":"releases/0.1.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_electricity</code>.</li> </ul>"},{"location":"releases/0.1.0/#dummy","title":"dummy","text":"<ul> <li>Added <code>dummy.NoChangeClassifier</code>.</li> <li>Added <code>dummy.PriorClassifier</code>.</li> <li>Added <code>dummy.StatisticRegressor</code>.</li> </ul>"},{"location":"releases/0.1.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added <code>feature_extraction.Differ</code>.</li> <li>Renamed <code>feature_extraction.GroupBy</code> to <code>feature_extraction.Agg</code>.</li> <li>Renamed <code>feature_extraction.TargetGroupBy</code> to <code>feature_extraction.TargetAgg</code>.</li> </ul>"},{"location":"releases/0.1.0/#feature_selection","title":"feature_selection","text":"<ul> <li>Added <code>feature_selection.SelectKBest</code>.</li> <li>Added <code>feature_selection.VarianceThreshold</code>.</li> </ul>"},{"location":"releases/0.1.0/#impute","title":"impute","text":"<ul> <li>Added <code>impute.StatImputer</code>.</li> <li>Removed <code>impute.CategoricalImputer</code>.</li> <li>Removed <code>impute.NumericImputer</code>.</li> </ul>"},{"location":"releases/0.1.0/#linear_model","title":"linear_model","text":"<ul> <li>Added <code>linear_model.PAClassifier</code>.</li> <li>Added <code>linear_model.PARegressor</code>.</li> <li>Added <code>linear_model.SoftmaxRegression</code>.</li> </ul>"},{"location":"releases/0.1.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.ConfusionMatrix</code>.</li> <li>Added <code>metrics.CrossEntropy</code>.</li> <li>Added <code>metrics.MacroF1</code>.</li> <li>Added <code>metrics.MacroPrecision</code>.</li> <li>Added <code>metrics.MacroRecall</code>.</li> <li>Added <code>metrics.MicroF1</code>.</li> <li>Added <code>metrics.MicroPrecision</code>.</li> <li>Added <code>metrics.MicroRecall</code>.</li> <li>Each metric now has a <code>bigger_is_better</code> property to indicate if a high value is better than a low one or not.</li> </ul>"},{"location":"releases/0.1.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.OptimalLR</code>.</li> <li>Added <code>optim.CrossEntropy</code>.</li> <li>Removed <code>optim.PassiveAggressiveI</code>.</li> <li>Removed <code>optim.PassiveAggressiveII</code>.</li> </ul>"},{"location":"releases/0.1.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Removed <code>preprocessing.Discarder</code>.</li> <li>Added <code>on</code> and <code>sparse</code> parameters to <code>preprocessing.OneHotEncoder</code>.</li> </ul>"},{"location":"releases/0.1.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.Covariance</code>.</li> <li>Added <code>stats.PearsonCorrelation</code>.</li> <li>Added <code>stats.SmoothMean</code>.</li> </ul>"},{"location":"releases/0.1.0/#utils","title":"utils","text":"<ul> <li>Added <code>utils.check_estimator</code>.</li> <li>Added <code>utils.Histogram</code>.</li> <li>Added <code>utils.SortedWindow</code>.</li> <li>Added <code>utils.Window</code>.</li> </ul>"},{"location":"releases/0.10.0/","title":"0.10.0 - 2022-02-04","text":""},{"location":"releases/0.10.0/#base","title":"base","text":"<ul> <li>Introduce <code>base.MiniBatchTransformer</code>. Add support for mini-batches to <code>compose.TransformerUnion</code>, <code>compose.Select</code>, and <code>preprocessing.OneHotEncoder</code>.</li> </ul>"},{"location":"releases/0.10.0/#checks","title":"checks","text":"<ul> <li>Created this module to store estimator unit testing, rather than having it in the <code>utils</code> module.</li> </ul>"},{"location":"releases/0.10.0/#compose","title":"compose","text":"<ul> <li>Split <code>compose.Renamer</code> into <code>compose.Prefixer</code> and <code>compose.Suffixer</code> that respectively prepend and append a string to the features' name.</li> <li>Changed <code>compose.Renamer</code> to allow feature renaming following a mapping.</li> </ul>"},{"location":"releases/0.10.0/#evaluate","title":"evaluate","text":"<ul> <li>Refactored <code>evaluate.progressive_validation</code> to work with <code>api.anomaly.base.AnomalyDetector</code>s.</li> </ul>"},{"location":"releases/0.10.0/#facto","title":"facto","text":"<ul> <li>Added <code>debug_one</code> method to <code>BaseFM</code>.</li> </ul>"},{"location":"releases/0.10.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Make the <code>by</code> parameter in <code>feature_extraction.Agg</code> and <code>feature_extraction.TargetAgg</code> to be optional, allowing to calculate aggregates over the whole data.</li> <li>Removed <code>feature_extraction.Lagger</code> and <code>feature_extraction.TargetLagger</code>. Their functionality can be reproduced by combining <code>feature_extraction.Agg</code> and <code>stats.Shift</code>.</li> <li><code>feature_extraction.Agg</code> and <code>feature_extraction.Target</code> now have a <code>state</code> property. It returns a <code>pandas.Series</code> representing the current aggregates values within each group.</li> </ul>"},{"location":"releases/0.10.0/#metrics","title":"metrics","text":"<ul> <li><code>metrics.ROCAUC</code> works with <code>base.AnomalyDetectors</code>s.</li> </ul>"},{"location":"releases/0.10.0/#misc","title":"misc","text":"<ul> <li>Created this module to store some stuff that was in the <code>utils</code> module but wasn't necessarily shared between modules.</li> <li>Implement <code>misc.CovMatrix</code>.</li> </ul>"},{"location":"releases/0.10.0/#reco","title":"reco","text":"<ul> <li>Renamed the <code>Recommender</code> base class into <code>Ranker</code>.</li> <li>Added a <code>rank</code> method to each recommender.</li> <li>Removed <code>reco.SurpriseWrapper</code> as it wasn't really useful.</li> <li>Added an <code>is_contextual</code> property to each ranker to indicate if a model makes use of contextual features or not.</li> </ul>"},{"location":"releases/0.10.0/#stats","title":"stats","text":"<ul> <li><code>stats.Mean</code>, <code>stats.Var</code>, and <code>stats.Cov</code> each now have an <code>update_many</code> method which accepts numpy arrays.</li> </ul>"},{"location":"releases/0.10.0/#utils","title":"utils","text":"<ul> <li>Removed <code>utils.Window</code> and use <code>collections.deque</code> instead where necessary.</li> </ul>"},{"location":"releases/0.10.1/","title":"0.10.1 - 2022-02-05","text":""},{"location":"releases/0.10.1/#evaluate","title":"evaluate","text":"<p><code>evaluate.progressive_val_score</code> can now handle models which use <code>**kwargs</code> in their <code>learn_one</code> and <code>predict_one</code> methods. For instance, this is useful for <code>reco.Ranker</code> models which require passing a user and an item.</p>"},{"location":"releases/0.11.0/","title":"0.11.0 - 2022-05-28","text":"<ul> <li>Moved all metrics in <code>metrics.cluster</code> except <code>metrics.Silhouette</code> to river-extra.</li> </ul>"},{"location":"releases/0.11.0/#anomaly","title":"anomaly","text":"<ul> <li>There is now a <code>anomaly.base.SupervisedAnomalyDetector</code> base class for supervised anomaly detection.</li> <li>Added <code>api.anomaly.GaussianScorer</code>, which is the first supervised anomaly detector.</li> <li>There is now a <code>anomaly.base.AnomalyFilter</code> base class for anomaly filtering methods. These allow to classify anomaly scores. They can also prevent models from learning on anomalous data, for instance by putting them as an initial step of a pipeline.</li> <li>Added <code>anomaly.ConstantFilter</code> and <code>QuantileFilter</code>, which are the first anomaly filters.</li> <li>Removed <code>anomaly.ConstantThresholder</code> and <code>anomaly.QuantileThresholder</code>, as they overlap with the new anomaly filtering mechanism.</li> </ul>"},{"location":"releases/0.11.0/#base","title":"base","text":"<ul> <li>Fixed an issue where the <code>_raw_memory_usage</code> property would spin into an infinite loop if a model's property was an <code>itertools.count</code>.</li> </ul>"},{"location":"releases/0.11.0/#dataset","title":"dataset","text":"<ul> <li>Added the <code>datasets.WaterFlow</code> dataset.</li> </ul>"},{"location":"releases/0.11.0/#dist","title":"dist","text":"<ul> <li>A <code>revert</code> method has been added to <code>stats.Gaussian</code>.</li> <li>A <code>revert</code> method has been added to <code>stats.Multinomial</code>.</li> <li>Added <code>dist.TimeRolling</code> to measure probability distributions over windows of time.</li> </ul>"},{"location":"releases/0.11.0/#drift","title":"drift","text":"<ul> <li>Add the <code>PeriodicTrigger</code> detector, a baseline capable of producing drift signals in regular or random intervals.</li> <li>The numpy usage was removed in <code>drift.KSWIN</code> in favor of <code>collections.deque</code>. Appending or deleting elements to numpy arrays imply creating another object.</li> <li>Added the seed parameter to <code>drift.KSWIN</code> to control reproducibility.</li> <li>The Kolmogorov-Smirnov test mode was changed to the default (<code>\"auto\"</code>) to suppress warnings (<code>drift.KSWIN</code>).</li> <li>Unnecessary usage of numpy was also removed in other concept drift detectors.</li> </ul>"},{"location":"releases/0.11.0/#ensemble","title":"ensemble","text":"<ul> <li>Streamline <code>SRP{Classifier,Regressor}</code>, remove unneeded numpy usage, make SRP variants robust against missing features, and fix bugs.</li> <li>Remove unneeded numpy usage <code>AdaptiveRandomForest{Classifier,Regressor}</code>.</li> </ul>"},{"location":"releases/0.11.0/#evaluate","title":"evaluate","text":"<ul> <li>Added a <code>iter_progressive_val_score</code> function, which does the same as <code>progressive_val_score</code>, except that it yields rather than prints results at each step, which give more control to the user.</li> </ul>"},{"location":"releases/0.11.0/#imblearn","title":"imblearn","text":"<ul> <li>Added <code>imblearn.ChebyshevUnderSampler</code> and <code>imblearn.ChebyshevOverSampler</code> for imbalanced regression.</li> </ul>"},{"location":"releases/0.11.0/#linear_model","title":"linear_model","text":"<ul> <li><code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code> now correctly apply the <code>l2</code> regularization when their <code>learn_many</code> method is used.</li> <li>Added <code>l1</code> regularization (implementation with cumulative penalty, see paper) for <code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code></li> </ul>"},{"location":"releases/0.11.0/#neighbors","title":"neighbors","text":"<ul> <li><code>neighbors.KNNADWINClassifier</code> and <code>neighbors.SAMKNNClassifier</code> have been deprecated.</li> <li>Introduced <code>neighbors.NearestNeighbors</code> for searching nearest neighbors.</li> <li>Vastly refactored and simplified the nearest neighbors logic.</li> </ul>"},{"location":"releases/0.11.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.Rolling</code> to measure a probability distribution over a window.</li> </ul>"},{"location":"releases/0.11.0/#rules","title":"rules","text":"<ul> <li>AMRules's <code>debug_one</code> explicitly indicates the prediction strategy used by each rule.</li> <li>Fix bug in <code>debug_one</code> (AMRules) where prediction explanations were incorrectly displayed when <code>ordered_rule_set=True</code>.</li> </ul>"},{"location":"releases/0.11.0/#time_series","title":"time_series","text":"<ul> <li>Added an <code>iter_evaluate</code> function to trace the evaluation at each sample in a dataset.</li> </ul>"},{"location":"releases/0.11.0/#tree","title":"tree","text":"<ul> <li>Fix bug in Naive Bayes-based leaf prediction.</li> <li>Remove unneeded numpy usage in <code>HoeffdingAdaptiveTree{Classifier,Regressor}</code>.</li> </ul>"},{"location":"releases/0.11.0/#stats","title":"stats","text":"<ul> <li>A <code>revert</code> method has been added to <code>stats.Var</code>.</li> </ul>"},{"location":"releases/0.11.1/","title":"0.11.1 - 2022-06-06","text":"<p>A small release to introduce benchmarks.</p>"},{"location":"releases/0.11.1/#anomaly","title":"anomaly","text":"<ul> <li>Fixed a bug where anomaly filters were never updated.</li> </ul>"},{"location":"releases/0.12.0/","title":"0.12.0 - 2022-09-02","text":"<ul> <li>Moved all the public modules imports from <code>river/__init__.py</code> to <code>river/api.py</code> and removed unnecessary dependencies between modules enabling faster cherry-picked import times (~3x).</li> <li>Adding wheels for Python 3.11.</li> </ul>"},{"location":"releases/0.12.0/#base","title":"base","text":"<ul> <li>Introduced an <code>mutate</code> method to the <code>base.Base</code> class. This allows setting attributes in a controlled manner, which paves the way for online AutoML. See the recipe for more information.</li> </ul>"},{"location":"releases/0.12.0/#compat","title":"compat","text":"<ul> <li>Moved the PyTorch wrappers to river-extra.</li> </ul>"},{"location":"releases/0.12.0/#covariance","title":"covariance","text":"<ul> <li>Created a new <code>covariance</code> module to hold everything related to covariance and inversion covariance matrix estimation.</li> <li>Moved <code>misc.CovarianceMatrix</code> to <code>covariance.EmpiricalCovariance</code>.</li> <li>Added <code>covariance.EmpiricalPrecision</code> to estimate the inverse covariance matrix.</li> </ul>"},{"location":"releases/0.12.0/#compose","title":"compose","text":"<ul> <li>Moved <code>utils.pure_inference_mode</code> to <code>compose.pure_inference_mode</code> and <code>utils.warm_up_mode</code> to <code>compose.warm_up_mode</code>.</li> <li>Pipeline parts can now be accessed by integer positions as well as by name.</li> </ul>"},{"location":"releases/0.12.0/#datasets","title":"datasets","text":"<ul> <li>Imports <code>synth</code>, enabling `from river import datasets; datasets.synth.</li> </ul>"},{"location":"releases/0.12.0/#drift","title":"drift","text":"<ul> <li>Refactor the concept drift detectors to match the remaining of River's API. Warnings are only issued by detectors that support this feature.</li> <li>Drifts can be assessed via the property <code>drift_detected</code>. Warning signals can be acessed by the property <code>warning_detected</code>. The <code>update</code> now returns <code>self</code>.</li> <li>Ensure all detectors automatically reset their inner states after a concept drift detection.</li> <li>Streamline <code>DDM</code>, <code>EDDM</code>, <code>HDDM_A</code>, and <code>HDDM_W</code>. Make the configurable parameters names match their respective papers.</li> <li>Fix bugs in <code>EDDM</code> and <code>HDDM_W</code>.</li> <li>Enable two-sided tests in <code>PageHinkley</code>.</li> <li>Improve documentation and update tests.</li> </ul>"},{"location":"releases/0.12.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Added a <code>tokenizer_pattern</code> parameter to <code>feature_extraction.BagOfWords</code> and <code>feature_extraction.TFIDF</code> to override the default pattern used for tokenizing text.</li> <li>Added a <code>stop_words</code> parameter to <code>feature_extraction.BagOfWords</code> and <code>feature_extraction.TFIDF</code> for removing stop words once the text has been tokenized.</li> </ul>"},{"location":"releases/0.12.0/#linear_model","title":"linear_model","text":"<ul> <li>After long ado, we've finally implemented <code>linear_model.BayesianLinearRegression</code>.</li> </ul>"},{"location":"releases/0.12.0/#metrics","title":"metrics","text":"<ul> <li>Removed dependency to <code>optim</code>.</li> <li>Removed <code>metrics.Rolling</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>metrics.TimeRolling</code>, due to the addition of <code>utils.Rolling</code>.</li> </ul>"},{"location":"releases/0.12.0/#proba","title":"proba","text":"<ul> <li>Removed <code>proba.Rolling</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>proba.TimeRolling</code>, due to the addition of <code>utils.Rolling</code>.</li> </ul>"},{"location":"releases/0.12.0/#rule","title":"rule","text":"<ul> <li>The default <code>splitter</code> was changed to <code>tree.splitter.TEBST</code> for memory and running time efficiency.</li> </ul>"},{"location":"releases/0.12.0/#stats","title":"stats","text":"<ul> <li>Removed <code>stats.RollingMean</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>stats.RollingVar</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>stats.RollingCov</code>, due to the addition of <code>utils.Rolling</code>.</li> <li>Removed <code>stats.RollingPearsonCorr</code>, due to the addition of <code>utils.Rolling</code>.</li> </ul>"},{"location":"releases/0.12.0/#stream","title":"stream","text":"<ul> <li><code>stream.iter_array</code> now handles text data.</li> <li>Added <code>stream.TwitterLiveStream</code>, to listen to a filtered live stream of Tweets.</li> </ul>"},{"location":"releases/0.12.0/#time_series","title":"time_series","text":"<ul> <li>Added <code>time_series.HorizonAggMetric</code>.</li> <li>Fixed a bug in <code>time_series.SNARIMAX</code> where the number of seasonal components was not correct when <code>sp</code> or <code>sq</code> were specified.</li> <li>Fixed the differencing logic in <code>time_series.SNARIMAX</code> when <code>d</code> or <code>sd</code> were specified.</li> </ul>"},{"location":"releases/0.12.0/#tree","title":"tree","text":"<ul> <li>Rename <code>split_confidence</code> and <code>tie_threshold</code> to <code>delta</code> and <code>tau</code>, respectively. This way, the parameters are not misleading and match what the research papers have used for decades.</li> <li>Refactor <code>HoeffdingAdaptiveTree{Classifier,Regressor}</code> to allow the usage of any drift detector. Expose the significance level of the test used to switch between subtrees as a user-defined parameter.</li> <li>Correct test used to switch between foreground and background subtrees in <code>HoeffdingAdaptiveTreeRegressor</code>. Due to the continuous and unbounded nature of the monitored errors, a z-test is now performed to decide which subtree to keep.</li> <li>The default <code>leaf_prediction</code> value was changed to <code>\"adaptive\"</code>, as this often results in the smallest errors in practice.</li> <li>The default <code>splitter</code> was changed to <code>tree.splitter.TEBST</code> for memory and running time efficiency.</li> </ul>"},{"location":"releases/0.12.0/#utils","title":"utils","text":"<ul> <li>Removed dependencies to <code>anomaly</code> and <code>compose</code>.</li> <li>Added <code>utils.Rolling</code> and <code>utils.TimeRolling</code>, which are generic wrappers for computing over a window (of time).</li> <li>Use binary search to speed-up element removal in <code>utils.SortedWindow</code>.</li> </ul>"},{"location":"releases/0.12.1/","title":"0.12.1 - 2022-09-02","text":""},{"location":"releases/0.12.1/#base","title":"base","text":"<ul> <li>Fix the way the <code>clone</code> method handles positional arguments.</li> </ul>"},{"location":"releases/0.13.0/","title":"0.13.0 - 2022-09-15","text":""},{"location":"releases/0.13.0/#compose","title":"compose","text":"<ul> <li><code>compose.TransformerUnion</code> parts can now be accessed by index as well as by name.</li> </ul>"},{"location":"releases/0.13.0/#stats","title":"stats","text":"<ul> <li>Added the <code>LossyCount</code> for tracking frequent itemsets. This implementation also supports a forgetting factor to reduce the influence of old elements.</li> <li>The following statistics are now implemented in Rust:</li> <li><code>Quantile</code></li> <li><code>EWMean</code></li> <li><code>EWVar</code></li> <li><code>IQR</code></li> <li><code>Kurtosis</code></li> <li><code>PeaktoPeak</code></li> <li><code>Skew</code></li> <li><code>RollingQuantile</code></li> <li><code>RollingIQR</code></li> </ul>"},{"location":"releases/0.13.0/#stream","title":"stream","text":"<ul> <li>Implemented <code>stream.TwitchChatStream</code>.</li> </ul>"},{"location":"releases/0.14.0/","title":"0.14.0 - 2022-10-26","text":"<ul> <li>Introducing the <code>bandit</code> module for running multi-armed bandits</li> <li>Introducing the <code>sketch</code> module with summarization tools and data sketches working in a streaming fashion!</li> </ul>"},{"location":"releases/0.14.0/#bandit","title":"bandit","text":"<ul> <li>Added <code>bandit.EpsilonGreedy</code>.</li> <li>Added <code>bandit.UCB</code>.</li> <li>Added <code>bandit.ThomsonSampling</code>.</li> <li>Added a <code>bandit.base</code> module.</li> <li>Added <code>bandit.envs.CandyCaneContest</code>, which implements the Gym interface.</li> <li>Added <code>bandit.envs.KArmedTestbed</code>, which implements the Gym interface.</li> <li>Added <code>bandit.evaluate</code> for basic benchmarking of bandit policies on a Gym environment.</li> </ul>"},{"location":"releases/0.14.0/#drift","title":"drift","text":"<ul> <li>Exposed more parameters in ADWIN: <code>clock</code>, <code>max_buckets</code>, <code>min_window_length</code>, and <code>grace_period</code>.</li> </ul>"},{"location":"releases/0.14.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.BanditRegressor</code>, which is a generic model selection method that works with any bandit policy.</li> <li>Removed <code>model_selection.EpsilonGreedyRegressor</code> due to the addition of <code>model_selection.BanditRegressor</code>.</li> <li>Removed <code>model_selection.UCBRegressor</code> due to the addition of <code>model_selection.BanditRegressor</code>.</li> </ul>"},{"location":"releases/0.14.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.Beta</code>.</li> <li>Added a <code>sample</code> method to each distribution.</li> <li>Added a <code>mode</code> property to each distribution.</li> <li>Replaced the <code>pmf</code> and <code>pdf</code> methods with a <code>__call__</code> method.</li> </ul>"},{"location":"releases/0.14.0/#sketch","title":"sketch","text":"<ul> <li>Moved <code>misc.Histogram</code> to <code>sketch.Histogram</code>.</li> <li>Moved <code>stats.LossyCount</code> to <code>sketch.HeavyHitters</code> and update its API to better match <code>collections.Counter</code>.</li> <li>Added missing return <code>self</code> in <code>HeavyHitters</code>.</li> <li>Added the Count-Min Sketch (<code>sketch.Counter</code>) algorithm for approximate element counting.</li> <li>Added an implementation of Bloom filter (<code>sketch.Set</code>) to provide approximate set-like operations.</li> </ul>"},{"location":"releases/0.15.0/","title":"0.15.0 - 2023-01-29","text":""},{"location":"releases/0.15.0/#active","title":"active","text":"<ul> <li>Created this module dedicated to online active learning.</li> <li>Added <code>api.active.EntropySampler</code>.</li> </ul>"},{"location":"releases/0.15.0/#base","title":"base","text":"<ul> <li>Fixed an issue where an estimator that has attribute a pipeline could not be cloned.</li> <li>Added a <code>base.DriftAndWarningDetector</code> to clarify the difference between drift detectors that have a <code>warning_detected</code> property and those that don't.</li> <li>Added <code>MultiLabelClassifier</code>.</li> <li>Added <code>MultiTargetRegressor</code>.</li> <li>Added <code>drift.BinaryDriftDetector</code>.</li> <li>Added <code>drift.BinaryDriftAndWarningDetector</code>.</li> </ul>"},{"location":"releases/0.15.0/#conf","title":"conf","text":"<ul> <li>Introduced this new module to perform conformal predictions.</li> <li>Added a <code>conf.Interval</code> dataclass to represent predictive intervals.</li> <li>Added <code>conf.RegressionJackknife</code>.</li> </ul>"},{"location":"releases/0.15.0/#datasets","title":"datasets","text":"<ul> <li>Removed unnecessary Numpy usage in the <code>synth</code> submodule.</li> <li>Changed <code>np.random.RandomState</code> to <code>np.random.default_rng</code> where necessary.</li> </ul>"},{"location":"releases/0.15.0/#drift","title":"drift","text":"<ul> <li>Added <code>drift.DriftRetrainingClassifier</code>.</li> <li>Renamed <code>drift.PeriodicTrigger</code> to <code>drift.DummyDriftDetector</code> to clarify it is a naive baseline.</li> <li>Created a <code>binary</code> submodule to organize all drift detectors which only apply to binary inputs.</li> </ul>"},{"location":"releases/0.15.0/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.ADWINBoostingClassifier</code>.</li> <li>Added <code>ensemble.BOLEClassifier</code>.</li> </ul>"},{"location":"releases/0.15.0/#evaluate","title":"evaluate","text":"<ul> <li><code>evaluate.progressive_val_score</code> and <code>evaluate.iter_progressive_val_score</code> will now also produce a report once the last sample has been processed, in addition to every <code>print_every</code> steps.</li> </ul>"},{"location":"releases/0.15.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li><code>feature_extraction.BagOfWords</code> now outputs a dictionary, and not a <code>collections.Counter</code>.</li> </ul>"},{"location":"releases/0.15.0/#forest","title":"forest","text":"<ul> <li>Created this new module to host all models based on an ensemble of decision trees.</li> <li>Moved <code>ensemble.AdaptiveRandomForestClassifier</code> to <code>forest.ARFClassifier</code>.</li> <li>Moved <code>ensemble.AdaptiveRandomForestRegressor</code> to <code>forest.ARFRegressor</code>.</li> <li>Added <code>forest.AMFClassifier</code>.</li> <li>Added <code>forest.OXTRegressor</code>.</li> </ul>"},{"location":"releases/0.15.0/#linear_model","title":"linear_model","text":"<ul> <li>Renamed <code>use_dist</code> to <code>with_dist</code> in <code>linear_model.BayesianLinearRegression</code>'s <code>predict_one</code> method.</li> </ul>"},{"location":"releases/0.15.0/#multiclass","title":"multiclass","text":"<ul> <li>Added a <code>coding_method</code> method to <code>multiclass.OCC</code> to control how the codes are randomly generated.</li> </ul>"},{"location":"releases/0.15.0/#multioutput","title":"multioutput","text":"<ul> <li>Added <code>MultiClassEncoder</code> to convert multi-label tasks into multi-class problems.</li> </ul>"},{"location":"releases/0.15.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>preprocessing.AdaptiveStandardScaler</code>.</li> </ul>"},{"location":"releases/0.15.0/#rules","title":"rules","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>rules.AMRules</code>.</li> </ul>"},{"location":"releases/0.15.0/#sketch","title":"sketch","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>sketch.HeavyHitters</code>.</li> </ul>"},{"location":"releases/0.15.0/#stats","title":"stats","text":"<ul> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>stats.Entropy</code>.</li> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>stats.EWMean</code>.</li> <li>Renamed <code>alpha</code> to <code>fading_factor</code> in <code>stats.EWVar</code>.</li> </ul>"},{"location":"releases/0.15.0/#stream","title":"stream","text":"<ul> <li>Upgraded <code>stream.iter_sql</code> to SQLAlchemy 2.0.</li> </ul>"},{"location":"releases/0.15.0/#tree","title":"tree","text":"<ul> <li>Remove <code>LabelCombinationHoeffdingTreeClassifier</code>. New code should use <code>multioutput.MulticlassEncoder</code> instead.</li> </ul>"},{"location":"releases/0.15.0/#utils","title":"utils","text":"<ul> <li>Removed artifacts from the merger.</li> </ul>"},{"location":"releases/0.16.0/","title":"0.16.0 - 2023-05-08","text":"<p>Added wheels for Python 3.11.</p>"},{"location":"releases/0.16.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li><code>feature_extraction.Agg</code> and <code>feature_extraction.TargetAgg</code> can now be passed an optional <code>t</code> in its <code>learn_one</code> method, which allows it to work with <code>utils.TimeRolling</code>.</li> </ul>"},{"location":"releases/0.16.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.MAPE</code>.</li> <li>Added <code>metrics.RollingROCAUC</code>.</li> </ul>"},{"location":"releases/0.16.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.GaussianRandomProjector</code>.</li> <li>Added <code>preprocessing.SparseRandomProjector</code>.</li> </ul>"},{"location":"releases/0.16.0/#stats","title":"stats","text":"<ul> <li>Fixed randomness issue with the first few outputs of <code>stats.Quantile</code>.</li> </ul>"},{"location":"releases/0.17.0/","title":"0.17.0 - 2023-05-27","text":""},{"location":"releases/0.17.0/#bandit","title":"bandit","text":"<ul> <li>Bandit policies now return a single arm when the <code>pull</code> method is called, instead of yielding or one more arms at a time. This is simpler to understand. We will move back to multi-armed pulls in the future.</li> <li>Added <code>bandit.Exp3</code>.</li> <li><code>bandit.UCB</code> and <code>bandit.Exp3</code> have an extra <code>reward_scaler</code> parameter, which can be any object that inherits from <code>compose.TargetTransformRegressor</code>. This allows scaling rewards before updating arms.</li> </ul>"},{"location":"releases/0.17.0/#compose","title":"compose","text":"<ul> <li><code>compose.TransformerProduct</code> now correctly returns a <code>compose.TransformerUnion</code> when a transformer is added to it.</li> <li>Fixed <code>compose.TransformerProduct</code>'s <code>transform_many</code> behavior.</li> <li><code>compose.TransformerUnion</code> and <code>compose.TransformerProduct</code> will now clone the provided estimators, so that shallow copies aren't shared in different places.</li> </ul>"},{"location":"releases/0.17.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.BanditClassifier</code>, which is the classification equivalent to <code>bandit.BanditRegressor</code>. Both are methods to perform online model selection via a bandit policy.</li> </ul>"},{"location":"releases/0.17.0/#multioutput","title":"multioutput","text":"<ul> <li><code>metrics.multioutput.MacroAverage</code> and <code>metrics.multioutput.MicroAverage</code> now loop over the keys of <code>y_true</code> instead of <code>y_pred</code>. This ensures a <code>KeyError</code> is correctly raised if <code>y_pred</code> is missing an output that is present in <code>y_true</code>.</li> </ul>"},{"location":"releases/0.17.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.TargetMinMaxScaler</code>, which operates the same as <code>preprocessing.TargetStandardScaler</code>, but instead uses min-max scaling.</li> </ul>"},{"location":"releases/0.18.0/","title":"0.18.0 - 2023-06-26","text":""},{"location":"releases/0.18.0/#bandit","title":"bandit","text":"<ul> <li>Added <code>bandit.BayesUCB</code>.</li> <li>Added <code>bandit.evaluate_offline</code>, for evaluating bandits on historical (logged) data.</li> </ul>"},{"location":"releases/0.18.0/#cluster","title":"cluster","text":"<ul> <li><code>DBStream</code> will now only recluster on demand, rather than at every call to <code>learn_one</code>.</li> </ul>"},{"location":"releases/0.18.0/#compat","title":"compat","text":"<ul> <li>The <code>predict_many</code> method scikit-learn models wrapped with <code>compat.convert_sklearn_to_river</code> raised an exception if the model had not been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River.</li> <li><code>compat.SKL2RiverRegressor</code> and <code>compat.SKL2RiverClassifier</code> didn't check whether features were ordered in the same way at each method call. They now store the list of feature names at the first function call, and align subsequent inputs in the same order.</li> </ul>"},{"location":"releases/0.18.0/#compose","title":"compose","text":"<ul> <li><code>compose.TransformerProduct</code> will now preserve the density of sparse columns.</li> <li>Added a <code>transform_many</code> method to <code>compose.FuncTransformer</code>, allowing it to be used in mini-batch pipelines.</li> <li>The <code>compose.pure_inference_mode</code> now works with mini-batching.</li> </ul>"},{"location":"releases/0.18.0/#neighbors","title":"neighbors","text":"<ul> <li>Added <code>neighbors.SWINN</code> to power-up approximate nearest neighbor search. SWINN uses graphs to speed up nearest neighbor search in large sliding windows of data.</li> <li>Renamed <code>neighbors.NearestNeighbors</code> to <code>neighbors.LazySearch</code>.</li> <li>Standardize and create base classes for generic nearest neighbor search utilities.</li> <li>The user can now select the nearest neighbor search engine to use in <code>neighbors.KNNClassifier</code> and <code>neighbors.KNNRegressor</code>.</li> </ul>"},{"location":"releases/0.18.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Rename <code>sparse</code> parameter to <code>drop_zeros</code> in <code>preprocessing.OneHotEncoder</code>.</li> <li>The <code>transform_many</code> method of <code>preprocessing.OneHotEncoder</code> will now return a sparse dataframe, rather than a dense one, which will consume much less memory.</li> </ul>"},{"location":"releases/0.18.0/#proba","title":"proba","text":"<ul> <li>Added a <code>cdf</code> method to <code>proba.Beta</code>.</li> </ul>"},{"location":"releases/0.18.0/#tree","title":"tree","text":"<ul> <li>Expose the <code>min_branch_fraction</code> parameter to avoid splits where most of the data goes to a single branch. Affects   classification trees.</li> <li>Added the <code>max_share_to_split</code> parameter to Hoeffding Tree classifiers. This parameters avoids splitting when the majority   class has most of the data.</li> </ul>"},{"location":"releases/0.18.0/#utils","title":"utils","text":"<ul> <li>Fixed <code>utils.math.minkowski_distance</code>.</li> </ul>"},{"location":"releases/0.19.0/","title":"0.19.0 - 2023-08-02","text":"<p>Calling <code>learn_one</code> in a pipeline will now update each part of the pipeline in turn. Before the unsupervised parts of the pipeline were updated during <code>predict_one</code>. This is more intuitive for new users. The old behavior, which yields better results, can be restored by calling <code>learn_one</code> with the new <code>compose.learn_during_predict</code> context manager.</p>"},{"location":"releases/0.19.0/#bandit","title":"bandit","text":"<ul> <li>Added a <code>bandit.datasets</code> submodule, which is meant to contain contextual bandit datasets.</li> <li>Added <code>bandit.base.ContextualPolicy</code>.</li> <li>Added <code>bandit.datasets.NewsArticles</code>.</li> <li>Added <code>bandit.LinUCBDisjoint</code>, which is River's first contextual bandit policy.</li> <li>Added <code>bandit.RandomPolicy</code>.</li> </ul>"},{"location":"releases/0.19.0/#compose","title":"compose","text":"<ul> <li>Removed the <code>compose.warm_up_mode</code> context manager.</li> <li>Removed the <code>compose.pure_inference_mode</code> context manager.</li> <li>The last step of a pipeline will be correctly updated if it is unsupervised, which wasn't the case before.</li> <li>Fixed an edge-case where <code>compose.TransformerProduct</code> would not work when chained more than twice.</li> </ul>"},{"location":"releases/0.19.0/#drift","title":"drift","text":"<ul> <li>Added a <code>datasets</code> submodule, which contains datasets that are useful for concept drift experiments.</li> <li>Fix bugs in <code>drift.binary.HDDM_A</code> and <code>drift.binary.HDDM_W</code>.</li> </ul>"},{"location":"releases/0.19.0/#linear_model","title":"linear_model","text":"<ul> <li>Added a <code>predict_many</code> method to <code>linear_model.BayesianLinearRegression</code>.</li> <li>Added a <code>smoothing</code> parameter to <code>linear_model.BayesianLinearRegression</code>, which allows it to cope with concept drift.</li> </ul>"},{"location":"releases/0.19.0/#forest","title":"forest","text":"<ul> <li>Fixed issue with <code>forest.ARFClassifier</code> which couldn't be passed a <code>CrossEntropy</code> metric.</li> <li>Fixed a bug in <code>forest.AMFClassifier</code> which slightly improves predictive accurary.</li> <li>Added <code>forest.AMFRegressor</code>.</li> </ul>"},{"location":"releases/0.19.0/#multioutput","title":"multioutput","text":"<ul> <li>Added <code>metrics.multioutput.SampleAverage</code>, which is equivalent to using <code>average='samples'</code> in scikit-learn.</li> </ul>"},{"location":"releases/0.19.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.OrdinalEncoder</code>, to map string features to integers.</li> <li>The <code>transform_many</code> method of <code>preprocessing.StandardScaler</code> now uses the dtype of the input for the output.</li> </ul>"},{"location":"releases/0.19.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.MultivariateGaussian</code>.</li> </ul>"},{"location":"releases/0.19.0/#stream","title":"stream","text":"<ul> <li><code>stream.iter_arff</code> now supports sparse data.</li> <li><code>stream.iter_arff</code> now supports multi-output targets.</li> <li><code>stream.iter_arff</code> now supports missing values indicated with question marks.</li> </ul>"},{"location":"releases/0.19.0/#utils","title":"utils","text":"<ul> <li>Added <code>utils.random.exponential</code> to retrieve random samples following an exponential distribution.</li> </ul>"},{"location":"releases/0.2.0/","title":"0.2.0 - 2019-05-27","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.2.0/#compose","title":"compose","text":"<ul> <li><code>compose.Pipeline</code> now has a <code>debug_one</code>.</li> <li><code>compose.Discard</code> and <code>compose.Select</code> now take variadic inputs, which means you don't have to provide a list of features to exclude/include.</li> </ul>"},{"location":"releases/0.2.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_bikes</code></li> </ul>"},{"location":"releases/0.2.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Classes that inherit from <code>feature_extraction.VectorizerMixin</code> can now directly be passed <code>str</code> instances instead of <code>dict</code> instances.</li> <li><code>feature_extraction.Agg</code> and <code>feature_extraction.TargetAgg</code> can now aggregate on multiple attributes.</li> </ul>"},{"location":"releases/0.2.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>RollingAccuracy</code></li> <li>Added <code>RollingCrossEntropy</code></li> <li>Added <code>RollingF1</code></li> <li>Added <code>RollingLogLoss</code></li> <li>Added <code>RollingMacroF1</code></li> <li>Added <code>RollingMacroPrecision</code></li> <li>Added <code>RollingMacroRecall</code></li> <li>Added <code>RollingMAE</code></li> <li>Added <code>RollingMicroF1</code></li> <li>Added <code>RollingMicroPrecision</code></li> <li>Added <code>RollingMicroRecall</code></li> <li>Added <code>RollingMSE</code></li> <li>Added <code>RollingPrecision</code></li> <li>Added <code>RollingRecall</code></li> <li>Added <code>RollingRMSE</code></li> <li>Added <code>RollingRMSLE</code></li> <li>Added <code>RollingSMAPE</code></li> </ul>"},{"location":"releases/0.2.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.online_qa_score</code>.</li> </ul>"},{"location":"releases/0.2.0/#proba","title":"proba","text":"<p>The <code>dist</code> module has been renamed to <code>proba</code> and is now public, for the moment it contains a single distribution called <code>proba.Gaussian</code>.</p>"},{"location":"releases/0.2.0/#naive_bayes","title":"naive_bayes","text":"<ul> <li>Added <code>naive_bayes.BernoulliNB</code>.</li> <li>Added <code>naive_bayes.ComplementNB</code>.</li> </ul>"},{"location":"releases/0.2.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.AdaBound</code>.</li> </ul>"},{"location":"releases/0.2.0/#tree","title":"tree","text":"<ul> <li>Added <code>tree.DecisionTreeClassifier</code>.</li> <li>Removed <code>tree.MondrianTreeClassifier</code> and <code>tree.MondrianTreeRegressor</code> because their performance wasn't good enough.</li> </ul>"},{"location":"releases/0.2.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.AutoCorrelation</code>.</li> <li>Added <code>stats.EWVar</code>.</li> <li>Rename <code>stats.Variance</code> to <code>stats.Var</code> and <code>stats.RollingVariance</code> to <code>stats.RollingVar</code>.</li> </ul>"},{"location":"releases/0.2.0/#stream","title":"stream","text":"<ul> <li>Added <code>stream.simulate_qa</code>.</li> </ul>"},{"location":"releases/0.2.0/#utils","title":"utils","text":"<ul> <li>Added <code>utils.SDFT</code>.</li> <li>Added <code>utils.Skyline</code>.</li> <li>Renamed the <code>window_size</code> parameter to <code>size</code> in <code>utils.Window</code> and <code>utils.SortedWindow</code>.</li> </ul>"},{"location":"releases/0.20.0/","title":"0.20.0 - 2023-11-09","text":"<ul> <li>River's mini-batch methods now support pandas v2. In particular, River conforms to pandas' new sparse API.</li> <li>Dropped support for Python 3.8.</li> <li>Added support for Python 3.12.</li> </ul>"},{"location":"releases/0.20.0/#anomaly","title":"anomaly","text":"<ul> <li>Added <code>api.anomaly.LocalOutlierFactor</code>, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation.</li> <li>Made <code>score_one</code> method of <code>api.anomaly.LocalOutlierFactor</code> stateless</li> <li>Defined default score for uninitialized detector</li> <li>Implementation of the <code>api.anomaly.StandardAbsoluteDeviation</code> algorithm, which is a uni-variate anomaly detection algorithm, based on the implementation in PySAD (Python Streaming Anomaly Detection)</li> </ul>"},{"location":"releases/0.20.0/#covariance","title":"covariance","text":"<ul> <li>Added <code>_from_state</code> method to <code>covariance.EmpiricalCovariance</code> to warm start from previous knowledge.</li> </ul>"},{"location":"releases/0.20.0/#clustering","title":"clustering","text":"<ul> <li>Add fixes to <code>cluster.DBSTREAM</code> algorithm, including:</li> <li>Addition of the <code>-</code> sign before the <code>fading_factor</code> in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed.</li> <li>The new <code>micro_cluster</code> is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (<code>len = 0</code>), a new micro cluster is added with key 0.</li> <li><code>cluster_is_up_to_date</code> is set to <code>True</code> at the end of the <code>self._recluster()</code> function.</li> <li>Shared density graph update timestamps are initialized with the current timestamp value</li> <li><code>neighbour_neighbours</code> are appended correctly to the <code>seed_set</code> when generating cluster labels</li> <li>When building weighted adjacency matrix the algorithm accounts for possibly orphaned entries in shared density graph</li> </ul>"},{"location":"releases/0.20.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.WebTraffic</code>, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs.</li> </ul>"},{"location":"releases/0.20.0/#drift","title":"drift","text":"<ul> <li>Add <code>drift.NoDrift</code> to allow disabling the drift detection capabilities of models. This detector does nothing and always returns <code>False</code> when queried whether or not a concept drift was detected.</li> </ul>"},{"location":"releases/0.20.0/#evaluate","title":"evaluate","text":"<ul> <li>Added a <code>yield_predictions</code> parameter to <code>evaluate.iter_progressive_val_score</code>, which allows including predictions in the output.</li> </ul>"},{"location":"releases/0.20.0/#forest","title":"forest","text":"<ul> <li>Simplify inner the structures of <code>forest.ARFClassifier</code> and <code>forest.ARFRegressor</code> by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole.</li> </ul>"},{"location":"releases/0.20.0/#metrics","title":"metrics","text":"<ul> <li><code>metrics.ConfusionMatrix</code> may now be used with <code>evaluate.progressive_val_score</code> and <code>evaluate.iter_progressive_val_score</code>.</li> </ul>"},{"location":"releases/0.20.0/#proba","title":"proba","text":"<ul> <li>Added <code>_from_state</code> method to <code>proba.MultivariateGaussian</code> to warm start from previous knowledge.</li> </ul>"},{"location":"releases/0.20.0/#tree","title":"tree","text":"<ul> <li>Fix a bug in <code>tree.splitter.NominalSplitterClassif</code> that generated a mismatch between the number of existing tree branches and the number of tracked branches.</li> <li>Fix a bug in <code>tree.ExtremelyFastDecisionTreeClassifier</code> where the split re-evaluation failed when the current branch's feature was not available as a split option. The fix also enables the tree to pre-prune a leaf via the tie-breaking mechanism.</li> </ul>"},{"location":"releases/0.20.0/#stats","title":"stats","text":"<ul> <li>Implementation of the incremental Kolmogorov-Smirnov statistics (at <code>stats.KolmogorovSmirnov</code>), with the option to calculate either the original KS or Kuiper's test.</li> </ul>"},{"location":"releases/0.20.0/#utils","title":"utils","text":"<ul> <li>Removed <code>utils.dict2numpy</code> and <code>utils.numpy2dict</code> functions. They were not used anywhere in the library.</li> <li><code>utils.TimeRolling</code> now works correctly if two samples with the same timestamp are added in a row.</li> </ul>"},{"location":"releases/0.20.1/","title":"0.20.1 - 2023-11-09","text":"<p>Dummy release to make wheels available. No actual difference with v0.20.0.</p>"},{"location":"releases/0.21.0/","title":"0.21.0 - 2023-12-04","text":"<ul> <li>The <code>learn_one</code> and <code>learn_many</code> methods of each estimator don't not return anything anymore. This is to emphasize that the estimators are stateful.</li> <li>The <code>update</code> and <code>revert</code> method of classes that have also cease to return anything.</li> <li><code>sample_weight</code> has been renamed to <code>w</code>.</li> </ul>"},{"location":"releases/0.21.0/#covariance","title":"covariance","text":"<ul> <li>Fixed an issue where <code>update_many</code> would reset <code>covariance.EmpiricalCovariance</code> each time it was called.</li> </ul>"},{"location":"releases/0.21.1/","title":"0.21.1 - 2024-03-28","text":"<p>This release should fix some of the installation issues when building the River wheel from scratch.</p>"},{"location":"releases/0.21.1/#anomaly","title":"anomaly","text":"<ul> <li>Added <code>PredictiveAnomalyDetection</code>, a semi-supervised technique that employs a predictive model for anomaly detection.</li> </ul>"},{"location":"releases/0.21.1/#drift","title":"drift","text":"<ul> <li>Added <code>FHDDM</code> drift detector.</li> <li>Added a <code>iter_polars</code> function to iterate over the rows of a polars DataFrame.</li> </ul>"},{"location":"releases/0.21.1/#neighbors","title":"neighbors","text":"<ul> <li>Simplified <code>neighbors.SWINN</code> to avoid recursion limit and pickling issues.</li> </ul>"},{"location":"releases/0.21.2/","title":"0.21.2 - 2024-07-08","text":"<p>This release makes Polars an optional dependency instead of a required one.</p>"},{"location":"releases/0.21.2/#cluster","title":"cluster","text":"<ul> <li>Added <code>ODAC</code> (Online Divisive-Agglomerative Clustering) for clustering time series.</li> </ul>"},{"location":"releases/0.21.2/#forest","title":"forest","text":"<ul> <li>Fix error in <code>forest.ARFClassifer</code> and <code>forest.ARFRegressor</code> where the algorithms would crash in case the number of features available for learning went below the value of the <code>max_features</code> parameter (#1560).</li> </ul>"},{"location":"releases/0.3.0/","title":"0.3.0 - 2019-06-23","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.3.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.load_chick_weights</code>.</li> </ul>"},{"location":"releases/0.3.0/#decomposition","title":"decomposition","text":"<ul> <li>Added <code>decomposition.LDA</code>.</li> </ul>"},{"location":"releases/0.3.0/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.HedgeRegressor</code>.</li> <li>Added <code>ensemble.StackingBinaryClassifier</code>.</li> </ul>"},{"location":"releases/0.3.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.FBeta</code></li> <li>Added <code>metrics.MacroFBeta</code></li> <li>Added <code>metrics.MicroFBeta</code></li> <li>Added <code>metrics.MultiFBeta</code></li> <li>Added <code>metrics.RollingFBeta</code></li> <li>Added <code>metrics.RollingMacroFBeta</code></li> <li>Added <code>metrics.RollingMicroFBeta</code></li> <li>Added <code>metrics.RollingMultiFBeta</code></li> <li>Added <code>metrics.Jaccard</code></li> <li>Added <code>metrics.RollingConfusionMatrix</code></li> <li>Added <code>metrics.RegressionMultiOutput</code></li> <li>Added <code>metrics.MCC</code></li> <li>Added <code>metrics.RollingMCC</code></li> <li>Added <code>metrics.ROCAUC</code></li> <li>Renamed <code>metrics.F1Score</code> to <code>metrics.F1</code>.</li> </ul>"},{"location":"releases/0.3.0/#multioutput","title":"multioutput","text":"<ul> <li>Added <code>multioutput.ClassifierChain</code>.</li> <li>Added <code>multioutput.RegressorChain</code>.</li> </ul>"},{"location":"releases/0.3.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.QuantileLoss</code></li> <li>Added <code>optim.MiniBatcher</code>.</li> </ul>"},{"location":"releases/0.3.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.Normalizer</code>.</li> </ul>"},{"location":"releases/0.3.0/#proba","title":"proba","text":"<ul> <li>Added <code>proba.Multinomial</code>.</li> </ul>"},{"location":"releases/0.4.1/","title":"0.4.1 - 2019-10-23","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.4.1/#base","title":"base","text":"<ul> <li>Tests are now much more extensive, thanks mostly to the newly added estimator tags.</li> </ul>"},{"location":"releases/0.4.1/#compose","title":"compose","text":"<ul> <li>Added <code>compose.Renamer</code>.</li> </ul>"},{"location":"releases/0.4.1/#datasets","title":"datasets","text":"<ul> <li>Added <code>fetch_kdd99_http</code>.</li> <li>Added <code>fetch_sms</code>.</li> <li>Added <code>fetch_trec07p</code>.</li> </ul>"},{"location":"releases/0.4.1/#ensemble","title":"ensemble","text":"<ul> <li>Removed <code>ensemble.HedgeBinaryClassifier</code> because its performance was subpar.</li> <li>Removed <code>ensemble.GroupRegressor</code>, as this should be a special case of <code>ensemble.StackingRegressor</code>.</li> </ul>"},{"location":"releases/0.4.1/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Fixed a bug where <code>feature_extraction.CountVectorizer</code> and <code>feature_extraction.TFIDFVectorizer</code> couldn't be pickled.</li> </ul>"},{"location":"releases/0.4.1/#linear_model","title":"linear_model","text":"<ul> <li><code>linear_model.LogisticRegression</code> and <code>linear_model.LinearRegression</code> now have an <code>intercept_lr</code> parameter.</li> </ul>"},{"location":"releases/0.4.1/#metrics","title":"metrics","text":"<ul> <li>Metrics can now be composed using the <code>+</code> operator, which is useful for evaluating multiple metrics at the same time.</li> <li>Added <code>metrics.Rolling</code>, which eliminates the need for a specific rolling implementation for each metric.</li> <li>Each metric can now be passed a <code>sample_weight</code> argument.</li> <li>Added <code>metrics.WeightedF1</code>.</li> <li>Added <code>metrics.WeightedFBeta</code>.</li> <li>Added <code>metrics.WeightedPrecision</code>.</li> <li>Added <code>metrics.WeightedRecall</code>.</li> </ul>"},{"location":"releases/0.4.1/#neighbors","title":"neighbors","text":"<ul> <li>Added <code>neighbors.KNeighborsRegressor</code>.</li> <li>Added <code>neighbors.KNeighborsClassifier</code>.</li> </ul>"},{"location":"releases/0.4.1/#optim","title":"optim","text":"<ul> <li>Added <code>optim.AdaMax</code>.</li> <li>The <code>optim</code> module has been reorganized into submodules; namely <code>optim.schedulers</code>, <code>optim.initializers</code>, and <code>optim.losses</code>. The top-level now only contains optimizers. Some classes have been renamed accordingly. See the documentation for details.</li> <li>Renamed <code>optim.VanillaSGD</code> to <code>optim.SGD</code>.</li> </ul>"},{"location":"releases/0.4.1/#stats","title":"stats","text":"<ul> <li>Added <code>stats.IQR</code>.</li> <li>Added <code>stats.RollingIQR</code>.</li> <li>Cythonized <code>stats.Mean</code> and <code>stats.Var</code>.</li> </ul>"},{"location":"releases/0.4.1/#stream","title":"stream","text":"<ul> <li>Added <code>stream.shuffle</code>.</li> <li><code>stream.iter_csv</code> now has <code>fraction</code> and <code>seed</code> parameters to sample rows, deterministically or not.</li> <li>Renamed <code>stream.iter_numpy</code> to <code>stream.iter_array</code>.</li> <li><code>stream.iter_csv</code> can now read from gzipped files.</li> </ul>"},{"location":"releases/0.4.1/#time_series","title":"time_series","text":"<ul> <li><code>time_series.Detrender</code> now has a <code>window_size</code> parameter for detrending with a rolling mean.</li> </ul>"},{"location":"releases/0.4.1/#tree","title":"tree","text":"<ul> <li>Added <code>tree.RandomForestClassifier</code>.</li> </ul>"},{"location":"releases/0.4.1/#utils","title":"utils","text":"<ul> <li>Fixed a bug where <code>utils.dot</code> could take longer than necessary.</li> </ul>"},{"location":"releases/0.4.3/","title":"0.4.3 - 2019-10-27","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.4.3/#base","title":"base","text":"<ul> <li>Model that inherit from <code>base.Wrapper</code> (e.g. <code>tree.RandomForestClassifier</code>) can now be pickled.</li> </ul>"},{"location":"releases/0.4.3/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.fetch_credit_card</code>.</li> </ul>"},{"location":"releases/0.4.3/#utils","title":"utils","text":"<ul> <li>Added the <code>utils.math</code> sub-module.</li> </ul>"},{"location":"releases/0.4.3/#tree","title":"tree","text":"<ul> <li>Fixed the <code>debug_one</code> method of <code>tree.DecisionTreeClassifier</code>.</li> </ul>"},{"location":"releases/0.4.4/","title":"0.4.4 - 2019-11-11","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul> <p>This release was mainly made to provide access to <code>wheels &lt;https://pythonwheels.com/&gt;</code>_ for Windows and MacOS.</p>"},{"location":"releases/0.4.4/#ensemble","title":"ensemble","text":"<ul> <li>Added <code>ensemble.AdaBoostClassifier</code>.</li> </ul>"},{"location":"releases/0.4.4/#linear_model","title":"linear_model","text":"<ul> <li>Added a <code>clip_gradient</code> parameter to <code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code>. Gradient clipping was already implemented, but the maximum absolute value can now be set by the user.</li> <li>The <code>intercept_lr</code> parameter of <code>linear_model.LinearRegression</code> and <code>linear_model.LogisticRegression</code> can now be passed an instance of <code>optim.schedulers.Scheduler</code> as well as a <code>float</code>.</li> </ul>"},{"location":"releases/0.4.4/#metrics","title":"metrics","text":"<ul> <li>Fixed <code>metrics.SMAPE</code>, the implementation was missing a multiplication by 2.</li> </ul>"},{"location":"releases/0.4.4/#optim","title":"optim","text":"<ul> <li>Added <code>optim.schedulers.Optimal</code> produces results that are identical to <code>sklearn.linear_model.SGDRegressor</code> and <code>sklearn.linear_model.SGDClassifier</code> when setting their <code>learning_rate</code> parameter to <code>'optimal'</code>.</li> </ul>"},{"location":"releases/0.4.4/#time_series","title":"time_series","text":"<ul> <li>Added <code>time_series.SNARIMAX</code>, a generic model which encompasses well-known time series models such as ARIMA and NARX.</li> </ul>"},{"location":"releases/0.5.0/","title":"0.5.0 - 2020-03-13","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.5.0/#compat","title":"compat","text":"<ul> <li>Added <code>compat.PyTorch2CremeRegressor</code>.</li> <li><code>compat.SKL2CremeRegressor</code> and <code>compat.SKL2CremeClassifier</code> now have an optional <code>batch_size</code> parameter in order to perform mini-batching.</li> </ul>"},{"location":"releases/0.5.0/#compose","title":"compose","text":"<ul> <li>Renamed <code>compose.Whitelister</code> to <code>compose.Select</code>.</li> <li>Renamed <code>compose.Blacklister</code> to <code>compose.Discard</code>.</li> </ul>"},{"location":"releases/0.5.0/#facto","title":"facto","text":"<ul> <li>Added <code>facto.FFMClassifier</code>.</li> <li>Added <code>facto.FFMRegressor</code>.</li> <li>Added <code>facto.FwFMClassifier</code>.</li> <li>Added <code>facto.FwFMRegressor</code>.</li> <li>Added <code>facto.HOFMClassifier</code>.</li> <li>Added <code>facto.HOFMRegressor</code>.</li> <li>Refactored <code>facto.FMClassifier</code>.</li> <li>Refactored <code>facto.FMRegressor</code>.</li> </ul>"},{"location":"releases/0.5.0/#feature_selection","title":"feature_selection","text":"<ul> <li>Added <code>feature_selection.PoissonInclusion</code>.</li> <li>Removed <code>feature_selection.RandomDiscarder</code> as it didn't make much sense.</li> </ul>"},{"location":"releases/0.5.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Renamed <code>feature_extraction.CountVectorizer</code> to <code>feature_extraction.BagOfWords</code>.</li> <li>Renamed <code>feature_extraction.TFIDFVectorizer</code> to <code>feature_extraction.TFIDF</code>.</li> <li>Added <code>preprocessor</code> and <code>ngram_range</code> parameters to <code>feature_extraction.BagOfWords</code>.</li> <li>Added <code>preprocessor</code> and <code>ngram_range</code> parameters to <code>feature_extraction.TFIDF</code>.</li> </ul>"},{"location":"releases/0.5.0/#datasets","title":"datasets","text":"<ul> <li>The <code>datasets</code> module has been overhauled. Each dataset is now a class (e.g. <code>fetch_electricity</code> has become <code>datasets.Elec2</code>).</li> <li>Added <code>datasets.TrumpApproval</code>.</li> <li>Added <code>datasets.MaliciousURL</code>.</li> <li>Added <code>datasets.gen.SEA</code>.</li> <li>Added <code>datasets.Higgs</code>.</li> <li>Added <code>datasets.MovieLens100K</code>.</li> <li>Added <code>datasets.Bananas</code>.</li> <li>Added <code>datasets.Taxis</code>.</li> <li>Added <code>datasets.ImageSegments</code>.</li> <li>Added <code>datasets.SMTP</code></li> </ul>"},{"location":"releases/0.5.0/#impute","title":"impute","text":"<ul> <li>Added <code>impute.PreviousImputer</code>.</li> </ul>"},{"location":"releases/0.5.0/#linear_model","title":"linear_model","text":"<ul> <li><code>linear_model.FMClassifier</code> has been moved to the <code>facto</code> module.</li> <li><code>linear_model.FMRegressor</code> has been  moved to the <code>facto</code> module.</li> <li>Added <code>linear_model.ALMAClassifier</code>.</li> </ul>"},{"location":"releases/0.5.0/#metrics","title":"metrics","text":"<ul> <li>Added <code>metrics.ClassificationReport</code>.</li> <li>Added <code>metrics.TimeRolling</code>.</li> <li>The implementation of <code>metrics.ROCAUC</code> was incorrect. Using the trapezoidal rule instead of Simpson's rule seems to be more robust.</li> <li><code>metrics.PerClass</code> has been removed; it is recommended that you use <code>metrics.ClassificationReport</code> instead as it gives a better overview.</li> </ul>"},{"location":"releases/0.5.0/#meta","title":"meta","text":"<ul> <li>Moved <code>meta.TransformedTargetRegressor</code> and <code>meta.BoxCoxRegressor</code> to this module (they were previously in the <code>compose</code> module).</li> <li>Added <code>meta.PredClipper</code></li> </ul>"},{"location":"releases/0.5.0/#model_selection","title":"model_selection","text":"<ul> <li>Added <code>model_selection.expand_param_grid</code> to generate a list of models from a grid of parameters.</li> <li>Added the <code>model_selection.successive_halving</code> method for selecting hyperparameters.</li> <li>The <code>online_score</code> and <code>online_qa_score</code> methods have been merged into a single method named <code>model_selection.progressive_val_score</code>.</li> </ul>"},{"location":"releases/0.5.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added <code>preprocessing.RBFSampler</code>.</li> <li>Added <code>preprocessing.MaxAbsScaler</code>.</li> <li>Added <code>preprocessing.RobustScaler</code>.</li> <li>Added <code>preprocessing.Binarizer</code>.</li> <li>Added <code>with_mean</code> and <code>with_std</code> parameters to <code>preprocessing.StandardScaler</code>.</li> </ul>"},{"location":"releases/0.5.0/#optim","title":"optim","text":"<ul> <li>Added <code>optim.losses.BinaryFocalLoss</code>.</li> <li>Added the <code>optim.AMSGrad</code> optimizer.</li> <li>Added the <code>optim.Nadam</code> optimizer.</li> <li>Added <code>optim.losses.Poisson</code>.</li> <li>Fixed a performance bug in <code>optim.NesterovMomentum</code>.</li> </ul>"},{"location":"releases/0.5.0/#reco","title":"reco","text":"<ul> <li>Added <code>reco.FunkMF</code>.</li> <li>Renamed <code>reco.SVD</code> to <code>reco.BiasedMF</code>.</li> <li>Renamed <code>reco.SGDBaseline</code> to <code>reco.Baseline</code>.</li> <li>Models now expect a <code>dict</code> input with <code>user</code> and <code>item</code> fields.</li> </ul>"},{"location":"releases/0.5.0/#sampling","title":"sampling","text":"<ul> <li>Added <code>sampling.RandomUnderSampler</code>.</li> <li>Added <code>sampling.RandomOverSampler</code>.</li> <li>Added <code>sampling.RandomSampler</code>.</li> <li>Added <code>sampling.HardSamplingClassifier</code>.</li> <li>Added <code>sampling.HardSamplingRegressor</code>.</li> </ul>"},{"location":"releases/0.5.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.AbsMax</code>.</li> <li>Added <code>stats.RollingAbsMax</code>.</li> </ul>"},{"location":"releases/0.5.0/#stream","title":"stream","text":"<ul> <li>Added <code>stream.iter_libsvm</code>.</li> <li><code>stream.iter_csv</code> now supports reading from '.zip' files.</li> <li>Added <code>stream.Cache</code>.</li> <li>Added a <code>drop</code> parameter to <code>stream.iter_csv</code> to discard fields.</li> </ul>"},{"location":"releases/0.5.1/","title":"0.5.1 - 2020-03-29","text":"<ul> <li>PyPI</li> <li>GitHub</li> </ul>"},{"location":"releases/0.5.1/#compose","title":"compose","text":"<ul> <li><code>compose.Pipeline</code> and <code>compose.TransformerUnion</code> now variadic arguments as input instead of a list. This doesn't change anything when using the shorthand operators <code>|</code> and <code>+</code>.</li> </ul>"},{"location":"releases/0.5.1/#model_selection","title":"model_selection","text":"<ul> <li>Removed <code>model_selection.successive_halving</code></li> <li>Added <code>model_selection.SuccessiveHalvingRegressor</code> and <code>model_selection.SuccessiveHalvingClassifier</code></li> </ul>"},{"location":"releases/0.5.1/#stream","title":"stream","text":"<ul> <li>Added a <code>copy</code> parameter to <code>stream.simulate_qa</code> in order to handle unwanted feature modifications.</li> </ul>"},{"location":"releases/0.5.1/#tree","title":"tree","text":"<ul> <li>Added a <code>curtail_under</code> parameter to <code>tree.DecisionTreeClassifier</code>.</li> <li>The speed and accuracy of both <code>tree.DecisionTreeClassifier</code> and <code>tree.RandomForestClassifier</code> has been slightly improved for numerical attributes.</li> <li>The esthetics of the <code>tree.DecisionTreeClassifier.draw</code> method have been improved.</li> </ul>"},{"location":"releases/0.6.0/","title":"0.6.0 - 2020-06-09","text":""},{"location":"releases/0.6.0/#base","title":"base","text":"<ul> <li>Added a new base class called <code>SupervisedTransformer</code> from which supervised transformers inherit from. Before this, supervised transformers has a <code>is_supervised</code> property.</li> </ul>"},{"location":"releases/0.6.0/#compose","title":"compose","text":"<ul> <li>Added <code>compose.SelectType</code>, which allows selecting feature subsets based on their type.</li> <li>Added a <code>score_one</code> method to <code>compose.Pipeline</code> so that estimators from the <code>anomaly</code> module can be pipelined.</li> <li>Added <code>compose.Grouper</code>, which allows applying transformers within different subgroups.</li> </ul>"},{"location":"releases/0.6.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.Music</code>, which is a dataset for multi-output binary classification.</li> <li>Added <code>datasets.synth.Friedman</code>, which is synthetic regression dataset.</li> <li>The <code>datasets.gen</code> module has been renamed to <code>datasets.synth</code></li> <li>Each dataset now has a <code>__repr__</code> method which displays some descriptive information.</li> <li>Added <code>datasets.Insects</code>, which has 10 variants.</li> </ul>"},{"location":"releases/0.6.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li><code>feature_extraction.Differ</code> has been deprecated. We might put it back in a future if we find a better design.</li> </ul>"},{"location":"releases/0.6.0/#impute","title":"impute","text":"<ul> <li><code>impute.StatImputer</code> has been completely refactored.</li> </ul>"},{"location":"releases/0.6.0/#metrics","title":"metrics","text":"<ul> <li>In <code>metrics.SMAPE</code>, instead of raising a <code>ZeroDivisionError</code>, the convention is now to use 0 when both <code>y_true</code> and <code>y_pred</code> are equal to 0.</li> </ul>"},{"location":"releases/0.6.0/#model_selection","title":"model_selection","text":"<ul> <li>Added the possibility to configure how the progress is printed in <code>model_selection.progressive_val_score</code>. For instance, the progress can now be printed to a file by providing the <code>file</code> argument.</li> </ul>"},{"location":"releases/0.6.0/#multiclass","title":"multiclass","text":"<ul> <li>Added <code>multiclass.OutputCodeClassifier</code>.</li> <li>Added <code>multiclass.OneVsOneClassifier</code>.</li> </ul>"},{"location":"releases/0.6.0/#multioutput","title":"multioutput","text":"<ul> <li>Fixed a bug where <code>multioutput.ClassifierChain</code> and <code>multioutput.RegressorChain</code> could not be pickled.</li> </ul>"},{"location":"releases/0.6.0/#stats","title":"stats","text":"<ul> <li>Added <code>stats.Shift</code>, which can be used to compute statistics over a shifted version of a variable.</li> <li>Added <code>stats.Link</code>, which can be used to compose univariate statistics. Univariate statistics can now be composed via the <code>|</code> operator.</li> <li>Renamed <code>stats.Covariance</code> to <code>stats.Cov</code>.</li> <li>Renamed <code>stats.PearsonCorrelation</code> to <code>stats.PearsonCorr</code>.</li> <li>Renamed <code>stats.AutoCorrelation</code> to <code>stats.AutoCorr</code>.</li> <li>Added <code>stats.RollingCov</code>, which computes covariance between two variables over a window.</li> <li>Added <code>stats.RollingPearsonCorr</code>, which computes the Pearson correlation over a window.</li> </ul>"},{"location":"releases/0.6.0/#stream","title":"stream","text":"<ul> <li>Added a <code>stream.iter_sql</code> utility method to work with SQLAlchemy.</li> <li>The <code>target_name</code> parameter of <code>stream.iter_csv</code> has been renamed to <code>target</code>. It can now be passed a list of values in order to support multi-output scenarios.</li> <li>Added <code>stream.iter_arff</code> for handling ARFF files.</li> </ul>"},{"location":"releases/0.6.0/#tree","title":"tree","text":"<ul> <li>Cancelled the behavior where <code>tree.DecisionTreeRegressor</code> would raise an exception when no split was found.</li> </ul>"},{"location":"releases/0.6.1/","title":"0.6.1 - 2020-06-10","text":""},{"location":"releases/0.6.1/#compose","title":"compose","text":"<ul> <li>Fixed a bug that occurred when part of a <code>compose.Transformer</code> was a <code>compose.Pipeline</code> and wasn't properly handled.</li> </ul>"},{"location":"releases/0.7.0/","title":"0.7.0 - 2021-04-16","text":"<p>Alas, no release notes for this one.</p>"},{"location":"releases/0.7.1/","title":"0.7.1 - 2021-06-13","text":"<p>Fixed an issue where scikit-learn was imported in <code>sam_knn.py</code> but wasn't specified as a dependency.</p>"},{"location":"releases/0.7.1/#expert","title":"expert","text":"<ul> <li>Each expert model will now raise a <code>NotEnoughModels</code> exception if only a single model is passed.</li> </ul>"},{"location":"releases/0.7.1/#stream","title":"stream","text":"<ul> <li>Added <code>drop_nones</code> parameter to <code>stream.iter_csv</code>.</li> </ul>"},{"location":"releases/0.8.0/","title":"0.8.0 - 2021-08-31","text":""},{"location":"releases/0.8.0/#base","title":"base","text":"<ul> <li>The <code>predict_many</code> and <code>predict_proba_many</code> methods have been removed from <code>base.Classifier</code>. They're part of <code>base.MiniBatchClassifier</code>.</li> </ul>"},{"location":"releases/0.8.0/#ensemble","title":"ensemble","text":"<ul> <li>Implemented <code>ensemble.VotingClassifier</code>.</li> <li>Implemented <code>ensemble.SRPRegressor</code>.</li> </ul>"},{"location":"releases/0.8.0/#meta","title":"meta","text":"<ul> <li>Renamed <code>meta.TransformedTargetRegressor</code> to <code>meta.TargetTransformRegressor</code>.</li> <li>Added <code>meta.TargetStandardScaler</code>.</li> </ul>"},{"location":"releases/0.8.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Added a <code>with_std</code> parameter to <code>StandardScaler</code>.</li> </ul>"},{"location":"releases/0.8.0/#rules","title":"rules","text":"<ul> <li>Added <code>rules.AMRules</code></li> </ul>"},{"location":"releases/0.8.0/#stats","title":"stats","text":"<ul> <li>Make <code>stats.RollingQuantile</code> match the default behavior of Numpy's <code>quantile</code> function.</li> </ul>"},{"location":"releases/0.8.0/#tree","title":"tree","text":"<ul> <li>Unifed base class structure applied to all tree models.</li> <li>Bug fixes.</li> <li>Added <code>tree.SGTClassifier</code> and <code>tree.SGTRegressor</code>.</li> </ul>"},{"location":"releases/0.9.0/","title":"0.9.0 - 2021-11-30","text":"<ul> <li>Wheels for Python 3.6 have been dropped.</li> <li>Wheels for Python 3.9 have been added.</li> </ul>"},{"location":"releases/0.9.0/#anomaly","title":"anomaly","text":"<ul> <li>Moved <code>api.anomaly.base.AnomalyDetector</code> to <code>anomaly.AnomalyDetector</code>.</li> <li>Implemented <code>anomaly.ConstantThresholder</code>.</li> <li>Implemented <code>anomaly.QuantileThresholder</code>.</li> <li>Implemented <code>api.anomaly.OneClassSVM</code>.</li> </ul>"},{"location":"releases/0.9.0/#base","title":"base","text":"<ul> <li>Renamed <code>base.WrapperMixin</code> to <code>base.Wrapper</code>.</li> <li>Introduced <code>base.WrapperEnsemble</code>.</li> <li>Clarified the difference between a <code>base.typing.Dataset</code> and a <code>base.typing.Stream</code>. A <code>Stream</code> is an instance of a <code>Dataset</code> and is stateful. A <code>Dataset</code> is stateless. It's essentially the same difference between an <code>Iterable</code> and an <code>Iterator</code> in the Python standard library.</li> </ul>"},{"location":"releases/0.9.0/#compat","title":"compat","text":"<ul> <li>Added <code>compat.PyTorch2RiverClassifier</code></li> <li>Implemented median absolute deviation in <code>stats.MAD</code>.</li> <li>Refactored <code>compat.PyTorch2RiverRegressor</code></li> <li>Fixed an issue where some statistics could not be printed if they had not seen any data yet.</li> </ul>"},{"location":"releases/0.9.0/#compose","title":"compose","text":"<ul> <li>You can now use a <code>list</code> as a shorthand to build a <code>TransformerUnion</code>.</li> <li>Fixed a visualization issue when using a pipeline with multiple feature unions.</li> <li>The prejudiced terms <code>blacklist</code> and <code>whitelist</code> have both been renamed to <code>keys</code>.</li> <li>Removed <code>learn_unsupervised</code> parameter from pipeline methods.</li> <li>Implemented <code>compose.TransformerProduct</code>.</li> </ul>"},{"location":"releases/0.9.0/#datasets","title":"datasets","text":"<ul> <li>Added <code>datasets.Keystroke</code>.</li> </ul>"},{"location":"releases/0.9.0/#ensemble","title":"ensemble","text":"<ul> <li>Bug fixes in <code>ensemble.SRPClassifier</code> and <code>ensemble.SRPRegressor</code>.</li> <li>Some estimators have been moved into the <code>ensemble</code> module.</li> </ul>"},{"location":"releases/0.9.0/#feature_extraction","title":"feature_extraction","text":"<ul> <li>Implemented <code>feature_extraction.Lagger</code>.</li> <li>Implemented <code>feature_extraction.TargetLagger</code>.</li> </ul>"},{"location":"releases/0.9.0/#meta","title":"meta","text":"<p>This module has been deleted.</p> <ul> <li>Move <code>meta.PredClipper</code> to the <code>preprocessing</code> module.</li> <li>Removed <code>meta.BoxCoxRegressor</code>.</li> <li>Moved <code>meta.TargetTransformRegressor</code> to <code>compose.TargetTransformRegressor</code>.</li> <li>Moved <code>meta.TargetStandardScaler</code> to <code>preprocessing.TargetStandardScaler</code>.</li> </ul>"},{"location":"releases/0.9.0/#model_selection","title":"model_selection","text":"<ul> <li>This new module replaces the <code>expert</code> module.</li> <li>Implemented <code>model_selection.GreedyRegressor</code>.</li> <li>Added <code>ModelSelector</code> base class.</li> </ul>"},{"location":"releases/0.9.0/#optim","title":"optim","text":"<ul> <li><code>optim.Adam</code> and <code>optim.RMSProp</code> now work with <code>utils.VectorDict</code>s as well as <code>numpy.ndarray</code>s.</li> <li>Added <code>optim.losses.Huber</code>.</li> </ul>"},{"location":"releases/0.9.0/#preprocessing","title":"preprocessing","text":"<ul> <li>Enabled <code>preprocessing.OneHotEncoder</code> to one-hot encode values that are list or sets.</li> </ul>"},{"location":"releases/0.9.0/#reco","title":"reco","text":"<ul> <li>Added a <code>debug_one</code> method to <code>reco.FMRegressor</code>.</li> </ul>"},{"location":"releases/0.9.0/#selection","title":"selection","text":"<ul> <li>This new module replaces the <code>expert</code> module.</li> <li>Implemented <code>selection.GreedyExpertRegressor</code>.</li> </ul>"},{"location":"releases/0.9.0/#stats","title":"stats","text":"<ul> <li>Fixed an issue where some statistics could not be printed if they had not seen any data yet.</li> <li>Implemented median absolute deviation in <code>stats.MAD</code>.</li> <li>The <code>stats.Mean</code> and <code>stats.Var</code> implementations have been made more numerically stable.</li> </ul>"},{"location":"releases/0.9.0/#time_series","title":"time_series","text":"<ul> <li><code>time_series.Detrender</code> and <code>time_series.GroupDetrender</code> have been removed as they overlap with <code>preprocessing.TargetStandardScaler</code>.</li> <li>Implemented a <code>time_series.evaluate</code> method, which performs progressive validation for time series scenarios.</li> <li>Implemented <code>time_series.HorizonMetric</code> class to evaluate the performance of a forecasting model at each time step along a horizon.</li> <li>Implemented <code>time_series.HoltWinters</code>.</li> </ul>"},{"location":"releases/0.9.0/#utils","title":"utils","text":"<ul> <li>Moved <code>model_selection.expand_param_grid</code> to <code>utils.expand_param_grid</code>.</li> <li>Added <code>utils.poisson</code>.</li> <li>Added the <code>utils.log_method_calls</code> context manager.</li> <li>Added the <code>utils.warm_up_mode</code> context manager.</li> <li>Added the <code>utils.pure_inference_model</code> context manager.</li> </ul>"},{"location":"releases/unreleased/","title":"Unreleased","text":"<ul> <li>The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged.</li> </ul>"}]}
\ No newline at end of file